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 |
|---|---|---|---|---|---|
oyataku1/InterPolations | demo-interpolation.ipynb | 1 | 59546 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"三ツ国拓真 線形補間"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"my_lin_interp (generic function with 1 method)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"include(\"Interpolation.jl\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"function my_lin_interp(grid, vals)\n",
" function func(x::Real)\n",
" a=searchsortedfirst(grid, x)\n",
" b=searchsortedlast(grid, x)\n",
" if a==length(grid)+1||b==0\n",
" return 0\n",
" elseif a==1\n",
" return vals[1]\n",
" else\n",
" return (vals[a]-vals[a-1])/(grid[a]-grid[a-1])*(x-grid[a-1])+vals[a-1]\n",
" end\n",
" end\n",
" \n",
"\n",
" function func{T<:Real}(x::AbstractVector{T})\n",
" n = length(x)\n",
" out = Array(Float64, n)\n",
" for i in 1:n\n",
" out[i] = func(x[i])\n",
" end\n",
" return out\n",
" end\n",
"\n",
" return func\n",
"end"
]
}
],
"source": [
";cat Interpolation.jl"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2-element Array{Int64,1}:\n",
" 2\n",
" 0"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid = [1, 2]\n",
"vals = [2, 0]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "LoadError",
"evalue": "LoadError: UndefVarError: g not defined\nwhile loading In[5], in expression starting on line 1",
"output_type": "error",
"traceback": [
"LoadError: UndefVarError: g not defined\nwhile loading In[5], in expression starting on line 1",
""
]
}
],
"source": [
"g([1.25, 1.5,1.3,3])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4-element Array{Float64,1}:\n",
" 1.5\n",
" 1.0\n",
" 1.4\n",
" 0.0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g = my_lin_interp(grid, vals)\n",
"grid = [0, 2, 4, 6, 8, 10]\n",
"vals = [1, 4, 5, 8, 9, 11]\n",
"g([1.25, 1.5,1.3,0])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g(11)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"グラフ"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"my_lin_interp (generic function with 1 method)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"include(\"Interpolation.jl\")"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x3236280d0>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"PyObject <matplotlib.legend.Legend object at 0x3276546d0>"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using PyPlot\n",
"x = linspace(0, 1, 100)\n",
"f(x) = 2 .* cos(6x) .+ sin(14x) .+ 2.5\n",
"\n",
"m_grid=linspace(0, 1, 4)\n",
"h_grid=linspace(0, 1, 10)\n",
"m_vals=f(m_grid)\n",
"h_vals=f(h_grid)\n",
"m = my_lin_interp(m_grid,m_vals)\n",
"h = my_lin_interp(h_grid,h_vals)\n",
"\n",
"\n",
"plot(x, f(x))\n",
"plot(x,m(x),label=\"m\")\n",
"plot(x,h(x),label=\"h\")\n",
"legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.4.5",
"language": "julia",
"name": "julia-0.4"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.4.5"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
rdhyee/nypl50 | rebuild_travis_on_repos.ipynb | 1 | 16032 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(2016.01.12) I **think** this notebook is about pushing ahead on getting the following in place. In one commit, I'd to be able to:\n",
"\n",
"* add `travis.deploy.api_key.txt`\n",
"* increase patch version number\n",
"* `git commit` with appropriate message\n",
"* `git tag`\n",
"* `git push`"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import print_function"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"import shutil\n",
"import sh\n",
"import yaml\n",
"from pandas import DataFrame, Series\n",
"from itertools import islice\n",
"\n",
"REPOS_LIST = \"/Users/raymondyee/C/src/gitenberg/Second-Folio/list_of_repos.txt\"\n",
"GITENBERG_DIR = \"/Users/raymondyee/C/src/gitenberg/\"\n",
"\n",
"METADATA_DIR = \"/Users/raymondyee/C/src/gitenberg-dev/giten_site/metadata\"\n",
"COVERS_DATA = \"/Users/raymondyee/C/src/gitenberg/Second-Folio/covers_data.json\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"import glob\n",
"import sh\n",
"import yaml\n",
"\n",
"from gitenberg import metadata\n",
"import jinja2\n",
"\n",
"from second_folio import (GITENBERG_DIR, \n",
" all_repos, \n",
" apply_to_repos, \n",
" travis_template, \n",
" travis_setup_releases, \n",
" git_pull,\n",
" apply_travis,\n",
" finish_travis,\n",
" repo_is_buildable,\n",
" has_travis_with_gitenberg_build,\n",
" slugify,\n",
" write_repo_token_file,\n",
" latest_epub,\n",
" repo_version\n",
" )\n",
"\n",
"from github_settings import (username, password)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from itertools import islice, izip\n",
"\n",
"# pick subset of repositories to calculate on\n",
"repos = list(islice(all_repos,0,None))\n",
"\n",
"# determine which repos are \"buildable\"\n",
"repos_statues = list(izip(repos, \n",
" apply_to_repos(repo_is_buildable, repos=repos), \n",
" apply_to_repos(has_travis_with_gitenberg_build, repos=repos) ))\n",
"\n",
"# we want to apply travis to repos that are buildable but that don't yet have .travis.yml. \n",
"\n",
"repos_to_travisfy = [repo[0] for repo in repos_statues if repo[1] and not repo[2]]\n",
"repos_to_travisfy"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Adventures-of-Huckleberry-Finn_76',\n",
" 'Don-Quixote_996',\n",
" 'Dubliners_2814',\n",
" 'Jane-Eyre_1260',\n",
" 'Moby-Dick--Or-The-Whale_2701',\n",
" 'Narrative-of-the-Life-of-Frederick-Douglass-an-American-Slave_23',\n",
" 'Pride-and-Prejudice_1342',\n",
" 'The-Adventures-of-Sherlock-Holmes_1661',\n",
" 'The-Brothers-Karamazov_28054',\n",
" 'The-Time-Machine_35',\n",
" 'Frankenstein_84',\n",
" 'Middlemarch_145',\n",
" 'A-Tale-of-Two-Cities_98',\n",
" 'The-Call-of-the-Wild_215',\n",
" 'Crime-and-Punishment_2554',\n",
" 'The-Strange-Case-of-Dr.-Jekyll-and-Mr.-Hyde_42',\n",
" 'Dracula_345',\n",
" 'Flatland--A-Romance-of-Many-Dimensions--Illustrated-_201',\n",
" 'Household-Stories-by-the-Brothers-Grimm_19068',\n",
" 'Heart-of-Darkness_219',\n",
" 'A-Journey-into-the-Interior-of-the-Earth_3748',\n",
" 'Jude-the-Obscure_153',\n",
" 'King-Solomon-s-Mines_2166',\n",
" 'Little-Women_514',\n",
" 'Madame-Bovary_2413',\n",
" 'The-Life-and-Adventures-of-Robinson-Crusoe_521',\n",
" 'The-Awakening-and-Selected-Short-Stories_160',\n",
" 'The-Jungle_140',\n",
" 'The-Jungle-Book_236',\n",
" 'Metamorphosis_5200',\n",
" 'The-Picture-of-Dorian-Gray_174',\n",
" 'The-Red-Badge-of-Courage_73',\n",
" 'The-Scarlet-Letter_33',\n",
" 'The-War-of-the-Worlds_36',\n",
" 'The-Wonderful-Wizard-of-Oz_55',\n",
" 'This-Side-of-Paradise_805',\n",
" 'Anna-Karenina_1399',\n",
" 'Gulliver-s-Travels_829',\n",
" 'Les-Mis-rables_135',\n",
" 'Swann-s-Way_7178',\n",
" 'The-Count-of-Monte-Cristo_1184',\n",
" 'The-Hunchback-of-Notre-Dame_6539',\n",
" 'The-Three-Musketeers_1257',\n",
" 'Through-the-Looking-Glass_12',\n",
" 'Twenty-Thousand-Leagues-under-the-Sea_164',\n",
" 'War-and-Peace_2600',\n",
" 'Winesburg-Ohio--A-Group-of-Tales-of-Ohio-Small-Town-Life_416',\n",
" 'My-Antonia_242',\n",
" 'Divine-Comedy-Longfellow-s-Translation-Hell_1001',\n",
" 'The-Works-of-Edgar-Allan-Poe-The-Raven-EditionTable-Of-Contents-And-Index-Of-The-Five-Volumes_25525']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_repos"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Dubliners_2814'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"repo = all_repos[2]\n",
"repo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[semantic_version 2.4.2 : Python Package Index](https://pypi.python.org/pypi/semantic_version/2.4.2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"list(apply_to_repos(repo_version,kwargs={'version_type':'patch'},repos=all_repos))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# templates\n",
"\n",
"template path? \n",
"\n",
"variables to fill:\n",
"\n",
"* `epub_title`\n",
"* `encrypted_key`\n",
"* `repo_name`"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def new_travis_template(repo, template, write_template=False):\n",
" \"\"\"\n",
" compute (and optionally write) .travis.yml based on the template and current metadata.yaml \n",
" \"\"\"\n",
" template_written = False\n",
" \n",
" sh.cd(os.path.join(GITENBERG_DIR, repo))\n",
"\n",
" metadata_path = os.path.join(GITENBERG_DIR, repo, \"metadata.yaml\")\n",
" travis_path = os.path.join(GITENBERG_DIR, repo, \".travis.yml\")\n",
" travis_api_key_path = os.path.join(GITENBERG_DIR, repo, \".travis.deploy.api_key.txt\") \n",
" \n",
" md = metadata.pandata.Pandata(metadata_path)\n",
" epub_title = slugify(md.metadata.get(\"title\"))\n",
" encrypted_key = open(travis_api_key_path).read().strip()\n",
" repo_name = md.metadata.get(\"_repo\")\n",
" \n",
" template_vars = {\n",
" 'epub_title': epub_title,\n",
" 'encrypted_key': encrypted_key,\n",
" 'repo_name': repo_name\n",
" }\n",
" \n",
" template_result = template.render(**template_vars)\n",
" \n",
" if write_template:\n",
" with open(travis_path, \"w\") as f:\n",
" f.write(template_result)\n",
" template_written = True\n",
" \n",
" return (template_result, template_written) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from itertools import izip\n",
"\n",
"template = template = travis_template()\n",
"\n",
"results = list(izip(all_repos, apply_to_repos(new_travis_template,\n",
" kwargs={'template':template},\n",
" repos=all_repos)))\n",
"[result for result in results if isinstance(result[1], Exception) ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": [],
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"import yaml\n",
"import pdb\n",
"\n",
"def commit_travis_api_key_and_update_travis(repo, template, write_updates=False):\n",
" \"\"\"\n",
" create .travis.deploy.api_key.txt and update .travis.yml; do git commit\n",
" \"\"\"\n",
" sh.cd(os.path.join(GITENBERG_DIR, repo))\n",
"\n",
" metadata_path = os.path.join(GITENBERG_DIR, repo, \"metadata.yaml\")\n",
" travis_path = os.path.join(GITENBERG_DIR, repo, \".travis.yml\")\n",
" travis_api_key_path = os.path.join(GITENBERG_DIR, repo, \".travis.deploy.api_key.txt\") \n",
" \n",
" # git add .travis.deploy.api_key.txt\n",
" \n",
" if write_updates:\n",
" sh.git.add(travis_api_key_path)\n",
" \n",
" # read the current metadata file and replace current_ver with next_ver\n",
"\n",
" (v0, v1, v_updated) = repo_version(repo, version_type='patch', write_version=write_updates)\n",
" if v_updated:\n",
" sh.git.add(metadata_path)\n",
" \n",
" # write new .travis.yml\n",
" (new_template, template_written) = new_travis_template(repo, template, write_template=write_updates)\n",
" if template_written:\n",
" sh.git.add(travis_path)\n",
" \n",
" if write_updates:\n",
" sh.git.commit(\"-m\", \"add .travis.deploy.api_key.txt; updated .travis.yml\")\n",
" \n",
" # add tag\n",
" if v_updated:\n",
" sh.git.tag(v1)\n",
" sh.git.push(\"origin\", \"master\", \"--tags\")\n",
"\n",
" return True\n",
"\n",
" else:\n",
" return False\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"problem_repos = ('The-Picture-of-Dorian-Gray_174',\n",
" 'The-Hunchback-of-Notre-Dame_6539', \n",
" 'Divine-Comedy-Longfellow-s-Translation-Hell_1001',\n",
" 'The-Works-of-Edgar-Allan-Poe-The-Raven-EditionTable-Of-Contents-And-Index-Of-The-Five-Volumes_25525'\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"repos = all_repos[36:][0:]\n",
"repos = [repo for repo in repos if repo not in problem_repos]\n",
"repos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"template = travis_template()\n",
"\n",
"# I wish there would be a way to figure out variables in a template from jinja2...but I don't see a way.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"results = list(apply_to_repos(commit_travis_api_key_and_update_travis, \n",
" kwargs={'template':template,\n",
" 'write_updates':True},\n",
" repos=repos))\n",
"results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import requests\n",
"\n",
"def url_status(url):\n",
" r = requests.get(url, allow_redirects=True, stream=True)\n",
" return r.status_code\n",
"\n",
"def repo_epub_status(repo):\n",
" return url_status(latest_epub(repo))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"list(izip(repos, apply_to_repos(repo_epub_status, \n",
" repos=repos)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"results = list(izip(all_repos, apply_to_repos(repo_epub_status, \n",
" repos=all_repos)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ok_repos = [result[0] for result in results if result[1] == 200 ]\n",
"not_ok_repos = [result[0] for result in results if result[1] <> 200 ]\n",
"len(ok_repos), len(not_ok_repos)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for (i, repo) in enumerate(ok_repos):\n",
" print (i+1, \"\\t\", repo, \"\\t\", latest_epub(repo))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"not_ok_repos"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Divine Comedy\n",
"\n",
" `Divine-Comedy-Longfellow-s-Translation-Hell_1001` / `/Users/raymondyee/C/src/gitenberg/Divine-Comedy-Longfellow-s-Translation-Hell_1001\n",
"`: there is a book.asciidoc but no .travis.yml \n",
"\n",
"Let's do this by hand and document the process...\n",
"\n",
"template"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from second_folio import TRAVIS_TEMPLATE_URL\n",
"\n",
"repo = \"Divine-Comedy-Longfellow-s-Translation-Hell_1001\""
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"u'Divine-Comedy-Longfellows-Translation-Hell'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"title = \"Divine Comedy, Longfellow's Translation, Hell\"\n",
"slugify(title)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"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
}
| apache-2.0 |
rouckas/NEVF138 | 000 Title page.ipynb | 1 | 2454 | {
"metadata": {
"name": "",
"signature": "sha256:b27c4ae520783f229d26e9528e0bc0a80cbf4d9b71f93e895471a6407cff70f5"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okruhy ot\u00e1zek z NEVF138\n",
"=======================\n",
"\n",
"+ Numerick\u00e1 derivace, dop\u0159edn\u00e9 diference, zp\u011btn\u00e9 diference, centr\u00e1ln\u00ed diference\n",
"+ Odhad chyby Taylorov\u00fdm rozvojem, asymptotick\u00e9 chov\u00e1n\u00ed chyby, Richardsonova extrapolace\n",
"+ Numerick\u00e1 integrace, obd\u00e9ln\u00edkov\u00e9 pravidlo, lichob\u011b\u017en\u00edkov\u00e9 pravidlo, glob\u00e1ln\u00ed vs. lok\u00e1ln\u00ed chyba\n",
"+ Integrace ODR, Explicitn\u00ed vs. Implicitn\u00ed metody.\n",
"+ Rungovy Kuttovy metody - nap\u0159 Eulerova metoda, Heunova metoda (explicitn\u00ed), Lichob\u011b\u017en\u00edkov\u00e1 metoda (implicitn\u00ed)\n",
"+ Leap Frog\n",
"+ Stabilita, vy\u0161et\u0159ov\u00e1n\u00ed stability - testovac\u00ed rovnice, tuh\u00e9 soustavy\n",
"+ Integrace PDR - diskretizace metodou kone\u010dn\u00fdch diferenc\u00ed (metoda s\u00edt\u00ed)\n",
"+ \u0158e\u0161en\u00ed okrajov\u00fdch \u00faloh - p\u0159\u00edm\u00e9 (Gaussova eliminace, LU dekompozice, Fourierova transformace), nep\u0159\u00edm\u00e9 (Relaxa\u010dn\u00ed metody - Jacobi, Gauss Seidel...)\n",
"+ Matice druh\u00fdch diferenc\u00ed - vlastn\u00ed \u010d\u00edsla a vlastn\u00ed vektory kvalitativn\u011b\n",
"+ Po\u010d\u00e1te\u010dn\u011b-okrajov\u00e9 \u00falohy (evolu\u010dn\u00ed rovnice) - \n",
"+ FTCS (forward time, cetered space), Laxova(-Friedrichsova) metoda, Crank Nicolson a pou\u017eitelnost pro parabolick\u00e9 (dif\u00faze) a hyperbolick\u00e9 (advekce) probl\u00e9my\n",
"+ Von Neumannova anal\u00fdza stability, Courant Friedrichs Lewyho podm\u00ednka, \n",
"+ Stru\u010dn\u011b princip metody kone\u010dn\u00fdch prvk\u016f: PDR $\\to$ slab\u00e1 formulace $\\to$ diskretizace prostoru funkc\u00ed $\\to$ \u0159e\u0161en\u00ed lin. rovnic\n",
"+ Podm\u00edn\u011bnost matice a jej\u00ed v\u00fdznam pro numerick\u00e9 metody"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | cc0-1.0 |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session04/Day0/TooBriefMachLearn.ipynb | 1 | 31193 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to Machine Learning:\n",
"Examples of Unsupervised and Supervised Machine-Learning Algorithms \n",
"========\n",
"\n",
"##### Version 0.1\n",
"\n",
"Broadly speaking, machine-learning methods constitute a diverse collection of data-driven algorithms designed to classify/characterize/analyze sources in multi-dimensional spaces. The topics and studies that fall under the umbrella of machine learning is growing, and there is no good catch-all definition. The number (and variation) of algorithms is vast, and beyond the scope of these exercises. While we will discuss a few specific algorithms today, more importantly, we will explore the scope of the two general methods: unsupervised learning and supervised learning and introduce the powerful (and dangerous?) Python package [`scikit-learn`](http://scikit-learn.org/stable/).\n",
"\n",
"***\n",
"By AA Miller\n",
"\n",
"2017 September 16"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem 1) Introduction to `scikit-learn`\n",
"\n",
"At the most basic level, `scikit-learn` makes machine learning extremely easy within `python`. By way of example, here is a short piece of code that builds a complex, non-linear model to classify sources in the Iris data set that we learned about earlier:\n",
"\n",
" from sklearn import datasets\n",
" from sklearn.ensemble import RandomForestClassifier\n",
" iris = datasets.load_iris()\n",
" RFclf = RandomForestClassifier().fit(iris.data, iris.target)\n",
"\n",
"Those 4 lines of code have constructed a model that is superior to any system of hard cuts that we could have encoded while looking at the multidimensional space. This can be fast as well: execute the dummy code in the cell below to see how \"easy\" machine-learning is with `scikit-learn`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# execute dummy code here\n",
"\n",
"from sklearn import datasets\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"iris = datasets.load_iris()\n",
"RFclf = RandomForestClassifier().fit(iris.data, iris.target)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generally speaking, the procedure for `scikit-learn` is uniform across all machine-learning algorithms. Models are accessed via the various modules (`ensemble`, `SVM`, `neighbors`, etc), with user-defined tuning parameters. The features (or data) for the models are stored in a 2D array, `X`, with rows representing individual sources and columns representing the corresponding feature values. [In a minority of cases, `X`, represents a similarity or distance matrix where each entry represents the distance to every other source in the data set.] In cases where there is a known classification or scalar value (typically supervised methods), this information is stored in a 1D array `y`. \n",
"\n",
"Unsupervised models are fit by calling `.fit(X)` and supervised models are fit by calling `.fit(X, y)`. In both cases, predictions for new observations, `Xnew`, can be obtained by calling `.predict(Xnew)`. Those are the basics and beyond that, the details are algorithm specific, but the documentation for essentially everything within `scikit-learn` is excellent, so read the docs.\n",
"\n",
"To further develop our intuition, we will now explore the Iris dataset a little further.\n",
"\n",
"**Problem 1a** What is the pythonic type of `iris`?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You likely haven't encountered a `scikit-learn Bunch` before. It's functionality is essentially the same as a dictionary. \n",
"\n",
"**Problem 1b** What are the keys of iris?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most importantly, iris contains `data` and `target` values. These are all you need for `scikit-learn`, though the feature and target names and description are useful.\n",
"\n",
"**Problem 1c** What is the shape and content of the `iris` data?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"print( # complete\n",
"# complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Problem 1d** What is the shape and content of the `iris` target?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print( # complete\n",
"# complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, as a baseline for the exercises that follow, we will now make a simple 2D plot showing the separation of the 3 classes in the iris dataset. This plot will serve as the reference for examining the quality of the clustering algorithms. \n",
"\n",
"**Problem 1e** Make a scatter plot showing sepal length vs. sepal width for the iris data set. Color the points according to their respective classes. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(iris.feature_names) # shows that sepal length is first feature and sepal width is second feature\n",
"\n",
"plt.scatter( # complete\n",
"# complete\n",
"# complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem 2) Unsupervised Machine Learning\n",
"\n",
"Unsupervised machine learning, sometimes referred to as clustering or data mining, aims to group or classify sources in the multidimensional feature space. The \"unsupervised\" comes from the fact that there are no target labels provided to the algorithm, so the machine is asked to cluster the data \"on its own.\" The lack of labels means there is no (simple) method for validating the accuracy of the solution provided by the machine (though sometimes simple examination can show the results are **terrible**). \n",
"\n",
"For this reason [*note* - this is my (AAM) opinion and there many be many others who disagree], unsupervised methods are not particularly useful for astronomy. Supposing one did find some useful clustering structure, an adversarial researcher could always claim that the current feature space does not accurately capture the physics of the system and as such the clustering result is not interesting or, worse, erroneous. The one potentially powerful exception to this broad statement is outlier detection, which can be a branch of both unsupervised and supervised learning. Finding weirdo objects is an astronomical pastime, and there are unsupervised methods that may help in that regard in the LSST era. \n",
"\n",
"To begin today we will examine one of the most famous, and simple, clustering algorithms: [$k$-means](https://en.wikipedia.org/wiki/K-means_clustering). $k$-means clustering looks to identify $k$ convex clusters, where $k$ is a user defined number. And here-in lies the rub: if we truly knew the number of clusters in advance, we likely wouldn't need to perform any clustering in the first place. This is the major downside to $k$-means. Operationally, pseudocode for the algorithm can be summarized as the following: \n",
"\n",
" initiate search by identifying k points (i.e. the cluster centers)\n",
" loop \n",
" assign each point in the data set to the closest cluster center\n",
" calculate new cluster centers based on mean position of all points within cluster\n",
" if diff(new center - old center) < threshold:\n",
" stop (i.e. clusters are defined)\n",
"\n",
"The threshold is defined by the user, though in some cases the total number of iterations is also. An advantage of $k$-means is that the solution will always converge, though the solution may only be a local minimum. Disadvantages include the assumption of convexity, i.e. difficult to capture complex geometry, and the curse of dimensionality.\n",
"\n",
"In `scikit-learn` the [`KMeans`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans) algorithm is implemented as part of the [`sklearn.cluster`](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster) module. \n",
"\n",
"**Problem 2a** Fit two different $k$-means models to the iris data, one with 2 clusters and one with 3 clusters. Plot the resulting clusters in the sepal length-sepal width plane (same plot as above). How do the results compare to the true classifications?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.cluster import KMeans\n",
"\n",
"Kcluster = KMeans( # complete\n",
"Kcluster.fit( # complete\n",
"\n",
"plt.figure()\n",
"plt.scatter( # complete\n",
"\n",
"# complete\n",
"# complete\n",
"# complete\n",
"# complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With 3 clusters the algorithm does a good job of separating the three classes. However, without the a priori knowledge that there are 3 different types of iris, the 2 cluster solution would appear to be superior. \n",
"\n",
"**Problem 2b** How do the results change if the 3 cluster model is called with `n_init = 1` and `init = 'random'` options? Use `rs` for the random state [this allows me to cheat in service of making a point].\n",
"\n",
"*Note - the respective defaults for these two parameters are 10 and `k-means++`, respectively. Read the docs to see why these choices are, likely, better than those in 2b. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rs = 14\n",
"Kcluster1 = KMeans( # complete\n",
"# complete\n",
"# complete\n",
"# complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**A random aside that is not particularly relevant here**\n",
"\n",
"$k$-means evaluates the Euclidean distance between individual sources and cluster centers, thus, the magnitude of the individual features has a strong effect on the final clustering outcome. \n",
"\n",
"**Problem 2c** Calculate the mean, standard deviation, min, and max of each feature in the iris data set. Based on these summaries, which feature is most important for clustering? "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"feature\\t\\t\\tmean\\tstd\\tmin\\tmax\")\n",
"for featnum, feat in enumerate(iris.feature_names):\n",
" print(\"{:s}\\t{:.2f}\\t{:.2f}\\t{:.2f}\\t{:.2f}\".format(feat, np.mean(iris.data[:,featnum]), \n",
" np.std(iris.data[:,featnum]), np.min(iris.data[:,featnum]),\n",
" np.max(iris.data[:,featnum])))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Petal length has the largest range and standard deviation, thus, it will have the most \"weight\" when determining the $k$ clusters. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The truth is that the iris data set is fairly small and straightfoward. Nevertheless, we will now examine the clustering results after re-scaling the features. [Some algorithms, *cough* Support Vector Machines *cough*, are notoriously sensitive to the feature scaling, so it is important to know about this step.] Imagine you are classifying stellar light curves: the data set will include contact binaries with periods of $\\sim 0.1 \\; \\mathrm{d}$ and Mira variables with periods of $\\gg 100 \\; \\mathrm{d}$. Without re-scaling, this feature that covers 4 orders of magnitude may dominate all others in the final model projections.\n",
"\n",
"The two most common forms of re-scaling are to rescale to a guassian with mean $= 0$ and variance $= 1$, or to rescale the min and max of the feature to $[0, 1]$. The best normalization is problem dependent. The [`sklearn.preprocessing`](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing) module makes it easy to re-scale the feature set. **It is essential that the same scaling used for the training set be used for all other data run through the model.** The testing, validation, and field observations cannot be re-scaled independently. This would result in meaningless final classifications/predictions. \n",
"\n",
"**Problem 2d** Re-scale the features to normal distributions, and perform $k$-means clustering on the iris data. How do the results compare to those obtained earlier? \n",
"\n",
"*Hint - you may find [`'StandardScaler()'`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler) within the `sklearn.preprocessing` module useful.*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"scaler = StandardScaler().fit( # complete\n",
"\n",
"# complete\n",
"# complete\n",
"# complete\n",
"# complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These results are almost identical to those obtained without scaling. This is due to the simplicity of the iris data set. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**How do I test the accuracy of my clusters?**\n",
"\n",
"Essentially - you don't. There are some methods that are available, but they essentially compare clusters to labeled samples, and if the samples are labeled it is likely that supervised learning is more useful anyway. If you are curious, `scikit-learn` does provide some [built-in functions for analyzing clustering](http://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation), but again, it is difficult to evaluate the validity of any newly discovered clusters. \n",
"\n",
"**What if I don't know how many clusters are present in the data?**\n",
"\n",
"An excellent question, as you will almost never know this a priori. Many algorithms, like $k$-means, do require the number of clusters to be specified, but some other methods do not. As an example [`DBSCAN`](https://en.wikipedia.org/wiki/DBSCAN). In brief, `DBSCAN` requires two parameters: `minPts`, the minimum number of points necessary for a cluster, and $\\epsilon$, a distance measure. Clusters are grown by identifying *core points*, objects that have at least `minPts` located within a distance $\\epsilon$. *Reachable points* are those within a distance $\\epsilon$ of at least one *core point* but less than `minPts` *core points*. Identically, these points define the outskirts of the clusters. Finally, there are also *outliers* which are points that are $> \\epsilon$ away from any *core points*. Thus, `DBSCAN` naturally identifies clusters, does not assume clusters are convex, and even provides a notion of outliers. The downsides to the algorithm are that the results are highly dependent on the two tuning parameters, and that clusters of highly different densities can be difficult to recover (because $\\epsilon$ and `minPts` is specified for all clusters. \n",
"\n",
"In `scitkit-learn` the \n",
"[`DBSCAN`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN) algorithm is part of the `sklearn.cluster` module. $\\epsilon$ and `minPts` are set by `eps` and `min_samples`, respectively. \n",
"\n",
"**Problem 2e** Cluster the iris data using `DBSCAN`. Play around with the tuning parameters to see how they affect the final clustering results. How does the use of `DBSCAN` compare to $k$-means? Can you obtain 3 clusters with `DBSCAN`? If not, given the knowledge that the iris dataset has 3 classes - does this invalidate `DBSCAN` as a viable algorithm?\n",
"\n",
"*Note - DBSCAN labels outliers as $-1$, and thus, `plt.scatter()`, will plot all these points as the same color.*\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# execute this cell\n",
"\n",
"from sklearn.cluster import DBSCAN\n",
"\n",
"dbs = DBSCAN(eps = 0.7, min_samples = 7)\n",
"dbs.fit(scaler.transform(iris.data)) # best to use re-scaled data since eps is in absolute units\n",
"\n",
"dbs_outliers = dbs.labels_ == -1\n",
"\n",
"plt.figure()\n",
"plt.scatter(iris.data[:,0], iris.data[:,1], c = dbs.labels_, s = 30, edgecolor = \"None\", cmap = \"viridis\")\n",
"plt.scatter(iris.data[:,0][dbs_outliers], iris.data[:,1][dbs_outliers], s = 30, c = 'k')\n",
"\n",
"\n",
"plt.xlabel('sepal length')\n",
"plt.ylabel('sepal width')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I was unable to obtain 3 clusters with `DBSCAN`. While these results are, on the surface, worse than what we got with $k$-means, my suspicion is that the 4 features do not adequately separate the 3 classes. [See - a nayseyer can always make that argument.] This is not a problem for `DBSCAN` as an algorithm, but rather, evidence that no single algorithm works well in all cases. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Challenge Problem) Cluster SDSS Galaxy Data\n",
"\n",
"The following query will select 10k likely galaxies from the SDSS database and return the results of that query into an [`astropy.Table`](http://docs.astropy.org/en/stable/table/) object. (For now, if you are not familiar with the SDSS DB schema, don't worry about this query, just know that it returns a bunch of photometric features.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from astroquery.sdss import SDSS # enables direct queries to the SDSS database\n",
"\n",
"GALquery = \"\"\"SELECT TOP 10000 \n",
" p.dered_u - p.dered_g as ug, p.dered_g - p.dered_r as gr, \n",
" p.dered_g - p.dered_i as gi, p.dered_g - p.dered_z as gz, \n",
" p.petroRad_i, p.petroR50_i, p.deVAB_i\n",
" FROM PhotoObjAll AS p JOIN specObjAll s ON s.bestobjid = p.objid\n",
" WHERE p.mode = 1 AND s.sciencePrimary = 1 AND p.clean = 1 AND p.type = 3\n",
" \"\"\"\n",
"SDSSgals = SDSS.query_sql(GALquery)\n",
"SDSSgals"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I have used my own domain knowledge to specifically choose features that may be useful when clustering galaxies. If you know a bit about SDSS and can think of other features that may be useful feel free to add them to the query. \n",
"\n",
"One nice feature of `astropy` tables is that they can readily be turned into `pandas DataFrames`, which can in turn easily be turned into a `sklearn X` array with `NumPy`. For example: \n",
"\n",
" X = np.array(SDSSgals.to_pandas())\n",
"\n",
"And you are ready to go. \n",
"\n",
"**Challenge Problem** Using the SDSS dataset above, identify interesting clusters within the data [this is intentionally very open ended, if you uncover anything especially exciting you'll have a chance to share it with the group]. Feel free to use the algorithms discussed above, or any other packages available via `sklearn`. Can you make sense of the clusters in the context of galaxy evolution? \n",
"\n",
"*Hint - don't fret if you know nothing about galaxy evolution (neither do I!). Just take a critical look at the clusters that are identified*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note - I was unable to get the galaxies to clusster using DBSCAN."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Problem 3) Supervised Machine Learning\n",
"\n",
"Supervised machine learning, on the other hand, aims to predict a target class or produce a regression result based on the location of labelled sources (i.e. the training set) in the multidimensional feature space. The \"supervised\" comes from the fact that we are specifying the allowed outputs from the model. As there are labels available for the training set, it is possible to estimate the accuracy of the model (though there are generally important caveats about generalization, which we will explore in further detail later).\n",
"\n",
"We will begin with a simple, but nevertheless, elegant algorithm for classification and regression: [$k$-nearest-neighbors](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) ($k$NN). In brief, the classification or regression output is determined by examining the $k$ nearest neighbors in the training set, where $k$ is a user defined number. Typically, though not always, distances between sources are Euclidean, and the final classification is assigned to whichever class has a plurality within the $k$ nearest neighbors (in the case of regression, the average of the $k$ neighbors is the output from the model). We will experiment with the steps necessary to optimize $k$, and other tuning parameters, in the detailed break-out problem.\n",
"\n",
"In `scikit-learn` the [`KNeighborsClassifer`](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) algorithm is implemented as part of the [`sklearn.neighbors`](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors) module. \n",
"\n",
"**Problem 3a** \n",
"\n",
"Fit two different $k$NN models to the iris data, one with 3 neighbors and one with 10 neighbors. Plot the resulting class predictions in the sepal length-sepal width plane (same plot as above). How do the results compare to the true classifications? Is there any reason to be suspect of this procedure?\n",
"\n",
"*Hint - after you have constructed the model, it is possible to obtain model predictions using the `.predict()` method, which requires a feature array, including the same features and order as the training set, as input.*\n",
"\n",
"*Hint that isn't essential, but is worth thinking about - should the features be re-scaled in any way?*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"\n",
"KNNclf = KNeighborsClassifier( # complete\n",
"preds = KNNclf.predict( # complete\n",
"plt.figure()\n",
"plt.scatter( # complete\n",
"\n",
"# complete\n",
"# complete\n",
"# complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These results are almost identical to the training classifications. However, we have cheated! In this case we are evaluating the accuracy of the model (98% in this case) using the same data that defines the model. Thus, what we have really evaluated here is the training error. The relevant parameter, however, is the generalization error: how accurate are the model predictions on new data? \n",
"\n",
"Without going into too much detail, we will test this using cross validation (CV). In brief, CV provides predictions on the training set using a subset of the data to generate a model that predicts the class of the remaining sources. Using [`cross_val_predict`](http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.cross_val_predict.html), we can get a better sense of the model accuracy. Predictions from `cross_val_predict` are produced in the following manner:\n",
"\n",
" from sklearn.cross_validation import cross_val_predict\n",
" CVpreds = cross_val_predict(sklearn.model(), X, y)\n",
"\n",
"where `sklearn.model()` is the desired model, `X` is the feature array, and `y` is the label array.\n",
"\n",
"**Problem 3b** \n",
"\n",
"Produce cross-validation predictions for the iris dataset and a $k$NN with 5 neighbors. Plot the resulting classifications, as above, and estimate the accuracy of the model as applied to new data. How does this accuracy compare to a $k$NN with 50 neighbors?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.cross_validation import cross_val_predict\n",
"\n",
"CVpreds = cross_val_predict( # complete\n",
"\n",
"plt.scatter( # complete\n",
"print(\"The accuracy of the kNN = 5 model is ~{:.4}\".format( # complete\n",
"\n",
"# complete\n",
"# complete\n",
"# complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While it is useful to understand the overall accuracy of the model, it is even more useful to understand the nature of the misclassifications that occur. \n",
"\n",
"**Problem 3c** \n",
"\n",
"Calculate the accuracy for each class in the iris set, as determined via CV for the $k$NN = 50 model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# complete\n",
"# complete\n",
"# complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We just found that the classifier does a much better job classifying setosa and versicolor than it does for virginica. The main reason for this is some viginica flowers lie far outside the main virginica locus, and within predominantly versicolor \"neighborhoods\". In addition to knowing the accuracy for the individual classes, it is also useful to know class predictions for the misclassified sources, or in other words where there is \"confusion\" for the classifier. The best way to summarize this information is with a confusion matrix. In a confusion matrix, one axis shows the true class and the other shows the predicted class. For a perfect classifier all of the power will be along the diagonal, while confusion is represented by off-diagonal signal. \n",
"\n",
"Like almost everything else we have encountered during this exercise, `scikit-learn` makes it easy to compute a confusion matrix. This can be accomplished with the following: \n",
"\n",
" from sklearn.metrics import confusion_matrix\n",
" cm = confusion_matrix(y_test, y_prep)\n",
"\n",
"**Problem 3d** \n",
"\n",
"Calculate the confusion matrix for the iris training set and the $k$NN = 50 model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"cm = confusion_matrix( # complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From this representation, we see right away that most of the virginica that are being misclassifed are being scattered into the versicolor class. However, this representation could still be improved: it'd be helpful to normalize each value relative to the total number of sources in each class, and better still, it'd be good to have a visual representation of the confusion matrix. This visual representation will be readily digestible. Now let's normalize the confusion matrix.\n",
"\n",
"**Problem 3e** \n",
"\n",
"Calculate the normalized confusion matrix. Be careful, you have to sum along one axis, and then divide along the other. \n",
"\n",
"*Anti-hint: This operation is actually straightforward using some array manipulation that we have not covered up to this point. Thus, we have performed the necessary operations for you below. If you have extra time, you should try to develop an alternate way to arrive at the same normalization.*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"normalized_cm = cm.astype('float')/cm.sum(axis = 1)[:,np.newaxis]\n",
"\n",
"normalized_cm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The normalization makes it easier to compare the classes, since each class has a different number of sources. Now we can procede with a visual representation of the confusion matrix. This is best done using `imshow()` within pyplot. You will also need to plot a colorbar, and labeling the axes will also be helpful. \n",
"\n",
"**Problem 3f** \n",
"\n",
"Plot the confusion matrix. Be sure to label each of the axeses.\n",
"\n",
"*Hint - you might find the [`sklearn` confusion matrix tutorial](http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#example-model-selection-plot-confusion-matrix-py) helpful for making a nice plot.*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# complete\n",
"# complete\n",
"# complete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now it is straight-forward to see that virginica and versicolor flowers are the most likely to be confused, which we could intuit from the very first plot in this notebook, but this exercise becomes far more important for large data sets with many, many classes. \n",
"\n",
"Thus concludes our introduction to `scikit-learn` and supervised and unsupervised learning. "
]
}
],
"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": 1
}
| mit |
mrphyja/bioinfo-intro-python | bioinformatics_intro_python.ipynb | 1 | 183332 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"from pprint import pprint\n",
"import random\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import YouTubeVideo"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "dfdc94f4-6768-4140-8109-8ac2e61fcdf4"
},
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# The Source Code of Life: Using Python to Explore Our DNA\n",
"\n",
"Researchers just found the gene responsible for mistakenly thinking we've found the gene for specific things. It's the region between the start and the end of every chromosome, plus a few segments in our mitochondria."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "b0746739-b0f7-4130-8773-2024b6d8858e"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Every good presentation starts with xkcd, right?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Bioinformatics\n",
"HINT: If you're viewing this notebook as slides, press the \"s\" key to see a bunch of extra notes.\n",
"\n",
"Image: http://www.sciencemag.org/sites/default/files/styles/article_main_large/public/images/13%20June%202014.jpg?itok=DPBy5nLZ"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "e4dc1f84-5612-4ef5-a45c-35f46c51c136"
},
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Today we're going to talk about part off the wonderful field of study known as bioinformatics. What is bioinformatics? According to [Wikipedia](https://en.wikipedia.org/wiki/Bioinformatics), its \"an interdisciplinary field that develops methods and software tools for understanding biological data.\" Here's another definition that more fits my experiences: \"The mathematical, statistical and computing methods that aim to solve biological problems using DNA and amino acid sequences and related information.\" ([Fredj Tekaia](http://slideplayer.com/slide/4800050/), Institut Pasteur)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## What is DNA?\n",
"A long string of A, C, G, and T (bases)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
" ACGTTCATGG <- Ten bases\n",
"\n",
" ACGTTCATGGATGTGACCAG <- Twenty bases\n",
"\n",
" etc..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"So what is it about biology that needs such computationally intensive stuff? I thought biology was a \"squishy\" science? Well guess what, nature has been playing with big data long before it became the buzzword it is now. Let's start by talking about DNA. DNA is your \"source code,\" but we'll get to more about how that works in a minute. First we're just going to talk about what it is. It's a string of four different types of molecules with long names that we're not going to worry about right now, so we'll just call them by their abbreviations (which is what everyone uses most of the time anyway): `A`, `C`, `G`, and `T`. And instead of molecules, we'll call them bases, because reasons. So if you have just an `A`, that's one base, or if you have `ACGTTCATGG`, that is ten bases."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Well, not really a string...\n",
"<img src=\"images/double_helix.jpg\" alt=\"Double helix\" style=\"width: 200px;\"/>\n",
"Two strings stuck together? But if you know one you know the other..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Ok, when I said it was a string that was a bit of a simplification, it's actually two strings stuck together. That's why pictures of DNA look like a twisted ladder (that's the \"double helix\" you may hear about): each side of the ladder is a string, and the \"rungs\" are where they stick together. But the nice thing is, each base has only one other base it can stick to. `A` always sticks to `T` and vice versa, same with `C` and `G`. So if you know that one side of the ladder is `ACGTTCATGG`, then we know the other side is `TGCAAGTACC`. This is really nice because it cuts the amount of information we actually have to know in half."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## The Human Genome\n",
"It's big. How big?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"So now that you know everything (just kidding) about DNA, it's time for a question: How many bases long would you guess the human genome (fancy word for all the DNA) is? It ends up being about 3.2 billion base pairs long. (Remember the double helix? That's why it's _pairs_)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"About 3,200,000,000bp (bp is base pairs). Actual file format commonly used in bioinformatics (FASTA):\n",
"\n",
"```\n",
">Sequence0\n",
"TTTCTGACTAACACTACAATTACCACTTGATGTTACCGACTAAGTGGTACGACTTGCTAGAACCGACTCTCGTACGTAT\n",
"CGCAGACTAGTGCGCGCGCTTAGTGACTATACTAGAATATACCTGGGGCCCAAGGAGTGTCGGGCGATCGTCCTTGAAA\n",
"TAAATATCTCAACCATCGTCATCTAGGGGGAACAGAGCGGTGGGCAGGTCCCAACCTGTTTATTTGTGTTGCTAACACT\n",
"ACGGCGCAGCTGCTCAAGTAGGTGCGATTATCGAGTAGAGGCTCCACCGGCTCTATGTGCCACGCATCTACTGAACCGA\n",
"ATTCTATCCCTGATACTCCAGAAGGTCGCAGGTTTACAGACACGTTTCAGCTCGAGAGGCCATCGATTATCTTAATATA\n",
"CCACACTGCCGAATAGCATGCCCGTAGAATCCAAGCCACGAGATAGCGTTACTTAATGAGTACCCAACGCAAATGAGGT\n",
"TGATTATCCCTAACCTGCAATCTAGGCCTTGTTCTGGAGGGGGTTATCCTTTATAGTTGATTACTTACACTCACCATGT\n",
"TCGTAGTCGGAACTCACCGATTAAGACCGATTTTACTATGGGAAGGCCAGGTTACACCTGTTTCGGGGGGGCCGCGGCG\n",
"GGTTACTTTAACCTGTCCATCCATCAGTCACTGGGCGCCAAGATTCTCCTATAGTTATATCCGCCCTTTGATTTAAACC\n",
"TAGGCCTACCTCAACGAACTGGGCCATGGGGTTCACACAGAAACAAGGGGGATAGACAGTCTTATTGAGCGCTTCTGAA\n",
"CAGCGTGTGTTCACGGTACGGCAATACCACCAGTAAACCGAGAACAGTGTTGAAGGTGATCGAACACGTGTTTTCTTCA\n",
"CCGTAGGGCTTCTAGGGAGTATCGCCCCCATATAGGCAGACGAGAAGGACTGTCACGCGCGGAGATCGATAATACGTAT\n",
"AACACAAGCACAGTAACTGCCCCGACCGGCTAAAGGACGTGGCCCAGTGTACCCAACGTACGTAATTGCAAGAGGTCTG\n",
"TCTGTCATCCCGAGGACTGCTTCTATAACTCGTTGAGGGCACTAGGCTTGAGACAATCAGCTTCGCTCGTCACGATTTT\n",
"ACTTTTTTCCTGGAAAAGCCCCCCCACAGACTATCAGGTCGCGCTTACCATACCAGTCCTTCTTGATAAGCCAATCCGT\n",
"ATTAGGTAGATTAAGCTGACAGTCGGGGCGACTCTTTGGAAACAGTATTCCCGTTTCGGGCACCTAGGATTCAGGCTTG\n",
"TACAACGATCATAGACGTCGCGGAAAGAAATAGCACAGTGTAGGAGCTGGTCGTGACCCGTGCTGTCAAGTTTATTGCA\n",
"CGGCTTGCTAAAAGGTACAGTGTAACGTTTCACAAACAAGCGAGACCCATTGTTGGTCTAACGCTATCGTACTTGATAC\n",
"CAGCCTGTGACGTCACGCGAAATCGTCTGTATAACTAGTTCTTCCCCGACTGCCACGGTATCCCAAAATTACATACTGA\n",
"CAGGACCTCTTCCATATTCATCAGGACTCGACGAAGCGCGCCCCGTGTAGTACGCGAAAATTATACCGTCCGTAGGTAC\n",
"\n",
"```\n",
"Now picture this 2 million more times. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"If you wrapped it in PEP 8-guideline adhering lines of 79 characters, it would still be over 40 million lines (although granted DNA is about as un-Pythonic of code as you can get). And we're not even all that special when it comes to genome length. A lot of plants have us beat, actually, with _paris japonica_ being a [top contender](http://blogs.biomedcentral.com/on-biology/2014/03/20/worlds-largest-sequenced-genome-unlocking-the-loblolly-pine/) for longest genome at 150 billion base pairs.\n",
"\n",
"So what does all of that code even do? Well, most of it doesn't appear to \"do\" much. (How much code in your codebase is like that? Although I'd be willing to bet there's a lot of it that we just don't understand what it does yet) But we'll stick with the ~1.5% of it that we have a pretty good idea about."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"\n",
"https://en.wikipedia.org/wiki/Central_dogma_of_molecular_biology"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"It's time to learn something called \"The Central Dogma of Molecular Biology,\" since that's a lot cooler-sounding than \"what DNA does.\" Don't worry, we'll keep to the very basics. There are two steps, \"transcription\" and \"translation.\" Transcription basically involves making a copy of a chunk of DNA, except that you only copy one side of it (it looks like half a strand of DNA). We call this copy RNA. Translation involves reading the pattern of the DNA into protein. What is protein? Basically most of what we're made of is either protein or is made by proteins (excluding water, of course). So that's basically how your source code works: your DNA tells your body how to make proteins, which are like parts of a machine. These little parts make little machines, which are part of bigger machines, etc. etc. until all the parts fit together to make you! (But how do the parts know how to fit together you ask? Umm...great question...that we're not going to cover today. Next slide!)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Sequencing DNA"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"image/jpeg": [
"/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAUDBAkJCAgICAkICAgICAgICAgICAgICAgICAgICAgI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\n"
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x7f00bbbe56a0>"
]
},
"metadata": {}
}
],
"source": [
"YouTubeVideo('fCd6B5HRaZ8', start=135)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Ok, now you know something about DNA, so we can start getting into some of the fun puzzles that this leads to. So how do we find out what someone's DNA sequence is? There are several methods, including some newer ones that I'm really excited about, but we'll stick with the most popular here: Illuimina's sequencing by synthesis. It's probably so popular because it's fast and keeps getting cheaper. The reason it's so fast is because it's parallelized. It works by breaking the DNA up into little chunks and then looking at all the chunks at the same time. Basically you have a whole bunch of these fragments on a slide and then flood the slide with a whole bunch of one base (say A). The base is modified slightly so that it fluoresces a certain color when illuminated by a certain wavelength and also so that no other bases can attach after it. Then the excess bases are washed off, the slide is imaged to see which fragments got a base added, and then the fluorescent part of the new addition as well as the part that blocks the next base are removed and the process is repeated with another base (say C). Repeat until you've got most of the fragments."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Sequence Alignment\n",
"Now things start to get fun"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"That's a nice-looking lake...\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Until now!\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"So now you've got a whole bunch of little pieces of DNA that you have to match up to a reference sequence. A quick analogy (credit for the analogy/pics to Aaron Quinlan, who has [all his slides](https://github.com/quinlan-lab/applied-computational-genomics) for a course in applied computational genomics freely available). What makes this lake puzzle so hard? So much blue and white! So we need to find a way to determine how well the piece we have actually matches the picture at that point. We'll call this aligning sequences."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### First step: Assign scores\n",
"If a base matches with itself, it gets a score of one. Otherwise it gets a score of zero.\n",
"\n",
"||A|C|G|T|\n",
"|-|-|-|-|-|\n",
"|__A__|1|0|0|0|\n",
"|__C__|0|1|0|0|\n",
"|__G__|0|0|1|0|\n",
"|__T__|0|0|0|1|\n",
"\n",
"So these two sequences:\n",
"```\n",
"AACTGTGGTAC\n",
"ACTTGTGGAAC\n",
"10011111011\n",
"```\n",
"have an alignment score of eight."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"This is going to seem like complete overkill at first, but you'll understand the advantages to doing it this way after a bit. Each base that aligns with itself gets a score of one, any other alignment is zero. But what if these are long segments, could we maybe get a better score by shifting one in reference to another? That's a valid question, but before we can tackle that, I need to introduce you to another unfortunate aspect of sequencing..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### What about gaps?\n",
"Yes, unfortunately there are going to be gaps.\n",
"\n",
"We'll add a \"gap penalty\" of -3.\n",
"\n",
"So the score for this alignment:\n",
"```\n",
"ACTACA-ACGTTGAC\n",
"A-TAGAAACGCT-AC\n",
"1 1101 11101 11\n",
"-3 -3 -3\n",
"```\n",
"is just one\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Sequencing DNA is great, but it's also kind of messy. You may end up with extra bases or missing bases in your sequences. Also, people don't all have the same DNA (unless you're identical twins!) so you may have bases that are actually missing or extra with respect to the \"reference\" sequence. But, gaps are problematic because when they're in a place that codes for a protein (remember earlier?) they are pretty good at making the protein not work. So we want to introduce gaps only when it's a lot better than the alternative. For now we'll have a \"gap penalty\" of -3."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Scoring matrix\n",
"\n",
"||-|A|C|G|T|T|T|G|T|C|G|C|\n",
"|-|\n",
"|__-__|0|-3|-6|-9|-12|-15|-18|-21|-24|-27|-30|-33|\n",
"|__A__|-3||||||||||||\n",
"|__C__|-6||||||||||||\n",
"|__T__|-9||||||||||||\n",
"|__T__|-12||||||||||||\n",
"|__T__|-15||||||||||||\n",
"|__C__|-18||||||||||||\n",
"|__T__|-21||||||||||||\n",
"|__G__|-24||||||||||||\n",
"|__C__|-27||||||||||||\n",
"\n",
"$$\n",
"S_{m,n}=\\left\\{\n",
" \\begin{array}{ll}\n",
" S_{m-1,n} + gap\\\\\n",
" S_{m,n-1} + gap\\\\\n",
" S_{m-1,n-1} + B(a,b)\\\\\n",
" \\end{array}\n",
"\\right.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"||-|A|C|G|T|T|T|G|T|C|G|C|\n",
"|-|\n",
"|__-__|0|-3|-6|-9|-12|-15|-18|-21|-24|-27|-30|-33|\n",
"|__A__|-3|1|-2|-5|-8|-11|-14|-17|-20|-23|-26|-29|\n",
"|__C__|-6||||||||||||\n",
"|__T__|-9||||||||||||\n",
"|__T__|-12||||||||||||\n",
"|__T__|-15||||||||||||\n",
"|__C__|-18||||||||||||\n",
"|__T__|-21||||||||||||\n",
"|__G__|-24||||||||||||\n",
"|__C__|-27||||||||||||"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"We need to keep track of where that score came from."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"||-|A|C|G|T|T|T|G|T|C|G|C|\n",
"|-|\n",
"|__-__|0|←-3|←-6|←-9|←-12|←-15|←-18|←-21|←-24|←-27|←-30|←-33|\n",
"|__A__|↑-3|↖1|←-2|←-5|←-8|←-11|←-14|←-17|←-20|←-23|←-26|←-29|\n",
"|__C__|↑-6||||||||||||\n",
"|__T__|↑-9||||||||||||\n",
"|__T__|↑-12||||||||||||\n",
"|__T__|↑-15||||||||||||\n",
"|__C__|↑-18||||||||||||\n",
"|__T__|↑-21||||||||||||\n",
"|__G__|↑-24||||||||||||\n",
"|__C__|↑-27||||||||||||"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"||-|A|C|G|T|T|T|G|T|C|G|C|\n",
"|-|\n",
"|__-__|0|←-3|←-6|←-9|←-12|←-15|←-18|←-21|←-24|←-27|←-30|←-33|\n",
"|__A__|↑-3|↖1|←-2|←-5|←-8|←-11|←-14|←-17|←-20|←-23|←-26|←-29|\n",
"|__C__|↑-6|↑-2|↖2|←1|←-4|←-7|←-10|←-13|←-16|↖-19|←-22|↖-25|\n",
"|__T__|↑-9|↑-5|↑-1|↖2|↖0|←-3|←-6|←-9|←-12|←-15|←-18|←-21|\n",
"|__T__|↑-12|↑-8|↑-4|↑-1|↖3|↖1|←-2|←-5|↖-8|←-11|←-14|←-17|\n",
"|__T__|↑-15|↑-11|↑-7|↖-4|↑0|↖4|↖2|←-1|↖-4|←-7|←-10|←-13|\n",
"|__C__|↑-18|↑-14|↖-10|↖-7|↑-3|↑1|↖4|↖2|↖-1|↖-3|←-6|←-9|\n",
"|__T__|↑-21|↑-17|↑-13|↑-10|↑-6|↑-2|↖2|↖4|↖3|←0|↖-3|←-6|\n",
"|__G__|↑-24|↑-20|↑-16|↖-12|↑-9|↑-5|↑-1|↖3|↖4|↖3|↖1|←-2|\n",
"|__C__|↑-27|↑-23|↖-19|↑-15|↖-12|↑-8|↑-4|↑0|↖3|↖5|↖3|↖2|\n",
"\n",
"Now just follow the arrows from that bottom-right corner back to the top-left zero.\n",
"```\n",
"ACGTTTGTCGC\n",
"|| ||| | ||\n",
"AC-TTTCT-GC\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"So how do we keep track of where we actually want gaps? We will use a scoring matrix. We start out with one sequence at the top and one on the side with an extra space added at the beginning of each. The two spaces align with a score of zero and we start form there. Each cell gets filled in with whatever ends up the highest of three possibilities:\n",
"1. The score above plus the gap penalty (remember the gap penalty is negative)\n",
"2. The score to the left plus the gap penalty\n",
"3. The score to the upper left plus the alignment score\n",
"\n",
"We can fill in the top and left columns right away since they don't have a score to their upper-left, so their only possible score is the gap penalty.\n",
"\n",
"But our choice affects the score of everything down and to the right, so besides just the score, we need to keep track of where that score came from.\n",
"\n",
"Once it's all filled out, just follow the arrows from the bottom-right corner to the top-left zero. Every time you go straight up, it's a gap in the left sequence. Every time you go left it's a gap in the top sequence. Every time you go up-left, the two bases align."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Let's code this!\n",
"See _Python for Bioinformatics_ for the inspiration for this demo.\n",
"\n",
"Here is the \"substitution matrix\" and its corresponding \"alphabet\":"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"dna_sub_mat = np.array(\n",
" [[ 1, 0, 0, 0],\n",
" [ 0, 1, 0, 0],\n",
" [ 0, 0, 1, 0],\n",
" [ 0, 0, 0, 1]])\n",
"\n",
"dbet = 'ACGT'"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Same as we defined above, with `dbet` being the \"dna alphabet\" used for this matrix (four-letter alphabet, could be in a different order, this is just the one we chose)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"And here are we calculate the scores and arrows as separate matrices"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"def nw_alignment(sub_mat, abet, seq1, seq2, gap=-8):\n",
" # Get the lengths of the sequences\n",
" seq1_len, seq2_len = len(seq1), len(seq2)\n",
" # Create the scoring and arrow matrices\n",
" score_mat = np.zeros((seq1_len+1, seq2_len+1), int)\n",
" arrow_mat = np.zeros((seq1_len+1, seq2_len+1), int)\n",
" # Fill first column and row of score matrix with scores based on gap penalty\n",
" score_mat[0] = np.arange(seq2_len+1) * gap\n",
" score_mat[:,0] = np.arange(seq1_len+1) * gap\n",
" # Fill top row of arrow matrix with ones (left arrow)\n",
" arrow_mat[0] = np.ones(seq2_len+1)\n",
" for seq1_pos, seq1_letter in enumerate(seq1):\n",
" for seq2_pos, seq2_letter in enumerate(seq2):\n",
" f = np.zeros(3)\n",
" # Cell above + gap penalty\n",
" f[0] = score_mat[seq1_pos,seq2_pos+1] + gap\n",
" # Cell to left + gap penalty\n",
" f[1] = score_mat[seq1_pos+1,seq2_pos] + gap\n",
" n1 = abet.index(seq1_letter)\n",
" n2 = abet.index(seq2_letter)\n",
" # Cell to upper-left + alignment score\n",
" f[2] = score_mat[seq1_pos,seq2_pos] + sub_mat[n1,n2]\n",
" score_mat[seq1_pos+1, seq2_pos+1] = f.max()\n",
" arrow_mat[seq1_pos+1, seq2_pos+1] = f.argmax()\n",
" return score_mat, arrow_mat"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"I'm calling this `nw_align` after the Needleman-Wunsch algorithm. It's hard to put score and directional information in just one matrix, so we make two matrices. We start with a matrix of zeros for each and then fill them in concurrently as we run through the possibilities."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"What does our result look like?"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0 -3 -6 -9 -12 -15 -18 -21 -24 -27 -30 -33]\n",
" [ -3 1 -2 -5 -8 -11 -14 -17 -20 -23 -26 -29]\n",
" [ -6 -2 2 -1 -4 -7 -10 -13 -16 -19 -22 -25]\n",
" [ -9 -5 -1 2 0 -3 -6 -9 -12 -15 -18 -21]\n",
" [-12 -8 -4 -1 3 1 -2 -5 -8 -11 -14 -17]\n",
" [-15 -11 -7 -4 0 3 1 -2 -5 -7 -10 -13]\n",
" [-18 -14 -10 -7 -3 1 4 1 -1 -4 -7 -10]\n",
" [-21 -17 -13 -9 -6 -2 1 5 2 -1 -3 -6]\n",
" [-24 -20 -16 -12 -9 -5 -2 2 5 3 0 -2]]\n",
"[[1 1 1 1 1 1 1 1 1 1 1 1]\n",
" [0 2 1 1 1 1 1 1 1 1 1 1]\n",
" [0 0 2 1 1 1 1 1 1 1 1 1]\n",
" [0 0 0 2 2 1 1 1 1 1 1 1]\n",
" [0 0 0 0 2 2 1 1 1 1 1 1]\n",
" [0 0 0 0 0 2 2 1 1 2 1 1]\n",
" [0 0 0 0 0 2 2 1 2 1 1 1]\n",
" [0 0 0 2 0 0 0 2 1 1 2 1]\n",
" [0 0 0 0 0 0 0 0 2 2 1 2]]\n"
]
}
],
"source": [
"s1 = 'ACTTCTGC'\n",
"\n",
"s2 = 'ACGTTTGTCGC'\n",
"\n",
"score_mat, arrow_mat = nw_alignment(dna_sub_mat, dbet, s1, s2, gap=-3)\n",
"print(score_mat)\n",
"print(arrow_mat)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Looks good, but not all that useful by itself."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Now we need a way to get the sequences back out of our scoring matrix:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"def backtrace(arrow_mat, seq1, seq2):\n",
" align1, align2 = '', ''\n",
" align1_pos, align2_pos = arrow_mat.shape\n",
" align1_pos -= 1\n",
" align2_pos -= 1\n",
" selected = []\n",
" while True:\n",
" selected.append((align1_pos, align2_pos))\n",
" if arrow_mat[align1_pos, align2_pos] == 0:\n",
" # Up arrow, add gap to align2\n",
" align1 += seq1[align1_pos-1]\n",
" align2 += '-'\n",
" align1_pos -= 1\n",
" elif arrow_mat[align1_pos, align2_pos] == 1:\n",
" # Left arrow, add gap to align2\n",
" align1 += '-'\n",
" align2 += seq2[align2_pos-1]\n",
" align2_pos -= 1\n",
" elif arrow_mat[align1_pos, align2_pos] == 2:\n",
" # Up-arrow arrow, no gap\n",
" align1 += seq1[align1_pos-1]\n",
" align2 += seq2[align2_pos-1]\n",
" align1_pos -= 1\n",
" align2_pos -= 1\n",
" if align1_pos==0 and align2_pos==0:\n",
" break\n",
" # reverse the strings\n",
" return align1[::-1], align2[::-1], selected"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AC-TTC-T-GC\n",
"ACGTTTGTCGC\n"
]
}
],
"source": [
"a1, a2, selected = backtrace(arrow_mat, s1, s2)\n",
"print(a1)\n",
"print(a2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Sometimes it's nice to see the scoring matrix, though, so here's a function to visualize it"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"def visual_scoring_matrix(seq1, seq2, score_mat, arrow_mat):\n",
" visual_mat = []\n",
" for i, row in enumerate(arrow_mat):\n",
" visual_mat_row = []\n",
" for j, col in enumerate(row):\n",
" if col == 0:\n",
" arrow = '↑'\n",
" elif col == 1:\n",
" arrow = '←'\n",
" else:\n",
" arrow = '↖'\n",
" visual_mat_row.append(arrow + ' ' + str(score_mat[i,j]))\n",
" visual_mat.append(visual_mat_row)\n",
" visual_mat = np.array(visual_mat, object)\n",
"\n",
" tab = plt.table(\n",
" cellText=visual_mat,\n",
" rowLabels=['-'] + list(s1),\n",
" colLabels=['-'] + list(s2),\n",
" loc='center')\n",
" tab.scale(2, 3)\n",
" tab.set_fontsize(30)\n",
" plt.axis('tight')\n",
" plt.axis('off')\n",
" \n",
" align1, align2, selected = backtrace(arrow_mat, seq1, seq2)\n",
" for pos in selected:\n",
" y, x = pos\n",
" tab._cells[(y+1, x)]._text.set_color('green')\n",
" tab._cells[(y+1, x)]._text.set_weight(1000)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAscAAAF2CAYAAACCiDVjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3XlcVOX+B/DPkWEGFBFlEbfBHRVF\nb9LVfi4grplbpra4YFeN1DSTLM0MFUgyLYM0TQ0BUcz0XpdcckFNxRTNTKBEc0tDECFFUQb4/v5w\nZi4DzDDbmYX7fb9e5/WqM895nufjOXPOd2bODAIRgTHGGGOMMQbUsvYEGGOMMcYYsxVcHDPGGGOM\nMabExTFjjDHGGGNKXBwzxhhjjDGmxMUxY4wxxhhjSlwcM8YYY4wxpiSx9gTKc3Z2zn78+HFDa89D\nLE5OTmWPHz+usS9IanK+mpwN4Hz2jvPZLycnpzsAUFOvfTV53wGcz945OTndKSoq8q64XrCl3zkW\nBIFsaT7mJggCOJ99qsnZAM5n7zif/RIEAQBqdL6amg3gfPZOmU+ouL7GvhpgjDHGGGPMUFwcM8YY\nY4wxpsTFMWOMMcYYY0pcHDPGGGOMMabExTFjjDHGGGNKXBwzxhhjjDGmxMUxY4wxxhhjSlwcM8YY\nY4wxpsTFMWOMMcYYY0pcHNdAkydPhiAImD17trWnIorU1FSMGTMGjRs3hlQqhbu7O/r374/4+HiU\nlpZae3omSU1NxSuvvIKmTZtCKpXC1dUVzz77LBYsWIC//vrL2tMziiAI1S7Nmze39jSNxvnsO58K\nP/fsH18b7Jet7TuJxUdkoioqKsLWrVsBAElJSVi6dCkkkpqzm1esWIHZs2cjODgYn3zyCXx8fJCf\nn48ffvgBU6dOhZubG4YPH27taRpl+fLlmDNnDvr06YPIyEi0bNkShYWFOHnyJL7++mukpaVh7969\n1p6mwVJTUzX+/8UXX0Tnzp2xcOFC9TqZTGbhWZkP57PvfAA/92oCvjbY3/GpYpP7johsZnk6nZrL\nEvmSkpIIAA0ePJgA0K5du0QfU0XsfEePHiVBEGjGjBlVPn758mX65ZdfRBlb7GyHDx8mQRBo1qxZ\nVT5eWFhIcXFxoo1vyeeej48PjR071mLjEXE+c6pp+WzhuWep/VfT9p0KXxviRBu/Jl/XidT5KtWj\nwtPHbIMgCGRL8zE3QRAgdr6BAwfizJkz+P333+Hj44MhQ4bg22+/FXVMFbHzDR48GKdPn8aff/4J\nJycn0capitjZBg0ahHPnzuHPP/+EVCoVbRxtLHFsqjRv3hw9e/bExo0bLTIewPnMqabls4XnHgCL\n7L+atu9U+Nognpp8XQfU+YSK6/me4xrk9u3bOHjwIF5++WV4enpixIgR2LlzJ/Lz8609NZOVlpbi\nyJEjGDBggFWeQGIqKSnB0aNH0b9/f6uc/Bj7X8XPPfvH1wb7Zcv7jovjGiQxMRFlZWWYMGECACAk\nJARPnjzBli1brDwz0929exdFRUXw8fGx9lTMLi8vD48fP4ZcLq/0WElJicbCGDMffu7ZP7422O/x\nacv77n+yOCaiGnFgVZSQkIA2bdrgueeeAwD069cPjRs3RkJCgpVnxnTR9pFVdnY2HB0dNZaacqwy\nZgv4ucdsGR+f1vM/WRwfPXq00oFl786cOYOMjAyMHDkSBQUFKCgowIMHDzBy5Eikpqbi0qVL1p6i\nSdzd3eHs7Izr169beypm5+HhAScnJ9y4caPS+jNnzuDMmTOYMmWKlWbHWM3Fzz37x9cG+z0+bXnf\n1Zzf+DJA165dcebMGWtPw6zi4+MBAJ988gk++eSTSo8nJCQgMjLS0tMyG4lEgqCgIBw4cABPnjyp\nMT8/BDzN1rt3bxw4cADFxcXqe8skEgkCAgIAALt377bmFBmrkfi5Z//42mC/x6ct77v/yXeO69at\ni4CAAI3FnhUXFyM5ORndunVDSkpKpaVLly5ITEy02LfZxTJ37lzk5eVhzpw5VT5+9epVXLhwwcKz\nMo/33nsPd+/exfvvv2/tqTD2P4Wfe/aPrw32y1b33f/kO8c1ze7du5GXl4fly5cjKCio0uOhoaGY\nOnUqjhw5gj59+lh+gmbSu3dvfPbZZ5g9ezYyMzMxceJEyOVy5Ofn49ChQ1i3bh02bdoEf39/a0/V\nYH379kV0dDTmzp2LCxcuYMKECWjRogUeP36MS5cuITk5GXXq1FH/7BNjzDz4uWf/+Npgv8enze67\nqn782FoL+I+AGGXYsGFUt25devjwYZWPFxQUkLOzM4WEhIgyvoql9t+JEydo1KhR5O3tTRKJhOrX\nr0/9+/enxMREKi0tFWVMS2U7fvw4jR49mho3bkyOjo5Ut25dCggIoI8++ohu374t2riWfO7V1D9E\noML5zM8S+az53LPU/qup+06Frw3mV5Ov60T8R0BsgiV/qN8aanK+mpwN4Hz2jvPZL0v+ERBrqMn7\nDuB89o7/CAhjjDHGGGPV4OKYMcYYY4wxJS6OGWOMMcYYU+LimDHGGGOMMSUujhljjDHGGFPi4pgx\nxhhjjDElLo4ZY4wxxhhT4uKYMcYYY4wxJS6OGWOMMcYYU+LimDHGGGOMMSUujhljjDHGGFPi4pgx\nxhhjjDElibk7dHZ2zn78+HFDY7Z1cnKCIAjmnpLN4Hz2qyZnAzifveN89svJyakMAARBqJFvVtXk\nfQdwPnunev5VJBCRWQcSBIGM7VMQBJh7PraE89mvmpwN4Hz2jvPZL1XhUZPz1dRsAOezd8p8lar/\nGvlKlTHGGGOMMWNwccwYY4wxxpgSF8eMMcYYY4wpcXHMGGOMMcaYEhfHjDHGGGOMKXFxzBhjjDHG\nmBIXx4wxxhhjjClxccwYY4wxxpgSF8eMMcYYY4wpcXFspLVr16Jdu3aQyWTw9fXF6tWrrT0ls7h+\n/TqGDx8OHx8fODs7w8PDA0FBQdi7d6+1p2ZWmZmZGD16NDw8PODs7AxfX1988cUX1p6Wya5evYpR\no0bBzc0NderUQZ8+fZCWlmbtaVXrgw8+wIABA+Du7g5BELBhw4ZKbf766y/MmzcPAQEBqFevHjw9\nPdG3b18cO3bM8hM2kD75ACAoKAiCIFRaVqxYYdkJG0DfbI8ePUJ4eDjatm0LZ2dnNGvWDBMmTMC1\na9csOl9DpKWl4Y033kC7du1Qu3ZtyOVyjB07FlevXq3U9rPPPsPQoUPRqFEjCIKAhQsXWn7CBjIk\nX3mbN2+GIAho2rSphWZqHH3zbdiwocrnnWrJzs62UgLdDLleP378GHPmzEGjRo3g7OyM5557zubP\nnfrmE6Nu4eLYCGvXrkVoaCheeukl7Nu3D6NHj8a0adPw1VdfWXtqJissLISHhwciIyOxZ88erF+/\nHi4uLhg8eDC2b99u7emZRVpaGrp164YnT55g3bp12LNnD8LCwlBaWmrtqZkkLy8PPXv2xMWLF7Fm\nzRokJycDAPr06YPMzEwrz0632NhYFBUVYciQIVrbnD17Flu2bMHw4cPx3XffYcOGDXByckJQUBB2\n795twdkaTp98Kv7+/khNTdVYXnnlFQvM0jj6Zps8eTI+/fRTTJkyBXv27EFkZCSOHTuGvn37orCw\n0EKzNUxycjLS09Mxc+ZM7N27F9HR0Th37hwCAgJw8+ZNjbZr165FTk4ORowYYaXZGs6QfCoFBQV4\n55134O3tbeHZGk7ffC+88EKl59zJkyfh7u6OZ5991mazGnK9njRpEtauXYvFixdj9+7daNSoEQYO\nHIjz589bafbV0zefKHULEZl1edqlcUzZ1lIUCgV5enrShAkTNNa//vrr5O7uTsXFxVq3tYd8VVEo\nFNS0aVMaMmSIznb2kK+0tJQ6dOhAI0aMMGg7e8gWERFBDg4OlJWVpV5XWFhIXl5eNHr0aJ3bWjtf\naWkpERFlZWURAIqLi6vUJj8/nxQKhcY6hUJBbdu2pV69euns3x7yEREFBgZSjx49DO7fmvn0yfbo\n0SNycHCgefPmaazfu3cvAaB9+/bpHMNa+XJyciqtu3btGgmCQAsWLNBYr/p3UCgUBIDCw8P1GgOA\nXeRTmTJlCg0YMIBCQkKoSZMm1Y5hzWPTmHwqx44dIwD05Zdf6mxn7XNLRVVdr8+fP08A6JtvvtFo\n17ZtWxo6dKjO/uwhnyntlPkq1bL/c+8cx8XF4cKFC0Zvn5qaitzcXIwbN05j/fjx45GXl4fjx4+b\nOkW9mJrDEBKJBPXq1YOjo6NFxgPEy3fkyBFkZGRg9uzZZu9bX2JlO3XqFNq0aYPWrVur19WpUwe9\nevXC7t27UVJSYlL/Yh5ztWpVfypyc3ODRCLRWCeRSNClSxfcunXL5DlYO5+YrJ2tpKQEpaWlcHV1\n1Vjv5uYGACgrKzNpDmLl8/T0rLTOx8cHnp6elY45MfexLeQDgBMnTmDjxo1YuXKlWedhK/nKi4+P\nh1QqNcunNta+Xu/cuROOjo54+eWXNdq98sor2L9/P548eWLSmNbOZ0o7bf6niuNVq1Zh0qRJOHDg\ngNF9pKenAwA6duyosd7Pzw8AkJGRYfwE9WSOHNUpKytDSUkJsrOzERERgUuXLmH69OmijVeemPlU\nL14eP36M7t27w9HREV5eXpg5cyaKiorMPl5FYmZzcHCAVCqttF4mk6GoqAhXrlwxum9LHHPGKC4u\nRmpqKtq3b29SP7aU7+eff1af1P39/bF+/XqT+rOFbHXr1sX48eMRExODlJQUFBYWIj09HXPmzEHn\nzp3Rt29fo/u2dL7MzEzk5OSYfMzpy1byKRQKvPHGG5gzZ47GC3BT2Uq+8oqKirB161YMGTIE7u7u\nJo1nC9fr9PR0tGjRArVr19bYzs/PD8XFxbh8+bLRY9tCPkPb6aWqt5NNWWCjt1XExMQQAIqIiDCp\nn6ioKAJARUVFGutVH6UtXrxY67bmyGeuHNUJCwtTf9zn4uJC27Ztq3Ybe8gXGhpKAKh+/fq0YMEC\nSklJoU8//ZScnZ113mphD9nmzJlDzs7OdPfuXfW60tJSat26NQGgkydPat1WVz5LHXNE1d92UNG8\nefNIEAQ6duyYznb2km/BggX09ddf05EjR+g///kPjRw5Uq+5actnS9lKSkpo2rRp6vMKAOrWrVuV\nH31XZAv5iJ6e53v37k2enp507949rW1gptsqbClfREQEtWrVSn3tM8dtFbaUr7xNmzYRANqxY0e1\nfdpCvuqu1/3796du3bpV2u7AgQMEQOf50x7yGdquPGi5rUK04risrIwUCoXGouckzS4jI0PjhFzd\nouukFhkZSQDo8ePHGustURybM0dJSYnGvlHdL6dy8+ZNOnPmDO3atYtGjx5NMpmMdu3apXN+9pBv\nypQpBIBmzJih0T46OpoAUHp6ut1mu3LlCslkMho8eDBdvnyZbt++TdOnTycHBwcCQKdOndLap7Z8\nljzmiAwrjpOSkkgQBProo4+qbWuP+VRGjBhBTk5O9ODBA4Py2Vq2uXPnkqurKy1btoyOHj1KiYmJ\n1Lp1a+ratSsVFhbq/DewhXxET19cSyQS2r9/v9a+zFUc21K+rKwscnJyor1796rXmVoc21K+igYO\nHEienp4m1S22dL3u168fde/evVK/P/zwAwHGFce2lM/QdlXks1xxnJKSUukfpzqmFiDalJWVqd+x\nWLRoEWVmZupccnNztfa1atUqAkC3b9/WWH/nzh0CdN+8b2o+c+YIDAzU+8BVtff19dXZxh7yzZ07\nlwDQzp07NdqfO3eOAFBSUpLdZiMi+u6776hhw4bqx5555hl69913CQBdv35da5/a8ln6mNO3eNy5\ncyc5OjrS5MmTdbaz13zlbdmyhQDD3/m3pWwXL14kALRu3TqN9ZcuXSIAtGLFCu3/ADaSb+7cuSQI\nAiUkJOicq7mKY1vK9/zzz9PgwYMpPz9fvbz66qvUuHFjys/Pp0ePHunMVxVbylfe7du3ycHBgWbN\nmqWzna3lq9i+/PV6zJgx1LZt20rtVOeWixcv2nU+U9pZvDi+f/8+nTlzRmOpjljFscpbb71FEomE\ntm7danQfR48eJQB04MABjfWqFwOHDx/Wuq258pkjx2+//aaxb27duqWzfVhYGDk4OOhsYw/5EhMT\nCUClV5Nnz54lALR58+Yq+7OHbColJSWUkZFBly9fJiKiN998k5o1a6azz+ryWeqY06d4PHjwIMlk\nMhozZozWd4gqsqd8FSUnJxMASk1N1dpGVz5byLZ582YCQL/88kulx9zc3Cg0NFTn+NbOp/rEMCYm\nptq+zP1rFbaQz8fHR+e7gm+//bbWsW3huWfI/lu6dCkBoJ9//lmvsW0hX0UVr9eLFi0iR0dHevjw\noUa78PBwkkqllT4JL88e8pnSTltxrPnVbzOqW7cuAgICxOreKLGxsZDJZFV+aUlfzz33HDw8PJCU\nlIR+/fqp12/cuBENGjRAjx49zDFVncyRw9fXV++2ZWVlOH78OFq1amX0eIYQM9/zzz8PmUyGffv2\nafwu6/79+wFA9GPWEvvOwcFB/WWT27dvY8uWLZgzZ47R4wGWP+a0SU1NxfDhw9G3b19s3LjRbL8Q\nYCv5qrJp0yY4OzujU6dORm1vC9lUvxN7+vRp+Pv7q9dfunQJBQUFaNKkidF9i50vJiYGH374IaKi\nojBjxgyjxzCWLeRLTk7G48ePNdZFR0fj7Nmz2Lp1q0l/DMQW8pWXkJAAf39/dOnSxej5lGcL1+th\nw4YhPDwcW7duRUhICICnvyCzZcsWDBgwADKZzOi52UI+U9ppI1pxbKuWLVtm0vaOjo6IiIjAtGnT\n0KRJE/Tr1w+HDx/GN998g9jYWJMOEEOYmkObhQsX4t69e+jRowe8vb2RnZ2N9evX4/Tp09i0aZMo\nY1ZFrHzu7u6YN28eIiIi4OrqiuDgYKSlpWHx4sUICQkx67ewtRErm0KhwHvvvYfAwEC4uroiPT0d\nS5YsgZ+fH8LCwkzuX6x5A8DRo0eRm5ur/ktUaWlpcHFxAQCMGjUKAPDbb7/hhRdegIeHB+bMmYOz\nZ89q9NG9e3eT5mDtfD/++COio6MxcuRING/eHH///Tfi4+Oxc+dOREdHo06dOkaPb+1svXr1QufO\nnREWFob8/HwEBATgxo0biIyMRL169dQXbGOJlS85ORmzZs3CoEGDEBwcjFOnTqkfc3V1RYcOHdT/\nn5aWhmvXrql/li4jIwPfffcdAGDw4MGVfinAENbOV9Vza8OGDZDJZAgKCjJ5HtbOp3Lu3DlcvHgR\ny5cvN+s8rH297tKlC15++WXMmjULCoUCLVq0wFdffYWrV68iKSnJ5HlYO58odUtVbyebssCEj59N\n2dbSVq9eTW3atCGpVEqtW7emlStXVruNPeTbsWMH9enThzw9PUkqlZJcLqehQ4fS8ePHq93WHvIR\nPb1Xavny5dSqVStydHQkuVxOCxYssPs/4KJQKOiFF14gLy8vkkql1LJlS5o/f36lj9KqYu18Fe85\nK7+oxMXF6fxoVxd7yJeVlUWDBg2ixo0bk1QqpTp16tBzzz1HmzZtqrZ/a+bTJxsR0d27d2n27NnU\nunVrcnJyoqZNm9KYMWPot99+q3YMa+ULCQnRmi0wMFDvtlevXtU6hj7Hr1gMyVfVtrb+R0AMzTdz\n5kySSCSUnZ2t9xjWzGfI9frRo0f0zjvvUMOGDUkmk9E///lPSklJqXYMe8hnhrqlUi0rPH3MfARB\nIGP7FAQB5p6PLeF89qsmZwM4n73jfPZLEAQAqNH5amo2gPPZO2U+oeL6/6k/AsIYY4wxxpguXBwz\nxhhjjDGmxMUxY4wxxhhjSlwcM8YYY4wxpsTFMWOMMcYYY0pcHDPGGGOMMabExTFjjDHGGGNKXBwz\nxhhjjDGmxMUxY4wxxhhjSlwcM8YYY4wxpsTFMWOMMcYYY0pcHDPGGGOMMaYkMXeHTk5OZYIgGFV0\nOzk5QRAEc0/JZnA++1WTswGcz95xPvvl5ORUBgDGXjdtXU3edwDns3eq519FAhGZdSBBEMjYPgVB\ngLnnY0s4n/2qydkAzmfvOJ/9UhUeNTlfTc0GcD57p8xXqfqvka9UGWOMMcYYMwYXx4wxxhhjjClx\nccwYY4wxxpgSF8eMMcYYY4wpcXHMGGOMMcaYEhfHjDHGGGOMKXFxzBhjjDHGmBIXx4wxxhhjjClx\nccwYY4wxxpiSxYrjyZMnQxAEzJ4921JDAgAKCgrw6quvYteuXWbp7/r16xg+fDh8fHzg7OwMDw8P\nBAUFYe/evWbp35pyH+bi7b1vo9u6bpBFyiAsEiAsEvDl6S+tPTWzSUtLwxtvvIF27dqhdu3akMvl\nGDt2LK5evWrtqZmFIAhVLufPn7f21Mzixo0bCAkJgVwuR+3atdG2bVt8+OGHePjwobWnptMHH3yA\nAQMGwN3dHYIgYMOGDVW2i4+Px0svvQQfHx8IgoCJEydadJ7G0DdbeSdPnkStWrUgCAJKSkrEn6QJ\n9Ml35MgRrc89QRBw6tQpy09cD4acD8vKyrBkyRI0b94cTk5O6Ny5M7Zt22aFWetP33wPHjzAmDFj\n0Lp1a9SpUwdubm7o1q0bNm7caKWZ68fQ69mtW7fwr3/9C97e3pDJZGjRogXmzZtn4Vnrz5B8d+/e\nxb/+9S94enrC2dkZ3bp1w/79+40eW2LKxPVVVFSErVu3AgCSkpKwdOlSSCTiD11QUID+/fsjLS0N\n27dvx7Zt2zBkyBCT+iwsLISHhwciIyPRtGlT3L9/H2vXrsXgwYOxbds2jBw50kyz19/Jmyfxzc/f\naKwLey4M7T3bG9TPrQe3EHM6xpxTsznJyclIT0/HzJkz4efnh1u3biEiIgIBAQE4f/48mjVrZu0p\nmmzixIkIDQ3VWNe2bVsrzcZ8Hj58iH79+kGhUCAiIgJyuRxnzpxBeHg4srKysGXLFmtPUavY2Fh0\n6dIFQ4YMQUJCgtZ2GzduRG5uLvr3768+Z9o6fbOpKBQKhIaGomHDhsjOzrbADE2jT75nnnkGqamp\nldZPmjQJ9+7dw7PPPiv2NI1iyPlwwYIFWLZsGaKiotC1a1ckJydj9OjR2L17NwYPHmzFFNrpm6+4\nuBgSiQTz5s1D8+bN8eTJE2zZsgXjx49Hbm4u3nnnHSsnqZoh++/atWvo0aMHWrRogZiYGDRs2BDX\nrl3D5cuXrZhAN33zPXnyBMHBwbh79y6WLl0Kb29vrF+/HkOGDMGBAwcQFBRk+OBEZNblaZeakpKS\nCAANHjyYANCuXbsqtaGnG1e53hj5+fkUEBBAcrmcpFIptWrViqRSKe3evdtsY6goFApq2rQpDRky\nRGc7c+Yrz2+lH2EhNJYDVw4Y3M/V/Kv0zr53KPnXZHpz15vqvmJ/itVre7HymVNOTk6lddeuXSNB\nEGjBggVat7OHbERP5zl//nyjtrN1+/fvJwC0f/9+jfXvv/8+OTg40MOHD7Vua+18paWlRESUlZVF\nACguLk5nOyKiJk2aUEhIiF79WzOfvtlUoqKiyM/Pjz744AMCQAqFotox7Cmfiuq88u677+psB8Bq\n+fQ9H965c4ekUil99NFHGm2Dg4OpU6dOOsew5r4z9nyv0r17d+rYsaPONvaSb+DAgfTss89ScXGx\nQWPYQ77ExEQCQCkpKep1ZWVl1KlTJ3r22Wd1jqHMV6mWtchtFfHx8ahfvz42bNgAZ2dnvd5dMNX6\n9euRk5ODlJQUODo6Yv78+Zg4cSLmzJlj9o/xJBIJ6tWrB0dHR7P2q6+Ssv/mmfHPGaBwQr+W/Qzu\np7lbc3w28DO83PFlNHRpaM4pGiwuLg4XLlwwe7+enp6V1vn4+MDT0xO3bt0y+3hVESubrRArX3Fx\nMQDA1dVVY72bmxvKyspUL86NJuZ+qVVLv1Otvu0MZQvZAODKlSuIiorCqlWrzHq+tJV85SUmJoKI\nEBISYvIcrH0+3L9/P4qLizFu3DiNtuPGjcOvv/5q8m1p1s6njbu7u1mOU2vnu3LlCvbv348ZM2aI\nUqdYO9+pU6fg7OyMwMBA9TpBEDBgwACcOXPGqGu76MXx7du3cfDgQbz88svw9PTEiBEjsHPnTuTn\n54s6blhYGNLS0tCyZUsAT/+hVq9ejWPHjpnllo6ysjKUlJQgOzsbERERuHTpEqZPn25yv8ZqWb8l\nvOp4IfZ0LGbunWm1eZjDqlWrMGnSJBw4cMAi42VmZiInJwft2xt2G4oxLJHtq6++gkwmQ+3atREc\nHIwff/xRtLEqEjNfv3790KZNG7z//vvIyMhAYWEhDh8+jC+++AJvvvkm6tSpY3Tflj7mLMmWsk2d\nOhWjRo1C7969zdanLeUrLyEhAc888ww6duxoUj+2cD5MT0+HTCZD69atNdr6+fkBADIyMowezxby\nqRARSkpKkJeXh6+//hr79+/HrFmzTBrPFvKdOHECAODs7Iz+/ftDJpOhfv36mDBhAvLy8kwazxby\nOTg4wNHREYIgaLSVyWQAgIsXLxo+UFVvJ5uyoMJb8NHR0QSATp48SURE+/btIwD01VdfaXt72+zq\n1Kmj90dh+goLC1N/HObi4kLbtm2rdhux8vnG+lJgXCBdvHORvD71IiwEzdgzw6Q+w1PCrXJbRUxM\nDAGgiIgIk/vSh0KhoN69e5Onpyfdu3dPazt7yTZu3DhKTk6mY8eOUWJiIvn7+5NEItH4uKkq9pLv\nzp071LNnT/VzDwBNnjxZ43aEqujKZ8ljzpCP5s1xW4UtZUtMTKT69evTnTt3iIgoPDzc5NsqbClf\neSdPniQA9MUXX1TbVnUcV8VWzodTpkyhhg0bVmqv+jdJSEjQ2qetPPeIqj/fx8bGqveHo6MjrVy5\nsto+7SHfkiVLCADVrVuXpk2bRocOHaI1a9ZQgwYNqGvXrjrPn/aQb+XKlQSAMjIyNNr36dOHANCm\nTZu09gktt1WIXhx36NCB2rRpo/7/kpISaty4MT333HPaJml2xhTHJSUlpFAo1EvFg+fmzZt05swZ\n2rVrF40ePZpkMpnWe6lVxMq3InUFrTu7jojIbAWyNYrjjIwMjaKnuiU8PFxrX9XtP5XQ0FCSSCSV\n7mOtyB6zERHdv3+f5HI59eivF/gHAAAgAElEQVTRQ+f87CFfUVERBQUFUdu2bSkxMZGOHj1Kn376\nKdWtW5fefPNNo/JZer9Ysji2pWx5eXnk5eWl8aaIqcWxLeWrKDQ0lBwdHSk3N1evbLaQTzXvqs6H\nkydPJm9v70rtL126ZHRxbEv5VHJycujMmTO0d+9emjp1KtWqVYtWr16tdVx7yRcVFUUAaOjQoRrr\nk5OTCQDt2bPHrvPl5+eTp6cnPfvss3ThwgXKzc2lqKgocnBwIACUnJxcXT7LFsenT58mAPT+++9T\nfn6+ennrrbcIAP3+++9VTdLsjCmOAwMD9d6xqva+vr4624iVryJzFMjWKI7Lyspo2rRpBIAWLVpE\nmZmZOhddFx599t/cuXNJEASdJ3YVe8tW3tSpU0kqlepsYw/5vvzySwJAly9f1mj/9ddfEwA6f/68\nwfksvV8sWRzbUrapU6dSp06dKC8vT30deP/99wkA3b17lwoLC+06X3mPHz+m+vXr04gRI3S2K5/N\nFvLpOh++9957JJPJqKysTGP9Tz/9RAB0ftHdVp57hpzvVUJCQsjFxUXnl9jsId/q1asJAMXExGis\nv3fvHgGgqKgou85HRHTs2DFq0aKFuo9WrVpRREQEAaCjR49Wl8+yxfH06dN1vpKo+K16WyqOf/vt\nNzpz5ox6uXXrls72YWFh5ODgoLONpYpjItMLZGvdVkFE9NZbb5FEIqGtW7ca3Ud1+y8yMrLKk4U2\n9pStojfffJNkMpnONvaQLzQ0lOrXr1+p/fnz5wkAbd68WWuf1eWz1H6x9G0VRLaRreLFr+IyfPhw\nnePber7yvv32WwJA//73v/UaW1txrGIL58P4+HgCQFlZWRrr4+LiCAD98ccfWse2heeeoed7FdVt\nFjdv3tTaxh7y/fjjjwSAYmM1r+Wq4njJkiVax7aHfCplZWV06dIlyszMpNLSUoqOjiZnZ2d68OCB\n1m20Fcei/dhwcXExkpOT0a1bN0RHR1d6/J133kFiYiIiIiIq3URtC3x9ffVuW1ZWhuPHj6NVq1Yi\nzsgwfl5+ODzhMIITgvHl6S/xst/L6CHvoXObMirDvaJ7AIBHikfq9Q+LH+Luo7sAAI/aHuJNWik2\nNhYymQxSqdToPnTtv5iYGHz44YeIiorCjBkzjB7DGGJnq+j+/fv4/vvv0a1bN6PHM4SY+by9vZGf\nn4/Lly9rfDHop59+AgA0adLE6DEtvV8syRayrVixAgUFBRrrNmzYgPj4eBw8eBANGxr/6zi2kK+8\n+Ph4uLu744UXXjBLf7ZwPhw0aBCkUimSkpIQHh6uXr9x40Z07NgRLVq0MHputpBPm6NHj8LFxQVe\nXl5Gz80W8nXv3h3e3t7Yt28f3nrrLfX6ffv2AYBJv8NtC/lUBEFAmzZtADz9mxRr167F+PHj4eLi\nYvCchKeFs/kIgkBEhO3bt+Oll17Chg0bqvwpm9WrV2Pq1Kk4fPgw+vTpo9oW5p4PALi4uODLL780\ny1+bWrhwIe7du4cePXrA29sb2dnZWL9+PQ4ePIhNmzbhlVde0bqtWPl0Sc9Jx9m/zmJC5wnVtr1W\ncA0tvtB9kqNw7fO3Rj5DJScn47XXXsPAgQM1TvLA058I69ChQ5Xb2UO2ZcuW4ffff0efPn3QuHFj\nXL9+Xb3u0KFD6NWrl9Zt7SHftWvX4O/vD29vb8yfPx9yuRxpaWmIiIhA27Ztcfr0aa0/u2XtfEeP\nHkVubi6ys7MxY8YMTJ8+Xf3D9KNGjVK3y8jIUH/zPzQ0FP7+/upfwQkMDKzyp40A6+bTN1tFCxcu\nxKJFi6BQKKr9BSF7yZeTk4MmTZpg6tSpiInR7w8qqd4cskY+Q86Hc+fOxYoVK/Dxxx/jmWeewZYt\nW7BmzRrs2LEDQ4cO1TqGNfedvvnWrFmDU6dOoV+/fmjatCny8vLw7bffYsuWLYiOjsb777+vdQx7\nyAc8fdGm+gNRI0eOxOXLlzF//nx06dIFhw8f1vompb3kmzdvHrp27QoPDw9cvnwZn376KWrVqoUT\nJ06gQYMGWsdQ5qscvqq3k01ZoHwLftiwYVS3bl2tP8xfUFBAzs7OGh8bopq3741lzl+r2LFjB/Xp\n04c8PT1JKpWSXC6noUOH0vHjx6vdVqx85nI1/2qlPyZScdHF1vMRPb2HDFo+2g0MDNS6nT1k27lz\nJ/3f//0fubu7k0QioQYNGtDQoUPpp59+qnZbe8hHRJSenk6jR4+mpk2bkpOTE7Vp04bCwsJ0/tII\nkfXz6bqtoDzVl9SqWnT94og18+mbrSJz/FqFJRiS77PPPiMAlJaWpnf/+vxbicWQ82FJSQlFRESo\n/7BWp06d9Poo3Zr7Tt98J06coOeff568vb1JKpVS48aNqW/fvnr90TB7yKeSkJBAfn5+JJVKydvb\nm9566y2dtxwQ2U++119/nZo0aUKOjo7UpEkTeuuttygvL6/aMaDltgrR3jk2clubf/fKFJzPftXk\nbADns3ecz35Z851jS6jJ+w7gfPZO2zvHFvkLeYwxxhhjjNkDLo4ZY4wxxhhT4uKYMcYYY4wxJS6O\nGWOMMcYYU+LimDHGGGOMMSUujhljjDHGGFPi4pgxxhhjjDElLo4ZY4wxxhhT4uKYMcYYY4wxJS6O\nGWOMMcYYU+LimDHGGGOMMSUujhljjDHGGFOSmLtDJyenMkEQjCq6nZycIAiCuadkMzif/arJ2QDO\nZ+84n/1ycnIqAwBjr5u2ribvO4Dz2TvV868igYjMOpAgCGRsn4IgwNzzsSWcz37V5GwA57N3nM9+\nqQqPmpyvpmYDOJ+9U+arVP3XyFeqjDHGGGOMGYOLY8YYY4wxxpS4OGaMMcYYY0yJi2PGGGOMMcaU\nuDhmjDHGGGNMiYtjxhhjjDHGlLg4ZowxxhhjTImLY8YYY4wxxpS4OGaMMcYYY0zJYsVxamoqxowZ\ng8aNG0MqlcLd3R39+/dHfHw8SktLRRu3oKAAr776Knbt2mXWfjMzMzF69Gh4eHjA2dkZvr6++OKL\nL8w6hj7MnS8tLQ1vvPEG2rVrh9q1a0Mul2Ps2LG4evWqWfq3pt2XdiPkPyFov7I96n9SHy4fu6Dj\nqo6Ye3Au7hXds/b0THbgygG8tu01tIppBWGRoF4KiwutPTVRLFmyBIIgoGfPntaeilksXLgQgiBU\nuTg5OVl7elp98MEHGDBgANzd3SEIAjZs2KC17dq1a9GuXTvIZDL4+vpi9erVlpuokfTN9/rrr6N9\n+/ZwdXWFi4sLOnfujNjYWFGvb+ZgyP7Lz8/HrFmzIJfLIZPJ0LRpU0ycONFiczWGvvkePXqEd955\nB02aNIGTkxM6deqEpKQky07WQPpery9duoS3334b/v7+cHFxQaNGjTBs2DD88ssvVpq5fgypR5o3\nb17lufM///mPUWNLTJ28PlasWIHZs2cjODgYn3zyCXx8fJCfn48ffvgBU6dOhZubG4YPH272cQsK\nCtC/f3+kpaVh+/bt2LZtG4YMGWJyv2lpaQgODkZQUBDWrVuHevXqISsrC4WFli1CxMiXnJyM9PR0\nzJw5E35+frh16xYiIiIQEBCA8+fPo1mzZmaavf5O3jyJb37+RmNd2HNhaO/Z3qB+vjz9JfZf2a+x\nLj03Hem56fg2/Vv8HPoz6jnVM3m+1vJ91vfYfHGztadhEX/88QeioqLg5eVl7amYzeTJkzFo0CCN\ndQ8fPsSgQYMwbNgwK82qerGxsejSpQuGDBmChIQEre3Wrl2L0NBQzJs3D/369cOhQ4cwbdo0EBGm\nTp1qwRkbRt98RUVFmDFjBlq1agVBELB//368/fbbuHz5slXeONGXvvny8/PRs2dPCIKAyMhING/e\nHLdv38aJEycsOFvD6Ztv5MiRSE1NRWRkJHx9fbF9+3aMGzcOZWVlGD9+vAVnrD99r9c//PADUlJS\nEBISgmeeeQYFBQVYunQpunXrhhMnTqBr165WTlI1Q+uRgQMHYuHChRrrfH19jRuciMy6PO3yv44e\nPUqCINCMGTOoKpcvX6ZffvmF6OnGVbYxRn5+PgUEBJBcLiepVEqtWrUiqVRKu3fvNqnf0tJS6tCh\nA40YMcLgbe0hX05OTqV1165dI0EQaMGCBTq3NWe+8vxW+hEWQmM5cOWAwf0M2zyMpu2eRmdvn6Ui\nRRGdunmKmn7WVN3n8pPLtW4rVjZzSvwlkaKORdGhPw5Rk+VN1LkePHlQ7bb2kK+8AQMG0BtvvEGB\ngYHUo0ePatvbWz6VhIQEAlDt89qa+UpLS4mIKCsriwBQXFxcpTYKhYI8PT1pwoQJGutff/11cnd3\np+LiYp1j2Ho+bV555RVycXHR2QaAXeQLDQ0luVxOf//9t0H9W/u5p0++H3/8scrHXnjhBWrUqBGV\nlJRo7d+a+fS9Xufm5lJZWZlGu4KCAnJzc6Px48frHMMe8hER+fj40NixYw0eQ5mvUi0r+m0V0dHR\naNCgAZYuXVrl461atYK/v7/Zx12/fj1ycnKQkpICR0dHzJ8/HxMnTsScOXNQUlJidL9HjhxBRkYG\nZs+ebcbZGk6sfJ6enpXW+fj4wNPTE7du3TJlykYrKftvnhn/nAEKJ/Rr2c/gfja+uBErX1iJZxo9\nAyeJE7o17Ya3u72tfvxS3iWzzFeb7ZnbkZWXJVr/4/zH4YNeHyC4RTAktSzyoVCV4uLicOHCBdH6\n37RpE86dO4clS5aINoYuYucrLz4+Hg0bNsTAgQNN6kfMOdeqVf1lJDU1Fbm5uRg3bpzG+vHjxyMv\nLw/Hjx83aQ7WzqeNu7s7JBLTn4vWzvfw4UMkJCRg8uTJcHV1NfscrJ3v1KlTAIDnn39eY/2gQYPw\n119/qR83llj59L1ee3h4QBAEjXb16tVD27ZtzXJdt3Y+MYhaHJeWluLIkSMYMGCAxe+ZCwsLQ1pa\nGlq2bAkAEAQBq1evxrFjx0w6WalO4o8fP0b37t3h6OgILy8vzJw5E0VFRWaZuz7EyleVzMxM5OTk\noH17w25jMKeW9VvCq44XYk/HYubemUb1UVdWt9K6xyWP1f/dzFW8W0a+Tf8WY7aOQejuUNHGsAWr\nVq3CpEmTcODAAVH6z8/PxzvvvIOlS5eiQYMGooyhi9j5yvvzzz+RkpKCsWPHmvSctuSctUlPTwcA\ndOzYUWO9n58fACAjI8Povm0hnwoRoaSkBAUFBdi2bRvi4+NNfiPFFvKdPXsWRUVFaNiwIUaNGgVn\nZ2e4uLhgxIgRJn8fxRbyOTg4AACkUqnGeplMBgC4ePGi0X1bOp++1+t79+7h4sWLJl/XbSnfrl27\nULt2bchkMnTv3t3o+40BiHtbRXZ2NgGguXPnGvL2ttnVqVPHoI/CdAkNDSUAVL9+fVqwYAGlpKTQ\np59+Ss7OztXeamEP+SpSKBTUu3dv8vT0pHv37ulsK1Y+31hfCowLpIt3LpLXp16EhaAZe6q+TccQ\nt+/fpoafNiQsBNWOqk03Cm5obWtKtm8vfkuSxRJqG9uWbt2/ZXQ/hvD53Mfit1XExMQQAIqIiDC5\nL20mTZpEPXv2VH9EaMnbKiyRr7yPP/6YAKhvO9NFWz5LzlnXx9ZRUVEEgIqKijTWKxQKAkCLFy/W\n2bet51PZtWuX+jYJQRBo3rx51faral8VW8m3efNmAkB169alMWPG0A8//EBJSUkkl8tJLpfT/fv3\ntfar67lnK/m+//57AkB79uzRWP/6668TAPr444+19msr+YgMu16/9tpr5OzsTFlZWTrb2Uu+t956\ni+Lj4+nYsWO0detWCgwMJACUmJios09oua2Ci2MtSkpKSKFQqBfVfUtTpkwhAJXuoY6OjiYAlJ6e\nrrVPe8hXUWhoKEkkEtq/f3+1fYqVb0XqClp3dh0RkdkK5BsFN6jdl+0IC0G1FtWirelbdbY3NltW\nXhZJFksq3TOtbQlPCTdqnIosXRxnZGSoL/L6LOHh4Vr70nZsHjt2jBwdHenXX39Vt7VUcWyJfBW1\na9eO/vGPf+g1v6ryWXrOuoqPyMhIAkCPHz/WWG9KcWxL+VQKCgrozJkzdPDgQZo3bx45OjrSBx98\nUG02W8+XlJREAMjf31/j3tVTp04RAFq1apXOfFWxpXwKhYLat29PrVu3ppMnT9K9e/do3bp15OTk\nRAAoOjra5vMR6X+9Vr3wXr9+vc529ppP1WdAQAA1bdpUn3yWLY4VCgU5OzvTq6++Wm2QcpM0O2OK\nR9Wrjoo7du7cuQSAdu7cqdH+3LlzBICSkpK09mkP+cqbO3cuCYJACQkJevUpVr6KTC2QM3Mzqdln\nzQgLQZLFEkq6oH2fqRib7XrBdXL52IWwEFRvST3y+dxH5/J56uda+7qaf7VSMR0YF1hlW0sXx2Vl\nZTRt2jQCQIsWLaLMzEydS25urta+tB2b7du3pzfffJPy8/PVS48ePah79+6Un59fqfCyt3zl/fTT\nTwSAVqxYodf8qspn6TnrKj5WrVpFAOj27dsa6+/cuUMA6Msvv7TrfNqEh4dTrVq16M8//9SZzdbz\n7du3jwDQ7NmzKz3m6upKU6ZM0ZmvKraUj4goPT2dOnfurO6jYcOG9PnnnxMAio+Pt/l8+l6vv/rq\nKwJAkZGROtvZa77yPvnkkyrPO1Xkq1TLivqtHYlEgqCgIBw4cABPnjxR379jD9asWYMHDx6o/79x\n48YA/nuPXMWb25/+G5v25Q1L0pZPJSoqCtHR0YiJibG5n7Hx8/LD4QmHEZwQjNjTsQCAmOdj9No2\n7XYank96Hncf3UVtx9rYOnorBrcZLNpc5fXk2PPaHgzeNBhOEifsGbsHHTw7iDaetQiCgJUrV6JW\nrVqIiIhAhw4dMGrUKKP60nZsZmZmIjMzs8rfxq1fvz4+//xzzJo1y7gA1bBEvvLi4+MhkUjw2muv\n2c2cdVGdN9PT09GoUSP1etW9xh06GP6csKV82gQEBKCsrAxXr15FkyZNDNrWlvJpu+6pGHPds6V8\nwNNj8Pz587h27RoePnyItm3bYvv27QCAHj16GNyfJfPpe71OTEzEtGnTEBYWhvnz5xs1FxVbzFeR\nqi7TdtxWu7E5F1R4lVHdT7n98ccfovyUW3nmvCf37t27JJPJaPr06RrrVR9T6Lp/xx7yERF98cUX\nBICioqIM2k6sfNqo3kEWFgp0/Prxatsf+uMQ1f24LmEhyP0Td0q9mar3WKZmO3btGLl87ELB8cEm\n9aPLw+KHlPswl3If5qrfGcdC0LX8a5T7MFfnO8jm3HdhYWG0Y8cOs/WnkpKSUmnp3LkzdezYkVJS\nUujmzZtat7WHfCpPnjyhBg0a0LBhw/Teprp8Ys+ZSPc7c8XFxeTh4UETJ07UWD9p0iRq0KABPXny\nRGfftp5Pm3fffZcEQaBbt7R/1wBa3jkuzxbyBQQEUKdOnTRuqzh58iQBoHXr1mntV5/nni3kq6i4\nuJh69uxJAwYM0NnO2vn0vV5v376dHBwcdL7LXxV7yVeRQqGgrl27klwu19kO1njnGAB69+6Nzz77\nDLNnz0ZmZiYmTpwIuVyO/Px8HDp0COvWrcOmTZtE+Tk3Mbi7u2PevHmIiIiAq6srgoODkZaWhsWL\nFyMkJAStW7e29hRNkpycjFmzZmHQoEEIDg7W+AkbV1dXo97hEYvqHeSzf51FD3n1r+wXH12MB8VP\nX53mFeXhufXPaTwe6BOIIxOPiDFV9PLphYPjD6Jl/Zai9A8AS08sxaKjiyqtb/5FcwBASOcQbBix\nQbTxVZYtWyZKv0FBQZXWubm5oaSkpMrHxCJWPpXdu3fj3r17CAkJMVufYs756NGjyM3NRXZ2NoCn\nfyTJxcUFANTvJDk6OiIiIgLTpk1DkyZN0K9fPxw+fBjffPMNYmNjK/1KgKGsne/7779HXFwchg4d\nCrlcjgcPHmDv3r34+uuvERoaavI70dbOBzz9WdaBAwdi1KhRmDx5MnJzczF//ny0a9fOpE84ANvI\nt2TJEvj4+KBx48a4ceMGVq5ciRs3bpjlj5yIlU/f6/WxY8fw6quvwt/fHxMnTtRoJ5PJ8I9//MOk\neVg73+bNm7Fjxw4MHjwYzZo1w507d7By5UqcPXsWmzcb+YexqqqYTVmg5VXGiRMnaNSoUeTt7U0S\niYTq169P/fv3p8TERPXN19q2NZW531ktKyuj5cuXU6tWrcjR0ZHkcjktWLDAaj9kb858ISEhWm+o\nDwwM1LmtWPnMJTAuUOeX4bTdv0tk+9mIiMJTwnXmC/l3iNZt7SFfVWriHwEZNmyYXu+mlmfNfBXv\nFyy/VLR69Wpq06YNSaVSat26Na1cuVKvMWw9X2ZmJr344ovUtGlTkkql5OXlRT169KCNGzdq/XKR\nirZ/K0sxZP/t2bOHAgICSCaTUYMGDWj8+PGUnZ2ts39rP/f0zTd//nxq3ry5ev9NmDCBbtzQ/gtG\nKtbMp+/1Ojw8XGs7Hx8fnWPYQ77U1FTq06cPeXl5kUQiIVdXV+rbty/t27ev2jGg5Z1jgZT3ZJiL\nIAhkbJ+CIMDc87ElnM9+1eRsAOezd5zPfqnuh6zJ+WpqNoDz2Ttlvko3JdvHt8cYY4wxxhizAC6O\nGWOMMcYYU+LimDHGGGOMMSUujhljjDHGGFPi4pgxxhhjjDElLo4ZY4wxxhhT4uKYMcYYY4wxJS6O\nGWOMMcYYU+LimDHGGGOMMSUujhljjDHGGFPi4pgxxhhjjDElLo4ZY4wxxhhTkpi7QycnpzJBEIwq\nup2cnCAIgrmnZDM4n/2qydkAzmfvOJ/9cnJyKgMAY6+btq4m7zuA89k71fOvIoGIzDqQIAhkbJ+C\nIMDc87ElnM9+1eRsAOezd5zPfqkKj5qcr6ZmAzifvVPmq1T918hXqowxxhhjjBmDi2PGGGOMMcaU\nuDhmjDHGGGNMiYtjxhhjjDHGlLg4ZowxxhhjTImLY8YYY4wxxpS4OGaMMcYYY0yJi2PGGGOMMcaU\nuDhmjDHGGGNMSbTiWBCEapfmzZuLNbxaQUEBXn31VezatctsfV69ehWjRo2Cm5sb6tSpgz59+iAt\nLc1s/RtCjHza9tf58+fNNoY+xMj2wQcfYMCAAXB3d4cgCNiwYYPZ+ra2v/76C/PmzUNAQADq1asH\nT09P9O3bF8eOHbP21MziXtE9zNo3Cz4rfCCLlKHx8sb4145/4ebfN609NbO4fv06hg8fDh8fHzg7\nO8PDwwNBQUHYu3evtadmVpmZmRg9ejQ8PDzg7OwMX19ffPHFF9aellnY0rVBX/qcE+353KLvOT8o\nKKjK696KFSssO2ED6Zvv0aNHCA8PR9u2beHs7IxmzZphwoQJuHbtmkXnq6+0tDS88cYbaNeuHWrX\nrg25XI6xY8fi6tWrldp+9tlnGDp0KBo1agRBELBw4UKTxxetOE5NTdVYvL29MXDgQI11//73v8Ua\nHsDT4qp///5ITk7GqFGjsHv3bpP7zMvLQ8+ePXHx4kWsWbMGycnJAIA+ffogMzPT5P4NIUY+lYkT\nJ1bah23btjVb/9URK1tsbCyKioowZMgQs/RnS86ePYstW7Zg+PDh+O6777BhwwY4OTkhKCjIrMeG\nIU7ePInJOydrLJm5hj9P/n78N3p80wNf/PQFbvx9A8Wlxfir8C/EnY9Dt3XdcL3gugizt6zCwkJ4\neHggMjISe/bswfr16+Hi4oLBgwdj+/bt1p6eWaSlpaFbt2548uQJ1q1bhz179iAsLAylpaXWnprJ\nbOnaYAh9zom2eG7RlyHnfH9//0rXvVdeecUCszSevvkmT56MTz/9FFOmTMGePXsQGRmJY8eOoW/f\nvigsLLTQbPWXnJyM9PR0zJw5E3v37kV0dDTOnTuHgIAA3Lyp+YbI2rVrkZOTgxEjRphvAkRk1uVp\nl5X5+PjQ2LFjq3xMRdu2xsjPz6eAgACSy+UklUqpVatWJJVKaffu3Sb1GxERQQ4ODpSVlaVeV1hY\nSF5eXjR69Gid29pDPqKn85w/f75R25mDmNlKS0uJiCgrK4sAUFxcnF7bmXPfVeX+/fuUmZmpsZSU\nlBjUR35+PikUCo11CoWC2rZtS7169dK5rVj5/Fb6ERZCYzlw5YDB/czeN1u9/Xs/vEd5j/Io5lSM\net1LW17Sub3Y+08sCoWCmjZtSkOGDNHZzh7ylZaWUocOHWjEiBEGb2sP+Yy9NgCwaj59zom2eG7R\nl77n/MDAQOrRo4fB/dtDvkePHpGDgwPNmzdPY/3evXsJAO3bt09r/9bKl5OTU2ndtWvXSBAEWrBg\ngcZ61b+BQqEgABQeHq73OMp8lWrZGnvP8fr165GTk4OUlBQ4Ojpi/vz5mDhxIubMmYOSkhKj+z11\n6hTatGmD1q1bq9fVqVMHvXr1wu7du03q2xBi5bMFYmarVcs2D/lXX30V7du311hyc3MN6sPNzQ0S\niURjnUQiQZcuXXDr1i1zTldvJWX/3V8z/jkDFE7o17KfQX0QEeJ/iQcA1HasjYjgCDRwboAZ3Wag\nZf2WAIAdv+9AflG++SauQ1xcHC5cuGCRsSQSCerVqwdHR0eLjAeIl+/IkSPIyMjA7Nmzzd63IcTK\nJ+a1QcxjTp9zotjnFmvnE5u185WUlKC0tBSurq4a693c3AAAZWVlRo8vVjZPT89K63x8fODp6Vnp\nmBNjH1v/qBFJWFgY0tLS0LLl04unIAhYvXo1jh07VulJbggHBwdIpdJK62UyGYqKinDlyhWj+zaE\nWPlUvvrqK8hkMtSuXRvBwcH48ccfTe5TX2Jns0Wqj7Xkcjn++OMPEBG8vb1N7re4uBipqalo3769\nyX0Zq2X9lvCq44XY07GYuXemwdtfLbiKvKI8AEDrBq0hdfjv88/P0w/A0yL85+yfzTNhHVatWoVJ\nkybhwIEDoo1RVlaGkpISZGdnIyIiApcuXcL06dNFG688MfMdP34cAPD48WN0794djo6O8PLywsyZ\nM1FUVGT28aoiZj6xrudI20cAACAASURBVA2WOOaMYa5ziy3l+/nnn9UvRv39/bF+/XqT+7SFfHXr\n1sX48eMRExODlJQUFBYWIj09HXPmzEHnzp3Rt29fo/q1dLbMzEzk5ORY5HpWY4tjoPIrD0EQ4OHh\nYVKfvr6+yMrKQl5ennpdWVkZTp8+DQC4d++eSf0bQox8ADBu3DisWrUKBw8exNdff428vDwEBwfj\nyJEjJvetL7Gy2bKxY8fC09MTQUFBVX7pwBgLFy7En3/+iffff98s/RmjmWszHJ5w2OgC+U7hHfV/\n15PV03isntN//z/nYY5pE61GbGwspk+fjsWLFyMsLEy0cd577z04OjqiUaNGWLp0KZKTk42+eBlC\n7Hy3b98GALz88ssYMGAADhw4gPfeew/r1q3Da6+9ZvbxKhI7nxjXBksdc8Ywx7nFlvL17t0bK1as\nwM6dO/Hdd9+hTZs2mDx5MiIjI43u05byxcXF4cUXX0RwcDDq1q2Ljh07QqFQ4MCBA1W+qKuOpbOV\nlJTgzTffhKenJyZNmiT6eDX2nuPy6tSpo/e9pSolJSWkUCjUi+qelitXrpBMJqPBgwfT5cuX6fbt\n2zR9+nRycHAgAHTq1CmtfdpDvqrcv3+f5HJ5tfdjiZFPrGy2ds9xYGAgTZ8+nfLz86lr164kl8vp\njz/+MKnPpKQkEgSBPvroo2rbipVvReoKWnd2HRERXbxzkbw+9SIsBM3YM0PvPk7eOKm+t7jXN5r3\nN47dNlb92OZfN2vtw9R8GRkZ6ntD9Vl03fNW3fF58+ZNOnPmDO3atYtGjx5NMpmMdu3apXN+9pBv\nypQpBIBmzNDc99HR0QSA0tPT7TqfsdcG1ZjWmHN5hpwTzXFuseV8KiNGjCAnJyd68OCB3eebO3cu\nubq60rJly+jo0aOUmJhIrVu3pq5du1JhYaFB+SydjYgoNDSUJBIJ7d+/X2tf5rznmItjLQIDA7Xu\n3O+++44aNmyofuyZZ56hd999lwDQ9evXtfZpL/mqMnXqVJJKpTrb2EpxrE82WyuO161bpy6AzFEg\n79y5kxwdHWny5Ml6tRc7n4oxBfKVe1fUBXCnVZ00Hhu6aaj6sUN/HNLah6n5ysrKaNq0aQSAFi1a\nVOnLkxWX3NxcrX0Z+twLDAwkX19fnW3sId/cuXMJAO3cuVOj/blz5wgAJSUl2XU+IuOuDdqKY0sf\nc/qeE811brHVfOVt2bKFANDJkyftOt/FixcJAK1bt05j/aVLlwgArVixwqB8ls42d+5cEgSBEhIS\ntPZDZN7iuGbewGkGa9aswYMHD9T/37hxY/V/v/TSSxgxYgQuXboEqVSKVq1aYerUqWjWrBnkcrk1\npmswXfmqQkQQBEHsaZmFodlsQfmPidzc3HDw4EH069cPQUFBOHLkCFq0aKF3X4cOHcLo0aPx4osv\nYs2aNWJM12h+Xn44POEwghOCEXs6FgAQ83yMzm1auLWAu7M78orycPneZRSXFqvvO07PTQcASGpJ\n8A/vf4g2b0EQsHLlStSqVQsRERHo0KEDRo0aZVRfhh6fAQEBov/WqiXy+fn5qccq7+n1SdwvTllq\n/5nz2mDNY04bc55bbDFfRapj05hrny3l+/XXXwEAzz77rMb6Nm3awM3NzeCfGrRktqioKERHRyMm\nJgbjx483agxjcHGsha+vr87HHRwc1DeF3759G1u2bMGcOXMsMTWzqC5feffv38f333+Pbt26iTgj\n8zEkm60ytkBOTU3F8OHD0bdvX2zcuNEmvqldUfkC+cvTX+Jlv5fRQ95Da3tBEBDSOQSfnfoMRSVF\nWHB4Aeb2nIuNFzbij/w/AADDfYejvnN90eceGxsLmUxm1D16KoYcn2VlZTh+/DhatWpl9HiGEDPf\n888/D5lMhn379mn8Juv+/fsBPH0RIDZL7D9zXxssfcxpI9a5xVbyVWXTpk1wdnZGp06djO7DFvKp\nvtx9+vRp+Pv7q9dfunQJBQUFaNKkiVH9ip0tJiYGH374IaKiojBjxgyjxzAGF8cGUigUeO+99xAY\nGAhXV1ekp6djyZIl8PPzs/oN9+awbNky/P777+jTpw8aN26M69evY9myZcjOzkZSUpK1p2eyo0eP\nIjc3F9nZ2QCe/lECFxcXADD6la9YKhbIv/zyi/qnd6ry22+/4YUXXoCHhwfmzJmDs2fPajzevXt3\nsaesN1WBfPavszoLY5WPAj/Cnst78Nvd37D05FIsPblU/Zi3izeWD1gu5nQ1LFu2TJR+Fy5ciHv3\n7qFHjx7w9vZGdnY21q9fj9OnT2PTpk2ijFkVsfK5u7tj3rx5iIiIgKurK4KDg5GWlobFixcjJCRE\n4yfQxCRWPjGvDWLNGdDvnCj2ucXa+X788UdER0dj5MiRaN68Of7++2/Ex8dj586diI6ORp06dUya\ng7Xz9erVC507d0ZYWBjy8/MREBCAGzduIDIyEvXq1UNISIjR44uVLTk5GbNmzcKgQYMQHByMU6dO\nqR9zdXVFhw4d1P+flpaGa9euqX+SLiMjA9999x0AYPDgwahdu7bhE6jqXgtTFtSQe461USgU9MIL\nL5CXlxdJpVJq2bIlzZ8/nx4+fFjttvaQb+fOnfR///d/5O7uThKJhBo0aEBDhw6ln376qdptxchn\nzmxEle9vKr/oIta+00d+fj6tX7++2nZxcXE6vxShizXz6SvvUR7N3DOT5J/LyXGxI3kv86aJ/5lI\nNwpuVLutPeTbsWMH9enThzw9PUkqlZJcLqehQ4fS8ePHq93WHvIRPb1Xcfny5dSqVStydHQkuVxO\nCxYsoOLiYp3b2UM+Y68N+jw/xaTPOdGezy365MvKyqJBgwZR48aNSSqVUp06dei5556jTZs2Vdu/\nPeQjov9n797joqrTP4B/jjIDCiJ3DXVQ8Y5p29JmSwpxMVNLXW95x8W8oyZZXnJFlKQyM0dNLUUU\nXSrtV14yEwXNZFPcLANaUdE20wUVUgyFYZ7fH86wDMMMczlnLuzzfr3Oqzyc8z3fzzlzzvdh5syB\nbt68SfPnz6dOnTqRm5sbtW3blkaNGkU//fST0fbtlW/SpEkGc4WHh5u8bFFRkdHtwMA9xwJp7qkR\niyAIZGmbgiBA7P44Es7nvBpzNoDzOTvO57y097M25nyNNRvA+ZydJp/eTeWOd0MiY4wxxhhjdsLF\nMWOMMcYYYxpcHDPGGGOMMabBxTFjjDHGGGMaXBwzxhhjjDGmwcUxY4wxxhhjGlwcM8YYY4wxpsHF\nMWOMMcYYYxpcHDPGGGOMMabBxTFjjDHGGGMaXBwzxhhjjDGmwcUxY4wxxhhjGi5iN+jm5qYWBMGi\notvNzQ2CIIjdJYfB+ZxXY84GcD5nx/mcl5ubmxoALB03HV1jPnYA53N22vOvLoGIRN2QIAhkaZuC\nIEDs/jgSzue8GnM2gPM5O87nvLSFR2PO11izAZzP2Wny6VX/jfI3VcYYY4wxxizBxTFjjDHGGGMa\nXBwzxhhjjDGmwcUxY4wxxhhjGlwcM8YYY4wxpsHFMWOMMcYYYxpcHDPGGGOMMabBxTFjjDHGGGMa\nXBwzxhhjjDGmIVlxLAhCg1P79u0tbr+srAxjxozB/v37Revz4sWL0b9/f/j6+kIQBGzfvl1vmevX\nr2PRokUIDQ1Fy5Yt4e/vj6ioKJw4cUK0fphDiv3w888/Y9KkSVAoFGjevDm6dOmC119/Hffu3RNt\nG6aSIl9tq1atgiAIePrppyVp3xh7vYad2eTJk9G9e3d4enrCw8MDvXv3hlKpRHV1tb27ZpWSeyWY\ne2gunvzwSbiudIWwXICwXMD60+vt3TXR5ObmYurUqejWrRuaN28OhUKBcePGoaioyN5dE42hse7c\nuXP27prVHGlcMIep18S0tDQMHz4cQUFBEAQBsbGxNu2npSy55p86dQpNmjSBIAhQqVTSd9JCpmTL\nzs42Wmf+4x//sGjbLlb23aCcnBydfw8bNgy9e/dGYmJizTxXV1eL2i4rK0NMTAxyc3Px6aefYu/e\nvRg8eLA13QUAKJVKPPbYYxg8eDB27NhR7zJnz57FRx99hMmTJ6NPnz6orKzExo0bERERgX379onS\nD1NJsR/u3buH6OhoVFVVYcWKFVAoFDhz5gyWLVuGwsJCfPTRRyL1vmFSHWety5cvIzk5GQEBAaK1\naSp7voadWUVFBeLj4xEcHAxBEHD48GHMnTsXFy9exHvvvWfz/pz69yls+26bzryEpxLQ3b+7We1c\nu3sN606vE7NrDicjIwN5eXmYM2cOQkJCcO3aNaxYsQKhoaE4d+4c2rVrZ+8uiiI2NhbTpk3Tmdel\nSxc79UYcjjQumMvUa2J6ejpKSkoQExODTz75xIY9tI651/yqqipMmzYNrVq1wo0bN2zQQ8uZku3x\nxx/XqzcBIC4uDrdv38YTTzxh2caJSNTpYZP6goKCaNy4cfX+TMvQurWVlpZSaGgoKRQKksvlFBwc\nTHK5nA4cONDgug2prq4mIqLCwkICQKmpqfVuv6qqSmdeVVUVdenShfr27Wu0fVPymUqq/XD48GEC\nQIcPH9aZ/9prr1HTpk3p3r17Btd1hny19e/fn6ZOnUrh4eEUFhZmdFlnyWbKa7g+Yuarz507d6ig\noEBnUqlUorT94osvkoeHh9FlpMoXsiGEkAid6cilI2a3U1RaRC9/+TJlnM+g6fun17Sl/FZp0vpS\nHz8xFBcX6827cuUKCYJAS5cuNbquM+QjetjPJUuWmL2Oo+dzlHHBEqZeE7XLERG1adOGJk2aZFL7\nzpJPKzk5mUJCQmjx4sUEQK+eqcue+Swdz7TXlVdeeaXBZTX59GpZp7vneOvWrSguLkZWVhZkMhmW\nLFmC2NhYLFiwwOqPB5o0aXh3eHl5wcVF9w13FxcXPPbYY7h27ZpV2zeHVPuhsrISAODp6akz38vL\nC2q1WvsLkOSkPM4AsHv3bvzzn//EqlWrROiteez9GraHMWPGoHv37jpTSUmJKG37+vrqnZO2olL/\n93jF/yketIwQ3THa7Hbae7XHmmfXYHTP0Wjl0UrMLpotNTUVP/zwg+jt+vv7680LCgqCv7+/Ta+d\nUuVzBFJlk3pckPKYmHpNlPLa6Qj5AODSpUtITk7Gxo0bIZPJRNm+o2SrbefOnSAiTJo0yfJtW7ym\nnSQkJCA3NxcdO3YE8PAer02bNuHEiRN2GyArKyuRk5OD7t3N+yjVGlLth+joaHTu3BmvvfYa8vPz\nUV5ejmPHjuG9997D9OnT4e7uLlYEo6Q8zqWlpXj55Zfx1ltvwcfHR4zumsURX8NSKy8vBwAoFApc\nvnwZRITWrVtb1BYRQaVSoaysDHv37kVaWhrmz58vZnfN0tG7IwLcA6A8rcScQ3Ps1g8xbNy4EXFx\ncThy5IhNtldQUIDi4mKbXTttke/999+Hq6srmjdvjsjISHz99deSbas2KbNJOS7Y+jVna46Ub8aM\nGRgxYgT69esnSnuOlK22HTt24PHHH0fPnj0tbsPpimNA/x0IQRDg5+dnp94AiYmJ+OWXX/Daa6/Z\ndLtS7Ac3NzecPHkSarUaISEhaNGiBaKiojB48GCsX2/bLwdJdZwXLFiALl262PULF472GraFcePG\nwd/fHxEREVZ9CevgwYOQyWTw9vbGyJEjER8fj6VLl4rYU/O082yHYxOPOX2BrFQqMWvWLCQlJSEh\nIUHy7alUKkyfPh3+/v6Ii4uTfHu2yDd+/Hhs3LgRmZmZ2LJlC27duoXIyEhkZ2dLsj0tqbNJNS7Y\n+jVna46ULz09Hbm5uXj77bdFac+RstWWk5ODwsJCq941BuB89xzX5u7ubvI9KFoqlYqqqqpqptr3\nGWmZc3/Lrl27SBAE+tvf/tbgsubmM5WY+6GiooIiIiKoS5cutHPnTjp+/Di9/fbb1KJFC5o+fbrR\nNp0h34kTJ0gmk9H58+drlrX1Pce1OcJrmEj6+8rCw8Np1qxZVFpaSn/84x9JoVDQ5cuXLWqrrKyM\nzpw5Q5mZmbRo0SKSyWS0ePFio+tIlW9tzlr68OyHRET0439+pIC3AwiJoPgv4i1uc1nWMpvfc5yf\nn19z76sp07Jlywy2Zcrrk4ho2rRp5OLioncfa32cMR/Rw3vtFQqF0euLdpuOnE2KccHWx8Sca6IY\n9xw7Ur5bt25RQEAAvf/++zXzli1bZvE9x46Ura5p06aRTCajkpKSBpetlU+/lq1vpjWToxfH4eHh\nDR40Uw/Evn37SCaT0ZQpU0zatiMVWIb2w/r16wkAXbx4UWf5LVu2EAA6d+6cwTadIV/37t1p+vTp\nVFpaWjOFhYVRnz59qLS0lO7fv19ve86QrTZHK44//PBD2r9/PxGRKAVybcuWLaMmTZrQL7/8YnAZ\nqfNpiVEg26M4VqvVNHPmTAJAy5cv1/vyZN3J2MBjyutz4cKFJAgC7dixw6T+OVu+2mbMmEFyudzg\nz60tjm2RTYpxwdbHxNbFsSPlmzFjBj366KN069atmnHvtddeIwB08+ZNKi8vNyufI2Wr7f79++Tt\n7U1Dhw41ulw9+fRq2cZ5g6MRmzdvxt27d2v+HRgYaFE7R48exciRIzFs2DBs3rxZrO7ZjKH9cP78\neXh7eyM4OFhn+T/96U8AHt4j2Lt3b9t11EKG8hUUFKCgoACbNm3SW8fb2xvvvvsu5s2bZ7N+WkKs\n17At1f7Y3MvLC5mZmYiOjkZERASys7PRoUMHi9sODQ2FWq1GUVER2rRpI0Z3LRYSEIJjE48hckck\nlKeVAIB1zzn+I9oEQcCGDRvQpEkTrFixAj169MCIESMsaquh12dycjJSUlKwbt06TJgwwap+m8qW\n+eoiIgiCYNG2TGGLbFKMC/Y8JrbgSPny8/Nx/vx5+Pr66v3Mz88PQ4YMwWeffWZye46UrbZ9+/ah\ntLTU+lsqIOFzjh1V165drW4jJycHQ4YMQVRUFNLT0x32CQHGGNoPrVu3RmlpKS5evIhOnTrVzP/2\n228BwO7Fh6kM5cvKytKbN2/ePFRXV0OpVOpkdlRivIbtTcwC+fjx4xAEoeYLjvZWu0Bef3o9RoeM\nRpgizOg6alLjdsVtAMDvVb/XzL9XeQ83f78JAPBrLv096UqlEq6urpDL5Ra3Yez1uW7dOrz++utI\nTk5GfHy8xduwlNT56rpz5w4OHjyIJ5980uLtmUrKbFKOC7Y+JrbmCPnWrl2LsrIynXnbt29HWloa\nMjMz0aqVZU/IcYRstaWlpcHX1xeDBg2yuq3/ueLYmOPHj6OkpKTmwdi5ubnw8PAAgJrfin766ScM\nGjQIfn5+WLBgAc6ePavTRp8+fWzbaZHFxsZizZo1GDhwIJYsWQKFQoHc3FysWLECf/zjHxEWZnyQ\nd3QRERF687y8vKBSqer9mbMx5TXsKOoWyN9//z28vLwMLn/w4EGkpqbi+eefh0KhwN27d3Ho0CFs\n2bIF06ZNc4h3i7S0BfLZ62cbLIwB4OfffkaH9/R/OVh4dCEWHl0IAKBlJHo/67N69WpJ2s3IyMC8\nefMwYMAAREZG6vzlKk9PT/To0UOS7dYlVb7Vq1fjX//6F5555hkEBgbi6tWrWL16NW7cuIFdu3ZJ\nss36+iAFqccFqfoNmH5NzM/PR35+PoCHf2zo6tWr2LNnDwAgPDy83kcRmsre+R577DG99bRfEg0P\nD7fqKUn2zqZVXFyMw4cPY8aMGeI8pq6+ey2smeDg9xwbU/fel9qTVmpqqtEbz40xN5+pxN4PeXl5\nNHLkSGrbti25ublR586dKSEhgW7fvm10PWfJV5ezfSHPGFNew/WRKp8pSktLaevWrQ0uV1BQQMOG\nDaO2bduSXC6ngIAACgsLo/T0dKNfiiKybz5TFJUW6f0xkbqTMY6ej4ho0qRJBl+b4eHhRtd1hnz7\n9u2jP//5z+Tr60suLi7k4+NDzz//PH377bdG1zPl/HQEjjYumMrUa6L2C2r1TVlZWQbbd5Z8dVnz\nhTxbMSfbmjVrCADl5uaatQ0YuOdYePgz8QiCQJa2KQiCzf7IhD1wPufVmLMBnM/ZcT7npb0fuTHn\na6zZAM7n7DT59L4U4Hw3yzLGGGOMMSYRLo4ZY4wxxhjT4OKYMcYYY4wxDS6OGWOMMcYY0+DimDHG\nGGOMMQ0ujhljjDHGGNPg4pgxxhhjjDENLo4ZY4wxxhjT4OKYMcYYY4wxDS6OGWOMMcYY0+DimDHG\nGGOMMQ0ujhljjDHGGNNwEbtBNzc3tSAIFhXdbm5uEARB7C45DM7nvBpzNoDzOTvO57zc3NzUAGDp\nuOnoGvOxAzifs9Oef3UJRCTqhgRBIEvbFAQBYvfHkXA+59WYswGcz9lxPuelLTwac77Gmg3gfM5O\nk0+v+m+Uv6kyxhhjjDFmCS6OGWOMMcYY0+DimDHGGGOMMQ0ujhljjDHGGNPg4pgxxhhjjDENLo4Z\nY4wxxhjT4OKYMcYYY4wxDS6OGWOMMcYY0+DimDHGGGOMMQ2bFcc5OTkYNWoUAgMDIZfL4evri5iY\nGKSlpaG6utrs9srKyjBmzBjs379ftD4uXrwY/fv3h6+vLwRBwPbt2+tdLiIiAoIg6E1r164VpR/2\nzJaWlobhw4cjKCgIgiAgNjZWtD5YQux9kZiYWO+xEwQBbm5uomzDVFIc59pWrVoFQRDw9NNPS9K+\npazN/cEHH6Bbt25wdXVF165dsWnTJpF7aD+TJ09G9+7d4enpCQ8PD/Tu3RtKpdKia6QjKblXgrmH\n5uLJD5+E60pXCMsFCMsFrD+93t5ds9p91X2s+noVnk1/FkFrg9AsuRlarW6Fp7c9jY9+/KjR/HUx\nQ9fNc+fO2btrVnOkccFcpo7tgHNeO03NJ/a108WKPpts7dq1mD9/PiIjI/Hmm28iKCgIpaWl+Oqr\nrzBjxgx4eXlhyJAhJrdXVlaGmJgY5Obm4tNPP8XevXsxePBgq/upVCrx2GOPYfDgwdixY4fRZXv1\n6oXNmzfrzGvfvr3VfbB3tvT0dJSUlCAmJgaffPKJ1du1hhT7YsqUKRgwYIDOvHv37mHAgAF44YUX\nrGrbHFIdZ63Lly8jOTkZAQEBorUpBmtzf/DBB5g2bRoWLVqE6OhoHD16FDNnzgQRYcaMGRL23DYq\nKioQHx+P4OBgCIKAw4cPY+7cubh48SLee+89m/fn1L9PYdt323TmJTyVgO7+3c1q59rda1h3ep2Y\nXXMYZffLsPjYYp1591X3UXyvGN/8+xt8e+1brHl2jZ16J67Y2FhMmzZNZ16XLl3s1BvxOMq4YAlT\nx3ZnvXaamk/0aycRiTo9bPK/jh8/ToIgUHx8PNXn4sWL9P333xM9XLneZWorLS2l0NBQUigUJJfL\nKTg4mORyOR04cKDBdRtSXV1NRESFhYUEgFJTU+tdLjw8nMLCwsxuv6F8jpBNuxwRUZs2bWjSpEkm\nb8OU42cqKfdFXTt27CAARtt2tmz9+/enqVOnmvxaFTOfIdbmrqqqIn9/f5o4caLO/MmTJ5Ovry9V\nVlYaXFfqfHfu3KGCggKdSaVSidL2iy++SB4eHkaXkSpfyIYQQiJ0piOXjpjdTlFpEb385cuUcT6D\npu+fXtOW8lulSevb4vVpqet3r1P7te1p/bfr6ZfffqE79+/QmlNrajI2Wd6EisuLDa4PwKHzaQGg\nJUuWWLSeGKQ8x+pjyrhAZP/XpiljuyNfOxtiau1SHzOunXq1rOS3VaSkpMDHxwdvvfVWvT8PDg5G\nr169TG5v69atKC4uRlZWFmQyGZYsWYLY2FgsWLAAKpXKqr42aWLfW7AdIZu994GWlPuirrS0NLRq\n1QrPPvusqO0aInW23bt345///CdWrVolQm/FY23unJwclJSUYPz48TrzJ0yYgFu3buHkyZNSdb1B\nY8aMQffu3XWmkpISUdr29fWFi4tNPuTTo1L/97jE/yketIwQ3THa7Hbae7XHmmfXYHTP0Wjl0UrM\nLprk04JPUXirUJK2fZv5omBWAWb9aRbaeLZBC9cWePmplxHiHwIAUJMal0ovSbLtulJTU/HDDz/Y\nZFu2JuU5Vh8xxwUpj4spY7bU10575zPEmmunpJVQdXU1srOz0b9/f9Hu20lISEBubi46duwI4OF9\nUJs2bcKJEydsOoB89913aNmyJWQyGXr16oWtW7da3aajZHMEttoXv/zyC7KysjBu3Dib7WMps5WW\nluLll1/GW2+9BR8fHzG6Kxprc+fl5QEAevbsqTM/JORhEZKfny9yj01XXl4OAFAoFLh8+TKICK1b\nt7aoLSKCSqVCWVkZ9u7di7S0NMyfP1/M7pqlo3dHBLgHQHlaiTmH5titH5b6OO9jjPpkFKYdmNbw\nwhaQNZXBzUV/fLuvul/z/20920qy7do2btyIuLg4HDlyRLJtvP/++3B1dUXz5s0RGRmJr7/+WrJt\n1SXmOdYQMccFWxyXhkh57XSEfFpiXjslLY5v3ryJiooKBAUFidquv7+/zr8FQYCfn5+o2zCmX79+\nWLt2Lfbt24c9e/agc+fOmDJlClauXGl12/bO5khssS927twJtVqNSZMmidpuQ6TKtmDBAnTp0sXu\nX6Q0xJrct2/fBgB4e3vrzNf+EqD9ub2MGzcO/v7+iIiIQFFRkcXtHDx4EDKZDN7e3hg5ciTi4+Ox\ndOlSEXtqnnae7XBs4jGnLJA/yfsE4z4dh2CfYKT/Jd1m29323baad4sHdxkseXGsVCoxa9YsJCUl\nISEhQZJtjB8/Hhs3bkRmZia2bNmCW7duITIyEtnZ2ZJsrz5inWMNEWtcsMVxMYVU105Hyacl5rXT\nMT5Dt6Hq6mqoVKqaSa1Wm91GUlISXnrpJYSHh2PIkCHYu3cvhg4diuTk5Jrfbu1BjGyNhan7YseO\nHfjDH/5g1q099mYo29dff40dO3bg/fffhyAIdu6l+EjzrX9Hzebl5YXMzEyrB+++ffvizJkzyMzM\nxMKFC7F69WosNOFHRQAAIABJREFUWbJE5N6aZkboDEzoNQEhASFOVyBfvH0RYz8dC5VahQu3LqDN\nmjY1T8mob0rMThRlu7vP78b0A9MBAJ18OmHrC9Z/qmhMQUEB5sx5eDyWLl1q8KkL2ikxMdFgW8au\nmzt37sTo0aPRt29fjB8/HidPnkRgYCBef/11SfPVZu05ZstxwVbHxRRSXDsdKZ+WmNdOSYtjX19f\nNGvWDFevXpVyM2aJioqCTCarmZKSkkRpd8yYMbh//z7Onz8vSnuWkCqbMzJlX5w+fRo//fSTzd81\ntpahbNOmTUNcXBzatm2LsrIylJWVQaVSobq6GmVlZXjw4IGde24dQ+9yaP9tz9tIJkyYgAEDBohS\nILds2RKhoaGIiorCG2+8gcWLFyMlJQXXrl2ToOfGze0zF3GPxwGA0xXI8qbymtsdWrq2RFDLIKOT\nl5uXwbaulF3RK6YjtkfoLbf+9HqM/3Q8qtRV6OLbpWZ/Salbt26YOXMmAGD58uUoKCgwOs2ePdtg\nW+aMIS1atMCgQYNw5swZ0TPVR4xzzJbjgr2OS32kuHY6Uj4tMa+dkt5k6eLigoiICBw5cgQPHjyA\nq6urlJszyebNm3H37t2afwcGBorSriO8qyVVNmdkyr5IS0uDi4sLxo4da8uuWc1QNu1FqL5nV3p7\ne+Pdd9/FvHnzbNZPsWnvj8vLy8MjjzxSM197v1yPHj3s0i8AiIuLq/l/7eAdHR2NiIgIZGdno0OH\nDha3HRoaCrVajaKiIrRp00aM7lpMWyBH7oiE8rQSALDuOcd8RJuipQJfjP0CA3cPhJuLG74Y9wV6\n+Ev3GlmevRyJxxMBAE8EPoGDYw/C393f+EoiEAQBGzZsQJMmTbBixQr06NEDI0aMsKgtc8cQIrLZ\nmCfGOWbLccGex6UuKa6djpTPEGuunZJ/A2nhwoWIiIjAggULsG6d/kW0qKgId+/etdnH2l27dpWk\n3d27d6NZs2Z49NFHJWnfFFJlc0YN7YvKykpkZGRg4MCBevfBOjpD2bKysvTmzZs3D9XV1VAqlejU\nqZPUXZPUU089BT8/P+zatQvR0f99YkJ6ejp8fHwQFhZmx97pErNAPn78OARBqPkio73VLpDXn16P\n0SGjEaYwvu/VpMbtiofvUv1e9XvN/HuV93Dz95sAAL/m4n+3om9Q35oCOf5QPI5OPGpRO+292oOW\n1f/HPIgIcw7NwfozD/+gSf/g/tg7ai885B4W99sSSqUSrq6ukMvlFrdhzhhy584dHDx4EE8++aTF\n27OGJeeYPcYFWx+X+kh57XSEfIZYc+2UvDju168f1qxZg/nz56OgoACxsbFQKBQoLS3F0aNH8eGH\nH2L37t0Occ/n8ePHUVJSghs3bgAAcnNz4eHx8AKn/Y3o66+/RkpKCv7yl7+gffv2+O2335CWloZ9\n+/YhJSUF7u7uduu/MaZkAx7+Jqn9bbKiogJXr17Fnj17AADh4eFOV0gacuDAAdy+fdvpbqkwJiIi\nQm+el5cXVCpVvT9zNjKZDCtWrMDMmTPRpk0bREdH49ixY9i2bRuUSqVVF2cp1B28v//+e3h5Gf7o\n/uDBg0hNTcXzzz8PhUKBu3fv4tChQ9iyZQumTZvmUJ8EaQvks9fPNlgYA8DPv/2MDu/pFy4Ljy7E\nwqMLAcBg8WmtvkF9kTkhEx29pfnl4upvV2sKYwD46tJXaLGqhc4yqUNSEftYrCTbr2316tWStfuv\nf/0LzzzzDAIDA3H16lWsXr0aN27cwK5duyTZpinMPccaItW4INVxAUwb26W+dto7nyTXzvoefmzN\nBAMPjP7mm29oxIgR1Lp1a3JxcSFvb2+KiYmhnTt31jzk2dC6hri7u5v1QOiGhIeH1zyQve6kVVhY\nSAMGDKDAwECSy+Xk7u5OTz31FO3evbvB9s3JZ49sRETLli0zuFxWVpbRbZh7/Ewl9r4gInrhhRfI\nx8eHHjx4YNLyzpStNkf6IyC1WZN706ZN1LlzZ5LL5dSpUyfasGFDg+vYOl9tpaWltHXr1gaXKygo\noGHDhlHbtm1JLpdTQEAAhYWFUXp6us4f56mPPfOZoqi0SO+PidSdjHHkfKZkS/0u1eD69V2HHc2+\nffvoz3/+M/n6+pKLiwv5+PjQ888/T99++22D69oim6nnWEPMHReI7P/aNHVsJ3K+ayeRaflEuHbq\n1bICifx33wVBIEvbFASh0fwd+vpwPufVmLMBnM/ZcT7npb1ntzHna6zZAM7n7DT59G6c/597lBtj\njDHGGGOGcHHMGGOMMcaYBhfHjDHGGGOMaXBxzBhjjDHGmAYXx4wxxhhjjGlwccwYY4wxxpgGF8eM\nMcYYY4xpcHHMGGOMMcaYBhfHjDHGGGOMaXBxzBhjjDHGmAYXx4wxxhhjjGlwccwYY4wxxpiGi9gN\nurm5qQVBsKjodnNzgyAIYnfJYXA+59WYswGcz9lxPufl5uamBgBLx01H15iPHcD5nJ32/KtLICJR\nNyQIAlnapiAIELs/joTzOa/GnA3gfM6O8zkvbeHRmPM11mwA53N2mnx61X+j/E2VMcYYY4wxS3Bx\nzBhjjDHGmAYXx4wxxhhjjGlwccwYY4wxxpgGF8eMMcYYY4xpcHHMGGOMMcaYBhfHjDHGGGOMaXBx\nzBhjjDHGmAYXx4wxxhhjjGlIVhwLgtDg1L59e4vbLysrw5gxY7B//37R+rx48WL0798fvr6+EAQB\n27dvr3e533//HcuWLUOXLl3QrFkztGvXDhMnTsSVK1dE6Yc9s9V26tQpNGnSBIIgQKVSidYXe+f7\n4IMP0K1bN7i6uqJr167YtGmTaP0wh9j7ITEx0eC55ubmJso2zCF2vqtXr2LIkCEICgpCs2bN4Ofn\nh4iICBw6dEiU9h1BWloahg8fjqCgIAiCgNjYWHt3SRJSXVvsqTEfuyOXjmDs3rEIXhcMYblQM5VX\nltu7a6L7v4L/08m4/vR6e3fJao42NpjKnuO6ZMVxTk6OztS6dWs8++yzOvP+7//+z6K2y8rKEBMT\ng4yMDIwYMQIHDhwQpc9KpRIVFRUYPHiw0eWmTJmCt99+Gy+99BK++OILrFy5EidOnEBUVBTKy627\nWNg7m1ZVVRWmTZuGVq1aibJ9LXvn++CDDzBt2jQMHz4cX375JUaOHImZM2fi/fffF6UfppJiP0yZ\nMkXvvMvMzISLiwteeOEFEXptOinylZeXw8/PDytXrsQXX3yBrVu3wsPDAwMHDsSnn34qQq/tLz09\nHZcuXUJMTAw8PT3t3R1JSHVtsTd7HbtT/z6FKfum6EwFJQWibuNg4UH8/ce/43LpZVHbdTR3HtzB\n7EOz7d0N0TnS2GAOu47rRCTq9LBJfUFBQTRu3Lh6f6ZlaN3aSktLKTQ0lBQKBcnlcgoODia5XE4H\nDhxocN2GVFdXExFRYWEhAaDU1FS9ZX7//Xdq2rQpLVq0SGf+oUOHCAB9+eWXBttvKJ+9s9WWnJxM\nISEhtHjxYgJAVVVVDW7D0fNVVVWRv78/TZw4UWf+5MmTydfXlyorKw22b8pr01RS7oe6duzYQQAa\nbNtZ81VVVVHbtm1p8ODBRpcTM1997ty5QwUFBTqTSqUyux3t65iIqE2bNjRp0iST1pMyn1jZtKS4\ntjgCa46dNflCNoQQEqEzHbl0xOL26rPz+52UfCKZjl4+Sm3eaVOznbsP7ja4rjMcO60ZB2YQEkHu\nye41GZXfKo2u40z5arPH2GAuqcd1opp8erWs091zvHXrVhQXFyMrKwsymQxLlixBbGwsFixYYPXH\nc02aNLw7VCoVqqur9d4Z8PLyAgCo1WqLt2/vbFqXLl1CcnIyNm7cCJlMZtV2a7N3vpycHJSUlGD8\n+PE68ydMmIBbt27h5MmTVvXBVFLuh7rS0tLQqlUrPPvss6K2a4wt87m4uKBly5aivk4tMWbMGHTv\n3l1nKikpMbsdc85TWxErGyDdtcVaqamp+OGHH6xqw17HTqX+7zkV/6d40DJCdMdoUbcxvtd4LO67\nGJEdIuHSxEXUtk3xacGnKLxVKOk2Tv37FDblbkJbz7aY9sdpkm7LEDFeh6YSa2yQss/2HNcd70rc\ngISEBOTm5qJjx44AHt7bvGnTJpw4cQIuLtKftC1atMCECROwbt06ZGVloby8HHl5eViwYAF69+6N\nqKgoi9u2dzatGTNmYMSIEejXr5+o7do7X15eHgCgZ8+eOvNDQkIAAPn5+ZL3AbDdfvjll1+QlZWF\ncePG2fT1I3U+tVoNlUqFGzduYMWKFbhw4QJmzZpldbvW0N5OpVAocPnyZRARWrdubdc+iUXMbFJd\nW6yxceNGxMXF4ciRI/buisU6endEgHsAlKeVmHNojr27I6qP8z7GqE9GYdoB6QrWquoqTN0/FQTC\nxoEb0cK1hWTbMsSWr0OxxgZHOHekGtedrjgGAH9/f51/C4IAPz8/m20/NTUVw4YNQ2RkJFq0aIGe\nPXuiqqoKR44cgVwut6pte2dLT09Hbm4u3n77bUnat2e+27dvAwC8vb115vv4+Oj83BZssR927twJ\ntVqNSZMmidquKaTM9+qrr0Imk+GRRx7BW2+9hYyMDKt+KRXLuHHj4O/vj4iICBQVFdm7O6ISI5vU\n1xZLKJVKzJo1C0lJSUhISLB3dyzWzrMdjk081ugK5E/yPsG4T8ch2CcY6X9Jl2w7KSdTkFeSh1Eh\no/B81+cl244htn4dijE2OMq5I9W47pTFsTWqq6uhUqlqJktug3j99deRnp6O1atX4/jx49i5cydu\n3bqF5557Dvfu3ZOg16axNtvt27eRkJCAN954AwEBARL10nLW5nt4e9HDQs2ZmbofduzYgT/84Q/o\n1auXjXtonYbyzZs3D2fOnMH+/fvx3HPPYezYsaJ9sdMaXl5eyMzMbJQFsrXZHPHaUlBQgDlzHhaR\nS5cubfDpSomJifbtsAEzQmdgQq8JCAkIaVQF8sXbFzH207FQqVW4cOsC2qxpo/MUibpTYnaiRdv5\n181/IfnrZHi7eWPdgHXihjCBmK9DW40N9uizIVKN6/9zxXFUVBRkMlnNlJSUZNb6eXl5SElJwZo1\na5CQkIB+/fph/Pjx+OKLL3D27Fl8+OGHEvW8YdZme/3119GqVSuMGjUKZWVlKCsrw/379wEAv/32\nm10Lf8D6fIZ+k9T+W/tzR2fKfjh9+jR++uknu7xrbK2G8rVt2xahoaEYPHgwPv74Y/Tp0wevvPKK\nnXr70IQJEzBgwIBGWSCLkc0Rry3dunXDzJkzAQDLly9HQUGB0Wn2bMd8isHcPnMR93gcAFhcIF8p\nu6JXbEZsj5Cw1w2TN5XDzeXhY8ZaurZEUMsgo5OXm5fBtozlW/n1SjyofoC//uGvuF5+HedunMON\n8hs16167cw3nbpyTLKeYr0NbjQ227rMxUo3rtr+z3s42b96Mu3fv1vw7MDDQrPXPnz8PAHjiiSd0\n5nfu3BleXl4oKBD3ETrmsDZbfn4+zp8/D19fX72f+fn5YciQIfjss8+s7qelrM2nvQcpLy8Pjzzy\nSM187T1JPXr0EKGX0jNlP6SlpcHFxQVjx461ZddEYe5xDg0Nxdq1a6XullFxcXE1/68tIqOjoxER\nEYHs7Gx06NDBjr2zjhjZHPHaIggCNmzYgCZNmmDFihXo0aMHRowYYdM+SEFbIEfuiITytBIAsO45\n278jai1FSwW+GPsFBu4eCDcXN3wx7gv08Bf/Gn33wcNrzTs57+CdnHf0fp7yTQpSvkkBLSPRtw2I\n+zq01dhg6z4bI9W4/j9XHHft2tWq9bVfRDl9+rTORxIXLlxAWVkZ2rRpY1X71rA229q1a1FWVqYz\nb/v27UhLS0NmZqbdn0tqbb6nnnoKfn5+2LVrF6Kj//tt7vT0dPj4+CAsLMzaLtpEQ/uhsrISGRkZ\nGDhwoN69v87AnOOsVqtx8uRJBAcHS9gj8zW2Ark2S7I58rVFqVTC1dXV6u+LOJLaBfL60+sxOmQ0\nwhSGr2/tvdobLf5+r/odv1f9DgBQ038/9r71+y3cV92Hm4sbPOQe4gXQ6BvUt6ZAjj8Uj6MTj1rU\nTkP5HIEYr0Nbjw226HNDpBrX/+eKY2OOHz+OkpIS3Ljx8COV3NxceHg8POG1vxX17dsXvXv3RkJC\nAkpLSxEaGoqff/4ZK1euRMuWLR32Y2xTsj322GN662VnZwMAwsPDbfrEA3OZkk8mk2HFihWYOXMm\n2rRpg+joaBw7dgzbtm2DUqlsNIPjgQMHcPv2bYd9LVoqMTERt2/fRlhYGFq3bo0bN25g69atOH36\nNHbv3m3v7umpW0R+//33NY98NCQ/P7/mHY+KigpcvXoVe/bsAfDwHHSUX3bMzebo15bVq1db3Yaj\nHTttgXz2+lmjhbEp3vrmLSw/vlxvfvv32gMAJvWehO1Dt1u1DUP6BvVF5oRMdPTuKEn7n72o/4lF\nYnZiTV7lc0rM/pNtbqkR43VojBRjg5R9tuu4Xt/Dj62ZIPEfAanN3d29wT9mYY7w8PCaB7LXnWq7\nefMmzZ8/nzp16kRubm7Utm1bGjVqFP30009G2zcnn72y1bVs2TJJHtRvz3ybNm2izp07k1wup06d\nOtGGDRsabN/c16apxN4PREQvvPAC+fj40IMHD0xexxnyff755/TMM8+Qv78/yeVyUigU9Pzzz9PJ\nkycbXFeqfKYoLS2lrVu3mrSs9nyrb8rKyjK4nr3ymZOtLqmuLfZizbFz9HzLspbp/aGR2tOk/5tk\ncF1Hz1af2nkb0x8BcaSxwRRSj+tEhv8IiEAk7kcNgiCQpW0KggCx++NIOJ/zaszZAM7n7Dif89J+\ny74x52us2QDO5+w0+fQedfE/97QKxhhjjDHGDOHimDHGGGOMMQ0ujhljjDHGGNPg4pgxxhhjjDEN\nLo4ZY4wxxhjT4OKYMcYYY4wxDS6OGWOMMcYY0+DimDHGGGOMMQ0ujhljjDHGGNPg4pgxxhhjjDEN\nLo4ZY4wxxhjT4OKYMcYYY4wxDRexG3Rzc1MLgmBR0e3m5gZBEMTuksPgfM6rMWcDOJ+z43zOy83N\nTQ0Alo6bjq4xHzuA8zk77flXl0BEom5IEASytE1BECB2fxwJ53NejTkbwPmcHedzXtrCozHna6zZ\nAM7n7DT59Kr/RvmbKmOMMcYYY5bg4pgxxhhjjDENLo4ZY4wxxhjT4OKYMcYYY4wxDS6OGWOMMcYY\n0+DimDHGGGOMMQ0ujhljjDHGGNPg4pgxxhhjjDENLo4ZY4wxxhjTsFlxnJOTgxdffBFt27aFXC6H\np6cnnnjiCSxduhTXr183u72ysjKMGTMG+/fvF6V/ubm5mDp1Krp164bmzZtDoVBg3LhxKCoq0lt2\nzZo1eP755/HII49AEAQkJiaK0gctsbMBwOLFi9G/f3/4+vpCEARs375db5ns7GwIgmBw+sc//iFK\nX+yVDwAmT56M7t27w9PTEx4eHujduzeUSiWqq6tF64s9FRUVYcSIEfDy8oK7uzueeeYZ5Obm2qUv\nUhxnACgoKMDIkSPh5+eHZs2aoWvXrnjvvfdE3YYp7HkNshdrMqelpWH48OEICgqCIAiIjY0Vv4N2\nFBERUe91c+3atfbumtWuX7+ORYsWITQ0FC1btoS/vz+ioqJw4sQJe3dNNKaOIc7o3Zx3EbYtDK1X\nt4Z8hRzNk5ujx4YeWPDVAtyuuG3v7olGzLHBJsXxO++8g7CwMJSUlGDlypXIzMxERkYGnn32WWzZ\nsgV//etfzWqvrKwMMTExyMjIwIgRI3DgwAGr+5iRkYG8vDzMmTMHhw4dQkpKCv75z38iNDQU//73\nv3WW/eCDD1BcXIyhQ4davd26pMgGAEqlEhUVFRg8eLDBZR5//HHk5OToTT169EDr1q3xxBNPWN0P\ne+YDgIqKCsTHx+OTTz7Bp59+iujoaMydOxfz588XpR/2dOvWLTz99NP48ccfsXnzZmRkZAAAnnnm\nGRQUFNi0L1Id59zcXDz55JN48OABPvzwQ3zxxRdISEiw+S839r4G2YO1mdPT03Hp0iXExMTA09NT\nol7aV69evfSuny+++KK9u2W1s2fP4qOPPsKQIUOwZ88ebN++HW5uboiIiBDt3LY3U8cQsZ369ylM\n2TdFZyooEfd6/fm/Psepf5/Cf+79B1XqKlSoKlBwswCrc1YjakcU1KQWdXv2IPrYQESiTg+b/K9j\nx46RIAg0b948qk95eTmlpqYSPVy53mVqKy0tpdDQUFIoFCSXyyk4OJjkcjkdOHCgwXWNKS4u1pt3\n5coVEgSBli5dqjO/urqaiIiqqqoIAC1btsykbTSUT6psRP/tc2FhIQGo2ecN0e6DV155pcFlnTEf\nEdGLL75IHh4eRpcx5bVpijt37lBBQYHOpFKpRGl7xYoV1LRpUyosLKyZV15eTgEBATRy5Eij64qV\nj0i641xdXU09evSgoUOHmr2uM+Qz5xpUl5j56iNGZu05SkTUpk0bmjRpksnrSplPrHMyPDycwsLC\nzF4PgOTHz1qlpaVUVVWlM6+qqoq6dOlCffv2Nbquo2fTsnQMsTZfyIYQQiJ0piOXjljVZl0bTm+g\no5ePUnF5Md2rvEef5H1C8hXymu19d/07g+s6w/ETYWzQq2Ulf+f4zTffhJ+fH9588816f+7u7m7W\nx2tbt25FcXExsrKyIJPJsGTJEsTGxmLBggVQqVQW99Pf319vXlBQEPz9/XHt2jWd+U2aSLPbpMoG\nWN7nnTt3gogwadIkq7YPOGY+APD19YWLi4tV2zfVmDFj0L17d52ppKRElLb/8Y9/oHPnzujUqVPN\nPHd3d/Tt2xcHDhyweh+bSqrjnJ2djfz8fLu/y+8I1yBbEyOzVNdNa0l5TjqK1NRU/PDDDxav7+Xl\npXeNdHFxwWOPPWbT16a1OYyx1+tTpf7v+RP/p3jQMkJ0x2hRtzHziZmI7BAJf3d/NJc1x4geI9Az\noGfNz2VNZKJuzxCpjp8UY4OkrwaVSoXjx48jJiYGcrlclDYTEhKQm5uLjh07AgAEQcCmTZtw4sQJ\n0QucgoICFBcXo3v37qK2a4gts5lqx44dePzxx9GzZ8+GF26Ao+QjIqhUKpSVlWHv3r1IS0uzWcFV\nXl4OAFAoFLh8+TKICK1btxal7aZNm9Z7nrm6uqKiogKXLl0SZTsNkeo4nzx5EgBw//599OnTBzKZ\nDAEBAZgzZw4qKipE6bspGvM1yBBHOXelIOY5+d1336Fly5aQyWTo1asXtm7dKmZXLbJx40bExcXh\nyJEjorZbWVmJnJwcm702pcrhCDp6d0SAewCUp5WYc2iOpNu6V3kPn+R9gh+LfwQARHWIQkhAiKTb\nBKQ9flKMDZIWx7du3cL9+/ehUCj0fqZSqXQmc9R9h0UQBPj5+VnV17pUKhWmT58Of39/xMXFidq2\nMbbIZqqcnBwUFhaK8q6xliPkO3jwIGQyGby9vTFy5EjEx8dj6dKlNtv+uHHj4O/vj4iICFG/bNW1\na1cUFhbi1q1bNfPUajVOnz4NALh923ZfvJDiOP/6668AgNGjR6N///44cuQIXn31VXz44YcYO3as\nVW2bqzFfgwxxhHNXKmKck/369cPatWuxb98+7NmzB507d8aUKVOwcuVKkXtrOqVSiVmzZiEpKQkJ\nCQmitp2YmIhffvkFr732mqjt1kfKHI6gnWc7HJt4TNICOftKNoTlAjxWeWDUnlGorK7E4C6DsW/M\nPtG3VZfUx0+KsUHS4vjh7Rz6bty4AZlMpjPZ6iPf6upqnaJcra7/RvTZs2fj1KlTSE9Ph7e3t036\nZi1Ts5kqLS0NMpnM5oWHIWLl69u3L86cOYPMzEwsXLgQq1evxpIlS0TurWFeXl7IzMy0eDA2tB+m\nT58OtVqNiRMn4tKlS7h+/TrmzJlT076jfqxdl6F82v+OHz8eSUlJiIiIwCuvvIJly5bhs88+Q35+\nvj27bbLGfA1yVtaekwCQlJSEl156CeHh4RgyZAj27t2LoUOHIjk5uebdaVsqKCjAnDkPi6ylS5ca\nfRKRuU9d2r17N1JSUrB06VL07dtXogQPiZlD7DFSDDNCZ2BCrwkICQiRvECu68CFA/jLR3+R9At5\ntjh+UowNko6Wfn5+cHNzw88//6w3/8yZMzhz5gxeeuklKbugJyoqSqcoT0pK0ltm0aJF2LJlC7Zt\n24b+/fvbtH/WMCWbqR48eICPP/4YgwYNcph3h8TK17JlS4SGhiIqKgpvvPEGFi9ejJSUFJvcOzdh\nwgQMGDDAqsHY0H7o2LEjdu3ahbNnz6JTp04IDAxETk4OXn75ZQDAI488IkkmsRnK5+vrCwCIiYnR\nWV57jp47d862HbVQY74GOSMxzklDxowZg/v37+P8+fMi9NQ83bp1w8yZMwEAy5cvR0FBgdFp9uzZ\nJrW7f/9+xMbGIi4uDsuXL5cyAgBxc4g5Roplbp+5iHv84SdDlhbIV8quQFgu6EwR2yN0loloHwFa\nRriz8A6+Gv8VAlsEAgAOXzqMz3/6XNRMtdni+EkxNkh6s5iLiwv69euHI0eOoLKysuZ+SBcXF4SG\nhgKAzR8Ds3nzZty9e7fm34GBgTo/T05ORkpKCtatW4cJEybYtG/WaiibOfbt24fS0lJRb6mwlpj5\nagsNDYVarUZRURHatGkjSpuG1P54XDsYR0dHIyIiAtnZ2ejQoUODbRjbD8OHD8fQoUNx4cIFyOVy\nBAcHY8aMGWjXrl29tzc5IkP5QkIe3hcnCILO8tpPqJzlnfHGfA1yRmKck4ZoX5t1X7O2IAgCNmzY\ngCZNmmDFihXo0aMHRowYYVWbR48exciRIzFs2DBs3rxZpJ4aJ2YOqcYQMWkL5MgdkVCeVgIA1j23\nTrT2W7i2QExwDEb2GIn3vn34DOALty6I1n5dtjh+UowNkn+T4tVXX0VMTAxee+01vPvuu1JvrkFd\nu3Y1+LOXKZCLAAAWg0lEQVR169bh9ddfR3JyMuLj423YK3EYy2autLQ0+Pr6YtCgQaK1aS0x89V2\n/PhxCIJQ82UjW7JkMG5oPzRt2rTmSzK//vorPvroIyxYsEC0PkvNUL7nnnsOrq6u+PLLL3WeRXr4\n8GEAqPmF29E15mtQYyBmgbx79240a9YMjz76qMi9NJ1SqYSrq6vVX4rPycnBkCFDEBUVhfT0dJv/\nMipGDqnGELHVLpDXn16P0SGjEaYIM7h8e6/2oGX138Z67sY5fPTjRxjWfRg6+3SGq4srTl87jb0F\ne2uWCfYJFj1DXVIePynGBsmL46ioKKSkpGDhwoX44YcfMHHiRHTo0AH379/HhQsXkJGRAXd3d7v8\nZl1bRkYG5s2bhwEDBiAyMlLnr8F5enqiR48eNf/Ozc3FlStXau5zyc/Px549ewAAAwcORPPmzW3b\neRMcP34cJSUluHHjBoCHGTw8PABA77e44uJiHD58GDNmzIBMZptHvFjLlHwHDx5Eamoqnn/+eSgU\nCty9exeHDh3Cli1bMG3aNLu9i1B3MP7+++/h5eVldjtVVVV49dVXER4eDk9PT+Tl5WHVqlUICQlp\nFF9i8fX1xaJFi7BixQp4enoiMjISubm5SEpKwqRJk3QeYeeMzLkGOaP8/Pyae/8qKipw9erVmutm\neHh4vY+ysxdzz8mvv/4aKSkp+Mtf/oL27dvjt99+Q1paGvbt24eUlBS4u7vbsPf6Vq9ebdX6P/30\nU80tdgsWLMDZs2d1ft6nTx+r2jeVtTmMMWeMtAVtgXz2+lmjhXFDyu6XIeWbFKR8k1Lvz8PahWFI\n1yEWt28OqY6fJGNDfQ8/tmaCgQdGnzx5kkaOHEmBgYEkk8moRYsWFBoaSn/729/o119/rf0wZpO5\nu7ub9ccejJk0aVLNw9jrTuHh4SYvW1RUZHAb5uQTMxvRwwfUG+pzXWvWrCEAlJuba9Y2HD1fQUEB\nDRs2jNq2bUtyuZwCAgIoLCyM0tPTdf5AQX3MfW1aorS0lLZu3Wrx+lVVVTRo0CAKCAgguVxOHTt2\npCVLltC9e/caXFeqfGIfZ7VaTe+88w4FBweTTCYjhUJBS5cupcrKSqPrOUM+c65Bddni9allaeZl\ny5YZzJeVlWV0XVvmq83Uc7KwsJAGDBhAgYGBJJfLyd3dnZ566inavXt3g+saug47ktTUVIPHrqG+\nO3o2LXPGyNocPd/Vsqs0+bPJ1H19d2q5qiU1Xd6UvFO86c9b/0zvnHqHKqoqjK7v6Pm0rBwb9GpZ\ngQw8UcJSgiCQpW0KgmDwCReNAedzXo05G8D5nB3nc17aT00bc77Gmg3gfM5Ok0/v1gXn+AYLY4wx\nxhhjNsDFMWOMMcYYYxpcHDPGGGOMMabBxTFjjDHGGGMaXBwzxhhjjDGmwcUxY4wxxhhjGlwcM8YY\nY4wxpsHFMWOMMcYYYxpcHDPGGGOMMabBxTFjjDHGGGMaXBwzxhhjjDGmwcUxY4wxxhhjGi5iN+jm\n5qYWBMGiotvNzQ2CIIjdJYfB+ZxXY84GcD5nx/mcl5ubmxoALB03HV1jPnYA53N22vOvLoGIRN2Q\nIAhkaZuCIEDs/jgSzue8GnM2gPM5O87nvLSFR2PO11izAZzP2Wny6VX/jfI3VcYYY4wxxizBxTFj\njDHGGGMaXBwzxhhjjDGmwcUxY4wxxhhjGlwcM8YYY4wxpsHFMWOMMcYYYxpcHDPGGGOMMabBxTFj\njDHGGGMaXBwzxhhjjDGmYbPiOCcnB6NGjUJgYCDkcjl8fX0RExODtLQ0VFdXm91eWVkZxowZg/37\n94vSv9zcXEydOhXdunVD8+bNoVAoMG7cOBQVFRld7+9//zsEQUDbtm1F6YeWPfOp1WqsWrUK7du3\nh5ubG3r37o29e/eK0g9A/GwAsHjxYvTv3x++vr4QBAHbt283uGxpaSnmzZsHhUIBV1dXtG3bFrGx\nsaL1xV75rl+/jkWLFiE0NBQtW7aEv78/oqKicOLECdH6YQ4p9kNRURFGjBgBLy8vuLu745lnnkFu\nbq5o7ZtDinyCINQ7nTt3TrRtmMpRrrG2ZGlmRzv3pBAREVHva3Pt2rX27pooJk+ejO7du8PT0xMe\nHh7o3bs3lEqlRfWJo/rggw/QrVs3uLq6omvXrti0aZO9u2SV+6r7WPX1Kjyb/iyC1gahWXIztFrd\nCk9vexof/fiRdX/Zj4hEnR42qevdd98lQRAoKiqKduzYQcePH6fPPvuMZs6cSc2aNaPPPvuMNH9z\nWm/d+pSWllJoaCgBILlcTvv37zdpPWMSEhLoz3/+M23YsIGys7Np165d1K1bN/Lx8aGff/7ZYD9a\ntWpFrVu3pjZt2jS4DWfJt3jxYpLL5fT222/TsWPHaOrUqSQIAh08eNDoNkzJJ0U2IiIPDw96+umn\naeLEiQSAUlNT613u9u3b1KNHDwoJCaG0tDQ6fvw4/f3vf6fZs2cbbd+ex47ItHz79++nDh06UFJS\nEn311Vd04MABGjhwIAmC0GA/TM1nKin2w82bNykwMJC6du1KGRkZtG/fPoqIiCAPDw/Kz883uq4z\n5CN62M/Y2FjKycnRme7du9fgemKy9zWoLrHz1ceazNaee7bIZ63w8HDq1auX3mvz+vXrRtdzhmxE\nRKNHj6YNGzbQl19+SYcPH6b58+eTIAg0Z84co+s5S74tW7aQIAi0ePFiOnbsGC1ZsoQEQaCNGzca\nXc+afN/8/A3FfR6nM+UXG79Wm+P63euERBicXv7y5Qbb0OTTr2Xrm2nNVHdHHj9+nARBoPj4+Ho7\ndvHiRfr+++9rd9Io7QVMoVCQXC6n4OBgksvldODAgQbXNaa4uFhv3pUrV0gQBFq6dGm967z00kvU\nv39/mjRpkmjFsb3z/ec//yG5XE5/+9vfdJaNjIykRx991Og2GsonVTYiourqaiIiKiwsNFocT5s2\njRQKBf32229mtW/PY0dkWr7S0lKqqqrSmVdVVUVdunShvn37Gm1fzAu8VPthxYoV1LRpUyosLKyZ\nV15eTgEBATRy5Eij6zpDPqKH/VyyZIlF64nF3teg+khdgFib2dpzT8p8d+7coYKCAp1JpVKZ3U54\neDiFhYWZvZ7Ux06sfPV58cUXycPDw+gyzlAcV1VVkb+/P02cOFFn/uTJk8nX15cqKysNrmtNvpAN\nIXoF65FLRyxur67rd69T+7Xtaf236+mX336hO/fv0JpTa2q21WR5Eyou17/u1Ga34vi5554jX19f\nqqioaDCoKQdh9erVpFAo6NKlS+Tu7k7btm2jqVOnUvfu3fUuTmIICAigv/71r3rzT548Sc2aNaPC\nwkJRi2N759uxYwcBoAsXLugst23bNgJAly9fNthWQ/lskc1Y8VheXk7NmjWjpKQks9t1lGPXUPFf\nn1GjRlHHjh2NLiPmBV6q/TBo0CDq1q2b3vzhw4dTs2bNjLbtDPmIHKM4tvc1qD5SFyBSZTb13JMy\n36BBg2q2oZ0aere3Po5aHIuVrz6zZs0iLy8vo8vYojjetm1bzZuIljhx4gQBoK+++kpn/rFjxwgA\nHTt2zOC61uTrquxaU6jGf1H/G6TWqFRVUkWVfm1ZuyjP+XeO0TYMFceS3nNcXV2N7Oxs9O/fH25u\nbqK0mZCQgNzcXHTs2BHAw3v0Nm3ahBMnTsDFxUWUbWgVFBSguLgY3bt315lfVVWFqVOnYsGCBejU\nqZOo27R3vry8PLi6uurlCgkJAQDk5+dbvD1bZqvP2bNnUVFRgVatWmHEiBFo1qwZPDw8MHToUFHu\ne7R3vvpUVlYiJydH7zUsJan2Q9OmTSGXy/Xmu7q6oqKiApcuXbK4bXNIfZzff/99uLq6onnz5oiM\njMTXX39tdZvmsPc1yB6kyGyPc68+5eXlAACFQoHLly+DiNC6dWuL2vruu+/QsmVLyGQy9OrVC1u3\nbhWzqxYRMx8RQaVSoaysDHv37kVaWhrmz58vZnfNtnHjRsTFxeHIkSMWt5GXlwcA6Nmzp858Mcb1\nhnT07ogA9wAoTysx59AcUduWNZXBzUW/tryvul/z/209Lfs+mKTF8c2bN1FRUYGgoCBR2/X399f5\ntyAI8PPzE3UbKpUK06dPh7+/P+Li4nR+9uabb+LBgwdYtGiRqNvUsme+27dvw8vLC4Ig6Czv4+NT\n83Nr2CKbIb/++isA4JVXXkHTpk2xb98+bNmyBd999x0iIiJw9+5dq7dhz3z1SUxMxC+//ILXXnvN\nptuVYj907doVhYWFuHXrVs08tVqN06dPA7D+tWkOqY7z+PHjsXHjRmRmZmLLli24desWIiMjkZ2d\nbXXb5rD3NdYexM5sr3OvPuPGjYO/vz8iIiIsfiOgX79+WLt2Lfbt24c9e/agc+fOmDJlClauXCly\nb80nRj4AOHjwIGQyGby9vTFy5EjEx8dj6dKlIvbUPEqlErNmzUJSUhISEhIsbkd7bfT29taZL9a4\nbkw7z3Y4NvGYZAVyXdu+24ZLpQ/fKBncZbBjFseOqLq6GiqVqmZSq9X1Ljd79mycOnUK6enpOi+o\nixcvIjk5GevXrxft3XAxWZuPiPQKY+18ezM1myHa5Tt06ICMjAzExMRg7Nix+Pjjj/Hzzz8jPT1d\nim6bzNp8de3evRspKSlYunQp+vbtK1IvpWdoP0yfPh1qtRoTJ07EpUuXcP36dcyZM6dmMGzSxDku\nZ8aO886dOzF69Gj07dsX48ePx8mTJxEYGIjXX3/djj02j7XXoMbA0c49Ly8vZGZmWlVAJiUl4aWX\nXkJ4eDiGDBmCvXv3YujQoUhOTq5599ZexMgHAH379sWZM2eQmZmJhQsXYvXq1ViyZInIvTVNQUEB\n5sx5WEguXbrU4JNstFNiYqLBtrTjd31ju5RmhM7AhF4TEBIQYpMCeff53Zh+YDoAoJNPJ2x9wfJP\nNiQdTXx9fdGsWTNcvXpVys2YJSoqCjKZrGZKSkrSW2bRokXYsmULtm3bhv79++v8bM6cOYiMjESf\nPn1QVlaGsrIyVFZWgohQVlaGiooKW0Wpl7X5fHx8UFpaqlcMl5aW1vzcXkzJZoyvry8AIDo6Wuci\n8eSTT8LT0xPfffedqP01l7X5atu/fz9iY2MRFxeH5cuXi9hL6RnaDx07dsSuXbtw9uxZdOrUCYGB\ngcjJycHLL78MAHjkkUfs2W2TmXOcW7RogUGDBuHMmTM27KF1rL0GOTtHO/cmTJiAAQMGiFZA1jZm\nzBjcv38f58+fF6GnlhEzX8uWLREaGoqoqCi88cYbWLx4MVJSUnDt2jUJem5ct27dMHPmTADA8uXL\nUVBQYHSaPXu2wbYMvUOs/bdU4/rcPnMR9/jDT4UsLZCvlF2BsFzQmSK2R+gtt/70eoz/dDyq1FXo\n4tulZluWkvRGSBcXF0RERODIkSN48OABXF1dpdycSTZv3qzz8XlgYKDOz5OTk5GSkoJ169ZhwoQJ\neuvn5+fj6tWr9b7T4e3tjblz59r1uY/W5gsJCcGDBw9w6dIlnfuOtfck9ejRQ6KeN6yhbA3R3l9l\n6Ldne7/zaG0+raNHj2LkyJEYNmwYNm/eLFb3bMbYfhg+fDiGDh2KCxcuQC6XIzg4GDNmzEC7du2g\nUCjs0V2zmXucDX2a46isvQY5M0c892rfsqItIKOjoxEREYHs7Gx06NDB4rbt9Y5kbVLmCw0NhVqt\nRlFREdq0aSNGd00mCAI2bNiAJk2aYMWKFejRowdGjBhhUVvasS8vL0/nTQRbj+vaAjlyRySUp5UA\ngHXPrbO63eXZy5F4PBEA8ETgEzg49iD83f2Nr9SQ+r6lZ80EMx/ldvnyZbMe5Vabu7u7Wd/ab8h7\n771HACg5OdngMjk5OZSVlaUzPfvss+Tn50dZWVk6j5mqyxnyaR/llpiYqDM/KiqKevbsabR9c/KJ\nnU2roac5hIaG0qOPPkpqtbpm3qlTpwgAffjhhwbbtfex02oo36lTp8jd3Z0GDhxo9PE8dZmbz1RS\n7Qeta9eukbe3N73xxhtGl3PWfL/99hu1a9eO+vXrZ3Q5Z8lnyjWoPlLlq4+lma0592yZj+jho+f+\n+Mc/kkKhMPoEooa88MIL1KxZMyovLze4jK2zEYmX75VXXiFBEOjatWsGl7FFvoSEBPr8888tXr+y\nspL8/PwoNjZWZ35cXBz5+PjQgwcPDK4rRb4f//MjBbwdQEKiQCevnrS4HbVaTbMPzq55MkX/nf3p\n7oO7ZrUBA0+rkPwr9P369fv/9u4ntKksDOPweyEmhdpSa8lIlSBSQSjKIF3UdhEVxUAJg5KVIHYh\nFFypYDfZDGSRLEI3KhRRo6hFcWVABcGASKEylKGLUkUG6WoiWYTisjLfLOY2jNo0f9v0ht8DWYTe\nc3LeJjf57ml6jqampnTt2jUtLS1pfHxcoVBIxWJRb9680Z07dzQzM6MjR45s9lA29OTJE125ckWR\nSEQnT57U3Nxc6Wfd3d2lK6vh4eGf2t6/f1+BQEDHjx/fquHWrNp8wWBQV69eVTKZVFdXl44ePaqn\nT58ql8vp+fPnrRp+RW/fvlWhUFA+n5f0325cO3fulKTvrrZTqZTOnDmjWCymS5cuqVAoKB6P69Ch\nQzp//nxLxl6NavJ9+PBBY2Nj6uvr0/Xr1zU/P/9dH+u9dr1kdXVVk5OTCofD6u7u1uLiopLJpAYH\nBxv6Z5XtIp1O6+PHjzpx4oT6+/u1vLysdDqtfD6vx48ft3p4Dav2PciLvHbu/TjDurCwoJ6enrLH\nv3v3TqlUSufOndP+/fu1srKiBw8eKJvNKpVKqbOzcwtHX1mt+V68eKFMJqNoNKpQKKSvX7/q1atX\nun37tiYmJur+K16zpNPphtrv2LFDiURCly9f1t69e3Xq1Cnlcjndu3dPN27cWHcVoM20NoM8//e8\nRkOjdfezvLKsm3/cLN1//ddrdSW7vjsm81tG47+O1975ehVzIzeVucqYnZ21WCxme/bsMZ/PZ7t2\n7bLTp0/bw4cPSxsclGtbTjNnNS5evPjTWolrt3A4XLFtM3fIW9OqfN++fbNEIlFaEP/w4cP27Nmz\nio9RS75mz0iFw+Gy+X708uVLGxoaskAgYL29vXbhwgXL5/Mb9t/K586sunyZTKbsMZXGX2u+ajXz\n97C6umpjY2MWDAbN7/fbgQMHLB6PV9w9zswb+bLZrI2MjNju3bvN5/NZb2+vRaNRe//+fcW2XsjX\nyHvsZuVbTz2ZGz33tjLf/xWLRbt7927F4z59+mSRSMT6+/vN7/dbZ2enHTt2zGZmZiq2bVU2s+rz\nLS0t2dmzZ23fvn3m9/stGAza6OioPXr0qFSflNPKfLWanp62gwcPmt/vt4GBAbt161bFNts53+fi\n5w13yNPvssyfmQ37UJmZY8eavAqB4zhWb5+O42yLVRE2C/m8q52zSeTzOvJ519r3dds5X7tmk8jn\ndW6+n7407421jwAAAIAtQHEMAAAAuCiOAQAAABfFMQAAAOCiOAYAAABcFMcAAACAi+IYAAAAcFEc\nAwAAAC6KYwAAAMBFcQwAAAC4KI4BAAAAF8UxAAAA4PI1u8OOjo4vjuP8UmfbfxzHaduCnXze1c7Z\nJPJ5Hfm8q6Oj44sk1fu5ud2183Mnkc/r1s6/HzlmttVjAQAAALaltr0aAAAAAGpFcQwAAAC4KI4B\nAAAAF8UxAAAA4KI4BgAAAFz/AuRWqpteGOQSAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0096cc7ac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"visual_scoring_matrix(s1, s2, score_mat, arrow_mat)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Let's generate some sequences and see how fast this is"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"def random_dna_seq(length=1000):\n",
" seq = [random.choice(dbet) for x in range(length)]\n",
" return ''.join(seq)\n",
"\n",
"def mutate_dna_seq(seq, chance=1/5):\n",
" mut_seq_base = [random.choice(dbet) if random.random() < chance else x for x in seq]\n",
" mut_seq_indel = [random.choice(('', x + random.choice(dbet))) if random.random() < chance else x for x in mut_seq_base]\n",
" return ''.join(mut_seq_indel)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AATCTACTTTCCTTAGTCTTCATGTAGCCCCTCGGTCAAGTCCTCTTATCCCTATCGTAAATGCCATCGTGAGTTTAGTTCATGTGCGTTGTGGTAACATGTATACGTATGCGGGAGCTGAGTCTCACGAAAACGAGTTCAAGCATTTCAATGAGTCCAATGTGCTTTATATTGACTGTTCTCAGTCTGTCATATCGCTGCGCCGCCTCTTATCAATGTAGGTAACGGCGTAGAGTGCACACGAATCGTCAAGTTACTCCCATCTCCATTTTTACGCGGTAATATGTCATGCACGCCGATACAGATAAAAGGGGCGTGGACGGGCCACGAATGACTACCCTGTGGGCGTATCGCAAGTTGCGTAAATCCGCTCACGAGTCCCATCGAGCATTCACCCATATGACCAGCCCGATAATTATGGACGTAATATAACCTCCCATTGGGGACGTACGCGACCTCCATTTGTGGAAAGATGGTTCGGTTGCTGACTCAAATGATAAAACTCCTGTACGGCGTTGACACAAACCTCCTTGCCCTGTCTCTATGGCATCCAACGTTGTTCTTCAACTGCGCCGTCTCGTAATGCAGCTATAAGTTCCACCTTGAACCGCGTTCAGCGGTTCTTCTTTCCACTGTGAATTTTAAAAAAGTTTCATCCTCGATGTATAGCGTTCCGACGGGCACTTAAGAAAGGGGCTTCGAGCCGGCCCATCGATAAAGACGATTAAGTACGGTAACGAGGCCCCGGGGGATGTATGTATAGCACGCTACTGCGTGGATGTTCCAGAACATGGGTTCGTAGTCCCGACGCGCTGCCGGCCCCTGTTTTCGTTCTTGCTATCTGAGCGCTTGTTTTCGCGTGTATCAGCTTTATGCACATAATAAAAAGTGATGCAGAGATAAGTTCTTCTAATCTGACAGGGAATAAATGAGTGCCCGATGGACTGGGTCACTTAATGGCATCTGCCTTCCGAGAGTAAAGGTGCCATTTCCACTCTTT\n",
"TATTATTTCCTCAGTCGTTCTATGTTTCCCCCGCGGTCAAGTCGCTTTAGTGCTATGCGTAAAGGTTACGTGAGTTTATTTAGGCGGTTGGTGGTAACAGTATCGTATTGCGGGAGCTGTAGCTCCACCAAATACGTTCAGCATATACAATGGCCAATTGCTTTTACAGTGGACTTCGCCCAGATGATACATACTCCTGCGCCATCTTATCTATGTGAGACGGCGTGAGTGCGCGACAATCGATCAGAATACTGCATCCCAGTTTACGCTGCATAGTCAGCACGCTGGTAACAGCGAAAAGTGCTATCGACCCCCAGAGATGCATGCCTGTTGGGCGATCATCGCAAGTGCGTAAATCCGCTCACGGGTCCAACGATGACTTCACCTAATATGACTGCGATAAAATATAGGACGAGAAGATCTTAATTGGGGGACGGACGCGAGCCTCGATTTGTGAAAAAATGGGGTTCGTTTGCGGACCCAAAAGATAAGCACACCTTGTACGGTTGGACTCATCCTCACTTGCCTCGTCACGTGGCATACCAACGTTGGTCACTTCAACGCGCCGTAGCCGTCAATGCCTGAATAGTACCATCGTGAATCCTCTTCAAGGTTCTAATCCTTGGGATTTTAAAAAGTTCATGGCTACGAGTAAGGTTCCGCTCGGGCACTTATAGTAGTGTGCTCGAACCGCCATCGAGGAAAGACCCATTAAGTACGGAACGTGGCCCCGGGGGAATGTATATATAGCACGCTAACTGCGTGGCTTTCCAAGCACGCGGTCATAGTTCGCCAGAGGGCTGTTTGCCCTTTTCGTTATACCTGATCTTCGCGCCTGTTTCGCTGCTGTTATCTAGCTTAATTACACTATGATGAAAACGGTGCATGCAGGAGATAGTTCGTTCAAATCGGAGGATCTTCGTGCCGTCATGGGTGCCTTAATGGCTCCGCACTTCAGAGTAAGGCCGGATTTCCTCTAT\n",
"11.7 s ± 3.06 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"852.0 years for the whole genome\n"
]
}
],
"source": [
"s1 = random_dna_seq()\n",
"s2 = mutate_dna_seq(s1)\n",
"print(s1)\n",
"print(s2)\n",
"a = %timeit -o nw_alignment(dna_sub_mat, dbet, s1, s2, gap=-3)\n",
"print('{:.1f} years for the whole genome'.format(a.average * 2300000000 / 60 / 60 / 24 / 365.25))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"If we wanted to shift this one position at a time along the whole genome and check the alignments, how long would it take?\n",
"\n",
"That's a long time!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Let's make it faster! (Just because)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"So in reality, we don't actually want to use this algorithm to align our fragments to the whole genome. It's too slow, and there's no good way to decide which alignment is \"best.\" It's still a good introduction to thinking about these types of problems, though. And since it's actually fairly easy and demonstrates how you can improve your code by understanding your algorithm, we'll do something to make it a bit faster."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"||-|A|C|G|T|T|T|G|T|C|G|C|\n",
"|-|\n",
"|__-__|0|←-3|←-6|←-9|←-12|←-15|←-18|←-21|←-24|←-27|←-30|←-33|\n",
"|__A__|↑-3|↖1|←-2||||||||||\n",
"|__C__|↑-6|↑-2|||||||||||\n",
"|__T__|↑-9||||||||||||\n",
"|__T__|↑-12||||||||||||\n",
"|__T__|↑-15||||||||||||\n",
"|__C__|↑-18||||||||||||\n",
"|__T__|↑-21||||||||||||\n",
"|__G__|↑-24||||||||||||\n",
"|__C__|↑-27||||||||||||\n",
"\n",
"We can't calculate a whole row or column at a time because the values depend on those in the same row/column. But what about diagonals?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"If you look at the diagonals, you know the values above and to the left, so you have everything you need to calculate your score."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"We'll \"get rid of\" our nested loop (really just abstract it into a faster numpy \"loop\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"This is going to take a couple more steps, but it will be worth it in the end.\n",
"\n",
"First we pre-calculate the \"upper-left score\" for each location."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
" [0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],\n",
" [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
" [0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def sub_values(sub_mat, abet, seq1, seq2):\n",
" # convert the sequences to numbers\n",
" seq1_ind = [abet.index(i) for i in seq1]\n",
" seq2_ind = [abet.index(i) for i in seq2]\n",
" sub_vals = np.array([[0] * (len(seq2)+1)] + [[0] + [sub_mat[y, x] for x in seq2_ind] for y in seq1_ind], int)\n",
" return sub_vals\n",
"\n",
"sub_values(dna_sub_mat, dbet, 'AACGTTA', 'AAGCTTAAAAAAAA')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Then we get a list of all the diagonals in the matrix."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[[array([1]), array([1])],\n",
" [array([2, 1]), array([1, 2])],\n",
" [array([3, 2, 1]), array([1, 2, 3])],\n",
" [array([4, 3, 2]), array([1, 2, 3])],\n",
" [array([5, 4, 3]), array([1, 2, 3])],\n",
" [array([6, 5, 4]), array([1, 2, 3])],\n",
" [array([6, 5]), array([2, 3])],\n",
" [array([6]), array([3])]]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def diags(l1, l2):\n",
" ys = np.array([np.arange(l1) + 1 for i in np.arange(l2)])\n",
" xs = np.array([np.arange(l2) + 1 for i in np.arange(l1)])\n",
" diag_ys = [np.flip(ys.diagonal(i), 0) for i in range(1-l2, l1)]\n",
" diag_xs = [xs.diagonal(i) for i in range(1-l1, l2)]\n",
" index_list = []\n",
" for y, x in zip(diag_ys, diag_xs):\n",
" index_list.append([y, x])\n",
" return index_list\n",
"\n",
"diags(6, 3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"And here's the actual function. It takes the same arguments and produces the same matrices."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(array([[ 0, -8, -16, ..., -7824, -7832, -7840],\n",
" [ -8, 0, -7, ..., -7815, -7823, -7831],\n",
" [ -16, -8, 1, ..., -7806, -7814, -7822],\n",
" ..., \n",
" [-7984, -7975, -7966, ..., 321, 328, 337],\n",
" [-7992, -7983, -7974, ..., 313, 321, 329],\n",
" [-8000, -7991, -7982, ..., 305, 313, 322]]),\n",
" array([[1, 1, 1, ..., 1, 1, 1],\n",
" [0, 2, 2, ..., 1, 1, 1],\n",
" [0, 0, 2, ..., 1, 1, 1],\n",
" ..., \n",
" [0, 0, 0, ..., 2, 0, 2],\n",
" [0, 0, 0, ..., 0, 2, 0],\n",
" [0, 0, 0, ..., 0, 0, 2]]))"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def FastNW(sub_mat, abet, seq1, seq2, gap=-8):\n",
" sub_vals = sub_values(sub_mat, abet, seq1, seq2)\n",
" # Get the lengths of the sequences\n",
" seq1_len, seq2_len = len(seq1), len(seq2)\n",
" # Create the scoring and arrow matrices\n",
" score_mat = np.zeros((seq1_len+1, seq2_len+1), int)\n",
" arrow_mat = np.zeros((seq1_len+1, seq2_len+1), int)\n",
" # Fill first column and row of score matrix with scores based on gap penalty\n",
" score_mat[0] = np.arange(seq2_len+1) * gap\n",
" score_mat[:,0] = np.arange(seq1_len+1) * gap\n",
" # Fill top row of arrow matrix with ones (left arrow)\n",
" arrow_mat[0] = np.ones(seq2_len+1)\n",
" # Get the list of diagonals\n",
" diag_list = diags(seq1_len, seq2_len)\n",
" # fill in the matrix\n",
" for diag in diag_list:\n",
" # Matrix to hold all three possible scores for every element in the diagonal\n",
" f = np.zeros((3, len(diag[0])), float)\n",
" # Cell above + gap penalty for every cell in the diagonal\n",
" x, y = diag[0]-1, diag[1]\n",
" f[0] = score_mat[x, y] + gap\n",
" # Cell to the left + gap penalty for every cell in the diagonal\n",
" x, y = diag[0], diag[1]-1\n",
" f[1] = score_mat[x, y] + gap\n",
" # Cell to the upper left + alignment score for every cell in the diagonal\n",
" x, y = diag[0]-1, diag[1]-1\n",
" f[2] = score_mat[x,y] + sub_vals[diag]\n",
" max_score = (f.max(0))\n",
" max_score_pos = f.argmax(0)\n",
" score_mat[diag] = max_score\n",
" arrow_mat[diag] = max_score_pos\n",
" return score_mat, arrow_mat\n",
"\n",
"FastNW(dna_sub_mat, dbet, s1, s2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"So how much faster is it?"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9.04 s ± 369 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"658.5 years for the whole genome\n",
"589 ms ± 2.68 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
"43.0 years for the whole genome\n"
]
}
],
"source": [
"s1 = random_dna_seq()\n",
"s2 = mutate_dna_seq(s1)\n",
"a = %timeit -o nw_alignment(dna_sub_mat, dbet, s1, s2)\n",
"print('{:.1f} years for the whole genome'.format(a.average * 2300000000 / 60 / 60 / 24 / 365.25))\n",
"a = %timeit -o FastNW(dna_sub_mat, dbet, s1, s2)\n",
"print('{:.1f} years for the whole genome'.format(a.average * 2300000000 / 60 / 60 / 24 / 365.25))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Now why did we use the substitution matrix?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"Here's how DNA translates to protein:\n",
"\n",
"[Wikipedia](https://en.wikipedia.org/wiki/DNA_codon_table)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"So we can align proteins, too!"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"blosum50 = np.array(\n",
" [[ 5,-2,-1,-2,-1,-1,-1, 0,-2,-1,-2,-1,-1,-3,-1, 1, 0,-3,-2, 0],\n",
" [-2, 7,-1,-2,-1, 1, 0,-3, 0,-4,-3, 3,-2,-3,-3,-1,-1,-3,-1,-3],\n",
" [-1,-1, 7, 2,-2, 0, 0, 0, 1,-3,-4,-0,-2,-4,-2,-1, 0,-4,-2,-3],\n",
" [-2,-2, 2, 8,-4, 0, 2,-1,-1,-4,-4,-1,-4,-5,-1, 0,-1,-5,-3,-4],\n",
" [-1,-4,-2,-4,13,-3,-3,-3,-3,-2,-2,-3,-2,-2,-4,-1,-1,-5,-3,-1],\n",
" [-1,-1, 0, 0,-3, 7, 2,-2, 1,-3,-2, 2, 0,-4,-1,-0,-1,-1,-1,-3],\n",
" [-1, 0, 0, 2,-3, 2, 6,-3, 0,-4,-3, 1,-2,-3,-1,-1,-1,-3,-2,-3],\n",
" [ 0,-3, 0,-1,-3,-2,-3, 8,-2,-4,-4,-2,-3,-4,-2, 0,-2,-3,-3,-4],\n",
" [-2, 0, 1,-1,-3, 1, 0,-2,10,-4,-3, 0,-1,-1,-2,-1,-2,-3,-1, 4],\n",
" [-1,-4,-3,-4,-2,-3,-4,-4,-4, 5, 2,-3, 2, 0,-3,-3,-1,-3,-1, 4],\n",
" [-2,-3,-4,-4,-2,-2,-3,-4,-3, 2, 5,-3, 3, 1,-4,-3,-1,-2,-1, 1],\n",
" [-1, 3, 0,-1,-3, 2, 1,-2, 0,-3,-3, 6,-2,-4,-1, 0,-1,-3,-2,-3],\n",
" [-1,-2,-2,-4,-2, 0,-2,-3,-1, 2, 3,-2, 7, 0,-3,-2,-1,-1, 0, 1],\n",
" [-3,-3,-4,-5,-2,-4,-3,-4,-1, 0, 1,-4, 0, 8,-4,-3,-2, 1, 4,-1],\n",
" [-1,-3,-2,-1,-4,-1,-1,-2,-2,-3,-4,-1,-3,-4,10,-1,-1,-4,-3,-3],\n",
" [ 1,-1, 1, 0,-1, 0,-1, 0,-1,-3,-3, 0,-2,-3,-1, 5, 2,-4,-2,-2],\n",
" [ 0,-1, 0,-1,-1,-1,-1,-2,-2,-1,-1,-1,-1,-2,-1, 2, 5,-3,-2, 0],\n",
" [-3,-3,-4,-5,-5,-1,-3,-3,-3,-3,-2,-3,-1, 1,-4,-4,-3,15, 2,-3],\n",
" [-2,-1,-2,-3,-3,-1,-2,-3, 2,-1,-1,-2, 0, 4,-3,-2,-2, 2, 8,-1],\n",
" [ 0,-3,-3,-4,-1,-3,-3,-4,-4, 4, 1,-3, 1,-1,-3,-2, 0,-3,-1, 5]])\n",
"pbet = 'ARNDCQEGHILKMFPSTWYV'"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAs8AAAG+CAYAAABs5OlOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0093c93550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"s1 = [random.choice(pbet) for _ in range(10)]\n",
"s2 = [random.choice(pbet) if random.random() < .25 else x for x in s1] + [random.choice(pbet) for _ in range(10)]\n",
"score_mat, arrow_mat = FastNW(blosum50, pbet, s1, s2)\n",
"visual_scoring_matrix(s1, s2, score_mat, arrow_mat)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Other things we can do with this algorithm:\n",
"* Local alignment\n",
"* Affine gap penalties"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Other useful bioinformatics Python packages:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"853.3953523635864 MiB\n",
"83257441 bp\n",
"TGTTATTCTTTTGCGCCTGTTGCTGTCCCTAGGATACCGGAGATACTCAGCCTGTGCTTGCCAAGGGTATACATATGTATTCAAATACGCACATCCTGTAAACAGCTGTGGTGGAAACCCAAGACCAGACTTGTAGAGACCTGGATCTATTTTGCAGGCCACAGTGAAGGGCTGTGTGAGCTTGAGTCCTCTCTGAGCCTCACGTTTTACATCTAAAACATGAGGGGCTGGTCCAACTGGTTTCCAAAAGTCCTTCCAACTGGGACAGCCTGTGGATCTGCCAGTGCTCCAGCTGCACCAGCATGCAGCACATGTGCCCACACGTGTACACACCTGCTTACGCACGGCCACCCACCATGCTGAAGAACCAGCCCTCGGCATCCTTGCAGCTCTTCTCCACCAATGTCTTGTCCTGGTCACGCATCTCATTCAGGATGCAGCTCAGGTTCACTCCAGGCACAGTGTCCATCTTCACACTGACATCCTCATCCACCTGACCT\n"
]
}
],
"source": [
"from pysam import FastaFile\n",
"from os.path import getsize\n",
"\n",
"print(getsize('Homo_sapiens.GRCh38.dna.primary_assembly.fasta.gz')/1024**2, 'MiB')\n",
"\n",
"\n",
"with FastaFile('Homo_sapiens.GRCh38.dna.primary_assembly.fasta.gz') as myfasta:\n",
" chr17len = myfasta.get_reference_length('17')\n",
" print(chr17len, 'bp')\n",
" seq = myfasta.fetch('17', int(chr17len/2), int(chr17len/2)+500)\n",
"print(seq)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"203 µs ± 1.11 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"with FastaFile('Homo_sapiens.GRCh38.dna.primary_assembly.fasta.gz') as myfasta:\n",
" chr17len = myfasta.get_reference_length('17')\n",
" %timeit myfasta.fetch('17', int(chr17len/2), int(chr17len/2)+500)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Seq('ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG', IUPACUnambiguousDNA())\n",
"Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*'))\n"
]
}
],
"source": [
"from Bio.Seq import Seq\n",
"from Bio.Alphabet import IUPAC\n",
"coding_dna = Seq(\"ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG\", IUPAC.unambiguous_dna)\n",
"print(coding_dna.__repr__())\n",
"print(coding_dna.translate().__repr__())"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Any questions?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Other possible topics:\n",
"How do you actually get from reads to something usable?\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Why does bioinformatics use flat files and not databases for everything?\n",
"The best answer I can give is that of Heng Li when talking about Tabix:\n",
"> It is straightforward to achieve overlap queries using the standard B-tree index (with or without binning) implemented in all SQL databases, or the R-tree index in PostgreSQL and Oracle. But there are still many reasons to use tabix. Firstly, tabix directly works with a lot of widely used TAB-delimited formats such as GFF/GTF and BED. We do not need to design database schema or specialized binary formats. Data do not need to be duplicated in different formats, either. Secondly, tabix works on compressed data files while most SQL databases do not. The GenCode annotation GTF can be compressed down to 4%. Thirdly, tabix is fast. The same indexing algorithm is known to work efficiently for an alignment with a few billion short reads. SQL databases probably cannot easily handle data at this scale. Last but not the least, tabix supports remote data retrieval. One can put the data file and the index at an FTP or HTTP server, and other users or even web services will be able to get a slice without downloading the entire file."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"What things can you do in bioinformatics?\n",
"* Look at variants - which ones matter?\n",
"* Assemble genomes for new organisms\n",
"* Compare genomes of different organisms\n",
"* Biomarker studies:\n",
" * Compare gene expression levels across case and control samples, see if you can find significantly different ones. (It's messy. Also, R is better for this than Python (shhh...))\n",
"* Molecular interaction networks"
]
}
],
"metadata": {
"anaconda-cloud": {},
"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": "3.5.4"
},
"nbpresent": {
"slides": {
"1a878cd3-6859-42a9-acfa-b96c07fa65f7": {
"id": "1a878cd3-6859-42a9-acfa-b96c07fa65f7",
"prev": null,
"regions": {
"3d4268fa-250f-4344-9516-ff77f4ec0e46": {
"attrs": {
"height": 0.4,
"width": 0.8,
"x": 0.1,
"y": 0.5
},
"content": {
"cell": "b0746739-b0f7-4130-8773-2024b6d8858e",
"part": "whole"
},
"id": "3d4268fa-250f-4344-9516-ff77f4ec0e46"
},
"867408ce-ca40-4626-97bb-e8a421eadcb8": {
"attrs": {
"height": 0.8,
"width": 0.8,
"x": 0.1,
"y": 0.1
},
"content": {
"cell": "dfdc94f4-6768-4140-8109-8ac2e61fcdf4",
"part": "whole"
},
"id": "867408ce-ca40-4626-97bb-e8a421eadcb8"
},
"c2dbf2de-6e36-4683-a5ea-dc1cf471d6f2": {
"attrs": {
"height": 0.4,
"width": 0.8,
"x": 0.1,
"y": 0.5
},
"content": {
"cell": "2df25111-1de8-447f-b03a-361cc5c97373",
"part": "whole"
},
"id": "c2dbf2de-6e36-4683-a5ea-dc1cf471d6f2"
}
}
},
"7fd1644c-8a23-4211-8e4e-6b0a84e6198a": {
"id": "7fd1644c-8a23-4211-8e4e-6b0a84e6198a",
"prev": "1a878cd3-6859-42a9-acfa-b96c07fa65f7",
"regions": {
"3223a531-ef8f-4311-a064-50ac87f65594": {
"attrs": {
"height": 0.8,
"width": 0.8,
"x": 0.1,
"y": 0.1
},
"content": {
"cell": "e4dc1f84-5612-4ef5-a45c-35f46c51c136",
"part": "whole"
},
"id": "3223a531-ef8f-4311-a064-50ac87f65594"
}
}
}
},
"themes": {
"default": "1629dac1-1f53-4f1b-8318-e8aaea63fd7a",
"theme": {}
}
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
IanHawke/maths-with-python | 05-classes-oop.ipynb | 1 | 18500 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Classes and Object Oriented Programming"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have looked at functions which take input and return output (or do things to the input). However, sometimes it is useful to think about *objects* first rather than the actions applied to them."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Think about a polynomial, such as the cubic\n",
"\n",
"$$ p(x) = 12 - 14 x + 2 x^3. $$\n",
"\n",
"This is one of the standard forms that we would expect to see for a polynomial. We could imagine representing this in Python using a container containing the coefficients, such as:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p_normal = (12, -14, 0, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The order of the polynomial is given by the number of coefficients (minus one), which is given by `len(p_normal)-1`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, there are many other ways it could be written, which are useful in different contexts. For example, we are often interested in the roots of the polynomial, so would want to express it in the form\n",
"\n",
"$$ p(x) = 2 (x - 1)(x - 2)(x + 3). $$\n",
"\n",
"This allows us to read off the roots directly. We could imagine representing this in Python using a container containing the roots, such as:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p_roots = (1, 2, -3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"combined with a single variable containing the leading term,"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p_leading_term = 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that the order of the polynomial is given by the number of roots (and hence by `len(p_roots)`). This form represents the same polynomial but requires two pieces of information (the roots and the leading coefficient)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The different forms are useful for different things. For example, if we want to add two polynomials the standard form makes it straightforward, but the factored form does not. Conversely, multiplying polynomials in the factored form is easy, whilst in the standard form it is not."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But the key point is that the object - the polynomial - is the same: the representation may appear different, but it's the object itself that we really care about. So we want to represent the object in code, and work with that object."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Classes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python, and other languages that include *object oriented* concepts (which is most modern languages) allow you to define and manipulate your own objects. Here we will define a *polynomial* object step by step."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Polynomial(object):\n",
" explanation = \"I am a polynomial\"\n",
" \n",
" def explain(self):\n",
" print(self.explanation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have defined a *class*, which is a single object that will represent a polynomial. We use the keyword `class` in the same way that we use the keyword `def` when defining a function. The definition line ends with a colon, and all the code defining the object is indented by four spaces.\n",
"\n",
"The name of the object - the general class, or type, of the thing that we're defining - is `Polynomial`. The convention is that class names start with capital letters, but this convention is frequently ignored.\n",
"\n",
"The type of object that we are building on appears in brackets after the name of the object. The most basic thing, which is used most often, is the `object` type as here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Class variables are defined in the usual way, but are only visible inside the class. Variables that are set outside of functions, such as `explanation` above, will be common to all class variables.\n",
"\n",
"Functions are defined inside classes in the usual way (using the `def` keyword, indented by four additional spaces). They work in a special way: they are not called directly, but only when you have a member of the class. This is what the `self` keyword does: it takes the specific *instance* of the class and uses its data. Class functions are often called *methods*.\n",
"\n",
"Let's see how this works on a specific example:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I am a polynomial\n",
"I am a polynomial\n",
"I change the string\n"
]
}
],
"source": [
"p = Polynomial()\n",
"print(p.explanation)\n",
"p.explain()\n",
"p.explanation = \"I change the string\"\n",
"p.explain()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first line, `p = Polynomial()`, creates an *instance* of the class. That is, it creates a specific `Polynomial`. It is assigned to the variable named `p`. We can access class variables using the \"dot\" notation, so the string can be printed via `p.explanation`. The method that prints the class variable also uses the \"dot\" notation, hence `p.explain()`. The `self` variable in the definition of the function is the instance itself, `p`. This is passed through automatically thanks to the dot notation.\n",
"\n",
"Note that we can change class variables in specific instances in the usual way (`p.explanation = ...` above). This only changes the variable for that instance. To check that, let us define two polynomials:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Changed the string a third time\n",
"I am a polynomial\n"
]
}
],
"source": [
"p = Polynomial()\n",
"p.explanation = \"Changed the string again\"\n",
"q = Polynomial()\n",
"p.explanation = \"Changed the string a third time\"\n",
"p.explain()\n",
"q.explain()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can of course make the methods take additional variables. We modify the class (note that we have to completely re-define it each time):"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Polynomial(object):\n",
" explanation = \"I am a polynomial\"\n",
" \n",
" def explain_to(self, caller):\n",
" print(\"Hello, {}. {}.\".format(caller,self.explanation))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then use this, remembering that the `self` variable is passed through automatically:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello, Alice. I am a polynomial.\n"
]
}
],
"source": [
"r = Polynomial()\n",
"r.explain_to(\"Alice\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At the moment the class is not doing anything interesting. To do something interesting we need to store (and manipulate) relevant variables. The first thing to do is to add those variables when the instance is actually created. We do this by adding a special function (method) which changes how the variables of type `Polynomial` are created:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Polynomial(object):\n",
" \"\"\"Representing a polynomial.\"\"\"\n",
" explanation = \"I am a polynomial\"\n",
" \n",
" def __init__(self, roots, leading_term):\n",
" self.roots = roots\n",
" self.leading_term = leading_term\n",
" self.order = len(roots)\n",
" \n",
" def explain_to(self, caller):\n",
" print(\"Hello, {}. {}.\".format(caller,self.explanation))\n",
" print(\"My roots are {}.\".format(self.roots))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This `__init__` function is called when a variable is created. There are a number of special class functions, each of which has two underscores before and after the name. This is another Python *convention* that is effectively a rule: functions surrounded by two underscores have special effects, and will be called by other Python functions internally. So now we can create a variable that represents a specific polynomial by storing its roots and the leading term:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello, Alice. I am a polynomial.\n",
"My roots are (1, 2, -3).\n",
"Hello, Bob. I am a polynomial.\n",
"My roots are (1, 1, 0, -2).\n"
]
}
],
"source": [
"p = Polynomial(p_roots, p_leading_term)\n",
"p.explain_to(\"Alice\")\n",
"q = Polynomial((1,1,0,-2), -1)\n",
"q.explain_to(\"Bob\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is always useful to have a function that shows what the class represents, and in particular what this particular instance looks like. We can define another method that explicitly `display`s the `Polynomial`:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Polynomial(object):\n",
" \"\"\"Representing a polynomial.\"\"\"\n",
" explanation = \"I am a polynomial\"\n",
" \n",
" def __init__(self, roots, leading_term):\n",
" self.roots = roots\n",
" self.leading_term = leading_term\n",
" self.order = len(roots)\n",
" \n",
" def display(self):\n",
" string = str(self.leading_term)\n",
" for root in self.roots:\n",
" if root == 0:\n",
" string = string + \"x\"\n",
" elif root > 0:\n",
" string = string + \"(x - {})\".format(root)\n",
" else:\n",
" string = string + \"(x + {})\".format(-root)\n",
" return string\n",
" \n",
" def explain_to(self, caller):\n",
" print(\"Hello, {}. {}.\".format(caller,self.explanation))\n",
" print(\"My roots are {}.\".format(self.roots))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2(x - 1)(x - 2)(x + 3)\n",
"-1(x - 1)(x - 1)x(x + 2)\n"
]
}
],
"source": [
"p = Polynomial(p_roots, p_leading_term)\n",
"print(p.display())\n",
"q = Polynomial((1,1,0,-2), -1)\n",
"print(q.display())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Where classes really come into their own is when we manipulate them as objects in their own right. For example, we can multiply together two polynomials to get another polynomial. We can create a method to do that:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Polynomial(object):\n",
" \"\"\"Representing a polynomial.\"\"\"\n",
" explanation = \"I am a polynomial\"\n",
" \n",
" def __init__(self, roots, leading_term):\n",
" self.roots = roots\n",
" self.leading_term = leading_term\n",
" self.order = len(roots)\n",
" \n",
" def display(self):\n",
" string = str(self.leading_term)\n",
" for root in self.roots:\n",
" if root == 0:\n",
" string = string + \"x\"\n",
" elif root > 0:\n",
" string = string + \"(x - {})\".format(root)\n",
" else:\n",
" string = string + \"(x + {})\".format(-root)\n",
" return string\n",
" \n",
" def multiply(self, other):\n",
" roots = self.roots + other.roots\n",
" leading_term = self.leading_term * other.leading_term\n",
" return Polynomial(roots, leading_term)\n",
" \n",
" def explain_to(self, caller):\n",
" print(\"Hello, {}. {}.\".format(caller,self.explanation))\n",
" print(\"My roots are {}.\".format(self.roots))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-2(x - 1)(x - 2)(x + 3)(x - 1)(x - 1)x(x + 2)\n"
]
}
],
"source": [
"p = Polynomial(p_roots, p_leading_term)\n",
"q = Polynomial((1,1,0,-2), -1)\n",
"r = p.multiply(q)\n",
"print(r.display())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now have a simple class that can represent polynomials and multiply them together, whilst printing out a simple string form representing itself. This can obviously be extended to be much more useful."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exercise: Equivalence classes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An *equivalence class* is a relation that groups objects in a set into related subsets. For example, if we think of the integers modulo $7$, then $1$ is in the same equivalence class as $8$ (and $15$, and $22$, and so on), and $3$ is in the same equivalence class as $10$. We use the tilde $3 \\sim 10$ to denote two objects within the same equivalence class.\n",
"\n",
"Here, we are going to define the positive integers programmatically from equivalent sequences."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 1\n",
"\n",
"Define a Python class `Eqint`. This should be\n",
"\n",
"1. Initialized by a sequence;\n",
"2. Store the sequence;\n",
"3. Have a `display` method that returns a string showing the integer length of the sequence;\n",
"4. Have an `equals` method that checks if two `Eqint`s are equal, which is `True` if, and only if, their sequences have the same length."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 2\n",
"\n",
"Define a `zero` object from the empty list, and three `one` objects, from a single object list, tuple, and string. For example\n",
"\n",
"```python\n",
"one_list = Eqint([1])\n",
"one_tuple = Eqint((1,))\n",
"one_string = Eqint('1')\n",
"```\n",
"\n",
"Check that none of the `one` objects equal the zero object, but all equal the other `one` objects. Display each object to check that the representation gives the integer length."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 3\n",
"\n",
"Redefine the class by including an `add` method that combines the two sequences. That is, if `a` and `b` are `Eqint`s then `a.add(b)` should return an `Eqint` defined from combining `a` and `b`s sequences.\n",
"\n",
"##### Note\n",
"\n",
"Adding two different *types* of sequences (eg, a list to a tuple) does not work, so it is better to either iterate over the sequences, or to convert to a uniform type before adding."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 4\n",
"\n",
"Check your addition function by adding together all your previous `Eqint` objects (which will need re-defining, as the class has been redefined). Display the resulting object to check you get `3`, and also print its internal sequence."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 5\n",
"\n",
"We will sketch a construction of the positive integers from *nothing*.\n",
"\n",
"1. Define an empty list `positive_integers`.\n",
"2. Define an `Eqint` called `zero` from the empty list. Append it to `positive_integers`.\n",
"3. Define an `Eqint` called `next_integer` from the `Eqint` defined by *a copy of* `positive_integers` (ie, use `Eqint(list(positive_integers))`. Append it to `positive_integers`.\n",
"4. Repeat step 3 as often as needed.\n",
"\n",
"Use this procedure to define the `Eqint` equivalent to $10$. Print it, and its internal sequence, to check."
]
}
],
"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"
},
"nbconvert": {
"title": "Classes and objects"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
adukic/nd101 | autoencoder/Convolutional_Autoencoder_Solution.ipynb | 2 | 94635 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convolutional Autoencoder\n",
"\n",
"Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"from tensorflow.examples.tutorials.mnist import input_data\n",
"mnist = input_data.read_data_sets('MNIST_data', validation_size=0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f4631f1a4e0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f463c6bbac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = mnist.train.images[2]\n",
"plt.imshow(img.reshape((28, 28)), cmap='Greys_r')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Network Architecture\n",
"\n",
"The encoder part of the network will be a typical convolutional pyramid. Each convolutional layer will be followed by a max-pooling layer to reduce the dimensions of the layers. The decoder though might be something new to you. The decoder needs to convert from a narrow representation to a wide reconstructed image. For example, the representation could be a 4x4x8 max-pool layer. This is the output of the encoder, but also the input to the decoder. We want to get a 28x28x1 image out from the decoder so we need to work our way back up from the narrow decoder input layer. A schematic of the network is shown below.\n",
"\n",
"\n",
"\n",
"Here our final encoder layer has size 4x4x8 = 128. The original images have size 28x28 = 784, so the encoded vector is roughlt 16% the size of the original image. These are just suggested sizes for each of the layers. Feel free to change the depths and sizes, but remember our goal here is to find a small representation of the input data.\n",
"\n",
"### What's going on with the decoder\n",
"\n",
"Okay, so the decoder has these \"Upsample\" layers that you might not have seen before. First off, I'll discuss a bit what these layers *aren't*. Usually, you'll see **deconvolutional** layers used to increase the width and height of the layers. They work almost exactly the same as convolutional layers, but it reverse. A stride in the input layer results in a larger stride in the deconvolutional layer. For example, if you have a 3x3 kernel, a 3x3 patch in the input layer will be reduced to one unit in a convolutional layer. Comparatively, one unit in the input layer will be expanded to a 3x3 path in a deconvolutional layer. Deconvolution is often called \"transpose convolution\" which is what you'll find the TensorFlow API, with [`tf.nn.conv2d_transpose`](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_transpose). \n",
"\n",
"However, deconvolutional layers can lead to artifacts in the final images, such as checkerboard patterns. This is due to overlap in the kernels which can be avoided by setting the stride and kernel size equal. In [this Distill article](http://distill.pub/2016/deconv-checkerboard/) from Augustus Odena, *et al*, the authors show that these checkerboard artifacts can be avoided by resizing the layers using nearest neighbor or bilinear interpolation (upsampling) followed by a convolutional layer. In TensorFlow, this is easily done with [`tf.image.resize_images`](https://www.tensorflow.org/versions/r1.1/api_docs/python/tf/image/resize_images), followed by a convolution. Be sure to read the Distill article to get a better understanding of deconvolutional layers and why we're using upsampling.\n",
"\n",
"> **Exercise:** Build the network shown above. Remember that a convolutional layer with strides of 1 and 'same' padding won't reduce the height and width. That is, if the input is 28x28 and the convolution layer has stride = 1 and 'same' padding, the convolutional layer will also be 28x28. The max-pool layers are used the reduce the width and height. A stride of 2 will reduce the size by 2. Odena *et al* claim that nearest neighbor interpolation works best for the upsampling, so make sure to include that as a parameter in `tf.image.resize_images` or use [`tf.image.resize_nearest_neighbor`]( `https://www.tensorflow.org/api_docs/python/tf/image/resize_nearest_neighbor)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')\n",
"targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')\n",
"\n",
"### Encoder\n",
"conv1 = tf.layers.conv2d(inputs_, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
"# Now 28x28x16\n",
"maxpool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same')\n",
"# Now 14x14x16\n",
"conv2 = tf.layers.conv2d(maxpool1, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
"# Now 14x14x8\n",
"maxpool2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same')\n",
"# Now 7x7x8\n",
"conv3 = tf.layers.conv2d(maxpool2, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
"# Now 7x7x8\n",
"encoded = tf.layers.max_pooling2d(conv3, (2,2), (2,2), padding='same')\n",
"# Now 4x4x8\n",
"\n",
"### Decoder\n",
"upsample1 = tf.image.resize_nearest_neighbor(encoded, (7,7))\n",
"# Now 7x7x8\n",
"conv4 = tf.layers.conv2d(upsample1, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
"# Now 7x7x8\n",
"upsample2 = tf.image.resize_nearest_neighbor(conv4, (14,14))\n",
"# Now 14x14x8\n",
"conv5 = tf.layers.conv2d(upsample2, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
"# Now 14x14x8\n",
"upsample3 = tf.image.resize_nearest_neighbor(conv5, (28,28))\n",
"# Now 28x28x8\n",
"conv6 = tf.layers.conv2d(upsample3, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
"# Now 28x28x16\n",
"\n",
"logits = tf.layers.conv2d(conv6, 1, (3,3), padding='same', activation=None)\n",
"#Now 28x28x1\n",
"\n",
"decoded = tf.nn.sigmoid(logits, name='decoded')\n",
"\n",
"loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\n",
"cost = tf.reduce_mean(loss)\n",
"opt = tf.train.AdamOptimizer(0.001).minimize(cost)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training\n",
"\n",
"As before, here wi'll train the network. Instead of flattening the images though, we can pass them in as 28x28x1 arrays."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sess = tf.Session()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"epochs = 20\n",
"batch_size = 200\n",
"sess.run(tf.global_variables_initializer())\n",
"for e in range(epochs):\n",
" for ii in range(mnist.train.num_examples//batch_size):\n",
" batch = mnist.train.next_batch(batch_size)\n",
" imgs = batch[0].reshape((-1, 28, 28, 1))\n",
" batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: imgs,\n",
" targets_: imgs})\n",
"\n",
" print(\"Epoch: {}/{}...\".format(e+1, epochs),\n",
" \"Training loss: {:.4f}\".format(batch_cost))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABawAAAEsCAYAAAAvofT2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f46266ffb38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n",
"in_imgs = mnist.test.images[:10]\n",
"reconstructed = sess.run(decoded, feed_dict={inputs_: in_imgs.reshape((10, 28, 28, 1))})\n",
"\n",
"for images, row in zip([in_imgs, reconstructed], axes):\n",
" for img, ax in zip(images, row):\n",
" ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n",
" ax.get_xaxis().set_visible(False)\n",
" ax.get_yaxis().set_visible(False)\n",
"\n",
"\n",
"fig.tight_layout(pad=0.1)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sess.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Denoising\n",
"\n",
"As I've mentioned before, autoencoders like the ones you've built so far aren't too useful in practive. However, they can be used to denoise images quite successfully just by training the network on noisy images. We can create the noisy images ourselves by adding Gaussian noise to the training images, then clipping the values to be between 0 and 1. We'll use noisy images as input and the original, clean images as targets. Here's an example of the noisy images I generated and the denoised images.\n",
"\n",
"\n",
"\n",
"\n",
"Since this is a harder problem for the network, we'll want to use deeper convolutional layers here, more feature maps. I suggest something like 32-32-16 for the depths of the convolutional layers in the encoder, and the same depths going backward through the decoder. Otherwise the architecture is the same as before.\n",
"\n",
"> **Exercise:** Build the network for the denoising autoencoder. It's the same as before, but with deeper layers. I suggest 32-32-16 for the depths, but you can play with these numbers, or add more layers."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')\n",
"targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')\n",
"\n",
"### Encoder\n",
"conv1 = tf.layers.conv2d(inputs_, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
"# Now 28x28x32\n",
"maxpool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same')\n",
"# Now 14x14x32\n",
"conv2 = tf.layers.conv2d(maxpool1, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
"# Now 14x14x32\n",
"maxpool2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same')\n",
"# Now 7x7x32\n",
"conv3 = tf.layers.conv2d(maxpool2, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
"# Now 7x7x16\n",
"encoded = tf.layers.max_pooling2d(conv3, (2,2), (2,2), padding='same')\n",
"# Now 4x4x16\n",
"\n",
"### Decoder\n",
"upsample1 = tf.image.resize_nearest_neighbor(encoded, (7,7))\n",
"# Now 7x7x16\n",
"conv4 = tf.layers.conv2d(upsample1, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
"# Now 7x7x16\n",
"upsample2 = tf.image.resize_nearest_neighbor(conv4, (14,14))\n",
"# Now 14x14x16\n",
"conv5 = tf.layers.conv2d(upsample2, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
"# Now 14x14x32\n",
"upsample3 = tf.image.resize_nearest_neighbor(conv5, (28,28))\n",
"# Now 28x28x32\n",
"conv6 = tf.layers.conv2d(upsample3, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
"# Now 28x28x32\n",
"\n",
"logits = tf.layers.conv2d(conv6, 1, (3,3), padding='same', activation=None)\n",
"#Now 28x28x1\n",
"\n",
"decoded = tf.nn.sigmoid(logits, name='decoded')\n",
"\n",
"loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\n",
"cost = tf.reduce_mean(loss)\n",
"opt = tf.train.AdamOptimizer(0.001).minimize(cost)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sess = tf.Session()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"epochs = 100\n",
"batch_size = 200\n",
"# Set's how much noise we're adding to the MNIST images\n",
"noise_factor = 0.5\n",
"sess.run(tf.global_variables_initializer())\n",
"for e in range(epochs):\n",
" for ii in range(mnist.train.num_examples//batch_size):\n",
" batch = mnist.train.next_batch(batch_size)\n",
" # Get images from the batch\n",
" imgs = batch[0].reshape((-1, 28, 28, 1))\n",
" \n",
" # Add random noise to the input images\n",
" noisy_imgs = imgs + noise_factor * np.random.randn(*imgs.shape)\n",
" # Clip the images to be between 0 and 1\n",
" noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n",
" \n",
" # Noisy images as inputs, original images as targets\n",
" batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: noisy_imgs,\n",
" targets_: imgs})\n",
"\n",
" print(\"Epoch: {}/{}...\".format(e+1, epochs),\n",
" \"Training loss: {:.4f}\".format(batch_cost))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Checking out the performance\n",
"\n",
"Here I'm adding noise to the test images and passing them through the autoencoder. It does a suprising great job of removing the noise, even though it's sometimes difficult to tell what the original number is."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABawAAAEsCAYAAAAvofT2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfibwjnPWAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4624778320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n",
"in_imgs = mnist.test.images[:10]\n",
"noisy_imgs = in_imgs + noise_factor * np.random.randn(*in_imgs.shape)\n",
"noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n",
"\n",
"reconstructed = sess.run(decoded, feed_dict={inputs_: noisy_imgs.reshape((10, 28, 28, 1))})\n",
"\n",
"for images, row in zip([noisy_imgs, reconstructed], axes):\n",
" for img, ax in zip(images, row):\n",
" ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n",
" ax.get_xaxis().set_visible(False)\n",
" ax.get_yaxis().set_visible(False)\n",
"\n",
"fig.tight_layout(pad=0.1)"
]
}
],
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
hamipy/learnpython | Alex B.ipynb | 1 | 293 | {
"metadata": {
"name": "Alex B"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
alistairwalsh/MLD | tests/Getting Started.ipynb | 1 | 4538 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import Image, HTML, IFrame"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Machine Learning with OpenCV for computer vision\n",
"These lessons have been written with a certain type of learner in mind. They assume you are comfortable with writing and using Python code. They also assume a general understanding of statistical concepts. Nothing too esoteric but you should have a working knowledge of regression and significance. \n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These lessons have been written with a certain type of learner in mind. They assume you are comfortable with writing and using Python code. They also assume a general understanding of statistical concepts. Nothing too esoteric but you should have a working knowledge of regression and significance. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'Image' 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-1-84423cf22012>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mImage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'images/OpenCV_logo.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'Image' is not defined"
]
}
],
"source": [
"Image('images/OpenCV_logo.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OpenCV (Open Source Computer Vision) is an open-source BSD-licensed library that includes hundreds of computer vision algorithms. It is:\n",
"\n",
"* Cross-platform: Windows, Mac, Linux (even Raspberry Pi)\n",
"* Languages: C++, C, Python & Java\n",
"* Wrappers: MATLAB/OCTAVE, C#, Ruby, etc\n",
"* Fast - utilizing threading, CUDA & OpenCL\n",
"* Python interface uses NumPy matrices for images\n",
"* Able to be integrated into iPhone and Android apps\n",
"* Includes machine learning algorithms (although we'll be using scikit-learn)\n",
"\n",
"It can be downloaded for free from their [website](http://opencv.org) or installed via a package manager like homebrew on Macs."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What is an image?"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## What is OpenCV?\n",
"\n",
"OpenCV (Open Source Computer Vision) is an open-source BSD-licensed library that includes hundreds of computer vision algorithms. It is:\n",
"\n",
"* Cross-platform: Windows, Mac, Linux (even Raspberry Pi)\n",
"* Languages: C++, C, Python & Java\n",
"* Wrappers: MATLAB/OCTAVE, C#, Ruby, etc\n",
"* Fast - utilizing threading, CUDA & OpenCL\n",
"* Python interface uses NumPy matrices for images\n",
"* Able to be integrated into iPhone and Android apps\n",
"* Includes machine learning algorithms (although we'll be using scikit-learn)\n",
"\n",
"It can be downloaded for free from their [website](http://opencv.org) or installed via a package manager like homebrew on Macs.\n",
"\n",
"What is machine learning?\n",
"Quick intro: statistics, training, un/supervised\n",
"\n",
"\n",
"What is an image?\n",
"Images, pixels, grayscale, colour, show pic of image matrix."
]
},
{
"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": "3.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
mdeff/ntds_2017 | assignments/03_feedback.ipynb | 1 | 10379 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# [NTDS'17] assignment 3: feedback\n",
"[ntds'17]: https://github.com/mdeff/ntds_2017\n",
"\n",
"[Michaël Defferrard](http://deff.ch), [EPFL LTS2](http://lts2.epfl.ch)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The below grading scheme was followed for the correction of the third assignment, on a total of 100 points plus 10 bonus points. You'll also find some comments and common mistakes.\n",
"\n",
"Thanks for your work! It was quite good in general. Some did very well, and I read quite interesting comments throughout.\n",
"\n",
"## General remark\n",
"\n",
"First, a general remark: use vectorized code (via numpy or pandas) instead of loops. It's much more efficient, because the loop is either written in optimized C (or Fortran) code, or the function is carried by the CPU's [single instruction, multiple data (SIMD)](https://en.wikipedia.org/wiki/SIMD) unit (c.f. MMX, SSE, AVX instructions for x86 CPUs from Intel and AMD).\n",
"\n",
"Below are some examples from your submissions:\n",
"* `err = np.sum(np.abs(labels - genres))` is better than `err = len([1 for i in range(len(labels)) if labels[i] != genres[i]])`.\n",
"* `np.mean(mfcc, axis=1)` is better than `[np.mean(x) for x in mfcc]`.\n",
"* `weights = np.exp(-distances**2 / kernel_width**2)` is better than\n",
" ```\n",
" for i in range(0,2000):\n",
" for j in range(i,2000):\n",
" weights[i,j] = math.exp(-math.sqrt(distances[i,j])/math.sqrt(kernel_width))\n",
" ```\n",
"\n",
"If, for some reason, you cannot vectorize your code, consider using [numba](https://numba.pydata.org/) or [Cython](http://cython.org/).\n",
"\n",
"If you wrote *any loop* for your submission, please look at my [solution] for ways to avoid them. It's both faster and makes the code easier to understand.\n",
"\n",
"[solution]: 03_solution.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data and features (25 points)\n",
"\n",
"### 1 Code: get genre from FMA (5 points)\n",
"* In Python, you can convert an object to an integer with `int()`, to a string with `str()`, etc.\n",
"\n",
"### 2 Code: fill table with genres (5 points)\n",
"* Doing a for loop on a pandas dataframe is quite inefficient. Again, try to vectorize. Here, the `apply` or `map` functions are handy.\n",
"* Do `.apply(get_genre)` instead of `.apply(lambda x: get_genre(x))`. The anonymous function is useless if you don't alter the argument.\n",
"\n",
"### 3 Code: MFCCs (5 points)\n",
"\n",
"### 4 Code: summary statistics (5 points)\n",
"\n",
"### 5 Code: feature selection (5 points)\n",
"* Some of you made great efforts to select the best features, even if that was not the focus of the assignment (as stated). Well done!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Graph (55 + 7 points)\n",
"\n",
"### 6 Question: what is the cosine distance (2 points)\n",
"* Beware the difference between a distance and a similarity measure. A distance is 0 if two elements are equal, while a similarity measure takes its maximum. The maximum can be any number. It's 1 in the case of the cosine similarity.\n",
"* Note that the range of the cosine similarity is $[-1, 1]$ ($1$ for vectors pointing in the same direction, $0$ for orthogonal vectors, and $-1$ for vectors pointing in opposite directions). The range of the cosine distance is thus $[0, 2]$.\n",
"\n",
"### 7 Code: distances (3 points)\n",
"\n",
"### 8 Question: distances equal to zero (4 points)\n",
"* Some of you investigated why the distance between a pair of songs was zero (or almost zero) and discovered they were duplicates. Good job! :)\n",
"\n",
"### 9 Bonus: alternative kernel (3 points)\n",
"* Think about the requirements for a valid kernel. Note that we want to transform a distance to a similarity measure. See the [solution] for more.\n",
"\n",
"### 10 Code: weights (4 points)\n",
"\n",
"### 11 Code: nearest neightbors (7 points)\n",
"* Some of you used algorithms with one or two loops. Try to vectorize as much as possible for your code to be efficient. (I still gave all the points if you used only one loop.)\n",
"\n",
"### 12 Bonus: adjacency matrix visualization (4 points)\n",
"* The \"block-diagonal\" view (see my [solution]) is the simplest visualization here. I put block-diagonal in quotes as the matrix is not exactly block-diagonal. If it was, our graph would be disconnected and a perfect separation would be easy. It shows the size of the clusters, and gives an indication of intra and inter cluster concentration of edges.\n",
"* Some of you plotted non-zero values in the adjacency matrix with different colors to indicate if they were intra or inter genre. As you observed, this is quite hard to visualize. Especially if we would have more than 2 genres!\n",
"* Some also plotted distributions of edge weights for each genre and between genre. This is a good idea, though it's more verbose and harder to interpret than the \"block-diagonal\" view.\n",
"\n",
"### 13 Code: degrees (3 points)\n",
"* No need to use NetworkX. A simple `W.sum(0)` will do.\n",
"\n",
"### 14 Question: choice of Laplacian (3 points)\n",
"* A lot of you said that they should use the normalized Laplacian, without justification. Both Laplacians are however valid choices. Clustering with the sign of the Fiedler vector of the combinatorial Laplacian is a relaxation of the RatioCut. A relaxation of the NormalizedCut is obtained with the normalized Laplacian. Both the RatioCut and the NormalizedCut are normalized versions of the MinCut which seek to impose balanced clusters.\n",
"\n",
"### 15 Code: Laplacian (4 points)\n",
"* When computing the normalized Laplacian $\\mathbf{I} - \\mathbf{D}^{-1/2} \\mathbf{W} \\mathbf{D}^{-1/2}$, don't do `np.linalg.inv(D)` to inverse the degree matrix. The inverse of a diagonal matrix is straightforward and can be computed with `np.diag(1 / np.sqrt(degrees))`.\n",
"* If the graph is weighted, we compute the Laplacian from the weighted adjacency and degree matrices. We only use the binary adjacency matrix if no weights are available. We would otherwise discard valuable information.\n",
"\n",
"### 16 Code: number of edges (3 points)\n",
"* If you use the Laplacian matrix, you should subtract the non-zero elements on the diagonal.\n",
"* You should divide the number of non-zero values by two as we are counting edges for an undirected graph.\n",
"\n",
"### 17 Question: which eigensolver (4 points)\n",
"* We are not using the routines from `scipy.sparse` because they allow us to choose the number of eigenvectors to return. We use them because they implement efficient algorithms for partial eigendecomposition of sparse matrices.\n",
"\n",
"### 18 Code: eigenvectors & eigenvalues (5 points)\n",
"* Using `eigsh` with `which='SM'`, `which='SA'`, or `sigma=0` are all correct approaches. See the [solution] for more information.\n",
"\n",
"### 19 Question: eigenvectors & eigenvalues (5 points)\n",
"\n",
"### 20 Question: connectedness (4 points)\n",
"* The least costly way to check if the graph is connected is to look at the multiplicity of eigenvalue 0. Some of you used NetworkX, which is costlier (because we have the eigenvalues already).\n",
"* While a $k$ nearest neighbor (kNN) graph ensures that each node is connected to at least $k$ nodes, it does not ensure connectedness. For example, two well separated clusters in feature space would not be connected together. We would end up with two graphs. That's not bad, it's very easy to cluster!\n",
"\n",
"### 21 Question: first eigenvector (4 points)\n",
"* Most of you correclty expected to get 0 here, but not all realized that it was not exactly zero because computers have finite memory, and can only approximate real numbers with a 32 or 64 bits floating point representation. Some numerical error in the eigendecomposition is likely a reason as well."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualization and clustering (20 + 3 points)\n",
"\n",
"### 22 Question: Why not the first eigenvector (3 points)\n",
"* The first eigenvector of the normalized Laplacian is not constant. It's value is $\\mathbf{D}^{1/2} \\mathbf{1}$, where $\\mathbf{D}$ is the diagonal weighted degree matrix and $\\mathbf{1}$ is the vector of all ones.\n",
"* The first eigenvector of the combinatorial Laplacian is not all ones, but $\\frac{1}{N} \\mathbf{1}$. While $\\mathbf{1}$ is also an eigenvector (by scaling by $N$), by convention we normalize the eigenvectors so that they form an orthonormal basis.\n",
"\n",
"### 23 Code: 2D graph embedding (3 points)\n",
"* While any symmetric matrix is diagonalizable, the main result here is that the spectral theorem says \n",
"\n",
"### 24 Question: appearance of genre (5 points)\n",
"\n",
"### 25 Code: classification with Fiedler vector (4 points)\n",
"* The separating plane should be vertical as the first eigenvector was also used for the x axis.\n",
"\n",
"### 26 Code: error rate (5 points)\n",
"* You should take into accounts that the labels may be reversed, i.e. -1 could correspond to either rock or hip-hop. Clustering with $\\operatorname{sign}(\\mathbf{u}_2)$ or $\\operatorname{sign}(-\\mathbf{u}_2)$, where $\\mathbf{u}_2$ is the Fiedler vector, should give the same error rate.\n",
"\n",
"### 27 Bonus: method name and goal (3 points)\n",
"* Some of you mentioned *spectral graph embedding* instead of *Laplacian eigenmaps*. The main point is that the embedding method minimizes $\\operatorname{tr}(\\mathbf{Y}^\\intercal\\mathbf{L}\\mathbf{Y})$ and thus preserves the distances between samples as much as possible given the dimension of the embedding space.\n",
"* Please don't copy Wikipedia, or any other source, without appropriate citation."
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
tpin3694/tpin3694.github.io | python/pandas_selecting_rows_on_conditions.ipynb | 1 | 6190 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Title: Selecting Pandas DataFrame Rows Based On Conditions \n",
"Slug: pandas_selecting_rows_on_conditions \n",
"Summary: Selecting Pandas DataFrame Rows Based On Conditions \n",
"Date: 2016-05-01 12:00 \n",
"Category: Python \n",
"Tags: Data Wrangling \n",
"Authors: Chris Albon "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preliminaries"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Import modules\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>first_name</th>\n",
" <th>nationality</th>\n",
" <th>age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Jason</td>\n",
" <td>USA</td>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Molly</td>\n",
" <td>USA</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>France</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>UK</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>UK</td>\n",
" <td>70</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" first_name nationality age\n",
"0 Jason USA 42\n",
"1 Molly USA 52\n",
"2 NaN France 36\n",
"3 NaN UK 24\n",
"4 NaN UK 70"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a dataframe\n",
"raw_data = {'first_name': ['Jason', 'Molly', np.nan, np.nan, np.nan], \n",
" 'nationality': ['USA', 'USA', 'France', 'UK', 'UK'], \n",
" 'age': [42, 52, 36, 24, 70]}\n",
"df = pd.DataFrame(raw_data, columns = ['first_name', 'nationality', 'age'])\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Method 1: Using Boolean Variables"
]
},
{
"cell_type": "code",
"execution_count": 40,
"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>first_name</th>\n",
" <th>nationality</th>\n",
" <th>age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Molly</td>\n",
" <td>USA</td>\n",
" <td>52</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" first_name nationality age\n",
"1 Molly USA 52"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create variable with TRUE if nationality is USA\n",
"american = df['nationality'] == \"USA\"\n",
"\n",
"# Create variable with TRUE if age is greater than 50\n",
"elderly = df['age'] > 50\n",
"\n",
"# Select all casess where nationality is USA and age is greater than 50\n",
"df[american & elderly]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Method 2: Using variable attributes "
]
},
{
"cell_type": "code",
"execution_count": 41,
"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>first_name</th>\n",
" <th>nationality</th>\n",
" <th>age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Jason</td>\n",
" <td>USA</td>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Molly</td>\n",
" <td>USA</td>\n",
" <td>52</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" first_name nationality age\n",
"0 Jason USA 42\n",
"1 Molly USA 52"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Select all cases where the first name is not missing and nationality is USA \n",
"df[df['first_name'].notnull() & (df['nationality'] == \"USA\")]"
]
}
],
"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 |
ioam/holoviews | examples/reference/containers/matplotlib/Overlay.ipynb | 1 | 2830 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n",
"<dl class=\"dl-horizontal\">\n",
" <dt>Title</dt> <dd>Overlay Container</dd>\n",
" <dt>Dependencies</dt> <dd>Matplotlib</dd>\n",
" <dt>Backends</dt> <dd><a href='../bokeh/Overlay.ipynb'>Bokeh</a></dd> <dd><a href='../matplotlib/Overlay.ipynb'>Matplotlib</a></dd> <dd><a href='../plotly/Overlay.ipynb'>Plotly</a></dd>\n",
"</dl>\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import holoviews as hv\n",
"hv.extension('matplotlib')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A Overlay is a collection of HoloViews objects that are related in some way, to be displayed simultanously, overlaid in the space space. Like [``Layout``](./Layout.ipynb) and unlike other containers such as [``HoloMap``](./HoloMap.ipynb) , [``GridSpace``](./GridSpace.ipynb) and [``NdOverlay``](./NdOverlay.ipynb) a ``Overlay`` is *not* dictionary like: it holds potentially heterogeneous types without any dimensioned keys.\n",
"\n",
"\n",
"A ``Overlay`` cannot contain any other container type other than ``NdOverlay`` but can contain any HoloViews elements. See [Building Composite Objects](../../../user_guide/06-Building_Composite_Objects.ipynb) for more details on how to compose containers. It is best to learn about ``Overlay`` and [``Layout``](./Layout.ipynb) together as they are very closely related objects that share many core concepts."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ``Overlay`` is a heterogeneous collection"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can build a ``Overlay`` between any two HoloViews objects (which can have different types) using the ``*`` operator:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xvals = [0.1* i for i in range(100)]\n",
"curve = hv.Curve((xvals, [np.sin(x) for x in xvals]))\n",
"scatter = hv.Scatter((xvals[::5], np.linspace(0,1,20)))\n",
"curve * scatter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example, we have a ``Overlay`` composed of a ``Curve`` element and a ``Scatter`` element.\n",
"\n",
"For more information about both ``Overlay`` and ``Layout``, see the [Composing_Elements](../../../user_guide/02-Composing_Elements.ipynb) user guide."
]
}
],
"metadata": {
"language_info": {
"name": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
brillliantz/Quantitative_Finance | IPythonNotebooks_for_Research/hdf_0705_ReSample.ipynb | 1 | 106358 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"sns.set_context('talk')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hft = pd.read_hdf('HFT_SR_RM_MA_TA.hdf')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/usr/lib/python2.7/dist-packages/tables/leaf.py:392: PerformanceWarning: The Leaf ``/HFT/block0_values`` is exceeding the maximum recommended rowsize (104857600 bytes);\n",
"be ready to see PyTables asking for *lots* of memory and possibly slow\n",
"I/O. You may want to reduce the rowsize by trimming the value of\n",
"dimensions that are orthogonal (and preferably close) to the *main*\n",
"dimension of this leave. Alternatively, in case you have specified a\n",
"very small/large chunksize, you may want to increase/decrease it.\n",
" PerformanceWarning)\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hft.head"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<bound method Panel.head of <class 'pandas.core.panel.Panel'>\n",
"Dimensions: 38 (items) x 2700120 (major_axis) x 4 (minor_axis)\n",
"Items axis: AveragePrice to volume\n",
"Major_axis axis: 2015-11-19 21:00:00 to 2015-12-31 15:00:00\n",
"Minor_axis axis: MA0001 to TA0001>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hft.items"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"Index([u'AveragePrice', u'LifeHigh', u'LifeLow', u'TotalAskLot',\n",
" u'TotalBidLot', u'askPrc_0', u'askPrc_1', u'askPrc_2', u'askPrc_3',\n",
" u'askPrc_4', u'askQty_0', u'askQty_1', u'askQty_2', u'askQty_3',\n",
" u'askQty_4', u'bidPrc_0', u'bidPrc_1', u'bidPrc_2', u'bidPrc_3',\n",
" u'bidPrc_4', u'bidQty_0', u'bidQty_1', u'bidQty_2', u'bidQty_3',\n",
" u'bidQty_4', u'close', u'high', u'highLimit', u'last', u'low',\n",
" u'lowLimit', u'open', u'openInterest', u'prevClose',\n",
" u'prevOpenInterest', u'prevSettle', u'settle', u'volume'],\n",
" dtype='object')"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# output cols to csv\n",
"cols = hft.items\n",
"cols = np.array(cols).astype(str)\n",
"np.savetxt('cols.csv', cols, delimiter=',', fmt='%s')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hft.major_axis"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"DatetimeIndex([ '2015-11-19 21:00:00', '2015-11-19 21:00:00.250000',\n",
" '2015-11-19 21:00:00.500000', '2015-11-19 21:00:00.750000',\n",
" '2015-11-19 21:00:01', '2015-11-19 21:00:01.250000',\n",
" '2015-11-19 21:00:01.500000', '2015-11-19 21:00:01.750000',\n",
" '2015-11-19 21:00:02', '2015-11-19 21:00:02.250000',\n",
" ...\n",
" '2015-12-31 14:59:57.750000', '2015-12-31 14:59:58',\n",
" '2015-12-31 14:59:58.250000', '2015-12-31 14:59:58.500000',\n",
" '2015-12-31 14:59:58.750000', '2015-12-31 14:59:59',\n",
" '2015-12-31 14:59:59.250000', '2015-12-31 14:59:59.500000',\n",
" '2015-12-31 14:59:59.750000', '2015-12-31 15:00:00'],\n",
" dtype='datetime64[ns]', length=2700120, freq=None)"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hft.minor_axis"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"Index([u'MA0001', u'RM0001', u'SR0001', u'TA0001'], dtype='object')"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"night_len = int(4*3600*2.5)\n",
"mor_len = int(4*3600*2.25)\n",
"aftn_len = int(4*3600*1.5)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 113
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mor_len"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"32400"
]
}
],
"prompt_number": 29
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# FOCUS on RM"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rm = hft.minor_xs('RM0001')\n",
"type(rm)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"pandas.core.frame.DataFrame"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## show columns"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rm.iloc[:5, :12]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AveragePrice</th>\n",
" <th>LifeHigh</th>\n",
" <th>LifeLow</th>\n",
" <th>TotalAskLot</th>\n",
" <th>TotalBidLot</th>\n",
" <th>askPrc_0</th>\n",
" <th>askPrc_1</th>\n",
" <th>askPrc_2</th>\n",
" <th>askPrc_3</th>\n",
" <th>askPrc_4</th>\n",
" <th>askQty_0</th>\n",
" <th>askQty_1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2015-11-19 21:00:00.000</th>\n",
" <td>1775.0</td>\n",
" <td>2322.0</td>\n",
" <td>1768.0</td>\n",
" <td>3103.0</td>\n",
" <td>2975.0</td>\n",
" <td>1776.0</td>\n",
" <td>1777.0</td>\n",
" <td>1778.0</td>\n",
" <td>1779.0</td>\n",
" <td>1780.0</td>\n",
" <td>21.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:00:00.250</th>\n",
" <td>1775.0</td>\n",
" <td>2322.0</td>\n",
" <td>1768.0</td>\n",
" <td>3424.0</td>\n",
" <td>3010.0</td>\n",
" <td>1776.0</td>\n",
" <td>1777.0</td>\n",
" <td>1778.0</td>\n",
" <td>1779.0</td>\n",
" <td>1780.0</td>\n",
" <td>20.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:00:00.500</th>\n",
" <td>1775.0</td>\n",
" <td>2322.0</td>\n",
" <td>1768.0</td>\n",
" <td>3648.0</td>\n",
" <td>3170.0</td>\n",
" <td>1776.0</td>\n",
" <td>1777.0</td>\n",
" <td>1778.0</td>\n",
" <td>1779.0</td>\n",
" <td>1780.0</td>\n",
" <td>8.0</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:00:00.750</th>\n",
" <td>1775.0</td>\n",
" <td>2322.0</td>\n",
" <td>1768.0</td>\n",
" <td>3830.0</td>\n",
" <td>3234.0</td>\n",
" <td>1775.0</td>\n",
" <td>1776.0</td>\n",
" <td>1777.0</td>\n",
" <td>1778.0</td>\n",
" <td>1779.0</td>\n",
" <td>38.0</td>\n",
" <td>37.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:00:01.000</th>\n",
" <td>1775.0</td>\n",
" <td>2322.0</td>\n",
" <td>1768.0</td>\n",
" <td>4012.0</td>\n",
" <td>3294.0</td>\n",
" <td>1775.0</td>\n",
" <td>1776.0</td>\n",
" <td>1777.0</td>\n",
" <td>1778.0</td>\n",
" <td>1779.0</td>\n",
" <td>96.0</td>\n",
" <td>88.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
" AveragePrice LifeHigh LifeLow TotalAskLot \\\n",
"2015-11-19 21:00:00.000 1775.0 2322.0 1768.0 3103.0 \n",
"2015-11-19 21:00:00.250 1775.0 2322.0 1768.0 3424.0 \n",
"2015-11-19 21:00:00.500 1775.0 2322.0 1768.0 3648.0 \n",
"2015-11-19 21:00:00.750 1775.0 2322.0 1768.0 3830.0 \n",
"2015-11-19 21:00:01.000 1775.0 2322.0 1768.0 4012.0 \n",
"\n",
" TotalBidLot askPrc_0 askPrc_1 askPrc_2 askPrc_3 \\\n",
"2015-11-19 21:00:00.000 2975.0 1776.0 1777.0 1778.0 1779.0 \n",
"2015-11-19 21:00:00.250 3010.0 1776.0 1777.0 1778.0 1779.0 \n",
"2015-11-19 21:00:00.500 3170.0 1776.0 1777.0 1778.0 1779.0 \n",
"2015-11-19 21:00:00.750 3234.0 1775.0 1776.0 1777.0 1778.0 \n",
"2015-11-19 21:00:01.000 3294.0 1775.0 1776.0 1777.0 1778.0 \n",
"\n",
" askPrc_4 askQty_0 askQty_1 \n",
"2015-11-19 21:00:00.000 1780.0 21.0 7.0 \n",
"2015-11-19 21:00:00.250 1780.0 20.0 7.0 \n",
"2015-11-19 21:00:00.500 1780.0 8.0 13.0 \n",
"2015-11-19 21:00:00.750 1779.0 38.0 37.0 \n",
"2015-11-19 21:00:01.000 1779.0 96.0 88.0 "
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rm.iloc[:5, 12:24]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>askQty_2</th>\n",
" <th>askQty_3</th>\n",
" <th>askQty_4</th>\n",
" <th>bidPrc_0</th>\n",
" <th>bidPrc_1</th>\n",
" <th>bidPrc_2</th>\n",
" <th>bidPrc_3</th>\n",
" <th>bidPrc_4</th>\n",
" <th>bidQty_0</th>\n",
" <th>bidQty_1</th>\n",
" <th>bidQty_2</th>\n",
" <th>bidQty_3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2015-11-19 21:00:00.000</th>\n",
" <td>58.0</td>\n",
" <td>30.0</td>\n",
" <td>262.0</td>\n",
" <td>1775.0</td>\n",
" <td>1774.0</td>\n",
" <td>1773.0</td>\n",
" <td>1772.0</td>\n",
" <td>1771.0</td>\n",
" <td>83.0</td>\n",
" <td>26.0</td>\n",
" <td>68.0</td>\n",
" <td>154.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:00:00.250</th>\n",
" <td>58.0</td>\n",
" <td>352.0</td>\n",
" <td>262.0</td>\n",
" <td>1775.0</td>\n",
" <td>1774.0</td>\n",
" <td>1773.0</td>\n",
" <td>1772.0</td>\n",
" <td>1771.0</td>\n",
" <td>83.0</td>\n",
" <td>56.0</td>\n",
" <td>68.0</td>\n",
" <td>159.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:00:00.500</th>\n",
" <td>80.0</td>\n",
" <td>362.0</td>\n",
" <td>263.0</td>\n",
" <td>1775.0</td>\n",
" <td>1774.0</td>\n",
" <td>1773.0</td>\n",
" <td>1772.0</td>\n",
" <td>1771.0</td>\n",
" <td>83.0</td>\n",
" <td>109.0</td>\n",
" <td>89.0</td>\n",
" <td>160.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:00:00.750</th>\n",
" <td>55.0</td>\n",
" <td>84.0</td>\n",
" <td>362.0</td>\n",
" <td>1774.0</td>\n",
" <td>1773.0</td>\n",
" <td>1772.0</td>\n",
" <td>1771.0</td>\n",
" <td>1770.0</td>\n",
" <td>183.0</td>\n",
" <td>90.0</td>\n",
" <td>164.0</td>\n",
" <td>88.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:00:01.000</th>\n",
" <td>77.0</td>\n",
" <td>84.0</td>\n",
" <td>369.0</td>\n",
" <td>1774.0</td>\n",
" <td>1773.0</td>\n",
" <td>1772.0</td>\n",
" <td>1771.0</td>\n",
" <td>1770.0</td>\n",
" <td>185.0</td>\n",
" <td>94.0</td>\n",
" <td>174.0</td>\n",
" <td>89.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
" askQty_2 askQty_3 askQty_4 bidPrc_0 bidPrc_1 \\\n",
"2015-11-19 21:00:00.000 58.0 30.0 262.0 1775.0 1774.0 \n",
"2015-11-19 21:00:00.250 58.0 352.0 262.0 1775.0 1774.0 \n",
"2015-11-19 21:00:00.500 80.0 362.0 263.0 1775.0 1774.0 \n",
"2015-11-19 21:00:00.750 55.0 84.0 362.0 1774.0 1773.0 \n",
"2015-11-19 21:00:01.000 77.0 84.0 369.0 1774.0 1773.0 \n",
"\n",
" bidPrc_2 bidPrc_3 bidPrc_4 bidQty_0 bidQty_1 \\\n",
"2015-11-19 21:00:00.000 1773.0 1772.0 1771.0 83.0 26.0 \n",
"2015-11-19 21:00:00.250 1773.0 1772.0 1771.0 83.0 56.0 \n",
"2015-11-19 21:00:00.500 1773.0 1772.0 1771.0 83.0 109.0 \n",
"2015-11-19 21:00:00.750 1772.0 1771.0 1770.0 183.0 90.0 \n",
"2015-11-19 21:00:01.000 1772.0 1771.0 1770.0 185.0 94.0 \n",
"\n",
" bidQty_2 bidQty_3 \n",
"2015-11-19 21:00:00.000 68.0 154.0 \n",
"2015-11-19 21:00:00.250 68.0 159.0 \n",
"2015-11-19 21:00:00.500 89.0 160.0 \n",
"2015-11-19 21:00:00.750 164.0 88.0 \n",
"2015-11-19 21:00:01.000 174.0 89.0 "
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rm.iloc[:5, 24:]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bidQty_4</th>\n",
" <th>close</th>\n",
" <th>high</th>\n",
" <th>highLimit</th>\n",
" <th>last</th>\n",
" <th>low</th>\n",
" <th>lowLimit</th>\n",
" <th>open</th>\n",
" <th>openInterest</th>\n",
" <th>prevClose</th>\n",
" <th>prevOpenInterest</th>\n",
" <th>prevSettle</th>\n",
" <th>settle</th>\n",
" <th>volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2015-11-19 21:00:00.000</th>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>1775.0</td>\n",
" <td>1866.0</td>\n",
" <td>1775.0</td>\n",
" <td>1775.0</td>\n",
" <td>1722.0</td>\n",
" <td>1775.0</td>\n",
" <td>587568.0</td>\n",
" <td>1779.0</td>\n",
" <td>587568.0</td>\n",
" <td>1794.0</td>\n",
" <td>1775.0</td>\n",
" <td>520.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:00:00.250</th>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>1776.0</td>\n",
" <td>1866.0</td>\n",
" <td>1776.0</td>\n",
" <td>1775.0</td>\n",
" <td>1722.0</td>\n",
" <td>1775.0</td>\n",
" <td>587566.0</td>\n",
" <td>1779.0</td>\n",
" <td>587568.0</td>\n",
" <td>1794.0</td>\n",
" <td>1775.0</td>\n",
" <td>522.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:00:00.500</th>\n",
" <td>80.0</td>\n",
" <td>0.0</td>\n",
" <td>1777.0</td>\n",
" <td>1866.0</td>\n",
" <td>1775.0</td>\n",
" <td>1775.0</td>\n",
" <td>1722.0</td>\n",
" <td>1775.0</td>\n",
" <td>587534.0</td>\n",
" <td>1779.0</td>\n",
" <td>587568.0</td>\n",
" <td>1794.0</td>\n",
" <td>1775.0</td>\n",
" <td>674.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:00:00.750</th>\n",
" <td>407.0</td>\n",
" <td>0.0</td>\n",
" <td>1777.0</td>\n",
" <td>1866.0</td>\n",
" <td>1775.0</td>\n",
" <td>1775.0</td>\n",
" <td>1722.0</td>\n",
" <td>1775.0</td>\n",
" <td>587648.0</td>\n",
" <td>1779.0</td>\n",
" <td>587568.0</td>\n",
" <td>1794.0</td>\n",
" <td>1775.0</td>\n",
" <td>1036.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:00:01.000</th>\n",
" <td>422.0</td>\n",
" <td>0.0</td>\n",
" <td>1777.0</td>\n",
" <td>1866.0</td>\n",
" <td>1775.0</td>\n",
" <td>1774.0</td>\n",
" <td>1722.0</td>\n",
" <td>1775.0</td>\n",
" <td>587652.0</td>\n",
" <td>1779.0</td>\n",
" <td>587568.0</td>\n",
" <td>1794.0</td>\n",
" <td>1775.0</td>\n",
" <td>1086.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
" bidQty_4 close high highLimit last low \\\n",
"2015-11-19 21:00:00.000 60.0 0.0 1775.0 1866.0 1775.0 1775.0 \n",
"2015-11-19 21:00:00.250 60.0 0.0 1776.0 1866.0 1776.0 1775.0 \n",
"2015-11-19 21:00:00.500 80.0 0.0 1777.0 1866.0 1775.0 1775.0 \n",
"2015-11-19 21:00:00.750 407.0 0.0 1777.0 1866.0 1775.0 1775.0 \n",
"2015-11-19 21:00:01.000 422.0 0.0 1777.0 1866.0 1775.0 1774.0 \n",
"\n",
" lowLimit open openInterest prevClose \\\n",
"2015-11-19 21:00:00.000 1722.0 1775.0 587568.0 1779.0 \n",
"2015-11-19 21:00:00.250 1722.0 1775.0 587566.0 1779.0 \n",
"2015-11-19 21:00:00.500 1722.0 1775.0 587534.0 1779.0 \n",
"2015-11-19 21:00:00.750 1722.0 1775.0 587648.0 1779.0 \n",
"2015-11-19 21:00:01.000 1722.0 1775.0 587652.0 1779.0 \n",
"\n",
" prevOpenInterest prevSettle settle volume \n",
"2015-11-19 21:00:00.000 587568.0 1794.0 1775.0 520.0 \n",
"2015-11-19 21:00:00.250 587568.0 1794.0 1775.0 522.0 \n",
"2015-11-19 21:00:00.500 587568.0 1794.0 1775.0 674.0 \n",
"2015-11-19 21:00:00.750 587568.0 1794.0 1775.0 1036.0 \n",
"2015-11-19 21:00:01.000 587568.0 1794.0 1775.0 1086.0 "
]
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ALL datatype are float64"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for col, ser in rm.iteritems():\n",
" print col, ser.dtype, ser.std(axis=0)\n",
" #ser.plot(marker='*', linestyle='-')\n",
" #plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"AveragePrice float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"54.434424448\n",
"LifeHigh float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"21.2264025889\n",
"LifeLow float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"23.243718561\n",
"TotalAskLot float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"4143.52344434\n",
"TotalBidLot float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"5431.52860007\n",
"askPrc_0 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"54.7464276683\n",
"askPrc_1 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"54.7464246804\n",
"askPrc_2 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"54.746428347\n",
"askPrc_3 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"55.0503771675\n",
"askPrc_4 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"58.4466642821\n",
"askQty_0 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"748.917923457\n",
"askQty_1 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"421.785572043\n",
"askQty_2 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"418.087426985\n",
"askQty_3 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"413.856144793\n",
"askQty_4 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"394.690490296\n",
"bidPrc_0 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"110.444738874\n",
"bidPrc_1 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"126.184326121\n",
"bidPrc_2 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"138.026853276\n",
"bidPrc_3 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"143.214587735\n",
"bidPrc_4 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"147.346242573\n",
"bidQty_0 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"390.154663217\n",
"bidQty_1 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"431.977763012\n",
"bidQty_2 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"397.773367011\n",
"bidQty_3 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"393.917308311\n",
"bidQty_4 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"382.138728729\n",
"close float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2.53507178231\n",
"high float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"52.5286801604\n",
"highLimit float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"55.9599265724\n",
"last float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"54.7501530753\n",
"low float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"57.651762213\n",
"lowLimit float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"51.8069267635\n",
"open float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"55.7954302954\n",
"openInterest float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"87128.4161217\n",
"prevClose float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"55.845033993\n",
"prevOpenInterest float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"87144.7523699\n",
"prevSettle float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"53.8828045932\n",
"settle float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"54.434424448\n",
"volume float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"288637.682074\n"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Standard Deviation"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rm_20 = rm.ix[night_len + mor_len + aftn_len + 1: night_len + mor_len + aftn_len +night_len + mor_len + aftn_len, :]\n",
"for col, ser in rm_20.iteritems():\n",
" print col, ser.dtype, ser.std(axis=0)\n",
" print ser.mean(axis=0)\n",
" #ser.plot(marker='*', linestyle='-')\n",
" #plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"AveragePrice float64 9.78584380897\n",
"1778.29026989\n",
"LifeHigh float64 0.0\n",
"2322.0\n",
"LifeLow float64 21.9351354691\n",
"1749.2633807\n",
"TotalAskLot float64 4756.83462798\n",
"17630.6455627\n",
"TotalBidLot float64 3782.283291\n",
"8361.70233003\n",
"askPrc_0 float64 30.1693681976\n",
"1762.12290137\n",
"askPrc_1 float64 30.1693681976\n",
"1763.12290137\n",
"askPrc_2 float64 30.1693681976\n",
"1764.12290137\n",
"askPrc_3 float64 30.1693681976\n",
"1765.12290137\n",
"askPrc_4 float64 30.1693681976\n",
"1766.12290137"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"askQty_0 float64 3464.61119866\n",
"1296.75515284\n",
"askQty_1 float64 383.672196245\n",
"566.726741408\n",
"askQty_2 float64 300.231166411\n",
"522.411326793\n",
"askQty_3 float64 329.549605872\n",
"523.192935477\n",
"askQty_4 float64 308.92017189\n",
"474.665229614\n",
"bidPrc_0 float64 481.373354751\n",
"1623.45279392\n",
"bidPrc_1 float64 560.935358248\n",
"1566.35854843\n",
"bidPrc_2 float64 616.038855766\n",
"1518.06502294\n",
"bidPrc_3 float64 638.889974348\n",
"1494.88815431\n",
"bidPrc_4 float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"656.518057259\n",
"1475.57688419\n",
"bidQty_0 float64 820.390116646\n",
"502.691007678\n",
"bidQty_1 float64 892.580542505\n",
"643.946377182\n",
"bidQty_2 float64 530.384970412\n",
"498.109745664\n",
"bidQty_3 float64 522.438849381\n",
"524.685940955\n",
"bidQty_4 float64 486.345020246\n",
"523.983210925\n",
"close float64 0.0\n",
"0.0\n",
"high float64 3.88692879213\n",
"1794.10597896\n",
"highLimit float64 0.0750551179951\n",
"1853.00043334\n",
"last float64 30.1111491551\n",
"1761.64696274\n",
"low float64 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"26.3038950071\n",
"1755.01507795\n",
"lowLimit float64 0.0750551179951\n",
"1709.00043334\n",
"open float64 0.01154694123\n",
"1776.99993333\n",
"openInterest float64 11622.1768732\n",
"538866.873599\n",
"prevClose float64 0.01154694123\n",
"1777.00006667\n",
"prevOpenInterest float64 237.047156511\n",
"546511.368615\n",
"prevSettle float64 0.0750551179951\n",
"1781.00043334\n",
"settle float64 9.78584380897\n",
"1778.29026989\n",
"volume float64 416314.41521\n",
"635516.389782\n"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rm_20['last'].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"<matplotlib.axes.AxesSubplot at 0x7f3e24283910>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEKCAYAAADuEgmxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f3e241e91d0>"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rm.ix[night_len + mor_len, :]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"AveragePrice 1782.0\n",
"LifeHigh 2322.0\n",
"LifeLow 1765.0\n",
"TotalAskLot 18021.0\n",
"TotalBidLot 9921.0\n",
"askPrc_0 1778.0\n",
"askPrc_1 1779.0\n",
"askPrc_2 1780.0\n",
"askPrc_3 1781.0\n",
"askPrc_4 1782.0\n",
"askQty_0 361.0\n",
"askQty_1 267.0\n",
"askQty_2 942.0\n",
"askQty_3 481.0\n",
"askQty_4 508.0\n",
"bidPrc_0 1777.0\n",
"bidPrc_1 1776.0\n",
"bidPrc_2 1775.0\n",
"bidPrc_3 1774.0\n",
"bidPrc_4 1773.0\n",
"bidQty_0 155.0\n",
"bidQty_1 476.0\n",
"bidQty_2 540.0\n",
"bidQty_3 95.0\n",
"bidQty_4 315.0\n",
"close 0.0\n",
"high 1798.0\n",
"highLimit 1866.0\n",
"last 1778.0\n",
"low 1765.0\n",
"lowLimit 1722.0\n",
"open 1775.0\n",
"openInterest 556792.0\n",
"prevClose 1779.0\n",
"prevOpenInterest 587568.0\n",
"prevSettle 1794.0\n",
"settle 1782.0\n",
"volume 767392.0\n",
"Name: 2015-11-20 11:29:59.500000, dtype: float64"
]
}
],
"prompt_number": 28
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Resample\n",
"datetimeindex format: 2015-11-20 09:30:00.000000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### In this data set, Monday's night trading begins at Sunday night."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rm.ix['2015-11-20 14:59:00': '2015-11-23 09:01:00', 'last'].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 120,
"text": [
"<matplotlib.axes.AxesSubplot at 0x7f3e20084f10>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEKCAYAAAD0Luk/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f3e200d2b90>"
]
}
],
"prompt_number": 120
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rm_23 = rm.ix['2015-11-22 21:00:00': '2015-11-23 15:00:00', :]\n",
"rm_23.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AveragePrice</th>\n",
" <th>LifeHigh</th>\n",
" <th>LifeLow</th>\n",
" <th>TotalAskLot</th>\n",
" <th>TotalBidLot</th>\n",
" <th>askPrc_0</th>\n",
" <th>askPrc_1</th>\n",
" <th>askPrc_2</th>\n",
" <th>askPrc_3</th>\n",
" <th>askPrc_4</th>\n",
" <th>...</th>\n",
" <th>last</th>\n",
" <th>low</th>\n",
" <th>lowLimit</th>\n",
" <th>open</th>\n",
" <th>openInterest</th>\n",
" <th>prevClose</th>\n",
" <th>prevOpenInterest</th>\n",
" <th>prevSettle</th>\n",
" <th>settle</th>\n",
" <th>volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>90004.000000</td>\n",
" <td>90004.0</td>\n",
" <td>90004.000000</td>\n",
" <td>90004.000000</td>\n",
" <td>90004.000000</td>\n",
" <td>90004.000000</td>\n",
" <td>90004.000000</td>\n",
" <td>90004.000000</td>\n",
" <td>90004.000000</td>\n",
" <td>90004.000000</td>\n",
" <td>...</td>\n",
" <td>90004.000000</td>\n",
" <td>90004.000000</td>\n",
" <td>90004.0</td>\n",
" <td>90004.0</td>\n",
" <td>90004.000000</td>\n",
" <td>90004.0</td>\n",
" <td>90004.0</td>\n",
" <td>90004.0</td>\n",
" <td>90004.000000</td>\n",
" <td>9.000400e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1778.288109</td>\n",
" <td>2322.0</td>\n",
" <td>1749.259277</td>\n",
" <td>17630.756733</td>\n",
" <td>8360.978768</td>\n",
" <td>1762.117684</td>\n",
" <td>1763.117684</td>\n",
" <td>1764.117684</td>\n",
" <td>1765.117684</td>\n",
" <td>1766.117684</td>\n",
" <td>...</td>\n",
" <td>1761.641805</td>\n",
" <td>1755.010655</td>\n",
" <td>1709.0</td>\n",
" <td>1777.0</td>\n",
" <td>538863.792909</td>\n",
" <td>1777.0</td>\n",
" <td>546510.0</td>\n",
" <td>1781.0</td>\n",
" <td>1778.288109</td>\n",
" <td>6.355760e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>9.788023</td>\n",
" <td>0.0</td>\n",
" <td>21.937622</td>\n",
" <td>4756.631191</td>\n",
" <td>3782.998119</td>\n",
" <td>30.172564</td>\n",
" <td>30.172564</td>\n",
" <td>30.172564</td>\n",
" <td>30.172564</td>\n",
" <td>30.172564</td>\n",
" <td>...</td>\n",
" <td>30.114289</td>\n",
" <td>26.306678</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>11625.633963</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>9.788023</td>\n",
" <td>4.163665e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1755.000000</td>\n",
" <td>2322.0</td>\n",
" <td>1709.000000</td>\n",
" <td>4594.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1709.000000</td>\n",
" <td>1710.000000</td>\n",
" <td>1711.000000</td>\n",
" <td>1712.000000</td>\n",
" <td>1713.000000</td>\n",
" <td>...</td>\n",
" <td>1709.000000</td>\n",
" <td>1709.000000</td>\n",
" <td>1709.0</td>\n",
" <td>1777.0</td>\n",
" <td>507074.000000</td>\n",
" <td>1777.0</td>\n",
" <td>546510.0</td>\n",
" <td>1781.0</td>\n",
" <td>1755.000000</td>\n",
" <td>4.900000e+02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1775.000000</td>\n",
" <td>2322.0</td>\n",
" <td>1742.000000</td>\n",
" <td>15060.000000</td>\n",
" <td>6522.000000</td>\n",
" <td>1746.000000</td>\n",
" <td>1747.000000</td>\n",
" <td>1748.000000</td>\n",
" <td>1749.000000</td>\n",
" <td>1750.000000</td>\n",
" <td>...</td>\n",
" <td>1745.000000</td>\n",
" <td>1742.000000</td>\n",
" <td>1709.0</td>\n",
" <td>1777.0</td>\n",
" <td>532352.000000</td>\n",
" <td>1777.0</td>\n",
" <td>546510.0</td>\n",
" <td>1781.0</td>\n",
" <td>1775.000000</td>\n",
" <td>2.731060e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1782.000000</td>\n",
" <td>2322.0</td>\n",
" <td>1765.000000</td>\n",
" <td>17409.000000</td>\n",
" <td>8422.000000</td>\n",
" <td>1778.000000</td>\n",
" <td>1779.000000</td>\n",
" <td>1780.000000</td>\n",
" <td>1781.000000</td>\n",
" <td>1782.000000</td>\n",
" <td>...</td>\n",
" <td>1778.000000</td>\n",
" <td>1776.000000</td>\n",
" <td>1709.0</td>\n",
" <td>1777.0</td>\n",
" <td>535940.000000</td>\n",
" <td>1777.0</td>\n",
" <td>546510.0</td>\n",
" <td>1781.0</td>\n",
" <td>1782.000000</td>\n",
" <td>5.432880e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1786.000000</td>\n",
" <td>2322.0</td>\n",
" <td>1765.000000</td>\n",
" <td>19278.000000</td>\n",
" <td>11218.000000</td>\n",
" <td>1787.000000</td>\n",
" <td>1788.000000</td>\n",
" <td>1789.000000</td>\n",
" <td>1790.000000</td>\n",
" <td>1791.000000</td>\n",
" <td>...</td>\n",
" <td>1787.000000</td>\n",
" <td>1776.000000</td>\n",
" <td>1709.0</td>\n",
" <td>1777.0</td>\n",
" <td>549208.000000</td>\n",
" <td>1777.0</td>\n",
" <td>546510.0</td>\n",
" <td>1781.0</td>\n",
" <td>1786.000000</td>\n",
" <td>9.371845e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1788.000000</td>\n",
" <td>2322.0</td>\n",
" <td>1765.000000</td>\n",
" <td>37160.000000</td>\n",
" <td>15932.000000</td>\n",
" <td>1797.000000</td>\n",
" <td>1798.000000</td>\n",
" <td>1799.000000</td>\n",
" <td>1800.000000</td>\n",
" <td>1801.000000</td>\n",
" <td>...</td>\n",
" <td>1796.000000</td>\n",
" <td>1777.000000</td>\n",
" <td>1709.0</td>\n",
" <td>1777.0</td>\n",
" <td>560880.000000</td>\n",
" <td>1777.0</td>\n",
" <td>546510.0</td>\n",
" <td>1781.0</td>\n",
" <td>1788.000000</td>\n",
" <td>1.436512e+06</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows \u00d7 38 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 122,
"text": [
" AveragePrice LifeHigh LifeLow TotalAskLot TotalBidLot \\\n",
"count 90004.000000 90004.0 90004.000000 90004.000000 90004.000000 \n",
"mean 1778.288109 2322.0 1749.259277 17630.756733 8360.978768 \n",
"std 9.788023 0.0 21.937622 4756.631191 3782.998119 \n",
"min 1755.000000 2322.0 1709.000000 4594.000000 0.000000 \n",
"25% 1775.000000 2322.0 1742.000000 15060.000000 6522.000000 \n",
"50% 1782.000000 2322.0 1765.000000 17409.000000 8422.000000 \n",
"75% 1786.000000 2322.0 1765.000000 19278.000000 11218.000000 \n",
"max 1788.000000 2322.0 1765.000000 37160.000000 15932.000000 \n",
"\n",
" askPrc_0 askPrc_1 askPrc_2 askPrc_3 askPrc_4 \\\n",
"count 90004.000000 90004.000000 90004.000000 90004.000000 90004.000000 \n",
"mean 1762.117684 1763.117684 1764.117684 1765.117684 1766.117684 \n",
"std 30.172564 30.172564 30.172564 30.172564 30.172564 \n",
"min 1709.000000 1710.000000 1711.000000 1712.000000 1713.000000 \n",
"25% 1746.000000 1747.000000 1748.000000 1749.000000 1750.000000 \n",
"50% 1778.000000 1779.000000 1780.000000 1781.000000 1782.000000 \n",
"75% 1787.000000 1788.000000 1789.000000 1790.000000 1791.000000 \n",
"max 1797.000000 1798.000000 1799.000000 1800.000000 1801.000000 \n",
"\n",
" ... last low lowLimit open \\\n",
"count ... 90004.000000 90004.000000 90004.0 90004.0 \n",
"mean ... 1761.641805 1755.010655 1709.0 1777.0 \n",
"std ... 30.114289 26.306678 0.0 0.0 \n",
"min ... 1709.000000 1709.000000 1709.0 1777.0 \n",
"25% ... 1745.000000 1742.000000 1709.0 1777.0 \n",
"50% ... 1778.000000 1776.000000 1709.0 1777.0 \n",
"75% ... 1787.000000 1776.000000 1709.0 1777.0 \n",
"max ... 1796.000000 1777.000000 1709.0 1777.0 \n",
"\n",
" openInterest prevClose prevOpenInterest prevSettle settle \\\n",
"count 90004.000000 90004.0 90004.0 90004.0 90004.000000 \n",
"mean 538863.792909 1777.0 546510.0 1781.0 1778.288109 \n",
"std 11625.633963 0.0 0.0 0.0 9.788023 \n",
"min 507074.000000 1777.0 546510.0 1781.0 1755.000000 \n",
"25% 532352.000000 1777.0 546510.0 1781.0 1775.000000 \n",
"50% 535940.000000 1777.0 546510.0 1781.0 1782.000000 \n",
"75% 549208.000000 1777.0 546510.0 1781.0 1786.000000 \n",
"max 560880.000000 1777.0 546510.0 1781.0 1788.000000 \n",
"\n",
" volume \n",
"count 9.000400e+04 \n",
"mean 6.355760e+05 \n",
"std 4.163665e+05 \n",
"min 4.900000e+02 \n",
"25% 2.731060e+05 \n",
"50% 5.432880e+05 \n",
"75% 9.371845e+05 \n",
"max 1.436512e+06 \n",
"\n",
"[8 rows x 38 columns]"
]
}
],
"prompt_number": 122
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rm_1min = rm.ix[::240, :]\n",
"rm_1min.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AveragePrice</th>\n",
" <th>LifeHigh</th>\n",
" <th>LifeLow</th>\n",
" <th>TotalAskLot</th>\n",
" <th>TotalBidLot</th>\n",
" <th>askPrc_0</th>\n",
" <th>askPrc_1</th>\n",
" <th>askPrc_2</th>\n",
" <th>askPrc_3</th>\n",
" <th>askPrc_4</th>\n",
" <th>...</th>\n",
" <th>last</th>\n",
" <th>low</th>\n",
" <th>lowLimit</th>\n",
" <th>open</th>\n",
" <th>openInterest</th>\n",
" <th>prevClose</th>\n",
" <th>prevOpenInterest</th>\n",
" <th>prevSettle</th>\n",
" <th>settle</th>\n",
" <th>volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2015-11-19 21:00:00</th>\n",
" <td>1775.0</td>\n",
" <td>2322.0</td>\n",
" <td>1768.0</td>\n",
" <td>3103.0</td>\n",
" <td>2975.0</td>\n",
" <td>1776.0</td>\n",
" <td>1777.0</td>\n",
" <td>1778.0</td>\n",
" <td>1779.0</td>\n",
" <td>1780.0</td>\n",
" <td>...</td>\n",
" <td>1775.0</td>\n",
" <td>1775.0</td>\n",
" <td>1722.0</td>\n",
" <td>1775.0</td>\n",
" <td>587568.0</td>\n",
" <td>1779.0</td>\n",
" <td>587568.0</td>\n",
" <td>1794.0</td>\n",
" <td>1775.0</td>\n",
" <td>520.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:01:00</th>\n",
" <td>1777.0</td>\n",
" <td>2322.0</td>\n",
" <td>1768.0</td>\n",
" <td>4754.0</td>\n",
" <td>3981.0</td>\n",
" <td>1778.0</td>\n",
" <td>1779.0</td>\n",
" <td>1780.0</td>\n",
" <td>1781.0</td>\n",
" <td>1782.0</td>\n",
" <td>...</td>\n",
" <td>1778.0</td>\n",
" <td>1774.0</td>\n",
" <td>1722.0</td>\n",
" <td>1775.0</td>\n",
" <td>586902.0</td>\n",
" <td>1779.0</td>\n",
" <td>587568.0</td>\n",
" <td>1794.0</td>\n",
" <td>1777.0</td>\n",
" <td>8562.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:02:00</th>\n",
" <td>1777.0</td>\n",
" <td>2322.0</td>\n",
" <td>1768.0</td>\n",
" <td>6424.0</td>\n",
" <td>4349.0</td>\n",
" <td>1776.0</td>\n",
" <td>1777.0</td>\n",
" <td>1778.0</td>\n",
" <td>1779.0</td>\n",
" <td>1780.0</td>\n",
" <td>...</td>\n",
" <td>1775.0</td>\n",
" <td>1774.0</td>\n",
" <td>1722.0</td>\n",
" <td>1775.0</td>\n",
" <td>587470.0</td>\n",
" <td>1779.0</td>\n",
" <td>587568.0</td>\n",
" <td>1794.0</td>\n",
" <td>1777.0</td>\n",
" <td>13102.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:03:00</th>\n",
" <td>1775.0</td>\n",
" <td>2322.0</td>\n",
" <td>1768.0</td>\n",
" <td>6589.0</td>\n",
" <td>4588.0</td>\n",
" <td>1773.0</td>\n",
" <td>1774.0</td>\n",
" <td>1775.0</td>\n",
" <td>1776.0</td>\n",
" <td>1777.0</td>\n",
" <td>...</td>\n",
" <td>1772.0</td>\n",
" <td>1770.0</td>\n",
" <td>1722.0</td>\n",
" <td>1775.0</td>\n",
" <td>588386.0</td>\n",
" <td>1779.0</td>\n",
" <td>587568.0</td>\n",
" <td>1794.0</td>\n",
" <td>1775.0</td>\n",
" <td>20892.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-11-19 21:04:00</th>\n",
" <td>1773.0</td>\n",
" <td>2322.0</td>\n",
" <td>1767.0</td>\n",
" <td>8793.0</td>\n",
" <td>4045.0</td>\n",
" <td>1768.0</td>\n",
" <td>1769.0</td>\n",
" <td>1770.0</td>\n",
" <td>1771.0</td>\n",
" <td>1772.0</td>\n",
" <td>...</td>\n",
" <td>1768.0</td>\n",
" <td>1767.0</td>\n",
" <td>1722.0</td>\n",
" <td>1775.0</td>\n",
" <td>589904.0</td>\n",
" <td>1779.0</td>\n",
" <td>587568.0</td>\n",
" <td>1794.0</td>\n",
" <td>1773.0</td>\n",
" <td>34234.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 38 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 95,
"text": [
" AveragePrice LifeHigh LifeLow TotalAskLot \\\n",
"2015-11-19 21:00:00 1775.0 2322.0 1768.0 3103.0 \n",
"2015-11-19 21:01:00 1777.0 2322.0 1768.0 4754.0 \n",
"2015-11-19 21:02:00 1777.0 2322.0 1768.0 6424.0 \n",
"2015-11-19 21:03:00 1775.0 2322.0 1768.0 6589.0 \n",
"2015-11-19 21:04:00 1773.0 2322.0 1767.0 8793.0 \n",
"\n",
" TotalBidLot askPrc_0 askPrc_1 askPrc_2 askPrc_3 \\\n",
"2015-11-19 21:00:00 2975.0 1776.0 1777.0 1778.0 1779.0 \n",
"2015-11-19 21:01:00 3981.0 1778.0 1779.0 1780.0 1781.0 \n",
"2015-11-19 21:02:00 4349.0 1776.0 1777.0 1778.0 1779.0 \n",
"2015-11-19 21:03:00 4588.0 1773.0 1774.0 1775.0 1776.0 \n",
"2015-11-19 21:04:00 4045.0 1768.0 1769.0 1770.0 1771.0 \n",
"\n",
" askPrc_4 ... last low lowLimit open \\\n",
"2015-11-19 21:00:00 1780.0 ... 1775.0 1775.0 1722.0 1775.0 \n",
"2015-11-19 21:01:00 1782.0 ... 1778.0 1774.0 1722.0 1775.0 \n",
"2015-11-19 21:02:00 1780.0 ... 1775.0 1774.0 1722.0 1775.0 \n",
"2015-11-19 21:03:00 1777.0 ... 1772.0 1770.0 1722.0 1775.0 \n",
"2015-11-19 21:04:00 1772.0 ... 1768.0 1767.0 1722.0 1775.0 \n",
"\n",
" openInterest prevClose prevOpenInterest prevSettle \\\n",
"2015-11-19 21:00:00 587568.0 1779.0 587568.0 1794.0 \n",
"2015-11-19 21:01:00 586902.0 1779.0 587568.0 1794.0 \n",
"2015-11-19 21:02:00 587470.0 1779.0 587568.0 1794.0 \n",
"2015-11-19 21:03:00 588386.0 1779.0 587568.0 1794.0 \n",
"2015-11-19 21:04:00 589904.0 1779.0 587568.0 1794.0 \n",
"\n",
" settle volume \n",
"2015-11-19 21:00:00 1775.0 520.0 \n",
"2015-11-19 21:01:00 1777.0 8562.0 \n",
"2015-11-19 21:02:00 1777.0 13102.0 \n",
"2015-11-19 21:03:00 1775.0 20892.0 \n",
"2015-11-19 21:04:00 1773.0 34234.0 \n",
"\n",
"[5 rows x 38 columns]"
]
}
],
"prompt_number": 95
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rm_1min_last = rm_1min.ix[:, 'last']\n",
"rm_1min_lastd = rm_1min_last.diff()\n",
"rm_1min_lastd"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 128,
"text": [
"2015-11-19 21:00:00.000 NaN\n",
"2015-11-19 21:01:00.000 3.0\n",
"2015-11-19 21:02:00.000 -3.0\n",
"2015-11-19 21:03:00.000 -3.0\n",
"2015-11-19 21:04:00.000 -4.0\n",
"2015-11-19 21:05:00.000 2.0\n",
"2015-11-19 21:06:00.000 1.0\n",
"2015-11-19 21:07:00.000 -4.0\n",
"2015-11-19 21:08:00.000 5.0\n",
"2015-11-19 21:09:00.000 3.0\n",
"2015-11-19 21:10:00.000 -1.0\n",
"2015-11-19 21:11:00.000 4.0\n",
"2015-11-19 21:12:00.000 0.0\n",
"2015-11-19 21:13:00.000 0.0\n",
"2015-11-19 21:14:00.000 1.0\n",
"2015-11-19 21:15:00.000 -1.0\n",
"2015-11-19 21:16:00.000 0.0\n",
"2015-11-19 21:17:00.000 0.0\n",
"2015-11-19 21:18:00.000 -1.0\n",
"2015-11-19 21:19:00.000 1.0\n",
"2015-11-19 21:20:00.000 -1.0\n",
"2015-11-19 21:21:00.000 -1.0\n",
"2015-11-19 21:22:00.000 0.0\n",
"2015-11-19 21:23:00.000 1.0\n",
"2015-11-19 21:24:00.000 1.0\n",
"2015-11-19 21:25:00.000 1.0\n",
"2015-11-19 21:26:00.000 2.0\n",
"2015-11-19 21:27:00.000 3.0\n",
"2015-11-19 21:28:00.000 -2.0\n",
"2015-11-19 21:29:00.000 2.0\n",
" ... \n",
"2015-12-31 14:30:30.250 1.0\n",
"2015-12-31 14:31:30.250 -1.0\n",
"2015-12-31 14:32:30.250 0.0\n",
"2015-12-31 14:33:30.250 1.0\n",
"2015-12-31 14:34:30.250 -1.0\n",
"2015-12-31 14:35:30.250 0.0\n",
"2015-12-31 14:36:30.250 0.0\n",
"2015-12-31 14:37:30.250 0.0\n",
"2015-12-31 14:38:30.250 0.0\n",
"2015-12-31 14:39:30.250 0.0\n",
"2015-12-31 14:40:30.250 -1.0\n",
"2015-12-31 14:41:30.250 0.0\n",
"2015-12-31 14:42:30.250 -1.0\n",
"2015-12-31 14:43:30.250 1.0\n",
"2015-12-31 14:44:30.250 1.0\n",
"2015-12-31 14:45:30.250 -2.0\n",
"2015-12-31 14:46:30.250 0.0\n",
"2015-12-31 14:47:30.250 1.0\n",
"2015-12-31 14:48:30.250 0.0\n",
"2015-12-31 14:49:30.250 0.0\n",
"2015-12-31 14:50:30.250 -1.0\n",
"2015-12-31 14:51:30.250 0.0\n",
"2015-12-31 14:52:30.250 1.0\n",
"2015-12-31 14:53:30.250 -1.0\n",
"2015-12-31 14:54:30.250 2.0\n",
"2015-12-31 14:55:30.250 -1.0\n",
"2015-12-31 14:56:30.250 0.0\n",
"2015-12-31 14:57:30.250 -1.0\n",
"2015-12-31 14:58:30.250 0.0\n",
"2015-12-31 14:59:30.250 2.0\n",
"Name: last, dtype: float64"
]
}
],
"prompt_number": 128
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rm_23_1min_lastd = rm_1min_lastd.ix['2015-11-22 21:00:00': '2015-11-23 15:00:00']\n",
"rm_23_1min_lastd.hist(bins=50)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 133,
"text": [
"<matplotlib.axes.AxesSubplot at 0x7f3e182d9110>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEICAYAAACzliQjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f3e18e63fd0>"
]
}
],
"prompt_number": 133
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prove that there is no missing points"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in xrange(len(inde) - 1):\n",
" if (inde[i + 1] - inde[i] != pd.Timedelta(250, 'ms') \n",
" and inde[i].strftime('%T') != '23:30:00'\n",
" and inde[i].strftime('%T') != '10:15:00'\n",
" and inde[i].strftime('%T') != '11:30:00'\n",
" and inde[i].strftime('%T') != '15:00:00'):\n",
" print inde[i], inde[i+1]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 68
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The number of ticks = the number of time intervals + 1"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.where(np.array(inde) == np.datetime64('2015-11-20T11:30:00.000000000+0000'))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 83,
"text": [
"(array([68402]),)"
]
}
],
"prompt_number": 83
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"suspect1 = rm.ix['2015-11-20 09:00:00.000000': '2015-11-20 10:15:00.000000', :]\n",
"suspect2 = rm.ix['2015-11-20 10:30:00.000000': '2015-11-20 11:30:00.000000', :]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 89
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"suspect1.index"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 90,
"text": [
"DatetimeIndex([ '2015-11-20 09:00:00', '2015-11-20 09:00:00.250000',\n",
" '2015-11-20 09:00:00.500000', '2015-11-20 09:00:00.750000',\n",
" '2015-11-20 09:00:01', '2015-11-20 09:00:01.250000',\n",
" '2015-11-20 09:00:01.500000', '2015-11-20 09:00:01.750000',\n",
" '2015-11-20 09:00:02', '2015-11-20 09:00:02.250000',\n",
" ...\n",
" '2015-11-20 10:14:57.750000', '2015-11-20 10:14:58',\n",
" '2015-11-20 10:14:58.250000', '2015-11-20 10:14:58.500000',\n",
" '2015-11-20 10:14:58.750000', '2015-11-20 10:14:59',\n",
" '2015-11-20 10:14:59.250000', '2015-11-20 10:14:59.500000',\n",
" '2015-11-20 10:14:59.750000', '2015-11-20 10:15:00'],\n",
" dtype='datetime64[ns]', length=18001, freq=None)"
]
}
],
"prompt_number": 90
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"suspect2.index"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 91,
"text": [
"DatetimeIndex([ '2015-11-20 10:30:00', '2015-11-20 10:30:00.250000',\n",
" '2015-11-20 10:30:00.500000', '2015-11-20 10:30:00.750000',\n",
" '2015-11-20 10:30:01', '2015-11-20 10:30:01.250000',\n",
" '2015-11-20 10:30:01.500000', '2015-11-20 10:30:01.750000',\n",
" '2015-11-20 10:30:02', '2015-11-20 10:30:02.250000',\n",
" ...\n",
" '2015-11-20 11:29:57.750000', '2015-11-20 11:29:58',\n",
" '2015-11-20 11:29:58.250000', '2015-11-20 11:29:58.500000',\n",
" '2015-11-20 11:29:58.750000', '2015-11-20 11:29:59',\n",
" '2015-11-20 11:29:59.250000', '2015-11-20 11:29:59.500000',\n",
" '2015-11-20 11:29:59.750000', '2015-11-20 11:30:00'],\n",
" dtype='datetime64[ns]', length=14401, freq=None)"
]
}
],
"prompt_number": 91
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
| cc0-1.0 |
BrownDwarf/ApJdataFrames | notebooks/Dupuy2012.ipynb | 1 | 25682 | {
"metadata": {
"name": "",
"signature": "sha256:45bda62eabf51c31685f2a036277b297b1e85b32981ad6d23f7afe8cd5113a58"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`ApJdataFrames` Dupuy2012\n",
"---\n",
"`Title`: THE HAWAII INFRARED PARALLAX PROGRAM. I. ULTRACOOL BINARIES AND THE L/T TRANSITION \n",
"`Authors`: Trent J Dupuy and Michael C Liu\n",
"\n",
"Data is from this paper: \n",
"http://iopscience.iop.org/0067-0049/201/2/19/ \n",
"and this website: \n",
"http://www.as.utexas.edu/~tdupuy/plx/Database_of_Ultracool_Parallaxes.html"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"import seaborn as sns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"pd.options.display.max_columns = 150"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The full sample"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"names = [\"Object name (LaTeX-able)\",\"Opt SpT\",\"NIR SpT\",\"SpT Refs\",\"flag\",\"RA (deg)\",\"Dec (deg)\",\n",
" \"Epoch (JD)\",\"plx\",\"eplx\",\"PMtot\",\"ePM\",\"PM_PA\",\"ePA\",\"PlxRef\",\"gmag\",\"egmag\",\"rmag\",\"ermag\",\n",
" \"imag\",\"eimag\",\"zmag\",\"ezmag\",\"Ref\",\"Ymag\",\"eYmag\",\"Jmag\",\"eJmag\",\"Hmag\",\"eHmag\",\"Kmag\",\"eKmag\",\n",
" \"Lmag\",\"eLmag\",\"Mmag\",\"eMmag\",\"J2mag\",\"eJ2mag\",\"H2mag\",\"eH2mag\",\"K2mag\",\"eK2mag\",\"MKO+2MASS Refs\",\n",
" \"CH1mag\",\"eCH1mag\",\"CH2mag\",\"eCH2mag\", \"CH3mag\",\"eCH3mag\", \"CH4mag\",\"eCH4mag\",\"W1mag\", \"eW1mag\",\"W2mag\", \n",
" \"eW2mag\",\"W3mag\", \"eW3mag\",\"W4mag\",\"eW4mag\", \"nb na cc ext var qual\",\"MIR Refs\", \"sysID\",\n",
" \"bin\", \"compsep\",\"HST/AO\",\"HST/AO Refs\"]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"url_address = \"http://www.as.utexas.edu/~tdupuy/plx/Database_of_Ultracool_Parallaxes_files/vlm-plx-all.txt\"\n",
"tbl = pd.read_csv(url_address, skiprows=1, names = names, sep='[ ]{2,}')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tbl.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Object name (LaTeX-able)</th>\n",
" <th>Opt SpT</th>\n",
" <th>NIR SpT</th>\n",
" <th>SpT Refs</th>\n",
" <th>flag</th>\n",
" <th>RA (deg)</th>\n",
" <th>Dec (deg)</th>\n",
" <th>Epoch (JD)</th>\n",
" <th>plx</th>\n",
" <th>eplx</th>\n",
" <th>PMtot</th>\n",
" <th>ePM</th>\n",
" <th>PM_PA</th>\n",
" <th>ePA</th>\n",
" <th>PlxRef</th>\n",
" <th>gmag</th>\n",
" <th>egmag</th>\n",
" <th>rmag</th>\n",
" <th>ermag</th>\n",
" <th>imag</th>\n",
" <th>eimag</th>\n",
" <th>zmag</th>\n",
" <th>ezmag</th>\n",
" <th>Ref</th>\n",
" <th>Ymag</th>\n",
" <th>eYmag</th>\n",
" <th>Jmag</th>\n",
" <th>eJmag</th>\n",
" <th>Hmag</th>\n",
" <th>eHmag</th>\n",
" <th>Kmag</th>\n",
" <th>eKmag</th>\n",
" <th>Lmag</th>\n",
" <th>eLmag</th>\n",
" <th>Mmag</th>\n",
" <th>eMmag</th>\n",
" <th>J2mag</th>\n",
" <th>eJ2mag</th>\n",
" <th>H2mag</th>\n",
" <th>eH2mag</th>\n",
" <th>K2mag</th>\n",
" <th>eK2mag</th>\n",
" <th>MKO+2MASS Refs</th>\n",
" <th>CH1mag</th>\n",
" <th>eCH1mag</th>\n",
" <th>CH2mag</th>\n",
" <th>eCH2mag</th>\n",
" <th>CH3mag</th>\n",
" <th>eCH3mag</th>\n",
" <th>CH4mag</th>\n",
" <th>eCH4mag</th>\n",
" <th>W1mag</th>\n",
" <th>eW1mag</th>\n",
" <th>W2mag</th>\n",
" <th>eW2mag</th>\n",
" <th>W3mag</th>\n",
" <th>eW3mag</th>\n",
" <th>W4mag</th>\n",
" <th>eW4mag</th>\n",
" <th>nb na cc ext var qual</th>\n",
" <th>MIR Refs</th>\n",
" <th>sysID</th>\n",
" <th>bin</th>\n",
" <th>compsep</th>\n",
" <th>HST/AO</th>\n",
" <th>HST/AO Refs</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> SDSS_J000013.54+255418.6</td>\n",
" <td> null</td>\n",
" <td> T4.5</td>\n",
" <td> 25</td>\n",
" <td> null</td>\n",
" <td> 0.05639</td>\n",
" <td> 25.90546</td>\n",
" <td> 2454302.13</td>\n",
" <td> 70.80</td>\n",
" <td> 1.90</td>\n",
" <td> 128.10</td>\n",
" <td> 1.30</td>\n",
" <td> 351.430</td>\n",
" <td> 0.65</td>\n",
" <td> 68</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",
" <td> 18.485</td>\n",
" <td> 0.032</td>\n",
" <td> 1</td>\n",
" <td> 15.798</td>\n",
" <td> 0.058 1</td>\n",
" <td> 14.730</td>\n",
" <td> 0.030 0</td>\n",
" <td> 14.740</td>\n",
" <td> 0.030 0</td>\n",
" <td> 14.820</td>\n",
" <td> 0.030 0</td>\n",
" <td> 13.030</td>\n",
" <td> 0.030 0</td>\n",
" <td> 13.280</td>\n",
" <td> 0.100 0</td>\n",
" <td> 15.063</td>\n",
" <td> 0.041 0</td>\n",
" <td> 14.731</td>\n",
" <td> 0.074 0</td>\n",
" <td> 14.836</td>\n",
" <td> 0.120 0</td>\n",
" <td> 57,68,135,148</td>\n",
" <td> 13.72</td>\n",
" <td> 0.03</td>\n",
" <td> 13.07</td>\n",
" <td> 0.03</td>\n",
" <td> 12.56</td>\n",
" <td> 0.09</td>\n",
" <td> 12.5</td>\n",
" <td> 0.03</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",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 0 0 null 0 null null</td>\n",
" <td> 148</td>\n",
" <td> 141</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" <td> K</td>\n",
" <td> 68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2MASSI_J0003422-$282241</td>\n",
" <td> M7.5</td>\n",
" <td> null</td>\n",
" <td> 52</td>\n",
" <td> over-L</td>\n",
" <td> 0.92770</td>\n",
" <td>-28.37834</td>\n",
" <td> 2455051.03</td>\n",
" <td> 25.70</td>\n",
" <td> 0.93</td>\n",
" <td> 314.46</td>\n",
" <td> 0.98</td>\n",
" <td> 116.750</td>\n",
" <td> 0.13</td>\n",
" <td> 253</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 21.576</td>\n",
" <td> 0.117</td>\n",
" <td> 19.380</td>\n",
" <td> 0.058</td>\n",
" <td> 1</td>\n",
" <td> 13.814</td>\n",
" <td> 0.055 1</td>\n",
" <td> 13.017</td>\n",
" <td> 0.024 1</td>\n",
" <td> 12.410</td>\n",
" <td> 0.028 1</td>\n",
" <td> 11.949</td>\n",
" <td> 0.025 1</td>\n",
" <td> NaN</td>\n",
" <td> NaN 9</td>\n",
" <td> NaN</td>\n",
" <td> NaN 9</td>\n",
" <td> 13.068</td>\n",
" <td> 0.024 0</td>\n",
" <td> 12.376</td>\n",
" <td> 0.028 0</td>\n",
" <td> 11.972</td>\n",
" <td> 0.025 0</td>\n",
" <td> 57,68</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",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 11.671</td>\n",
" <td> 0.025</td>\n",
" <td> 11.502</td>\n",
" <td> 0.023</td>\n",
" <td> 10.971</td>\n",
" <td> 0.099</td>\n",
" <td> 9.017</td>\n",
" <td> NaN</td>\n",
" <td> 1 0 dd00 0 011n AAAU</td>\n",
" <td> 260</td>\n",
" <td> 242</td>\n",
" <td> 1</td>\n",
" <td> 66.000</td>\n",
" <td> null</td>\n",
" <td> null</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> GJ_1001B</td>\n",
" <td> null</td>\n",
" <td> L5</td>\n",
" <td> 68</td>\n",
" <td> null</td>\n",
" <td> 1.14519</td>\n",
" <td>-40.73497</td>\n",
" <td> 2451392.79</td>\n",
" <td> 76.86</td>\n",
" <td> 3.97</td>\n",
" <td> 1627.00</td>\n",
" <td> 1.80</td>\n",
" <td> 156.700</td>\n",
" <td> 0.12</td>\n",
" <td> 111</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",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> null</td>\n",
" <td> NaN</td>\n",
" <td> NaN 9</td>\n",
" <td> 13.764</td>\n",
" <td> 0.038 0</td>\n",
" <td> 12.820</td>\n",
" <td> 0.035 0</td>\n",
" <td> 12.064</td>\n",
" <td> 0.038 1</td>\n",
" <td> NaN</td>\n",
" <td> NaN 9</td>\n",
" <td> NaN</td>\n",
" <td> NaN 9</td>\n",
" <td> 13.813</td>\n",
" <td> 0.034 1</td>\n",
" <td> 12.735</td>\n",
" <td> 0.032 1</td>\n",
" <td> 12.100</td>\n",
" <td> 0.035 0</td>\n",
" <td> 57,68,147</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",
" <td> NaN</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",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 0 0 null 0 null null</td>\n",
" <td> 200,260</td>\n",
" <td> 637</td>\n",
" <td> 2</td>\n",
" <td> 18.600</td>\n",
" <td> NV</td>\n",
" <td> 68,98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> GJ_1001C</td>\n",
" <td> null</td>\n",
" <td> L5</td>\n",
" <td> 68</td>\n",
" <td> null</td>\n",
" <td> 1.14519</td>\n",
" <td>-40.73497</td>\n",
" <td> 2451392.79</td>\n",
" <td> 76.86</td>\n",
" <td> 3.97</td>\n",
" <td> 1627.00</td>\n",
" <td> 1.80</td>\n",
" <td> 156.700</td>\n",
" <td> 0.12</td>\n",
" <td> 111</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",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> null</td>\n",
" <td> NaN</td>\n",
" <td> NaN 9</td>\n",
" <td> 13.864</td>\n",
" <td> 0.040 0</td>\n",
" <td> 12.970</td>\n",
" <td> 0.037 0</td>\n",
" <td> 12.164</td>\n",
" <td> 0.040 1</td>\n",
" <td> NaN</td>\n",
" <td> NaN 9</td>\n",
" <td> NaN</td>\n",
" <td> NaN 9</td>\n",
" <td> 13.913</td>\n",
" <td> 0.036 1</td>\n",
" <td> 12.885</td>\n",
" <td> 0.034 1</td>\n",
" <td> 12.200</td>\n",
" <td> 0.037 0</td>\n",
" <td> 57,68,147</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",
" <td> NaN</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",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 0 0 null 0 null null</td>\n",
" <td> 200,260</td>\n",
" <td> 637</td>\n",
" <td> 2</td>\n",
" <td> 18.600</td>\n",
" <td> NV</td>\n",
" <td> 68,98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> LSR_J0011+5908</td>\n",
" <td> M6.5</td>\n",
" <td> null</td>\n",
" <td> 160</td>\n",
" <td> null</td>\n",
" <td> 2.88260</td>\n",
" <td> 59.14446</td>\n",
" <td> 2451492.72</td>\n",
" <td> 108.30</td>\n",
" <td> 1.40</td>\n",
" <td> 1472.28</td>\n",
" <td> NaN</td>\n",
" <td> 217.670</td>\n",
" <td> NaN</td>\n",
" <td> 160</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",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> null</td>\n",
" <td> NaN</td>\n",
" <td> NaN 9</td>\n",
" <td> NaN</td>\n",
" <td> NaN 9</td>\n",
" <td> NaN</td>\n",
" <td> NaN 9</td>\n",
" <td> NaN</td>\n",
" <td> NaN 9</td>\n",
" <td> NaN</td>\n",
" <td> NaN 9</td>\n",
" <td> NaN</td>\n",
" <td> NaN 9</td>\n",
" <td> 9.945</td>\n",
" <td> 0.023 0</td>\n",
" <td> 9.393</td>\n",
" <td> 0.026 0</td>\n",
" <td> 9.093</td>\n",
" <td> 0.021 0</td>\n",
" <td> 57</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",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 8.864</td>\n",
" <td> 0.023</td>\n",
" <td> 8.633</td>\n",
" <td> 0.019</td>\n",
" <td> 8.407</td>\n",
" <td> 0.025</td>\n",
" <td> 7.927</td>\n",
" <td> 0.117</td>\n",
" <td> 1 0 h000 0 0010 AAAB</td>\n",
" <td> 260</td>\n",
" <td> 161</td>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" <td> null</td>\n",
" <td> null</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
" Object name (LaTeX-able) Opt SpT NIR SpT SpT Refs flag RA (deg) \\\n",
"0 SDSS_J000013.54+255418.6 null T4.5 25 null 0.05639 \n",
"1 2MASSI_J0003422-$282241 M7.5 null 52 over-L 0.92770 \n",
"2 GJ_1001B null L5 68 null 1.14519 \n",
"3 GJ_1001C null L5 68 null 1.14519 \n",
"4 LSR_J0011+5908 M6.5 null 160 null 2.88260 \n",
"\n",
" Dec (deg) Epoch (JD) plx eplx PMtot ePM PM_PA ePA PlxRef \\\n",
"0 25.90546 2454302.13 70.80 1.90 128.10 1.30 351.430 0.65 68 \n",
"1 -28.37834 2455051.03 25.70 0.93 314.46 0.98 116.750 0.13 253 \n",
"2 -40.73497 2451392.79 76.86 3.97 1627.00 1.80 156.700 0.12 111 \n",
"3 -40.73497 2451392.79 76.86 3.97 1627.00 1.80 156.700 0.12 111 \n",
"4 59.14446 2451492.72 108.30 1.40 1472.28 NaN 217.670 NaN 160 \n",
"\n",
" gmag egmag rmag ermag imag eimag zmag ezmag Ref Ymag \\\n",
"0 NaN NaN NaN NaN NaN NaN 18.485 0.032 1 15.798 \n",
"1 NaN NaN NaN NaN 21.576 0.117 19.380 0.058 1 13.814 \n",
"2 NaN NaN NaN NaN NaN NaN NaN NaN null NaN \n",
"3 NaN NaN NaN NaN NaN NaN NaN NaN null NaN \n",
"4 NaN NaN NaN NaN NaN NaN NaN NaN null NaN \n",
"\n",
" eYmag Jmag eJmag Hmag eHmag Kmag eKmag Lmag \\\n",
"0 0.058 1 14.730 0.030 0 14.740 0.030 0 14.820 0.030 0 13.030 \n",
"1 0.055 1 13.017 0.024 1 12.410 0.028 1 11.949 0.025 1 NaN \n",
"2 NaN 9 13.764 0.038 0 12.820 0.035 0 12.064 0.038 1 NaN \n",
"3 NaN 9 13.864 0.040 0 12.970 0.037 0 12.164 0.040 1 NaN \n",
"4 NaN 9 NaN NaN 9 NaN NaN 9 NaN NaN 9 NaN \n",
"\n",
" eLmag Mmag eMmag J2mag eJ2mag H2mag eH2mag K2mag \\\n",
"0 0.030 0 13.280 0.100 0 15.063 0.041 0 14.731 0.074 0 14.836 \n",
"1 NaN 9 NaN NaN 9 13.068 0.024 0 12.376 0.028 0 11.972 \n",
"2 NaN 9 NaN NaN 9 13.813 0.034 1 12.735 0.032 1 12.100 \n",
"3 NaN 9 NaN NaN 9 13.913 0.036 1 12.885 0.034 1 12.200 \n",
"4 NaN 9 NaN NaN 9 9.945 0.023 0 9.393 0.026 0 9.093 \n",
"\n",
" eK2mag MKO+2MASS Refs CH1mag eCH1mag CH2mag eCH2mag CH3mag eCH3mag \\\n",
"0 0.120 0 57,68,135,148 13.72 0.03 13.07 0.03 12.56 0.09 \n",
"1 0.025 0 57,68 NaN NaN NaN NaN NaN NaN \n",
"2 0.035 0 57,68,147 NaN NaN NaN NaN NaN NaN \n",
"3 0.037 0 57,68,147 NaN NaN NaN NaN NaN NaN \n",
"4 0.021 0 57 NaN NaN NaN NaN NaN NaN \n",
"\n",
" CH4mag eCH4mag W1mag eW1mag W2mag eW2mag W3mag eW3mag W4mag \\\n",
"0 12.5 0.03 NaN NaN NaN NaN NaN NaN NaN \n",
"1 NaN NaN 11.671 0.025 11.502 0.023 10.971 0.099 9.017 \n",
"2 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
"4 NaN NaN 8.864 0.023 8.633 0.019 8.407 0.025 7.927 \n",
"\n",
" eW4mag nb na cc ext var qual MIR Refs sysID bin compsep HST/AO HST/AO Refs \n",
"0 NaN 0 0 null 0 null null 148 141 1 NaN K 68 \n",
"1 NaN 1 0 dd00 0 011n AAAU 260 242 1 66.000 null null \n",
"2 NaN 0 0 null 0 null null 200,260 637 2 18.600 NV 68,98 \n",
"3 NaN 0 0 null 0 null null 200,260 637 2 18.600 NV 68,98 \n",
"4 0.117 1 0 h000 0 0010 AAAB 260 161 1 NaN null null "
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Table for reference numbers"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"names = [\"Ref\", \"Excel code\", \"ADS Bibtex Key\" ]\n",
"url_refs = \"http://www.as.utexas.edu/~tdupuy/plx/Database_of_Ultracool_Parallaxes_files/vlm-plx-refs.txt\"\n",
"refs = pd.read_csv(url_refs, skiprows=1, names = names, sep='[ ]{2,}')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"refs.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Ref</th>\n",
" <th>Excel code</th>\n",
" <th>ADS Bibtex Key</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> Adel2009</td>\n",
" <td> 2009ApJS..182..543A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> Adel2012</td>\n",
" <td> 2012ApJS..203...21A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> Alle2008</td>\n",
" <td> 2008AJ....135.2024A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> Alle2009</td>\n",
" <td> 2009ApJ...697..824A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5</td>\n",
" <td> Andr2010</td>\n",
" <td> 2011AJ....141...54A</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
" Ref Excel code ADS Bibtex Key\n",
"0 1 Adel2009 2009ApJS..182..543A\n",
"1 2 Adel2012 2012ApJS..203...21A\n",
"2 3 Alle2008 2008AJ....135.2024A\n",
"3 4 Alle2009 2009ApJ...697..824A\n",
"4 5 Andr2010 2011AJ....141...54A"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**The end.**"
]
}
],
"metadata": {}
}
]
} | mit |
dgleich/diffusion-tutorial | HeatKernelDiffusion.ipynb | 1 | 12414332 | null | mit |
DominikDitoIvosevic/Uni | STRUCE/2018/SU-2018-LAB02-0036477171.ipynb | 2 | 518911 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sveučilište u Zagrebu \n",
"Fakultet elektrotehnike i računarstva \n",
" \n",
"## Strojno učenje 2018/2019\n",
"http://www.fer.unizg.hr/predmet/su"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"------------------------------\n",
"\n",
"### Laboratorijska vježba 2: Linearni diskriminativni modeli\n",
"\n",
"*Verzija: 1.2 \n",
"Zadnji put ažurirano: 26. listopada 2018.*\n",
"\n",
"(c) 2015-2018 Jan Šnajder, Domagoj Alagić \n",
"\n",
"Objavljeno: **26. listopada 2018.** \n",
"Rok za predaju: **5. studenog 2018. u 07:00h**\n",
"\n",
"------------------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Upute\n",
"\n",
"Prva laboratorijska vježba sastoji se od šest zadataka. U nastavku slijedite upute navedene u ćelijama s tekstom. Rješavanje vježbe svodi se na **dopunjavanje ove bilježnice**: umetanja ćelije ili više njih **ispod** teksta zadatka, pisanja odgovarajućeg kôda te evaluiranja ćelija. \n",
"\n",
"Osigurajte da u potpunosti **razumijete** kôd koji ste napisali. Kod predaje vježbe, morate biti u stanju na zahtjev asistenta (ili demonstratora) preinačiti i ponovno evaluirati Vaš kôd. Nadalje, morate razumjeti teorijske osnove onoga što radite, u okvirima onoga što smo obradili na predavanju. Ispod nekih zadataka možete naći i pitanja koja služe kao smjernice za bolje razumijevanje gradiva (**nemojte pisati** odgovore na pitanja u bilježnicu). Stoga se nemojte ograničiti samo na to da riješite zadatak, nego slobodno eksperimentirajte. To upravo i jest svrha ovih vježbi.\n",
"\n",
"Vježbe trebate raditi **samostalno**. Možete se konzultirati s drugima o načelnom načinu rješavanja, ali u konačnici morate sami odraditi vježbu. U protivnome vježba nema smisla."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"# Učitaj osnovne biblioteke...\n",
"import sklearn\n",
"import mlutils\n",
"import matplotlib.pyplot as plt\n",
"%pylab inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zadatci"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Linearna regresija kao klasifikator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"U prvoj laboratorijskoj vježbi koristili smo model linearne regresije za, naravno, regresiju. Međutim, model linearne regresije može se koristiti i za **klasifikaciju**. Iako zvuči pomalo kontraintuitivno, zapravo je dosta jednostavno. Naime, cilj je naučiti funkciju $f(\\mathbf{x})$ koja za negativne primjere predviđa vrijednost $1$, dok za pozitivne primjere predviđa vrijednost $0$. U tom slučaju, funkcija $f(\\mathbf{x})=0.5$ predstavlja granicu između klasa, tj. primjeri za koje vrijedi $h(\\mathbf{x})\\geq 0.5$ klasificiraju se kao pozitivni, dok se ostali klasificiraju kao negativni.\n",
"\n",
"Klasifikacija pomoću linearne regresije implementirana je u razredu [`RidgeClassifier`](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.RidgeClassifier.html). U sljedećim podzadatcima **istrenirajte** taj model na danim podatcima i **prikažite** dobivenu granicu između klasa. Pritom isključite regularizaciju ($\\alpha = 0$, odnosno `alpha=0`). Također i ispišite **točnost** vašeg klasifikacijskog modela (smijete koristiti funkciju [`metrics.accuracy_score`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html)). Skupove podataka vizualizirajte korištenjem pomoćne funkcije ``plot_clf_problem(X, y, h=None)`` koja je dostupna u pomoćnom paketu `mlutils` (datoteku `mlutils.py` možete preuzeti sa stranice kolegija). `X` i `y` predstavljaju ulazne primjere i oznake, dok `h` predstavlja funkciju predikcije modela (npr. `model.predict`). \n",
"\n",
"U ovom zadatku cilj je razmotriti kako se klasifikacijski model linearne regresije ponaša na linearno odvojim i neodvojivim podatcima.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression, RidgeClassifier\n",
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (a)\n",
"\n",
"Prvo, isprobajte *ugrađeni* model na linearno odvojivom skupu podataka `seven` ($N=7$)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"seven_X = np.array([[2,1], [2,3], [1,2], [3,2], [5,2], [5,4], [6,3]])\n",
"seven_y = np.array([1, 1, 1, 1, 0, 0, 0])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0\n"
]
},
{
"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": [
"clf = RidgeClassifier().fit(seven_X, seven_y)\n",
"predicted_y = clf.predict(seven_X)\n",
"score = accuracy_score(y_pred=predicted_y, y_true=seven_y)\n",
"print(score)\n",
"\n",
"mlutils.plot_2d_clf_problem(X=seven_X, y=predicted_y, h=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Kako bi se uvjerili da se u isprobanoj implementaciji ne radi o ničemu doli o običnoj linearnoj regresiji, napišite kôd koji dolazi do jednakog rješenja korištenjem isključivo razreda [`LinearRegression`](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html). Funkciju za predikciju, koju predajete kao treći argument `h` funkciji `plot_2d_clf_problem`, možete definirati lambda-izrazom: `lambda x : model.predict(x) >= 0.5`."
]
},
{
"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": [
"\n",
"lr = LinearRegression().fit(seven_X, seven_y)\n",
"predicted_y_2 = lr.predict(seven_X)\n",
"\n",
"\n",
"mlutils.plot_2d_clf_problem(X=seven_X, y=seven_y, h= lambda x : lr.predict(x) >= 0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q:** Kako bi bila definirana granica između klasa ako bismo koristili oznake klasa $-1$ i $1$ umjesto $0$ i $1$?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Probajte isto na linearno odvojivom skupu podataka `outlier` ($N=8$):"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"outlier_X = np.append(seven_X, [[12,8]], axis=0)\n",
"outlier_y = np.append(seven_y, 0)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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": [
"lr2 = LinearRegression().fit(outlier_X, outlier_y)\n",
"predicted_y_2 = lr2.predict(outlier_X)\n",
"\n",
"mlutils.plot_2d_clf_problem(X=outlier_X, y=outlier_y, h= lambda x : lr2.predict(x) >= 0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q:** Zašto model ne ostvaruje potpunu točnost iako su podatci linearno odvojivi?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (c)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Završno, probajte isto na linearno neodvojivom skupu podataka `unsep` ($N=8$):"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"unsep_X = np.append(seven_X, [[2,2]], axis=0)\n",
"unsep_y = np.append(seven_y, 0)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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": [
"lr3 = LinearRegression().fit(unsep_X, unsep_y)\n",
"predicted_y_2 = lr3.predict(unsep_X)\n",
"\n",
"mlutils.plot_2d_clf_problem(X=unsep_X, y=unsep_y, h= lambda x : lr3.predict(x) >= 0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q:** Očito je zašto model nije u mogućnosti postići potpunu točnost na ovom skupu podataka. Međutim, smatrate li da je problem u modelu ili u podacima? Argumentirajte svoj stav."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Višeklasna klasifikacija"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Postoji više načina kako se binarni klasifikatori mogu se upotrijebiti za višeklasnu klasifikaciju. Najčešće se koristi shema tzv. **jedan-naspram-ostali** (engl. *one-vs-rest*, OVR), u kojoj se trenira po jedan klasifikator $h_j$ za svaku od $K$ klasa. Svaki klasifikator $h_j$ trenira se da razdvaja primjere klase $j$ od primjera svih drugih klasa, a primjer se klasificira u klasu $j$ za koju je $h_j(\\mathbf{x})$ maksimalan.\n",
"\n",
"Pomoću funkcije [`datasets.make_classification`](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html) generirajte slučajan dvodimenzijski skup podataka od tri klase i prikažite ga koristeći funkciju `plot_2d_clf_problem`. Radi jednostavnosti, pretpostavite da nema redundantnih značajki te da je svaka od klasa \"zbijena\" upravo u jednu grupu."
]
},
{
"cell_type": "code",
"execution_count": 10,
"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": [
"from sklearn.datasets import make_classification\n",
"\n",
"x, y = sklearn.datasets.make_classification(n_samples=100, n_informative=2, n_redundant=0, n_repeated=0, n_features=2, n_classes=3, n_clusters_per_class=1)\n",
"\n",
"#print(dataset)\n",
"mlutils.plot_2d_clf_problem(X=x, y=y, h=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Trenirajte tri binarna klasifikatora, $h_1$, $h_2$ i $h_3$ te prikažite granice između klasa (tri grafikona). Zatim definirajte $h(\\mathbf{x})=\\mathrm{argmax}_j h_j(\\mathbf{x})$ (napišite svoju funkciju `predict` koja to radi) i prikažite granice između klasa za taj model. Zatim se uvjerite da biste identičan rezultat dobili izravno primjenom modela `RidgeClassifier`, budući da taj model za višeklasan problem zapravo interno implementira shemu jedan-naspram-ostali.\n",
"\n",
"**Q:** Alternativna shema jest ona zvana **jedan-naspram-jedan** (engl, *one-vs-one*, OVO). Koja je prednost sheme OVR nad shemom OVO? A obratno?"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 360x1080 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(5,15))\n",
"fig.subplots_adjust(wspace=0.2)\n",
"\n",
"y_ovo1 = [ 0 if i == 0 else 1 for i in y]\n",
"lrOvo1 = LinearRegression().fit(x, y_ovo1)\n",
"fig.add_subplot(3,1,1)\n",
"mlutils.plot_2d_clf_problem(X=x, y=y_ovo1, h= lambda x : lrOvo1.predict(x) >= 0.5)\n",
"\n",
"y_ovo2 = [ 0 if i == 1 else 1 for i in y]\n",
"lrOvo2 = LinearRegression().fit(x, y_ovo2)\n",
"fig.add_subplot(3,1,2)\n",
"mlutils.plot_2d_clf_problem(X=x, y=y_ovo2, h= lambda x : lrOvo2.predict(x) >= 0.5)\n",
"\n",
"y_ovo3 = [ 0 if i == 2 else 1 for i in y]\n",
"lrOvo3 = LinearRegression().fit(x, y_ovo3)\n",
"fig.add_subplot(3,1,3)\n",
"mlutils.plot_2d_clf_problem(X=x, y=y_ovo3, h= lambda x : lrOvo3.predict(x) >= 0.5)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Logistička regresija"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ovaj zadatak bavi se probabilističkim diskriminativnim modelom, **logističkom regresijom**, koja je, unatoč nazivu, klasifikacijski model.\n",
"\n",
"Logistička regresija tipičan je predstavnik tzv. **poopćenih linearnih modela** koji su oblika: $h(\\mathbf{x})=f(\\mathbf{w}^\\intercal\\tilde{\\mathbf{x}})$. Logistička funkcija za funkciju $f$ koristi tzv. **logističku** (sigmoidalnu) funkciju $\\sigma (x) = \\frac{1}{1 + \\textit{exp}(-x)}$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Definirajte logističku (sigmoidalnu) funkciju $\\mathrm{sigm}(x)=\\frac{1}{1+\\exp(-\\alpha x)}$ i prikažite je za $\\alpha\\in\\{1,2,4\\}$."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x240baf3b668>]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAT8AAANSCAYAAAATU8kGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzs3XuYHHWd9/33d3pOmZwmyUzIYRJyIAhBUXAIQVBRQANqsu7talB2UbnIo7vs6qP3Xou3yrK4ez2363XvwWtRNysuKkhE713JYlw8gZpAQsKZJMT05DSThMwhk8NkztPf54/uhGbSk+mZqZ7q7vq8LobprvpN9bdrej6pql9V/czdERGJmpKwCxARCYPCT0QiSeEnIpGk8BORSFL4iUgkKfxEJJIUfiISSQo/EYkkhZ+IRFJpWC9cU1PjCxYsCOvlRaRIPfPMM63uXjtcu9DCb8GCBWzbti2slxeRImVm+7Npp91eEYkkhZ+IRJLCT0QiSeEnIpGk8BORSFL4iUgkDRt+ZvYdM2s2s5eHmG9m9nUzi5vZi2Z2efBliogEK5stv/uBFeeYfyOwJPW1Bvjm2MsSEcmtYU9ydvffmtmCczRZBXzPk4OBbDazajOb7e6HA6pRRHLM3ekbcPoGEvT2J+gbSNCT+t47kKCv3+kdGKC3P9lmIOEk3Ek4DCQcd2cg9dzdU/MhkdYu4Y4nXwxPfsPTH6fqSDVJfsf5SP18plaVBf6eg7jCYy7QmPa8KTXtrPAzszUktw6ZP39+AC8tIpAMjY6efo519nG8K/l1rLOPY129yeedfWfmHevq5VhnHye6+jjZ3U9PfzLg8tV7ls7K2/CzDNMyDgnn7muBtQD19fUaNk5kFLr7Bvj9kZPsPHyCHYdOsPPwSXa+eoKT3f1D/kx5aQnVE8qoripj6oQy6qZVUT23jMmVpVSUxiiPGeWlJZTFSl73vXzQ87KYUR4rIVZixEqMEjPMOPM4+QUllpyfPs8ADIzkdAMsNd1S0zFeNw+gqiyWk/UYRPg1AfPSntcBhwJYrkikuTstJ3vYcTgZcMnvJ9jT0kEitelQVR7jolmTWfnmOZw/o4rqCeVMrSqjekJZ6ns51VVlVOYoQApZEOG3HrjDzNYBVwLHdbxPZHQOHeviB1sO8HzjMXYePkHbqd4z8+ZWT+Di2ZO58Y2zWDp7ChfPnsL86VWUlGTa+ZLhDBt+ZvYQcC1QY2ZNwF8DZQDu/i1gA3ATEAc6gU/kqliRYrWv9RTffKKB/3iuiYTDxbMn8+6LZrJ0TjLkLp41JSfHvaIsm97em4eZ78CfBVaRSITsevUk33gizn+9cIjSWAk3L5vPmncsom5aVdilFb3Q7ucnEmUvNh3jX34d5+c7jjCxPMbtb1/EbW9fyMzJlWGXFhkKP5FxtGVPG//yeJzf7W5lSmUpn7luCZ+4egHVVeVhlxY5Cj+RHHN3fvP7Fu59PM7Wfe3UTCrnr1ZcxC3L5zO5UsfxwqLwE8mRRML5+Y4j3Pt4nJcOHmf21Eru/sBSPnLFfCaU69STsCn8RHLA3fnyIy/z4JYDLJhRxVf/x5v44GV1lJfqRkr5QuEnkgP3bdzLg1sOcPvbF/JXKy6iNKbQyzcKP5GA/Xz7q/zdhp3c9KZZfOHGi3UScp7SP0ciAXqp6TifWfc8l9ZV8w8ffouCL48p/EQCcvh4F7d9dyvTJ5bzb3/yVl1Pm+e02ysSgFM9/Xzy/m109g7w408v08nKBUDhJzJGAwnnLx56jt8fOcl9t9Zz0awpYZckWdBur8gY/e1Pd/CrV5q5e+UlXPuGmWGXI1lS+ImMwfee2se/b9rHJ69eyB8vPz/scmQEFH4io/TErmbuXr+d6y+eyRffd3HY5cgIKfxERuGVV09wxw+e46JZU/jn1ZcR0yktBUfhJzJCzSe7+eS/b2ViRYz7Pl7PxAr1GxYi/dZERqCrd4Dbv7uN9s4+fvSpq5g9dULYJckoKfxEspRIOJ97+HlePHictX9czxvnTg27JBkD7faKZOnvH9vFz15+lS/edDE3LD0v7HJkjBR+Ill4eFsj3/pNAx+7cj63XbMw7HIkAAo/kWF09w3wdz/dyfJF0/mblZecGUxbCpvCT2QYP33xMMe7+viL65bovnxFRL9JkWE8sGU/i2onctWiGWGXIgFS+Imcw/ZDx3nuwDE+duX52t0tMgo/kXN4cMsBKkpL+NDldWGXIgFT+IkM4WR3Hz957iAfePMcplZpiMlio/ATGcJPnjtIZ+8At+huLUVJ4SeSgbvz4JYDvHHuFN5cpys5ipHCTySDZ/a388qrJ9XRUcQUfiIZPLB5P5MrSln1ljlhlyI5ovATGeToqV42vPQqf3j5XKrKde+PYqXwExnkR9sa6R1I8DF1dBQ1hZ9ImkTC+cHTB1i2YDoXnjc57HIkhxR+Imk2xlvZ39bJx5bPD7sUybGsws/MVpjZLjOLm9mdGebPN7PHzew5M3vRzG4KvlSR3Htg835mTCxnxRtnhV2K5Niw4WdmMeBe4EZgKXCzmS0d1OxLwMPufhmwGvhG0IWK5Nrh4138cucR/qh+HhWlsbDLkRzLZstvGRB39z3u3gusA1YNauPA6WHqpwKHgitRZHyse7oRBz66TLu8UZBNP/5coDHteRNw5aA2dwM/N7M/ByYC1wdSncg46RtIsG7rAd6xpJb5M6rCLkfGQTZbfplOb/dBz28G7nf3OuAm4PtmdtayzWyNmW0zs20tLS0jr1YkR361s5kjJ3p0HW+EZBN+TcC8tOd1nL1bexvwMIC7PwVUAjWDF+Tua9293t3ra2trR1exSA48uGU/c6ZW8u6LZoZdioyTbMJvK7DEzBaaWTnJDo31g9ocAK4DMLOLSYafNu2kIOxtPcXvdreyetl8YiW6jjcqhg0/d+8H7gAeA3aS7NXdbmb3mNnKVLPPA7eb2QvAQ8DH3X3wrrFIXnro6QOUlhirr5g3fGMpGllduOjuG4ANg6bdlfZ4B3B1sKWJ5F533wA/2tbIey45j5lTKsMuR8aRrvCQSNvw0mHaO/v42JXq6IgahZ9E2oNbDrCoZiJvW6yR2aJG4SeRtfPwCZ7Z385Hr5yvG5ZGkMJPIuuBzfuTI7O9VSOzRZHCTyKpo6efnzx3kPdfOofqqvKwy5EQKPwkkn7y3EFO9Q5wi25dFVkKP4kcd+eBzftZOnsKb5lXHXY5EhKFn0TOsweO8cqrJ7lluUZmizKFn0TOg5v3M0kjs0Wewk8i5XhnH4++dJgPXjaXiRUamS3KFH4SKRvjrfT2J/iDy+aGXYqETOEnkbIx3srkilLeXDc17FIkZAo/iZRN8VauXDSD0pg++lGnT4BERuPRTg4c7eSaC3Qdryj8JEI2xVsBuGbJWTcZlwhS+ElkbIy3ct6UChbXTgq7FMkDCj+JhETCebKhjasvqNGJzQIo/CQidr56gqOnerl6sXZ5JUnhJ5Fw+njf1Rco/CRJ4SeRsDHexgUzJzFrqsbpkCSFnxS9nv4Btu49yjXa6pM0Cj8pes8dOEZX34B2eeV1FH5S9DbFW4mVGFcumh52KZJHFH5S9DbGW7m0bipTKsvCLkXyiMJPitqJ7j5eaDym431yFoWfFLXNDW0kXKe4yNkUflLUnmxoY0JZjMvma6wOeT2FnxS1jfFWli2cTkVpLOxSJM8o/KRovXq8m3hzB1frFlaSgcJPipYuaZNzUfhJ0doUb2X6xHIunjUl7FIkDyn8pCi5Oxvjrbxt8QxKSnQLKzmbwk+KUry5g+aTPTq/T4ak8JOipON9MhyFnxSljfE25k+vYt70qrBLkTyVVfiZ2Qoz22VmcTO7c4g2HzazHWa23cx+EGyZItnrH0iweU+btvrknEqHa2BmMeBe4AagCdhqZuvdfUdamyXAF4Cr3b3dzGbmqmCR4bzQdJyOnn4d75NzymbLbxkQd/c97t4LrANWDWpzO3Cvu7cDuHtzsGWKZG9TvBUzuGqxTm6WoWUTfnOBxrTnTalp6S4ELjSzTWa22cxWZFqQma0xs21mtq2lpWV0FYsMY2O8lUvmTGH6xPKwS5E8lk34ZTpJygc9LwWWANcCNwPfNrOzriR397XuXu/u9bW1tSOtVWRYnb39PHegXaO0ybCyCb8mYF7a8zrgUIY2j7h7n7vvBXaRDEORcfX03qP0Dbg6O2RY2YTfVmCJmS00s3JgNbB+UJufAO8CMLMakrvBe4IsVCQbm+KtlMdKuGKBblkv5zZs+Ll7P3AH8BiwE3jY3beb2T1mtjLV7DGgzcx2AI8Df+nubbkqWmQoG+NtvPX8aUwo1y2s5NyGPdUFwN03ABsGTbsr7bEDn0t9iYSitaOHnYdP8JfvfUPYpUgB0BUeUjSebEjubOh4n2RD4SdF48l4K5MrS3nT3KlhlyIFQOEnRcHd+d3uVq5aNIOYbmElWVD4SVE4cLSTg8e6uGaJdnklOwo/KQobdQsrGSGFnxSFTfFWZk+tZFHNxLBLkQKh8JOCN5Bwnmxo422LazDT8T7JjsJPCt6OQyc41tnHNUt0FxfJnsJPCt6mhtTxPt3MQEZA4ScFb1O8lQvPm8TMKZVhlyIFROEnBa27b4Cn9x5VL6+MmMJPCtqz+9vp6U/olvUyYgo/KWgb463ESoxlC3ULKxkZhZ8UtE3xVt4yr5rJlWVhlyIFRuEnBet4Zx8vHTyu430yKgo/KVhP7Wkj4eh4n4yKwk8K1qZ4K1XlMd4y76yxskSGpfCTgrUp3sqVC6dTXqqPsYycPjVSkA4e62JP6ykd75NRU/hJQdqkW1jJGCn8pCA9GW+lZlI5bzhvctilSIFS+EnBcXc2xpO3sCrRLetllBR+UnB+f6SD1o4eneIiY6Lwk4Jz5pb1Gq9DxkDhJwVnU7yVBTOqmFs9IexSpIAp/KSg9A0k2LKnTb28MmYKPykoLzQe41TvgI73yZgp/KSgbIy3YgZXLdZ4HTI2Cj8pKJvirbxp7lSqq8rDLkUKnMJPCkZHTz/PHTim430SCIWfFIyn97bRn3CN0iaBUPhJwdgUb6O8tIT6BdPCLkWKgMJPCsameCtXLJhGZVks7FKkCCj8pCA0n+zmlVdP6nifBCar8DOzFWa2y8ziZnbnOdp9yMzczOqDK1EEnmpoA3TLegnOsOFnZjHgXuBGYClws5ktzdBuMvAXwJagixTZuLuVqRPKuGTO1LBLkSKRzZbfMiDu7nvcvRdYB6zK0O4rwN8D3QHWJ4K7syneylWLZhDTLawkINmE31ygMe15U2raGWZ2GTDP3R8NsDYRAPa1dXLoeLfu4iKByib8Mv1T62dmmpUA/wh8ftgFma0xs21mtq2lpSX7KiXSTt/CSsf7JEjZhF8TMC/teR1wKO35ZOCNwBNmtg9YDqzP1Onh7mvdvd7d62tra0dftUTKpt2tzK2ewIIZVWGXIkUkm/DbCiwxs4VmVg6sBtafnunux929xt0XuPsCYDOw0t235aRiiZSBhPNkQytXXzADMx3vk+AMG37u3g/cATwG7AQedvftZnaPma3MdYESbS8fPM6J7n6d3yeBK82mkbtvADYMmnbXEG2vHXtZIkmbGpLH+96m63klYLrCQ/LapngrF82aTO3kirBLkSKj8JO81d03wNZ97drllZxQ+Ene2ravnd7+hE5xkZxQ+Ene2hhvpbTEWLZwetilSBFS+EneerKhlcvnT2NiRVb9ciIjovCTvHSss5eXDh7X8T7JGYWf5KWnGtpwh6sv0ChtkhsKP8lLG+OtTCyP8eZ51WGXIkVK4Sd5aVO8leWLZlAW00dUckOfLMk7Te2d7Gvr1PE+ySmFn+SdJ+OpW9br/n2SQwo/yTsb463UTq5gycxJYZciRUzhJ3klkUjesv7qxbqFleSWwk/yyq4jJ2k71avjfZJzCj/JK5tSt6xX+EmuKfwkr2yKt7KodiJzqieEXYoUOYWf5I3e/gRb9h7VXVxkXCj8JG8833iMzt4B7fLKuFD4Sd7YGG+lxGD5Il3PK7mn8JO8sSneypvqqpk6oSzsUiQCFH6SF0529/F84zGu0V1cZJwo/CQvPL33KAMJ1/E+GTcKP8kLG+OtVJaVcPn8aWGXIhGh8JPQuTu/2dXCFQumU1kWC7sciQiFn4Ruy96j7Gk9xQfePCfsUiRCFH4Suge3HGBKZSkfuFThJ+NH4SehajnZw3+/fJgPvXUeE8q1yyvjR+EnoXp4WyN9A85Hr5wfdikSMQo/Cc1Awnno6QNctWgGF+jGpTLOFH4Smt/+voWm9i5uWX5+2KVIBCn8JDQPbN5PzaQKblh6XtilSAQp/CQUTe2d/HpXM6uvmEd5qT6GMv70qZNQrHu6EQNuVkeHhEThJ+Outz/Buq2NvOsNM5mrOzZLSBR+Mu5+seMIrR096uiQUGUVfma2wsx2mVnczO7MMP9zZrbDzF40s1+ZmT7VMqQHNu+nbtoE3nFhbdilSIQNG35mFgPuBW4ElgI3m9nSQc2eA+rd/VLgx8DfB12oFId4cwdP7Wnj5mXziZVoXF4JTzZbfsuAuLvvcfdeYB2wKr2Buz/u7p2pp5uBumDLlGLxgy0HKIsZH7liXtilSMRlE35zgca0502paUO5DfjZWIqS4tTVO8CPn2lkxRtnUzOpIuxyJOJKs2iTad/EMzY0uwWoB945xPw1wBqA+fN1ikPU/NeLhzjR3c/HdHqL5IFstvyagPR9lDrg0OBGZnY98EVgpbv3ZFqQu69193p3r6+t1cHuqHlwywGWzJzElQunh12KSFbhtxVYYmYLzawcWA2sT29gZpcB/0oy+JqDL1MK3csHj/NC4zE+duV8zNTRIeEbNvzcvR+4A3gM2Ak87O7bzeweM1uZavY1YBLwIzN73szWD7E4iagHNu9nQlmMD16uvjDJD9kc88PdNwAbBk27K+3x9QHXJUXkRHcfjzx/iJVvnqMxeSVv6AoPybn/fPYgXX0DuqJD8orCT3LK3Xlg834urZvKm+qmhl2OyBkKP8mprfva2d3cwS1XaqtP8ovCT3Lqgc37mVxZqmEpJe8o/CRnWjt6+NnLh/kfl9dpZDbJOwo/yZkfbWuib8C5Zbmu6JD8o/CTnEgknB88vZ/li6ZzwczJYZcjchaFn+TEb3a30Hi0i4+po0PylMJPcuLBzQeomVTBey+ZFXYpIhkp/CRwB4918etXjvCRK+o0MpvkLX0yJXDrnj6AA6uvUEeH5C+FnwSqb+C1kdnmTa8KuxyRISn8JFB/++gOWk72cOvbFoRdisg5KfwkMPdv2st3n9rPmncs4p0amU3ynMJPAvHrV45wz6M7eM/S8/irFReFXY7IsBR+MmY7Dp3gjh88xyVzpvJPq9+iISmlICj8ZEyOnOjmtu9uZeqEMr59az1V5VndH1ckdAo/GbXO3n5u++5WTnT1cd+tV3DelMqwSxLJmv6ZllEZSDifWfc8Ow6d4Nu31rN0zpSwSxIZEW35yah89b9f4Rc7jnDX+5fy7ovOC7sckRFT+MmI/WDLAdb+dg+3XnU+H796YdjliIyKwk9G5He7W/jyIy9z7Rtq+fL7l4ZdjsioKfwka7uPnORPH3iWJTMn8S8fvZzSmD4+Urj06ZWstJzs4RP3b6WyPMZ9H7+CSRXqK5PCpvCTYXX3DbDm+9to7ejhvlvrmVs9IeySRMZM/3zLOSUSzud/9ALPNx7jmx97K5fWVYddkkggtOUn5/QPv/g9P33xMHeuuIgVb9RdmaV4aMtPMmo/1cu//nYP3/pNA6uvmMeadywKuySRQCn85HWaT3Tzb7/bw4NbDtDZO8AHL5vLV/7gjZjpZgVSXBR+AkDj0U7+9bcNPLytif6BBCvfPIc/fdcFXHiehp2U4qTwi7iGlg6+8XgDjzx/EDP40Fvr+NQ7F3P+jIlhlyaSUwq/iNpx6AT3PhFnw0uHqSgt4Zbl57PmHYuYo9NYJCIUfhHz7IF27v11nF+90sykilI+/c7FfPKahdRMqgi7NJFxpfArcu7OkRM9vNB0jO8+uY8nG9qorirjczdcyK1XLWBqVVnYJYqEQuFXRHr7E8SbO9h5+AQ7Dp9gZ+qrvbMPgNrJFXzxpov56JXzmajL0yTisvoLMLMVwD8DMeDb7v6/B82vAL4HvBVoAz7i7vuCLVXSHT3Veybcdhw+wY5DJ2ho6aBvwAGoKC3holmTee8ls7h49hQunj2FS+umUlkWC7lykfwwbPiZWQy4F7gBaAK2mtl6d9+R1uw2oN3dLzCz1cBXgY/kouBi4+509PRzrLOP412vfR3r7ONYV2/yeWf6tD7aOnpoPtlzZhkzJ1ewdM4U3nXRTC6ePYWlsyezYMZE3XVF5Byy2fJbBsTdfQ+Ama0DVgHp4bcKuDv1+MfAv5iZubsHVej9m/biQPoS0xc+1Eu5g+Op769/nv6zg+cnPHlda8JTj91JJJyBVNuEOwOJ17frTzi9/Ql6BxL09ifoS33vTfved2Zesm1X3wADiaFXU3lpCdUTypg6oYzqqjLmVk/gjXOmcOF5k1NbdJOZoc4KkRHLJvzmAo1pz5uAK4dq4+79ZnYcmAG0pjcyszXAGoD58+ePqNC/eXQHwUVpdkoMYiWGmSUfm1FiRklJ8vngx2WxEspiRnlpjPKYUV5aQnlpCZMqSymLJR+Xx5JfZaVGeSzGhPISqieUM3VCGVOrXgu509Mqy0p0dYVIDmQTfpn+8gbHUDZtcPe1wFqA+vr6EUXZs1+6gdMZYOkvl/4w82TMkj9hlvzZM8ux15aVfPxa2xKNPStS1LIJvyZgXtrzOuDQEG2azKwUmAocDaTClGkTy4NcnIhEXDZHxLcCS8xsoZmVA6uB9YParAduTT3+EPDrII/3iYgEbdgtv9QxvDuAx0ie6vIdd99uZvcA29x9PXAf8H0zi5Pc4ludy6JFRMYqq/P83H0DsGHQtLvSHncDfxRsaSIiuaMTwUQkkhR+IhJJCj8RiSSFn4hEksJPRCLJwjodz8xagP0j/LEaBl0yF4J8qAHyow7V8Jp8qEM1JJ3v7rXDNQot/EbDzLa5e33Ua8iXOlRDftWhGkZGu70iEkkKPxGJpEILv7VhF0B+1AD5UYdqeE0+1KEaRqCgjvmJiASl0Lb8REQCofATkUjKu/Azsz8ys+1mljCz+kHzvmBmcTPbZWbvHeLnF5rZFjPbbWY/TN2DcCz1/NDMnk997TOz54dot8/MXkq12zaW18yw7LvN7GBaHTcN0W5Fat3EzezOIGtILf9rZvaKmb1oZv9pZtVDtAt8XQz33sysIvW7iqd+/wuCeN205c8zs8fNbGfq8/mZDG2uNbPjab+nuzItK4Bazrl+LenrqXXxopldHvDrvyHtPT5vZifM7LOD2ozLuhiT5OA9+fMFXAy8AXgCqE+bvhR4AagAFgINQCzDzz8MrE49/hbw6QBr+z/AXUPM2wfU5Gid3A38z2HaxFLrZBFQnlpXSwOu4z1AaerxV4Gvjse6yOa9AX8KfCv1eDXww4Df+2zg8tTjycDvM9RwLfBoLj4DI1m/wE3Az0iOzLAc2JLDWmLAqyRPLB73dTGWr7zb8nP3ne6+K8OsVcA6d+9x971AnOTIcmdYcqSfd5McQQ7gu8AfBFFXatkfBh4KYnk5cGaUPXfvBU6PshcYd/+5u/ennm4mOaTBeMjmva0i+fuG5O//Ogtw5Cd3P+zuz6YenwR2khy4Kx+tAr7nSZuBajObnaPXug5ocPeRXq0VurwLv3PINIrc4A/fDOBY2h9opjaj9XbgiLvvHmK+Az83s2dSo9QF7Y7ULsx3zGxahvnZrJ8gfZLk1kUmQa+LbN7b60YQBE6PIBi41C71ZcCWDLOvMrMXzOxnZnZJLl6f4dfveH4WVjP0BsF4rItRy+pOzkEzs18CszLM+qK7PzLUj2WYNqpR5EZZz82ce6vvanc/ZGYzgV+Y2Svu/tvhXjubGoBvAl8h+V6+QnL3+5ODF5HhZ0d8HlM268LMvgj0Aw8OsZgxrYtMZWWYFsjvfsSFmE0C/i/wWXc/MWj2syR3/zpSx2V/AiwJugaGX7/jtS7KgZXAFzLMHq91MWqhhJ+7Xz+KH8tmFLlWkpv4pal//TO1GXE9lhyR7g+Bt55jGYdS35vN7D9J7qpl/Qef7Toxs38DHs0wK5v1M+Y6zOxW4P3AdZ46uJNhGWNaFxnkxQiCZlZGMvgedPf/GDw/PQzdfYOZfcPMatw90Av9s1i/gXwWsnAj8Ky7H8lQ47isi7EopN3e9cDqVK/eQpL/ijyd3iD1x/g4yRHkIDmi3FBbkiNxPfCKuzdlmmlmE81s8unHJDsGXg7gdU8vP/14zQeHWHY2o+yNtY4VwF8BK929c4g2uVgXoY8gmDp+eB+w093/YYg2s04fZzSzZST/vtqCqiG13GzW73rgT1K9vsuB4+5+OMg6UobcGxqPdTFmYfe4DP4i+cfdBPQAR4DH0uZ9kWSv3y7gxrTpG4A5qceLSIZiHPgRUBFATfcDnxo0bQ6wIe01X0h9bSe5ixjkOvk+8BLwIskP9uzBNaSe30SyF7Ih6BpSy4+TPJb0fOrrW4PryNW6yPTegHtIBjFAZer3HU/9/hcF/N6vIbnr+GLa+78J+NTpzwZwR+o9v0CyQ+htOfgdZFy/g+ow4N7UunqJtLMmAqyjimSYTU2bNq7rYqxfurxNRCKpkHZ7RUQCo/ATkUhS+IlIJCn8RCSSFH4iEkkKPxGJJIWfiESSwk9EIknhJyKRpPATkUhS+IlIJCn8RCSSFH4iEkkKPxGJJIWfiESSwk9EIknhJyKRpPATkUhS+IlIJCn8RCSSFH4iEkkKPxGJJIWfiESSwk9EIknhJyKRpPATkUhS+IlIJCn8RCSSFH4iEkkKPxGJJIWfiERSaVgvXFNT4wsWLAjr5UWkSD3zzDOt7l47XLvQwm/BggVs27YtrJcXkSJlZvuzaafdXhGJJIWfiESSwk9EIknhJyKRpPATkUgaNvzM7Dtm1mxmLw8x38zs62YWN7MXzezy4MsUEQlWNlt+9wMrzjH/RmBJ6msN8M2xlyUiklvDhp+7/xY4eo4JW1sxAAAgAElEQVQmq4DvedJmoNrMZgdVoIhILgRxkvNcoDHteVNq2uEAli1SsAYSzqFjXXT1DdDdN0B3XyL1fYCe/tTj/gQ9g+b39CcYcMcd/PR3kt8Tqcep/0icmZ9sm0nmqYXjr9+/lJlTKgNfbhDhZxmmZVzfZraG5K4x8+fPD+ClRfJHIuHsfPUEm/cc5amGNp7e28aJ7v6sf77EoLIsRkVpCbESA4wSAzOwM4+Tf25mUGKWmpecbpD5r3HoyQWhpz+Rk+UGEX5NwLy053XAoUwN3X0tsBagvr6+0P9Bkohzd35/pIOnGlp5ak8bW/Ye5VhnHwALZlTxvktn8+a6aiZVllJZGqOyLEZlWQkVpcnvlWUxKlLfK0tjlMXsTLhJ7gURfuuBO8xsHXAlcNzdtcsrRcfdaWg5xVN72tjc0MbmPW20neoFoG7aBG64+DyuWjyD5YtmMKd6QsjVynCGDT8zewi4Fqgxsybgr4EyAHf/FrABuAmIA53AJ3JVrEhY7n08zv1P7qPlZA8As6dW8s4La1m+eAZXLZrBvOlVIVcoIzVs+Ln7zcPMd+DPAqtIJM9s3XeUrz22i6svmMHnb7iQ5YtmcP6MKu2iFrjQbmklUgj6BxJ8+ScvM2dqJf/2J/VUletPpljo8jaRc7j/yX288upJ7vrAUgVfkVH4iQzhyIlu/umXu3nnhbW895JZYZcjAVP4iQzh7366k96BBH+z8hId3ytCCj+RDJ6Mt7L+hUN86p2LWVAzMexyJAcUfiKD9PYn+PIjLzNv+gT+9NrFYZcjOaIjuCKD3LdxLw0tp/jOx+upLIuFXY7kiLb8RNIcPNbF13+1mxuWnse7Lzov7HIkhxR+Imn+9tEdOM5d718adimSYwo/kZTf/L6Fn738Kne86wJdrhYBCj8RoLtvgL9+5GUW1Uzk9ncsCrscGQfq8BAB1v52D/vaOvn+bcuoKFUnRxRoy08ir/FoJ/c+Hud9b5rN25fUhl2OjBOFn0Te3eu3EysxvvT+i8MuRcaRwk8i7Zc7jvCrV5r57PVLmD1VNyCNEoWfRFZX7wB3/9d2LjxvEp+4emHY5cg4U4eHRNY3nojT1N7FujXLKYtpOyBq9BuXSNrbeop//c0e/uAtc1i+aEbY5UgIFH4SOe7OXY+8TEVpCf/rferkiCqFn0TOf7/8Kr/b3crn3nMhMycHPxi2FAaFn0RKd98A9zy6g6Wzp/DHy88PuxwJkcJPIuXlg8c5fLybv7huCaXq5Ig0/fYlUuLNHQBcMmdKyJVI2BR+Einx5g4qy0qYW60TmqNO4SeRsru5g0U1kygp0YBEUafwk0iJN3dwwcxJYZcheUDhJ5HR2dvPwWNdLFH4CQo/iZA9LacAtOUngMJPIuR0T6/CT0DhJxESb+4gVmKcP0ODkIvCTyJkd/NJzp9RRXmpPvai8JMIiTd3cEGtdnklSeEnkdA3kGB/WydLzlP4SZLCTyJhf9sp+hOuzg45Q+EnkXCmp7d2csiVSL7IKvzMbIWZ7TKzuJndmWH+fDN73MyeM7MXzeym4EsVGb3dR5Lht3imenoladjwM7MYcC9wI7AUuNnMlg5q9iXgYXe/DFgNfCPoQkXGIt7SwdzqCVSVa9gaScpmy28ZEHf3Pe7eC6wDVg1q48DpewRNBQ4FV6LI2MWbO1is432SJpvwmws0pj1vSk1Ldzdwi5k1ARuAPw+kOpEAJBJOQ0uHrumV18km/DLd+8cHPb8ZuN/d64CbgO+b2VnLNrM1ZrbNzLa1tLSMvFqRUTh4rIvuvoR6euV1sgm/JmBe2vM6zt6tvQ14GMDdnwIqgZrBC3L3te5e7+71tbW1o6tYZITiLbqmV86WTfhtBZaY2UIzKyfZobF+UJsDwHUAZnYxyfDTpp3khfiR06e5KPzkNcOGn7v3A3cAjwE7Sfbqbjeze8xsZarZ54HbzewF4CHg4+4+eNdYJBTx5g5mTCxn2sTysEuRPJJVv7+7byDZkZE+7a60xzuAq4MtTSQY8Rb19MrZdIWHFDV3J96snl45m8JPilprRy/Hu/rU2SFnUfhJUdPdm2UoCj8pavHmk4DCT86m8JOiFm/uYFJFKbOmVIZdiuQZhZ8UtXhLB4trJ2KmQcrl9RR+UtSSg5TrHn5yNoWfFK0T3X0cOdGj432SkcJPilaDenrlHBR+UrR2K/zkHBR+UrQamjsoj5Uwb9qEsEuRPKTwk6IVb+5gYc1ESmP6mMvZ9KmQohVv6eACjdMrQ1D4SVHq7hug8Win7uEnQ1L4SVHa23qKhKuzQ4am8JOipJ5eGY7CT4pSvLmDEoOFNRqkXDJT+ElRamjuYN70KirLYmGXInlK4SdFSXdvluEo/KTo9A8k2Nt6SuN2yDkp/KToNLZ30TuQ0Gkuck4KPyk6u4/o7s0yPIWfFJ14S/I0F+32yrko/KToxJs7OG9KBVMqy8IuRfKYwk+KTkNzB0t092YZhsJPioq709BySsf7ZFgKPykqr57opqOnX8f7ZFgKPykqu4+krunVaS4yDIWfFJW4bmggWVL4SVGJt3QwdUIZNZPKwy5F8pzCT4rK6Wt6NUi5DEfhJ0WloblDu7ySFYWfFI2jp3ppO9Wr8JOsKPykaJzu7NBpLpINhZ8UjTM9vTrNRbKQVfiZ2Qoz22VmcTO7c4g2HzazHWa23cx+EGyZIsOLN3cwoSzG3GoNUi7DKx2ugZnFgHuBG4AmYKuZrXf3HWltlgBfAK5293Yzm5mrgkWGEm/pYPHMiZSUqKdXhpfNlt8yIO7ue9y9F1gHrBrU5nbgXndvB3D35mDLFBleQ3OHdnkla9mE31ygMe15U2pauguBC81sk5ltNrMVmRZkZmvMbJuZbWtpaRldxSIZnOrp5+CxLvX0StayCb9M+xA+6HkpsAS4FrgZ+LaZVZ/1Q+5r3b3e3etra2tHWqvIkBpadFmbjEw24dcEzEt7XgccytDmEXfvc/e9wC6SYSgyLnRNr4xUNuG3FVhiZgvNrBxYDawf1OYnwLsAzKyG5G7wniALFTmXeHMHpSXG+TM0SLlkZ9jwc/d+4A7gMWAn8LC7bzeze8xsZarZY0Cbme0AHgf+0t3bclW0yGDx5g4W1EykLKZTVyU7w57qAuDuG4ANg6bdlfbYgc+lvkTGXbylgwt163oZAf0zKQWvtz/B/rZOHe+TEVH4ScHb13aKgYQr/GREFH5S8NTTK6Oh8JOCdzr8FtWqp1eyp/CTghdv7qBu2gSqyrPqvxMBFH5SBOK6e7OMgsJPCtpAwmlo0Q0NZOQUflLQDrZ30dOf0JafjJjCTwpavOUkoJ5eGTmFnxQ0neYio6Xwk4IWb+6gZlIF1VUapFxGRuEnBS3Z06vz+2TkFH5SsNyd3TrNRUZJ4ScFq+VkDye7+3Wai4yKwk8K1mudHbqVlYycwk8KVlzjdsgYKPykYMWbO5hcUcp5UyrCLkUKkMJPCla8uYPFMydhpkHKZeQUflKw1NMrY6Hwk4J0vKuPlpM9Cj8ZNYWfFKQzPb06zUVGSeEnBalB1/TKGCn8pCDFWzooLy1h3vSqsEuRAqXwk4IUb+5gUc1EYiXq6ZXRUfhJQdrdfFK7vDImCj8pON19AzS1dyn8ZEwUflJwGlo6cFdnh4yNwk8Kju7eLEFQ+EnBaWjuoMRgYY1uYiqjp/CTghNv6eD8GROpKI2FXYoUMIWfFJx4cweLdWWHjJHCTwpK/0CCva2ndLxPxkzhJwVl/9FO+gZc4SdjpvCTgqKeXglKVuFnZivMbJeZxc3sznO0+5CZuZnVB1eiyGtOh9/iWvX0ytgMG35mFgPuBW4ElgI3m9nSDO0mA38BbAm6SJHTGpo7mD21ksmVZWGXIgUumy2/ZUDc3fe4ey+wDliVod1XgL8HugOsT+R14i26e7MEI5vwmws0pj1vSk07w8wuA+a5+6MB1ibyOomE6zQXCUw24ZfpnkF+ZqZZCfCPwOeHXZDZGjPbZmbbWlpasq9SBDh8opvO3gFt+Ukgsgm/JmBe2vM64FDa88nAG4EnzGwfsBxYn6nTw93Xunu9u9fX1taOvmqJJPX0SpCyCb+twBIzW2hm5cBqYP3pme5+3N1r3H2Buy8ANgMr3X1bTiqWyFL4SZCGDT937wfuAB4DdgIPu/t2M7vHzFbmukCR0+LNHUyrKmPGxPKwS5EiUJpNI3ffAGwYNO2uIdpeO/ayRM7WkBqnV4OUSxB0hYcUDN26XoKk8JOC0NbRQ3tnn05zkcAo/KQgqLNDgqbwk4IQb1H4SbAUflIQ4s0dVJXHmDN1QtilSJFQ+ElBOH1ZW4kGKZeAKPykIJw+zUUkKAo/yXsdPf0cOt6t8JNAKfwk7zWcuYGpwk+Co/CTvKfTXCQXFH6S9+ItHZSWGOfPqAq7FCkiCj/Je/HmDhbWTKQspo+rBEefJsl76umVXFD4SV7r6R9gX5sGKZfgKfwkr+1r7STh6uyQ4Cn8JK/FdZqL5IjCT/JavLkDM4WfBE/hJ3kt3tJB3bQJTCiPhV2KFBmFn+S1eHMHF2irT3JA4Sd5ayDh7GnRaS6SGwo/yVtN7Z309CcUfpITCj/JW7qmV3JJ4Sd560z41U4OuRIpRgo/yVvx5g5qJ1cwtaos7FKkCCn8JG/FW9TTK7mj8JO85O7J01x0vE9yROEnean5ZA8nu/sVfpIzCj/JS+rplVxT+EleUvhJrin8JC/FmzuYXFHKzMkVYZciRUrhJ3kp3tzBBedNwkyDlEtuKPwkL+k0F8k1hZ/kneOdfbSc7NHxPskphZ/knXjLSUCdHZJbCj/JO+rplfGQVfiZ2Qoz22VmcTO7M8P8z5nZDjN70cx+ZWbnB1+qREW8uYPy0hLqpmmQcsmdYcPPzGLAvcCNwFLgZjNbOqjZc0C9u18K/Bj4+6ALleiIN3ewuHYSsRL19EruZLPltwyIu/sed+8F1gGr0hu4++Pu3pl6uhmoC7ZMiZK47t4s4yCb8JsLNKY9b0pNG8ptwM/GUpREV1fvAE3tXTrNRXKuNIs2mfY9PGNDs1uAeuCdQ8xfA6wBmD9/fpYlSpQ0tHTgGqRcxkE2W35NwLy053XAocGNzOx64IvASnfvybQgd1/r7vXuXl9bWzuaeqXINbSop1fGRzbhtxVYYmYLzawcWA2sT29gZpcB/0oy+JqDL1OiIt7cQYnBghr19EpuDRt+7t4P3AE8BuwEHnb37WZ2j5mtTDX7GjAJ+JGZPW9m64dYnMg5xZs7OH/GRCpKNUi55FY2x/xw9w3AhkHT7kp7fH3AdUlE6e7NMl50hYfkjb6BBPvaTin8ZFwo/CRv7G/rpG/AdZqLjAuFn+QNXdMr40nhJ3nj9GkuixV+Mg4UfpI34s0dzJ5ayaSKrPrhRMZE4Sd5Qz29Mp4UfpIXEgmnQTc0kHGk8JO8cOh4F529Awo/GTcKP8kLZ3p6dZqLjBOFn+QFneYi403hJ3mhoaWDaVVlzJikQcplfCj8JC/sPtLBkpmTwy5DIkThJ6F77kA7zxxo58pF08MuRSJE4SehGkg4X37kZWZOruD/eefisMuRCFH4Sage3LKflw+e4EvvW6orO2RcKfwkNC0ne/jaY7u45oIa3n/p7LDLkYhR+Elo/r+f7aS7b4C/WXUJZhqjV8aXwk9C8fTeo/zHswe5/e2LWKwTmyUECj8Zd/0DCe565GXmVk/gjndfEHY5ElEKPxl39z+5j1dePcldH1hKVbk6OSQcCj8ZV0dOdPNPv9zNtW+o5T1Lzwu7HIkwhZ+Mq7/96U56BxL8zUp1cki4FH4ybjbFW/mvFw7x6Xcu5vwZE8MuRyJO4Sfjorc/2ckxf3oVn75WV3JI+HS0WcbFfRv30tByin//+BVUlsXCLkdEW36SewePdfH1X+3mPUvP410XzQy7HBFA4Sfj4Cv/tQPHuesDS8MuReQMhZ/k1BO7mvnv7a/y5+9eQt20qrDLETlD4Sc50903wF+v386i2onc/vZFYZcj8jrq8JCcWfvbPexv6+SB266kvFT/zkp+0SdScqLxaCf3Ph7nfZfO5polNWGXI3IWhZ/kxN3rt1NaYnz5ferkkPyk8JPA/WLHEX71SjOfvf5CZk2tDLsckYx0zE8C036qly172/jKozu58LxJfPzqBWGXJDKkrMLPzFYA/wzEgG+7+/8eNL8C+B7wVqAN+Ii77wu2VMk3xzv72LK3jaf2tPFUQxuvvHoSgMkVpXz95rdQFtOOheSvYcPPzGLAvcANQBOw1czWu/uOtGa3Ae3ufoGZrQa+CnwkFwVLeE5097F171E270kG3vZDJ3CHitIS6hdM4/M3XMhVi2dwaV21encl72Wz5bcMiLv7HgAzWwesAtLDbxVwd+rxj4F/MTNzdw+wVsmxvoEE3X0DdPclv/f0D9DU3sVTe9rY3NDGSwePk3Aoj5Vw2fxqPnPdEq5aNIO3zK+molTX60phySb85gKNac+bgCuHauPu/WZ2HJgBtAZRJMD/+8PnKcYsHeodneutOuDuyZ91SLjjDs7p76n5/vq2vf1p4dY/QE/f6ecDdPcnGEhkftGymPGWedXc8a4LWL54BpfPn6abE0jByyb8Mt1xcvBfSTZtMLM1wBqA+fPnZ/HSr3nuQPuQQVHohrql57lu9mmp/5WYYYAZGJb8nj4tbXp5rISq8lKmTyyhoixGZWmMyrISKstS30tjZx5XlMaoKCuhdlIFb5lfrdvNS9HJ5hPdBMxLe14HHBqiTZOZlQJTgaODF+Tua4G1APX19SPKsif+8l0jaS4ick7ZHJXeCiwxs4VmVg6sBtYParMeuDX1+EPAr3W8T0Ty2bBbfqljeHcAj5E81eU77r7dzO4Btrn7euA+4PtmFie5xbc6l0WLiIxVVgdy3H0DsGHQtLvSHncDfxRsaSIiuaOTsUQkkhR+IhJJCj8RiSSFn4hEksJPRCJJ4ScikWRhnYtsZi3A/hH+WA0BXi88SvlQA+RHHarhNflQh2pIOt/da4drFFr4jYaZbXP3+qjXkC91qIb8qkM1jIx2e0UkkhR+IhJJhRZ+a8MugPyoAfKjDtXwmnyoQzWMQEEd8xMRCUqhbfmJiAQi78LPzP7IzLabWcLM6gfN+4KZxc1sl5m9d4ifX2hmW8xst5n9MHUPwrHU80Mzez71tc/Mnh+i3T4zeynVbttYXjPDsu82s4Npddw0RLsVqXUTN7M7g6whtfyvmdkrZvaimf2nmVUP0S7wdTHcezOzitTvKp76/S8I4nXTlj/PzB43s52pz+dnMrS51syOp/2e7sq0rABqOef6taSvp9bFi2Z2ecCv/4a09/i8mZ0ws88OajMu62JMkmM95M8XcDHwBuAJoD5t+lLgBaACWAg0ALEMP/8wsDr1+FvApwOs7f8Adw0xbx9Qk6N1cjfwP4dpE0utk0VAeWpdLQ24jvcApanHXwW+Oh7rIpv3Bvwp8K3U49XADwN+77OBy1OPJwO/z1DDtcCjufgMjGT9AjcBPyM52sFyYEsOa4kBr5I8t27c18VYvvJuy8/dd7r7rgyzVgHr3L3H3fcCcZIjy51hyUEv3k1yBDmA7wJ/EERdqWV/GHgoiOXlwJlR9ty9Fzg9yl5g3P3n7t6ferqZ5JAG4yGb97aK5O8bkr//6+xcg6CMkLsfdvdnU49PAjtJDtyVj1YB3/OkzUC1mc3O0WtdBzS4+0gvWAhd3oXfOWQaRW7wh28GcCztDzRTm9F6O3DE3XcPMd+Bn5vZM6mBmoJ2R2oX5jtmNi3D/GzWT5A+SXLrIpOg10U27+11IwgCp0cQDFxql/oyYEuG2VeZ2Qtm9jMzuyQXr8/w63c8PwurGXqDYDzWxaiFMiSXmf0SmJVh1hfd/ZGhfizDtFGNIjfKem7m3Ft9V7v7ITObCfzCzF5x998O99rZ1AB8E/gKyffyFZK7358cvIgMPzvirvxs1oWZfRHoBx4cYjFjWheZysowLZDf/YgLMZsE/F/gs+5+YtDsZ0nu/nWkjsv+BFgSdA0Mv37Ha12UAyuBL2SYPV7rYtRCCT93v34UP5bNKHKtJDfxS1P/+mdqM+J6LDki3R8Cbz3HMg6lvjeb2X+S3FXL+g8+23ViZv8GPJphVjbrZ8x1mNmtwPuB6zx1cCfDMsa0LjIIbATBsTCzMpLB96C7/8fg+elh6O4bzOwbZlbj7oFe65rF+g3ks5CFG4Fn3f1IhhrHZV2MRSHt9q4HVqd69RaS/Ffk6fQGqT/Gx0mOIAfJEeWG2pIcieuBV9y9KdNMM5toZpNPPybZMfByAK97evnpx2s+OMSysxllb6x1rAD+Cljp7p1DtMnFugh9BMHU8cP7gJ3u/g9DtJl1+jijmS0j+ffVFlQNqeVms37XA3+S6vVdDhx398NB1pEy5N7QeKyLMQu7x2XwF8k/7iagBzgCPJY274ske/12ATemTd8AzEk9XkQyFOPAj4CKAGq6H/jUoGlzgA1pr/lC6ms7yV3EINfJ94GXgBdJfrBnD64h9fwmkr2QDUHXkFp+nOSxpOdTX98aXEeu1kWm9wbcQzKIASpTv+946ve/KOD3fg3JXccX097/TcCnTn82gDtS7/kFkh1Cb8vB7yDj+h1UhwH3ptbVS6SdNRFgHVUkw2xq2rRxXRdj/dIVHiISSYW02ysiEhiFn4hEksJPRCJJ4ScikaTwE5FIUviJSCQp/EQkkhR+IhJJCj8RiSSFn4hEksJPRCJJ4ScikaTwE5FIUviJSCQp/EQkkhR+IhJJCj8RiSSFn4hEksJPRCJJ4ScikaTwE5FIUviJSCQp/EQkkhR+IhJJCj8RiSSFn4hEksJPRCJJ4ScikaTwE5FIUviJSCQp/EQkkhR+IhJJpWG9cE1NjS9YsCCslxeRIvXMM8+0unvtcO1CC78FCxawbdu2sF5eRIqUme3Ppp12e0UkkhR+IhJJCj8RiSSFn4hEksJPRCJp2PAzs++YWbOZvTzEfDOzr5tZ3MxeNLPLgy9TRCRY2Wz53Q+sOMf8G4Elqa81wDfHXpaISG4NG37u/lvg6DmarAK+50mbgWozmx1UgSIiuRDESc5zgca0502paYcDWLZIqBqPdvLErmae2NXC4ePdOODuuIOT/J5wxwHSHp+eLmP30O3LmTe9KvDlBhF+lmFaxt+6ma0huWvM/PnzA3hpkWD19ifYuu8oj7/SzOO7mmloOQXA+TOquKB2EmZGiYEZGEZJSfI7BiVmGMl5px9n/OuQEakoy02/bBDh1wTMS3teBxzK1NDd1wJrAerr6/XPouSFQ8e6eGJXC0/samZTvJVTvQOUx0q4ctF0Pnbl+bzropksrJkYdpkSsCDCbz1wh5mtA64Ejru7dnklb/UNJHhmf/uZwHvl1ZMAzK2ewAcvn8u1F87kbRfMoKo8tEvfZRwM+9s1s4eAa4EaM2sC/hooA3D3bwEbgJuAONAJfCJXxYqMVfupXm74x9/Q2tFLaYlxxYLp/K+bLuJdb5jJBTOTu7USDcOGn7vfPMx8B/4ssIpEcuiVV0/S2tHLF268iI9eOZ/JlWVhlyQh0RUeEilN7Z0ArHjjLAVfxCn8JFIa27swg9lTJ4RdioRM4SeR0tTeyewplZSX6qMfdfoESKQ0He2iblrwJ8xK4VH4SaQ0tXdSN127vKLwkwjp7U9w+ES3tvwEUPhJhBw+3oU7zJumLT9R+EmENB7tAtCWnwAKP4mQ0+f4zdMxP0HhJxHS2N5JrMSYNaUy7FIkDyj8JDKa2ruYU11JaUwfe1H4SYQ0Hu2krlrH+yRJ4SeR0dTepeN9cobCTyKhu2+A5pM96umVMxR+EglN7cnTXLTlJ6cp/CQSTp/moi0/OU3hJ5HQeHrLT+EnKQo/iYSm9k7KYyXMnFwRdimSJxR+EglNR7uYO20CJSUao0OSFH4SCU3tndTphgaSRuEnkdDYrpuYyusp/KTonerp5+ipXp3mIq+j8JOid/ocP235STqFnxS9M7ey0jE/SaPwk6LXeFQnOMvZFH5S9Jrau6gsK6FmUnnYpUgeUfhJ0Wts76RuWhVmOsdPXqPwk6LX1N6l431yFoWfFL3Go5063idnUfhJUTve1ceJ7n6d4ydnUfhJUdOtrGQoCj8pak26lZUMQeEnRe21c/y02yuvp/CTotbU3sWkilKqq8rCLkXyjMJPitrpW1npHD8ZLKvwM7MVZrbLzOJmdmeG+fPN7HEze87MXjSzm4IvVWTkGo/qVlaS2bDhZ2Yx4F7gRmApcLOZLR3U7EvAw+5+GbAa+EbQhYqMlLvrJqYypGy2/JYBcXff4+69wDpg1aA2DkxJPZ4KHAquRJHRae/s41TvAPOma8tPzpZN+M0FGtOeN6WmpbsbuMXMmoANwJ9nWpCZrTGzbWa2raWlZRTlimTvtXP8tOUnZ8sm/DIdKfZBz28G7nf3OuAm4Ptmdtay3X2tu9e7e31tbe3IqxUZgcajOsdPhpZN+DUB89Ke13H2bu1twMMA7v4UUAnUBFGgyGid2fLTpW2SQTbhtxVYYmYLzaycZIfG+kFtDgDXAZjZxSTDT/u1EqrG9k6mTihjSqXO8ZOzDRt+7t4P3AE8Buwk2au73czuMbOVqWafB243sxeAh4CPu/vgXWORcdXU3qUbGsiQSrNp5O4bSHZkpE+7K+3xDuDqYEsTGZvGo50smTk57DIkT+kKDylKyXP8tOUnQ1P4SVFq6eihpz+hqztkSAo/KUpnbmWlLT8ZgsJPipKGq5ThKPykKJ3e8tPVHTIUhZ8Upab2TmZMLKeqPKsTGiSCFH5SlJrau6jTDQ3kHBR+UpSSw1Vql1eGpvCTopNIOAePdemGBnJOCj8pOkdOdtM34Nryk3NS+EnROXMrKx3zk3NQ+EnR0U1MJRsKPyk6p7f85lYr/GRoCj8pOk3tncycXEFlWSzsUsOkgYMAABH9SURBVCSPKfyk6DS2d+p4nwxL4SdFp6m9S8f7ZFgKPykq/QMJDh/v1jl+MiyFnxSVw8e7GUi4bmUlw1L4SVFpbNetrCQ7Cj8pKmduYqrwk2Eo/KSoNB3tpMRgdnVl2KVInlP4SVFpau9i9tQJlMX00ZZz0ydEikpjeydzdZqLZEHhJ0WlqV23spLsKPykaPT0D/DqiW6d4CxZUfhJ0Th8rBt33cpKsqPwk6LRqFtZyQgo/KRovDZQubb8ZHgKPykajUc7KS0xZk3ROX4yPIWfFI2m9i7mVE8gVmJhlyIFQOEnRaOxXcNVSvYUflI0Go/qHD/JnsJPikJ33wCtHT3a8pOsKfykKJwesU09vZKtrMLPzFaY2S4zi5vZnUO0+bCZ7TCz7Wb2g2DLFDm3xtRpLtryk2yVDtfAzGLAvcANQBOw1czWu/uOtDZLgC8AV7t7u5nNzFXBIpk0HdWWn4xMNlt+y4C4u+9x915gHbBqUJvbgXvdvR3A3ZuDLVPk3JrauygvLaF2UsX/397dx8hR33ccf3/uzj4f2NiAL2BsA6Y4gJu2IlwpLalKAm0Niuy2Co2RqkZKJCutUBulrUKEhBD8U0IflKq0KW2iNlFUIGmTWMiI9MH0SYJiKA81xuUgoF0MxubWD+T27Hv49o+Zs5fLnm9vd3Z3xvt5SSt2Z343+93Z9Yffzvx2ft0uxQqikfBbC5RqHpfTZbU+CHxQ0n9JelLS5qwKNGtEqTLOulVD9HmMnzVowa+9QL1PU9TZzkbgBmAd8B+SPhQRh9+3IWk7sB3g4osvXnSxZvMpV6q+jp8tSiM9vzKwvubxOmB/nTbfi4jJiPgBsI8kDN8nIh6MiJGIGBkeHm62ZrMfURrzROW2OI2E39PARkkbJC0FtgE75rT5LvBRAEmrSb4Gv5ZloWbzee/4FJXxSQ9wtkVZMPwiYgq4HXgc2As8EhF7JN0jaUva7HHgXUkvAbuAP4iId9tVtFmtsi9lZU1o5JgfEbET2Dln2V019wP4fHoz66jymC9lZYvnX3hY4fkiptYMh58VXrlSZWhJP+efvbTbpViBOPys8EpjyaWsJI/xs8Y5/KzwypWqj/fZojn8rPB8EVNrhsPPCu1IdZJjE1Me42eL5vCzQiuN+UyvNcfhZ4Xmi5hasxx+VmhlX8TUmuTws0IrjY2zYnCAlUNLul2KFYzDzwpt9lJWHuNni+Xws0IrVXwpK2uOw88KKyIoV6o+3mdNcfhZYY398ATjJ6Y9xs+a4vCzwvKZXmuFw88Kq+QxftYCh58Vlnt+1gqHnxVWaWycVWctYcUyj/GzxXP4WWGVK1Wf7LCmOfyssHwpK2uFw88KKSJ40xcxtRY4/KyQDh47zvGpGff8rGkOPyukUnqm18f8rFkOPyskT1RurXL4WSGdGuPnnp81x+FnhVQaG2f18qUMLe3vdilWUA4/K6Tkai7u9VnzHH5WSB7jZ61y+FnhTM8E+w97jJ+1xuFnhXPg6AST0+Gen7XE4WeFU/YYP8uAw88KxxOVWxYcflY4sz2/tQ4/a4HDzwqnVBnngnMGGRzwGD9rnsPPCqdcGffxPmtZQ+EnabOkfZJGJd1xmnafkBSSRrIr0ez9SmOertJat2D4SeoHHgBuBjYBt0naVKfdCuB3gKeyLtJs1uT0DG8d8Rg/a10jPb9rgdGIeC0iTgAPAVvrtLsX+BIwkWF9Zu/z9pEJZsJneq11jYTfWqBU87icLjtJ0tXA+oh4NMPazH7E7DAXH/OzVjUSfqqzLE6ulPqAPwV+b8ENSdsl7Za0++DBg41XaZbypawsK42EXxlYX/N4HbC/5vEK4EPAE5JeB64DdtQ76RERD0bESESMDA8PN1+19axSZZw+wZpVy7pdihVcI+H3NLBR0gZJS4FtwI7ZlRFxJCJWR8SlEXEp8CSwJSJ2t6Vi62nlSpU1K4dY0u9RWtaaBT9BETEF3A48DuwFHomIPZLukbSl3QWa1SqN+VJWlo2BRhpFxE5g55xld83T9obWyzKrr1ypcv3lq7tdhp0B/N3BCuP41DQHjk2w/jz3/Kx1Dj8rjP2HJ4jwmV7LhsPPCuPUGD/3/Kx1Dj8rjJNj/PzTNsuAw88Ko1QZZ0m/uPAcj/Gz1jn8rDDKlSoXrRqiv6/ej47MFsfhZ4XhMX6WJYefFUa5UvUFDSwzDj8rhOqJaQ69d9w9P8uMw88K4c3D6TAXn+m1jDj8rBBKY7OXsnLPz7Lh8LNCKFd8EVPLlsPPCqFUqbJ0oI/Vywe7XYqdIRx+VgjlSjLMpc9j/CwjDj8rhGS6Sn/ltew4/KwQkonKfbLDsuPws9x77/gUlfFJ9/wsUw4/y72TZ3p9EVPLkMPPcu/UGD/3/Cw7Dj/LvVNj/Nzzs+w4/Cz3SmNVhpb0c97ZS7tdip1BHH6We+XKOOvPG0LyGD/LjsPPcq9U8Rg/y57Dz3LPY/ysHRx+lmtHxic5NjHlnp9lzuFnuVbyGD9rE4ef5drsMBf3/CxrDj/Ltdm5en0dP8uaw89yrTQ2zorBAc4ZGuh2KXaGcfhZrpUqVdadd5bH+FnmHH6Wa7MXMTXLmsPPcisiKI15rl5rD4ef5dbYD09QnZx2z8/awuFnuVWaPdPruXqtDRx+llunxvi552fZayj8JG2WtE/SqKQ76qz/vKSXJL0g6V8kXZJ9qdZrZi9i6p6ftcOC4SepH3gAuBnYBNwmadOcZv8DjETETwLfBr6UdaHWe8qVcc49awnLBz3Gz7LXSM/vWmA0Il6LiBPAQ8DW2gYRsSsixtOHTwLrsi3TepEvZWXt1Ej4rQVKNY/L6bL5fAZ4rN4KSdsl7Za0++DBg41XaT1p9iKmZu3QSPjVG1ofdRtKvwGMAPfXWx8RD0bESESMDA8PN16l9ZyZmaDsnp+1USMHU8rA+prH64D9cxtJugm4E/iFiDieTXnWqw69d5wTUzO+iKm1TSM9v6eBjZI2SFoKbAN21DaQdDXwV8CWiHgn+zKt15R8KStrswXDLyKmgNuBx4G9wCMRsUfSPZK2pM3uB5YD35L0nKQd82zOrCEnL2XlY37WJg2NIYiIncDOOcvuqrl/U8Z1WY8rjSU9v7Wr3POz9vAvPCyXypUqq5cPMrS0v9ul2BnK4We5VPKlrKzNHH6WS+VK1T9rs7Zy+FnuTM8E+w9X3fOztnL4We4cODrB5HT4IqbWVg4/y53ZM73u+Vk7Ofwsd8q+iKl1gMPPcqdUGUeCi1Yt63YpdgZz+FnulCtVLlixjMEBj/Gz9nH4We6UxjzGz9rP4We54zF+1gkOP8uVyekZ3jriMX7Wfg4/y5W3j0wwE3iMn7Wdw89yxWP8rFMcfpYrHuNnneLws1wpVcbpE1y40mP8rL0cfpYr5UqVNSuHWNLvj6a1lz9hlise42ed4vCzXPEYP+sUh5/lxvGpaQ4cm3DPzzrC4We58WalSniMn3WIw89yw8NcrJMcfpYbpyYq99deaz+Hn+VGuVJlSb+44ByP8bP2c/hZbpTGxrlo1RD9fep2KdYDHH6WG+VK1Sc7rGMcfpYbZU9Ubh3k8LNcqJ6Y5tB7J3ym1zrG4We5UPaZXuswh5/lwuwYv3U+5mcd4vCzXJgd47fePT/rEIef5UK5UmVwoI/hFYPdLsV6hMPPcqE0Ns7ac4eQPMbPOmOg2wVYb3vzcJUn9r3DM29UuGrNOd0ux3pIQ+EnaTPwZaAf+JuI+MM56weBrwPXAO8Cn4yI17Mt1c4Ek9MzPPNGhV373uGJlw+y78AxANauGuLWkXVdrs56yYLhJ6kfeAD4RaAMPC1pR0S8VNPsM0AlIi6XtA24D/hkOwq24jlwdIJ/23eQXfve4T9fOcSx41Ms6Rc/fel53HnNVXz0ymF+bHi5v/JaRzXS87sWGI2I1wAkPQRsBWrDbytwd3r/28CfS1JERIa1WkFMTc/wXOlw0rvbd5A9+48CcOE5y/j4T63hhis+wPWXr2b5oI+6WPc08ulbC5RqHpeBn5mvTURMSToCnA8cyqJIgI/90RM4STsvIpgJCIII0lsQwEyky9J2EcmyickZqpPT9PeJay45ly9svpIbrhjmygtXuHdnudFI+NX7tM7NoUbaIGk7sB3g4osvbuCpT/mJdStxP7I7+gSSkEAofQx96TLmLBvo6+OaS87lIxtXs3JoSbfLN6urkfArA+trHq8D9s/TpixpAFgJjM3dUEQ8CDwIMDIysqgo+/K2qxfT3MzstBoZ5/c0sFHSBklLgW3AjjltdgCfSu9/AvhXH+8zszxbsOeXHsO7HXicZKjL1yJij6R7gN0RsQP4KvANSaMkPb5t7SzazKxVDZ1ui4idwM45y+6quT8B3JptaWZm7eOft5lZT3L4mVlPcviZWU9y+JlZT3L4mVlPcviZWU9St8YiSzoIvLHIP1tNhr8XblIeaoB81OEaTslDHa4hcUlEDC/UqGvh1wxJuyNipNdryEsdriFfdbiGxfHXXjPrSQ4/M+tJRQu/B7tdAPmoAfJRh2s4JQ91uIZFKNQxPzOzrBSt52dmlonchZ+kWyXtkTQjaWTOui9KGpW0T9Ivz/P3GyQ9JekVSQ+n1yBspZ6HJT2X3l6X9Nw87V6X9GLabncrz1ln23dLerOmjlvmabc53Tejku7IsoZ0+/dLelnSC5K+I2nVPO0y3xcLvTZJg+l7NZq+/5dm8bw1218vaZekvenn83frtLlB0pGa9+muetvKoJbT7l8l/izdFy9I+nDGz39FzWt8TtJRSZ+b06Yj+6IlydwL+bkBVwFXAE8AIzXLNwHPA4PABuBVoL/O3z8CbEvvfwX4rQxr+2PgrnnWvQ6sbtM+uRv4/QXa9Kf75DJgabqvNmVcxy8BA+n9+4D7OrEvGnltwG8DX0nvbwMezvi1rwE+nN5fAfxfnRpuAB5tx2dgMfsXuAV4jGR6ieuAp9pYSz/wNsnYuo7vi1Zuuev5RcTeiNhXZ9VW4KGIOB4RPwBGSWaWO0nJ7DgfI5lBDuDvgF/Joq50278O/H0W22uDk7PsRcQJYHaWvcxExPcjYip9+CTJlAad0Mhr20ryfkPy/t+oDGdLioi3IuLZ9P4xYC/JxF15tBX4eiSeBFZJWtOm57oReDUiFvuDha7LXfidRr1Z5OZ++M4HDtf8A63Xplk/DxyIiFfmWR/A9yU9k07UlLXb068wX5N0bp31jeyfLH2apHdRT9b7opHX9r4ZBIHZGQQzl36lvhp4qs7qn5X0vKTHJP14O56fhfdvJz8L25i/Q9CJfdG0rkycKumfgQvrrLozIr4335/VWdbULHJN1nMbp+/1XR8R+yV9APgnSS9HxL8v9NyN1AD8JXAvyWu5l+Tr96fnbqLO3y76VH4j+0LSncAU8M15NtPSvqhXVp1lmbz3iy5EWg78A/C5iDg6Z/WzJF//3kuPy34X2Jh1DSy8fzu1L5YCW4Av1lndqX3RtK6EX0Tc1MSfNTKL3CGSLv5A+n//em0WXY+SGel+DbjmNNvYn/73HUnfIfmq1vA/+Eb3iaS/Bh6ts6qR/dNyHZI+BXwcuDHSgzt1ttHSvqgjsxkEWyFpCUnwfTMi/nHu+towjIidkv5C0uqIyPS3rg3s30w+Cw24GXg2Ig7UqbEj+6IVRfrauwPYlp7V20Dyf5H/rm2Q/mPcRTKDHCQzys3Xk1yMm4CXI6Jcb6WksyWtmL1PcmLgfzN43tnt1x6v+dV5tt3ILHut1rEZ+AKwJSLG52nTjn3R9RkE0+OHXwX2RsSfzNPmwtnjjJKuJfn39W5WNaTbbWT/7gB+Mz3rex1wJCLeyrKO1LzfhjqxL1rW7TMuc28k/7jLwHHgAPB4zbo7Sc767QNurlm+E7govX8ZSSiOAt8CBjOo6W+Bz85ZdhGws+Y5n09ve0i+Ima5T74BvAi8QPLBXjO3hvTxLSRnIV/NuoZ0+6Mkx5KeS29fmVtHu/ZFvdcG3EMSxADL0vd7NH3/L8v4tX+E5KvjCzWv/xbgs7OfDeD29DU/T3JC6Ofa8B7U3b9z6hDwQLqvXqRm1ESGdZxFEmYra5Z1dF+0evMvPMysJxXpa6+ZWWYcfmbWkxx+ZtaTHH5m1pMcfmbWkxx+ZtaTHH5m1pMcfmbWk/4fK23Il5r9aDYAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 360x1080 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"def sigm(alpha):\n",
" def f(x):\n",
" return 1 / (1 + exp(-alpha*x))\n",
" \n",
" return f\n",
"\n",
"ax = list(range(-10, 10))\n",
"ay1 = list(map(sigm(1), ax))\n",
"ay2 = list(map(sigm(2), ax))\n",
"ay3 = list(map(sigm(4), ax))\n",
"\n",
"fig = plt.figure(figsize=(5,15))\n",
"p1 = fig.add_subplot(3, 1, 1)\n",
"p1.plot(ax, ay1)\n",
"p2 = fig.add_subplot(3, 1, 2)\n",
"p2.plot(ax, ay2)\n",
"p3 = fig.add_subplot(3, 1, 3)\n",
"p3.plot(ax, ay3)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q**: Zašto je sigmoidalna funkcija prikladan izbor za aktivacijsku funkciju poopćenoga linearnog modela? \n",
"</br>\n",
"\n",
"**Q**: Kakav utjecaj ima faktor $\\alpha$ na oblik sigmoide? Što to znači za model logističke regresije (tj. kako izlaz modela ovisi o normi vektora težina $\\mathbf{w}$)?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Implementirajte funkciju \n",
"\n",
"> `lr_train(X, y, eta=0.01, max_iter=2000, alpha=0, epsilon=0.0001, trace=False)` \n",
"\n",
"za treniranje modela logističke regresije gradijentnim spustom (*batch* izvedba). Funkcija uzima označeni skup primjera za učenje (matrica primjera `X` i vektor oznaka `y`) te vraća $(n+1)$-dimenzijski vektor težina tipa `ndarray`. Ako je `trace=True`, funkcija dodatno vraća listu (ili matricu) vektora težina $\\mathbf{w}^0,\\mathbf{w}^1,\\dots,\\mathbf{w}^k$ generiranih kroz sve iteracije optimizacije, od 0 do $k$. Optimizaciju treba provoditi dok se ne dosegne `max_iter` iteracija, ili kada razlika u pogrešci unakrsne entropije između dviju iteracija padne ispod vrijednosti `epsilon`. Parametar `alpha` predstavlja faktor regularizacije.\n",
"\n",
"Preporučamo definiranje pomoćne funkcije `lr_h(x,w)` koja daje predikciju za primjer `x` uz zadane težine `w`. Također, preporučamo i funkciju `cross_entropy_error(X,y,w)` koja izračunava pogrešku unakrsne entropije modela na označenom skupu `(X,y)` uz te iste težine.\n",
"\n",
"**NB:** Obratite pozornost na to da je način kako su definirane oznake ($\\{+1,-1\\}$ ili $\\{1,0\\}$) kompatibilan s izračunom funkcije gubitka u optimizacijskome algoritmu."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import PolynomialFeatures as PolyFeat\n",
"from sklearn.metrics import log_loss\n",
"\n",
"def loss_function(h_x, y):\n",
" return -y * np.log(h_x) - (1 - y) * np.log(1 - h_x)\n",
"\n",
"def lr_h(x, w):\n",
" Phi = PolyFeat(1).fit_transform(x.reshape(1,-1))\n",
" return sigm(1)(Phi.dot(w))\n",
" \n",
"def cross_entropy_error(X, y, w):\n",
" Phi = PolyFeat(1).fit_transform(X)\n",
" return log_loss(y, sigm(1)(Phi.dot(w)))\n",
"\n",
"\n",
"def lr_train(X, y, eta = 0.01, max_iter = 2000, alpha = 0, epsilon = 0.0001, trace= False):\n",
" w = zeros(shape(X)[1] + 1)\n",
" N = len(X)\n",
" w_trace = [];\n",
" error = epsilon**-1\n",
" \n",
" for i in range(0, max_iter):\n",
" dw0 = 0; dw = zeros(shape(X)[1]);\n",
" new_error = 0\n",
" \n",
" for j in range(0, N):\n",
" h = lr_h(X[j], w)\n",
" dw0 += h - y[j]\n",
" dw += (h - y[j])*X[j]\n",
" \n",
" new_error += loss_function(h, y[j])\n",
"\n",
" if abs(error - new_error) < epsilon: \n",
" print('stagnacija na i = ', i)\n",
" break\n",
" \n",
" else: error = new_error\n",
" \n",
" w[0] -= eta*dw0\n",
" w[1:] = w[1:] * (1-eta*alpha) - eta*dw\n",
" \n",
" w_trace.extend(w)\n",
" \n",
" if trace:\n",
" return w, w_trace\n",
" \n",
" else: return w\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (c)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Koristeći funkciju `lr_train`, trenirajte model logističke regresije na skupu `seven`, prikažite dobivenu granicu između klasa te izračunajte pogrešku unakrsne entropije. \n",
"\n",
"**NB:** Pripazite da modelu date dovoljan broj iteracija."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.07751718252492557\n",
"[ 6.44150959 -2.11012128 0.53522851]\n"
]
},
{
"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": [
"trained = lr_train(seven_X, seven_y)\n",
"print(cross_entropy_error(seven_X, seven_y, trained))\n",
"print(trained)\n",
"\n",
"h3c = lambda x: lr_h(x, trained) > 0.5\n",
"\n",
"figure()\n",
"mlutils.plot_2d_clf_problem(seven_X, seven_y, h3c)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q:** Koji kriterij zaustavljanja je aktiviran?\n",
"\n",
"**Q:** Zašto dobivena pogreška unakrsne entropije nije jednaka nuli?\n",
"\n",
"**Q:** Kako biste utvrdili da je optimizacijski postupak doista pronašao hipotezu koja minimizira pogrešku učenja? O čemu to ovisi?\n",
"\n",
"**Q:** Na koji način biste preinačili kôd ako biste htjeli da se optimizacija izvodi stohastičkim gradijentnim spustom (*online learning*)?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prikažite na jednom grafikonu pogrešku unakrsne entropije (očekivanje logističkog gubitka) i pogrešku klasifikacije (očekivanje gubitka 0-1) na skupu `seven` kroz iteracije optimizacijskog postupka. Koristite trag težina funkcije `lr_train` iz zadatka (b) (opcija `trace=True`). Na drugom grafikonu prikažite pogrešku unakrsne entropije kao funkciju broja iteracija za različite stope učenja, $\\eta\\in\\{0.005,0.01,0.05,0.1\\}$."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stagnacija na i = 1544\n",
"stagnacija na i = 1128\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x1080 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import zero_one_loss\n",
"\n",
"eta = [0.005, 0.01, 0.05, 0.1]\n",
"[w3d, w3d_trace] = lr_train(seven_X, seven_y, trace=True)\n",
"\n",
"\n",
"Phi = PolyFeat(1).fit_transform(seven_X)\n",
"h_3d = lambda x: x >= 0.5\n",
"\n",
"error_unakrs = []\n",
"errror_classy = []\n",
"errror_eta = []\n",
"\n",
"for k in range(0, len(w3d_trace), 3):\n",
" error_unakrs.append(cross_entropy_error(seven_X, seven_y, w3d_trace[k:k+3]))\n",
" errror_classy.append(zero_one_loss(seven_y, h_3d(sigm(1)(Phi.dot(w3d_trace[k:k+3])))))\n",
" \n",
"for i in eta:\n",
" err = []\n",
" [w3, w3_trace] = lr_train(seven_X, seven_y, i, trace=True)\n",
" \n",
" for j in range(0, len(w3_trace), 3):\n",
" err.append(cross_entropy_error(seven_X, seven_y, w3_trace[j:j+3]))\n",
" \n",
" errror_eta.append(err)\n",
" \n",
"figure(figsize(12, 15))\n",
"subplots_adjust(wspace=0.1)\n",
"subplot(2,1,1)\n",
"grid()\n",
"plot(error_unakrs); plot(errror_classy);\n",
"\n",
"subplot(2,1,2)\n",
"grid()\n",
"for i in range(0, len(eta)):\n",
" plot(errror_eta[i], label = 'eta = ' + str(i))\n",
"legend(loc = 'best');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"**Q:** Zašto je pogreška unakrsne entropije veća od pogreške klasifikacije? Je li to uvijek slučaj kod logističke regresije i zašto?\n",
"\n",
"**Q:** Koju stopu učenja $\\eta$ biste odabrali i zašto?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Upoznajte se s klasom [`linear_model.LogisticRegression`](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) koja implementira logističku regresiju. Usporedite rezultat modela na skupu `seven` s rezultatom koji dobivate pomoću vlastite implementacije algoritma.\n",
"\n",
"**NB:** Kako ugrađena implementacija koristi naprednije verzije optimizacije funkcije, vrlo je vjerojatno da Vam se rješenja neće poklapati, ali generalne performanse modela bi trebale. Ponovno, pripazite na broj iteracija i snagu regularizacije."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"reg3e = LogisticRegression(max_iter=2000, tol=0.0001, C=0.01**-1, solver='lbfgs').fit(seven_X,seven_y)\n",
"h3e = lambda x : reg3e.predict(x)\n",
"\n",
"figure(figsize(7, 7))\n",
"mlutils.plot_2d_clf_problem(seven_X,seven_y, h3e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4. Analiza logističke regresije"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Koristeći ugrađenu implementaciju logističke regresije, provjerite kako se logistička regresija nosi s vrijednostima koje odskaču. Iskoristite skup `outlier` iz prvog zadatka. Prikažite granicu između klasa."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q:** Zašto se rezultat razlikuje od onog koji je dobio model klasifikacije linearnom regresijom iz prvog zadatka?"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
" logReg4 = LogisticRegression(solver='liblinear').fit(outlier_X, outlier_y)\n",
" mlutils.plot_2d_clf_problem(X=outlier_X, y=outlier_y, h= lambda x : logReg4.predict(x) >= 0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Trenirajte model logističke regresije na skupu `seven` te na dva odvojena grafikona prikažite, kroz iteracije optimizacijskoga algoritma, (1) izlaz modela $h(\\mathbf{x})$ za svih sedam primjera te (2) vrijednosti težina $w_0$, $w_1$, $w_2$.\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x1008 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"[w4b, w4b_trace] = lr_train(seven_X, seven_y, trace = True)\n",
"\n",
"w0_4b = []; w1_4b = []; w2_4b = [];\n",
"\n",
"for i in range(0, len(w4b_trace), 3):\n",
" w0_4b.append(w4b_trace[i])\n",
" w1_4b.append(w4b_trace[i+1])\n",
" w2_4b.append(w4b_trace[i+2])\n",
" \n",
"h_gl = []\n",
"\n",
"for i in range(0, len(seven_X)):\n",
" h = []\n",
"\n",
" for j in range(0, len(w4b_trace), 3):\n",
" h.append(lr_h(seven_X[i], w4b_trace[j:j+3]))\n",
" \n",
" h_gl.append(h)\n",
"\n",
"\n",
"figure(figsize(7, 14))\n",
"subplot(2,1,1)\n",
"grid()\n",
"for i in range(0, len(h_gl)):\n",
" plot(h_gl[i], label = 'x' + str(i))\n",
"\n",
"legend(loc = 'best') ;\n",
" \n",
"subplot(2,1,2)\n",
"grid()\n",
"plot(w0_4b); plot(w1_4b); plot(w2_4b);\n",
"legend(['w0', 'w1', 'w2'], loc = 'best');\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (c)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ponovite eksperiment iz podzadatka (b) koristeći linearno neodvojiv skup podataka `unsep` iz prvog zadatka."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q:** Usporedite grafikone za slučaj linearno odvojivih i linearno neodvojivih primjera te komentirajte razliku."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stagnacija na i = 1315\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x1008 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"unsep_y = np.append(seven_y, 0)\n",
"[w4c, w4c_trace] = lr_train(unsep_X, unsep_y, trace = True)\n",
"\n",
"w0_4c = []; w1_4c = []; w2_4c = [];\n",
"\n",
"for i in range(0, len(w4c_trace), 3):\n",
" w0_4c.append(w4c_trace[i])\n",
" w1_4c.append(w4c_trace[i+1])\n",
" w2_4c.append(w4c_trace[i+2])\n",
" \n",
"h_gl = []\n",
"\n",
"for i in range(0, len(unsep_X)):\n",
" h = []\n",
"\n",
" for j in range(0, len(w4c_trace), 3):\n",
" h.append(lr_h(unsep_X[i], w4c_trace[j:j+3]))\n",
" \n",
" h_gl.append(h)\n",
" \n",
"\n",
"figure(figsize(7, 14))\n",
"subplots_adjust(wspace=0.1)\n",
"subplot(2,1,1)\n",
"grid()\n",
"for i in range(0, len(h_gl)):\n",
" plot(h_gl[i], label = 'x' + str(i))\n",
"\n",
"legend(loc = 'best') ;\n",
" \n",
"subplot(2,1,2)\n",
"grid()\n",
"plot(w0_4c); plot(w1_4c); plot(w2_4c);\n",
"legend(['w0', 'w1', 'w2'], loc = 'best');\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5. Regularizirana logistička regresija"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Trenirajte model logističke regresije na skupu `seven` s različitim faktorima L2-regularizacije, $\\alpha\\in\\{0,1,10,100\\}$. Prikažite na dva odvojena grafikona (1) pogrešku unakrsne entropije te (2) L2-normu vektora $\\mathbf{w}$ kroz iteracije optimizacijskog algoritma."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q:** Jesu li izgledi krivulja očekivani i zašto?\n",
"\n",
"**Q:** Koju biste vrijednost za $\\alpha$ odabrali i zašto?"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"from numpy.linalg import norm"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stagnacija na i = 772\n",
"stagnacija na i = 230\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x1008 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"alpha5 = [0, 1, 10, 100]\n",
"\n",
"err_gl = []; norm_gl = [];\n",
"\n",
"for a in alpha5:\n",
" [w5, w5_trace] = lr_train(seven_X, seven_y, alpha = a, trace = True)\n",
" err = []; L2_norm = [];\n",
" \n",
" for k in range(0, len(w5_trace), 3):\n",
" err.append(cross_entropy_error(seven_X, seven_y, w5_trace[k:k+3]))\n",
" L2_norm.append(linalg.norm(w5_trace[k:k+1]))\n",
" \n",
" err_gl.append(err)\n",
" norm_gl.append(L2_norm)\n",
" \n",
"figure(figsize(7, 14))\n",
"subplot(2,1,1)\n",
"grid()\n",
"for i in range(0, len(err_gl)):\n",
" plot(err_gl[i], label = 'alpha = ' + str(alpha5[i]) )\n",
" \n",
"legend(loc = 'best') ;\n",
"\n",
"subplot(2,1,2)\n",
"grid()\n",
"for i in range(0, len(err_gl)):\n",
" plot(norm_gl[i], label = 'alpha = ' + str(alpha5[i]) )\n",
" \n",
"legend(loc = 'best');\n",
"\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 6. Logistička regresija s funkcijom preslikavanja"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Proučite funkciju [`datasets.make_classification`](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html). Generirajte i prikažite dvoklasan skup podataka s ukupno $N=100$ dvodimenzijskih ($n=2)$ primjera, i to sa dvije grupe po klasi (`n_clusters_per_class=2`). Malo je izgledno da će tako generiran skup biti linearno odvojiv, međutim to nije problem jer primjere možemo preslikati u višedimenzijski prostor značajki pomoću klase [`preprocessing.PolynomialFeatures`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html), kao što smo to učinili kod linearne regresije u prvoj laboratorijskoj vježbi. Trenirajte model logističke regresije koristeći za preslikavanje u prostor značajki polinomijalnu funkciju stupnja $d=2$ i stupnja $d=3$. Prikažite dobivene granice između klasa. Možete koristiti svoju implementaciju, ali se radi brzine preporuča koristiti `linear_model.LogisticRegression`. Regularizacijski faktor odaberite po želji.\n",
"\n",
"**NB:** Kao i ranije, za prikaz granice između klasa koristite funkciju `plot_2d_clf_problem`. Funkciji kao argumente predajte izvorni skup podataka, a preslikavanje u prostor značajki napravite unutar poziva funkcije `h` koja čini predikciju, na sljedeći način:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 504x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.preprocessing import PolynomialFeatures\n",
"\n",
"[x6, y6] = make_classification(n_samples=100, n_features=2, n_redundant=0, n_classes=2, n_clusters_per_class=2)\n",
"\n",
"figure(figsize(7, 5))\n",
"mlutils.plot_2d_clf_problem(x6, y6)\n",
"\n",
"d = [2,3]\n",
"j = 1\n",
"figure(figsize(12, 4))\n",
"subplots_adjust(wspace=0.1)\n",
"for i in d:\n",
" subplot(1,2,j)\n",
" poly = PolynomialFeatures(i)\n",
" Phi = poly.fit_transform(x6)\n",
"\n",
" model = LogisticRegression(solver='lbfgs')\n",
" model.fit(Phi, y6)\n",
" h = lambda x : model.predict(poly.transform(x))\n",
"\n",
" mlutils.plot_2d_clf_problem(x6, y6, h)\n",
" title('d = ' + str(i))\n",
" j += 1\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# Vaš kôd ovdje..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Q:** Koji biste stupanj polinoma upotrijebili i zašto? Je li taj odabir povezan s odabirom regularizacijskog faktora $\\alpha$? Zašto?"
]
}
],
"metadata": {
"anaconda-cloud": {},
"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 |
rhellmuth/humanumexmachina | content/downloads/notebooks/.ipynb_checkpoints/Untitled0-checkpoint.ipynb | 1 | 27513 | {
"metadata": {
"name": "",
"signature": "sha256:664e83dcb13a6be804c5689979f751f061929155800d5b53073acfe1ec8a0d8f"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Aqui come\u00e7a o Ipython Notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Posso escrever em Markdown qualquer coisa, p\u00f4r figuras, links, $\\LaTeX$, qualquer coisa.\n",
"\n",
"Vou plotar um gr\u00e1fico com o matplotlib."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.linspace(0, 3*np.pi, 500)\n",
"plt.plot(x, np.sin(x**2))\n",
"plt.title('$sen(x^2)$')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"<matplotlib.text.Text at 0x10af5e2d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAENCAYAAADjW7WQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIiIiDbC/wef0YSCwgNEVgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10ae716d0>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
Joshuaalbert/IonoTomo | src/ionotomo/notebooks/ProgressBarClass.ipynb | 1 | 3073 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import sys\n",
"import re\n",
"from time import clock\n",
"\n",
"\n",
"class ProgressBar(object):\n",
" DEFAULT = 'Progress: %(bar)s %(percent)3d%%'\n",
" FULL = '%(bar)s %(current)d/%(total)d (%(percent)3d%%) %(remaining)d to go %(timeleft)f s'\n",
"\n",
" def __init__(self, total, width=40, fmt=DEFAULT, symbol='=',\n",
" output=sys.stderr):\n",
" assert len(symbol) == 1\n",
"\n",
" self.total = total\n",
" self.width = width\n",
" self.symbol = symbol\n",
" self.output = output\n",
" self.fmt = re.sub(r'(?P<name>%\\(.+?\\))d',\n",
" r'\\g<name>%dd' % len(str(total)), fmt)\n",
"\n",
" self.current = 0\n",
" self.startTime = clock()\n",
"\n",
" def __call__(self,current=None):\n",
" '''Assumes current is iterations done '''\n",
" if current is not None:\n",
" self.current = current\n",
" if self.current == 0:\n",
" return\n",
" percent = self.current / float(self.total)\n",
" size = int(self.width * percent)\n",
" remaining = self.total - self.current\n",
" remainingTime = float(remaining) * (clock() - self.startTime)/float(self.current)\n",
" bar = '[' + self.symbol * size + ' ' * (self.width - size) + ']'\n",
"\n",
" args = {\n",
" 'total': self.total,\n",
" 'bar': bar,\n",
" 'current': self.current,\n",
" 'percent': percent * 100,\n",
" 'remaining': remaining,\n",
" 'timeleft':remainingTime\n",
" }\n",
" print('\\r' + self.fmt % args, file=self.output, end='')\n",
"\n",
" def done(self):\n",
" self.current = self.total\n",
" self()\n",
" print('', file=self.output)\n",
" print('Completed in {} seconds.'.format(clock() - self.startTime), file=self.output, end='')\n",
" print('', file=self.output)\n",
"if __name__ == '__main__':\n",
" from time import sleep\n",
"\n",
" progress = ProgressBar(80, fmt=ProgressBar.FULL)\n",
"\n",
" for x in range(progress.total):\n",
" progress(x+1)\n",
" sleep(0.1)\n",
" progress.done()"
]
}
],
"metadata": {
"anaconda-cloud": {},
"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": 1
}
| apache-2.0 |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session04/Day0/TooBriefMLSolutions.ipynb | 1 | 541331 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to Machine Learning:\n",
"Examples of Unsupervised and Supervised Machine-Learning Algorithms \n",
"========\n",
"\n",
"##### Version 0.1\n",
"\n",
"Broadly speaking, machine-learning methods constitute a diverse collection of data-driven algorithms designed to classify/characterize/analyze sources in multi-dimensional spaces. The topics and studies that fall under the umbrella of machine learning is growing, and there is no good catch-all definition. The number (and variation) of algorithms is vast, and beyond the scope of these exercises. While we will discuss a few specific algorithms today, more importantly, we will explore the scope of the two general methods: unsupervised learning and supervised learning and introduce the powerful (and dangerous?) Python package [`scikit-learn`](http://scikit-learn.org/stable/).\n",
"\n",
"***\n",
"By AA Miller\n",
"\n",
"2017 September 16"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem 1) Introduction to `scikit-learn`\n",
"\n",
"At the most basic level, `scikit-learn` makes machine learning extremely easy within `python`. By way of example, here is a short piece of code that builds a complex, non-linear model to classify sources in the Iris data set that we learned about earlier:\n",
"\n",
" from sklearn import datasets\n",
" from sklearn.ensemble import RandomForestClassifier\n",
" iris = datasets.load_iris()\n",
" RFclf = RandomForestClassifier().fit(iris.data, iris.target)\n",
"\n",
"Those 4 lines of code have constructed a model that is superior to any system of hard cuts that we could have encoded while looking at the multidimensional space. This can be fast as well: execute the dummy code in the cell below to see how \"easy\" machine-learning is with `scikit-learn`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# execute dummy code here\n",
"\n",
"from sklearn import datasets\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"iris = datasets.load_iris()\n",
"RFclf = RandomForestClassifier().fit(iris.data, iris.target)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generally speaking, the procedure for `scikit-learn` is uniform across all machine-learning algorithms. Models are accessed via the various modules (`ensemble`, `SVM`, `neighbors`, etc), with user-defined tuning parameters. The features (or data) for the models are stored in a 2D array, `X`, with rows representing individual sources and columns representing the corresponding feature values. [In a minority of cases, `X`, represents a similarity or distance matrix where each entry represents the distance to every other source in the data set.] In cases where there is a known classification or scalar value (typically supervised methods), this information is stored in a 1D array `y`. \n",
"\n",
"Unsupervised models are fit by calling `.fit(X)` and supervised models are fit by calling `.fit(X, y)`. In both cases, predictions for new observations, `Xnew`, can be obtained by calling `.predict(Xnew)`. Those are the basics and beyond that, the details are algorithm specific, but the documentation for essentially everything within `scikit-learn` is excellent, so read the docs.\n",
"\n",
"To further develop our intuition, we will now explore the Iris dataset a little further.\n",
"\n",
"**Problem 1a** What is the pythonic type of `iris`?"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"sklearn.datasets.base.Bunch"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(iris)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You likely haven't encountered a `scikit-learn Bunch` before. It's functionality is essentially the same as a dictionary. \n",
"\n",
"**Problem 1b** What are the keys of iris?"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['target_names', 'data', 'target', 'DESCR', 'feature_names']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most importantly, iris contains `data` and `target` values. These are all you need for `scikit-learn`, though the feature and target names and description are useful.\n",
"\n",
"**Problem 1c** What is the shape and content of the `iris` data?"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(150, 4)\n",
"[[ 5.1 3.5 1.4 0.2]\n",
" [ 4.9 3. 1.4 0.2]\n",
" [ 4.7 3.2 1.3 0.2]\n",
" [ 4.6 3.1 1.5 0.2]\n",
" [ 5. 3.6 1.4 0.2]\n",
" [ 5.4 3.9 1.7 0.4]\n",
" [ 4.6 3.4 1.4 0.3]\n",
" [ 5. 3.4 1.5 0.2]\n",
" [ 4.4 2.9 1.4 0.2]\n",
" [ 4.9 3.1 1.5 0.1]\n",
" [ 5.4 3.7 1.5 0.2]\n",
" [ 4.8 3.4 1.6 0.2]\n",
" [ 4.8 3. 1.4 0.1]\n",
" [ 4.3 3. 1.1 0.1]\n",
" [ 5.8 4. 1.2 0.2]\n",
" [ 5.7 4.4 1.5 0.4]\n",
" [ 5.4 3.9 1.3 0.4]\n",
" [ 5.1 3.5 1.4 0.3]\n",
" [ 5.7 3.8 1.7 0.3]\n",
" [ 5.1 3.8 1.5 0.3]\n",
" [ 5.4 3.4 1.7 0.2]\n",
" [ 5.1 3.7 1.5 0.4]\n",
" [ 4.6 3.6 1. 0.2]\n",
" [ 5.1 3.3 1.7 0.5]\n",
" [ 4.8 3.4 1.9 0.2]\n",
" [ 5. 3. 1.6 0.2]\n",
" [ 5. 3.4 1.6 0.4]\n",
" [ 5.2 3.5 1.5 0.2]\n",
" [ 5.2 3.4 1.4 0.2]\n",
" [ 4.7 3.2 1.6 0.2]\n",
" [ 4.8 3.1 1.6 0.2]\n",
" [ 5.4 3.4 1.5 0.4]\n",
" [ 5.2 4.1 1.5 0.1]\n",
" [ 5.5 4.2 1.4 0.2]\n",
" [ 4.9 3.1 1.5 0.1]\n",
" [ 5. 3.2 1.2 0.2]\n",
" [ 5.5 3.5 1.3 0.2]\n",
" [ 4.9 3.1 1.5 0.1]\n",
" [ 4.4 3. 1.3 0.2]\n",
" [ 5.1 3.4 1.5 0.2]\n",
" [ 5. 3.5 1.3 0.3]\n",
" [ 4.5 2.3 1.3 0.3]\n",
" [ 4.4 3.2 1.3 0.2]\n",
" [ 5. 3.5 1.6 0.6]\n",
" [ 5.1 3.8 1.9 0.4]\n",
" [ 4.8 3. 1.4 0.3]\n",
" [ 5.1 3.8 1.6 0.2]\n",
" [ 4.6 3.2 1.4 0.2]\n",
" [ 5.3 3.7 1.5 0.2]\n",
" [ 5. 3.3 1.4 0.2]\n",
" [ 7. 3.2 4.7 1.4]\n",
" [ 6.4 3.2 4.5 1.5]\n",
" [ 6.9 3.1 4.9 1.5]\n",
" [ 5.5 2.3 4. 1.3]\n",
" [ 6.5 2.8 4.6 1.5]\n",
" [ 5.7 2.8 4.5 1.3]\n",
" [ 6.3 3.3 4.7 1.6]\n",
" [ 4.9 2.4 3.3 1. ]\n",
" [ 6.6 2.9 4.6 1.3]\n",
" [ 5.2 2.7 3.9 1.4]\n",
" [ 5. 2. 3.5 1. ]\n",
" [ 5.9 3. 4.2 1.5]\n",
" [ 6. 2.2 4. 1. ]\n",
" [ 6.1 2.9 4.7 1.4]\n",
" [ 5.6 2.9 3.6 1.3]\n",
" [ 6.7 3.1 4.4 1.4]\n",
" [ 5.6 3. 4.5 1.5]\n",
" [ 5.8 2.7 4.1 1. ]\n",
" [ 6.2 2.2 4.5 1.5]\n",
" [ 5.6 2.5 3.9 1.1]\n",
" [ 5.9 3.2 4.8 1.8]\n",
" [ 6.1 2.8 4. 1.3]\n",
" [ 6.3 2.5 4.9 1.5]\n",
" [ 6.1 2.8 4.7 1.2]\n",
" [ 6.4 2.9 4.3 1.3]\n",
" [ 6.6 3. 4.4 1.4]\n",
" [ 6.8 2.8 4.8 1.4]\n",
" [ 6.7 3. 5. 1.7]\n",
" [ 6. 2.9 4.5 1.5]\n",
" [ 5.7 2.6 3.5 1. ]\n",
" [ 5.5 2.4 3.8 1.1]\n",
" [ 5.5 2.4 3.7 1. ]\n",
" [ 5.8 2.7 3.9 1.2]\n",
" [ 6. 2.7 5.1 1.6]\n",
" [ 5.4 3. 4.5 1.5]\n",
" [ 6. 3.4 4.5 1.6]\n",
" [ 6.7 3.1 4.7 1.5]\n",
" [ 6.3 2.3 4.4 1.3]\n",
" [ 5.6 3. 4.1 1.3]\n",
" [ 5.5 2.5 4. 1.3]\n",
" [ 5.5 2.6 4.4 1.2]\n",
" [ 6.1 3. 4.6 1.4]\n",
" [ 5.8 2.6 4. 1.2]\n",
" [ 5. 2.3 3.3 1. ]\n",
" [ 5.6 2.7 4.2 1.3]\n",
" [ 5.7 3. 4.2 1.2]\n",
" [ 5.7 2.9 4.2 1.3]\n",
" [ 6.2 2.9 4.3 1.3]\n",
" [ 5.1 2.5 3. 1.1]\n",
" [ 5.7 2.8 4.1 1.3]\n",
" [ 6.3 3.3 6. 2.5]\n",
" [ 5.8 2.7 5.1 1.9]\n",
" [ 7.1 3. 5.9 2.1]\n",
" [ 6.3 2.9 5.6 1.8]\n",
" [ 6.5 3. 5.8 2.2]\n",
" [ 7.6 3. 6.6 2.1]\n",
" [ 4.9 2.5 4.5 1.7]\n",
" [ 7.3 2.9 6.3 1.8]\n",
" [ 6.7 2.5 5.8 1.8]\n",
" [ 7.2 3.6 6.1 2.5]\n",
" [ 6.5 3.2 5.1 2. ]\n",
" [ 6.4 2.7 5.3 1.9]\n",
" [ 6.8 3. 5.5 2.1]\n",
" [ 5.7 2.5 5. 2. ]\n",
" [ 5.8 2.8 5.1 2.4]\n",
" [ 6.4 3.2 5.3 2.3]\n",
" [ 6.5 3. 5.5 1.8]\n",
" [ 7.7 3.8 6.7 2.2]\n",
" [ 7.7 2.6 6.9 2.3]\n",
" [ 6. 2.2 5. 1.5]\n",
" [ 6.9 3.2 5.7 2.3]\n",
" [ 5.6 2.8 4.9 2. ]\n",
" [ 7.7 2.8 6.7 2. ]\n",
" [ 6.3 2.7 4.9 1.8]\n",
" [ 6.7 3.3 5.7 2.1]\n",
" [ 7.2 3.2 6. 1.8]\n",
" [ 6.2 2.8 4.8 1.8]\n",
" [ 6.1 3. 4.9 1.8]\n",
" [ 6.4 2.8 5.6 2.1]\n",
" [ 7.2 3. 5.8 1.6]\n",
" [ 7.4 2.8 6.1 1.9]\n",
" [ 7.9 3.8 6.4 2. ]\n",
" [ 6.4 2.8 5.6 2.2]\n",
" [ 6.3 2.8 5.1 1.5]\n",
" [ 6.1 2.6 5.6 1.4]\n",
" [ 7.7 3. 6.1 2.3]\n",
" [ 6.3 3.4 5.6 2.4]\n",
" [ 6.4 3.1 5.5 1.8]\n",
" [ 6. 3. 4.8 1.8]\n",
" [ 6.9 3.1 5.4 2.1]\n",
" [ 6.7 3.1 5.6 2.4]\n",
" [ 6.9 3.1 5.1 2.3]\n",
" [ 5.8 2.7 5.1 1.9]\n",
" [ 6.8 3.2 5.9 2.3]\n",
" [ 6.7 3.3 5.7 2.5]\n",
" [ 6.7 3. 5.2 2.3]\n",
" [ 6.3 2.5 5. 1.9]\n",
" [ 6.5 3. 5.2 2. ]\n",
" [ 6.2 3.4 5.4 2.3]\n",
" [ 5.9 3. 5.1 1.8]]\n"
]
}
],
"source": [
"print(np.shape(iris.data))\n",
"print(iris.data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Problem 1d** What is the shape and content of the `iris` target?"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(150,)\n",
"[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\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2]\n"
]
}
],
"source": [
"print(np.shape(iris.target))\n",
"print(iris.target)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, as a baseline for the exercises that follow, we will now make a simple 2D plot showing the separation of the 3 classes in the iris dataset. This plot will serve as the reference for examining the quality of the clustering algorithms. \n",
"\n",
"**Problem 1e** Make a scatter plot showing sepal length vs. sepal width for the iris data set. Color the points according to their respective classes. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x110a40650>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEPCAYAAABY9lNGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110a086d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(iris.feature_names) # shows that sepal length is first feature and sepal width is second feature\n",
"\n",
"plt.scatter(iris.data[:,0], iris.data[:,1], c = iris.target, s = 30, edgecolor = \"None\", cmap = \"viridis\")\n",
"plt.xlabel('sepal length')\n",
"plt.ylabel('sepal width')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem 2) Unsupervised Machine Learning\n",
"\n",
"Unsupervised machine learning, sometimes referred to as clustering or data mining, aims to group or classify sources in the multidimensional feature space. The \"unsupervised\" comes from the fact that there are no target labels provided to the algorithm, so the machine is asked to cluster the data \"on its own.\" The lack of labels means there is no (simple) method for validating the accuracy of the solution provided by the machine (though sometimes simple examination can show the results are **terrible**). \n",
"\n",
"For this reason [*note* - this is my (AAM) opinion and there many be many others who disagree], unsupervised methods are not particularly useful for astronomy. Supposing one did find some useful clustering structure, an adversarial researcher could always claim that the current feature space does not accurately capture the physics of the system and as such the clustering result is not interesting or, worse, erroneous. The one potentially powerful exception to this broad statement is outlier detection, which can be a branch of both unsupervised and supervised learning. Finding weirdo objects is an astronomical pastime, and there are unsupervised methods that may help in that regard in the LSST era. \n",
"\n",
"To begin today we will examine one of the most famous, and simple, clustering algorithms: [$k$-means](https://en.wikipedia.org/wiki/K-means_clustering). $k$-means clustering looks to identify $k$ convex clusters, where $k$ is a user defined number. And here-in lies the rub: if we truly knew the number of clusters in advance, we likely wouldn't need to perform any clustering in the first place. This is the major downside to $k$-means. Operationally, pseudocode for the algorithm can be summarized as the following: \n",
"\n",
" initiate search by identifying k points (i.e. the cluster centers)\n",
" loop \n",
" assign each point in the data set to the closest cluster center\n",
" calculate new cluster centers based on mean position of all points within cluster\n",
" if diff(new center - old center) < threshold:\n",
" stop (i.e. clusters are defined)\n",
"\n",
"The threshold is defined by the user, though in some cases the total number of iterations is also. An advantage of $k$-means is that the solution will always converge, though the solution may only be a local minimum. Disadvantages include the assumption of convexity, i.e. difficult to capture complex geometry, and the curse of dimensionality.\n",
"\n",
"In `scikit-learn` the [`KMeans`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans) algorithm is implemented as part of the [`sklearn.cluster`](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster) module. \n",
"\n",
"**Problem 2a** Fit two different $k$-means models to the iris data, one with 2 clusters and one with 3 clusters. Plot the resulting clusters in the sepal length-sepal width plane (same plot as above). How do the results compare to the true classifications?"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x115c4a2b0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCsmbLUE8aQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1123e1b70>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAEB7AYo0EKq0d7cVAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111ea4940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.cluster import KMeans\n",
"\n",
"Kcluster = KMeans(n_clusters = 2)\n",
"Kcluster.fit(iris.data)\n",
"\n",
"plt.figure()\n",
"plt.scatter(iris.data[:,0], iris.data[:,1], c = Kcluster.labels_, s = 30, edgecolor = \"None\", cmap = \"viridis\")\n",
"plt.xlabel('sepal length')\n",
"plt.ylabel('sepal width')\n",
"\n",
"Kcluster = KMeans(n_clusters = 3)\n",
"Kcluster.fit(iris.data)\n",
"\n",
"plt.figure()\n",
"plt.scatter(iris.data[:,0], iris.data[:,1], c = Kcluster.labels_, s = 30, edgecolor = \"None\", cmap = \"viridis\")\n",
"plt.xlabel('sepal length')\n",
"plt.ylabel('sepal width')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With 3 clusters the algorithm does a good job of separating the three classes. However, without the a priori knowledge that there are 3 different types of iris, the 2 cluster solution would appear to be superior. \n",
"\n",
"**Problem 2b** How do the results change if the 3 cluster model is called with `n_init = 1` and `init = 'random'` options? Use `rs` for the random state [this allows me to cheat in service of making a point].\n",
"\n",
"*Note - the respective defaults for these two parameters are 10 and `k-means++`, respectively. Read the docs to see why these choices are, likely, better than those in 2b. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x115d6ab00>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKKWUctKkoJRSyun/AYSkFl8jyKmeAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115c56710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rs = 14\n",
"Kcluster1 = KMeans(n_clusters = 3, n_init = 1, init = 'random', random_state = rs)\n",
"Kcluster1.fit(iris.data)\n",
"\n",
"plt.figure()\n",
"plt.scatter(iris.data[:,0], iris.data[:,1], c = Kcluster1.labels_, s = 30, edgecolor = \"None\", cmap = \"viridis\")\n",
"plt.xlabel('sepal length')\n",
"plt.ylabel('sepal width')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**A random aside that is not particularly relevant here**\n",
"\n",
"$k$-means evaluates the Euclidean distance between individual sources and cluster centers, thus, the magnitude of the individual features has a strong effect on the final clustering outcome. \n",
"\n",
"**Problem 2c** Calculate the mean, standard deviation, min, and max of each feature in the iris data set. Based on these summaries, which feature is most important for clustering? "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"feature\t\t\tmean\tstd\tmin\tmax\n",
"sepal length (cm)\t5.84\t0.83\t4.30\t7.90\n",
"sepal width (cm)\t3.05\t0.43\t2.00\t4.40\n",
"petal length (cm)\t3.76\t1.76\t1.00\t6.90\n",
"petal width (cm)\t1.20\t0.76\t0.10\t2.50\n"
]
}
],
"source": [
"print(\"feature\\t\\t\\tmean\\tstd\\tmin\\tmax\")\n",
"for featnum, feat in enumerate(iris.feature_names):\n",
" print(\"{:s}\\t{:.2f}\\t{:.2f}\\t{:.2f}\\t{:.2f}\".format(feat, np.mean(iris.data[:,featnum]), \n",
" np.std(iris.data[:,featnum]), np.min(iris.data[:,featnum]),\n",
" np.max(iris.data[:,featnum])))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Petal length has the largest range and standard deviation, thus, it will have the most \"weight\" when determining the $k$ clusters. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The truth is that the iris data set is fairly small and straightfoward. Nevertheless, we will now examine the clustering results after re-scaling the features. [Some algorithms, *cough* Support Vector Machines *cough*, are notoriously sensitive to the feature scaling, so it is important to know about this step.] Imagine you are classifying stellar light curves: the data set will include contact binaries with periods of $\\sim 0.1 \\; \\mathrm{d}$ and Mira variables with periods of $\\gg 100 \\; \\mathrm{d}$. Without re-scaling, this feature that covers 4 orders of magnitude may dominate all others in the final model projections.\n",
"\n",
"The two most common forms of re-scaling are to rescale to a guassian with mean $= 0$ and variance $= 1$, or to rescale the min and max of the feature to $[0, 1]$. The best normalization is problem dependent. The [`sklearn.preprocessing`](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing) module makes it easy to re-scale the feature set. **It is essential that the same scaling used for the training set be used for all other data run through the model.** The testing, validation, and field observations cannot be re-scaled independently. This would result in meaningless final classifications/predictions. \n",
"\n",
"**Problem 2d** Re-scale the features to normal distributions, and perform $k$-means clustering on the iris data. How do the results compare to those obtained earlier? \n",
"\n",
"*Hint - you may find [`'StandardScaler()'`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler) within the `sklearn.preprocessing` module useful.*"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x115f65208>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1123fe668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"scaler = StandardScaler().fit(iris.data)\n",
"\n",
"Kcluster = KMeans(n_clusters = 3)\n",
"Kcluster.fit(scaler.transform(iris.data))\n",
"\n",
"plt.figure()\n",
"plt.scatter(iris.data[:,0], iris.data[:,1], c = Kcluster.labels_, s = 30, edgecolor = \"None\", cmap = \"viridis\")\n",
"plt.xlabel('sepal length')\n",
"plt.ylabel('sepal width')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These results are almost identical to those obtained without scaling. This is due to the simplicity of the iris data set. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**How do I test the accuracy of my clusters?**\n",
"\n",
"Essentially - you don't. There are some methods that are available, but they essentially compare clusters to labeled samples, and if the samples are labeled it is likely that supervised learning is more useful anyway. If you are curious, `scikit-learn` does provide some [built-in functions for analyzing clustering](http://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation), but again, it is difficult to evaluate the validity of any newly discovered clusters. \n",
"\n",
"**What if I don't know how many clusters are present in the data?**\n",
"\n",
"An excellent question, as you will almost never know this a priori. Many algorithms, like $k$-means, do require the number of clusters to be specified, but some other methods do not. As an example [`DBSCAN`](https://en.wikipedia.org/wiki/DBSCAN). In brief, `DBSCAN` requires two parameters: `minPts`, the minimum number of points necessary for a cluster, and $\\epsilon$, a distance measure. Clusters are grown by identifying *core points*, objects that have at least `minPts` located within a distance $\\epsilon$. *Reachable points* are those within a distance $\\epsilon$ of at least one *core point* but less than `minPts` *core points*. Identically, these points define the outskirts of the clusters. Finally, there are also *outliers* which are points that are $> \\epsilon$ away from any *core points*. Thus, `DBSCAN` naturally identifies clusters, does not assume clusters are convex, and even provides a notion of outliers. The downsides to the algorithm are that the results are highly dependent on the two tuning parameters, and that clusters of highly different densities can be difficult to recover (because $\\epsilon$ and `minPts` is specified for all clusters. \n",
"\n",
"In `scitkit-learn` the \n",
"[`DBSCAN`](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN) algorithm is part of the `sklearn.cluster` module. $\\epsilon$ and `minPts` are set by `eps` and `min_samples`, respectively. \n",
"\n",
"**Problem 2e** Cluster the iris data using `DBSCAN`. Play around with the tuning parameters to see how they affect the final clustering results. How does the use of `DBSCAN` compare to $k$-means? Can you obtain 3 clusters with `DBSCAN`? If not, given the knowledge that the iris dataset has 3 classes - does this invalidate `DBSCAN` as a viable algorithm?\n",
"\n",
"*Note - DBSCAN labels outliers as $-1$, and thus, `plt.scatter()`, will plot all these points as the same color.*\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x115c4fb38>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115f934a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# execute this cell\n",
"\n",
"from sklearn.cluster import DBSCAN\n",
"\n",
"dbs = DBSCAN(eps = 0.7, min_samples = 7)\n",
"dbs.fit(scaler.transform(iris.data)) # best to use re-scaled data since eps is in absolute units\n",
"\n",
"dbs_outliers = dbs.labels_ == -1\n",
"\n",
"plt.figure()\n",
"plt.scatter(iris.data[:,0], iris.data[:,1], c = dbs.labels_, s = 30, edgecolor = \"None\", cmap = \"viridis\")\n",
"plt.scatter(iris.data[:,0][dbs_outliers], iris.data[:,1][dbs_outliers], s = 30, c = 'k')\n",
"\n",
"\n",
"plt.xlabel('sepal length')\n",
"plt.ylabel('sepal width')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I was unable to obtain 3 clusters with `DBSCAN`. While these results are, on the surface, worse than what we got with $k$-means, my suspicion is that the 4 features do not adequately separate the 3 classes. [See - a nayseyer can always make that argument.] This is not a problem for `DBSCAN` as an algorithm, but rather, evidence that no single algorithm works well in all cases. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Challenge Problem) Cluster SDSS Galaxy Data\n",
"\n",
"The following query will select 10k likely galaxies from the SDSS database and return the results of that query into an [`astropy.Table`](http://docs.astropy.org/en/stable/table/) object. (For now, if you are not familiar with the SDSS DB schema, don't worry about this query, just know that it returns a bunch of photometric features.)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<Table length=10000>\n",
"<table id=\"table4664138272\" class=\"table-striped table-bordered table-condensed\">\n",
"<thead><tr><th>ug</th><th>gr</th><th>gi</th><th>gz</th><th>petroRad_i</th><th>petroR50_i</th><th>deVAB_i</th></tr></thead>\n",
"<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>1.744738</td><td>1.880987</td><td>2.618439</td><td>3.098848</td><td>4.734287</td><td>2.280206</td><td>0.6594689</td></tr>\n",
"<tr><td>1.64506</td><td>0.6405602</td><td>0.9067268</td><td>1.131088</td><td>2.879089</td><td>1.368413</td><td>0.5958594</td></tr>\n",
"<tr><td>0.5541306</td><td>1.367058</td><td>2.234196</td><td>2.648424</td><td>3.24823</td><td>1.391062</td><td>0.5536546</td></tr>\n",
"<tr><td>1.750225</td><td>1.017056</td><td>2.236155</td><td>2.476191</td><td>7.35715</td><td>1.656455</td><td>0.3708669</td></tr>\n",
"<tr><td>-0.4647484</td><td>3.276148</td><td>4.653189</td><td>5.100149</td><td>3.155632</td><td>1.3742</td><td>0.3997748</td></tr>\n",
"<tr><td>1.92227</td><td>1.048584</td><td>1.480556</td><td>1.818228</td><td>4.623463</td><td>2.050763</td><td>0.8584502</td></tr>\n",
"<tr><td>2.932352</td><td>1.911509</td><td>2.750689</td><td>3.06374</td><td>4.154721</td><td>1.771925</td><td>0.8026502</td></tr>\n",
"<tr><td>1.752588</td><td>0.8249168</td><td>1.33543</td><td>1.658825</td><td>4.75457</td><td>2.082502</td><td>0.224614</td></tr>\n",
"<tr><td>2.269855</td><td>1.570141</td><td>2.396418</td><td>2.819241</td><td>2.498447</td><td>1.520789</td><td>0.8498095</td></tr>\n",
"<tr><td>1.846529</td><td>1.31798</td><td>2.55481</td><td>3.117262</td><td>3.405702</td><td>1.412154</td><td>0.3636903</td></tr>\n",
"<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
"<tr><td>1.852249</td><td>0.8217258</td><td>1.189435</td><td>1.440802</td><td>3.5701</td><td>1.496244</td><td>0.835227</td></tr>\n",
"<tr><td>1.75761</td><td>0.6210327</td><td>0.9024334</td><td>1.077242</td><td>4.522943</td><td>2.017324</td><td>0.8735518</td></tr>\n",
"<tr><td>2.200459</td><td>1.88521</td><td>2.588902</td><td>2.789146</td><td>9.380934</td><td>2.429901</td><td>0.4929135</td></tr>\n",
"<tr><td>1.719664</td><td>2.761608</td><td>3.795464</td><td>4.383556</td><td>4.556319</td><td>2.062559</td><td>0.7050323</td></tr>\n",
"<tr><td>2.056519</td><td>1.169394</td><td>1.663309</td><td>1.950346</td><td>4.492199</td><td>2.091322</td><td>0.9831492</td></tr>\n",
"<tr><td>1.605412</td><td>0.7955189</td><td>1.24593</td><td>1.611261</td><td>6.397684</td><td>2.953169</td><td>0.190593</td></tr>\n",
"<tr><td>1.799215</td><td>1.163046</td><td>1.631817</td><td>1.920591</td><td>3.615045</td><td>1.63818</td><td>0.906157</td></tr>\n",
"<tr><td>1.546144</td><td>0.7393208</td><td>1.109642</td><td>1.34337</td><td>5.175054</td><td>2.079152</td><td>0.8696824</td></tr>\n",
"<tr><td>4.863503</td><td>1.659336</td><td>2.598026</td><td>2.916479</td><td>4.734004</td><td>1.889737</td><td>0.6406367</td></tr>\n",
"<tr><td>1.058554</td><td>0.7011375</td><td>1.09726</td><td>1.275547</td><td>5.904384</td><td>2.444521</td><td>0.5284007</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table length=10000>\n",
" ug gr gi gz petroRad_i petroR50_i deVAB_i \n",
" float64 float64 float64 float64 float64 float64 float64 \n",
"---------- --------- --------- -------- ---------- ---------- ---------\n",
" 1.744738 1.880987 2.618439 3.098848 4.734287 2.280206 0.6594689\n",
" 1.64506 0.6405602 0.9067268 1.131088 2.879089 1.368413 0.5958594\n",
" 0.5541306 1.367058 2.234196 2.648424 3.24823 1.391062 0.5536546\n",
" 1.750225 1.017056 2.236155 2.476191 7.35715 1.656455 0.3708669\n",
"-0.4647484 3.276148 4.653189 5.100149 3.155632 1.3742 0.3997748\n",
" 1.92227 1.048584 1.480556 1.818228 4.623463 2.050763 0.8584502\n",
" 2.932352 1.911509 2.750689 3.06374 4.154721 1.771925 0.8026502\n",
" 1.752588 0.8249168 1.33543 1.658825 4.75457 2.082502 0.224614\n",
" 2.269855 1.570141 2.396418 2.819241 2.498447 1.520789 0.8498095\n",
" 1.846529 1.31798 2.55481 3.117262 3.405702 1.412154 0.3636903\n",
" ... ... ... ... ... ... ...\n",
" 1.852249 0.8217258 1.189435 1.440802 3.5701 1.496244 0.835227\n",
" 1.75761 0.6210327 0.9024334 1.077242 4.522943 2.017324 0.8735518\n",
" 2.200459 1.88521 2.588902 2.789146 9.380934 2.429901 0.4929135\n",
" 1.719664 2.761608 3.795464 4.383556 4.556319 2.062559 0.7050323\n",
" 2.056519 1.169394 1.663309 1.950346 4.492199 2.091322 0.9831492\n",
" 1.605412 0.7955189 1.24593 1.611261 6.397684 2.953169 0.190593\n",
" 1.799215 1.163046 1.631817 1.920591 3.615045 1.63818 0.906157\n",
" 1.546144 0.7393208 1.109642 1.34337 5.175054 2.079152 0.8696824\n",
" 4.863503 1.659336 2.598026 2.916479 4.734004 1.889737 0.6406367\n",
" 1.058554 0.7011375 1.09726 1.275547 5.904384 2.444521 0.5284007"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from astroquery.sdss import SDSS # enables direct queries to the SDSS database\n",
"\n",
"GALquery = \"\"\"SELECT TOP 10000 \n",
" p.dered_u - p.dered_g as ug, p.dered_g - p.dered_r as gr, \n",
" p.dered_g - p.dered_i as gi, p.dered_g - p.dered_z as gz, \n",
" p.petroRad_i, p.petroR50_i, p.deVAB_i\n",
" FROM PhotoObjAll AS p JOIN specObjAll s ON s.bestobjid = p.objid\n",
" WHERE p.mode = 1 AND s.sciencePrimary = 1 AND p.clean = 1 AND p.type = 3\n",
" \"\"\"\n",
"SDSSgals = SDSS.query_sql(GALquery)\n",
"SDSSgals"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I have used my own domain knowledge to specifically choose features that may be useful when clustering galaxies. If you know a bit about SDSS and can think of other features that may be useful feel free to add them to the query. \n",
"\n",
"One nice feature of `astropy` tables is that they can readily be turned into `pandas DataFrames`, which can in turn easily be turned into a `sklearn X` array with `NumPy`. For example: \n",
"\n",
" X = np.array(SDSSgals.to_pandas())\n",
"\n",
"And you are ready to go. \n",
"\n",
"**Challenge Problem** Using the SDSS dataset above, identify interesting clusters within the data [this is intentionally very open ended, if you uncover anything especially exciting you'll have a chance to share it with the group]. Feel free to use the algorithms discussed above, or any other packages available via `sklearn`. Can you make sense of the clusters in the context of galaxy evolution? \n",
"\n",
"*Hint - don't fret if you know nothing about galaxy evolution (neither do I!). Just take a critical look at the clusters that are identified*"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0, 3.5)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlYAAAHWCAYAAAC1/cdaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115bb22e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Xgal = np.array(SDSSgals.to_pandas())\n",
"\n",
"galScaler = StandardScaler().fit(Xgal)\n",
"\n",
"dbs = DBSCAN(eps = .25, min_samples=55)\n",
"\n",
"dbs.fit(galScaler.transform(Xgal))\n",
"\n",
"cluster_members = dbs.labels_ != -1\n",
"outliers = dbs.labels_ == -1\n",
"\n",
"plt.figure(figsize = (10,8))\n",
"plt.scatter(Xgal[:,0][outliers], Xgal[:,3][outliers], \n",
" c = \"k\", \n",
" s = 4, alpha = 0.1)\n",
"plt.scatter(Xgal[:,0][cluster_members], Xgal[:,3][cluster_members], \n",
" c = dbs.labels_[cluster_members], \n",
" alpha = 0.4, edgecolor = \"None\", cmap = \"viridis\")\n",
"\n",
"plt.xlim(-1,5)\n",
"plt.ylim(-0,3.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note - I was unable to get the galaxies to clusster using DBSCAN."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Problem 3) Supervised Machine Learning\n",
"\n",
"Supervised machine learning, on the other hand, aims to predict a target class or produce a regression result based on the location of labelled sources (i.e. the training set) in the multidimensional feature space. The \"supervised\" comes from the fact that we are specifying the allowed outputs from the model. As there are labels available for the training set, it is possible to estimate the accuracy of the model (though there are generally important caveats about generalization, which we will explore in further detail later).\n",
"\n",
"We will begin with a simple, but nevertheless, elegant algorithm for classification and regression: [$k$-nearest-neighbors](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) ($k$NN). In brief, the classification or regression output is determined by examining the $k$ nearest neighbors in the training set, where $k$ is a user defined number. Typically, though not always, distances between sources are Euclidean, and the final classification is assigned to whichever class has a plurality within the $k$ nearest neighbors (in the case of regression, the average of the $k$ neighbors is the output from the model). We will experiment with the steps necessary to optimize $k$, and other tuning parameters, in the detailed break-out problem.\n",
"\n",
"In `scikit-learn` the [`KNeighborsClassifer`](http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html) algorithm is implemented as part of the [`sklearn.neighbors`](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors) module. \n",
"\n",
"**Problem 3a** \n",
"\n",
"Fit two different $k$NN models to the iris data, one with 3 neighbors and one with 10 neighbors. Plot the resulting class predictions in the sepal length-sepal width plane (same plot as above). How do the results compare to the true classifications? Is there any reason to be suspect of this procedure?\n",
"\n",
"*Hint - after you have constructed the model, it is possible to obtain model predictions using the `.predict()` method, which requires a feature array, including the same features and order as the training set, as input.*\n",
"\n",
"*Hint that isn't essential, but is worth thinking about - should the features be re-scaled in any way?*"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x11d6634a8>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNLkrpVQU0uSulFJR6P8BYoW6EbxGy3gAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10889d358>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d8dfe48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"\n",
"KNNclf = KNeighborsClassifier(n_neighbors = 3).fit(iris.data, iris.target)\n",
"preds = KNNclf.predict(iris.data)\n",
"plt.figure()\n",
"plt.scatter(iris.data[:,0], iris.data[:,1], \n",
" c = preds, cmap = \"viridis\", s = 30, edgecolor = \"None\")\n",
"\n",
"KNNclf = KNeighborsClassifier(n_neighbors = 10).fit(iris.data, iris.target)\n",
"preds = KNNclf.predict(iris.data)\n",
"plt.figure()\n",
"plt.scatter(iris.data[:,0], iris.data[:,1], \n",
" c = preds, cmap = \"viridis\", s = 30, edgecolor = \"None\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These results are almost identical to the training classifications. However, we have cheated! In this case we are evaluating the accuracy of the model (98% in this case) using the same data that defines the model. Thus, what we have really evaluated here is the training error. The relevant parameter, however, is the generalization error: how accurate are the model predictions on new data? \n",
"\n",
"Without going into too much detail, we will test this using cross validation (CV). In brief, CV provides predictions on the training set using a subset of the data to generate a model that predicts the class of the remaining sources. Using [`cross_val_predict`](http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.cross_val_predict.html), we can get a better sense of the model accuracy. Predictions from `cross_val_predict` are produced in the following manner:\n",
"\n",
" from sklearn.cross_validation import cross_val_predict\n",
" CVpreds = cross_val_predict(sklearn.model(), X, y)\n",
"\n",
"where `sklearn.model()` is the desired model, `X` is the feature array, and `y` is the label array.\n",
"\n",
"**Problem 3b** \n",
"\n",
"Produce cross-validation predictions for the iris dataset and a $k$NN with 5 neighbors. Plot the resulting classifications, as above, and estimate the accuracy of the model as applied to new data. How does this accuracy compare to a $k$NN with 50 neighbors?"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The accuracy of the kNN = 5 model is ~0.9867\n",
"The accuracy of the kNN = 50 model is ~0.8867\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/adamamiller/miniconda3/envs/DSFP/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"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD8CAYAAACMwORRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d648a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.cross_validation import cross_val_predict\n",
"\n",
"CVpreds = cross_val_predict(KNeighborsClassifier(n_neighbors=5), iris.data, iris.target)\n",
"plt.figure()\n",
"plt.scatter(iris.data[:,0], iris.data[:,1], \n",
" c = preds, cmap = \"viridis\", s = 30, edgecolor = \"None\")\n",
"print(\"The accuracy of the kNN = 5 model is ~{:.4}\".format( sum(CVpreds == iris.target)/len(CVpreds) ))\n",
"\n",
"CVpreds50 = cross_val_predict(KNeighborsClassifier(n_neighbors=50), iris.data, iris.target)\n",
"\n",
"print(\"The accuracy of the kNN = 50 model is ~{:.4}\".format( sum(CVpreds50 == iris.target)/len(CVpreds50) ))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While it is useful to understand the overall accuracy of the model, it is even more useful to understand the nature of the misclassifications that occur. \n",
"\n",
"**Problem 3c** \n",
"\n",
"Calculate the accuracy for each class in the iris set, as determined via CV for the $k$NN = 50 model."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The accuracy for class setosa is ~1.0000\n",
"The accuracy for class versicolor is ~0.9200\n",
"The accuracy for class virginica is ~0.7400\n"
]
}
],
"source": [
"for iris_type in range(3):\n",
" iris_acc = sum( (CVpreds50 == iris_type) & (iris.target == iris_type)) / sum(iris.target == iris_type)\n",
"\n",
" print(\"The accuracy for class {:s} is ~{:.4f}\".format(iris.target_names[iris_type], iris_acc))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We just found that the classifier does a much better job classifying setosa and versicolor than it does for virginica. The main reason for this is some viginica flowers lie far outside the main virginica locus, and within predominantly versicolor \"neighborhoods\". In addition to knowing the accuracy for the individual classes, it is also useful to know class predictions for the misclassified sources, or in other words where there is \"confusion\" for the classifier. The best way to summarize this information is with a confusion matrix. In a confusion matrix, one axis shows the true class and the other shows the predicted class. For a perfect classifier all of the power will be along the diagonal, while confusion is represented by off-diagonal signal. \n",
"\n",
"Like almost everything else we have encountered during this exercise, `scikit-learn` makes it easy to compute a confusion matrix. This can be accomplished with the following: \n",
"\n",
" from sklearn.metrics import confusion_matrix\n",
" cm = confusion_matrix(y_test, y_prep)\n",
"\n",
"**Problem 3d** \n",
"\n",
"Calculate the confusion matrix for the iris training set and the $k$NN = 50 model."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[50 0 0]\n",
" [ 1 46 3]\n",
" [ 0 13 37]]\n"
]
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"cm = confusion_matrix(iris.target, CVpreds50)\n",
"print(cm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From this representation, we see right away that most of the virginica that are being misclassifed are being scattered into the versicolor class. However, this representation could still be improved: it'd be helpful to normalize each value relative to the total number of sources in each class, and better still, it'd be good to have a visual representation of the confusion matrix. This visual representation will be readily digestible. Now let's normalize the confusion matrix.\n",
"\n",
"**Problem 3e** \n",
"\n",
"Calculate the normalized confusion matrix. Be careful, you have to sum along one axis, and then divide along the other. \n",
"\n",
"*Anti-hint: This operation is actually straightforward using some array manipulation that we have not covered up to this point. Thus, we have performed the necessary operations for you below. If you have extra time, you should try to develop an alternate way to arrive at the same normalization.*"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. , 0. , 0. ],\n",
" [ 0.02, 0.92, 0.06],\n",
" [ 0. , 0.26, 0.74]])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"normalized_cm = cm.astype('float')/cm.sum(axis = 1)[:,np.newaxis]\n",
"\n",
"normalized_cm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The normalization makes it easier to compare the classes, since each class has a different number of sources. Now we can procede with a visual representation of the confusion matrix. This is best done using `imshow()` within pyplot. You will also need to plot a colorbar, and labeling the axes will also be helpful. \n",
"\n",
"**Problem 3f** \n",
"\n",
"Plot the confusion matrix. Be sure to label each of the axeses.\n",
"\n",
"*Hint - you might find the [`sklearn` confusion matrix tutorial](http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#example-model-selection-plot-confusion-matrix-py) helpful for making a nice plot.*"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVAAAAEYCAYAAAAK467YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNTMr6P/aoorgAAAABUlEQVQAa6svpcEm9EAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d648b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(normalized_cm, interpolation = 'nearest', cmap = 'bone_r')# complete\n",
"\n",
"tick_marks = np.arange(len(iris.target_names))\n",
"plt.xticks(tick_marks, iris.target_names, rotation=45)\n",
"plt.yticks(tick_marks, iris.target_names)\n",
"\n",
"\n",
"plt.ylabel( 'True')# complete\n",
"plt.xlabel( 'Predicted' )# complete\n",
"plt.colorbar()\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now it is straight-forward to see that virginica and versicolor flowers are the most likely to be confused, which we could intuit from the very first plot in this notebook, but this exercise becomes far more important for large data sets with many, many classes. \n",
"\n",
"Thus concludes our introduction to `scikit-learn` and supervised and unsupervised learning. "
]
}
],
"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": 1
}
| mit |
aarrasmi/Dimension-Reduction-Preliminary | FIM_eigenspectra-cosine_basis.ipynb | 1 | 104359 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from scipy.io import FortranFile"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following block builds the TT binning matrix (W) following the formula in the Planck 2015 likelihood paper. The Wprime is the binning matrix that binns twice as much (except for the upper end of the scale, there where an odd number of binns.)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"W=np.zeros([215,2479])\n",
"l=30\n",
"for b in range(215):\n",
" s=0\n",
" if b < 14:\n",
" for i in range(l,l+5):\n",
" s+=i*(i+1)\n",
" m=l\n",
" for l in range(m,m+5):\n",
" W[b,l-30]=(l*(l+1))/float(s)\n",
" l=l+1\n",
" elif b<170:\n",
" for i in range(l,l+9):\n",
" s+=i*(i+1)\n",
" m=l\n",
" for l in range(m,m+9):\n",
" W[b,l-30]=l*(l+1)/float(s)\n",
" l=l+1\n",
" elif b<200:\n",
" for i in range(l,l+17):\n",
" s+=i*(i+1)\n",
" m=l\n",
" for l in range(m,m+17):\n",
" W[b,l-30]=l*(l+1)/float(s)\n",
" l=l+1\n",
" elif b<215:\n",
" for i in range(l,l+33):\n",
" s+=i*(i+1)\n",
" m=l\n",
" for l in range(m,m+33):\n",
" W[b,l-30]=l*(l+1)/float(s)\n",
" l=l+1\n",
" else:\n",
" print \"Fail\"\n",
"Wprime=np.zeros([115,2479])\n",
"l=30\n",
"for b in range(115):\n",
" s=0\n",
" if b < 7:\n",
" for i in range(l,l+10):\n",
" s+=i*(i+1)\n",
" m=l\n",
" for l in range(m,m+10):\n",
" Wprime[b,l-30]=l*(l+1)/float(s)\n",
" l=l+1\n",
" elif b<85:\n",
" for i in range(l,l+18):\n",
" s+=i*(i+1)\n",
" m=l\n",
" for l in range(m,m+18):\n",
" Wprime[b,l-30]=l*(l+1)/float(s)\n",
" l=l+1\n",
" elif b<100:\n",
" for i in range(l,l+34):\n",
" s+=i*(i+1)\n",
" m=l\n",
" for l in range(m,m+34):\n",
" Wprime[b,l-30]=l*(l+1)/float(s)\n",
" l=l+1\n",
" elif b<115:\n",
" for i in range(l,l+33):\n",
" s+=i*(i+1)\n",
" m=l\n",
" for l in range(m,m+33):\n",
" Wprime[b,l-30]=l*(l+1)/float(s)\n",
" l=l+1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the derivative vectors from file"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dcls=np.loadtxt(\"xmat_cosines.txt\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load, rearrange, and slice the Planck data inverse covariance matrix"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f=FortranFile('cinv','r')\n",
"cinv=f.read_reals(dtype='float').reshape(613,613)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in range (cinv.shape[0]):\n",
" for j in range(i):\n",
" cinv[j,i]=cinv[i,j]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cinv=cinv[0:215,0:215]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#need to rebinn the cinv for the prime case\n",
"#\"unbinning\"- use the pseudo inverse of W\n",
"rebinn=np.dot(Wprime,np.linalg.pinv(W))\n",
"cinvp=np.dot(rebinn,np.dot(cinv,rebinn.T))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dclsb=np.dot(W,dcls)\n",
"dclsbp=np.dot(Wprime,dcls)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"evals,evecs=np.linalg.eigh(np.dot(dclsb.T,np.dot(cinv,dclsb)))\n",
"evalsp,evecsp=np.linalg.eigh(np.dot(dclsbp.T,np.dot(cinvp,dclsbp)))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.gca().set_yscale('log')\n",
"plt.plot(evals,\".\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEDCAYAAADZUdTgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f7c01b9c810>"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.gca().set_yscale('log')\n",
"plt.plot(evalsp,\".\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEDCAYAAADZUdTgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUI7Ye+wAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f7c31847d90>"
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(evecs[:,4])\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEACAYAAABbMHZzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f7c037eca10>"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(evecsp[:,4])\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEACAYAAABbMHZzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQAp/EREHUviLiDjQ/wMvJNXLbmyPGgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f7c01c747d0>"
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(evecs[:,4]-evecsp[:,4])\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEACAYAAABCl1qQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nd+cAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f7c01cc5690>"
]
}
],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(np.dot(dcls,evecs[:,4].T))\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEACAYAAABPiSrXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH7+0SJrWj73cAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f7c026a0ad0>"
]
}
],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(np.dot(dcls,evecsp[:,4].T))\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEACAYAAABPiSrXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f7c01ae6cd0>"
]
}
],
"prompt_number": 59
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(np.dot(dcls,(evecs[:,4].T-evecsp[:,4].T)))\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEACAYAAAC6d6FnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiKTYgAgIjKp/wcb4lUon80VCQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f7c31834d10>"
]
}
],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
tepickering/mmtwfs | notebooks/zernike testing.ipynb | 2 | 488029 | {
"cells": [
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib\n",
"matplotlib.use('nbagg')\n",
"\n",
"from zernike import ZernikeVector\n",
"import astropy.units as u\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"z = ZernikeVector(Z4=100.0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"$[0,~0,~100] \\; \\mathrm{nm}$"
],
"text/plain": [
"<Quantity [ 0., 0., 100.] nm>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"z.array"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"z['Z55'] = 100.0"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"z['Z5'] = 1000.0\n",
"z['Z6'] = 100\n",
"z['Z12'] = 55"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'Z04': <Quantity 100.0 nm>,\n",
" 'Z05': <Quantity 1000.0 nm>,\n",
" 'Z06': <Quantity 100.0 nm>,\n",
" 'Z12': <Quantity 55.0 nm>,\n",
" 'Z55': <Quantity 100.0 nm>}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"z.coeffs"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Z04: 100 nm \t Defocus (2, 0)\n",
" Z05: 1e+03 nm \t Primary Astig at 45˚ (2, -2)\n",
" Z06: 100 nm \t Primary Astig at 0˚ (2, 2)\n",
" Z12: 55 nm \t Secondary Astigmatism at 0˚ (4, 2)\n",
" Z55: 100 nm\n",
"\n"
]
}
],
"source": [
"z.denormalize()\n",
"print(z)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" Z04: 100 nm \t Defocus (2, 0)\n",
" Z05: 1e+03 nm \t Primary Astig at 45˚ (2, -2)\n",
" Z06: 100 nm \t Primary Astig at 0˚ (2, 2)\n",
" Z12: 55 nm \t Secondary Astigmatism at 0˚ (4, 2)\n",
" Z55: 100 nm"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"z.units = u.nm\n",
"z"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"$[0,~0,~57.735027,~408.24829,~40.824829,~0,~0,~0,~0,~0,~17.392527,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~0,~22.36068] \\; \\mathrm{nm}$"
],
"text/plain": [
"<Quantity [ 0. , 0. , 57.73502692, 408.24829046,\n",
" 40.82482905, 0. , 0. , 0. ,\n",
" 0. , 0. , 17.39252713, 0. ,\n",
" 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. ,\n",
" 0. , 0. , 0. , 0. ,\n",
" 0. , 22.36067977] nm>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"z.denormalize()\n",
"z.norm_array"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Z04 100.0 nm\n",
"Z06 100.0 nm\n",
"Z05 1000.0 nm\n",
"Z55 100.0 nm\n",
"Z12 54.99999999999999 nm\n"
]
}
],
"source": [
"for c in z:\n",
" print(c, z[c])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'Z04' in z"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"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",
" this.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, fig.canvas.height);\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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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 overriden (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"
},
{
"data": {
"text/latex": [
"$501.19255 \\; \\mathrm{nm}$"
],
"text/plain": [
"<Quantity 501.1925542272196 nm>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"z.denormalize()\n",
"z.ignore('Z05')\n",
"z.units = u.nm\n",
"z.plot_map()\n",
"z.peak2valley"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"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",
" this.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, fig.canvas.height);\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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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 overriden (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"
},
{
"data": {
"text/latex": [
"$325.82856 \\; \\mathrm{nm}$"
],
"text/plain": [
"<Quantity 325.82856074961865 nm>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"z.restore('Z05')\n",
"z.plot_map()\n",
"z.rms"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"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",
" this.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, fig.canvas.height);\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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\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 overriden (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=\"800\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"z.plot_surface()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"b = z.phase_map()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/latex": [
"$1175.8988 \\; \\mathrm{nm}$"
],
"text/plain": [
"<Quantity 1175.8988139720632 nm>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b[4].max()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" Z04: 100 nm \t Defocus (2, 0)\n",
" Z05: 1e+03 nm \t Primary Astig at 45˚ (2, -2)\n",
" Z06: 100 nm \t Primary Astig at 0˚ (2, 2)\n",
" Z12: 55 nm \t Secondary Astigmatism at 0˚ (4, 2)\n",
" Z55: 100 nm"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"z"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ref = ZernikeVector(Z04=-500, Z20=20)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" Z04: -500 nm \t Defocus (2, 0)\n",
" Z20: 20 nm \t X Pentafoil (5, 5)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ref"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" Z04: -400 nm \t Defocus (2, 0)\n",
" Z05: 1e+03 nm \t Primary Astig at 45˚ (2, -2)\n",
" Z06: 100 nm \t Primary Astig at 0˚ (2, 2)\n",
" Z12: 55 nm \t Secondary Astigmatism at 0˚ (4, 2)\n",
" Z20: 20 nm \t X Pentafoil (5, 5)\n",
" Z55: 100 nm"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"z + ref"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" Z04: -600 nm \t Defocus (2, 0)\n",
" Z05: -1e+03 nm \t Primary Astig at 45˚ (2, -2)\n",
" Z06: -100 nm \t Primary Astig at 0˚ (2, 2)\n",
" Z12: -55 nm \t Secondary Astigmatism at 0˚ (4, 2)\n",
" Z20: 20 nm \t X Pentafoil (5, 5)\n",
" Z55: -100 nm"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new = ref - z\n",
"\n",
"new"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" Z04: -3e+03 nm \t Defocus (2, 0)\n",
" Z05: -5e+03 nm \t Primary Astig at 45˚ (2, -2)\n",
" Z06: -500 nm \t Primary Astig at 0˚ (2, 2)\n",
" Z12: -275 nm \t Secondary Astigmatism at 0˚ (4, 2)\n",
" Z20: 100 nm \t X Pentafoil (5, 5)\n",
" Z55: -500 nm"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new * 5"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" Z04: -0.00833 1 / nm \t Defocus (2, 0)\n",
" Z05: -0.005 1 / nm \t Primary Astig at 45˚ (2, -2)\n",
" Z06: -0.05 1 / nm \t Primary Astig at 0˚ (2, 2)\n",
" Z12: -0.0909 1 / nm \t Secondary Astigmatism at 0˚ (4, 2)\n",
" Z20: 0.25 1 / nm \t X Pentafoil (5, 5)\n",
" Z55: -0.05 1 / nm"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"5/ new"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" Z04: -1.2e+03 nm \t Defocus (2, 0)\n",
" Z05: -2e+03 nm \t Primary Astig at 45˚ (2, -2)\n",
" Z06: -200 nm \t Primary Astig at 0˚ (2, 2)\n",
" Z12: -110 nm \t Secondary Astigmatism at 0˚ (4, 2)\n",
" Z20: 40 nm \t X Pentafoil (5, 5)\n",
" Z55: -200 nm"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new * 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"2 + new"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"set(z.keys() & ref.keys())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for k in sorted(z.keys()):\n",
" print(k)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"(1.0 * u.nm / (1. * u.mm).to(u.nm)).value"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"u.di"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:astroconda]",
"language": "python",
"name": "conda-env-astroconda-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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
folivetti/LINKEDOPT | LineDistance.ipynb | 1 | 9375 | {
"metadata": {
"name": "",
"signature": "sha256:90558b3f76b3866b70273a3bafc33ee002b81710bc05c3aa3d85a73cef2c330a"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some sample codes can be found at another [notebook](http://nbviewer.ipython.org/github/folivetti/LINKEDOPT/blob/master/LineDistanceSrc.ipynb)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Line Distance\n",
"==============\n",
"\n",
"The Line Distance was first proposed in [deFranca][1] and measures the likelihood of two points being located in the basis of attraction of the same local optima. It does so by approximating the curve through a line and checking the approximation error.\n",
"\n",
"Given a multimodal function *f(x)* and two points, *x1* and *x2*, de Line Distance *LD(x1,x2)* can be calculated as:\n",
"\n",
"$$ LD(x1,x2) = \\|P_{proj}\\| $$\n",
"\n",
"with\n",
"\n",
"$$P_{proj} = \\overline{P_1P_m} - (\\overline{P_1P_m} \\cdot v)v$$\n",
"\n",
"and\n",
"\n",
"$$\\begin{align}\n",
"P_1 &= [x1, f(x1)] \\\\\n",
"v &= \\frac{[x2, f(x2)] - P_1}{\\|[x2, f(x2)] - P_1\\|} \\\\\n",
"P_m &= [\\frac{x1+x2}{2}, f(\\frac{x1+x2}{2})] \\\\\n",
"\\overline{P_1P_m} &= [\\frac{x2-x1}{2}, f(\\frac{x1+x2}{2})-f(x1)]\n",
"\\end{align}$$\n",
"\n",
"as the projection of the middle point between *[x1, f(x1)]* and *[x2, f(x2)]* to the line segment defined by those two points.\n",
"\n",
"\n",
"[1]: http://dl.acm.org/citation.cfm?doid=1068009.1068057 \"deFranca\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can simplify and generalize this equation to a d-dimensional vector. Let us define:\n",
"\n",
"$$\\begin{align}\n",
"\\overline{P_1P_m} &= [\\frac{x_1+x_2}{2}-x_1, y_m-y_1] = [\\frac{x_2-x_1}{2}, y_m-y_1] \\\\\n",
"\\overline{P_1P_2} &= [x_2-x_1, y_2-y_1] \\\\\n",
"\\|\\overline{P_1P_2}\\| &= \\sqrt{\\sum_{i}{(x_2-x_1)^{2}} + (y_2-y_1)^{2}} \\\\\n",
"\\end{align}$$\n",
"\n",
"$LD(P_1,P_2)$ is then:\n",
"\n",
"$$\\begin{align}\n",
"LD(P_1,P_2) &= \\|\\overline{P_1P_m} - (\\overline{P_1P_m} \\cdot \\overline{P_1P_2})\\frac{\\overline{P_1P_2}}{\\|\\overline{P_1P_2}\\|^{2}}\\| \\\\\n",
"LD(P_1,P_2) &= \\|\\frac{\\|\\overline{P_1P_2}\\|^{2}\\overline{P_1P_m} - (\\overline{P_1P_m} \\cdot \\overline{P_1P_2})\\overline{P_1P_2}}{\\|\\overline{P_1P_2}\\|^{2}}\\| \\\\\n",
"\\end{align}$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given that\n",
"\n",
"$$(\\overline{P_1P_m} \\cdot \\overline{P_1P_2}) = \\frac{1}{2}\\sum_{i}{(x_2-x_1)^{2}} + (y_m-y_1)(y_2-y_1)$$\n",
"\n",
"The $LD(P_1,P_2)$ becomes\n",
"\n",
"$$LD(P_1,P_2) = \\|\\frac{\\sum_{i}{(x_2-x_1)^{2}}(\\overline{P_1P_m} - \\frac{1}{2}\\overline{P_1P_2}) + (y_2-y_1)( (y_2-y_1)\\overline{P_1P_m} - (y_m-y_1)\\overline{P_1P_2} )}{\\|\\overline{P_1P_2}\\|^{2}}\\|$$\n",
"\n",
"By developing the first term of the sum we have\n",
"\n",
"$$\\begin{align}\n",
"& \\sum_{i}{(x_2-x_1)^{2}}[\\frac{x_2-x_1}{2}-\\frac{x_2-x_1}{2}, (y_m-y_1)-\\frac{1}{2}(y_2-y_1)] \\\\\n",
"&= \\sum_{i}{(x_2-x_1)^{2}}[0, y_m - \\frac{y_2-y_1}{2}] \\\\\n",
"\\end{align}$$\n",
"\n",
"The second term becomes\n",
"\n",
"$$\\begin{align}\n",
"& (y_2-y_1)[(y_2-y_1)\\frac{x_2-x_1}{2} - (y_m-y_1)(x_2-x_1), (y_2-y_1)(y_m-y_1) - (y_m-y_1)(y_2-y_1)] \\\\\n",
"&= (y_2-y_1)[(x_2-x_1)(\\frac{y_1+y_2}{2} - y_m), 0]\n",
"\\end{align}$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And summing the terms we have:\n",
"$$\\begin{align}\n",
"LD(P_1,P_2) &= \\frac{\\|[(x_2-x_1)(y_2-y_1)(\\frac{y_1+y_2}{2} - y_m), \\sum_{i}{(x_2-x_1)^{2}}(\\frac{y_1+y_2}{2} - y_m)]\\|}{\\|\\overline{P_1P_2}\\|^{2}} \\\\\n",
"LD(P_1,P_2) &= \\sqrt{\\frac{(y_2-y_1)^{2}(\\frac{y_1+y_2}{2} - y_m)^{2}\\sum_{i}{(x_2-x_1)^2} + (\\sum_{i}{(x_2-x_1)^{2}})^{2}(\\frac{y_1+y_2}{2} - y_m)^2}{(\\sum_{i}{(x_2-x_1)^2)} + (y_2-y_1)^2)^2}} \\\\\n",
"LD(P_1,P_2) &= \\sqrt{\\frac{((\\frac{y_1+y_2}{2} - y_m)^{2}\\sum_{i}{(x_2-x_1)^2})(\\sum_{i}{(x_2-x_1)^{2}} + (y_2-y_1)^{2})}{(\\sum_{i}{(x_2-x_1)^2)} + (y_2-y_1)^2)^2}} \\\\\n",
"LD(P_1,P_2) &= \\sqrt{\\frac{((\\frac{y_1+y_2}{2} - y_m)^{2}\\sum_{i}{(x_2-x_1)^2})}{\\sum_{i}{(x_2-x_1)^2)} + (y_2-y_1)^2}} \\\\\n",
"\\end{align}$$\n",
"\n",
"From this equation we can see that as two points get closer to each other onto the same basis of attraction, the objective-function curve can be approximated by a line and thus the distance becomes closer to zero."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Hypothesis 1:** If two points *x1* e *x2* are located on the basis of attraction of the same local optima, scaling the function by a factor K will have an insignificant effect on *LD(x1,x2)*.\n",
"\n",
"**Proof:**\n",
"\n",
"From the Line Distance final equation:\n",
"\n",
"$$LD(P_1,P_2) = \\sqrt{\\frac{((\\frac{y_1+y_2}{2} - y_m)^{2}\\sum_{i}{(x_2-x_1)^2})}{\\sum_{i}{(x_2-x_1)^2)} + (y_2-y_1)^2}}$$\n",
"\n",
"If we scale the function by a factor K we have:\n",
"\n",
"$$\\begin{align}\n",
"LD(P_1,P_2) &= \\sqrt{\\frac{K^{2}((\\frac{y_1+y_2}{2} - y_m)^{2}\\sum_{i}{(x_2-x_1)^2})}{\\sum_{i}{(x_2-x_1)^2)} + K^{2}(y_2-y_1)^2}} \\\\\n",
"LD(P_1,P_2) &= \\sqrt{\\frac{K^{2}((\\frac{y_1+y_2}{2} - y_m)^{2}\\sum_{i}{(x_2-x_1)^2})}{ K^{2}(\\sum_{i}{(\\frac{x_2-x_1}{K})^2)} +(y_2-y_1)^2})} \\\\\n",
"LD(P_1,P_2) &= \\sqrt{\\frac{((\\frac{y_1+y_2}{2} - y_m)^{2}\\sum_{i}{(x_2-x_1)^2})}{(\\sum_{i}{(\\frac{x_2-x_1}{K})^2)} +(y_2-y_1)^2})} \\\\\n",
"\\end{align}$$\n",
"\n",
"In this situation, the expression $\\frac{x_2-x_1}{K}$ tends to zero as $K$ tends to infinity. So, this distance is bounded to a value as the objective-function is scaled to infinity. This allow us to simplify this measure while maintaining the closest points to a distance close to zero while amplifying the farthest points:\n",
"\n",
"$$LD(P_1,P_2) = \\sqrt{\\frac{((\\frac{y_1+y_2}{2} - y_m)^{2}\\sum_{i}{(x_2-x_1)^2})}{(y_2-y_1)^2}}$$\n",
"\n",
"Finally, we can work with the squared Line Distance that not only removes the square root from the calculation but also makes the distance always positive, with a value of $0$ whenever $x1=x2$. Also, an $\\epsilon$ should be included in the denominator to avoid a discontinuity whenever $y_2=y_1$.\n",
"\n",
"$$LDS(P_1,P_2) = \\frac{((\\frac{y_1+y_2}{2} - y_m)^{2}\\sum_{i}{(x_2-x_1)^2})}{(y_2-y_1)^2 + \\epsilon}$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Local Optima\n",
"\n",
"The minimum value possible for this distance function is $0$ that is obtained whenever:\n",
"\n",
"- $x_2 = x_1$\n",
"- $\\frac{y_1+y_2}{2} = y_m$\n",
"- $(y_2-y_1)^2 \\to \\infty$ (like that's gonna happen)\n",
"\n",
"The first case is the expected solution in which both points are the same. \n",
"\n",
"The second is a situation that we should be aware of, if the $x_1$ and $x_2$ are located at different basis of attraction but, the middle point intersects $(y_1+y_2)/2$, their distance will become $0$ even though they are not at the same basis. This could be alleviated by taking different middle points with different average weightings.\n",
"\n",
"The third case will only happens if the function can tend to inifinity."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to study the maximization of this function, let us fix a certain point $x_1$ and a random direction $d$ described by an unit vector. Let us define $x_2 = x_1 + \\alpha d$ with $0 < \\alpha < r$, and $r$ being a radius that encloses only one peak. Our goal is to find the value of $\\alpha$ that maximizes $LDS(x_1,x_2)$.\n",
"\n",
"$$LDS(P_1,P_2) = \\alpha^{2}\\frac{((\\frac{y_1+y_2}{2} - y_m)^{2}\\sum_{i}{d_i^2})}{(y_2-y_1)^2 + \\epsilon}$$\n",
"\n",
"As $d$ is an unit vector, then $\\sum_{i}{d_i^2}=1$:\n",
"\n",
"$$LDS(P_1,P_2) = \\alpha^{2}\\frac{((\\frac{y_1+y_2}{2} - y_m)^{2}}{(y_2-y_1)^2 + \\epsilon}$$\n",
"\n",
"From the definition of $LDS()$ we can assert that the maximum possible value is obtained when $y_2 - y_1$ is close to zero and $\\frac{y_1+y_2}{2} - y_m$ is maximized. Replacing $y_2$ with $y_1$ and taking the derivative, we have:\n",
"\n",
"$$\\frac{d y_1 - y_m}{d\\alpha} = 0$$\n",
"\n",
"$$y'_m = 0$$\n",
"\n",
"So, the maximum is obtained whenever $y'_m$ is a local optima."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
mjirik/ndnoise | examples/space_noise_method_design.ipynb | 1 | 344089 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Noise generator design"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy\n",
"import scipy.ndimage\n",
"import sed3\n",
"import plotly.plotly as py"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# [mm]\n",
"sample_spacing = [1.0, 1.0, 1.0]\n",
"\n",
"# [mm]\n",
"lambda_start = 1.0\n",
"lambda_stop = 15.0\n",
"\n",
"exponent = 0.0\n",
"data_shape = [100,100,100]"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lambda0 = lambda_start * np.asarray(sample_spacing)\n",
"lambda1 = lambda_stop * np.asarray(sample_spacing)"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def noise_normalization(data, std_factor=1.0):\n",
" data0n = (data - np.mean(data)) * 1.0 / (std_factor * np.var(data)**0.5)\n",
" return data0n"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = np.random.rand(*data_shape)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.57074361798e-16\n",
"1.0\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAS0AAAD/CAYAAACkYoB+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112546310>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"data0 = scipy.ndimage.filters.gaussian_filter(data, sigma=lambda0)\n",
"data0 = noise_normalization(data0)\n",
"plt.imshow(data0[:,:,50], cmap=\"gray\")\n",
"plt.colorbar()\n",
"print np.mean(data0)\n",
"print np.var(data0)\n"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 1.00000000e+01, 2.40000000e+01, 1.56000000e+02,\n",
" 7.39000000e+02, 3.29500000e+03, 1.30220000e+04,\n",
" 3.87130000e+04, 8.77970000e+04, 1.51783000e+05,\n",
" 1.99186000e+05, 2.00750000e+05, 1.55636000e+05,\n",
" 9.10270000e+04, 4.03930000e+04, 1.31710000e+04,\n",
" 3.46000000e+03, 6.37000000e+02, 1.64000000e+02,\n",
" 2.50000000e+01, 1.20000000e+01]),\n",
" array([-5.27338817, -4.7471906 , -4.22099302, -3.69479545, -3.16859788,\n",
" -2.6424003 , -2.11620273, -1.59000516, -1.06380758, -0.53761001,\n",
" -0.01141244, 0.51478514, 1.04098271, 1.56718028, 2.09337786,\n",
" 2.61957543, 3.145773 , 3.67197058, 4.19816815, 4.72436572,\n",
" 5.25056329]),\n",
" <a list of 20 Patch objects>)"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEACAYAAACtVTGuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJoNCktRkUEiSmgwKSVKTQSFJavp/qBZZvo1NV/UAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113020f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(data0.ravel(), 20)"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.3304561719e-13\n",
"1.0\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAATYAAAD/CAYAAAB7LPphAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOC1gW7Ro0YHTArZFixYdOP1/CqWM9fbsONEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110965250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"data1 = scipy.ndimage.filters.gaussian_filter(data, sigma=lambda1)\n",
"data1 = noise_normalization(data1)\n",
"plt.imshow(data1[:,:,50], cmap=\"gray\")\n",
"plt.colorbar()\n",
"print np.mean(data1)\n",
"print np.var(data1)"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 1104., 4565., 42201., 192650., 333620., 297564.,\n",
" 97856., 23888., 4807., 1745.]),\n",
" array([-4.28640401, -3.39345973, -2.50051545, -1.60757117, -0.71462689,\n",
" 0.17831739, 1.07126167, 1.96420595, 2.85715023, 3.7500945 ,\n",
" 4.64303878]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEACAYAAABYq7oeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110970950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"plt.hist(data1.ravel())"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x116fc9310>]"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDH1JaoihL0kNMfQlqSGGviQ15H8B8xcZuZ6W72kAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112230710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(0,10)\n",
"y = np.exp(-0.0 * x)\n",
"plt.plot(x,y)"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5 0.5\n"
]
}
],
"source": [
"\n",
"w0 = np.exp(exponent * lambda_start)\n",
"w1 = np.exp(exponent * lambda_stop)\n",
"wsum = w0 + w1\n",
"w0 = w0 / wsum\n",
"w1 = w1 / wsum\n",
"print w0, w1"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.66328219268e-14\n",
"0.528346224359\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAATYAAAD/CAYAAAB7LPphAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntgK2VVu1VXvq2v8Hptja7Zo+E0EAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112c50ad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"data = ( data0 * w0 + \n",
" data1 * w1)\n",
"plt.imshow(data[:,:,50], cmap=\"gray\")\n",
"plt.colorbar()\n",
"print np.mean(data)\n",
"print np.var(data)"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 8.00000000e+00, 6.30000000e+01, 3.28000000e+02,\n",
" 1.42900000e+03, 6.58900000e+03, 2.71350000e+04,\n",
" 8.10090000e+04, 1.66180000e+05, 2.34611000e+05,\n",
" 2.29322000e+05, 1.51185000e+05, 6.91040000e+04,\n",
" 2.38250000e+04, 6.68200000e+03, 1.77200000e+03,\n",
" 5.65000000e+02, 1.54000000e+02, 3.30000000e+01,\n",
" 4.00000000e+00, 2.00000000e+00]),\n",
" array([-3.93513174, -3.49488151, -3.05463129, -2.61438107, -2.17413085,\n",
" -1.73388063, -1.29363041, -0.85338019, -0.41312997, 0.02712025,\n",
" 0.46737047, 0.90762069, 1.34787092, 1.78812114, 2.22837136,\n",
" 2.66862158, 3.1088718 , 3.54912202, 3.98937224, 4.42962246,\n",
" 4.86987268]),\n",
" <a list of 20 Patch objects>)"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEACAYAAACtVTGuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noJAkNRkUkqQmg0KS1PT/AQEroT+mrhh7AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ee08fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"plt.hist(data.ravel(), 20)"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def noises_fast(shape, sample_spacing=None, exponent=0.0, \n",
" lambda_start=0, lambda_stop=1, **kwargs):\n",
" \n",
" data0 = 0\n",
" data1 = 0\n",
" w0 = 0\n",
" w1 = 0\n",
" \n",
" lambda1 = lambda_stop * np.asarray(sample_spacing)\n",
" \n",
" if lambda_start is not None:\n",
" lambda0 = lambda_start * np.asarray(sample_spacing)\n",
" data0 = np.random.rand(*shape)\n",
" data0 = scipy.ndimage.filters.gaussian_filter(data0, sigma=lambda0)\n",
" data0 = noise_normalization(data0)\n",
" w0 = np.exp(exponent * lambda_start)\n",
" \n",
" if lambda_stop is not None:\n",
" lambda1 = lambda_stop * np.asarray(sample_spacing)\n",
" data1 = np.random.rand(*shape)\n",
" data1 = scipy.ndimage.filters.gaussian_filter(data1, sigma=lambda1)\n",
" data1 = noise_normalization(data1)\n",
" w1 = np.exp(exponent * lambda_stop)\n",
" \n",
" wsum = w0 + w1\n",
" if wsum > 0:\n",
" w0 = w0 / wsum\n",
" w1 = w1 / wsum\n",
" \n",
" print w0, w1\n",
" print np.mean(data0), np.var(data0)\n",
" print np.mean(data1), np.var(data1)\n",
" \n",
" data = ( data0 * w0 + data1 * w1)\n",
" \n",
" # plt.figure()\n",
" # plt.imshow(data0[:,:,50], cmap=\"gray\")\n",
" # plt.colorbar()\n",
" # plt.figure()\n",
" # plt.imshow(data1[:,:,50], cmap=\"gray\")\n",
" # plt.colorbar()\n",
" return data\n"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5 0.5\n",
"6.20445916866e-16 1.0\n",
"-1.22386154544e-14 1.0\n",
"var 0.494328133612\n",
"mean -5.79871084483e-15\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAS0AAAEACAYAAADm0SAGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112a00b10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAATYAAAD/CAYAAAB7LPphAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nENuCBQt2HRZiW7Bgwa7D/wNwrOx5okiWQwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112cb90d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAATYAAAD/CAYAAAB7LPphAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110454d50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"noise = noises_fast(\n",
" shape=data_shape, \n",
" sample_spacing=sample_spacing, \n",
" exponent=exponent, \n",
" lambda_start=lambda_start,\n",
" lambda_stop=lambda_stop\n",
")\n",
"\n",
"plt.figure()\n",
"plt.imshow(noise[:,:,50], cmap=\"gray\")\n",
"plt.colorbar()\n",
"print \"var \", np.var(noise.ravel())\n",
"print \"mean \", np.mean(noise.ravel())"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
ocefpaf/system-test | Theme_1_Baseline/Scenario_1E_Salinity/Scenario_1E_Salinity.ipynb | 2 | 2454683 | null | unlicense |
arongdari/almc | notebooks/plot_posterior_variance.ipynb | 1 | 40522 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import re\n",
"import os\n",
"import numpy as np\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"from collections import defaultdict\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"color = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120), \n",
" (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150), \n",
" (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148), \n",
" (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199), \n",
" (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)] \n",
" \n",
"# Scale the RGB values to the [0, 1] range, which is the format matplotlib accepts. \n",
"for i in range(len(color)): \n",
" r, g, b = color[i] \n",
" color[i] = (r / 255., g / 255., b / 255.)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"models = ['brescal', 'bcomp_mul', 'bcomp_add', 'bcomp_mul_10', \n",
" 'bcomp_add_10', 'bcomp_mul_comp_10', 'bcomp_add_comp_10',\n",
" 'bcomp_mul_var_1_comp_10','bcomp_add_var_1_comp_10']\n",
"\n",
"models = ['brescal', 'bcomp_mul', 'bcomp_add']\n",
"\n",
"model_colors = {'brescal':color[0], 'amdc_pop':color[8], 'amdc_pred':color[7], \n",
" 'bcomp_mul':color[2], 'bcomp_add':color[3], 'logit':color[5], \n",
" 'rescal':'grey',\n",
" 'brescal_passive':'grey', 'bcomp_mul_10': color[8], 'bcomp_add_10':color[9], \n",
" 'bcomp_mul_comp_10':color[11], 'bcomp_add_comp_10':color[13], \n",
" 'bcomp_mul_var_1_comp_10': color[15], 'bcomp_add_var_1_comp_10': color[17]}\n",
"\n",
"model_names = {'rescal':'rescal', 'brescal':'pnormal-ts', 'amdc_pop':'amdc_pop', 'amdc_pred':'amdc_pred', \n",
" 'bcomp_mul':'pcomp-mul-ts', 'bcomp_add':'pcomp-add-ts', 'logit':'blogit', \n",
" 'brescal_passive':'brescal_passive'}\n",
"\n",
"data_lim = {'kinship':2000, 'umls':2000, 'nation':1000}\n",
"cumsum_lim = {'kinship':10000, 'umls':10000, 'nation':4000}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAACsCAYAAACNUJzhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQuHC/D/zn74/I1BZUAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1947ea3c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"datasets = ['nation','kinship','umls']\n",
"datasets = ['kinship']\n",
"for dataset in datasets:\n",
"#dataset = 'nation'\n",
"\n",
" x_lim = data_lim[dataset]\n",
" second_start = x_lim/2\n",
"\n",
" file_name = {'brescal':'brescal_var_%s.out' % (dataset), \n",
" 'bcomp_mul':'bcomp_mul_var_%s.out' % (dataset),\n",
" 'bcomp_add':'bcomp_add_var_%s.out' % (dataset),\n",
"# 'bcomp_mul':'bcomp_mul_var_10_comp_var_1000_%s.out' % (dataset),\n",
"# 'bcomp_add':'bcomp_add_var_10_comp_var_1000_%s.out' % (dataset),\n",
" 'bcomp_mul_10':'bcomp_mul_var_er_10_%s.out' % (dataset),\n",
" 'bcomp_add_10':'bcomp_add_var_er_10_%s.out' % (dataset), \n",
" 'bcomp_mul_comp_10':'bcomp_mul_comp_var_10_%s.out' % (dataset),\n",
" 'bcomp_add_comp_10':'bcomp_add_comp_var_10_%s.out' % (dataset),\n",
" 'bcomp_mul_var_1_comp_10': 'bcomp_mul_var_1_comp_var_10_%s.out' % (dataset),\n",
" 'bcomp_add_var_1_comp_10': 'bcomp_add_var_1_comp_var_10_%s.out' % (dataset)\n",
" }\n",
"\n",
" legend_size = 9\n",
"\n",
"# fig = plt.figure(figsize=(18,5))\n",
" fig = plt.figure(figsize=(6,2.5))\n",
" for model in models:\n",
" filepath = '../result_posterior_variance/%s' % file_name[model]\n",
" if os.path.exists(filepath):\n",
" logs = open(filepath, 'r').readlines()\n",
" e_p = re.compile(r'Mean variance E, ([0-9]+), ([0-9]+.[0-9]+|[0-9]+)')\n",
" r_p = re.compile(r'Mean variance R, ([0-9]+), ([0-9]+.[0-9]+|[0-9]+)')\n",
" e_vars = defaultdict(list)\n",
" for line in logs:\n",
" m = e_p.search(line)\n",
" if m is not None:\n",
" en = int(m.group(1))\n",
" e_vars[en].append(float(m.group(2)))\n",
" mean_var = np.zeros(x_lim)\n",
" cnt = 0\n",
" for key in e_vars.keys():\n",
" mean_var += e_vars[key][::10][:x_lim]\n",
" cnt += 1.\n",
" mean_var /= cnt\n",
" c = model_colors[model]\n",
" model_name = model_names[model].upper()\n",
" plt.subplot(1, 2, 1) \n",
" plt.plot(mean_var, label=model_name, color=c)\n",
" plt.subplot(1, 2, 2)\n",
"# plt.plot(mean_var[second_start:], label=model_name, color=c)\n",
" plt.plot(mean_var[200:700], label=model_name, color=c) \n",
" plt.xticks(np.arange(0, 500, 250), np.arange(200, 700, 250))\n",
" plt.subplot(1, 2, 1) \n",
" plt.legend(loc='upper right', numpoints=1, frameon=False, prop={'size':legend_size})\n",
"# plt.title(dataset.upper())\n",
" plt.savefig('../paper/cikm2016/images/posterior_variance_trace_%s.pdf' % dataset, format='PDF', bbox_inches='tight', pad_inches=0.1)\n",
"\n",
"# plt.subplot(1, 3, 3)\n",
" \n",
"# x_lim = cumsum_lim[dataset]\n",
"# for model in models:\n",
"# filepath = '../result_posterior_variance/%s' % file_name[model]\n",
"# if os.path.exists(filepath):\n",
"# logs = open(filepath, 'r').readlines()\n",
"# p_p = re.compile(r'population: ([0-9]+)/([0-9]+)')\n",
"\n",
"# population = list()\n",
"# for line in logs:\n",
"# m = p_p.search(line)\n",
"# if m is not None:\n",
"# population.append(int(m.group(1)))\n",
"\n",
"# c = model_colors[model]\n",
"# model_name = model_names[model].upper()\n",
"# plt.plot(population[:x_lim*2:2], label=model_name, color=c)\n",
"# print(model, len(population), population[-1])\n",
"# plt.legend(loc='upper left', numpoints=1, frameon=False, prop={'size':legend_size})\n",
"# #plt.savefig('../paper/cikm2016/images/.pdf', format='PDF', bbox_inches='tight', pad_inches=0.1)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Beta-Bernoulli Model Posterior Variance Trace Plot"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11c1f9240>]"
]
},
"execution_count": 153,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEACAYAAABYq7oeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x129ca8e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"theta = 0.9\n",
"alpha = 0.1\n",
"beta = 0.1\n",
"\n",
"max_trial = 200\n",
"max_test = 100\n",
"\n",
"vars = np.zeros([max_test, max_trial])\n",
"for n_test in range(max_test):\n",
" obs = list()\n",
" for i in range(max_trial):\n",
" x = np.random.binomial(1, theta, size=1)\n",
" obs.append(x)\n",
" new_alpha = i+1-np.sum(obs) + alpha\n",
" new_beta = np.sum(obs) + beta\n",
" var = new_alpha*new_beta/((new_alpha+new_beta)*(new_alpha+new_beta)*(new_alpha+new_beta+1))\n",
" vars[n_test,i] = var\n",
"\n",
"plt.plot(np.mean(vars, 0))"
]
},
{
"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
}
| gpl-2.0 |
chi-learn/chi-learn | viewer/notebooks/grab_community_area_numbers_from_geojson.ipynb | 1 | 2004096 | null | mit |
ledeprogram/algorithms | class5/homework/Kromrei_Georgia_05_01.ipynb | 1 | 70341 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the data from heights_weights_genders.csv to create a simple predictor that takes in a person's height and guesses their weight based on a model using all the data, regardless of gender. To do this, find the parameters (lm.params) and use those in your function (i.e. don't generate a model each time)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt # package for doing plotting (necessary for adding the line)\n",
"import statsmodels.formula.api as smf # package we'll be using for linear regression"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.read_csv(\"heights_weights_genders.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10cec65f8>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEPCAYAAAC3NDh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ce3acf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(kind=\"scatter\",x=\"Height\",y=\"Weight\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lm = smf.ols(formula=\"Weight~Height\",data=df).fit() #notice the formula regresses Y on X (Y~X)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Intercept -350.737192\n",
"Height 7.717288\n",
"dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lm.params"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"intercept, slope = lm.params"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x10ce3a6d8>]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEPCAYAAAC3NDh4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ce3a898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(kind=\"scatter\",x=\"Height\",y=\"Weight\")\n",
"#give it df['Height'] as X, and \"slope*df[\"Height\"]+intercept\" as Y, this is y = mx + b. magical!\n",
"plt.plot(df[\"Height\"],slope*df[\"Height\"]+intercept,\"-\",color=\"orange\") #we create the best fit line from the values in the fit model"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_weight(height):\n",
" return slope*height+intercept"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"174.03836776126673"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_weight(6)"
]
},
{
"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 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
}
| gpl-3.0 |
cernbox/entf | ROOT_testing/converted_notebooks/math/mathcoreCDF.C.nbconvert.ipynb | 1 | 76519 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mathcore C D F\n",
"<hr style=\"border-top-width: 4px; border-top-color: #34609b;\">\n",
"Example describing how to use the different cumulative distribution functions in ROOT.\n",
"The macro shows four of them with\n",
"respect to their two variables. In order to run the macro type:\n",
"\n",
"```cpp\n",
" root [0] .x mathcoreCDF.C\n",
"```\n",
"\n",
"\n",
"\n",
"\n",
"**Author:** Lorenzo Moneta \n",
"<i><small>This notebook tutorial was automatically generated with <a href= \"https://github.com/root-mirror/root/blob/master/documentation/doxygen/converttonotebook.py\">ROOTBOOK-izer (Beta)</a> from the macro found in the ROOT repository on Thursday, January 19, 2017 at 04:33 PM.</small></i>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[?1034h"
]
}
],
"source": [
"%%cpp -d\n",
"#include \"TSystem.h\"\n",
"#include \"TF2.h\"\n",
"#include \"TCanvas.h\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"gSystem->Load(\"libMathCore\");\n",
"TF2 *f1a = new TF2(\"f1a\",\"ROOT::Math::breitwigner_cdf_c(x, y)\",-10,10,0,10);\n",
"TF2 *f2a = new TF2(\"f2a\",\"ROOT::Math::cauchy_cdf(x,y)\",0,20, 0,20);\n",
"TF2 *f3a = new TF2(\"f3a\",\"ROOT::Math::normal_cdf(x,y)\",-10,10,0,5);\n",
"TF2 *f4a = new TF2(\"f4a\",\"ROOT::Math::exponential_cdf_c(x,y)\",0,10,0,5);\n",
"\n",
"TCanvas *c1 = new TCanvas(\"c1\",\"c1\",800,650);\n",
"\n",
"c1->Divide(2,2);\n",
"c1->cd(1); f1a->SetLineWidth(1);\n",
"f1a->Draw(\"surf1\");\n",
"c1->cd(2); f2a->SetLineWidth(1);\n",
"f2a->Draw(\"surf1\");\n",
"c1->cd(3); f3a->SetLineWidth(1);\n",
"f3a->Draw(\"surf1\");\n",
"c1->cd(4); f4a->SetLineWidth(1);\n",
"f4a->Draw(\"surf1\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAxwAAAJuCAIAAAC1+JMhAAAABmJLR0QAAAAAAAD5Q7t/AAAgAElE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\nqDIMwzAMw0iAiSrDMAzDMIwE/H/KpVWTaxaztwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gROOT->GetListOfCanvases()->Draw()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "ROOT C++",
"language": "c++",
"name": "root"
},
"language_info": {
"codemirror_mode": "text/x-c++src",
"file_extension": ".C",
"mimetype": " text/x-c++src",
"name": "c++"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| agpl-3.0 |
cchuang2009/DyLabIPy | DiffEq/3__2D_poisson.ipynb | 1 | 2299398 | null | gpl-3.0 |
imatge-upc/activitynet-2016-cvprw | notebooks/16 Visualization of Results.ipynb | 1 | 440589 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate some validation videos random, download them from the server and then use them to visualize the results."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import random\n",
"import os\n",
"import numpy as np\n",
"from work.dataset.activitynet import ActivityNetDataset\n",
"\n",
"dataset = ActivityNetDataset(\n",
" videos_path='../dataset/videos.json',\n",
" labels_path='../dataset/labels.txt'\n",
")\n",
"videos = dataset.get_subset_videos('validation')\n",
"videos = random.sample(videos, 8)\n",
"\n",
"examples = []\n",
"for v in videos:\n",
" file_dir = os.path.join('../downloads/features/', v.features_file_name)\n",
" if not os.path.isfile(file_dir):\n",
" os.system('scp imatge:~/work/datasets/ActivityNet/v1.3/features/{} ../downloads/features/'.format(v.features_file_name))\n",
" features = np.load(file_dir)\n",
" examples.append((v, features))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the trained model with its weigths"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using Theano backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"____________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"====================================================================================================\n",
"features (InputLayer) (1, 1, 4096) 0 \n",
"____________________________________________________________________________________________________\n",
"batchnormalization_1 (BatchNormaliz(1, 1, 4096) 8192 features[0][0] \n",
"____________________________________________________________________________________________________\n",
"lstm1 (LSTM) (1, 1, 512) 9439232 batchnormalization_1[0][0] \n",
"____________________________________________________________________________________________________\n",
"lstm2 (LSTM) (1, 1, 512) 2099200 lstm1[0][0] \n",
"____________________________________________________________________________________________________\n",
"fc (TimeDistributed) (1, 1, 201) 103113 lstm2[0][0] \n",
"====================================================================================================\n",
"Total params: 11649737\n",
"____________________________________________________________________________________________________\n"
]
}
],
"source": [
"from keras.layers import Input, BatchNormalization, LSTM, TimeDistributed, Dense\n",
"from keras.models import Model\n",
"\n",
"input_features = Input(batch_shape=(1, 1, 4096,), name='features')\n",
"input_normalized = BatchNormalization(mode=1)(input_features)\n",
"lstm1 = LSTM(512, return_sequences=True, stateful=True, name='lstm1')(input_normalized)\n",
"lstm2 = LSTM(512, return_sequences=True, stateful=True, name='lstm2')(lstm1)\n",
"output = TimeDistributed(Dense(201, activation='softmax'), name='fc')(lstm2)\n",
"model = Model(input=input_features, output=output)\n",
"model.load_weights('../work/scripts/training/lstm_activity_classification/model_snapshot/lstm_activity_classification_02_e100.hdf5')\n",
"model.summary()\n",
"model.compile(loss='categorical_crossentropy', optimizer='rmsprop')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Extract the predictions for each video and print the scoring"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/Alberto/Development/DeepLearning/frameworks/Keras/keras/keras/backend/theano_backend.py:514: UserWarning: theano.function was asked to create a function computing outputs given certain inputs, but the provided input variable at index 1 is not part of the computational graph needed to compute the outputs: keras_learning_phase.\n",
"To make this warning into an error, you can pass the parameter on_unused_input='raise' to theano.function. To disable it completely, use on_unused_input='ignore'.\n",
" **kwargs)\n"
]
}
],
"source": [
"predictions = []\n",
"for v, features in examples:\n",
" nb_instances = features.shape[0]\n",
" X = features.reshape((nb_instances, 1, 4096))\n",
" model.reset_states()\n",
" prediction = model.predict(X, batch_size=1)\n",
" prediction = prediction.reshape(nb_instances, 201)\n",
" class_prediction = np.argmax(prediction, axis=1)\n",
" predictions.append((v, prediction, class_prediction))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the global classification results"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Video ID: unI7FhokvbM\t\tGround truth: Installing carpet\n",
"0.5446\tVacuuming floor\n",
"0.1337\tIroning clothes\n",
"0.0953\tDoing nails\n"
]
},
{
"data": {
"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEABALDA4MChAODQ4SERATGCgaGBYWGDEjJR0oOjM9PDkz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\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAP/Z\n",
"text/html": [
"\n",
" <iframe\n",
" width=\"400\"\n",
" height=\"300\"\n",
" src=\"https://www.youtube.com/embed/unI7FhokvbM\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x1224cff28>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: 7p99ez6MEeo\t\tGround truth: Volleyball\n",
"0.7736\tPlaying beach volleyball\n",
"0.1720\tPlaying kickball\n",
"0.0376\tVolleyball\n"
]
},
{
"data": {
"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEABALDA4MChAODQ4SERATGCgaGBYWGDEjJR0oOjM9PDkz\nODdASFxOQERXRTc4UG1RV19iZ2hnPk1xeXBkeFxlZ2MBERISGBUYLxoaL2NCOEJjY2NjY2NjY2Nj\nY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY//AABEIAWgB4AMBIgACEQED\nEQH/xAAbAAACAwEBAQAAAAAAAAAAAAAAAQIDBAUGB//EAEYQAAEEAAQDAwkFBwMCBQUAAAEAAgMR\nBBIhMQVBURMiYRcyZXGBkaTh4hQVI0OiBjNCUlNjoTZygmKSJDREVMEmNbHR8P/EABgBAQEBAQEA\nAAAAAAAAAAAAAAABAgME/8QAHxEBAQEAAgMBAAMAAAAAAAAAABEBAiESMUEDBFFx/9oADAMBAAIR\nAxEAPwD5+hCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCE\nAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEAhCEHtvJ76U+H+pPyeelPh/qXtwF\nILcxi68P5O/Snw/1J+Tv0r8P9S9wmkwuvDeTv0p8P9SPJ16V+H+pe5TSYXXhfJ16V+H+pPyc+lfh\n/qXuU0mF14Xyc+lfh/qR5OfSvw/1L3dISYXXhPJz6V+H+pPycelfh/qXuk0mLdeE8nHpX4f6keTj\n0r8P9S92hJhdeE8nHpX4f6keTf0t8P8AUveISYV4Pyb+lvh/qT8m/pb4f6l7tNJivB+Tf0t8P9SP\nJv6W+H+pe8CaTB80n/YjseKQYL7xvtWk5+x29mZbvJt6W+G+pegxw/8AqjA/7HLuqTB4Pybelvhv\nqR5NvS3w31L3qEmDwXk29LfDfUjya+lvhvqXvUUkweC8mvpb4b6keTX0t8N9S99SEmK8D5NfS3w3\n1I8mvpb4b6l75CTB4Hya+lvhvqR5NfS3w31L3yEg8F5NfS3w31JeTX0t8N9S98hIPA+TX0t8N9Sf\nk19LfDfUveoSDwXk19LfDfUjya+lvhvqXvkkg8F5NfS3w31I8mvpb4b6l71CQeC8mvpb4b6kvJr6\nW+G+pe+QkHgfJr6W+G+pHk19LfDfUvfISDwPk19LfDfUjya+lvhvqXvklIjwXk29LfDfUjybelvh\nvqXvUJB4LybelvhvqR5NvS3w31L3iEHgvJt6W+G+pHk29LfDfUveoQeC8m3pb4b6keTf0t8P9S96\nkg8H5N/S3w/1I8m/pb4f6l7xCDwXk39LfD/Ujycelfh/qXvEIPB+Tj0r8P8AUl5OfSvw/wBS94kU\nFSaaYWmBSKTTQKkUmmilSE0UgKQmikCTTpCKSaE0CpFKSECpCaaikhNFKjhY/wD1Rgf9jl3aXCx/\n+p8D/scu6oopCaECTTpJAIpNCBUik6RSBUik0IEhNCBIpNCgSE0IEhNCoSE0IEhNCgihNCBITQgj\nSKTQiEkmhAkJpIEhMpIBJNCKSRTSKiIUnSAmFWQik00CpNCFVCYQmgiE00KAVc80eGhdNM8Mjbu4\n8lYk5jXtLXtDmncHmiqo8Xh5HZWTMJABrNyKvXnuJYKecTk8Lw725gGuz5XFo5pyxCF+YPxsJe1r\njIx2doA8FVj0CazYF874rnyHXuuafOHVakISE0UoEmnSEHB4j/qfAf7XLvBcLiP+puH/AO1y7wQK\nk6QhAUik0IFSE0IpIpNCIVIpNCBUik0KKVIpNJUCEIQCEIQCEIUCQmkgEimkUAhCEoSEIRCQmkgE\nk0JSEkmklAhCy4ziOEwIvEzsj8DuUGlIqvDYhmKgbNHeR4sWrCaFnQIIgKSQTRk0ITRQhCaAQhCA\nTSTRQhCaCLvMPqUYR+Cz1KbvNPqUYP3LPUipphCaATSQgaEiQ0WTQCAQRYII8EHD4n/qTh3qcu6u\nFxX/AFFw0/7l3UAmkmgEJIQNCSEDQkhShoSQlDQkhKGkhCAQhCAQhJKGhUSYqNjwwW5x2DVRHxOG\nTEdleVwNEO0KLGuSVkTczzQ2SY/OOV9LXB45xt+BxbIiwdk8U13j1W2HG4PI0w7UCX5SLSkdRCiH\nAtzBUnENz9nICwnQE7FKL0LnsxpYXZnhzQdL0r2rWMTE5pcx2ev5dUItSUIpmTNtjr6jmFNAFJNJ\nQCSErRDSRaEEZM3ZuymnVovN4Xgn2iU4riUJfiM4ov0seoL0j3ta3M4gALmYriPaOEeGBc691aNs\ns8GEjANNAGjQubJNice/LGMsanDgHPJkxLrdey6Aa1hytAApQTbdW5SBB2XmpOMYlzeyEeUE1ncd\nR7F28EXiJrSWOAHnA7pWY1pqKaqmmooc4NFuNBKJIUWuDmhzTYPMJoJISRmA3KUNNRBB2KaVQ7zS\noYf9wz1KZ80qvDf+XZ6lFXIQhA0JIQV4lpfhpGjctIXP/Z3CTYPAOjnaWuLyaLrXTd5p9SGHuD1K\nji8W/wBQcMPi5d1cHi//AN94YfEruqBoUUWgkhZ58SII+0cxxZ1aLpYpOO4PI4wydqQLoApSOqhZ\nocZFNG14cBm5EolxuHiBLpBpyCUjSi1V2zDGJL7vVSDg4WDYKUiVotJCEO0WkkhErSSQgZdQtUun\nLXtaW+f5uu6m9wykWPauVxR7omGWCV+ePvcqJ6aqVcx18wur16LDxCxG8yyvYy7zDkFyeE8dinf2\n87Qx8jspGayOi63Eix+FILw3NoH5cxHiixz8HiuzxMmGaWyZRnjc06vBH+KWbHcRZhONxysiEpxE\nWV4NgNrxXCbJ908RxLcJiGyx5aLtnFU4jiUzoYwZ2SEA00t29fVEdBmPkOOc/EYMdsLcxzneYNxQ\nVsvGeKTskbhmgxxfvZBWnSlyYcYx+Bex9unmlFi9mgdVqwMmFwLJnxYhsjomhwdZAJ6Ac0HbwmOx\nEGBBxZkY9zqDpXc+hUMbxRmPwskDL7ZpsOadPYvP4vicWMnc6RzwLFXqBpqs+BmDpbxGJqNmou9S\noPUcE4e3H0/Hv+0NaO4M1AeFc16SGCOAkRMDG9BsvN8N4rhGxNc4BrwRrGND4Lpfbp8Sbw7ajHnS\nE7JVjdJh/wDxLZ4zlcdHVzV4Omu689BjsfPiTo54A81oAodfFdnD5i2+2Lh0LapWkaUilaLSpAki\n1VNiY4G3I6vBCLVkxXEIoO6O+/oFlfPicacsIMcf8x5q2DBxQDMe8/eyiM4ixOOdmmcWR3stQhiw\nzWhgrvbq4vsnL13VU41YSbOdKRa4lwdyFrHi8d9mlDWYd8jyNNVpklAa/LqQuFxPiDsPLJldHKao\ntOhCi64EnEJWSUS5sgA798l6nhWNw8cPflzzE6nXT1rwTW9tIDM/M8mjmd/8rpYfibuHOczB4gzN\nkFHTVqrD6DFiopayPDr6K+14h/HcQ3CMcQY9CAMtFyoj/aOTOO0fITVAAeceSK96XACyss+KjdFT\n2Oyu0vLYXn5uMY0Rh00GRhaMuZ3+dFpgOKxmHD8Y4MaDbWsPnIqvh3E5MHinYRwkkBNs02au67iO\nHZXaOLSeoK8jjcYMPxJk0L+zAOVzHXsr8BxGRzZJGuzhsml60EHqPtsMrD2E8ZfysrzON43ixM5k\nhEdOyEhulLRjn4Y4mB+IDSxxpz81UfUFT+07MsETjI06VqNT7eagu4Zx2OH8PEYrtK0b3K//AIL0\nMONilYX6taDqSvmc2OzCjbjVAnQ0Nl6L9n+Jx/Z3TYntZnsNADUBUexLu7Y1tV4Q/wDhmLkYLjTM\nZiKF5hoI2mz7VuwE5fGI9LbvXJGo6FotQtFpSJotQtFpSJO80+pQbI1sbczgNEOJymui4+Kkx+Ud\nllDA2iXN3UpC4w4ffPDHA2MxXdteI4nNisDNg5p4gSx2ZrWusHXZdGL9rsJLgZHvJimaayHf2JSP\nSucQaDSVUZHRvuSmsPO1yp+JCDDNcyW5nebGat2i52K/anDyYLs5Indo8U5uw96VY6OM4pDi8Z9g\nhnawg95xGniuJx6HDsDJcDiQJZNHRtPnDr/heXMxbiA+M95psA6pS46R+LM4yte4a5BQ26Izcetw\nOOwcWHwrZczczc0ha63eA8Ftk4pDh+2fgYMxLvxC51u0HIFeGE4du1uo6LrcH4k6EPZURdoA924R\nc13OF8QmmeZp8TkistkaQKA5UvQnEx4YMEsgAfo1eT4zxWJsrsPCyGQFlOIaNDXVcvB4t0UjZMVP\n27RQy7mvC1Gn0oOBFg2i1xuD8ROKiHZgOb1JAI9i69pUiVotRtK1SJ2qMS8huQOy3uegVlrl8R4q\nzDzPw8cMk02XZg0HrKEVSzOEL2YOFkrWG3ukdQd6lyeIYbGYrCCR74sNh/O7EPzLnY/HYpsb5nxi\nMuOTKGbLHxJ09sfIXgOYNCb18UN1ljxMmFnilBFs1F6her/Zji32pksGKdmaD3CNgDyXje0zed1V\nmFxj8LI50by0OGU0OSsZzXd4jwQDFTyRzOEMQ7mgJKnio8NxLhML8PNGZMOMr2ublJXP4PxxmDe9\nmIbnhIraysrca1/GPtLw3sy+yw7FDpTiGPgmDQHAGi0HfVWYwDEPfMxga0Afxeb4KviJacQS1lHz\nrvcLMH2XAusE2jKYFvDc1X7Vc9rmRkP7rGmrH8SrwDC+XugkAakclfJiHOlkd5rQNAUHQ4ThoQGS\n8Rke3C7gHQE+C6UXFHyyyRYc9nhc3nu2A8V5+OdkkTo5JnBoIIHj6kR4gwl7XOzNJGajVqa1mveY\nfE4GJrIGzuc4/wATQQCUcQZJP2QixPZtBuQDTOPWvOv/AGhhhwbzGM0zyRkOwHIrnw4riGKcOzc9\n7W6VtQRbj6FFiYHdyOQEjSgrHStYMznADxXlYcVJh4WmVzYq96tZisz2umBdFehLt0K68mOkmOTC\ntv8A6ylHg25u0nd2j/HZWRvZkHZAZfBSvQlxtCf2k6QN2oAf4WPEY7smFzYXSDTdwCniZCRkY3Mb\nXC4tHK+ImXutAvK0a+0oa2j9oGNxDmYnLFTtrtUYjjEf2lg+05xebKNK9q8xiIJGW50TmWdBmWFx\np25KRK9VNxD7XPNGXtrTKGupUSuge9kRqMkWSNwfWvOCd8bw6M5SNVKfFSTkukeXP6qxKzvmz5dB\nopw4swvD2ABwNgrIDafPwWmXSxnGMTjmMZO/M1mwAoKtuIeYxG4ktGoAOxWVpBblAA6lTY0624aK\nDfDjn2xr+8xv8NWvTYPiuGw0cYnY1sbxQJqwPUF4q61CYfaDv8YfhZSZcLiHPANmJ4OnqKzQYmKP\nhcrdRiHHSuYXNGKkaS3MQkx7M1uG3K0HQmc3sbLbfYuTkR6lRicbiJhklmc5rPNHIKqHiEkeHfE0\nAh2muunRZnnUEJFrsYTFYeOEwYlhkDzpJsWr0GB4thDD9mgjbh2ObWeqc+huAvFCQFtFSdK+JwDX\nDbXLyQer4LisDgIpcRPrLu1x1JtXcA4tJnmbLlZncX6rxY7z6buV28FDJh8N2rJWmV1ghx7oHUpF\nr3kGNiljaY5A/rVrQJgdivIcM4iyD8M4gSSOAsAbldpuNBrMC2uaeK+TrdoE+0XObiA4WDopdt4q\n+K+eHjJGOe0HO6ubX5Q1eWx/GThm3hsWXOOhYHlwA9oV/FIoHSOfHK+PKCHirDj42vK5ZHG+ztvM\nAqRN5O/xfjOF4k2Awh+WLRxOhOy4GKjZG5r2TtkDtSADbfXaTSBFJWyoc6xopE3W9mLBmZiHSDNE\nLDS3zj0VeLxbsdi3TEBrn/wjYLnkm91JpAHirErQw0/PmIO6T8pJLBQ6KkO1ooaaHgg0sjcMO95F\ngGt0R4nsw4UO82tRapz5g7kSo5aAPVBqikYSO0caO4WmOXCtqmOc4j+I6NNrl2TVcluwsDpGGohI\nXDQ56y+xSLXpeGR4XFm2EtxrPNeJKbXK167D52QsbI/O8DU9V8+4fP2FRzRQxszDUtzOrnS9xFLW\ngNsIsKR0zcbcyWZZ+1R2qRrpozLJJ2LO1MjgAdTarxWMbDG47uq6C84eKCaQdoDHiGnui9/G0h0t\n4vxrC5RhmMBArvVXq3XJnweDbhGzOxL5ZnGy1t90FLi7o8djXNZCTMf4g6uStwUDTwx7nuEM0Nlw\neLsVoq577ct+CLMG3EOzNzOIAreliZG+RzzG0lrdSeivmxMuIcc8lM3pUte5g7pocxyVYVOBDiOa\nQ0KkSdTSgRsD7URZn2zd47UrpGtyNp2tWRWytlwsUfDYMQHkvedRyAWZr6twdryRVgkc1rgxpDed\ntUJZDI/M6uQpEuLllaGufY5+KnBgsTiadHEco3J0ARFYJYQ5u6TGyySBrGuc93IBdEYbB4SjjMTn\nePy4tf8AKg/i72gxYKFsLdr3JCKuh4UyJna8SlETf5L1WuTiJEQdhIxHG45TM/U+5cNs7jL2kn4j\ngdS7VTf2kjxJ5mc6aU0IPSM4TFMRK/Ficg2Xu0AHQLU4YTCRSNw4DtRetgFcE4b7MWGbGscDQIjN\nkKvD4lsWJLoXZX5tHu1zexFzcezw+LZFAPxO0FchsVqZIJGkn3FeTHGMQA+CRol11Pm37lOCWRrR\nMYpbce8G6ABI1XpcVjIMM0GWQN10XmeK8Sz4ljo3FzA3QtHNb2YjDv70kuYk6GtSs8kzoTJWHbHb\ne7zKRN1xMXM7EO7SQlvINqvcskjWFtNLs45dVdMZZ5XOldmcNKpUTW92waQdANEYZySHUd1K2iyU\nPH8Rv19VHLYFqjOFY0Uq7Ck11+oILAaGik43VKrNopB4CgmQ4DUjVIDWzpokHC9UnOvkgZeNzqVE\nnmonUpWVRMOISBsqI1THdQTsIPJRz3yCkDem55ILsrS4ZDlGm5Uo5JNWBwq7IvdVNkdpbQK02Wts\nLnNEuZozbAIOtwrhcs+SYTtblOrRyXocNhZYbz4h0hPULiYHFsib+E4NNahXv4maP4y6ZiV158Qz\nDMtxN8m72qMRO1+Hc7tjG48g6iF57HcSkfUbZM3NzqXLxDnE5rslZ3R1Me/sgSQ2RjxYc52rVxxK\n9hc+N5HI0q+2IdmIB9agZOml7rKtPaZoXb6dVQ5w05AJtdbHqt1bIGaA05pNJaSR0VjQ3MQ990NC\nFEnLXUoI6k6CqUxWXXZJr+4W0DfOkcvBBIUQTVJmsrTy6KtpKCQTQUFrnNddmiPNob+tMPIGZo73\nVUNkyp5jZIJFqjoYXF4gNDIwHtsaOrT1dF1/v3iLcoMBY0Gi7zl5yFjnOylt2NyarxXawsOJjwpD\nZWSMLgAQfNNq4V6fDY7PEx0rmtc7YBaJJi1hI5LLhWRYeARx0ANa6WpSkPoZqvTddInlrlzRTY95\nkdNlANWOi5eLbEyYskntrf4wK1XfcWRHIwiuhXGfLlnkm+yMxEZpu+jfUsbh5awsx8ZcWzucS03H\nKxoBHrVOO4i7FT9o7cgBx5OrwUZ3QNmePs5HQZqpZso380dFlauhkit3aWL5gbFUnbTWkra3QIbs\nAoGTlZXNVuBq60UsoLyL0tOuT9RyQdmVmHPBMH9oe9jaJBY2zuscZ4XG05m4ic+PdWvHx3wHCOHm\nihS4+nVTF1tbxKOFtYXBRRH+Zxzn/Konx+IxF9rM4joNAqHsrXdV9VUWAB2qGOCgHdEAEAhBqhil\nmLQxurjorjHPk7J5cGnbXRZGOc0UCdVfFinNYWV3TqqO1w3hOGfHIXW+6yucK9a6EHDcJFlc5jQ9\nvQ6LzbMXIKpxpa4Me6MNc4kiyFc3GddyTDMkkc5oDXgb8vcrcjYmaPJbd0TouV95RZc9Ve6pkxzJ\n4yDYWukutmYwyl0LWuBca1WbHYvFNiOcMaXVqN3Iw8zBCc1BoB1C5U2JaXaW8De1FoONlY1sbXZH\nN6D/AOVldiH2QTetkka+9RlcHvLgAL5BV7rLQJtyugaJMRGzq4LOdFt4SztOJwDlmtBz5Y6eAOav\nijEdgndeg4TwaIxCXFtzF3mt6LNxfhLIfxcO4ho3b0VmmbjmNhYRqdvBREEZO9IyOrRyMjqokrK9\nLWYeO7ztU3QR5D3m+xZxGRzKl2fiU7OifhMozB4PqVfZEbkaq3s6Ni1LswdwUGYxkDZKdnZtFGyV\nsEYvQFV4iBzx3LJ6KowArXhg40dAOpVTcLLmpzaC19jbAGmk1Y1xGFhz20uQ9kZl8/KDqVligcx1\nuNhXlocdrNUs7V6TbFGdRLSg+Nt6SUL3Kk2OtgPcmYyRVCvUp2dMrzlJo3SqJJJtbDGCayD2JHBm\nTuhh6mlcSMOW9EjEehW+LBW9wiDiANk3Yd29andUjC1pDXAgqD2EDVbZoywMsVasfBnJzC1Uc5th\nPmFv+yCh1UfshBJOigxv1y+GiGtJsE1S1iEZT1H+U/s2d2VrhpugxEEJBp6LojA0NHC0fYnmrLfe\ngwxwFzhpY5gKfYPZoSB4Hktn2J4NggeFqL8DI4jvN96UZC12tuvwGy7PCOKQ4eSNk0NsAIc4al3R\nYDw6T+ZvvTHD5B/E33pR3MVxhssxdgsMRC0amTQ2suK4y50n4Tco6LOIZOx7MkVXVZ/scgJ7wI9a\neWrMEmOlkeC9535KceKlnY2IAuY0+aFjjwr3gyOJbroKXUgDomghjAedJdIz4gwFuZzCZuZJ0WMx\n2C7+EdFungdM4uuiU4oMsWQtB9alI5mQam0MHeGlrpTsj7o7MX4KhzPxNG03TREY8Q0wSUaKTKeb\nJIB/wt01SEtLa00sbLNHw3FZe1Yy4ubr2Vztrlxjo8RD28FwdyF1nza0Gi5OxK7fEo5DhMHhw0Fw\nB/8AwuYIWiSVkuhaBuUz0kUFpDQ48+Shks3yV4jdLE0tNkIGGl5tKqKzCAe6VY1rKAItW9hKDpGS\n1Bwkx1MZRFQyt2Sc5nMqwYOWhmBb4lDYWMJD2hx62oudoNINUbpW5/wWjxKo7KpO6KB5K0xPDRVo\nh56aUNfWqr7OQkijojs3CwQkEy/NQVbqN6aK2OKqJc0etVOYbOtoK60soy6FwBpSylTIPYEDcqii\nsxXV/Z6EO4gHfytJXM7OQUSw0dl3P2biLZ5XuFd0UoO2KEZoaBYsQ4HRwsFXSYuDDxVNK1mY6WsU\n0jXvaWkOadbBXRMciSMMkcBySDAU53/jv9arEi5a0s7MKQYAq+0KYeTup2LAweKkGDqVVnKefxU7\nVbQ6lUucbJClnNbqrNRBVr0/hxzc3damYTuBznmzrSnlaNFGbENa0G9Es9tBHNZrPPKnQQWijV+9\nUtlzWpB6duK4VXP3p6ePvVIkRnTsXBgB3PvXpcKRFwhkkMWdxFkcyvLdotcUuLxEHYwzZGMG2arW\nuGz2jfgCWYg5MCGZ3d5wfdKPFiGYnuEd4LiRYjEwT/hhzX+BWgiZ0jWTON8r5LXLemtxRjnZnRnm\nCtRlJ2I9yrnwbnmxKKGwLVAZho4gnqAsbmxF/aP/AJx7ki+TlIP+1VWhZVGaPul1i/AK3DuqKyG2\n43soONtKUZpoHRPg1doK81qO0/ts96ozIDgoq/tD/TZ70B55xM96qDgjOEGgPP8ASYgTAi+zCo7R\nISckGjtWf0h7lowUJxcuSOJorUuI2WDOunwjFxxh8JdT36ha45dNQxXCHjEARyNMR86hRCjI6GF+\nR8dkdAFtLnE3mItczG0XtIcCTut8uOZieVTM2H/pf4Ue1wx/LIWTXqkb6rmtaD9le4OyuXW4dgsO\n9gnyAk7XyXAbmNAFerwzezgYwfwtW+LOnJg8NLRlha4jYkKTo48mTI0Mrza0Tc6lWX5pGjwK6s9u\nFxNrsDMZs9wFtCMjQFcWLE4OcvfJC6CQ7vYcw9xXp+OMDsI1x2Dl45z6xjy3ujwWNa45W2DCRiRr\nsPimuad2vGUlbxhiBoxrvU5ceOa5mknnRW1rqtZ3V3Mxs7Igaxf5QQ4DSM0s4meNnH3qQxUo/MKz\nUTc4gWWOXPxJdLJniieRVHulW4viuKhc0RyUTvYBVDeOY7MC6YEf7Qt5RZgw5vdkwr3WfOorf9kc\n6qicAubLx3GOfccgaK2IBVuD4ri5nFr5boaUKTajoswDrNRvs+KkOFudVscD4rN9rxBP71yX2if+\nq/3qUazwt55gdFV9yU798L8VT28p3e73qLnl3nFx9qeSRobwRhFOxVeoKxnBoWD/AMwXewLDTTuF\nZDE2SQMAonRXyI2O4ThnedK6vFwUQ2fh73Ow0bJoarR+qjNw453RW4t22VEuBHD/AMISF43N6UlW\nJ8ZiqPN0VPDWA4ewNbXVx8bXwOB6LkcLmax7oid1v6nxnmP4zvWq7XVlhwwec4FlQEOE2pY3NRzb\n8Ew4fy/5XR+z4O+fvKPs+D5X7yk0c+2/yp2z+UrecLheRI9pR9mwnMn3pNVgttHQhUErqPwuFDTl\nc6601XKfopHr/j71qp5NVei0wOJi1OgWZ63YSFr8OXueGpD9OkI2CycwCuDQP4wq8Kxs8zmOfkA5\ngWtZwDOWJNeICR5uXtQG/wDUE8n/AFBXjh8Z/wDUn/tCl93M/wDcn/tSazWcM/6gt2Hw0Iw5knkc\nCToGi1QeHxtaScSdPBUv4qGxtYGVlFALO7vH46cOPlroskiaT2OHNAXbzVrK7HfacS0GMMIPIqJg\nxE2HEhmAcReQLntDxKN2vtazeW+2t45OtduRwFrC5/e0VzoMTqO0jIVX2PEk2Hxha1yRzlPMVMYL\nE83xJ/Y8R/PEsRarz2DaQdl0pXfYp+sfvSODxBOmQ+1WFVdp4I7QdFYcJiR/A32FL7Pif6X6lIVH\nOOiO0HRSMGJ/o/qS7LEf0D7wpFo7QILtSl2U/wDQd7wpCGf+g73hWFRzKwtuKN1WM9KBimG8DlpM\n8MeBijcC2RkmZ1jkrmGa7BiY+MON90civOdpdnqV3G8V4f2eX7XHdbLidmbOVjiL0NLXNks6YcbR\nkI/Kcpx4eSQ6MLR1K5xagJC0hwOo2XpOF4mTEYXtpnNsmui4zMFE3944uPTZaA9jGBjBTRyXXjx1\nN3I7Rka46OB9qzzTshmie85WWQSeS47p443jM8Ncdlm4vxGURNjLLH897rUTHS4rxLCYnDvgieXO\nzDvAUF5SSL8Z4vxUXDNqx93yO6O/bcworLW5MKFv4mYnYrqAilgA7F9b2Fs0I85TliVPRGnVV6fz\nBJzw1pNhc4M2NYC7OD4UsZWt4MkbnXQGw6rIuvH0aQ3WzDRvZMwgjXf1LGujCfMrommY1jdO1UTp\nulZ6rnEW5ijMVTbkWUgtzrTgQ6TFRhu+ZYtV0uEyRxTF8jgCwXRTMV6j8QCwWkrzPGcwxby6rNHR\ndnD8QjxDpWMJJjrMuJxyS8S6ugW99Jx08VxGJ8DmMz5iNLC4mGxAGIrKC7kSVqcy7XN+zzh+YRnd\naHa7R0nedulSqw7rjFiip5hZFqKnVIUc9jQFBzmqoIJgqDpWNIBcAfWkYwR3ySfcmIomnMGNaeoC\nCbnDISOi5r6sre93dNBcx8zL0Kzr1fhuZmokBXwZ3QPjDS4DYrG6a9grsBMWYiidHaFXMT9OfFsw\nUT45O+KsLcSkKtB9VKuFSCNFFt3YSI11KiHLRicOoXPwmGZM53a2QPGl0AL0AtUwt7J8g8Uazemg\naaDks8cJdO6R/LYK8EblBLbtESBSJAKjmFp6EVSosvTdK1AADkE6HRQSJobph3KyoFoP8ITy10QT\nzOG5KdnqVXSZYD1HqQWZjpqUsxB3UCBtaGtHU+9BYXHqUW7qq8tnmPalls2SUVbmcuRxWV4mAvcL\npdh2rg1uez0cVkfhXPmcyRtBpqzqUP8AHG5r0kMrjAw3yCwHCRdrkDe6SrcRG2DDPyF2g07yvtmR\nHEcRe19RHY6nqtMXE24im+a/p1XCD7FKBdRsHVbzIy9E6bXdRfPlaSuSMbbRm84c+qm+Y5ddintC\nkf2jrcbSeZHwuaO8G60eSoB7ytie/PlZ/HoVntvpQ3vAU1XywdjEx16nddOHC9kKaQL8FIwgnvuD\nhyBaoayYp3bYjDU3bc9V0e2afy2qgw1sR7lEsm5OZ7kSNXaMP5TVilAmxjW5WtYNSAFYGz/zs9yK\nmB2YVFPHPhGDl/Ca0kUCAvPldTiT5BAGuDRZ5FcsrWMhdbh7WMkaJWhwrZcgLpYGbPMG86TVx2Lw\n39Ee5IjDn8ke5U2eiMx/lKykWlmHP5aiYsMfy1HN4FIvaN1RMw4b+Uj2rBjmtaGAecbsrbmBFrDj\nnZpWtHRa4Z2m+meOaaC+zkezNvlNWujC1szM0xc7TQk7rDiGZZWt5UAt4LWNAuqV5w4sRxLhJ3a1\nUHyOc6r3VTGufLXRXNb+M3SwFiukxrEbG1zKmB0UQQQnaMn7EwehULzeCdgeKCe4SrTRRzHoU75l\nBJcLEsMc72+K7g1XK4oypmu6hUZApxOyytPQqtSZ549aI9CzvNBHNTAI5qqE/ht15Ky7NFRUrtIe\nCDSdghFIE2jKLJ5lP1IJ5BAFtahGW90XWnNAvmigBMDqok66/wCFIEEUgK1RSBtuneiB7JbpWmDo\noGi1GxakEBSEA60lVm7QSHW1E7o2KAgtw0gjnabSxZyzu1BvXRVkJZQOSDU7DRfdgxGT8S9/aubj\nDeEk9S3sxMsbMjXd3oQudxR1YV22pGyo4myLStC2wdLZEyJ8ALnSZhyDVjWnDPOoUu56XMzetRdG\nWOzUcp2JG6uwbc2IaemqtxUZGFjvkocP/fH1KW9rI6l6bIvwUb6JEqCea0+SrtAKCSaii0RzOJ5z\nKLaQ3YHqueQuvj9Qy9kYJrTG4EAi+YVpHLijfK8Mjbmc7YLRgbbi2AiuStxWHjbMCwZQRsr8JBDl\nbIG98c7RI26JFCFFFIIQmRaISqLbkutlbXRI6m8tIKZWBzwaVqYaCLJ1QAgxwRDtSfBTdGWOzBMN\nvmm7tKrQqRqlmB9aYvqohjjuAphtDdaZCALTr2ptUUUghM7p6Ugi0ErBxSMl7XgaAUV0Mw5LNOc5\ny9VLFzK5KbQcw0K6kmEiGtJwxBzugCtPFow19m3MFbdKLaB3Q/dES3KK1Q2j4KRItFIG0wQDqNUv\nYnysoAjW0ZuqgTabbpA9CmFEe9MDqimatA1OugQaRvsglui65KNkaJoDS9E6KWybXDluoAOAKfio\nk0b2Su9QgkXdUAlR9ald8kDzBP1KLqHgkDZ0QOuZK5WPnEvdGjR/lbMa8xwOo6nRYsDB28pe8d1n\nLqVcGc4GbsO2rS9BzVcsLoZMjtwvQWAqJcNFM/O9pJ9atZjhK7DHLO3otuLw0UcNtbraz4eNrp2g\nq1MxuxAzxOA9ax4U/jtpdLIK1XPhZ2eNyHqVlt0BrzTCAKTRAiqCLQaPrQJA8U6RoiM+MZnj03BR\nhBlh9qte2xSTWlopFZsaLyO9iuwoqAXz1UpozIwDohgpoA5InxaDoi1EWnsgknajaLRE7Qo2gEIJ\nJEJgpFBnvwUuWpUQSi9d1RI+tKr3QegRYCCQronY9SiDe6V+pBI0dlEO12RumRpaipDXkqMl4j1K\n5os7qEQtz3FTW+KuYkuyjmrIgGNpRb55JU8ymYctWAX4IIN6EKF3zTvSlphKyOaRN81EE8lOrGqB\nh1BOzSWXoEqRTBrlaY18FEo22CCR3UgQojZF8kASCaUtlDS9FLQ6UgZNIBSqjrsj2IqRGiiLBsos\n9E66oAusbJByKHRMaHVQMG9kico1SAJdvokeg96IbTmQKCB67UT53gisnEH9o5kbd7WmCMRRhjeX\nNZ52VIHDrata/NsVRdfVFqG+6MwCIpxwJh05FZMILnHgtuJ70LvUsuCH4jj0CDoA6LDKQ3GtO2q7\nmFhEED8VOGOZksDcjVc+XCOxjpnwQ0xrtJCd/CkDsIzAqtjiWjMNVMV0QSCK5pa8kxaINUJkKJ02\nCB0CgnLoUNJ6KRGmqCKEj4FAJukQx4oIKZ0UST1QSSKiNd0E0UEqQlaKQS0QSo+tFikFICXrCVWm\nNOaoecJg2lSLpA3EDkgNDhoo5lMNsamkCohGpHNDqagOvRAWrGPZkyAGuagQOWqKJ1CzrWbDcxp2\nSy6aoBKeaiqiIaeikGlGYnqptIG7UDDSApAaKBeDsUrPNBYXBVlxJ0THikTSomNRSTbB1CTTaeXm\nCUEiRdFRy3sk4Ud0wSOWiipAZfBRJPIpZrO6noRogg3NeqnZ5qOVyACNygldjRKyjTklsdNUDBN2\nSmTzUS7StE2gIGNUnepSIsaFVHNmooLAKFpDXZIWw97ZO2nRqCDmNcKI96GtDfNA9ilRG7rSO+gQ\nPVQLdbU69iKAQVSW5haOazRZo3OAG63d0bJU0oGZu2ZDDLKY2sdZAHnLXPjm4ZoEAzMOgZ0WQNIR\ndbIikOzuJI3KsDUw4Hkg6blFKq5p0ix1QK5lEB06lMO8EwUib2QF+CENtBrrSBHxKYpKgmNEQEqJ\nITJFKNBUMDomVH3p2gLKjzRae6gYKTmpnuqJOuiDPlvmfemYnAaSG+iBQ2U2jnaoiO1aaIb67UpC\n4DRhd6k9TuEnU7mgqBcD5pUhiWt3BUsnVWUMuqCp08b+vuSGJYNC6vYnttopd3Yi/YgYkafN1RmU\nmV/KAFIkbUoqsPFqy214qNMO4CgII+Wb/uKC4DUIeQNAqTFWxd/3JCN+ay810KCwElWGxyVZafy3\nUfFVf+KHIHxtBcfEJtHRNtub3iFAF7by5CgsfmrzUg4cyqg+UuosB8bVluDbLBaCdjkVEHXUpNLj\nqGUEs1ElzHac0E7HgnfTVU/aIvEKejhYtFWBx/lUXG1ESNZz96k1wfzBCBsBG5TsclHJY3pMMyjQ\noEH3pSkBY00UctIPrQS12SzAGlAnkpMFb6oJ5rUDW5BSuuqYIQAo7WEWL1KfLdQLq5IJl1DqgEEW\nk2zuKClQrRAqsoIyjfVGqZ/wghZKeW99EUL6IN79EQFGW9yhpz8kC2jUIGB4JHTkEw8HRBGuiADj\nzCD1Sooy+KB3oog+CmokdECocjSKI2KgXkHZPtAiJG+iBfRGYJWqGdOSjYRn12SOvJBKgRugDxUS\nK6pWAgsOqigFJBEChoEjaAaPM+KeYIEHDZSAB5JZvBPNQsoEW2iw3ZAJd6kOad7QSzWo6t5e1LKR\nzFKeYVSB2CFAjXTVNo6EINk6EKAcGD1+tNtHzUAHnSBY2ACKYITcQRSgQP4gmMo6oECBzpWtcD6l\nWRf8KB3RdaIJHU3yQDbsoGiQIfumQG7EoJENjFgaqJeTsk7vc0gA06lBNt81N2UDVQvSwouFi0Eh\nQuhQ8EBwCTHjZMtbvSALweQQHAcgkWt5BAjHqQSLglfRHZt6oLG8rRUgU7UQBanogTo2uGoVRgp4\ncyVzR03VhKKtEHLUkqoi9i5TcC0oFu2I0QUls27ZPYWpN7e9hfirxYOtUm5ubUIM755WGnRD12k3\nEk/l+4rTQy+KQDW6tpBX2kh/9O/3qxjjerS31p5ieaWUk7hAOA3R6kEA7FHeA2QR7XK/JRLvBSt4\nOoTB5pE2UAaJuh7FB8nZ/wALqUwNdDoh1IKPtbDuCpDExgbn3KRA0BaCpBvuQVnExn+Ij1gpfaI+\nbgFb7SovGYa6jxQQOIh3zgpdtB/M1WBjdqHuUHwRE+YPYiDtogdwmcRGP42qBwsX8ql2MTRXZj3K\niWcb2PejMCLQNBoKSJBGov1oGHjmQomRp2IPtUXsjd5zR7lV9ljvzRSDR2jRuR71HtW3oR71QcFF\nelj2qBwI5EoL91Kso5lDWgb6qRN80ELJOysbWXvJe1Gg3CBv27qi3NzRnCeUOQBcFA76FTyNGw/y\nnma3ogQJqmhDbJ1IUg61GgCgmTSG95VgA8v8qxv+FFDg66akcw3r2KTj03VebqgZeRySD+Sk2zzQ\nTR0FoG0ozVoVEOd0KlmA3QSyjduiiQB52pUvHZQy6+cgCa0Ug60xVapZR/CaQMi+SkAAOqhRPNMu\nIGqBmgfFItIKbCHbJObZ85AyTWyGu02UQHDf/CeYoDLZ0JCNQd0XqApHKdCgiXEKTSb1UWsp1jVN\nzjaCbiFAGtAkOZUS+ignraZJJtRaSVIkAaoIi+oU7PJVkhMOA2QKnZlJzsvL2ped60701QNpDhaC\n6jsoZidtEWeaBknkkH2aKYCi8dBSCwChooGydkgXEbJ61qgQaAVKzShY6KTTogRKQHUoc0E+KMoR\nDA8UjqjbkiwUBZRm6p3poogg7qiV6JXXJLTkgoHQcEsoGyjmdyCmL5oEdd0rUio2NkECK5pjVVgq\nQcb0CCRab86lPYKGh56pm63QIjXQJtzDkhpoEpBznHQFBbyVZj5gqYHXdRJylBDI8b6pgOtSDrTL\ngECDa3UrrZIODinsgTjY0UWkc082qnvyUAXDJ0UWkdVLUcknC9QimQb3TNEWQq9c2+iuBptIIEmt\ndlEP12Sc7XVSaTWg0QO9NlIVV0o1ztAcAKVAXdApDUdFWSmCbUBZG4A8VLMCPOQ6iACo5W9EE9PE\no0s//tRqxokNQgllHK0iKO6dkDUKtz9QCCguadFF4vnqotrqVKKRkb7fF2g6ZqQDI3uddaeCH219\nEUtE2PEkgLIA0DnazYmeZ50Y3LzduVRK8wvkFB1grRG38IdwnoVAtu65f4UFGZNos7q9kJleGsaC\nTstknCjExpz27nQVGHM1gVZeCdVfPhjGaLSRe/VU9mMo6qBHlRJU7US0gE2EjmF6ahAU+7sUpgjm\nqwXk7bILXE6ILLtI7Kq3A6hHaIJFvNIu1oqPagoLwgmCTyRqPFQEgpMSIHnFpkDmlYIsJAWbREtk\ntCkVBj9aA1VFiEb76Jc9Cgd0najQ5FHtQS5KIIR7UqQYPvAf0f1fJP7y/tfq+SwIQbxxKvyv1fJB\n4npXZfq+SwIQbvvH+1+pSHE6/J/V8lz0IOh96f2f1fJB4nf5P6vkuehBvHEgNof1fJMcT/s/q+S5\n6EHQ+8xf7n9XySPE7/K/V8lgQg3feP8Aa/Upt4pX5P6/kuchB0vvX+z+r5Jfen9n9XyXOQg6H3p/\nZ/V8kxxU/wBH9XyXOQg6P3r/AGf1fJP710/ca/7vkuahB0PvQ/0v1fJI8Sv8r9XyWBCDf95f2v1f\nJA4kQf3X6vksCEHQPFL/ACa/5fJL7zNfu/1fJYEIOi3imUfudf8Af8kfep/o/q+S5yEHSHFiBrDf\n/L5IPFr3g/X8lzUIOieKg/kfr+SR4mD+T+r5LnoQbxxIg/uv1fJS+9dK7H9XyXOQg6b+L5wB2FAD\n+f5I+9zp+Dp0zfJcxCDrw8cMMrXjDg5TdZ/ktsn7WZwQMFV/3fkvNoQdvEftE6ZmVuGDBz793/hU\nffJ5wX/z+S5aEHUdxcE39nrwz/JI8WaRph6/5/JcxCDpjiwH5H6/kn97i/8Ay/6/kuWhB0zxYHaC\nv+fyUTxQHeD9XyXOQg6H3k3/ANv+v5JHiQP5Nf8AP5LAhBuPEG8oCP8An8kfeDdPwT/3/JYUIN33\nj0iI/wCXyR94/wBr9XyWFCDd95H+n+r5I+8T/T/V8lhQg6H3noQYbPXN8lH7xP8AT/UsKEG77x0/\ndfqR94/2v1fJYUIN/wB4/wBr9XyR94/2v1fJYEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIB\nCEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIBCEIB\nCEIBCEIP/9k=\n",
"text/html": [
"\n",
" <iframe\n",
" width=\"400\"\n",
" height=\"300\"\n",
" src=\"https://www.youtube.com/embed/7p99ez6MEeo\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x118d385f8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: E50qKeeMbgU\t\tGround truth: Elliptical trainer\n",
"0.6777\tElliptical trainer\n",
"0.2239\tAssembling bicycle\n",
"0.0558\tSpinning\n"
]
},
{
"data": {
"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEABALDA4MChAODQ4SERATGCgaGBYWGDEjJR0oOjM9PDkz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\n/9k=\n",
"text/html": [
"\n",
" <iframe\n",
" width=\"400\"\n",
" height=\"300\"\n",
" src=\"https://www.youtube.com/embed/E50qKeeMbgU\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x1224cff28>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: j56eH9M0ObY\t\tGround truth: Breakdancing\n",
"0.4643\tBreakdancing\n",
"0.2776\tTango\n",
"0.2189\tBelly dance\n"
]
},
{
"data": {
"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDABALDA4MChAODQ4SERATGCgaGBYWGDEjJR0oOjM9PDkz\nODdASFxOQERXRTc4UG1RV19iZ2hnPk1xeXBkeFxlZ2P/2wBDARESEhgVGC8aGi9jQjhCY2NjY2Nj\nY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2P/wAARCAFoAeADASIA\nAhEBAxEB/8QAGgAAAgMBAQAAAAAAAAAAAAAAAgMAAQQFBv/EADMQAAICAQQBBAIBAwMEAgMAAAAB\nAhEDBBIhMUEFEyJRMmFxFEKBI1KhBiQzwWKRFSVD/8QAGQEAAwEBAQAAAAAAAAAAAAAAAAECAwQF\n/8QAIREBAQEBAAMBAAIDAQAAAAAAAAERAgMSITETQSIyUWH/2gAMAwEAAhEDEQA/AOF/aIkmpWaF\nyDOFobqBHlBNgbWmVUvoMLcOixkXyZ1uXgZCXJfKmmJGSFMmR0v2dHIWml2A18rFpNczkGnFcbrf\n8Fc20NePmBVBY1UL+yeTWchnzxtokVyN1FKhEoy/KEk/0LrAY0yqBhk8Ph/sa+iN0FtAMPI6EuTf\ngx6uDRMGS4K3EclRjaC65HKCaErlmmKSRKoipdFqmBOSXgH3OOiaobiVtj5A9wvdfgkajxJ9Gfbt\nma10Z8qe6wRVw7YwTGVBe4B6YQHei00xHqyFN14LaoD1T6FPsY3wKbtgloXRC49EfQQyMn5BQByd\nlQlRdSeUCpWESEYO6ipOhTk2+gPTt6BnJUAUk2BInckaF0KhDmxwHoZPahW9t8BTdspLixFap2yu\nS2/2U8iQ8LVwbTdjb4EKakxkZWBpONozvG7NO79FNICrNsZFis0bUVQFjM4NFfLwaZK0Z5XFgXq0\nwlY7ijJa+x8JcGkpwxRTLr9FJkTL2KRxVCWvkaYuxeZUrRUwh43Rbknz9CITTXKCTcpUujSA7HBZ\nZfZtxafCl+Nsz4IqHZp9xRXZ0c+sC3FLhKkDXIKypyDcqL9oa5YIZY/Iy5tEo8421RqjlQe9P+B/\n40nL23xN2zRFXEHUx2z3LoXHcvxkzLqYSs35Cal4GTu+XYzFwuDCzTwj2ZS5K9t9Gq2SzO8nhEMV\nDHwghM53wZ1UA3ciVyVJbY2ZJ6iW6iKm9NlMi4Mb1UqGYszycMRezZCVlZI2gYug4uwX+s8oSvgi\nhI0osQxmeNxBtpmqXKM7VSAsMi+CSkDu4Ak7YBG23wXsl9B4o9jW6EJEj0S+Cygh4VOFsBwaNBHy\nV+ljMrTGp8C8iplRmkIlzfIeOCa5FvljocIDgnCIKggrIB4iVAzlRb6FS7AIlYnPJxXA5NoXlg5o\nciOmNZZ32XKT+xiwNMP+nQM8oMDds0XQMMSgFaQlxakGmKtbhngDFYDLBAJfIE4oNIpoAv2UEotL\ngX7kgo5Gux4UMSf0XQMcgXuRHOaa+vJLvsm+LL3RH60B2ou6XCCqJe1N8DztSlOSXZHNvyxkdPKX\nSI9NlXcGX69gEZtPsZKba7F7JRfMWg2uOg3qGFybfdBxyyS7A2SfgntyRM66n4WCeVyVMS4U/jJo\nNY5EeOSL9+/7ICi/LGxkkqQO1+SbWRe6qC3MpyJRKF7nUbpGe/0PatCfakTqaqTbjRmlglJ2mao4\n5Elil9i1F51heCQ3DilCXJo9uRFjkgKc4JvgHHIv25MkMdCaw1MuykiUI03foXNN9BkGMI2tFxxt\nvkaWBYkU4oPsHlEpsRrJYFstWAFZSKLGQXBSFvFyO48gsCBGFDfBX+SXH7A1kKuP2Va+wC2uBM+G\nN3L7Kc4/QETUmElOuhnux+inm/Q4ReyX0T2pf7gnmKeVsRLWJ+ZWX7S+xbySK3yfkAP21d2FwhVy\nKe59gDrX2U3H7E8koCN3xXkpziL2guKAH+0ye2zRRaRWq9Wf22XsZopF0glPGbYy9kjRRdFzoYQk\nxkFK7QdL6CVfRrz3Bg8GTURl8Y2bXq9Rs+WExQyzh+Mg3qMnW43nm5heuhyZpyf/AI2jJOUlk+SO\nhHLJ90xOWKcroXXXNPMVDUqK5xst6mL/AP5sJyTXSBuukhzrmFRR1EPONl5s2Nx4xtAqdeEXOamq\n2pBfLyWMsp/SFubNDxp+QHiX2c/XUpwncypTl4NHsop4fpmVNn3zJ7kh3s/snssignfIm+Q32WT2\nWIEb8hFOfkf7Evsr2H9gCvckT3JDfYf2T2GA+lb5E3yGewyexL7AfSvcl9lrJIZ7DJ7DEAe5Ir3J\nDfYZXsMAFZJE9yQfssntS+wMHusp5ZB+zL7K9iX2ELAe7IrfIZ7Evsv2WMYS5SKTZoWH7LWCihjP\nZZo9mJaxRFRjNS8lUjX7Ufontx+iR6sqS8F7TUoRXgvbH6GMY9r+ibX4RrpfRKQKxk2y/wBpPbka\n6RAT6svtSJ7UzUQB6svtzfZfss0FdAPVnWFlvCh1sFoCwr2UT2YjaJQDB2WYHlkGs0jX0L3bSGP3\npBLUPyE4P3ayzMtSvJP6tLwP+Oj2aSIzrVp9oJaqC8D/AI6fscE+xH9VjfgZHUYZySjwL+OqlPj0\nSZqw6JZIqSmXLRtz270bTxU2Io6Gb02UI25o5zcYTcWxXxdFqyFwipPhhvBJL8hfw9UvaFkI4uPb\nsBszvh6g2CLF7kX7kF2T6U9guSALJArcvsiyjRvkqv0CsiRTyr7ENNI+RXv4ye/B9CP4ZX7IL96J\nXvx+wLTSgPeg+2R5sbA9GQH3IfZPcgvIDYMli/eg/JPch9iLYZ2QBTx0+Se5BurA9gymRNFWhyGh\nCJosNwlELILREIUWPYaEKaZa/gWkhCO/oqmGkhGXTJyL2GgIFyZ1ld9P49B7DTiDYLdFMkoBpaUU\nM2gyVINGgKI+ipNIpNqEFSmivdSBPsVVlpBpFNHRrPFJBbCkhig7DYeBeIntDnjnSoHZkH7T/p4V\nsX0TZ+gqaCi/sqZ/08K2BYYr3kM22HijWVFT/wAOOnglJtRuqLUm81WwcC+YTVamJ1ca1huXLJxa\nbZzZQi8jdG/IuDFJ1Jh316p/R44qPktv92BjdsjtCnkKxU19CnF/Y67BmqI68gyE7GDst0Mk3YeJ\nPcmc3fYwel9NyaiVLg1aj0fJihad/wCDXotVCDpRdnRyZ4vHcotnJfJ9PHjsuKcJVJVQpwvydnXP\nHOdqNHOpN8FTrRjN7aIo0aNgMo0URNWV7f7GJDIQ3Coxn2cfZSx19nRx6bcmDLTNEWllc1wkv7mV\nUq/I2ZcbXgzzTXaH7aRUVJP8i+fslk3jTVrd1YzFF71bFxmkMwS3TiI+a3bUi9qIywdEUooCctoy\nxeSiaVB7n6LWQFbS9qZKdGpplqcfJSgiba6F8Gj3x+y/cihVMragGm+7DyX7sEZ5RXBHFfYy1p93\nGT3sZicf2Dtb8hiW15ofQENRiUk3jv8A9mbbXkJRDDaZatTfwhtoVLUSfgKOL42gHGn0VJqKv3ZP\nwC5uXkXqcmyG1Omc3LLJH5KZXqm9OptnJdlSVJt+DFotZJz2ydmzNJLHIILfjJPV44ypropa3D/t\nMMlc5MGn9FyM9dyuClGxm10HCHk06dGLw4TRjxRQMZbV0HF/RjaqQ3ZFoTkxxDTb8lSyQTqUqYSq\nwjJirwJca8GxverVNCcyUFy0aSkWuug8auaAxyu/0Mxc5Im/H+0KOljhtxuTAq9Tv/to1QismJwu\nnQlwbm8UfHFndk1Q8kVKCaOVqXWVo7ksajiUG/l5X0cLWNLUteTLz/6kvDGx0heLoZPoz8d/xLC7\ne5A5Lcgq+SJ/eY91Ui4Rofja3coSuRmOlLl9nN5Gjt+nyi5r4o7UsUXj/FHnvTssFnUG+Tt4NZDO\npxj/AGPazltxl1+uL6lijjnLjs4uTHFuTO/6quXfLOFm8pPsvirxn8ANWxgKNdTiLFZcI+3QUbCk\n/iibTxqg7aYeofwE43bXgLUySjVmVT1HDza/OpyXhMX/AFk525LkdPAm2/t2CsC5ZtPjKwj3m30U\nssn3EbLCvAGxlpD7jvodppbs8RLVcjtLDbqI/QDmOp2Kz6hYUr8he9jUqU02ZddW6Dl0xN9adNqY\nZu+xetyKCSMWCXtalJcpmrXJceSKm9FrIrGLKqM+22q8l7X/AM0CNalliwlNf7jN7bZFja8snDaX\nkj/uBeVfZmeN32yKLuipBtPlkXFMFyvyJlB8UxM1OlTHhbW5S3FvkXh/G30uzLn9QjGe2MW0vP2E\nh3rG5cjYRTkjn6fWqbSa7Z1cEN2SKQuhLrdpNJvwylXSMmTFWZr9HoNDGMMDi12Y82m3Z3t5Ig6e\nO1+VvUST/G+DLKTd2aPUYyhrMkZKlFmfHjy5GtsW7OiOeppm/fVI6Ool/p8cAYtLLFVx+R09H6f/\nAFqyxcktsHIjfq5+PNS/JkLl+clVUyu+uTWVnXpH1YieqUXtRyo6vLGbpvkuDudyb5F+t/5Hbw5P\ncQ2eSONXLg5+k1GPG6bK9UzVCKi+Cav2joYtTFv+ejBr1JZ1K+GLhmSw2/AepzLPgi4roUHsfopy\ncWn0K9R3b402ol6PKsSSkB6jnjPao80UWmaR22lyjUpRhmhv4SZg0GSMJfLgmsk56lxT+NGnFyj2\ndf1HUvDHHkwv8kzNh9RnCbyN+VZj0yyZ8ns3cYRtGv07Rf1vqGHTz43do6P5NP2emzYHs96PLcbZ\n5HJOTzSlP8nJ2ev/AOoNZp/SsCxrtRpI8Pl1Ep5nkardyHk79uSnTq4HcRsjmQ1+xUlZrwaj+pxd\nci56yYrTf7kVfzKjKCdTfQiWsxLNtRn1TnTUi5SUe3XkB8dBLHat9UY9LtL0eqlD1PHJTtT4PSYM\nkNNjzzm6cpWzxqlLDqVPY3GLOxrtQsuhjT520cvXKZWp+ox1NqUqa4MOVx3OuTkZ9z9vbKtvZpwO\neLSzySVruyuZh2nJ9lHInlyu8t8DY6mUomuJ95uOomSX4Ix4tZGEal2a9/uRVEYv2jRAz67Oo0Z9\nZkyPJHHF1wYJyyYclTe4Xoi9xrhNyC5kxGDOuzPnzTyZlt4oeJ9o2zTiA2nRkjqpRybZD9++n9FJ\n9pQyimv8jYfnw/AvFKMYzlLy+Q9LOK1DUuU+gEA9ilau2MzXmhjxt00ytjWr9v8AuQj1BLHklGL5\nj2NVpmSMcOSLUraY3UZk0jkNycW3KzqvHFYMbTvgMZzrUwZVPJGKNEcT7/ZlwY/+43LujfCW1dkV\nU+i2ovYmim/lNJWHtUcVfohdZcmXHB/JmrTenZNdpJ6jTNNR7OJnjeWTfJ1PSJaiOkyQxzcYPhxK\n/EqwY4vFNz4cTDmz4lKos2eo6bPljUJVGK8eTlLAvK67K5sqLsasesuFY1cn2jJmxfL4rnyhuDH7\neVtdBNt5JOi8Ky1m02LI88fi+z1mhx7ZRbOHo8E5zWR3SPRaWHj/ACZ9L55sdjDG8XHgyanW4tHC\nWab6NOoyx02kb/ua4PE+s6iWTLtk7DnkuqTqckvU9a8m3am+R+bU4tI/awRuSRzoZ5QtR7Hen4nm\n1KlLwaM59rfpf6zLL3Jw4Jqtfk0l+0nFyTTOpCaitq6Of6tBZYJpEteucjgx+fb5ZeFRjkaZsekh\n7asPSaSGTT5slfiy9Y2BenjCb3PvwZ82N4nxbTNWad5PkDqZqSSKirIyqM9ob9zNGn4HynFRTrpC\nsc3GXAijThxR9rbNmzDpMbwOpCcWN5cMmuwcWaeJNXwgjX5C9S/bdUZu+WN1UvclusmmwrNKrLxj\nRYcHvK0+i8uHJimm3dm2OFaePxBjcstZel0JUlD6dklj1iclXFHWmva1K1eF7Zx6MEcSc1JdmyTf\ns0GtIxet6rJr80c+aV0q2nNhFz45bf8Awb8ihOKTCxLHg+aV0Pdgsc/HH28jjNGj0/JeonXCroHL\nP3sjyJGvFjxQ0Ms3Uxy4hy8ubJLUTjbVPgnxjy+ZF2p3KvkJae62Kp9sa/6+XiP/ACHj12Wdpvgw\nbR2n4kzOn72uhLU0uUq+jTHNCbcG0r6RilBSiJzS9rHuT+Xgzs1tz0e4JZpQlK78F41k1DyaaK24\n0uzDGcoqOZyud8nW0eohOOSaVcchh7rnPT5Ywljq7DyaOeDTKT5ZTyzyOUo5Kin+Jsy5vd0WyqZW\npvMzXL06U7c/Bv0MnPDf0ZsWjltlyP0sJYcUlfNi0cwvWym9XFRTuhGWORZFa5Zry6mGLMnONyoG\neswtKlcmCrIXh0eRQ5kkU8Gy23Y5ZZZVw0FaUabVsDvPLmzwt5rNs9uLTUo3L7DlGFE3xjjtqx6z\n9JvwvQaV5prf+D5aEat+1q3sW1Lo6unkngc48HE1rc88pXwODyf4xtjqcUtW8zdPbdGbDt1Oui8n\nU3yjGo8xNGjl/wBzFpdcFM5dbtX6djWdzxP4LwDCcc0ZpcRhHg0a2UsWkdPk5KzPHjnCPcqJ/VWe\nrdujpsCnJ3Kf/Bu0uPfp1N+TiOby5IJu0vB2sGZqMVfC8EdQ/HXS9Oz6bT5pT1OJzTj0gVoc2q0+\nbWY41p0/PFImp1emngxrBicJxXys4Oo9X1csc8Mczjgb/FEyK6oNTkxPUVh5+2bvS/UoYceTTTS+\nb4ZyMcW4uUeI/YqacJ7W+/P0aXjYx98r12s0r0m1PNGfuR3KK8Hn9TUZyTdL6EYdTOE98skpVxyx\nOXJLJklK+GLnmRV7Ojmt/R0PdwvRyUV8/s5CfgZhjKU1HdSfZaee67uibWngm0rOhiyuMbTqlRjh\nDFix46B1GpSVRIsdMvwz1DXzyJJy4SPOanI55pSvs35m5xbs5WR1NlcufyVE3ydH0edSlatnOj4N\nfpkqztDqOL9d2Pe6+/AvLJODtWVKVJAZX/pszrqrPlW3E34Q/QqEPScspSpTkJ1VrSSl4ow6rLOG\nnxYYuotWEmsa16jHK1uFZse6Jr1slkapmdul2aVWFyj/AKaRWmWxtMbFpqmVk23wA9WrSJQyN+GP\n1OGEYNR7kJ08ksfQc8u6VfQ4XTmSW1tWMwS2+Qci/wBT7NOHHh2pylya1jLhkZtsqWSUXaovJKKV\nQFLO1LmKaFOV+7Tj1Lk0mjZv2YG35MG+E3GqTDy5ZShs6K9Ee+MzbWVy8BtrZ/JUVcqfRJxp8K0L\n0xU7DGW0mSTnhkvFjIxi1+wVBu4+GOc2lei5qMcSX6EOCkuTqf0e7C5f+jBlwyjwL1qZ0Gk2kTLL\nZOKQuLcXbfQGWdyuiLD9v+NGLLuyE9UxZcclHLGr5Fab89x6HRZsGqwZ/wCujGc1H4/ozvxrzvUc\nDLC9PBxS3NqzVp8MscMuO29yHPFGM18fi1wJy5c2LNuxc/oWtPWwt4oY4qKfzvlGeepliyyx3wnw\nglgyzbz3bbM+sTWdyaab/XY0dWtOPM8kV8qGw1Dakkro52N5Z0o42x+GOfGpL23T8iwparJJ5Zts\nXJNyS7Lx5JRz7dt7nQzUyeHK8ce67Hh3ocJOGoSb4oapKTVd3yY8F5c0bfRsUFB8LsKfuuUV3Y6K\nUko/ozThKT/OjTpqc4kr46N1K9rTxUe2jhZN3uNfZ3dVLc6apJHD1FRyNp2VyjzUtjtM6zw/kS+r\nG4IyeWEoq6Y7WPP9Ot6luWn4+jiHc109+mT6SRwxRp5PtTw+TuabRZZemR1E/wAWcnHp99Nyo7mn\n1clplp0rgvBPVV44RrM7xaX23+UlSOMscm/0ev02k00vSc8tXG885VD/AOJ5nV6XJo6i3xJumPil\n3zQO4Y6AnD3na7KuShzzYuMpQfxdl5jH6kri6fgjXFmh4nPHd8sGOkyNdiOSlRHwdO7oCeJ4vNi5\nzSVNDH46WnzXFxTsbCCl+VoxemZcWPLc1Z0Z545ZfFKJNbc3Uy4o4dNOafg4cpXGl9nY1knPT+1V\nfbORKLjOkn/9Diegdvk2+l3HVb64RjUJOuHb/R2dJgWDEr5cv10CeIPPmuTX2BhyzncW6Jmgt6lf\nXgBRlGakub/4IbNHqOGf9NBKLdvk52rbeSMPbdJHVjrXuUJcpeBizYJym3GN/QJ9dcbFlm1XZpxQ\n3q2K00axtGvDjk4NR7Kp8KWKKF5MaTNkdNJxuXDEvDCF0KVt1PgIyUVQve1PjkVKbcpL6GYsjijb\nlxdX6XO9xcU6LbthJWuTac6xq1Jk48oJQQ2MDaeNGhw425pxQ7JH5U+wsa2NNLkdljvhur5B61Ws\nbiXGSvax0cfAuWF3aF1yJ0P2Vj+VjtLhWTPFS8szbGlbNOmk6UquiTu17BekYI6DfxdHlNdjjHJK\nG02y9TzPBtv4oyxg8z3tEdXGvHOuPn03FowZFtdHos2OMIOzgar5ZW4+DHdPrn1M02SC7Oliy6fG\n2073ROLFv7NESLNXz1ZG5arbk+S+NcIJazEm2oV/JgZRONOe6349dgjLmFt+Bi1emzrbPCm31x0c\ntsbhT9xUF+Ln10L9vG44saS+y9ryY5QaVtDseP3IK2Vk1GPTrZVpEWqvMcLDp5YdXPevx6E6xS95\nzfk7LxSzbs0VSo5Oqly49Oi5WHfOL9PjeWT+jbl7Qj02PxkzRlXLDU8griw9O1jzxcvxAbqIvUTU\nUnViq4b6nqI7ZKHk5FudD82VZImdpJjiPJdW/o0aLJs1Ed3T4MwzTxcsqSKqeXS9Xltxxhjfx7Zy\nbfg06mc6kpdGRqhQ+7tNjlkjoaDUqK5fJyuQ4MVmpnWPUf8A5HFk0qUnsknZyfV80cueDjPckuf0\nZm20Z5rnkU4xd70zJJNJIQ7S7Luimy2dasEksUnJ9C555tVGQuL4YO1yYK9hPJKfbBcuPuyykCTI\nZHCviHi1E1kj+hJeN/NL9gJW7UarcueLEafVezm3uKmv2aNVjUMDl5aOa3aErq49FGenzwUseNJj\ncmSOza1TOR6fljhn8+UO1eeO5NPhhjXmzD4Q+bblY3DHxKPQvRtThwzfg9nFiludTkqSMOriy9dp\ncWD0v+oSqT6OHizOSb/uPQf9TRni9N0kJPzZ5zHxbXTZpx9jLq40uWxOhml1mzIrAlG0zLCLU3wX\nir16u9PK549yZysuol7hojm2aemYY/Kdjkwd+TYttykMi6CjBIrbbOjiOS0cENSSAhEcovwdHHLK\n0UFfg148Ng6bTym+EdbT6NuSW023Es2LTfasbLS+aO/pfTLjbRqfparoxvlkU8fPTuKMs1JM9Jq9\nHsvg4+oxJMqfS3HLnJt8nQ9OcFiaaMWaNMLS6hYo1Iz75xt4+m7Ve2tPPZ2gNBqN+np+PIqebHmx\nyjHyjHoZup40+mc3Tq56mupq6ngtLtHmcnGSS/Z6aPy07b8I8zmdzb/bMoPKSvjLs0RlfRnfQWGV\nXY2cp92RclJ8MqL5EuVrxYFLk1Y8UYNOjNgnSNGO5yqv8/RFbRrit6qDoz5lHd0bIQ9qHD3Mw5JK\nXNk400zLlhHT0lXBwNRLdNtI6WSdxas504reVHN5Omz03jEM1L2u1zYWCMcWETmyrJKKXgf9km74\nEqocgS4tFuTcaAmfJiUoylH/AOjKkvLOjGKjgk35Oa1udoaekoZgbjO06ALh+Q0T4ZqJb2k30VpM\nLzSafgDInvOh6ZCouQL/AFzssHHI0ClXk1a2G3K39mVAmw1TaKl8uQLJbAKTuVUTtN/QeCClk5Cz\nxjDK4gF44RWFzb78Ct1codmSWKMUIdR4AllFlAaUHhV5l/II3TQ3ZuBUNOvn8Yx/RgXZr9RTjkiv\n0ZBw+jE9vQM25LsjYF8oKUr0HpGB+zGT53HQfpspamMsjS54V9GX0/jR46ZqlPZinklks5evtdHP\n4w/9VvItRhUsinFRpJfo4MZyUdqNGvnLNlcnPd/6M0W2dHPyMO3WpNiJ46mmjTtaQmbqaQSurvkO\nSDcBMVtZqyvbFeTPLs15jk7+NEH8WDFbmyoO1Q3HDk6OYxv0WOHBoxxp012THDg0YoKzo5ZV0NBG\nMeTvaOEXRwNO9p2tFmqrH1PglehxJLGqCTZmw6iLilYWXVQiuGefeLreWMvqKVNnmtVFOTOzrNTv\nZxdT23Z2eOZGPVcrUY6Zina6N+pdmGal9F1O0n3JwbkTSZJe+qX5MrIm1T4H6XbiqT5o5PJHT466\netcdPp+6bXR5vPOMsjcf8mjX6uepycvjqjPDFKfjj7Mfxt31rO3T4KSo0ZcO3pCX+w3WWCjkoL3R\nSVk/wJcrV/UxXEYmjT6uKmuK+znqe3ojnXNCxU8mPST1CnjXty5M2pk44G3+RxYanJHp0d/0eEc+\nF5dVzBEWY1579nC/qMkuH5LSUJ3I6HqWPSx1G7TtOMv+DE4Nyt8oqM+ph0tRHZVmeE0p2/JHi8lu\nMWlTGEzahRVIHFncpNsksUW7ZftLw6EmGah3pWc59I6GXnC4ffkwyg06AWKaLxcTsucJRKxpuVIc\nTRZHc7OloEv6Y5VNuqO1o8M44EnEmr4jPrYJ4zmU1wdvPjvG74OROL3ugg8kD0VK0uC3Ft8FyUqq\nq/ZSF4m3liTJzlmvtkwL/WVuki8i25nb8jC862xgv0JRq1kXcLVKjMlyIllMjdE7QAcvxQ3QtrPG\n+RTfxD07lGdw5Ea9XNyzy56M6HZMUnKUn2xPT5GKspkSJ4YCOx6fnrBQzNqJ5IuK6Zz9FPaqNykq\noysyt+aw5/8ATx19mRM16+S3JUYmjWMOv16eWFtGLV4/bi5HQWRuqM+vxPJFMiPR8nPxiT34UxTX\nI/HhkotEeJ2bc1wdc1WKNs1QVCoQ2miK4N+emfqZFprgbjdMTBDoo156R1G3AzZizNROXHJtHQyu\njadMbHaxal0uSsuqbT5ObDK6CeVi+F9aJ5bVmPPO1wXOfAhuxWzDktZp/JiZrg1y7EZDPqtueNYc\nydcGeUpqJ0WuORcoo5+62nDmNNctBY8s3wlwa5RX0BSXRhq5wFp18jLlwu7NgD57FqrxrBJSTK3N\nGyeFSXBlyYJQdhrO84AhUV9l0OJUjsZtYsHp8MON9Lk44WTLKYrNE6wyU2pKnx2hkNRt7EQfAxAe\n2nqXgpR5fAMXFMdGpPgG3P0DTImMlFp8A1ZKvVaVoy5cct11wa4/FlZXuQ5ReWenk+JI4/Zn/Iyq\nJdjZXlnpuV15OjjzTUIqzI0aI/gTV8zEyZZNcszqMXbYyXQuuRyDr6XSgHs9xJLySuR8Z7Y0VqfV\nglilHJsXZo9pTnGuQu52FPmQaXqXrXu2v6MlU0bckbhyYZJbuhIsxUuwolyqyl3wNC39Gv06Kc7Z\nja5On6VC4tsVa8zQaqNTdHOnFuR1dTGsjObP/wAg4O5gEQhBVDXooWzWoNN2J9MjcJHQcFbZFbcz\n45Gt/wDIjMaNatuoaM5cZdfr0+PHJRsXky09rG5dRtx0uzFNuUrfYnZO7TvejVKIEql0gE0i4jTf\nooxGroBOw0aSsryOPAy7FoJGsrO8jUb8joIUh0XZrOmd5NXCQTjJ9IuGNujdg0rmg9k450k/oDbR\n0c+n2oxSjTC1XMjPJC5QcvBpcQ8UN7I1p+MfsuuhbxW+jtPS1jujHkx0R1zq+enNnjSRnnD/AAdH\nJDgzTgYWNmNxKpIe48gSiSZfBTgpdsJxsrYwHUYNTBY5UmK6S8mnXQ5MsfN+Co5evi/5Bvnsv8+E\nFp8Ly5NgVMmm6bBkzyagv8mjNos+nVzj8a7OxodJ7OKo0m12aNRh93TuLkpNITfnxa8zVu7LjNro\nW24zaZNy8DZ/jTjm5SpjP54+/wBGOOTa7OnpILWadyh/5oeP9xFa8dM7i1JxfgFqhn2+v19ALlkt\nf0O39k2h0SitThTQ5fjQLQSAYCSBoY+yKIFhTVMgyUSq4DSwsKLTlySiVyMsMnGLj2Y8mK3wjRK1\n5Fu0COudZp0mUlXNGlLFLsuUYOqGz9SIxs6mg+GPowJV0bdPKoE1pz8VqvyOZk/M6GeVtnPm/mVI\nnuguyLspFx7HfxnP11vRo704nUeJrijD/wBOx3ZGju5MXyaOa3/J1cz48h6iv+6fFcGV8G71WDjr\nH/BikuDo5vxh067nZEDVF3QnRBouuSo8+BiGdFHgNABIekMJAWFF2y5SpiH6eNyoRE14PzXBrzWP\nbr+m6NZZqztaXS+3uRn9EgqcmddRSt/YuuvrFwPUIKMmcnPV8Hb9W5m0kcXPFxavyiv2HGauR+FK\nUlQlquex+mdSToci9+Orj07lpufBytVj23R6LFFf0Ld9o4OpdSd8jt1PLnZEZciNWQzTV2Ydc46e\nSGuQJRGNFSaMa2hO0CUR9Jp8i5flVCOsmqj/AKRzumdXV0oU2cuXMnx0VHJ5f1V8m70uEZ5W2YfJ\nt9Nfzu6BHN+vQYpKKpMOU7i+TJjyQT+UhWu1ePFD4u2K11XqY4mTJWVoFNuXBJQlN7kqNOnwqrbH\nrm9WeTcUuDX6XqlpsryTf5CdTOMXt29GfvhgP9XpnHTa3H8ZpT8GDPinjftz4ryc3DmeDLCcW+Gd\n5zjq8SUuJV2RW/HWsFFMuS2umUDVUVTLIXQAM+KDg+AZR3VyFGNLsCwMgGNkhb5ArFA1QVFJV5Gn\nFS5QLXAb5KoZAolBgsE2Bock1DgVQ+P4gMZZqUrsRKMrNshUlyUz6jK4griSHuFgPG7QW/EZ9dT0\nrI8abR2IazIm2/o5PpuH48s06xzxweyO7js57Nrpn45/rDeTOpvyjnGvPmeVJTjyjPsV2dHP45+n\nQbCS3NfoVCXuOo/5HxSXC6Jx0y6YuEWgUEgVggkCEgLBIOHYtDIdlwqbHs3aePziYodm7S8SNuWP\nb1PpEaxs6b6OV6XLhHVatE9/rBxvUFFSX80cbX/LM9vg7WvdZWef1MrnIvn5BCW2aNL2jJZp0svm\nipV/09Tih/8Ar+fKPO65JWj0GPIl6e/4PM63J/qNiiOf1hySEN9lyFSMuq6+QyZUmimRtRRlWgbQ\nueaME2xOfOl0YM2X3BI68mL1OpeaXAi7VfRNxS4ZTn6u1flG3RxajYjBiuVs6GKNoVq/Hyr5OQOT\nTrI6kPqiWZ66fWWMuWEYwrokJqMUVq4XKNeBSnCfDfJUY35cJ1PyybhSkPzY6X6EP6iimPQr3Nfy\ndrBBwSlu8HP9O008uX5LhGxylHJ1+Iq08UsTM90+CkuESfMrL8EulVEKIILCQJaA0Ytoaxb7AqoE\nJlDSooKrKoZYElBFAWKoZH8QUMj0GjCpCmaJIU48j1FhdFeUHRcVyhJ9XT0EXsK105w4jyi9JLah\nmaS2szl+tP6cTK7d1z5FmnO7kIrk31hWrTJRhfkcuSlGK5QaJdMi0WmUWhUxrkJAoljA0Mj2LQce\nyoR8HybtOubOfDs6OGUVRrz+su3e9Nm1VeDrPNJRurOJoppVR0ZZ6gadTXLWLXZLnKX0cLUNOTa8\nnS1uZNzORklaGvku+TRp/wAkZL5G4ZVNcilaWfHoVla0El2cDUTbtpWbff8A+3acjnZsnxZVqOZ9\nZpS/Qpy4sOXRm1E9sTCuj2xfuJsy5tSraRmnqGpCG9zuzNHXk1MmRyf2Lcq/tClBpKi8eJyBj9oY\nwUq47NeHTr+5BYcKTTNeVriSXfBOtueCtsfxSr9hYbSplTXFgRzoTSfD7plNgbrI2LGmqauSXZgz\nxePK2jdJtLchM9ueH/yRTHr80nHqFKoyia8eDG47oxtvwc2cHGVPhjtPqcmF/aBHPTp48j09y29e\nAIz3bm/IueZZYbl2+yR4ihNoZ2qJQKkEnYlpRRZLEEospMtMDSwWuQimIUDRAgSoSXRVk48l8DSo\nosoQS6Gx6Ejk7QAEnyCFJUwRgLJHhhNF0BYfiltQcsloUnSJJonBfxmzO5cCaHTpsCuTSMbGwJAl\n9EOjRotAplocAmQqwkxwCQaAQaGDIm/C3ZgibMUqNuajp19JJpm1zqJy9NkprmjZkyfHhm/65Op9\nZNTz5OdkjybcvPkxZFT7JrTkrbyHFWwPPZalTIaHuD2mXKqY7e2uzPkfPY6UBn4jwcjUNuXJ05S+\n+TJmxLJ+qMrT6c2VWClulyMzY3B8RsW1K76IZY0rCqVDYQSQjBkatSH+OGC4ZZVglfsmtEbrsBwU\nW2FNbo2Lbb8gS1JeRkZKhKja/gK6EemqScWZ8MqyzsLcwYxqVjLRZKyJoyPG0a+L+ibLfQ04TgT5\ns0JFxxfSD2OJNq+YpMJUiqvovZITSRe6iJEUGEkJQaoiYbiDVeQC0gWHaAbGFFFlDJCiyUwLAFol\nFqIiQNSpA0U2AW3yCyWU+RhEMj2L6DiwIy0lyLcgmLYFQTYCaCkDSGitN8hWAuxi6JaLQYslgoxl\noWmEmOAxMZF9ikwhg6Mh8JmRDoPk05T06Wnnb5NWXMlHg5eN88Dk265Ojlz9Q7LMx5JWxk2Jk+RW\nq5DZTkRMrcRatbnSFSlYxu0KkK1UgGxM7vgbJinyZarNJnG+zHOLRvYtoWpvj1gSk3/BqwW1yMqi\nULROMVRXIxcl7RKwpW3RNqGbSOIFeS3FLoFodVlOIHITRaVBtIrZYJwKhYSTsii0F5BU5Fbii4y3\ndlfyEkmJeKSoPdQNURckni3JsiJtCSURmgElYdl1YAimShsotAOxkAgdWDQErnwTkuiUMKLREir5\nEWICw2VQEEhdEoZBoJIlFoRIwWGLkMrQsopog0aeuw0/0LQRLSCssFhMFLoJFIsZRaDAQQ1DQyIt\nBplylmtEJDsfHbMuOXJoUt3RtzWXUXKVsTJ8hSfItu2TaOYhCgbI1eClKkKkwpcoWxWqVJ34AfAT\nBl4IoA3+gaDoponVAkiq/ZcnRVWLQvd+iJ2VtIlQ9AyPkCwgCFPksgDFbS6rotuyuhaLFUVtCsga\naUmXRSL8hpr2/siVEISEbKXL7LJSQwLiirKTIATcyf4IRAEaBoJlDJRTLI2ACTaRKgrGSqKaLbKs\nCUSypFDSshRBEsBhAjhVVEIyhpwaDAQaYliRdlJkAxbqLXIKf6LsDGmHYtBoZjQSBRaY4DYDYyoQ\nmNi/0a8p6RsFsnYIqUWV4KbB3cdEmjAYV2C2SanZRcrS7ATbJoDLsrstq2QlUU1QITBAKKLolfsZ\nKGdgUvsJOl9gFPwWU1bsJCVF7aBb+y7JSYgB0F2FtRGqGEUUR8A27LsAtBcLsBFvkRitA0yqJbGF\npMtWioyfJG2BLaB8luQIDRMEuwbHCWVRCAEolEKYBHwUXf6KBKiE7KoQQoKl9lV+yk1RCdFWNKmV\nyRlWBGFohBKGiyEA1kIQDGi75RCDMd8BJkIEEGhkeiENeS6RAS7IQVKKBRCEmEFkII1XZTVEIKgP\nkGyEJVEshCCCNAshBpDTCukQggtSsJEICooiZCCNNxN1kIMIWyEAJRRCAEJuIQAu7IQgEqi6IQAg\nLIQcFUQhAJLJ2QgEhRCAFIhCCCtjLqiEKTQsEhBpUUQgE//Z\n",
"text/html": [
"\n",
" <iframe\n",
" width=\"400\"\n",
" height=\"300\"\n",
" src=\"https://www.youtube.com/embed/j56eH9M0ObY\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x118d385f8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: CRzaKuaCXr8\t\tGround truth: Cumbia\n",
"0.5613\tBreakdancing\n",
"0.1487\tBrushing teeth\n",
"0.0881\tSnatch\n"
]
},
{
"data": {
"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEABALDA4MChAODQ4SERATGCgaGBYWGDEjJR0oOjM9PDkz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\n4zlVHmUFto9Bj7ELABoYW/4bK4AAAAAAAAAAAAAAAAAAAAAAB//Z\n",
"text/html": [
"\n",
" <iframe\n",
" width=\"400\"\n",
" height=\"300\"\n",
" src=\"https://www.youtube.com/embed/CRzaKuaCXr8\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x12279ff98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: DP9hfhq8sro\t\tGround truth: Table soccer\n",
"0.5665\tPainting furniture\n",
"0.1129\tLaying tile\n",
"0.1023\tPaintball\n"
]
},
{
"data": {
"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEABALDA4MChAODQ4SERATGCgaGBYWGDEjJR0oOjM9PDkz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\nAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgP/9k=\n",
"text/html": [
"\n",
" <iframe\n",
" width=\"400\"\n",
" height=\"300\"\n",
" src=\"https://www.youtube.com/embed/DP9hfhq8sro\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x11ab5c860>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: xSWpGhhM1H8\t\tGround truth: Tug of war\n",
"0.9300\tTug of war\n",
"0.0140\tRafting\n",
"0.0082\tHitting a pinata\n"
]
},
{
"data": {
"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEABALDA4MChAODQ4SERATGCgaGBYWGDEjJR0oOjM9PDkz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\nFAUUUUBRRRQFFFFAUUUUBRRRQFFFFAUUUUH/2Q==\n",
"text/html": [
"\n",
" <iframe\n",
" width=\"400\"\n",
" height=\"300\"\n",
" src=\"https://www.youtube.com/embed/xSWpGhhM1H8\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x12279ff98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: WQmJrfjOF7o\t\tGround truth: Laying tile\n",
"0.5013\tInstalling carpet\n",
"0.2710\tLaying tile\n",
"0.2099\tVacuuming floor\n"
]
},
{
"data": {
"image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEABALDA4MChAODQ4SERATGCgaGBYWGDEjJR0oOjM9PDkz\nODdASFxOQERXRTc4UG1RV19iZ2hnPk1xeXBkeFxlZ2MBERISGBUYLxoaL2NCOEJjY2NjY2NjY2Nj\nY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY2NjY//AABEIAWgB4AMBIgACEQED\nEQH/xAAbAAACAgMBAAAAAAAAAAAAAAAAAgEDBAUGB//EAEIQAAIBAgQDBQUECQMEAgMAAAABAgMR\nBBIhMQVBUSJhcZLSBhMWMoFTYpGhFBczQkNSVLHRI0TBFTRyggfxJaLw/8QAGAEBAQEBAQAAAAAA\nAAAAAAAAAAECAwT/xAAeEQEBAQEAAgMBAQAAAAAAAAAAARECEiEDMVFBE//aAAwDAQACEQMRAD8A\n8/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA6D4Q4h9thvNL0k/B3EPtsL5pe\nkmxcc8B0PwdxD7bC+aXpJ+DeI/bYXzS9I2GOdA6NexfEX/Gwvml6Q+C+I/bYXzy9I2GOcA6T4K4l\n9vhPPL0h8E8S+3wnnl6RsMc2B0nwTxL7fCeeXpD4J4l9vhPPL0jYY5sDpfgjiX2+E88vSHwRxL7f\nCeeXpGwxzQHS/BHE/t8J55ekn4H4n9vhPPL0jYY5kDpvgfif2+E88vSHwNxP7fCeeXpGwxzIHTfA\n3E/t8J55ekH7DcTX8fCeeXpGwxzIHSr2I4k/4+E88vSS/YbiaV/f4Tzy9I2GOZA6VexHE3G/v8J5\n5ekj4K4lmcffYS6V/nl6RsMc2BvMV7LY3C03OpWw1l0lL/Br/wDptb+aD+r/AMDTGGBkvA1F+9D8\nWS8BW+7+ZUYoGQsHUbtmh+IfolS9rxfgBjgZP6DVte8SP0Of80PxAxwLv0eXWIywlRuycfxAxwMp\n4GonZzpr6v8AwWUuFVqrtGpSv3t/4AwQNvS9nMXVmoqrQTfWT/wZlL2K4lVqRhGvhby6zl6SbFxz\ngHXfq64v/U4Hzz9Ifq64v/U4Hzz9I1HIgdd+rri/9TgfPP0kL/484s21+kYLT78/SNHJAdd+rvi/\n9RgfPP0h+rvi/wDUYHzz9I0ciB136u+L/wBRgvPP0kfq84t/UYLzz9I2DkgOt/V5xb+owXnn6Q/V\n7xb+owXnn6Ro5IDrP1e8W/qMF55+kP1fcW/qMF55+kaOTA6v9X/Ff6jBeefpD4A4r/UYLzy9I0co\nB1XwBxX+owXnl6Q+AeK/1GD88vSNHKgdT8BcU/qMH55ekPgPin2+D88vSNHLAdR8CcU+3wfnl6SP\ngXif2+D88vSNHMAdP8DcT+3wnnl6SPgfid7e/wAJ55ekaOZA6RexPEm/2+E88vSHwTxL7fCeeXpG\njsUMiCTm2kZCkoByUKhkAyJIRIEkkEgCGsRYE7BU2AlA1zQQIYVElBzJ3I2ZKArnG2qJg8ysWbrU\nplFwldbEU0dE0RFL31R35JBKVssuT0YR/bTfVIqNPx+F8FV5WV0ccqjd3bfqd1xqjKpg55FrY4Sc\nXGVlstCwqY73kkug0rLVyKn2WrN28SyDi9m34mmVSbz9V3DZv/5IscFGWkXqLpfawEqSts/GxVU0\n5IeUebfghJRvrqFVx02UWRft66EtJc9hW+4IeU4mVhZXkv7mBpz/AAMzDdr6gjfUJ2lGR03C3nxF\nJnKYf9nE6ngXaqw7kzDTfgABkFdPeT7ywSl8r8QpwAAIIJZDAggkgoghkkMBWQSyAIYrGFYEMVjM\nRgQxWMxWBAl7Rkxm7JsWekUgEitGDGWiIYGIBIGVQSgAB0xluVodMKZDIVEoBiSEAQ8XyJaK3pqP\nCVwqFoWJkONxbOLAaUeaIjLkxoyuS4pgQ9wIWmjJ0exUTcGk1Zit2JTAqy705bPYim7ytL5krMtq\nRzRut0VWvUhVXTLJEU1WKnScepw3E8HKhWqK2ibsdzN5U/E1OIowrV6sakVKLf8AwNxeefJxMe0t\nEtCrM1LRnR4vglJXdGo435PVGLS9n8RW5xgur5mvKH+XTDpRlJq87ro2WV6Ljsl9C6pgauDqZZxT\nS/euDfvOX4srNln217i3fYR2S1ZfVWRuxjyi/EqK5NX0BWZZGk2rv+wskEVyytmVg5JSStqzEukz\nLwcf9SO1rgb2irRslY6b2c1nfpE56EVkTOj9m1rUfRGG2+QAFwyh7MWn8iJn8jCGkF4ASAABDIZJ\nDAgglkFEEMligQQSQArIJYrAhijMVgKyCWQwFkrpLqxJ6zsPftruVxN22AEMkWWwGKSKSZVIEEhU\njIVDIBhkKSAwwhIDENW2BEgNCd9CyxQ1rdDwnfR7gS49Boy5PclroRa+4DNXFcOjJV1o/wASboor\n7pfiK24PX/7Lmk9xJRsrPWJBKlp3FM04t5eeq8Sdabve8eo9lJAV13mw8pLpc1zlduXWTZm4lunF\n9Jp/iafCV1UjFN6qOviSu3xfa2nHPPXkPVlOhezd2lltzZNPstdDKSi7PRkehiVqE6lKM60Um49q\nDV2jUVMLGKsm0u432LqRhh5NvWxooYicpO8W77WRqOHye2BWw+ur07zEnQaeiOk/QJ1oqTWXuYq4\nVaV56mtcfCtDRwVetK0IyZky4JVUM03lR01GnCjBRikivFtum8q8CeTrPjk+3GV8GqT+b8i7A0bV\nLNa30L8TTdR3fNmbwzBuc1/cuuNntl0ovLFczp+AU8kKj6pGop4S078jf8Kjlpz8UQrPAAYZJV/Z\nsZbC1flS7xgAAACCGSQwIZBJBRArGFAgglkAKxRmKwqGKxmKwFZBLFeib6BC30lL6CrYHpTiuurJ\n5AQJIdiN6gYPvJLeN/AZVYS0vZ9GazAY5YqCipZKyWsHs/AzPeW0qxt37ozisqwFMEt6ctO5lik+\neoU9xkxMyZIFiJETJTAckUkBrkoUALEQ481uQnYZMCYTtpIsK3G6IjJx0lsBcgSFC/UBg8SLk30C\nEatyuiqpUVCN2m4dVyMhNMSUdHpdPdBWJi6kZ4e6d+aOdw8Hh8RWV9JScl4G6xWGdJ3hf3b5dGam\nsnrb5lsGublZkK2xdGqaylUzR7y6Mn1I9Gsuo41VaaUl0CjTp0opRjsUR15luqQWLZ1VFXIhXpy3\nkkY7vU7NiVhqbnF2bvugX9XylBfvr8SqtCVSm0tCVgsPGr7xUlmWzuXlxw6+S1rqXCoN5pSeY2dC\nhCjG0ERHctRXM8UbbhqtQk/vGqijb8PVsN4thGSDAAhKm8V3jCy/aRGAAAAAgkgBWQyWQUQKxiGA\npDJIYUorGYrAVkEsVgQxKmsbdXYdivWce7UISWs/ACVq2yAIYqi2NuS52A88yqk/e53njtK+xs8J\nx5ZMuLg9P348/FGoyufPN38kLGnJvLHbqdrzKxrrqFbD4mHvKE0++JkJyXNSONgp0XejKUZdU7GZ\nDjmLoWU1Cr4qzOd4/G/J1F0+qJWZfLqjnl7TQj8+Gkn92Vxl7U4bLmdGsvC3+TPjV2OgVX+ZWHUo\nvZmhp+1OAnpP3sP/AChf+xlUuNcLrfLioR/8uz/cmU2NsnYZSMSlWpVNaNeE/CSZbnkt0RWQmSY3\nvY9bFkaqAuJQkZNq6JuwLE2tiXaWxXdjKVgJjPK7PYt32KpJTRFOTi8rfgBdsQ1ziTe6Xy/iS0kU\nUybWq5boaFVNDaX7yp0rS7mA88sotPZnL4jSrK2yZ0Fe8aU9dkaCcryd0QUaJ3Q/vkuok+qEl1DU\n6rKp4haXRZKrLL2XYwYvkXU6ltHsF8qaE5RqbvU2dBpxvzNc1dpozsNe1mEtXkDEBlMPmLUVQ3LU\nUWRNxglbCw+v9zTxN1hVbDU/AItAACEf7VeAwq/ay8BgAAAAIJIYCkEkAQxWMxWUQKyWQFQxWPa5\nGTqEVhZssskK5dAIydRZKO+zByYjAXZWIsMRcBWrFbQ8hWB56puUrZfouRfl7JCikrRVu4ZJ8j0M\nEafQqdPPK/Iysl9WJO1rIDAr00rpGLKNqfgbKUMyMarS7DINdJWYpfKm34oqyyTtzMAjeLunZ9xs\nsJxnG4ayhiZ26Sd1+ZrVcLMiumo+1FS1q9GlU74vKzPocewdTeVSi/vK6/I4qzGg5J6OxMlXa9Mw\nGOo4hZYVoTfLLIzbrocDwiNlHEPSafZaZ2mCxMcZQUk7VI6MxfTUZdrq8dUCK7vNb5Z/kx4zUnll\n2ZEU6RLinuhb5XZjxYAotfK7royXCVtLr8xlYZAU5ai5X8GCzPn+JeK0VGJXpTdGa3ujnZtt66Nb\np8jqZrQ5rFVV/wBUr0ZxyveL6oKocbvRizjaGpbPLayZVOGeO97dSChPUsTK3HLzRKYWMmhO0rPY\n21CPZujSU320brCS7AKtsA7VxGETHcsiVx3LIlFsTdUFahD/AMUaWJvKelOK7glMAEBCx+eQwsP3\nn3jAAAAAQySGArIGDKwEZFizKuZDaRQmRhlSCU2I9QJckthJSZIrAVijMgBWKMVTrU4u17vogJs2\nQ2olU6tRrs2j+bMdpyb95Nvu2QF08RFO0byfRalMp1Z7JRX4ssUUo2irIixNXHEKafUsjUa0sUrR\nuL+hYpfielzPmuQ0QtRlowEcdCqcLoyrXYrj3AaudOzZVaNtUjY1adzDnDLIzZox3Ri+qFdFX3L7\nX52RDdnojm0qdGKdrkql2rLQZK8rseOkgCjiJ0pqDlpfSx0XDMVKhUjUi201quqNDDLPRxWZtam4\npJJJtpJbsVY7CLhiKUZJ7rRivTs1fpLmjn+EcbpLG/orl/py+Wb/AJjprRqKz3MWY0qzyprtdqHU\naMudPtL+XmQ4TpN5dY80J7tS7VF2fODIMmE1LYsTManUzaSVpLruXxf1AsuDITuBQsjluPSmuK0m\notxhHdHUTdjnOLS95ipJqyireIXmbWLTqU566EyldaWMWUbaqz70RGdnrma7mRvwWOF3eyEaystz\n0lFt1JRt1Ec42vni49QnjURlaSNvg6l4milVhmtFmywFXZBG4ixnqVp6DJhkLcsiJzGRRdDdG9Wx\no6Ws4+JvAlSQwIezCIp/KMLT+RDgQBIARYmwXIbANEQ5dCGKANtisYVlCsgkhtLd2QEENCOsnfKs\n39hXmkruVl0j/kCZyjH5nYqlVf7kX4si0U3Za9RW3mW4C9qT7cm+7ZEPaySJeruR+8Aktk0hJQb1\nvcsaV5Re7BaatgUwzJ2GbfQdpJ9m6FzdrVX6kxXFK1WEpONpx6cyta2ZTSrSjK2Zp8mZDlKorxSz\nc0elzPAaxVC6epZmYDx2Gtcr3WmjLIu++4FM4WZjVqd3c2MlmWpjTp8rEGorUcz0dilwnHWM8xsq\ntMwatFqTcbox1P61LFar69uJZCpF6plMqdR94vuZ9DKMh1/dSutZEPEVKqtOba6X0KlQqPkW0sM3\nJXlYBoXzLLe99LHoHBcVVr4OEcQrV4rXvXU5rAYajShmSvLqzaYetKnUU4brqZtajp4SUtHuJUoa\n5oaMqo1Y1qUakGZNOamrPcy0pTUnlqK0lsyyzW/4ompBPR/RiKUqek9Y9QLFtqTZ9QTT1WwaLmAk\n722RpcbQi6s3dX3Zu5bHO8a99GvLLJKEkr23TFb+O5WvxMMtnHnzKUm9XHXqimp76nFZZtpcmXYD\nERquUZ2i+hJXXZVkVeN9Jx/MSdGMr+7/AAMWrWrYLFT0cqUndW5GZTmsRBSpNZnqi09Viyg1oX4O\ns6c0pE1JSnK80k1poVOJjXOx0uHqKcEXGj4fi3GShN+DN3CWZXN/bFhkMhRkVGRhda0F95G8NJgt\ncTDxN1cJUkS+VhckIErKxJAASQAABDJIYEEBKSitWkVyq6dlX8QHK51oQdm9eiKZzlK2r15CuKgF\nWTqyvosq/MxasXP5m34lk3fdlfXmAUm/lu7jtvZiQdn01HrWuvyAWUXuQ3dWuS6nZ1T+gqir6u3e\nUDXQRpW5XLHZ63Yjtm05hCu1k+gSjazJqRtrfsvfuE97GKs25NgTFrYWpZa3SK5S7WjT8CuUm1qR\nXCTiPSntraS2YQd9HuQ4Wex6HNkXlJ5oK/VdBk/xMenVamltL+5f2VHMm9eXQoti1oOlqVRZdB3A\nfcSUb6FsUuQNIisSpTVtjCr0zaVI31MWtTumEa3KCiXOHasMqaOVmVqKVG+xbRpXmhow6oy8HRvP\nM4uyIrMorLGKcfqXRg9bP8xlFRjduy7yieIpQ2lmfRGWmywGIlhqlp3yS3/ybrpODujj546b0gre\nJtuB8Tc5fo2Ildv5JP8AsTVx0MJqpHvIatyuih3hLNH6mTCaqRTQRQ4NO9N/QlTzK0kXOHQVwvuv\nqBVb+WTXiY2KwqrwcaiTXW+xmNSW1n3MpqSa0lBoo5SUN0YlTD2qe8p9mS/M2uNpKniZKPyvVGLJ\nHL6rrqtv3lLLUh2upVCkoO8LrwL0ico1dV2Cw7iRYiFym14bis3+lUfa5Pqa2w0NGmtyy4lmuiGR\nh4LFKqsk321+ZlnSXWMxlYD/ALuBuUafh7TxCd1ombdbFZpguiuU7aIaFrbgOAA5KK1YQElbqfyr\n8RG3L5m/BEU8qkYu17vohJVJPZZfEi1thU7t9BooacZr3juuUn/yPa5Y0mrPVdCh/wCjfdwfPnEC\nUla4LtXzMItfTkGW90nZ9SiurZJFCbTvyHmpe8S3ZHNx0TAmnLt2tfQm+VNNXEWiLcnazOVn37AT\nC1lYElKdrAprItFfa6Kv3ul+hQTlGEsuZdyIzqyVrd/MWVHNrrmRMo3atuiAb0eZX8RJpZb6DNtS\n3IkrtOyS6AUttNW3DMnO7RdKEbWbKai00e2wHJ4vBKUnOmknzXUw7NSyyTTXU6GeuisYtfCKqrSW\nvcbneM2NJVp3i9NSaNSUJ2lq/wC6MuthKlJ7Zo/mYdRZXbly/wAHWXWcZebNbKtOt9UPGRg06soy\nyt3v+aMmMqUY3jt3vVFGTGXRlildamPGS5FjehBPPuEnHkOtSJd4Vg1oWldBGz1sWVtUTCDskkY7\nIrk7b3ZdTxE6cMtNJd5l0sI1G843b/IxqtL3c2raHG10kVynOo7yk2QkMkSkYbQkNG6aadmiUibB\nXTcJ4gsZS93Uf+tBa/eXUz0nCWaP1Rx1GpOhVjUpu0ovRnWcPxkMbRUlZTXzR6GpdZsZsJqcboYo\nacZZofVdSyE86ut+aKyZiNDBYopdKFROM4pp7nN4vBypVKuR5o05WfVHTyutOpoVU/8AzUpRfZnL\n3dSD/Jks1ZcaxLUlI2+P4XvUw6t1j18DUo52Y3LqLEZRySKTKCQ9ibAKm4tNaNG3pVKlTDvNHt23\nNZTipTS7za0l2dzfK5rFwuIlhMT2pNeHM3a4jUnlcI9jrLQwqdJPRxT8TIjh0463ua9r4RsMPWVa\nDktGt0XRqZXpqaqnN0KskrOPO5soa2a5lcuucZKk5LoRaxF7BcywhkXsF0Q7hUybadtH1EjmUbSW\no1iShLEWLBQKHTlC7p7c4/4FUrrSz8TIbUVd6FNSn7x5o9l9eoRRKUm8umhDyuF2TUg4PVWE1Voy\n26lEZ+0o6LvGvfd3Fjk944b95fOFt0vFAJBrMlybHyxUnl1syuSy6Mm9rW0QFjsrN7lU4uLvsROT\nd9x07q0/oBTKTb7Tdgd7jSgs13qS7vZ3QCW/Mrq05NX212LG7fiTe+r1QGjak9Fcn3by6oyNAlG9\nrAYkqSkrMwcTgqcrvKr9UbSUbyElT1dy7g52thpU4WlHTlLoJHsPV68mb6pDsuOXc11fAc4bdGdJ\n3+s2Mb/VWV+9ptNc9C2nUle0rfiIqNSnF3i7vZP+4sc19U0bZX59XYJTzR3K7a7FtPDyqOyVhaqp\nRlOajFXZssPh/drM1dl2Gwcaa53e7MhUlF9Tj1dakVwWmxFfDRq02tFIyLR6EW00MtNHKGVtNbEW\nM/F0L9uK8TEynO+nSK0hso9iUiBEi7C16mFrRqU3Zr8xMtibBXXYTEQxVGNWnz3XQeUWnmho/wC5\nzXDsXLDVlr2XudFQxdKu5QjJe8j80TcuudmLoSVSO2q3RLT5MrlpK60ZPvMy0+boaQ7WbKVzpwlU\n+SN07t2JjVUtF83QaMbLXcIhxTiaTiGAhGteno5atcjeydlqYtSlnblLdkrpxNrn/wBFnysyPcVF\n+6bidGzZXkto9jOR18Wq91U/kZPuKz2j+JtlS7h4x5JaDxh4tVRwdaNTM2jZ4en2dUWxhbZWQ8Ha\n6NLIeEUtbFykrFUZchZzf0KtQ6fvaq156mzp6I1eFlGeIcXLVLY2i2I4d32e4ashDJhhCJsTZENq\nOrYE2IRVOvFfLqV55Oau9LlRkNrZaitqKvJ2RYl2r25EVKaktVcCqdK6um33C3aSuki1zcezpoUV\nmlaT+bqA07OWWaurGLWp+7711MiTvK99LaETs4+IGHTjnnoX5XGDttzuQkoy02Y2a/ZAi99ORCv7\nprZthJOmtrkQu7q1yCGrxZMFeDz7oSpoteYsZXa6tFFqSlcSMdyYRYVW8tkAlTTRkrSKW4Rd1qiX\nrZWA1zsmRnu9FcXcaCs7AQ1rqS6SS6J8yyFFzk5PZDVI3slyAxvc5npsvzFeEzybe3IzIwS3LHoU\na14O5XPBwTTNo1ZMrcbxVkBr1hYtawQ8aEUtEjLdN2vYj3bexBjrs6XHvfYsVKXOOo2RrdWAr5Ep\n9yYyi+mgWs9Vb6BUOKmmmkanE0fdVGuXI2rduTKq9L3sGra8jNmrLjUk2Gccraa1IZzdEEoiwXAu\nwzjHEU3P5VJXOj9zRquNSUE5raS0kvqcwmbTh3EFG1Ou0uk3/wAmuaz1G5lRclZVWvFJirDzXzVf\nwjYI16eW6qqS7hXUc/mlkh0e7OjC2jpHM5ZvvNcizMt27Ix5Vc2kPxESbd2yNz47ftkZlKV3tyBu\nLW5S5Jcw06h1kxM4rqUuC3LHkS1ZXKrBfQLqVHmFrO6MStxKhT3nd9EYc+M/Z02/HQmprcsrk0no\nzTf9TxU3aEIrxuwX6VWlepWkl0joTyhrdyrKEb6Gvq4t1W4025d/IpjQVu05S/8AJ3L4wSWiJehG\nHUqclJSeZcze4LExrws9JrdGlVlZc3sZuGweKk1OMfdrrLf8Bzrn1jcEZl4k5Ox2ndlcdsv9zo5H\nTcpNbWIqJuLWXVhmWnInNdq7u0Bi092nyEmrS0et7mQ4xc8y0MetTcZvvYGZCetm9bFqRjKSjJNL\nkXU5uSAipFtMxNXJp8jNbco95RWi12gFSTgnvYSpF35C021O3IyJLRvoBjqFo67lak1LX8S6UtXb\noVNt6t3AmTzyS6FsI5u1szFbabS2LYScE9eQFdda2W6FhHK77/Usm7yV0tRpR7MXHTuAWnN31RFT\ntbWTGtdtoV6MCFTy67onRO1tkNfW/N7hOLVmrNhGrUUtdbl1Gn2rkKDlokX0YqPiFS2lFiJZnexL\nkpStyGtLlsioHGy7xZ5sqSRYmuaGa00QGLNS0vf6BCEcurf1MuEP5tyn3e/a0CkjFyfu42v1IlTc\nHZZr8wacZJp6rYvTzJNx37gKFT17WnMNE9VfxMpqLW5RKLv2dUBDcLaxX0IeRrYtioaMlxjZ2QGP\nlvsh/d20yoZpaPTwGTyrZteAGr4jhdPewj4mtsdPJKcWmtGaLF4d0KrVuy9jn1P66c3+MRoixY0Q\n0YbVhce2gtgGhXq01aE3HwZb+n4hfv38UY9iLF0ZceJ4mP70fwGXF6/7yT/IwrBlGm1sFxZrelf/\nANgfF+lH/wDY1/u22PGi2NXavnxStL5YRj+ZROrXr/POTXQtjQXQuhSS5DRiQwz5mRTw6XIyFTGj\nG7yxTlLpFXZAkKSXIujG5kQwOJlNRyqCa+aT/wCDNw3DaEda16z+9t+Bqc1m9xraNOdaWWjCVR92\n347Gxo8Ino8RUyr+WGv5mwoxhF9nRLZbWL2bnMc73VGGwtGgv9Kmk/5t2/qXbciVukkTvfKaZRJ2\ntZXFlFcl3DtrmDsopgU1I3j3rUqb1XIybXWuzKZLLLTYCvN8yYN54p2Wu5VVbUtOZNF2j/e+hBdk\nVkmWNSS0toVKorRvqXJ3fLUAp5rahNXiDlGnvzB3auijEbUalki7P2NN+YlZNT2Fei63IB9ZIrya\n35DSm29QulyAqlfkiLPXQutcWayoBLpSTepZrmTvo3cTRvYlTvptYAjaU9dEStJ9ULKVtbd1yb33\nsAracrpc9ib2V1qLou13lsfdrdasClxy6JW/5Kettx6s5N5U7J8xJS2SWhagpxe1xpOSk1unzFXy\nuWa3gMna2t2FCi3K23iZMNI66lVnZJ9NxnNKNr36hEufa20KqkVfs6kuWbbRirW6tbq7jVL7uU0n\nzva1y2i504tOzXRiO+lmWQjGad7p8gJu5XSSTbJhG7s7vwDsuOjbaGhJbX1ArdK8rJ6eIKFtN7Fn\nZzbteAbLRPxIEtBS7SsRLTVLTuLH9CMtteYENX0V/qjHxmHVam09GtjLv1sS1meVKwHLyg4yae5G\nU23EsL/FiteZrspysx2l2KHEjIXuOgZDLTHyXDIZGUnKUUKkMqfUuURlEgqjTXQdRtyHis08kYuU\nukVczqHDK89alqcfxZqS1LZGDltqX0MJXr/sqbt/M9EbejgMPQabi5yXOWpldmytqanLF7/GvocI\nSd8RUcvuw0RsKGHpUValFQXchlbe9iGvvP8AE3MjFtppqLfiRGNotIVK2wKLV+0yoE2m9EWq7Wju\nyuCTW6tzGWl7aIB1J5ddb6DvSDKn1uNq2ugQ2jt16CJPLZ8tic6zZenMd6rYio6dxTUUs+hY32rI\nRuWa/QDGqxbaTRFFN1LWuXSm1OOaz5iXea6bWu3cAyVktHbZ3L01yK6cW1dvmNJaXQEVXZKyuFm4\nbkvVDbxKKXdO0tUVz3VuRfKKa2MWV4vtbATUSlHewkQlJNWEzNabIgtvpchu73FzfgS30QCyjpdb\nor+ZJqTTLoy11RXJK+1kAKSk7S0Y9nptoiiMlmtyL5JaZXeL/IClya7OhYnd/kVSupdpbjQd0lzA\nrUHK2lk9wnFbO5dKC+aOi6CzUU8xRVFXWVKyvuW5bNOy011ETd2krjWaV5fQgeMk5athUjCS7N0y\npvLq9WxlJ2TutSg91zb1HilZ3V+gr1srjXatdkDKCk23o+4RqSdlsSpO+nXcWrJgFOTjUvLSPMul\nKKdowbKbdlap+JZGCcHFvZ82A1rr5LDWb2aUeieoKEUt+11RLSj+9cohxbtZWBxkuehDV+bJtFQ5\n95ALXkPqvAS6WkW0vC42aKdnJpMBnCM45Wro0OLw7oVmracjfJpfK0UYvDKvSdl2lqidTYvNytDY\nEh3FqTTWxGreWEXOX8sVdnJ2LYNFu7GZR4bWq2c5Rpd3zP8AwbCjgKVFqWRSmv3pas1Oaze41VHC\n4jEfs6Vo/wA09EZ1LhCTTrTc/urRGxho7lmaVtEjc5jF6tV06NOlG1KEYLolYd6/ToQlJhZt8isp\nTcd7rxIeR6tK5MoSdsz0QyhBR3Ahp2so3EblFfLr0uWZ1ybQLXqAiqN7xaY/afJZRJ6bP8hPeWVl\nJ3vtbQCxQlGo2ksr0aGVmur3K1Ulbm/qTGVpXa/BlFrT5oFJ6j7rYW1mAWt0uxr7iZrLTe4PZ2AG\nnu76CuWaenIsS0syuokne2oCYlWincrzWcVrqrMmrJuSs7IWc8soO3cwLqd4wklrYbNprzFgmrtE\nzayvQBkSxIS0Q1r6gUzquMttCuf+pDMhq6WXRalMJ5dGtH+QCyjlaHtGdNRTtLvJkk1e4ttuQAoz\nu4t7EJ5XqXu183Ux6qjndpMgmT6IWW4t3m7iJz6AKvm20HhJqb0shW+xbr0Jhe9gHm1ezV/AhRbV\nkttCucm5aPwHi5wd3fwCBK87PYiu7JLoTtJctRKkJ5nZ3TKCjFrtNluy2uxKd4q0nZkykqdnd3YV\nVJLM2PdOO7TQl3mcrkpq17O5A0uiZZCLZTmu9i+lJWAnLrbTUjL2u1yGntnXIV3cr22AnlyV3oS0\n46aaDqndJtbFNV9NQGinLbbuLMtl3iUG4xyy+hapLm2Aqhz0HyOSu/7i3V9ib3QEpLlNd5FRxts7\n+BORuL/mKZxk3rL8Ch4z67eBLm7dlCqDSV/zGjB9bkGPUwEK1T3lTN3pO1y6nTp0YZacVCPcW5Xt\nLYV0U3oAXV1tbvHfcxHTctNR6d0trd4E2urNjKCirsi6/m/Im99nfxAm6Sul+ZN8y0vcXVK9rXCM\nlGd7sAyT3d/qCi1zsTKonuyO/T6APk6y1IUlG6zCZrvQmKS3/MCZy0VlmuK4yeyLIpPuYSur2aYF\nahmkln16WEkpRdtF39Rr9ta2fgS4yzXm7gNh5y/f1RcpLWxjxUYx036E06nay8wLbK129CLJaLUM\n13aS0uENyiUpJ9xXN2lq9B3OzcUI0pR7wKaibnsTaTltz5jSWsddNrkqaTa77ATB9qSvbQiTu7Wv\noLLtVXZAotTV5APF5krImVRQ35hLb5bvoU1XdZZJsCK8s0E1yexQneMm9CyULKy2/sJG0oWWvcA8\nVdXsri21ETSkr3t0LXkto7vvRBMGrq6X1FcFKzSS8Ailms+exYllTV1t+BRTkW6/Ax2r6vfoZEp5\nHbR6dSizvcgheOo7SUezbvuxE2m3r3kuV33gQ4q610GzZtHsF1az1HjJRbtrcqEp6t3ZZB55O2xw\nK9suIp39zhfLL1DfGvEU7qhhF4Ql6i4a7ppKbUuhEu1G6Rwj9suIttujhW392XqJ+NOI2t7jC+WX\nqGGu1ho7L8B5WS2aOFXtjxFPSjhvLL1Ev2y4i96OF8svUTKa7ZWk9C2KlGL1OFXtpxFfwMJp9yXq\nJftrxF70MJ5JeoYa7n3rejSv1Hg5N9xwUfbTiMXdUML5Jeole23El/Awnkl6hlNegyqJK19BIU1N\n31OBftrxJpL3GE0+5L1DL254nFO1DCa/cl6hlNd7Hd8kuQ0HrrdHAfHHE/sMJ5Jeon464n9hg/JL\n1DKa9AafT8RoxXI89+OuJ3/YYTyS9RK9vOKLbD4PyS9RcNehKC1zOxHZjZJHnz9vOKP/AG+D8kvU\nL8ccS/p8H5JeoZTXojvJJxRKTavpc86+OOJr+DhPJL1B8ccTvf3OF8svUTKa9Dlmt3BGWt2eeP25\n4m/4GE8kvUHxzxOyXuMJp92XqHjTXo6lpewjmm9/BHni9uuKL+DhPJL1B8c8Tv8A9vg1/wCkvUMp\nr0Tw3GsedfHnE/6fBeSXqJ+PeKWt7jB+SXqGU16Neb3jdC5NdrI87Xt9xVbUMH5JeoH7fcVf8DB+\nSXqGU16I07f5Fkm7aa9zPPfj3ilv+3wfkl6gft7xR/7fBr/0n6hhr0C0tb3fgF3fS7Z5+/b7irWu\nHwfkl6gXt7xRO/6PgvJL1DDXosczjeyQ6d97L6nnP6wOK2t+j4LyT9RD9vuKv/b4PyS9RcNekOy3\nSYqnJ+HI84+PeK2/YYPyS9QL2+4otsPg/JL1DKa9GqR0TX1RQ4K6avdHBP8A+QOKt3eHwXkl6hfj\n3in9PgvJL1DDXo9Kd04u9yed+Z5v8e8Uvf8AR8Gv/SXqJft/xV/7fBeSXqGGvRbS1FjLMnF/MeeL\n2/4qv9vg/JL1EfHvFM1/0fB+SXqGGvQ7xyWd1rcHdx7O+554/b3ij3w+C8kvUC9vOKLbD4PyS9Qw\n16JH9ohGm6slroeffHvFLp/o+D0+5L1E/H3Fbt/o+Du/uS9Qw16Hd6ajXzR3uecy9vOKSVnh8H5J\neoWPt3xSO1DCeSXqGGvQ75W4pPxFhFKLu9Tz/wCPOKfYYPyS9QvxxxO/7DCeSXqGGu+yLXUiDV9e\nRwT9uOJP/b4PyS9Qi9teJJ/scL5JeoYa9BaT5kNOLsnv3HAr234kv4GE8kvUM/bnibd/cYPyS9RM\npruKl8ycVyLFGVk+qOB+N+JXv7jCeSXqJftzxNxS9xhLL7kvUMNdxUVltaT3XUqUVfXc4t+2/E2r\nOjhfJL1C/GnEt/cYS/8A4S9RcNd1lSjdap8nyIbjFXadzh/jbieWyo4XyS9QvxlxHf3OFv1yy9Qw\n1zoABpkAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAH\n/9k=\n",
"text/html": [
"\n",
" <iframe\n",
" width=\"400\"\n",
" height=\"300\"\n",
" src=\"https://www.youtube.com/embed/WQmJrfjOF7o\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
" ></iframe>\n",
" "
],
"text/plain": [
"<IPython.lib.display.YouTubeVideo at 0x11ab5c860>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
}
],
"source": [
"from IPython.display import YouTubeVideo, display\n",
"\n",
"for v, prediction, class_prediction in predictions:\n",
" print('Video ID: {}\\t\\tGround truth: {}'.format(v.video_id, v.get_activity()))\n",
" class_means = np.mean(prediction, axis=0)\n",
" top_3 = np.argsort(class_means[1:])[::-1][:3] + 1\n",
" scores = class_means[top_3]/np.sum(class_means[1:])\n",
" for index, score in zip(top_3, scores):\n",
" if score == 0.:\n",
" continue\n",
" label = dataset.labels[index][1]\n",
" print('{:.4f}\\t{}'.format(score, label))\n",
" vid = YouTubeVideo(v.video_id)\n",
" display(vid)\n",
" print('\\n')\n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now show the temporal prediction for the activity happening at the video."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Video ID: unI7FhokvbM\n",
"Main Activity: Installing carpet\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSQNZOJAkSZIkSQNZOJAkSZIkSQP9P/1FZb5bCIrQAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118da8358>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npd4YDGREWQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ab7acc0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: 7p99ez6MEeo\n",
"Main Activity: Volleyball\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAD8JJREFUeJzt3X2wXVV5x/HvL1AckZcRqFgSE6soTKkYk5jaQi1KK4Gx\nxtFpTehQodaJ1mintSNox6K2WrFYq4NoomlGqDZYrSW2iGkrrYUCRklAJWmiaExCEoHiK6ON8PSP\nsy+enNyTe0LOy+Xe72fmTPZe5zl7P3dmzbo5z117rVQVkiRJkiRJ45kx6gQkSZIkSdLkZeFAkiRJ\nkiR1ZeFAkiRJkiR1ZeFAkiRJkiR1ZeFAkiRJkiR1ZeFAkiRJkiR1ZeFAkiQdkiTfSPL8Ed5/e5Ln\njur+kiRNdRYOJEma5JIsSXJLkh8k2Z3k5iSvHnVeE0lyXZLvJ/lekv9L8uPm+HtJrnyE17w6yZ/1\nO1dJktSdhQNJkiaxJK8H3gNcBpxYVU8EXgX8SpKf6fKZSfH7varOq6qjq+oY4KPAZVV1TPP6g874\nJIcNP0tJkjSRSfEfC0mStL8kxwBvBV5dVZ+qqh8CVNXtVXVBVe1t4lYnuTLJvyT5PnBWkmOSXJXk\n282jBH/adt1Lk1zddj4nyUNjBYckNyR5W5Ibm9kB1yc5ri3+giTfTHJPkjcdws93dpPbG5PsAlYm\neUWSG9piDmtym93MsngZ8KYmr0+2XW5+kjuS3J/ko92KKpIk6eBZOJAkafL6ZeAIYG0PsUuBP6+q\no4GbgCuAo4EnA2cBv5vkorb46vh85/lS4OXAzwKPAf4EIMkvAFcCvwOcBBwPzOz1BxrHLOBI4EnA\n2CyEcXOrqg8A1wDvaGYtvLQt5reAs4GnAAuACw4hJ0mS1MbCgSRJk9cJwL1V9dBYQ5Kbmr+qP5Dk\nzLbYa6vqluZ4L62/zF9SVQ9U1Tbg3Rzcl+nVVfX1qvox8HFgbtP+UuDTVXVTM+Phzez/Rf9g7AXe\nWlU/ae41nvRwnfdU1T1VdT/wz235SpKkQ2ThQJKkyes+4IT2NQuq6oyqenzzXvvv8e1txycAhwPf\namvbxsHNDNjddvwAcFRzfFL7varqgSaXR2pPVf3kED7/8HXajtvzlSRJh8jCgSRJk9fNwI+BxT3E\ntv/V/15af8mf09Y2B9jZHP+Q1uMBY37uIHLaReuxAgCSHEnrcYVHqnO2wni5tcccyuwGSZL0CFg4\nkCRpkqqq7wJvA65M8tIkR6VlLvt+ue783EO0Hi94e/OZOcAfAWMLIm4EnpvkSUmOBS45iLQ+Abww\nydiuDm+jt0cJenU7cHqS05I8FujcenEPrXUMJEnSkFg4kCRpEquqvwL+GHgDrccHdgMfaM7/+wAf\nfR2tKft3AZ8H/q6qVjfX/DdaiwzeAawHPt152wPkcyfwGuDvgbtpPaawo5cfpYcYqmoT8A7gP4FN\nzb/tPgzMTXJfko8fzLUlSdIjk6oD/65Nsgp4Ia1nEE/vEvM+4Fxa0wsvrKqN/U5UkiRJkiQNXy8z\nDlYD53R7M8m5wFOr6mnAMuCDfcpNkiRJkiSN2ISFg6q6Ebj/ACGLgaua2FuBY5Oc2J/0JEmSJEnS\nKPVjjYOZ7LsF1E4ObrsnSZIkSZI0Sbk4oiRJkiRJ6urwPlxjJ237OQOz+Ok+0ftI4qrHkiRJkiRN\nUlW13zbLvRYOQvc9mtfS2pbpmiTPAb5TVXu6XehLPd5Q6ocVtFbs1PQ078KOhtfve/qPv3jufp9Z\nwav2OV/3uRftG/CWjg/8100dDauAV/SWoNQX9jkNm31Ow2af07BN5z535ritExYOknwMOAs4Psm3\ngEuBI4CqqpVVdV2S85J8jdZ2jBf1LWdJkiRJkjRSExYOqur8HmKW9ycdSZIkSZI0mbg4oqa0+aNO\nQNPQs0adgKYd+5yGzT6nYbPPadjsc50sHGhKWzDqBDQNzRt1App27HMaNvuchs0+p2Gzz3XqqXCQ\nZFGSzUm2JLl4nPePT/KZJBuTfDnJhX3PVJIkSZIkDd2EhYMkM4ArgHOA04ClSU7tCFsObKyqucDz\ngHcn6cdWj5IkSZIkaYR6mXGwENhaVduqai+wBljcEbMbOLo5Phq4r6p+0r80JUmSJEnSKPQyK2Am\nsL3tfAetYkK7DwH/nuRu4CjgZf1JT5IkSZIkjVK/Fkd8I3B7VZ1EawnK9yc5qk/XliRJkiRJI9LL\njIOdwOy281lNW7szgLcDVNXXk3wDOBX4YufFVrQdz8dV7yVJkiRJGo3bgA0TRvVSOFgPnJxkDrAL\nWAIs7YjZBPw6cFOSE4GnA3eNd7FlPdxQkiRJkiQN2jz23X5y9bhRExYOqurBJMuBdbQebVhVVZuS\nLGu9XSuBvwRWJ7kdCPCGqvrfQ/wJJEmSJEnSiPW0ZWJVXQ+c0tG2ou34XuA3+5uaJEmSJEkatX4t\njihJkiRJkqYgCweSJEmSJKkrCweSJEmSJKmrngoHSRYl2ZxkS5KLu8SclWRDkq8kuaG/aUqSJEmS\npFGYcHHEJDOAK4CzgbuB9UmurarNbTHHAu8HXlBVO5OcMKiEJUmSJEnS8PQy42AhsLWqtlXVXmAN\nsLgj5nzgk1W1Ex7eZUGSJEmSJD3K9VI4mAlsbzvf0bS1ezpwXJIbkqxPckG/EpQkSZIkSaMz4aMK\nB3GdecDzgccBNye5uaq+1hm4ou14PrCgTwlIkiRJkqSDcRuwYcKoXgoHO4HZbeezmrZ2O4B7q+pH\nwI+SfB54JrBf4WBZDzeUJEmSJEmDNq95jVk9blQvjyqsB05OMifJEcASYG1HzLXAmUkOS3Ik8EvA\npoPOWZIkSZIkTSoTzjioqgeTLAfW0So0rKqqTUmWtd6ulVW1OclngTuAB4GVVXXnQDOXJEmSJEkD\n19MaB1V1PXBKR9uKjvPLgcv7l5okSZIkSRq1Xh5VkCRJkiRJ05SFA0mSJEmS1FVPhYMki5JsTrIl\nycUHiHt2kr1JXtK/FCVJkiRJ0qhMWDhIMgO4AjgHOA1YmuTULnHvBD7b7yQlSZIkSdJo9DLjYCGw\ntaq2VdVeYA2weJy41wKfAL7dx/wkSZIkSdII9VI4mAlsbzvf0bQ9LMlJwIur6gNA+peeJEmSJEka\npZ62Y+zB3wDtax90LR607+E4H1jQpwQkSZIkSdLBuA3YMGFUL4WDncDstvNZTVu7BcCaJAFOAM5N\nsreq1nZebFkPN5QkSZIkSYM2r3mNWT1uVC+Fg/XAyUnmALuAJcDS9oCqesrYcZLVwKfHKxpIkiRJ\nkqRHlwkLB1X1YJLlwDpaayKsqqpNSZa13q6VnR8ZQJ6SJEmSJGkEelrjoKquB07paFvRJfb3+pCX\nJEmSJEmaBHrZVUGSJEmSJE1TFg4kSZIkSVJXPRUOkixKsjnJliQXj/P++Ulub143JnlG/1OVJEmS\nJEnDNmHhIMkM4ArgHOA0YGmSUzvC7gKeW1XPBP4C+FC/E5UkSZIkScPXy4yDhcDWqtpWVXuBNcDi\n9oCquqWqvtuc3gLM7G+akiRJkiRpFHopHMwEtred7+DAhYHfBz5zKElJkiRJkqTJoaftGHuV5HnA\nRcCZ/byuJEmSJEkajV4KBzuB2W3ns5q2fSQ5HVgJLKqq+7tdbEXb8XxgQW95SpIkSZKkvroN2DBh\nVC+Fg/XAyUnmALuAJcDS9oAks4FPAhdU1dcPdLFlPdxQkiRJkiQN2rzmNWb1uFETFg6q6sEky4F1\ntNZEWFVVm5Isa71dK4E3A8cBVyYJsLeqFh7iTyBJkiRJkkaspzUOqup64JSOthVtx68EXtnf1CRJ\nkiRJ0qj1squCJEmSJEmapiwcSJIkSZKkrnoqHCRZlGRzki1JLu4S874kW5NsTDK3v2lKkiRJkqRR\nmLBwkGQGcAVwDnAasDTJqR0x5wJPraqn0do44YMDyFU6aF8cdQKahm4bdQKaduxzGjb7nIbNPqdh\ns8916mXGwUJga1Vtq6q9wBpgcUfMYuAqgKq6FTg2yYl9zVR6BL406gQ0DU28D67UX/Y5DZt9TsNm\nn9Ow2ec69VI4mAlsbzvf0bQdKGbnODGSJEmSJOlRxsURJUmSJElSV6mqAwckzwHeUlWLmvNLgKqq\ny9piPgjcUFXXNOebgV+rqj0d1zrwzSRJkiRJ0shUVTrbDu/hc+uBk5PMAXYBS4ClHTFrgdcA1zSF\nhu90Fg26JSBJkiRJkiavCQsHVfVgkuXAOlqPNqyqqk1JlrXerpVVdV2S85J8DfghcNFg05YkSZIk\nScMw4aMKkiRJkiRp+hra4ohJFiXZnGRLkouHdV9NX0m+meT2JBuSfGHU+WjqSbIqyZ4kd7S1PT7J\nuiT/k+SzSY4dZY6aWrr0uUuT7EhyW/NaNMocNXUkmZXkc0m+muTLSV7XtDvOaSDG6XOvbdod5zQQ\nSR6T5Nbm+8JXk7yjaXec6zCUGQdJZgBbgLOBu2mtm7CkqjYP/OaatpLcBcyvqvtHnYumpiRnAj8A\nrqqq05u2y4D7qupdTZH08VV1ySjz1NTRpc9dCny/qv56pMlpyknyROCJVbUxyVHAl4DFtB5JdZxT\n3x2gz70MxzkNSJIjq+qBJIcBNwGvB16E49w+hjXjYCGwtaq2VdVeYA2tQUAapOCWoxqgqroR6CxM\nLQY+0hx/BHjxUJPSlNalz0FrvJP6qqp2V9XG5vgHwCZgFo5zGpAufW5m87bjnAaiqh5oDh9D67vD\n/TjO7WdYX6pmAtvbznfw00FAGpQC/jXJ+iSvHHUymjaeMLarTFXtBp4w4nw0PSxPsjHJh51OqUFI\n8mRgLnALcKLjnAatrc/d2jQ5zmkgksxIsgHYDfxHVd2J49x+/GusprIzqmoecB7wmmaKrzRsrkCr\nQbsSeEpVzaX1nx6n8qqvminjnwD+sPkrcOe45jinvhqnzznOaWCq6qGqehatGVW/muQsHOf2M6zC\nwU5gdtv5rKZNGpiq2tX8ew/wKVqPzEiDtifJifDws5rfHnE+muKq6p766YJFHwKePcp8NLUkOZzW\nF7irq+raptlxTgMzXp9znNMwVNX3gOuABTjO7WdYhYP1wMlJ5iQ5AlgCrB3SvTUNJTmyqVaT5HHA\nC4CvjDYrTVFh3+cu1wIXNscvB67t/IB0iPbpc81/aMa8BMc69dffAndW1Xvb2hznNEj79TnHOQ1K\nkhPGHn1J8ljgN4ANOM7tZyi7KkBrO0bgvbSKFauq6p1DubGmpSQ/T2uWQQGHAx+1z6nfknwMOAs4\nHtgDXAr8E/APwJOAbcBvV9V3RpWjppYufe55tJ4Dfgj4JrBs7LlM6VAkOQP4PPBlWr9PC3gT8AXg\n4zjOqc8O0OfOx3FOA5DkGbQWPxxbVP3qqro8yXE4zu1jaIUDSZIkSZL06OPiiJIkSZIkqSsLB5Ik\nSZIkqSsLB5IkSZIkqSsLB5IkSZIkqSsLB5IkSZIkqSsLB5IkSZIkqSsLB5IkSZIkqSsLB5IkSZIk\nqav/B8aCqfirDC4OAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ab6e8d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nktSTiQNJkiRJktSTiQNJkiRJktTT/wM4040CnNMPUAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118cf74a8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: E50qKeeMbgU\n",
"Main Activity: Elliptical trainer\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAR71AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11aafd278>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6un/ADSdG4MMIB3MAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12278aa20>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: j56eH9M0ObY\n",
"Main Activity: Breakdancing\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11aa64be0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncCBJkiRJkrr6f94fpwbJdDGhAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12249fcc0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: CRzaKuaCXr8\n",
"Main Activity: Cumbia\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRJ0kD/D84VgDb2wXMvAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12275b860>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x122878dd8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: DP9hfhq8sro\n",
"Main Activity: Table soccer\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118c007b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11acb7470>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: xSWpGhhM1H8\n",
"Main Activity: Tug of war\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1/8BuF7qGmt55FQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12224fc88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIkSZI6+j8ZqxpTMuTCbAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ad8a438>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: WQmJrfjOF7o\n",
"Main Activity: Laying tile\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGsjCgSRJkiRJGuj/AR7/d+kSZ50AAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11aa957f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ae7e0f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import matplotlib\n",
"normalize = matplotlib.colors.Normalize(vmin=0, vmax=201)\n",
"\n",
"for v, prediction, class_prediction in predictions:\n",
" v.get_video_instances(16, 0)\n",
" ground_truth = np.array([instance.output for instance in v.instances])\n",
" nb_instances = len(v.instances)\n",
" \n",
" print('Video ID: {}\\nMain Activity: {}'.format(v.video_id, v.get_activity()))\n",
" plt.figure(num=None, figsize=(18, 1), dpi=100)\n",
" plt.contourf(np.broadcast_to(ground_truth, (2, nb_instances)), norm=normalize, interpolation='nearest')\n",
" plt.title('Ground Truth')\n",
" plt.show()\n",
" \n",
" plt.figure(num=None, figsize=(18, 1), dpi=100)\n",
" plt.contourf(np.broadcast_to(class_prediction, (2, nb_instances)), norm=normalize, interpolation='nearest')\n",
" plt.title('Prediction')\n",
" plt.show()\n",
"\n",
" print('\\n')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Video ID: unI7FhokvbM\n",
"Main Activity: Installing carpet\n",
"0.5446\tVacuuming floor\n",
"0.1337\tIroning clothes\n",
"0.0953\tDoing nails\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2tx9+nmCm9AAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b050940>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11add6a20>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEkDmTiQJEmSJEkD/X9XZJ1GzYQlfgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11af862e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: 7p99ez6MEeo\n",
"Main Activity: Volleyball\n",
"0.7736\tPlaying beach volleyball\n",
"0.1720\tPlaying kickball\n",
"0.0376\tVolleyball\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b119048>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11af4ba90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n84M3r0sAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b1395c0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: E50qKeeMbgU\n",
"Main Activity: Elliptical trainer\n",
"0.6777\tElliptical trainer\n",
"0.2239\tAssembling bicycle\n",
"0.0558\tSpinning\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJHVl40CSJEmSJHVl40CSJEmSJHX1f4GQ4oox0h7EAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118c007b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1224bd2e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12279def0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: j56eH9M0ObY\n",
"Main Activity: Breakdancing\n",
"0.4643\tBreakdancing\n",
"0.2776\tTango\n",
"0.2189\tBelly dance\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nST3ZOJAkSZIkST3ZOJAkSZIkST39P9ir/sVfg/LWAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1224e4240>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIkSZLU0/8HndnTK3dgxAMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1227840f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x122770898>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: CRzaKuaCXr8\n",
"Main Activity: Cumbia\n",
"0.5613\tBreakdancing\n",
"0.1487\tBrushing teeth\n",
"0.0881\tSnatch\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b109550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x122747898>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAErJJREFUeJzt3X/0nnVdx/Hna3LIBETQHLG5oU6ZrRStlmU/wEoGaluW\nwSxUTFtHp53KnFYerPzZUSuaKKNF4I8G/mijUlgp5xQVNgoIZYuhMNkYUwTyB9mZ490f1zW8du++\nv9977N73/n7Z83HOfbivz/W5Ptf7ur58zr37fX+uzydVhSRJkiRJUj+zxh2AJEmSJEmavkwcSJIk\nSZKkgUwcSJIkSZKkgUwcSJIkSZKkgUwcSJIkSZKkgUwcSJIkSZKkgUwcSJJ0kJKcl+SDD/HYlyX5\n5wn2fzLJOf3qJvl6kpMeynn7nOepSa5P8j9JVo6izekqyfwkDySZ0n8HJbk6ySum8pySJI2CiQNJ\n0mEpye1J7k/ytSQ7k1yc5FEH0WQdimOr6syq+mC/ulV1TFXdDtDG/4cHEcMbgM9U1bFVtfog2pkp\nBt7zJLclee7BNN4mky49mDYkSZouTBxIkg5XBTy/qh4NPAv4IeD3+1VMkqkMbEzmA59/KAcmecTB\nnPhgj59qMy1eSZIOlokDSdLhLABVtRP4FPD98OCQ8rcmuSbJN4EnJvneJBuSfDXJLUle2dPWdydZ\n145guC7J0x88SbIqya3tvs8lWdZz7Kwkf57kviQ3d3/tnmh4ezvc/klJXgX8MvCG9hwbkrw+ycd6\n6p+f5E/6tPNp4DTgfe3xC5I8OsmlSb7c/gL/e536L2vvzXuT3A2c16fNRya5JMk9ST6f5HeS3NHZ\nf1uSNyS5EfhGkllJntZe771JbkrywkH3oc9jGw8kWdH+be5Jsrqzb1aSdyf5SpJbgef3u59t3UuB\necDftvfi9Z1HG16RZBvw6SQ/1b2ezjU9N8npwO8CZ7WPk1zfqXZSe+++luTKJMcPikWSpOnCxIEk\n6bCX5AnAmcB/dop/BXglcAzwJWBd+98TgBcDb09yaqf+zwGXAccBfw2s7/wyfSvwnHZ0wx8AH0oy\nu3PsjwBbgccCbwE+keQxQ4ReAFV1EfBh4I+r6tFVtRT4EHB6kke31/gI4Czgkv0aqfpp4J+B17TH\n3wqsbq/9JOBU4KVJzu2J+Vbg8cDb+sT2Fpov4CcBP0tzP3sfDzgbOAN4DM2/Sa4ArgS+B3gd8OEk\nT5ns+jueD/wg8Azgl5I8ry3/NZq/7zNoRpb84sAGq15K83d+QXsv3t3Z/ZPAQuD0Aeff28ZVwNuB\ny9rHSZ7Z2b0ceFl7jd8FvH6C65MkaVowcSBJOpytT3IP8E/A1cA7Ovv+qqq2VNUDNMmCHwNWVdXu\nqroR+AvgpZ36/1FVf1NVe4D3Ao8Eng1QVR+vql3t+4/SJAkWd47dVVXnV9Weqroc+G8m+FW8Y+Aj\nFFV1F00y4MVt0RnAV6rqhkkbbSYNPAt4Y1XdX1XbgPcA53Sq7aiqC6rqgar6vz7NvBh4W1V9raru\nBM7vU+fPqurO9vhnA0dV1buq6ttVdTXwdzRftIf1jqr6elXdQfP3PKUTy5+257qPff/Og/Te2wLO\nq6r/HXC9w7q4qr7QtnF5J0ZJkqYtEweSpMPZ0qo6vqqeWFWv7flC2B2GfiJwT1Xd3ynbBszpV7+q\nCtjeHkeSl6ZZseDeJPcCi4DHdY7d0RPXtr3HHqRLaX7ph+ZRhmFXfngccATNL+/dmPpe7wAn0tyD\niep395/Yp07vOSezq/P+fuDoAW1vO4A2u7ZPXmVSd3Xed2OUJGnaMnEgSTqcTTTpYXcY+p3A8UmO\n6pTNY98v/E94sNFmMsW5wJ1J5gFrgFdX1XFVdRzNJITdc/d+OZ7XnvNA9Bs2vx54epJFwAtoHmcY\nxt3AbpoJE/eaz77XO9kqEnfS3IO95vWp03uPn9Czv3uPvwl0V704YZLzd+3saXv+oIp94hpUvk88\n7aMg3zNEG5IkzTgmDiRJmkRVbQf+FXhHku9qJz78Vfb9Bf8Hkyxrv0D+JvAt4FrgKOAB4O52kr5z\naSdh7Jid5LVJjkjyYprn6P/+AMPcBTypJ+5vAZ8APgJ8tr2OYa73AZph9G9LcnSS+e01DTtiAeCj\nwJuSPCbJHOA1k9T/LHB/O2HiEe38ES+gmS8C4AbgRUm+O8kCmvs/rMuB1yWZk+Q4YNUk9e+i516y\nf5LpFuCRSc5IcgTNihxHdvbvopkI8XBYkUOS9DBn4kCSdLia6BfhfvuWA0+k+WX848Cb2+fw99pA\nMy/AvTSPBfx8O2fBZpr5Aa6l+UK6CLimp+1rgafQ/NL/R8AvtM/iH0ica4FF7YoCn+iUXwL8AM1j\nCxPpPc/raIbSf5FmDogPVdXFk7TR9Yc0owVuAzbSJBK6j4Lsc76q2g28kGYSw7tpJmc8p6q2tlX+\nhGYUxF3AxTSTP04Uf3f7IuAq4EbgOpq/30TeCby5vZe/NSDerwGvprnv24Gvs++jDB+lSTZ8Ncl1\nA2KUJGlGSPMY5gQVkrU0Gf9dVfX0AXXOp5l06ZvAy4eZeEmSJB16SeYCW4ATquobY4zj14Gzquq0\nccUgSZIemmFGHFzMd5Yd2k+SM4AnV9VTgBXAB0YUmyRJOgjt6givB9ZNddIgyQlJfiyNk4Hfpnls\nQpIkzTBHTFahqq5pn20cZCnt8Meq+mySY5PM3rvslCRJmnpJHkXznP1tNKMCp9qRwIXAScB9NHMV\nvH8McUiSpIM0aeJgCHPYd4mjHW2ZiQNJksakXTrymDGe/0s0cytIkqQZzskRJUmSJEnSQKMYcbCD\nfddGnsu+6zw/KImzCUuSJEmSNE1V1X5LCQ+bOAj7r1+81xU0azNfluTZwH0Tz29wICs5SRpsPbBs\n3EFIDxP2J2l07E/S6NifDp12+p/vnQ07+319/dSBtfWLs+E0mP/qLQBsu2AhvJW27QNpa9zO7Vs6\naeIgyUeAU4HHJvkScB7NhEdVVWuq6pNJzkxyK81yjP3PJEmSJEmSZpxhVlV4yRB1Vo4mHEmSJEmS\nNJ04OaI0Yy0cdwDSw4j9SRod+5M0OvYnTQ8mDqQZyw8SaXTsT9Lo2J+k0bE/aXoYKnGQZEmSLUlu\nSbKqz/7HJvlUkhuS3JTk5SOPVJIkSZIkTblJEwdJZgGrgdOBRcDyJL2pr5XADVV1CnAa8J4ko1jq\nUZIkSZIkjdEwIw4WA1uraltV7QbWAUt76twFHNO+Pwb4alV9e3RhSpIkSZKkcRhmVMAc4I7O9naa\nZELXRcCnk9wJHA2cNZrwJEmSJEnSOI1qcsQ3ATdW1YnAM4H3JTl6RG1LkiRJkqQxGWbEwQ5gXmd7\nblvW9RzgbQBV9YUkt9FMAXrd/s2t77xfiDOFSpIkSZI0Dlva18SGSRxsAhYkmQ/sBM4GlvfU2Qz8\nDPAvSWYDTwW+2L+5ZUOcUpIkSZIkHVq9P+Zv6Ftr0sRBVe1JshLYSPNow9qq2pxkRbO71gDvAC5O\nciMQ4A1Vdc9BXoEkSZIkSRqzoZZMrKorgZN7yi7svL8beOFoQ5MkSZIkSeM2qskRJUmSJEnSw5CJ\nA0mSJEmSNJCJA0mSJEmSNNBQiYMkS5JsSXJLklUD6pya5Pokn0ty9WjDlCRJkiRJ4zDp5IhJZgGr\ngZ8G7gQ2JdlQVVs6dY4F3gc8r6p2JHncoQpYkiRJkiRNnWFGHCwGtlbVtqraDawDlvbUeQnw8ara\nAQ+usiBJkiRJkma4YRIHc4A7Otvb27KupwLHJ7k6yaYk54wqQEmSJEmSND6TPqpwAO08C3gucBTw\nb0n+rapu3b/q+s77he1LkiRJkiRNrS3ta2LDJA52APM623Pbsq7twN1V9S3gW0n+CXgG0CdxsGyI\nU0qSJEmSpEOr98f8DX1rDfOowiZgQZL5SY4Ezgau6KmzAfjxJI9I8ijgR4DNBxyzJEmSJEmaViYd\ncVBVe5KsBDbSJBrWVtXmJCua3bWmqrYkuQr4L2APsKaqbj6kkUuSJEmSpENuqDkOqupK4OSesgt7\ntt8NvHt0oUmSJEmSpHEb5lEFSZIkSZJ0mDJxIEmSJEmSBhoqcZBkSZItSW5JsmqCej+cZHeSF40u\nREmSJEmSNC6TJg6SzAJWA6cDi4DlSRYOqPdO4KpRBylJkiRJksZjmBEHi4GtVbWtqnYD64Clfeq9\nFvgY8OURxidJkiRJksZomMTBHOCOzvb2tuxBSU4EllXV+4GMLjxJkiRJkjROQy3HOIQ/BbpzH0yQ\nPFjfeb+wfUmSJEmSpKm1pX1NbJjEwQ5gXmd7blvW9UPAuiQBHgeckWR3VV2xf3PLhjilJEmSJEk6\ntHp/zN/Qt9YwiYNNwIIk84GdwNnA8m6FqnrS3vdJLgb+tn/SQJIkSZIkzSSTJg6qak+SlcBGmjkR\n1lbV5iQrmt21pveQQxCnJEmSJEkag6HmOKiqK4GTe8ouHFD3FSOIS5IkSZIkTQPDrKogSZIkSZIO\nUyYOJEmSJEnSQEMlDpIsSbIlyS1JVvXZ/5IkN7ava5L8wOhDlSRJkiRJU23SxEGSWcBq4HRgEbA8\nycKeal8EfrKqngG8Fbho1IFKkiRJkqSpN8yIg8XA1qraVlW7gXXA0m6Fqrq2qv6n3bwWmDPaMCVJ\nkiRJ0jgMkziYA9zR2d7OxImBVwKfOpigJEmSJEnS9DDUcozDSnIacC7w46NsV5IkSZIkjccwiYMd\nwLzO9ty2bB9Jng6sAZZU1b2Dm1vfeb+wfUmSJEmSpKm1pX1NbJjEwSZgQZL5wE7gbGB5t0KSecDH\ngXOq6gsTN7dsiFNKkiRJkqRDq/fH/A19a02aOKiqPUlWAhtp5kRYW1Wbk6xodtca4M3A8cAFSQLs\nrqrFB3kFkiRJkiRpzIaa46CqrgRO7im7sPP+VcCrRhuaJEmSJEkat2FWVZAkSZIkSYcpEweSJEmS\nJGmgoRIHSZYk2ZLkliSrBtQ5P8nWJDckOWW0YUqSJEmSpHGYNHGQZBawGjgdWAQsT7Kwp84ZwJOr\n6inACuADhyBWSfuYfNkUScOyP0mjY3+SRsf+pOlhmBEHi4GtVbWtqnYD64ClPXWWApcCVNVngWOT\nzB5ppJJ6+EEijY79SRod+5M0OvYnTQ/DJA7mAHd0tre3ZRPV2dGnjiRJkiRJmmGcHFGSJEmSJA2U\nqpq4QvJs4C1VtaTdfiNQVfWuTp0PAFdX1WXt9hbgp6pqV09bE59MkiRJkiSNTVWlt+yIIY7bBCxI\nMh/YCZwNLO+pcwXwGuCyNtFwX2/SYFAAkiRJkiRp+po0cVBVe5KsBDbSPNqwtqo2J1nR7K41VfXJ\nJGcmuRX4JnDuoQ1bkiRJkiRNhUkfVZAkSZIkSYevKZscMcmSJFuS3JJk1VSdV3q4SHJ7khuTXJ/k\n39uy45JsTPLfSa5Kcuy445SmoyRrk+xK8l+dsoH9J8mbkmxNsjnJ88YTtTT9DOhL5yXZnuQ/29eS\nzj77kjRAkrlJPpPk80luSvK6ttzPJ007U5I4SDILWA2cDiwClidZOBXnlh5GHgBOrapnVtXituyN\nwD9W1cnAZ4A3jS06aXq7mOYzqKtv/0nyfcAvAU8DzgAuSOIcPVKjX18CeG9VPat9XQmQ5GnYl6SJ\nfBv4rapaBPwo8Jr2O5KfT5p2pmrEwWJga1Vtq6rdwDpg6RSdW3q4CPv32aXAJe37S4BlUxqRNENU\n1TXAvT3Fg/rPzwHrqurbVXU7sJXmc0w67A3oS9B8RvVain1JGqiq7qqqG9r33wA2A3Px80nT0FQl\nDuYAd3S2t7dlkoZXwD8k2ZTklW3Z7L0rmFTVXcDjxxadNPM8fkD/6f3M2oGfWdJkVia5IclfdIZV\n25ekISU5CTgFuJbB/76zT2lspmyOA0kH7TlV9SzgTJqhbD9Bk0zocrZT6aGz/0gPzQXAk6rqFOAu\n4D1jjkeaUZIcDXwM+I125IH/vtO0M1WJgx3AvM723LZM0pCqamf7368A62mGpu1KMhsgyQnAl8cX\noTTjDOo/O4AndOr5mSVNoKq+Ut9ZpusivjN02r4kTSLJETRJgw9W1Ya22M8nTTtTlTjYBCxIMj/J\nkcDZwBVTdG5pxkvyqDYbTZKjgOcBN9H0o5e31V4GbOjbgCRonsHuPoc9qP9cAZyd5MgkTwQWAP8+\nVUFKM8A+fan9YrPXi4DPte/tS9Lk/hK4uar+rFPm55OmnSOm4iRVtSfJSmAjTbJibVVtnopzSw8T\ns4G/SVI0/fbDVbUxyXXA5UleAWyjmWlXUo8kHwFOBR6b5EvAecA7gY/29p+qujnJ5cDNwG7g1Z1f\nU6XD2oC+dFqSU2hW/7kdWAH2JWkySZ4D/DJwU5LraR5J+F3gXfT59519SuMU/1+TJEmSJEmDODmi\nJEmSJEkayMSBJEmSJEkayMSBJEmSJEkayMSBJEmSJEkayMSBJEmSJEkayMSBJEmSJEkayMSBJEmS\nJEkayMSBJEmSJEka6P8BFGiy/q0IeGAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1223db8d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: DP9hfhq8sro\n",
"Main Activity: Table soccer\n",
"0.5665\tPainting furniture\n",
"0.1129\tLaying tile\n",
"0.1023\tPaintball\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1227bd9b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11afa5588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngUwcSJIkSZKkgf4/CcvDst1NhRgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x122885ba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: xSWpGhhM1H8\n",
"Main Activity: Tug of war\n",
"0.9300\tTug of war\n",
"0.0140\tRafting\n",
"0.0082\tHitting a pinata\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ae97b38>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLBxIkiRJkqSe/j+W8TW4ZZ96WgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b1a9748>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ab0e748>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Video ID: WQmJrfjOF7o\n",
"Main Activity: Laying tile\n",
"0.5013\tInstalling carpet\n",
"0.2710\tLaying tile\n",
"0.2099\tVacuuming floor\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b1cfb70>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11aa2e240>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAABjCAYAAAAb+0LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIkSdJA/x+veTV0QM+C1QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11aa84f60>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
}
],
"source": [
"normalize = matplotlib.colors.Normalize(vmin=0, vmax=1)\n",
"\n",
"for v, prediction, class_prediction in predictions:\n",
" v.get_video_instances(16, 0)\n",
" ground_truth = np.array([instance.output for instance in v.instances])\n",
" nb_instances = len(v.instances)\n",
" output_index = dataset.get_output_index(v.label)\n",
" \n",
" print('Video ID: {}\\nMain Activity: {}'.format(v.video_id, v.get_activity()))\n",
"\n",
" class_means = np.mean(prediction, axis=0)\n",
" top_3 = np.argsort(class_means[1:])[::-1][:3] + 1\n",
" scores = class_means[top_3]/np.sum(class_means[1:])\n",
" for index, score in zip(top_3, scores):\n",
" if score == 0.:\n",
" continue\n",
" label = dataset.labels[index][1]\n",
" print('{:.4f}\\t{}'.format(score, label))\n",
" \n",
" plt.figure(num=None, figsize=(18, 1), dpi=100)\n",
" plt.contourf(np.broadcast_to(ground_truth/output_index, (2, nb_instances)), norm=normalize, interpolation='nearest')\n",
" plt.title('Ground Truth')\n",
" plt.show()\n",
" \n",
" # print only the positions that predicted the global ground truth category\n",
" temp = np.zeros((nb_instances))\n",
" temp[class_prediction==output_index] = 1\n",
" plt.figure(num=None, figsize=(18, 1), dpi=100)\n",
" plt.contourf(np.broadcast_to(temp, (2, nb_instances)), norm=normalize, interpolation='nearest')\n",
" plt.title('Prediction of the ground truth class')\n",
" plt.show()\n",
" \n",
" plt.figure(num=None, figsize=(18, 1), dpi=100)\n",
" plt.contourf(np.broadcast_to(prediction[:,output_index], (2, nb_instances)), norm=normalize, interpolation='nearest')\n",
" plt.title('Probability for ground truth')\n",
" plt.show()\n",
"\n",
" print('\\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": "2.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
matthew-brett/brole | tests/tinypages/more_notebooks/more_mini_stripped.ipynb | 1 | 687 | {
"metadata": {
"name": "",
"signature": "sha256:b0249a14c73f2fe78ee857dee48a113cd2cc1b231b430d383bb6adea505366bb"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Another heading"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some different text with $\\sum = 1$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"my text \" + \"extended\")"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | bsd-3-clause |
mercybenzaquen/foundations-homework | databases_hw/db04/Homework_4.ipynb | 2 | 20821 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Homework #4\n",
"\n",
"These problem sets focus on list comprehensions, string operations and regular expressions.\n",
"\n",
"## Problem set #1: List slices and list comprehensions\n",
"\n",
"Let's start with some data. The following cell contains a string with comma-separated integers, assigned to a variable called `numbers_str`:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"numbers_str = '496,258,332,550,506,699,7,985,171,581,436,804,736,528,65,855,68,279,721,120'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following cell, complete the code with an expression that evaluates to a list of integers derived from the raw numbers in `numbers_str`, assigning the value of this expression to a variable `numbers`. If you do everything correctly, executing the cell should produce the output `985` (*not* `'985'`)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"985"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_list = numbers_str.split(\",\") \n",
"\n",
"numbers = [int(item) for item in new_list]\n",
"\n",
"max(numbers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! We'll be using the `numbers` list you created above in the next few problems.\n",
"\n",
"In the cell below, fill in the square brackets so that the expression evaluates to a list of the ten largest values in `numbers`. Expected output:\n",
"\n",
" [506, 528, 550, 581, 699, 721, 736, 804, 855, 985]\n",
" \n",
"(Hint: use a slice.)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[506, 528, 550, 581, 699, 721, 736, 804, 855, 985]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#len(numbers)\n",
"sorted(numbers)[10:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the cell below, write an expression that evaluates to a list of the integers from `numbers` that are evenly divisible by three, *sorted in numerical order*. Expected output:\n",
"\n",
" [120, 171, 258, 279, 528, 699, 804, 855]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[120, 171, 258, 279, 528, 699, 804, 855]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted([item for item in numbers if item % 3 == 0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay. You're doing great. Now, in the cell below, write an expression that evaluates to a list of the square roots of all the integers in `numbers` that are less than 100. In order to do this, you'll need to use the `sqrt` function from the `math` module, which I've already imported for you. Expected output:\n",
"\n",
" [2.6457513110645907, 8.06225774829855, 8.246211251235321]\n",
" \n",
"(These outputs might vary slightly depending on your platform.)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[2.6457513110645907, 8.06225774829855, 8.246211251235321]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from math import sqrt\n",
"# your code here\n",
"squared = []\n",
"\n",
"for item in numbers:\n",
" if item < 100:\n",
" squared_numbers = sqrt(item)\n",
" squared.append(squared_numbers)\n",
"squared\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem set #2: Still more list comprehensions\n",
"\n",
"Still looking good. Let's do a few more with some different data. In the cell below, I've defined a data structure and assigned it to a variable `planets`. It's a list of dictionaries, with each dictionary describing the characteristics of a planet in the solar system. Make sure to run the cell before you proceed."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"planets = [\n",
" {'diameter': 0.382,\n",
" 'mass': 0.06,\n",
" 'moons': 0,\n",
" 'name': 'Mercury',\n",
" 'orbital_period': 0.24,\n",
" 'rings': 'no',\n",
" 'type': 'terrestrial'},\n",
" {'diameter': 0.949,\n",
" 'mass': 0.82,\n",
" 'moons': 0,\n",
" 'name': 'Venus',\n",
" 'orbital_period': 0.62,\n",
" 'rings': 'no',\n",
" 'type': 'terrestrial'},\n",
" {'diameter': 1.00,\n",
" 'mass': 1.00,\n",
" 'moons': 1,\n",
" 'name': 'Earth',\n",
" 'orbital_period': 1.00,\n",
" 'rings': 'no',\n",
" 'type': 'terrestrial'},\n",
" {'diameter': 0.532,\n",
" 'mass': 0.11,\n",
" 'moons': 2,\n",
" 'name': 'Mars',\n",
" 'orbital_period': 1.88,\n",
" 'rings': 'no',\n",
" 'type': 'terrestrial'},\n",
" {'diameter': 11.209,\n",
" 'mass': 317.8,\n",
" 'moons': 67,\n",
" 'name': 'Jupiter',\n",
" 'orbital_period': 11.86,\n",
" 'rings': 'yes',\n",
" 'type': 'gas giant'},\n",
" {'diameter': 9.449,\n",
" 'mass': 95.2,\n",
" 'moons': 62,\n",
" 'name': 'Saturn',\n",
" 'orbital_period': 29.46,\n",
" 'rings': 'yes',\n",
" 'type': 'gas giant'},\n",
" {'diameter': 4.007,\n",
" 'mass': 14.6,\n",
" 'moons': 27,\n",
" 'name': 'Uranus',\n",
" 'orbital_period': 84.01,\n",
" 'rings': 'yes',\n",
" 'type': 'ice giant'},\n",
" {'diameter': 3.883,\n",
" 'mass': 17.2,\n",
" 'moons': 14,\n",
" 'name': 'Neptune',\n",
" 'orbital_period': 164.8,\n",
" 'rings': 'yes',\n",
" 'type': 'ice giant'}]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, in the cell below, write a list comprehension that evaluates to a list of names of the planets that have a diameter greater than four earth radii. Expected output:\n",
"\n",
" ['Jupiter', 'Saturn', 'Uranus']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Jupiter', 'Saturn', 'Uranus', 'Neptune']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[item['name'] for item in planets if item['diameter'] > 2]\n",
"#I got one more planet!\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the cell below, write a single expression that evaluates to the sum of the mass of all planets in the solar system. Expected output: `446.79`"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"446.79"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#sum([int(item['mass']) for item in planets])\n",
"sum([item['mass'] for item in planets])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Good work. Last one with the planets. Write an expression that evaluates to the names of the planets that have the word `giant` anywhere in the value for their `type` key. Expected output:\n",
"\n",
" ['Jupiter', 'Saturn', 'Uranus', 'Neptune']"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import re"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Jupiter', 'Saturn', 'Uranus', 'Neptune']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"planet_with_giant= [item['name'] for item in planets if re.search(r'\\bgiant\\b', item['type'])]\n",
"\n",
"planet_with_giant"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*EXTREME BONUS ROUND*: Write an expression below that evaluates to a list of the names of the planets in ascending order by their number of moons. (The easiest way to do this involves using the [`key` parameter of the `sorted` function](https://docs.python.org/3.5/library/functions.html#sorted), which we haven't yet discussed in class! That's why this is an EXTREME BONUS question.) Expected output:\n",
"\n",
" ['Mercury', 'Venus', 'Earth', 'Mars', 'Neptune', 'Uranus', 'Saturn', 'Jupiter']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem set #3: Regular expressions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following section, we're going to do a bit of digital humanities. (I guess this could also be journalism if you were... writing an investigative piece about... early 20th century American poetry?) We'll be working with the following text, Robert Frost's *The Road Not Taken*. Make sure to run the following cell before you proceed."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import re\n",
"poem_lines = ['Two roads diverged in a yellow wood,',\n",
" 'And sorry I could not travel both',\n",
" 'And be one traveler, long I stood',\n",
" 'And looked down one as far as I could',\n",
" 'To where it bent in the undergrowth;',\n",
" '',\n",
" 'Then took the other, as just as fair,',\n",
" 'And having perhaps the better claim,',\n",
" 'Because it was grassy and wanted wear;',\n",
" 'Though as for that the passing there',\n",
" 'Had worn them really about the same,',\n",
" '',\n",
" 'And both that morning equally lay',\n",
" 'In leaves no step had trodden black.',\n",
" 'Oh, I kept the first for another day!',\n",
" 'Yet knowing how way leads on to way,',\n",
" 'I doubted if I should ever come back.',\n",
" '',\n",
" 'I shall be telling this with a sigh',\n",
" 'Somewhere ages and ages hence:',\n",
" 'Two roads diverged in a wood, and I---',\n",
" 'I took the one less travelled by,',\n",
" 'And that has made all the difference.']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the cell above, I defined a variable `poem_lines` which has a list of lines in the poem, and `import`ed the `re` library.\n",
"\n",
"In the cell below, write a list comprehension (using `re.search()`) that evaluates to a list of lines that contain two words next to each other (separated by a space) that have exactly four characters. (Hint: use the `\\b` anchor. Don't overthink the \"two words in a row\" requirement.)\n",
"\n",
"Expected result:\n",
"\n",
"```\n",
"['Then took the other, as just as fair,',\n",
" 'Had worn them really about the same,',\n",
" 'And both that morning equally lay',\n",
" 'I doubted if I should ever come back.',\n",
" 'I shall be telling this with a sigh']\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Then took the other, as just as fair,',\n",
" 'Had worn them really about the same,',\n",
" 'And both that morning equally lay',\n",
" 'I doubted if I should ever come back.',\n",
" 'I shall be telling this with a sigh']"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[item for item in poem_lines if re.search(r'\\b[a-zA-Z]{4}\\b \\b[a-zA-Z]{4}\\b', item)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Good! Now, in the following cell, write a list comprehension that evaluates to a list of lines in the poem that end with a five-letter word, regardless of whether or not there is punctuation following the word at the end of the line. (Hint: Try using the `?` quantifier. Is there an existing character class, or a way to *write* a character class, that matches non-alphanumeric characters?) Expected output:\n",
"\n",
"```\n",
"['And be one traveler, long I stood',\n",
" 'And looked down one as far as I could',\n",
" 'And having perhaps the better claim,',\n",
" 'Though as for that the passing there',\n",
" 'In leaves no step had trodden black.',\n",
" 'Somewhere ages and ages hence:']\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['And be one traveler, long I stood',\n",
" 'And looked down one as far as I could',\n",
" 'And having perhaps the better claim,',\n",
" 'Though as for that the passing there',\n",
" 'In leaves no step had trodden black.',\n",
" 'Somewhere ages and ages hence:']"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[item for item in poem_lines if re.search(r'\\b[a-zA-Z]{5}\\b.?$',item)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, now a slightly trickier one. In the cell below, I've created a string `all_lines` which evaluates to the entire text of the poem in one string. Execute this cell."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"all_lines = \" \".join(poem_lines)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, write an expression that evaluates to all of the words in the poem that follow the word 'I'. (The strings in the resulting list should *not* include the `I`.) Hint: Use `re.findall()` and grouping! Expected output:\n",
"\n",
" ['could', 'stood', 'could', 'kept', 'doubted', 'should', 'shall', 'took']"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['could', 'stood', 'could', 'kept', 'doubted', 'should', 'shall', 'took']"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"re.findall(r'[I] (\\b\\w+\\b)', all_lines)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, something super tricky. Here's a list of strings that contains a restaurant menu. Your job is to wrangle this plain text, slightly-structured data into a list of dictionaries."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"entrees = [\n",
" \"Yam, Rosemary and Chicken Bowl with Hot Sauce $10.95\",\n",
" \"Lavender and Pepperoni Sandwich $8.49\",\n",
" \"Water Chestnuts and Peas Power Lunch (with mayonnaise) $12.95 - v\",\n",
" \"Artichoke, Mustard Green and Arugula with Sesame Oil over noodles $9.95 - v\",\n",
" \"Flank Steak with Lentils And Tabasco Pepper With Sweet Chilli Sauce $19.95\",\n",
" \"Rutabaga And Cucumber Wrap $8.49 - v\"\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You'll need to pull out the name of the dish and the price of the dish. The `v` after the hyphen indicates that the dish is vegetarian---you'll need to include that information in your dictionary as well. I've included the basic framework; you just need to fill in the contents of the `for` loop.\n",
"\n",
"Expected output:\n",
"\n",
"```\n",
"[{'name': 'Yam, Rosemary and Chicken Bowl with Hot Sauce ',\n",
" 'price': 10.95,\n",
" 'vegetarian': False},\n",
" {'name': 'Lavender and Pepperoni Sandwich ',\n",
" 'price': 8.49,\n",
" 'vegetarian': False},\n",
" {'name': 'Water Chestnuts and Peas Power Lunch (with mayonnaise) ',\n",
" 'price': 12.95,\n",
" 'vegetarian': True},\n",
" {'name': 'Artichoke, Mustard Green and Arugula with Sesame Oil over noodles ',\n",
" 'price': 9.95,\n",
" 'vegetarian': True},\n",
" {'name': 'Flank Steak with Lentils And Tabasco Pepper With Sweet Chilli Sauce ',\n",
" 'price': 19.95,\n",
" 'vegetarian': False},\n",
" {'name': 'Rutabaga And Cucumber Wrap ', 'price': 8.49, 'vegetarian': True}]\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great work! You are done. Go cavort in the sun, or whatever it is you students do when you're done with your homework"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'Yam, Rosemary and Chicken Bowl with Hot Sauce',\n",
" 'price': '10.95',\n",
" 'vegetarian': False},\n",
" {'name': 'Lavender and Pepperoni Sandwich',\n",
" 'price': '8.49',\n",
" 'vegetarian': False},\n",
" {'name': 'Water Chestnuts and Peas Power Lunch (with mayonnaise)',\n",
" 'price': '12.95',\n",
" 'vegetarian': True},\n",
" {'name': 'Artichoke, Mustard Green and Arugula with Sesame Oil over noodles',\n",
" 'price': '9.95',\n",
" 'vegetarian': True},\n",
" {'name': 'Flank Steak with Lentils And Tabasco Pepper With Sweet Chilli Sauce',\n",
" 'price': '19.95',\n",
" 'vegetarian': False},\n",
" {'name': 'Rutabaga And Cucumber Wrap', 'price': '8.49', 'vegetarian': True}]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"menu = []\n",
"\n",
"for item in entrees:\n",
" entrees_dictionary= {}\n",
" match = re.search(r'(.*) .(\\d*\\d\\.\\d{2})\\ ?( - v+)?$', item)\n",
" \n",
" if match:\n",
" name = match.group(1)\n",
" price= match.group(2)\n",
" #vegetarian= match.group(3)\n",
" if match.group(3):\n",
" entrees_dictionary['vegetarian']= True\n",
" else:\n",
" entrees_dictionary['vegetarian']= False\n",
" \n",
" entrees_dictionary['name']= name\n",
" entrees_dictionary['price']= price\n",
" \n",
" menu.append(entrees_dictionary)\n",
"\n",
"menu\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"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 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 |
childresslab/MicrocavityExp1 | tools/Start Ramp Signal.ipynb | 1 | 2985 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calibration\n",
"\n",
"## Start a simple ramp signal\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Start ramp signal\n",
"Amplitude = 0.05 # V\n",
"Offset = -1.0 #Amplitude/2.0 # V\n",
"Freq = 40\n",
"mynicard.set_up_ramp_output(Amplitude,Offset,Freq)\n",
"mynicard.start_ramp()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mynicard.stop_ramp()\n",
"mynicard.close_ramp()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mynicard._write_cavity_ao(np.array([-3.75],dtype=float), start=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"manager.startModule('logic','cavitylogic')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cavitylogic._get_scope_data()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cavitylogic._save_raw_data()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"1/16.4e-3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.arange(-11 / 2, 11 / 2, dtype=int)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Qudi",
"language": "python",
"name": "qudi"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": "3.6.0"
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
hbjornoy/DataAnalysis | Backup (not final delivery)/Homework 1.ipynb | 1 | 30114 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Table of Contents\n",
" <p><div class=\"lev1\"><a href=\"#Task-1.-Compiling-Ebola-Data\"><span class=\"toc-item-num\">Task 1. </span>Compiling Ebola Data</a></div>\n",
" <div class=\"lev1\"><a href=\"#Task-2.-RNA-Sequences\"><span class=\"toc-item-num\">Task 2. </span>RNA Sequences</a></div>\n",
" <div class=\"lev1\"><a href=\"#Task-3.-Class-War-in-Titanic\"><span class=\"toc-item-num\">Task 3. </span>Class War in Titanic</a></div></p>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Imports\n",
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import glob\n",
"import csv\n",
"import calendar\n",
"import webbrowser\n",
"from datetime import datetime\n",
"\n",
"# Constants\n",
"DATA_FOLDER = 'Data/'\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 1. Compiling Ebola Data\n",
"\n",
"The `DATA_FOLDER/ebola` folder contains summarized reports of Ebola cases from three countries (Guinea, Liberia and Sierra Leone) during the recent outbreak of the disease in West Africa. For each country, there are daily reports that contain various information about the outbreak in several cities in each country.\n",
"\n",
"Use pandas to import these data files into a single `Dataframe`.\n",
"Using this `DataFrame`, calculate for *each country*, the *daily average per month* of *new cases* and *deaths*.\n",
"Make sure you handle all the different expressions for *new cases* and *deaths* that are used in the reports."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''\n",
"Functions needed to solve task 1\n",
"'''\n",
"\n",
"#function to import excel file into a dataframe\n",
"def importdata(path,date):\n",
" allpathFiles = glob.glob(DATA_FOLDER+path+'/*.csv')\n",
" list_data = []\n",
" for file in allpathFiles:\n",
" excel = pd.read_csv(file,parse_dates=[date])\n",
" list_data.append(excel)\n",
" return pd.concat(list_data)\n",
"\n",
"#function to add the month on a new column of a DataFrame\n",
"def add_month(df):\n",
" copy_df = df.copy()\n",
" months = [calendar.month_name[x.month] for x in copy_df.Date]\n",
" copy_df['Month'] = months\n",
" return copy_df\n",
"\n",
"#founction which loc only the column within a country and a specified month\n",
"#return a dataframe\n",
"def chooseCountry_month(dataframe,country,descr,month):\n",
" df = dataframe.loc[(dataframe['Country']==country) & (dataframe['Description']==descr)]\n",
" #df = add_month(df)\n",
" df_month = df.loc[(df['Month']==month)]\n",
" return df_month\n",
"\n",
"# Create a dataframe with the number of death, the new cases and the daily infos for a country and a specified month \n",
"def getmonthresults(dataframe,country,month):\n",
" if country =='Liberia':\n",
" descr_kill ='Total death/s in confirmed cases'\n",
" descr_cases ='Total confirmed cases'\n",
" if country =='Guinea':\n",
" descr_kill ='Total deaths of confirmed'\n",
" descr_cases ='Total cases of confirmed'\n",
" if country == 'Sierra Leone': \n",
" descr_kill ='death_confirmed'\n",
" descr_cases ='cum_confirmed'\n",
" \n",
" df_kill = chooseCountry_month(dataframe,country,descr_kill,month)\n",
" df_cases = chooseCountry_month(dataframe,country,descr_cases,month)\n",
" \n",
" #calculate the number of new cases and of new deaths for the all month\n",
" res_kill = int(df_kill.iloc[len(df_kill)-1].Totals)-int(df_kill.iloc[0].Totals)\n",
" res_cases = int(df_cases.iloc[len(df_cases)-1].Totals)-int(df_cases.iloc[0].Totals)\n",
" #calculate the number of days counted which is last day of register - first day of register\n",
" nb_day = df_kill.iloc[len(df_kill)-1].Date.day-df_kill.iloc[0].Date.day \n",
" \n",
"\n",
" # Sometimes the values in the dataframe are wrong due to the excelfiles which are not all the same!\n",
" # We then get negative results. Therefor we replace them all by NaN ! \n",
" if(res_cases < 0)&(res_kill <0):\n",
" monthreport = pd.DataFrame({'New cases':[np.nan],'Deaths':[np.nan],'daily average of New cases':[np.nan],'daily average of Deaths':[np.nan],'month':[month],'Country':[country]})\n",
" elif(res_cases >= 0) &( res_kill <0):\n",
" monthreport = pd.DataFrame({'New cases':[res_cases],'Deaths':[np.nan],'daily average of New cases':[res_cases/nb_day],'daily average of Deaths':[np.nan],'month':[month],'Country':[country]})\n",
" elif(res_cases < 0) & (res_kill >= 0):\n",
" monthreport = pd.DataFrame({'New cases':[np.nan],'Deaths':[res_kill],'daily average of New cases':[np.nan],'daily average of Deaths':[res_kill/nb_day],'month':[month],'Country':[country]})\n",
" elif(nb_day == 0):\n",
" monthreport = pd.DataFrame({'New cases':'notEnoughdatas','Deaths':'notEnoughdatas','daily average of New cases':'notEnoughdatas','daily average of Deaths':'notEnoughdatas','month':[month],'Country':[country]})\n",
" else: \n",
" monthreport = pd.DataFrame({'New cases':[res_cases],'Deaths':[res_kill],'daily average of New cases':[res_cases/nb_day],'daily average of Deaths':[res_kill/nb_day],'month':[month],'Country':[country]})\n",
" return monthreport\n",
"\n",
"#check if the month and the country is in the dataframe df\n",
"def checkData(df,month,country):\n",
" check = df.loc[(df['Country']==country)& (df['Month']== month)]\n",
" return check\n",
"\n",
"#return a dataframe with all the infos(daily new cases, daily death) for each month and each country\n",
"def getResults(data):\n",
" Countries = ['Guinea','Liberia','Sierra Leone']\n",
" Months = ['January','February','March','April','May','June','July','August','September','October','November','December']\n",
" results=[]\n",
" compteur =0\n",
" for country in Countries:\n",
" for month in Months:\n",
" if not(checkData(data,month,country).empty) : #check if the datas for the month and country exist \n",
" res = getmonthresults(data,country,month)\n",
" results.append(res) \n",
" return pd.concat(results)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# import data from guinea\n",
"path_guinea = 'Ebola/guinea_data/'\n",
"data_guinea = importdata(path_guinea,'Date')\n",
"\n",
"# set the new order / change the columns / keep only the relevant datas / add the name of the country\n",
"data_guinea = data_guinea[['Date', 'Description','Totals']]\n",
"data_guinea['Country'] = ['Guinea']*len(data_guinea)\n",
"\n",
"#search for New cases and death!! \n",
"#descr(newcases): \"Total cases of confirmed\" // descr(deaths): \"Total deaths of confirmed\"\n",
"data_guinea = data_guinea.loc[(data_guinea.Description=='Total cases of confirmed')|(data_guinea.Description=='Total deaths of confirmed')]\n",
"\n",
" \n",
"#import data from liberia\n",
"path_liberia = 'Ebola/liberia_data/'\n",
"data_liberia = importdata(path_liberia,'Date')\n",
"# set the new order / change the columns / keep only the relevant datas / add the name of the country\n",
"data_liberia = data_liberia[['Date', 'Variable','National']]\n",
"data_liberia['Country'] = ['Liberia']*len(data_liberia)\n",
"\n",
"#search for New cases and death!! \n",
"#descr(newcases): \"Total confirmed cases\" // descr(deaths): \"Total death/s in confirmed cases\" \n",
"data_liberia = data_liberia.loc[(data_liberia.Variable=='Total confirmed cases')|(data_liberia.Variable=='Total death/s in confirmed cases')]\n",
"\n",
"#change the name of the columns to be able merge the 3 data sets\n",
"data_liberia = data_liberia.rename(columns={'Date': 'Date', 'Variable': 'Description','National':'Totals'})\n",
"\n",
" \n",
"#import data from sierra leonne\n",
"path_sl = 'Ebola/sl_data/'\n",
"data_sl = importdata(path_sl,'date')\n",
"# set the new order / change the columns / keep only the relevant datas / add the name of the country\n",
"data_sl = data_sl[['date', 'variable','National']]\n",
"data_sl['Country'] = ['Sierra Leone']*len(data_sl)\n",
"\n",
"#search for new cases and death \n",
"#descr(newcases): \"cum_confirmed\" // descr(deaths): \"death_confirmed\"\n",
"data_sl = data_sl.loc[(data_sl.variable=='cum_confirmed')|(data_sl.variable=='death_confirmed')]\n",
"#change the name of the columns to be able merge the 3 data sets\n",
"data_sl = data_sl.rename(columns={'date': 'Date', 'variable': 'Description','National':'Totals'})\n",
"\n",
"\n",
"#merge the 3 dataframe into ONE which we'll apply our analysis\n",
"dataFrame = [data_guinea,data_liberia,data_sl]\n",
"data = pd.concat(dataFrame)\n",
"\n",
"# Replace the NaN by 0;\n",
"data = data.fillna(0)\n",
"#add a column with the month\n",
"data = add_month(data)\n",
"\n",
"#get the results from the data set -> see the function\n",
"results = getResults(data)\n",
"\n",
"#print the resuults\n",
"results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 2. RNA Sequences\n",
"\n",
"In the `DATA_FOLDER/microbiome` subdirectory, there are 9 spreadsheets of microbiome data that was acquired from high-throughput RNA sequencing procedures, along with a 10<sup>th</sup> file that describes the content of each. \n",
"\n",
"Use pandas to import the first 9 spreadsheets into a single `DataFrame`.\n",
"Then, add the metadata information from the 10<sup>th</sup> spreadsheet as columns in the combined `DataFrame`.\n",
"Make sure that the final `DataFrame` has a unique index and all the `NaN` values have been replaced by the tag `unknown`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Sheet10_Meta = pd.read_excel(DATA_FOLDER +'microbiome/metadata.xls') \n",
"allFiles = glob.glob(DATA_FOLDER + 'microbiome' + \"/MID*.xls\")\n",
"allFiles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" 2. Creating and filling the DataFrame\n",
"In order to iterate only once over the data folder, we will attach the metadata to each excel spreadsheet right after creating a DataFrame with it. This will allow the code to be shorter and clearer, but also to iterate only once on every line and therefore be more efficient. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Creating an empty DataFrame to store our data and initializing a counter.\n",
"Combined_data = pd.DataFrame()\n",
"K = 0\n",
"while (K < int(len(allFiles))):\n",
" \n",
" #Creating a DataFrame and filling it with the excel's data\n",
" df = pd.read_excel(allFiles[K], header=None)\n",
" \n",
" #Getting the metadata of the corresponding spreadsheet\n",
" df['BARCODE'] = Sheet10_Meta.at[int(K), 'BARCODE']\n",
" df['GROUP'] = Sheet10_Meta.at[int(K), 'GROUP']\n",
" df['SAMPLE'] = Sheet10_Meta.at[int(K),'SAMPLE']\n",
" \n",
" #Append the recently created DataFrame to our combined one\n",
" Combined_data = Combined_data.append(df)\n",
" \n",
" K = K + 1\n",
" \n",
"#Renaming the columns with meaningfull names\n",
"Combined_data.columns = ['Name', 'Value','BARCODE','GROUP','SAMPLE']\n",
"Combined_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" 3. Cleaning and reindexing\n",
"At first we get rid of the NaN value, we must replace them by \"unknown\". In order to have a more meaningful and single index, we will reset it to be the name of the RNA sequence."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Replacing the NaN values with unknwown\n",
"Combined_data = Combined_data.fillna('unknown')\n",
"\n",
"#Reseting the index\n",
"Combined_data = Combined_data.set_index('Name')\n",
"\n",
"#Showing the result\n",
"Combined_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 3. Class War in Titanic\n",
"\n",
"Use pandas to import the data file `Data/titanic.xls`. It contains data on all the passengers that travelled on the Titanic."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## For each of the following questions state clearly your assumptions and discuss your findings:\n",
"1. Describe the *type* and the *value range* of each attribute. Indicate and transform the attributes that can be `Categorical`. \n",
"2. Plot histograms for the *travel class*, *embarkation port*, *sex* and *age* attributes. For the latter one, use *discrete decade intervals*. \n",
"3. Calculate the proportion of passengers by *cabin floor*. Present your results in a *pie chart*.\n",
"4. For each *travel class*, calculate the proportion of the passengers that survived. Present your results in *pie charts*.\n",
"5. Calculate the proportion of the passengers that survived by *travel class* and *sex*. Present your results in *a single histogram*.\n",
"6. Create 2 equally populated *age categories* and calculate survival proportions by *age category*, *travel class* and *sex*. Present your results in a `DataFrame` with unique index."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 3.1\n",
"\n",
"##### Describe the *type* and the *value range* of each attribute. Indicate and transform the attributes that can be `Categorical`. \n",
"\n",
"Assumptions: \n",
" - \"For each exercise, please provide both a written explanation of the steps you will apply to manipulate the data, and the corresponding code.\" We assume that \"written explanation can come in the form of commented code as well as text\"\n",
" - We assume that we must not describe the value range of attributes that contain string as we dont feel the length of strings or ASCI-values don't give any insight"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"''' \n",
"Here is a sample of the information in the titanic dataframe\n",
"''' \n",
"\n",
"# Importing titanic.xls info with Pandas\n",
"titanic = pd.read_excel('Data/titanic.xls')\n",
"\n",
"# printing only the 30 first and last rows of information\n",
"print(titanic.head)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"To describe the INTENDED values and types of the data we will show you the titanic.html file that was provided to us\n",
"Notice:\n",
" - 'age' is of type double, so someone can be 17.5 years old, mostly used with babies that are 0.x years old\n",
" - 'cabin' is stored as integer, but it har characters and letters\n",
" - By this model, embarked is stored as an integer, witch has to be interpreted as the 3 different embarkation ports\n",
" - It says that 'boat' is stored as a integer even though it has spaces and letters, it should be stored as string\n",
" \n",
"PS: it might be that the information stored as integer is supposed to be categorical data,\n",
" ...because they have a \"small\" amount of valid options\n",
"''' \n",
"\n",
"# Display html info in Jupyter Notebook\n",
"from IPython.core.display import display, HTML\n",
"htmlFile = 'Data/titanic.html'\n",
"display(HTML(htmlFile))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"''' \n",
"The default types of the data after import:\n",
"Notice:\n",
" - the strings and characters are imported as objects\n",
" - 'survived' is imported as int instead of double (which is in our opinion better since it's only 0 and 1\n",
" - 'sex' is imported as object not integer because it is a string\n",
"'''\n",
"\n",
"titanic.dtypes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"''' \n",
"Below you can see the value range of the different numerical values.\n",
"\n",
"name, sex, ticket, cabin, embarked, boat and home.dest is not included because they can't be quantified numerically.\n",
"''' \n",
"\n",
"titanic.describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"'''\n",
"Additional information that is important to remember when manipulation the data\n",
"is if/where there are NaN values in the dataset\n",
"'''\n",
"\n",
"# This displays the number of NaN there is in different attributes\n",
"print(pd.isnull(titanic).sum())\n",
"\n",
"'''\n",
"Some of this data is missing while some is meant to describe 'No' or something of meaning.\n",
"Example:\n",
" Cabin has 1014 NaN in its column, it might be that every passenger had a cabin and the data is missing.\n",
" Or it could mean that most passengers did not have a cabin or a mix. The displayed titanic.html file \n",
" give us some insight if it is correct. It says that there are 0 NaN in the column. This indicates that\n",
" there are 1014 people without a cabin. Boat has also 823 NaN's, while the titanic lists 0 NaN's. \n",
" It is probably because most of those who died probably weren't in a boat.\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"What attributes should be stored as categorical information?\n",
"\n",
"Categorical data is essentially 8-bit integers which means it can store up to 2^8 = 256 categories\n",
"Benefit is that it makes memory usage lower and it has a performance increase in calculations.\n",
"'''\n",
"\n",
"print('Number of unique values in... :')\n",
"for attr in titanic:\n",
" print(\" {attr}: {u}\".format(attr=attr, u=len(titanic[attr].unique())))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"We think it will be smart to categorize: 'pclass', 'survived', 'sex', 'cabin', 'embarked' and 'boat'\n",
"because they have under 256 categories and don't have a strong numerical value like 'age'\n",
"'survived' is a bordercase because it might be more practical to work with integers in some settings\n",
"'''\n",
"\n",
"# changing the attributes to categorical data\n",
"titanic.pclass = titanic.pclass.astype('category')\n",
"titanic.survived = titanic.survived.astype('category')\n",
"titanic.sex = titanic.sex.astype('category')\n",
"titanic.cabin = titanic.cabin.astype('category')\n",
"titanic.embarked = titanic.embarked.astype('category')\n",
"titanic.boat = titanic.boat.astype('category')\n",
"\n",
"#Illustrate the change by printing out the new types\n",
"titanic.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 3.2\n",
"###### \"Plot histograms for the *travel class*, *embarkation port*, *sex* and *age* attributes. For the latter one, use *discrete decade intervals*. \"\n",
"\n",
"assumptions: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"#Plotting the ratio different classes(1st, 2nd and 3rd class) the passengers have\n",
"pc = titanic.pclass.value_counts().sort_index().plot(kind='bar')\n",
"pc.set_title('Travel classes')\n",
"pc.set_ylabel('Number of passengers')\n",
"pc.set_xlabel('Travel class')\n",
"pc.set_xticklabels(('1st class', '2nd class', '3rd class'))\n",
"plt.show(pc)\n",
"\n",
"#Plotting the amount of people that embarked from different cities(C=Cherbourg, Q=Queenstown, S=Southampton)\n",
"em = titanic.embarked.value_counts().sort_index().plot(kind='bar')\n",
"em.set_title('Ports of embarkation')\n",
"em.set_ylabel('Number of passengers')\n",
"em.set_xlabel('Port of embarkation')\n",
"em.set_xticklabels(('Cherbourg', 'Queenstown', 'Southampton'))\n",
"plt.show(em)\n",
"\n",
"#Plotting what sex the passengers are\n",
"sex = titanic.sex.value_counts().plot(kind='bar')\n",
"sex.set_title('Gender of the passengers')\n",
"sex.set_ylabel('Number of Passengers')\n",
"sex.set_xlabel('Gender')\n",
"sex.set_xticklabels(('Female', 'Male'))\n",
"plt.show(sex)\n",
"\n",
"#Plotting agegroup of passengers\n",
"bins = [0,10,20,30,40,50,60,70,80]\n",
"age_grouped = pd.DataFrame(pd.cut(titanic.age, bins))\n",
"ag = age_grouped.age.value_counts().sort_index().plot.bar()\n",
"ag.set_title('Age of Passengers ')\n",
"ag.set_ylabel('Number of passengers')\n",
"ag.set_xlabel('Age groups')\n",
"plt.show(ag)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 3.3\n",
"###### Calculate the proportion of passengers by *cabin floor*. Present your results in a *pie chart*.\n",
"\n",
"assumptions: \n",
"- Because we are tasked with categorizing persons by the floor of their cabin it was problematic that you had cabin input: \"F E57\" and \"F G63\". There were only 7 of these instances with conflicting cabinfloors. We also presumed that the was a floor \"T\". Even though there was only one instance, so it might have been a typo.\n",
"- We assume that you don't want to include people without cabinfloor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"Parsing the cabinfloor, into floors A, B, C, D, E, F, G, T and display in a pie chart\n",
"\n",
"'''\n",
"#Dropping NaN (People without cabin)\n",
"cabin_floors = titanic.cabin.dropna()\n",
"\n",
"# removes digits and spaces\n",
"cabin_floors = cabin_floors.str.replace(r'[\\d ]+', '')\n",
"# removes duplicate letters and leave unique (CC -> C) (FG -> G)\n",
"cabin_floors = cabin_floors.str.replace(r'(.)(?=.*\\1)', '')\n",
"# removes ambigous data from the dataset (FE -> NaN)(FG -> NaN)\n",
"cabin_floors = cabin_floors.str.replace(r'([A-Z]{1})\\w+', 'NaN' )\n",
"\n",
"# Recategorizing (Since we altered the entries, we messed with the categories)\n",
"cabin_floors = cabin_floors.astype('category')\n",
"# Removing NaN (uin this case ambigous data)\n",
"cabin_floors = cabin_floors.cat.remove_categories('NaN')\n",
"cabin_floors = cabin_floors.dropna()\n",
"\n",
"# Preparing data for plt.pie\n",
"numberOfCabinPlaces = cabin_floors.count()\n",
"grouped = cabin_floors.groupby(cabin_floors).count()\n",
"sizes = np.array(grouped)\n",
"labels = np.array(grouped.index)\n",
"\n",
"# Plotting the pie chart\n",
"plt.pie(sizes, labels=labels, autopct='%1.1f%%', pctdistance=0.75, labeldistance=1.1)\n",
"print(\"There are {cabin} passengers that have cabins and {nocabin} passengers without a cabin\"\n",
" .format(cabin=numberOfCabinPlaces, nocabin=(len(titanic) - numberOfCabinPlaces)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 3.4\n",
"###### For each *travel class*, calculate the proportion of the passengers that survived. Present your results in *pie charts*.\n",
"\n",
"assumptions: "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# function that returns the number of people that survived and died given a specific travelclass\n",
"def survivedPerClass(pclass):\n",
" survived = len(titanic.survived[titanic.survived == 1][titanic.pclass == pclass])\n",
" died = len(titanic.survived[titanic.survived == 0][titanic.pclass == pclass])\n",
" return [survived, died]\n",
"\n",
"# Fixing the layout horizontal\n",
"the_grid = plt.GridSpec(1, 3)\n",
"labels = [\"Survived\", \"Died\"]\n",
"\n",
"# Each iteration plots a pie chart\n",
"for p in titanic.pclass.unique():\n",
" sizes = survivedPerClass(p)\n",
" plt.subplot(the_grid[0, p-1], aspect=1 )\n",
" plt.pie(sizes, labels=labels, autopct='%1.1f%%')\n",
" \n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 3.5\n",
"##### \"Calculate the proportion of the passengers that survived by travel class and sex. Present your results in a single histogram.\"\n",
"\n",
"assumptions: \n",
" 1. By \"proportions\" We assume it is a likelyhood-percentage of surviving"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# group by selected data and get a count for each category\n",
"survivalrate = titanic.groupby(['pclass', 'sex', 'survived']).size()\n",
"\n",
"# calculate percentage\n",
"survivalpercentage = survivalrate.groupby(level=['pclass', 'sex']).apply(lambda x: x / x.sum() * 100)\n",
"\n",
"# plotting in a histogram\n",
"histogram = survivalpercentage.filter(like='1', axis=0).plot(kind='bar')\n",
"histogram.set_title('Proportion of the passengers that survived by travel class and sex')\n",
"histogram.set_ylabel('Percent likelyhood of surviving titanic')\n",
"histogram.set_xlabel('class/gender group')\n",
"plt.show(histogram)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 3.6\n",
"##### \"Create 2 equally populated age categories and calculate survival proportions by age category, travel class and sex. Present your results in a DataFrame with unique index.\"\n",
"\n",
"assumptions: \n",
"1. By \"proportions\" we assume it is a likelyhood-percentage of surviving\n",
"2. To create 2 equally populated age categories; we will find the median and round up from the median to nearest whole year difference before splitting."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#drop NaN rows\n",
"age_without_nan = titanic.age.dropna()\n",
"\n",
"#categorizing\n",
"age_categories = pd.qcut(age_without_nan, 2, labels=[\"Younger\", \"Older\"])\n",
"\n",
"#Numbers to explain difference\n",
"median = int(np.float64(age_without_nan.median()))\n",
"amount = int(age_without_nan[median])\n",
"print(\"The Median age is {median} years old\".format(median = median))\n",
"print(\"and there are {amount} passengers that are {median} year old \\n\".format(amount=amount, median=median))\n",
"\n",
"print(age_categories.groupby(age_categories).count())\n",
"print(\"\\nAs you can see the pd.qcut does not cut into entirely equal sized bins, because the age is of a discreet nature\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# imported for the sake of surpressing some warnings\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# extract relevant attributes\n",
"csas = titanic[['pclass', 'sex', 'age', 'survived']]\n",
"csas.dropna(subset=['age'], inplace=True)\n",
"\n",
"# Defining the categories\n",
"csas['age_group'] = csas.age > csas.age.median()\n",
"csas['age_group'] = csas['age_group'].map(lambda age_category: 'older' if age_category else \"younger\")\n",
"\n",
"# Converting to int to make it able to aggregate and give percentage\n",
"csas.survived = csas.survived.astype(int)\n",
"\n",
"g_categories = csas.groupby(['pclass', 'age_group', 'sex'])\n",
"result = pd.DataFrame(g_categories.survived.mean()).rename(columns={'survived': 'survived proportion'})\n",
"\n",
"# reset current index and spesify the unique index\n",
"result.reset_index(inplace=True)\n",
"unique_index = result.pclass.astype(str) + ': ' + result.age_group.astype(str) + ' ' + result.sex.astype(str)\n",
"\n",
"# Finalize the unique index dataframe\n",
"result_w_unique = result[['survived proportion']]\n",
"result_w_unique.set_index(unique_index, inplace=True)\n",
"print(result_w_unique)\n"
]
}
],
"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
}
| apache-2.0 |
lmoresi/UoM-VIEPS-Intro-to-Python | Notebooks/Introduction/2 - Introduction to ipython.ipynb | 1 | 5992 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## ipython \n",
"\n",
"[ipython](https://ipython.org) is an _interactive_ version of the python interpreter. It provides a number of extras which are helpful when writing code. `ipython` code is almost always `python` code, and the differences are generally only important when editing a code in a live (interactive) environment. \n",
"\n",
"The `jupyter` notebook is a fine example of an interactive environment - you are changing the code as it runs and checking answers as you go. Because you may have a lot of half-completed results in an interactive script, you probably want to make as few mistakes as you can. This is the purpose of `ipython`.\n",
"\n",
"`ipython` provides access to the help / documentation system, provides tab completion of variable and function names, allows you see see what methods live inside a module ... "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"## Try the autocomplete ... it works on functions that are in scope\n",
"\n",
"# pr\n",
"\n",
"# it also works on variables\n",
"\n",
"# long_but_helpful_variable_name = 1\n",
"\n",
"# long_b"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"---\n",
"\n",
"It works on modules to list the available methods and variables. Take the `math` module, for example:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import math\n",
"\n",
"# math.is # Try completion on this\n",
"\n",
"help(math.isinf)\n",
"\n",
"# try math.isinf() and hit shift-tab while the cursor is between the parentheses \n",
"# you should see the same help pop up.\n",
"\n",
"# math.isinf()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"---\n",
"\n",
"It works on functions that take special arguments and tells you what you need to supply.\n",
"\n",
"Try this and try tabbing in the parenthesis when you use this function yourself:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import string\n",
"string.capwords(\"the quality of mercy is not strained\")\n",
"\n",
"# string.capwords()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"It also provides special operations that allow you to drill down into the underlying shell / filesystem (but these are not standard python code any more). "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# execute simple unix shell commands \n",
"\n",
"!ls\n",
"\n",
"!echo \"\"\n",
"\n",
"!pwd"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"---\n",
"\n",
"Another way to do this is to use the __cell magic__ functionality to direct the notebook to change the cell to something different (here everything in the cell is interpreted as a unix shell ) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%%sh \n",
"\n",
"ls -l\n",
"\n",
"echo \"\"\n",
"\n",
"pwd"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"---\n",
"\n",
"I don't advise using this too often as the code becomes more difficult to convert to python. \n",
"\n",
" - A `%` is a one-line magic function that can go anywhere in the cell. \n",
" - A `%%` is a cell-wide function \n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%magic # to see EVERYTHING in the magic system !"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"\n",
"Useful magic functions:\n",
"\n",
" - `%matplotlib inline` makes plots appear in the notebook and not an external window\n",
" - `%run FILE` runs the contents of the file in place of the given cell \n",
" - `%%timeit` times how long the cell takes to run\n",
" \n",
"---\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"You can also run ipython in the terminal / shell on this machine. You will see that some of the interactivity still works in a text environment but not all of the pop up help is as helpful as in the notebooks.\n",
"\n",
"<a href=\"/terminals/1\"> Terminal </a>"
]
}
],
"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 |
joshnsolomon/phys202-2015-work | assignments/assignment04/MatplotlibExercises.ipynb | 1 | 40978 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualization 1: Matplotlib Basics Exercises"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scatter plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Learn how to use Matplotlib's `plt.scatter` function to make a 2d scatter plot.\n",
"\n",
"* Generate random data using `np.random.randn`.\n",
"* Style the markers (color, size, shape, alpha) appropriately.\n",
"* Include an x and y label and title."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f9582328c10>"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAH4CAYAAACbjOPoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f958218d190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,8))\n",
"plt.scatter(np.random.randn(100),np.random.randn(100),s=50,c='b',marker='d',alpha=.7)\n",
"plt.xlabel('x-coordinate')\n",
"plt.ylabel('y-coordinate')\n",
"plt.title('100 Random Points')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Histogram"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Learn how to use Matplotlib's `plt.hist` function to make a 1d histogram.\n",
"\n",
"* Generate randpom data using `np.random.randn`.\n",
"* Figure out how to set the number of histogram bins and other style options.\n",
"* Include an x and y label and title."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f958213d710>"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAH4CAYAAAAPakoaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKEBmGJYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9582310910>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,8))\n",
"p=plt.hist(np.random.randn(100000),bins=50,color='g')\n",
"plt.xlabel('value')\n",
"plt.ylabel('frequency')\n",
"plt.title('Distrobution 100000 Random Points with mean of 0 and variance of 1')"
]
}
],
"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": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
Snyder005/StatisticalMethods | notes/Priors.ipynb | 4 | 3900 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Assigning Priors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*\"There are only two problems in inference: how to assign probability distributions, and how to do integrals.\" - John Skilling*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The topic of how to assign priors will re-appear throughout this course, including in this week's homework.\n",
"\n",
"\n",
"* One way to assign priors is to *plead ignorance.* Not knowing the value of a parameter up to an additive constant indicates that a *uniform* (i.e. top hat) prior might be appropriate. \n",
"\n",
"\n",
"* Not knowing the value of a parameter up to an multiplicative constant indicates that a prior *uniform in the log of the parameter* might be appropriate. Equating ${\\rm Pr}(x)\\,dx = {\\rm Pr}(\\log{x})\\,d \\log{x}$ and assigning the above uniform PDF leads to ${\\rm Pr}(x) \\propto 1/x$, which is sometimes known loosely as the \"Jeffreys Prior\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* One problem with uninformative priors is that they can lead to parameter space volumes that are unmanageably large.\n",
"\n",
"#### Q: Consider a uniform joint prior PDF in N parameter dimensions. What fraction of the *a priori* allowed volume is in a hypercubic shell that has thickness *f* of the side length?"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Volume fraction for f =', 0.01, ' is')\n"
]
}
],
"source": [
"f = 0.01\n",
"N = 2\n",
"# Compute difference between two hypervolumes:\n",
"\n",
"print(\"Volume fraction for f =\",f,\" is\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This effect can cause real computational problems when attempting to characterize posterior PDFs - you've seen it already, just in our attempts with two-dimensional grids!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Informative Priors\n",
"\n",
"* The prior PDF is an *opportunity* - to include *more information* into our analysis.\n",
"\n",
"\n",
"* One good prior PDF is the *posterior PDF* from a previous analysis.\n",
"\n",
"${\\rm Pr}(x|d,H) \\propto {\\rm Pr}(d|x,H)\\;{\\rm Pr}(x|c,H)$\n",
"\n",
"* Note that ${\\rm Pr}(x|c,H)\\propto {\\rm Pr}(c|x,H)\\;{\\rm Pr}(x|H)$ by Bayes Theorem, so that *using posteriors as priors is equivalent to joint inference from multiple datasets*:\n",
"\n",
"${\\rm Pr}(x|d,H) \\propto {\\rm Pr}(d|x,H)\\;{\\rm Pr}(c|x,H)\\;{\\rm Pr}(x|H)$\n",
"\n",
"\n",
"* Another good prior PDF is *one that accurately represents your beliefs.* Such \"subjective priors\" are typically treated with justified caution, because scientists aspire to perform *objective* analyses. However, a prior PDF assignment is just one type of assumption in the many that are involved in the model, and they can and should be tested in the same way. We'll return to this in week 3."
]
},
{
"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
}
| gpl-2.0 |
UNH-CORE/RM2-tow-tank | Documents/Lab notebooks/2015.02.05.ipynb | 1 | 5432 | {
"metadata": {
"name": "",
"signature": "sha256:628641eb3c6d285d2ef6a54726baaeca65ec030433c73eacf7356aa8fd9fba73"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"2015.02.05"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Torqued servo gearhead to mounting block.\n",
"* Torqued lower half of upper servo coupling to 70 Nm.\n",
"* Applied dielectric grease to cabinet end of servo power and feedback cabled, and attached these to the servo and cabinet connectors. \n",
"* Confirmed AKD parameters\n",
" * Slider tuning at 5\n",
" * Regen set to external\n",
" * Emulated encoder ouput at 5,000 lines per rev, or 1e5 including the gearbox.\n",
"* Jogged turbine axis at 5 RPM and things looked good. Recorded some video.\n",
"* Wired up torque transducer to 9205 module and 12 VDC power supply.\n",
"* Attached Vectrino sync leads; applied dielectric grease to terminals.\n",
"* Homed z-axis.\n",
"* Checked Vectrino comms; good.\n",
"* Checked Vectrino velocity measurement; very noisy, but probably due to no seeding. \n",
"* z-axis has some trouble sticking when reversing direction. Will need to do some tuning. \n",
"* Looked at torque, drag and RPM NI signals while rotating turbine at 10 RPM; look good.\n",
"* Homed y-axis; looks good.\n",
"* Jogged turbine at 15 RPM.\n",
"* Torque transducer and arm measurements appear to be tracking eachother, which is good."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vectrino trigger was not working initially. Moved positive lead to \"Synch -\" and it worked fine."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Have done a couple shakedown runs at 0.5 m/s:\n",
"\n",
"| $\\lambda$ | $C_P$ |\n",
"|-----------|-------|\n",
"| 2.6 | 0.13 | \n",
"| 2.7 | 0.17 | \n",
"| 2.8 | 0.21 | \n",
"| 2.9 | 0.24 | \n",
"| 3.0 | 0.27 |\n",
"| 3.1 | 0.29 |\n",
"| 3.2 | 0.31 |\n",
"| 3.3 | 0.32 |\n",
"| 3.4 | 0.32 |\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Greased z-axis ball screw to aleviate binding.\n",
"\n",
"To-do\n",
"-----\n",
"* ~~Re-tape Vectrino cable~~\n",
"* ~~Zero turbine angle~~\n",
"* ~~Take more pictures~~\n",
"* ~~Measure YZ traverse geometry~~\n",
"* Measure salinity\n",
"* Add seeding\n"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Confirming YZ traverse geometry"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The distance shown below was measured by tape measure to be 70 3/16\" when the controller thinks $y=0$."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import Image\n",
"Image(url=\"https://unh.box.com/shared/static/qldih5xxwvp2mie9ph87xsl6zemkvusl.png\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<img src=\"https://unh.box.com/shared/static/qldih5xxwvp2mie9ph87xsl6zemkvusl.png\"/>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"<IPython.core.display.Image at 0x37478b0>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The measurement corresponding to the image below was 74 1/8\" via tape measure, so things look pretty good."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Image(url=\"https://unh.box.com/shared/static/h2lxd3k0ptr6poz0f7a5ecljbvqil1i3.png\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<img src=\"https://unh.box.com/shared/static/h2lxd3k0ptr6poz0f7a5ecljbvqil1i3.png\"/>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"<IPython.core.display.Image at 0x37477b0>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zeroed turbine angle +/- a couple degrees. It's not that easy to tell even with the pointer bracket.\n",
"\n",
"Tranferred Nikon camera images and video to Box. \n",
"\n",
"Did another shakedown run at $\\lambda=4.0$. Power coefficient was 0.25. At a tip speed ratio of 5, $C_P$ went down to 0.02. At 5.5, -0.10. Judging from the RVAT $Re$-dependence data, this means we'd be pretty close to zero at the higher Reynolds number. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$C_P$ went up to 0.34 for $U_\\infty = 0.6$ and $\\lambda = 3.3$. Hit 0.35 at $U_\\infty = 0.7$ and 0.37 at $U_\\infty = 0.8$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calling it quits for tonight. Will resume tomorrow."
]
}
],
"metadata": {}
}
]
} | mit |
DataKind-SG/healthcare_ASEAN | notebooks/MY_dengue_viz.ipynb | 1 | 113399 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.read_csv(\"./../Data/interim/disease_MY/weekly-dengue.csv\")"
]
},
{
"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>year</th>\n",
" <th>week</th>\n",
" <th>cases</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2010</td>\n",
" <td>8</td>\n",
" <td>1121.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2010</td>\n",
" <td>9</td>\n",
" <td>1074.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2010</td>\n",
" <td>10</td>\n",
" <td>1039.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2010</td>\n",
" <td>11</td>\n",
" <td>898.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2010</td>\n",
" <td>12</td>\n",
" <td>797.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year week cases\n",
"0 2010 8 1121.0\n",
"1 2010 9 1074.0\n",
"2 2010 10 1039.0\n",
"3 2010 11 898.0\n",
"4 2010 12 797.0"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABEgAAAJ4CAYAAAB20dZBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/w/YtIaf5X2XEgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1149a5510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"colors = ['r', 'g', 'b', 'm','c']\n",
"markers = ['*', 's', 'p', '8','h']\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(18.5, 10.5);\n",
"for i, year in enumerate(range(2010, 2015)):\n",
" denguef_year_data = df[df['year']==year]\n",
" plt.plot(denguef_year_data.week, denguef_year_data.cases, color = colors[i], marker = markers[i], label=str(year))\n",
" plt.legend()\n",
" plt.xlabel('Week number')\n",
" plt.ylabel('Number of cases')\n",
" plt.title('Dengue Fever weekly trend')"
]
},
{
"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": 0
}
| mit |
krosaen/ml-study | python-ml-book/ch10/ch10.ipynb | 1 | 1745866 | null | mit |
machlearn/ipython-notebooks | ML Algorithm - Linear Regression.ipynb | 1 | 5123 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linear Regression\n",
"\n",
"`LinearRegression` fits a linear model to the dataset by adjusting a set of parameters in order to make the sum of the squared residuals of the model as small as possible\n",
"\n",
"## An example \n",
"Load `diabetes` dataset:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(442, 10)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"from sklearn import datasets\n",
"import matplotlib.pyplot as plt\n",
"\n",
"diabetes = datasets.load_diabetes()\n",
"diabetes.data.shape"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Use only one feature\n",
"diabetes_X = diabetes.data[:, np.newaxis, 2]\n",
"\n",
"## Divide the dataset into train and test set\n",
"diabetes_X_train = diabetes_X[:-20]\n",
"diabetes_X_test = diabetes_X[-20:]\n",
"diabetes_y_train = diabetes.target[:-20]\n",
"diabetes_y_test = diabetes.target[-20:]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import linear_model\n",
"regr = linear_model.LinearRegression()\n",
"regr.fit(diabetes_X_train, diabetes_y_train)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'copy_X': True, 'fit_intercept': True, 'n_jobs': 1, 'normalize': False}"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get parameters\n",
"regr.get_params()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 938.23786125]\n"
]
}
],
"source": [
"print(regr.coef_)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2548.0723987259703"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the mean square error\n",
"np.mean((regr.predict(diabetes_X_test)-diabetes_y_test) ** 2)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.4725754479822713"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# variance score: 1 is perfect prediction, 0 means that there is no linear relationship between X and y\n",
"regr.score(diabetes_X_test, diabetes_y_test)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Plot output\n",
"plt.scatter(diabetes_X_test, diabetes_y_test, color='black')\n",
"plt.plot(diabetes_X_test, regr.predict(diabetes_X_test), color = 'blue', linewidth=3)\n",
"\n",
"plt.xticks(())\n",
"plt.yticks(())\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## scikit-learn Linear Regression Usage\n",
"- More info here: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression\n",
"- "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reference\n",
"- http://scikit-learn.org/stable/tutorial/statistical_inference/supervised_learning.html\n"
]
},
{
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
ghvn7777/ghvn7777.github.io | content/fluent_python/15_with.ipynb | 1 | 20147 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"本章讨论其他语言不常见的流程控制，用户可能会忽略这些特性：\n",
"\n",
"- with 语句和上下文管理器\n",
"- for while 和 try 语句的 else 子句\n",
"\n",
"with 语句会设置一个临时的上下文，交给上下文管理器对象控制，并负责清理上下文。这么做能避免错误并减少样板代码，因此 API 更安全，更易于使用。除了自动关闭文件之外，with 块还有很多用途\n",
"\n",
"else 子句和 with 没关系，不过这两个都内容比较短，所以放到了一个逻辑\n",
"\n",
"## 先做这个，再做那个： if 之外的 else 块\n",
"\n",
"else 子句不仅能在 if 语句中使用，还能在 for，while，try 语句中使用\n",
"\n",
"else 子句行为如下：\n",
"\n",
"for: 仅当 for 循环运行完毕时（即 for 循环没有被 break 语句终止）才运行 else\n",
"\n",
"try: 仅当 try 块中没有异常时候才运行 else 块，else 子句抛出的异常不会由前面的 except 子句处理\n",
"\n",
"在所有情况下，如果异常或者 return, break 或 continue 语句导致控制权跳到了复合语句之外，else 也会被跳过\n",
"\n",
"for 循环用 else 如下："
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# for item in my_list:\n",
"# if item.flavor == 'banana':\n",
"# break\n",
"# else:\n",
"# raise ValueError('No banana flavor found!')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"一开始你可能觉得没必要在 try/except 中使用 else 子句，毕竟下面代码中只有 dangerous_cal() 不抛出异常 after_call() 才会执行"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# try:\n",
"# dangerous_call()\n",
"# after_call()\n",
"# except OSError:\n",
"# log('OSError...')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"然而，after_call() 不应该放在 try 块中。为了清晰准确，try 块应该只抛出预期异常的语句，因此像下面这样写更好："
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# try:\n",
"# dangerous_call()\n",
"# except OSError:\n",
"# log('OSError...')\n",
"# else:\n",
"# after_call()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"现在很明确，try 为了捕获的是 dangerous_call() 的异常。\n",
"\n",
"Python 中，try/except 不仅用于处理错误，还用于控制流程，为此，官方定义了几个缩略词：\n",
"\n",
"EAFP:\n",
" 取得原谅比获得许可容易(easier to ask for forgiveness than\n",
"permission)。这是一种常见的 Python 编程风格,先假定存在有效\n",
"的键或属性,如果假定不成立,那么捕获异常。这种风格简单明\n",
"快,特点是代码中有很多 try 和 except 语句。与其他很多语言一\n",
"样(如 C 语言),这种风格的对立面是 LBYL 风格。\n",
"\n",
"LBYL\n",
" 三思而后行(look before you leap)。这种编程风格在调用函数\n",
"或查找属性或键之前显式测试前提条件。与 EAFP 风格相反,这种\n",
"风格的特点是代码中有很多 if 语句。在多线程环境中,LBYL 风\n",
"格可能会在“检查”和“行事”的空当引入条件竞争。例如,对 if\n",
"key in mapping: return mapping[key] 这段代码来说,如果\n",
"在测试之后,但在查找之前,另一个线程从映射中删除了那个键,\n",
"那么这段代码就会失败。这个问题可以使用锁或者 EAFP 风格解\n",
"决。\n",
"如果选择使用 EAFP 风格,那就要更深入地了解 else 子句，并在 try/except 中合理使用\n",
"\n",
"## 上下文管理器和 with 块\n",
"\n",
"上下文管理器对象存在的目的是管理 with 语句，就像迭代器存在是为了管理 for 语句。\n",
"\n",
"with 语句目的是为了简化 try/finally 模式。上下文管理器协议包含 `__enter__` 和 `__exit__` 方法，with 开始时，会调用 `__enter__` 方法，结束时候会调用 `__exit__` 方法\n",
"\n",
"最常见的是打开文件："
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"60"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open('with.ipynb') as fp:\n",
" src = fp.read(60)\n",
"len(src)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<_io.TextIOWrapper name='with.ipynb' mode='r' encoding='UTF-8'>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fp"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(True, 'UTF-8')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fp.closed, fp.encoding"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "I/O operation on closed file.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-a91ac20aa9d8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# fp 虽然可用，但不能执行 I/O 操作，\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# 因为在 with 末尾，调用 TextIOWrapper.__exit__ 关闭了文件\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mValueError\u001b[0m: I/O operation on closed file."
]
}
],
"source": [
"# fp 虽然可用，但不能执行 I/O 操作，\n",
"# 因为在 with 末尾，调用 TextIOWrapper.__exit__ 关闭了文件\n",
"fp.read(60) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with 的 as 子句是可选的，对 open 来说，必须加 as 子句，以便获取文件的引用。不过，有些上下文管理器会返回 None，因为没有什么有用的对象能提供给用户\n",
"\n",
"下面是一个精心制作的上下文管理器执行操作，以此强调上下文管理器与 `__enter__` 方法返回的对象之间的区别"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class LookingGlass:\n",
" def __enter__(self): # enter 只有一个 self 参数\n",
" import sys\n",
" self.original_write = sys.stdout.write # 保存供日后使用\n",
" sys.stdout.write = self.reverse_write # 打猴子补丁，换成自己方法\n",
" return 'JABBERWOCKY' # 返回的字符串讲存入 with 语句的 as 后的变量\n",
" \n",
" def reverse_write(self, text): #取代 sys.stdout.write，反转 text\n",
" self.original_write(text[::-1])\n",
" \n",
" # 正常传的参数是 None， None， None，有异常传如下异常信息\n",
" def __exit__(self, exc_type, exc_value, traceback):\n",
" import sys # 重复导入不会消耗很多资源，Python 会缓存导入模块\n",
" sys.stdout.write = self.original_write # 还原 sys.stdout.write 方法\n",
" if exc_type is ZeroDivisionError: # 如果有除 0 异样，打印消息\n",
" print('Please DO NOT divide by zero')\n",
" return True # 返回 True 告诉解释器已经处理了异常\n",
"# 如果 __exit__ 方法返回 None，或者 True 之外的值，with 块中的任何异常都会向上冒泡"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pordwonS dna yttiK ,ecilA\n",
"YKCOWREBBAJ\n"
]
}
],
"source": [
"with LookingGlass() as what:\n",
" print('Alice, Kitty and Snowdrop') #打印出的内容是反向的\n",
" print(what)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'JABBERWOCKY'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# with 执行完毕，可以看出 __enter__ 方法返回的值 -- 即存储在 what 变量中的值是 'JABBERWOCKY' \n",
"what "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Back to normal\n"
]
}
],
"source": [
"print('Back to normal') # 输出不再是反向的了"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 在实际应用中，如果程序接管了标准输出，可能会把 sys.stdout 换成类似文件的其他对象，然后再切换成原来的版本。contextlib.redirect_stdout 上下文管理器就是这么做的\n",
"\n",
"解释器调用 __enter__ 方法时，除了隐式的 self 之外，不会传入任何参数，传给 `__exit__` 的三个参数如下：\n",
"\n",
"exc_type: 异常类（例如 ZeroDivisionError）\n",
"\n",
"exc_value: 异常实例。有时好有参数传给异常构造方法，例如错误消息，参数可以通过 exc_value.args 获取\n",
"\n",
"traceback： traceback 对象\n",
"\n",
"上下文管理器具体工作方式如下："
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# In [2]: manager = LookingGlass()\n",
"# ...: manager\n",
"# ...: \n",
"# Out[2]: <__main__.LookingGlass at 0x7f586d4aa1d0>\n",
"\n",
"# In [3]: monster = manager.__enter__()\n",
"\n",
"# In [4]: monster == 'JABBERWOCKY'\n",
"# Out[4]: eurT\n",
"\n",
"# In [5]: monster\n",
"# Out[5]: 'YKCOWREBBAJ'\n",
"\n",
"# In [6]: manager.__exit__(None, None, None)\n",
"\n",
"# In [7]: monster\n",
"# Out[7]: 'JABBERWOCKY'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"上面在命令行执行的，因为在 jupyter notebook 的输出有时候有莫名其妙的 bug\n",
"\n",
"## contextlib 模块中的实用工具\n",
"\n",
"自定义上下文管理器类之前，先看一下 Python 标准库文档中的 contextlib。除了前面提到的 redirect_stdout 函数，contextlib 模块中还有一些类和其它函数，实用范围更广\n",
"\n",
"closing: 如过对象提供了 close() 方法，但没有实现 `__enter__/__exit__` 协议，可以实用这个函数构建上下文管理器\n",
"\n",
"suppress: 构建临时忽略指定异常的上下文管理器\n",
"\n",
"@contextmanager: 这个装饰器把简单的生成器函数变成上下文管理器，这样就不用创建类去实现管理协议了\n",
"\n",
"ContextDecorator: 这是个基类，用于定义基于类的上下文管理器。这种上下文管理器也能用于装饰函数，在受管理的上下文中运行整个函数\n",
"\n",
"ExitStack: 这个上下文管理器能进入多个上下文管理器，with 块结束时，ExitStack 按照后进先出的顺序调用栈中各个上下文管理器的 `__exit__` 方法。如果事先不知道 with 块要进入多少个上下文管理器，可以使用这个类。例如同时打开任意一个文件列表中的所有文件\n",
"\n",
"这些工具中使用最广泛的是 @contextmanager 装饰器，因此要格外小心，这个装饰器也有迷惑人的一面，因为它与迭代无关，却使用 yield 语句，由此可以引出协程\n",
"\n",
"## 使用 @contextmanager\n",
"\n",
"@contextmanager 装饰器能减少创建上下文管理器的样板代码量，因为不用编写一个完整的类，定义 `__enter__` 和 `__exit__` 方法，而只需实现一个有 yield 语句的生成器，生成想让 `__enter__` 方法返回的值\n",
"\n",
"在使用 @contextmanager 装饰器能减少创建上下文管理器的样板代码量，因为不用编写一个完整的类，定义 `__enter__` 和 `__exit__` 方法，而只需实现有一个 yield 语句的生成器，生成想让 `__enter__` 方法返回的值\n",
"\n",
"在使用 @contextmanager 装饰器的生成器中，yield 语句的作用是把函数的定义体分成两个部分：yield 语句前面所有代码在 with 块开始时（即解释器调用 `__enter__` 方法时）执行，yield 语句后面的代码在 with 块结束时（即调用 `__exit__` 方法时）执行"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import contextlib\n",
"\n",
"@contextlib.contextmanager\n",
"def looking_glass():\n",
" import sys\n",
" original_write = sys.stdout.write\n",
" \n",
" def reverse_write(text):\n",
" original_write(text[::-1])\n",
" \n",
" sys.stdout.write = reverse_write\n",
" # 产生一个值，这个值会绑定到 with 语句的 as 子句后的目标变量上\n",
" # 执行 with 块中的代码时，这个函数会在这一点暂停\n",
" yield 'JABBERWOCKY' \n",
" # 控制权一旦跳出 with 块，继续执行 yield 语句后的代码\n",
" sys.stdout.write = original_write"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pordwonS dna yttiK ,ecilA\n",
"YKCOWREBBAJ\n"
]
}
],
"source": [
"with looking_glass() as what:\n",
" print('Alice, Kitty and Snowdrop')\n",
" print(what)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"其实，contextlib.contextmanager 装饰器会把函数包装成实现 `__enter__` 和 `__exit__` 方法的类\n",
"\n",
"这个类的 `__enter__` 作用如下：\n",
"\n",
"- 调用生成器函数，保存生成器对象（这里称为 gen）\n",
"- 调用 next(gen)，执行到 yield 关键字位置\n",
"- 返回 next(gen) 产生的值，以便把产生的值绑定到 `with/as` 语句中目标变量上\n",
"\n",
"with 块终止时，`__exit__` 方法会做以下几件事\n",
"\n",
"- 检查有没有把异常传给 exc_type, 如果有，调用 gen.throw(exception), 在生成器函数定义体中包含 yield 关键字的那一行跑出异常\n",
"\n",
"- 否则，调用 next(gen)，继续执行生成器函数体中 yield 语句之后的代码\n",
"\n",
"上面的例子其实有一个严重的错误，如果在 with 块中抛出了异常，Python 解释器会将其捕获，然后在 looking_glass 函数的 yield 表达式再次跑出，但是，那里没有处理错误的代码，因此 looking_glass 函数会终止，永远无法恢复成原来的 sys.stdout.write 方法，导致系统处于无效状态，下面添加了一些代码，用于处理 ZeroDivisionError 异常，这样就比较健壮了"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import contextlib\n",
"\n",
"@contextlib.contextmanager\n",
"def looking_glass():\n",
" import sys\n",
" original_write = sys.stdout.write\n",
" \n",
" def reverse_write(text):\n",
" original_write(text[::-1])\n",
" \n",
" sys.stdout.write = reverse_write\n",
" msg = ''\n",
" try:\n",
" yield 'JABBERWOCKY' \n",
" except ZeroDivisionError:\n",
" msg = 'Please DO NOT divide by zero'\n",
" finally:\n",
" sys.stdout.write = original_write\n",
" if msg:\n",
" print(msg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"前面说过，为了告诉解释器异常已经处理了，`__exit__` 方法返回 True，此时解释器会压制异常。如果 `__exit__` 方法没有显式返回一个值，那么解释器得到的是 None，然后向上冒泡异常。使用 @contextmanager 装饰器时，默认行为是相反的，装饰器提供的 `__exit__` 方法假定发给生成器的所有异常都得到处理了，因此应该压制异常。如果不想让 `@contextmanager` 压制异常，必须在装饰器的函数中显式重新跑出异常\n",
"\n",
"> 把异常发给生成器的方式是使用 throw 方法，下章讲\n",
"\n",
"> 这样的约定的原因是，创建上下文时，生成器无法返回值，只能产出值。不过现在可以返回值了，见下章\n",
"\n",
"> 使用 @contextmanager 装饰器时，要把 yield 语句放到 try/finally 语句中（或者放在 with 语句中），这是无法避免的，因为我们永远不知道上下文管理器用户会在 with 块中做什么\n",
"\n",
"除了标准库中举得例子外，Martijin Pieters 实现原地文件重写上下文管理器是 @contextmanager 不错的使用实例，如下："
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# import csv\n",
"# with inplace(csvfilename, 'r', newline='') as (infh, outfh):\n",
"# reader = csv.reader(infh)\n",
"# writer = csv.writer(outfh)\n",
"# for row in reader:\n",
"# row += ['new', 'columns']\n",
"# writer.writerow(row)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"inplace 函数是个上下文管理器,为同一个文件提供了两个句柄(这个示例中的 infh 和 outfh),以便同时读写同一个文件。这比标准库中的 fileinput.input 函数更易用\n",
"\n",
"注意,在 @contextmanager 装饰器装饰的生成器中,yield 与迭代没有任何关系。在本节所举的示例中,生成器函数的作用更像是协程:执行到某一点时暂停,让客户代码运行,直到客户让协程继续做事。下章会全面讨论协程。"
]
}
],
"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
}
| apache-2.0 |
pucdata/pythonclub | sessions/09-numba_cython/Faster_computations.ipynb | 1 | 285372 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"\n",
"In order to run these examples, it is recommended to use gcc as the default compiler, with OpenMP installed.\n",
"\n",
"Numba and Cython can be installed easily with conda:\n",
"#### conda install numba\n",
"#### conda install cython\n",
"\n",
"The include path of the NumPy C header files might have to be added to the .bashrc (Linux) or .bash_profile (Mac) files to make Numba run:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"export CFLAGS=\"-I /home/ghajdu/bin/miniconda2/lib/python2.7/site-packages/numpy/core/include/ $CFLAGS\"\n"
]
}
],
"source": [
"import numpy as np\n",
"print \"export CFLAGS=\\\"-I\",np.__path__[0]+'/core/include/ $CFLAGS\\\"'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Append the output of the print command above\n",
"to the .bashrc (Linux) or .bash_profile (Mac) file in the default user library, if Numba does not work out of the box."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Faster computations in Python\n",
"\n",
"Python's dynamically typed nature makes it easy to quickly write code\n",
"that works, however, this comes at the cost of execution speed, as\n",
"each time an operation is executed on a variable, its type has to be\n",
"checked by the interpreter, in order to execute the appropriate subroutine\n",
"for the given combination of variable type and operation.\n",
"\n",
"The speed of computations can be greatly increased by utilizing NumPy,\n",
"where the data are stored in homogeneous C arrays inside array objects.\n",
"NumPy also provides specialized commands to do calculations quickly on\n",
"these arrays.\n",
"\n",
"In this example, we will compare different implementations of a truncated\n",
"Fourier sum, which is calculated for a number of different positions.\n",
"In astronomical situations, these positions can be, for example, times of\n",
"measurement of the magnitude of a star. The sum has the form:\n",
"\n",
"### $m_i (t_i) = \\sum_{j=1}^{n} A_j \\cdot \\sin( 2 \\cdot \\pi \\cdot f_j \\cdot t_i +\\phi_j )$,\n",
"\n",
"where $m_i$ is the $i$th magnitude of the star at time $t_i$, $n$ is the\n",
"number of Fourier terms, $A_j$ is the amplitude, $f_j$ is the frequency, and $\\phi_j$ is the phase\n",
"of the $j$th Fourier term."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preparation\n",
"\n",
"First, we import the packages that we will be using, and prepare the data.\n",
"We store the $t_i$, $A_j$, $f_j$ and $\\phi_j$ parameters in NumPy arrays.\n",
"\n",
"We also define two functions, one which does the above sum using two for cycles,\n",
"and another one exploiting array operations of NumPy.\n",
"\n",
"Furthermore, we prepare for the usage of Cython within the Notebook by loading\n",
"the Cython magic commands into it."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The size of the time array: 7000\n"
]
}
],
"source": [
"import numpy as np\n",
"from numba import jit, autojit\n",
"%load_ext Cython\n",
"\n",
"\n",
"times=np.arange(0,70,0.01)\n",
"print \"The size of the time array:\", times.size\n",
"\n",
"freq = np.arange(0.1,6.0,0.1)*1.7763123\n",
"freq[-20:] = freq[:20]*0.744\n",
"amp = 0.05/(freq**2)+0.01\n",
"phi = freq\n",
"\n",
"def fourier_sum_naive(times, freq, amp, phi):\n",
" mags = np.zeros_like(times)\n",
" for i in xrange(times.size):\n",
" for j in xrange(freq.size):\n",
" mags[i] += amp[j] * np.sin( 2 * np.pi * freq[j] * times[i] + phi[j] )\n",
" \n",
" return mags\n",
" \n",
"def fourier_sum_numpy(times, freq, amp, phi):\n",
" return np.sum(amp.T.reshape(-1,1) * np.sin( 2 * np.pi * freq.T.reshape(-1,1) * times.reshape(1,-1) + phi.T.reshape(-1,1)), axis=0)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Numba\n",
"\n",
"We use the autojit function from Numba to prepare the translation of the Python function to machine code.\n",
"By default usage, the functions get translated during runtime (in a Just-In-Time JIT manner), when the\n",
"first call is made to the function. Numba produces optimized machine code, taking into account the type of\n",
"input the function receives when called for the first time.\n",
"\n",
"Alternatively, the function can be defined like normal, but preceded by the @jit decorator, in order to\n",
"notify Numba about the functions to optimize, as show in the commented area below.\n",
"\n",
"Note that Numba can be called eagerly, telling it the type of the expected variable, as well as the return\n",
"type of the function. This can be used to fine-tune the behavior of the function. See more in the Numba\n",
"documentation:\n",
"http://numba.pydata.org/numba-doc/dev/user/jit.html\n",
"\n",
"Note that functions can also be compiled ahead of time. For more information, see:\n",
"http://numba.pydata.org/numba-doc/0.32.0/reference/aot-compilation.html"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fourier_sum_naive_numba = autojit(fourier_sum_naive)\n",
"fourier_sum_numpy_numba = autojit(fourier_sum_numpy)\n",
"\n",
"#@jit\n",
"#def fourier_sum_naive_numba(times, freq, amp, phi):\n",
"# mags = np.zeros_like(times)\n",
"# for i in xrange(times.size):\n",
"# for j in xrange(freq.size):\n",
"# mags[i] += amp[j] * np.sin( 2 * np.pi * freq[j] * times[i] + phi[j] )\n",
"# \n",
"# return mags\n",
"\n",
"#@jit()\n",
"#def fourier_sum_numpy_numba(times, freq, amp, phi):\n",
"# return np.sum(amp.T.reshape(-1,1) * np.sin( 2 * np.pi * freq.T.reshape(-1,1) * times.reshape(1,-1) + phi.T.reshape(-1,1)), axis=0)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Cython\n",
"\n",
"Cython works different than Numba: It produces C code that gets compiled before calling the function.\n",
"NumPy arrays store the data internally in simple C arrays, which can be accessed with the\n",
"Typed Memoryview feature of Cython. This allows operations on these arrays that completely bypass Python.\n",
"We can also import C functions, as we could writing pure C by importing the corresponding header files.\n",
"\n",
"In the example implementation of the Fourier sum below, we use define two funtions.\n",
"The first one handles interactions with Python, while the second one handles the actual calculations.\n",
"Note that we also pass a temp array, in order to provide a reserved space for the function to work\n",
"on, eliminating the need to create a NumPy array within the function.\n",
"\n",
"#### Important note\n",
"Normal usage of Cython involves creating a separate .pyx file, with the corresponding Cython code inside,\n",
"which then gets translated into a source object (.so on Unix-like systems, .dll on Windows), which can\n",
"be imported into Python like a normal .py file. See the Cython documentation for more information:\n",
"http://docs.cython.org/en/latest/src/quickstart/build.html"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<!DOCTYPE html>\n",
"<!-- Generated by Cython 0.25.2 -->\n",
"<html>\n",
"<head>\n",
" <meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n",
" <title>Cython: _cython_magic_ec5a80ebe17c2abd87108e86b8d3c94e.pyx</title>\n",
" <style type=\"text/css\">\n",
" \n",
"body.cython { font-family: courier; font-size: 12; }\n",
"\n",
".cython.tag { }\n",
".cython.line { margin: 0em }\n",
".cython.code { font-size: 9; color: #444444; display: none; margin: 0px 0px 0px 8px; border-left: 8px none; }\n",
"\n",
".cython.line .run { background-color: #B0FFB0; }\n",
".cython.line .mis { background-color: #FFB0B0; }\n",
".cython.code.run { border-left: 8px solid #B0FFB0; }\n",
".cython.code.mis { border-left: 8px solid #FFB0B0; }\n",
"\n",
".cython.code .py_c_api { color: red; }\n",
".cython.code .py_macro_api { color: #FF7000; }\n",
".cython.code .pyx_c_api { color: #FF3000; }\n",
".cython.code .pyx_macro_api { color: #FF7000; }\n",
".cython.code .refnanny { color: #FFA000; }\n",
".cython.code .trace { color: #FFA000; }\n",
".cython.code .error_goto { color: #FFA000; }\n",
"\n",
".cython.code .coerce { color: #008000; border: 1px dotted #008000 }\n",
".cython.code .py_attr { color: #FF0000; font-weight: bold; }\n",
".cython.code .c_attr { color: #0000FF; }\n",
".cython.code .py_call { color: #FF0000; font-weight: bold; }\n",
".cython.code .c_call { color: #0000FF; }\n",
"\n",
".cython.score-0 {background-color: #FFFFff;}\n",
".cython.score-1 {background-color: #FFFFe7;}\n",
".cython.score-2 {background-color: #FFFFd4;}\n",
".cython.score-3 {background-color: #FFFFc4;}\n",
".cython.score-4 {background-color: #FFFFb6;}\n",
".cython.score-5 {background-color: #FFFFaa;}\n",
".cython.score-6 {background-color: #FFFF9f;}\n",
".cython.score-7 {background-color: #FFFF96;}\n",
".cython.score-8 {background-color: #FFFF8d;}\n",
".cython.score-9 {background-color: #FFFF86;}\n",
".cython.score-10 {background-color: #FFFF7f;}\n",
".cython.score-11 {background-color: #FFFF79;}\n",
".cython.score-12 {background-color: #FFFF73;}\n",
".cython.score-13 {background-color: #FFFF6e;}\n",
".cython.score-14 {background-color: #FFFF6a;}\n",
".cython.score-15 {background-color: #FFFF66;}\n",
".cython.score-16 {background-color: #FFFF62;}\n",
".cython.score-17 {background-color: #FFFF5e;}\n",
".cython.score-18 {background-color: #FFFF5b;}\n",
".cython.score-19 {background-color: #FFFF57;}\n",
".cython.score-20 {background-color: #FFFF55;}\n",
".cython.score-21 {background-color: #FFFF52;}\n",
".cython.score-22 {background-color: #FFFF4f;}\n",
".cython.score-23 {background-color: #FFFF4d;}\n",
".cython.score-24 {background-color: #FFFF4b;}\n",
".cython.score-25 {background-color: #FFFF48;}\n",
".cython.score-26 {background-color: #FFFF46;}\n",
".cython.score-27 {background-color: #FFFF44;}\n",
".cython.score-28 {background-color: #FFFF43;}\n",
".cython.score-29 {background-color: #FFFF41;}\n",
".cython.score-30 {background-color: #FFFF3f;}\n",
".cython.score-31 {background-color: #FFFF3e;}\n",
".cython.score-32 {background-color: #FFFF3c;}\n",
".cython.score-33 {background-color: #FFFF3b;}\n",
".cython.score-34 {background-color: #FFFF39;}\n",
".cython.score-35 {background-color: #FFFF38;}\n",
".cython.score-36 {background-color: #FFFF37;}\n",
".cython.score-37 {background-color: #FFFF36;}\n",
".cython.score-38 {background-color: #FFFF35;}\n",
".cython.score-39 {background-color: #FFFF34;}\n",
".cython.score-40 {background-color: #FFFF33;}\n",
".cython.score-41 {background-color: #FFFF32;}\n",
".cython.score-42 {background-color: #FFFF31;}\n",
".cython.score-43 {background-color: #FFFF30;}\n",
".cython.score-44 {background-color: #FFFF2f;}\n",
".cython.score-45 {background-color: #FFFF2e;}\n",
".cython.score-46 {background-color: #FFFF2d;}\n",
".cython.score-47 {background-color: #FFFF2c;}\n",
".cython.score-48 {background-color: #FFFF2b;}\n",
".cython.score-49 {background-color: #FFFF2b;}\n",
".cython.score-50 {background-color: #FFFF2a;}\n",
".cython.score-51 {background-color: #FFFF29;}\n",
".cython.score-52 {background-color: #FFFF29;}\n",
".cython.score-53 {background-color: #FFFF28;}\n",
".cython.score-54 {background-color: #FFFF27;}\n",
".cython.score-55 {background-color: #FFFF27;}\n",
".cython.score-56 {background-color: #FFFF26;}\n",
".cython.score-57 {background-color: #FFFF26;}\n",
".cython.score-58 {background-color: #FFFF25;}\n",
".cython.score-59 {background-color: #FFFF24;}\n",
".cython.score-60 {background-color: #FFFF24;}\n",
".cython.score-61 {background-color: #FFFF23;}\n",
".cython.score-62 {background-color: #FFFF23;}\n",
".cython.score-63 {background-color: #FFFF22;}\n",
".cython.score-64 {background-color: #FFFF22;}\n",
".cython.score-65 {background-color: #FFFF22;}\n",
".cython.score-66 {background-color: #FFFF21;}\n",
".cython.score-67 {background-color: #FFFF21;}\n",
".cython.score-68 {background-color: #FFFF20;}\n",
".cython.score-69 {background-color: #FFFF20;}\n",
".cython.score-70 {background-color: #FFFF1f;}\n",
".cython.score-71 {background-color: #FFFF1f;}\n",
".cython.score-72 {background-color: #FFFF1f;}\n",
".cython.score-73 {background-color: #FFFF1e;}\n",
".cython.score-74 {background-color: #FFFF1e;}\n",
".cython.score-75 {background-color: #FFFF1e;}\n",
".cython.score-76 {background-color: #FFFF1d;}\n",
".cython.score-77 {background-color: #FFFF1d;}\n",
".cython.score-78 {background-color: #FFFF1c;}\n",
".cython.score-79 {background-color: #FFFF1c;}\n",
".cython.score-80 {background-color: #FFFF1c;}\n",
".cython.score-81 {background-color: #FFFF1c;}\n",
".cython.score-82 {background-color: #FFFF1b;}\n",
".cython.score-83 {background-color: #FFFF1b;}\n",
".cython.score-84 {background-color: #FFFF1b;}\n",
".cython.score-85 {background-color: #FFFF1a;}\n",
".cython.score-86 {background-color: #FFFF1a;}\n",
".cython.score-87 {background-color: #FFFF1a;}\n",
".cython.score-88 {background-color: #FFFF1a;}\n",
".cython.score-89 {background-color: #FFFF19;}\n",
".cython.score-90 {background-color: #FFFF19;}\n",
".cython.score-91 {background-color: #FFFF19;}\n",
".cython.score-92 {background-color: #FFFF19;}\n",
".cython.score-93 {background-color: #FFFF18;}\n",
".cython.score-94 {background-color: #FFFF18;}\n",
".cython.score-95 {background-color: #FFFF18;}\n",
".cython.score-96 {background-color: #FFFF18;}\n",
".cython.score-97 {background-color: #FFFF17;}\n",
".cython.score-98 {background-color: #FFFF17;}\n",
".cython.score-99 {background-color: #FFFF17;}\n",
".cython.score-100 {background-color: #FFFF17;}\n",
".cython.score-101 {background-color: #FFFF16;}\n",
".cython.score-102 {background-color: #FFFF16;}\n",
".cython.score-103 {background-color: #FFFF16;}\n",
".cython.score-104 {background-color: #FFFF16;}\n",
".cython.score-105 {background-color: #FFFF16;}\n",
".cython.score-106 {background-color: #FFFF15;}\n",
".cython.score-107 {background-color: #FFFF15;}\n",
".cython.score-108 {background-color: #FFFF15;}\n",
".cython.score-109 {background-color: #FFFF15;}\n",
".cython.score-110 {background-color: #FFFF15;}\n",
".cython.score-111 {background-color: #FFFF15;}\n",
".cython.score-112 {background-color: #FFFF14;}\n",
".cython.score-113 {background-color: #FFFF14;}\n",
".cython.score-114 {background-color: #FFFF14;}\n",
".cython.score-115 {background-color: #FFFF14;}\n",
".cython.score-116 {background-color: #FFFF14;}\n",
".cython.score-117 {background-color: #FFFF14;}\n",
".cython.score-118 {background-color: #FFFF13;}\n",
".cython.score-119 {background-color: #FFFF13;}\n",
".cython.score-120 {background-color: #FFFF13;}\n",
".cython.score-121 {background-color: #FFFF13;}\n",
".cython.score-122 {background-color: #FFFF13;}\n",
".cython.score-123 {background-color: #FFFF13;}\n",
".cython.score-124 {background-color: #FFFF13;}\n",
".cython.score-125 {background-color: #FFFF12;}\n",
".cython.score-126 {background-color: #FFFF12;}\n",
".cython.score-127 {background-color: #FFFF12;}\n",
".cython.score-128 {background-color: #FFFF12;}\n",
".cython.score-129 {background-color: #FFFF12;}\n",
".cython.score-130 {background-color: #FFFF12;}\n",
".cython.score-131 {background-color: #FFFF12;}\n",
".cython.score-132 {background-color: #FFFF11;}\n",
".cython.score-133 {background-color: #FFFF11;}\n",
".cython.score-134 {background-color: #FFFF11;}\n",
".cython.score-135 {background-color: #FFFF11;}\n",
".cython.score-136 {background-color: #FFFF11;}\n",
".cython.score-137 {background-color: #FFFF11;}\n",
".cython.score-138 {background-color: #FFFF11;}\n",
".cython.score-139 {background-color: #FFFF11;}\n",
".cython.score-140 {background-color: #FFFF11;}\n",
".cython.score-141 {background-color: #FFFF10;}\n",
".cython.score-142 {background-color: #FFFF10;}\n",
".cython.score-143 {background-color: #FFFF10;}\n",
".cython.score-144 {background-color: #FFFF10;}\n",
".cython.score-145 {background-color: #FFFF10;}\n",
".cython.score-146 {background-color: #FFFF10;}\n",
".cython.score-147 {background-color: #FFFF10;}\n",
".cython.score-148 {background-color: #FFFF10;}\n",
".cython.score-149 {background-color: #FFFF10;}\n",
".cython.score-150 {background-color: #FFFF0f;}\n",
".cython.score-151 {background-color: #FFFF0f;}\n",
".cython.score-152 {background-color: #FFFF0f;}\n",
".cython.score-153 {background-color: #FFFF0f;}\n",
".cython.score-154 {background-color: #FFFF0f;}\n",
".cython.score-155 {background-color: #FFFF0f;}\n",
".cython.score-156 {background-color: #FFFF0f;}\n",
".cython.score-157 {background-color: #FFFF0f;}\n",
".cython.score-158 {background-color: #FFFF0f;}\n",
".cython.score-159 {background-color: #FFFF0f;}\n",
".cython.score-160 {background-color: #FFFF0f;}\n",
".cython.score-161 {background-color: #FFFF0e;}\n",
".cython.score-162 {background-color: #FFFF0e;}\n",
".cython.score-163 {background-color: #FFFF0e;}\n",
".cython.score-164 {background-color: #FFFF0e;}\n",
".cython.score-165 {background-color: #FFFF0e;}\n",
".cython.score-166 {background-color: #FFFF0e;}\n",
".cython.score-167 {background-color: #FFFF0e;}\n",
".cython.score-168 {background-color: #FFFF0e;}\n",
".cython.score-169 {background-color: #FFFF0e;}\n",
".cython.score-170 {background-color: #FFFF0e;}\n",
".cython.score-171 {background-color: #FFFF0e;}\n",
".cython.score-172 {background-color: #FFFF0e;}\n",
".cython.score-173 {background-color: #FFFF0d;}\n",
".cython.score-174 {background-color: #FFFF0d;}\n",
".cython.score-175 {background-color: #FFFF0d;}\n",
".cython.score-176 {background-color: #FFFF0d;}\n",
".cython.score-177 {background-color: #FFFF0d;}\n",
".cython.score-178 {background-color: #FFFF0d;}\n",
".cython.score-179 {background-color: #FFFF0d;}\n",
".cython.score-180 {background-color: #FFFF0d;}\n",
".cython.score-181 {background-color: #FFFF0d;}\n",
".cython.score-182 {background-color: #FFFF0d;}\n",
".cython.score-183 {background-color: #FFFF0d;}\n",
".cython.score-184 {background-color: #FFFF0d;}\n",
".cython.score-185 {background-color: #FFFF0d;}\n",
".cython.score-186 {background-color: #FFFF0d;}\n",
".cython.score-187 {background-color: #FFFF0c;}\n",
".cython.score-188 {background-color: #FFFF0c;}\n",
".cython.score-189 {background-color: #FFFF0c;}\n",
".cython.score-190 {background-color: #FFFF0c;}\n",
".cython.score-191 {background-color: #FFFF0c;}\n",
".cython.score-192 {background-color: #FFFF0c;}\n",
".cython.score-193 {background-color: #FFFF0c;}\n",
".cython.score-194 {background-color: #FFFF0c;}\n",
".cython.score-195 {background-color: #FFFF0c;}\n",
".cython.score-196 {background-color: #FFFF0c;}\n",
".cython.score-197 {background-color: #FFFF0c;}\n",
".cython.score-198 {background-color: #FFFF0c;}\n",
".cython.score-199 {background-color: #FFFF0c;}\n",
".cython.score-200 {background-color: #FFFF0c;}\n",
".cython.score-201 {background-color: #FFFF0c;}\n",
".cython.score-202 {background-color: #FFFF0c;}\n",
".cython.score-203 {background-color: #FFFF0b;}\n",
".cython.score-204 {background-color: #FFFF0b;}\n",
".cython.score-205 {background-color: #FFFF0b;}\n",
".cython.score-206 {background-color: #FFFF0b;}\n",
".cython.score-207 {background-color: #FFFF0b;}\n",
".cython.score-208 {background-color: #FFFF0b;}\n",
".cython.score-209 {background-color: #FFFF0b;}\n",
".cython.score-210 {background-color: #FFFF0b;}\n",
".cython.score-211 {background-color: #FFFF0b;}\n",
".cython.score-212 {background-color: #FFFF0b;}\n",
".cython.score-213 {background-color: #FFFF0b;}\n",
".cython.score-214 {background-color: #FFFF0b;}\n",
".cython.score-215 {background-color: #FFFF0b;}\n",
".cython.score-216 {background-color: #FFFF0b;}\n",
".cython.score-217 {background-color: #FFFF0b;}\n",
".cython.score-218 {background-color: #FFFF0b;}\n",
".cython.score-219 {background-color: #FFFF0b;}\n",
".cython.score-220 {background-color: #FFFF0b;}\n",
".cython.score-221 {background-color: #FFFF0b;}\n",
".cython.score-222 {background-color: #FFFF0a;}\n",
".cython.score-223 {background-color: #FFFF0a;}\n",
".cython.score-224 {background-color: #FFFF0a;}\n",
".cython.score-225 {background-color: #FFFF0a;}\n",
".cython.score-226 {background-color: #FFFF0a;}\n",
".cython.score-227 {background-color: #FFFF0a;}\n",
".cython.score-228 {background-color: #FFFF0a;}\n",
".cython.score-229 {background-color: #FFFF0a;}\n",
".cython.score-230 {background-color: #FFFF0a;}\n",
".cython.score-231 {background-color: #FFFF0a;}\n",
".cython.score-232 {background-color: #FFFF0a;}\n",
".cython.score-233 {background-color: #FFFF0a;}\n",
".cython.score-234 {background-color: #FFFF0a;}\n",
".cython.score-235 {background-color: #FFFF0a;}\n",
".cython.score-236 {background-color: #FFFF0a;}\n",
".cython.score-237 {background-color: #FFFF0a;}\n",
".cython.score-238 {background-color: #FFFF0a;}\n",
".cython.score-239 {background-color: #FFFF0a;}\n",
".cython.score-240 {background-color: #FFFF0a;}\n",
".cython.score-241 {background-color: #FFFF0a;}\n",
".cython.score-242 {background-color: #FFFF0a;}\n",
".cython.score-243 {background-color: #FFFF0a;}\n",
".cython.score-244 {background-color: #FFFF0a;}\n",
".cython.score-245 {background-color: #FFFF0a;}\n",
".cython.score-246 {background-color: #FFFF09;}\n",
".cython.score-247 {background-color: #FFFF09;}\n",
".cython.score-248 {background-color: #FFFF09;}\n",
".cython.score-249 {background-color: #FFFF09;}\n",
".cython.score-250 {background-color: #FFFF09;}\n",
".cython.score-251 {background-color: #FFFF09;}\n",
".cython.score-252 {background-color: #FFFF09;}\n",
".cython.score-253 {background-color: #FFFF09;}\n",
".cython.score-254 {background-color: #FFFF09;}\n",
".cython .hll { background-color: #ffffcc }\n",
".cython { background: #f8f8f8; }\n",
".cython .c { color: #408080; font-style: italic } /* Comment */\n",
".cython .err { border: 1px solid #FF0000 } /* Error */\n",
".cython .k { color: #008000; font-weight: bold } /* Keyword */\n",
".cython .o { color: #666666 } /* Operator */\n",
".cython .ch { color: #408080; font-style: italic } /* Comment.Hashbang */\n",
".cython .cm { color: #408080; font-style: italic } /* Comment.Multiline */\n",
".cython .cp { color: #BC7A00 } /* Comment.Preproc */\n",
".cython .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */\n",
".cython .c1 { color: #408080; font-style: italic } /* Comment.Single */\n",
".cython .cs { color: #408080; font-style: italic } /* Comment.Special */\n",
".cython .gd { color: #A00000 } /* Generic.Deleted */\n",
".cython .ge { font-style: italic } /* Generic.Emph */\n",
".cython .gr { color: #FF0000 } /* Generic.Error */\n",
".cython .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
".cython .gi { color: #00A000 } /* Generic.Inserted */\n",
".cython .go { color: #888888 } /* Generic.Output */\n",
".cython .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
".cython .gs { font-weight: bold } /* Generic.Strong */\n",
".cython .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
".cython .gt { color: #0044DD } /* Generic.Traceback */\n",
".cython .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
".cython .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
".cython .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
".cython .kp { color: #008000 } /* Keyword.Pseudo */\n",
".cython .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
".cython .kt { color: #B00040 } /* Keyword.Type */\n",
".cython .m { color: #666666 } /* Literal.Number */\n",
".cython .s { color: #BA2121 } /* Literal.String */\n",
".cython .na { color: #7D9029 } /* Name.Attribute */\n",
".cython .nb { color: #008000 } /* Name.Builtin */\n",
".cython .nc { color: #0000FF; font-weight: bold } /* Name.Class */\n",
".cython .no { color: #880000 } /* Name.Constant */\n",
".cython .nd { color: #AA22FF } /* Name.Decorator */\n",
".cython .ni { color: #999999; font-weight: bold } /* Name.Entity */\n",
".cython .ne { color: #D2413A; font-weight: bold } /* Name.Exception */\n",
".cython .nf { color: #0000FF } /* Name.Function */\n",
".cython .nl { color: #A0A000 } /* Name.Label */\n",
".cython .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\n",
".cython .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
".cython .nv { color: #19177C } /* Name.Variable */\n",
".cython .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\n",
".cython .w { color: #bbbbbb } /* Text.Whitespace */\n",
".cython .mb { color: #666666 } /* Literal.Number.Bin */\n",
".cython .mf { color: #666666 } /* Literal.Number.Float */\n",
".cython .mh { color: #666666 } /* Literal.Number.Hex */\n",
".cython .mi { color: #666666 } /* Literal.Number.Integer */\n",
".cython .mo { color: #666666 } /* Literal.Number.Oct */\n",
".cython .sa { color: #BA2121 } /* Literal.String.Affix */\n",
".cython .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
".cython .sc { color: #BA2121 } /* Literal.String.Char */\n",
".cython .dl { color: #BA2121 } /* Literal.String.Delimiter */\n",
".cython .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
".cython .s2 { color: #BA2121 } /* Literal.String.Double */\n",
".cython .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */\n",
".cython .sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
".cython .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */\n",
".cython .sx { color: #008000 } /* Literal.String.Other */\n",
".cython .sr { color: #BB6688 } /* Literal.String.Regex */\n",
".cython .s1 { color: #BA2121 } /* Literal.String.Single */\n",
".cython .ss { color: #19177C } /* Literal.String.Symbol */\n",
".cython .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
".cython .fm { color: #0000FF } /* Name.Function.Magic */\n",
".cython .vc { color: #19177C } /* Name.Variable.Class */\n",
".cython .vg { color: #19177C } /* Name.Variable.Global */\n",
".cython .vi { color: #19177C } /* Name.Variable.Instance */\n",
".cython .vm { color: #19177C } /* Name.Variable.Magic */\n",
".cython .il { color: #666666 } /* Literal.Number.Integer.Long */\n",
" </style>\n",
" <script>\n",
" function toggleDiv(id) {\n",
" theDiv = id.nextElementSibling\n",
" if (theDiv.style.display != 'block') theDiv.style.display = 'block';\n",
" else theDiv.style.display = 'none';\n",
" }\n",
" </script>\n",
"</head>\n",
"<body class=\"cython\">\n",
"<p><span style=\"border-bottom: solid 1px grey;\">Generated by Cython 0.25.2</span></p>\n",
"<p>\n",
" <span style=\"background-color: #FFFF00\">Yellow lines</span> hint at Python interaction.<br />\n",
" Click on a line that starts with a \"<code>+</code>\" to see the C code that Cython generated for it.\n",
"</p>\n",
"<div class=\"cython\"><pre class=\"cython line score-0\"> <span class=\"\">01</span>: </pre>\n",
"<pre class=\"cython line score-11\" onclick='toggleDiv(this)'>+<span class=\"\">02</span>: <span class=\"k\">cimport</span> <span class=\"nn\">cython</span></pre>\n",
"<pre class='cython code score-11 '> __pyx_t_1 = <span class='py_c_api'>PyDict_New</span>(); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) __PYX_ERR(0, 2, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
"</pre><pre class=\"cython line score-8\" onclick='toggleDiv(this)'>+<span class=\"\">03</span>: <span class=\"k\">import</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span></pre>\n",
"<pre class='cython code score-8 '> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_Import</span>(__pyx_n_s_numpy, 0, -1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 3, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) __PYX_ERR(0, 3, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
"</pre><pre class=\"cython line score-0\"> <span class=\"\">04</span>: <span class=\"k\">from</span> <span class=\"nn\">libc.math</span> <span class=\"k\">cimport</span> <span class=\"n\">sin</span><span class=\"p\">,</span> <span class=\"n\">M_PI</span></pre>\n",
"<pre class=\"cython line score-0\"> <span class=\"\">05</span>: </pre>\n",
"<pre class=\"cython line score-77\" onclick='toggleDiv(this)'>+<span class=\"\">06</span>: <span class=\"k\">def</span> <span class=\"nf\">fourier_sum_cython</span><span class=\"p\">(</span><span class=\"n\">times</span><span class=\"p\">,</span> <span class=\"n\">freq</span><span class=\"p\">,</span> <span class=\"n\">amp</span><span class=\"p\">,</span> <span class=\"n\">phi</span><span class=\"p\">,</span> <span class=\"n\">temp</span><span class=\"p\">):</span></pre>\n",
"<pre class='cython code score-77 '>/* Python wrapper */\n",
"static PyObject *__pyx_pw_46_cython_magic_ec5a80ebe17c2abd87108e86b8d3c94e_1fourier_sum_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n",
"static PyMethodDef __pyx_mdef_46_cython_magic_ec5a80ebe17c2abd87108e86b8d3c94e_1fourier_sum_cython = {\"fourier_sum_cython\", (PyCFunction)__pyx_pw_46_cython_magic_ec5a80ebe17c2abd87108e86b8d3c94e_1fourier_sum_cython, METH_VARARGS|METH_KEYWORDS, 0};\n",
"static PyObject *__pyx_pw_46_cython_magic_ec5a80ebe17c2abd87108e86b8d3c94e_1fourier_sum_cython(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
" PyObject *__pyx_v_times = 0;\n",
" PyObject *__pyx_v_freq = 0;\n",
" PyObject *__pyx_v_amp = 0;\n",
" PyObject *__pyx_v_phi = 0;\n",
" PyObject *__pyx_v_temp = 0;\n",
" PyObject *__pyx_r = 0;\n",
" <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
" <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"fourier_sum_cython (wrapper)\", 0);\n",
" {\n",
" static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_times,&__pyx_n_s_freq,&__pyx_n_s_amp,&__pyx_n_s_phi,&__pyx_n_s_temp,0};\n",
" PyObject* values[5] = {0,0,0,0,0};\n",
" if (unlikely(__pyx_kwds)) {\n",
" Py_ssize_t kw_args;\n",
" const Py_ssize_t pos_args = <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args);\n",
" switch (pos_args) {\n",
" case 5: values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n",
" case 4: values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
" case 3: values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
" case 2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
" case 1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
" case 0: break;\n",
" default: goto __pyx_L5_argtuple_error;\n",
" }\n",
" kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
" switch (pos_args) {\n",
" case 0:\n",
" if (likely((values[0] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_times)) != 0)) kw_args--;\n",
" else goto __pyx_L5_argtuple_error;\n",
" case 1:\n",
" if (likely((values[1] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_freq)) != 0)) kw_args--;\n",
" else {\n",
" <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"fourier_sum_cython\", 1, 5, 5, 1); __PYX_ERR(0, 6, __pyx_L3_error)\n",
" }\n",
" case 2:\n",
" if (likely((values[2] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_amp)) != 0)) kw_args--;\n",
" else {\n",
" <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"fourier_sum_cython\", 1, 5, 5, 2); __PYX_ERR(0, 6, __pyx_L3_error)\n",
" }\n",
" case 3:\n",
" if (likely((values[3] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_phi)) != 0)) kw_args--;\n",
" else {\n",
" <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"fourier_sum_cython\", 1, 5, 5, 3); __PYX_ERR(0, 6, __pyx_L3_error)\n",
" }\n",
" case 4:\n",
" if (likely((values[4] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_temp)) != 0)) kw_args--;\n",
" else {\n",
" <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"fourier_sum_cython\", 1, 5, 5, 4); __PYX_ERR(0, 6, __pyx_L3_error)\n",
" }\n",
" }\n",
" if (unlikely(kw_args > 0)) {\n",
" if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, \"fourier_sum_cython\") < 0)) __PYX_ERR(0, 6, __pyx_L3_error)\n",
" }\n",
" } else if (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args) != 5) {\n",
" goto __pyx_L5_argtuple_error;\n",
" } else {\n",
" values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
" values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
" values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
" values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
" values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n",
" }\n",
" __pyx_v_times = values[0];\n",
" __pyx_v_freq = values[1];\n",
" __pyx_v_amp = values[2];\n",
" __pyx_v_phi = values[3];\n",
" __pyx_v_temp = values[4];\n",
" }\n",
" goto __pyx_L4_argument_unpacking_done;\n",
" __pyx_L5_argtuple_error:;\n",
" <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"fourier_sum_cython\", 1, 5, 5, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); __PYX_ERR(0, 6, __pyx_L3_error)\n",
" __pyx_L3_error:;\n",
" <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_ec5a80ebe17c2abd87108e86b8d3c94e.fourier_sum_cython\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
" <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
" return NULL;\n",
" __pyx_L4_argument_unpacking_done:;\n",
" __pyx_r = __pyx_pf_46_cython_magic_ec5a80ebe17c2abd87108e86b8d3c94e_fourier_sum_cython(__pyx_self, __pyx_v_times, __pyx_v_freq, __pyx_v_amp, __pyx_v_phi, __pyx_v_temp);\n",
"\n",
" /* function exit code */\n",
" <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
" return __pyx_r;\n",
"}\n",
"\n",
"static PyObject *__pyx_pf_46_cython_magic_ec5a80ebe17c2abd87108e86b8d3c94e_fourier_sum_cython(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_times, PyObject *__pyx_v_freq, PyObject *__pyx_v_amp, PyObject *__pyx_v_phi, PyObject *__pyx_v_temp) {\n",
" PyObject *__pyx_r = NULL;\n",
" <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
" <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"fourier_sum_cython\", 0);\n",
"/* … */\n",
" /* function exit code */\n",
" __pyx_L1_error:;\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_2);\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_3);\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_4, 1);\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1);\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_6, 1);\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_7, 1);\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_8, 1);\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_9);\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_10);\n",
" <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_ec5a80ebe17c2abd87108e86b8d3c94e.fourier_sum_cython\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
" __pyx_r = NULL;\n",
" __pyx_L0:;\n",
" <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
" <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
" return __pyx_r;\n",
"}\n",
"/* … */\n",
" __pyx_tuple__14 = <span class='py_c_api'>PyTuple_Pack</span>(5, __pyx_n_s_times, __pyx_n_s_freq, __pyx_n_s_amp, __pyx_n_s_phi, __pyx_n_s_temp); if (unlikely(!__pyx_tuple__14)) __PYX_ERR(0, 6, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_tuple__14);\n",
" <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_tuple__14);\n",
"/* … */\n",
" __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_46_cython_magic_ec5a80ebe17c2abd87108e86b8d3c94e_1fourier_sum_cython, NULL, __pyx_n_s_cython_magic_ec5a80ebe17c2abd87); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 6, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_fourier_sum_cython, __pyx_t_1) < 0) __PYX_ERR(0, 6, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
" __pyx_codeobj__15 = (PyObject*)<span class='pyx_c_api'>__Pyx_PyCode_New</span>(5, 0, 5, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__14, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_home_ghajdu_cache_ipython_cytho, __pyx_n_s_fourier_sum_cython, 6, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__15)) __PYX_ERR(0, 6, __pyx_L1_error)\n",
"</pre><pre class=\"cython line score-55\" onclick='toggleDiv(this)'>+<span class=\"\">07</span>: <span class=\"k\">return</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">asarray</span><span class=\"p\">(</span><span class=\"n\">fourier_sum_purec</span><span class=\"p\">(</span><span class=\"n\">times</span><span class=\"p\">,</span> <span class=\"n\">freq</span><span class=\"p\">,</span> <span class=\"n\">amp</span><span class=\"p\">,</span> <span class=\"n\">phi</span><span class=\"p\">,</span> <span class=\"n\">temp</span><span class=\"p\">))</span></pre>\n",
"<pre class='cython code score-55 '> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
" __pyx_t_2 = <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_n_s_np); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
" __pyx_t_3 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_t_2, __pyx_n_s_asarray); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_3);\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
" __pyx_t_4 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_ds_double</span>(__pyx_v_times);\n",
" if (unlikely(!__pyx_t_4.memview)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
" __pyx_t_5 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_ds_double</span>(__pyx_v_freq);\n",
" if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
" __pyx_t_6 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_ds_double</span>(__pyx_v_amp);\n",
" if (unlikely(!__pyx_t_6.memview)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
" __pyx_t_7 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_ds_double</span>(__pyx_v_phi);\n",
" if (unlikely(!__pyx_t_7.memview)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
" __pyx_t_8 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_ds_double</span>(__pyx_v_temp);\n",
" if (unlikely(!__pyx_t_8.memview)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
" __pyx_t_2 = __pyx_f_46_cython_magic_ec5a80ebe17c2abd87108e86b8d3c94e_fourier_sum_purec(__pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_4, 1);\n",
" __pyx_t_4.memview = NULL;\n",
" __pyx_t_4.data = NULL;\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1);\n",
" __pyx_t_5.memview = NULL;\n",
" __pyx_t_5.data = NULL;\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_6, 1);\n",
" __pyx_t_6.memview = NULL;\n",
" __pyx_t_6.data = NULL;\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_7, 1);\n",
" __pyx_t_7.memview = NULL;\n",
" __pyx_t_7.data = NULL;\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_8, 1);\n",
" __pyx_t_8.memview = NULL;\n",
" __pyx_t_8.data = NULL;\n",
" __pyx_t_9 = NULL;\n",
" if (CYTHON_UNPACK_METHODS && unlikely(<span class='py_c_api'>PyMethod_Check</span>(__pyx_t_3))) {\n",
" __pyx_t_9 = <span class='py_macro_api'>PyMethod_GET_SELF</span>(__pyx_t_3);\n",
" if (likely(__pyx_t_9)) {\n",
" PyObject* function = <span class='py_macro_api'>PyMethod_GET_FUNCTION</span>(__pyx_t_3);\n",
" <span class='pyx_macro_api'>__Pyx_INCREF</span>(__pyx_t_9);\n",
" <span class='pyx_macro_api'>__Pyx_INCREF</span>(function);\n",
" <span class='pyx_macro_api'>__Pyx_DECREF_SET</span>(__pyx_t_3, function);\n",
" }\n",
" }\n",
" if (!__pyx_t_9) {\n",
" __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_CallOneArg</span>(__pyx_t_3, __pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" } else {\n",
" #if CYTHON_FAST_PYCALL\n",
" if (<span class='py_c_api'>PyFunction_Check</span>(__pyx_t_3)) {\n",
" PyObject *__pyx_temp[2] = {__pyx_t_9, __pyx_t_2};\n",
" __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyFunction_FastCall</span>(__pyx_t_3, __pyx_temp+1-1, 1+1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_9); __pyx_t_9 = 0;\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
" } else\n",
" #endif\n",
" #if CYTHON_FAST_PYCCALL\n",
" if (<span class='pyx_c_api'>__Pyx_PyFastCFunction_Check</span>(__pyx_t_3)) {\n",
" PyObject *__pyx_temp[2] = {__pyx_t_9, __pyx_t_2};\n",
" __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyCFunction_FastCall</span>(__pyx_t_3, __pyx_temp+1-1, 1+1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_9); __pyx_t_9 = 0;\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
" } else\n",
" #endif\n",
" {\n",
" __pyx_t_10 = <span class='py_c_api'>PyTuple_New</span>(1+1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_10);\n",
" <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_9); <span class='py_macro_api'>PyTuple_SET_ITEM</span>(__pyx_t_10, 0, __pyx_t_9); __pyx_t_9 = NULL;\n",
" <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_2);\n",
" <span class='py_macro_api'>PyTuple_SET_ITEM</span>(__pyx_t_10, 0+1, __pyx_t_2);\n",
" __pyx_t_2 = 0;\n",
" __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_Call</span>(__pyx_t_3, __pyx_t_10, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 7, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_10); __pyx_t_10 = 0;\n",
" }\n",
" }\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;\n",
" __pyx_r = __pyx_t_1;\n",
" __pyx_t_1 = 0;\n",
" goto __pyx_L0;\n",
"</pre><pre class=\"cython line score-0\"> <span class=\"\">08</span>: </pre>\n",
"<pre class=\"cython line score-0\"> <span class=\"\">09</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
"<pre class=\"cython line score-0\"> <span class=\"\">10</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
"<pre class=\"cython line score-3\" onclick='toggleDiv(this)'>+<span class=\"\">11</span>: <span class=\"k\">cdef</span> <span class=\"nf\">fourier_sum_purec</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">times</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">freq</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">amp</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">phi</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">temp</span><span class=\"p\">):</span></pre>\n",
"<pre class='cython code score-3 '>static PyObject *__pyx_f_46_cython_magic_ec5a80ebe17c2abd87108e86b8d3c94e_fourier_sum_purec(__Pyx_memviewslice __pyx_v_times, __Pyx_memviewslice __pyx_v_freq, __Pyx_memviewslice __pyx_v_amp, __Pyx_memviewslice __pyx_v_phi, __Pyx_memviewslice __pyx_v_temp) {\n",
" int __pyx_v_i;\n",
" int __pyx_v_j;\n",
" int __pyx_v_irange;\n",
" int __pyx_v_jrange;\n",
" PyObject *__pyx_r = NULL;\n",
" <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
" <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"fourier_sum_purec\", 0);\n",
"/* … */\n",
" /* function exit code */\n",
" __pyx_L1_error:;\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n",
" <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_ec5a80ebe17c2abd87108e86b8d3c94e.fourier_sum_purec\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
" __pyx_r = 0;\n",
" __pyx_L0:;\n",
" <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
" <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
" return __pyx_r;\n",
"}\n",
"</pre><pre class=\"cython line score-0\"> <span class=\"\">12</span>: <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">i</span><span class=\"p\">,</span> <span class=\"nf\">j</span><span class=\"p\">,</span> <span class=\"nf\">irange</span><span class=\"p\">,</span> <span class=\"nf\">jrange</span></pre>\n",
"<pre class=\"cython line score-6\" onclick='toggleDiv(this)'>+<span class=\"\">13</span>: <span class=\"n\">irange</span><span class=\"o\">=</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">times</span><span class=\"p\">)</span></pre>\n",
"<pre class='cython code score-6 '> __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_times, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 13, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" __pyx_t_2 = <span class='py_c_api'>PyObject_Length</span>(__pyx_t_1); if (unlikely(__pyx_t_2 == -1)) __PYX_ERR(0, 13, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
" __pyx_v_irange = __pyx_t_2;\n",
"</pre><pre class=\"cython line score-6\" onclick='toggleDiv(this)'>+<span class=\"\">14</span>: <span class=\"n\">jrange</span><span class=\"o\">=</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">freq</span><span class=\"p\">)</span></pre>\n",
"<pre class='cython code score-6 '> __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_freq, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 14, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" __pyx_t_2 = <span class='py_c_api'>PyObject_Length</span>(__pyx_t_1); if (unlikely(__pyx_t_2 == -1)) __PYX_ERR(0, 14, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
" __pyx_v_jrange = __pyx_t_2;\n",
"</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">15</span>: <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"nb\">xrange</span><span class=\"p\">(</span><span class=\"n\">irange</span><span class=\"p\">):</span></pre>\n",
"<pre class='cython code score-0 '> __pyx_t_3 = __pyx_v_irange;\n",
" for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {\n",
" __pyx_v_i = __pyx_t_4;\n",
"</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">16</span>: <span class=\"n\">temp</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span><span class=\"o\">=</span><span class=\"mf\">0</span></pre>\n",
"<pre class='cython code score-0 '> __pyx_t_5 = __pyx_v_i;\n",
" *((double *) ( /* dim=0 */ (__pyx_v_temp.data + __pyx_t_5 * __pyx_v_temp.strides[0]) )) = 0.0;\n",
"</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">17</span>: <span class=\"k\">for</span> <span class=\"n\">j</span> <span class=\"ow\">in</span> <span class=\"nb\">xrange</span><span class=\"p\">(</span><span class=\"n\">jrange</span><span class=\"p\">):</span></pre>\n",
"<pre class='cython code score-0 '> __pyx_t_6 = __pyx_v_jrange;\n",
" for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_6; __pyx_t_7+=1) {\n",
" __pyx_v_j = __pyx_t_7;\n",
"</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">18</span>: <span class=\"n\">temp</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\">+=</span> <span class=\"n\">amp</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">sin</span><span class=\"p\">(</span> <span class=\"mf\">2</span> <span class=\"o\">*</span> <span class=\"n\">M_PI</span> <span class=\"o\">*</span> <span class=\"n\">freq</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">times</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">phi</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">]</span> <span class=\"p\">)</span></pre>\n",
"<pre class='cython code score-0 '> __pyx_t_8 = __pyx_v_j;\n",
" __pyx_t_9 = __pyx_v_j;\n",
" __pyx_t_10 = __pyx_v_i;\n",
" __pyx_t_11 = __pyx_v_j;\n",
" __pyx_t_12 = __pyx_v_i;\n",
" *((double *) ( /* dim=0 */ (__pyx_v_temp.data + __pyx_t_12 * __pyx_v_temp.strides[0]) )) += ((*((double *) ( /* dim=0 */ (__pyx_v_amp.data + __pyx_t_8 * __pyx_v_amp.strides[0]) ))) * sin(((((2.0 * M_PI) * (*((double *) ( /* dim=0 */ (__pyx_v_freq.data + __pyx_t_9 * __pyx_v_freq.strides[0]) )))) * (*((double *) ( /* dim=0 */ (__pyx_v_times.data + __pyx_t_10 * __pyx_v_times.strides[0]) )))) + (*((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_11 * __pyx_v_phi.strides[0]) ))))));\n",
" }\n",
" }\n",
"</pre><pre class=\"cython line score-1\" onclick='toggleDiv(this)'>+<span class=\"\">19</span>: <span class=\"k\">return</span> <span class=\"n\">temp</span></pre>\n",
"<pre class='cython code score-1 '> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
" __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_temp, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 19, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" __pyx_r = __pyx_t_1;\n",
" __pyx_t_1 = 0;\n",
" goto __pyx_L0;\n",
"</pre></div></body></html>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%cython -a\n",
"\n",
"cimport cython\n",
"import numpy as np\n",
"from libc.math cimport sin, M_PI\n",
"\n",
"def fourier_sum_cython(times, freq, amp, phi, temp):\n",
" return np.asarray(fourier_sum_purec(times, freq, amp, phi, temp))\n",
"\n",
"@cython.boundscheck(False)\n",
"@cython.wraparound(False)\n",
"cdef fourier_sum_purec(double[:] times, double[:] freq, double[:] amp, double[:] phi, double[:] temp):\n",
" cdef int i, j, irange, jrange\n",
" irange=len(times)\n",
" jrange=len(freq)\n",
" for i in xrange(irange):\n",
" temp[i]=0\n",
" for j in xrange(jrange):\n",
" temp[i] += amp[j] * sin( 2 * M_PI * freq[j] * times[i] + phi[j] )\n",
" return temp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We called the cython command with the -a argument, that makes it produce an html summary of the translated code.\n",
"White parts show code that don't interact with Python at all. Optimizing Cython involves minimizing the code\n",
"that is \"yellow\", making the code \"whiter\", that is, executing more code in C.\n",
"\n",
"#### Cython + OpenMP\n",
"\n",
"We can parallelize the execution of the code using OpenMP in the parts where the code is executed\n",
"completely outside of Python, but we need to release the Global Interpreter Lock (GIL) first.\n",
"The prange command replaces the range or xrange command in the for cycle we would like to execute in\n",
"parallel. We can also call OpenMP functions, for example, to get the number of processor cores of the\n",
"system.\n",
"\n",
"Note that the number of threads, the scheduler and the chunksize can have a large effect on the\n",
"performance of the code. While optimizing, you should try different chunksizes (the default is 1)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<!DOCTYPE html>\n",
"<!-- Generated by Cython 0.25.2 -->\n",
"<html>\n",
"<head>\n",
" <meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n",
" <title>Cython: _cython_magic_1cd56569f3d2a9008e919ac54e3d0f01.pyx</title>\n",
" <style type=\"text/css\">\n",
" \n",
"body.cython { font-family: courier; font-size: 12; }\n",
"\n",
".cython.tag { }\n",
".cython.line { margin: 0em }\n",
".cython.code { font-size: 9; color: #444444; display: none; margin: 0px 0px 0px 8px; border-left: 8px none; }\n",
"\n",
".cython.line .run { background-color: #B0FFB0; }\n",
".cython.line .mis { background-color: #FFB0B0; }\n",
".cython.code.run { border-left: 8px solid #B0FFB0; }\n",
".cython.code.mis { border-left: 8px solid #FFB0B0; }\n",
"\n",
".cython.code .py_c_api { color: red; }\n",
".cython.code .py_macro_api { color: #FF7000; }\n",
".cython.code .pyx_c_api { color: #FF3000; }\n",
".cython.code .pyx_macro_api { color: #FF7000; }\n",
".cython.code .refnanny { color: #FFA000; }\n",
".cython.code .trace { color: #FFA000; }\n",
".cython.code .error_goto { color: #FFA000; }\n",
"\n",
".cython.code .coerce { color: #008000; border: 1px dotted #008000 }\n",
".cython.code .py_attr { color: #FF0000; font-weight: bold; }\n",
".cython.code .c_attr { color: #0000FF; }\n",
".cython.code .py_call { color: #FF0000; font-weight: bold; }\n",
".cython.code .c_call { color: #0000FF; }\n",
"\n",
".cython.score-0 {background-color: #FFFFff;}\n",
".cython.score-1 {background-color: #FFFFe7;}\n",
".cython.score-2 {background-color: #FFFFd4;}\n",
".cython.score-3 {background-color: #FFFFc4;}\n",
".cython.score-4 {background-color: #FFFFb6;}\n",
".cython.score-5 {background-color: #FFFFaa;}\n",
".cython.score-6 {background-color: #FFFF9f;}\n",
".cython.score-7 {background-color: #FFFF96;}\n",
".cython.score-8 {background-color: #FFFF8d;}\n",
".cython.score-9 {background-color: #FFFF86;}\n",
".cython.score-10 {background-color: #FFFF7f;}\n",
".cython.score-11 {background-color: #FFFF79;}\n",
".cython.score-12 {background-color: #FFFF73;}\n",
".cython.score-13 {background-color: #FFFF6e;}\n",
".cython.score-14 {background-color: #FFFF6a;}\n",
".cython.score-15 {background-color: #FFFF66;}\n",
".cython.score-16 {background-color: #FFFF62;}\n",
".cython.score-17 {background-color: #FFFF5e;}\n",
".cython.score-18 {background-color: #FFFF5b;}\n",
".cython.score-19 {background-color: #FFFF57;}\n",
".cython.score-20 {background-color: #FFFF55;}\n",
".cython.score-21 {background-color: #FFFF52;}\n",
".cython.score-22 {background-color: #FFFF4f;}\n",
".cython.score-23 {background-color: #FFFF4d;}\n",
".cython.score-24 {background-color: #FFFF4b;}\n",
".cython.score-25 {background-color: #FFFF48;}\n",
".cython.score-26 {background-color: #FFFF46;}\n",
".cython.score-27 {background-color: #FFFF44;}\n",
".cython.score-28 {background-color: #FFFF43;}\n",
".cython.score-29 {background-color: #FFFF41;}\n",
".cython.score-30 {background-color: #FFFF3f;}\n",
".cython.score-31 {background-color: #FFFF3e;}\n",
".cython.score-32 {background-color: #FFFF3c;}\n",
".cython.score-33 {background-color: #FFFF3b;}\n",
".cython.score-34 {background-color: #FFFF39;}\n",
".cython.score-35 {background-color: #FFFF38;}\n",
".cython.score-36 {background-color: #FFFF37;}\n",
".cython.score-37 {background-color: #FFFF36;}\n",
".cython.score-38 {background-color: #FFFF35;}\n",
".cython.score-39 {background-color: #FFFF34;}\n",
".cython.score-40 {background-color: #FFFF33;}\n",
".cython.score-41 {background-color: #FFFF32;}\n",
".cython.score-42 {background-color: #FFFF31;}\n",
".cython.score-43 {background-color: #FFFF30;}\n",
".cython.score-44 {background-color: #FFFF2f;}\n",
".cython.score-45 {background-color: #FFFF2e;}\n",
".cython.score-46 {background-color: #FFFF2d;}\n",
".cython.score-47 {background-color: #FFFF2c;}\n",
".cython.score-48 {background-color: #FFFF2b;}\n",
".cython.score-49 {background-color: #FFFF2b;}\n",
".cython.score-50 {background-color: #FFFF2a;}\n",
".cython.score-51 {background-color: #FFFF29;}\n",
".cython.score-52 {background-color: #FFFF29;}\n",
".cython.score-53 {background-color: #FFFF28;}\n",
".cython.score-54 {background-color: #FFFF27;}\n",
".cython.score-55 {background-color: #FFFF27;}\n",
".cython.score-56 {background-color: #FFFF26;}\n",
".cython.score-57 {background-color: #FFFF26;}\n",
".cython.score-58 {background-color: #FFFF25;}\n",
".cython.score-59 {background-color: #FFFF24;}\n",
".cython.score-60 {background-color: #FFFF24;}\n",
".cython.score-61 {background-color: #FFFF23;}\n",
".cython.score-62 {background-color: #FFFF23;}\n",
".cython.score-63 {background-color: #FFFF22;}\n",
".cython.score-64 {background-color: #FFFF22;}\n",
".cython.score-65 {background-color: #FFFF22;}\n",
".cython.score-66 {background-color: #FFFF21;}\n",
".cython.score-67 {background-color: #FFFF21;}\n",
".cython.score-68 {background-color: #FFFF20;}\n",
".cython.score-69 {background-color: #FFFF20;}\n",
".cython.score-70 {background-color: #FFFF1f;}\n",
".cython.score-71 {background-color: #FFFF1f;}\n",
".cython.score-72 {background-color: #FFFF1f;}\n",
".cython.score-73 {background-color: #FFFF1e;}\n",
".cython.score-74 {background-color: #FFFF1e;}\n",
".cython.score-75 {background-color: #FFFF1e;}\n",
".cython.score-76 {background-color: #FFFF1d;}\n",
".cython.score-77 {background-color: #FFFF1d;}\n",
".cython.score-78 {background-color: #FFFF1c;}\n",
".cython.score-79 {background-color: #FFFF1c;}\n",
".cython.score-80 {background-color: #FFFF1c;}\n",
".cython.score-81 {background-color: #FFFF1c;}\n",
".cython.score-82 {background-color: #FFFF1b;}\n",
".cython.score-83 {background-color: #FFFF1b;}\n",
".cython.score-84 {background-color: #FFFF1b;}\n",
".cython.score-85 {background-color: #FFFF1a;}\n",
".cython.score-86 {background-color: #FFFF1a;}\n",
".cython.score-87 {background-color: #FFFF1a;}\n",
".cython.score-88 {background-color: #FFFF1a;}\n",
".cython.score-89 {background-color: #FFFF19;}\n",
".cython.score-90 {background-color: #FFFF19;}\n",
".cython.score-91 {background-color: #FFFF19;}\n",
".cython.score-92 {background-color: #FFFF19;}\n",
".cython.score-93 {background-color: #FFFF18;}\n",
".cython.score-94 {background-color: #FFFF18;}\n",
".cython.score-95 {background-color: #FFFF18;}\n",
".cython.score-96 {background-color: #FFFF18;}\n",
".cython.score-97 {background-color: #FFFF17;}\n",
".cython.score-98 {background-color: #FFFF17;}\n",
".cython.score-99 {background-color: #FFFF17;}\n",
".cython.score-100 {background-color: #FFFF17;}\n",
".cython.score-101 {background-color: #FFFF16;}\n",
".cython.score-102 {background-color: #FFFF16;}\n",
".cython.score-103 {background-color: #FFFF16;}\n",
".cython.score-104 {background-color: #FFFF16;}\n",
".cython.score-105 {background-color: #FFFF16;}\n",
".cython.score-106 {background-color: #FFFF15;}\n",
".cython.score-107 {background-color: #FFFF15;}\n",
".cython.score-108 {background-color: #FFFF15;}\n",
".cython.score-109 {background-color: #FFFF15;}\n",
".cython.score-110 {background-color: #FFFF15;}\n",
".cython.score-111 {background-color: #FFFF15;}\n",
".cython.score-112 {background-color: #FFFF14;}\n",
".cython.score-113 {background-color: #FFFF14;}\n",
".cython.score-114 {background-color: #FFFF14;}\n",
".cython.score-115 {background-color: #FFFF14;}\n",
".cython.score-116 {background-color: #FFFF14;}\n",
".cython.score-117 {background-color: #FFFF14;}\n",
".cython.score-118 {background-color: #FFFF13;}\n",
".cython.score-119 {background-color: #FFFF13;}\n",
".cython.score-120 {background-color: #FFFF13;}\n",
".cython.score-121 {background-color: #FFFF13;}\n",
".cython.score-122 {background-color: #FFFF13;}\n",
".cython.score-123 {background-color: #FFFF13;}\n",
".cython.score-124 {background-color: #FFFF13;}\n",
".cython.score-125 {background-color: #FFFF12;}\n",
".cython.score-126 {background-color: #FFFF12;}\n",
".cython.score-127 {background-color: #FFFF12;}\n",
".cython.score-128 {background-color: #FFFF12;}\n",
".cython.score-129 {background-color: #FFFF12;}\n",
".cython.score-130 {background-color: #FFFF12;}\n",
".cython.score-131 {background-color: #FFFF12;}\n",
".cython.score-132 {background-color: #FFFF11;}\n",
".cython.score-133 {background-color: #FFFF11;}\n",
".cython.score-134 {background-color: #FFFF11;}\n",
".cython.score-135 {background-color: #FFFF11;}\n",
".cython.score-136 {background-color: #FFFF11;}\n",
".cython.score-137 {background-color: #FFFF11;}\n",
".cython.score-138 {background-color: #FFFF11;}\n",
".cython.score-139 {background-color: #FFFF11;}\n",
".cython.score-140 {background-color: #FFFF11;}\n",
".cython.score-141 {background-color: #FFFF10;}\n",
".cython.score-142 {background-color: #FFFF10;}\n",
".cython.score-143 {background-color: #FFFF10;}\n",
".cython.score-144 {background-color: #FFFF10;}\n",
".cython.score-145 {background-color: #FFFF10;}\n",
".cython.score-146 {background-color: #FFFF10;}\n",
".cython.score-147 {background-color: #FFFF10;}\n",
".cython.score-148 {background-color: #FFFF10;}\n",
".cython.score-149 {background-color: #FFFF10;}\n",
".cython.score-150 {background-color: #FFFF0f;}\n",
".cython.score-151 {background-color: #FFFF0f;}\n",
".cython.score-152 {background-color: #FFFF0f;}\n",
".cython.score-153 {background-color: #FFFF0f;}\n",
".cython.score-154 {background-color: #FFFF0f;}\n",
".cython.score-155 {background-color: #FFFF0f;}\n",
".cython.score-156 {background-color: #FFFF0f;}\n",
".cython.score-157 {background-color: #FFFF0f;}\n",
".cython.score-158 {background-color: #FFFF0f;}\n",
".cython.score-159 {background-color: #FFFF0f;}\n",
".cython.score-160 {background-color: #FFFF0f;}\n",
".cython.score-161 {background-color: #FFFF0e;}\n",
".cython.score-162 {background-color: #FFFF0e;}\n",
".cython.score-163 {background-color: #FFFF0e;}\n",
".cython.score-164 {background-color: #FFFF0e;}\n",
".cython.score-165 {background-color: #FFFF0e;}\n",
".cython.score-166 {background-color: #FFFF0e;}\n",
".cython.score-167 {background-color: #FFFF0e;}\n",
".cython.score-168 {background-color: #FFFF0e;}\n",
".cython.score-169 {background-color: #FFFF0e;}\n",
".cython.score-170 {background-color: #FFFF0e;}\n",
".cython.score-171 {background-color: #FFFF0e;}\n",
".cython.score-172 {background-color: #FFFF0e;}\n",
".cython.score-173 {background-color: #FFFF0d;}\n",
".cython.score-174 {background-color: #FFFF0d;}\n",
".cython.score-175 {background-color: #FFFF0d;}\n",
".cython.score-176 {background-color: #FFFF0d;}\n",
".cython.score-177 {background-color: #FFFF0d;}\n",
".cython.score-178 {background-color: #FFFF0d;}\n",
".cython.score-179 {background-color: #FFFF0d;}\n",
".cython.score-180 {background-color: #FFFF0d;}\n",
".cython.score-181 {background-color: #FFFF0d;}\n",
".cython.score-182 {background-color: #FFFF0d;}\n",
".cython.score-183 {background-color: #FFFF0d;}\n",
".cython.score-184 {background-color: #FFFF0d;}\n",
".cython.score-185 {background-color: #FFFF0d;}\n",
".cython.score-186 {background-color: #FFFF0d;}\n",
".cython.score-187 {background-color: #FFFF0c;}\n",
".cython.score-188 {background-color: #FFFF0c;}\n",
".cython.score-189 {background-color: #FFFF0c;}\n",
".cython.score-190 {background-color: #FFFF0c;}\n",
".cython.score-191 {background-color: #FFFF0c;}\n",
".cython.score-192 {background-color: #FFFF0c;}\n",
".cython.score-193 {background-color: #FFFF0c;}\n",
".cython.score-194 {background-color: #FFFF0c;}\n",
".cython.score-195 {background-color: #FFFF0c;}\n",
".cython.score-196 {background-color: #FFFF0c;}\n",
".cython.score-197 {background-color: #FFFF0c;}\n",
".cython.score-198 {background-color: #FFFF0c;}\n",
".cython.score-199 {background-color: #FFFF0c;}\n",
".cython.score-200 {background-color: #FFFF0c;}\n",
".cython.score-201 {background-color: #FFFF0c;}\n",
".cython.score-202 {background-color: #FFFF0c;}\n",
".cython.score-203 {background-color: #FFFF0b;}\n",
".cython.score-204 {background-color: #FFFF0b;}\n",
".cython.score-205 {background-color: #FFFF0b;}\n",
".cython.score-206 {background-color: #FFFF0b;}\n",
".cython.score-207 {background-color: #FFFF0b;}\n",
".cython.score-208 {background-color: #FFFF0b;}\n",
".cython.score-209 {background-color: #FFFF0b;}\n",
".cython.score-210 {background-color: #FFFF0b;}\n",
".cython.score-211 {background-color: #FFFF0b;}\n",
".cython.score-212 {background-color: #FFFF0b;}\n",
".cython.score-213 {background-color: #FFFF0b;}\n",
".cython.score-214 {background-color: #FFFF0b;}\n",
".cython.score-215 {background-color: #FFFF0b;}\n",
".cython.score-216 {background-color: #FFFF0b;}\n",
".cython.score-217 {background-color: #FFFF0b;}\n",
".cython.score-218 {background-color: #FFFF0b;}\n",
".cython.score-219 {background-color: #FFFF0b;}\n",
".cython.score-220 {background-color: #FFFF0b;}\n",
".cython.score-221 {background-color: #FFFF0b;}\n",
".cython.score-222 {background-color: #FFFF0a;}\n",
".cython.score-223 {background-color: #FFFF0a;}\n",
".cython.score-224 {background-color: #FFFF0a;}\n",
".cython.score-225 {background-color: #FFFF0a;}\n",
".cython.score-226 {background-color: #FFFF0a;}\n",
".cython.score-227 {background-color: #FFFF0a;}\n",
".cython.score-228 {background-color: #FFFF0a;}\n",
".cython.score-229 {background-color: #FFFF0a;}\n",
".cython.score-230 {background-color: #FFFF0a;}\n",
".cython.score-231 {background-color: #FFFF0a;}\n",
".cython.score-232 {background-color: #FFFF0a;}\n",
".cython.score-233 {background-color: #FFFF0a;}\n",
".cython.score-234 {background-color: #FFFF0a;}\n",
".cython.score-235 {background-color: #FFFF0a;}\n",
".cython.score-236 {background-color: #FFFF0a;}\n",
".cython.score-237 {background-color: #FFFF0a;}\n",
".cython.score-238 {background-color: #FFFF0a;}\n",
".cython.score-239 {background-color: #FFFF0a;}\n",
".cython.score-240 {background-color: #FFFF0a;}\n",
".cython.score-241 {background-color: #FFFF0a;}\n",
".cython.score-242 {background-color: #FFFF0a;}\n",
".cython.score-243 {background-color: #FFFF0a;}\n",
".cython.score-244 {background-color: #FFFF0a;}\n",
".cython.score-245 {background-color: #FFFF0a;}\n",
".cython.score-246 {background-color: #FFFF09;}\n",
".cython.score-247 {background-color: #FFFF09;}\n",
".cython.score-248 {background-color: #FFFF09;}\n",
".cython.score-249 {background-color: #FFFF09;}\n",
".cython.score-250 {background-color: #FFFF09;}\n",
".cython.score-251 {background-color: #FFFF09;}\n",
".cython.score-252 {background-color: #FFFF09;}\n",
".cython.score-253 {background-color: #FFFF09;}\n",
".cython.score-254 {background-color: #FFFF09;}\n",
".cython .hll { background-color: #ffffcc }\n",
".cython { background: #f8f8f8; }\n",
".cython .c { color: #408080; font-style: italic } /* Comment */\n",
".cython .err { border: 1px solid #FF0000 } /* Error */\n",
".cython .k { color: #008000; font-weight: bold } /* Keyword */\n",
".cython .o { color: #666666 } /* Operator */\n",
".cython .ch { color: #408080; font-style: italic } /* Comment.Hashbang */\n",
".cython .cm { color: #408080; font-style: italic } /* Comment.Multiline */\n",
".cython .cp { color: #BC7A00 } /* Comment.Preproc */\n",
".cython .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */\n",
".cython .c1 { color: #408080; font-style: italic } /* Comment.Single */\n",
".cython .cs { color: #408080; font-style: italic } /* Comment.Special */\n",
".cython .gd { color: #A00000 } /* Generic.Deleted */\n",
".cython .ge { font-style: italic } /* Generic.Emph */\n",
".cython .gr { color: #FF0000 } /* Generic.Error */\n",
".cython .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
".cython .gi { color: #00A000 } /* Generic.Inserted */\n",
".cython .go { color: #888888 } /* Generic.Output */\n",
".cython .gp { color: #000080; font-weight: bold } /* Generic.Prompt */\n",
".cython .gs { font-weight: bold } /* Generic.Strong */\n",
".cython .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
".cython .gt { color: #0044DD } /* Generic.Traceback */\n",
".cython .kc { color: #008000; font-weight: bold } /* Keyword.Constant */\n",
".cython .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */\n",
".cython .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */\n",
".cython .kp { color: #008000 } /* Keyword.Pseudo */\n",
".cython .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */\n",
".cython .kt { color: #B00040 } /* Keyword.Type */\n",
".cython .m { color: #666666 } /* Literal.Number */\n",
".cython .s { color: #BA2121 } /* Literal.String */\n",
".cython .na { color: #7D9029 } /* Name.Attribute */\n",
".cython .nb { color: #008000 } /* Name.Builtin */\n",
".cython .nc { color: #0000FF; font-weight: bold } /* Name.Class */\n",
".cython .no { color: #880000 } /* Name.Constant */\n",
".cython .nd { color: #AA22FF } /* Name.Decorator */\n",
".cython .ni { color: #999999; font-weight: bold } /* Name.Entity */\n",
".cython .ne { color: #D2413A; font-weight: bold } /* Name.Exception */\n",
".cython .nf { color: #0000FF } /* Name.Function */\n",
".cython .nl { color: #A0A000 } /* Name.Label */\n",
".cython .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */\n",
".cython .nt { color: #008000; font-weight: bold } /* Name.Tag */\n",
".cython .nv { color: #19177C } /* Name.Variable */\n",
".cython .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */\n",
".cython .w { color: #bbbbbb } /* Text.Whitespace */\n",
".cython .mb { color: #666666 } /* Literal.Number.Bin */\n",
".cython .mf { color: #666666 } /* Literal.Number.Float */\n",
".cython .mh { color: #666666 } /* Literal.Number.Hex */\n",
".cython .mi { color: #666666 } /* Literal.Number.Integer */\n",
".cython .mo { color: #666666 } /* Literal.Number.Oct */\n",
".cython .sa { color: #BA2121 } /* Literal.String.Affix */\n",
".cython .sb { color: #BA2121 } /* Literal.String.Backtick */\n",
".cython .sc { color: #BA2121 } /* Literal.String.Char */\n",
".cython .dl { color: #BA2121 } /* Literal.String.Delimiter */\n",
".cython .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */\n",
".cython .s2 { color: #BA2121 } /* Literal.String.Double */\n",
".cython .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */\n",
".cython .sh { color: #BA2121 } /* Literal.String.Heredoc */\n",
".cython .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */\n",
".cython .sx { color: #008000 } /* Literal.String.Other */\n",
".cython .sr { color: #BB6688 } /* Literal.String.Regex */\n",
".cython .s1 { color: #BA2121 } /* Literal.String.Single */\n",
".cython .ss { color: #19177C } /* Literal.String.Symbol */\n",
".cython .bp { color: #008000 } /* Name.Builtin.Pseudo */\n",
".cython .fm { color: #0000FF } /* Name.Function.Magic */\n",
".cython .vc { color: #19177C } /* Name.Variable.Class */\n",
".cython .vg { color: #19177C } /* Name.Variable.Global */\n",
".cython .vi { color: #19177C } /* Name.Variable.Instance */\n",
".cython .vm { color: #19177C } /* Name.Variable.Magic */\n",
".cython .il { color: #666666 } /* Literal.Number.Integer.Long */\n",
" </style>\n",
" <script>\n",
" function toggleDiv(id) {\n",
" theDiv = id.nextElementSibling\n",
" if (theDiv.style.display != 'block') theDiv.style.display = 'block';\n",
" else theDiv.style.display = 'none';\n",
" }\n",
" </script>\n",
"</head>\n",
"<body class=\"cython\">\n",
"<p><span style=\"border-bottom: solid 1px grey;\">Generated by Cython 0.25.2</span></p>\n",
"<p>\n",
" <span style=\"background-color: #FFFF00\">Yellow lines</span> hint at Python interaction.<br />\n",
" Click on a line that starts with a \"<code>+</code>\" to see the C code that Cython generated for it.\n",
"</p>\n",
"<div class=\"cython\"><pre class=\"cython line score-0\"> <span class=\"\">01</span>: </pre>\n",
"<pre class=\"cython line score-11\" onclick='toggleDiv(this)'>+<span class=\"\">02</span>: <span class=\"k\">cimport</span> <span class=\"nn\">cython</span></pre>\n",
"<pre class='cython code score-11 '> __pyx_t_1 = <span class='py_c_api'>PyDict_New</span>(); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 2, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) __PYX_ERR(0, 2, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
"</pre><pre class=\"cython line score-0\"> <span class=\"\">03</span>: <span class=\"k\">cimport</span> <span class=\"nn\">openmp</span></pre>\n",
"<pre class=\"cython line score-8\" onclick='toggleDiv(this)'>+<span class=\"\">04</span>: <span class=\"k\">import</span> <span class=\"nn\">numpy</span> <span class=\"k\">as</span> <span class=\"nn\">np</span></pre>\n",
"<pre class='cython code score-8 '> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_Import</span>(__pyx_n_s_numpy, 0, -1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 4, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) __PYX_ERR(0, 4, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
"</pre><pre class=\"cython line score-0\"> <span class=\"\">05</span>: <span class=\"k\">from</span> <span class=\"nn\">libc.math</span> <span class=\"k\">cimport</span> <span class=\"n\">sin</span><span class=\"p\">,</span> <span class=\"n\">M_PI</span></pre>\n",
"<pre class=\"cython line score-0\"> <span class=\"\">06</span>: <span class=\"k\">from</span> <span class=\"nn\">cython.parallel</span> <span class=\"k\">import</span> <span class=\"n\">parallel</span><span class=\"p\">,</span> <span class=\"n\">prange</span></pre>\n",
"<pre class=\"cython line score-0\"> <span class=\"\">07</span>: </pre>\n",
"<pre class=\"cython line score-77\" onclick='toggleDiv(this)'>+<span class=\"\">08</span>: <span class=\"k\">def</span> <span class=\"nf\">fourier_sum_cython_omp</span><span class=\"p\">(</span><span class=\"n\">times</span><span class=\"p\">,</span> <span class=\"n\">freq</span><span class=\"p\">,</span> <span class=\"n\">amp</span><span class=\"p\">,</span> <span class=\"n\">phi</span><span class=\"p\">,</span> <span class=\"n\">temp</span><span class=\"p\">):</span></pre>\n",
"<pre class='cython code score-77 '>/* Python wrapper */\n",
"static PyObject *__pyx_pw_46_cython_magic_1cd56569f3d2a9008e919ac54e3d0f01_1fourier_sum_cython_omp(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/\n",
"static PyMethodDef __pyx_mdef_46_cython_magic_1cd56569f3d2a9008e919ac54e3d0f01_1fourier_sum_cython_omp = {\"fourier_sum_cython_omp\", (PyCFunction)__pyx_pw_46_cython_magic_1cd56569f3d2a9008e919ac54e3d0f01_1fourier_sum_cython_omp, METH_VARARGS|METH_KEYWORDS, 0};\n",
"static PyObject *__pyx_pw_46_cython_magic_1cd56569f3d2a9008e919ac54e3d0f01_1fourier_sum_cython_omp(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {\n",
" PyObject *__pyx_v_times = 0;\n",
" PyObject *__pyx_v_freq = 0;\n",
" PyObject *__pyx_v_amp = 0;\n",
" PyObject *__pyx_v_phi = 0;\n",
" PyObject *__pyx_v_temp = 0;\n",
" PyObject *__pyx_r = 0;\n",
" <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
" <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"fourier_sum_cython_omp (wrapper)\", 0);\n",
" {\n",
" static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_times,&__pyx_n_s_freq,&__pyx_n_s_amp,&__pyx_n_s_phi,&__pyx_n_s_temp,0};\n",
" PyObject* values[5] = {0,0,0,0,0};\n",
" if (unlikely(__pyx_kwds)) {\n",
" Py_ssize_t kw_args;\n",
" const Py_ssize_t pos_args = <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args);\n",
" switch (pos_args) {\n",
" case 5: values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n",
" case 4: values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
" case 3: values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
" case 2: values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
" case 1: values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
" case 0: break;\n",
" default: goto __pyx_L5_argtuple_error;\n",
" }\n",
" kw_args = <span class='py_c_api'>PyDict_Size</span>(__pyx_kwds);\n",
" switch (pos_args) {\n",
" case 0:\n",
" if (likely((values[0] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_times)) != 0)) kw_args--;\n",
" else goto __pyx_L5_argtuple_error;\n",
" case 1:\n",
" if (likely((values[1] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_freq)) != 0)) kw_args--;\n",
" else {\n",
" <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"fourier_sum_cython_omp\", 1, 5, 5, 1); __PYX_ERR(0, 8, __pyx_L3_error)\n",
" }\n",
" case 2:\n",
" if (likely((values[2] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_amp)) != 0)) kw_args--;\n",
" else {\n",
" <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"fourier_sum_cython_omp\", 1, 5, 5, 2); __PYX_ERR(0, 8, __pyx_L3_error)\n",
" }\n",
" case 3:\n",
" if (likely((values[3] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_phi)) != 0)) kw_args--;\n",
" else {\n",
" <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"fourier_sum_cython_omp\", 1, 5, 5, 3); __PYX_ERR(0, 8, __pyx_L3_error)\n",
" }\n",
" case 4:\n",
" if (likely((values[4] = <span class='py_c_api'>PyDict_GetItem</span>(__pyx_kwds, __pyx_n_s_temp)) != 0)) kw_args--;\n",
" else {\n",
" <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"fourier_sum_cython_omp\", 1, 5, 5, 4); __PYX_ERR(0, 8, __pyx_L3_error)\n",
" }\n",
" }\n",
" if (unlikely(kw_args > 0)) {\n",
" if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, \"fourier_sum_cython_omp\") < 0)) __PYX_ERR(0, 8, __pyx_L3_error)\n",
" }\n",
" } else if (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args) != 5) {\n",
" goto __pyx_L5_argtuple_error;\n",
" } else {\n",
" values[0] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 0);\n",
" values[1] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 1);\n",
" values[2] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 2);\n",
" values[3] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 3);\n",
" values[4] = <span class='py_macro_api'>PyTuple_GET_ITEM</span>(__pyx_args, 4);\n",
" }\n",
" __pyx_v_times = values[0];\n",
" __pyx_v_freq = values[1];\n",
" __pyx_v_amp = values[2];\n",
" __pyx_v_phi = values[3];\n",
" __pyx_v_temp = values[4];\n",
" }\n",
" goto __pyx_L4_argument_unpacking_done;\n",
" __pyx_L5_argtuple_error:;\n",
" <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>(\"fourier_sum_cython_omp\", 1, 5, 5, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); __PYX_ERR(0, 8, __pyx_L3_error)\n",
" __pyx_L3_error:;\n",
" <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_1cd56569f3d2a9008e919ac54e3d0f01.fourier_sum_cython_omp\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
" <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
" return NULL;\n",
" __pyx_L4_argument_unpacking_done:;\n",
" __pyx_r = __pyx_pf_46_cython_magic_1cd56569f3d2a9008e919ac54e3d0f01_fourier_sum_cython_omp(__pyx_self, __pyx_v_times, __pyx_v_freq, __pyx_v_amp, __pyx_v_phi, __pyx_v_temp);\n",
"\n",
" /* function exit code */\n",
" <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
" return __pyx_r;\n",
"}\n",
"\n",
"static PyObject *__pyx_pf_46_cython_magic_1cd56569f3d2a9008e919ac54e3d0f01_fourier_sum_cython_omp(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_times, PyObject *__pyx_v_freq, PyObject *__pyx_v_amp, PyObject *__pyx_v_phi, PyObject *__pyx_v_temp) {\n",
" PyObject *__pyx_r = NULL;\n",
" <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
" <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"fourier_sum_cython_omp\", 0);\n",
"/* … */\n",
" /* function exit code */\n",
" __pyx_L1_error:;\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_2);\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_3);\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_4, 1);\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1);\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_6, 1);\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_7, 1);\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_8, 1);\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_9);\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_10);\n",
" <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_1cd56569f3d2a9008e919ac54e3d0f01.fourier_sum_cython_omp\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
" __pyx_r = NULL;\n",
" __pyx_L0:;\n",
" <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
" <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
" return __pyx_r;\n",
"}\n",
"/* … */\n",
" __pyx_tuple__14 = <span class='py_c_api'>PyTuple_Pack</span>(5, __pyx_n_s_times, __pyx_n_s_freq, __pyx_n_s_amp, __pyx_n_s_phi, __pyx_n_s_temp); if (unlikely(!__pyx_tuple__14)) __PYX_ERR(0, 8, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_tuple__14);\n",
" <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_tuple__14);\n",
"/* … */\n",
" __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_46_cython_magic_1cd56569f3d2a9008e919ac54e3d0f01_1fourier_sum_cython_omp, NULL, __pyx_n_s_cython_magic_1cd56569f3d2a9008e); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 8, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_fourier_sum_cython_omp, __pyx_t_1) < 0) __PYX_ERR(0, 8, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
" __pyx_codeobj__15 = (PyObject*)<span class='pyx_c_api'>__Pyx_PyCode_New</span>(5, 0, 5, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__14, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_home_ghajdu_cache_ipython_cytho, __pyx_n_s_fourier_sum_cython_omp, 8, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__15)) __PYX_ERR(0, 8, __pyx_L1_error)\n",
"</pre><pre class=\"cython line score-55\" onclick='toggleDiv(this)'>+<span class=\"\">09</span>: <span class=\"k\">return</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">asarray</span><span class=\"p\">(</span><span class=\"n\">fourier_sum_purec_omp</span><span class=\"p\">(</span><span class=\"n\">times</span><span class=\"p\">,</span> <span class=\"n\">freq</span><span class=\"p\">,</span> <span class=\"n\">amp</span><span class=\"p\">,</span> <span class=\"n\">phi</span><span class=\"p\">,</span> <span class=\"n\">temp</span><span class=\"p\">))</span></pre>\n",
"<pre class='cython code score-55 '> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
" __pyx_t_2 = <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_n_s_np); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 9, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
" __pyx_t_3 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_t_2, __pyx_n_s_asarray); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 9, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_3);\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
" __pyx_t_4 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_ds_double</span>(__pyx_v_times);\n",
" if (unlikely(!__pyx_t_4.memview)) __PYX_ERR(0, 9, __pyx_L1_error)\n",
" __pyx_t_5 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_ds_double</span>(__pyx_v_freq);\n",
" if (unlikely(!__pyx_t_5.memview)) __PYX_ERR(0, 9, __pyx_L1_error)\n",
" __pyx_t_6 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_ds_double</span>(__pyx_v_amp);\n",
" if (unlikely(!__pyx_t_6.memview)) __PYX_ERR(0, 9, __pyx_L1_error)\n",
" __pyx_t_7 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_ds_double</span>(__pyx_v_phi);\n",
" if (unlikely(!__pyx_t_7.memview)) __PYX_ERR(0, 9, __pyx_L1_error)\n",
" __pyx_t_8 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_ds_double</span>(__pyx_v_temp);\n",
" if (unlikely(!__pyx_t_8.memview)) __PYX_ERR(0, 9, __pyx_L1_error)\n",
" __pyx_t_2 = __pyx_f_46_cython_magic_1cd56569f3d2a9008e919ac54e3d0f01_fourier_sum_purec_omp(__pyx_t_4, __pyx_t_5, __pyx_t_6, __pyx_t_7, __pyx_t_8); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 9, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_2);\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_4, 1);\n",
" __pyx_t_4.memview = NULL;\n",
" __pyx_t_4.data = NULL;\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_5, 1);\n",
" __pyx_t_5.memview = NULL;\n",
" __pyx_t_5.data = NULL;\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_6, 1);\n",
" __pyx_t_6.memview = NULL;\n",
" __pyx_t_6.data = NULL;\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_7, 1);\n",
" __pyx_t_7.memview = NULL;\n",
" __pyx_t_7.data = NULL;\n",
" __PYX_XDEC_MEMVIEW(&__pyx_t_8, 1);\n",
" __pyx_t_8.memview = NULL;\n",
" __pyx_t_8.data = NULL;\n",
" __pyx_t_9 = NULL;\n",
" if (CYTHON_UNPACK_METHODS && unlikely(<span class='py_c_api'>PyMethod_Check</span>(__pyx_t_3))) {\n",
" __pyx_t_9 = <span class='py_macro_api'>PyMethod_GET_SELF</span>(__pyx_t_3);\n",
" if (likely(__pyx_t_9)) {\n",
" PyObject* function = <span class='py_macro_api'>PyMethod_GET_FUNCTION</span>(__pyx_t_3);\n",
" <span class='pyx_macro_api'>__Pyx_INCREF</span>(__pyx_t_9);\n",
" <span class='pyx_macro_api'>__Pyx_INCREF</span>(function);\n",
" <span class='pyx_macro_api'>__Pyx_DECREF_SET</span>(__pyx_t_3, function);\n",
" }\n",
" }\n",
" if (!__pyx_t_9) {\n",
" __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_CallOneArg</span>(__pyx_t_3, __pyx_t_2); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 9, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" } else {\n",
" #if CYTHON_FAST_PYCALL\n",
" if (<span class='py_c_api'>PyFunction_Check</span>(__pyx_t_3)) {\n",
" PyObject *__pyx_temp[2] = {__pyx_t_9, __pyx_t_2};\n",
" __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyFunction_FastCall</span>(__pyx_t_3, __pyx_temp+1-1, 1+1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 9, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_9); __pyx_t_9 = 0;\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
" } else\n",
" #endif\n",
" #if CYTHON_FAST_PYCCALL\n",
" if (<span class='pyx_c_api'>__Pyx_PyFastCFunction_Check</span>(__pyx_t_3)) {\n",
" PyObject *__pyx_temp[2] = {__pyx_t_9, __pyx_t_2};\n",
" __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyCFunction_FastCall</span>(__pyx_t_3, __pyx_temp+1-1, 1+1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 9, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_9); __pyx_t_9 = 0;\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_2); __pyx_t_2 = 0;\n",
" } else\n",
" #endif\n",
" {\n",
" __pyx_t_10 = <span class='py_c_api'>PyTuple_New</span>(1+1); if (unlikely(!__pyx_t_10)) __PYX_ERR(0, 9, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_10);\n",
" <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_9); <span class='py_macro_api'>PyTuple_SET_ITEM</span>(__pyx_t_10, 0, __pyx_t_9); __pyx_t_9 = NULL;\n",
" <span class='refnanny'>__Pyx_GIVEREF</span>(__pyx_t_2);\n",
" <span class='py_macro_api'>PyTuple_SET_ITEM</span>(__pyx_t_10, 0+1, __pyx_t_2);\n",
" __pyx_t_2 = 0;\n",
" __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyObject_Call</span>(__pyx_t_3, __pyx_t_10, NULL); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 9, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_10); __pyx_t_10 = 0;\n",
" }\n",
" }\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_3); __pyx_t_3 = 0;\n",
" __pyx_r = __pyx_t_1;\n",
" __pyx_t_1 = 0;\n",
" goto __pyx_L0;\n",
"</pre><pre class=\"cython line score-0\"> <span class=\"\">10</span>: </pre>\n",
"<pre class=\"cython line score-0\"> <span class=\"\">11</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">boundscheck</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
"<pre class=\"cython line score-0\"> <span class=\"\">12</span>: <span class=\"nd\">@cython</span><span class=\"o\">.</span><span class=\"n\">wraparound</span><span class=\"p\">(</span><span class=\"bp\">False</span><span class=\"p\">)</span></pre>\n",
"<pre class=\"cython line score-3\" onclick='toggleDiv(this)'>+<span class=\"\">13</span>: <span class=\"k\">cdef</span> <span class=\"nf\">fourier_sum_purec_omp</span><span class=\"p\">(</span><span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">times</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">freq</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">amp</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">phi</span><span class=\"p\">,</span> <span class=\"n\">double</span><span class=\"p\">[:]</span> <span class=\"n\">temp</span><span class=\"p\">):</span></pre>\n",
"<pre class='cython code score-3 '>static PyObject *__pyx_f_46_cython_magic_1cd56569f3d2a9008e919ac54e3d0f01_fourier_sum_purec_omp(__Pyx_memviewslice __pyx_v_times, __Pyx_memviewslice __pyx_v_freq, __Pyx_memviewslice __pyx_v_amp, __Pyx_memviewslice __pyx_v_phi, __Pyx_memviewslice __pyx_v_temp) {\n",
" int __pyx_v_i;\n",
" int __pyx_v_j;\n",
" CYTHON_UNUSED int __pyx_v_irange;\n",
" int __pyx_v_jrange;\n",
" PyObject *__pyx_r = NULL;\n",
" <span class='refnanny'>__Pyx_RefNannyDeclarations</span>\n",
" <span class='refnanny'>__Pyx_RefNannySetupContext</span>(\"fourier_sum_purec_omp\", 0);\n",
"/* … */\n",
" /* function exit code */\n",
" __pyx_L1_error:;\n",
" <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_t_1);\n",
" <span class='pyx_c_api'>__Pyx_AddTraceback</span>(\"_cython_magic_1cd56569f3d2a9008e919ac54e3d0f01.fourier_sum_purec_omp\", __pyx_clineno, __pyx_lineno, __pyx_filename);\n",
" __pyx_r = 0;\n",
" __pyx_L0:;\n",
" <span class='refnanny'>__Pyx_XGIVEREF</span>(__pyx_r);\n",
" <span class='refnanny'>__Pyx_RefNannyFinishContext</span>();\n",
" return __pyx_r;\n",
"}\n",
"</pre><pre class=\"cython line score-0\"> <span class=\"\">14</span>: <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">i</span><span class=\"p\">,</span> <span class=\"nf\">j</span><span class=\"p\">,</span> <span class=\"nf\">irange</span><span class=\"p\">,</span> <span class=\"nf\">jrange</span></pre>\n",
"<pre class=\"cython line score-6\" onclick='toggleDiv(this)'>+<span class=\"\">15</span>: <span class=\"n\">irange</span><span class=\"o\">=</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">times</span><span class=\"p\">)</span></pre>\n",
"<pre class='cython code score-6 '> __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_times, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 15, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" __pyx_t_2 = <span class='py_c_api'>PyObject_Length</span>(__pyx_t_1); if (unlikely(__pyx_t_2 == -1)) __PYX_ERR(0, 15, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
" __pyx_v_irange = __pyx_t_2;\n",
"</pre><pre class=\"cython line score-6\" onclick='toggleDiv(this)'>+<span class=\"\">16</span>: <span class=\"n\">jrange</span><span class=\"o\">=</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">freq</span><span class=\"p\">)</span></pre>\n",
"<pre class='cython code score-6 '> __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_freq, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 16, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" __pyx_t_2 = <span class='py_c_api'>PyObject_Length</span>(__pyx_t_1); if (unlikely(__pyx_t_2 == -1)) __PYX_ERR(0, 16, __pyx_L1_error)\n",
" <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n",
" __pyx_v_jrange = __pyx_t_2;\n",
"</pre><pre class=\"cython line score-0\"> <span class=\"\">17</span>: <span class=\"c\">#print openmp.omp_get_num_procs()</span></pre>\n",
"<pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">18</span>: <span class=\"k\">with</span> <span class=\"k\">nogil</span><span class=\"p\">,</span> <span class=\"n\">parallel</span><span class=\"p\">(</span><span class=\"n\">num_threads</span><span class=\"o\">=</span><span class=\"mf\">4</span><span class=\"p\">):</span></pre>\n",
"<pre class='cython code score-0 '> {\n",
" #ifdef WITH_THREAD\n",
" PyThreadState *_save;\n",
" Py_UNBLOCK_THREADS\n",
" #endif\n",
" /*try:*/ {\n",
" {\n",
" #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))))\n",
" #undef likely\n",
" #undef unlikely\n",
" #define likely(x) (x)\n",
" #define unlikely(x) (x)\n",
" #endif\n",
" #ifdef _OPENMP\n",
" #pragma omp parallel num_threads(4)\n",
" #endif /* _OPENMP */\n",
" {\n",
"/* … */\n",
" /*finally:*/ {\n",
" /*normal exit:*/{\n",
" #ifdef WITH_THREAD\n",
" Py_BLOCK_THREADS\n",
" #endif\n",
" goto __pyx_L5;\n",
" }\n",
" __pyx_L5:;\n",
" }\n",
" }\n",
"</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">19</span>: <span class=\"k\">for</span> <span class=\"n\">i</span> <span class=\"ow\">in</span> <span class=\"n\">prange</span><span class=\"p\">(</span><span class=\"n\">irange</span><span class=\"p\">,</span> <span class=\"n\">schedule</span><span class=\"o\">=</span><span class=\"s\">'dynamic'</span><span class=\"p\">,</span> <span class=\"n\">chunksize</span><span class=\"o\">=</span><span class=\"mf\">10</span><span class=\"p\">):</span></pre>\n",
"<pre class='cython code score-0 '> __pyx_t_3 = __pyx_v_irange;\n",
" if (1 == 0) abort();\n",
" {\n",
" __pyx_t_5 = (__pyx_t_3 - 0 + 1 - 1/abs(1)) / 1;\n",
" if (__pyx_t_5 > 0)\n",
" {\n",
" #ifdef _OPENMP\n",
" #pragma omp for firstprivate(__pyx_v_i) lastprivate(__pyx_v_i) lastprivate(__pyx_v_j)\n",
"/* … */\n",
" __pyx_t_3 = __pyx_v_irange;\n",
" if (1 == 0) abort();\n",
" {\n",
" __pyx_t_5 = (__pyx_t_3 - 0 + 1 - 1/abs(1)) / 1;\n",
" if (__pyx_t_5 > 0)\n",
" {\n",
" #ifdef _OPENMP\n",
" #pragma omp for firstprivate(__pyx_v_i) lastprivate(__pyx_v_i) lastprivate(__pyx_v_j) schedule(dynamic, __pyx_t_6)\n",
" #endif /* _OPENMP */\n",
" for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_5; __pyx_t_4++){\n",
" {\n",
" __pyx_v_i = (int)(0 + 1 * __pyx_t_4);\n",
" /* Initialize private variables to invalid values */\n",
" __pyx_v_j = ((int)0xbad0bad0);\n",
"</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">20</span>: <span class=\"n\">temp</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span><span class=\"o\">=</span><span class=\"mf\">0</span></pre>\n",
"<pre class='cython code score-0 '> __pyx_t_7 = __pyx_v_i;\n",
" *((double *) ( /* dim=0 */ (__pyx_v_temp.data + __pyx_t_7 * __pyx_v_temp.strides[0]) )) = 0.0;\n",
"</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">21</span>: <span class=\"k\">for</span> <span class=\"n\">j</span> <span class=\"ow\">in</span> <span class=\"nb\">xrange</span><span class=\"p\">(</span><span class=\"n\">jrange</span><span class=\"p\">):</span></pre>\n",
"<pre class='cython code score-0 '> __pyx_t_8 = __pyx_v_jrange;\n",
" for (__pyx_t_9 = 0; __pyx_t_9 < __pyx_t_8; __pyx_t_9+=1) {\n",
" __pyx_v_j = __pyx_t_9;\n",
"</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">22</span>: <span class=\"n\">temp</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\">+=</span> <span class=\"n\">amp</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">sin</span><span class=\"p\">(</span> <span class=\"mf\">2</span> <span class=\"o\">*</span> <span class=\"n\">M_PI</span> <span class=\"o\">*</span> <span class=\"n\">freq</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">times</span><span class=\"p\">[</span><span class=\"n\">i</span><span class=\"p\">]</span> <span class=\"o\">+</span> <span class=\"n\">phi</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">]</span> <span class=\"p\">)</span></pre>\n",
"<pre class='cython code score-0 '> __pyx_t_10 = __pyx_v_j;\n",
" __pyx_t_11 = __pyx_v_j;\n",
" __pyx_t_12 = __pyx_v_i;\n",
" __pyx_t_13 = __pyx_v_j;\n",
" __pyx_t_14 = __pyx_v_i;\n",
" *((double *) ( /* dim=0 */ (__pyx_v_temp.data + __pyx_t_14 * __pyx_v_temp.strides[0]) )) += ((*((double *) ( /* dim=0 */ (__pyx_v_amp.data + __pyx_t_10 * __pyx_v_amp.strides[0]) ))) * sin(((((2.0 * M_PI) * (*((double *) ( /* dim=0 */ (__pyx_v_freq.data + __pyx_t_11 * __pyx_v_freq.strides[0]) )))) * (*((double *) ( /* dim=0 */ (__pyx_v_times.data + __pyx_t_12 * __pyx_v_times.strides[0]) )))) + (*((double *) ( /* dim=0 */ (__pyx_v_phi.data + __pyx_t_13 * __pyx_v_phi.strides[0]) ))))));\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" #if ((defined(__APPLE__) || defined(__OSX__)) && (defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))))\n",
" #undef likely\n",
" #undef unlikely\n",
" #define likely(x) __builtin_expect(!!(x), 1)\n",
" #define unlikely(x) __builtin_expect(!!(x), 0)\n",
" #endif\n",
" }\n",
"</pre><pre class=\"cython line score-1\" onclick='toggleDiv(this)'>+<span class=\"\">23</span>: <span class=\"k\">return</span> <span class=\"n\">temp</span></pre>\n",
"<pre class='cython code score-1 '> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);\n",
" __pyx_t_1 = __pyx_memoryview_fromslice(__pyx_v_temp, 1, (PyObject *(*)(char *)) __pyx_memview_get_double, (int (*)(char *, PyObject *)) __pyx_memview_set_double, 0);; if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 23, __pyx_L1_error)\n",
" <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n",
" __pyx_r = __pyx_t_1;\n",
" __pyx_t_1 = 0;\n",
" goto __pyx_L0;\n",
"</pre></div></body></html>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%cython --compile-args=-fopenmp --link-args=-fopenmp --force -a\n",
"\n",
"cimport cython\n",
"cimport openmp\n",
"import numpy as np\n",
"from libc.math cimport sin, M_PI\n",
"from cython.parallel import parallel, prange\n",
"\n",
"def fourier_sum_cython_omp(times, freq, amp, phi, temp):\n",
" return np.asarray(fourier_sum_purec_omp(times, freq, amp, phi, temp))\n",
"\n",
"@cython.boundscheck(False)\n",
"@cython.wraparound(False)\n",
"cdef fourier_sum_purec_omp(double[:] times, double[:] freq, double[:] amp, double[:] phi, double[:] temp):\n",
" cdef int i, j, irange, jrange\n",
" irange=len(times)\n",
" jrange=len(freq)\n",
" #print openmp.omp_get_num_procs()\n",
" with nogil, parallel(num_threads=4):\n",
" for i in prange(irange, schedule='dynamic', chunksize=10):\n",
" temp[i]=0\n",
" for j in xrange(jrange):\n",
" temp[i] += amp[j] * sin( 2 * M_PI * freq[j] * times[i] + phi[j] )\n",
" return temp "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Comparison\n",
"\n",
"Finally, we compare the execution times of the implementations of the funtions."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5 loops, best of 5: 887 ms per loop\n",
"10 loops, best of 10: 20.1 ms per loop\n",
"10 loops, best of 10: 19 ms per loop\n",
"10 loops, best of 10: 20 ms per loop\n",
"10 loops, best of 10: 19.3 ms per loop\n",
"10 loops, best of 10: 9.35 ms per loop\n"
]
}
],
"source": [
"temp=np.zeros_like(times)\n",
"\n",
"amps_naive = fourier_sum_naive(times, freq, amp, phi)\n",
"amps_numpy = fourier_sum_numpy(times, freq, amp, phi)\n",
"amps_numba1 = fourier_sum_naive_numba(times, freq, amp, phi)\n",
"amps_numba2 = fourier_sum_numpy_numba(times, freq, amp, phi)\n",
"amps_cython = fourier_sum_cython(times, freq, amp, phi, temp)\n",
"amps_cython_omp = fourier_sum_cython_omp(times, freq, amp, phi, temp)\n",
"\n",
"%timeit -n 5 -r 5 fourier_sum_naive(times, freq, amp, phi)\n",
"%timeit -n 10 -r 10 fourier_sum_numpy(times, freq, amp, phi)\n",
"%timeit -n 10 -r 10 fourier_sum_naive_numba(times, freq, amp, phi)\n",
"%timeit -n 10 -r 10 fourier_sum_numpy_numba(times, freq, amp, phi)\n",
"%timeit -n 10 -r 10 fourier_sum_cython(times, freq, amp, phi, temp)\n",
"%timeit -n 10 -r 10 fourier_sum_cython_omp(times, freq, amp, phi, temp)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0. 0. 0. ..., 0. 0. 0.]\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA7QAAAKvCAYAAABaoYNCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSimRDK0mSJEkqkg2tJEmSJKlI/w8NejStA3lYEgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f78fdef6d90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pylab as plt\n",
"\n",
"print amps_numpy-amps_cython\n",
"\n",
"fig=plt.figure()\n",
"fig.set_size_inches(16,12)\n",
"\n",
"plt.plot(times,amps_naive ,'-', lw=2.0)\n",
"#plt.plot(times,amps_numpy - 2,'-', lw=2.0)\n",
"plt.plot(times,amps_numba1 - 4,'-', lw=2.0)\n",
"#plt.plot(times,amps_numba2 - 6,'-', lw=2.0)\n",
"plt.plot(times,amps_cython - 8,'-', lw=2.0)\n",
"#plt.plot(times,amps_cython_omp -10,'-', lw=2.0)\n",
"\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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
pgr-me/metis_projects | 03-water_1/get_pums-p_data.ipynb | 1 | 23059 | {
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import csv\n",
"import urllib\n",
"import urllib2\n",
"import os\n",
"import pandas as pd\n",
"import zipfile\n",
"import fnmatch\n",
"import shutil\n",
"import glob"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[['01', 'AL', 'Alabama', '01779775'],\n",
" ['02', 'AK', 'Alaska', '01785533'],\n",
" ['04', 'AZ', 'Arizona', '01779777']]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"states_url = 'http://www2.census.gov/geo/docs/reference/state.txt'\n",
"response = urllib2.urlopen(states_url)\n",
"cr = csv.reader(response)\n",
"\n",
"states_list_1 = []\n",
"for row in cr:\n",
" states_list_1.append(row)\n",
"states_list_1.pop(0)\n",
"\n",
"states_list_2 = []\n",
"for element in states_list_1:\n",
" for string in element:\n",
" split_string = string.split(\"|\")\n",
" states_list_2.append(split_string)\n",
"\n",
"states_list_2[:3]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>abr</th>\n",
" <th>name</th>\n",
" <th>num</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>01</td>\n",
" <td>al</td>\n",
" <td>Alabama</td>\n",
" <td>01779775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>02</td>\n",
" <td>ak</td>\n",
" <td>Alaska</td>\n",
" <td>01785533</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>04</td>\n",
" <td>az</td>\n",
" <td>Arizona</td>\n",
" <td>01779777</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id abr name num\n",
"0 01 al Alabama 01779775\n",
"1 02 ak Alaska 01785533\n",
"2 04 az Arizona 01779777"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_states = pd.DataFrame(states_list_2)\n",
"df_states.columns = ['id', 'abr', 'name', 'num']\n",
"df_states['abr'] = df_states['abr'].str.lower()\n",
"df_states.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# make directories\n",
"\n",
"data_dir = 'data'\n",
"if not os.path.exists(data_dir):\n",
" os.makedirs(data_dir)\n",
" \n",
"pums_dir = 'data/pums-p'\n",
"if not os.path.exists(pums_dir):\n",
" os.makedirs(pums_dir)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": true
},
"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>abr</th>\n",
" <th>name</th>\n",
" <th>num</th>\n",
" <th>url</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>01</td>\n",
" <td>al</td>\n",
" <td>Alabama</td>\n",
" <td>01779775</td>\n",
" <td>http://www2.census.gov/programs-surveys/acs/da...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>02</td>\n",
" <td>ak</td>\n",
" <td>Alaska</td>\n",
" <td>01785533</td>\n",
" <td>http://www2.census.gov/programs-surveys/acs/da...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>04</td>\n",
" <td>az</td>\n",
" <td>Arizona</td>\n",
" <td>01779777</td>\n",
" <td>http://www2.census.gov/programs-surveys/acs/da...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id abr name num \\\n",
"0 01 al Alabama 01779775 \n",
"1 02 ak Alaska 01785533 \n",
"2 04 az Arizona 01779777 \n",
"\n",
" url \n",
"0 http://www2.census.gov/programs-surveys/acs/da... \n",
"1 http://www2.census.gov/programs-surveys/acs/da... \n",
"2 http://www2.census.gov/programs-surveys/acs/da... "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# construct the urls\n",
"# example url: http://www2.census.gov/programs-surveys/acs/data/pums/2014/1-Year/\n",
"# http://www2.census.gov/programs-surveys/acs/data/pums/2014/1-Year/csv_pak.zip\n",
"base_url = 'http://www2.census.gov/programs-surveys/acs/data/pums/'\n",
"year = '2014'\n",
"middle_url = '/1-Year/csv_p'\n",
"end_url = '.zip'\n",
"df_states['url'] = base_url + year + middle_url + df_states['abr'] + end_url\n",
"df_states.head(3)"
]
},
{
"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>id</th>\n",
" <th>abr</th>\n",
" <th>name</th>\n",
" <th>num</th>\n",
" <th>url</th>\n",
" <th>path</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>01</td>\n",
" <td>al</td>\n",
" <td>Alabama</td>\n",
" <td>01779775</td>\n",
" <td>http://www2.census.gov/programs-surveys/acs/da...</td>\n",
" <td>data/pums-p/al.zip</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>02</td>\n",
" <td>ak</td>\n",
" <td>Alaska</td>\n",
" <td>01785533</td>\n",
" <td>http://www2.census.gov/programs-surveys/acs/da...</td>\n",
" <td>data/pums-p/ak.zip</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>04</td>\n",
" <td>az</td>\n",
" <td>Arizona</td>\n",
" <td>01779777</td>\n",
" <td>http://www2.census.gov/programs-surveys/acs/da...</td>\n",
" <td>data/pums-p/az.zip</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id abr name num \\\n",
"0 01 al Alabama 01779775 \n",
"1 02 ak Alaska 01785533 \n",
"2 04 az Arizona 01779777 \n",
"\n",
" url path \n",
"0 http://www2.census.gov/programs-surveys/acs/da... data/pums-p/al.zip \n",
"1 http://www2.census.gov/programs-surveys/acs/da... data/pums-p/ak.zip \n",
"2 http://www2.census.gov/programs-surveys/acs/da... data/pums-p/az.zip "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# construct the paths\n",
"df_states['path'] = 'data/pums-p/' + df_states['abr'] + '.zip'\n",
"df_states.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# download zipped files and save to pums directory\n",
"for index, row in df_states.iterrows():\n",
" urllib.urlretrieve(row['url'], row['path'])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%bash\n",
"rm data/pums-p/as.zip\n",
"rm data/pums-p/mp.zip\n",
"rm data/pums-p/um.zip\n",
"rm data/pums-p/gu.zip\n",
"rm data/pums-p/vi.zip"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# unzip files\n",
"\n",
"rootPath = r\"/Users/peter/Dropbox/ds/metis/notebooks/projects/mcnulty/data/pums-p\"\n",
"pattern = '*.zip'\n",
"\n",
"for root, dirs, files in os.walk(rootPath):\n",
" for filename in fnmatch.filter(files, pattern):\n",
" #print(os.path.join(root, filename))\n",
" zipfile.ZipFile(os.path.join(root, filename)).extractall(os.path.join(root, os.path.splitext(filename)[0]))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['data/pums-p/ak/ss14pak.csv',\n",
" 'data/pums-p/al/ss14pal.csv',\n",
" 'data/pums-p/ar/ss14par.csv',\n",
" 'data/pums-p/az/ss14paz.csv',\n",
" 'data/pums-p/ca/ss14pca.csv']"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# make list of tuples of csvs\n",
"\n",
"base_path = 'data/pums-p/*/*.'\n",
"csv_list = glob.glob(base_path + 'csv')\n",
"\n",
"csv_list[:5]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/peter/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2902: DtypeWarning: Columns (95,104,126) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
}
],
"source": [
"# make dataframe of all the csvs\n",
"df = pd.DataFrame()\n",
"temp_list = []\n",
"\n",
"for csv in csv_list:\n",
" dataframe = pd.read_csv(csv, index_col=None, header=0)\n",
" temp_list.append(dataframe)\n",
"df = pd.concat(temp_list)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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>RT</th>\n",
" <th>SERIALNO</th>\n",
" <th>SPORDER</th>\n",
" <th>PUMA</th>\n",
" <th>ST</th>\n",
" <th>ADJINC</th>\n",
" <th>PWGTP</th>\n",
" <th>AGEP</th>\n",
" <th>CIT</th>\n",
" <th>CITWP</th>\n",
" <th>...</th>\n",
" <th>pwgtp71</th>\n",
" <th>pwgtp72</th>\n",
" <th>pwgtp73</th>\n",
" <th>pwgtp74</th>\n",
" <th>pwgtp75</th>\n",
" <th>pwgtp76</th>\n",
" <th>pwgtp77</th>\n",
" <th>pwgtp78</th>\n",
" <th>pwgtp79</th>\n",
" <th>pwgtp80</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>P</td>\n",
" <td>315</td>\n",
" <td>1</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" <td>1008425</td>\n",
" <td>161</td>\n",
" <td>30</td>\n",
" <td>4</td>\n",
" <td>2008</td>\n",
" <td>...</td>\n",
" <td>165</td>\n",
" <td>156</td>\n",
" <td>44</td>\n",
" <td>43</td>\n",
" <td>243</td>\n",
" <td>213</td>\n",
" <td>56</td>\n",
" <td>138</td>\n",
" <td>243</td>\n",
" <td>163</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>P</td>\n",
" <td>315</td>\n",
" <td>2</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" <td>1008425</td>\n",
" <td>155</td>\n",
" <td>34</td>\n",
" <td>1</td>\n",
" <td></td>\n",
" <td>...</td>\n",
" <td>178</td>\n",
" <td>141</td>\n",
" <td>47</td>\n",
" <td>45</td>\n",
" <td>253</td>\n",
" <td>227</td>\n",
" <td>39</td>\n",
" <td>134</td>\n",
" <td>278</td>\n",
" <td>144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>P</td>\n",
" <td>315</td>\n",
" <td>3</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" <td>1008425</td>\n",
" <td>255</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td></td>\n",
" <td>...</td>\n",
" <td>289</td>\n",
" <td>251</td>\n",
" <td>68</td>\n",
" <td>71</td>\n",
" <td>438</td>\n",
" <td>386</td>\n",
" <td>64</td>\n",
" <td>230</td>\n",
" <td>396</td>\n",
" <td>230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>P</td>\n",
" <td>315</td>\n",
" <td>4</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" <td>1008425</td>\n",
" <td>204</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td></td>\n",
" <td>...</td>\n",
" <td>222</td>\n",
" <td>194</td>\n",
" <td>56</td>\n",
" <td>63</td>\n",
" <td>350</td>\n",
" <td>344</td>\n",
" <td>60</td>\n",
" <td>217</td>\n",
" <td>322</td>\n",
" <td>180</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>P</td>\n",
" <td>315</td>\n",
" <td>5</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" <td>1008425</td>\n",
" <td>180</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td></td>\n",
" <td>...</td>\n",
" <td>192</td>\n",
" <td>143</td>\n",
" <td>56</td>\n",
" <td>52</td>\n",
" <td>304</td>\n",
" <td>248</td>\n",
" <td>52</td>\n",
" <td>165</td>\n",
" <td>255</td>\n",
" <td>176</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 284 columns</p>\n",
"</div>"
],
"text/plain": [
" RT SERIALNO SPORDER PUMA ST ADJINC PWGTP AGEP CIT CITWP ... \\\n",
"0 P 315 1 200 2 1008425 161 30 4 2008 ... \n",
"1 P 315 2 200 2 1008425 155 34 1 ... \n",
"2 P 315 3 200 2 1008425 255 7 1 ... \n",
"3 P 315 4 200 2 1008425 204 5 1 ... \n",
"4 P 315 5 200 2 1008425 180 4 1 ... \n",
"\n",
" pwgtp71 pwgtp72 pwgtp73 pwgtp74 pwgtp75 pwgtp76 pwgtp77 pwgtp78 \\\n",
"0 165 156 44 43 243 213 56 138 \n",
"1 178 141 47 45 253 227 39 134 \n",
"2 289 251 68 71 438 386 64 230 \n",
"3 222 194 56 63 350 344 60 217 \n",
"4 192 143 56 52 304 248 52 165 \n",
"\n",
" pwgtp79 pwgtp80 \n",
"0 243 163 \n",
"1 278 144 \n",
"2 396 230 \n",
"3 322 180 \n",
"4 255 176 \n",
"\n",
"[5 rows x 284 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# remove unnecessary files in pums folder\n",
"shutil.rmtree('data/pums-p')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pums_dir = 'data/pums-p'\n",
"if not os.path.exists(pums_dir):\n",
" os.makedirs(pums_dir)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# http://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMSDataDict14.pdf\n",
"\n",
"cols_list = [\n",
" #'RT' # record type, H indicates house\n",
" 'SERIALNO' # housing unit / GQ person serial number...unique identifier\n",
" #,'SPORDER' # person order\n",
" #,'PUMA' # public use microdata area code, based on 2010 census defn, 100k people per puma\n",
" #,'ST' # state code\n",
" #,'PWGTP' # person weight\n",
" #,'ADJINC' # adj for income & earnings to 2014 values, divide by 1 million to do conversion\n",
" ,'ESR' # employment status of recode: b n/a, less than 16 years old, 1 civilian employed, 2 civilian employed w/ job but not at work, 3 unemployed, 4 armed forces at work, 5 armed forces w/ job but not at work, 6 not in labor force\n",
" ,'CIT' # citizenship status\n",
" ,'RAC1P' # recorded detailed race code: 1 white alone, 2 black or aa alone, 3 american indian alone, 4 alaska native alone, 5 american indian / alaska native tribe specified or not, 6 asian alone, 7 native hawaiian / pacific islander alone, 8 some other race alone, 9 two or more races\n",
" ,'HISP' # hispanic: 0 is no and all other numbers yes\n",
" ,'SCHL' # educational attainment\n",
" ,'HICOV' # health insurance coverage recode\n",
" #,'ESP' # employment status of parents\n",
" ,'DIS' # disability recode: 1 disability, 2 no disability\n",
" ,'AGEP' # age: 00 is under one year, max of 99\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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>SERIALNO</th>\n",
" <th>ESR</th>\n",
" <th>CIT</th>\n",
" <th>RAC1P</th>\n",
" <th>HISP</th>\n",
" <th>SCHL</th>\n",
" <th>HICOV</th>\n",
" <th>DIS</th>\n",
" <th>AGEP</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>315</td>\n",
" <td>6.0</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>19.0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>315</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>20.0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>315</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3.0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SERIALNO ESR CIT RAC1P HISP SCHL HICOV DIS AGEP\n",
"0 315 6.0 4 1 1 19.0 1 2 30\n",
"1 315 1.0 1 1 1 20.0 1 2 34\n",
"2 315 NaN 1 1 1 3.0 1 2 7"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select desired columns\n",
"df1 = df[cols_list]\n",
"df1.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3164116, 9)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.shape"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# h5 dataframe (pickling didn't work, perhaps because dataset too large)\n",
"hdf = pd.HDFStore('data/pums-p/pums-p.h5')\n",
"hdf.put('d1', df1, format='table', data_columns=True)"
]
}
],
"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
}
| gpl-3.0 |
CompPhysics/ComputationalPhysics2 | doc/LectureNotes/_build/jupyter_execute/linearregression.ipynb | 1 | 201849 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linear Regression and more Advanced Regression Analysis\n",
"\n",
"\n",
"\n",
"## Why Linear Regression (aka Ordinary Least Squares and family)\n",
"\n",
"Fitting a continuous function with linear parameterization in terms of the parameters $\\boldsymbol{\\beta}$.\n",
"* Method of choice for fitting a continuous function!\n",
"\n",
"* Gives an excellent introduction to central Machine Learning features with **understandable pedagogical** links to other methods like **Neural Networks**, **Support Vector Machines** etc\n",
"\n",
"* Analytical expression for the fitting parameters $\\boldsymbol{\\beta}$\n",
"\n",
"* Analytical expressions for statistical propertiers like mean values, variances, confidence intervals and more\n",
"\n",
"* Analytical relation with probabilistic interpretations \n",
"\n",
"* Easy to introduce basic concepts like bias-variance tradeoff, cross-validation, resampling and regularization techniques and many other ML topics\n",
"\n",
"* Easy to code! And links well with classification problems and logistic regression and neural networks\n",
"\n",
"* Allows for **easy** hands-on understanding of gradient descent methods\n",
"\n",
"* and many more features\n",
"\n",
"For more discussions of Ridge and Lasso regression, [Wessel van Wieringen's](https://arxiv.org/abs/1509.09169) article is highly recommended.\n",
"Similarly, [Mehta et al's article](https://arxiv.org/abs/1803.08823) is also recommended.\n",
"\n",
"\n",
"\n",
"## Regression analysis, overarching aims\n",
"\n",
"Regression modeling deals with the description of the sampling distribution of a given random variable $y$ and how it varies as function of another variable or a set of such variables $\\boldsymbol{x} =[x_0, x_1,\\dots, x_{n-1}]^T$. \n",
"The first variable is called the **dependent**, the **outcome** or the **response** variable while the set of variables $\\boldsymbol{x}$ is called the independent variable, or the predictor variable or the explanatory variable. \n",
"\n",
"A regression model aims at finding a likelihood function $p(\\boldsymbol{y}\\vert \\boldsymbol{x})$, that is the conditional distribution for $\\boldsymbol{y}$ with a given $\\boldsymbol{x}$. The estimation of $p(\\boldsymbol{y}\\vert \\boldsymbol{x})$ is made using a data set with \n",
"* $n$ cases $i = 0, 1, 2, \\dots, n-1$ \n",
"\n",
"* Response (target, dependent or outcome) variable $y_i$ with $i = 0, 1, 2, \\dots, n-1$ \n",
"\n",
"* $p$ so-called explanatory (independent or predictor) variables $\\boldsymbol{x}_i=[x_{i0}, x_{i1}, \\dots, x_{ip-1}]$ with $i = 0, 1, 2, \\dots, n-1$ and explanatory variables running from $0$ to $p-1$. See below for more explicit examples. \n",
"\n",
" The goal of the regression analysis is to extract/exploit relationship between $\\boldsymbol{y}$ and $\\boldsymbol{x}$ in or to infer causal dependencies, approximations to the likelihood functions, functional relationships and to make predictions, making fits and many other things.\n",
"\n",
"\n",
"\n",
"\n",
"## Regression analysis, overarching aims II\n",
"\n",
"\n",
"Consider an experiment in which $p$ characteristics of $n$ samples are\n",
"measured. The data from this experiment, for various explanatory variables $p$ are normally represented by a matrix \n",
"$\\mathbf{X}$.\n",
"\n",
"The matrix $\\mathbf{X}$ is called the *design\n",
"matrix*. Additional information of the samples is available in the\n",
"form of $\\boldsymbol{y}$ (also as above). The variable $\\boldsymbol{y}$ is\n",
"generally referred to as the *response variable*. The aim of\n",
"regression analysis is to explain $\\boldsymbol{y}$ in terms of\n",
"$\\boldsymbol{X}$ through a functional relationship like $y_i =\n",
"f(\\mathbf{X}_{i,\\ast})$. When no prior knowledge on the form of\n",
"$f(\\cdot)$ is available, it is common to assume a linear relationship\n",
"between $\\boldsymbol{X}$ and $\\boldsymbol{y}$. This assumption gives rise to\n",
"the *linear regression model* where $\\boldsymbol{\\beta} = [\\beta_0, \\ldots,\n",
"\\beta_{p-1}]^{T}$ are the *regression parameters*. \n",
"\n",
"Linear regression gives us a set of analytical equations for the parameters $\\beta_j$.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Examples\n",
"In order to understand the relation among the predictors $p$, the set of data $n$ and the target (outcome, output etc) $\\boldsymbol{y}$,\n",
"consider the model we discussed for describing nuclear binding energies. \n",
"\n",
"There we assumed that we could parametrize the data using a polynomial approximation based on the liquid drop model.\n",
"Assuming"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"BE(A) = a_0+a_1A+a_2A^{2/3}+a_3A^{-1/3}+a_4A^{-1},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"we have five predictors, that is the intercept, the $A$ dependent term, the $A^{2/3}$ term and the $A^{-1/3}$ and $A^{-1}$ terms.\n",
"This gives $p=0,1,2,3,4$. Furthermore we have $n$ entries for each predictor. It means that our design matrix is a \n",
"$p\\times n$ matrix $\\boldsymbol{X}$.\n",
"\n",
"Here the predictors are based on a model we have made. A popular data set which is widely encountered in ML applications is the\n",
"so-called [credit card default data from Taiwan](https://www.sciencedirect.com/science/article/pii/S0957417407006719?via%3Dihub). The data set contains data on $n=30000$ credit card holders with predictors like gender, marital status, age, profession, education, etc. In total there are $24$ such predictors or attributes leading to a design matrix of dimensionality $24 \\times 30000$. This is however a classification problem and we will come back to it when we discuss Logistic Regression.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## General linear models\n",
"Before we proceed let us study a case from linear algebra where we aim at fitting a set of data $\\boldsymbol{y}=[y_0,y_1,\\dots,y_{n-1}]$. We could think of these data as a result of an experiment or a complicated numerical experiment. These data are functions of a series of variables $\\boldsymbol{x}=[x_0,x_1,\\dots,x_{n-1}]$, that is $y_i = y(x_i)$ with $i=0,1,2,\\dots,n-1$. The variables $x_i$ could represent physical quantities like time, temperature, position etc. We assume that $y(x)$ is a smooth function. \n",
"\n",
"Since obtaining these data points may not be trivial, we want to use these data to fit a function which can allow us to make predictions for values of $y$ which are not in the present set. The perhaps simplest approach is to assume we can parametrize our function in terms of a polynomial of degree $n-1$ with $n$ points, that is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"y=y(x) \\rightarrow y(x_i)=\\tilde{y}_i+\\epsilon_i=\\sum_{j=0}^{n-1} \\beta_j x_i^j+\\epsilon_i,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $\\epsilon_i$ is the error in our approximation.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Rewriting the fitting procedure as a linear algebra problem\n",
"For every set of values $y_i,x_i$ we have thus the corresponding set of equations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{align*}\n",
"y_0&=\\beta_0+\\beta_1x_0^1+\\beta_2x_0^2+\\dots+\\beta_{n-1}x_0^{n-1}+\\epsilon_0\\\\\n",
"y_1&=\\beta_0+\\beta_1x_1^1+\\beta_2x_1^2+\\dots+\\beta_{n-1}x_1^{n-1}+\\epsilon_1\\\\\n",
"y_2&=\\beta_0+\\beta_1x_2^1+\\beta_2x_2^2+\\dots+\\beta_{n-1}x_2^{n-1}+\\epsilon_2\\\\\n",
"\\dots & \\dots \\\\\n",
"y_{n-1}&=\\beta_0+\\beta_1x_{n-1}^1+\\beta_2x_{n-1}^2+\\dots+\\beta_{n-1}x_{n-1}^{n-1}+\\epsilon_{n-1}.\\\\\n",
"\\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rewriting the fitting procedure as a linear algebra problem, more details\n",
"Defining the vectors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{y} = [y_0,y_1, y_2,\\dots, y_{n-1}]^T,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\beta} = [\\beta_0,\\beta_1, \\beta_2,\\dots, \\beta_{n-1}]^T,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\epsilon} = [\\epsilon_0,\\epsilon_1, \\epsilon_2,\\dots, \\epsilon_{n-1}]^T,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and the design matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}=\n",
"\\begin{bmatrix} \n",
"1& x_{0}^1 &x_{0}^2& \\dots & \\dots &x_{0}^{n-1}\\\\\n",
"1& x_{1}^1 &x_{1}^2& \\dots & \\dots &x_{1}^{n-1}\\\\\n",
"1& x_{2}^1 &x_{2}^2& \\dots & \\dots &x_{2}^{n-1}\\\\ \n",
"\\dots& \\dots &\\dots& \\dots & \\dots &\\dots\\\\\n",
"1& x_{n-1}^1 &x_{n-1}^2& \\dots & \\dots &x_{n-1}^{n-1}\\\\\n",
"\\end{bmatrix}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"we can rewrite our equations as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta}+\\boldsymbol{\\epsilon}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above design matrix is called a [Vandermonde matrix](https://en.wikipedia.org/wiki/Vandermonde_matrix).\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Generalizing the fitting procedure as a linear algebra problem\n",
"\n",
"We are obviously not limited to the above polynomial expansions. We\n",
"could replace the various powers of $x$ with elements of Fourier\n",
"series or instead of $x_i^j$ we could have $\\cos{(j x_i)}$ or $\\sin{(j\n",
"x_i)}$, or time series or other orthogonal functions. For every set\n",
"of values $y_i,x_i$ we can then generalize the equations to"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{align*}\n",
"y_0&=\\beta_0x_{00}+\\beta_1x_{01}+\\beta_2x_{02}+\\dots+\\beta_{n-1}x_{0n-1}+\\epsilon_0\\\\\n",
"y_1&=\\beta_0x_{10}+\\beta_1x_{11}+\\beta_2x_{12}+\\dots+\\beta_{n-1}x_{1n-1}+\\epsilon_1\\\\\n",
"y_2&=\\beta_0x_{20}+\\beta_1x_{21}+\\beta_2x_{22}+\\dots+\\beta_{n-1}x_{2n-1}+\\epsilon_2\\\\\n",
"\\dots & \\dots \\\\\n",
"y_{i}&=\\beta_0x_{i0}+\\beta_1x_{i1}+\\beta_2x_{i2}+\\dots+\\beta_{n-1}x_{in-1}+\\epsilon_i\\\\\n",
"\\dots & \\dots \\\\\n",
"y_{n-1}&=\\beta_0x_{n-1,0}+\\beta_1x_{n-1,2}+\\beta_2x_{n-1,2}+\\dots+\\beta_{n-1}x_{n-1,n-1}+\\epsilon_{n-1}.\\\\\n",
"\\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note that we have $p=n$ here. The matrix is symmetric. This is generally not the case!**\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Generalizing the fitting procedure as a linear algebra problem\n",
"We redefine in turn the matrix $\\boldsymbol{X}$ as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}=\n",
"\\begin{bmatrix} \n",
"x_{00}& x_{01} &x_{02}& \\dots & \\dots &x_{0,n-1}\\\\\n",
"x_{10}& x_{11} &x_{12}& \\dots & \\dots &x_{1,n-1}\\\\\n",
"x_{20}& x_{21} &x_{22}& \\dots & \\dots &x_{2,n-1}\\\\ \n",
"\\dots& \\dots &\\dots& \\dots & \\dots &\\dots\\\\\n",
"x_{n-1,0}& x_{n-1,1} &x_{n-1,2}& \\dots & \\dots &x_{n-1,n-1}\\\\\n",
"\\end{bmatrix}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and without loss of generality we rewrite again our equations as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta}+\\boldsymbol{\\epsilon}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The left-hand side of this equation is kwown. Our error vector $\\boldsymbol{\\epsilon}$ and the parameter vector $\\boldsymbol{\\beta}$ are our unknow quantities. How can we obtain the optimal set of $\\beta_i$ values?\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Optimizing our parameters\n",
"We have defined the matrix $\\boldsymbol{X}$ via the equations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{align*}\n",
"y_0&=\\beta_0x_{00}+\\beta_1x_{01}+\\beta_2x_{02}+\\dots+\\beta_{n-1}x_{0n-1}+\\epsilon_0\\\\\n",
"y_1&=\\beta_0x_{10}+\\beta_1x_{11}+\\beta_2x_{12}+\\dots+\\beta_{n-1}x_{1n-1}+\\epsilon_1\\\\\n",
"y_2&=\\beta_0x_{20}+\\beta_1x_{21}+\\beta_2x_{22}+\\dots+\\beta_{n-1}x_{2n-1}+\\epsilon_1\\\\\n",
"\\dots & \\dots \\\\\n",
"y_{i}&=\\beta_0x_{i0}+\\beta_1x_{i1}+\\beta_2x_{i2}+\\dots+\\beta_{n-1}x_{in-1}+\\epsilon_1\\\\\n",
"\\dots & \\dots \\\\\n",
"y_{n-1}&=\\beta_0x_{n-1,0}+\\beta_1x_{n-1,2}+\\beta_2x_{n-1,2}+\\dots+\\beta_{n-1}x_{n-1,n-1}+\\epsilon_{n-1}.\\\\\n",
"\\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we noted above, we stayed with a system with the design matrix \n",
" $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times n}$, that is we have $p=n$. For reasons to come later (algorithmic arguments) we will hereafter define \n",
"our matrix as $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$, with the predictors refering to the column numbers and the entries $n$ being the row elements.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Our model for the nuclear binding energies\n",
"\n",
"In our [introductory notes](https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/html/How2ReadData.html) we looked at the so-called [liquid drop model](https://en.wikipedia.org/wiki/Semi-empirical_mass_formula). Let us remind ourselves about what we did by looking at the code.\n",
"\n",
"We restate the parts of the code we are most interested in."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'DataFiles/MassEval2016.dat'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-58d214e24281>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavefig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig_id\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\".png\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0minfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"MassEval2016.dat\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'r'\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 34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'DataFiles/MassEval2016.dat'"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"# Common imports\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import display\n",
"import os\n",
"\n",
"# Where to save the figures and data files\n",
"PROJECT_ROOT_DIR = \"Results\"\n",
"FIGURE_ID = \"Results/FigureFiles\"\n",
"DATA_ID = \"DataFiles/\"\n",
"\n",
"if not os.path.exists(PROJECT_ROOT_DIR):\n",
" os.mkdir(PROJECT_ROOT_DIR)\n",
"\n",
"if not os.path.exists(FIGURE_ID):\n",
" os.makedirs(FIGURE_ID)\n",
"\n",
"if not os.path.exists(DATA_ID):\n",
" os.makedirs(DATA_ID)\n",
"\n",
"def image_path(fig_id):\n",
" return os.path.join(FIGURE_ID, fig_id)\n",
"\n",
"def data_path(dat_id):\n",
" return os.path.join(DATA_ID, dat_id)\n",
"\n",
"def save_fig(fig_id):\n",
" plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
"\n",
"infile = open(data_path(\"MassEval2016.dat\"),'r')\n",
"\n",
"\n",
"# Read the experimental data with Pandas\n",
"Masses = pd.read_fwf(infile, usecols=(2,3,4,6,11),\n",
" names=('N', 'Z', 'A', 'Element', 'Ebinding'),\n",
" widths=(1,3,5,5,5,1,3,4,1,13,11,11,9,1,2,11,9,1,3,1,12,11,1),\n",
" header=39,\n",
" index_col=False)\n",
"\n",
"# Extrapolated values are indicated by '#' in place of the decimal place, so\n",
"# the Ebinding column won't be numeric. Coerce to float and drop these entries.\n",
"Masses['Ebinding'] = pd.to_numeric(Masses['Ebinding'], errors='coerce')\n",
"Masses = Masses.dropna()\n",
"# Convert from keV to MeV.\n",
"Masses['Ebinding'] /= 1000\n",
"\n",
"# Group the DataFrame by nucleon number, A.\n",
"Masses = Masses.groupby('A')\n",
"# Find the rows of the grouped DataFrame with the maximum binding energy.\n",
"Masses = Masses.apply(lambda t: t[t.Ebinding==t.Ebinding.max()])\n",
"A = Masses['A']\n",
"Z = Masses['Z']\n",
"N = Masses['N']\n",
"Element = Masses['Element']\n",
"Energies = Masses['Ebinding']\n",
"\n",
"# Now we set up the design matrix X\n",
"X = np.zeros((len(A),5))\n",
"X[:,0] = 1\n",
"X[:,1] = A\n",
"X[:,2] = A**(2.0/3.0)\n",
"X[:,3] = A**(-1.0/3.0)\n",
"X[:,4] = A**(-1.0)\n",
"# Then nice printout using pandas\n",
"DesignMatrix = pd.DataFrame(X)\n",
"DesignMatrix.index = A\n",
"DesignMatrix.columns = ['1', 'A', 'A^(2/3)', 'A^(-1/3)', '1/A']\n",
"display(DesignMatrix)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With $\\boldsymbol{\\beta}\\in {\\mathbb{R}}^{p\\times 1}$, it means that we will hereafter write our equations for the approximation as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\tilde{y}}= \\boldsymbol{X}\\boldsymbol{\\beta},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"throughout these lectures. \n",
"\n",
"\n",
"\n",
"## Optimizing our parameters, more details\n",
"With the above we use the design matrix to define the approximation $\\boldsymbol{\\tilde{y}}$ via the unknown quantity $\\boldsymbol{\\beta}$ as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\tilde{y}}= \\boldsymbol{X}\\boldsymbol{\\beta},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and in order to find the optimal parameters $\\beta_i$ instead of solving the above linear algebra problem, we define a function which gives a measure of the spread between the values $y_i$ (which represent hopefully the exact values) and the parameterized values $\\tilde{y}_i$, namely"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"C(\\boldsymbol{\\beta})=\\frac{1}{n}\\sum_{i=0}^{n-1}\\left(y_i-\\tilde{y}_i\\right)^2=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)\\right\\},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or using the matrix $\\boldsymbol{X}$ and in a more compact matrix-vector notation as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"C(\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This function is one possible way to define the so-called cost function.\n",
"\n",
"\n",
"\n",
"It is also common to define\n",
"the function $C$ as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"C(\\boldsymbol{\\beta})=\\frac{1}{2n}\\sum_{i=0}^{n-1}\\left(y_i-\\tilde{y}_i\\right)^2,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"since when taking the first derivative with respect to the unknown parameters $\\beta$, the factor of $2$ cancels out.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Interpretations and optimizing our parameters\n",
"\n",
"The function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"C(\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"can be linked to the variance of the quantity $y_i$ if we interpret the latter as the mean value. \n",
"When linking (see the discussion below) with the maximum likelihood approach below, we will indeed interpret $y_i$ as a mean value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"y_{i}=\\langle y_i \\rangle = \\beta_0x_{i,0}+\\beta_1x_{i,1}+\\beta_2x_{i,2}+\\dots+\\beta_{n-1}x_{i,n-1}+\\epsilon_i,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $\\langle y_i \\rangle$ is the mean value. Keep in mind also that\n",
"till now we have treated $y_i$ as the exact value. Normally, the\n",
"response (dependent or outcome) variable $y_i$ the outcome of a\n",
"numerical experiment or another type of experiment and is thus only an\n",
"approximation to the true value. It is then always accompanied by an\n",
"error estimate, often limited to a statistical error estimate given by\n",
"the standard deviation discussed earlier. In the discussion here we\n",
"will treat $y_i$ as our exact value for the response variable.\n",
"\n",
"In order to find the parameters $\\beta_i$ we will then minimize the spread of $C(\\boldsymbol{\\beta})$, that is we are going to solve the problem"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in\n",
"{\\mathbb{R}}^{p}}}\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In practical terms it means we will require"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\beta_j} = \\frac{\\partial }{\\partial \\beta_j}\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}\\right)^2\\right]=0,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"which results in"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\beta_j} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}x_{ij}\\left(y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}\\right)\\right]=0,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or in a matrix-vector form as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right).\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interpretations and optimizing our parameters\n",
"We can rewrite"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial C(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right),\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}^T\\boldsymbol{y} = \\boldsymbol{X}^T\\boldsymbol{X}\\boldsymbol{\\beta},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and if the matrix $\\boldsymbol{X}^T\\boldsymbol{X}$ is invertible we have the solution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\beta} =\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We note also that since our design matrix is defined as $\\boldsymbol{X}\\in\n",
"{\\mathbb{R}}^{n\\times p}$, the product $\\boldsymbol{X}^T\\boldsymbol{X} \\in\n",
"{\\mathbb{R}}^{p\\times p}$. In the above case we have that $p \\ll n$,\n",
"in our case $p=5$ meaning that we end up with inverting a small\n",
"$5\\times 5$ matrix. This is a rather common situation, in many cases we end up with low-dimensional\n",
"matrices to invert. The methods discussed here and for many other\n",
"supervised learning algorithms like classification with logistic\n",
"regression or support vector machines, exhibit dimensionalities which\n",
"allow for the usage of direct linear algebra methods such as **LU** decomposition or **Singular Value Decomposition** (SVD) for finding the inverse of the matrix\n",
"$\\boldsymbol{X}^T\\boldsymbol{X}$.\n",
"\n",
"\n",
"\n",
"**Small question**: Do you think the example we have at hand here (the nuclear binding energies) can lead to problems in inverting the matrix $\\boldsymbol{X}^T\\boldsymbol{X}$? What kind of problems can we expect?\n",
"\n",
"\n",
"\n",
"\n",
"## Some useful matrix and vector expressions\n",
"\n",
"The following matrix and vector relation will be useful here and for the rest of the course. Vectors are always written as boldfaced lower case letters and \n",
"matrices as upper case boldfaced letters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2\n",
"6\n",
" \n",
"<\n",
"<\n",
"<\n",
"!\n",
"!\n",
"M\n",
"A\n",
"T\n",
"H\n",
"_\n",
"B\n",
"L\n",
"O\n",
"C\n",
"K"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2\n",
"7\n",
" \n",
"<\n",
"<\n",
"<\n",
"!\n",
"!\n",
"M\n",
"A\n",
"T\n",
"H\n",
"_\n",
"B\n",
"L\n",
"O\n",
"C\n",
"K"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2\n",
"8\n",
" \n",
"<\n",
"<\n",
"<\n",
"!\n",
"!\n",
"M\n",
"A\n",
"T\n",
"H\n",
"_\n",
"B\n",
"L\n",
"O\n",
"C\n",
"K"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial \\log{\\vert\\boldsymbol{A}\\vert}}{\\partial \\boldsymbol{A}} = (\\boldsymbol{A}^{-1})^T.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interpretations and optimizing our parameters\n",
"The residuals $\\boldsymbol{\\epsilon}$ are in turn given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\epsilon} = \\boldsymbol{y}-\\boldsymbol{\\tilde{y}} = \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and with"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)= 0,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"we have"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}^T\\boldsymbol{\\epsilon}=\\boldsymbol{X}^T\\left( \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)= 0,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"meaning that the solution for $\\boldsymbol{\\beta}$ is the one which minimizes the residuals. Later we will link this with the maximum likelihood approach.\n",
"\n",
"\n",
"\n",
"\n",
"Let us now return to our nuclear binding energies and simply code the above equations. \n",
"\n",
"\n",
"## Own code for Ordinary Least Squares\n",
"\n",
"It is rather straightforward to implement the matrix inversion and obtain the parameters $\\boldsymbol{\\beta}$. After having defined the matrix $\\boldsymbol{X}$ we simply need to \n",
"write"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# matrix inversion to find beta\n",
"beta = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Energies)\n",
"# and then make the prediction\n",
"ytilde = X @ beta"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, you can use the least squares functionality in **Numpy** as"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"fit = np.linalg.lstsq(X, Energies, rcond =None)[0]\n",
"ytildenp = np.dot(fit,X.T)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And finally we plot our fit with and compare with data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"Masses['Eapprox'] = ytilde\n",
"# Generate a plot comparing the experimental with the fitted values values.\n",
"fig, ax = plt.subplots()\n",
"ax.set_xlabel(r'$A = N + Z$')\n",
"ax.set_ylabel(r'$E_\\mathrm{bind}\\,/\\mathrm{MeV}$')\n",
"ax.plot(Masses['A'], Masses['Ebinding'], alpha=0.7, lw=2,\n",
" label='Ame2016')\n",
"ax.plot(Masses['A'], Masses['Eapprox'], alpha=0.7, lw=2, c='m',\n",
" label='Fit')\n",
"ax.legend()\n",
"save_fig(\"Masses2016OLS\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Adding error analysis and training set up\n",
"\n",
"We can easily test our fit by computing the $R2$ score that we discussed in connection with the functionality of **Scikit-Learn** in the introductory slides.\n",
"Since we are not using **Scikit-Learn** here we can define our own $R2$ function as"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"def R2(y_data, y_model):\n",
" return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and we would be using it as"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"print(R2(Energies,ytilde))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can easily add our **MSE** score as"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"def MSE(y_data,y_model):\n",
" n = np.size(y_model)\n",
" return np.sum((y_data-y_model)**2)/n\n",
"\n",
"print(MSE(Energies,ytilde))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and finally the relative error as"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"def RelativeError(y_data,y_model):\n",
" return abs((y_data-y_model)/y_data)\n",
"print(RelativeError(Energies, ytilde))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The $\\chi^2$ function\n",
"\n",
"Normally, the response (dependent or outcome) variable $y_i$ is the\n",
"outcome of a numerical experiment or another type of experiment and is\n",
"thus only an approximation to the true value. It is then always\n",
"accompanied by an error estimate, often limited to a statistical error\n",
"estimate given by the standard deviation discussed earlier. In the\n",
"discussion here we will treat $y_i$ as our exact value for the\n",
"response variable.\n",
"\n",
"Introducing the standard deviation $\\sigma_i$ for each measurement\n",
"$y_i$, we define now the $\\chi^2$ function (omitting the $1/n$ term)\n",
"as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\chi^2(\\boldsymbol{\\beta})=\\frac{1}{n}\\sum_{i=0}^{n-1}\\frac{\\left(y_i-\\tilde{y}_i\\right)^2}{\\sigma_i^2}=\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)^T\\frac{1}{\\boldsymbol{\\Sigma^2}}\\left(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}}\\right)\\right\\},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where the matrix $\\boldsymbol{\\Sigma}$ is a diagonal matrix with $\\sigma_i$ as matrix elements.\n",
"\n",
"\n",
"\n",
"\n",
"## The $\\chi^2$ function\n",
"\n",
"In order to find the parameters $\\beta_i$ we will then minimize the spread of $\\chi^2(\\boldsymbol{\\beta})$ by requiring"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_j} = \\frac{\\partial }{\\partial \\beta_j}\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(\\frac{y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}}{\\sigma_i}\\right)^2\\right]=0,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"which results in"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_j} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}\\frac{x_{ij}}{\\sigma_i}\\left(\\frac{y_i-\\beta_0x_{i,0}-\\beta_1x_{i,1}-\\beta_2x_{i,2}-\\dots-\\beta_{n-1}x_{i,n-1}}{\\sigma_i}\\right)\\right]=0,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or in a matrix-vector form as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{A}^T\\left( \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{\\beta}\\right).\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where we have defined the matrix $\\boldsymbol{A} =\\boldsymbol{X}/\\boldsymbol{\\Sigma}$ with matrix elements $a_{ij} = x_{ij}/\\sigma_i$ and the vector $\\boldsymbol{b}$ with elements $b_i = y_i/\\sigma_i$.\n",
"\n",
"\n",
"\n",
"\n",
"## The $\\chi^2$ function\n",
"\n",
"We can rewrite"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\boldsymbol{\\beta}} = 0 = \\boldsymbol{A}^T\\left( \\boldsymbol{b}-\\boldsymbol{A}\\boldsymbol{\\beta}\\right),\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{A}^T\\boldsymbol{b} = \\boldsymbol{A}^T\\boldsymbol{A}\\boldsymbol{\\beta},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and if the matrix $\\boldsymbol{A}^T\\boldsymbol{A}$ is invertible we have the solution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\beta} =\\left(\\boldsymbol{A}^T\\boldsymbol{A}\\right)^{-1}\\boldsymbol{A}^T\\boldsymbol{b}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The $\\chi^2$ function\n",
"\n",
"If we then introduce the matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{H} = \\left(\\boldsymbol{A}^T\\boldsymbol{A}\\right)^{-1},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"we have then the following expression for the parameters $\\beta_j$ (the matrix elements of $\\boldsymbol{H}$ are $h_{ij}$)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\beta_j = \\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}\\frac{y_i}{\\sigma_i}\\frac{x_{ik}}{\\sigma_i} = \\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}b_ia_{ik}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We state without proof the expression for the uncertainty in the parameters $\\beta_j$ as (we leave this as an exercise)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\sigma^2(\\beta_j) = \\sum_{i=0}^{n-1}\\sigma_i^2\\left( \\frac{\\partial \\beta_j}{\\partial y_i}\\right)^2,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"resulting in"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\sigma^2(\\beta_j) = \\left(\\sum_{k=0}^{p-1}h_{jk}\\sum_{i=0}^{n-1}a_{ik}\\right)\\left(\\sum_{l=0}^{p-1}h_{jl}\\sum_{m=0}^{n-1}a_{ml}\\right) = h_{jj}!\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The $\\chi^2$ function\n",
"The first step here is to approximate the function $y$ with a first-order polynomial, that is we write"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"y=y(x) \\rightarrow y(x_i) \\approx \\beta_0+\\beta_1 x_i.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By computing the derivatives of $\\chi^2$ with respect to $\\beta_0$ and $\\beta_1$ show that these are given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_0} = -2\\left[ \\frac{1}{n}\\sum_{i=0}^{n-1}\\left(\\frac{y_i-\\beta_0-\\beta_1x_{i}}{\\sigma_i^2}\\right)\\right]=0,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial \\chi^2(\\boldsymbol{\\beta})}{\\partial \\beta_1} = -\\frac{2}{n}\\left[ \\sum_{i=0}^{n-1}x_i\\left(\\frac{y_i-\\beta_0-\\beta_1x_{i}}{\\sigma_i^2}\\right)\\right]=0.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The $\\chi^2$ function\n",
"\n",
"For a linear fit (a first-order polynomial) we don't need to invert a matrix!! \n",
"Defining"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\gamma = \\sum_{i=0}^{n-1}\\frac{1}{\\sigma_i^2},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\gamma_x = \\sum_{i=0}^{n-1}\\frac{x_{i}}{\\sigma_i^2},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\gamma_y = \\sum_{i=0}^{n-1}\\left(\\frac{y_i}{\\sigma_i^2}\\right),\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\gamma_{xx} = \\sum_{i=0}^{n-1}\\frac{x_ix_{i}}{\\sigma_i^2},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\gamma_{xy} = \\sum_{i=0}^{n-1}\\frac{y_ix_{i}}{\\sigma_i^2},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"we obtain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\beta_0 = \\frac{\\gamma_{xx}\\gamma_y-\\gamma_x\\gamma_y}{\\gamma\\gamma_{xx}-\\gamma_x^2},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\beta_1 = \\frac{\\gamma_{xy}\\gamma-\\gamma_x\\gamma_y}{\\gamma\\gamma_{xx}-\\gamma_x^2}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This approach (different linear and non-linear regression) suffers\n",
"often from both being underdetermined and overdetermined in the\n",
"unknown coefficients $\\beta_i$. A better approach is to use the\n",
"Singular Value Decomposition (SVD) method discussed below. Or using\n",
"Lasso and Ridge regression. See below.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Fitting an Equation of State for Dense Nuclear Matter\n",
"\n",
"Before we continue, let us introduce yet another example. We are going to fit the\n",
"nuclear equation of state using results from many-body calculations.\n",
"The equation of state we have made available here, as function of\n",
"density, has been derived using modern nucleon-nucleon potentials with\n",
"[the addition of three-body\n",
"forces](https://www.sciencedirect.com/science/article/pii/S0370157399001106). This\n",
"time the file is presented as a standard **csv** file.\n",
"\n",
"The beginning of the Python code here is similar to what you have seen\n",
"before, with the same initializations and declarations. We use also\n",
"**pandas** again, rather extensively in order to organize our data.\n",
"\n",
"The difference now is that we use **Scikit-Learn's** regression tools\n",
"instead of our own matrix inversion implementation. Furthermore, we\n",
"sneak in **Ridge** regression (to be discussed below) which includes a\n",
"hyperparameter $\\lambda$, also to be explained below.\n",
"\n",
"\n",
"## The code"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# Common imports\n",
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.pyplot as plt\n",
"import sklearn.linear_model as skl\n",
"from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
"\n",
"# Where to save the figures and data files\n",
"PROJECT_ROOT_DIR = \"Results\"\n",
"FIGURE_ID = \"Results/FigureFiles\"\n",
"DATA_ID = \"DataFiles/\"\n",
"\n",
"if not os.path.exists(PROJECT_ROOT_DIR):\n",
" os.mkdir(PROJECT_ROOT_DIR)\n",
"\n",
"if not os.path.exists(FIGURE_ID):\n",
" os.makedirs(FIGURE_ID)\n",
"\n",
"if not os.path.exists(DATA_ID):\n",
" os.makedirs(DATA_ID)\n",
"\n",
"def image_path(fig_id):\n",
" return os.path.join(FIGURE_ID, fig_id)\n",
"\n",
"def data_path(dat_id):\n",
" return os.path.join(DATA_ID, dat_id)\n",
"\n",
"def save_fig(fig_id):\n",
" plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
"\n",
"infile = open(data_path(\"EoS.csv\"),'r')\n",
"\n",
"# Read the EoS data as csv file and organize the data into two arrays with density and energies\n",
"EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n",
"EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n",
"EoS = EoS.dropna()\n",
"Energies = EoS['Energy']\n",
"Density = EoS['Density']\n",
"# The design matrix now as function of various polytrops\n",
"X = np.zeros((len(Density),4))\n",
"X[:,3] = Density**(4.0/3.0)\n",
"X[:,2] = Density\n",
"X[:,1] = Density**(2.0/3.0)\n",
"X[:,0] = 1\n",
"\n",
"# We use now Scikit-Learn's linear regressor and ridge regressor\n",
"# OLS part\n",
"clf = skl.LinearRegression().fit(X, Energies)\n",
"ytilde = clf.predict(X)\n",
"EoS['Eols'] = ytilde\n",
"# The mean squared error \n",
"print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, ytilde))\n",
"# Explained variance score: 1 is perfect prediction \n",
"print('Variance score: %.2f' % r2_score(Energies, ytilde))\n",
"# Mean absolute error \n",
"print('Mean absolute error: %.2f' % mean_absolute_error(Energies, ytilde))\n",
"print(clf.coef_, clf.intercept_)\n",
"\n",
"# The Ridge regression with a hyperparameter lambda = 0.1\n",
"_lambda = 0.1\n",
"clf_ridge = skl.Ridge(alpha=_lambda).fit(X, Energies)\n",
"yridge = clf_ridge.predict(X)\n",
"EoS['Eridge'] = yridge\n",
"# The mean squared error \n",
"print(\"Mean squared error: %.2f\" % mean_squared_error(Energies, yridge))\n",
"# Explained variance score: 1 is perfect prediction \n",
"print('Variance score: %.2f' % r2_score(Energies, yridge))\n",
"# Mean absolute error \n",
"print('Mean absolute error: %.2f' % mean_absolute_error(Energies, yridge))\n",
"print(clf_ridge.coef_, clf_ridge.intercept_)\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.set_xlabel(r'$\\rho[\\mathrm{fm}^{-3}]$')\n",
"ax.set_ylabel(r'Energy per particle')\n",
"ax.plot(EoS['Density'], EoS['Energy'], alpha=0.7, lw=2,\n",
" label='Theoretical data')\n",
"ax.plot(EoS['Density'], EoS['Eols'], alpha=0.7, lw=2, c='m',\n",
" label='OLS')\n",
"ax.plot(EoS['Density'], EoS['Eridge'], alpha=0.7, lw=2, c='g',\n",
" label='Ridge $\\lambda = 0.1$')\n",
"ax.legend()\n",
"save_fig(\"EoSfitting\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above simple polynomial in density $\\rho$ gives an excellent fit\n",
"to the data. \n",
"\n",
"We note also that there is a small deviation between the\n",
"standard OLS and the Ridge regression at higher densities. We discuss this in more detail\n",
"below.\n",
"\n",
"\n",
"\n",
"## Splitting our Data in Training and Test data\n",
"\n",
"It is normal in essentially all Machine Learning studies to split the\n",
"data in a training set and a test set (sometimes also an additional\n",
"validation set). **Scikit-Learn** has an own function for this. There\n",
"is no explicit recipe for how much data should be included as training\n",
"data and say test data. An accepted rule of thumb is to use\n",
"approximately $2/3$ to $4/5$ of the data as training data. We will\n",
"postpone a discussion of this splitting to the end of these notes and\n",
"our discussion of the so-called **bias-variance** tradeoff. Here we\n",
"limit ourselves to repeat the above equation of state fitting example\n",
"but now splitting the data into a training set and a test set."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import train_test_split\n",
"# Where to save the figures and data files\n",
"PROJECT_ROOT_DIR = \"Results\"\n",
"FIGURE_ID = \"Results/FigureFiles\"\n",
"DATA_ID = \"DataFiles/\"\n",
"\n",
"if not os.path.exists(PROJECT_ROOT_DIR):\n",
" os.mkdir(PROJECT_ROOT_DIR)\n",
"\n",
"if not os.path.exists(FIGURE_ID):\n",
" os.makedirs(FIGURE_ID)\n",
"\n",
"if not os.path.exists(DATA_ID):\n",
" os.makedirs(DATA_ID)\n",
"\n",
"def image_path(fig_id):\n",
" return os.path.join(FIGURE_ID, fig_id)\n",
"\n",
"def data_path(dat_id):\n",
" return os.path.join(DATA_ID, dat_id)\n",
"\n",
"def save_fig(fig_id):\n",
" plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
"\n",
"def R2(y_data, y_model):\n",
" return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)\n",
"def MSE(y_data,y_model):\n",
" n = np.size(y_model)\n",
" return np.sum((y_data-y_model)**2)/n\n",
"\n",
"infile = open(data_path(\"EoS.csv\"),'r')\n",
"\n",
"# Read the EoS data as csv file and organized into two arrays with density and energies\n",
"EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n",
"EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n",
"EoS = EoS.dropna()\n",
"Energies = EoS['Energy']\n",
"Density = EoS['Density']\n",
"# The design matrix now as function of various polytrops\n",
"X = np.zeros((len(Density),5))\n",
"X[:,0] = 1\n",
"X[:,1] = Density**(2.0/3.0)\n",
"X[:,2] = Density\n",
"X[:,3] = Density**(4.0/3.0)\n",
"X[:,4] = Density**(5.0/3.0)\n",
"# We split the data in test and training data\n",
"X_train, X_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2)\n",
"# matrix inversion to find beta\n",
"beta = np.linalg.inv(X_train.T.dot(X_train)).dot(X_train.T).dot(y_train)\n",
"# and then make the prediction\n",
"ytilde = X_train @ beta\n",
"print(\"Training R2\")\n",
"print(R2(y_train,ytilde))\n",
"print(\"Training MSE\")\n",
"print(MSE(y_train,ytilde))\n",
"ypredict = X_test @ beta\n",
"print(\"Test R2\")\n",
"print(R2(y_test,ypredict))\n",
"print(\"Test MSE\")\n",
"print(MSE(y_test,ypredict))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Boston housing data example\n",
"\n",
"The Boston housing \n",
"data set was originally a part of UCI Machine Learning Repository\n",
"and has been removed now. The data set is now included in **Scikit-Learn**'s \n",
"library. There are 506 samples and 13 feature (predictor) variables\n",
"in this data set. The objective is to predict the value of prices of\n",
"the house using the features (predictors) listed here.\n",
"\n",
"The features/predictors are\n",
"1. CRIM: Per capita crime rate by town\n",
"\n",
"2. ZN: Proportion of residential land zoned for lots over 25000 square feet\n",
"\n",
"3. INDUS: Proportion of non-retail business acres per town\n",
"\n",
"4. CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
"\n",
"5. NOX: Nitric oxide concentration (parts per 10 million)\n",
"\n",
"6. RM: Average number of rooms per dwelling\n",
"\n",
"7. AGE: Proportion of owner-occupied units built prior to 1940\n",
"\n",
"8. DIS: Weighted distances to five Boston employment centers\n",
"\n",
"9. RAD: Index of accessibility to radial highways\n",
"\n",
"10. TAX: Full-value property tax rate per USD10000\n",
"\n",
"11. B: $1000(Bk - 0.63)^2$, where $Bk$ is the proportion of [people of African American descent] by town\n",
"\n",
"12. LSTAT: Percentage of lower status of the population\n",
"\n",
"13. MEDV: Median value of owner-occupied homes in USD 1000s\n",
"\n",
"## Housing data, the code\n",
"We start by importing the libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt \n",
"\n",
"import pandas as pd \n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and load the Boston Housing DataSet from **Scikit-Learn**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"from sklearn.datasets import load_boston\n",
"\n",
"boston_dataset = load_boston()\n",
"\n",
"# boston_dataset is a dictionary\n",
"# let's check what it contains\n",
"boston_dataset.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we invoke Pandas"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"boston = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n",
"boston.head()\n",
"boston['MEDV'] = boston_dataset.target"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and preprocess the data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# check for missing values in all the columns\n",
"boston.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can then visualize the data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# set the size of the figure\n",
"sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
"\n",
"# plot a histogram showing the distribution of the target values\n",
"sns.distplot(boston['MEDV'], bins=30)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is now useful to look at the correlation matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# compute the pair wise correlation for all columns \n",
"correlation_matrix = boston.corr().round(2)\n",
"# use the heatmap function from seaborn to plot the correlation matrix\n",
"# annot = True to print the values inside the square\n",
"sns.heatmap(data=correlation_matrix, annot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the above coorelation plot we can see that **MEDV** is strongly correlated to **LSTAT** and **RM**. We see also that **RAD** and **TAX** are stronly correlated, but we don't include this in our features together to avoid multi-colinearity"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"plt.figure(figsize=(20, 5))\n",
"\n",
"features = ['LSTAT', 'RM']\n",
"target = boston['MEDV']\n",
"\n",
"for i, col in enumerate(features):\n",
" plt.subplot(1, len(features) , i+1)\n",
" x = boston[col]\n",
" y = target\n",
" plt.scatter(x, y, marker='o')\n",
" plt.title(col)\n",
" plt.xlabel(col)\n",
" plt.ylabel('MEDV')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we start training our model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"X = pd.DataFrame(np.c_[boston['LSTAT'], boston['RM']], columns = ['LSTAT','RM'])\n",
"Y = boston['MEDV']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We split the data into training and test sets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"# splits the training and test data set in 80% : 20%\n",
"# assign random_state to any value.This ensures consistency.\n",
"X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5)\n",
"print(X_train.shape)\n",
"print(X_test.shape)\n",
"print(Y_train.shape)\n",
"print(Y_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we use the linear regression functionality from **Scikit-Learn**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.metrics import mean_squared_error, r2_score\n",
"\n",
"lin_model = LinearRegression()\n",
"lin_model.fit(X_train, Y_train)\n",
"\n",
"# model evaluation for training set\n",
"\n",
"y_train_predict = lin_model.predict(X_train)\n",
"rmse = (np.sqrt(mean_squared_error(Y_train, y_train_predict)))\n",
"r2 = r2_score(Y_train, y_train_predict)\n",
"\n",
"print(\"The model performance for training set\")\n",
"print(\"--------------------------------------\")\n",
"print('RMSE is {}'.format(rmse))\n",
"print('R2 score is {}'.format(r2))\n",
"print(\"\\n\")\n",
"\n",
"# model evaluation for testing set\n",
"\n",
"y_test_predict = lin_model.predict(X_test)\n",
"# root mean square error of the model\n",
"rmse = (np.sqrt(mean_squared_error(Y_test, y_test_predict)))\n",
"\n",
"# r-squared score of the model\n",
"r2 = r2_score(Y_test, y_test_predict)\n",
"\n",
"print(\"The model performance for testing set\")\n",
"print(\"--------------------------------------\")\n",
"print('RMSE is {}'.format(rmse))\n",
"print('R2 score is {}'.format(r2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# plotting the y_test vs y_pred\n",
"# ideally should have been a straight line\n",
"plt.scatter(Y_test, y_test_predict)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reducing the number of degrees of freedom, overarching view\n",
"\n",
"Many Machine Learning problems involve thousands or even millions of\n",
"features for each training instance. Not only does this make training\n",
"extremely slow, it can also make it much harder to find a good\n",
"solution, as we will see. This problem is often referred to as the\n",
"curse of dimensionality. Fortunately, in real-world problems, it is\n",
"often possible to reduce the number of features considerably, turning\n",
"an intractable problem into a tractable one.\n",
"\n",
"Later we will discuss some of the most popular dimensionality reduction\n",
"techniques: the principal component analysis (PCA), Kernel PCA, and\n",
"Locally Linear Embedding (LLE). \n",
"\n",
"\n",
"Principal component analysis and its various variants deal with the\n",
"problem of fitting a low-dimensional [affine\n",
"subspace](https://en.wikipedia.org/wiki/Affine_space) to a set of of\n",
"data points in a high-dimensional space. With its family of methods it\n",
"is one of the most used tools in data modeling, compression and\n",
"visualization.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Preprocessing our data\n",
"\n",
"Before we proceed however, we will discuss how to preprocess our\n",
"data. Till now and in connection with our previous examples we have\n",
"not met so many cases where we are too sensitive to the scaling of our\n",
"data. Normally the data may need a rescaling and/or may be sensitive\n",
"to extreme values. Scaling the data renders our inputs much more\n",
"suitable for the algorithms we want to employ.\n",
"\n",
"**Scikit-Learn** has several functions which allow us to rescale the\n",
"data, normally resulting in much better results in terms of various\n",
"accuracy scores. The **StandardScaler** function in **Scikit-Learn**\n",
"ensures that for each feature/predictor we study the mean value is\n",
"zero and the variance is one (every column in the design/feature\n",
"matrix). This scaling has the drawback that it does not ensure that\n",
"we have a particular maximum or minimum in our data set. Another\n",
"function included in **Scikit-Learn** is the **MinMaxScaler** which\n",
"ensures that all features are exactly between $0$ and $1$. The\n",
"\n",
"\n",
"## More preprocessing\n",
"\n",
"\n",
"The **Normalizer** scales each data\n",
"point such that the feature vector has a euclidean length of one. In other words, it\n",
"projects a data point on the circle (or sphere in the case of higher dimensions) with a\n",
"radius of 1. This means every data point is scaled by a different number (by the\n",
"inverse of it’s length).\n",
"This normalization is often used when only the direction (or angle) of the data matters,\n",
"not the length of the feature vector.\n",
"\n",
"The **RobustScaler** works similarly to the StandardScaler in that it\n",
"ensures statistical properties for each feature that guarantee that\n",
"they are on the same scale. However, the RobustScaler uses the median\n",
"and quartiles, instead of mean and variance. This makes the\n",
"RobustScaler ignore data points that are very different from the rest\n",
"(like measurement errors). These odd data points are also called\n",
"outliers, and might often lead to trouble for other scaling\n",
"techniques.\n",
"\n",
"\n",
"\n",
"\n",
"## Simple preprocessing examples, Franke function and regression"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# Common imports\n",
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import sklearn.linear_model as skl\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import MinMaxScaler, StandardScaler, Normalizer\n",
"\n",
"# Where to save the figures and data files\n",
"PROJECT_ROOT_DIR = \"Results\"\n",
"FIGURE_ID = \"Results/FigureFiles\"\n",
"DATA_ID = \"DataFiles/\"\n",
"\n",
"if not os.path.exists(PROJECT_ROOT_DIR):\n",
" os.mkdir(PROJECT_ROOT_DIR)\n",
"\n",
"if not os.path.exists(FIGURE_ID):\n",
" os.makedirs(FIGURE_ID)\n",
"\n",
"if not os.path.exists(DATA_ID):\n",
" os.makedirs(DATA_ID)\n",
"\n",
"def image_path(fig_id):\n",
" return os.path.join(FIGURE_ID, fig_id)\n",
"\n",
"def data_path(dat_id):\n",
" return os.path.join(DATA_ID, dat_id)\n",
"\n",
"def save_fig(fig_id):\n",
" plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
"\n",
"\n",
"def FrankeFunction(x,y):\n",
"\tterm1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))\n",
"\tterm2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))\n",
"\tterm3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))\n",
"\tterm4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)\n",
"\treturn term1 + term2 + term3 + term4\n",
"\n",
"\n",
"def create_X(x, y, n ):\n",
"\tif len(x.shape) > 1:\n",
"\t\tx = np.ravel(x)\n",
"\t\ty = np.ravel(y)\n",
"\n",
"\tN = len(x)\n",
"\tl = int((n+1)*(n+2)/2)\t\t# Number of elements in beta\n",
"\tX = np.ones((N,l))\n",
"\n",
"\tfor i in range(1,n+1):\n",
"\t\tq = int((i)*(i+1)/2)\n",
"\t\tfor k in range(i+1):\n",
"\t\t\tX[:,q+k] = (x**(i-k))*(y**k)\n",
"\n",
"\treturn X\n",
"\n",
"\n",
"# Making meshgrid of datapoints and compute Franke's function\n",
"n = 5\n",
"N = 1000\n",
"x = np.sort(np.random.uniform(0, 1, N))\n",
"y = np.sort(np.random.uniform(0, 1, N))\n",
"z = FrankeFunction(x, y)\n",
"X = create_X(x, y, n=n) \n",
"# split in training and test data\n",
"X_train, X_test, y_train, y_test = train_test_split(X,z,test_size=0.2)\n",
"\n",
"\n",
"clf = skl.LinearRegression().fit(X_train, y_train)\n",
"\n",
"# The mean squared error and R2 score\n",
"print(\"MSE before scaling: {:.2f}\".format(mean_squared_error(clf.predict(X_test), y_test)))\n",
"print(\"R2 score before scaling {:.2f}\".format(clf.score(X_test,y_test)))\n",
"\n",
"scaler = StandardScaler()\n",
"scaler.fit(X_train)\n",
"X_train_scaled = scaler.transform(X_train)\n",
"X_test_scaled = scaler.transform(X_test)\n",
"\n",
"print(\"Feature min values before scaling:\\n {}\".format(X_train.min(axis=0)))\n",
"print(\"Feature max values before scaling:\\n {}\".format(X_train.max(axis=0)))\n",
"\n",
"print(\"Feature min values after scaling:\\n {}\".format(X_train_scaled.min(axis=0)))\n",
"print(\"Feature max values after scaling:\\n {}\".format(X_train_scaled.max(axis=0)))\n",
"\n",
"clf = skl.LinearRegression().fit(X_train_scaled, y_train)\n",
"\n",
"\n",
"print(\"MSE after scaling: {:.2f}\".format(mean_squared_error(clf.predict(X_test_scaled), y_test)))\n",
"print(\"R2 score for scaled data: {:.2f}\".format(clf.score(X_test_scaled,y_test)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The singular value decomposition\n",
"\n",
"\n",
"The examples we have looked at so far are cases where we normally can\n",
"invert the matrix $\\boldsymbol{X}^T\\boldsymbol{X}$. Using a polynomial expansion as we\n",
"did both for the masses and the fitting of the equation of state,\n",
"leads to row vectors of the design matrix which are essentially\n",
"orthogonal due to the polynomial character of our model. Obtaining the inverse of the design matrix is then often done via a so-called LU, QR or Cholesky decomposition. \n",
"\n",
"\n",
"\n",
"This may\n",
"however not the be case in general and a standard matrix inversion\n",
"algorithm based on say LU, QR or Cholesky decomposition may lead to singularities. We will see examples of this below.\n",
"\n",
"There is however a way to partially circumvent this problem and also gain some insights about the ordinary least squares approach, and later shrinkage methods like Ridge and Lasso regressions. \n",
"\n",
"This is given by the **Singular Value Decomposition** algorithm, perhaps\n",
"the most powerful linear algebra algorithm. Let us look at a\n",
"different example where we may have problems with the standard matrix\n",
"inversion algorithm. Thereafter we dive into the math of the SVD.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Linear Regression Problems\n",
"\n",
"One of the typical problems we encounter with linear regression, in particular \n",
"when the matrix $\\boldsymbol{X}$ (our so-called design matrix) is high-dimensional, \n",
"are problems with near singular or singular matrices. The column vectors of $\\boldsymbol{X}$ \n",
"may be linearly dependent, normally referred to as super-collinearity. \n",
"This means that the matrix may be rank deficient and it is basically impossible to \n",
"to model the data using linear regression. As an example, consider the matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{align*}\n",
"\\mathbf{X} & = \\left[\n",
"\\begin{array}{rrr}\n",
"1 & -1 & 2\n",
"\\\\\n",
"1 & 0 & 1\n",
"\\\\\n",
"1 & 2 & -1\n",
"\\\\\n",
"1 & 1 & 0\n",
"\\end{array} \\right]\n",
"\\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The columns of $\\boldsymbol{X}$ are linearly dependent. We see this easily since the \n",
"the first column is the row-wise sum of the other two columns. The rank (more correct,\n",
"the column rank) of a matrix is the dimension of the space spanned by the\n",
"column vectors. Hence, the rank of $\\mathbf{X}$ is equal to the number\n",
"of linearly independent columns. In this particular case the matrix has rank 2.\n",
"\n",
"Super-collinearity of an $(n \\times p)$-dimensional design matrix $\\mathbf{X}$ implies\n",
"that the inverse of the matrix $\\boldsymbol{X}^T\\boldsymbol{X}$ (the matrix we need to invert to solve the linear regression equations) is non-invertible. If we have a square matrix that does not have an inverse, we say this matrix singular. The example here demonstrates this"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{align*}\n",
"\\boldsymbol{X} & = \\left[\n",
"\\begin{array}{rr}\n",
"1 & -1\n",
"\\\\\n",
"1 & -1\n",
"\\end{array} \\right].\n",
"\\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see easily that $\\mbox{det}(\\boldsymbol{X}) = x_{11} x_{22} - x_{12} x_{21} = 1 \\times (-1) - 1 \\times (-1) = 0$. Hence, $\\mathbf{X}$ is singular and its inverse is undefined.\n",
"This is equivalent to saying that the matrix $\\boldsymbol{X}$ has at least an eigenvalue which is zero.\n",
"\n",
"\n",
"\n",
"## Fixing the singularity\n",
"\n",
"If our design matrix $\\boldsymbol{X}$ which enters the linear regression problem"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto1\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\boldsymbol{\\beta} = (\\boldsymbol{X}^{T} \\boldsymbol{X})^{-1} \\boldsymbol{X}^{T} \\boldsymbol{y},\n",
"\\label{_auto1} \\tag{1}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"has linearly dependent column vectors, we will not be able to compute the inverse\n",
"of $\\boldsymbol{X}^T\\boldsymbol{X}$ and we cannot find the parameters (estimators) $\\beta_i$. \n",
"The estimators are only well-defined if $(\\boldsymbol{X}^{T}\\boldsymbol{X})^{-1}$ exits. \n",
"This is more likely to happen when the matrix $\\boldsymbol{X}$ is high-dimensional. In this case it is likely to encounter a situation where \n",
"the regression parameters $\\beta_i$ cannot be estimated.\n",
"\n",
"A cheap *ad hoc* approach is simply to add a small diagonal component to the matrix to invert, that is we change"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}^{T} \\boldsymbol{X} \\rightarrow \\boldsymbol{X}^{T} \\boldsymbol{X}+\\lambda \\boldsymbol{I},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $\\boldsymbol{I}$ is the identity matrix. When we discuss **Ridge** regression this is actually what we end up evaluating. The parameter $\\lambda$ is called a hyperparameter. More about this later. \n",
"\n",
"\n",
"\n",
"\n",
"## Basic math of the SVD\n",
"\n",
"\n",
"From standard linear algebra we know that a square matrix $\\boldsymbol{X}$ can be diagonalized if and only it is \n",
"a so-called [normal matrix](https://en.wikipedia.org/wiki/Normal_matrix), that is if $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times n}$\n",
"we have $\\boldsymbol{X}\\boldsymbol{X}^T=\\boldsymbol{X}^T\\boldsymbol{X}$ or if $\\boldsymbol{X}\\in {\\mathbb{C}}^{n\\times n}$ we have $\\boldsymbol{X}\\boldsymbol{X}^{\\dagger}=\\boldsymbol{X}^{\\dagger}\\boldsymbol{X}$.\n",
"The matrix has then a set of eigenpairs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"(\\lambda_1,\\boldsymbol{u}_1),\\dots, (\\lambda_n,\\boldsymbol{u}_n),\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and the eigenvalues are given by the diagonal matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\Sigma}=\\mathrm{Diag}(\\lambda_1, \\dots,\\lambda_n).\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The matrix $\\boldsymbol{X}$ can be written in terms of an orthogonal/unitary transformation $\\boldsymbol{U}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X} = \\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with $\\boldsymbol{U}\\boldsymbol{U}^T=\\boldsymbol{I}$ or $\\boldsymbol{U}\\boldsymbol{U}^{\\dagger}=\\boldsymbol{I}$.\n",
"\n",
"Not all square matrices are diagonalizable. A matrix like the one discussed above"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X} = \\begin{bmatrix} \n",
"1& -1 \\\\\n",
"1& -1\\\\\n",
"\\end{bmatrix}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"is not diagonalizable, it is a so-called [defective matrix](https://en.wikipedia.org/wiki/Defective_matrix). It is easy to see that the condition\n",
"$\\boldsymbol{X}\\boldsymbol{X}^T=\\boldsymbol{X}^T\\boldsymbol{X}$ is not fulfilled. \n",
"\n",
"\n",
"\n",
"## The SVD, a Fantastic Algorithm\n",
"\n",
"\n",
"However, and this is the strength of the SVD algorithm, any general\n",
"matrix $\\boldsymbol{X}$ can be decomposed in terms of a diagonal matrix and\n",
"two orthogonal/unitary matrices. The [Singular Value Decompostion\n",
"(SVD) theorem](https://en.wikipedia.org/wiki/Singular_value_decomposition)\n",
"states that a general $m\\times n$ matrix $\\boldsymbol{X}$ can be written in\n",
"terms of a diagonal matrix $\\boldsymbol{\\Sigma}$ of dimensionality $m\\times n$\n",
"and two orthognal matrices $\\boldsymbol{U}$ and $\\boldsymbol{V}$, where the first has\n",
"dimensionality $m \\times m$ and the last dimensionality $n\\times n$.\n",
"We have then"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X} = \\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As an example, the above defective matrix can be decomposed as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X} = \\frac{1}{\\sqrt{2}}\\begin{bmatrix} 1& 1 \\\\ 1& -1\\\\ \\end{bmatrix} \\begin{bmatrix} 2& 0 \\\\ 0& 0\\\\ \\end{bmatrix} \\frac{1}{\\sqrt{2}}\\begin{bmatrix} 1& -1 \\\\ 1& 1\\\\ \\end{bmatrix}=\\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with eigenvalues $\\sigma_1=2$ and $\\sigma_2=0$. \n",
"The SVD exits always! \n",
"\n",
"The SVD\n",
"decomposition (singular values) gives eigenvalues \n",
"$\\sigma_i\\geq\\sigma_{i+1}$ for all $i$ and for dimensions larger than $i=p$, the\n",
"eigenvalues (singular values) are zero.\n",
"\n",
"In the general case, where our design matrix $\\boldsymbol{X}$ has dimension\n",
"$n\\times p$, the matrix is thus decomposed into an $n\\times n$\n",
"orthogonal matrix $\\boldsymbol{U}$, a $p\\times p$ orthogonal matrix $\\boldsymbol{V}$\n",
"and a diagonal matrix $\\boldsymbol{\\Sigma}$ with $r=\\mathrm{min}(n,p)$\n",
"singular values $\\sigma_i\\geq 0$ on the main diagonal and zeros filling\n",
"the rest of the matrix. There are at most $p$ singular values\n",
"assuming that $n > p$. In our regression examples for the nuclear\n",
"masses and the equation of state this is indeed the case, while for\n",
"the Ising model we have $p > n$. These are often cases that lead to\n",
"near singular or singular matrices.\n",
"\n",
"The columns of $\\boldsymbol{U}$ are called the left singular vectors while the columns of $\\boldsymbol{V}$ are the right singular vectors.\n",
"\n",
"\n",
"## Economy-size SVD\n",
"\n",
"If we assume that $n > p$, then our matrix $\\boldsymbol{U}$ has dimension $n\n",
"\\times n$. The last $n-p$ columns of $\\boldsymbol{U}$ become however\n",
"irrelevant in our calculations since they are multiplied with the\n",
"zeros in $\\boldsymbol{\\Sigma}$.\n",
"\n",
"The economy-size decomposition removes extra rows or columns of zeros\n",
"from the diagonal matrix of singular values, $\\boldsymbol{\\Sigma}$, along with the columns\n",
"in either $\\boldsymbol{U}$ or $\\boldsymbol{V}$ that multiply those zeros in the expression. \n",
"Removing these zeros and columns can improve execution time\n",
"and reduce storage requirements without compromising the accuracy of\n",
"the decomposition.\n",
"\n",
"If $n > p$, we keep only the first $p$ columns of $\\boldsymbol{U}$ and $\\boldsymbol{\\Sigma}$ has dimension $p\\times p$. \n",
"If $p > n$, then only the first $n$ columns of $\\boldsymbol{V}$ are computed and $\\boldsymbol{\\Sigma}$ has dimension $n\\times n$.\n",
"The $n=p$ case is obvious, we retain the full SVD. \n",
"In general the economy-size SVD leads to less FLOPS and still conserving the desired accuracy.\n",
"\n",
"\n",
"## Codes for the SVD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"# SVD inversion\n",
"def SVDinv(A):\n",
" ''' Takes as input a numpy matrix A and returns inv(A) based on singular value decomposition (SVD).\n",
" SVD is numerically more stable than the inversion algorithms provided by\n",
" numpy and scipy.linalg at the cost of being slower.\n",
" '''\n",
" U, s, VT = np.linalg.svd(A)\n",
"# print('test U')\n",
"# print( (np.transpose(U) @ U - U @np.transpose(U)))\n",
"# print('test VT')\n",
"# print( (np.transpose(VT) @ VT - VT @np.transpose(VT)))\n",
" print(U)\n",
" print(s)\n",
" print(VT)\n",
"\n",
" D = np.zeros((len(U),len(VT)))\n",
" for i in range(0,len(VT)):\n",
" D[i,i]=s[i]\n",
" UT = np.transpose(U); V = np.transpose(VT); invD = np.linalg.inv(D)\n",
" return np.matmul(V,np.matmul(invD,UT))\n",
"\n",
"\n",
"X = np.array([ [1.0, -1.0, 2.0], [1.0, 0.0, 1.0], [1.0, 2.0, -1.0], [1.0, 1.0, 0.0] ])\n",
"print(X)\n",
"A = np.transpose(X) @ X\n",
"print(A)\n",
"# Brute force inversion of super-collinear matrix\n",
"#B = np.linalg.inv(A)\n",
"#print(B)\n",
"C = SVDinv(A)\n",
"print(C)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The matrix $\\boldsymbol{X}$ has columns that are linearly dependent. The first\n",
"column is the row-wise sum of the other two columns. The rank of a\n",
"matrix (the column rank) is the dimension of space spanned by the\n",
"column vectors. The rank of the matrix is the number of linearly\n",
"independent columns, in this case just $2$. We see this from the\n",
"singular values when running the above code. Running the standard\n",
"inversion algorithm for matrix inversion with $\\boldsymbol{X}^T\\boldsymbol{X}$ results\n",
"in the program terminating due to a singular matrix.\n",
"\n",
"\n",
"\n",
"\n",
"## Mathematical Properties\n",
"\n",
"There are several interesting mathematical properties which will be\n",
"relevant when we are going to discuss the differences between say\n",
"ordinary least squares (OLS) and **Ridge** regression.\n",
"\n",
"We have from OLS that the parameters of the linear approximation are given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\tilde{y}} = \\boldsymbol{X}\\boldsymbol{\\beta} = \\boldsymbol{X}\\left(\\boldsymbol{X}^T\\boldsymbol{X}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The matrix to invert can be rewritten in terms of our SVD decomposition as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}^T\\boldsymbol{X} = \\boldsymbol{V}\\boldsymbol{\\Sigma}^T\\boldsymbol{U}^T\\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the orthogonality properties of $\\boldsymbol{U}$ we have"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}^T\\boldsymbol{X} = \\boldsymbol{V}\\boldsymbol{\\Sigma}^T\\boldsymbol{\\Sigma}\\boldsymbol{V}^T = \\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with $\\boldsymbol{D}$ being a diagonal matrix with values along the diagonal given by the singular values squared. \n",
"\n",
"This means that"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"(\\boldsymbol{X}^T\\boldsymbol{X})\\boldsymbol{V} = \\boldsymbol{V}\\boldsymbol{D},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"that is the eigenvectors of $(\\boldsymbol{X}^T\\boldsymbol{X})$ are given by the columns of the right singular matrix of $\\boldsymbol{X}$ and the eigenvalues are the squared singular values. It is easy to show (show this) that"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"(\\boldsymbol{X}\\boldsymbol{X}^T)\\boldsymbol{U} = \\boldsymbol{U}\\boldsymbol{D},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"that is, the eigenvectors of $(\\boldsymbol{X}\\boldsymbol{X})^T$ are the columns of the left singular matrix and the eigenvalues are the same. \n",
"\n",
"Going back to our OLS equation we have"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}\\boldsymbol{\\beta} = \\boldsymbol{X}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T \\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}=\\boldsymbol{U\\Sigma V^T}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T \\right)^{-1}(\\boldsymbol{U\\Sigma V^T})^T\\boldsymbol{y}=\\boldsymbol{U}\\boldsymbol{U}^T\\boldsymbol{y}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will come back to this expression when we discuss Ridge regression. \n",
"\n",
"\n",
"$$ \\tilde{y}^{OLS}=\\boldsymbol{X}\\hat{\\beta}^{OLS}=\\sum_{j=1}^p \\boldsymbol{u}_j\\boldsymbol{u}_j^T\\boldsymbol{y}$$ and for Ridge we have \n",
"\n",
"$$ \\tilde{y}^{Ridge}=\\boldsymbol{X}\\hat{\\beta}^{Ridge}=\\sum_{j=1}^p \\boldsymbol{u}_j\\frac{\\sigma_j^2}{\\sigma_j^2+\\lambda}\\boldsymbol{u}_j^T\\boldsymbol{y}$$ . \n",
"\n",
"It is indeed the economy-sized SVD, note the summation runs up tp $$p$$ only and not $$n$$. \n",
"\n",
"Here we have that $$\\boldsymbol{X} = \\boldsymbol{U}\\boldsymbol{\\Sigma}\\boldsymbol{V}^T$$, with $$\\Sigma$$ being an $$ n\\times p$$ matrix and $$\\boldsymbol{V}$$ being a $$ p\\times p$$ matrix. We also have assumed here that $$ n > p$$. \n",
"\n",
"\n",
"\n",
"## Ridge and LASSO Regression\n",
"\n",
"Let us remind ourselves about the expression for the standard Mean Squared Error (MSE) which we used to define our cost function and the equations for the ordinary least squares (OLS) method, that is \n",
"our optimization problem is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in {\\mathbb{R}}^{p}}}\\frac{1}{n}\\left\\{\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)^T\\left(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\right)\\right\\}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"or we can state it as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in\n",
"{\\mathbb{R}}^{p}}}\\frac{1}{n}\\sum_{i=0}^{n-1}\\left(y_i-\\tilde{y}_i\\right)^2=\\frac{1}{n}\\vert\\vert \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\vert\\vert_2^2,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where we have used the definition of a norm-2 vector, that is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\vert\\vert \\boldsymbol{x}\\vert\\vert_2 = \\sqrt{\\sum_i x_i^2}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By minimizing the above equation with respect to the parameters\n",
"$\\boldsymbol{\\beta}$ we could then obtain an analytical expression for the\n",
"parameters $\\boldsymbol{\\beta}$. We can add a regularization parameter $\\lambda$ by\n",
"defining a new cost function to be optimized, that is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in\n",
"{\\mathbb{R}}^{p}}}\\frac{1}{n}\\vert\\vert \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\vert\\vert_2^2+\\lambda\\vert\\vert \\boldsymbol{\\beta}\\vert\\vert_2^2\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"which leads to the Ridge regression minimization problem where we\n",
"require that $\\vert\\vert \\boldsymbol{\\beta}\\vert\\vert_2^2\\le t$, where $t$ is\n",
"a finite number larger than zero. By defining"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"C(\\boldsymbol{X},\\boldsymbol{\\beta})=\\frac{1}{n}\\vert\\vert \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\vert\\vert_2^2+\\lambda\\vert\\vert \\boldsymbol{\\beta}\\vert\\vert_1,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"we have a new optimization equation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"{\\displaystyle \\min_{\\boldsymbol{\\beta}\\in\n",
"{\\mathbb{R}}^{p}}}\\frac{1}{n}\\vert\\vert \\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta}\\vert\\vert_2^2+\\lambda\\vert\\vert \\boldsymbol{\\beta}\\vert\\vert_1\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"which leads to Lasso regression. Lasso stands for least absolute shrinkage and selection operator. \n",
"\n",
"Here we have defined the norm-1 as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\vert\\vert \\boldsymbol{x}\\vert\\vert_1 = \\sum_i \\vert x_i\\vert.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## More on Ridge Regression\n",
"\n",
"Using the matrix-vector expression for Ridge regression,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"C(\\boldsymbol{X},\\boldsymbol{\\beta})=\\frac{1}{n}\\left\\{(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta})^T(\\boldsymbol{y}-\\boldsymbol{X}\\boldsymbol{\\beta})\\right\\}+\\lambda\\boldsymbol{\\beta}^T\\boldsymbol{\\beta},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"by taking the derivatives with respect to $\\boldsymbol{\\beta}$ we obtain then\n",
"a slightly modified matrix inversion problem which for finite values\n",
"of $\\lambda$ does not suffer from singularity problems. We obtain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\beta}^{\\mathrm{Ridge}} = \\left(\\boldsymbol{X}^T\\boldsymbol{X}+\\lambda\\boldsymbol{I}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with $\\boldsymbol{I}$ being a $p\\times p$ identity matrix with the constraint that"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\sum_{i=0}^{p-1} \\beta_i^2 \\leq t,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with $t$ a finite positive number. \n",
"\n",
"We see that Ridge regression is nothing but the standard\n",
"OLS with a modified diagonal term added to $\\boldsymbol{X}^T\\boldsymbol{X}$. The\n",
"consequences, in particular for our discussion of the bias-variance tradeoff \n",
"are rather interesting.\n",
"\n",
"Furthermore, if we use the result above in terms of the SVD decomposition (our analysis was done for the OLS method), we had"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"(\\boldsymbol{X}\\boldsymbol{X}^T)\\boldsymbol{U} = \\boldsymbol{U}\\boldsymbol{D}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can analyse the OLS solutions in terms of the eigenvectors (the columns) of the right singular value matrix $\\boldsymbol{U}$ as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}\\boldsymbol{\\beta} = \\boldsymbol{X}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T \\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}=\\boldsymbol{U\\Sigma V^T}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T \\right)^{-1}(\\boldsymbol{U\\Sigma V^T})^T\\boldsymbol{y}=\\boldsymbol{U}\\boldsymbol{U}^T\\boldsymbol{y}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For Ridge regression this becomes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}\\boldsymbol{\\beta}^{\\mathrm{Ridge}} = \\boldsymbol{U\\Sigma V^T}\\left(\\boldsymbol{V}\\boldsymbol{D}\\boldsymbol{V}^T+\\lambda\\boldsymbol{I} \\right)^{-1}(\\boldsymbol{U\\Sigma V^T})^T\\boldsymbol{y}=\\sum_{j=0}^{p-1}\\boldsymbol{u}_j\\boldsymbol{u}_j^T\\frac{\\sigma_j^2}{\\sigma_j^2+\\lambda}\\boldsymbol{y},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with the vectors $\\boldsymbol{u}_j$ being the columns of $\\boldsymbol{U}$. \n",
"\n",
"\n",
"## Interpreting the Ridge results\n",
"\n",
"Since $\\lambda \\geq 0$, it means that compared to OLS, we have"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\sigma_j^2}{\\sigma_j^2+\\lambda} \\leq 1.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ridge regression finds the coordinates of $\\boldsymbol{y}$ with respect to the\n",
"orthonormal basis $\\boldsymbol{U}$, it then shrinks the coordinates by\n",
"$\\frac{\\sigma_j^2}{\\sigma_j^2+\\lambda}$. Recall that the SVD has\n",
"eigenvalues ordered in a descending way, that is $\\sigma_i \\geq\n",
"\\sigma_{i+1}$.\n",
"\n",
"For small eigenvalues $\\sigma_i$ it means that their contributions become less important, a fact which can be used to reduce the number of degrees of freedom.\n",
"Actually, calculating the variance of $\\boldsymbol{X}\\boldsymbol{v}_j$ shows that this quantity is equal to $\\sigma_j^2/n$.\n",
"With a parameter $\\lambda$ we can thus shrink the role of specific parameters. \n",
"\n",
"\n",
"\n",
"## More interpretations\n",
"\n",
"For the sake of simplicity, let us assume that the design matrix is orthonormal, that is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}^T\\boldsymbol{X}=(\\boldsymbol{X}^T\\boldsymbol{X})^{-1} =\\boldsymbol{I}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case the standard OLS results in"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\beta}^{\\mathrm{OLS}} = \\boldsymbol{X}^T\\boldsymbol{y}=\\sum_{i=0}^{p-1}\\boldsymbol{u}_j\\boldsymbol{u}_j^T\\boldsymbol{y},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\beta}^{\\mathrm{Ridge}} = \\left(\\boldsymbol{I}+\\lambda\\boldsymbol{I}\\right)^{-1}\\boldsymbol{X}^T\\boldsymbol{y}=\\left(1+\\lambda\\right)^{-1}\\boldsymbol{\\beta}^{\\mathrm{OLS}},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"that is the Ridge estimator scales the OLS estimator by the inverse of a factor $1+\\lambda$, and\n",
"the Ridge estimator converges to zero when the hyperparameter goes to\n",
"infinity.\n",
"\n",
"We will come back to more interpreations after we have gone through some of the statistical analysis part. \n",
"\n",
"For more discussions of Ridge and Lasso regression, [Wessel van Wieringen's](https://arxiv.org/abs/1509.09169) article is highly recommended.\n",
"Similarly, [Mehta et al's article](https://arxiv.org/abs/1803.08823) is also recommended.\n",
"\n",
"\n",
"\n",
"## A better understanding of regularization\n",
"\n",
"The parameter $\\lambda$ that we have introduced in the Ridge (and\n",
"Lasso as well) regression is often called a regularization parameter\n",
"or shrinkage parameter. It is common to call it a hyperparameter. What does it mean mathemtically?\n",
"\n",
"Here we will first look at how to analyze the difference between the\n",
"standard OLS equations and the Ridge expressions in terms of a linear\n",
"algebra analysis using the SVD algorithm. Thereafter, we will link\n",
"(see the material on the bias-variance tradeoff below) these\n",
"observation to the statisical analysis of the results. In particular\n",
"we consider how the variance of the parameters $\\boldsymbol{\\beta}$ is\n",
"affected by changing the parameter $\\lambda$.\n",
"\n",
"\n",
"## Decomposing the OLS and Ridge expressions\n",
"\n",
"We have our design matrix\n",
" $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$. With the SVD we decompose it as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X} = \\boldsymbol{U\\Sigma V^T},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with $\\boldsymbol{U}\\in {\\mathbb{R}}^{n\\times n}$, $\\boldsymbol{\\Sigma}\\in {\\mathbb{R}}^{n\\times p}$\n",
"and $\\boldsymbol{V}\\in {\\mathbb{R}}^{p\\times p}$.\n",
"\n",
"The matrices $\\boldsymbol{U}$ and $\\boldsymbol{V}$ are unitary/orthonormal matrices, that is in case the matrices are real we have $\\boldsymbol{U}^T\\boldsymbol{U}=\\boldsymbol{U}\\boldsymbol{U}^T=\\boldsymbol{I}$ and $\\boldsymbol{V}^T\\boldsymbol{V}=\\boldsymbol{V}\\boldsymbol{V}^T=\\boldsymbol{I}$.\n",
"\n",
"\n",
"\n",
"\n",
"## Introducing the Covariance and Correlation functions\n",
"\n",
"Before we discuss the link between for example Ridge regression and the singular value decomposition, we need to remind ourselves about\n",
"the definition of the covariance and the correlation function. These are quantities \n",
"\n",
"Suppose we have defined two vectors\n",
"$\\hat{x}$ and $\\hat{y}$ with $n$ elements each. The covariance matrix $\\boldsymbol{C}$ is defined as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{C}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n",
" \\mathrm{cov}[\\boldsymbol{y},\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{y},\\boldsymbol{y}] \\\\\n",
" \\end{bmatrix},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where for example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] =\\frac{1}{n} \\sum_{i=0}^{n-1}(x_i- \\overline{x})(y_i- \\overline{y}).\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With this definition and recalling that the variance is defined as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mathrm{var}[\\boldsymbol{x}]=\\frac{1}{n} \\sum_{i=0}^{n-1}(x_i- \\overline{x})^2,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"we can rewrite the covariance matrix as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{C}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} \\mathrm{var}[\\boldsymbol{x}] & \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n",
" \\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}] & \\mathrm{var}[\\boldsymbol{y}] \\\\\n",
" \\end{bmatrix}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The covariance takes values between zero and infinity and may thus\n",
"lead to problems with loss of numerical precision for particularly\n",
"large values. It is common to scale the covariance matrix by\n",
"introducing instead the correlation matrix defined via the so-called\n",
"correlation function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}]=\\frac{\\mathrm{cov}[\\boldsymbol{x},\\boldsymbol{y}]}{\\sqrt{\\mathrm{var}[\\boldsymbol{x}] \\mathrm{var}[\\boldsymbol{y}]}}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The correlation function is then given by values $\\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}]\n",
"\\in [-1,1]$. This avoids eventual problems with too large values. We\n",
"can then define the correlation matrix for the two vectors $\\boldsymbol{x}$\n",
"and $\\boldsymbol{y}$ as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{K}[\\boldsymbol{x},\\boldsymbol{y}] = \\begin{bmatrix} 1 & \\mathrm{corr}[\\boldsymbol{x},\\boldsymbol{y}] \\\\\n",
" \\mathrm{corr}[\\boldsymbol{y},\\boldsymbol{x}] & 1 \\\\\n",
" \\end{bmatrix},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the above example this is the function we constructed using **pandas**.\n",
"\n",
"\n",
"## Correlation Function and Design/Feature Matrix\n",
"\n",
"In our derivation of the various regression algorithms like **Ordinary Least Squares** or **Ridge regression**\n",
"we defined the design/feature matrix $\\boldsymbol{X}$ as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}=\\begin{bmatrix}\n",
"x_{0,0} & x_{0,1} & x_{0,2}& \\dots & \\dots x_{0,p-1}\\\\\n",
"x_{1,0} & x_{1,1} & x_{1,2}& \\dots & \\dots x_{1,p-1}\\\\\n",
"x_{2,0} & x_{2,1} & x_{2,2}& \\dots & \\dots x_{2,p-1}\\\\\n",
"\\dots & \\dots & \\dots & \\dots \\dots & \\dots \\\\\n",
"x_{n-2,0} & x_{n-2,1} & x_{n-2,2}& \\dots & \\dots x_{n-2,p-1}\\\\\n",
"x_{n-1,0} & x_{n-1,1} & x_{n-1,2}& \\dots & \\dots x_{n-1,p-1}\\\\\n",
"\\end{bmatrix},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$, with the predictors/features $p$ refering to the column numbers and the\n",
"entries $n$ being the row elements.\n",
"We can rewrite the design/feature matrix in terms of its column vectors as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}=\\begin{bmatrix} \\boldsymbol{x}_0 & \\boldsymbol{x}_1 & \\boldsymbol{x}_2 & \\dots & \\dots & \\boldsymbol{x}_{p-1}\\end{bmatrix},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"with a given vector"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{x}_i^T = \\begin{bmatrix}x_{0,i} & x_{1,i} & x_{2,i}& \\dots & \\dots x_{n-1,i}\\end{bmatrix}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With these definitions, we can now rewrite our $2\\times 2$\n",
"correaltion/covariance matrix in terms of a moe general design/feature\n",
"matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$. This leads to a $p\\times p$\n",
"covariance matrix for the vectors $\\boldsymbol{x}_i$ with $i=0,1,\\dots,p-1$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{C}[\\boldsymbol{x}] = \\begin{bmatrix}\n",
"\\mathrm{var}[\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_{p-1}]\\\\\n",
"\\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & \\mathrm{var}[\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_{p-1}]\\\\\n",
"\\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_1] & \\mathrm{var}[\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{cov}[\\boldsymbol{x}_2,\\boldsymbol{x}_{p-1}]\\\\\n",
"\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n",
"\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n",
"\\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_1] & \\mathrm{cov}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_{2}] & \\dots & \\dots & \\mathrm{var}[\\boldsymbol{x}_{p-1}]\\\\\n",
"\\end{bmatrix},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and the correlation matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{K}[\\boldsymbol{x}] = \\begin{bmatrix}\n",
"1 & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_0,\\boldsymbol{x}_{p-1}]\\\\\n",
"\\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & 1 & \\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_2] & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_1,\\boldsymbol{x}_{p-1}]\\\\\n",
"\\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_0] & \\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_1] & 1 & \\dots & \\dots & \\mathrm{corr}[\\boldsymbol{x}_2,\\boldsymbol{x}_{p-1}]\\\\\n",
"\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n",
"\\dots & \\dots & \\dots & \\dots & \\dots & \\dots \\\\\n",
"\\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_0] & \\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_1] & \\mathrm{corr}[\\boldsymbol{x}_{p-1},\\boldsymbol{x}_{2}] & \\dots & \\dots & 1\\\\\n",
"\\end{bmatrix},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Covariance Matrix Examples\n",
"\n",
"\n",
"The Numpy function **np.cov** calculates the covariance elements using\n",
"the factor $1/(n-1)$ instead of $1/n$ since it assumes we do not have\n",
"the exact mean values. The following simple function uses the\n",
"**np.vstack** function which takes each vector of dimension $1\\times n$\n",
"and produces a $2\\times n$ matrix $\\boldsymbol{W}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{W} = \\begin{bmatrix} x_0 & y_0 \\\\\n",
" x_1 & y_1 \\\\\n",
" x_2 & y_2\\\\\n",
" \\dots & \\dots \\\\\n",
" x_{n-2} & y_{n-2}\\\\\n",
" x_{n-1} & y_{n-1} & \n",
" \\end{bmatrix},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"which in turn is converted into into the $2\\times 2$ covariance matrix\n",
"$\\boldsymbol{C}$ via the Numpy function **np.cov()**. We note that we can also calculate\n",
"the mean value of each set of samples $\\boldsymbol{x}$ etc using the Numpy\n",
"function **np.mean(x)**. We can also extract the eigenvalues of the\n",
"covariance matrix through the **np.linalg.eig()** function."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# Importing various packages\n",
"import numpy as np\n",
"n = 100\n",
"x = np.random.normal(size=n)\n",
"print(np.mean(x))\n",
"y = 4+3*x+np.random.normal(size=n)\n",
"print(np.mean(y))\n",
"W = np.vstack((x, y))\n",
"C = np.cov(W)\n",
"print(C)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Correlation Matrix\n",
"\n",
"The previous example can be converted into the correlation matrix by\n",
"simply scaling the matrix elements with the variances. We should also\n",
"subtract the mean values for each column. This leads to the following\n",
"code which sets up the correlations matrix for the previous example in\n",
"a more brute force way. Here we scale the mean values for each column of the design matrix, calculate the relevant mean values and variances and then finally set up the $2\\times 2$ correlation matrix (since we have only two vectors)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"n = 100\n",
"# define two vectors \n",
"x = np.random.random(size=n)\n",
"y = 4+3*x+np.random.normal(size=n)\n",
"#scaling the x and y vectors \n",
"x = x - np.mean(x)\n",
"y = y - np.mean(y)\n",
"variance_x = np.sum(x@x)/n\n",
"variance_y = np.sum(y@y)/n\n",
"print(variance_x)\n",
"print(variance_y)\n",
"cov_xy = np.sum(x@y)/n\n",
"cov_xx = np.sum(x@x)/n\n",
"cov_yy = np.sum(y@y)/n\n",
"C = np.zeros((2,2))\n",
"C[0,0]= cov_xx/variance_x\n",
"C[1,1]= cov_yy/variance_y\n",
"C[0,1]= cov_xy/np.sqrt(variance_y*variance_x)\n",
"C[1,0]= C[0,1]\n",
"print(C)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that the matrix elements along the diagonal are one as they\n",
"should be and that the matrix is symmetric. Furthermore, diagonalizing\n",
"this matrix we easily see that it is a positive definite matrix.\n",
"\n",
"The above procedure with **numpy** can be made more compact if we use **pandas**.\n",
"\n",
"\n",
"## Correlation Matrix with Pandas\n",
"\n",
"We whow here how we can set up the correlation matrix using **pandas**, as done in this simple code"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"n = 10\n",
"x = np.random.normal(size=n)\n",
"x = x - np.mean(x)\n",
"y = 4+3*x+np.random.normal(size=n)\n",
"y = y - np.mean(y)\n",
"X = (np.vstack((x, y))).T\n",
"print(X)\n",
"Xpd = pd.DataFrame(X)\n",
"print(Xpd)\n",
"correlation_matrix = Xpd.corr()\n",
"print(correlation_matrix)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We expand this model to the Franke function discussed above.\n",
"\n",
"\n",
"## Correlation Matrix with Pandas and the Franke function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# Common imports\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"\n",
"def FrankeFunction(x,y):\n",
"\tterm1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))\n",
"\tterm2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))\n",
"\tterm3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))\n",
"\tterm4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)\n",
"\treturn term1 + term2 + term3 + term4\n",
"\n",
"\n",
"def create_X(x, y, n ):\n",
"\tif len(x.shape) > 1:\n",
"\t\tx = np.ravel(x)\n",
"\t\ty = np.ravel(y)\n",
"\n",
"\tN = len(x)\n",
"\tl = int((n+1)*(n+2)/2)\t\t# Number of elements in beta\n",
"\tX = np.ones((N,l))\n",
"\n",
"\tfor i in range(1,n+1):\n",
"\t\tq = int((i)*(i+1)/2)\n",
"\t\tfor k in range(i+1):\n",
"\t\t\tX[:,q+k] = (x**(i-k))*(y**k)\n",
"\n",
"\treturn X\n",
"\n",
"\n",
"# Making meshgrid of datapoints and compute Franke's function\n",
"n = 4\n",
"N = 100\n",
"x = np.sort(np.random.uniform(0, 1, N))\n",
"y = np.sort(np.random.uniform(0, 1, N))\n",
"z = FrankeFunction(x, y)\n",
"X = create_X(x, y, n=n) \n",
"\n",
"Xpd = pd.DataFrame(X)\n",
"# subtract the mean values and set up the covariance matrix\n",
"Xpd = Xpd - Xpd.mean()\n",
"covariance_matrix = Xpd.cov()\n",
"print(covariance_matrix)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We note here that the covariance is zero for the first rows and\n",
"columns since all matrix elements in the design matrix were set to one\n",
"(we are fitting the function in terms of a polynomial of degree $n$).\n",
"\n",
"This means that the variance for these elements will be zero and will\n",
"cause problems when we set up the correlation matrix. We can simply\n",
"drop these elements and construct a correlation\n",
"matrix without these elements. \n",
"\n",
"\n",
"\n",
"## Rewriting the Covariance and/or Correlation Matrix\n",
"\n",
"We can rewrite the covariance matrix in a more compact form in terms of the design/feature matrix $\\boldsymbol{X}$ as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{C}[\\boldsymbol{x}] = \\frac{1}{n}\\boldsymbol{X}^T\\boldsymbol{X}= \\mathbb{E}[\\boldsymbol{X}^T\\boldsymbol{X}].\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To see this let us simply look at a design matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{2\\times 2}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}=\\begin{bmatrix}\n",
"x_{00} & x_{01}\\\\\n",
"x_{10} & x_{11}\\\\\n",
"\\end{bmatrix}=\\begin{bmatrix}\n",
"\\boldsymbol{x}_{0} & \\boldsymbol{x}_{1}\\\\\n",
"\\end{bmatrix}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we then compute the expectation value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mathbb{E}[\\boldsymbol{X}^T\\boldsymbol{X}] = \\frac{1}{n}\\boldsymbol{X}^T\\boldsymbol{X}=\\begin{bmatrix}\n",
"x_{00}^2+x_{01}^2 & x_{00}x_{10}+x_{01}x_{11}\\\\\n",
"x_{10}x_{00}+x_{11}x_{01} & x_{10}^2+x_{11}^2\\\\\n",
"\\end{bmatrix},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"which is just"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{C}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] = \\boldsymbol{C}[\\boldsymbol{x}]=\\begin{bmatrix} \\mathrm{var}[\\boldsymbol{x}_0] & \\mathrm{cov}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] \\\\\n",
" \\mathrm{cov}[\\boldsymbol{x}_1,\\boldsymbol{x}_0] & \\mathrm{var}[\\boldsymbol{x}_1] \\\\\n",
" \\end{bmatrix},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where we wrote $$\\boldsymbol{C}[\\boldsymbol{x}_0,\\boldsymbol{x}_1] = \\boldsymbol{C}[\\boldsymbol{x}]$$ to indicate that this the covariance of the vectors $\\boldsymbol{x}$ of the design/feature matrix $\\boldsymbol{X}$.\n",
"\n",
"It is easy to generalize this to a matrix $\\boldsymbol{X}\\in {\\mathbb{R}}^{n\\times p}$.\n",
"\n",
"\n",
"\n",
"## Linking with SVD\n",
"\n",
"See lecture september 11. More text to be added here soon.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Where are we going?\n",
"\n",
"Before we proceed, we need to rethink what we have been doing. In our\n",
"eager to fit the data, we have omitted several important elements in\n",
"our regression analysis. In what follows we will\n",
"1. look at statistical properties, including a discussion of mean values, variance and the so-called bias-variance tradeoff\n",
"\n",
"2. introduce resampling techniques like cross-validation, bootstrapping and jackknife and more\n",
"\n",
"This will allow us to link the standard linear algebra methods we have discussed above to a statistical interpretation of the methods. \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Resampling methods\n",
"Resampling methods are an indispensable tool in modern\n",
"statistics. They involve repeatedly drawing samples from a training\n",
"set and refitting a model of interest on each sample in order to\n",
"obtain additional information about the fitted model. For example, in\n",
"order to estimate the variability of a linear regression fit, we can\n",
"repeatedly draw different samples from the training data, fit a linear\n",
"regression to each new sample, and then examine the extent to which\n",
"the resulting fits differ. Such an approach may allow us to obtain\n",
"information that would not be available from fitting the model only\n",
"once using the original training sample.\n",
"\n",
"Two resampling methods are often used in Machine Learning analyses,\n",
"1. The **bootstrap method**\n",
"\n",
"2. and **Cross-Validation**\n",
"\n",
"In addition there are several other methods such as the Jackknife and the Blocking methods. We will discuss in particular\n",
"cross-validation and the bootstrap method.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Resampling approaches can be computationally expensive\n",
"\n",
"Resampling approaches can be computationally expensive, because they\n",
"involve fitting the same statistical method multiple times using\n",
"different subsets of the training data. However, due to recent\n",
"advances in computing power, the computational requirements of\n",
"resampling methods generally are not prohibitive. In this chapter, we\n",
"discuss two of the most commonly used resampling methods,\n",
"cross-validation and the bootstrap. Both methods are important tools\n",
"in the practical application of many statistical learning\n",
"procedures. For example, cross-validation can be used to estimate the\n",
"test error associated with a given statistical learning method in\n",
"order to evaluate its performance, or to select the appropriate level\n",
"of flexibility. The process of evaluating a model’s performance is\n",
"known as model assessment, whereas the process of selecting the proper\n",
"level of flexibility for a model is known as model selection. The\n",
"bootstrap is widely used.\n",
"\n",
"\n",
"\n",
"\n",
"## Why resampling methods ?\n",
"**Statistical analysis.**\n",
"\n",
"\n",
"* Our simulations can be treated as *computer experiments*. This is particularly the case for Monte Carlo methods\n",
"\n",
"* The results can be analysed with the same statistical tools as we would use analysing experimental data.\n",
"\n",
"* As in all experiments, we are looking for expectation values and an estimate of how accurate they are, i.e., possible sources for errors.\n",
"\n",
" \n",
"\n",
"\n",
"## Statistical analysis\n",
"\n",
"* As in other experiments, many numerical experiments have two classes of errors:\n",
"\n",
" * Statistical errors\n",
"\n",
" * Systematical errors\n",
"\n",
"\n",
"* Statistical errors can be estimated using standard tools from statistics\n",
"\n",
"* Systematical errors are method specific and must be treated differently from case to case.\n",
"\n",
" \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Linking the regression analysis with a statistical interpretation\n",
"\n",
"\n",
"The\n",
"advantage of doing linear regression is that we actually end up with\n",
"analytical expressions for several statistical quantities. \n",
"Standard least squares and Ridge regression allow us to\n",
"derive quantities like the variance and other expectation values in a\n",
"rather straightforward way.\n",
"\n",
"\n",
"It is assumed that $\\varepsilon_i\n",
"\\sim \\mathcal{N}(0, \\sigma^2)$ and the $\\varepsilon_{i}$ are\n",
"independent, i.e.:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{align*} \n",
"\\mbox{Cov}(\\varepsilon_{i_1},\n",
"\\varepsilon_{i_2}) & = \\left\\{ \\begin{array}{lcc} \\sigma^2 & \\mbox{if}\n",
"& i_1 = i_2, \\\\ 0 & \\mbox{if} & i_1 \\not= i_2. \\end{array} \\right.\n",
"\\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The randomness of $\\varepsilon_i$ implies that\n",
"$\\mathbf{y}_i$ is also a random variable. In particular,\n",
"$\\mathbf{y}_i$ is normally distributed, because $\\varepsilon_i \\sim\n",
"\\mathcal{N}(0, \\sigma^2)$ and $\\mathbf{X}_{i,\\ast} \\, \\boldsymbol{\\beta}$ is a\n",
"non-random scalar. To specify the parameters of the distribution of\n",
"$\\mathbf{y}_i$ we need to calculate its first two moments. \n",
"\n",
"Recall that $\\boldsymbol{X}$ is a matrix of dimensionality $n\\times p$. The\n",
"notation above $\\mathbf{X}_{i,\\ast}$ means that we are looking at the\n",
"row number $i$ and perform a sum over all values $p$.\n",
"\n",
"\n",
"\n",
"## Assumptions made\n",
"\n",
"The assumption we have made here can be summarized as (and this is going to be useful when we discuss the bias-variance trade off)\n",
"that there exists a function $f(\\boldsymbol{x})$ and a normal distributed error $\\boldsymbol{\\varepsilon}\\sim \\mathcal{N}(0, \\sigma^2)$\n",
"which describe our data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{y} = f(\\boldsymbol{x})+\\boldsymbol{\\varepsilon}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We approximate this function with our model from the solution of the linear regression equations, that is our\n",
"function $f$ is approximated by $\\boldsymbol{\\tilde{y}}$ where we want to minimize $(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2$, our MSE, with"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\tilde{y}} = \\boldsymbol{X}\\boldsymbol{\\beta}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Expectation value and variance\n",
"\n",
"We can calculate the expectation value of $\\boldsymbol{y}$ for a given element $i$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{align*} \n",
"\\mathbb{E}(y_i) & =\n",
"\\mathbb{E}(\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}) + \\mathbb{E}(\\varepsilon_i)\n",
"\\, \\, \\, = \\, \\, \\, \\mathbf{X}_{i, \\ast} \\, \\beta, \n",
"\\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"while\n",
"its variance is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{align*} \\mbox{Var}(y_i) & = \\mathbb{E} \\{ [y_i\n",
"- \\mathbb{E}(y_i)]^2 \\} \\, \\, \\, = \\, \\, \\, \\mathbb{E} ( y_i^2 ) -\n",
"[\\mathbb{E}(y_i)]^2 \\\\ & = \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\,\n",
"\\beta + \\varepsilon_i )^2] - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \\\\ &\n",
"= \\mathbb{E} [ ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2 \\varepsilon_i\n",
"\\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} + \\varepsilon_i^2 ] - ( \\mathbf{X}_{i,\n",
"\\ast} \\, \\beta)^2 \\\\ & = ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 + 2\n",
"\\mathbb{E}(\\varepsilon_i) \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta} +\n",
"\\mathbb{E}(\\varepsilon_i^2 ) - ( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta})^2 \n",
"\\\\ & = \\mathbb{E}(\\varepsilon_i^2 ) \\, \\, \\, = \\, \\, \\,\n",
"\\mbox{Var}(\\varepsilon_i) \\, \\, \\, = \\, \\, \\, \\sigma^2. \n",
"\\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hence, $y_i \\sim \\mathcal{N}( \\mathbf{X}_{i, \\ast} \\, \\boldsymbol{\\beta}, \\sigma^2)$, that is $\\boldsymbol{y}$ follows a normal distribution with \n",
"mean value $\\boldsymbol{X}\\boldsymbol{\\beta}$ and variance $\\sigma^2$ (not be confused with the singular values of the SVD). \n",
"\n",
"\n",
"## Expectation value and variance for $\\boldsymbol{\\beta}$\n",
"\n",
"With the OLS expressions for the parameters $\\boldsymbol{\\beta}$ we can evaluate the expectation value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mathbb{E}(\\boldsymbol{\\beta}) = \\mathbb{E}[ (\\mathbf{X}^{\\top} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1}\\mathbf{X}^{T} \\mathbb{E}[ \\mathbf{Y}]=(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\mathbf{X}^{T}\\mathbf{X}\\boldsymbol{\\beta}=\\boldsymbol{\\beta}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This means that the estimator of the regression parameters is unbiased.\n",
"\n",
"We can also calculate the variance\n",
"\n",
"The variance of $\\boldsymbol{\\beta}$ is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{eqnarray*}\n",
"\\mbox{Var}(\\boldsymbol{\\beta}) & = & \\mathbb{E} \\{ [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})] [\\boldsymbol{\\beta} - \\mathbb{E}(\\boldsymbol{\\beta})]^{T} \\}\n",
"\\\\\n",
"& = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} - \\boldsymbol{\\beta}]^{T} \\}\n",
"\\\\\n",
"% & = & \\mathbb{E} \\{ [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}] \\, [(\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y}]^{T} \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
"% \\\\\n",
"% & = & \\mathbb{E} \\{ (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\mathbf{Y} \\, \\mathbf{Y}^{T} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
"% \\\\\n",
"& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\mathbb{E} \\{ \\mathbf{Y} \\, \\mathbf{Y}^{T} \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
"\\\\\n",
"& = & (\\mathbf{X}^{T} \\mathbf{X})^{-1} \\, \\mathbf{X}^{T} \\, \\{ \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\, \\mathbf{X}^{T} + \\sigma^2 \\} \\, \\mathbf{X} \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
"% \\\\\n",
"% & = & (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T \\, \\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^T \\, \\mathbf{X}^T \\, \\mathbf{X} \\, (\\mathbf{X}^T % \\mathbf{X})^{-1}\n",
"% \\\\\n",
"% & & + \\, \\, \\sigma^2 \\, (\\mathbf{X}^T \\mathbf{X})^{-1} \\, \\mathbf{X}^T \\, \\mathbf{X} \\, (\\mathbf{X}^T \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\boldsymbol{\\beta}^T\n",
"\\\\\n",
"& = & \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} + \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1} - \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T}\n",
"\\, \\, \\, = \\, \\, \\, \\sigma^2 \\, (\\mathbf{X}^{T} \\mathbf{X})^{-1},\n",
"\\end{eqnarray*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where we have used that $\\mathbb{E} (\\mathbf{Y} \\mathbf{Y}^{T}) =\n",
"\\mathbf{X} \\, \\boldsymbol{\\beta} \\, \\boldsymbol{\\beta}^{T} \\, \\mathbf{X}^{T} +\n",
"\\sigma^2 \\, \\mathbf{I}_{nn}$. From $\\mbox{Var}(\\boldsymbol{\\beta}) = \\sigma^2\n",
"\\, (\\mathbf{X}^{T} \\mathbf{X})^{-1}$, one obtains an estimate of the\n",
"variance of the estimate of the $j$-th regression coefficient:\n",
"$\\boldsymbol{\\sigma}^2 (\\boldsymbol{\\beta}_j ) = \\boldsymbol{\\sigma}^2 \\sqrt{\n",
"[(\\mathbf{X}^{T} \\mathbf{X})^{-1}]_{jj} }$. This may be used to\n",
"construct a confidence interval for the estimates.\n",
"\n",
"\n",
"In a similar way, we can obtain analytical expressions for say the\n",
"expectation values of the parameters $\\boldsymbol{\\beta}$ and their variance\n",
"when we employ Ridge regression, allowing us again to define a confidence interval. \n",
"\n",
"It is rather straightforward to show that"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big]=(\\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I}_{pp})^{-1} (\\mathbf{X}^{\\top} \\mathbf{X})\\boldsymbol{\\beta}^{\\mathrm{OLS}}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see clearly that \n",
"$\\mathbb{E} \\big[ \\boldsymbol{\\beta}^{\\mathrm{Ridge}} \\big] \\not= \\boldsymbol{\\beta}^{\\mathrm{OLS}}$ for any $\\lambda > 0$. We say then that the ridge estimator is biased.\n",
"\n",
"We can also compute the variance as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{Ridge}}]=\\sigma^2[ \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1} \\mathbf{X}^{T} \\mathbf{X} \\{ [ \\mathbf{X}^{\\top} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and it is easy to see that if the parameter $\\lambda$ goes to infinity then the variance of Ridge parameters $\\boldsymbol{\\beta}$ goes to zero. \n",
"\n",
"With this, we can compute the difference"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mbox{Var}[\\boldsymbol{\\beta}^{\\mathrm{OLS}}]-\\mbox{Var}(\\boldsymbol{\\beta}^{\\mathrm{Ridge}})=\\sigma^2 [ \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}[ 2\\lambda\\mathbf{I} + \\lambda^2 (\\mathbf{X}^{T} \\mathbf{X})^{-1} ] \\{ [ \\mathbf{X}^{T} \\mathbf{X} + \\lambda \\mathbf{I} ]^{-1}\\}^{T}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The difference is non-negative definite since each component of the\n",
"matrix product is non-negative definite. \n",
"This means the variance we obtain with the standard OLS will always for $\\lambda > 0$ be larger than the variance of $\\boldsymbol{\\beta}$ obtained with the Ridge estimator. This has interesting consequences when we discuss the so-called bias-variance trade-off below. \n",
"\n",
"\n",
"\n",
"## Resampling methods\n",
"\n",
"With all these analytical equations for both the OLS and Ridge\n",
"regression, we will now outline how to assess a given model. This will\n",
"lead us to a discussion of the so-called bias-variance tradeoff (see\n",
"below) and so-called resampling methods.\n",
"\n",
"One of the quantities we have discussed as a way to measure errors is\n",
"the mean-squared error (MSE), mainly used for fitting of continuous\n",
"functions. Another choice is the absolute error.\n",
"\n",
"In the discussions below we will focus on the MSE and in particular since we will split the data into test and training data,\n",
"we discuss the\n",
"1. prediction error or simply the **test error** $\\mathrm{Err_{Test}}$, where we have a fixed training set and the test error is the MSE arising from the data reserved for testing. We discuss also the \n",
"\n",
"2. training error $\\mathrm{Err_{Train}}$, which is the average loss over the training data.\n",
"\n",
"As our model becomes more and more complex, more of the training data tends to used. The training may thence adapt to more complicated structures in the data. This may lead to a decrease in the bias (see below for code example) and a slight increase of the variance for the test error.\n",
"For a certain level of complexity the test error will reach minimum, before starting to increase again. The\n",
"training error reaches a saturation.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Resampling methods: Jackknife and Bootstrap\n",
"\n",
"Two famous\n",
"resampling methods are the **independent bootstrap** and **the jackknife**. \n",
"\n",
"The jackknife is a special case of the independent bootstrap. Still, the jackknife was made\n",
"popular prior to the independent bootstrap. And as the popularity of\n",
"the independent bootstrap soared, new variants, such as **the dependent bootstrap**.\n",
"\n",
"The Jackknife and independent bootstrap work for\n",
"independent, identically distributed random variables.\n",
"If these conditions are not\n",
"satisfied, the methods will fail. Yet, it should be said that if the data are\n",
"independent, identically distributed, and we only want to estimate the\n",
"variance of $\\overline{X}$ (which often is the case), then there is no\n",
"need for bootstrapping. \n",
"\n",
"\n",
"## Resampling methods: Jackknife\n",
"\n",
"The Jackknife works by making many replicas of the estimator $\\widehat{\\theta}$. \n",
"The jackknife is a resampling method where we systematically leave out one observation from the vector of observed values $\\boldsymbol{x} = (x_1,x_2,\\cdots,X_n)$. \n",
"Let $\\boldsymbol{x}_i$ denote the vector"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{x}_i = (x_1,x_2,\\cdots,x_{i-1},x_{i+1},\\cdots,x_n),\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"which equals the vector $\\boldsymbol{x}$ with the exception that observation\n",
"number $i$ is left out. Using this notation, define\n",
"$\\widehat{\\theta}_i$ to be the estimator\n",
"$\\widehat{\\theta}$ computed using $\\vec{X}_i$. \n",
"\n",
"\n",
"\n",
"## Jackknife code example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"from numpy import *\n",
"from numpy.random import randint, randn\n",
"from time import time\n",
"\n",
"def jackknife(data, stat):\n",
" n = len(data);t = zeros(n); inds = arange(n); t0 = time()\n",
" ## 'jackknifing' by leaving out an observation for each i \n",
" for i in range(n):\n",
" t[i] = stat(delete(data,i) )\n",
"\n",
" # analysis \n",
" print(\"Runtime: %g sec\" % (time()-t0)); print(\"Jackknife Statistics :\")\n",
" print(\"original bias std. error\")\n",
" print(\"%8g %14g %15g\" % (stat(data),(n-1)*mean(t)/n, (n*var(t))**.5))\n",
"\n",
" return t\n",
"\n",
"\n",
"# Returns mean of data samples \n",
"def stat(data):\n",
" return mean(data)\n",
"\n",
"\n",
"mu, sigma = 100, 15\n",
"datapoints = 10000\n",
"x = mu + sigma*random.randn(datapoints)\n",
"# jackknife returns the data sample \n",
"t = jackknife(x, stat)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Resampling methods: Bootstrap\n",
"Bootstrapping is a nonparametric approach to statistical inference\n",
"that substitutes computation for more traditional distributional\n",
"assumptions and asymptotic results. Bootstrapping offers a number of\n",
"advantages: \n",
"1. The bootstrap is quite general, although there are some cases in which it fails. \n",
"\n",
"2. Because it does not require distributional assumptions (such as normally distributed errors), the bootstrap can provide more accurate inferences when the data are not well behaved or when the sample size is small. \n",
"\n",
"3. It is possible to apply the bootstrap to statistics with sampling distributions that are difficult to derive, even asymptotically. \n",
"\n",
"4. It is relatively simple to apply the bootstrap to complex data-collection plans (such as stratified and clustered samples).\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Resampling methods: Bootstrap background\n",
"\n",
"Since $\\widehat{\\theta} = \\widehat{\\theta}(\\boldsymbol{X})$ is a function of random variables,\n",
"$\\widehat{\\theta}$ itself must be a random variable. Thus it has\n",
"a pdf, call this function $p(\\boldsymbol{t})$. The aim of the bootstrap is to\n",
"estimate $p(\\boldsymbol{t})$ by the relative frequency of\n",
"$\\widehat{\\theta}$. You can think of this as using a histogram\n",
"in the place of $p(\\boldsymbol{t})$. If the relative frequency closely\n",
"resembles $p(\\vec{t})$, then using numerics, it is straight forward to\n",
"estimate all the interesting parameters of $p(\\boldsymbol{t})$ using point\n",
"estimators. \n",
"\n",
"\n",
"\n",
"## Resampling methods: More Bootstrap background\n",
"\n",
"In the case that $\\widehat{\\theta}$ has\n",
"more than one component, and the components are independent, we use the\n",
"same estimator on each component separately. If the probability\n",
"density function of $X_i$, $p(x)$, had been known, then it would have\n",
"been straight forward to do this by: \n",
"1. Drawing lots of numbers from $p(x)$, suppose we call one such set of numbers $(X_1^*, X_2^*, \\cdots, X_n^*)$. \n",
"\n",
"2. Then using these numbers, we could compute a replica of $\\widehat{\\theta}$ called $\\widehat{\\theta}^*$. \n",
"\n",
"By repeated use of (1) and (2), many\n",
"estimates of $\\widehat{\\theta}$ could have been obtained. The\n",
"idea is to use the relative frequency of $\\widehat{\\theta}^*$\n",
"(think of a histogram) as an estimate of $p(\\boldsymbol{t})$.\n",
"\n",
"\n",
"## Resampling methods: Bootstrap approach\n",
"\n",
"But\n",
"unless there is enough information available about the process that\n",
"generated $X_1,X_2,\\cdots,X_n$, $p(x)$ is in general\n",
"unknown. Therefore, [Efron in 1979](https://projecteuclid.org/euclid.aos/1176344552) asked the\n",
"question: What if we replace $p(x)$ by the relative frequency\n",
"of the observation $X_i$; if we draw observations in accordance with\n",
"the relative frequency of the observations, will we obtain the same\n",
"result in some asymptotic sense? The answer is yes.\n",
"\n",
"\n",
"Instead of generating the histogram for the relative\n",
"frequency of the observation $X_i$, just draw the values\n",
"$(X_1^*,X_2^*,\\cdots,X_n^*)$ with replacement from the vector\n",
"$\\boldsymbol{X}$. \n",
"\n",
"\n",
"## Resampling methods: Bootstrap steps\n",
"\n",
"The independent bootstrap works like this: \n",
"\n",
"1. Draw with replacement $n$ numbers for the observed variables $\\boldsymbol{x} = (x_1,x_2,\\cdots,x_n)$. \n",
"\n",
"2. Define a vector $\\boldsymbol{x}^*$ containing the values which were drawn from $\\boldsymbol{x}$. \n",
"\n",
"3. Using the vector $\\boldsymbol{x}^*$ compute $\\widehat{\\theta}^*$ by evaluating $\\widehat \\theta$ under the observations $\\boldsymbol{x}^*$. \n",
"\n",
"4. Repeat this process $k$ times. \n",
"\n",
"When you are done, you can draw a histogram of the relative frequency\n",
"of $\\widehat \\theta^*$. This is your estimate of the probability\n",
"distribution $p(t)$. Using this probability distribution you can\n",
"estimate any statistics thereof. In principle you never draw the\n",
"histogram of the relative frequency of $\\widehat{\\theta}^*$. Instead\n",
"you use the estimators corresponding to the statistic of interest. For\n",
"example, if you are interested in estimating the variance of $\\widehat\n",
"\\theta$, apply the etsimator $\\widehat \\sigma^2$ to the values\n",
"$\\widehat \\theta ^*$.\n",
"\n",
"\n",
"\n",
"## Code example for the Bootstrap method\n",
"\n",
"The following code starts with a Gaussian distribution with mean value\n",
"$\\mu =100$ and variance $\\sigma=15$. We use this to generate the data\n",
"used in the bootstrap analysis. The bootstrap analysis returns a data\n",
"set after a given number of bootstrap operations (as many as we have\n",
"data points). This data set consists of estimated mean values for each\n",
"bootstrap operation. The histogram generated by the bootstrap method\n",
"shows that the distribution for these mean values is also a Gaussian,\n",
"centered around the mean value $\\mu=100$ but with standard deviation\n",
"$\\sigma/\\sqrt{n}$, where $n$ is the number of bootstrap samples (in\n",
"this case the same as the number of original data points). The value\n",
"of the standard deviation is what we expect from the central limit\n",
"theorem."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"from numpy import *\n",
"from numpy.random import randint, randn\n",
"from time import time\n",
"import matplotlib.mlab as mlab\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Returns mean of bootstrap samples \n",
"def stat(data):\n",
" return mean(data)\n",
"\n",
"# Bootstrap algorithm\n",
"def bootstrap(data, statistic, R):\n",
" t = zeros(R); n = len(data); inds = arange(n); t0 = time()\n",
" # non-parametric bootstrap \n",
" for i in range(R):\n",
" t[i] = statistic(data[randint(0,n,n)])\n",
"\n",
" # analysis \n",
" print(\"Runtime: %g sec\" % (time()-t0)); print(\"Bootstrap Statistics :\")\n",
" print(\"original bias std. error\")\n",
" print(\"%8g %8g %14g %15g\" % (statistic(data), std(data),mean(t),std(t)))\n",
" return t\n",
"\n",
"\n",
"mu, sigma = 100, 15\n",
"datapoints = 10000\n",
"x = mu + sigma*random.randn(datapoints)\n",
"# bootstrap returns the data sample \n",
"t = bootstrap(x, stat, datapoints)\n",
"# the histogram of the bootstrapped data \n",
"n, binsboot, patches = plt.hist(t, 50, normed=1, facecolor='red', alpha=0.75)\n",
"\n",
"# add a 'best fit' line \n",
"y = mlab.normpdf( binsboot, mean(t), std(t))\n",
"lt = plt.plot(binsboot, y, 'r--', linewidth=1)\n",
"plt.xlabel('Smarts')\n",
"plt.ylabel('Probability')\n",
"plt.axis([99.5, 100.6, 0, 3.0])\n",
"plt.grid(True)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Various steps in cross-validation\n",
"\n",
"When the repetitive splitting of the data set is done randomly,\n",
"samples may accidently end up in a fast majority of the splits in\n",
"either training or test set. Such samples may have an unbalanced\n",
"influence on either model building or prediction evaluation. To avoid\n",
"this $k$-fold cross-validation structures the data splitting. The\n",
"samples are divided into $k$ more or less equally sized exhaustive and\n",
"mutually exclusive subsets. In turn (at each split) one of these\n",
"subsets plays the role of the test set while the union of the\n",
"remaining subsets constitutes the training set. Such a splitting\n",
"warrants a balanced representation of each sample in both training and\n",
"test set over the splits. Still the division into the $k$ subsets\n",
"involves a degree of randomness. This may be fully excluded when\n",
"choosing $k=n$. This particular case is referred to as leave-one-out\n",
"cross-validation (LOOCV). \n",
"\n",
"\n",
"## How to set up the cross-validation for Ridge and/or Lasso\n",
"\n",
"* Define a range of interest for the penalty parameter.\n",
"\n",
"* Divide the data set into training and test set comprising samples $\\{1, \\ldots, n\\} \\setminus i$ and $\\{ i \\}$, respectively.\n",
"\n",
"* Fit the linear regression model by means of ridge estimation for each $\\lambda$ in the grid using the training set, and the corresponding estimate of the error variance $\\boldsymbol{\\sigma}_{-i}^2(\\lambda)$, as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{align*}\n",
"\\boldsymbol{\\beta}_{-i}(\\lambda) & = ( \\boldsymbol{X}_{-i, \\ast}^{T}\n",
"\\boldsymbol{X}_{-i, \\ast} + \\lambda \\boldsymbol{I}_{pp})^{-1}\n",
"\\boldsymbol{X}_{-i, \\ast}^{T} \\boldsymbol{y}_{-i}\n",
"\\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Evaluate the prediction performance of these models on the test set by $\\log\\{L[y_i, \\boldsymbol{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]\\}$. Or, by the prediction error $|y_i - \\boldsymbol{X}_{i, \\ast} \\boldsymbol{\\beta}_{-i}(\\lambda)|$, the relative error, the error squared or the R2 score function.\n",
"\n",
"* Repeat the first three steps such that each sample plays the role of the test set once.\n",
"\n",
"* Average the prediction performances of the test sets at each grid point of the penalty bias/parameter. It is an estimate of the prediction performance of the model corresponding to this value of the penalty parameter on novel data. It is defined as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{align*}\n",
"\\frac{1}{n} \\sum_{i = 1}^n \\log\\{L[y_i, \\mathbf{X}_{i, \\ast}; \\boldsymbol{\\beta}_{-i}(\\lambda), \\boldsymbol{\\sigma}_{-i}^2(\\lambda)]\\}.\n",
"\\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cross-validation in brief\n",
"\n",
"For the various values of $k$\n",
"\n",
"1. shuffle the dataset randomly.\n",
"\n",
"2. Split the dataset into $k$ groups.\n",
"\n",
"3. For each unique group:\n",
"\n",
"a. Decide which group to use as set for test data\n",
"\n",
"b. Take the remaining groups as a training data set\n",
"\n",
"c. Fit a model on the training set and evaluate it on the test set\n",
"\n",
"d. Retain the evaluation score and discard the model\n",
"\n",
"\n",
"5. Summarize the model using the sample of model evaluation scores\n",
"\n",
"## Code Example for Cross-validation and $k$-fold Cross-validation\n",
"\n",
"The code here uses Ridge regression with cross-validation (CV) resampling and $k$-fold CV in order to fit a specific polynomial."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import KFold\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"\n",
"# A seed just to ensure that the random numbers are the same for every run.\n",
"# Useful for eventual debugging.\n",
"np.random.seed(3155)\n",
"\n",
"# Generate the data.\n",
"nsamples = 100\n",
"x = np.random.randn(nsamples)\n",
"y = 3*x**2 + np.random.randn(nsamples)\n",
"\n",
"## Cross-validation on Ridge regression using KFold only\n",
"\n",
"# Decide degree on polynomial to fit\n",
"poly = PolynomialFeatures(degree = 6)\n",
"\n",
"# Decide which values of lambda to use\n",
"nlambdas = 500\n",
"lambdas = np.logspace(-3, 5, nlambdas)\n",
"\n",
"# Initialize a KFold instance\n",
"k = 5\n",
"kfold = KFold(n_splits = k)\n",
"\n",
"# Perform the cross-validation to estimate MSE\n",
"scores_KFold = np.zeros((nlambdas, k))\n",
"\n",
"i = 0\n",
"for lmb in lambdas:\n",
" ridge = Ridge(alpha = lmb)\n",
" j = 0\n",
" for train_inds, test_inds in kfold.split(x):\n",
" xtrain = x[train_inds]\n",
" ytrain = y[train_inds]\n",
"\n",
" xtest = x[test_inds]\n",
" ytest = y[test_inds]\n",
"\n",
" Xtrain = poly.fit_transform(xtrain[:, np.newaxis])\n",
" ridge.fit(Xtrain, ytrain[:, np.newaxis])\n",
"\n",
" Xtest = poly.fit_transform(xtest[:, np.newaxis])\n",
" ypred = ridge.predict(Xtest)\n",
"\n",
" scores_KFold[i,j] = np.sum((ypred - ytest[:, np.newaxis])**2)/np.size(ypred)\n",
"\n",
" j += 1\n",
" i += 1\n",
"\n",
"\n",
"estimated_mse_KFold = np.mean(scores_KFold, axis = 1)\n",
"\n",
"## Cross-validation using cross_val_score from sklearn along with KFold\n",
"\n",
"# kfold is an instance initialized above as:\n",
"# kfold = KFold(n_splits = k)\n",
"\n",
"estimated_mse_sklearn = np.zeros(nlambdas)\n",
"i = 0\n",
"for lmb in lambdas:\n",
" ridge = Ridge(alpha = lmb)\n",
"\n",
" X = poly.fit_transform(x[:, np.newaxis])\n",
" estimated_mse_folds = cross_val_score(ridge, X, y[:, np.newaxis], scoring='neg_mean_squared_error', cv=kfold)\n",
"\n",
" # cross_val_score return an array containing the estimated negative mse for every fold.\n",
" # we have to the the mean of every array in order to get an estimate of the mse of the model\n",
" estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)\n",
"\n",
" i += 1\n",
"\n",
"## Plot and compare the slightly different ways to perform cross-validation\n",
"\n",
"plt.figure()\n",
"\n",
"plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')\n",
"plt.plot(np.log10(lambdas), estimated_mse_KFold, 'r--', label = 'KFold')\n",
"\n",
"plt.xlabel('log10(lambda)')\n",
"plt.ylabel('mse')\n",
"\n",
"plt.legend()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The bias-variance tradeoff\n",
"\n",
"\n",
"We will discuss the bias-variance tradeoff in the context of\n",
"continuous predictions such as regression. However, many of the\n",
"intuitions and ideas discussed here also carry over to classification\n",
"tasks. Consider a dataset $\\mathcal{L}$ consisting of the data\n",
"$\\mathbf{X}_\\mathcal{L}=\\{(y_j, \\boldsymbol{x}_j), j=0\\ldots n-1\\}$. \n",
"\n",
"Let us assume that the true data is generated from a noisy model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{y}=f(\\boldsymbol{x}) + \\boldsymbol{\\epsilon}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $\\epsilon$ is normally distributed with mean zero and standard deviation $\\sigma^2$.\n",
"\n",
"In our derivation of the ordinary least squares method we defined then\n",
"an approximation to the function $f$ in terms of the parameters\n",
"$\\boldsymbol{\\beta}$ and the design matrix $\\boldsymbol{X}$ which embody our model,\n",
"that is $\\boldsymbol{\\tilde{y}}=\\boldsymbol{X}\\boldsymbol{\\beta}$. \n",
"\n",
"Thereafter we found the parameters $\\boldsymbol{\\beta}$ by optimizing the means squared error via the so-called cost function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"C(\\boldsymbol{X},\\boldsymbol{\\beta}) =\\frac{1}{n}\\sum_{i=0}^{n-1}(y_i-\\tilde{y}_i)^2=\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right].\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can rewrite this as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\frac{1}{n}\\sum_i(f_i-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2+\\frac{1}{n}\\sum_i(\\tilde{y}_i-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2+\\sigma^2.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The three terms represent the square of the bias of the learning\n",
"method, which can be thought of as the error caused by the simplifying\n",
"assumptions built into the method. The second term represents the\n",
"variance of the chosen model and finally the last terms is variance of\n",
"the error $\\boldsymbol{\\epsilon}$.\n",
"\n",
"To derive this equation, we need to recall that the variance of $\\boldsymbol{y}$ and $\\boldsymbol{\\epsilon}$ are both equal to $\\sigma^2$. The mean value of $\\boldsymbol{\\epsilon}$ is by definition equal to zero. Furthermore, the function $f$ is not a stochastics variable, idem for $\\boldsymbol{\\tilde{y}}$.\n",
"We use a more compact notation in terms of the expectation value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\mathbb{E}\\left[(\\boldsymbol{f}+\\boldsymbol{\\epsilon}-\\boldsymbol{\\tilde{y}})^2\\right],\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and adding and subtracting $\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right]$ we get"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\mathbb{E}\\left[(\\boldsymbol{f}+\\boldsymbol{\\epsilon}-\\boldsymbol{\\tilde{y}}+\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right]-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2\\right],\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"which, using the abovementioned expectation values can be rewritten as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mathbb{E}\\left[(\\boldsymbol{y}-\\boldsymbol{\\tilde{y}})^2\\right]=\\mathbb{E}\\left[(\\boldsymbol{y}-\\mathbb{E}\\left[\\boldsymbol{\\tilde{y}}\\right])^2\\right]+\\mathrm{Var}\\left[\\boldsymbol{\\tilde{y}}\\right]+\\sigma^2,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"that is the rewriting in terms of the so-called bias, the variance of the model $\\boldsymbol{\\tilde{y}}$ and the variance of $\\boldsymbol{\\epsilon}$.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Example code for Bias-Variance tradeoff"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.utils import resample\n",
"\n",
"np.random.seed(2018)\n",
"\n",
"n = 500\n",
"n_boostraps = 100\n",
"degree = 18 # A quite high value, just to show.\n",
"noise = 0.1\n",
"\n",
"# Make data set.\n",
"x = np.linspace(-1, 3, n).reshape(-1, 1)\n",
"y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2) + np.random.normal(0, 0.1, x.shape)\n",
"\n",
"# Hold out some test data that is never used in training.\n",
"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
"\n",
"# Combine x transformation and model into one operation.\n",
"# Not neccesary, but convenient.\n",
"model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))\n",
"\n",
"# The following (m x n_bootstraps) matrix holds the column vectors y_pred\n",
"# for each bootstrap iteration.\n",
"y_pred = np.empty((y_test.shape[0], n_boostraps))\n",
"for i in range(n_boostraps):\n",
" x_, y_ = resample(x_train, y_train)\n",
"\n",
" # Evaluate the new model on the same test data each time.\n",
" y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel()\n",
"\n",
"# Note: Expectations and variances taken w.r.t. different training\n",
"# data sets, hence the axis=1. Subsequent means are taken across the test data\n",
"# set in order to obtain a total value, but before this we have error/bias/variance\n",
"# calculated per data point in the test set.\n",
"# Note 2: The use of keepdims=True is important in the calculation of bias as this \n",
"# maintains the column vector form. Dropping this yields very unexpected results.\n",
"error = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )\n",
"bias = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )\n",
"variance = np.mean( np.var(y_pred, axis=1, keepdims=True) )\n",
"print('Error:', error)\n",
"print('Bias^2:', bias)\n",
"print('Var:', variance)\n",
"print('{} >= {} + {} = {}'.format(error, bias, variance, bias+variance))\n",
"\n",
"plt.plot(x[::5, :], y[::5, :], label='f(x)')\n",
"plt.scatter(x_test, y_test, label='Data points')\n",
"plt.scatter(x_test, np.mean(y_pred, axis=1), label='Pred')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Understanding what happens"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.pipeline import make_pipeline\n",
"from sklearn.utils import resample\n",
"\n",
"np.random.seed(2018)\n",
"\n",
"n = 40\n",
"n_boostraps = 100\n",
"maxdegree = 14\n",
"\n",
"\n",
"# Make data set.\n",
"x = np.linspace(-3, 3, n).reshape(-1, 1)\n",
"y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n",
"error = np.zeros(maxdegree)\n",
"bias = np.zeros(maxdegree)\n",
"variance = np.zeros(maxdegree)\n",
"polydegree = np.zeros(maxdegree)\n",
"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
"\n",
"for degree in range(maxdegree):\n",
" model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))\n",
" y_pred = np.empty((y_test.shape[0], n_boostraps))\n",
" for i in range(n_boostraps):\n",
" x_, y_ = resample(x_train, y_train)\n",
" y_pred[:, i] = model.fit(x_, y_).predict(x_test).ravel()\n",
"\n",
" polydegree[degree] = degree\n",
" error[degree] = np.mean( np.mean((y_test - y_pred)**2, axis=1, keepdims=True) )\n",
" bias[degree] = np.mean( (y_test - np.mean(y_pred, axis=1, keepdims=True))**2 )\n",
" variance[degree] = np.mean( np.var(y_pred, axis=1, keepdims=True) )\n",
" print('Polynomial degree:', degree)\n",
" print('Error:', error[degree])\n",
" print('Bias^2:', bias[degree])\n",
" print('Var:', variance[degree])\n",
" print('{} >= {} + {} = {}'.format(error[degree], bias[degree], variance[degree], bias[degree]+variance[degree]))\n",
"\n",
"plt.plot(polydegree, error, label='Error')\n",
"plt.plot(polydegree, bias, label='bias')\n",
"plt.plot(polydegree, variance, label='Variance')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summing up\n",
"\n",
"\n",
"\n",
"\n",
"The bias-variance tradeoff summarizes the fundamental tension in\n",
"machine learning, particularly supervised learning, between the\n",
"complexity of a model and the amount of training data needed to train\n",
"it. Since data is often limited, in practice it is often useful to\n",
"use a less-complex model with higher bias, that is a model whose asymptotic\n",
"performance is worse than another model because it is easier to\n",
"train and less sensitive to sampling noise arising from having a\n",
"finite-sized training dataset (smaller variance). \n",
"\n",
"\n",
"\n",
"The above equations tell us that in\n",
"order to minimize the expected test error, we need to select a\n",
"statistical learning method that simultaneously achieves low variance\n",
"and low bias. Note that variance is inherently a nonnegative quantity,\n",
"and squared bias is also nonnegative. Hence, we see that the expected\n",
"test MSE can never lie below $Var(\\epsilon)$, the irreducible error.\n",
"\n",
"\n",
"What do we mean by the variance and bias of a statistical learning\n",
"method? The variance refers to the amount by which our model would change if we\n",
"estimated it using a different training data set. Since the training\n",
"data are used to fit the statistical learning method, different\n",
"training data sets will result in a different estimate. But ideally the\n",
"estimate for our model should not vary too much between training\n",
"sets. However, if a method has high variance then small changes in\n",
"the training data can result in large changes in the model. In general, more\n",
"flexible statistical methods have higher variance.\n",
"\n",
"\n",
"You may also find this recent [article](https://www.pnas.org/content/116/32/15849) of interest.\n",
"\n",
"\n",
"## Another Example from Scikit-Learn's Repository"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"============================\n",
"Underfitting vs. Overfitting\n",
"============================\n",
"\n",
"This example demonstrates the problems of underfitting and overfitting and\n",
"how we can use linear regression with polynomial features to approximate\n",
"nonlinear functions. The plot shows the function that we want to approximate,\n",
"which is a part of the cosine function. In addition, the samples from the\n",
"real function and the approximations of different models are displayed. The\n",
"models have polynomial features of different degrees. We can see that a\n",
"linear function (polynomial with degree 1) is not sufficient to fit the\n",
"training samples. This is called **underfitting**. A polynomial of degree 4\n",
"approximates the true function almost perfectly. However, for higher degrees\n",
"the model will **overfit** the training data, i.e. it learns the noise of the\n",
"training data.\n",
"We evaluate quantitatively **overfitting** / **underfitting** by using\n",
"cross-validation. We calculate the mean squared error (MSE) on the validation\n",
"set, the higher, the less likely the model generalizes correctly from the\n",
"training data.\n",
"\"\"\"\n",
"\n",
"print(__doc__)\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"\n",
"def true_fun(X):\n",
" return np.cos(1.5 * np.pi * X)\n",
"\n",
"np.random.seed(0)\n",
"\n",
"n_samples = 30\n",
"degrees = [1, 4, 15]\n",
"\n",
"X = np.sort(np.random.rand(n_samples))\n",
"y = true_fun(X) + np.random.randn(n_samples) * 0.1\n",
"\n",
"plt.figure(figsize=(14, 5))\n",
"for i in range(len(degrees)):\n",
" ax = plt.subplot(1, len(degrees), i + 1)\n",
" plt.setp(ax, xticks=(), yticks=())\n",
"\n",
" polynomial_features = PolynomialFeatures(degree=degrees[i],\n",
" include_bias=False)\n",
" linear_regression = LinearRegression()\n",
" pipeline = Pipeline([(\"polynomial_features\", polynomial_features),\n",
" (\"linear_regression\", linear_regression)])\n",
" pipeline.fit(X[:, np.newaxis], y)\n",
"\n",
" # Evaluate the models using crossvalidation\n",
" scores = cross_val_score(pipeline, X[:, np.newaxis], y,\n",
" scoring=\"neg_mean_squared_error\", cv=10)\n",
"\n",
" X_test = np.linspace(0, 1, 100)\n",
" plt.plot(X_test, pipeline.predict(X_test[:, np.newaxis]), label=\"Model\")\n",
" plt.plot(X_test, true_fun(X_test), label=\"True function\")\n",
" plt.scatter(X, y, edgecolor='b', s=20, label=\"Samples\")\n",
" plt.xlabel(\"x\")\n",
" plt.ylabel(\"y\")\n",
" plt.xlim((0, 1))\n",
" plt.ylim((-2, 2))\n",
" plt.legend(loc=\"best\")\n",
" plt.title(\"Degree {}\\nMSE = {:.2e}(+/- {:.2e})\".format(\n",
" degrees[i], -scores.mean(), scores.std()))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## More examples on bootstrap and cross-validation and errors"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# Common imports\n",
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.utils import resample\n",
"from sklearn.metrics import mean_squared_error\n",
"# Where to save the figures and data files\n",
"PROJECT_ROOT_DIR = \"Results\"\n",
"FIGURE_ID = \"Results/FigureFiles\"\n",
"DATA_ID = \"DataFiles/\"\n",
"\n",
"if not os.path.exists(PROJECT_ROOT_DIR):\n",
" os.mkdir(PROJECT_ROOT_DIR)\n",
"\n",
"if not os.path.exists(FIGURE_ID):\n",
" os.makedirs(FIGURE_ID)\n",
"\n",
"if not os.path.exists(DATA_ID):\n",
" os.makedirs(DATA_ID)\n",
"\n",
"def image_path(fig_id):\n",
" return os.path.join(FIGURE_ID, fig_id)\n",
"\n",
"def data_path(dat_id):\n",
" return os.path.join(DATA_ID, dat_id)\n",
"\n",
"def save_fig(fig_id):\n",
" plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
"\n",
"infile = open(data_path(\"EoS.csv\"),'r')\n",
"\n",
"# Read the EoS data as csv file and organize the data into two arrays with density and energies\n",
"EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n",
"EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n",
"EoS = EoS.dropna()\n",
"Energies = EoS['Energy']\n",
"Density = EoS['Density']\n",
"# The design matrix now as function of various polytrops\n",
"\n",
"Maxpolydegree = 30\n",
"X = np.zeros((len(Density),Maxpolydegree))\n",
"X[:,0] = 1.0\n",
"testerror = np.zeros(Maxpolydegree)\n",
"trainingerror = np.zeros(Maxpolydegree)\n",
"polynomial = np.zeros(Maxpolydegree)\n",
"\n",
"trials = 100\n",
"for polydegree in range(1, Maxpolydegree):\n",
" polynomial[polydegree] = polydegree\n",
" for degree in range(polydegree):\n",
" X[:,degree] = Density**(degree/3.0)\n",
"\n",
"# loop over trials in order to estimate the expectation value of the MSE\n",
" testerror[polydegree] = 0.0\n",
" trainingerror[polydegree] = 0.0\n",
" for samples in range(trials):\n",
" x_train, x_test, y_train, y_test = train_test_split(X, Energies, test_size=0.2)\n",
" model = LinearRegression(fit_intercept=True).fit(x_train, y_train)\n",
" ypred = model.predict(x_train)\n",
" ytilde = model.predict(x_test)\n",
" testerror[polydegree] += mean_squared_error(y_test, ytilde)\n",
" trainingerror[polydegree] += mean_squared_error(y_train, ypred) \n",
"\n",
" testerror[polydegree] /= trials\n",
" trainingerror[polydegree] /= trials\n",
" print(\"Degree of polynomial: %3d\"% polynomial[polydegree])\n",
" print(\"Mean squared error on training data: %.8f\" % trainingerror[polydegree])\n",
" print(\"Mean squared error on test data: %.8f\" % testerror[polydegree])\n",
"\n",
"plt.plot(polynomial, np.log10(trainingerror), label='Training Error')\n",
"plt.plot(polynomial, np.log10(testerror), label='Test Error')\n",
"plt.xlabel('Polynomial degree')\n",
"plt.ylabel('log10[MSE]')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The same example but now with cross-validation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"# Common imports\n",
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import KFold\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"\n",
"# Where to save the figures and data files\n",
"PROJECT_ROOT_DIR = \"Results\"\n",
"FIGURE_ID = \"Results/FigureFiles\"\n",
"DATA_ID = \"DataFiles/\"\n",
"\n",
"if not os.path.exists(PROJECT_ROOT_DIR):\n",
" os.mkdir(PROJECT_ROOT_DIR)\n",
"\n",
"if not os.path.exists(FIGURE_ID):\n",
" os.makedirs(FIGURE_ID)\n",
"\n",
"if not os.path.exists(DATA_ID):\n",
" os.makedirs(DATA_ID)\n",
"\n",
"def image_path(fig_id):\n",
" return os.path.join(FIGURE_ID, fig_id)\n",
"\n",
"def data_path(dat_id):\n",
" return os.path.join(DATA_ID, dat_id)\n",
"\n",
"def save_fig(fig_id):\n",
" plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
"\n",
"infile = open(data_path(\"EoS.csv\"),'r')\n",
"\n",
"# Read the EoS data as csv file and organize the data into two arrays with density and energies\n",
"EoS = pd.read_csv(infile, names=('Density', 'Energy'))\n",
"EoS['Energy'] = pd.to_numeric(EoS['Energy'], errors='coerce')\n",
"EoS = EoS.dropna()\n",
"Energies = EoS['Energy']\n",
"Density = EoS['Density']\n",
"# The design matrix now as function of various polytrops\n",
"\n",
"Maxpolydegree = 30\n",
"X = np.zeros((len(Density),Maxpolydegree))\n",
"X[:,0] = 1.0\n",
"estimated_mse_sklearn = np.zeros(Maxpolydegree)\n",
"polynomial = np.zeros(Maxpolydegree)\n",
"k =5\n",
"kfold = KFold(n_splits = k)\n",
"\n",
"for polydegree in range(1, Maxpolydegree):\n",
" polynomial[polydegree] = polydegree\n",
" for degree in range(polydegree):\n",
" X[:,degree] = Density**(degree/3.0)\n",
" OLS = LinearRegression()\n",
"# loop over trials in order to estimate the expectation value of the MSE\n",
" estimated_mse_folds = cross_val_score(OLS, X, Energies, scoring='neg_mean_squared_error', cv=kfold)\n",
"#[:, np.newaxis]\n",
" estimated_mse_sklearn[polydegree] = np.mean(-estimated_mse_folds)\n",
"\n",
"plt.plot(polynomial, np.log10(estimated_mse_sklearn), label='Test Error')\n",
"plt.xlabel('Polynomial degree')\n",
"plt.ylabel('log10[MSE]')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cross-validation with Ridge"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import KFold\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"\n",
"# A seed just to ensure that the random numbers are the same for every run.\n",
"np.random.seed(3155)\n",
"# Generate the data.\n",
"n = 100\n",
"x = np.linspace(-3, 3, n).reshape(-1, 1)\n",
"y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.normal(0, 0.1, x.shape)\n",
"# Decide degree on polynomial to fit\n",
"poly = PolynomialFeatures(degree = 10)\n",
"\n",
"# Decide which values of lambda to use\n",
"nlambdas = 500\n",
"lambdas = np.logspace(-3, 5, nlambdas)\n",
"# Initialize a KFold instance\n",
"k = 5\n",
"kfold = KFold(n_splits = k)\n",
"estimated_mse_sklearn = np.zeros(nlambdas)\n",
"i = 0\n",
"for lmb in lambdas:\n",
" ridge = Ridge(alpha = lmb)\n",
" estimated_mse_folds = cross_val_score(ridge, x, y, scoring='neg_mean_squared_error', cv=kfold)\n",
" estimated_mse_sklearn[i] = np.mean(-estimated_mse_folds)\n",
" i += 1\n",
"plt.figure()\n",
"plt.plot(np.log10(lambdas), estimated_mse_sklearn, label = 'cross_val_score')\n",
"plt.xlabel('log10(lambda)')\n",
"plt.ylabel('MSE')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Ising model\n",
"\n",
"The one-dimensional Ising model with nearest neighbor interaction, no\n",
"external field and a constant coupling constant $J$ is given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto2\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" H = -J \\sum_{k}^L s_k s_{k + 1},\n",
"\\label{_auto2} \\tag{2}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $s_i \\in \\{-1, 1\\}$ and $s_{N + 1} = s_1$. The number of spins\n",
"in the system is determined by $L$. For the one-dimensional system\n",
"there is no phase transition.\n",
"\n",
"We will look at a system of $L = 40$ spins with a coupling constant of\n",
"$J = 1$. To get enough training data we will generate 10000 states\n",
"with their respective energies."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"import seaborn as sns\n",
"import scipy.linalg as scl\n",
"from sklearn.model_selection import train_test_split\n",
"import tqdm\n",
"sns.set(color_codes=True)\n",
"cmap_args=dict(vmin=-1., vmax=1., cmap='seismic')\n",
"\n",
"L = 40\n",
"n = int(1e4)\n",
"\n",
"spins = np.random.choice([-1, 1], size=(n, L))\n",
"J = 1.0\n",
"\n",
"energies = np.zeros(n)\n",
"\n",
"for i in range(n):\n",
" energies[i] = - J * np.dot(spins[i], np.roll(spins[i], 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we use ordinary least squares\n",
"regression to predict the energy for the nearest neighbor\n",
"one-dimensional Ising model on a ring, i.e., the endpoints wrap\n",
"around. We will use linear regression to fit a value for\n",
"the coupling constant to achieve this.\n",
"\n",
"\n",
"## Reformulating the problem to suit regression\n",
"\n",
"A more general form for the one-dimensional Ising model is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto3\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" H = - \\sum_j^L \\sum_k^L s_j s_k J_{jk}.\n",
"\\label{_auto3} \\tag{3}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we allow for interactions beyond the nearest neighbors and a state dependent\n",
"coupling constant. This latter expression can be formulated as\n",
"a matrix-product"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto4\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\boldsymbol{H} = \\boldsymbol{X} J,\n",
"\\label{_auto4} \\tag{4}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $X_{jk} = s_j s_k$ and $J$ is a matrix which consists of the\n",
"elements $-J_{jk}$. This form of writing the energy fits perfectly\n",
"with the form utilized in linear regression, that is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto5\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta} + \\boldsymbol{\\epsilon},\n",
"\\label{_auto5} \\tag{5}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We split the data in training and test data as discussed in the previous example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"X = np.zeros((n, L ** 2))\n",
"for i in range(n):\n",
" X[i] = np.outer(spins[i], spins[i]).ravel()\n",
"y = energies\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linear regression\n",
"\n",
"In the ordinary least squares method we choose the cost function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto6\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" C(\\boldsymbol{X}, \\boldsymbol{\\beta})= \\frac{1}{n}\\left\\{(\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y})^T(\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y})\\right\\}.\n",
"\\label{_auto6} \\tag{6}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then find the extremal point of $C$ by taking the derivative with respect to $\\boldsymbol{\\beta}$ as discussed above.\n",
"This yields the expression for $\\boldsymbol{\\beta}$ to be"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\beta} = \\frac{\\boldsymbol{X}^T \\boldsymbol{y}}{\\boldsymbol{X}^T \\boldsymbol{X}},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"which immediately imposes some requirements on $\\boldsymbol{X}$ as there must exist\n",
"an inverse of $\\boldsymbol{X}^T \\boldsymbol{X}$. If the expression we are modeling contains an\n",
"intercept, i.e., a constant term, we must make sure that the\n",
"first column of $\\boldsymbol{X}$ consists of $1$. We do this here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"X_train_own = np.concatenate(\n",
" (np.ones(len(X_train))[:, np.newaxis], X_train),\n",
" axis=1\n",
")\n",
"X_test_own = np.concatenate(\n",
" (np.ones(len(X_test))[:, np.newaxis], X_test),\n",
" axis=1\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"def ols_inv(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n",
" return scl.inv(x.T @ x) @ (x.T @ y)\n",
"beta = ols_inv(X_train_own, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Singular Value decomposition\n",
"\n",
"Doing the inversion directly turns out to be a bad idea since the matrix\n",
"$\\boldsymbol{X}^T\\boldsymbol{X}$ is singular. An alternative approach is to use the **singular\n",
"value decomposition**. Using the definition of the Moore-Penrose\n",
"pseudoinverse we can write the equation for $\\boldsymbol{\\beta}$ as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{\\beta} = \\boldsymbol{X}^{+}\\boldsymbol{y},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where the pseudoinverse of $\\boldsymbol{X}$ is given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\boldsymbol{X}^{+} = \\frac{\\boldsymbol{X}^T}{\\boldsymbol{X}^T\\boldsymbol{X}}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using singular value decomposition we can decompose the matrix $\\boldsymbol{X} = \\boldsymbol{U}\\boldsymbol{\\Sigma} \\boldsymbol{V}^T$,\n",
"where $\\boldsymbol{U}$ and $\\boldsymbol{V}$ are orthogonal(unitary) matrices and $\\boldsymbol{\\Sigma}$ contains the singular values (more details below).\n",
"where $X^{+} = V\\Sigma^{+} U^T$. This reduces the equation for\n",
"$\\omega$ to"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto7\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\boldsymbol{\\beta} = \\boldsymbol{V}\\boldsymbol{\\Sigma}^{+} \\boldsymbol{U}^T \\boldsymbol{y}.\n",
"\\label{_auto7} \\tag{7}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that solving this equation by actually doing the pseudoinverse\n",
"(which is what we will do) is not a good idea as this operation scales\n",
"as $\\mathcal{O}(n^3)$, where $n$ is the number of elements in a\n",
"general matrix. Instead, doing $QR$-factorization and solving the\n",
"linear system as an equation would reduce this down to\n",
"$\\mathcal{O}(n^2)$ operations."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"def ols_svd(x: np.ndarray, y: np.ndarray) -> np.ndarray:\n",
" u, s, v = scl.svd(x)\n",
" return v.T @ scl.pinv(scl.diagsvd(s, u.shape[0], v.shape[0])) @ u.T @ y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"beta = ols_svd(X_train_own,y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When extracting the $J$-matrix we need to make sure that we remove the intercept, as is done here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"J = beta[1:].reshape(L, L)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A way of looking at the coefficients in $J$ is to plot the matrices as images."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(20, 14))\n",
"im = plt.imshow(J, **cmap_args)\n",
"plt.title(\"OLS\", fontsize=18)\n",
"plt.xticks(fontsize=18)\n",
"plt.yticks(fontsize=18)\n",
"cb = fig.colorbar(im)\n",
"cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is interesting to note that OLS\n",
"considers both $J_{j, j + 1} = -0.5$ and $J_{j, j - 1} = -0.5$ as\n",
"valid matrix elements for $J$.\n",
"In our discussion below on hyperparameters and Ridge and Lasso regression we will see that\n",
"this problem can be removed, partly and only with Lasso regression. \n",
"\n",
"In this case our matrix inversion was actually possible. The obvious question now is what is the mathematics behind the SVD?\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## The one-dimensional Ising model\n",
"\n",
"Let us bring back the Ising model again, but now with an additional\n",
"focus on Ridge and Lasso regression as well. We repeat some of the\n",
"basic parts of the Ising model and the setup of the training and test\n",
"data. The one-dimensional Ising model with nearest neighbor\n",
"interaction, no external field and a constant coupling constant $J$ is\n",
"given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto8\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" H = -J \\sum_{k}^L s_k s_{k + 1},\n",
"\\label{_auto8} \\tag{8}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $s_i \\in \\{-1, 1\\}$ and $s_{N + 1} = s_1$. The number of spins in the system is determined by $L$. For the one-dimensional system there is no phase transition.\n",
"\n",
"We will look at a system of $L = 40$ spins with a coupling constant of $J = 1$. To get enough training data we will generate 10000 states with their respective energies."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"import seaborn as sns\n",
"import scipy.linalg as scl\n",
"from sklearn.model_selection import train_test_split\n",
"import sklearn.linear_model as skl\n",
"import tqdm\n",
"sns.set(color_codes=True)\n",
"cmap_args=dict(vmin=-1., vmax=1., cmap='seismic')\n",
"\n",
"L = 40\n",
"n = int(1e4)\n",
"\n",
"spins = np.random.choice([-1, 1], size=(n, L))\n",
"J = 1.0\n",
"\n",
"energies = np.zeros(n)\n",
"\n",
"for i in range(n):\n",
" energies[i] = - J * np.dot(spins[i], np.roll(spins[i], 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A more general form for the one-dimensional Ising model is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto9\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" H = - \\sum_j^L \\sum_k^L s_j s_k J_{jk}.\n",
"\\label{_auto9} \\tag{9}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we allow for interactions beyond the nearest neighbors and a more\n",
"adaptive coupling matrix. This latter expression can be formulated as\n",
"a matrix-product on the form"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto10\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" H = X J,\n",
"\\label{_auto10} \\tag{10}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $X_{jk} = s_j s_k$ and $J$ is the matrix consisting of the\n",
"elements $-J_{jk}$. This form of writing the energy fits perfectly\n",
"with the form utilized in linear regression, viz."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto11\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" \\boldsymbol{y} = \\boldsymbol{X}\\boldsymbol{\\beta} + \\boldsymbol{\\epsilon}.\n",
"\\label{_auto11} \\tag{11}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We organize the data as we did above"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"X = np.zeros((n, L ** 2))\n",
"for i in range(n):\n",
" X[i] = np.outer(spins[i], spins[i]).ravel()\n",
"y = energies\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.96)\n",
"\n",
"X_train_own = np.concatenate(\n",
" (np.ones(len(X_train))[:, np.newaxis], X_train),\n",
" axis=1\n",
")\n",
"\n",
"X_test_own = np.concatenate(\n",
" (np.ones(len(X_test))[:, np.newaxis], X_test),\n",
" axis=1\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will do all fitting with **Scikit-Learn**,"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"clf = skl.LinearRegression().fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When extracting the $J$-matrix we make sure to remove the intercept"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"J_sk = clf.coef_.reshape(L, L)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then we plot the results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(20, 14))\n",
"im = plt.imshow(J_sk, **cmap_args)\n",
"plt.title(\"LinearRegression from Scikit-learn\", fontsize=18)\n",
"plt.xticks(fontsize=18)\n",
"plt.yticks(fontsize=18)\n",
"cb = fig.colorbar(im)\n",
"cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The results perfectly with our previous discussion where we used our own code.\n",
"\n",
"\n",
"## Ridge regression\n",
"\n",
"Having explored the ordinary least squares we move on to ridge\n",
"regression. In ridge regression we include a **regularizer**. This\n",
"involves a new cost function which leads to a new estimate for the\n",
"weights $\\boldsymbol{\\beta}$. This results in a penalized regression problem. The\n",
"cost function is given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1\n",
"3\n",
"6\n",
" \n",
"<\n",
"<\n",
"<\n",
"!\n",
"!\n",
"M\n",
"A\n",
"T\n",
"H\n",
"_\n",
"B\n",
"L\n",
"O\n",
"C\n",
"K"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"_lambda = 0.1\n",
"clf_ridge = skl.Ridge(alpha=_lambda).fit(X_train, y_train)\n",
"J_ridge_sk = clf_ridge.coef_.reshape(L, L)\n",
"fig = plt.figure(figsize=(20, 14))\n",
"im = plt.imshow(J_ridge_sk, **cmap_args)\n",
"plt.title(\"Ridge from Scikit-learn\", fontsize=18)\n",
"plt.xticks(fontsize=18)\n",
"plt.yticks(fontsize=18)\n",
"cb = fig.colorbar(im)\n",
"cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LASSO regression\n",
"\n",
"In the **Least Absolute Shrinkage and Selection Operator** (LASSO)-method we get a third cost function."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto13\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
" C(\\boldsymbol{X}, \\boldsymbol{\\beta}; \\lambda) = (\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y})^T(\\boldsymbol{X}\\boldsymbol{\\beta} - \\boldsymbol{y}) + \\lambda \\sqrt{\\boldsymbol{\\beta}^T\\boldsymbol{\\beta}}.\n",
"\\label{_auto13} \\tag{13}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finding the extremal point of this cost function is not so straight-forward as in least squares and ridge. We will therefore rely solely on the function ``Lasso`` from **Scikit-Learn**."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"clf_lasso = skl.Lasso(alpha=_lambda).fit(X_train, y_train)\n",
"J_lasso_sk = clf_lasso.coef_.reshape(L, L)\n",
"fig = plt.figure(figsize=(20, 14))\n",
"im = plt.imshow(J_lasso_sk, **cmap_args)\n",
"plt.title(\"Lasso from Scikit-learn\", fontsize=18)\n",
"plt.xticks(fontsize=18)\n",
"plt.yticks(fontsize=18)\n",
"cb = fig.colorbar(im)\n",
"cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=18)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is quite striking how LASSO breaks the symmetry of the coupling\n",
"constant as opposed to ridge and OLS. We get a sparse solution with\n",
"$J_{j, j + 1} = -1$.\n",
"\n",
"\n",
"\n",
"\n",
"## Performance as function of the regularization parameter\n",
"\n",
"We see how the different models perform for a different set of values for $\\lambda$."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"lambdas = np.logspace(-4, 5, 10)\n",
"\n",
"train_errors = {\n",
" \"ols_sk\": np.zeros(lambdas.size),\n",
" \"ridge_sk\": np.zeros(lambdas.size),\n",
" \"lasso_sk\": np.zeros(lambdas.size)\n",
"}\n",
"\n",
"test_errors = {\n",
" \"ols_sk\": np.zeros(lambdas.size),\n",
" \"ridge_sk\": np.zeros(lambdas.size),\n",
" \"lasso_sk\": np.zeros(lambdas.size)\n",
"}\n",
"\n",
"plot_counter = 1\n",
"\n",
"fig = plt.figure(figsize=(32, 54))\n",
"\n",
"for i, _lambda in enumerate(tqdm.tqdm(lambdas)):\n",
" for key, method in zip(\n",
" [\"ols_sk\", \"ridge_sk\", \"lasso_sk\"],\n",
" [skl.LinearRegression(), skl.Ridge(alpha=_lambda), skl.Lasso(alpha=_lambda)]\n",
" ):\n",
" method = method.fit(X_train, y_train)\n",
"\n",
" train_errors[key][i] = method.score(X_train, y_train)\n",
" test_errors[key][i] = method.score(X_test, y_test)\n",
"\n",
" omega = method.coef_.reshape(L, L)\n",
"\n",
" plt.subplot(10, 5, plot_counter)\n",
" plt.imshow(omega, **cmap_args)\n",
" plt.title(r\"%s, $\\lambda = %.4f$\" % (key, _lambda))\n",
" plot_counter += 1\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that LASSO reaches a good solution for low\n",
"values of $\\lambda$, but will \"wither\" when we increase $\\lambda$ too\n",
"much. Ridge is more stable over a larger range of values for\n",
"$\\lambda$, but eventually also fades away.\n",
"\n",
"\n",
"## Finding the optimal value of $\\lambda$\n",
"\n",
"To determine which value of $\\lambda$ is best we plot the accuracy of\n",
"the models when predicting the training and the testing set. We expect\n",
"the accuracy of the training set to be quite good, but if the accuracy\n",
"of the testing set is much lower this tells us that we might be\n",
"subject to an overfit model. The ideal scenario is an accuracy on the\n",
"testing set that is close to the accuracy of the training set."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"editable": true
},
"outputs": [],
"source": [
"fig = plt.figure(figsize=(20, 14))\n",
"\n",
"colors = {\n",
" \"ols_sk\": \"r\",\n",
" \"ridge_sk\": \"y\",\n",
" \"lasso_sk\": \"c\"\n",
"}\n",
"\n",
"for key in train_errors:\n",
" plt.semilogx(\n",
" lambdas,\n",
" train_errors[key],\n",
" colors[key],\n",
" label=\"Train {0}\".format(key),\n",
" linewidth=4.0\n",
" )\n",
"\n",
"for key in test_errors:\n",
" plt.semilogx(\n",
" lambdas,\n",
" test_errors[key],\n",
" colors[key] + \"--\",\n",
" label=\"Test {0}\".format(key),\n",
" linewidth=4.0\n",
" )\n",
"plt.legend(loc=\"best\", fontsize=18)\n",
"plt.xlabel(r\"$\\lambda$\", fontsize=18)\n",
"plt.ylabel(r\"$R^2$\", fontsize=18)\n",
"plt.tick_params(labelsize=18)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the above figure we can see that LASSO with $\\lambda = 10^{-2}$\n",
"achieves a very good accuracy on the test set. This by far surpasses the\n",
"other models for all values of $\\lambda$."
]
}
],
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
} | cc0-1.0 |
NLP-Deeplearning-Club/Classic-ML-Methods-Algo | ipynbs/appendix/model_evaluation/clustering_metrics.ipynb | 2 | 9770 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 聚类的评估指标\n",
"\n",
"聚类的评估不像分类那么容易,我们训练的时候只知道特征没有标签,那自然评估的时候和标签会对不上.下面是几种常见的聚类性能评估方式"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 有标签参考的情况下\n",
"\n",
"有时候我们会拿带标签数据去掉标签做聚类,这种时候以下的评估指标就可以有用了\n",
"\n",
"### 调整兰德系数(Adjusted Rand Index)\n",
"\n",
"\n",
"调整兰德系数,用于体现不考虑标签顺序的相似性.\n",
"\n",
"\n",
"$$ \\text{RI} = \\frac{a + b}{C_2^{n_{samples}}} $$\n",
"\n",
"\n",
"其中 $C_2^{n_{samples}}$ 是数据集中可能的数据对(pairs)的总数(不排序).\n",
"\n",
"然而,RI评分不能保证随机标签任务(random label assignments)将获得接近零的值(特别是如果簇的数量与采样数量相同的数量级).\n",
"\n",
"\n",
"为了抵消这种影响,我们可以通过定义`adjusted Rand index`(调整后的Rand index)来折现(discount)随机标签的预期$RI E[\\text{RI}]$ ,如下所示:\n",
"\n",
"$$ \\text{ARI} = \\frac{\\text{RI} - E[\\text{RI}]}{\\max(\\text{RI}) - E[\\text{RI}]}$$\n",
"\n",
"`adjusted rand score`是对称的(symmetric)-- 交换参数不会改变得分.它可以作为共识度量(consensus measure),\n",
"\n",
"其取值范围在[-1,1],而负数或者得分接近0说明效果不佳;越大说明效果越好.\n",
"\n",
"它的缺点是必须要有真实标签,这在现实中怕是比较难做到."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### 互信息(Mutual Information)\n",
"\n",
"互信息常常作为离散数据关联程度的度量,针对聚类效果评估,常见的有:\n",
"\n",
"+ Normalized Mutual Information(NMI) \n",
"+ Adjusted Mutual Information(AMI)\n",
"\n",
"\n",
"假设两个标签分配(相同的N个对象), 和 V.它们的熵是一个分区集合的不确定性量,定义如下:\n",
"\n",
"$$ H(U) = - \\sum_{i=1}^{|U|}P(i)\\log(P(i)) $$\n",
"\n",
"其中 $P(i) = \\frac{|U_i|}{N}$ 是从U中随机选取的对象到类$ U_i$的概率.\n",
"\n",
"同样对于 V:\n",
"\n",
"$$ H(V) = - \\sum_{j=1}^{|V|}P'(j)\\log(P'(j)) $$\n",
"\n",
"使用 $P'(j) = |V_j| / N$.U和V之间的mutual information(MI)由下式计算:\n",
"\n",
"$$ \\text{MI}(U, V) = \\sum_{i=1}^{|U|}\\sum_{j=1}^{|V|}P(i, j)\\log\\left(\\frac{P(i,j)}{P(i)P'(j)}\\right) $$\n",
"\n",
"其中$ P(i, j) = |U_i \\cap V_j| / N $是随机选择的对象落入两个类的概率 $U_i$ 和$ V_j $。\n",
"\n",
"也可以用设定的基数表达式表示:\n",
"\n",
"$$\\text{MI}(U, V) = \\sum_{i=1}^{|U|} \\sum_{j=1}^{|V|} \\frac{|U_i \\cap V_j|}{N}\\log\\left(\\frac{N|U_i \\cap V_j|}{|U_i||V_j|}\\right)$$\n",
"\n",
"normalized mutual information 被定义为\n",
"\n",
"$$ \\text{NMI}(U, V) = \\frac{\\text{MI}(U, V)}{\\sqrt{H(U)H(V)}}$$\n",
"\n",
"mutual information 的值以及 normalized variant(标准化变量)的值不会因 chance而被调整,随着不同标签簇的数量的增加,不管标签分配之间的\"mutual information\"的实际数量如何,都会趋向于增加.\n",
"\n",
"mutual information 的期望值可以用 Vinh,Epps 和 Bailey,(2009) 的以下公式来计算.在这个方程式中,$ a_i = |U_i| $($U_i$中元素的数量) 和 $b_j = |V_j| $($V_j$ 中元素的数量).\n",
"\n",
"$$ E[\\text{MI}(U,V)]=\\sum_{i=1}^|U| \\sum_{j=1}^|V| \\sum_{n_{ij}=(a_i+b_j-N)^+\n",
"}^{\\min(a_i, b_j)} \\frac{n_{ij}}{N}\\log \\left( \\frac{ N.n_{ij}}{a_i b_j}\\right)\n",
"\\frac{a_i!b_j!(N-a_i)!(N-b_j)!}{N!n_{ij}!(a_i-n_{ij})!(b_j-n_{ij})!\n",
"(N-a_i-b_j+n_{ij})!} $$\n",
"\n",
"使用期望值, 然后可以使用与 adjusted Rand index 相似的形式来计算调整后的 mutual information:\n",
"\n",
"$$ \\text{AMI} = \\frac{\\text{MI} - E[\\text{MI}]}{\\max(H(U), H(V)) - E[\\text{MI}]}$$\n",
"\n",
"互信息的取值范围是[0,1],它的缺点和上面一样,得有个真实标签才可以"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 同质性(homogeneity),完整性(completeness)和 V-measure\n",
"\n",
"有点类似分类问题的精确率,召回率和f1.\n",
"\n",
"\n",
"同质性和completeness的得分由下面公式给出:\n",
"\n",
"$$h = 1 - \\frac{H(C|K)}{H(C)}$$\n",
"\n",
"$$c = 1 - \\frac{H(K|C)}{H(K)}$$\n",
"\n",
"其中$ H(C|K) $是 给定簇分配的类的条件熵,由下式给出:\n",
"\n",
"$$ H(C|K) = - \\sum_{c=1}^{|C|} \\sum_{k=1}^{|K|} \\frac{n_{c,k}}{n}\n",
"\\cdot \\log\\left(\\frac{n_{c,k}}{n_k}\\right) $$\n",
"\n",
"并且H(C)是类的熵,并且由下式给出:\n",
"\n",
"$$H(C) = - \\sum_{c=1}^{|C|} \\frac{n_c}{n} \\cdot \\log\\left(\\frac{n_c}{n}\\right)$$\n",
"\n",
"n个样本总数,$ n_c$ 和$ n_k $分别属于 c 类和簇 k 的样本数,最后$ n_{c,k}$ 分配给簇 k 的类 c 的样本数.\n",
"\n",
"给定类的条件熵$ H(K|C)$ 和 簇的熵$ H(K)$ 以对称方式定义.\n",
"\n",
"Rosenberg 和 Hirschberg 进一步定义 V-measure 作为同质性和完整性的调和平均数:\n",
"\n",
"$$ v = 2 \\cdot \\frac{h \\cdot c}{h + c} $$\n",
"\n",
"这几个参数与上面一样取之范围为[0,1],越大说明越好."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fowlkes-Mallows 分数\n",
"\n",
"Fowlkes-Mallows 分数被定义为 成对的准确率和 recall召回率的几何平均值:\n",
"\n",
"$$ \\text{FMI} = \\frac{\\text{TP}}{\\sqrt{(\\text{TP} + \\text{FP}) (\\text{TP} + \\text{FN})}} $$\n",
"\n",
"取值范围为[0,1].较高的值代表效果越好.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 无标签参考情况下的度量\n",
"\n",
"更多的情况下,聚类就是为了处理无标签数据的,这种时候压根就没有标签,聚类效果的度量就只能从数据自己身上找了."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Silhouette 系数\n",
"\n",
"该系数基于两个集合间的平均距离.\n",
"\n",
"$$ s = \\frac{b - a}{max(a, b)} $$\n",
"\n",
"\n",
"+ a: 样本与同一类别中所有其他点之间的平均距离。\n",
"+ b: 样本与 下一个距离最近的簇 中的所有其他点之间的平均距离。\n",
"\n",
"Silhouette系数取值为[-1,1],越高说明聚类效果越好\n",
"\n",
"silhouette系数的缺点是凸簇通常比其他类型的簇更高."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calinski-Harabaz 指数\n",
"\n",
"对于k簇,Calinski-Harabaz得分s是作为簇间色散平均值与群内色散之间的比值给出的:\n",
"\n",
"$$ s(k) = \\frac{\\mathrm{Tr}(B_k)}{\\mathrm{Tr}(W_k)} \\times \\frac{N - k}{k - 1} $$\n",
"\n",
"其中$B_K$是 组间色散矩阵,$W_K$是由以下定义的群内色散矩阵:\n",
"\n",
"$$ W_k = \\sum_{q=1}^k \\sum_{x \\in C_q} (x - c_q) (x - c_q)^T$$\n",
"\n",
"$$ B_k = \\sum_q n_q (c_q - c) (c_q - c)^T $$\n",
"\n",
"N为数据中的点数,$C_q$为簇q中的点集$c_q$为簇q的中心,c 为 E 的中心,$n_q$为簇q中的点数.\n",
"\n",
"该指标的取值范围为\n",
"\n",
"这个指数的优点是:\n",
"\n",
"+ 当簇密集且分离较好时,分数更高\n",
"+ 得分计算很快\n",
"\n",
"缺点和上面一样:\n",
"\n",
"+ 凸的簇的该指数通常高于其他类型的簇."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ***使用sklearn做模型评估***\n",
"\n",
"sklearn提供了一些接口来做聚类模型的评估\n",
"\n",
"接口|说明\n",
"---|---\n",
"`metrics.adjusted_mutual_info_score(…)`|调整互信息指数\n",
"`metrics.adjusted_rand_score(labels_true, …)`|调整兰德系数\n",
"`metrics.calinski_harabaz_score(X, labels)`|计算calinski和harabaz评分\n",
"`metrics.completeness_score(labels_true, …)`|给定一个基本事实的集群标注的完备度量\n",
"`metrics.fowlkes_mallows_score(labels_true, …)`|Fowlkes-Mallows分数\n",
"`metrics.homogeneity_completeness_v_measure(…)`|计算同质性(homogeneity),完整性(completeness)和 V-measure\n",
"`metrics.homogeneity_score(labels_true, …)`|计算同质性(homogeneity)\n",
"`metrics.mutual_info_score(labels_true, …)`|互信息指数\n",
"`metrics.normalized_mutual_info_score(…)`|标准化互信息指数\n",
"`metrics.silhouette_score(X, labels[, …])`|计算Silhouette系数\n",
"`metrics.silhouette_samples(X, labels[, metric])`|为每个样本计算Silhouette系数\n",
"`metrics.v_measure_score(labels_true, labels_pred)`|计算V-measure"
]
}
],
"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": 2
}
| mit |
calroc/joypy | docs/4. Replacing Functions in the Dictionary.ipynb | 1 | 9652 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preamble"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from notebook_preamble import D, J, V"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### A long trace"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" . [23 18] average\n",
" [23 18] . average\n",
" [23 18] . [sum 1.0 *] [size] cleave /\n",
" [23 18] [sum 1.0 *] . [size] cleave /\n",
" [23 18] [sum 1.0 *] [size] . cleave /\n",
" [23 18] [sum 1.0 *] [size] . [i] app2 [popd] dip /\n",
" [23 18] [sum 1.0 *] [size] [i] . app2 [popd] dip /\n",
"[23 18] [[sum 1.0 *] [23 18]] [i] . infra first [[size] [23 18]] [i] infra first [popd] dip /\n",
" [23 18] [sum 1.0 *] . i [[23 18]] swaack first [[size] [23 18]] [i] infra first [popd] dip /\n",
" [23 18] . sum 1.0 * [[23 18]] swaack first [[size] [23 18]] [i] infra first [popd] dip /\n",
" 41 . 1.0 * [[23 18]] swaack first [[size] [23 18]] [i] infra first [popd] dip /\n",
" 41 1.0 . * [[23 18]] swaack first [[size] [23 18]] [i] infra first [popd] dip /\n",
" 41.0 . [[23 18]] swaack first [[size] [23 18]] [i] infra first [popd] dip /\n",
" 41.0 [[23 18]] . swaack first [[size] [23 18]] [i] infra first [popd] dip /\n",
" [23 18] [41.0] . first [[size] [23 18]] [i] infra first [popd] dip /\n",
" [23 18] 41.0 . [[size] [23 18]] [i] infra first [popd] dip /\n",
" [23 18] 41.0 [[size] [23 18]] . [i] infra first [popd] dip /\n",
"[23 18] 41.0 [[size] [23 18]] [i] . infra first [popd] dip /\n",
" [23 18] [size] . i [41.0 [23 18]] swaack first [popd] dip /\n",
" [23 18] . size [41.0 [23 18]] swaack first [popd] dip /\n",
" [23 18] . 0 swap [pop ++] step [41.0 [23 18]] swaack first [popd] dip /\n",
" [23 18] 0 . swap [pop ++] step [41.0 [23 18]] swaack first [popd] dip /\n",
" 0 [23 18] . [pop ++] step [41.0 [23 18]] swaack first [popd] dip /\n",
" 0 [23 18] [pop ++] . step [41.0 [23 18]] swaack first [popd] dip /\n",
" 0 23 [pop ++] . i [18] [pop ++] step [41.0 [23 18]] swaack first [popd] dip /\n",
" 0 23 . pop ++ [18] [pop ++] step [41.0 [23 18]] swaack first [popd] dip /\n",
" 0 . ++ [18] [pop ++] step [41.0 [23 18]] swaack first [popd] dip /\n",
" 1 . [18] [pop ++] step [41.0 [23 18]] swaack first [popd] dip /\n",
" 1 [18] . [pop ++] step [41.0 [23 18]] swaack first [popd] dip /\n",
" 1 [18] [pop ++] . step [41.0 [23 18]] swaack first [popd] dip /\n",
" 1 18 [pop ++] . i [41.0 [23 18]] swaack first [popd] dip /\n",
" 1 18 . pop ++ [41.0 [23 18]] swaack first [popd] dip /\n",
" 1 . ++ [41.0 [23 18]] swaack first [popd] dip /\n",
" 2 . [41.0 [23 18]] swaack first [popd] dip /\n",
" 2 [41.0 [23 18]] . swaack first [popd] dip /\n",
" [23 18] 41.0 [2] . first [popd] dip /\n",
" [23 18] 41.0 2 . [popd] dip /\n",
" [23 18] 41.0 2 [popd] . dip /\n",
" [23 18] 41.0 . popd 2 /\n",
" 41.0 . 2 /\n",
" 41.0 2 . /\n",
" 20.5 . \n"
]
}
],
"source": [
"V('[23 18] average')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Replacing `sum` and `size` with \"compiled\" versions.\n",
"\n",
"Both `sum` and `size` are [catamorphisms](https://en.wikipedia.org/wiki/Catamorphism), they each convert a sequence to a single value."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Given a quoted sequence of numbers return the sum.\n",
"\n",
"sum == 0 swap [+] step\n",
"\n"
]
}
],
"source": [
"J('[sum] help')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 swap [pop ++] step\n",
"\n"
]
}
],
"source": [
"J('[size] help')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use \"compiled\" versions (they're not really compiled in this case, they're hand-written in Python) to speed up evaluation and make the trace more readable. The `sum` function is already in the library. It gets shadowed by the definition version above during `initialize()`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from joy.library import SimpleFunctionWrapper, primitives\n",
"from joy.utils.stack import iter_stack\n",
"\n",
"\n",
"@SimpleFunctionWrapper\n",
"def size(stack):\n",
" '''Return the size of the sequence on the stack.'''\n",
" sequence, stack = stack\n",
" n = 0\n",
" for _ in iter_stack(sequence):\n",
" n += 1\n",
" return n, stack\n",
"\n",
"\n",
"sum_ = next(p for p in primitives if p.name == 'sum')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we replace them old versions in the dictionary with the new versions and re-evaluate the expression."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"old_sum, D['sum'] = D['sum'], sum_\n",
"old_size, D['size'] = D['size'], size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can see that `size` and `sum` now execute in a single step."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" . [23 18] average\n",
" [23 18] . average\n",
" [23 18] . [sum 1.0 *] [size] cleave /\n",
" [23 18] [sum 1.0 *] . [size] cleave /\n",
" [23 18] [sum 1.0 *] [size] . cleave /\n",
" [23 18] [sum 1.0 *] [size] . [i] app2 [popd] dip /\n",
" [23 18] [sum 1.0 *] [size] [i] . app2 [popd] dip /\n",
"[23 18] [[sum 1.0 *] [23 18]] [i] . infra first [[size] [23 18]] [i] infra first [popd] dip /\n",
" [23 18] [sum 1.0 *] . i [[23 18]] swaack first [[size] [23 18]] [i] infra first [popd] dip /\n",
" [23 18] . sum 1.0 * [[23 18]] swaack first [[size] [23 18]] [i] infra first [popd] dip /\n",
" 41 . 1.0 * [[23 18]] swaack first [[size] [23 18]] [i] infra first [popd] dip /\n",
" 41 1.0 . * [[23 18]] swaack first [[size] [23 18]] [i] infra first [popd] dip /\n",
" 41.0 . [[23 18]] swaack first [[size] [23 18]] [i] infra first [popd] dip /\n",
" 41.0 [[23 18]] . swaack first [[size] [23 18]] [i] infra first [popd] dip /\n",
" [23 18] [41.0] . first [[size] [23 18]] [i] infra first [popd] dip /\n",
" [23 18] 41.0 . [[size] [23 18]] [i] infra first [popd] dip /\n",
" [23 18] 41.0 [[size] [23 18]] . [i] infra first [popd] dip /\n",
"[23 18] 41.0 [[size] [23 18]] [i] . infra first [popd] dip /\n",
" [23 18] [size] . i [41.0 [23 18]] swaack first [popd] dip /\n",
" [23 18] . size [41.0 [23 18]] swaack first [popd] dip /\n",
" 2 . [41.0 [23 18]] swaack first [popd] dip /\n",
" 2 [41.0 [23 18]] . swaack first [popd] dip /\n",
" [23 18] 41.0 [2] . first [popd] dip /\n",
" [23 18] 41.0 2 . [popd] dip /\n",
" [23 18] 41.0 2 [popd] . dip /\n",
" [23 18] 41.0 . popd 2 /\n",
" 41.0 . 2 /\n",
" 41.0 2 . /\n",
" 20.5 . \n"
]
}
],
"source": [
"V('[23 18] average')"
]
}
],
"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-3.0 |
danielc2112/notebooks | tf_idf.ipynb | 1 | 24793 | {
"metadata": {
"name": "",
"signature": "sha256:ca12b831350080a857fed200d698e72b020d58f5e998ef7008bfd80204831596"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"def freq(word, doc):\n",
" return doc.count(word)\n",
" \n",
" \n",
"def word_count(doc):\n",
" return len(doc.split())\n",
" \n",
" \n",
"def tf(word, doc):\n",
" return ( freq(word, doc) / float(word_count(doc)) ) \n",
" \n",
" \n",
"def num_docs_containing(word, list_of_docs):\n",
" count = 0\n",
" for document in list_of_docs:\n",
" if freq(word, document) > 0:\n",
" count += 1\n",
" return 1 + count\n",
" \n",
" \n",
"def idf(word, list_of_docs):\n",
" num = len(list_of_docs)\n",
" #print num\n",
" den = float( num_docs_containing(word, list_of_docs) )\n",
" #print den\n",
" return math.log( num / den )\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 84
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d0 = \"\"\"Python is a 2000 made-for-TV horror movie directed by Richard\n",
"Clabaugh. The film features several cult favorite actors, including William\n",
"Zabka of The Karate Kid fame, Wil Wheaton, Casper Van Dien, Jenny McCarthy,\n",
"Keith Coogan, Robert Englund (best known for his role as Freddy Krueger in the\n",
"A Nightmare on Elm Street series of films), Dana Barron, David Bowe, and Sean\n",
"Whalen. The film concerns a genetically engineered snake, a python, that\n",
"escapes and unleashes itself on a small town. It includes the classic final\n",
"girl scenario evident in films like Friday the 13th. It was filmed in Los Angeles,\n",
" California and Malibu, California. Python was followed by two sequels: Python\n",
" II (2002) and Boa vs. Python (2004), both also made-for-TV films.\"\"\"\n",
"\n",
"d1 = \"\"\"Python is a genus of\n",
"nonvenomous pythons found in Africa and Asia. Currently, 7 species are\n",
"recognised. A member of this genus, P. reticulatus, is among the longest\n",
"snakes known. This snake is also very scary and I never want to see one up close. \n",
"Which is scariest, pythons, sharks or ninjas?\"\"\"\n",
"\n",
"d2 = \"\"\"The Colt Python is a .357 Magnum caliber revolver formerly\n",
"manufactured by Colt's Manufacturing Company of Hartford, Connecticut.\n",
"It is sometimes referred to as a \"Combat Magnum\". It was first introduced\n",
"in 1955, the same year as Smith & Wesson's M29 .44 Magnum. The now discontinued\n",
"Colt Python targeted the premium revolver market segment. Some firearm\n",
"collectors and writers such as Jeff Cooper, Ian V. Hogg, Chuck Hawks, Leroy\n",
"Thompson, Renee Smeets and Martin Dougherty have described the Python as the\n",
"finest production revolver ever made.\"\"\"\n",
"\n",
"#d3 = \"\"\"I am a truly small text\"\"\"\n",
"d3 = \"\"\"The fossil record of snakes is relatively poor because snake skeletons \n",
"are typically small and fragile making fossilization uncommon. Fossils readily \n",
"identifiable as snakes (though often retaining hind limbs) first appear in the \n",
"fossil record during the Cretaceous period. The earliest known true snake \n",
"fossils (members of the crown group Serpentes) come from the marine simoliophiids, \n",
"the oldest of which is the Late Cretaceous (Cenomanian age) Haasiophis terrasanctus,\n",
"dated to between 112 and 94 million years old. Based on comparative anatomy, there \n",
"is consensus that snakes descended from lizards. Pythons and boas\u2014primitive \n",
"groups among modern snakes\u2014have vestigial hind limbs: tiny, clawed digits known as anal \n",
"spurs, which are used to grasp during mating. The families Leptotyphlopidae \n",
"and Typhlopidae also possess remnants of the pelvic girdle, appearing as horny projections \n",
"when visible.\"\"\"\n",
"\n",
"d4 = \"\"\"The potato is a starchy, tuberous crop from the perennial nightshade \n",
"Solanum tuberosum L. The word \"potato\" may refer either to the plant itself \n",
"or to the edible tuber. In the Andes, where the species is indigenous, there\n",
"are some other closely related cultivated potato species. Potatoes were introduced\n",
"outside the Andes region approximately four centuries ago, and have since \n",
"become an integral part of much of the world's food supply. It is the world's \n",
"fourth-largest food crop, following maize, wheat, and rice.\"\"\"\n",
"\n",
"corpus = [d0,d1,d2,d3,d4]\n",
"\n",
"def all_freq(word):\n",
" for i,doc in enumerate(corpus):\n",
" print 'document',i, 'has the word \"'+word+'\":\\t', freq(word,doc),'times out of',word_count(doc)\n",
" \n",
"def all_tf(word):\n",
" for i,doc in enumerate(corpus):\n",
" print 'document',i, 'has TF for word \"'+word+'\":\\t', tf(word,doc)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 91
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"word = 'Python'\n",
"#all_freq(word)\n",
"print ''\n",
"all_tf(word)\n",
"print ''\n",
"print word,'idf 3 docs:\\t',idf(word,corpus[:3]),'\\tidf all docs:',idf(word,corpus)\n",
"print '\\n'\n",
"\n",
"word = 'the'\n",
"#all_freq(word)\n",
"print ''\n",
"all_tf(word)\n",
"print ''\n",
"print word,'idf 3 docs:\\t',idf(word,corpus[:3]),'\\tidf all docs:',idf(word,corpus)\n",
"print '\\n'\n",
"\n",
"word = 'truly'\n",
"#all_freq(word)\n",
"print ''\n",
"all_tf(word)\n",
"print ''\n",
"print word,'idf 3 docs:\\t',idf(word,corpus[:3]),'\\tidf all docs:',idf(word,corpus)\n",
"print '\\n'"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"document 0 has TF for word \"Python\":\t0.0327868852459\n",
"document 1 has TF for word \"Python\":\t0.0192307692308\n",
"document 2 has TF for word \"Python\":\t0.0348837209302\n",
"document 3 has TF for word \"Python\":\t0.00763358778626\n",
"document 4 has TF for word \"Python\":\t0.0\n",
"\n",
"Python idf 3 docs:\t-0.287682072452 \tidf all docs: 0.0\n",
"\n",
"\n",
"\n",
"document 0 has TF for word \"the\":\t0.0245901639344\n",
"document 1 has TF for word \"the\":\t0.0192307692308\n",
"document 2 has TF for word \"the\":\t0.046511627907\n",
"document 3 has TF for word \"the\":\t0.0610687022901\n",
"document 4 has TF for word \"the\":\t0.132530120482\n",
"\n",
"the idf 3 docs:\t-0.287682072452 \tidf all docs: -0.182321556794\n",
"\n",
"\n",
"\n",
"document 0 has TF for word \"truly\":\t0.0\n",
"document 1 has TF for word \"truly\":\t0.0\n",
"document 2 has TF for word \"truly\":\t0.0\n",
"document 3 has TF for word \"truly\":\t0.0\n",
"document 4 has TF for word \"truly\":\t0.0\n",
"\n",
"truly idf 3 docs:\t1.09861228867 \tidf all docs: 1.60943791243\n",
"\n",
"\n"
]
}
],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def treat(doc):\n",
" doc = doc.lower()\n",
" doc = doc.replace('.','')\n",
" doc = doc.replace('-', ' ')\n",
" doc = doc.replace(',','')\n",
" doc = doc.replace('?','')\n",
" return doc"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from operator import itemgetter\n",
"\n",
"res = {}\n",
"for doc in corpus:\n",
" doc = treat(doc)\n",
" for word in doc.split():\n",
" word_idf = idf(word, corpus)\n",
" word_tf = tf(word, doc)\n",
" res[word] = word_idf * word_tf\n",
"final = sorted(res.items(), key=itemgetter(1))\n",
"\n",
"for xx in final[::-1]:\n",
" print xx"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(u'magnum', 0.05614318299188722)\n",
"(u'colt', 0.05614318299188722)\n",
"(u'potato', 0.04363289199400738)\n",
"(u'film', 0.04363289199400738)\n",
"(u'andes', 0.03831995029605)\n",
"(u'this', 0.03524195122592905)\n",
"(u'genus', 0.03524195122592905)\n",
"(u'fossil', 0.03497292869748684)\n",
"(u'tuber', 0.03272466899550554)\n",
"(u'revolver', 0.03196363018165657)\n",
"(u'africa', 0.03095072908527116)\n",
"(u'asia', 0.03095072908527116)\n",
"(u'currently', 0.03095072908527116)\n",
"(u'wil', 0.025546633530700004)\n",
"(u'california', 0.025546633530700004)\n",
"(u'tv', 0.025546633530700004)\n",
"(u'cretaceous', 0.024571571182200002)\n",
"(u\"world's\", 0.02181644599700369)\n",
"(u'crop', 0.02181644599700369)\n",
"(u'films', 0.02181644599700369)\n",
"(u'four', 0.02181644599700369)\n",
"(u'food', 0.02181644599700369)\n",
"(u'for', 0.020270858085952012)\n",
"(u'solanum', 0.019159975148025)\n",
"(u'potatoes', 0.019159975148025)\n",
"(u'\"combat', 0.01871439433062907)\n",
"(u\"colt's\", 0.01871439433062907)\n",
"(u\"wesson's\", 0.01871439433062907)\n",
"(u'leroy', 0.01871439433062907)\n",
"(u'smeets', 0.01871439433062907)\n",
"(u'dougherty', 0.01871439433062907)\n",
"(u'hartford', 0.01871439433062907)\n",
"(u'connecticut', 0.01871439433062907)\n",
"(u'company', 0.01871439433062907)\n",
"(u'jeff', 0.01871439433062907)\n",
"(u'hawks', 0.01871439433062907)\n",
"(u'm29', 0.01871439433062907)\n",
"(u'renee', 0.01871439433062907)\n",
"(u'manufacturing', 0.01871439433062907)\n",
"(u'cooper', 0.01871439433062907)\n",
"(u'martin', 0.01871439433062907)\n",
"(u'chuck', 0.01871439433062907)\n",
"(u'thompson', 0.01871439433062907)\n",
"(u'smith', 0.01871439433062907)\n",
"(u'hogg', 0.01871439433062907)\n",
"(u'magnum\"', 0.01871439433062907)\n",
"(u'python', 0.017819498503464794)\n",
"(u'longest', 0.017620975612964523)\n",
"(u'very', 0.017620975612964523)\n",
"(u'want', 0.017620975612964523)\n",
"(u'nonvenomous', 0.017620975612964523)\n",
"(u'scary', 0.017620975612964523)\n",
"(u'scariest', 0.017620975612964523)\n",
"(u'recognised', 0.017620975612964523)\n",
"(u'one', 0.017620975612964523)\n",
"(u'never', 0.017620975612964523)\n",
"(u'see', 0.017620975612964523)\n",
"(u'found', 0.017620975612964523)\n",
"(u'reticulatus', 0.017620975612964523)\n",
"(u'ninjas', 0.017620975612964523)\n",
"(u'sharks', 0.017620975612964523)\n",
"(u'snakes', 0.015597728969953914)\n",
"(u'which', 0.013989171478994734)\n",
"(u'hind', 0.013989171478994734)\n",
"(u'typhlopidae', 0.013989171478994734)\n",
"(u'record', 0.013989171478994734)\n",
"(u'appear', 0.013989171478994734)\n",
"(u'old', 0.013989171478994734)\n",
"(u'fossils', 0.013989171478994734)\n",
"(u'during', 0.013989171478994734)\n",
"(u'group', 0.013989171478994734)\n",
"(u'nightmare', 0.012773316765350002)\n",
"(u'englund', 0.012773316765350002)\n",
"(u'kid', 0.012773316765350002)\n",
"(u'dien', 0.012773316765350002)\n",
"(u'william', 0.012773316765350002)\n",
"(u'wheaton', 0.012773316765350002)\n",
"(u'bowe', 0.012773316765350002)\n",
"(u'coogan', 0.012773316765350002)\n",
"(u'jenny', 0.012773316765350002)\n",
"(u'dana', 0.012773316765350002)\n",
"(u'zabka', 0.012773316765350002)\n",
"(u'freddy', 0.012773316765350002)\n",
"(u'malibu', 0.012773316765350002)\n",
"(u'whalen', 0.012773316765350002)\n",
"(u'van', 0.012773316765350002)\n",
"(u'angeles', 0.012773316765350002)\n",
"(u'david', 0.012773316765350002)\n",
"(u'keith', 0.012773316765350002)\n",
"(u'friday', 0.012773316765350002)\n",
"(u'mccarthy', 0.012773316765350002)\n",
"(u'robert', 0.012773316765350002)\n",
"(u'richard', 0.012773316765350002)\n",
"(u'karate', 0.012773316765350002)\n",
"(u'sean', 0.012773316765350002)\n",
"(u'street', 0.012773316765350002)\n",
"(u'krueger', 0.012773316765350002)\n",
"(u'barron', 0.012773316765350002)\n",
"(u'clabaugh', 0.012773316765350002)\n",
"(u'elm', 0.012773316765350002)\n",
"(u'casper', 0.012773316765350002)\n",
"(u'based', 0.012285785591100001)\n",
"(u'(cenomanian', 0.012285785591100001)\n",
"(u'haasiophis', 0.012285785591100001)\n",
"(u'serpentes)', 0.012285785591100001)\n",
"(u'leptotyphlopidae', 0.012285785591100001)\n",
"(u'species', 0.012162514851571207)\n",
"(u'region', 0.010908222998501846)\n",
"(u'integral', 0.010908222998501846)\n",
"(u'starchy', 0.010908222998501846)\n",
"(u'may', 0.010908222998501846)\n",
"(u'plant', 0.010908222998501846)\n",
"(u'largest', 0.010908222998501846)\n",
"(u'nightshade', 0.010908222998501846)\n",
"(u'following', 0.010908222998501846)\n",
"(u'other', 0.010908222998501846)\n",
"(u'edible', 0.010908222998501846)\n",
"(u'word', 0.010908222998501846)\n",
"(u'perennial', 0.010908222998501846)\n",
"(u'part', 0.010908222998501846)\n",
"(u'since', 0.010908222998501846)\n",
"(u'tuberosum', 0.010908222998501846)\n",
"(u'much', 0.010908222998501846)\n",
"(u'\"potato\"', 0.010908222998501846)\n",
"(u'approximately', 0.010908222998501846)\n",
"(u'cultivated', 0.010908222998501846)\n",
"(u'ago', 0.010908222998501846)\n",
"(u'become', 0.010908222998501846)\n",
"(u'maize', 0.010908222998501846)\n",
"(u'wheat', 0.010908222998501846)\n",
"(u'outside', 0.010908222998501846)\n",
"(u'tuberous', 0.010908222998501846)\n",
"(u'centuries', 0.010908222998501846)\n",
"(u'rice', 0.010908222998501846)\n",
"(u'related', 0.010908222998501846)\n",
"(u'where', 0.010908222998501846)\n",
"(u'either', 0.010908222998501846)\n",
"(u'fourth', 0.010908222998501846)\n",
"(u'closely', 0.010908222998501846)\n",
"(u'were', 0.010908222998501846)\n",
"(u'indigenous', 0.010908222998501846)\n",
"(u'supply', 0.010908222998501846)\n",
"(u'discontinued', 0.010654543393885524)\n",
"(u'sometimes', 0.010654543393885524)\n",
"(u'segment', 0.010654543393885524)\n",
"(u'such', 0.010654543393885524)\n",
"(u'caliber', 0.010654543393885524)\n",
"(u'same', 0.010654543393885524)\n",
"(u'44', 0.010654543393885524)\n",
"(u'production', 0.010654543393885524)\n",
"(u'ian', 0.010654543393885524)\n",
"(u'market', 0.010654543393885524)\n",
"(u'firearm', 0.010654543393885524)\n",
"(u'357', 0.010654543393885524)\n",
"(u'formerly', 0.010654543393885524)\n",
"(u'&', 0.010654543393885524)\n",
"(u'described', 0.010654543393885524)\n",
"(u'manufactured', 0.010654543393885524)\n",
"(u'targeted', 0.010654543393885524)\n",
"(u'collectors', 0.010654543393885524)\n",
"(u'referred', 0.010654543393885524)\n",
"(u'1955', 0.010654543393885524)\n",
"(u'premium', 0.010654543393885524)\n",
"(u'writers', 0.010654543393885524)\n",
"(u'finest', 0.010654543393885524)\n",
"(u'snake', 0.010220315327368387)\n",
"(u'member', 0.009823569687807515)\n",
"(u'7', 0.009823569687807515)\n",
"(u'close', 0.009823569687807515)\n",
"(u'2000', 0.0072721486656678975)\n",
"(u'scenario', 0.0072721486656678975)\n",
"(u'engineered', 0.0072721486656678975)\n",
"(u'includes', 0.0072721486656678975)\n",
"(u'several', 0.0072721486656678975)\n",
"(u'genetically', 0.0072721486656678975)\n",
"(u'actors', 0.0072721486656678975)\n",
"(u'concerns', 0.0072721486656678975)\n",
"(u'(2004)', 0.0072721486656678975)\n",
"(u'fame', 0.0072721486656678975)\n",
"(u'13th', 0.0072721486656678975)\n",
"(u'escapes', 0.0072721486656678975)\n",
"(u'vs', 0.0072721486656678975)\n",
"(u'evident', 0.0072721486656678975)\n",
"(u'ii', 0.0072721486656678975)\n",
"(u'directed', 0.0072721486656678975)\n",
"(u'final', 0.0072721486656678975)\n",
"(u'two', 0.0072721486656678975)\n",
"(u'classic', 0.0072721486656678975)\n",
"(u'films)', 0.0072721486656678975)\n",
"(u'features', 0.0072721486656678975)\n",
"(u'followed', 0.0072721486656678975)\n",
"(u'boa', 0.0072721486656678975)\n",
"(u'favorite', 0.0072721486656678975)\n",
"(u'(best', 0.0072721486656678975)\n",
"(u'sequels:', 0.0072721486656678975)\n",
"(u'movie', 0.0072721486656678975)\n",
"(u'girl', 0.0072721486656678975)\n",
"(u'series', 0.0072721486656678975)\n",
"(u'like', 0.0072721486656678975)\n",
"(u'horror', 0.0072721486656678975)\n",
"(u'town', 0.0072721486656678975)\n",
"(u'(2002)', 0.0072721486656678975)\n",
"(u'role', 0.0072721486656678975)\n",
"(u'unleashes', 0.0072721486656678975)\n",
"(u'filmed', 0.0072721486656678975)\n",
"(u'including', 0.0072721486656678975)\n",
"(u'both', 0.0072721486656678975)\n",
"(u'typically', 0.006994585739497367)\n",
"(u'mating', 0.006994585739497367)\n",
"(u'retaining', 0.006994585739497367)\n",
"(u'age)', 0.006994585739497367)\n",
"(u'fossilization', 0.006994585739497367)\n",
"(u'poor', 0.006994585739497367)\n",
"(u'when', 0.006994585739497367)\n",
"(u'skeletons', 0.006994585739497367)\n",
"(u'relatively', 0.006994585739497367)\n",
"(u'comparative', 0.006994585739497367)\n",
"(u'often', 0.006994585739497367)\n",
"(u'112', 0.006994585739497367)\n",
"(u'pelvic', 0.006994585739497367)\n",
"(u'snakes\\u2014have', 0.006994585739497367)\n",
"(u'anatomy', 0.006994585739497367)\n",
"(u'making', 0.006994585739497367)\n",
"(u'grasp', 0.006994585739497367)\n",
"(u'girdle', 0.006994585739497367)\n",
"(u'94', 0.006994585739497367)\n",
"(u'uncommon', 0.006994585739497367)\n",
"(u'crown', 0.006994585739497367)\n",
"(u'earliest', 0.006994585739497367)\n",
"(u'families', 0.006994585739497367)\n",
"(u'modern', 0.006994585739497367)\n",
"(u'digits', 0.006994585739497367)\n",
"(u'tiny', 0.006994585739497367)\n",
"(u'lizards', 0.006994585739497367)\n",
"(u'readily', 0.006994585739497367)\n",
"(u'simoliophiids', 0.006994585739497367)\n",
"(u'period', 0.006994585739497367)\n",
"(u'dated', 0.006994585739497367)\n",
"(u'descended', 0.006994585739497367)\n",
"(u'appearing', 0.006994585739497367)\n",
"(u'spurs', 0.006994585739497367)\n",
"(u'identifiable', 0.006994585739497367)\n",
"(u'groups', 0.006994585739497367)\n",
"(u'boas\\u2014primitive', 0.006994585739497367)\n",
"(u'(members', 0.006994585739497367)\n",
"(u'(though', 0.006994585739497367)\n",
"(u'projections', 0.006994585739497367)\n",
"(u'vestigial', 0.006994585739497367)\n",
"(u'clawed', 0.006994585739497367)\n",
"(u'terrasanctus', 0.006994585739497367)\n",
"(u'used', 0.006994585739497367)\n",
"(u'marine', 0.006994585739497367)\n",
"(u'because', 0.006994585739497367)\n",
"(u'anal', 0.006994585739497367)\n",
"(u'consensus', 0.006994585739497367)\n",
"(u'fragile', 0.006994585739497367)\n",
"(u'visible', 0.006994585739497367)\n",
"(u'possess', 0.006994585739497367)\n",
"(u'between', 0.006994585739497367)\n",
"(u'late', 0.006994585739497367)\n",
"(u'true', 0.006994585739497367)\n",
"(u'million', 0.006994585739497367)\n",
"(u'pythons', 0.006994585739497367)\n",
"(u'horny', 0.006994585739497367)\n",
"(u'oldest', 0.006994585739497367)\n",
"(u'limbs)', 0.006994585739497367)\n",
"(u'years', 0.006994585739497367)\n",
"(u'limbs:', 0.006994585739497367)\n",
"(u'remnants', 0.006994585739497367)\n",
"(u'introduced', 0.0060812574257856035)\n",
"(u'itself', 0.0060812574257856035)\n",
"(u'refer', 0.0060812574257856035)\n",
"(u'some', 0.0060812574257856035)\n",
"(u'there', 0.0060812574257856035)\n",
"(u'from', 0.0060812574257856035)\n",
"(u'made', 0.005939832834488264)\n",
"(u'was', 0.005939832834488264)\n",
"(u'by', 0.005939832834488264)\n",
"(u'year', 0.005939832834488264)\n",
"(u'up', 0.00429122214065788)\n",
"(u'los', 0.004054171617190403)\n",
"(u'cult', 0.004054171617190403)\n",
"(u'that', 0.0038994322424884785)\n",
"(u'come', 0.0038994322424884785)\n",
"(u'among', 0.0038994322424884785)\n",
"(u'first', 0.0038994322424884785)\n",
"(u'small', 0.0038994322424884785)\n",
"(u'known', 0.003406771775789462)\n",
"(u'have', 0.0026564708489786874)\n",
"(u'ever', 0.002594692457141974)\n",
"(u'his', 0.0017709805659857918)\n",
"(u'also', 0.001703385887894731)\n",
"(u'as', 0.0)\n",
"(u'it', 0.0)\n",
"(u'are', 0.0)\n",
"(u'now', 0.0)\n",
"(u'of', -0.004340989447475109)\n",
"(u'is', -0.0065114841712126635)\n",
"(u'and', -0.008681978894950218)\n",
"(u'or', -0.008681978894950218)\n",
"(u'on', -0.012525908482027415)\n",
"(u'in', -0.013022968342425327)\n",
"(u'to', -0.013022968342425327)\n",
"(u'an', -0.015193463066162882)\n",
"(u'v', -0.019080162920297575)\n",
"(u'p', -0.021037102706994763)\n",
"(u'the', -0.028216431408588212)\n",
"(u'l', -0.03906890502727598)\n",
"(u'i', -0.05609894055198603)\n",
"(u'a', -0.060773852264651526)\n"
]
}
],
"prompt_number": 94
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br><br> Assim temos uma m\u00e9trica de relev\u00e2ncia para termos contidos em nosso corpus!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br><br>\n",
"Podemos tamb\u00e9m rapidamente escrever uma fun\u00e7\u00e3o geradora de n-grams"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"input_list = ['all', 'this', 'happened', 'more', 'or', 'less']\n",
"\n",
"def find_ngrams(input_list, n):\n",
" return zip(*[input_list[i:] for i in range(n)])\n",
"\n",
"print find_ngrams(input_list, 2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[(u'all', u'this'), (u'this', u'happened'), (u'happened', u'more'), (u'more', u'or'), (u'or', u'less')]\n"
]
}
],
"prompt_number": 95
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
hughperkins/pub-prototyping | jupyter/matplotlib.ipynb | 1 | 10559 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# matplotlib"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## shared subplot axes"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEPCAYAAABWc+9sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8c360a37f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"N = 2\n",
"\n",
"np.random.seed(123)\n",
"f, axes = plt.subplots(N, N, sharex=True, sharey=True)\n",
"ax = f.add_subplot(1, 1, 1, frameon=False)\n",
"ax.set_xlim([-3, 3])\n",
"ax.set_ylim([-3, 3])\n",
"# ax.set_xlabel('my x label')\n",
"\n",
"plt.xlabel(\"common X\")\n",
"for i in range(N):\n",
" for j in range(N):\n",
" ax = axes[i][j]\n",
"# ax.set_ylim(ax.get_ylim()[::-1])\n",
" img = np.random.randn(10, 10).astype(np.float32)\n",
" img = np.flipud(img)\n",
" ax.pcolor(img)\n",
"# plt.gca().invert_yaxis()\n",
" ax.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off')\n",
"# ax.set_ylim(ax.get_ylim()[::-1])\n",
"# ax.invert_yaxis()\n",
"\n",
"# f.subplots_adjust(hspace=0, wspace=0)\n",
"plt.show()\n"
]
}
],
"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 |
vivek-bala/radical.entk-0.6 | design_helpers/cpu_memory/separate_data_objects/3D_schematic_tasks_stages_pipes_objects.ipynb | 4 | 7099 | {
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using matplotlib backend: TkAgg\n"
]
}
],
"source": [
"# Imports\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"from matplotlib import cm\n",
"import numpy as np\n",
"import matplotlib.ticker as mticker\n",
"\n",
"\n",
"# Settings\n",
"FONTSIZE=28\n",
"LABELPAD=18\n",
"TICKPAD=8\n",
"\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "raw",
"metadata": {
"deletable": true,
"editable": true
},
"source": []
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" tasks stages pipelines cpu memory\n",
"0 1 1 1 0.000543 0.003\n",
"1 10 1 1 0.000458 0.017\n",
"2 100 1 1 0.004154 0.159\n",
"3 1000 1 1 0.037974 1.536\n",
"4 10000 1 1 0.297402 15.531\n",
"5 100000 1 1 3.147311 154.300\n",
"6 1000000 1 1 32.636946 1534.992\n"
]
}
],
"source": [
"#x, y = np.meshgrid(num_tasks, num_stages)\n",
"#x = np.log10(x)\n",
"#y = np.log10(y)\n",
"\n",
"df = pd.read_csv('./monitor_task_variation.csv', skipinitialspace=True)\n",
"\n",
"num_pipes = 1\n",
"num_stages = 1\n",
"num_tasks = [1,10,100,1000,10000,100000,1000000] \n",
"\n",
"print df\n",
"fig,axes = plt.subplots(2,1)\n",
"\n",
"df.plot(ax=axes[0], x=['tasks'], y = ['cpu'], loglog=True)\n",
"axes[0].set_ylabel('CPU consumption (seconds)', fontsize=FONTSIZE-6, labelpad=LABELPAD)\n",
"axes[0].set_xlabel('')\n",
"axes[0].legend(fontsize=FONTSIZE)\n",
"for tick in axes[0].yaxis.get_major_ticks():\n",
" tick.label.set_fontsize(FONTSIZE)\n",
"for tick in axes[0].xaxis.get_major_ticks():\n",
" tick.label.set_fontsize(FONTSIZE)\n",
" \n",
"df.plot(ax=axes[1], x=['tasks'], y = ['memory'], loglog=True)\n",
"axes[1].set_ylabel('Memory consumption (MB)', fontsize=FONTSIZE-6,labelpad=LABELPAD)\n",
"axes[1].set_xlabel('Tasks per stage', fontsize=FONTSIZE, labelpad=LABELPAD)\n",
"axes[1].legend(fontsize=FONTSIZE)\n",
"for tick in axes[1].yaxis.get_major_ticks():\n",
" tick.label.set_fontsize(FONTSIZE)\n",
"for tick in axes[1].xaxis.get_major_ticks():\n",
" tick.label.set_fontsize(FONTSIZE)\n",
" \n",
" \n",
"axes[0].set_title('Memory + CPU consumption as a function of tasks(Pipelines = 1, Stages=1)', fontsize=FONTSIZE)\n",
"fig.set_size_inches(18.5, 10.5)\n",
"plt.savefig('./plot_task_variation.png')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#x, y = np.meshgrid(num_tasks, num_stages)\n",
"#x = np.log10(x)\n",
"#y = np.log10(y)\n",
"\n",
"df = pd.read_csv('./monitor_stage_variation.csv', skipinitialspace=True)\n",
"\n",
"fig,axes = plt.subplots(2,1)\n",
"\n",
"df.plot(ax=axes[0], x=['stages'], y = ['cpu'], loglog=True)\n",
"axes[0].set_ylabel('CPU consumption (seconds)', fontsize=FONTSIZE-6, labelpad=LABELPAD)\n",
"axes[0].set_xlabel('')\n",
"axes[0].legend(fontsize=FONTSIZE)\n",
"for tick in axes[0].yaxis.get_major_ticks():\n",
" tick.label.set_fontsize(FONTSIZE)\n",
"for tick in axes[0].xaxis.get_major_ticks():\n",
" tick.label.set_fontsize(FONTSIZE)\n",
" \n",
"df.plot(ax=axes[1], x=['stages'], y = ['memory'], loglog=True)\n",
"axes[1].set_ylabel('Memory consumption (MB)', fontsize=FONTSIZE-6,labelpad=LABELPAD)\n",
"axes[1].set_xlabel('Stages per pipeline', fontsize=FONTSIZE, labelpad=LABELPAD)\n",
"axes[1].legend(fontsize=FONTSIZE)\n",
"for tick in axes[1].yaxis.get_major_ticks():\n",
" tick.label.set_fontsize(FONTSIZE)\n",
"for tick in axes[1].xaxis.get_major_ticks():\n",
" tick.label.set_fontsize(FONTSIZE)\n",
" \n",
" \n",
"axes[0].set_title('Memory + CPU consumption as a function of stages(Pipelines = 1, Tasks per stage =1)', fontsize=FONTSIZE)\n",
"fig.set_size_inches(18.5, 10.5)\n",
"plt.savefig('./plot_stage_variation.png')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#x, y = np.meshgrid(num_tasks, num_stages)\n",
"#x = np.log10(x)\n",
"#y = np.log10(y)\n",
"\n",
"df = pd.read_csv('./monitor_pipeline_variation.csv', skipinitialspace=True)\n",
"\n",
"fig,axes = plt.subplots(2,1)\n",
"\n",
"df.plot(ax=axes[0], x=['pipelines'], y = ['cpu'], loglog=True)\n",
"axes[0].set_ylabel('CPU consumption (seconds)', fontsize=FONTSIZE-6, labelpad=LABELPAD)\n",
"axes[0].set_xlabel('')\n",
"axes[0].legend(fontsize=FONTSIZE)\n",
"for tick in axes[0].yaxis.get_major_ticks():\n",
" tick.label.set_fontsize(FONTSIZE)\n",
"for tick in axes[0].xaxis.get_major_ticks():\n",
" tick.label.set_fontsize(FONTSIZE)\n",
" \n",
"df.plot(ax=axes[1], x=['pipelines'], y = ['memory'], loglog=True)\n",
"axes[1].set_ylabel('Memory consumption (MB)', fontsize=FONTSIZE-6,labelpad=LABELPAD)\n",
"axes[1].set_xlabel('Pipelines', fontsize=FONTSIZE, labelpad=LABELPAD)\n",
"axes[1].legend(fontsize=FONTSIZE)\n",
"for tick in axes[1].yaxis.get_major_ticks():\n",
" tick.label.set_fontsize(FONTSIZE)\n",
"for tick in axes[1].xaxis.get_major_ticks():\n",
" tick.label.set_fontsize(FONTSIZE)\n",
" \n",
" \n",
"axes[0].set_title('Memory + CPU consumption as a function of pipelines(Stages=1, Tasks per stage =1)', fontsize=FONTSIZE)\n",
"fig.set_size_inches(18.5, 10.5)\n",
"plt.savefig('./plot_pipeline_variation.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": 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": 1
}
| mit |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_linear_model_patterns.ipynb | 1 | 4496 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n# Linear classifier on sensor data with plot patterns and filters\n\n\nDecoding, a.k.a MVPA or supervised machine learning applied to MEG and EEG\ndata in sensor space. Fit a linear classifier with the LinearModel object\nproviding topographical patterns which are more neurophysiologically\ninterpretable [1] than the classifier filters (weight vectors).\nThe patterns explain how the MEG and EEG data were generated from the\ndiscriminant neural sources which are extracted by the filters.\nNote patterns/filters in MEG data are more similar than EEG data\nbecause the noise is less spatially correlated in MEG than EEG.\n\n[1] Haufe, S., Meinecke, F., G\u00f6rgen, K., D\u00e4hne, S., Haynes, J.-D.,\nBlankertz, B., & Bie\u00dfmann, F. (2014). On the interpretation of\nweight vectors of linear models in multivariate neuroimaging.\nNeuroImage, 87, 96\u2013110. doi:10.1016/j.neuroimage.2013.10.067\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Authors: Alexandre Gramfort <[email protected]>\n# Romain Trachel <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom mne import io\nfrom mne.datasets import sample\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\n\n# import a linear classifier from mne.decoding\nfrom mne.decoding import LinearModel\n\nprint(__doc__)\n\ndata_path = sample.data_path()"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Set parameters\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\ntmin, tmax = -0.2, 0.5\nevent_id = dict(aud_l=1, vis_l=3)\n\n# Setup for reading the raw data\nraw = io.read_raw_fif(raw_fname, preload=True)\nraw.filter(2, None, method='iir') # replace baselining with high-pass\nevents = mne.read_events(event_fname)\n\n# Read epochs\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,\n decim=4, baseline=None, preload=True)\n\nlabels = epochs.events[:, -1]\n\n# get MEG and EEG data\nmeg_epochs = epochs.copy().pick_types(meg=True, eeg=False)\nmeg_data = meg_epochs.get_data().reshape(len(labels), -1)\neeg_epochs = epochs.copy().pick_types(meg=False, eeg=True)\neeg_data = eeg_epochs.get_data().reshape(len(labels), -1)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Decoding in sensor space using a LogisticRegression classifier\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"clf = LogisticRegression()\nsc = StandardScaler()\n\n# create a linear model with LogisticRegression\nmodel = LinearModel(clf)\n\n# fit the classifier on MEG data\nX = sc.fit_transform(meg_data)\nmodel.fit(X, labels)\n# plot patterns and filters\nmodel.plot_patterns(meg_epochs.info, title='MEG Patterns')\nmodel.plot_filters(meg_epochs.info, title='MEG Filters')\n\n# fit the classifier on EEG data\nX = sc.fit_transform(eeg_data)\nmodel.fit(X, labels)\n# plot patterns and filters\nmodel.plot_patterns(eeg_epochs.info, title='EEG Patterns')\nmodel.plot_filters(eeg_epochs.info, title='EEG Filters')"
],
"outputs": [],
"metadata": {
"collapsed": false
}
}
],
"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": "2.7.12",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | bsd-3-clause |
mzwiessele/notebook_playground | GPExplanationAnimation.ipynb | 1 | 51650550 | null | bsd-2-clause |
nontas/trafficsignrecognition | notebooks/Guide.ipynb | 1 | 10633215 | null | bsd-3-clause |
jupyter-widgets/ipywidgets | tests/test_sanitizer.ipynb | 1 | 2544 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from ipywidgets import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Checkbox(description=\"<p><i>italic</i></p> <p><b>bold</b></p>\", description_allow_html=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Checkbox(description=\"<p><i>italic</i></p> <p><b>bold</b></p>\", description_allow_html=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Text(description=\"<style type='type/css'>SPAN {text-decoration:underline;}</style><img src='jlogo-small.png' title='àçeù' /> <span style='color: orange'>NOT styled</span>\", description_allow_html=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Text(description=\"<style type='type/css'>SPAN {text-decoration:underline;}</style><img src='jlogo-small.png' title='àçeù' /> <span style='color: orange'>styled</span>\", description_allow_html=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Textarea(description=\"<style type='type/css'>SPAN {color: blue;}</style><img src='jlogo-small.png' title='àçeù' /> <span style='text-decoration:underline;''>underlined</span>\", description_allow_html=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"VBox((Text(description=\"<strong>$ y < a, x $</strong>\", allow_html=True),\n",
" Text(description=\"$ <strong>y < a, x</strong> $\", description_allow_html=True)\n",
" ))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"VBox((Label(\"$ y < a, x > a$\"), HTMLMath(\"<strong>$ y < a, x > a$</strong>\")))"
]
}
],
"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": "3.8.1"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| bsd-3-clause |
AlbertoAlfredo/exercicios-cursos | ExerciciosGerais/slides/37.Prática em Python/scripts/5.metricas.ipynb | 1 | 3189 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Formação Cientista de Dados - Fernando Amaral e Jones Granatyr\n",
"# Igraph"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Importação das bibliotecas\n",
"from igraph import Graph\n",
"from igraph import plot\n",
"import igraph"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Carregamento de grafo no formato graphml\n",
"grafo = igraph.load('Grafo.graphml')\n",
"print(grafo)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot(grafo, bbox = (0,0,600,600))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Visualização do grau de entrada, saída e entrada + saída do grafo\n",
"grafo.degree(mode = 'all')\n",
"#grafo.degree(mode = 'in')\n",
"#grafo.degree(mode = 'out')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Obtendo e imprimindo somente os graus de entrada\n",
"grau = grafo.degree(mode = 'in')\n",
"print(grau)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#gerando o grafo com vertice proporcional ao grau\n",
"plot(grafo, vertex_size = grau ,bbox = (0,0,600,600))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Retorno do diâmetro do grafo (maior distância entre os vértices)\n",
"grafo.diameter(directed = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Retorno dos vértices que possuem a maior distância entre os pontos do grafo\n",
"grafo.get_diameter()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Retorno dos vizinhos de cada vértice\n",
"grafo.neighborhood()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Verificar se o grafo é isomórfico\n",
"grafo2 = grafo\n",
"grafo.isomorphic(grafo2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"jupytext": {
"cell_metadata_filter": "-all",
"main_language": "python",
"notebook_metadata_filter": "-all"
},
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
wegamekinglc/alpha-mind | notebooks/Example 2 - Strategy Analysis.ipynb | 1 | 48643 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 请在环境变量中设置`DB_URI`指向数据库"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"from PyFin.api import *\n",
"from alphamind.api import *\n",
"from alphamind.strategy.strategy import Strategy, RunningSetting\n",
"from matplotlib import pyplot as plt\n",
"\n",
"plt.style.use('ggplot')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Back test parameter settings\n",
"start_date = '2020-01-01'\n",
"end_date = '2020-02-21'\n",
"\n",
"freq = '10b'\n",
"industry_name = 'sw'\n",
"industry_level = 1\n",
"turn_over_target = 0.4\n",
"batch = 1\n",
"horizon = map_freq(freq)\n",
"weights_bandwidth = 0.01\n",
"universe = Universe('hs300')\n",
"data_source = os.environ['DB_URI']\n",
"benchmark_code = 300\n",
"method = 'risk_neutral'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Model settings\n",
"alpha_factors = {\n",
" 'f01': CSQuantiles(LAST('EMA5D')),\n",
" 'f02': CSQuantiles(LAST('EMV6D')),\n",
" }\n",
"\n",
"weights = dict(f01=1.,\n",
" f02=1.)\n",
"\n",
"alpha_model = ConstLinearModel(features=alpha_factors, weights=weights)\n",
"\n",
"data_meta = DataMeta(freq=freq,\n",
" universe=universe,\n",
" batch=1,\n",
" neutralized_risk=None,\n",
" pre_process=None,\n",
" post_process=None,\n",
" data_source=data_source)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Constraintes settings\n",
"\n",
"industry_names = industry_list(industry_name, industry_level)\n",
"constraint_risk = ['SIZE', 'SIZENL', 'BETA']\n",
"total_risk_names = constraint_risk + ['benchmark', 'total']\n",
"all_styles = risk_styles + industry_styles + macro_styles\n",
"\n",
"b_type = []\n",
"l_val = []\n",
"u_val = []\n",
"\n",
"previous_pos = pd.DataFrame()\n",
"rets = []\n",
"turn_overs = []\n",
"leverags = []\n",
"\n",
"for name in total_risk_names:\n",
" if name == 'benchmark':\n",
" b_type.append(BoundaryType.RELATIVE)\n",
" l_val.append(0.8)\n",
" u_val.append(1.0)\n",
" else:\n",
" b_type.append(BoundaryType.ABSOLUTE)\n",
" l_val.append(0.0)\n",
" u_val.append(0.0)\n",
"\n",
"bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Running settings\n",
"running_setting = RunningSetting(weights_bandwidth=weights_bandwidth,\n",
" rebalance_method=method,\n",
" bounds=bounds,\n",
" turn_over_target=turn_over_target)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2021-01-10 02:23:10,565 - ALPHA_MIND - INFO - alpha factor data loading finished ...\n",
"2021-01-10 02:23:13,606 - ALPHA_MIND - INFO - industry data loading finished ...\n",
"2021-01-10 02:23:27,549 - ALPHA_MIND - INFO - benchmark data loading finished ...\n",
"2021-01-10 02:23:43,474 - ALPHA_MIND - INFO - risk_model data loading finished ...\n",
"2021-01-10 02:24:14,284 - ALPHA_MIND - INFO - returns data loading finished ...\n",
"2021-01-10 02:24:16,913 - ALPHA_MIND - INFO - starting backting ...\n",
"2021-01-10 02:24:16,925 - ALPHA_MIND - INFO - alpha models training finished ...\n",
"2021-01-10 02:24:16,930 - ALPHA_MIND - INFO - 2020-01-02 00:00:00 re-balance: 300 codes\n",
"2021-01-10 02:24:16,959 - ALPHA_MIND - INFO - 2020-01-16 00:00:00 re-balance: 300 codes\n",
"2021-01-10 02:24:17,011 - ALPHA_MIND - INFO - 2020-02-07 00:00:00 re-balance: 300 codes\n",
"2021-01-10 02:24:17,054 - ALPHA_MIND - INFO - 2020-02-21 00:00:00 re-balance: 300 codes\n"
]
}
],
"source": [
"# Strategy\n",
"strategy = Strategy(alpha_model,\n",
" data_meta,\n",
" universe=universe,\n",
" start_date=start_date,\n",
" end_date=end_date,\n",
" freq=freq,\n",
" benchmark=benchmark_code)\n",
"\n",
"strategy.prepare_backtest_data()\n",
"ret_df, positions = strategy.run(running_setting=running_setting)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1008x504 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ret_df[['turn_over', 'excess_return']].cumsum().plot(figsize=(14, 7), secondary_y='turn_over')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env: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": "3.8.5"
},
"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": 4
}
| mit |
juditacs/morph-segmentation-experiments | notebooks/sandbox/seq_lstm_padding_hello_world.ipynb | 2 | 8278 | {
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from keras.utils.np_utils import to_categorical\n",
"from keras.layers import Input, Dense, Embedding, Masking, Bidirectional\n",
"from keras.layers.recurrent import LSTM\n",
"from keras.models import Model\n",
"from keras.layers.wrappers import TimeDistributed\n",
"import keras"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Toy data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"samples = [\n",
" (u\"autót\", \"BEEEB\"),\n",
" (u\"autót\", \"BEEEB\"),\n",
" (u\"autót\", \"BEEEB\"),\n",
" (u\"autókat\", \"BEEEBEB\"),\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Featurizing the toy dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"maxlen = max(len(s[0]) for s in samples)\n",
"vocab_x = {'PAD': 0}\n",
"vocab_y = {'PAD': 0}\n",
"\n",
"def pad_sample(sample):\n",
" return [0] * (maxlen - len(sample)) + sample\n",
"\n",
"data_x = [pad_sample([vocab_x.setdefault(c, len(vocab_x)) for c in sample[0]]) for sample in samples]\n",
"data_y = [to_categorical(pad_sample([vocab_y.setdefault(c, len(vocab_y)) for c in sample[1]])) for sample in samples]\n",
"\n",
"data_x = np.array(data_x)\n",
"data_y = np.array(data_y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model parameters"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"timesteps = maxlen\n",
"batch_size = 4\n",
"vocab_size = len(vocab_x)\n",
"embedding_size = 10\n",
"seq_size = 50\n",
"mlp_size = len(vocab_y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model definition"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"xin = Input(batch_shape=(batch_size, timesteps), dtype='int32')\n",
"xemb = Embedding(vocab_size, embedding_size)(xin)\n",
"xemb = Masking(mask_value=0.)(xemb)\n",
"seq = Bidirectional(LSTM(seq_size, return_sequences=True))(xemb)\n",
"mlp = TimeDistributed(Dense(mlp_size, activation='softmax'))(seq)\n",
"model = Model(inputs=xin, outputs=mlp)\n",
"model.compile(optimizer='Adam', loss='categorical_crossentropy')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Training and testing"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7fd4f8bbcfd0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(data_x, data_y, epochs=500, verbose=0)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4/4 [==============================] - 0s\n"
]
},
{
"data": {
"text/plain": [
"0.00095214252360165119"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.evaluate(data_x, data_y)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model.save('/tmp/toy_model')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_1 (InputLayer) (4, 7) 0 \n",
"_________________________________________________________________\n",
"embedding_1 (Embedding) (4, 7, 10) 60 \n",
"_________________________________________________________________\n",
"masking_1 (Masking) (4, 7, 10) 0 \n",
"_________________________________________________________________\n",
"bidirectional_1 (Bidirection (4, 7, 100) 24400 \n",
"_________________________________________________________________\n",
"time_distributed_1 (TimeDist (4, 7, 3) 303 \n",
"=================================================================\n",
"Total params: 24,763\n",
"Trainable params: 24,763\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"m = keras.models.load_model('/tmp/toy_model')\n",
"m.summary()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[ 9.99483585e-01, 4.77622438e-04, 3.86894644e-05],\n",
" [ 9.99053895e-01, 8.44825641e-04, 1.01257952e-04],\n",
" [ 4.58437018e-04, 9.99414802e-01, 1.26775732e-04],\n",
" [ 8.96619881e-08, 1.07046770e-04, 9.99892831e-01],\n",
" [ 3.00282021e-11, 3.59821314e-07, 9.99999642e-01],\n",
" [ 2.60060840e-09, 3.54139105e-04, 9.99645829e-01],\n",
" [ 2.98374744e-07, 9.98308778e-01, 1.69083721e-03]],\n",
"\n",
" [[ 9.99483585e-01, 4.77622438e-04, 3.86894644e-05],\n",
" [ 9.99053895e-01, 8.44825641e-04, 1.01257952e-04],\n",
" [ 4.58437018e-04, 9.99414802e-01, 1.26775732e-04],\n",
" [ 8.96619881e-08, 1.07046770e-04, 9.99892831e-01],\n",
" [ 3.00282021e-11, 3.59821314e-07, 9.99999642e-01],\n",
" [ 2.60060840e-09, 3.54139105e-04, 9.99645829e-01],\n",
" [ 2.98374744e-07, 9.98308778e-01, 1.69083721e-03]],\n",
"\n",
" [[ 9.99483585e-01, 4.77622438e-04, 3.86894644e-05],\n",
" [ 9.99053895e-01, 8.44825641e-04, 1.01257952e-04],\n",
" [ 4.58437018e-04, 9.99414802e-01, 1.26775732e-04],\n",
" [ 8.96619881e-08, 1.07046770e-04, 9.99892831e-01],\n",
" [ 3.00282021e-11, 3.59821314e-07, 9.99999642e-01],\n",
" [ 2.60060840e-09, 3.54139105e-04, 9.99645829e-01],\n",
" [ 2.98374744e-07, 9.98308778e-01, 1.69083721e-03]],\n",
"\n",
" [[ 1.08423491e-03, 9.98783171e-01, 1.32616784e-04],\n",
" [ 2.84857265e-07, 6.36511186e-06, 9.99993324e-01],\n",
" [ 1.73078574e-09, 7.95891566e-08, 9.99999881e-01],\n",
" [ 8.77982188e-07, 1.09988404e-03, 9.98899221e-01],\n",
" [ 1.55240559e-06, 9.97682333e-01, 2.31608399e-03],\n",
" [ 8.73340369e-08, 5.22461394e-03, 9.94775295e-01],\n",
" [ 3.47519801e-07, 9.95841086e-01, 4.15855786e-03]]], dtype=float32)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m.predict(data_x)"
]
}
],
"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 |
decisionstats/pythonfordatascience | data+wrangling+titanic+dataset.ipynb | 1 | 74902 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Title\n",
"## This is our code for Week 12\n",
"- Input\n",
"- Explore\n",
"- Analyze"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'commit_hash': '5c9c918',\n",
" 'commit_source': 'installation',\n",
" 'default_encoding': 'cp1252',\n",
" 'ipython_path': 'C:\\\\Users\\\\Dell\\\\Anaconda3\\\\lib\\\\site-packages\\\\IPython',\n",
" 'ipython_version': '5.1.0',\n",
" 'os_name': 'nt',\n",
" 'platform': 'Windows-7-6.1.7600-SP0',\n",
" 'sys_executable': 'C:\\\\Users\\\\Dell\\\\Anaconda3\\\\python.exe',\n",
" 'sys_platform': 'win32',\n",
" 'sys_version': '3.5.2 |Anaconda custom (64-bit)| (default, Jul 5 2016, '\n",
" '11:41:13) [MSC v.1900 64 bit (AMD64)]'}\n"
]
}
],
"source": [
"import IPython\n",
"print(IPython.sys_info())"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titanic=pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/datasets/Titanic.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1313 entries, 0 to 1312\n",
"Data columns (total 7 columns):\n",
"Unnamed: 0 1313 non-null int64\n",
"Name 1313 non-null object\n",
"PClass 1313 non-null object\n",
"Age 756 non-null float64\n",
"Sex 1313 non-null object\n",
"Survived 1313 non-null int64\n",
"SexCode 1313 non-null int64\n",
"dtypes: float64(1), int64(3), object(3)\n",
"memory usage: 71.9+ KB\n"
]
}
],
"source": [
"titanic.info()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>Unnamed: 0</th>\n",
" <th>Name</th>\n",
" <th>PClass</th>\n",
" <th>Age</th>\n",
" <th>Sex</th>\n",
" <th>Survived</th>\n",
" <th>SexCode</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Allen, Miss Elisabeth Walton</td>\n",
" <td>1st</td>\n",
" <td>29.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Allison, Miss Helen Loraine</td>\n",
" <td>1st</td>\n",
" <td>2.00</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Allison, Mr Hudson Joshua Creighton</td>\n",
" <td>1st</td>\n",
" <td>30.00</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Allison, Mrs Hudson JC (Bessie Waldo Daniels)</td>\n",
" <td>1st</td>\n",
" <td>25.00</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Allison, Master Hudson Trevor</td>\n",
" <td>1st</td>\n",
" <td>0.92</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Name PClass Age \\\n",
"0 1 Allen, Miss Elisabeth Walton 1st 29.00 \n",
"1 2 Allison, Miss Helen Loraine 1st 2.00 \n",
"2 3 Allison, Mr Hudson Joshua Creighton 1st 30.00 \n",
"3 4 Allison, Mrs Hudson JC (Bessie Waldo Daniels) 1st 25.00 \n",
"4 5 Allison, Master Hudson Trevor 1st 0.92 \n",
"\n",
" Sex Survived SexCode \n",
"0 female 1 1 \n",
"1 female 0 1 \n",
"2 male 0 0 \n",
"3 female 0 1 \n",
"4 male 1 0 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titanic=titanic.drop('Unnamed: 0',1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"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>PClass</th>\n",
" <th>Age</th>\n",
" <th>Sex</th>\n",
" <th>Survived</th>\n",
" <th>SexCode</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Allen, Miss Elisabeth Walton</td>\n",
" <td>1st</td>\n",
" <td>29.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Allison, Miss Helen Loraine</td>\n",
" <td>1st</td>\n",
" <td>2.00</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Allison, Mr Hudson Joshua Creighton</td>\n",
" <td>1st</td>\n",
" <td>30.00</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Allison, Mrs Hudson JC (Bessie Waldo Daniels)</td>\n",
" <td>1st</td>\n",
" <td>25.00</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Allison, Master Hudson Trevor</td>\n",
" <td>1st</td>\n",
" <td>0.92</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name PClass Age Sex \\\n",
"0 Allen, Miss Elisabeth Walton 1st 29.00 female \n",
"1 Allison, Miss Helen Loraine 1st 2.00 female \n",
"2 Allison, Mr Hudson Joshua Creighton 1st 30.00 male \n",
"3 Allison, Mrs Hudson JC (Bessie Waldo Daniels) 1st 25.00 female \n",
"4 Allison, Master Hudson Trevor 1st 0.92 male \n",
"\n",
" Survived SexCode \n",
"0 1 1 \n",
"1 0 1 \n",
"2 0 0 \n",
"3 0 1 \n",
"4 1 0 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"titanic2=titanic.copy()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3rd 711\n",
"1st 322\n",
"2nd 279\n",
"* 1\n",
"Name: PClass, dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.value_counts(titanic.PClass)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"male 851\n",
"female 462\n",
"Name: Sex, dtype: int64"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.value_counts(titanic.Sex)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 863\n",
"1 450\n",
"Name: Survived, dtype: int64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.value_counts(titanic.Survived)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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>PClass</th>\n",
" <th>Age</th>\n",
" <th>Sex</th>\n",
" <th>Survived</th>\n",
" <th>SexCode</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Allison, Miss Helen Loraine</td>\n",
" <td>1st</td>\n",
" <td>2.0</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Allison, Mr Hudson Joshua Creighton</td>\n",
" <td>1st</td>\n",
" <td>30.0</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name PClass Age Sex Survived SexCode\n",
"1 Allison, Miss Helen Loraine 1st 2.0 female 0 1\n",
"2 Allison, Mr Hudson Joshua Creighton 1st 30.0 male 0 0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.iloc[1:3,:]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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>PClass</th>\n",
" <th>Age</th>\n",
" <th>Sex</th>\n",
" <th>Survived</th>\n",
" <th>SexCode</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Allen, Miss Elisabeth Walton</td>\n",
" <td>1st</td>\n",
" <td>29.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Allison, Miss Helen Loraine</td>\n",
" <td>1st</td>\n",
" <td>2.00</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Allison, Mr Hudson Joshua Creighton</td>\n",
" <td>1st</td>\n",
" <td>30.00</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Allison, Mrs Hudson JC (Bessie Waldo Daniels)</td>\n",
" <td>1st</td>\n",
" <td>25.00</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Allison, Master Hudson Trevor</td>\n",
" <td>1st</td>\n",
" <td>0.92</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Anderson, Mr Harry</td>\n",
" <td>1st</td>\n",
" <td>47.00</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Andrews, Miss Kornelia Theodosia</td>\n",
" <td>1st</td>\n",
" <td>63.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name PClass Age Sex \\\n",
"0 Allen, Miss Elisabeth Walton 1st 29.00 female \n",
"1 Allison, Miss Helen Loraine 1st 2.00 female \n",
"2 Allison, Mr Hudson Joshua Creighton 1st 30.00 male \n",
"3 Allison, Mrs Hudson JC (Bessie Waldo Daniels) 1st 25.00 female \n",
"4 Allison, Master Hudson Trevor 1st 0.92 male \n",
"5 Anderson, Mr Harry 1st 47.00 male \n",
"6 Andrews, Miss Kornelia Theodosia 1st 63.00 female \n",
"\n",
" Survived SexCode \n",
"0 1 1 \n",
"1 0 1 \n",
"2 0 0 \n",
"3 0 1 \n",
"4 1 0 \n",
"5 1 0 \n",
"6 1 1 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.head(7)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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>PClass</th>\n",
" <th>Age</th>\n",
" <th>SexCode</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1st</td>\n",
" <td>29.00</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1st</td>\n",
" <td>2.00</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1st</td>\n",
" <td>30.00</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1st</td>\n",
" <td>25.00</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1st</td>\n",
" <td>0.92</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PClass Age SexCode\n",
"0 1st 29.00 1\n",
"1 1st 2.00 1\n",
"2 1st 30.00 0\n",
"3 1st 25.00 1\n",
"4 1st 0.92 0"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic[['PClass','Age','SexCode']].head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 29.00\n",
"1 2.00\n",
"2 30.00\n",
"3 25.00\n",
"4 0.92\n",
"Name: Age, dtype: float64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.Age.head()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tpy=titanic.values"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([['Allen, Miss Elisabeth Walton', '1st', 29.0, 'female', 1, 1],\n",
" ['Allison, Miss Helen Loraine', '1st', 2.0, 'female', 0, 1],\n",
" ['Allison, Mr Hudson Joshua Creighton', '1st', 30.0, 'male', 0, 0],\n",
" ..., \n",
" ['Zenni, Mr Philip', '3rd', 22.0, 'male', 0, 0],\n",
" ['Lievens, Mr Rene', '3rd', 24.0, 'male', 0, 0],\n",
" ['Zimmerman, Leo', '3rd', 29.0, 'male', 0, 0]], dtype=object)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tpy"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os as os"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'C:\\\\Users\\\\Dell'"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"os.chdir('C:\\\\Users\\\\Dell\\\\Desktop')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['.Rhistory',\n",
" 'BigDiamonds.csv',\n",
" 'Class-3-Public-Primary-Certification-Authority.pem.txt',\n",
" 'Data Analysis (1)',\n",
" 'DataWrangling.pdf',\n",
" 'desktop.ini',\n",
" 'Diamond (7).csv',\n",
" 'dump',\n",
" 'GoToWebinar.lnk',\n",
" 'kushal.jpg',\n",
" 'Pythonajay.docx',\n",
" 'SUINV.png',\n",
" '~$thonajay.docx']"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.listdir()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"titanic.to_csv('C:\\\\Users\\\\Dell\\\\Desktop\\\\titanic2.csv', index=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['.Rhistory',\n",
" 'BigDiamonds.csv',\n",
" 'Class-3-Public-Primary-Certification-Authority.pem.txt',\n",
" 'Data Analysis (1)',\n",
" 'DataWrangling.pdf',\n",
" 'desktop.ini',\n",
" 'Diamond (7).csv',\n",
" 'dump',\n",
" 'GoToWebinar.lnk',\n",
" 'kushal.jpg',\n",
" 'Pythonajay.docx',\n",
" 'SUINV.png',\n",
" 'titanic2.csv',\n",
" '~$thonajay.docx']"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.listdir()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"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>PClass</th>\n",
" <th>Age</th>\n",
" <th>Sex</th>\n",
" <th>Survived</th>\n",
" <th>SexCode</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Allen, Miss Elisabeth Walton</td>\n",
" <td>1st</td>\n",
" <td>29.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Allison, Miss Helen Loraine</td>\n",
" <td>1st</td>\n",
" <td>2.00</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Allison, Mr Hudson Joshua Creighton</td>\n",
" <td>1st</td>\n",
" <td>30.00</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Allison, Mrs Hudson JC (Bessie Waldo Daniels)</td>\n",
" <td>1st</td>\n",
" <td>25.00</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Allison, Master Hudson Trevor</td>\n",
" <td>1st</td>\n",
" <td>0.92</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name PClass Age Sex \\\n",
"0 Allen, Miss Elisabeth Walton 1st 29.00 female \n",
"1 Allison, Miss Helen Loraine 1st 2.00 female \n",
"2 Allison, Mr Hudson Joshua Creighton 1st 30.00 male \n",
"3 Allison, Mrs Hudson JC (Bessie Waldo Daniels) 1st 25.00 female \n",
"4 Allison, Master Hudson Trevor 1st 0.92 male \n",
"\n",
" Survived SexCode \n",
"0 1 1 \n",
"1 0 1 \n",
"2 0 0 \n",
"3 0 1 \n",
"4 1 0 "
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.head()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"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>PClass</th>\n",
" <th>Age</th>\n",
" <th>Sex</th>\n",
" <th>Survived</th>\n",
" <th>SexCode</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Allen, Miss Elisabeth Walton</td>\n",
" <td>1st</td>\n",
" <td>29.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Allison, Master Hudson Trevor</td>\n",
" <td>1st</td>\n",
" <td>0.92</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Anderson, Mr Harry</td>\n",
" <td>1st</td>\n",
" <td>47.00</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Andrews, Miss Kornelia Theodosia</td>\n",
" <td>1st</td>\n",
" <td>63.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Appleton, Mrs Edward Dale (Charlotte Lamson)</td>\n",
" <td>1st</td>\n",
" <td>58.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Astor, Mrs John Jacob (Madeleine Talmadge Force)</td>\n",
" <td>1st</td>\n",
" <td>19.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Aubert, Mrs Leontine Pauline</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Barkworth, Mr Algernon H</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Baxter, Mrs James (Helene DeLaudeniere Chaput)</td>\n",
" <td>1st</td>\n",
" <td>50.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Beckwith, Mr Richard Leonard</td>\n",
" <td>1st</td>\n",
" <td>37.00</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Beckwith, Mrs Richard Leonard (Sallie Monypeny)</td>\n",
" <td>1st</td>\n",
" <td>47.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Behr, Mr Karl Howell</td>\n",
" <td>1st</td>\n",
" <td>26.00</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Bishop, Mr Dickinson H</td>\n",
" <td>1st</td>\n",
" <td>25.00</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Bishop, Mrs Dickinson H (Helen Walton)</td>\n",
" <td>1st</td>\n",
" <td>19.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Bjornstrm-Steffansson, Mr Mauritz Hakan</td>\n",
" <td>1st</td>\n",
" <td>28.00</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Blank, Mr Henry</td>\n",
" <td>1st</td>\n",
" <td>39.00</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Bonnell, Miss Caroline</td>\n",
" <td>1st</td>\n",
" <td>30.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Bonnell, Miss Elizabeth</td>\n",
" <td>1st</td>\n",
" <td>58.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Bowen, Miss Grace Scott</td>\n",
" <td>1st</td>\n",
" <td>45.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>Bowerman, Miss Elsie Edith</td>\n",
" <td>1st</td>\n",
" <td>22.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Bradley, Mr George</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Brown, Mrs James Joseph (Margaret Molly\" Tobin)\"</td>\n",
" <td>1st</td>\n",
" <td>44.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>Brown, Mrs John Murray (Caroline Lane Lamson)</td>\n",
" <td>1st</td>\n",
" <td>59.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Bucknell, Mrs William Robert (Emma Eliza Ward)</td>\n",
" <td>1st</td>\n",
" <td>60.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>Calderhead, Mr Edward P</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>Candee, Mrs Edward (Helen Churchill Hungerford)</td>\n",
" <td>1st</td>\n",
" <td>53.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>Cardeza, Mrs James Warburton Martinez (Charlot...</td>\n",
" <td>1st</td>\n",
" <td>58.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Cardeza, Mr Thomas Drake Martinez</td>\n",
" <td>1st</td>\n",
" <td>36.00</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Carter, Mr William Ernest</td>\n",
" <td>1st</td>\n",
" <td>36.00</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>Carter, Mrs William Ernest (Lucile Polk)</td>\n",
" <td>1st</td>\n",
" <td>36.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</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",
" </tr>\n",
" <tr>\n",
" <th>265</th>\n",
" <td>Thorne, Mrs Gertrude Maybelle</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>266</th>\n",
" <td>Tucker, Mr Gilbert Milligan, jr</td>\n",
" <td>1st</td>\n",
" <td>31.00</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>271</th>\n",
" <td>Warren, Mrs Frank Manley (Anna S Atkinson)</td>\n",
" <td>1st</td>\n",
" <td>60.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>273</th>\n",
" <td>White, Mrs J Stuart (Ella Holmes)</td>\n",
" <td>1st</td>\n",
" <td>55.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>277</th>\n",
" <td>Wick, Mrs George Dennick (Martha Hitchcock)</td>\n",
" <td>1st</td>\n",
" <td>45.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>278</th>\n",
" <td>Wick, Miss Mary Natalie</td>\n",
" <td>1st</td>\n",
" <td>31.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>280</th>\n",
" <td>Widener, Mrs George Dunton (Eleanor Elkins)</td>\n",
" <td>1st</td>\n",
" <td>50.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>282</th>\n",
" <td>Willard, Miss Constance</td>\n",
" <td>1st</td>\n",
" <td>20.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>285</th>\n",
" <td>Williams, Mr Richard Norris II</td>\n",
" <td>1st</td>\n",
" <td>21.00</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>286</th>\n",
" <td>Woolner, Mr Hugh</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>288</th>\n",
" <td>Young, Miss Marie Grice</td>\n",
" <td>1st</td>\n",
" <td>36.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>289</th>\n",
" <td>Barber, Ms</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>290</th>\n",
" <td>Bazzani, Ms Albina</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>291</th>\n",
" <td>Bidois, Miss Rosalie</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>292</th>\n",
" <td>Bird, Ms Ellen</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>293</th>\n",
" <td>Bissetti, Ms Amelia</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>294</th>\n",
" <td>Burns, Ms Elizabeth Margaret</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>295</th>\n",
" <td>Chaudanson, Ms Victorine</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>296</th>\n",
" <td>Cleaver, Ms Alice</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>297</th>\n",
" <td>Daniels, Ms Sarah</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>298</th>\n",
" <td>Endres, Miss Caroline Louise</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>301</th>\n",
" <td>Francatelli, Ms Laura Mabel</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>303</th>\n",
" <td>Geiger, Miss Emily</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308</th>\n",
" <td>Icabad (Icabod), Ms</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>310</th>\n",
" <td>Kenchen, Ms Amelia</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>311</th>\n",
" <td>LeRoy, Miss Berthe</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>313</th>\n",
" <td>Maloney, Ms</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>315</th>\n",
" <td>Pericault, Ms</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>318</th>\n",
" <td>Segesser, Mlle Emma</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>321</th>\n",
" <td>Wilson, Ms Helen</td>\n",
" <td>1st</td>\n",
" <td>NaN</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>193 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" Name PClass Age Sex \\\n",
"0 Allen, Miss Elisabeth Walton 1st 29.00 female \n",
"4 Allison, Master Hudson Trevor 1st 0.92 male \n",
"5 Anderson, Mr Harry 1st 47.00 male \n",
"6 Andrews, Miss Kornelia Theodosia 1st 63.00 female \n",
"8 Appleton, Mrs Edward Dale (Charlotte Lamson) 1st 58.00 female \n",
"11 Astor, Mrs John Jacob (Madeleine Talmadge Force) 1st 19.00 female \n",
"12 Aubert, Mrs Leontine Pauline 1st NaN female \n",
"13 Barkworth, Mr Algernon H 1st NaN male \n",
"15 Baxter, Mrs James (Helene DeLaudeniere Chaput) 1st 50.00 female \n",
"18 Beckwith, Mr Richard Leonard 1st 37.00 male \n",
"19 Beckwith, Mrs Richard Leonard (Sallie Monypeny) 1st 47.00 female \n",
"20 Behr, Mr Karl Howell 1st 26.00 male \n",
"22 Bishop, Mr Dickinson H 1st 25.00 male \n",
"23 Bishop, Mrs Dickinson H (Helen Walton) 1st 19.00 female \n",
"24 Bjornstrm-Steffansson, Mr Mauritz Hakan 1st 28.00 male \n",
"26 Blank, Mr Henry 1st 39.00 male \n",
"27 Bonnell, Miss Caroline 1st 30.00 female \n",
"28 Bonnell, Miss Elizabeth 1st 58.00 female \n",
"30 Bowen, Miss Grace Scott 1st 45.00 female \n",
"31 Bowerman, Miss Elsie Edith 1st 22.00 female \n",
"32 Bradley, Mr George 1st NaN male \n",
"36 Brown, Mrs James Joseph (Margaret Molly\" Tobin)\" 1st 44.00 female \n",
"37 Brown, Mrs John Murray (Caroline Lane Lamson) 1st 59.00 female \n",
"38 Bucknell, Mrs William Robert (Emma Eliza Ward) 1st 60.00 female \n",
"40 Calderhead, Mr Edward P 1st NaN male \n",
"41 Candee, Mrs Edward (Helen Churchill Hungerford) 1st 53.00 female \n",
"42 Cardeza, Mrs James Warburton Martinez (Charlot... 1st 58.00 female \n",
"43 Cardeza, Mr Thomas Drake Martinez 1st 36.00 male \n",
"47 Carter, Mr William Ernest 1st 36.00 male \n",
"48 Carter, Mrs William Ernest (Lucile Polk) 1st 36.00 female \n",
".. ... ... ... ... \n",
"265 Thorne, Mrs Gertrude Maybelle 1st NaN female \n",
"266 Tucker, Mr Gilbert Milligan, jr 1st 31.00 male \n",
"271 Warren, Mrs Frank Manley (Anna S Atkinson) 1st 60.00 female \n",
"273 White, Mrs J Stuart (Ella Holmes) 1st 55.00 female \n",
"277 Wick, Mrs George Dennick (Martha Hitchcock) 1st 45.00 female \n",
"278 Wick, Miss Mary Natalie 1st 31.00 female \n",
"280 Widener, Mrs George Dunton (Eleanor Elkins) 1st 50.00 female \n",
"282 Willard, Miss Constance 1st 20.00 female \n",
"285 Williams, Mr Richard Norris II 1st 21.00 male \n",
"286 Woolner, Mr Hugh 1st NaN male \n",
"288 Young, Miss Marie Grice 1st 36.00 female \n",
"289 Barber, Ms 1st NaN female \n",
"290 Bazzani, Ms Albina 1st NaN female \n",
"291 Bidois, Miss Rosalie 1st NaN female \n",
"292 Bird, Ms Ellen 1st NaN female \n",
"293 Bissetti, Ms Amelia 1st NaN female \n",
"294 Burns, Ms Elizabeth Margaret 1st NaN female \n",
"295 Chaudanson, Ms Victorine 1st NaN female \n",
"296 Cleaver, Ms Alice 1st NaN female \n",
"297 Daniels, Ms Sarah 1st NaN female \n",
"298 Endres, Miss Caroline Louise 1st NaN female \n",
"301 Francatelli, Ms Laura Mabel 1st NaN female \n",
"303 Geiger, Miss Emily 1st NaN female \n",
"308 Icabad (Icabod), Ms 1st NaN female \n",
"310 Kenchen, Ms Amelia 1st NaN female \n",
"311 LeRoy, Miss Berthe 1st NaN female \n",
"313 Maloney, Ms 1st NaN female \n",
"315 Pericault, Ms 1st NaN female \n",
"318 Segesser, Mlle Emma 1st NaN female \n",
"321 Wilson, Ms Helen 1st NaN female \n",
"\n",
" Survived SexCode \n",
"0 1 1 \n",
"4 1 0 \n",
"5 1 0 \n",
"6 1 1 \n",
"8 1 1 \n",
"11 1 1 \n",
"12 1 1 \n",
"13 1 0 \n",
"15 1 1 \n",
"18 1 0 \n",
"19 1 1 \n",
"20 1 0 \n",
"22 1 0 \n",
"23 1 1 \n",
"24 1 0 \n",
"26 1 0 \n",
"27 1 1 \n",
"28 1 1 \n",
"30 1 1 \n",
"31 1 1 \n",
"32 1 0 \n",
"36 1 1 \n",
"37 1 1 \n",
"38 1 1 \n",
"40 1 0 \n",
"41 1 1 \n",
"42 1 1 \n",
"43 1 0 \n",
"47 1 0 \n",
"48 1 1 \n",
".. ... ... \n",
"265 1 1 \n",
"266 1 0 \n",
"271 1 1 \n",
"273 1 1 \n",
"277 1 1 \n",
"278 1 1 \n",
"280 1 1 \n",
"282 1 1 \n",
"285 1 0 \n",
"286 1 0 \n",
"288 1 1 \n",
"289 1 1 \n",
"290 1 1 \n",
"291 1 1 \n",
"292 1 1 \n",
"293 1 1 \n",
"294 1 1 \n",
"295 1 1 \n",
"296 1 1 \n",
"297 1 1 \n",
"298 1 1 \n",
"301 1 1 \n",
"303 1 1 \n",
"308 1 1 \n",
"310 1 1 \n",
"311 1 1 \n",
"313 1 1 \n",
"315 1 1 \n",
"318 1 1 \n",
"321 1 1 \n",
"\n",
"[193 rows x 6 columns]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.query(\"PClass=='1st' and Survived ==1\")"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.5993788819875776"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"193/322"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Name 138\n",
"PClass 138\n",
"Age 78\n",
"Sex 138\n",
"Survived 138\n",
"SexCode 138\n",
"dtype: int64"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.query(\"PClass=='3rd' and Survived==1\").count()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.1940928270042194"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"138/711"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Survived</th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>PClass</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>*</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1st</th>\n",
" <td>129</td>\n",
" <td>193</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2nd</th>\n",
" <td>160</td>\n",
" <td>119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3rd</th>\n",
" <td>573</td>\n",
" <td>138</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Survived 0 1\n",
"PClass \n",
"* 1 0\n",
"1st 129 193\n",
"2nd 160 119\n",
"3rd 573 138"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(titanic.PClass,titanic.Survived)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Survived</th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>All</th>\n",
" </tr>\n",
" <tr>\n",
" <th>PClass</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>*</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1st</th>\n",
" <td>129</td>\n",
" <td>193</td>\n",
" <td>322</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2nd</th>\n",
" <td>160</td>\n",
" <td>119</td>\n",
" <td>279</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3rd</th>\n",
" <td>573</td>\n",
" <td>138</td>\n",
" <td>711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All</th>\n",
" <td>863</td>\n",
" <td>450</td>\n",
" <td>1313</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Survived 0 1 All\n",
"PClass \n",
"* 1 0 1\n",
"1st 129 193 322\n",
"2nd 160 119 279\n",
"3rd 573 138 711\n",
"All 863 450 1313"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(titanic.PClass,titanic.Survived,margins=True)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>Survived</th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>PClass</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>*</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1st</th>\n",
" <td>0.400621</td>\n",
" <td>0.599379</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2nd</th>\n",
" <td>0.573477</td>\n",
" <td>0.426523</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3rd</th>\n",
" <td>0.805907</td>\n",
" <td>0.194093</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Survived 0 1\n",
"PClass \n",
"* 1.000000 0.000000\n",
"1st 0.400621 0.599379\n",
"2nd 0.573477 0.426523\n",
"3rd 0.805907 0.194093"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(titanic.PClass,titanic.Survived,normalize='index')"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Name 143\n",
"PClass 143\n",
"Age 101\n",
"Sex 143\n",
"Survived 143\n",
"SexCode 143\n",
"dtype: int64"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.query(\"PClass=='1st' and Sex=='female'\").count()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Name 134\n",
"PClass 134\n",
"Age 96\n",
"Sex 134\n",
"Survived 134\n",
"SexCode 134\n",
"dtype: int64"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.query(\"PClass=='1st' and Sex=='female' and Survived==1\").count()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.9370629370629371"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"134/143"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Name 58\n",
"PClass 58\n",
"Age 32\n",
"Sex 58\n",
"Survived 58\n",
"SexCode 58\n",
"dtype: int64"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.query(\"PClass=='3rd' and Sex=='male' and Survived==1\").count()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Name 499\n",
"PClass 499\n",
"Age 216\n",
"Sex 499\n",
"Survived 499\n",
"SexCode 499\n",
"dtype: int64"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.query(\"PClass=='3rd' and Sex=='male' \").count()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.11623246492985972"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"58/499"
]
},
{
"cell_type": "code",
"execution_count": 72,
"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>Survived</th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>All</th>\n",
" </tr>\n",
" <tr>\n",
" <th>PClass</th>\n",
" <th>Sex</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>*</th>\n",
" <th>male</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">1st</th>\n",
" <th>female</th>\n",
" <td>9</td>\n",
" <td>134</td>\n",
" <td>143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>male</th>\n",
" <td>120</td>\n",
" <td>59</td>\n",
" <td>179</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2nd</th>\n",
" <th>female</th>\n",
" <td>13</td>\n",
" <td>94</td>\n",
" <td>107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>male</th>\n",
" <td>147</td>\n",
" <td>25</td>\n",
" <td>172</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">3rd</th>\n",
" <th>female</th>\n",
" <td>132</td>\n",
" <td>80</td>\n",
" <td>212</td>\n",
" </tr>\n",
" <tr>\n",
" <th>male</th>\n",
" <td>441</td>\n",
" <td>58</td>\n",
" <td>499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All</th>\n",
" <th></th>\n",
" <td>863</td>\n",
" <td>450</td>\n",
" <td>1313</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Survived 0 1 All\n",
"PClass Sex \n",
"* male 1 0 1\n",
"1st female 9 134 143\n",
" male 120 59 179\n",
"2nd female 13 94 107\n",
" male 147 25 172\n",
"3rd female 132 80 212\n",
" male 441 58 499\n",
"All 863 450 1313"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab([titanic.PClass, titanic.Sex], titanic.Survived, margins=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 79,
"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>PClass</th>\n",
" <th>Age</th>\n",
" <th>Sex</th>\n",
" <th>Survived</th>\n",
" <th>SexCode</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Allen, Miss Elisabeth Walton</td>\n",
" <td>1st</td>\n",
" <td>29.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Allison, Miss Helen Loraine</td>\n",
" <td>1st</td>\n",
" <td>2.00</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Allison, Mr Hudson Joshua Creighton</td>\n",
" <td>1st</td>\n",
" <td>30.00</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Allison, Mrs Hudson JC (Bessie Waldo Daniels)</td>\n",
" <td>1st</td>\n",
" <td>25.00</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Allison, Master Hudson Trevor</td>\n",
" <td>1st</td>\n",
" <td>0.92</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name PClass Age Sex \\\n",
"0 Allen, Miss Elisabeth Walton 1st 29.00 female \n",
"1 Allison, Miss Helen Loraine 1st 2.00 female \n",
"2 Allison, Mr Hudson Joshua Creighton 1st 30.00 male \n",
"3 Allison, Mrs Hudson JC (Bessie Waldo Daniels) 1st 25.00 female \n",
"4 Allison, Master Hudson Trevor 1st 0.92 male \n",
"\n",
" Survived SexCode \n",
"0 1 1 \n",
"1 0 1 \n",
"2 0 0 \n",
"3 0 1 \n",
"4 1 0 "
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic2.head()"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titanic.loc[titanic.Survived==1,'Survived2']='Alive'"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titanic.loc[titanic.Survived!=1,'Survived2']='Dead'"
]
},
{
"cell_type": "code",
"execution_count": 78,
"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>PClass</th>\n",
" <th>Age</th>\n",
" <th>Sex</th>\n",
" <th>Survived</th>\n",
" <th>SexCode</th>\n",
" <th>Survived2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Allen, Miss Elisabeth Walton</td>\n",
" <td>1st</td>\n",
" <td>29.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Alive</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Allison, Miss Helen Loraine</td>\n",
" <td>1st</td>\n",
" <td>2.00</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Dead</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Allison, Mr Hudson Joshua Creighton</td>\n",
" <td>1st</td>\n",
" <td>30.00</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Dead</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Allison, Mrs Hudson JC (Bessie Waldo Daniels)</td>\n",
" <td>1st</td>\n",
" <td>25.00</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Dead</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Allison, Master Hudson Trevor</td>\n",
" <td>1st</td>\n",
" <td>0.92</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Alive</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name PClass Age Sex \\\n",
"0 Allen, Miss Elisabeth Walton 1st 29.00 female \n",
"1 Allison, Miss Helen Loraine 1st 2.00 female \n",
"2 Allison, Mr Hudson Joshua Creighton 1st 30.00 male \n",
"3 Allison, Mrs Hudson JC (Bessie Waldo Daniels) 1st 25.00 female \n",
"4 Allison, Master Hudson Trevor 1st 0.92 male \n",
"\n",
" Survived SexCode Survived2 \n",
"0 1 1 Alive \n",
"1 0 1 Dead \n",
"2 0 0 Dead \n",
"3 0 1 Dead \n",
"4 1 0 Alive "
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.head()"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titanic = titanic.assign(e=pd.Series(np.random.randn(len(titanic))).values)\n"
]
},
{
"cell_type": "code",
"execution_count": 87,
"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>PClass</th>\n",
" <th>Age</th>\n",
" <th>Sex</th>\n",
" <th>Survived</th>\n",
" <th>SexCode</th>\n",
" <th>Survived2</th>\n",
" <th>e</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Allen, Miss Elisabeth Walton</td>\n",
" <td>1st</td>\n",
" <td>29.00</td>\n",
" <td>female</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Alive</td>\n",
" <td>0.960077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Allison, Miss Helen Loraine</td>\n",
" <td>1st</td>\n",
" <td>2.00</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Dead</td>\n",
" <td>-2.777595</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Allison, Mr Hudson Joshua Creighton</td>\n",
" <td>1st</td>\n",
" <td>30.00</td>\n",
" <td>male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Dead</td>\n",
" <td>0.294452</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Allison, Mrs Hudson JC (Bessie Waldo Daniels)</td>\n",
" <td>1st</td>\n",
" <td>25.00</td>\n",
" <td>female</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Dead</td>\n",
" <td>-0.249450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Allison, Master Hudson Trevor</td>\n",
" <td>1st</td>\n",
" <td>0.92</td>\n",
" <td>male</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Alive</td>\n",
" <td>0.950757</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name PClass Age Sex \\\n",
"0 Allen, Miss Elisabeth Walton 1st 29.00 female \n",
"1 Allison, Miss Helen Loraine 1st 2.00 female \n",
"2 Allison, Mr Hudson Joshua Creighton 1st 30.00 male \n",
"3 Allison, Mrs Hudson JC (Bessie Waldo Daniels) 1st 25.00 female \n",
"4 Allison, Master Hudson Trevor 1st 0.92 male \n",
"\n",
" Survived SexCode Survived2 e \n",
"0 1 1 Alive 0.960077 \n",
"1 0 1 Dead -2.777595 \n",
"2 0 0 Dead 0.294452 \n",
"3 0 1 Dead -0.249450 \n",
"4 1 0 Alive 0.950757 "
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.head()"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(titanic.Name)"
]
},
{
"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
}
| apache-2.0 |
bspalding/research_public | lectures/drafts/Conditional probability and Bayes' formula.ipynb | 6 | 5654 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# This notebook is very raw and needs more work!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conditional probability\n",
"By Evgenia \"Jenny\" Nitishinskaya and Delaney Granizo-Mackenzie\n",
"\n",
"Notebook released under the Creative Commons Attribution 4.0 License.\n",
"\n",
"---\n",
"\n",
"Conditional probability refers to probability that takes some extra information about the state of the world into account. For instance, we can ask, \"what is the probability that I have the flu, given that I am sneezing?\" or, \"what is the probability that this stock will have a return above the risk-free rate, given that it has positive return?\" In both cases, the extra information changes the probability. We write the probability of $A$ given $B$ as $P(A|B)$. When looking at conditional probability, we are only considering situations where $B$ occurs, and we don't care about the situations where it does not.\n",
"\n",
"If we compute conditional probability empirically, we can use simple counting. Just look at the set of data points for which $B$ is true, and compute the fraction of them that have $A$ true as well. For instance, we can ask, \"what is the probability that a stock is in the top decile of returns this month, given that it was in the top decile last month?\" To compute this, we count the number of stocks that were in the top decile both months, and divide it by the number of stocks in the top decile last month.\n",
"\n",
"The following formula holds for conditional probabilities:\n",
"\n",
"$$ P(A|B) = \\frac{P(A\\text{ and } B)}{P(B)} $$\n",
"\n",
"This can be written as $P(A|B)P(B) = P(A\\text{ and } B)$, that is, the probability that $A$ and $B$ both occur is the probability that $B$ occurs, times the probability that $A$ occurs given that $B$ occurs. Notice that the equation above is symmetric, so we can also say $P(A\\text{ and } B) = P(B|A)P(A)$. \n",
"\n",
"Conditional probabilitites satisfy the <i>total probability rule</i>, which states that if $S_1, S_2, \\ldots, S_n$ are mutually exclusive and exhaustive events, then\n",
"$$ P(A) = P(A|S_1)P(S_1) + P(A|S_2)P(S_2) + \\ldots + P(A|S_n)P(S_n) $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bayes' formula\n",
"\n",
"We can also use conditional probability for inference. This is useful when we want to know the state of a variable which is unobservable, because we can infer it from observable ones. In this case, the equation for conditional probability above isn't simply a true statement about known quantities, but a way to compute an unknown.\n",
"\n",
"A useful restatement of the equation is\n",
"\n",
"$$ P(A|B) = \\frac{P(B|A)P(A)}{P(B)} $$\n",
"\n",
"This is known as Bayes' formula. $P(A)$ is called the <i>prior probability</i> and $P(A|B)$ is called the <i>posterior probability</i> (the probability after taking into account the observation of $B$).\n",
"\n",
"As an example, imagine that a certain test for disease X comes up positive for 99% of subjects with the disease, and for 5% of healthy subjects. Statistically, 1% of the population have the disease. So, your prior probability for having the disease before you get tested is 1%. If your test comes up positive, you have to update the probability that you have the disease. Say $B$ is the test coming up positive, and $A$ is having the disease. Then $P(B|A) = .99$ and $P(A) = .01$. To compute $P(B)$, we can use the total probability rule, and we get that $P(B) = .99 \\cdot .06$. Then the posterior probability that you have the disease, given that your test comes up positive, is $P(A|B) = 16.7\\%$.\n",
"\n",
"Bayes' formula, and conditional probability in general, work for continuous distributions as well as for discrete probabilities, so that we can compute the distribution of random variable $A$ given the distribution of random variable $B$ (which will be two-dimensional), or the distribution of $A$ given a particular observation $b$ of $B$. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Online estimation\n",
"\n",
"Bayesian inference is very useful for online estimation and prediction, where we update our estimate as new evidence comes in. Examples of this include neural networks and Kalman filters. This allows us to have some estimate to work with at all times, while continually improving it using the newest observations. The process is for this is:\n",
"\n",
"1. Start with a prior, based either on your own prior knowledge or estimated from past data using statistical techniques.\n",
"2. When a new data point comes in, use Bayes' formula to update the prior distribution to a posterior distribution.\n",
"3. The posterior distribution is now your new prior. Go to step 2."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# TODO example\n",
"# sklearn.naive_bayes?"
]
}
],
"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": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
Oslandia/open-data-bikes-analysis | notebooks/Clustering-Bordeaux.ipynb | 1 | 509789 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bicycle Sharing Stations Bordeaux"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Analyze the daily profile for each station from 2017-07-09 to 2017-09-26."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.cluster import KMeans"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"sns.set_context('notebook')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Watermark for the versions of used libs"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%load_ext watermark"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2017-11-20 \n",
"\n",
"CPython 3.5.2\n",
"IPython 6.2.1\n",
"\n",
"numpy 1.13.3\n",
"pandas 0.20.3\n",
"sklearn 0.18.1\n",
"matplotlib 2.0.2\n",
"seaborn 0.8.1\n",
"\n",
"compiler : GCC 5.4.0 20160609\n",
"system : Linux\n",
"release : 4.4.0-98-generic\n",
"machine : x86_64\n",
"processor : x86_64\n",
"CPU cores : 4\n",
"interpreter: 64bit\n",
"Git hash : 213e0344fc447e7cc4c6119eec239ca29346e680\n",
"watermark 1.5.0\n"
]
}
],
"source": [
"%watermark -d -v -p numpy,pandas,sklearn,matplotlib,seaborn -g -m -w"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Timeseries data\n",
"DATA = \"../data/bordeaux.csv\""
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"raw = pd.read_csv(DATA, parse_dates=['ts'])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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>gid</th>\n",
" <th>ident</th>\n",
" <th>type</th>\n",
" <th>name</th>\n",
" <th>state</th>\n",
" <th>available_stand</th>\n",
" <th>available_bike</th>\n",
" <th>ts</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>83</td>\n",
" <td>1</td>\n",
" <td>VLS</td>\n",
" <td>Meriadeck</td>\n",
" <td>CONNECTEE</td>\n",
" <td>18</td>\n",
" <td>2</td>\n",
" <td>2017-07-09 00:03:04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>84</td>\n",
" <td>2</td>\n",
" <td>VLS</td>\n",
" <td>St Bruno</td>\n",
" <td>CONNECTEE</td>\n",
" <td>7</td>\n",
" <td>13</td>\n",
" <td>2017-07-09 00:03:04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>41</td>\n",
" <td>3</td>\n",
" <td>VLS</td>\n",
" <td>Place Tartas</td>\n",
" <td>CONNECTEE</td>\n",
" <td>17</td>\n",
" <td>1</td>\n",
" <td>2017-07-09 00:03:04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>42</td>\n",
" <td>4</td>\n",
" <td>VLS</td>\n",
" <td>St Seurin</td>\n",
" <td>CONNECTEE</td>\n",
" <td>18</td>\n",
" <td>2</td>\n",
" <td>2017-07-09 00:03:04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>43</td>\n",
" <td>5</td>\n",
" <td>VLS</td>\n",
" <td>Place Gambetta</td>\n",
" <td>CONNECTEE</td>\n",
" <td>37</td>\n",
" <td>2</td>\n",
" <td>2017-07-09 00:03:04</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gid ident type name state available_stand \\\n",
"0 83 1 VLS Meriadeck CONNECTEE 18 \n",
"1 84 2 VLS St Bruno CONNECTEE 7 \n",
"2 41 3 VLS Place Tartas CONNECTEE 17 \n",
"3 42 4 VLS St Seurin CONNECTEE 18 \n",
"4 43 5 VLS Place Gambetta CONNECTEE 37 \n",
"\n",
" available_bike ts \n",
"0 2 2017-07-09 00:03:04 \n",
"1 13 2017-07-09 00:03:04 \n",
"2 1 2017-07-09 00:03:04 \n",
"3 2 2017-07-09 00:03:04 \n",
"4 2 2017-07-09 00:03:04 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some Cleaning"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3761880, 8)\n"
]
}
],
"source": [
"print(raw.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Get only `CONNECTEE` station (i.e. not closed)\n",
"* Rename some columns\n",
"* Drop duplicates"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = (raw.copy()\n",
" .query('state == \"CONNECTEE\"')\n",
" .drop(['gid', 'available_stand', 'type', 'state'], axis=1)\n",
" .rename_axis({\"available_bike\": \"bikes\", \"ident\": \"station\"}, axis=1)\n",
" .drop_duplicates()\n",
" .sort_values([\"station\", \"ts\"]))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(3607233, 4)\n"
]
},
{
"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>station</th>\n",
" <th>name</th>\n",
" <th>bikes</th>\n",
" <th>ts</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Meriadeck</td>\n",
" <td>2</td>\n",
" <td>2017-07-09 00:03:04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>1</td>\n",
" <td>Meriadeck</td>\n",
" <td>2</td>\n",
" <td>2017-07-09 00:04:04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>348</th>\n",
" <td>1</td>\n",
" <td>Meriadeck</td>\n",
" <td>2</td>\n",
" <td>2017-07-09 00:09:04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>522</th>\n",
" <td>1</td>\n",
" <td>Meriadeck</td>\n",
" <td>2</td>\n",
" <td>2017-07-09 00:14:03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>696</th>\n",
" <td>1</td>\n",
" <td>Meriadeck</td>\n",
" <td>2</td>\n",
" <td>2017-07-09 00:19:04</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" station name bikes ts\n",
"0 1 Meriadeck 2 2017-07-09 00:03:04\n",
"174 1 Meriadeck 2 2017-07-09 00:04:04\n",
"348 1 Meriadeck 2 2017-07-09 00:09:04\n",
"522 1 Meriadeck 2 2017-07-09 00:14:03\n",
"696 1 Meriadeck 2 2017-07-09 00:19:04"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(data.shape)\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Timeseries resampling"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get data every 5 minutes."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = (data.set_index(\"ts\")\n",
" .groupby(\"station\")[\"bikes\"]\n",
" .resample(\"5T\")\n",
" .mean()\n",
" .bfill()\n",
" .unstack(0))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(22932, 171)\n"
]
},
{
"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>station</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>...</th>\n",
" <th>165</th>\n",
" <th>166</th>\n",
" <th>167</th>\n",
" <th>168</th>\n",
" <th>169</th>\n",
" <th>170</th>\n",
" <th>171</th>\n",
" <th>172</th>\n",
" <th>173</th>\n",
" <th>174</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ts</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>2017-07-09 00:00:00</th>\n",
" <td>2.0</td>\n",
" <td>13.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>12.0</td>\n",
" <td>2.5</td>\n",
" <td>6.0</td>\n",
" <td>...</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>10.0</td>\n",
" <td>8.0</td>\n",
" <td>16.0</td>\n",
" <td>7.0</td>\n",
" <td>6.0</td>\n",
" <td>19.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-09 00:05:00</th>\n",
" <td>2.0</td>\n",
" <td>13.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>12.0</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>...</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>10.0</td>\n",
" <td>8.0</td>\n",
" <td>16.0</td>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" <td>19.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-09 00:10:00</th>\n",
" <td>2.0</td>\n",
" <td>13.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>12.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>10.0</td>\n",
" <td>8.0</td>\n",
" <td>16.0</td>\n",
" <td>7.0</td>\n",
" <td>10.0</td>\n",
" <td>19.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-09 00:15:00</th>\n",
" <td>2.0</td>\n",
" <td>13.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>12.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>10.0</td>\n",
" <td>8.0</td>\n",
" <td>16.0</td>\n",
" <td>7.0</td>\n",
" <td>10.0</td>\n",
" <td>19.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-09 00:20:00</th>\n",
" <td>1.0</td>\n",
" <td>13.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>12.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>10.0</td>\n",
" <td>8.0</td>\n",
" <td>16.0</td>\n",
" <td>7.0</td>\n",
" <td>12.0</td>\n",
" <td>19.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 171 columns</p>\n",
"</div>"
],
"text/plain": [
"station 1 2 3 4 5 6 7 8 9 10 ... \\\n",
"ts ... \n",
"2017-07-09 00:00:00 2.0 13.0 1.0 2.0 3.0 2.0 3.0 12.0 2.5 6.0 ... \n",
"2017-07-09 00:05:00 2.0 13.0 1.0 2.0 4.0 2.0 3.0 12.0 3.0 6.0 ... \n",
"2017-07-09 00:10:00 2.0 13.0 0.0 2.0 3.0 2.0 3.0 12.0 3.0 5.0 ... \n",
"2017-07-09 00:15:00 2.0 13.0 0.0 1.0 5.0 2.0 3.0 12.0 3.0 5.0 ... \n",
"2017-07-09 00:20:00 1.0 13.0 0.0 0.0 5.0 2.0 2.0 12.0 3.0 5.0 ... \n",
"\n",
"station 165 166 167 168 169 170 171 172 173 174 \n",
"ts \n",
"2017-07-09 00:00:00 7.0 15.0 14.0 10.0 8.0 16.0 7.0 6.0 19.0 0.0 \n",
"2017-07-09 00:05:00 7.0 15.0 14.0 10.0 8.0 16.0 7.0 7.0 19.0 0.0 \n",
"2017-07-09 00:10:00 7.0 15.0 14.0 10.0 8.0 16.0 7.0 10.0 19.0 0.0 \n",
"2017-07-09 00:15:00 7.0 15.0 14.0 10.0 8.0 16.0 7.0 10.0 19.0 0.0 \n",
"2017-07-09 00:20:00 7.0 15.0 14.0 10.0 8.0 16.0 7.0 12.0 19.0 0.0 \n",
"\n",
"[5 rows x 171 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(df.shape)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get rid of saturday and sunday"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"weekday = df.index.weekday"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mask = weekday < 5"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16308"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mask.sum()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = df[mask]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(16308, 171)\n"
]
}
],
"source": [
"print(df.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get the daily profile"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['hour'] = df.index.hour"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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>station</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>...</th>\n",
" <th>166</th>\n",
" <th>167</th>\n",
" <th>168</th>\n",
" <th>169</th>\n",
" <th>170</th>\n",
" <th>171</th>\n",
" <th>172</th>\n",
" <th>173</th>\n",
" <th>174</th>\n",
" <th>hour</th>\n",
" </tr>\n",
" <tr>\n",
" <th>ts</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>2017-07-10 00:00:00</th>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>9.0</td>\n",
" <td>3.0</td>\n",
" <td>17.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>12.0</td>\n",
" <td>10.0</td>\n",
" <td>16.0</td>\n",
" <td>8.0</td>\n",
" <td>20.0</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-10 00:05:00</th>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>9.0</td>\n",
" <td>3.0</td>\n",
" <td>17.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>12.0</td>\n",
" <td>10.0</td>\n",
" <td>16.0</td>\n",
" <td>8.0</td>\n",
" <td>20.0</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-10 00:10:00</th>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>9.0</td>\n",
" <td>4.0</td>\n",
" <td>17.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>12.0</td>\n",
" <td>10.0</td>\n",
" <td>16.0</td>\n",
" <td>8.0</td>\n",
" <td>20.0</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-10 00:15:00</th>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>9.0</td>\n",
" <td>4.0</td>\n",
" <td>18.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>12.0</td>\n",
" <td>10.0</td>\n",
" <td>16.0</td>\n",
" <td>8.0</td>\n",
" <td>20.0</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-07-10 00:20:00</th>\n",
" <td>2.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>9.0</td>\n",
" <td>4.0</td>\n",
" <td>18.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>12.0</td>\n",
" <td>10.0</td>\n",
" <td>16.0</td>\n",
" <td>8.0</td>\n",
" <td>20.0</td>\n",
" <td>10.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 172 columns</p>\n",
"</div>"
],
"text/plain": [
"station 1 2 3 4 5 6 7 8 9 10 ... \\\n",
"ts ... \n",
"2017-07-10 00:00:00 2.0 4.0 1.0 6.0 0.0 1.0 9.0 3.0 17.0 1.0 ... \n",
"2017-07-10 00:05:00 2.0 4.0 1.0 6.0 0.0 1.0 9.0 3.0 17.0 1.0 ... \n",
"2017-07-10 00:10:00 2.0 4.0 1.0 7.0 0.0 1.0 9.0 4.0 17.0 1.0 ... \n",
"2017-07-10 00:15:00 2.0 4.0 1.0 7.0 0.0 1.0 9.0 4.0 18.0 1.0 ... \n",
"2017-07-10 00:20:00 2.0 4.0 1.0 6.0 0.0 1.0 9.0 4.0 18.0 1.0 ... \n",
"\n",
"station 166 167 168 169 170 171 172 173 174 hour \n",
"ts \n",
"2017-07-10 00:00:00 15.0 14.0 12.0 10.0 16.0 8.0 20.0 10.0 0.0 0 \n",
"2017-07-10 00:05:00 15.0 14.0 12.0 10.0 16.0 8.0 20.0 10.0 0.0 0 \n",
"2017-07-10 00:10:00 15.0 14.0 12.0 10.0 16.0 8.0 20.0 10.0 0.0 0 \n",
"2017-07-10 00:15:00 15.0 14.0 12.0 10.0 16.0 8.0 20.0 10.0 0.0 0 \n",
"2017-07-10 00:20:00 15.0 14.0 12.0 10.0 16.0 8.0 20.0 10.0 0.0 0 \n",
"\n",
"[5 rows x 172 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"profile = df.groupby(\"hour\").mean()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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>station</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>...</th>\n",
" <th>165</th>\n",
" <th>166</th>\n",
" <th>167</th>\n",
" <th>168</th>\n",
" <th>169</th>\n",
" <th>170</th>\n",
" <th>171</th>\n",
" <th>172</th>\n",
" <th>173</th>\n",
" <th>174</th>\n",
" </tr>\n",
" <tr>\n",
" <th>hour</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>3.266082</td>\n",
" <td>5.775585</td>\n",
" <td>6.897661</td>\n",
" <td>6.171053</td>\n",
" <td>2.808480</td>\n",
" <td>1.837719</td>\n",
" <td>5.251462</td>\n",
" <td>8.002924</td>\n",
" <td>10.951754</td>\n",
" <td>6.609649</td>\n",
" <td>...</td>\n",
" <td>8.904971</td>\n",
" <td>10.245614</td>\n",
" <td>10.975146</td>\n",
" <td>9.798246</td>\n",
" <td>6.932749</td>\n",
" <td>7.517544</td>\n",
" <td>12.219298</td>\n",
" <td>4.355263</td>\n",
" <td>12.076023</td>\n",
" <td>1.084795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2.932749</td>\n",
" <td>6.281433</td>\n",
" <td>6.978801</td>\n",
" <td>5.461988</td>\n",
" <td>1.983918</td>\n",
" <td>1.653509</td>\n",
" <td>4.508772</td>\n",
" <td>9.185673</td>\n",
" <td>12.275585</td>\n",
" <td>7.353801</td>\n",
" <td>...</td>\n",
" <td>9.017544</td>\n",
" <td>10.320906</td>\n",
" <td>11.001462</td>\n",
" <td>9.900585</td>\n",
" <td>7.119883</td>\n",
" <td>7.799708</td>\n",
" <td>13.330409</td>\n",
" <td>4.561404</td>\n",
" <td>12.076023</td>\n",
" <td>1.080409</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.620614</td>\n",
" <td>6.640351</td>\n",
" <td>7.149123</td>\n",
" <td>4.676901</td>\n",
" <td>1.755117</td>\n",
" <td>1.627193</td>\n",
" <td>3.379386</td>\n",
" <td>9.966374</td>\n",
" <td>13.274854</td>\n",
" <td>7.755848</td>\n",
" <td>...</td>\n",
" <td>9.046784</td>\n",
" <td>10.340643</td>\n",
" <td>11.054094</td>\n",
" <td>9.978070</td>\n",
" <td>7.359649</td>\n",
" <td>8.231725</td>\n",
" <td>13.790936</td>\n",
" <td>4.934211</td>\n",
" <td>12.428363</td>\n",
" <td>1.153509</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2.754386</td>\n",
" <td>6.750000</td>\n",
" <td>7.087719</td>\n",
" <td>4.429825</td>\n",
" <td>1.866959</td>\n",
" <td>1.852339</td>\n",
" <td>3.064327</td>\n",
" <td>9.919591</td>\n",
" <td>13.483918</td>\n",
" <td>8.046784</td>\n",
" <td>...</td>\n",
" <td>9.204678</td>\n",
" <td>10.583333</td>\n",
" <td>11.115497</td>\n",
" <td>9.991228</td>\n",
" <td>7.557018</td>\n",
" <td>8.410819</td>\n",
" <td>13.845029</td>\n",
" <td>4.921053</td>\n",
" <td>12.571637</td>\n",
" <td>1.209064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.517544</td>\n",
" <td>6.780702</td>\n",
" <td>7.456140</td>\n",
" <td>4.483918</td>\n",
" <td>2.038743</td>\n",
" <td>2.352339</td>\n",
" <td>2.844298</td>\n",
" <td>10.122076</td>\n",
" <td>13.527778</td>\n",
" <td>8.469298</td>\n",
" <td>...</td>\n",
" <td>9.298977</td>\n",
" <td>11.719298</td>\n",
" <td>11.269006</td>\n",
" <td>10.020468</td>\n",
" <td>7.625731</td>\n",
" <td>8.523392</td>\n",
" <td>13.746345</td>\n",
" <td>4.761696</td>\n",
" <td>12.756579</td>\n",
" <td>1.270468</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 171 columns</p>\n",
"</div>"
],
"text/plain": [
"station 1 2 3 4 5 6 7 \\\n",
"hour \n",
"0 3.266082 5.775585 6.897661 6.171053 2.808480 1.837719 5.251462 \n",
"1 2.932749 6.281433 6.978801 5.461988 1.983918 1.653509 4.508772 \n",
"2 2.620614 6.640351 7.149123 4.676901 1.755117 1.627193 3.379386 \n",
"3 2.754386 6.750000 7.087719 4.429825 1.866959 1.852339 3.064327 \n",
"4 3.517544 6.780702 7.456140 4.483918 2.038743 2.352339 2.844298 \n",
"\n",
"station 8 9 10 ... 165 166 \\\n",
"hour ... \n",
"0 8.002924 10.951754 6.609649 ... 8.904971 10.245614 \n",
"1 9.185673 12.275585 7.353801 ... 9.017544 10.320906 \n",
"2 9.966374 13.274854 7.755848 ... 9.046784 10.340643 \n",
"3 9.919591 13.483918 8.046784 ... 9.204678 10.583333 \n",
"4 10.122076 13.527778 8.469298 ... 9.298977 11.719298 \n",
"\n",
"station 167 168 169 170 171 172 \\\n",
"hour \n",
"0 10.975146 9.798246 6.932749 7.517544 12.219298 4.355263 \n",
"1 11.001462 9.900585 7.119883 7.799708 13.330409 4.561404 \n",
"2 11.054094 9.978070 7.359649 8.231725 13.790936 4.934211 \n",
"3 11.115497 9.991228 7.557018 8.410819 13.845029 4.921053 \n",
"4 11.269006 10.020468 7.625731 8.523392 13.746345 4.761696 \n",
"\n",
"station 173 174 \n",
"hour \n",
"0 12.076023 1.084795 \n",
"1 12.076023 1.080409 \n",
"2 12.428363 1.153509 \n",
"3 12.571637 1.209064 \n",
"4 12.756579 1.270468 \n",
"\n",
"[5 rows x 171 columns]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"profile.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the KMeans algorithm on the daily profile."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_clusters = 4"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Normalization\n",
"df_norm = profile / profile.max()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(df_norm.T)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"labels = pd.Series(kmeans.labels_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Number of stations for each cluster (i.e. usage pattern)."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 56\n",
"1 76\n",
"2 27\n",
"3 12\n",
"dtype: int64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label_count = labels.groupby(labels).count()\n",
"label_count"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Choose some colors."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"colors = sns.color_palette('Set1', n_clusters)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAPQAAABLCAYAAABHs6peAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5fed0f72e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.palplot(colors)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f5fe8d4b160>"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEaCAYAAAD+E0veAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS1KIiIhakkJERNT+P5k4ZnUSFeB1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5fec85c160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.barplot(x=label_count.index, y=label_count, palette=colors)\n",
"plt.xlabel('Cluster')\n",
"plt.ylabel('Number of stations')\n",
"plt.title('Number of stations for each cluster')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot the daily profile of available bikes (%) for each cluster."
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"pd.DataFrame(kmeans.cluster_centers_).to_csv(\"../data/bordeaux_clusters.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.85612079, 0.90144269, 0.92701865, 0.93529502, 0.93635042,\n",
" 0.92984217, 0.91218976, 0.85143622, 0.67839165, 0.53811147,\n",
" 0.48767876, 0.45683496, 0.44343193, 0.4332927 , 0.42625061,\n",
" 0.42879971, 0.428392 , 0.46259526, 0.54899295, 0.64206451,\n",
" 0.67905327, 0.69504767, 0.72619311, 0.78652168],\n",
" [ 0.88266194, 0.90293344, 0.92038195, 0.93411959, 0.94003019,\n",
" 0.9348597 , 0.9163827 , 0.90072124, 0.88410779, 0.87966202,\n",
" 0.87229049, 0.86141732, 0.85165133, 0.83614936, 0.83599821,\n",
" 0.83479042, 0.81898354, 0.80056498, 0.80174642, 0.81273062,\n",
" 0.81905851, 0.82179479, 0.82779762, 0.84520065],\n",
" [ 0.31643453, 0.3001893 , 0.29684107, 0.29803988, 0.30018171,\n",
" 0.30801922, 0.31882587, 0.35971205, 0.55797902, 0.85811351,\n",
" 0.91821378, 0.91469655, 0.85458968, 0.83590916, 0.87430805,\n",
" 0.83412739, 0.73269849, 0.54653792, 0.3934498 , 0.33190782,\n",
" 0.32354833, 0.32679378, 0.31894644, 0.30408257],\n",
" [ 0.54669465, 0.3821515 , 0.26250428, 0.22803816, 0.22026069,\n",
" 0.22212548, 0.28425895, 0.3464735 , 0.35105263, 0.35510855,\n",
" 0.45170944, 0.52078828, 0.61404514, 0.6912073 , 0.67561088,\n",
" 0.66128805, 0.66127012, 0.70026712, 0.72902505, 0.7660174 ,\n",
" 0.85840817, 0.96024107, 0.93159552, 0.77444184]])"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"kmeans.cluster_centers_"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAmwAAAF/CAYAAAD0P5WNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUjgObEREREQKx4GNiIiISOH+H5h6ktlLB9Q5AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5fed0b1c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with sns.axes_style(\"darkgrid\", {'xtick.major.size': 8.0}):\n",
" fig, ax = plt.subplots(figsize=(10,6))\n",
"\n",
"for k, label, color in zip(kmeans.cluster_centers_, range(n_clusters), colors):\n",
" plt.plot(100*k, color=color, label=label)\n",
" \n",
"plt.legend()\n",
"plt.xlabel('Hour')\n",
"plt.xticks(np.linspace(0, 24, 13))\n",
"plt.yticks(np.linspace(0, 100, 11))\n",
"plt.ylabel(\"available bikes%\")\n",
"sns.despine()\n",
"plt.savefig(\"../images/bordeaux-pattern.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Map"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the station lat/lon coordinates."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"locations = pd.read_csv(\"../data/bordeaux-stations.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 37,
"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>ident</th>\n",
" <th>nom</th>\n",
" <th>lat</th>\n",
" <th>lon</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>138</td>\n",
" <td>Lycée Brémontier</td>\n",
" <td>44.824055</td>\n",
" <td>-0.570243</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>135</td>\n",
" <td>Eglise Ste Croix</td>\n",
" <td>44.831312</td>\n",
" <td>-0.561393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>79</td>\n",
" <td>Buttinière</td>\n",
" <td>44.864276</td>\n",
" <td>-0.524200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>Porte de Bourgogne</td>\n",
" <td>44.837789</td>\n",
" <td>-0.567156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8</td>\n",
" <td>Doyen Brus</td>\n",
" <td>44.800385</td>\n",
" <td>-0.609857</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ident nom lat lon\n",
"0 138 Lycée Brémontier 44.824055 -0.570243\n",
"1 135 Eglise Ste Croix 44.831312 -0.561393\n",
"2 79 Buttinière 44.864276 -0.524200\n",
"3 3 Porte de Bourgogne 44.837789 -0.567156\n",
"4 8 Doyen Brus 44.800385 -0.609857"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"locations.head()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dflabel = pd.DataFrame({\"label\": kmeans.labels_}, index=profile.columns)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>label</th>\n",
" </tr>\n",
" <tr>\n",
" <th>station</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" label\n",
"station \n",
"1 2\n",
"2 2\n",
"3 1\n",
"4 2\n",
"5 2"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dflabel.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the label, i.e. the cluster id, for each station."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"locations = locations.merge(dflabel, right_index=True, left_on='ident')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"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>ident</th>\n",
" <th>nom</th>\n",
" <th>lat</th>\n",
" <th>lon</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>138</td>\n",
" <td>Lycée Brémontier</td>\n",
" <td>44.824055</td>\n",
" <td>-0.570243</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>135</td>\n",
" <td>Eglise Ste Croix</td>\n",
" <td>44.831312</td>\n",
" <td>-0.561393</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>79</td>\n",
" <td>Buttinière</td>\n",
" <td>44.864276</td>\n",
" <td>-0.524200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>Porte de Bourgogne</td>\n",
" <td>44.837789</td>\n",
" <td>-0.567156</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8</td>\n",
" <td>Doyen Brus</td>\n",
" <td>44.800385</td>\n",
" <td>-0.609857</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ident nom lat lon label\n",
"0 138 Lycée Brémontier 44.824055 -0.570243 0\n",
"1 135 Eglise Ste Croix 44.831312 -0.561393 0\n",
"2 79 Buttinière 44.864276 -0.524200 1\n",
"3 3 Porte de Bourgogne 44.837789 -0.567156 1\n",
"4 8 Doyen Brus 44.800385 -0.609857 2"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"locations.head()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"locations[\"nom\"] = locations['nom'].str.replace(\"'\", \"'\")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import folium"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Bordeaux (France) position.\n",
"position = [44.836151, -0.580816]"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"mp = folium.Map(location=position, zoom_start=12, tiles='cartodbpositron')"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hex_colors = colors.as_hex()"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for _,row in locations.iterrows():\n",
" folium.CircleMarker(\n",
" location=[row['lat'], row['lon']],\n",
" radius=5,\n",
" popup=row['nom'],\n",
" color=hex_colors[row['label']],\n",
" fill=False,\n",
" fill_opacity=0.5,\n",
" fill_color=hex_colors[row['label']]\n",
" ).add_to(mp)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mp.save(\"../images/bordeaux-map-n_clusters-{}.html\".format(n_clusters))"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,<!DOCTYPE html>
<head>    
    <meta http-equiv="content-type" content="text/html; charset=UTF-8" />
    <script>L_PREFER_CANVAS = false; L_NO_TOUCH = false; L_DISABLE_3D = false;</script>
    <script src="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.js"></script>
    <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js"></script>
    <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js"></script>
    <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>
    <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/leaflet@1.2.0/dist/leaflet.css" />
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css" />
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css" />
    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.6.3/css/font-awesome.min.css" />
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css" />
    <link rel="stylesheet" href="https://rawgit.com/python-visualization/folium/master/folium/templates/leaflet.awesome.rotate.css" />
    <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>
    <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>
    
            <style> #map_0d86e08075254804a191840c1a8961f7 {
                position : relative;
                width : 100.0%;
                height: 100.0%;
                left: 0.0%;
                top: 0.0%;
                }
            </style>
        
</head>
<body>    
    
            <div class="folium-map" id="map_0d86e08075254804a191840c1a8961f7" ></div>
        
</body>
<script>    
    

            
                var bounds = null;
            

            var map_0d86e08075254804a191840c1a8961f7 = L.map(
                                  'map_0d86e08075254804a191840c1a8961f7',
                                  {center: [44.836151,-0.580816],
                                  zoom: 12,
                                  maxBounds: bounds,
                                  layers: [],
                                  worldCopyJump: false,
                                  crs: L.CRS.EPSG3857
                                 });
            
        
    
            var tile_layer_3924c5bd903f4e0691c6c3a7b0ed4fe6 = L.tileLayer(
                'https://cartodb-basemaps-{s}.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png',
                {
  "attribution": null,
  "detectRetina": false,
  "maxZoom": 18,
  "minZoom": 1,
  "noWrap": false,
  "subdomains": "abc"
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
        
    
            var circle_marker_4dcc2e7fc7434dc5b82c453a8812fb10 = L.circleMarker(
                [44.82405483453179,-0.570243491180582],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_cd1af4ba213a4fcc89e80dc427dbac15 = L.popup({maxWidth: '300'});

            
                var html_d181c01fb7924308b4865059e50fa9e7 = $('<div id="html_d181c01fb7924308b4865059e50fa9e7" style="width: 100.0%; height: 100.0%;">Lycée Brémontier</div>')[0];
                popup_cd1af4ba213a4fcc89e80dc427dbac15.setContent(html_d181c01fb7924308b4865059e50fa9e7);
            

            circle_marker_4dcc2e7fc7434dc5b82c453a8812fb10.bindPopup(popup_cd1af4ba213a4fcc89e80dc427dbac15);

            
        
    
            var circle_marker_2b8e28c9fd37435aa66a9bb7ad43007b = L.circleMarker(
                [44.8313123071108,-0.561392805485366],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_72b3b61678294794a42954d7f99fff02 = L.popup({maxWidth: '300'});

            
                var html_49a9a706126e41caaeec44e656078271 = $('<div id="html_49a9a706126e41caaeec44e656078271" style="width: 100.0%; height: 100.0%;">Eglise Ste Croix</div>')[0];
                popup_72b3b61678294794a42954d7f99fff02.setContent(html_49a9a706126e41caaeec44e656078271);
            

            circle_marker_2b8e28c9fd37435aa66a9bb7ad43007b.bindPopup(popup_72b3b61678294794a42954d7f99fff02);

            
        
    
            var circle_marker_590056b733ca45cabfb2fc055dc314b0 = L.circleMarker(
                [44.8642758580301,-0.524200242527949],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_754536ca01754a83859472219c2d3bb1 = L.popup({maxWidth: '300'});

            
                var html_4e3c7a2ef5d645fca33c394ddaedc4c5 = $('<div id="html_4e3c7a2ef5d645fca33c394ddaedc4c5" style="width: 100.0%; height: 100.0%;">Buttinière</div>')[0];
                popup_754536ca01754a83859472219c2d3bb1.setContent(html_4e3c7a2ef5d645fca33c394ddaedc4c5);
            

            circle_marker_590056b733ca45cabfb2fc055dc314b0.bindPopup(popup_754536ca01754a83859472219c2d3bb1);

            
        
    
            var circle_marker_49502ad52acd4c7da4d3612db960943a = L.circleMarker(
                [44.837788570071105,-0.5671555557555621],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_42ea812e900e4c77b621129cfa46ebfe = L.popup({maxWidth: '300'});

            
                var html_6c261d5b93754a0ab1764c7b7997951c = $('<div id="html_6c261d5b93754a0ab1764c7b7997951c" style="width: 100.0%; height: 100.0%;">Porte de Bourgogne</div>')[0];
                popup_42ea812e900e4c77b621129cfa46ebfe.setContent(html_6c261d5b93754a0ab1764c7b7997951c);
            

            circle_marker_49502ad52acd4c7da4d3612db960943a.bindPopup(popup_42ea812e900e4c77b621129cfa46ebfe);

            
        
    
            var circle_marker_f00876df83444709ad4aa423bd3a5be5 = L.circleMarker(
                [44.8003846074376,-0.609856530171509],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_4639188cabbc4414aa441d17a24995cb = L.popup({maxWidth: '300'});

            
                var html_4652b11bc13f426689ba4b6a23ece7bb = $('<div id="html_4652b11bc13f426689ba4b6a23ece7bb" style="width: 100.0%; height: 100.0%;">Doyen Brus</div>')[0];
                popup_4639188cabbc4414aa441d17a24995cb.setContent(html_4652b11bc13f426689ba4b6a23ece7bb);
            

            circle_marker_f00876df83444709ad4aa423bd3a5be5.bindPopup(popup_4639188cabbc4414aa441d17a24995cb);

            
        
    
            var circle_marker_27312b687bca4cf0a6c6cc2d611265f3 = L.circleMarker(
                [44.86328744461021,-0.5999496122171569],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_800037486e904dc5b981d9236688dc4b = L.popup({maxWidth: '300'});

            
                var html_c7d3445c9905413c899f85b482c8c3fa = $('<div id="html_c7d3445c9905413c899f85b482c8c3fa" style="width: 100.0%; height: 100.0%;">Le Bouscat Mairie</div>')[0];
                popup_800037486e904dc5b981d9236688dc4b.setContent(html_c7d3445c9905413c899f85b482c8c3fa);
            

            circle_marker_27312b687bca4cf0a6c6cc2d611265f3.bindPopup(popup_800037486e904dc5b981d9236688dc4b);

            
        
    
            var circle_marker_c3f66ba304414a86b4225e0d7a106d14 = L.circleMarker(
                [44.85416906214729,-0.5865861955488361],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_dcab17d124794d158e37e2e8526bf849 = L.popup({maxWidth: '300'});

            
                var html_1e8187444b544cc0b7248bdd5712f99d = $('<div id="html_1e8187444b544cc0b7248bdd5712f99d" style="width: 100.0%; height: 100.0%;">Parc Rivière</div>')[0];
                popup_dcab17d124794d158e37e2e8526bf849.setContent(html_1e8187444b544cc0b7248bdd5712f99d);
            

            circle_marker_c3f66ba304414a86b4225e0d7a106d14.bindPopup(popup_dcab17d124794d158e37e2e8526bf849);

            
        
    
            var circle_marker_46573c07d8bc4f9f984d6eafc32a5e20 = L.circleMarker(
                [44.832618305563,-0.570974436712359],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_ede864716be8404e80ef584eccccd653 = L.popup({maxWidth: '300'});

            
                var html_9b62b814210c4bfdbd8cbe89761589a7 = $('<div id="html_9b62b814210c4bfdbd8cbe89761589a7" style="width: 100.0%; height: 100.0%;">Rue du Mirail</div>')[0];
                popup_ede864716be8404e80ef584eccccd653.setContent(html_9b62b814210c4bfdbd8cbe89761589a7);
            

            circle_marker_46573c07d8bc4f9f984d6eafc32a5e20.bindPopup(popup_ede864716be8404e80ef584eccccd653);

            
        
    
            var circle_marker_a340ecd136184c36a248eb8e69b44da2 = L.circleMarker(
                [44.7966723128786,-0.617040553455776],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_defb9ee91d4b4443874293585e299a05 = L.popup({maxWidth: '300'});

            
                var html_eb8a6017113847bd8ad518917786f885 = $('<div id="html_eb8a6017113847bd8ad518917786f885" style="width: 100.0%; height: 100.0%;">Montaigne Montesquieu</div>')[0];
                popup_defb9ee91d4b4443874293585e299a05.setContent(html_eb8a6017113847bd8ad518917786f885);
            

            circle_marker_a340ecd136184c36a248eb8e69b44da2.bindPopup(popup_defb9ee91d4b4443874293585e299a05);

            
        
    
            var circle_marker_847525d26ec34380957f8c6fd62dd11c = L.circleMarker(
                [44.8279738044959,-0.593490369291754],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_66e17c20a35444ad9c3df11db1bff1e9 = L.popup({maxWidth: '300'});

            
                var html_c7ce18cdeada498381620302e63ad3df = $('<div id="html_c7ce18cdeada498381620302e63ad3df" style="width: 100.0%; height: 100.0%;">Xaintrailles</div>')[0];
                popup_66e17c20a35444ad9c3df11db1bff1e9.setContent(html_c7ce18cdeada498381620302e63ad3df);
            

            circle_marker_847525d26ec34380957f8c6fd62dd11c.bindPopup(popup_66e17c20a35444ad9c3df11db1bff1e9);

            
        
    
            var circle_marker_845822f9aa0b4f37b7d0e09da647afdc = L.circleMarker(
                [44.8494651911446,-0.545253248846396],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_6e6d203068ea4e3ba636ccc617321f4b = L.popup({maxWidth: '300'});

            
                var html_496c1dff1202419c9e5e1e6977f8dfed = $('<div id="html_496c1dff1202419c9e5e1e6977f8dfed" style="width: 100.0%; height: 100.0%;">Galin</div>')[0];
                popup_6e6d203068ea4e3ba636ccc617321f4b.setContent(html_496c1dff1202419c9e5e1e6977f8dfed);
            

            circle_marker_845822f9aa0b4f37b7d0e09da647afdc.bindPopup(popup_6e6d203068ea4e3ba636ccc617321f4b);

            
        
    
            var circle_marker_2fb617960dc04ae9974121f8f80626aa = L.circleMarker(
                [44.887498870215396,-0.51762894718184],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_62af503c77d540249a0e452233958a75 = L.popup({maxWidth: '300'});

            
                var html_339777b063f14491916fd0cd53492238 = $('<div id="html_339777b063f14491916fd0cd53492238" style="width: 100.0%; height: 100.0%;">La Gardette</div>')[0];
                popup_62af503c77d540249a0e452233958a75.setContent(html_339777b063f14491916fd0cd53492238);
            

            circle_marker_2fb617960dc04ae9974121f8f80626aa.bindPopup(popup_62af503c77d540249a0e452233958a75);

            
        
    
            var circle_marker_cd2dfa1fe8e34700bab009463ad001f9 = L.circleMarker(
                [44.8773814879661,-0.54433270185457],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_0885094e4fe443109c6339a7053ee201 = L.popup({maxWidth: '300'});

            
                var html_1da00d533a6843e28628475707789c32 = $('<div id="html_1da00d533a6843e28628475707789c32" style="width: 100.0%; height: 100.0%;">Claveau</div>')[0];
                popup_0885094e4fe443109c6339a7053ee201.setContent(html_1da00d533a6843e28628475707789c32);
            

            circle_marker_cd2dfa1fe8e34700bab009463ad001f9.bindPopup(popup_0885094e4fe443109c6339a7053ee201);

            
        
    
            var circle_marker_bde52214e3e0476491a8392ea6d426a4 = L.circleMarker(
                [44.8441478155752,-0.581633153292761],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_fc3a3f95543b4ddbae41150d095641ec = L.popup({maxWidth: '300'});

            
                var html_e52095504d26445f819cacc1dc5c1778 = $('<div id="html_e52095504d26445f819cacc1dc5c1778" style="width: 100.0%; height: 100.0%;">Huguerie</div>')[0];
                popup_fc3a3f95543b4ddbae41150d095641ec.setContent(html_e52095504d26445f819cacc1dc5c1778);
            

            circle_marker_bde52214e3e0476491a8392ea6d426a4.bindPopup(popup_fc3a3f95543b4ddbae41150d095641ec);

            
        
    
            var circle_marker_d684c3dff95e4c56b1000c41eedddcc5 = L.circleMarker(
                [44.8517457831629,-0.614383180249531],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a69aeec4abb54c9188b393ca0a60e12d = L.popup({maxWidth: '300'});

            
                var html_94813466a3124be68cd731aa16fa8479 = $('<div id="html_94813466a3124be68cd731aa16fa8479" style="width: 100.0%; height: 100.0%;">Caudéran</div>')[0];
                popup_a69aeec4abb54c9188b393ca0a60e12d.setContent(html_94813466a3124be68cd731aa16fa8479);
            

            circle_marker_d684c3dff95e4c56b1000c41eedddcc5.bindPopup(popup_a69aeec4abb54c9188b393ca0a60e12d);

            
        
    
            var circle_marker_f1eb61b1451d422d81eca87515e1ebd3 = L.circleMarker(
                [44.8541785581174,-0.5128187356398309],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_44d1c8c08dfd4b66ba05feba94b17662 = L.popup({maxWidth: '300'});

            
                var html_addf1736252d40b08d0b7843fda3940a = $('<div id="html_addf1736252d40b08d0b7843fda3940a" style="width: 100.0%; height: 100.0%;">Jean Zay</div>')[0];
                popup_44d1c8c08dfd4b66ba05feba94b17662.setContent(html_addf1736252d40b08d0b7843fda3940a);
            

            circle_marker_f1eb61b1451d422d81eca87515e1ebd3.bindPopup(popup_44d1c8c08dfd4b66ba05feba94b17662);

            
        
    
            var circle_marker_915704ed59b14ef3b0cb7c4cb35d557c = L.circleMarker(
                [44.8422055855349,-0.5848222650121421],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_53f7ce1a1ec343619a39cf32953488ff = L.popup({maxWidth: '300'});

            
                var html_6749ac415e02494296054b1cc76285f2 = $('<div id="html_6749ac415e02494296054b1cc76285f2" style="width: 100.0%; height: 100.0%;">St Seurin</div>')[0];
                popup_53f7ce1a1ec343619a39cf32953488ff.setContent(html_6749ac415e02494296054b1cc76285f2);
            

            circle_marker_915704ed59b14ef3b0cb7c4cb35d557c.bindPopup(popup_53f7ce1a1ec343619a39cf32953488ff);

            
        
    
            var circle_marker_c737ce1bc35b44b2844b0b482d530d3a = L.circleMarker(
                [44.8414957860903,-0.580803720206641],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_42d69906763343ea8080b04b294d59b1 = L.popup({maxWidth: '300'});

            
                var html_f98f9afaab774b5cbcad0f82cc0c0056 = $('<div id="html_f98f9afaab774b5cbcad0f82cc0c0056" style="width: 100.0%; height: 100.0%;">Place Gambetta</div>')[0];
                popup_42d69906763343ea8080b04b294d59b1.setContent(html_f98f9afaab774b5cbcad0f82cc0c0056);
            

            circle_marker_c737ce1bc35b44b2844b0b482d530d3a.bindPopup(popup_42d69906763343ea8080b04b294d59b1);

            
        
    
            var circle_marker_027819a46c594f45b6444183f72bce04 = L.circleMarker(
                [44.826535861290004,-0.601842356968657],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_37db3d72e91f45148f3065dccca1bd64 = L.popup({maxWidth: '300'});

            
                var html_c70ab352e63f451fafcef464d1fa14a3 = $('<div id="html_c70ab352e63f451fafcef464d1fa14a3" style="width: 100.0%; height: 100.0%;">Bordeaux II</div>')[0];
                popup_37db3d72e91f45148f3065dccca1bd64.setContent(html_c70ab352e63f451fafcef464d1fa14a3);
            

            circle_marker_027819a46c594f45b6444183f72bce04.bindPopup(popup_37db3d72e91f45148f3065dccca1bd64);

            
        
    
            var circle_marker_18bbde66444e476a8c04e97174cdaa71 = L.circleMarker(
                [44.844227022453204,-0.5743444501730071],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_3ad8a2a589a2497aaf90e02398daac71 = L.popup({maxWidth: '300'});

            
                var html_9fc9ecc6b85c48e7b42a4fe8a89905a6 = $('<div id="html_9fc9ecc6b85c48e7b42a4fe8a89905a6" style="width: 100.0%; height: 100.0%;">Quinconces</div>')[0];
                popup_3ad8a2a589a2497aaf90e02398daac71.setContent(html_9fc9ecc6b85c48e7b42a4fe8a89905a6);
            

            circle_marker_18bbde66444e476a8c04e97174cdaa71.bindPopup(popup_3ad8a2a589a2497aaf90e02398daac71);

            
        
    
            var circle_marker_4014a4cbc6c04cc89aed8bd869512f2a = L.circleMarker(
                [44.8416628527762,-0.599695197744329],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_23dd1a13bb5948318a2f6f4c291e8772 = L.popup({maxWidth: '300'});

            
                var html_6bb9fe6335974d51a4a9823047dcb477 = $('<div id="html_6bb9fe6335974d51a4a9823047dcb477" style="width: 100.0%; height: 100.0%;">Cité Administrative</div>')[0];
                popup_23dd1a13bb5948318a2f6f4c291e8772.setContent(html_6bb9fe6335974d51a4a9823047dcb477);
            

            circle_marker_4014a4cbc6c04cc89aed8bd869512f2a.bindPopup(popup_23dd1a13bb5948318a2f6f4c291e8772);

            
        
    
            var circle_marker_429e5a6c9f5f4a6bb7a063b70afafc5c = L.circleMarker(
                [44.8431439888232,-0.5772404414507191],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_5c7acd1d364f4b2eb9f1cd7d6f09b002 = L.popup({maxWidth: '300'});

            
                var html_80d5fff429fc4271a61d804527a0848a = $('<div id="html_80d5fff429fc4271a61d804527a0848a" style="width: 100.0%; height: 100.0%;">Grands Hommes</div>')[0];
                popup_5c7acd1d364f4b2eb9f1cd7d6f09b002.setContent(html_80d5fff429fc4271a61d804527a0848a);
            

            circle_marker_429e5a6c9f5f4a6bb7a063b70afafc5c.bindPopup(popup_5c7acd1d364f4b2eb9f1cd7d6f09b002);

            
        
    
            var circle_marker_608216114b3a44c49e58cf1e791ad5f2 = L.circleMarker(
                [44.8588988431287,-0.581080543691782],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_08eb6e4e5de84e6e86d345c03edfcd17 = L.popup({maxWidth: '300'});

            
                var html_7df3857b9eba44c49ac0b9fc981d6d7d = $('<div id="html_7df3857b9eba44c49ac0b9fc981d6d7d" style="width: 100.0%; height: 100.0%;">Place de l&apos;Europe</div>')[0];
                popup_08eb6e4e5de84e6e86d345c03edfcd17.setContent(html_7df3857b9eba44c49ac0b9fc981d6d7d);
            

            circle_marker_608216114b3a44c49e58cf1e791ad5f2.bindPopup(popup_08eb6e4e5de84e6e86d345c03edfcd17);

            
        
    
            var circle_marker_e08496ee995e40dcb1b8d64d9eea7661 = L.circleMarker(
                [44.826421204936,-0.557323394027949],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_8f6cccd940dc4008b8c7a1265ed5be06 = L.popup({maxWidth: '300'});

            
                var html_fcdabd15e39b416caada9898124e8a45 = $('<div id="html_fcdabd15e39b416caada9898124e8a45" style="width: 100.0%; height: 100.0%;">Rue St Vincent de Paul</div>')[0];
                popup_8f6cccd940dc4008b8c7a1265ed5be06.setContent(html_fcdabd15e39b416caada9898124e8a45);
            

            circle_marker_e08496ee995e40dcb1b8d64d9eea7661.bindPopup(popup_8f6cccd940dc4008b8c7a1265ed5be06);

            
        
    
            var circle_marker_31f6e5b5b34f4932b678500e38e3ec1c = L.circleMarker(
                [44.83027805669379,-0.526965614882518],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_0f2ed3e9daa247cbba9861d20457fccd = L.popup({maxWidth: '300'});

            
                var html_9fdc002620e24caf9634efdc3f748e66 = $('<div id="html_9fdc002620e24caf9634efdc3f748e66" style="width: 100.0%; height: 100.0%;">François Mitterrand</div>')[0];
                popup_0f2ed3e9daa247cbba9861d20457fccd.setContent(html_9fdc002620e24caf9634efdc3f748e66);
            

            circle_marker_31f6e5b5b34f4932b678500e38e3ec1c.bindPopup(popup_0f2ed3e9daa247cbba9861d20457fccd);

            
        
    
            var circle_marker_ea5668595f1b47a8b9e8c24ed5752525 = L.circleMarker(
                [44.8411382995454,-0.562235533069282],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_3faba93f87f04c58ba376a7db1108a34 = L.popup({maxWidth: '300'});

            
                var html_89916822f2d44499af98ceb2a49f7f95 = $('<div id="html_89916822f2d44499af98ceb2a49f7f95" style="width: 100.0%; height: 100.0%;">Gare d&apos;Orléans</div>')[0];
                popup_3faba93f87f04c58ba376a7db1108a34.setContent(html_89916822f2d44499af98ceb2a49f7f95);
            

            circle_marker_ea5668595f1b47a8b9e8c24ed5752525.bindPopup(popup_3faba93f87f04c58ba376a7db1108a34);

            
        
    
            var circle_marker_699137032542461cbf5191e9f7c731b9 = L.circleMarker(
                [44.831826305618705,-0.5596603635595601],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_30afa587ba0040d6a65e9f41dd5f2298 = L.popup({maxWidth: '300'});

            
                var html_1068bbd121ed41f8a0ef7597765bda6c = $('<div id="html_1068bbd121ed41f8a0ef7597765bda6c" style="width: 100.0%; height: 100.0%;">Conservatoire</div>')[0];
                popup_30afa587ba0040d6a65e9f41dd5f2298.setContent(html_1068bbd121ed41f8a0ef7597765bda6c);
            

            circle_marker_699137032542461cbf5191e9f7c731b9.bindPopup(popup_30afa587ba0040d6a65e9f41dd5f2298);

            
        
    
            var circle_marker_61e0f929c666457f8a3a250adb33c57f = L.circleMarker(
                [44.8953027136185,-0.7146676441038691],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_8686b84eeeca49b1a2c1a2ebc2ceab31 = L.popup({maxWidth: '300'});

            
                var html_aba69e4ca12c475798618ff27440e936 = $('<div id="html_aba69e4ca12c475798618ff27440e936" style="width: 100.0%; height: 100.0%;">St Médard République</div>')[0];
                popup_8686b84eeeca49b1a2c1a2ebc2ceab31.setContent(html_aba69e4ca12c475798618ff27440e936);
            

            circle_marker_61e0f929c666457f8a3a250adb33c57f.bindPopup(popup_8686b84eeeca49b1a2c1a2ebc2ceab31);

            
        
    
            var circle_marker_91357685fd02487b87d7d54e21604550 = L.circleMarker(
                [44.8738860218516,-0.5740119377009429],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_c8d1e080980442bd82d818df21a79282 = L.popup({maxWidth: '300'});

            
                var html_e08397456ba1489faa4e336d5843341e = $('<div id="html_e08397456ba1489faa4e336d5843341e" style="width: 100.0%; height: 100.0%;">Les Aubiers</div>')[0];
                popup_c8d1e080980442bd82d818df21a79282.setContent(html_e08397456ba1489faa4e336d5843341e);
            

            circle_marker_91357685fd02487b87d7d54e21604550.bindPopup(popup_c8d1e080980442bd82d818df21a79282);

            
        
    
            var circle_marker_988633e8b571493ba5c54bfbc386e4c1 = L.circleMarker(
                [44.8299539022041,-0.5681171033556129],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_2aef36bdeca74010935a57a86ae6ed55 = L.popup({maxWidth: '300'});

            
                var html_3cafcfec1f5540a99bf26f5f79041f2e = $('<div id="html_3cafcfec1f5540a99bf26f5f79041f2e" style="width: 100.0%; height: 100.0%;">Capucins</div>')[0];
                popup_2aef36bdeca74010935a57a86ae6ed55.setContent(html_3cafcfec1f5540a99bf26f5f79041f2e);
            

            circle_marker_988633e8b571493ba5c54bfbc386e4c1.bindPopup(popup_2aef36bdeca74010935a57a86ae6ed55);

            
        
    
            var circle_marker_26f1677284444d46b8d90532592be3fe = L.circleMarker(
                [44.8059145174294,-0.60232316231959],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_0e6f5305a12d45b29db8a531e190a847 = L.popup({maxWidth: '300'});

            
                var html_b543b4279f8e461eadd95f922e7642a2 = $('<div id="html_b543b4279f8e461eadd95f922e7642a2" style="width: 100.0%; height: 100.0%;">Arts et Métiers</div>')[0];
                popup_0e6f5305a12d45b29db8a531e190a847.setContent(html_b543b4279f8e461eadd95f922e7642a2);
            

            circle_marker_26f1677284444d46b8d90532592be3fe.bindPopup(popup_0e6f5305a12d45b29db8a531e190a847);

            
        
    
            var circle_marker_fc58ad443a2743f08da7b9c0786a7c9b = L.circleMarker(
                [44.7968805718968,-0.6018688664835501],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_10bbb207013a4fba92547a8aba409283 = L.popup({maxWidth: '300'});

            
                var html_c1384d17aeda4218ac0ed72e4cc149ac = $('<div id="html_c1384d17aeda4218ac0ed72e4cc149ac" style="width: 100.0%; height: 100.0%;">Ecole de Management</div>')[0];
                popup_10bbb207013a4fba92547a8aba409283.setContent(html_c1384d17aeda4218ac0ed72e4cc149ac);
            

            circle_marker_fc58ad443a2743f08da7b9c0786a7c9b.bindPopup(popup_10bbb207013a4fba92547a8aba409283);

            
        
    
            var circle_marker_229fe4366a5e4e2c8cd5ddf61d6b6208 = L.circleMarker(
                [44.8452123554279,-0.5920629394044891],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a79dc2b2fac2441d85557b2ad8e2bbf4 = L.popup({maxWidth: '300'});

            
                var html_c1ffa044eb164f15b1a7d3b4f26c2504 = $('<div id="html_c1ffa044eb164f15b1a7d3b4f26c2504" style="width: 100.0%; height: 100.0%;">Rue de la Croix Blanche</div>')[0];
                popup_a79dc2b2fac2441d85557b2ad8e2bbf4.setContent(html_c1ffa044eb164f15b1a7d3b4f26c2504);
            

            circle_marker_229fe4366a5e4e2c8cd5ddf61d6b6208.bindPopup(popup_a79dc2b2fac2441d85557b2ad8e2bbf4);

            
        
    
            var circle_marker_bd654b4f232d4730a9e6b316029d3a5e = L.circleMarker(
                [44.8446454582444,-0.577447981270094],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_5c21af07cb364158b462086334d74e84 = L.popup({maxWidth: '300'});

            
                var html_a508f2dff7ff4ced810079f2f8bcf082 = $('<div id="html_a508f2dff7ff4ced810079f2f8bcf082" style="width: 100.0%; height: 100.0%;">Place Tourny</div>')[0];
                popup_5c21af07cb364158b462086334d74e84.setContent(html_a508f2dff7ff4ced810079f2f8bcf082);
            

            circle_marker_bd654b4f232d4730a9e6b316029d3a5e.bindPopup(popup_5c21af07cb364158b462086334d74e84);

            
        
    
            var circle_marker_6a6ad531c48f4b79aa5ee8697debce41 = L.circleMarker(
                [44.834828248887206,-0.580185208518229],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_5df4d1e3578a4089b54375b643585eeb = L.popup({maxWidth: '300'});

            
                var html_461cba41f6004cd8ac09e1fb390fcd62 = $('<div id="html_461cba41f6004cd8ac09e1fb390fcd62" style="width: 100.0%; height: 100.0%;">République</div>')[0];
                popup_5df4d1e3578a4089b54375b643585eeb.setContent(html_461cba41f6004cd8ac09e1fb390fcd62);
            

            circle_marker_6a6ad531c48f4b79aa5ee8697debce41.bindPopup(popup_5df4d1e3578a4089b54375b643585eeb);

            
        
    
            var circle_marker_1d32e3bc5dea462bb926d988122e74e0 = L.circleMarker(
                [44.793131985816096,-0.6054743774272769],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_ffc9091d28db4a8597901c069d10e484 = L.popup({maxWidth: '300'});

            
                var html_7ccb7091583243d9b5efb1aa212de630 = $('<div id="html_7ccb7091583243d9b5efb1aa212de630" style="width: 100.0%; height: 100.0%;">Compostelle</div>')[0];
                popup_ffc9091d28db4a8597901c069d10e484.setContent(html_7ccb7091583243d9b5efb1aa212de630);
            

            circle_marker_1d32e3bc5dea462bb926d988122e74e0.bindPopup(popup_ffc9091d28db4a8597901c069d10e484);

            
        
    
            var circle_marker_2e0e0d7ecd0643e5b85f9c9130dfc771 = L.circleMarker(
                [44.8262833624718,-0.5724843902242801],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_fc989a13df8743d8be81d92d717e4a36 = L.popup({maxWidth: '300'});

            
                var html_aa3887181dfa49f78a033583f76c99f3 = $('<div id="html_aa3887181dfa49f78a033583f76c99f3" style="width: 100.0%; height: 100.0%;">Cours de la Somme</div>')[0];
                popup_fc989a13df8743d8be81d92d717e4a36.setContent(html_aa3887181dfa49f78a033583f76c99f3);
            

            circle_marker_2e0e0d7ecd0643e5b85f9c9130dfc771.bindPopup(popup_fc989a13df8743d8be81d92d717e4a36);

            
        
    
            var circle_marker_94438e7ef8cd4bd8986ba07597116b85 = L.circleMarker(
                [44.8203170209881,-0.571854647158152],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_704de00249264684957e2a8bd3cfaf6e = L.popup({maxWidth: '300'});

            
                var html_a9adb0320efe4ceca39306d2eeddf58a = $('<div id="html_a9adb0320efe4ceca39306d2eeddf58a" style="width: 100.0%; height: 100.0%;">Nansouty</div>')[0];
                popup_704de00249264684957e2a8bd3cfaf6e.setContent(html_a9adb0320efe4ceca39306d2eeddf58a);
            

            circle_marker_94438e7ef8cd4bd8986ba07597116b85.bindPopup(popup_704de00249264684957e2a8bd3cfaf6e);

            
        
    
            var circle_marker_f1b47985570a408ab1fd935919af366d = L.circleMarker(
                [44.85170753450279,-0.574271942562023],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_d34c5b53c40c4fc5adfa63081a87af9c = L.popup({maxWidth: '300'});

            
                var html_437ab51fc2f448dd9b8ffcc6c5451a78 = $('<div id="html_437ab51fc2f448dd9b8ffcc6c5451a78" style="width: 100.0%; height: 100.0%;">Place Paul Doumer</div>')[0];
                popup_d34c5b53c40c4fc5adfa63081a87af9c.setContent(html_437ab51fc2f448dd9b8ffcc6c5451a78);
            

            circle_marker_f1b47985570a408ab1fd935919af366d.bindPopup(popup_d34c5b53c40c4fc5adfa63081a87af9c);

            
        
    
            var circle_marker_ed4b0094077d4c37896a82b63bfa4c42 = L.circleMarker(
                [44.8500457661933,-0.5820039119777061],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_f7b2b432ee5841d38d5e5b4b43df159e = L.popup({maxWidth: '300'});

            
                var html_34c78f33b48e4438b508f201dca506c4 = $('<div id="html_34c78f33b48e4438b508f201dca506c4" style="width: 100.0%; height: 100.0%;">Place Longchamps</div>')[0];
                popup_f7b2b432ee5841d38d5e5b4b43df159e.setContent(html_34c78f33b48e4438b508f201dca506c4);
            

            circle_marker_ed4b0094077d4c37896a82b63bfa4c42.bindPopup(popup_f7b2b432ee5841d38d5e5b4b43df159e);

            
        
    
            var circle_marker_d1fe8a5b524040df81931c242838f502 = L.circleMarker(
                [44.80445898386721,-0.632668180740745],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_9db09e53021a49a6aa8d9ec0782df60a = L.popup({maxWidth: '300'});

            
                var html_5f6f7da95bbc42c9a0003c2d35425457 = $('<div id="html_5f6f7da95bbc42c9a0003c2d35425457" style="width: 100.0%; height: 100.0%;">Pessac Centre</div>')[0];
                popup_9db09e53021a49a6aa8d9ec0782df60a.setContent(html_5f6f7da95bbc42c9a0003c2d35425457);
            

            circle_marker_d1fe8a5b524040df81931c242838f502.bindPopup(popup_9db09e53021a49a6aa8d9ec0782df60a);

            
        
    
            var circle_marker_b98a457010c14281879d8817ce79f337 = L.circleMarker(
                [44.792962631100906,-0.6463612905802328],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_0a1a4957b56f4854b4ac58356975e941 = L.popup({maxWidth: '300'});

            
                var html_2016a0d1ab6d410a864afa17c9f91263 = $('<div id="html_2016a0d1ab6d410a864afa17c9f91263" style="width: 100.0%; height: 100.0%;">Châtaigneraie</div>')[0];
                popup_0a1a4957b56f4854b4ac58356975e941.setContent(html_2016a0d1ab6d410a864afa17c9f91263);
            

            circle_marker_b98a457010c14281879d8817ce79f337.bindPopup(popup_0a1a4957b56f4854b4ac58356975e941);

            
        
    
            var circle_marker_0f7e2a1cdc9640048ecb885cd6dd2cdc = L.circleMarker(
                [44.7821503214335,-0.5661566288543921],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_7c8581ae255541bcab1a3adec1466920 = L.popup({maxWidth: '300'});

            
                var html_1e04ccc9795f487b9043a9093ea32ac9 = $('<div id="html_1e04ccc9795f487b9043a9093ea32ac9" style="width: 100.0%; height: 100.0%;">Pont de la Maye (retirée le 19 novembre 2015 en raison des travaux d&apos;extension du tram C)</div>')[0];
                popup_7c8581ae255541bcab1a3adec1466920.setContent(html_1e04ccc9795f487b9043a9093ea32ac9);
            

            circle_marker_0f7e2a1cdc9640048ecb885cd6dd2cdc.bindPopup(popup_7c8581ae255541bcab1a3adec1466920);

            
        
    
            var circle_marker_02a7f9dac23b4d6dab8ef17626f99286 = L.circleMarker(
                [44.82670073497211,-0.549830125548165],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_ddefc895e09d4e14b6778f74527aac2b = L.popup({maxWidth: '300'});

            
                var html_2a17966fb5804ff0b05d68708972de4a = $('<div id="html_2a17966fb5804ff0b05d68708972de4a" style="width: 100.0%; height: 100.0%;">Quai de Paludate</div>')[0];
                popup_ddefc895e09d4e14b6778f74527aac2b.setContent(html_2a17966fb5804ff0b05d68708972de4a);
            

            circle_marker_02a7f9dac23b4d6dab8ef17626f99286.bindPopup(popup_ddefc895e09d4e14b6778f74527aac2b);

            
        
    
            var circle_marker_f52d56fde52842179c11d3ec7c610bdb = L.circleMarker(
                [44.84819450642961,-0.591969726529606],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a4e929b2be0947f7953b568f7e3a3c67 = L.popup({maxWidth: '300'});

            
                var html_b2bf5e6be7c1433ebce5ed6ccb2d8c4a = $('<div id="html_b2bf5e6be7c1433ebce5ed6ccb2d8c4a" style="width: 100.0%; height: 100.0%;">Dubreuil Turenne</div>')[0];
                popup_a4e929b2be0947f7953b568f7e3a3c67.setContent(html_b2bf5e6be7c1433ebce5ed6ccb2d8c4a);
            

            circle_marker_f52d56fde52842179c11d3ec7c610bdb.bindPopup(popup_a4e929b2be0947f7953b568f7e3a3c67);

            
        
    
            var circle_marker_80c571f66e44415e936850733fb4947d = L.circleMarker(
                [44.8383834550559,-0.616893430308816],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_dbbd4fd72e4e41f5ad8e5dfc6969d54a = L.popup({maxWidth: '300'});

            
                var html_a945ca325e5c46929ab66a499c73722e = $('<div id="html_a945ca325e5c46929ab66a499c73722e" style="width: 100.0%; height: 100.0%;">Place Mondésir</div>')[0];
                popup_dbbd4fd72e4e41f5ad8e5dfc6969d54a.setContent(html_a945ca325e5c46929ab66a499c73722e);
            

            circle_marker_80c571f66e44415e936850733fb4947d.bindPopup(popup_dbbd4fd72e4e41f5ad8e5dfc6969d54a);

            
        
    
            var circle_marker_459aec8a7e634c08973f6ddb1601de71 = L.circleMarker(
                [44.85326197252461,-0.591721825634013],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_4ca5ad3ff94c4b42b1fd510cd70e5369 = L.popup({maxWidth: '300'});

            
                var html_b10103e9c0e540f4b09dfae9984d43cc = $('<div id="html_b10103e9c0e540f4b09dfae9984d43cc" style="width: 100.0%; height: 100.0%;">Barrière du Médoc</div>')[0];
                popup_4ca5ad3ff94c4b42b1fd510cd70e5369.setContent(html_b10103e9c0e540f4b09dfae9984d43cc);
            

            circle_marker_459aec8a7e634c08973f6ddb1601de71.bindPopup(popup_4ca5ad3ff94c4b42b1fd510cd70e5369);

            
        
    
            var circle_marker_bcabebdaf5f341eeba0132c13591f996 = L.circleMarker(
                [44.83478298225079,-0.5646641958573271],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_61b0a9284e4f4574978cd1383cfa6dfc = L.popup({maxWidth: '300'});

            
                var html_61541d866576459ea6d6b332492e3bf6 = $('<div id="html_61541d866576459ea6d6b332492e3bf6" style="width: 100.0%; height: 100.0%;">Place St Michel</div>')[0];
                popup_61b0a9284e4f4574978cd1383cfa6dfc.setContent(html_61541d866576459ea6d6b332492e3bf6);
            

            circle_marker_bcabebdaf5f341eeba0132c13591f996.bindPopup(popup_61b0a9284e4f4574978cd1383cfa6dfc);

            
        
    
            var circle_marker_4417374ec77e4bfd9099de49ec5ffd39 = L.circleMarker(
                [44.8310036470032,-0.5873420830366],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_f3386ce4cf994dd1be78123741acd664 = L.popup({maxWidth: '300'});

            
                var html_93475e924dce4e03aeb8416f9da1ed78 = $('<div id="html_93475e924dce4e03aeb8416f9da1ed78" style="width: 100.0%; height: 100.0%;">François de Sourdis</div>')[0];
                popup_f3386ce4cf994dd1be78123741acd664.setContent(html_93475e924dce4e03aeb8416f9da1ed78);
            

            circle_marker_4417374ec77e4bfd9099de49ec5ffd39.bindPopup(popup_f3386ce4cf994dd1be78123741acd664);

            
        
    
            var circle_marker_80e589a6a3f8463e9cc894fd12cbf5a6 = L.circleMarker(
                [44.773526429667996,-0.614317927760213],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_59ce389d32564c9c8cd51ccd8032cf05 = L.popup({maxWidth: '300'});

            
                var html_c78aafea5a444945ba9adf1bfd62c9d9 = $('<div id="html_c78aafea5a444945ba9adf1bfd62c9d9" style="width: 100.0%; height: 100.0%;">Place Bernard Roumegoux</div>')[0];
                popup_59ce389d32564c9c8cd51ccd8032cf05.setContent(html_c78aafea5a444945ba9adf1bfd62c9d9);
            

            circle_marker_80e589a6a3f8463e9cc894fd12cbf5a6.bindPopup(popup_59ce389d32564c9c8cd51ccd8032cf05);

            
        
    
            var circle_marker_01a3729fbad44ad19cd425342aa3fddc = L.circleMarker(
                [44.793087635670204,-0.6576450248561739],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_b1fe3f2441d442629dea20d9c4fa541e = L.popup({maxWidth: '300'});

            
                var html_8300ce6d41c0404dbfd606e2836b4219 = $('<div id="html_8300ce6d41c0404dbfd606e2836b4219" style="width: 100.0%; height: 100.0%;">Gare Pessac Alouette</div>')[0];
                popup_b1fe3f2441d442629dea20d9c4fa541e.setContent(html_8300ce6d41c0404dbfd606e2836b4219);
            

            circle_marker_01a3729fbad44ad19cd425342aa3fddc.bindPopup(popup_b1fe3f2441d442629dea20d9c4fa541e);

            
        
    
            var circle_marker_3fab29f3fe64481b815fa28be549e7cd = L.circleMarker(
                [44.8416130882014,-0.646520450493249],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_754894fafeec4422baddd3b31cf869cd = L.popup({maxWidth: '300'});

            
                var html_ba2a45f119ed40ebba5627d6665670f0 = $('<div id="html_ba2a45f119ed40ebba5627d6665670f0" style="width: 100.0%; height: 100.0%;">Mérignac Centre</div>')[0];
                popup_754894fafeec4422baddd3b31cf869cd.setContent(html_ba2a45f119ed40ebba5627d6665670f0);
            

            circle_marker_3fab29f3fe64481b815fa28be549e7cd.bindPopup(popup_754894fafeec4422baddd3b31cf869cd);

            
        
    
            var circle_marker_f901cae8661f4faaab7b9191c7eb3b25 = L.circleMarker(
                [44.815026281156996,-0.5509087022557521],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_4e0a89fbe62e4590bb92778330d3ba3a = L.popup({maxWidth: '300'});

            
                var html_5fc6a51fb0254186809da41cac435065 = $('<div id="html_5fc6a51fb0254186809da41cac435065" style="width: 100.0%; height: 100.0%;">Terres Neuves</div>')[0];
                popup_4e0a89fbe62e4590bb92778330d3ba3a.setContent(html_5fc6a51fb0254186809da41cac435065);
            

            circle_marker_f901cae8661f4faaab7b9191c7eb3b25.bindPopup(popup_4e0a89fbe62e4590bb92778330d3ba3a);

            
        
    
            var circle_marker_fb5934c78838418d85c579ccfd40d195 = L.circleMarker(
                [44.850096272665795,-0.5855818728558331],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_3c8ea4035ef14383a8f5f4c32b9af8ef = L.popup({maxWidth: '300'});

            
                var html_7bfb361d76ee4f84a1335803501d2436 = $('<div id="html_7bfb361d76ee4f84a1335803501d2436" style="width: 100.0%; height: 100.0%;">Place Marie Brizard (supprimée le 11 mars 2016 en raison des travaux tram D)</div>')[0];
                popup_3c8ea4035ef14383a8f5f4c32b9af8ef.setContent(html_7bfb361d76ee4f84a1335803501d2436);
            

            circle_marker_fb5934c78838418d85c579ccfd40d195.bindPopup(popup_3c8ea4035ef14383a8f5f4c32b9af8ef);

            
        
    
            var circle_marker_64d27912112c438d8df37ad815871c02 = L.circleMarker(
                [44.8324154122993,-0.645936661160173],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_42aaaca4ddbe4199a328e2903fba00e6 = L.popup({maxWidth: '300'});

            
                var html_4d4f15ff15af4cfab2d74236bf96a08d = $('<div id="html_4d4f15ff15af4cfab2d74236bf96a08d" style="width: 100.0%; height: 100.0%;">Quatre Chemins</div>')[0];
                popup_42aaaca4ddbe4199a328e2903fba00e6.setContent(html_4d4f15ff15af4cfab2d74236bf96a08d);
            

            circle_marker_64d27912112c438d8df37ad815871c02.bindPopup(popup_42aaaca4ddbe4199a328e2903fba00e6);

            
        
    
            var circle_marker_11b4d6be7e83481aa82de456d029e219 = L.circleMarker(
                [44.85848777302589,-0.569659370398398],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_7c29d40b28ed48d4b6f7c943ef4ff726 = L.popup({maxWidth: '300'});

            
                var html_5592d2f5ad694cff999b05407f70bd06 = $('<div id="html_5592d2f5ad694cff999b05407f70bd06" style="width: 100.0%; height: 100.0%;">St Louis Médoc</div>')[0];
                popup_7c29d40b28ed48d4b6f7c943ef4ff726.setContent(html_5592d2f5ad694cff999b05407f70bd06);
            

            circle_marker_11b4d6be7e83481aa82de456d029e219.bindPopup(popup_7c29d40b28ed48d4b6f7c943ef4ff726);

            
        
    
            var circle_marker_636605f80e224a05b0b3dc3c057a818a = L.circleMarker(
                [44.8298604334843,-0.603509959107344],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_c48f92431868492f91e378c324e4a01b = L.popup({maxWidth: '300'});

            
                var html_1eafd2c308ab4d87b8e7aac87d68dfd0 = $('<div id="html_1eafd2c308ab4d87b8e7aac87d68dfd0" style="width: 100.0%; height: 100.0%;">Hôpital Pellegrin</div>')[0];
                popup_c48f92431868492f91e378c324e4a01b.setContent(html_1eafd2c308ab4d87b8e7aac87d68dfd0);
            

            circle_marker_636605f80e224a05b0b3dc3c057a818a.bindPopup(popup_c48f92431868492f91e378c324e4a01b);

            
        
    
            var circle_marker_9329b483bfad41b6a2470fded23731c8 = L.circleMarker(
                [44.862601640429105,-0.5757641511520151],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_079847c71cd2492f840eba7bfe12f536 = L.popup({maxWidth: '300'});

            
                var html_94f1ba9547224b66818944fff579e452 = $('<div id="html_94f1ba9547224b66818944fff579e452" style="width: 100.0%; height: 100.0%;">Grand Parc</div>')[0];
                popup_079847c71cd2492f840eba7bfe12f536.setContent(html_94f1ba9547224b66818944fff579e452);
            

            circle_marker_9329b483bfad41b6a2470fded23731c8.bindPopup(popup_079847c71cd2492f840eba7bfe12f536);

            
        
    
            var circle_marker_3d3e4614ea5d42b6b3fe4d24ca43365b = L.circleMarker(
                [44.8464146714062,-0.5801979888173471],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_f8acf962366d4615b57c4cf1b1260424 = L.popup({maxWidth: '300'});

            
                var html_7f4212863da94a79ace3005f105bb4ef = $('<div id="html_7f4212863da94a79ace3005f105bb4ef" style="width: 100.0%; height: 100.0%;">Place Charles Gruet</div>')[0];
                popup_f8acf962366d4615b57c4cf1b1260424.setContent(html_7f4212863da94a79ace3005f105bb4ef);
            

            circle_marker_3d3e4614ea5d42b6b3fe4d24ca43365b.bindPopup(popup_f8acf962366d4615b57c4cf1b1260424);

            
        
    
            var circle_marker_c5b70318138d4bf99b5b78f5bad8c661 = L.circleMarker(
                [44.830636168306796,-0.573213220578234],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_673ea52c855e420199867e39607701a0 = L.popup({maxWidth: '300'});

            
                var html_099c48ad329d4439bcd02c5458bdbf4b = $('<div id="html_099c48ad329d4439bcd02c5458bdbf4b" style="width: 100.0%; height: 100.0%;">Place de la Victoire</div>')[0];
                popup_673ea52c855e420199867e39607701a0.setContent(html_099c48ad329d4439bcd02c5458bdbf4b);
            

            circle_marker_c5b70318138d4bf99b5b78f5bad8c661.bindPopup(popup_673ea52c855e420199867e39607701a0);

            
        
    
            var circle_marker_c6447d19f31941cfa34ac62392bc95b8 = L.circleMarker(
                [44.8282573643245,-0.5623690058149289],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_fc0cafee63294a35b504956a17e03cd7 = L.popup({maxWidth: '300'});

            
                var html_096cf25b061c4f52b74762d60f6bf887 = $('<div id="html_096cf25b061c4f52b74762d60f6bf887" style="width: 100.0%; height: 100.0%;">Place André Meunier</div>')[0];
                popup_fc0cafee63294a35b504956a17e03cd7.setContent(html_096cf25b061c4f52b74762d60f6bf887);
            

            circle_marker_c6447d19f31941cfa34ac62392bc95b8.bindPopup(popup_fc0cafee63294a35b504956a17e03cd7);

            
        
    
            var circle_marker_fd6ac55dcef44b26b6ae1edd6eaf5819 = L.circleMarker(
                [44.8511475804554,-0.608609297008604],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_2c4db72e692e4d8d880864a0352b2f4e = L.popup({maxWidth: '300'});

            
                var html_d318006a01c54f1e8c5db8742eb237e5 = $('<div id="html_d318006a01c54f1e8c5db8742eb237e5" style="width: 100.0%; height: 100.0%;">Grand Lebrun</div>')[0];
                popup_2c4db72e692e4d8d880864a0352b2f4e.setContent(html_d318006a01c54f1e8c5db8742eb237e5);
            

            circle_marker_fd6ac55dcef44b26b6ae1edd6eaf5819.bindPopup(popup_2c4db72e692e4d8d880864a0352b2f4e);

            
        
    
            var circle_marker_642b1a48f5b8438081e2dbc985e43762 = L.circleMarker(
                [44.7938887827872,-0.63505075796394],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_48a4c41d184e4fa68a323eab5b44911c = L.popup({maxWidth: '300'});

            
                var html_f663e07220564f29b2426c6df08382c7 = $('<div id="html_f663e07220564f29b2426c6df08382c7" style="width: 100.0%; height: 100.0%;">Bougnard</div>')[0];
                popup_48a4c41d184e4fa68a323eab5b44911c.setContent(html_f663e07220564f29b2426c6df08382c7);
            

            circle_marker_642b1a48f5b8438081e2dbc985e43762.bindPopup(popup_48a4c41d184e4fa68a323eab5b44911c);

            
        
    
            var circle_marker_18da4b32265a421db442c5cad3ec16c7 = L.circleMarker(
                [44.843774390805294,-0.5575143358321251],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_c85b95e6250b444eae771b22bb72329c = L.popup({maxWidth: '300'});

            
                var html_9cca078b139c4a948d74c9b92c815b0c = $('<div id="html_9cca078b139c4a948d74c9b92c815b0c" style="width: 100.0%; height: 100.0%;">Allée de Serr Abadie</div>')[0];
                popup_c85b95e6250b444eae771b22bb72329c.setContent(html_9cca078b139c4a948d74c9b92c815b0c);
            

            circle_marker_18da4b32265a421db442c5cad3ec16c7.bindPopup(popup_c85b95e6250b444eae771b22bb72329c);

            
        
    
            var circle_marker_bbeb7740fc8e41b59fb21068de078a21 = L.circleMarker(
                [44.841221945764396,-0.5756676590783549],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_57d7f060b47f47de8984eb9d429796ce = L.popup({maxWidth: '300'});

            
                var html_52da1feed85242c2bc72ffda1eb00537 = $('<div id="html_52da1feed85242c2bc72ffda1eb00537" style="width: 100.0%; height: 100.0%;">Puy Paulin</div>')[0];
                popup_57d7f060b47f47de8984eb9d429796ce.setContent(html_52da1feed85242c2bc72ffda1eb00537);
            

            circle_marker_bbeb7740fc8e41b59fb21068de078a21.bindPopup(popup_57d7f060b47f47de8984eb9d429796ce);

            
        
    
            var circle_marker_1cb2f2dab7e64f3da2c59169a00b1b49 = L.circleMarker(
                [44.8408779999531,-0.59104139136587],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_fd7775f039c84089b110fdda18dfed67 = L.popup({maxWidth: '300'});

            
                var html_4ead8190ea8b49d5aac5c48d605cc76c = $('<div id="html_4ead8190ea8b49d5aac5c48d605cc76c" style="width: 100.0%; height: 100.0%;">Place Tartas</div>')[0];
                popup_fd7775f039c84089b110fdda18dfed67.setContent(html_4ead8190ea8b49d5aac5c48d605cc76c);
            

            circle_marker_1cb2f2dab7e64f3da2c59169a00b1b49.bindPopup(popup_fd7775f039c84089b110fdda18dfed67);

            
        
    
            var circle_marker_b2ba1fb61bdc41b388196c03329c34eb = L.circleMarker(
                [44.8343893623561,-0.58812343829027],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_0f0e0f900b6e4d369d6cdb749960651d = L.popup({maxWidth: '300'});

            
                var html_cf5948928ab14ff68c3984bdf2087007 = $('<div id="html_cf5948928ab14ff68c3984bdf2087007" style="width: 100.0%; height: 100.0%;">Patinoire</div>')[0];
                popup_0f0e0f900b6e4d369d6cdb749960651d.setContent(html_cf5948928ab14ff68c3984bdf2087007);
            

            circle_marker_b2ba1fb61bdc41b388196c03329c34eb.bindPopup(popup_0f0e0f900b6e4d369d6cdb749960651d);

            
        
    
            var circle_marker_9651f3ffa8db4ce9be27e6cbdcbfec64 = L.circleMarker(
                [44.833033292653,-0.592892735150062],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_7cbf4a60b08b4380995304e721517e5b = L.popup({maxWidth: '300'});

            
                var html_97a2b50bf84e4447b699cb781f9f1072 = $('<div id="html_97a2b50bf84e4447b699cb781f9f1072" style="width: 100.0%; height: 100.0%;">Gaviniès</div>')[0];
                popup_7cbf4a60b08b4380995304e721517e5b.setContent(html_97a2b50bf84e4447b699cb781f9f1072);
            

            circle_marker_9651f3ffa8db4ce9be27e6cbdcbfec64.bindPopup(popup_7cbf4a60b08b4380995304e721517e5b);

            
        
    
            var circle_marker_f643507f854d4e6c9403248b5880b87e = L.circleMarker(
                [44.836058835347096,-0.575427382768788],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_46c575f8d7b94d5e88bf575309a32a56 = L.popup({maxWidth: '300'});

            
                var html_076e2136050b4cc1b7dde6278d3444f3 = $('<div id="html_076e2136050b4cc1b7dde6278d3444f3" style="width: 100.0%; height: 100.0%;">Musée d&apos;Aquitaine</div>')[0];
                popup_46c575f8d7b94d5e88bf575309a32a56.setContent(html_076e2136050b4cc1b7dde6278d3444f3);
            

            circle_marker_f643507f854d4e6c9403248b5880b87e.bindPopup(popup_46c575f8d7b94d5e88bf575309a32a56);

            
        
    
            var circle_marker_e2e37e9469694f65a65d970052eec43f = L.circleMarker(
                [44.837108327472706,-0.5727796298560601],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_2835f7af9a384d9cbfb2ee1bca8f53aa = L.popup({maxWidth: '300'});

            
                var html_cbbd01a6b783429491325c7aa54620ef = $('<div id="html_cbbd01a6b783429491325c7aa54620ef" style="width: 100.0%; height: 100.0%;">St Paul</div>')[0];
                popup_2835f7af9a384d9cbfb2ee1bca8f53aa.setContent(html_cbbd01a6b783429491325c7aa54620ef);
            

            circle_marker_e2e37e9469694f65a65d970052eec43f.bindPopup(popup_2835f7af9a384d9cbfb2ee1bca8f53aa);

            
        
    
            var circle_marker_e596927a6f4d45efbc705de5ac6f99eb = L.circleMarker(
                [44.859988515489995,-0.5887606764126709],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_68b11297475847548547b8daa80b3ee6 = L.popup({maxWidth: '300'});

            
                var html_2b382e3a7d254320b629c1bdbb2f06e5 = $('<div id="html_2b382e3a7d254320b629c1bdbb2f06e5" style="width: 100.0%; height: 100.0%;">Mandron Godard</div>')[0];
                popup_68b11297475847548547b8daa80b3ee6.setContent(html_2b382e3a7d254320b629c1bdbb2f06e5);
            

            circle_marker_e596927a6f4d45efbc705de5ac6f99eb.bindPopup(popup_68b11297475847548547b8daa80b3ee6);

            
        
    
            var circle_marker_8846b2eab3ea42228acd7a4a7f3725a4 = L.circleMarker(
                [44.849782481805605,-0.570243742432327],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a428fa25989f4290afabafdd64dee4fd = L.popup({maxWidth: '300'});

            
                var html_a4711502866a42bbb18caf1e0eb75387 = $('<div id="html_a4711502866a42bbb18caf1e0eb75387" style="width: 100.0%; height: 100.0%;">CAPC</div>')[0];
                popup_a428fa25989f4290afabafdd64dee4fd.setContent(html_a4711502866a42bbb18caf1e0eb75387);
            

            circle_marker_8846b2eab3ea42228acd7a4a7f3725a4.bindPopup(popup_a428fa25989f4290afabafdd64dee4fd);

            
        
    
            var circle_marker_7915c6d576074490b6ea894bb6f3b82e = L.circleMarker(
                [44.807704530607005,-0.5482081246107151],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a8d92c586bca4939a8e214d5e83f6339 = L.popup({maxWidth: '300'});

            
                var html_479606a52b1e431cb0f6a8e5aafd9d21 = $('<div id="html_479606a52b1e431cb0f6a8e5aafd9d21" style="width: 100.0%; height: 100.0%;">Place du 14 juillet</div>')[0];
                popup_a8d92c586bca4939a8e214d5e83f6339.setContent(html_479606a52b1e431cb0f6a8e5aafd9d21);
            

            circle_marker_7915c6d576074490b6ea894bb6f3b82e.bindPopup(popup_a8d92c586bca4939a8e214d5e83f6339);

            
        
    
            var circle_marker_8b10756020f847e28c26967187bbdefe = L.circleMarker(
                [44.840303327410496,-0.5691361390534],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_19435c274fc24a489f3632ef70d2b6e0 = L.popup({maxWidth: '300'});

            
                var html_ebf61074001c41d58c9222be1ee9938a = $('<div id="html_ebf61074001c41d58c9222be1ee9938a" style="width: 100.0%; height: 100.0%;">Place de la Bourse</div>')[0];
                popup_19435c274fc24a489f3632ef70d2b6e0.setContent(html_ebf61074001c41d58c9222be1ee9938a);
            

            circle_marker_8b10756020f847e28c26967187bbdefe.bindPopup(popup_19435c274fc24a489f3632ef70d2b6e0);

            
        
    
            var circle_marker_6735d41725184c0ca0bc78e4445ffc8c = L.circleMarker(
                [44.8600704540937,-0.5544677944219699],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_b3810963441540e8baea55e598e93873 = L.popup({maxWidth: '300'});

            
                var html_fdb93dc0b0ec4c4aa8270a5fa7a7dd6d = $('<div id="html_fdb93dc0b0ec4c4aa8270a5fa7a7dd6d" style="width: 100.0%; height: 100.0%;">Bassins à flot</div>')[0];
                popup_b3810963441540e8baea55e598e93873.setContent(html_fdb93dc0b0ec4c4aa8270a5fa7a7dd6d);
            

            circle_marker_6735d41725184c0ca0bc78e4445ffc8c.bindPopup(popup_b3810963441540e8baea55e598e93873);

            
        
    
            var circle_marker_b936d4e49eca485e812d1a7a2a3b9932 = L.circleMarker(
                [44.857868173886004,-0.55808025998087],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_c5e7759b617c45f59dce5dedb35930b4 = L.popup({maxWidth: '300'});

            
                var html_6cf583f7c4984802bc41bb2e80d3d1fd = $('<div id="html_6cf583f7c4984802bc41bb2e80d3d1fd" style="width: 100.0%; height: 100.0%;">Les Hangars</div>')[0];
                popup_c5e7759b617c45f59dce5dedb35930b4.setContent(html_6cf583f7c4984802bc41bb2e80d3d1fd);
            

            circle_marker_b936d4e49eca485e812d1a7a2a3b9932.bindPopup(popup_c5e7759b617c45f59dce5dedb35930b4);

            
        
    
            var circle_marker_5652f9bb49eb400b8882b2dcc73ca5e7 = L.circleMarker(
                [44.8338711646842,-0.562710232600152],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_c6828885b7c445168b21130c01895ce5 = L.popup({maxWidth: '300'});

            
                var html_8a19e9d4c1914494b68c06c4f4d071e5 = $('<div id="html_8a19e9d4c1914494b68c06c4f4d071e5" style="width: 100.0%; height: 100.0%;">Parc des Sports</div>')[0];
                popup_c6828885b7c445168b21130c01895ce5.setContent(html_8a19e9d4c1914494b68c06c4f4d071e5);
            

            circle_marker_5652f9bb49eb400b8882b2dcc73ca5e7.bindPopup(popup_c6828885b7c445168b21130c01895ce5);

            
        
    
            var circle_marker_c1486e523f654c308fa5b55a5192a48e = L.circleMarker(
                [44.8392607368341,-0.572034212881664],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_e078be23225b4c91a887219fac88886e = L.popup({maxWidth: '300'});

            
                var html_7ddb2a8f20e84249a389c45d1c18da41 = $('<div id="html_7ddb2a8f20e84249a389c45d1c18da41" style="width: 100.0%; height: 100.0%;">Camille Jullian</div>')[0];
                popup_e078be23225b4c91a887219fac88886e.setContent(html_7ddb2a8f20e84249a389c45d1c18da41);
            

            circle_marker_c1486e523f654c308fa5b55a5192a48e.bindPopup(popup_e078be23225b4c91a887219fac88886e);

            
        
    
            var circle_marker_0e1d0c30cf354661a919823ed1b93c20 = L.circleMarker(
                [44.838868754385395,-0.574807040123963],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a11ae469155a4f5b95e874f4e4bff03f = L.popup({maxWidth: '300'});

            
                var html_b163a8feb2c24a07b8e13c3dcccb0183 = $('<div id="html_b163a8feb2c24a07b8e13c3dcccb0183" style="width: 100.0%; height: 100.0%;">Place St Projet</div>')[0];
                popup_a11ae469155a4f5b95e874f4e4bff03f.setContent(html_b163a8feb2c24a07b8e13c3dcccb0183);
            

            circle_marker_0e1d0c30cf354661a919823ed1b93c20.bindPopup(popup_a11ae469155a4f5b95e874f4e4bff03f);

            
        
    
            var circle_marker_95fde55f31a44048a8b162dc5482817f = L.circleMarker(
                [44.822792955966605,-0.552970659841354],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a2d4924c997b442e952d5ac7b2a381eb = L.popup({maxWidth: '300'});

            
                var html_0a6390562adc4cf684ec8c83c6abc941 = $('<div id="html_0a6390562adc4cf684ec8c83c6abc941" style="width: 100.0%; height: 100.0%;">Belcier</div>')[0];
                popup_a2d4924c997b442e952d5ac7b2a381eb.setContent(html_0a6390562adc4cf684ec8c83c6abc941);
            

            circle_marker_95fde55f31a44048a8b162dc5482817f.bindPopup(popup_a2d4924c997b442e952d5ac7b2a381eb);

            
        
    
            var circle_marker_1f4432238dfa4021a32dbd5b59f08ee4 = L.circleMarker(
                [44.82789351594621,-0.5860458753106851],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_4640485566f74e7787b580b1e731823e = L.popup({maxWidth: '300'});

            
                var html_238b378cbc4d4c7a814890a1955a6ae6 = $('<div id="html_238b378cbc4d4c7a814890a1955a6ae6" style="width: 100.0%; height: 100.0%;">Magendie</div>')[0];
                popup_4640485566f74e7787b580b1e731823e.setContent(html_238b378cbc4d4c7a814890a1955a6ae6);
            

            circle_marker_1f4432238dfa4021a32dbd5b59f08ee4.bindPopup(popup_4640485566f74e7787b580b1e731823e);

            
        
    
            var circle_marker_7158da76817b482ba12a61e58651c798 = L.circleMarker(
                [44.85320907028171,-0.56726525454553],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_588258d8eeda446987edbd335aec8050 = L.popup({maxWidth: '300'});

            
                var html_305bac23e89947ab9f023cb15fcc37bc = $('<div id="html_305bac23e89947ab9f023cb15fcc37bc" style="width: 100.0%; height: 100.0%;">Chartrons</div>')[0];
                popup_588258d8eeda446987edbd335aec8050.setContent(html_305bac23e89947ab9f023cb15fcc37bc);
            

            circle_marker_7158da76817b482ba12a61e58651c798.bindPopup(popup_588258d8eeda446987edbd335aec8050);

            
        
    
            var circle_marker_8d564e93f32442a7b736df710011d48f = L.circleMarker(
                [44.8275103799747,-0.575890929152947],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_dec5866716ab4cbda64bb84b3e0206c5 = L.popup({maxWidth: '300'});

            
                var html_bd7847a8725143609d117918b5899f1d = $('<div id="html_bd7847a8725143609d117918b5899f1d" style="width: 100.0%; height: 100.0%;">St Nicolas</div>')[0];
                popup_dec5866716ab4cbda64bb84b3e0206c5.setContent(html_bd7847a8725143609d117918b5899f1d);
            

            circle_marker_8d564e93f32442a7b736df710011d48f.bindPopup(popup_dec5866716ab4cbda64bb84b3e0206c5);

            
        
    
            var circle_marker_d1763a2169394a04ba38c82d7e3bab0b = L.circleMarker(
                [44.842997409923804,-0.5503284302991149],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_5eb84954966f4b20ad2de66ea6381adf = L.popup({maxWidth: '300'});

            
                var html_675f29daa9db44189151cdddb8aea65a = $('<div id="html_675f29daa9db44189151cdddb8aea65a" style="width: 100.0%; height: 100.0%;">Cours Le Rouzic</div>')[0];
                popup_5eb84954966f4b20ad2de66ea6381adf.setContent(html_675f29daa9db44189151cdddb8aea65a);
            

            circle_marker_d1763a2169394a04ba38c82d7e3bab0b.bindPopup(popup_5eb84954966f4b20ad2de66ea6381adf);

            
        
    
            var circle_marker_125b803729a84711bd82e4eb2e539a64 = L.circleMarker(
                [44.85260945908279,-0.599229535668728],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_6e524cb7fd4849cfbebce55dc5b7522c = L.popup({maxWidth: '300'});

            
                var html_fee68f7acda44d7a91941eaa14ec9717 = $('<div id="html_fee68f7acda44d7a91941eaa14ec9717" style="width: 100.0%; height: 100.0%;">Parc Bordelais</div>')[0];
                popup_6e524cb7fd4849cfbebce55dc5b7522c.setContent(html_fee68f7acda44d7a91941eaa14ec9717);
            

            circle_marker_125b803729a84711bd82e4eb2e539a64.bindPopup(popup_6e524cb7fd4849cfbebce55dc5b7522c);

            
        
    
            var circle_marker_180a21a2a3eb4cd4815696f54b6434ad = L.circleMarker(
                [44.824714650998004,-0.578146900858999],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_96f1413062a54dd49e94330088515503 = L.popup({maxWidth: '300'});

            
                var html_479e1097100b4636b13f71e2f3bfbe0a = $('<div id="html_479e1097100b4636b13f71e2f3bfbe0a" style="width: 100.0%; height: 100.0%;">Bergonié</div>')[0];
                popup_96f1413062a54dd49e94330088515503.setContent(html_479e1097100b4636b13f71e2f3bfbe0a);
            

            circle_marker_180a21a2a3eb4cd4815696f54b6434ad.bindPopup(popup_96f1413062a54dd49e94330088515503);

            
        
    
            var circle_marker_070ff085cb0c40b49d07d8ed1bc18c89 = L.circleMarker(
                [44.854617894494496,-0.574346916974448],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_eee2eefabe0e4ae8853a4248dd8ab7cd = L.popup({maxWidth: '300'});

            
                var html_fca94086c0ba4f6c8d573e79703ba931 = $('<div id="html_fca94086c0ba4f6c8d573e79703ba931" style="width: 100.0%; height: 100.0%;">Camille Godard</div>')[0];
                popup_eee2eefabe0e4ae8853a4248dd8ab7cd.setContent(html_fca94086c0ba4f6c8d573e79703ba931);
            

            circle_marker_070ff085cb0c40b49d07d8ed1bc18c89.bindPopup(popup_eee2eefabe0e4ae8853a4248dd8ab7cd);

            
        
    
            var circle_marker_0c36bd13b61b463387a13173c9311269 = L.circleMarker(
                [44.873691458564,-0.6775348189370001],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_18b1d93366db430bb3c79c2494308e3a = L.popup({maxWidth: '300'});

            
                var html_536c917ed9d44f8f8086172b5a72d9d3 = $('<div id="html_536c917ed9d44f8f8086172b5a72d9d3" style="width: 100.0%; height: 100.0%;">Mairie du Haillan</div>')[0];
                popup_18b1d93366db430bb3c79c2494308e3a.setContent(html_536c917ed9d44f8f8086172b5a72d9d3);
            

            circle_marker_0c36bd13b61b463387a13173c9311269.bindPopup(popup_18b1d93366db430bb3c79c2494308e3a);

            
        
    
            var circle_marker_31da9e10bac9497b88b277e305b204c8 = L.circleMarker(
                [44.862559315754794,-0.583247050910222],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_2ffcc3ded04e4b369bb528f01f95d835 = L.popup({maxWidth: '300'});

            
                var html_a0961d80e812460fad0b9d82ada5dacf = $('<div id="html_a0961d80e812460fad0b9d82ada5dacf" style="width: 100.0%; height: 100.0%;">Place Ampère</div>')[0];
                popup_2ffcc3ded04e4b369bb528f01f95d835.setContent(html_a0961d80e812460fad0b9d82ada5dacf);
            

            circle_marker_31da9e10bac9497b88b277e305b204c8.bindPopup(popup_2ffcc3ded04e4b369bb528f01f95d835);

            
        
    
            var circle_marker_33a65810b0024035939b5debfd05966d = L.circleMarker(
                [44.883957964802896,-0.649921125977042],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_10fa492c83d44ff4a5a3e676dd077548 = L.popup({maxWidth: '300'});

            
                var html_4dbda5e40f3545768e859da8cd846daa = $('<div id="html_4dbda5e40f3545768e859da8cd846daa" style="width: 100.0%; height: 100.0%;">Eysines Centre</div>')[0];
                popup_10fa492c83d44ff4a5a3e676dd077548.setContent(html_4dbda5e40f3545768e859da8cd846daa);
            

            circle_marker_33a65810b0024035939b5debfd05966d.bindPopup(popup_10fa492c83d44ff4a5a3e676dd077548);

            
        
    
            var circle_marker_5517fa7127c14f1ca2a4bf61de8323c9 = L.circleMarker(
                [44.8317574259134,-0.598683495255674],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_198b763d661d4fa5af8953482b35a6b9 = L.popup({maxWidth: '300'});

            
                var html_72ffea810c404663bdeda1ed7e950b4f = $('<div id="html_72ffea810c404663bdeda1ed7e950b4f" style="width: 100.0%; height: 100.0%;">Stade Chaban Delmas</div>')[0];
                popup_198b763d661d4fa5af8953482b35a6b9.setContent(html_72ffea810c404663bdeda1ed7e950b4f);
            

            circle_marker_5517fa7127c14f1ca2a4bf61de8323c9.bindPopup(popup_198b763d661d4fa5af8953482b35a6b9);

            
        
    
            var circle_marker_10c55fcf4bcd4dce9772cd5edad0844c = L.circleMarker(
                [44.8257830793101,-0.589760256971077],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_c257f568c73b4d658165a196496e17fe = L.popup({maxWidth: '300'});

            
                var html_3b5dab10ac704987a9260d6ace017eab = $('<div id="html_3b5dab10ac704987a9260d6ace017eab" style="width: 100.0%; height: 100.0%;">Barrière de Pessac</div>')[0];
                popup_c257f568c73b4d658165a196496e17fe.setContent(html_3b5dab10ac704987a9260d6ace017eab);
            

            circle_marker_10c55fcf4bcd4dce9772cd5edad0844c.bindPopup(popup_c257f568c73b4d658165a196496e17fe);

            
        
    
            var circle_marker_f9df84143e3847adb320587ea00a80a3 = L.circleMarker(
                [44.848398031151596,-0.598236967662844],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_fcf695e9e8ef421a884b7ab35c422778 = L.popup({maxWidth: '300'});

            
                var html_11b76363b24e48d796ad0c3a3a1cab79 = $('<div id="html_11b76363b24e48d796ad0c3a3a1cab79" style="width: 100.0%; height: 100.0%;">Barriere St Medard</div>')[0];
                popup_fcf695e9e8ef421a884b7ab35c422778.setContent(html_11b76363b24e48d796ad0c3a3a1cab79);
            

            circle_marker_f9df84143e3847adb320587ea00a80a3.bindPopup(popup_fcf695e9e8ef421a884b7ab35c422778);

            
        
    
            var circle_marker_7d02d8e069744e5a8d73ce75c8b3fce8 = L.circleMarker(
                [44.8515505178619,-0.571631042414094],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_7392d0dbf9b843ed89fa0a5e57930c6e = L.popup({maxWidth: '300'});

            
                var html_73a222228a5a4d6ab622a766a02c7d88 = $('<div id="html_73a222228a5a4d6ab622a766a02c7d88" style="width: 100.0%; height: 100.0%;">Eglise St Louis</div>')[0];
                popup_7392d0dbf9b843ed89fa0a5e57930c6e.setContent(html_73a222228a5a4d6ab622a766a02c7d88);
            

            circle_marker_7d02d8e069744e5a8d73ce75c8b3fce8.bindPopup(popup_7392d0dbf9b843ed89fa0a5e57930c6e);

            
        
    
            var circle_marker_9a75da0e17d2484988690cfb8b938a3d = L.circleMarker(
                [44.8395959043342,-0.556447738178034],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_7da22786eeaf40c0bd3a9214091d2cd0 = L.popup({maxWidth: '300'});

            
                var html_9620faac06794e259265780eec49f39d = $('<div id="html_9620faac06794e259265780eec49f39d" style="width: 100.0%; height: 100.0%;">La Benauge</div>')[0];
                popup_7da22786eeaf40c0bd3a9214091d2cd0.setContent(html_9620faac06794e259265780eec49f39d);
            

            circle_marker_9a75da0e17d2484988690cfb8b938a3d.bindPopup(popup_7da22786eeaf40c0bd3a9214091d2cd0);

            
        
    
            var circle_marker_81a5f531e8ce438595022beb73e5f712 = L.circleMarker(
                [44.7862005825706,-0.638735601385109],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_10a65d6e644244908b9b07919e563c58 = L.popup({maxWidth: '300'});

            
                var html_56db25e600344e87af81eefc47fca62c = $('<div id="html_56db25e600344e87af81eefc47fca62c" style="width: 100.0%; height: 100.0%;">Pessac Bersol</div>')[0];
                popup_10a65d6e644244908b9b07919e563c58.setContent(html_56db25e600344e87af81eefc47fca62c);
            

            circle_marker_81a5f531e8ce438595022beb73e5f712.bindPopup(popup_10a65d6e644244908b9b07919e563c58);

            
        
    
            var circle_marker_534a9cc12b8e4626bc062e508836b6fb = L.circleMarker(
                [44.836280554731104,-0.687128208972081],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_9aba41139b16407788ca122fc62f4858 = L.popup({maxWidth: '300'});

            
                var html_3046974b9914443db5dced7f91c82dab = $('<div id="html_3046974b9914443db5dced7f91c82dab" style="width: 100.0%; height: 100.0%;">Kennedy Parc Hôtelier</div>')[0];
                popup_9aba41139b16407788ca122fc62f4858.setContent(html_3046974b9914443db5dced7f91c82dab);
            

            circle_marker_534a9cc12b8e4626bc062e508836b6fb.bindPopup(popup_9aba41139b16407788ca122fc62f4858);

            
        
    
            var circle_marker_2f3e6e93f6864204be19b315e04fc1c3 = L.circleMarker(
                [44.8123770192435,-0.591030931391491],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_4489e4a144764f678b86072e82272a41 = L.popup({maxWidth: '300'});

            
                var html_7884e92083284b9c97f799bbb6800ad7 = $('<div id="html_7884e92083284b9c97f799bbb6800ad7" style="width: 100.0%; height: 100.0%;">Forum</div>')[0];
                popup_4489e4a144764f678b86072e82272a41.setContent(html_7884e92083284b9c97f799bbb6800ad7);
            

            circle_marker_2f3e6e93f6864204be19b315e04fc1c3.bindPopup(popup_4489e4a144764f678b86072e82272a41);

            
        
    
            var circle_marker_be8713699ad04685a4b0591c38d90f36 = L.circleMarker(
                [44.866941563060294,-0.576077830856061],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_d9dd3dc5860e4a3dad53d70659280af8 = L.popup({maxWidth: '300'});

            
                var html_7256ab14cef2444d895d845d70d938d5 = $('<div id="html_7256ab14cef2444d895d845d70d938d5" style="width: 100.0%; height: 100.0%;">Le Bouscat Ravezies</div>')[0];
                popup_d9dd3dc5860e4a3dad53d70659280af8.setContent(html_7256ab14cef2444d895d845d70d938d5);
            

            circle_marker_be8713699ad04685a4b0591c38d90f36.bindPopup(popup_d9dd3dc5860e4a3dad53d70659280af8);

            
        
    
            var circle_marker_62a9c85a20564ce488b7ea4c1ef43adc = L.circleMarker(
                [44.8221810827779,-0.5630395536365821],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_73b7dd793c36456b95beeaf3a03d2263 = L.popup({maxWidth: '300'});

            
                var html_4619f4cb3b6545349f26f0cb104dc031 = $('<div id="html_4619f4cb3b6545349f26f0cb104dc031" style="width: 100.0%; height: 100.0%;">Sacré Coeur</div>')[0];
                popup_73b7dd793c36456b95beeaf3a03d2263.setContent(html_4619f4cb3b6545349f26f0cb104dc031);
            

            circle_marker_62a9c85a20564ce488b7ea4c1ef43adc.bindPopup(popup_73b7dd793c36456b95beeaf3a03d2263);

            
        
    
            var circle_marker_72d9d9b77331455daebecf47b54e2889 = L.circleMarker(
                [44.8378395440189,-0.590279568888706],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_295bf0ff2fd4491f86a635a2e43d7453 = L.popup({maxWidth: '300'});

            
                var html_910c5089acf54b5e8323d881b4a625fb = $('<div id="html_910c5089acf54b5e8323d881b4a625fb" style="width: 100.0%; height: 100.0%;">St Bruno</div>')[0];
                popup_295bf0ff2fd4491f86a635a2e43d7453.setContent(html_910c5089acf54b5e8323d881b4a625fb);
            

            circle_marker_72d9d9b77331455daebecf47b54e2889.bindPopup(popup_295bf0ff2fd4491f86a635a2e43d7453);

            
        
    
            var circle_marker_facee4d770d74236817b1e18f4bf52d1 = L.circleMarker(
                [44.8130803058232,-0.564195813094978],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_ec70af59f3074eacacc0a54a45df616d = L.popup({maxWidth: '300'});

            
                var html_5c37a927915d479282a546f0ac078ab8 = $('<div id="html_5c37a927915d479282a546f0ac078ab8" style="width: 100.0%; height: 100.0%;">Barrière de Bègles</div>')[0];
                popup_ec70af59f3074eacacc0a54a45df616d.setContent(html_5c37a927915d479282a546f0ac078ab8);
            

            circle_marker_facee4d770d74236817b1e18f4bf52d1.bindPopup(popup_ec70af59f3074eacacc0a54a45df616d);

            
        
    
            var circle_marker_9b6214b7b58041328b823a1e1ec2d999 = L.circleMarker(
                [44.826297061892205,-0.557071873036146],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_81ae46c0d97f4b07b9a13e19492a9448 = L.popup({maxWidth: '300'});

            
                var html_bbfa6ca3f8b440098c98491ddb66169e = $('<div id="html_bbfa6ca3f8b440098c98491ddb66169e" style="width: 100.0%; height: 100.0%;">Gare St Jean</div>')[0];
                popup_81ae46c0d97f4b07b9a13e19492a9448.setContent(html_bbfa6ca3f8b440098c98491ddb66169e);
            

            circle_marker_9b6214b7b58041328b823a1e1ec2d999.bindPopup(popup_81ae46c0d97f4b07b9a13e19492a9448);

            
        
    
            var circle_marker_e8054a4bbb264831995d25286da59fcf = L.circleMarker(
                [44.8145020960259,-0.5706738859723299],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_e29d658513324695a90f9077060d6dad = L.popup({maxWidth: '300'});

            
                var html_68ca7c75750e4c7f92fe646378b44fce = $('<div id="html_68ca7c75750e4c7f92fe646378b44fce" style="width: 100.0%; height: 100.0%;">Barrière de Toulouse</div>')[0];
                popup_e29d658513324695a90f9077060d6dad.setContent(html_68ca7c75750e4c7f92fe646378b44fce);
            

            circle_marker_e8054a4bbb264831995d25286da59fcf.bindPopup(popup_e29d658513324695a90f9077060d6dad);

            
        
    
            var circle_marker_91719254364d48f2900e5bc14a669a13 = L.circleMarker(
                [44.8068109085645,-0.592453956058217],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_feed20d6ea6c4ecf8c3d8e8b99e37738 = L.popup({maxWidth: '300'});

            
                var html_8b6aae914f9848e0b45ca47f8d36b4e1 = $('<div id="html_8b6aae914f9848e0b45ca47f8d36b4e1" style="width: 100.0%; height: 100.0%;">Peixotto</div>')[0];
                popup_feed20d6ea6c4ecf8c3d8e8b99e37738.setContent(html_8b6aae914f9848e0b45ca47f8d36b4e1);
            

            circle_marker_91719254364d48f2900e5bc14a669a13.bindPopup(popup_feed20d6ea6c4ecf8c3d8e8b99e37738);

            
        
    
            var circle_marker_40972fab511e4ddc835044d2ddb6f211 = L.circleMarker(
                [44.8403384742305,-0.559553791883195],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_abac233cf3e54ee08ad314154925d1bd = L.popup({maxWidth: '300'});

            
                var html_d32027e32d6045e6a04849b05edcbdbf = $('<div id="html_d32027e32d6045e6a04849b05edcbdbf" style="width: 100.0%; height: 100.0%;">Stalingrad</div>')[0];
                popup_abac233cf3e54ee08ad314154925d1bd.setContent(html_d32027e32d6045e6a04849b05edcbdbf);
            

            circle_marker_40972fab511e4ddc835044d2ddb6f211.bindPopup(popup_abac233cf3e54ee08ad314154925d1bd);

            
        
    
            var circle_marker_6968b1dd9ee541a4a8de7352d20e9028 = L.circleMarker(
                [44.846253879295396,-0.5649509762224311],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_b1c15641b8b041df991db30ad2e41cc0 = L.popup({maxWidth: '300'});

            
                var html_ca6d16a10c7c48cba2053ac3d5072e74 = $('<div id="html_ca6d16a10c7c48cba2053ac3d5072e74" style="width: 100.0%; height: 100.0%;">Parc aux Angeliques</div>')[0];
                popup_b1c15641b8b041df991db30ad2e41cc0.setContent(html_ca6d16a10c7c48cba2053ac3d5072e74);
            

            circle_marker_6968b1dd9ee541a4a8de7352d20e9028.bindPopup(popup_b1c15641b8b041df991db30ad2e41cc0);

            
        
    
            var circle_marker_f753edae4ec7409b8d26d812521900c2 = L.circleMarker(
                [44.8013759766058,-0.597153198537933],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_b0a88c9a8d724c2f81c85bc91c8eb710 = L.popup({maxWidth: '300'});

            
                var html_179b20e8bd7648c48826f14b07f4c24e = $('<div id="html_179b20e8bd7648c48826f14b07f4c24e" style="width: 100.0%; height: 100.0%;">CREPS</div>')[0];
                popup_b0a88c9a8d724c2f81c85bc91c8eb710.setContent(html_179b20e8bd7648c48826f14b07f4c24e);
            

            circle_marker_f753edae4ec7409b8d26d812521900c2.bindPopup(popup_b0a88c9a8d724c2f81c85bc91c8eb710);

            
        
    
            var circle_marker_d6544db155cb408785b9cb48bf201152 = L.circleMarker(
                [44.842949616892206,-0.570372793782736],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_659970ec01e845bba6863a5c1040872b = L.popup({maxWidth: '300'});

            
                var html_825ed89398bb4994bad02224166c7a39 = $('<div id="html_825ed89398bb4994bad02224166c7a39" style="width: 100.0%; height: 100.0%;">Place Jean Jaurès</div>')[0];
                popup_659970ec01e845bba6863a5c1040872b.setContent(html_825ed89398bb4994bad02224166c7a39);
            

            circle_marker_d6544db155cb408785b9cb48bf201152.bindPopup(popup_659970ec01e845bba6863a5c1040872b);

            
        
    
            var circle_marker_0d48320bbc884ea4947753db2e9995ed = L.circleMarker(
                [44.8538162121191,-0.5886820298771109],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a2624814902a42b09fae0ae774b0cc9f = L.popup({maxWidth: '300'});

            
                var html_e81d89d3940b4ec3b6cbcc5fef91765a = $('<div id="html_e81d89d3940b4ec3b6cbcc5fef91765a" style="width: 100.0%; height: 100.0%;">Tivoli</div>')[0];
                popup_a2624814902a42b09fae0ae774b0cc9f.setContent(html_e81d89d3940b4ec3b6cbcc5fef91765a);
            

            circle_marker_0d48320bbc884ea4947753db2e9995ed.bindPopup(popup_a2624814902a42b09fae0ae774b0cc9f);

            
        
    
            var circle_marker_9e2e44aa51744c67beb7b5dfe20c7565 = L.circleMarker(
                [44.9173004397779,-0.623816093549365],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_faefa5045db840318a89f438fe2b78d9 = L.popup({maxWidth: '300'});

            
                var html_f491fa243c3a427680a6c9efa4ac9d99 = $('<div id="html_f491fa243c3a427680a6c9efa4ac9d99" style="width: 100.0%; height: 100.0%;">Gare de Blanquefort (réouverture prévue fin 2016)</div>')[0];
                popup_faefa5045db840318a89f438fe2b78d9.setContent(html_f491fa243c3a427680a6c9efa4ac9d99);
            

            circle_marker_9e2e44aa51744c67beb7b5dfe20c7565.bindPopup(popup_faefa5045db840318a89f438fe2b78d9);

            
        
    
            var circle_marker_2a06343fcf48471fa39f2f895e9f9589 = L.circleMarker(
                [44.7958766715951,-0.6673257911302409],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_1f19583a6b7d4d4a92ca12b48a697411 = L.popup({maxWidth: '300'});

            
                var html_1ec766426e694fab853642d24e298d12 = $('<div id="html_1ec766426e694fab853642d24e298d12" style="width: 100.0%; height: 100.0%;">Morin Cazalet</div>')[0];
                popup_1f19583a6b7d4d4a92ca12b48a697411.setContent(html_1ec766426e694fab853642d24e298d12);
            

            circle_marker_2a06343fcf48471fa39f2f895e9f9589.bindPopup(popup_1f19583a6b7d4d4a92ca12b48a697411);

            
        
    
            var circle_marker_e9d0034af3dd493cbb4a54f2143c8ad9 = L.circleMarker(
                [44.850965930590704,-0.644753529917565],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a03e361535c44bad9baf568e70228c08 = L.popup({maxWidth: '300'});

            
                var html_3c1d567abc0240f29789bd0068db6a5f = $('<div id="html_3c1d567abc0240f29789bd0068db6a5f" style="width: 100.0%; height: 100.0%;">Capeyron</div>')[0];
                popup_a03e361535c44bad9baf568e70228c08.setContent(html_3c1d567abc0240f29789bd0068db6a5f);
            

            circle_marker_e9d0034af3dd493cbb4a54f2143c8ad9.bindPopup(popup_a03e361535c44bad9baf568e70228c08);

            
        
    
            var circle_marker_6e49cec055274986853f00cd2f6326bb = L.circleMarker(
                [44.832632938696605,-0.567111855410022],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_3438d3ae6e684f62ad278f7ccf9174f4 = L.popup({maxWidth: '300'});

            
                var html_c971e80dbd1e4de5b980b24bae50997e = $('<div id="html_c971e80dbd1e4de5b980b24bae50997e" style="width: 100.0%; height: 100.0%;">Place du Maucaillou</div>')[0];
                popup_3438d3ae6e684f62ad278f7ccf9174f4.setContent(html_c971e80dbd1e4de5b980b24bae50997e);
            

            circle_marker_6e49cec055274986853f00cd2f6326bb.bindPopup(popup_3438d3ae6e684f62ad278f7ccf9174f4);

            
        
    
            var circle_marker_2fb51527f1e64662ab1124363cd0fd65 = L.circleMarker(
                [44.8331199635521,-0.576952524846592],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_f12b4d3bb7ff4aab9f2e051065f5c07c = L.popup({maxWidth: '300'});

            
                var html_3799c810af794d74b9e94a65e474f476 = $('<div id="html_3799c810af794d74b9e94a65e474f476" style="width: 100.0%; height: 100.0%;">Place Ste Eulalie</div>')[0];
                popup_f12b4d3bb7ff4aab9f2e051065f5c07c.setContent(html_3799c810af794d74b9e94a65e474f476);
            

            circle_marker_2fb51527f1e64662ab1124363cd0fd65.bindPopup(popup_f12b4d3bb7ff4aab9f2e051065f5c07c);

            
        
    
            var circle_marker_b0d87107f67b44b19b9fc54103b5d98a = L.circleMarker(
                [44.8351755836624,-0.5720496800131321],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_15a6e3a4009e43f4bba3016a84a2fadd = L.popup({maxWidth: '300'});

            
                var html_c197cc08dc844cd2b56867e7df5ccb96 = $('<div id="html_c197cc08dc844cd2b56867e7df5ccb96" style="width: 100.0%; height: 100.0%;">Grosse Cloche (fermée depuis le 26/09/2016)</div>')[0];
                popup_15a6e3a4009e43f4bba3016a84a2fadd.setContent(html_c197cc08dc844cd2b56867e7df5ccb96);
            

            circle_marker_b0d87107f67b44b19b9fc54103b5d98a.bindPopup(popup_15a6e3a4009e43f4bba3016a84a2fadd);

            
        
    
            var circle_marker_4f3bfec0691b4477a5b4b49d65074112 = L.circleMarker(
                [44.85560206029321,-0.563193934178502],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_448f728572d1445eb282eaa4dae70993 = L.popup({maxWidth: '300'});

            
                var html_d0a59e102e904b11bc530c1ab02e0375 = $('<div id="html_d0a59e102e904b11bc530c1ab02e0375" style="width: 100.0%; height: 100.0%;">Cours du Médoc</div>')[0];
                popup_448f728572d1445eb282eaa4dae70993.setContent(html_d0a59e102e904b11bc530c1ab02e0375);
            

            circle_marker_4f3bfec0691b4477a5b4b49d65074112.bindPopup(popup_448f728572d1445eb282eaa4dae70993);

            
        
    
            var circle_marker_70b37a17cc1e4da7a4bd21544b2aaf84 = L.circleMarker(
                [44.8484148322112,-0.575741583710469],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_744b84b33e534b6bb63941e2f57fe04b = L.popup({maxWidth: '300'});

            
                var html_4fb85b7baa5942dbb2f768bc9efc99bd = $('<div id="html_4fb85b7baa5942dbb2f768bc9efc99bd" style="width: 100.0%; height: 100.0%;">Jardin Public</div>')[0];
                popup_744b84b33e534b6bb63941e2f57fe04b.setContent(html_4fb85b7baa5942dbb2f768bc9efc99bd);
            

            circle_marker_70b37a17cc1e4da7a4bd21544b2aaf84.bindPopup(popup_744b84b33e534b6bb63941e2f57fe04b);

            
        
    
            var circle_marker_db15c70c571144bc990c0a5cfaf54306 = L.circleMarker(
                [44.8561372751007,-0.533637420460925],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_2b13b438ca6240d6a19d34eeb923fe2f = L.popup({maxWidth: '300'});

            
                var html_fa7a336e3d3541bfb5b01c458cd2c25b = $('<div id="html_fa7a336e3d3541bfb5b01c458cd2c25b" style="width: 100.0%; height: 100.0%;">Cenon Gare</div>')[0];
                popup_2b13b438ca6240d6a19d34eeb923fe2f.setContent(html_fa7a336e3d3541bfb5b01c458cd2c25b);
            

            circle_marker_db15c70c571144bc990c0a5cfaf54306.bindPopup(popup_2b13b438ca6240d6a19d34eeb923fe2f);

            
        
    
            var circle_marker_b27184789ca7450ba5720042d3247577 = L.circleMarker(
                [44.8377941712661,-0.581662430453886],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_1c23c89831ae43c4ba4f7a702bd44912 = L.popup({maxWidth: '300'});

            
                var html_f5c1af1329b7482f890a2cc09af9fb1d = $('<div id="html_f5c1af1329b7482f890a2cc09af9fb1d" style="width: 100.0%; height: 100.0%;">Square André Lhote</div>')[0];
                popup_1c23c89831ae43c4ba4f7a702bd44912.setContent(html_f5c1af1329b7482f890a2cc09af9fb1d);
            

            circle_marker_b27184789ca7450ba5720042d3247577.bindPopup(popup_1c23c89831ae43c4ba4f7a702bd44912);

            
        
    
            var circle_marker_7331e43478b540b285a1d3ef9697dff5 = L.circleMarker(
                [44.8626120471668,-0.567184098919909],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_da95cb162664417dacf702020a0ebd44 = L.popup({maxWidth: '300'});

            
                var html_8b00befd1eab469c9a2cfd08e5999f2f = $('<div id="html_8b00befd1eab469c9a2cfd08e5999f2f" style="width: 100.0%; height: 100.0%;">Saint Louis Haussmann</div>')[0];
                popup_da95cb162664417dacf702020a0ebd44.setContent(html_8b00befd1eab469c9a2cfd08e5999f2f);
            

            circle_marker_7331e43478b540b285a1d3ef9697dff5.bindPopup(popup_da95cb162664417dacf702020a0ebd44);

            
        
    
            var circle_marker_9b8780335c17485d8381fe8df98651ba = L.circleMarker(
                [44.8380294186168,-0.584364976111836],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_fba83fad52604b4aaf1c72c2d4fbdbc9 = L.popup({maxWidth: '300'});

            
                var html_11057d241a0a43f7a55529fff899bcf2 = $('<div id="html_11057d241a0a43f7a55529fff899bcf2" style="width: 100.0%; height: 100.0%;">Mériadeck</div>')[0];
                popup_fba83fad52604b4aaf1c72c2d4fbdbc9.setContent(html_11057d241a0a43f7a55529fff899bcf2);
            

            circle_marker_9b8780335c17485d8381fe8df98651ba.bindPopup(popup_fba83fad52604b4aaf1c72c2d4fbdbc9);

            
        
    
            var circle_marker_d5456c122718499e9d1b3be80f9fcb1e = L.circleMarker(
                [44.8359397316541,-0.58211488103064],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_8b5886bf92274c95a8f90a877ae57ec1 = L.popup({maxWidth: '300'});

            
                var html_0b66f7d7635d47f6b53eff221d09956b = $('<div id="html_0b66f7d7635d47f6b53eff221d09956b" style="width: 100.0%; height: 100.0%;">Palais de Justice</div>')[0];
                popup_8b5886bf92274c95a8f90a877ae57ec1.setContent(html_0b66f7d7635d47f6b53eff221d09956b);
            

            circle_marker_d5456c122718499e9d1b3be80f9fcb1e.bindPopup(popup_8b5886bf92274c95a8f90a877ae57ec1);

            
        
    
            var circle_marker_ae4e0dcf75914104a7f15b0a840a4270 = L.circleMarker(
                [44.8468975914944,-0.582190313794346],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_4083ecac080d49d290f4150fdb6289ad = L.popup({maxWidth: '300'});

            
                var html_4fba64ee626447a4a58bf347e106edd1 = $('<div id="html_4fba64ee626447a4a58bf347e106edd1" style="width: 100.0%; height: 100.0%;">Palais Gallien</div>')[0];
                popup_4083ecac080d49d290f4150fdb6289ad.setContent(html_4fba64ee626447a4a58bf347e106edd1);
            

            circle_marker_ae4e0dcf75914104a7f15b0a840a4270.bindPopup(popup_4083ecac080d49d290f4150fdb6289ad);

            
        
    
            var circle_marker_02254bc78cf746228c02c7ad10b70eea = L.circleMarker(
                [44.832505316290394,-0.610466692761722],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_0896829c084849dab215c9063d3ebce7 = L.popup({maxWidth: '300'});

            
                var html_64a6bb9bc8044235915e3e6c20f4505a = $('<div id="html_64a6bb9bc8044235915e3e6c20f4505a" style="width: 100.0%; height: 100.0%;">St Augustin</div>')[0];
                popup_0896829c084849dab215c9063d3ebce7.setContent(html_64a6bb9bc8044235915e3e6c20f4505a);
            

            circle_marker_02254bc78cf746228c02c7ad10b70eea.bindPopup(popup_0896829c084849dab215c9063d3ebce7);

            
        
    
            var circle_marker_01ccb4c170ea49118169d1e83b937c40 = L.circleMarker(
                [44.8471835107084,-0.5713111276547099],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_81fe070e89b84b76ba73f9c75bc0c6dd = L.popup({maxWidth: '300'});

            
                var html_110d253c2bb9429b86104eba63486ca0 = $('<div id="html_110d253c2bb9429b86104eba63486ca0" style="width: 100.0%; height: 100.0%;">Allées de Chartres</div>')[0];
                popup_81fe070e89b84b76ba73f9c75bc0c6dd.setContent(html_110d253c2bb9429b86104eba63486ca0);
            

            circle_marker_01ccb4c170ea49118169d1e83b937c40.bindPopup(popup_81fe070e89b84b76ba73f9c75bc0c6dd);

            
        
    
            var circle_marker_76d079a17bd84f4d891c22184bd254e6 = L.circleMarker(
                [44.8429268147365,-0.573898729949943],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_bb558bc707a644468dc71bb2a35967bb = L.popup({maxWidth: '300'});

            
                var html_2129a7675c894bbe9fdf638a663f83db = $('<div id="html_2129a7675c894bbe9fdf638a663f83db" style="width: 100.0%; height: 100.0%;">Grand Théâtre</div>')[0];
                popup_bb558bc707a644468dc71bb2a35967bb.setContent(html_2129a7675c894bbe9fdf638a663f83db);
            

            circle_marker_76d079a17bd84f4d891c22184bd254e6.bindPopup(popup_bb558bc707a644468dc71bb2a35967bb);

            
        
    
            var circle_marker_7ff7a570b2ca4bbcb729bcc59a926006 = L.circleMarker(
                [44.84297697868261,-0.555732111587463],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_61dbb49b67de4a569399b76f5d21853d = L.popup({maxWidth: '300'});

            
                var html_14278a5a81b54beca904df84a21d9e13 = $('<div id="html_14278a5a81b54beca904df84a21d9e13" style="width: 100.0%; height: 100.0%;">Thiers Jardin Botanique</div>')[0];
                popup_61dbb49b67de4a569399b76f5d21853d.setContent(html_14278a5a81b54beca904df84a21d9e13);
            

            circle_marker_7ff7a570b2ca4bbcb729bcc59a926006.bindPopup(popup_61dbb49b67de4a569399b76f5d21853d);

            
        
    
            var circle_marker_ba070d84946d47bda79a2fb9b7673f66 = L.circleMarker(
                [44.8077684559174,-0.560054594815583],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_13aab69b01b94ad08ff70277d05bcfa4 = L.popup({maxWidth: '300'});

            
                var html_68e30893590c419f992f78b8a6b99a60 = $('<div id="html_68e30893590c419f992f78b8a6b99a60" style="width: 100.0%; height: 100.0%;">Bègles Poste</div>')[0];
                popup_13aab69b01b94ad08ff70277d05bcfa4.setContent(html_68e30893590c419f992f78b8a6b99a60);
            

            circle_marker_ba070d84946d47bda79a2fb9b7673f66.bindPopup(popup_13aab69b01b94ad08ff70277d05bcfa4);

            
        
    
            var circle_marker_3a6d881a07ef48cd913cb648df029d63 = L.circleMarker(
                [44.8377883500726,-0.5703384430356229],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_e856dfcff2744b85be1d2fb333e5db04 = L.popup({maxWidth: '300'});

            
                var html_d5d6b123ddef44a2a63f54b7f000f949 = $('<div id="html_d5d6b123ddef44a2a63f54b7f000f949" style="width: 100.0%; height: 100.0%;">Place du Palais</div>')[0];
                popup_e856dfcff2744b85be1d2fb333e5db04.setContent(html_d5d6b123ddef44a2a63f54b7f000f949);
            

            circle_marker_3a6d881a07ef48cd913cb648df029d63.bindPopup(popup_e856dfcff2744b85be1d2fb333e5db04);

            
        
    
            var circle_marker_994a56fd157e4fac85b16c39deecba98 = L.circleMarker(
                [44.8153457349536,-0.634437084515722],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a0008cd8e1db45f5855b151b9cd3e80c = L.popup({maxWidth: '300'});

            
                var html_25b52ae9a674471cb252a7b8b528bd46 = $('<div id="html_25b52ae9a674471cb252a7b8b528bd46" style="width: 100.0%; height: 100.0%;">Le Burck</div>')[0];
                popup_a0008cd8e1db45f5855b151b9cd3e80c.setContent(html_25b52ae9a674471cb252a7b8b528bd46);
            

            circle_marker_994a56fd157e4fac85b16c39deecba98.bindPopup(popup_a0008cd8e1db45f5855b151b9cd3e80c);

            
        
    
            var circle_marker_461681a8630b49c0b90eefd4d609020e = L.circleMarker(
                [44.8265044749032,-0.6257739119014971],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_6739c175a993427c97fd8476f4e2517b = L.popup({maxWidth: '300'});

            
                var html_15b12f0111e74eb8b564185c997b9c71 = $('<div id="html_15b12f0111e74eb8b564185c997b9c71" style="width: 100.0%; height: 100.0%;">Fontaine d&apos;Arlac</div>')[0];
                popup_6739c175a993427c97fd8476f4e2517b.setContent(html_15b12f0111e74eb8b564185c997b9c71);
            

            circle_marker_461681a8630b49c0b90eefd4d609020e.bindPopup(popup_6739c175a993427c97fd8476f4e2517b);

            
        
    
            var circle_marker_d7730dde01dc4eddb708fba4567ca309 = L.circleMarker(
                [44.8814488592023,-0.613374329926722],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_6a534b6bf7704affb5468ee76e5d5e83 = L.popup({maxWidth: '300'});

            
                var html_3761c6610d874df8bb3a4020f63e784a = $('<div id="html_3761c6610d874df8bb3a4020f63e784a" style="width: 100.0%; height: 100.0%;">Bruges Hôtel de Ville</div>')[0];
                popup_6a534b6bf7704affb5468ee76e5d5e83.setContent(html_3761c6610d874df8bb3a4020f63e784a);
            

            circle_marker_d7730dde01dc4eddb708fba4567ca309.bindPopup(popup_6a534b6bf7704affb5468ee76e5d5e83);

            
        
    
            var circle_marker_626a65976117436ebb004f46e1b01863 = L.circleMarker(
                [44.8220935136601,-0.581781925139518],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_6b60f6a1ac2b4fda80f4fb6aaab0e9c1 = L.popup({maxWidth: '300'});

            
                var html_c282f5138a2943f886085f25204e77be = $('<div id="html_c282f5138a2943f886085f25204e77be" style="width: 100.0%; height: 100.0%;">Barrière St Genès</div>')[0];
                popup_6b60f6a1ac2b4fda80f4fb6aaab0e9c1.setContent(html_c282f5138a2943f886085f25204e77be);
            

            circle_marker_626a65976117436ebb004f46e1b01863.bindPopup(popup_6b60f6a1ac2b4fda80f4fb6aaab0e9c1);

            
        
    
            var circle_marker_5da139f8f5ce45dab73246c6a5341816 = L.circleMarker(
                [44.858907740747604,-0.565842813677209],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_d37bd3d78b0b45148c1e20e6cb1df728 = L.popup({maxWidth: '300'});

            
                var html_dd8f4ca5a6fe429aa2c1e9b98c1a12fc = $('<div id="html_dd8f4ca5a6fe429aa2c1e9b98c1a12fc" style="width: 100.0%; height: 100.0%;">Place St Martial</div>')[0];
                popup_d37bd3d78b0b45148c1e20e6cb1df728.setContent(html_dd8f4ca5a6fe429aa2c1e9b98c1a12fc);
            

            circle_marker_5da139f8f5ce45dab73246c6a5341816.bindPopup(popup_d37bd3d78b0b45148c1e20e6cb1df728);

            
        
    
            var circle_marker_99ea2adb08f54a3aa69acd4b971358cf = L.circleMarker(
                [44.8332348401948,-0.5828331038628051],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.4,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 3,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_83d03eb58937447d9f0eead3e2bf4842 = L.popup({maxWidth: '300'});

            
                var html_1b2ce4b29fac4339a2131270e9b7af9d = $('<div id="html_1b2ce4b29fac4339a2131270e9b7af9d" style="width: 100.0%; height: 100.0%;">Libération</div>')[0];
                popup_83d03eb58937447d9f0eead3e2bf4842.setContent(html_1b2ce4b29fac4339a2131270e9b7af9d);
            

            circle_marker_99ea2adb08f54a3aa69acd4b971358cf.bindPopup(popup_83d03eb58937447d9f0eead3e2bf4842);

            
        
    
            var circle_marker_18a2894f34ea4ad7a87d97bbbbf0c17a = L.circleMarker(
                [44.82405483453179,-0.570243491180582],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_8a1954765f484db098d2f5732e29369d = L.popup({maxWidth: '300'});

            
                var html_9aac1b25621c4919b44fdca979c77746 = $('<div id="html_9aac1b25621c4919b44fdca979c77746" style="width: 100.0%; height: 100.0%;">Lycée Brémontier</div>')[0];
                popup_8a1954765f484db098d2f5732e29369d.setContent(html_9aac1b25621c4919b44fdca979c77746);
            

            circle_marker_18a2894f34ea4ad7a87d97bbbbf0c17a.bindPopup(popup_8a1954765f484db098d2f5732e29369d);

            
        
    
            var circle_marker_8fac5ee23dc2484aa6d36e94b01af927 = L.circleMarker(
                [44.8313123071108,-0.561392805485366],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_56299247b4cd416e8f957271bcd6accf = L.popup({maxWidth: '300'});

            
                var html_3d412364666f438685cec50fbe26fb49 = $('<div id="html_3d412364666f438685cec50fbe26fb49" style="width: 100.0%; height: 100.0%;">Eglise Ste Croix</div>')[0];
                popup_56299247b4cd416e8f957271bcd6accf.setContent(html_3d412364666f438685cec50fbe26fb49);
            

            circle_marker_8fac5ee23dc2484aa6d36e94b01af927.bindPopup(popup_56299247b4cd416e8f957271bcd6accf);

            
        
    
            var circle_marker_1fc0465f63ad4ba885d0cad9cadb2d54 = L.circleMarker(
                [44.8642758580301,-0.524200242527949],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a7e3f6823f244c84b79f04a8aaa0b53a = L.popup({maxWidth: '300'});

            
                var html_60c78f55440a4380be131e97eeabc6a3 = $('<div id="html_60c78f55440a4380be131e97eeabc6a3" style="width: 100.0%; height: 100.0%;">Buttinière</div>')[0];
                popup_a7e3f6823f244c84b79f04a8aaa0b53a.setContent(html_60c78f55440a4380be131e97eeabc6a3);
            

            circle_marker_1fc0465f63ad4ba885d0cad9cadb2d54.bindPopup(popup_a7e3f6823f244c84b79f04a8aaa0b53a);

            
        
    
            var circle_marker_d05be7d58d9849aa889e54bc805ba9de = L.circleMarker(
                [44.837788570071105,-0.5671555557555621],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_ba7bd5bcaa7c432090bd655e149740e5 = L.popup({maxWidth: '300'});

            
                var html_bb24dae28a37421f840fe0533fcc2a47 = $('<div id="html_bb24dae28a37421f840fe0533fcc2a47" style="width: 100.0%; height: 100.0%;">Porte de Bourgogne</div>')[0];
                popup_ba7bd5bcaa7c432090bd655e149740e5.setContent(html_bb24dae28a37421f840fe0533fcc2a47);
            

            circle_marker_d05be7d58d9849aa889e54bc805ba9de.bindPopup(popup_ba7bd5bcaa7c432090bd655e149740e5);

            
        
    
            var circle_marker_7ce9c6e13dca47c1940c1612a06c31c7 = L.circleMarker(
                [44.8003846074376,-0.609856530171509],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_937fe3ba0d764ffea2d7cde77353d783 = L.popup({maxWidth: '300'});

            
                var html_c7a9c2d284ec48c6a580a681e3172683 = $('<div id="html_c7a9c2d284ec48c6a580a681e3172683" style="width: 100.0%; height: 100.0%;">Doyen Brus</div>')[0];
                popup_937fe3ba0d764ffea2d7cde77353d783.setContent(html_c7a9c2d284ec48c6a580a681e3172683);
            

            circle_marker_7ce9c6e13dca47c1940c1612a06c31c7.bindPopup(popup_937fe3ba0d764ffea2d7cde77353d783);

            
        
    
            var circle_marker_9c782733f0ae4f8384d3ab540d2d54f1 = L.circleMarker(
                [44.86328744461021,-0.5999496122171569],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_1593934398b14151b17cc627b55eb88b = L.popup({maxWidth: '300'});

            
                var html_77cb7bef10384a63b20bf65bfc8f00fb = $('<div id="html_77cb7bef10384a63b20bf65bfc8f00fb" style="width: 100.0%; height: 100.0%;">Le Bouscat Mairie</div>')[0];
                popup_1593934398b14151b17cc627b55eb88b.setContent(html_77cb7bef10384a63b20bf65bfc8f00fb);
            

            circle_marker_9c782733f0ae4f8384d3ab540d2d54f1.bindPopup(popup_1593934398b14151b17cc627b55eb88b);

            
        
    
            var circle_marker_40c0efcd1ab2493d8e739437379e8249 = L.circleMarker(
                [44.85416906214729,-0.5865861955488361],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_c311b2dd3f604d778c90c79c9f95999d = L.popup({maxWidth: '300'});

            
                var html_a282a444101446b6b20aface2929da31 = $('<div id="html_a282a444101446b6b20aface2929da31" style="width: 100.0%; height: 100.0%;">Parc Rivière</div>')[0];
                popup_c311b2dd3f604d778c90c79c9f95999d.setContent(html_a282a444101446b6b20aface2929da31);
            

            circle_marker_40c0efcd1ab2493d8e739437379e8249.bindPopup(popup_c311b2dd3f604d778c90c79c9f95999d);

            
        
    
            var circle_marker_ac15c411462f4c4c9291b3094465b66d = L.circleMarker(
                [44.832618305563,-0.570974436712359],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_eac7d0d6982b484882b823355886652c = L.popup({maxWidth: '300'});

            
                var html_c208a887c4a748078d151396529be822 = $('<div id="html_c208a887c4a748078d151396529be822" style="width: 100.0%; height: 100.0%;">Rue du Mirail</div>')[0];
                popup_eac7d0d6982b484882b823355886652c.setContent(html_c208a887c4a748078d151396529be822);
            

            circle_marker_ac15c411462f4c4c9291b3094465b66d.bindPopup(popup_eac7d0d6982b484882b823355886652c);

            
        
    
            var circle_marker_ddc63e85e6c843ac9f1b343dc2ddd41d = L.circleMarker(
                [44.7966723128786,-0.617040553455776],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_99c4f26c88074d7b84df6f546f86b7fd = L.popup({maxWidth: '300'});

            
                var html_4f66a2a9403b41b394de2f62ff35f3ea = $('<div id="html_4f66a2a9403b41b394de2f62ff35f3ea" style="width: 100.0%; height: 100.0%;">Montaigne Montesquieu</div>')[0];
                popup_99c4f26c88074d7b84df6f546f86b7fd.setContent(html_4f66a2a9403b41b394de2f62ff35f3ea);
            

            circle_marker_ddc63e85e6c843ac9f1b343dc2ddd41d.bindPopup(popup_99c4f26c88074d7b84df6f546f86b7fd);

            
        
    
            var circle_marker_b691e52d49b84269803a0b5c01e4f5ee = L.circleMarker(
                [44.8279738044959,-0.593490369291754],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_326894674a9d4814b035a273f1e1e715 = L.popup({maxWidth: '300'});

            
                var html_2b32494fc0014e04aa7c118c23020def = $('<div id="html_2b32494fc0014e04aa7c118c23020def" style="width: 100.0%; height: 100.0%;">Xaintrailles</div>')[0];
                popup_326894674a9d4814b035a273f1e1e715.setContent(html_2b32494fc0014e04aa7c118c23020def);
            

            circle_marker_b691e52d49b84269803a0b5c01e4f5ee.bindPopup(popup_326894674a9d4814b035a273f1e1e715);

            
        
    
            var circle_marker_57ffbfe1122b4b5d9812f2760f33fddb = L.circleMarker(
                [44.8494651911446,-0.545253248846396],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_323acad0d7f544d6bd4f33a6576132b6 = L.popup({maxWidth: '300'});

            
                var html_72761cac3b334583bf23b986e2fa0dec = $('<div id="html_72761cac3b334583bf23b986e2fa0dec" style="width: 100.0%; height: 100.0%;">Galin</div>')[0];
                popup_323acad0d7f544d6bd4f33a6576132b6.setContent(html_72761cac3b334583bf23b986e2fa0dec);
            

            circle_marker_57ffbfe1122b4b5d9812f2760f33fddb.bindPopup(popup_323acad0d7f544d6bd4f33a6576132b6);

            
        
    
            var circle_marker_2b4363f9b79a4620b1ed9b5bc527ea89 = L.circleMarker(
                [44.887498870215396,-0.51762894718184],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_b130ab4a0054450c88b49dbb3a590efc = L.popup({maxWidth: '300'});

            
                var html_44b3ed5bb4864b51ae1d6f1d979ad7a5 = $('<div id="html_44b3ed5bb4864b51ae1d6f1d979ad7a5" style="width: 100.0%; height: 100.0%;">La Gardette</div>')[0];
                popup_b130ab4a0054450c88b49dbb3a590efc.setContent(html_44b3ed5bb4864b51ae1d6f1d979ad7a5);
            

            circle_marker_2b4363f9b79a4620b1ed9b5bc527ea89.bindPopup(popup_b130ab4a0054450c88b49dbb3a590efc);

            
        
    
            var circle_marker_ab066e0ef6fa47618dcb3364b3a56f82 = L.circleMarker(
                [44.8773814879661,-0.54433270185457],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_f605fae99efd4daba2b36482db998112 = L.popup({maxWidth: '300'});

            
                var html_8baaff2746af463a84476a12bdb08ba6 = $('<div id="html_8baaff2746af463a84476a12bdb08ba6" style="width: 100.0%; height: 100.0%;">Claveau</div>')[0];
                popup_f605fae99efd4daba2b36482db998112.setContent(html_8baaff2746af463a84476a12bdb08ba6);
            

            circle_marker_ab066e0ef6fa47618dcb3364b3a56f82.bindPopup(popup_f605fae99efd4daba2b36482db998112);

            
        
    
            var circle_marker_67fa351a768840d4abcb48d65a3f2ed4 = L.circleMarker(
                [44.8441478155752,-0.581633153292761],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a7145d76ae684ae49015eee3cbf5ad67 = L.popup({maxWidth: '300'});

            
                var html_fd42dd6177fb4a768bb30d72ba896ca9 = $('<div id="html_fd42dd6177fb4a768bb30d72ba896ca9" style="width: 100.0%; height: 100.0%;">Huguerie</div>')[0];
                popup_a7145d76ae684ae49015eee3cbf5ad67.setContent(html_fd42dd6177fb4a768bb30d72ba896ca9);
            

            circle_marker_67fa351a768840d4abcb48d65a3f2ed4.bindPopup(popup_a7145d76ae684ae49015eee3cbf5ad67);

            
        
    
            var circle_marker_1931f570945a4b178739f3f477b30e27 = L.circleMarker(
                [44.8517457831629,-0.614383180249531],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_5abb0fc29e3b468ba972de60d08eec3a = L.popup({maxWidth: '300'});

            
                var html_5272e78f4e384cdd8b72546c007c0a78 = $('<div id="html_5272e78f4e384cdd8b72546c007c0a78" style="width: 100.0%; height: 100.0%;">Caudéran</div>')[0];
                popup_5abb0fc29e3b468ba972de60d08eec3a.setContent(html_5272e78f4e384cdd8b72546c007c0a78);
            

            circle_marker_1931f570945a4b178739f3f477b30e27.bindPopup(popup_5abb0fc29e3b468ba972de60d08eec3a);

            
        
    
            var circle_marker_975a817086be4de88b36fd5c5d1d69c7 = L.circleMarker(
                [44.8541785581174,-0.5128187356398309],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_fbdd64debff447c484b981ece8c5744a = L.popup({maxWidth: '300'});

            
                var html_ff915538457b44ccbe87d80da2dd209d = $('<div id="html_ff915538457b44ccbe87d80da2dd209d" style="width: 100.0%; height: 100.0%;">Jean Zay</div>')[0];
                popup_fbdd64debff447c484b981ece8c5744a.setContent(html_ff915538457b44ccbe87d80da2dd209d);
            

            circle_marker_975a817086be4de88b36fd5c5d1d69c7.bindPopup(popup_fbdd64debff447c484b981ece8c5744a);

            
        
    
            var circle_marker_aee42a2f37dc4b7e811799a5831266ee = L.circleMarker(
                [44.8422055855349,-0.5848222650121421],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_7a82f36cb6da415ca78d69ed99ddefe1 = L.popup({maxWidth: '300'});

            
                var html_a1429402287a4c6c8523e786323ffb66 = $('<div id="html_a1429402287a4c6c8523e786323ffb66" style="width: 100.0%; height: 100.0%;">St Seurin</div>')[0];
                popup_7a82f36cb6da415ca78d69ed99ddefe1.setContent(html_a1429402287a4c6c8523e786323ffb66);
            

            circle_marker_aee42a2f37dc4b7e811799a5831266ee.bindPopup(popup_7a82f36cb6da415ca78d69ed99ddefe1);

            
        
    
            var circle_marker_9c262e8f54f446cbb64ea6e03d12bb2f = L.circleMarker(
                [44.8414957860903,-0.580803720206641],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_577b2ca6fcc549d7bae40a23fea0cfad = L.popup({maxWidth: '300'});

            
                var html_d84de5d11c7148cc9a6bd54154859c1a = $('<div id="html_d84de5d11c7148cc9a6bd54154859c1a" style="width: 100.0%; height: 100.0%;">Place Gambetta</div>')[0];
                popup_577b2ca6fcc549d7bae40a23fea0cfad.setContent(html_d84de5d11c7148cc9a6bd54154859c1a);
            

            circle_marker_9c262e8f54f446cbb64ea6e03d12bb2f.bindPopup(popup_577b2ca6fcc549d7bae40a23fea0cfad);

            
        
    
            var circle_marker_b9fe03104f4843ad93c7654fe541dd24 = L.circleMarker(
                [44.826535861290004,-0.601842356968657],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_851c3361754e41de8697faf9a6fe4110 = L.popup({maxWidth: '300'});

            
                var html_742ca39f77604dd0a192149c2cded764 = $('<div id="html_742ca39f77604dd0a192149c2cded764" style="width: 100.0%; height: 100.0%;">Bordeaux II</div>')[0];
                popup_851c3361754e41de8697faf9a6fe4110.setContent(html_742ca39f77604dd0a192149c2cded764);
            

            circle_marker_b9fe03104f4843ad93c7654fe541dd24.bindPopup(popup_851c3361754e41de8697faf9a6fe4110);

            
        
    
            var circle_marker_48386db97e4c45f2ba2fb5832e381dfc = L.circleMarker(
                [44.844227022453204,-0.5743444501730071],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_b8bf9558535242ea98c2eb83b5403c7d = L.popup({maxWidth: '300'});

            
                var html_369ec4b7d22c4ceb9d69b5f0968f04da = $('<div id="html_369ec4b7d22c4ceb9d69b5f0968f04da" style="width: 100.0%; height: 100.0%;">Quinconces</div>')[0];
                popup_b8bf9558535242ea98c2eb83b5403c7d.setContent(html_369ec4b7d22c4ceb9d69b5f0968f04da);
            

            circle_marker_48386db97e4c45f2ba2fb5832e381dfc.bindPopup(popup_b8bf9558535242ea98c2eb83b5403c7d);

            
        
    
            var circle_marker_a80172d678a44ea4970a777ce9415754 = L.circleMarker(
                [44.8416628527762,-0.599695197744329],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_026be5d53c6e4eee8a340492a0eb4ae1 = L.popup({maxWidth: '300'});

            
                var html_f32c5b1cc5d44556bd97c415ad60a05e = $('<div id="html_f32c5b1cc5d44556bd97c415ad60a05e" style="width: 100.0%; height: 100.0%;">Cité Administrative</div>')[0];
                popup_026be5d53c6e4eee8a340492a0eb4ae1.setContent(html_f32c5b1cc5d44556bd97c415ad60a05e);
            

            circle_marker_a80172d678a44ea4970a777ce9415754.bindPopup(popup_026be5d53c6e4eee8a340492a0eb4ae1);

            
        
    
            var circle_marker_d3b18249c021430b87aac210eca4258e = L.circleMarker(
                [44.8431439888232,-0.5772404414507191],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_c912179d1ab04feb869b0dfc3d8bb8bb = L.popup({maxWidth: '300'});

            
                var html_c71f7b471b0149b99bef4ea457da690e = $('<div id="html_c71f7b471b0149b99bef4ea457da690e" style="width: 100.0%; height: 100.0%;">Grands Hommes</div>')[0];
                popup_c912179d1ab04feb869b0dfc3d8bb8bb.setContent(html_c71f7b471b0149b99bef4ea457da690e);
            

            circle_marker_d3b18249c021430b87aac210eca4258e.bindPopup(popup_c912179d1ab04feb869b0dfc3d8bb8bb);

            
        
    
            var circle_marker_4223ff40d3894aab82ed3de43c250c7a = L.circleMarker(
                [44.8588988431287,-0.581080543691782],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_33b68b8a1b27416ebf6341ea581003a0 = L.popup({maxWidth: '300'});

            
                var html_bbe90bced79c4dd7848563af151cde44 = $('<div id="html_bbe90bced79c4dd7848563af151cde44" style="width: 100.0%; height: 100.0%;">Place de l&apos;Europe</div>')[0];
                popup_33b68b8a1b27416ebf6341ea581003a0.setContent(html_bbe90bced79c4dd7848563af151cde44);
            

            circle_marker_4223ff40d3894aab82ed3de43c250c7a.bindPopup(popup_33b68b8a1b27416ebf6341ea581003a0);

            
        
    
            var circle_marker_edb96b681af2413880a512261b52ed0d = L.circleMarker(
                [44.826421204936,-0.557323394027949],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_8ff8f01eda21415b80a4d33b25bb5bf2 = L.popup({maxWidth: '300'});

            
                var html_8853a544ba204e75b4bebe1f658a81cc = $('<div id="html_8853a544ba204e75b4bebe1f658a81cc" style="width: 100.0%; height: 100.0%;">Rue St Vincent de Paul</div>')[0];
                popup_8ff8f01eda21415b80a4d33b25bb5bf2.setContent(html_8853a544ba204e75b4bebe1f658a81cc);
            

            circle_marker_edb96b681af2413880a512261b52ed0d.bindPopup(popup_8ff8f01eda21415b80a4d33b25bb5bf2);

            
        
    
            var circle_marker_d42adc79c3f64c0d8b04582253f4cd89 = L.circleMarker(
                [44.83027805669379,-0.526965614882518],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_afc77b1cfa5446b29bf54919627b77c1 = L.popup({maxWidth: '300'});

            
                var html_3eddd3b1f4b946c6a1e85c5790a72a78 = $('<div id="html_3eddd3b1f4b946c6a1e85c5790a72a78" style="width: 100.0%; height: 100.0%;">François Mitterrand</div>')[0];
                popup_afc77b1cfa5446b29bf54919627b77c1.setContent(html_3eddd3b1f4b946c6a1e85c5790a72a78);
            

            circle_marker_d42adc79c3f64c0d8b04582253f4cd89.bindPopup(popup_afc77b1cfa5446b29bf54919627b77c1);

            
        
    
            var circle_marker_2dd6209524be41df821d132b3b0f8807 = L.circleMarker(
                [44.8411382995454,-0.562235533069282],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_71d7c53108f94a4c9d1818812c020cd8 = L.popup({maxWidth: '300'});

            
                var html_baf687cc3a5644e287cca76eb74278d5 = $('<div id="html_baf687cc3a5644e287cca76eb74278d5" style="width: 100.0%; height: 100.0%;">Gare d&apos;Orléans</div>')[0];
                popup_71d7c53108f94a4c9d1818812c020cd8.setContent(html_baf687cc3a5644e287cca76eb74278d5);
            

            circle_marker_2dd6209524be41df821d132b3b0f8807.bindPopup(popup_71d7c53108f94a4c9d1818812c020cd8);

            
        
    
            var circle_marker_21059f2a9232477383f5d6dde9e26a7a = L.circleMarker(
                [44.831826305618705,-0.5596603635595601],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_d9df36fcb202489aaaccd270ae086b1c = L.popup({maxWidth: '300'});

            
                var html_b5a352c8677b4e3aa59fb2c8d34c2a62 = $('<div id="html_b5a352c8677b4e3aa59fb2c8d34c2a62" style="width: 100.0%; height: 100.0%;">Conservatoire</div>')[0];
                popup_d9df36fcb202489aaaccd270ae086b1c.setContent(html_b5a352c8677b4e3aa59fb2c8d34c2a62);
            

            circle_marker_21059f2a9232477383f5d6dde9e26a7a.bindPopup(popup_d9df36fcb202489aaaccd270ae086b1c);

            
        
    
            var circle_marker_8be4e27068e04128a8808346dee2ca40 = L.circleMarker(
                [44.8953027136185,-0.7146676441038691],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_90b0b45cab0749eeaedec8dea2b5befe = L.popup({maxWidth: '300'});

            
                var html_c607aeaa42fe4bab9a3b82d6559837ba = $('<div id="html_c607aeaa42fe4bab9a3b82d6559837ba" style="width: 100.0%; height: 100.0%;">St Médard République</div>')[0];
                popup_90b0b45cab0749eeaedec8dea2b5befe.setContent(html_c607aeaa42fe4bab9a3b82d6559837ba);
            

            circle_marker_8be4e27068e04128a8808346dee2ca40.bindPopup(popup_90b0b45cab0749eeaedec8dea2b5befe);

            
        
    
            var circle_marker_64d144c25290409799db9d98fdc4ad99 = L.circleMarker(
                [44.8738860218516,-0.5740119377009429],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_67c58e3efa4940beb09ee3f851bb30e9 = L.popup({maxWidth: '300'});

            
                var html_6468d55423724f35adb7115a44bbdff3 = $('<div id="html_6468d55423724f35adb7115a44bbdff3" style="width: 100.0%; height: 100.0%;">Les Aubiers</div>')[0];
                popup_67c58e3efa4940beb09ee3f851bb30e9.setContent(html_6468d55423724f35adb7115a44bbdff3);
            

            circle_marker_64d144c25290409799db9d98fdc4ad99.bindPopup(popup_67c58e3efa4940beb09ee3f851bb30e9);

            
        
    
            var circle_marker_67e0707e8b9449449ecc8819bbdf7c74 = L.circleMarker(
                [44.8299539022041,-0.5681171033556129],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_6f8476a105d64a2ab1fdfb791dae894f = L.popup({maxWidth: '300'});

            
                var html_3e4ef752494943608493f55d72e15025 = $('<div id="html_3e4ef752494943608493f55d72e15025" style="width: 100.0%; height: 100.0%;">Capucins</div>')[0];
                popup_6f8476a105d64a2ab1fdfb791dae894f.setContent(html_3e4ef752494943608493f55d72e15025);
            

            circle_marker_67e0707e8b9449449ecc8819bbdf7c74.bindPopup(popup_6f8476a105d64a2ab1fdfb791dae894f);

            
        
    
            var circle_marker_e1a946a83b9249458c6fe7188a5f17ff = L.circleMarker(
                [44.8059145174294,-0.60232316231959],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_ab5ba2a00e8647918eebc95c7d241a96 = L.popup({maxWidth: '300'});

            
                var html_3d4b0e38853640a5b2dc14ac60121c0e = $('<div id="html_3d4b0e38853640a5b2dc14ac60121c0e" style="width: 100.0%; height: 100.0%;">Arts et Métiers</div>')[0];
                popup_ab5ba2a00e8647918eebc95c7d241a96.setContent(html_3d4b0e38853640a5b2dc14ac60121c0e);
            

            circle_marker_e1a946a83b9249458c6fe7188a5f17ff.bindPopup(popup_ab5ba2a00e8647918eebc95c7d241a96);

            
        
    
            var circle_marker_28278d2ca0ac4176882f0f41e034e87f = L.circleMarker(
                [44.7968805718968,-0.6018688664835501],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_c2762525682745ed9afc700bdb87eaa9 = L.popup({maxWidth: '300'});

            
                var html_e8d8a69547c94905aa1f3541733773e6 = $('<div id="html_e8d8a69547c94905aa1f3541733773e6" style="width: 100.0%; height: 100.0%;">Ecole de Management</div>')[0];
                popup_c2762525682745ed9afc700bdb87eaa9.setContent(html_e8d8a69547c94905aa1f3541733773e6);
            

            circle_marker_28278d2ca0ac4176882f0f41e034e87f.bindPopup(popup_c2762525682745ed9afc700bdb87eaa9);

            
        
    
            var circle_marker_202fb0c2cb334e2ca6d7d90e9d16e0fd = L.circleMarker(
                [44.8452123554279,-0.5920629394044891],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a4d0d3be2b914dc4b41de61aebce166a = L.popup({maxWidth: '300'});

            
                var html_e06b3141bccd4de1a70cbed46c1dc3b0 = $('<div id="html_e06b3141bccd4de1a70cbed46c1dc3b0" style="width: 100.0%; height: 100.0%;">Rue de la Croix Blanche</div>')[0];
                popup_a4d0d3be2b914dc4b41de61aebce166a.setContent(html_e06b3141bccd4de1a70cbed46c1dc3b0);
            

            circle_marker_202fb0c2cb334e2ca6d7d90e9d16e0fd.bindPopup(popup_a4d0d3be2b914dc4b41de61aebce166a);

            
        
    
            var circle_marker_9e94361f36234c24b977a40e96131ab9 = L.circleMarker(
                [44.8446454582444,-0.577447981270094],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_d6a5e5cf80634c3eb8f2418e91394f7f = L.popup({maxWidth: '300'});

            
                var html_b0c24fcf5f2f4a58b13ca426dbad289f = $('<div id="html_b0c24fcf5f2f4a58b13ca426dbad289f" style="width: 100.0%; height: 100.0%;">Place Tourny</div>')[0];
                popup_d6a5e5cf80634c3eb8f2418e91394f7f.setContent(html_b0c24fcf5f2f4a58b13ca426dbad289f);
            

            circle_marker_9e94361f36234c24b977a40e96131ab9.bindPopup(popup_d6a5e5cf80634c3eb8f2418e91394f7f);

            
        
    
            var circle_marker_235fb0e6ef9545e0ab32bb46776b76c2 = L.circleMarker(
                [44.834828248887206,-0.580185208518229],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_5bfd362ca20d45ccb7da3af1230aebcf = L.popup({maxWidth: '300'});

            
                var html_eff0c53c03ae431bac68a5af26988ac5 = $('<div id="html_eff0c53c03ae431bac68a5af26988ac5" style="width: 100.0%; height: 100.0%;">République</div>')[0];
                popup_5bfd362ca20d45ccb7da3af1230aebcf.setContent(html_eff0c53c03ae431bac68a5af26988ac5);
            

            circle_marker_235fb0e6ef9545e0ab32bb46776b76c2.bindPopup(popup_5bfd362ca20d45ccb7da3af1230aebcf);

            
        
    
            var circle_marker_730c5ac6004347b0974e0b76bc9cc4f1 = L.circleMarker(
                [44.793131985816096,-0.6054743774272769],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_26fc064e2fbe4008b4a47f98d010b41f = L.popup({maxWidth: '300'});

            
                var html_86ee19c4c7974b89a7ed287b136709ed = $('<div id="html_86ee19c4c7974b89a7ed287b136709ed" style="width: 100.0%; height: 100.0%;">Compostelle</div>')[0];
                popup_26fc064e2fbe4008b4a47f98d010b41f.setContent(html_86ee19c4c7974b89a7ed287b136709ed);
            

            circle_marker_730c5ac6004347b0974e0b76bc9cc4f1.bindPopup(popup_26fc064e2fbe4008b4a47f98d010b41f);

            
        
    
            var circle_marker_3c3d126555a645c0aa73be4ca295f761 = L.circleMarker(
                [44.8262833624718,-0.5724843902242801],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_aa6492df4a39459c977783be2f32e6ec = L.popup({maxWidth: '300'});

            
                var html_2eb7deb83c1742c1adcbecaeda4d12e1 = $('<div id="html_2eb7deb83c1742c1adcbecaeda4d12e1" style="width: 100.0%; height: 100.0%;">Cours de la Somme</div>')[0];
                popup_aa6492df4a39459c977783be2f32e6ec.setContent(html_2eb7deb83c1742c1adcbecaeda4d12e1);
            

            circle_marker_3c3d126555a645c0aa73be4ca295f761.bindPopup(popup_aa6492df4a39459c977783be2f32e6ec);

            
        
    
            var circle_marker_02ca6db6b51b4910b66156e4cf338078 = L.circleMarker(
                [44.8203170209881,-0.571854647158152],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_45f3ba426eeb41adb114b76170dc6d48 = L.popup({maxWidth: '300'});

            
                var html_71f416f50d66464ea2b569d281ae66d4 = $('<div id="html_71f416f50d66464ea2b569d281ae66d4" style="width: 100.0%; height: 100.0%;">Nansouty</div>')[0];
                popup_45f3ba426eeb41adb114b76170dc6d48.setContent(html_71f416f50d66464ea2b569d281ae66d4);
            

            circle_marker_02ca6db6b51b4910b66156e4cf338078.bindPopup(popup_45f3ba426eeb41adb114b76170dc6d48);

            
        
    
            var circle_marker_d137ce64f45d4363862fd95cd7f026c5 = L.circleMarker(
                [44.85170753450279,-0.574271942562023],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_89644cfff7f94c0f91f2f75c722ecba1 = L.popup({maxWidth: '300'});

            
                var html_2e0fd505b05f455aaae11e31bf5b4e86 = $('<div id="html_2e0fd505b05f455aaae11e31bf5b4e86" style="width: 100.0%; height: 100.0%;">Place Paul Doumer</div>')[0];
                popup_89644cfff7f94c0f91f2f75c722ecba1.setContent(html_2e0fd505b05f455aaae11e31bf5b4e86);
            

            circle_marker_d137ce64f45d4363862fd95cd7f026c5.bindPopup(popup_89644cfff7f94c0f91f2f75c722ecba1);

            
        
    
            var circle_marker_3d1d998830ff44fdb4527e83bda7964d = L.circleMarker(
                [44.8500457661933,-0.5820039119777061],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_871d88d7acd44f448b17083a661844dc = L.popup({maxWidth: '300'});

            
                var html_c116a12e0c224f76b362f366514faacb = $('<div id="html_c116a12e0c224f76b362f366514faacb" style="width: 100.0%; height: 100.0%;">Place Longchamps</div>')[0];
                popup_871d88d7acd44f448b17083a661844dc.setContent(html_c116a12e0c224f76b362f366514faacb);
            

            circle_marker_3d1d998830ff44fdb4527e83bda7964d.bindPopup(popup_871d88d7acd44f448b17083a661844dc);

            
        
    
            var circle_marker_ad183e84619a4d77a2da609048c060fa = L.circleMarker(
                [44.80445898386721,-0.632668180740745],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_1e4f2c66adaa48cb82d4fd94f1b81708 = L.popup({maxWidth: '300'});

            
                var html_109e14c2be4b4056a4ca82a174a4964e = $('<div id="html_109e14c2be4b4056a4ca82a174a4964e" style="width: 100.0%; height: 100.0%;">Pessac Centre</div>')[0];
                popup_1e4f2c66adaa48cb82d4fd94f1b81708.setContent(html_109e14c2be4b4056a4ca82a174a4964e);
            

            circle_marker_ad183e84619a4d77a2da609048c060fa.bindPopup(popup_1e4f2c66adaa48cb82d4fd94f1b81708);

            
        
    
            var circle_marker_3c95d75cc7c94c4c9614206e3a8a352b = L.circleMarker(
                [44.792962631100906,-0.6463612905802328],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_2129529fc84a4c948a7433b704ceb5ff = L.popup({maxWidth: '300'});

            
                var html_af5586f0b1334c96b3feeddb2a6f03e8 = $('<div id="html_af5586f0b1334c96b3feeddb2a6f03e8" style="width: 100.0%; height: 100.0%;">Châtaigneraie</div>')[0];
                popup_2129529fc84a4c948a7433b704ceb5ff.setContent(html_af5586f0b1334c96b3feeddb2a6f03e8);
            

            circle_marker_3c95d75cc7c94c4c9614206e3a8a352b.bindPopup(popup_2129529fc84a4c948a7433b704ceb5ff);

            
        
    
            var circle_marker_a1b43c09718e4bc1afd821a595f2bea5 = L.circleMarker(
                [44.7821503214335,-0.5661566288543921],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_31ccab84f68248599916f20918a828de = L.popup({maxWidth: '300'});

            
                var html_d88a628d41924f9ca21b4b86b7fe57fa = $('<div id="html_d88a628d41924f9ca21b4b86b7fe57fa" style="width: 100.0%; height: 100.0%;">Pont de la Maye (retirée le 19 novembre 2015 en raison des travaux d&apos;extension du tram C)</div>')[0];
                popup_31ccab84f68248599916f20918a828de.setContent(html_d88a628d41924f9ca21b4b86b7fe57fa);
            

            circle_marker_a1b43c09718e4bc1afd821a595f2bea5.bindPopup(popup_31ccab84f68248599916f20918a828de);

            
        
    
            var circle_marker_faa37d394dfd4cd682410bdcca010fcb = L.circleMarker(
                [44.82670073497211,-0.549830125548165],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_97ab634e7023496a8dc21b2adb329fbd = L.popup({maxWidth: '300'});

            
                var html_fd90a546b0ce49138f42f2d2dd924d8f = $('<div id="html_fd90a546b0ce49138f42f2d2dd924d8f" style="width: 100.0%; height: 100.0%;">Quai de Paludate</div>')[0];
                popup_97ab634e7023496a8dc21b2adb329fbd.setContent(html_fd90a546b0ce49138f42f2d2dd924d8f);
            

            circle_marker_faa37d394dfd4cd682410bdcca010fcb.bindPopup(popup_97ab634e7023496a8dc21b2adb329fbd);

            
        
    
            var circle_marker_ec6cbe04b3784020a1761e0f8b11c616 = L.circleMarker(
                [44.84819450642961,-0.591969726529606],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_449cd7e936da426ca5d43bc6bcf3514b = L.popup({maxWidth: '300'});

            
                var html_2e14b9bba0a34253bc5311c9ad1f5612 = $('<div id="html_2e14b9bba0a34253bc5311c9ad1f5612" style="width: 100.0%; height: 100.0%;">Dubreuil Turenne</div>')[0];
                popup_449cd7e936da426ca5d43bc6bcf3514b.setContent(html_2e14b9bba0a34253bc5311c9ad1f5612);
            

            circle_marker_ec6cbe04b3784020a1761e0f8b11c616.bindPopup(popup_449cd7e936da426ca5d43bc6bcf3514b);

            
        
    
            var circle_marker_e40c211a35ad431199b6c1b8c426e30e = L.circleMarker(
                [44.8383834550559,-0.616893430308816],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_3a89315ce8c341449e39cab5ce386a27 = L.popup({maxWidth: '300'});

            
                var html_decc9547f5dd4f45961ebc24cc5ff483 = $('<div id="html_decc9547f5dd4f45961ebc24cc5ff483" style="width: 100.0%; height: 100.0%;">Place Mondésir</div>')[0];
                popup_3a89315ce8c341449e39cab5ce386a27.setContent(html_decc9547f5dd4f45961ebc24cc5ff483);
            

            circle_marker_e40c211a35ad431199b6c1b8c426e30e.bindPopup(popup_3a89315ce8c341449e39cab5ce386a27);

            
        
    
            var circle_marker_20dc695683234f3e8bc14a5769aa3e45 = L.circleMarker(
                [44.85326197252461,-0.591721825634013],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_fea8f1e73a0b411ab26d3353f47ee220 = L.popup({maxWidth: '300'});

            
                var html_2cdd8c98e40b493aad4902486ab4df38 = $('<div id="html_2cdd8c98e40b493aad4902486ab4df38" style="width: 100.0%; height: 100.0%;">Barrière du Médoc</div>')[0];
                popup_fea8f1e73a0b411ab26d3353f47ee220.setContent(html_2cdd8c98e40b493aad4902486ab4df38);
            

            circle_marker_20dc695683234f3e8bc14a5769aa3e45.bindPopup(popup_fea8f1e73a0b411ab26d3353f47ee220);

            
        
    
            var circle_marker_0c788a0463dc4b0ab61e9850af857d68 = L.circleMarker(
                [44.83478298225079,-0.5646641958573271],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_2bebe489b5da41618ed9caae54e1c583 = L.popup({maxWidth: '300'});

            
                var html_306f8fea594e49f3ac44f9d1dd232432 = $('<div id="html_306f8fea594e49f3ac44f9d1dd232432" style="width: 100.0%; height: 100.0%;">Place St Michel</div>')[0];
                popup_2bebe489b5da41618ed9caae54e1c583.setContent(html_306f8fea594e49f3ac44f9d1dd232432);
            

            circle_marker_0c788a0463dc4b0ab61e9850af857d68.bindPopup(popup_2bebe489b5da41618ed9caae54e1c583);

            
        
    
            var circle_marker_1d8c9424ca6b40ba863b0468d6518be4 = L.circleMarker(
                [44.8310036470032,-0.5873420830366],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_59a27c1b0fe74d47aa969a59c834b075 = L.popup({maxWidth: '300'});

            
                var html_e8cfabbfbd1f480caafc098e4156a2f9 = $('<div id="html_e8cfabbfbd1f480caafc098e4156a2f9" style="width: 100.0%; height: 100.0%;">François de Sourdis</div>')[0];
                popup_59a27c1b0fe74d47aa969a59c834b075.setContent(html_e8cfabbfbd1f480caafc098e4156a2f9);
            

            circle_marker_1d8c9424ca6b40ba863b0468d6518be4.bindPopup(popup_59a27c1b0fe74d47aa969a59c834b075);

            
        
    
            var circle_marker_1dcc3df883544aafb177d86ea7db9c45 = L.circleMarker(
                [44.773526429667996,-0.614317927760213],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_5f828124d273414abab01d70a7d75ab5 = L.popup({maxWidth: '300'});

            
                var html_0018e747c02c4ad5b939d0c8eb916e0b = $('<div id="html_0018e747c02c4ad5b939d0c8eb916e0b" style="width: 100.0%; height: 100.0%;">Place Bernard Roumegoux</div>')[0];
                popup_5f828124d273414abab01d70a7d75ab5.setContent(html_0018e747c02c4ad5b939d0c8eb916e0b);
            

            circle_marker_1dcc3df883544aafb177d86ea7db9c45.bindPopup(popup_5f828124d273414abab01d70a7d75ab5);

            
        
    
            var circle_marker_83b8db0d993140a684b10c780df525b4 = L.circleMarker(
                [44.793087635670204,-0.6576450248561739],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_2d7fedb74dfe4b1b870f8a35b77fe7f3 = L.popup({maxWidth: '300'});

            
                var html_efe8ecc8fabb4b23b16fc9097d5ced41 = $('<div id="html_efe8ecc8fabb4b23b16fc9097d5ced41" style="width: 100.0%; height: 100.0%;">Gare Pessac Alouette</div>')[0];
                popup_2d7fedb74dfe4b1b870f8a35b77fe7f3.setContent(html_efe8ecc8fabb4b23b16fc9097d5ced41);
            

            circle_marker_83b8db0d993140a684b10c780df525b4.bindPopup(popup_2d7fedb74dfe4b1b870f8a35b77fe7f3);

            
        
    
            var circle_marker_c3eb9bd9f1c5456692070614a82ddb22 = L.circleMarker(
                [44.8416130882014,-0.646520450493249],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_58647be30d424948a4100c41b4c9f6e9 = L.popup({maxWidth: '300'});

            
                var html_ecb4432115ce45c3986e840dcbef1896 = $('<div id="html_ecb4432115ce45c3986e840dcbef1896" style="width: 100.0%; height: 100.0%;">Mérignac Centre</div>')[0];
                popup_58647be30d424948a4100c41b4c9f6e9.setContent(html_ecb4432115ce45c3986e840dcbef1896);
            

            circle_marker_c3eb9bd9f1c5456692070614a82ddb22.bindPopup(popup_58647be30d424948a4100c41b4c9f6e9);

            
        
    
            var circle_marker_5e77cc48d85b4716af530a89bdb0a815 = L.circleMarker(
                [44.815026281156996,-0.5509087022557521],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_efb126281ae745f199e361861f91fc29 = L.popup({maxWidth: '300'});

            
                var html_1120050e9f964bbfb7e24de42f18774f = $('<div id="html_1120050e9f964bbfb7e24de42f18774f" style="width: 100.0%; height: 100.0%;">Terres Neuves</div>')[0];
                popup_efb126281ae745f199e361861f91fc29.setContent(html_1120050e9f964bbfb7e24de42f18774f);
            

            circle_marker_5e77cc48d85b4716af530a89bdb0a815.bindPopup(popup_efb126281ae745f199e361861f91fc29);

            
        
    
            var circle_marker_8c1bf5a6350f422a8cdf6e01e51185b3 = L.circleMarker(
                [44.850096272665795,-0.5855818728558331],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_5774eeb6101c4b84bccc518fa6155b64 = L.popup({maxWidth: '300'});

            
                var html_1664a59733d5479e94ca953595f4ccab = $('<div id="html_1664a59733d5479e94ca953595f4ccab" style="width: 100.0%; height: 100.0%;">Place Marie Brizard (supprimée le 11 mars 2016 en raison des travaux tram D)</div>')[0];
                popup_5774eeb6101c4b84bccc518fa6155b64.setContent(html_1664a59733d5479e94ca953595f4ccab);
            

            circle_marker_8c1bf5a6350f422a8cdf6e01e51185b3.bindPopup(popup_5774eeb6101c4b84bccc518fa6155b64);

            
        
    
            var circle_marker_7b2dcadb607b4b8bafa47e32f6a43b0f = L.circleMarker(
                [44.8324154122993,-0.645936661160173],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_36d26729e5054cc98f7af0746ae8ae7b = L.popup({maxWidth: '300'});

            
                var html_1ba8dce4f3c14532a9d2dc58fd8c4425 = $('<div id="html_1ba8dce4f3c14532a9d2dc58fd8c4425" style="width: 100.0%; height: 100.0%;">Quatre Chemins</div>')[0];
                popup_36d26729e5054cc98f7af0746ae8ae7b.setContent(html_1ba8dce4f3c14532a9d2dc58fd8c4425);
            

            circle_marker_7b2dcadb607b4b8bafa47e32f6a43b0f.bindPopup(popup_36d26729e5054cc98f7af0746ae8ae7b);

            
        
    
            var circle_marker_b7972a38f581430fb3afee772de95e2a = L.circleMarker(
                [44.85848777302589,-0.569659370398398],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_5b85a2d9d45f44bb9eb021129efe2fb6 = L.popup({maxWidth: '300'});

            
                var html_1773c39bb8544da3b24e628f6b8a2089 = $('<div id="html_1773c39bb8544da3b24e628f6b8a2089" style="width: 100.0%; height: 100.0%;">St Louis Médoc</div>')[0];
                popup_5b85a2d9d45f44bb9eb021129efe2fb6.setContent(html_1773c39bb8544da3b24e628f6b8a2089);
            

            circle_marker_b7972a38f581430fb3afee772de95e2a.bindPopup(popup_5b85a2d9d45f44bb9eb021129efe2fb6);

            
        
    
            var circle_marker_db136d1b0a8b4cf6a67ec3e2931e8c5a = L.circleMarker(
                [44.8298604334843,-0.603509959107344],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_5c2187c8655d4081bb6d1e9298e57020 = L.popup({maxWidth: '300'});

            
                var html_b9c7317f4dee4360bc37ebdd0de908f7 = $('<div id="html_b9c7317f4dee4360bc37ebdd0de908f7" style="width: 100.0%; height: 100.0%;">Hôpital Pellegrin</div>')[0];
                popup_5c2187c8655d4081bb6d1e9298e57020.setContent(html_b9c7317f4dee4360bc37ebdd0de908f7);
            

            circle_marker_db136d1b0a8b4cf6a67ec3e2931e8c5a.bindPopup(popup_5c2187c8655d4081bb6d1e9298e57020);

            
        
    
            var circle_marker_2782eff3029e46edbe99ab0f4964ccbe = L.circleMarker(
                [44.862601640429105,-0.5757641511520151],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_0b626681c0724ab4bd767ce3b779fedb = L.popup({maxWidth: '300'});

            
                var html_ee58b433e07349f4b80a77d556ab311e = $('<div id="html_ee58b433e07349f4b80a77d556ab311e" style="width: 100.0%; height: 100.0%;">Grand Parc</div>')[0];
                popup_0b626681c0724ab4bd767ce3b779fedb.setContent(html_ee58b433e07349f4b80a77d556ab311e);
            

            circle_marker_2782eff3029e46edbe99ab0f4964ccbe.bindPopup(popup_0b626681c0724ab4bd767ce3b779fedb);

            
        
    
            var circle_marker_8f7ce1e1d74243efb349fd0d98abf13a = L.circleMarker(
                [44.8464146714062,-0.5801979888173471],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_0adfa7513930478d93e32eb9bcf3ab70 = L.popup({maxWidth: '300'});

            
                var html_360a58e51209437c9ca87c9693845295 = $('<div id="html_360a58e51209437c9ca87c9693845295" style="width: 100.0%; height: 100.0%;">Place Charles Gruet</div>')[0];
                popup_0adfa7513930478d93e32eb9bcf3ab70.setContent(html_360a58e51209437c9ca87c9693845295);
            

            circle_marker_8f7ce1e1d74243efb349fd0d98abf13a.bindPopup(popup_0adfa7513930478d93e32eb9bcf3ab70);

            
        
    
            var circle_marker_9ea78533d96d4112b33f73ef5b79ebb2 = L.circleMarker(
                [44.830636168306796,-0.573213220578234],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_ea94b35a86fb48cbb04b59b45e0b5a64 = L.popup({maxWidth: '300'});

            
                var html_ef23c9152a514e289aa8caa37615ab20 = $('<div id="html_ef23c9152a514e289aa8caa37615ab20" style="width: 100.0%; height: 100.0%;">Place de la Victoire</div>')[0];
                popup_ea94b35a86fb48cbb04b59b45e0b5a64.setContent(html_ef23c9152a514e289aa8caa37615ab20);
            

            circle_marker_9ea78533d96d4112b33f73ef5b79ebb2.bindPopup(popup_ea94b35a86fb48cbb04b59b45e0b5a64);

            
        
    
            var circle_marker_7b33654391d94297a762ed6dbbcf56dd = L.circleMarker(
                [44.8282573643245,-0.5623690058149289],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_2b73f247f8674ffb9acf9c175d51c6f8 = L.popup({maxWidth: '300'});

            
                var html_76a9664d4d7c4efbb46d9e35da79e55b = $('<div id="html_76a9664d4d7c4efbb46d9e35da79e55b" style="width: 100.0%; height: 100.0%;">Place André Meunier</div>')[0];
                popup_2b73f247f8674ffb9acf9c175d51c6f8.setContent(html_76a9664d4d7c4efbb46d9e35da79e55b);
            

            circle_marker_7b33654391d94297a762ed6dbbcf56dd.bindPopup(popup_2b73f247f8674ffb9acf9c175d51c6f8);

            
        
    
            var circle_marker_7816c18ddfc94d41a2c1fda6903d1a3b = L.circleMarker(
                [44.8511475804554,-0.608609297008604],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_5978c6f4d22d48d7bd36361897cd4c3b = L.popup({maxWidth: '300'});

            
                var html_9dc0638701b14c71b3c1d574a021e45d = $('<div id="html_9dc0638701b14c71b3c1d574a021e45d" style="width: 100.0%; height: 100.0%;">Grand Lebrun</div>')[0];
                popup_5978c6f4d22d48d7bd36361897cd4c3b.setContent(html_9dc0638701b14c71b3c1d574a021e45d);
            

            circle_marker_7816c18ddfc94d41a2c1fda6903d1a3b.bindPopup(popup_5978c6f4d22d48d7bd36361897cd4c3b);

            
        
    
            var circle_marker_a62bb3fd3be046feb33001e719edc0eb = L.circleMarker(
                [44.7938887827872,-0.63505075796394],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_41fe8c1ce2c4440b8e3f431a0ac98166 = L.popup({maxWidth: '300'});

            
                var html_54b253c14a964c48ad95e73fcbe96f3a = $('<div id="html_54b253c14a964c48ad95e73fcbe96f3a" style="width: 100.0%; height: 100.0%;">Bougnard</div>')[0];
                popup_41fe8c1ce2c4440b8e3f431a0ac98166.setContent(html_54b253c14a964c48ad95e73fcbe96f3a);
            

            circle_marker_a62bb3fd3be046feb33001e719edc0eb.bindPopup(popup_41fe8c1ce2c4440b8e3f431a0ac98166);

            
        
    
            var circle_marker_498d57d8aef04f1fac1cf7fe5465ce51 = L.circleMarker(
                [44.843774390805294,-0.5575143358321251],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_ecb388410cad4847ab47852a3a835568 = L.popup({maxWidth: '300'});

            
                var html_d04ccd0919c24cbebac91940c54e52d6 = $('<div id="html_d04ccd0919c24cbebac91940c54e52d6" style="width: 100.0%; height: 100.0%;">Allée de Serr Abadie</div>')[0];
                popup_ecb388410cad4847ab47852a3a835568.setContent(html_d04ccd0919c24cbebac91940c54e52d6);
            

            circle_marker_498d57d8aef04f1fac1cf7fe5465ce51.bindPopup(popup_ecb388410cad4847ab47852a3a835568);

            
        
    
            var circle_marker_9082405a9a084e66b57f8196f0fd3c00 = L.circleMarker(
                [44.841221945764396,-0.5756676590783549],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_2743ec289ba04d54a537c8cee9ec1065 = L.popup({maxWidth: '300'});

            
                var html_94b6ca0b9d174443aadab5b361547a05 = $('<div id="html_94b6ca0b9d174443aadab5b361547a05" style="width: 100.0%; height: 100.0%;">Puy Paulin</div>')[0];
                popup_2743ec289ba04d54a537c8cee9ec1065.setContent(html_94b6ca0b9d174443aadab5b361547a05);
            

            circle_marker_9082405a9a084e66b57f8196f0fd3c00.bindPopup(popup_2743ec289ba04d54a537c8cee9ec1065);

            
        
    
            var circle_marker_4d32d790dc9a4cdf999930dce921e2c5 = L.circleMarker(
                [44.8408779999531,-0.59104139136587],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_e05a43413baf4971a025a4159b57aefc = L.popup({maxWidth: '300'});

            
                var html_6399eeaece5946bfb2eca4e985e91c98 = $('<div id="html_6399eeaece5946bfb2eca4e985e91c98" style="width: 100.0%; height: 100.0%;">Place Tartas</div>')[0];
                popup_e05a43413baf4971a025a4159b57aefc.setContent(html_6399eeaece5946bfb2eca4e985e91c98);
            

            circle_marker_4d32d790dc9a4cdf999930dce921e2c5.bindPopup(popup_e05a43413baf4971a025a4159b57aefc);

            
        
    
            var circle_marker_714b212a87524000be971695ef6f5847 = L.circleMarker(
                [44.8343893623561,-0.58812343829027],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_86009e69d6f04f5d8612148c7f9a3e4f = L.popup({maxWidth: '300'});

            
                var html_da0f0b7c94a246028cadb57804925253 = $('<div id="html_da0f0b7c94a246028cadb57804925253" style="width: 100.0%; height: 100.0%;">Patinoire</div>')[0];
                popup_86009e69d6f04f5d8612148c7f9a3e4f.setContent(html_da0f0b7c94a246028cadb57804925253);
            

            circle_marker_714b212a87524000be971695ef6f5847.bindPopup(popup_86009e69d6f04f5d8612148c7f9a3e4f);

            
        
    
            var circle_marker_58e4a1554061400f9643299a2735fd0f = L.circleMarker(
                [44.833033292653,-0.592892735150062],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_06801ad2f8034b9eae2499471a68ff02 = L.popup({maxWidth: '300'});

            
                var html_f1941746e8cb4cd0b920587021091bb3 = $('<div id="html_f1941746e8cb4cd0b920587021091bb3" style="width: 100.0%; height: 100.0%;">Gaviniès</div>')[0];
                popup_06801ad2f8034b9eae2499471a68ff02.setContent(html_f1941746e8cb4cd0b920587021091bb3);
            

            circle_marker_58e4a1554061400f9643299a2735fd0f.bindPopup(popup_06801ad2f8034b9eae2499471a68ff02);

            
        
    
            var circle_marker_653bffffed8a4e6399b1ac6a4c80d135 = L.circleMarker(
                [44.836058835347096,-0.575427382768788],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_937989c7d7df44569ab38b9237017fd3 = L.popup({maxWidth: '300'});

            
                var html_c41a1aefb60b49b4814f19a70e6c0e71 = $('<div id="html_c41a1aefb60b49b4814f19a70e6c0e71" style="width: 100.0%; height: 100.0%;">Musée d&apos;Aquitaine</div>')[0];
                popup_937989c7d7df44569ab38b9237017fd3.setContent(html_c41a1aefb60b49b4814f19a70e6c0e71);
            

            circle_marker_653bffffed8a4e6399b1ac6a4c80d135.bindPopup(popup_937989c7d7df44569ab38b9237017fd3);

            
        
    
            var circle_marker_2b12258c5c9542b4b2e4630bc66873a7 = L.circleMarker(
                [44.837108327472706,-0.5727796298560601],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a68915091cab40f3962aa75600dd11b9 = L.popup({maxWidth: '300'});

            
                var html_5ce80e0edbed4385b8b046a46fd9dbfb = $('<div id="html_5ce80e0edbed4385b8b046a46fd9dbfb" style="width: 100.0%; height: 100.0%;">St Paul</div>')[0];
                popup_a68915091cab40f3962aa75600dd11b9.setContent(html_5ce80e0edbed4385b8b046a46fd9dbfb);
            

            circle_marker_2b12258c5c9542b4b2e4630bc66873a7.bindPopup(popup_a68915091cab40f3962aa75600dd11b9);

            
        
    
            var circle_marker_d1ddc7cf9f914483920044cd6f43a82a = L.circleMarker(
                [44.859988515489995,-0.5887606764126709],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_26c215e7af2f4de4baacb1aee7fa2cf6 = L.popup({maxWidth: '300'});

            
                var html_0d852db508554890b91b78807bc62486 = $('<div id="html_0d852db508554890b91b78807bc62486" style="width: 100.0%; height: 100.0%;">Mandron Godard</div>')[0];
                popup_26c215e7af2f4de4baacb1aee7fa2cf6.setContent(html_0d852db508554890b91b78807bc62486);
            

            circle_marker_d1ddc7cf9f914483920044cd6f43a82a.bindPopup(popup_26c215e7af2f4de4baacb1aee7fa2cf6);

            
        
    
            var circle_marker_975c8546fc504c2fb9bf5764740a1375 = L.circleMarker(
                [44.849782481805605,-0.570243742432327],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_3475c60728a443d3baa7298e00f87318 = L.popup({maxWidth: '300'});

            
                var html_8a425feef93449e6bd496655914ef141 = $('<div id="html_8a425feef93449e6bd496655914ef141" style="width: 100.0%; height: 100.0%;">CAPC</div>')[0];
                popup_3475c60728a443d3baa7298e00f87318.setContent(html_8a425feef93449e6bd496655914ef141);
            

            circle_marker_975c8546fc504c2fb9bf5764740a1375.bindPopup(popup_3475c60728a443d3baa7298e00f87318);

            
        
    
            var circle_marker_e5824d10ff4d45efb7b74489cbd9d425 = L.circleMarker(
                [44.807704530607005,-0.5482081246107151],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a4561488e1a147b3a179cae75c7c0797 = L.popup({maxWidth: '300'});

            
                var html_d0be13ea4f4f40f199d691f797296a56 = $('<div id="html_d0be13ea4f4f40f199d691f797296a56" style="width: 100.0%; height: 100.0%;">Place du 14 juillet</div>')[0];
                popup_a4561488e1a147b3a179cae75c7c0797.setContent(html_d0be13ea4f4f40f199d691f797296a56);
            

            circle_marker_e5824d10ff4d45efb7b74489cbd9d425.bindPopup(popup_a4561488e1a147b3a179cae75c7c0797);

            
        
    
            var circle_marker_af44ae8d4f2e47ac86ba444f20e2bb25 = L.circleMarker(
                [44.840303327410496,-0.5691361390534],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_6d27c3588e8d44deb48b72e373ea186b = L.popup({maxWidth: '300'});

            
                var html_72ef88bd577442f1aaf6a5e24a7a61b4 = $('<div id="html_72ef88bd577442f1aaf6a5e24a7a61b4" style="width: 100.0%; height: 100.0%;">Place de la Bourse</div>')[0];
                popup_6d27c3588e8d44deb48b72e373ea186b.setContent(html_72ef88bd577442f1aaf6a5e24a7a61b4);
            

            circle_marker_af44ae8d4f2e47ac86ba444f20e2bb25.bindPopup(popup_6d27c3588e8d44deb48b72e373ea186b);

            
        
    
            var circle_marker_9d72cbd4fa3744c8bab103166c1c64c5 = L.circleMarker(
                [44.8600704540937,-0.5544677944219699],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_6ca5833f4d2f457e846e423e558d3cae = L.popup({maxWidth: '300'});

            
                var html_affe97e261994bbc8b9fffa1f9b4a97a = $('<div id="html_affe97e261994bbc8b9fffa1f9b4a97a" style="width: 100.0%; height: 100.0%;">Bassins à flot</div>')[0];
                popup_6ca5833f4d2f457e846e423e558d3cae.setContent(html_affe97e261994bbc8b9fffa1f9b4a97a);
            

            circle_marker_9d72cbd4fa3744c8bab103166c1c64c5.bindPopup(popup_6ca5833f4d2f457e846e423e558d3cae);

            
        
    
            var circle_marker_1bf0e45b489a42c2b1b374c06d5ae488 = L.circleMarker(
                [44.857868173886004,-0.55808025998087],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_db477bde5a7e438e98e194317f9a6b45 = L.popup({maxWidth: '300'});

            
                var html_8e10883c78454dbaaae39eb290d031ad = $('<div id="html_8e10883c78454dbaaae39eb290d031ad" style="width: 100.0%; height: 100.0%;">Les Hangars</div>')[0];
                popup_db477bde5a7e438e98e194317f9a6b45.setContent(html_8e10883c78454dbaaae39eb290d031ad);
            

            circle_marker_1bf0e45b489a42c2b1b374c06d5ae488.bindPopup(popup_db477bde5a7e438e98e194317f9a6b45);

            
        
    
            var circle_marker_372c42d348d84e3e831c0172764b0fb3 = L.circleMarker(
                [44.8338711646842,-0.562710232600152],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_908d63d7c8c749b5a33ad318e08e32c0 = L.popup({maxWidth: '300'});

            
                var html_84fd44cfbff047328599bdbb2bbe8dc6 = $('<div id="html_84fd44cfbff047328599bdbb2bbe8dc6" style="width: 100.0%; height: 100.0%;">Parc des Sports</div>')[0];
                popup_908d63d7c8c749b5a33ad318e08e32c0.setContent(html_84fd44cfbff047328599bdbb2bbe8dc6);
            

            circle_marker_372c42d348d84e3e831c0172764b0fb3.bindPopup(popup_908d63d7c8c749b5a33ad318e08e32c0);

            
        
    
            var circle_marker_08a41876c9a64ce5a8101881e3b14ff1 = L.circleMarker(
                [44.8392607368341,-0.572034212881664],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_4ca75332c24444458279337826d89e01 = L.popup({maxWidth: '300'});

            
                var html_fec3514fc2754e60b298c401e4b65ea6 = $('<div id="html_fec3514fc2754e60b298c401e4b65ea6" style="width: 100.0%; height: 100.0%;">Camille Jullian</div>')[0];
                popup_4ca75332c24444458279337826d89e01.setContent(html_fec3514fc2754e60b298c401e4b65ea6);
            

            circle_marker_08a41876c9a64ce5a8101881e3b14ff1.bindPopup(popup_4ca75332c24444458279337826d89e01);

            
        
    
            var circle_marker_17fb54c322474d4498d5827f227d5c2d = L.circleMarker(
                [44.838868754385395,-0.574807040123963],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_5e61f9ba70fc4f2e930b085682e8ab10 = L.popup({maxWidth: '300'});

            
                var html_f8e1144886fb4adda40e36dec7f4ce07 = $('<div id="html_f8e1144886fb4adda40e36dec7f4ce07" style="width: 100.0%; height: 100.0%;">Place St Projet</div>')[0];
                popup_5e61f9ba70fc4f2e930b085682e8ab10.setContent(html_f8e1144886fb4adda40e36dec7f4ce07);
            

            circle_marker_17fb54c322474d4498d5827f227d5c2d.bindPopup(popup_5e61f9ba70fc4f2e930b085682e8ab10);

            
        
    
            var circle_marker_78db9f4fc70f453e8299c5e2a19639a6 = L.circleMarker(
                [44.822792955966605,-0.552970659841354],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_cdf99a5cf3b14266a6c927d093709785 = L.popup({maxWidth: '300'});

            
                var html_d99b8bab8c4c49b99cb15ea929a8bf19 = $('<div id="html_d99b8bab8c4c49b99cb15ea929a8bf19" style="width: 100.0%; height: 100.0%;">Belcier</div>')[0];
                popup_cdf99a5cf3b14266a6c927d093709785.setContent(html_d99b8bab8c4c49b99cb15ea929a8bf19);
            

            circle_marker_78db9f4fc70f453e8299c5e2a19639a6.bindPopup(popup_cdf99a5cf3b14266a6c927d093709785);

            
        
    
            var circle_marker_c3612b4606644e44ab03abdad7b4fe91 = L.circleMarker(
                [44.82789351594621,-0.5860458753106851],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_31ba2c4408444137b65e047b491fae4e = L.popup({maxWidth: '300'});

            
                var html_70fa36be32e54b7aa364b505c044fc67 = $('<div id="html_70fa36be32e54b7aa364b505c044fc67" style="width: 100.0%; height: 100.0%;">Magendie</div>')[0];
                popup_31ba2c4408444137b65e047b491fae4e.setContent(html_70fa36be32e54b7aa364b505c044fc67);
            

            circle_marker_c3612b4606644e44ab03abdad7b4fe91.bindPopup(popup_31ba2c4408444137b65e047b491fae4e);

            
        
    
            var circle_marker_c8ae18ba022144768feb40fcff2e1e20 = L.circleMarker(
                [44.85320907028171,-0.56726525454553],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_7624b664f41d4636aeba4fec68ca1337 = L.popup({maxWidth: '300'});

            
                var html_44fc05728b91474b886218212dae6692 = $('<div id="html_44fc05728b91474b886218212dae6692" style="width: 100.0%; height: 100.0%;">Chartrons</div>')[0];
                popup_7624b664f41d4636aeba4fec68ca1337.setContent(html_44fc05728b91474b886218212dae6692);
            

            circle_marker_c8ae18ba022144768feb40fcff2e1e20.bindPopup(popup_7624b664f41d4636aeba4fec68ca1337);

            
        
    
            var circle_marker_72e1ba033c2a4129a7b3dae8d5c8ab1c = L.circleMarker(
                [44.8275103799747,-0.575890929152947],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_1ff0c2cf29bd4cadbb70c970102d8943 = L.popup({maxWidth: '300'});

            
                var html_72327f9205574fb3bf76752f26003dff = $('<div id="html_72327f9205574fb3bf76752f26003dff" style="width: 100.0%; height: 100.0%;">St Nicolas</div>')[0];
                popup_1ff0c2cf29bd4cadbb70c970102d8943.setContent(html_72327f9205574fb3bf76752f26003dff);
            

            circle_marker_72e1ba033c2a4129a7b3dae8d5c8ab1c.bindPopup(popup_1ff0c2cf29bd4cadbb70c970102d8943);

            
        
    
            var circle_marker_59fea234e2cf49fe974a5728e13ca915 = L.circleMarker(
                [44.842997409923804,-0.5503284302991149],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_01078a0ff46440daad2213bc0145507e = L.popup({maxWidth: '300'});

            
                var html_4a8df3c1e136435fad9135c227665c44 = $('<div id="html_4a8df3c1e136435fad9135c227665c44" style="width: 100.0%; height: 100.0%;">Cours Le Rouzic</div>')[0];
                popup_01078a0ff46440daad2213bc0145507e.setContent(html_4a8df3c1e136435fad9135c227665c44);
            

            circle_marker_59fea234e2cf49fe974a5728e13ca915.bindPopup(popup_01078a0ff46440daad2213bc0145507e);

            
        
    
            var circle_marker_e16494b52e31412385de7c3ef1fd5a1e = L.circleMarker(
                [44.85260945908279,-0.599229535668728],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_c6d69b8417664c2c9eaa40e4ac567525 = L.popup({maxWidth: '300'});

            
                var html_c23352180be54cdda367fe068d0d35d9 = $('<div id="html_c23352180be54cdda367fe068d0d35d9" style="width: 100.0%; height: 100.0%;">Parc Bordelais</div>')[0];
                popup_c6d69b8417664c2c9eaa40e4ac567525.setContent(html_c23352180be54cdda367fe068d0d35d9);
            

            circle_marker_e16494b52e31412385de7c3ef1fd5a1e.bindPopup(popup_c6d69b8417664c2c9eaa40e4ac567525);

            
        
    
            var circle_marker_1b2d86b7bc9a4123884411c873778d3f = L.circleMarker(
                [44.824714650998004,-0.578146900858999],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_c2b263af3a684cb6bbaa39eef9ac8f09 = L.popup({maxWidth: '300'});

            
                var html_b003f1c1b9d1452aaf2cba98f9a89ce7 = $('<div id="html_b003f1c1b9d1452aaf2cba98f9a89ce7" style="width: 100.0%; height: 100.0%;">Bergonié</div>')[0];
                popup_c2b263af3a684cb6bbaa39eef9ac8f09.setContent(html_b003f1c1b9d1452aaf2cba98f9a89ce7);
            

            circle_marker_1b2d86b7bc9a4123884411c873778d3f.bindPopup(popup_c2b263af3a684cb6bbaa39eef9ac8f09);

            
        
    
            var circle_marker_40a7da154fe643fba1772c2351ab6db7 = L.circleMarker(
                [44.854617894494496,-0.574346916974448],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_33593c5beef547ad86af692531fb6837 = L.popup({maxWidth: '300'});

            
                var html_fcbe6a92827a4b2c9f0ccec6071c77c3 = $('<div id="html_fcbe6a92827a4b2c9f0ccec6071c77c3" style="width: 100.0%; height: 100.0%;">Camille Godard</div>')[0];
                popup_33593c5beef547ad86af692531fb6837.setContent(html_fcbe6a92827a4b2c9f0ccec6071c77c3);
            

            circle_marker_40a7da154fe643fba1772c2351ab6db7.bindPopup(popup_33593c5beef547ad86af692531fb6837);

            
        
    
            var circle_marker_32c6533eee444f5792149645cefb5553 = L.circleMarker(
                [44.873691458564,-0.6775348189370001],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_346ce8a8dcba4d83a87fbb7db0a08502 = L.popup({maxWidth: '300'});

            
                var html_2108d1d36b724581bc0517ee7e5fee86 = $('<div id="html_2108d1d36b724581bc0517ee7e5fee86" style="width: 100.0%; height: 100.0%;">Mairie du Haillan</div>')[0];
                popup_346ce8a8dcba4d83a87fbb7db0a08502.setContent(html_2108d1d36b724581bc0517ee7e5fee86);
            

            circle_marker_32c6533eee444f5792149645cefb5553.bindPopup(popup_346ce8a8dcba4d83a87fbb7db0a08502);

            
        
    
            var circle_marker_4dab6e6478014056866d6e94782dc3a5 = L.circleMarker(
                [44.862559315754794,-0.583247050910222],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_67009b4f86c14ff78b126f164e67cd0e = L.popup({maxWidth: '300'});

            
                var html_a4dab87e6bb74847ad7bfe907ff4c0c4 = $('<div id="html_a4dab87e6bb74847ad7bfe907ff4c0c4" style="width: 100.0%; height: 100.0%;">Place Ampère</div>')[0];
                popup_67009b4f86c14ff78b126f164e67cd0e.setContent(html_a4dab87e6bb74847ad7bfe907ff4c0c4);
            

            circle_marker_4dab6e6478014056866d6e94782dc3a5.bindPopup(popup_67009b4f86c14ff78b126f164e67cd0e);

            
        
    
            var circle_marker_a5c724e75214476f8ed301f12a099123 = L.circleMarker(
                [44.883957964802896,-0.649921125977042],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_389090b00bab46c6833914a78ae39545 = L.popup({maxWidth: '300'});

            
                var html_f1d7adbf84924039ad9f8c968b6ff1af = $('<div id="html_f1d7adbf84924039ad9f8c968b6ff1af" style="width: 100.0%; height: 100.0%;">Eysines Centre</div>')[0];
                popup_389090b00bab46c6833914a78ae39545.setContent(html_f1d7adbf84924039ad9f8c968b6ff1af);
            

            circle_marker_a5c724e75214476f8ed301f12a099123.bindPopup(popup_389090b00bab46c6833914a78ae39545);

            
        
    
            var circle_marker_9faed6f773e440bf87f555f4cd5e3bb8 = L.circleMarker(
                [44.8317574259134,-0.598683495255674],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_8a7ffeffba694dd99b3666ba76f2f31d = L.popup({maxWidth: '300'});

            
                var html_01868b484e164a7c9a381c6f50db3cf2 = $('<div id="html_01868b484e164a7c9a381c6f50db3cf2" style="width: 100.0%; height: 100.0%;">Stade Chaban Delmas</div>')[0];
                popup_8a7ffeffba694dd99b3666ba76f2f31d.setContent(html_01868b484e164a7c9a381c6f50db3cf2);
            

            circle_marker_9faed6f773e440bf87f555f4cd5e3bb8.bindPopup(popup_8a7ffeffba694dd99b3666ba76f2f31d);

            
        
    
            var circle_marker_4c3659d1558a449fa25b4fa3425d4edd = L.circleMarker(
                [44.8257830793101,-0.589760256971077],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_5d8a4e09b8bb4fef9cae979b8a038aca = L.popup({maxWidth: '300'});

            
                var html_4769d7e20a73472cb519df14119c12cb = $('<div id="html_4769d7e20a73472cb519df14119c12cb" style="width: 100.0%; height: 100.0%;">Barrière de Pessac</div>')[0];
                popup_5d8a4e09b8bb4fef9cae979b8a038aca.setContent(html_4769d7e20a73472cb519df14119c12cb);
            

            circle_marker_4c3659d1558a449fa25b4fa3425d4edd.bindPopup(popup_5d8a4e09b8bb4fef9cae979b8a038aca);

            
        
    
            var circle_marker_0a7eb23424014d16bedfe90d5df1398e = L.circleMarker(
                [44.848398031151596,-0.598236967662844],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_d64df67cfbdc452a9b077afc88219174 = L.popup({maxWidth: '300'});

            
                var html_0c527351daca43b1bfd35cf15fd853db = $('<div id="html_0c527351daca43b1bfd35cf15fd853db" style="width: 100.0%; height: 100.0%;">Barriere St Medard</div>')[0];
                popup_d64df67cfbdc452a9b077afc88219174.setContent(html_0c527351daca43b1bfd35cf15fd853db);
            

            circle_marker_0a7eb23424014d16bedfe90d5df1398e.bindPopup(popup_d64df67cfbdc452a9b077afc88219174);

            
        
    
            var circle_marker_c33cfb8af68e415c8c51094b037464bc = L.circleMarker(
                [44.8515505178619,-0.571631042414094],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_11ea87cf89924de6812d84d9f5ff8d85 = L.popup({maxWidth: '300'});

            
                var html_2bbd82c2742d497f99121cc908326e70 = $('<div id="html_2bbd82c2742d497f99121cc908326e70" style="width: 100.0%; height: 100.0%;">Eglise St Louis</div>')[0];
                popup_11ea87cf89924de6812d84d9f5ff8d85.setContent(html_2bbd82c2742d497f99121cc908326e70);
            

            circle_marker_c33cfb8af68e415c8c51094b037464bc.bindPopup(popup_11ea87cf89924de6812d84d9f5ff8d85);

            
        
    
            var circle_marker_0bddde39875841e08ecf3d2789de2a83 = L.circleMarker(
                [44.8395959043342,-0.556447738178034],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_38833f87e5ba45b4bed6a85d317d2eed = L.popup({maxWidth: '300'});

            
                var html_6995d7fa8c26416c826488bfd1b7f4b9 = $('<div id="html_6995d7fa8c26416c826488bfd1b7f4b9" style="width: 100.0%; height: 100.0%;">La Benauge</div>')[0];
                popup_38833f87e5ba45b4bed6a85d317d2eed.setContent(html_6995d7fa8c26416c826488bfd1b7f4b9);
            

            circle_marker_0bddde39875841e08ecf3d2789de2a83.bindPopup(popup_38833f87e5ba45b4bed6a85d317d2eed);

            
        
    
            var circle_marker_5416c8cb599841c88e5f9683bdb0163a = L.circleMarker(
                [44.7862005825706,-0.638735601385109],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_93f56a0df0e84d6eaf5b764d3aa7cc27 = L.popup({maxWidth: '300'});

            
                var html_db646ca0751a4be4866ae8cd56123323 = $('<div id="html_db646ca0751a4be4866ae8cd56123323" style="width: 100.0%; height: 100.0%;">Pessac Bersol</div>')[0];
                popup_93f56a0df0e84d6eaf5b764d3aa7cc27.setContent(html_db646ca0751a4be4866ae8cd56123323);
            

            circle_marker_5416c8cb599841c88e5f9683bdb0163a.bindPopup(popup_93f56a0df0e84d6eaf5b764d3aa7cc27);

            
        
    
            var circle_marker_bad9284725ec4781b97e774b0b7209eb = L.circleMarker(
                [44.836280554731104,-0.687128208972081],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_32dfdcd18d74468da84f18059b37a68d = L.popup({maxWidth: '300'});

            
                var html_0d541cff212c49359fb6943e09937a0f = $('<div id="html_0d541cff212c49359fb6943e09937a0f" style="width: 100.0%; height: 100.0%;">Kennedy Parc Hôtelier</div>')[0];
                popup_32dfdcd18d74468da84f18059b37a68d.setContent(html_0d541cff212c49359fb6943e09937a0f);
            

            circle_marker_bad9284725ec4781b97e774b0b7209eb.bindPopup(popup_32dfdcd18d74468da84f18059b37a68d);

            
        
    
            var circle_marker_ab09cfbe1ced415d8095e47755b1387c = L.circleMarker(
                [44.8123770192435,-0.591030931391491],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_826f0401b061470eb2bf507a68f07c7c = L.popup({maxWidth: '300'});

            
                var html_67529e749aaa4db0acc790603a2fa2fc = $('<div id="html_67529e749aaa4db0acc790603a2fa2fc" style="width: 100.0%; height: 100.0%;">Forum</div>')[0];
                popup_826f0401b061470eb2bf507a68f07c7c.setContent(html_67529e749aaa4db0acc790603a2fa2fc);
            

            circle_marker_ab09cfbe1ced415d8095e47755b1387c.bindPopup(popup_826f0401b061470eb2bf507a68f07c7c);

            
        
    
            var circle_marker_716b2031bf5e4921ba3d4527fba8006a = L.circleMarker(
                [44.866941563060294,-0.576077830856061],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_55c0f005ed7f4ad7aeb0524b77b13c2a = L.popup({maxWidth: '300'});

            
                var html_3a0d9c65bdeb46c6aa375d4a65210f6e = $('<div id="html_3a0d9c65bdeb46c6aa375d4a65210f6e" style="width: 100.0%; height: 100.0%;">Le Bouscat Ravezies</div>')[0];
                popup_55c0f005ed7f4ad7aeb0524b77b13c2a.setContent(html_3a0d9c65bdeb46c6aa375d4a65210f6e);
            

            circle_marker_716b2031bf5e4921ba3d4527fba8006a.bindPopup(popup_55c0f005ed7f4ad7aeb0524b77b13c2a);

            
        
    
            var circle_marker_685a904d4d624f2d8eb04b1d37c74959 = L.circleMarker(
                [44.8221810827779,-0.5630395536365821],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_9138cb8b005f447d9de84bb6929c1b5b = L.popup({maxWidth: '300'});

            
                var html_b921c5c8ec0749e087b137d6a11c69a7 = $('<div id="html_b921c5c8ec0749e087b137d6a11c69a7" style="width: 100.0%; height: 100.0%;">Sacré Coeur</div>')[0];
                popup_9138cb8b005f447d9de84bb6929c1b5b.setContent(html_b921c5c8ec0749e087b137d6a11c69a7);
            

            circle_marker_685a904d4d624f2d8eb04b1d37c74959.bindPopup(popup_9138cb8b005f447d9de84bb6929c1b5b);

            
        
    
            var circle_marker_7ae55cc7fc9d4d0c8d44b51994b45315 = L.circleMarker(
                [44.8378395440189,-0.590279568888706],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_6c0ca2bb49d7461bb1c7480c4bf6e1de = L.popup({maxWidth: '300'});

            
                var html_c4eb51281bcc4bca8ca63a0a13157c3f = $('<div id="html_c4eb51281bcc4bca8ca63a0a13157c3f" style="width: 100.0%; height: 100.0%;">St Bruno</div>')[0];
                popup_6c0ca2bb49d7461bb1c7480c4bf6e1de.setContent(html_c4eb51281bcc4bca8ca63a0a13157c3f);
            

            circle_marker_7ae55cc7fc9d4d0c8d44b51994b45315.bindPopup(popup_6c0ca2bb49d7461bb1c7480c4bf6e1de);

            
        
    
            var circle_marker_0444ba41941243cf8e4f8c5520e8338f = L.circleMarker(
                [44.8130803058232,-0.564195813094978],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_22c9746d703a4e659a700948aba65e00 = L.popup({maxWidth: '300'});

            
                var html_5b024726212a4b53aac878ea5f47c8bc = $('<div id="html_5b024726212a4b53aac878ea5f47c8bc" style="width: 100.0%; height: 100.0%;">Barrière de Bègles</div>')[0];
                popup_22c9746d703a4e659a700948aba65e00.setContent(html_5b024726212a4b53aac878ea5f47c8bc);
            

            circle_marker_0444ba41941243cf8e4f8c5520e8338f.bindPopup(popup_22c9746d703a4e659a700948aba65e00);

            
        
    
            var circle_marker_fb925b26c6d349dbb741b746785d0879 = L.circleMarker(
                [44.826297061892205,-0.557071873036146],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_54a9cf39e0dc4711abec77e1320eb5cb = L.popup({maxWidth: '300'});

            
                var html_b27e87205276447eb5911af20c55cf65 = $('<div id="html_b27e87205276447eb5911af20c55cf65" style="width: 100.0%; height: 100.0%;">Gare St Jean</div>')[0];
                popup_54a9cf39e0dc4711abec77e1320eb5cb.setContent(html_b27e87205276447eb5911af20c55cf65);
            

            circle_marker_fb925b26c6d349dbb741b746785d0879.bindPopup(popup_54a9cf39e0dc4711abec77e1320eb5cb);

            
        
    
            var circle_marker_5cdbd41aee294a678447cccd96c27d68 = L.circleMarker(
                [44.8145020960259,-0.5706738859723299],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_6e1bf1ce6dda423694ecbd418d2d74cd = L.popup({maxWidth: '300'});

            
                var html_10914eda7ea34a1585d7b91031b574d7 = $('<div id="html_10914eda7ea34a1585d7b91031b574d7" style="width: 100.0%; height: 100.0%;">Barrière de Toulouse</div>')[0];
                popup_6e1bf1ce6dda423694ecbd418d2d74cd.setContent(html_10914eda7ea34a1585d7b91031b574d7);
            

            circle_marker_5cdbd41aee294a678447cccd96c27d68.bindPopup(popup_6e1bf1ce6dda423694ecbd418d2d74cd);

            
        
    
            var circle_marker_7ead512fa03d4ff8b768af7fa4f1dc35 = L.circleMarker(
                [44.8068109085645,-0.592453956058217],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_3c288701959f4fea8c016b7b377e7dbb = L.popup({maxWidth: '300'});

            
                var html_2f6cc68c4b8c48c899a867b440fface1 = $('<div id="html_2f6cc68c4b8c48c899a867b440fface1" style="width: 100.0%; height: 100.0%;">Peixotto</div>')[0];
                popup_3c288701959f4fea8c016b7b377e7dbb.setContent(html_2f6cc68c4b8c48c899a867b440fface1);
            

            circle_marker_7ead512fa03d4ff8b768af7fa4f1dc35.bindPopup(popup_3c288701959f4fea8c016b7b377e7dbb);

            
        
    
            var circle_marker_b74358179cad49459951c6d5891ae215 = L.circleMarker(
                [44.8403384742305,-0.559553791883195],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_7ad6c4c74bbc4077abbde6d7b24e2bd7 = L.popup({maxWidth: '300'});

            
                var html_a4c31d6e7caa42308b7c880479cb47d7 = $('<div id="html_a4c31d6e7caa42308b7c880479cb47d7" style="width: 100.0%; height: 100.0%;">Stalingrad</div>')[0];
                popup_7ad6c4c74bbc4077abbde6d7b24e2bd7.setContent(html_a4c31d6e7caa42308b7c880479cb47d7);
            

            circle_marker_b74358179cad49459951c6d5891ae215.bindPopup(popup_7ad6c4c74bbc4077abbde6d7b24e2bd7);

            
        
    
            var circle_marker_9fbc47e0a2ba4bc7a7595a56753ecc8c = L.circleMarker(
                [44.846253879295396,-0.5649509762224311],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_1870165101b84451872d234ab19298be = L.popup({maxWidth: '300'});

            
                var html_e941cf601a6b4c8390afbd57d2ae2cd6 = $('<div id="html_e941cf601a6b4c8390afbd57d2ae2cd6" style="width: 100.0%; height: 100.0%;">Parc aux Angeliques</div>')[0];
                popup_1870165101b84451872d234ab19298be.setContent(html_e941cf601a6b4c8390afbd57d2ae2cd6);
            

            circle_marker_9fbc47e0a2ba4bc7a7595a56753ecc8c.bindPopup(popup_1870165101b84451872d234ab19298be);

            
        
    
            var circle_marker_e38e0868d1ac4c40a3525aaaebb7ee44 = L.circleMarker(
                [44.8013759766058,-0.597153198537933],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a025056a99cc4627b697c7d736def927 = L.popup({maxWidth: '300'});

            
                var html_206dcb358221448cb0db1feb4a416864 = $('<div id="html_206dcb358221448cb0db1feb4a416864" style="width: 100.0%; height: 100.0%;">CREPS</div>')[0];
                popup_a025056a99cc4627b697c7d736def927.setContent(html_206dcb358221448cb0db1feb4a416864);
            

            circle_marker_e38e0868d1ac4c40a3525aaaebb7ee44.bindPopup(popup_a025056a99cc4627b697c7d736def927);

            
        
    
            var circle_marker_e348369e6587425f826bfc95cd1790a3 = L.circleMarker(
                [44.842949616892206,-0.570372793782736],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_c8937e34c4d64b2e8b0dcb048abc9832 = L.popup({maxWidth: '300'});

            
                var html_a9366a8649eb4e48845663336a3c4cf2 = $('<div id="html_a9366a8649eb4e48845663336a3c4cf2" style="width: 100.0%; height: 100.0%;">Place Jean Jaurès</div>')[0];
                popup_c8937e34c4d64b2e8b0dcb048abc9832.setContent(html_a9366a8649eb4e48845663336a3c4cf2);
            

            circle_marker_e348369e6587425f826bfc95cd1790a3.bindPopup(popup_c8937e34c4d64b2e8b0dcb048abc9832);

            
        
    
            var circle_marker_4596678b602d40b1a4c00abbed3cebdb = L.circleMarker(
                [44.8538162121191,-0.5886820298771109],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_34b0196dcb49446fbb993dc32040feac = L.popup({maxWidth: '300'});

            
                var html_044daffeb0c349f2af5f0c5e72bd0355 = $('<div id="html_044daffeb0c349f2af5f0c5e72bd0355" style="width: 100.0%; height: 100.0%;">Tivoli</div>')[0];
                popup_34b0196dcb49446fbb993dc32040feac.setContent(html_044daffeb0c349f2af5f0c5e72bd0355);
            

            circle_marker_4596678b602d40b1a4c00abbed3cebdb.bindPopup(popup_34b0196dcb49446fbb993dc32040feac);

            
        
    
            var circle_marker_972c2391e4bd4a0c92a56f32767f8690 = L.circleMarker(
                [44.9173004397779,-0.623816093549365],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_1fc0d89b2639420489ad55997a8a1434 = L.popup({maxWidth: '300'});

            
                var html_b790a3941c4147ea8dc2a8c11e68bc01 = $('<div id="html_b790a3941c4147ea8dc2a8c11e68bc01" style="width: 100.0%; height: 100.0%;">Gare de Blanquefort (réouverture prévue fin 2016)</div>')[0];
                popup_1fc0d89b2639420489ad55997a8a1434.setContent(html_b790a3941c4147ea8dc2a8c11e68bc01);
            

            circle_marker_972c2391e4bd4a0c92a56f32767f8690.bindPopup(popup_1fc0d89b2639420489ad55997a8a1434);

            
        
    
            var circle_marker_23375b0cb7614a2e9033e2f8c7de6fba = L.circleMarker(
                [44.7958766715951,-0.6673257911302409],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_5161f47099c149fb8d1a90d00474a6ef = L.popup({maxWidth: '300'});

            
                var html_d0921b5cbd524ac183062d18eadf59f8 = $('<div id="html_d0921b5cbd524ac183062d18eadf59f8" style="width: 100.0%; height: 100.0%;">Morin Cazalet</div>')[0];
                popup_5161f47099c149fb8d1a90d00474a6ef.setContent(html_d0921b5cbd524ac183062d18eadf59f8);
            

            circle_marker_23375b0cb7614a2e9033e2f8c7de6fba.bindPopup(popup_5161f47099c149fb8d1a90d00474a6ef);

            
        
    
            var circle_marker_74dc2609f17a42aaafb71776da9701a9 = L.circleMarker(
                [44.850965930590704,-0.644753529917565],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_b2c65ff073f64718923b6ff25f42c8b4 = L.popup({maxWidth: '300'});

            
                var html_2cdae43bed1a46d09bbfe246e7fe5af6 = $('<div id="html_2cdae43bed1a46d09bbfe246e7fe5af6" style="width: 100.0%; height: 100.0%;">Capeyron</div>')[0];
                popup_b2c65ff073f64718923b6ff25f42c8b4.setContent(html_2cdae43bed1a46d09bbfe246e7fe5af6);
            

            circle_marker_74dc2609f17a42aaafb71776da9701a9.bindPopup(popup_b2c65ff073f64718923b6ff25f42c8b4);

            
        
    
            var circle_marker_936be5196e9a4589816502ca1ef35f63 = L.circleMarker(
                [44.832632938696605,-0.567111855410022],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_2242b8f7bada44a9ad5c0dd90eb4456b = L.popup({maxWidth: '300'});

            
                var html_0cc3658854324eb8a45c3c8dcb9dac18 = $('<div id="html_0cc3658854324eb8a45c3c8dcb9dac18" style="width: 100.0%; height: 100.0%;">Place du Maucaillou</div>')[0];
                popup_2242b8f7bada44a9ad5c0dd90eb4456b.setContent(html_0cc3658854324eb8a45c3c8dcb9dac18);
            

            circle_marker_936be5196e9a4589816502ca1ef35f63.bindPopup(popup_2242b8f7bada44a9ad5c0dd90eb4456b);

            
        
    
            var circle_marker_1af1489e9394489c967123a65e11dc2d = L.circleMarker(
                [44.8331199635521,-0.576952524846592],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_b11d046827df4ce3be141fb5a4fd5652 = L.popup({maxWidth: '300'});

            
                var html_959f8f3218bf4a2181e8b25568175736 = $('<div id="html_959f8f3218bf4a2181e8b25568175736" style="width: 100.0%; height: 100.0%;">Place Ste Eulalie</div>')[0];
                popup_b11d046827df4ce3be141fb5a4fd5652.setContent(html_959f8f3218bf4a2181e8b25568175736);
            

            circle_marker_1af1489e9394489c967123a65e11dc2d.bindPopup(popup_b11d046827df4ce3be141fb5a4fd5652);

            
        
    
            var circle_marker_7ae1bc57be774e2dbaafa9fd25cdff86 = L.circleMarker(
                [44.8351755836624,-0.5720496800131321],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_9e2784c6d28f432ea574bf62939b03e1 = L.popup({maxWidth: '300'});

            
                var html_f6a7962e2de8413eb0ab79b2aa63df06 = $('<div id="html_f6a7962e2de8413eb0ab79b2aa63df06" style="width: 100.0%; height: 100.0%;">Grosse Cloche (fermée depuis le 26/09/2016)</div>')[0];
                popup_9e2784c6d28f432ea574bf62939b03e1.setContent(html_f6a7962e2de8413eb0ab79b2aa63df06);
            

            circle_marker_7ae1bc57be774e2dbaafa9fd25cdff86.bindPopup(popup_9e2784c6d28f432ea574bf62939b03e1);

            
        
    
            var circle_marker_9794bb3e13604b6aac10017c910f18d6 = L.circleMarker(
                [44.85560206029321,-0.563193934178502],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_d16b889328d14415be4412f4be22353f = L.popup({maxWidth: '300'});

            
                var html_e371c6da9fcd41a5934dcf2244c4d7ff = $('<div id="html_e371c6da9fcd41a5934dcf2244c4d7ff" style="width: 100.0%; height: 100.0%;">Cours du Médoc</div>')[0];
                popup_d16b889328d14415be4412f4be22353f.setContent(html_e371c6da9fcd41a5934dcf2244c4d7ff);
            

            circle_marker_9794bb3e13604b6aac10017c910f18d6.bindPopup(popup_d16b889328d14415be4412f4be22353f);

            
        
    
            var circle_marker_a9c238c503c94bb0b98161a369607d11 = L.circleMarker(
                [44.8484148322112,-0.575741583710469],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_ae823cb14e714d148a226947b0d4c6a0 = L.popup({maxWidth: '300'});

            
                var html_a1a93b8b9d804a19b2c7b3660fb6845f = $('<div id="html_a1a93b8b9d804a19b2c7b3660fb6845f" style="width: 100.0%; height: 100.0%;">Jardin Public</div>')[0];
                popup_ae823cb14e714d148a226947b0d4c6a0.setContent(html_a1a93b8b9d804a19b2c7b3660fb6845f);
            

            circle_marker_a9c238c503c94bb0b98161a369607d11.bindPopup(popup_ae823cb14e714d148a226947b0d4c6a0);

            
        
    
            var circle_marker_6fed3f8eaa094ff48fcd7bf18ccb26a4 = L.circleMarker(
                [44.8561372751007,-0.533637420460925],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_107647d3771c401e9b223849c6d96e49 = L.popup({maxWidth: '300'});

            
                var html_36478372539346aaa46fa35172e2376c = $('<div id="html_36478372539346aaa46fa35172e2376c" style="width: 100.0%; height: 100.0%;">Cenon Gare</div>')[0];
                popup_107647d3771c401e9b223849c6d96e49.setContent(html_36478372539346aaa46fa35172e2376c);
            

            circle_marker_6fed3f8eaa094ff48fcd7bf18ccb26a4.bindPopup(popup_107647d3771c401e9b223849c6d96e49);

            
        
    
            var circle_marker_cb3acc0b3690430e9663a9a5b588784f = L.circleMarker(
                [44.8377941712661,-0.581662430453886],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_48042d8b47be48c79aed17325069a751 = L.popup({maxWidth: '300'});

            
                var html_7e30193182384ef18ed2380ba6da3ffe = $('<div id="html_7e30193182384ef18ed2380ba6da3ffe" style="width: 100.0%; height: 100.0%;">Square André Lhote</div>')[0];
                popup_48042d8b47be48c79aed17325069a751.setContent(html_7e30193182384ef18ed2380ba6da3ffe);
            

            circle_marker_cb3acc0b3690430e9663a9a5b588784f.bindPopup(popup_48042d8b47be48c79aed17325069a751);

            
        
    
            var circle_marker_9b27dd6eb72a4dcd8aea8f596fd05d1c = L.circleMarker(
                [44.8626120471668,-0.567184098919909],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_1dee2f38d88c41c39bc4218050a54e71 = L.popup({maxWidth: '300'});

            
                var html_6a01f2889aa0414b873e6629b52c596e = $('<div id="html_6a01f2889aa0414b873e6629b52c596e" style="width: 100.0%; height: 100.0%;">Saint Louis Haussmann</div>')[0];
                popup_1dee2f38d88c41c39bc4218050a54e71.setContent(html_6a01f2889aa0414b873e6629b52c596e);
            

            circle_marker_9b27dd6eb72a4dcd8aea8f596fd05d1c.bindPopup(popup_1dee2f38d88c41c39bc4218050a54e71);

            
        
    
            var circle_marker_d3232bffadec49efb5a1b94f49e41842 = L.circleMarker(
                [44.8380294186168,-0.584364976111836],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a5e433f9e88e48d08a69a9bf80704395 = L.popup({maxWidth: '300'});

            
                var html_cd5978b6940d4f7ea1c0854725deac43 = $('<div id="html_cd5978b6940d4f7ea1c0854725deac43" style="width: 100.0%; height: 100.0%;">Mériadeck</div>')[0];
                popup_a5e433f9e88e48d08a69a9bf80704395.setContent(html_cd5978b6940d4f7ea1c0854725deac43);
            

            circle_marker_d3232bffadec49efb5a1b94f49e41842.bindPopup(popup_a5e433f9e88e48d08a69a9bf80704395);

            
        
    
            var circle_marker_489190f526af41c18cc8461ee1af462f = L.circleMarker(
                [44.8359397316541,-0.58211488103064],
                {
  "bubblingMouseEvents": true,
  "color": "#984ea3",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#984ea3",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_cdf8980cc900436aa08de00d332d089f = L.popup({maxWidth: '300'});

            
                var html_5b515066ab7c40708880b2caabca6a88 = $('<div id="html_5b515066ab7c40708880b2caabca6a88" style="width: 100.0%; height: 100.0%;">Palais de Justice</div>')[0];
                popup_cdf8980cc900436aa08de00d332d089f.setContent(html_5b515066ab7c40708880b2caabca6a88);
            

            circle_marker_489190f526af41c18cc8461ee1af462f.bindPopup(popup_cdf8980cc900436aa08de00d332d089f);

            
        
    
            var circle_marker_831428a8b6a04f8e865a8d0feffb3c1b = L.circleMarker(
                [44.8468975914944,-0.582190313794346],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_3440262421c84d86a2ea766b7d397404 = L.popup({maxWidth: '300'});

            
                var html_6fec57703c234a11a6ab07e5abb9bfdb = $('<div id="html_6fec57703c234a11a6ab07e5abb9bfdb" style="width: 100.0%; height: 100.0%;">Palais Gallien</div>')[0];
                popup_3440262421c84d86a2ea766b7d397404.setContent(html_6fec57703c234a11a6ab07e5abb9bfdb);
            

            circle_marker_831428a8b6a04f8e865a8d0feffb3c1b.bindPopup(popup_3440262421c84d86a2ea766b7d397404);

            
        
    
            var circle_marker_41541cae65734014bdbe862e13316bcb = L.circleMarker(
                [44.832505316290394,-0.610466692761722],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_23cf4095bbd14324acf4d0a556c7a962 = L.popup({maxWidth: '300'});

            
                var html_e737c65809394c3aaffee0a5fe490015 = $('<div id="html_e737c65809394c3aaffee0a5fe490015" style="width: 100.0%; height: 100.0%;">St Augustin</div>')[0];
                popup_23cf4095bbd14324acf4d0a556c7a962.setContent(html_e737c65809394c3aaffee0a5fe490015);
            

            circle_marker_41541cae65734014bdbe862e13316bcb.bindPopup(popup_23cf4095bbd14324acf4d0a556c7a962);

            
        
    
            var circle_marker_a0f8295a5a9d48efb93b838f4b281caf = L.circleMarker(
                [44.8471835107084,-0.5713111276547099],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_17b78528055b43bd8ebb6907c1920d36 = L.popup({maxWidth: '300'});

            
                var html_82584837c368498da8b1c009cbcab235 = $('<div id="html_82584837c368498da8b1c009cbcab235" style="width: 100.0%; height: 100.0%;">Allées de Chartres</div>')[0];
                popup_17b78528055b43bd8ebb6907c1920d36.setContent(html_82584837c368498da8b1c009cbcab235);
            

            circle_marker_a0f8295a5a9d48efb93b838f4b281caf.bindPopup(popup_17b78528055b43bd8ebb6907c1920d36);

            
        
    
            var circle_marker_60fd47e9220b434dbc6b4ca4ff537d00 = L.circleMarker(
                [44.8429268147365,-0.573898729949943],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_bff81e0d5bb34394af8862edea3ce161 = L.popup({maxWidth: '300'});

            
                var html_6f1f3c1d11a5404f8a3f37156885a1e7 = $('<div id="html_6f1f3c1d11a5404f8a3f37156885a1e7" style="width: 100.0%; height: 100.0%;">Grand Théâtre</div>')[0];
                popup_bff81e0d5bb34394af8862edea3ce161.setContent(html_6f1f3c1d11a5404f8a3f37156885a1e7);
            

            circle_marker_60fd47e9220b434dbc6b4ca4ff537d00.bindPopup(popup_bff81e0d5bb34394af8862edea3ce161);

            
        
    
            var circle_marker_d4db4b9074da493e87b7a46fc1a9ed8a = L.circleMarker(
                [44.84297697868261,-0.555732111587463],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_10ac4a2dcbdb4b37b6a8740436224e43 = L.popup({maxWidth: '300'});

            
                var html_4436ea2506e4477b94f1cd12596987cd = $('<div id="html_4436ea2506e4477b94f1cd12596987cd" style="width: 100.0%; height: 100.0%;">Thiers Jardin Botanique</div>')[0];
                popup_10ac4a2dcbdb4b37b6a8740436224e43.setContent(html_4436ea2506e4477b94f1cd12596987cd);
            

            circle_marker_d4db4b9074da493e87b7a46fc1a9ed8a.bindPopup(popup_10ac4a2dcbdb4b37b6a8740436224e43);

            
        
    
            var circle_marker_c5ca96f439484845831d76a3e9469031 = L.circleMarker(
                [44.8077684559174,-0.560054594815583],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_b11f005e6959454e800eb81eadaa0d4a = L.popup({maxWidth: '300'});

            
                var html_46b419b6458e458fbfe00b16579b8ce4 = $('<div id="html_46b419b6458e458fbfe00b16579b8ce4" style="width: 100.0%; height: 100.0%;">Bègles Poste</div>')[0];
                popup_b11f005e6959454e800eb81eadaa0d4a.setContent(html_46b419b6458e458fbfe00b16579b8ce4);
            

            circle_marker_c5ca96f439484845831d76a3e9469031.bindPopup(popup_b11f005e6959454e800eb81eadaa0d4a);

            
        
    
            var circle_marker_11f6c771e28d4ef59032eefc9531005d = L.circleMarker(
                [44.8377883500726,-0.5703384430356229],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_d412c095321d48bfa629497c2218dd6b = L.popup({maxWidth: '300'});

            
                var html_6f6a99c0ef7045e89e1c099ff50cbddb = $('<div id="html_6f6a99c0ef7045e89e1c099ff50cbddb" style="width: 100.0%; height: 100.0%;">Place du Palais</div>')[0];
                popup_d412c095321d48bfa629497c2218dd6b.setContent(html_6f6a99c0ef7045e89e1c099ff50cbddb);
            

            circle_marker_11f6c771e28d4ef59032eefc9531005d.bindPopup(popup_d412c095321d48bfa629497c2218dd6b);

            
        
    
            var circle_marker_334841c9bd1d41ec811eaf22ff5fb576 = L.circleMarker(
                [44.8153457349536,-0.634437084515722],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_384f73d28db6473ea87812e4b40f5f8e = L.popup({maxWidth: '300'});

            
                var html_6156ec64127447f0a03ab5897046a419 = $('<div id="html_6156ec64127447f0a03ab5897046a419" style="width: 100.0%; height: 100.0%;">Le Burck</div>')[0];
                popup_384f73d28db6473ea87812e4b40f5f8e.setContent(html_6156ec64127447f0a03ab5897046a419);
            

            circle_marker_334841c9bd1d41ec811eaf22ff5fb576.bindPopup(popup_384f73d28db6473ea87812e4b40f5f8e);

            
        
    
            var circle_marker_d9439b6252024edc8dd59049c24113aa = L.circleMarker(
                [44.8265044749032,-0.6257739119014971],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_952c1d97faef4804a54ca9fc4a02f943 = L.popup({maxWidth: '300'});

            
                var html_39bed55c486c4820bf5195661fcf4509 = $('<div id="html_39bed55c486c4820bf5195661fcf4509" style="width: 100.0%; height: 100.0%;">Fontaine d&apos;Arlac</div>')[0];
                popup_952c1d97faef4804a54ca9fc4a02f943.setContent(html_39bed55c486c4820bf5195661fcf4509);
            

            circle_marker_d9439b6252024edc8dd59049c24113aa.bindPopup(popup_952c1d97faef4804a54ca9fc4a02f943);

            
        
    
            var circle_marker_3308c21ff234456cbdb12fe913f1746e = L.circleMarker(
                [44.8814488592023,-0.613374329926722],
                {
  "bubblingMouseEvents": true,
  "color": "#4daf4a",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#4daf4a",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_57617503c296400dbefa5dbd97b01af1 = L.popup({maxWidth: '300'});

            
                var html_3b5d716753404a05bea73e9e5e13d260 = $('<div id="html_3b5d716753404a05bea73e9e5e13d260" style="width: 100.0%; height: 100.0%;">Bruges Hôtel de Ville</div>')[0];
                popup_57617503c296400dbefa5dbd97b01af1.setContent(html_3b5d716753404a05bea73e9e5e13d260);
            

            circle_marker_3308c21ff234456cbdb12fe913f1746e.bindPopup(popup_57617503c296400dbefa5dbd97b01af1);

            
        
    
            var circle_marker_ff61fed62b5d49adbb73600b70822b0d = L.circleMarker(
                [44.8220935136601,-0.581781925139518],
                {
  "bubblingMouseEvents": true,
  "color": "#377eb8",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#377eb8",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a89531bf623947acbc494ba2459674c1 = L.popup({maxWidth: '300'});

            
                var html_e2610ca29a8047f4acca0f8b77e00b63 = $('<div id="html_e2610ca29a8047f4acca0f8b77e00b63" style="width: 100.0%; height: 100.0%;">Barrière St Genès</div>')[0];
                popup_a89531bf623947acbc494ba2459674c1.setContent(html_e2610ca29a8047f4acca0f8b77e00b63);
            

            circle_marker_ff61fed62b5d49adbb73600b70822b0d.bindPopup(popup_a89531bf623947acbc494ba2459674c1);

            
        
    
            var circle_marker_c3a13cad5b2b4ceead645ab44848d94f = L.circleMarker(
                [44.858907740747604,-0.565842813677209],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_a3907ec8f5c248ceb74a542588f1bdb5 = L.popup({maxWidth: '300'});

            
                var html_c9aa72a2c21c45ad8e9ae90a55a9e6ce = $('<div id="html_c9aa72a2c21c45ad8e9ae90a55a9e6ce" style="width: 100.0%; height: 100.0%;">Place St Martial</div>')[0];
                popup_a3907ec8f5c248ceb74a542588f1bdb5.setContent(html_c9aa72a2c21c45ad8e9ae90a55a9e6ce);
            

            circle_marker_c3a13cad5b2b4ceead645ab44848d94f.bindPopup(popup_a3907ec8f5c248ceb74a542588f1bdb5);

            
        
    
            var circle_marker_3002a85769df4620b63390c309bdba64 = L.circleMarker(
                [44.8332348401948,-0.5828331038628051],
                {
  "bubblingMouseEvents": true,
  "color": "#e41a1c",
  "dashArray": null,
  "dashOffset": null,
  "fill": false,
  "fillColor": "#e41a1c",
  "fillOpacity": 0.5,
  "fillRule": "evenodd",
  "lineCap": "round",
  "lineJoin": "round",
  "opacity": 1.0,
  "radius": 5,
  "stroke": true,
  "weight": 3
}
                ).addTo(map_0d86e08075254804a191840c1a8961f7);
            
    
            var popup_d231072cd5c2492cb43201bd23d95b87 = L.popup({maxWidth: '300'});

            
                var html_1724bcba611541f28a8c41360b153b6e = $('<div id="html_1724bcba611541f28a8c41360b153b6e" style="width: 100.0%; height: 100.0%;">Libération</div>')[0];
                popup_d231072cd5c2492cb43201bd23d95b87.setContent(html_1724bcba611541f28a8c41360b153b6e);
            

            circle_marker_3002a85769df4620b63390c309bdba64.bindPopup(popup_d231072cd5c2492cb43201bd23d95b87);

            
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f5fe8b66a90>"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mp"
]
}
],
"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 |
MadDataScience/DAT4 | DS_Lec06-ggplot2.ipynb | 1 | 322640 | {
"metadata": {
"celltoolbar": "Slideshow",
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Data Science\n",
"====\n",
"### Visualizing Data with R and ggplot2\n",
"\n",
"Alessandro Gagliardi \n",
"Sr. Data Scientist, [Glassdoor.com](Glassdoor.com)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Agenda\n",
"------\n",
"\n",
"1. Data: Recap\n",
"2. Science: Exploration & Visualization \n",
"3. Lab"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Data Recap\n",
"-----\n",
"\n",
"1. Python\n",
"1. Big Data / Unstructured Data\n",
" 1. Hadoop\n",
" 2. IPython.parallel & StarCluster\n",
"2. APIs & NoSQL / Semi-Structured Data\n",
"3. SQL & Normalization / Structured Data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Day 1 \n",
"-----\n",
" \n",
"### Birthday Paradox\n",
"Moral: Test Assumptions, Be Rigorous"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Data Science Workflow:\n",
"\n",
"1. Obtain\n",
"2. Scrub\n",
"3. Explore\n",
"4. Model\n",
"5. Interpret"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Python\n",
"\n",
"* Working with strings!\n",
"* You wrote an app!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Day 2\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Big Data\n",
"\n",
"* Volume\n",
"* Velocity\n",
"* Variety"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Map/Reduce\n",
"\n",
"* Moving Code to Data\n",
"* Word Count Example"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Elastic MapReduce\n",
"\n",
"* You scripted and ran a Hadoop job!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Day 3\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Amazon Web Services\n",
"\n",
"- IAM: Identity and Access Management\n",
"- S3: Simple Storage Service\n",
"- EC2: Elastic Cloud Compute\n",
"- EMR: Elastic MapReduce"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### IPython.parallel and StarCluster\n",
"\n",
"* Bringing data to code\n",
"* You processed 500GB of wikipedia data!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Day 4\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"<H3>RESTful web API HTTP methods</H3>\n",
"<TABLE>\n",
"<TR><TH>Resource</TH><TH>GET</TH><TH>PUT</TH><TH>POST</TH><TH>DELETE</TH></TR>\n",
"<TR><TH>Collection URI, such as http://example.com/resources</TH><TD><B>List</B> the URIs and perhaps other details of the collection's members.</TD><TD><B>Replace</B> the entire collection with another collection.</TD><TD><B>Create</B> a new entry in the collection. The new entry's URI is assigned automatically and is usually returned by the operation.</TD><TD><B>Delete</B> the entire collection.</TD></TR>\n",
"<TR><TH>Element URI, such as http://example.com/resources/item17</TH><TD><B>Retrieve</B> a representation of the addressed member of the collection, expressed in an appropriate Internet media type.</TD><TD><B>Replace</B> the addressed member of the collection, or if it doesn't exist, create it.</TD><TD>Not generally used. Treat the addressed member as a collection in its own right and create a new entry in it.</TD><TD><B>Delete</B> the addressed member of the collection.</TD></TR>\n",
"</TABLE>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### (non-relational) Database Management Systems\n",
"\n",
"* ACID\n",
" - Atomicity\n",
" - Consistency\n",
" - Isolation\n",
" - Durability\n",
"* CAP\n",
" - Consistency\n",
" - Availability\n",
" - Partition Tolerance\n",
"* CAP Theorem\n",
" - Pick two!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Twitter and Mongo\n",
"\n",
"* You pulled data from Twitter's API and stored it in Mongo!\n",
"* You then queried Mongo to discover facts about Twitter data!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Day 5\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Normalization\n",
"\n",
"* *\"[Every] non-key [attribute] must provide a fact about the key, the whole key, and nothing but the key (so help me Codd).\"*\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Munging Data with Pandas\n",
"\n",
"* Using Pandas you transformed and plotted Enron's email data\n",
"\n",
"This brings us to..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Science!!!\n",
"======="
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"but first...any questions?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Projects\n",
"--------\n",
"\n",
"* Code\n",
" * must be in Github along with at least one visualization\n",
"* Data\n",
" * If small (<1 MB) put in Github.\n",
" * If medium (i.e. [1, 10) MB) put in S3 with instructions on how to retrieve in Github\n",
" * If large, put small or medium sample in Github or S3 (your choice)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Key Objectives\n",
"----\n",
"\n",
"- Become familiar with the R environment\n",
"- Explore data in R\n",
"- Visualize data using ggplot2"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Visualization as a Medium"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%load_ext rmagic"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"anscombe1 <- anscombe[,c('x1', 'y1')]"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Consider the following dataset:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"plot(y1 ~ x1, data = anscombe, col = \"red\", pch = 21, bg = \"orange\", \n",
" cex = 1.2, xlim = c(3, 19), ylim = c(3, 13))\n",
"abline(lm(y1 ~ x1, data = anscombe), col = \"blue\")\n",
"t(anscombe1)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]\n",
"x1 10.00 8.00 13.00 9.00 11.00 14.00 6.00 4.00 12.00 7.00 5.00\n",
"y1 8.04 6.95 7.58 8.81 8.33 9.96 7.24 4.26 10.84 4.82 5.68\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAYAAAB91L6VAAAEJGlDQ1BJQ0MgUHJvZmlsZQAAOBGF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\nRgABBBDwX+D/AZz3aZUlRTvjAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"apply(anscombe1, 2, mean)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" x1 y1 \n",
"9.000000 7.500909 \n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"apply(anscombe1, 2, var)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" x1 y1 \n",
"11.000000 4.127269 \n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"cor(anscombe$x1, anscombe$y1)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"[1] 0.8164205\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"lm(y1 ~ x1, data = anscombe)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"\n",
"Call:\n",
"lm(formula = y1 ~ x1, data = anscombe)\n",
"\n",
"Coefficients:\n",
"(Intercept) x1 \n",
" 3.0001 0.5001 \n",
"\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Q: Does everyone know what the mean is?\n",
"Q: Does anyone know what the variance is?\n",
"Q: More importantly, do you know what the variance tells you?\n",
"Q: Does everyone know what correl is?\n",
"Q: More importantly, do you know what correl tells you?\n",
"Q: Does everyone know what a line of best fit is?\n",
"Q: More importantly, do you know what the line of best fit tells you? (note: we will look at this next time)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"ff <- y ~ x\n",
"mods <- setNames(as.list(1:4), paste0(\"lm\", 1:4))\n",
"for(i in 1:4) {\n",
" ff[2:3] <- lapply(paste0(c(\"y\",\"x\"), i), as.name)\n",
" ## or ff[[2]] <- as.name(paste0(\"y\", i))\n",
" ## ff[[3]] <- as.name(paste0(\"x\", i))\n",
" mods[[i]] <- lmi <- lm(ff, data = anscombe)\n",
" print(anova(lmi))\n",
"}"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"Analysis of Variance Table\n",
"\n",
"Response: y1\n",
" Df Sum Sq Mean Sq F value Pr(>F) \n",
"x1 1 27.510 27.5100 17.99 0.00217 **\n",
"Residuals 9 13.763 1.5292 \n",
"---\n",
"Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n",
"Analysis of Variance Table\n",
"\n",
"Response: y2\n",
" Df Sum Sq Mean Sq F value Pr(>F) \n",
"x2 1 27.500 27.5000 17.966 0.002179 **\n",
"Residuals 9 13.776 1.5307 \n",
"---\n",
"Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n",
"Analysis of Variance Table\n",
"\n",
"Response: y3\n",
" Df Sum Sq Mean Sq F value Pr(>F) \n",
"x3 1 27.470 27.4700 17.972 0.002176 **\n",
"Residuals 9 13.756 1.5285 \n",
"---\n",
"Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n",
"Analysis of Variance Table\n",
"\n",
"Response: y4\n",
" Df Sum Sq Mean Sq F value Pr(>F) \n",
"x4 1 27.490 27.4900 18.003 0.002165 **\n",
"Residuals 9 13.742 1.5269 \n",
"---\n",
"Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Now, suppose I give you\n",
"three more datasets\n",
"with exactly the same\n",
"characteristics\u2026\n",
"\n",
"Q: how similar are these datasets?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"anscombe"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" x1 x2 x3 x4 y1 y2 y3 y4\n",
"1 10 10 10 8 8.04 9.14 7.46 6.58\n",
"2 8 8 8 8 6.95 8.14 6.77 5.76\n",
"3 13 13 13 8 7.58 8.74 12.74 7.71\n",
"4 9 9 9 8 8.81 8.77 7.11 8.84\n",
"5 11 11 11 8 8.33 9.26 7.81 8.47\n",
"6 14 14 14 8 9.96 8.10 8.84 7.04\n",
"7 6 6 6 8 7.24 6.13 6.08 5.25\n",
"8 4 4 4 19 4.26 3.10 5.39 12.50\n",
"9 12 12 12 8 10.84 9.13 8.15 5.56\n",
"10 7 7 7 8 4.82 7.26 6.42 7.91\n",
"11 5 5 5 8 5.68 4.74 5.73 6.89\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"mydata=with(anscombe, data.frame(xVal=c(x1,x2,x3,x4), yVal=c(y1,y2,y3,y4), \n",
" group=gl(4,nrow(anscombe))))\n",
"aggregate(.~group, data=mydata, mean)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" group xVal yVal\n",
"1 1 9 7.500909\n",
"2 2 9 7.500909\n",
"3 3 9 7.500000\n",
"4 4 9 7.500909\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"aggregate(.~group, data=mydata, var)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" group xVal yVal\n",
"1 1 11 4.127269\n",
"2 2 11 4.127629\n",
"3 3 11 4.122620\n",
"4 4 11 4.123249\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R -w 960 -h 480 -u px\n",
"library(ggplot2)\n",
"ggplot(mydata,aes(x=xVal, y=yVal)) + geom_point(col = \"red\", size=4) + \n",
" geom_smooth(method='lm', alpha=0, fullrange=TRUE) + facet_wrap(~group)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAA8AAAAHgCAYAAABq5QSEAAAEJGlDQ1BJQ0MgUHJvZmlsZQAAOBGF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\nVwELQAEKUIACFKAABShAAQpQgAKJEPj/VKifSBlQK5EAAAAASUVORK5CYII=\n"
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"Q: how similar are these datasets?\n",
"\n",
"A: not very!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"visualing distributions"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"R\n",
"=="
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Like Pandas, R uses Data Frames."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R -o anscombe\n",
"is(anscombe)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"[1] \"data.frame\" \"list\" \"oldClass\" \"vector\" \n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Data Frames (or '`data.frame`'s) are derived from `list`s which are derived from `oldClass`es, which are derived from `vectors`. Therefore a `data.frame` is a `list` which is an `oldClass` which is a `vector`. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"`is()` is similar to `type()` in Python and can be used to tell you what *type* an object *is*."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Linear Models:\n",
"\n",
"* Independent (Explanatory) Variables\n",
"* Dependant (Response) Variables"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Another important concept in R are *formulas*. \n",
"`y1 ~ x1` models a relationship between `y1` and `x1` such that `y1` is determined by `x1`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"lm(y1 ~ x1, data = anscombe)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"\n",
"Call:\n",
"lm(formula = y1 ~ x1, data = anscombe)\n",
"\n",
"Coefficients:\n",
"(Intercept) x1 \n",
" 3.0001 0.5001 \n",
"\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"One of the most useful functions in both R and Pandas is `plot()`. "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"One of the biggest differences between R and Python is that in R, objects don't have methods. So instead of calling `DataFrame.plot()` as we would in Python, we call `plot(data.frame)`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"plot(y1 ~ x1, data = anscombe)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAYAAAB91L6VAAAEJGlDQ1BJQ0MgUHJvZmlsZQAAOBGF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\n"
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"`plot()` accepts many arguments that can be used to make it prettier and easier to understand."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"plot(y1 ~ x1, data = anscombe, col = \"red\", pch = 21, bg = \"orange\", \n",
" cex = 1.2, xlim = c(3, 19), ylim = c(3, 13))"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAYAAAB91L6VAAAEJGlDQ1BJQ0MgUHJvZmlsZQAAOBGF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\nAAII2Bf4X5LGMOXJlRqkAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"R is all about models. One of the simplest is the linear model which tries to find a linear relationship between the independant (or explanatory) and dependant (or response) variables."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Plotting a linear regression:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"plot(y1 ~ x1, data = anscombe, col = \"red\", pch = 21, bg = \"orange\", \n",
" cex = 1.2, xlim = c(3, 19), ylim = c(3, 13))\n",
"abline(lm(y1 ~ x1, data = anscombe), col = \"blue\")"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAYAAAB91L6VAAAEJGlDQ1BJQ0MgUHJvZmlsZQAAOBGF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\nRgABBBDwX+D/AZz3aZUlRTvjAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Correlation"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"cor.test(anscombe$x1, anscombe$y1)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"\n",
"\tPearson's product-moment correlation\n",
"\n",
"data: anscombe$x1 and anscombe$y1\n",
"t = 4.2415, df = 9, p-value = 0.00217\n",
"alternative hypothesis: true correlation is not equal to 0\n",
"95 percent confidence interval:\n",
" 0.4243912 0.9506933\n",
"sample estimates:\n",
" cor \n",
"0.8164205 \n",
"\n"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"### Analysis of Variance (ANOVA)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"anova(lm(y1 ~ x1, data = anscombe))"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
"Analysis of Variance Table\n",
"\n",
"Response: y1\n",
" Df Sum Sq Mean Sq F value Pr(>F) \n",
"x1 1 27.510 27.5100 17.99 0.00217 **\n",
"Residuals 9 13.763 1.5292 \n",
"---\n",
"Signif. codes: 0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n"
]
}
],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"R likes tables long and narrow:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"anscombe"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" x1 x2 x3 x4 y1 y2 y3 y4\n",
"1 10 10 10 8 8.04 9.14 7.46 6.58\n",
"2 8 8 8 8 6.95 8.14 6.77 5.76\n",
"3 13 13 13 8 7.58 8.74 12.74 7.71\n",
"4 9 9 9 8 8.81 8.77 7.11 8.84\n",
"5 11 11 11 8 8.33 9.26 7.81 8.47\n",
"6 14 14 14 8 9.96 8.10 8.84 7.04\n",
"7 6 6 6 8 7.24 6.13 6.08 5.25\n",
"8 4 4 4 19 4.26 3.10 5.39 12.50\n",
"9 12 12 12 8 10.84 9.13 8.15 5.56\n",
"10 7 7 7 8 4.82 7.26 6.42 7.91\n",
"11 5 5 5 8 5.68 4.74 5.73 6.89\n"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"with(anscombe, data.frame(xVal=c(x1,x2,x3,x4), yVal=c(y1,y2,y3,y4), \n",
" group=gl(4,nrow(anscombe))))"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" xVal yVal group\n",
"1 10 8.04 1\n",
"2 8 6.95 1\n",
"3 13 7.58 1\n",
"4 9 8.81 1\n",
"5 11 8.33 1\n",
"6 14 9.96 1\n",
"7 6 7.24 1\n",
"8 4 4.26 1\n",
"9 12 10.84 1\n",
"10 7 4.82 1\n",
"11 5 5.68 1\n",
"12 10 9.14 2\n",
"13 8 8.14 2\n",
"14 13 8.74 2\n",
"15 9 8.77 2\n",
"16 11 9.26 2\n",
"17 14 8.10 2\n",
"18 6 6.13 2\n",
"19 4 3.10 2\n",
"20 12 9.13 2\n",
"21 7 7.26 2\n",
"22 5 4.74 2\n",
"23 10 7.46 3\n",
"24 8 6.77 3\n",
"25 13 12.74 3\n",
"26 9 7.11 3\n",
"27 11 7.81 3\n",
"28 14 8.84 3\n",
"29 6 6.08 3\n",
"30 4 5.39 3\n",
"31 12 8.15 3\n",
"32 7 6.42 3\n",
"33 5 5.73 3\n",
"34 8 6.58 4\n",
"35 8 5.76 4\n",
"36 8 7.71 4\n",
"37 8 8.84 4\n",
"38 8 8.47 4\n",
"39 8 7.04 4\n",
"40 8 5.25 4\n",
"41 19 12.50 4\n",
"42 8 5.56 4\n",
"43 8 7.91 4\n",
"44 8 6.89 4\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"ggplot2\n",
"-------"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### ggplot2 layers geometric elements"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"ggplot(subset(mydata, group==1), aes(x=xVal, y=yVal)) + geom_point(col = \"red\", size=4)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAYAAAB91L6VAAAEJGlDQ1BJQ0MgUHJvZmlsZQAAOBGF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\nAElFTkSuQmCC\n"
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### `facet_wrap` allows you to split your plot by group:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"ggplot(mydata,aes(x=xVal, y=yVal)) + geom_point(col = \"red\", size=4) + \n",
" facet_wrap(~group)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAYAAAB91L6VAAAEJGlDQ1BJQ0MgUHJvZmlsZQAAOBGF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\n"
}
],
"prompt_number": 23
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### geom_smooth can display a linear model fit:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"ggplot(mydata,aes(x=xVal, y=yVal)) + geom_point(col = \"red\", size=4) + \n",
" geom_smooth(method='lm', alpha=0, fullrange=TRUE) + facet_wrap(~group) "
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAYAAAB91L6VAAAEJGlDQ1BJQ0MgUHJvZmlsZQAAOBGF\nVd9v21QUPolvUqQWPyBYR4eKxa9VU1u5GxqtxgZJk6XtShal6dgqJOQ6N4mpGwfb6baqT3uBNwb8\nAUDZAw9IPCENBmJ72fbAtElThyqqSUh76MQPISbtBVXhu3ZiJ1PEXPX6yznfOec7517bRD1fabWa\nGVWIlquunc8klZOnFpSeTYrSs9RLA9Sr6U4tkcvNEi7BFffO6+EdigjL7ZHu/k72I796i9zRiSJP\nwG4VHX0Z+AxRzNRrtksUvwf7+Gm3BtzzHPDTNgQCqwKXfZwSeNHHJz1OIT8JjtAq6xWtCLwGPLzY\nZi+3YV8DGMiT4VVuG7oiZpGzrZJhcs/hL49xtzH/Dy6bdfTsXYNY+5yluWO4D4neK/ZUvok/17X0\nHPBLsF+vuUlhfwX4j/rSfAJ4H1H0qZJ9dN7nR19frRTeBt4Fe9FwpwtN+2p1MXscGLHR9SXrmMgj\nONd1ZxKzpBeA71b4tNhj6JGoyFNp4GHgwUp9qplfmnFW5oTdy7NamcwCI49kv6fN5IAHgD+0rbyo\nBc3SOjczohbyS1drbq6pQdqumllRC/0ymTtej8gpbbuVwpQfyw66dqEZyxZKxtHpJn+tZnpnEdrY\nBbueF9qQn93S7HQGGHnYP7w6L+YGHNtd1FJitqPAR+hERCNOFi1i1alKO6RQnjKUxL1GNjwlMsiE\nhcPLYTEiT9ISbN15OY/jx4SMshe9LaJRpTvHr3C/ybFYP1PZAfwfYrPsMBtnE6SwN9ib7AhLwTrB\nDgUKcm06FSrTfSj187xPdVQWOk5Q8vxAfSiIUc7Z7xr6zY/+hpqwSyv0I0/QMTRb7RMgBxNodTfS\nPqdraz/sDjzKBrv4zu2+a2t0/HHzjd2Lbcc2sG7GtsL42K+xLfxtUgI7YHqKlqHK8HbCCXgjHT1c\nAdMlDetv4FnQ2lLasaOl6vmB0CMmwT/IPszSueHQqv6i/qluqF+oF9TfO2qEGTumJH0qfSv9KH0n\nfS/9TIp0Wboi/SRdlb6RLgU5u++9nyXYe69fYRPdil1o1WufNSdTTsp75BfllPy8/LI8G7AUuV8e\nk6fkvfDsCfbNDP0dvRh0CrNqTbV7LfEEGDQPJQadBtfGVMWEq3QWWdufk6ZSNsjG2PQjp3ZcnOWW\ning6noonSInvi0/Ex+IzAreevPhe+CawpgP1/pMTMDo64G0sTCXIM+KdOnFWRfQKdJvQzV1+Bt8O\nokmrdtY2yhVX2a+qrykJfMq4Ml3VR4cVzTQVz+UoNne4vcKLoyS+gyKO6EHe+75Fdt0Mbe5bRIf/\nwjvrVmhbqBN97RD1vxrahvBOfOYzoosH9bq94uejSOQGkVM6sN/7HelL4t10t9F4gPdVzydEOx83\nGv+uNxo7XyL/FtFl8z9ZAHF4bBsrEwAAQABJREFUeAHsnQd8FEX/xp+r6T2hd5QmCCrYARXsICr2\nCiqKvYIdC+9reV/0r74iKEUEK4j0IggiFsSCBaSoIAoB0kivl+T+Mxs33iV3l8tl7/bKM59Psnu7\nszPz+87u/nZ3Zp4x2EUAAwmQAAmQAAmQQEAJGAOaGzMjARIgARIgARJQCNAB80QgARIgARIgAR0I\n0AHrAJ1ZkgAJkAAJkAAdMM8BEiABEiABEtCBAB2wDtCZJQmQAAmQAAnQAfMcIAESIAESIAEdCNAB\n6wCdWZIACZAACZAAHTDPARIgARIgARLQgQAdsA7QmSUJkAAJkAAJ0AHzHCABEiABEiABHQjQAesA\nnVmSAAmQAAmQAB0wzwESIAESIAES0IEAHbAO0JklCZAACZAACZhDAcHGjRtDoZgsIwkENYEhQ4Y4\nlY/XlRMO/iABnwk0vLa8TYhvwN6SYjwSIAESIAES0JAAHbCGMJkUCZAACZAACXhLgA7YW1KMRwIk\nQAIkQAIaEqAD1hBmpCZ18OBBPPLII5FqPu0mAc0J7Ny5E08//bRyXa1YsULz9JlgcBAIiU5YwYGK\npXBFYPPmzZg5cyZsNpur3dxGAiTgA4FXX30VkyZNQlpaGu6//34MHDgQrVu39iElHhLMBPgGHMy1\nEwJlKy8vx/PPPx8CJWURSSB0CEjn26pVK5hMJqXQhw8fDp3Cs6ReE+AbsNeoGNEVgdNOOw3V1dWu\ndnEbCZCAjwSk85Xh/fffR0JCAnr37u1jSjwsmAnQAQdz7bBsJEACEUvgzTffxN69e5VP0RELIcwN\npwMO8wqmeSRAAqFHQDrfgoICPPHEEzAa2VIYejXoXYnpgL3jxFgkQAIkEBACubm5mDdvHjp37oxx\n48YpeY4fPx6DBg0KSP7MJHAE6IADxzpsczKbzZgzZ07Y2kfDSCCQBNLT07F+/fpAZsm8dCLAbxs6\ngWe2JEACJEACkU2ADjiy65/WkwAJkAAJ6ETAYBdBp7y9zjY/P9/ruN5GVDs21NbWentIWMWLdPvl\n+EpZ9yFw+mt23qWkpDil5Y/rymAwKJ2GampqnPKKlB+Rbn8k3lesVivi4uJ8OsVDog24sLDQJ+M8\nHZSYmAiLxQJ/3IQ85avuk+2mMug1hlYq7Ej1qqKiIrVIAV3Kk1bmr5cDbNu2rVL3FRUVAbVbzSw6\nOhqBzruhA/bHdSXrNSMjA5mZmaqpAV9GRUWhsrIy4PnKDOPj4xETEwO9hDPkg6V0gnop06WmpkI+\nfPnj3PKmQuX5J++pgXyxknXuqwPmJ2hvapVxSIAESIAESEBjAnTAGgNlciRAAiRAAiTgDQE6YG8o\nMQ4JkAAJkAAJaEyADlhjoEyOBEiABEiABLwhQAfsDSXGIQESIAESIAGNCdABawyUyZEACZAACZCA\nNwTogL2hxDgkQAIkQAIkoDEBOmCNgTI5EiABEiABEvCGAB2wN5QYhwRIgARIgAQ0JkAHrDFQJkcC\nJEACJEAC3hCgA/aGEuOQAAmQAAmQgMYE6IA1BsrkSIAESIAESMAbAnTA3lBiHBIgARIgARLQmAAd\nsMZAmRwJkAAJkAAJeEOADtgbSoxDAiRAAiRAAhoToAPWGCiTIwESIAESIAFvCNABe0OJcUiABEiA\nBEhAYwJ0wBoDZXIkQAIkQAIk4A0BOmBvKDEOCZAACZAACWhMgA5YY6BMjgRIgARIgAS8IUAH7A0l\nxiEBEiABEiABjQnQAWsMlMmRAAmQAAmQgDcE6IC9ocQ4JEACJEACJKAxATpgjYEyORIgARIgARLw\nhgAdsDeUGIcESIAESIAENCZAB6wxUCZHAiRAAiRAAt4QMHsTSc84drsd0dHRmhfBbDbDZDL5JW1v\nCms01j37yHLoEaTt/mLrjT0yf5WBN/G1jmMwGGC1WrVO1uv0ZL3747z2VIDa2lon5v7I32KxQLL1\nR9qebHPcJ88tWQY9grRfz/uKtFv+yTLoEVT2etW/zF/+yXtbKAR97v7NICNPpoqKimYc4V1UefP1\nV9relEB1vNXV1d5E1zxOXFwcZN7+YOtNYSV/m82m24UiL9Cqqird7Jc3qECzb/jA44/8pZOXbP2R\ntjfnlYwTFRWFyspKb6NrGk9e1/JPL/ul85H1LK8tPUJsbCxqamp0s1/eV+R9TZ6HgQrx8fE+Z8VP\n0D6j44EkQAIkQAIk4DsBOmDf2fFIEiABEiABEvCZAB2wz+h4IAmQAAmQAAn4ToAO2Hd2PJIESIAE\nSIAEfCZAB+wzOh5IAiRAAiRAAr4ToAP2nR2PJAESIAESIAGfCdAB+4yOB5IACZAACZCA7wTogH1n\nxyNJgARIgARIwGcCdMA+o+OBJEACJEACJOA7ATpg39nxSBIgARIgARLwmQAdsM/oeCAJkAAJkAAJ\n+E6ADth3djySBEiABEiABHwmQAfsMzoeSAIkQAIkQAK+E6AD9p0djyQBEiABEiABnwnQAfuMjgeS\nAAmQAAmQgO8E6IB9Z8cjSYAESIAESMBnAnTAPqPjgSRAAiRAAiTgOwE6YN/Z8UgSIAESIAES8JkA\nHbDP6HggCZAACZAACfhOgA7Yd3Y8kgRIgARIgAR8JkAH7DM6HkgC+hMoLjNi45ZY/QvCEpBAGBGo\nqQHWbY6D3e5fo8z+TZ6pkwAJ+INATS2w6osEzF2WjGN7l2PwMWUwGPyRE9MkgcgisGVHNKYvSEWU\n1a5cWymJ4mLzU6AD9hNYJksC/iLw0y5xg/gwFUaDHY/fko1+R1T6KyumSwIRQ+BAjhkzFqZi514r\nxl5QgDNPKvH7Qy0dcMScXjQ01AkcypM3iBRs+z0aYy7Ix9knl8DIRqRQr1aWX2cC5RUGvLs6Gcs/\nS8CIIcWYMCYHsdF+/vb8t810wDpXPrMngaYIVFQa8MHHSVj8aSLOOaUYs5/aj7iYwNwgmiob95NA\nqBKQ7bufiHbeNxen4MjOVZj68AG0a1UdUHMC4oCXLl2Kbt26oW/fvopx33//PbZs2QKTyYQhQ4bg\niCOOCKjRzIwEQoXA+m/iMGtRCrq2r8IrDx1Ax9aBvUGECieWkwSaQ2DHH1GYNj8V5eLh9v7rcnFc\nn4rmHK5ZXL864MrKSnzwwQfYtWsXOnXqpBS6tLQUGzZswN133w25f+rUqZgwYYL41s4eJJrVKhMK\neQK//mnFNNERpKjEhLuuysMJ/cpD3iYaQAJ6E8grMGG2eOPdvC0GV59XgAuGFosXQf1K5VcHLJ3t\nwIEDkZKSUm+h1WrFzTffDLPZjBrR17uwsFB09bbXO+DDhw9D/qkhJiYGUVFR6k/NlvLtW/75I21v\nCinzlkFdenOMlnFkvpK7XvZbLBbRfmlUyqClXd6mJR/4ZBkkAz2Cu7wPFxpFO28CPt8SjWtHlmD0\nsFJxrcgSan8N+KPupV2SrT/S9raeZBn0CvK+Js9rveyXeat/ejCQ9xU961/WvSxDba1zz+UqGzD/\n4zi8vzoeZ5xQjrefyUFSgrz2W35dteTl0a8OODU1FfJv9+7d9eeCBCT/qqurMW/ePJx11lnKCaNG\n2L59OzZu3Kj+VN6cL7vssvrfWq3Ik1SCS0tL0yrJZqWjVppeDkCepPJhKDo6ulnl1iqytF8v26UN\n8kaZmJioWxka2m8TN4i3lxsx80MThp9Ui+XTqpGWLOvGf/Xjj3Nf2iXPLX+k7e2515Ctt8dpEU91\nfpFqv6x7PR/sXdX9uq8NmDLbjDYZdsx5pgY9u8oHtFQtqltJo7zc969TfnXA7iysqqrC7Nmz0aNH\nDwwdOtQp2qmnngr55xj27t3r+FOTdXnzlQ8CeXl5mqTX3ESkA5BBPojoEeQNwibu+kVFRXpkrzh/\nmb9eTrht27bIz89HRYU+bT/ywUfNe/PWGLwuhhUlJ1TjmTuzcWSnKlSWAQfEn5ahS5cuTskdOHDA\n6bcWP+RDXUZGBvyRtrflk2+fsnlLjxAfHw/51S4nJ0eP7JWHH/kQIK8tPYJ84VK/bOqRvzz/5D1V\nvgHvPWBR2nkP5low7uIcDD627oLS+rSXde5rCLgDlmBmzJiB448/HoMGDfK13DyOBEKewF8HLYrj\n/VMsb7woH6cPKg15m2gACehNoKjUiLeWJGPdN/EYPbwQk2/PhtWiT1NTUywC7oB//PFH5ZN0cXEx\n1q1bp5TvrrvuQmws5fSaqizuDw8CJUI+csZHCVj5eQwuGlakiGlEC9UdBhIgAd8JSHW4ReviMGdp\nPI4T6nCvP56JjBShKRnEISAOeOTIkfUIjj32WMg/BhKINAKyX8iqL+OFfGQKjullw/THMtE6Lbhv\nEJFWR7Q3NAn8sFOVjwSeujUHfbrp07TUXHoBccDNLRTjk0C4Efj5VzHucEFdh79Hb8rG8UcbRBsw\nnW+41TPtCSyBg7lmvCHU4XbsicbYUfk4f0iVaP+VbcCBLYevudEB+0qOx5GAFwSy8kyYuSgVP/8a\njetG5uPcU1T5SP/1bvaiWIxCAiFNQMpHvi/U4ZZuSMR5g4vxwHV16nBGozVknK+sADrgkD4NWfhg\nJVBRZcD8NUmiTSpR0Wye9WQm4mND5LE8WKGyXBFPQA7bl9MEzl6SgiM6VuFVIR/ZPsDykVpWAh2w\nljSZFgkIAp9+Wycf2bmtDS9PPIhOYslAAiTQMgK7xCxFr81PQ2m5Afddk4eBR/k+/rZlJdHuaDpg\n7VgypQgn8NtfQj5S6MsWFJtwxxV5OPHo0L9BRHiV0vwgIJBX+Ld8pBgvf/V5hRg5tAhmHeUjtURC\nB6wlTaYVkQQKio14U3wS++KHWFxxTiEuPL0IFl5ZEXku0GjtCEj5yEXrk5SZwIYOLMXMJzKFWE14\nNePwNqHd+cKUIoxAtejEvERMEfjuqiScPKBMuUGkJIbXDSLCqpTmBgmBTT/FiN7NqUKOtQb/vfcQ\nuov23nAMdMDhWKu0ye8EvhGzqUj5yMT4Gjx7VxZ6iPlEGUiABFpGQMpHyutqf5YFN118GEOP01iP\ntWXF0/xoOmDNkTJBSSB6zTrEfrQYpqws1HTsiNIrL0XlSSeGPJx9WWa8LqYJ/CPTqshHnnE85SND\nvlJpgO4EioU63NxlQj5ys5CPHFaIJ8dnIyoC1OHogHU/9cKvAMlP/gvxb739j2E//ozYZStQOPE+\nFN968z/bQ2hN9rx8Z2UyVn2RoLTxPjYuB9FRlI8MoSpkUYOQgJSPXPl5AuYuT8axvYR8pFCHy0iN\nHIEaOuAgPClDuUjRa9c5O18HYxL/8yIqTj4Jtv79HLYG96pU1Pn4q3ihL5uCvkdUYNpjB9AmTZ8Z\nrIKbFEtHAs0j8OOuOvlIs8mOJ27JFteXPjNYNa/U2samA9aWZ8SnFvvRErcMDGJP7OKlKAwRB7z1\n9yhMF8OKau0GPHJjDvr3DA19WbcVwB0kEAQEDgn5yBkfpWDb79EYI+Qjzz5JVYcLgsIFuAh0wAEG\nHu7ZmQ5leTSxqf0eDw7QzuzDdfKRPwqB9+tGFuDcU4thMgYoc2ZDAmFKoKJSyEeuTsISIR8pr6n7\nrq2TjwxTc70yiw7YK0yM5C2Bmk4dgR9/chtd2e92r747KoV85IK1ifhoXRKGn1iCWU9lIoHykfpW\nCnMPeQJSPnL9N0IdbnEKuneowv8eOoAOrdmMIyuWDjjkT+/gMqDkyssQu3S5y0LZTSaUXnqxy316\nb/zsu1hl0oQOrW34vwkHIWUkGUiABFpGQMpHSnW4kjIT7hXykYPCQD6yZUScj6YDdubBXy0kUHXi\n8Sh48AEkPT8Fss1XDdL55j/3L1Qf0V3dFBTL3/dZlXZeKXd322V5OKk/5SODomJYiJAmcFhcT28u\nScamn2NxpVCHGyXU4cJFPlLLiqED1pIm01IIlIy/CZWnnIjYRUsh23xrOnZA6WWjUd29W9AQOlwI\nvDAnUXwaixbykQW46AzKRwZN5bAgIUvAJr4sv7M8CnMWJ2HIceEpH6ll5dABa0mTadUTsPXri0Lx\nF2xBykfKOUTfW23Fyf0rMEPoy6YlRc64w2CrD5YnfAhs+jkGsxbFK/KR/xHykXK6QAbPBOiAPfPh\n3jAi8N0vMZguVKziY2swbZINHTIKUVFB5xtGVUxTdCDw50GLcl3tE/KR915bgTNOqERhIZ2vN1VB\nB+wNJcYJaQL7hXykFHaX7b03XJiPYUI+sl27tsjPD2mzWHgS0JWAlI98WyhYrf06XmnCkfKRbduk\noIbPtF7XCx2w16gYMdQIOMpHjjqtCA/fkIOYaMpHhlo9srzBRUDKR0pJVqndPKBXBaYL+chWESQf\nqWVt0AFrSZNpBZSAoaAACXPmwbr5O8gu15UnnYCS669FTXwC1myqk4/s003IRz4q5CPTOe4woJXD\nzMKSwE+/1slHGg12PC7kI/tFoHyklhVLB6wlTaYVMAKmP/9CxhXXwuygvBW9aTN+Xb4X/z5lNmwG\nKx4Sb7wDKB8ZsDphRuFL4FCekI9c+Ld85AVCPvLkyJWP1LKW6YC1pMm0AkYg9b4HnZzvoej2eKHn\nM/gqfThu2TsLQ2eOonxkwGqDGYUrASkf+cHHSVj8aSLOOaUYs5+ifKSWdR30DtgudMysVquWNitp\nmYQwhPzzR9reFNZorBMXVpfeHKNlHJmvnvabzb6feqa9fyJqyw8KjgpjNOZ0vRdvir+RB97Byo19\nkWTLR27hUNS2buUWmcFggMViQa2c7kiHoAd7eS1Ju9Xgj3NfMpV5+CNttdxNLfVgq5ZJntfy2tLL\nfpm35O9Yz2rZmrv85OsYvPFhIrp1sGHa4zno1Eb2rrJ4TEa9n+llv6x7abs810Mh+H4XDKB11dXa\nt9/JG6/880fa3qCRJ4oMNTp1GZQnqJ72ywtV2u7LhWLcn6mw+7jNxZjS61l0Kt2Nt78+DUeWbFe2\ny3/2zExUp6XW/264IvOV+etZ/3rlrbLwR/6yXiVbf6StlruppSyDXvmr57Re+UvbW2r/r39a8doH\nKSgsMeLuq/NwQr+6WcC8uQ3rfV+Vzley9+W+0tR55W5/Sx42gt4BS6CyUrUOsoLknz/S9qas8iKR\nQa/89bZfvVB9uVB2mo/AzOPX4FBMBzy4YwKGZy11Qi6ffW1t2zbJVi2D08EB+qHHudfwrcgf556a\nproMEE6nbPRgqxZA7+tKffvzhX9+kRGzl6Tgqx9jcdW5/8hHNvf2G8n81fPA22XQO2BvDWG88Ccg\nn8jfWpqCDd91wtj42bhxwwhYaxsP+K84azhqPbz9hj8pWkgC3hOQ8pGyjVdOFXjqMWWY9WQmkhO0\nf+nxvkSRE5MOOHLqOmQtlV/pl36WgHdWJuOEvuWYMSkTGZX9YLiqPbDnDye7qnr1QP6zk5228QcJ\nkIBrApu3xuD1D1OFw63Bc3dn4chOjR9oXR/JrVoQoAPWgiLT8BuB77ZHKzeImCg7/nV7Fnp1rbtB\n1KI1slYsQtz8hYj6VowDFgOB5Tjg0tEXAVHad9rzm4FMmAR0IPCXkI+UjlfKSN54UT5OH1SqQymY\nJR0wz4GgJHAg24zXhXzkb6JDyFghHzn8hFLRu7FBUaOjUXrd1cpfgz38SQIk4IJAiZSPXJGMj78S\n8pHDihQxjWhraPQYdmFOyG+iAw75KgwvA8oqDHhvVTKWb0zAyKFFeGgs5SPDq4ZpjSMB885dYkjd\nj7BbLeILzomoad/Ocbdm67Ij1aov44V8ZAqO7lEnH9k6jaLNmgH2MSE6YB/B8TBtCYgO6Vgr5CPf\nXJqMXl2qMPWRA2iXof3wM21LzdRIwDcChvJypEx4GLErVtcnYBcjI4pvuQlFE++r36bFys+/RmHa\ngjQlqUdvyhYOuFKLZJmGBgTogDWAyCRaRmD7HnGDmJ+KKpsBE8bk4lgh8M5AAiFFQDxBRq9dj5gv\nN8FssyFOdAYsu+Qi2GNjXZqR/NiTTs5XRjKI19TEaW8oPfhLbhwjNzUOlVWI+WQ9zLv3oDY1BeXD\nz0Btm9aN44ktWXkmzFyUip+FfvN1I/Nx7imUj3QJSseNdMA6wo/0rHMLxA3io2R8K+bpvfb8AowY\nUizUuSKdCu0POQLCKaaNvwMxGzbWFz1FrCW8MRu582ahumuX+u1yxSSEZGI/WuK0zfFHwmuvo2TM\ntSKi88Vg/vU3pN90K8z79tdHT578DAomPYLSq6+s3yblI98R7byL1icKzeZiZVhRfCyHFdUDCqIV\nOuAgqoxIKYp8052/Jl6MO4wXvS9LMFuMO0yM5w0iUuo/3OxM+r+XnZyvap9ZqLGl3n4Pspd/BCFP\npW6GZftOOXmX22A6nA/TwUOo6SCG2amhogLpN9wCc+YBdYuyNFTZkPzYU6ju0gWVp5yE9d/EYoZ4\nqO3cpgqvPHgQHdvYnOLzR3ARoAMOrvoI+9J88YO4QYhZVdpm1OLFBw6hSzuOOwz7Sg9nA8Vn47h3\n57u10LpjJ6w//Iiq446tj2OPc/1Zuj6CWGn46Tp2+cpGzleNL535/pkb8eymi4R8pAn3XF2AgX2K\n1d1cBjEBOuAgrpxwKtofmRbRESQVWblmjBudjzNOqIZNtJXJzlcMJBCqBIz5BTAWe3Z25j/3OTng\nqmMHoDYpCcbCQpdmVw7or7TvOu607PrN8Wf9ep41Ay/3eBprLRfjsv5lGD28VAyDN4prqz4KV4KY\nwD/fRYK4kCyaGwKi7SnurbeRNmacMjdu0jP/UT5duYmty+YiIR/5v/dScf8LbXGMmJt3xhMHFLk7\nXQrDTEmgGQSMeYch210Npe5FKmoTE2CPivKYak2rDKf99pgY5D/5KFw9e9aKse0FTz/uFF/+kPk4\nBpvBLGYAu0f0m9gq0jFg0f5RuPTMIlj4SuWIKejXWV1BX0WuC2goLkHG1WNg3bqtPkLU5m8R9/4C\n5L41A1XHDKjfrseKlI+UY3nnic4gg44qxxtCPjI9WWxkIIEgJ2D6ax9SHpmEKNGjWX7etYspFktF\nj+bCxx5q9GlYzGmJshHnIm7hYpdWVbdtg8rjBzbaV37hBchLTkbiCy/Dsu0XpcNV5cknovChCbD1\n6dU4vtA3T3zxFaU8n2Wcg//0/i9SqnIx45vz0LdoC4ruvBVFjY6q2xD11ddKpy9TVhaqO3VC6RWX\nwNavr5vY3BxIAnTAgaStYV5Jz01xcr5q0vJzWOqd9+PQ+lUQk5KqmwO63LIjGtPF5+YoobDz9G3Z\n6NON4w4DWgHMzGcCxpxctLr0apiys+vTMIjvufHvzYf5z7+Q+/abYryQcxeqwkcfhPWnrbD8vrv+\nGLlSK4YgHX5pitvrsOK0IZB/qBL9IGSP5wa9nh0Tq+7ZA1vHTcLUH47D7/FH4b5dj+L8gx8oUap6\n91LGDzvGV9cTn38BidNnqD/F8ivEvfeBeMuehNJr/uk57RCBqwEkQAccQNiaZSVeL2MXO0/B55i2\n7H0p34YrB5/iuNnv6wdyzKKDVSp27hXykRcU4MyTShreq/xeBmZAAi0hkPD6TCfn65hWtHiTjBZj\ncCvOHOa4GbUpKchePB/xc+Yh7vMvYRRNQ2VH9UbxuBtQ07mTU1yXP5p4UC4tNyjykatzHsIlJ3yH\n/3x+BRIKfkG1UM0qP/9c5e3XHhfXKOnoTz9r4HzrohhEx4vkJyajctBxkI6dQT8CdMD6sfc5Z4N4\nyzWWlXk83nTokMf9Wu4sl/KRYiqzZZ8lKmN5J4zJQWy0qxYuLXNlWiSgPYGorzZ5TFR+zm3ogOUB\n0gEW3z4e9gcfQIxo4y3IyfGYjjc7RQdrrBaazXIKzn5HVmDaYwfQJq0Vih94G567fdWlHismKnEX\npOiH/Gxe+MhEd1G4PQAE6IADAFnrLOyJiahNSPDY+7KmYwets22UnuzB/MnmOLy5OAVHdhbykQ8L\n+chWlI9sBIobQoaAodpzPwVDdWDO762/SfnIVDFKwIBHbspBf6Hf3NxgPuA8Zrjh8aYm9jeMz9/a\nE6AD1p6p/1MUg/pLLxuNhFlzXOZl69ZVfF5q3PHDZWQfN+74o04+slyo7tx/XS6O69P8G4SPWfMw\nEvAbgcqBx8Ly2+9u05efbf0Zsg9LdbhU/LhLykcW4NxThTqcj2NVqtu3h/XnfzppNix3TTv/TPzQ\nMB/+dk+ADtg9m6DeU/jAPUJRZweiN212KmdNejrypr7ksUOH0wFe/DAeylJ6V1t+/x1ZSV3xSvJd\n2JTVClefV4ALhlI+0guEjBIiBIpvvRlS9MJYXNKoxFVH9UH5eec02q7FhooqAxasSVLkI888sQSz\nnspEQgvlI8suvRixqz52WTw58UPp6Atd7uPGwBGgAw4ca21zEuMFZY/MmGUrEPOp0KAVUnW2o/ui\n5KrLYRfDG7QKUV98JXRu70R1uQ1vdbkbs7o9gHN/mo8PB2wFztB21hatysx0SMBXArLpJkdcV6n3\nPQiLmPBADeWit3L+lOcAs/a3zA3fxYlJE1LQSchGvjThIDq11UZFo+L0oSgaP65RRyy76MVd8NTj\n7IClVq6OS+3PJh2NibisxVNs+aiRyp8/bDcUFCDttruxPn4Ypgx8Dm3K92PO5uHoVSycrxi6eHhA\nV2XGF3/kzTRJQC8CtqP7IWvtCliEjKQcllTdtQtqOnXUvDi/77Mqs4AdLjLh9svzcNLR5ZrnUfTg\n/cpoCDn5gxxaVd2xI8cBa07Z9wTpgH1nF/ZHHpr/LSb1+gB/xXXHAzsfxtmHhKi8Q4h79wM6YAce\nXA1OAnI4Ttzcd2De+ydqM9JRdsEIlF55medmGvGWaOvT2y8GFRQbMWdJCjZuicMV5xTgojP8q2Al\nBT7kX7OCGOpo+WU7jEIFzNarpzLUqlnHM7JXBOiAvcIUXpFMQlDA9MmnsBsNMPbrq9yUHC0sLjVi\n7rJkrPvpGtxw+D947fuLEFXbWEzDvG+f42FcJ4GgI5Dw6jQkCbWp+iCccNS33yN64+fIm/Y/z064\n/iBtVmQH6yWfJuLdVUk4Weg2z3xCzJaU5LnXtTY5Ny+V6HWfIuXRJ2DKqhMjUdqLr74CBY8+BKGu\n07zEGNsjATpgj3jCbKdQ3El5/CnI8YFSy0deSjFCSq9IjF8svvt21Ihxhys+F/KRy5NxXO9yvN1v\nGnqsfNYthJpWrdzu4w4S0JuAnPZPSj26CjFr1yPugw9RKvpMBCJ8sy0Gb3yYioS4GjxzZxZ6dgnO\nWcCsQsAn7ebbIccJq0Gux897V9HEzn/heXUzlxoQ0MUBb9u2DZs2bRIyqhacddZZaMfu8BpUZdNJ\nJD/1DOIaDM6XMntJL/0P3+BovFR0uagTO54cn42julfCeOgU2F+ywiCUfVyFsotHudrMbSQQFARi\nRG9mZ9FI52LFLF3hdwe8L8usON49+6244UIxC9jxpUGtDpc05f+cnK8jsTjRjlx8y02o7nGk42au\nt4CAjyPMfM+xRrQtLF++HGPGjME555yDJUuW+J4Yj/SagFF0wJAasA3DvpiuuPuYDzB52zm46LQC\nvDLxoOJ8ZbzaNq2R/+y/YHehUVs+7HSUjLm2YXL8TQJBQ8CUm+exLKbcXI/7W7JTyke+Iea9vuu5\ndujesQqznszEsBOC2/lC3Jut3//g0Wz5+Z5BOwIBfwM2iZu5XUgoZQq94mzhFKIaTOVVJT6TVlb+\n094o4xtFb1+tg0F0spB//kjbm7Kq+apLb45pSZyoX3ZAasCqocwUhze6P4h3O9+Ky/6aiX9vvQml\nTyxGjdm5t2eFGCuYK55444TOrVkIFNSmpihjIcvF7DAtKbs8Vv7Jc0GvoJZBj/z1zFu1V5ZB66Cm\nqS61Tt+b9FS2Nd27eoxe3b1bi85hV4lL5arF68z43zsdlAfZ6Y8fRNt02c4r38U9vY+7Sq3521Tb\nfeYvzwnhiN0Fgxf3Y73vqz7b7s5oP24X9+TA3gFlditXrsQvv/yC8vJyXHjhhejfv3+9iatXr4b8\nU0P37t1xxx13qD+59JGAff0G1A4/V5mDdFm7q/B/Pf+F3kU/YeKOCehSVqf8YzzwBwxt2viYAw8L\ndgLyxugYAnzpO2YdkHW70EOv7dEPKGksqiELYFyzAobhZ2hWlu/F0Lx/TwdkZ6tHbwFO+Oe2plke\n/k6oRtwjIO4VLoM4fYw7t8Jw5BEud0fqxuLiYiQKeWBfQsDfgPeJnrN//fUXJk6cKB60ajB58mT0\n7t1bzJxX17tOfpaWf45h7969jj81WZfAZBt0Xp7nz1SaZOYiEfPfA/qrA6Qta2jXBrlth+D5zpNR\nbEnG5K3jcWrumvqSySnNsuWXhz//rN/mzxVZ3zbR/qyXE2jbti3y8/OFfkmFP810m3a0EFIJdN5d\nunRxKs+ffqhrWa8ZGRnKFy6nzAL4Q35VU7+iRQlVuLTb74axpLS+BFKIovDhCSg5srsm53uOlI8U\nQhpbdsZg3OhyXHGeAYcP5wTqUqq3S66oXwzlteVLsIjOmK2++BKGqsbHl1xzFQqsFo/MUlNTlft6\nYWGhL9m3+Bh5/sl7aq1DJ7IWJ9pEAvHx8U3EcL874A5YApKzhcggPxWo6+6LyD0tJZBXaBITJnTE\n1wOX4ratj+PKP6fBYv9HVF5OOC6VcfQOlh9/Rsy6T5XelnJS8rKR54thD1F6F4v5hzCByiGnirmx\nP1am71TGAaenoWzEeajW4C2uUspHrk3ER+uSMFzIR84W8pFtW8UKJ1h3fwtFbFKEJGfem0h57Ml6\nTexacb8uGTcWRXfdHoomBXWZA+6A24hPnCli/sy5c+cqn6AHDx5c//Yb1KRCsHDyIXaxGHf4/upk\nDDmuFDP/nY02m3oC/+0M/L5b+RxddewxKHzsQVQdM0A/C0WzRPKkpxH/9ntOZUh85TXkzpmBajG5\nBAMJ+EpAim+UiLl5tQyffR+rTJrQobUNLz5wEF3aNX5j1DK/QKZVdfxAZK1ZDtO+/cqXA1u3LnwQ\n9lMFBNwBSztGjRqlfH5UP5f4ybaITnbTT2Lc4cJUpImB/v+995DSE1MCqThruPKXZrbAJhxfUc0/\nb8J6AZMqRQ2dryyLWdwA0m65A1mrRE95sy6nql5ImG+QEtgt5SPFNIF5BSbcdpmQj+yvvXxksJgu\ndbHdd8cKllKGdjl0u6vJ9lcG7Qn8edCC6eIGsT/LgpsuOoyhA8tcZ5IkOg3IdqKiItf7A7g14c25\nbnOziDf1qC83oXLoYLdxuIME/E1Ayke+tSwFn4mJEy4/u1DIRxZCNocykEBLCOjmgFtSaB7bmEBx\nmVFRsPrk63hcPKxQEdOIsuo3xKdxCd1sER3xzEIa01OQs9LQAXsixH3+IiB7NC/dUCcfeWK/cswQ\n8pHyqxIDCWhBgA7YG4rCScSsXQfLTz/DHiV6r4qpyWwDjvbmSL/HkfKRq75IULSbB/SqwOuPZSIj\nNYRuEGJcYW1SEoweek3WpKX6nSMzCA0CZvFFJGH6TDHR/FbYY8S1KKbcK75xLOwJvvdEdWf5d7/E\nYPqHKYiLsePfdwSvfKS78nN78BOgA26ijqSCVPrYW2DdvqM+ZtLLr6L00tHIf26y7Mpdvz3QKz/t\nilbao0xGOx6/JRv9jvhHwCTQZWlJfmUjzkX8O++7TKI2Pg4V/Pzskk2kbZRzU6ffdKuQRv3nPLf+\nvE3Mib0SOQveVURitGCyX8pHiv4TcrpAKR85LMjlI7WwmWnoQ4AOuAnuaXfc6+R81ehxCxaiunNH\nFIuJDAIdDuWZMUPI3G37PRpjLsjH2SeX6Pkc0GLzCyfciyghAi/bex2DnIUl/5mnYU9OdtzM9Ugk\nIMZrp9470cn5qhgse/5A0r+fQ0snCpDyke+uSsZKMSHJBacV4eEbchATHQLNOCoILkOOgH6vbyGA\nyiI+c3nSPo2XnYdET+JAhYpKA95amoxb/9UOrdOqxbjD/Tj31NB2vpKdXXyCzv7oA2VWJlvPHqhu\n3x7lord2zofvolyOBWaIeALRX38DT9rNsStWCwkq33r0S82G1V/G48YnO+BgjhmvPXoAY0cV0PlG\n/FnnfwB8A/bA2PL7Hg97hepM3mEY8ws0+/QlM5NtXKacHFQL1aKatm2U/KWP//TbOMwSajtd21fh\nlYcOoGNr3242Hg3Scadswyt64B7lT8diMOsgJWAU14SnID9LG4TkZHO/lvyyO0ppxrHZDHhwbA6O\nEf0oGEggUATogD2QlhMPeAp2MTa1Ni7OUxSv91l27ELK/Q/CumNn/THlQqd2810vYurqLigqNeHu\nq/NwfN/wHXdYbzhXSKABgequnsVYalKSlS8pDQ5z+zMn36Q80H6/IwbXjijA+YOLYeL3QLe8uMM/\nBOiAPXCtPGEQaoQTNh3OdxlLOkhE1WlYu4zg5UbTwUNIv+p6mAoK6o/ItbbCS1mX45Op7XHlxSUY\nNawEZlP9bq6QQEQRqDp2AKr6CM14h86QjgBKr71KTDZkcNzkcr1KvOlK+ciFnySJzlVCPlJME5gQ\n98/k8y4P4kYS8BMBPvN5AGsXGqj5z4n5cF2oMFWLuXILJz3i4WjvdyVMn1HvfG0GC2Z3vRcjh/wM\nk70GKz7tg2vK36Pz9R4nY4YjAdEhL++1l1HdqWMj68rOPRtFd97WaHvDDRu3xOKmp9pj62/Rinzk\n7VccpvNtCIm/A0qAb8BN4K44cxiyF76HxFenw/qTGHsYHaWMAy6641ZIjVktgvXb75RkPm11Hv7T\n6z9Ir8zCrG/OQZ+iH5XtJd98j/JRI7XIimmQQMgSqOncCYc+Xoa4RUth2bpNXIt1Y/LlhAuewp79\nFtHOmwb52fmWSw7jlAFu1OE8JcJ9JOAHAnTAXkCVM4TkvTHVi5i+Rdlj6Y7/G/hf7I7vjft2PYrz\nDs53TsjY9Kc15wP4iwTClIBwuqVXXgbIvyZCYYmQj1yagg1CPvKyswoVhTjKRzYBjbsDSoAOOKC4\nnTMrKTNgzpJUrOnwLq779QW8tOVyxNQ27mRVedIJzgfyFwmQgFsCQrgOi9Yn4J2VyThBdFqcMUnI\nRyaHkDqcW8u4I9wI0AHrUKNy3OHyz+Lw5pJEHN1DyEfe8yuOvmoaTK6c76DjUH7OWTqUklmSQOgR\n+G57tFCxSkO0tRb/uj0LvbpWhZ4RLHHEEKADDnBV//yrHHeYpnTYfGJ8Hvp0k+1Riche8A5SH3pM\nUYSSRbILjeSyUSNQ8OTjuspdBhgPsyMBnwgcyDbjdSEf+dufVoy7pASnHVfgTadon/LiQSSgFQE6\nYK1INpFOVp4JMxel4udfo3HdyHyMHFqhyEeq4j01XToj5/15MGZlCSGOXKW3pz1RTBnIQAIk4JZA\nWYWQjxSfmlcI+ciRQ4vwkBDTSE6ywkEu2u2x3EECehOgA/ZzDVRUGTD/4yTRJpUoNJuLMUuMO4yP\nrRXO1zX62tatIf8YSIAE3BOQ6nBrN8Vj9pIU9O5aiamPHEC7jPBSh3NvPfeECwHXXiBcrNPZDlU+\nsnNbG16eeBCdxJKBBEigZQS2/y0fKUU1Joo33mMpH9kyoDxaNwJ0wH5A/9tfVkybn4qCYhPuvDIP\nJ4iJvBlIgARcEzCKJheDaIupEeI2nhpucwvq5CO/FfP0Xnt+AUYMEfKRVIdzDZVbQ4IAHbCG1VRQ\nbBQ9m1PwxQ+xuOKcQlx4ehEsJKwhYSYVTgSivtyE5MnPwrLrV8Ws6nZtUXT/PSi7eJSTmfJN98NP\nEvHh2iSc8bd8ZGI85SOdIPFHSBKge9Cg2qrFEMMlnyaKuUSTcLJQ2Zn5RCZSEnmD0AAtkwhTAlFf\nfIX062+CQY7J+zuYDxxEqpiQxFBWhtJrrlS2yodZOfd1m/RqvHD/QTEbGJtxVF5chj4BOuAW1uHm\nrTFi3GEqEuNr8OxdWejRmeMOW4iUh0cAgeSn/u3kfB1NTnpuCradcCmmLW+PrMNmjBudj1OPoXyk\nIyOuhwcBOmAf63FflhnTF6Rib6YVN12cj9MHlfqYEg8jgcgiIIfaWcS8165CgSUV/+v8BJb9rzMu\nPbcEo4cXwWoRXZ4ZSCAMCdABN7NSS8sNeHtFMlZ/maC08T4+LgfRUbxBNBMjo0cwAUNV48/I1QYT\n3u90C1474jEMyVmNt85ZjIRzB0UwJZoeCQSC3gHbxYA/kx+6OhrE3KFGMcWZt2nLpqpVXwr5yMVJ\n6HdkJd6YdEi0S0l9Wd9mdJR5y+Bt/kpkDf81134Ns1aSUtnL+tUrqGXQI3/JP9B1L1nLfNXgj/zV\nNNWlmpfTUkwpWNMqA6bsHGXzV2nD8FzvKYitKcVr31+E/sXfIfukL1Hr43WvZ73KvPWoW5WvzF9P\n+4PhvqLWgcokmJdB74AlPMebhhYwzaLXpVVM8WeqrYG1dy/Yjh/oMdmtv1kx9f1k1Agn/PgthzGg\nZ+Xf8f+5mXlMwMVO1SZ16SKKXzep+apLv2bmInGZr155y+Ko+etVBjV/F2gCtskftqtpqkuXxgjH\nWiJ6Oxc+NUuZfnNr8kDcu+txjMqcB3lFlY67AXYx1afvV5f29wyXdnjY6NF+D8e1dJear7psaXq+\nHq9n/nrm3VxeQe+AJcxqVa+xudY1jC9eY5OfmIy4t9+rv7jTRJwKMZ9o3qsvwZ4Q73RE9uE6+cgf\nd0r5yAKce6oYdyheXLUqjsxMM9ucSt70j1rBQv7plb98SpV56/UGLPOtEdPm6GW/2WwOeN4Nb0z+\nsF3Wqwye0pbyka9bb8Ly0+/AFXun4/nPxiCupgR2cWzxmGtROPG+Fl1k8u3bU/5NXx2+x5DXlDy3\n9Mpf2q5eW75b4fuR0na97yvyupZlCIUQ9A5YS4gJr72OeOF8G4bojV8g5aHHcHjqS8quSikfuaZO\nPvLME0sw66lMJAj5SAYSIAHfCYh7M9Z+HS/GyiejpxgtMHVSDtpHn4GyH1JRXlWFqgFHozYjw/cM\neCQJhBiByHHA4m0rYcabbqsnduVqFP61D+uzeyqTJnRobcP/TTgIKSPJQAIk0DICO/4Qs4AJdbiK\nSgMeuC4Xx/WpUBK0IwGVQwe3LHEeTQIhSiBiHLDp0CEYi4rcVtOOxP54esaRyDWk4LbL83DS0ZSP\ndAuLO0jASwJ5Uj5ycQq+2RaDa4R85EjKR3pJjtEigUDEOODa+HjI/rYNO3Yctqbj5R5P4eM2l+DK\nzntxwZgKykdGwplPG/1KQI40WrguCQtEU87pg0QzjpgFLInykX5lzsRDj0Bdj4nQK3ezS2xPTkaV\nQ29nm8GMuV3uFILuW2EzWLFk2xCMvjaGzrfZZHkACTgT+GSTAeOebo8fdkRjyn0HxYQkh+l8nRHx\nFwkoBCLmDVham//0JLS69Cp8GXUynhfjDhNt+Xj92xHoW/wD8qa9AluUlacFCZCAjwT+yLQIWdY0\nIR9pwk0X5WDwsZSP9BElD4sQAhHlgPcm98Gj1+3C7j0G3LfrUYw48B6qjumPnHvnoWrQcRFS5TST\nBLQlUFRixNzlyVj/TTwuP6cEt19tRG4Ona+2lJlaOBKICAcs5SPfWZmMVV8kYNRpRXj4zkK0yvg3\nKi3/QV5eXjjWK20iAb8TEMMtseLzBMX5DuxTLtThMtGulQlR1hi/580MSCAcCIS1A5ZjsddskuMO\nU3BU9wpMe/SAMq1ZOFQcbSABPQlsEeI0r4vJSORECU/fmo0+3VV1OJOexWLeJBBSBMLWAf+yu27c\noa3agIdvzBHykXXjDkOqdlhYEggyAgdzzaKdNwU79kRj7Kh8nHVSiZD1DLJCsjgkECIEws4B5+SL\ncYeLUvD9jhhcO6IA5w+uk48MkfpgMUkgKAmUC/nI91YnYdlnico19cB1+xEXo99EGkEJiYUigWYS\nCBsHLOUjP/wkEQs/ScKwE0owW4w7TIjTTz7Ssu0XRfbS/Mde1KSno+yC81Fx9pnNrB5GJwF9CUj5\nyHWb4zBbiGkc0akKrz58AO1bVetbKOZOAmFCIGwc8OQ3MlBdY8CLDxxEl3b6ykfGzv8QKQ9PgsFB\nEFxKXZZeOhr5//l3mJw6NCMSCLy7KgkbvovDfUI+cuDf8pHu7JazjMWuXQ+UlyOuXRvx0Dmy0QQn\n7o7ldhKIRAJh44AnjslFYhAo7Zj+/Aspjz7p5HzVEytuwUJUnng8yi4epW7ikgSCmsCFpxfhirML\nxdzFnouZ8PJUJL70v3qluRQRPfGlV5E7czps/ft5Pph7SSBCCYSNElYwOF95DsUuWQ6Dh/kKYz/8\nKEJPNZodigRkO29Tzjdm9RokOThf1U5Tbh7Sx90GQ0mJuolLEiABBwJh44AdbNJ11XTwoMf8TYey\nPO7nThIINQLxb851W2RTTg5iRPMLAwmQQGMCdMCNmbRoS02H9h6Pb2q/x4O5kwSCkIB5zx8eS2XZ\n7Xm/x4O5kwTCmAAdsMaVW3bhBbB70JQuvfxSjXNkciSgL4Ha1FSPBahJlS3CDCRAAg0J0AE3JNLC\n3zXt2+HwlOdht1gapVR8w/UoP/+cRtu5gQRCmUDZiHPdFt8uGpDLzznL7X7uIIFIJhDyvaCNoo0p\net0GmA4fhu2I7qg4fSjgwvkFspLLxQ0pq08vxL2/AHXjgNNQPvJ8VJ58YiCLwbxIICAEim++EdGf\nfY6o739olF/hIxNR07lTo+3cQAIkAIS0A479QIy3fWIyDJWqDi1g69oFeTNeQ3X3brrWb3W3rpA3\nHwYSCHsCUVHIeWcOEma/hdhVa2AuLERF1y4ouXEMKgefEvbm00AS8JVAyDrgqE1fI+Whx+rHHaoA\nLEJ5Kn3MOBz6ZCUgbgwMJEACASAgrrXiW29G5d13ICMjA3mZmQHIlFmQQGgTCNk24PjXZzVyvmpV\nmPdnInb5KvUnlyRAAiRAAiQQdARC1gFbd+7yCNPSxH6PB3MnCZAACZAACfiZQMg64NqEBI9omtrv\n8WDuJAESIAESIAE/E9DNAdeKiQreeOMNHGxCOcqd/eVnDXe3C3axp+LMYW73cwcJkAAJkAAJ6E1A\nNwe8fv167N27F9UedJM9wSm+dRxsPXu4jFI8/ibYevd0uY8bSYAESIAESCAYCBjsIgS6IPv378eG\nDRsU5zts2DB07Nixvghr166F/FNDt27dMG7cOPWn09IuhjvgKTG9n5hlCEL4Hb17AXffDsP11zrF\nc/XDYDAom3UwX8mX+evL32g0Qn6F0SvI+g/0uWdqMKtCTU2NX8yPRLYqSFmv8k+vc4v3lcDfV0pL\nS5GYmKieAs1aBtwB22w25dPz2LFjMX/+fDR0wOViLtGysrJ6IyxCVKOoqKj+t1Yr8fHxQq/Dgvz8\nfK2SbFY66s3QXzfBpgqTkpICWRclOs1UI9nL/PUKrVq1QqF4gKt0GEMeyLJEiWE7gc67Q4cOTibK\nB2Gtg9VqhTy3srL0m3RElqGqqkpr07xKLzY2FtHR0TgshIH0CPLhR/75+mWxpWVOSkpSHj6Ki4tb\nmpRPx8v7irQ9kA+3sr7T09N9Km/AxwGvWbMG8iT97rvvkJ2djS1btijjBqURMsTExCh/jtb442SW\nT6jyT68TVbVPr/z1tl/eJOTDRyAvFJW5XMp8Zf568TebzbrlrXLwh+2yXmXwR9pquZtayodbvfKX\n15U8t/TKX9quZ/4ybz3vq+p9RZYhFELAHXCfPn3QunVrhY28CUnHq342CQVgLCMJkAAJkAAJaEEg\n4A64a9eukH8ybNu2DdIhy89xDCRAAiRAAiQQSQQC7oAd4Y4ZM8bxJ9dJgARIgARIIGII6DYMKWII\n01ASIAESIAEScEGADtgFFG4iARIgARIgAX8ToAP2N2GmTwIkQAIkQAIuCOjaBuyiPNxEAiQQJAQs\n23fA/Nc+VLdpA9vRfSEGmAZJyVgMEggPAnTA4VGPtIIENCNgEk437c57Yf15W32atu7dcPiVF2Dr\n07t+G1dIgARaRoCPtC3jx6NJIKwIGISsXsZVY5ycrzTQsnsP0q8eC6MQz2EgARLQhgAdsDYcmQoJ\nhAWBuA8+hDkz06UtpoICJMx+y+U+biQBEmg+ATrg5jPjESQQtgSsP/zo0TbrFs/7PR7MnSRAAk4E\n6ICdcPAHCUQ2AbsQs/cUmtrv6VjuIwEScCZAB+zMg79IIKIJVJ56ikf7m9rv8WDuJAEScCJAB+yE\ngz9IILIJlI0agcrjjnEJQfaELrnuKpf7uJEESKD5BOiAm8+MR5BA+BIQ09nlvjUTJVdcCnuUVbHT\nLmYtKxtxLnI+eBv2uLjwtZ2WkUCACXAccICBMzsSCHYC0skWPDsZBU8+BlN2DmrTUmEXc3gzkAAJ\naEuADlhbnkyNBMKHgJgmtKZjh/Cxh5aQQJAR4CfoIKsQFocESIAESCAyCHj9Bvztt9/ixhtv9Ehl\nypQpOOusszzG4U4SIAESIAESIAHAawfco0cPTJs2zSOzXr16edzv606jH0Tg7XY7amtrhb68Ph8B\nDAaDgkOv/KXtkoFe+Uv7Zd6yDHqEmpoaXfnLzPVir/L2R/6yXqurq8PSNpWbpyXvK/reV2TdqPcW\nT/Wk5b6WXEcGccJodgesqKhAdHS0lrYxLRIgARIgARIISwI+vf6tW7cOQ4YMQb9+/dC3b1/07t0b\nrVu3xooVK8ISEo0iARIgARIgAa0J+PQGLD9HX3fdddi+fbvigOPj4/HOO+9g06ZNun960hoQ0yMB\nEiABEiABfxBo9huw/GJdWFiIRx99FGeffTaqqqpw1113YdiwYVi9erU/ysg0SYAESIAESCDsCHjd\nCUu1XDZwx4mB+tIJ9+/fH/PmzVN2paam4q+//lKjabrcuHGjpukxMRKIRAKy2cgx8LpypMF1EvCd\nQMNry9uUmu2AZcLjx4/H0UcfjT179mDv3r24/PLLsX79enz99dfe5st4JEACJEACJBDRBJr9CVrS\nmjhxIpYvXw6z0IiVHbJkZ6xly5ahe/fuEQ2TxpMACZAACZCAtwS8dsDy7VYKcciOVjLIN2AZOnfu\njMceewwnnnii8pv/IouAfAB7+OGH8eCDD2Lfvn2RZTytJQE/E5BNfBxd4mfIOibvtQM+8sgj0apV\nK1x66aVKz+eXXnoJeXl5OhadWetNoLi4GIsWLcK//vUvpVf81KlT9S4S8yeBsCGwbds2LF68GCUl\nJWFjEw1xJuC1A05LS8Ozzz6rdLR68cUXIaUpjzjiCFx55ZXKZ2gN9TycS8hfQUsgISEBr776qqJm\nJW8WGRkZQVtWFowEQolAWVkZ3nrrLVxxxRWhVGyWtZkEvHbAarpSdkvqPctxv3/++acy/OiZZ55B\nz5498d1336nRuIwgAvItWD6pH3OM64ncIwgFTSUBTQjIr0ljx46lsqAmNIM3kWY7YEdTLBYL5FtQ\nUlISKisrlTHBjvu5HhkEZLPE3LlzFa1wKUfKQAIk4DuBLVu24Ndff8WuXbvwww8/QH5dki87DOFH\noNkOWIr4f/LJJxgzZgzatWuHOXPm4KqrrsJvv/2Gk08+OfwI0SK3BA4ePIjJkyfX75eKaLJnPAMJ\nkIDvBNq0aYPLLrsM8nqKEnMyW61WyJcdhvAj4PXdMicnB8899xzee+895aS44YYb8PPPP6Njx47h\nR4UWeUWgbdu2kH0DJk2apHwBueaaa+iAvSLHSCTgnoB8sZF/MsgvSrI9WP3t/ijuCUUCXjtg+YZ7\n4MAB5VOjlJ2UilgMJHDbbbcpzlc+pfOc4PlAAtoSGDlypLYJMrWgIuC1A5afl9VPzFdffbXSEWv0\n6NHKZ5KgsoiFCTgB+ZmMgQRIgARIoHkEmt0GLJOXnxqlElaXLl2UtuANGzboNrF688xlbBIgARIg\nARIIDgI+OeBzzz0XCxYswO+//668FcvxwXJM8FNPPaV8pg4O01gKEiABEiABEgheAl5/gnZlgpyI\nQbYNS0csOwlIZaSTTjpJ6awlBTq0Cp06ddIqqfp0EhMTlZ6Feql5qb2Fq6ur68sUyBXZecpms2Gk\nOAMAADl3SURBVKGoqCiQ2dbnJduMZf56CbjIDmT5+flKJ5f6QgVwJTo6Wre8VTP9cV3JepWCLJmZ\nmWo2AV/KJhE5LFKPIHsux8TEQHZa1SOYTCZlTnZ5bekR5Kx4NTU1ymx5euQvzz95T5WjdQIVZJ37\nGnx6A3755ZcVOcoRI0YoXeTlPMCff/45pkyZonTSkkpZDCRAAiRAAiRAAu4J+PQGLKchfOGFF3Dm\nmWcqT1uOyR911FF45ZVXHDdxnQRIgARIgARIoAEBnxywfAN2F9LT0yH/GEiABEiABEiABNwT8OkT\ntPvkuIcESIAESIAESMAbAnTA3lBiHBIgARIgARLQmAAdsMZAmRwJkAAJkAAJeEOADtgbSoxDAiRA\nAiRAAhoToAPWGCiTIwESIAESIAFvCNABe0OJcUiABEiABEhAYwJ0wBoDZXIkQAIkQAIk4A0BOmBv\nKDEOCZAACZAACWhMgA5YY6BMjgRIgARIgAS8IUAH7A0lxiEBEiABEiABjQnQAWsMlMmRAAmQAAmQ\ngDcE6IC9ocQ4JEACJEACJKAxATpgjYEyORIgARIgARLwhgAdsDeUGIcESIAESIAENCZAB6wxUCZH\nAiRAAiRAAt4QoAP2hhLjkAAJkAAJkIDGBOiANQbK5EiABEiABEjAGwJ0wN5QYhwSIAESIAES0JgA\nHbDGQJkcCZAACZAACXhDgA7YG0qMQwIkQAIkQAIaE6AD1hgokyMBEiABEiABbwjQAXtDiXFIgARI\ngARIQGMCdMAaA2VyJEACJEACJOANAbM3kfSMY7fbYbVaNS+CyWSC/PNH2t4U1mise/ZRl94co2Uc\nma+e9pvN+p56BoMBFosFtbW1WmL1Oi092MtrSdqtBn+c+5KpzMMfaavlbmqpB1u1TPK8lteWXvbL\nvCV/x3pWyxaIpXo/08t+WffSdnmuh0LQ9y7oJSGbzeZlTO+j1dTUKA7IH2l7Uwr1RKmurvYmuuZx\npOORDPSyX14k0na9LhSZr8xfL/tl/euVt3oy+SN/9ebnj7TVcje1lE5Ar/zlNSWvLb3yD4b7ip72\ny3NDrYOmzhOt9rfkYSPoHbB6QWsFyzEdeRPWywHIcuidv1oGRyaBWldtj1T+qv2B4i3zkdeSY/AH\nezVNdemYX6DW9WCr2qbarS7V7YFaynz9ec/0xg49+cvy6Z2/N4zUOGwDVklwSQIkQAIkQAIBJEAH\nHEDYzIoESIAESIAEVAJ0wCoJLkmABEiABEgggATogAMIm1mRAAmQAAmQgEqADlglwSUJkAAJkAAJ\nBJAAHXAAYTMrEiABEiABElAJ0AGrJLgkARIgARIggQASoAMOIGxmRQIkQAIkQAIqATpglQSXJEAC\nJEACJBBAAnTAAYTNrEiABEiABEhAJUAHrJLgkgRIgARIgAQCSIAOOICwmRUJkAAJkAAJqASCfjIG\ntaBchjkBMTNR7IeLELPuUxiKS2A7qjdKxl6Lmg4dwtxwmkcCJBCpBOiAI7Xmg8nuykqkXz8O0Zu/\nqS+VXI97fwHyZr6GypNOrN/OFRIgARIIFwL8BB0uNRnCdiS+9KqT81VNMZaVIfWu+2EQSwYSIAES\nCDcCdMDhVqMhaE/cwsVuS23KzUP0Z5+73c8dJEACJKA5gZoamHf9CsuOXYBoHvNX4Cdof5Flut4R\nECe6KSfHY1zTwUMe93MnCZAACWhFIPajJUh69j+QD/8y1CQno2jCvSi96nKtsqhPh2/A9Si4ogsB\nkwnVbVp7zLq6Q3uP+7mTBEiABLQgIJ1v6v0P1jtfmaapoAApjz6BuHnvapGFUxp0wE44+EMPAqWX\nX+o2W+mcK4YOdrufO0iABEhAEwLia1zSs/91m1TSf18ERIdRLQMdsJY0mZZPBIpvvwXlZ5zW6Nja\nxEQcfu1lICqq0T5uqCNgtwN/ZFqIgwRIoIUEzLv3iDff3PpUfko+vn5drhjF8EjrL9udtrX0B9uA\nW0qQx7ecgMUihhtNQ8zKjxH9yToYS0th6yPGAV99JWoz0luefpimsH1PFKYvSFWse3niQRgMYWoo\nzSKBQBCorVVy2Rt7BJ7vPQU7E4/G/K9ORkalQx8U+cSrYaAD1hAmk2oBAeE9ys8/R/lrQSoRcWhu\ngQmzF6fgm20xuOb8AowcUkznGxE1TyP9SaCw/ZF4a8BLWJhxNa7ZOxUv/HgVYmv+GQJZGxeHqqP6\naFoEOmBNcTIxEvAfgSqbAQs/ScSCtUk4fVAJZj+ZicT4uqd2/+XKlEkgvAnIF9+PN8VjzpIU9O97\nBj5aOAgdyvc2MrronjuA6OhG21uygQ64JfR4LAkEiMAXP8RixsIUtE6vxpT7DqJbB1uAcmY2JBC+\nBLb9XteMU11jwCM35qB/Tyviel2P2udfFG2+xYrh8s236N47UXLjGM1BBMQBL126FN26dUPfvn0V\nA77//nts2bIFJjEEZciQITjiiCM0N4wJkkA4ENiz34LpH6YiK9eMcaPzceox/3wSCwf7aAMJ6EEg\n57AJMxelYMvOGFw3ogDnDS6G6e8uyaWi70npJaNh3b4DsNeiSvRH0frNV7XZrw64UnTZ/uCDD7Br\n1y506tRJybNUdLDZsGED7r77btGjuxJTp07FhAkTRBsWe5ColcIlCRSVGPHWsmR8+m08Lj2zEKNv\nL4LVom0HEFImgUgjUFllEE04ifhoXRKGnyiacZ7KREKsi2acKCuqjunvdzx+dcDS2Q4cOBApKSn1\nhlitVtx8880wm82oEeOuCgsLYRc9y1QHXF5ejoqKivr4Mp58U9Y6yPyMRqNf0vamrKpN0nY9gt72\nq+z1sl8yV8ugB393eYtLAks/i8e8ZYk4vl8FZj15COkpYiO0HzGonoNa2q+mqS61TNvbtNyx9fb4\nlsSTectrSy/7Zb6yDLV/9+htiS2+HBss9xVZjoZhw3cxohknGe1bVePlB7PQpZ2UmJTxtPcvDfN2\n99sgboB+9wDLli1Dx44dMWDAgPpyVAt9zTlz5uDII4/E0KFD67evWbMG8k8N8tP1+PHj1Z+aLmUl\nBcB8TcusVWKRbLtkGIz2f/UD8OwbBsSIfh6P3GzHAPHlS8sgb8yOwV836WBk62i3v9cj2f5gtH3H\nHuCZ1w3IFsqSE2+yY5jGk6uVlJQgUWgW+BJ0ccBVVVWYPXs2evTogTPOOKPJcu/du7fJOM2NIIFZ\n5PjTvDq9z+Ye39L48s1eBvkgokdIS0uDzWZDUVGRHtlDfgmR+ev1ANS2bVvk5+c7fW0JJIho0ZtS\n/dJzINuMNxamYtefVowdVYAzxacxFw/wLS5ely5dnNLwx3Ul6zUjIwOZmZlOeQXyR5QQbpHNW3qE\n+Ph4xMTEIKcJfXN/lU19A5bXlh4hNTW1/sumHvnL80/eU+XDZUGxbMZJwWffxeHyswtx0RmFohlH\n+1LJOk9P902vwK+foF2ZKsHMmDEDxx9/PAYNGuQqCreRQEQQKKsw4L1VyVi+MQEjxFjeiWNzEBvt\n9w9SEcGWRkYugWrRYrN4fSLeXZWEE/uVY8YTmUhLks04wRcC7oB//PFH7N69G8Wii/e6desUInfd\ndRdiY2ODjw5LRAJ+ICAbfVZ9EYM3PkxHz85VmPrwAbQT7VIMJEACLSPwzdYovPp+BuJjavDvO7LQ\ns0tVyxL089EBccAjR46sN+PYY4+F/GMggUgkoMpHVtpMmHB9Lo7t/U+Hw0jkQZtJQAsC+7PqmnF2\n74vCjRflK0I1/mjG0aKsjmkExAE7Zsh1EohEAlI+cpYYd/jtL3XykZecZUO1jc43Es8F2qwdgdJy\nA95ZmSy+KCVg1GlFeGJ8ISxmm2gD1i4Pf6ZEB+xPukw74gm4k480m6KFA454PARAAj4RkA52jZCP\nfFPIRx7VvQLTHj2ANkIlrq4Tlk9J6nIQHbAu2JlpJBBwlI984f6D6NqeHjcS6p02+pfAL7ujMG1+\nKmzVBjws5CMH9AzdL0l0wP49V5h6BBKgfGQEVjpN9juBnPy6Zpzvd8TgWiEfeb6DfKTfM/dTBnTA\nfgLLZCOPAOUjI6/OabH/CUj5yA+FfORCIR857IS6WcAS4kKkkbcJPHTATQDibhJoioCUj1wmxvK+\nvSIZx/ctxxuTMpGeHJzjDpuyhftJIJgIbNwSi5kfpaJdhg0vPnBQyEeGVzMOHXAwnW0sS8gR2LIj\nGtMXpCI6yo6nb8tGn276KDCFHDgWmAQ8ENi9z4pp4rqSowfGX3IYJw8Iz1nA6IA9nATcRQLuCARK\nPtJd/txOAuFIoFDMAjZnqf/lI4OFHR1wsNQEyxESBJzkI4dSPjIkKo2FDHoCdbOAJYoxvUk4Icjl\nI7WESQesJU2mFbYEpHzk2q/luMPkOvnIR4R8ZAblI8O2wmlYwAh8t72uGScuxo5/3Z6FXl2DWz5S\nSzB0wFrSZFphSUCVj6yoNFA+MixrmEbpQSBTzAL2+oep+P0vK264MF/0cC71yyxgetjmbZ50wN6S\nYryII+AoH3nt+QXKjEVivnMGEiCBFhCQ8pHvilnAVn6egAuEfOTDN+SIObAjcxYwOuAWnEg8NDwJ\nuJOPDE9raRUJBIaAlI+sa8ZJQe9uFXhNyEe2FfKRkRzogCO59ml7IwKUj2yEhBtIoMUEtgv5yNfE\nsCKbzYAHxbzXx/QKXfnIFsNwSIAO2AEGVyOXAOUjI7fuabn/CDjJR4pmnPOHFMNk9F9+oZYyHXCo\n1RjLqykBykdqipOJkYBCQDbjLJDykZ8I+cjjw0s+UssqpgPWkibTChkC1UIpcuHaWDGsKIPykSFT\nayxoKBBYv9mCl99uH7bykVrWAR2wljSZVkgQkPKRM5+xiIm7jZSPDIkaYyFDgYBsxnn4f7HIyjPg\nlkvycEqYykdqWRd0wFrSZFpBTcBRPvLe62sweEAeKiup3RzUlcbCBT0BKR85d1kyPv02HmMurMLl\n55SjvCw8tZu1rgw6YK2JMr2gI+BKPrJ71zbIzw+6orJAJBAyBOrkIxOEfGQyThCzgM0Qs4Ad2S0J\ncnt5yFihb0HpgPXlz9z9SIDykX6Ey6QjmsD3Uj5SqFjFiFnAJt+ejd5d+SXJlxOCDtgXajwm6AlQ\nPjLoq4gFDEECshnn9YWp+O1PK8YK+cjhESgfqWW10QFrSZNp6U6A8pG6VwELEIYEZDPOu+JT8woh\nHzlyaBEeEmIakSofqWX10gFrSZNp6UaA8pG6oWfGYUxAacbZJGYBW5qMXl2qMJWzgGla20HvgO3i\nDDCbtS+m0WiE/PNH2t7UkElnVf9gsF/Wrfxradj4fYyYVSUJbYSu7EsTs9Gtg9SXlXI77iV3DAYD\nZB3oVf96nHuStbRbDf6wXU1TXap5BXKpB1vVPpm3ZKyX/Vrm/8vvVkz9IBmVVQY8dEM+juujtvO6\nvx9L2/XmL69rWYZQCO5JhkLpWcaIJiDHHU79IAlZuWaMv7QQg49l38uIPiFovCYEcvONmLEwGd9s\ni8Z1FxTigqGl4mFVk6SZSAMCQe+A5RNVdbX2M2bUiqk55J8/0m7A2ONPvfLX2375hCpt9+UNuJF8\n5G1FsFrsIj2PqJ12ynxrxHgJvfjLN6RA5+349ith+CN/9c3DH2k7VaCHH/INSK/85XUlzy298lff\n/nzJXzbjfPhJIj5cm4QzhHzkrCf3IzFe2iPPFQ/AHXZJ2/W8r8rzT17XsgyhEILeAYcCRJYxMATk\n+MJlGxPw9opkykcGBjlziRAC6ixgshnnhfsPomt7W4RYrq+ZdMD68mfuXhKQ8pHTxXRm0WLc4dO3\nZaNPN7U9yssEGI0ESKARAWUWMHFdZR02Y9zofJx6DBWsGkHy4wY6YD/CZdItJ+AoHzl2VAHOPLFE\ndHJpebpMgQQimYBTM85ZhRg9rK4ZJ5KZ6GE7HbAe1JlnkwRcyUfGRre8x3STGTMCCYQxgYbNOFI+\nMi1ZtO0w6EKADlgX7MzUHQHZ4WPt12Lc4ZJk9OzMcYfuOHE7CTSXgJSPfF3IR8pmHMpHNpeef+LT\nAfuHK1P1gQDlI32AxkNIoAkCbMZpApCOu+mAdYTPrOsIUD6SZwIJaE+AzTjaM9U6RTpgrYkyPa8J\nVImRDnLM4QLxd/qgEsx+MlMZd+h1AoxIAiTQiIBsxlkj5SPZjNOITbBtoAMOthqJkPJs/D4a0z5o\nhdbpNo47jJA6p5n+J7B9txWvzU9BeQUw4fpcHNtbrDAELQE64KCtmvAsmDLuUHQEyT5swc2XHMYp\nAzjuMDxrmlYFkoBsxpm9OAXfbosR8pHFOO+UAspHBrICfMyLDthHcDyseQScxh2eWYgrz8uHATZF\n5q55KTWObSgugUgM9vj4xju5hQQkASFNaCwoRG2COEcslrBh0nAWsDmTDyE5EbBRyCok6pgOOCSq\nKXQL2XDc4Rti3GG6GHdotVhbfJOI+uxzJD0/BdYduxRAVX2PQuEjE1B50omhC4wl15aA8ESJL09F\n/NvvwVhYCHuUFWXnnYPCRx9CbVqqtnkFODVVPrK1kI+cct9BMQuYTZnhy9MsYAEuIrNrggAdcBOA\nuNt3Ao7jDrWWj4xZvQapt90Ng+xx8newbvsF6dfcgNzZr6Ny6GB1M5cRTCDtjnsRs+aTegKGyirE\nLVoK609bkb14AezyjTjEwh+ZFkyT8pFiFjDKR4ZY5TUoLh1wAyD82XICfh93KF6rkydNdnK+aqkN\n4lNjyuNP4dBna8VnaWpWqlwicSm/kDg6X0cGlj1/IGHWmyi6507HzUG93rAZZ/TtlI8M6grzonB0\nwF5AYhTvCARq3KFl+w6YcnLcFsq8bz/M4gZb3b2b2zjcEf4Eojds9Gik3B8KDlg24ywXs4DNE7OA\nDTqqHGozjkfjuDMkCNABh0Q1BXch5VfgQMpHGiqangnJUMHhF8F91vi/dE2dAwY5VifIgzoLWJSV\ns4AFeVX5VDw6YJ+w8SCVgB7ykbZePWG3WmCQSh4uQm1sLGxHdHexh5siiYDt6H7A+wvcmlwl9wdp\nOJBjxoyFqdi514qxF4hZwE7iLGBBWlUtKhYdcIvwRe7BespHyo4zxTeMQeL0GS4roHj8TUBUlMt9\n3Bg5BEovugCJL/0PpuzGzRW1ZjOU8yTIcCjNOKuTsfyzBIwYUowJY3LAWcCCrJI0LA4dsIYwIyGp\nhuMO9ZKPLHrgHhgqKxE/Z159Zyy7yYTicWNRfMetkVAVtLEJAsaiIihjxF3EM1ZXw3TgYND0E5DN\nOJ9sjlPENHrIWcAePoB2rapdlJybwokAHXA41aafbXEcd/jC/QfRtb3rT8B+LkZd8sLZFk56BCU3\njoH1+y2ix7MRlYOOQ22b1gHJnpkEP4G4hUtgLC93W9D4ee+gcvApbvcHaseOP6IwbX4qyisNeOC6\nXBzXJ/jbpgPFJtzzoQMO9xrWwD5VPjIrL/jGHda0b4dy8cdAAg0JmP/Y23CT02/zHs/7nSL74Ufe\n3/KRm4V85NXnFeCCocWUj/QD52BOkg44mGtH57I1Gnc43Mtxh2Isbtx78xH70RKYsrJQ3bEjSq+6\nDOUjz9fZImYfSQRqMtI9mluTkeFxv792yr6DC9eJWcDW1M0CNkvMApYUX+uv7JhuEBOgAw7iytGr\naO7kI70qj2jMSr37fsQuX1Uf3Zx5ANFfb0bxDz8pn43rd3CFBPxIoHzEeUh47XUpE+4ylF8Q+AfC\nL3+MxRsLU9A69R/5SJeF48aIIEAHHBHV7L2RLZWPjFm63Mn5Ouac8OZclJ81HFUnHu+4mesk4BcC\ntt49UTThXiT99/8apS/Pw9LLL2m03V8bpHzkdCEfeUjIR950cT4GH8tZwPzFOpTSpQMOpdryY1m1\nko90fPN1VdzY5SvpgF2B4Ta/ECi+7RZUHdVHvAm/AfPeP1GbnorSKy5D6dVXiDkLjH7J0zHR4lIj\n3lqWjPXfxOMSMQvYZMpHOuKJ+HVdHPC2bduwadMmMSuYBWeddRbatWMnGr3ORK3lI415hz2a0tR+\njwdzJwk0k4Dp4CEkvjoNUd+JnvIyZGfDOGM2bH16oeq4Y+u2+eF/jWjSXSHkI+cuT8bAPuV4/fFM\nZKQITUkGEnAgEHAHXCMaGJcvX477778feXl5WLRoEW69leM2HeokIKty3OGKjVa89n579JTjDh8R\n4w4zXI87tH79DeTnY/Pvu1GbkoxyMZ1bybVXuZxXtbpbV0T98KNbG6jP7BYNd2hNQMx8lH7tDbDs\n3uOUstQKT79+HLJWLEJN505O+7T4sflnI56f2U5MuSnkI2/NRp/uTUunapEv0wg9AgF3wCYxftMu\n7v6ZmZniYTRbCBY5KxZ9/PHHkH9q6NatG2677Tb1p6ZLg5gtJy4uTtM0vU1M5i2DZBHo8OMO4P4X\nDZCSyv+dYMfJx0SLIrj+CmEXalP22+92KmLU9z8g+fMvYVi1FAar1WmffeJ9sC9aokyA7rRD/oiO\nRtJ9dyO5UycxUZFBF9vVMsn8W7Vqpf4M+FJv+6XBnUQ9+CNI2/yVtjflVdnaxUOjvYHzVY83lpai\n7Tvvwzj9VXVTi5f7DgFPvGbAjzuBe66z4+Iz5YRcgR+XrtrfYoN8SEDNOykpyYejW36Imn/LU/I+\nhZKSEu8jN4gpplMNrAeQ2a1cuRK//PILysUg+QsvvBD9+/evL1alUDeqcBDSNwvJuMOHPX/WrD+4\nGSvx8fGwCufhj7S9KYZ8EJFBfhEIVMjNN2HGR0n4dls0brm8GheeUY7ysmK32ZvEm0LG0DNhEKpB\nrkLRg/ej9PbxjXbFzP8QSQ9PgsH2j1CH1GcuePX/UDn8DCW+bH6oFukG+PSrL6t0voVignZ5vukR\n5INnoPPuKIaDOYZ9+/Y5/tRkXV5Tqamis9Eh4Y10CrIMVVVVSHroMcS++4HbUth69kDu2hVu93u7\no7zCgHdXJWLJp/G4aHglxl9uR2V5nreHaxrPKNq15Z+8tvQIycnJqBXDEIuECpkeQY/7SkxMDNLT\nPQ95c8ci4G/A8qL/66+/MHHiRMX5TJ48Gb1791acoSykvDE1fCvO8TD1nDvDmtoub/zyRAmkA3Qs\nk3xSkyEQ+TeUj5TjDrt2ToHN5tn+mBWr3DpfWfboJctQNH6cXHUKJaMvQvkJgxC7bCVMh+Q44A4o\nGzUCtXLc5d8PHPIBRNqulwOWBdaz/vXMW60sf5x7aprqUs0rkEuVbe3f15jbvMU9oCXllK8u6/6W\njzyiUxVeFfKRPbtFQ96Qy0oC92Dtyr6W2OUqPW+36X1flfcVWf/yLxRCwB2wfDqVJ6gM8klNXQ8F\nWKFYxpbIR5pyPT/Fe9pf06EDim+9ORSRsczhQuDvr0zuzLG3oBf0zj9E/4n5aYp85H1CPnIg5SPd\nYeZ2DwQC7oDbtGmDlJQUzJ0rxoSKT9CDBw+uf/v1UE7uaiYBd/KRhrIyxM5fCMu27UL2TnyqOuF4\nlElBAvGpv2Gwde3ScJPT7+om9jtF5g8SCDSBJpoXHJtIvC2aIh+5JAWbt1I+0ltmjOeeQOO7rvu4\nmu0ZNWqU+PxpEw7ApLwFa5YwE4In+UjTftGme9UYyF6gakgVzjju7feQO3cm7KJd3DHI3s41U/4P\nJjdDi0quu8YxOtdJIKgI2P/+0uauUPa4WHe7Gm2X8pEfCfnI+UI+8rSBpaB8ZCNE3OADAf+PRHdT\nKNlYLj9BM2hDQDatLv40ATc82V58FjPijUmZuPLcQmUohJpD6l0PODlfdbscNpT89LPqz/qlPTEB\nea+/ihrRsaJhKBICB+Ujzm24mb9JIGgIVJw2xGNZmtqvHvyVkI+8eXJ7fL8jBlPuO4i7rsqjdrMK\nh8sWEdDlDbhFJebBjQg4ykdOvj0bvbs27tlr3rnL4/jc2CVLUfDUY2j41iDFCg59uhpxYmIF82+/\ni3HAKWIc8Nmw9T2qUTm4gQSCiUDl0MGK9GnMmk8aFUs2rxTfdEOj7Y4b9h6ok488kGMR8pGHMYTy\nkY54uK4BATpgDSDqlURz5CMdPzu7Kq9BfGNTeix37dJot128AZfccH2j7dxAAsFOIE8MfUt8eSri\nRTOLUQw7s0dZUXbu2Sh87GHYE5ybXFRbpHzkXCEfuU7IR44eXoinhJhGlDXw4/XV8nAZvgTogEOw\nbh3lIy/o9xee7voO4r4rFGMf+6FyyKly9H8jq2qamKjeLocFNTF9W6NEuYEEgp2AaOoqeuAeFN13\nF4z5BagVzSpCA9dlqRX5yM8TME/IRx7Xm/KRLiFxo6YE6IA1xenfxOS4w7Vfx+PNJclCPrIScxMn\nodfkKU7TrVUeMwB5b0wVovNpToWRn4xtRx4Bi/iM7CqUnzmsUScsV/G4jQRCjoCQpIz+bCPMf+wV\n10U6yoedBvlVxzH8sDNama3IIuQjnxyfjaMoH+mIh+t+IkAH7CewWie7fU8Ups1PRWWVAROuz8WQ\nTa8jee6URtnIDlWpd92H3Hffct4n3ooPv/wC0q++HibxJuAYbEK/ueDpSY6buE4CYUHAsn0n0m65\nHeb9mfX21MbHIf/5fyua5gfF9IAzxPy82/dEY8wF+TjrpBLRObQ+KldIwK8E6ID9irfliecWmDBr\nUQq+/SUG155fgBFDisXwLSBh7Cy3iUdv2gzL1m2w9evrFEfOj5q1eikSZr+F2J+2wW4xo1TMzVty\n3dV8+3UixR/hQMBQXIL0sTfDJDTnHYOxpBTR9z2B6QeGY/EvnXDe4GLc///tnQl4FEXax/+TSSb3\nQRISLgO4COESFkGR9QJFQMFj+VC8OFTEFT8/UfFaWVyBVZZ13ZVVDkFc5VMEQQOoiBJYwF11Ac2C\nCopyE66QkPvO9tthhplkMsmEPpLpfz3PPFNdVV1v1a+76+2u460xhxAZznFed07060+AClh/xo2S\nUNN85OuK+ciYqGrzajZlMon9mGejUlNIyO4faylgSVMpNpCfnILghAR1LXaeSTZba5aXxySgNYGI\nFe/XUr6iYle3uR0vdZmBzl9n4m/TjqBtUrnWopkfCTSIABVwgzAZkygoOxth6zdi055k/O3ECCS1\ntePFRzPRse3ZTQ2kJLJUqEqxXFXXJgmSpjImRv7oSMCyBBxK97O72xHbF3/o9mfkB8di+o77cUn0\nDziWtNY9Cf0kYCgBjnYYirtuYeFrPkbu0Ifw6PI+mLvrUjzx+b14c2VPdD79n9onKfa0iwdeWTv8\nTEhldDRKLr2kznhGkIAVCASd2cTlRGgrPN3zNUzstxrDMpdj5ZaLcNnJdbCdwzZyVuDHOupPgApY\nf8b1Sij89268Mj8YYy/8EP2z0rFqc28MPqYYvlD2TE4cdx9kLKumy/ndU6ioMdNZ0lQpk62yZ0yr\nc41jzXx4TAKBSqA0OAwLz38MIy7PgKOyBGs29cSYfXMQUlXd5WzzWD8QqBRYr6ZMgF3QJl4dp/nI\nt5cPwBVBWVi9uReSSjI9SmRX3uJlLKtg3F0e4bLb0PFVKxDz55cRtvEfsCl7KJcqk67yHnoAJf35\n9esBiweWI/CvjHAsDF2IVi13440vByM1r3ZPUkVivOW4sMJNiwAVsEnXY+u3ocp2ZnEIc1RizsHx\nuGjnyjpL4lDMSBZ4ia1o3QrZs//gJYZBJGBNAmI+csGKeBw8ascDvTZg1B9G1Ami5NL+dcYxggSM\nIEAFrCNl+779CFEW/0tXcVn3brIBMs6ajwxV7MuexqB+uUj6aq/PUsiYLh0JkEDdBPIKz5iP/DIK\ntwwpwLSJpxBxPAJVzysdzWLBxosr7d7VSyiDSMA4AlTAOrAOOnES8Y89ibBNW1y55/yiO+aMWopV\nu1Iw/Mo8PD0hGxFhVShXhqPEClXo9q9daWt6JJ6OBEigNgExH/mRYj7yTcV8ZJ9UxXzkM4fRrnUw\nSkqqEPHBqjqVr+QU8eFaFP36ptqZMoQEDCJABaw1aEWjJo69F47vq5dAyLv3B23H4C/tn0PP9K8x\n7+kcJPdqiWBlGZHT5Y+7ExFrP4EjY4czyPWff9stKL24r+uYHhIggWoC3+yuNh8ZbK9SvniPo0cn\n5y5g1c9W8IFDPlEF7z/gM56RJKA3gbNaQG9JFsk//MOPXcr3m7hL8Lyy7rAoKALPZ9yDAVnrkf/u\nLcjp9ZwnjbAwnFBMR8quLRFpqxF0MgvlHdqrFqoK7rrdMy2PSMDiBI6K+ciVLbBzj2I+8sZsDKnD\nfGRFcpJPUvVtUOLzZEaSgAYEqIA1gOieRejW7TgW2gZ/7jITm1sOwaQ9M3DrgfkIrqpQk0m8N1cV\nEYHTT01Rf97iGUYCVidQXGLD0rWxSNsYg2GX5eGRu3ybjyy8cTiiX50PW2W1Bbma/ApvuqFmEI9J\nwFACXAfcQNxi/jH0iy/h+DoDig1Hr2eJ+cjXi2/EDZd/g6jyXHy0qQfu2P+qS/nKSVXBiiFnOhIg\ngQYTkDlU67+MxN3PtsVPhxyY8+QR3Dcyu17bzeWdfoGcqcq+v14kFdw0AoUjOf7rBQ2DDCTAL+D6\nYCtjurGzX0KUsoGB0/RjRUI8cqY9g6IR17nO3vJ1hLqrSutwB95aOxCd8791xbl7Sn41wP2QfhIg\nAR8Edu9zqLuA5RfaMfnOLPTrXuQjde0oWT8vKxCilryjbkcoe17Ll3HRiOu97ptdOweGkIB+BKiA\n62EbN+MFRP19iUcqe9Ypdcu/rMgIfNd5sLqP6LFTwZigvJVf1rsYiXtjgS0ep6gHMiaVN/He2hEM\nIQES8CBw6rQdr3/QAl/sCMftw07jhqty0djOo9J+F+GU8qMjgaZGgArYxxWxHz6CyDf/32uK0yHx\neGVROD5Mbo1Rg09j5DW5cCibeSuv1cha8ApiZ85C5PIVsJWWqV1gJVdchuyZzyobgid4zY+BJEAC\nyuiOsizv/fQYZaw3DldcVICF0w4jLtr7GC55kUBzJ0AF7OMKOpS1uTUX8Zfb7FiaMhGvdnoGV5xY\ni4WzdiG+XYRHLrJbUc6MZ3H6t0/AfiQTlfEtUNmihUcaHpAACXgSEPORYsUqIbYCf5x8FJ3OK/VM\nwCMSCDACTV4BVykzMGzKBgOaO2WDA8nXV942xXKVu/s88RrMSp2NiIoCzN12E3rlfIUjLbapGyC4\np3P5lZnNFcpEEHHealCffFc+Onp81V9HsfWy11O2M28z+Zshu+azpMe1d+bp/Hey9vW/PzME85a1\nwMFjIYp1uGxc1bfwTHJvT42vnKrjarKVzUzCP1rrGgMuHjYEFW1a159RI1I46+38b0QW53SKU67z\n/5wya+TJItvK8v3B1uQVsFQmJCTEnzr5TBusLAOKfm4mQrZVW55KPr8jCh5/FCVuE6qcGVQOuFTd\nd/egoz3+mDoLO+L6YvLuqbjx8FuqQi27sCeCG/llG3RGuZt1o4p8u92uKVsnt4b8i2xRCGY54S7G\nULS8t/ypi/A3S7aznHrIF6bCtiF55xXY8EZaND75ZwRGXZuPmQ9lI9QhpTu3592dbci/tyH2nvsR\nlJXlrDaqZr2IPGU4qPj20a4wrTxyX7vL1yrfhuYjsoW/me2KlLUh17+hdfInnfCXupvZtvhT3iav\ngAVmaak2XVEORfm2uH0sbG7LiIJ/3ovY+x/EqezpKBw9yoNdYXgLvPY/aXg/62KM3j8fszLGIbKi\nemvAKuVC5zz1WKPL5rSEVS62KE1wlcrayAplOyat2DamCmXKdTDrQRG5It+s+ktDabTsmo2yXvKF\nra+8xXzkx1sU85Gr49A7tVg1H9kyvnqdvBaPurPNCMrORsK4CQjKyfG4PW2KkOjHf4viDh1Q2reP\nR9y5HjiUvbpF+fiq/7nK8HW+8wVA7m0znNntivCXNlXKYZQTmY11TV4BN7Zi3s6Le3aGh/J1TxM3\n8wV1WVFVZKSiFIBP/xWFxavikNohEYuT56HrFy8g6IzyLVOsVOX8fiq3/XMHSD8JNIBAhmI+cu7y\neNiDqjBVMR/Z02U+sgEn+5kkYsUHsNdQvs4sZG6HLC08pbECdubPfxJoCAHLKGAx7+j49rs6mQTl\nF8Cx/Rt83XaQuu6wpNSGKWNPok/XYuWckTjywAjI13KVYjayQlHAdCRAAg0ncDRLMR+54oz5yBsU\n85ED8mVzMF1dyI97fOZfX7zPkxlJAhoQCAwFrHSl2gqLUBUdVScSW4nTULv3JGI+8vnNl+CLU0m4\n6/ocDL8iTxkjdUsrXRupXdwC6CUBEqiPgGo+8hPFfOSGhpmPrC8/f+KrwkJ9JpdhJDoSMJNAs1bA\nQSdOIG7GLIR/sg62klJUJCmGLu4eg/wJd6t777qDlc3rKxITYT950j0YJUGhWNxxMhaf/wgGtSrC\n6w8dRkyUceMHHoXhAQkECAEZxtnw70gser8FOrYtxcuK+cjzko2d71CFej6xGzfJOkCuEKvRFAg0\nWwUsEyySfj0awYcOuzjajx9H3At/QvC+/ch5frorXPUo/V25D09Ci2d+7wpfl3wT/pT6AtoV7cP8\n+FlIvHuCK44eEiCBxhHY+aMNz73SCrkFdvzfHVm4uId/5iMbJ7X2WUFFvuXaxOoHHQmYSKDZKuDo\nv83zUL7uDKOWLkfBraNQ1vtC92AU3HEbgk7nInPRBszqNAtHwlMwZdcTGHBVCLKf+51HWh6QAAn4\nR0DMR/5dmdn8zww7Rg/JxY0DG28+0j/J3lOLzXZfjlbpfNFhnBEEmq0CDtu4ySef8I3/qKWAT+cH\nYU6Lp7DhihkYc8F/8HLnLcjv8r/IPq+dz7wYSQIkUDcBd/ORV/UrwupXy1FckFv3CQbFFF0/FNHz\nXvNqBEeKUDhksEEloRgS8E6g2SpgW6HTWo73ismkLKdT5mhh9aZoLPkwTu0Oe23aEXRMaY/gkE6o\ncFug70zPfxIggYYR+Nd/qs1HxsdUYNbDR9G9E5AQ1xKHCxp2vp6pbAWFdSpfkWsrkhUOdCRgHoFm\nq4DLevZA8NFjdZIrVeLFbfsuDPPfi0dYaBWmTzqOrh19z4auM0NGkAAJuAiI+Uh5rg4cVcxH3izm\nI50at/FGCVyZa+QJ/3S9z5zCP0tH/gP3+UzDSBLQk0CzVcC5D0xEWPpG2OTztoYr63wBfrroOiyY\nm4Td+x0Yf2MOBvcX2881EvKQBEjALwJ5hUFYsiYOn34RhZsH5eJ3ijGNMId5JkV9FT4ov9pqXV1p\n6ouv6zyGk4BWBOqZp6+VGO3zkQlWWa/8BZWxyt67bi679yV4/s61mDQ7Bee1LsOiZw/j2kupfN0Q\n0UsCfhMQ85FrlGGce6a1RXauHfOeOYy7huc0WeUrFSytZ91+WddUvznwBBLQkkCz/QIWCMXKJIrM\nywYg9PMvYDuVjTVVA7EwoxdSC0rxytNH0KYllxloebMwL2sSyPghDPMU85E2m2I+8j7FfOQFzWMY\np3DkzYh5dQHsir2Amq5K2TQib8L4msE8JgFDCTRrBSykxHZz8bVXY/qCljiojEedNR9pKEcKI4GA\nJLDisxgsWxeLsYr5yKEGmI/UEqJYxjv5xmtImDjJY8liZVQksmfNRFmP7lqKY14k4DeBZq+AnTW+\n56ZsJCeUe5qPdEbynwRIoFEErupXgKG/ykNkeNMc562vUmXdUnH0s48RpixLDN67H5UtE1F09VWo\niour71TGk4DuBAJGAbdJYnez7ncLBViOQEJs7UmOzQ6CssmwDFfRkUBTI9BsJ2E1NZAsDwmQAAmQ\nAAn4Q4AK2B9aTEsCJEACJEACGhGgAtYIJLMhARIgARIgAX8IUAH7Q4tpSYAESIAESEAjAlTAGoFk\nNiRAAiRAAiTgDwEqYH9oMS0JkAAJkAAJaESAClgjkMyGBEiABEiABPwhQAXsDy2mJQESIAESIAGN\nCFABawSS2ZAACZAACZCAPwSogP2hxbQkQAIkQAIkoBEB0xRwZWUlFixYgMzMTI2qwmxIgARIgARI\noPkQME0Bp6enY9++fSgvpw3n5nO7sKQkQAIkQAJaETBlM4ZDhw7h6NGj6Ny5c616bNmyBfJzupSU\nFIwcOdJ5qNm/3W5X9je1weFwaJanPxmJbHFVVebsMhOs7IcqsiOV7RzNcEFBQap8s+ofEhKC+Ph4\n0/hL/aUXyEzXunVrzcXLfS3Plh55N7SwZrIV2fIzq/5mtyty7cVFREQ09HJpmk7YS5tiZLtSXFzc\n6DoYroDLysqQlpaG8ePHY9myZbUK3q1bNyQnJ7vCw8PDkZ2d7TrWyiM3iCih3NxcrbL0Kx/njVpR\nYc5uMzExMWrvQ2FhoV/l1iqxsDez90OUb35+PkpLS7Wqkl/5yIuf0bJbtWrlUUY9nit5sYmNjdXl\nmfUovI8DKYO0M2Y4aa9CQ0ORk5NjhnhV+YsSNqtdiY6OVl8sCwoKTKm/tCtSdyMVsNxvjXWGK+B1\n69apb0dbt27F8ePHsX37drRs2RJhYWFqHaRhlJ+7k65qrZ00gHKjnsvby7mUSW4UcWYpIfnyFdlm\n1V/4SyNp5IPifr1ErihAs+ovZTFTtl7y5ate2JpZN5FfUlLifrkN88tzLT+z6i8v9vIVaNYLiHzY\niAI0q/7Srki7ZmTvkrMtb8xNZrgCdv/ClYKL4nV2mzSmAjyHBEiABEiABJojAcMVcMeOHSE/cTt3\n7oQoZOmyoSMBEiABEiABKxEwXAG7wx03bpz7If0kQAIkQAIkYBkCpi1DsgxhVpQESIAESIAEvBCg\nAvYChUEkQAIkQAIkoDcBKmC9CTN/EiABEiABEvBCgArYCxQGkQAJkAAJkIDeBKiA9SbM/EmABEiA\nBEjACwEqYC9QGEQCJEACJEACehOgAtabMPMnARIgARIgAS8EqIC9QGEQCZAACZAACehNgApYb8LM\nnwRIgARIgAS8EKAC9gKFQSRAAiRAAiSgNwEqYL0JM38SIAESIAES8EKACtgLFAaRAAmQAAmQgN4E\nqID1Jsz8SYAESIAESMALASpgL1AYRAIkQAIkQAJ6E6AC1psw8ycBEiABEiABLwSogL1AYRAJkAAJ\nkAAJ6E2AClhvwsyfBEiABEiABLwQsFUpzkt4wAdt2rQJmZmZuPXWWwO+rt4quGzZMiQnJ+PKK6/0\nFh3wYS+//DKGDBmCLl26BHxdjazggQMHsHTpUjz++ONGim0ysr766ivs2rULY8aMaTJlMrIgaWlp\nCA8Px7XXXmuk2GYry7JfwBUVFSgrK2u2F+5cC15eXg75WdWVlpaisrLSqtXXrd7CtKSkRLf8m3rG\n8kzJvWVVJ22qldsVf6+7ZRWwv6CYngRIgARIgAS0JEAFrCVN5kUCJEACJEACDSRg2THgkydPori4\nGO3atWsgqsBKdujQIYSFhSExMTGwKtbA2vz0009ISkpCdHR0A89gsoYQKCwshNxbnTt3bkjygEuT\nnZ2NvLw8pKSkBFzdGlIhmVdjt9vVZ6sh6a2exrIK2OoXnvUnARIgARIwlwC7oM3lT+kkQAIkQAIW\nJUAFbNELz2qTAAmQAAmYSyDYXPHmSF+3bh127NihCo+JicGECRPMKYjBUmV5wPz58zF+/HhERESo\nywWWL1+OY8eOqWN21113ncElMlZcVlYW3nvvPUycOFEV/P333+Ojjz5yFeLee+9FbGys65ge/whY\nmeeqVatw/vnno0ePHiq0DRs2qG2MtC+jR49W51v4R7P5pK7ZrsgY+IIFC1wVkPX2Ti6uQHpUApZU\nwN9++y3uv/9+OBwO2Gw2S9wKx48fx9tvv40jR47AaXslPT0drVq1wi233II333xTNSCQmpoakDy+\n++47rFmzxmON4o8//ojBgweja9euap1DQkICsu5GVcqKPGXN87vvvovdu3e7Jl7t3bsXP//8MyZN\nmoQtW7bg008/xYgRI4y6DIbK8dauyCS8jh07uuosk7LovBOwXBe0GAooKCjAzp07sW3bNssYY8jN\nzVXfxFu3bu26E2QmcJ8+fdRZi7/85S/xww8/uOICzSMNpbx0uTtpKGTW7ubNm1FUVOQeRX8jCFiR\np7Qlffv2Rf/+/V3E5Lnq1auX+lxJXCA/V97aFbkPgoODsXHjRpw4cQJBQZZTM657oT6P5chI90hU\nVJT6y8/Px7x58+pjFBDxnTp1Ur923SuTk5OjdkVLmHRJS2MSqE5eMCIjIz2q52wYpJvwpZdesrQF\nJw8wjTywIs/4+Hh069bNg5j7cyVmGQP5ufLWroglMOlZbNOmDd555x1IzwiddwKW64KWMb6HH35Y\npdG9e3ds374dp0+ftuTYnzQO8mUoXa/y0MiLiZWc+xfxwYMH1TE7+WKhaxwB8qzm5nyu5EhMM1pt\nrfn111/vuoFkuGvr1q244IILXGH0nCVguS9gMcAh453inHZbrfaAOC+/GCHZt2+feijjVm3btnVG\nBfy/DEXMnTvXZbdXJqLJGztd4wiQ51luNZ8rq91XK1euxP79+1UgfK7O3hfefJb7AhbLT9IVuXDh\nQnV84pprrrHsGMXVV1+NFStWqGOg8hU8fPhwb/dIQIZJd6mMf8t9IF8psjOU1RpKLS8seZ6l2bNn\nT3VCo6w4kDFS56z7sykC29evXz/IrHAZBxZrg1ZZZdKYq2pZS1jS6EqjwRl6UL8CZUa4FZ18ucm9\nEBoaasXqa15n8jyLVIZ1rPpcCQVRvmLulq5uApZVwHUjYQwJkAAJkAAJ6E/AcmPA+iOlBBIgARIg\nARKonwAVcP2MmIIEmiWBOXPm4OKLL65V9sOHD0Nm6sqExLrc559/jgsvvLCuaIaTAAloQIAKWAOI\nzIIEmiKB2267DRkZGRDDEO5O1mYOGzbMsltRurOgnwTMJEAFbCZ9yiYBDQjIEjKZebtnzx41t8WL\nF2PUqFFISEhQFe3SpUs9pLz11lsYO3asGiYmOgcOHKiug2/fvr1qkMQjMQ9IgAR0I0AFrBtaZkwC\nxhAQu7ti03ry5Mmqre8pU6bgiSeeUK0RiaJ1V8CyCYlsmu7ceOPOO+9U/WIjXKyBybmnTp0ypuCU\nQgIWJ0AFbPEbgNUPDALTp09XLXkNHTpUXXfptOglVolE4Yrtc3FLlizBHXfcoVo/k2PZteaRRx5R\nl2F16NBBHRsW+710JEAC+hOgAtafMSWQgO4ExLjMb37zG1XRPvjggy55sg5VtsOTcV9Zoys7Yjm7\nnyWRKNvLL78cSUlJeOyxx1BRUWGZDUpckOghAZMIUAGbBJ5iSUBLArIBwF//+lcMGjQITz75pEfW\nonCXLVumbo0n48K9e/dW46WreeTIkXj00UfVruv169erW1U6t6v0yIQHJEACmhOgAtYcKTMkAeMJ\niBKV7mcxLfrZZ59h7dq1rkKIaUAxCzh79myMGzfOFS67gYkTc6xisUi+ksV6kVgGoyMBEtCfgOVs\nQeuPlBJIwFgC6enpSEtLU+0Py25fL774orr3sYz7One4kq/gqVOnYtGiRa7CpaSkqN3RsnetfBnL\ntnqyr63sX0u72C5M9JCAbgRoilI3tMyYBJoHAdmvVvZvlT2h6UiABIwjQAVsHGtKIgESIAESIAEX\nAY4Bu1DQQwIkQAIkQALGEaACNo41JZEACZAACZCAiwAVsAsFPSRAAiRAAiRgHAEqYONYUxIJkAAJ\nkAAJuAhQAbtQ0EMCJEACJEACxhGgAjaONSWRAAmQAAmQgIsAFbALBT0kQAIkQAIkYBwBKmDjWFMS\nCZAACZAACbgI/BcGejTuLouYvwAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"ggplot(mydata,aes(x=xVal, y=yVal)) + geom_point(col = \"red\", size=4) + \n",
" geom_smooth(method='lm') + facet_wrap(~group) "
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAYAAAB91L6VAAAEJGlDQ1BJQ0MgUHJvZmlsZQAAOBGF\nVd9v21QUPolvUqQWPyBYR4eKxa9VU1u5GxqtxgZJk6XtShal6dgqJOQ6N4mpGwfb6baqT3uBNwb8\nAUDZAw9IPCENBmJ72fbAtElThyqqSUh76MQPISbtBVXhu3ZiJ1PEXPX6yznfOec7517bRD1fabWa\nGVWIlquunc8klZOnFpSeTYrSs9RLA9Sr6U4tkcvNEi7BFffO6+EdigjL7ZHu/k72I796i9zRiSJP\nwG4VHX0Z+AxRzNRrtksUvwf7+Gm3BtzzHPDTNgQCqwKXfZwSeNHHJz1OIT8JjtAq6xWtCLwGPLzY\nZi+3YV8DGMiT4VVuG7oiZpGzrZJhcs/hL49xtzH/Dy6bdfTsXYNY+5yluWO4D4neK/ZUvok/17X0\nHPBLsF+vuUlhfwX4j/rSfAJ4H1H0qZJ9dN7nR19frRTeBt4Fe9FwpwtN+2p1MXscGLHR9SXrmMgj\nONd1ZxKzpBeA71b4tNhj6JGoyFNp4GHgwUp9qplfmnFW5oTdy7NamcwCI49kv6fN5IAHgD+0rbyo\nBc3SOjczohbyS1drbq6pQdqumllRC/0ymTtej8gpbbuVwpQfyw66dqEZyxZKxtHpJn+tZnpnEdrY\nBbueF9qQn93S7HQGGHnYP7w6L+YGHNtd1FJitqPAR+hERCNOFi1i1alKO6RQnjKUxL1GNjwlMsiE\nhcPLYTEiT9ISbN15OY/jx4SMshe9LaJRpTvHr3C/ybFYP1PZAfwfYrPsMBtnE6SwN9ib7AhLwTrB\nDgUKcm06FSrTfSj187xPdVQWOk5Q8vxAfSiIUc7Z7xr6zY/+hpqwSyv0I0/QMTRb7RMgBxNodTfS\nPqdraz/sDjzKBrv4zu2+a2t0/HHzjd2Lbcc2sG7GtsL42K+xLfxtUgI7YHqKlqHK8HbCCXgjHT1c\nAdMlDetv4FnQ2lLasaOl6vmB0CMmwT/IPszSueHQqv6i/qluqF+oF9TfO2qEGTumJH0qfSv9KH0n\nfS/9TIp0Wboi/SRdlb6RLgU5u++9nyXYe69fYRPdil1o1WufNSdTTsp75BfllPy8/LI8G7AUuV8e\nk6fkvfDsCfbNDP0dvRh0CrNqTbV7LfEEGDQPJQadBtfGVMWEq3QWWdufk6ZSNsjG2PQjp3ZcnOWW\ning6noonSInvi0/Ex+IzAreevPhe+CawpgP1/pMTMDo64G0sTCXIM+KdOnFWRfQKdJvQzV1+Bt8O\nokmrdtY2yhVX2a+qrykJfMq4Ml3VR4cVzTQVz+UoNne4vcKLoyS+gyKO6EHe+75Fdt0Mbe5bRIf/\nwjvrVmhbqBN97RD1vxrahvBOfOYzoosH9bq94uejSOQGkVM6sN/7HelL4t10t9F4gPdVzydEOx83\nGv+uNxo7XyL/FtFl8z9ZAHF4bBsrEwAAQABJREFUeAHsnQmcFNW1/093z77BwAwwrMMAgyCK4ooo\nuKJG3OK+RMXELTGaPBNNYmKeL3l5L3lJXl5e8rL+ExUT44KoAdwRUdlFZBMRkH2GYZl9n+n5319B\nDT093T3V3be6evndz6ene6pu3eV7q+rc5ZxzXV0qCAMJkAAJkAAJkEBMCbhjmhszIwESIAESIAES\nMAhQAPNGIAESIAESIAEHCFAAOwCdWZIACZAACZAABTDvARIgARIgARJwgAAFsAPQmSUJkAAJkAAJ\nUADzHiABEiABEiABBwhQADsAnVmSAAmQAAmQAAUw7wESIAESIAEScIAABbAD0JklCZAACZAACVAA\n8x4gARIgARIgAQcIUAA7AJ1ZkgAJkAAJkAAFMO8BEiABEiABEnCAAAWwA9CZJQmQAAmQAAmkJQKC\nJUuWJEIxWUYSiGsC06dP71E+Plc9cPAfEoiYgP+zZTUhjoCtkmI8EiABEiABEtBIgAJYI0wmRQIk\nQAIkQAJWCVAAWyXFeCRAAiRAAiSgkQAFsEaYqZpURUWFfO9730vV6rPeJKCdwObNm+Xf/u3fjOdq\nwYIF2tNngvFBICGUsOIDFUsRiMCKFSvkz3/+s7S3twc6zWMkQAIREPjNb34jjz32mAwcOFAeeugh\nOfXUU2Xw4MERpMRL4pkAR8Dx3DoJULbm5mb56U9/mgAlZRFJIHEIQPgOGjRIPB6PUejDhw8nTuFZ\nUssEOAK2jIoRAxE499xzpaOjI9ApHiMBEoiQAIQvwj/+8Q/Jz8+XCRMmRJgSL4tnAhTA8dw6LBsJ\nkEDKEvjrX/8qO3bsMKaiUxZCklecAjjJG5jVIwESSDwCEL41NTXywx/+UNxurhQmXgtaKzEFsDVO\njEUCJEACMSFw8OBBmTNnjowaNUruuusuI897771XTjvttJjkz0xiR4ACOHaskzantLQ0eeKJJ5K2\nfqwYCcSSQFFRkSxatCiWWTIvhwhwbsMh8MyWBEiABEggtQlQAKd2+7P2JEACJEACDhFwdangUN6W\ns62urrYc12pEU7HB6/VavSSp4qV6/WFfibZPgNtf231XWFjYIy07niuXy2UoDXV2dvbIK1X+SfX6\np+J7JSMjQ3JzcyO6xRNiDbi2tjaiyoW6qKCgQNLT08WOl1CofM1zWDdFcMqGFh524L2qrq7OLFJM\nv3HTIn+nBGBJSYnR9i0tLTGtt5lZVlaWxDpvfwFsx3OFdi0uLpa9e/eaVY35d2ZmprS2tsY8X2SY\nl5cn2dnZ4pTjDHQsIQSd8kw3YMAAQefLjnvLSoPi/sM7NZYDK7R5pAKYU9BWWpVxSIAESIAESEAz\nAQpgzUCZHAmQAAmQAAlYIUABbIUS45AACZAACZCAZgIUwJqBMjkSIAESIAESsEKAAtgKJcYhARIg\nARIgAc0EKIA1A2VyJEACJEACJGCFAAWwFUqMQwIkQAIkQAKaCVAAawbK5EiABEiABEjACgEKYCuU\nGIcESIAESIAENBOgANYMlMmRAAmQAAmQgBUCFMBWKDEOCZAACZAACWgmQAGsGSiTIwESIAESIAEr\nBCiArVBiHBIgARIgARLQTIACWDNQJkcCJEACJEACVghQAFuhxDgkQAIkQAIkoJkABbBmoEyOBEiA\nBEiABKwQoAC2QolxSIAESIAESEAzAQpgzUCZHAmQAAmQAAlYIUABbIUS45AACZAACZCAZgIUwJqB\nMjkSIAESIAESsEKAAtgKJcYhARIgARIgAc0EKIA1A2VyJEACJEACJGCFAAWwFUqMQwIkQAIkQAKa\nCVAAawbK5EiABEiABEjACgEKYCuUGIcESIAESIAENBOgANYMlMmRAAmQAAmQgBUCaVYiORmnq6tL\nsrKytBchLS1NPB6PLWlbKazbfaTvg3I4EVB3u9haqQ/yNxlYia87jsvlkoyMDN3JWk4P7W7HfR2q\nAF6vtwdzO/JPT08XsLUj7VB18z2HewtlcCKg/k6+V1BvfFAGJ4LJ3qn2R/744N2WCMGZt38YZHAz\ntbS0hHGFtah4+dqVtpUSmIK3o6PDSnTtcXJzcwV528HWSmHBv7293bEHBQ9oW1ubY/XHCyrW7P07\nPHbkDyEPtnakbeW+QpzMzExpbW21Gl1rPDzX+DhVfwgftDOeLSdCTk6OdHZ2OlZ/vFfwXsN9GKuQ\nl5cXcVacgo4YHS8kARIgARIggcgJUABHzo5XkgAJkAAJkEDEBCiAI0bHC0mABEiABEggcgIUwJGz\n45UkQAIkQAIkEDEBCuCI0fFCEiABEiABEoicAAVw5Ox4JQmQAAmQAAlETIACOGJ0vJAESIAESIAE\nIidAARw5O15JAiRAAiRAAhEToACOGB0vJAESIAESIIHICVAAR86OV5IACZAACZBAxAQogCNGxwtJ\ngARIgARIIHICFMCRs+OVJEACJEACJBAxAQrgiNHxQhIgARIgARKInAAFcOTseCUJkAAJkAAJREyA\nAjhidLyQBEiABEiABCInQAEcOTteSQIkQAIkQAIRE6AAjhgdLyQBEiABEiCByAlQAEfOjleSAAmQ\nAAmQQMQEKIAjRscLSYAESIAESCByAhTAkbPjlSRAAiRAAiQQMQEK4IjR8UISIAESIAESiJwABXDk\n7HglCZAACZAACURMgAI4YnS8kARIgARIgAQiJ0ABHDk7XkkCJEACJEACEROgAI4YHS8kARIgARIg\ngcgJpEV+qfUrX3nlFSkrK5NJkyYZF3344YeyZs0a8Xg8Mn36dBk7dqz1xBiTBEiABEiABJKAgK0j\n4NbWVnnqqadkxYoV0tHRYeBqbGyUxYsXy+zZs+WGG26QF198Ubq6upIAJatAAiRAAiRAAtYJ2DoC\nhrA99dRTpbCwsLtEGRkZcvfdd0taWpp0dnZKbW2tIYBdLpcR58CBA1JVVdUdPzc3V7Kzs7v/1/Uj\nPT3dKIMdaVspI0b/CGDgRAB/BKfqj/xNBk7U3+12S2Zmppj3XazLgPvPqbzNutrR9mhX1MuOtM1y\n9/UNtmhfJwLyxn3tVP1Rb/A3n+9YM0DdUQan6o964xPLQV0095qtAnjAgAGCz7Zt27rvA9yg+GBE\nPGfOHJk5c2aPh2Xr1q2ydOnS7vgjRoyQK664ovt/XT9wo+BG7devn64kw0rHfPnG8kbxLaB5o6It\nnAi4aVF3p+qP9rerc2eFJ+rv9XqtRLUtjh33Pu5r1M2OtK2CcJKtKYCcqn88vFfwTDvVAXDivYKZ\n3kiDrQI4WKHa2trkL3/5i5SXl8uMGTN6RJs6darg4xt27Njh+6+W3wUFBUZH4NChQ1rSCzcR8wY1\np+bDvT7a+AMHDpT29napq6uLNqmIrsdMCPJ3SgCXlJRIdXW1tLS0RFT+aC/KysqKed6lpaU9il1Z\nWdnjfx3/oF2Li4vFjrStlg8zG9G8FK3mEyheXl6eMfrDTJ4TwewA4NlyImDAZc5sOpE/7j+8U2PZ\nuUWbRxpiLoAB5k9/+pOcfvrpctppp0Vabl5HAiRAAiRAAglNIOYCeO3atcaUdH19vbz99tsGvAce\neEBycnISGiQLTwIkQAIkQALhEIiJAL788su7yzRlyhTBh4EESIAESIAEUpmAM6qCqUycdScBEiAB\nEiABRYACmLdBwhOAUh8DCZCAXgJQpnLKTFJvTeI3NQrg+G0blswCgaamJtm7d6+FmIxCAiRglQA6\ntXiunLLSsFrORI8XkzXgRIfE8scngZqaGnHKjCw+ibBUJBA9AXRq9+/fz9Fv9Cj7TIECuE9EjBBv\nBGA7fPDgQcOLGspmOh+It3KyPCSQaATgmRDPllP2+YnGK9ryUgBHS5DXx5QA1qTg5KG5uTmm+TIz\nEkhmAv6d2mSuazzVjQI4nlqDZQlJAOtSEL5UugqJiSdJICwC7NSGhUtrZApgrTiZmF0EuC5lF1mm\nm8oE0JmtqKgw3MKmMgen6k4B7BT5JM83+5X5kjv3JfHsr5KOEcOl8abrpeX8cyOqtalsxXWpiPDx\nIhIISACdWswoxdJvcsCCpPBBCuAUbny7ql74yKOS+9zc7uTTP90i2W8tkrqv3yd1//Jg9/G+fkDg\nwqm9UxtG9FU+nieBRCXATm18tBztgOOjHZKmFNmvvt5D+PpWrOB/fycZaz7yPRT0N9al9u3bR+Eb\nlBBPkED4BMxOLTWdw2dnxxUUwHZQTeE0c176Z8ja93UeF2Mrud27d1PTOSRJniSB8AiYnVqYGjHE\nBwFOQcdHOyRNKdxVofdBdR84GLKuDQ0NUlVVxXWpkJR4kgTCI4BOLZSt6NkqPG52x+YI2G7CKZZ+\nx+hRIWvcUToy6PnDhw8bHnioFBIUEU+QQNgE0KmlW8mwscXkAgrgmGBOnUwab7lRuoJUtys9XZqu\nv7bXWaxLQRsTApiazr3w8AAJREwAzxQ1nSPGZ/uFFMC2I06tDNpOmSI1P3xUulyuHhXvykiXw7/4\nqXSMLu1xvL29Xfbs2SPopTOQAAnoIYBZJLNTqydFpmIHAa4B20E1xdNsvONL0nrWmZLz8nzxqJFt\nx4gR0nTNVdKp7IF9A9xJYl0KyiEMJEACegiYOxlh3ZchvglQAMd3+yRs6TrKx0ndt78ZtPywQ+TU\nWFA8PEECEREwPcbRXWtE+GJ+EQVwzJGndoamHSJeFFzvTe17gbXXSwCd2urqar2JMjVbCVAA24qX\nifsSgAkERr0tLS2SlsZbz5cNf5NApASw3guPcfX19eJ2u7k9Z6QgHbiOb0EHoKdilhC6EL60Q0zF\n1med7SLg26m1Kw+max8BCmD72DLlowTs2uTbrTSnM99fKumf75Diz3dJ69QzpOErd4i3sJDsSSDp\nCZjrvVRiTNympgBO3LaL+5Kb6712bKbgUR618v78F3E3NIrL2yWZH601PjkvviQHnvtbL43ruIfF\nApJAGASw1ku7+TCAxWlU2gHHacMkerEwNQb7XjuEL9jkvvCiIXz9OaVV7pfChx/1P8z/SSApCKBT\ni6WcQ4cOUYkxCVo07kfAuOEyMjK0o/Z4PIKPHWlbKSyUJRDMbyvX6IyDfO2qf2Njo/GSwNRYMGWr\naOrtPnhQPHv2GjhaJFvezb5MTpA1Yrr+yFq+QrIOHRZvyZCgyFzKUUi68szllNtLu9gHrbA6gWcJ\n9TaDHfc+mCIPO9I2y93XtxNszTLhfse9bUf9YVoEu3l8B3uuwN78mGWK5FtNKsnS9UNk4KBMyc+3\n/v41n2s76m+lHmh71B/3eiKEuBfAgAhvSboDhAMay460rZTVvFGcUkqC4AED3fXHtBg+fQU8JChD\nJA+Kq7rWcHf5fuYl8o+c+2R0+2ZpSCuQ/I667my9aivD9qKB3f/7/0C+YK+7/v75BPvfyXvPLJMd\ndTdffnakbZa7r28IAafyxzOF+1p3/ujU7t+/v88OoykAo+lYbt3TT154p1w6vW45Y/JBGdDP+vsX\n+dpR/77a3Pe82Qa+x+z8HU1nI+4FsPlA2wEQL+FIBICusjidP+qhq/546PCCwIvCSjDrHkn+WzvK\n5MWCP0qTO0/uafh3mdy6oofwhRvMjuHD+qybWQYr5dUdx4m88Sz5hkjY+14f6LeZpvkdKI7dx5xg\na9bJrLf5bR6P9BvpoENr1b4X8SN9Z1bXZ8pLS8bJ5p0D5LKztsvZJ+6RkUNGqOcoM6ziowy66h9W\nxkcjO51/OGWOewEcTmUY1xkCcHkH4Wu3953ahgx5+f2xsn5bsVzV/3m5dNfvxSO93Vg2X3YJNaGd\nuRWYq0YCGMlhvRcuW+0Mbe1ueWv1KFn04Sg5fWKF/PDOpZKT1WFnlkz7KAEKYN4KURGAkhWcANjZ\n423vcMk7a0bJGytL5ZTxlfLY7KXSz5sr8tciEaV05RvaTpwk1T/+V99D/E0CCUcAQhedWruXqNZs\nGSTz3i2XQYWN8tCNq6SkyNoMVsIBjdMCUwDHacPEe7Ew5XxQKUPZpeVs1n/d1mKZ++446Z/XKt+4\nfrUMH3Rk1ySv5ErdV++RjLXrDDvgxtJSww646YrLRGmomJfzmwQSjkAsTIz2VOXJ8++Ml5qGTLn2\nvE9l8tgDCccpGQrMN1UytGKM64CpZkyN2TnlXHEwV15YXC5V1bly9fQtMmV8Ve9aKiW6tlNOlvZT\np0j1mDG9z/MICSQQAUw5V1VVWdajiKRqDc3p8s8PxsiHm4fIzNN3yHlTdkp6WmJoDEdS33i/hgI4\n3lsozsoHf7OYcsYI2I7Q1JImC5aWyYpNQ+X8U3bKPVd+LBnp9uRlR/mZJgmEQyBdzeDAiUyT2i97\nt9pBrLWwfziXW47b6XXJko+Gy8LlZXLCmAPygzvUMk5em+XrGdEeAhTA9nBNulTtnnKGPP9g/XCZ\n/0GZHDfqsDx6+zIpzOd+pkl3I7FCBgGXshYY8I1vS/Zbi6RqyGA5OGKY5CgtdfdZUwVKhDrDph0D\nZK6abs7K7JCvffEjKS05Zq6nMx+mFT4BCuDwmaXcFXZrOW/ZVaimm8eL2+WVu69cJ2OG1aQcY1Y4\nwQmoHmT2gtck54Ol4lF+C/KOGy+NN1wnXQX5AStW+N3HJH3xEtlePlbq+/frjpO1dJl05edJy/Sz\nu4/5/nCptNM3bBIPFB9zc6Vt4gSl8R941FxVnS0vqmWcXVUFcuXZWw0NZz9LNN+k+dsBAhTADkBP\npCyxkQLc3tkx5XyoNktefHesbN1TKJerF8TUSfuUEE4kOiwrCSgCygyv6M57JGvp8m4cEIn5/+8J\nOfD0X6VjbE/9BM/uPeJ9d4lsmTRR2tXUs3/IXPK+tJwzTZRBb49TnopKyZvzN3HXHhvBZr/2hjRf\nOlNa1MjZDC1tHjWTNFre/3i4TD9pj8y+bINkZvQ21zPj89s5AhTAzrGP65ztVAhpVXaHC5aOVnaH\nw2TaCXsNu8NsNT3GQAKJSKDfz37ZQ/iadfDsr5KBX/uG7H/1ZficNQ7DXK9m/Xo5MH6cwGFMoOBW\nJkhu1fH19j82snUpxce8J58Wt9LB6BGOjrw7i4ulbexY5T6yRF5+b4yMLqmR735phRT1t9eGuEdZ\n+E/YBCiAw0aW/BfYaYO46pMh8tJ7Y2V4caM8cusqw/4w+YmyhklLQGku5z4/N2j10rd8JhkffiRt\np51iuKeEbW+nMpPLCyJ8zYS6Mnt6n0pfv6G38DUjq+9di6tkzsqbpb3DrUa8m6R8xEGfs/wZrwQo\ngOO1ZRwoF3rncHtXU1Oj3bHGzsp85V9WrYspM4ibL/xEJpfXGr6oVZYMJJCwBNw1tUowHrFND1aJ\ntF275aAa8cJu3ljKGTlCvDnZ4m4KPDrtGDVSurKzeySH0XSgcNhdJM/kfFU+rjtLLj11n8w4eZ+k\neUQ9W4Fi81i8EaAAjrcWCaM8LjVVlTvn75L1zhJxtbRIu/ICVf+V2RHthQubXtggtqh0dIa6xgx5\nRbmP/Fg51LjkjM/l3JN3q00wIHV56+nkzLT0E8CaK3be6lACs6vfMUUp35y8SsnKm5Ul7iDPTYca\n7e5SSlY16tnqDupY8xWXS+4/nus+ZP7oUju/NV2unMn4ha6cnB5H2iRDFmTfJAuybpFpba/Lz9K+\nJl0n33Z0d7XAU9s9EuA/cUGAb8G4aIbwC+FSLiCLb/ySZHzyaffFmWs/lpy5L8nBv/7RmPLqPtHH\nD4x4MfLVqWjV0emSxWtGyusrS+WkcVWG+8j8HOu7qvRRZJ4mAdsIpG3bLoXf+b5krl5j5NGl1m+b\nrrpcah7/gXTl5fXMV22/2Ky8r+U+13sauk4J3l1TTpKa4UN7XqP+azvheKXFfIdkKTOktF17RDxq\nB6dxY6T54osE67n+of34CZL95tvG4ZUZ58rfcu6XQd598ljdvTKyc7s0n3meBOs6p3+qpsGVrTHW\nj72FhdJ6+qlGp8I/D/4fewIUwLFnriXH/j/5WQ/haybqhn3hgw9J5TtviFJ9NA8H/MaWafvUtn1N\nTU0Bz0d6cP32IsP8IT+nTb5+7RoZOdhPcSTShHkdCdhMwK1GqsU33CoetZ+0GVxK0Sn3xZclTe1B\nfeCZp7oVqszzNY8+IunrN6rncbNxqFMJ7IpRI+Tg8GHScOtNSriqOeEAob1stLTf/eUAZ3ofglDe\nctZN8vyGqXLAXSK3NP2vnN72rhGxc9hQaYXWdICQveDVngpiO3Yawhi2xr6a0wEu5aEYEKAAjgFk\n7VmoBZ7sVxYETTZNTZ1lrlgprUFsCXEhRry6hW/l4RzD4L/iUK5cNf0zOfW4nhslBC0wT5BAnBDI\n/92feghf32JlrlwtWWoU2qJGqb6hq6BAql78h+Q9/YyIirNXTUs3jBgurWdPFW+QqWvf6/v6DfeR\n85X7yNWfnysXT1gl36z4jmS17pPOoiJpVyPplhnnSJcaifuHdNUh8DWN8j0Pm+X20aOls2SI72H+\njjEBCuAYA9eRnUtNJcFUIVTwKG3LQAG7q2CtFxt365pyhvvIV5WLu6XrhyrfsrvkrivoPjIQex6L\nfwJZy1aELCTO+wtgXOBVa7efXzlLWi+5SNIxLe1vLhQy1cAn4T7yPWXLu3BZmUwsPSjfV97h+ivv\ncK1ym/r0HTLWrA0ZCdPSzSV6vW6FzJAnexGgAO6FJP4PoMftVR+3WgcOFjpGjux1ytepRp7/Wlav\n2H0f8CpdqqXrhxm983EjqpX7yOUyoCDYSlTf6TEGCThOoC+1fDUd7R+whAP/6FjSyVIKWTrCJzvh\nPrJc+UHvlHuvWitlQ2vDTtajbIlDhVDvj1DX8Zw+AhTA+ljGLiU1em286TrJ/8P/C5hn+7ix0qZ2\nCDIDNJzxgtC5sffWPf2N7cyQx5dnrZNxI2rM7PhNAglLoFXZ68J2N1hoPf207lNwVgPTImxQoisc\nqFHuI9X2mzsq+skVyjvcmcdX+DvEspxVp1K48uzdFzS+r6OPoJF4wlYCFMC24rUv8dpvPihpm7dI\n9rvv9cikQzl2P/R//2MofsCuF3uLQss5mulmt7oe618eNXV9MGO4PNN5m2yuHimzpm2TaSfupfvI\nHi3AfxKZQP19d0vO/FcNT1T+9WibfILh9hHHIXQhfCGEdYRW5T7ytRWjZcna4XLO5D1y+6UbJStK\n95HohGds2Bi4eC63sZVn4JM8GisCFMCxIq07H6XhfEiZG2W//qayA35XXM0tghdE4/XXGs7cMdrF\nqDfaPXsxGsj927PSpjzsvJx9q7yafYPMaFkgPxv/R+maPFN3rZgeCThKABrFB/7+pBR+65EeVgbN\nMy+Uwz/9sbRh1Kv0K3RZDmDGe8WmEsNWfuTgOvnOrSukuDC0fodVQJgJaz7/PMle9E7PS5QXrsar\nLw9o7tQzIv+zmwAFsN2E7UxfPUjNl8w0PmY2ULI6pF4QOqbF3GptC84Clruny9/7f02Gde6Qf6u9\nS33vFPlIpLG0qMdUt1kGfpNAIhNon3icVC1UZkdbt4lHjXLbS0ulY1Dxkdmk3bu1eYn7fF+BsQtY\nS1uafOnijTKh9Jjpky5+LRecqzaDKJMMte8w1nw7BxSqZ/YU6Rw8SFcWTCcKAhTAUcCLp0sx3Qwl\nK50ONfYtOyC/yvyV1LoHyuzGX8iU9g96VDlz1WpHBbBH2VfmKA9B+fmBt3zrUVj+k7IEstQsUd5T\nf5M0ZQPrLS6SpitmScNttyhnbKFff9jFCJ+GhgY5pAQvlKx0hJqGDLVhwjjZoOzlvzB1u7FjkccN\n73D2BLi2xMdqgIVEttLk7r97r+TtUeZOygmIN4BzEKvpMV5wAqHvwODX8UwcEcB0GNajrE43Yy9R\nN9aG1DqQe/gwZatY0KM29U3p8k/lPvKjTdPkyra/yqUtzyrHkb13K8LacKxDmnpp5qp9UPHJVv5y\nXX04tY91+ZhffBEo+O9fS8Gv/+9YofZVSMbH6wVb/h368+9CCmHsg43nSpfyYnuHS+0ANkreXFUq\npxxXaewClpetR6gfq2BkvyB00ZnFczXw/aUy8LF/U6P/Q0Zi2LWp6fprpFp5AhO/TSIiy41XmQQo\ngE0SCfiNFwT26rW8HqWmp3PnvWxMR6G68M/TTwnhlhlnS/NFFyiFEuU+cu0IeW35aDlxzAH58Um/\nlyFvvhCUTFdBYP+4QS+I8IQpdGE6BaHLQAJWCKSrTma+r/D1uQjKi7nPPCeNX7rZ5+iRn1CswnOF\nZRzMLOkIaz8rlnlLxkmhsuP9xvWrZfig0Bs46MizrzRMoYvnCsIX/2eqPY2L1BaKLp9643fusy+I\nS20ecfjXv+grWZ4Pg4AjAnjDhg2ybNkyw2B95syZMnRob1+pYdQh5aJinRfazXVqTSecF0TuPxd0\nC99uaF1eyVq8RNa1nSDPVF0puVntcv81H8moIXXKd2ypdL2TLq6OwL30VuXn1q4AZwbojePloMu2\n0q6yMt34JABvT64QRctRbhp9BTAsBbCMg2crGqsB3yz3Hcw1dgGDedHVMz6TKeU+mzL4RozRbwhZ\n87mC0PWfQSr4+a96CF/fYuWo90fdV++WjuPG+x7m7ygIxFwAo3c5f/58eeihh4xe5rx58+S+++6L\nogqpcyleCjApisSsyF1XLxlHncv7Eqtwj5A5uQ/Izs3lcsWl2+W0Cfu77Q69am216dqrlaN5NQr2\nc0DQdvxEaT3zdN+kov5tjnSxpkuhGzXOlE/A159zIBjuo/6e0YlFZxaCF51bHaGxOU0WLBsjK5WG\n8wWn7jScaWSk93bioSOvvtIwR7p4rgIJ3e7rVd0z1n7c/W+gH5kfrqEADgQmwmMxF8BQnMENv3fv\nXsMlYqbfmgLWW3y3xMNLGdfoDuj54ca0I20rZTXztTKC9e2Zm3aHKHs4Af6hfUOTK0fmZd8pi7Ku\nkJnNL8gD9T+Q9qH3idczwDeadCjTpoYhgyRj6QrDDtires3wP9t+0mQJrwQ9kjV63qgDPhjl4uUQ\n6+llJ9vfybzNljDvQfN/Hd9mmua3jjTDTcNk26l2FwoVOseUGcs3mG42FaxwbTShq8slb68aLHMX\nDVNazYfkB7NXGNPOR9KMLm0r5UL5zVEtRrqm0LVUL+hT4F0bohPiSs8I+c50+r1qtr3JwAozJ+Oo\n6X2fyf4YlATZLVy4UDZu3GgoN1x11VUyefLk7pzfeOMNwccMZWVlcu+995r/av1GI8W4+mGVH4IX\nLwf4bjZfEGEl4BtZbUnW9atfi1dNyr2beZk8l3OvjG//WG5u+o3a1qzCiOn62U9EPbG+V9nyGy/n\nfspJff/+/Y0XhBMPS7y3vR3g/V/CuqZZ/csaL2y71HPTNf5EUcNb/yJKbWF/2f/H30qL2pFIV9i4\nLU+eXDBCKVd3ye2X7Zbxoxp1JW0pHbQvhG6h8oCF5yuSTpD3kstFjm572CtT9b50fbpeXKrjEizE\nS9sHK58dx6ElX6BcA0cSYj4C3q3U+Xft2iUPP/yw4UXmRz/6kUyYMEEylDNzBKwJ4+MbduzY4fuv\nlt8AhnVGCDgnAkb2CIGmvNAp0D0lJoX9pCr3VHkq7avSqXSa769/TI7vWNNd9U61Dl8HMwu1S5Id\nAQ8mpr/QI8dLAu2N9TZMpzsRSkpKjClH39mWWJYDU+yxzru0tLRHFfEc6g5o12JlsoIZLqcCZtWg\noIiQ+ftfy8B7H+j2m16j7GD3K83/w2pppVXt16vjfj9YmyXz3i2XbXv7yzXn7ZRzTz0sjQ31OpK2\nhBD3Ep4rzCSBP+oeKf/0B78mg5SGuOsoP98C1N/xJalNV++tEPfNgAEDjPc6nm0nAuqPd6pdnctA\ndQL3SEPMBTAAmVON6LGZvyOtQDJdh5sGghdCKZBgjrSu1fWZ8tKSSbI5b4ZcV/NbuaD1ZTV97LMe\nleZRtpGXRZp8yOt8Xw599cixRp391tviamiU9okTjE3Qu5TQZiCBSAm0Tj1TKha/Lh1q+84amBQp\nkzssoXQOHBhpkt3Xtba75XXlPvLdj0YYLllvVc40CvulK9esvbcG7L5I0w8MHiB08cFvXaFd6XYc\neOZJ6f/9xyVj0ydGst78PKlX+xbXf/UeXdkwnaMEYi6AhwwZYkyRPPXUU8YU9DnnnNM9+k3VVsG6\nLnqM+JhrvDpYtKkXxFurRxm2h6dPrJDH7loh/XflSNebygvO0TVh7AnafOlMgQs+XSHsl4Ma8Rd+\n9weGqYNvGfL/7w9y8Ik/Gc4QfI/zNwlYIWDOJNUqc6K2aWdaucRSHCzarfpkiOFMY/igenn41pUy\nuLDp6LX6hKF/YTBgiYW+RNvJJ0nVgnnirtwv7sZG6Rg5QpSU9y8O/9dAIOYCGGW+8sorjTVNjIj8\n16U01ClhksC6rrmbiu4pkzVbBqlpsXFS3L9ZHrpxlZQUHVmPah9fLvjkqZmITvUmaca0s4aAKWZM\nLWNqH1PN4YS8vz7VS/ji+jS1k8vAe+6X/a//M6TDhHDyYtzkJ4BOLDSa0aHVOZMEcjsrC4xdwLAH\n9s0zN8nxo+1fwsIsoTnFHMv3pVdt7OIzT5b8N44DNXREAKOeOqdNHOAWVZbQ9IaRf6PqXeoc8aJQ\ne6ryDP+ymHa+9rwtMnnsgcBlVQJYZS6qJxT4vMWjWG8zp8L6mmIOlmTek08HOyXp2z+XLOWZp+Xc\n6UHj8AQJgAA8wUHo4vmy6hXOKrm6RriPHCvrthXLJWd+LueetFspOelx0hGoDNARwXNl6qoEisNj\niU/AMQGc+OjCqwGmw6AthxcElG8iFVbBcm1oVu4jPxgjH24eIhedtkPOP2WnpCttTDuCORWGl0PU\n9rqqE5C2a3fIYqYpISwUwCEZpfJJdGTxXJke4XR27juUd7hFH46UN1aOlinj98tjs5dKfk50ndZg\nbWUqKpqzSPifIbkJUABbaV81Ssx+7Q3Dh2wXtFdnnCNtp51i5UpjCgwvByhX6R7togCdXpexh+jC\nZWUyqeygfP+OpdI/r81S2cKNBAU6vBzw0TYVppYhOpU5kieENnRnUfQKM+HWlfHjk0Da5k+l4Hd/\nEvfGTXJY7VBUedYZUq86Z3gudYd124rkxcXl0i+vVR68/kMZodZ77QjojJvPlc7Ogx1lZZp6CVAA\n98HTo5SVimbfLemfbumOWfDb36v9NK+Q6v/6jyOG691njv1ArxxCF71yu2yNP9kxwJhuzsrokK99\n8SMpLelt73isRJH9MnvlsNm1S2O9+fIvSN6cvwcsIDQwOf0cEE3KHcxU/ptzvvktOaTuxZqBA8Tb\n2SGe9z6Qgo2fSP09XxFvXq4WJhWHcmXuO+VSeTjXcB95ihr52hEwewR7XShWcbRrB+H4T5MCuI82\nGnD/N3sIXzN67rxXpEPZVdY/8FXzkDHaxdouBG/UjjO6U+3940B1tsxVdoe79hfIFWdvlTOUhrPu\n2SqzV44XhGmz3Lskeo7UfuubkqG2NszYfKyTg5S71DpY9U//XbrUiJshdQlAQbFe2et7/vAn2Tdu\nbC8QbmW7nr3wNWm8/ppe58I5AMWqBWomacXGoXLelF1y95Ufi273kRC0ELh4rqJevgmncowblwQo\ngEM0Szq2LVujdp4PEvKeeErq7r9XGtUoF4LXztEuitDS5jF2Knrv4+HGHqKzv7BBMjOUIpXGgCkw\n00OVtmnmPsrXVZAvB154RvL+/ISyA14kLjV70K72IIXtYfsJk/q4mqeTlYCprAjdCc8nn0oelAaD\nhAzlWa/Re7WotZEgMYIfVvJdPlg/TOYrHYrykdXyvduWy4CCluAXRHAmlh3aCIrHSxwiQAEcAnz6\ntu1Bz7Yo04DDanp0p1qL6sjWt/7kge2dEuadRUXiVe7yEJT+lizfWCKvqD16S0tq5TtfWmGYFwUt\nXAQn0BuH4I3Gq0sE2XZf0qVMmOqVFx58GFKXAGaO0JnFx3cWKU0J4ZCho9Pw3tQV5naVW3YXqt2K\nyo0ZpLuuWCdjh+v1zIbZI7iGxBovp5lDtmBKnqQADtHsnWqdyTd0qIcJa0/VRQOlKVfZuirliY4M\nPQbqnooKtevQXLXhwTGzoXY13bbhnC/Lc8snS1u7R267dKNMGKXXVSRsdvGCsGt915cff5NAIALQ\nkYDAxUgXo95AOhPe4tCKeNgkJBzhewjuI9X+vFv3FMqsadvkrBP2Kg9WgUoX2TEoLOK54vpuZPxS\n5SoK4BAt3XrGadKuhG2jmqOC0K1Xbuy6fBZb2yYeF1QJK0SyvU65a2olX02/upR5khkOu4vkHxU3\nydqXT5ZLZ+yRc07eJx63PrMi9MixDuU7yjDz5jcJ2E0AQtacYobCYl+OaDpGjjS8tXmUc5ZAoXXq\nGYEO9zoG73CvryyVxWtGGkL3h3culexMPVsQIjPMJA0bNqzbF3WvAvAACfgQoAD2gWH+NF8O6JHv\nfvz7kvP0M0ecVpgR1LdXOXJvvuxSnyOR/8xa8l638G2TDFmYfZPMz7pFzmp7XX5x+Hpxey6SNveJ\nkWfgcyW8VcFh+lC1+QKELwWwDxz+tJ0ANgqAkiKEbrheqhpuvkHy/jpHPMqns29oO/EEaTlvhu+h\ngL+PuI8cq7zCNci3b1kpQwaY7iMDRg/rIBzS4LkaPHiwMZt04MCxmaywEmLklCJAAezT3HCQgamw\nHi+H8nHScd/dkrV4iaTt2StdajeQdnWsVdkeYtpLR0jbeWRXmpUZM+TvOfdLkbdSHqu7T0Z2bjOS\nb925U9pOik4Am4LXf/9lHeVnGiQQigC8UqEz67+uG+qaQOe8Skeh7utflYx16488i2r5p2P8OIE/\n81Bh1/58eX7ReGlozpAbL9xs2MuHih/OOTxP5lRzONcxLgmAQMoLYPTIzZdDsB55Z8kQabzpetvu\nmN1dpfK3gkelyj1UblH7857etrhnXq7wNTvNBLC2O1Dt/EKTB5MIv2NBADMreK7wMbcG1JKv2rmr\nbcpJxqev9OA+8pX3x8jHWwfJxWco95En75Y0Te4jYS2A58oppcW+6s7ziUEgJQUwXgjY5B5mQ4dt\n2v/WSvM3NqcZ/mVXec+WWe1Pybebv6UmoHt7seoYE7qHHygv9Mzxggh3Y4RAafEYCVghYApdbKe5\nb1/gtVor6UQbB+4j31o90tgqcPLYKvnBHcukILf3cxVJPtRqjoQarwlGIGUEMIQuppbRI8eUGEaG\nuv0xB4Psfxx2h+99PEz5bh4tE0sPyWM3L5ERf3lR7dHb+yXRPrpU2o6f6J9E0P/xgsBaFJSsGEjA\nbgKm0DVHurj/sImAU2HDduU+Ujmpyctuk69fu0ZGDtbjPhI28TDTw3QzzYmcat3kyzepBTDWdCF0\n8dG9O0qkt8LmnXAfWS4ZaqOEr35xnZQOqVZJuQ1XejnzXpZu22P1wGNfzqZZ1hS9zBcEXhKxcqAR\nKQNel9gE8CyZzxWesXgIlYdzZK56rioO5sk1530uJ4/bq6VYpucqzCahc8FAAjoJJNUdBe1lvBDQ\nG8f0cjxp+B6syVY983HyeUU/5T5ym0w7cb9h/G8694HTjYY7bxd3Q6PhCQpa1l1qGtlKwDoUXhB0\n5G6FFuNEQsB/BimSNOy4Bu4jX10+WpYqT1ZwH3nX5eskN8ejnv3oc4PeRJFyiEP9iehZMoXABJJG\nAGPdCZ9gilSBq2//0VblPhJ2h+9+NELOPnGvfOmSjcruUHntcXkCZm44lLfoVB7rvHhB0IlGQJQ8\nqIEAOrIwqYmnziyq5VUm8RC6cB85dni1PHq7r/vIwM+WVRxcxrFKivGiJZA0Ahgj33gSvnAfCbvD\nl94bJyMH1ckjt66QQYXN0baXcT2mmLHOC0caXI/SgpSJBCGA6eZ4E75b9/SX598Zb7hovXPWeikf\ngWWc6AOeJehOYDaJyzjR82QKfRNIGgHcd1VjF2NHRYHxgmhpTZNbZ26SiaMPacsc080Y9XI9ShtS\nJuQwAXedUpTydirnNkd8nwcrzuG6I+4jt+w64j5yGtxHRm6h1yMbzCYNGjRI8M1AArEiQAGskXRt\nA+wOx8q6bcXyhTO3GzsWeTTaHRYXF9OsSGN7MSlnCaR/tlWyF7wqngNHPFt5lfZ080UXSNspJ/co\nGNxHvrmqVN5R7iPPOH6fwH1kTpYe95GcTeqBmv/EmAAFsAbg7R0u9XIYJW+otd5TxisvVrOXSn6O\nBi0QVTZMi2GqGVPOnBbT0FhMIi4IQPjmPfn0ka2+jpYIu4DlvviSuNS0t+nb+cNPB8s8pbw4ZGCj\nPHTTKilR37oCbOQx6uVski6iTCdcAhTA4RLzi79ua7Gh3dwvr1W+cf1qGT6oj23T/K4P9S92VMEL\nglqYoSjxXCISwMjXWMQNUPjsN96SbSNnyHPvTZK6pgy5/oLNcuKYnv6fA1xm+RDs/7GM46S9suXC\nMmJSE6AAjrB5Kw7lyly1j+j+6ly5evoWmTK+KsKUel+GUS/seTHqpZJVbz48ktgEsOZrTjv716TO\n1V+eS79bls89XWZO3WmYFulyH4m84BMdSzkc9fqT5/9OEKAADpM67A4XLC2TFZuGyvmn7JS7r/xY\nMtKVaytNgaNeTSCZTPwSUApX/qFDPPJG1nXyUvbtckrbe/L4jL9LzuTh/tEi/p+j3ojR8UIbCcS9\nAIZzDSu9VTxg4biWxMgSH6vXHHEfOdRwH3ncyGr5weyVMqCgVTUNdvEO3+4w0HouRr2YGovFqBf5\n42OFrR33H7ijbfFxIpht71T9nWAP1r73lpW6I47VZwTtaN7XIa9RMztQuMKaL8La9DPl6dwHJNdb\nL4/UfVPGeLdIXfnDEe+1bbatkbj6g7VebBNopb7mNZF+o/7IPxZ5BSqj0/mj7k7c2yYL5I17D9+J\nEOJeANsB0V1RKZ4dO8WtpKpH7XTUWRZ6s4NPdym7w0XjxO3qknuu2iDjhtdqLRYe1iFDhtChhlaq\nTEwHAVs6SOol3XrxhVLz4mqZowTvzrRxclPj72Sa2v8a3dnW6WeL2mYo6uJDGGC6GUqMDCQQjwTi\nXgDjIbLiYKNT+XTEJ2RQI4CcV+ZL5srV3dFy1a/2MWXSePON0pXV0wbwUO0Ru8Otewrl8mlbZeoJ\n+5QQFpVP9+VR/0DvHC8J9Nis1DPqDI8m4FWdD3ximadv2c362vKC980oyG/ki/vFqfqj0xXrvPEs\n+QYr+Vt6rnwSNfMI9Sw2t3rk1brr5IOBX5eLm5+XB6p/IFmifEqrbTdbzp4qzTMvjOohw70Ft6xQ\nYMSSjpV6+lQhqp94pnBvxTJP3wKboz+n8kfdnX6v4N5DGRIhxL0A1gkx650lPYSvmTY2QMiZO08a\nb7nRONSq7A5hUrRY2R2epYz9YXeYnanH7tDMEy8JvCDoRtIkwu9kJwD3kcs3DDVs5ccMq5Hvzl4l\nxdkDpGPXDdKgXpqdw4eJNzcnKgzoAEB5EQ5rzM5AVAnyYhKwkUDqCGDVI8p6f2lQlBmbPpFmtTfw\niv0TlfvIsTKsqEEevmWlDB7QFPSaSE9A6A4bNsxYJ3Kqpxpp2XkdCURCYNvefvKCch/Z0emW2Zet\nl/FKjwKhS+2A3T62LJIke12DUS/WejHljM0jGEgg3gmkjAB219WJqzX41mnbPcfJX186RxqkQG66\ncLNMKtNnd2jeBOiRYz9RfJxS0jDLwm8SiAWB6vpMeWnJOME2nF84a7ucc+IetdyiP2eMeDGjhJkl\nBhJIFAIpI4CDbe1X6yqUZ3PulVUZM+TS4Rtl+gWNSotOv2YuBC5655xyTpRHg+WMhgDcR761epQs\n+nCUnD6xwvAOl5utdxkH5YPAheUANlFgIIFEI5A6AlhN+7aPLpX0z3cYbdQhafJa1vXycvZtcnrb\nYvlZ+5dFzlcfpcKuO8D4H73zkKYZujNleiTgEIEVGwplzsKJUty/WR66UbmPLNLnPtK3SlCwQqeW\nGyj4UuHvRCKQMgIYjdJ8xSxJ+/2f5SPvFMPusJ+3Wr5X96CM9n4mDbfcIO1peoUvppyxtRnsexlI\nINkJ7KnKk7nvHie1Ddly7XmbZfLYA7ZVGSNejHw55WwbYiYcAwIpJYD3po2SueV/k8rKTLmpBXaH\nb6pR8Qipv+BO6Rg5Qitu07aXfpy1YmVicUigoTldOagZIx9uHiIXn7lLrj6vRhrqD9tSUk4524KV\niTpEICUEMNxHvrq8TJauH2r4lr3rmk3Sr2CGtHvOl4YGfZsnmG3IKWeTBL+TmUCn1yVL1g6XhcvK\nDKXF79+xVIr6eyU9Ld+WamPKGQ5r8M1AAslAIKkFMOwOl64fJvNV73zs8Gp59Pblyn1kcE3oaBvU\ntEGEljMDCSQzgU07BsjcxeMlK6NDvvbFj6S0pO5ode15pWDnItNhTTJzZd1Si4A9T0scMNy6R7mP\nVHaHCF+etU7GjaixtVTUcrYVLxOPEwIHqrPVOm+57NpfIFecvVXOUBrOfg62tJYUnVoIXmo5a8XK\nxOKEQNIJ4MN1R9xHbtlVKLOmbZNpypOV3aaBMC3C1Bi1nOPkrmYxtBNoaVPuI5ePlvc/Hi7TT9oj\ns7+wQTIzNPpkDVBiONbAc0Ut5wBweCgpCCSNAIb7SGwT+I5yH3nG8fsM95E5WfrtDv1bHRrO0HRG\nT903pK9bL3lP/0PSPv9cOpW2ZvPlX5DmL1ziG4W/SSDuCSjXvrJsQ4nhPrK0pFa+86UVhnmR3QWn\nHoXdhJl+PBBIGgH8m+fHSXu7V75100oZMlC/+0j/xoI2Jmx74YHHP+T843kp/N5j4sLb62jIee0N\nabzmaqn+r58op/M9hbUZh98kEG8E5i4aLO9/lC+3X7pRjhsVWrPZU7lfMpVLV/UgSmb/ftI2eXKv\nDU76qh86stChgD9nBhJIdgJJI4DvuXqr2kFF7zaBwRo/lDYmtjks/MHjPYSvmU6u2vChdeoZ0nTN\nVeYhfpNAXBO47OwDcs4Jm8TjPtaZDFTg7LffkaxFi7tPYUuFrLcXS8NttxibLHSfCPEDSzjo1GL0\ny0ACqUAgaRyn5mXbux5l3gwY8Q4fPjyoKUTOy/PF1RF86hu7LjGQQKIQyM5Ue2b3IXwzNm7qIXzN\nurkbGyVvzt+VD/a+N0bAOi+eKwpfkx6/U4FA0ghguxsLU2NY64VSSCjvO57KypBFwTQdAwkkE4HM\npcuDVset7OzT128Meh4n0KnF7mBQumIggVQikDRT0HY2GqbG4HM2J6fvvUo7RwwPWZS+zoe8mCdJ\nIA4JuA+G3jnME+S82amlq9Y4bFQWKSYEOALuA7M5NWZF+CKppquukGA7L+F8443X4YuBBJKGQFdu\nb0VE38p1BVjTRae2pKSEftJ9QfF3yhGgAA7R5JFMjXUOLZHDv/ypdGX0nk6r/8psab704hA58hQJ\nJB6BthOPD15oZS3QdvzEHufD7dT2uJj/kEASEUj4KWh3VZVkv/WO5DTUS+uQwdI+vjzqLQUxNQYz\niEhdSsLet3LiBMn7x3OStn2HdBYXSdPll0nbmacn0a3DqpDAEQItZ0+T9C1bJW3nrl5ImtSz4B1w\nzDUrXUr2QsQDKUwgoQVw7jPPSf/Hf6y0LNukbmyZtKsH3asEZ8NtNyuhVxxRs0LBCuu90WpjdpaO\nktrvfDuiMvAiEkgoAmlpUn/nHZK1dJlAI9rT3CztyvlM67Sp0j6mzKhKtJ3ahOLBwpKARQIJK4Az\nly2X/nB24VdR9+HDkvfE01L7za+LqBdDOAH2vViXojZmONQYlwQUAbWXdsv0s6Xj/HMFo9yG6upu\nLOjUwnrAqh5F94X8QQJJTiBh14Dzf//nXsLXbCt3TY1krNtg/mvpGyNemkJYQsVIJGCZADq1I0aM\noPC1TIwRU4lAeEPEOCKT/umWkKXx7Ldub4u1Xtj4MpAACegjgBEvlnO4SYk+pkwpuQgkrAD2FhSI\nZ39V0NboysoKes48YU6NBfLnbMbhNwmQQPgE2KkNnxmvSD0Cjk1Be71e+eMf/ygVFRURUW+eeWHI\n69onHBfyPPbvLS0tDbiZQsgLeZIESCAoAShbYcqZM0pBEfEECXQTcEwAL1q0SHbs2CEdIfwmd5cy\nwI/6e78ibccpk6MAAcogncokKVjIUqPj0aNHC/bxZSABEtBDAJ1aCN9Izff0lIKpkEDiEHBkCnrP\nnj1SqXwml5f3FqBvvvmmvPHGG90Ey8rK5J577un+3/dH17Il0vX4v4s8/6Io1WWR4cPEdcH5kq3s\nbYOJVrwc8JLA9DN669GaG/mWJ5F+o+5darvEVHUDiPpj551UDiNHjuyz+uikYraqr4D1XnRqYUEA\ntlbS7ivNRD2fyvU33yv9+vVL1OYLu9wNyt95pEFtWeuzaW2kqYRxXbvaKxRTz7Nnz5bnnntOLrjg\nAkMgmkk0KxtCfMyAXnVtbd/bDEKg19fXm5f1+saNgWkxs3eOdV9oaB5WZktOBNQLIdIZgGjLDA7I\nOxSzaPMIdT3Y416I8e3XXSQIX9xXrRZ26um+SOMPeIOKdd7oePqG3bt3+/4b8He1Mic6GMSXs3kB\nzI6gbIVnDO0KJzZ4Hp0KTrA164oOPWbYDh06ZB6K6TcU3jC4wLPlRECHHh22uro6J7I37j+816x0\nGnUVEJ3UImX3HkmI+QgYo1v0llevXi1VyovVmjVrpFg5zcBNi4DK+E8NWxGSnZ2dQaHjhkQeELqI\nh4AXPxrJ/N84GMM/eFkhOJW/0/VHvfFxSgCDvZPt72TeqDuClXsP5cQnUMA9bHqMM+OYaZrfga6z\n+xjydip/cMA97VT+YOtk/vHyXjHvR7vvtWjTj7kAnjhxotFbRsExCoTgNYVRtJUJdD3yGDp0aND9\newNdw2MkQAKhCaBTi1kEWhCE5sSzJBCKQMwFMNaJ8EHYsGGDQCBjysiOAOEOz1a0Q7SDLtNMVQLo\n1OK5suu5TVWurHfqEYi5APZFfMcdd/j+q/V3gbITxrSznaNrrQVmYiSQAATQqYVbSVOHIQGKzCKS\nQNwScFQA20HFd13KjvSZJgmkKgEoW2HamZ3aVL0DWG/dBJJKAGNdSsdORrohMz0SSHQCULbCh4EE\nSEAfgaQRwJgSw2YKXJfSd3MwJRLAaBdTzlS24r1AAvoJJI0AjtQOSz9SpkgCyUEgfdMnMmTXbulQ\nArj9xEmiDEyTo2KsBQnECYGkEcBxwpPFIIGEJ+BRQnfg17/ZY0vP9jFlcvjXv5D2iRMSvn6sAAnE\nCwF2aeOlJVgOEogDAq7GRim++Y4ewhfFSt+2XYpumS1u5TyHgQRIQA8BCmA9HJkKCSQFgdxnX5C0\nvXsD1sVTUyP5f3ky4DkeJAESCJ8ABXD4zHgFCSQtgYyP1oasW8aa0OdDXsyTJEACPQhQAPfAwX9I\nILUJdGFXsRChr/MhLuUpEiABPwIUwH5A+C8JpDKB1rOnhax+X+dDXsyTJEACPQhQAPfAwX9IILUJ\nNF05S1pPOTkgBGhCN9x2c8BzPEgCJBA+AQrg8JnxChJIXgJqP9mDT/5ZGm68TroyM4x6diknN02z\nLpUDzz4tXWq/WwYSIAE9BGgHrIcjUyGBpCEAIVvzHz+Smn/9vniqDoh34ADpUnt4M5AACeglQAGs\nlydTI4HkIaC2Ce0cMTx56sOakECcEeAUdJw1CItDAiRAAiSQGgQsj4BXrVolX/7yl0NS+fnPfy4z\nZ84MGYcnSYAESIAESIAERCwL4PLycvnd734Xktlxxx0X8nykJ7HNoO7Q1dUlXq9X+ZfXn7aVspp7\nqjqVP+oOBk7lj/ojb5TBidDZ2Wlk61T9kbmTeduVP9q1o6MjKetm3DB9/OF7xdn3CprHfLf00VTa\nTkfzHLvUDaPtDdjS0iJZWVnaKsaESIAESIAESCBZCUQ0/Hv77bdl+vTpcsIJJ8ikSZNkwoQJMnjw\nYFmwYEGycmK9SIAESIAESEArgYhGwJiOvu2222TTpk2GAMZm3X/7299k2bJljk89aaXDxEiABDpG\njVYAAEAASURBVEiABEjAJgJhj4AxY11bWyuPPvqoXHzxxdLW1iYPPPCAXHDBBfLaa6/ZVEwmSwIk\nQAIkQALJRcCyEpZZbSxw5ypDfQjhyZMny5w5c4xTAwYMkF27dpnRtH4vWbJEa3pMjARSkQCWjXwD\nnytfGvxNApET8H+2rKYUtgBGwvfee6+ceOKJsn37dtmxY4fccMMNsmjRIlm+fLnVfBmPBEiABEiA\nBFKaQNhT0KD18MMPy/z58yVN+YiFQhaUsf75z3/KmDFjUhomK08CJEACJEACVglYFsAY3cIRBxSt\nEDACRhg1apR8//vflzPPPNP4n39SiwA6YN/97nflkUcekd27d6dW5VlbErCZAJb4aF1iM2QHk7cs\ngMeNGyeDBg2S6667ztB8/tWvfiWHDh1ysOjM2mkC9fX1Mm/ePPnxj39saMX/9re/dbpIzJ8EkobA\nhg0b5KWXXpKGhoakqRMr0pOAZQE8cOBA+Y//+A9D0eqXv/ylwDXl2LFj5aabbjKmoTX68+hZQv4X\ntwTy8/PlN7/5jeHNCi+L4uLiuC0rC0YCiUSgqalJnnzySbnxxhsTqdgsa5gELAtgM1243YK/Z9j9\n7ty50zA/+slPfiLjx4+X1atXm9H4nUIEMApGT/3kkwNv5J5CKFhVEtBCALNJs2fPpmdBLTTjN5Gw\nBbBvVdLT0wWjoH79+klra6thE+x7nr9TgwCWJZ566inDVzjckTKQAAlETmDNmjWyZcsW+fTTT+Wj\njz4SzC5hsMOQfATCFsBw4v/WW2/JHXfcIUOHDpUnnnhCbr75Zvnss8/krLPOSj5CrFFQAhUVFfKj\nH/2o+zw8okEznoEESCByAkOGDJHrr79e8Dxlqj2ZMzIyBIMdhuQjYPlteeDAAfnP//xPeeaZZ4yb\n4s4775R169bJiBEjko8Ka2SJQElJiUA34LHHHjNmQG699VYKYEvkGIkEghPAwAYfBMwoYT3Y/D/4\nVTyTiAQsC2CMcPft22dMNcLtJDxiMZDAV7/6VUP4opfOe4L3AwnoJXD55ZfrTZCpxRUBywIY08vm\nFPMtt9xiKGJdc801xjRJXNWIhYk5AUyTMZAACZAACYRHIOw1YCSPqUZ4wiotLTXWghcvXuzYxurh\nVZexSYAESIAESCA+CEQkgC+99FJ5/vnnZevWrcaoGPbBsAl+/PHHjWnq+KgaS0ECJEACJEAC8UvA\n8hR0oCpgIwasDUMQQ0kAnpGmTp1qKGvBQYeuMHLkSF1JdadTUFBgaBY65c3L1Bbu6OjoLlMsf0B5\nqr29Xerq6mKZbXdeWDNG/k45cIECWXV1taHk0l2oGP7IyspyLG+zmnY8V2hXOGTZu3evmU3Mv7Ek\nArNIJwI0l7OzswVKq04Ej8dj7MmOZ8uJgF3xOjs7jd3ynMgf9x/eqbDWiVVAm0caIhoB/8///I/h\njnLWrFmGijz2AX7vvffk5z//uaGkBU9ZDCRAAiRAAiRAAsEJRDQCxjaEv/jFL+Siiy4yelu+yR9/\n/PHy61//2vcQf5MACZAACZAACfgRiEgAYwQcLBQVFQk+DCRAAiRAAiRAAsEJRDQFHTw5niEBEiAB\nEiABErBCgALYCiXGIQESIAESIAHNBCiANQNlciRAAiRAAiRghQAFsBVKjEMCJEACJEACmglQAGsG\nyuRIgARIgAQSnwDsme0OFMB2E2b6JEACJEACCUUAjkzgTMZuR0ERmSElFEkWlgRIgARIgAQsEoAX\nNex1HgsvhRTAFhuF0UiABEiABJKbQGNjo+zfvz9mriwpgJP7fmLtSIAESIAELBCora2VgwcP2j7t\n7FsUCmBfGvxNAiRAAiSQcgSwKQ82Z4l1oACONXHmRwIkQAIkEBcEoGSFKeeGhgZHykMB7Ah2ZkoC\nJEACJOAkAZgZQdmqpaXFsWJQADuGnhmTAAmQAAk4QQBmRhC+bW1tTmTfnScFcDcK/iABEiABEkh2\nAhjxQvjGwtFGXywpgPsixPMkQAIkQAJJQQBrvVVVVTEzM+oLGgVwX4R4ngRIgARIIOEJ1NTUCLSd\n7fZuFQ4oCuBwaDEuCZAACZBAQhGAwIV9L+x84y1QAMdbi7A8JEACJEACWgh4vV7DzAgeruIxUADH\nY6uwTCRAAiRAAlERiAczo74qQAHcFyGeJwESIAESSCgCMC+CpjPMjeI5UADHc+uwbCRAAiRAAmER\naGpqihszo74KTgHcFyGeJwESIAESSAgCdXV1sm/fvrgxM+oLGgVwX4R4ngRIgARIIO4JYDOFeNR0\nDgUu7gUwVMgzMzND1SGicx6PR/CxI20rBXK73UY0lMGJgHztYmulPsjf5XJZiWpLHOSdnp7umE2g\nE/ceNELN+w5Q7bj3wRRs7Ujb6o2Qlubcaw15g7FT9Qd7fHzb2So3HfHM5zqW9cd7DBsqYPSLeqP+\nKIeOgHogPbuCc3eqxRqh8q2trRZjW48GsGgsO9K2UgrzJdHR0WEluvY40BBE3k7VPyMjw1CQwMPj\nREC+UNBwqv523dehWPq/SOyoO7jiY0faoermf86p/NEBwcep/CF48F5zSvkI7xV8YlV/5AXhi3Vf\nBLxX0dHER0dAPfyfG/900d6RhrgXwJFWjNeRAAn0JNDXi6RnbP5HAvFNAAMIaDrHStjbQYMC2A6q\nTJMESIAESMA2AhC6EL5OzSDqqhgFsC6STIcESIAESMB2Aphurqys1DbNbHuBQ2RAARwCDk+RAAmQ\nAAnEDwFoOcOvs1O6I7pJUADrJsr0SIAESIAEtBOA4MWORskUKICTqTVZFxIgARJIMgLxvqFCNLgp\ngKOhx2tJgARIgARsIwAzIyhbtbS02JaHkwlTADtJn3mTAAmQAAkEJJAoGyoELLzFgxTAFkExGgmQ\nAAmQQGwINDc3G5rOGAEnc6AATubWZd1IgARIIMEI1NfXS1VVVdJoOofCTwEcig7PkQAJkAAJxIzA\n4cOHBZ9UCRTAqdLSrCcJkAAJxCkB2PVi1IvRbyoFCuBUam3WlQRIgATijADWeeHZCuu+qRYogFOt\nxeO1vsq3a97Tz0jWW++Iu6FB2o+fIPVfmS0dY8fEa4lZLhIggSgJYNcmmBlB4zkVAwVwKrZ6nNXZ\npXy7Ft1yh2SuXdddsowNGyXnpVfk0O/+V1rOm9F9nD9IgASSg0CqaDqHaq0ju8KHisFzJGAzgYJf\n/rqH8DWzc7W2SeG/PCKu+gbzEL9JgASSgADWevft22fsHZwE1Ym4ChTAEaPjhboIYKQbLHiU79es\nd98LdprHSYAEEowAtJz3798f32ZGaq/htF27JWP1GnHZuDbNKegEu3mTrrhKAcNzKLTZgUdpRzKQ\nAAkkNgFoOh84cEDq6uriuiKZK1ZK9utviUvppQxatUa6srOl7hv3S8Ndd2ovN0fA2pEywbAIeDzS\nMWxoyEs6Ro4IeZ4nSYAE4psANlSAslXcC9+VqyXnlQWG8DWJupWOSv+f/Ezy/vQX85C2bwpgbSiZ\nUKQEGm+5KeilHcOHScv0s4Oe5wkSIIH4JgBN5z179kiTEmRxHVQnIevNt4IWseBXv9E+HU0BHBQ3\nT8SKQP3dd0rTrEt7ZddZNFAO/eE3IhkZvc7xAAmQQPwTgKYzhG8imBl5qg6Iu+mYLfL69NN6AMZI\nOH3TJz2ORfsP14CjJcjroyegpqEP/+9/S+O1X5TsN98WV2OjtE+cII03XCtdBQXRp88USIAEYk4g\n4Xw6u10Gox2ecTIn9xty2F0kF2e8JMWtlcfYufWOWSmAj6HlL4cJtM44R/BhIAESSGwC1dXVcujQ\noYSqRE3uUHmu/6Oy3DVdrmieI5e2PCtFbfu76+DNz5e2iRO7/9fxgwJYB0WmQQIkQAIkYJgWwb43\nkYRvZ6dLlqwdLguXl8lJw7bIf226WQq7enceah/5F5FMvcthFMB8aEiABEiABKImAJ/OO3bskAbl\nSjZRwqbPB8rcxeWSldkhX/viR1JaUicZa88V76uvGy5xUY/OAYVS961vSuNN12uvVkwE8CuvvCJl\nZWUyadIkowIffvihrFmzRjxq7W/69OkyduxY7RVjgiRAAiRAArEhAE1njHwzEkRhsqo6Wwne8bKn\nKl+uPOczOW1CpbiOLAFL20knStvkE8Rz4KBUDhggnfBHn2aPqLQn1aNt3qoMmZ999ln59NNPZeTI\nkcbRRqVgs3jxYnnwwQcF53/729/Kt7/9bVX5o7WPzf3CXEiABEiABDQQ8PXpHO8CuLnVI6+pqeb3\n1w2TGSfvljsvW69mlTt7U1DyqHNQsXSMGWOrbLJVAEPYnnrqqVJYWNhdQTTQ3XffrToUaYYf0Nra\nWmPdwBTAmL7wncJA/PT09O7rdf1wK202fOxI20oZMfpHMOtt5RqdcVB3lMGp+qP94RnHqQDuKINT\n9XeSvcncjrqDKYIdaZvl7usbZYDjBycC2tXJ94r5XotV3eFYA24lEVB3PFfmuyVWZfDNB3kj+L9X\nvepVs2zDEHl5yRgpG1orj96xSor7txy99Mi7+Og/Pb5wH/un1SOC+qev8/7xff+3VQAPUMN3fLZt\n29adJyqET4fytTlnzhyZOXOm0WBmhJUrV8rbb79t/iulpaUye/bs7v91/QA0fDIzM3UlGVY6ZqM5\nJYRwoyLv3NzcsMqtKzLq71TdUQe8LNAxdKoMTtcfDAYPHowv7QFs7UrbSmGdZIu88XGq/sgbIRb3\nNTxbwblGvtIONoOZv1MjYTN/3/p/ujNX5iwcIe0dbrn/+h0yaUy9Ki6UqfpWqEI7mmmadfT/jsbB\niK0C2L+g5v8wyv7LX/4i5eXlMmNGz63mzj//fMHHN2BhX3coUPal6Ag4pa1njhTQEXEiDBw4ULBu\n45RrODygyN/3QYklh5KSEoGpREuL2QuOZe4iWVlZMc+7VHVmfQMcJOgOaNfi4mLZu3ev7qQtp4dO\nNZa3nAh5eXmSrXwHw+exE8EcgePZsitgdgGjXsxw+gd06HEe09JOBHP2A2Wors+Ul98bJ5/sGCBf\nmLpdzpm8Rw32RD331kuGZ6QvAYw2jzTEXAADzJ/+9Cc5/fTT5bTTenoaibQSvI4ESIAESMB+Ahgw\nYOTrVAfHSg0x0n1j5Sh5e/UoOX1ChTw2e6nkZjsz0OmrvDEXwGvXrjWmpOElxZxqfuCBByQnJ6ev\nsvI8CZAACZCAQwQgdCF8nZq1s1LtDz8tlrnvlElRv2b5lxtXydCi3qN0K+nEKk5MBPDll1/eXZ8p\nU6YIPgwkQAIkQAKJQQDTzZh2xgxmPIY9VXmGPe/h+my59rzP5MQxibGFaUwEcDw2GMtEAiRAAiTQ\nNwHoShw+fNgxfY1QJWxoTpf5H4yR1ZuHyEWn7ZCZZ+wVj7tTdRRCXRU/5yiA46ctWJIICcADD5RP\nGEiABPQRgIJkVVWVYLkw3kKn1yXvHXUfefzog/L9O5ZK/7w2w7QwUYQvmFIAx9udxfKERQAa9ZWV\nld2OXsK6mJFJgAQCEkCnFs+VU9rMAQt19CC0ml9QXqyyMjrkvqs+ktFD60JFj+tzFMBx3TwsXCgC\nWJdCDz1e16VClZ3nSCBeCZid2njbw/eAch/54rvlsqOyQLmP3CpnTKxQJkLxStFauSiArXFirDgj\nUFNTY9hwY5qsLzu9OCs6i0MCcUsATiWgbIURcLyElja4jxwt7308XKaftEdu/8IGNfqNn/JFw4kC\nOBp6vDbmBCBw4eTAKQciMa8wMySBGBGAW+CDBw/GjbKVetRlxaYS5UxjrJQOqZPvfGmFch/pjIMP\nu5qAAtguskxXO4F4XpfSXlkmSAIxIhCPndrt+/rJC++US1u7R267dKNMGHU4RjRimw0FcGx5M7cI\nCWA9Ck4A7HSxF2HReBkJJCwBdGox5RyNP2Odla+B+8j3x8rGz4vkC2cq95Fqytnjdm7TFp11C5QW\nBXAgKjwWVwRMZat4WpeKK0AsDAlEQCCeOrVwH/n2hyPlrVWlcupxlYb7yLxs+/xZR4DLlksogG3B\nykR1EfBVttKVJtMhgVQnEE+d2rWfFcs8pd08QLmP/OYNq2VYcUPKNA8FcMo0dWJVNB7XpRKLIEtL\nAoEJxEundu+BPGXPWy6HarPl6umfycnlieE+MjDVyI5SAEfGjVfZSABTzVjvdWqrQBurxqRJwDEC\n8dKphfvIBUvLZNUnJXLhqTvlgqvXSnpagviO1Nx6FMCagTK56AhgxxV44KGyVXQceTUJ+BLADkZ4\nrpzs1MJ95PsfD5MFy8bIxFLlPvL2ZdI/35l9m33ZOPmbAthJ+sy7B4GGhgZ6tupBhP+QQPQEIHQh\nfJ3cRnDzTriPLJcMNdK996q1Uja0NvqKJUEKFMBJ0IjJUAXstoIPAwmQgD4C2EgBjmucctd6oAbu\nI8fJjop+csXZW+XM4xPffaS+1uFmDDpZMq0ICODFADtEaGUykAAJ6CNw6NAhwVaCToRWuI9cMVqW\nqB2Lzp6s3EcqZxrJ4j5SJ0+OgHXSZFphEcA67759+yTenL6HVQlGJoE4I4BOLZ4rJ5xrwH3k0nXF\n8vyiUTJyUJ08cusKGVSYXO4jdTY3BbBOmkzLMgGMePfu3evoupTlwjIiCSQIAXRmndpGcEdFgcx9\nd4K0tHrk1os3KkUrLin1ddtQAPdFiOe1E8C0GBy/07OVdrRMMIUJoFOLzRRivd5b25BhuI9cv61Y\nrpqxR847Rc1qtXLUa+VWpAC2QolxtBDAiwEKIVAMSUvjracFKhMhAUUACozo2GJrzlhtz9ne4ZJF\nH46SN5X7yFOU+8gf3rlUBhdlqA4Am8QqAb4FrZJivKgIYL0XU2Ow82UgARLQQwCd2qqqKoEJH0Ks\nhO/HW4sN7eZCZcf7jetXy/BBpvvIDD0VS5FUKIBTpKGdrGY8bvLtJA/mTQI6CJjrvbFUYqw4mCvP\nvzNeYF509YzPZEoKuo/U0XZmGhTAJgl+20IA02KYHoMbPAYSIAE9BGK9mUJjc5rhwWrlphI5/5Sd\nhjONjHTONUfbmhTA0RLk9QEJ+E+NBYzEgyRAAmETgH0vNlSIRacW67nvrRsuC5Xv5uNGHZZHlftI\nTDsz6CEQ9wIYN5kdCjtut1vwsSNtK03j8XisRLMtjp31x5QYNlPAd7B6In8dLxAMrDfvLJTx48O7\nlbFWhrI51f5O3Hvg7btGaEfdzTTNb9tu4BAJO8HWLA7yBmM76g+rAehRYEkH+QQKyNu3jQPFsXps\n887+yp53nHjcXXLf1RtkzHDTfWTwdxfyRtmCPfdW8440nll/XfmjHXXxDFSn8N5agVKIwTEdL2r/\nYpppmt/+5+3+38zX/LY7P//0zXzNb//zkf4PDWcohfRlCmHma35Hkt+Oinx5blG5NLeky7lTD0t+\nrvVpbuRrfiLJO9prnMzbLHs07M00/L/NNM1v//Ox+j/Z8ofyIjq1VjYpgcCIpv4Ha7Jk7uJxsm1f\nP7ny7G0y9YQKcbtEpWm99aLJ33ouwWPqyl9XOsFKGvcCGDeTHfaiAAshYUfawWD7Hjd7VU7lr7v+\nSM80hfCtZ7Df6CWDfyQ3eF2jsjt8b6ysU3aHl5z5uZx38h7JySpTbRkst8DHnWx/1DvWbW/ecyYN\nO/I30zS/zbxi+e1ku5r3tM7619XVGeZ7Vp4V87lCOcINre1ueV25j3z3oxEy7cS9ypnGBsnOVA+V\nErxei8IXZUTekeQfbnkDxUf9zTIEOh/uMbSj/3MTbhqh4se9AA5VeJ6LDwLYZQX+nJub7TW+7+iE\n3eFIeWPlaJkyfr88Nnup5Oe02/qAxAdhliIVCUCQwG4eAtjOoLJRe/MOUZ3acTJsUL08fOtKGVzY\nZGeWTPsoAQpg3gpREYDQhfC1e6uzdduK5EW1nVm/vFZ58PoPZYR6UTCQQLISiJXd/M7KAsOsqKkl\nTW6euUmOH30oWZHGZb0ogOOyWRKjULEwMao4lCtz3ymX/dW5ctX0z+QUNfJlIIFkJgCnGhj56pzG\n9uflv4xz7km7leKUxXlm/8SS7H9MOWdlZUlOTo7ts2sUwEl288SiOngxYNRr524r6JEvWFYmKzYO\nlfOm7JK7r/xYaHcYi9ZlHk4RwJSzaWJkVxmCLePYlV+ipAtt59zcXMnOzjY+urSo+6o/BXBfhHi+\nB4GWlhZD+FrRxuxxocV/oDvywfrhMv+DMhk/8rB877blMqCgxeLVjEYCiUkASzgwMcLzZVfgMs4x\nsr6jXIx0MzMzj52M4S8K4BjCTvSs7J5y3rKrUF5Q67xuV5fcdcU6GTu8JtGRsfwk0CcBu71acRnn\nSBNglAtha36gMe10oAB2ugUSIH+7p5wP1WbJvCXjZOueQrl82lZld7jPsDtMADQsIglETMDuKedU\nX8bBKBcjW0wtOznKDXWDUACHosNzhmmRXVrOht3h8lJZDLvDE/Ya25llZ3aQOgkkPQEs4eC5smPK\nGcs4732cmss4WLuFwDWVqGK1lhvpDUsBHCm5JL8OvXM41rDD5yzsDldsHKS2MyuTYUUN8vAtyu5w\nAO0Ok/yWYvWOEoBd78GDB21xVrFll3IfqawGXOJNiWUcjHIzMjK6R7kFBQWGSaRTjkDCvckpgMMl\nlgLx7eyd76zMlxfUdmaNLRly80Wbld3hwRQgyiqSgPImpYamvnv36mTiu4xz5Tnb5Yzj9yTtMg5G\ntdBWNtdy7fC7rbNtQqVFARyKTgqes6t3DrvDV94fK9jI+5IzPpcLT6tQdDvC8i8brDlcym7Sfbha\nvAMKg0Xh8VQnoLSMPQcOSmdhf1HzkzGnAYc1EL66rQewjPPGSrWMs2aknKWWcR7/yjLllhUudmNe\nRVsz9B3lYnoZI99kCBTAydCKGuoARSu8IKCRqTPA7vAd9XKAj9kp5cfcR3o8aVG/JNI3bZbs19+U\ntKoDMnT1GmkvHyc133tYWmeco7MKTCuRCbS2Sb9f/Epyn3lW3A2N0qU0YZtnXig1//qoeIuLba+Z\nnUs5cB/5kvKJPtRnGeeIZm/iCyfUA4LWVKBKT0+3va2cyIAC2AnqcZYnhC487+h2J7leuY+c+265\nFOS0yQPXfSgjB+tzH5mxboPkPvt8D5LpWz6Totl3y6E//lZaLjy/xzn+k5oEBt73dcl+593uyrvU\nSDhn4WuSsWGj7P/nXOlSa4Z2BexghE4tvnWG7mWc5nS56cLNMqksOZZx4tFMSGe7BUqLAjgQlRQ5\nZq5J6Xb2XnkoR9nzjpdK5UYS7iNPPU6z+0i1lpa9YGHAVnIpDa/+//pjqbzgPFHzVAHj8GBqEMha\ntLiH8PWtddqu3ZL/x79I3be+4XtYy2+MeqG8CCVGncpAR5ZxxqhlnEHGMs65Jye++0hTWxkjXaec\nYWhp9AgToQCOEFyiX4ZR765du7TutNLL7vAKe9xHetS+qJhODBbS9u6TtG3bpWPsmGBReDwFCGQt\neT9kLXFetwBua2sz9u2FlrOugGUcrPG+rtZ6TxpX1b0LmK70Y5kOppahQGVOLSeyApUObhTAOigm\nUBrokcPfLNZ88dERYHf4wfphyn3kGCkfWW27+0hXR9/ldqkXIUOKE+jjHtB9j2DUi46tTqGyfvuR\nXcDy1TLO169do3UZJ1Z3hzm1DKEL4RsPHqhiVfe+8qEA7otQEp3H5glYk8Jab15enpaabdmt3EfC\n7lDN9sbKfWTnkMEiaR6lRB1YEHvzcqV9DEe/Who4gRNpO+lEkWeeC1oD43zQs9ZPYNSL5wpONTCl\nqiNUHs5Ru4CNF7iRtGUZR0chQ6SB6WSYCUHo6mISIruEPUUBnLBNZ73gGOli1KtzrdfX7nDWtG2G\nCYQ7RkuuXerhbpl+tmQtejcghLqv3SeSmRHwHA+mDoGmK6+Qgl/+r6Qpj1P+wau0auvv+Yr/4bD+\nx1ov/KNj5KtrrRfLOK8uL5Ol64/sAnaXTcs4YVXUQmRzc4OSkhJjlAuzK4a+CVAA980ooWPU19cb\nXnd0TTe3KbtDrEWZdoc/vHOpOOE+svn880TaOyTr/WWqfY6MhLvwUr3vLmm458sJ3WYsvB4CbiUc\n3bW1ARNzK1eQaTt3Scfo0oDn+zoIAQPLAYx+dQSv8g63dJ1axlk6RsaNqJZHb4//XcAwlYxRLmbT\n8I3/BwwYYCxtUQBbuysogK1xSrhYMPjHC0LXnr1wH7l68zG7w28r95FDnHQfqea8my+ZKa1nnyXQ\naD14153SdsoU8Q4ckHBtxQLbQyD3xZfEHWJ7v9ynn5GWc6eHlTk6stBuxmwSRsA6wtY9cB853kjq\ny7PWKQEcv7uAYT0X08rmem6yOMTQ0Y6RpEEBHAm1OL7GjmmxeLY79Kred/vxE6WFa75xfFc6U7S0\nHbtCZpy2Y2fI8/4nIXRNBUb/c5H8f7juyC5g2IbzcizjnLg3Lt1HwgkGRrlcz42klUNfQwEcmk9C\nnW1QLhnxgtDl7s7X7vBi5T7yPKt2h2pkkLVshWSs/VhctXWGi8jW006VtiknaeWJ3jeUPXQplGkt\nHBNznEDnoNCervo6b1YAylUwK9K1cxGWcd5cVSqLPhwpUyftM3YBy8mKr13AzOcKQhduIBnsIUAB\nbA/XmKaKdSi8IHRNN0dld6iEb97fn5X0TZ90M3CrjgGmidN275GmK2d1H4/kB4SuaUeIl4NOk49I\nysNr4pdA0xWXSf7//UHgnCVQaLrq8kCHu4/ZMd28evNgeUntfV0ysFG+fbNaxhkYH7uAmZ1ZPFPo\n0Car68fuxo2THxTAcdIQkRTDjhdEtHaHGWvX9RC+vvXKXLlK2idNVCZCZb6H+/xtCl1zGize9/js\ns0KMEBMCHco3eK3yDd7/33/aK7+mWZdK03XX9DqOA6YnK2g361Je3LX/yC5gdU0ZcsMFm+WEMfoc\ndQSshIWDeK5gImQ+V+zMWoCmOYojAnjDhg2ybNkyo5c1c+ZMGTp0qOZqJXdydrwg4D5yrnIfGa3d\nIXzshgrp6rwVAUyhG4oiz1kl0PCV2dI+8TjJ/+0fBGu+3oEDpeHmG6TphmsDuiqF1QCWcXT5Ra9v\nSjd2Aftoy+Aj7iOn7FIm7IFH5FbrFE08Ct1o6Om/NuYCGD3K+fPny0MPPWTc6PPmzZP77lN2mwx9\nEoDgxQsCtoe61nlhd7hwWZks2zBUzj9ll3KmEZ37SFdj6Cm1UOcpdPu8BRghTAKePXul4L/+WzLV\nzIwR9lVIwa8PSkfpKGk78/Tu1GA2g2UcXRsndMJ95Ecj5DW1C9jksQcM95EFuXpMlroLbfEHha5F\nUA5Ei7kAxvQhBMnevXsN7zFY7GfomwAUrGD+oNPucPGawTJv8cg+7Q7Tt26XzA+Wikdt+9eVmyNt\nk46XlrOmHvFG5Vd0LxRfdu/2O3rsX+P8sX+NfT3NaTBMhXF62QcOf0ZHQO1CVHTblyX98x090kmr\nqJSiO++RqvkvSsPQEuO50qU/gYw+/qyfPL1wkuRmt8v916yRUUP07QLWoyJ9/OP7XHF6uQ9YDp1W\n+glBNBRsKhCyW7hwoWzcuFHQ67zqqqtk8uTJ3bm9+uqrgo8ZxijzkgceeMD8N+W+YfpQoTYf0GnY\nvunzPHly/jCD5e2z9srE0Q1BuXapHWW6nnuh93m10YHrwfvF5bdPZ5dStOr6yc/UQppyEO0flDal\n6/EfiKuw0DDcL1Tf/fv3p8KHPyeb/sdIyDfE+NH3zTomv73/7wnpuivw7FpzTrbsv/N2qVcfXWHf\ngUx5auEw2VWZLTdfvE+mTa6O+YZcELrmc8XBja6WDZ0OBkf5+fmhIwU5G/MR8G41OsIuPA8//LCh\n4PCjH/1IJkyY0K3qfuGFF8r06ceM4zEiwjW6A4BB0w+jSieCOdILpuQBp+6YatZl+oA6Gu4j3x0r\nW3b3l2vO3yNnT66Q9rZWlU9gAi7FpuD5FwOf3LpNml+ZL63nn9vzfF6upF/3Rcl5cV4PX81wH9lx\n602SpTzlgD0852C6r7Kysuf1Mfpv8ODBUqu8JOnkG07R8XLUNd1pNd+RI0f2iGrHcwWTlYFqnRWd\nRqcCyoCZon6L3pFcv0I0qfuualiJ1Bb2l07lCash2M3vd12of5tbPbJw6Wh5f91QufC0Crn/2s/U\nc1WnXFSGukrfObxL8EyZz5W5zai+HKynhA418tfp9tZ67mK80/FO1eUa1EreeJeBfSQh5gIYDwfM\nSBDMral8Cw6h6K8CbwdM9P7xsSNt3/oE+23uCOKfPwQvOgU6X86wO3xDuY98R21pdtYJ+4z1qEFF\nWaoDJCHrn7V+Q+CR7NFKpa/bIM0BPAm1Tj5B2tUaGxSy0mtqJXdAoWRcdKFkYBOFo8Fk7+QoDOz9\n+Zvls/vbrL/d+YRK3466m2ma36Hyt+ucybZLCSbfAKG7Y9yYY4eifP7hPnKZ8tn8zw/GytjhR3YB\nG6Zucby/WlsCzAAdyznqXxC6MBnCi998nyJRcMe7JR74R13JCBIw297J+odT7JgL4CFDhhhTJE89\n9ZQxrXrOOed0j37DKXgyxcVNg2kMjHh1rfGafHrYHYbpPjKUwhTSdzUGnrrGCyBn2FDJP268MdL1\nn/o0y8ZvErCTQJfflHunn0COxu0U3EdiFzBvl1vuvGy9sQ3nkbro2Q0pEBdTSRFCF8LX7MQHistj\niUEg5gIYWK688kpDixe9uFS+idBLg60hpkJ1aTWbt51pd1gPu8MLld1h2RG7Q5ea9s1c9aF41Kb1\nYN85ulTaTlZr8P4vJ5VQZ1GRmVzAb6/feUyrFhQUGHaF5hR7wAt5kARiQMCl/KGHDJgCCjNU12fK\nvHfHyae7Bsisadtl2gl71HMUZiJhRseIGs8VBC+VqcKEF+fRHRHAYOI/zRznnLQWDzaGdgleuI/8\n5wdjZO1ng+Ti03fIuT52h241tZ2vFFPcalrYDLnKXWSWcpBRf+cd0pXVUyO9/YTjxfvm2wJPVoFC\n61lnGlrL0F7GC4JKH4Eo8ZhTBLqOLnUFzT8MCwws47y1epRyHzlKzphov/tIdI4xysVz5TvFHLQu\nPJGQBBwTwAlJK8pCQ5MZo12s8+oe+RvuI5Xd4euG3WGV/OCOZeJvd5j37As9hK9ZHYyGc+YvlMZr\nrzYPGd9QnGr40s2S99TfxK3K7Btc586QQrUlIIQvp5h9yfB3vBBoOW+G5P/1qaDFaRtfHvSc74k1\nnw6SeUvKZXBhozx00yrDjaTveZ2/zVkkjHZ1vyN0lpNp6SFAAayHY9BUMM2M9V0IXp2KVb4Zbthe\npLxYlUtedpt8/do1MnJwb7tDT+V+gVOCYCFj3XqB79wupSTnGzqHD5O6f3lQMj5WDjr2H5B8tdF9\nlhK+brUDEQMJxDOB1nOmSdNll0jOgtd6FbOzuFhapylb9hBhT1WesU1gbUOmXHfep3KicqhhR+Bo\n1w6qiZEmBbBN7fT/2zsT+KqqO4//87JvBJKQQICQgAQIgqKA0tE6gmwKtaMfFKtVcFR0YKxLrXam\n1M44n06nHduq049K9WOLVjGIigsgaEDAHVNbVlHZZQkEQkK2l7yXOb8L9+W9l7cluffd5N3f+Xze\n593l3HPu+d7lf885/wXKVFDFx88sjbwjJ+A+skQOH8+Qqy/9WsaPDG7Sg+DkIZOaD3MoL1suZULi\nn5KyeknWrJnaHBR7u/50uN6dCZz4/f9K87BhkvH8X7TTbFXzqc1jRkv9jKmCEZ5ACe4jodlc8WW+\nTJ2gooCpaZzEBOPdR+pzuxhmps5EoCsR+9sogA28xro2M4SukY4z/E8R7iNXfTxEPlQmEJNLtsvC\nPm9KyqE6aYkbIM0YVvPT/sTx7qws/2J81+OU6YIaTtYTBC3moLLUcZyD0qnwv8cRSEiQ2h8tOPNT\nHrCqT6vRoQDPB9oF95HvfzFIPVvFMmboMTWN86FkZRjvPhLPE54rPF/8oO1xd5ShJ0wBbABODC1D\n6GKoOZhjDQOqUSYPooTuAHlLKVnB7vCRgl/KgA3v+BTtUuY/p2++0UeYIoNLudxzKTtcDEUHSs5z\nR2o9AgyHYf6JHqoCUeK2HklAxfNNfXeduA7slyTlfa1l5AhxK5eq3mn7nhxtNClVxeVdcM1fpah/\njffuLi9D0EJfAs8VlRW7jDNmCqAA7uSlhKBFYAT8zJrb9T412B0uWzdc23TrzC0yZt/bkrrKV/hi\nJxSq0pcuk1oVBcY/1akIMBnQgj7tq1CF+TDn97+neTDClzmVP/zJcb2nEkD0rZz5CyVBBWGIy82R\n9CFF0qoUDuuvuVqcaij66Mk0eVVN4xyszFTTOF9p0zhBOsidQoChZf2DliZEnUIY0wdRAHfg8mKI\nGRrMELpw3o51s9OJmhSlgTlMdu3vo+wOv1F2h99qdofJf/kwaNUJe/ZqCldQoPJOrrw8qb17oSR/\n+LEkqWAYKKi1uEhSZ14pfZVNL4fDvGlxuacTiKupldx5d0j88SqfpsA+OK5stbx+YIps3D1CLht7\nQHOmkZzUcbtgn4K9ViBs8THLD1ovKFxsR4ACuB2S9hvgLxhC1+whZu+aYXe49rMiZXdYKBPP9bU7\njFPmTFCYCpXiKyvFXwAjP4beGqZMknQlcDEcho8ICt5QJLmvpxJIX/5aO+HrFjXPm3yVlKXNl3P2\n7ZOf/vATye3dYFgTIXj791fTPWqEjM+VYVhjtiAK4CCXFp6p9CFmo71UBalSGxpO2LlTNu8vlmWH\nZ0h+fpM88INPpV+Ob4xdzVQInqtCePIJ5oQA80+IljJ48GDN+xbmrplIIBYJJG7f4dOsHennya+y\nfivNkiQLan8hIx17pKb3j3zydHYFGs34oC0oKNDcrx47Zo7JUmfPj8d1TwIUwF7XBR6q0MuN1ryu\nV9XKzvbvcnTFDnk+eaGccuTIrXWPyPl1W+T0xBvEJQXeWTW3kU7lZzlp23bf7WfXWlVIspYhxT77\nEAQjW0UigiIIEwnYgYDDb+i5Lr6XfLfxbbmi6XVROv/ibur6s4AeLz5oYUqEHi97vXa4s4xro+0F\nMIaKIHTxw1BzNOZ1/S9f3ddV8srKYfJJ2h3y/YY/y/TGMkmQFhHlMTJjyQtSc+/dyk2kr5P3hiun\nS8L+A+2HotVLoF4pVOk2jhC8eEFAEYSJBGxFQPVKvdO4mo2Sn3ugbZNveOS27REsQbkKPV78KHQj\nAMYsAQnYUgBD6CLyEBSpKtVcqRVCF1cDdofvbS6U1Ru/I+Nb18mj1XMkq9XXYQY0lpMrvpBG5XfZ\nO7l7Z0ntgvmS8t46Sdz1lYizWZvzhfu9lsGFmtN27y9z72O5TAJ2IODKzwvZzNb0jveAYSEAxSo8\nW7QWCImXOyMgYBsBDKELDWb0dOEkI0X1KPEVa5Xw3bo7W5aVD5P0FKc8KItkWN3HQS9X/NHAtrtu\n1atFb9c74aXQhy8IbyRctikBBAvJeOGloK1vPmdI0H3+O9DLxSgSpnFoTuRPh+udJRDTAhhzurrQ\ntWJ4OV4pYjiOVUlrZoa0wCRIPcRHz7qP/PZ4plxz2Tdy4fBDkvl0cB/NuLDulNSw1xcvCMzv5ihX\nknxBhMXFDDYg4Dx3lCAmcFwQc0FXfn5EFOC5KldZDdCBRkS4mKkDBGJOAENjGUIXPyuELtjDRCit\nbLkk7t7juRSnswdIWcnDsmnvcC1E4B1Xb5PkJLemyNxcOlKbz/Vk9ltoLh3ht8V3Fb15vCDwz0QC\nJHCGQNrrbwQVvsiRtHWbOC84PyguaDbjg5aKi0ERcUcXCcSMAIbmMiIOQehamtRQd8afnve4fITd\n4frkWVLWeoeM2P53+dmcPdJn8Jnhb/08GydeJInbdkjCAS8FkbM7my4ar83p6nm9/9HTxZAYNDCZ\nSIAEfAkkHAg9soT42IGSNo2j5nipYBWIDrcZSSBmBLDe4zUSTmfKStqyzSN8dyacJ0vS71EGDw65\nu3aRlLb8VZwVF0jd4Kt9i1aC9PQ/3yIp6zZo5kgONU/tys2RposmSNOEcb551RqGmyF08XVORZB2\neLiBBDQCrn6hlbACBShBgASMJqH3y0QCZhOIGQFsNqhIy0/Yt1+OO/LlpbR/ka2J42R2/TMyqWmF\nZneIMuL3te/lYjvCpDVMnaz9sB4sYZi5r/LdzPmoYIS4nQTOEKi/epZk/uFpiVMxuQMl59i24WcO\nNwcixG1mE6AAjpCwQ5ksxR86ogSlstAdoBSqEpQnKr8E95FrqmfI2qxL5LtNK+W3yqwovdXPZSQ8\nWHUioaeLHq9u8N+JIngICdiKQMvQIVL9H4uk98//s91cMISv8/wx2mgSzIrwbNGe11a3R7doLAVw\nuMug5nRTV6+VlI8+Ud3UM1/S7rRUaVABDJznjfEc/bkK3v3a+8Okf8ox+UXNHTLQtdezz3uhedhQ\n79WIljksFhEmZiKBdgTqbrpBmkeNlHRljuRUnrGahxRpUZCco0dpo0h5KkAJR5PaYeOGKBGgAA4D\nOu3tVZL8yWc+uRz1DZKutJzhk3l3znh5ZV2JnKpLlusm71SBvI8rJSyFVfnG8E/urF7S9N1L/DcH\nXYedMuaj6MUqKCLuIIGwBLTerurxajG7leMdbTSJSlZhuTGD+QQogEMwdlRXtxO+evZTcb3l5dUl\n8knihTLtoj1y+QX71aj0mfCEp9VXd9rqNZL0WYXEtTRrhzQPL5H6780Ud5pvIHC9PP9/mD5grhdC\nmIkESMAYAtChQK8XLlqZSMBqAhTAIa4AfC37pxaJlzUps+W11LkyrmGD/PyWddIr28+prNJqrldD\n1PXTp4rjVI20Ks3K1pRk/6ICrkPgQvDS9jAgHm4kgU4TSFMfvxhN4lxvpxHyQIMJdHsBDFeRkZja\nIE8k+Tz8lDtKpJDH+PU+v0icKC+k/6uku2vkoZp7ZKhrp5zK+HdVSBAHGPjK7qsC3at6/ES0Vrd/\n/ZjrhfCNhicrvITwC9l+z1kav6BfL6tcgersrWq/FezB2lv4mNF2vUz93/g7J3yJ/mzj1Edw6tsr\nJWHPPnGp57FxxjRxDRoYvqBO5EDd/vV3ophOH2J1/Thxu7e/Ixev2wtgNCYSgYSeYyQPffzefZL8\n5tsSf/CMkX6mcmTRNG2KtCiNSP/UipB+SrAfkoGa4N0fP0zm1D8p/+B8RxOoroEDxRHhkLJ/2bhJ\nkfBS1Od6oY0ZraQ/JJGwNeOccK2sHF5H+1G/le23qm79eppRP5iCrRll6+cd7h/3ll5/4qefSdat\nd4pDBV/RU69f/1ZqH3lYGn/4A32TYf9Wtx9t198thjWqAwXp72CdfwcONSSrfu2t/LDvSEO6vQDG\nzeR0OsO2CS4o4fs5VEpQwjft2T8p58ptdoHwhpP60staZCR/pxcNjkxZXvLfsqFyrExrWKY500iR\ns5624hxSP2Nq2DqDnY8ufDAXhTkp2CFG0s5g5XV0u1sxQICKaNbpf464ZlY9KKgX9VvVfrwool23\n/4vZrPrB1qyy/e+hQOv6O8Nx4qTk3nK7OGpqfLLFqeue+dDPpLFYaUQHcHTjk7mDK3ieo/0se58i\n3iu4t3BvW5Gsfq+AP+QAziNaqSv6BN1eABsJMe3NlT7C17vs1FXvaHaB0Gx2K12qj7YWyJubzpGh\nA6rl4YJnZdCHK1Sv94zwxTBWg1Koaika7F1Eh5bxkoAbSc5JdQgbM5NAxATSlr/WTvjqB2P8KeO5\nJXLCYAGsl89/EoiEgG0EMOLqxh85EpRJnOplw4vVzrRxKkxgiRLCDrn1qi1SUoihq1Kp/sfhEq96\ny/BY5VZBuLuS8IU8QDnzgEZmuF57V+rhsSRgZwKJX38Tsvnh9oc8mDtJwAACsSGA1XBLnIp+FDKF\nGZ6ucuTJks2TZMfJIrnqO7vlkjEHMf3bltTQjkspSHU1occLRauuDFt09Rx4PAnYgUBrcmjLg9YA\n3uzswIVt7D4EerQAdqhA9b3/61eSuuZdcRUOEkfhQGn6zkRphLMLNcTrndy9s8StbGsR6MA7OSVJ\n3ky9SValzJGLso/Iw//0oaSlhJ5L9j4+0mXMy0Dw0qlGpMSYjwS6RgCxgEOmM2b7IbNwJwmYSaDH\nCmAoWORde4MkfHvIwwfDzBDG8VUnpO4av4hDKlfjFZMEMUL19HHSJHkxbYH0dx2Qfxv8hPSZ1j7y\nkJ63K/9wddevXz9GWOkKRB5LAh0k4GgIHZo0LsyoWAerY3YS6DCBHiuAM//vSR/h693ypM8rpGn8\nhdLiZ+uHbXGNDXK0/KAsSVkoJx25cnPd72X02AbNcYZ3GUYtw7QI7iT9NVCNKp/lkAAJBCbgyskO\nvOPsVpd6LplIwEoCPVYAp6zfEJJb4q5d7QRwbX2ivNh4u1Tk5MnMoRUyo3ilNPUbL/VqeNrohCFn\nmBfRo5XRZFkeCURGoGHWlZL51B+DOsFpmD4lsoKYiwRMItBjBXDcWU9WQbk0t83julxx8v4Xg2TV\nx8UqWMIxWTT3I+nXN145Yhgjbr854aDldWAHh5w7AItZScAkAnE1tUGFL6p0hFPcNOm8WCwJ6AR6\nrABuHjNaEo4c1dvR7t+FmL0qbduTI8vXl2iKVQuu+asU9deN8lPbHWPEBg45G0GRZZBA1wmkvlse\nspCU99ZL7YI7Q+bhThIwk0CPFcA1C+ZLSvl6FW2oraerg3Ll58uBggvl1ddGyMHKTLn60q9k/Mgj\n/orRenZD/qnlbAhGFkIChhHwt3jwLzjcfv/8XCcBowl4W7oaXbap5aEHXPWHx8TVx9cpRu3AEvnz\niP+RX794sQzoWys/v/UDmVBqrvCFTe9A5ReaJkamXnIWTgIdIuAcOSJk/ubSkSH3cycJmE2gx/aA\nAaZx6mQ5cslESf7oEzlVVSWrTk+U17eNl6GN1fLTmz+W3KzQZghGwIWSFZSt0ANmIgES6D4E6q/9\nvvR6crHEH61sd1KtKmRo7e3z2m3nBhKIJoEeLYABqlVFI2qcfLk8+nimHDqWLPOU+8jhmvtIczHC\nrCgnJ0d6d9EtpblnydJJwL4EWtXH8bE//VFy5y8U79je7swMOfnrX0rzqFL7wmHLuwWBHi+AdYrX\nX7FfUhKqfN1H6jsN/keorXw1z5yaao4il8Gny+JIwLYEWkYMlyNr35aU9zcKoqFp8YAvv0xaoxj6\n07bw2fCwBGJGAOdlN4kJFkXtAELoQvhaFe+y3QlxAwmQQGgCSkejccrk0Hm4lwQsIBAzAjga7Ghi\nFA3KrIMESIAE7EGAAjiC64z5XgRS6NWrVwS5mYUESIAESIAEwhOgAA7DCEPNCKSA2L1MJEACJEAC\nJGAUAQrgECQx3wvhG69iATORAAmQAAmQgJEEKICD0OR8bxAw3EwCJEACJGAIAQpgP4yY70X4QAhg\nJhIgARIgARIwiwAFsBdZ2vd6weAiCZAACZCAqQQogM/ihZIV5ntp32vq/cbCSYAESIAEzhKgAFYg\nEEQB/pwx/MxEAiRAAiRAAtEgYHsBDH/Offr0iQZr1kECJEACJEACHgKWhfBxu92yePFiOXz4sOdk\norkA06LCwkIK32hCZ10kQAIkQAIeApYJ4PLyctm7d6+0tLR4TiZaC4mJiVJUVMT4vdECznpIgARI\ngATaEbBkCPrgwYNy5MgRKSkpaXdCmzZtEvz0NGjQIJk9e7a+GvS/ubk5IgUqxO9FzzdJOWjHnG9y\ncnLQMmN5B0YAWltbBTysSGCP+q1KULbD9INV52B1+8G9oKDAcPxoF9iaUXakJ2slW8QFx8+u7dff\nK+np6ZFeLkPzWXHtGxoaOt2GqAtgCMoVK1bIvHnzpKysrN2Jl5aWatGG9B3wRlVVVaWvBv2vra2V\nurq6oPuxA7a98Od86tQpSVNxhNETxrIVSfeu5XK5rKheY4HRh3DMzDo5sEf9VglACF/cM06n06wm\nhiwXH4DRrrt///4+5xTJc+VzQAQruK6IkW1G2RFUr2Wxgq1+bnhf4aO+urpa3xTVf7xXIISsGFlE\nQ/F+xfTi6WiEpgtAFvcf3qk4h2gl3G+dTVEXwGvWrNGE3+bNm6WyslIqKiq0QAe6r+Xs7GzBzzth\nqDpcwssMwj1Qwg2pO9fQX3p4SPCl2tTUFOgQ07fp5k5WPSi4SVG3Ve2H4MX1skoA6/Vb1X7ck1bV\nrd/cZtQPrviZUbZ+3pH8W1U/BAB+VtUPAYz3WrB3YSTsupIH7xX8rGo/7j2816IpgHG9O5uiLoC9\ne7gQQhC8eBmZlXBDwr4XX6ZMJEACJEACJNBdCERdABcXFwt+SFu3bhUIZLPmYTE0gGG3rnyhdJcL\nxfMgARIgARKILQJRF8De+ObOneu9augy5njR88VwDBMJkAAJkAAJdDcClgpgs2AwkpFZZFkuCZAA\nCZCAUQRiSgB7K1sZBYjlkAAJkAAJkIAZBGJGAOu2d1S2MuM2YZkkQAIkQAJGE4gZAdy3b19TtamN\nBs/ySIAESIAE7E0gZjSUzDRlsvctwtaTAAmQAAmYQSBmBLAZcFgmCZAACZAACZhFgALYLLIslwRI\ngARIgARCEKAADgGHu0iABEiABEjALAIUwGaRZbkkQAIkQAIkEIIABXAIONxFAiRAAiRAAmYRoAA2\niyzLJQESIAESIIEQBCiAQ8DhLhIgARIgARIwiwAFsFlkWS4JkAAJkAAJhCBAARwCDneRAAmQAAmQ\ngFkEKIDNIstySYAESIAESCAEAQrgEHC4iwRIgARIgATMIkABbBZZlksCJEACJEACIQjEtaoUYn/M\n7tqwYYMcPnxYrr/++phtY6iGlZWVSX5+vlx22WWhssXsvscff1ymTZsmw4cPj9k2WtGw/fv3y9Kl\nS+UnP/mJFdVbXuenn34qO3fulJtvvtnyc7HiBFasWCEICTt16lQrqu9xddq2B+xyuaS5ubnHXTCj\nTrilpUXws2tyOp3idrvt2nzT2g2mTU1NppXf3QvGM4V7y64J71Q7v1c6et1tK4A7Cor5SYAESIAE\nSMBIAhTARtJkWSRAAiRAAiQQIQHbzgEfP35cGhsbZeDAgRGiiq1sBw8elJSUFMnNzY2thkXYmm++\n+Uby8vIkMzMzwiOYLRIC9fX1gnurpKQkkuwxl+fkyZNSW1srhYWFMde2SBoEvZr4+Hjt2Yokv93z\n2FYA2/3Cs/0kQAIkQALWEuAQtLX8WTsJkAAJkIBNCVAA2/TCs9kkQAIkQALWEkiwtnpral+zZo1s\n2bJFq7xXr15y++23W3MiUa4V5gFPP/20zJs3T9LS0jRzgWXLlsnRo0e1Obsrr7wyymcU3eqqqqrk\nlVdekfnz52sV79ixQ1auXOk5idtuu02ysrI861zoGAE783zjjTdkyJAhcu6552rQ1q1bp71j8H6Z\nM2eOpm/RMZo9J7f/ewVz4IsXL/Y0APb2OhfPRi5oBGwpgLdt2yZ33nmnJCUlSVxcnC1uhcrKSnnx\nxRfl0KFDovteKS8vl379+sl1110nS5Ys0RwIjBgxIiZ5bN++Xd566y0fG8WvvvpKpkyZIiNHjtTa\nnJiYGJNtj1aj7MgTNs8vv/yyfPnllx7Fqz179sju3btlwYIFsmnTJlm7dq3MmjUrWpchqvUEeq9A\nCa+4uNjTZihlMQUmYLshaDgKqKurk61bt8rnn39uG2cMNTU12pd4//79PXcCNIEvuOACTWtx7Nix\nsmvXLs++WFvAixIfXd4JLwpo7W7cuFEaGhq8d3G5EwTsyBPvknHjxsnFF1/sIYbn6rzzztOeK+yL\n5ecq0HsF90FCQoKsX79ejh07Jg6H7cSM514It2A7MhgeycjI0H6nT5+Wp556KhyjmNh/zjnnaL1d\n78ZUV1drQ9HYhiFpvExiNeEDIz093ad5+osBw4S/+93vbO3ByQdMJ1fsyDM7O1tKS0t9iHk/V3DL\nGMvPVaD3CjyBYWSxoKBAXnrpJcHICFNgArYbgsYc3z333KP9s8ueAAAERElEQVTRGDVqlFRUVMip\nU6dsOfeHlwN6hhh6xUODDxM7Je8e8YEDB7Q5O/RYmDpHgDzPcNOfK6zBNaPdbM2vuuoqzw2E6a7N\nmzfLsGHDPNu40EbAdj1gOODAfCeS7rfVbg+IfvnhhGTv3r3aKuatBgwYoO+K+X9MRTz55JMev71Q\nRMMXO1PnCJBnGzf/58pu99Wrr74q+/bt04DwuWq7LwIt2a4HDM9PGIp85plntPmJK664wrZzFJMn\nT5bly5drc6DoBc+cOTPQPRKT2zBcivlv3AfopSAylN1elEZeWPJsozl69GhNoREWB5gj1bXu23LE\n9tL48eMFWuGYB4a3QbtYmXTmqtrWExZeunhpUENPtF4gNMLtmNBzw72QnJxsx+Yb3mbybEOKaR27\nPlegAOELd7dMwQnYVgAHR8I9JEACJEACJGA+AdvNAZuPlDWQAAmQAAmQQHgCFMDhGTEHCfRIAk88\n8YRMmDCh3bl/++23Ak1dKCQGSx988IGMGTMm2G5uJwESMIAABbABEFkECXRHAjfccIP87W9/EziG\n8E6wzZwxY4ZtQ1F6s+AyCVhJgALYSvqsmwQMIAATMmjefv3111ppzz33nMyePVtycnI0Qbt06VKf\nWp5//nm55ZZbtG1w0Xn55ZdrdvCDBw/WHJL4ZOYKCZCAaQQogE1Dy4JJIDoE4HcXPq3vvfdezdf3\nAw88IA8++KDmjQiC1lsAIwgJgqbrgTduuukmbRk+wuENDMeeOHEiOifOWkjA5gQogG1+A7D5sUHg\nkUce0Tx5TZ8+XbO71D16wSsRBC58nyO98MILcuONN2rez7COqDX33XefZoZVVFSkzQ3Dfy8TCZCA\n+QQogM1nzBpIwHQCcC5z1113aYJ24cKFnvpgh4pweJj3hY0uImLpw8/IBGF76aWXSl5envz4xz8W\nl8tlmwAlHkhcIAGLCFAAWwSe1ZKAkQQQAOCxxx6TSZMmyUMPPeRTNARuWVmZFhoP88Lnn3++th9D\nzddee63cf//92tD1e++9p4Wq1MNV+hTCFRIgAcMJUAAbjpQFkkD0CUCIYvgZrkXfffddWb16teck\n4BoQbgF/85vfyNy5cz3bEQ0MCe5Y4bEIvWR4L4JnMCYSIAHzCdjOF7T5SFkDCUSXQHl5uaxYsULz\nP4xoX48++qgW+xjzvnqEK/SCFy1aJM8++6zn5AoLC7XhaMSuRc8YYfUQ1xbxa+kX24OJCyRgGgG6\nojQNLQsmgZ5BAPFqEb8VMaGZSIAEokeAAjh6rFkTCZAACZAACXgIcA7Yg4ILJEACJEACJBA9AhTA\n0WPNmkiABEiABEjAQ4AC2IOCCyRAAiRAAiQQPQIUwNFjzZpIgARIgARIwEOAAtiDggskQAIkQAIk\nED0CFMDRY82aSIAESIAESMBDgALYg4ILJEACJEACJBA9AhTA0WPNmkiABEiABEjAQ+D/AbINkHKn\nduKWAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 25
},
{
"cell_type": "heading",
"level": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Lab"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Discussion"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### ggplot2 vs. other visualization libraries\n",
"\n",
"* vs. the native R plot library\n",
"* vs. matplotlib\n",
"* vs. D3.js\n",
"* vs. Google Viz\n",
"* vs. Tableau\n",
"* vs. ???\n",
"\n",
"Ease of Use vs. Expressive Power (vs. Interactivity (vs. ?))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Next Time: \n",
"\n",
"# PROJECTS LIVE!\n",
"\n",
"# REGRESSION AND REGULARIZATION"
]
}
],
"metadata": {}
}
]
} | artistic-2.0 |
picklecai/OMOOC2py | _src/exercise/day1.ipynb | 2 | 42672 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. _ _builtin_ _ 模块"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.1 apply:使用元组或字典中的参数调用函数"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Python 允许你实时地创建函数参数列表. 只要把所有的参数放入一个元组中，然后通过内建的 apply 函数调用函数."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def function(a,b):\n",
" print a, b"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"wheather Canada?\n"
]
}
],
"source": [
"apply(function, (\"wheather\", \"Canada?\"))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 8\n"
]
}
],
"source": [
"apply(function, (1, 3+5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"效果等同于："
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"wheather Canada?\n"
]
}
],
"source": [
"function(\"wheather\", \"Canada?\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 8\n"
]
}
],
"source": [
"function(1, 3+5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**那为什么要使用apply呢？**"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"35cm 12cm\n"
]
}
],
"source": [
"apply(function, (), {\"a\":\"35cm\", \"b\":\"12cm\"})"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"v love\n"
]
}
],
"source": [
"apply(function, (\"v\",), {\"b\":\"love\"})"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-21-075685bc83e8>, line 1)",
"output_type": "error",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-21-075685bc83e8>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m apply(function, ( ,\"v\"), {\"a\":\"hello\"})\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"apply(function, ( ,\"v\"), {\"a\":\"hello\"})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** 何谓“关键字参数”？ **\n",
"\n",
"apply使用字典传递关键字参数，实际上就是字典的键是函数的参数名，字典的值是函数的实际参数值。（相对于形参和实参）\n",
"\n",
"根据上面的例子看，如果部分传递，只能传递后面的关键字参数，不能传递前面的。？？？？"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> apply 函数的一个常见用法是把构造函数参数从子类传递到基类, 尤其是构造函数需要接受很多参数的时候.\n",
"\n",
"子类和基类是什么概念？"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Rectangle:\n",
" def __init__(self, color=\"white\", width=10, height=10):\n",
" print \"Create a \", color, self, \"sized\", width, \"X\", height"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class RoundRectangle:\n",
" def __init__(self, **kw):\n",
" apply(Rectangle.__init__, (self,), kw)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Create a green <__main__.Rectangle instance at 0x0000000003684E48> sized 200 X 156\n"
]
}
],
"source": [
"rect = Rectangle(color=\"green\", width=200, height=156)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "unbound method __init__() must be called with Rectangle instance as first argument (got RoundRectangle instance instead)",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-34-799235dba81c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mrect\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRoundRectangle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"brown\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m<ipython-input-23-ac7804c40e49>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, **kw)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mRoundRectangle\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRectangle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m: unbound method __init__() must be called with Rectangle instance as first argument (got RoundRectangle instance instead)"
]
}
],
"source": [
"rect = RoundRectangle(color=\"brown\", width=20, height=15)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**第二个函数不知道如何使用 ？？？？**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"修改，子类要以父类为参数！！！"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class RoundRectangle(Rectangle):\n",
" def __init__(self, **kw):\n",
" apply(Rectangle.__init__, (self,), kw)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Create a blue <__main__.RoundRectangle instance at 0x00000000036E4348> sized 23 X 10\n"
]
}
],
"source": [
"rect2 = RoundRectangle(color= \"blue\", width=23, height=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> 使用 \\* 来标记元组, \\*\\* 来标记字典.\n",
"\n",
"apply的第一个参数是函数名，第二个参数是元组，第三个参数是字典。所以用上面这个表达最好不过。"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"er haha\n"
]
}
],
"source": [
"args = (\"er\",)\n",
"kwargs = {\"b\":\"haha\"}\n",
"function(*args, **kwargs)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"er haha\n"
]
}
],
"source": [
"apply(function, args, kwargs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"以上等价。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"用这个意思引申："
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Create a brown <__main__.RoundRectangle instance at 0x00000000036E1D88> sized 123 X 34\n"
]
}
],
"source": [
"kw = {\"color\":\"brown\", \"width\":123, \"height\": 34}\n",
"rect3 = RoundRectangle(**kw)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Create a brown <__main__.Rectangle instance at 0x00000000036E1EC8> sized 123 X 34\n"
]
}
],
"source": [
"rect4 = Rectangle(**kw)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Create a yellow <__main__.Rectangle instance at 0x00000000036E1DC8> sized 45 X 23\n"
]
}
],
"source": [
"arg=(\"yellow\", 45, 23)\n",
"rect5 = Rectangle(*arg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.2 import"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import glob, os"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"modules =[]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for module_file in glob.glob(\"*-plugin.py\"):\n",
" try:\n",
" module_name, ext = os.path.splitext(os.path.basename(module_file))\n",
" module = __import__(module_name)\n",
" modules.append(module)\n",
" except ImportError:\n",
" pass #ignore broken modules"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-56-0755481c0a01>, line 3)",
"output_type": "error",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-56-0755481c0a01>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m example-plugin says hello\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"for module in modules:\n",
" module.hello()\n",
"example-plugin says hello"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def hello():\n",
" print \"example-plugin says hello\""
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def getfunctionname(module_name, function_name):\n",
" module = __import__(module_name)\n",
" return getattr(module, function_name)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<function open at 0x00000000036A7DD8>\n"
]
}
],
"source": [
"print repr(getfunctionname(\"dumbdbm\",\"open\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. os模块"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"import string"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def replace(file, search_for, replace_with):\n",
" back = os.path.splitext(file)[0] + \".bak\"\n",
" temp = os.path.splitext(file)[0] + \".tmp\"\n",
" \n",
" try:\n",
" os.remove(temp)\n",
" except os.error:\n",
" pass\n",
" \n",
" fi = open(file)\n",
" fo = open(temp, \"w\")\n",
" \n",
" for s in fi.readlines():\n",
" fo.write(string.replace(s, search_for, replace_with))\n",
" \n",
" fi.close()\n",
" fo.close()\n",
" \n",
" try:\n",
" os.remove(back)\n",
" except os.error:\n",
" pass\n",
" \n",
" os.rename(file, back)\n",
" os.rename(temp, file)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"file = \"samples/sample.txt\""
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"replace(file, \"hello\", \"tjena\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"replace(file, \"tjena\", \"hello\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"os.path.splitext：切扩展名"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"os.remove：remove a file。上面的程序里为什么要remove呢？"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def replace1(file, search_for, replace_with):\n",
" back = os.path.splitext(file)[0] + \".bak\"\n",
" temp = os.path.splitext(file)[0] + \".tmp\"\n",
" \n",
" try:\n",
" os.remove(temp)\n",
" except os.error:\n",
" pass\n",
" \n",
" fi = open(file)\n",
" fo = open(temp, \"w\")\n",
" \n",
" for s in fi.readlines():\n",
" fo.write(string.replace(s, search_for, replace_with))\n",
" \n",
" fi.close()\n",
" fo.close()\n",
" \n",
" try:\n",
" os.remove(back)\n",
" except os.error:\n",
" pass\n",
"\n",
" \n",
" os.rename(file, back)\n",
" os.rename(temp, file)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"replace1(file, \"hello\", \"tjena\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"replace1(file, \"tjena\", \"hello\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"doc = os.path.splitext(file)[0] + \".doc\""
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sample.bak\n",
"sample.txt\n"
]
}
],
"source": [
"for file in os.listdir(\"samples\"):\n",
" print file"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cwd = os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 I:\\github\\omooc2py\\_src\\exercise\n"
]
}
],
"source": [
"print 1, cwd"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"os.chdir(\"samples\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 I:\\github\\omooc2py\\_src\\exercise\\samples\n"
]
}
],
"source": [
"print 2, os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3 I:\\github\\omooc2py\\_src\\exercise\n"
]
}
],
"source": [
"os.chdir(os.pardir)\n",
"print 3, os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. stat模块"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import stat"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os, time"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"st = os.stat(\"samples/sample.txt\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"os.stat是将文件的相关属性读出来，然后用stat模块来处理，处理方式有多重，就要看看stat提供了什么了。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. string模块"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import string"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"text = \"Monty Python's Flying Circus\""
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"upper => MONTY PYTHON'S FLYING CIRCUS\n"
]
}
],
"source": [
"print \"upper\", \"=>\", string.upper(text)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"lower => monty python's flying circus\n"
]
}
],
"source": [
"print \"lower\", \"=>\", string.lower(text)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"split => ['Monty', \"Python's\", 'Flying', 'Circus']\n"
]
}
],
"source": [
"print \"split\", \"=>\", string.split(text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"分列变成了list"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"join => Monty Python's Flying Circus\n"
]
}
],
"source": [
"print \"join\", \"=>\", string.join(string.split(text))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"join和split相反。"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"replace => Monty Cplus's Flying Circus\n"
]
}
],
"source": [
"print \"replace\", \"=>\", string.replace(text, \"Python\", \"Cplus\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"replace的参数结构是： \n",
"1. 整字符串 2. 将被替换的字符串 3. 替换后的字符串"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"find => 6\n"
]
}
],
"source": [
"print \"find\", \"=>\", string.find(text, \"Python\")"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"find => 6 -1\n"
]
}
],
"source": [
"print \"find\", \"=>\", string.find(text, \"Python\"), string.find(text, \"Cplus\")"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Monty Python's Flying Circus\n"
]
}
],
"source": [
"print text"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"上面replace的结果，没有影响原始的text。\n",
"\n",
"find时，能找到就显示位置，不能找到就显示-1"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" count => 3\n"
]
}
],
"source": [
"print \"count\", \"=>\", string.count(text,\"n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"和数学运算一样，这些方法也分一元的和多元的： \n",
"\n",
"upper, lower, split, join 都是一元的。其中join的对象是一个list。\n",
"\n",
"replace, find, count则需要除了text之外的参数。replace需要额外两个，用以指明替换关系。find只需要一个被查找对象。count则需要一个需要计数的字符。\n",
"\n",
"特别注意： **replace不影响原始字符串对象。（好奇怪）**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"23\n"
]
}
],
"source": [
"print string.atoi(\"23\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"int"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(string.atoi(\"23\"))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"234"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"int(\"234\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"int"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(int(\"234\"))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"float"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(float(\"334\"))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"334.0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"float(\"334\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"456.0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"string.atof(\"456\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. re模块"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import re"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"text = \"The Attila the Hun Show\""
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'.' => 'T'\n"
]
}
],
"source": [
"m = re.match(\".\", text)\n",
"if m:\n",
" print repr(\".\"), \"=>\", repr(m.group(0))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. math模块和cmath模块"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import math"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3.141592653589793"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"math.pi"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.718281828459045"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"math.e"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5.0\n"
]
}
],
"source": [
"print math.hypot(3,4)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5.0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"math.sqrt(25)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import cmath"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1j\n"
]
}
],
"source": [
"print cmath.sqrt(-1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 10. operator模块"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import operator"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"operator.add(3,5)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"seq = 1,5,7,9"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"22"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reduce(operator.add,seq)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-20"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reduce(operator.sub, seq)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"315"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reduce(operator.mul, seq)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"float(reduce(operator.div, seq))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"operator下的这四则运算，都是针对两个数进行的，即参数只能是两个。为了能对多个数进行连续运算，就需要用reduce，它的意思是两个运算后，作为一个数，与下一个继续进行两个数运算，直到数列终。感觉和apply作用有点差不多，第一个参数是函数，第二个参数是数列（具体参数）。"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'erterui'"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"operator.concat(\"ert\", \"erui\")"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"operator.getitem(seq,1)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"operator.indexOf(seq, 5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"getitem和indexOf为一对逆运算，前者求指定位置上的值，后者求给定值的位置。注意，后者是大写的字母ｏ。"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"operator.sequenceIncludes(seq, 5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"判断序列中是否包含某个值。结果是布尔值。"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import UserList"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def dump(data):\n",
" print data,\":\"\n",
" print type(data),\"=>\",\n",
" if operator.isCallable(data):\n",
" print \"is a CALLABLE data.\"\n",
" if operator.isMappingType(data):\n",
" print \"is a MAP data.\"\n",
" if operator.isNumberType(data):\n",
" print \"is a NUMBER data.\"\n",
" if operator.isSequenceType(data):\n",
" print \"is a SEQUENCE data.\""
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 :\n",
"<type 'int'> => is a NUMBER data.\n"
]
}
],
"source": [
"dump(0)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3, 4, 5, 6] :\n",
"<type 'list'> => is a SEQUENCE data.\n"
]
}
],
"source": [
"dump([3,4,5,6])"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"weioiuernj :\n",
"<type 'str'> => is a SEQUENCE data.\n"
]
}
],
"source": [
"dump(\"weioiuernj\")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'a': '155cm', 'b': '187cm'} :\n",
"<type 'dict'> => is a MAP data.\n"
]
}
],
"source": [
"dump({\"a\":\"155cm\", \"b\":\"187cm\"})"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<built-in function len> :\n",
"<type 'builtin_function_or_method'> => is a CALLABLE data.\n"
]
}
],
"source": [
"dump(len)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<module 'UserList' from 'C:\\Users\\admin\\Anaconda2\\lib\\UserList.pyc'> :\n",
"<type 'module'> =>\n"
]
}
],
"source": [
"dump(UserList)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'UserList.UserList'> :\n",
"<class 'abc.ABCMeta'> => is a CALLABLE data.\n"
]
}
],
"source": [
"dump(UserList.UserList)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[] :\n",
"<class 'UserList.UserList'> => is a SEQUENCE data.\n"
]
}
],
"source": [
"dump(UserList.UserList())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 15. types模块"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import types"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def check(object):\n",
" if type(object) is types.IntType:\n",
" print \"INTEGER\",\n",
" if type(object) is types.FloatType:\n",
" print \"FLOAT\",\n",
" if type(object) is types.StringType:\n",
" print \"STRING\",\n",
" if type(object) is types.ClassType:\n",
" print \"CLASS\",\n",
" if type(object) is types.InstanceType:\n",
" print \"INSTANCE\",\n",
" print"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INTEGER\n"
]
}
],
"source": [
"check(0)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"FLOAT\n"
]
}
],
"source": [
"check(0.0)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"STRING\n"
]
}
],
"source": [
"check(\"picklecai\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class A:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CLASS\n"
]
}
],
"source": [
"check(A)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = A()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INSTANCE\n"
]
}
],
"source": [
"check(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"types 模块在第一次引入的时候会破坏当前的异常状态. 也就是说, 不要在异常处理语句块中导入该模块 ( 或其他会导入它的模块 ) ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 16. gc模块"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"gc 模块提供了到内建循环垃圾收集器的接口。"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import gc"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Node:\n",
" def __init__(self, name):\n",
" self.name = name\n",
" self.patrent = None\n",
" self.children = []\n",
" \n",
" def addchild(self, node):\n",
" node.parent = self\n",
" self.children.append(node)\n",
" \n",
" def __repr__(self):\n",
" return \"<Node %s at %x\" % (repr(self.name), id(self))\n",
" \n",
"root = Node(\"monty\")\n",
"\n",
"root.addchild(Node(\"eric\"))\n",
"root.addchild(Node(\"john\"))\n",
"root.addchild(Node(\"michael\"))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"root.__init__(\"eric\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"<Node 'eric' at 3737448\""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"root.__repr__()"
]
},
{
"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 |
flowersteam/naminggamesal | notebooks/7_Design_newVocabulary.ipynb | 1 | 5108 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('..')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Types of vocabularies: Existing ones, designing new ones"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vocabularies are found in the submodule ngvoc.\n",
"\n",
"For the moment, \"matrix\" and \"sparse_matrix\" are available. they both have fixed size (M,W), only difference is how the information is stored (numpy matrix and scipy sparse.lil_sparse).\n",
"\n",
"To define a vocabulary type, the following things are needed:\n",
"\n",
"* def get_known_words\n",
"* def get_known_meanings\n",
"* def exists\n",
"* def get_content\n",
"* def add\n",
"* def rm\n",
"* def get_unknown_words\n",
"* def get_unknown_meanings\n",
"* def get_new_unknown_m\n",
"* def get_new_unknown_w\n",
"* def get_random_known_m\n",
"* def get_random_known_w\n",
"\n",
"you can add a *visual* method to visualize some aspects of your vocabulary.\n",
"All those methods are inheritable from sparse_matrix.VocSparseMatrix and matrix.VocMatrix.\n",
"\n",
"If you want to design a new Vocabulary class, just add it in the ngvoc folder, in a \\*.py file. Example is given in TEST.py.\n",
"\n",
"To call it in your experiments, simply give as *voc_cfg* a configuration dict containing the key/value: 'voc\\_type':'\\$pyfile.\\$classname'. You can use any other keys you need, it will be fed directly to the \\_\\_init__ of your class.\n",
"\n",
"You should use the \\_\\_init__ of the parent class, via super(), so do not forget to give the appropriate arguments.\n",
"\n",
"###Example:\n",
"\n",
"Let's test the vocabulary class VocTest, in TEST.py.\n",
"The class is a child of VocMatrix, uses its \\_\\_init__ with M = number/2 and W = 15 (constant). The *test* method prints the 'testkey' key-value pair in the *voc_cfg* object. Feel free to test other modifications. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from lib import ngvoc"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"voc_cfg = {\n",
" 'voc_type':'TEST.VocTest',\n",
" 'testkey':'Test',\n",
" 'number':12\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"voc_test = ngvoc.Vocabulary(**voc_cfg)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<lib.ngvoc.TEST.VocTest at 0x7ff67aab4290>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"voc_test"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"testkey:TeSt\n"
]
}
],
"source": [
"voc_test.test()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Words\n",
" [[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
"Meanings [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
" [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
"\n"
]
}
],
"source": [
"print(voc_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Designing a Vocabulary with adaptable matrix size"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"We need to define different things for this: \n",
" * Label the meanings and words globally, outside the vocabulary, being \n",
" * Refer to these global labels "
]
}
],
"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": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| agpl-3.0 |
cathalmccabe/PYNQ | boards/Pynq-Z2/logictools/notebooks/boolean_generator.ipynb | 4 | 5015 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Boolean Generator\n",
"This notebook will show how to use the boolean generator to generate a boolean combinational function. The function that is implemented is a 2-input XOR."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 1: Download the `logictools` overlay"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"\n",
"require(['notebook/js/codecell'], function(codecell) {\n",
" codecell.CodeCell.options_default.highlight_modes[\n",
" 'magic_text/x-csrc'] = {'reg':[/^%%microblaze/]};\n",
" Jupyter.notebook.events.one('kernel_ready.Kernel', function(){\n",
" Jupyter.notebook.get_cells().map(function(cell){\n",
" if (cell.cell_type == 'code'){ cell.auto_highlight(); } }) ;\n",
" });\n",
"});\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pynq.overlays.logictools import LogicToolsOverlay\n",
"\n",
"logictools_olay = LogicToolsOverlay('logictools.bit')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2: Specify the boolean function of a 2-input XOR \n",
"The logic is applied to the on-board pushbuttons and LED, pushbuttons **PB0** and **PB3** are set as inputs and LED **LD2** is set as an output"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"function = ['LD2 = PB3 ^ PB0']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 3: Instantiate and setup of the boolean generator object. \n",
"The logic function defined in the previous step is setup using the `setup()` method "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"boolean_generator = logictools_olay.boolean_generator\n",
"boolean_generator.setup(function)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Find the On-board pushbuttons and LEDs__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 4: Run the boolean generator verify operation"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"boolean_generator.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Verify the operation of the XOR function\n",
"\n",
"| PB0 | PB3 | LD2 |\n",
"|:---:|:---:|:---:|\n",
"| 0 | 0 | 0 |\n",
"| 0 | 1 | 1 |\n",
"| 1 | 0 | 1 |\n",
"| 1 | 1 | 0 |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 5: Stop the boolean generator"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"boolean_generator.stop()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 6: Re-run the entire boolean function generation in a single cell\n",
"**Note**: The boolean expression format can be `list` or `dict`. We had used a `list` in the example above. We will now use a `dict`. \n",
"<font color=\"DodgerBlue\">**Alternative format:**</font> \n",
"```python\n",
"function = {'XOR_gate': 'LD2 = PB3 ^ PB0'}\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from pynq.overlays.logictools import LogicToolsOverlay\n",
"\n",
"logictools_olay = LogicToolsOverlay('logictools.bit')\n",
"boolean_generator = logictools_olay.boolean_generator\n",
"\n",
"function = {'XOR_gate': 'LD2 = PB3 ^ PB0'}\n",
"\n",
"boolean_generator.setup(function)\n",
"boolean_generator.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Stop the boolean generator__"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"boolean_generator.stop()"
]
}
],
"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"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 1,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| bsd-3-clause |
JudoWill/ResearchNotebooks | SadiVariation.ipynb | 2 | 71898 | {
"metadata": {
"name": "SadiVariation"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import os, os.path\n",
"import sys\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"os.chdir('/home/will/SadiVariation/')\n",
"sys.path.append('/home/will/PySeqUtils/')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from GeneralSeqTools import fasta_reader, fasta_writer, WebPSSM_V3_series\n",
"import glob"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"files = [('x4_seqs.fasta.old', 'x4_seqs.fasta'),\n",
" ('r5_seqs.fasta.old', 'r5_seqs.fasta')]\n",
"for ifile, ofile in files:\n",
" with open(ifile) as handle:\n",
" with open(ofile, 'w') as ohandle:\n",
" for name, seq in fasta_reader(handle):\n",
" fasta_writer(ohandle, [(name, seq[1:-1])])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"subtype_files = glob.glob('/home/will/WLAHDB_data/SubtypeGuess/*.gb')\n",
"subtypes = []\n",
"for f in subtype_files:\n",
" gb = f.rsplit(os.sep, 1)[-1].split('.')[0]\n",
" with open(f) as handle:\n",
" subtype = handle.next().strip()\n",
" if subtype != 'Unk':\n",
" subtypes.append((int(gb), subtype))\n",
"subtype_df = pd.DataFrame(subtypes, columns = ['GI', 'Subtype'])\n",
"\n",
"subtype_ser = subtype_df.groupby('GI')['Subtype'].first()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-4-5d88017929c9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mgb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrsplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0msubtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0msubtype\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'Unk'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0msubtypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msubtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"with open('hxb2.needle') as handle:\n",
" aligned_seqs = list(fasta_reader(handle))"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy.stats import linregress\n",
"\n",
"hxb2_inds = [350, 364, 375, 388, 398, 456]\n",
"our_inds = [-103, -99, -85, -74, -63, 0]\n",
"m, b, _, _, _ = linregress(hxb2_inds, our_inds)\n",
"new_our_inds = np.arange(0, 634)\n",
"new_hxb2_inds = np.ceil(m*new_our_inds+b)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"starts = range(0, len(aligned_seqs), 2)\n",
"aligned = []\n",
"for s in starts:\n",
" _, hxb2_seq = aligned_seqs[s]\n",
" gi, gi_seq = aligned_seqs[s+1]\n",
" aseq = ''.join(q for q, r in zip(gi_seq, hxb2_seq) if r.isalpha())\n",
" aligned.append((int(gi), np.array(list(aseq))))"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"aligned_ser = pd.DataFrame(aligned, columns=['GI', 'alignment']).groupby('GI').first()['alignment']\n",
"subs, _ = subtype_ser.align(aligned_ser, join='right')"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"subs.value_counts()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"aset = set(gi for gi, _ in aligned)\n",
"with open('/home/will/WLAHDB_data/SeqDump/B_v3.fasta') as handle:\n",
" v3_seqs = []\n",
" for gi, seq in fasta_reader(handle):\n",
" if int(gi) in aset:\n",
" v3_seqs.append((int(gi), seq))\n",
" print len(v3_seqs)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"trop_dict = dict((int(gi), 'X4' if trop > 0.5 else 'R5') for gi, trop in WebPSSM_V3_series(v3_seqs))\n",
"v3_ser = pd.Series(trop_dict)\n",
"trop_data, _ = v3_ser.align(aligned_ser, join='right')"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"trop_data.value_counts()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lanl_data = pd.read_csv('/home/will/HIVTropism/R5Cluster/LANLResults.tsv', sep = '\\t')"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"wtissue, _ = lanl_data['NewSimpleTissue'].dropna().align(aligned_ser, join = 'right')\n",
"wcor_receptor, _ = lanl_data['Coreceptor'].dropna().align(aligned_ser, join = 'right')"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"check_seqs = [('CEBP-US2', 'ATTTCATCA', -170, -162),\n",
" ('ATF-CREB', 'CTGACATCG', -123, -115),\n",
" ('CEBP-US1', 'AGCTTTCTACAA', -114, -103),\n",
" ('NFKB-II', 'AGGGACTTTCC', -103, -93),\n",
" ('NFKB-I', 'GGGGACTTTCC', -99, -89),\n",
" ('SP-III', 'GAGGCGTGG', -85, -77),\n",
" ('SP-II', 'TGGGCGGGA', -74, -66),\n",
" ('SP-I', 'GGGGAGTGG', -63, -55),\n",
" ('AP1-I', 'TTGAGTGCT', 85, 93),\n",
" ('AP1-II', 'TGTTGTGTGAC', 121, 138),\n",
" ('AP1-III', 'TTTAGTCAG', 153, 161),\n",
" ('DS3-B', 'TCAGTGTGGAAAATC', 158, 175),\n",
" ('DS3-C', 'GTAGTGTGGAAAATC', 158, 175),\n",
" ('DS3-D', 'TCAGTGTGGAAAATC', 158, 175),\n",
" ('DS3-A', 'ACTGTGTAAAAATC', 158, 175)\n",
" ]\n",
"slop = 20\n",
"check_dict = {'Subs':subs, 'tissue': wtissue, 'Coreceptor': trop_data}\n",
"for name, seq, start, stop in check_seqs:\n",
" mask = (new_hxb2_inds>(start-slop)) & (new_hxb2_inds<(stop+slop))\n",
" extracted = aligned_ser.map(lambda x: ''.join(x[mask]).replace('-', ''))\n",
" \n",
" check_dict[name] = extracted.map(lambda x: seq in x).map(float)\n",
" check_dict[name][extracted.map(len)==0] = np.nan\n",
"df = pd.DataFrame(check_dict)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print pd.pivot_table(df, rows = 'Subs', aggfunc='mean').T*100"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 9.0/290"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print pd.pivot_table(df, rows = 'Coreceptor', aggfunc='mean').T*100"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def find_best(tf_seq, ltr_seq):\n",
" width = len(tf_seq)\n",
" scores = []\n",
" for start in range(0, len(ltr_seq)-width):\n",
" scores.append(sum(s == t for s, t in zip(tf_seq, ltr_seq[start:(start+width)])))\n",
" if scores:\n",
" return max(scores)/float(width)\n",
" else:\n",
" return np.nan\n",
"\n",
"best_dict = {'Subs':subs, 'tissue': wtissue, 'Coreceptor': trop_data}\n",
"for name, seq, start, stop in check_seqs:\n",
" print name\n",
" mask = (new_hxb2_inds>(start-slop)) & (new_hxb2_inds<(stop+slop))\n",
" extracted = aligned_ser.map(lambda x: ''.join(x[mask]).replace('-', ''))\n",
" best_dict[name] = extracted.map(lambda x: find_best(seq, x))\n",
"bdf = pd.DataFrame(best_dict)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print (1-pd.pivot_table(bdf, rows = 'Subs')).T*100"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print (1-pd.pivot_table(bdf, rows = 'Coreceptor')).T*100"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"slop=5\n",
"mask = (new_hxb2_inds>(159-slop)) & (new_hxb2_inds<(174+slop))\n",
"ds3_ser = aligned_ser.map(lambda x: ''.join(x[mask]).replace('-', ''))\n",
"ds3_ser[ds3_ser.map(len)==0] = np.nan\n",
"with open('r5_seqs.fasta', 'w') as handle:\n",
" fasta_writer(handle, ds3_ser[trop_data == 'R5'].dropna().to_dict().items())\n",
"\n",
"with open('x4_seqs.fasta', 'w') as handle:\n",
" fasta_writer(handle, ds3_ser[trop_data == 'X4'].dropna().to_dict().items())\n",
" \n",
"with open('subC_seqs.fasta', 'w') as handle:\n",
" fasta_writer(handle, ds3_ser[subs == 'C'].dropna().to_dict().items())\n",
" \n",
"#print ds3_ser[trop_data == 'X4'].dropna()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 109
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"quick_seqs = []\n",
"with open('r5_seqs.fasta') as handle:\n",
" for name, seq in fasta_reader(handle):\n",
" quick_seqs.append({\n",
" 'Name':name,\n",
" 'Trop':'R5',\n",
" 'Seq':seq\n",
" })\n",
"with open('x4_seqs.fasta') as handle:\n",
" for name, seq in fasta_reader(handle):\n",
" quick_seqs.append({\n",
" 'Name':name,\n",
" 'Trop':'X4',\n",
" 'Seq':seq\n",
" })"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from Bio.Seq import Seq\n",
"from Bio import Motif\n",
"from StringIO import StringIO\n",
"from itertools import groupby\n",
"from operator import methodcaller\n",
"from Bio.Alphabet import IUPAC\n",
"\n",
"def yield_motifs():\n",
" with open('/home/will/LTRtfAnalysis/Jaspar_PWMs.txt') as handle:\n",
" for key, lines in groupby(handle, methodcaller('startswith', '>')):\n",
" if key:\n",
" name = lines.next().strip().split()[-1].lower()\n",
" else:\n",
" tmp = ''.join(lines)\n",
" mot = Motif.read(StringIO(tmp), 'jaspar-pfm')\n",
" yield name, mot\n",
" yield name+'-R', mot.reverse_complement()\n",
"pwm_dict = {}\n",
"for num, (name, mot) in enumerate(yield_motifs()):\n",
" if num % 100 == 0:\n",
" print num\n",
" pwm_dict[name] = mot"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0\n",
"100\n",
"200\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from functools import partial\n",
"from scipy.stats import ttest_ind, gaussian_kde, chi2_contingency\n",
"\n",
"def score_seq(mot, seq):\n",
" bseq = Seq(seq, alphabet=IUPAC.unambiguous_dna)\n",
" scores = mot.scanPWM(bseq)\n",
" return np.max(scores)\n",
"\n",
"def make_cdfs(kde, points):\n",
" \n",
" cdf = []\n",
" for point in points:\n",
" cdf.append(kde.integrate_box_1d(-np.inf, point))\n",
" return 1-np.array(cdf)\n",
" \n",
" \n",
"wanted_mots = ['cebpa-R',\n",
" #'nfatc2',\n",
" 'nfatc2-R']\n",
"fig, axs = plt.subplots(2,1, sharex=True, figsize = (10, 5))\n",
"quick_seqs_df = pd.DataFrame(quick_seqs)\n",
"\n",
"r5_mask = quick_seqs_df['Trop'] == 'R5'\n",
"x4_mask = quick_seqs_df['Trop'] == 'X4'\n",
"for ax, mot in zip(axs.flatten(), wanted_mots):\n",
" quick_seqs_df[mot] = quick_seqs_df['Seq'].map(partial(score_seq, pwm_dict[mot]))\n",
" \n",
" r5_vals = quick_seqs_df[mot][r5_mask].dropna().values\n",
" x4_vals = quick_seqs_df[mot][x4_mask].dropna().values\n",
" \n",
" r5_kde = gaussian_kde(r5_vals)\n",
" x4_kde = gaussian_kde(x4_vals)\n",
" \n",
" points = np.linspace(0, 15)\n",
" ax.plot(points, make_cdfs(r5_kde, points), 'b', label = 'R5')\n",
" ax.plot(points, make_cdfs(x4_kde, points), 'g', label = 'X4')\n",
" ax.set_title(mot)\n",
" if ax.is_last_row():\n",
" ax.set_xlabel('TF Score')\n",
" else:\n",
" ax.legend()\n",
" \n",
" ax.set_ylabel('Frac Sequences')\n",
" thresh = Motif.Thresholds.ScoreDistribution(pwm_dict[mot], precision = 100).threshold_fpr(0.005)\n",
" ax.vlines(thresh, 0, 1)\n",
" \n",
" ch2table = [[(r5_vals>thresh).sum(), (r5_vals<=thresh).sum()],\n",
" [(x4_vals>thresh).sum(), (x4_vals<=thresh).sum()],]\n",
" \n",
" _, pval, _, _ = chi2_contingency(ch2table)\n",
" print mot, np.mean(r5_vals), np.mean(x4_vals), pval\n",
"plt.tight_layout()\n",
"#plt.savefig('/home/will/Dropbox/Wigdahl HIV Lab/SadiTFFigure/TFscores.png', dpi = 300)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"cebpa-R 3.25429779065 2.42559581026 0.000490673533494\n",
"nfatc2-R 9.56427351272 11.4786407471 0.243616994612\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAFiCAYAAAAEBkVdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSBYRERERucf/A97DgFJ5yO5xAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x40cb8d0>"
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ax.vlines?"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn.svm import OneClassSVM\n",
"\n",
"data = np.array([2,5,30,4,8,3,5,4,2,5,3,4,5,4]).reshape((-1, 1))\n",
"tmp = OneClassSVM().fit(data).predict(data)\n",
"data[tmp>0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 119,
"text": [
"array([[5],\n",
" [5],\n",
" [5],\n",
" [5]])"
]
}
],
"prompt_number": 119
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"count = 0\n",
"for num, f in enumerate(glob.glob('/home/will/WLAHDB_data/RegionSplit/ltr/*.fasta')):\n",
" if num % 5000 == 0:\n",
" print num, count\n",
" with open(f) as handle:\n",
" if len(handle.read()) > 10:\n",
" count += 1"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0 0\n",
"5000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 98\n",
"10000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 175\n",
"15000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 250\n",
"20000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 346\n",
"25000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 444\n",
"30000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 536\n",
"35000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 623\n",
"40000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 706\n",
"45000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 793\n",
"50000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 887\n",
"55000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 970\n",
"60000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1055\n",
"65000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1151\n",
"70000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1247\n",
"75000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1343\n",
"80000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1445\n",
"85000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1541\n",
"90000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1636\n",
"95000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1729\n",
"100000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1837\n",
"105000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1933\n",
"110000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2030\n",
"115000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2128\n",
"120000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2227\n",
"125000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2297\n",
"130000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2395\n",
"135000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2494\n",
"140000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2588\n",
"145000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2678\n",
"150000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2767\n",
"155000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2870\n",
"160000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2962\n",
"165000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 3050\n",
"170000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 3130\n",
"175000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 3224\n",
"180000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 3327\n",
"185000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 3416\n",
"190000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 3511\n",
"195000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 3602\n",
"200000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 3704\n",
"205000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 3804\n",
"210000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 3905\n",
"215000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 3997\n",
"220000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 4077\n",
"225000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 4166\n",
"230000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 4256\n",
"235000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 4350\n",
"240000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 4442\n",
"245000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 4524\n",
"250000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 4605\n",
"255000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 4706\n",
"260000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 4812\n",
"265000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 4917\n",
"270000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5013\n",
"275000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5119\n",
"280000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5222\n",
"285000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5304\n",
"290000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5404\n",
"295000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5506\n",
"300000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5607\n",
"305000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5693\n",
"310000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5793\n",
"315000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5881\n",
"320000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5988\n",
"325000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 6090\n",
"330000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 6198\n",
"335000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 6300\n",
"340000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 6398\n",
"345000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 6507\n",
"350000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 6584\n",
"355000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 6659\n",
"360000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 6763\n",
"365000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 6863\n",
"370000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 6959\n",
"375000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 7053\n",
"380000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 7141\n",
"385000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 7223\n",
"390000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 7328\n",
"395000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 7421\n",
"400000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 7532\n",
"405000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 7631\n",
"410000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 7755\n",
"415000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 7846\n",
"420000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 7958\n",
"425000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 8072\n",
"430000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 8173\n",
"435000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 8271\n",
"440000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 8349\n",
"445000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 8454\n",
"450000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 8544\n",
"455000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 8631\n",
"460000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 8744\n",
"465000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 8834\n",
"470000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 8935\n",
"475000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 9032\n",
"480000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 9132\n",
"485000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 9220\n",
"490000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 9330\n",
"495000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 9406\n",
"500000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 9509\n",
"505000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 9614\n",
"510000"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 9712\n"
]
}
],
"prompt_number": 121
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
basnijholt/holoviews | examples/gallery/demos/matplotlib/verhulst_mandelbrot.ipynb | 2 | 3902 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## verhulst mandelbrot "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most examples work across multiple plotting backends, this example is also available for:\n",
"\n",
"* [Bokeh - verhulst_mandelbrot ](../bokeh/verhulst_mandelbrot.ipynb)\n",
"\n",
"Example showing how bifurcation diagram for the logistic map relates to the Mandelbrot set according to a linear transformation. Inspired by [this illustration](https://en.wikipedia.org/wiki/Mandelbrot_set#/media/File:Verhulst-Mandelbrot-Bifurcation.jpg) on Wikipedia."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from itertools import islice\n",
"\n",
"import numpy as np\n",
"import holoviews as hv\n",
"from holoviews import opts\n",
"\n",
"hv.extension('matplotlib')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defining Mandelbrot and Logistic Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Area of the complex plane\n",
"bounds = (-2,-1.4,0.8,1.4)\n",
"# Growth rates used in the logistic map\n",
"growth_rates = np.linspace(0.9, 4, 1000)\n",
"# Bifurcation points\n",
"bifurcations = [1, 3, 3.4494, 3.5440, 3.5644, 3.7381, 3.7510, 3.8284, 3.8481]\n",
"\n",
"def mandelbrot_generator(h,w, maxit, bounds=bounds):\n",
" \"Generator that yields the mandlebrot set.\"\n",
" (l,b,r,t) = bounds\n",
" y,x = np.ogrid[b:t : h*1j, l:r:w*1j]\n",
" c = x+y*1j\n",
" z = c\n",
" divtime = maxit + np.zeros(z.shape, dtype=int)\n",
" for i in range(maxit):\n",
" z = z**2 + c\n",
" diverge = z*np.conj(z) > 2**2\n",
" div_now = diverge & (divtime==maxit)\n",
" divtime[div_now] = i\n",
" z[diverge] = 2\n",
" yield divtime\n",
" \n",
"def mandelbrot(h,w, n, maxit):\n",
" \"Returns the mandelbrot set computed to maxit\"\n",
" iterable = mandelbrot_generator(h,w, maxit)\n",
" return next(islice(iterable, n, None))\n",
"\n",
"def mapping(r):\n",
" \"Linear mapping applied to the logistic bifurcation diagram\"\n",
" return (r /2.0) * ( 1 - (r/2.0))\n",
"\n",
"def logistic_map(gens=20, init=0.5, growth=0.5):\n",
" population = [init]\n",
" for gen in range(gens-1):\n",
" current = population[gen]\n",
" population.append(current * growth * (1 - current))\n",
" return population"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"bifurcation_diagram = hv.Points([(mapping(rate), pop) for rate in growth_rates for \n",
" (gen, pop) in enumerate(logistic_map(gens=200, growth=rate))\n",
" if gen>=100]) # Discard the first 100 generations to view attractors more easily\n",
"\n",
"img = hv.Image(mandelbrot(800,800, 45, 46).copy(), bounds=(-2, -1.4, 0.8, 1.4))\n",
"\n",
"vlines = hv.Overlay([hv.Curve([(mapping(pos),0), ((mapping(pos),1.4))]) for pos in bifurcations])\n",
"\n",
"(img * bifurcation_diagram * hv.HLine(0) * vlines).opts(\n",
" opts.Curve(color='teal', linewidth=1),\n",
" opts.HLine(color='k', linestyle='--'),\n",
" opts.Image(cmap='Reds', fig_size=200, logz=True, xaxis=None, yaxis=None),\n",
" opts.Points(s=1, color='g'))"
]
}
],
"metadata": {
"language_info": {
"name": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| bsd-3-clause |
amccaugh/phidl | docs/tutorials/quickstart.ipynb | 1 | 192196 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Quick start"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PHIDL allows you to create complex designs from simple shapes, and can output the result as GDSII files. The basic element of PHIDL is the `Device`, which is just a GDS cell with some additional functionality (for those unfamiliar with GDS designs, it can be thought of as a blank area to which you can add polygon shapes). The polygon shapes can also have `Port`s on them--these allow you to snap shapes together like Lego blocks. You can either hand-design your own polygon shapes, or there is a large library of pre-existing shapes you can use as well."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Brief introduction\n",
"\n",
"This first section is an extremely short tutorial meant to give you an idea of what PHIDL can do. For a more detailed tutorial, please read the following \"The basics of PHIDL\" section and the other tutorials.\n",
"\n",
"We'll start with some boilerplate imports:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from phidl import Device\n",
"from phidl import quickplot as qp # Rename \"quickplot()\" to the easier \"qp()\"\n",
"import phidl.geometry as pg"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then let's create a blank Device (essentially an empty GDS cell with some special features)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"D = Device('mydevice')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next let's add a custom polygon using lists of x points and y points. You can also add polygons pair-wise like `[(x1,y1), (x2,y2), (x3,y3), ... ]`. We'll also image the shape using the handy `quickplot()` function (imported here as `qp()`)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD4CAYAAADxeG0DAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQUUlEQVR4nO3dbWydZ3nA8f8V59VN45SmhbpulwpQoWJirQyjdEMM2BReRLdpm0ACMYTkL8DKxoTKpglp0qR9mBB8QNMsKCDRFU2FCsRYaMeLKqQRaEq1tU3ZupDS1G2Syo3jxCFJm2sffDqlIY0dn/s8z7kf/39SFfvk9L6vO2n/ffr4nDgyE0lSvda0PYAkqT+GXJIqZ8glqXKGXJIqZ8glqXJr29h027ZtuX379sb2O3LkCFu2bGlsv6adOnWKdevWtT3GQHT5bOD5atf0+Xbv3v10Zl529uOthHz79u3cd999je23c+dOduzY0dh+TZuZmWF8fLztMQaiy2cDz1e7ps8XEY+d63FvrUhS5Qy5JFXOkEtS5Qy5JFXOkEtS5Qy5JFWuSMgjYmtE3BkRj0TEnoi4scS6kqSllXod+WeBnZn5RxGxHhgttK4kaQl9hzwixoA3AX8KkJkngZP9ritJWp4SV+TXAIeAL0bEa4HdwC2ZeezMJ0XEFDAFMDExwczMTIGtl2dubq7R/Zo2Ozvb9ggD0+Wzgeer3bCcr0TI1wI3AB/NzF0R8VngVuBvznxSZk4D0wCTk5PZ5Ntax8bGOv02YaDT5+vy2cDz1W4Yzlfii537gf2Zuav3+Z0shl2S1IC+Q56ZTwGPR8S1vYfeCjzc77qSpOUp9aqVjwK3916xshf4YKF1JUlLKBLyzHwAmCyxliTpwvjOTkmqnCGXpMoZckmqnCGXpMoZckmqnCGXpMoZckmqnCGXpMoZckmqnCGXpMoZckmqnCGXpMoZckmqnCGXpMoZckmqnCGXpMoZckmqnCGXpMoZckmqXJHv2RkR+4B54Dng2cz0+3dKUkOKhLzndzLz6YLrSZKWwVsrklS5UlfkCdwdEQn8U2ZOn/2EiJgCpgAmJiaYmZkptPXS5ubmGt2vabOzs22PMDBdPht4vtpkJgsLCxw6dIinDh5gZM0aXjf5urbHKhby38rMJyLicuCeiHgkM+898wm9uE8DTE5O5vj4eKGtlzY2NkaT+7Why+fr8tnA8w2rzOTw4cM89thj7N33cx7eu4f/3vcoC88dZ9MVmzm9OXjFiQlufvfNbY9aJuSZ+UTvx4MRcRfweuDe8/9dkjQclor2updtYuxVL+Hlb7ueTVtGiQjmD83Bd+fbHh0oEPKIuAhYk5nzvY9/D/jbvieTpAFYSbSHXYkr8pcCd/UOuxb458zcWWBdSepLF6N9Ln2HPDP3Aq8tMIskrdhqifa5lHwduSQ1YjVH+1wMuaShZrSXZsglDQ2jvTKGXFIrjHY5hlzSwBntwTLkkooy2s0z5JJWbKloX3311Sy86jmjPWCGXNKyrORKe/Ph9RzderLt0TvPkEv6Fd4eqYshl1Y5o10/Qy6tIka7mwy51FFGe/Uw5FIHGO3VzZBLlVlWtF99qdFeRQy5NMSMtpbDkEtDwmhrpQy51ILno71v3z5+9ONdRlt9MeTSgJ3vSvvl176CQ6NzRlt9MeRSQRd6e+TiuQ1s8y3s6lOxkEfECHAf8ERmvqvUutKw8p62hkXJK/JbgD3AloJrSkPBaGuYFQl5REwA7wT+DviLEmtKbTHaqk2pK/LPAJ8ALn6xJ0TEFDAFMDExwczMTKGtlzY3N9fofk2bnZ1te4SBGfTZMpOFhQUOHTrEUwcPsP/AfmYOPsWJ0ydZf8lG1m5dz+i1F3PDjW9k/aYNL4x2AnP97b/xWLe/TNXl840cHyU2rxmKtvT9qxwR7wIOZubuiHjziz0vM6eBaYDJyckcHx/vd+tlGxsbo8n92tDl85U627KutK++lK03voyXnHWlfQo4xakic5yt639ed1fPN39qgTg6PxT/7pX4z+VNwLsj4h3ARmBLRHwlM99XYG1pRbw9otWk75Bn5ieBTwL0rsj/0oirSUZbq113b2Cpk4y29KuKhjwzfwD8oOSaWr0yk2PHjvHAAw8Ybek8vCLXUHixK+1LL72Up0cOG23pPAy5Gncht0cuO72V8UsG82oRqSsMuQaq33vacdgrb2kphlzF+IVIqR2GXCuyZLSvGGXs1X6PSKkJhlxLMtrScDPkegGjLdXHkK9iRlvqBkO+ShhtqbsMeQcZbWl1MeSVW/Jt7EZb6jxDXpEl38ZutKVVyZAPqQu5PeLb2KXVzZAPgX7vafs2dml1M+QN8wuRkkoz5ANktCU1wZAXYrQltcWQr4DRljRMDPkSjLakYdd3yCNiI3AvsKG33p2Z+al+122D0ZZUoxJX5CeAt2Tm0YhYB/wwIv4tM39UYO2BMdqSuqLvkGdmAkd7n67r/ZX9rltSZvLMM88YbUmdVOQeeUSMALuBVwCfy8xd53jOFDAFMDExwczMTImtl3T8+HHu/t493P3j77H+ko2svWQDo9du5oYb38j6TRteGO0E5hoZq6iNx7r7pY4unw08X81Gjo8Sm9c01rLzKfKrnJnPAb8REVuBuyLiNZn54FnPmQamASYnJ3N8fLzE1ks6cOAATy/M8usf/u0XRPsUcIruvK396NaTbY8wMF0+G3i+Ws2fWiCOztNUy85nTcnFMvMw8H1gR8l1S/B2iaSu6jvkEXFZ70qciNgE/C7wSL/rSpKWp8StlSuAL/fuk68B/iUzv1VgXUnSMpR41cp/AtcXmEWStAJF75FLkppnyCWpcoZckipnyCWpcoZckipnyCWpcoZckipnyCWpcoZckipnyCWpcoZckipnyCWpcoZckipnyCWpcoZckipnyCWpcoZckipnyCWpcoZckirXd8gj4qqI+H5EPBwRD0XELSUGkyQtT9/ffBl4Fvh4Zt4fERcDuyPinsx8uMDakqQl9H1FnplPZub9vY/ngT3Alf2uK0lanhJX5P8vIrYD1wO7zvFzU8AUwMTEBDMzMyW3flFzc3OMjW5h8+H1jezXho3Hiv42DpUunw08X81Gjo8Sm9c01rLzKfarHBGbga8BH8vMI2f/fGZOA9MAk5OTOT4+Xmrr8xoZGWFu4QhHt55sZL+2dPl8XT4beL5azZ9aII7O01TLzqfIq1YiYh2LEb89M79eYk1J0vKUeNVKAF8A9mTmp/sfSZJ0IUpckd8EvB94S0Q80PvrHQXWlSQtQ9/3yDPzh0AUmEWStAK+s1OSKmfIJalyhlySKmfIJalyhlySKmfIJalyhlySKmfIJalyhlySKmfIJalyhlySKmfIJalyhlySKmfIJalyhlySKmfIJalyhlySKmfIJalyhlySKlck5BFxW0QcjIgHS6wnSVq+UlfkXwJ2FFpLknQBioQ8M+8FZkusJUm6MGub2igipoApgImJCWZmZhrZd25ujrHRLWw+vL6R/dqw8Vhjv42N6/LZwPPVbOT4KLF5TWMtO5/GfpUzcxqYBpicnMzx8fFG9h0ZGWFu4QhHt55sZL+2dPl8XT4beL5azZ9aII7O01TLzsdXrUhS5Qy5JFWu1MsP7wD+A7g2IvZHxIdKrCtJWlqRe+SZ+d4S60iSLpy3ViSpcoZckipnyCWpcoZckipnyCWpcoZckipnyCWpcoZckipnyCWpcoZckipnyCWpcoZckipnyCWpcoZckipnyCWpcoZckipnyCWpcoZckipnyCWpcqW++fKOiPhZRDwaEbeWWFOStDx9hzwiRoDPAW8HrgPeGxHX9buuJGl51hZY4/XAo5m5FyAivgrcDDxcYO0iTp8+zdyBZ9oeY2DWHL+IuRPH2h5jILp8NvB8NVuYPcrFRNtjAGVCfiXw+Bmf7wd+8+wnRcQUMAVw+eWXs3PnzgJbL+3EiROs/eUanr7jfxvZrw2/3HgRR3/ZzX9Zunw28Hy1u2TbeGMtO58SIV+WzJwGpgEmJydzx44dTW3Nhg0baHK/ps3MzDA+Pt72GAPR5bOB56vdsJyvxBc7nwCuOuPzid5jkqQGlAj5T4BXRsQ1EbEeeA/wzQLrSpKWoe9bK5n5bER8BPgOMALclpkP9T2ZJGlZitwjz8xvA98usZYk6cL4zk5Jqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqpwhl6TKGXJJqlxfIY+IP46IhyLidERMlhpKkrR8/V6RPwj8IXBvgVkkSSuwtp+/OTP3AEREmWkkSResr5BfiIiYAqYAJiYmmJmZaWpr5ubmGt2vabOzs22PMDBdPht4vtoNy/mWDHlE/DvwsnP81F9n5jeWu1FmTgPTAJOTkzk+Pr7sIfs1NjZGk/u1ocvn6/LZwPPVbhjOt2TIM/NtTQwiSVoZX34oSZXr9+WHfxAR+4EbgX+NiO+UGUuStFz9vmrlLuCuQrNIklbAWyuSVDlDLkmVM+SSVDlDLkmVM+SSVLnIzOY3jTgEPNbgltuApxvcr2ldPl+Xzwaer3ZNn+/XMvOysx9sJeRNi4j7MrOzf8xul8/X5bOB56vdsJzPWyuSVDlDLkmVWy0hn257gAHr8vm6fDbwfLUbivOtinvkktRlq+WKXJI6y5BLUuU6HfKI2BERP4uIRyPi1rbnKSkiroqI70fEwxHxUETc0vZMgxARIxHx04j4VtuzlBYRWyPizoh4JCL2RMSNbc9UUkT8ee+fzQcj4o6I2Nj2TP2IiNsi4mBEPHjGYy+JiHsi4n96P17SxmydDXlEjACfA94OXAe8NyKua3eqop4FPp6Z1wFvAD7csfM97xZgT9tDDMhngZ2Z+SrgtXTonBFxJfBnwGRmvgYYAd7T7lR9+xKw46zHbgW+m5mvBL7b+7xxnQ058Hrg0czcm5knga8CN7c8UzGZ+WRm3t/7eJ7FCFzZ7lRlRcQE8E7g823PUlpEjAFvAr4AkJknM/Nwq0OVtxbYFBFrgVGg6u+Anpn3Amd/t+WbgS/3Pv4y8PtNzvS8Lof8SuDxMz7fT8dC97yI2A5cD+xqeZTSPgN8Ajjd8hyDcA1wCPhi79bR5yPioraHKiUznwD+AfgF8CQwl5l3tzvVQLw0M5/sffwU8NI2huhyyFeFiNgMfA34WGYeaXueUiLiXcDBzNzd9iwDsha4AfjHzLweOEZL/1s+CL17xTez+B+sceCiiHhfu1MNVi6+lruV13N3OeRPAFed8flE77HOiIh1LEb89sz8etvzFHYT8O6I2MfibbG3RMRX2h2pqP3A/sx8/v+i7mQx7F3xNuDnmXkoM08BXwfe2PJMg3AgIq4A6P14sI0huhzynwCvjIhrImI9i19o+WbLMxUTEcHi/dU9mfnptucpLTM/mZkTmbmdxd+772VmZ67oMvMp4PGIuLb30FuBh1scqbRfAG+IiNHeP6tvpUNfzD3DN4EP9D7+APCNNobo65svD7PMfDYiPgJ8h8WvmN+WmQ+1PFZJNwHvB/4rIh7oPfZXmfnt9kbSBfoocHvvQmMv8MGW5ykmM3dFxJ3A/Sy+wuqnDMnb2VcqIu4A3gxsi4j9wKeAvwf+JSI+xOIfzf0nrczmW/QlqW5dvrUiSauCIZekyhlySaqcIZekyhlySaqcIZekyhlySarc/wGxWkD9XdnIMQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"xpts = (0,10,10, 0)\n",
"ypts = (0, 0, 5, 3)\n",
"poly1 = D.add_polygon( [xpts, ypts], layer = 0)\n",
"\n",
"qp(D) # quickplot it!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also create new geometry using the built-in geometry library:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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"
},
{
"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": [
"T = pg.text('Hello!', layer = 1)\n",
"A = pg.arc(radius = 25, width = 5, theta = 90, layer = 3)\n",
"\n",
"qp(T) # quickplot it!\n",
"qp(A) # quickplot it!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can easily add these new geometries to `D`, which currently contains our custom polygon. (For more details about references see below, or the tutorial called \"Understanding References\".)"
]
},
{
"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": [
"text1 = D.add_ref(T) # Add the text we created as a reference\n",
"arc1 = D.add_ref(A) # Add the arc we created\n",
"\n",
"qp(D) # quickplot it!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that the geometry has been added to `D`, we can move and rotate everything however we want:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAuT0lEQVR4nO3dd3xUVf7/8dcnvRBCKOlA6FUBiYCdVVTW1bUrFnRtuN+vXb+7a/vu6rrN/X7V3d+6RRRd/Vp3LYuCoqCUtYCgFIEAhhAgmTQy6WVS5vz+yAQDBFJm7pSbz/PxyIOZO3fuOSd35s3NmTPniDEGpZRS9hQW6AoopZSyjoa8UkrZmIa8UkrZmIa8UkrZmIa8UkrZWESgK9DR4MGDTVZWliXHbm5uJjIy0pJj+5Md2lFdXU3//v0DXQ2v2OE8gD3aoW2Ar7766oAxZkhnjwVVyGdlZbFhwwZLju1wOEhPT7fk2P5kh3YsW7aMuXPnBroaXrHDeQB7tEPbACKy92iPaXeNUkrZmIa8UkrZmNchLyIxIvKliGwWkW0i8qhn+wgRWSciuSLyhohEeV9dpZRSPeGLK3kXcKYxZgowFZgrIrOAx4GnjDGjgQrgJh+UpZRSqge8DnnTptZzN9LzY4AzgTc9218ELvK2LKWUUj3jk9E1IhIOfAWMBv4M7AYqjTEtnl0KgIyjPHcBsAAgMzMTh8Phiyodwel0WnJcf7NDO6qqqiw7z/5ih/MA9miHtuHYfBLyxphWYKqIDADeAcb34LkLgYUA2dnZxsqhUKE+zKpdqLcjMTEx5NsAoX8e2tmhHdqGo/Pp6BpjTCWwEjgJGCAi7f+JZAKFvixLKaVU13wxumaI5woeEYkFzgZyaAv7yzy7XQ8s9rYspZRSPeOL7po04EVPv3wY8A9jzBIR2Q68LiK/AjYCi3xQllJKqR7wOuSNMVuAaZ1szwNmeHt8pZRSvaffeFVKKRvTkFdKKRvTkFdKKRvTkFdKKRvTkFdKKRvTkFdKKRvTkFdKKRvTkFdKKRvTkFdKKRvTkFdKKRvTkFdKKRvTkFdKKRvTkFdKKRvTkFdKKRvTkFdKKRvzxcpQQ0VkpYhsF5FtInKXZ/sjIlIoIps8P+d5X12llFI94YuVoVqA+4wxX4tIAvCViCz3PPaUMeZ/fVCGUkqpXvDFylBFQJHndo2I5AAZ3h5XKaWU93xxJX+QiGTRthTgOuAU4HYRuQ7YQNvVfkUnz1kALADIzMzE4XD4skoHOZ1OS47rb3ZoR1VVlWXn2V/scB7AHu3QNhybz0JeRPoBbwF3G2OqReSvwGOA8fz7BHDj4c8zxiwEFgJkZ2eb9PR0X1XpCFYe259CvR2JiYkh3wYI/fPQzg7t0DYcnU9CXkQiaQv4V4wxbwMYY0o6PP4ssMQXZSmllK/8dtUq3t66lZ0HDhAdEcGsoUP57bnnMjk1NdBV8xlfjK4RYBGQY4x5ssP2tA67XQxs9bYspZTypVV5efznrFl8/uMf88nNNxMRFsacRYtw1tcHumo+44sr+VOA+cA3IrLJs+1B4CoRmUpbd00+cKsPylJKKZ/58MZDe5D/74orSHz0UT7bu5cLJkwIUK18yxejaz4FpJOH3vf22Eop5U81LhduY0iKjQ10VXxGv/GqlFIedy1ZwtS0NE4aNgwAt9vN8m+/pbCqKsA16z0NeaWUAu5dsoRP8/N569prCQ8Lw+1286uVK3ngww954t//DnT1es2n4+SVUioU3bNkCa9v2cLKm29m5MCBuN1u7nzvPdbu3w9AbVNTgGvYexrySqk+7a733uONLVtYecstjE9OBuBrh+NgwIc6DXmlVJ912+LF/N/Gjfxr/nySYmMprqkBYGhiImkJCRR57ocyDXmlVJ/1l7VrATjruecO2f6Ls87iJ6efzr1LlwaiWj6lIa+U6jMeWbGClpYWGhoaALh3xoxO96upqeHPa9ZQUNE23VZTUxP3vfNOt8sJCwsjPj6eR+bM8b7SXtKQV0r1KTk52ygs3Elk5LHjr4Zw6ogBoLLeyZdlud0uo76+lXPPvdCrevqKhrxSqk9paWlm9MgEMjOSLCtj5erg+dBWQ14p1Wc8MmcOz+XnkJVZQvb0fpaVU73sAD8/80zLjt8T+mUopVSf0tLcTHh4ZzOx+E5rqyEiIjiuoTXklVJ9SktLMxHh4ZYd3xhDa4ubcAvL6AkNeaVUn9LU3ER4hHXRZ4whLDyctlnYA09DXinVp9TXVxMXG2nZ8Zub3UREWnf8ntKQV0r1KfV1tcTFRVt2/IaGJuJi4yw7fk9pyCul+pS6uhpi46y70q6rbyI+PsGy4/eUL5b/GyoiK0Vku4hsE5G7PNsHishyEfnW8691g1KVUqobjDE0NNQTFxtlWRkNDU3ExVk3PLOnfHEl3wLcZ4yZCMwCbhORicD9wMfGmDHAx577SikVMA0NDURGCuHh1nVi1Nc3ER/f37Lj95TXLTXGFBljvvbcrgFygAzgQuBFz24vAhd5W5ZSSnnD6XQyYECMpWXU1zcR3y/R0jJ6wqej9UUkC5gGrANSjDFFnoeKgZSjPGcBsAAgMzMTh8Phyyod5HQ6LTmuv9mhHVVVVZadZ3+xw3kAe7SjJ20oKCggI2MYZeXWfTBa35hEQr+BPXqNW3kefBbyItIPeAu42xhT3XGMqDHGiIjp7HnGmIXAQoDs7GyTnp7uqyodwcpj+1OotyMxMTHk2wChfx7a2aEd3W1Dbm4uEWElDBnU6TWnT1RVFDB0xGk9/r1adR580jElIpG0Bfwrxpi3PZtLRCTN83gaUOqLspRSqrecznISE639JmpVjZukpOAZZ+KL0TUCLAJyjDFPdnjoXeB6z+3rgcXelqWUUt4oK9vPwKR4S8uoqGyyV8gDpwDzgTNFZJPn5zzgd8DZIvItMMdzXymlAsZRmE9KsrUjX6qqmhk4cKClZfSE133yxphPgaNN0nCWt8dXSilfMMZQXFJIcnK2ZWW0tLTS0NBK//42GkKplFKhwOl0EhsDMTHWfdu1qqqBxMQkwsKCJ1qDpyZKKWWh4uJihgyxbs4agJLSGlJSh1laRk9pyCul+oSCggJSh1g7O2RpaTVpaVmWltFTGvJKqT4hf08OQ4da21deUuYiI2O4pWX0lIa8UqpP2LMnh6GZ1g5tLCtrJjU11dIyekpDXille3V1ddTUlJOcbO0UwCWlDaSlpVlaRk9pyCulbC8/P5+M9FhLl+SrqW1EJIaEhOCZSx405JVSfUBu7rcMG2rt7JP791cwbPjooFnbtZ2GvFLK9nbkbGD0qEGWlrG/oJIRIyZaWkZvaMgrpWytqamJvXt3MSJrsKXlFBQ2MmLEaEvL6A0NeaWUre3evZu01Giio326fMYRCgobGD48uIZPgoa8UsrmduzYzqiR1vbHV1U1YExMUE1M1k5DXilla1u2fM64scmWlrF3XznDs8YF3YeuoCGvlLKxiooKDpTtY0SWtR+67t5dwfjx0ywto7es7aRSSvXaTTfcQ2lJfZf7JafEseiFp/xQo9CzZcsWxo2Jt3xWyLz8Bs44a7ylZfSWhrxSQaq0pJ7ssc90ud+GXbf6oTahadPGzzluorX95LW1LpwVJig/dAXfrfH6vIiUisjWDtseEZHCw1aLUkopv3C5XOzatYnx46ydSyZvTxmjx0wmPNzatWN7y1d/w/wdmNvJ9qeMMVM9P+/7qCyllOrSpk2byBoeRVxclKXl5O6uYMKEEywtwxs+6a4xxqwRkSxfHEsFv4KCAv71zkcYd9f7zj3vNMaMGdPjMtas+ZzNG3d0uV9UdDjzr7ucuLi4Hpeh7G3d2pVMm2LtF6AAcnfXc+bZEywvp7es7pO/XUSuAzYA9xljKg7fQUQWAAsAMjMzcTgcllTE6XQese0vT79AVZWry+cmJkbzn7ff0KtyfV1GZ+3wt507d7Jzq4vEfsd+YVdU72bEqJ3Ex8cfsr2qqqrL87x5Yw57diUSF3PsN2lVwzry8vL8Pj7ZH+cha0QKiYO7fj9kNaf0+n0TDK8nb3XWhsbGRioqSklPH0tZuXXdKDXVjfRLGEFkZKRX2WXlebAy5P8KPAYYz79PADcevpMxZiGwECA7O9ukp6dbVqHDj715077ufbC16dYjnttdVpRh5e+oO/Ly8igviWdw7Oxj7ldRWkVYWNgR9U1MTOyyDTXVjTRUnkjikOOPuV9x4WqSkpIC8juxusz8PSUMjuy6jPw9JV7VJdCvJ184vA2rV68maUA56WnWLsW3fdsu0jOmkZGR4fWxrDoPlo0rMsaUGGNajTFu4FlghlVlKaVUR59/9iHTplj7BSiA7TtqmDr1RMvL8YZlIS8iHWfOvxjYerR9lVLKVwoLCykry2XSRGsX72hoaGJ/gYsJE4K3Px581F0jIq8Bs4HBIlIA/AKYLSJTaeuuyQd0MK9SynKrV39M9vQBln8BasfOYsaOnUJ0dLSl5XjLV6Nrrupk8yJfHFsppbqrqamJdWs/4u7brb+63rrdybQTLrS8HG/p3DVKKdtYv349melCUlJ81zt7weVqYcfOOqZPn25pOb6gIa+UsgVjDMuXv80pJ1s/WmjrtkLGjj2Bfv36WV6WtzTklVK2sGPHDpoaC5gw3tppDAA2bi5n5qzvWV6OL+gEZSGiurqav/3lZUBwOmstK2f0mHR+dMOVREToS0OFlmUfvM0Zp6VaPqd7XZ2LPfkubr9zqqXl+Iq+k0NAdXU1//3QkxzYP5XxE4ZSX9LfsrKWbV9OdfVz3HnXzRr0KmQUFBSwf/9m5l810/KyNm0p4PgppxATY+1qU76i7+Ig1x7wzsJsxmadT0K8A3eSdX2OAxJH8PnKhYAGvQodH3ywmJNnDSIiwvqZINdvKOeSy26xvBxf0T75INYx4EcPO98vZYaHRTBx5AI+X+nm//3xOVpaWvxSrlK9VVFRwdZvVnPqySMtL6ugsIK6+ngmT55seVm+oiEfpAIR8O006FUoWb/+c04/dRAxMZGWl7V23X5OP+N8y79o5Uv6t3iQ+t1v/hqQgG93MOg/eYaUlH9xzbWXBaQegZaXl8dfnn6dxobmQ7bPmDWO666fd/B+SUkJj/92IU2uQ+dfnnR8Frfcco12e1mksLCQgoId3PKjUZaX5XK1sPmbWn55xemWl+VL+soLUoUFTjJSTg1oHcLDIhiUeBJFjvUBrUeg5OXl8dD9fyEh7EoS+n33OUhNXRH79n5+yL4VFRXs2x3B8JTrDm4zxvDx0nepq13I3fcs0KC3wOLFbzB5Qn+io63/3X69cR/jxp1IUlKS5WX5Uuj8zaGUH7UH/MDoG8hIPZH+/TIO/sTHdj67YURE9CH7JSZkMnHkAtauhj88tVC7vXwsNzeX3d9+wbhx1s82aYxh7ZcHOGN2ZwvgBTcNeaUO0zHgkwdN8upY7d1eGvS+ZYzh9dee47y5mX4ZUZO35wDNLQND6gPXdhrySnXgy4Bvp0Hve2vXrqW1ZS8nTLN2UZB2q1bv49y5l1v+RSsraCdhCHMbN1/nPEZUTEOvnh8l4xg/onfLGtqRFQHf7rugX8gf0D56b7hcLt56cxHXzhvll9AtLqmiwBHGnSefbHlZVtBXWTc0NcRxwXm9mw6/qcHCBaaNISyqmOde/E2Pn7p//35+9fMlFlQqNFkZ8O006H1j6ZJ3GT60maws6xfpBlj9772cedYlREZaP0TTCr5aNOR54Hyg1Bgz2bNtIPAGkEXboiFXdLaQdyg4ecpTga7CMfXm0/6qqioLahKa/BHw7TTovVNYWMia1W9y793T/FJedU0jW7c1cM38s/xSnhV81Sf/d+Dwj53vBz42xowBPvbcVyqo+DPg22kffe8YY3jpxb9yztkp9E/wz7wxq1bnMuukuSExpfDR+CTkjTFrAOdhmy8EXvTcfhG4yBdlKeUrgQj4dhr0Pbdq1Upamndz0swRfimvuqaRDV/X8oMfBP/qT8di5d+JKcaYIs/tYiDFwrLUYerr67nz9p9TXdXc6ePGuGlpyvJvpYKIMYZfPPwnBkbf3OOAr6zZQ8ZhX6GPjo7G1VyGq6mW6KjuXfV9N33EXxg3/iMuuOC8HtWjL3E6nfzrnUX8x4Lxfhvh8sknuZx8yvkMGDDAL+VZxS+dgcYYIyKms8dEZAGwACAzMxOHw2FJHZzOw//QgKwRKSQOtqa8nspqTjmk7ekZA0mKLSMqov6Q/eL6f9cOY9wMd6d0+jurq6sjPn4wE7I6W363jUgYYXLs9rdG1hPfL/qQMlwuFxnDI7r83WW4we12H1G/qqqqLs9zQv8YUjLqSOx/7P2GuhOpqKjo8RvfGMOAAf0ZlZ4EdP81cKByF2mj1nHaGbMPaUNUVBTX/Ggm//5kEUkDLiAyovvdCWPDxlJfV3vE76S7r8/DXzs90dn7ItgYY3j33Tc5/bQxhEekUVZ+6OPVNb5fSLu+von9jiSuvuYkyzKpIyvPg5UhXyIiacaYIhFJA0o728kYsxBYCJCdnW3S062bRvfwY+fvKWFwpPVLhXVH/p6SQ+rnKHRiEoYQGz3giH2rDrTt53a3sje/5Ih2AVRWVlKwt4KB4cO9qldJaTH9h7gOKSMvL4/CvS30k2P/7gr3QlhY2BH1S0xM7LTOHdVUN1JSGI80HXu//XurSEpK6vJ4h3O73W3nP6r7z9tfvJbWqA/4zeN3Y4w5osxLL03HuBfzygsvM3HEvd2+onfs287xJx55vO6+Pg9/7fSUle85X1i+/EMOlH7BlZfMICysvtN9hgzqfHtvvbVmCxMmnMOoUdbPidPOqvNg5Zeh3gWu99y+HlhsYVlKWaot4N/hN4/fTVpaWqf7iAiXXX4h19xwPNv3PImryboVvPoKh8PBkvde4OorJ/tt5kens47N3zRy7twf+KU8q/nktyYirwFfAONEpEBEbgJ+B5wtIt8Cczz3lQo53Qn4dhr0vtPc3MyzC59i7tkpDBrkv9EtS97fxdnnXElCQoLfyrSST7prjDFH6/gN3cGlHXy++R6iYnv352BTQ1zQj7M/GmMMlTU7SOgfG+iqBEyjq5qKxtd4+qn7uwz4du1B73K5WPLmm0wY8SNrK2lTr732EgMHlDJr5lS/lblnzwH2F0bx49u+77cyrabfwuiGqNh63nv/mV49t7fflA00Ywzf7n2b4eP2cO38uwNdnYBpdTeRlNSv2wHfTkSYNHksS99eZ1HN7O2LLz5n+9Zl3H3HDL+NpjHGsHhJLpdedjdRUVF+KdMfdIIydYT2gM8YvYNfPHo38fHxga6S6kMcDgevv/Y01107yS+rPbXb8NVeIqNGMnOm9YuB+5OGvDqEBrwKpPr6ev7858c579xk0tMG+K1cl6uFZR85mHfVTSE50+SxaHeNjbmNm6bmuh49J79wmQa8Cgi3280zf/sjo7JqmTnjOL+W/eHynUyYNNuvQyb9RUPepqKiohiSIuSVPtyj542ZnMHPHtCAbxcRHo2zvI78/HyysrK6/TxjDOu/3ALu0J3zxN9ef/1lmlzfcNE12X4tt6Cwgo2bXfzysfl+LddfNORtKi4ujoXP/T7Q1Qh50VEJpCTcyIM/e5rfPH57t4LeGMMLz7/KssUlTBx5p/WVtIFVqz7hm81LuOM/s/02Hh7a/nr451s7ufSy22wzZPJw2ievVBdShxxPYsR1PPizp8nPzz/mvu0Bv+StQiaOvLNH0xv0VVu2bOFf7/yNG68/jrg4/45q+fSzPOITxnPKKaf6tVx/0it5pbohdcjxUHbdwSv6zobY+Trgk1Pi2LCr6yG4ySkWLkxjsd27d7Poud9yw/yxDBni3yvpysp6Vqws48GHHrbdh60dacjbwIEDByw9fkJCAtHRvZsEqq6u7oj6dbZt0KBBvX6jVVRUdFm/yMhIEhMTe3X8dh2D/u77rjgi6N9d/KFPr+AXvRCaX6LrLofDwdN/epR5lw/z2ypP7YwxvPHmNs4+5ypSU1P9Wra/aciHMJEwTHMGP77JujBobnYx5/sTuOOOm3r83NjoIbzx8lLeeHnTIdujYut47aWvD95vaa3hvx+9nunTp/e4jDCTySMPvdb1fpFOXn71jweDWURITulPYfF6MlJP7HZ57UH/2v+tYP++Q6djCnOn9zjgW1pc1DRuYuCgKd1+jh2Ul5fz1JOPcP73BzFhfM++aOYLn32eR0vrcM477wK/l+1vGvIhTESYPqFno2d6ylH6Na6G9b167rD0k4EjFz+udi2jf+p3C4nt3Pt8rxfNmDiye//5fJ17J263++B9EeGx39zN/T95isJiehz0iYMHMyDcu1kDW1pcbMt7mrk/HMI559hiBpBuKS8v539+/9+cfko000/wbpbU3igtreGjjw/w0MMPEx4e7vfy/U0/eFV9Vnp6Or/7n3toDP8HhcW9+4+st9oD/twfDuaWBfP9OqIkkNoD/uRZYZx2qv/HpLe2unn1jW1cfMnNpKT0jXWM+sYrS6mjCETQa8CHcfqpowNSh+Wf7GJA0lRmzz4zIOUHQt94dSl1DP4M+r4a8KWlpfz+8YcDGvC5u0v5cn0jP7rhx7YeTXM47ZMPUkOHDSI/ZzWjh/0wYC/IVncLB6o+44yMQ/ueBw0aRH3LEuoaDhAfa82oiPTMQXy99lOGDJpIeJj1L9P2oO9NH3139TTgb7rhHkpLup7iOjklLqhH4uzbt48//uER5nwvgZNm+WcR7sNV1zTy6hu53HTzL0J+zdae0pAPUj974D/4+cNPsnufMGrYBX4P+lZ3C9t2/5XT5kRx5byLDnlswoQJ3HHPXP705BOMzbzPkqC/dv5llJU9w7o1zzJx5C0hH/S9uYIvLakne2zXU1x3Zyx9oOTk5PDM337NpRemcdxxGQGpg9vt5uVXtjD7e/OYNKlni7bbgeV/K4pIvoh8IyKbRGSD1eXZRUJCAr/81b0MHraR3fvew5hO10G3RMeAv+POmzsdgXDWWbO5495z2VXwBHUNvh+nHxERwT333srM091sz3uWVnfvRt/0lBVdN321i2bDhg0889dHufaqYQELeIAPPtxBTNwkzj//ooDVIZD89Wr7njFmqjHGvzMPhbhABH13Ar6dBn3X+mLAG2NYuvRdXnvlcW6+cRyjRyUHrC7btjvYtMVwy4I7+8TvvjPaXRPk2oP+5w8/ya78ZsbHDqPMWW1ZeUXly7sV8O3OOms2AH968gnSk64mLKzrRR5awkpx1e04eL/BVXXUfduD/imeYd3qhaQO6t2oCNNhjHx3dOy62bO/ln7xh35hR2JqunUeHAeW9qmAb25u5oUXnsFR8Cl33n4CiQFcOrK4pIp/vrWPO+76tW0nH+sOf4S8AT4SEQM8Y4xZ2PFBEVkALADIzMzE4XBYUgmn03nEtilTh3Gg6uddPnfK1GG9rpevyrj9zmtZ/tG/gW3ED3L1qi7dcVJaCmfMPpmSkpJuP2fChLHcfo+bzRs/79b+B5xFDB7YcPD+qLA4YmJijtn+eVf9kNTUzygtWd3tenU0MW4a5eXlPf5s46cPXM8nH39Bc9OuQ7bH94vu1nk4begITj1tJsXFxT0qFyBrRAqJg7t+3WU1p/T69dnZ+6K36uvr+eD9fxETfYB5V8ymqTmMsnKfHf6oqmuOnNKisbGZpR9UcuHFC4iNjbUsV3zFl+fhcGJ1F4CIZBhjCkUkGVgO3GGMWdPZvtnZ2WbDBmu67R0OB+np3n1DMRjYoR3Lli1j7ty5Xe8YxPxxHi4479Zuf/Da2zWIfdWOnTt3svCZ3zMjO5Jz5ozz60CBsvI4hgz6bhRSS0srzzy7gfETL+bSy67wWz284e15EJGvjtYdbvmVvDGm0PNvqYi8A8wAOg15pVRoMcbw/vtLWP7RS8y7fBTjxwV2si9jDG++s5XEgdlcfMllAa1LsLA05EUkHggzxtR4bp8D/NLKMpVS/lFbW8uiRX+munIj99xxAomJget/b7dy9beUlA7mZ/ff1ic+A+kOq6/kU4B3PH+6RQCvGmOWWVymUspi33zzDS/+/Y8cP1m49soZhIcHPlDXfZnP2i/dPPDgg8TE6GIt7SwNeWNMHtC35lBVykeCcdEQl8vFP/7xKls2fcC8y0cHdHhkR1u3OfhwRSU//dnvSEpKCnR1gooOoVQqSAXbVAW7d+9m0aI/kJFWzb13ZRMb69+l+o6mpKSaf75dyD33/tr2C4D0hoa8UuqY6uvreeutN/j6q/e56IJhTDl+WqCrdFCho5JVa2q49ccPdWuR9b5IQ14p1SljDBs2bOCN1//GuDGGn9xzot8X2j6WouIqnn1+G5dcegcTJ04MdHWCloa8UuoIRUVF/OONlygp/oprrhzLiBH+XYO1K0XFVTzz3Fauvua/yMwcGujqBDUNeaXUQXV1dSxe/BbrvljC984YwjVXziQiIriWyOsY8DNmzAz6b7MGmoa8UoqWlhZWrfqEpUteZvLECH5y73T69TtyuoBAOzzgVdc05JXqw1pbW1m9ehVL3nuF1ORGbr15NKkpiYGuVqf27XPy/Es5GvA9pCGvVB/U2trKF198wWefLgezm2vnjWD48EGBrtZR7dxVwqtv7OGGGx9i6tSpga5OSNGQV6oPaWxs5NNP/83yj95iQP8aTjttGpMnBvcyDxs37ePdJWXcfsdjjBkzJtDVCTka8kr1AZWVlXzyyXJWr1rMyBHhXH3FMIYPH09ZeRzQ9TqygfLZ53msXFPPfT/5LZmZmYGuTkjSkFfKpowx7Ny5k9WrP2LrN58ybUoCd/zHeAYP7hfoqnXJGMP7y3LYmhPF/Q/8jsGDg2sIZyjRkFfKZmpqavjss3+zZvVShHJmzRjMBT/JDqovMh2Ly9XCq29sprl1FA8+eF+fXtXJFzTklbKBxsZGNm7cyLq1K8nN3cTECXFccUkmw4eP9usCHt6qrKxn0YubGTnqLObPv5GICI0ob+lvUKkQ1dDQwLZt29iw4VO+2fIFI7OimTZ1MFddPoPo6NB7a+fnH+ClV3Yy97ybOPvsc0PqP6dgFnqvBKX6sLKyMjZv3symTZ+xJ28bWcNjmDQxkQvmnkB8fPB9eak7jDF8sW4PH61wcsONDzNlis5O7ksa8koFMafTya5du8jJ2cyOnK9wuSoYP64fM6cPZv680Lxi78jlauHNd76htGwwDzz4BCkpKYGuku1Y/goRkbnAH4Fw4DljzO+sLlOpUNTY2Mi+ffvIz89nz57t7MnLoaHBycgR8Ywa0Y+TrssgJXmCbboxikuqeOnlbYweexYP3XoDUVGh8cFwqLF6jddw4M/A2UABsF5E3jXGbLeyXKWCWWNjIyUlJRQVFVFU5KDIkUdh4R6czmLSUmPJzIhm5LAEzjglnZTk8bYJ9Y6++nov7y4t4vIr7uCUU061ZRuDhdVX8jOAXM8ygIjI68CFgIa8ClmPrFhBVVUVMTEx/PTkk2ltbaW1tZXm5mYaGxupr6+nvr6euro66upqqawso7y8iApnGU7nAdxuF4MHxTJkSCRDBkcycVwCs08bQkryqKBYK9VK9fVNvPXOVorLkvjJT/9Xv+DkB1aHfAawv8P9AuCQmYVEZAGwACA5OZlly6xZ57uqqorExOCceKkn7NCOjRs3BroKPdLa2kplZSWVlZWUOQ+wpLqKFlcTuJuoXvoaYWGCCISFCRERQmREGBFRQlSEEBkpREdHEBsTycARkUyaOIyoyDDocOVaUQUVVc3s/Lbc722rq4siPr7JL2WVldWweUs5mUMncvLJU9m6dStbt271+rh2eE9Y2YaAf2pjjFkILATIzs42c+fOtaQch8NBenq6Jcf2J7u0w6rz7K3m5mb279/P3r172bnnW3L27KCguJCoQbFEp8cTO64fsbWJ1G4vZUJWLL++MrRXJCorj2PIIGunNWhqamHJ+zns+jaM225/yOerONnhPWFlG6wO+UKg47ItmZ5tSgVcl4Ge2o+kuamcmDqB8A4LZ+SuL6dW+5C7ZXdeGf98axcjR5/Bo7+8gfj4+EBXqc+xOuTXA2NEZARt4T4PuNriMpU6Qm8DXfVOXZ2L997fQe7ucK66+n6mT58e6Cr1WZaGvDGmRURuBz6kbQjl88aYbVaWqVR7oO/bt48debs00P3IGMPXG/fx3vsFnDjjfB771RXExsYGulp9muV98saY94H3rS5H9U0a6MGjpKSaf723k4bGNO6863FGjhwZ6CopguCDV6W6SwM9ONXVufhw+S42b3Hxgwvmc9ZZZxMerr//YKEhr4KSBnrwa2118+lnu1m5uozsGefxq99cqtMCByENeRVwGuihxRjDN1sLeX/ZPlLTpvHT+x8K+SGMdqYhr/yusrKSVatWaaCHGGMM27YX8dGK/URFD2f+9b9g0qRJga6W6oKGvPKr+vp63lu+lMgp/TXQQ4QxhpwdxXy4fB8Sns4ll/2MKVOm6HwzIUJDXvmV2+3GhMOkC0Nj3PTGb6vZW9x4yLZ9pY00hkWT2twaoFr5h9vtZus2BytXO2hxJ3PRRf/FCSecoOEeYjTklTqGTbm1VNe3HLKtuq6FFgnngH+mfPG7pqYWvlyfz5rPyhgwYBQXXPhfTJ06lbAwe0+eZlca8kp1Is/RwOrNFewraaSusZUBCRHExxzapRQbZgJUO2tUVTfw+dp81q6rYMyYGSy49W5GjRqlV+4hTkNeqU64mt2kDoxi+tgEXv+k5OD2701NYlt+HdU7ahhshzUujGHHzmK+WOcgb08LM2bO4cGHfqArNNmIhrxSnZgwPJ4Jw9sm0/rHyhJOnTyAMZlxpA2KptjZhDGtxITwF36qaxpZ92U+u/fE42oSZs++if+4bSYxMTGBrpryMQ15pbohLjqctEGhuVB2u8bGZr7ZWsimLU727msm+8Qzmfv9E3WkjM1pyCtlYy0treTkFPP15lJ2fVvP+PEncsb3rmbKlClER0fjcDg04G1OQ14pm6mpbSQnp5jtOyrI3V3P8OETmTnrJm5eMF3nc++DNOSVCnHGGBxFVeTsKCZnRy3FJS1MmnQiJ868nBtvPo7+/fsHuooqgDTklQoxxhiKiqvYvbuM3Lw68vbUkpCQzKTJJ3HJZdmMHTuWyMjIQFdTBQnLQl5EHgFuAco8mx70zC2vVNBzNbspr2oGwAAVtc04DriIjfb/F4Jqa13sL3Cyv6CSgsJG8vfWER8/hHHjT+DkU4/nhpvGMWDAAL/XS4UGq6/knzLG/K/FZSjlcwWljTzz3nfLES/f4GT5BifTxyaQlGDNVXJLSysHymspKammpLSWomIXBQUNNDSGM3z4aEaMPJPTZ4/iRyNHMnDgQEvqoOxHu2uU6sSojDh+/+MxnT720fryXh3T7XZTU+OisrKeyqp6KirqqapqoryildIyF5UVTQwclEJ6+nDS0mcy6+ThZGVlkZycrCNgVK9ZHfK3i8h1wAbgPmNMhcXlqRBgjJv8r3IDXY1eqywyuFtbcTpdfPpZLq2tblpa3LS63bS2unE1GhoaDQ2NburrW2lodNPQ0EJtbRP9+iWSNHAwA5NSSBqYSmpmCsdNG0JqairJyclEROh1l/Itr15RIrICSO3koYeAvwKP0dal+RjwBHBjJ8dYACwAyMzMxOFweFOlo3I6nZYc199CvR1ut5vjRk0itS50F5koaDpAS3IC4WFh1DZmEhYWQXhEOJFh4USHhTNgUBQxMTFER0cTFfXd7ZiYmGMui1daWurHVrQJ9dcTaBu64lXIG2PmdGc/EXkWWHKUYywEFgJkZ2cbK1eYscvqNaHejhOmnsDcuXMDXY1e27diBTU1NSQkJDBvTrfeAkEt1F9PoG04FitH16QZY4o8dy8GtlpVllL+9MicOTgcDlsEi7I/KzsAfy8iU2nrrskHbrWwLKWUUp2wLOSNMfOtOrZSSqnu0aVelFLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxrwKeRG5XES2iYhbRLIPe+wBEckVkZ0icq531VRKKdUb3q4MtRW4BHim40YRmQjMAyYB6cAKERlrjGn1sjyllFI94NWVvDEmxxizs5OHLgReN8a4jDF7gFxghjdlKaWU6jmr1njNANZ2uF/g2XYEEVkALADIzMzE4XBYUiGn02nJcf3NDu2oqqqy7Dz7ix3OA9ijHdqGY+sy5EVkBZDayUMPGWMWe1sBY8xCYCFAdna2SU9P9/aQR2Xlsf0p1NuRmJgY8m2A0D8P7ezQDm3D0XUZ8saYOb04biEwtMP9TM82pZRSfmTVEMp3gXkiEi0iI4AxwJcWlaWUUuoovB1CebGIFAAnAUtF5EMAY8w24B/AdmAZcJuOrFFKKf/z6oNXY8w7wDtHeezXwK+9Ob5SSinv6DdelVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxsQYE+g6HCQiZcBeiw4/GDhg0bH9yQ7t0DYEDzu0Q9sAw40xQzp7IKhC3koissEYk931nsHNDu3QNgQPO7RD23Bs2l2jlFI2piGvlFI21pdCfmGgK+AjdmiHtiF42KEd2oZj6DN98kop1Rf1pSt5pZTqczTklVLKxmwf8iLyPyKyQ0S2iMg7IjKgw2MPiEiuiOwUkXMDWM1jEpHLRWSbiLhFJPuwx0KiDQAiMtdTz1wRuT/Q9ekuEXleREpFZGuHbQNFZLmIfOv5NymQdeyKiAwVkZUist3zWrrLsz3U2hEjIl+KyGZPOx71bB8hIus8r603RCQq0HXtioiEi8hGEVniuW9JG2wf8sByYLIx5nhgF/AAgIhMBOYBk4C5wF9EJDxgtTy2rcAlwJqOG0OpDZ56/Rn4PjARuMpT/1Dwd9p+vx3dD3xsjBkDfOy5H8xagPuMMROBWcBtnt9/qLXDBZxpjJkCTAXmisgs4HHgKWPMaKACuClwVey2u4CcDvctaYPtQ94Y85ExpsVzdy2Q6bl9IfC6McZljNkD5AIzAlHHrhhjcowxOzt5KGTaQFu9co0xecaYJuB12uof9IwxawDnYZsvBF703H4RuMifdeopY0yRMeZrz+0a2sIlg9BrhzHG1HruRnp+DHAm8KZne9C3Q0QygR8Az3nuCxa1wfYhf5gbgQ88tzOA/R0eK/BsCyWh1IZQqmt3pBhjijy3i4GUQFamJ0QkC5gGrCME2+Hp5tgElNL2l/puoLLDxVwovLb+APwUcHvuD8KiNni1kHewEJEVQGonDz1kjFns2ech2v5kfcWfdeuu7rRBBSdjjBGRkBiLLCL9gLeAu40x1W0XkG1CpR3GmFZgqufztXeA8YGtUc+IyPlAqTHmKxGZbXV5tgh5Y8ycYz0uIj8CzgfOMt99MaAQGNpht0zPtoDoqg1HEVRt6EIo1bU7SkQkzRhTJCJptF1VBjURiaQt4F8xxrzt2Rxy7WhnjKkUkZXAScAAEYnwXAkH+2vrFOCHInIeEAP0B/6IRW2wfXeNiMyl7c+iHxpj6js89C4wT0SiRWQEMAb4MhB19EIotWE9MMYzgiCKtg+M3w1wnbzxLnC95/b1QFD/teXp810E5BhjnuzwUKi1Y0j7CDkRiQXOpu3zhZXAZZ7dgrodxpgHjDGZxpgs2t4HnxhjrsGqNhhjbP1D24eR+4FNnp+/dXjsIdr683YC3w90XY/Rhotp66NzASXAh6HWBk9dz6NthNNu2rqhAl6nbtb7NaAIaPach5to60P9GPgWWAEMDHQ9u2jDqbR9QLmlw3vhvBBsx/HARk87tgI/92wfSdsFTi7wTyA60HXtZntmA0usbINOa6CUUjZm++4apZTqyzTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxjTklVLKxv4/HHr9AWg9n8AAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"text1.movey(5)\n",
"text1.movex(-20)\n",
"arc1.rotate(-90)\n",
"arc1.move([10,22.5])\n",
"poly1.ymax = 0\n",
"\n",
"qp(D) # quickplot it!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also connect shapes together using their `Port`s, allowing us to snap shapes together like Legos. Let's add another arc and snap it to the end of the first arc:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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": [
"arc2 = D.add_ref(A) # Add a second reference the arc we created earlier\n",
"arc2.connect(port = 1, destination = arc1.ports[2])\n",
"\n",
"qp(D) # quickplot it!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's it for the very basics! Keep reading for a more detailed explanation of each of these, or see the other tutorials for topics such as using Groups, creating smooth Paths, and more."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The basics of PHIDL\n",
"\n",
"This is a longer tutorial meant to explain the basics of PHIDL in a little more depth. Further explanation can be found in the other tutorials as well.\n",
"\n",
"PHIDL allows you to create complex designs from simple shapes, and can output the result as GDSII files. The basic element of PHIDL is the `Device`, which can be thought of as a blank area to which you can add polygon shapes. The polygon shapes can also have `Port`s on them--these allow you to snap shapes together like Lego blocks. You can either hand-design your own polygon shapes, or there is a large library of pre-existing shapes you can use as well.\n",
"\n",
"### Creating a custom shape\n",
"\n",
"Let's start by trying to make a rectangle shape with ports on either end."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"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": [
"import numpy as np\n",
"from phidl import quickplot as qp\n",
"from phidl import Device\n",
"import phidl.geometry as pg\n",
"\n",
"\n",
"# First we create a blank device `R` (R can be thought of as a blank \n",
"# GDS cell with some special features). Note that when we\n",
"# make a Device, we usually assign it a variable name with a capital letter\n",
"R = Device('rect')\n",
"\n",
"# Next, let's make a list of points representing the points of the rectangle\n",
"# for a given width and height\n",
"width = 10\n",
"height = 3\n",
"points = [(0, 0), (width, 0), (width, height), (0, height)]\n",
"\n",
"# Now we turn these points into a polygon shape using add_polygon()\n",
"R.add_polygon(points)\n",
"\n",
"# Let's use the built-in \"quickplot\" function to display the polygon we put in D\n",
"qp(R)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, let's add `Port`s to the rectangle which will allow us to connect it to other shapes easily"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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": [
"# Ports are defined by their width, midpoint, and the direction (orientation) they're facing\n",
"# They also must have a name -- this is usually a string or an integer\n",
"R.add_port(name = 'myport1', midpoint = [0,height/2], width = height, orientation = 180)\n",
"R.add_port(name = 'myport2', midpoint = [width,height/2], width = height, orientation = 0)\n",
"\n",
"# The ports will show up when we quickplot() our shape\n",
"qp(R) # quickplot it!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can check to see that our Device has ports in it using the `print` command:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Device (name \"rect\" (uid 4), ports ['myport1', 'myport2'], aliases [], 1 polygons, 0 references)\n"
]
}
],
"source": [
"print(R)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks good!\n",
"\n",
"### Library & combining shapes\n",
"\n",
"Since this Device is finished, let's create a new (blank) Device and add several shapes to it. Specifically, we will add an arc from the built-in geometry library and two copies of our rectangle Device. We'll then then connect the rectangles to both ends of the arc. The `arc()` function is contained in the `phidl.geometry` library which as you can see at the top of this example is imported with the name `pg`.\n",
"\n",
"This process involves adding \"references\". These references allow you to create a Device shape once, then reuse it many times in other Devices. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"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": [
"# Create a new blank Device\n",
"E = Device('arc_with_rectangles')\n",
"\n",
"# Also create an arc from the built-in \"pg\" library\n",
"A = pg.arc(width = 3)\n",
"\n",
"# Add a \"reference\" of the arc to our blank Device\n",
"arc_ref = E.add_ref(A)\n",
"\n",
"# Also add two references to our rectangle Device\n",
"rect_ref1 = E.add_ref(R)\n",
"rect_ref2 = E.add_ref(R)\n",
"\n",
"# Move the shapes around a little\n",
"rect_ref1.move([-10,0])\n",
"rect_ref2.move([-5,10])\n",
"\n",
"qp(E) # quickplot it!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can see we have added 3 shapes to our Device \"E\": two references to our rectangle Device, and one reference to the arc Device. We can also see that all the references have `Port`s on them, shown as the labels \"myport1\", \"myport2\", \"1\" and \"2\".\n",
"\n",
"Next, let's snap everything together like Lego blocks using the `connect()` command."
]
},
{
"cell_type": "code",
"execution_count": 12,
"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": [
"# First, we recall that when we created the references above we saved\n",
"# each one its own variable: arc_ref, rect_ref1, and rect_ref2\n",
"# We'll use these variables to control/move the reference shapes.\n",
"\n",
"# First, let's move the arc so that it connects to our first rectangle.\n",
"# In this command, we tell the arc reference 2 things: (1) what port\n",
"# on the arc we want to connect, and (2) where it should go\n",
"arc_ref.connect(port = 1, destination = rect_ref1.ports['myport2'])\n",
"\n",
"qp(E) # quickplot it!"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD4CAYAAADvsV2wAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAyBklEQVR4nO3deXhb1Z3/8feRLHnf9y375pA9TsKSkD0EBpLCQAi0gbZAYAoUfgxlKTOUodPSdqZlmNIWAmWAspY2i0NogAAJSSCAk5iskDiOg215i/fdlnx+f0g2tmMnsX1tydL39Tx+JN17de85vtJHV0fnnqu01gghhPB+JncXQAghxOCQwBdCCB8hgS+EED5CAl8IIXyEBL4QQvgIP3cX4GxiYmL0iBEj3F0MWlpasFgs7i6G4by1XtXV1YSFhbm7GIbz1v0l9TLW3r17T2utY7ub59GBP2LECDIzM91dDGw2G0lJSe4uhuG8tV5bt25l+fLl7i6G4bx1f0m9jKWUOtXTPGnSEUIIHyGBL4QQPkICXwghfIQEvhBC+AgJfCGE8BES+EII4SMk8IUQwkdI4AshhI+QwBdCCB8hgS+EED7ivANfKfWCUqpEKXWow7THlFIFSqks198VPTx3uVLqa6VUtlLqISMKLoQQond6c4T/ItDdACVPaq2nuf7e6TpTKWUG/gBcDkwEblBKTexLYYUQQvTdeQe+1vpjoLwP25gNZGutc7TWzcAbwMo+rEcIIUQ/GDFa5l1KqZuATOBftdYVXeYnA3kdHucDc3pamVJqLbAWICUlBZvNZkAR+6e8vC+fc57PW+tVVVXlEa8bo3nr/pJ6DZ7+Bv6fgJ8D2nX7W+CH/Vmh1nodsA4gPT1de8qwqZ5SDqN5Y73Cw8O9sl7gnfsLpF6DpV+9dLTWxVprh9a6FXgOZ/NNVwVAaofHKa5pQgghBlG/Al8pldjh4dXAoW4W+wIYq5QaqZSyAquBjP5sVwghRO+dd5OOUup1YAEQo5TKB34GLFBKTcPZpJML3O5aNgl4Xmt9hdbarpS6C3gXMAMvaK0PG1kJIYQQ53bega+1vqGbyX/uYVkbcEWHx+8AZ3TZFEIIMXjkTFshhPAREvhCCOEjJPCFEMJHSOALIYSPkMAXQggfIYEvhBA+QgJfCCF8hAS+EEL4CAl8IYTwERL4QgjhIyTwhRDCR0jgCyGEj5DAF0IIHyGBL4QQPkICXwghfIQEvhBC+Ij+XsRcCCFELy1Yt45J8fE8vXJl+7QvCwv51fbt7Dp1itN1dQyLiOCW9HTunzcPk8mYY3MJfCGEGCR2hwNzD+G9t6CA2OBg/rJqFcMiIvg8L4/b1q/H3trKTxcuNGT7vbmm7QvAlUCJ1nqSa9p/AVcBzcAJ4Ada68punpsL1AAOwK61Tu93yYUQwmXBunWkxcURZLHwf3v3YjaZ+LeFC7ljzhzu27KFV7OyCPP35xfLlrFmxgwWPfccE+PieHrlSlocDt47fpwZSUmM/u//5pVVq7hm0iRG/PrXfH/mTLLLyth45AghViv3z5vH/Zde2r7dbyoruWfzZrZlZwOwdOxY/veqq0gJDwfgsW3b+NuhQ9w/bx4///BDcisquPqCC9hx8iQ7Tp7kD3v2AHDygQf4YXrnWBwVFcU+m42/Hzo0+IEPvAg8DbzcYdr7wMOuC5X/GngYeLCH5y/UWp/uUymFEOIcXs3K4r65c/nsRz8i4+hR7n37bbYeO8bycePIvPNOXtq3j1vXr2fJmDHcNmsWd2Zk8Kvly1l/+DCHCwp4Zf9+gq1WrkpLa1/n73bt4sH583l00SI+ysnh7s2bGRUVxTWTJtHa2srKl18m0GLho9tuA+CujAy+85e/8MWdd7av42R5Oa9lZfHWjTdiNZtJjYjAVl3NhNhYfnnZZQDEBgd3W6fqxkYiAwMN+x+dd8OQ1vpjoLzLtPe01nbXwz1AimElE0KIXrggLo7HlixhbEwM982dS0xwMBazmXsuuYQxMTE8ungxGth96hTXTJqESSn+nJnJzgMHyHjvbd47epSpCQk0Oxzt65yTmsojCxcyLjaW2+fM4abp0/ndrl0AfHDiBAeKinht9WrSU1JIT0nhtdWr2Wez8cGJE+3raHY4+MuqVcxITmZSQgLhAQFYzWaCLBYSQkNJCA3ttplnX0EBL+7bx79ceKFh/yMj2/B/CLzZwzwNvKeU0sCzWut1Pa1EKbUWWAuQkpKCzWYzsIh9U15efu6FhiBvrVdVVZVHvG6M5q37y4h6NTc3MzoiotN+j/L3Z0RwcKdp4VYrxwoKKIuO5upx43jl00+ZUFdM+LgUvrLVY6mp4akPP2RVWhoOh4NJkZGdnp8WFsbfDx7EZrOxJzub+OBgrA0N2BoaAAgA4oOC+PT4cRJSU6mpqSExJARHTQ22mppO5a2rq+vxdZpdUcF169dz65QpXBQVZdjr2ZDAV0o9AtiBV3tYZK7WukApFQe8r5T6yvWN4QyuD4N1AOnp6TopKcmIIvabp5TDaN5Yr/DwcK+sF3jn/oL+18tqtRIRGtppPRaLhagurwWz2UxYWBhJSUksjIzk+f37uejyOFpyWggLMnOq8DhhFgsR4eGYzGZCu6wzoqAAZTKRlJRE+MmTmM3mM8puNpuJCA8nKiqK0NBQwgIDz1jGarUSHBzcbb2/Kinh+k2buHH6dJ688sp+/V+66nfgK6W+j/PH3MVaa93dMlrrAtdtiVJqAzAb6DbwhRBioO3Zs4f1b79ISupojpTAsbx6LpwYjqM5mINZRwCobWpi96lTnZ+Xl0daXBwAaXFx2Kqrya2oYERkJAA55eXYamqY6FqmJ1azGUc3cXmkuJhFzz/PqsmTDQ976GfgK6WWAw8A87XW9T0sEwyYtNY1rvvLgMf7s10hhOirk7m5bN/zNmlr0jGVK9Z/XIrZBJdOjaSipoUs4GDWERqtUXxy6hT/sW0bN06bxvacHF7et49Xr78egCVjxjAlIYHvvvkmT7nC+e6MDGYkJbFo9GgKCwt7LMOIyEg+z8sjt6KCEKuVqMBAjpaWsuj551k4ahQ/XbiQog5NQAmhoYbUvTfdMl8HFgAxSql84Gc4e+X442ymAdijtb5DKZUEPK+1vgKIBza45vsBr2mttxpSeiGE6IWWlhY+yNzBVWvSCYuLYGpkK5t2lzJlVCgBVhOJ0f4wIYIsIPtAJWPDwtn81Vf8186dBFutPL50KddOnkxjSwsF1dX871VX8fiHH7Jg3TqUUiwZM4bfr1iBK+96dP+8edz81ltMfPJJGlpaOPnAA7x18CAltbW8eeAAbx440Gl5/cQThtRf9dAK4xHS09N1Zmamu4uBzWbzyrZTb63X1q1bWb58ubuLYThv3V+DVa89e/bw36/9L2musAeoqrPzxCsnuWNFCiMSv+3+WFjWxJ825hHWVM+C1DHMnzmTm2fOJNhqBWDVa6+R0+XH5ikJCbxw7bWDXq+ulFJ7ezrXSc60FUJ4va5h73Bo6pscbP3sNEkx/p3CHiAx2h+rxURodAQHvznSPr0t9B2trWdsY0x09IDXo78k8IUQXq27I/vcogae3VxATLiF7y1N7PZ5ZpMiKS4Qv9jU9h9yAS4aPpzi2tpOyy4dM4YH588fsDoYRQJfCOG1ugt7gNHJQfzmjrFnfe7D3xsJOJt3snD+kFvd0sJTn3xCsNWKydVOv3TMGP5z2bIex8jxJBL4Qgiv1FPY91bbD7mftbTy8VEb/tZAGoOCiA4O5rKxY4dM2IOMhy+E8EJGhX2bxGh/Zk+KJDwqkIbmBmrq64kNCuLhBQuGTNiDBL4QwssYHfZtkmICWLU4gfi4IExNNYTV1/PK/v3UNTcbto2BJk06QgivMVBh3yY5JoCr58eT9ZU/h7882t6O37HLpieTwBdCeIWBDvs2STEBqLRIspTq1HtnKIS+BL4QYsgbrLBv0/GM3KEU+hL4QoghbbDDvs1QDH0JfCHEkOWusG9zttD3RBL4Qoghyd1h36an0F/mgeMeSeALIYYcTwn7Nt2FfmVVFfckJnpU844EvhBiSPG0sG/TXei/FB7uUW36EvhCiCHDU8O+TcfQP/V1Hjv2Oqd7SuhL4AshhgRPD/s2baF/tNmPg0eOtF8MxRNCX4ZWEEJ4vKES9m0So/2ZMSICS4Q/hSWFlNXXd7pkobvIEb4QwqMNtbAH0FqjcwuZ2RRA6sXzuG7yZEZ7wAVSenWEr5R6QSlVopQ61GFalFLqfaXUcddtZA/Pvdm1zHGl1M39LbgQwvsN1bA/ujWL8MoAnn7w3/nJggVMiItzd7GA3jfpvAh0vVjoQ8AHWuuxwAeux50opaJwXvR8DjAb+FlPHwxCCAFDO+zjikNYtfI6goKCCPH3d3ex2vUq8LXWHwPlXSavBF5y3X8J+E43T70MeF9rXa61rgDe58wPDiGEAIZ+2P/sJ/+OvwcFfRsj2vDjtdaFrvtFQHw3yyQDeR0e57umnUEptRZYC5CSkoLNZjOgiP1TXt71M847eGu9qqqqPOJ1YzRv3V9d63Xs2DE27dzCRdfOJ8gaDJXuKVdvaK3JzzrJhMZUVt10HZWVlR65vwz90VZrrZVSup/rWAesA0hPT9dJHnJ6sqeUw2jeWK/w8HCvrBd45/6Cb+u1Z88enl6/jrQ16bTGWajF8y8u0vHI/u6f3E9QUFD7PE/bX0Z0yyxWSiUCuG5LulmmAEjt8DjFNU0IIQDvaMbpGPaeyIjAzwDaet3cDGzqZpl3gWVKqUjXj7XLXNOEEELCfpD0tlvm68CnwHilVL5S6hbgV8BSpdRxYInrMUqpdKXU8wBa63Lg58AXrr/HXdOEED7u2LFjEvaDpFdt+FrrG3qYtbibZTOBWzs8fgF4oVelE0J4tT179rBp5xYJ+0EiQysIIdyirRknZf4YCftBIoEvhBh0Hdvsg8KD3V2c8zLUwx4k8IUQg0x+oHUfCXwhxKCRsHcvCXwhxKCQsHc/CXwhxICTsPcMEvhCiAElYe85JPCFEANGwt6zSOALIQaEhL3nkcAXQhhOwt4zSeALIQwlYe+5JPCFEIaRsPdsEvhCCENI2Hs+CXwhRL9J2A8NEvhCiH6RsB86JPCFEH0mYT+0SOALIfpEwn7okcAXQvSahP3Q1O/AV0qNV0pldfirVkrd22WZBUqpqg7LPNrf7Qoh3EPCfujq1TVtu6O1/hqYBqCUMgMFwIZuFt2ptb6yv9sTQriPhP3QZnSTzmLghNb6lMHrFUK4mYT90NfvI/wuVgOv9zDvIqXUl4ANuF9rfbi7hZRSa4G1ACkpKdhsNoOL2Hvl5eXuLsKA8NZ6VVVVecTrxmju3F/Hjh1j084tXHTtfIKswVBp3LoD6oyOISetNflZJ5nQmMqqm66jsrKSysrKAdlWdzzx/WXYf1opZQVWAA93M3sfMFxrXauUugLYCIztbj1a63XAOoD09HSdlJRkVBH7xVPKYTRvrFd4eLhX1gvcs7/27NnD0+vXkbYmndY4C7U0G76N2ghj19nxyP7un9zvtiN7T3sdGtmkczmwT2td3HWG1rpaa13ruv8OYFFKxRi4bSHEAJBmHO9iZODfQA/NOUqpBKWUct2f7dpumYHbFkIYTMLe+xjSpKOUCgaWArd3mHYHgNb6GeBa4F+UUnagAVittdZGbFsIYTwJe+9kSOBrreuA6C7Tnulw/2ngaSO2JYQYWBL23kvOtBVCtJOw924S+EIIQMLeF0jgCyEk7H2EBL4QPk7C3ndI4AvhwyTsfYsEvhA+SsLe90jgC+GDJOx9kwS+ED5Gwt53SeAL4UMk7H2bBL4QPkLCXkjgC+EDJOwFGH8BFCGEh+ku7D/cV86hk7WUVrbgZ1YMiw/g8jnRJET5u7ewLhL2A0OO8IXwYj0d2efYGrjognDuvDqFtVclY1KwbnMB9Y0O9xXWRcJ+4MgRvhBepr65mYNFRWibrcdmnFuvTO70ePXiBB594QS5RQ1MHBEyiKXtTMJ+YEngC+FF6pubuW/LFvbm5BCQd4gF59lm39TcitYQ6G8e+EL2QMJ+4EngC+FF/vOjj9j91Vfk5OcSMyKZ4mYLYefxvIzdpSRF+zM8PmDAy9gdCfvBIW34QniRC+wtlJUXYvb3o8xWxrbM0xzPrzvrczZ/UsrJogbWXJaIyaQGqaTfkrAfPBL4QniJf7z7D9a//xqrVgwndXwCZpOZktySs4Z+xu5SsrJruP2qFKLDLINcYmfY52edlLAfJIY16SilcoEawAHYtdbpXeYr4CngCqAe+L7Wep9R2xfCV2mt+evf3+KtTzYy5YcXERQezIqIYDKA/GPFztB3LTs2Jbj9eZt2lfLliRpuX5FCXKTVLeU+ujWLCY2p3P2T+yXsB4HRbfgLtdane5h3OTDW9TcH+JPrVgjRRy0tLTzz52fZmf85026di3+wsw0+KszKiotjewz9DTtL2HeshpuXJxLkb6Km3g6A1WLC3zLwX/w7NuOsuuk6CftBMpg/2q4EXtZaa2CPUipCKZWotS4cxDII4TXq6ur49f/+FydMBcz4/lzMls5v57OF/qeHqwBn3/uOlsyMYtms6AEtd9c2+8rKygHdnviWkYGvgfeUUhp4Vmu9rsv8ZCCvw+N817ROga+UWgusBUhJScFmsxlYxL4pLy93dxEGhLfWq6qqyiNeN0bruL/Kysr4+5YNOBLNXDLtUlRd9z+2hmDlhklW3mo2UWyroLawhh2fmbl7wTDSEkIBqG92sCGrmLyKBsqK7bz+djFmk2LxhGiGRwUaWoe2NvsJjamsuuk6KisrvfZ16In1MjLw52qtC5RSccD7SqmvtNYf93Ylrg+KdQDp6ek6KSnJwCL2naeUw2jeWK/w8HCvrBc499eePXv4n1eeJm7ZSFKnjqSOlrM+xz8CrgiLJuOTVvKPFVOQV8jbZgeNIXbGpgRzuqqZ3Jp68IOSpqb2552y1xEdYVy//I5H9l3b7L15f3kSwwJfa13gui1RSm0AZgMdA78ASO3wOMU1TQhxHhwOB6+88SobP9vC+O/NICIx6ryfe7bmnfCQM3vnjE0OYtqYUGMKjnS99BSGBL5SKhgwaa1rXPeXAY93WSwDuEsp9QbOH2urpP1eiPNTVFTEq397nQMNXzP99nlYA3s/yFl3of8Pu8bRqjCZv20SGpscxPLZ0Yb1yZew9xxGHeHHAxucPS/xA17TWm9VSt0BoLV+BngHZ5fMbJzdMn9g0LaF8Fpaa3bu2skf31jHhIVTmJZ+Ma73WZ90DP1Tx0qwFddjtloIDfYj0N8sYe/lDAl8rXUOMLWb6c90uK+BO43YnhC+oKKiguf/8mc+z89iwvdnEG+No1Y193u9baH/RqODU7Z6Wuyayho7KTH+EvZeTs60FcLDaK3Z8fEO7nr0Xr4OLWDm7fMNv2hJVJiVxTOi8LM6j/laW1vJL2kku+DswzCcLwl7zySDpwnhQWw2G8/95c8crjrOuDVTCY+PHJDtFJU38eH+SqLDLJTXtGBvslNTWce2zNMopTqdkdtbEvaeSwJfCA9QX1/P3zetJ2PXFmLmpjLzwksxmQbmC3hReRMbdpbSbG/FbFZEhVqwB5lpKq2n9FQp21y/EfQl9CXsPZsEvhBu5HA42LlrJ/+3/i8wyp8pP5pLQIixJzt11DHs21wwIoT08SFs/tR61rF3zkXC3vNJ4AvhBlprPv/8c15a/woVQXWMvD6NyOSBHdKgsbmVjV3CHiA8xI/ocP+zjr1zLhL2Q4MEvhCDqLW1lf379/NaxhvktRQzfNl4Ro5JHJRtmxQEB5qx12kAHK3O20Crs+noXAOu9UTCfuiQwBdiENjtdj799FPe2PIWp01VJM0bRXrahH71qe8tq8XEmmVn/3Dpbehrrfndi1+TZA9h1087h/09mzez+9QpDhUXkxASQu6DDxpYG9EXEvhCDKCqqip27NzBxg/epjHKQcploxk+cuqgBn1vnW/o2x2tfP3ul1ibQ5g9M/2MI/tWrbl5xgwOFhXx3vHjg1cB0SMJfCEMprXm+PHj/OPDd9n15ScEpUWScv248x775plN+cRFWrH4KTK/rsakFItnRLI4OZYNO0vYf7yGAKuJy2ZHM3NcGM9m5BMfaeU78+La19HY7ODnL59k9aIEJo8K4YlXTjJzfBhl1S0cPlmL1WJi/tRI5k/7tttnRU0LGbtLOV5QD8CI+ADiR8VSnFNKSW4Jb9XYUSYTS2dGs21fGRXVLYw1hZCn4dnMTJ7NzATg5AMPMCIykt+vWAHAf3/8sQS+h5DAF8IgxcXF7PpkN+9+so0qVUPk9ESm3zsfS0Dvrya1/3gN86ZEcNfVqRw5VUfGJ6fJTmxkzMgAfvzPqez9uoa/bS9hbHIQs9PC2birhCsvjsHP7GyPz8quxepnYuLwb4/Idx6oZMG0SJZcO4wTtgY27SohKszC5FEhtGrNS1sLsfgpbr8qBXBeEavZ3kry2DgKjpdQV1WP3RLA7kMVXBJUTUxjEL+45yf885tvMiE2ll9edhkAscF978MvBpYEvhB9pLWmqKiIvfv3seOLnZw8/Q2hE6NJumYUYxIj+9VsEx9lbb8QyaXhFrbvr8CsFHOnOI/Il8yMYntWOblFDUweFcym3XDoZF37CJdffFXFzPGhmDsMijYszp/FM53fMmIjrOSVNLLzQAWTR4WQnV9PYXkTD94wgijXtW1vWJLAb17LZcG0OJRSZJ+oQLdqmqvqCCSIpx9yttlbzWaCLBYSQo0bXVMMDAl8IXqhqamJ7OxsDhw+yM69uylpKCNkfCSxc5OYNWqRYSdLJUZ9+61AKUVIoJmkiG9HyDSbFYH+ZmobHPiZTcwYG8YXX1UzbUwoReVN5JU0sWphQqd1Dkvo3L9/eHwAh07WAlBS2UJYkF972ANEh1kIC/ajvkmz4uJYnq9opqyiCV1rpyxtPPWtrUh/nKFFAl+Is6irqyM3N5fjJ46TeXg/X+d+jTUhmIDhISR8ZzgjEqcNyA+w5m4GMDN3sx3tup2dFsaTb31DRU0LX3xVzfD4AOINujC5AiJC/IhprqXaFMAF49O4b948YkJCDFm/GDwS+ELg7B9fVlZGYWEhNpuN49+c4MiJo5RUlhKYGIIlMYjoWfHM+OeF+PmfecEQd0uI8mdYXACfH61m//EaLpt95klc3xQ3nvE4LsL5oRAXYaG63k55dUv7UX5ZdQvVdXZiw/04+PfPSdAJOKLDeGTRIpaOHdu+HqvZjENrhOeTwD+Hx7Zto6amhtDQUB5bssTdxRHnsGDdOsrKyvhlbi7v3nwzdrsdu91OQ0MDdXV17X+VlZUUlhVReLqI4tMlFJ8uRgX64R8TiF+MP0FxoURPG8mI2GkDNqaN0WanhbH+41LMJpg6+sz29G+KG/lwXzmTR4WQY2tg77EablgcD8DYlCASo/x5/YMiVlwSC8Cm3aUkxVip3fkl04LGM/LCqWw4erRT2AOMiIzk87w8cisqCLFaiQoMxGQykX36NLXNzdiqq2l2OMhyXWd4YlwcVj+JHneQ//p5+GJfJsUVpex/5U/uLoqhhicN45TtG3cXw1AHIpNptTsoLCvmu/d+H7PFDCaFX4AFc4AZU6Afyt+EKdRCQHgggRcEExExjOTIiR555N4bU0eHsml3KVNGhRJgPfNDat6UCArLm/hwXzlWi4lls6KY4vpgUEpx8/JEMnaX8uzmfABGJwQwpjiPOfHTuGvtj/jP7du73e798+Zx81tvMfHJJ2loaWnvlnnr+vXsOHmyfbnpv/898G23TTH4lPbgr2Lp6ek609W3110e27aNf3zwLmEz49p7TXiLkEortRH9v6CGJ/nDhlNUnDhN9Ng4/mVl6rmfMISca39V1dl54pWT3LEihRGJnX+gfeKVk1w8KaJTv/uzqauo5dCrn7NyxnK+e/2NA/otx2azedzFvo3grnoppfZqrdO7mydH+EIMcQ6Hpr7JwdbPTpMU439G2PdWRUEZx97Yx9oVP2DZkmUGlVJ4gn4HvlIqFXgZ53VtNbBOa/1Ul2UWAJuAtu9367XWXS9yLoTog9yiBp7dXEBMuIXvLe3fQGyFX+Vje/s4P/3BT5gxY4ZBJRSewogjfDvwr1rrfUqpUGCvUup9rfWRLsvt1FpfacD2hHCrZzblkxDVeSgD2+kmPsoqJ7ewkbpGBxEhfsyeEMal0yIxDfC4OaOTg/jNHWPPuszD3xt51vlaa47vOIxjfw2//H//wahRo4wsovAQ/Q58rXUhUOi6X6OUOgokA10DX4ghzdGq6en63gWnGwkJMLN6UTwRoRbyShr5245iWjUsmnF+Y+i4S0tjC4c3fMFIRxL3P/YoERER7i6SGCCGtuErpUYA04HPupl9kVLqS8AG3K+1PtzDOtYCawFSUlKwubpyuUtNTQ1BAYFYG82EVBpzIounCKjzjJ9wnvzgJAnh/ljNJj49WYlJwfKJscwbE8nf9xfxxakqAixmVkyOY87ICP7nw1wSw/25fua3zRcNLQ4e2vg1UUF+WC1W8oqb2L6zktLaZg4U1ODvZ2LxhGiWTohpf055XTNv7Sviq2LnhbsnJASzakYikUHO3jpvHyxhf341S8ZH84/DpZTVtzAtOYycwgZyChv45HAVAD+/ciwLE2Kh7cTWVhgeE0zJ6BYOH69jxajOZ7z21UDsr4aqOmy7TnD5yAXMnzuf+vp66uvrDd/O2ZSXlw/q9gaLJ9bLsFeQUioE+Dtwr9a6usvsfcBwrXWtUuoKYCPQ7XdQrfU6YB04e+m4+9f70NBQ6hsb8AsI9boeLYBH1Mnhp/n8VBXzpkRw5zUpHDlVx98+LeLg6WrGpwZx9yznYGGvfGEjdZyVmZND2birhMsWRLYPFrbnSBVWiwn/QKhsaYagAD44VsaCaZH8+KJU12BhxYTEmdoHC/vj+3lY/BRrVyQDzsHC/vjpKX58TSpKKZoDHJyua2aPrZIblyfgZ1aEB/tR/k4zsRFWLp/j7LVlCdDUms78P9aYWvAPUob+j41cV/7BXIq25nDX6tuZN3eeYevtC3e/zweKp9XLkL5WSikLzrB/VWu9vut8rXW11rrWdf8dwKKUium6nPBdbYOFxUZYuXRKBMEBZkwm52BhMeFWlsyMAnT7YGFKOQcLa9M2WFjHYQ7aBguLjbBy4cRwZowLY+eBCoD2wcJuWJxAalwAqXEB3LAkAVtpE9kFDe3rcLRqVi+KJyU2gIQofwL9zZhNCqufIjTIj9AgP0zdtPPklzay9+saLrogfOD+aX1kb2rh4IYvcOyq4jc/+YXbw14Mnn4HvnK+w/4MHNVa/66HZRJcy6GUmu3abll/ty28R3eDhXWc1tNgYUD7YGGzJnQO1+4GCyuucB4hn22wsLZlAMKDnaHeGyWVzfzfP2zMnRzB5FGeNYJkRf5p9j77MXPDZ/Db//gNw4cPd3eRxCAyoknnEmANcFApleWa9lNgGIDW+hngWuBflFJ2oAFYrT35jC8x6LobLKy7I+fBGiysjdWvd8dEJRXNPLs5n6mjQ7niQs/5Etva2krO7q+o/+w0D6y5h9mzZ7u7SMINjOils4vO75HulnkaeLq/2xKizUAOFhZ3jg8Os1nR2s3hSnF5E89uLmDq6JD28Wg8QXVJJcc2fkla6GjufuxhoqO964xxcf48o5uGEH1wvoOFXTAimNyixvMbLCzWnzHJZz9TNTLUj7ySRsqrW/C3mAgMMFFS0cy6zQWMTgpk0Ywoaurt7cv3tknIKK2OVrI/Pkx9Zhl3XPsDFsxf4NHX0hUDTwJfDFnnM1hYdkE9735RhtVPnXOwsLHJQaycG3vOUJw/NZI3Pyrmt389RYtd89CNIzhwopbaBgdfnqjlyxO1nZY/10lRA6GioIzjG79kRuJkbn/834mK8uxzAcTgkMAXbnfHypQzpv3r9Wf+mPjozZ3P/mxobsVu18yeENY+rbbBQaPVH0crZBfU0+LQJMU4rxRl9TOhtW4P9MhQCzcv77nb3LJZ0d0OmBcbYeWuq1PPa9nB1lzfxPFth2g93sB9N9zJhRdeKEf1op0EPmB3OMgsKKChpeWMeSfLy6lGYW1udUPJRHeaWlo5UVDPJ4eqiA6zYG/VZBc4TxaqbWjF7ueH1lBe09KpOSXzmLNXz8WTwr0uBFtbW8n97Dind+Vx1cXLufaWawkKkgsQis4k8IGNR47wqx07up1nq66mChOWOnu388Xg+2BvOduzKjCbFVFhfmzZc7p9XrNdo1XPPWsyj1UzLjWI2AjvOWu6NKeIk/84wqTo8Tzy0G9ISTnzG5MQIIEPwKyUFML8/aluaupxmWoJfI8xa0IYp4obaLaf2VXGpMChNf5WE9PGnPlDbnCAmchQ73jZV9rKydl2hLDqQB5YdQ/p6ele981FGMs7Xvn9NDwykj+uXMmPNm3qMfTDguVf5SliI6ysvCSOTbtLzgh9k8k58mNEiB9XXuQ5XSONVFNaRc6HR/ErcHDryu8y/9L5+MklA8V5kFeJy4S4OP64ciXf++tfAfjjypVMiI3lie3beX/HB4SH93z5u+6GywXn2Cy5xQ0UlzcTGmg+5xC14uxybA3s+LKCgtJGqusdLE2P4sgp5/AK1y+MJyLEj+c251GZW02gf7CbS2u8xpp6Dn60n9YTDay+/FqW3rMUf39/dxdLDCES+B1MiItj05o1fFVayqyUFJRS+Pv5Yab7sz7PNlwugEaTPi6MwvImjucN7giE3qippZWEKCszx4Xy5kfFRIZYuGlZIuU1LSREOYPPZFIoDd7UslFRUMY3O4+T3BTDd6Ys47LblhEc7H0faGLgeWTgq4cf3g4cjRs3jqjHH8dsMvFvCxdyx5w53LdlC69mZRHm788vli1jzYwZLHruOSbGxfH0ypXt66hubCThl7/klVWruGbSJEb8+td8f+ZMssvK2HjkCCFWK/fPm8f9l17a/pxvKiu5Z/NmtmVnA7B07FhGdBgb/L0vyjiYU8v8qZFs21dORU0Lk0aGnDFc7kM3jiAqzMJ35jqP+HdkVUjgGyBteDBpw51B99ePigFn18rI0KF98fHuaK0pPVFEwe4TBFVZWLP8WiaMGy9j34h+8cjAd/mu2W5n149+RMbRo9z79ttsPXaM5ePGkXnnnby0bx+3rl/PkjFjuG3WLO7MyOC3//RP+LvaMl//8ktCrFauSktrX+Hvdu3iwfnzeXTRIj7KyeHuzZsZFRXFNZMm0draysqXXybQYuGj224D4K6MDPZ88w0d+zyU17SwP7uG7y39drjc6jp7p+FygwPMg/ZPEt6lpbGFvP05lO8rJN4SzZ1X3MKcOXPw8/Nz+7UhxNA3cJei77/DSTk5jI2J4b65c4kJDsZiNnPPJZcwJiaGRxcvRgO7T53imkmTMCnFhsPfXlPlhcxMbpoxA4v52/Cdk5rKIwsXMi42ltvnzOGm6dP53a5dAHxw4gQHiop4bfVq0lNSSE9J4bXVqymsqaG6w1BBfR0uV4izqSqq4HDGXvb/z3bGVyTx81v/jaf+83dccskl8oOsMIwnv5IOABeC8zT4uOBgJsfHt8+0mM1EBgZSUluLv58fa6ZP54XMTFZPncrh4mI+z8/nxeuu67TCi4YNO+PxeteHxNGSEpLCwhgRGdk+f1RUFKH+/jQ0fdsc05fhcoXoTmNtA/kHTlF1qISgeitXL/on5q+ZL5cYFAPGk5Or02mvSqlOR+vgHKKz1TXK8q2zZjHlqaf4prKSFzIzuWjYMNLiOveaMUJvh8sVoiN7s53Co3mUHSzEkd/IvBkXs+DGm0lLS8NkkteWGFieHPi9ckF8PHNSU3nu8895JSuLXyxbdsYye775pvPjvLz2D4W0uDhs1dXkVlS0H+XnlJdT09TEuS5S1tNwuUKAc3ybwqN5VB07Tf3JaqaPm8KNi29l+vTp0q1SDCqvCXyA22bN4o6NG7GYzVw/ZcoZ8/fk5fHE9u1cO2kS23NyeHnfPl69/noAlowZw5SEBL775ps8deWVANydkUFiaChhNRVn3W53w+WalOJ0VTPNLZrqejv2Vo3ttPOkrrhIK35maefvraaWVsqqnF/8NFBR24LtdBOB/iaP6qmjtaamtIriYzbqjldgL2pk1uSZzF2wgsl3T5YulcJtvCrwr58yhR9v3sx1kycT2s2R031z53KgsJBffPQRwVYrjy9dyrWTJwPOJqNNN93EjzdvZuFzzwHOD4GLhg1j9+7ux9lp091wuVFhFv62vYScwm+vj/o/f3N+w2ibL3onv6SRZzcXtD9+P7Oc9zPLmTkulOsXJbixZFBXXkNpThE1OeXUn6omKjCC2ZPSmX3NLNLS0rBYZH8L9/PIwNdPPLEAIP399+9sm3bo3nvPWK7okUc6Pa5sbKTBbueW9PRu1xtitfL6DTf0uN1hERFsXLOm07THtm1rv9+b4XKh+2F/Rd+NTg5yy9jyXdmbWqiwlVORV0pTYT0NBTUEE8CMidOZcdE/MeEHE4iJ8ZzLGwrRxpDAV0otB54CzMDzWutfdZnvD7wMzMR58fLrtda5RmwboMXhoKy+np+++y7TExO5ZMQIo1YtfFhrayt1ZTXUlFRRXVKFvayRppJ6HBVNjEodybzR0xk/fxwjRowgLi5OBi4THq/fga+UMgN/AJYC+cAXSqkMrfWRDovdAlRorccopVYDvwau7++22+w+dYqFzz3H2Oho/nrjjUatVngxrTUtjc001zfTUF1PQ2Ud9ZV1OGqacVS3YK9qoqmigdiIGIYnD+PC5DRSx6WSnJxMcnKy9I0XQ5IRr9rZQLbWOgdAKfUGsBLoGPgrgcdc9/8GPK2UUlprQ/q2LBg1Cv3EE2ddJvfBB43YlBgSNHWltRx8/TNoBe3QaIcD3dKKvcGOvaGF1iY7gQGBBAeFEBMZzZiYeJJiEogZHkNUVBRRUVHEx8djtXrPuPlCGBH4yUBeh8f5wJyeltFa25VSVUA0cLrLciil1gJrAeLi4ti6dasBRey77NxclAMKd+Swd8t+t5bFaJFhEVRUV7q7GIaqj4rHarYQbg5mfuwETCZT+5/ZbMZqtWK1WrFYLN02wTQ1NVFYWEhhYSGHO5y57QmqqqoIDw93dzEMJ/UaPB73vVRrvQ5YB5Cenq6XL1/u1vLs2baN+Ph4QkNDeWzJEreWxWg2m42kpHOdZTC0LFi3jvLycqKiorjlllvcXRxDeeP+AqnXYDIi8AuAjl1UUlzTulsmXynlB4Tj/PHW4z22ZIlH7jjRve1r17J161bcfaAghCcy4lzuL4CxSqmRSikrsBrI6LJMBnCz6/61wIdGtd8LIYQ4P/0+wne1yd8FvIuzW+YLWuvDSqnHgUytdQbwZ+AvSqlsoBznh4IQQohBZEgbvtb6HeCdLtMe7XC/Ebiu6/OEEEIMHhmeTwghfIQEvhBC+AgJfCGE8BES+EII4SMk8IUQwkdI4AshhI+QwBdCCB8hgS+EED5CAl8IIXyEBL4QQvgICXwhhPAREvhCCOEjJPCFEMJHSOALIYSPkMAXQggfIYEvhBA+QgJfCCF8hAS+EEL4iH5d4lAp9V/AVUAzcAL4gda6spvlcoEawAHYtdbp/dmuEEKI3uvvEf77wCSt9RTgGPDwWZZdqLWeJmEvhBDu0a/A11q/p7W2ux7uAVL6XyQhhBADoV9NOl38EHizh3kaeE8ppYFntdbrelqJUmotsBYgJSUFm81mYBH7pry83N1FGBDeWq+qqiqPeN0YzVv3l9Rr8Jwz8JVS24CEbmY9orXe5FrmEcAOvNrDauZqrQuUUnHA+0qpr7TWH3e3oOvDYB1Aenq6TkpKOo9qDDxPKYfRvLFe4eHhXlkv8M79BVKvwXLOwNdaLznbfKXU94ErgcVaa93DOgpctyVKqQ3AbKDbwBdCCDEw+tWGr5RaDjwArNBa1/ewTLBSKrTtPrAMONSf7QohhOi9/vbSeRoIxdlMk6WUegZAKZWklHrHtUw8sEsp9SXwObBFa721n9sVQgjRS/360VZrPaaH6TbgCtf9HGBqf7YjhBCi/+RMWyGE8BES+EII4SMk8IUQwkdI4AshhI+QwBdCCB8hgS+EED5CAl8IIXyEBL4QQvgI1cPwNx5BKVUKnHJ3OYAY4LS7CzEApF5Di9RraHFXvYZrrWO7m+HRge8plFKZ3njhFqnX0CL1Glo8sV7SpCOEED5CAl8IIXyEBP756fEKXUOc1GtokXoNLR5XL2nDF0IIHyFH+EII4SMk8IUQwkdI4J+FUuo6pdRhpVSrUiq9y7yHlVLZSqmvlVKXuauM/aWUekwpVeC6YlmWUuoKd5epP5RSy137JFsp9ZC7y2MUpVSuUuqgax9lurs8faWUekEpVaKUOtRhWpRS6n2l1HHXbaQ7y9gXPdTL495bEvhndwi4hi4XXFdKTQRWAxcAy4E/KqXMg188wzyptZ7m+nvn3It7Jtc++ANwOTARuMG1r7zFQtc+8qi+3b30Is73TEcPAR9orccCH7geDzUvcma9wMPeWxL4Z6G1Pqq1/rqbWSuBN7TWTVrrk0A2MHtwSye6MRvI1lrnaK2bgTdw7ivhIbTWHwPlXSavBF5y3X8J+M5glskIPdTL40jg900ykNfhcb5r2lB1l1LqgOtr6ZD7Ot2Bt+2XjjTwnlJqr1JqrbsLY7B4rXWh634REO/OwhjMo95bPh/4SqltSqlD3fx5zZHhOer4J2A0MA0oBH7rzrKKHs3VWs/A2Vx1p1LqUncXaCBoZz9xb+kr7nHvLT93F8DdtNZL+vC0AiC1w+MU1zSPdL51VEo9B7w9wMUZSENqv/SG1rrAdVuilNqAs/nq47M/a8goVkolaq0LlVKJQIm7C2QErXVx231PeW/5/BF+H2UAq5VS/kqpkcBY4HM3l6lPXG+wNlfj/KF6qPoCGKuUGqmUsuL8YT3DzWXqN6VUsFIqtO0+sIyhvZ+6ygBudt2/GdjkxrIYxhPfWz5/hH82Sqmrgd8DscAWpVSW1voyrfVhpdRfgSOAHbhTa+1wZ1n74TdKqWk4v0bnAre7tTT9oLW2K6XuAt4FzMALWuvDbi6WEeKBDUopcL5nX9Nab3VvkfpGKfU6sACIUUrlAz8DfgX8VSl1C87h0Fe5r4R900O9Fnjae0uGVhBCCB8hTTpCCOEjJPCFEMJHSOALIYSPkMAXQggfIYEvhBA+QgJfCCF8hAS+EEL4iP8Pe1K2VOBsWewAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Then we want to move the second rectangle reference so that\n",
"# it connects to port 2 of the arc\n",
"rect_ref2.connect('myport1', arc_ref.ports[2])\n",
"\n",
"qp(E) # quickplot it!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks great!\n",
"\n",
"### Going a level higher\n",
"\n",
"Now we've made a (somewhat) complicated bend-shape from a few simple shapes. But say we're not done yet -- we actually want to combine together 3 of these bend-shapes to make an even-more complicated shape. We could recreate the geometry 3 times and manually connect all the pieces, but since we already put it together once it will be smarter to just reuse it multiple times.\n",
"\n",
"We will start by abstracting this bend-shape. As shown in the quickplot, there are ports associated with each reference in our bend-shape Device `E`: \"myport1\", \"myport2\", \"1\", and \"2\". But when working with this bend-shape, all we really care about is the 2 ports at either end -- \"myport1\" from `rect_ref1` and \"myport2\" from `rect_ref2`. It would be simpler if we didn't have to keep track of all of the other ports.\n",
"\n",
"First, let's look at something: let's see if our bend-shape Device `E` has any ports in it:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Device (name \"arc_with_rectangles\" (uid 5), ports [], aliases [], 0 polygons, 3 references)\n"
]
}
],
"source": [
"print(E)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It has no ports apparently! Why is that, when we clearly see ports in the quickplots above?\n",
"\n",
"The answer is that Device `E` *itself* doesn't have ports -- the references inside `E` do have ports, but we never actually added ports to `E`. Let's fix that now, adding a port at either end, setting the names to the integers 1 and 2."
]
},
{
"cell_type": "code",
"execution_count": 15,
"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": [
"# Rather than specifying the midpoint/width/orientation, we can instead\n",
"# copy ports directly from the references since they're already in the right place\n",
"E.add_port(name = 1, port = rect_ref1.ports['myport1'])\n",
"E.add_port(name = 2, port = rect_ref2.ports['myport2'])\n",
"\n",
"qp(E) # quickplot it!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we look at the quickplot above, we can see that there are now red-colored ports on both ends. Ports that are colored red are owned by the Device, ports that are colored blue-green are owned by objects inside the Device. This is good! Now if we want to use this bend-shape, we can interact with its ports named 1 and 2. \n",
"\n",
"Let's go ahead and try to string 3 of these bend-shapes together:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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": [
"# Create a blank Device\n",
"D = Device('triple-bend')\n",
"\n",
"# Add 3 references to our bend-shape Device `E`:\n",
"bend_ref1 = D.add_ref(E) # Using the function add_ref()\n",
"bend_ref2 = D << E # Using the << operator which is identical to add_ref()\n",
"bend_ref3 = D << E\n",
"\n",
"# Let's mirror one of them so it turns right instead of left\n",
"bend_ref2.mirror()\n",
"\n",
"# Connect each one in a series\n",
"bend_ref2.connect(1, bend_ref1.ports[2])\n",
"bend_ref3.connect(1, bend_ref2.ports[2])\n",
"\n",
"# Add ports so we can use this shape at an even higher-level\n",
"D.add_port(name = 1, port = bend_ref1.ports[1])\n",
"D.add_port(name = 2, port = bend_ref3.ports[2])\n",
"\n",
"qp(D) # quickplot it!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Saving as a GDSII file\n",
"\n",
"Saving the design as a GDS file is simple -- just specify the Device you'd like to save and run the `write_gds()` function:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'triple-bend.gds'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D.write_gds('triple-bend.gds')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some useful notes about writing GDS files:\n",
"\n",
"- The default unit is 1e-6 (micrometers aka microns), with a precision of 1e-9 (nanometer resolution)\n",
"- PHIDL will automatically handle naming of all the GDS cells to avoid name-collisions.\n",
"- Unless otherwise specified, the top-level GDS cell will be named \"toplevel\"\n",
"\n",
"All of these parameters can be modified using the appropriate arguments of `write_gds()`:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'triple-bend.gds'"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D.write_gds(filename = 'triple-bend.gds', # Output GDS file name\n",
" unit = 1e-6, # Base unit (1e-6 = microns)\n",
" precision = 1e-9, # Precision / resolution (1e-9 = nanometers)\n",
" auto_rename = True, # Automatically rename cells to avoid collisions\n",
" max_cellname_length = 28, # Max length of cell names\n",
" cellname = 'toplevel' # Name of output top-level cell\n",
" )"
]
}
],
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
catalystcomputing/DSIoT-Python-sessions | Session2/code/01 Working with text.ipynb | 1 | 27895 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Working with text\n",
"\n",
"Sample from [http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html](http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.htm)\n",
"\n",
"The data set is '20 newsgroups dataset' a dataset used for testing machine learning accuracy described at:\n",
"[20 newsgroups dataset website](http://people.csail.mit.edu/jrennie/20Newsgroups/).\n",
"\n",
"We will be using this data to show scikit learn."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To make the samples run more quickly we will be limiting the example data set to just 4 categories."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"categories = ['alt.atheism', 'soc.religion.christian','comp.graphics', 'sci.med']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load in the training set of data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.datasets import fetch_20newsgroups\n",
"twenty_train = fetch_20newsgroups(subset='train',categories=categories, shuffle=True, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['alt.atheism', 'comp.graphics', 'sci.med', 'soc.religion.christian']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"twenty_train.target_names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note target names not in same order as in the categories array\n",
"\n",
"Count of documents"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2257"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(twenty_train.data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show the first 8 lines of text from one of the documents formated with line breaks"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"From: [email protected] (Michael Collier)\n",
"Subject: Converting images to HP LaserJet III?\n",
"Nntp-Posting-Host: hampton\n",
"Organization: The City University\n",
"Lines: 14\n",
"\n",
"Does anyone know of a good way (standard PC application/PD utility) to\n",
"convert tif/img/tga files into LaserJet III format. We would also like to\n"
]
}
],
"source": [
"print(\"\\n\".join(twenty_train.data[0].split(\"\\n\")[:8]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Path to file on your machine"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'C:\\\\Users\\\\Peter\\\\scikit_learn_data\\\\20news_home\\\\20news-bydate-train\\\\comp.graphics\\\\38440'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"twenty_train.filenames[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show the the targets categories of first 10 documents. As a list and show there names."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 1 3 3 3 3 3 2 2 2]\n",
"comp.graphics\n",
"comp.graphics\n",
"soc.religion.christian\n",
"soc.religion.christian\n",
"soc.religion.christian\n",
"soc.religion.christian\n",
"soc.religion.christian\n",
"sci.med\n",
"sci.med\n",
"sci.med\n"
]
}
],
"source": [
"print(twenty_train.target[:10])\n",
"for t in twenty_train.target[:10]:\n",
" print(twenty_train.target_names[t])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets look at a document in the training data."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"From: [email protected] (Michael Collier)\n",
"Subject: Converting images to HP LaserJet III?\n",
"Nntp-Posting-Host: hampton\n",
"Organization: The City University\n",
"Lines: 14\n",
"\n",
"Does anyone know of a good way (standard PC application/PD utility) to\n",
"convert tif/img/tga files into LaserJet III format. We would also like to\n",
"do the same, converting to HPGL (HP plotter) files.\n",
"\n",
"Please email any response.\n",
"\n",
"Is this the correct group?\n",
"\n",
"Thanks in advance. Michael.\n",
"-- \n",
"Michael Collier (Programmer) The Computer Unit,\n",
"Email: [email protected] The City University,\n",
"Tel: 071 477-8000 x3769 London,\n",
"Fax: 071 477-8565 EC1V 0HB.\n",
"\n"
]
}
],
"source": [
"print(\"\\n\".join(twenty_train.data[0].split(\"\\n\")))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extracting features from text files\n",
"\n",
"So for machine learning to be used text must be turned into numerical feature vectors.\n",
"\n",
"**What is a feature vector?**\n",
"\n",
"- Each word is assigned an integer identifier\n",
"- Each document is assigned an integer identifier\n",
"\n",
"The results are stored in scipy.sparse matrices."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tokenizing text with scikit-learn\n",
"\n",
"Using `CountVectorizer` we load the training data into a spare matrix.\n",
"\n",
"**What is a spare matrix?**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2257, 35788)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.feature_extraction.text import CountVectorizer\n",
"count_vect = CountVectorizer()\n",
"X_train_counts = count_vect.fit_transform(twenty_train.data)\n",
"X_train_counts.shape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"scipy.sparse.csr.csr_matrix"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train_counts.__class__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using a CountVectorizer method we can get the integer identifier of a word."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5285"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"count_vect.vocabulary_.get(u'application')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With this identifier we can get the count of the word in a given document."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Word count for application in first document: 1 and last document: 0 \n"
]
}
],
"source": [
"print(\"Word count for application in first document: {0} and last document: {1} \").format(\n",
" X_train_counts[0, 5285], X_train_counts[2256, 5285])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"31077"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"count_vect.vocabulary_.get(u'subject')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Word count for email in first document: 1 and last document: 1 \n"
]
}
],
"source": [
"print(\"Word count for email in first document: {0} and last document: {1} \").format(\n",
" X_train_counts[0, 31077], X_train_counts[2256, 31077])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"32493"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"count_vect.vocabulary_.get(u'to')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Word count for email in first document: 4 and last document: 0 \n"
]
}
],
"source": [
"print(\"Word count for email in first document: {0} and last document: {1} \").format(\n",
" X_train_counts[0, 32493], X_train_counts[2256, 32493])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**What are two problems with using a word count in a document?**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### From occurrences to frequencies\n",
"\n",
"$\\text{Term Frequencies tf} = \\text{occurrences of each word} / \\text{total number of words}$\n",
"\n",
"**tf-idf** is \"Term Frequencies times Inverse Document Frequency\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Calculating tfidf"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2257, 35788)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.feature_extraction.text import TfidfTransformer\n",
"tf_transformer = TfidfTransformer(use_idf=False).fit(X_train_counts)\n",
"X_train_tfidf_2stage = tf_transformer.transform(X_train_counts)\n",
"X_train_tfidf_2stage.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`.fit(..)` to fit estimator to the data\n",
"`.transform(..)` to transform the count matrix to **tf-idf**\n",
"\n",
"It is possible to merge the fit and transform stages using .fit_transform(..)\n",
"\n",
"#### Calculate tfidf"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2257, 35788)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tfidf_transformer = TfidfTransformer()\n",
"X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n",
"X_train_tfidf.shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In first document tf-idf for application: 0.0841345440909 subject: 0.0167978060212 to: 0.0728377394162\n"
]
}
],
"source": [
"print(\"In first document tf-idf for application: {0} subject: {1} to: {2}\").format(\n",
" X_train_tfidf[0, 5285], X_train_tfidf[0, 31077], X_train_tfidf[0, 32493])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training a classifier\n",
"\n",
"So we now have features. We can train a classifier to try to predict the category of a post. First we will try the\n",
"[naïve Bayes](http://scikit-learn.org/stable/modules/naive_bayes.html#naive-bayes) classifier."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.naive_bayes import MultinomialNB\n",
"clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here tfidf_transformer is used to classify"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"'God is love' => soc.religion.christian\n",
"'Heart attacks are common' => sci.med\n",
"'Disbelief in a proposition' => alt.atheism\n",
"'Disbelief in a proposition means that one does not believe it to be true' => soc.religion.christian\n",
"'OpenGL on the GPU is fast' => comp.graphics\n"
]
}
],
"source": [
"docs_new = ['God is love', 'Heart attacks are common', 'Disbelief in a proposition', 'Disbelief in a proposition means that one does not believe it to be true', 'OpenGL on the GPU is fast']\n",
"X_new_counts = count_vect.transform(docs_new)\n",
"X_new_tfidf = tfidf_transformer.transform(X_new_counts)\n",
"\n",
"predicted = clf.predict(X_new_tfidf)\n",
"\n",
"for doc, category in zip(docs_new, predicted):\n",
" print('%r => %s' % (doc, twenty_train.target_names[category]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see it get some right but not all.\n",
"\n",
"## Building a pipeline\n",
"\n",
"Here we can put all the stages together in a pipeline. The names 'vect', 'tfidf' and 'clf' are arbitrary. "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.pipeline import Pipeline\n",
"text_clf_bayes = Pipeline([('vect', CountVectorizer()),\n",
" ('tfidf', TfidfTransformer()),\n",
" ('clf', MultinomialNB()),\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"text_clf_bayes_fit = text_clf_bayes.fit(twenty_train.data, twenty_train.target)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.83488681757656458"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"twenty_test = fetch_20newsgroups(subset='test',\n",
" categories=categories, shuffle=True, random_state=42)\n",
"docs_test = twenty_test.data\n",
"predicted_bayes = text_clf_bayes_fit.predict(docs_test)\n",
"np.mean(predicted_bayes == twenty_test.target) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Try a support vector machine instead"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.9127829560585885"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import SGDClassifier\n",
"text_clf_svm = Pipeline([('vect', CountVectorizer()),\n",
" ('tfidf', TfidfTransformer()),\n",
" ('clf', SGDClassifier(loss='hinge', penalty='l2',\n",
" alpha=1e-3, n_iter=5, random_state=42)),])\n",
"text_clf_svm_fit = text_clf_svm.fit(twenty_train.data, twenty_train.target)\n",
"predicted_svm = text_clf_svm_fit.predict(docs_test)\n",
"np.mean(predicted_svm == twenty_test.target) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see the support vector machine got a higher number than naïve Bayes. What does it mean? We move on to metrics."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using metrics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Classification report & Confusion matix\n",
"\n",
"Here we will use a simple example to show classification reports and confusion matrices.\n",
"\n",
"- y_true is the test data\n",
"- y_pred is the prediction"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" ant 0.67 1.00 0.80 2\n",
" bird 1.00 0.50 0.67 2\n",
" cat 0.67 0.67 0.67 3\n",
"\n",
"avg / total 0.76 0.71 0.70 7\n",
"\n"
]
}
],
"source": [
"from sklearn import metrics\n",
"\n",
"y_true = [\"cat\", \"ant\", \"cat\", \"cat\", \"ant\", \"bird\", \"bird\"]\n",
"y_pred = [\"ant\", \"ant\", \"cat\", \"cat\", \"ant\", \"cat\", \"bird\"]\n",
"print(metrics.classification_report(y_true, y_pred,\n",
" target_names=[\"ant\", \"bird\", \"cat\"]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we can see that the predictions:\n",
"- found ant 3 times and should have found it twice hence precision of 0.67.\n",
"- never predicted ant when shouldn't have hence recall of 1.\n",
"- f1 source is the mean of precision and recall\n",
"- support of 2 meaning there were 2 in the true data set.\n",
"\n",
"[http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Confusion matix**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"metrics.confusion_matrix(y_true, y_pred, labels=[\"ant\", \"bird\", \"cat\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the confusion_matrix the labels give the order of the rows.\n",
"\n",
"- ant was correctly categorised twice and was never miss categorised\n",
"- bird was correctly categorised once and was categorised as cat once\n",
"- cat was correctly categorised twice and was categorised as an ant once\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Back to '20 newsgroups dataset'**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(metrics.classification_report(twenty_test.target, predicted_svm,\n",
" target_names=twenty_test.target_names))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see where the 91% score came from."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# We got the evaluation score this way before:\n",
"print(np.mean(predicted_svm == twenty_test.target))\n",
"# We get the same results using metrics.accuracy_score\n",
"print(metrics.accuracy_score(twenty_test.target, predicted_svm, normalize=True, sample_weight=None))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now lets see the confusion matrix. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(twenty_train.target_names)\n",
"\n",
"metrics.confusion_matrix(twenty_test.target, predicted_bayes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we can see the naïve Bayes classifier got a lot more correct in some cases but also included a higher proportion in the last category."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"metrics.confusion_matrix(twenty_test.target, predicted_svm)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"We can see that atheism is miss categorised as Christian and science and medicine as computer graphics a high proportion of the time using the support vector machine."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parameter tuning\n",
"\n",
"Transformation and classifiers can have various parameters. Rather than manually tweaking each parameter in the pipeline it is possible to use [grid search](http://scikit-learn.org/stable/modules/grid_search.html) instead.\n",
"\n",
"Here we try a couple of options for each stage. The more options the longer the grid search will take."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.grid_search import GridSearchCV\n",
"parameters = {'vect__ngram_range': [(1, 1), (1, 2)],\n",
" 'tfidf__use_idf': (True, False),\n",
" 'clf__alpha': (1e-3, 1e-4),\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gs_clf = GridSearchCV(text_clf_svm_fit, parameters, n_jobs=-1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Running the search on all the data will take a little while 10-30 seconds on a new ish desktop with 8 cores. If you don't want to wait that long uncomment the line with `:400` and comment out the other."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#gs_clf_fit = gs_clf.fit(twenty_train.data[:400], twenty_train.target[:400])\n",
"gs_clf_fit = gs_clf.fit(twenty_train.data, twenty_train.target)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"best_parameters, score, _ = max(gs_clf_fit.grid_scores_, key=lambda x: x[1])\n",
"for param_name in sorted(parameters.keys()):\n",
" print(\"%s: %r\" % (param_name, best_parameters[param_name]))\n",
"score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Well that is a significant improvement. Lets use these new parameters."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"text_clf_svm_tuned = Pipeline([('vect', CountVectorizer(ngram_range=(1, 2))),\n",
" ('tfidf', TfidfTransformer(use_idf=True)),\n",
" ('clf', SGDClassifier(loss='hinge', penalty='l2',\n",
" alpha=0.0001, n_iter=5, random_state=42)),\n",
"])\n",
"text_clf_svm_tuned_fit = text_clf_svm_tuned.fit(twenty_train.data, twenty_train.target)\n",
"predicted_tuned = text_clf_svm_tuned_fit.predict(docs_test)\n",
"metrics.accuracy_score(twenty_test.target, predicted_tuned, normalize=True, sample_weight=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Why has this only give a .93 instead of .97?**\n",
"\n",
"[http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for x in gs_clf_fit.grid_scores_:\n",
" print x[0], x[1], x[2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Moving on from that lets see where the improvements where made."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(metrics.classification_report(twenty_test.target, predicted_svm,\n",
" target_names=twenty_test.target_names))\n",
"\n",
"metrics.confusion_matrix(twenty_test.target, predicted_svm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(metrics.classification_report(twenty_test.target, predicted_tuned,\n",
" target_names=twenty_test.target_names))\n",
"\n",
"metrics.confusion_matrix(twenty_test.target, predicted_tuned)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"We see comp.graphics is the only category to see a drop in prediction the other have improved.\n",
"\n",
"## Conclusion\n",
"\n",
"We can see that scikit learn can do a good job in classification with the amount of training and test data in this simple example.\n",
"\n",
"1. Can you see a use in your project?\n",
"2. What issues can you see with the training and test data?"
]
},
{
"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
}
| apache-2.0 |
rossant/spiky | experimental/hdf5-test.ipynb | 1 | 7552 | {
"metadata": {
"name": "hdf5-test"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import tables\n",
"filename = \"test_data/n6mab031109.h5\"\n",
"f = tables.openFile(filename)\n",
"data = f.root.RawData\n",
"nsamples, nchannels = data.shape\n",
"freq = 20000.\n",
"dt = 1. / freq\n",
"duration = (data.shape[0] - 1) * dt\n",
"MAXSIZE = 5000\n",
"step = nsamples // MAXSIZE"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "HDF5ExtError",
"evalue": "HDF5 error back trace\n\n File \"..\\..\\hdf5-1.8.10-patch1\\src\\H5F.c\", line 1582, in H5Fopen\n unable to open file\n File \"..\\..\\hdf5-1.8.10-patch1\\src\\H5F.c\", line 1400, in H5F_open\n file close degree doesn't match\n\nEnd of HDF5 error back trace\n\nUnable to open/create file 'test_data/n6mab031109.h5'",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mHDF5ExtError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-44-99b54e824a11>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtables\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"test_data/n6mab031109.h5\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtables\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopenFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mroot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRawData\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mnsamples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnchannels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Python27\\lib\\site-packages\\tables\\file.pyc\u001b[0m in \u001b[0;36mopenFile\u001b[1;34m(filename, mode, title, rootUEP, filters, **kwargs)\u001b[0m\n\u001b[0;32m 228\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mfilehandle\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 229\u001b[0m \u001b[1;31m# Finally, create the File instance, and return it\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 230\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrootUEP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 231\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 232\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Python27\\lib\\site-packages\\tables\\file.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filename, mode, title, rootUEP, filters, **kwargs)\u001b[0m\n\u001b[0;32m 493\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 494\u001b[0m \u001b[1;31m# Now, it is time to initialize the File extension\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 495\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_g_new\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 496\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 497\u001b[0m \u001b[1;31m# Check filters and set PyTables format version for new files.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mC:\\Python27\\lib\\site-packages\\tables\\hdf5Extension.pyd\u001b[0m in \u001b[0;36mtables.hdf5Extension.File._g_new (tables\\hdf5Extension.c:3182)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mHDF5ExtError\u001b[0m: HDF5 error back trace\n\n File \"..\\..\\hdf5-1.8.10-patch1\\src\\H5F.c\", line 1582, in H5Fopen\n unable to open file\n File \"..\\..\\hdf5-1.8.10-patch1\\src\\H5F.c\", line 1400, in H5F_open\n file close degree doesn't match\n\nEnd of HDF5 error back trace\n\nUnable to open/create file 'test_data/n6mab031109.h5'"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def fun():\n",
" # Slicing access with PyTables on this data set appears slow\n",
" samples = data[::50000, 0]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%lprun -f fun fun()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import h5py"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f = h5py.File(filename)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = f['RawData']\n",
"nsamples, nchannels = data.shape\n",
"freq = 20000.\n",
"dt = 1. / freq\n",
"duration = (data.shape[0] - 1) * dt\n",
"MAXSIZE = 5000\n",
"step = nsamples // MAXSIZE"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def fun():\n",
" # Slicing access with PyTables on this data set appears slow\n",
" samples = data[::MAXSIZE, :]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%timeit -r 1 -n 1 fun()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": "*"
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f.close()"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | bsd-3-clause |
kwinkunks/timefreak | reassignment.ipynb | 1 | 3505099 | null | apache-2.0 |
johnson1228/pymatgen | examples/Getting crystal structures from online sources.ipynb | 4 | 8918 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"\n",
"Pymatgen supports reading of most common file formats, including the Crystallographic Information File and various input and output files of computational codes like VASP. However, it is often easier and quicker to directly query for structures from online sources. Though private databases such as the Inorganic Crystal Structure Database are not open, there are open sources such as the Materials Project and the Crystallographic Open Database (COD) where one can obtain crystal structures. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Materials Project\n",
"\n",
"Pymatgen contains a high-level interface to the Materials Project, which can be used to query for structures very easily."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pymatgen.ext.matproj import MPRester\n",
"# Note that you will need to add your Materials API key in your .pmgrc.yaml file as \"PMG_MAPI_KEY\". \n",
"# Alternatively, you will need to supply the API key as an arg to MPRester.\n",
"mpr = MPRester() "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Structure Summary\n",
"Lattice\n",
" abc : 3.2910717923597561 3.2910718996250861 3.2910720568557887\n",
" angles : 60.129710432884849 60.129709521376753 60.129703130390972\n",
" volume : 25.279668381289056\n",
" A : 2.91738857 0.097894369999999994 1.5200046599999999\n",
" B : 0.96463405999999996 2.7550356100000002 1.5200046599999999\n",
" C : 0.13320635 0.097894430000000004 3.28691771\n",
"PeriodicSite: O (0.0000, 0.0000, 0.0000) [0.0000, 0.0000, 0.0000]\n",
"PeriodicSite: Li (3.0121, 2.2136, 4.7463) [0.7502, 0.7502, 0.7502]\n",
"PeriodicSite: Li (1.0031, 0.7372, 1.5806) [0.2498, 0.2498, 0.2498], Structure Summary\n",
"Lattice\n",
" abc : 5.1517948200000001 3.1404278300000001 5.9334081599999999\n",
" angles : 90.0 90.0 90.0\n",
" volume : 95.995660249910003\n",
" A : 5.1517948200000001 0.0 0.0\n",
" B : 0.0 3.1404278300000001 0.0\n",
" C : 0.0 0.0 5.9334081599999999\n",
"PeriodicSite: Li (0.0631, 0.7851, 0.9438) [0.0122, 0.2500, 0.1591]\n",
"PeriodicSite: Li (0.7268, 0.7851, 3.4367) [0.1411, 0.2500, 0.5792]\n",
"PeriodicSite: Li (1.8490, 2.3553, 0.4700) [0.3589, 0.7500, 0.0792]\n",
"PeriodicSite: Li (2.5128, 2.3553, 3.9105) [0.4878, 0.7500, 0.6591]\n",
"PeriodicSite: Li (2.6390, 0.7851, 2.0229) [0.5122, 0.2500, 0.3409]\n",
"PeriodicSite: Li (3.3027, 0.7851, 5.4634) [0.6411, 0.2500, 0.9208]\n",
"PeriodicSite: Li (4.4249, 2.3553, 2.4967) [0.8589, 0.7500, 0.4208]\n",
"PeriodicSite: Li (5.0887, 2.3553, 4.9896) [0.9878, 0.7500, 0.8409]\n",
"PeriodicSite: O (1.2677, 2.3553, 2.3664) [0.2461, 0.7500, 0.3988]\n",
"PeriodicSite: O (1.3082, 0.7851, 5.3331) [0.2539, 0.2500, 0.8988]\n",
"PeriodicSite: O (3.8436, 2.3553, 0.6003) [0.7461, 0.7500, 0.1012]\n",
"PeriodicSite: O (3.8841, 0.7851, 3.5670) [0.7539, 0.2500, 0.6012]]\n"
]
}
],
"source": [
"# Querying by formula only.\n",
"structures = mpr.get_structures(\"Li2O\")\n",
"print(structures)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Li2 O4\n",
"Li2 O1\n",
"Li8 O4\n",
"Li4 O4\n",
"Li1 O3\n",
"Li16 O16\n"
]
}
],
"source": [
"# Querying by chemical system only.\n",
"structures = mpr.get_structures(\"Li-O\")\n",
"for s in structures:\n",
" print(s.formula)\n",
"# A number of Li-O structures are returned with different Li:O ratios."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## COD\n",
"\n",
"To obtain structures from COD by the COD id, you just need to know the id. However, most sophisticated searches require that you have installed mysql given that the COD does not support a proper REST API at this time."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pymatgen.ext.cod import COD\n",
"cod = COD()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Full Formula (Li8 O4)\n",
"Reduced Formula: Li2O\n",
"abc : 4.610000 4.610000 4.610000\n",
"angles: 90.000000 90.000000 90.000000\n",
"Sites (12)\n",
" # SP a b c\n",
"--- ---- ---- ---- ----\n",
" 0 Li+ 0.25 0.25 0.25\n",
" 1 Li+ 0.25 0.75 0.75\n",
" 2 Li+ 0.75 0.25 0.75\n",
" 3 Li+ 0.75 0.75 0.25\n",
" 4 Li+ 0.75 0.75 0.75\n",
" 5 Li+ 0.75 0.25 0.25\n",
" 6 Li+ 0.25 0.75 0.25\n",
" 7 Li+ 0.25 0.25 0.75\n",
" 8 O2- 0 0 0\n",
" 9 O2- 0 0.5 0.5\n",
" 10 O2- 0.5 0 0.5\n",
" 11 O2- 0.5 0.5 0\n"
]
}
],
"source": [
"structure = cod.get_structure_by_id(1010064)\n",
"print(structure)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/shyue/repos/pymatgen/pymatgen/io/cif.py:801: UserWarning: LI parsed as L\n",
" warnings.warn(\"{} parsed as {}\".format(sym, v))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"COD ID: 1010064, Formula: Li8 O4, Spacegroup: F m -3 m\n",
"COD ID: 1011372, Formula: Li8 O4, Spacegroup: F m -3 m\n",
"COD ID: 9009059, Formula: Li8 O4, Spacegroup: F m -3 m\n",
"COD ID: 4311895, Formula: L8 O4, Spacegroup: F m -3 m\n",
"COD ID: 1514086, Formula: Li8 O4, Spacegroup: F m -3 m\n",
"COD ID: 1514087, Formula: Li8 O4, Spacegroup: F m -3 m\n",
"COD ID: 1514088, Formula: Li6 O3, Spacegroup: R -3 m :H\n",
"COD ID: 4121514, Formula: Li8 O4, Spacegroup: F m -3 m\n",
"COD ID: 4121515, Formula: Li8 O4, Spacegroup: F m -3 m\n",
"COD ID: 1514092, Formula: Li8 O4, Spacegroup: F m -3 m\n",
"COD ID: 1514093, Formula: Li7.84 O4, Spacegroup: F m -3 m\n",
"COD ID: 1514094, Formula: Li7.9984 O4, Spacegroup: F m -3 m\n",
"COD ID: 1514095, Formula: Li8.0008 O4, Spacegroup: F m -3 m\n",
"COD ID: 1514096, Formula: Li8 O4, Spacegroup: F m -3 m\n",
"COD ID: 1514097, Formula: Li8.0008 O4, Spacegroup: F m -3 m\n",
"COD ID: 1514098, Formula: Li8 O4, Spacegroup: F m -3 m\n"
]
}
],
"source": [
"structures = cod.get_structure_by_formula(\"Li2O\")\n",
"for d in structures:\n",
" print(\"COD ID: %d, Formula: %s, Spacegroup: %s\" % (d[\"cod_id\"], d[\"structure\"].formula, d[\"sg\"]))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Full Formula (Li8 O4)\n",
"Reduced Formula: Li2O\n",
"abc : 4.610000 4.610000 4.610000\n",
"angles: 90.000000 90.000000 90.000000\n",
"Sites (12)\n",
" # SP a b c\n",
"--- ---- ---- ---- ----\n",
" 0 Li+ 0.25 0.25 0.25\n",
" 1 Li+ 0.25 0.75 0.75\n",
" 2 Li+ 0.75 0.25 0.75\n",
" 3 Li+ 0.75 0.75 0.25\n",
" 4 Li+ 0.75 0.75 0.75\n",
" 5 Li+ 0.75 0.25 0.25\n",
" 6 Li+ 0.25 0.75 0.25\n",
" 7 Li+ 0.25 0.25 0.75\n",
" 8 O2- 0 0 0\n",
" 9 O2- 0 0.5 0.5\n",
" 10 O2- 0.5 0 0.5\n",
" 11 O2- 0.5 0.5 0\n"
]
}
],
"source": [
"print(structures[0][\"structure\"])"
]
},
{
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
transedward/ml-playground | reinforcement/value_iteration.ipynb | 1 | 3664 | {
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"import numpy as np\n",
"import sys\n",
"if \"../\" not in sys.path:\n",
" sys.path.append(\"../\")\n",
"\n",
"from envs.gridworld import GridworldEnv\n",
"from value_iteration import value_iteration\n",
"\n",
"# for auto-reloading external modules\n",
"# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"env = GridworldEnv()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"policy, v = value_iteration(env)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Policy Probability Distribution:\n",
"[[ 1. 0. 0. 0.]\n",
" [ 0. 0. 0. 1.]\n",
" [ 0. 0. 0. 1.]\n",
" [ 0. 0. 1. 0.]\n",
" [ 1. 0. 0. 0.]\n",
" [ 1. 0. 0. 0.]\n",
" [ 1. 0. 0. 0.]\n",
" [ 0. 0. 1. 0.]\n",
" [ 1. 0. 0. 0.]\n",
" [ 1. 0. 0. 0.]\n",
" [ 0. 1. 0. 0.]\n",
" [ 0. 0. 1. 0.]\n",
" [ 1. 0. 0. 0.]\n",
" [ 0. 1. 0. 0.]\n",
" [ 0. 1. 0. 0.]\n",
" [ 1. 0. 0. 0.]]\n",
"\n",
"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\n",
"[[0 3 3 2]\n",
" [0 0 0 2]\n",
" [0 0 1 2]\n",
" [0 1 1 0]]\n",
"\n",
"Value Function:\n",
"[ 0. -1. -2. -3. -1. -2. -3. -2. -2. -3. -2. -1. -3. -2. -1. 0.]\n",
"\n",
"Reshaped Grid Value Function:\n",
"[[ 0. -1. -2. -3.]\n",
" [-1. -2. -3. -2.]\n",
" [-2. -3. -2. -1.]\n",
" [-3. -2. -1. 0.]]\n",
"\n"
]
}
],
"source": [
"policy, v = value_iteration(env)\n",
"\n",
"print(\"Policy Probability Distribution:\")\n",
"print(policy)\n",
"print(\"\")\n",
"\n",
"print(\"Reshaped Grid Policy (0=up, 1=right, 2=down, 3=left):\")\n",
"print(np.reshape(np.argmax(policy, axis=1), env.shape))\n",
"print(\"\")\n",
"\n",
"print(\"Value Function:\")\n",
"print(v)\n",
"print(\"\")\n",
"\n",
"print(\"Reshaped Grid Value Function:\")\n",
"print(v.reshape(env.shape))\n",
"print(\"\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Test the value function\n",
"expected_v = np.array([ 0, -1, -2, -3, -1, -2, -3, -2, -2, -3, -2, -1, -3, -2, -1, 0])\n",
"np.testing.assert_array_almost_equal(v, expected_v, decimal=2)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [py35]",
"language": "python",
"name": "Python [py35]"
},
"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 |
metpy/MetPy | v1.0/_downloads/f1c8c5b9729cd7164037ec8618030966/upperair_declarative.ipynb | 1 | 3115 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Upper Air Analysis using Declarative Syntax\n\nThe MetPy declarative syntax allows for a simplified interface to creating common\nmeteorological analyses including upper air observation plots.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from datetime import datetime\n\nimport pandas as pd\n\nfrom metpy.cbook import get_test_data\nimport metpy.plots as mpplots\nfrom metpy.units import units"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Getting the data**\n\nIn this example, data is originally from the Iowa State Upper-air archive\n(https://mesonet.agron.iastate.edu/archive/raob/) available through a Siphon method.\nThe data are pre-processed to attach latitude/longitude locations for each RAOB site.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = pd.read_csv(get_test_data('UPA_obs.csv', as_file_obj=False))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Plotting the data**\n\nUse the declarative plotting interface to create a CONUS upper-air map for 500 hPa\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Plotting the Observations\nobs = mpplots.PlotObs()\nobs.data = data\nobs.time = datetime(1993, 3, 14, 0)\nobs.level = 500 * units.hPa\nobs.fields = ['temperature', 'dewpoint', 'height']\nobs.locations = ['NW', 'SW', 'NE']\nobs.formats = [None, None, lambda v: format(v, '.0f')[:3]]\nobs.vector_field = ('u_wind', 'v_wind')\nobs.reduce_points = 0\n\n# Add map features for the particular panel\npanel = mpplots.MapPanel()\npanel.layout = (1, 1, 1)\npanel.area = (-124, -72, 20, 53)\npanel.projection = 'lcc'\npanel.layers = ['coastline', 'borders', 'states', 'land', 'ocean']\npanel.plots = [obs]\n\n# Collecting panels for complete figure\npc = mpplots.PanelContainer()\npc.size = (15, 10)\npc.panels = [panel]\n\n# Showing the results\npc.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": "3.9.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
drwalshaw/sc-python | 01-analysing-data.ipynb | 1 | 22877 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## analysing tabular data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"numpy.loadtxt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"numpy.loadtxt(fname='data/weather-01.csv' delimiter = ',')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"numpy.loadtxt(fname='data/weather-01.csv'delimiter=',')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"numpy.loadtxt(fname='data/weather-01.csv',delimiter=',')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# variables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"weight_kg=55"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print (weight_kg)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print('weight in pounds:',weight_kg*2.2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"numpy.loadtxt(fname='data/weather-01.csv',delimiter=',')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"numpy.loadtxt(fname='data/weather-01.csv',delimiter=',')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"numpy.loadtxt(fname='data/weather-01.csv',delimiter=',')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%whos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data=numpy.loadtxt(fname='data/weather-01.csv',delimiter=',')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%whos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%whos"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(data.dtype)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(data.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# this is 60 by 40\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print (\"first value in data:\",data [0,0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print ('A middle value:',data[30,20])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# lets get the first 10 columns for the firsst 4 rows\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"print(data[0:4, 0:10])\n",
"# start at index 0 and go up to but not including index 4"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print (data[0:4, 0:10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#we dont need to start slicng at 0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print (data[5:10,7:15])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#we dont even need to inc upper and lower limits"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"smallchunk=data[:3,36:]\n",
"print(smallchunk)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#arithmetic on arrays"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"doublesmallchunk=smallchunk*2.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(doublesmallchunk)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"triplesmallchunk=smallchunk+doublesmallchunk"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(triplesmallchunk)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(numpy.mean(data))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print (numpy.max(data))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print (numpy.min(data))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#get a set of data for the first station\n",
"#this is shorthand for \"all the columns\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"station_0=data[0,:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(numpy.max(station_0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#we dont need to create @temporary@ array slices\n",
"#we can refer to what we call array axes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(numpy.mean(data, axis=0))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(numpy.mean(data, axis=1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#axis = 0 gets mean down eaach column\n",
"#axis=1 gets the mean across each row so the mean temp\n",
"#for each station for all periods\n",
"#see above"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#do some simple vissualisations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"image=matplotlib.pyplot.imshow(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#lets look at the average tempp over time"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"avg_temperature=numpy.mean(data,axis=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"avg_plot=matplotlib.pyplot.plot(avg_temperature)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data=numpy.loadtxt(fname='data/weather-01.csv',delimiter=',')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#create a wide figure to hold sub plots"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig=matplotlib.pyplot.figure (figsize=(10.0,3.0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#create placeholders for plots"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "raw",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig=matplotlib.pyplot.figure (figsize=(10.0,3.0))\n",
"subplot1=fig.add_subplot (1,3,1)\n",
"subplot2=fig.add_subplot (1,3,2)\n",
"subplot3=fig.add_subplot (1,3,3)\n",
"\n",
"subplot1.set_ylabel('average')\n",
"subplot1.plot(numpy.mean(data, axis=0))\n",
"\n",
"subplot2.set_ylabel('minimum')\n",
"subplot2.plot(numpy.min(data, axis=0))\n",
"\n",
"subplot3.set_ylabel('maximum')\n",
"subplot3.plot(numpy.max(data, axis=0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# this is fine for small numbers of datasets, what if wwe have hundreds or thousands? we need more automaation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#loops"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"word='notebook'\n",
"print (word[4])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#see aabove note diff between squaare and normaal brackets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for char in word:\n",
" # colon before word or indentation v imporetaant\n",
" #indent is 4 spaces\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for char in word:\n",
" print (char)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#reading filenames\n",
"# get a list of all the filenames from disk"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import glob"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#global..something~"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(glob.glob('data/weather*.csv'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#putting it all together"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"filenames=sorted(glob.glob('data/weather*.csv'))\n",
"filenames=filenames[0:3]\n",
"\n",
"for f in filenames:\n",
" print (f)\n",
" data=numpy.loadtxt(fname=f, delimiter=',')\n",
" \n",
"#next bits need indenting\n",
"\n",
"\n",
" fig=matplotlib.pyplot.figure (figsize=(10.0,3.0))\n",
" subplot1=fig.add_subplot (1,3,1)\n",
" subplot2=fig.add_subplot (1,3,2)\n",
" subplot3=fig.add_subplot (1,3,3)\n",
"\n",
" subplot1.set_ylabel('average')\n",
" subplot1.plot(numpy.mean(data, axis=0))\n",
"\n",
" subplot2.set_ylabel('minimum')\n",
" subplot2.plot(numpy.min(data, axis=0))\n",
"\n",
" subplot3.set_ylabel('maximum')\n",
" subplot3.plot(numpy.max(data, axis=0))\n",
" \n",
" fig.tight_layout()\n",
" matplotlib.pyplot.show"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"num=37\n",
"if num>100:\n",
" print('greater')\n",
"else:\n",
" print('not greater')\n",
" print ('done')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"num=107\n",
"if num>100:\n",
" print('greater')\n",
"else:\n",
" print('not greater')\n",
" print ('done')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# didnt print \"done\" due to break in indentation sequence"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"num=-3\n",
"\n",
"if num>0:\n",
" print (num, \"is positive\")\n",
"elif num ==0:\n",
" print (num, \"is zero\")\n",
"else:\n",
" print (num, \"is negative\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# elif eqauls else if, always good to finish a chain with an else"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"filenames=sorted(glob.glob('data/weather*.csv'))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"filenames=sorted(glob.glob('data/weather*.csv'))\n",
"filenames=filenames[0:3]\n",
"\n",
"for f in filenames:\n",
" print (f)\n",
" data=numpy.loadtxt(fname=f, delimiter=',') == 0 \n",
" if numpy.max (data, axis=0)[0] ==0 and numpy.max (data, axis=0)[20] ==20:\n",
" print ('suspicious looking maxima')\n",
" elif numpy.sum(numpy.min(data, axis=0)) ==0:\n",
" print ('minimum adds to zero')\n",
" else:\n",
" print ('data looks ok')\n",
" \n",
" \n",
" \n",
"#next bits need indenting\n",
"\n",
"\n",
" fig=matplotlib.pyplot.figure (figsize=(10.0,3.0))\n",
" subplot1=fig.add_subplot (1,3,1)\n",
" subplot2=fig.add_subplot (1,3,2)\n",
" subplot3=fig.add_subplot (1,3,3)\n",
"\n",
" subplot1.set_ylabel('average')\n",
" subplot1.plot(numpy.mean(data, axis=0))\n",
"\n",
" subplot2.set_ylabel('minimum')\n",
" subplot2.plot(numpy.min(data, axis=0))\n",
"\n",
" subplot3.set_ylabel('maximum')\n",
" subplot3.plot(numpy.max(data, axis=0))\n",
" \n",
" fig.tight_layout()\n",
" matplotlib.pyplot.show"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#something went wrong with the above"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def fahr_to_kelvin(temp):\n",
" return((temp-32)*(5/9)+ 273.15)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print ('freezing point of water:', fahr_to_kelvin(32))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print ('boiling point of water:', fahr_to_kelvin(212))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#using functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def analyse (filename):\n",
" data=numpy.loadtxt(fname=filename,)......"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#unfinsinshed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def detect_problems (filename):\n",
" data=numpy.loadtxt(fname=filename, delimiter=',')\n",
" \n",
" if numpy.max (data, axis=0)[0] ==0 and numpy.max (data, axis=0)[20] ==20:\n",
" print ('suspicious looking maxima')\n",
" elif numpy.sum(numpy.min(data, axis=0)) ==0:\n",
" print ('minimum adds to zero')\n",
" else:\n",
" print ('data looks ok')\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for f in filenames [0:5]:\n",
" print (f)\n",
" analyse (f)\n",
" detect_problems (f)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def analyse (filename):\n",
" data=numpy.loadtxt(fname=filename,delimiter=',')\n",
" \n",
" fig=matplotlib.pyplot.figure (figsize=(10.0,3.0))\n",
" subplot1=fig.add_subplot (1,3,1)\n",
" subplot2=fig.add_subplot (1,3,2)\n",
" subplot3=fig.add_subplot (1,3,3)\n",
"\n",
" subplot1.set_ylabel('average')\n",
" subplot1.plot(numpy.mean(data, axis=0))\n",
"\n",
" subplot2.set_ylabel('minimum')\n",
" subplot2.plot(numpy.min(data, axis=0))\n",
"\n",
" subplot3.set_ylabel('maximum')\n",
" subplot3.plot(numpy.max(data, axis=0))\n",
" \n",
" fig.tight_layout()\n",
" matplotlib.pyplot.show\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for f in filenames [0:5]:\n",
" print (f)\n",
" analyse (f)\n",
" detect_problems (f)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"help(numpy.loadtxt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"help(detect_problems)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"\"\"\"some of our temperature files haave problems, check for these\n",
"\n",
"this function reads a file and reports on odd looking maxima and minimia that add to zero\n",
"the function does not return any data\n",
"\"\"\"\n",
"\n",
"def detect_problems (filename):\n",
" data=numpy.loadtxt(fname=filename, delimiter=',')\n",
" \n",
" if numpy.max (data, axis=0)[0] ==0 and numpy.max (data, axis=0)[20] ==20:\n",
" print ('suspicious looking maxima')\n",
" elif numpy.sum(numpy.min(data, axis=0)) ==0:\n",
" print ('minimum adds to zero')\n",
" else:\n",
" print ('data looks ok')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def analyse (filename):\n",
" data=numpy.loadtxt(fname=filename,delimiter=',')\n",
" \n",
" \"\"\" this function analyses a dataset and outputs plots for maax min and ave\n",
" \"\"\"\n",
" \n",
" fig=matplotlib.pyplot.figure (figsize=(10.0,3.0))\n",
" subplot1=fig.add_subplot (1,3,1)\n",
" subplot2=fig.add_subplot (1,3,2)\n",
" subplot3=fig.add_subplot (1,3,3)\n",
"\n",
" subplot1.set_ylabel('average')\n",
" subplot1.plot(numpy.mean(data, axis=0))\n",
"\n",
" subplot2.set_ylabel('minimum')\n",
" subplot2.plot(numpy.min(data, axis=0))\n",
"\n",
" subplot3.set_ylabel('maximum')\n",
" subplot3.plot(numpy.max(data, axis=0))\n",
" \n",
" fig.tight_layout()\n",
" matplotlib.pyplot.show"
]
},
{
"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": 1
}
| mit |
curtisalexander/learning | julia/checking-checks.ipynb | 1 | 19250 | {
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from pandas import Series, DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"checking_file = \"/Users/calex/Downloads/Checking1.csv\"\n",
"\n",
"header = [\"date\", \"amt\", \"_a\", \"_b\", \"desc\"]\n",
"keep = list(filter(lambda x: not x.startswith('_'), header))\n",
"\n",
"df = pd.read_csv(\"/Users/calex/Downloads/Checking1.csv\",\n",
" names=header,\n",
" usecols=keep)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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>date</th>\n",
" <th>amt</th>\n",
" <th>desc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10/12/2018</td>\n",
" <td>-64.04</td>\n",
" <td>PURCHASE AUTHORIZED ON 10/10 AMZN Mktp US*MT5V...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10/12/2018</td>\n",
" <td>-6.48</td>\n",
" <td>PURCHASE AUTHORIZED ON 10/10 AMZN Mktp US*MT68...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10/12/2018</td>\n",
" <td>-5.55</td>\n",
" <td>PURCHASE AUTHORIZED ON 10/10 AMZN Mktp US*MT87...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10/12/2018</td>\n",
" <td>4698.52</td>\n",
" <td>AMERICREDIT DIRECT DEP 181012 941607572332VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>10/12/2018</td>\n",
" <td>23.44</td>\n",
" <td>YMCA of Metropol Payroll 000101 IMM00000336542...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date amt desc\n",
"0 10/12/2018 -64.04 PURCHASE AUTHORIZED ON 10/10 AMZN Mktp US*MT5V...\n",
"1 10/12/2018 -6.48 PURCHASE AUTHORIZED ON 10/10 AMZN Mktp US*MT68...\n",
"2 10/12/2018 -5.55 PURCHASE AUTHORIZED ON 10/10 AMZN Mktp US*MT87...\n",
"3 10/12/2018 4698.52 AMERICREDIT DIRECT DEP 181012 941607572332VDT ...\n",
"4 10/12/2018 23.44 YMCA of Metropol Payroll 000101 IMM00000336542..."
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>date</th>\n",
" <th>amt</th>\n",
" <th>desc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10/12/2018</td>\n",
" <td>4698.52</td>\n",
" <td>AMERICREDIT DIRECT DEP 181012 941607572332VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>09/28/2018</td>\n",
" <td>4698.51</td>\n",
" <td>AMERICREDIT DIRECT DEP 180928 564044839211VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>09/14/2018</td>\n",
" <td>4698.52</td>\n",
" <td>AMERICREDIT DIRECT DEP 180914 600045724949VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td>08/31/2018</td>\n",
" <td>4698.52</td>\n",
" <td>AMERICREDIT DIRECT DEP 180831 400035112359VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>185</th>\n",
" <td>08/17/2018</td>\n",
" <td>4546.04</td>\n",
" <td>AMERICREDIT DIRECT DEP 180817 931507536879VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>231</th>\n",
" <td>08/03/2018</td>\n",
" <td>4341.90</td>\n",
" <td>AMERICREDIT DIRECT DEP 180803 944907359007VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>288</th>\n",
" <td>07/20/2018</td>\n",
" <td>4341.90</td>\n",
" <td>AMERICREDIT DIRECT DEP 180720 931107473232VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>332</th>\n",
" <td>07/06/2018</td>\n",
" <td>4341.88</td>\n",
" <td>AMERICREDIT DIRECT DEP 180706 940507122326VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>381</th>\n",
" <td>06/22/2018</td>\n",
" <td>4240.87</td>\n",
" <td>AMERICREDIT DIRECT DEP 180622 635067534972VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>431</th>\n",
" <td>06/08/2018</td>\n",
" <td>4240.87</td>\n",
" <td>AMERICREDIT DIRECT DEP 180608 624068065452VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>432</th>\n",
" <td>06/08/2018</td>\n",
" <td>3283.50</td>\n",
" <td>AMERICREDIT DIRECT DEP 180608 624068065453VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>479</th>\n",
" <td>05/25/2018</td>\n",
" <td>4240.85</td>\n",
" <td>AMERICREDIT DIRECT DEP 180525 931006698961VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>523</th>\n",
" <td>05/11/2018</td>\n",
" <td>4240.87</td>\n",
" <td>AMERICREDIT DIRECT DEP 180511 730034367801VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>563</th>\n",
" <td>04/27/2018</td>\n",
" <td>4240.86</td>\n",
" <td>AMERICREDIT DIRECT DEP 180427 602044712515VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>584</th>\n",
" <td>04/20/2018</td>\n",
" <td>1261.46</td>\n",
" <td>GM FINANCIAL AP DISBURS 180419 SAS CONFERENCE ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>599</th>\n",
" <td>04/13/2018</td>\n",
" <td>4240.86</td>\n",
" <td>AMERICREDIT DIRECT DEP 180413 485046051387VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>642</th>\n",
" <td>03/30/2018</td>\n",
" <td>4022.64</td>\n",
" <td>AMERICREDIT DIRECT DEP 180330 935006473036VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>651</th>\n",
" <td>03/27/2018</td>\n",
" <td>1242.35</td>\n",
" <td>PAYPAL TRANSFER 180327 5BD22AF52GFQ6 CURTIS AL...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>688</th>\n",
" <td>03/16/2018</td>\n",
" <td>4022.64</td>\n",
" <td>AMERICREDIT DIRECT DEP 180316 609043454573VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>709</th>\n",
" <td>03/09/2018</td>\n",
" <td>21825.55</td>\n",
" <td>AMERICREDIT DIRECT DEP 180309 775069122837VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>731</th>\n",
" <td>03/02/2018</td>\n",
" <td>4022.65</td>\n",
" <td>AMERICREDIT DIRECT DEP 180302 487546814825VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>775</th>\n",
" <td>02/16/2018</td>\n",
" <td>4022.64</td>\n",
" <td>AMERICREDIT DIRECT DEP 180216 934606289560VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>820</th>\n",
" <td>02/02/2018</td>\n",
" <td>4022.64</td>\n",
" <td>AMERICREDIT DIRECT DEP 180202 405031800646VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>845</th>\n",
" <td>01/19/2018</td>\n",
" <td>3894.66</td>\n",
" <td>AMERICREDIT DIRECT DEP 180119 571031361881VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>887</th>\n",
" <td>01/05/2018</td>\n",
" <td>3894.67</td>\n",
" <td>AMERICREDIT DIRECT DEP 180105 595042112820VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>932</th>\n",
" <td>12/22/2017</td>\n",
" <td>4223.64</td>\n",
" <td>AMERICREDIT DIRECT DEP 171222 285070518861VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>974</th>\n",
" <td>12/08/2017</td>\n",
" <td>4223.65</td>\n",
" <td>AMERICREDIT DIRECT DEP 171208 939704624547VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1037</th>\n",
" <td>11/24/2017</td>\n",
" <td>3924.31</td>\n",
" <td>AMERICREDIT DIRECT DEP 171124 706071237525VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1082</th>\n",
" <td>11/10/2017</td>\n",
" <td>3896.95</td>\n",
" <td>AMERICREDIT DIRECT DEP 171110 932304944050VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1131</th>\n",
" <td>10/27/2017</td>\n",
" <td>3896.96</td>\n",
" <td>AMERICREDIT DIRECT DEP 171027 372546011435VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1183</th>\n",
" <td>10/16/2017</td>\n",
" <td>3306.99</td>\n",
" <td>ATM CHECK DEPOSIT ON 10/15 1900 WEST EVERMAN P...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1185</th>\n",
" <td>10/13/2017</td>\n",
" <td>3896.95</td>\n",
" <td>AMERICREDIT DIRECT DEP 171013 563030277944VDT ...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date amt desc\n",
"3 10/12/2018 4698.52 AMERICREDIT DIRECT DEP 181012 941607572332VDT ...\n",
"38 09/28/2018 4698.51 AMERICREDIT DIRECT DEP 180928 564044839211VDT ...\n",
"89 09/14/2018 4698.52 AMERICREDIT DIRECT DEP 180914 600045724949VDT ...\n",
"130 08/31/2018 4698.52 AMERICREDIT DIRECT DEP 180831 400035112359VDT ...\n",
"185 08/17/2018 4546.04 AMERICREDIT DIRECT DEP 180817 931507536879VDT ...\n",
"231 08/03/2018 4341.90 AMERICREDIT DIRECT DEP 180803 944907359007VDT ...\n",
"288 07/20/2018 4341.90 AMERICREDIT DIRECT DEP 180720 931107473232VDT ...\n",
"332 07/06/2018 4341.88 AMERICREDIT DIRECT DEP 180706 940507122326VDT ...\n",
"381 06/22/2018 4240.87 AMERICREDIT DIRECT DEP 180622 635067534972VDT ...\n",
"431 06/08/2018 4240.87 AMERICREDIT DIRECT DEP 180608 624068065452VDT ...\n",
"432 06/08/2018 3283.50 AMERICREDIT DIRECT DEP 180608 624068065453VDT ...\n",
"479 05/25/2018 4240.85 AMERICREDIT DIRECT DEP 180525 931006698961VDT ...\n",
"523 05/11/2018 4240.87 AMERICREDIT DIRECT DEP 180511 730034367801VDT ...\n",
"563 04/27/2018 4240.86 AMERICREDIT DIRECT DEP 180427 602044712515VDT ...\n",
"584 04/20/2018 1261.46 GM FINANCIAL AP DISBURS 180419 SAS CONFERENCE ...\n",
"599 04/13/2018 4240.86 AMERICREDIT DIRECT DEP 180413 485046051387VDT ...\n",
"642 03/30/2018 4022.64 AMERICREDIT DIRECT DEP 180330 935006473036VDT ...\n",
"651 03/27/2018 1242.35 PAYPAL TRANSFER 180327 5BD22AF52GFQ6 CURTIS AL...\n",
"688 03/16/2018 4022.64 AMERICREDIT DIRECT DEP 180316 609043454573VDT ...\n",
"709 03/09/2018 21825.55 AMERICREDIT DIRECT DEP 180309 775069122837VDT ...\n",
"731 03/02/2018 4022.65 AMERICREDIT DIRECT DEP 180302 487546814825VDT ...\n",
"775 02/16/2018 4022.64 AMERICREDIT DIRECT DEP 180216 934606289560VDT ...\n",
"820 02/02/2018 4022.64 AMERICREDIT DIRECT DEP 180202 405031800646VDT ...\n",
"845 01/19/2018 3894.66 AMERICREDIT DIRECT DEP 180119 571031361881VDT ...\n",
"887 01/05/2018 3894.67 AMERICREDIT DIRECT DEP 180105 595042112820VDT ...\n",
"932 12/22/2017 4223.64 AMERICREDIT DIRECT DEP 171222 285070518861VDT ...\n",
"974 12/08/2017 4223.65 AMERICREDIT DIRECT DEP 171208 939704624547VDT ...\n",
"1037 11/24/2017 3924.31 AMERICREDIT DIRECT DEP 171124 706071237525VDT ...\n",
"1082 11/10/2017 3896.95 AMERICREDIT DIRECT DEP 171110 932304944050VDT ...\n",
"1131 10/27/2017 3896.96 AMERICREDIT DIRECT DEP 171027 372546011435VDT ...\n",
"1183 10/16/2017 3306.99 ATM CHECK DEPOSIT ON 10/15 1900 WEST EVERMAN P...\n",
"1185 10/13/2017 3896.95 AMERICREDIT DIRECT DEP 171013 563030277944VDT ..."
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['amt'] > 1000]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>0</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>0</th>\n",
" <td>10/12/2018</td>\n",
" <td>-64.04</td>\n",
" <td>*</td>\n",
" <td>NaN</td>\n",
" <td>PURCHASE AUTHORIZED ON 10/10 AMZN Mktp US*MT5V...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10/12/2018</td>\n",
" <td>-6.48</td>\n",
" <td>*</td>\n",
" <td>NaN</td>\n",
" <td>PURCHASE AUTHORIZED ON 10/10 AMZN Mktp US*MT68...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10/12/2018</td>\n",
" <td>-5.55</td>\n",
" <td>*</td>\n",
" <td>NaN</td>\n",
" <td>PURCHASE AUTHORIZED ON 10/10 AMZN Mktp US*MT87...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10/12/2018</td>\n",
" <td>4698.52</td>\n",
" <td>*</td>\n",
" <td>NaN</td>\n",
" <td>AMERICREDIT DIRECT DEP 181012 941607572332VDT ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>10/12/2018</td>\n",
" <td>23.44</td>\n",
" <td>*</td>\n",
" <td>NaN</td>\n",
" <td>YMCA of Metropol Payroll 000101 IMM00000336542...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 \\\n",
"0 10/12/2018 -64.04 * NaN \n",
"1 10/12/2018 -6.48 * NaN \n",
"2 10/12/2018 -5.55 * NaN \n",
"3 10/12/2018 4698.52 * NaN \n",
"4 10/12/2018 23.44 * NaN \n",
"\n",
" 4 \n",
"0 PURCHASE AUTHORIZED ON 10/10 AMZN Mktp US*MT5V... \n",
"1 PURCHASE AUTHORIZED ON 10/10 AMZN Mktp US*MT68... \n",
"2 PURCHASE AUTHORIZED ON 10/10 AMZN Mktp US*MT87... \n",
"3 AMERICREDIT DIRECT DEP 181012 941607572332VDT ... \n",
"4 YMCA of Metropol Payroll 000101 IMM00000336542... "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-10-d41bed48ce61>, line 1)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-10-d41bed48ce61>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m df.filter(df.1 == \"10/12/2018\")\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"df.filter( == \"10/12/2018\")"
]
},
{
"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": []
}
],
"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 |
ethen8181/machine-learning | deep_learning/softmax_tensorflow.ipynb | 1 | 74951 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": true
},
"source": [
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Tensorflow\" data-toc-modified-id=\"Tensorflow-1\"><span class=\"toc-item-num\">1 </span>Tensorflow</a></span><ul class=\"toc-item\"><li><span><a href=\"#Hello-World\" data-toc-modified-id=\"Hello-World-1.1\"><span class=\"toc-item-num\">1.1 </span>Hello World</a></span></li><li><span><a href=\"#Linear-Regression\" data-toc-modified-id=\"Linear-Regression-1.2\"><span class=\"toc-item-num\">1.2 </span>Linear Regression</a></span></li><li><span><a href=\"#MNIST-Using-Softmax\" data-toc-modified-id=\"MNIST-Using-Softmax-1.3\"><span class=\"toc-item-num\">1.3 </span>MNIST Using Softmax</a></span></li></ul></li><li><span><a href=\"#Reference\" data-toc-modified-id=\"Reference-2\"><span class=\"toc-item-num\">2 </span>Reference</a></span></li></ul></div>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>\n",
"@import url('http://fonts.googleapis.com/css?family=Source+Code+Pro');\n",
"@import url('http://fonts.googleapis.com/css?family=Vollkorn');\n",
"@import url('http://fonts.googleapis.com/css?family=Arimo');\n",
"@import url('http://fonts.googleapis.com/css?family=Fira_sans');\n",
" \n",
" div.cell {\n",
" width: 1000px;\n",
" margin-left: 0% !important;\n",
" margin-right: auto;\n",
" }\n",
" div.text_cell code {\n",
" background: transparent;\n",
" color: #000000;\n",
" font-weight: 600;\n",
" font-size: 12pt;\n",
" font-style: bold;\n",
" font-family: 'Source Code Pro', Consolas, monocco, monospace;\n",
" }\n",
" h1 {\n",
" font-family: 'Open sans',verdana,arial,sans-serif;\n",
"\t}\n",
"\t\n",
" div.input_area {\n",
" background: #F6F6F9;\n",
" border: 1px solid #586e75;\n",
" }\n",
"\n",
" .text_cell_render h1 {\n",
" font-weight: 200;\n",
" font-size: 30pt;\n",
" line-height: 100%;\n",
" color:#c76c0c;\n",
" margin-bottom: 0.5em;\n",
" margin-top: 1em;\n",
" display: block;\n",
" white-space: wrap;\n",
" text-align: left;\n",
" } \n",
" h2 {\n",
" font-family: 'Open sans',verdana,arial,sans-serif;\n",
" text-align: left;\n",
" }\n",
" .text_cell_render h2 {\n",
" font-weight: 200;\n",
" font-size: 16pt;\n",
" font-style: italic;\n",
" line-height: 100%;\n",
" color:#c76c0c;\n",
" margin-bottom: 0.5em;\n",
" margin-top: 1.5em;\n",
" display: block;\n",
" white-space: wrap;\n",
" text-align: left;\n",
" } \n",
" h3 {\n",
" font-family: 'Open sans',verdana,arial,sans-serif;\n",
" }\n",
" .text_cell_render h3 {\n",
" font-weight: 200;\n",
" font-size: 14pt;\n",
" line-height: 100%;\n",
" color:#d77c0c;\n",
" margin-bottom: 0.5em;\n",
" margin-top: 2em;\n",
" display: block;\n",
" white-space: wrap;\n",
" text-align: left;\n",
" }\n",
" h4 {\n",
" font-family: 'Open sans',verdana,arial,sans-serif;\n",
" }\n",
" .text_cell_render h4 {\n",
" font-weight: 100;\n",
" font-size: 14pt;\n",
" color:#d77c0c;\n",
" margin-bottom: 0.5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
" white-space: nowrap;\n",
" }\n",
" h5 {\n",
" font-family: 'Open sans',verdana,arial,sans-serif;\n",
" }\n",
" .text_cell_render h5 {\n",
" font-weight: 200;\n",
" font-style: normal;\n",
" color: #1d3b84;\n",
" font-size: 16pt;\n",
" margin-bottom: 0em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
" white-space: nowrap;\n",
" }\n",
" div.text_cell_render{\n",
" font-family: 'Fira sans', verdana,arial,sans-serif;\n",
" line-height: 125%;\n",
" font-size: 115%;\n",
" text-align:justify;\n",
" text-justify:inter-word;\n",
" }\n",
" div.output_wrapper{\n",
" margin-top:0.2em;\n",
" margin-bottom:0.2em;\n",
" }\n",
"\n",
" code{\n",
" font-size: 70%;\n",
" }\n",
" .rendered_html code{\n",
" background-color: transparent;\n",
" }\n",
" ul{\n",
" margin: 2em;\n",
" }\n",
" ul li{\n",
" padding-left: 0.5em; \n",
" margin-bottom: 0.5em; \n",
" margin-top: 0.5em; \n",
" }\n",
" ul li li{\n",
" padding-left: 0.2em; \n",
" margin-bottom: 0.2em; \n",
" margin-top: 0.2em; \n",
" }\n",
" ol{\n",
" margin: 2em;\n",
" }\n",
" ol li{\n",
" padding-left: 0.5em; \n",
" margin-bottom: 0.5em; \n",
" margin-top: 0.5em; \n",
" }\n",
" ul li{\n",
" padding-left: 0.5em; \n",
" margin-bottom: 0.5em; \n",
" margin-top: 0.2em; \n",
" }\n",
" a:link{\n",
" font-weight: bold;\n",
" color:#447adb;\n",
" }\n",
" a:visited{\n",
" font-weight: bold;\n",
" color: #1d3b84;\n",
" }\n",
" a:hover{\n",
" font-weight: bold;\n",
" color: #1d3b84;\n",
" }\n",
" a:focus{\n",
" font-weight: bold;\n",
" color:#447adb;\n",
" }\n",
" a:active{\n",
" font-weight: bold;\n",
" color:#447adb;\n",
" }\n",
" .rendered_html :link {\n",
" text-decoration: underline; \n",
" }\n",
" .rendered_html :hover {\n",
" text-decoration: none; \n",
" }\n",
" .rendered_html :visited {\n",
" text-decoration: none;\n",
" }\n",
" .rendered_html :focus {\n",
" text-decoration: none;\n",
" }\n",
" .rendered_html :active {\n",
" text-decoration: none;\n",
" }\n",
" .warning{\n",
" color: rgb( 240, 20, 20 )\n",
" } \n",
" hr {\n",
" color: #f3f3f3;\n",
" background-color: #f3f3f3;\n",
" height: 1px;\n",
" }\n",
" blockquote{\n",
" display:block;\n",
" background: #fcfcfc;\n",
" border-left: 5px solid #c76c0c;\n",
" font-family: 'Open sans',verdana,arial,sans-serif;\n",
" width:680px;\n",
" padding: 10px 10px 10px 10px;\n",
" text-align:justify;\n",
" text-justify:inter-word;\n",
" }\n",
" blockquote p {\n",
" margin-bottom: 0;\n",
" line-height: 125%;\n",
" font-size: 100%;\n",
" }\n",
"</style>\n",
"<script>\n",
" MathJax.Hub.Config({\n",
" TeX: {\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",
" scale:100,\n",
" availableFonts: [],\n",
" preferredFont:null,\n",
" webFont: \"TeX\",\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": [
"# code for loading the format for the notebook\n",
"import os\n",
"\n",
"# path : store the current path to convert back to it later\n",
"path = os.getcwd()\n",
"os.chdir(os.path.join('..', 'notebook_format'))\n",
"\n",
"from formats import load_style\n",
"load_style(plot_style = False)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ethen 2018-09-15 15:33:48 \n",
"\n",
"CPython 3.6.4\n",
"IPython 6.4.0\n",
"\n",
"numpy 1.14.1\n",
"matplotlib 2.2.2\n",
"keras 2.2.2\n",
"tensorflow 1.7.0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"os.chdir(path)\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# 1. magic for inline plot\n",
"# 2. magic to print version\n",
"# 3. magic so that the notebook will reload external python modules\n",
"# 4. magic to enable retina (high resolution) plots\n",
"# https://gist.github.com/minrk/3301035\n",
"%matplotlib inline\n",
"%load_ext watermark\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"import tensorflow as tf\n",
"from keras.datasets import mnist\n",
"from keras.utils import np_utils\n",
"\n",
"%watermark -a 'Ethen' -d -t -v -p numpy,matplotlib,keras,tensorflow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tensorflow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"TensorFlow provides multiple APIs. The lowest level API--TensorFlow Core-- provides you with complete programming control. We recommend TensorFlow Core for machine learning researchers and others who require fine levels of control over their models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hello World"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can think of TensorFlow Core programs as consisting of two discrete sections:\n",
"\n",
"- Building the computational graph.\n",
"- Running the computational graph."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'Const:0' shape=() dtype=string>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# note that this is simply telling tensorflow to \n",
"# create a constant operation, nothing gets\n",
"# executed until we start a session and run it\n",
"hello = tf.constant('Hello, TensorFlow!')\n",
"hello"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"b'Hello, TensorFlow!'\n"
]
}
],
"source": [
"# start the session and run the graph\n",
"with tf.Session() as sess:\n",
" print(sess.run(hello))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can think of tensorflow as a system to define our computation, and using the operation that we've defined it will construct a computation graph (where each operation becomes a node in the graph). The computation graph that we've defined will not be `run` unless we give it some context and explicitly tell it to do so. In this case, we create the `Session` that encapsulates the environment in which the objects are evaluated (execute the operations that are defined in the graph).\n",
"\n",
"Consider another example that simply add and multiply two constant numbers."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mutiply: 6.0\n",
"add: 5.0\n",
"add: 5.0\n"
]
}
],
"source": [
"a = tf.constant(2.0, tf.float32)\n",
"b = tf.constant(3.0) # also tf.float32 implicitly\n",
"c = a + b\n",
"\n",
"with tf.Session() as sess:\n",
" print('mutiply: ', sess.run(a * b))\n",
" print('add: ', sess.run(c)) # note that we can define the add operation outside \n",
" print('add: ', sess.run(a + b)) # or inside the .run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The example above is not especially interesting because it always produces a constant result. A graph can be parameterized to accept external inputs, known as `placeholders`. Think of it as the input data we would give to machine learning algorithm at some point.\n",
"\n",
"We can do the same operation as above by first defining a `placeholder` (note that we must specify the data type). Then `feed` in values using `feed_dict` when we `run` it."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mutiply: 6.0\n",
"add: 5.0\n"
]
}
],
"source": [
"a = tf.placeholder(tf.float32)\n",
"b = tf.placeholder(tf.float32)\n",
"\n",
"# define some operations\n",
"add = a + b\n",
"mul = a * b\n",
"\n",
"with tf.Session() as sess:\n",
" print('mutiply: ', sess.run(mul, feed_dict = {a: 2, b: 3}))\n",
" print('add: ', sess.run(add, feed_dict = {a: 2, b: 3}))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some matrix operations are the same compared to numpy. e.g. \t"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[3. 4.]\n",
" [5. 6.]\n",
" [6. 7.]]\n",
"[3.5 5.5 6.5]\n",
"[1 1 1]\n",
"[3.5 5.5 6.5]\n",
"[1 1 1]\n"
]
}
],
"source": [
"c = np.array([[3.,4], [5.,6], [6.,7]])\n",
"print(c)\n",
"print(np.mean(c, axis = 1))\n",
"print(np.argmax(c, axis = 1))\n",
"\n",
"with tf.Session() as sess:\n",
" result = sess.run(tf.reduce_mean(c, axis = 1))\n",
" print(result)\n",
" print(sess.run(tf.argmax(c, axis = 1)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The functionality of `numpy.mean` and `tensorflow.reduce_mean` are the same. When axis argument parameter is 1, it computes mean across (3,4) and (5,6) and (6,7), so 1 defines across which axis the mean is computed (axis = 1, means the operation is along the column, so it will compute the mean for each row). When it is 0, the mean is computed across(3,5,6) and (4,6,7), and so on. The same can be applied to argmax which returns the index that contains the maximum value along an axis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linear Regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll start off by writing a simple linear regression model. To do so, we first need to understand the difference between `tf.Variable` and `tf.placeholder`. \n",
"\n",
"> [Stackoverflow](http://stackoverflow.com/questions/36693740/whats-the-difference-between-tf-placeholder-and-tf-variable). The difference is that with `tf.Variable` you have to provide an initial value when you declare it. With `tf.placeholder` you don't have to provide an initial value and you can specify it at run time with the `feed_dict` argument inside `Session.run`.\n",
"> In short, we will use `tf.Variable` for trainable variables such as weights (W) and biases (B) for our model. On the other hand, `tf.placeholder` is used to feed actual training examples.\n",
"\n",
"Also note that, constants are automatically initialized when we call `tf.constant`, and their value can never change. By contrast, variables are not initialized when we call `tf.Variable`. To initialize all the variables in a TensorFlow program, we must explicitly call a special operation called `tf.global_variables_initializer()`. Things will become clearer with the example below."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Parameters\n",
"learning_rate = 0.01 # learning rate for the optimizer (gradient descent)\n",
"n_epochs = 1000 # number of iterations to train the model\n",
"display_epoch = 100 # display the cost for every display_step iteration"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# make up some trainig data\n",
"X_train = np.asarray([3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, \n",
" 2.167, 7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1], dtype = np.float32)\n",
"y_train = np.asarray([1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, \n",
" 1.221, 2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3], dtype = np.float32)\n",
"\n",
"# placeholder for the input data\n",
"X = tf.placeholder(tf.float32)\n",
"Y = tf.placeholder(tf.float32)\n",
"\n",
"# give the model's parameter a randomized initial value\n",
"W = tf.Variable(np.random.randn(), tf.float32, name = 'weight')\n",
"b = tf.Variable(np.random.randn(), tf.float32, name = 'bias')\n",
"\n",
"# Construct the formula for the linear model\n",
"# we can also do\n",
"# pred = tf.add(tf.multiply(X, W), b)\n",
"pred = W * X + b\n",
"\n",
"# we then define the loss function that the model is going to optimize on,\n",
"# here we use the standard mean squared error, which is sums the squares of the\n",
"# prediction and the true y divided by the number of observations, note\n",
"# that we're computing the difference between the prediction and the y label\n",
"# from the placeholder\n",
"cost = tf.reduce_mean(tf.pow(pred - Y, 2))\n",
"\n",
"# after defining the model structure and the function to optimize on,\n",
"# tensorflow provides several optimizers that can do optimization task\n",
"# for us, the simplest one being gradient descent\n",
"optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n",
"train = optimizer.minimize(cost)\n",
"\n",
"# initializing the variables\n",
"init = tf.global_variables_initializer()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 100, cost: 0.58211350440979\n",
"Epoch: 200, cost: 0.41723793745040894\n",
"Epoch: 300, cost: 0.3158383071422577\n",
"Epoch: 400, cost: 0.2534768581390381\n",
"Epoch: 500, cost: 0.21512417495250702\n",
"Epoch: 600, cost: 0.19153699278831482\n",
"Epoch: 700, cost: 0.17703068256378174\n",
"Epoch: 800, cost: 0.1681092530488968\n",
"Epoch: 900, cost: 0.16262249648571014\n",
"Epoch: 1000, cost: 0.15924812853336334\n",
"Optimization Finished!\n",
"Training cost: 0.15924812853336334, W: 0.2810680568218231, b: 0.5901336073875427\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 361,
"width": 488
}
},
"output_type": "display_data"
}
],
"source": [
"# change default figure and font size\n",
"plt.rcParams['figure.figsize'] = 8, 6 \n",
"plt.rcParams['font.size'] = 12\n",
"\n",
"# Launch the graph\n",
"with tf.Session() as sess:\n",
" sess.run(init)\n",
"\n",
" # Fit on all the training data\n",
" feed_dict = {X: X_train, Y: y_train}\n",
" for epoch in range(n_epochs):\n",
" sess.run(train, feed_dict = feed_dict)\n",
"\n",
" # Display logs per epoch step\n",
" if (epoch + 1) % display_epoch == 0:\n",
" # run the cost to obtain the value for the cost function at each step\n",
" c = sess.run(cost, feed_dict = feed_dict)\n",
" print(\"Epoch: {}, cost: {}\".format(epoch + 1, c))\n",
"\n",
" print(\"Optimization Finished!\")\n",
" c = sess.run(cost, feed_dict = feed_dict)\n",
" weight = sess.run(W)\n",
" bias = sess.run(b)\n",
" print(\"Training cost: {}, W: {}, b: {}\".format(c, weight, bias))\n",
"\n",
" # graphic display\n",
" plt.plot(X_train, y_train, 'ro', label = 'Original data')\n",
" plt.plot(X_train, weight * X_train + bias, label = 'Fitted line')\n",
" plt.legend()\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MNIST Using Softmax"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MNIST is a simple computer vision dataset. It consists of images of handwritten digits like these:\n",
"\n",
"<img src='images/mnist.png'>\n",
"\n",
"Each image is 28 pixels by 28 pixels, which is essentially a $28 \\times 28$ array of numbers. To use it in a context of a machine learning problem, we can flatten this array into a vector of $28 \\times 28 = 784$, this will be the number of features for each image. It doesn't matter how we flatten the array, as long as we're consistent between images. Note that, flattening the data throws away information about the 2D structure of the image. Isn't that bad? Well, the best computer vision methods do exploit this structure. But the simple method we will be using here, a softmax regression (defined below), won't.\n",
"\n",
"The dataset also includes labels for each image, telling us the each image's label. For example, the labels for the above images are 5, 0, 4, and 1. Here we're going to train a softmax model to look at images and predict what digits they are. The possible label values in the MNIST dataset are numbers between 0 and 9, hence this will be a 10-class classification problem."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"60000 train samples\n",
"10000 test samples\n"
]
}
],
"source": [
"n_class = 10\n",
"n_features = 784 # mnist is a 28 * 28 image\n",
"\n",
"# load the dataset and some preprocessing step that can be skipped\n",
"(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
"X_train = X_train.reshape(60000, n_features)\n",
"X_test = X_test.reshape(10000, n_features)\n",
"X_train = X_train.astype('float32')\n",
"X_test = X_test.astype('float32')\n",
"\n",
"# images takes values between 0 - 255, we can normalize it\n",
"# by dividing every number by 255\n",
"X_train /= 255\n",
"X_test /= 255\n",
"\n",
"print(X_train.shape[0], 'train samples')\n",
"print(X_test.shape[0], 'test samples')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# convert class vectors to binary class matrices (one-hot encoding)\n",
"# note: you HAVE to to this step\n",
"Y_train = np_utils.to_categorical(y_train, n_class)\n",
"Y_test = np_utils.to_categorical(y_test , n_class)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the following code chunk, we define the overall computational graph/structure for the softmax classifier using the cross entropy cost function as the objective. Recall that the formula for this function can be denoted as:\n",
"\n",
"$$L = -\\sum_i y'_i \\log(y_i)$$\n",
"\n",
"Where y is our predicted probability distribution, and y′ is the true distribution."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# define some global variables\n",
"learning_rate = 0.1 \n",
"n_iterations = 400\n",
"\n",
"# define the input and output \n",
"# here None means that a dimension can be of any length,\n",
"# which is what we want, since the number of observations\n",
"# we have can vary;\n",
"# note that the shape argument to placeholder is optional, \n",
"# but it allows TensorFlow to automatically catch bugs stemming \n",
"# from inconsistent tensor shapes\n",
"X = tf.placeholder(tf.float32, [None, n_features])\n",
"y = tf.placeholder(tf.float32, [None, n_class])\n",
"\n",
"# initialize both W and b as tensors full of zeros. \n",
"# these are parameters that the model is later going to learn,\n",
"# Notice that W has a shape of [784, 10] because we want to multiply \n",
"# the 784-dimensional image vectors by it to produce 10-dimensional \n",
"# vectors of evidence for the difference classes. b has a shape of [10] \n",
"# so we can add it to the output.\n",
"W = tf.Variable(tf.zeros([n_features, n_class]))\n",
"b = tf.Variable(tf.zeros([n_class]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"# to define the softmax classifier and cross entropy cost\n",
"# we can do the following\n",
"\n",
"# matrix multiplication using the .matmul command\n",
"# and add the softmax output\n",
"output = tf.nn.softmax(tf.matmul(X, W) + b)\n",
"\n",
"# cost function: cross entropy, the reduce mean is simply the average of the\n",
"# cost function across all observations\n",
"cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(output), axis = 1))\n",
"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From <ipython-input-14-f12deee807bb>:4: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"\n",
"Future major versions of TensorFlow will allow gradients to flow\n",
"into the labels input on backprop by default.\n",
"\n",
"See tf.nn.softmax_cross_entropy_with_logits_v2.\n",
"\n"
]
}
],
"source": [
"# but for numerical stability reason, the tensorflow documentation\n",
"# suggests using the following function\n",
"output = tf.matmul(X, W) + b\n",
"cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y, logits = output))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we defined the structure of our model, we'll:\n",
"\n",
"1. Define a optimization algorithm the train it. In this case, we ask TensorFlow to minimize our defined cross_entropy cost using the gradient descent algorithm with a learning rate of 0.5. There are also other off the shelf [optimizers](https://www.tensorflow.org/api_guides/python/train#optimizers) that we can use that are faster for more complex models.\n",
"2. We'll also add an operation to initialize the variables we created\n",
"3. Define helper \"function\" to evaluate the prediction accuracy"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)\n",
"init = tf.global_variables_initializer()\n",
"\n",
"# here we're return the predicted class of each observation using argmax\n",
"# and see if the ouput (prediction) is equal to the target variable (y)\n",
"# since equal is a boolean type tensor, we cast it to a float type to compute\n",
"# the actual accuracy\n",
"correct_prediction = tf.equal(tf.argmax(y, axis = 1), tf.argmax(output, axis = 1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now it's time to run it. During each step of the loop, we get a \"batch\" of one hundred random data points (defined by `batch_size`) from our training set. We run train_step feeding in the batches data to replace the placeholders.\n",
"\n",
"Using small batches of random data is called stochastic training -- in this case, stochastic gradient descent. Ideally, we'd like to use all our data for every step of training because that would give us a better sense of what we should be doing, but that's expensive. So, instead, we use a different subset every time. Doing this is cheap and has much of the same benefit."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.09871667\n",
"0.84145\n",
"0.8613333\n",
"0.87055\n",
"0.87721664\n",
"0.88168335\n",
"0.88545\n",
"0.8884\n",
"test: 0.8906\n"
]
}
],
"source": [
"with tf.Session() as sess: \n",
" # initialize the variable, train the \"batch\" gradient descent\n",
" # for a specified number of iterations and evaluate on accuracy score\n",
" # remember the key to the feed_dict dictionary must match the variable we use\n",
" # as the placeholder for the data in the beginning\n",
" sess.run(init)\n",
" for i in range(n_iterations):\n",
" # X_batch, y_batch = mnist.train.next_batch(batch_size)\n",
" _, acc = sess.run([train_step, accuracy], feed_dict = {X: X_train, y: Y_train})\n",
" \n",
" # simply prints the training data's accuracy for every n iteration\n",
" if i % 50 == 0:\n",
" print(acc)\n",
" \n",
" # after training evaluate the accuracy on the testing data\n",
" acc = sess.run(accuracy, feed_dict = {X: X_train, y: Y_train})\n",
" print('test:', acc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that we did not have to worry about computing the gradient to update the model, the nice thing about Tensorflow is that, once we've defined the structure of our model it has the capability to automatically differentiate mathematical expressions. This means we no longer need to compute the gradients ourselves! In this example, our softmax classifier obtained pretty nice result around 90%. But we can certainly do better with more advanced techniques such as convolutional deep learning."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reference"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- [Blog: What is a TensorFlow Session?](http://danijar.com/what-is-a-tensorflow-session/)\n",
"- [Github: Tensorflow Examples - Linear Regression](https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/2_BasicModels/linear_regression.ipynb)\n",
"- [Tensorflow Documentation: Getting Started With TensorFlow](https://www.tensorflow.org/get_started/get_started)\n",
"- [TensorFlow Documentation: MNIST For ML Beginners](https://www.tensorflow.org/get_started/mnist/beginners)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"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": "3.6.4"
},
"toc": {
"nav_menu": {
"height": "169px",
"width": "252px"
},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": true,
"toc_position": {},
"toc_section_display": "block",
"toc_window_display": true
},
"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": 1
}
| mit |
baklanovp/pystella | ipnote/nb_pymc_cburns.ipynb | 1 | 5566 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# PyMC https://users.obs.carnegiescience.edu/cburns/ipynbs/PyMC.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from numpy import *\n",
"Nobs = 20\n",
"x_true = random.uniform(0,10, size=Nobs)\n",
"y_true = random.uniform(-1,1, size=Nobs)\n",
"alpha_true = 0.5\n",
"beta_x_true = 1.0\n",
"beta_y_true = 10.0\n",
"eps_true = 0.5\n",
"z_true = alpha_true + beta_x_true*x_true + beta_y_true*y_true\n",
"z_obs = z_true + random.normal(0, eps_true, size=Nobs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"plt.figure(figsize=(12,6))\n",
"plt.subplot(1,2,1)\n",
"plt.scatter(x_true, z_obs, c=y_true, marker='o')\n",
"plt.colorbar()\n",
"plt.xlabel('X')\n",
"plt.ylabel('Z')\n",
"plt.subplot(1,2,2)\n",
"plt.scatter(y_true, z_obs, c=x_true, marker='o')\n",
"plt.colorbar()\n",
"plt.xlabel('Y')\n",
"plt.ylabel('Z')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pymc\n",
"# define the parameters with their associated priors\n",
"alpha = pymc.Uniform('alpha', -100,100, value=median(z_obs))\n",
"betax = pymc.Uniform('betax', -100,100, value=std(z_obs)/std(x_true))\n",
"betay = pymc.Uniform('betay', -100,100, value=std(z_obs)/std(y_true))\n",
"eps = pymc.Uniform('eps', 0, 100, value=0.01)\n",
"\n",
"# Now define the model\n",
"@pymc.deterministic\n",
"def model(alpha=alpha, betax=betax, betay=betay, x=x_true, y=y_true):\n",
" return alpha + betax*x + betay*y\n",
"\n",
"# pymc parametrizes the width of the normal distribution by tau=1/sigma**2\n",
"@pymc.deterministic\n",
"def tau(eps=eps):\n",
" return power(eps, -2)\n",
"\n",
"# Lastly relate the model/parameters to the data\n",
"data = pymc.Normal('data', mu=model, tau=tau, value=z_obs, observed=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sampler = pymc.MCMC([alpha,betax,betay,eps,model,tau,z_obs,x_true,y_true])\n",
"sampler.use_step_method(pymc.AdaptiveMetropolis, [alpha,betax,betay,eps],\n",
" scales={alpha:0.1, betax:0.1, betay:1.0, eps:0.1})\n",
"sampler.sample(iter=10000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pymc.Matplot.plot(sampler)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sampler.sample(iter=10000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"alpha.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"m_alpha = median(alpha.trace())\n",
"m_betax = median(betax.trace())\n",
"m_betay = median(betay.trace())\n",
"m_eps = median(eps.trace())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.figure(figsize=(12,6))\n",
"plt.subplot(1,2,1)\n",
"plt.plot(x_true, z_obs-m_alpha-m_betay*y_true, 'o')\n",
"plt.xlabel('X')\n",
"plt.ylabel('Z - alpha - beta_y y')\n",
"# Now plot the model\n",
"xx = array([x_true.min(), x_true.max()])\n",
"plt.plot(xx, xx*m_betax)\n",
"plt.plot(xx, xx*m_betax + m_eps, '--', color='k')\n",
"plt.plot(xx, xx*m_betax - m_eps, '--', color='k')\n",
"plt.subplot(1,2,2)\n",
"plt.plot(y_true, z_obs-m_alpha-m_betax*x_true, 'o')\n",
"plt.xlabel('Y')\n",
"plt.ylabel('Z - alpha - beta_x x')\n",
"yy = array([y_true.min(), y_true.max()])\n",
"plt.plot(yy, yy*m_betay)\n",
"plt.plot(yy, yy*m_betay + m_eps, '--', color='k')\n",
"plt.plot(yy, yy*m_betay - m_eps, '--', color='k')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"samples = array([alpha.trace(),betax.trace(),betay.trace(),eps.trace()]).T\n",
"samples = samples[0]\n",
"import corner\n",
"tmp = corner.corner(samples[:,:], labels=['alpha','betax','betay','eps'], \n",
" truths=[alpha_true, beta_x_true, beta_y_true, eps_true])"
]
},
{
"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": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
rcmorehead/ASTRO585 | ASTRO_585_Lab_4_R_Morehead.ipynb | 1 | 101539 | {
"metadata": {
"language": "Julia",
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Lab 4 Feb 7, 2014 \n",
"#Robert Morehead"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##1a.\n",
"Let's use Eric's code and the crummy code written by the clearly untalented student. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"include(\"CLASS_REPO/Astro585/lab4/HW4_leapfrog.jl\")\n",
"include(\"CLASS_REPO/Astro585/lab4/HW4_leapfrog_student.jl\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"test_leapfrog_student (generic function with 2 methods)"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##1b.\n",
"I suspect the memory re-allocation for the log array due to the vcat() calls each loop is what made this so terrible. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##1c."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Profile.clear()\n",
"for i in 0:50\n",
" @profile orbit = integrate_leapfrog!([1.0,0.0,00.0,1.0],2pi/200,6pi);\n",
" #println(\"X:\",orbit[1][end],\" Y:\",orbit[2][end],\" Vx:\",orbit[3][end],\" Vy:\",orbit[4][end])\n",
"end\n",
"Profile.print()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING: the profile data buffer is full; profiling probably terminated\n",
"before your program finished. To profile for longer runs, call Profile.init()\n",
"with a larger buffer and/or larger delay."
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1 ...rc/execute_request.jl; execute_request_0x535c5df2; line: 132\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 1 loading.jl; include_string; line: 83\n",
" 1 no file; anonymous; line: 14\n",
" 1 ...lab4/HW4_leapfrog.jl; integrate_leapfrog!; line: 95\n",
" 1 deepcopy.jl; deepcopy_internal; line: 45\n",
"24 .../lab4/HW4_leapfrog.jl; advance_leapfrog!; line: 46\n",
"45 .../lab4/HW4_leapfrog.jl; update_derivs_pos!; line: 6\n",
"60 .../lab4/HW4_leapfrog.jl; update_derivs_vel!; line: 18\n",
"21 abstractarray.jl; checkbounds; line: 63\n",
"27 abstractarray.jl; checkbounds; line: 74\n",
"12 abstractarray.jl; checkbounds; line: 95\n",
"226 abstractarray.jl; trailingsize; line: 54\n",
"9 array.jl; copy!; line: 48\n",
"26 array.jl; getindex; line: 342\n",
"13 array.jl; setindex!; line: 453\n",
"25 array.jl; unsafe_copy!; line: 31\n",
"11 array.jl; unsafe_copy!; line: 38\n",
"24 deepcopy.jl; _deepcopy_array_t; line: 52\n",
"68 deepcopy.jl; deepcopy_internal; line: 45\n",
"16 int.jl; <; line: 228\n",
"12 operators.jl; >; line: 19\n",
"21 range.jl; Range1; line: 27\n",
"131 range.jl; colon; line: 38\n",
"12 range.jl; maximum; line: 101\n",
"5 range.jl; minimum; line: 100\n",
"7 reflection.jl; isbits; line: 36\n",
" 43235 multi.jl; anonymous; line: 1308\n",
" 43235 ...Julia/src/IJulia.jl; eventloop; line: 92\n",
" 43235 ...Julia/src/IJulia.jl; eventloop; line: 68\n",
" 43235 ...xecute_request.jl; execute_request_0x535c5df2; line: 132\n",
" 43235 loading.jl; include_string; line: 83\n",
" 6 no file; anonymous; line: 10\n",
" 43229 no file; anonymous; line: 14\n",
" 5 ...HW4_leapfrog.jl; integrate_leapfrog!; line: 67\n",
" 8 ...HW4_leapfrog.jl; integrate_leapfrog!; line: 70\n",
" 12 ...HW4_leapfrog.jl; integrate_leapfrog!; line: 73\n",
" 1 ...HW4_leapfrog.jl; integrate_leapfrog!; line: 74\n",
" 1 .../HW4_leapfrog.jl; update_derivs!; line: 36\n",
" 75 ...HW4_leapfrog.jl; integrate_leapfrog!; line: 78\n",
" 4 deepcopy.jl; deepcopy_internal; line: 46\n",
" 2 deepcopy.jl; _deepcopy_array_t; line: 52\n",
" 2 deepcopy.jl; _deepcopy_array_t; line: 53\n",
" 2 array.jl; copy!; line: 51\n",
" 2 array.jl; unsafe_copy!; line: 38\n",
" 2 array.jl; unsafe_copy!; line: 31\n",
" 27 array.jl; setindex!; line: 465\n",
" 8 ...HW4_leapfrog.jl; integrate_leapfrog!; line: 82\n",
" 17 ...HW4_leapfrog.jl; integrate_leapfrog!; line: 84\n",
" 21591 ...HW4_leapfrog.jl; integrate_leapfrog!; line: 87\n",
" 183 ...4_leapfrog.jl; advance_leapfrog!; line: 46\n",
" 58 ...4_leapfrog.jl; advance_leapfrog!; line: 49\n",
" 3987 ...4_leapfrog.jl; advance_leapfrog!; line: 51\n",
" 132 range.jl; colon; line: 38\n",
" 61 range.jl; Range1; line: 27\n",
" 50 range.jl; Range1; line: 28\n",
" 877 ...4_leapfrog.jl; advance_leapfrog!; line: 52\n",
" 119 ...4_leapfrog.jl; update_derivs_vel!; line: 18\n",
" 11 ...4_leapfrog.jl; update_derivs_vel!; line: 19\n",
" 99 ...4_leapfrog.jl; update_derivs_vel!; line: 21\n",
" 3 ...4_leapfrog.jl; update_derivs_vel!; line: 22\n",
" 19 ...4_leapfrog.jl; update_derivs_vel!; line: 23\n",
" 111 ...4_leapfrog.jl; update_derivs_vel!; line: 24\n",
" 79 ...4_leapfrog.jl; update_derivs_vel!; line: 25\n",
" 105 ...4_leapfrog.jl; update_derivs_vel!; line: 26\n",
" 77 ...4_leapfrog.jl; update_derivs_vel!; line: 27\n",
" 65 ...4_leapfrog.jl; update_derivs_vel!; line: 28\n",
" 60 ...4_leapfrog.jl; update_derivs_vel!; line: 29\n",
" 1 ...4_leapfrog.jl; update_derivs_vel!; line: 30\n",
" 3181 ...4_leapfrog.jl; advance_leapfrog!; line: 53\n",
" 43 range.jl; colon; line: 38\n",
" 37 range.jl; Range1; line: 27\n",
" 313 ...4_leapfrog.jl; advance_leapfrog!; line: 54\n",
" 202 ...4_leapfrog.jl; update_derivs_pos!; line: 6\n",
" 1 ...4_leapfrog.jl; update_derivs_pos!; line: 7\n",
" 101 ...4_leapfrog.jl; update_derivs_pos!; line: 8\n",
" 3 ...4_leapfrog.jl; update_derivs_pos!; line: 11\n",
" 2825 ...4_leapfrog.jl; advance_leapfrog!; line: 55\n",
" 22 range.jl; colon; line: 38\n",
" 15 range.jl; Range1; line: 27\n",
" 6 range.jl; colon; line: 38\n",
" 20 ...HW4_leapfrog.jl; integrate_leapfrog!; line: 88\n",
" 103 ...HW4_leapfrog.jl; integrate_leapfrog!; line: 91\n",
" 1 ...HW4_leapfrog.jl; integrate_leapfrog!; line: 93\n",
" 3880 ...HW4_leapfrog.jl; integrate_leapfrog!; line: 94\n",
" 250 abstractarray.jl; trailingsize; line: 54\n",
" 3594 array.jl; getindex; line: 342\n",
" 1 range.jl; Range1; line: 28\n",
" 12 range.jl; colon; line: 38\n",
" 5 range.jl; Range1; line: 27\n",
" 4 range.jl; Range1; line: 28\n",
" 17419 ...HW4_leapfrog.jl; integrate_leapfrog!; line: 95\n",
" 127 abstractarray.jl; trailingsize; line: 54\n",
" 2344 deepcopy.jl; deepcopy_internal; line: 45\n",
" 4422 deepcopy.jl; deepcopy_internal; line: 46\n",
" 2870 deepcopy.jl; _deepcopy_array_t; line: 52\n",
" 160 reflection.jl; isbits; line: 36\n",
" 330 deepcopy.jl; _deepcopy_array_t; line: 53\n",
" 92 array.jl; copy!; line: 48\n",
" 207 array.jl; copy!; line: 51\n",
" 199 array.jl; unsafe_copy!; line: 38\n",
" 77 array.jl; unsafe_copy!; line: 31\n",
" 61 array.jl; unsafe_copy!; line: 33\n",
" 3 array.jl; unsafe_copy!; line: 44\n",
" 1060 deepcopy.jl; _deepcopy_array_t; line: 55\n",
" 51 deepcopy.jl; _deepcopy_array_t; line: 62\n",
" 36 deepcopy.jl; deepcopy_internal; line: 48\n",
" 43 range.jl; colon; line: 38\n",
" 36 range.jl; Range1; line: 27\n",
" 1 range.jl; Range1; line: 28\n",
" 26 array.jl; setindex!; line: 453\n",
" 4923 array.jl; setindex!; line: 454\n",
" 9 abstractarray.jl; checkbounds; line: 63\n",
" 10 abstractarray.jl; checkbounds; line: 75\n",
" 192 abstractarray.jl; checkbounds; line: 95\n",
" 95 abstractarray.jl; checkbounds; line: 63\n",
" 4648 abstractarray.jl; checkbounds; line: 96\n",
" 4615 abstractarray.jl; checkbounds; line: 74\n",
" 184 range.jl; maximum; line: 101\n",
" 136 range.jl; minimum; line: 100\n",
" 1 int.jl; <; line: 228\n",
" 48 int.jl; <; line: 228\n",
" 51 operators.jl; >; line: 19\n",
" 10 abstractarray.jl; checkbounds; line: 75\n",
" 5 range.jl; maximum; line: 101\n",
" 3 range.jl; minimum; line: 100\n",
" 29 array.jl; setindex!; line: 462\n",
" 17 array.jl; setindex!; line: 464\n",
" 1180 array.jl; setindex!; line: 465\n",
" 24 array.jl; setindex!; line: 469\n",
" 4 deepcopy.jl; deepcopy_internal; line: 46\n",
" 11 range.jl; colon; line: 38\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Profile.clear()\n",
"for i in 0:50\n",
" @profile orbit = integrate_leapfrog_student([1.0,0.0,0.0,1.0],2pi/200,6pi);\n",
" #println(\"X:\",orbit[1][end],\" Y:\",orbit[2][end],\" Vx:\",orbit[3][end],\" Vy:\",orbit[4][end])\n",
"end\n",
"Profile.print()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING: "
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"the profile data buffer is full; profiling probably terminated\n",
"before your program finished. To profile for longer runs, call Profile.init()\n",
"with a larger buffer and/or larger delay.\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 6466 multi.jl; anonymous; line: 1308\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 6466 ...Julia/src/IJulia.jl; eventloop; line: 92\n",
" 6466 ...Julia/src/IJulia.jl; eventloop; line: 68\n",
" 6466 ...xecute_request.jl; execute_request_0x535c5df2; line: 132\n",
" 6466 loading.jl; include_string; line: 83\n",
" 6466 no file; anonymous; line: 14\n",
" 6384 ...frog_student.jl; integrate_leapfrog_student; line: 31\n",
" 1 ...frog_student.jl; leapfrog_update_pos; line: 11\n",
" 6383 ...frog_student.jl; leapfrog_update_pos; line: 13\n",
" 4 abstractarray.jl; vcat; line: 706\n",
" 17 ...frog_student.jl; integrate_leapfrog_student; line: 32\n",
" 2 ...pfrog_student.jl; derrivatives; line: 4\n",
" 15 ...pfrog_student.jl; derrivatives; line: 7\n",
" 2 abstractarray.jl; vcat; line: 706\n",
" 2 ...frog_student.jl; integrate_leapfrog_student; line: 33\n",
" 2 ...pfrog_student.jl; leapfrog_update_both; line: 20\n",
" 1 abstractarray.jl; vcat; line: 706\n",
" 22 ...frog_student.jl; integrate_leapfrog_student; line: 36\n",
" 1 abstractarray.jl; vcat; line: 706\n",
" 4 abstractarray.jl; vcat; line: 925\n",
" 1 abstractarray.jl; cat_t; line: 875\n",
" 1 abstractarray.jl; cat_t; line: 893\n",
" 1 base.jl; Array; line: 149\n",
" 1 abstractarray.jl; cat_t; line: 918\n",
" 1 abstractarray.jl; cat_t; line: 919\n",
" 1 base.jl; append_any; line: 110\n",
" 16 array.jl; vcat; line: 1164\n",
" 15 ...frog_student.jl; integrate_leapfrog_student; line: 37\n",
" 1 abstractarray.jl; vcat; line: 706\n",
" 6 abstractarray.jl; vcat; line: 925\n",
" 6 abstractarray.jl; cat_t; line: 912\n",
" 5 array.jl; vcat; line: 1164\n",
" 3 array.jl; vcat; line: 1172\n",
" 15 ...frog_student.jl; integrate_leapfrog_student; line: 38\n",
" 2 abstractarray.jl; vcat; line: 706\n",
" 6 array.jl; vcat; line: 1164\n",
" 11 ...frog_student.jl; integrate_leapfrog_student; line: 39\n",
" 2 abstractarray.jl; vcat; line: 706\n",
" 4 array.jl; vcat; line: 1164\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##1d.\n",
"Yep it was the vcat calls, interesting how the increase by ~an order of magnitude in each successive line. Your code spends most of it's time on the deepcopy at line 95\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##1e.\n",
"Pre-allocating the log array might help, changing a value should be a much cheaper process. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#2 \n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##2a."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"function f(x::Vector{Float64})\n",
" return exp(-0.5.*x.^2)/sqrt(2pi)\n",
"end"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"f (generic function with 2 methods)"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = linspace(-3,3,1000)\n",
"y = f(x)\n",
"using PyPlot\n",
"plot(x,y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAqwAAAINCAYAAAD7gi17AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3XlYlWXixvHvQQzBfRdMx0JtczeNktJyISltUVQEzFxbx6UptZmaaX42TauO7WgSi6GGWWqWRYtOauq4tBNmY+SWlZqIioLn98eTTAToOWzPWe7PdXE5vec9570ZLuDmPc/icDqdTkREREREPFSA7QAiIiIiImeiwioiIiIiHk2FVUREREQ8mgqriIiIiHg0FVYRERER8WgqrCIiIiLi0VRYRURERMSjqbCKiIiIiEdTYRURERERj+ZWYc3Pz2fatGmEhYUREhJCREQEmZmZbl90/PjxBAQEMGjQoFIfX7duHZGRkdSuXZvQ0FAmTZpEXl6e29cREREREe/nVmEdPXo0s2bNIiEhgTlz5lCjRg2io6NZu3aty6/xn//8h+TkZGrVqoXD4Sjx+LZt2+jbty/Hjx9n1qxZjBs3jsTERGJiYtyJKiIiIiI+wuF0Op2unLhx40YiIiJ44oknmDp1KmDuuHbo0IFmzZq5VFqdTie9evXikksuITMzk44dO7Js2bJi50RHR/Ppp5+SlZVFnTp1AHjppZcYP348q1aton///u5+jiIiIiLixVy+w5qRkUFgYCATJkwoOhYUFMTYsWNZv349u3fvPutrpKam8uWXXzJz5kxK68mHDx8mMzOT+Pj4orIKMGrUKOrUqcPixYtdjSsiIiIiPsLlwrp161bat29frEgC9OjRAzBv5Z9Jbm4u06ZN4/7776d58+alnvPZZ59RUFDApZdeWux4zZo16dKlC1u3bnU1roiIiIj4iEBXT9y7dy+hoaEljp8+tmfPnjM+/+9//zu1a9dmypQpZ7zGb1/zt1q0aMFHH31U6vN++uknVq1aRZs2bQgODj5jDhERERGpfseOHWPnzp1ERUXRpEkTt57rcmE9duwYQUFBJY7XqlWr6PGyZGdnM2fOHBYuXEjNmjXPeA2gzOuUdY1Vq1YRHx9/xvwiIiIiYl9aWhpxcXFuPcflwhocHEx+fn6J48ePHy96vCyTJk2iV69e3HTTTWe9BlDmdUJCQkp93nnnnQeY/wMuuuiiM15D7Jg8eTKzZ8+2HUNKoa+NXYWFkJ0NmzbBjh3w3Xewbx8cOwZ5eZOpW3c29epB06bQrp356NEDWrWynVz0veO59LXxTF999RXx8fFFvc0dLhfW0NDQUt/2P/02flhYWKnPe//991m1ahWvvfYaO3fuLDpeUFDA0aNH+e6772jUqBF169YtGgpw+jV/f52yrnH6Lu9FF11Et27dXP2UpBo1aNBAXxsPpa9N9XM64aOPIDkZli6FAwcgJAQ6doSuXeEPf4A6dSA1tQHx8d04cMAU2c8+gyVLTMlt1w5uvBHGjYP27W1/Rv5J3zueS18bz3a6t7nD5cLatWtXPvzwQ3Jzc6lbt27R8Q0bNgDQpUuXUp+Xk5MDwM0331zisT179nDeeecxe/Zs/vjHP9KhQwcCAwPZtGkTQ4cOLTrvxIkTbNu2jREjRrgaV0TE45w8CQsWwD//CV9/DW3awO23Q1QUXHYZnHNO8fM/+gimTSt+7MgReP99ePNNeOklePxxuPpqc96AAVDK8tYiIl7P5VUChg4dSmFhIYmJiUXH8vPzSUpKIiIigpYtWwKwb98+srKyKCgoAKBv3768/vrrxT6WLl1K06ZN6dGjB6+//jrXX389APXr16dfv36kpaVx5MiRouukpqaSl5enzQNExCs5nZCWZu6K3norXHwxfPCBGQIwcyZceWXJslqWOnVg8GB48UXYvdu87tGjcO21cNVV8O9/V+3nIiJig8t3WHv27ElMTAwzZsxg//79hIeHk5ycTE5ODklJSUXnTZ8+nZSUFHbu3Enr1q1p1aoVrUoZbDVp0iSaN2/O4MGDix1/+OGHueKKK+jduzfjx49n165dPPXUU0RFRTFgwIAKfKoiItXvk0/grrvM3dIhQ2DFCujQoXJeu1YtiIuDkSPh7bfhgQdMaR01Cp54wox7FRHxBW5tzZqSksLkyZNJTU1l0qRJFBYWsmLFCiIjI4vOcTgcpW65+ntlndO1a1cyMzMJDg5m6tSpzJs3j3HjxpGRkeFOVPEwsbGxtiNIGfS1qRoFBfDww3DppWaM6nvvQUaGe2XV1a+NwwEDB8LGjTB3LixfDhdeaK4nVUffO55LXxvf4/LWrJ5sy5YtdO/enc2bN2uQtYhY99135q7nxx/DjBnw4IOuv+VfGfbvhzvvNIV1wgSYNctM6hIRsakifc2tO6wiInJmq1ebu6q7d8OaNWaManWWVYBmzWDxYnO3NTXVTOj6zSItIiJeR4VVRKSSPP889OsHnTvD5s3Qq5e9LA6HWfJq0yYzKatnTzOOVkTEG6mwiohUkNMJf/4z3HGHeSv+7behcWPbqYxLLoENG+Cii+Caa+DVV20nEhFxnwqriEgFFBSYcaL/+IeZmT97NgS6vP5K9WjSBN59F2JiYMQImD/fdiIREfd42I9VERHvUVgIo0fDwoVm16pRo2wnKts555jxrPXqwdixZgOCP/7RdioREdeosIqIlMOpU+bOanq6+Rg2zHaiswsIgOeeM5sPTJoEQUEwcaLtVCIiZ6fCKiLiJqfTbAaQlAQpKd5RVk9zOOCxx+DECbMtbHCwZ98ZFhEBFVYREbf9+c9mRYB58yA+3nYa9zkcZm3Wo0fNVrENG8KgQbZTiYiUTZOuRETcMHcuPPKImWA1dqztNOUXEAAvvAA33mgmYm3ebDuRiEjZVFhFRFz0zjvmbfQ77oCpU22nqbgaNcxErA4d4PrrISfHdiIRkdKpsIqIuOCzz2DoUIiKgn/9y7yt7gtCQmDZMjOWNToafvnFdiIRkZJUWEVEzuLgQfPW+fnnw6JFnrfOakU1bw4rV8KuXXDLLWYFBBERT6LCKiJyBqdOmVn0Bw/C0qVmSShfdOGFkJYGb7xhVhEQEfEkKqwiImfwj3/Am2/CggVw3nm201St66+Hv/zFrIKQmWk7jYjI/6iwioiUYdUqePBB+NvfYOBA22mqx9/+Bv36QWwsfP+97TQiIoYKq4hIKfbtg4QEuPZac9fRX9SoAa+8YiZhxcWZ7WdFRGxTYRUR+R2n0yyoHxAAL79s/vUnjRubIRBr18I//2k7jYiICquISAlPPw1vv23KarNmttPYceWVMGMG/PWvsGGD7TQi4u9UWEVEfuOzz+C++2DSJDMcwJ/99a/QvTuMHAm5ubbTiIg/U2EVEfnViRMQHw/t2umtcICaNc3QgP37fWNnLxHxXiqsIiK/euQR+PJLs11prVq203iGtm3h8cdh3jx4913baUTEX6mwiogAn34KM2eacZtduthO41kmTICrr4bx4zU0QETsUGEVEb9XUGBWBbjwQv9awspVAQHmDuuPP5pCLyJS3VRYRcTvPf44bNsG8+fDOefYTuOZzj/fDJl49llYvdp2GhHxNyqsIuLXduyAhx6CP/0JevSwncaz3XUX9OoFEydCfr7tNCLiT1RYRcRvOZ2mhLVoYZZwkjMLCIAXXoBvvoEnn7SdRkT8iQqriPitpUvNBgFz5kBIiO003qFDB5gyxUxQ27nTdhoR8RcqrCLil44cMZsDDBoEgwfbTuNd/vpXaNTI/P8nIlIdVFhFxC/9/e/w00/wr3/ZTuJ96tSB2bNh2TLzISJS1VRYRcTvfP45zJpllrA67zzbabzTkCEQFQV//CMcPWo7jYj4OhVWEfErTqd5K/u888zKAFI+Dgc88wzs3QuPPWY7jYj4OhVWEfErK1bA++/DU09BUJDtNN6tbVuYPNkU1t27bacREV+mwioifuPECXNXtV8/uO4622l8w/33mzGt999vO4mI+DIVVhHxGy+8ANu3mzVEHQ7baXxD/fpmAltKCvznP7bTiIivUmEVEb9w4AD87W8wdix06mQ7jW8ZNw4uuQSmTjVjhEVEKpsKq4j4hf/7Pzh50vwrlSsw0Ny1/ve/zWYMIiKVTYVVRHze9u1mRvuMGWYbVql8UVEwcCDcey/k59tOIyK+RoVVRHzeX/5iiuqUKbaT+LbHHzfbtc6dazuJiPgaFVYR8WlbtsDixWY70eBg22l82yWXQEICzJwJeXm204iIL1FhFRGf9uc/Q/v2MHq07ST+4W9/MxPctOWtiFQmFVYR8Vlr1sDbb5uJVoGBttP4hzZt4LbbzGYCBw7YTiMivkKFVUR8ktNpJll17QpDh9pO41/+/GcoKNCWrSJSeVRYRcQnvfkmrFsH//gHBOgnXbVq3txs2TpnDuzdazuNiPgC/RgXEZ9z6pS5y3fVVWa5Jal+f/oT1KqldW9FpHKosIqIz1m6FD79FB5+WFuw2tKgAUybBvPmQU6O7TQi4u1UWEXEp5w6Zfa279sXIiNtp/Fvd94J9evDP/9pO4mIeDu3Cmt+fj7Tpk0jLCyMkJAQIiIiyMzMPOvz1qxZw+DBg2ndujXBwcE0b96cvn378tZbb5U4t0+fPgQEBJT4GDhwoDtRRcRPvfGGubv617/aTiJ16sA995i7rN9/bzuNiHgztxZ6GT16NEuWLGHKlCm0a9eOpKQkoqOj+eCDD+jVq1eZz9u+fTuBgYHcfvvttGjRggMHDpCWlsZ1111HamoqcXFxxc5v1aoVjzzySLFjYWFh7kQVET/kdJq7q1dfDVdeaTuNgLnL+vjj5i7rs8/aTiMi3srhdDqdrpy4ceNGIiIieOKJJ5g6dSpg7rh26NCBZs2asXbtWrcufOzYMc4//3zat2/P6tWri4736dOHAwcO8Omnn7r8Wlu2bKF79+5s3ryZbt26uZVDRHzHsmVwww3w4YfQu7ftNHLaP/4BDz0EO3bAuefaTiMitlSkr7k8JCAjI4PAwEAmTJhQdCwoKIixY8eyfv16du/e7daFg4ODadKkCTVr1izxmNPppLCwkCNHjrj1miLiv5xOU4quukpl1dPcdRfUrq2xrCJSfi4X1q1bt9K+fXvq1KlT7HiPHj0A2LZt21lf4/Dhw/z0009kZWVx//33k52dzT333FPivOzsbGrXrk29evUIDQ3lwQcfpKCgwNWoIuKH3nwTtmzR2FVPVK+eGcs6dy64eW9DRARwYwzr3r17CQ0NLXH89LE9e/ac9TWGDRvGO++8A0Dt2rVZvHhxiclUbdu2pW/fvnTs2JG8vDxeffVVZs6cSXZ2NgsXLnQ1roj4kdNjVyMjzfhV8Tx33w1PPml2v/rXv2ynERFv43JhPXbsGEFBQSWO16pVq+jxs3n00Ue59957ycnJ4YUXXmDEiBEsW7aM/v37F50zb968Ys+Ji4tj4sSJzJ07lylTpnDZZZeV+fqTJ0+mQYMGxY7FxsYSGxt71mwi4r1WrYJNm+Ddd7XuqqeqV8+U1scfhwcegCZNbCcSkaqUnp5Oenp6sWOHDh0q9+u5POmqQ4cOhIaG8u677xY7/uWXX9KhQwdefPFFxo8f7/KFT548Sbdu3cjPzyc7O/uM52ZnZ3PhhRcyc+ZM7r///hKPa9KViH/r3Rvy82H9ehVWT/bTT/CHP5hdsB56yHYaEalu1TLpKjQ0tNS3/ff+ulG0u8tO1axZk0GDBvHNN9+ctXGf++u00gMHDrh1DRHxfR9/DGvWmF2VVFY9W5MmMH48PP00aE6tiLjD5cLatWtXsrOzyc3NLXZ8w4YNAHTp0sXti58eRhAQcOYY3377LQBNmzZ1+xoi4tsefRQuuMAsZyWeb+pUyM01E7BERFzlcmEdOnQohYWFJCYmFh3Lz88nKSmJiIgIWrZsCcC+ffvIysoqNqt///79JV7v0KFDLFmyhI4dO1KvXj0AcnNzyc/PL3ae0+lk5syZOBwOoqKi3PvsRMSnffUVvP463HcfnOXvXvEQrVtDXJyZgPW7H/ciImVyedJVz549iYmJYcaMGezfv5/w8HCSk5PJyckhKSmp6Lzp06eTkpLCzp07ad26NQADBw6kVatW9OzZk2bNmhU958cff+Tll18ueu7mzZuJjY1l5MiRhIeHc+zYMZYuXcq6deuYOHFiue7iiojvevxxCAszBUi8x7RpkJwMCxbAmDG204iIN3Bra9aUlBQeeOABUlNTOXjwIJ07d2bFihVERkYWneNwOHD8biDZ2LFjWbhwIbNnz+bQoUM0btyYyMhIZsyYUWzQbZs2bbjqqqtYunQp+/btIyAggIsvvtjtCV0i4vt27YK0NLOLUikLmIgHu+giuPFGs8TVLbdAjRq2E4mIp3N5lQBPplUCRPzPn/4E8+ZBTo5ZMkm8y8cfw+WXw5IlcPPNttOISHWollUCREQ8xcGD8OKLcMcdKqveKiIC+vQx27V6/20TEalqKqwi4nWeew5OnoRJk2wnkYq47z6z4cPatbaTiIinU2EVEa+Sn2/W8bzlFmje3HYaqYioKDOe9amnbCcREU+nwioiXmXhQvjhB5gyxXYSqaiAAPN1fP112LHDdhoR8WQqrCLiNZxOmDULBg6ECy+0nUYqQ3w8NG4M//qX7SQi4slUWEXEa6xeDZ98orurviQ42Eyemz/fTKYTESmNCquIeI1Zs6BDB+jXz3YSqUx33GEm0Wm7VhEpiwqriHiFb76B5cth8mT43d4k4uWaNzdDA+bMMcVVROT3VFhFxCv8619mrKO2YfVNU6bA7t2weLHtJCLiiVRYRcTjHToESUlw++1Qq5btNFIVOnSAAQPMElfaSEBEfk+FVUQ83rx55q3iO+6wnUSq0tSpsGUL/PvftpOIiKdRYRURj1ZQYDYKiI2FFi1sp5Gq1L8/XHABPPOM7SQi4mlUWEXEo732GuTkaCkrfxAQAHfdZb7mu3bZTiMinkSFVUQ82pw50KcPdO5sO4lUh1GjzNqsL75oO4mIeBIVVhHxWNu2wdq1cPfdtpNIdalXD0aPhsREyM+3nUZEPIUKq4h4rGefhXPPhcGDbSeR6nTnnbB/P7z6qu0kIuIpVFhFxCMdPAgLFsBtt0FgoO00Up0uvNBMwNLkKxE5TYVVRDxSUpJZIWDcONtJxIa774YNG2DTJttJRMQTqLCKiMc5dQqeew5iYsy2neJ/oqOhTRvdZRURQ4VVRDzOO+/Ajh1mLKP4pxo1zNd/4UIznlVE/JsKq4h4nGeega5d4fLLbScRm8aMMcV13jzbSUTENhVWEfEo//0vrFxp7q45HLbTiE2NGkFcHDz/vBnPLCL+S4VVRDzK889DgwZmK1aRO+80u14tX247iYjYpMIqIh7j2DF46SW49VYICbGdRjxBly4QEaGdr0T8nQqriHiMhQvN+qu33247iXiSiRNh1Sr49lvbSUTEFhVWEfEYzz8PUVHQtq3tJOJJhg0zw0QSE20nERFbVFhFxCNs3WoWib/tNttJxNOEhMCoUTB/Ppw4YTuNiNigwioiHiExEcLC4LrrbCcRTzRxIvz4IyxdajuJiNigwioi1h05AgsWwNixEBhoO414oosvhiuv1OQrEX+lwioi1i1caErr2LG2k4gnu+02+OAD+Ppr20lEpLqpsIqIdS++CAMHwh/+YDuJeLIhQ6BxY02+EvFHKqwiYtWWLfCf/5gxiiJnEhRk1uh9+WWzZq+I+A8VVhGxKjERWraE6GjbScQbTJgABw5ARobtJCJSnVRYRcQaTbYSd7VrB337avKViL9RYRURa9LT4ehRTbYS90ycCGvXwuef204iItVFhVVErElMNJOtWre2nUS8yQ03QPPm8MILtpOISHVRYRURK05PtpowwXYS8TbnnANjxkBamiZfifgLFVYRseLFFzXZSspvzBj45Rd47TXbSUSkOqiwiki1y82FV17RZCspv7ZtoXdvmDfPdhIRqQ4qrCJS7RYvhrw8TbaSihk7Fj78EHbssJ1ERKqaCquIVLuXXoKoKE22kooZMgTq14f5820nEZGqpsIqItXqq69g/XozBlGkIkJCYORIs/NVQYHtNCJSlVRYRaRazZ9v9oMfPNh2EvEFY8fCnj3w9tu2k4hIVVJhFZFqc/IkpKRAfLzZF16korp1g86dzTATEfFdKqwiUm3efBP279dwAKk8DgeMGwcrVsAPP9hOIyJVRYVVRKrN/Plw6aXQqZPtJOJL4uKgRg1z915EfJNbhTU/P59p06YRFhZGSEgIERERZGZmnvV5a9asYfDgwbRu3Zrg4GCaN29O3759eeutt0o9f926dURGRlK7dm1CQ0OZNGkSeXl57kQVEQ+zdy+sXKm7q1L5GjaEm282a7I6nbbTiEhVcKuwjh49mlmzZpGQkMCcOXOoUaMG0dHRrF279ozP2759O4GBgdx+++0899xz3HfffRw4cIDrrruOBQsWFDt327Zt9O3bl+PHjzNr1izGjRtHYmIiMTEx7n92IuIxUlKgZk2IjbWdRHzR2LGQnQ1n+XUkIl7K4XS69vfoxo0biYiI4IknnmDq1KmAuePaoUMHmjVrdtbS+nvHjh3j/PPPp3379qxevbroeHR0NJ9++ilZWVnUqVMHgJdeeonx48ezatUq+vfvX+K1tmzZQvfu3dm8eTPdunVzK4eIVD2nEy68EHr0MPu/i1S2U6f+t/tVUpLtNCJSmor0NZfvsGZkZBAYGMiECROKjgUFBTF27FjWr1/P7t273bpwcHAwTZo0oWbNmkXHDh8+TGZmJvHx8UVlFWDUqFHUqVOHxYsXu3UNEfEMa9eau1/a2UqqSkCAGW6yeDEcPmw7jYhUNpcL69atW2nfvn2xIgnQo0cPwLyVfzaHDx/mp59+Iisri/vvv5/s7Gzuueeeosc/++wzCgoKuPTSS4s9r2bNmnTp0oWtW7e6GldEPMj8+XDeeebul0hVGT0ajh+HhQttJxGRyuZyYd27dy+hoaEljp8+tmfPnrO+xrBhw2jWrBkXX3wxTz/9NIsXL2bgwIHFrvHb1/ytFi1auHQNEfEsubnmrteYMeYumEhVOfdcs+Wv1mQV8T2Brp547NgxgkpZ6btWrVpFj5/No48+yr333ktOTg4vvPACI0aMYNmyZUXjUk+/RlnXOds1Jk+eTIMGDYodi42NJVazPESsWbwYjh6FW26xnUT8wdixMHQofPklXHyx7TQi/is9PZ309PRixw4dOlTu13O5sAYHB5Ofn1/i+PHjx4seP5vOnTsX/e/4+Hi6devGnXfeSXZ2drHXKOs6ISEhZ3z92bNna9KViId56SVz16tVK9tJxB9cfz00agTJyfDoo7bTiPiv0m4Ynp50VR4uv0EXGhpa6lvyp9/GDwsLc+vCNWvWZNCgQXzzzTdFjfv0UIDTr/n767h7DRGx66uvYP16rb0q1ScoCEaOhNRUKCiwnUZEKovLhbVr165kZ2eTm5tb7PiGDRsA6NKli9sXP/0Wf8CvA9s6dOhAYGAgmzZtKnbeiRMn2LZtW7muISL2vPyyuds1eLDtJOJPRo82G1W8+67tJCJSWVwurEOHDqWwsJDExMSiY/n5+SQlJREREUHLli0B2LdvH1lZWRT85k/b/fv3l3i9Q4cOsWTJEjp27Ei9evUAqF+/Pv369SMtLY0jR44UnZuamkpeXp42DxDxIgUF5i7XyJHmrpdIdenWDS65xPzBJCK+weUxrD179iQmJoYZM2awf/9+wsPDSU5OJicnh6TfrNI8ffp0UlJS2LlzJ61btwZg4MCBtGrVip49e9KsWbOi5/z444+8/LufKA8//DBXXHEFvXv3Zvz48ezatYunnnqKqKgoBgwYUDmftYhUucxMc5dLk62kujkc5i7rX/4CBw+arVtFxLu5tchMSkoKkydPJjU1lUmTJlFYWMiKFSuIjIwsOsfhcOBwOIo9b+zYsRw4cIDZs2dzxx13MHfuXCIiIli3bh3XXHNNsXO7du1KZmYmwcHBTJ06lXnz5jFu3DgyMjIq8GmKSHVLTjaztMs5vl6kQuLizF3+RYtsJxGRyuDy1qyeTFuziniWX36BFi3goYfgvvtspxF/df318NNP8PHHtpOICFTT1qwiIq5avBhOnID4eNtJxJ/dcgts2ABZWbaTiEhFqbCKSKVLTob+/UEr0YlNgwaZ8avJybaTiEhFqbCKSKX65htYu1aTrcS+WrUgNhZSUqCw0HYaEakIFVYRqVQpKVCvHtx4o+0kIma1gD17zKoVIuK9VFhFpNKcOmUK67Bh4MJuzSJV7tJL4aKLtCariLdTYRWRSrNmDXz3nYYDiOc4vSbr66/Dr7uAi4gXUmEVkUqTnAzh4dCrl+0kIv8TH29WrVi82HYSESkvFVYRqRRHjsCrr8KoUeauloinCAuDqCgNCxDxZiqsIlIpXnsN8vJMYRXxNLfcAuvXw9df204iIuWhwioilSI5Gfr0gTZtbCcRKemGG6B+fTMpUES8jwqriFRYTg588IEmW4nnqlULRowwhfXUKdtpRMRdKqwiUmGpqWYZqyFDbCcRKduoUbBrF6xebTuJiLhLhVVEKsTpNMMBhgyBunVtpxEp2+WXm1UsUlNtJxERd6mwikiFfPwxbN+u4QDi+RwOs8RVRgYcPWo7jYi4Q4VVRCokORlatYKrr7adROTs4uMhNxeWLbOdRETcocIqIuWWnw+LFpkSEKCfJuIF2rY1QwM0LEDEu+hXjIiU28qVZrvLhATbSURcl5AAq1bBDz/YTiIirlJhFZFyS0uDbt3gootsJxFx3bBh5h2B9HTbSUTEVSqsIlIuBw/CihVmOICIN2ncGK67TsMCRLyJCquIlEtGBhQUmMXYRbxNQgJs2QJffmk7iYi4QoVVRMplwQLo1w9CQ20nEXHfdddBw4a6yyriLVRYRcRtOTlmt6C4ONtJRMonKMiMZV2wQFu1ingDFVYRcdsrr5itWG+6yXYSkfJLSIDvv9dWrSLeQIVVRNzidJq3UW+8UVuxine74go4/3wNCxDxBiqsIuKWTz4xE1W0OoB4O23VKuI9VFhFxC3iFsqdAAAgAElEQVQLFkCTJtC/v+0kIhWnrVpFvIMKq4i4rLDQjF8dMQJq1rSdRqTi2rWDiAgNCxDxdCqsIuKyDz+EPXs0HEB8i7ZqFfF8Kqwi4rK0NGjbFnr2tJ1EpPIMH262al240HYSESmLCquIuOTYMViyxNxddThspxGpPI0bQ3S0hgWIeDIVVhFxyfLlZnKKNgsQX5SQAJs3w1df2U4iIqVRYRURl6SlwWWXmSEBIr7m+uuhQQPdZRXxVCqsInJWP/0Eb72lyVbiu7RVq4hnU2EVkbN69VWzw9Xw4baTiFSdhATIyYE1a2wnEZHfU2EVkbNKS4OoKGja1HYSkarTqxecd56GBYh4IhVWETmjb7+Fdes0HEB832+3aj1+3HYaEfktFVYROaMFC6BOHbjhBttJRKpeXBwcPmxWxRARz6HCKiJlcjpNYb35ZggJsZ1GpOpdcAH06GGGwYiI51BhFZEybd4MX3+t4QDiX+LjYeVKszqGiHgGFVYRKVNaGrRoAddcYzuJSPUZMcK8u/Dqq7aTiMhpKqwiUqqCAkhPh9hYqFHDdhqR6tOsmVkVQ6sFiHgOFVYRKdV778H+/RoOIP4pPh7Wr4cdO2wnERFQYRWRMqSlwUUXQdeutpOIVL8bbjCrYyxYYDuJiIAKq4iU4sgReO01s8SPw2E7jUj1CwmBIUPMH25Op+00IqLCKiIlvPEGHD0KI0faTiJiT3w8bN8OGzfaTiIiKqwiUkJaGkRGmm0qRfzV1VdDaKjWZBXxBG4V1vz8fKZNm0ZYWBghISFERESQmZl51ue99957jBkzhvbt21O7dm3Cw8MZP348+/btK3Funz59CAgIKPExcOBAd6KKSDn98AO8+64mW4nUqGHeZVi4EE6etJ1GxL8FunPy6NGjWbJkCVOmTKFdu3YkJSURHR3NBx98QK9evcp83rRp0zh06BAxMTG0a9eOHTt28Mwzz7BixQq2bdtG8+bNi53fqlUrHnnkkWLHwsLC3IkqIuW0aBEEBEBMjO0kIvYlJMCTT8I778B119lOI+K/XC6sGzduZNGiRTzxxBNMnToVgISEBDp06MB9993H2rVry3zu7NmziYyMLHbs2muvpXfv3jzzzDP83//9X7HH6tevz0gNnhOxIi0NoqOhUSPbSUTs69QJOnQwa7KqsIrY4/KQgIyMDAIDA5kwYULRsaCgIMaOHcv69evZvXt3mc/9fVkFuPLKK2nUqBFZWVklHnM6nRQWFnLkyBFX44lIJcjOhk2bNBxA5DSHw3w/vPEGHD5sO42I/3K5sG7dupX27dtTp06dYsd79OgBwLZt29y68JEjR8jNzaVJkyYlHsvOzqZ27drUq1eP0NBQHnzwQQoKCtx6fRFx34IFUK8eXH+97SQinmPkSMjPN0u9iYgdLg8J2Lt3L6GhoSWOnz62Z88ety48e/ZsTp48yfDhw4sdb9u2LX379qVjx47k5eXx6quvMnPmTLKzs1m4cKFb1xAR1zmdZjhATAzUqmU7jYjnaNUK+vQx3x+jR9tOI+KfXC6sx44dIygoqMTxWr/+Zjt27JjLF12zZg0PPfQQw4cPp0+fPsUemzdvXrH/jouLY+LEicydO5cpU6Zw2WWXlfm6kydPpkGDBsWOxcbGEhsb63I2EX/18cfw7bfwu29BEcEMCxg3DnbtgnPPtZ1GxPOlp6eTnp5e7NihQ4fK/XouF9bg4GDy8/NLHD9+/HjR467IysripptuolOnTiXKaVnuuece5s6dy3vvvXfGwjp79my6devm0muKSHFpaeYXce/etpOIeJ4hQ+COOyA9He6913YaEc9X2g3DLVu20L1793K9nstjWENDQ0t923/v3r2Aa8tOff/99wwYMICGDRuycuVKateu7dK1z/31z9kDBw64GldE3HDypFnOauRIs6SViBRXvz4MHqxNBERscflXU9euXcnOziY3N7fY8Q0bNgDQpUuXMz7/559/ZsCAAZw8eZJVq1aVWHv1TL799lsAmjZt6vJzRMR1q1bBzz9rdQCRM0lIgE8/NR8iUr1cLqxDhw6lsLCQxMTEomP5+fkkJSURERFBy5YtAdi3bx9ZWVnFZvXn5eURHR3N3r17WblyJeHh4aVeIzc3t8SwA6fTycyZM3E4HERFRbn1yYmIa9LSoGNH8yEipYuKgsaNdZdVxAaXx7D27NmTmJgYZsyYwf79+wkPDyc5OZmcnBySkpKKzps+fTopKSns3LmT1q1bA2bi1KZNmxgzZgxffPEFX3zxRdH5devW5YYbbgBg8+bNxMbGMnLkSMLDwzl27BhLly5l3bp1TJw48ax3cUXEfYcPmzUmH3rIdhIRz3bOOTB8OLzyCjzyiNm6VUSqh1tbs6akpPDAAw+QmprKwYMH6dy5MytWrCi2MYDD4cDhcBR73ieffILD4WD+/PnMnz+/2GNt2rQpKqxt2rThqquuYunSpezbt4+AgAAuvvhiXnzxRcaPH1/ez1FEzmDpUrPGpBbTEDm7+Hh47jlYvRquucZ2GhH/4XA6nU7bISrq9KyzzZs3a5UAETf17w+FhfD++7aTiHg+pxPatYOrroLf3X8RkbOoSF/TfGARP7ZnD7z3niZbibjq9FatGRlw9KjtNCL+Q4VVxI+lp5txeUOG2E4i4j3i4iA3F5Yvt51ExH+osIr4sbQ0GDTIrDEpIq5p1w4uu0yrBYhUJxVWET/1xRewbZuGA4iUR0ICvP02/Pij7SQi/kGFVcRPLVgAjRrBwIG2k4h4n2HDzL+LFtnNIeIvVFhF/NCpU6awxsSYMawi4p6mTeHaazUsQKS6qLCK+KGPPoKcHA0HEKmI+HjYsAG2b7edRMT3qbCK+KEFC6BNG7jiCttJRLzX4MFQt675fhKRqqXCKuJn8vNh8WKzNE+AfgKIlFtwMAwdaoYFeP8WPCKeTb+uRPzMypVw6JAprCJSMfHxsGMHfPyx7SQivk2FVcTPpKVBt25w0UW2k4h4v969oWVLTb4SqWoqrCJ+5NAhWLFCk61EKkuNGjBypFne6sQJ22lEfJcKq4gfyciAggIYMcJ2EhHfkZAAP/8Mq1bZTiLiu1RYRfxIWhr06wehobaTiPiOjh2hUydITbWdRMR3qbCK+ImcHFi9WpOtRKpCfDwsWwa//GI7iYhvUmEV8RPp6WYZnptusp1ExPfExpoxrEuW2E4i4ptUWEX8gNNp3q684Qaz0LmIVK5zz4VrrtFqASJVRYVVxA98+il88YWZHCIiVSM+Hj78EL7/3nYSEd+jwiriB9LSoGlT6N/fdhIR33XzzRAUBK+8YjuJiO9RYRXxcYWF5hfoiBFQs6btNCK+q149M+wmNVVbtYpUNhVWER/3wQewZ482CxCpDgkJZvjNp5/aTiLiW1RYRXxcWhq0awc9ethOIuL7BgyAJk20JqtIZVNhFfFhR4+aZXbi48HhsJ1GxPfVrGmG37zyihmOIyKVQ4VVxIctWwZHjmizAJHqFB8Pe/ea4TgiUjlUWEV8WFoaXH45hIfbTiLiP3r2NMNwtCarSOVRYRXxUT/+CG+/rclWItXN4TDfd0uWmGE5IlJxKqwiPmrRIvOLc9gw20lE/E9cnBmO88YbtpOI+AYVVhEflZYG0dFmxrKIVK/wcDMcR8MCRCqHCquID9q+HTZs0HAAEZsSEmDVKti/33YSEe+nwirigxYsMLvuXH+97SQi/mvYMDMsZ+FC20lEvJ8Kq4iPcTrN25BDh0JwsO00Iv6rcWMzLEfDAkQqToVVxMds2AA7dmg4gIgniI+HTZvg669tJxHxbiqsIj4mLQ3OPRd697adREQGDTLDcxYssJ1ExLupsIr4kJMnzXi5kSMhQN/dItbVqgUxMeYPSafTdhoR76VfaSI+ZNUq+PlnDQcQ8STx8fDf/8K6dbaTiHgvFVYRH5KWBp06QceOtpOIyGlXXQWtWmnylUhFqLCK+IhffjG76ujuqohnCQgwO18tWgQnTthOI+KdVFhFfMRrr0F+PsTG2k4iIr8XHw8HD8LKlbaTiHgnFVYRH5GWBldfbVYIEBHPcskl0KWLhgWIlJcKq4gP2LULPvhAwwFEPFl8PCxfDocO2U4i4n1UWEV8QHo6BAXBzTfbTiIiZYmNhYICyMiwnUTE+6iwiviAtDQYPBjq17edRETKEhYGfftCaqrtJCLeR4VVxMt9+qn50HAAEc8XHw9r1sB339lOIuJdVFhFvNyCBdC4MURF2U4iImdz000QHAyvvGI7iYh3UWEV8WKnTpnCOnw4nHOO7TQicjZ165rSmpqqrVpF3KHCKuLFPvwQdu/WcAARbxIfD199BVu32k4i4j3cKqz5+flMmzaNsLAwQkJCiIiIIDMz86zPe++99xgzZgzt27endu3ahIeHM378ePbt21fq+evWrSMyMpLatWsTGhrKpEmTyMvLcyeqiF9ISYG2bSEiwnYSEXFV//7QtKnWZBVxh1uFdfTo0cyaNYuEhATmzJlDjRo1iI6OZu3atWd83rRp01izZg1Dhgzh6aefZsSIESxevJiuXbvyww8/FDt327Zt9O3bl+PHjzNr1izGjRtHYmIiMTEx7n92Ij7syBGzPM6oUeBw2E4jIq4KDDRLXKWnm2WuRMQFThdt2LDB6XA4nE8++WTRsePHjzvbtm3rvOKKK8743H//+98ljq1Zs8bpcDicf/nLX4odHzhwoLNly5bO3NzcomPz5s1zOhwO5zvvvFPq62/evNkJODdv3uzqpyPi9VJSnE5wOv/7X9tJRMRdmzaZ799Vq2wnEak+FelrLt9hzcjIIDAwkAkTJhQdCwoKYuzYsaxfv57du3eX+dzIyMgSx6688koaNWpEVlZW0bHDhw+TmZlJfHw8derUKTo+atQo6tSpw+LFi12NK+LzUlKgd29o08Z2EhFxV/fucMEFGhYg4iqXC+vWrVtp3759sSIJ0KNHD8C8le+OI0eOkJubS5MmTYqOffbZZxQUFHDppZcWO7dmzZp06dKFrRqhLgKYrVjfe88MBxAR7+NwmMlXr70GmqIhcnYuF9a9e/cSGhpa4vjpY3v27HHrwrNnz+bkyZMMHz682DV++5q/1aJFC7evIeKr0tKgVi0YOtR2EhEpr7g4U1Zff912EhHPF+jqiceOHSMoKKjE8Vq1ahU97qo1a9bw0EMPMXz4cPr06VPsGkCZ1znbNSZPnkyDBg2KHYuNjSU2NtblbCKezuk0wwFuugnq1bOdRkTK67zzIDLS/AEaF2c7jUjlSk9PJz09vdixQ4cOlfv1XC6swcHB5Ofnlzh+/PjxosddkZWVxU033USnTp2YN29eiWsAZV4nJCTkjK89e/ZsunXr5lIOEW+1ebNZw3HWLNtJRKSi4uPhjjvghx+geXPbaUQqT2k3DLds2UL37t3L9XouDwkIDQ0t9S3502/jh4WFnfU1vv/+ewYMGEDDhg1ZuXIltWvXLnGN377m76/jyjVEfF1yMoSGQr9+tpOISEXFxECNGmaJKxEpm8uFtWvXrmRnZ5Obm1vs+IYNGwDo0qXLGZ//888/M2DAAE6ePMmqVatoXsqfkh06dCAwMJBNmzYVO37ixAm2bdt21muI+LoTJ8wvtvh480tORLxbo0Zw3XVaLUDkbFwurEOHDqWwsJDExMSiY/n5+SQlJREREUHLli0B2LdvH1lZWRT8ZjXkvLw8oqOj2bt3LytXriQ8PLzUa9SvX59+/fqRlpbGkSNHio6npqaSl5enzQPE7731Fvz8s1YHEPElCQn/G+ojIqVzeQxrz549iYmJYcaMGezfv5/w8HCSk5PJyckhKSmp6Lzp06eTkpLCzp07ad26NQBxcXFs2rSJMWPG8MUXX/DFF18UnV+3bl1uuOGGov9++OGHueKKK+jduzfjx49n165dPPXUU0RFRTFgwIDK+JxFvFZKCnTtCh062E4iIpUlOhoaNIAFC2DmTNtpRDyTy4UVICUlhQceeIDU1FQOHjxI586dWbFiRbGNARwOB47f7RP5ySef4HA4mD9/PvPnzy/2WJs2bYoV1q5du5KZmcm0adOYOnUq9erVY9y4cTzyyCPl+fxEfMbPP8Py5fDYY7aTiEhlqlXLjGVNS4O//x0C3No0XcQ/OJxOp9N2iIo6Pets8+bNWiVAfNZzz8Ef/wh79kCzZrbTiEhlWrPG7Fy3Zg1ceaXtNCJVoyJ9TX/HiXiJlBQYOFBlVcQXRUbCH/6gyVciZVFhFfECX38NGzZospWIrwoIMJsHLF4MpSxFLuL3VFhFvEBKipmUMWiQ7SQiUlXi4uDQIXjzTdtJRDyPCquIhzt1ClJTYfhwMzlDRHzTxRdDt24aFiBSGhVWEQ+3ejV8/72GA4j4g4QEc4f1wAHbSUQ8iwqriIdLToa2beHyy20nEZGqNmIEFBRARobtJCKeRYVVxIPl5ZlfXKNGwe+WNxYRH9SiBfTvb4YBicj/qLCKeLAlS0xpjY+3nUREqktCAnz0EezYYTuJiOdQYRXxYElJcPXVcN55tpOISHW56SaoVw9eftl2EhHPocIq4qG+/RY+/BBuvdV2EhGpTiEhZlWQ5GQoLLSdRsQzqLCKeKjkZKhbF4YMsZ1ERKrbmDFmdZD337edRMQzqLCKeKBTp0xhHT7c3G0REf9y2WVw4YVmWJCIqLCKeKQPPoDvvtNwABF/5XCY7//XXoODB22nEbFPhVXEAyUlwQUXaO1VEX+WkGDWZF240HYSEftUWEU8zC+/mOWsRo/W2qsi/iw0FAYO1LAAEVBhFfE4ixbBiRPailVEzLCATZvgiy9sJxGxS4VVxMPMnw9RURAWZjuJiNh2/fXQpInusoqosIp4kK++gg0bNNlKRIxzzoG4OLNV68mTttOI2KPCKuJBkpKgUSMYPNh2EhHxFLfeCvv3w8qVtpOI2KPCKuIhCgrMXZSRIyEoyHYaEfEUnTtDt24aFiD+TYVVxEO8/Tbs26fhACJS0q23wptvmjutIv5IhVXEQyQlQadO0LWr7SQi4mlGjoSAAEhLs51ExA4VVhEP8NNPsHy52T9ca6+KyO81agQ33mj+sHU6bacRqX4qrCIeYMEC829cnN0cIuK5br0VPv8cNm+2nUSk+qmwiniApCQYNMistygiUpr+/aFlS7NWs4i/UWEVsWzLFvjkE022EpEzq1HD7ICXng7Hj9tOI1K9VFhFLJs71+xqde21tpOIiKe79VY4dAiWLrWdRKR6qbCKWJSXB6+8YiZbBQbaTiMinq5dO7jySnjpJdtJRKqXCquIRRkZcPiwKawiIq4YPx7eew927LCdRKT6qLCKWDR3LvTrB+edZzuJiHiLoUOhQQPdZRX/osIqYslXX8HateZuiYiIq4KDIT7erC5y8qTtNCLVQ4VVxJKXXoLGjeGGG2wnERFvM26c2cr5zTdtJxGpHiqsIhbk50NyslmiJijIdhoR8TadO0OPHmZYkYg/UGEVsWDZMrMd67hxtpOIiLcaPx7efhu+/952EpGqp8IqYsG8eXDFFXDxxbaTiIi3GjHCjGdNSrKdRKTqqbCKVLOdO+Hdd3V3VUQqpm5diI014+ELC22nEalaKqwi1Wz+fKhTB4YNs51ERLzduHGQk2P+CBbxZSqsItWosNAU1pEjoXZt22lExNv17AkdO2rylfg+FVaRarRqFezereEAIlI5HA4z+WrZMvjhB9tpRKqOCqtINZo7F7p0ge7dbScREV8RHw+BgWapPBFfpcIqUk327YPly83dVYfDdhoR8RUNG5rtWufNA6fTdhqRqqHCKlJN5s+Hc84x41dFRCrTuHGwfTusXm07iUjVUGEVqQaFhZCYaNZNbNjQdhoR8TVXXQXt22vylfguFVaRarBqFXz3Hdx2m+0kIuKLHA5zl3XJEvj5Z9tpRCqfCqtINXjhBeja1ez9LSJSFW65BU6dgpQU20lEKp8Kq0gVy8mBN980d1c12UpEqkqzZjBkiPkDWZOvxNe4VVjz8/OZNm0aYWFhhISEEBERQWZm5lmft2/fPqZPn87VV19N3bp1CQgIYHUZI8P79OlDQEBAiY+BAwe6E1XEY8ybZzYJiI21nUREfN3tt0N2Nrz/vu0kIpUr0J2TR48ezZIlS5gyZQrt2rUjKSmJ6OhoPvjgA3r16lXm87Kysnjsscdo3749nTp1Yv369TjOcKupVatWPPLII8WOhYWFuRNVxCOcPGkKa3y82fdbRKQqXXklXHIJPP889O1rO41I5XG5sG7cuJFFixbxxBNPMHXqVAASEhLo0KED9913H2vXri3zuZdeeikHDhygQYMGZGRksH79+jNeq379+ozU2j/iA5Yvh717NdlKRKqHw2F+3kyeDHv2gO71iK9weUhARkYGgYGBTJgwoehYUFAQY8eOZf369ezevbvM59apU4cGDRq4HMrpdFJYWMiRI0dcfo6IJ3rhBbjiCujUyXYSEfEXCQkQFGTe3RHxFS4X1q1bt9K+fXvq1KlT7HiPX6c9b9u2rdJCZWdnU7t2berVq0doaCgPPvggBQUFlfb6ItXhm2/g3Xd1d1VEqlf9+hAXZ9Z+1q9O8RUuDwnYu3cvoaGhJY6fPrZnz55KCdS2bVv69u1Lx44dycvL49VXX2XmzJlkZ2ezcOHCSrmGSHVITIRGjcyWiSIi1em228wmAitWwI032k4jUnEuF9Zjx44RFBRU4nitWrWKHq8M8373HkZcXBwTJ05k7ty5TJkyhcsuu6zM506ePLnE0IPY2FhiNT1bqll+vtmKdfRoCA62nUZE/E23btCzpxmWpMIqNqSnp5Oenl7s2KFDh8r9ei4X1uDgYPLz80scP378eNHjVeWee+5h7ty5vPfee2csrLNnz6Zbt25VlkPEVRkZZreZ3wz5FhGpVrffDrfeCjt2QHi47TTib0q7Ybhlyxa6d+9ertdzeQxraGhoqW/77927F6jaZafOPfdcAA4cOFBl1xCpTC+8ANdcAxdcYDuJiPir4cOhYUN48UXbSUQqzuXC2rVrV7Kzs8nNzS12fMOGDQB06dKlcpP9xrfffgtA06ZNq+waIpXl88/ho4802UpE7AoONsOS5s+HX98MFfFaLhfWoUOHUlhYSGJiYtGx/Px8kpKSiIiIoGXLloDZ1SorK6tcs/pzc3NLDDtwOp3MnDkTh8NBVFSU268pUt2efx6aN4cbbrCdRET83W23meFJGRm2k4hUjMtjWHv27ElMTAwzZsxg//79hIeHk5ycTE5ODklJSUXnTZ8+nZSUFHbu3Enr1q2Ljs+cOROAL774AoCUlBTWrFkDwF/+8hcANm/eTGxsLCNHjiQ8PJxjx46xdOlS1q1bx8SJE6v0Lq5IZfjlF0hOhnvugXPOsZ1GRPxd+/Zmx6vnnzc77ol4K7e2Zk1JSeGBBx4gNTWVgwcP0rlzZ1asWEFkZGTROQ6Ho9RtVx988EEcDgdOpxOHw8H8+fOLzj9dWNu0acNVV13F0qVL2bdvHwEBAVx88cW8+OKLjB8/viKfp0i1SEkxKwRMnGg7iYiIcfvtZnm9Tz/VJibivRxOp9NpO0RFnZ51tnnzZq0SINacOgUXXQRdu4KWDBYRT3HyJLRpA9dfrwlYYldF+prLY1hF5MwyMyE7G+66y3YSEZH/qVnTjGVNS4ODB22nESkfFVaRSvLMM9C5M/TqZTuJiEhxEyaYbVp/HY0n4nVUWEUqwbffmi0Q77oLShnCLSJiVfPmMGyY+cO6sNB2GhH3qbCKVILnn4cGDWDkSNtJRERK98c/ws6d8OabtpOIuE+FVaSCjh6Fl16CsWMhJMR2GhGR0vXoAZddBk8/bTuJiPtUWEUq6JVX4NAhs3SMiIgnu/tuM0H0yy9tJxFxjwqrSAU4nWZM2HXXwfnn204jInJmMTHQooX5uSXiTVRYRSrgo4/gk0/MXQsREU93zjlmY5PkZPPOkIi3UGEVqYBnnjFbH/brZzuJiIhrJk6EEyfgN7uqi3g8FVaRctq1C157De68EwL0nSQiXiI0VEtciffRr1mRcnrmGQgOhtGjbScREXHP3Xeb9aPfest2EhHXqLCKlENeHiQmwvjxUK+e7TQiIu657DK49FItcSXeQ4VVpBySk+GXXzTZSkS8k8NhNhJ45x0tcSXeQYVVxE2nTsHs2XDzzdCmje00IiLlM3y4Gc86e7btJCJnp8Iq4qaVK2H7dpgyxXYSEZHyO+ccuOsuSEmBH3+0nUbkzFRYRdw0axb07AmXX247iYhIxUycaFY5ef5520lEzkyFVcQNn3wC779v7q46HLbTiIhUTOPGcOut8OyzcPy47TQiZVNhFXHD7Nlw7rkwZIjtJCIilWPSJDMk4JVXbCcRKZsKq4iL9u0zP9Dvvhtq1rSdRkSkcrRvD4MGwVNPgdNpO41I6VRYRVz0/PMQGGjWXhUR8SVTp8IXX5hlrkQ8kQqriAuOHzeFdcwYaNjQdhoRkcp11VXQrZu5yyriiVRYRVywYAH89JMZ6yUi4mscDrjnHnOH9fPPbacRKUmFVeQsTp2CJ580Y7zatrWdRkSkasTEQMuWZuk+EU+jwipyFm++CV99BffdZzuJiEjVqVnTbNealgY//GA7jUhxKqwiZ/HYY3DFFdCrl+0kIiJVa/x4U1yffdZ2EpHiVFhFzmDdOvjoI91dFRH/0LAhjBtnCuuRI7bTiPyPCqvIGTz+OFx4oRm/KiLiD6ZOhcOHYd4820lE/keFVaQMWVnwxhtw771mr20REX/QujWMHGkmm544YTuNiKFfwyJlePJJaNEC4uJsJxERqV733Qe7dmm7VvEcKqwipdi7F1JSYPJkCAqynUZEpHpdcgkMHgyPPmqW9hOxTYVVpBRz5piiOnGi7SQiInZMn26GRi1fbjuJiAqrSAmHD5ttWG+7DerXt51GRMSOyy+HK6+ERx4Bpy9H6vsAACAASURBVNN2GvF3Kqwiv5OYCEePmuEAIiL+bPp02LAB1qyxnUT8nQqryG8cP24mW40aBWFhttOIiNg1cCB07Aj//KftJOLvVFhFfmP+fNi/39xVEBHxdw6H+Xn49tuwbZvtNOLPVFhFfnXypJkRO2IEtG1rO42IiGcYNgzatDE/H0VsUWEV+VVaGuTkwIwZtpOIiHiOwED4059g8WLYscN2GvFXKqwiQGGhmQl7003QoYPtNCIinmXMGGja1PycFLFBhVUEePVV2L4d/vxn20lERDxPcLDZpjo5Gb77znYa8UcqrOL3Tp2Chx+Ga6+F7t1tpxER8Uy33QYNGmjFALFDhVX83vLl8PnnursqInImtWvDPffASy/B99/bTiP+RoVV/JrTCTNnQu/eEBlpO42IiGe7806oWxcee8x2EvE3Kqzi1955B/7zH91dFRFxRd26MGUKzJ0Le/bYTiP+RIVV/JbTCQ89BD17Qr9+ttOIiHiHu++GWrXg8cdtJxF/osIqfuudd2D9elNaHQ7baUREvEP9+jB5Mrz4Ivzwg+004i9UWMUvOZ3w4INw+eUQFWU7jYiId5k0yWwo8OSTtpOIv3CrsObn5zNt2jTCwsIICQkhIiKCzMzMsz5v3759TJ8+nauvvpq6desSEBDA6tWryzx/3bp1REZGUrt2bUJDQ5k0aRJ5eXnuRBU5o7fego0bdXdVRKQ8GjY0QwOefRZ+/NF2GvEHbhXW0aNHM2vWLBISEpgzZw41atQgOjqatWvXnvF5WVlZPPbYY+zdu5dOnToB4CijJWzbto2+ffty/PhxZs2axbhx40hMTCQmJsadqCJlOn13NTJSY1dFRMpryhQICNCKAVI9Al09cePGjSxatIgnnniCqVOnApCQkPD/7d15XFZl/v/x142YsrgNmoC5hWKlJrjikqGOmjbmt1LMXFpM6jctKuZappXZZmaO4zRaIYhpSppWtmGaVqSmUKYZlpqlqLkwoggB3r8/roGJRLyR5dzL+/l48CAP53C/6dTth+tc1+eiVatWTJw4scSitX379pw8eZLatWuTmJhIcnLyRc+dOnUqAQEBbNy4EX9/fwCaNGnC6NGj+eSTT+jdu7ejkUWK9e67sH07fPqpRldFRC5X3bqmaH3xRYiJgaAgqxOJO3N4hDUxMRFvb2+io6MLj1WrVo1Ro0aRnJzMoUOHLnqtv78/tWvXvuRrnD59mqSkJIYPH15YrAKMHDkSf39/VqxY4WhckWLZ7TB9OkRGQo8eVqcREXFtMTGmY8CsWVYnEXfncMGakpJCaGhokUISoEOHDoB5lF9WO3fuJC8vj/bt2xc5XrVqVcLCwkhJSSnza4hne+cdSE01c1dFRKRsateGiRNNx4Cff7Y6jbgzhwvW9PR0gooZ7y84drgcOginp6cX+Z5/FBgYWC6vIZ7r/HkzutqrF3TvbnUaERH38PDDpnCdOdPqJOLOHJ7Deu7cOapVq3bB8erVqxd+vawKvsfFXudSrzF27NgLph4MHTqUoUOHljmbuL5Vq2DnTvj8c6uTiIi4D39/mDIFJkyASZOgWTOrE4kzWLZsGcuWLStyLCMj47K/n8MFq4+PDzk5ORccz87OLvx6WRV8j4u9jq+vb4nXz507l7Zt25Y5h7ifvDx4/HHTc7VrV6vTiIi4lwcegNmzzXSrJUusTiPOoLgBwx07dtCuXbvL+n4OTwkICgoq9pF8wWP84ODgywrw59f44/f88+uUx2uIZ1q8GH74AZ591uokIiLux8fHDAosXQq7d1udRtyRwwVreHg4aWlpZGZmFjm+ZcsWAMLCwsocplWrVnh7e7Nt27Yix3///XdSU1PL5TXE85w7BzNmwB13QHi41WlERNzTqFHQqJFZKyBS3hwuWAcNGkR+fj4LFy4sPJaTk0NsbCwRERE0aNAAMLta7dmzh7y8vFKHqVWrFn/9619JSEjgzJkzhceXLFnC2bNntXmAXJb5881+108/bXUSERH3dcUVplhNTDS9rkXKk8NzWDt27MjgwYOZMmUKx44dIyQkhLi4OA4ePEhsbGzheZMnTyY+Pp4DBw7QqFGjwuMz/7t8cNeuXQDEx8ezadMmAB5//PHC85555hm6dOnCjTfeyOjRo/n111+ZM2cOffv2pU+fPmX7acXjZGSYaQCjR2shgIhIRRsxwmwkMHEiJCVpcxYpPw4XrGCKzGnTprFkyRJOnTpFmzZteO+99+jWrVvhOTabrdhtV5944glsNht2ux2bzcYbb7xReP4fC9bw8HCSkpKYNGkSMTEx1KxZk/vuu49nNflQLsMLL0BODkybZnUSERH35+0Nzz0HAwfCRx/BTTdZnUjchc1ut9utDlFWBavOtm/fri4BUig9HUJCzNaBzzxjdRoREc9gt5te16dPw44dUKWK1YnEWZSlXnN4DquIq3nqKbNl4IQJVicREfEcNpuZFvDtt6ZrgEh5UMEqbmnvXli0CKZONTuwiIhI5YmIgNtvN9Ox/tuuXaRMVLCKW5o6FYKC4MEHrU4iIuKZZs2CQ4dMpxaRslLBKm7n889NW5VZs0wzaxERqXyhoRAdbdYQnDxpdRpxdSpYxa2cPw8xMdCuHQwbZnUaERHPNn065OZql0EpOxWs4laWLYNt22DOHPDSf90iIpaqX98sfP3HP+DAAavTiCvTX+niNrKyYPJkuO0201JFRESsN348BASYzQRELpcKVnEbc+aYLViff97qJCIiUsDf32wmsHIl/HeDS5FSU8EqbiE93bwhPvywtmAVEXE2w4ZBx44wdizk51udRlyRClZxC9OmmU0C/rDLr4iIOAkvL3jlFUhJgcWLrU4jrkgFq7i8b76BN96AGTOgTh2r04iISHEiIsxI69SpZttWkdJQwSouzW6HRx4x/f7uv9/qNCIiUpLnnoMzZ2DmTKuTiKtRwSoubflyM4l/3jyoWtXqNCIiUpKrroJJk2DuXPjxR6vTiCtRwSouKzMTHn3UtLHq08fqNCIi4ohHH4XAQPNZxFEqWMVlzZxptvubM8fqJCIi4ihfX3jxRVizBj780Oo04ipUsIpL2rMHXn7ZTN5v3NjqNCIiUhpRUdCzJzz0EGRnW51GXIEKVnE5BQutGjY0W/6JiIhrsdngn/+Egwe12Ys4RgWruJx33oFPPjGT9qtXtzqNiIhcjmuuMYMOzz6rBVhyaSpYxaWcOWN2SunfH/72N6vTiIhIWTz2mFmA9dBD5umZyMWoYBWXMmMG/PYb/OMf5pGSiIi4Ll9f837+0UewapXVacSZqWAVl5GSYqYBTJ8OV19tdRoRESkPAwbALbfAmDGmXaFIcVSwikvIz4foaLj2WoiJsTqNiIiUp1deMW0Kn3rK6iTirFSwiktYsAC2b4eFC7WjlYiIu2nSBKZNM+0KU1OtTiPOSAWrOL1ffzUT8x94ADp3tjqNiIhUhPHjzVO0UaMgL8/qNOJsVLCK03vkEfDzg1mzrE4iIiIV5Yor4PXXzQjryy9bnUacjQpWcWqrVsHq1WZ+U+3aVqcREZGK1LGjaV34xBOwd6/VacSZqGAVp3XiBPy//wcDB8LgwVanERGRyvDUUxAUZBbaqjerFFDBKk5rzBjIzYV//Us9V0VEPIWfHyxaBBs3wmuvWZ1GnIUKVnFKa9fC0qVmKkBQkNVpRESkMvXqBffeC48+CocOWZ1GnIEKVnE6p06ZjgA33wzDh1udRkRErDB7thltHT1aUwNEBas4oXHjICsL/v1vTQUQEfFUdeqYKQEffKCpAaKCVZzMunUQFwdz5kCDBlanERERK/XvD/fdZ3Y43L/f6jRiJRWs4jSOHzcNo2+6Ce65x+o0IiLiDF56CQIC4O674fx5q9OIVVSwilOw2808pdxceOMNTQUQERGjZk1YvBg2bYK5c61OI1ZRwSpOITYW3nkHFi5UVwARESkqMtJsKDB1KuzebXUasYIKVrHcTz+Znqv33AO33WZ1GhERcUazZkHTpqZ7TE6O1WmksqlgFUvl5cGIEVCvnum5KiIiUhwfH9Of+7vv4LHHrE4jlU0Fq1jquedgyxZYsgRq1LA6jYiIOLO2bc3fGy+9BB9+aHUaqUwqWMUyX3wBM2bAlCnQtavVaURExBWMHQt9+8Jdd8HRo1ankcqiglUsceIEDB0KERGmaBUREXGEl5fp1w1qdeVJVLBKpbPbzQKrs2dh2TLw9rY6kYiIuJL69U3R+uGHanXlKVSwSqWbOxfefdf01WvY0Oo0IiLiim66yeyANXkybN1qdRqpaCpYpVJt3QqTJpk3mQEDrE4jIiKu7NlnzUKswYPNVDNxXypYpdJkZMAdd0B4uHmTERERKYsrroAVK8wUs+HDNZ/VnalglUpx/rx5Mzl1CpYvN28yIiIiZdWoEbz5Jnz0EcycaXUaqSgqWKVSPPUUrFtn3lSaNrU6jYiIuJM+fWD6dNN15uOPrU4jFaFUBWtOTg6TJk0iODgYX19fIiIiSEpKcujajIwMoqOjqVevHv7+/vTs2ZOUlJQLzouMjMTLy+uCj379+pUmqjiR996DJ580Ratuo4iIVIRp00zheued8MsvVqeR8laqhkJ33303b7/9NuPGjaN58+bExsbSv39/NmzYQNcSOr+fP3+em2++mW+//ZaJEycSEBDAggULiIyMZPv27TRr1qzI+Q0bNuTZP01yDA4OLk1UcRJ795qpAAMHwtSpVqcRERF35eUFCQnQrh3ceits3my2cxX34HDBunXrVt566y1mz55NTEwMACNGjKBVq1ZMnDiRL7744qLXJiYmkpycTGJiIrfddhsAUVFRhIaGMn36dJYuXVrk/Fq1anHnnXdezs8jTuTMGbjttv/1y/PSBBQREalAdevCO++Y3RNHjYKlS8FmszqVlAeHS4jExES8vb2Jjo4uPFatWjVGjRpFcnIyhw4dKvHawMDAwmIVoG7dukRFRbFmzRpyc3OLnG+328nPz+fMmTOl+VnEiZw/b3YgOXAAVq+GWrWsTiQiIp4gPNwMkixbBs8/b3UaKS8OF6wpKSmEhobi7+9f5HiHDh0ASE1NLfHatm3bXnC8Q4cOZGVlkZaWVuR4Wloafn5+1KxZk6CgIJ544gny8vIcjSpOYNo0WLXKPJ657jqr04iIiCcZPNj8PTR1qtmoRlyfw1MC0tPTCQoKuuB4wbHDhw+XeG1kZGSJ17Zs2RKAZs2a0atXL1q3bs3Zs2dZuXIlM2fOJC0tjeXLlzsaVywUHw+zZsGLL5q5qyIiIpVtxgzYuROGDYOvvtLgiatzuGA9d+4c1apVu+B49erVC79+MdnZ2Q5f+9prrxU5Z9iwYdx///0sWrSIcePG0alTp4u+ztixY6ldu3aRY0OHDmXo0KEXvUbK1+efw+jRZu7Q+PFWpxEREU/l5WUGULp0gb/9zRStV15pdSrPsWzZMpYtW1bkWEZGxmV/P4cLVh8fH3Jyci44np2dXfj1irgWYPz48SxatIj169eXWLDOnTu32KkHUjn27TMrMzt3hgULNNFdRESsVaOGaa0YEWG2A9+wAXx9rU7lGYobMNyxYwft2rW7rO/n8BzWoKCgYh/7p6enAyW3nSrLtQBXXXUVACdPnnQ0rlSykyfNb7C1a8Pbb2snKxERcQ6NG5uiddcu06M1P9/qRHI5HC5Yw8PDSUtLIzMzs8jxLVu2ABAWFnbRa8PCwtixYwd2u/2Ca/38/AgNDS3xtfft2wdAvXr1HI0rlSgry/zm+ttv8P77EBBgdSIREZH/adcOVqwwC7DGjoU/lSPiAhwuWAcNGkR+fj4LFy4sPJaTk0NsbCwRERE0aNAAgCNHjrBnz54iq/oHDRrE0aNHWbVqVeGx48ePs3LlSgYMGEDVqlUByMzMvGDqgN1uZ+bMmdhsNvr27Xt5P6VUmLw8GDoUUlNNsXqJ3z1EREQs0b8//OtfMH8+vPyy1WmktByew9qxY0cGDx7MlClTOHbsGCEhIcTFxXHw4EFiY2MLz5s8eTLx8fEcOHCARo0aAaZgjYiI4J577mH37t2FO13Z7XaefPLJwmu3b9/O0KFDufPOOwkJCeHcuXOsXr2aL7/8kvvvv7/EUVypfHY7/P3vplB9913o2NHqRCIiIhcXHW36g48fbza1GTbM6kTiqFJtzRofH8+0adNYsmQJp06dok2bNrz33nt069at8BybzYbtT6ttvLy8WLduHRMmTGDevHmcO3eOjh07Eh8fT/PmzQvPa9KkCd27d2f16tUcOXIELy8vrrvuOv79738zevToMv6oUt5mzIBFi2DxYujXz+o0IiIil/bMM3DkCNx1F9Ssaaa0ifOz2f88sdQFFaw62759u7oEVJJ582DMGHj2WZg82eo0IiIijsvLgyFDzBPCDz6AHj2sTuQZylKvaXd3KbWFC02xOmECTJpkdRoREZHS8faGN9+E7t3hlltg2zarE8mlqGCVUomPhwcegIceMns0q9eqiIi4omrVYPVqaN0abroJvvvO6kRSEhWs4rAVK+Cee8wuVq+8omJVRERcm5+fmRbQsCH07Gl6tYpzUsEqDlmzxqymHDoUXn3VbHknIiLi6urUgaQkCA42c1k10uqcVHbIJa1cCYMGmW1XFy+GKlWsTiQiIlJ+6taF9etN0dqzp4pWZ6SCVUqUkAB33GFWU775ppmoLiIi4m4CAkzR2qCBilZnpIJVLuq112DkSLj7boiLU7EqIiLuLSDATA9o0AAiI9U9wJmoYJVi/fOfMHq06QiwaJGmAYiIiGcICIBPPzVbjffsCRs2WJ1IQAWr/IndDtOnm7ZVMTGmcNUCKxER8SR16sAnn0CXLmYnx3fesTqRqBSRQnl5cP/98NRTZger2bPVukpERDyTnx+8+y4MHAi3324WHYt1NCtRAMjKMi2r3n8fYmPNvFURERFPdsUVZsFx7dqmD/mhQzB1qgZzrKCCVThxwmxNl5pqfpvs18/qRCIiIs6hShXTfzw4GB5/HPbtM3+uWtXqZJ5FBauH270bBgyA06fNJPNOnaxOJCIi4lxsNrO+4+qrzW6PP/8MiYlm5FUqh+awerAPP4TOncHXF7ZuVbEqIiJSkhEj4OOPYft26NoVDhywOpHnUMHqgex2mDcPbr4ZbrgBvvgCmja1OpWIiIjzi4yE5GTIzob27c3TSal4Klg9THY2REfDmDEwbhysWQM1a1qdSkRExHVcc415Mtm2LfTuDXPmmMEgqTgqWD3I/v3mEUZCgukEMHu2NgQQERG5HAEBsG4djB9vPoYPNx13pGKoYPUQ69ZBu3aQkQFffqm2VSIiImXl7Q0vvADLl5vNBTp3hh9+sDqVe1LB6uby8mDaNDNftVs3+PprCA+3OpWIiIj7GDLEzGvNyTGDQ0uWWJ3I/ahgdWP790P37mbXqlmzzG9/depYnUpERMT9XH+9GRS6/XYYOdI8yTxzxupU7kMFq5tKSIA2beDIEdi8GaZMAS/dbRERkQrj7w9xceYjMdF0Edi+3epU7kEljJvJyIBhw0yvuIEDze5VnTtbnUpERMRzjBxpClVfX4iIgCefhNxcq1O5NhWsbuS996BlS/M5IcHMoVHLKhERkcrXogVs2QJTp8LTT5vBo927rU7lulSwuoETJ0w7jQEDzDSA774zo6wiIiJinapVzehqcjKcPWv6tj7/vEZbL4cKVhdmt8OKFWZU9f33YfFi87lhQ6uTiYiISIEOHWDHDnj4YTPi2q6dKWLFcSpYXdQPP0DfvqaVRsFjhrvuApvN6mQiIiLyZz4+8OKLppNA9epmI5+//92sPZFLU8HqYs6eNb+dtW4NP/4I774Lq1dDUJDVyURERORSwsPN6Oorr5j1Jtdeaz6fP291MuemgtVF2O2wciVcd53Zs3jqVNi1C/72N6uTiYiISGlUqWKmB+zebTb1GTHCdBP48kurkzkvFawuYPNm89g/KsqMrO7aBTNmmMcLIiIi4pquusoMRm3caHam7NoV7rgDfv7Z6mTORwWrE/v+e/i//zO7VeXmwvr1pmVVSIjVyURERKS83HijmdsaGwuffWZaYk2YAMePW53MeahgdUI//QSjRpnR1G++gTffhG3boGdPq5OJiIhIRfDyMtu57t0LkybBq69C06YwbZoWZoEKVqeyd6/5j7VFC9Oe6sUXYc8eGDpU26qKiIh4An9/07t1/37TReCll6BJE7P5wOnTVqezjsogJ/D992Ybt2uugY8/Nv9x7tsH48ZBtWpWpxMREZHKVreu2WRg3z4zmPXMM6bP+uTJkJ5udbrKp4LVInY7JCVB//5m5f/69TB3rpkOMGaM2X9YREREPFtgoKkP9u2DBx6ABQvMiGt0NKSlWZ2u8qhgrWQ5ORAXB2Fh0Ls3HDpk/rxvn2lxoZX/IiIi8mfBwWbE9Zdf4KmnTB/2a64x27J/+KH793FVwVpJ0tLg0UehQQMztH/VVWZUNTXVTAfQo38RERG5lFq1zKKs/fth0SL49Vfo1w9CQ82UwpMnrU5YMVSwVqCcHHjrLbO6v0UL065i5EgzZ/X9981xbaUqIiIipVW9uukotGMHfPEFdOoEU6aYAbERI8y0Q3cadVXBWs7On4dNm8zcksBA0wA4NxeWLDGP/+fMMUP4IiIiImVls0GXLrB0qZkuMG0abN1qph02aQKPPeYec11VsJYDux2++85sl9q0qWkA/PHHph3F7t1mp6rhw81vQyIiIiIVoX59M8q6Zw8kJ5uF3f/8p3nK27mzGTQ7eNDqlJdHBetlOn8evvrKzCO55hrT5P/VV808ks2bzSKqZ56Ba6+1OqmIiIh4EpsNIiJMXZKeDsuXQ716ppht3NhMH5g928yDdRXeVgdwJVlZ5nH/2rWwZg0cPmz6pA0caCY69+6txVMiIiLiPHx8YMgQ83H6tNniPTHRTB2YMAHatDEjsf37myLX20krQyeN5Rzsdti5Ez76yDzi37zZLKRq3BiiouDWW6FrV6hSxeqkIiIiIiWrWRPuvNN8ZGbCunVmEfiiRfDss1CnDvTta54W9+xpFnA5CxWsf2C3m3kfmzebj6QkOHLE/HYSGWn6n/XpY6YAaHW/iIiIuKoaNf438pqfD19/bQrYdevgrrvMOc2aQY8epgbq0QOCgqzL69EFa06O6YP6+eemQP38czhxwoyYhoebthB9+5pRVC2YEhEREXdUpYqZ19qpEzz5JPz2m5kCuWGD+Vi0yJzXooXpSFBwbqtWlTeFwGMK1txcs5L/66//97FzpzlevbqZt/H3v8MNN5h/rlHD6sQiIiIila9ePbj9dvMBcPQofPYZbNxoFpzHx5tRWV9faN/+fwVsWJjpluRVAUv63a5gtdtNv9PvvjMfu3aZzzt3mhFVLy9o2dL8Cx41ynwOC4MrrrA6uYiIiIjzqV/frN2JijJ/zsqC7dthyxZTwC5bBi++aL7m7286J7VpA9dfbz63bl32gcBS1cA5OTlMmjSJ4OBgfH19iYiIICkpyaFrMzIyiI6Opl69evj7+9OzZ09SUlKKPffLL7+kW7du+Pn5ERQUxJgxYzh79uwlX+Oee6B2bWjY0EwYnj7dFKytWsELL5idIDIz4dtv4Y03zIhqx44qVivDsmXLrI4gF6F747x0b5yb7o/z0r2pWL6+5on0o4+ajgO//GLaZ334oek+0LSpmWb58MNmWmXNmqY2e/DBy3/NUhWsd999Ny+//DIjRoxg3rx5VKlShf79+/PFF1+UeN358+e5+eabWbZsGY888ggvvPACx44dIzIykh9//LHIuampqfTq1Yvs7Gxefvll7rvvPhYuXMjgwYMvma9BA9NjbO1a0wc1M9Ps9hAbC488YuZd+PqW5ieW8qI3D+ele+O8dG+cm+6P89K9qXyBgWbdz8SJZtetnTvh7FmzdWxcnNlAqSytPx2eErB161beeustZs+eTUxMDAAjRoygVatWTJw4scSiNTExkeTkZBITE7ntttsAiIqKIjQ0lOnTp7N06dLCc6dOnUpAQAAbN27E398fgCZNmjB69Gg++eQTevfufdHXmTkT2rZ19CcSERERkYpSrZpZxB4ebv68Ywe0a3d538vhEdbExES8vb2Jjo7+Q5BqjBo1iuTkZA4dOlTitYGBgYXFKkDdunWJiopizZo15ObmAnD69GmSkpIYPnx4YbEKMHLkSPz9/VmxYkWpfjgRERERcX0OF6wpKSmEhoYWKSQBOnToAJhH+SVd27aYoc8OHTqQlZVFWloaADt37iQvL4/27dsXOa9q1aqEhYVddM6riIiIiLgvh6cEpKenE1RMx9iCY4cPHy7x2sjIyBKvbdmyJenp6UWO/1FgYCCff/55sd8/OzsbgO+//77kH0Isk5GRwY4dO6yOIcXQvXFeujfOTffHeeneOKeCOu3cuXOlvtbhgvXcuXNUK2a2bPX/dtQv6cWzs7Mdurbg88XOvdhr7N+/H4Dhw4eX9COIxdpd7sQVqXC6N85L98a56f44L90b53XgwAG6du1aqmscLlh9fHzIycm54HjB6KaPj0+Zry34fLFzfS+yxL9v374kJCTQpEmTEnOIiIiIiDWys7PZv38/ffv2LfW1DhesQUFBxT72L3iMHxwcXOZrC6YCFBz/87kXe426desybNiwS/wEIiIiImKlLl26XNZ1Di+6Cg8PJy0tjczMzCLHt2zZAkBYWNhFrw0LC2PHjh3Y7fYLrvXz8yM0NBSAVq1a4e3tzbZt24qc9/vvv5Oamlria4iIiIiIe3K4YB00aBD5+fksXLiw8FhOTg6xsbFERETQoEEDAI4cOcKePXvIy8srcu3Ro0dZtWpV4bHjx4+zcuVKBgwYQNWqVQGoVasWf/3rX0lISODMmTOF5y5ZsoSzZ886tHmAiIiIiLgXm/3Pw54lGDJkCKtXr2bcuHGElJbKlwAACHZJREFUhIQQFxfH119/zfr16+nWrRtgdsOKj4/nwIEDNGrUCDA7XXXr1o3vvvuOCRMmEBAQwIIFC/j111/Ztm0bzZs3L3yNlJQUunTpwnXXXcfo0aP59ddfmTNnDjfeeCMffPBBOf/4IiIiIuLsSrU1a3x8PGPHjmXJkiWMGTOG/Px83nvvvcJiFcBms2Gz2Yq+iJcX69atY8iQIcybN4+JEydy5ZVX8umnnxYpVsFMPUhKSsLHx4eYmBhee+017rvvPhITEx3OuWnTJm655RYaNWqEj48P9evXp1evXip4ncD69eu59957CQ0Nxc/Pj5CQEEaPHs2RI0esjubxjhw5wuTJk+nRowc1atTAy8uLzz77zOpYHicnJ4dJkyYRHByMr68vERERJCUlWR1LgLNnzzJ9+nRuuukm/vKXv+Dl5UVcXJzVsQTYtm0bDz30EC1btsTf35/GjRszZMgQ9u7da3U0j7dr1y4GDx5MSEgIfn5+BAQE0KVLlyK7nDqiVCOsruL111/n/fffp0OHDgQGBnLy5EkSEhL45ptvWLJkiRZoWah9+/ZkZGQwePBgmjdvzk8//cT8+fPx9fUlNTWV+vXrWx3RY23cuJGePXsSGhpKQEAAycnJbNy4ke7du1sdzaMMHTqUt99+m3HjxtG8eXNiY2PZtm0bGzZsKHUbGClfBw4c4Oqrr6Zx48Y0bdqUjRs3snjxYkaOHGl1NI83aNAgkpOTGTx4MNdffz3p6enMnz+fM2fO8NVXX9GyZUurI3qsDz74gHnz5tGlSxeCg4PJysoiMTGRzZs38/TTT/PYY4859o3sHiIrK8seGBho7969u9VRPNrmzZsvOLZp0ya7zWazP/744xYkkgKZmZn2U6dO2e12u33lypV2m81m/+yzzyxO5Vm2bNlit9ls9pdeeqnwWHZ2tr1Zs2b2Ll26WJhM7Ha7PScnx3706FG73W63f/3113abzWaPi4uzOJXY7Xb7l19+ac/NzS1ybO/evfbq1avbhw8fblEquZj8/Hx7WFiYvVGjRg5fU6opAa7Mx8eHunXrFi7wEmv8cfpIgRtuuIG//OUv7Nmzx4JEUsDf35/atWtbHcOjJSYm4u3tTXR0dOGxatWqMWrUKJKTkzl06JCF6eSKK67gyiuvBLig641Yq3Pnznh7F+3U2axZM6677jr93eKEvLy8uOqqq0pVkznch9UVnT59mt9//53jx48THx9PWloaL7zwgtWx5E/OnDlDZmYmdevWtTqKiKVSUlIIDQ3F39+/yPEOHToAkJqaWtiRRURKZrfbOXr0KK1bt7Y6igBZWVlkZWXxn//8h7Vr1/LRRx8xf/58h69364I1KiqKjz/+GAA/Pz9WrFhBv379LE4lfzZ37lxyc3MZMmSI1VFELJWenl64gcofFRwrbgMWESne0qVLOXz4MDNnzrQ6igAxMTGFrVG9vb2ZN29ekadJl+L0Bavdbi92q9biVK9evcifn3/+eSZMmMDBgwd59dVXueOOO1i7di29e/euiKgepyz3psCmTZt48sknGTJkCJGRkeWYzrOVx72Rynfu3DmqVat2wfGCe3Tu3LnKjiTikvbs2cODDz5Ily5duOuuu6yOI8C4ceOIiori8OHDLF26lIceeggfHx+H74/TF6yfffYZPXv2dOjcPXv2FO6aBdCmTZvCfx4+fDht27blwQcfJC0trdxzeqKy3JuCY7feeivXX389r732WkVE9FhlvTdiDR8fn2J/0cjOzi78uoiU7MiRI9x8883UqVOHxMTEC1ptijVatGhBixYtAFOT9e3bl7FjxxIVFeXQe5vTF6zXXnstixcvdujcwMDAi36tatWqDBgwgOeee46MjAwtLikHZbk3v/zyC3369KFOnTqsW7cOPz+/Ckjoucrr/xupXEFBQcU+9k9PTwcgODi4siOJuJT//Oc/9OvXj9OnT7N582a9vzmx22+/nU8++YQffviBsLCwS57v9AVr/fr1y63HXcHjNC8vj2mOUKEu996cOHGCPn36kJuby4YNG9R7tQKU5/83UnnCw8PZuHEjmZmZ1KhRo/D4li1bABx6UxfxVNnZ2QwYMIAff/yRpKQkrrnmGqsjSQlKW5O5ZeV27NixC45lZGTw9ttv07p1a2rWrGlBKgGzU0z//v1JT09n3bp1hISEWB1JxGkMGjSI/Pz8woUJYHa+io2NJSIiQh0CRC4iPz+fIUOGsGXLFlauXEmnTp2sjiT/9dtvv11wLDc3l/j4eAICAhze1MHpR1gvR79+/WjYsCEdO3bkyiuv5ODBg8TGxvLbb785/JhUKsawYcPYtm0b9957L7t27WLXrl2FX6tRowYDBw60MJ0UrKYtuC/x8fFs2rQJgMcff9yyXJ6iY8eODB48mClTpnDs2DFCQkKIi4srfA8T682fP5+MjIzCqRtr167l4MGDADzyyCMaELHI+PHjeffddxkwYADHjx8nISGhyNeHDx9uUTKJjo4mMzOT7t27ExwczJEjR1i6dClpaWnExsZSpUoVh76PW27NumDBApYvX86ePXvIyMggICCAbt26MWXKFNq2bWt1PI/WtGlTDh48WGzT7SZNmrBv3z4LUkkBLy8vbDYbdru98DOAzWYjPz/f4nSeIScnh2nTppGQkMCpU6do06YNTz/9tLqbOImmTZvy888/AxQu5in4/2X//v00atTIyngeq0ePHmzatKnYv1v0/mWtt956i9dff52dO3dy4sQJatasSadOnYiJiaFXr14Ofx+3LFhFRERExH245RxWEREREXEfKlhFRERExKmpYBURERERp6aCVUREREScmgpWEREREXFqKlhFRERExKmpYBURERERp6aCVUREREScmgpWEREREXFq/x+YAR8kXwZU6gAAAABJRU5ErkJggg==",
"svg": [
"<?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 (http://matplotlib.org/) -->\n",
"<svg height=\"377pt\" version=\"1.1\" viewBox=\"0 0 492 377\" width=\"492pt\" 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:square;stroke-linejoin:round;}\n",
" </style>\n",
" </defs>\n",
" <g id=\"figure_1\">\n",
" <g id=\"patch_1\">\n",
" <path d=\"\n",
"M0 377.615\n",
"L492.81 377.615\n",
"L492.81 0\n",
"L0 0\n",
"z\n",
"\" style=\"fill:#ffffff;\"/>\n",
" </g>\n",
" <g id=\"axes_1\">\n",
" <g id=\"patch_2\">\n",
" <path d=\"\n",
"M36.3306 357.338\n",
"L482.731 357.338\n",
"L482.731 11.7384\n",
"L36.3306 11.7384\n",
"z\n",
"\" style=\"fill:#ffffff;\"/>\n",
" </g>\n",
" <g id=\"line2d_1\">\n",
" <path clip-path=\"url(#p57b995488c)\" d=\"\n",
"M36.3306 353.509\n",
"L42.5865 352.428\n",
"L48.3955 351.191\n",
"L53.7577 349.816\n",
"L58.673 348.327\n",
"L63.1414 346.76\n",
"L67.6099 344.965\n",
"L72.0784 342.918\n",
"L76.1 340.838\n",
"L80.1216 338.514\n",
"L84.1432 335.924\n",
"L88.1649 333.049\n",
"L92.1865 329.869\n",
"L95.7613 326.769\n",
"L99.336 323.398\n",
"L102.911 319.742\n",
"L106.486 315.789\n",
"L110.06 311.525\n",
"L113.635 306.94\n",
"L117.21 302.025\n",
"L121.232 296.089\n",
"L125.253 289.714\n",
"L129.275 282.893\n",
"L133.296 275.623\n",
"L137.318 267.905\n",
"L141.786 258.809\n",
"L146.255 249.18\n",
"L150.723 239.037\n",
"L155.639 227.32\n",
"L161.001 213.927\n",
"L166.81 198.789\n",
"L173.513 180.668\n",
"L182.45 155.806\n",
"L197.642 113.462\n",
"L203.898 96.7165\n",
"L209.26 82.9992\n",
"L213.729 72.1521\n",
"L217.75 62.9328\n",
"L221.325 55.23\n",
"L224.9 48.0404\n",
"L228.028 42.2068\n",
"L231.156 36.8301\n",
"L234.284 31.9373\n",
"L236.965 28.1475\n",
"L239.646 24.7458\n",
"L242.327 21.745\n",
"L244.561 19.559\n",
"L246.795 17.6651\n",
"L249.03 16.0685\n",
"L251.264 14.7734\n",
"L253.498 13.7835\n",
"L255.286 13.2129\n",
"L257.073 12.8403\n",
"L258.86 12.6663\n",
"L260.648 12.6912\n",
"L262.435 12.9149\n",
"L264.223 13.337\n",
"L266.01 13.9569\n",
"L267.797 14.7734\n",
"L270.032 16.0685\n",
"L272.266 17.6651\n",
"L274.5 19.559\n",
"L276.734 21.745\n",
"L278.968 24.2175\n",
"L281.65 27.5533\n",
"L284.331 31.2793\n",
"L287.012 35.3816\n",
"L290.14 40.6227\n",
"L293.268 46.3287\n",
"L296.395 52.4712\n",
"L299.97 59.9871\n",
"L303.992 69.0179\n",
"L308.014 78.5901\n",
"L312.482 89.7721\n",
"L317.844 103.81\n",
"L324.547 122.051\n",
"L334.824 150.787\n",
"L346.442 183.117\n",
"L353.592 202.334\n",
"L359.401 217.331\n",
"L364.763 230.572\n",
"L369.678 242.132\n",
"L374.147 252.124\n",
"L378.615 261.595\n",
"L383.084 270.527\n",
"L387.105 278.096\n",
"L391.127 285.216\n",
"L395.149 291.888\n",
"L399.17 298.116\n",
"L403.192 303.908\n",
"L407.214 309.274\n",
"L410.788 313.696\n",
"L414.363 317.803\n",
"L417.938 321.607\n",
"L421.513 325.119\n",
"L425.087 328.352\n",
"L429.109 331.675\n",
"L433.131 334.683\n",
"L437.152 337.396\n",
"L441.174 339.836\n",
"L445.195 342.023\n",
"L449.217 343.975\n",
"L453.686 345.893\n",
"L458.154 347.571\n",
"L463.069 349.168\n",
"L467.985 350.534\n",
"L473.347 351.793\n",
"L479.156 352.921\n",
"L482.731 353.509\n",
"L482.731 353.509\" style=\"fill:none;stroke:#0000ff;\"/>\n",
" </g>\n",
" <g id=\"matplotlib.axis_1\">\n",
" <g id=\"xtick_1\">\n",
" <g id=\"line2d_2\">\n",
" <defs>\n",
" <path d=\"\n",
"M0 0\n",
"L0 -4\" id=\"mcb557df647\" style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\"/>\n",
" </defs>\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"36.330625\" xlink:href=\"#mcb557df647\" y=\"357.3384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_3\">\n",
" <defs>\n",
" <path d=\"\n",
"M0 0\n",
"L0 4\" id=\"mdad270ee8e\" style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\"/>\n",
" </defs>\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"36.330625\" xlink:href=\"#mdad270ee8e\" y=\"11.7384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_1\">\n",
" <!-- \u22123 -->\n",
" <defs>\n",
" <path d=\"\n",
"M10.5938 35.5\n",
"L73.1875 35.5\n",
"L73.1875 27.2031\n",
"L10.5938 27.2031\n",
"z\n",
"\" id=\"BitstreamVeraSans-Roman-2212\"/>\n",
" <path d=\"\n",
"M40.5781 39.3125\n",
"Q47.6562 37.7969 51.625 33\n",
"Q55.6094 28.2188 55.6094 21.1875\n",
"Q55.6094 10.4062 48.1875 4.48438\n",
"Q40.7656 -1.42188 27.0938 -1.42188\n",
"Q22.5156 -1.42188 17.6562 -0.515625\n",
"Q12.7969 0.390625 7.625 2.20312\n",
"L7.625 11.7188\n",
"Q11.7188 9.32812 16.5938 8.10938\n",
"Q21.4844 6.89062 26.8125 6.89062\n",
"Q36.0781 6.89062 40.9375 10.5469\n",
"Q45.7969 14.2031 45.7969 21.1875\n",
"Q45.7969 27.6406 41.2812 31.2656\n",
"Q36.7656 34.9062 28.7188 34.9062\n",
"L20.2188 34.9062\n",
"L20.2188 43.0156\n",
"L29.1094 43.0156\n",
"Q36.375 43.0156 40.2344 45.9219\n",
"Q44.0938 48.8281 44.0938 54.2969\n",
"Q44.0938 59.9062 40.1094 62.9062\n",
"Q36.1406 65.9219 28.7188 65.9219\n",
"Q24.6562 65.9219 20.0156 65.0312\n",
"Q15.375 64.1562 9.8125 62.3125\n",
"L9.8125 71.0938\n",
"Q15.4375 72.6562 20.3438 73.4375\n",
"Q25.25 74.2188 29.5938 74.2188\n",
"Q40.8281 74.2188 47.3594 69.1094\n",
"Q53.9062 64.0156 53.9062 55.3281\n",
"Q53.9062 49.2656 50.4375 45.0938\n",
"Q46.9688 40.9219 40.5781 39.3125\" id=\"BitstreamVeraSans-Roman-33\"/>\n",
" </defs>\n",
" <g transform=\"translate(28.601875 370.2446875)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-2212\"/>\n",
" <use x=\"83.7890625\" xlink:href=\"#BitstreamVeraSans-Roman-33\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_2\">\n",
" <g id=\"line2d_4\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"110.730625\" xlink:href=\"#mcb557df647\" y=\"357.3384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_5\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"110.730625\" xlink:href=\"#mdad270ee8e\" y=\"11.7384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_2\">\n",
" <!-- \u22122 -->\n",
" <defs>\n",
" <path d=\"\n",
"M19.1875 8.29688\n",
"L53.6094 8.29688\n",
"L53.6094 0\n",
"L7.32812 0\n",
"L7.32812 8.29688\n",
"Q12.9375 14.1094 22.625 23.8906\n",
"Q32.3281 33.6875 34.8125 36.5312\n",
"Q39.5469 41.8438 41.4219 45.5312\n",
"Q43.3125 49.2188 43.3125 52.7812\n",
"Q43.3125 58.5938 39.2344 62.25\n",
"Q35.1562 65.9219 28.6094 65.9219\n",
"Q23.9688 65.9219 18.8125 64.3125\n",
"Q13.6719 62.7031 7.8125 59.4219\n",
"L7.8125 69.3906\n",
"Q13.7656 71.7812 18.9375 73\n",
"Q24.125 74.2188 28.4219 74.2188\n",
"Q39.75 74.2188 46.4844 68.5469\n",
"Q53.2188 62.8906 53.2188 53.4219\n",
"Q53.2188 48.9219 51.5312 44.8906\n",
"Q49.8594 40.875 45.4062 35.4062\n",
"Q44.1875 33.9844 37.6406 27.2188\n",
"Q31.1094 20.4531 19.1875 8.29688\" id=\"BitstreamVeraSans-Roman-32\"/>\n",
" </defs>\n",
" <g transform=\"translate(103.121875 370.2446875)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-2212\"/>\n",
" <use x=\"83.7890625\" xlink:href=\"#BitstreamVeraSans-Roman-32\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_3\">\n",
" <g id=\"line2d_6\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"185.130625\" xlink:href=\"#mcb557df647\" y=\"357.3384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_7\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"185.130625\" xlink:href=\"#mdad270ee8e\" y=\"11.7384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_3\">\n",
" <!-- \u22121 -->\n",
" <defs>\n",
" <path d=\"\n",
"M12.4062 8.29688\n",
"L28.5156 8.29688\n",
"L28.5156 63.9219\n",
"L10.9844 60.4062\n",
"L10.9844 69.3906\n",
"L28.4219 72.9062\n",
"L38.2812 72.9062\n",
"L38.2812 8.29688\n",
"L54.3906 8.29688\n",
"L54.3906 0\n",
"L12.4062 0\n",
"z\n",
"\" id=\"BitstreamVeraSans-Roman-31\"/>\n",
" </defs>\n",
" <g transform=\"translate(177.475 370.0871875)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-2212\"/>\n",
" <use x=\"83.7890625\" xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_4\">\n",
" <g id=\"line2d_8\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"259.530625\" xlink:href=\"#mcb557df647\" y=\"357.3384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_9\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"259.530625\" xlink:href=\"#mdad270ee8e\" y=\"11.7384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_4\">\n",
" <!-- 0 -->\n",
" <defs>\n",
" <path d=\"\n",
"M31.7812 66.4062\n",
"Q24.1719 66.4062 20.3281 58.9062\n",
"Q16.5 51.4219 16.5 36.375\n",
"Q16.5 21.3906 20.3281 13.8906\n",
"Q24.1719 6.39062 31.7812 6.39062\n",
"Q39.4531 6.39062 43.2812 13.8906\n",
"Q47.125 21.3906 47.125 36.375\n",
"Q47.125 51.4219 43.2812 58.9062\n",
"Q39.4531 66.4062 31.7812 66.4062\n",
"M31.7812 74.2188\n",
"Q44.0469 74.2188 50.5156 64.5156\n",
"Q56.9844 54.8281 56.9844 36.375\n",
"Q56.9844 17.9688 50.5156 8.26562\n",
"Q44.0469 -1.42188 31.7812 -1.42188\n",
"Q19.5312 -1.42188 13.0625 8.26562\n",
"Q6.59375 17.9688 6.59375 36.375\n",
"Q6.59375 54.8281 13.0625 64.5156\n",
"Q19.5312 74.2188 31.7812 74.2188\" id=\"BitstreamVeraSans-Roman-30\"/>\n",
" </defs>\n",
" <g transform=\"translate(256.5071875 370.2446875)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_5\">\n",
" <g id=\"line2d_10\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"333.930625\" xlink:href=\"#mcb557df647\" y=\"357.3384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_11\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"333.930625\" xlink:href=\"#mdad270ee8e\" y=\"11.7384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_5\">\n",
" <!-- 1 -->\n",
" <g transform=\"translate(331.32625 370.0871875)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_6\">\n",
" <g id=\"line2d_12\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"408.330625\" xlink:href=\"#mcb557df647\" y=\"357.3384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_13\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"408.330625\" xlink:href=\"#mdad270ee8e\" y=\"11.7384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_6\">\n",
" <!-- 2 -->\n",
" <g transform=\"translate(405.55375 370.2446875)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-32\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"xtick_7\">\n",
" <g id=\"line2d_14\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"482.730625\" xlink:href=\"#mcb557df647\" y=\"357.3384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_15\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"482.730625\" xlink:href=\"#mdad270ee8e\" y=\"11.7384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_7\">\n",
" <!-- 3 -->\n",
" <g transform=\"translate(479.8515625 370.2446875)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-33\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"matplotlib.axis_2\">\n",
" <g id=\"ytick_1\">\n",
" <g id=\"line2d_16\">\n",
" <defs>\n",
" <path d=\"\n",
"M0 0\n",
"L4 0\" id=\"mc8fcea1516\" style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\"/>\n",
" </defs>\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"36.330625\" xlink:href=\"#mc8fcea1516\" y=\"357.3384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_17\">\n",
" <defs>\n",
" <path d=\"\n",
"M0 0\n",
"L-4 0\" id=\"m0d5b0a6425\" style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\"/>\n",
" </defs>\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"482.730625\" xlink:href=\"#m0d5b0a6425\" y=\"357.3384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_8\">\n",
" <!-- 0.00 -->\n",
" <defs>\n",
" <path d=\"\n",
"M10.6875 12.4062\n",
"L21 12.4062\n",
"L21 0\n",
"L10.6875 0\n",
"z\n",
"\" id=\"BitstreamVeraSans-Roman-2e\"/>\n",
" </defs>\n",
" <g transform=\"translate(7.2 361.70625)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n",
" <use x=\"95.41015625\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" <use x=\"159.033203125\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_2\">\n",
" <g id=\"line2d_18\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"36.330625\" xlink:href=\"#mc8fcea1516\" y=\"314.1384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_19\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"482.730625\" xlink:href=\"#m0d5b0a6425\" y=\"314.1384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_9\">\n",
" <!-- 0.05 -->\n",
" <defs>\n",
" <path d=\"\n",
"M10.7969 72.9062\n",
"L49.5156 72.9062\n",
"L49.5156 64.5938\n",
"L19.8281 64.5938\n",
"L19.8281 46.7344\n",
"Q21.9688 47.4688 24.1094 47.8281\n",
"Q26.2656 48.1875 28.4219 48.1875\n",
"Q40.625 48.1875 47.75 41.5\n",
"Q54.8906 34.8125 54.8906 23.3906\n",
"Q54.8906 11.625 47.5625 5.09375\n",
"Q40.2344 -1.42188 26.9062 -1.42188\n",
"Q22.3125 -1.42188 17.5469 -0.640625\n",
"Q12.7969 0.140625 7.71875 1.70312\n",
"L7.71875 11.625\n",
"Q12.1094 9.23438 16.7969 8.0625\n",
"Q21.4844 6.89062 26.7031 6.89062\n",
"Q35.1562 6.89062 40.0781 11.3281\n",
"Q45.0156 15.7656 45.0156 23.3906\n",
"Q45.0156 31 40.0781 35.4375\n",
"Q35.1562 39.8906 26.7031 39.8906\n",
"Q22.75 39.8906 18.8125 39.0156\n",
"Q14.8906 38.1406 10.7969 36.2812\n",
"z\n",
"\" id=\"BitstreamVeraSans-Roman-35\"/>\n",
" </defs>\n",
" <g transform=\"translate(7.45125 318.50625)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n",
" <use x=\"95.41015625\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" <use x=\"159.033203125\" xlink:href=\"#BitstreamVeraSans-Roman-35\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_3\">\n",
" <g id=\"line2d_20\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"36.330625\" xlink:href=\"#mc8fcea1516\" y=\"270.9384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_21\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"482.730625\" xlink:href=\"#m0d5b0a6425\" y=\"270.9384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_10\">\n",
" <!-- 0.10 -->\n",
" <g transform=\"translate(7.2 275.30625)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n",
" <use x=\"95.41015625\" xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n",
" <use x=\"159.033203125\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_4\">\n",
" <g id=\"line2d_22\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"36.330625\" xlink:href=\"#mc8fcea1516\" y=\"227.7384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_23\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"482.730625\" xlink:href=\"#m0d5b0a6425\" y=\"227.7384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_11\">\n",
" <!-- 0.15 -->\n",
" <g transform=\"translate(7.45125 232.10625)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n",
" <use x=\"95.41015625\" xlink:href=\"#BitstreamVeraSans-Roman-31\"/>\n",
" <use x=\"159.033203125\" xlink:href=\"#BitstreamVeraSans-Roman-35\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_5\">\n",
" <g id=\"line2d_24\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"36.330625\" xlink:href=\"#mc8fcea1516\" y=\"184.5384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_25\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"482.730625\" xlink:href=\"#m0d5b0a6425\" y=\"184.5384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_12\">\n",
" <!-- 0.20 -->\n",
" <g transform=\"translate(7.2 188.90625)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n",
" <use x=\"95.41015625\" xlink:href=\"#BitstreamVeraSans-Roman-32\"/>\n",
" <use x=\"159.033203125\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_6\">\n",
" <g id=\"line2d_26\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"36.330625\" xlink:href=\"#mc8fcea1516\" y=\"141.3384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_27\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"482.730625\" xlink:href=\"#m0d5b0a6425\" y=\"141.3384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_13\">\n",
" <!-- 0.25 -->\n",
" <g transform=\"translate(7.45125 145.70625)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n",
" <use x=\"95.41015625\" xlink:href=\"#BitstreamVeraSans-Roman-32\"/>\n",
" <use x=\"159.033203125\" xlink:href=\"#BitstreamVeraSans-Roman-35\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_7\">\n",
" <g id=\"line2d_28\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"36.330625\" xlink:href=\"#mc8fcea1516\" y=\"98.1384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_29\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"482.730625\" xlink:href=\"#m0d5b0a6425\" y=\"98.1384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_14\">\n",
" <!-- 0.30 -->\n",
" <g transform=\"translate(7.2 102.50625)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n",
" <use x=\"95.41015625\" xlink:href=\"#BitstreamVeraSans-Roman-33\"/>\n",
" <use x=\"159.033203125\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_8\">\n",
" <g id=\"line2d_30\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"36.330625\" xlink:href=\"#mc8fcea1516\" y=\"54.9384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_31\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"482.730625\" xlink:href=\"#m0d5b0a6425\" y=\"54.9384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_15\">\n",
" <!-- 0.35 -->\n",
" <g transform=\"translate(7.45125 59.30625)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n",
" <use x=\"95.41015625\" xlink:href=\"#BitstreamVeraSans-Roman-33\"/>\n",
" <use x=\"159.033203125\" xlink:href=\"#BitstreamVeraSans-Roman-35\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"ytick_9\">\n",
" <g id=\"line2d_32\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"36.330625\" xlink:href=\"#mc8fcea1516\" y=\"11.7384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"line2d_33\">\n",
" <g>\n",
" <use style=\"stroke:#000000;stroke-linecap:butt;stroke-width:0.5;\" x=\"482.730625\" xlink:href=\"#m0d5b0a6425\" y=\"11.7384375\"/>\n",
" </g>\n",
" </g>\n",
" <g id=\"text_16\">\n",
" <!-- 0.40 -->\n",
" <defs>\n",
" <path d=\"\n",
"M37.7969 64.3125\n",
"L12.8906 25.3906\n",
"L37.7969 25.3906\n",
"z\n",
"\n",
"M35.2031 72.9062\n",
"L47.6094 72.9062\n",
"L47.6094 25.3906\n",
"L58.0156 25.3906\n",
"L58.0156 17.1875\n",
"L47.6094 17.1875\n",
"L47.6094 0\n",
"L37.7969 0\n",
"L37.7969 17.1875\n",
"L4.89062 17.1875\n",
"L4.89062 26.7031\n",
"z\n",
"\" id=\"BitstreamVeraSans-Roman-34\"/>\n",
" </defs>\n",
" <g transform=\"translate(7.2 16.10625)scale(0.12 -0.12)\">\n",
" <use xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" <use x=\"63.623046875\" xlink:href=\"#BitstreamVeraSans-Roman-2e\"/>\n",
" <use x=\"95.41015625\" xlink:href=\"#BitstreamVeraSans-Roman-34\"/>\n",
" <use x=\"159.033203125\" xlink:href=\"#BitstreamVeraSans-Roman-30\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <g id=\"patch_3\">\n",
" <path d=\"\n",
"M36.3306 11.7384\n",
"L482.731 11.7384\" style=\"fill:none;stroke:#000000;\"/>\n",
" </g>\n",
" <g id=\"patch_4\">\n",
" <path d=\"\n",
"M482.731 357.338\n",
"L482.731 11.7384\" style=\"fill:none;stroke:#000000;\"/>\n",
" </g>\n",
" <g id=\"patch_5\">\n",
" <path d=\"\n",
"M36.3306 357.338\n",
"L482.731 357.338\" style=\"fill:none;stroke:#000000;\"/>\n",
" </g>\n",
" <g id=\"patch_6\">\n",
" <path d=\"\n",
"M36.3306 357.338\n",
"L36.3306 11.7384\" style=\"fill:none;stroke:#000000;\"/>\n",
" </g>\n",
" </g>\n",
" </g>\n",
" <defs>\n",
" <clipPath id=\"p57b995488c\">\n",
" <rect height=\"345.6\" width=\"446.4\" x=\"36.330625\" y=\"11.7384375\"/>\n",
" </clipPath>\n",
" </defs>\n",
"</svg>\n"
],
"text": [
"Figure(PyObject <matplotlib.figure.Figure object at 0x110a55850>)"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"1-element Array{Any,1}:\n",
" PyObject <matplotlib.lines.Line2D object at 0x110d90e10>"
]
}
],
"prompt_number": 30
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Looks normal to me! "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##2b. -loop"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"function loop_interate(f, a::Float64, b::Float64, integration_number::Int=1000)\n",
"\n",
" @assert a < b\n",
" interval_bounds = linspace(a,b,integration_number+1)\n",
" value_array = zeros(Float64,integration_number) \n",
"\n",
" for i = 2:integration_number\n",
" #trapizoid rule, cause that is enough for a homework \n",
" value_array[i] = (interval_bounds[i] - interval_bounds[i-1]) *.5*(f(interval_bounds[i-1] )+f(interval_bounds[i] ) ) \n",
" end\n",
" \n",
" return sum(value_array)\n",
"\n",
"end"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 83,
"text": [
"loop_interate (generic function with 3 methods)"
]
}
],
"prompt_number": 83
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Does it obey the 68\u201395\u201399.7 rule?\n",
"println(loop_interate(f,-1.0,1.0))\n",
"println(loop_interate(f,-2.0,2.0))\n",
"println(loop_interate(f,-3.0,3.0))\n",
"println(loop_interate(f,-100.0,100.0))\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.6822049054334063"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"0.9542826178365158\n",
"0.9972732918302741\n",
"1.0000000000000004\n"
]
}
],
"prompt_number": 118
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##2c. -vector"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"function vector_interate(f, a::Float64, b::Float64, integration_number::Int=1000)\n",
"\n",
" @assert a < b\n",
" bounds = linspace(a,b,integration_number+1)\n",
" lower_bounds = bounds[1:integration_number]\n",
" upper_bounds = bounds[2:end]\n",
"\n",
" value_array = (upper_bounds .- lower_bounds) *.5.*(f(lower_bounds ).+f(upper_bounds)) \n",
"\n",
" return sum(value_array)\n",
"\n",
"end"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 108,
"text": [
"vector_interate (generic function with 2 methods)"
]
}
],
"prompt_number": 108
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Does it obey the 68\u201395\u201399.7 rule?\n",
"println(vector_interate(f,-1.0,1.0))\n",
"println(vector_interate(f,-2.0,2.0))\n",
"println(vector_interate(f,-3.0,3.0))\n",
"println(vector_interate(f,-100.0,100.0))\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.682689330823248"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"0.9544994481518971\n",
"0.9973001241637558\n",
"1.0\n"
]
}
],
"prompt_number": 120
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##2d. -map + reduce"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"function trapazoid_rule(a::Float64,b::Float64)\n",
" #If f is not alrady complied you are out of luck here. \n",
" return (b - a) * .5 * (f(a) + f(b))\n",
" \n",
"end\n",
"\n",
"function map_plus_reduce_interate(f, a::Float64, b::Float64, integration_number::Int=1000)\n",
"\n",
" #Don't actually call f anymore\n",
" @assert a < b\n",
" bounds = linspace(a,b,integration_number+1)\n",
" lower_bounds = bounds[1:integration_number]\n",
" upper_bounds = bounds[2:end]\n",
"\n",
" value_array = map(trapazoid_rule,lower_bounds,upper_bounds)\n",
"\n",
" return reduce(+,value_array)\n",
"\n",
"end\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 143,
"text": [
"map_plus_reduce_interate (generic function with 2 methods)"
]
}
],
"prompt_number": 143
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Does it obey the 68\u201395\u201399.7 rule?\n",
"println(map_plus_reduce_interate(f,-1.0,1.0))\n",
"println(map_plus_reduce_interate(f,-2.0,2.0))\n",
"println(map_plus_reduce_interate(f,-3.0,3.0))\n",
"println(map_plus_reduce_interate(f,-100.0,100.0))\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.682689330823248"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"0.9544994481518971\n",
"0.9973001241637558\n",
"1.0\n"
]
}
],
"prompt_number": 144
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##2e. -mapreduce"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#mapreduce can't handle multiple itr, so just tweaking map_plus_reduce wont do it. errgh, tired and grumpy, \n",
"#don't want to code no more today\n",
"\n",
"function trapazoid_rule(bounds)\n",
" #If f is not alrady complied you are out of luck here. \n",
" return (b - a) * .5 * (f(a) + f(b))\n",
" \n",
"end\n",
"\n",
"function mapreduce_interate(f, a::Float64, b::Float64, integration_number::Int=1000)\n",
"\n",
" #Don't actually call f anymore \n",
" @assert a < b\n",
" bounds = linspace(a,b,integration_number+1)\n",
" #lower_bounds = bounds[1:integration_number]\n",
" #upper_bounds = bounds[2:end]\n",
"\n",
" return mapreduce(trapazoid_rule,+,bounds)\n",
"\n",
"\n",
"end\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 159,
"text": [
"mapreduce_interate (generic function with 2 methods)"
]
}
],
"prompt_number": 159
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Does it obey the 68\u201395\u201399.7 rule?\n",
"println(mapreduce_interate(f,-1.0,1.0))\n",
"println(mapreduce_interate(f,-2.0,2.0))\n",
"println(mapreduce_interate(f,-3.0,3.0))\n",
"println(mapreduce_interate(f,-100.0,100.0))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "LoadError",
"evalue": "no method trapazoid_rule(Float64,)\nat In[149]:2",
"output_type": "pyerr",
"traceback": [
"no method trapazoid_rule(Float64,)\nat In[149]:2",
" in mr_pairwise at reduce.jl:135",
" in mr_pairwise at reduce.jl:153 (repeats 3 times)",
" in mapreduce at reduce.jl:162",
" in mapreduce_interate at In[148]:15",
" in mapreduce_interate at In[148]:10"
]
}
],
"prompt_number": 149
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##2f. -devectorize"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#blah! looks interesting...loops faster than vectors? Strange."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 151
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##2g."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"println(\"Loop: \",@elapsed loop_interate(f,-1.0,1.0,1000000))\n",
"println(\"Vector: \",@elapsed vector_interate(f,-1.0,1.0,1000000))\n",
"println(\"Map+Reduce: \",@elapsed map_plus_reduce_interate(f,-1.0,1.0,1000000))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Loop: 0."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"440510142\n",
"Vector: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.188881352\n",
"Map+Reduce: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.216344906\n"
]
}
],
"prompt_number": 160
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##2h. \n",
"Since I only made three versions, let's profile all of them."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Profile.clear()\n",
"@profile loop_interate(f,-1.0,1.0,1000000)\n",
"Profile.print()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1 array.jl; setindex!; line: 412\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 372 multi.jl; anonymous; line: 1308\n",
" 372 ...IJulia/src/IJulia.jl; eventloop; line: 92\n",
" 372 ...Julia/src/IJulia.jl; eventloop; line: 68\n",
" 372 ...execute_request.jl; execute_request_0x535c5df2; line: 132\n",
" 372 loading.jl; include_string; line: 83\n",
" 371 profile.jl; anonymous; line: 14\n",
" 14 In[83]; loop_interate; line: 4\n",
" 14 array.jl; linspace; line: 238\n",
" 2 In[83]; loop_interate; line: 5\n",
" 2 array.jl; fill!; line: 187\n",
" 1 In[83]; loop_interate; line: 8\n",
" 353 In[83]; loop_interate; line: 10\n",
" 53 In[17]; f; line: 2\n",
" 1 array.jl; setindex!; line: 412\n",
" 3 float.jl; *; line: 136\n",
" 2 float.jl; +; line: 132\n",
" 1 In[83]; loop_interate; line: 13\n",
" 1 abstractarray.jl; sum; line: 1487\n",
" 1 abstractarray.jl; sum_pairwise; line: 1481\n",
" 1 abstractarray.jl; sum_pairwise; line: 1481\n",
" 1 abstractarray.jl; sum_pairwise; line: 1481\n",
" 1 abstractarray.jl; sum_pairwise; line: 1481\n",
" 1 abstractarray.jl; sum_pairwise; line: 1481\n",
" 1 abstractarray.jl; sum_pairwise; line: 1481\n",
" 1 ...ractarray.jl; sum_pairwise; line: 1481\n",
" 1 ...ractarray.jl; sum_pairwise; line: 1481\n",
" 1 ...actarray.jl; sum_pairwise; line: 1481\n",
" 1 ...actarray.jl; sum_pairwise; line: 1481\n",
" 1 ...actarray.jl; sum_pairwise; line: 1481\n",
" 1 ...ctarray.jl; sum_pairwise; line: 1481\n",
" 1 ...ctarray.jl; sum_pairwise; line: 1481\n",
" 1 ...ctarray.jl; sum_pairwise; line: 135\n"
]
}
],
"prompt_number": 161
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Profile.clear()\n",
"@profile vector_interate(f,-1.0,1.0,1000000)\n",
"Profile.print()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 160 multi.jl; anonymous; line: 1308\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 160 ...IJulia/src/IJulia.jl; eventloop; line: 92\n",
" 160 ...Julia/src/IJulia.jl; eventloop; line: 68\n",
" 160 ...execute_request.jl; execute_request_0x535c5df2; line: 132\n",
" 160 loading.jl; include_string; line: 83\n",
" 160 profile.jl; anonymous; line: 14\n",
" 15 In[108]; vector_interate; line: 4\n",
" 15 array.jl; linspace; line: 238\n",
" 22 In[108]; vector_interate; line: 6\n",
" 22 array.jl; getindex; line: 294\n",
" 122 In[108]; vector_interate; line: 13\n",
" 3 array.jl; .*; line: 135\n",
" 3 broadcast.jl; ##broadcast_T_-#215; line: 189\n",
" 3 broadcast.jl; ##-_inner!#217; line: 166\n",
" 92 In[29]; f; line: 2\n",
" 4 array.jl; .*; line: 135\n",
" 14 array.jl; ./; line: 135\n",
" 46 array.jl; .^; line: 920\n",
" 28 operators.jl; exp; line: 236\n",
" 20 broadcast.jl; .*; line: 277\n",
" 20 broadcast.jl; ##broadcast_T_*#221; line: 189\n",
" 17 broadcast.jl; broadcast_args; line: 87\n",
" 17 broadcast.jl; calc_loop_strides; line: 63\n",
" 1 broadcast.jl; ##*_inner!#223; line: 136\n",
" 2 broadcast.jl; ##*_inner!#223; line: 166\n",
" 4 broadcast.jl; .+; line: 276\n",
" 4 broadcast.jl; ##broadcast_T_+#209; line: 189\n",
" 1 broadcast.jl; ##+_inner!#211; line: 136\n",
" 3 broadcast.jl; ##+_inner!#211; line: 166\n",
" 1 In[108]; vector_interate; line: 15\n",
" 1 abstractarray.jl; sum; line: 1487\n",
" 1 abstractarray.jl; sum_pairwise; line: 1481\n",
" 1 abstractarray.jl; sum_pairwise; line: 1481\n",
" 1 abstractarray.jl; sum_pairwise; line: 1481\n",
" 1 abstractarray.jl; sum_pairwise; line: 1481\n",
" 1 ...ractarray.jl; sum_pairwise; line: 1481\n",
" 1 ...ractarray.jl; sum_pairwise; line: 1481\n",
" 1 ...actarray.jl; sum_pairwise; line: 1481\n",
" 1 ...actarray.jl; sum_pairwise; line: 1481\n",
" 1 ...actarray.jl; sum_pairwise; line: 1481\n",
" 1 ...ctarray.jl; sum_pairwise; line: 1481\n",
" 1 ...ctarray.jl; sum_pairwise; line: 1481\n",
" 1 ...tarray.jl; sum_pairwise; line: 1481\n",
" 1 ...tarray.jl; sum_pairwise; line: 1481\n",
" 1 ...tarray.jl; sum_pairwise; line: 135\n"
]
}
],
"prompt_number": 162
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Profile.clear()\n",
"@profile map_plus_reduce_interate(f,-1.0,1.0,1000000) \n",
"Profile.print()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 263 multi.jl; anonymous; line: 1308\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 263 ...IJulia/src/IJulia.jl; eventloop; line: 92\n",
" 263 ...Julia/src/IJulia.jl; eventloop; line: 68\n",
" 263 ...execute_request.jl; execute_request_0x535c5df2; line: 132\n",
" 263 loading.jl; include_string; line: 83\n",
" 262 profile.jl; anonymous; line: 14\n",
" 14 In[143]; map_plus_reduce_interate; line: 11\n",
" 14 array.jl; linspace; line: 238\n",
" 189 In[143]; map_plus_reduce_interate; line: 15\n",
" 19 abstractarray.jl; map; line: 1696\n",
" 170 abstractarray.jl; map; line: 1702\n",
" 170 abstractarray.jl; map_to!; line: 1690\n",
" 52 In[148]; trapazoid_rule; line: 3\n",
" 49 In[17]; f; line: 2\n",
" 1 array.jl; setindex!; line: 412\n",
" 59 In[143]; map_plus_reduce_interate; line: 17\n",
" 59 reduce.jl; reduce; line: 179\n",
" 59 reduce.jl; r_pairwise; line: 174\n",
" 59 reduce.jl; r_pairwise; line: 174\n",
" 59 reduce.jl; r_pairwise; line: 174\n",
" 59 reduce.jl; r_pairwise; line: 174\n",
" 59 reduce.jl; r_pairwise; line: 174\n",
" 59 reduce.jl; r_pairwise; line: 174\n",
" 59 reduce.jl; r_pairwise; line: 174\n",
" 59 reduce.jl; r_pairwise; line: 174\n",
" 59 reduce.jl; r_pairwise; line: 174\n",
" 59 reduce.jl; r_pairwise; line: 174\n",
" 59 reduce.jl; r_pairwise; line: 174\n",
" 59 reduce.jl; r_pairwise; line: 174\n",
" 59 reduce.jl; r_pairwise; line: 174\n",
" 59 reduce.jl; r_pairwise; line: 135\n"
]
}
],
"prompt_number": 163
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
eaton-lab/toytree | manuscript/Manuscript-code.ipynb | 1 | 5268165 | null | bsd-3-clause |
kkhenriquez/python-for-data-science | Week-8-NLP-Databases/Working with Databases.ipynb | 2 | 14860 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Access a Database with Python - Iris Dataset\n",
"\n",
"The Iris dataset is a popular dataset especially in the Machine Learning community, it is a set of features of 50 Iris flowers and their classification into 3 species.\n",
"It is often used to introduce classification Machine Learning algorithms.\n",
"\n",
"First let's download the dataset in `SQLite` format from Kaggle:\n",
"\n",
"<https://www.kaggle.com/uciml/iris/>\n",
"\n",
"Download `database.sqlite` and save it in the `data/iris` folder."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p><img src=\"https://upload.wikimedia.org/wikipedia/commons/4/49/Iris_germanica_%28Purple_bearded_Iris%29%2C_Wakehurst_Place%2C_UK_-_Diliff.jpg\" alt=\"Iris germanica (Purple bearded Iris), Wakehurst Place, UK - Diliff.jpg\" height=\"145\" width=\"114\"></p>\n",
"\n",
"<p><br> From <a href=\"https://commons.wikimedia.org/wiki/File:Iris_germanica_(Purple_bearded_Iris),_Wakehurst_Place,_UK_-_Diliff.jpg#/media/File:Iris_germanica_(Purple_bearded_Iris),_Wakehurst_Place,_UK_-_Diliff.jpg\">Wikimedia</a>, by <a href=\"//commons.wikimedia.org/wiki/User:Diliff\" title=\"User:Diliff\">Diliff</a> - <span class=\"int-own-work\" lang=\"en\">Own work</span>, <a href=\"http://creativecommons.org/licenses/by-sa/3.0\" title=\"Creative Commons Attribution-Share Alike 3.0\">CC BY-SA 3.0</a>, <a href=\"https://commons.wikimedia.org/w/index.php?curid=33037509\">Link</a></p>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First let's check that the sqlite database is available and display an error message if the file is not available (`assert` checks if the expression is `True`, otherwise throws `AssertionError` with the error message string provided):"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"data_iris_folder_content = os.listdir(\"data/iris\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"error_message = \"Error: sqlite file not available, check instructions above to download it\"\n",
"assert \"database.sqlite\" in data_iris_folder_content, error_message"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Access the Database with the sqlite3 Package"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use the `sqlite3` package from the Python standard library to connect to the `sqlite` database:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sqlite3"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"conn = sqlite3.connect('data/iris/database.sqlite')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cursor = conn.cursor()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"sqlite3.Cursor"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(cursor)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `sqlite3.Cursor` object is our interface to the database, mostly throught the `execute` method that allows to run any `SQL` query on our database.\n",
"\n",
"First of all we can get a list of all the tables saved into the database, this is done by reading the column `name` from the `sqlite_master` metadata table with:\n",
"\n",
" SELECT name FROM sqlite_master\n",
" \n",
"The output of the `execute` method is an iterator that can be used in a `for` loop to print the value of each row."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"('Iris',)\n"
]
}
],
"source": [
"for row in cursor.execute(\"SELECT name FROM sqlite_master\"):\n",
" print(row)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"a shortcut to directly execute the query and gather the results is the `fetchall` method:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('Iris',)]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cursor.execute(\"SELECT name FROM sqlite_master\").fetchall()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Notice**: this way of finding the available tables in a database is specific to `sqlite`, other databases like `MySQL` or `PostgreSQL` have different syntax."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we can execute standard `SQL` query on the database, `SQL` is a language designed to interact with data stored in a relational database. It has a standard specification, therefore the commands below work on any database.\n",
"\n",
"If you need to connect to another database, you would use another package instead of `sqlite3`, for example:\n",
"\n",
"* [MySQL Connector](https://dev.mysql.com/doc/connector-python/en/) for MySQL\n",
"* [Psycopg](http://initd.org/psycopg/docs/install.html) for PostgreSQL\n",
"* [pymssql](http://pymssql.org/en/stable/) for Microsoft MS SQL\n",
"\n",
"then you would connect to the database using specific host, port and authentication credentials but then you could execute the same exact `SQL` statements.\n",
"\n",
"Let's take a look for example at the first 3 rows in the Iris table:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sample_data = cursor.execute(\"SELECT * FROM Iris LIMIT 20\").fetchall()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'list'>\n"
]
},
{
"data": {
"text/plain": [
"[(1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'),\n",
" (2, 4.9, 3, 1.4, 0.2, 'Iris-setosa'),\n",
" (3, 4.7, 3.2, 1.3, 0.2, 'Iris-setosa'),\n",
" (4, 4.6, 3.1, 1.5, 0.2, 'Iris-setosa'),\n",
" (5, 5, 3.6, 1.4, 0.2, 'Iris-setosa'),\n",
" (6, 5.4, 3.9, 1.7, 0.4, 'Iris-setosa'),\n",
" (7, 4.6, 3.4, 1.4, 0.3, 'Iris-setosa'),\n",
" (8, 5, 3.4, 1.5, 0.2, 'Iris-setosa'),\n",
" (9, 4.4, 2.9, 1.4, 0.2, 'Iris-setosa'),\n",
" (10, 4.9, 3.1, 1.5, 0.1, 'Iris-setosa'),\n",
" (11, 5.4, 3.7, 1.5, 0.2, 'Iris-setosa'),\n",
" (12, 4.8, 3.4, 1.6, 0.2, 'Iris-setosa'),\n",
" (13, 4.8, 3, 1.4, 0.1, 'Iris-setosa'),\n",
" (14, 4.3, 3, 1.1, 0.1, 'Iris-setosa'),\n",
" (15, 5.8, 4, 1.2, 0.2, 'Iris-setosa'),\n",
" (16, 5.7, 4.4, 1.5, 0.4, 'Iris-setosa'),\n",
" (17, 5.4, 3.9, 1.3, 0.4, 'Iris-setosa'),\n",
" (18, 5.1, 3.5, 1.4, 0.3, 'Iris-setosa'),\n",
" (19, 5.7, 3.8, 1.7, 0.3, 'Iris-setosa'),\n",
" (20, 5.1, 3.8, 1.5, 0.3, 'Iris-setosa')]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(type(sample_data))\n",
"sample_data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Id',\n",
" 'SepalLengthCm',\n",
" 'SepalWidthCm',\n",
" 'PetalLengthCm',\n",
" 'PetalWidthCm',\n",
" 'Species']"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[row[0] for row in cursor.description]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is evident that the interface provided by `sqlite3` is low-level, for data exploration purposes we would like to directly import data into a more user friendly library like `pandas`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import data from a database to `pandas`"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"iris_data = pd.read_sql_query(\"SELECT * FROM Iris\", conn)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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>SepalLengthCm</th>\n",
" <th>SepalWidthCm</th>\n",
" <th>PetalLengthCm</th>\n",
" <th>PetalWidthCm</th>\n",
" <th>Species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n",
"0 1 5.1 3.5 1.4 0.2 Iris-setosa\n",
"1 2 4.9 3.0 1.4 0.2 Iris-setosa\n",
"2 3 4.7 3.2 1.3 0.2 Iris-setosa\n",
"3 4 4.6 3.1 1.5 0.2 Iris-setosa\n",
"4 5 5.0 3.6 1.4 0.2 Iris-setosa"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Id int64\n",
"SepalLengthCm float64\n",
"SepalWidthCm float64\n",
"PetalLengthCm float64\n",
"PetalWidthCm float64\n",
"Species object\n",
"dtype: object"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_data.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`pandas.read_sql_query` takes a `SQL` query and a connection object and imports the data into a `DataFrame`, also keeping the same data types of the database columns. `pandas` provides a lot of the same functionality of `SQL` with a more user-friendly interface.\n",
"\n",
"However, `sqlite3` is extremely useful for downselecting data **before** importing them in `pandas`.\n",
"\n",
"For example you might have 1 TB of data in a table stored in a database on a server machine. You are interested in working on a subset of the data based on some criterion, unfortunately it would be impossible to first load data into `pandas` and then filter them, therefore we should tell the database to perform the filtering and just load into `pandas` the downsized dataset."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"iris_setosa_data = pd.read_sql_query(\"SELECT * FROM Iris WHERE Species == 'Iris-setosa'\", conn)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(50, 6)\n",
"(150, 6)\n"
]
}
],
"source": [
"iris_setosa_data\n",
"print(iris_setosa_data.shape)\n",
"print(iris_data.shape)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"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": 1
}
| mit |
yunfeiz/py_learnt | pandas/pandas_utils.ipynb | 1 | 16275 | {
"cells": [
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"data = {'BoolCol': [1, 2, 3, 3, 4],\n",
" 'attr': [22, 33, 22, 44, 66],\n",
" 'BoolC': [1, 2, 3, 3, 4],\n",
" 'att': [22, 33, 22, 44, 66],\n",
" 'Bool': [1, 2, 3, 3, 4]\n",
" }\n",
"index= pd.Index(data=[1,2,3,4,5],name=\"index_new\")\n",
"df=pd.DataFrame(data, index=index)\n",
"\n",
"#df.index, df.index.name"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"#随机生成3000个test号\n",
"#random.sample(range(0,10),6)从0-9这十位数中随机选出6位\n",
"test_list=[]\n",
"for i in range(3000):\n",
" test_list.append(\"123456\"+\"\".join(str(s) for s in random.sample(range(0,10),6)))\n",
"#生成3000个1-200的随机浮点数，且保留两位小数\n",
"test_list2 = [round(random.uniform(1,200),2) for i in range(3000)]\n",
"data = {\n",
" 'date':pd.date_range(\"2000\",freq= 'D',periods=3000),\n",
" 'aa':test_list,\n",
" 'test2':test_list2,\n",
" 'label':[random.randint(0,1) for _ in range(3000)]\n",
"}\n",
"df_test = pd.DataFrame(data)\n",
"\n",
"#date_1= pd.date_range(\"2000\",freq= 'D',periods=3000).year"
]
},
{
"cell_type": "code",
"execution_count": 92,
"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>test2</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3000.000000</td>\n",
" <td>3000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>100.821863</td>\n",
" <td>0.512000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>56.557216</td>\n",
" <td>0.499939</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.140000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>52.022500</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>101.300000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>150.087500</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>199.900000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" test2 label\n",
"count 3000.000000 3000.000000\n",
"mean 100.821863 0.512000\n",
"std 56.557216 0.499939\n",
"min 1.140000 0.000000\n",
"25% 52.022500 0.000000\n",
"50% 101.300000 1.000000\n",
"75% 150.087500 1.000000\n",
"max 199.900000 1.000000"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test.describe()"
]
},
{
"cell_type": "code",
"execution_count": 93,
"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>date</th>\n",
" <th>aa</th>\n",
" <th>test2</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2000-01-01</td>\n",
" <td>123456253640</td>\n",
" <td>51.40</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2000-01-02</td>\n",
" <td>123456652438</td>\n",
" <td>65.38</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2000-01-03</td>\n",
" <td>123456549183</td>\n",
" <td>35.39</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2000-01-04</td>\n",
" <td>123456792183</td>\n",
" <td>165.18</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2000-01-05</td>\n",
" <td>123456785963</td>\n",
" <td>36.03</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date aa test2 label\n",
"0 2000-01-01 123456253640 51.40 1\n",
"1 2000-01-02 123456652438 65.38 1\n",
"2 2000-01-03 123456549183 35.39 0\n",
"3 2000-01-04 123456792183 165.18 1\n",
"4 2000-01-05 123456785963 36.03 0"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"date=df_test.pop('date')\n",
"df_test.insert(0,'date',date)\n",
"df_test.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"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>date</th>\n",
" <th>aa</th>\n",
" <th>test2</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2000-01-01</td>\n",
" <td>123456253640</td>\n",
" <td>51.40</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2000-01-02</td>\n",
" <td>123456652438</td>\n",
" <td>65.38</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date aa test2 label\n",
"0 2000-01-01 123456253640 51.40 1\n",
"1 2000-01-02 123456652438 65.38 1"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test[:2]"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" c1 c3 c5\n",
"B 0.012703 0.048813 0.508066\n",
"D 0.200248 0.192892 0.293228\n",
" c1 c3\n",
"B 0.012703 0.048813\n",
" c1 c3 c5\n",
"A 0.700437 0.676514 0.951458\n",
"B 0.012703 0.048813 0.508066\n"
]
}
],
"source": [
"import numpy as np \n",
"import pandas as pd \n",
"from pandas import Series, DataFrame \n",
"np.random.seed(666) \n",
"df = pd.DataFrame(np.random.rand(25).reshape(5, 5), \n",
" index=['A', 'B', 'D', 'E', 'F'], \n",
" columns=['c1', 'c2', 'c3', 'c4', 'c5']) \n",
"#print(df.shape) # (5, 5) # 返回前五行 \n",
"#df.head() # 返回后五行 \n",
"#df.tail() # 访问 某几个 列 \n",
"#print(df[['c1', 'c4']]) \n",
"'''\n",
" c1 c4\n",
"A 0.700437 0.727858\n",
"B 0.012703 0.099929\n",
"D 0.200248 0.700845\n",
"E 0.774479 0.110954\n",
"F 0.023236 0.197503\n",
"''' # 赋值于一个新的 dataframe \n",
"sub_df = df[['c1', 'c3', 'c5']] \n",
"'''\n",
" c1 c3 c5\n",
"A 0.700437 0.676514 0.951458\n",
"B 0.012703 0.048813 0.508066\n",
"D 0.200248 0.192892 0.293228\n",
"E 0.774479 0.112858 0.247668\n",
"F 0.023236 0.340035 0.909180\n",
"''' # 查看前五行 \n",
"#print(sub_df.head(5)) \n",
"'''\n",
" c1 c3 c5\n",
"A 0.700437 0.676514 0.951458\n",
"B 0.012703 0.048813 0.508066\n",
"D 0.200248 0.192892 0.293228\n",
"E 0.774479 0.112858 0.247668\n",
"F 0.023236 0.340035 0.909180\n",
"''' # 查看中间 几行 的数据 使用 方法 iloc \n",
"print(sub_df.iloc[1:3, :]) # iloc : index location 用索引定位， 前包含后不包含\n",
"'''\n",
" c1 c3 c5\n",
"B 0.012703 0.048813 0.508066\n",
"D 0.200248 0.192892 0.293228\n",
"''' # 过滤 列 \n",
"print(sub_df.iloc[1:2, 0:2]) # 和python的用法一样，但是 该方法 是 基于 index 信息的 \n",
"'''\n",
" c1 c3\n",
"B 0.012703 0.048813\n",
"''' # loc 方法， 通过label 名称来过滤 \n",
"print(sub_df.loc['A':'B', 'c1':'c5']) # 基于 label 选择 ， 包含前后"
]
},
{
"cell_type": "code",
"execution_count": 96,
"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>c1</th>\n",
" <th>c2</th>\n",
" <th>c3</th>\n",
" <th>c4</th>\n",
" <th>c5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>0.012703</td>\n",
" <td>0.413588</td>\n",
" <td>0.048813</td>\n",
" <td>0.099929</td>\n",
" <td>0.508066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D</th>\n",
" <td>0.200248</td>\n",
" <td>0.744154</td>\n",
" <td>0.192892</td>\n",
" <td>0.700845</td>\n",
" <td>0.293228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>E</th>\n",
" <td>0.774479</td>\n",
" <td>0.005109</td>\n",
" <td>0.112858</td>\n",
" <td>0.110954</td>\n",
" <td>0.247668</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" c1 c2 c3 c4 c5\n",
"B 0.012703 0.413588 0.048813 0.099929 0.508066\n",
"D 0.200248 0.744154 0.192892 0.700845 0.293228\n",
"E 0.774479 0.005109 0.112858 0.110954 0.247668"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[[1,2,3]]"
]
},
{
"cell_type": "code",
"execution_count": 97,
"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>c1</th>\n",
" <th>c2</th>\n",
" <th>c3</th>\n",
" <th>c4</th>\n",
" <th>c5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>A</th>\n",
" <td>0.700437</td>\n",
" <td>0.844187</td>\n",
" <td>0.676514</td>\n",
" <td>0.727858</td>\n",
" <td>0.951458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B</th>\n",
" <td>0.012703</td>\n",
" <td>0.413588</td>\n",
" <td>0.048813</td>\n",
" <td>0.099929</td>\n",
" <td>0.508066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>D</th>\n",
" <td>0.200248</td>\n",
" <td>0.744154</td>\n",
" <td>0.192892</td>\n",
" <td>0.700845</td>\n",
" <td>0.293228</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" c1 c2 c3 c4 c5\n",
"A 0.700437 0.844187 0.676514 0.727858 0.951458\n",
"B 0.012703 0.413588 0.048813 0.099929 0.508066\n",
"D 0.200248 0.744154 0.192892 0.700845 0.293228"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[[\"A\",\"B\",\"D\"]]"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7004371218578347\n",
"0.7004371218578347\n",
"0.41358769878652346\n"
]
}
],
"source": [
"print(df.loc[\"A\",\"c1\"])\n",
"\n",
"print(df.at[\"A\",\"c1\"])\n",
"\n",
"print(df.iat[1,1])"
]
},
{
"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": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
simpleig/FCN-semantic-segmentation | FCN-KITTI.ipynb | 1 | 62981 | {
"metadata": {
"name": "",
"signature": "sha256:e674afd5acd25e3059053467d2bb72b4ce6a8d3dfcd9b39924bf576a9a652cd3"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"# extract the variables that would be used for KITTI-finetuning\n",
"\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import os\n",
"import scipy.io\n",
"import glob\n",
"import random\n",
"import BatchDatsetReader as dataset\n",
"import scipy.misc as misc\n",
"from six.moves import cPickle as pickle\n",
"\n",
"# reset the graph\n",
"tf.reset_default_graph()\n",
"\n",
"# reset tf.flags.FLAGS\n",
"import argparse\n",
"tf.reset_default_graph()\n",
"tf.flags.FLAGS = tf.python.platform.flags._FlagValues()\n",
"tf.flags._global_parser = argparse.ArgumentParser()\n",
"\n",
"# set tf.flags.FLAGS\n",
"FLAGS = tf.flags.FLAGS\n",
"tf.flags.DEFINE_integer(\"batch_size\",\"2\",\"batch size for training\")\n",
"tf.flags.DEFINE_string(\"logs_dir\",\"logs/\",\"path to logs directory\")\n",
"tf.flags.DEFINE_string(\"data_dir\",\"Data_zoo/MIT_SceneParsing/\",\"path to dataset\")\n",
"tf.flags.DEFINE_string(\"pickle_name\",\"MITSceneParsing.pickle\",\"pickle file of the data\")\n",
"tf.flags.DEFINE_string(\"data_url\",\"http://sceneparsing.csail.mit.edu/data/ADEChallengeData2016.zip\",\"url of the data\")\n",
"tf.flags.DEFINE_float(\"learning_rate\",\"1e-4\",\"learning rate for the optimizier\")\n",
"tf.flags.DEFINE_string(\"model_dir\",\"Model_zoo/\",\"path to vgg model mat\")\n",
"tf.flags.DEFINE_bool(\"debug\",\"True\",\"Debug model: True/False\")\n",
"tf.flags.DEFINE_string(\"mode\",\"train\",\"Mode: train/ valid\")\n",
"tf.flags.DEFINE_integer(\"max_iters\",\"100001\",\"max training iterations of batches\")\n",
"tf.flags.DEFINE_integer(\"num_classes\",\"151\",\"mit_sceneparsing with (150+1) classes\")\n",
"tf.flags.DEFINE_integer(\"image_size\",\"224\",\"can be variable in deed\")\n",
"tf.flags.DEFINE_string(\"model_weights\",\"http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat\",\"pretrained weights of the CNN in use\")\n",
"tf.flags.DEFINE_string(\"full_model\",\"full_model/\",\"trained parameters of the whole network\")\n",
"tf.flags.DEFINE_string(\"full_model_file\",\"100000_model.ckpt\",\"pretrained parameters of the whole network\")\n",
"tf.flags.DEFINE_bool(\"load\",\"True\",\"load in pretrained parameters\")\n",
"\n",
"# check if the CNN weights folder exist\n",
"if not os.path.exists(FLAGS.model_dir):\n",
" os.makedirs(FLAGS.model_dir)\n",
"\n",
"# check if the CNN weights file exist\n",
"weights_file = os.path.join(FLAGS.model_dir,FLAGS.model_weights.split('/')[-1])\n",
"if not os.path.exists(weights_file):\n",
" print(\"\\ndownloading \"+weights_file+\" ...\")\n",
" os.system(\"wget \"+FLAGS.model_weights+\" -P \"+FLAGS.model_dir)\n",
" print(\"download finished!\\n\")\n",
"else:\n",
" print(\"\\n\"+weights_file+\" has already been downloaded.\\n\")\n",
"\n",
"# load the weights file\n",
"print(\"\\nloading pretrained weights from: \"+weights_file)\n",
"pretrain_weights = scipy.io.loadmat(weights_file)\n",
"print(\"loading finished!\\n\")\n",
"\n",
"# the mean RGB\n",
"mean = pretrain_weights['normalization'][0][0][0] # shape(224,224,3)\n",
"mean_pixel = np.mean(mean,axis=(0,1)) # average on (height,width) to compute the mean RGB \n",
"\n",
"# the weights and biases\n",
"weights_biases = np.squeeze(pretrain_weights['layers'])\n",
"\n",
"# network input data\n",
"dropout_prob = tf.placeholder(tf.float32,name=\"dropout_probability\")\n",
"images = tf.placeholder(tf.float32,shape=[None,FLAGS.image_size,FLAGS.image_size,3],name=\"input_images\")\n",
"annotations = tf.placeholder(tf.int32,shape=[None,FLAGS.image_size,FLAGS.image_size,1],name=\"input_annotations\")\n",
"\n",
"# subtract the mean image\n",
"processed_image = images - mean_pixel\n",
"\n",
"# construct the semantic_seg network\n",
"with tf.variable_scope(\"semantic_seg\"):\n",
" # convs of the vgg net\n",
" net = {}\n",
" layers = [\n",
" 'conv1_1','relu1_1','conv1_2','relu1_2','pool1',\n",
" 'conv2_1','relu2_1','conv2_2','relu2_2','pool2',\n",
" 'conv3_1','relu3_1','conv3_2','relu3_2','conv3_3','relu3_3','conv3_4','relu3_4','pool3',\n",
" 'conv4_1','relu4_1','conv4_2','relu4_2','conv4_3','relu4_3','conv4_4','relu4_4','pool4',\n",
" 'conv5_1','relu5_1','conv5_2','relu5_2','conv5_3' #,'relu5_3','conv5_4','relu5_4','pool5'\n",
" ]\n",
" current = processed_image\n",
"\n",
" # sanity check\n",
" print(\"processed_image: {}\".format(processed_image.get_shape()))\n",
"\n",
" for i,name in enumerate(layers):\n",
" type = name[:4]\n",
" if type == 'conv':\n",
" # matconvnet weights: (width, height, in_channels, out_channels)\n",
" # tensorflow weights: (height, width, in_channels, out_channels)\n",
" weights, biases = weights_biases[i][0][0][0][0]\n",
"\n",
" weights = np.transpose(weights,(1,0,2,3)) \n",
" biases = np.squeeze(biases)\n",
" \n",
" init = tf.constant_initializer(weights,dtype=tf.float32)\n",
" weights = tf.get_variable(initializer=init,shape=weights.shape,name=name+\"_w\")\n",
" \n",
" init = tf.constant_initializer(biases,dtype=tf.float32)\n",
" biases = tf.get_variable(initializer=init,shape=biases.shape,name=name+\"_b\")\n",
" \n",
" current = tf.nn.conv2d(current,weights,strides=[1,1,1,1],padding=\"SAME\")\n",
" current = tf.nn.bias_add(current,biases,name=name)\n",
"\n",
" # sanity check\n",
" print(\"{}: {}\".format(name,current.get_shape()))\n",
" elif type == 'relu':\n",
" current = tf.nn.relu(current,name=name)\n",
" if FLAGS.debug:\n",
" tf.histogram_summary(current.op.name+\"/activation\",current)\n",
" tf.scalar_summary(current.op.name+\"/sparsity\",tf.nn.zero_fraction(current))\n",
" # sanity check\n",
" print(\"{}: {}\".format(name,current.get_shape())) \n",
" elif type == 'pool':\n",
" if name == 'pool5':\n",
" current = tf.nn.max_pool(current,ksize=[1,2,2,1],strides=[1,2,2,1],padding=\"SAME\",name=name)\n",
" else:\n",
" current = tf.nn.avg_pool(current,ksize=[1,2,2,1],strides=[1,2,2,1],padding=\"SAME\",name=name)\n",
" # sanity check\n",
" print(\"{}: {}\".format(name,current.get_shape()))\n",
" net[name] = current\n",
" \n",
" net['pool5'] = tf.nn.max_pool(net['conv5_3'],ksize=[1,2,2,1],strides=[1,2,2,1],padding=\"SAME\",name=name)\n",
" # sanity check\n",
" print(\"pool5: {}\".format(net['pool5'].get_shape()))\n",
" \n",
" # fcn6\n",
" init = tf.truncated_normal(shape=[7,7,512,4096],stddev=0.02)\n",
" fcn6_w = tf.get_variable(initializer=init,name=\"fcn6_w\")\n",
"\n",
" init = tf.constant(0.0,shape=[4096])\n",
" fcn6_b = tf.get_variable(initializer=init,name=\"fcn6_b\")\n",
"\n",
" fcn6 = tf.nn.conv2d(net['pool5'],fcn6_w,strides=[1,1,1,1],padding=\"SAME\")\n",
" fcn6 = tf.nn.bias_add(fcn6,fcn6_b,name=\"fcn6\")\n",
"\n",
" relu6 = tf.nn.relu(fcn6,name=\"relu6\")\n",
" if FLAGS.debug:\n",
" tf.histogram_summary(\"relu6/activation\", relu6, collections=None, name=None)\n",
" tf.scalar_summary(\"relu6/sparsity\", tf.nn.zero_fraction(relu6), collections=None, name=None)\n",
" dropout6 = tf.nn.dropout(relu6, keep_prob=dropout_prob, noise_shape=None, seed=None, name=\"dropout6\")\n",
" # sanity check\n",
" print(\"dropout6: {}\".format(dropout6.get_shape()))\n",
"\n",
" # fcn7\n",
" init = tf.truncated_normal(shape=[1,1,4096,4096],stddev=0.02)\n",
" fcn7_w = tf.get_variable(initializer=init,name=\"fcn7_w\")\n",
"\n",
" init = tf.constant(0.0,shape=[4096])\n",
" fcn7_b = tf.get_variable(initializer=init,name=\"fcn7_b\")\n",
"\n",
" fcn7 = tf.nn.conv2d(dropout6, fcn7_w, strides=[1,1,1,1], padding=\"SAME\", use_cudnn_on_gpu=None, data_format=None, name=None)\n",
" fcn7 = tf.nn.bias_add(fcn7, fcn7_b, data_format=None, name=\"fcn7\")\n",
"\n",
" relu7 = tf.nn.relu(fcn7,name=\"relu7\")\n",
" if FLAGS.debug:\n",
" tf.histogram_summary(\"relu7/activation\", relu7, collections=None, name=None)\n",
" tf.scalar_summary(\"relu7/sparsity\", tf.nn.zero_fraction(relu7), collections=None, name=None)\n",
" dropout7 = tf.nn.dropout(relu7, keep_prob=dropout_prob, noise_shape=None, seed=None, name=\"dropout7\")\n",
" # sanity check\n",
" print(\"dropout7: {}\".format(dropout7.get_shape()))\n",
"\n",
" # fcn8\n",
" init = tf.truncated_normal(shape=[1,1,4096,FLAGS.num_classes],stddev=0.02)\n",
" fcn8_w = tf.get_variable(initializer=init,name=\"fcn8_w\")\n",
"\n",
" init = tf.constant(0.0,shape=[FLAGS.num_classes])\n",
" fcn8_b = tf.get_variable(initializer=init,name=\"fcn8_b\")\n",
"\n",
" fcn8 = tf.nn.conv2d(dropout7, fcn8_w, strides=[1,1,1,1], padding=\"SAME\", use_cudnn_on_gpu=None, data_format=None, name=None)\n",
" fcn8 = tf.nn.bias_add(fcn8, fcn8_b, data_format=None, name=\"fcn8\")\n",
" # sanity check\n",
" print(\"fcn8: {}\".format(fcn8.get_shape()))\n",
"\n",
" # deconv1 + net['pool4']: x32 -> x16\n",
" s = 2\n",
" k = 2*s\n",
" in_channel = FLAGS.num_classes\n",
" out_channel = net['pool4'].get_shape()[3].value\n",
" out_shape = tf.shape(net['pool4'])\n",
"\n",
" init = tf.truncated_normal(shape=[k,k,out_channel,in_channel],stddev=0.02)\n",
" deconv1_w = tf.get_variable(initializer=init,name=\"deconv1_w\")\n",
"\n",
" init = tf.constant(0.0,shape=[out_channel])\n",
" deconv1_b = tf.get_variable(initializer=init,name=\"deconv1_b\")\n",
"\n",
" # sanity check\n",
" print(\"deconv1 output_shape: {}\".format(net['pool4'].get_shape()))\n",
"\n",
" deconv1 = tf.nn.conv2d_transpose(fcn8, deconv1_w, output_shape=out_shape, strides=[1,s,s,1], padding='SAME', name=None)\n",
" deconv1 = tf.nn.bias_add(deconv1, deconv1_b, data_format=None, name=\"deconv1\")\n",
"\n",
" fuse1 = tf.add(deconv1, net['pool4'], name=\"fuse1\")\n",
" \n",
" # deconv2 + net['pool3']: x16 -> x8\n",
" s = 2\n",
" k = 2*s\n",
" in_channel = out_channel\n",
" out_channel = net['pool3'].get_shape()[3].value\n",
" out_shape = tf.shape(net['pool3'])\n",
"\n",
" init = tf.truncated_normal(shape=[k,k,out_channel,in_channel],stddev=0.02)\n",
" deconv2_w = tf.get_variable(initializer=init,name=\"deconv2_w\")\n",
"\n",
" init = tf.constant(0.0,shape=[out_channel])\n",
" deconv2_b = tf.get_variable(initializer=init,name=\"deconv2_b\")\n",
"\n",
" deconv2 = tf.nn.conv2d_transpose(fuse1, deconv2_w, output_shape=out_shape, strides=[1,s,s,1], padding='SAME', name=None)\n",
" deconv2 = tf.nn.bias_add(deconv2, deconv2_b, data_format=None, name=\"deconv2\")\n",
"\n",
" fuse2 = tf.add(deconv2,net['pool3'],name=\"fuse2\")\n",
"\n",
" # deconv3: x8 -> image_size\n",
" s = 8\n",
" k = 2*s\n",
" in_channel = out_channel\n",
" out_channel = FLAGS.num_classes\n",
" out_shape = tf.pack([tf.shape(processed_image)[0],tf.shape(processed_image)[1],tf.shape(processed_image)[2],out_channel])\n",
" \n",
" init = tf.truncated_normal(shape=[k,k,out_channel,in_channel],stddev=0.02)\n",
" deconv3_w = tf.get_variable(initializer=init,name=\"deconv3_w\")\n",
"\n",
" init = tf.constant(0.0,shape=[out_channel])\n",
" deconv3_b = tf.get_variable(initializer=init,name=\"deconv3_b\")\n",
"\n",
" deconv3 = tf.nn.conv2d_transpose(fuse2, deconv3_w, output_shape=out_shape, strides=[1,s,s,1], padding='SAME', name=None)\n",
" deconv3 = tf.nn.bias_add(deconv3, deconv3_b, data_format=None, name=\"deconv3\")\n",
"\n",
" # per-pixel prediction\n",
" annotations_pred = tf.argmax(deconv3, dimension=3, name=None)\n",
" annotations_pred = tf.expand_dims(annotations_pred, dim=3, name=\"prediction\")\n",
"\n",
"# log images, annotations, annotations_pred\n",
"tf.image_summary(\"images\", images, max_images=2, collections=None, name=None)\n",
"tf.image_summary(\"annotations\", tf.cast(annotations,tf.uint8), max_images=2, collections=None, name=None)\n",
"tf.image_summary(\"annotations_pred\", tf.cast(annotations_pred,tf.uint8), max_images=2, collections=None, name=None)\n",
"\n",
"# construct the loss\n",
"loss = tf.nn.sparse_softmax_cross_entropy_with_logits(deconv3, tf.squeeze(annotations, squeeze_dims=[3]))\n",
"loss = tf.reduce_mean(loss, reduction_indices=None, keep_dims=False, name=\"pixel-wise_cross-entropy_loss\")\n",
"\n",
"# log the loss\n",
"tf.scalar_summary(\"pixel-wise_cross-entropy_loss\", loss, collections=None, name=None)\n",
"\n",
"# log all the trainable variables\n",
"trainabel_vars = tf.trainable_variables()\n",
"if FLAGS.debug:\n",
" for var in trainabel_vars:\n",
" tf.histogram_summary(var.op.name+\"/values\", var, collections=None, name=None)\n",
" tf.add_to_collection(\"sum(t ** 2) / 2 of all trainable_vars\", tf.nn.l2_loss(var))\n",
" \n",
"# construct the optimizier\n",
"optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)\n",
"gradients = optimizer.compute_gradients(loss,trainabel_vars)\n",
"if FLAGS.debug:\n",
" # log the gradients\n",
" for grad, var in gradients:\n",
" tf.histogram_summary(var.op.name+\"/gradients\", grad, collections=None, name=None)\n",
"train_op = optimizer.apply_gradients(gradients)\n",
"\n",
"# initialize the variables\n",
"print(\"\\nInitializing the variables ...\\n\")\n",
"sess = tf.InteractiveSession()\n",
"tf.initialize_all_variables().run()\n",
"\n",
"# set up the saver\n",
"print(\"\\nSetting up the Saver ...\\n\")\n",
"saver = tf.train.Saver()\n",
"if FLAGS.load:\n",
" print(\"\\nLoading pretrain parameters of the whole network ...\\n\")\n",
" saver.restore(sess, os.path.join(FLAGS.full_model,FLAGS.full_model_file))\n",
"\n",
"# set the summary writer\n",
"print(\"\\nSetting the summary writers ...\\n\")\n",
"summary_op = tf.merge_all_summaries()\n",
"if not os.path.exists(FLAGS.logs_dir):\n",
" os.system(\"mkdir \"+FLAGS.logs_dir)\n",
"if FLAGS.mode == 'train':\n",
" if os.path.exists(FLAGS.logs_dir+\"/train\"):\n",
" os.system(\"rm -r \"+FLAGS.logs_dir+\"/train\")\n",
" if os.path.exists(FLAGS.logs_dir+\"/valid\"):\n",
" os.system(\"rm -r \"+FLAGS.logs_dir+\"/valid\")\n",
" train_writer = tf.train.SummaryWriter(FLAGS.logs_dir+\"/train\",sess.graph)\n",
" valid_writer = tf.train.SummaryWriter(FLAGS.logs_dir+\"/valid\")\n",
"elif FLAGS.mode == 'valid':\n",
" if os.path.exists(FLAGS.logs_dir+\"/complete_valid\"):\n",
" os.system(\"rm -r \"+FLAGS.logs_dir+\"/complete_valid\")\n",
" valid_writer = tf.train.SummaryWriter(FLAGS.logs_dir+\"/complete_valid\") \n",
"\n",
"# read data_records from *.pickle\n",
"print(\"\\nReading in and reprocessing all images ...\\n\")\n",
"# check if FLAGS.data_dir folder exist\n",
"if not os.path.exists(FLAGS.data_dir):\n",
" os.makedirs(FLAGS.data_dir)\n",
"# check if the *.pickle file exist\n",
"pickle_file = os.path.join(FLAGS.data_dir,FLAGS.pickle_name)\n",
"if not os.path.exists(pickle_file):\n",
" # check if the *.zip exist\n",
" zip_file = os.path.join(FLAGS.data_dir,FLAGS.data_url.split('/')[-1])\n",
" if not os.path.exists(zip_file):\n",
" # download the *.zip\n",
" print(\"downloading \"+zip_file+\" ..\")\n",
" os.system(\"wget \"+FLAGS.data_url+\" -P \"+FLAGS.data_dir)\n",
" print(\"download finished!\")\n",
" # unzip the file\n",
" print(\"unzipping \"+zip_file+\" ..\")\n",
" os.system(\"unzip \"+zip_file+\" -d \"+FLAGS.data_dir)\n",
" print(\"unzipping finished!\")\n",
" # pack data into *.pickle\n",
" source_datadir = zip_file.split('.')[0]\n",
" if not os.path.exists(source_datadir):\n",
" print(\"Error: source_datadir not found!!!\")\n",
" exit()\n",
" else:\n",
" data_types = ['training','validation']\n",
" data_list = {}\n",
" for data_type in data_types:\n",
" image_list = []\n",
" data_list[data_type] = []\n",
" # find all images\n",
" image_names = os.path.join(source_datadir,\"images\",data_type,'*.jpg')\n",
" print(\"\\nimage_names: %s\\n\"%(image_names))\n",
" image_list.extend(glob.glob(image_names))\n",
" if not image_list:\n",
" print(\"Error: no images found for \"+data_type+\"!!!\")\n",
" exit()\n",
" else:\n",
" # find corresponding annotations\n",
" for i in image_list:\n",
" image_name = (i.split('/')[-1]).split('.')[0]\n",
" annotation_name = os.path.join(source_datadir,\"annotations\",data_type,image_name+\".png\")\n",
" if os.path.exists(annotation_name):\n",
" # record this data tuple\n",
" record = {'image':i,'annotation':annotation_name,'filename':image_name}\n",
" data_list[data_type].append(record)\n",
" # shuffle all tuples\n",
" random.shuffle(data_list[data_type])\n",
" print(\"Number of %s tuples: %d\"%(data_type,len(data_list[data_type])))\n",
" print(\"Packing data into \"+pickle_file+\" ...\")\n",
" with open(pickle_file,'wb') as f:\n",
" pickle.dump(data_list,f,pickle.HIGHEST_PROTOCOL)\n",
" print(\"pickle finished!!!\")\n",
"# load data_records from *.pickle\n",
"with open(pickle_file,'rb') as f:\n",
" pickle_records = pickle.load(f)\n",
" train_records = pickle_records['training']\n",
" valid_records = pickle_records['validation']\n",
" del pickle_records\n",
" \n",
"# initialize the data reader\n",
"print(\"Initializing the data reader...\")\n",
"reader_options = {'resize':True,'resize_size':FLAGS.image_size}\n",
"if FLAGS.mode == 'train':\n",
" train_reader = dataset.BatchDatset(train_records[:10],reader_options)\n",
"valid_reader = dataset.BatchDatset(valid_records[:10],reader_options)\n",
"\n",
"# check if FLAGS.full_model exist\n",
"if not os.path.exists(FLAGS.full_model):\n",
" os.makedirs(FLAGS.full_model)\n",
"\n",
"# check variables' names\n",
"print(\"\\n-------------all vairables' names:-----------\\n\")\n",
"idx = 0\n",
"for variable_name in tf.trainable_variables():\n",
" print(\"%d-th: %s\"%(idx,variable_name.op.name))\n",
" idx += 1\n",
"\n",
"# define saver for KITTI\n",
"all_vars = tf.trainable_variables()\n",
"saver_KITTI = tf.train.Saver({\"semantic_seg/conv1_1_w\":all_vars[0],\"semantic_seg/conv1_1_b\":all_vars[1],\"semantic_seg/conv1_2_w\":all_vars[2],\"semantic_seg/conv1_2_b\":all_vars[3],\n",
" \"semantic_seg/conv2_1_w\":all_vars[4],\"semantic_seg/conv2_1_b\":all_vars[5],\"semantic_seg/conv2_2_w\":all_vars[6],\"semantic_seg/conv2_2_b\":all_vars[7],\n",
" \"semantic_seg/conv3_1_w\":all_vars[8],\"semantic_seg/conv3_1_b\":all_vars[9],\"semantic_seg/conv3_2_w\":all_vars[10],\"semantic_seg/conv3_2_b\":all_vars[11],\"semantic_seg/conv3_3_w\":all_vars[12],\"semantic_seg/conv3_3_b\":all_vars[13],\"semantic_seg/conv3_4_w\":all_vars[14],\"semantic_seg/conv3_4_b\":all_vars[15],\n",
" \"semantic_seg/conv4_1_w\":all_vars[16],\"semantic_seg/conv4_1_b\":all_vars[17],\"semantic_seg/conv4_2_w\":all_vars[18],\"semantic_seg/conv4_2_b\":all_vars[19],\"semantic_seg/conv4_3_w\":all_vars[20],\"semantic_seg/conv4_3_b\":all_vars[21],\"semantic_seg/conv4_4_w\":all_vars[22],\"semantic_seg/conv4_4_b\":all_vars[23],\n",
" \"semantic_seg/conv5_1_w\":all_vars[24],\"semantic_seg/conv5_1_b\":all_vars[25],\"semantic_seg/conv5_2_w\":all_vars[26],\"semantic_seg/conv5_2_b\":all_vars[27],\"semantic_seg/conv5_3_w\":all_vars[28],\"semantic_seg/conv5_3_b\":all_vars[29],\n",
" \"semantic_seg/fcn6_w\":all_vars[30],\"semantic_seg/fcn6_b\":all_vars[31],\"semantic_seg/fcn7_w\":all_vars[32],\"semantic_seg/fcn7_b\":all_vars[33],\n",
" \"semantic_seg/deconv2_w\":all_vars[38],\"semantic_seg/deconv2_b\":all_vars[39]})\n",
"\n",
"# start training/ validation\n",
"print(\"\\nStarting training/ validation...\\n\")\n",
"if FLAGS.mode == 'train':\n",
" \n",
" # extrac the variables for KITTI-finetuning\n",
" snapshot_name = os.path.join(FLAGS.full_model,\"100000_model_KITTI.ckpt\")\n",
" saver_KITTI.save(sess,snapshot_name)\n",
" \n",
"elif FLAGS.mode == 'valid':\n",
" # quantitative results\n",
" valid_images,valid_annotations=valid_reader.get_records()\n",
" feed_dict = {images:valid_images[:20],annotations:valid_annotations[:20],dropout_prob:1.0}\n",
" valid_loss,valid_summary = sess.run([loss,summary_op],feed_dict=feed_dict)\n",
" valid_writer.add_summary(valid_summary,FLAGS.max_iters)\n",
" print(\"==============================\")\n",
" print(\"Step: %d, valid_loss: %f\"%(FLAGS.max_iters,valid_loss))\n",
" print(\"==============================\")\n",
" # qualitative results\n",
" valid_images,valid_annotations=valid_reader.get_random_batch(FLAGS.batch_size)\n",
" feed_dict = {images:valid_images,annotations:valid_annotations,dropout_prob:1.0}\n",
" annotations_pred_results = sess.run(annotations_pred,feed_dict=feed_dict)\n",
" \n",
" valid_annotations = np.squeeze(valid_annotations,axis=3)\n",
" annotations_pred_results = np.squeeze(annotations_pred_results,axis=3)\n",
" \n",
" for n in xrange(FLAGS.batch_size):\n",
" print(\"Saving %d-th valid tuples for qualitative comparisons...\"%(n))\n",
" misc.imsave(FLAGS.logs_dir+\"/complete_valid/\"+str(n)+\"_image.png\",valid_images[n].astype(np.uint8))\n",
" misc.imsave(FLAGS.logs_dir+\"/complete_valid/\"+str(n)+\"_annotation.png\",valid_annotations[n].astype(np.uint8))\n",
" misc.imsave(FLAGS.logs_dir+\"/complete_valid/\"+str(n)+\"_prediction.png\",annotations_pred_results[n].astype(np.uint8))\n",
" print(\"saving finished!!!\")"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# prepare the *_gt.png for KITTI dataset\n",
"\n",
"import scipy.io \n",
"import numpy as np \n",
"import scipy.misc as misc\n",
"import glob\n",
"import pylab as pl\n",
"import os\n",
"\n",
"'''\n",
"Label number / Object class / RGB\n",
"0 - NOT GROUND TRUTHED - 0 0 0\n",
"1 - building - 153 0 0\n",
"2 - sky - 0 51 102\n",
"3 - road - 160 160 160\n",
"4 - vegetation - 0 102 0\n",
"5 - sidewalk - 255 228 196\n",
"6 - car - 255 200 50\n",
"7 - pedestrian - 255 153 255\n",
"8 - cyclist - 204 153 255\n",
"9 - signage - 130 255 255\n",
"10 - fence - 193 120 87\n",
"'''\n",
"\n",
"dataset_types = ['trn','test']\n",
"dataset_idices = {'trn':['0009','0010','0011','0019'],\n",
" 'test':['0000','0004','0005','0013']}\n",
"\n",
"for dataset_type in dataset_types:\n",
" for dataset_idx in dataset_idices[dataset_type]:\n",
" \n",
" # find all images of a sequence\n",
" image_names = \"KITTI_public/image_02/\"+str(dataset_type)+\"/\"+str(dataset_idx)+\"/*.mat\"\n",
" image_list = []\n",
" image_list.extend(glob.glob(image_names))\n",
"\n",
" # sort images by time-stamp\n",
" image_list.sort()\n",
"\n",
" # for each image annotation\n",
" for k in xrange(len(image_list)):\n",
" # (height=375, width=1242)\n",
" # 0019 (374,1238)\n",
" image_train = scipy.io.loadmat(image_list[k]) \n",
" image_train = np.array(image_train['truth'])\n",
" height = image_train.shape[0]\n",
" width = image_train.shape[1]\n",
"\n",
" # data info\n",
" if k == 0:\n",
" print(\"dataset name: %s\"%(image_names))\n",
" print(\"image number: %d\"%(len(image_list)))\n",
" print(\"image_size(height,width): (%d,%d)\"%(height,width))\n",
" # index range\n",
" min_idx = np.min(image_train)\n",
" max_idx = np.max(image_train)\n",
" print(\"min_idx: %d\"%(min_idx))\n",
" print(\"max_idx: %d\"%(max_idx))\n",
"\n",
" # Create an empty image\n",
" image_train_color = np.zeros((height, width, 3), dtype=np.uint8)\n",
"\n",
" # index to rgb map\n",
" idx2rgb = [(0,0,0),(153,0,0),(0,51,102),(160,160,160),(0,102,0),\n",
" (255,228,196),(255,200,50),(255,153,255),\n",
" (204,153,255),(130,255,255),(193,120,87)]\n",
"\n",
" # color the index\n",
" for i in xrange(image_train.shape[0]):\n",
" for j in xrange(image_train.shape[1]):\n",
" # map index to RGB\n",
" idx = image_train[i][j]\n",
" image_train_color[i][j] = idx2rgb[idx]\n",
"\n",
"# # Display the image\n",
"# pl.imshow(image_train_color)\n",
"# pl.xlabel(\"[%s-%s]: %s/%s\"%(dataset_type,dataset_idx,image_list[k].split('.')[0].split('/')[-1],image_list[-1].split('.')[0].split('/')[-1])) \n",
"# pl.pause(0.0000001)\n",
"# pl.draw()\n",
"\n",
" # save the *_mask.png\n",
" misc.imsave(image_list[k].split('.')[0]+\"_mask.png\",image_train_color)\n",
" \n",
" # save the *_gt.png\n",
" misc.imsave(image_list[k].split('.')[0]+\"_gt.png\",image_train.astype(np.uint8))\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"dataset name: KITTI_public/image_02/trn/0009/*.mat\n",
"image number: 41\n",
"image_size(height,width): (375,1242)\n",
"min_idx: 0\n",
"max_idx: 10\n",
"dataset name: KITTI_public/image_02/trn/0010/*.mat"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"image number: 30\n",
"image_size(height,width): (375,1242)\n",
"min_idx: 0\n",
"max_idx: 6\n",
"dataset name: KITTI_public/image_02/trn/0011/*.mat"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"image number: 38\n",
"image_size(height,width): (375,1242)\n",
"min_idx: 0\n",
"max_idx: 9\n",
"dataset name: KITTI_public/image_02/trn/0019/*.mat"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"image number: 31\n",
"image_size(height,width): (374,1238)\n",
"min_idx: 0\n",
"max_idx: 8\n",
"dataset name: KITTI_public/image_02/test/0000/*.mat"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"image number: 16\n",
"image_size(height,width): (375,1242)\n",
"min_idx: 0\n",
"max_idx: 9\n",
"dataset name: KITTI_public/image_02/test/0004/*.mat"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"image number: 32\n",
"image_size(height,width): (375,1242)\n",
"min_idx: 0\n",
"max_idx: 9\n",
"dataset name: KITTI_public/image_02/test/0005/*.mat"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"image number: 30\n",
"image_size(height,width): (375,1242)\n",
"min_idx: 0\n",
"max_idx: 6\n",
"dataset name: KITTI_public/image_02/test/0013/*.mat"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"image number: 34\n",
"image_size(height,width): (375,1242)\n",
"min_idx: 0\n",
"max_idx: 9\n"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# prepare the original rgb images from KITTI\n",
"\n",
"import scipy.io \n",
"import numpy as np \n",
"import scipy.misc as misc\n",
"import glob\n",
"import pylab as pl\n",
"import os\n",
"import sys\n",
"\n",
"dataset_types = ['trn','test']\n",
"dataset_idices = {'trn':['0009','0010','0011','0019'],\n",
" 'test':['0000','0004','0005','0013']}\n",
"fake2true = {'0009':'0036','0010':'0056','0011':'0059','0019':'0071',\n",
" '0000':'0005','0004':'0014','0005':'0015','0013':'0091'}\n",
"\n",
"for dataset_type in dataset_types:\n",
" for dataset_idx in dataset_idices[dataset_type]:\n",
" # find all images of a sequence\n",
" image_names = \"KITTI_public/image_02/\"+str(dataset_type)+\"/\"+str(dataset_idx)+\"/*.mat\"\n",
" image_list = []\n",
" image_list.extend(glob.glob(image_names))\n",
"\n",
" # log info\n",
" print(\"processing: %s\"%(image_names))\n",
" \n",
" # sort images by time-stamp\n",
" image_list.sort()\n",
"\n",
" # to find each original image from KITTI\n",
" for k in xrange(len(image_list)):\n",
" # name of its original image\n",
" image_name = \"KITTI/\"+fake2true[dataset_idx]+\"_\"+dataset_idx+\"/*/*_sync/image_02/data/*\"+image_list[k].split('.')[0].split('/')[-1]+\".png\"\n",
" \n",
" # find the full path\n",
" full_path = glob.glob(image_name)\n",
"\n",
" # current path\n",
" target_path = image_list[k].split('.')[0]+\".png\"\n",
" \n",
" # copy the image to current folder\n",
" os.system(\"cp \"+str(full_path[0])+\" \"+str(target_path))\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"processing: KITTI_public/image_02/trn/0009/*.mat\n",
"processing: KITTI_public/image_02/trn/0010/*.mat"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"processing: KITTI_public/image_02/trn/0011/*.mat"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"processing: KITTI_public/image_02/trn/0019/*.mat"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"processing: KITTI_public/image_02/test/0000/*.mat"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"processing: KITTI_public/image_02/test/0004/*.mat"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"processing: KITTI_public/image_02/test/0005/*.mat"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"processing: KITTI_public/image_02/test/0013/*.mat"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# pack the KITTI dataset into \"Data_zoo/KITTI/KITTI.pickle\"\n",
"\n",
"import os\n",
"import random\n",
"from six.moves import cPickle as pickle\n",
"import glob\n",
"\n",
"data_types = ['trn','test']\n",
"data_list = {}\n",
"for data_type in data_types:\n",
" image_list = []\n",
" data_list[data_type] = []\n",
" # find all images\n",
" image_names = \"KITTI_public/image_02/\"+data_type+\"/00*/*.png\"\n",
" print(\"\\nimage_names: %s\\n\"%(image_names))\n",
" image_list.extend(glob.glob(image_names))\n",
" if not image_list:\n",
" print(\"Error: no images found for \"+data_type+\"!!!\")\n",
" exit()\n",
" else:\n",
" # find corresponding annotations\n",
" for i in image_list:\n",
" annotation_name = i.split('.')[0]+\"_gt.png\"\n",
" if os.path.exists(annotation_name):\n",
" # record this data tuple\n",
" record = {'image':i,'annotation':annotation_name}\n",
" data_list[data_type].append(record)\n",
" # shuffle all tuples\n",
" random.shuffle(data_list[data_type])\n",
" print(\"Number of %s tuples: %d\"%(data_type,len(data_list[data_type])))\n",
"\n",
"pickle_file = \"Data_zoo/KITTI/KITTI.pickle\"\n",
"print(\"Packing data into \"+pickle_file+\" ...\")\n",
"with open(pickle_file,'wb') as f:\n",
" pickle.dump(data_list,f,pickle.HIGHEST_PROTOCOL)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"image_names: KITTI_public/image_02/trn/00*/*.png\n",
"\n",
"Number of trn tuples: 140\n",
"\n",
"image_names: KITTI_public/image_02/test/00*/*.png\n",
"\n",
"Number of test tuples: 112\n",
"Packing data into Data_zoo/KITTI/KITTI.pickle ...\n"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# fine-tuning on KITTI\n",
"\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import os\n",
"import scipy.io\n",
"import glob\n",
"import random\n",
"import BatchDatsetReader as dataset\n",
"import scipy.misc as misc\n",
"from six.moves import cPickle as pickle\n",
"\n",
"# reset the graph\n",
"tf.reset_default_graph()\n",
"\n",
"# reset tf.flags.FLAGS\n",
"import argparse\n",
"tf.reset_default_graph()\n",
"tf.flags.FLAGS = tf.python.platform.flags._FlagValues()\n",
"tf.flags._global_parser = argparse.ArgumentParser()\n",
"\n",
"# set tf.flags.FLAGS\n",
"FLAGS = tf.flags.FLAGS\n",
"tf.flags.DEFINE_integer(\"batch_size\",\"1\",\"batch size for training\")\n",
"tf.flags.DEFINE_string(\"logs_dir\",\"logs/KITTI\",\"path to logs directory\")\n",
"tf.flags.DEFINE_string(\"data_dir\",\"Data_zoo/KITTI/\",\"path to dataset\")\n",
"tf.flags.DEFINE_string(\"pickle_name\",\"KITTI.pickle\",\"pickle file of the data\")\n",
"tf.flags.DEFINE_float(\"learning_rate\",\"1e-4\",\"learning rate for the optimizier\")\n",
"tf.flags.DEFINE_string(\"model_dir\",\"Model_zoo/\",\"path to vgg model mat\")\n",
"tf.flags.DEFINE_bool(\"debug\",\"True\",\"Debug model: True/False\")\n",
"tf.flags.DEFINE_string(\"mode\",\"train\",\"Mode: train/ valid\")\n",
"tf.flags.DEFINE_integer(\"max_iters\",\"100001\",\"max training iterations of batches\")\n",
"tf.flags.DEFINE_integer(\"num_classes\",\"11\",\"mit_sceneparsing with (150+1) classes\")\n",
"tf.flags.DEFINE_string(\"model_weights\",\"http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat\",\"pretrained weights of the CNN in use\")\n",
"tf.flags.DEFINE_string(\"full_model\",\"full_model/\",\"trained parameters of the whole network\")\n",
"tf.flags.DEFINE_string(\"full_model_file\",\"100000_model.ckpt\",\"pretrained parameters of the whole network\")\n",
"tf.flags.DEFINE_bool(\"load\",\"True\",\"load in pretrained parameters\")\n",
"tf.flags.DEFINE_string(\"name\",\"KITTI\",\"dataset name\")\n",
"\n",
"# check if the CNN weights folder exist\n",
"if not os.path.exists(FLAGS.model_dir):\n",
" os.makedirs(FLAGS.model_dir)\n",
" \n",
"# check if the CNN weights file exist\n",
"weights_file = os.path.join(FLAGS.model_dir,FLAGS.model_weights.split('/')[-1])\n",
"if not os.path.exists(weights_file):\n",
" print(\"\\ndownloading \"+weights_file+\" ...\")\n",
" os.system(\"wget \"+FLAGS.model_weights+\" -P \"+FLAGS.model_dir)\n",
" print(\"download finished!\\n\")\n",
"else:\n",
" print(\"\\n\"+weights_file+\" has already been downloaded.\\n\")\n",
" \n",
"# load the weights file\n",
"print(\"\\nloading pretrained weights from: \"+weights_file)\n",
"pretrain_weights = scipy.io.loadmat(weights_file)\n",
"print(\"loading finished!\\n\")\n",
"\n",
"# the mean RGB\n",
"mean = pretrain_weights['normalization'][0][0][0] # shape(224,224,3)\n",
"mean_pixel = np.mean(mean,axis=(0,1)) # average on (height,width) to compute the mean RGB \n",
"\n",
"# the weights and biases\n",
"weights_biases = np.squeeze(pretrain_weights['layers'])\n",
"\n",
"# network input data\n",
"dropout_prob = tf.placeholder(tf.float32,name=\"dropout_probability\")\n",
"images = tf.placeholder(tf.float32,shape=[None,None,None,3],name=\"input_images\")\n",
"annotations = tf.placeholder(tf.int32,shape=[None,None,None,1],name=\"input_annotations\")\n",
"\n",
"# subtract the mean image\n",
"processed_image = images - mean_pixel\n",
"\n",
"# construct the semantic_seg network\n",
"with tf.variable_scope(\"semantic_seg\"):\n",
" # convs of the vgg net\n",
" net = {}\n",
" layers = [\n",
" 'conv1_1','relu1_1','conv1_2','relu1_2','pool1',\n",
" 'conv2_1','relu2_1','conv2_2','relu2_2','pool2',\n",
" 'conv3_1','relu3_1','conv3_2','relu3_2','conv3_3','relu3_3','conv3_4','relu3_4','pool3',\n",
" 'conv4_1','relu4_1','conv4_2','relu4_2','conv4_3','relu4_3','conv4_4','relu4_4','pool4',\n",
" 'conv5_1','relu5_1','conv5_2','relu5_2','conv5_3' #,'relu5_3','conv5_4','relu5_4','pool5'\n",
" ]\n",
" current = processed_image\n",
"\n",
" # sanity check\n",
" print(\"processed_image: {}\".format(processed_image.get_shape()))\n",
"\n",
" for i,name in enumerate(layers):\n",
" type = name[:4]\n",
" if type == 'conv':\n",
" # matconvnet weights: (width, height, in_channels, out_channels)\n",
" # tensorflow weights: (height, width, in_channels, out_channels)\n",
" weights, biases = weights_biases[i][0][0][0][0]\n",
"\n",
" weights = np.transpose(weights,(1,0,2,3)) \n",
" biases = np.squeeze(biases)\n",
" \n",
" init = tf.constant_initializer(weights,dtype=tf.float32)\n",
" weights = tf.get_variable(initializer=init,shape=weights.shape,name=name+\"_w\")\n",
" \n",
" init = tf.constant_initializer(biases,dtype=tf.float32)\n",
" biases = tf.get_variable(initializer=init,shape=biases.shape,name=name+\"_b\")\n",
" \n",
" current = tf.nn.conv2d(current,weights,strides=[1,1,1,1],padding=\"SAME\")\n",
" current = tf.nn.bias_add(current,biases,name=name)\n",
"\n",
" # sanity check\n",
" print(\"{}: {}\".format(name,current.get_shape()))\n",
" elif type == 'relu':\n",
" current = tf.nn.relu(current,name=name)\n",
" if FLAGS.debug:\n",
" tf.histogram_summary(current.op.name+\"/activation\",current)\n",
" tf.scalar_summary(current.op.name+\"/sparsity\",tf.nn.zero_fraction(current))\n",
" # sanity check\n",
" print(\"{}: {}\".format(name,current.get_shape())) \n",
" elif type == 'pool':\n",
" if name == 'pool5':\n",
" current = tf.nn.max_pool(current,ksize=[1,2,2,1],strides=[1,2,2,1],padding=\"SAME\",name=name)\n",
" else:\n",
" current = tf.nn.avg_pool(current,ksize=[1,2,2,1],strides=[1,2,2,1],padding=\"SAME\",name=name)\n",
" # sanity check\n",
" print(\"{}: {}\".format(name,current.get_shape()))\n",
" net[name] = current\n",
" \n",
" net['pool5'] = tf.nn.max_pool(net['conv5_3'],ksize=[1,2,2,1],strides=[1,2,2,1],padding=\"SAME\",name=name)\n",
" # sanity check\n",
" print(\"pool5: {}\".format(net['pool5'].get_shape()))\n",
" \n",
" # fcn6\n",
" init = tf.truncated_normal(shape=[7,7,512,4096],stddev=0.02)\n",
" fcn6_w = tf.get_variable(initializer=init,name=\"fcn6_w\")\n",
"\n",
" init = tf.constant(0.0,shape=[4096])\n",
" fcn6_b = tf.get_variable(initializer=init,name=\"fcn6_b\")\n",
"\n",
" fcn6 = tf.nn.conv2d(net['pool5'],fcn6_w,strides=[1,1,1,1],padding=\"SAME\")\n",
" fcn6 = tf.nn.bias_add(fcn6,fcn6_b,name=\"fcn6\")\n",
"\n",
" relu6 = tf.nn.relu(fcn6,name=\"relu6\")\n",
" if FLAGS.debug:\n",
" tf.histogram_summary(\"relu6/activation\", relu6, collections=None, name=None)\n",
" tf.scalar_summary(\"relu6/sparsity\", tf.nn.zero_fraction(relu6), collections=None, name=None)\n",
" dropout6 = tf.nn.dropout(relu6, keep_prob=dropout_prob, noise_shape=None, seed=None, name=\"dropout6\")\n",
" # sanity check\n",
" print(\"dropout6: {}\".format(dropout6.get_shape()))\n",
"\n",
" # fcn7\n",
" init = tf.truncated_normal(shape=[1,1,4096,4096],stddev=0.02)\n",
" fcn7_w = tf.get_variable(initializer=init,name=\"fcn7_w\")\n",
"\n",
" init = tf.constant(0.0,shape=[4096])\n",
" fcn7_b = tf.get_variable(initializer=init,name=\"fcn7_b\")\n",
"\n",
" fcn7 = tf.nn.conv2d(dropout6, fcn7_w, strides=[1,1,1,1], padding=\"SAME\", use_cudnn_on_gpu=None, data_format=None, name=None)\n",
" fcn7 = tf.nn.bias_add(fcn7, fcn7_b, data_format=None, name=\"fcn7\")\n",
"\n",
" relu7 = tf.nn.relu(fcn7,name=\"relu7\")\n",
" if FLAGS.debug:\n",
" tf.histogram_summary(\"relu7/activation\", relu7, collections=None, name=None)\n",
" tf.scalar_summary(\"relu7/sparsity\", tf.nn.zero_fraction(relu7), collections=None, name=None)\n",
" dropout7 = tf.nn.dropout(relu7, keep_prob=dropout_prob, noise_shape=None, seed=None, name=\"dropout7\")\n",
" # sanity check\n",
" print(\"dropout7: {}\".format(dropout7.get_shape()))\n",
"\n",
" # fcn8\n",
" init = tf.truncated_normal(shape=[1,1,4096,FLAGS.num_classes],stddev=0.02)\n",
" fcn8_w = tf.get_variable(initializer=init,name=\"fcn8_w\")\n",
"\n",
" init = tf.constant(0.0,shape=[FLAGS.num_classes])\n",
" fcn8_b = tf.get_variable(initializer=init,name=\"fcn8_b\")\n",
"\n",
" fcn8 = tf.nn.conv2d(dropout7, fcn8_w, strides=[1,1,1,1], padding=\"SAME\", use_cudnn_on_gpu=None, data_format=None, name=None)\n",
" fcn8 = tf.nn.bias_add(fcn8, fcn8_b, data_format=None, name=\"fcn8\")\n",
" # sanity check\n",
" print(\"fcn8: {}\".format(fcn8.get_shape()))\n",
"\n",
" # deconv1 + net['pool4']: x32 -> x16\n",
" s = 2\n",
" k = 2*s\n",
" in_channel = FLAGS.num_classes\n",
" out_channel = net['pool4'].get_shape()[3].value\n",
" out_shape = tf.shape(net['pool4'])\n",
"\n",
" init = tf.truncated_normal(shape=[k,k,out_channel,in_channel],stddev=0.02)\n",
" deconv1_w = tf.get_variable(initializer=init,name=\"deconv1_w\")\n",
"\n",
" init = tf.constant(0.0,shape=[out_channel])\n",
" deconv1_b = tf.get_variable(initializer=init,name=\"deconv1_b\")\n",
"\n",
" # sanity check\n",
" print(\"deconv1 output_shape: {}\".format(net['pool4'].get_shape()))\n",
"\n",
" deconv1 = tf.nn.conv2d_transpose(fcn8, deconv1_w, output_shape=out_shape, strides=[1,s,s,1], padding='SAME', name=None)\n",
" deconv1 = tf.nn.bias_add(deconv1, deconv1_b, data_format=None, name=\"deconv1\")\n",
"\n",
" fuse1 = tf.add(deconv1, net['pool4'], name=\"fuse1\")\n",
" \n",
" # deconv2 + net['pool3']: x16 -> x8\n",
" s = 2\n",
" k = 2*s\n",
" in_channel = out_channel\n",
" out_channel = net['pool3'].get_shape()[3].value\n",
" out_shape = tf.shape(net['pool3'])\n",
"\n",
" init = tf.truncated_normal(shape=[k,k,out_channel,in_channel],stddev=0.02)\n",
" deconv2_w = tf.get_variable(initializer=init,name=\"deconv2_w\")\n",
"\n",
" init = tf.constant(0.0,shape=[out_channel])\n",
" deconv2_b = tf.get_variable(initializer=init,name=\"deconv2_b\")\n",
"\n",
" deconv2 = tf.nn.conv2d_transpose(fuse1, deconv2_w, output_shape=out_shape, strides=[1,s,s,1], padding='SAME', name=None)\n",
" deconv2 = tf.nn.bias_add(deconv2, deconv2_b, data_format=None, name=\"deconv2\")\n",
"\n",
" fuse2 = tf.add(deconv2,net['pool3'],name=\"fuse2\")\n",
"\n",
" # deconv3: x8 -> image_size\n",
" s = 8\n",
" k = 2*s\n",
" in_channel = out_channel\n",
" out_channel = FLAGS.num_classes\n",
" out_shape = tf.pack([tf.shape(processed_image)[0],tf.shape(processed_image)[1],tf.shape(processed_image)[2],out_channel])\n",
" \n",
" init = tf.truncated_normal(shape=[k,k,out_channel,in_channel],stddev=0.02)\n",
" deconv3_w = tf.get_variable(initializer=init,name=\"deconv3_w\")\n",
"\n",
" init = tf.constant(0.0,shape=[out_channel])\n",
" deconv3_b = tf.get_variable(initializer=init,name=\"deconv3_b\")\n",
"\n",
" deconv3 = tf.nn.conv2d_transpose(fuse2, deconv3_w, output_shape=out_shape, strides=[1,s,s,1], padding='SAME', name=None)\n",
" deconv3 = tf.nn.bias_add(deconv3, deconv3_b, data_format=None, name=\"deconv3\")\n",
"\n",
" # per-pixel prediction\n",
" annotations_pred = tf.argmax(deconv3, dimension=3, name=None)\n",
" annotations_pred = tf.expand_dims(annotations_pred, dim=3, name=\"prediction\")\n",
"\n",
"# log images, annotations, annotations_pred\n",
"tf.image_summary(\"images\", images, max_images=1, collections=None, name=None)\n",
"tf.image_summary(\"annotations\", tf.cast(annotations,tf.uint8), max_images=1, collections=None, name=None)\n",
"tf.image_summary(\"annotations_pred\", tf.cast(annotations_pred,tf.uint8), max_images=1, collections=None, name=None)\n",
"\n",
"# construct the loss\n",
"loss = tf.nn.sparse_softmax_cross_entropy_with_logits(deconv3, tf.squeeze(annotations, squeeze_dims=[3]))\n",
"loss = tf.reduce_mean(loss, reduction_indices=None, keep_dims=False, name=\"pixel-wise_cross-entropy_loss\")\n",
"\n",
"# log the loss\n",
"tf.scalar_summary(\"pixel-wise_cross-entropy_loss\", loss, collections=None, name=None)\n",
"\n",
"# log all the trainable variables\n",
"trainabel_vars = tf.trainable_variables()\n",
"if FLAGS.debug:\n",
" for var in trainabel_vars:\n",
" tf.histogram_summary(var.op.name+\"/values\", var, collections=None, name=None)\n",
" tf.add_to_collection(\"sum(t ** 2) / 2 of all trainable_vars\", tf.nn.l2_loss(var))\n",
" \n",
"# construct the optimizier\n",
"optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)\n",
"gradients = optimizer.compute_gradients(loss,trainabel_vars)\n",
"if FLAGS.debug:\n",
" # log the gradients\n",
" for grad, var in gradients:\n",
" tf.histogram_summary(var.op.name+\"/gradients\", grad, collections=None, name=None)\n",
"train_op = optimizer.apply_gradients(gradients)\n",
"\n",
"# initialize the variables\n",
"print(\"\\nInitializing the variables ...\\n\")\n",
"sess = tf.InteractiveSession()\n",
"tf.initialize_all_variables().run()\n",
"\n",
"# set up the saver\n",
"print(\"\\nSetting up the Saver ...\\n\")\n",
"saver = tf.train.Saver()\n",
"all_vars = tf.trainable_variables()\n",
"saver_KITTI = tf.train.Saver({\"semantic_seg/conv1_1_w\":all_vars[0],\"semantic_seg/conv1_1_b\":all_vars[1],\"semantic_seg/conv1_2_w\":all_vars[2],\"semantic_seg/conv1_2_b\":all_vars[3],\n",
" \"semantic_seg/conv2_1_w\":all_vars[4],\"semantic_seg/conv2_1_b\":all_vars[5],\"semantic_seg/conv2_2_w\":all_vars[6],\"semantic_seg/conv2_2_b\":all_vars[7],\n",
" \"semantic_seg/conv3_1_w\":all_vars[8],\"semantic_seg/conv3_1_b\":all_vars[9],\"semantic_seg/conv3_2_w\":all_vars[10],\"semantic_seg/conv3_2_b\":all_vars[11],\"semantic_seg/conv3_3_w\":all_vars[12],\"semantic_seg/conv3_3_b\":all_vars[13],\"semantic_seg/conv3_4_w\":all_vars[14],\"semantic_seg/conv3_4_b\":all_vars[15],\n",
" \"semantic_seg/conv4_1_w\":all_vars[16],\"semantic_seg/conv4_1_b\":all_vars[17],\"semantic_seg/conv4_2_w\":all_vars[18],\"semantic_seg/conv4_2_b\":all_vars[19],\"semantic_seg/conv4_3_w\":all_vars[20],\"semantic_seg/conv4_3_b\":all_vars[21],\"semantic_seg/conv4_4_w\":all_vars[22],\"semantic_seg/conv4_4_b\":all_vars[23],\n",
" \"semantic_seg/conv5_1_w\":all_vars[24],\"semantic_seg/conv5_1_b\":all_vars[25],\"semantic_seg/conv5_2_w\":all_vars[26],\"semantic_seg/conv5_2_b\":all_vars[27],\"semantic_seg/conv5_3_w\":all_vars[28],\"semantic_seg/conv5_3_b\":all_vars[29],\n",
" \"semantic_seg/fcn6_w\":all_vars[30],\"semantic_seg/fcn6_b\":all_vars[31],\"semantic_seg/fcn7_w\":all_vars[32],\"semantic_seg/fcn7_b\":all_vars[33],\n",
" \"semantic_seg/deconv2_w\":all_vars[38],\"semantic_seg/deconv2_b\":all_vars[39]})\n",
"if FLAGS.load:\n",
" print(\"\\nLoading pretrain parameters of the whole network ...\\n\")\n",
" saver_KITTI.restore(sess, os.path.join(FLAGS.full_model,FLAGS.full_model_file))\n",
" \n",
"# set the summary writer\n",
"print(\"\\nSetting the summary writers ...\\n\")\n",
"summary_op = tf.merge_all_summaries()\n",
"if not os.path.exists(FLAGS.logs_dir):\n",
" os.system(\"mkdir \"+FLAGS.logs_dir)\n",
"if FLAGS.mode == 'train':\n",
" if os.path.exists(FLAGS.logs_dir+\"/train\"):\n",
" os.system(\"rm -r \"+FLAGS.logs_dir+\"/train\")\n",
" if os.path.exists(FLAGS.logs_dir+\"/valid\"):\n",
" os.system(\"rm -r \"+FLAGS.logs_dir+\"/valid\")\n",
" train_writer = tf.train.SummaryWriter(FLAGS.logs_dir+\"/train\",sess.graph)\n",
" valid_writer = tf.train.SummaryWriter(FLAGS.logs_dir+\"/valid\")\n",
"elif FLAGS.mode == 'valid':\n",
" if os.path.exists(FLAGS.logs_dir+\"/complete_valid\"):\n",
" os.system(\"rm -r \"+FLAGS.logs_dir+\"/complete_valid\")\n",
" valid_writer = tf.train.SummaryWriter(FLAGS.logs_dir+\"/complete_valid\")\n",
" \n",
"# read data_records from *.pickle\n",
"print(\"\\nReading in and reprocessing all images ...\\n\")\n",
"# check if FLAGS.data_dir folder exist\n",
"if not os.path.exists(FLAGS.data_dir):\n",
" os.makedirs(FLAGS.data_dir)\n",
"# check if the *.pickle file exist\n",
"pickle_file = os.path.join(FLAGS.data_dir,FLAGS.pickle_name)\n",
"# load data_records from *.pickle\n",
"with open(pickle_file,'rb') as f:\n",
" pickle_records = pickle.load(f)\n",
" train_records = pickle_records['trn']\n",
" valid_records = pickle_records['test']\n",
" del pickle_records\n",
"\n",
"# initialize the data reader\n",
"print(\"Initializing the data reader...\")\n",
"reader_options = {'different_size':True}\n",
"if FLAGS.mode == 'train':\n",
" train_reader = dataset.BatchDatset(train_records,reader_options)\n",
"valid_reader = dataset.BatchDatset(valid_records,reader_options)\n",
"\n",
"# check if FLAGS.full_model exist\n",
"if not os.path.exists(os.path.join(FLAGS.full_model,FLAGS.name)):\n",
" os.makedirs(os.path.join(FLAGS.full_model,FLAGS.name))\n",
"\n",
"# start training/ validation\n",
"print(\"\\nStarting training/ validation...\\n\")\n",
"if FLAGS.mode == 'train':\n",
" for itr in xrange(FLAGS.max_iters):\n",
" # read next batch\n",
" train_images, train_annotations = train_reader.next_image(FLAGS.batch_size)\n",
" feed_dict = {images:train_images,annotations:train_annotations,dropout_prob:0.85}\n",
" # training\n",
" sess.run(train_op,feed_dict=feed_dict)\n",
" # log training info\n",
" if itr % 10 == 0:\n",
" train_loss, train_summary = sess.run([loss,summary_op],feed_dict=feed_dict)\n",
" train_writer.add_summary(train_summary,itr)\n",
" print(\"Step: %d, train_loss: %f\"%(itr,train_loss))\n",
" # log valid info\n",
" if itr % 100 == 0:\n",
" valid_images, valid_annotations = valid_reader.next_image(FLAGS.batch_size)\n",
" feed_dict = {images:valid_images,annotations:valid_annotations,dropout_prob:1.0}\n",
" valid_loss, valid_summary = sess.run([loss,summary_op],feed_dict=feed_dict)\n",
" valid_writer.add_summary(valid_summary,itr)\n",
" print(\"==============================\")\n",
" print(\"Step: %d, valid_loss: %f\"%(itr,valid_loss))\n",
" print(\"==============================\")\n",
" # save snapshot\n",
" if itr % 500 == 0:\n",
" snapshot_name = os.path.join(os.path.join(FLAGS.full_model,FLAGS.name),str(itr)+\"_model.ckpt\")\n",
" saver.save(sess,snapshot_name)\n",
"elif FLAGS.mode == 'valid':\n",
" # quantitative results\n",
" valid_images,valid_annotations=valid_reader.next_image(FLAGS.batch_size)\n",
" feed_dict = {images:valid_images[:20],annotations:valid_annotations[:20],dropout_prob:1.0}\n",
" valid_loss,valid_summary = sess.run([loss,summary_op],feed_dict=feed_dict)\n",
" valid_writer.add_summary(valid_summary,FLAGS.max_iters)\n",
" print(\"==============================\")\n",
" print(\"Step: %d, valid_loss: %f\"%(FLAGS.max_iters,valid_loss))\n",
" print(\"==============================\")\n",
" # qualitative results\n",
" valid_images,valid_annotations=valid_reader.next_image(FLAGS.batch_size)\n",
" feed_dict = {images:valid_images,annotations:valid_annotations,dropout_prob:1.0}\n",
" annotations_pred_results = sess.run(annotations_pred,feed_dict=feed_dict)\n",
" \n",
" valid_annotations = np.squeeze(valid_annotations,axis=3)\n",
" annotations_pred_results = np.squeeze(annotations_pred_results,axis=3)\n",
" \n",
" for n in xrange(FLAGS.batch_size):\n",
" print(\"Saving %d-th valid tuples for qualitative comparisons...\"%(n))\n",
" misc.imsave(FLAGS.logs_dir+\"/complete_valid/\"+str(n)+\"_image.png\",valid_images[n].astype(np.uint8))\n",
" misc.imsave(FLAGS.logs_dir+\"/complete_valid/\"+str(n)+\"_annotation.png\",valid_annotations[n].astype(np.uint8))\n",
" misc.imsave(FLAGS.logs_dir+\"/complete_valid/\"+str(n)+\"_prediction.png\",annotations_pred_results[n].astype(np.uint8))\n",
" print(\"saving finished!!!\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Model_zoo/imagenet-vgg-verydeep-19.mat has already been downloaded.\n",
"\n",
"\n",
"loading pretrained weights from: Model_zoo/imagenet-vgg-verydeep-19.mat\n",
"loading finished!\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"processed_image: (?, ?, ?, 3)\n",
"conv1_1: (?, ?, ?, 64)\n",
"relu1_1: (?, ?, ?, 64)\n",
"conv1_2: (?, ?, ?, 64)\n",
"relu1_2: (?, ?, ?, 64)\n",
"pool1: (?, ?, ?, 64)\n",
"conv2_1: (?, ?, ?, 128)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"relu2_1: (?, ?, ?, 128)\n",
"conv2_2: (?, ?, ?, 128)\n",
"relu2_2: (?, ?, ?, 128)\n",
"pool2: (?, ?, ?, 128)\n",
"conv3_1: (?, ?, ?, 256)\n",
"relu3_1: (?, ?, ?, 256)\n",
"conv3_2: (?, ?, ?, 256)\n",
"relu3_2: (?, ?, ?, 256)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"conv3_3: (?, ?, ?, 256)\n",
"relu3_3: (?, ?, ?, 256)\n",
"conv3_4: (?, ?, ?, 256)\n",
"relu3_4: (?, ?, ?, 256)\n",
"pool3: (?, ?, ?, 256)\n",
"conv4_1: (?, ?, ?, 512)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"relu4_1: (?, ?, ?, 512)\n",
"conv4_2: (?, ?, ?, 512)\n",
"relu4_2: (?, ?, ?, 512)\n",
"conv4_3: (?, ?, ?, 512)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"relu4_3: (?, ?, ?, 512)\n",
"conv4_4: (?, ?, ?, 512)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"relu4_4: (?, ?, ?, 512)\n",
"pool4: (?, ?, ?, 512)\n",
"conv5_1: (?, ?, ?, 512)\n",
"relu5_1: (?, ?, ?, 512)\n",
"conv5_2: (?, ?, ?, 512)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"relu5_2: (?, ?, ?, 512)\n",
"conv5_3: (?, ?, ?, 512)\n",
"pool5: (?, ?, ?, 512)\n",
"dropout6: (?, ?, ?, 4096)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"dropout7: (?, ?, ?, 4096)\n",
"fcn8: (?, ?, ?, 11)\n",
"deconv1 output_shape: (?, ?, ?, 512)\n",
"\n",
"Initializing the variables ...\n"
]
}
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
molgor/spystats | notebooks/Sandboxes/TensorFlow/GPFlow_examples.ipynb | 1 | 808227 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import sys\n",
"sys.path.append('/apps')\n",
"import django\n",
"django.setup()\n",
"from drivers.graph_models import TreeNode, Order, Family, graph, pickNode\n",
"from traversals.strategies import sumTrees, UniformRandomSampleForest"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GPFlow first approximation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Import modules\n",
"import numpy as np\n",
"import scipy.spatial.distance as sp\n",
"from matplotlib import pyplot as plt\n",
"plt.style.use('ggplot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulating Data\n",
"* Simulate random uniform 4-d vector. Give N of this."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Parameter definitions\n",
"N = 1000\n",
"phi = 0.05\n",
"sigma2 = 1.0\n",
"beta_0 = 10.0\n",
"beta_1 = 1.5\n",
"beta_2 = -1.0\n",
"# AL NAGAT\n",
"nugget = 0.03"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X = np.random.rand(N,4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Calculate distance\n",
"X can be interpreted as covariate matrix in which the first two columns are the longitud and latitude.\n",
"GPFlow requires that all the covariates (including spatio-temporal coordinates) are in X.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"points = X[:,0:2]\n",
"dist_points = sp.pdist(points)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fee2fc4b5d0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAARsAAAEECAYAAAAYrZBvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fee68368990>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"## Reshape the vector to square matrix \n",
"distance_matrix = sp.squareform(dist_points)\n",
"correlation_matrix = np.exp(- distance_matrix / phi)\n",
"covariance_matrix = correlation_matrix * sigma2\n",
"plt.imshow(covariance_matrix)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulate the Gaussian Process $S$\n",
"\n",
"Remmember that for a stationary Gaussian Process, the value at Z is independent of the betas (Covariate weights).\n",
"Mean 0's $\\Sigma$ Correlation matrix\n",
"$$S = MVN(0,\\Sigma) + \\epsilon$$\n",
"$ \\epsilon \\sim N(0,\\sigma^{2}) $\n",
"\n",
"S is a realization of a spatial process."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"S = np.random.multivariate_normal(np.zeros(N), correlation_matrix) +\\\n",
" np.random.normal(size = N) * nugget"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7fee2dc75d10>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEECAYAAAA8tB+vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fee2f9c3d90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"S.shape\n",
"# We convert to Matrix [1 column]\n",
"S = S.reshape(N,1)\n",
"## Plot x, y using as color the Gaussian process\n",
"plt.scatter(X[:, 0], X[:, 1], c = S)\n",
"plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Simulate the Response Variable $y$ "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$y_1(x_1,x_2) = S(x_1,x_2) $$\n",
"$$y_2(x_1,x_2) = \\beta_0 + x_3\\beta_1 + x_4\\beta_2 + S(x_1,x_2)$$"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7fee2db66910>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEECAYAAAA4Qc+SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fee2dc99d10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# remmember index 0 is 1\n",
"mu = beta_0 + beta_1 * X[:, 2] + beta_2 * X[:, 3]\n",
"mu = mu.reshape(N,1)\n",
"Y1 = S\n",
"Y2 = mu + S\n",
"plt.scatter(X[:, 0], X[:, 1], c = Y2)\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1000, 1)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"S.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GP Model !\n",
"This model is without covariates"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import GPFlow\n",
"import GPflow as gf"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Defining the model Matern function with \\kappa = 0.5 \n",
"k = gf.kernels.Matern12(2, lengthscales=1, active_dims = [0,1] )"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"GPflow.kernels.Matern12"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(k)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model for $y_1$"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name.kern.\u001b[1mlengthscales\u001b[0m transform:+ve prior:None\n",
"[ 1.]\n",
"name.kern.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n",
"[ 1.]\n",
"name.likelihood.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n",
"[ 0.001]\n",
"\n"
]
}
],
"source": [
"m = gf.gpr.GPR(points, Y1, k)\n",
"## First guess\n",
"init_nugget = 0.001\n",
"m.likelihood.variance = init_nugget\n",
"print(m)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Like in tensorflow, m is a graph and has at least three nodes: *lengthscale, kern variance and likelihood variance*"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name.kern.\u001b[1mlengthscales\u001b[0m transform:+ve prior:None\n",
"[ 0.04899438]\n",
"name.kern.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n",
"[ 1.0912394]\n",
"name.likelihood.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n",
"[ 1.04964184e-06]\n",
"\n"
]
}
],
"source": [
"# Estimation using symbolic gradient descent\n",
"m.optimize()\n",
"print(m)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"compare with original parameters (made from the simulation)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0.05, 1.0, 0.03)\n"
]
}
],
"source": [
"print(phi,sigma2,nugget)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1000, 2)\n",
"(1000, 1)\n"
]
}
],
"source": [
"print points.shape\n",
"print Y1.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" it was close enough"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## GAUSSIAN PROCESS WITH LINEAR TREND \n",
"### Defining the model"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"k = gf.kernels.Matern12(2, lengthscales=1, active_dims = [0,1] )"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"gf.mean_functions.Linear()\n",
"meanf = gf.mean_functions.Linear(np.ones((4,1)), np.ones(1))"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name.kern.\u001b[1mlengthscales\u001b[0m transform:+ve prior:None\n",
"[ 1.]\n",
"name.kern.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n",
"[ 1.]\n",
"name.likelihood.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n",
"[ 0.001]\n",
"name.mean_function.\u001b[1mA\u001b[0m transform:(none) prior:None\n",
"[[ 1.]\n",
" [ 1.]\n",
" [ 1.]\n",
" [ 1.]]\n",
"name.mean_function.\u001b[1mb\u001b[0m transform:(none) prior:None\n",
"[ 1.]\n"
]
}
],
"source": [
"m = gf.gpr.GPR(X, Y2, k, meanf)\n",
"m.likelihood.variance = init_nugget\n",
"print(m)"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name.kern.\u001b[1mlengthscales\u001b[0m transform:+ve prior:None\n",
"[ 0.05029038]\n",
"name.kern.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n",
"[ 0.98037623]\n",
"name.likelihood.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n",
"[ 0.0059287]\n",
"name.mean_function.\u001b[1mA\u001b[0m transform:(none) prior:None\n",
"[[ 0.44958183]\n",
" [ 0.03042917]\n",
" [ 1.52097183]\n",
" [-0.97419863]]\n",
"name.mean_function.\u001b[1mb\u001b[0m transform:(none) prior:None\n",
"[ 9.60799153]\n"
]
}
],
"source": [
"# Estimation\n",
"m.optimize()\n",
"print(m)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Original parameters\n",
"* phi = 0.05 ---> lengthscale\n",
"* sigma2 = 1.0 ---> variance transform\n",
"* nugget = 0.03 ---> likelihood variance\n",
"* beta_0 = 10.0 ---> mean_function b\n",
"* beta_1 = 1.5 ---> mean_fucntionA [2]\n",
"* beta_2 = -1.0 ---> mean_functionA [3]\n",
"* mean_functionA[0] and mean_functionA[1] are the betas for for x and y (coordinates respectively)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Without spatial coordinates as covariates"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Defining the model\n",
"k = gf.kernels.Matern12(2, lengthscales=1, active_dims = [0,1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Custom made mean function (Erick Chacón )"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from GPflow.mean_functions import MeanFunction, Param\n",
"import tensorflow as tf\n",
"class LinearG(MeanFunction):\n",
" \"\"\"\n",
" y_i = A x_i + b\n",
" \"\"\"\n",
" def __init__(self, A=None, b=None):\n",
" \"\"\"\n",
" A is a matrix which maps each element of X to Y, b is an additive\n",
" constant.\n",
" If X has N rows and D columns, and Y is intended to have Q columns,\n",
" then A must be D x Q, b must be a vector of length Q.\n",
" \"\"\"\n",
" A = np.ones((1, 1)) if A is None else A\n",
" b = np.zeros(1) if b is None else b\n",
" MeanFunction.__init__(self)\n",
" self.A = Param(np.atleast_2d(A))\n",
" self.b = Param(b)\n",
"\n",
" def __call__(self, X):\n",
" Anew = tf.concat([np.zeros((2,1)),self.A],0)\n",
" return tf.matmul(X, Anew) + self.b\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Now we can use the special mean function *without* the coordinates (covariates)."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"meanf = LinearG(np.ones((2,1)), np.ones(1))"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'X' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m\u001b[0m",
"\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-5f379da93da6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mY2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
]
}
],
"source": [
"X.shape\n",
"Y2.shape"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name.kern.\u001b[1mlengthscales\u001b[0m transform:+ve prior:None\n",
"[ 1.]\n",
"name.kern.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n",
"[ 1.]\n",
"name.likelihood.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n",
"[ 0.1]\n",
"name.mean_function.\u001b[1mA\u001b[0m transform:(none) prior:None\n",
"[[ 1.]\n",
" [ 1.]]\n",
"name.mean_function.\u001b[1mb\u001b[0m transform:(none) prior:None\n",
"[ 1.]\n"
]
}
],
"source": [
"m = gf.gpr.GPR(X, Y2, k, meanf)a\n",
"m.likelihood.variance = 0.1\n",
"print(m)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Only 2 parameters now!"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"name.kern.\u001b[1mlengthscales\u001b[0m transform:+ve prior:None\n",
"[ 0.05198912]\n",
"name.kern.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n",
"[ 1.08595541]\n",
"name.likelihood.\u001b[1mvariance\u001b[0m transform:+ve prior:None\n",
"[ 1.04187191e-06]\n",
"name.mean_function.\u001b[1mA\u001b[0m transform:(none) prior:None\n",
"[[ 1.47377333]\n",
" [-1.12897884]]\n",
"name.mean_function.\u001b[1mb\u001b[0m transform:(none) prior:None\n",
"[ 10.28939636]\n"
]
}
],
"source": [
"# Estimation\n",
"m.optimize()\n",
"print(m)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Original parameters\n",
"* phi = 0.05 ---> lengthscale\n",
"* sigma2 = 1.0 ---> variance transform\n",
"* nugget = 0.03 ---> likelihood variance\n",
"* beta_0 = 10.0 ---> mean_function b\n",
"* beta_1 = 1.5 ---> mean_fucntionA [2]\n",
"* beta_2 = -1.0 ---> mean_functionA [3]"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"predicted_x = np.linspace(0.0,1.0,100)"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from external_plugins.spystats.models import makeDuples\n",
"predsX = makeDuples(predicted_x)"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pX = np.array(predsX)"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tt = np.ones((10000,2)) *0.5"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Concatenate with horizontal stack\n",
"SuperX = np.hstack((pX,tt))"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(10000, 4)"
]
},
"execution_count": 193,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SuperX.shape"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mean, variance = m.predict_y(SuperX)\n",
"minmean = min(mean)\n",
"maxmean = max(mean)"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f65aa1925d0>"
]
},
"execution_count": 195,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEECAYAAADHzyg1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhosCIYQQGy4KhBBCbP4fdZdp1LPkg80AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f65aa25e1d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#plt.figure(figsize=(12, 6))\n",
"plt.scatter(pX[:,0], pX[:,1])\n"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PolyCollection at 0x7f65a8854610>"
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEECAYAAADEVORYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f65a8a64490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Xx, Yy = np.meshgrid(predicted_x,predicted_x)\n",
"plt.pcolor(Xx,Yy,mean.reshape(100,100),cmap=plt.cm.Accent)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAssAAAHjCAYAAADVH1IdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nP2oAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3c79ccea50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Nn = 300\n",
"predicted_x = np.linspace(0.0,1.0,Nn)\n",
"Xx, Yy = np.meshgrid(predicted_x,predicted_x)\n",
"## Predict\n",
"from external_plugins.spystats.models import makeDuples\n",
"predsX = makeDuples(predicted_x)\n",
"pX = np.array(predsX)\n",
"tt = np.ones((Nn**2,2)) *0.5\n",
"SuperX = np.hstack((pX,tt))\n",
"mean, variance = m.predict_y(SuperX)\n",
"minmean = min(mean)\n",
"maxmean = max(mean)\n",
"width = 12\n",
"height = 8\n",
"minz = minmean\n",
"maxz = maxmean\n",
"plt.figure(figsize=(width, height))\n",
"plt.subplot(1,2,1)\n",
"scat = plt.scatter(X[:, 0], X[:, 1], c = Y2)\n",
"#plt.axis('equal')\n",
"plt.xlim((0,1))\n",
"plt.ylim((0,1))\n",
"plt.clim(minz,maxz)\n",
"#plt.colorbar()\n",
"plt.subplot(1,2,2)\n",
"#field = plt.imshow(mean.reshape(100,100).transpose().transpose(),interpolation=None)\n",
"plt.pcolor(Xx,Yy,mean.reshape(Nn,Nn).transpose())\n",
"plt.colorbar()\n",
"plt.clim(minz,maxz)\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f65ac029b90>"
]
},
"execution_count": 132,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEECAYAAAAMOA6OAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbIVCoVgl/P9aOxVGV9F2kgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f65ab7b6090>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(nrows=1, ncols=2)\n",
"scat = plt.scatter(X[:, 0], X[:, 1], c = Y2)\n",
"\n",
"field = plt.imshow(mean.reshape(100,100),interpolation=None)\n",
"#fig.subplots_adjust(right=0.8)\n",
"#cbar_ax = fig.add_axes([0.85, 0.05])\n",
"fig.colorbar(field, ax=axes.ravel().tolist())\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f65c37aca90>"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAATwAAAEDCAYAAACoKbh+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvUmsbclZ7/mLZjW7O829eW+2JAnYPFe5sM2AEiW7EIZJ\n4YlnSacnbAED8MDAAFtCBUZyyROMoLAwDBAMQMIg4aw3YWhTyCUhTKOijKiC94wfZOO83Wl2s7qI\nqEHEt1bsfS84r31xPuMT0tI5Z5/VxIod8Y+v+X/fp0IIgat21a7aVfs6aPr17sBVu2pX7ap9tdoV\n4F21q3bVvm7aFeBdtat21b5u2hXgXbWrdtW+btoV4F21q3bVvm7aFeBdtat21b5u2lcEeH/913/N\nT/3UT/H+97+fF1544TVf97nPfe4reezr0r7W+vy11l+46vNV+7dvXzbgee/5rd/6LX7u536Oj370\no3zmM5/hxRdffE3Xfi1Okq+1Pn+t9Reu+nzV/u2b/XIv/Id/+AeefPJJbty4AcDb3/52/vzP/5yn\nn376kXXuql21q/bvu3384x/nL//yLzk+PuaXfumXAPjEJz7BZz/7WZRSHB8f8773vY+Tk5O96+7c\nucPHPvYxzs/PUUrxvd/7vbzrXe/6ks/7sgHv7t27XL9+ffz72rVr/MM//MOXe7urdtWu2tdhe+c7\n38n3fd/38bGPfWz87N3vfjff//3fD8Af//Ef84d/+If8+I//+N51xhh+5Ed+hOeee46mafjABz7A\nW9/61i8pcL0uTovnn3/+9XjsV9S+1vr8tdZfuOrz10I7+8d/fKT3e9Ob3sRisdj7rK7r8fe2bVFK\n3XfdyckJzz333Hj+008/zd27d7/k875sCe/atWvcvn17/Pvu3btcu3btvvM+97nP7dk5nn/+eXj5\n0/D5T0ObPgyAIcJvmY4qHqEEinj4QuGtpreWzha0uqSloqcY768IKDw6HQaPwWHCQMFAEXps57Gd\nQ7VAE58fCvClppmXNLOKDXO2LAgoFJ6bfBsXfBbLgGXA4FizZMOCu1zjDte511/jrDtls1kS7mrC\nXQ0b4tECHTAALr2zNJOO9J7j+9fZMYs/9aJHzwfqomFmdsz1lgUbahoMDo1nzZI38Cz/l2/Y+jlD\nsEBAq4DRDqNdGqeQxsiNh7yb3Evj05kKh2HA0lOkUbDjmSHEs0OIk1N+5i2g4uch/v/w3O+ylj91\nfRwcNV4Uv9WgCE7jnQanCU6BU3E8vYpj6tP5PrtWZYcBdIAioGxAVwO6cpSmozAdSgUCGuc1zluc\nNwSv9t5FaRnDAasc/5Ou+SyXaSxdmm9prtFT0u0dFS0V7X2fF/Tj9SG9vEePs1k+0zhM8NRdR913\n6HWAyzS/fHrHY/BHim1Zs61qdmrGjhlv4j/yB3/wB+O7vPnNb+bNb37zfd/Tv9ZOnnuO/+0BAPSg\n9nNfQZj+7//+7/Mnf/InLBYLfuEXfuFfPffVV1/lC1/4Am984xu/5H2/bMB7wxvewCuvvMKtW7c4\nPT3lM5/5DO9///vvO++Bg/qfPw3/xy/CWfpbMS3yJXACHMdDrYB5PMwsHv2ipF3OOK+WnKlTLtUK\nlRAkLr+4cGXSFXRUdCzCBuW36LWHex51B7gNOFAzcCea7ukZ66fnfJHHeZXH8Wg0jpIf5mU+zoIN\nS9bUNNziKV7iKf6W/57/h/+Bv2/+A/94a8Gdf7bw/wJ/D3wxHZfZxOzjM8d3r4BSwSK961E6ToBr\nB8dNUDcCx0cN1xd3uFHc4nG+yHV1Z1xIL/Mk/wtLfkPf5Uyf0FGOm4BlwAZ5eByvgp5KtdQ0zNhR\n0VDTjtAm20dDzYYFm7Bgy5wt87hcQ4ELFhciQHivJ0DP1oaAh/ca7xXBa7yfQPJ/nWs+3HRoFUDt\nL5bgNa4tcE0Zx7BV0KhpI+nTIRuKT0cOdiaN80zBLKBPQJ84lvMtq9kF2gQ6CjpX0exq2qbGO4Uf\nJkXI2IGidpRlR100fLBU/O/hDKOGEbhmKo7jgg3HnHPMOUdccMw5p9zjhDOOwgVHXLAKa5ZcosIW\ny4BiiFuQMgzK0mNxKm4sAZW2mR7V7yg3W9StEOfXOr13DeEZhas15+Y6r4Zr3FWn3OOUN/EfH4lE\nWnzpU77i9gM/8AP8wA/8AC+88AJ//Md//C/2u2kafvmXf5n3vOc9e5Lhv9S+bMDTWvOjP/qjfPjD\nHyaEwPd8z/fwzDPPvLaLO+AeEWxyqaZjAoCaOJlLJsV7iJ8VnWPRN4SFxtcFVIyLUiS8XGoRSWVs\nsgDkuS7+VBa0jqBQ07Jgw4BNUl5IXbA01PQUXLLaO3btnOGugVeIxxeBV9OxTkcfwHnwSfxQCqyK\n/dmoKMk1xMWbg4ZK41BqQlHQ6ZpNsaQ0HaXqQEFFS0HPJaskK+hRaov9j8+U//mgsWogoNB4CvoE\n8AFLT52kEY9mwOLRbJnj0XSU7PyMtq9ouwofNCFJeLKxKwWogNY+SU+AAF6S1oLXBBe/4FBpfGfx\nOqBy0BPprjXQatgp2KVxatK8EdBzaZ54JklaJD1DnPEzYA6hM/i2pFksCYsCZQIuGJwzDE2Bby2h\nVzAoSKDsC81QGUJtcVVFe1KydkfYsqPXLaXp4vrAU9HuAVVFM4Lgqb/HqTtn2W+ou5Zy6NHeo73H\nF55QOLrSo0tPb+JLxPkd57ErFP1ME049hgCn8TWHStOc1uxmM+4VR5yrIy5ZsWH+JZfla20PAxpf\nqUT5jne8g4985CMPBDznHB/96Ef5ru/6Lr7jO77jNd3vywY8gLe97W386q/+6sNfKIB3i3HyIaqr\nSX93TIAHcQK3gAbbOkzv0D7gtYbK01HuqbYizQjYKfwIWmimyS+AV4CyAWM8BQN1mpwdJQM23SPg\nMOyYEVD3Ad62neHuWHiZfcC7RQS7DTB44ooMqSPpUGpSXTserJYZoNBQaPqyZr1YoUuHNVEKEwlj\nw2JcaDIOjLeLsl4fCoaQJAelMLgR3CP4DVS0zNnG85OsrPE4DF2IgNe0Nc22jqAk4lwgApaOYKeN\nw9hJqvRO45zBD5rgDAjgOY3rC0gAiU7fl4+AR2cT4AFb4s8dk9ScS3iHP+V7F8CbQWgNoTE0q4J2\ntwQT4nukjZVWxXt209QNhWGoCoY60C0C7QwuuyMKGqqiYNAtWgUsjp4ChwHApPEUwLvu73G9u8ui\n2WI2oHchahou9W0OZu5in0ycBg4zzmFXKDpjQIOuHMqDK6ArNJfljPPymHtJvhTTy6Nqs4c497VI\nlCEE8ix1r7zyCk888QTAv8r8+PjHP84zzzzzmryz0r4iwPuyWw1cJy5qATqx3S2JI1qk3gkmKEb7\njBpAdRH46qHBObBqoFE1QakkkTFODrGu9KpAa48vHX7pMNqjKg8eQqlwM0NXF3SUe5NLQKKlGm1Z\nPQW3eYw7XOeuu8Y9f8Jms2C4a/eB7pJku2vB7yCIGJIjmY4SRJ8Ml9aCSSCo2MNFdAQVFyx9qNme\nrrBLj5sbStVR6XaUQHe+pvMVg7d4Z6JElQ4XDN4bTNEzVBYsFKan19MiLeiZscNhRlufw9C6inY7\np90uGHYFYVvs91VB0AEseOOhSPanBIBKB5QPUYDrFaHT8VqvoDdgNEEkvEC01fUKtipKd2IX3aYj\nt48KwAn4CRDKkOs0v2YqfjdHwEIR5kyrwTFtuHK9rMcizd8FhCNFOIVhU8Xvr9IErzA2bkKNrkd7\nZ0BhkhRd0VAOLUUzYDcBvQUl4N0A10AVmYCbWQiRea0UThvaIkp4Hk1nLDtTcW6OOVcR7M44YUM0\nQTyq9ihV2l/91V/lb//2b7m8vOQnfuIneP755/nLv/xLXnrpJbTW3LhxY/TQ3rt3j9/8zd/kgx/8\nIH/3d3/Hn/7pn/Lss8/ysz/7syil+MEf/EHe9ra3/avPe30AbwY8QVRdIU52AT2xY1VMEp8sdmke\n6EF3nrpvwDm08QRFclNEuQ4msHJYegJBgasHXDlgZw57FKUtZw2DsTS2oKUaJaT8HgIkDTVb5tzm\nMW7zGHd9dFasN0v6Oxng3WZStXwD4R5xFeVei8zINRyDO066IPHlc7AT0B/AO0M3zAiDJmhFWxXU\npqGmSVbLgp2f0w0lfV/QdwWus1FK6gzBR2eBnXX0K4OqPaVqmSXAEzVsxo4hTRONZ8DSDjXN5Zzm\n7oLQ6GhLy6XmzAkTihB9CQpUMaB0QHtP0IqAIXQKdiae74Ah2f9ktXsi2HVEwNsSwU4k5twhlKu0\nAlQ5cIlgLY6gI6KtWP622Rh32fV9NveKNH9X6b7Pgjsv4rxzGoeKs0311LoaNYSQ1NGo2naUrsfu\nPFok1S1wTgThIvYtBJXN32ickDnpsHSqTIKBSvOyYqvmXKhoNbxkxQVHySJb8ajaowSNB9n93/nO\ndz7w3NPTUz74wQ8C0bv7iU984qGf9/oA3hJ4mjhpxMBsmXbP5KQYJ6KAYRKIQlJFVZm8bWpyVMQ5\nHW1uAnoOg1cah0ZTYIxHG48xDl1GVdVrjdMmGq2ZvL9yOAwRTiLYXbLkjJOoMvglu6GmH4roRRRv\nIUxew9GEmIsfB5b1UECooNOwqSOIHEq6cr+gCR4GXdLUc1ylGcqCvigYfEGvK3Zbz9AVDJ1laCy+\nSSpha8ZHD6tkLQiORvdsTceMHTs1Y8ucOdsJ5MOcbZiz62d0mxJ/r5gARUBOAG+U2gMhRMkE45KK\n67F6wHlHX8Hgov0vjoFKY6VEj4vPaBLYrYmgcMlkF23Zl+5kiHsmqUlAZWBkAHACXKTfy2ycYR8w\nu+z7rIib8mkC+T72LdQapz2BQOPjtlvSsdMzOl0mG6gZDStBKXyhcFX6+o3CaY2vDBwH1AzaoqBV\n5Wiu6SijxzZtiIqQ5rWJNlVmbFhwzjEXHHHJkjWrNIcf3VL/ajgt/q3a6wN4R8CzxEkrE1UkhJII\ncjIpM1rKeE76O9QKX2m80cn+HzLjvEhmotwqAuVIsQhkKhiMgpZcE/26E3FgwLJlTkPNJUsuWbFm\nyZolja8ZXBEnY6kiWC/T4YmLLVgIM/YNSyKSSEcaYAuDieCn7f5CzAEvNV9o+nmFrwx+buhnhqGr\n6IuK3ZnFt5rQaHyjYJeksYYR8HxnCKqiV55dOWDKgVJFW6BJxi9x1NwLp1z4I3b9nGFTRLAQQMjB\nTjYv+S6DISiNrjqscRS2wwSHM4YtAW8UfrCgi0nCkvcc0v3T0Ixgd5EO8XznG0sOeDlInqXvQhgB\np+nI7ccmfR0iJQqgyyY2S/N3YNqw09foG0twii7tYZaebTGn0fU4h4Q7MBSGfqHRVQT44DStL+lC\niaoCqg50RUFjIpFFQE8cThNVKHpyW6o9wBNV9pLlCLSPqr0+oPFo2uvS92GhCc9o1Dag+oDqxKPH\nuFhCWughieyhUAQDGBWxwyqGQtMVhk7bZN+Y2GUuHRMbT4+8MeGSORUlPxA6ix9/imSXA96OyGeK\nzLfFCIA9RQRQE+ktYcWkLsmi6y2oOtnwduyvZkGzRAwMFQwOGh89t0ZN5j5Z0BAXYKHxM423Br+E\nYalxbcmwsvR3inRLNXk0Gw74j5pQafqyoqkdqvAUZkAbj1NRchhUXFBn/pR1t6JpZwyt3feQ5tQP\nAb2KqI76xL2roldWK0+lW7zS9D6OcWj0pN174jX5ELVM4HXBpP5dZs+Xd0omj1HCE6A8I6rAAsjC\nw5QNRcwnKo2xC9P7CeAt030XiftnAtgQf3pFaCxD/JWSlp2as7OzUa0cpTVb0KgSHzKTialpTD0y\n7/L5l2saOejJOaJ9RJBbRfoQC3bMkqbz6ADvSsJ7yNbOS/onKoq+R/ce03u0T045DcFEgchb8Fbh\nrMHZKCV4pfBaJxVU0xvDgImUiLTjTaTYqOgKyOWqQZtJcMAeAbSgH+8xfRolPAG9hhmdeOEMmMJh\nZg53FAjXiAtsQ1J5iC/Tz8B3TMYiWdGCZl129NHtttOgzAEYZIMpANMr/LIgLDRhMPANKjpN2uzI\nDfuywBOQ+NrQlTUo0LPAUBl2esZaL8cNYj2s2GyP6dYzfG8niVOcwH32Kia9d8koKQ2FpakrDD2z\ncodVA1YPGOOik0JU2JxOIgJxRwQvke4EvLbpnNzOmY+Tf8A9RcBWWZ8PKUACcCOhOcTDAYsEzgtg\nBurEoZcDvjGEncU3GkJJH2Y0dsa2jpzFDQsaorS3UzPQjI4ghxnVV9FJhmwuT3R3OwKeQzNk81nm\npmzQh3bsR9WuAO8hW1OXbOo5dWiwvaMYBowLCHPBq4DXCm/AWc2gLb2JElnOrpt2OzPKdvlOKFKd\nLFjZZRvqBFrxp8anqbIbgS+XBLukhmyZsUuE2x0zekocGnSInrmZwx9FTtQIeDuSnclE4KLifsCT\nljNoe/ADtMW0GPNFHA5+7hRhYQmL9JVeIzpOcoO+mA1hn/u4g7DWDLbCK4MPmlaVbO2C0rR4TFSb\nujnNZsmwqadu54Ai/TnkvSUbmjMGbysq3xKWGl04ojM2RDqGOCgmBsukMoqEd0mU7kQ9FWn1UCWV\nFtjvk9zTJ/DaAd5HbmTIT1TcLxWF6HHuQ/SWJzuzDg696MCXuJ2BPnIcB1PSLOsR7PLDKEdnygcC\nmzRxvuXz3R/M/z7Fc4iEJ4DqMjVWtJ5H1R6GlvLfWntdAG/Nki/yBDUthekpVI81HusTX055UIGg\nFV4pej1RRfKdbtibAGb0LuabdQQ9Q5+MujtmxJiCScKziSMVUCOFQOgEudwXgW4+gmUvpGQdMHbA\nVA619FGVPWVSv0Qa0dIr0Z1y8Qgm8SsZkEIP3kxSIkzqmqhjYpAXisUq3fqbmCS8PKRNJKE6/Z2r\noRpC0AxdSdhqhqKiLVxkMQaNawrcpY3AI+TorLt7joPccWPTcU8TblvamwvOb2jU3NN1FUNX4bYW\nHkvj5Zkk0Nx+dwHcJXq/7zA5E8R2KLa43ObZsU9kF46dJwIdHsIlhHMmD7pwV8R1mxkljYJKxXsm\nX4zWDlsMuJmK0SROx7GsPYONvM0LjrjDNWZsMTiOuGDGDssw2uNydoEi3Pf5gw7RWGS++oxdkFuv\nH2W7suE9ZFuz4Is8ESMDdEepu2TOjZAmdjTggV/qkAy1uboqKmoOiSbdI3qxirS7Lu+7lzxHAG8C\nun37yb5KWzMk4V4rj7UOXTvUMkTAEw/iGVH1ES/zuJIN/zLgibW8iosyEEFNhL/c6ygUjQumkDyb\nPhcJL6dkCCgIKOXdUdF4PmwrhrJKjqOkano1qZXbg/se8tYOQ70E/G5rONW0F5aun0fqRQ90Kl67\nTO8hIFak6w8B7w4RzCV2dE7Ep9x2KH6gQ8ATP1EPBA+uT1/Si0xeNJMNplAF0pdo9OT0sOkxxlMU\nPczA6RgyB0DhGYxJjq4Vd7iOxeExHHHBkjUV7Qhf0g5BKne05Q6Lw7UxxjWPgBcNPZqwJzR/pe1K\npX3ItmHBKzxOlbyB5UgG6ce/BfRETRXwEWBrKelHz9cEULnjIQdN8TTumI3X5FOqpUIiKcTNn0+m\nliqGjzHbk/R21GzdnNZVDEMRIz+ERJ1LHCbRGJSCkLubc36UuKJjn0YDVhgi4Aw6Ln5ZsDuiU0Ps\nWuKtLJiiWXIvpmKK5hA2jESbFGp6fC5BllP0xN7nIjnmx6HUmZNpG6AcoBrgSU140cJ1M0lnAxFj\nJIesfN6nd7hLjF65FeB2gDs+julcw1zFdzpO71Zl/cu4i2P/tLxQQ9wZ7pACntPAyCAlgqiaw6yM\ntrvHgRvpWRWgYhic0oGi7NHaEUIMsTPG4Y2m8TXn6jgOoYob7xExlnbOdoyBFj1FZLosdiX7bfo7\nbtBmPDsHQyCDyUcr5V1JeA/ZNkmlncCuH7NIVEnhLOnG0DCxpe1GS9vk9cq5ciKt5ZlAgNHA2yfw\nEnUgguOAAhpiHOl+DotylCZbqpHEeQh4G7dg19b0/QMATzSi3AM4Ap7wNqQV2UliHEtSnzfxOlFp\nc6+2JF0ge27LJA3tmMjdi+z6nD4i1B91cO98huQOhDwKIeer7YgYcpkdZ+lwQ4w2uWnhnzQ8YSJw\nLNLzvgn4ZybpTFTaNRG8XyZKdnc83B2g1mkjSfa0UyZpT4CWNJQdUUrM9xQaokHwDhFN1+nhR8RQ\nIBM963oJSwM3NTxJBLwTxr1KqRCjSMxAWfg483wCH61oQ8U5x+z0jA1L7nHKcUomcMQFi5SbR7Kp\nyKYtECZN5rTGj+CWS3USQJk3Ac69WPKvsF1JeA/ZNix4mSewuD3Ak2wd8+QeqGko6Efw2bBgzXJU\nLSPoTZKeS1JbvsMB41cuiq5MCTETtyk6oaDfA1AB0RzwdmHGJiwTAXdO08/Ybef0mxluXRDWOi7u\nC+L6EerG6CWUp0tgZ44yeyIh+y5HJk9hTjobfFJXFZyXsKwihnbEtXyXuPgFX0P6XdS8HOxs9sic\nZiIt57jlHt9DztoeFSTE8TgP0LuoQg7Efp8RnStH6TnnwH9lwnyRNuVed5loKCHZ0o6Itr+ngWfD\nZHoTYrJS8XlbJn/RKOHJOIpRU8TjY1DHYJYwr2Fh4YkEds8Qf14PsPQoE7C6p9QtVvUUaoh31jF8\nryfGLO92C3bNkkYvuFAnbMoLttWCrZ1zzDkrLkfSkxhqRFabEmL4cc7GN9if55ObRTb8aeO/kvBi\ne92cFq/wJJKhI2bmaJilgP0FmzEkf8ZuBDdhkOccOMkuJqE3ueFXWq4gxJjGeLZY8Uo6NinTiFyf\ne80E8M45ZsuCtV+ydkvazZx2vWC4sAwXFn+p4TIB3i2i4HBBBIAc9IBpRRfZ78K2zhFInBvu4Egi\nVRhgCNELvDmGsyou+pbJYSJcM4laEZVPkjPI4wTcpDsPCunLQ7cG/mXQE21xDTQBOh+9zmGArYZX\nwmTjPErPPwQ80vMERC/Tz6ChVnCs4HEVSexvIEqIMnx3iADpsmvlf6PzSDadJVFsS/mV1DHoJ6C4\nBtcsPKHgKfaPGx59MqAKKEJDrXZUKhpbNIGgFJ2Km/TQW9rzmu7WjK31aOvYHS1oTytau29PjubL\n+FfUxtUo8YmDIwexKAWqUcbTB1a+3Pr3qNqVhPeQbR0WvByeRIWAVQOGgTrlY1uoDUsuOeKSY86Z\nsx3d7RLKNQJeqGlDNeVjI1IqXDBRAkhJJsf9TQMqUOgheoZV/Fmqbk+Fhom7JGE5XSi5CMds3IJN\nu2SzW9LfndHdreEsgVweBSDq5D2mRe8PgStfdCKC5aCX68KHPBShtAwJD9UEDJoJmMLBkV+a294E\nW0XgyXltOanXZdfndJSc83Z47y5RQERP7U2kn4gtcB0TDXBJtOHtgazalyp7FdNpVUTp8EnQzwwU\nz/aYZ4dIUNeKwRYMuojjccEkzebhY6M94Dh70RmYFcxPYTWPkuM3AU+FCHQ3A+qGRz82YI87TGGo\nw46ZSgfNOI/ELjyEgm63oLs3iyq4Ba80YREIZUBpj1aihwiT1I1zUOOT9bodJT8BwWiuiZu6GT/J\nrXrTz0fVrmgpD9k2YckrwxPRsKscWvnRWztnmyIA16OEJ+rlRTL1CoO8DRW9K+hdgfMxj5nzMQtI\nGHRMO+Sj3SsooHBgo0etsD2F7ShMBLwqKce5ynDoMLkcVmzaJc1ZnLzuVQuvqghqAni5/UpUygti\n1ITP3Zki8onumINdDno54JGuySP0xaUpIhzT2pUA+ZpJmMzoFHtAkn92KOEdUtLECSCAmj9TnAQ5\nCI79r7N+2/isLZNNUTzLDwoKkPuG9A4rolD2JJRPdZzcvMPsxganYlacdX/Epj/Gn5spLrvMhnM0\nISzS2M3jP1QNVQ2P1RHg3piOJ4HHQV9zmOOOYtVRLhpKU7Ngm46onYgTQgDPY+j9jLVsBg0MtWXX\nzLDVgso2VLZLukSP5JEWnp3BsWCDZJ+emKVDOsfiURimrDYTGPrRU/uo2pWE95BtN8y5tbtBcHr0\ncBk9YM1AraJaO1dbFnpLrZqRfLlhwTos2YUZTajpfMkwWNyQwG6IGSv8kKSIzkTPpgNUgHqA2mHr\nnqLqKcqOougpdUel2qiSqJZSd4ne4kYPbR8KNsOSzTZKdsMrNbxEPO4SAW/NlLJIKCPnJA+qAF6e\nyuPQVSqGtSyaXWV65X20FknLVIAt7k+nVTHFfZIuybOCiAPk0JuZ29B09shcxc0J0LmTIXfOSKcV\nKa1VCSolOJT1Jx5cfPx5z+3fVCXHhCRJFSfLEVHCewzsYz2La5ccHd+LG1Qo6ZuSbbuCpU7Zd9S+\nTwiYzAk1KA/agClhbiOYfmNAvcGj/zuPuhngesCseuy8oaxa6qKhsIY52zF/4orL0QkhppaWmrU+\nQRk/JhR1vabtSuwwY2vmY/LWgimHo8y9mP7dU7MbpT1hNAxYImcvrhGT4HLfb3tlw5P2+sTSXpZ0\nLylCr1AxmBCt4pfc6Tk7s6Iseoq6x5ZDTCWkFd1Q0vUl/WAZhiImkQwq1lPwOpE+E33jMAU4wErD\nUuHnin5u8GVJbx1tMbCzA0XZUddbZtWU5rxPe6n3hn5b05/X+Ns2OvX+GfgCk+q65X6KhkQDDETe\nFzCJWzlXRFLEpGSAyoBOi138F5oIAIoEdAIGyTN8TJR8ZkxCi3Du5FF5IgbYj24L2e/j85i0aznk\nWgE5n71O7sVdMyVwlXMtk2o8qqmeuDuoNJBDunAOoQZfwFBMIK2Z9ocSBmtZqwUhSGZlhTeKUAao\nw0Rbkb6PjhlNTHOdYmFLDTMdHbSPg3oqMP/GDfM3rjGrATsfoAr4ApTxFLpHs0gSVZS6JMnnCWf0\nWHbMuTRHzI42FE/tUpr6IpoNi4ky1VGNSToFpCRcbJ7KCtgk6V3jHhJ3IVSsKFRPoZQ6Azv9qL20\nX8OI9/oA3kVB91+LCAiHNQhkF5faDkvuC1EabT9Cis9VIBGixFMo+ca8gmsaroFfpkB7UXGqADOP\nmXcsdMlQmaQiOAZhsHtDvykZzuqJxfBPwH8hMv/vspeFZC/qYIBJHyN7Ifl9RlStEv9L6fsyw+yZ\n87QWxutZ5W3oAAAgAElEQVSkpgo3VtQ3Abw91ZJ9iU1AThJcSuQFB+cKuMC+6ir/t9n5IjU2TGqk\nnCOaupwjORRGwNNpYBviF5+KQnkds0jkUmRG/RmsZaOWuKCilKR6vFVQeZh5mJt9yTZ/QaUnZ/mC\nOOdukADPM3tuzekbXqUq2zHksKViwCQgmQofFckBtmTNKfdwGNasONMn1McbyvmWbjPDb824eQjZ\nvU0udJd9AUKBGii4yS0sAwu2nHJv7xyYwE5szrkD45F7aa8A7yHbWkXj9IZ9G34edDAjzvcTJk/i\nIaFVOKIV02IQkMnTgKfKZFQq3ksWnEgqFqg1fmFpmzkMGl0rTOUZMLShjipzo/eJvmdEoDsPsAnR\nOD86B5KTwud6oSC5hjHltnhnS9BFVP2s2ufI7RGZ1f0Sl5wrY5EnU82dCQJ6OVjBJJnJuOSqarLj\nj+MkICzXaaYcc4aIUwsm/D7J+pDH727SGN4hSqu9iJ3ysFz/VPsmt3l2fxNBwzlD31u0dVijMOVA\nvdzSn9QM1yvCXTMVQmqJTh55P+nXnCglXydy+lYQKoXXZi8HnbgSVBokkdJaKrbMucfJaIY555hW\nVSgTqGgjdxCFMh5T9hjrUIrRD9tn4CQx3svEWDjx5zze3uIb+pfZlhW7ssZrPdoKczpKTkl51K0w\nX/qc19oeVIj7d3/3d/mLv/gLrLU8/vjj/ORP/iTz+f0Zm7fbLb/xG7/BP/3TP6GU4id+4ie+ZOWy\n1wfwNkTAO2dfIsrTctfATeLkE+K7AJ7QHSBO0KQeYJmAU4LNRRoUY3fFFK4kz9VApQhLS+cMg6ow\nJ3FCSkrzwRv8Tk+51SSA/S6xItnWR3oIJK6cBKMf5h4SQ1oezZ4WtlYR0A5KVY4SnHDMDsx8ews3\nB6VcwsvzjeaPhsnJkBe/cQfniE9EcFuaqMDSv5DGW9TrG9wfU7tJY3c7/W9QsMkBT2LgMpFMAE/A\nTrJiWwE8zTBYCj0QjMJWPbNii9qBXxe46ybOpXvsp2yHSYoWCU822qOYb9Epk1Gd7o9ciIAX7W0b\n5jg021RX5IKjWBrAKCrdpu85IAknjPGgGQFSbHIxH3Tkqa645IgLTvwZjze3eWb9MndXx9wpjugo\nMMzJozLyCNx/k0iLR4gaDyrE/Za3vIUf+qEfQmvN7/3e7/HCCy/wQz/0Q/dd+9u//dt8+7d/Oz/z\nMz+Dc462be87576+P7quP0QT9ryUlsu5XHkanw1xkufxmmPKdCYVKl+cOSfsMC2QSBbCS5PzdKI5\ntIpQgqs0jZqjtcehY00IZXE7NUUYiL9B1qVRWdB8zuPIjPZy5OmeclTJpbpDoJuzb+bL05LLbXJh\nUjyZOQXlcIyli3kqJMf96rgAmk7Pl/t64nio9PkJUCSg74AzBWs9AbKA8SVRspunz4MCnQLz9Qp8\nEulVHZ0cEr0ips5UhCdn7ygd7cCWWHJypnYoHWgWc7Y3Wpr1nObWjOE8GRWL7B3EwiCp21fx3kEp\nur5mfXmEqy2utCgdoxy80khWbal1IpE6QkuRLNlb5rRU8RrjsWWfojNi5mehpOQRQtE5MdWxjYwB\ny4Vdcqu6zoVdcqmWbFnQUI2+3YmHmifReLSAVzy6bPG86U1v4tatW3ufveUtbxl/f+Mb38if/dmf\n3Xfddrvl7/7u73jf+94HgDHmgVLgYXt9AC8Hr7wWgdjkZGGIiiWSQQ4yssBFlcq5WznvTM6Te4lt\nXJEBHpPklxZTR40LNjpMvMLNLV6cEjk5f0VUj5yKC32sOSuSidp3FhwGUeTtIDHHCHaiIoq6mINe\nLm2p7Kd4aGWsJfedjE+eykn+zgFPspRITjtJSrAkqnsyduJIkFjWZUBVQxRwzw1s9PT9yPudE+kn\nElXnFbgyAc9xsumljcHo6EyQsZD3zjJiqyKgrceagUq3zFNh8hkNzXzGxl5w3pxy79UbDJflVJdC\nnCaySeS+IwvBKZrtnOGsoDuqaXWJLfrRwZYDnsRq5/YyoYyI53gINlaIs5GKZXBYNaDHMLIpg6Nk\n8KlpsPQMGC7NipfqJ2LFMlvQqyLFh0uYZcwBOYWYTZPskWZM+Sqixqc+9Sne/va33/f5q6++ymq1\n4td//df5whe+wDd/8zfz3ve+l7IsH3CXqb0+gJcb0kX9lJaHPQkNLbdVwbRIRb3KjdEiqeQG7pzC\nJvGV8rckx1BMi3yjcLbAmSnleJCA/NxLKFJNUClkSyTATGfMJa6KqLIK6B2SgnMnQC7RLJgkDwG9\nBwGeNNEIF0wqe66+w+Sw+NdavmlYUHOHueZQjwXc2uDXiUu3ZspGsmAK4TLAIoANUMT6I8oG1HGA\nJYSZIhSGYHWMFa6BYzOlfZLNShwzItXl6n0FqgzJdjeMnMplSsA/L7bUxRZOFZsnjtjsQrpeTYCe\nveMoNQagVbjLAnenwAfFoDVl3WJth9UDSgWcMjShRoWQCovHbCmx6LjGqSgNBq3wGoyUrVRCMJ64\nnxJNsV9EvkcR6Kg4U8e4QnNWHCF5vKdEoFO+IcnwfRh69sjaVwk1/uiP/ghjDO94xzvu+5/3ns9/\n/vP86I/+KN/yLd/C7/zO7/DCCy98ybKQrw/gPchoLsAm3kZZ5Ev2dt2963KPYS4h5vc9DE7I8+TI\nPMilD1F989TfgSi9iOqce5FFpRNnq6RxEluxIeVQY7K7iefyUKoSgJGFl0s1+XhIKUtZnDnYy7uK\nE0P6kgN93nIzohijczJxwQi29nSgfmKDvu5owoL2wkZp7UWmlFE7RXA2XkN679KjygFTOmw5oOYB\nVuCPDcOqwi3L2McVMbJB+uyzvojpU94rU/FVFTBFBDyrhtFbOktJKEo6utmM4ok2UoNmKmaByQv7\n5BxwxRSal1aI8wUdCr80FDODK7uYBl8bGlcTnML1sWBSaA2hTUXGhS5ZD+hqQBUdRgeMciOgCegV\n408BwKgaewwb5gxY7nGaiMhb5mxHoITJDhjzQk6A98jbQ6DGl1uI+9Of/jR/9Vd/xc///M8/8P/X\nrl3j+vXrfMu3fAsA3/md38kLL7zwJe/7+gBeLpnJBBMbyqE0I4C3AMoAqVKZgFpwyXbWqXjkgQmH\nUVyHnuCcG5ZLkHlARM5XE2CumABPJMSCKcNxp/bPP+QVS8DBYSlBURMPbXkPGhcBvJwXJyDRZ8+E\nfVU2P2DafETCzAIhRgrJKr6vmjmKWYeuBrpQx3c9J2ZWEooRejIzpHx6qvRQO0zdUVYtWsf8Hv7E\n0i08/TzgtgZODDyn9u2Nh+UVZSyOgFVALQJ65iLgaalaIseAIcaillWLud4TUyubyUYsccd57VqY\nAmGIz/fK4guL14pgAt7E9OxeaTpX4gZD31T0u4qwNtF2OSRVvPbYsMNaT2Gj99VmadGKTNKbomrj\n7yaFmLXUtEx1bmPV2fOUyrYlTyjgHwB2j1TKewgv7ZdTiPuv//qv+U//6T/xi7/4ixTFg+M6Tk5O\nuH79Oi+99BJPPfUUf/M3f8MzzzzzJZ/1+gCeFNwWm5wnTmLx7An/LrdZzUHNBvQsSgraRBFq6Cyu\ntYSdifUfNmq6pwDKYeKRnEeWg8thzL60B9EXDtOlS/k+IRqLAyMLitgDL7GpNUSVMGT3y1VJkWhk\nkUtW4xzw5Py8XoUwOvIKXgKsbXbOg6IjpC/yd0rf5C4Ktp9fol/09K+UMcrkTnqHgikV1Cr1S7Iv\npzmrlcckO1tFh649/ppl0BUXuxM4PYa3pvuId13GaMjG/wbwBOibA+ZaT7FsKIoOmxBqjG4gOp16\nStZ6gSsUZtbjl4pwNHHh9nL75UlWBXBbxrKOoY/k9uBjNu6Awvt4hEHFMphrDXdVvP6YWEo0RK9s\noYcx5YWEoB1Kepa8MsuwZxeUanKSIko8u3Ga+vssFXkSgUfWHiFqPKgQ9yc/+UmGYeDDH/4wEB0X\nP/ZjP7ZXiBvgve99L7/2a7/GMAwjfeWr2PWHaCVJ5WGyLR2HSBuQRLOHBvoZ6KXDrlps1cUIDK8J\nTYWT9EFrDSqke6qpXqos7gdJNgJ4DyL45uflNp4c7MSeN2Pi/Qng5cTonBt3xETP2DCp0WK7OlS1\nc8A7DvGnAJ44d8bIhcx2CPuRbDnoSX/yMThMByV9TzxHd1Hg2iI+6x6RkiOEawH1WfruRlI3o6Sk\nlcdqR21iGFZlW3QdGFYFThswx/C2EB0aa7VPHm9IERFkgOew19oR8EwGeA1VAruCNlSs9QJfKEzd\nw8LgjorpuxZwFdqTgJwU+d4S52sCQe8iwCkdE30Gn34OOtUUVilTS4jPWCSpLkVnVKrdAz3JCyk2\nvf3KFfuB//KOEis7YMf/Sw492akmj6/mUWZL2ctZ+xW2L7cQN8Bzzz3HRz7ykYd63usDeHOirSYP\nwzrysfrTwqNmsS6n2LtUFdClp6631NWGmd0y0w1BK9ZVqg0bFjTMY8WuTk+LWpwNh8Ali3F2cOQe\nQKFi5Jxhkf4OCb9CHRHAy1UkUW3z0KqQXStOF/EiC+BKngChgpyAOnXo0wE98+jSx4LOg8b3Bt8Y\nMGZyrIhke5gdpWcCd+l7nqw0V3Nz54Es/i0RlG4zgcQhB1LAqoRgNKos8EXB4CyDKhi0RcpSD9rC\n0mH0gHki4EoLW7WfXdkF1Nyh5o7FkxvmT23RRwPMYrolq2PqpD6Jky0VGkcfCrpQsXEL2m6G62KY\n4PgdCmk9r00rG5BIldmmqMtAUQ0UZR9VaFVSFw3eaPplYNDRrudrDQHM8UBx3DNfrpmZNQu1HWNv\nDyvlCeBNhUWn7N15U4Q9CTAvixA9xz6ZiOMXJ/d5ZO0q0uIhmwCeGIkNqIVDL3pMNWAKhzIhMhN0\npBwY61iaS1b6giN1wZG6JKC4q69h7cAZ0KoipojbKdipjCPHPkctD0LPHQM54OURHLI4cmAQMM1B\nM09nLhKVSHpk7wuTcyLn0gngif3NZ89NgKevD5jrLXY2YIu0yNuCvi1QpiQoM1FtDiMpckkP9tVl\ncQgI6Ang5ry8hin11UtMmU2ELyznCeCNThpDqDSujDHQg46gZ1RcyE4b1NJjVI95MuDnhiCJU8WG\npwLq2oC51rFcnHFjeQssdDpKh3mdVgG9sW5rKGlcTdvXuK6I0S+yicnGAxPdSXLwSUVN2agKUKXH\nlgNl2cUykwzURUMgpnTvq4F+XjCcWpQKlLOOum5YmgtWWpJ8Nnvq7GSz6/dU2Cnle0jT536uXl7D\nTKIrGBXeGGUbz3uEDowrwHu4puc9s8cbSttRmZbSdpRVQ1HtUDbgtWZQqU6FKsfkATF33ZTOCWCm\ndpGLVM6wvselhfUv2uJye5o4EOaHR4jxtXXMaKt0QBUGvXTRflPoGKZWqyTVqUm6y6M7HkT2PSQI\n5xw96XPurFgCxwF96tCPDcyPL5kvLrHlgLYeFww7NaPVNV2APujkvNHRE5l7e0XFF1JyfoiKnKdQ\nyoFOAEikHymqI5JRHqWR177YqvReCm8tfVHSUmOrAXRcmkEpVJGiQZdDXMozE+1lQ3RKKR0wpwPF\nSceyvOR6eZtB27HOyICNPLeUA9E5y+AMbigYhoK+KXGbEnYmJpWQ/h4GwUicroyRSHaPD5iTgWq+\nY1bsqPU2gdSclbpEEWL9ZG1izeTSxILjRUtVtCmDyiapsYdS3X7l2akmmRRbFFZdeAD4SR69/H8T\n2D1ySgo8lNPiv7X2ugCemfesinssizUn6owTdcZcb5ibLU5pGhUrqJ9zzCUrWlXRqTLtYnlG18BY\nD8O2lFVHV1p8EQgCdrkh/tBJkUt3ecjSAqgcqoqgoo1DVxV61ROcxleG0JloJ+z1fhWxmgdLeof5\n46RvQqTOgUlqVByRSj4GzPUOe7NlVZ9zvb6NNQNBKVqqyNY3nuAVgzeE1oLV+6r2in3HRu4cWjFx\n+3KpVvq+Tn8LVQf2S0eKVJR7iYXak9lFnbF4W8f6D9YRrBoXudKJiFu3UQPvbUzz5VIGHB0wi56y\n6liaNafq3ljfRALwO8qUD1HTNyVdW+JbS2htLJLd6OhUkI0oN3Hkm+AyjXvPuAmYGz3V9S2zxYaZ\n3aQaxj0lRxxxHoEqpTqTdza4mHpstNc1BwCXe5TdHuBNKup+QZ8cwGJaqPshLRxA5iNvVxLew7Wi\n6jit7vBYcZsneIUneIUTf8ZJOKdRFXfVdV5VNzA4hmDHkB35wnU2CTR+yqlnHbrw91NeDr2euRo6\nJ5Jjl8AyRBviwqHLHl32GOMwxmGtpp5vI7m0MtFWmCIsXGMZdoZQmRQLqybJ57Cylzvoj9j3ZKEN\n6ffrjMHu6iRQHHfUqw1H9pxr9i5GOQbsuOAdhqG0dK7AVwqM3c+iIplQ5PkSuTFSXXx899pHiVYH\nfKfxnSZUKgKoZFXepP5JFIY4oYRuE5gAMfsOgtIEZelVibH1JD3r9B3iMUXMQ6IKj3dC4FUY5anr\nHXO7ZaZivZOY/20YF/4QksrcWbptSb8tYWtjSvlGT1J3nsxA7HYaKAIYj9YeHRKltwzowlOtttTL\nDfMi5mqUou0V1zjlXlRP1YBVPVIiVNTUiV/XM2UxmTKZHILcocMir0cGjJST/YjZPPOdgJ3ekwof\nWbsCvIdrlem4wS2e5kWe4x95jn/kcf9FbrpXuVBHfME+h1V9yjxxigoBKbIdF8Z+RbJYgDjprjnH\nT3bwQ8eBqLFCfVkRnSYrh5n3FIsOazsK26P1gFGe0hSs1GXyyikIilgvPLBr5myqBb0tQZt4yLMO\nPcV5Ldc80kOqAopX+hqxJOB10Meeat6yKi450hcccY6w70c7lSppTYUtBobC7kesCA+xYpIwRX1P\nkp5eecyixc57rB3Q2tG3JV1T4usEonkWl5pYPEckaBlTkRDFDha/pBSwr8BrvDZ0ZYk2YvwXoyJo\n47FhIFgHId0+BKxyzM2Ghd5Q0o4QIEtbEaKE2xW0mzpGgVym0Dbx+IozKe9z7rSwHj0bsHVHVbSU\nto3lAExPVeyobaxdUaeiAxL6dZ27zJAcig15+Sfh1f3LwBMwBxt4Dni5M2KS6PZhc7LiTf5dOd/v\nsbcfUbsCvIdrtW64yas8E/6Zb+LzfGv4/3iye4Unm1e4bR5jmFnOymNm7OKX5jSDi8prR02rW3aq\nA6VSbYsZvSvxgyEEvc8ty200+WJdAMswgp0+GjDLnmq+o5o1lLqjNB1GxelUMmdlLoFph5WJFGyg\nUbE6VSzCHKYIC5FwDtVrUQFF8hKHSSCC8AlwI0QJb+Up6o652Y0LTvpRUoxSRGF6bDHgi2h7HNX2\noCbnhyJSd0TCXQXUUcCsOqpFQ1HvKIseYwaaZkZoPUMZUCX4wkx8w2vAOvLS6FUCM6a1laebkmJB\nfSSHe2UZSk9vS4JuUJZI7cCAV2gT0MpFSUvFBV8wpJp1a8qU0TXuYRkoBPC9YdiVqZiShgs1FVIS\ncrfMiTyCpwatPWbWUawa5tWaebXZo4+IZ1UcDgJ8j3F7TO8eU7w3ew4JCQE7PFyaFGoPxiZnRX5M\nADYVjBcO3pR+tGBI50XQi7a82B6h0+IR0lK+2u11AbwVlzzJyzzFSzwzvMg3DC9yfHZJdXvAzAL6\n8UAoo+Sy8zOa3ZxmvcS7kp1bcq+4TlXtCAaaMKMJM7qhonMVbldEQ3ceHykZhwT4RLo7CnDiMUc9\n5aqhWjTM7I7a7MbCPjLhKo454nzPRzal80m0AROm6IRD50QeDSFqVa5mi3dUcsqdEEHl2MHSocqo\n2rikxmoCA8Uo2WoVVbFRrRepRSanJF+QxZ4Az6x6iqMuGuPLDXWxo9BRLbPFgFGOTvX0RYWbWfyR\ngkahugAduKbE7YopbZZwEXMVXlrKIxgGjdcFQfcx4XAZ6HcVnapo3SzliRsoQzeqiCX9GE5VpvhS\ng6dIANTQYcOAHkKkJV2qqXLZGVENl/7IxrdiikqpQXtHWXTMqw0rc8ExF3vSnHDmpNKdVEm+wS1O\nOGPFBUdcMmNLTZsoJnHGTBXw8rgKi0+aSzQxTxJe7rndp5zoPYDLaygftljL7N/AaXEl4T1cW7Lm\nSV7iqfAyTw0v83T7MuWZQ78Uuaf6JOqgAzEwu9nOae4taJpV3KVrBysHpScWp1axYM+goyNBpAsB\nPOHTkT6bJ5vdkYfjAXPcUi+2zGebcVHFqSQ0AUfFTY64GO1lLsTsGAGNlrAYTQx/UwG0igCoib/D\nBHY5HT4HPOH/HZEAL8Q+Lhyq8OPCaZiNPjgBPEVMN6TxKJsBnqR3kvUgwDoD6oA56qmOtszLNQu9\nYaZ2SA0FbT3aBmzhaGeO/sjihkiyjSmNoF+DXxvCF3UMMbujpozTuXe3Z4oy8ZpQaEJlUXMFNQyb\nit5WdP1AZTzahBHMigxg5uzGgHqNy5Z7AgZHrGWyJgLeLSJfUABPaCgVU8xusp9q7yltx7zcxoSb\n3MvKvkfQE2mrpBv5dDe5xWPc4iSccRzOWLCN1ctCDypK4h0lvSpH6OzUVOh9SEqt2PUE7MowVdOT\nctsSMCdv3agpQ8vkyouHfPbI279nL+2dO3f42Mc+xvn5OUopvvd7v5d3vetdrNdrfuVXfoVbt25x\n8+ZNfvqnf/o15aMCOOKCp3iZG+FVVu0GcxFQifNU2I4jdcE17nHMOUu/ZrM9Rt2G8F+IKdUrBU/q\nmLJdCrRIxhIhxuYOgjz7ShFQ1z3quqM4bqhWO2azLXO7Zc4mlfjeUSb6gJA6a1qOuMBhaaho0DR9\nxb3+lE2/wAWDKRym6lEefG1wrcEXhmBt7J8UhIZ9CfTQS5tsizFONNba0HYKEM/Z9XtZMVKONSVz\nPI8mgQjECVz1rMfMB+pqx8xMjoCSbgTTMRGlGig1FKqLOdxCz0ztqOjZLFZs7Irtdsn2bInTdopF\nlkJGIvUJ0CTVcrgoWH/+iHZWs3Nz+A+gWlDzQKk6lmyo2QGMToDccB9fzzOGYoUBNfh4f4nxfSn9\n3DDFUcs45yTsGYRThesMzluUAqscVQI2UVclwqGgZ8Ulc7Y8zss8ycusLjcsL7dUTUfRxU0DC6FQ\nuKrHVQ1tXdDUJa2KFj8BPZe8PQJ4BbFWcx0aKtdhe4cZImXLK82uqNgWNSYpsVLHQlpu5xPF+JG1\nf88SnjGGH/mRH+G5556jaRo+8IEP8Na3vpVPfepTfNu3fRvvfve7eeGFF/jkJz/JD//wD7+mhx5x\nwZO8zE1eZdmsI+Al435Z9BzpC65xNwJeWHO27eJu/X8D/yeR//YGEwsw3yDmZxOKxxkx7Eky2ObJ\nMiUm99Shr3VUyy2r2QXLYs1cbUewq9mNKozsvzUNR1zQURKITP62r7m3O6X3BT5obDFQzVqMcXRt\nAW0BpoogIHw2GXGhyWjur0mRPKdq6bEzR1H2MTMu9wOeOHMgcbNUZhzPoy1yDmIRMPOBYtFQlRHw\n5mo7GtxloQjgWRVpLwZHGSIAnHDGkjX37Cln81PuXNykreoJ8CQq44IpJb70J2VWGV4pWA/HqBDw\ncx2l2h2oZxPgqTUzdsQC1WqPhDuq8snmJYCnBx/H+YxYHuPFdKyZYpuFeiSAl/Iahq3G9bECHik5\nQEnLjC0rLjhJzqKeAsvACWfM2fIEr/AM/8x83TF7qcece9QmxO94pAUpwgqaE0tTWna6pkzF5EXN\nlSb2vzlbFmHDbGgptx7bBYKBYBSXzCjsjKCgT9vzYU3lfZ/vFeDBa+j6yckJJycnANR1zdNPP82d\nO3f47Gc/y4c+9CEAvvu7v5sPfehDrxnwZmHHKfc48hfUfYvagVoDF1DalpP2nBvDLW7o21xXd7hb\nPx5jSE9CXBSoKeWRkHzzOFBR28RonxwUejWgV456taFabVlWlxzZS+Z6w4xmtMnM2GY2m8Smp+GE\n8zGjrcUxmIJQqlj4GzA22r2CjsVVGj2jYUFjAs5bXG+jfSmvA5sD3sgFDFAHVOXRVYyo0NrtSTe5\nqhKlsLjoi9DT6QGlfPSqFnrfpmgDWI+yDmMHjKRUUv0IeFKWcqCgD0UkBovLVE0RDLswZ9MvueyO\nadYz/D0dgeaCVHycifOWJ0eQiI1e4Z3ZC2UT0DbKjWqjLFmJOZV3PgRBhGfYEG13/wy83MDtXZQw\nQwW6iOTjrZ74gkml9dc0/WVFf1Tj6+TpTi/dU7JJFcqAUcWufIu63LBa7yj/80Dx9x59lynR7DFx\nzj4WY8VLP6C0h1oRyuhgO/TASnWLcuip+55qPWDvekyKwgkWqustrgg0tsaqmJsPpg2xo0yulYnm\n/Mjav2eVNm+vvvoqX/jCF/jWb/1Wzs/PRyA8OTnh/Pz8Nd+npoFwwdKvKfsB1Ya4AG5BqTtOtmfc\nGG7xuP0iN/UtXlpuUI8HwrPEosg7IjjksaLikBDqhWQ1SRKTOgqYk57itGFZXXBUnadQtfXIqapp\nmCcb3mE2ixk7TrhHR0lFG3OtFR0rczlRYlQ0KfaqYG2XrPWSSz0QSuj8LHqRczU7/z2v1zCLUR5U\nKe1Rch5oNUl5ubUmN3KXdDQm6vOqTBSavGCNjjZGbWIMqknJKOUdLQM9MZFkF2LgvUh5oh5JSvNN\nWHKnucnty5v0d6pYmPwWU41eCcTPHUbyfe2Y1PosXC+Wh/Vjn0SdnLEb31PsVxM/M5VmDJKxhAnw\nvriDi9spqcIR6GVK8po2nh1j6J5/zDCc13SnLYOx+GpSCbfJkic2w6gJNFShpbi3o35xwPytR/1N\nnMds4/tIBTR2wABWRbtoOB7wpsObiUovKdpHx8XgKLaO4syjvpjGNZHmrRmoV4FSd8mMEcZ79MRM\nyOI3bhJh5pG1f88SnrSmafjlX/5l3vOe91DX9w+eUuoBV8HnPvc5Pve5z41/P//881znrYTwPuZq\nhz4OcRFeA56FYhEdF8/oBW9XT/FN6km++/SUlxc7ukXJ8OaCIEV5JLGmhEMJsTb3RhYhJSAAMzPY\nus5hYSgAACAASURBVGCmF8y0plSLjDrgRsDIQ35Eunia5yj4n8cd1CnDYCy9KZKEEUbvbU/BTtU0\nakaja3alpU/B5f7xkAiwGY0jj+9Nh6pAl4airCi0pqSm4DijKfhRxhMVVDyBbV3xP6LQ1wPeeYKD\nIDZOrUBpbFljtKGkomRFyWNUtGh8kg4qelWM3j+NR/lE9A4BEri0bkZLTThV8K1MCTzFcRTY90iL\nai0eafl9Ad/9HNihwhbHzIPlSK3GzUdsi0K5yL2UbVrQu2JGc3NOI/PhOWBTQn+UpMsalCXW82Ui\nTYtH/DEFNwxFuWRlvpHl/8/eu4Vqt6V3nb8xxjy+x7XWd6p9qEpZFel0gZgmqfSFYEcv7QsLaQQj\n9o23elEQlAhKGmySG1slghAIUfBGAhpQvM0GO7RgoOyWxLqItEmd9v5Oa613vYd5GmP0xTOeOee7\n9lfuvatWandX14D3W4dvrfed651z/sdz+P//D5fjxje3Z9JRjI94xYX547D4KtnVkfDHoV9APEIY\nwGaQrSBLOmi2YNdgl1DlFmcc1axzq+dxZAbkHdkyhcgWKd2k9yy7tJAbtnaJNWsuEiCrZ15HkVot\nFV2K1jHfuyHn2XpA7PxBr48FeN57/t7f+3v86T/9p/nyl78MSFR3c3Mzftxut2/83Te9qaf4fxC7\nX2LR3MCLZAd0h0QFV3I9ttkl79f/Lb9XfIn/y/xJ/k/7J3k/vM0H3Wfo9pV433nOjTJVcD8X6yez\nTrvy1PZAXd/xxLzkMS9S5DBRDioa1tyJNfhIgeiSfO1/4j/xL1A7bR14PJClInM3dlBv2fABn+ED\nnvGSR7wyj7nxl9y2F3S7CnaZWAipuYCm4KnWaFYet+ooTMMiO4xRznQ84v02b1xo5CWTs5YYPsv/\nFhuafkHflvi2IPYOnbGRLT3ZomdRHFmVexbuOEZReqM0seIU6xEsBegg9hZ/LPB3Od0fWro/NPB7\nwH9M53AOZnMJ3VyrOh9UtEBIzBfwvx48Wd3zeHPL28W3eexesmHHiv24EXUJdo7UHFlyx5pbttyE\nS16fnnLzagG/BfwL4PkRjs/TNZEuLlfIrEElnX8OAccfB/4bKL4QuHz7xEV9y4o9a/aoUeeGWx7x\nms/yDX6Cr/PI/4/wB7/M4j9es/ttuP7f4fgSWg/lEj7zFjz+LPAF4IvAu8A74J842scZx7xijwzj\n0fOnpJULbnnENcvDiewPItn7cWz8NF8s2W9KPsie8S3e4QWPueaSHduk2l3xnKc85yl3rDjGJf+z\n+XiGnB+5fthT2n/8j/8x7777Ln/uz/258Xs/9VM/xXvvvcdXvvIV3nvvPX76p3/6E7yox/iBTMca\nqvrBglnINVn6gW245Zl5n3eKK26KDfGZ4biqOOw3+Luc0DpRPhhD1HmtwZxHFqo4SDehFvX1HtSl\nqaF2x5YcWLMbqSklLRtuZyKfKa0s0qXak7NnzYIr6eaaitaUQqbIa7K6YxgyordEY6dOsgJBGdOM\nhoAtBlze4+x5pOlmUOtmoZOmQrnpsZlMvFpWB4JNdNbUfdRyV2gzehvpTE+blWBgMNIMUSF+HzNC\nsOMjBitecG1Of1fhX5eyWX0baRC8QP4mbb7M3/t7riNn2lVN5TPwxhHbgrarOGYLDm55RgUp0sYy\nV6OOnWpNj2cjHEX5Ush1YTLJCvRaUcAdLaEM3IG/yTisVoRlpDE1R7ukyhrKrMGYyJIjvZk4kOoQ\n44/Q3sHxDpqYXLmUNaAqj/ng+Tg1GJRqIoOACvmuk/MZFpZi5cnWAXJDzGFXLtm5Bdf2ghumxy1b\nDizZxzUv/RM+8J9hH1bSBV997Fv0v75+mFPar3/96/zbf/tv+dznPsff+Bt/A2MMf+kv/SW+8pWv\n8Pf//t/nt37rt3jy5Alf/epXP/aL6pQmYNrx5zpXA1kc2HDHU56nnWuDLxzBGK6LKw6rFU1f0w3i\nsRZwhOjE+sebCfhSQTxGw9DnNKeaXb7G5oHOFGMkp239DTsBCw5suR35XTofVIgDAGbkuRcpsWop\ncSklXHBMcqN2VEHkRc9QDQxtLrU8wyQtUy1nETG5EIidlfqanYGdQ11wfTqSmIInAWvS5yUtC3uk\nK3KGvsDH8syJOUQxDOpNxckGfHD0WY41gRAtPjp6n9OHjOAdwVuCFw1xPGWEvZsMMkM6b1dMgvv5\n/Ny5E8w9f7mzn7UQgyM00NcVp2rJfsZ9m4OcDr4WBl4+fn8EPnU72dXQJQ1cXkJZQG2lbqfu0RdM\nIyNPEF5aqbneObpiyb4YqLZHyosjFIbanFJUtmAwGRQZLA2Li8iTx7ANMLSQ1bBSqeB8sFHqtHrj\nUm6QUnJqjizG1Lal4mQX1MuG4t2e7Mon5yDL7XbFzq14wROe85RXPOKGS27YsmPLbdzy6ij11bYr\nGYZc6t8PsR4Q8L6fQdz/4T/8B/7JP/knxBj5M3/mz/CVr3zl+z/0n/iJn+Cf//N//sb/+9t/+29/\n5Au8aakgGpgALykBYtI2ujiwinc8JuOWLXuW+MIRC0O1PPGKR+z8BtvWNF0l7hpdDoMDn4T9M1fi\nGA19lxFMjYuBaBy9kS6kNw7vHLntEblWR82RDbtEwo1j3WbeHZ1Ir/IQIb/lyOIM7DIGMjeQ5z19\n4fFlmAjB87Qv1w6qkH6dCzij0il/xjk758rJLZKPYCgR3sIdObmaznn6EKau5CAvLO9BJNo4pubq\nuhKDwXuhaPhBPsbeEbvU5VS3Zm1K1IihwHwmh1KC1EhBP+qNn3NekrBAcMTW0XclR78gjy0O6Tqf\nx7lTCt/FxGWL4pRCSM+5AW5qONaTukRVNuoWo440M8CLrxzdoaZ7WY+Nr8IfqBZ7smxgbe/YmxUH\nlkKZKXNYweISFk/lOmbP5HA9n+SWuqzBWryZ1yEnwFNb0DsjqXq5aMkXsvFqY2LHhh0bXnMpYBcF\n7G645DpechMuuT084vbVFbFxsjE9FOA9YEr7vQ7iDiHwa7/2a/ydv/N3uLy85Bd+4Rf48pe/zDvv\nvPNffb1Pxw+PONaDULG/rlJAL2biopExUHPkglv2vBrdQZSKkWc9znhaW9KaSLA5XrXo0c7sigzx\nlOExkm7EnO64YH93wWJ1YP3Hdvhnjke8QkTpEwFU9Zo5w9gqEM7w1OzI6ekopbt5xv8XSoAPAhrB\nW7E7UkODN674XT6fUm+YancqS1IR+vnPB6GoqNJER1UmDmA0Dm9K4uDwucc45XKR0liTXEvmDjHp\nubQUsWEyK1Wt6qwmOQKaAuF9rbNeA4maQge+c3RDQRMqStOSm34sJWgaOzYsYsVhWHBsFvS3uSgr\nQOqCMR1f5MNzfud2YApKjsnp5ZoxDfddTpsvOT5as1+uuSvX3LEWv8ZVIZ3YzyGd1C2TpdYjpFP7\nSL4fkxVXKC2dnRoLEyFqMX5vrpaYD9nWrvGJBbdsuGXLLm64Cxvuhg37bs2h3dDsaqkVazr9UOsB\nUeN7HcT9+7//+7z11ls8efIEgD/1p/4U//7f//v/dwKeuQ9481A/h1DILiiAJ9SELbecqOkoz2o2\nNg9jbS5aQ28QekJACvTBypzY3hA78UZrTwXtKbJ/HjHfidTPjhxWrzDP/JhSiI22gNycBzbWbQD1\nM8uS1vNEGAFPgW70y4jJkNLbKd3+Ht+7ydrRjN09rXG92SRSuG1jCq1DfIx4dfjo8H0ukjQXwESi\nDfoDo8uJyPf02I08hwJehoDHHAznztH3UrrxbZwD/z3Aa4eS3FeUriGnS7HsVLMbAS9UHIcFx9OC\n4TY7B7ySydVlXjOcz7jVJophUoncIRFsKgF4mxM2GadixaFYfQjw4jNE3XFCUuQ7JiOILUkXzTgB\nzpeW3ua0lGdgN39oyn6i4pjoJXrF6bV1YCGWCnHFYVhxbJd0hwX9vibuDFGbY/Npfd/v+gGixncb\nxP369WsePXo0fn11dcXv//7vf+TzfTrlxzh7pCghOAg5+Mwy5JYuz+mt1OYy/NipXHPHqIkw0orP\nrcwXUD7S6CuknwekIL0HDhD3Rj7eAQF6m59pG+9rEuWQhQgy32n7VFw+pO/s2PABz3jBE17xiOt4\nyW3YchfXnJol/bEiHHLiyZ5PNhvddg04K9FWL7SXwWRCf6Ggm3Vm9YZXekbE0FCxi2sOXuZ8vG4C\ne7+mPS2E9DzvlCofbjBJY2qJQzoGG6XYDzOHl1QXxQi1RVNWOLehVyBXwCtjco+W2uSo80XS5jg4\nMVFVW/okvwuVExPPomQoc7zLkohqKvRPM74cIVrZ6GI64CWTCkePUTv58/nAc0qTZgMnJjK0dv0r\nQ3Tncq2OQpy565ITBcWTgezOYwsE8DxTCn0FXMCwcvS145hXHO15RHdM4KX8Ob3OD8kn5hgXdD6n\nDwX9UDD0Bd1Q0vQFbV/R9SVdVzAcC8LJTTLL+fjJh1g/ILeU/9og7u91fSqAF/Uf7aLm4DPwuaHP\nLH2W09qCzhZjM2HqnN6NF8Se1ST/Ml7MJDWS0QtWb+weOfnXCPCp1GgBcW3wufY97XhDzcFNDteN\nxM4+OZXo10OqNT7n6Qh6r7niJlxwO2xpTkv6uxp/zMSMcu6YMoKQAWOImcPnGUOWMWR5Kmz7MbLT\nqO5+FKlv67FfsmPD87sFQ1MQupzQJwDTmqlGU7rze8Tk05HIybP3b25tZWbPIXm9REk6M+P+zxRA\nHaDyMow7lxBQpnxJgyI2Vs6NRlgN+MIS1zldWTBkOT53qL2lOsfNTdHHZoVmDOt7xzcfhD7n/819\nEzWVPTDZiClQXMzet6Q20SbKqSo55iU8AncIUotVM9e5ndYl9GvHsS455DUHO0VzGuEdEs1GjLDk\nMX4dV5yGilNf408lw6kiHB3hYAmtJfSW0BmibiBas9Vz8lDrE6DGH+Ug7pcvX45fv379mqurq498\n3k8F8Lxx+KzAlYHoJDUaMivkXJdJxGWn7puma/eVEOrqQZR6k++lo4i3KV2OYANYO6Vzc/cOLSqv\nIzaf5D1zuoOKusOsNtecuaRNKckNF7zkMS/CE973b/F8eMbtacvptKLflYRdBo07l1vBdOOl6CQ6\ni89yBufpXUHnBrnJjNStHF66qrrb+5zB5zK9zFvau5J+WXL6gwJOmaSjGtVpdximTWdIrz2OlTTn\nMx5cnIAwC+AiRiOmua+fR6JqC8ZFcQsuPK7osWVHlomZphjXpbkTVcnQlPQuh6KQ96EFBkvoMnyf\nM4RsvA7sCHhT9O1MEJPOoqXfWHhiiWtDbA229LhFwJYCRCb5OGCF/mTc1OkOgxGX56PDr5x0okPi\nIF4FzDLgigFjp+skGEvnchpXUNQDcW1kPKOS4AuICwhbiBtDs8gF7NyHwU4BT0FOGxN3fs2+33Do\nVjTHkvZYEvYFfp/LaNID0xzdudW+ZlF6Lh9qfQLU+KMaxP3jP/7jvP/++7x48YLLy0t++7d/+40j\nH7+PQ3+4NZiMQ1kxZFY4dMYwWMegnVOTn0VXcqDChVNLnvl0pxgtQ5/TnkpCn0mahBGdUpZ4V1m6\nyzW8167dFuw6khVDsk/kQ4AXMAy40ShofnHqBbpjw3XqmL0Mj3nRPuXV8RHdTU13WxEPjqhypvlc\nW5i6lenYorWQZfispM+kkeAzSXFBCMD9kNO2JUObE5qc0GTEkyGeDP7GEd918J+iDBiyTAV6jXIm\nmcaH1RDaSJhrki1gI8YNYCfIOStPxBSkWukyZ4U4Gpe2oXINuenIbT+qNYaY0ZQ1p3rBIVtDmYuO\nuLXJXUYaJX4GeJOeWJZL3MM6b7CLSPM4Qh0JvcX3lqLoqBZt0iMnCVYaoG2TXG8014yOzud0bUmz\nq4XgnizH7MVAtu1wdYvJtFsqvfMudVqHrCPqYCd9T0uIS/Abw7CxNHnJwei1I+Slw73anUZ5O21I\nDFsO+w2nuyX+1hF2jnhr4da82R1Iz+H9SPah1gOC5/c6iNtay1/9q3+Vv/t3/y4xRv7sn/2zvPvu\nux/5ep8K4PUm45Av6PJ8RrycJgDMLdvntthzy6J592qIjqHPGE459E6oKTq0OUTQBslchaE1phWw\nkKEyhkDAJSGTiK+1K9tTsGfJSYvELBGLyA03XHDNJdf+kuv+iuvmipvdBbvdFl45eTRmqtspQGjq\nrXQN/T9riM4RXKR3BdFGfMxEyB8s0Vv6RuY2+GNO3GdwyCa1ymuJKvjPTOMSHyH1rC0j13EEvXmU\nphHB3EA0SfSMFUK0zQQ8rA0fOkeYJP53gbzoxVAz1V/nDjQZAx4nRXe/lu6zXeHWEd9mUBqZeZHS\nRy1taA1TX9MSRhDNzIDdBOxiYPAZ3jvKvKWuTuRuMtLUyPC+fHDA0VBz7BeY2hNW0ugKvcMtB7Jl\nS1b2WKtehLopZmO0NwKN+g+mLvCwcDR1xsmUHMxUozsHOn2kKC+s2fkN+9Oa025N96qeTE2v0+PA\nqNNNb8w53Wf+eKj1gKjx/Qzi/smf/En+4T/8h5/o9T4dwKPgjg1ZonnEVI/S7qZGVeoAq0sLuifq\nsYPVUtLGkqEr5KbvrDQoLIxDqTszufAqGXnO+s+EzKlgd2TBXYratFT+lIpbLsb/u2MtnKcU1b3i\nEbfNJbvXFxyvVzSvK3idwbWRC1NBBc5rjMpHU7PO2fEFZ6U7iGHovHDkOgedxR8k5Yp7K3bmd0yA\ndwP8GPAd5O9epudXpv18cI1OMZvP0dXj0mPJk+FANpCXHXnRpSHUA2YGQsAYPRkbyeyQZHnNGJ3P\nb289pzuzwVaRylxSXXlOdgE5AjKLViacpYgKsrGqqnxOx0CJRG6la2lNKfXPkJG5gcJ0Z+CmnW61\nay9TkWLASXRlN+SVWHK1bUnXlbiqIyt6XObPnEnieDSiLY46qU5pOgXE2tDnOSdTcjR1ksTJNnBM\nwHcY09sJ8Pb9isNpTXO7YHjpRMmiA9BfIuCngKfNL609KgVHP///aJf2odenBHg5e1bjbqucqoZq\nvCwBdPqTLi3iamerjRKFtaHEtzkcnaRwLYCZSYaYHHiV8DvvWDoIRmyeWmQHFn3mxUj2bKi4GQFv\nzS5uRqB7EZ/wIjzhbn/B6fma/tvV5LR7g9AV5vSb+ynjPKUF6ZRmgLPiL4eTWqSJydooEwvzXXru\nHZMl0xz4XjCNirxiqi1VmnsyDRJqzfT+zGs+DnFvQdxViqKjrBqxwLeTBT4p4taIRwJF4VGqHlkb\nTyqEsnjuWFPZhq4shH570dFkOVjIyg5XdJhMrhMfJaLKUsHQmDjGVwU91ngK11I5afRoGjynFc1H\nIqodmJp7DjjuWFMkF5OhyDC5x7fINLVcGmMwGTbMDTajN7K5JjJ2TFlEWAjr4GRqjmZxBnDzx4kF\nxziltcd+xfGwpLup4bUVsPsAkfE9T49jJHmkJuWKmSbStUwT675HGtQb1w+7lvahl4jsxZpah9FJ\na76mTVEeiOIhS1tTxIzF3DvWEunFBY2v6doC3yT2v97wmp4NTDNSFQg0hUxj+8Jg6X3OKdbszZob\nLsbXzVN34UTNa64S4G24ZcMLnvAiPuHm9pLD9Zb2Owv8HzrRlr5EUg/17NNa2FxTOq+vaEQFE+Bk\npM6puJMQEaC7YzI6vUkPnSdx5DzC2DB2CM1lxD3ucE8HhmHuz2cm3pkqJbRuVyBlARtwTupxS3MY\nHZJVXqfDBPsk7Jfv6vwF6TLPOYMqkBclykS3IXEG1e7JB0sfc+FtAsQo4xDT1ihvV8Sjg26krhfT\n/2k0Nx+E8+FRiH7c2CyTeDBgCVE1xI6hDwxZRmdlQLxu0Gobq/QgPX+hNgwLS7MoOBSLs+7rZAch\nQNeMbiflmLX0fU48Oongd0Y2N7Wt/w5ynTUR+tSJclYA74LzOchaPnmo9cPulvLQy+NoqFCrH4+V\n3U0jt0T0UctxrdrNW/QHlhzjglNfSfFe5U4KBBrZdAiw6UzSnsknL30deksfcpoozhU3RshbA9kY\nFRxZ8IpHnKjHYvKL+IQP4jMOuw3NH64Y/ksO/8XIhajDY7S8Ned/3R/aoxGfXpjzdLtIDRftgt6m\nv+8V50NqdrO/UzuEyXaJy/S4CmSPO/JnJzhWhKMh7oDWTRtFy7klfg2EiHGBzPVUtmFl9uPtOq/H\nqb71hKelIlCOPEGXAE/rsOcV22EEGQypCyrUlRDdRLlJPDtPT2EmI1ABMIE5AFWiTCYR53Ndz32A\np+GGasBwZrSaxnIGP0nB+nyypRLAqxIpPgFequGF2tAuM46LgoOpuWPDnjUHvX5nXDvl4qlyRMo0\nOeF+NP+aybDhD4FuXpjO5dxp+jovoTzknf6jlPaTrft0j8muO4w1Mzk46cTqft6RSwqru6Av6bsK\nfyyJx0xC+wNycahDRcsUZenjErkY1vL/obMMTU7TVhzyJXd2nZoilixJy44seM3VBHhxy+50wfG4\noXte4z/I4JtWZm58E7k4D5zPq/Czh6awc+DrZ8d7ZOos63jBnqlYrYCntZzd7LkXTDWdmdrBFGCL\ngMs91kSh7zT2PDXukHRIrwwHrvBkVUtdnli6A2tzlyyb7kZNSY+47B5Zjh1P8WIrxqbUaGA6o9fs\n2HATLnjZPKU1Fc11RjgWAjIm0LuM6HL6LBALcQoJZUMsLNFJh195cdLYkJBGYzn1RY7ENwLaMB63\nKBt2bLgZLrluHnFoNil7KAkuEFygqSJ3dSAve5b2wNEteMETttySLz3504GFP2HzgK8sx7LiYGuu\nuZSOK5uzTXtejx4fvqAbCoY2I7ZGGl56TRyRNLaNMAQILWddi97BbildK6UgacT+UOtHKe0nW2qH\no6AypTlDOqghMUemi1N96Pqxg1rShYqhLfGHknhMjYlkFT9ajGvtbj5QJiCAkxoZsTP0TU7TVBzN\ngrt8Pd5AOjjmRH0W4e3ilt1hy+nVCv88k6ld3wL+b+APmKZkqUhdU+xw76ERoPIEtd6Yzb6nqWbP\nlMIq2Gkh+4bz2ptGi+pIosaiWcQ5LyKUwUrNc8cUJQ5MMrH0+670VIuWRXlkZfds2HGRhiypOac6\nfmT4MYKP0dDFYnTgDVh6k3Oi5pYtEdiz5i6suTk+prULmlcRfxCvwxjBxyhboJP3Mq4hri3RTnw6\nBbxp8tekRuHeZ/Puvh6Xprp6XHd+y/5uy/FmLby8zmAc+CwSF0JQxwSq4sTRLXjOM1YcyJaBrBxY\nxoPYOjnL0S3Yszyzb9ol10WN7u4DXu9zUUy0GVG7+6d7j94jljsNk710Jwz+u6fQLKbzr6MOHmr9\nKML7ZOv+TAaN7GQcooiHZMmFGRMPTi7WKUL0wYknXuOmutX9lFBrGXM51ZwSgqRK3juGIacN4l93\nf2aCNi2aULPza3bdhma/wN/kxJ2Va06nc6lYu5+9/psIofHe16PRAZMFus6/0Dmvt0xpu0Zl+tqa\nKmuGowCob2ln8PuM/nWFf53BawM3Zqpt6kQ17dSmlDYrB6qsYeGOrBDAu+SaK16PJXeh6yyxBHpy\njiyQcYowJAsvcTip2LMmjwPRG5q+5nSqOb5c49cO/w0zan2jlHLlvlY1RBYZ8hxTFBgTsLluigJ4\nWpebX2v3V8+sJgiSrgZH09cc+hWnuyXt+zXDy2I8bzEBx7CE2MF+7Xm17rhb13ybt6hsAzmE3LDm\njlI6ZyNvc+TUpfrvIVGcTimNVXaCXNcZvneE3smgcy3F6HUE0tgqjIykDEV6k3ziLxq5lu6Xch5q\n/QjwvpdlUKmQFrHnQ4iBVOyWKWEeO9Z6xoEnwRC7FO7rTaHqCYdcIDUTGOi1r84YWk+zBlJhug8y\nO9SkDrLePB0FOzacQs1du2Z/WOP3pehyNX3WyFE1pnMZ1/wx58DNl5ZjdHbtHKi1Fqkgpx+1STEX\n4ZvZRxXpdxD2hv5FhT/lQk6+NRIl7pjmT4zAwuhGnJUDlZVu5oo9W2654pqnvEhagNtE07kYO9p3\nrClNSxZ7uj6nOVV0seQYg/gTeCEY+53D3zqG5zl8HikJxHSOtkyblr4HvRgLxLYYCc5TRMfI19N1\nf3PVrwOGGKUp0Q85XVvQH0qG25LhZUH4ZiYdUd00Es0jLiz+rqDZrnjtDXe15Vu8S1F0Y9S4YUfN\naQT/lvJMPXHLhlNKoaVJV4xswIgRw1XvCIOB3pxThnRDL6xM72MBbQFRL/TIKHadK2Aekpbyo5T2\nk6/p3owpwpMCtiasBsbRc9rt06B/5FQZjzFeKBvqZKv1q4pzMfgKASL1bdty7lOWPPP0olVQ1ZS6\niwWHuKTxNadmQXeo4OAmlrt2RRdICpszTcRSHefcC27erJgyr+mYY/qo0ZlOZ1MKinZVlUoC5yqJ\nWbo3PufR4oPFH/LzIrjWG7VhYYEyYhYBu/YUVUttT8xJFCvuEvC95hGvqGgBw5El15zE0ik1fKI3\n9G0uKbS3qTNsxbpIa5EfpHPyrfQ+BSb+oP5NyPeDFx2u9xLlDzE1NdJ0tbPoDRkcHiXvJSR6i4+O\n4DNCcHSngq7JCTc58VUGz50czwdMNVb10jtZ4tHS9wZfWE6PIi94Ql2IGDhg2bNiySHxTG1yNRHy\ny441e9ZjGjs3NR2NKiJEHSyvkb1Gdwa5zmuTrjMHeQF9nhw40q6rtKf5tfVQ60dd2k++7lsY6aBl\n0cy2Z3UY7aMtOHGasZgqdyJbdpiuJwZVWMz4dwoWGjUpUChP6SJ9rCLkAeMC0Uoh286iTz2G1osb\nRTjlQnI+zaJLlarpjFzV7I7dVqaIci750QgOzrWtmp7rLq0Nl/tgp+CoF7hSX8y951VhfJi9Fwp6\n10z1u1V6rjXk25bismGx3LNwMhRb5mko+aRjyZ4LbujJElF7opmMlNwIxARyJwtHIyn0LVMd8iUy\nU+I1k6WS2k/pe1Uzif1JXVxv8daBlWbIvCFBRGglMUVN2m0NTiKoLid0GaFxhJMl7pzItfZMOmeA\n8AAAIABJREFUKeA8jdRz0wPGEuuM0EQOrHm9vCI3QrXRbnVOP147SjY+JSZCN7o0y7s1ejzisZEp\nNVXAU9AjneM1sjlputpaaEqxQiM54yyYuu0PGZX9KML7ZOs8yZhAT28S7c7m9HgcJS0BmywPlZ++\np3YHikWDDT2hN1LLU+WCpmUuTjY/eySqUKLvIo6AZwuxLopmqhGOqXOKCjpf0PWFSJ9ObqqPpGts\ndMUomP5vTnKeO3PMtY5vivL0MW9knDivt83VI3PVhr6OPu+cj6g/e2Jq5MzdY4oIRcReBoqLhuV2\nxyK/o2Ya1K3nyZwRi1XbPJ1Po3m2Wkt1VubB3pqp8TJXD+wR8LVMgKMF9zKO0ZaxTFy9IAAWjcHG\nKZUNYQK6kVaSosIwiItzPGVwyqeNS6k5c6mWbpjMzokHnCHuMkIbOMYl11yOlq8a1ekM3YAdqSun\nxDUdxpqdMv8mpoIJUYxWeyOEcG286bXmmGyn9JhODu7cREI2TBnMQwPej2p4n2xpFcWmJoSmkfPu\nmfKiYipElzMTbC2cb+2O62LPbnGiWxi6Op+inDnI6F+ZBi6f1WUWAbuUSVlZ0Y+ay7k+VGO9EMyU\nHsEETprKwhSJ6M77ptra/XU/tdWlQKV0FQU97TZr3Q2mSHEOrPMUek5/UR2xuv2qlVEDpgpkn+ko\nvtiweXTDhX3Nmh01zXhOOgrx2+MRAcc+1e9epslZe1YjR+1sa5sD+Jx+o1QitWZSlYD+vVpPdFE0\nvbnHZl60vCZOwKZ+eBEBtmDTLA5LTEAXvSX28uBkpyaTblAaWetrKsDd503CZA4as1Gg1lBRpjkc\nPv2gEuun5oRax7rZ1jEDvAHZGPZmisTnkb3KBevZOW7S8erGlazPzgxOH2r9CPA+6YpnzQeR7Yu3\nsDi81qMUSCMKHX7dcmLJni077uwtq3JPZU+EOqfTC2DOP5qrBpacd6uKmGpVnqwaKIqOzKnD2pRS\nj4XuKGnRCGD3AU91jCumbqsWjecd2nDv+4bzFFTXvImhAHCcPU5M6fR9udoc5O5/T5spc8BLxgam\njOTPWqof37NxNzyyL2XoeLqJI5aOkj1rDCL3e8GTUf/5JsAbQW/ehVb1iwL4HRPg6d97TN9TioxL\npYdMhhyJ+0k6FcEkVUQiJw9OhjsN0/AhBid1Me18jhQPpg1q1A8z2V9p8+g+4KXzM8SMNs2E1WHd\n9h7gjfM3zjy0z/0XQaqApkfAeJ866HPQU4BW1+ZVOoeq5smZOrr368cPtX4EeJ9sKbP9w0abkyWT\ngtxo8ImnI09QlAYx+5KmXdCcVgxNcd7FcikFyiMUybo8MzAwmoTa3GNLT1b2FEVL4cS+aG6VLqmt\nHE/mBkKRESoPy0Gcek1KPRSY9DEOzLn30ItRf+Z+FDhPced1SP1d5Rqe4vS7IGmjRnXaiNFC+3yG\ng9YRN1GO+TLCuwHXDOS+p96euPjiKy6y16yMzOidDw0ayDiyQJxOlmN8ojw8KcxvRiLv3PkGOG+s\n6LEukFLACmkobZiil5knn8mCmIgWA1k+jKCHEcCD5IvoHaHP0sMSBzs1TAaTzoWZaqX3rbE0uoMJ\n8O4bPSiYOCaQTaqQYOTv1fdLjDG0I6s6X+UjyKMPBW1b0bQV+1drwgcWPjBTuq+qGgU8fe80atZr\naq7Wub/pPdT6UQ3vky3VLo5teCZzTd0JI6iR+5gsdBTjRdKT0/qS02HFcbcW2/S508fYLJAUiMyP\nwGJsxJqIywfxbMt7iqyjcOrZ0s2ONSUcJpBnAyEODNUgBE8L1O7cXXb+cf79eVSjQ3Tgnnkm53U9\n7v2c/r42LLQep7K0+4CnEjqN4lYk94w4a5hIpzvLGurqyLa65a3Vt/iMeX+sq05Rmhkdlg8smTzh\nJoGWGqK+EfDmYKfgUSEFeIc0Kp4hzSQF59mNa3KPzQc5b7lMWLM2JAnaND/XDwnw2iLx2FIDQMFr\nXou7T/7W49L3fg548/dXZ2I46Q6HRHHxRjZtk56wT6YYen3Ph21OE8tKmlCzP2057Db4lxnhO24y\nCXjBmwFPo7kmHa820PTYZ+YYD3qn/yjC+2RL57zqzaBt+ZZyTI0GHPuzROE0VvFec8VLHvN6uOJ4\nWBBu8wk07kV2JveYbBC/u+TVZlzEWonY1IW3sD256UYnZV3avHB4ctsTMkuojNwcpSMuEzm0N9I4\n6cz4tXTZEo9KGfOajrrxBaboTVNvjWzM7PP7FJNhgNinVLgAlyU7dQTk1A5qjURz2wibgFt4XO3J\nsoEs6ylcQ5mdqIsji/LIJt/xiFds2I03pp4nLbBP8yTy0TtQAVGlWk2U8Yk+6KzYODs3TJK7JVO9\naYkM3NHjXiKNpSpgUiSelw15LrXWMbrDMNhsalR4R+gcsU3GCDqacg5wWlqADzsCa5SnHdo58XkO\neNpBTlG4XsvKvdON4UT9oRRW39suFDKAqFlxulnQvahEJ/tNhKLzbQT01PtOAVujfTinMWmJRTv1\nfxQR3g9opsUfxfpUAC9LTQgtgg8p0lNN4y1bGkp0BmuV/NR0N3zOU77JO3zgn3E8LITeMKd4ZExg\nlwvYOSd+ctYFnPXjx8yIr1tuJnGPsuSBkZNnEWfd6AymirjCSwHcW6kTDRbfu4khr2lUn246jez2\nTF1kj4BgTB9rzpsNhg+bDWiKG/v0ZIBdQZ7JDajRnFIXNgjYXXrsdqBYtpTLhoU5UtsjG7NjY25Z\n2T0Lp51YsXOa1+H0BpWmUja+U6fkcqOZ1HjTx5w+5AI+0SRD1nhO0amZblCfjv0RAn464WspNVZb\n9+R1Q1WfyO1AbvpJQ2ucUFJSahsGK5tOa6aGxNx4ldnnb+qaz8m6Gg3O011Naav0dfp/nzYCuXal\ni7VnxYmaSfMR06kXiVrnC47NgsNuyfCiEJD7JmIM8A3k61dMHXndMFSFox8VBEnv67yJpdHgQ60H\nRI03DeL+d//u3/Ebv/EbfPOb3+SXfumX+MIXvvBdfz+EwC/8wi9wdXXF3/ybf/MjX+9TAbycnpoj\nw9iZtbjEA+gRreUd63E3VL2m7p4v42NexcfswpYulNNO7YBs1snLB0mBnB9BzzmPswn4zOSNpu4d\nxSylnXh4SEqL3GTWBlz0I8dLeF2Woc+gzzFDwASEipGALzaG0FhibYmFESsfmG6onsm0UetXWu/T\nLm3LVKR2CEM1ZwK5NQIU2/RcFxEuIubC47Yd+bZhsTiyqI/JZOuOR7zkKS9YczfaPfXJ7bmnGGuq\nc4LslI7lY+1Ob+cQLUNMtuw+wweR8xsXiJlNkbc5d/XQq1B5jDUSla4jZuVxy468bqnKhjo/pSpY\nn94+K8RjI7NSTEQaFJ1Nm4yZVDByUs8bRApk40bCh1Pe+4CXI2UBJWnDWMMT26h6pOdId7YY2xST\na0tq2AVH35ZCZL92UrfTCO87A7zspXHRp11PwVbfszlv837qrVG0Gkg81HpA1HjTIO7Pfe5z/PzP\n/zy/+qu/+pG//2/+zb/hnXfe4XQ6feTPwqcEeAUtK/YMKXoCaKgpE+HY487qQApEmhbchi1HX9PH\njFBZiWJ0hy4Cpki1nuRQ6xTsrMdamWMwTcxQYoB0jpVXNp+foE0UlbxpxKeRhSUQjJGB10B0PjkC\ne3QITN/lDG3GsCzwVSHHrZGOpnPKrVLysHZgNbJTPiHAMYdulRoQuQDFUyQlfJye61GEK0920bLY\nHFgsDixyIW4r4D3hJW/zLbbsRiuuW7Z4NsRUs9PYV6e3Te9BRKekjmAYMnqf4RPY+ZDoFs4TMjdq\nUs8AT1P2kqlhsY6w9thVT7GUyG6RicpDu/d6bnryFGE5cUsanBBxtcs7p5soaM072/OHrvvd9HlK\nO0apISl0JLIcTEaX1EEqS/SJVqXbhdKxRPebNsbeCo9OpX4fIKD36gTHG0nH4+VUtlhy1jQZa7ha\n/tANUK8pvb4eaj0garxpEPfbb7/9sX731atXfO1rX+Mv/IW/wL/+1//6Y/3OpwJ4ZehYxz2DyWWj\nNXAy9UhsBWhjkVwlNmMEpvbhx7ig9RU+ZsTSwEqvZuQizDw2ky5elg3YLAjgGY+zw3izns1iQBtc\n8QwEXSrLz91cDBFr5GcGsmT5bccKFzZQli1F0Sqhhb4Xy5+uruhK8FUUu6MkEYp7c95wUEPQ2shD\nUylNUfY5NPlU7L8iAV4cAc888pjLnmLbsFzesal2ozRM/Dr2XHLNE16yYUdGPwLcId0hc9c4UQNM\n/XS9kVVRMJDho2XwOcOQCW8xdU+NldppPGsopTd+3hldxwR4AbP2ZKuWqm5YVpNdpra3SKl2a0p5\nfZNzigGClXR2rkaZv45+PgJgvAeCcbog5g0lg3T6tUacx5RWC+B5m9FF+aMCTmZsMA1rFzMFuUbE\nuXnAxYD1TJ6EczL2bQ/tXv4eu5p02ivOh5srUCsBfoNE+xr16+cPtOLH7NJ+N9rpQ61/+k//KX/l\nr/wVjsfjR/9wWp8O4LU9l92ewTmKYqDIu7HYfcuWGklbPBmnWOOM7I7AWOsLWLkwi0EuCJXjZImM\nSjJuDBYT0qSqsVk4dR/nhWSYzCOVizeRZ7Wf7Me/QzuTXV/QtSXDKSecHMZEsotAVvQjtSbahpBZ\n+rqgtTVtVTKsc4ZThj85SXeNGcdWSqHHpoHKTkBNh/BcMRkWKL9wwxTdPYmwgJyGanNkVd9xkd2w\nYZcqbqdk4Cl6T3WgdokuoYabGtWqfZd+T4IJNe6cW3iJ+UIYJL0XIrCcFzHS5MNE7Dl3UKVkC3Ar\nT75qqOsjy0yVqGKbqTVdmEi9Wg8+mhWYIBHe7SydtZzP0pWTPAFvjnD8cpkSpw2u6I1Yt2simmYH\ny+jKCFai3sHLxD2xgDfT/juCXUdJx9x8VOg8lZRzAlKnu2GyNusLiBvILdS5ANdlOv9aQ9SIU8E6\n55yKpN359Xe/Hz/p8h8TNf4owUXrfp///Of53d/93bMxj5/WMX3XVbY95XFPyA2F6cjzZiyEv+Zq\ndNL10Qng4XFmsntQ+3BsxBQeY3qJJIIVIwHtmkWDCfKweqGim3Uc61OTg4ZlbgslXEFGmoFKp2Aq\nQXssXV9y3C/xdwXsLc4O5FWP30pNrOZIZgecDfRZzqmuOMWaJtY0oWLoMoZeI6IJfOPgMCdDPDih\nauyQi/2GiTBrmLhsF+lxFQTwqobV6o6LXNzY1LZoPt93DnhaTlDAmzvX6NwKuVFJSewEeNK5LTAx\nCum3f8OlFcx5l3ReP9ObNnVu3XKgWjYsquPo0rJKoFcnoZb6KZ6SPU1HQW466Qh3UQBPGUZatFed\nNZzTTEowZcCWPTYfMDrSUaVpwSaJnD2LWjEChH5w9DaX/7OAkU1Vm3MF/fi5vq8WT8NCNvM3Ad5Q\nAFuJKtfFaNXPFecDeubOP3PDWU1pFfweaH0SwPteB3F/1Pr617/O7/zO7/C1r32Nrus4nU78o3/0\nj/hrf+2vfeQx/cCXuQPeB5NHyu0A24ZjdeRQHlg5cf1fJzeOo1mMqaza+mANIfNEJhCUuQMRHROI\nYZQcGa/lIo1EUkIx5PR9SYwGawcy1+MzR+/ysaFh0txSZc2P9Zmk24zRSEdW3T9eG6J3dFSYPlKv\nO1jfkdueOhGmlrOCf29zQm4J1mAimDgNwxl8TpdXdHVFv8zpL3O6q4J+lxMaN81vLRA98HrAbQbc\nqqcoM1Zxz8IeqUwzRkXaALIJzLtEnWgpR7DvKDBESlrW3I3J/3yKnKa+6nun3w/OMpSZzHTwiRvX\nZ4Qhk4lrjZm0ofNmgtahTAQXcEoTMs3Iw5wG7gj4qRv1iZqcnmAtd+WW62WDrwp8mTPOAnH3HppW\n52BL6QJnVU9etGRZh0sza0MUGykfnfDsQvKrS8BnjEsbVUYM0qkPmSVkousubEdtT2N5RFkAanuW\n+x57CrKZvQS+HWHXgpeUHTKwmdhBafdaSdorJsXFXEJWyvUgj4CpA6ZSC6Hvf7Xlx9OplXxvg7g/\nzvq5n/s5fu7nfg6A3/u93+Nf/at/9ZFgB58WhfAO+KbQx4p2IAue1fbEPhfAk8rdjq255chCOF9m\nMkjEGIKb0gZDxHuH2OLOVpSIySOFZf1pXdIdWxC8xeQ9rujpq5zOpq6amcgYAZe6/J7RNiomN47B\nJMCzcA2hsXRDRegcq7fviCtDQT9aBo2AZpx0Fk3EZJDFYUzNlJd4jEsOQeZ3HMOC/WGFPyyJp0Lc\ncL3UlEwRcauefNlQFQ1lUbNCAK9O5BEFuzwBRUxR2QE7Ata8WaOcRK3bqfuJNjNayvH5Rpmgc/Rl\nTswNw5CN0avvskkIrw+lVWjDKSLpqPNYN1CY0cw/SQvFPGKTNkOdeXKixhLoTMGr4hFFPNHVhlDm\nkkbPtbD3HwXYUsZBllVDlTcUriUzci5ilE1WnU36kNP7nH6QOiVGN1uLH2Ag4vMMXzhMEVhkR6Kd\nnFD071CtbeYHbJMA7wVCQ9m34G8RgNokwDOT9dgVUzdegU+J0PNHEaDwozrloQDPu4eTWrxpEPdy\nueTXf/3X2e12/PIv/zKf//zn+Vt/62+dDeL+XtenA3g74BtgcjA+Yl2kzFuWqwPb/JZLrnlkXo22\n4NqxVZa/NWEqk2SpupJwTAXkOnwlYiAYoknTp1wCGBuTn1pKV3xGSAaY4+wDE3Ax4kJgKHOiMWTZ\nMNb9VLuJFRWAWRm4AtsH8m1PuWpYlXds0gyILbdjB1hdWSIGlxog2n2cA8oh+cPsU2K3sxt2xYZ2\nWTN0pRB7rcFkUUi5ZUvpWnKbUZsTRQItfcyHUevf0aXugXYWFRiLFIcWKZ4TiZ/87lwoX6TalEGi\na+sChenGgTSnY0bfG3El0VGSe6Z0Uxs0Oi6yEwlYjBN/RKJLHckooHHBDVtuOFEz4NiZDUu3py5O\nxFVOfxUnba5yGsd6XUzOMAFX9hRlS5WfqN2JyjVn50IAT77TmpLGCHBE1VV7S+wyGCAGGKyFzNJU\nkcOqI6uF61mZhmjURVuGvvshI+wNXAdpUtz10O0gXoPZgFtCbSegeww8i/L5lUT12XrA1V7MUPPk\nAp1HcB6ygHXyf4KQ3//yPBzgvWkQN8DP/MzPfOh79wdx6/rSl77El770pY/1ep8O4N0ixEqt29SQ\nr3qW4TCaSh5YjnSQXfLVbZLSYuyuKrFy1g4K3uF9qoGN7hlGWAk+YG2UC8CldMUGSXsQ08Whywh+\naoIYLzXA/iKAtWRZT0uBOr7F1Cgxix7rgvjImZ7l4sB6seOqfMUj8yr9BUL9UDcWrdVlqVWgo8Vj\nAqGWIiVy9ZjI3WZbbu2WQ7niFGoZkmMcGMiSFlh82RZjhDYNoZ40sRrHhZEdZsfapSGSp5rTij0L\nJEpU89WIGY9JX2Pe1S7oqEzD3q2wMTBQcVJlgI6Y1Alp2rSYmZRyMoRFhg95Unloh5ixW17QsuWG\nd/g2R2r2rHnBE5b2yMId6dc15mkk6vjKeZQ31rs8pvS4oqPIW7GxN9PfKjQdudCUmuOMJ1ojqbtJ\nt89gpcuaiM4hpAluC8udCcQScttTGRmzbRNHpqOgHUrCnYNXEXYNnPYQriG+hsxC/mhKZR8j0ru3\nkG78k0C2aak3R8qiJbcduR1wZsBZT6qRSO3aAnzuAW5eGB4Q8H7Q69MBvA654HOSRx3kzcCyO7Ep\n9lzaa062RjWYeqPJ8O4JLKKRR0AmWKm0yEYRkWsdT7uFVgkBDqzxGBcxRZTfUxCIRgruQ7IQ6i0M\njqFqOfQrTBc52AUnuxBfs5iBiRRVi60kSqtsw0V5zWVxzTOe85Tnqeg+CfHngDdRF0RWHrCUtCxw\nRG4Qof6KPUuu3RVLdxinX52o8GQTzSHx4nIuKWnQqWL6vulmoZZE8tMTqCigKTl8i3R31+ypQ0Me\ne2K07O2SO7s6r9/N65zGyMAeU2OHkFQmRiRSOlpSAa9g0oTuDFSGUGd0q4K2KGUOrFXh/bkG9TQb\nb9gb6UxkdsDVA+bCC8E7M8Jlg8kpJkcioaInzztK11LZJsXTh7NOsBKIT9RgYDCS2o5W8j5FpY0Z\nLffD4OgbA1WAEsqqpSg7XJS4eh9WXA9X3B22dLsSdhG0bGczsCUsclgboRu9BbwTse967Gc92WVH\nftmxWO1ZLnfU+SlRnLvxOhi9HFNv+KGW/5Rg4yHWp3Pkc6NKgB6yNlCfetb5gcvyltaWY8rlxlM2\n3Lth002rBGA7q0PZKFZOPsplGQ3GeZwLcoHnYiMfg8VHK/NPg2Poc/ouEznY0ckYw9YwrHNevPoM\nN9UVQ5Ez5Bk+B59DmbdURcPCCr9ta2555F7yiFc8Gi3QJQk8MxVlGlFJ6nSKcYLcnAXd2Fk9pLRW\naRlavzqwRAfAKJBqRKe8RgGl6XUN54xDje5C6hxk9FQ0bLjjitdccc0FNyzDibIXwLvNV9zYzVl6\nrImzNj1UBRE6K7NVr5HC/HOEb6aWRiWTzvg1Miynyjgta2wutl1F0Y0glKfUrKMcJ8l9m7dHm6po\nDOQBu+ikBmcz0TZrgyRx7mweyIqBwvVUpknW9VJDrlNUq1HlgQWOAY9NyolZpqF1wvmYzQ5ibxmq\nklNmuN0OhNxwNAtuzQWHbs31/jG7mwtONwvYJ07OwkGshWh8VcFbJXwe+CKYLwSyz7cUn2vY1DvW\n1S3rfMfaCb+yYrLVN6njPg1CfzhDvIdMaX/Q62MD3n3N2n6/5x/8g3/AixcvePr0KV/96ldZLBYf\n/UT6qiqPSfWbrA9kTWBZNWyzW7ps8vkfrcKJowZRd/qefOKCmeSEQsDEgLWGaA3ReGKEzHmcG6iz\nE3XeJM4U+OjoY043FDQx4gdLGLJkwmhhD/4q4/a/PJqkXMsI6wGz6qltQ12fuMpf84QXPOEFz/iA\nJzxPg/luxwtRtaZ6/CrMDxjp2ibgyulZhT1Pwwu24Za9W7K3y3FWhD5fSTsK1XXp79c046toGgVM\nNypTk8LMamQVLUsObNgJYIfXXIZrVt2Rqu0J0VHYFpf3Y61RIyBNdU1UwMsJrZNI/gYBvPejgN6J\npKk1Ur87IYBXgq8dYVVhS09pW4pcRGyFkairJ+c6XhEhjVe84DZu2ZuV0IjygKt7orHiTtxBHBBq\njAFsFEJ6NpA76QZPgLdjlXiK2pnOEwdIlT8aMQOTKmNu09RAHCx+VxAKxz4L9FXGway5Nh3N3ZLd\n60u6l/VEM8pKuJwp898GvgDmiwHzxUD+xzoW7+5ZvL3jsXnJY/OSrblhyy1LjmNEr8elm4/2uR9q\n/f8C8O5r1n7zN3+TP/En/gR//s//eX7zN3+Tf/kv/yV/+S//5Y/3ZDVSdFUZjG4+Pbh+oI4nNuzO\nrIWkkJ+NPTsxCZ1SqRDPWf3Z2GX1uMLjoqcwLYXtqFxDZRpM+t2enJOpOLkFtpCIpXeWPuRyoz5H\n6idfQy7sR+nrtyzxrRyXQbno2LDjKc95l2/wDt/iLb6TJFx7VJPbUXKkpqE+swwaUhdYI7M1d1z2\nNzzdv2Tb3LFbL9mtlyP4695d0oxzElQgpzfoguP4c+qxq+Cmn2sqrZ8vOKQ09nYcxbhtdyyPDWU7\n4AahjWR5L2kas6FKKbLQ8+WjSM1Cayf79ucI4L3vE+A5WKbi/yn9TA0sLHGZ4cuCNq85Vh2lEYXv\nIJRqUYTEJcdYcwoL2ljinSNYi80CZdXibMQ7z9BKpzj2bqzZYiUTyIyAvErutuxSAeGAujVKo0bB\nbrKSlwuOqZ6s2YuSnT3Eg2FwJXGwDKGiCZ7hpsC/yKQre40A5QaJ5pRf9zbwY5D9WE/9+T3Lt3dc\nbV5zaaQufMVrLlP0vWJPxYlibLTYsw1o/4BEvPYBo8Uf9PpYgPcmzdrv/M7v8Iu/+IsA/OzP/iy/\n+Iu/+MkA7xGTSkCDkx6yfmARjoTIGdh5k2gaLDiwlKglceGUKxWjGRnymRsoXUtp25Fsq4TVStUP\nKRVrTMmBFZkdiIVhSNPuBx+JCnifQQDvBLwDvGsgOFg67CJShjYB3gd8lm/wY/wBn43fGGdweBy9\nydKxb7XPiyGOjix5Ygtu2PGYlzwZXvH09jWbuz0rt2S5XmBjgjUjkrecnjvWoymn0GdiivBOYzlg\nrojQNVeNZAxjk2LLLdt4y4W55lF8xaptqG97XBMwEUIRyRfDGc1l3hTRtF24hG8CvADf8ZLClgZW\ndpqzcU3Sf1pYWvyioF1VuCh66tyIi0vGwG3c8jI+5hCW+MFhYhRQdB02CxSZfBzyAbKCaMEbI0ag\nwUgt30Yy01OiEZ5QXtbcseSASgs7cvYsRwrO2VICtY1SL1Q3ExAgO1oGXzIcS1r1SNRJbc+RqFYB\n7ypOBPK3gc9C9k7P8u0djx495y3e5zO8P25G+nEd99QcR835QJbU0mt2ZkM+83j8ftcPfQ3vTZq1\n29tbLi4uALi4uOD29vbjv2qJSF0Mkzd/kv24ECjbgWgajLsVl2EjaeDBLJPsTHbZGE0Sqxf0bUHX\nlWR5T1ZItKGRkvL60umnTORPlbMdWHDHhoqTCP4NUOS0iyVRHTEC0+wFZa8nW3IzgI3aoexHgC1p\nqXxL5Tt6K0TUzA7jTTTvbMIkZ9P6nMsG4ioQXMRVPXVsWJu70cVEa30atWmqPI/wFOx0nVmuM6W3\natCQpxTYEcj8QB56nPcy+jCDkIGvLF2ep4E00xDpkFrmcz2ys0LeZi7QN4l4FxF1jNa+OibaSrI2\nD2tLty6wdUVXFPRlPr1PppHOt+3wmSNGI11q+vH9GUzGkGV0VUnrSrqiom8LfJfj8o7CtSztka2Z\nhotf8XpUdOhWMZfSxRT3YUkcyghlkOaFZyI7q9uKWv3r36hNO7Xor9L7oprXJwGeBOonJxaPj1xe\nvuZJ/T5P+YBn6bFNpRI93nW3Z3FsKPqBUBh84XiVXZLnfaLAPJyy9Yc6pb2vWftuy5gZN31QAAAg\nAElEQVRP8IaqV5vh3A7JgvMR2wxkMVAUHWXR0DvhP92yPZucFaLF+0yE+W1J34hawOXDWWr4mJc8\n5uV4QSvlQFPjO9ajKB1gsI62WGIWMwugwFSHqhHAGzWPERNnlAw0qkyA17XYLCdagxoszd1aYNKt\nFnQjLcI5D6uIryIu76ljYMNdkjPN6cAOlXb1ZLixhnc6i+zmlvrjeSPOXrs/6/LlYaDoezI/SIMn\ng7AAXxvafEor5yahqju2BIlCjcfaOIuCEJqEySZQGJhE/jpFbQZ4/aaAlaezJUOZjXXdigZjIrU5\niQHorFOtf6+3IvlqXUleVJzKgWiWeAxZ1lO6lqWVeuUlN1yla0TPgZYdtIOuTifeyKQ0YyLkIQGe\n8AfHJoZaSw1Mcjq1+VLAC0xGqE/S4+2AeXug3t7xePWCZ+X7vOO+zWf4Dk95zpM0AH09NpVes+kO\n1NcdxWmAFfiVI696QgbBmLMa7/e7fqgB702atV/5lV/h4uKCm5ub8eN2+2ZS4+/+7u+eAeVf/It/\nEZ7+7LmQO5ObSQXkxgqboDKQB4e1SzaseMoFX+SSW7YjLeMYZDh2MJaQO6wzOFNQkbOkYsWKDRes\neMY6UUNUwD0vuDdUHM1iTJn365x91dEvIv7djP9hY+HnkYs0Cdx5AjyGxXbNKn+XS5Y84V2u+DJb\nbqnZkWU90fY4aymtw1BQUHGRCCPzi2fuPlLQUbiOouwwRUcRwYZIScYFGU9NyedtzcEuOCSTSe36\nGiJf4G0qhLw5tSUMU/vnPMqbA592gS/cLcHc0G8GwkLI3sEZvMsoXMmWioKaq9GMXx4NFScjZPFj\nvqD9bE6zAL4A/PeIxlV5eDCaBvzsf8fk2Dy6NVvY5rjSsHCRBeX4SiTy9mwC7hn1JmLkukgANZAx\nGMewHvCLhiKLlLZgY56wpWDNM1bsqTmN0Zw2xo4s2LNix4YbLjjYiq5wfNkYssvI4D2hM4T5TBN1\nTJ7PzdCv1edQZW95xK4DZhXI65a87qhzWLglK/uMFUuWvD1yIrWcUIeGOpzIhwE3BKmxIufpsV1S\nmiXP0jUND6Nt/aHm4b1Js/bX//pf55/9s3/Ge++9x1e+8hXee+89fvqnf/qNv//GN3X/Hjz/X9JM\nCKCCWEJQf68k/jc9xP+HvTeJte067zt/q9nN6e657Xt8fKRaK3FSiBzDSFkBUk4ZziRGBhmUBGRk\nAR7JHhgJCpEmKSDzKIoBQUlGyTQ1CDTwIKPSIMmggAyqSiVHjhuKEvlIvu42p9vtWjX41rf3vpeU\nSMpPRdvhEg6u3uW5p9nNt77m3wTPZnXO28sL/pjP8sd8ltf4NK/zSd6sXuXJE8fVswXxYIkHsBcR\ne6/jYnnJw9mbfNK+zqd4jU/zAz7JDyj5YeJgdhxYpf1RACRPOecx93jCBY/cy7wVX+aqPef6+QmR\nkn/6X5F+i96UnwRqOI177s0f88n8B3yOP+TTvMYr/IiXecTKbFnZLZ0R2IbeNFuWQ4bkUlaivlZK\noZqzZ2F2LOKOedMwr2tcbyE4qmJOPV/xPD/nDV7lCRfD6MDRs+DX+b/5vQGaoBNh5U1okDCT8Cfq\nQj2LZIP5kDd5xbzJykk/CwONzTnYGTesuOGIS05uPa4Tp0TB4lf9mmfXD3j6wxnxDwx8HwGdv4UA\n0JUOndoa//R/R/pXCrB9Ffg0ZK/23Du55F75iAe8xQPeIkuYQf1+Ksc/hf5McXsVBY0p6Kzk2Ssr\n7Y6XeTQMmR7wFidcDpNYGTmt0uz9grd5iTd5yBMuuLZrMv4y33IbtmZFtVtQXy8IGz96BytnWAPc\n1K1ONewWYNaBIt+Tnx+4l7/DPfcOJ+Y5J+aKU2T6f8az4Uhrq4ZmQ3m4wX6vxvwfUY7r/wjxlyzb\ni3PevnfOY3ePd7jHL/K/fCBu6/utv/A9vPdaf//v/32+8Y1v8J3vfIeLiwv+4T/8hx/4b2MB4Qhw\n0g8KhaXPLH1mBqEAEsOhw7M3MoUce0VpR28M4Ykj/DAbSoVgHXGeEXOHLQI5DUt2HMdLLvpnvNQ/\nHiectsG6iLG3P58zfeJsB/LjFvNyJJud4f8q9NdepsHGyo2ZC2G+NRlVooJtWHLDmqXZCSyCUfpc\naXIqbtqSDcKnwC0Qb2s8lSmwIVBUPe4q4g89HHrRuzsJxKWhLXNc3g/Nagl4O855RnMni72b5U09\nFtp0OVQUHMxMCPQE1m7OMtukAYvYaG5ZsmE1ZNtblkmtTr5XE/ORJeGM9ELnjKrMKlCrzm6TwdXg\nzHaD4Pf2BmojNEQCOe2QU2r5Og3gwHsEvCTQZDJRXoZhEqt9sXvxMRfhKcfxCh9FHcZYiNZSm2JQ\nkSmoWZtrnpgLFrzCib2UY1k42qIgVAKFEc15xrJdS1uluXnEs+M4Yk87iosDy5MrTs1T7pu3OTWX\nHHGTKHTXA+vl7rDIEqSPOIN+aehmjibPOLhiUFzuPi5pgQ8Z8KacteVyyT/5J//kp3rTMIP+FHpn\nabKcJstorZed1yhP1dHnljbmPMtOecYp16wTb9JLr+7QY9+MkjXozplb4toTZw7mIzd0EQ4sqgPL\nqhrKt66oqcsdrR0xf5pp5TQSluYHivsNc1sy+yuB3WYBu0xc0pZyBHvraBMNbCpmtGE1lM8gF0pF\nyf6Oq5fcxPWtZr8EIYHhmADLTS1TvafyyJYdy/sHzH2DuQ+rfDP00kAmvfd5e1CRs6lx3eNoyAiT\nYNChRtIC9pY+YjWcry1LlmxwCcyqZktqyTh9bAdOyYJNXLGJK+pYINw3RgHLFZLlqCKxQjt6Rmc2\n9WLdg2kS7zodI0NM/dKxpzsqV4dxSpw2Ae3FybUjZbxm0Wc8kwwqPOe0vWTdbbBBKIddkVEXOUu2\nBCxz9jzgLa5Z8wM+xYq/xinP5fVdwSGfi36d9TK8UFyeBrypBLtK2Z90uNOG+WzLqb3khKuBJ6y5\nsrI/RgxgkuXy0JYG+6rF/k+RcLDsH5Rs7y94Xh5zaU7YcDRIaL2I9SJBzP9/r48kN+0LS+UzOuc4\nuBkHW9KYYii3pr4JNflQLj3nlJt08nrELSxeInLY2hS+sLCxxGOR8VH2gKXH9QHXjOYGzgXyWDPn\nQJtkkLLEMpghWMB5uacoK1b9fY4/2RB2hub5nPbKD6KLfXTUsaAK0gfcmVGuUjmpWmqN380P31Xa\nOFM/+uR/EAMmGJGBujJSrvwQ+BH4o4DfBFy7xbuWlbuhi54ueqI3LMstF/EpuWsxNqIm5/UkI3pX\nD5MZHRmejv2Eh6sZndKVtKe1YzFAHzTAb+OSbVywjSs2/RG7ZkXT5RLcEm96ECzVkk5lojRxaJBy\n8IbRoLuTHubIuulT9r4dBgwKxp7SqqbHWgOeBk3tVQ4Qj3DFUbdl2e4xQZRwSt+kYU59a/q+ZMue\neWKk3HDNmtzW2KzF+Ixo4u1enfbzFGyfAp5ZBexxJ34j5Y61uR6ofKvE+tBgJzxsBZGnc+g8lcnp\nLyzuONJFz2W+5jI/Rrk+G1bs+ICkgA+w/kL38H4Wq3YFV+aYxmYSIFK2U0+mflPyvOx1xzzhHo+5\nxxXHbFgJ/azwUippszhN/LrGcwizoby8Msdclmue26Phc7RZRuMk2/H0Q7Nag90FTzjmihMuOTef\n4zP5U4q+5ensPlf1bOB/dpXnUJXs3ZyDn1G58g5cww5ZoyqNdGma2uNYpGJwqlmnkJbZoWZ2VTN/\nu4LXgT9Jj2P5vq4NlLsW/3YgtEaMp08N5Wdr1mFDv7LUhQQoZVso3UgD3hQAHSalbkXJhtWQ1Wnm\n9OMC3hYJdrt+wb5ZcqiWVNs5/S4bZdZVkVd9SGaMPS4VsmzS7xLPmmSe7kzP6LAh2Z0yQrSZP6qc\nAEw9OcaAp9AZPc4KW5qzJ4tJOdGmoauzKTOfccMRHX7YBFTIQmFA3nRYm3CI8U7A04dCsBLbyM4C\n+axmNtsz8/sU2G4r3Ix0MamHJXPNqBGp+NoUGCcmRlUseebOeDYQAk+kd8mL81b877KH96dZtcu5\ncmtqitRFWXBIXZnpDagX1TVHXLPmKRc85oJ94o82LqcvkzCi0BjGgFd7Dv2MTVxxxZpLe8yz4ph1\ncXzrs6hwu+L2ZtpcSsDRM55xap5zZn+FT+U/oCPjcFhyVVzI+x2grzxVXbLPJVutXHkrW1VS/pw9\nhjiU5T5RzbS0mtpElhxY9nvWhz3Lq0oAqj8CXgP+CFG+BVwXcdctLNvRCvJTwP2GdbelmXn2xWzE\nLqZSeVrmaXAWAYGRV1tTsGE1nIufGPBi6oj1C/btgkO1oL2Z0d2Uo0QTjKZFgTG7qxld20rGKWcF\n1KI7aJwwIvJJwJtxEKB0vE49rg1FVL8LWQ05tRmvqc744TtqVriI0oSYxQofRdG5d/II9vax0D6o\nQppghBSpaKgxUdzTtHc3NWjXatAL5MnOAsW8Zj6b6hY2k7xUQd0jlS2mfjBAY3IRNMBTu5INSx5z\nwVPO2SWNaN3IXtT676aH96KWTiv3zN/V+9HhxGgBWKTEfskNKw7Mxx5e1mOPo0z0njIGvC3Um4Lr\n7THvFC+xyPbYLLLhiMfcH6Afyp1UjTqXmAt5bOQRGqIxHOwMbwQq4kI/qn8k5kCIDhY5fZGL+OME\nFCwwj8NQkvQ4Sio6PDkNATv0ZvTC1vzLhw5bhxGv5ZFA9yqjfLcColPwpUJYLD3EzogowpAxj/mj\nZjtTOMdd4yLVytOhBiRnMPJJD0/yo227ZFcvqaoZzb6g2+eEvZPPtmfs06kzWcntUq9L3+0vp++R\nSj73akf+6YrVvWvWi8uhga9Z2TFXnIQrTtorVu0Od+jwVT9Ix/d5Q1dUtLmn9hmN9wNuUWFAs3ig\n6GuyrsWF5CQ2+J8ocnGk4E2d3KYCEKoFaF0gaEmr50UzvTmjYksZsUVH7poE5qkn18IITNc1nUhP\n8ZTCw9Zh0ioNjqabrhTxL2p9HPA+5NKAt2U5gTOskwF3OdnXJPDt082lFCpIklFZhz0OEvBapOfT\nA1tobgrazZo4i/hFT5PlPOYer/PJAcelJH9Vz9WglNOwDFuWYUtvHBuzJDOiweZjh20nAe8tiJmj\nPzN0y4K+1ExJgogqjxTptQN2QPBr9ldQT/ioo0duFjpsk95LM6ITRqUZ8XeWoNIzKgk/kH/H3ohP\n7HsEPB1W9JPPOk6KR/6oZqlqnKTlrZ6/G464Rnp1u+2KbpMTN464S3JJOpSY+m8oBhNue8FqwNNs\nbwnupY75p7ccXVyytlccoz2uUVT1NFxy1lyz2u7gCsxVHGTc48LAEbQLR2X9wPxVCIslUISGoqvI\nu14cxJKMHDBsAHpelNGix3FQ4UY43M71UtYaEQMdps5TY2+LCJCWIQW8etgUVV9QVQqZBDwYp/1h\nOEtacheJM7saBmJNms5ON+AXsT7u4X3I1WM5ULJlwTVHPOeEqzSb0nJPjXp6vJK0BsjFAKmwydR6\namhiEWmevSVe5xyKJU/DBU2f88TcY2b2FE1L3rQ8KB5xNX+de9k7w2RMJ7WqqnywZSqDEAaDbfBl\ng13VxCNHXDvxklh05KXwOKfZWrKhpk9BR0qRmG4eoSkpbkxunJha5D3ORfwi4M567CFiIxjN4mBs\n8k+Q/TFC/akMFjnP4zGP/QVPuJeEqk644Yg981t4PDMUs5rhdUx1B/vJLa+ZxIYVm7DiplmzrY+o\nr2d01wXhxsHGCpRE+1YKvB1MZpLXQh4Gc3RLID/POPofdvi+F0mosmG23rFc33CcPecBj6TFwHPJ\n9OI163DNst5S3NT4qyA4yUvGTaFLKjopqrrY07leqH5GwoCLHS5EbC/YTxPA9OBCJDctZaYyrPtB\nFeZuADFEnO3xvsUXHd1cxGDZMvYoewYLRbvq8Mua+XzHwu+SOsv+PYPeXaiNZuUS7FTTMB+YNkMA\nRvWazZCFvoj1IvuB//Jf/suBzfXP/tk/A/jASky/93u/x3e+8x2MMXziE5/gt37rt/D+J4e0jyTg\nBRw15QBruExzsktObuHtppM2tUtUnqR4QniCt6Mb+4xBoYKDgWtLk824DOds+iNRgjUddmOwG8Pj\n9Y/Y+CWbbMnLPBouJEugtRmVKbkxKypmiApJT+Za/KLGx5q+yulbh7voKI73lPMdhR8vWM0I+lQa\nKqksDkEGImos5FCxRkj7egZ+LYbiWRawqzg29KfIfc0cUtCvXirgOOexOeOt7CUe8YDH3OM5p0nK\nYD5kBmboaEnvaoR3vJuS1pIN6hsbVlzHNbv9mt3Vmv7SES6d4ObUD1YBtgrFUCzeOsBRwC5bspkI\nY2amJZ8tOFs+powVS7vhyG5Y+g3zQkj9Guwk4F2yjlcchRsW9R5/00lbQwOemtmAYCptJEck+5si\nYvK0cRoj3rAxjpp2LTgnx7VwLfPywNxIn3XPHM/iVqk5KNDYnixrcWWHXUb6dTpX0/OUrDb9Ucfs\naM9isWHpNsM0VlkUtzB2d8paCWECFe+H2sAz8pm5FQ7l+X82S9pf/dVf5e/+3b/LN7/5zeF3H0SJ\n6fnz5/yH//Af+Bf/4l/gvecb3/gG//k//2f+9t/+2z/x/T4a17IUBATPtRxQR1rS3pWF0gBHlL8V\nwYCMqinp1DNOLf7kj0TAc2cIuaE2lrovk49oFFHPzpKFmmW8ZsYuQWbF1T43jfRpTJ6yIQFtFjQs\n7Ybj4pIzHtOez2hNSXFyoFzsOMmeszLjxDCfFFAjRiwOoUYSn+luLRCVFuGpVrakLgq2riKjJy8D\ndBHTJbn6ELF9kJ4iIprZzjxP1mcsihP+yP4cr/MJ3uF+gvSsEiPUD/AOLaNHVZUpEU0eei70MzYh\nZ9/P2DZLqpsZ7dMcLq3wjHeMFpJyskcD8VnAnASy45p8XVEu98zLHbNCbvIj+4BX568no57rJHu0\nQzwspkIQAsJd9HvmTUV+aHGbKC0NZTjAeHUnjJ8hplI1Ms2QTMKRY3psjNhONhZrIlnWUc4TfNc0\nZKYbNgQtN7XszY1I2/d5Rlx6XOjFbGopQrQmkIyxI/P1ltXsipNMFE+mCi13g55j2s/TTUjeWUVw\np+KuZuhSCnxHkZ0var3Ikvbnf/7nefLkya3ffVAlphACVVUxm82o65qTk5P3fb+PJOBJ5sMw7bvm\nmBvWbFkmMO5UCViDQjqp0dC2GXVd0O5K2iob6Ukqy6M9oQq5AbDQxlTmpP7OItDPLLUrUqgTMPAs\n9fIUv6V9GkNgxp4Tc0nlS6wJ1Gcz6sWMvKgo8wMnJhG5JzAJLVHUh7Sb3IXd8FFH9N1dEr43UvL5\nRY/Pe0yImBDxsSOLLXlsyaKQMnduwdYveSt7wCfNS/xf/AI/4lWecsaWFRVFygH6YTIsfcbRJ3Xq\nXiY/QW+vYcobHHVdst8t6C9zeGKEJnbDgJlDOaLKkS7AHnXYi5bF+obj+SXr4oq1u2ZlRNX5LOZ8\nzvw35uyHHp2wKcIAFyrTZHbOgbJvyOsev4+YHRJsaySbUsxfUraJc6Eudt7QWk9j8yEr6lxHb1pi\n12JNi+vjoILji55iKXJb3vWDLNe0K6YDjYKKBQ6bR7KjjqYs6I8tobGYKMHW5BGTRVbFDSf5s1tZ\nqwb0KV82SzRIBRvr9vhefOjR83b82dGn8HdHw+9PsX7WsJQPosR0enrK3/t7f4/f+q3foigKPv/5\nz/P5z3/+fV/7owl4QVQnGlOwjUueR0GD71gkFsA4Rpcgl05zEOextsqpd4Xguyp3m4Sdms50yMXv\n0kURTHKtirDqMYueWEaxFRzp+tTpIYHIDAHKEViyFeMWB6WrqPIZ1apM4GKRRFee49QXYapAIowH\nly5dhUSrwGl2e7Cg1pTOYV3AFCO4YFRlqQZg8xVrruMxb4RXKMNL/L914J3mPtdhLT1Q6yiKiiIX\nlZHSVFgTBnjGqOJ7W/HNpFCnNxTB0NUZzbaESyeQmalZDozWiz5Kqb0Cv24oT3Ycr57xUvY29/wT\nERjlmoKaUx7wGV6j5MAijapUx001/qbsCh96XBexWuanKX2MEEtDODb0a0u/9PQzQ++hy2STq00+\ngNud6XG2p8xa2rym9DV57PBtlzKzOAwxNF2M6ZzpZ1M9QYDMt8z8gW42Tux1U9FjKOos49RZN0ml\njr2bPnY7A9c9XUHUdpLR3X6EIcd7UevDlLQvQqzgvZSYdrsd/+W//Be+9a1vMZ/P+frXv85/+k//\nib/1t/7WT3ytjyTgFb1gwipfctWvedzd45BsGPtJJqeinjGa5P/qiL2jP3jCzsHGJZ4ltyEO05tO\nb7zEiiBDGuVlTZY3eDteUFOlDV2K6M9oOOFqoF2tuR4YClIa9ikzuUmS27thh7aTC1AxeWOPcoQX\ndPgB7HvDalBBUbtKbVArpGWaQaq72zVr3mwe8gnu80dvLNm9vaLeF/TWE2bQPsipHxTMswNkDBQ6\nvVk141SGypSIHxD8WR4bXIt4fjy3wnTRzEppUxnSYlgiopbnkcXJjtPZEx74R7xi3+ABb3HOU465\nwtNxzF/jIW8O8CA9dhpYVKcPEnfWWqI3g8rOcM49tMeO+lXPbrVgM1vRZBnBCqA4WJt6kir6JIEv\n8x2zZc2R3XDPPuMkv6Kd5+yynL2bUZkR6tFN+sx6ncj57ojshrCoIq0awPV8qUBEmTI5CXAJ+jRk\naNNWgwYtzf0NU8O+Hnvr7zLaoXWhHrgvan2YgPfTiBV8ECWm7373u9y7d4/lcgnAL//yL/MHf/AH\nfzYDXt63GGM4UHLTH/G0PaeN0ieLKr8dTZJtT36ywUDrxf/zYKV82RnJKia7+/BQkvY04GlpZXt8\n0ZBlTbI0HPXppoWCwhZU2POEy6QksknBTqbHGhA0eCjJe0p30h1aL0i9WQLJA5cR47ZhyVPOpe8W\n1wP4t4kJWhLFAHtupMQrjAS8TVyxjSveql7mb5p7/OBHFv6rE1qaB44DtcnxJzmn5jnGR3LTDDei\n9ugUsjK9yUCy0ZpKREHbCAcvfbt3GKlhqh+oQ4olcBow93vmR1suZk946N/kk7zOw/gm93jMCZcY\nImuueWDeulWujVNJhnMk7QAnRubeEjIj8no+Qm6IDqrTnO3LJU9npzzhYvB0mGZBamC5S/xnMiiz\nirPsksz0LLMd+1nJJpuzs4tbwPipBqC+bpGGDJLxNymQVczjXkZy8ZJl2LEIWzLTinOeHfAvstWb\ncVA0le16r8043SiQeuLTDHIquy+PP5tDC4AYEzMlrV/6pV96XyWm8/Nz/vAP/5CmaciyjO9+97t8\n9rOffd/3+kgC3o1fcomUWXjBocXe0PdOgls0xF4Cnf4Us2Mrw4ipe/2eFPzSQxHt6j+qODAlbpdg\nYsSaKIHPdENJOhvCWMVdXmtOwynPAYb+3iHt0xogps7yOrTQvpf0IOWkagZz9yL2zGXeQsGGI55F\n8Ty7CsfUbUndFHSHjG6f4XyQ6Wbe4H2DNYGqnlE1czZPjghLB39shIa2QYDKUY6ZlmBTalZGk0r6\nMuUaGWK+Pc1OreAJTYM1/egpW3B7SKGbTA5mFsmXNfnRnuPyilMjUkcv8TYPwyMetO9wGi5pMk/p\nhdKnAWXsaWobPg7T+hZPdI4uz1gc7yk+WeNPO0LvaIPnRw9e4Yf+YYLjnKL4xxkHFgkCogFPeNpn\nNORYAku35Wa25m1/n7awtMYN2bMKWOjUHcagPEvnfpUEsoTnu2cVt9w7POX+4Sn545r8nQZXBsxp\ngHUcRFU7m9G6DDXbHAvYsZAee6t6VQk3WPF5HlGEniogjlvGi1n1C4Sl/O7v/i6///u/z2az4Stf\n+Qpf+tKXfqwS0+XlJf/6X/9rvva1r/FzP/dzfOELX+CrX/0qzjk+9alP8Xf+zt953/f7SALexi25\n5FhoOg5y14gBdrDECKG3xN6lElYmqvRupCEpFm3PGPD0pz5HCdqK6FfYRiKtCzdTrB+zgc6lDqf1\n0BvR0iqn4YRLRBVZLp5tou5oqIRRKn1qUD2deWrXTgSeb/dWRFXFUJOzYcVzzngn3udZf05VlVT7\nkvjcw3MvwWQdMYsOVzQYG+i3Bf02J75hifct/DHyqBA4hG4AjGZBYlxzg6cbLB/VCFwzX/W2jVjU\n+9RpwFNsHZA+/mhoU4CZR/JFxfLohrW95NQ8mwS8N3mleZuz9ooNJd7LJFbLeAkqfij7pzClmoLW\n5RxcwTLbcXS0oQwVTZpw/6H7DP+P+zzPOOOGo1QyJ140T4dg2uJ5zhlv8YAtS1oyClvzfHbKm+VL\nZEaeddeZTYPyFBeX0bJkywVPucc7SSp+zzrc8Mr+bR5evo35g4j5HrCO8HMQX4HuHFpv2WVz9k55\n5UUqtydMDuD2TFazv4Dya9sJlEWhRVMs34tYLzLD+53f+Z33/P17KTGdnJzwta99bfj3F7/4Rb74\nxS9+qPf7SALe1iy5Zj3sqHP2qR9jCcESgiMEk4KdE0/RDsnuNKBVk4fqp2njXGEJDimpQKZ1yTsh\ntobQW/pgh36O9si0n6MBb4BvhJ6jbsesqTD7QKygOGqZHR3Y2ZGzqKWE9v1GL90x5OlFO+3TZLTs\nWKSeTjNgsCJiYNT1GV1TSOCPNpnDAL2j9wnRt/PEXRKfPEvf/wSJz+dgX+6ZHx9YFNfDdHSeCPc6\nCVXvBp9G31q2KZ1MaIArmpiinE/H1jDSx3QlJd/cN8ztXh6pb5VT422HyQLBBIwbm+56c6pGn7JE\nNEhpjuOMfM7CiROdp0OEPkv+G3+J1/mEqBOzJKcZBlDKmVZRgRtWA9unpsCZntoU3HA0bIQwshym\nYqMqP6aDCBHrfCz9yfYZq2rHarvj5PE1xTsN5m2kDRCAd8QjRDouBuYVdi54PmMjjYlAjsAZxwMb\nGUtz0pXV3YI+jRrQP7kc/unWx9SyD7n0xhG138CCHb11dD6jD46+Sx8rGMnulL2gVM4AACAASURB\nVIdYv8dDy9otAo1QAxjNNk6QG3JilBxbQ99Zea8JHEQ5vDUlStzR0tPFnmW9Z7XZyVTyCopXG8rl\nnswKa6IjGy5EzQx1TjfdlbVE0aVgajXanqGqGQ3eSMA0qYdJTP04hd1UkocSolC5tNQP6Xs/QErO\n++AeBhYXO07L55zYS9ZcMWc/wIR0kqifHRhu7oDlhiOec8oVa9G4U3knFW8IvCvgmSySuXYA/mhA\nd/REG2lzQ505gr0tzz7tJ1ZD7j1q2mkmGm8FAjMExzd5yCNeZsOSQ5JxAslslZKmr7tlxRXHXHIy\nCALsmXPJyVD+5nc2Ie2xqehExLDmmjOecS8FvHvdU1ZXe5ZPKrI3O3gDuXYqJDt+AsaJnYGxEWKD\nyzpMFon2bj53GyGpMGMQ1ee7AOUx8L3YYAcfU8s+9DogEuECMnYSEMzYsAUk2AUjpijTYYQGP5WD\n0mxPg95lerQJMn8AfAY2Kb6WEA6WvvF0XUab5UmkQMoVzeg001E7R0PEhZ686yZldcBVHbEQ6Eib\n3sOkqa0Ces1klx2zvNv9GGDAyHkNmDpQMcKCIBq5UXJzZyptxmMynZYmGpNZBeyDjuLBgaPja+75\nx5zzhHOeseKGknrIXrR8zWiHiaQGhuuw5nk85aZdU3eFvL8Ci2vGSWlEyKgeTJEsMyeAG6+lvhE9\ntzqVrnn6DFMam5aSoqhTpvZBMTByptNSDZAtGZecsEkCFXWacheIwXiVuMQqXvGsO+NJd8FVOKWO\nBRHDzq4o7Z4jd8OR2zAz+3cNonRzOOEST8d93uYBj7jfPOa8es7J9Q3zt2rKR61Msh8xmvdYYAcm\nmRXZBfgiYBaBxnR418lQA4Wg6DR/DF86UR9z8pEbfRen9yLXx/JQH3Ip9GI/2Px5+uhSD08ns2aE\nmGigU07mVHYnubwPmd4Vsos2DXCdzGLWsM3kCllC3Dn6Kqeb5TRFkehSSzaMNpTqOqZ804gZe1OJ\ntuRjxO57Ohq6ck9js6H0uO0nFt+1+07/3U9udJUpn07aPF0aEkTxPY0pR5y63StvVYOdR6TUC7Cn\nPdkrFfOLG04Wz7jPO7yUZM0LKixxyORkWCE3dMAO8lw3rLmOa67bNZvmiKYpbgOMkzrJ8LU0EJYR\n53syM8o6efrhe7eTcrVM7z+VrRqNpJeDJNUu5V3S5yveNTzoceyZ002ywB5LRZGYM/I3W5Y84Zx3\n2vu8s3+Z6/aY0CfetG/JfE01K+lmnhUjP1Vlm7TcvcfjQQn5ZR5xcXjKydMbFm9XuDeDmG0/SQ89\nT553VSm2AlODc1GobsN3GvRzbvUxG4pho5oqOk+vpRHE/uLWxyXth1xaRshFO6OhoI1SzoY+DSrU\n8m4qHzTF2envNNPR7EaVcptW/lEB3UKefyG/igdLbDxdn9HEPN0Iouo7DhIMjjDumsbQOUeXWdws\nYBqwLmLbSNG1dLHCRYGbBDOFFLybqqXr9sWrBfCIgRt4rTFgQkyTZtFRI0bpaU43Bt0QFIZzFOEo\n4i5ayos9y9Mb1ubqFpXJ0w1N8angpE9doYqCTVjxLJ5y3a7ZVUuqak7fTBguntvDixxR8533uHmH\nz9qJyqGWhqMwwRTiMQ12FQUHylvae6qsvGHFPs45xBkBK+Bhc7v/p0MnwaxpOTr24vZJ1PMmrLlp\nj9hUK+kZRwNZh81bfNaRR4EvSX4p5WOessWSahCKfal7h5e6dzi+umH16EDxw04Uqt9ENuIrxgl2\nxrhhNxLoTAOxFeGC9yrta2ED02OH60qD/FTReVRRmbKiP9bDg48o4DVpCqkB78CMOhS0bUbfemKX\n1BfV2PhuoBuhYT9hJXyEsi6mZXAnvcHQe7qYDSVtRXlLMW4akHrjaPOcImYUXUdme5n6esH1ZbGF\nGOiMiAOMAFH5LHd7LOOgxNGkG7sappPKNklg4+iItYWtgVmPmfXEzkKcHCMd9mqm5YFFgJOebF0J\nlclcsjC7gZmhXqUaiP2QR+jQIums9TM27RG7akVzmNHvCmLtxpI2OW+hNLISzFnAHzcUiz1FPgXX\nTtVk5HbULEZxiONEVLkHY2anwe6GI7Zhya4X+8G52zNzo33hCMqwwzBpxp41N/hUrms5jAWbt7jQ\nCcA9mKEXOWVU6GBnFB/dUFIN7YHz3XOOrzcs3qhxrwWBBL2OBDwdpqnIRYbwajUrj3ceDMYEwyBm\nlE7LbrVDlO98t7QdBV0V6fliVj3sbH/+1kejeByLsaSNCQjSF7TdJODpsOK9MjwNeGbyuMuqcEYC\ngjrda1N9CJp3xTFFfrGhuqXWMsg/GkuV5RxMjo0Rn40ik8ZKsCAGrBGDcF2DwKa5y1oYDWZUyn4K\nR+iHcGAJ0RJqIz1KG6HsRVG3syPkRo/HhL9qlgF70pIdVSyKDUf2ZhBI0AY/MExkVUxA1XZlr/BU\nfcmuERXjfl8Q9142DlVCGcQBEOmnRSqj1zXFfE/hDoMe4G0wdkBZA5qVjKXsaHY0eAVPzJEGleV+\nKdLvth16bEu2twKBQkbUYilL09w+BQTjAlnekIWaLhhC58AK3cyaEf4hw6huwFqu2FDGinOkTXC8\n27J+siN7o4fXIPwxhNcgPpJr17Rg12DWYBbIMZxez7eCnWFarlYp01XglAY8PWfyXUcpr2lJq9fR\ni1of9/A+5GrJ2PVz9mFO0+c0fU7XZPSNJ7RObuRpiXY3s5sGOMXaLRgHGD1QFxCP5GZc5XAO3EP6\nWvMIeQDXD0qP8c7FMu6W2RAAawoOtsTmgB1vhGDlFWwIQ3AblmG42KYw0H5AACoKsBhuwlGUM03b\nYsQ0UXCGV5bYZlJ2TXdtDXYuHY8CsqOWcrFjWW4pnbAxOhz7pGenWDwFSJM+pQ5NdCrpbMBnHc73\nBBvGt54ef/13CSwjZt2Tz2rJvOxt+pRAdcbyUrOYuzaWyZn3Vt9ObnoZOlgbmHt51rl9OujkHXEz\nudntUN6qapxNrQqDOJ+t3Iaz7BmlaWiyghA8xvY427LOrzkyNwM/WnNNtXjMaTjqthx3W+Y3NfZJ\nELOlN6B6E549gZsrKAKUPSwzWJbgdaPSczc5f8HeztZ0cLQb3E9mQ8BT3KfCm5hcX2Pg06rjxayP\nS9oPuSTgifdB22Z0rSd0AjSmdVJy3g14OrTQdWeAwGLyHAfUOfSZ/LcTI8HuAplcLgLkPXgR+dTd\ndFSRnUJVxv5STSGUoDxCJmKSLvZDj03EJEfu5/SzTuEFOott0y14V4V4uhtborx+iwS8tyy8bSWw\nnCE4Q+WuKrVrAZSQ2ZbFcsuy3KbhhHjXioCnKC7MEsxXgdIyMOlu9dqc6/Gxw/mObpQCHo+/Dkky\nZDNZBuyRCKLO3ChqebeHN96c/scGvB1ztiwG75Ndmrq2ZFgTWPgda6455yn3E9j3hMtbwUIBwroi\nDDi6gpqV3RByyzw/UMeCHo+ylpdmzCvHYDf+zGnw7ZbjeoO7DpgnyDT2R1C9AY+ewltXUr0eAfdL\nmE8VZd6jQgnGTq4PP4Cp98yHPqa2SEYntVFEYprpTQP/i1ofB7wPuUK09NEL8DdYYVYEi7C7zeSR\n/mB6Ueinzhl7H9ObL0mD06XXmCFYvDPgIXAObt3hFiIe4OxoexffY3ecAk1rCnGm0tvUdGQRXBCW\nhIkR00cwCWpsDMbG9yhnxxt9FHaXrG4KdtZJ4NztadcF9aslfe4JpQdr5HsqdzX1zigl4JgcjO2H\nG6hNZZFKt2tWNQI96iHTk2GBZFcNuVg/JhGHadkF6bjrRLYAu+hxq4ZyfqDMKkqjEkei7tyg/r2i\nglJRYojsWLCmSOLtqzTUWrJPBk/a35RMWBgIeWLKzDhwzBUXPOE8mRMqz7mdZOjT89vhmbMfpu+e\njgU7GiNZ9uB3kY6EdA03Q56pUlWOHhs6XBsxU8TADro9bFp4lo5XTopzer50sy4Rc/oCQmHovKM1\nYy9uarqklpl6LS3YpVskplskILS0sXXyoie1H+PwPuQKWPpoB/hJjCnYacC7u6bBTpvketPpxEvd\nsFaMrvY2/e4YCXrnwAW4k55iWVMUgvafDhTeq7SdmlVPCdkRM7hUWROwIUiFHCPRQrQigKD/0zW9\n8eRmVPzUiKDXUDjjwMLtaM4KDrMS1jPiqSjHTF5wtDwswSx6TAa4nmBHKpZNQWd680ytIcukuqI9\no8FUKWb00Y3YyLutBeXTzsDOA/miplwcKJ1qunXpRnQD1EQFFHQjODDjfuIQT60fxzJOwcflcMxi\n6gWWSb3mPFG6znk6lMMjXMMleMcoB6ZCWFraqzF6YDT40QxKy1kVhlDetY8dse9Eb3GKCz1AV8O2\nExFm1VHoLETNhlOwo4Q4g76ALje0TtS85boYnU6mAW/kGduhJ0q6fiSjnXIsXvSU9uMe3odawwkw\nMbWhUtpg0l00JaBPVWvvwi50lyyRwDYFJ2t/aR6w614eRwG3ChSrHUW+p3QVmRk9P+9meNOySGXa\nhylmYkBoNhYxmAi2i+mjGnojH3zM6sxkWjblQo5QimZS1g7Zi73ClT1FVlHbGU05p2szuuCELRIT\n2i83xMxgyw7rHTb2RGvSVC8g7lZSHmUsBsTbdDLt6IfvfM1agLuhoOsy+lbMz2+BnvVcZAEKceFy\neYv3rdgWMso5TSeOolaSDRvMAG5mnXIp6ZJpo765M8zRI6iS5mqgnqfzo4ZJ+v+nAU++tZz3PP2d\nBjvNBFXya2rAXSbhgSLp8cl1w1iB3LkesxKOK3gJOPdw6mG+lMEF+kjg8LgQxepDJuKk06xUChgV\naZXrRTdLDegj/KafTGpv4/Fe1Pq4pP2QKwLGiM+ANQFjI9FGwZbZKFneNNjp/9dJa4cEs/eCqWg8\nSVmfXQSydU22rMiKlrxoybOK3NfMjCiaaNCC8cacBrsm4Z9uBTxt6DNyYY3OQUJKVN14U47DiFFO\nSDp4gnWziD+B+vOqPNOcPY6eI3dDZz3VakZVzjgEKbaqWFJHmX1KKeMwvsd5YY1EYxJ42iSOcLj1\nGKVPmxTwugSHsEO36hBm1E1OV3uZFk/1B7X35AMm7zFZK6Y8ZuqjOmVB5EMZKzenHGstWTXgjX7F\ns6G/eZtEH1OJnCUYj5x4DWiiI9IPx1lRjRJ0WxoEG/jjLBdHWbCpjXczlP4a7KJJJaoOb5ZIRXEG\n8yv45AGO97Au4GgO5Qm4C6SnfAacyvPjkaUpc/aupDLFQEqUy1+C9tTZTDcP1fVTO00dTGkp/LMI\neDrd//O4Pprc1EgWZ0yUHpeNYIPgnwTCJgRDkH9rQLtrXNNPeko2ggOb9fi8w+Y9Lu/JZg35Yk8+\nq8hcKyY8TAn+YznwkzO8cUe9rTUm/783TjonUeSnBtK/Gf9exU1H3FsPiXlgCdQUww2/Z47QrWrm\nZk9hJNM4uBmHcjZi0bTsizOaPqcNYszsXD4EvC54ulSSRi1L0/H0dGSmJbMt3jVC6E99x5qSOhY0\nfUHX5oTaix9IxYgfUxkoGzG+x/ggEZ8RVqEtAeUq+0FeKA7hVhVSrjhO32megn85ZF8KINbzpaRA\n9YHQHqSUyiHR7m8DdOWYNsNnmn6uaUC9y4l2k3OujnPKpWl9Rp3n+EWHPwkyHHsIRQ3nEY5zKBfy\nMA/lv/EAuA/hzNAfW5qlZ1+U7Owi4e1kkzVEMsTbeJkGThVCf1MxhI6MKn1vO3yu8Tr+uIc3ro+2\npAURPjSBYM2Y5RGkvHWMfaNpw1yzvAFwGwfsWbnYM19smGd75m5P6SuyrMa7dgImHelfevEC77pQ\nlPI1ZQFktEPgG/ZR05PZDu8Cwcq+3FlP48QIqE4UoHGXFTjooMSScFQHZjzhgmecsWeOJSQFtpsE\nbn06yBMp2f0yGUzecMTezjmYGZ3xOLPERsEE9r2j7xx944iNl8CVRFOtCVgfcHmHm7W4ssXnLd73\ndNHTRU/b5WmCbsUNTnUHJwGPkLjQRr5dFz21KYY+pMo9aS9KrwHlMCvcQtRNFrckotqUuVp6yrRR\n5TSDuvRDHvESb3OcFKmzITjpgCnc2symkI+pPsxIC5z6Q7zbPUyzdOWkbP2CzLQs1hXzusJXAQLY\nEopzyK7Bq43oS0iwexl4BboLy/64ZDefsfELNixRm4EeoRlqRpml7NumzXLKspCfIjSrF/oUh/dx\nD0/WR/7JhywvSpaXfilTSF3RStDTFbgNXYkMAS9b1SxObjj1zzjnGQuzxacenQYqndxp0/o2QXsa\n8Hxqcvt3ZXhTcVBnU3gMYG2LNZHG5VRu7I6pcM+U1XC3lN7EFY+5x9NwTh1KAbfaDXO34yXe5tO8\nNsATLjnhKecs2VIivcjcyI2hsvMu9kPAa5qc9pATdjlxlwvHeMsYsBY9Zt3igkyGc1tP5PWTJNVE\ncWYIeHeZAsHQd57W5MO5nZmKgzlQG8EaahY2qg2P5etlPE5T2TFABgTbWKQhgo40znnCfR7zILzF\ny/EtTsNzVDU4Mw25EYl4ayQICIBc8/LR8lzP+d2NcHqedXVkQ8bZkNOajBu/FIjTkSGLHSa2GB8x\na8gegtkyQIfiWXrcM4T7lvq44Ga24LpYDcfhNhZz9PLQviREuskASK/pd/teTHvGL2593MP7KZYx\nEWuDTDGtwQT5KdmdGZ6jMwyT/m1MJAZD6CyxdYTGQWMHEYGuzDh0MzLb8pJ9i3OeAcIYuOGIG46G\nXTEO4cagBBxPRkc3lDkZosgxLc/0pp3yHTs8lZ2R5T0xGiqXT5BnKkBgh1JJw2nE8IQL3uE+b4eX\neDM85On2guZ6Joq5pxVnR88wxMHRas01ZdKxM8Th5tOgDAzv0SGSUrGT7C7urQS7J4iPq8JZ1oYY\nPAHojZhVO9fjXQdFQ4yGrsnpdzmh9TIJbxiEA2LniHvoOkvwGb0raOycxi8wM0OYmYEFodmdCJ0e\nJZqhDCiec8YhzmhjThsk2FkXyEw7wEGOkwzTyzziFd7g5fYRDzaPOT1cQbpGXBFwRU/0kegivTP0\nxtMZPwl2fji307LPECZZok7kSRnpjOm0ec+cZ0ktuS8y+iPHwuwpy4rsrMNuEPP0NNzpji3tsaNa\nFRyWMzbFkmu34obl0Mccedi3TXlAS+0wzG/1CtbSdfq3MIX6/dnUw/tpjbifPXvGN7/5Ta6vrzHG\n8Gu/9mv8+q//+vu+30ca8IyN2BCJNhCtwUQVoo7jf09DDWNDGnJEQhA5+L7OMNEk6XeggnaZcWjn\nZL7jgXmbh+aNoSluiEMDXAGagkIfk38Ndtpgb2lp087akg03wbQhrwHH2gA5Q/k7arf5IeBNRT8V\nQqAB763wgEftyzy9uU/zaMaaG9b5FbujxRDwXMpHZhxQ4r8qimgw1mmdCmLGKHSpUDspSTXgvUmy\nrARqA9YRPfSZx5Qd1vY424m1oOsxbSSsHDR+zOjUQKezhJ0lVBJwGwP4iMt7gjF0M8uSLac8R2l1\natF5zXoIeM/iGXUs6HpP36celmnkWiAw58Caay54wiu8wWf4Ex42b3H/8hnrq80w4TcrYAVdaWhz\n6Tu1ftq3ux3wpm0WHXSMAU9LYpmY6lBJs9KnnIviS+EIuaGfgzltsVWHUc+VNNzpVo7DKuPGL6Qd\nYdaDRalujnrlaY95Os3XEZhygBQeNf0OU0DKz0IA9EX28H5aI27nHL/xG7/Bpz71Kaqq4qtf/Sq/\n8Au/wMOHD3/i+33kJa0uY8CYkLK8lAG6gLUB63qcD4MiRozijdrR0zSW1mRyUd1Abz1NO6M6WbA/\nWbCfSYmgu7E2+bcsh96cTdNC3SGnJY1AGsYMT4ObBsyO0dQ6GnMrANbkw001FRHQHVu5nlccS2+u\nn1E1Jc0hp994WpuxaxdcccwzznjMPZYJsCFKu82gtjudKrZkqbeYAmvCCgJjoNKpouLo7EiYNzbi\nbKCwNQuzw1s5OmHuac5K6rJkX805NDN65+itH9sO6iKXRBoCUJ3M4QQerTqao5w8bzA+0pCx7Vbs\nugVVM6fK5mxuDF2vWZfB5T0sSD4akdw2yaj7hpP+irPwnJPtNeWTGvdOHOFMa2APbh5hBq4QWcTe\n9+JDay2dlRI3WEswdjh/eg3YIW8KQ4bnaVHBAx18DNeSEfxc5QoOZsbcHPA+4NswlCn7smDvC67t\nURKcF+H5DUsUcq6m8OMQJaY+sxmecxcnqhNbgWSO1zBwqxp5EetF9vB+WiPu4+Pjwbu2LEsePnzI\n8+fP//wEPEzE2oiaF2nAc67HZx3eC/ZNT2KIliYG+kNBC3KTXUNfZ4Rrz6FZspkdcTOTEramGACt\n04Cnk78w9EzCcLHogEIDY4vH44cdUwKb9IemZO/RXzYfmuKkZ03byDJRM4MdY90XtE1Of/DEnaF3\njl0355JjnnDBWzzgHo6CZujtqG8GMHxm/Z2jw5nuFkRkWDmj/H2GXAlmhAs531MYkUBamB0Lt8PM\nocszdicLnvbnxP6Uuirpa8SycZ9KXTXl3kJsDPXxnPa4oH5Y8sSck69qciuKJoe2pD7M6LcF1XzO\n9u1SsH4JoOsWMjXuswrrxENXA95xuOKsec56e0P2uBdFYYUw7YED2GUi6s8CsYjEAvrcEDIjZa5L\nvT0NeozwjTFDGjmNd6WtekRX78Bs4Lzu7ZydWTC3e7K8JYudviB7M2dvRGNQBk9iw71hNbznmutJ\n4FXHO9Hpm4Li7wY8Ba8rOVJf70UGO/jZw1I+iBH3dD1+/JjXX3+dz33uc+/72h9RwItDP87YOPBQ\nwx06mUnPsybgJg5jgwqGs1jXC7fLpkFHsMQWbtojftS9yr4vwUATc561FzxrzqlCSRVKemuJzpD5\nli7L6L1NFdEYTKYYLeU/TM2QmVz4Wgqr2ocoOkvAu41514tQWQaJN2qWBGuws554mqhhpeyoIsS5\nHly3puRxtYZUbJY2sDOTPpEVIHCXZ4TcEwsnrAwNeJ6kdAL4iPXSuyttzZw9x0lDr3Q11gUOccY8\n7shCwyZbsclXtMxoO0M0yRj9xiQpfENYO8La0TeGKuT445Zs3mIstHVGV8kmFS4s/Q+TWVMmnymc\nRLrc081kc7JupNyVsaYMDXnoBAmjcCU1cTKIttygthOJHZg2EnNwHoIzBN8TnJWHMYIY0J8p7Cgo\n+bawgTjW3bACzNDXq0xJZWTopBxlXfs0Zd9MXNAUXqSb4RTvlyc1ap0Na2ulThqOOuioEhLAMOoa\n/qyC3ocpaX9WRty6qqrin//zf86Xv/xlyrJ839f6SALeMICwSWHEGkJPUjuW0srGQIxjeTGlWw3l\npPFYm0QACgszM0xrr9wxfxg+xxvdy2k44thu12w3a/rW0jcOMkMsIVvUtAtP8KPasI4bNOBp9hZS\nPgi629phhiZwgjJJGy2S6q4feobjiGQMltqDOzDjxq2wRcSfNITcYW2DW8mksSUbpJFUBglI8IwN\npzxHGQtS0oZU3uY0tiHLa7qZp5sZ+iobp6vKQ06CA6YA5wPetRSmZp6GBBc8Zc2V8E3JKU2Ftx1P\ni3Oc79n20Hc5/Y2TTatC+oRvMTDnYysDpu7EE5YFeAitJdaGeGOhMKIftyMNUiD2hm7paY8ygrVD\nUPB0uNiLkowBowwHnSDX6XV0TYQobCdCm9ZBdJGYQfQ9wQd6D72X7DraMYuqEuv4Kpn93LBiywIx\nATpChA/KAShdUwwG59OAN/poLIdAp3+j17lecyOFTr+3XPcjxWzUBqwTNk/6u9WQFEzvnxe1PkxJ\n+7My4gbo+56vf/3r/Mqv/Ap/42/8jQ/02h/ok+/3e/7Vv/pX/OhHP8IYw1e+8hUePHjwvpOUH7ck\ncwtYa9Kw4g7BPrwbp6d9qjFo6IsldoaPkKeZVIRDPae9vo9rOxkm9IbmckZzOR+ltecRVtAf+4EZ\n4V1P5kasl6Do8lsZnJSv3STQFUNWp161CpydqnVEzAD1GIjd0Q5/vzVLogM/b4llR25r8kKAydPh\nxIaVQFESnUg5n6pEos126T/WQkTPMvrSE2eOvg4jaJvEainjkOEZFxLUphsgESJ4ueOIa3rj2HDE\nzixEysh74szT9gX1AsLcEwszsmMakt+IJRYQG0dQqIaqVd8g+LR3kKHKUv42rgyxlWvi7uRS9fRQ\nfrWqCOtrTumJaUpqFAigGEILdBEyCHnERqSYtErokqx5n477FWuuUmZ2nbyVLzmhIWfGgUNyQ9Ms\nTOW39LpXGbD9BHozhcTAbXc0LVczkblO15Z8lmuOuO6Ouerk/XGBzLZgDM5KgNNX0VbQi1gvGpby\n0xhxg0x4X3nllQ80ndX1gQLev/k3/4Zf/MVf5B/9o39E3/fUdc2///f//n0nKT9uGWTaGhmzvIFx\nERB590SztTYMwRBG0PI4PxsDI4ZB3Th0jm5b0rskj95Af+Wlv6TKx2vgHPra0ZgSTCSbtcLOGEhL\ncvkqAd8hEktu2PWVZTkGOwXN6sU9Tn0zAfMGTx9cwriNDegm5vTRpb5lLzpyrh4C21QXLeDIaG5J\nS+mFqAGhoJbwYC3BJ95tmdHMOxFHNXaU3FIRT1VMHs7UqKxyYIaCeIEUBLdUFHSFpzUemkBTz+nq\nXI6xqtr0jAFOTWxUALNBsjENjFsGZzZjRJ4qy1oR5Jz0QFE8epqAGr0P78r/93ce+h3VZW3oG0tX\nxDmJgfJSLpWPc25YpUAng4bnCSD+hAsaChZsqdkOwwY9dtOWxlSKXZ+nm5MGc83O9HzD6B73nNNk\nHC6g8+f1GVfbM5qY4cqWvDiIU5yVrFI3RjfpQ/5p14sMeD+tEff3v/99/uN//I984hOf4B//43+M\nMYZ/8A/+AX/9r//1n/h+7xvw9vs93//+9/nt3/5tQMbB8/n8A01SftySABYwWKyRktY6URuJwRKM\n3FI2YfWsGS/yqWjklJs6rAbxrWjlBkdNUyoki9gw/u7MwAFisLRZTsygEsoknwAAIABJREFUMjV5\nUZOZFPBMOwQ8sfCLA/5Je2+HQSxolnZuAYM2cXxUUfqGTchpu5y+T967cQo0BCzkviHPakpXDVkb\ncCvgdXgyxCBaDW609NYbp6QGUj/KObrM05Q5pi9S0LJjQBBncCDhh+OEe2ryJDjQDmVSQFzARDop\np8s8XeYSGDmp4BhDXBri1sDOQGZGqqCW1FMzpn5ybpKasvUiPpplbZoW3w56wUSClSCF0hI7xsxu\nqqt4V1lYqzwt6xMrziSiDzBM3NXz5IajycDhhH2c8zReUMeC2uQEI9f0iJGLw+RUB12jBLscSz2/\n08xVszw1ONol5ZfnnHIZT7niWB7NMdebE9ro8bGmtI7ctsz9HkMc1Gpua3r96daLDHg/rRH3z//8\nz/Pv/t2/+9Dv974B7/Hjx6xWK771rW/x+uuv85nPfIYvf/nLH3qScnfJpCswjVfGRLzriblcNHle\nk7ua3Ix4JOUw6gwtRhlU0CY4xAbJ4hT/NH3s0u+nDW5VMvCW4DyVmWGKXpy2slFzoiEfjJr1Qlag\nqAa8Kv2sU3ZXU1C1JVVb0h4Kuiqnrz197QjdmAUMU9IyQBlwZSB6g3HTAYc8V8uhA2Jes2PBU865\n4nh4T8WQLdgNN16PpTWeOiuoZzV9Z+iaZFgz4SrHztC3nrbNqWzJ3s0TYb0Z3l+VTnRSPOdAj8cA\nRdEyO6k4ZAvqo5zm5ZxuU9Dd5GOGpcE9MJaeHVJSnyE9v6RhaNeBbNZS+orc3paHjzYSnSH45MKp\nJXSbXkuDnsqHFemnPhdGiE4mU+Hgk9e5GbnVUl7mE5+NxRD8agou2xP2zRyTReb5gWCmJerthvt0\naKV9abkfbgPSNThWlCjUqaLkOafSQwxHbPsldVsKCD8k69Eso/M+IQNimtjHFxqk6oEL/edvvW/A\nCyHw2muv8Zu/+Zt89rOf5d/+23/Lt7/97Xc97ydNUt71XEYYpzXIjZ2gEDHp4TnTS6BLk0Zvusnf\nM/TDhPKUvB3U9+EZYzZ3YNQo08CnS/s9GAEMF466KOmWBj/ryH0zSBwpsV+NmmOayo2GM2MZq45b\nVSw5tHP2+znhKiNeZ7C18pm0j606djNg1cO6E95vwa1MRm8+hQRoxnnNmsfc45r1cFzFqKZjyZac\nBkOgRWSH6qyiNgV14+nrgqi85JTxxE6oYU2bU2US8DTLnEpkKURcp8TK+Szzilm+Z3u8ZBcX7Nol\n1ZWhu8rHDUcHC8rU0OCXIwEPRHHkHMw6ks8bSi8qIbeA3waCg+AN0SfudZx8H9VKvBv09Hca6KcB\nzyVpxnQ5S8CTMlQNpxTWtGE1BLzNfkU5qzjJLu/05N6dWU3hSTpMmGZ3U/aEtkI0s9fMbtMfsWuX\n1F0hHhzBEBpP53O6QpEBpHaIHRg4L2L9haaWnZ6ecnZ2xmc/+1kAvvCFL/Dtb3/7A09Svve97/G9\n731v+PeXvvQl/iYz/lcgGN3qU6YztON0ByxvDSx0Dfgjn1EvPa2PUBo4ifCykWB3YAws71XOKEtA\n8WgnBo4tZpljrKfAUbAchgF/hTU9vzz0sKb9mS4m82jtz/WOEISwHxpH3zuitzA3Yi40ZzTAUS5r\nDswczCw+h8J6SmbMOWbOy4Pyrh4bvRm0v6QcXw1Af40LZvzV4WbRSfDBlVR2RrfM6LxMK2mRlMaA\ncQ6TzbAxJ48z8ng6SIjnZmyAT0n5ek56LMFMcGIGuryiOYk0q5a293S96OqJWZNJfTYDHfzP50jg\nN8gQZRbJ5pbZYsbcnHOMZ80Faz4n3hX2hoW5wa1rzGci3IsD/m4YYhTyiHl6+AQ3sYlwFQFrRLDV\nQLRG/M6NIcdxxIyMGQsWXLDgM0nwXeXmP0/J/1ZCn3Us3ZKVfZUFp7cmtJrFSStgGtbuUtrGTF5Z\nQJph6ght6BnbGXs/p19mhCxVOt5ifU6Rn1BScsSaI/7SgGqAFwMT+Qsd8I6Pjzk7O+PRo0e8/PLL\nfPe73+WVV17hlVde+UCTlPc6qP8nO36XZ5NibcwO9TQ7Rh0LvWj02erytQ1LrqoTtpcWfuDgNSfW\neH8CvI1wRXeMm6z2efQm1xviAvj/2HuTWFuyu9zzt5podnvOPbfJtNM2CTbWM1WYJzBNURLgkhkg\nWTYqlZlQEwQTjFDJEhITA7ZEp5LAFkhMEFYhlQQzPEAUiOLZEp7Q6BmVlUjgeu/xcJOZtznd7qJb\na9VgrX+s2Ocm5bzJLacL5ZK2TrfPjtg7Ir74N9/3/b8FeDvw7zTqnbB4pmP5zIaljhMVfgbF/8aX\nU1/EHp2Ebajio69oG01/KPA7i98WhCtFuFLwUEWaxnXaJ5+2PSebQd4D7imqewOLu1tOi3Pu8JC7\nPOKMR9zm0fh5HdId/5oTzjnjwCyVsS94C1/mGd7Nf+T/5JJTzjnjPnd5yB3O1Rnn3GZ3WLO/KAjS\nJOjIN4GVRq9gduqY3WpYmyvW6iqZNu3HBD5e0FnULmacOt1RPFF5cGHPONdnnIczzv0ttuGEw25F\nv63y+MIeKOHjX03HaRVHTC7shmfql3mWF3krX+KtfIm38c+8lS/xJl7EqZc4dRvqXY85d9kUQaK6\nJbACv4LBRjfh3hb0uhj1KV6leiORhxdS3VOaBI+4zcvc42WeieYO3OERt7nklP+Zgk/pr4CCuyqa\nGYjN/JrrkYsna0oSnoLd9Ks0u6QuvEvp8/XoD7Pmsj/lqjH0O4XfmgjcpcfUA0u1Z1ldc4sLzrhI\nEbnjf+I7XxNN5Ob6N28P9RM/8RP89m//NsMw8Mwzz/DhD38Y7/0rdlJe7dIcD7tRKof62cciKwcK\nulT6iUNmFIFBW6qyoa1rnCnx3sS7+yVw4eDCx1RWpaq2zE2VFNek72siCDUx4gjRnz1K2BL0Oix7\nFpFG4ks6V9G7kn6o6F1B7wuGztIfCtzeEnYWNgYu0v7cJ1IurogXpCOC3TLtwwCUCmYQVhrfGwZf\n0KuSVuUxfXEFOqqRDyUdvmViZolp6HSalSE2fxQhDgWKL5OJujJYxsRP2StFV1RQBlQZCFbRmZK9\nnjFTMc6QapbU8oQ9VtKNsraAYmU2LM01JQ3xklaEoAkm4AuDL1PzZKbhto++hnOHWvWUs5baSuVs\nlxyH08AhlSLdwlAsHENwMAd1ImmuwtUGNzP0taWrDW1p6XVJp0tEopUjreOoa8COKeRV0rse0uB4\nj8HgscpF+R2DtBHGG8+KzZEeFvIMj6mETdJaiYx3LIhUxniMxzGNYc4uzNmHBd1Q47oimjZIUdQr\ngjN0XcX+sEDpwGAKStVhdX+znPia1795e6jnn3+eX/u1X3vs96/USXk1a0rCPf6d+JBloCsTq76k\nHTtdYyFWW5qqpp3VdDbVt5KmlmsPuzb+bCsoJnclGbaiicBzc4qUIY5hVIGpnGfPnD4UHIaappsx\nNBXDIRaNfa8JncI3mnBQsNOJe0YcavCACHgXxCjEMUYfYyE/qR/CXjH0lt4V0WZKRbDLpgMOkcIV\n9OMc1njvv6ZOxNP/1yVp/ZDe/z59LlJlsOCKksYYwkwzzCz7YkZpeyqT5zycYVNEHi/uRZomK4OB\nDC5N+NqggJ4SZy3DwuAtdGVJqC3BKZhbuB2lZLoaYrOiapkZAbwIejXNGPE7dBx6s1SoUqGGgBrA\nFXEYTmsrWlPTmIpGV7S6olPxzJr6Hvqjr1k5IzPLojHparRjmnbD58S5uydcp97tOWecs2Q7vlrW\nbPixOytgKDcsyRggRlF7FmPdtkkqjoOfs3dzur7Gtal+JwolH2+U3aHGec1QWtqiprA9heqeIuD9\nG4/wnvZ6JcDTPK6oKJJ4v0r6BZM6ptIlGpRlb+dUZYsrqly0lmaEFK5THWfs0KnJ32riXIE1sPaY\nlcMse6qypVJC+lW4YGLa6qoonN/P8bsSt6uiNYikyRIt7YmAd0UEuA0xipTHdMC42KTP4374vY7c\nwCGC3UHPxrReLhKJjqfOKwIwYlw69UqbplBjZC1mqkIH6dLBSPvvrQFtaD24oOmKKnLidLwJHdQi\n8gWLA3O1R5x5JRKbjcAUCS6XnMZU2DRUuqHXaf5rEaei2XrG7GSPUp6i6CmLjqWNZQXRFJxwNYJe\n1LxoOl2gqoArEq8wKHpr6WzBQc3S5LM8kLKffCY3dak3na7l/zYpft6zOHJgFlrJMQVdWlnNY1Cq\nyOaiJR11aKh9iw2Og6456BqZHUzq1LaJA7kPcw79nKab0x8qfJMG1nsY0SwYXADnNL4z9LaiKHts\nOUQ1zVNYbwDeEy6J5qbr2NJmGu25UTspcxeEud5TUKtIV2jNkLtvFig1zKsIJGsdD7aAnnQLS+Lv\n3ww8D/qtnupNe2Z3diyrDUu1TRFeBIrOl7R9Tb+rcVc1YaszYVaGB01lTTtiii3DwSFTJYSLJvUm\nGOkYYafpm4K2ryLY2ax2mF44uQIaEFPNeDNYjSahMgjneEjNDctvPXkIP61N+27AB8swKJwu0d7T\nBU+jHQfTMj85sD6J6gtNnpEhF35UgXQpigljB9MoF0nVVY8vItG8sopTfZEcpDsK3XOqLzlVl6xH\nuu8FJ1ynIdgR+DtVMegCpUJ0PkHT6mrUm4rMTwBPyEbD13j0iQfXHrky16MF+1QZDcIPzcUY4UrK\n5yKfzYrN+H3tW+Zdg/WObTFnW8Yq6QVnY3e1Sdve+SWHZkG7XeAPBg4T4riaHMNE03JBE0KBKyt0\n7Z8a4LXdqzQP+AYcffE6R3jq6Hc3L+KbfKWKOMhEEegoR36e1QPa+tTpJB7YzsSup3C7TojRXEkE\nO9FrLoFnA3wT6Occ9d09q9PLcf5ovMvX+KBiitmVDIcKvylz5CaMfonausk2xChTpq0JPWJaPxPN\nZ3IY8XtFaCx9V9IWNU3S9MoFJJHFNG0VJQYwqiIuOB0vlpaKIRT4m4A3ITyPKb4n8hpT6h9IJG5C\n6uhGKlFfljSz2Si3i5FO9nHLFJLjub+oGJnWpolyr3Q+1FScFeeTemAXXVG4ZJ2iuxOuWLIZwd9j\naJVNw6tl4oUdaUL7JMU6TD8HsoN1j8zcKEYZYPw6HdFZjFQjeXdSx4qvkyPCaYxX0Y6d7JugV4b4\n/mrXsehbrPMUOrKvS7p0eqT6XYj1u/0wp93PGC7rTNYeSzByIqhRNhh6g+vj+Mcxen8Kyw2vEjbe\nALx/eU3vktKKlwivH3u18nM+OUU56gsdwe4O8E3EjidEQLtHnA4lEZ6AkVBCbgV4zqPv9cwW0VFY\n/IoluvNB0/cFfVvgW51nkAqgSZQnkZvw/+R3Om1/GumJumCqEOiAVhNaC51B1TnSlfL6VH4k9JjR\ndTnZuzfUXHMygt9UfuZl0Dkcg5268fXmSu9BmTgcqZ7tqRd7ZIxhdgcZUMRuusckdcLpSJYWR5A8\n+S1e5HOW3OP+BDLa0eFZOsRinCBAMtWc5glkxciZE1cTAcAY4UWg60ZYzfI8ubHcrOtJtW1qFyZE\n7GvWyHCgkkzdgZz+PZap+JbKd1R9j+kdwWn6UvZ7noaQR71t6yOBvd3XDFc28kxvUqvkGEpZR865\nqYrnKS03vJHSPtGS6GSa1E5TA0d0uhXQGyZ3Xinu9hT0IZ1aymbAu00EPJ/E8LeIg1Pu8jjgSTow\nB+549K2e2XzHCVeID1oXqhgRTQAvdDqnrpKyTsFNUkIh2Ep0JwT1Kcj0jLKmMeJrdZwO1lu0n5JS\n89eRfE0Wmo8RFCTAW48XsbioDMEmc4bpAQlJSJp+1kRDBjURmsIowNe1p1g31KstlTpQq6wqnrqD\ndKmSJVZIU8CTzqXUvyJg3uYuD5izH19LXlfqgnP2Y5ooN7zRlmn8L/l5CniZFC6fSSsMwxC3LnSQ\nKaDJGtJ55sL01hONHwTwCnoKNaVR+fSxZbATsK9DSzW0FIPD9DB4Q+/LEaTFAbohShKbbka7r/AC\neFIaqdP3xcT8VM7r6Y1sPPH+9esNwHsNa0qylK+i3RTH3ukdt6McU6WeIroXhyWNn9G7Am9MTGXv\nAQWowqOWA7P1ntNbVyxXW6yNqe+mW3HdreNds6/whULNB1ThaXScjRr3CfZuwaZf0VMybD1ha2Gv\nI2iKjE24X+LAATnFleaIIQJyTwTYw+QxBcmU1kagDOiQGzkSi8ySlEvSKbnYJRqRlH+Xuny5dqdQ\nyVtQWQdl9Nuj0jlKhXhWVMAswAJU7dC1Q5cDpnCUZcus2rNQW2rVjHHQNOoKqNRZnKdUMErRaprx\nORDGFHiRhhHd4SEnXCXQO1DRYelTA6QfgXvqSDJVuhxDr0xyzSqYZhI/js42YcHOLWiGGf1QMXib\nxn760YR2zDy8TfbzljBouqrmqj/jYOYoG6LuV08nnT1entH46MxtC6I7fkHjZ7xU3uPlxJfcshzf\n6zjtTk2OjaSxQrAuA1Q+ugZNXa0rFR1wisDTyjGH/g3Ae6L1SvWn7DeXZ7fmlDaqBaQmNDpYhDmN\nq+ldNO9knt7RKaiFw9zqWC6veG72X3m2fJEqSdW+Ep7jK+E5LptT3GHF4C26dKjC05qKS07HFsph\nmLFvFvSmoN8q2NrM5t+SOH/p4YkAclO2VE4eoh9t0v8X5NcTm6Q0BlGFbNU9bQbMaMYLXWqMYigQ\nNa3RhmjHYkx9p1OwtHJo66Aa4gVRq8hBnHavKyJAz0HNHbpuKeuWsuyoi4a52rNUEaREfieddwHY\nJhFUJKqKdbomPSdeNPKe1myoabjLA27zKA4wStGcnAsFAy3VeO68EuBJV3Vq7iXPaY+iv3o0z7z2\nazbDikO7oN/PYgRTOHTRR8fiKvvZDYOl60pcWxA6S7cquNydocsOM3eU9rh+OU2Mp6QXpwylLXFG\nsy/nbFjzsnqGl7nHI26zZTnWG8doU5HJ8hK9yXlVBFSZbmLxohpnECvjY437KQGed98wlbAnXq8b\n4JlE9xA+2SjSdgXDYAlOo0KI5M7UsTPaYfUQ62mhoHUVh27O0FZxlGABFB7mAbPsqE/3LOYblvaa\nld2MdaMZe0parBtQXSD4yKFzveLQKpxNg2+Com9Kun2FXxnCI2CTpGtXRCXHQyLH7gHQO7Au3m1n\nBhY21hRvE2uJIikTsBRdZ0GuJ4qu1EXzS7GPOu4eFslEMl6sl+6UjV/iVbysCjPEubiqHOt9EPXJ\ncbZphysa3FwzeIcLBcGY6GHnQK88eukxc4eZO2zVYouWsmipio7KNOPchZy+lmxY4TDIcKEmGSlM\noxQhlgvlURodVbIxOuFqjPLi7A43RnOiQpCUUgAtR2/1Y7ZcUzPNaWQoaf6BGYd+zmG/pNnMcFcl\nvjewdOhFNNBTNlmXQbTYcgbXG0JjCbVm2JWoVtPpmraqaEwEU5knOzV+FeAbVByyHpQeu8cd4qKS\nm1KaOMulKHqKqqNfRLrJWJgrA1QBVTl02ccbd3ITByAotHbRE3K0uP5XrjdS2idbufAudZLcqOiG\niuZQM3RFtOsBjO0xdhhnW4SgGJxhGAq6tmJoS4K3yegxgHUU845FvWNR7KhVM9Z9IAOuCgGcxneW\n0EfXZacqWhXGbqVvNH6vo1rjPplbd0UGuhfTY+fAN9H15JkZvNlGUHuG2DQ5Iaa5G2JkWJKLzgJ6\nMgUsRMBzPs/DFT8+jRtlZRf+FufdGZthBSqgNNRFgyuzD9vUhCBGTJ5QRjeW1gy0tmZYFHAa6SFm\n1lPMesqqpSo7StNS6I5SdZQ6U06mJqQN9ejYEg0LwhiBDQms4mc/1TIEREZYpqhoxYbbPOKMc9ZE\nB54rTtiwGpsLoucQh+k2fTbTqG7acc3dWHv0O3lu19d02xnDeUV4mJx3BkMwimAHQqVhOgjJK4IY\nVgzAThNUgasKhlXWN0/lkDePg8NQEccFyA0hDti+2Y6Ldvulip93GKJcD5dcDkyAwqGqAVMO2HIY\nh18J8JkEmk9tNW9EeE+0pGgdU6zo6qBCiJFbV9BvKvp9GUXlAXRVoupoimmKCHjeaVxv8J3FdzYe\nfKce6zZ6ZWhVPaZ3BNj6JY2v6ZsSvzOwNYRGEXqNnyoQRIVwIN4cXySmm9fp8ZCoorggAuBOxX2e\nqwhu0qyYE+uLK46cSUZmjsyXkP1eEd070PS+4NDP2OolVg8cUmS380seudtc9rfYNCfshznKxFTV\nmGEEGEmJxdLK0uOVSc7OHSUVpe4YakvwUQFQFB1F2VObJtoyJfrPNEaaOkLbdCwl3RQlgYBL9i7M\nRXOBgZt/y/WubGkuN0Oxzp+mpzdNVqfbPOriI+zOV9KgxAhOmTjDFh/QhccULqoUdJcm6qXnFApV\nQRgs2ipsNUQHb+NxytBQYRLpTd7NzdRWyOEah8jHhvFzmHbm42ySyrRUZYNbGAZtCIONkZYOYOJ1\nUZQDtugxxkWzVBXlhEbF13lq6+mZJ3/d1+sGeFFQHTCU9HiaUMcUrjGEKwOXJto9OfALhVoa+iLg\nSk8cLA1h0PheRba5yGucgmAYipJDN+fKxBOgMXnAx8P+Do/a2xyulwwPizhwplGxaTBVHog774HY\n6f0KmVC8IQPdIT0XDUUFVYDSjpPAjgjJ4twicyTsZJsCeCkFdkbTDDWh9fgykmk1Hh0cTT9j08a6\nU9fWeF+gy56gQ2y6pg5oHugyjE2FgKLCMqOgN5ahKkbyr8ZjdRz8U2mZ1JHZalOd83GS3TM1rhTA\ny4aXoyF7gjMzxjuKkJouUb63YZW6sDEyvGbFJs1/kGL+zdR1aqopj9EcIN39snY1/rVIMWNXNvSr\nPUF7+rIAryjWHeW6oyoO1MUBpVN9Ulms6RmKHldZTAUzvY3llqrFmxjtiiJ8JFqPHXbPVA1jEviJ\n316XqDxyncgxrOioVRujTaOi48xgU1TvMTZ6OFozYPUwapmntcSntp4y4P3Jn/wJf/EXfwHwLw7U\nfuGFF/j93/99nHOs12t+6Zd+6TVt63UBvFhHO6T6TBwWvQ/zZN6oomfcuckuGiea4CAU4Asy121K\n9pXgoY9CdKcKWj1DBQilYWdT/SLA1f6Eze6E4aIkPCrgUh/z5kQm1pEB798RAe9AprUI8bhJ+2RM\n1OzWZAqMIpORI8Zn7t0cWISkDkndNVKEWIAfNN2hwqEY6oKmnhO81BYL2kON6wtwOpovFLFOJyf7\nVOpkE1jFpTIYpGldkP33polgnpPhRtDMxAx3BCCinpF63TF7cErziPZHAgEQaTQy4/WSUxxxlnBA\njc2FTfKgayaNCYFhieBy9z9HVxLZTc0phCbiE4czaDBFR1dXhAD1rKWqm0i5UYfx/BpMisaKgqE2\nlMqyqq+iG7OKtcnOV7Q+dc61JWghZWfmn2i0o8FCdjeO0elxvXNsWOk22ldZFU0+EwFYa5/mN/sI\ndpMbXTaP+MaM8L70pS/xH/7Df+DXf/3XMcbwq7/6q3zXd30XzzzzzPic/X7P7/3e7/HRj36Us7Mz\nrq+vX/P2XhfAK+k44SrRDBIjXs2xJnGX+hCBRcBE+EbCWRPL9o7j6VsqPa8Gv7f0u5owN7hZyb4Y\n0lWgaK4r3KYkPDTRtkmitIYMdKKJFcDbEC2nmhu/F8896ZjNiLrcNAUM4dq1RKCETP9Ixp9qMaAW\njtAaQhvNHMW9JZQWVyq6yuLKKroSO4V3JgrHjYLao6uBqt4zn+1Ymi0lZiTr3vRlg2M6EOS6pnDG\n4jyPPN1ewO0YQiT/l3VMMcq/DSPkTdW8Eul1qBHALjlF48eivzQ/DsliXRxLpEOdi/z5PeRaWY5q\njiMc6X67FDk1LMyOgQKnInM3Cu77ZPHfjQAqFKlBWdCKFSXP8PLIJugpOPRzdocVKgTcrCDUjKod\nIUYbSqLUzo3RYE+cY+zIOt0YpeWbj9xElPYom2gvKsROufJHYHdT8fLUVv+1n/Jq11e+8hXe8Y53\nUBRRJfSud72Lv/qrv+IDH/jA+JzPfe5zfO/3fi9nZ2cArNfr17y91w3w1lxR0rFPEcSVPsncpank\nasdxjStM/iagNxXgi+Hj3OC2BjePk7SoyNHVJRHkztNjSwYvIQwfbjwE8DqOScU9Wd5Tk2t1AniS\n0rZkYq/MkCiBVUCdOfStDr8tCddmIlmLs3eDtfgS+oJsOCDp79yh6h5Td9T1gVW9SbZEs9EqSpoL\nU8A7Bq88+jBeMFKzGyZ8suPp9Rkwp4QLfRTVAUcAOS3ai0WSPF/SuUgJ0mOHczrMRow3mxAd+aYd\nTYtLXegc2dxsGkzBsGDAJSXNQsd3HYyC8vi5chMQU04BLa8MSnkW3OIZ7qPwowvytj9huzvBBUuw\nCl0PnHB11CHOlcXszHIzNYcsq5TZZdFY1USXcHP82U75mlN53tOeWvY0sfOtb30rf/iHf8h2u6Uo\nCj7/+c+PZsOyvvrVr+Kc4+Mf/zhN0/AjP/Ij/MAP/MBr2t7rREs5vngg1yusdah5yD5xUvPaklNY\ncSKZ2oULETNFeMwZeWTURICR15KmgwCfvI6Qb2U2xoZjzt1Dcr1tWucT63ABvOVk+yL7mdgujVyq\nCrCK4A2+LePcVnfj9QVQxbBUssLEv9IzTzlvqWc71jbrTmesOOOc6TDo6UkvTQFxhLvJ9cv/k73c\nJHKbDlF6pYqePFeePwUOuXjzBZ07uOIFJzAg3UyhnGxZsg1LDn7GwUfVBgqs6qNHn8oOJVOvuSnQ\nysox5vGaAvL0hiDvVcjUHQVCq6nSNqWaWaoWrSLtyqppgyc3f+T9TQn3N6uiDplsliPYfPym0XJ+\nr9NjKHI/edWntp7gpb6Ww/Jzzz3HBz/4QX75l3+Zuq55/vnn0fpY1yhjJn7xF3+Rtm356Ec/yjvf\n+U6effbZJ971bwjiMWTAM8ahZj6CxtSnbktOJ7dkRYJEeVOpzWzyELCT5oCA54bjGtzN2p10XwXw\ndkTAm2oTBYCFSyfGBUtyhCfTwORxE/AKCMEkuVokih41Tlx6//2mn1OpAAAgAElEQVRkOwJ8FZiZ\no5y1LGY7VmrDaZqaOuMuZ5wfqRXsmNZlOJL63BToxNFYOrIxncvUIUnrpmJ5iVzkIp4CnpDFpylz\nTNl8iuJikd6jR7v6+LNiSMRq0ZduWbD3cw79DK80SgVq07A21ziyY4s0aOTcmjYN8nvPRf1prWv6\nHmRJBLZjQXTyiU0wy5CsqtwIWoXq0HpABYVRwysA3pBi08cB7/FI7/EOthxDSdNvRuhyLOfskyff\nU05pm1f/1FfjsPze976X9773vQD8wR/8Abdv3z76+9nZGavVirIsKcuSd73rXfzTP/3T/38AL6BH\nyZjwqKRYG2Q46FQHKJGU2JGLukFUCR05RRSQE8CT5oGIqgPZHkqAr59sSzChT89reug6cGUEJGOP\nB8MoYs3uhDh45jR9v+I40hOnlqnyQvibXsXXFnCbrmkHV03+xwYoPbrqqe2Bpd6y5opbXHDKJTMO\nnPGIGYdJHS5fyBLhjTZFIlsPexZhxzzsKdxAOQwEHeePDCYNAtKT2QrMMDgaqrHOlRsVCoPGMtxo\nGsil7FLdyqa3moeSx/TY0oZo8XTwC3bNkn27oNtVdLuSACgDYW4pTgfKRaxxZajNsegU0CQ2rTIT\n7wiQhEN4nBiWo0pDNK7AKOHT+BGstfEs6mjKets+Gi3fzzhnlZz1BICm7z2DnqVLQH9kbRViZ7oP\nBV7F+E4rPzmePjVjGpZsxwm6QhV6auspd2mvr69Zr9c8fPiQv/7rv+ZXfuVXjv7+3d/93XzqU5/C\ne0/f93zxi1/k/e9//2va1usCeDFNqQkwSoFaskvF0TUvhp4yV/aSGGndJ4LelmzTfjO6E8C7OZZv\nGiluyZIw0SgWk+d1HfhrYAXBQLC5XidgKgaip0SzgpP085Ic7RU3HkIwhgxqybwSyAA8NelU5M6v\n8VA6TNFTm8N4gou9+Jw9t1NKm9PSDAMSLUjqI+L8FRtWfsPSbSlaT9F6QpoM1hWWQxFNKkXcPlVy\nCJSYBK8mleXjlnM9ago+03mt4j7iMCPINNRR6zosOWyXNJcL/LnGn+tRuqduK9qioVtE2/bHE293\ntO0M8uNoI/KAzegWvU/AtmE1Rq/XyQRBppVBHFl4ySmiX+4ooQis9DU1B+7pl4mCsfvc4/64LWmA\n+HSQYzRrRgKQmI9eJzqO3GBcMPiQ6qOJCC2EH3HjmyXAO+OcuzxAXHSe2nrKgPcbv/EbbLdbjDH8\n1E/9FPP5nD//8z9HKcX73vc+nnvuOb7jO76Dn/u5n0Nrzfve9z7e8pa3vKZtvY6AV6U7er5rReui\nG+aUU7slicoE+C4C7AK0IWpCS51TRQE/ASXhxEFOXaUzK/ZMevI/4jV2DWw0WBX/JoNhFsTobU6M\n5gT0TiaP1eR5knKPj0RHSXbyY7rriduaepxNB0lDNiOwAW0cVvXUyFDsa5bJbn3Fhhn7McLK00Ls\nmN7JbIol2zgmxm1YHbYs93vMIWD3Eed9CX1lKOc9Zd1jE+8LclSX+7pSSI/mSJI+StRnKcbty/+K\nZnQaTTXUHPyMXb9kf1jSXc/oz2fRKv9++ixK8LSoZzgCuH+pFmeOAC9GtYsE35L+i+lrPEdNcjxe\nccEpV+GUjc+A1+iay3A6ntdOGZQOWN2n47HhFhfc5QHP8tK4f9KI0ekOliE6lwyaMGMblmz8isbP\n6HxM9YNiHFCPJkZ5Kve+Nbl5I9K/b1RaCsDHP/7xx373wz/8w0c/f+ADHzjq3L7W9Q0AeGVux4dU\npJUoR6Kb6aCZKV2kCVG/2vs4ULRTsFfH4neJxCT9lLR1CiCSntZksLoDvBP45xL+0xoqC2cqm4bK\nY3Xj5/WN30mkKQAmX2WbRQDrY8TW65ijjTZR5ChPlrxGmuWrtMckOkIEvUjaMLgEZgemRNdpI8He\nuPjn7JkPe6qrHvswoLfALu6SLqFYePRJh1kH1DKgrBCNDTJ3ocONTSmdoMMeXWwxXSzIw6blXJim\ntBLR7N2Cw35Ju5njL20uZzxKL1eCWXiqrk2zJbqjGpmsabf4JtWjSu+/osMwjM2CgejKc8UJ59yK\nA8/9KTu3pPUVSgcOzDj3kS6hdADF+Loxgm5Gudyb+eoYwR2Y0WNTHQ+myb4QS5pQs/dzdt2Ctp3R\nD0U0AjBudHHBBpTxWBWLBFODV9mO3FCe2nqKtJSv93rdAU9M2/tQMDiLd2nG5nSO7LQjKl8FsFyU\nAo2RoPyTKCcqlWtmUheETOswHDcUKjJQ1UQ276ZInnnpdyuOo7oFuVmxBJYh1+6mBORImpp0az2q\n8ig7oO1AsAYKSwgaP+jsOuzUJB0O2R3DJLnTjcKfFLwPzBDFRbx3ZKuoacFbAKCkpfItxd5hpUZ6\nHT8bZUGvA9Y7jAoxQq0CvSroVJy9USAStiyn0uSiusQwEeRimNBRjrQPNwJnnTzqatqhpmsqhm0V\na6oJhNlPjuUQ0P64EysgMo3xGPcsU6GndBn57GTodtQrx8juItziItziajhl3y3ohhKlAm1dcd1G\nOzFtPcZ6KtOAJnH6wKhItF9znWqAJBkeE7Cbdr4T6AVLF0p6X0an7b5AWYe2GuNddENJ76nHYVVB\nr4qjlFgaRE+1hvcUg8Wv93rdAO9mrUZcUlxv4oBmqWlNl4CURGO1gt5AN3U5TAUvp6ORplO58zoF\nOEkx5V+mndEDObq0RI+9NfAmMiCuJo9pre5mo8ImcHBJZyvLeEzZo2c9ZdlSFS3OG7y39EVFp2u8\nTvK0Pbnul8BVzaLeU2mP13qsMd3nHpaBR9zm7/m2sYsYCcjNSG8Q1zjxF4xk1xsXhShEpvrfEnQV\nKOsB6pbWthyKGKfL+Ey5eF+pdmaJvtVihil0EwFj8avrQsng0w1wmAj1B7K3YJpJ4laGQxkH7Ui0\nGi/0OGxXoroMu3mvYmpZjVFpjOpmafbYbR5xmwtuceVOuR7W7Jol7W6Ga0vwAXeroDuPNzFV+jhM\nqLb4WrMrFlyrdRrxuJg4T099ke1RIj7Vrigdo0Vre2xR4FLtzg8mKm58DA68V2OpRBtHyXJMm8Vw\n4pUUxK95vaGlfbIlEZ7D0IXUBfMFfR8Bz087ljfJ/AJ4M2IUdzA3npCQy4c0TSwknp7KNTP5Kr5i\nU8AT0rH8bIluySsi4FkyyI2AF3K9bgHMAqpKkZj2oD2h1dCa8e6oTMCUPcXswKLaMy/3OBUJqE2x\nwCtNr3Xc3kxNaDcBZmBqh0nOGF5lq3FFYAiWc874ex85T3N23NEPOVWXiMW4aDwLemqaScMogbKk\n/qInlkHdC1CLQDl3mFmgnrXURTNK0BQeoSkbQopVpkbqLZFMXBLQbFiNYBTPhzQ7QiL+QRP61MUW\nmo4AXlK1uKVlb2dYv6JWDTN1GBszsW3iJ53jHElFQIgDRuT3TVJ0nKdhi4/CHS4442o4idrl3YLh\nuibsCujBFdDdL5IJRDw2bmUJBnZ2wYY11+okTTybT/rB1VjKmVJQZD8DCqUC1gwUvqcverSP6hrv\nNAjgeYVLWVFQCqXA4AlommSaUal4zJ/aegJayjfaet0Ar6FmCJF20Piapp3T72a4XRntzaeRhaxp\nZFaSCcZTDt24UogmQ01e4U9HVI+xgZD+Ll3fabq74vGUdgWsA6w8eukoFj12FvmE2rpknx7o2oqu\nqXBNQWgM1g+szIZVecHabFhzPXYD98WC3XJFW9QMSxslZCmIVUWUFJliwBQ9lY1T2zxRc9qFkn2z\n5JIz/tPFO6GBebnj8vQWp8vzNO4wNijWXI+pZ0nLnCW17rDzA/WJyzplcWqWqDXZiKshULqeeTiw\nULvRQXibQE3qYznJipGen8Rb06YCEP3/vKUfyuic05RxhohY5TM5ZulYud7Q7mfsto66aqiqLrVL\n5Om5aTElUQ+p0SJxVnSPnrNhGSO8cMYDf4+H7g7XuxO6qznuqoQrnelQa6ICZ9RGg3d6nCfclDU7\nveA+9/gnnh+3PfXjy3ZWuf4mnW3xwzPaYUycfUGaSRKcitGe9tAX+KZk0DUdc7bqlFm9Y1btWKjY\ngX9q640I78mWRHgdJQc/o3E1bbug383wO5sjCqnVCejJiS5cNunEDgr24RUALyGbnzRCIEd10zre\ntCs6rRkK8BXkKG7NcVd2HWDtMKuWanmgqhpKFdM3WbtygbexQO1bNQLe3eoBt7jklroYu5O7YsHG\nbtkv5nShogvFmLEbHFpFGZXIqaQLu2NOT8FVc4cLbvNfXj6DK5jNd5yXp5wtH3KHR9zmEaJDFY+8\nmoYde2amYTbr4/t6JUWJ3AQCqD5Q+D4ROGoW7NixoKYd09aK7iilLRiISsDHu6gQAW/wlr4vYu3u\nUMYJdFPAkxtQ+kz8EAEv7KBSDWXVpadlxcVNsMv1wjzysqUcqSiR3HPGQ3eHB8Nd2u2C7tEcf54a\nJwJ4zxG7xpoY9R9i7S7UBW2ZzECLBQ+4O6pXCnpEMjclbkuzJKQ78RHgpUaFuLbgTPRyTJVARywP\nNwS2BLTyzNgwr7accPkNzcP7eq7XB/BCBLzWV7TdLHagtiXhWsNWHQPezXmuU7Cbzo0QsvKg48kA\nxGq7ujHIhBzhhcnPjjw/VqaJQb6whLIindiTAKcedeoplg3FsmU+27Mot8ztfkzf5GJqiliI7ynx\numCmGp6tv8I99dI4flDGCm7Vkq2SqVUVMgMVsiRMvsbdV4hxU0dF58t4QQWTPgLHxq1RvYudRJOn\nw+X+YKqxaUdRO4oQL8HCDCgHIVlaqSI+MPEzKhrHjJ5VucOXml5H3zoxgMpysn6kRkxh7hiQPMEp\nhs4yHIp489sbOKhjowh5KFIJQhH2Grcv6Ot406hoj+pjYlklcjUzATrRybbJa3ATVlz4My6GW1xv\nT2k2C4b7FeElA49U5n/2wDuAr6bPY6XiTRGFryx9UbMvFlzWp8iQdKmd5uNmx8heygxTIwdLsnpK\nEd6gipy1DEmZk6zRQirNBA8+BDpToSpHXRwYjH3lSXSvZb0BeE+2PJoulBHw2pp2O8df2wR4ZBG/\nnOCyp9KplHGHAlrTtLTVsbZH+r1O9S9FFt6HG//L5PcyU1a2OSdHgVNzgBPQpw51q6de7FnONqyK\nDWt9neYxHEZdp8HhjGGoDa6w+IVhRsOz9qvc4/7Ivt+ySAnnKhW554ksIUOfI0DJLkukIh25A/PI\nZxMuY3JwcYXh4GfQOXQRwKgRYo57hHHAT1F3lLZhYRts7Qgh5I8qZuhj3bNoPKbrYLHFmg6no2U5\nCLFWjxGedEJvyrambirBKfquyIC31RHwpueDHEc5hgdgp+GgcX32DD6mHZsR3KZAFw99hJZ9qt9d\nhzUX7ozL7pTmakl3f054SRNeVJH0fkFW6FwTbcMKcgdZa5grhnnFbrHiItGEILqmiL71JmFbaEPy\neUzHghsdIzxNSPJDAb3JtSCfS+rsD6WFeYwevX6KEd4btJQnW4E4Fd15i+striniSX3TEEDAbspb\nK3m8gysNiDnRNLSbqBUkXYXj+bGSrs7T68Yde2UgFeLygtSgCLD22FVHuTqwrDaclFecmEtOx2HR\nWxbsxhM2zkQIJKYCNQ1nnHPKxcjwX7BMJODdOFpQqmA5IlPjZzhNizapKA7pKSrE+R464EsYlKEd\nKnZ6EedlKMWgIlFWYgmHiZIlA14rFjQsTIxGvAIbBmrfUQ09th+wvUP1AdNDpXtU6Viz5cRcx6lc\nqRspEZ7s91TQL5e6lk6iU9HFurXQmON67pSqBBn8SmloqEnN95iuM9X2Ts0K4g0jknJ2YR4rm+6E\nzf6E/XbF8LDCv2zhJRVrdfeJHMDrdC5dEQGvJtuLLRTsFO5QsG8W6LZHm0AwaqynSZNnGvWOzYob\nEZ7U8LRKJ6XXGdyE0TDKEj26chg9MKt31HrLSm3GqPKprDdoKU++pJ0eBpWlYyLUFyPOQI7cAhns\n5DwWQKvIICZ3upsUlKmkTBxRRHtrJv8nF5eQlSV9LsjR3cqj1gPl8sCy3rC215zoq1T1uRj1rCdc\nHTHeM9k1S5uqRFCVWpqIvsXRNz7bIr4fmT6rxuL3gRklHVecxLjNJPCoE0JYF3lh3tAMNV5pnDE0\nth59NUTxIgDbMKOycYYFxDhwxp5b4Yq1u2bZ7VjoA7oDdQBjPFURmIeWdbXB6aiiEUOBqduykGJz\nbW9CFPaK0Mch5PHmxbGJwtSIQf5WpeOnQyJh9zfIHsdC/ek+CPG5oWLLik1Ys+lXHDYL+gcz/H0T\na3QvkwHvPrGO16SvXyGWOvq0j7fi34bWsN/PcTtgpqKDdfqcZxwoU01P+sdTS62bEZ5OMzVidC0R\nHhn44j+iqkCxaqlWe27XjzirH7I0W+Za0pansN7o0j7pykmNyr86Vj9IVCdBjXRPX6mBMaWwyPOn\nMq5phCfmoU16nuhmp8afPcc0kzlxOlTqyOr1gFl11LMDy3LLSscu6wnRnuksMbhucXHklHFz+I1O\nIYtQEmwCwhn7MTqS0YtZeZovWJFi7ZlT0HOR1ADOFlhOqeZd7NVo4rAXFJ0rGXzBYAqaYU6nKw56\nxkHXNLrmoGp2zLniNH2W0WvOqoFb6oKWF/EOtIoW6fbgselC1C5Q+Eiy7bCIG0k8XOGx6EpE+9P6\nHl4TUkF+TNemNzjL8TmgQFUeMxuiQ7E9jOWEaRR1s2khSXwe9TjjEGbs3IJ9u6Dd1LjzMk+luz/5\neh+4DPkm/UDOp1Q+uYyPcKLpbMWgNcrFZkZvy2jCoIrkVXhs3XUT7Eri8CRLj1Fp8JREeDfrmTaS\nn4tVx+zOjhN1wTPq5XGI1VNbb9TwnmxpAlY7rO3piwEqB7WJ1ubwONlVaCfTrqpQUyQFnf5e6nxT\nhxIptBuOgU+kavvJzx25AyzuJ1X8Xp84ynVLvdyxtFsWapd8eI8fIsafestNB+AIZ01NAE9SmYoW\nMdM8dnSLaD6NlMSto6SL5pjUzMsDC9bcWV3hVHLgUJbBF7i2pGtLnKvovKcrK/azOftqzqGYs7Er\nHnKXGQf2bsbezTnRV9w257xFfRmNjyMzbUxFZ8sOrVp8pfAzhStV1JIy4FPIk2tmyeEjve+eIkU6\n3QiIflqrmB7vKeiJmWuK9IrTjsVz16xuX3Fr9ojTZJowS6AnDigAPt9igQx+A5bOxc+mO6ThTjJo\n/XzyuCS79AwhN7uUylnBA2LEFxTsNeFQ0JwtcV1JWBrCUuGNunEkw9hIEfCXG8SApaamDR2Nd8d1\nu2ma70GFgFU9lWpZqB1rrseGyVNbb9Twnmxp5bGqx2IjebYeCHMIS5Mt0cf0dKKUMJOHJacQkNNP\nAbp5GEnAVKDS79Q8naSDIlxDuFBwpeLJvVNZwyrbOSV2ZStidHfiqJYt8/mO+Tht4fgxZ888fa2T\ndiAPCZQUbqo59SmhiemY1HFCiCHMmIapXKMKKELIgFfT0qqKQVlm5YEVb+Fe+XISodccwpxDN+ew\nrxn2NUOKcg+LGRsfzeAPasaVPqFUHSa4OAJyOONZ+xLfZP4rQAR0taWwHaWJyo2i6PFWMRSawagx\nKoTuuAOb3q38Ls5Ma7EhhgweHecLC4VIAG8a7YeQI/c6oGaB8rRlde+Ss5OHqZxwMZYO8jwHmaam\nU6tGHaW5DsPgLV1f0h8Kwl7nkZwXjFHb6MN4iEeBkM4Xr/JNVVx3+kiMD42h6wo6H2usoY4a6IKB\nQg1HkbDcDKYANWCok77YMsTPQIZWTTmlAng4St2NxgglHfppAt4bNbwn3ejAnH08Gat4cDtK+rLE\nNTbOduh1FNPLPAhRQEztlSa1iyNrqDmxzrZymIXD1AO2GiiLnqLoCT7WELt1RXNS018X0Vr9Og0O\n2pPvnskhRZmArnts1VLaZlL1asYoLqdnw3hRhRsRBeSoQqVnCecqFu8V4jSi8VEjGgJO62h4STyp\ntQM9BMJg6HzBjnMW1Z5b9QVf5jlu806+mf8S61LJenyjBnwoafxy7EYHrwmhoBtmbIZAPysoTBx6\n3vgadJzUJSL6S0654NboNKJshGR0IGg1En6lIZE7sG4sYgjg7ZgTiFO+tm5Jo2oOncc3Oh7vV6rb\naaLapE5Oz/MDJ4tLzqroN3fCFWuuR5LzMfVEIz57Ylxg6bmZ8hLI3U5xu5bZJYF8c01a1vH5e3JW\nsufYt7GJfxus5TCfUdHQ2wJnjqkoon2VSFjmCDsMgy5o6gW79QG/L3DSbZsokoKPDcFcm6yJxPJv\n3KllX8/1ugJeoXpM5bBlz76cs1/OIA3WplXR9qlVea6EdEulLiezLGTkoVg1LUGdePRJj111VHWc\n6TlX+9GLLKDZtkvc4QS3meMvVRwPeU2M9gRkJS3WAVN12LqhNO2kHjedkNqOF1CMKF4J8I4Tmelv\nhJohA/6K0FP4HhuGGKcojQ4BFQLF4Ckbj2kDoVd0ynKqLrhXv8QpF9zhB/hm/onz1EYpVY9Smj3r\nCORCv+mjUUE3aPpg2YcZthgoin6shw7KJiH9mgtuccplima3aBNNDKTCKFwyoaBMJ13Ikq5smQi4\nTajjrGBqDm3AH24obQToBGiWHpaOcrlnvbzgVnnOmXrI7RsGm2JKcBPshgRsMeq2R4A3gp40sG4C\nHmSTiU5lNxtHnrOyJdf7LtPPqenWzwv86YzaHuh0iTPHN4ibVuzxs4xuyr0u2NZLSr2MzJPBHjcv\niMGfC7HzLGoOcW95ausNwHuyJV5tTtmUih2YF3sOqqazFX1ZMcwK3BBpK0NjcY3Bbw1hbmPquSOe\ngJboIJIaDHrp0MsBu+woFi1F0npWth1Jn5IiuiJaGxnl6U3NUJW4usDXNnvliVGBjfY/WnuMenwy\n1LEUPEcLMo5wCm9izxR/ml6SUsnSyBkcO3MBExx6cJgU2dkmYPceLQ2YEDhlgzaeQ7FgUey4p+7H\n11Zx24OyXNlbqKoHawgqajLpIBw0wUQ/+lAafGWwRZxmDzfVCcmlWkljpUh7LXW4TK8Axkpktokv\nODDnkb/Nfx6+hb/vvo2Xtm/GlQXuPBD2Kt7oBPAktU212WLeUywPrGbXnNpLbulzzpL6dcmOJbuj\n+uiU5zbgJ38TS6pUY9U9he2x1YCbGfyCWNK4TW6ECai1Kt8QF0xoIoDviDWDALYGU8XO7TNEgnRr\ncc6mkkUGO4l8p2eEeOSNn6mONxhlA8oGQhEm9U2PmjlMkU1WpzfTp7beqOE92YpTyzYjj6yjpDNl\ntA+3FW1VJUlVSecrmraibSuGRYXb6ph6luQ5DyVjZKcXPXaRZoqWDZVtqPS0cZBTBm8iOdfagUM1\n0MxnUIGvbE5t5UKzoH3AaJ/kXJluUpC6aEnvIDNa5VQTwANGsJvGeVLjMokbE6c8RLt7r+JX7T3a\nBUwHpg/oPSiZ6rYD4z0Lc6AqO3bzJdbuuKMeHnVGW1XyoNxhZh2+LAimzJGMUqCSAWsda2lKgSny\n7Tzz/sTDMBMndHpfN/lkmW/nx8uvo2TLgvvuHv/YvpP/a/dumkdL3LLAPeKYWyZgB2NkVcw6Fsst\n6+KaU33JGRecEUEv+jDvx/c8VfHGfbFjr1v2c7R41x1l2VHUPWFe4NdEoLpHboTtyBlHQ9ZWS8ml\nB4YW1GU0sAi34j8+C2wi4IXORgeYoDiO7v/lORcuUZOCUmgdRsUM4vOoA5QeVQ/YUhpjuYY55ST+\nq1f79F4KXt0g7k996lP83d/9HVVV8TM/8zM8//zzr2lbrwrw/viP/5jPfOYzKKV429vexoc//GGa\npuGTn/wkDx484N69e3zkIx9hPp+/qo1GwIvDdEfyZ/Ly6nR2uxVftIOZcShqOjOjr2rczOJXOl4Y\nNqCKWLxWtaesG4o6RnSVbal0BrpcwI4RS63T3VWkO9rREOIQ7FrjaxNPEwOqDOgQ5T1WCSl0CnrH\ng49lJqpEePF7zXFCexPwIgElkKdUKRWjSuWjdkEHMA7UTXK0AW0dVgdK1Y4ebAL0IyUjKEKILhtH\noNKr6CijkyGlUng74Cs9Kjekm5ltAPI7n9bKBGxuXsDZjilWAFtV4bQhaD3uUxjUcdNqSjNK6a01\nA5VtWZgdJ1xxxvk4N2KeKCkSa0/rWNNhPjHijC85liRUy8wcaKuasLa4O0UcroSKsrE1mRwvw9gX\nxAhwR6Y/9TraqKgQVRei9JnQaUadLG5yforbjB1rcC1VGkK+YseCdqgZuiK6yEzPARtQpUPXA8aI\nsiXTcJ7q+joP4v785z/Pyy+/zG/91m/xxS9+kd/93d99bO7Fq11fE/DOz8/50z/9Uz75yU9ireUT\nn/gEn/vc5/jyl7/Mt3/7t/PBD36QT3/60/zRH/0RP/7jP/6qNiqDuKfhu9zNjuy9ZWCKqTnoGY2Z\ncZjN6dcl/RAlVEp5tA4RiIyjMC2laSl0n/hLmYc1LUxPi8SF6in0QFFGLamuHW1V0tVl7IYpUKWO\ne2iHUbgv3bVXSm2zjOrxFE+0ErIfksBISjwlx47nqu7QwRF0ICRDTsrxAyVocGtNt9B4C3YSNYwE\n5TCnH2p8U0S7KrF8khqZFOq1Bg2+1LjB4EwEveiabCZAl3WgsqYXl3zeQj6WaHDPPBpg6sBJdcWz\n4SUezp+FYhnBQabICRjDqN3FgwmOInTM2bPmepzdcJcHY011CnTH6gUxuBcEVenZLTN1YK73dGWJ\nO41cOW8sYVbkTu2GPAtlQ6Sf3CWN3Ez7eaigO4nR17qIafEiHa8iEsO1cWNppBx1134sakj3fcsi\nNZ5WbPyKXbek3dWExsYaYgJRpTzGunh+6iFlCzC1xXpq6+s8iPtv/uZv+MEf/EEAvvVbv5X9fs/l\n5SWnp6dPvL1XFeF572mahtlsRtd1nJ2d8elPf5qPfexjAPzQD/0QH/vYx54Y8CTyyBwsPQKegN1O\nLWKEx4yDnY0XS3b7EDqHO4o5pvrNKeH08VTLMShLqyJgaflNpb4AACAASURBVONjaqBnBBui5xgK\nZYt4IhnxpHWjMqIeH5nsmpWqOcKbgsH0e/kMZJ8lBpHJVMAY6Wnj0NZn/pVoya2mXVjaMnLuisn7\nnUZXzhuCuMeMYJq+JroOPaA1oTdR+mcsg7UjSfcgJgcsj97DdE0jWFnTUYdblnS6oNAdq3LDdXkr\nU4HEvXrKxYyhWHLHEc54dhK+y0Oe5eXxxrNNJphSd4ylhNiflThP9rSlivlE8tLripJ+UTAYw2BK\n3DzgTzT+SsO1go2KX6+I6ew9sqt2BewttGm63R0iIJ7EfVdlQNnoeqLVcdR5k0y+8Ssuwwkbt2bn\nluz7Be12htuWEexalbXigXTz92gVjs75rJt+SuvrPIj7/Pz8aHTj2dkZ5+fn/98A3tnZGe9///v5\n8Ic/TFVVvPvd7+bd7343V1dX4wZPT0+5urp61RstaTnhCpnnKfZGIvcR1xBhtsn3kuaKCiE3A44H\nSgvgTMXquUNnxnueqB4CarTDJqVg2nrU3EcTSm8wRkem+yQNkRmuc/aJf5dTSKmbxOs1G4k/ntDm\niE9SWgGGqRlk0Amgimj8p7VHFTFFdUYzWMu+jEqJvZpTHrVQJsae2secuDTRMXqaNkI2/VTgW0No\nC3pb0oZ4E9qx5Jo155xR0I/HRLqxipvG6uHo2OYULb5OR3U8mhNyXbFJ+yNk8VSz8ic6DX2KdcMZ\nB27ziDfx4tGxl2hSPueCYwMDAQVxV6kmkZ4v4vHuTUW3KOlWFd1pidsUsYZ8pSIReUmsz1Vk6aFI\nIwtiDfBOeqxBzQOmcGg9ldrp8aY0YNkxj9by7pTz/oxds6Lbz+j2NW5v4aBznVMi/QKC0/iQbzFT\nsHvMzfpfs77Og7if5vqagLfb7fjbv/1bfud3fof5fM5v/uZv8pd/+ZePPU+pV76DvPDCC7zwwgvj\nzz/2Yz/Gt/A8S/6Ho7qaHCaR+4zNjARuMsd2jH6mFzESYUztE/OSgy7/K/AjoAWM22qpaFVFXxT0\nRYyIfND896rAssKGGXNmzNUtVtxjyXZ0wCjpUvSXO2LJMmCs1MmJdxz/TPu3x8Lxgh6r+kRQjVZB\n2g5RYhTi5660RmtDSYGiRFOz4Dv5b1jxzSkS27BiY1Zczk+5LoBFINwlFtbzjsbIT1Lc0kCpKYtT\nKlUzC6fMeY652o3NAeEfZmqHz/XHyXEIqPGC7hLwvT0s+G9Zc61P2K7WvEcBzxMjIlG8TCe1JZ5l\nuVhSYrnlLXfUGc+ob+ZN/HvOOB+3v2bB3XEv54hryzTiF09GucGON1RdMmiLKwxullpRtzR9r/Ad\nhDYQmgB7+KFTBf8juWEhvn2iAxe9dhoDoNcavS6YlacsTMGc28zZjy7UMotEMpwDc3pVEqwhVDo7\n+DDZRgGqMqi6xJSKmTHMWDDjLGUdsc0EXxuAXtV6AsB7WoO4Hz16NP786NEjzs7OXv1OTNbXBLwv\nfOEL3Lt3j+Uypi/f8z3fwz/8wz9weno65tGXl5ecnJy84v+/0of6Mn/PP/C/TyRY25HKkWsXy+TJ\nu0pEg8V4D8525MdNgJwoxrgpp3O52zVQjBIvUUY4TBzBxwnXrNmwHjUTMmRIcYv/1W+oOfAm/SJv\nVi/yVr7E2/jnUTcrM2CjciJXjPox4b4pac8Dqz16/EtJn1Lkw6jakEtXqDVSkB6CoaWkJc+KvWbN\nW1jyN/wfXHCLB9zlJZ7lRZ7lqzzHi+FNdH1Nd6jxnY3a1VZlYwXBqTSnozodmJ02nNRXnJlzbhuZ\n9vBIqkujXngq3JdIS25QchFfcjLu14u8mZcGw+VFzf9i4OMvkPlrQtgVM4g097d8e6D8FnjbrSu+\ndfmf+bbyBf49f8c7+L/TzWdP9K055ZoVm2R9LwAtZQbhKN7nHi/zDA+5wzlnXLMeMwl5HyMoDjOa\nw5z+uoLzSAL9+D8Sa3zSvZUUXEZ6roggfgcK31PUB07MJWf6Ebe4ZM01Mw7jjf48TRe+2gSuXi5p\nLwrCJmQqlhCgA5lofxpQdzzVrY6zxSVn9tF4jJapEvh+/rtXBUBfcz1lWsrXGsT9nve8hz/7sz/j\n+7//+/nHf/xHFovFa0pn4VUA3p07d/jiF79I13UURcEXvvAF3v72t1PXNZ/97Gf50R/9UT772c/y\nnve851VvtEhgEwX34ioS62INNRtW1DQj1UMuJonChOkGPBbR+QnQSaI7pU9EXWKTxxImoX6XUuma\nJqWTaaqqUngV0x8fFD5E3l6dxu9NnVFkMI00CaQuKXMLcnczj6L2EwCUek5JT7b8lppj0lqq6GYr\nqWL0wpMGT45o5H1LZ1jjKFXPvNqzLq/ZGY+rIsi5VoGejIeU+plWoBRDVXKo53EGrs6eLSHVv6Yp\nYTl+mv1jgBe9/pY84oyH3OEBd3kY7nDlT2i6OgKEdD83ZBlXS7y401ziblbTLSou9RkPq7u8XD7D\nV3kzC3accQ7ETMEkgnsU4fcs2VLTjFmE1MvKJIGbNs/kWFW0LNhR2A5rB7T2DNowKEPwRQSc28R9\nFy32dPznjJjqnhIJ8XWI83wNOBXNUiHQUI0V6GtO2LDmsF8wPKgJ94vsJDQ1xYVsVlsqQptmXvhJ\n7XfML75xaSlfaxD3d37nd/L5z3+en/3Zn6Wua376p3/6NW/rawLeO97xDr7v+76Pn//5n8cYw/PP\nP8/73vc+mqbhE5/4BJ/5zGe4e/cuH/nIR55owzfpC0U/UPcdRgdcYeiNHe2KBLwkBZ0eTLkDy8pk\nzWPAk4tOrMenXVVgbDpIWttRxtNeRXC1YWCptyxD5Lfd4z5nnKcYNLL7RQfbU4xfp2l5PwLfVFUr\nPxdj4iuTtyK4i2VQ1Nka5cZao0vUhU2yLNinmPXAbLSNkmgYFCrNry3p6GxHUZaEweK7gjCVUzVE\nmko8UHhjGHRF6x0b70etqDd6fF/iUHIT+CLVN89JbanYhWX0kxnucN2e0uwXDPsiAoPMHN5xLM1q\nSdGSGlPvXb/mRfUWrHPY2tGUNfd4mXs8GM+Pko45B5bdjpP9hsp1HOYl+1l91NTYM2MXYmaxDUs6\nXzIEG2ulOjCoNDNZE40TqgE/N9FF5yRdSWJnNQW8mqjnXgVYeFSdmk46pKFNJQrG7GMIloOvaV3J\n4Gyub0rzRswuhFIkEXmtovFtr0fJm4xeF6eep7Zeh0HcP/mTP/lUtvWqurQf+tCH+NCHPnT0u+Vy\nyS/8wi+8po1OiZVS0yp6x2zfYWxg0DYOQaGhSYDH5O475TBNTRSBMVU8dhs5rvlJbWzawYwNiIaW\nagTD2DHtI+AxsNIbTrniDg94lhc5SymdOKTo1HCQVFXArqE+ihqOKcu5dgj5Iq05EI0/IyfPMCAD\nccpE7Oop0gyGOApQmjzTYdayP/Le5QIodI8tBpxxOAJh6pYr6o3kERiUxSkdgxetGLTGK4Uzud56\nbJZQjVpbudCkhidd2vNwxqPhDofDivZ6HsX61WTbWyLgiS3ThnFIDknnvGVFU/0/7Z1JjCXZVf5/\n996Y3pBDZQ3dbTdNAxb/DTILA7YEAoyRkCwWbAEheQnNIFtCQiyQt7CwMaLVxiuMQGxYYMksWGFL\nYDa2aEtIqC1a2Aa7hxpzeFNM9/4X556I+15l21100VWtfqcUyqzMl/FuxIv44gzf+U5F7Spa6zgv\n5ryXV7jHq0O64ip3xANvzrlyek5ZN7jrU/qJwcTCRk05VJ0vwpyFn9P34imZLAxqwQEzAF5WdLST\nXHJ0R4wipLuAN4hZeJj2mFKGOxnLVgVZaTstOZu+kjm0PsM7K96jyqM1jPM0tP3OxPNS2y3AU5kp\nfYA+NNt3WjyY1ZTc5WS4yO5wlSv2jCvZOcZ51qZgFW9WVdfQhHP6VZ9cY/cEQ8g45se0OpqyMOSn\n0m8oFBcBuZG3l26qOqswojO3uoGmMUHlnlRuXYWiFHh2w+p0loEmy3WtMlSnGsL3EPN7CpHSD2yj\naOVcxglydQhntdJ9yvGQE1WvT9vAvBJ9exNHICaEX80T6U1mpQujJ4/NmgY7hTCxeGdFNMDo+XbD\nZ6b8RD2PE1Z47OAVL8wBnStoshKvRS8dGqSdCxrenjECYRwk1Btp0TpdnJA99QzNtZLz+TG3Z9di\nFuweC6RHd2XnLIpzCtNy7macM+e7vJfXeJLXww1uh+vc666waI5Yt3O8TgYrLH3hBk+vDw4MIrde\ndJg8w8wDITejAnEiZWWKAKXHVh2mkr7xzEpqQkNozTir7uHQTVF4zMwTNn6koawZOZNrxrBZW/G8\nkUF5hOFxrzqLD832aikPZhsqXuUpYCwtXM/v8IS9ycSssa7HxzyY5lq09Ur4V+3w1JqxjHkZAYtd\nSodS3NPKoYbTGyoMgZpyCCxTmU0bLxqdtKrKvC0FFxxQxiS9Aox4DPK9elvqwSnI7fZ2tmRb3pjS\nYteRQttFD03nu05YD17phgnnHHKL67zOE1EBTsB1zYQ7nAyh7YL51oyM1ue0bU7fOIJWRNMOBxUY\nUOkhRFml7wpoLcteJOJDaTDFOCtCc42aelCPVR9MU9ayPlPhM0uoDG2f05YVg/59CnqaU1RvZoMo\nD/v4uzPY3K64/UNPsvzBA+4+fZVXZk9xjdtc43akI1/nqDjj4HhBHlpW+ZQFs1jIeYrv+vfyqn+K\n080Jy8UhTV1Fby7Ql46ud7hcSL0Bgw9WxCSKFpvl2GmHL6w8PNKRoBZM5rF5jys6skJEGZzpYs6Q\neI2G5CoV9ejctPTTnO6why7OM27NKGgA24K1iUaeCSMvYKz278UD4BEB3poJt7i+Febdcyfcc8dD\nxSpth1JLuXYKelGrdgA89Z80TWvi36U1UR3aIp6aiR5aPyR2R/7SuClcKrfrlGMpZGBYMROpJMIQ\npqjXpgCc7ivNJ6aAp7lDkLB7SR2pCZPhb+R4xJO84JDXkYT9qzwV/T8ByBVT7nEyUEDWTFmFKTUV\nTShou0JalGpHaMw24HXJ9wp6PdBaQmvpuhjSZlba7PJ+8Loz0w0UCwE8yUnOWA6JhtzIHF2soS0y\nNn3FsrBgq0HXbVhHOmOkQwarn8bvNx5OPf09WF7MWK8mLLsp97JjLvIjTvMTbpsbvGrOmJklE1Y4\n20t/9qbiXrjCPX+F2+1V7rbXWK4OaM8r+roYRCmCFw5nFqJOYfTMrPWQB2nnmzb4PrbrJeNAjQlC\nFHeeLG/Js07EU41erSkTU753pic3rfTKVoZwYGl8oPd5TDuY6O0hwKeioy5EOlEYWtfiKtBGtodm\ne8B7MKspOeNoK5HfRK9JSATng+c2ctv6mMq4n2OniX1LIJ0GZYen3NgZkdEN76t5kyUtotcG/hIe\noHpoMo0LVGBIPLvJUPVV7zHlEmpfhpiSZsx9gJf2D+tAZgELmfu6iG1GSvrtyLjggNtc47u8l1tc\nJ+X5aUh7X7uen1D3FW1b4OuC0GTQ2BHw1GvQHHdazNBQtzcE5+izksZVrCcthW2ozIYGlaWXT8oh\nQ761Kn/IOcecUdBiTWBjS5bZHJ8X4A5HdWMVtpQTLje2gvAmCH+waeC0lsHY5xXhXkZ7XuIvHP64\nYHF0zM2spnAxZ2mlyto7F4caVWzais2qYr2a0K5z/MbFIgAwBW8cIioT6LXTJYa3Noa2RdkMyifp\nQ1or6tZ4nO3IbC8DeXbISbtdKVroyfKObNaxMRPWZkpjKzBRD005fipecIiEthkCevHe0BylXrsP\nxfY5vAczBbeRpuFYU3HOIQdcsGbCEWcccDEotgp1IAz5s1HfTELDbVZbGP6fhUTKyTfkQULQ1uR4\n49iYisy0aHtbRyYjJJHugjrEENDkLMNM8oPG0Jh86DwojK5vZPGnMkkes/NEJwHXEfCUXlL7itoX\neO8wPlCEliVzLswRd9w1Xren9MaxZMYpx7zOE5xyjDV9bFcKrJlwyrGcnyCg1/hChp53JV1T4Ouk\np1bBTgFPixYaOuaMrwuGUGT4wtJMKkzXUWYbNq5iEh8O6hWbmMubsOaYU25wk8kQ1k64ba5xyz3B\nMj/enjCXzjdR2X71/NoArYdl5NGcWrjICRcFXVvSNSX1EzPObjCqDw/D1sM43EmPd2kkN6jerENA\nxADOEnKLd54+62QQdgQ6YwLO9hRFHPwdB+2kHpZeCZpPU2DbFu4fr9kU/IqsocxqMtfKGAsnmdGA\nkSLJBMjMCHpVwOQ+TsgbAU/TGg/NHjIt5e20RzOIOz7fNOQRTc+RnaYfvHpB6iVpu9B2c5b0V2hw\nbAb3IBEICA2lbyjWHcW6I3cBn/XUec152WEcQ+5NaL5zLvoDzrtD6rai7UrqquK0PiF3Detiwiqf\nsTBrzmOHhfLOUnEC/To2rvcDVIs3aIcQWMPgJTNWzUyoGssCcxFw656lO+S2e4rp8QXT4wXk0BpZ\n8yLMWVORZR1ZJu1vIsE0l4dCyCRP1hcyB7gp6dc5IZ0KloKdFgs0pFTA0d7WDFFLnkC3zAizik1V\niayXK0eKBdosNyqX6DjJLl566nk7149irmr6UerP72vmkfo1vZUxn1rVncXjWLJd4dydAQEii6U/\nTzT3hgKED1tD27WnWXqq+6GYZfHSy5pcl2xdp5dv2w/oPp4PyV5Ld1DG1C4pqoYLGtZhxjqfEqqM\nULpBScbMPHba4qoal41DkVIJtodm+5D2wWykpBjGMSXdEHLu9sAK2Dk6xnAhvWw8NqpN1PHV/ZC5\nGDoXuppi6SlOPRQ9FLCa1eRZB45YBKjiMOYZF90BZ80hzXpCt6mocdxbZLi8pWJGla0HSSH1IC+7\neNNKr9ZmUyqN0Fe0WjuRLpPmiNXZEc2tSsYDniLtQ3nAPbMhKzbYSY+NT/LeSxhbmIYiE0lwBReP\nkKW7PqNtctpNRbsuYO2Et1UbaMK2Z6eApyGsAp6qlxTx95Whm2Z0ywkbu6Yuxj7n7WlrsimpPAW8\njI7cRNGGFNhSUEpnWmxZ/IPOwsqOgKfrO0/+RknVDZL87wKUBkrJlQ0dCzp4vQX6sJ1DBDAB6+LA\ncgRYSrMZH2ZmBLI3YynwKfhrf7buY+0mFK4lL1ru5YF6luFLCIUdiiRmLoCXVTXOSYpGuJpjWPvQ\nbB/SPpgpUJmYc3P0A29rhkwB0zatbdWHEeC0HatPtg6LS/JlKolj8RgXMEVDNvO0RUZbZJyXM07d\nURz7Ignse/2JNGyfH9CeTehXhXDEfsDAqw5fQjszhGlG6yZsnHql/XCxG+uxWYfLOoq8Ic8aSjMS\ncYfXI+ICw2DBvqLuS9pNjl/aqMqB3MgWgjP4IqdzwthXEciQGSgCpheYNwQ659i0lfQCdw7fZvRN\nRq8DruuEhtKZ7RkOF/E9NZenuaJkHODws7mFNfRlTu2LITxPt3V8iPj4aFtTsWAWB40LDy0YFUdg\nHJA+ZxQNUBAEucl7RUEkvCvsOFBdj2MY/MPIX1vGc7o0wp87MnDC2Kuropoq9RT7io0LOOdl2p4R\nkB4AjzpmLbdD1PTa3X5Aj4IBeo2maiYVwssbyPEmjuwzCP2FwGpyyGoSCxkBbCm5xCrfUDodN/B/\npIm3p6U8mKUdFvpUm7Iacnbp6D6lC49Eke1gIA17R8kfhuR9wEhuxQay0lPSUhcZy7LizM65Z4/G\n8dlBwO7O+irdvZLu9YpwYWEVb45XHKGytLOMblqK8GiehLBSQhX5n6rFVlJwmGTrCOiboaiShu4b\nLSj0FZumkuT50grwKActejzeZqJGXMX3K4ngEL1iF72+MmOzqejbDN9mhNZBEyuySm9IK7Ma1qak\nX/X2FIA07NMiwgTxrNaWfpbThHSk0ViIWUfPVYo0BRtKlszZMBkpRLuAp8ojOp4TRsBrYqUSI8KA\nBQJ66YzhNVqglHVvGB8eryNKJ0/ELc3ZqTebjvW0YKwAnnPbMmQCeJfrLqYFjLEyn074GDO7qfsq\nUlaBIt4XA2nYhDhQCsIkZzM5oI80Ilv2lGXDpBCZK6EBjdHSQ7V9SPtglj5txr6I+3Xr0hYyfa1P\nIDDl1m3D4UhArk0hhQFT0OQVK7Nhkc1YZDNeNU/yOk9wkxvc4jp3/VUulkc0pxP8aznhO050z1bA\nDxDzQ4awgaAehFYV04HRzsM0w85ywrGjPy4IRYbNobftcEx6MWprWR9iH6Q3o5rt4NXEbWXg3BAa\nory3kFNNbFeSv7fSDrYq8U2cANe4cQpcSvlQxQ31hjziCd1BQGOFgJ3SuNS7m8a1xN7Xfp5RNwW1\nK9nYUoZ6R9WPPFbapVpcU1PFSWrzSIqu6EJ0z3TMofagah+t5vKyBKwxY95Nw9G0SLHr1KTDgFSw\nE8b8njJE0lGgmYds9Nhz2w7enc57reKDbDtPu309p7xLzd92jLJlPgj9xPSBri9YdXPm2YKD4pzM\nNQQMldkQzJlw9KqczeGUts8gEHukzziy9zjiPPYN10NUsUvxeku2B7wHM9U10esxpXMoNw9GMFRL\n28PM8HvPrgaJ8uHWTPBYNkzITMd5JtSACys326u8h1d4j7DteYLb/TVWiwP8zYLwioX/Zhy6/H5k\nCr3mtRpGD0LDQQUSa2Du8AeWduPoQ4k9COTzBm/HdPbu14ErZYYDHKe0Kai2CMgMrwuYqsPOOkwe\nvbw2wzuHX7tYmNjppNDig94DOhdEr4ZlPFaVMp+zndSvGJvZ42v6taOpC+qsoi7GUHbJbPhchH7c\nDPnFcw5jD/CUNuQjqGm4PGVsxk+BeXcwu65J83Al4wD28YIbHxpKOzmIr09fp/schrp7TN5h8w7n\nWrKopJ32Cm9LZI0CZingpVQoMHSoVqKTwDXI0KquddxbO8zGMp9ccGjvcuhk9OSUFXnsUqmrkkUW\nzxswtwuu5DKqUiKlxcCBSMH3odg+h/dgpnmMcYxhykvLB89HVWD1r7Znf22rFqd9tarQoaolDYXk\nP4x4BCIBdRAlga5zN5xw6o9YtHPqdUm4yATozpCvK8a80MB1YqzuqSyQAgQGDg1ckaRxX+a02YZu\nKsiVeq96nBaPtTIzw8apVCHNI2nj+CL5XgsK0QsLnQVn8JtM5kQsIkG1MQmHju0cTBqeKlgYttu7\nsvhVAUiLG8nmN5auyWnKKBjqRtBTOkYdfVktXpyGY87DAat+StvnI9CnPaiTZL3qXaby7/pQqNie\nTVwlx0L8Xo9zHtetoJh+punDxoHNe2zZxsHjwjcsotCE5smUKL/dlridP6sph+u5S708n9H4krqr\naDc53San3xT064J1mLCpclqXYa0M167YUJia4/wedV7iMTh65iy4GufyKi9U7xO93x6a7T28B7OR\nJed38hojp07YD9vTr9KsSJrJG1j+jFJSwKCdpx0M6j2ex2b7u5xwyjEXfs6mm9A2Ob6xIzDA+LTX\nGyvJ6wyeXarscSf+/CqwiFXAAwhTQ3dFAW/sAHGRp5bR0Vvp2/RlTl9W+IIxZNYc1IJxDu8BUBtC\nyOhrF/s2kRC2MJL/S3N1Kk6ZpnXmDL2p1Eiu8iDuv43vqwUDBUv1sOpx/6G19E1G14m3IlSIcrjR\nTXLbrWNL3N1wlfP+iFU7o23zMd+mhQMFPW1vUy6gdn6kHl4KaCn47aYdtD0t1f3Tv0lBzwGZqBPn\nZSucOCNS/pMIKGUMZVNp/3TTB7dqHSqtqqEcIpq2L9hsKjabCX6d4Ve5PLhaQ5dlLNcznG0oiw25\nFaTJ6AZgK2kGxW0JY9f3pXW0pXFvj5iWolXadNCIkiXH+GIcdAhjo8xlHl4qvd6SD7kL7ZLQToYz\njjjlmFOOOeeQZZix6Uq6JsN39v7wTcMszRPpcG71NDYIuNwGXmX0jDrgioEb0F9xtE0hoyGDkFRz\nFzC2j9SGGu8MwVn6oqApe5Eesoj6xRoBVAWdKQJOGys3yBqCri8g+mvnbBcl0j5ZGEM7PT7Nm80Y\nK6Qbtikhnu0iRwzlQ2cInaP3jj7oQCZpazPxAaafmg71PgtHLPo5m66i6zK2PvYU9PRnGnqn+TbD\nNuCpZzeNx6PhrbZeacVW58pu2M5hqlmph7hciMVlVsvMC7QAtY6A11KxHgTAdgsaer2PYX3Bmh5C\nVPfuc5pNSbOoYOlkHkYMwbs8w9cVWT5j6dZU2WboUT7kgoqbHHPGFe4NBH0zpA60YFShIrZ7e4Qh\nrfKwOjJSzTRgeEKNahL3h7Hb7PTRZVGCa0c2hI6qbKLEXlUyURWROlR0fY73ov5BiYCJggQIgPwg\n4v1oBe8UuTjPGL2O3YpnDD27ZcH63gFm0UNnhLJysCbM11JVC7Uw9+npXca6nEFZygSxjnGavYaR\nM2Regs5HXcQ1HzHOgFiy7ZWlnDIFvgsEEAISGnfI8R8nr9FCQloI6JP9pTy1pOCUpik0hJOc6tg3\nLIUaE/tQk/2l9JfdHJ7m5L6Xh6fAraF6hXjbJgj/cG3GSWRpiJ/uOwtkrqdwNRO7HiSnFPSKWAmt\n2IzdPEOPdz14eUKMl+/1GiRA5zO6tsCvM7hwMqtCZyE7CL3B95beS7dMKug6jWKn7+EVfoDvMOcC\nS6Al45Qr0nmDH6InMyQq3932yDy8EfDGUoOa3jJy04yaebsdC9uZwEjHiN6iNuzrz9pILE7BThvz\n61DS+jjRyxq5OQ5JZjsEAZdnGImpemPUSOVQe04VXGpgHYawr1sVdPdKuZFqcEWDdwY7k7utNPUQ\nkreu4qzsoPBSAOlieKq6cAvkZl4hYKcqwVflvSgZOw12AU43XWfa3aDy5LuAN1RIk2NXT2+XmMt2\na50C3giA2eBpN6GgC05m5e6uMwU8BaBdtd/dHJ4C3oRhMLt8DSLAOZeGf5ZWzqEWQNIQNwAhgDUY\nB851lBHwdKxUFbVsNIzVKq1KvaoHqIUMIdhLWC9FnG4AvLbN8ascFtn48FJaTGcInaXvxWvuyYZr\nesaKa9zmGf6b/8c3OOKMHhG2eIVmOP+qoPhQixZvfL6kRAAAIABJREFUumrx5kD2srnXWXY/NL38\n8sv80R/9ER//+Mf54Ac/+ADrHe2RAZ4STnPagXeUxYtFpYTUk5MqWD38PBVIT3tTtTND2tLMFhCm\nHkc65LhBlG29t9IArglz4teiFx2zAzi2pxRVSzAQgmXVz1m3M8I9Myb8NSzSsEznls4R76IzkuRv\nHe1pRWN77Awm0/UA8q3LOauOqKcV/aTAV1YA7ogRaHROqwKehnGq87hbnNimeo0/0wKFykHpvAQF\nQgVD9fJSUvDBuNlZj5s0FEVNYZutz2lM3qf6hGPBChgfLmnhQD26NITdCTuH16RjEqvkaxVgBnbe\nkR/U2Lyn9RVd7UZ+3pJxFm4V38t1mLKlytZxbsc5R5wzY8FkJ2dXxj7qfPh/PQjuy/wRyTEHDHc5\nkc+4zWnWE9rzCn/hBIDVe9c1tAY6R+gz+rAtGruJBSGdmZfH/es1vS2A8ZBby9501eL7A95lc6+/\n8pWvDHNo1bz3/O3f/i0//uM//r9Y72iPrJdWn/oSegpI5cPFsh44TiokmQ67HpPC/Q6gjTdQ6hXu\ngp5KjacXhw8xXnNmvGE8mKnHzluyg8CV+W1mxUKyij7jThPY1FOC0hu0yAD3A54CVPT+QrC0WSW3\nvoHpdDWEPXVWMqlWLKZzQuXwVZQ/P2QsFmiCv0EATwfG1IwVZAVGk2xq6qkp4CnlpWMbXLLkWJQu\norm++bi5maeY1pQR8Lbn88r59yjvbFdKgW3AY+f9Uy8zvdcUkNUjSkEvDXGn4GY91cEKl3WExtEt\nqrEYtIzn0CMA7oMMyy5bymzNnEUc7XTGfFDxkSeDenb6vV6nQqQXPoAK1NaU8XtL1xU0ywnt+YRw\nET14rYAPx2qgswTv6MO2aGwd/Ugd8aRyammBrmFb8efh2Zv18N6cYEE697qua65cuXLfa/7xH/+R\nD33oQ7z88ssPsM777ZF5eDoVXsvnedRMO4wqKZP4ZExvD/UYUg9vewZqGg63lBFQJQRRVeJRFaUN\nuYSzfUHfZ+BlaA9ZEFUNG7DTjmzWYnNDbhqKTCrAvndkVQvTICHTIXBsZP5ohbQr3QB7pcMc9ITg\n8LVLKryG0Br6OqPpSpZhRmEkBJKZq4aQFk4OkLBak/TJ2MLB4zpk1EdTwEg9JtgOx8e60Pb/9W/V\ng9RKZxHXcS3Ak7Ll11vy6w3l8ZqyXDJ3F8zNInYIrCkHxnIYKoVDe5UxIrPkvHSsOMb8W7oWGIGZ\nYXfbx5ceZ8qli8cXGkt3XuL7DP96Jt0WryEdF0sEaOJDydiAyzuKsmaWLTg2p5xwl2vcZsqSMoaq\nPW54GGt+TfuIVbtQr1dgiCxWTKnbin6ZEc5jeK35Uw3bhxypEeHVIHw9Fbg442jwJh09R5yR0dGR\ncZeTYfqa9C5L+ubh2fr7vwSQC/J72xvNvU7t7t27fPWrX+WTn/zkOxnwSgKWAy4wBEpq5nEK2BVO\nmbIcvIOxwUxsW5pTQG8cdT1WbjP6WJQTj67kcKicqQxU08vm+0z6M40ZGfa5SHPnVYu1GcaIq6FN\n4jbzmEkQsDlCwE5zQifAjYC91pHPN3RdQQglIXej1+IhtIa1n3KPK0PIvgwz2r6QMFs9zoO4zxK5\njgoEGNLKsXo1ClAaUaSAoC5V2skRktelr20ZJdXVizpE2rGeCZhnesrjJbMrC6p8TZWvOLAXHJrz\nWDWsh5SFFo7UAoZgIrhknl4BT7srdD2XX0D3A+Ku6e9ibrVfZGxOp5hlwH83g1cQwLvFGNLHCrdx\nnrzoqCYb5m7BFe5xDRncpFPQQGTONNpI55QUNLSDPL88gnW6nI41WLcT+qUbc7Lr5Jh28q0hiCRG\nSzH0Iasijs4IOeBiCJ/TPLUoXQtD4eHZm2cef785uLtzrz/1qU/xL//yL/zMz/zM8JrPf/7z/Pqv\n//rw/xD+9/nIRwJ4ygzqYjI3iz2Dx5xyzd/hur/DLCzABoKNLHWTD+STlJu327uoRQ3NhwG0MQTQ\napq2qHVdTlNXtJsS32SiugExlAqQ9dIXa2MLWCjY+DglzRs6k20Dks4PbpGk/1VgwqiEa8w2EGUQ\nrGUdKk79MUVoyEzHop9TNxV9kxOCHcmxBwjQzeP/DxkrkUrfuCz/loKY2m4hI/WWAiKLVAdYeFn3\noRVVkWOw1zuqJ1dMnlxyNDvlcHpKZUe6xnSgbIgnrlo4oJSkcYochijs0IuW2ywSqNmp2mo6gOT/\nhlG+So8x9ZA8QunpIdSO/twJdejbSBeNdoukld4CTB7Iio4q23BozrnGbZ7gdZ7i1YHnpsUXDRhT\niX4lI4uCj1yTOvv2TnuNs/aY9XJKf5GN3t0m+dyGh5Gcg+ANvhcKS21KVla8NS1i1JRx5u4GFyvB\n9ZDjmwzA+PDszTOPv98c3N251x/84Af5xje+sQV4//Vf/8VnPvMZQghcXFzw4osvkmXZA42GVXsk\ngFex4ZBzGorhwpiz4IS73Ohv8VR9i3lY0BWGNrei+Bom1CZSGeKdmQJdSlPR3JH+TEMJGdK8RkVE\n26aguZjQrif41o0Dn+P9ZqyQhH0vIcXCz9kYmYMRvGHtJwJmGSNNJEP2E0cK9jYnXFiCiRLghjHX\nV4AvDRs74aw9wkVF3E09ZbOc4VcFvnNjGKueVhf3f4KA4IQdgU7G/Jea2dm0Epp2jAxgF99j08Oi\nhSqTavHUyCDsay3XrtzixsGrHOenHJt7SaGp3/pM9M11cpqy1Dpyes3fmoAtOmzWYefQh1yAKpWb\nV68vVV8e6COMHEN9oKTCCNqZosIB/wN8izHnpwWYq/LVVIEsa6nshiPOuMHrvIdXeJrvUFKjGoYL\n5gPIqzjCiikBSdH0WE45piXnWzzLN3mWVzZPc3p+jfW9Of15NobTLSNBOiWGe6Gn9J2j62SaX22r\nrXROh+OC+RC9tEnebpzjvFuxeiv28HrL3mjudWrPP//88P0LL7zABz7wgf8V2MEjBLxjTqkpOYhS\nUAfE0MHf4Wp7l3m/pDGW2mZkVrTShCwv2by0KKGW6oploRs8vY6MmVkOg7cHdeLe0NU5/SaPT1Wt\nGEZXycjPlAu1aqeiaIvwxppuIoNbMjMSfmOxQ5L7RkYcrrPt4oGGhxmE3FCHEt8cYqKqS7cpaJcT\noSukg3Q0d6eTqjR/N0l+p16PFhpS28112WTfKcdu8KwCBA/WC4ctVoqzKy1HB6c8OX11mA6m3lyP\npaVAh4inZPJBat6XkQpU0HdSIbdZL21cM4/3GaGNlJ7ajORgpf5om9+uBHzK1SsZvTfD2A1zB+Hf\nXSApiCPkwXElfj0kDsuWQTozlnJdhts8wWuUNBKemgk6SOqAi2E85uj9TYauikWY863wLP8dfpC7\nyxss7h3R3S2Ev3lBnAPM6PnvpBuCN/S9peszOiuFCC3oiYdXYCMzwQdL6wu6EAVYjbu8aPWW7OH1\nlu3Ovf6hH/ohfvEXf3FrEPfDtEcCeFNWPMlrdGTDTNeDOOtgFpZkvsN4cF0gsx4bZau3IzL5X0pY\nhm1ysgsjx23COr6P6nQsKN0aW7TQ9eM8z+GmN8OFBhl94WgbqQaE3oC39JtsHMunN5l6SQpqKajo\nQzZ92HojoetSWCsmBPwqw19YuUnXJMKVjOGahj86jFmBVkO58YS88aYeEtx/MxjgwMFTBVy3cGKH\ntjMzAZuNys0wqliLN10MOaw23vxrJpzFqRYX/ZxlLWG77zNCsGRVIyrCRU9X9JAb2TQfqevtGTX7\nNPemailpu5kCv/Lt1kiBQtvnCuC9wNOMudDYkhZKQ+8kb4Z8+pRhw8yvmJg1rckG4dcJa25wE5Dr\nes4iMg+goeSMw0F27G53wup8gr9tJbS+g4CeHkfFdg4v5TYGg/eGPowPEOWa6uCmlpzGF9Sbirqu\nwAWCi4PDs/4h3u0PVz3gsrnXu4O41Z577rm39F6PBPDkInmdgBkYQwcRjKYx5MSD7QOuC1inBIaR\nXnLZpjaAXvBDQWQS+w0PjIDdjAWlXePyRhSQg9nRXjMQcychenlNXQgQdlZG59V2fNhpFVM9EVU5\nSYm6aing9eDrLBZNkP2tjNyomsxOelaHME9vCu2RVUJ0uu/05t/9XfL+W8A3nGYjgGfc6AFpzrAK\nkneLL9aOGFX+qKOwp8wGkVySDAw/4CIcsGxnLFdz2k0JncMayLJOlIRzyeWF3ENuxXtOAU+LQkrj\n6BnVmPVz0M9A70sfX6uV0EPgqQA/jGwFwxyIEIHHZzZWlePYydAw9SumZkXvLG3k4k1Yc51bWPzQ\n03oRmXsXlFxwyO1wjbvdVU7Xx/Tn5TbgLRiHaWu6YyBzxzWFCHjB4sN237l2FDVB5las+ynr9YzN\ncoKNKi95KQovD+9uf7NV2sfPHgnggSqEhEHZwWM555DCddTViqJoCdbQOcvCzVgyFXY+RUzWijx8\nj8y60P4LlYR3oceEgOs91teYYLnI10zz1RDazrIV8+mC2kxow5TO2xGc+pgc6j3BxrkJTS43RG/l\n953Z9qhSgEk12XY9rtT0/VLpJlVdUQUWHUZ9zgh+2turINgy5qT0hk+LGJcVLXbfP1U7PmXkER4i\nOa5IyO66jHN/xE2eYB1ngGj42qhoQIgioL5k3U5ZtxM2bUXdTmhWJX4ZZeYb0X1uN5XkbM8zQhNl\n24PZppgopUf5h+vkXGgvsLb1kZzP1EPO4rEcgznuMcc9k8maWb7EmEATckwWmE3kwZjRC2/TlKxt\nhTMdMjPcDAWytJUMxvbGYSB7mFNvJvjzknDq4K6R6vDrjGIQ6TGQHK8Dk4U4JS2AMYOHJz3KcS5K\nX7Bup2w2Fd0qh7UjtOBzGasZWjcS6t+yvXPlUh4p4Gkf4pQVAcM5h9gs0LiMMjQEI0TVlRmrTQ3l\nkIANSaFie4KEk8nxHrK+J2s6Cg/nZhUBTyqJs3zJPFuwNjMWbU7XFjFkNHLDeaSX1UriONQx7E29\nq85s0yQU5DQUg20P7z7AM+PsBPXW1mxLTqWApz8vGFui9MbWPKIq/6bV+zSkhvtpKQq0qrB8inh1\nqp5ywNAB0LUZZ/4Q6Fgy44KDuEuTEGNLNqFi4yds6gmbVYVf54RVRlhawtJIP+taclRNU9JehfY8\npitSzzNt7tcHiAKedkjMkvOgoX86kEi991k8PxHw7HHDbHbKjeoWxgWWTPHGMbVL5nGWrgLexk0o\nIvE9ZQSovnPKt2soBlLwMszZbCb05wWcGskh3kZoMStG7qFKYaW5yHgdWRcl/Y0SuAUlhzRCX7Fa\nT2iWFWGVwdoSnCVkAd9kdIUfWQRv2d65gniPcKaFHb7X0voFB2CgNRkFzfA7FePRfguQsHUUHVBu\n3lghNEYuEO8cee5Fd8yOc3B73DhHIc0P6U1jzHYo2CO6ciEBvN1h0XA5CfayhLECjWf0FHfnwqaa\ncwp+Kvuuai0wypKrJ5QeSwp06VrS6qzmwZT6ojdG2j6mqiod+LVjs5gRMsPGzLiwtSTXMfS9o+ud\nUH66grbNaVcF7SqHVWyQ35jxGHWNpSGsIZzH49FijVabtXXugDG3mTOOkNRuF1WyScGxZ/R8dYbr\ngXRf5NOGsqop8hrn+ug1maH6avFDkeKCORriGqQP3FANqjsXHAwcuAsOYuftjHWY0LW5ALyqtOxW\nkdOKc+wWMZXHVD2ubHB5Fye7hcjLkxC3C9nQk9u3OaGNfNJBjEG+hrB7Ab4V23t4D2QpZVifUKoi\noaKd+rSUlhzx7DRRq2CXRVIrwK5qSjBGQmLb4ZzokK2i7HjaUtaR0QdH8HZbRgm2wUGrg7vcsJTz\nlQKk2dnHZYCX/n3qnaSgl4LfGvHy7sLAI9UeV1Uz0U90VxxAvb2U66VA7xnpGcR9HCf7mzP2CbfQ\nLx0bN6XpS5auxzk/VBNDawi1bH5jCRuLX5sR6LQiqedMAU17Sc/ZLsBodVq9IC3SBAS8dMjPnK3C\nw/AZqJek+bEYzjIPuFlHManJchnQnU7NU5kxQ4ik4UmcAudidVSu04IZt7jGOYfc4wpnHG2B34op\nm1DR9dkIdJqD3eXPppJYEzAT6U/OqoasaCS/aeT+ISBg1zu6NqdtFOzcwN8brrHwUEu07D28B7QU\n6FThWFvNFPy0NxAYiJTqmdkYDhfUhOjhyTYmzzuT05iCDJkU35ENMxRUKWXVTUX4c1PgdWxhKh9u\nEP6ZAkRKfN2tpKXg+EZVUZLXp+HkrseYbj33A+uQZ+RysDTJ/jK2wS7d0m4FDR0VXI6S99L+VOL+\nF5a+j1Xq1APuEQ9GvVENv9VLTflzMOautDtEw3nNOypgpcrFmscrGAHEMooF6P7SSm2XvNc8wAmY\nw4Cb9ORZ9JwQtmAWaU3KYtMe2AsOuMtVVkzR9sQNFUcc8BpPcsEBZxxxxvEAeoswZ9VPqdtS9P7S\nwormYHXtmsMrAkwCzAJu2lJUG/KiJsvkOg6YsYDhLV2X0caJdL6LueXd6+Shenew9/Ae0FQtJew8\ndfTnKhKgPDotM2hnrSpUSNeFRWWkejJyWtrIU5K/9zH8yETdOFbQzjhi0R6yWh5Snyes99TzSCt+\naR5uF+j0NbuAslsoYOfvdwsHl0kupe+jN/qMUZ7cJvtULzS90dNwadcDVUBRDy9VQ0lzg2lYrJXR\nNds8PwWzVOpeN/VY064O9WTSvKOCtoboqTiAgoLyD1UoQZ2NVDxAz0taIU9B5qqHY4+pvBQhIoCF\niNzpHDwlE59yHN9GKiI6Z/cpDvku703aueacR/rNwksLWV2X9F08WSplpRPZVOwhlaafesxhTz6v\nmVYr8ryJPNRR8Ue6Lyx96+jbjL5zkT2wcw2l20OzvYf3QKZN1iOxIcpdk9+njOLoB69MeydyOrSt\nRhvRtUSvihW7o/LEwzsYkuwXHLCs52xOJ6JTp7MrYLwBU6WO3W6E1GuC+z28XU9v13Y9vd2LcxdU\nFYA13NNwU/etifw0lGnZVgy2yX5Smop6hKmXmUq4pzeQFgK09Wnw7Bg1+JbIw0NVSHaBU1VXVCRA\njytds55b7bE1QBfk93MjeTid1THQgELCfwzbD5x4vswkwGGPPexwVYczYxvitgDt2KWzZjJEHxbt\nxR7D3Fd5zyAmq4OLFsxZ+Rl1W8nogD4BPAXswNhBomBcIh7epMdVLUVRk2VRFTy40cPzlr5z9HHm\ncGhdzAWb8WGXXrOpVNhbtj0t5YGsZxToFBpJPjRhp5JQKqKoFVqtyYbh0a0VK0tHQ0cWA+Vxepnm\n9vSJnNIF1qsJ/nU3jiRU7yLNeSkYKChdBmK7xYAUSHb5b+GS74fQ45KTpftQryctkKREY4+Ai7a1\nKWCpthpse60pbUbXl4KvenHa57lhGwTbnU3DV60ma3FFHyIwgpHe8CT/T+dtqLes4WwRRMwhAlHY\nOBkmnobImqvLQxRODRjnx8/JgnEBW3jcrCWbtuRFjTPbI0LV1JMS7blqyNkJN0B+rsD2Gk+yYhof\n0XmUVp9Koc1n+D5qLSrYTxgfAKmHqj3SuVRjU46pj3Qr7yWU7XtH3zvhifZOcnc6UD3laqZk7Idm\new/vgUxZ4ZKPy2iQkT4awgIxNM2p2AxUh7RVafQMx3xgR7O1n7Q0ImMbhfW3DOLlbVYV/S0Ht8yY\nltACQApWadiYenBqu69NwXAX8HQfu8B3GY0k3a8CngKCXtBdGEPZlRm9L83npSFtuq+B6hEuX7uC\nCGb07FLBUQW4Xc9Oh12fBTgPsAqiMZiZUUvvwIz7V7BTHb4UnHOkpW3ioYxCDq4XPcHajoBHPJ48\nQCQuG9djssStMWCtx2aeomgoymaQ49oFuxFghF5iCOgsCv15Oi7gJjdiyiXqLQYhX9ehpPM5oY8n\nVgFPlZq0B1i/V8DL5EOQXJ2j94HeuGFucd+7wbsLcaOz9+d7L0tJPBTb5/AeyJSgOspA+uRZxnCB\njbNlBb5SIck0nzEOacwHkNsV/mzJuMcJd8MVzvpDVv2Epivw4QGuhN38HFyeF9v1AncLFru2C5jq\nianXonkpZeGrN6SzLG7H/bgILmlVeVc9ZPc9Nbx10YuKyjASbjnIrFB0FGS1Y0E7QdT70zBWx1su\nGqjPoFuBn0E/A5OBzUR1ZTentAvwevx5FBaoWlzWY7OekPX4vCV0ltg9iM0C1vW4vCPLZH5sZlq8\niTkvRv29LOtkmLZph4giHSnQx+uyjxVakWgf5x2rgOyK6SD7VEd+qMfQhZy2z+lb8e4IVopfGspr\n9VzzljDqG+aAt4R1RmsnLDqDKzrpuzZsAZ7vnOw/5YaqV6efv+Z0d/uq35LtPbwHMs3hjWFnmkMZ\nte1kxF+HG6BNLin/BmCnlV2b3E36RK4pY5v7Fc76I1bNlLbLHwzwdsFt1zMyO69LPb1dD+6y/abV\n0uGG39m0Kqnelo6HXCOV1UPkplIPL6WA7BZB0nA5k4HTxIolwRByBPDUI00B75xtIrQCoALepoFw\nG8Id4DoYC6YC60bATo9/Ny0QixWmCLiyw5UNWd6R5QJ2fSHeTvBG1FYyT+46CldTuprSCP1ZAUpF\nOQMGZ7avHL1exghCJ1HIddklt4kyCzQ10sVe4U0EPFHDFimnrovhpoazWgH3jA8ydUItoxqOt7C2\nNMHStgW2bHFlK2AfQ1rfSUiLtwnnjvsBTwUUHirg7T28BzLVRPOXAh4DaGmOzw1kFAl99WfyT/al\naq/pAKC0GLKhGvhROhd0yJsoOCgBN1Ez2WppSr/fAr5wP8Dt5vnSSmmPeE27+9nNr6WAV7KdM9P1\naXU1kIwkTN57tyByWdhsPJg4Lzf2EEv7nL28mpwWNXZzekOBQ/kWkRwXcmQqjtk+r+m53Sr6aKgd\nwEQiedxsJmISIa7NEHCuJ7M9pdWZGs1W8Wr020yS6tjWU5RDHB+mei2mlgJemoeWYkJMoAQ7TGIL\nACZsf57KmdQUhX4WBTJPWItRwRICeO+gi6Mug41Dj9SzM2MBST+j9PNKP+uHZnsP74FMgcjGT+Gy\nApKJcOgGSLRIC1kYwM5FCOzph4LF+B7bskQ67X7BnMYU0eEImKN442iLUhpOpWGlSX72/Tw8uPwC\nC2wDUco3S/Nr6aahrFYvFVgU6CZxPzpnQit9lwHeZV6mCQOoAHIjtS5uZhsk06LGG4XMw/41KWUZ\nRPuslfxU+jBJRRYu86B3zusAfC5gIuJZPFn02nIzDg8aVfhC4rNtP1TTiCAtEADD1/QBqoC37TXK\n67TJP3grnliQiAUbYrqBse0vUb2ObzKel/R8oPt09AOJGNFW9PZ7c+x2z+dDs72H90A2Vp++dzjp\nt+pU48Wnd57kVNL9piyNbXLz1kVq7OhR6RNXL7zdXNplebndm/GyEPYyM8ki0/D3+21pXuu+PJ8Z\nZ1joDZOu7Y2e+FsWxteSeHhvFIZfRqO5r/CiC9IhG270at8o77l7PuDSczp4elaTG2OC47Icrv7f\nb31IYes124cnex2nrW3njdNt97QOVKt4LqXJIdz/OeqxpaFm6vGm5yDI/kwYgXX7jd/gwts9pw/N\n3rm0FBPeikD83va2t729g+yhFqvfrKWDPd4p9k5b8zttvbBf897+7+2RAN7e9ra3vT0K2wPe3va2\nt3eNPRLA251N+U6wd9qa32nrhf2a9/Z/b/uixd72trd3je1D2r3tbW/vGtsD3t72trd3jb3txOOv\nf/3rfP7znyeEwIc//GF+5Vd+5e1ewve0O3fu8Pzzz3N2doYxho985CN89KMfZbFY8JnPfIZbt25x\n48YNPvGJTzCdTr//Dt8m897zh3/4h5ycnPAHf/AHj/16V6sVf/EXf8H//M//YIzht37rt3jqqace\n6zX/wz/8A1/60pcwxvDMM8/w3HPPsdlsHus1723Hwttofd+H3/md3wk3b94MbduG3//93w/f+c53\n3s4lfF+7d+9e+OY3vxlCCGG9Xoff+73fC9/5znfCX//1X4cvfOELIYQQ/v7v/z78zd/8zSNc5f32\nxS9+MfzZn/1Z+OM//uMQQnjs1/v888+Hf/qnfwohhNB1XVgul4/1mu/cuRN++7d/O7RtG0II4dOf\n/nT40pe+9FiveW/329sa0r788ss89dRTXL9+nSzL+Omf/mm++tWvvp1L+L52fHzMs88+C0BVVbz3\nve/lzp07fO1rX+Pnfu7nAPj5n//5x2rdd+7c4cUXX+QjH/nI8LPHeb2r1YqXXnqJD3/4wwA455hO\np4/1mkG86M1mQ9/3NE3DycnJY7/mvW3b2xrS3r17l6tXx+GYJycnvPzyy2/nEh7Ibt68ybe//W1+\n9Ed/lLOzM46PZa7B8fExZ2dnj3h1o/3VX/0Vv/Ebv8FqNcoLP87rvXnzJgcHB7zwwgt8+9vf5od/\n+If52Mc+9liv+eTkhF/+5V/mueeeoyxL3v/+9/P+97//sV7z3u63fdHiDWyz2fDpT3+aj33sY1RV\ndd/vjXmo3dj/a/u3f/s3jo6OePbZZwnfg2H0uKwXxFP65je/yS/90i/xJ3/yJ5RlyRe+8IX7Xvc4\nrXm5XPK1r32NF154gc997nPUdc0///M/3/e6x2nNe7vf3lYP7+TkhNu3bw//v3v3LicnJ2/nEt6U\n9X3Ppz71KX72Z3+Wn/zJnwTk6X16ejp8PTo6esSrFHvppZf42te+xosvvkjTNKzXa/78z//8sV0v\nyHVw9epVfuRHfgSAD33oQ3zhC194rNf87//+79y4cYP5XIZx/NRP/RTf+MY3Hus17+1+e1s9vPe9\n73289tpr3Lp1i67r+MpXvsJP/MRPvJ1LeFP22c9+lqeffpqPfvSjw88+8IEP8OUvfxmAL3/5y4/N\nun/t136Nz372szz//PN8/OMf58d+7Mf43d/93cd2vSAPj6tXr/LKK68AAiZPP/30Y73ma9eu8Z//\n+Z80TUMI4R2x5r3db297p8XXv/51/vIv/5JUEeMPAAAA1klEQVQQAr/wC7/w2NFSXnrpJT75yU/y\nzDPPYIzBGMOv/uqv8r73vY8//dM/5fbt21y/fp1PfOITzGazR73cLfuP//gPvvjFLw60lMd5vd/6\n1rf43Oc+R9d1PPHEEzz33HN47x/rNf/d3/0d//qv/4pzjmeffZbf/M3fZLPZPNZr3tu27VvL9ra3\nvb1rbF+02Nve9vausT3g7W1ve3vX2B7w9ra3vb1rbA94e9vb3t41tge8ve1tb+8a2wPe3va2t3eN\n7QFvb3vb27vG9oC3t73t7V1j/x8SFGBMvwgO3wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f65ae66c1d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(mean.reshape(100,100),interpolation=None)\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Django Shell-Plus",
"language": "python",
"name": "django_extensions"
},
"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
}
| bsd-2-clause |
0111001101111010/cs595-f13 | assignment6/q1/11notes.ipynb | 1 | 611724 | {
"metadata": {
"name": "11notes"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"#use regular expression to parse log entry\n",
"import re\n",
"#http://docs.python.org/2/library/re.html\n",
"#awkward historical log format:\n",
"log_entry='proxy4.utsa.edu.au 151.217.6.9 - -|- [11/Apr/2013:23:57:14 -0400] [Mozilla/5.0 (Windows NT 6.1; rv:19.0) Gecko/20100101 Firefox/19.0|-=151.217.60.103|0|http://arxiv.org/|proxy4.utas.edu.au.1364191674910933] \"GET /find/all/1/all:+2013arXiv13011419D/0/1/0/all/0/1 HTTP/1.0\" 200 10737'\n",
"#re.match pulls out the objects in ()\n",
"mm = re.match(r\"(\\S+) (\\S+) (\\S+) (\\S+?)\\|(\\S+) \\[(.*?)\\] \\[(.*)\\|(.*?)=(.*?)\\|(\\d+)\\|(.*)\\|(.*?)\\] \\\"(.*)\\\" (\\d+) (\\S+)\",log_entry)\n",
"keys=['host','ip','logname','tapiruid','tapirsid','datetime','ua','xfrom','xfor','delay','referer','cookie','request','status','bytes']\n",
"#mm.groups() is the list of matching objects\n",
"entry=dict(zip(keys,mm.groups()))\n",
"for k in keys: print k+':',entry[k]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"host: proxy4.utsa.edu.au\n",
"ip: 151.217.6.9\n",
"logname: -\n",
"tapiruid: -\n",
"tapirsid: -\n",
"datetime: 11/Apr/2013:23:57:14 -0400\n",
"ua: Mozilla/5.0 (Windows NT 6.1; rv:19.0) Gecko/20100101 Firefox/19.0\n",
"xfrom: -\n",
"xfor: 151.217.60.103\n",
"delay: 0\n",
"referer: http://arxiv.org/\n",
"cookie: proxy4.utas.edu.au.1364191674910933\n",
"request: GET /find/all/1/all:+2013arXiv13011419D/0/1/0/all/0/1 HTTP/1.0\n",
"status: 200\n",
"bytes: 10737\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#next need to parse the datetime\n",
"import time\n",
"#http://docs.python.org/2/library/time.html"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def timestr_utc(time_string):\n",
" #wants timezone names instead of time offset:\n",
" ts=time_string.replace('-0400','EDT').replace('-0500','EST')\n",
" try: \n",
" return int(time.mktime(time.strptime(ts,'%d/%b/%Y:%H:%M:%S %Z')))\n",
" except ValueError:\n",
" print \"bad time\",time_string\n",
" return(None)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#current time\n",
"print 'current time is',time.time()\n",
"time_string=time.strftime('%d/%b/%Y:%H:%M:%S %Z')\n",
"print 'current time string is',time_string\n",
"utc_time = timestr_utc(time_string)\n",
"print 'and converts back to',utc_time,'seconds'\n",
"print \"in years that's roughly\",utc_time/(60*60*24*365.25),'years'"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"current time is 1366481747.77\n",
"current time string is 20/Apr/2013:14:15:47 EDT\n",
"and converts back to 1366481747 seconds\n",
"in years that's roughly 43.3011935952 years\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#recall that on 31 Dec 1969 at 7pm eastern time the ball at Times Square descended\n",
"#with great fanfare and announced to the world \"0 Unix time\"\n",
"for ts in [\"31/Dec/1969:18:59:59 -0500\",\"31/Dec/1969:19:00:00 -0500\",\"31/Dec/1969:19:00:01 -0500\"]:\n",
" print ts,'converts to',timestr_utc(ts)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"31/Dec/1969:18:59:59 -0500 converts to -1\n",
"31/Dec/1969:19:00:00 -0500 converts to 0\n",
"31/Dec/1969:19:00:01 -0500 converts to 1\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#http://imgs.xkcd.com/comics/bug.png\n",
"from urllib2 import urlopen\n",
"bug = urlopen('http://imgs.xkcd.com/comics/bug.png').read()\n",
"from IPython.display import Image \n",
"Image(bug)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAQ8AAAECCAAAAADF2g08AAAABGdBTUEAALGOfPtRkwAAAxhpQ0NQ\nUGhvdG9zaG9wIElDQyBwcm9maWxlAAB42mNgYJ7g6OLkyiTAwFBQVFLkHuQYGREZpcB+noGNgZmB\ngYGBgSExubjAMSDAh4GBgSEvPy+VARUwMjB8u8bAyMDAwHBZ19HFyZWBNMCaXFBUwsDAcICBgcEo\nJbU4mYGB4QsDA0N6eUlBCQMDYwwDA4NIUnZBCQMDYwEDA4NIdkiQMwMDYwsDAxNPSWpFCQMDA4Nz\nfkFlUWZ6RomCoaWlpYJjSn5SqkJwZXFJam6xgmdecn5RQX5RYklqCgMDA9QOBgYGBl6X/BIF98TM\nPAUjA1UGKoOIyCgFCAsRPggxBEguLSqDByUDgwCDAoMBgwNDAEMiQz3DAoajDG8YxRldGEsZVzDe\nYxJjCmKawHSBWZg5knkh8xsWS5YOlluseqytrPfYLNmmsX1jD2ffzaHE0cXxhTOR8wKXI9cWbk3u\nBTxSPFN5hXgn8QnzTeOX4V8soCOwQ9BV8IpQqtAP4V4RFZG9ouGiX8QmiRuJX5GokJSTPCaVLy0t\nfUKmTFZd9pZcn7yL/B+FrYqFSnpKb5XXqhSomqj+VDuo3qURqqmk+UHrgPYknVRdKz1BvVf6RwwW\nGNYaxRjbmsibMpu+NLtgvtNiieUEqzrrXJs420A7V3trB2NHHSc1ZyUXBVd5NwV3ZQ91T10vE28b\nH3ffYL8E//yA+sCJQUuDd4VcDH0ZzhQhF2kVFRFdETMzdk/cgwS2RN2ksOSGlDWpN9M5MiwyM7Pm\nZl/MZc+zz68o2FT4rli7JKt0VdmbCv3KkqpdNYy1XnVT6x826jXVNJ9tlWsrbD/aKd1V1H26V7Wv\nsf/uRJtJsyf/nRo/7fAMjZn9s77PSZh7er75gqWLRBa3Lvm2LHP5vZUhq06vcVm7b73lhm2bTDZv\n2WqybfsOq537d7vuObsvbP+DgzmHfh5pPyZ+fMVJ61PnziSf/XV+0kXtS0evJF79d33OTZtbd+/U\n31O+f+Jh3mOxJ/ufZb4QeXnwdf5b+XcXPjR9Mv386uuC7+E/BX6d+tP6z/H/fwANAA80+pbxXQAA\nACBjSFJNAAB6JQAAgIMAAPn/AACA6QAAdTAAAOpgAAA6mAAAF2+SX8VGAAAACXBIWXMAAAsTAAAL\nEwEAmpwYAAAoxUlEQVR42u2dZ0AUyfP3awNRBSRKBlEEBcWAiAlFMWDO2TMd6qkoZ87ZMyfMERPq\nqegZMIJijgiCgIgoqKDknNnv82J2YUFWGEF+y//ZfrOzsz3dM5/t6a6qrq4m4shScSIiV88jssSk\nk0uJ6ApkSZSeE9FlGQYZDxkPGQ8ZDxkPGQ8ZDxkPGQ8ZDxkPGQ8ZDxkPGQ8Zj2pOBan/X/CIT69g\nxovOUswjP7/k97wHh/6NALJ3T5sy79XP/mTmI2t3MgTM4cD7FbyrFd1Kn8lY1syd7bMJCoDs8xO7\n7S+oPI+g6KLDScvOWfx58PjlG48FECB+oQHxiLcJe4hTi8OZ+V1i0UNm5QOAO0UcmQgACNGLBABc\namO7WgAgd084AODz2TuJAICkC37xAgDovLfw8IQ5x1OKW1YbaqmwFdl7hjp1nxoGAAnhuQCAu9vP\n3P4OxH4DgC9r0vHtNfBmQx6AmyMQ2YQ6tqV27yvNw9q26G82mbKBGuho8qkdPKfiEM16GPd8KgXf\npvsC/9FU/7OkojtTr+/AI3l6cYB8AGA6U+YD+Sb2dArA39R8w4UMRJoQqTofSAP6EfEbt+k8JMjx\nYVtS4pNT0R87WuMBlhjiHjVydNDhrgPQn3SmZgDP6xCPDL7AaQoAXKJXcLEDXGgLgEUmmFHvKXC1\n/qFK85hP74RHoYrvVrVHTlb0+1x4qhYc7QIAaLEyvpY/AA/6U1LRblTH4CXaWyv5on4bABHKXgAg\naNU6D706ACdphYkyOSJI2ffhaidySINZ34sz+jh3WJknOEirMhNPdBOxvlUnGAgbhjP184CCqUpf\nEKIxwYE2A21bf4o9vyETjm0B4ArvCzr1BFry64QBO4zTLb0AIL+w0jze0Azh0X4tbGwjPP6gHHKm\nGfPnrxOoXQQAV8V4CUXvUH9mavGH7jX+LVyhU4BbVwCAF/c1cGwiYD0FKx560eVc3ecArtIMtJhe\n1B4alyhqlCOAgmxstQaAENV3OGpaiHbDEEMbmRwTzAHAm/cRbQcC7ZfVXgwcbvTaMK6q+lO7psKD\nXgOxuZXwOE3d+4oFAFxVDoPBMQC4yQkvvigtKSmhaAzxVk4PrUs3vtAZoJtG2j06BwDoPxlAbjYi\nVF8BwLh2uUbnAeAAeTrNFF3r0LLEzVi6Mp/zHFLepuGZaiRcHYBlWgiiEyiMywbW6OYCeCgfgcF9\nkazr72qcj6PNXpinVxWPFcrJAIAcnc043eRjhM97ALB2j2i1e9upJbVXI1XFEwAi5JhB47NjffOG\nqnLyPDX70bPCAOC5Qije3kOG2hbgCTnqNM0FgAIzBgtOGuYAgBe9s9kEAOjbxXmWqPYpdPHu/OUR\nwm9Z2sKxxa23Hek3UOgBDOgHnKewENp70oy6Z2K3VhaA2wqR6D8c6SavX3Nf4FiTQOt0IGFph943\nKs3Dn/MUyBAgXOEhfElBkfYAQJOdUUbNiGpvAVLqXgWAFBUv5lVytnXo0mfkoAEd1EluCwBEKD0D\nADSeD2ACqTPcvtfxYyoYMxwAEKcX6OjCvJg2A+3+GnkKAPBQm0idmmaLeBxlDsZP+keJlNakAnbG\nrsNH8rbn1eNRtwVyI7GN2ja3tqpT+ws6jkVibV+YT8Mpo1dN04Bl1FV5eqV5pNXdhtB6e3Bb/hNO\nNg58HlIIIFPrWLyOv4e6XjSQpnoYAJJrnyp1ZfonppEmKN8GADhOAvCW5838GqUoFEI6rGCEhJaB\nE3sAALZ2n0Bc2sb8GufzCvflAyBso6uYg26rEWxHwUCKLulZzrfsBo/+PsAGejqNhi+YN/tPtUTY\nTkC2/n/4Sy75hmGAYTSQ/j2/4d7Ky2OO7bGJTLDTOB+edsJzqWqXv+l9wRNlByBbZwsABCtIksli\nVZ8CALo7oqg1Aelq+5iDTr2ZXMaf1unlA8CYP8aMzhYK6jkFAKJqPRWN/n2Fndp64A2NAoJrPwTQ\nrxFzOlghdHoHAPCum4qmk5BnuAPveXsfqX/Q8QKAMK3wyvM4qPyt0yitS31GAMethALnJ+7dtHrB\nwHXaj3S1LQCwX0FSF/5eLQQAMFUnC8jSPig8be8AAIKAYXQAABY1wJZWAPAPeY1j5Lav+9J7bAfg\nYZRYNPo/BBCXbekOYKpSHJbYAMAOpZT4EACHLNGtFwC4a2Sh2XjAaj3Qu8nuBujaMAfAtLZVIK8/\n43ZviQFNtO4B55t/zgzeNczwWar83VTVZwDG6mXGcHYDmY90B0gq2leX+bPP0g0gS32F8LQHbQJy\nptOpCbQ+8+Ms8sAGSwAbaTgWNoh/f+/oREWbbLuuKNxGJ0VFJdVv/bXwJO+szd8AnpInLNcBwL8U\ntIkXgCzrTejL/w5gBycWLccALeYAl7k6g+BHg3PzntS9VQU8Uk3pGC5RkzzglhzVUdCwnlcYQXeS\nle4DeESnCld1VLNRomZfJBW904L5jFGyLQR2+ovOD6aFi40U/0POGJIjWgJM1noXtICmFWIXcYlI\neUQSVpGjOS0oFqMuc+SVORvzGiwC8E1+eCj/FQAE6r2PN6kzsd5IYLh6BgBv8oF5W8B5LJBtTjsB\nD462Ci2vEn3O/y6APU8AINLL40UmgIjmV+GbCQBB8cDeFp3/PpstseinXsKDpbNKqmZTVHi2IQDw\nau++UAA9iYj+AeA7YemOa5FZAiBvuWaPZ+IXvVg5zhcFbiEA0Hx+xr1CAMiLB2KmW7kXAKO2AYAg\nOjun8TbA+yaAe5MSATw+7HE1U8rtH9kpJVTmtFsnfT/9oFX/xGyQW4YSLih6tworc2sy+5iMh4xH\n9fFILGCT++PbX6slJjrnf85j3JHi47hQAAi9in19pm/YdfCk91eExwFAfIObwizffHcsW3r9UyFy\nc5GeLJTfQo96v4pLSvuQnhssAID+w8QscHv2bt11+t4nCDx79F73LAd4HPT8gf/nby/zijNd9QMQ\nP4hHertzkf/4nXCkSA8oy+AcXWwGiwiJDLjj+xk5o0x7HsmsEh4dRPdeuCdvnVkBgEUNQmoRhyMn\np0DHBdo9AeA2PWEyPdYkIiLyxqT5mO0AABi7dxwR8XmqdOwBPQCQW09Mr5pGXG1teaK+e6muoSIZ\nHn5fl4iIy1WLKs40sBcQb6V+6u5UmoAgHtWxHLH4BI6qUGfRGOPpfqjNeH8AiGtBp9/5/Tt67uOQ\n8PrE01Cl9pjHa2tKpveqgsfc+sIh7J1G7E7NLABrFN/VGRwflRSXEJofp0rrAdykB0KtTHnXm4+R\nTzqcRZ+eWE8RAD7WPj3O8MaRvetO7I64T6MBpKtuKC5/Pz0pzP4etkvRhS4g9ZYzLTDt9iDwzp4T\nAWLtY5EFCls3+ApgF4XC131Rb0sato1GHVB8KVKzSGW6Lc3MxUfD7v14yqQgR0RKtP5JTNKnT9fp\nCPDUSje1CnicqMW0enhpYG0jAFirnmc/Qvjrd01z5ZfAWWI0pafEWChy8+DSHdFyuwAcNcfQzsLs\nj0k5GMg32i0mYSkz5o1mI9XfAEDL5eauP9zEIXO8Ij8AmKiVAQBYZnFIZQ8+8roKxYy2Jl+AI3LD\nMcAsNcPQ7lPWENXbD0/ymWbbVy8XQIJvQRXweCMvVFyXmWKWLQCsaQ3nSSKpR/eo6mTAgxg7/HEq\nkiY3GRfCzhHAlElwGSQ8ea+ehgsAi0Vib7vuOwDIs9ha9wGQu1UjpP1UkQT7OCzYzzMIwFVL+NGc\nL0mBG6kX01j1rrR3BbCJsccAnboAwH9ytzVPAqvr5WAKLwFvawUBAAZxh3Rx3JhbJf1HnKIHc9B+\nNsbbPDzifmtib/Qbj4LsdABofnuubhZuCNvHHQoEgKyvCdhhAexQjIWg0Q7MsQuPKACA0+0200MU\nGi8tLj9Jww8ANnWKqDXOe4NG3duwmIGENADYSHweUeNc4Io10jsRh09KtExoRbrFDQOAPozOjT49\nACBac2ijLOCsQgKW0Xe8Ujz78l0BEOHS84/udKxqxts2TFvIML6JoUxnORB9yMhIpQsAtPUOplUI\n494FAIRzrwIhA+tRNxxoAbymBwiX88UcIloHAIc6waot0PBgcfGZDTqPnjeziUFEXG0i/b+jATtV\nA7IHgPsed2773XkO4HQbIPfKPzueb6cAAHij8HKRdg4AXOMy1oQe/QEg3VSr4dIdx/orfMEe3nc8\nISIS2uHQrWvV8BjHzCw8MkhGn0bnAvx9mkyCpdnQfmM9AaD1ZXSn17F0mulO+AeAy5r2PQ2xqRWQ\nXncn3OtlYYLSkgV+ALClC/6l7QI9sZ4+x9xYx6jRlCgkahpT03cAWtYeuex6yXtwF00D9Wdm7MY3\nw6SGTPOqw0yqWM8FgK8qZGNaX19H7jV2cr/htvyl2yeE3R+2W1YNjxVGhVh6F0uGADZTAMBxAlRF\nA2aB6T48og7RCoyNotB0BIA83G2GQ8Z5QIte6DgN2GwtzL68E9Ch3guTCLH2YeSLQgBIVb/mqaQb\nDNT/+4d7WN9eaEHV2QkAcaonMN6cGdFMFzOzZVsB4DIpR6FQ8L32RczjJcJLMQEAwm28ALj2Q3Ji\nFfA4L5fyiupEDNqKPL2ZANB9JuydREZSncvALpqvelo4qckJAYBJs/GvUS6w3OCo+jtgidCgh38c\ngAfkYCB2XxkGvsL5YJOzeExOgNPoH+5hpbCtP6FXAOCumoipygkAkKW7GwC+KXoCCDA0bS4AkKu/\nDi7KqbhIQYVpSXinwd2FMK2zsBhXBTzCuT6DBuq0MApEZt2JANB8OrpOQT6Qk4nE2nOBPGsj/n+i\nAXUIgGiL93hikAL4EGcugGEtRO2+EwBnaiami+caCA3ROfV2ALPoMzqNQeaXTyGrxaSFBe2EtiVe\nHADYTAX20uQCIG+oQQwARHCuIHYdx60vM5LZmmFsQ+AqKWmryJluSx5COtS2MMXyQlXoL7YtLbPP\n0UAgz2gsADRZjoHquoa2Fno6QZ95lwAcJRKZfBfSmYznrRYD3lrJwCelel8AOHHa2Dn0ad8Pe6YC\n8KHJYqVHagqH6KyWV4C7dCOYGrRX4xJNFIM2p4dwckI/G8Aa1WggdTjp/vWPiW4YI5gr1tHi6h39\nLMf01GsV8sdZAxl3dm7au321D+C/dGcakF1YFTz60ikIzn8CEJoAAC/SsHzIqD7Ne0w/nJ+0Lw8A\nnLqJakqzJQVyzADCLgIQTLwDAJ7mbQc4t7OfnZ+aAkCw96tY6V9FXWduNICP9a58H9P6j/2eHmfF\nxFMEfGA+F18B8IEmAIDgQiflxnOE/geB9ebsu5SGgGFM0bF38cr/d+m3c6eWeVogbqoqdnf4uuvw\nhWIHBQGTq0BQwfsQpJaf86KdUEIUpBcJnDmxMvuHzB4k4yHjIeMh4yHjIeMh4yHjIeMh4yHjUS2p\nsFDGQzydd5fxEE9Td8p4iKeBZ2U8xFOH/2Q8xFNHGY8SqZOMR4lk6y3jIZYKrO/JeIinltdkPMRT\nO1n/USLZbZbxKCGPucl4iKeFLjIe4ul4TxkP8fSyRa40PHe+QEp4xBmF/v5KvqdKWC5SZHwZuE9K\neBSae1Y8c5bfDXfXAa07d52RBsGPq85ztw/ZmAjRGkS/c6LzH3XrGPRYdlO4cisvo7hV9D4qPLJy\nkhIe6Dav4nm9iciw66z5w8wiLznpNOj295EoxMzuMuUu8/dPJxWy+LaiH5N5hoLIGyuM366PARG/\nxdiHgH+LRvEAgoZGATm624VZ+jWTFh5zJ7HI3NkmoACAAL6kMWbc4DYKta525/VoTLMA4DHtwHP9\nNbu1swAA6/giX5DvChdR8GzbqMY0HkfIWmUsgGt0AsgxEFlf/nCUFh4ebAaY0cL/Hmna/wBA9p98\neW9gOW0GMKgtgCnXH6klAwA2mRX1kVaLmc+AT3doETbQXeCbogeQVk/klOTWXlp4BLRmsZZlwEDR\nUfdpAIAD1BMAJtIrfND2B/Cm8JUmE2FiU4Oiy0YNKlKnXYGc+j2BPKMDQIKycFU05nSRFh657cIr\nnrn/4OykhAwAGMU4Y04hFwBI05mOfcKoE+9VZx6eNaVfu4b2RZfNaS48eGUQA+AMXYDA8jIQo/VG\n+MPf3aSFR37HdxXPPIzk5MlWAMCtB4DCCc2cxgAA5rdGP2HH/EGZiKem162xQ9Fl+7STmLF1hX1h\nVmLAaf54wPIq8KZupDDH7LbSwqPA/nXFMzt1OOm59wQALO5+et1Ss1pBLiMBABc6Jqv5MHme6b+M\nTMrIx+Tiv/wCaZpYGPyRhVGkoibHJTJOQWNv4CwJ/RXhuUxaeKDz84rnbVW0ZH1b2yEqjZZH4S9m\n3Yf7wHt1k5hfbukzC8JGFgfpukNOvdtMfoiMhn9Oc9vv88KLrsDyIuBBH6VNXgeG+FQ4a55pkfVo\nXl9kFwDYXb8QALpsvyISN04bMB10rzFF1/kS4wvup890tYIGc9DoFHBQMUn6ePQ9X+GsqfWLgmm4\n/MF83pcLBnBeLeoas89CLrYL4wSI8XhJzOKHNaJXaKA9Gm4CNuuLnH0fu/hJC4/eFY9+9a5OEFLe\nPn9y62bWQOFisiT1Do+CltIWZNnpeDy+Ptc65UBbxjt3ZLFI8UrIo71o/dH59ehiC2zlLll57F1U\nJlIakmWetLSPilsMo2vZNFIgDofo0eC5wnNX1In4GwF8bEVE6ieRLQypsMNRUMzDCwCi1F4Wl7Vk\nG7CFeFwiMksIJjLLlhIeQyv+vsRZWDvP/++l/8uXuBQmOpn04AajqeU8v3U/TWzgKv7DU85lA0Cy\ne6k2EOOXEHtt1W4X/9R6NFdq+o8jFc4qiP9d3hHPbgqkhUcfL5l9rIQMfk3GQzyNeCLjIZ56vJHx\nENdf2vjLeIilNKc4GQ+x9KZ3ddSSOedLDeHx3/DqqGW9bmYN4eE1pTpeSt01NeV9uTSxGirxNEis\nKTyujqyGSjovrDH96cnxv7+O+zrfagyPfXN+fx2dBtWc8Xb93N9ehY9maM3hsXX2764hTv/Xm+D/\nwfeloHfX3BrE4+Bvfl/i+hlH1yR5/c68Knz26+7L51+KFHd+CTZQD6tR+suHRVVUULaXsxIT96rx\n1uKoF92NXtUsfS52etWU88COOG2WXXv1YGe/uqS8jfEBgSAmu4bpt3l/pVdFMauI+okMKelXWpLJ\nsxqq72NSVTjUHSAljxIKi7aiXw3l4VoFK8au8/QDSp55QQNrKI9zCypdREIDuaelz42gxTWTR+bo\nSu+m5kpLfjj33Zbu1EgeWHi4kgVEKVsn/3g2XN46p0byeFlZC2o/8iq71RyrkTzwx5FKXX6RxpR5\nPoC61EwekT2SKnP5ALpUtsBqpvq9RvLAjvWVuDhGqX2BpPfouZTx+Ho9CxFzy10SlhFciTrOSLzh\nTTRXynjsp7qtlKlx/u9sXC4kaaoplLpKGY99/PkuWwbWSv6d+o9xU4k/mXWWMh476qYDCxV/5/a1\nn4SRx8vqUPXbSheP2BbyMcAcE8Fv5PGBekjsaXldpIrHAT4pvgeGWFRcdo8J9Q/5zsrcGS55B9H3\nEgST/xGP0zRuH/kDjq0q2pyWahAR8TV67I1n0T6mSn6YWdLEo6UhXtMLoE+dCvUfX/6qQwYTVx/x\nWDvbhmoPD6owD4mN4AItlCYeLrqCIMVgYIp2RWx2R3VIa64QXIH/En3qHVgxEYfXTvJov1uaeLzh\nPAvhvgYmVaQ/3UyK0xLEvudu0xLG+i8vtdCRZHCcTY+kiUe+8dIY8gbm1kkrN28AX720Rec+T6dC\nlsS5JMHAVmBXN1GaeKBX6yyVY8A5+lRezlx7uv3DydsV00+fU9M8CT9U3kJWpTxWq8cZrAZOU7nq\nyTIaXcbZ6RV7oAWivV9KpUX0Xrp4PKN7LVyAc/Ix5Sn8yqpl6SDJ5soxFagmTa9hWc5huWbtIV08\nIuhYt5GAJyeynIx7aJCETrZCA8R2WlGm3ntQynh8pOPj+gI3qTxZYgiVbUKNUXaqSD0pDcsyGLbR\nSZUyHvG8w4vaAM+onO270w0ppOxf/ix3wR8A4EktpdK7VOMQrYGU8fjO9zpkJsBbuvnzfG+pg0SR\n/3iFarqpQWtLNS0N+3xIXf9x7kKdFLwnj5/nu1Xm6AIAJ6iCO7U806P+4qNJaoe6QZA2HkF07ar8\ndyQr7vh5vmO0U+JQer+CdUXZk8K4h0JDauJ5S2a1pXTxeEmvz6mlIUd318/zraO7En7pU7SAutyU\nvV6fQ2arQrNTLvaqRSoekD4ejyjioloqBKZTf55vI72WMHBoNWVhSsoNXqxGXO06VHuw1zdIIQ8f\nTswtegs0dS5PfngqoQBi6WwXvrZHs8F3ri/IgjTy8OV/CyVv4A/rn+fbRxKWjM2j6xWr6eubZ2f3\nrxxm3dJ5otu6M6Oo8TOp5EEhEXQcmG9eXr6yozulmRiU/Uen3b/lffqk+8r1q0b/OXToCGdbeSIi\n4iopcxgPMsk25v8lDz96F0D3gbF6eT/NF0j9yjzvzmyYVZYcXyJxdFpPWb52x/nw5KQ3D+/eueiu\nQBUel6q3Pw1/QI+BaSo/dxFLV7cr67SghaR+NtZt8uwVR32ePLrj/zbwdVhUKftbojrRICnk8YL8\nwug6sJNbzryyM/mXKZyu+7V6Mw10OynHSh+Pl+QbTScBLyrH0HWVHH4cV3Mt1X9xpWFhY61zVaHc\nVn378I8lb+Aytzypajid8y1tvNlJ03614mbKKZ0s86SOx10K/0L7gWMaGeW1JEVFskguOYSqa379\n1YptFbL96IjU8bhFkZm11wH7a6eUl7UVkULJOFOjJAzCFUm9ePGwbl4ofTxC0GAicIr7uTxRu5HY\nBsYAgK00qIznqeAjTqYP2EpnpO99eYqmw4AXdKu8rNOJaEjx148DqWcl3n8XXjxSdNpI3/jyCMZT\ngQAqP6RFfyKyupyYDwAPpmvR2ORKVDxKLglYWxUqfxXzuAeLPgyX8mXZie71iCwnjO3UgEMaRfpH\nwa/4SvRVSAKSNDtJGY/H9BBWjsBLelBu3nvkgWSvua0V5AyabptNohhLOY5uvwCkk2YmgMX0Upp4\nZF6wp6PX1F2AMMUX5eYeWzseAAqS4vOBWSQKRzqNaCr71W8j5eMB+NNEKeKxW03TXpv0+GOBEO7D\ncrM7FPd+kS34pMRItAdozT5e2zS2dS9izGod1ZKkhsdpWp+JTzoTDHt+Sf2XHpebv2lRHHb/+g6r\nif4BgCTNvsBJ9n758+XiAOAY7ZAaHh26ACg0+cuRuErkXL69qv5fwoNntRwwRVOzLwCsoDAglDax\nrXxivVwASKxnIy08cnXdAOTpTsL7J/cfle8Pk68vXGZ5jjql5tebP6G+AMjW6wsgz5C1V9xIRWa0\n3iLRUP0beQgSP4Td379m5UT7xt1cdp71unrS/aTP3+SFiE9hGg5+iw5nI6/cLjFenrHBe9KfWdhH\n79ZTDHCD0UEsWQtWneUZ15p4Dadq5RHrd2z+ZGtFIg6Hp85TMzNSUOByOTwi4izJbkbyckREpFJf\nzyqinJLe8h8DwH80HkALR3jSA2A6M/Hb2i7qBLunGCAndDXaXoGeq6p4ZM0wkiOiWq0mXb93+30C\n8vIKkBkbEhgUG/3BPwVYZqTVcayr64DBVvaDzn/PzfsWGvoh7F1SZkY+BD844J/nfwbgx+2eDaQq\nr4AP3UGWgQMAYDCptmPJQ9g+EMUdXl080h3Jaf2V4MBIAVIeH9h7aM/aHlZ2HRo1NrGw7Th2+vSR\no6b1bTdxyvAeDp3trfT0NbX0+BwOl8Oppa5u2rKZWWlReoF2DhCp51wAIJAO4i7dwUWhn0s/4tJE\nVpOxU3kiS1JfSTPlVc0jrT2531w5pltrAwNtxrpNqqb2vcb+0byJpUMrK7uupkrqunwlg1atzBs5\nzPpjzPCxg916t5uy13VAH2sjSxpWqriB3YFoU8t4RnqIRDCdxzjeZwAosNDXGWlg8ZbFUywm0XzU\nffq7eni40u3NREp69Xs4dxyz2uvR2bMPU0u8BXkp6VlRiTll7NgpKIRlKdedAqMJCDPSZZ7ZehQQ\nSv/C3JYRz+TstT/7sVq6sZyKQhh0oTfVwSOIZqJLE7/EvIJfMnAalzIFfqEDccbazBLaCDoLPCGf\nWGIWonpRb2scp4csyl9NYcU2y3nVwWMl9xMGtxWOt7ExHxOzMzPzUJiVlRIa8PrEP2sOHVy/YuW5\np29evf2enpsrAJCe9Dk89OO7S7s3LRk9kltq04ZHdK6ljtBodJbCgEP09qBwbBivMKIvOjRk04Es\noOIgBkfvVgePodbAEpr/14B2bSxry/FJXk9T26SluY4OE2JBUYH4cnwi4hJXRVe3UQvbxmp80dwZ\n1VExKtWfniEDBdHI6GxWALhR/ChL5lWy1Hde/Ynd1MMUeoeqSRXm0Ri4SkR8DSNDK9tpc8bZtLRr\nZNHKyn7MGNfjNwNTY4NjUiPuXr9wfP+hlcPdJo/q3nvA6rVbDrnPP3L/fkhaYmn5bD7RNuFhcu05\nQKaxRUpdxknmq1z7ple2cj6y0vdVUquZxxidHNwl18cfsvKZZxMAheXvdnygX9nne5J7sZHxDHCT\nxoXReuH30aYf+7NSRHLMqmq6ssI83Gql4U4FnbvE0ky1ssMIbmlfNCPxN0UDs8jnqFCtHcGdYRqm\nOoBNLU+JWuRVL49N9AWfeaydcEbqS7hP0QuUd115NJBjLp8ygz4DQJS8/ZBmC9lNpswhy4o6SlQV\nj/W8OAhMZ7Itvl05C9oeNCHuA+ACucBRL5exo9TlE/VhM5fyTcPgKo2rXh7n6C3QgfXyRcshiH1x\nYp8kf/Y9pD03WAAMpMdo0J/pGusc4+wPY2Uy3E+z4Cj3rVp5BJAfsFqTrV9Sc1KVIw6ZlDlfFzmA\n2kcDQKxKY6QrbwKA2zTpgQLLeFlD6Clukle18rhLT4E7vA/sShc01l27+8TbzWU6cG/k0wzGcLSD\ntiBOwYPR5SKms6Vu07AA6XUnVSsPP7oNnKBItu/LMADYTT8qZ1HO1EAkSdrRW3wkDwBPqVeh4Wh2\ndXyiUQD662VUJ493dBQ4zFpXsq3vtWWaW1PlH17uCxrkIjKj/0vtgGRldyC5JS84XI5lfPb/aA+A\ngxRQnTw+0F4giMM2hlMP4pCyGr9bKS0w3Y0Mi3fFcaLrQL7RKHzpRTux24BllJfF9AjAx9IO7b+X\nxxfOaiCc2G5u3Lnpw4CkzMRS9uWILtS7WMC+zmwZNpa31Iz+QqEV20G9p3E+AEGTxoJq5IFWLYEP\nPLZST+cy5O5CD23SW7V595bVG7Zu3X7uWWtmmjG2I6kdBLyJpSvpVwVmp5zZ9KI6efTXzcUbYrsV\nxWBx+TTt1c0TnmcnNuIQccTdJwcLbUSfUwHMNGK5bGO7cHXJY5pfnTz66OXiCbHdimKh2AZW/6oL\nn99y6qSW2lwirm6TBkYaZCMej/Itn62PYUdtoXNn0xbVyWMJPwFv6V+WxV8W8zScSbaW1GaEgryD\nvQqR3qCDASn52Rnxj0s4Sk7VZRndMJxE2xLOpahq5LGRovCWtYL7iq4WHa+lhWvJTov4qi3H73lV\ntp6XpsF2iv5g0Wtyh45WI49JdTMQQ1tYFv+WvIuOfUlRjuoOcXO27WzVYvjQjq07LTm2eb9vCQeY\nS9xAljVMLVrlnqY2uBp5jNTMQobKDJbFfxBzcvuqTtx99ydrEK+VS6dmVi0dbDTk5eTk6pk4/blX\nJOcNYj0h3Z1XJDM7aGRUH4+uJgUoMGKrJESLL6UbQn1O1qEB5z+JWkRmYmKc//qVg5tRHUYDjpC4\nkExSSlQuntnZUflwBhXn4aIcBzRk6zX/udguCPSmNvINyxqhCu3lGOPyZrkYlhX4igEPouHVx+MO\n9U8r1Ge790Ja7eLpsjAFIsuyHVOTWzfMAZCq05/t/buJh2BrpZZQbTxwi2/2RpGtcFBo7CJmtiH5\nJxJ1srsAtkkIxPczsVk8RN8GulZ9PPDisGibTBbJemxxz0dkJWl2bw0FAFkmtmxVkDgF8Tf4ReUd\n6lj5f7wV7w0q+P8Vxfa8z1Hm1JfgOPTJuGkBsJK9d8912i/2LdeoebXy+EasY6eb/yE6WklLh8pf\nCC3DMhq3T5MOAMH8Iaxvf1XJlTau5UdiqUoeidyVLIsvMPirSDpQTltLRPVnnn1avK5DkPn+7HgV\n0r8M3GogaRXqz0yFRiX43qy0iMqKR2ottjaXLM1VwqNA6olpWgdnt1EgUrJxdl0w23XBn/2aqxPx\nbOYl4rED/UJwqG+KJf3F0rU7ViePL6xvOUtzo0gfpL2YqZsHJHy4scC5mWJtVR5pmeh0XfVvFvB+\nFClM/QXHnpu0vOSJyZUNmcOKR5zcHpbFZ2u4CQ+MdZKwgl8U/6n5eNgKnTa+n+lE1DLwV+5+E90r\neeIG49dbTTyyNVnHfzMTjreeNAXYTOGMVBJ+uK6akwZZNOvRVa8ujxQmPPy1tU191EpN7MTVsqlG\nHmjUnW35jYRjhh29AG6QL4CQkWYcIg09PbK2MzPpN2vuxc+/ePNR3B/CsPemwGrkYcN6fDdn5KV3\nNAHATboD4Lqy2fTDPE+cJz8UFFTm5nf9uK71WOke5ffyaMW6fTAq1hy6D8CbfAAgIw9POf64Tecr\n17ZhY/GDPJuuZ5VffTws2Oq3BcaDAUBgbiUAcK/IOnSA9w1vKxt9MaKsSKj9KbT6eJizXpHVqBMA\nvGFiP70sIuCqlIKP5Fo5HkfKMuduqmgEoqrg0YB11IC2RnkANjMvSjgJ9yZM1XIACszsKsdjUFnT\nyf6SYkT8Fh6s91jppZ4BoJ1qHGMdGsqcvUweANazV+9LSIeKZY2tmfqaKdXGo2FP1u2joQBIrMNM\njXzjCeOjL+THAojgd6sMj0tlr/cfXqltT1i2D9YWbCf9HOCeME58plYzppdtwPRDE9QrYwBeQWXO\nrh+oVNBgdjwsh7ItfzIvAVhLk4VSumEu048wcT2vVGqDko4mZc7hhFcqdg4rHuLWvwobKD4DQ+ik\nUL7mfgGAs0z3ikTjBr/eQKJpVNk/NOdHVBOPLA3W8byXUgxwpYdQJpjA+FWPVElmvp+gvb985960\ntewflrFfj/ir9o/a29iWv4WiS7zy3gDS64kMYVn1G/7y9nlukhyC3hYtbv7dPL6yni7CYRLfPvEC\nbQbgXiyo76ONv3rnTSQ+9YhKuE6x4vGYTrDnIe6lkh8UCWQbt4fg+33f+z73XkVbGf/avvCIEPXR\nP6bztLR6eFwgH7ble/74Xz2iftNNmZWYpKZbfqiQstM+yb1EZCVGGFY8DgjtOSzS7R/mVJ/2JSL9\noStOe586tneIJvX8tdiDM34yWdtH8Wu18NipyDreu0+pKDY547nUdvudLDH969ciqU34SSexWVJc\ntyrmsVWH9WjwtJSOcpW4FuNnuQ7pZdfQwKqxdcfRvTj05684BkbdlXxV/Lxv1cJjvi7rVSYPS7WP\nyC7KRKq1lez6TRw6bPp4mwY2tUix8mHkqyyx4jHGmLXtaR95ljwRokM0OaNoqWqEE1E71FAeHS3L\nE+jTo2+e2OT65+ztno9Cbt8Oik3pVqvkDGJmU70zU0XiOxCpabhXxzi7hvKwckRcanJ89tcP0Z/C\nA6OCnrx8G+rv9+Chj/exncvH97NpVL82EXH4PEUOhxQ5RHLKpXfsCaTOS6zpQNFwzH0RqW2RW0N5\nOCgNraWqoqjJJy6HSJFDxOXyOFwul0PE0W45dKTTwt3HboTEJCS+j3j1PPCu14UdPqUUtmzXhrZu\nj4q6woSh6rymb2rq+/J+bP1O7Vv0HOi24vi2ZQdu/nd637Frzx+9DAx7GRgeX1E5s+QMQ0FIYA5q\nKg+gQCAoxP/l9L/Zz1PGQ8ZDxkPGQ8ZDxkPGQ8ZDlmQ8ZDxkPGQ8ZDxkPGQ8ZDxkPGQ8pJXHFRmG\nEjzWPPaRJSY93EOyVDL9PzBkqYtZrQhUAAAAAElFTkSuQmCC\n",
"prompt_number": 20,
"text": [
"<IPython.core.display.Image at 0x10b9cd210>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#now check what happens when the clocks were turned back at 2a.m., and 1:30a.m. occurred twice:\n",
"time_string1 = \"04/Nov/2012:01:30:13 -0400\"\n",
"time_string2 = \"04/Nov/2012:01:30:13 -0500\"\n",
"utc1=timestr_utc(time_string1)\n",
"utc2=timestr_utc(time_string2)\n",
"print time_string1,'converts to',utc1\n",
"print time_string2,'converts to',utc2\n",
"print utc1,'-',utc2,'=',utc2-utc1,'seconds difference, and convert back to:'\n",
"#then see how they're translated back\n",
"print time.strftime('%d/%b/%Y:%H:%M:%S %Z',time.localtime(utc1))\n",
"print time.strftime('%d/%b/%Y:%H:%M:%S %Z',time.localtime(utc2))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"04/Nov/2012:01:30:13 -0400 converts to 1352007013\n",
"04/Nov/2012:01:30:13 -0500 converts to 1352010613\n",
"1352007013 - 1352010613 = 3600 seconds difference, and convert back to:\n",
"04/Nov/2012:01:30:13 EDT\n",
"04/Nov/2012:01:30:13 EST\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#some examples from http://networkx.github.io/documentation/latest/examples/\n",
"import networkx as nx"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"G=nx.Graph()\n",
"G.add_node(\"spam\")\n",
"G.add_edge(1,2)\n",
"print 'nodes:',G.nodes()\n",
"print 'edges:',G.edges()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"nodes: [1, 2, 'spam']\n",
"edges: [(1, 2)]\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#http://networkx.github.io/documentation/latest/examples/drawing/house_with_colors.html\n",
"G=nx.house_graph()\n",
"# explicitly set positions\n",
"pos={0:(0,0),\n",
" 1:(1,0),\n",
" 2:(0,1),\n",
" 3:(1,1),\n",
" 4:(0.5,2.0)}\n",
"nx.draw_networkx_nodes(G,pos,node_size=2000,nodelist=[4])\n",
"nx.draw_networkx_nodes(G,pos,node_size=3000,nodelist=[0,1,2,3],node_color='b')\n",
"nx.draw_networkx_edges(G,pos,alpha=0.5,width=6)\n",
"axis('off')\n",
"None"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD9CAYAAAC7iRw+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n"
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#http://networkx.github.io/documentation/latest/examples/drawing/ego_graph.html\n",
"###just draw friends network of highest degree node in preferential attachment network\n",
"# Create a BA model graph\n",
"n=1000\n",
"m=2\n",
"from operator import itemgetter\n",
"G=nx.generators.barabasi_albert_graph(n,m)\n",
" # find node with largest degree\n",
"node_and_degree=G.degree()\n",
"(largest_hub,degree)=sorted(node_and_degree.items(),key=itemgetter(1))[-1]\n",
" # Create ego graph of main hub\n",
"hub_ego=nx.ego_graph(G,largest_hub)\n",
" # Draw graph\n",
"figure(figsize=(4,4))\n",
"pos=nx.spring_layout(hub_ego)\n",
"nx.draw(hub_ego,pos,node_color='b',node_size=50,with_labels=False)\n",
" # Draw ego as large and red\n",
"nx.draw_networkx_nodes(hub_ego,pos,nodelist=[largest_hub],node_size=300,node_color='r')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 10,
"text": [
"<matplotlib.collections.PathCollection at 0x10824da10>"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAU8AAAE+CAYAAAANs5KWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4P8BJDSPzJ8kaAYAAAAASUVORK5CYII=\n"
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#http://networkx.github.io/documentation/latest/examples/drawing/random_geometric_graph.html\n",
"#random geometric graph\n",
"#\n",
"G=nx.random_geometric_graph(200,0.125)\n",
"# position is stored as node attribute data for random_geometric_graph\n",
"pos=nx.get_node_attributes(G,'pos')\n",
"\n",
"# find node near center (0.5,0.5)\n",
"dmin=1\n",
"ncenter=0\n",
"for n in pos:\n",
" x,y=pos[n]\n",
" d=(x-0.5)**2+(y-0.5)**2\n",
" if d<dmin:\n",
" ncenter=n\n",
" dmin=d\n",
"\n",
"# color by path length from node near center\n",
"p=nx.single_source_shortest_path_length(G,ncenter)\n",
"\n",
"figure(figsize=(8,8))\n",
"nx.draw_networkx_edges(G,pos,nodelist=[ncenter],alpha=0.4)\n",
"nx.draw_networkx_nodes(G,pos,nodelist=p.keys(),\n",
" node_size=80,\n",
" node_color=p.values(),\n",
" cmap=plt.cm.Reds_r)\n",
"\n",
"xlim(-0.05,1.05)\n",
"ylim(-0.05,1.05)\n",
"axis('off')\n",
"None"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAd8AAAHYCAYAAAAMBeLgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nz0T4CoIgCIKeifAVBEEQBD37Pw9sKQ1qEje+AAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#http://networkx.github.io/documentation/latest/examples/graph/degree_sequence.html\n",
"#Random graph from given degree sequence.\n",
"\n",
"z=[5,3,3,3,3,2,2,2,1,1,1]\n",
"print nx.is_valid_degree_sequence(z)\n",
"\n",
"print(\"Configuration model\")\n",
"G=nx.configuration_model(z) # configuration model\n",
"degree_sequence=list(nx.degree(G).values()) # degree sequence\n",
"print(\"Degree sequence %s\" % degree_sequence)\n",
"print(\"Degree histogram\")\n",
"hist={}\n",
"for d in degree_sequence:\n",
" if d in hist:\n",
" hist[d]+=1\n",
" else:\n",
" hist[d]=1\n",
"print(\"degree #nodes\")\n",
"for d in hist:\n",
" print('%d %d' % (d,hist[d]))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"True\n",
"Configuration model\n",
"Degree sequence [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]\n",
"Degree histogram\n",
"degree #nodes\n",
"1 3\n",
"2 3\n",
"3 4\n",
"5 1\n"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#http://networkx.github.io/documentation/latest/examples/graph/erdos_renyi.html\n",
"#Create an G{n,m} random graph with n nodes and m edges and report some properties.\n",
"\n",
"n=10 # 10 nodes\n",
"m=20 # 20 edges\n",
"\n",
"G=nx.gnm_random_graph(n,m)\n",
"\n",
"# some properties\n",
"print(\"node degree clustering\")\n",
"for v in nx.nodes(G):\n",
" print('%s %d %f' % (v,nx.degree(G,v),nx.clustering(G,v)))\n",
"\n",
"# print the adjacency list to terminal\n",
"nx.write_adjlist(G,sys.stdout)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"node degree clustering\n",
"0 2 0.000000\n",
"1 2 0.000000\n",
"2 4 0.500000\n",
"3 5 0.500000\n",
"4 5 0.500000\n",
"5 5 0.200000\n",
"6 4 0.666667\n",
"7 5 0.300000\n",
"8 5 0.200000\n",
"9 3 0.000000\n",
"# gnm_random_graph(10,20)\n",
"0 8 5 \n",
"1 9 7 \n",
"2 8 4 6 7 \n",
"3 8 4 5 6 7 \n",
"4 8 5 6 \n",
"5 9 7 \n",
"6 7 \n",
"7 \n",
"8 9 \n",
"9 \n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#http://networkx.github.io/documentation/latest/examples/graph/karate_club.html\n",
"#Zachary's Karate Club graph\n",
"#Data file from:\n",
"#http://vlado.fmf.uni-lj.si/pub/networks/data/Ucinet/UciData.htm\n",
"\n",
"G=nx.karate_club_graph()\n",
"figure(figsize=(8,8))\n",
"nx.draw_networkx(G)\n",
"axis('off')\n",
"print(\"Node Degree\")\n",
"for v in G: print('%s %s' % (v,G.degree(v)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Node Degree\n",
"0 16\n",
"1 9\n",
"2 10\n",
"3 6\n",
"4 3\n",
"5 4\n",
"6 4\n",
"7 4\n",
"8 5\n",
"9 2\n",
"10 3\n",
"11 1\n",
"12 2\n",
"13 5\n",
"14 2\n",
"15 2\n",
"16 2\n",
"17 2\n",
"18 2\n",
"19 3\n",
"20 2\n",
"21 2\n",
"22 2\n",
"23 5\n",
"24 3\n",
"25 3\n",
"26 2\n",
"27 4\n",
"28 3\n",
"29 4\n",
"30 4\n",
"31 6\n",
"32 12\n",
"33 17\n"
]
},
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAHcCAYAAAD7kvLzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEMJESGgLIYQQJkJCWwghhDAR/w86+Yu+gWzIogAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#http://networkx.github.io/documentation/latest/examples/drawing/giant_component.html\n",
"#illustrates sudden appearance of giant connected component in a binomial random graph.\n",
"\n",
"layout=nx.graphviz_layout\n",
"n=150 # 150 nodes\n",
"# p value at which giant component (of size log(n) nodes) is expected\n",
"p_giant=1.0/(n-1)\n",
"# p value at which graph is expected to become completely connected\n",
"p_conn=math.log(n)/float(n)\n",
"\n",
"# the following range of p values should be close to the threshold\n",
"pvals=[0.003, 0.006, 0.008, 0.015]\n",
"\n",
"figure(figsize=(8,8))\n",
"region=220 # for pylab 2x2 subplot layout\n",
"subplots_adjust(left=0,right=1,bottom=0,top=0.95,wspace=0.01,hspace=0.01)\n",
"for p in pvals:\n",
" G=nx.binomial_graph(n,p)\n",
" pos=layout(G)\n",
" region+=1\n",
" subplot(region)\n",
" title(\"p = %6.3f\"%(p))\n",
" nx.draw(G,pos, with_labels=False, node_size=10)\n",
" # identify largest connected component\n",
" Gcc=nx.connected_component_subgraphs(G)\n",
" G0=Gcc[0]\n",
" nx.draw_networkx_edges(G0,pos, with_labels=False,\n",
" edge_color='r', width=6.0)\n",
" # show other connected components\n",
" for Gi in Gcc[1:]:\n",
" if len(Gi)>1: nx.draw_networkx_edges(Gi,pos,with_labels=False,\n",
" edge_color='r',alpha=0.3,width=5.0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAncAAAJLCAYAAACFcrC0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUl7k/wFf7XjWViGt3gAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#http://networkx.github.io/documentation/latest/examples/graph/napoleon_russian_campaign.html\n",
"#Minard's data from Napoleon's 1812-1813 Russian Campaign.\n",
"#http://www.math.yorku.ca/SCS/Gallery/minard/minard.txt\n",
"\n",
"import string\n",
"def minard_graph():\n",
" data1=\"\"\"\\\n",
"24.0,54.9,340000,A,1\n",
"24.5,55.0,340000,A,1\n",
"25.5,54.5,340000,A,1\n",
"26.0,54.7,320000,A,1\n",
"27.0,54.8,300000,A,1\n",
"28.0,54.9,280000,A,1\n",
"28.5,55.0,240000,A,1\n",
"29.0,55.1,210000,A,1\n",
"30.0,55.2,180000,A,1\n",
"30.3,55.3,175000,A,1\n",
"32.0,54.8,145000,A,1\n",
"33.2,54.9,140000,A,1\n",
"34.4,55.5,127100,A,1\n",
"35.5,55.4,100000,A,1\n",
"36.0,55.5,100000,A,1\n",
"37.6,55.8,100000,A,1\n",
"37.7,55.7,100000,R,1\n",
"37.5,55.7,98000,R,1\n",
"37.0,55.0,97000,R,1\n",
"36.8,55.0,96000,R,1\n",
"35.4,55.3,87000,R,1\n",
"34.3,55.2,55000,R,1\n",
"33.3,54.8,37000,R,1\n",
"32.0,54.6,24000,R,1\n",
"30.4,54.4,20000,R,1\n",
"29.2,54.3,20000,R,1\n",
"28.5,54.2,20000,R,1\n",
"28.3,54.3,20000,R,1\n",
"27.5,54.5,20000,R,1\n",
"26.8,54.3,12000,R,1\n",
"26.4,54.4,14000,R,1\n",
"25.0,54.4,8000,R,1\n",
"24.4,54.4,4000,R,1\n",
"24.2,54.4,4000,R,1\n",
"24.1,54.4,4000,R,1\"\"\"\n",
" data2=\"\"\"\\\n",
"24.0,55.1,60000,A,2\n",
"24.5,55.2,60000,A,2\n",
"25.5,54.7,60000,A,2\n",
"26.6,55.7,40000,A,2\n",
"27.4,55.6,33000,A,2\n",
"28.7,55.5,33000,R,2\n",
"29.2,54.2,30000,R,2\n",
"28.5,54.1,30000,R,2\n",
"28.3,54.2,28000,R,2\"\"\"\n",
" data3=\"\"\"\\\n",
"24.0,55.2,22000,A,3\n",
"24.5,55.3,22000,A,3\n",
"24.6,55.8,6000,A,3\n",
"24.6,55.8,6000,R,3\n",
"24.2,54.4,6000,R,3\n",
"24.1,54.4,6000,R,3\"\"\"\n",
" cities=\"\"\"\\\n",
"24.0,55.0,Kowno\n",
"25.3,54.7,Wilna\n",
"26.4,54.4,Smorgoni\n",
"26.8,54.3,Moiodexno\n",
"27.7,55.2,Gloubokoe\n",
"27.6,53.9,Minsk\n",
"28.5,54.3,Studienska\n",
"28.7,55.5,Polotzk\n",
"29.2,54.4,Bobr\n",
"30.2,55.3,Witebsk\n",
"30.4,54.5,Orscha\n",
"30.4,53.9,Mohilow\n",
"32.0,54.8,Smolensk\n",
"33.2,54.9,Dorogobouge\n",
"34.3,55.2,Wixma\n",
"34.4,55.5,Chjat\n",
"36.0,55.5,Mojaisk\n",
"37.6,55.8,Moscou\n",
"36.6,55.3,Tarantino\n",
"36.5,55.0,Malo-Jarosewii\"\"\"\n",
" c={}\n",
" for line in cities.split('\\n'):\n",
" x,y,name=line.split(',')\n",
" c[name]=(float(x),float(y))\n",
"\n",
" g=[] \n",
"\n",
" for data in [data1,data2,data3]:\n",
" G=nx.Graph()\n",
" i=0\n",
" G.pos={} # location\n",
" G.pop={} # size\n",
" last=None\n",
" for line in data.split('\\n'):\n",
" x,y,p,r,n=line.split(',')\n",
" G.pos[i]=(float(x),float(y))\n",
" G.pop[i]=int(p)\n",
" if last is None:\n",
" last=i\n",
" else:\n",
" G.add_edge(i,last,{r:int(n)})\n",
" last=i\n",
" i=i+1\n",
" g.append(G) \n",
"\n",
" return g,c \n",
"\n",
"\n",
"(g,city)=minard_graph()\n",
"\n",
"figure(1,figsize=(11,5))\n",
"\n",
"colors=['b','g','r']\n",
"for G in g:\n",
" c=colors.pop(0)\n",
" node_size=[int(G.pop[n]/300.0) for n in G]\n",
" nx.draw_networkx_edges(G,G.pos,edge_color=c,width=4,alpha=0.5)\n",
" nx.draw_networkx_nodes(G,G.pos,node_size=node_size,node_color=c,alpha=0.5)\n",
" nx.draw_networkx_nodes(G,G.pos,node_size=5,node_color='k')\n",
"\n",
"for c in city:\n",
" x,y=city[c]\n",
" text(x,y+0.1,c)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAApIAAAE1CAYAAABUeQCtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_evoked_topomap_delayed_ssp.ipynb | 3 | 3690 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n# Create topographic ERF maps in delayed SSP mode\n\n\nThis script shows how to apply SSP projectors delayed, that is,\nat the evoked stage. This is particularly useful to support decisions\nrelated to the trade-off between denoising and preserving signal.\nIn this example we demonstrate how to use topographic maps for delayed\nSSP application.\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Authors: Denis Engemann <[email protected]>\n# Christian Brodbeck <[email protected]>\n# Alexandre Gramfort <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport numpy as np\nimport mne\nfrom mne import io\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Set parameters\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\nevent_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'\necg_fname = data_path + '/MEG/sample/sample_audvis_ecg_proj.fif'\nevent_id, tmin, tmax = 1, -0.2, 0.5\n\n# Setup for reading the raw data\nraw = io.Raw(raw_fname)\nevents = mne.read_events(event_fname)\n\n# delete EEG projections (we know it's the last one)\nraw.del_proj(-1)\n# add ECG projs for magnetometers\n[raw.add_proj(p) for p in mne.read_proj(ecg_fname) if 'axial' in p['desc']]\n\n# pick magnetometer channels\npicks = mne.pick_types(raw.info, meg='mag', stim=False, eog=True,\n include=[], exclude='bads')\n\n# We will make of the proj `delayed` option to\n# interactively select projections at the evoked stage.\n# more information can be found in the example/plot_evoked_delayed_ssp.py\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,\n baseline=(None, 0), reject=dict(mag=4e-12), proj='delayed')\n\nevoked = epochs.average() # average epochs and get an Evoked dataset."
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Interactively select / deselect the SSP projection vectors\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# set time instants in seconds (from 50 to 150ms in a step of 10ms)\ntimes = np.arange(0.05, 0.15, 0.01)\n\nevoked.plot_topomap(times, proj='interactive')\n# Hint: the same works for evoked.plot and evoked.plot_topo"
],
"outputs": [],
"metadata": {
"collapsed": false
}
}
],
"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": "2.7.11",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | bsd-3-clause |
ajgpitch/qutip-notebooks | examples/qip-processor-DJ-algorithm.ipynb | 1 | 90817 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Running the Deutsch–Jozsa algorithm on the noisy device simulator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Author: Boxi Li ([email protected])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this example, we demonstrate how to simulate simple quantum algorithms on a qauntum hardware with QuTiP. The simulators are defined in the class `Processor`(and its sub-classes). `Processor` represents a general quantum device. The interaction of the quantum systems such as qubits is defined by the control Hamiltonian. One can set the amplitude of the interaction by the attribute `coeff` which corresponds to the pulse intensity of the control system. For more details please refer to [the introductory notebook](qip-noisy-device-simulator.ipynb).\n",
"\n",
"In this example, we won't set the interaction strength by ourselves. Instead, we give it a sequence of gates, i.e. a `QubitCircuit`, and let the `Processor` find the desired pulses. The `Processor` class has a method `load_circuit` that can transfer a `QubitCircuit` object into a control pulse sequence. Different sub-class of `Processor` find their pulses in different ways. We show two examples here, one is based on a physical model and the other uses the `qutip.control` module. For each case, we also compare the result with or without noise by defining the `t1` and `t2` time of the device.\n",
"\n",
"## The Deutsch–Jozsa algorithm\n",
"The Deutsch–Jozsa algorithm is the simplest quantum algorithm that offers an exponential speed-up compared to the classical one. It assumes that we have a function $f:\\{0,1\\}^n \\rightarrow \\{0,1\\}$ which is either balanced or constant. Constant means that $f(x)$ is either 1 or 0 for all inputs while balanced means that $f(x)$ is 1 for half of the input domain and 0 for the other half. A more rigorous definition can be found at https://en.wikipedia.org/wiki/Deutsch-Jozsa_algorithm.\n",
"\n",
"The implementation of the Deutsch–Jozsa algorithm inclues $n$ input qubits and 1 ancilla initialised in state $1$. At the end of the algorithm, the first $n$ qubits are measured on the computational basis. If the function is constant, the result will be $0$ for all $n$ qubits. If balanced, $\\left|00...0\\right\\rangle$ will never be measured.\n",
"The following example is implemented for the balanced function $f:\\{00,01,10,11\\} \\rightarrow \\{0,1\\}$, where $f(00)=f(11)=0$ and $f(01)=f(10)=1$. This function is balanced, so the probability of measuring state $\\left|00\\right\\rangle$ should be 0."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from qutip.qip.device import OptPulseProcessor, LinearSpinChain\n",
"from qutip.qip.circuit import QubitCircuit\n",
"from qutip.operators import sigmaz, sigmax, identity\n",
"from qutip.tensor import tensor\n",
"from qutip.states import basis\n",
"from qutip.qobj import ptrace\n",
"basis00 = tensor([basis(2,0), basis(2,0)])"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"qc = QubitCircuit(N=3)\n",
"qc.add_gate(\"SNOT\", targets=0)\n",
"qc.add_gate(\"SNOT\", targets=1)\n",
"qc.add_gate(\"SNOT\", targets=2)\n",
"\n",
"# function f(x)\n",
"qc.add_gate(\"CNOT\", controls=0, targets=2)\n",
"qc.add_gate(\"CNOT\", controls=1, targets=2)\n",
"\n",
"qc.add_gate(\"SNOT\", targets=0)\n",
"qc.add_gate(\"SNOT\", targets=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using the optimal control module to find the pulse\n",
"This feature integrated into the sub-class `OptPulseProcessor` which use methods in the optimal control module to find the optimal pulse sequence for the desired gates. It can find the optimal pulse either for the whole unitary evolution or for each gate. Here we choose the second option."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"********** Gate 0 **********\n",
"Final fidelity error 1.9392365491199826e-10\n",
"Final gradient normal 7.815011344686465e-06\n",
"Terminated due to function converged\n",
"Number of iterations 15\n",
"********** Gate 1 **********\n",
"Final fidelity error 6.092241155997158e-10\n",
"Final gradient normal 1.2203284511145281e-05\n",
"Terminated due to function converged\n",
"Number of iterations 16\n",
"********** Gate 2 **********\n",
"Final fidelity error 4.0477787788262276e-10\n",
"Final gradient normal 1.2685480550581627e-05\n",
"Terminated due to function converged\n",
"Number of iterations 19\n",
"********** Gate 3 **********\n",
"Final fidelity error 1.254523354754511e-06\n",
"Final gradient normal 0.00021647097373448686\n",
"Terminated due to function converged\n",
"Number of iterations 139\n",
"********** Gate 4 **********\n",
"Final fidelity error 2.349601027262782e-05\n",
"Final gradient normal 0.003121190621829365\n",
"Terminated due to function converged\n",
"Number of iterations 36\n",
"********** Gate 5 **********\n",
"Final fidelity error 2.9691660241581985e-10\n",
"Final gradient normal 1.0759808980245661e-05\n",
"Terminated due to function converged\n",
"Number of iterations 16\n",
"********** Gate 6 **********\n",
"Final fidelity error 2.81108802901997e-10\n",
"Final gradient normal 9.019307538532246e-06\n",
"Terminated due to function converged\n",
"Number of iterations 16\n"
]
}
],
"source": [
"setting_args = {\"SNOT\": {\"num_tslots\": 5, \"evo_time\": 1},\n",
" \"CNOT\": {\"num_tslots\": 12, \"evo_time\": 5}}\n",
"processor = OptPulseProcessor(N=3)\n",
"processor.add_control(sigmaz(), cyclic_permutation=True)\n",
"processor.add_control(sigmax(), cyclic_permutation=True)\n",
"processor.add_control(tensor([sigmax(), sigmax(), identity(2)]))\n",
"processor.add_control(tensor([identity(2), sigmax(), sigmax()]))\n",
"processor.load_circuit(qc, setting_args=setting_args, merge_gates=False, verbose=True,\n",
" amp_ubound=5, amp_lbound=0);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To quickly visualize the pulse, `Processor` has a method called `plot_pulses`. In the figure bellow, each colour represents the pulse sequence of one control Hamiltonian in the system as a function of time. In each time interval, the pulse remains constant."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 800x400 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"processor.plot_pulses(title=\"Control pulse of OptPulseProcessor\", figsize=(8, 4), dpi=100);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To simulate the evolution, we only need to call the method `run_state` which calls one of the open system solvers in QuTiP and calculate the time evolution.\n",
"### Without decoherence"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Probability of measuring state 00:\n",
"7.686362456299262e-07\n"
]
}
],
"source": [
"psi0 = tensor([basis(2, 0), basis(2, 0), basis(2, 1)])\n",
"result = processor.run_state(init_state=psi0)\n",
"print(\"Probability of measuring state 00:\")\n",
"print(np.real((basis00.dag() * ptrace(result.states[-1], [0,1]) * basis00)[0,0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### With decoherence"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Probability of measuring state 00:\n",
"0.09532818184024891\n"
]
}
],
"source": [
"processor.t1 = 100\n",
"processor.t2 = 30\n",
"psi0 = tensor([basis(2, 0), basis(2, 0), basis(2, 1)])\n",
"result = processor.run_state(init_state=psi0)\n",
"print(\"Probability of measuring state 00:\")\n",
"print(np.real((basis00.dag() * ptrace(result.states[-1], [0,1]) * basis00)[0,0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that under noisy evolution their is a none zero probability of measuring state 00."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generating pulse based on quantum computing model\n",
"Below, we simulate the same quantum circuit using one sub-class `LinearSpinChain`. It will find the pulse based on the Hamiltonian available on a quantum computer of the linear spin chain system.\n",
"Please refer to [the notebook of the spin chain model](https://nbviewer.jupyter.org/github/qutip/qutip-notebooks/blob/master/examples/spin-chain-model.ipynb) for more details."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"processor2 = LinearSpinChain(3)\n",
"processor2.load_circuit(qc);"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"processor2.plot_pulses(title=\"Control pulse of Spin chain\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first three pulse periods (from $t=0$ to $t\\approx5$) are for the three Hadamard gates, they are followed by two long periods for the CNOT gates and then again two Hadamard. Different colours represent different kinds of interaction, as shown in the legend."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Without decoherence"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Probability of measuring state 00:\n",
"1.8002112771931593e-09\n"
]
}
],
"source": [
"psi0 = tensor([basis(2, 0), basis(2, 0), basis(2, 1)])\n",
"result = processor2.run_state(init_state=psi0)\n",
"print(\"Probability of measuring state 00:\")\n",
"print(np.real((basis00.dag() * ptrace(result.states[-1], [0,1]) * basis00)[0,0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### With decoherence"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Probability of measuring state 00:\n",
"0.1661582845316164\n"
]
}
],
"source": [
"processor2.t1 = 100\n",
"processor2.t2 = 30\n",
"psi0 = tensor([basis(2, 0), basis(2, 0), basis(2, 1)])\n",
"result = processor2.run_state(init_state=psi0)\n",
"print(\"Probability of measuring state 00:\")\n",
"print(np.real((basis00.dag() * ptrace(result.states[-1], [0,1]) * basis00)[0,0]))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table><tr><th>Software</th><th>Version</th></tr><tr><td>QuTiP</td><td>4.5.0.dev0+4ad874f6</td></tr><tr><td>Numpy</td><td>1.17.5</td></tr><tr><td>SciPy</td><td>1.2.1</td></tr><tr><td>matplotlib</td><td>2.2.4</td></tr><tr><td>Cython</td><td>0.29.14</td></tr><tr><td>Number of CPUs</td><td>12</td></tr><tr><td>BLAS Info</td><td>Generic</td></tr><tr><td>IPython</td><td>7.11.1</td></tr><tr><td>Python</td><td>3.6.7 (default, Dec 6 2019, 07:03:06) [MSC v.1900 64 bit (AMD64)]</td></tr><tr><td>OS</td><td>nt [win32]</td></tr><tr><td colspan='2'>Tue Jan 28 23:31:08 2020 W. Europe Standard Time</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from qutip.ipynbtools import version_table\n",
"version_table()"
]
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| lgpl-3.0 |
thompsonj/deepspeechsynthesis | SampleDBM.ipynb | 1 | 8796 | {
"metadata": {
"name": "Sample DBM"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": "#!/usr/bin/env python\n__authors__ = \"Ian Goodfellow\"\n__copyright__ = \"Copyright 2012, Universite de Montreal\"\n__credits__ = [\"Ian Goodfellow\"]\n__license__ = \"3-clause BSD\"\n__maintainer__ = \"Ian Goodfellow\"\n\"\"\"\n\nUsage: python show_samples <path_to_a_saved_DBM.pkl>\nDisplays a batch of data from the DBM's training set.\nThen interactively allows the user to run Gibbs steps\nstarting from that seed data to see how the DBM's MCMC\nsampling changes the data.\n\n\"\"\"\n\nfrom pylearn2.utils import serial\nimport sys\nfrom pylearn2.config import yaml_parse\nfrom pylearn2.gui.patch_viewer import PatchViewer\nimport time\nfrom theano import function\nfrom theano.sandbox.rng_mrg import MRG_RandomStreams\nimport numpy as np\nfrom pylearn2.expr.basic import is_binary\nimport scipy.io.wavfile",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": "rows = 10\ncols = 10\nm = rows * cols\n\nmodel_path = 'timit_gdbm_pcd.pkl'\n\nprint 'Loading model...'\nmodel = serial.load(model_path)\nmodel.set_batch_size(m)\n\n\ndataset_yaml_src = model.dataset_yaml_src\n\nprint 'Loading data (used for setting up visualization and seeding gibbs chain) ...'\ndataset = yaml_parse.load(dataset_yaml_src)",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Loading model...\nLoading data (used for setting up visualization and seeding gibbs chain) ..."
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\n"
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": "# vis_batch = dataset.get_batch_topo(m)\n\n# _, patch_rows, patch_cols, channels = vis_batch.shape\n\n# assert _ == m\n\n# mapback = hasattr(dataset, 'mapback_for_viewer')\n\n# pv = PatchViewer((rows,cols*(1+mapback)), (patch_rows,patch_cols), is_color = (channels==3))\n\n# def show():\n# display_batch = dataset.adjust_for_viewer(vis_batch)\n# if display_batch.ndim == 2:\n# display_batch = dataset.get_topological_view(display_batch)\n# if mapback:\n# design_vis_batch = vis_batch\n# if design_vis_batch.ndim != 2:\n# design_vis_batch = dataset.get_design_matrix(design_vis_batch)\n# mapped_batch_design = dataset.mapback_for_viewer(design_vis_batch)\n# mapped_batch = dataset.get_topological_view(mapped_batch_design)\n# for i in xrange(rows):\n# row_start = cols * i\n# for j in xrange(cols):\n# pv.add_patch(display_batch[row_start+j,:,:,:], rescale = False)\n# if mapback:\n# pv.add_patch(mapped_batch[row_start+j,:,:,:], rescale = False)\n# pv.show()\n\n\nif hasattr(model.visible_layer, 'beta'):\n beta = model.visible_layer.beta.get_value()\n# #model.visible_layer.beta.set_value(beta * 100.)\n# print 'beta: ',(beta.min(), beta.mean(), beta.max())\n\n# print 'showing seed data...'\n# show()\n\n# print 'How many Gibbs steps should I run with the seed data clamped? (negative = ignore seed data) '\n# x = int(input())\nx=15\n\n\n# Make shared variables representing the sampling state of the model\nlayer_to_state = model.make_layer_to_state(m)\n# Seed the sampling with the data batch\nvis_sample = layer_to_state[model.visible_layer]\n\ndef validate_all_samples():\n # Run some checks on the samples, this should help catch any bugs\n layers = [ model.visible_layer ] + model.hidden_layers\n\n def check_batch_size(l):\n if isinstance(l, (list, tuple)):\n map(check_batch_size, l)\n else:\n assert l.get_value().shape[0] == m\n\n\n for layer in layers:\n state = layer_to_state[layer]\n space = layer.get_total_state_space()\n space.validate(state)\n if 'DenseMaxPool' in str(type(layer)):\n p, h = state\n p = p.get_value()\n h = h.get_value()\n assert np.all(p == h)\n assert is_binary(p)\n if 'BinaryVisLayer' in str(type(layer)):\n v = state.get_value()\n assert is_binary(v)\n if 'Softmax' in str(type(layer)):\n y = state.get_value()\n assert is_binary(y)\n s = y.sum(axis=1)\n assert np.all(s == 1 )\n if 'Ising' in str(type(layer)):\n s = state.get_value()\n assert is_binary((s + 1.) / 2.)\n\n\n\nvalidate_all_samples()\n\n# if x >= 0:\n# if vis_sample.ndim == 4:\n# vis_sample.set_value(vis_batch)\n# else:\n# vis_sample.set_value(dataset.get_design_matrix(vis_batch))",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": "validate_all_samples()\n\ntheano_rng = MRG_RandomStreams(2012+9+18)\n\nif x > 0:\n sampling_updates = model.get_sampling_updates(layer_to_state, theano_rng,\n layer_to_clamp = { model.visible_layer : True }, num_steps = x)\n\n t1 = time.time()\n sample_func = function([], updates=sampling_updates)\n t2 = time.time()\n print 'Clamped sampling function compilation took',t2-t1\n sample_func()\n\n\n# Now compile the full sampling update\nsampling_updates = model.get_sampling_updates(layer_to_state, theano_rng)\nassert layer_to_state[model.visible_layer] in sampling_updates\n\nt1 = time.time()\nsample_func = function([], updates=sampling_updates)\nt2 = time.time()\n\nprint 'Sampling function compilation took',t2-t1\n\n# while True:\n# print 'Displaying samples. How many steps to take next? (q to quit, ENTER=1)'\n# while True:\n# x = raw_input()\n# print x\n# if x == 'q':\n# quit()\n# if x == '':\n# x = 1\n# break\n# else:\n# try:\n# x = int(x)\n# break\n# except:\n# print 'Invalid input, try again'",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Clamped sampling function compilation took 2.19332385063\nSampling function compilation took"
},
{
"output_type": "stream",
"stream": "stdout",
"text": " 12.7259421349\n"
},
{
"output_type": "stream",
"stream": "stderr",
"text": "/usr/local/Cellar/python/2.7.6/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/Theano-0.6.0-py2.7.egg/theano/sandbox/rng_mrg.py:1169: UserWarning: MRG_RandomStreams Can't determine #streams from size ((Elemwise{add,no_inplace}.0,)), guessing 60*256\n nstreams = self.n_streams(size)\n"
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": "print 'Displaying samples. How many steps to take next?' \nx = raw_input()\nx = int(x)\nfor i in xrange(x):\n print i\n sample_func()\n\nvalidate_all_samples()\n\nvis_batch = vis_sample.get_value()\n #show()\n\n# if 'Softmax' in str(type(model.hidden_layers[-1])):\n# state = layer_to_state[model.hidden_layers[-1]]\n# value = state.get_value()\n# y = np.argmax(value, axis=1)\n# assert y.ndim == 1\n# for i in xrange(0, y.shape[0], cols):\n# print y[i:i+cols]",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Displaying samples. How many steps to take next?\n"
},
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": "15\n"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "0\n1\n2\n3\n4\n5\n6\n7\n8\n9"
},
{
"output_type": "stream",
"stream": "stdout",
"text": "\n10\n11\n12\n13\n14\n"
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": "scipy.io.wavfile.write(\"vis_sample1F.wav\", 16000, vis_batch.flatten(order='F'))",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": " ",
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | bsd-3-clause |
Kuwamai/probrobo_note | monte_calro_localization/notebook_demo.ipynb | 1 | 61090 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Notebook Demo \n",
"文章と式で説明してもわかりにくいので図を添えます。 \n",
"まずは色々設定します。 "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import math, random # 計算用、乱数の生成用ライブラリ\n",
"import matplotlib.pyplot as plt # 描画用ライブラリ"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Landmarks:\n",
" def __init__(self, array):\n",
" self.positions = array # array = [[1個めの星のx座標, 1個めの星のy座標], [2個めの星のx座標, 2個めの星のy座標]...]\n",
" \n",
" def draw(self):\n",
" # ランドマークの位置を取り出して描画\n",
" xs = [e[0] for e in self.positions]\n",
" ys = [e[1] for e in self.positions]\n",
" plt.scatter(xs,ys,s=300,marker=\"*\",label=\"landmarks\",color=\"orange\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def Movement(pos, fw, rot):\n",
" # 移動モデル\n",
" # posからfw前進、rot回転した位置をリストで返す\n",
" \n",
" # 雑音の入った前進、回転の動き\n",
" actual_fw = random.gauss(fw, fw * 0.2) # 20%の標準偏差でばらつく\n",
" actual_rot = random.gauss(rot, rot * 0.2) # 20%の標準偏差でばらつく\n",
" dir_error = random.gauss(0.0, math.pi / 180.0 * 3.0) # 3[deg]の標準偏差\n",
" \n",
" # 異動前の位置を保存\n",
" px, py, pt = pos\n",
"\n",
" # 移動後の位置を計算\n",
" x = px + actual_fw * math.cos(pt + dir_error)\n",
" y = py + actual_fw * math.sin(pt + dir_error)\n",
" t = pt + dir_error + actual_rot # dir_errorを足す\n",
"\n",
" # 結果を返す\n",
" return [x,y,t]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def Observation(pos, landmark):\n",
" # 観測モデル\n",
" # posから見えるランドマークの距離と方向をリストで返す\n",
" \n",
" obss = []\n",
" \n",
" # センサの計測範囲\n",
" # 距離0.1 ~ 1\n",
" # 角度90 ~ -90[deg]\n",
" sensor_max_range = 1.0\n",
" sensor_min_range = 0.1\n",
" sensor_max_angle = math.pi / 2\n",
" sensor_min_angle = -math.pi / 2\n",
" \n",
" # ロボットやパーティクルの位置姿勢を保存\n",
" rx, ry, rt = pos\n",
" \n",
" # ランドマークごとに観測\n",
" for lpos in landmark.positions:\n",
" true_lx, true_ly = lpos\n",
" # 観測が成功したらresultをTrue\n",
" result = True\n",
"\n",
" # ロボットとランドマークの距離を計算\n",
" # センサの範囲外であればresultがFalseに\n",
" distance = math.sqrt((rx - true_lx) ** 2 + (ry - true_ly) ** 2)\n",
" if distance > sensor_max_range or distance < sensor_min_range:\n",
" result = False\n",
"\n",
" # ロボットから見えるランドマークの方向を計算\n",
" # こちらもセンサの範囲外であればresultがFalseに\n",
" direction = math.atan2(true_ly - ry, true_lx - rx) - rt\n",
" if direction > math.pi: direction -= 2 * math.pi\n",
" if direction < - math.pi: direction += 2 * math.pi\n",
" if direction > sensor_max_angle or direction < sensor_min_angle:\n",
" result = False\n",
"\n",
" # 雑音の大きさを設定\n",
" # これは尤度計算に使う正規分布関数の分散になる\n",
" sigma_d = distance * 0.2 # 20%の標準偏差\n",
" sigma_f = math.pi * 3 / 180 # 3degの標準偏差\n",
"\n",
" # 雑音を混ぜる\n",
" d = random.gauss(distance, sigma_d)\n",
" f = random.gauss(direction, sigma_f)\n",
" \n",
" # 観測データを保存\n",
" z = []\n",
" z.append([d, f, sigma_d, sigma_f, result])\n",
" \n",
" return z"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Robot:\n",
" def __init__(self, x, y, rad):\n",
" random.seed()\n",
" \n",
" # ステップごとにロボットの姿勢の真値が入った配列\n",
" self.actual_poses = [[x,y,rad]]\n",
"\n",
" def move(self,fw,rot):\n",
" # ロボットの位置を記録する(軌跡を残すために配列に入れてる)\n",
" self.actual_poses.append(Movement(self.actual_poses[-1], fw, rot))\n",
" \n",
" def observation(self, landmarks):\n",
" # 現在地から見た観測データの保存\n",
" self.z = Observation(self.actual_poses[-1], landmarks)\n",
" \n",
"\n",
" # 矢印の描画に必要な位置と方向を計算して描画\n",
" def draw(self, sp):\n",
" xs = [e[0] for e in self.actual_poses]\n",
" ys = [e[1] for e in self.actual_poses]\n",
" vxs = [math.cos(e[2]) for e in self.actual_poses]\n",
" vys = [math.sin(e[2]) for e in self.actual_poses]\n",
" plt.quiver(xs,ys,vxs,vys,color=\"red\",scale=15,angles='xy',scale_units='xy',alpha = 0.3)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def draw(i):\n",
" # グラフの設定\n",
" fig = plt.figure(i, figsize=(8,8))\n",
" sp = fig.add_subplot(111,aspect='equal')\n",
" sp.set_xlim(-0.5,2.0)\n",
" sp.set_ylim(-0.5,0.5)\n",
" \n",
" # ロボット、ランドマークの描画\n",
" for robot in robots:\n",
" robot.draw(sp)\n",
" \n",
" if i:\n",
" for robot in robots:\n",
" for obs in robot.z:\n",
" d = obs[0]\n",
" f = obs[1]\n",
" x = d * math.cos(f)\n",
" y = d * math.sin(f)\n",
" plt.plot(x, y, \"o\")\n",
" \n",
" actual_landmarks.draw()\n",
" \n",
" plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 移動モデル \n",
"ロボットを100台出してみます。 \n",
"どのロボットも1前進という命令を出しましたが、バラバラしていることがわかります。 "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAADVCAYAAABDjRnUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bf2edd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"actual_landmarks = Landmarks([[1.0,0.0]])\n",
"robots = []\n",
"\n",
"for i in range(100):\n",
" robots.append(Robot(0,0,0))\n",
"\n",
"for robot in robots:\n",
" robot.move(1.0, 0)\n",
" \n",
"draw(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 観測モデル \n",
"同様にロボットを100台出して、1先に見える星を観測させます。 \n",
"カラフルなドットがロボットから見える星の位置です。 "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAADVCAYAAABDjRnUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bf2e828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"actual_landmarks = Landmarks([[1.0,0.0]])\n",
"robots = []\n",
"\n",
"for i in range(100):\n",
" robots.append(Robot(0,0,0))\n",
" \n",
"for robot in robots:\n",
" robot.observation(actual_landmarks)\n",
" \n",
"draw(1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 尤度計算 \n",
"今回は尤度計算に正規分布を使っています。 \n",
"ロボットの観測データ$d, \\varphi$と、パーティクルの観測データ$d', \\varphi'$の差が大きいほど小さな値になります。 \n",
"\n",
"$$\n",
"p(z|x_t) \\propto \\frac {\\exp (- \\frac {1}{2\\sigma_d}(d-d')^2)}{\\sigma_d \\sqrt{2 \\pi}} \\frac {\\exp (- \\frac {1}{2\\sigma_\\varphi}(\\varphi-\\varphi')^2)}{\\sigma_\\varphi \\sqrt{2 \\pi}}\n",
"$$\n",
" \n",
"下のグラフは$d$と$\\varphi$についてそれぞれ別々に描画した正規分布のグラフです。 \n",
"$d$がオレンジ、$\\varphi$が青のグラフです。 \n",
"上の観測モデルと同じように、ランドマークはロボットから見て、0°の向きに1離れた位置にあります。 \n",
"なので$d$、distanceのグラフは、1に近いほど値が大きく、$\\varphi$、directionのグラフは0に近いほど値が大きくなっています。 "
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x10f78d978>"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlEAAAD8CAYAAABXYfHHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngEKUiIiISAv8fztUvi4arZPmAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f661c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rd = 1\n",
"rf = 0\n",
"sigma_rd = 1.0 * 0.2\n",
"sigma_rf = math.pi * 3 / 180\n",
"\n",
"pd = np.arange(-3, 3, 0.01)\n",
"pf = np.arange(-3, 3, 0.01)\n",
"\n",
"d = np.exp(-(rd - pd) ** 2 / (2 * (sigma_rd ** 2))) / (sigma_rd * np.sqrt(2 * np.pi))\n",
"f = np.exp(-(rf - pf) ** 2 / (2 * (sigma_rf ** 2))) / (sigma_rf * np.sqrt(2 * np.pi))\n",
"\n",
"fig = plt.figure(figsize=(10,4))\n",
"sp = fig.add_subplot(111)\n",
"sp.set_xlim(-0.5,2.0)\n",
"sp.set_ylim(-0.5,8)\n",
"\n",
"plt.plot(pd, d, color = \"orange\",label=\"distance\")\n",
"plt.plot(pf, f, color = \"blue\",label=\"direction\")\n",
"\n",
"plt.legend()"
]
},
{
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ksakloth/KnowledgeNetworks | doc/technology review/Selenium/SeleniumForCompendex - Rahul.ipynb | 1 | 6333 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import selenium\n",
"from selenium import webdriver\n",
"from selenium.webdriver.common.keys import Keys\n",
"from selenium.webdriver.support.ui import Select\n",
"from selenium.webdriver.support.ui import WebDriverWait\n",
"from selenium.webdriver.support import expected_conditions as EC\n",
"from selenium.webdriver.common.by import By"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def compendex(username,password,keywords,no_of_pages,pg_no):\n",
" \"\"\"\n",
" Function to download results from Compendex\n",
" \"\"\"\n",
" #Initializing driver\n",
" driver = webdriver.Chrome()\n",
" driver.get(\"https://www-engineeringvillage-com.offcampus.lib.washington.edu/search/quick.url?CID=quickSearch&database=compendex\")\n",
" #UW Net ID Login\n",
" if (driver.title == \"UW NetID Weblogin\"):\n",
" username_textbox = driver.find_element_by_id('weblogin_netid')\n",
" username_textbox.send_keys(username)\n",
" password_textbox = driver.find_element_by_id('weblogin_password')\n",
" password_textbox.send_keys(password)\n",
" submit_button = driver.find_element_by_name('submit').click()\n",
" #Search keywords\n",
" driver.find_element_by_id(\"srchWrd1\").send_keys(keywords[0])\n",
" driver.find_element_by_id(\"addsearchllink\").click()\n",
" for i in range(len(keywords)-1):\n",
" if i<2:\n",
" dropdown1 = driver.find_element_by_id(\"cbnt%d\"%(i+1))\n",
" Select(dropdown1).select_by_visible_text(\"OR\")\n",
" driver.find_element_by_id(\"srchWrd%d\"%(i+2)).send_keys(keywords[i+1])\n",
" else:\n",
" dropdown1 = driver.find_element_by_id('connector')\n",
" Select(dropdown1).select_by_visible_text(\"OR\")\n",
" driver.find_element_by_name(\"searchWords\").send_keys(keywords[i+1])\n",
" driver.find_element_by_id(\"addsearchllink\").click()\n",
" driver.find_element_by_xpath('//*[@id=\"advancedOptionstoggleAnchor\"]').click()\n",
" WebDriverWait(driver, 30).until(EC.visibility_of_element_located((By.XPATH,'//*[@id=\"startyrrange\"]')));\n",
" dropdown1 = driver.find_element_by_xpath('//*[@id=\"startyrrange\"]')\n",
" Select(dropdown1).select_by_visible_text(\"2000\")\n",
" driver.find_element_by_id(\"submitsearch_tool\").click()\n",
" #Increase number of results in page to 100\n",
" WebDriverWait(driver,5).until(EC.presence_of_element_located((By.ID, \"pageSizeVal_top\")))\n",
" dropdown1=driver.find_element_by_id(\"pageSizeVal_top\")\n",
" Select(dropdown1).select_by_visible_text(\"100\")\n",
" #Page number\n",
" WebDriverWait(driver,5).until(EC.presence_of_element_located((By.XPATH, '//*[@id=\"gotopage_top\"]/span[2]/input[1]')))\n",
" driver.find_element_by_xpath('//*[@id=\"gotopage_top\"]/span[2]/input[1]').clear()\n",
" driver.find_element_by_xpath('//*[@id=\"gotopage_top\"]/span[2]/input[1]').send_keys(pg_no)\n",
" driver.find_element_by_xpath('//*[@id=\"gotopage_top\"]/span[2]/input[2]').click()\n",
" #Select all results\n",
" WebDriverWait(driver,5).until(EC.presence_of_element_located((By.ID, \"pageckbx\")))\n",
" driver.find_element_by_id(\"pageckbx\").click()\n",
" for i in range(4):\n",
" driver.find_element_by_class_name(\"blackpipeleft\").click()\n",
" WebDriverWait(driver,5).until(EC.presence_of_element_located((By.ID, \"pageckbx\")))\n",
" driver.find_element_by_id(\"pageckbx\").click()\n",
" WebDriverWait(driver,5).until(EC.presence_of_element_located((By.ID, \"downloadli\")))\n",
" WebDriverWait(driver, 30).until(EC.visibility_of_element_located((By.ID,'downloadli')));\n",
" driver.find_element_by_id(\"downloadli\").click()\n",
" WebDriverWait(driver,5).until(EC.presence_of_element_located((By.ID, \"rdCsv\")))\n",
" driver.find_element_by_id(\"rdCsv\").click()\n",
" driver.find_element_by_id(\"rdDet\").click()\n",
" driver.find_element_by_id(\"savePrefsButton\").click()\n",
" for i in range(no_of_pages-1):\n",
" driver.find_element_by_id(\"clearbasket\").click()\n",
" #WebDriverWait(driver,5).until(EC.presence_of_element_located((By.XPATH,\"/html/body/div[12]/div[3]/div/button[1]\")))\n",
" driver.find_element_by_xpath(\"/html/body/div[12]/div[3]/div/button[1]\").click()\n",
" for i in range(5):\n",
" driver.find_element_by_class_name(\"blackpipeleft\").click()\n",
" WebDriverWait(driver,5).until(EC.presence_of_element_located((By.ID, \"pageckbx\")))\n",
" driver.find_element_by_id(\"pageckbx\").click()\n",
" WebDriverWait(driver,5).until(EC.presence_of_element_located((By.ID, \"downloadli\")))\n",
" WebDriverWait(driver, 30).until(EC.visibility_of_element_located((By.ID,'downloadli')));\n",
" driver.find_element_by_id(\"downloadli\").click()\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"username = '<username>'\n",
"password = '<password>'\n",
"keywords = ['nuclear','atomic','uranium','centrifuge']\n",
"#Each page has 500 results\n",
"no_of_pages = 8\n",
"#Page Number\n",
"pg_no = 1\n",
"compendex(username,password,keywords,no_of_pages,pg_no)"
]
}
],
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
stereoboy/Study | tools/torch.ipynb | 1 | 2158 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pytorch\n",
"* References\n",
" * http://pytorch.org/about/\n",
" \n",
"* tensorflow와 다르게 기본 데이터 구조인 tensor가 cpu tensor(torch.Tensor)와 gput tensor(torch.cuda.Tensor)로 나뉜다.\n",
" * WHY???\n",
" * torch VS Others\n",
" * https://www.reddit.com/r/MachineLearning/comments/5w3q74/d_so_pytorch_vs_tensorflow_whats_the_verdict_on/\n",
" \n",
"* Dynamic Computational Graph???\n",
" * https://medium.com/intuitionmachine/pytorch-dynamic-computational-graphs-and-modular-deep-learning-7e7f89f18d1\n",
" \n",
" ```\n",
" dynamic computational graphs are valuable for situations where you cannot determine the computation. One clear example of this are recursive computations that are based on variable data.\n",
" ```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## load/save\n",
"* Model Architecture/ Only the model parameters\n",
"\n",
"```python\n",
"#============================ Save and load the model ============================#\n",
"# Save and load the entire model.\n",
"torch.save(resnet, 'model.pkl')\n",
"model = torch.load('model.pkl')\n",
"\n",
"# Save and load only the model parameters(recommended).\n",
"torch.save(resnet.state_dict(), 'params.pkl')\n",
"resnet.load_state_dict(torch.load('params.pkl'))\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## multigpus\n",
"* Reference\n",
" * http://pytorch.org/tutorials/beginner/former_torchies/parallelism_tutorial.html"
]
}
],
"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 |
iRipVanWinkle/ml | Data Science UA - September 2017/Lecture 05 - Modeling Techniques and Regression/Linear_Regression.ipynb | 1 | 115545 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to Linear Regression\n",
"\n",
"*Adapted from Chapter 3 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)*\n",
"\n",
"Predictive modeling, using a data samples to make predictions about unobserved or future events, is a common data analytics task. Predictive modeling is considered to be a form of machine learning.\n",
"\n",
"Linear regression is a technique for predicting a response/dependent variable based on one or more explanatory/independent variables, or features. The term \"linear\" refers to the fact that the method models data as a linear combination of explanatory variables. \n",
"\n",
"Linear regression, in its simplest form, fits a straight line to the response variable data so that the line minimizes the squared differences (also called errors or residuals) between the actual obbserved response and the predicted point on the line. Since linear regression fits the observed data with a line, it is most effective when the response and the explanatory variable do have a linear relationship.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Motivating Example: Advertising Data\n",
"\n",
"Let us look at data depicting the money(in thousands of dollars) spent on TV, Radio and newspaper ads for a product in a given market, as well as the corresponding sales figures."
]
},
{
"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>TV</th>\n",
" <th>Radio</th>\n",
" <th>Newspaper</th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>230.1</td>\n",
" <td>37.8</td>\n",
" <td>69.2</td>\n",
" <td>22.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>44.5</td>\n",
" <td>39.3</td>\n",
" <td>45.1</td>\n",
" <td>10.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17.2</td>\n",
" <td>45.9</td>\n",
" <td>69.3</td>\n",
" <td>9.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>151.5</td>\n",
" <td>41.3</td>\n",
" <td>58.5</td>\n",
" <td>18.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>180.8</td>\n",
" <td>10.8</td>\n",
" <td>58.4</td>\n",
" <td>12.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TV Radio Newspaper Sales\n",
"1 230.1 37.8 69.2 22.1\n",
"2 44.5 39.3 45.1 10.4\n",
"3 17.2 45.9 69.3 9.3\n",
"4 151.5 41.3 58.5 18.5\n",
"5 180.8 10.8 58.4 12.9"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# imports\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"\n",
"# read data into a DataFrame\n",
"data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The **features**?\n",
"- TV: advertising dollars spent on TV (for a single product, in a given market) \n",
"- Radio: advertising dollars spent on Radio (for a single product, in a given market) \n",
"- Newspaper: advertising dollars spent on Newspaper (for a single product, in a given market) \n",
"\n",
"What is the **response**?\n",
"- Sales: sales of a single product in a given market (in thousands of widgets)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(200, 4)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# print the size of the DataFrame object, i.e., the size of the dataset\n",
"data.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 200 **observations**, corresponding to 200 markets.\n",
"\n",
"We can try to discover if there is any relationship between the money spend on a specific type of ad, in a given market, and the sales in that market by plotting the sales figures against each category of advertising expenditure."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fd69f930510>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA7QAAAHuCAYAAACvTUAWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XuQXNdh3/nfJdEzfYl5ALDbelGcpkhRoExCHHDBojcs\nE5AJJaWtXavsrWCRxyrJlIosGpK8kdfyateiaxG6TMeOQ8uLAFRGgb3lGYy9dqwkRXskrAZyZJfc\nUxREOBlCliL36GFZ3Ym9CGENjaF49o/uxvT0877vufd+P1VTJGamu0/33PO759xz7jmOMUYAAAAA\nAGTNLWkXAAAAAACAIOjQAgAAAAAyiQ4tAAAAACCT6NACAAAAADKJDi0AAAAAIJPo0AIAAAAAMinW\nDq3jOJOO4/yR4ziXHcf5j47j/Gz7+/sdx/mU4zhfchxn1XGc2TjLAQAAAADIHyfufWgdx7nNGPMd\nx3FulfQHkj4k6X+Q9F+MMT/vOM6HJe03xvxUrAUBAAAAAORK7FOOjTHfaf/vZPv1/lLSD0v61fb3\nf1XSe+IuBwAAAAAgX2Lv0DqOc4vjOJcl/bmkS8aYDUmvM8Z8W5KMMX8u6fviLgcAAAAAIF/2xP0C\nxpjXJM07jjMjadVxnKOSeuc5D5z37DhOvPOhAWSWMcZJuwxRIesADEPWASiCMFmX2CrHxpj/Kul5\nSf+NpG87jvM6SXIc5/WSGiMel+rXU089VfgypP36lIEy9H7lUdqfqa1/6yyUydZy2VgmW8tlY5mM\nIeuK/Pe3sUy2lsvGMtlaLhvLZEz4rIt7lePv7axg7DiOK+m4pMuS/o2kf9D+tfdK+mSc5QAAAAAA\n5E/cU47fIOlXHcdx1Oo8/9/GmP+3fU/tbziO848kbUr62zGXAwAAAACQM7F2aI0xfyzp8IDv/4Wk\nx+J87agcPXo07SKkXoa0X58yUAakw8a/tY1lkuwsl41lkuwsl41lQnJs/PvbWCbJznLZWCbJznLZ\nWKYoxL4PbRiO4xibywcgHY7jyORsoRSyDkAvsg5AEYTNusQWhQIAAAAAIEp0aAEAAAAAmUSHFgAA\nAACQSXRoAQAAAACZRIcWAAAAAJBJdGgBAAAAAJlEhxYAAAAAkEl0aAEAAAAAmUSHFgAAAACQSXRo\nAQAAAACZRIcWAAAAAJBJdGgBAAAAAJlEhxYAAAAAkEl0aAEAAAAAmUSHFgAAAACQSXRoAQAAAACZ\nRIcWAAAAAJBJdGgBAAAAAJlEhxYAAAAAkEl0aAEAAAAAmUSHFgAAAACQSXRoAQAAAACZRIcWAAAA\nAJBJdGgBAAAAAJlEhxYAAAAAkEl0aAEAAAAAmUSHFgAAAACQSXRoAQAAAACZRIcWAAAAAJBJdGgB\nAAAAAJlEhxYAAAAAkEl0aAEAAAAAmUSHFgAAAACQSXRoAQAAAACZRIcWAAAAAJBJdGgBAAAAAJlE\nhxYomGazqfX1dTWbzbSLAgCJIwOB8KhHsAkdWqBAlpdXNDd3UMePP6G5uYNaXl5Ju0gAkBgyEAiP\negTbOMaYtMswlOM4xubyAVnSbDY1N3dQW1trkg5JuiLXPabNzauqVCppF88Xx3FkjHHSLkdUyDog\nflnMQLIOtsliPYL9wmYdI7RAQdTrdU1MVNU6AUnSIZVKc6rX6+kVCgASQgYC4VGPYCM6tEBBVKtV\n3bhRl3Sl/Z0r2t7eVLVaTa9QAJAQMhAIj3oEG9GhBQqiUqlocfGMXPeYZmYOy3WPaXHxDFOEABQC\nGQiERz2CjbiHFiiYZrOper2uarWa2RMQ95UBCCpLGUjWwVZZqkewX9iso0MLIHNo5AEoArIOQBGw\nKBQAAAAAoJDo0AIAAAAAMokOLQAAAAAgk+jQAgAAAAAyiQ4tgJuazabW19fVbDbTLgqAgiOPAKSF\n/MkWOrQAJEnLyyuamzuo48ef0NzcQS0vr6RdJAAFRR4BSAv5kz1s2wNAzWZTc3MHtbW1JumQpCty\n3WPa3Lxq5f5ybGUB5FfW8ihOZB2QLPInHWzbAyC0er2uiYmqWuEtSYdUKs2pXq+nVygAhUQeAUgL\n+ZNNdGgBqFqt6saNuqQr7e9c0fb2pqrVanqFAlBI5BGAtJA/2USHFoAqlYoWF8/IdY9pZuawXPeY\nFhfPML0GQOLIIwBpIX+yiXtoAdzUbDZVr9dVrVatDm/uKwPyLyt5FCeyDkgH+ZOssFlHhxZA5tDI\nA1AEZB2AImBRKAAAAABAIdGhBQAAAABkEh1aoOCazabW19fVbDbTLgqAAiOLAMSNnMknOrRAgS0v\nr2hu7qCOH39Cc3MHtby8knaRABQQWQQgbuRMfrEoFGChsKvreXl8s9nU3NxBbW2tqbWB+BW57jG9\n8MLndP36datX9mOhFCA/hmXR5ubVmxkU5YqjWVq9lKyzQ5aOmTQF+ZyS+my95AzSw6JQQM6EvYLo\n9fH1el0TE1W1gl3t/75R8/MPc/USQGIGZdHW1n6dO/dxSdGOqjBCA784ZrwJ8jkl+dkOyplSaU71\nej2210RyGKEFLBL2CqKfxw/6XelhSc9LOur7tZPEqAWQH4Oz6KjKZaMvfOEP9eCDj0QyqpLFERqy\nLl1ZPGbSEORzSvqz5W9pN0ZogRwJewXRz+MrlYoWF8/IdY9pZuawJicfleu+Xq3OrP/XBoAgKpWK\nPvKRD0n6AUmHJR2T9C80MXGnarVaZKMqjNDAL44Zb4J8Tkl/tr1tHtc9psXFM3Rmc4IOLWCRarWq\nGzfqao1QSNIVbW9vqlqtxvL4kydPaHPzqi5ePKfLl/9Q0rXArw0AQT3++PtULk9I+glJVyXdq+3t\nTT300EOhMrFb2HxF8XDMeBPkc0rjs+1u82xuXtXJkydiey0kK9YOreM4tzuO8xnHcf6j4zh/7DjO\n+9vff8pxnG84jvOF9tffirMcQFaEvYIY5PGVSkVHjhzRvffey9VLAKmoVCr6xCfOynXfr5mZv3kz\nf6LMJUZo4BfHjDdB2x5pfLadNg9/w3yJ9R5ax3FeL+n1xpgvOo4zJekFST8s6YSkl40x/2zM4zN1\nrwUQlSRWOY7rtZPAfWVAPg3LH1Y5zoesZl2Wjpk02bzKMewWNusSXRTKcZzfkfQxSY9Ium6M+cUx\nv5/J4AMQLxp5AIqArANQBJlZFMpxnKqkByT9UftbpxzH+aLjOP/ScZzZpMoBAAAAAMiHPUm8SHu6\n8f8j6YPGmOuO45yR9H8aY4zjOP9E0j+TtDDosUePHlW1WlW1WtXRo0d19OjRJIoMwCKXLl3SpUuX\nVK/Xc7u6JFkHgKwDUARRZ13sU44dx9kj6d9J+l1jzLMDfj4n6d8aYw4N+BlTU5BZ3BcSH6bhAQgq\nS9lM1qUnS8cJkHVZmHL8CUkb3Z3Z9mJRHT8i6T8kUA4gMcvLK5qbO6jjx5/Q3NxBLS+vpF0kACg8\nshlecJwA2RL3Ksd/Q9LvS/pjSab99RFJf0et+2lfk1SX9Lgx5tsDHp+ZK3lAR7PZ1NzcQW1tram1\nYfgVue4xbW5e5SpvRBi1AOBXFrOZrEteFo8TIOvCZl2s99AaY/5A0q0DfvR7cb4ukKZ6va6Jiaq2\ntjqz6A+pVJpTvV7nZAgAKSGb4QXHCZA9ia1yDBRFtVrVjRt1SVfa37mi7e1NVavV9AoFAAVHNsML\njhMge+jQAhGrVCpaXDwj1z2mmZnDct1jWlw8Y+WV3WazqfX1dTWbzbSLAiDHbMiaLGUz0sNxYhcb\nsgP2i32V4zCycK8FMIztKyQuL69oYeFJTUy0rkYvLp7RyZMn0i6WJ9xXBmSHbVljezZ3I+vSk6Xj\nJK9syw7EJ2zW0aEFCijri17QyAOyIetZkzayDkVFdhRLFrbtARAzv1NyOotetE4SUveiFwAQlaBZ\nwzRDoNguX76sW26pSHpD+zu0UzAcHVog44Lsl8eiFwCSECRr2AMUKLbl5RW95z0n9Vd/9V1Jb5O0\nItopGIUpx0CGhZmS07k3pVSa0/b2ZqbuTWEaHpAdfrKGaYa7kXUomkEZIP2AyuUJfeITZzPTToE/\nVu9DCyBeYfbLO3nyhB577J0segEgVn6yhj1AgWIblAF7996t3/7tf6p3vetdqZYN9qJDC2TY7ul8\nrSuZfqbkVCoVGokAYuc1a8JmGoBsG5QBr732Dc3Pz6dbMFiNe2iBDGO/PAB5QqYBxUYGIAjuoQVC\nsmGvujjLYMP768V9ZUC64s6FpHLHxnzrRtYhLWnXjbRfP89s/GzZtgeI2ajtI2xZjbNSqejIkSOR\nB5Mt7w+APZLIhbgyrdu5cx/Xm998j37ohx4n34AuNpz7vWYAW3z5Y8PfNg6M0AIjdFbnnJho3dPR\nvTpn3lfjtPn9MWoBpMPmXPDj3LmP64knPijp87L5fZB1SFqW6vioNhr62fy3ZYQWiEmz2dTCwpPa\n2lrTtWsvaGtrTQsLT968CthZia8VClLeNv3O+/sD4F8ecqHZbOqDH/wJSfeo+33s2XNHpt4HEIes\n1PFxbTT0y8rfNgg6tMAQ4yr+7pX4pLytxpn39wfAvzzkQivb5yR9Xd3v48aNeqbeBxCHrNTxPHfO\n4pKVv20QdGiBIcZV/LyvxJf39wfAvzzkQrVa1auvflPShyUdk/QOSQ/r2Wd/PlPvA4hDVup4njtn\nccnK3zYI7qEFRujcn1EqzWl7e3Pg/Rk2rhYXJRvfH/eVAemyMRf86GT7rbe+UdvbX9Ozz/6CHn/8\nfWkXqw9Zh7RkoY57aaOhn41/27BZR4cWGMPGil90NPIAhJWFbCfrgNGyUI8xHh1aAIVDIw9AEZB1\nAIqAVY6BHGE/NQBFQ+4B1AMgDDq0gCXyutk1AAxD7gHUAyAsphwDFrB5s2sbMQ0PyD5ybzyyLv+o\nBwBTjoFcYD81AEVD7gHUAyAKdGgBC7CfGoCiIfcA6gEQBTq0gAXyvNk1AAxC7gHUAyAK3EMLWIT9\n1LzhvjIgP8i94ci64qAeoMjYhxZA4dDIA1AEZB2AImBRKAAAAABAIdGhBXwat/k5m6MDgD9BcpOs\nRR7ZelzbWi5AokML+DJu83M2RwcAf4LkJlmLPLL1uLa1XEAH99ACQ/Qu0DBu83M2R08O95UB+RAk\nN/0+JsuL7ZB1xWFrGyLJcmW5riIc7qEFYjDoauS4zc/ZHB0A/AmSm34ew8gSssLWNkRS5aKuIgw6\ntECPZrOphYUntbW1pmvXXtDW1poWFp7U1NTUyM3PdzZHvyRpXdIlNkcHgBF2cnNwrnZ037/n5zGD\nspx7AGEjr8f1KHHc5xpFucahriIsOrRAj2FXI69fvz5y8/NKpaKFhb8v6d2S/p6kd2th4e9FNm2G\nBRkA2CKqPKpUKiNzVeofubl48TNjHyPZO+IFDOKlLowS1whn2HJ5YWtdpd2VHdxDC/Twcq/soHs8\n4rzPZHl5RQsLT2pionWldHHxjE6ePBHqObOM+8qA9MSRR0FyVdLI++1svSfRD7KueILcR5rEsR7n\n/a021lXaXckKnXXGGGu/WsUDkre0dMG47gEzMzNvXPeAWVq6MPYxtVrNzM4eNpK5+TUzM29qtVrf\n7zYaDVOr1Uyj0Rj7vI1Gw7juASO92H7eF43rHvD02LxqZ0PqGRXVF1mHrBiVR35yzSs/uTpIkCy3\nCVkHL8LWExukWVd7s4t2V/LCZh1TjoEBTp48oc3Nq7p48Zw2N696uirn9T4Tv9OCbJ2KA6B4huXR\nuXMfj2W6Y9j794JkOZA1SdznGre06mqQRUBhH6YcAxHqTFEplea0vb3ZN0UliS0qioBpeEA6huWR\nMa/plVc+qzgyalyu5hlZB6+KXE+CGpZnL7zwOT344CO0uxIUNuv2RFkYoOhOnjyhxx5759D7TDpX\n/ba2+q/6DQvJzoIMCwvHdp2oCFUASRuURx/5yIf0C7/wW3rlFe+55se4XAVAPQliWJusswgo7a7s\nYIQWSJCX0dZRi6Nwomph1AJIV3ceSerKtTdI+rTK5R/T1772J4XPqrDIOnSjHRCtoIuAInphs457\naIEEjVv+ftT9tZVKRUeOHCFUAaSuO486uVYqPSKpKukpvfaa0cWLn0m5lEB+xLUtT5GNa5PR7soO\nRmiBiPi5kjfod7lX1jtGLQC7xJFfjI6QdWiJq31AHWvhc0gfI7SABfxeOR101Y9V9QBkVdT5xWgU\nsCOO9gF1bAcjsdnHCC0QUlRXThmh9Y5RC8AuUeYXWbiDrIMUfZ2gjsE2jNACKfNz5bTZbGp9fV3N\nZrPvZ+Pu5QAAW1UqFf3SL/2cJicf1fT0fKj8YrYKsFuU7YNms6nnn39ee/bMiTqGvGCEFgjJ65XO\nzh5xExOtDdCH7RFny70ctpRjEEYtgBZb6mkn3/bseZNu3NjUs8/+gh5//H2BnovRox1kHbqFre/d\n9fTll78i6fOKasTXhhxCdoXOOmOMtV+t4gH2W1q6YFz3gJmZmTeue8AsLV3Y9fNGo2Fc94CRXjSS\nMdKLxnUPmEajkVKJR+u8n9nZwwPfT9ra2ZB6RkX1RdYhCFvqaRz5Ni5Ti4KsQ1T66+kzRnLN9PQD\noeqYLTmEbAubdYzQAhEZdYVyfX1dx48/oWvXXrj5vZmZw7p48ZyOHDmSdFFHysLoCKMWKDqb6mlc\n+caoD1mH6Ayqp1NT9+tXfuUn9O53v5t7cZEq7qEFAhp1P2sQo1bJq1Zb04ylK+3vXNH29qaq1Wok\nrx0l7l8D7GdTPY0r3/yuPBp1pgN5Mqiefve7fxa4Myulm0PUd3SjQ4tCSnq5+iwt+JSlzjdQVDbV\nUxvyjS1IgNHiqKdp5RD1Hb2YcozCSXOKTFam0HUWjiiV5rS9vTl0Aau0MA0PsK+eppVveZ72SNYh\nalHX06RzKM/1vcjCZh0dWhROlu5nTZPNnW8aeUCLzfU0KXnOdLIOWZBkDuW5vhdZ2KzbE2VhgCzY\nPUWmdXWPKbX9KpVKYRvIQFZQT8l0IG1J5hD1HYNwDy0Kx4b7vQAA0SDTgeKgvmMQphyjsIo0VS9v\n75VpeMBueavjQeTxMyDrgME69X1qakrXr1/PVb0vIrbtAQLyuyVEVrEaIJBv1PGWomQ6gFZ9/8pX\nvqoHH3yk8NkHRmhRcDZd0Y+jLGFXA7Tp8+nGqAWKrLteSoplxU/b6r5t5UkKWZesl156SbVaTQ89\n9JDuvffeXT8r6jFoK1Y7zhdGaIGAbBrViKssYTY9t+nzAdDSWy/Pnft44Dru9TXSrvu2lQf59P73\n/7je/vYH9Q/+wc/q7W9/UO9//wdv/oxj0D5h2jfIH0ZoUUg2XdmLsyxBn9umz2cQRi1QRIPqZbn8\nqBznlsjqqm1137byJI2sS8ZLL72kt7/9QUmfV+c4kx7WxsYL+t7v/d5CH4O2Kno25A0jtEAANl3Z\ni7MsQVcDtOnzAdAyqF5OTNypj3zkQ5Gt+Glb3betPMinWq0m6c3qPs6k21Wr1TgGLcVqx+jGCC0K\nyaYre0mUxe+9PzZ9PoMwaoEiGlUvJUVyf59tdd+28iSNrEsGI7TZxb3N+cAILRCATVf2kiiL39U/\nbfp8ALSMqpdRrfBrW923rTzIp3vvvVenTr1P0sOS7pH0sE6dep/uvfdejkHLsbo5JEZoUXA2Xdmz\nqSwdNpZJYtQCxZZEvbSt7ttWnqSQdclilWMgHWGzjg4tkAFeTqRFOtnSyAN2G1T/i5QJeUXW5RPn\ndGA3phwDOedluwC2FACKa1D9JxMAO3FOB6IX6wit4zi3S/o1Sa+T9JqkjxtjftlxnP2SViTNSapL\n+tvGmGsDHs+VPGRa2CusXhZEKeKiKYxaAC1RbeXDaJCdyLp8ydo5nVxAUmwfoX1V0j82xny/pB+Q\n9GOO4xyU9FOSLhpj3ibpM5L+t5jLASQuiiusXrYLYEsBoLgG1f9bb/0+3XLL7i1IRmUCo0FAMrJ0\nTicXkCWxdmiNMX9ujPli+/+vS3pJ0u2SfljSr7Z/7VclvSfOcgBJazabWlh4Ultba7p27QVtba1p\nYeFJNZtNX89TrVZ140ZdrS0EJOmKtrc3Va1Wff0OgHwaVP+/+92GXnvt6/KSCVFlFYDxsnJOJxeQ\nNYndQ+s4TlXSA2pt8vU6Y8y3pVanV9L3JVUOIAlRXWH1sl0AWwoAxTWo/n/iE2c9Z4Ito0FAEWTl\nnE4uIGsSWeXYcZwpSZcknTbGfNJxnL8wxhzo+vl/McZ8z4DHmUcffVTValXValVHjx7V0aNHYy8v\nEFbU98D03sdStBVNL126pEuXLqler6ter+uzn/1s7u4rI+swyrj6HTQTbLpfD2SdzaI8x9q+yjG5\ngLhFnXWxd2gdx9kj6d9J+l1jzLPt770k6agx5tuO47xe0pox5t4Bjy304gHItuXlFS0sPKlSaU7b\n25taXDyjkydPRPa8ExOtaUlRPW+WsFAKiiTuOh9XViE8ss4ORTzvkgtIkvX70DqO82uS/rMx5h93\nfe8ZSX9hjHnGcZwPS9pvjPmpAY/NZPABHVGPpHLVtIVGHooizjrfnUWScjvDI8vIuvQV+byb55lf\nQfB5xMfqVY4dx/kbkv6upHc6jnPZcZwvOI7ztyQ9I+m44zhfkvRDkn4uznKgOJrNptbX161YuGBQ\n8IVdNZD7WoBiiavO92bRxYufUbVaVb1eTy0/bcpvoHM8Xr58ubDn3UqloiNHjvjqvOW1HrPqs+WM\nMdZ+tYoHeLO0dMG47gEzO3vYuO4Bs7R0waqyNBoN47oHjPSikYyRXjSue8A0Gg3PzxvFc+RBOxtS\nz6iovsg6DBNHnR/0nKXSdKr5aVN+24SsS0fv8VgqTRX+vOtFXusxba/4hc261MNtZOEyEnxIn01h\nM6wsq6urZnb2cPt7ra+ZmXlTq9V8PX/nhDEzM5+rE4YfNPJQJFHX+Vqt1pNFDSPdllp+2pTftiHr\nkjfoeJyYmDXl8r5Cn3fHyXM97s/MYO03DBc26/YkPSIMxKEzLW9rq39KUNL3OdTrde3ZM6fe6UmS\nuvaWa92HE2RvuZMnT+ixx97JfRxAQURd53fvc3lI0qclvVGDplQmkS825Tcw6Hgsl9+i3/zNn9P+\n/fs57w6RdD1O8n7W/sxMfm9gjJbYPrRAnGzYiLzjC1/4ol5++WpfWebn5yPbWy7IfS0AsivKOt+7\nz2W5/GPas+fb6s6sV175amL5aVN+A8OOx/n5ec67IyRZj5O+n9WGvYExWiL70AaVxdXwkB4blpjf\nWQ3xw2qtfXa7pC/r7Nln9fjj77v5O4yuhsPKn0B4nSyamprSO97xkLa3S5Kqkuoqlbb1zW9+NbGM\nsiG/bUTWpYPjMZgkPrc0V52m/RYf67ftCSMrwYdwkt6sPM7nWV9f1/HjT+jatRckNSXVNTX1j/SZ\nz3xCR44cCVwe7EYjD4jOTm79nqS6pKpmZv6mLl48dzO3kmjIRfEaeWtwknXpyduxlJS4PzcveRU3\njo3oWb1tDzBO1NNGopiWF6ZMu6fcVCRN6rvf/TOmzgGw1k5ufUvSEUnf2jVVMKnpfWHzm201ECVu\n7Qkm7s+tWq3qO9/5sqS3SXpC0tu0tfXlxNpZ5IydGKFFamzcrDyKMjFVKX6MWgDRGpZbNub0IFkp\np19kHbBbs9nU7be/VTdu/L46dX1i4gf1jW98OZEpx3nMGRuEzTpWOUZq4l4RL8iUEC9lGve8rEIM\nIEuazabuvvsteuGFz+n69eu7ciuqnI57ih4rJQPRCltn46rz9XpdrnuXbtzYvQp1EnWdnLEXU46R\nmjhXxAs6JWRcmbw+L1OVAGRBd6Y9+OAj+spXdi8EFUVOJzFFj5WSgeiErbNx1vk06zo5Y7Ewm9jG\n/aUMbMCNcJaWLhjXPRDpZuVhN/ceVqa4Nw1vNBqmVqsNfL5RPysihdyA27Yvsg5edHJgY2Mjkjzw\nmmlhcjru3IyqnLYi67InzvN1Em2BsHU2iTqfZl3PY87YIGzWpR5uIwtXgOBD9AFdq9XM7OzhdpC2\nvmZm5k2tVgtVpiied5hOQM7OHu4LyFE/KyoaeSiaTg647luM5BrXvT90HvjJtKA5HWduDpK3i39k\nXbbEeb5Oqi0Qts4mVefTrOt5yxkbhM06FoVC7sR1034azyuJBQgGYKEUFMlORvyWpB+VFE0eJLHA\nCYuohEPWZUecx3qS9Sjsa1HnEQTb9gA9KpWKFhfPyHWPaWbmsFz3mBYXz4QO0riet7PIQCv4pe5F\nBkb9DEAx7OTAXklVRZUHcWVa0q8B2CDO83WSbYGwdZY6jzQwQovcimuFvaiflxFa/xi1QJHENULb\n/fxxr8qexGvkEVmXHXkZoe1+TRtXOUY+sW0PMESlUoklRKN+3s7VzIWFY7v2gOy8xqifAci/nYz4\nURkzo1deeViue5ekP4ts9kncmZLEawBpGncut/W5R71mmOenziNJjNAiE4pwpW/UeyzC+/eDUQsU\nUScHpqam+vaLzSqybTSyLnviPKapLy18DvkTNuvo0MJ6y8srWlh4UhMTrf2/FhfP6OTJE2kX6yaC\nNXk08oDBspRHtme7Dci6bMhSvcs6ciOf6NAi12xYLW/UiYpgTQeNPKDfoDx67LF3WtnQtiHbs4Cs\ns1/e2wE2ddbJjfxilWPkWtqr/C4vr2hu7qCOH39Cc3MHtby8cvNnzWZTCwtPamtrTdeuvaCtrTUt\nLDypZrOZSNkAoGNQHr33ve8bml9pSzvbgSjkvR0wqg2UBnIDw9ChhdWq1dYVT+lK+ztXtL29qWq1\nGvtrjztREawAbNGfR2/Q9vZ3rW1op5ntQFTy3A6wsbNObmAYOrSwWpr7mY07URGsAGzRn0eflvRG\n2drQZq9K5EGe2wE2dtbJDQzDPbTIhDTu4fByr0bn3pnuZfTzdO+MrbivDOjXnUc3bvypXnvN6MaN\n35fN95rZdH+ejcg6++W1HWDz/arkRv6wKBQyJ44giivcvJyoBr02YRsvGnkoMq9bfF28+JnYG9px\nZR0Z2kLW2av7GJVk3fEaRR1Ko7NO3S+m0FlnjLH2q1U85MnS0gXjugfM7Oxh47oHzNLSBSufs1uj\n0TC1Ws0BAWd7AAAgAElEQVQ0Gg0rygNj2tmQekZF9UXWwSu/+eI3v+IsS9rPm0VknZ1sP0ajLF+c\nGdLL9s8V8QmbdYzQIjFxTF+xbUqMbeXJK0YtUEQ25UtcZbHpPdqArLOP7ceo7eUbJqvlRjTYtgeZ\nEccCA7YtWmBbeQDkh035EldZbHqPwCC2H6O2l2+YrJYbdqBDi8TEsRqgbSsM2lYeAPlhU77EVRab\n3iMwiO3HqO3lGyar5YYd6NAiMXEst27bEu62lQdAftiUL3GVxab3CAxi+zFqe/mGyWq5YQfuoUXi\nsrTKsY3lse29poH7ymCzuOuoTRnAKsfxIuvsZfsx2inf1NSUrl+/bm05e9n+uSIebNsDFEhnCf2J\nidbUnLzsd+cXjTzYijqKKJF1CIM8QlbQoQUKghUAd9DIg42oo4gaWYegyCNkCascAxZpNptaX19X\ns9mM/Lm9rgAYZxkA9OvUucuXL/tepZP6CqRrVB3Mcv1k1WAUCR1aWC/JE0qY11peXtHc3EEdP/6E\n5uYOanl5JdKyeVkBMO4yANitu8798A+f0NbWf5LXVTqjqq9JZWSWG/fAIKPq4KCfxVkHon5uVg1G\noRhjrP1qFQ9FtrR0wbjuATM7e9i47gGztHTBytdqNBrGdQ8Y6UUjGSO9aFz3gGk0GrGUcWZmvq+M\nSZXBBu1sSD2jovoi67JpUJ0rlaaG1tFxjw1SX5PKyCSzGDvIuviMqoOD6/Z0bHUgrvo1qs0A2CRs\n1qUebiMLZ1HwIR6NRsPUarWBjbgkO2hhX6tWq5nZ2cPtx7a+ZmbmTa1Wi6Wsgz6zJMuQNhp5sMGw\nOre6ujo018Y91k99TSojk3idUeeCIiPr4jOqDvb/rGGk22KpA3HXr7TqFnUafoTNOqYcIzXjpttF\nff/HqOk8YV9rZ2rPJUnrki7FNrWnUqnoyJEjfYs6ML0ISNawOjc/Pz+wjnabmprSK698Ra3M2Hms\nn/qaVEbGfS8et0ogDaPOmf0/+7SkNyqOOhB3/RrWZvAqyFToOOs0tz5goDC94bi/ZNGVPETLyxXJ\nKK9ajpvOE8VrnTr1QSO5RrrHSK45deoDvssZVlGmF4lRC1giSJ3rPMZ17zeSa8rlaqD6mlRGxjmC\nVKRbJYIg6+I1qv52/6xc3mcmJmYLVweCTIWO8/1w60N+hc261MNtZOEsCz5Ex+t0uyg6aF7DNcxr\n2XRCKsI0Hxp5sImfOjcoKyYn95nPfe5zgeptUhkZ18WyIt0qEQRZF79xtz51fha0DnjJBxsvRgdt\n18RVp21qZyF6dGiRSX6CycvJYNTv+AnXoJ3B06efNtLdNMoSQiMPWTUoj1z3PjM5ORN41MFvh7r3\nd71mZBwXy2ikjkbW2cVvHfAzomjbxeigHdMwdTqqthyyhw4tMiuqK5JJTCcepdFomHJ5n5H20yhL\nCI08ZNWgPGrdqrAWe3YMy8q0O5U2jk7ZgqzLrrTrVVhhyh/mVoy02nJIFx1aZFrYK5JJTCceZ+eq\n4QUjHTDSvJFuM6dPPx3Za2A3GnnIsu48mpzcZ1z3zthHHcZlZdqdSttGp2xB1mVXHkYUw96KFeZW\njKTbckhX2KxzWs9hJ8dxjM3lK7pms6l6va5qtRp49byw1tfXdfz4E7p27YWb35uZOayLF8/pyJEj\nu343rvI2m03NzR3U1taapDdI+rTK5R/T1772J6l9LnnnOI6MMU7a5YgKWVc8nTyamprSgw8+0s6P\nQ5KuyHWPaXPzaqT54SUrbcj0OGT5fZF12bW7bRBf3Y5b3PWn2Wzq+eef1/vf/6xefvkLN7+fdFsO\n6QqbdXuiLAyKY3l5RQsLT2piorW0/eLiGZ08eSLxcuxeWr91whi29UWlUokl/CqVihYXz2hh4ZhK\npTltb29qcfEsQQtgqO486s+PM5Hnh5esjCsj02TLuQrFM7htEH3djlucudCpn3v2vEkvv/wVpdmW\nQ7YxQgvfbLvq2AnE7hNGVA2W3iuBo64MctUwOYxaIEu85EgS+RFnVtrItnNVEGRd9nXPxrh+/Xqq\nbQSb2in99fPnJf2Mpqffpldf/Vru8wm7hc66MPOV4/5Sge61yBIb7wuJ4/6r3gUKTp36APufWULc\nV4aM6M+RD6aaI0W6V9XGc5VfZF0+2LB/qg1l6Daofk5N3WfOnz9fiHzCbmGzjhFa+JaHq97jDHqP\n0sOSnpd0VHl8z1nCqAWyoD9HLkl6t6TPK6/ZaZM8nKvIuuyz4Ti0oQxZKBPSEzbrbomyMCiGzn0h\nrntMMzOH5brHMnlfyCj1el0TE1W1Qlbt/94uae/Nf5dKc6rX6ymUDkAW9OfIXklvVneukCPxKcK5\nCvYb1J5Iut7bUIZe1E9EiRFaBGbTvRhRY4TWboxaIAsYobVDls9VZF322TASaUMZRpUtq/UT0WGE\nFqmpVCo6cuRILgNo0JXDU6feJ9f9Ua4kAvCkP0d+tJ0jjEgkKc/nKtjPhpFIG8owqmzUT4TFCC0w\ngp9VjpEcRi2QJeQIgiLr8sOGem9DGYBBwmYdHVqgoLJ8YqORB7+yfLyjuMg6pIG8RNKYcgzAt+Xl\nFc3NHdTx409obu6glpdX0i4SEBuOdwDwhrxEFjFCi8C4gpdNNi8O4RWjFvAqD8d7HnH+8IasQ5Ly\nlJdkTLYwQotU+LmC12w2tb6+rmazmWAJkxH1e0visxq2fP/ly5dz+3dCce0c72+QtC7pDSO3qwhT\nB/OcdcMEec+MACErghzfceZA3Blj4/Y+QaSZMUU8D1jBGGPtV6t4sE2j0TCue8BILxrJGOlF47oH\nTKPR6PvdpaULxnUPmNnZw8Z1D5ilpQsplDgeUb+3pD6rQX+/Umk6U3+ndjaknlFRfZF18Wk0GqZU\nmjbSfiMdNtJ+UypNRZ5Xec66YYK8Zz/nD5B1aQpyfMeZA0lkTB7qZ5rvoYjngaiEzbrUw21k4TIU\nfEVSq9XM7OzhdlC0vmZm5k2tVtv1e3kIxmGifm9Jf1ad0J2ZmTfl8j4zMTGbqb8TjTx41Wg0+o7v\niYnZvuM7TB3Mc9YNE/Q9ez1/oIWsS0eQ4zvOHEgyY7rbB1nslKWVMUU8D0QpbNYx5Ri+VatV3bhR\nl3Sl/Z0r2t7eVLVa3fV7eZm6MkjU783vtMiwTp48oc3Nq7p48Zw++ckVue5dyuPfCajX633Hd7n8\nlpvHd2d62OXLlwPX6Txn3TBB37PX8weQpiDHd5w54PW5o5ju2t0+2Ny8qpMnTwR+rjSklTFFPA/Y\nhA4tfPO6QXeeGy5Rv7dqtarvfOfLkt4m6QlJb9PW1pdj/aw6m5nPz8/n9u8EjKqr3fdZvec9J/Wd\n7/zJwN8L8xp5FfQ9ez1/AGkKcnzHmQNenjvK+0Y77YMs1su0MqaI5wGrhBnejftLGZmaUlSNRsPU\narWR0ymyMnXFy3vpFeV78zotMi5Z+Tt1iGl48GHQ8T1oetjExKwpl/cFqge90/hPn34691PNwuRG\nkMwtIrIuPX6O787xfPbsc7GdS0eVh+mu/dLImKy1pWwSNut8b9vjOM4tkqaMMf810p714NcyfssH\n+/hZOj3pZdabzabOnfu4fvZnf1ETE62ra4uLZzxPsQlS3kGPWV9f1/HjT+jatRdu/t7MzGFdvHhO\nR44c8f/GAsjSEvdsZQG/eo/vYXXuN3/z527+e35+3lddaDab+sVf/CX983/+LzQxcadefXXTV574\nKb8tbC1XXpB16fJyfC8vr2hh4cmbbYhf+qWf0+HDD0RaJzrlmJqa0vXr1/ue+1Of+pR+5Ed+Un/1\nV1+8+b2k2xC9ipoNRX3fYYXOOi+9XklLkmYk7ZW0Iekbkv7XMD1pj68bTbcfmZD06nBLSxdMubzP\nSLclvhhT73vk6qo/YtQCIQ2rc50RliA5dPbsc0ZyI6/HrJxZXGSd3ZI4d4+r/2m0ZcKWGegVNuu8\nBtAX2//9u5J+UVJJ0pUwL+zxdaP+vGCppDt0O6/36+2tPEzsq+GNe49MVfGORh6i0FvnOp3ZoCsd\nT07OGOkdu/JkevqBUHnCxa5iI+vsFveKuuPq/+6fXzCt7cnuSrUNQWYhiLBZ53VRqJLjOCVJ75H0\nb4wx25KyM2cEnqW1IXTSq8PtvN5xSXUlcRP/uPeY9ZUFgazprXOHDz8QcqXjOUlfV3ee3LhRD5Un\nrJyZ3nkJGCfuhYDG1f/dPz8h6Uvau/dW/c7vLKfWhkhyRWagw2uH9pxarf69kn7fcZw5SbHfQ4tk\nRblCnl9Jrw6383rfknRG0lFJd8e6Gp6X95jllQWBLOquc2FyqFqt6tVXvynpw5KOSXqHpIf17LM/\nH6o+F33lzDTPS8A4ca+oO67+9//8W3rttf+s+fn5SF4/iKRXZAYkBV/lWNKeMEPDHl8jwsFsjBLX\nFBE/q8wlPeV21Kqkca2Ox7TiaIhpeIjJuJVER+VC57FTU/eZyckZc/bsc7GXKU5pr0TM1EWyLiuG\n1ZUo6tC4+m9ju8LWFZnTzjQMFzbrvAbQ6yQtSvrd9r/fLmnBw+MWJX1bXffbSnpKrUWlvtD++lsj\nHh/X54YecdwHEmRRgKTDZtDrxb2YAYEaHo08xClMLsRVv5PODRsWdYn7/sQsIOuyK8o6NK7+29iu\nGFamtOq1DZmG4cJmnadtexzH+V1J/0rS/26MeYfjOHskXTbG3D/mcY9Iui7p14wxh9rfe0rSy8aY\nf+bhdY2X8iG8ZrOpubmD2tpaU+u+hyty3WPa3LwaaOpM1M+XlKyWu2jYygJJKlou2PJ+bSlHmsi6\nbOLYHS6Nz4a/h/3CZp3Xe2i/1xjzG5JekyRjzKuSvjvuQcaYz0n6ywE/yk0450XU94GEWcgkzYUC\nirgACwszIGuSPmazkgtRfS62vN+4708EgvBSz2ypQzZKo17z9ygAL8O4ki5J+h5JX2j/+2FJn/X4\n2Dn1Tzn+U0lflPQvJc2OeGw049jwLKppK0HvkYhrSojX6TobGxuFumcrq1NwxDS8wkrjmN3Y2Ghv\nybMWOBfinhIY9fRGm3LQxumUSSHr7OLn1oOJidlddWhiYjaWY9hP/bCpLiVZFtsyDf3CZp3XADos\n6Q8kXWv/908kHfL42N4ObUW6OdX5n0haHPHYmD42JMHvQgVxBY6XTcm7f37q1AesW2AhDlkOeBp5\nxZTGMdvJB9e930iuKZervnMhifvyo/5cbFxopojIOnv4qWeNRsOUSlPtfWHnjbTflEpTsS006SVb\nsnoBOypkmt0S6dC2Xkd7JH2/pPsklXw8bleH1uvP2j83jz76qHnve99rnnrqKbO2thb9J4hY+bkC\nF8dCAf42Jd/5+cbGRi4WdhlldXXV7N37jkwsuLK2tmaeeuop8973vtc8+uijuWzkkXXjxXXMjlqh\ntDcfJif3mY2NDV/PHXcnPK5FVmzKq6Ig6+zlp57t/G7DSDUjNQLXST/5NKqDbdMF7LSyhUyzR9RZ\nNy54fmTUl6cXkKqS/rjr36/v+v//RdLSiMfG9TkWRpYqbxyBO+4ElORqezZdHV1aumDK5X1Gus2a\nE5wfeWzkYbS4jtlh9bLRaJjz58+b6en5UPmQRMbY1ljNiiycH8k6e8TZgRx2LI5qNwTrYMff1hn1\nfsa9JxRX3B3afzXi6xNjn1xakvRnkv5a0tck/UNJv6bWbstflPQ7kl434vGxfnh5l8XQiHpKiJcR\n2iTuc7Gpwbm7LBfaU6LuyswxYgyNvKKJ65gdVi/Pnn3OuO4BMz3dmmYcpt4mVfeZTudPVs6PZJ1d\n/NQzr7876qJakBlmaY/QjqpbNrWFYJfEphyn8ZX14EtTf2ismcnJGV9T5dLSvUBTFFfPx23wncR9\nLjbtp9hflobZu/ces7q6mnhZgqKRVyxxHbOD6uXU1H1mcnJfV3Y+YyTXTE8/ELjjMyyDoh4hzMKI\nYxK8LAKYlUY1WWefKBdhGnVbg5d2QxQd7ChzY1zdsqktBLskeQ/tfyfpJyV9tPMV5oU9vmbUn1dh\n7A6NC0Y6YKR7zOTkPmuvRHcLcvV8VCiP3+A7/H0u48pmSwPKprIERSOvWOI6Zgc3Jmf6phlPTd1n\nzp8/H6oB2/v9rIwQZo2XzzVLjWqyLt8GHYvSW83k5MzNmSLjci9MBzvqHBpWt1ZXVwu5kwS8S2qV\n47PtqcJfV2vbnT8etTpxVF8EX3A7DbW1dmc2O+ERpPEaNJTTmIZjw5RAm8oSBI284onrmO19Xq+N\nyFHP5WVLDxp10fP6uWbp8yfr8m3Qsdhqs63tyqM4ztVx1INBz1kqTe/KxaLsJAF/kurQXun575Sk\nfx/mhT2+bsQfV7EsLV1o7514TyauRHf4vXo+KpS9XLlMsnNn05RAm8riF428YorrmB02atHdyR33\nun4ah1kaIcwSP59rVi7qkXX512qr7TPSW9ud2Qu7jt0ocm/Qc8SVQ911q1ze17dOievGt5MEsiup\nDu0ftf/7eUlvlFSW9JUwL+zxdaP+vApnY2Oj534we69Ed/i9ajgslE+fftrzqG2WO3dFRCMPcetk\nQmeEZFyO+GkcZmmEMEuiWlnWJmRdMbTaajPtWXXRZkLQRafC6NSt1dVVLt7Bk6Q6tD8taV97u55v\ntb9Oh3lhj68b+QdWRFm5Et3NT5mHhXJriw8ajHlEIw9JiHMV0Szmchbk7XMl64ojjmN3XC7FXV+4\neAevwmad03qOwRzHOSLp68aYP2//+3+W9PckXZX0M8aYvxj64Ag4jmNGlQ/eNZtN1et1VatVVSqV\nTLyen+dYXl7RwsKTKpXmtL29qY985EP6hV/4LV279sLN35mZOayLF8/pyJEjgcoDeziOI2OMk3Y5\nokLW2Wl9fV3Hjz/hOUd6c2hx8YxOnjwx9PmTzsmiyNNnQtblh5fjMupj10uGxV1f/OYiiils1o3r\n0H5B0mPGmL9wHOcHJV2Q9H5JD0i61xjzPwZ9YU+FK3DwZVknvCYmqrpxo55YeHWHsiTNzR3U1taa\npEOSrmhy8lFdvvyHuvfee2MvC+JFIw9RGNeQazabfTniuse0uXk1sQbpKGllLZJD1iUvjjqcZrvI\nb4bFVY68XGRCPEJn3ajhW0kvdv3//6XWqGzn318MMzTs5UtMTbGG13uNbJpe0plK47r3Gck1rntn\nLqaggWl4CM/risS2TmENuxgesoGsS5bfHRO81LW020W2ZhjQLWzWjQue/yBpT/v/r0r6we6fhXlh\nT4WzPPiKwk/A27Z6Z5wLLSA9NPIQRh4WD4piMTzYj6xLTtD74KNcMC4uNmYY0C1s1t0yZgB3WdJn\nHcf5pKQtSf++PSx8t6RrgYeFkRnNZlMLC09qa2tN1669oK2tNS0sPKlmsznw96vV1nQa6Ur7O1e0\nvb15cxpw0q5fv65y+W5JR9vfOaRSaU71ej2V8gBIX71e18REVa0peNK4XKhUKjpy5IhVU+WGZe3T\nT/9Tz3kNYIefXPDTNrKhXWRjhgFRGtmhNcY8LelDks5LeqTdg+487v3xFg02CNLwW1w8I9c9ppmZ\nw3LdY1pcPJNaiNpwIgFglzzkwqCs/chHPqTJybfIa14D2OEnF/y0jWxrFwF5NHJRqLRlYfGAvAu6\noIBNCwCwwl7+sFAKwspLLoxbDC+NBWAQHbIuWV5zwfYF44CsiXWV47TZHnxFkYeGHyeSfKGRhyjk\nMRfykNfYQdYlz2suUNeA6NChxS5xNdDy2PBDdtHIA4bLc17n+b0NQtbZLU/HY57eC7KHDi1uYk9C\nFAWNPKB4iniOI+uQhCLWLdiFDi0k2bN5NpAEGnlAsRT1HEfWIW5FrVuwS9isG7dtDzLC72rEtmk2\nm1pfX8/19hK977EI7xnwq1MvXnrppVjqB/Uum7J+jkN+dWdKFvMlK3Uri58tkkOHNieyvA3F8vKK\n5uYO6vjxJzQ3d1DLyytpFylyve/x/e//8dy/Z8CvTj159NH/SW9/+4N69NGFSOtHEbImr7J8jkN+\ndWfK7be/VW9601syly9ZqFtkN8Yyxlj71SoevFpaumBc94CZmZk3rnvALC1dGPn7jUbD1Go102g0\nEirh4DK47gEjvWgkY6QXjeseSLVMUet/j2tGcnP9nuPWzobUMyqqL7Kuu56sGSn6TChC1iQhzfOG\n33NcHpB19hqUKdJ+IzUyly9J1i2/GUJ2F0PYrGOENkdOnjyhzc2runjxnDY3r468oT/uq11ep4Zc\nvnxZt9zyZtk+1SWM/uk8eyUNfs9MqUERNZtNPf/889qz501q1Y+qos6EqKfVNZtNfepTn9KnPvWp\nwtTXtEdJ/JzjgLgNypRWdtUlHdItt9yuy5cvj3wOW875SdWtIBmSlSnRSFmY3nDcX8rRlTybxH21\nq3Olb3b28MgrfUtLF0y5vM9It+X6ypvXEdqzZ5/z9LmBUYs86eTF9PR8u178tPUjtEtLF8zExKyR\n7jbSbaZUmsp9fWWUJB1knb0ajUY7B7pHaGdujtBKt5lyed/INlCRzvlBM4TsKYawWZd6uI0sXI6C\nzya1Ws3Mzh5uB0Pra2Zm3tRqtdDP7TV4dv/ehfY0nbtyG+q903lOnfrArn93OrMEtjc08vJh8JQ9\n10xOvtlIrnHd+yLNhCim1Q2bZlgu78t1fY3zvIHhyDp7NRoNUypNtdsv8+3/Thip2v7/Cx7bQMU4\n54fJkCLeblA0YbNuT9Ijwkjf7gUAWku0R7UAQGdqyNZW/9SQ7uXfd//eIUnv1N69j+i3f3tZ73rX\nu0KXwzYnT57QY4+9c9em5R/96P9x899ePzcgTwYd99PTb9PHPvbjeuihh3T9+vWb9SUKg+phkDL3\n3iYhVXXrrX+V6/oa53kDyKJ6va7bbrtH1679nlrTjKu67bYf0muvXdMrr3xJUisLxreBpCKc88Nk\nSBTZjXyjQ1tAlUpFi4tntLBwTKXSnLa3N7W4eCaSgPAaWP2/9y299tp/1vz8fOgy2KpSqez6jHv/\nTWMRRTMoL1599Wt697vfHVuDpbfe+VWtVvXaa19Xd5mlur77XZPr+hrneQPIop38+pakI5KuyJhv\nSnqt/b2KvLeB8n/OD5shYbMbORdmeDfuL+VoaoqNOivNbWxsRLpq5bipIZ3X7UyzTXoKSVSrdEa9\n2idTarwT0/ByY9Bx76VupbHabnd2te6duyuWe2htWIF+GJvLlkdknd0G5ZfXc3mU5/yw9TLJeh3F\nayWdQ+Re/MJmXerhNrJwOQs+G8W1KMGwyt/7emfPPpdoiJ4+/bQpl/eFfr9Jf25Js6Ucw9DIs1uQ\nbRk6v+8lI9JYTGVQuVZXV83q6mqk9aRoC8VgNLLOPr35trGxYc6fP282NjaG/o7X5woibGZkLXOS\nLm/WPp+sokOLwJJelCDNRRA6gdRalXT0Yg3j5H0xhyyEN408e4U5fvrr1jNGcs309O7R26TrX1Kv\nmfdsgX9knV16862zwGNa58uwmZG1zClSu7VowmYd+9AWWNJ7e6W1l1iz2dTCwpPa2lqT9GVJlyQ9\nKekNgV7f9j3Rwuxr1/1ZXbv2gra21rSw8GTqe+TBXt3HW9jjZ3fdakp6RtLn9fLLX7j5XJcvX068\n/iVV523PFqDIBuXbr/zKx7W19VupnS/DZkYUmZPkXrpFabfCPzq0OTcqaHYvSiDFvShB3K837L0O\n3vx8TtKnA71+0p+bH0E2Le9GeMOP3uPt3LmPhzp+dtetuqTdqwnv2XOHvvSlL+mv//qrSrL+JVXn\nbc2WJBusgK0GtyVul7T35r+Dni+D1rGwmRH28WHbHL3GfQ55a7ciQmGGd+P+UsanpqTNy9S/pBci\niuv1Rr3XwftGjt7wPK33EUYUU2OyMr1GTMNL3bBjpVzeF+r46dStqan7jOT27VE7PX2/mZiYNaXS\nVKL1L6k6b1u2ZOEWhDwj6+wxbN9saS3U+TKqe2CDZkbQx0fdXvD6OeSl3YrdwmZd6uE2snAZDr40\ndC8u4Cdosr5anJf32htIp08/bd0qx2GfL8ym5d2yEN408tI37Hg7ffrpQKsWd+tdCX16+oF2w/GZ\nXXU86gWZvJYr7te0ZVG2cdlqSznzjKyzS+/5sXMPbdDzZZBO4aB6l8Yqx1G1OTqv7+dzyHq7Ne+C\nfF50aGGM6b+ydfr005EFzTC2VHCvoWpLeQeJYhQkyqulNn9WxtDIs8Go423UqsVBGnznz58309P3\nh84z249r24zKVkZuk0HW2ac3R8Lkit9OYRR5GlUGRtnmiLJzjHQFPUbp0GJgqJTL+2KdOmpTYyYr\n02SHibL8WRhdjQKNPDt42XM6imM7iuexKbOyYtjnvrGxkenMzRKyLt/8zqYLU+/iyMCo2hxZb8eh\nJczfkQ4tfE39i4KNwZPljtzuv1/DSDUzNXVf4CuTRRiFopFnj1HT31ZXVyO76h6mjtuYWVkx6HNn\nNCU5ZF3+ec22MPUuzim9UbU5styOQ0uYY5QOLTxP/YtKHI2ZKMpp0z0Vfk8Grb/fM0Y6YKR3GMk1\nZ88+F0exc4FGnr16RwFKpSnTWjRl1Uj/wpTL+xK9z8uY7sxqXTCSGnTAfBg0xZILBMkg64qhdw2U\nQTk3asbEuMf6abelOZulCBfk84wRWoIvtCSvbEXdmMniVMBRZQ7yfs6efc70ruhKA3E4Gnl2GpQN\nt96610iTRrrNSHeZPXumE6/jjUbDlErTRtpvpMNG2m9KpSnqVwiMpiSDrCuWce2H/kWpPnjz90ul\naTMxMet5t4dBbQwuViGsoOcGOrS4KckrW0W+b2LciHiQ91Or1cz09DxT+DyikWen/lGARvtCzf5U\n63ij0TATE7O7yjAxMWt1zmQBoynxI+uKw0+ns1ar9dzL3hibs17abdxOgCikscrxnuA72MI2lUpF\nlUql7/vNZlP1el3VanXgz4M4efKEHnvsnaGft7NR+dbWzkblnY3Joypr1EaVWVKg91OtVvXqq5tq\nbbo+OMUAACAASURBVN59SEE2747j7wz4MTU1pVde+ap2juNPS/oeSd/X/rckHdItt9yeaB2v1+ty\n3bt048ZOGcrlt1idM6PYUteHnXMAjNdbj722hzr1bn19vev31yXdqe6c7X2sl3ZbtVrVjRt1hWmL\nAGmcG25J9NWQuOXlFc3NHdTx409obu6glpdXInvuSqWiI0eOhDpod4enlIXwHFXmoO+nUqlocfGM\nXPeYZmYOy3WPaXHxjOfPNs6/M+DF8vKKHnzwEd1yy35JD8t171e5/GMqla5J+lN114nXXvtGonU8\nizkzDHUdyL5B9bharWpr6z+pO6deeeWrQ3Nqd65V1ZuzgzJuXLstbFsESE2Y4d24v8TUlFCSms7r\nZTGDUbJ4L9aoMvt9P2E/vyxO2w5LTMOzSqPRMOXyPiP9envq25qZnJwxGxsbZmnpQnthqNY9tBMT\ns6nU8e56WS7vM6dPPz22jtg2pbaIdb3oyDp7RJUHoxZ3amXlfiPNe7rXvzvXSqUpMzExG0lbKmy7\nBN7w2e4Im3Wph9vIwmU4+OLktQIkcS9E9wIGoxYkGCeLlTqKVY6jWBDLz985i5/zIDTy0td9LJ0+\n/XS7w3rYtFbqvrDrGGw0GmZ1ddWsrq6meux1yuqlztm2WF2j0TDnz5+P5V77vORCHpF1dogyD4ad\ns8+fPz9yNfZOPf3c5z5nzp8/bzY2NnZ9P47Op205mCd8trvRoS0YPxUg7oVQdl9lHL8gAXaLarTF\n6/PkKTxp5KVr8NY8O8eftD/U9jxxyepKn53Pe3r6fhP1auh5yoU8IuvSF3UejBqhHfY6nXpaKt3R\nzoC3Gsk1p059IOJ3O76ctuV6FvHZ9qNDWyB+K0Brqwp/01f82H2VsdYenTGpr4zXvQKgDXvbRrEv\n3DjjpjnnLTxp5KVn0LHUGp1tdB3Ld5mf+qmPeKo7SU5t81rnbFrps//zfsZIrpmefiCSaYV5yoU8\nIuvSNygPpqcf8JwHg3Jt2Dl70Pd36ulaewZMd/a6N0dqw+otZ5w5WPRZITadY2xBhzanBlV2vxVg\n5/cHT1+JooyjRmgnJ/dFFrRedU4GrvsWI7nGde8P3OiLYuSie2RlcnLGnD373M2fxXHVd9gJIm/h\nSSMvPYOOJeku07p/dqeRVSpN+5rW23vLgpd7XP3K4gjtoM97auo+c/78+dDlyVsudMtLg5msS9/g\ni3juzfP5qGNtWDti1G0YwzuW/QMH0lvN+fPnQ7/HQeWMIwc7t32Uy/sKPSvEpnOMLejQ5tCoAPQ7\nQht3hRm0IIHr3tfuTN6ZaFiNuorp931H8dntPMcz7fK8Y9dJ0JjkFsTKW3jSyEvPsMadVDbSofax\nvtdnp7H/gph0mymX90VeJ7zWOVsWq4uz7uYtFzryNI2arLPD2bPPtXOuk3HPGNc9YM6efW7osTas\nfo16zCBxj9COyoEoc7DzXNLd7by/kJvMCcKWc4wt6NDmzLgGht8KEEeF6b162P3vjY0NMzk50w7e\nZBtIo65i+h11iGLkolarte956179tTVy3f15JDWSkKfwpJGXrkGNu1aH9peNtOKp/o27ZaF1m8Sv\nR9p58zu12ZZRvjjrbp5ywZj8ddLJOjvsnM9rpnN7xdTUfWZycl/fsda53Wl1dbVvltz09APtNpK/\n43PnHto3t7P3bhPVPbTj2jtR3XrVfyH0gIl65mDW2HKOsQEd2pzx0pHyWwGCVJhhjxl35TvNKWxx\nj9D6nULdaDTMrbfeZnpXf/Vz703U8hKeNPLS1d+4u9A+zu830Y3QRtfY6c2ts2efy1w9iLPu5iUX\njMnfNGqyzg6D2wQzfauOu+59ZnJyxszOHjbl8j5z661729l22Ej7za237m1np//js1NPe1c5juO9\n9WZ22IwYfKtKtBctkW10aHPGhqvLYaY8p13+TtnL5appTXu+L/T9r0GnUDcaDVMq7b4Sa+vqr1lD\nIy9d4zujrXto/UzrLZWm2vXlLhPldLThiyrlY0QSu6V9DooaWWeP3tkMnanD/bdfrLX/vWZ6VyUv\nlWbae3bbc3yO2ys8iin8g0do47mtBNlEhzaHwkwBGzUd2OvjhzUGvF75TnsKW6esUaxyHGYK9bDF\nc06ffjpwedBCIy99O/dDvcm0pr/tHOdTU+8wq6urgVY57iwYElV+7K6HDRN29sYwGxsbkY6aILi0\nz0FRIuvs0tum6j7WJif3Gde9sysLa0a6p6/N1NkLu/f4THKmRHc7affFyV/fddE9ygtEvfUyjoX/\nkF10aHMqSLD1XkU7deqDvq+qjeq0+gk2L+XPwjS3MNPX8jZSYBMaeXZYXV01t912V18nsfs+8Shv\neQhidz2smdbibP7r8yinTn2wPRJzj4l7b0h4k4Xzixdknf0ajdaKxSsrKz3n/LW+EdpOG2BYxziJ\nhcy6X6u/E747E6Oewp+Xeono0aGFMWZQ52l4kPp7nnCLUg2TlVUow3ZK8zRSYBMaeXbYqR+dlbwP\nme6VvG2p551yTE3dFygXR9nY2Oh7zij3hkSxkXX22739WGu3h845/9SpD4xtAyR58Xvw1N/uadK7\nX5sL80gKHVoYYwZdRRs81cXLVbVxnbCwV9iyFpBhO6VckYwejTx7dHcWu/datq2ed+ph5763qC4y\nnT9/vi9ro9obEiDr7DYo58rlfbv2lx3XBkhyIbNBr9VZyGpYJnJhHkkIm3V7hFyoVqu6caMu6Yqk\nQ5L+StLXu/59Rdvbm6pWq2Of6+TJE3rssXeqXq+rWq2qUqns+nmlUun7niQ1m82hj+n+neeff157\n9sy1yyVJh1Qqzalerw99XJrGfR7jDPu8gDwYVj/q9bomJqra2tqp53v23KHnn39e7373u/vqhJf8\nCKNTD48cOaIf+ZH3RPZaDz30kHqzVvpG+/sA8mxQzk1M3Kn9+/ffzJZxbYD+9tvw9lqYnGw2m/rL\nv/zLvteS/kyXL39e169fH/i8YdtAQCLC9Ibj/lLOruTFrfcqmpepLlG/9qiphZ3faS1ZH+20PxSL\nGLWw3rCpbdPT9/dlhC1Tk4M6deoD7Ux7K/fQIlJknd2imoniZRQ0TE6OmhadtbxFPoXNOqf1HHZy\nHMfYXL4oRD0q0f18knT58mVJ0vz8fGxX1ZrNpubmDmpra02dK36ue0ybm1dvvmb/7/y8pJ/R9PTb\n9OqrX9Pi4hmdPHkilvIhfxzHkTHGSbscUclr1i0vr2hh4Unt2XOHXn75S5J+RtJPqjsjJI3Nj6QF\nyeWXXnpJtVpNDz30kO69996YS5htcY/G5wlZZ79OzpVKc9re3rzZnukc51NTU0NHP7uNqhde2lmj\nnrf3seXyo/rkJ1dibRsCfoTOujC94bi/lLMreb3Onn3OTE7uM3v37r73LApJjnh4uf9j0O9MTd1n\nzp8/z8gsfBOjFpnRaDTM+fPn2zMz+jMiyfvHvMj6aLEtht032Pv5nj37HGsMjEDW2WXYcT1s1WLX\nbc1I87uPfa8wOWlbxqaJNU3sFTbrUg+3kYXLePCNcvbsc+0pap3VQd+xa3XQMJJejMXL69m2QAyy\njUZetoyq/zZlg01lybJhFwX6P99n2tPQmfo4DFlnD68XuwbfbnHASGuB8yRMNpFrLVystBsd2gxq\nNBpmcnLGSN9vRu3fGFQa+4b5uf+D+zYQFo287BlV/71kQxJX1hnJCG9U43n359voO/8VsZE9DlmX\nnu7M8dMpHJQj0ryRaqHyJEwbqujtLzr19qNDm0G1Wq09/W6mPTK7E3rT0w+Ebjz5qbidwN7Y2PA0\nPWxUCHppcGZ1ukdWy51XNPLs4LdejPr9UT/bWVBu3kxO7hs4kyWKOkqjJ7xRFwV2f761vvMfFw/6\nkXXp6G37nD79tOeLXVGO0Pbm2qCc85p9eW6jjcPFSvvRoc2gnbD7aRPXar9+RkyH3eNB466FaSr2\noZGXvqTqxbDVkrs7tVGWpegjGWGNO29071vMavfjkXXJG7a3rJ/20E77qnWcl8vVUCsTx7HycZzP\nZRvas/ajQ5tRneCYnHxzO+y+P7IAGTfq2vmdcVcQbb6ildRVRELQTjTy0rVTL9baI23B7w0bpzWj\nZb5n+t4hMzk5E+k9uL3TC/M4SpGUcRcFOp/v2bPPcfFgDLIuecPaPqdPP913vHqZdTKqLTZM0muT\nBJnZl7V85GKl3ejQZtigsAsbFF6vsHm5x6M/4NbM5OSM2djYCPO2Q7NtBWckj0Zeumq1mnHdt7Qv\ngh020gFTLlfH1osg+dZac2Bf38W3qan7IlslOc8jE2nxeoEgq43jpJB1yRu3iF3neI0zN4LuHhGk\nfdJojF6JvpuX92xznba5bEVHhzZHwoaj3ytsXu7x6JSpXL6zPS35/lQbfEmPmDJCaycaeena2Njo\nmy4quSMvdoXJt51V4Q+1c+qZXQ3MMHWUOh4vLhaEQ9alw8ssgzhzI6kR2u71CXZ23gj+etR3BEWH\nNieiCCa/V+u83uOxsbHRN0ISdYOv0WiY1dVVs7q6OvJ50xgxZZqKfWjkpWtlZcWUSm8xrZVqW/XQ\nde8bWg+jyLfWvt0zZmrqvr56GKaOMgsjPlwsCI+sS8+o0bxBubF37yGzuroa2ev73T2iXN5nTp9+\nOtTFPMkdmLHGjM9K6jvCoEObE1E0qoKEiZd7POJu8C0tXTATE7NGuttIt5lSacrX/m5JBCbTVOxC\nIy89p059sH0l/y4j7TfShbH1MMqpcVFPXaURFh8uFoRH1tlpcGfwNlMu74v0orfXlYk79/f6GRkd\nVD+npx8w58+f9zyzrzsrqe8Igw5tTkTVqIpjNDHOBt/gk8J+Uy4P34+XEVPQyEvH4KnG4xtxtnca\nyZR42P53zwKyzl6d3PBzcS8OQetZkMeNykrqO8KwukMraVHStyVd6frefkmfkvQlSauSZkc8PoaP\nzF5nzz5nJiamjOu+JdRVvjhGE+Nq8NVqNbN37+69CKV5s3fvPbumsQTddw35RCMveY1Gw3z0ox81\n0j099fVu88u//MtjH297p9G2TOktj23l88r2v7vtyDq7ra6umr1732a6b7+IalTS64yUMCOjvfXT\ny5TlUeWiviMo2zu0j0h6oKdD+4ykn2z//4cl/dyIx0f/iVlqaemCKZWmjXSbke42pdKMdUEQR4Nq\n3AgtCwxgEBp5yerUw717+/cOHbcYVLesdsqS1pt7p059INM5yN89OLLObnGNSo5q+/T+rLP9VZiF\n8U6fftqUy/siyRjqO4KwukPbKp/mejq0VyW9rv3/r5d0dcRjI//AbNRoNEy5vK89ZSXaUMxCsOzc\nQ3uX6b6HlukrGIZGXnL66+Ez7U7tW43kmlOnPpBauWzPtiCGLdTS2vOXHCwass4ug3In6lHJUW2f\nYT8Ls6czbS3YIGzW3aLkfZ8x5tvtVPtzSd+XQhmsUq/Xdeutr5N0p6RD7e8e0i233K56vR74eZeX\nVzQ3d1DHjz+hubmDWl5eiaC00Tt58oS+8Y0va3X1jFZX/7W++c2v6uTJE6rX65qYqKr7MymV5kJ9\nJgD86a+HP6m9e+/SRz96UhsbL+hjH3s28TJlJduCGJR70u2S9t78NzkIJG9Y7pw8eUKbm1d18eI5\nbW5e1cmTJ0K9zqi2z7CfHT78QOAy0NZCHjitTnGML+A4c5L+rTHmUPvff2GMOdD18/9ijPmeIY81\njz76qKrVqqrVqo4ePaqjR4/GWt40NJtN3XHHPXrlFUfSJbVC5Ypc95g2N6+qUqkEes65uYPa2lqL\n5PnSkIf3gGhcunRJly5dunlC/+xnPytjjJN2uaJic9bZVg9tK0/UBr0/6WFJz0s6qry9X+xG1tkp\nydwZ9VqSIi9H3jMVdoo868IM73r5Uv+U45e0e8rxSyMeG9VItvVa99BOte+hvctMTMyOXTW0d7ud\nqBYJ8LKVT1JYYACDiGl4iQpTD71ODfb6e6O2muhklg3ZFUbv5925h5YcLB6yzg7D9p1dWVmJLGu6\nM3BU5sbRLup9zrNnn/P1vvJ6CwiSEzbrkgivqqQ/7vr3M5I+3P5/FoXq0mg0zOrqqlldXfW0bLrr\n3m8k17junaZUmjYTE7OhFwkY9NxpN6AISvSikZe8IPXQ66JufhZ/G3aPabk8ZyTXTEzca012hZGX\nVY4RDllnh8G5M9nOmvtDZ82gDIxj3+1ROs/ZaT96XSCKxTsRBas7tJKWJP2ZpL+W9DVJ/1CtbXsu\nqrVtz6ck7Rvx+Fg+NFt53UC7P1QPGGk29CIBg597n5H+NQsEwCo08tLhpxHldaGRMHshTk8/0F4w\n6afbOdibi2uZzi46sSDr7LF739nZge2uIHV1WAZGNdMkjtwO+vvAMGGzLtZFoYwxf8cY80ZjzKQx\n5g5jzL8yxvylMeYxY8zbjDHvMsb8f3GWIW3NZlPr6+tqNpu7/r+X10VOhi8Y8nqFXSRg8HNXJL1X\nxsywQABQYMvLK7rjjnt07Njf1x133DN2ISavC40EWZCkswjLxz7245qevlvSf6/WZKDu7JqTtDcz\ni5v0nh/ysvDVqPMeYKNhx+zJkyf0O7+zrL1790j6DUlvUe9CnpcvX/b9eoMy0JhZzc//t6Hrv98c\n8ZvHLCgFa4TpDcf9pQxfyTNm9zSMiYlZUypNDZyS4ecKl58RWr9XyIZvF/HTxs9ek0DcxKhFohqN\nRnuf7P1GOmyk/aZUmvI9mySqEdr+x65leoQ26n0lbcFUxPDIumSNO2Z3MuenTf+e3LeZcnmf7+O8\nPwPX+p47qjbduOdhhBZpCZt1qYfbyMJZHnyjDO4c7jdSo6/C+13Aaec+1/tM696xqimVpszExOzA\n6cV+ppucPftcO0gPtRuEzxjpgCmXD3paUApIAo28ZK2srJjW/WJruxpvq6urIx/ndfGSMIucdB5b\nLldN9z205XI1E52oQeeKyckZMz097/mcYCMautEg65Lj9ZjdaSc9024n3dVu313w1AEc1B7rzsDJ\nyZn2Oibh6n/QxUH95jGLdyIKdGgt0RtSg4JEmjdSrS9Ugl5FG7TKce+iUn6vkNdqNbN3733tcjba\n5TlkJidnaIjAGjTykrO0dMFMTu4z0lvbjbcL7Vy4a2yH1pjwqxx7XVugOw+ztMrxoHPF1NR97c88\nu53BMCvtB5HX+43JumQ0Gg1z/vx5TxeSarVa1++tGultXe2l4ce5l9HfTn5FcTEo7OyXoq1ynIf3\nkGV0aC0wbHU6ryO03c8R5gpXFNPWhk07Pnv2uUCfDRAHGnnJGH6Lw5qZmJiN/cRfhCmrwxqdfhf1\ns02SI7R5Pk7IuvjtLDJ3v/Ey1Xf3sd1ot+2ivbUiqlFPRk+9yXOGZAUd2pSNWp3u9OmnbwZJ5x7a\nUaES5upQlNPWulcQnZzcR2cW1qGRl4zBM03eaiYnZ2I/4RdpyuqwRqctIwZBy5FEYzrvxwlZF6/+\n4+cZI7lmevoBz7dJjLrlqyPIjIWo6r8tOWKDQZ9F3jMkK+jQpmxQSJXLd5rJyX1mdvawKZf3mdOn\nn745JTiuUIl62hoBCJvRyEvG4Atl+xJZIC7pKatpszVzw45cxP2+8n6ckHXxGtZ2On/+vK/bJMYd\n53Sa0jcsy/KeIVlBhzZlca1OF74c+Zi2BgxCIy85aU1ZowGYviz8DbJQxjDIunilMTWe9ljyRv2d\n854hWRE262Ldh7YIKpWKFhfPyHWPaWbmsCYnf1iue7eS3pOrtxyue0yLi2f0+OPv87UXLQB06+z5\nmnSGDMu0SqWSyOsjG3tMcpwgjCSPn7SyFKOzjAzJB6fVKbaT4zjG5vJ1azabqtfrmpqa0oMPPqKt\nrTW1Ks4Vue4xbW5eTaRydMpRrVapjMgtx3FkjHHSLkdUspR1SSPT0tNsNjU3dzC185kfeT1OyLpk\n5PX4QYuXLOMYSFfYrKNDG4Pl5RUtLDypUmlO29ubWlw8w5U4IEI08oBkcD5LF1kHRIMssxsdWkt1\nj9hev37diis+XH1CXtDIix95URzj/tYcC+kh67IhyjpCfYsPn629wmYd99BGpNlsan19Xc1mU1Lr\nvoyvfOWrevDBR3T8+BOamzuo5eWVyF/Hq+XlFc3NHRxalqDPCyB/OnnxQz/0uN785nt07tzHI3le\ncsY+484NUut8duTIkdQagBw3sJmXOhTVc/mtC9Sd3dLOMr/4+/kQZkWpuL9k2Wp4wwxaCjyOVdOC\nbp8wrixsKI2sESt/xmZQXkhu6P2oyRn7ZGF1z6IfN2Sd3aKsQ1G31Yped7KuaH+/sFmXeriNLFwG\ngm9YAK2urka6r9W4oBu1B9qoPbay0KABetHIi0+tVjPT0/O78kI6ZCYnZwLnAjljJ9v3X+S4Iets\nF2Ud8ttWG7UnOHUn24r49wubdUw5DmnYUuCSdONGXdKV9vevaHt7U9VqNdLXqdfrY6eoVKvVoWXJ\nwrYMAJLTyos/VXdeSN9QqXRH4FxoPe5N6s4Z6Y3kzAhJTDUbdW6wAecn2C7KOuS3rfbXf13R/PzD\nA6c4J113mBobLbLPPzq0IQ0LoD/90029+uoNST8g6W5NTPxgqH2thr3O1NSUFhae1NbWmq5de0Fb\nW2taWHjy/2/v/qMkK+s7j3++I9PdBTMNzFqaqKELRZ3RONIDAyiYmVZGzXgUDmTBWTXGzGZl8VdW\njaKeXTe75yTZ7JqNB53ww4ZolGaysglmY05wwgxGXaQDDS3MD4narRCEMrITBnqaAb77x73NVPdU\ndVd13brPc6ver3P6TPftmr7funWfT92nnnufOy9UFrvHVuwHNADyVS6X9dnP/qGkcyS9WtKIpI/r\n6af/adm5sGrVKs3M/KNqc2Zm5gdatWpVFiV3nSyvyVtM7Pdf5P0JscuyDbV6rCb9s2Znbz7mmE/K\nt+3klVe9hOxbhnaGdzv9pYKcmjJ3nvvg4LCXSmv8qquuqTlV4BGXvuIDAyct+5qKudNNFq7nhhtu\nbOl0l0anJdf7u0DMxGl4HXfVVdd4f/+gr1r1y23nwh133OGl0qkurXFp2KU1PjBQyezU1sUuuSia\nEKeaxbz9ev39iawrhiza0Nzf2Lt3b8Njtf7+k1x6aZqlNy56zJdH2+nFU2Pz0mvZ127WBQ+3RYsr\nUPDVhllW11Q0mmyqNuiyCpOYD2iAhTjIy0dWuXA0p3a7dIdLuzM76Om2iTNiv641hF5+fyLrekOz\nObZ3717v7x9Ms3TpY75Otx3yqrN6KfvazTruQ9sB1WpVQ0NrNTOzW8n575MqlUY0Pb2/6dNQWvkb\n3CwavYZ7MxZPJ3Iqi6yNTTc+JywfWdf9Wm3zMR3zkVfISrtZd1yWxSAxdx3E9u0j8wKnlcY9d0H4\nzMyxF4Qv/Dvbtl2q889/PTeLBhCtTuRUKzlZFFm8fwAojlZzLKZjPvIKsWCEtoOq1eqyA6fep179\n/Zs0MfEdrVu3riP1AkXBqEVY7WRb1nV06+hALNsYYZF13W+pHCtCFhShRsSt3axjluMOKpfL2rhx\nY9uz3ZVKr5J0jlasOFlnnHEeM8gBCCamGS1jn6W3He28fwAojsVyLKa8XQx5hdAYoc1QJz6h2rdv\nn4aHz9Hs7M2SNqubRiCA5WLUIoxYR0QZHYgDr0P2yLresbD9HM3bmySdIOlxlUoXB89boBMYoY1E\npz5FO3TokAYGTlPSmZW4uTKAUGK92TujA+EVZSQJiNXCHEty9SRJF0u6TNLFch8MnrdAjOjQZqBa\nrWr79ss1M7NbBw/eqZmZ3XVvdL0c3FwZQCzII9TTyfdAoFetWrVKMzMPSdot6U5Ju3X48MNatWpV\n4MqA+NChzUAnRy26+RoxAMVCHqGeWEfugSI7dOiQSqXTVNuuSqWX6NChQyHLAqLENbQZyOO6Mq5N\nAo7iurKwyCPUivXa6m5A1vUu2hV6CfehjUAe9+Eql8sEGIAokEeoxb0ogezRroDmMUKboWq1qomJ\nCUnS8PBwU6HDSAfQOkYt4tSredarz3shtkP2yLru0ah9LNVuaFfoBcxyHJFdu27VhRdu0yWXfKKp\nWR6ZFRJAt+jVPOvV510Ps00D9TXKiWbyg3YFLI0R2oy0eq0D10YAy8eoRVx6Nc969XkjP2Rd8TXK\niTvv/JbOOOM88gMQI7TRaHWWR2aFBNAtejXPevV5A2heo5y44447yA8gI3Rol6larWp8fPzZ++wt\ndX/GVh8PALFZmGNzejXPevV512q0TwBINMqJs846K2h+dKrtkgkIwt2j/UrKi88NN9zopdIaP/HE\nDV4qrfEbbrhx3vLBweG6y5t9PIDFpdkQPKOy+oo162o1yrGFv++1POvV5+2+9D6B9pF13WGp48O8\n86NTbZdMwHK1m3VcQ9uiffv2aXj4tZqdvU31rnmonY1OkiYmJnTBBZfq8OGlH881E0BzuK4sX81e\nK5p3nsWSn7HUkaciXz9cpNeLrOsey53lOOt1dqrtFjkTWlGk/CgSrqHN0djYTg0Pn6PZ2eep0TUP\nc7PR7dp1q4aG1uqiiz6mw4eflLRv0cfTKADEqtlrRfPMs5hmF+7FHC/q9cMx7TfoLY1yolP50Whf\n71TbLWomtIL8iBcjtE06+snTTZIultT4E6h6n1JJmyUdkPRQV35iBeSJUYt8xfbJe2z19KIivgZF\nrJmsw3Istq9LYoR2Gbr9+YXGCG1Ojn7ytFnSDkkjkl6m/v5NGh3d8ezpw+Pj45qYmDjmUyppjU44\n4TyVSiPPPh4AiqBcLmt0dIdKpRENDm4InmNFGQno5slRYtsnmlGU/QZo12L7eqfabqt/t2j5SH7E\njRHaJh37ycwe9fdfoImJ27Vu3TqNje3U9u2Xq68vmc3uqaee1JEj39bcpzj9/Zv0pS9drZGRkajf\n8IEiYNQijGq1qomJCUnS8PBwsCwrwiflC98TRkd3aNu2S0OXlbkiXU9WhP1mIbKud7XTtprZdmra\nuQAAGipJREFU1zvVdpv5u0XMxyLmR5G0nXXtzCjV6S9FNhteo9noHnnkES+V1rh0j0vu0j3e13ei\nDwyc5KXSL7tU8lLpVGZ8AzIiZv4MIqYZLGOeXbjee0KptMYfeeSR0KX1vJj3m3rIut6URdbGuq8X\nOR9j3abdoN2sY4S2RfU+eRofH9eWLZfp4ME7n33c4OAGXXvtFfr1X/8tzc7erORUZT7NAbLAqEX+\nYvx0OtbRwUbvCbt2Xa2NGzcGrAxSvPtNPWRd78kya2Pc14uejzFu027QbtYdl2UxvaBcLh+zAx+9\nafYeSSdIelxHjkzrpJNO0sDAaZqd3Zw+cv41DABQFHPXD83M1L8mK4SFeRzLgcbR94RJzR2QHjky\n/ezt3BBWvfdxIBZLZW0rORfjvl70fIxxm4JJoTJRLpe1ffu7JG2V9E5JW7V9+zs1PDxc02ilojVa\nAJgz/yBEii3PYrqdQhEnTAIQh8WyNqacWy7yEZ3AKcctavUm1bt23art2y/XypVDOnJkuhAXvgOx\n4zS8MOYm8ogtz7I4Ra8To7uxjBiH0uvPPwtkXW+ql7Wnn75ew8Ov1ezsbYrlso92xDLJIOLAbXty\n1MpNqmdmTtbVV1+rbdsuTTu2V2t6en8UB38AsByx5lm7t1Po1KhHuVzWxo0be/JArRtGkoBQFmat\nJA0Pn6PZ2eepW24bs2vXrbrwwm265JJPkBFoGyO0TWr1JtXSZg0MuH784+/35MEM0EmMWqBWOyO0\nMU52VXRs0+yQdTjanm6SdLGk4rcrMgILMUKbk6VuUv3JT35E0mskbZA0IulP1Nd3amE/OQOAomjn\nmqx2R3dxLLYpkJ2j7WmzpB1KjjFfpv7+TYW99pSMQNbo0DZpqQlR3vve39LAQJ+kj0raL2ldVBOm\nAEA3W+7p0LFPdlVEbFMgO/Pb06WSblJ//8OamPhONJd9tIqMQNbo0DZpqRGAcrms6667SqXSBzQ4\n+CZmbQOAnC3nmlVm3Mwe2xTIzrHt6WJdf/01WrduXejSlo2MQNa4hrZFS83ayKyOQOdxXRmyRnZn\nj23aPrIOc7qxPXXjc8LytJt1dGgBFA4HeQB6AVkHoBcwKRQAAAAAoCfRoQUAAAAAFBId2oxUq1WN\nj4+rWq2GLgUAgiIPASA+ZDO6FR3aDIyN7dTQ0Fpt2XKZhobWamxsZ+iSACAI8hAA4kM2o5sxKVSb\nqtWqhobWamZmt5IbRE+qVBrR9PR+ZmwDOoSJUuJEHgLZIuuQBbIZsWNSqJwtPF1jampKfX0VJQEh\nSeu1cuWQpqamAlUIAGGQh53HKYNAZ3VjGyOb0e3o0LZg4ekaV199rR599FE9+eSUpMn0UZM6cmRa\nlUolXKEAEEClUilkHhblAJZTBoHO6tY2Fls2FyVzURycctykeqdrSOdo9erTdPjwj2T2HA0MvFhH\njkxrdHSHtm27NHDFQPfiNLx4jY3t1Pbtl2vlyqFC5OFcvX19yQFfrPVyymBvIuvy0+1tLJZsLkrm\nIl/tZh0d2iaNj49ry5bLdPDgnTVLXy3pC5L6NTCwSTffvFPDw8NdEXxAzDjIi1u1WtXU1JQqlUrU\neVikA9h670GrVw/ryit/W1u3bo2uXmSDrMtPvTY2OLhBu3ZdrY0bNwasLDtZZfNy/06RMhf54hra\nnFQqFc3M/EC1p2tI05Iqktarr+9UnXzyyTRIAD2vXC5r48aN0edhka4rq3fK4GOPHdAHPvCZrjo1\nEgglttNyOyGLbG7ntOwiZS6KhQ5tC9yflrRZ0ob036fS33Rf6AFAtyvSAWy5XNbo6A6VSiNavXpY\n0jmS/rMee2xSMzO7tX375VyPBrShto0NDm5QqTSi0dEd0X8wl6dqtart2y/XzMxuHTx4Z8vZU6TM\nRbHQoW3SxMSE+vpeKOnbkq6WdEBSWQMDZ2pgYBOhB6DrdPvEHUU7gN227VJNT+/XlVf+tlavPk3S\nx9LfHDvK0e2vHdAJc21s166rNT29v+lrO0O2tzzX3e4Ia9EyF8VBh7YJY2M7deGF2/T4409LOlfS\nDyVdL+lhrVhxkszYjAC6S7fO9rnQcg9gQymXy9q6daueeupBNRrl6JXXDuiEVk/LDdne8l53FiOs\nRctcFAOTQi2h0ezGidvFRe1A/pgopbOYuCN+jWYs5bXrLmRd3EK2t1DrjmW2ZHSXdrPuuCyLaYWZ\nTUk6KOkZSUfc/axQtSxm7vSKmZmjp1cMDLxQK1YcryeeOPaUCw4YABRdvdwj4+KybdulOv/81x8z\n0yivHZCfkO0t1LobZQ8QUrAOrZKO7GZ3fzRgDUuaf3rF3Ajtz+S+Yt4yLmoH0C3q5R4ZF59yuXzM\nwSSvHZCfkO0t5LrrZQ8QUsiLPy3w+ptS7wL26667iovaAXQtJu4oLl47ID8h2xttHTgq2DW0ZvZD\nSf9P0tOSrnH3a+s8JpprLerdRDqrG1QDaA3XleWDjCsuXrvuQNYVQ8j2RltHN2g360J2aH/R3R8y\ns7Kkb0h6v7t/a8FjfNOmTapUKqpUKtq8ebM2b94cpN45BAeQvz179mjPnj2amprS1NSUbrvttq47\nyIst6xZDDgKdQdaFQ64B+ck666KY5djMPi3pMXf/owXLo/okb25mt76+5LoFZnYDwmDUIhxyEMgP\nWZcPcg0Iq5AjtGZ2vKQV7n7IzE6QdIuk33X3WxY8Lprg41YIQDw4yAuDHATyRdZ1HrkGhNdu1oWa\nlOn5kr5lZhNKbub6Vws7s7GZmx49CTupdnp0AOgF5CCAbkOuAcUX5LY97v4jSaeHWPdycSsEAL2O\nHATQbcg1oPiiv21OLJgeHUCvIwcBdBtyDSi+KCaFaiTWay2YBQ8Ii+vKwiIHgXyQdfkh14BwCjkp\nVLNiDj4A4XCQB6AXkHUAekFRJ4UCAAAAAKAtdGgBAAAAAIVEhxYAAAAAUEh0aAEAAAAAhUSHFgAA\nAABQSHRoAQAAAACFRIcWAAAAAFBIdGgBAAAAAIVEhxYAAAAAUEh0aAEAAAAAhUSHFgAAAABQSHRo\nAQAAAACFRIcWAAAAAFBIdGgBAAAAAIVEhxYAAAAAUEh0aAEAAAAAhUSHtgnValXj4+OqVquhSwEA\nLEBGA+g0cgaIFx3aJYyN7dTQ0Fpt2XKZhobWamxsZ+iSAAApMhpAp5EzQNzM3UPX0JCZecj6qtWq\nhobWamZmt6T1kiZVKo1oenq/yuVysLqAXmdmcncLXUdWQmddUZHR6HZkXXjkDNB57WYdI7SLmJqa\nUl9fRUmASdJ6rVw5pKmpqXBFAQAkkdEAOo+cAeJHh3YRlUpFTz45JWkyXTKpI0emValUwhUFAJBE\nRgPoPHIGiB8d2kWUy2WNju5QqTSiwcENKpVGNDq6g1NMACACZDSATiNngPhxDW0TqtWqpqamVKlU\nCDAgAlxXhlpkNLoVWRcPcgbonHazjg4tgMLhIA9ALyDrAPQCJoUCAAAAAPQkOrQAAAAAgEKiQwsA\nAAAAKCQ6tAAAAACAQqJDCwAAAAAoJDq0AAAAAIBCokMLAAAAACgkOrQAAAAAgEKiQwsAAAAAKCQ6\ntAAAAACAQqJDCwAAAAAoJDq0AAAAAIBCokMLAAAAACgkOrQAAAAAgEKiQwsAAAAAKCQ6tAAAAACA\nQqJDu4hqtarx8XFVq9XQpQAAUFi8n6JbsW8D4dGhbWBsbKeGhtZqy5bLNDS0VmNjO0OXBABA4fB+\nim7Fvg3Ewdw9dA0NmZmHqK9arWpoaK1mZnZLWi9pUqXSiKan96tcLudeD4D5zEzubqHryEqorAM6\njffT9pB18WLfBrLTbtYxQlvH1NSU+voqSgJKktZr5cohTU1NhSsKAICC4f0U3Yp9G4gHHdo6KpWK\nnnxyStJkumRSR45Mq1KphCsKAICC4f0U3Yp9G4gHHdo6yuWyRkd3qFQa0eDgBpVKIxod3cEpJAAA\ntID3U3Qr9m0gHlxDu4hqtaqpqSlVKhUCCogI15UBxcL76fKQdfFj3wba127W0aEFUDgc5AHoBWQd\ngF7ApFAAAAAAgJ5EhxYAAAAAUEh0aAEAAAAAhUSHFgAAAABQSHRoAQAAAACFRIcWAAAAAFBIdGgB\nAAAAAIVEhxYAAAAAUEh0aAEAAAAAhUSHFgAAAABQSHRoAQAAAACFRIcWAAAAAFBIdGgBAAAAAIVE\nhxYAAAAAUEh0aAEAAAAAhRSsQ2tmbzaz/Wb2fTP7eKg6lrJnz57QJQSvIfT6qYEaEEaMr3WMNUlx\n1hVjTVKcdcVYE/IT4+sfY01SnHXFWJMUZ10x1pSFIB1aM1sh6XOS3iTplZK2mdnaELUsJYYXPnQN\noddPDdSAMGJ8rWOsSYqzrhhrkuKsK8aakJ8YX/8Ya5LirCvGmqQ464qxpiyEGqE9S9L97j7t7kck\n3SjpgkC1AAAAAAAKKFSH9oWSflLz8wPpMgAAAAAAmmLunv9KzS6W9CZ3/3fpz++UdJa7f3DB4/Iv\nDkAhuLuFriErZB2ARsg6AL2gnaw7LstCWvCgpFNqfn5RumyebgpxAGiErAPQC8g6AJ0Q6pTjcUmn\nmdmQmfVJerukrwWqBQAAAABQQEFGaN39aTN7v6RblHSqR919X4haAAAAAADFFOQaWgAAAAAA2hXq\nlOOGzOzTZvaAmd2Vfr255nefMLP7zWyfmb2xw3W82cz2m9n3zezjnVzXgvVOmdk9ZjZhZneky042\ns1vM7ICZ/a2ZnZjxOkfN7GEzm6xZ1nCdnXgdGtSQ275gZi8ys1vN7D4z+56ZfTBdntt2qFPDB9Ll\neW6HfjP7brr/3Wdmv5cuz3M7NKohimzIkpn9mpnda2ZPm9mGBb8L9pxC5V+dOlrKppxqajkrcqip\n5XabJzNbkbbZr8VSlwV4r22iphPN7H+lbf4+Mzs7dE1ZMbM/TJ/X3WZ2k5kN1vyOrIswV2pqi7H9\nRtdW0v34PjObNLOvmFlfiJpafd/Mo/01qCnbTHD3qL4kfVrSh+ssXydpQslp0hVJ/6h0hLkDNaxI\n//6QpJWS7pa0Nqfn/0NJJy9Y9t8kfSz9/uOS/iDjdZ4n6XRJk0utU9IrOvE6NKght31B0i9IOj39\nfpWkA5LW5rkdFqkh1zYh6fj03+dIul3SuQH2h3o1BM+GrL8kvVzSSyXdKmlDDM9JAfOvTi1NZ1OO\nNbWUFTnW1XS7DfA6/gdJX5b0tRhew3S9ub/XNlHTn0p6T/r9cZJODF1Ths/tfEkr0u//QNLvp993\n5D2kyZpiyroocyVdb4ztN6q2ku5DP5TUl/68U9K7Q9SkCI7pm6wp00yIboQ2VW8WvAsk3ejuT7n7\nlKT7JZ3VofWfJel+d5929yOSbkzXnwfTsSPnF0j6Yvr9FyVdmOUK3f1bkh5tcp1vUwdehwY1SDnt\nC+7+U3e/O/3+kKR9Smbfzm07NKhh7v7MubUJd38i/bZfyb74qPLfH+rVIIXPhky5+wF3v1/HPq+Q\nzylk/s3TYjblVVOrWZFXXa2029yY2YskbZX0hZrFwetSgPfaRYtJRide5+7XS1La9g+GrClL7r7L\n3Z9Jf7xdSZuROvQe0qSYsi7KXImx/UbaVv5F0pOSTjCz4ySVlNy9JfeaYjimb6amrDMh1g7t+9Mh\n6C/UDIu/UNJPah7zoI4e7Gdt4boe6OC6FnJJ3zCzcTP7t+my57v7w1ISepKel0Mdz2uwzjxfBynA\nvmBmFSWfJN2uxts+rxq+my7KbTukpxdNSPqppD3uvlc5b4cGNUjhsyEvIZ9TyPxrRqNsyl2TWZFX\nLa202zz9T0m/o+S9bU4MdcXyXjvnVEk/M7Pr09M7rzGz4wPX1Cm/Kenr6fdk3QIx5YribL/RtRV3\nf1TSZyT9WMk+fNDdd4WsaYFYjukbaTsTgnRozewb6Tnmc1/fS/99q6Qdkl7s7qcreWP+TIgaAzrX\n3Tco+UTsfWb2Os0PEtX5OQ8h1pn7vmBmqyR9VdKH0k9Jc9/2dWrIdTu4+zPuPqzk07LXmdlm5bwd\nFtTwK2a2SQXNhiXyDu0LMrNhDFkxb2URtNuFzOwtkh5OR54Wu/9oiNcwtvfa4yRtkPT5tK7HJV0R\nuKaWNJN1ZvYpSUfcfSxgqdGKKVcibr/RtRUze7GSU7OHJL1AyUjtO0LWtIRY6sgsE0LdtmdLkw+9\nVtJfpd8/KOmXan73onRZJzwo6ZSc1jWPuz+U/ls1s79UMsz+sJk9390fNrNfkPRIDqU0Wmdur4O7\nV2t+7Pi+kJ4m8lVJf+buN6eLc90O9WrIezvMcfd/MbOvSzpTgfaHtIa/lnSmu99W86tQ2dCyFvKu\nVsjnFCz/mhQiD+dpMSty1WS7zcu5kt5mZluVnIK32sz+TNJPQ2+riN5r5zwg6Sfu/g/pzzcpOUgP\n/Ro2bamsM7PfUPIBwutrFpN1qQhzJdb2G2NbOVPSt93955JkZn8h6bWBa6oV/Ji+niwzIbpTjtMN\nPeciSfem339N0tstmTXsVEmnSbqjQ2WMSzrNzIbMrE/S29P1d5SZHZ9+OiczO0HSGyV9L133b6QP\ne7ekm+v+gTZXr/mfwDVaZydfh3k1BNgXrpO0190/W7Ms7+1wTA15bgcze+7cqbxmVpK0RcnF+blt\nhwY13B1JNnTSwvYX6jkFyb9FNJtNeWolKzpuGe02F+7+SXc/xd1frGQ/utXd36Xkw6hgdQV+r60r\nPR3wJ2b2snTRGyTdF7KmLFkyK/3vSHqbu8/W/IqsOyqqXIm1/UbaVg5IOsfMBszM0pr2BqwphmP6\nRWvKPBM855nJlvqS9CVJk0pmm/tLJeefz/3uE0pmu9on6Y0druPNSnbQ+yVdkdNzPzV93hNK3lyv\nSJevkbQrrecWSSdlvN4bJP2TpFkl5/+/R9LJjdbZidehQQ257QtKPol8umb735XuAw23fY415Lkd\nXpWud0LSPZI+utQ+mGMNUWRDll9KJmb4iaQZSQ9J+psYnlOI/GtQR0vZlFNNLWdFDjW13G4DvJab\ndHSW1KB1KdB7bRN1vVpJJ+tuSf9bycyt0byGbT63+yVNp/vpXZJ21PyOrIswVxbUF037TWuIrq0o\n6Zzdp+Q45YtKZs7OvaZW3zfzaH8Naso0Eyz9jwAAAAAAFEp0pxwDAAAAANAMOrQAAAAAgEKiQwsA\nAAAAKCQ6tAAAAACAQqJDCwAAAAAoJDq0AAAAAIBCokOLqJjZGjObMLO7zOwhM3sg/flpM9uy4LEf\nMrPPh6oVABaT5tZdZjZpZjeZ2Qkt/v9Pm9mH0+9/18xe35lKASBhZs+Y2X+v+fkjZvafQtYELIUO\nLaLi7j9392F33yDpKkl/5O7Dkt4raduCh79dyc2aASBGj7v7BndfL+kxJTm2LO7+aXe/NbvSAKCu\nWUkXmdma0IVkycyeE7oGdA4dWhTFTZK2mtlxkmRmQ5J+0d2/HbYsAGjK/5X0EkkysxPMbJeZ/YOZ\n3WNmb5t7kJl9yswOmNk3Jb28Zvn1ZnZR+v0b0pHfe8zsC2a2Mu8nA6BrPSXpGkkfXvgLM3uumX3V\nzL6bfr0mXT5pZoPp9z8zs3em338xzatXpI+/y8zuNrOXmNmQme0zsy+b2V4z+3MzG0j/339MHz9p\nZlfVrH+3mf1xeubepJltTJcfb2ajZna7md1pZm9Nl7/bzG42s7+TtKvD2w0B0aFFIbj7o5LukPSr\n6aK3S/rzcBUBwJJMenZkYIuk+9LlhyVd6O5nSnq9pM+kjztD0iWS1kt6i6SNx/xBs35J10v61+7+\nakkrJf37zj4NAD3EJX1e0jvMbPWC331WyZlzZ0v6NUmj6fJvSTrXzF4p6QeSXpcuf42k70i6TNIf\np2ffnSnpgfT3L5f0OXd/hZKzWC5Pl1/p7menZ7ccb2ZvqamhlJ659z5J16XLPiXp79z9HCWZ+j/M\nrJT+bljSRe4+ssztgQKgQ4siuVFJR1bpv2MBawGApZTM7C5JD0n6JSWXUUhJR/f3zeweJaMGLzCz\n50k6T9JfuPusuz8m6Wt1/ubLJf3Q3X+Q/vxFSb/SyScBoLe4+yEl2fKhBb86X9LnzGxCST6tMrPj\nlXRoNynJoqskvcrMXiDp5+4+o+QMlU+Z2cckVdx9Nv17P3b329Pvv6wkAyXpDelo66SkEUmvrKlh\nLK3x7yWtTkeG3yjpirSuPZL6JJ2SPv4b7n6wvS2C2NGhRZHcrCTkhpV8QjcRuiAAWMQT6YjEKUpG\nZedOLX6HpOdKGk5HGh6RNNDC37VMqwSAY31W0nZJtZPZmaSz07lOht39FHd/QtI3lYzKnidpt6Sf\nKRnB/XtJcvcxSW+VNCPp62a2ucE6PT0L5fNKRlXXS/qC5uejL/w/aV0X19R1qrsfSH//+DKeOwqG\nDi0Kw90fV/LJ23VidBZA/EyS3P2wkpGO30uXnyjpEXd/xsxGdHQk4ZuSLjSz/vRUv7fW+ZsHJA2Z\n2YvTn98l6bZOPQEAPWcutx5VcmnX9prf3aKaUVsze3X62AeUfEj3UnefUjJi+1ElmSYzO9Xdf+Tu\nVyoZnFif/olTzOzs9Pt/k/6/ASWd1H82s1VKOsa1Lk3/5nmSDqZns/ytpA/W1HV6G88fBUSHFkUz\npiQI6dACiN2zIwnufrek+83sUklfkbQxPeX4nZL2p4+ZUHIAOSnpr5XMGzDvb6Wn6r1H0lfT//+0\njp7KDADtqh0B/Yykf1Wz7EOSzkwnpLtX82duv13JB25SMjL7AiUdVEm6xMzuTU8JfqWkL6XLD0h6\nn5ntlXSSpD9JTw++VsmcA3+j+TkoSYfTSzl2SPrNdNl/lbQynSjqXkn/ZXlPHUVl7gtH7gEAAACg\nM9K7Vfwfd39VC/9nt6SPuPtdnasMRcQILQAAAIC8tTqqxigc6mKEFgAAAABQSIzQAgAAAAAKiQ4t\nAAAAAKCQ6NACAAAAAAqJDi0AAAAAoJDo0AIAAAAACun/A6/xIIinyfgFAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd6a40abb10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(1, 3, sharey=True)\n",
"data.plot(kind='scatter', x='TV', y='Sales', ax=axs[0], figsize=(16, 8))\n",
"data.plot(kind='scatter', x='Radio', y='Sales', ax=axs[1])\n",
"data.plot(kind='scatter', x='Newspaper', y='Sales', ax=axs[2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Questions \n",
"\n",
"How can the company selling the product decide on how to spend its advertising money in the future? We first need to answer the following question: \"Based on this data, does there apear to be a relationship between ads and sales?\"\n",
"\n",
"If yes, \n",
"1. Which ad types contribute to sales?\n",
"2. How strong is the relationship between each ad type and sales?\n",
"4. What is the effect of each ad type of sales?\n",
"5. Given ad spending in a particular market, can sales be predicted?\n",
"\n",
"We will use Linear Regression to try and asnwer these questions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simple Linear Regression\n",
"\n",
"Simple linear regression is an approach for modeling the relatrionship between a **dependent variable** (a \"response\") and an **explanatory variable**, also known as a \"predictor\" or \"feature\". The relationship is modeled as a linear function $y = \\beta_0 + \\beta_1x$ whose parameters are estimated from the available data.\n",
"\n",
"In the equation above:\n",
"- $y$ is called the response, regressand, endogenous variable, dependent variable, etc.\n",
"- $x$ is the feature, regressor, exogenous variable, explanatory variables, predictor, etc.\n",
"- $\\beta_0$ is known as the intercept\n",
"- $\\beta_1$ is the regression coefficient, effect, etc. \n",
"\n",
"Together, $\\beta_0$ and $\\beta_1$ are called **paramaters**, **model/regression coefficients**, or **effects**. To create a model, we must discover/learn/estimate the values of these coefficients. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimating/Learning Model/Regression Coefficients\n",
"Regression coefficients are estimated using a variety of methods. The **least squares method**, which finds the line which minimizes the **sum of squared residuals** (or \"sum of squared errors\") is among the most oftenly used."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the pictures below:\n",
"- The blue dots are the **observed values** of x and y.\n",
"- The red line is the **least squares line**.\n",
"- The **residuals** are the distances between the observed values and the least squares line.\n",
"\n",
"- $\\beta_0$ is the **intercept** of the least squares line (the value of $y$ when $x$=0)\n",
"- $\\beta_1$ is the **slope** of the least squares line, i.e. the ratio of the vertical change (in $y$) and the horizontal change (in $x$)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use the **statsmodels** package to estimate the model coefficients for the advertising data:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Intercept 7.032594\n",
"TV 0.047537\n",
"dtype: float64\n"
]
}
],
"source": [
"import statsmodels.formula.api as sf\n",
"\n",
"#create a model with Sales as dependent variable and TV as explanatory variable\n",
"model = sf.ols('Sales ~ TV', data)\n",
"\n",
"#fit the model to the data \n",
"fitted_model = model.fit()\n",
"\n",
"# print the coefficients\n",
"print(fitted_model.params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Interpreting Model Coefficients\n",
"\n",
"Q: How do we interpret the coefficient ($\\beta_1$) of the explanatory variable \"TV\"?\n",
"\n",
"A: A unit (a thousand dollars) increase in TV ad spending is **associated with** a 0.047537 unit (a thousand widgets) increase in Sales, i.e., an additional $1000 spent on TV ads is **associated with** an increase in sales of ~47.5 widgets.\n",
"\n",
"Note that it is, in general, possible to have a negative effect, e.g., an increase in TV ad spending to be associated with a **decrease** in sales. $\\beta_1$ would be **negative** in this case."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using the Model for Prediction\n",
"\n",
"Can we use the model we develop to guide advertising spending decisions? For example, if the company spends $50,000 on TV advertising in a new market, what would the model predict for the sales in that market?\n",
"\n",
"$$y = \\beta_0 + \\beta_1x$$\n",
"$$y = 7.032594 + 0.047537 \\times 50$$"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"9.409444"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"7.032594 + 0.047537*50"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The predicted Sales in that market are of **9.409444 * 1000 =~ 9409 widgets**\n",
"\n",
"Using Statsmodels:"
]
},
{
"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>TV</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>50</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TV\n",
"0 50"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a DataFrame to use with the Statsmodels formula interface\n",
"New_TV_spending = pd.DataFrame({'TV': [50]})\n",
"\n",
"#check the newly created DataFrame\n",
"New_TV_spending.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 9.40942557]\n"
]
}
],
"source": [
"# use the model created above to predict the sales to be generated by the new TV ad money\n",
"sales = fitted_model.predict(New_TV_spending)\n",
"print(sales)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting the Least Squares Line\n",
"\n",
"Let's make predictions for the **smallest and largest observed values of money spent on TV ads**, and then use the predicted values to plot the least squares line:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" TV\n",
"0 0.7\n",
"1 296.4\n"
]
}
],
"source": [
"# create a DataFrame with the minimum and maximum values of TV ad money\n",
"New_TV_money = pd.DataFrame({'TV': [data.TV.min(), data.TV.max()]})\n",
"print(New_TV_money.head())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 7.0658692 21.12245377]\n"
]
}
],
"source": [
"# make predictions for those x values and store them\n",
"sales_predictions = fitted_model.predict(New_TV_money)\n",
"print(sales_predictions)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fd68a8927d0>]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSaDEICIiCf4/9gKXm7/Jq3sAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd6a40ab490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot the observed data\n",
"data.plot(kind='scatter', x='TV', y='Sales')\n",
"\n",
"# plot the least squares line\n",
"plt.plot(New_TV_money, sales_predictions, c='red', linewidth=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Confidence in Linear Regression Models\n",
"\n",
"**Q:** Is linear regression a high bias/low variance model, or a low variance/high bias model?\n",
"\n",
"**A:** High bias/low variance. Under repeated sampling, the line will stay roughly in the same place (low variance), but the average of those models won't do a great job capturing the true relationship (high bias). (A low variance is a useful characteristic when limited training data is available.)\n",
"\n",
"We can use Statsmodels to calculate 95% confidence intervals for the model coefficients, which are interpreted as follows: If the population from which this sample was drawn was **sampled 100 times**, approximately **95 of those confidence intervals** would contain the \"true\" coefficient."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 1\n",
"Intercept 6.129719 7.935468\n",
"TV 0.042231 0.052843\n"
]
}
],
"source": [
"# print the confidence intervals for the model coefficients\n",
"print(fitted_model.conf_int())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we only have a **single sample of data**, and not the **entire population** the \"true\" value of the regression coefficient is either within this interval or it isn't, but there is no way to actually know. \n",
"\n",
"We estimate the regression coefficient using the data we have, and then we characterize the uncertainty about that estimate by giving a confidence interval, an interval that will \"probably\" contain the value coefficient. Note that there is no probability associated with the true value of the regression coefficient being in the given confidence interval!\n",
"\n",
"Also note that using 95% confidence intervals is simply a convention. One can create 90% confidence intervals (narrower intervals), 99% confidence intervals (wider intervals), etc."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Hypothesis Testing and p-values\n",
"\n",
"Closely related to confidence intervals is **hypothesis testing**. Generally speaking, you start with a **null hypothesis** and an **alternative hypothesis** (that is opposite the null). Then, you check whether the data supports **rejecting the null hypothesis** or **failing to reject the null hypothesis**.\n",
"\n",
"(Note that \"failing to reject\" the null is not the same as \"accepting\" the null hypothesis. The alternative hypothesis may indeed be true, except that you just don't have enough data to show that.)\n",
"\n",
"As it relates to model coefficients, here is the conventional hypothesis test:\n",
"- **null hypothesis:** There is no relationship between TV ads and Sales (and thus $\\beta_1$ equals zero)\n",
"- **alternative hypothesis:** There is a relationship between TV ads and Sales (and thus $\\beta_1$ is not equal to zero)\n",
"\n",
"How do we test this hypothesis? Intuitively, we reject the null (and thus believe the alternative) if the 95% confidence interval **does not include zero**. Conversely, the **p-value** represents the probability that the coefficient is actually zero:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Intercept 1.406300e-35\n",
"TV 1.467390e-42\n",
"dtype: float64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# print the p-values for the model coefficients\n",
"fitted_model.pvalues"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the 95% confidence interval **includes zero**, the p-value for that coefficient will be **greater than 0.05**. If the 95% confidence interval **does not include zero**, the p-value will be **less than 0.05**. Thus, a p-value less than 0.05 is one way to decide whether there is likely a relationship between the feature and the response. (Again, using 0.05 as the cutoff is just a convention.)\n",
"\n",
"In this case, the p-value for TV is far less than 0.05, and so we **believe** that there is a relationship between TV ads and Sales.\n",
"\n",
"Note that we generally ignore the p-value for the intercept."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How Well Does the Model Fit the data?\n",
"\n",
"The most common way to evaluate the overall fit of a linear model to the available data is by calculating the **R-squared** (a.k.a, \"coefficient of determination\") value. \n",
"\n",
"R-squared has several interpretations:\n",
"(1) R-squared ×100 percent of the variation in the dependent variable ($y$) is reduced by taking into account predictor $x$\n",
"(2) R-squared is the proportion of variance in the observed data that is \"explained\" by the model.\n",
"\n",
"R-squared is between 0 and 1, and, generally speaking, higher is considered to be better because more variance is accounted for (\"explained\") by the model.\n",
"\n",
"Note, however, that R-squared does not indicate whether a regression model is actually good. You can have a low R-squared value for a good model, or a high R-squared value for a model that does not fit the data!\n",
"\n",
"One should evaluate the adequacy of a model by looking at R-squared values as well as residual (i.e., observed value - fitted value) plots, other model statistics, and subject area knowledge."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The R-squared value for our simple linear regression model is:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.61187505085007099"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# print the R-squared value for the model\n",
"fitted_model.rsquared"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Is that a \"good\" R-squared value? One cannot generally assess that. What a \"good\" R-squared value is depends on the domain and therefore R-squared is most useful as a tool for **comparing different models**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multiple Linear Regression\n",
"\n",
"Simple linear regression can be extended to include multiple explanatory variables:\n",
"\n",
"$y = \\beta_0 + \\beta_1x_1 + ... + \\beta_nx_n$\n",
"\n",
"Each $x$ represents a different predictor/feature, and each predictor has its own coefficient. In our case:\n",
"\n",
"$y = \\beta_0 + \\beta_1 \\times TV + \\beta_2 \\times Radio + \\beta_3 \\times Newspaper$\n",
"\n",
"Let's use Statsmodels to estimate these coefficients:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Intercept 2.938889\n",
"TV 0.045765\n",
"Radio 0.188530\n",
"Newspaper -0.001037\n",
"dtype: float64\n"
]
}
],
"source": [
"# create a model with all three features\n",
"multi_model = sf.ols(formula='Sales ~ TV + Radio + Newspaper', data=data)\n",
"fitted_multi_model = multi_model.fit()\n",
"\n",
"# print the coefficients\n",
"print(fitted_multi_model.params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How do we interpret the coefficients? For a given amount of Radio and Newspaper ad spending, an increase of a **unit** ($1000 dollars) in TV ad spending is associated with an **increase in Sales of 45.765 widgets**.\n",
"\n",
"Other information is available in the model summary output:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Sales</td> <th> R-squared: </th> <td> 0.897</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.896</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 570.3</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Mon, 13 Feb 2017</td> <th> Prob (F-statistic):</th> <td>1.58e-96</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>18:03:58</td> <th> Log-Likelihood: </th> <td> -386.18</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 200</td> <th> AIC: </th> <td> 780.4</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 196</td> <th> BIC: </th> <td> 793.6</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 3</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 2.9389</td> <td> 0.312</td> <td> 9.422</td> <td> 0.000</td> <td> 2.324 3.554</td>\n",
"</tr>\n",
"<tr>\n",
" <th>TV</th> <td> 0.0458</td> <td> 0.001</td> <td> 32.809</td> <td> 0.000</td> <td> 0.043 0.049</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Radio</th> <td> 0.1885</td> <td> 0.009</td> <td> 21.893</td> <td> 0.000</td> <td> 0.172 0.206</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Newspaper</th> <td> -0.0010</td> <td> 0.006</td> <td> -0.177</td> <td> 0.860</td> <td> -0.013 0.011</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>60.414</td> <th> Durbin-Watson: </th> <td> 2.084</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td> 151.241</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td>-1.327</td> <th> Prob(JB): </th> <td>1.44e-33</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td> 6.332</td> <th> Cond. No. </th> <td> 454.</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Sales R-squared: 0.897\n",
"Model: OLS Adj. R-squared: 0.896\n",
"Method: Least Squares F-statistic: 570.3\n",
"Date: Mon, 13 Feb 2017 Prob (F-statistic): 1.58e-96\n",
"Time: 18:03:58 Log-Likelihood: -386.18\n",
"No. Observations: 200 AIC: 780.4\n",
"Df Residuals: 196 BIC: 793.6\n",
"Df Model: 3 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"Intercept 2.9389 0.312 9.422 0.000 2.324 3.554\n",
"TV 0.0458 0.001 32.809 0.000 0.043 0.049\n",
"Radio 0.1885 0.009 21.893 0.000 0.172 0.206\n",
"Newspaper -0.0010 0.006 -0.177 0.860 -0.013 0.011\n",
"==============================================================================\n",
"Omnibus: 60.414 Durbin-Watson: 2.084\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 151.241\n",
"Skew: -1.327 Prob(JB): 1.44e-33\n",
"Kurtosis: 6.332 Cond. No. 454.\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# print a summary of the fitted model\n",
"fitted_multi_model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- TV and Radio have significant **p-values**, whereas Newspaper does not. Thus we reject the null hypothesis for TV and Radio (that there is no association between those features and Sales), and fail to reject the null hypothesis for Newspaper.\n",
"- TV and Radio ad spending are both **positively associated** with Sales, whereas Newspaper ad spending is **slightly negatively associated** with Sales. \n",
"- This model has a higher **R-squared** (0.897) than the previous model, which means that this model provides a better fit to the data than a model that only includes TV."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature Selection\n",
"\n",
"How do I decide **which features to include** in a linear model? \n",
"- Try different models and check whether the R-squared value goes up when you add new predictors.\n",
"\n",
"What are the **drawbacks** to this approach?\n",
"- Linear models rely upon a lot of **assumptions** (such as the predictors/features being independent), and if those assumptions are violated (which they usually are), R-squared are less reliable.\n",
"- R-squared is susceptible to **overfitting**, and thus there is no guarantee that a model with a high R-squared value will generalize well to new data. For example:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.897194261083\n"
]
}
],
"source": [
"# only include TV and Radio in the model\n",
"model1 = sf.ols(formula='Sales ~ TV + Radio', data=data).fit()\n",
"print(model1.rsquared)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.897210638179\n"
]
}
],
"source": [
"# add Newspaper to the model (which we believe has no association with Sales)\n",
"model2 = sf.ols(formula='Sales ~ TV + Radio + Newspaper', data=data).fit()\n",
"print(model2.rsquared)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**R-squared will always increase as you add more features to the model**, even if they are unrelated to the response. Thus, selecting the model with the highest R-squared is not a reliable approach for choosing the best linear model.\n",
"\n",
"There is alternative to R-squared called **adjusted R-squared** that penalizes model complexity (to control for overfitting), but this approach has its own set of issues.\n",
"\n",
"Is there a better approach to feature selection? **Cross-validation**, which provides a more reliable estimate of out-of-sample error, and thus is better at choosing which model will better **generalize** to out-of-sample data. Cross-validation can be applied to any type of model, not just linear models."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linear Regression in scikit-learn\n",
"\n",
"The work done using Statsmodels can also be using scikit-learn:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.93888936946\n",
"[ 0.04576465 0.18853002 -0.00103749]\n"
]
}
],
"source": [
"# create a DataFrame\n",
"feature_cols = ['TV', 'Radio', 'Newspaper']\n",
"X = data[feature_cols]\n",
"y = data.Sales\n",
"\n",
"\n",
"from sklearn.linear_model import LinearRegression\n",
"lm = LinearRegression()\n",
"lm.fit(X, y)\n",
"\n",
"# print intercept and coefficients\n",
"print(lm.intercept_)\n",
"print(lm.coef_)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('TV', 0.045764645455397601), ('Radio', 0.18853001691820442), ('Newspaper', -0.0010374930424763007)]\n"
]
}
],
"source": [
"# pair the feature names with the coefficients\n",
"print(zip(feature_cols, lm.coef_))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 12.20266701])"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# predict for a new observation\n",
"lm.predict([[100, 25, 25]])"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.89721063817895208"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# calculate the R-squared\n",
"lm.score(X, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Handling Categorical Predictors with Two Categories\n",
"\n",
"What if one of the predictors was categorical?\n",
"\n",
"Let's create a new feature called **Size**, and randomly assign observations to be **small or large**:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"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>TV</th>\n",
" <th>Radio</th>\n",
" <th>Newspaper</th>\n",
" <th>Sales</th>\n",
" <th>Size</th>\n",
" <th>IsLarge</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>230.1</td>\n",
" <td>37.8</td>\n",
" <td>69.2</td>\n",
" <td>22.1</td>\n",
" <td>large</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>44.5</td>\n",
" <td>39.3</td>\n",
" <td>45.1</td>\n",
" <td>10.4</td>\n",
" <td>large</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17.2</td>\n",
" <td>45.9</td>\n",
" <td>69.3</td>\n",
" <td>9.3</td>\n",
" <td>small</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>151.5</td>\n",
" <td>41.3</td>\n",
" <td>58.5</td>\n",
" <td>18.5</td>\n",
" <td>small</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>180.8</td>\n",
" <td>10.8</td>\n",
" <td>58.4</td>\n",
" <td>12.9</td>\n",
" <td>small</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TV Radio Newspaper Sales Size IsLarge\n",
"1 230.1 37.8 69.2 22.1 large 0\n",
"2 44.5 39.3 45.1 10.4 large 1\n",
"3 17.2 45.9 69.3 9.3 small 0\n",
"4 151.5 41.3 58.5 18.5 small 1\n",
"5 180.8 10.8 58.4 12.9 small 1"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"# create a Series of booleans in which roughly half are True\n",
"\n",
"#generate len(data) numbers between 0 and 1\n",
"numbers = np.random.rand(len(data))\n",
"\n",
"#create and index of 0s and 1s by based on whether the corresponding random number\n",
"#is greater than 0.5. \n",
"index_for_large = (numbers > 0.5)\n",
"\n",
"#create a new data column called Size and set its values to 'small'\n",
"data['Size'] = 'small'\n",
"\n",
"# change the values of Size to 'large' whenever the corresponding value of the index is 1 \n",
"data.loc[index_for_large, 'Size'] = 'large'\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When using scikit-learn, we need to represent all data **numerically**. For example, if the feature we want to represent has only two categories, we create a **dummy variable** that represents the categories as a binary value:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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>TV</th>\n",
" <th>Radio</th>\n",
" <th>Newspaper</th>\n",
" <th>Sales</th>\n",
" <th>Size</th>\n",
" <th>IsLarge</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>230.1</td>\n",
" <td>37.8</td>\n",
" <td>69.2</td>\n",
" <td>22.1</td>\n",
" <td>large</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>44.5</td>\n",
" <td>39.3</td>\n",
" <td>45.1</td>\n",
" <td>10.4</td>\n",
" <td>large</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17.2</td>\n",
" <td>45.9</td>\n",
" <td>69.3</td>\n",
" <td>9.3</td>\n",
" <td>small</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>151.5</td>\n",
" <td>41.3</td>\n",
" <td>58.5</td>\n",
" <td>18.5</td>\n",
" <td>small</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>180.8</td>\n",
" <td>10.8</td>\n",
" <td>58.4</td>\n",
" <td>12.9</td>\n",
" <td>small</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TV Radio Newspaper Sales Size IsLarge\n",
"1 230.1 37.8 69.2 22.1 large 1\n",
"2 44.5 39.3 45.1 10.4 large 1\n",
"3 17.2 45.9 69.3 9.3 small 0\n",
"4 151.5 41.3 58.5 18.5 small 0\n",
"5 180.8 10.8 58.4 12.9 small 0"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a new Series called IsLarge\n",
"data['IsLarge'] = data.Size.map({'small':0, 'large':1})\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The multiple linear regression including the **IsLarge** predictor:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('TV', 0.045740960942262938),\n",
" ('Radio', 0.18850759734740805),\n",
" ('Newspaper', -0.0010357306151786705),\n",
" ('IsLarge', 0.051545593754182793)]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create X and y\n",
"feature_cols = ['TV', 'Radio', 'Newspaper', 'IsLarge']\n",
"X = data[feature_cols]\n",
"y = data.Sales\n",
"\n",
"# instantiate, fit\n",
"lm = LinearRegression()\n",
"lm.fit(X, y)\n",
"\n",
"# print coefficients\n",
"list(zip(feature_cols, lm.coef_))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How do we interpret the coefficient of **IsLarge**? For a given amount of TV/Radio/Newspaper ad spending, a large market is associated with an average **increase** in Sales of 51.55 widgets (as compared to sales in a Small market).\n",
"\n",
"If we reverse the 0/1 encoding and created the feature 'IsSmall', the coefficient would be the same in absolute value, but **negative** instead of positive. All that changes is the **interpretation** of the coefficient."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Handling Categorical Predictors with More than Two Categories\n",
"\n",
"Let's create a new feature called **Area**, and randomly assign observations to be **rural, suburban, or urban**:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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>TV</th>\n",
" <th>Radio</th>\n",
" <th>Newspaper</th>\n",
" <th>Sales</th>\n",
" <th>Size</th>\n",
" <th>IsLarge</th>\n",
" <th>Area</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>230.1</td>\n",
" <td>37.8</td>\n",
" <td>69.2</td>\n",
" <td>22.1</td>\n",
" <td>large</td>\n",
" <td>1</td>\n",
" <td>rural</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>44.5</td>\n",
" <td>39.3</td>\n",
" <td>45.1</td>\n",
" <td>10.4</td>\n",
" <td>large</td>\n",
" <td>1</td>\n",
" <td>urban</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17.2</td>\n",
" <td>45.9</td>\n",
" <td>69.3</td>\n",
" <td>9.3</td>\n",
" <td>small</td>\n",
" <td>0</td>\n",
" <td>rural</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>151.5</td>\n",
" <td>41.3</td>\n",
" <td>58.5</td>\n",
" <td>18.5</td>\n",
" <td>small</td>\n",
" <td>0</td>\n",
" <td>urban</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>180.8</td>\n",
" <td>10.8</td>\n",
" <td>58.4</td>\n",
" <td>12.9</td>\n",
" <td>small</td>\n",
" <td>0</td>\n",
" <td>suburban</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TV Radio Newspaper Sales Size IsLarge Area\n",
"1 230.1 37.8 69.2 22.1 large 1 rural\n",
"2 44.5 39.3 45.1 10.4 large 1 urban\n",
"3 17.2 45.9 69.3 9.3 small 0 rural\n",
"4 151.5 41.3 58.5 18.5 small 0 urban\n",
"5 180.8 10.8 58.4 12.9 small 0 suburban"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# set a seed for reproducibility\n",
"np.random.seed(123456)\n",
"\n",
"# assign roughly one third of observations to each group\n",
"nums = np.random.rand(len(data))\n",
"mask_suburban = (nums > 0.33) & (nums < 0.66)\n",
"mask_urban = nums > 0.66\n",
"data['Area'] = 'rural'\n",
"data.loc[mask_suburban, 'Area'] = 'suburban'\n",
"data.loc[mask_urban, 'Area'] = 'urban'\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have to represent Area numerically, but an encoding such as 0=rural, 1=suburban, 2=urban would not work because that would imply that there is an **ordered relationship** between suburban and urban. Instead, we can create another **dummy variable**."
]
},
{
"cell_type": "code",
"execution_count": 37,
"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>TV</th>\n",
" <th>Radio</th>\n",
" <th>Newspaper</th>\n",
" <th>Sales</th>\n",
" <th>Size</th>\n",
" <th>IsLarge</th>\n",
" <th>Area</th>\n",
" <th>Area_suburban</th>\n",
" <th>Area_urban</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>230.1</td>\n",
" <td>37.8</td>\n",
" <td>69.2</td>\n",
" <td>22.1</td>\n",
" <td>large</td>\n",
" <td>1</td>\n",
" <td>rural</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>44.5</td>\n",
" <td>39.3</td>\n",
" <td>45.1</td>\n",
" <td>10.4</td>\n",
" <td>large</td>\n",
" <td>1</td>\n",
" <td>urban</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>17.2</td>\n",
" <td>45.9</td>\n",
" <td>69.3</td>\n",
" <td>9.3</td>\n",
" <td>small</td>\n",
" <td>0</td>\n",
" <td>rural</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>151.5</td>\n",
" <td>41.3</td>\n",
" <td>58.5</td>\n",
" <td>18.5</td>\n",
" <td>small</td>\n",
" <td>0</td>\n",
" <td>urban</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>180.8</td>\n",
" <td>10.8</td>\n",
" <td>58.4</td>\n",
" <td>12.9</td>\n",
" <td>small</td>\n",
" <td>0</td>\n",
" <td>suburban</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TV Radio Newspaper Sales Size IsLarge Area Area_suburban \\\n",
"1 230.1 37.8 69.2 22.1 large 1 rural 0.0 \n",
"2 44.5 39.3 45.1 10.4 large 1 urban 0.0 \n",
"3 17.2 45.9 69.3 9.3 small 0 rural 0.0 \n",
"4 151.5 41.3 58.5 18.5 small 0 urban 0.0 \n",
"5 180.8 10.8 58.4 12.9 small 0 suburban 1.0 \n",
"\n",
" Area_urban \n",
"1 0.0 \n",
"2 1.0 \n",
"3 0.0 \n",
"4 1.0 \n",
"5 0.0 "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create three dummy variables using get_dummies, then exclude the first dummy column\n",
"area_dummies = pd.get_dummies(data.Area, prefix='Area').iloc[:, 1:]\n",
"\n",
"# concatenate the dummy variable columns onto the original DataFrame (axis=0 means rows, axis=1 means columns)\n",
"data = pd.concat([data, area_dummies], axis=1)\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- **rural** is coded as Area_suburban=0 and Area_urban=0\n",
"- **suburban** is coded as Area_suburban=1 and Area_urban=0\n",
"- **urban** is coded as Area_suburban=0 and Area_urban=1\n",
"\n",
"Only two dummies are needed to captures all of the information about the Area feature.(In general, for a categorical feature with k levels, we create k-1 dummy variables.)\n",
"\n",
"Let's include the two new dummy variables in the model:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('TV', 0.045778985741549899),\n",
" ('Radio', 0.18759070701897618),\n",
" ('Newspaper', -0.0010091530994697506),\n",
" ('IsLarge', 0.052973742106136937),\n",
" ('Area_suburban', -0.10968426641317629),\n",
" ('Area_urban', 0.26063319133651908)]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# read data into a DataFrame\n",
"#data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)\n",
"\n",
"# create X and y\n",
"feature_cols = ['TV', 'Radio', 'Newspaper', 'IsLarge', 'Area_suburban', 'Area_urban']\n",
"X = data[feature_cols]\n",
"y = data.Sales\n",
"\n",
"# instantiate, fit\n",
"lm = LinearRegression()\n",
"lm.fit(X, y)\n",
"\n",
"# print coefficients\n",
"list(zip(feature_cols, lm.coef_))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How do we interpret the coefficients?\n",
"- All other variables being fixed, being a **suburban** area is associated with an average **decrease** in Sales of 109.68 widgets (as compared to the baseline level, which is the rural area).\n",
"- Being an **urban** area is associated with an average **increase** in Sales of 260.63 widgets (as compared to the rural area)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that Linear Regression can only make good predictions if there is indeed a **linear relationship** between the features and the response. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What Didn't We Cover?\n",
"\n",
"- Detecting collinearity\n",
"- Diagnosing model fit\n",
"- Transforming predictors to fit non-linear relationships\n",
"- Interaction terms\n",
"- Assumptions of linear regression\n",
"- And so much more!\n",
"\n",
"Please see lecture slides for more details. It's a good way to **start your modeling process** when working a regression problem. However, it is limited by the fact that it can only make good predictions if there is a **linear relationship** between the features and the response, which is why more complex methods (with higher variance and lower bias) will often outperform linear regression.\n",
"\n",
"Therefore, we want you to understand linear regression conceptually, understand its strengths and weaknesses, be familiar with the terminology, and know how to apply it. However, we also want to spend time on many other machine learning models, which is why we aren't going deeper here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Resources\n",
"\n",
"- Chapter 3 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)\n",
"\n",
"- [related videos](http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) \n",
"- [quick reference guide](http://www.dataschool.io/applying-and-interpreting-linear-regression/) \n",
"- Statsmodels: [simple linear regression](http://www.datarobot.com/blog/ordinary-least-squares-in-python/) and [multiple linear regression](http://www.datarobot.com/blog/multiple-regression-using-statsmodels/).\n",
"- [introduction to linear regression](http://people.duke.edu/~rnau/regintro.htm) \n",
"- [assumptions of linear regression](http://pareonline.net/getvn.asp?n=2&v=8)."
]
}
],
"metadata": {
"anaconda-cloud": {},
"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"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
quantopian/research_public | notebooks/tutorials/2_pipeline_lesson2/notebook.ipynb | 4 | 14722 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Creating a Pipeline\n",
"In this lesson, we will take a look at creating an empty pipeline. First, let's import the Pipeline class:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from quantopian.pipeline import Pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In a new cell, let's define a function to create our pipeline. Wrapping our pipeline creation in a function sets up a structure for more complex pipelines that we will see later on. For now, this function simply returns an empty pipeline:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def make_pipeline():\n",
" return Pipeline()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In a new cell, let's instantiate our pipeline by running `make_pipeline()`:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"my_pipe = make_pipeline()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Running a Pipeline\n",
"\n",
"Now that we have a reference to an empty Pipeline, `my_pipe` let's run it to see what it looks like. Before running our pipeline, we first need to import `run_pipeline`, a research-only function that allows us to run a pipeline over a specified time period."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from quantopian.research import run_pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's run our pipeline for one day (2015-05-05) with `run_pipeline` and display it. Note that the 2nd and 3rd arguments are the start and end dates of the simulation, respectively."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"result = run_pipeline(my_pipe, '2015-05-05', '2015-05-05')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A call to `run_pipeline` returns a [pandas DataFrame](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) indexed by date and securities. Let's see what the empty pipeline looks like:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"61\" valign=\"top\">2015-05-05 00:00:00+00:00</th>\n",
" <th>Equity(2 [AA])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(21 [AAME])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(24 [AAPL])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(25 [AA_PR])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(31 [ABAX])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(39 [DDC])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(41 [ARCB])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(52 [ABM])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(53 [ABMD])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(62 [ABT])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(64 [ABX])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(66 [AB])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(67 [ADSK])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(69 [ACAT])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(70 [VBF])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(76 [TAP])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(84 [ACET])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(86 [ACG])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(88 [ACI])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(100 [IEP])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(106 [ACU])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(110 [ACXM])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(112 [ACY])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(114 [ADBE])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(117 [AEY])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(122 [ADI])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(128 [ADM])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(134 [SXCL])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(149 [ADX])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(153 [AE])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48961 [NYMT_O])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48962 [CSAL])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48963 [PAK])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48969 [NSA])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48971 [BSM])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48972 [EVA])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48981 [APIC])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48989 [UK])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48990 [ACWF])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48991 [ISCF])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48992 [INTF])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48993 [JETS])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48994 [ACTX])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48995 [LRGF])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48996 [SMLF])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48997 [VKTX])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48998 [OPGN])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(48999 [AAPC])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(49000 [BPMC])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(49001 [CLCD])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(49004 [TNP_PRD])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(49005 [ARWA_U])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(49006 [BVXV])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(49007 [BVXV_W])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(49008 [OPGN_W])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(49009 [PRKU])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(49010 [TBRA])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(49131 [OESX])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(49259 [ITUS])</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Equity(49523 [TLGT])</th>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8236 rows × 0 columns</p>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: []\n",
"Index: [(2015-05-05 00:00:00+00:00, Equity(2 [AA])), (2015-05-05 00:00:00+00:00, Equity(21 [AAME])), (2015-05-05 00:00:00+00:00, Equity(24 [AAPL])), (2015-05-05 00:00:00+00:00, Equity(25 [AA_PR])), (2015-05-05 00:00:00+00:00, Equity(31 [ABAX])), (2015-05-05 00:00:00+00:00, Equity(39 [DDC])), (2015-05-05 00:00:00+00:00, Equity(41 [ARCB])), (2015-05-05 00:00:00+00:00, Equity(52 [ABM])), (2015-05-05 00:00:00+00:00, Equity(53 [ABMD])), (2015-05-05 00:00:00+00:00, Equity(62 [ABT])), (2015-05-05 00:00:00+00:00, Equity(64 [ABX])), (2015-05-05 00:00:00+00:00, Equity(66 [AB])), (2015-05-05 00:00:00+00:00, Equity(67 [ADSK])), (2015-05-05 00:00:00+00:00, Equity(69 [ACAT])), (2015-05-05 00:00:00+00:00, Equity(70 [VBF])), (2015-05-05 00:00:00+00:00, Equity(76 [TAP])), (2015-05-05 00:00:00+00:00, Equity(84 [ACET])), (2015-05-05 00:00:00+00:00, Equity(86 [ACG])), (2015-05-05 00:00:00+00:00, Equity(88 [ACI])), (2015-05-05 00:00:00+00:00, Equity(100 [IEP])), (2015-05-05 00:00:00+00:00, Equity(106 [ACU])), (2015-05-05 00:00:00+00:00, Equity(110 [ACXM])), (2015-05-05 00:00:00+00:00, Equity(112 [ACY])), (2015-05-05 00:00:00+00:00, Equity(114 [ADBE])), (2015-05-05 00:00:00+00:00, Equity(117 [AEY])), (2015-05-05 00:00:00+00:00, Equity(122 [ADI])), (2015-05-05 00:00:00+00:00, Equity(128 [ADM])), (2015-05-05 00:00:00+00:00, Equity(134 [SXCL])), (2015-05-05 00:00:00+00:00, Equity(149 [ADX])), (2015-05-05 00:00:00+00:00, Equity(153 [AE])), (2015-05-05 00:00:00+00:00, Equity(154 [AEM])), (2015-05-05 00:00:00+00:00, Equity(157 [AEG])), (2015-05-05 00:00:00+00:00, Equity(161 [AEP])), (2015-05-05 00:00:00+00:00, Equity(162 [AEPI])), (2015-05-05 00:00:00+00:00, Equity(166 [AES])), (2015-05-05 00:00:00+00:00, Equity(168 [AET])), (2015-05-05 00:00:00+00:00, Equity(185 [AFL])), (2015-05-05 00:00:00+00:00, Equity(192 [ATAX])), (2015-05-05 00:00:00+00:00, Equity(197 [AGCO])), (2015-05-05 00:00:00+00:00, Equity(216 [HES])), (2015-05-05 00:00:00+00:00, Equity(225 [AHPI])), (2015-05-05 00:00:00+00:00, Equity(239 [AIG])), (2015-05-05 00:00:00+00:00, Equity(247 [AIN])), (2015-05-05 00:00:00+00:00, Equity(253 [AIR])), (2015-05-05 00:00:00+00:00, Equity(266 [AJG])), (2015-05-05 00:00:00+00:00, Equity(270 [AKRX])), (2015-05-05 00:00:00+00:00, Equity(273 [ALU])), (2015-05-05 00:00:00+00:00, Equity(283 [ALCO])), (2015-05-05 00:00:00+00:00, Equity(289 [MATX])), (2015-05-05 00:00:00+00:00, Equity(300 [ALK])), (2015-05-05 00:00:00+00:00, Equity(301 [ALKS])), (2015-05-05 00:00:00+00:00, Equity(311 [ALOG])), (2015-05-05 00:00:00+00:00, Equity(312 [ALOT])), (2015-05-05 00:00:00+00:00, Equity(328 [ALTR])), (2015-05-05 00:00:00+00:00, Equity(332 [ALX])), (2015-05-05 00:00:00+00:00, Equity(337 [AMAT])), (2015-05-05 00:00:00+00:00, Equity(351 [AMD])), (2015-05-05 00:00:00+00:00, Equity(353 [AME])), (2015-05-05 00:00:00+00:00, Equity(357 [TWX])), (2015-05-05 00:00:00+00:00, Equity(366 [AVD])), (2015-05-05 00:00:00+00:00, Equity(368 [AMGN])), (2015-05-05 00:00:00+00:00, Equity(371 [HWAY])), (2015-05-05 00:00:00+00:00, Equity(392 [AMS])), (2015-05-05 00:00:00+00:00, Equity(393 [AMSC])), (2015-05-05 00:00:00+00:00, Equity(397 [AMSW_A])), (2015-05-05 00:00:00+00:00, Equity(405 [AMWD])), (2015-05-05 00:00:00+00:00, Equity(410 [AN])), (2015-05-05 00:00:00+00:00, Equity(412 [ANAT])), (2015-05-05 00:00:00+00:00, Equity(430 [ANN])), (2015-05-05 00:00:00+00:00, Equity(438 [AON])), (2015-05-05 00:00:00+00:00, Equity(447 [AP])), (2015-05-05 00:00:00+00:00, Equity(448 [APA])), (2015-05-05 00:00:00+00:00, Equity(450 [CLFD])), (2015-05-05 00:00:00+00:00, Equity(451 [APB])), (2015-05-05 00:00:00+00:00, Equity(455 [APC])), (2015-05-05 00:00:00+00:00, Equity(460 [APD])), (2015-05-05 00:00:00+00:00, Equity(465 [APH])), (2015-05-05 00:00:00+00:00, Equity(468 [API])), (2015-05-05 00:00:00+00:00, Equity(474 [APOG])), (2015-05-05 00:00:00+00:00, Equity(484 [ATU])), (2015-05-05 00:00:00+00:00, Equity(508 [AIRM])), (2015-05-05 00:00:00+00:00, Equity(510 [ARG])), (2015-05-05 00:00:00+00:00, Equity(523 [AAN])), (2015-05-05 00:00:00+00:00, Equity(526 [AROW])), (2015-05-05 00:00:00+00:00, Equity(535 [ARTW])), (2015-05-05 00:00:00+00:00, Equity(538 [ARW])), (2015-05-05 00:00:00+00:00, Equity(542 [ASA])), (2015-05-05 00:00:00+00:00, Equity(547 [ASB])), (2015-05-05 00:00:00+00:00, Equity(548 [ASBI])), (2015-05-05 00:00:00+00:00, Equity(553 [ASEI])), (2015-05-05 00:00:00+00:00, Equity(557 [ASGN])), (2015-05-05 00:00:00+00:00, Equity(559 [ASH])), (2015-05-05 00:00:00+00:00, Equity(567 [ASMI])), (2015-05-05 00:00:00+00:00, Equity(576 [ASR])), (2015-05-05 00:00:00+00:00, Equity(579 [ASTE])), (2015-05-05 00:00:00+00:00, Equity(595 [GAS])), (2015-05-05 00:00:00+00:00, Equity(600 [OA])), (2015-05-05 00:00:00+00:00, Equity(607 [ATML])), (2015-05-05 00:00:00+00:00, Equity(610 [ATNI])), (2015-05-05 00:00:00+00:00, Equity(612 [ATO])), ...]\n",
"\n",
"[8236 rows x 0 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The output of an empty pipeline is a DataFrame with no columns. In this example, our pipeline has an index made up of all 8000+ securities (truncated in the display) for May 5th, 2015, but doesn't have any columns.\n",
"\n",
"In the following lessons, we'll take a look at how to add columns to our pipeline output, and how to filter down to a subset of securities."
]
}
],
"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
} | apache-2.0 |
pobch/Facebook_Data | 2_Cleaning/Cleaner.ipynb | 2 | 5702 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cleaning data\n",
"- Remove unwanted '\\r' in 'message' column\n",
"- Fill NAs with 0 and empty strings\n",
"- Add label and sub-label column to group the same posts together\n",
"- Organize data to reduce files' size"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import re\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pages = ['DramaAdd', 'ejeab', 'cartooneggcat', 'BBCThai', 'khobsanam', '1447102878929950',\n",
" 'powerofhusbands', 'basementkaraoke', 'cartoon5natee', 'AjahnBuddhadasa', 'Toodsdiary', 'ceclip', 'beargirlfriend',\n",
" 'jaytherabbitofficial', 'Darlingboredom', 'v.vajiramedhi', '334236760084743', 'kingdomoftigers', 'underbedstar', 'pantipded',\n",
" 'Pantip.KratooDed', 'nut.ped', '9gaginthai', 'in.one.zaroop']\n",
"\n",
"#exclude: 'HighlightsHD.tv'"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DramaAdd ......... DONE !!\n",
"ejeab ......... DONE !!\n",
"cartooneggcat Has no MSG COLUMN !!\n",
"cartooneggcat ......... DONE !!\n",
"BBCThai ......... DONE !!\n",
"khobsanam ......... DONE !!\n",
"1447102878929950 ......... DONE !!\n",
"powerofhusbands ......... DONE !!\n",
"basementkaraoke ......... DONE !!\n",
"cartoon5natee ......... DONE !!\n",
"AjahnBuddhadasa ......... DONE !!\n",
"Toodsdiary ......... DONE !!\n",
"ceclip ......... DONE !!\n",
"beargirlfriend ......... DONE !!\n",
"jaytherabbitofficial ......... DONE !!\n",
"Darlingboredom ......... DONE !!\n",
"v.vajiramedhi ......... DONE !!\n",
"334236760084743 ......... DONE !!\n",
"kingdomoftigers ......... DONE !!\n",
"underbedstar ......... DONE !!\n",
"pantipded ......... DONE !!\n",
"Pantip.KratooDed ......... DONE !!\n",
"nut.ped ......... DONE !!\n",
"9gaginthai ......... DONE !!\n",
"in.one.zaroop ......... DONE !!\n"
]
}
],
"source": [
"for page in pages:\n",
" df = pd.read_csv(page + '.csv', encoding='utf_8_sig')\n",
" pattern = r'(\\r)+'\n",
" if 'message' in df.columns:\n",
" # there are unwanted '\\r' in 'message' column because of Windows-Python incompatibility \n",
" for index, row in df.iterrows():\n",
" if pd.isnull(row['message']):\n",
" pass\n",
" else:\n",
" df.loc[index, 'message'] = re.sub(pattern, '', df.loc[index, 'message'])\n",
" elif 'link' in df.columns:\n",
" print(page + ' Has no MSG COLUMN !!')\n",
" df['message'] = ''\n",
" else:\n",
" raise Exception('What happened ??')\n",
" \n",
" # fill NA :\n",
" df[['comment_count', 'like_count', 'reaction_count', 'share_count']] = \\\n",
" df[['comment_count', 'like_count', 'reaction_count', 'share_count']].fillna(0).astype(int)\n",
" df[['message', 'link']] = df[['message', 'link']].fillna('').astype(str)\n",
" for colname in ['created_time', 'from', 'id', 'time_checked', 'type', 'updated_time']:\n",
" df[colname] = df[colname].apply(lambda x: str(x) if pd.notnull(x) else x)\n",
" \n",
" # create a new dataframe containing 'id', 'message' and 'link' column. Then, drop duplicates :\n",
" newdf = df.drop_duplicates(subset=['id', 'message', 'link'])\n",
" newdf = newdf.sort_values(by = ['created_time', 'id', 'time_checked'])\n",
" newdf.reset_index(drop=True, inplace=True)\n",
" # label each row by 'id' :\n",
" mapdf = newdf.loc[:, ['id', 'message']].drop_duplicates(subset = 'id')\n",
" mapdf.reset_index(drop=True, inplace=True)\n",
" mapdf['label1'] = np.arange(len(mapdf)) + 1\n",
" newdf = newdf.merge(mapdf[['id', 'label1']], on='id', how='left')\n",
" # sub-label each row by 'message' and 'link' :\n",
" newdf['label2'] = newdf.groupby('label1').cumcount() + 1\n",
" newdf['label2'] = newdf['label2'].astype(int)\n",
" # create new csv files containing 'id', 'message', 'link', 'label1' and 'label2' column :\n",
" newdf.to_csv(page + 'ID.csv', index=False, encoding='utf_8_sig')\n",
" # remove 'message' and 'link' column from original csv files to reduce the size : \n",
" df = df.merge(newdf[['id', 'message', 'link', 'label1', 'label2']], on=['id', 'message', 'link'], how='left')\n",
" df = df.drop(['message', 'link'], axis=1)\n",
" df.to_csv(page + '.csv', index = False, encoding='utf_8_sig')\n",
" print(page + ' ......... DONE !!')"
]
},
{
"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 |
egentry/lamat-2016-solutions | day3/animated.ipynb | 1 | 2094 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MP4 Animation\n",
"Alternatively, you can create plots for each frame, save them as saparate files, and combine them using `ffmpeg`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"\n",
"from matplotlib import animation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n_steps = 10\n",
"size = 20\n",
"\n",
"data = np.random.random((n_steps, size, size))\n",
"data = np.array(data.round(), dtype=int)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, ax = plt.subplots()\n",
"img = ax.matshow(data[0,:,:], cmap=\"binary\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def update_img(step):\n",
" img.set_data(data[step,:,:])\n",
" return img"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"anim = animation.FuncAnimation(fig, update_img, frames=n_steps)\n",
"anim.save(\"animation.mp4\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
brettavedisian/phys202-2015-work | assignments/assignment11/OptimizationEx01.ipynb | 1 | 52042 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Optimization Exercise 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import scipy.optimize as opt"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Hat potential"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"The following potential is often used in Physics and other fields to describe symmetry breaking and is often known as the \"hat potential\":\n",
"\n",
"$$ V(x) = -a x^2 + b x^4 $$\n",
"\n",
"Write a function `hat(x,a,b)` that returns the value of this function:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [],
"source": [
"def hat(x,a,b):\n",
" return -a*x**2+b*x**4"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "7204bd97cd003430289f171b6ba70d63",
"grade": true,
"grade_id": "optimizationex01a",
"points": 2
}
},
"outputs": [],
"source": [
"assert hat(0.0, 1.0, 1.0)==0.0\n",
"assert hat(0.0, 1.0, 1.0)==0.0\n",
"assert hat(1.0, 10.0, 1.0)==-9.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Plot this function over the range $x\\in\\left[-3,3\\right]$ with $b=1.0$ and $a=5.0$:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"a = 5.0\n",
"b = 1.0"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAjEAAAGNCAYAAADgjyBnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrnY69DyrAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f77941b7cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x=np.linspace(-3,3,100)\n",
"plt.figure(figsize=(9,6))\n",
"plt.xlabel('Range'), plt.ylabel('V(x)'), plt.title('Hat Potential')\n",
"plt.plot(x, hat(x,a,b))\n",
"plt.box(False)\n",
"plt.grid(True)\n",
"plt.tick_params(axis='x', top='off', direction='out')\n",
"plt.tick_params(axis='y', right='off', direction='out');"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "bd49ce2f030e3366ee640213f26fdaa6",
"grade": true,
"grade_id": "optimizationex01b",
"points": 2
}
},
"outputs": [],
"source": [
"assert True # leave this to grade the plot"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Write code that finds the two local minima of this function for $b=1.0$ and $a=5.0$.\n",
"\n",
"* Use `scipy.optimize.minimize` to find the minima. You will have to think carefully about how to get this function to find both minima.\n",
"* Print the x values of the minima.\n",
"* Plot the function as a blue line.\n",
"* On the same axes, show the minima as red circles.\n",
"* Customize your visualization to make it beatiful and effective."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Local minima: -1.581140, 1.581140\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAp8AAAGNCAYAAABXHhoFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f77940555c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"res1 = opt.minimize_scalar(hat, bounds=(-3,0), args=(a,b), method='bounded')\n",
"res2 = opt.minimize_scalar(hat, bounds=(0,3), args=(a,b), method='bounded')\n",
"print('Local minima: %f, %f' % (res1.x, res2.x))\n",
"plt.figure(figsize=(9,6))\n",
"plt.xlabel('Range'), plt.ylabel('V(x)')\n",
"plt.plot(x, hat(x,a,b), label=\"Potential\")\n",
"plt.scatter(res1.x, res1.fun, marker=\"o\", color=\"r\")\n",
"plt.scatter(res2.x, res2.fun, marker=\"o\", color=\"r\")\n",
"plt.title('Finding Local Minima of Hat Potential')\n",
"plt.box(False), plt.grid(True), plt.xlim(-2.5,2.5), plt.ylim(-8,4)\n",
"plt.tick_params(axis='x', top='off', direction='out')\n",
"plt.tick_params(axis='y', right='off', direction='out')\n",
"plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.);"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "235361d4c954cf9fd6a8ecef309b3a44",
"grade": true,
"grade_id": "optimizationex01c",
"points": 4
}
},
"outputs": [],
"source": [
"assert True # leave this for grading the plot"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"To check your numerical results, find the locations of the minima analytically. Show and describe the steps in your derivation using LaTeX equations. Evaluate the location of the minima using the above parameters."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false,
"nbgrader": {
"checksum": "d7d37614ffa0d469a42ff3fd121335f2",
"grade": true,
"grade_id": "optimizationex01d",
"points": 2,
"solution": true
}
},
"source": [
"To find the local minima of the hat potential analytically, I needed to take the first derivative with respect to $x$ and set that equal to zero.\n",
"\n",
"$$ V(x) = -ax^2 + bx^4 $$\n",
"\n",
"$$ \\frac{dV}{dx} = -2ax + 4bx^3 = 0 $$\n",
"\n",
"A solution we will not use is the $x=0$ because that corresponds to a maximum.\n",
"\n",
"Add $-2ax$ to the other side and cancel out an $x$ to get:\n",
"\n",
"$$ 4bx^2 = 2a $$\n",
"\n",
"Divide by $4b$ and reduce the fraction:\n",
"\n",
"$$ x^2 = \\frac{a}{2b} $$\n",
"\n",
"Take the square root:\n",
"\n",
"$$ x = \\pm \\sqrt{\\frac{a}{2b}} $$\n",
"\n",
"Plugging $a=5.0$ and $b=1.0$, we get:\n",
"\n",
"$$ x = -\\sqrt{\\frac{5}{2}} \\: or \\: \\sqrt{\\frac{5}{2}} $$\n",
"\n",
"Or\n",
"\n",
"$$ x = -1.581140 \\: or \\: 1.581140 $$"
]
}
],
"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 |
Mdround/fastai-deeplearning1 | deeplearning1/nbs/lesson5.ipynb | 2 | 56112 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:29:30.212787Z",
"start_time": "2017-06-27T20:29:27.260118Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10). Please switch to the gpuarray backend. You can get more information about how to switch at this URL:\n",
" https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29\n",
"\n",
"Using gpu device 0: GeForce GTX 1070 (CNMeM is disabled, cuDNN 5110)\n"
]
}
],
"source": [
"from theano.sandbox import cuda"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:29:31.166304Z",
"start_time": "2017-06-27T20:29:30.213889Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using Theano backend.\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import utils #; reload(utils)\n",
"from utils import *\n",
"from __future__ import division, print_function\n",
"import pickle\n",
"import utils_MDR"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:29:31.170146Z",
"start_time": "2017-06-27T20:29:31.167906Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model_path = 'data/imdb/models/'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MDR: and needs by GPU-fan code, too..."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:29:31.174309Z",
"start_time": "2017-06-27T20:29:31.171621Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"import utils_MDR\n",
"from utils_MDR import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're going to look at the IMDB dataset, which contains movie reviews from IMDB, along with their sentiment. Keras comes with some helpers for this dataset."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:29:36.042126Z",
"start_time": "2017-06-27T20:29:35.965181Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"from keras.datasets import imdb\n",
"idx = imdb.get_word_index()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the word list:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:29:36.895566Z",
"start_time": "2017-06-27T20:29:36.842046Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['the', 'and', 'a', 'of', 'to', 'is', 'br', 'in', 'it', 'i']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idx_arr = sorted(idx, key=idx.get)\n",
"idx_arr[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"...and this is the mapping from id to word"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:29:40.411231Z",
"start_time": "2017-06-27T20:29:40.390654Z"
},
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"## idx2word = {v: k for k, v in idx.iteritems()} ## Py 2.7\n",
"idx2word = {v: k for k, v in idx.items()} ## Py 3.x"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We download the reviews using code copied from keras.datasets:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:29:44.912020Z",
"start_time": "2017-06-27T20:29:42.786854Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"path = get_file('imdb_full.pkl',\n",
" origin='https://s3.amazonaws.com/text-datasets/imdb_full.pkl',\n",
" md5_hash='d091312047c43cf9e4e38fef92437263')\n",
"f = open(path, 'rb')\n",
"(x_train, labels_train), (x_test, labels_test) = pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:29:44.916221Z",
"start_time": "2017-06-27T20:29:44.913372Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"25000"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(x_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's the 1st review. As you see, the words have been replaced by ids. The ids can be looked up in idx2word."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:29:46.801119Z",
"start_time": "2017-06-27T20:29:46.797098Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'23022, 309, 6, 3, 1069, 209, 9, 2175, 30, 1, 169, 55, 14, 46, 82, 5869, 41, 393, 110, 138, 14, 5359, 58, 4477, 150, 8, 1, 5032, 5948, 482, 69, 5, 261, 12, 23022, 73935, 2003, 6, 73, 2436, 5, 632, 71, 6, 5359, 1, 25279, 5, 2004, 10471, 1, 5941, 1534, 34, 67, 64, 205, 140, 65, 1232, 63526, 21145, 1, 49265, 4, 1, 223, 901, 29, 3024, 69, 4, 1, 5863, 10, 694, 2, 65, 1534, 51, 10, 216, 1, 387, 8, 60, 3, 1472, 3724, 802, 5, 3521, 177, 1, 393, 10, 1238, 14030, 30, 309, 3, 353, 344, 2989, 143, 130, 5, 7804, 28, 4, 126, 5359, 1472, 2375, 5, 23022, 309, 10, 532, 12, 108, 1470, 4, 58, 556, 101, 12, 23022, 309, 6, 227, 4187, 48, 3, 2237, 12, 9, 215'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"', '.join(map(str, x_train[0]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first word of the first review is 23022. Let's see what that is."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:29:49.364425Z",
"start_time": "2017-06-27T20:29:49.360055Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'bromwell'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"idx2word[23022]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's the whole review, mapped from ids to words."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-26T20:52:59.286335Z",
"start_time": "2017-06-26T20:52:59.280337Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"\"bromwell high is a cartoon comedy it ran at the same time as some other programs about school life such as teachers my 35 years in the teaching profession lead me to believe that bromwell high's satire is much closer to reality than is teachers the scramble to survive financially the insightful students who can see right through their pathetic teachers' pomp the pettiness of the whole situation all remind me of the schools i knew and their students when i saw the episode in which a student repeatedly tried to burn down the school i immediately recalled at high a classic line inspector i'm here to sack one of your teachers student welcome to bromwell high i expect that many adults of my age think that bromwell high is far fetched what a pity that it isn't\""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"' '.join([idx2word[o] for o in x_train[0]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The labels are 1 for positive, 0 for negative."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-26T20:53:01.975490Z",
"start_time": "2017-06-26T20:53:01.969934Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels_train[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reduce vocab size by setting rare words to max index."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:30:00.702077Z",
"start_time": "2017-06-27T20:29:59.206042Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"vocab_size = 5000\n",
"\n",
"trn = [np.array([i if i<vocab_size-1 else vocab_size-1 for i in s]) for s in x_train]\n",
"test = [np.array([i if i<vocab_size-1 else vocab_size-1 for i in s]) for s in x_test]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Look at distribution of lengths of sentences."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:30:00.712484Z",
"start_time": "2017-06-27T20:30:00.703002Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"(2493, 10, 237.71364)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## create an array of 'len'...gths\n",
"# lens = np.array(map(len, trn)) ## only works in Py2.x, not 3.x ... \n",
"## 'map in Python 3 return an iterator, while map in Python 2 returns a list'\n",
"## (https://stackoverflow.com/questions/35691489/error-in-python-3-5-cant-add-map-results-together)\n",
"\n",
"# This is a quick fix - not really a proper P3x approach.\n",
"\n",
"lens = np.array(list(map(len, trn))) ## wrapped a list around it\n",
"\n",
"(lens.max(), lens.min(), lens.mean())"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Expect:\n",
"(2493, 10, 237.71364)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pad (with zero) or truncate each sentence to make consistent length."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:30:04.767746Z",
"start_time": "2017-06-27T20:30:04.534133Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"seq_len = 500\n",
"\n",
"trn = sequence.pad_sequences(trn, maxlen=seq_len, value=0)\n",
"test = sequence.pad_sequences(test, maxlen=seq_len, value=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This results in nice rectangular matrices that can be passed to ML algorithms. Reviews shorter than 500 words are pre-padded with zeros, those greater are truncated."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:30:06.469567Z",
"start_time": "2017-06-27T20:30:06.465540Z"
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"(25000, 500)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trn.shape"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Expect:\n",
"(25000, 500)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create simple models"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"### Single hidden layer NN"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"The simplest model that tends to give reasonable results is a single hidden layer net. So let's try that. Note that we can't expect to get any useful results by feeding word ids directly into a neural net - so instead we use an embedding to replace them with a vector of 32 (initially random) floats for each word in the vocab."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-26T20:53:55.946097Z",
"start_time": "2017-06-26T20:53:55.746808Z"
},
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"model = Sequential([\n",
" Embedding(vocab_size, 32, input_length=seq_len),\n",
" Flatten(),\n",
" Dense(100, activation='relu'),\n",
" Dropout(0.7),\n",
" Dense(1, activation='sigmoid')])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-26T20:53:58.440129Z",
"start_time": "2017-06-26T20:53:58.300771Z"
},
"hidden": true,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"____________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"====================================================================================================\n",
"embedding_1 (Embedding) (None, 500, 32) 160000 embedding_input_1[0][0] \n",
"____________________________________________________________________________________________________\n",
"flatten_1 (Flatten) (None, 16000) 0 embedding_1[0][0] \n",
"____________________________________________________________________________________________________\n",
"dense_1 (Dense) (None, 100) 1600100 flatten_1[0][0] \n",
"____________________________________________________________________________________________________\n",
"dropout_1 (Dropout) (None, 100) 0 dense_1[0][0] \n",
"____________________________________________________________________________________________________\n",
"dense_2 (Dense) (None, 1) 101 dropout_1[0][0] \n",
"====================================================================================================\n",
"Total params: 1,760,201\n",
"Trainable params: 1,760,201\n",
"Non-trainable params: 0\n",
"____________________________________________________________________________________________________\n"
]
}
],
"source": [
"model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-26T20:54:15.034750Z",
"start_time": "2017-06-26T20:54:08.623956Z"
},
"hidden": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/2\n",
"25000/25000 [==============================] - 2s - loss: 0.5320 - acc: 0.6807 - val_loss: 0.2968 - val_acc: 0.8726\n",
"Epoch 2/2\n",
"25000/25000 [==============================] - 2s - loss: 0.2233 - acc: 0.9152 - val_loss: 0.3010 - val_acc: 0.8757\n"
]
}
],
"source": [
"set_gpu_fan_speed(90)\n",
"model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=2, batch_size=64)\n",
"set_gpu_fan_speed(0)"
]
},
{
"cell_type": "raw",
"metadata": {
"hidden": true
},
"source": [
"JH's result:\n",
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/2\n",
"25000/25000 [==============================] - 1s - loss: 0.4651 - acc: 0.7495 - val_loss: 0.2830 - val_acc: 0.8804\n",
"Epoch 2/2\n",
"25000/25000 [==============================] - 1s - loss: 0.1969 - acc: 0.9265 - val_loss: 0.3195 - val_acc: 0.8694"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"The [stanford paper](http://ai.stanford.edu/~amaas/papers/wvSent_acl2011.pdf) that this dataset is from cites a state of the art accuracy (without unlabelled data) of 0.883. So we're short of that, but on the right track."
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"### Single conv layer with max pooling"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"A CNN is likely to work better, since it's designed to take advantage of ordered data. We'll need to use a 1D CNN, since a sequence of words is 1D."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-26T20:54:22.482449Z",
"start_time": "2017-06-26T20:54:22.332900Z"
},
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"conv1 = Sequential([\n",
" Embedding(vocab_size, 32, input_length=seq_len, dropout=0.2),\n",
" Dropout(0.2),\n",
" Convolution1D(64, 5, border_mode='same', activation='relu'),\n",
" Dropout(0.2),\n",
" MaxPooling1D(),\n",
" Flatten(),\n",
" Dense(100, activation='relu'),\n",
" Dropout(0.7),\n",
" Dense(1, activation='sigmoid')])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-26T20:54:23.378810Z",
"start_time": "2017-06-26T20:54:23.366854Z"
},
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"conv1.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-26T20:55:06.926497Z",
"start_time": "2017-06-26T20:54:25.599995Z"
},
"hidden": true,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/4\n",
"25000/25000 [==============================] - 8s - loss: 0.4949 - acc: 0.7320 - val_loss: 0.3057 - val_acc: 0.8719\n",
"Epoch 2/4\n",
"25000/25000 [==============================] - 8s - loss: 0.2976 - acc: 0.8811 - val_loss: 0.2695 - val_acc: 0.8867\n",
"Epoch 3/4\n",
"25000/25000 [==============================] - 8s - loss: 0.2577 - acc: 0.8978 - val_loss: 0.2588 - val_acc: 0.8941\n",
"Epoch 4/4\n",
"25000/25000 [==============================] - 8s - loss: 0.2378 - acc: 0.9068 - val_loss: 0.2556 - val_acc: 0.8941\n"
]
}
],
"source": [
"set_gpu_fan_speed(90)\n",
"conv1.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=4, batch_size=64)\n",
"set_gpu_fan_speed(0)"
]
},
{
"cell_type": "raw",
"metadata": {
"hidden": true
},
"source": [
"JH's result:\n",
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/4\n",
"25000/25000 [==============================] - 4s - loss: 0.4984 - acc: 0.7250 - val_loss: 0.2922 - val_acc: 0.8816\n",
"Epoch 2/4\n",
"25000/25000 [==============================] - 4s - loss: 0.2971 - acc: 0.8836 - val_loss: 0.2681 - val_acc: 0.8911\n",
"Epoch 3/4\n",
"25000/25000 [==============================] - 4s - loss: 0.2568 - acc: 0.8983 - val_loss: 0.2551 - val_acc: 0.8947\n",
"Epoch 4/4\n",
"25000/25000 [==============================] - 4s - loss: 0.2427 - acc: 0.9029 - val_loss: 0.2558 - val_acc: 0.8947"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"That's well past the Stanford paper's accuracy - another win for CNNs!"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-26T20:55:12.107196Z",
"start_time": "2017-06-26T20:55:12.039833Z"
},
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"conv1.save_weights(model_path + 'conv1.h5')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:36:04.646375Z",
"start_time": "2017-06-27T20:36:04.360053Z"
},
"collapsed": true,
"hidden": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'conv1' 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-32-1f275b7c9b27>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mconv1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'conv1.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'conv1' is not defined"
]
}
],
"source": [
"conv1.load_weights(model_path + 'conv1.h5')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pre-trained vectors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You may want to look at wordvectors.ipynb before moving on.\n",
"\n",
"In this section, we replicate the previous CNN, but using <strong>pre-trained</strong> embeddings."
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:43:29.818370Z",
"start_time": "2017-06-27T21:43:29.815797Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"def load_vectors(loc):\n",
" return (load_array(loc+'.dat'),\n",
" pickle.load(open(loc+'_words.pkl','rb')),\n",
" pickle.load(open(loc+'_idx.pkl','rb')))"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:52:55.440843Z",
"start_time": "2017-06-27T21:52:55.135336Z"
}
},
"outputs": [],
"source": [
"#vecs, words, wordidx = load_vectors('data/glove/results/6B.50d') ## JH's original\n",
"vecs, words, wordidx = load_vectors('data/glove/results/6B.100d') ## MDR's experiment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The glove word ids and imdb word ids use different indexes. So we create a simple function that creates an embedding matrix using the indexes from imdb, and the embeddings from glove (where they exist)."
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:52:56.309532Z",
"start_time": "2017-06-27T21:52:56.300213Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"def create_emb():\n",
" n_fact = vecs.shape[1]\n",
" emb = np.zeros((vocab_size, n_fact))\n",
"\n",
" for i in range(1,len(emb)):\n",
" word = idx2word[i]\n",
" if word and re.match(r\"^[a-zA-Z0-9\\-]*$\", word):\n",
" src_idx = wordidx[word]\n",
" emb[i] = vecs[src_idx]\n",
" else:\n",
" # If we can't find the word in glove, randomly initialize\n",
" emb[i] = normal(scale=0.6, size=(n_fact,))\n",
"\n",
" # This is our \"rare word\" id - we want to randomly initialize\n",
" emb[-1] = normal(scale=0.6, size=(n_fact,))\n",
" emb/=3\n",
" return emb"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:52:56.791262Z",
"start_time": "2017-06-27T21:52:56.773990Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"emb = create_emb()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We pass our embedding matrix to the Embedding constructor, and set it to non-trainable."
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:53:05.349213Z",
"start_time": "2017-06-27T21:53:05.172166Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model = Sequential([\n",
" #Embedding(vocab_size, 50, \n",
" Embedding(vocab_size, 100, \n",
" input_length=seq_len, dropout=0.2, weights=[emb], trainable=False),\n",
" Dropout(0.25), ## JH (0.25)\n",
" Convolution1D(64, 5, border_mode='same', activation='relu'),\n",
" Dropout(0.25), ## JH (0.25)\n",
" MaxPooling1D(),\n",
" Flatten(),\n",
" Dense(100, activation='relu'),\n",
" Dropout(0.3), ## JH (0.7)\n",
" Dense(1, activation='sigmoid')])"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:53:06.439113Z",
"start_time": "2017-06-27T21:53:06.427094Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I get better results with the 100d embedding than I do with the 50d embedding, after 4 epochs. - MDR"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:53:53.246889Z",
"start_time": "2017-06-27T21:53:18.477268Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/4\n",
"25000/25000 [==============================] - 8s - loss: 0.5658 - acc: 0.6951 - val_loss: 0.4579 - val_acc: 0.7869\n",
"Epoch 2/4\n",
"25000/25000 [==============================] - 7s - loss: 0.4530 - acc: 0.7906 - val_loss: 0.4927 - val_acc: 0.7446\n",
"Epoch 3/4\n",
"25000/25000 [==============================] - 7s - loss: 0.4276 - acc: 0.8032 - val_loss: 0.3745 - val_acc: 0.8446\n",
"Epoch 4/4\n",
"25000/25000 [==============================] - 7s - loss: 0.4013 - acc: 0.8186 - val_loss: 0.3630 - val_acc: 0.8491\n"
]
}
],
"source": [
"# model.optimizer.lr = 1e-3 ## MDR: added to the 50d for marginally faster training than I was getting\n",
"set_gpu_fan_speed(90)\n",
"model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=4, batch_size=64)\n",
"set_gpu_fan_speed(0)\n",
"model.save_weights(model_path+'glove100_wt1.h5') ## care, with the weight count!"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"MDR's result (50d embedding):\n",
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/4\n",
"25000/25000 [==============================] - 8s - loss: 0.5619 - acc: 0.7015 - val_loss: 0.4735 - val_acc: 0.7981\n",
"Epoch 2/4\n",
"25000/25000 [==============================] - 8s - loss: 0.4817 - acc: 0.7703 - val_loss: 0.4583 - val_acc: 0.7811\n",
"Epoch 3/4\n",
"25000/25000 [==============================] - 8s - loss: 0.4535 - acc: 0.7903 - val_loss: 0.4082 - val_acc: 0.8293\n",
"Epoch 4/4\n",
"25000/25000 [==============================] - 8s - loss: 0.4331 - acc: 0.7967 - val_loss: 0.4409 - val_acc: 0.8093"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"JH's result (50d embedding?):\n",
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/2\n",
"25000/25000 [==============================] - 4s - loss: 0.5217 - acc: 0.7172 - val_loss: 0.2942 - val_acc: 0.8815\n",
"Epoch 2/2\n",
"25000/25000 [==============================] - 4s - loss: 0.3169 - acc: 0.8719 - val_loss: 0.2662 - val_acc: 0.8978"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:49:43.830482Z",
"start_time": "2017-06-27T20:49:43.818089Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model.load_weights(model_path+'glove50_wt1.h5')"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:58:48.232160Z",
"start_time": "2017-06-27T21:58:48.210481Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model.load_weights(model_path+'glove100_wt1.h5')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MDR: so my initial results were nowhere near as good, but we're not overfitting yet."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MDR: my results are nowhere near JH's! [] Investigate this!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We already have beaten our previous model! But let's fine-tune the embedding weights - especially since the words we couldn't find in glove just have random embeddings."
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:57:25.146194Z",
"start_time": "2017-06-27T21:57:25.142735Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model.layers[0].trainable=True"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:57:28.568235Z",
"start_time": "2017-06-27T21:57:28.562411Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model.optimizer.lr=1e-4"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:58:11.358229Z",
"start_time": "2017-06-27T21:57:39.403029Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/4\n",
"25000/25000 [==============================] - 8s - loss: 0.3819 - acc: 0.8287 - val_loss: 0.3785 - val_acc: 0.8316\n",
"Epoch 2/4\n",
"25000/25000 [==============================] - 7s - loss: 0.3709 - acc: 0.8315 - val_loss: 0.3816 - val_acc: 0.8361\n",
"Epoch 3/4\n",
"25000/25000 [==============================] - 7s - loss: 0.3541 - acc: 0.8413 - val_loss: 0.3516 - val_acc: 0.8484\n",
"Epoch 4/4\n",
"25000/25000 [==============================] - 7s - loss: 0.3400 - acc: 0.8511 - val_loss: 0.3947 - val_acc: 0.8204\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f36ee1a8d30>"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=4, batch_size=64)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"MDR result (50d embedding)\n",
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/4\n",
"25000/25000 [==============================] - 8s - loss: 0.3990 - acc: 0.8183 - val_loss: 0.3893 - val_acc: 0.8295\n",
"Epoch 2/4\n",
"25000/25000 [==============================] - 8s - loss: 0.3947 - acc: 0.8213 - val_loss: 0.4191 - val_acc: 0.8040\n",
"Epoch 3/4\n",
"25000/25000 [==============================] - 8s - loss: 0.3746 - acc: 0.8301 - val_loss: 0.3859 - val_acc: 0.8282\n",
"Epoch 4/4\n",
"25000/25000 [==============================] - 8s - loss: 0.3680 - acc: 0.8343 - val_loss: 0.3931 - val_acc: 0.8174"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\"As expected, that's given us a nice little boost. :)\" - \n",
"MDR: actually made it worse! For both 50d and 100d cases!"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:51:16.431166Z",
"start_time": "2017-06-27T20:51:16.410766Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model.save_weights(model_path+'glove50.h5')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multi-size CNN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is an implementation of a multi-size CNN as shown in Ben Bowles' [excellent blog post](https://quid.com/feed/how-quid-uses-deep-learning-with-small-data)."
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:58:59.831106Z",
"start_time": "2017-06-27T21:58:59.825574Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"from keras.layers import Merge"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We use the functional API to create multiple conv layers of different sizes, and then concatenate them."
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T22:02:06.688609Z",
"start_time": "2017-06-27T22:02:06.597988Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"#graph_in = Input ((vocab_size, 50))\n",
"graph_in = Input ((vocab_size, 100)) ## MDR - for 100d embedding\n",
"convs = [ ] \n",
"for fsz in range (3, 6): \n",
" x = Convolution1D(64, fsz, border_mode='same', activation=\"relu\")(graph_in)\n",
" x = MaxPooling1D()(x) \n",
" x = Flatten()(x) \n",
" convs.append(x)\n",
"out = Merge(mode=\"concat\")(convs) \n",
"graph = Model(graph_in, out) "
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T22:02:07.382958Z",
"start_time": "2017-06-27T22:02:07.364930Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"emb = create_emb()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then replace the conv/max-pool layer in our original CNN with the concatenated conv layers."
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T22:02:08.861312Z",
"start_time": "2017-06-27T22:02:08.614651Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model = Sequential ([\n",
" #Embedding(vocab_size, 50, \n",
" Embedding(vocab_size, 100, \n",
" input_length=seq_len, dropout=0.2, weights=[emb]),\n",
" Dropout (0.2),\n",
" graph,\n",
" Dropout (0.5),\n",
" Dense (100, activation=\"relu\"),\n",
" Dropout (0.7),\n",
" Dense (1, activation='sigmoid')\n",
" ])"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T22:02:09.574814Z",
"start_time": "2017-06-27T22:02:09.559120Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MDR: it turns out that there's no improvement, in this expt, for using the 100d embedding over the 50d."
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T22:03:02.590511Z",
"start_time": "2017-06-27T22:02:13.009457Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/2\n",
"25000/25000 [==============================] - 22s - loss: 0.5049 - acc: 0.7261 - val_loss: 0.2961 - val_acc: 0.8833\n",
"Epoch 2/2\n",
"25000/25000 [==============================] - 22s - loss: 0.3106 - acc: 0.8742 - val_loss: 0.2761 - val_acc: 0.8838\n"
]
}
],
"source": [
"set_gpu_fan_speed(90)\n",
"model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=2, batch_size=64)\n",
"set_gpu_fan_speed(0)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"MDR's results (50d):\n",
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/2\n",
"25000/25000 [==============================] - 18s - loss: 0.4930 - acc: 0.7455 - val_loss: 0.3141 - val_acc: 0.8803\n",
"Epoch 2/2\n",
"25000/25000 [==============================] - 18s - loss: 0.3153 - acc: 0.8732 - val_loss: 0.2724 - val_acc: 0.8936"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"JH's results (50d?):\n",
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/2\n",
"25000/25000 [==============================] - 11s - loss: 0.3997 - acc: 0.8207 - val_loss: 0.3032 - val_acc: 0.8943\n",
"Epoch 2/2\n",
"25000/25000 [==============================] - 11s - loss: 0.2882 - acc: 0.8832 - val_loss: 0.2646 - val_acc: 0.9029"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Interestingly, I found that in this case I got best results when I started the embedding layer as being trainable, and then set it to non-trainable after a couple of epochs. I have no idea why!\n",
"\n",
"MDR: (does it limit overfitting, maybe?) ... anyway, my running of the same code achieved nearly the same results, so much happier."
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:55:16.761928Z",
"start_time": "2017-06-27T20:55:16.719021Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model.save_weights(model_path+'glove50_conv2_wt1.h5')"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:58:24.735108Z",
"start_time": "2017-06-27T20:58:24.720123Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model.load_weights(model_path+'glove50_conv2_wt1.h5')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MDR: I want to test this statement from JH, above, by running another couple of epochs. First let's reduce the LR."
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:58:25.876884Z",
"start_time": "2017-06-27T20:58:25.871280Z"
}
},
"outputs": [],
"source": [
"model.optimizer.lr = 1e-5"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T20:59:03.169373Z",
"start_time": "2017-06-27T20:58:26.403414Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/2\n",
"25000/25000 [==============================] - 18s - loss: 0.2815 - acc: 0.8887 - val_loss: 0.2644 - val_acc: 0.8961\n",
"Epoch 2/2\n",
"25000/25000 [==============================] - 17s - loss: 0.2622 - acc: 0.8949 - val_loss: 0.2681 - val_acc: 0.8893\n"
]
}
],
"source": [
"set_gpu_fan_speed(90)\n",
"model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=2, batch_size=64)\n",
"set_gpu_fan_speed(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Okay, so that didn't help. Reload the weights from before."
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:01:28.858732Z",
"start_time": "2017-06-27T21:01:28.824671Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model.load_weights(model_path+'glove50_conv2_wt1.h5')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MDR: following JH's plan, from this point."
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:01:30.328857Z",
"start_time": "2017-06-27T21:01:30.323085Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model.layers[0].trainable=False"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:01:31.145448Z",
"start_time": "2017-06-27T21:01:31.140511Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model.optimizer.lr=1e-5"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:02:49.410601Z",
"start_time": "2017-06-27T21:01:36.916524Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/4\n",
"25000/25000 [==============================] - 17s - loss: 0.2802 - acc: 0.8879 - val_loss: 0.2646 - val_acc: 0.8928\n",
"Epoch 2/4\n",
"25000/25000 [==============================] - 18s - loss: 0.2593 - acc: 0.8984 - val_loss: 0.2579 - val_acc: 0.8976\n",
"Epoch 3/4\n",
"25000/25000 [==============================] - 18s - loss: 0.2480 - acc: 0.8998 - val_loss: 0.2541 - val_acc: 0.8960\n",
"Epoch 4/4\n",
"25000/25000 [==============================] - 17s - loss: 0.2350 - acc: 0.9072 - val_loss: 0.2550 - val_acc: 0.8965\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f370f2f65f8>"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set_gpu_fan_speed(90)\n",
"model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=4, batch_size=64)\n",
"set_gpu_fan_speed(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This more complex architecture has given us another boost in accuracy.\n",
"\n",
"MDR: although I didn't see a huge advantage, personally."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LSTM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We haven't covered this bit yet!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MDR: so, there's no preloaded embedding, here - it's a fresh, random set?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = Sequential([\n",
" Embedding(vocab_size, 32, input_length=seq_len, mask_zero=True,\n",
" W_regularizer=l2(1e-6), dropout=0.2),\n",
" LSTM(100, consume_less='gpu'),\n",
" Dense(1, activation='sigmoid')])\n",
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
"model.summary()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"JH's result:\n",
"____________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"====================================================================================================\n",
"embedding_13 (Embedding) (None, 500, 32) 160064 embedding_input_13[0][0] \n",
"____________________________________________________________________________________________________\n",
"lstm_13 (LSTM) (None, 100) 53200 embedding_13[0][0] \n",
"____________________________________________________________________________________________________\n",
"dense_18 (Dense) (None, 1) 101 lstm_13[0][0] \n",
"====================================================================================================\n",
"Total params: 213365\n",
"____________________________________________________________________________________________________\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MDR: hang on! These summary() outputs look quite different, to me! Not least that this is apparently the 13th lstm he's produced (in this session?) - and yet I've fot a higher numbered dense layer than him. Eh?\n",
"\n",
"But then I reach better results in fewer epochs than he does, this time around. Compare the times, and the more stable convergence in my results. Weird. Still, that's my first LSTM!!"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:14:45.761301Z",
"start_time": "2017-06-27T21:06:29.330705Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/5\n",
"25000/25000 [==============================] - 82s - loss: 0.5113 - acc: 0.7419 - val_loss: 0.5116 - val_acc: 0.7670\n",
"Epoch 2/5\n",
"25000/25000 [==============================] - 83s - loss: 0.3604 - acc: 0.8495 - val_loss: 0.4464 - val_acc: 0.8411\n",
"Epoch 3/5\n",
"25000/25000 [==============================] - 82s - loss: 0.3048 - acc: 0.8774 - val_loss: 0.3181 - val_acc: 0.8662\n",
"Epoch 4/5\n",
"25000/25000 [==============================] - 83s - loss: 0.2825 - acc: 0.8858 - val_loss: 0.2985 - val_acc: 0.8759\n",
"Epoch 5/5\n",
"25000/25000 [==============================] - 83s - loss: 0.2588 - acc: 0.8964 - val_loss: 0.2927 - val_acc: 0.8795\n"
]
}
],
"source": [
"set_gpu_fan_speed(90)\n",
"model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=5, batch_size=64)\n",
"set_gpu_fan_speed(0)"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": true
},
"source": [
"JH's result:\n",
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/5\n",
"25000/25000 [==============================] - 100s - loss: 0.5007 - acc: 0.7446 - val_loss: 0.3475 - val_acc: 0.8531\n",
"Epoch 2/5\n",
"25000/25000 [==============================] - 100s - loss: 0.3524 - acc: 0.8507 - val_loss: 0.3602 - val_acc: 0.8453\n",
"Epoch 3/5\n",
"25000/25000 [==============================] - 99s - loss: 0.3750 - acc: 0.8342 - val_loss: 0.4758 - val_acc: 0.7710\n",
"Epoch 4/5\n",
"25000/25000 [==============================] - 99s - loss: 0.3238 - acc: 0.8652 - val_loss: 0.3094 - val_acc: 0.8725\n",
"Epoch 5/5\n",
"25000/25000 [==============================] - 99s - loss: 0.2681 - acc: 0.8920 - val_loss: 0.3018 - val_acc: 0.8776"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:16:06.054276Z",
"start_time": "2017-06-27T21:16:06.045783Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model.save_weights(model_path+'glove50_lstm1_wt1.h5')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MDR: let's see if it's possible to improve on that."
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T21:24:25.734066Z",
"start_time": "2017-06-27T21:17:29.269083Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/5\n",
"25000/25000 [==============================] - 82s - loss: 0.2439 - acc: 0.9009 - val_loss: 0.3326 - val_acc: 0.8594\n",
"Epoch 2/5\n",
"25000/25000 [==============================] - 82s - loss: 0.2322 - acc: 0.9080 - val_loss: 0.3005 - val_acc: 0.8785\n",
"Epoch 3/5\n",
"25000/25000 [==============================] - 84s - loss: 0.2064 - acc: 0.9188 - val_loss: 0.3347 - val_acc: 0.8700\n",
"Epoch 4/5\n",
"25000/25000 [==============================] - 83s - loss: 0.2020 - acc: 0.9184 - val_loss: 0.3178 - val_acc: 0.8694\n",
"Epoch 5/5\n",
"25000/25000 [==============================] - 83s - loss: 0.1983 - acc: 0.9221 - val_loss: 0.3009 - val_acc: 0.8744\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f370fa32048>"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.optimizer.lr = 1e-5\n",
"model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=5, batch_size=64)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"MDR: Conclusion: that may be all that's achievable with this dataset, of course. It's sentiment, after all! "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## MDR's lstm + preloaded embeddings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"God knows whether this will work. Let's see if I can create an LSTM layer on top of pretrained embeddings..."
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T22:21:34.976964Z",
"start_time": "2017-06-27T22:21:34.901177Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model2 = Sequential([\n",
" Embedding(vocab_size, 100, input_length = seq_len,\n",
" #mask_zero=True, W_regularizer=l2(1e-6), ## used in lstm above - not needed?\n",
" dropout=0.2, weights=[emb], trainable = False),\n",
" LSTM(100, consume_less = 'gpu'),\n",
" Dense(100, activation = 'sigmoid')\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T22:21:39.618058Z",
"start_time": "2017-06-27T22:21:39.478659Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"____________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"====================================================================================================\n",
"embedding_22 (Embedding) (None, 500, 100) 500000 embedding_input_22[0][0] \n",
"____________________________________________________________________________________________________\n",
"lstm_4 (LSTM) (None, 100) 80400 embedding_22[0][0] \n",
"____________________________________________________________________________________________________\n",
"dense_40 (Dense) (None, 100) 10100 lstm_4[0][0] \n",
"====================================================================================================\n",
"Total params: 590,500\n",
"Trainable params: 90,500\n",
"Non-trainable params: 500,000\n",
"____________________________________________________________________________________________________\n"
]
}
],
"source": [
"model2.summary()"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T22:21:48.074841Z",
"start_time": "2017-06-27T22:21:48.037106Z"
},
"collapsed": true
},
"outputs": [],
"source": [
"model2.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T22:23:39.790314Z",
"start_time": "2017-06-27T22:22:13.740536Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/4\n",
"25000/25000 [==============================] - 21s - loss: 0.2042 - acc: 0.9190 - val_loss: 0.2563 - val_acc: 0.8962\n",
"Epoch 2/4\n",
"25000/25000 [==============================] - 21s - loss: 0.2022 - acc: 0.9200 - val_loss: 0.2583 - val_acc: 0.8943\n",
"Epoch 3/4\n",
"25000/25000 [==============================] - 21s - loss: 0.1895 - acc: 0.9257 - val_loss: 0.2540 - val_acc: 0.8965\n",
"Epoch 4/4\n",
"25000/25000 [==============================] - 21s - loss: 0.1796 - acc: 0.9276 - val_loss: 0.2564 - val_acc: 0.8958\n"
]
}
],
"source": [
"set_gpu_fan_speed(90)\n",
"model.fit(trn, labels_train, validation_data=(test, labels_test), nb_epoch=4, batch_size=64)\n",
"set_gpu_fan_speed(0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"ExecuteTime": {
"end_time": "2017-06-27T22:21:22.610842Z",
"start_time": "2017-06-27T22:21:22.606357Z"
}
},
"source": [
"MDR: OMFG. It needs one epoch to be 90% accurate."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"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"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "142px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false,
"widenNotebook": false
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
fercarozzi/myseismicjulia | examples/Plotting_Examples.ipynb | 3 | 639710 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"using PyPlot,Seismic"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 109k 100 109k 0 0 222k 0 --:--:-- --:--:-- --:--:-- 223k\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f12e226af10>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of traces: 50\n",
"number of samples per trace: 501\n"
]
}
],
"source": [
"download(\"http://seismic.physics.ualberta.ca/data/data_with_noise.su\",\"data_with_noise.su\");\n",
"SegyToSeis(\"data_with_noise.su\",\"data_with_noise\",format=\"su\");\n",
"d,h,ext = SeisRead(\"data_with_noise\");\n",
"plotpar = Dict(:style=>\"wiggles\",\n",
" :vmin=>-5,:vmax=>5,\n",
" :xlabel=>\"X\",:dx=>1,\n",
" :ylabel=>\"Time\",:yunits=>\"(seconds)\",:oy=>0,:dy=>h[1].d1,\n",
" :cmap=>\"gray\",\n",
" :title=>\"data_with_noise.su\");\n",
"SeisPlot(d;plotpar...);"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
" 15 88464 15 14073 0 0 66077 0 0:00:01 --:--:-- 0:00:01 66070"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f12df626050>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of traces: 38\n",
"number of samples per trace: 522\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"100 88464 100 88464 0 0 283k 0 --:--:-- --:--:-- --:--:-- 283k\n"
]
}
],
"source": [
"download(\"http://seismic.physics.ualberta.ca/data/syn_cmp.su\",\"syn_cmp.su\");\n",
"SegyToSeis(\"syn_cmp.su\",\"syn_cmp\",format=\"su\");\n",
"d,h,ext = SeisRead(\"syn_cmp\");\n",
"plotpar = Dict(:style=>\"wiggles\",\n",
" :vmin=>-2,:vmax=>2,\n",
" :xlabel=>\"X\",:dx=>1,\n",
" :ylabel=>\"Time\",:yunits=>\"(seconds)\",:oy=>0,:dy=>h[1].d1,\n",
" :cmap=>\"gray\",\n",
" :title=>\"syn_cmp.su\");\n",
"SeisPlot(d;plotpar...);"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 20800 100 20800 0 0 43939 0 --:--:-- --:--:-- --:--:-- 43974\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of traces: 20\n",
"number of samples per trace: 200\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"\r",
" 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f12df461ad0>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of traces: 1\n",
"number of samples per trace: 45\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"100 420 100 420 0 0 2179 0 --:--:-- --:--:-- --:--:-- 2187\n"
]
}
],
"source": [
"download(\"http://seismic.physics.ualberta.ca/data/small_stack.su\",\"small_stack.su\");\n",
"SegyToSeis(\"small_stack.su\",\"small_stack\",format=\"su\");\n",
"d,h,ext = SeisRead(\"small_stack\");\n",
"nt = size(d,1);\n",
"dt = h[1].d1;\n",
"download(\"http://seismic.physics.ualberta.ca/data/wavelet_for_small_stack.su\",\n",
" \"wavelet_for_small_stack.su\");\n",
"SegyToSeis(\"wavelet_for_small_stack.su\",\"wavelet_for_small_stack\",format=\"su\");\n",
"w,h_wav,e_wav = SeisRead(\"wavelet_for_small_stack\");\n",
"nt_wav = size(w,1);\n",
"plotpar = Dict(:xlabel=>\"X\",:dx=>1,\n",
" :ylabel=>\"Time\",:yunits=>\"(seconds)\",:oy=>0,:dy=>h[1].d1,\n",
" :cmap=>\"PuOr\",:fignum=>1,\n",
" :title=>\"small_stack.su\");\n",
"\n",
"subplot(122);\n",
"wlong = hcat(w[:]', zeros(nt-nt_wav)');\n",
"ax = gca();\n",
"ax[:invert_yaxis](); \n",
"\n",
"plot(wlong',collect(nt-1:-1:0)*dt);\n",
"title(\"wavelet_for_small_stack.su\");\n",
"suptitle(\"Stack and Wavelet\");\n",
"ax = gca();\n",
"ax[:invert_yaxis](); \n",
"\n",
"subplot(121);\n",
"SeisPlot(d;pclip=200,plotpar...);"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 650k 100 650k 0 0 1238k 0 --:--:-- --:--:-- --:--:-- 1237k\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f12df4963d0>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of traces: 92\n",
"number of samples per trace: 1751\n"
]
}
],
"source": [
"download(\"http://seismic.physics.ualberta.ca/data/gom_cdp_nmo.su\",\"gom_cdp_nmo.su\");\n",
"SegyToSeis(\"gom_cdp_nmo.su\",\"gom_cdp_nmo\",format=\"su\");\n",
"d,h,ext = SeisRead(\"gom_cdp_nmo\");\n",
"nx = length(h);\n",
"dx = abs.(h[end].h - h[1].h)/nx;\n",
"plotpar = Dict( :aspect=>5000,\n",
" :xlabel=>\"Offset\",:xunits=>\"(ft)\",:dx=>dx,\n",
" :ylabel=>\"Time\",:yunits=>\"(seconds)\",:oy=>0,:dy=>h[1].d1,\n",
" :cmap=>\"gray\",\n",
" :title=>\"gom_cdp_nmo.su\");\n",
"SeisPlot(d;plotpar...);"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"\r",
" 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x7f12db53ead0>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of traces: 1\n",
"number of samples per trace: 35\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
"100 380 100 380 0 0 1730 0 --:--:-- --:--:-- --:--:-- 1735\n"
]
},
{
"data": {
"text/plain": [
"4-element Array{Float64,1}:\n",
" -20.0 \n",
" 20.0 \n",
" 0.136\n",
" 0.0 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"download(\"http://seismic.physics.ualberta.ca/data/min_phase_wavelet.su\",\"min_phase_wavelet.su\");\n",
"SegyToSeis(\"min_phase_wavelet.su\",\"min_phase_wavelet\",format=\"su\",swap_bytes=false,input_type=\"ieee\");\n",
"d,h,ext = SeisRead(\"min_phase_wavelet\");\n",
"nt = size(d,1);\n",
"dt = h[1].d1;\n",
"plot(d,collect(0:nt-1)*dt,color=\"k\");\n",
"title(\"min_phase_wavelet.su\");\n",
"ax = gca();\n",
"ax[:invert_yaxis]();\n",
"ylabel(\"Time (s)\");\n",
"axis([-20,20,(nt-1)*dt,0.])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.6.0",
"language": "julia",
"name": "julia-0.6"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
kspekkens/HI_analysis_course | chapter_08_sof_sop/Parameterisation.ipynb | 1 | 136664 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import HTML\n",
"HTML('../style/course.css')\n",
"HTML('../style/code_toggle.html')\n",
"\n",
"import os\n",
"import string\n",
"import subprocess\n",
"import shutil\n",
"\n",
"# simple script to run command line commands from python\n",
"def Run(command,verb1=1,verb2=0):\n",
" if verb1: print command\n",
" yodel = subprocess.Popen(string.split(command), stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()\n",
" result=string.split(yodel[0],'\\n')\n",
" if '### Fatal Error' in yodel[1]:\n",
" print 'Miriad error:'\n",
" print yodel[1]\n",
" raise Exception()\n",
" if verb2:\n",
" for jj in result: print jj\n",
" return result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic source parameterisation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Overview"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [Observational and physical source parameters](#Observational-and-physical-source-parameters)\n",
" * [Important preliminary remarks](#Important-preliminary-remarks)\n",
" * [Source position in the sky](#Source-position-in-the-sky)\n",
" * [Celestial coordinates](#Celestial-coordinates)\n",
" * [Conversion between coordinate systems](#Conversion-between-coordinate-systems)\n",
" * [Redshift and radial velocity](#Redshift-and-radial-velocity)\n",
" * [Converting between frequency and velocity](#Converting-between-frequency-and-velocity)\n",
" * [Velocity rest frames](#Velocity-rest-frames)\n",
" * [Flux density, brightness temperature and column density](#Flux-density,-brightness-temperature-and-column-density)\n",
" * [Integrated flux and HI mass](#Integrated-flux-and-HI-mass)\n",
" * [Spectral line width](#Spectral-line-width)\n",
" * [Angular size and orientation](#Angular-size-and-orientation)\n",
" * [Ellipse fitting](#Ellipse-fitting)\n",
" * [Fitting of an elliptical Gaussian](#Fitting-of-an-elliptical-Gaussian)\n",
"* [Creation of advanced data products](#Creation-of-advanced-data-products)\n",
" * [Moment maps](#Moment-maps)\n",
" * [Peak flux density map](#Peak-flux-density-map)\n",
" * [Simple source masks: Miriad example](#Simple-source-masks:-Miriad-example)\n",
" * [Slices through the data cube](#Slices-through-the-data-cube)\n",
" * [Data cube images](#Data-cube-images)\n",
" * [PV-diagrams](#PV-diagrams)\n",
" * [Integrated spectra](#Integrated-spectra)\n",
"* [Model fitting to spectral line profiles](#Model-fitting-to-spectral-line-profiles)\n",
" * [Fitting a Gaussian function](#Fitting-a-Gaussian-function)\n",
" * [Fitting a Busy Function](#Fitting-a-Busy-Function)\n",
" * [Alternative models for double-horn profiles](#Alternative-models-for-double-horn-profiles)\n",
"* [Systematic biases in source parameterisation](#Systematic-biases-in-source-parameterisation)\n",
" * [Source finding threshold](#Source-finding-threshold)\n",
" * [Impact of noise](#Impact-of-noise)\n",
"* [Correction factors at higher redshift](#Correction-factors-at-higher-redshift)\n",
" * [Velocity width](#Velocity-width)\n",
" * [Further information](#Further-information)\n",
"* [Determination of uncertanties](#Determination-of-uncertanties)\n",
" * [Statistical uncertainties](#Statistical-uncertainties)\n",
" * [Determination of meaningful uncertainties](#Determination-of-meaningful-uncertainties)\n",
"* [List of commonly used symbols](#List-of-commonly-used-symbols)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Observational and physical source parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When dealing with HI emission-line data, we typically wish to analyse the physical properties of the sources contained within the data set. However, while we are actually interested in parameters such as mass or recession velocity, the raw data cube will usually only contain flux density values as a function of position in the sky and frequency. In this section we will explore how to turn that information into physically meaningful properties that can ultimately be used in astrophysical studies."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Important preliminary remarks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this section we will make the following assumptions:\n",
"\n",
"* Data cubes are **three-dimensional** with the first two dimensions, $x$ and $y$, being longitude (e.g. right ascension) and latitude (e.g. declination) and the third dimension, $z$, being the spectral axis (e.g. observing frequency or radial velocity).\n",
"* Data cubes are spatially convolved with an elliptical **Gaussian beam**, while the frequency resolution in the spectral domain is equal to the width of a spectral channel.\n",
"* The statistical **noise** in the data cube is Gaussian and constant across the extent of the source.\n",
"* Accurate **source masks** from a previous source finding step are available to inform which elements of a data cube are occupied by a particular source.\n",
"* Sources are **compact**, i.e. their angular size is sufficiently small such that the small-angle approximations of trigonometry apply.\n",
"* Sources are located in the local universe at **redshift zero**. The required corrections for sources at higher redshift are discussed in a separate section on [Correction factors at higher redshift](Correction-factors-at-higher-redshift).\n",
"\n",
"While these assumptions are usually met by most real-world HI data cubes, some of the results derived in this section may not be generally valid, should one or more of the assumptions above not apply."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Several of the parameterisation steps are illustrated with the help of simple Python coding examples. For this purpose we include a small test data cube from HIPASS, called [`data_cube.fits`](01_figures/data_cube.fits), and an accompanying mask cube, called [`data_cube_mask.fits`](01_figures/data_cube_mask.fits), that was generated by running [SoFiA](https://github.com/SoFiA-Admin/SoFiA/) on the cube. The cube and mask contain two galaxies in the nearby universe, NGC 3887 and HIPASS J1150−17, whose parameters are measured throughout this section."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Source position in the sky"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One of the most fundamental pieces of information that we are interested in is the location of our sources in the sky. The most basic way of defining source position is to simply pick the position of the pixel with either the highest flux density in the data cube or the highest flux in the integrated column density map. However, as these positions may be arbitrary, a more meaningful method is to calculate the flux density-weighted **centroid** of the source,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\langle \\vec{p} \\rangle = \\frac{\\sum_{i} \\vec{p}_{i} S \\! \\left( \\vec{p}_{i} \\right)}{\\sum_{i} S \\! \\left( \\vec{p}_{i} \\right)} , $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where the summation is over all pixels, $i$, covered by emission from the source, and $S \\! \\left( \\vec{p}_{i} \\right)$ is the flux density at the position $\\vec{p}_{i} = (x_{i}, y_{i}, z_{i})$. Alternatively, setting $S \\! \\left( \\vec{p}_{i} \\right) \\equiv 1$ will yield the geometric mean position of the source within the source mask defined by the set of $\\vec{p}_{i}$. Note that the above method will work in any number of dimensions; in the case of a standard HI data cube, two of the components of the vector $\\langle \\vec{p} \\rangle$ will contain the spatial position, while the third component will contain the spectral centroid of the source."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Celestial coordinates"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The source position derived above will initially be defined in the native pixel grid of the data cube and will need to be converted into an appropriate celestial coordinate system. In the case of FITS data cubes, this is usually achieved by storing the details of such a transformation in a **World Coordinate System** (WCS) defined in the header of the cube. These WCS header elements contain the information required to assign the correct physical coordinate to any pixel in the cube. Detailed information on WCS definitions, keywords and conventions can be found on the [FITS World Coordinate System](http://fits.gsfc.nasa.gov/fits_wcs.html) website of the FITS Support Office."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most astronomical data analysis and display software are capable of automatically converting pixel coordinates into WCS coordinates as specified in the FITS header. This includes source finding packages such as [Duchamp](www.atnf.csiro.au/people/Matthew.Whiting/Duchamp/) and [SoFiA](https://github.com/SoFiA-Admin/SoFiA/). In addition, several WCS conversion libraries are available to astronomers who intend to write their own data analysis software, including\n",
"\n",
"* [WCSLIB](http://www.atnf.csiro.au/people/mcalabre/WCS/) by Mark Calabretta (C/C++, Fortran)\n",
"* [WCSTools](http://tdc-www.harvard.edu/software/wcstools.html) by Doug Mink (C/C++)\n",
"* [AST](http://starlink.eao.hawaii.edu/starlink/AST/) library (C/C++, Fortran, Python, Java, Perl, UNIX shell)\n",
"* [Astropy](http://www.astropy.org/)’s `wcs` module (Python)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An example of how to use **Astropy** for WCS conversion of a small HIPASS data cube in Python is shown below. Note that displaying celestial coordinate axes with matplotlib in the example below will only work if the [WCSAxes](https://github.com/astrofrog/wcsaxes/) package is installed and will need to be explicitly switched on in the source code. Otherwise, only plain pixel coordinates will be displayed by default."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Extracting WCS information from a data cube\n",
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt\n",
"from astropy import wcs\n",
"from astropy.io import fits\n",
"\n",
"# Open example FITS cube and extract WCS header keywords\n",
"hdulist = fits.open(\"figures/data_cube.fits\")\n",
"wcshead = wcs.WCS(hdulist[0].header)\n",
"\n",
"# Pixel coordinates to be converted\n",
"x = 22\n",
"y = 19\n",
"z = 10\n",
"\n",
"# Convert pixel coordinates to world coordinates\n",
"equ_coords = wcshead.wcs_pix2world(x, y, z, 0)\n",
"\n",
"# Extract right ascension, declination and velocity\n",
"ra = equ_coords[0] / 15.0;\n",
"dec = equ_coords[1]\n",
"vel = equ_coords[2] / 1000.0\n",
"\n",
"# Print coordinates and plot channel map\n",
"print(\"Pixel coordinates: x = %i, y = %i, z = %i\" % (x, y, z))\n",
"print(\"WCS coordinates: RA = %.3f h, Dec = %.3f°, cZ = %.1f km/s\" % (ra, dec, vel))\n",
"\n",
"wcsheadcel = wcshead.celestial # only required if one wants to display celestial coordinates\n",
"fig = plt.figure() # Note: WCS in matplotlib only works if WCSAxes is installed!\n",
"fig.add_subplot(111, projection=wcsheadcel) # Activate these lines if you have WCSAxes on your system.\n",
"plt.imshow(hdulist[0].data[z], origin=\"lower\", cmap=\"jet\", interpolation=\"nearest\")\n",
"plt.xlabel(\"Right ascension (J2000)\")\n",
"plt.ylabel(\"Declination (J2000)\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Conversion between coordinate systems"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another common requirement is the conversion from one type of celestial coordinate to another, e.g. from J2000 equatorial coordinates to Galactic coordinates. In spherical geometry, this is simply achieved by rotating the ccordinate vector, $(\\alpha, \\beta)$, to derive the transformed coordinates, $(\\alpha', \\beta')$. For arbitrary celestial coordinate systems, this rotation can be expressed as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\begin{eqnarray}\n",
" \\tan(\\alpha_{0}' - \\alpha') & = & \\frac{\\cos(\\beta) \\sin(\\alpha - \\alpha_{0})}{\\sin(\\beta) \\cos(\\beta_{0}) - \\cos(\\beta) \\sin(\\beta_{0}) \\cos(\\alpha - \\alpha_{0})} , \\\\\n",
" \\sin(\\beta') & = & \\sin(\\beta) \\sin(\\beta_{0}) + \\cos(\\beta) \\cos(\\beta_{0}) \\cos(\\alpha - \\alpha_{0}) ,\n",
"\\end{eqnarray} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $\\alpha_{0}$ and $\\beta_{0}$ are the coordinates of the target coordinate system’s north pole (in source coordinates), and $\\alpha_{0}'$ is the longitude of the source coordinate system’s north pole (in target coordinates). For a conversion from J2000 equatorial coordinates to Galactic coordinates the corresponding values are"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\begin{eqnarray}\n",
" \\alpha_{0} & \\approx & 192.8595^{\\circ} \\\\\n",
" \\beta_{0} & \\approx & \\phantom{0}27.1284^{\\circ} \\\\\n",
" \\alpha_{0}' & \\approx & 122.9320^{\\circ} ,\n",
"\\end{eqnarray} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"while for a conversion from B1950 equatorial coordinates to Galactic coordinates we get"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\begin{eqnarray}\n",
" \\alpha_{0} & = & 192.25^{\\circ} \\\\\n",
" \\beta_{0} & = & \\phantom{0}27.40^{\\circ} \\\\\n",
" \\alpha_{0}' & = & 123.00^{\\circ} .\n",
"\\end{eqnarray} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that in the latter case the numerical values are exact, as the definition of the Galactic coordinate system was originally based on the B1950 coordinate system. Similar to the examples above, conversion between any two coordinate systems (including ecliptic or Magellanic coordinates) can be carried out as long as the positions of the two north poles are known in both coordinate systems. A simple J2000-to-Galactic coordinate converter in Python is included below. This can easily be expanded to include additional coordinate systems."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Conversion between equatorial and Galactic coordinates\n",
"from math import sin, cos, asin, atan2, pi, degrees, radians\n",
"\n",
"# Input\n",
"alpha = radians(10.6847929) # RA (J2000) in deg\n",
"delta = radians(41.2690650) # Dec (J2000) in deg\n",
"\n",
"# Definition of north poles\n",
"a0 = radians(192.859496) # RA of Galactic north pole in deg\n",
"d0 = radians( 27.128353) # Dec of Galactic north pole in deg\n",
"l0 = radians(122.932000) # Galactic longitude of J2000 north pole in deg\n",
"\n",
"# Coordinate transformation\n",
"l = l0 - atan2(cos(delta) * sin(alpha - a0), sin(delta) * cos(d0) - cos(delta) * sin(d0) * cos(alpha - a0))\n",
"b = asin(sin(delta) * sin(d0) + cos(delta) * cos(d0) * cos(alpha - a0))\n",
"\n",
"# Output\n",
"print(\"Input J2000: (RA, Dec) = (%.4f°, %.4f°)\" % (degrees(alpha), degrees(delta)))\n",
"print(\"Output Galactic: (l, b) = (%.4f°, %.4f°)\" % (degrees(l) % 360.0, degrees(b)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Redshift and radial velocity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The observed frequency of the HI emission of a source is another fundamental parameter that can be directly extracted from the observation. As stated in the [previous section](#Source-position-in-the-sky), the usual way of determining the spectral position of a source is to calculate the flux density-weighted **centroid** of the integrated spectrum along the spectral axis, hence"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\langle z \\rangle = M_{1} = \\frac{\\sum_{z} z \\, S(z)}{\\sum_{z} S(z)} , $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where the summation is over all spectral channels, $z$, covered by the source, and $S(z_{i})$ is the flux density at the corresponding channel. This is also known as the **first moment**, $M_{1}$, of the spectrum. Alternatively, one can fit a function, e.g. a Gaussian, to the spectral line and derive the centroid from the fit (see the section on <a href=\"#Model-fitting-to-spectral-line-profiles\">Model fitting to spectral-line profiles</a> for details)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As discussed in the [previous section](#Source-position-in-the-sky), the WCS keywords in the header of a FITS data cube must be used to convert spectral channel numbers into physical coordinates such as frequency or velocity. HI data cubes are usually provided in units of either topocentric or barycentric frequency, as this is the way in which the data are recorded at the telescope. Prior to data analysis, the recorded frequency axis is often converted to more convenient [parameters](#Converting-between-frequency-and-velocity) and/or [reference systems](#Velocity-rest-frames), e.g. velocity in the Galactic standard-of-rest frame, as detailed below. HI surveys at higher redshift also often use redshift, $Z$, rather than frequency or velocity to characterise the spectral coordinate of sources. Redshift is defined as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ Z = \\frac{f_{0}}{f} - 1 = \\frac{\\lambda}{\\lambda_{0}} - 1 $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $f_{0}$ is the rest frequency of the observed emission line ($f_{0} \\approx 1.42~\\mathrm{GHz}$ in the case of the HI line), $f$ denotes the observed frequency of the emission, and the parameters $\\lambda_{0}$ and $\\lambda$ denote the respective wavelengths. The observed redshift, $Z_{\\rm obs}$, is usually made up of several components, most notably the cosmological redshift, $Z_{\\rm cos}$, caused by the “expansion” of the universe, the velocity redshift, $Z_{\\rm pec}$, caused by the peculiar velocity of the source with respect to the observer, and the gravitational redshift, $Z_{\\rm grav}$, caused the presence of gravitational fields. The gravitational redshift is negligible in most cases, so that we get"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ 1 + Z_{\\rm obs} = (1 + Z_{\\rm cos}) \\times (1 + Z_{\\rm pec}) . $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the commonly encountered equation $Z_{\\rm obs} = Z_{\\rm cos} + Z_{\\rm pec}$ is incorrect and just an approximation to the correct equation above for small redshifts of $Z_{\\rm cos} \\ll 1$ and $Z_{\\rm pec} \\ll 1$. Another factor that may need to be included is the redshift caused by the peculiar motion of the earth itself (and hence the observer) with respect to the CMB."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Converting between frequency and velocity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Under the assumption that the observed frequency shift (or redshift) of a source is caused by the Doppler effect due to the source’s motion with respect to the observer, one can convert the frequency shift into a radial velocity. The special relativistic equation for conversion of the observed frequency, $f$, of a source into radial velocity, $v$, under the assumption that the object is moving towards or away from the observer reads"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\frac{v}{\\mathrm{c}} = \\frac{f_{0}^{2} - f^{2}}{f_{0}^{2} + f^{2}} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $\\mathrm{c}$ denotes the speed of light, and $f_{0}$ is the rest frequency of the observed line transition. This equation assumes that the observed redshift of a source is due to its relativistic velocity along the line of sight towards or away from the observer (relativistic Doppler effect). Note, however, that the observed redshift of distant sources is largely due to the “cosmological expansion” of space, not due to their velocity with respect to the observer. Hence, the above relation will not yield sensible results for sources at higher redshift; to characterise sources beyond redshift zero, frequency (or redshift) rather than velocity should be used. Also note that the equation above is only strictly valid for objects moving radially towards or away from the observer, and additional corrections would have to be applied if the object had a significant tangential velocity component as well."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are two commonly used approximations to this equation which are accurate for small velocities of up to a few hundred kilometres per second. The so-called **optical definition** reads"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\frac{v_{\\rm opt}}{\\mathrm{c}} = \\frac{f_{0}}{f} - 1 = Z $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and the so-called **radio definition** is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\frac{v_{\\rm rad}}{\\mathrm{c}} = 1 - \\frac{f}{f_{0}} = \\frac{Z}{1 + Z} . $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The advantage of the radio definition is that equal increments in frequency correspond to equal increments in radial velocity. However, the radio definition is deprecated by the International Astronomical Union (IAU), and the optical defintion should be used instead. Note that $v_{\\rm opt} \\propto Z$ in the optical definition. Hence, $v_{\\rm opt} = \\mathrm{c} Z$ is often used in extragalactic surveys as a convenient representation of redshift in units of velocity, often referred to as “recession velocity”. However, the resulting values must not be mistaken for velocities, as the observed redshift of distant galaxies is dominated by the “expansion” of the universe and does not reflect their actual space velocity."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While the observed redshift is dominated by the “expansion” of the universe at higher redshifts, it is still possible to determine the relative radial velocity, $\\Delta v_{\\rm pec}$, between two objects under the assumption that they are at the same distance from the observer and thus experience the same cosmological redshift. In this case,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\frac{v_{\\rm pec}}{\\mathrm{c}} = \\frac{Z_{\\rm obs} - Z_{\\rm cos}}{1 + Z_{\\rm cos}} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"([<cite data-cite=\"2014MNRAS.442.1117D\">Davis & Scrimgeour 2014</cite>](http://adsabs.harvard.edu/abs/2014MNRAS.442.1117D)) and thus"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\frac{\\Delta v_{\\rm pec}}{\\mathrm{c}} = \\frac{\\Delta Z_{\\rm obs}}{1 + Z_{\\rm cos}} , $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $\\Delta Z_{\\rm obs}$ is the difference between the observed redshifts of the two sources (assumed to be equal to the difference in their peculiar velocity redshifts), and $\\mathrm{c}$ denotes the speed of light in vacuum. Note that this is the non-relativistic approximation for small peculiar velocities of $v_{\\rm pec} \\ll \\mathrm{c}$; for large peculiar velocities the full, special-relativistic version of the equation must be used."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Velocity rest frames"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The observed radial velocity of an astronomical object is subject to several projection effects such as the rotation and the orbital motion of the Earth, the motion of the Solar System about the Galactic centre, the motion of our Galaxy within the Local Group, etc. Hence, depending on the location of the object under study, conversion of its measured velocity into an appropriate reference system will be required."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A useful rest frame is the so-called barycentric rest frame which uses the **barycentre of the Solar System** as reference point and corrects for lower-order motions of the Earth–Moon system. It is the most commonly used reference system for objects located beyond the Local Group where “recession velocities” due to the expansion of the universe by far exceed peculiar motions found within the Local Group. In most cases, the spectra observed with a radio telescope are already provided in the barycentric frame. Note that the barycentric frame is often referred to as the heliocentric rest frame. The latter, however, uses the barycentre of the Sun instead of the Solar System barycentre as its reference point. The difference between barycentric and heliocentric velocities, however, is rather small and negligible in most cases. Conversion of topocentric velocities/frequencies (as seen by the telescope) into the barycentric rest frame requires knowledge of the geographic location of the telescope and the exact time of the observation. Hence, no general conversion formula can be given here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For objects located in the Milky Way one usually uses the **local standard-of-rest** (LSR) frame as the reference for radial velocities. The LSR frame accounts for the peculiar motion of the Solar System of about $16.55~\\mathrm{km \\, s^{-1}}$ with respect to the regular rotation of the Galaxy. Radial velocities in the LSR frame, $v_{\\rm LSR}$, can be calculated from barycentric velocities, $v_{\\rm bar}$, via"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ v_{\\rm LSR} = v_{\\rm bar} + 9 \\cos(l) \\cos(b) + 12 \\sin(l) \\cos(b) + 7 \\sin(b) $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $l$ and $b$ are the Galactic longitude and latitude of the source, respectively ([<cite data-cite=\"1999AJ....118..337C\">Courteau & van den Bergh 1999</cite>](http://adsabs.harvard.edu/abs/1999AJ....118..337C)). This definition is the so-called “dynamical defintion” (also referred to as the LSRD) as specified by the IAU. There is an alternative “kinematical definition” (referred to as LSRK) which results in a slightly higher velocity of about $20~\\mathrm{km \\, s^{-1}}$ in the direction of $(\\alpha, \\delta) = (270^{\\circ}, 30^{\\circ})$ in the B1900 system. However, the LSRD definition is the one most commonly used and usually referred to as the LSR."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the description of circumgalactic objects it is useful to also correct for the rotation velocity of our Milky Way of about $220~\\mathrm{km \\, s^{-1}}$. The corresponding reference frame, the so-called **Galactic standard-of-rest** (GSR) frame, is derived from the LSR frame via"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ v_{\\rm GSR} = v_{\\rm LSR} + 220 \\sin(l) \\cos(b) . $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For objects spread across the Local Group, a reference frame accounting for the motion of our Milky Way with respect to the Local Group barycentre would be more suitable. The corresponding radial velocities in the so-called **Local Group standard-of-rest** (LGSR) frame can be calculated from the GSR velocities via"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ v_{\\rm LGSR} = v_{\\rm GSR} - 88 \\cos(l) \\cos(b) + 64 \\sin(l) \\cos(b) - 43 \\sin(b) . $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that this definition uses the apex vector calculated by [<cite data-cite=\"1999AJ....118..337C\">Courteau & van den Bergh (1999)</cite>](http://adsabs.harvard.edu/abs/1999AJ....118..337C) which differs slighly from the apex vector of [<cite data-cite=\"1996AJ....111..794K\">Karachentsev & Makarov (1996)</cite>](http://adsabs.harvard.edu/abs/1996AJ....111..794K) used by the [NASA/IPAC Extragalactic Database](https://ned.ipac.caltech.edu/). Overall, the solar apex with respect to the Local Group is not well defined, and the LGSR frame is therefore best avoided. In principal, one can correct the radial velocity for rest frames of even higher order in the hierarchy of the universe all the way up to the cosmic microwave background. The reference frames mentioned above, however, are the ones most frequently used."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A simple velocity converter in Python is included below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Simple velocity calculator\n",
"from math import cos, sin, pi, radians\n",
"\n",
"# Input\n",
"vbar = -300.0 # Barycentric velocity in km/s\n",
"l = radians(121.1744) # Galactic longitude in deg\n",
"b = radians(-21.5729) # Galactic latitude in deg\n",
"\n",
"# Conversion\n",
"vlsr = vbar + 9.0 * cos(l) * cos(b) + 12.0 * sin(l) * cos(b) + 7.0 * sin(b)\n",
"vgsr = vlsr + 220.0 * sin(l) * cos(b)\n",
"vlgsr = vgsr - 88.0 * cos(l) * cos(b) + 64.0 * sin(l) * cos(b) - 43.0 * sin(b)\n",
"\n",
"# Output\n",
"print(\"Bar.: %.1f km/s\" % vbar)\n",
"print(\"LSR: %.1f km/s\" % vlsr)\n",
"print(\"GSR: %.1f km/s\" % vgsr)\n",
"print(\"LGSR: %.1f km/s\" % vlgsr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Flux density, brightness temperature and column density"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The peak **flux density** of a source is often simply taken as the maximum flux density value encountered, hence"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ S_{\\rm peak} = \\max \\limits_{i} \\! \\left[ S \\! \\left( \\vec{p}_{i} \\right) \\right] . $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note, however, that this approach will result in a positive bias due to the contribution of noise to the measured signal (see the section on [Biases in source parameterisation](#Biases-in-source-parameterisation) below). This bias can be significant for faint sources near the detection limit of the data. Therefore, a better approach would be to fit a mathematical model to the source (e.g. a Gaussian) and then extract the peak flux density from the fit."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Flux densities are usually specified in units of Jansky, with $1~\\mathrm{Jy} = 10^{-26}~\\mathrm{W \\, m^{-2} \\, Hz^{-1}}$. In general, flux density, $S$, can be converted to **brightness temperature**, $T_{\\rm B}$, via"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ T_{\\rm B} = \\frac{\\lambda^{2} S}{2 \\mathrm{k_{b}} \\Omega_{\\rm PSF}} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $\\lambda$ is the wavelength of the emission, $\\Omega_{\\rm PSF}$ is the solid angle of the telecope beam, and $\\mathrm{k_{B}}$ denotes the Boltzmann constant. For HI observations at redshift zero, where $\\lambda \\approx 21.1~\\mathrm{cm}$, the equation can be simplified to"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\frac{T_{\\rm B}}{\\mathrm{K}} \\approx \\frac{606}{\\theta_{a} \\times \\theta_{b}} \\times \\frac{S}{\\mathrm{mJy}} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $\\theta_{a}$ and $\\theta_{b}$ are the major and minor axis of the telescope beam (assumed to be Gaussian) in units of arcseconds. Under the additional assumption that the gas is optically thin ($\\tau \\ll 1$), we can directly derive the **HI column density**, $N_{\\rm HI}$, from the brightness temperature via"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\frac{N_{\\rm HI}}{\\mathrm{cm^{-2}}} \\approx \\frac{1.823 \\times 10^{18}}{\\mathrm{K \\, km \\, s^{-1}}} \\times \\! \\int \\limits_{v_{1}}^{v_{2}} \\! T_{\\rm B} \\mathrm{d} v $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $v$ denotes radial velocity, and the integration is over the entire spectral profile width of the emission."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Integrated flux and HI mass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The **total flux** of a source can be derived by summing the flux density values across the entire source mask and multiplying by the width, $\\Delta z$, of a spectral channel:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ S_{\\rm tot} = \\Delta z \\sum_{i} S \\! \\left( \\vec{p}_{i} \\right) . $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, as neighbouring pixels are usually correlated due to spatial oversampling of the data, we still need to divide by the solid angle of the point spread function (PSF) in order to derive the **integrated flux**:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ S_{\\rm int} = \\frac{S_{\\rm tot}}{\\Omega_{\\rm PSF}} , $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\Omega_{\\rm PSF} = \\frac{\\pi \\, \\theta_{a} \\theta_{b}}{4 \\ln(2)} \\approx 1.13309 \\, \\theta_{a} \\theta_{b} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"is the solid angle of a two-dimensional **Gaussian PSF** with FWHM $\\theta_{a} \\times \\theta_{b}$. For the purpose of flux correction, $\\theta_{a}$ and $\\theta_{b}$ must be specified in pixels rather than radians. Note that the size of the beam will generally change with frequency and that calculation of the integrated flux will become complicated in the case of non-Gaussian beams."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In practice, integrated flux is often specified in units of $\\mathrm{Jy \\, km \\, s^{-1}}$, but $\\mathrm{Jy \\, Hz}$ is occasionally encountered as well and should be the preferred unit, as the former does not strictly constitute an integrated flux. Note that the accuracy of the resulting flux measurements depends on the accuracy of the source mask. If the mask is too small, then the measured flux will be too small as well. If the mask is too large, then the statistical uncertainty of the flux measurement will become unnecessarily large. Such issues can be minimised through careful **mask optimisation** prior to source parameterisation (see section on [Biases in source parameterisation](#Biases-in-source-parameterisation) below for details)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Under the assumption that the HI gas is optically thin and at redshift zero, the integrated flux of a source can be directly converted into an **HI mass**, $M_{\\rm HI}$, if the distance, $d$, to the object is known:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\frac{M_{\\rm HI}}{M_{\\odot}} = 0.236 \\times \\frac{S_{\\rm int}}{\\mathrm{Jy \\, km \\, s^{-1}}} \\times \\left( \\! \\frac{d}{\\mathrm{kpc}} \\! \\right)^{\\!2} . $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Spectral line width"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Measurement of the width of the HI spectral line profile can provide information about the microscopic and macroscopic motions of gas particles in the source. This, in turn, may give information about important physical parameters such as the kinetic temperature of the gas or the rotation velocity and dynamical mass of a galaxy. Several measures of line width are in use, the most common ones of which are the **standard deviation** ($\\sigma$), the **full width at half maximum** ($w_{50}$) and the **full width at 20% of the maximum** ($w_{20}$)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One of the most basic ways of measuring the line width of sources across a large area on the sky is through calculation of the **second moment** of each spectrum in the data cube. The second moment is defined via the flux-weighted square of the radial velocity,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ M_{2} = \\sqrt{\\frac{\\sum_{z} (z - M_{1})^{2} S(z)}{\\sum_{z} S(z)}} , $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $S(z)$ is the flux density in channel $z$, and $M_{1}$ denotes the [first moment ⤵](#sof_sop:sec:moments) <!--\\ref{sof_sop:sec:moments}--> of the spectrum. Hence, the second moment is equivalent to the standard deviation about $M_{1}$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As measurement of the second moment is easily affected by noise, in particular where the HI emission is faint (e.g. near the edge of a source), more robust and sophisticated methods of measuring line width are often used. These include the fitting of model functions, e.g. a Gaussian function, to the spectral profile. Parameters such as $w_{50}$ or $w_{20}$ can then be derived from the fit and are less susceptible to the influence of noise."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the case of extended sources, such as the Milky Way or high-velocity clouds, measurement of the profile width at individual locations across the source will enable estimates of the upper limit of the **kinetic gas temperature**. Under the assumptions that the gas can be approximated as an ideal gas and that the velocities of the gas particle follow a Maxwell–Boltzmann distribution, we can derive the kinetic temperature of the gas from the measured FWHM, $w_{50}$, of the spectral line:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ T_{\\rm kin} \\lesssim \\frac{M_{\\rm H} w_{50}^{2}}{8 \\, \\mathrm{k_{B}} \\ln(2)} . $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, $M_{\\rm H}$ is the mass of a hydrogen atom and $\\mathrm{k_{B}}$ denotes the Boltzmann constant. The measurement of $T_{\\rm kin}$ constitutes an **upper limit** only, as non-thermal motions could have contributed to the observed line width, e.g. from turbulent motions or the superposition of multiple gas clouds along the line of sight."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Angular size and orientation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Ellipse fitting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For sources that are spatially resolved we might also be interested in some measure of the angular size and orientation of the source in the sky. One of the most basic methods to derive size information is to **fit an ellipse** to the integrated flux map of a source. This can be achieved through a basic moment analysis of the image. The second-order moments of the two-dimensional source image are defined as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\begin{eqnarray}\n",
" M_{xx} & = & \\frac{\\sum_{i} \\, (x_{i} - \\langle x \\rangle)^{2} \\, S(x_{i}, y_{i})}{\\sum_{i} S(x_{i}, y_{i})} , \\\\\n",
" M_{yy} & = & \\frac{\\sum_{i} \\, (y_{i} - \\langle y \\rangle)^{2} \\, S(x_{i}, y_{i})}{\\sum_{i} S(x_{i}, y_{i})} , \\\\\n",
" M_{xy} & = & \\frac{\\sum_{i} \\, (x_{i} - \\langle x \\rangle) \\, (y_{i} - \\langle y \\rangle) \\, S(x_{i}, y_{i})}{\\sum_{i} S(x_{i}, y_{i})} ,\n",
"\\end{eqnarray} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where the summation is over all pixels of the source mask, and $\\langle \\vec{p} \\rangle = (\\langle x \\rangle, \\langle y \\rangle)$ is the flux density-weighted centroid of the source (equivalent to the first-order moments in $x$ and $y$; see section on [Source position in the sky](#Source-position-in-the-sky)). Note that one could alternatively set $S(x_{i}, y_{i}) \\equiv 1$ to assign equal weight to all pixels, in which case the geometric outline of the source mask would be fitted. With the second-order moments defined, we can then derive the major and minor axes, $a$ and $b$, as well as the position angle, $\\phi$, of the source in the sky,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\begin{eqnarray}\n",
"\ta & = & \\sqrt{2 \\left( M_{xx} + M_{yy} + \\sqrt{(M_{xx} - M_{yy})^{2} + 4 M_{xy}^{2}} \\right) } , \\\\\n",
"\tb & = & \\sqrt{2 \\left( M_{xx} + M_{yy} - \\sqrt{(M_{xx} - M_{yy})^{2} + 4 M_{xy}^{2}} \\right) } , \\\\\n",
" \\phi & = & \\frac{1}{2} \\, \\arctan \\left( \\! \\frac{2 M_{xy}}{M_{xx} - M_{yy}} \\! \\right)\n",
"\\end{eqnarray} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(see [<cite data-cite=\"1995MNRAS.272..821B\">Banks, Dodd & Sullivan 1995</cite>](http://adsabs.harvard.edu/abs/1995MNRAS.272..821B)). In the case of a source of Gaussian shape, $a$ and $b$ will correspond to twice the standard deviation, $\\sigma$, of the Gaussian. Note that the major and minor axis will be specified in pixels and will need to be converted to angles in the sky. The position angle will be relative to the pixel grid (not the celestial coordinate system of the image) and defined in the mathematically correct sense, i.e. $\\phi = 0^{\\circ}$ points to the right rather than up. Conversions will need to be done to change the reference coordinate system or orientation of the position angle."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Fitting of an elliptical Gaussian"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For sources that can be approximated by a **two-dimensional, elliptical Gaussian** function we can alternatively perform a Gaussian fit to derive the source size and orientation in the sky. A two-dimensional Gaussian function can be defined as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ G(x, y) = A \\exp \\left( \\! -\\frac{a^{2}(x,y)}{2 \\sigma_{a}^{2}} - \\frac{b^{2}(x,y)}{2 \\sigma_{b}^{2}} \\! \\right) , $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $A$ is the amplitude of the Gaussian, $\\sigma_{a}$ and $\\sigma_{b}$ denote the standard deviation of the Gaussian along the major and minor axis, respectively, and the functions $a$ and $b$ are defined as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\begin{eqnarray}\n",
" a(x,y) & = & (x - x_{0}) \\cos(\\phi) & + & (y - y_{0}) \\sin(\\phi) , \\\\\n",
" b(x,y) & = & (x - x_{0}) \\sin(\\phi) & - & (y - y_{0}) \\cos(\\phi) .\n",
"\\end{eqnarray} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The parameter $\\phi$ describes the position angle of the major axis with respect to the pixel coordinate grid, and $(x_{0}, y_{0})$ denotes the position of the centre of the Gaussian function. Thus, we end up with six free parameters of the fit overall: amplitude ($A$), position ($x_{0}$ and $y_{0}$), width ($\\sigma_{a}$ and $\\sigma_{b}$) and position angle ($\\phi$)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Python script below calculates and plots a two-dimensional, elliptical Gaussian function. It then attempts to fit an ellipse to the image, using the second-order moment analysis introduced above, to derive the size and orientation angle of the Gaussian. Lastly, the fitted ellipse is plotted on top of the original Gaussian. The resulting ellipse parameters should recover the input parameters of the Gaussian, i.e. a major axis of $a_{\\rm ell} = 2 \\sigma_{a}$, a minor axis of $b_{\\rm ell} = 2 \\sigma_{b}$ and a position angle of $\\phi_{\\rm ell} \\equiv \\phi_{\\rm Gauss}~(\\mathrm{mod}~180^{\\circ})$, unless the Gaussian extends beyond the edges of the image, in which case the derived ellipse sizes will of course be too small."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Fitting an ellipse to a 2D elliptical Gaussian\n",
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.patches import Ellipse\n",
"from numpy import empty\n",
"from math import sqrt, exp, sin, cos, atan2, pi\n",
"\n",
"# Input parameters\n",
"(dx, dy) = 201, 201 # image size\n",
"(x0, y0) = 100.0, 100.0 # centre position\n",
"(sa, sb) = 25.0, 15.0 # widths\n",
"amp = 1.0 # amplitude\n",
"phi = 20.0 * pi / 180.0 # position angle\n",
"\n",
"# Additional variables\n",
"(mxx, mxy, myy) = 0.0, 0.0, 0.0\n",
"sint = 0.0\n",
"\n",
"# Create image\n",
"img = empty((dx, dy))\n",
"\n",
"# Generate Gaussian function\n",
"for x in range(dx):\n",
" for y in range(dy):\n",
" a = (x - x0) * cos(phi) + (y - y0) * sin(phi)\n",
" b = (x - x0) * sin(phi) - (y - y0) * cos(phi)\n",
" img[y][x] = amp * exp(-(a * a / (2.0 * sa * sa)) - (b * b / (2.0 * sb * sb)))\n",
"\n",
"# Calculate second-order moments for ellipse fit\n",
"for x in range(dx):\n",
" for y in range(dy):\n",
" mxx += (x - x0) * (x - x0) * img[y][x]\n",
" mxy += (x - x0) * (y - y0) * img[y][x]\n",
" myy += (y - y0) * (y - y0) * img[y][x]\n",
" sint += img[y][x]\n",
"mxx /= sint; mxy /= sint; myy /= sint\n",
"\n",
"# Determine ellipse parameters\n",
"emaj = sqrt(2.0 * (mxx + myy + sqrt((mxx - myy) * (mxx - myy) + 4.0 * mxy * mxy)))\n",
"emin = sqrt(2.0 * (mxx + myy - sqrt((mxx - myy) * (mxx - myy) + 4.0 * mxy * mxy)))\n",
"epos = (0.5 * atan2(2.0 * mxy, mxx - myy))\n",
"\n",
"# Print result of ellipse fit\n",
"print(\"Ellipse fit: a = %.1f, b = %.1f, phi = %.1f°\" % (emaj, emin, (180.0 * epos / pi)))\n",
"\n",
"# Plot image\n",
"plt.imshow(img, origin=\"lower\", cmap=\"plasma\", interpolation=\"bicubic\")\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"y\")\n",
"axes = plt.gca()\n",
"axes.add_patch(Ellipse((x0, y0), emaj, emin, (180.0 * epos / pi), edgecolor=\"white\", fill=False, linewidth=1.5, linestyle=\"dashed\", antialiased=True))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creation of advanced data products"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Moment maps<a id='sof_sop:sec:moments'></a><!--\\label{sof_sop:sec:moments}-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Among the most commonly required data products are moment maps of the sources detected in a data cube. Moment maps condense the spectral information of the cube into a two-dimensional image that can then be analysed further. The **zeroth moment** of the cube simply integrates over the spectral axis to create an integrated flux map,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ M_{0}(x, y) = \\Delta v \\sum_{z} S(x, y, z) , $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $\\Delta v$ is the velocity width of a spectral channel (assumed to be constant across the spectral extent of the source), $S$ is the measured flux density, and the summation is over all spectral channels, $z$, covered by the source. If a source mask is unavailable for the purpose of defining the spectral boundaries of the emission, a flux threshold can instead be used, although the result will not be optimal in this case. Fluxes in the moment-0 map are usually specified in $\\mathrm{Jy \\, km \\, s^{-1}}$, but can be converted to HI column density as specified in the section on [Flux density, brightness temperature and column density](#Flux-density,-brightness-temperature-and-column-density). Similarly, the **first moment**\n",
"is defined as the flux density-weighted velocity at each pixel of the map,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ M_{1}(x, y) = \\frac{\\sum_{z} v(z) \\, S(x, y, z)}{\\sum_{z} S(x, y, z)} , $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $v(z)$ is the velocity associated with spectral channel $z$, and the summation is again over all spectral channels covered by the source. Thus, the moment-1 image will show the radial velocity centroid of the HI emission at each pixel of the map. Alternatively, if $v(z)$ is unknown, $z$ can be used instead to derive the first moment in units of channel number rather than velocity. Lastly, we can calculate the **second moment** to derive a map of spectral line widths,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ M_{2}(x, y) = \\sqrt{\\frac{\\sum_{z} [v(z) - M_{1}(x, y)]^{2} \\, S(x, y, z)}{\\sum_{z} S(x, y, z)}} . $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The moment-2 image shows the standard deviation about the first moment and thus encodes the velocity dispersion of the gas in each pixel of the map. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Peak flux density map"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another convenient data product, particularly for display purposes, is the **peak flux density** map. It is simply created by displaying in each pixel the highest flux density encountered along the spectral axis,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ S_{\\rm max}(x, y) = \\max \\limits_{z}[S(x, y, z)] . $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The advantage of the peak flux density map is that it is capable of revealing even faint sources within the data cube without requiring a-priori knowledge of the sources’ location, while for a high-contrast moment-zero image accurate source masks would be required, as the noise would increase with the square root of the number of channels added."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A short Python script that calculates and displays the three moments and the peak flux density map of the example data cube is included below. Note that this is a greatly simplified version that does not utilise source masks, but instead uses a simple, user-defined flux threshold to generate moment maps. Moreover, all maps in the example are generated from scratch by looping over all three dimensions of the data cube multiple times. While this may be an efficient method in compiled programming languages like C++, it will be far too slow in Python for large data cubes. Instead, special libraries such as NumPy should be used to efficiently handle large data arrays in Python."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Generating moment and peak flux density maps from a data cube\n",
"%matplotlib notebook\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from astropy import wcs\n",
"from astropy.io import fits\n",
"from math import sqrt\n",
"\n",
"def genmomaps(incubus = '', maskcube = '', threshold = 0.025, minmaxpeak = [0., 0.1], minmaxmom0 = [0.,1.], minmaxmom1 = [0., 200.], minmaxmom2 = [0., 50.]):\n",
" \"\"\"\n",
" Generating moment and peak flux density maps from a data cube\n",
" incubus (string) : Input data cube\n",
" maskcube (string): Mask data cube \n",
" threshold (float): Additional threshold, below which pixels are flagged\n",
" minmaxpeak (list): Minum and maximum for colour scale in peak flux map\n",
" minmaxmom0 (list): Minimum and maximum for colour scale in Moment-0 map\n",
" minmaxmom1 (list): Minimum and maximum for colour scale in Moment-1 map\n",
" minmaxmom2 (list): Minimum and maximum for colour scale in Moment-2 map \n",
" \"\"\" \n",
" # Check if mask exists and read in mask\n",
" if maskcube != '':\n",
" hdulist = fits.open(maskcube)\n",
" maskdata = hdulist[0].data \n",
" \n",
" # Open example FITS cube and extract WCS header keywords and data\n",
" hdulist = fits.open(incubus)\n",
" wcshead = wcs.WCS(hdulist[0].header)\n",
" scidata = hdulist[0].data\n",
" \n",
" # Read cube dimensions and create arrays for maps\n",
" nz, ny, nx = scidata.shape\n",
" \n",
" if maskcube == '':\n",
" maskdata = np.ones((nz,ny,nx), dtype=float)\n",
" \n",
" for x in range(nx):\n",
" for y in range(ny):\n",
" for z in range(nz):\n",
" if scidata[z][y][x] < threshold:\n",
" maskdata[z][y][x] = 0.\n",
"\n",
" mom0 = [[0 for x in range(nx)] for y in range(ny)]\n",
" mom1 = [[0 for x in range(nx)] for y in range(ny)]\n",
" mom2 = [[0 for x in range(nx)] for y in range(ny)]\n",
" peak = [[0 for x in range(nx)] for y in range(ny)]\n",
"\n",
" # Convert channel numbers to velocity using WCS\n",
" velo = [0 for z in range(nz)]\n",
" for z in range(nz):\n",
" equ_coords = wcshead.wcs_pix2world(0, 0, z, 0)\n",
" velo[z] = equ_coords[2] / 1000.0\n",
" dv = abs(velo[1] - velo[0])\n",
"\n",
" # Create moment-0 and peak flux density maps\n",
" for x in range(nx):\n",
" for y in range(ny):\n",
" mom0[y][x] = 0.0;\n",
" peak[y][x] = -10000000.0;\n",
" for z in range(nz):\n",
" if scidata[z][y][x] > peak[y][x] and maskdata[z][y][x]: peak[y][x] = scidata[z][y][x]\n",
" if scidata[z][y][x] > threshold and maskdata[z][y][x]: mom0[y][x] += scidata[z][y][x]\n",
"\n",
" # Create moment-1 map\n",
" for x in range(nx):\n",
" for y in range(ny):\n",
" mom1[y][x] = 0.0;\n",
" for z in range(nz):\n",
" if scidata[z][y][x] > threshold and maskdata[z][y][x]: mom1[y][x] += velo[z] * scidata[z][y][x]\n",
" if mom0[y][x] > 0.0: mom1[y][x] /= mom0[y][x]\n",
"\n",
" # Create moment-2 map\n",
" for x in range(nx):\n",
" for y in range(ny):\n",
" mom2[y][x] = 0.0;\n",
" for z in range(nz):\n",
" if scidata[z][y][x] > threshold and maskdata[z][y][x]: \n",
" mom2[y][x] += (velo[z] - mom1[y][x]) * (velo[z] - mom1[y][x]) * scidata[z][y][x]\n",
" if mom0[y][x] > 0.0: mom2[y][x] = sqrt(mom2[y][x] / mom0[y][x])\n",
"\n",
" # Lastly, multiply moment-0 map with dv\n",
" for x in range(nx):\n",
" for y in range(ny):\n",
" mom0[y][x] *= dv\n",
"\n",
" wcsheadcel = wcshead.celestial # only required if one wants to display celestial coordinates\n",
" fig = plt.figure() # Note: WCS in matplotlib only works if WCSAxes is installed!\n",
" fig.add_subplot(221, projection=wcsheadcel) # Activate these lines if you have WCSAxes on your system.\n",
" \n",
" # Plot all maps\n",
" plt.imshow(peak, origin=\"lower\", cmap=\"jet\", interpolation=\"nearest\", vmin=minmaxpeak[0], vmax=minmaxpeak[1])\n",
" plt.colorbar()\n",
" plt.xlabel(\"Right ascension (J2000)\")\n",
" plt.ylabel(\"Declination (J2000)\")\n",
" plt.text(1, 1, \"S_max (Jy)\", color=\"white\")\n",
" fig.add_subplot(222, projection=wcsheadcel) # Activate these lines if you have WCSAxes on your system.\n",
" plt.imshow(mom0, origin=\"lower\", cmap=\"jet\", interpolation=\"nearest\", vmin=minmaxmom0[0], vmax=minmaxmom0[1])\n",
" plt.colorbar()\n",
" plt.xlabel(\"Right ascension (J2000)\")\n",
" plt.ylabel(\"Declination (J2000)\")\n",
" plt.text(1, 1, \"Mom 0 (Jy km/s)\", color=\"white\")\n",
" \n",
" fig.add_subplot(223, projection=wcsheadcel) # Activate these lines if you have WCSAxes on your system.\n",
" plt.imshow(mom1, origin=\"lower\", cmap=\"jet\", interpolation=\"nearest\", vmin=minmaxmom1[0], vmax=minmaxmom1[1])\n",
" plt.colorbar()\n",
" plt.xlabel(\"Right ascension (J2000)\")\n",
" plt.ylabel(\"Declination (J2000)\")\n",
" plt.text(1, 1, \"Mom 1 (km/s)\", color=\"white\")\n",
" fig.add_subplot(224, projection=wcsheadcel) # Activate these lines if you have WCSAxes on your system.\n",
" plt.imshow(mom2, origin=\"lower\", cmap=\"jet\", interpolation=\"nearest\", vmin=minmaxmom2[0], vmax=minmaxmom2[1])\n",
" plt.colorbar()\n",
" plt.xlabel(\"Right ascension (J2000)\")\n",
" plt.ylabel(\"Declination (J2000)\")\n",
" plt.text(1, 1, \"Mom 2 (km/s)\", color=\"white\")\n",
" plt.show()\n",
"\n",
" \n",
"genmomaps(incubus = 'figures/data_cube.fits', maskcube = '', threshold = 0.025, \n",
" minmaxpeak = [0., 0.25], minmaxmom0 = [0.,40.], minmaxmom1 = [1100., 1300.], minmaxmom2 = [0., 100.])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Simple source masks: Miriad example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The importance of source masks has been pointed out above. Here, we include a minimalistic example of how to derive an image mask using the Miriad software suite. We read in a fits file containing an observation of UGCA 105, a gas-rich dwarf galaxy. Subsequently, we convolve the image with a Gaussian to derive the image mask from the resulting data cube by clipping, using the task maths. \n",
"\n",
"The reason for this filtering is that we know that most emission in the cube is of extended nature, well extending the original beam size of $40^{\\prime\\prime}$. By convolving the data cube with a Gaussian of $60^{\\prime\\prime}$ we hence enhance the signal-to-noise ratio. \n",
"\n",
"This becomes clearer in a thought experiment. \n",
"\n",
"Imagine convolving a data cube with statistically independent pixels and uniform rms noise not with a Gaussian but with a (not normalised) box function, the box containing n pixels. Then, as the result is simply a sum over n pixels per resulting pixel, the rms is proportional to the square root of the number of pixels. Let us assume that the source is as extended as possible, i.e. the intensity is constant across the field. Then, the signal increases by the number of pixels and hence the signal-to-noise ratio decreases by the square root of the number of pixels. If the source is the opposite of extended, i.e. a single pixel, the sum stays at the value of the single pixel and the signal-to-noise ratio decreases by the square root of the number of pixels contained in the boxcar. In reality the maths is a bit more complicated but the result stays basically.\n",
"\n",
"Filtering (convolving) a cube to enhance the signal-to-noise ratio to find sources is a valid technique. However, the optimal kernel depends on the source structure and it might vary over an image. Whether source finding by clipping (creating a mask is the same as source finding) should be done at high or low resolution can be determined a-priori only by knowing more about the source structure. We know the optical extent of UGCA 105, we know that it is gas-rich, and we know that the gas in gas-rich galaxies entends over the optical disk. We hence infer that we best filter at low resolution in the given case.\n",
"\n",
"Handling interferometric radio data, we encounter an additional trap: filtering in the image domain is equivalent to tapering in the uv-domain. In other words, visibilities get downweighted by filtering (the larger the filter the more), and the result is that the rms increases overproportionally.\n",
"\n",
"Modern source finders have to take into account all these facts and if they are based on a filtering and clip algorithm, what is mostly done is to apply multiple filters by default. In a second step, source candidates are often de-selected employing statistical means or a second prior knowledge.\n",
"\n",
"In our case we make use of the Miriad task mafia, with which we can count pixels on \"islands\". If an island contains less than a certain number of pixels, we de-select the island. This again relies on the fact that we expect contiguous, large emitting fields to be observed in this nearby galaxy. Finally, we assume that detections have a certain line width, and we enforce an island to be real only if it overlaps with an island in a neighbouring channel."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# Executing MIRIAD commands\n",
"fits in=figures/UGCA105cube.fits out=bla_incubus op=xyin\n",
"convol map=bla_incubus out=bla_convolved fwhm=40\n",
"maths exp=\"<bla_incubus>\" out=bla_clipped mask=<bla_convolved>.gt.0.00175\n",
"mafia in=bla_clipped options=mask pixels=75\n",
"mafia in=bla_clipped options=3dim,blink,mask\n",
"immask in=bla_clipped flag=true logic=and region=images(8,55)\n",
"maths exp=<bla_clipped>*0+1 out=bla_binary_masked\n",
"imblr in=bla_binary_masked out=bla_binary\n",
"fits in=bla_binary out=figures/UGCA105_mask.fits op=xyout\n",
"# Done\n"
]
}
],
"source": [
"import os\n",
"def getmaskmir(incubus = '', outcube = '', fwhm = '40', clip = '0.00175', minpix = '75', images = '1,100'):\n",
" \"\"\"\n",
" Create a mask using miriad tasks\n",
" \n",
" Parameters:\n",
" incubus (string): Input data cube to be masked\n",
" outcube (string): Output data cube to be masked\n",
" fwhm (string) : Full-width-at-half-maximum of Gaussian to convolve original cube with\n",
" clip (string) : Clip level to create mask\n",
" minpix (string) : Minimum number of pixels in islands\n",
" images (string) : Planes to include in mask (all others flagged)\n",
" \"\"\" \n",
"\n",
" print '# Executing MIRIAD commands'\n",
"\n",
" if incubus == '':\n",
" raise Exception('Ey, give me an input cube')\n",
" if outcube == '':\n",
" raise Exception('Ey, give me an output cube')\n",
" \n",
" # Delete file (directory) if it exists\n",
" if os.path.exists('bla_incubus'): shutil.rmtree('bla_incubus')\n",
"\n",
" # Read in fits file \n",
" run_fits = Run('fits in='+incubus+' out=bla_incubus op=xyin')\n",
"\n",
" # Convolve\n",
" if os.path.exists('bla_convolved'): shutil.rmtree('bla_convolved')\n",
" run_convol = Run('convol map=bla_incubus out=bla_convolved fwhm='+fwhm)\n",
"\n",
" # Clip\n",
" if os.path.exists('bla_clipped'): shutil.rmtree('bla_clipped')\n",
" run_maths = Run('maths exp=\"<bla_incubus>\" out=bla_clipped mask=<bla_convolved>.gt.'+clip)\n",
"\n",
" # Exclude islands smaller than n pixels\n",
" run_mafia = Run('mafia in=bla_clipped options=mask pixels=' + minpix)\n",
"\n",
" # Exclude islands isolated in v\n",
" run_mafia = Run('mafia in=bla_clipped options=3dim,blink,mask')\n",
"\n",
" # Exclude Milky Way\n",
" run_immask = Run('immask in=bla_clipped flag=true logic=and region=images('+images+')')\n",
"\n",
" # Create binary mask \n",
" if os.path.exists('bla_binary_masked'): shutil.rmtree('bla_binary_masked')\n",
" run_maths = Run('maths exp=<bla_clipped>*0+1 out=bla_binary_masked')\n",
"\n",
" # Replace masked pixels with 0s\n",
" if os.path.exists('bla_binary'): shutil.rmtree('bla_binary') \n",
" run_imblr = Run('imblr in=bla_binary_masked out=bla_binary')\n",
" \n",
" # Write fits cube\n",
" if os.path.exists(outcube): os.remove(outcube)\n",
" run_fits = Run('fits in=bla_binary out='+outcube+' op=xyout')\n",
"\n",
" # Clean up\n",
" shutil.rmtree('bla_incubus')\n",
" shutil.rmtree('bla_convolved')\n",
" shutil.rmtree('bla_clipped') \n",
" shutil.rmtree('bla_binary_masked') \n",
" shutil.rmtree('bla_binary')\n",
" \n",
" print '# Done'\n",
"\n",
"getmaskmir(incubus = 'figures/UGCA105cube.fits', outcube = 'figures/UGCA105_mask.fits', images = '8,55')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is clear, however, that using a software package as above a lot of overhead is created by input and output to the mass storage device. In an automated pipeline dealing with larger amounts of data, it is a requirement to create a mask kept in memory. This is, however, a discussion to be continued at a different place."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us look how the moment maps turn out if we use the mask instead of a threshold:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from astropy import wcs\n",
"from astropy.io import fits\n",
"import math\n",
"import os\n",
"\n",
"def genmomaps_tofits(incubus = '', maskcube = '', peakmapname = '', totinsname = '', mom1name = '', \n",
" mom2name = '', threshold = 0.025, minmaxpeak = [0., 0.1], minmaxmom0 = [0.,1.], \n",
" minmaxmom1 = [0., 200.], minmaxmom2 = [0., 50.]):\n",
" \"\"\"\n",
" Generate moment maps and write into fits files\n",
" \n",
" incubus (string) : Input data cube\n",
" maskcube (string) : Mask data cube \n",
" peakmapname (string): Name of output peak intensity map\n",
" totinsname (string) : Name of total-intensity map\n",
" mom1name (string) : Name of moment-1 map\n",
" mom2name (string) : Name of moment-2 map\n",
" threshold (float) : Additional threshold, below which pixels are flagged\n",
"\n",
" \"\"\"\n",
" # Check if mask exists and read in mask\n",
" if maskcube != '':\n",
" hdulist = fits.open(maskcube)\n",
" maskdata = hdulist[0].data \n",
" \n",
" # Open example FITS cube and extract WCS header keywords and data\n",
" hdulist = fits.open(incubus)\n",
" wcshead = wcs.WCS(hdulist[0].header)\n",
" scidata = hdulist[0].data\n",
" \n",
" # Read cube dimensions and create arrays for maps\n",
" nz, ny, nx = scidata.shape\n",
" \n",
" if maskcube == '':\n",
" maskdata = np.ones((nz,ny,nx), dtype=float)\n",
" \n",
" for x in range(nx):\n",
" for y in range(ny):\n",
" for z in range(nz):\n",
" if scidata[z][y][x] < threshold:\n",
" maskdata[z][y][x] = 0.\n",
"\n",
" mom0 = [[0 for x in range(nx)] for y in range(ny)]\n",
" mom1 = [[0 for x in range(nx)] for y in range(ny)]\n",
" mom2 = [[0 for x in range(nx)] for y in range(ny)]\n",
" peak = [[0 for x in range(nx)] for y in range(ny)]\n",
"\n",
" # Convert channel numbers to velocity using WCS\n",
" velo = [0 for z in range(nz)]\n",
" for z in range(nz):\n",
" equ_coords = wcshead.wcs_pix2world(0, 0, z, 0)\n",
" velo[z] = equ_coords[2] / 1000.0\n",
" dv = abs(velo[1] - velo[0])\n",
"\n",
" # Create moment-0 and peak flux density maps\n",
" if peakmapname != '':\n",
" for x in range(nx):\n",
" for y in range(ny):\n",
" mom0[y][x] = 0.0;\n",
" peak[y][x] = -10000000.0;\n",
" for z in range(nz):\n",
" if scidata[z][y][x] > peak[y][x] and maskdata[z][y][x]: peak[y][x] = scidata[z][y][x]\n",
" if scidata[z][y][x] > threshold and maskdata[z][y][x]: mom0[y][x] += scidata[z][y][x]\n",
" if peak[y][x] == -10000000.0:\n",
" peak[y][x] = float('nan')\n",
"\n",
" # Create moment-1 map\n",
" for x in range(nx):\n",
" for y in range(ny):\n",
" mom1[y][x] = 0.;\n",
" for z in range(nz):\n",
" if scidata[z][y][x] > threshold and maskdata[z][y][x]: mom1[y][x] += velo[z] * scidata[z][y][x]\n",
" if math.isnan(peak[y][x]): mom1[y][x] = peak[y][x]\n",
" if mom0[y][x] > 0.0: mom1[y][x] /= mom0[y][x]\n",
"\n",
" # Create moment-2 map\n",
" for x in range(nx):\n",
" for y in range(ny):\n",
" mom2[y][x] = 0.0;\n",
" for z in range(nz):\n",
" if scidata[z][y][x] > threshold and maskdata[z][y][x]: mom2[y][x] += (velo[z] - mom1[y][x]) * (velo[z] - mom1[y][x]) * scidata[z][y][x]\n",
" if mom0[y][x] > 0.0: mom2[y][x] = math.sqrt(mom2[y][x] / mom0[y][x])\n",
" if math.isnan(peak[y][x]): mom2[y][x] = peak[y][x]\n",
"\n",
" # Lastly, multiply moment-0 map with dv\n",
" for x in range(nx):\n",
" for y in range(ny):\n",
" mom0[y][x] *= dv\n",
" \n",
" # Get header and make a twodimensional data set with same coordinate frame\n",
" hdulist[0].header.pop('NAXIS3', 0)\n",
" hdulist[0].header.pop('NAXIS4', 0)\n",
" hdulist[0].header.pop('CTYPE3', 0)\n",
" hdulist[0].header.pop('CTYPE4', 0)\n",
" hdulist[0].header.pop('CRPIX3', 0)\n",
" hdulist[0].header.pop('CRPIX4', 0)\n",
" hdulist[0].header.pop('CRVAL3', 0)\n",
" hdulist[0].header.pop('CRVAL4', 0)\n",
" hdulist[0].header.pop('CDELT3', 0)\n",
" hdulist[0].header.pop('CDELT4', 0)\n",
" hdulist[0].header.pop('DATAMIN', 0)\n",
" hdulist[0].header.pop('DATAMAX', 0)\n",
" hdulist[0].header.set('NAXIS', 2)\n",
" \n",
" # Write peak intensity map\n",
" if peakmapname != '':\n",
" hdulist[0].header.set('BUNIT', 'JY/BEAM')\n",
" hdulist[0].data = peak\n",
" if os.path.exists(peakmapname): os.remove(peakmapname)\n",
" hdulist.writeto(peakmapname)\n",
"\n",
" # Write total intensity map\n",
" if totinsname != '':\n",
" hdulist[0].header.set('BUNIT', 'JY.KM/S')\n",
" hdulist[0].data = mom0\n",
" if os.path.exists(totinsname): os.remove(totinsname)\n",
" hdulist.writeto(totinsname)\n",
"\n",
" # Write velocity field\n",
" if mom1name != '':\n",
" hdulist[0].header.set('BUNIT', 'KM/S')\n",
" hdulist[0].data = mom1\n",
" if os.path.exists(mom1name): os.remove(mom1name)\n",
" hdulist.writeto(mom1name)\n",
"\n",
" # Write total intensity map\n",
" if mom2name != '':\n",
" hdulist[0].header.set('BUNIT', 'KM/S')\n",
" hdulist[0].data = mom2\n",
" if os.path.exists(mom2name): os.remove(mom2name)\n",
" hdulist.writeto(mom2name)\n",
"\n",
"genmomaps_tofits(incubus = 'figures/UGCA105cube.fits', maskcube = 'figures/UGCA105_mask.fits', peakmapname = 'figures/UGCA105_peak.fits', \n",
" totinsname = 'figures/UGCA105_m0.fits', \n",
" mom1name = 'figures/UGCA105_m1.fits', mom2name = 'figures/UGCA105_m2.fits', \n",
" threshold = -1000.)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import aplpy\n",
"import matplotlib.pyplot as plt\n",
"from astropy import wcs\n",
"from astropy.io import fits\n",
"\n",
"def plotmomaps_aplpy(peakmapname = '', totinsname = '', mom1name = '', mom2name ='',\n",
" minmaxpeak = [0., 0.1], minmaxtotins = [0.,1.], \n",
" minmaxmom1 = [0., 200.], minmaxmom2 = [0., 50.], peakmaplevs = 5, \n",
" totinslevs = 5, mom1levs = 5, mom2levs = 5):\n",
" \"\"\"\n",
" Use aplpy to plot several maps to inspect data\n",
" \n",
" The maps are in principle arbitrary but given names here to indicate the purpose of this script.\n",
" \n",
" peakmapname (string) : Name of peak flux density map\n",
" totinsname (string) : Name of total intensity map\n",
" mom1name (string) : Name of velocity field\n",
" mom2name (string) : Name of moment-2 map\n",
" minmaxpeak (list) : Minum and maximum for colour scale in peak flux map\n",
" minmaxmom0 (list) : Minimum and maximum for colour scale in Moment-0 map\n",
" minmaxmom1 (list) : Minimum and maximum for colour scale in Moment-1 map\n",
" minmaxmom2 (list) : Minimum and maximum for colour scale in Moment-2 map\n",
" peakmaplevs (int or list): Either explicit contour levels or number of contour levels for peak flux density map\n",
" totinslevs (int or list) : Either explicit contour levels or number of contour levels for total intensity map\n",
" mom1levs (int or list) : Either explicit contour levels or number of contour levels for velocity field\n",
" mom2levs (int or list) : Either explicit contour levels or number of contour levels for moment-2 map\n",
" \"\"\"\n",
" \n",
" fig = plt.figure(figsize=(10,12))\n",
"\n",
" border = 0.09\n",
" dx = (1.0-3*border)/float(2)\n",
" dy = (1.0-3*border)/float(2)\n",
"\n",
" \n",
" names = [peakmapname, totinsname, mom1name, mom2name]\n",
" vmins = [minmaxpeak[0], minmaxtotins[0], minmaxmom1[0], minmaxmom2[0]]\n",
" vmaxs = [minmaxpeak[1], minmaxtotins[1], minmaxmom1[1], minmaxmom2[1]]\n",
" levs = [peakmaplevs, totinslevs, mom1levs, mom2levs]\n",
" \n",
" appfigs = []\n",
" for j in range(2):\n",
" for i in range(2):\n",
" appfig = aplpy.FITSFigure(names[j*2+i], figure = fig, subplot=[(i+1)*border+i*dx,1.0-(j+1)*(border+dy),dx,dy])\n",
" if i > 0:\n",
" appfig.hide_yaxis_label()\n",
" appfig.hide_ytick_labels()\n",
" if j < 1:\n",
" appfig.hide_xaxis_label()\n",
" appfig.hide_xtick_labels()\n",
" appfig.show_colorscale(vmin = vmins[j*2+i], vmax = vmaxs[j*2+i])\n",
" appfig.add_colorbar()\n",
" appfig.show_contour(colors = 'black', levels = levs[2*j+i])\n",
"\n",
" appfig.axis_labels.set_font(size='small')\n",
" appfig.axis_labels.set_xpad(0.03)\n",
" appfig.axis_labels.set_ypad(0.03)\n",
" appfig.tick_labels.set_font(size='small')\n",
" appfig.ticks.set_color('black')\n",
" appfig.colorbar.set_font(size='small')\n",
" appfig.colorbar.set_width(0.075)\n",
" appfig.colorbar.set_pad(0.03)\n",
" appfigs.append(appfig)\n",
"\n",
" plt.show()\n",
"# plt.canvas.draw()\n",
"\n",
"plotmomaps_aplpy(peakmapname = 'figures/UGCA105_peak.fits', totinsname = 'figures/UGCA105_m0.fits', \n",
" mom1name = 'figures/UGCA105_m1.fits', mom2name = 'figures/UGCA105_m2.fits',\n",
" minmaxpeak = [-0.02, 0.1], minmaxtotins = [-0.75, 3.], minmaxmom1 = [0., 200.], \n",
" minmaxmom2 = [0., 40.], peakmaplevs = 5, totinslevs = [0.1, 0.2, 0.4, 0.8, 1.6, 3.2], \n",
" mom1levs = list(np.arange(40.,180.,10.)), mom2levs = list(np.arange(10.,30.,2.)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Slices through the data cube"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A data cube is a threedimensional structure. Some programs can display a data cube as a threedimensional structure, and this can be helpful to interpret the data at hand."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from wand.image import Image as WImage\n",
"img = WImage(filename='figures/UGCA105cube.fits.eps')\n",
"img"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div align=\"center\">Visualisation of a data cube of an HI observation of the galaxy UGCA 105. The yellow axis denotes declination, blue axis right ascension, red axis velocity.\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Data cube images"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To avoid the complication of interpreting 3-D data all in one go, however, one can resort to looking at the intersection of planes in a data cube. The simplest realisation is to inspect the planes of a data cube itself."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import aplpy\n",
"import matplotlib.pyplot as plt\n",
"from astropy import wcs\n",
"from astropy.io import fits\n",
"\n",
"def plotcube(incubus = '', border = 0.15, nx = 3, ny = 4, level = 0.001, factor = 4, contours = 5, \n",
" ming = -0.01, mang = 0.005):\n",
" \"\"\"\n",
" \n",
" Plot a cube\n",
" \n",
" border (float): border to bottom and left in relative coordinates (1: entire width)\n",
" nx (int): number of images in x-direction\n",
" ny (int): number of images in y-direction\n",
" level (float): lowest contour level, will draw -level, level, level*factor, level*factor*factor, ...\n",
" factor (float): see above\n",
" contours: (int): number of contour levels\n",
" ming (float): minimum for greyscale map\n",
" mang (float): maximum for greyscale map\n",
" \"\"\"\n",
" \n",
" # Sizes of panels in x and y\n",
" dx = (1.0-border)/float(nx)\n",
" dy = (1.0-border)/float(ny)\n",
"\n",
" # Just read the number of channels (maybe better to use the header)\n",
" channels = fits.open(incubus)[0].data.shape[0]\n",
"\n",
" channelsep = channels/(nx*ny) # channel increment for plotting\n",
"\n",
" contlevels = [-level]\n",
" \n",
" for i in range(contours-1):\n",
" contlevels.append(level*pow(factor,i))\n",
" \n",
" fig = plt.figure(figsize=(10,12))\n",
" appfigs = []\n",
"\n",
" for j in range(ny):\n",
" for i in range(nx):\n",
" appfig = aplpy.FITSFigure(incubus, figure = fig, slices=[((i+1)+j*nx)*channelsep], \n",
" subplot=[border+i*dx,1.0-(j+1)*dy,dx,dy])\n",
" if i > 0:\n",
" appfig.hide_yaxis_label()\n",
" appfig.hide_ytick_labels()\n",
" if j < ny - 1:\n",
" appfig.hide_xaxis_label()\n",
" appfig.hide_xtick_labels()\n",
" appfig.axis_labels.set_font(size='x-small')\n",
" appfig.tick_labels.set_font(size='x-small')\n",
" appfigs.append(appfig)\n",
"\n",
" for i in range(len(appfigs)):\n",
" appfigs[i].show_grayscale(vmin=ming, vmax=mang)\n",
" appfigs[i].show_contour(colors = 'black', levels = contlevels)\n",
" plt.show()\n",
" \n",
"plotcube(incubus = 'figures/UGCA105cube.fits')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### PV-diagrams"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The other, often used to reduce the data amount that the eye has to digest, is the position-velocity diagram (PV-diagram). Along a line on the sky spectra are drawn in equidistant spaces, such that one axis of the PV-diagram is a spatial distance, the distance along the line, with some origin, the other is frequency or velocity. Hence, a PV-diagram is simply another slice through a data cube, geometrically equivalent to single image planes. A PV-diagram is meaningful in combination with the line(s) plotted on some image of the source.\n",
"\n",
"In the following example, four PV diagrams are extracted from a data cube of the galaxy UGCA 105. The PV diagrams are extracted along four axes, which are also shown, overlaid on the Moment-0 map shown above."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"import astropy\n",
"import pvextractor\n",
"import matplotlib\n",
"import aplpy\n",
"import numpy as np\n",
"\n",
"def pvhelper(w, hdu, centra, centdec, pa, majlen, minlen, mindist):\n",
" \"\"\"\n",
" Helper shared between pvdiagrams and plotvlines\n",
" \"\"\"\n",
" \n",
" # find a list of endpoints\n",
" \n",
" # Find spatial pixels corresponding to position\n",
" central_pix_x, central_pix_y = w.sub([wcs.WCSSUB_CELESTIAL]).wcs_world2pix(centra,centdec, 0)\n",
" \n",
" # this is how to convert arcsec to pixel\n",
" scale_xy = hdu.header['cdelt2']*3600.\n",
"\n",
" majlen = majlen/scale_xy\n",
" minlen = minlen/scale_xy\n",
" mindist = mindist/scale_xy\n",
" \n",
" # Make a grid with origin 0, for the single cuts, in x, start cut 1, end cut 1, start cut 2, end cut 2, ...\n",
" endpoints_y_b = np.array([majlen/2., -majlen/2., mindist , mindist , 0., 0.,\n",
" -mindist, -mindist]) \n",
" # Make a grid with origin 0, for the single cuts, in y, start cut 1, end cut 1, start cut 2, end cut 2, ...\n",
" endpoints_x_b = np.array([ 0., 0., -minlen/2., minlen/2., -minlen/2., minlen/2., \n",
" -minlen/2., minlen/2.])\n",
"\n",
" # Rotate all points\n",
" endpoints_x = endpoints_x_b*np.cos(np.pi*pa/180.)-endpoints_y_b*np.sin(np.pi*pa/180.)+central_pix_x\n",
" endpoints_y = endpoints_x_b*np.sin(np.pi*pa/180.)+endpoints_y_b*np.cos(np.pi*pa/180.)+central_pix_y\n",
" \n",
" return endpoints_x, endpoints_y\n",
"\n",
"def createpvdiagrams(incubus = '', outnameprefix = 'bla', centra = '30.', \n",
" centdec = '60.', pa = 45., majlen = 100., minlen = 10., mindist = 50.):\n",
" \"\"\"\n",
" Produce foue PV-diagrams from incubus\n",
" \n",
" incubus (string) : Input data cube\n",
" outnameprefix (string): Prefix for 4 output PV diagrams (outnameprefix+suffix), see below\n",
" centra (float) : Right ascension of centre (degrees)\n",
" centdec (float) : Declination of centre (degrees)\n",
" pa (float) : Position angle (degrees)\n",
" majlen (float) : Length of major axis cut (arcsec)\n",
" minlen (float) : Length of minor axis cut (arcsec)\n",
" mindist (float) : Distance between centre and outer minor axis cuts (arcsec)\n",
" \n",
" Will produce four pv diagrams, one along the \"major\" axis, defined by the central coordinates \n",
" centra (deg), centdec (deg), position angle (pa in degrees), and length majlen in arcsec, three \n",
" perpendicular with respect to the major axis, one through the centre, two (at each side) at a \n",
" distance of mindist (in arcsec) from the centre, of the length minlen. Output PV diagrams will \n",
" be called outnameprefix+'_pvmaj.fits', outnameprefix+'_pvmin_cent.fits', outnameprefix+'_pvmin_left.fits', \n",
" outnameprefix+'_pvmin_right.fits'. Plotoutname is a plot of file overplotname together with the \n",
" slice positions. Position angle should be defined as the receding side major axis.\n",
" \"\"\"\n",
"\n",
" fitsobj = astropy.io.fits.open(incubus)\n",
" hdu = fitsobj[0]\n",
" w = astropy.wcs.WCS(hdu.header, fitsobj)\n",
" \n",
" # find a list of endpoints\n",
" endpoints_x, endpoints_y = pvhelper(w, hdu, centra, centdec, pa, majlen, minlen, mindist)\n",
" \n",
" i = -1\n",
" for name in ['_pvmaj.fits', '_pvmin_left.fits', '_pvmin_cent.fits', '_pvmin_right.fits']:\n",
" i = i + 1\n",
" endpoints = [(endpoints_x[2*i],endpoints_y[2*i]),(endpoints_x[2*i+1],endpoints_y[2*i+1])]\n",
"\n",
" # This generates a path from which the pv diagram will be drawn, see pvextractor docu\n",
" xy = pvextractor.geometry.Path(endpoints)\n",
" # This generates a pyfits object from the input cube which is a pv diagram, see pvextractor docu\n",
" pv = pvextractor.extract_pv_slice(hdu, xy)\n",
"\n",
" # Change pixel to central pixel\n",
" pixels = pv.header['NAXIS1']\n",
" pv.header['CRPIX1'] = pixels/2\n",
" # \n",
" pv.header['CDELT1'] = pv.header['CDELT1']*3600.\n",
" pv.header['CRVAL1'] = pv.header['CRVAL1']*3600.\n",
" pv.header['CUNIT1'] = 'ARCSEC'\n",
"# pv.header['CTYPE1'] = 'POSITION'\n",
" \n",
" # And make y axis a unit with three digits less\n",
" pv.header['CDELT2'] = pv.header['CDELT2']/1000.\n",
" pv.header['CRVAL2'] = pv.header['CRVAL2']/1000.\n",
" pv.header['CUNIT2'] = 'KM/S'\n",
" # This is just a trick to make aplpy not recognise the coordinate type, otherwise it will enforce m/s as a unit\n",
" pv.header['CTYPE2'] = 'VELOCITY'\n",
" \n",
" # Write out file, delete if it already exists (that's the clobber option switched on)\n",
" pv.writeto(outnameprefix+name, clobber = True)\n",
" return\n",
"\n",
"def plotpvlines(aplpyobj = False, pyfitsobj = False, centra = '30.', centdec = '60.', pa = 45., majlen = 100., minlen = 10., \n",
" mindist = 50., colour = 'red', plotoutname = ''):\n",
" \"\"\"\n",
" Overplot lines corresponding to pv diagrams on given aplpy figure\n",
" aplpyobj : Open aplpy FITSFigure \n",
" pyfitsobj : pyfits \n",
" centra (float) : Right ascension of centre (degrees)\n",
" centdec (float) : Declination of centre (degrees)\n",
" pa (float) : Position angle (degrees)\n",
" majlen (float) : Length of major axis cut (arcsec)\n",
" minlen (float) : Length of minor axis cut (arcsec)\n",
" mindist (float) : Distance between centre and outer minor axis cuts (arcsec)\n",
" color (string) : Color of lines\n",
" plotoutname (string): Name of output file (dummy at the moment)\n",
" \n",
" centra = '30.', centdec = '60.', pa = 45., majlen = 100., minlen = 10., \n",
" mindist = 50., color = 'red', plotoutname = ''\n",
" \n",
" \"\"\"\n",
" hdu = pyfitsobj[0]\n",
" w = astropy.wcs.WCS(hdu.header, pyfitsobj)\n",
" \n",
" endpoints_x, endpoints_y = pvhelper(w, hdu, centra, centdec, pa, majlen, minlen, mindist)\n",
" \n",
" i = -1\n",
" for name in ['_pvmaj.fits', '_pvmin_left.fits', '_pvmin_cent.fits', '_pvmin_right.fits']:\n",
" i = i + 1\n",
" endpoints = [(endpoints_x[2*i],endpoints_y[2*i]),(endpoints_x[2*i+1],endpoints_y[2*i+1])]\n",
" \n",
" # Calculate coordinates of end points again\n",
" endpoints_wcs = w.sub([wcs.WCSSUB_CELESTIAL]).wcs_pix2world([[x,y] for x,y in endpoints], 0)\n",
" aplpyobj.show_lines([endpoints_wcs.T],color=colour)\n",
" \n",
" # aplpyobj.save(plotoutname)\n",
" return\n",
"\n",
"def plotpvdiagrams(greyscaleprefix = '', color1contourprefix = '', colour1 = 'red', color2contourprefix = '', \n",
" colour2 = 'blue', contours = 5, vminmax = [0., 0.01]):\n",
" \"\"\"\n",
" Plotting four PVdiagrams as generated by createpvdiagrams\n",
" \n",
" greyscaleprefix (string) : Prefix to the names of the datasets from which greyscale images will be generated\n",
" color1contourprefix (string): Prefix to the names of the datasets from which color1 contours will be generated (ignored if empty)\n",
" color1 (string) : Color 1\n",
" color2contourprefix (string): Prefix to the names of the datasets from which color2 contours will be generated (ignored if empty)\n",
" color2 (string) : Color 2\n",
" contours (int or list) : Contour levels if given as list, number of contours if given as list.\n",
" vminmax (list) : Minimum and maximum for greyscale wedge\n",
" \n",
" createpvdiagrams will generate four PV diagrams, outnameprefix+'_pvmaj.fits', outnameprefix+'_pvmin_cent.fits', \n",
" outnameprefix+'_pvmin_left.fits', outnameprefix+'_pvmin_right.fits'. It is assumed that the input prefixes in \n",
" this funtion are identical to some input in createpvdiagrams, i.e. that the following 12 data sets exist (unless \n",
" the input is an empty string): greyscaleprefix+'_pvmaj.fits', greyscaleprefix+'_pvmin_cent.fits', \n",
" greyscaleprefix+'_pvmin_left.fits', greyscaleprefix+'_pvmin_right.fits', color1contourprefix+'_pvmaj.fits', ..., \n",
" color2contourprefix+_pvmin_right.fits. For each PV cut (major, min_left, min_cent, min_right), a greyscale plot will\n",
" be generated overlaid with contours.\n",
" \"\"\"\n",
" \n",
" nx = 2\n",
" ny = 2\n",
" border = 0.15\n",
" \n",
" # Sizes of panels in x and y\n",
" dx = (1.0-border)/float(nx)\n",
" dy = (1.0-border)/float(ny)\n",
" \n",
" fig = plt.figure(figsize=(10,12))\n",
" greyscales = []\n",
" for name in ['_pvmaj.fits', '_pvmin_left.fits', '_pvmin_cent.fits', '_pvmin_right.fits']:\n",
" greyscales.append(greyscaleprefix+name)\n",
" color1conts= []\n",
" if color1contourprefix != '':\n",
" for name in ['_pvmaj.fits', '_pvmin_left.fits', '_pvmin_cent.fits', '_pvmin_right.fits']:\n",
" color1conts.append(color1contourprefix+name)\n",
" color2conts= []\n",
" if color2contourprefix != '':\n",
" for name in ['_pvmaj.fits', '_pvmin_left.fits', '_pvmin_cent.fits', '_pvmin_right.fits']:\n",
" color2conts.append(color2contourprefix+name)\n",
"\n",
" appfigs = []\n",
" for j in range(ny):\n",
" for i in range(nx):\n",
" appfig = aplpy.FITSFigure(greyscales[j*nx+i], figure = fig, subplot=[border+i*dx,1.0-(j+1)*dy,dx,dy])\n",
" appfig.show_grayscale(vmin = vminmax[0], vmax = vminmax[1], aspect = 'auto')\n",
" if color1conts != []:\n",
" appfig.show_contour(data = color1conts[j*nx+i], levels = contours, colors = colour1)\n",
" if color2conts != []:\n",
" appfig.show_contour(data = color2conts[j*nx+i], levels = contours, colors = colour2)\n",
" if i > 0:\n",
" appfig.hide_yaxis_label()\n",
" appfig.hide_ytick_labels()\n",
" if j < ny - 1:\n",
" appfig.hide_xaxis_label()\n",
" appfig.hide_xtick_labels()\n",
" appfig.axis_labels.set_font(size='x-small')\n",
" appfig.tick_labels.set_font(size='x-small')\n",
" appfigs.append(appfig)\n",
"\n",
" plt.show()\n",
" for i in appfigs:\n",
" i.close()\n",
" \n",
"if __name__ == \"__main__\":\n",
"\n",
" # This is the stuff we want to use to extract PV-diagrams\n",
" cube = 'figures/UGCA105cube.fits'\n",
" \n",
" # This has been generated before, it is used to plot cuts on\n",
" mom0 = 'figures/UGCA105_m0.fits'\n",
" \n",
" # Centre of cut\n",
" centre_ra= 78.5620851964\n",
" centre_dec= 62.5800017496\n",
"\n",
" # Position angle, anticlockwise from North (receding side)\n",
" posa = 203.\n",
" \n",
" # Length of major axis cut\n",
" len_maj = 1500.\n",
" \n",
" # Length of minor axis cuts\n",
" len_min = 850.\n",
" dist_min = 250.\n",
" \n",
" # Prefix of output PV diagram names\n",
" outputname = 'figures/UGCA_105_pvorig'\n",
" \n",
" # Base contour level, number of contours and factor to multiply base contour level by\n",
" level = 0.001\n",
" contours = 6\n",
" factor = 3\n",
" \n",
" createpvdiagrams(incubus = cube, outnameprefix = outputname, centra = centre_ra, \n",
" centdec = centre_dec, pa = posa, majlen = len_maj, minlen = len_min, mindist = dist_min)\n",
" \n",
" fitsobj = astropy.io.fits.open(mom0)\n",
" \n",
" aplpobj = aplpy.FITSFigure(mom0)\n",
" aplpobj.show_grayscale(vmin=-0.1, vmax=7.5)\n",
" matplotlib.pyplot.show()\n",
"\n",
" plotpvlines(aplpyobj = aplpobj, pyfitsobj = fitsobj, centra = centre_ra, \n",
" centdec = centre_dec, pa = posa, majlen = len_maj, minlen = len_min, mindist = dist_min, colour = 'yellow')\n",
" \n",
" contlevels = [-level]\n",
" \n",
" for i in range(contours-1):\n",
" contlevels.append(level*pow(factor,i))\n",
" \n",
" plotpvdiagrams(greyscaleprefix = 'figures/UGCA_105_pvorig', color1contourprefix = '', \n",
" color2contourprefix = 'figures/UGCA_105_pvorig', colour2 = 'yellow', contours = contlevels, \n",
" vminmax = [-0.01,0.05])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Integrated spectra"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another data product frequently required, e.g. for the accurate determination of spectral line widths, is the integrated HI spectrum of a source, which can be extracted by summing over the spatial extent of the emission,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ S_{\\rm int}(z) = \\frac{1}{\\Omega_{\\rm PSF}} \\sum_{x} \\sum_{y} S(x, y, z) , $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where, as in the case of the [integrated flux](#Integrated-flux-and-HI-mass), we need to divide by the solid angle of the telescope’s point spread function (measured in pixel units rather than radians). Again, a detailed source mask will be required to obtain a useful spectrum, as otherwise we might either miss flux or unnecessarily increase the statistical noise in the resulting spectrum."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A Python example for creating the integrated spectrum of a source based on a source mask is included below. As before, the example simply loops through the entire data cube to generate the spectrum; this is inefficient for large data cubes in Python, and numerical libraries such as NumPy should instead be used for arithmetic operations on data arrays."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Creating an integrated spectrum from a data cube\n",
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt\n",
"from astropy import wcs\n",
"from astropy.io import fits\n",
"from math import pi, log\n",
"\n",
"# Select source to be analysed\n",
"source_id = 1 # Must be 1 or 2!\n",
"\n",
"# Open example FITS cube and extract WCS header keywords and data\n",
"hdulist = fits.open(\"figures/data_cube.fits\")\n",
"wcshead = wcs.WCS(hdulist[0].header)\n",
"scidata = hdulist[0].data\n",
"\n",
"# Read cube dimensions and create array for spectrum\n",
"nz, ny, nx = scidata.shape\n",
"spectrum = [0 for z in range(nz)]\n",
"\n",
"# Convert channel numbers to velocity using WCS\n",
"velo = [0 for z in range(nz)]\n",
"for z in range(nz):\n",
" equ_coords = wcshead.wcs_pix2world(0, 0, z, 0)\n",
" velo[z] = equ_coords[2] / 1000.0 # velocity in km/s\n",
"\n",
"# Read beam information from header\n",
"bmaj = float(hdulist[0].header[\"bmaj\"])\n",
"bmin = float(hdulist[0].header[\"bmin\"])\n",
"pix_size = float(hdulist[0].header[\"cdelt2\"])\n",
"omega = pi * bmaj * bmin / (4.0 * log(2.0) * pix_size * pix_size)\n",
"print(\"Beam size: %.1f x %.1f arcmin\" % (60.0 * bmaj, 60.0 * bmin))\n",
"print(\"Pixel size: %.1f arcmin\" % (60.0 * pix_size))\n",
"\n",
"# Open mask cube\n",
"hdulist2 = fits.open(\"figures/data_cube_mask.fits\")\n",
"mask = hdulist2[0].data\n",
"\n",
"# Create integrated spectrum\n",
"for z in range(nz):\n",
" spectrum[z] = 0.0\n",
" for y in range(ny):\n",
" for x in range(nx):\n",
" if mask[z][y][x] == source_id: spectrum[z] += scidata[z][y][x]\n",
" spectrum[z] /= omega # Correct for beam size\n",
" spectrum[z] *= 1000.0 # Convert from Jy to mJy\n",
"\n",
"# Plot integrated spectrum\n",
"plt.plot(velo, spectrum, drawstyle=\"steps-mid\")\n",
"plt.axhline(0.0, color=\"grey\", linestyle=\"dashed\")\n",
"plt.xlabel(\"Radial velocity (km/s)\")\n",
"plt.ylabel(\"Flux density (mJy)\")\n",
"plt.title(\"Spectrum of source %i\" % source_id)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model fitting to spectral line profiles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At a frequency of 1.4 GHz the angular resolution of classical radio telescopes is often relatively poor and of the order of 1 arcmin or above. Hence, more distant galaxies beyond the local volume are usually poorly resolved by HI observations, while the spectral resolution remains high enough to resolve the galaxy’s spectral profile. In such cases it may be desirable to not parameterise the galaxy in the full, three-dimensional data cube, but rather [generate an integrated spectrum](#Integrated-spectra) of the object and then determine the galaxy’s parameters from an analysis of the one-dimensional spectral profile."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Fitting a Gaussian function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One of the most basic functions commonly used to fit HI spectral profiles is the Gaussian function. In its most basic form it can be written as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ G(z) = A \\exp \\left( \\! -\\frac{\\left[ z - z_{0} \\right]^{2}}{2 \\, \\sigma^{2}} \\! \\right) $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $A$ is the amplitude, $z_{0}$ is the centroid, and $\\sigma$ is the width of the Gaussian. From the three free parameters of the fit we can immediately derive basic observational parameters of the fitted HI source. The velocity or frequency centroid is directly given by $z_{0}$, while the full width at half maximum (FWHM) of the spectral profile, $w_{50}$, is given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ w_{50} = 2 \\sqrt{2 \\ln(2)} \\, \\sigma \\approx 2.3548 \\, \\sigma . $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, the integrated flux, $S_{\\rm int}$, of the source can be derived from integrating over the fitted Gaussian:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ S_{\\rm int} = \\! \\int \\limits_{-\\infty}^{\\infty} \\! G(z) \\, \\mathrm{d}z = \\sqrt{2 \\pi} \\, A \\sigma \\approx 2.5066 \\, A \\sigma . $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Gaussian fitting is particularly useful for sources with simple, single-component line profiles, including dwarf galaxies, face-on galaxies and Galactic HI clouds (e.g. high-velocity clouds)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Fitting a Busy Function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Spiral galaxies seen at intermediate or high inclination usually possess a characteristic so-called “double-peak” or “double-horn” profile. This profile shape can be fitted and described by the so-called Busy Function, $B(z)$, where $z$ denotes the spectral coordinate, e.g. spectral channel, frequency or velocity ([<cite data-cite=\"2014MNRAS.438.1176W\">Westmeier et al. 2014</cite>](http://adsabs.harvard.edu/abs/2014MNRAS.438.1176W)). In its most general form, the Busy Function is the product of two error functions, $\\mathrm{erf}(z)$, describing the steep outer flanks of the spectrum and a polynomial to characterise the central trough:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ B(z) = \\frac{a}{4} \\times [\\mathrm{erf}(b_{1} (w + z - z_{\\rm e})) + 1] \\times [\\mathrm{erf}(b_{2} (w - z + z_{\\rm e})) + 1] \\times \\left[ c \\, \\left| z - z_{\\rm p} \\right|^{\\,n} + 1 \\right] . $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The eight free parameters of the Busy Function are:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* $a$ – Amplitude of the function.\n",
"* $b_{1}$, $b_{2}$ – These two parameters control the steepness of spectrum’s outer flanks. A larger value of $b_{x}$ results in a steeper flank.\n",
"* $c$ – Amplitude of the polynomial trough.\n",
"* $n$ – Degree of the polynomial trough. A larger value of $n$ will result in a broader trough with steeper flanks.\n",
"* $w$ – This parameter corresponds to half the width of the profile, i.e. the separation between the two error functions is equal to $2w$.\n",
"* $z_{\\rm e}$, $z_{\\rm p}$ – Centroid of the two error functions and the polynomial trough, respectively. By choosing $z_{\\rm e} \\ne z_{\\rm p}$ one can shift the trough with respect to the error functions and thus create an asymmetric line profile."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Busy Function is flexible enough to describe a wide range of symmetric and asymmetric spectral profiles commonly found in galaxies, from simple single-component profiles (Gaussian, top-hat, etc.) to complex, asymmetric double-horn profiles. A Python implementation of the Busy Function that uses the error function provided by SciPy is shown below. The reader is encouraged to experiment with different input parameter settings to explore their impact on the overall shape of the Busy Function."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Plotting the Busy Function\n",
"%matplotlib notebook\n",
"import numpy\n",
"import matplotlib.pyplot as plt\n",
"from scipy.special import erf\n",
"\n",
"# Parameter settings\n",
"a = 1.0\n",
"b1 = 0.2\n",
"b2 = 0.1\n",
"c = 0.0005\n",
"n = 2.0\n",
"w = 50.0\n",
"ze = 0.0\n",
"zp = 5.0\n",
"\n",
"# Calculation of B(z)\n",
"z = numpy.linspace(-100, 100, 201)\n",
"bf = (a / 4.0) * (erf(b1 * (w + z - ze)) + 1.0) * (erf(b2 * (w - z + ze)) + 1.0) * (c * pow(abs(z - zp), n) + 1.0)\n",
"\n",
"# Plotting of B(z)\n",
"plt.plot(z, bf)\n",
"plt.xlabel(\"z\")\n",
"plt.ylabel(\"B(z)\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As the Busy Function is a heuristic function, the up to eight free parameters of the function have no immediate physical meaning, but instead need to be converted into more meaningful galaxy parameters, e.g. the integrated flux, systemic velocity or profile width. Unfortunately, the Busy Function is too complex for such parameters to be calculated analytically, and numerical calculation of physical parameters is thus required. For example, the integrated flux of a galaxy can be determined by numerically integrating over the fitted Busy Function."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"As the Busy Function is a simple analytic function, any data analysis software can be used to fit a Busy Function to spectral profiles, including the Python package [LMFIT](https://lmfit.github.io/lmfit-py/) or the stand-alone software [QtiPlot](http://www.qtiplot.com/). In addition, two Busy Function fitting routines specifically developed for HI spectral-line fitting are available: [BF_dist](https://github.com/RussellJurek/busy-function-fitting/) and [BusyFit](http://www.atnf.csiro.au/people/Tobias.Westmeier/tools_software_busyfit.php)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Alternative models for double-horn profiles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another option for describing the double-horn profile of galaxies is to model the outer flanks and the central trough of the spectrum separately. A common approach is to use Gaussian functions for the flanks and a polynomial for the trough:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ S(z) = \\begin{cases}\n",
"a_{\\rm G} \\exp \\left( \\! -\\frac{[z - (z_{0} - w)]^{2}}{2 \\sigma^{2}} \\! \\right) & \\text{if } z < z_{0} - w \\\\\n",
"a_{\\rm C} + \\frac{a_{\\rm G} - a_{\\rm C}}{w^{2}} (z - z_{0})^{2} & \\text{if } z_{0} - w \\le z \\le z_{0} + w \\\\\n",
"a_{\\rm G} \\exp \\left( \\! -\\frac{[z - (z_{0} + w)]^{2}}{2 \\sigma^{2}} \\! \\right) & \\text{if } z > z_{0} + w \\\\\n",
"\\end{cases} $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(e.g. [<cite data-cite=\"1607.01393\">Tiley et al. 2016</cite>](http://arxiv.org/abs/1607.01393/)). An advantage of this approach is that the generally sharp peaks of HI profiles are more accurately modelled, although the function is not differentiable at the position where the Gaussian and polynomial join."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Systematic biases in source parameterisation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"HI data cubes not only contain signal from the sources we intend to study, but also statistical noise. This noise limits the accuracy with which we can extract observational and physical parameters of sources in the data cube and hence determines the statistical uncertainty of our measurement. However, the presence of noise can also result in systematic errors and biases in some of the measured parameters. These biases can be roughly divided into two categories:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Biases caused by the source finding threshold.\n",
"2. Biases created by the presence of noise."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This section describes the most common biases in source parameterisation and explores methods to reduce or avoid them."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Source finding threshold"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most source finding algorithms are based on extracting emission above a user-defined flux threshold. The problem with this approach, however, is that those parts of a source that fall below the flux threshold, e.g. the outer HI disc of a galaxy, will be excluded from the source mask and thus the measurement of source parameters. The most obvious effect created by this type of bias is that of **missing flux** in the integrated flux measurement. This is illustrated in the figure below from [<cite data-cite=\"2012PASA...29..276W\">Westmeier et al. (2012)</cite>](http://adsabs.harvard.edu/abs/2012PASA...29..276W), which shows the ratio of measured versus true integrated flux as a function of signal-to-noise ratio for a set of point source models with Gaussian spectral line profiles. Due to the finite detection threshold, integrated flux measurements are systematically too low, in particular at the lower end of the flux density range, as the faint outer parts of the source images (after convolution with a telescope beam) are excluded from the source mask."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<figure>\n",
" <img src=\"01_figures/int_flux_bias.png\" width=\"640\" height=\"434\" alt=\"Integrated flux bias\" \\>\n",
" <figcaption>*Ratio of measured versus true integrated flux as a function of peak signal-to-noise ratio for a set of point source models with Gaussian spectral line profiles. Due to the finite detection threshold, integrated flux measurements are systematically too low, in particular at the lower end of the flux density range. The image was taken from [<cite data-cite=\"2012PASA...29..276W\">Westmeier et al. (2012)</cite>](http://adsabs.harvard.edu/abs/2012PASA...29..276W).*</figcaption>\n",
"</figure>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A simple script that analytically calculates and plots the expected integrated flux error for a one-dimensional source of Gaussian shape is included below. This situation applies when, e.g., an HI spectrum is searched for emission or absorption lines, but the concept can easily be expanded to two-dimensional or three-dimensional sources of Gaussian or other shape. In the one-dimensional example below, the typical flux measurement of a source with a peak flux density of 3 × the flux threshold is about 14% too low, while for a source of 2 × the flux threshold the defect already increases to 24%."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Simulating biases in integrated flux measurements\n",
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt\n",
"from numpy import linspace, sqrt, log, exp\n",
"from scipy.special import erf\n",
"from math import pi\n",
"\n",
"# Input\n",
"snr = 3.0 # Ratio of peak flux to flux threshold, must be >= 1.0\n",
"\n",
"# Calculate boundary and integrals over Gaussian\n",
"x = sqrt(-2.0 * log(1.0 / snr))\n",
"int_full = sqrt(2.0 * pi)\n",
"int_part = -sqrt(pi / 2.0) * (erf(-x / sqrt(2.0)) - erf(x / sqrt(2.0)))\n",
"\n",
"# Calculate missing flux from ratio of integrals\n",
"flux_error = 100.0 * (1.0 - int_part / int_full)\n",
"\n",
"# Generate plot\n",
"z = linspace(-4, 4, 201)\n",
"gauss = exp(-z*z / 2.0)\n",
"plt.fill_between(z, gauss, color=\"lavender\")\n",
"plt.plot(z, gauss, color=\"black\")\n",
"plt.axhline(1.0 / snr, color=\"blue\", linestyle=\"dashed\")\n",
"plt.axvline(-x, color=\"red\")\n",
"plt.axvline(x, color=\"red\")\n",
"plt.xlabel(\"x\")\n",
"plt.ylabel(\"G(x)\")\n",
"plt.text(-3.8, 1.0 / snr + 0.015, \"flux threshold\", color=\"blue\")\n",
"plt.text(-(x / 2.0) - 2.0, 1.0 / (2.0 * snr) - 0.015, \"missing flux\", color=\"red\", horizontalalignment=\"center\")\n",
"plt.text( (x / 2.0) + 2.0, 1.0 / (2.0 * snr) - 0.015, \"missing flux\", color=\"red\", horizontalalignment=\"center\")\n",
"plt.text(0.0, 1.0 / (2.0 * snr) - 0.015, \"measured flux\", color=\"green\", horizontalalignment=\"center\")\n",
"plt.text(-3.8, 0.94, \"Flux defect: %.1f%%\" % flux_error, color=\"black\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"There are several ways to mitigate the systematic bias in source parameterisation caused by a finite detection threshold. One method, which has been implemented in the [Duchamp](http://www.atnf.csiro.au/people/Matthew.Whiting/Duchamp/) source finder, is to apply a secondary **growth threshold**. The source finder is first run with a regular flux threshold (e.g. 3σ) to generate a catalogue of sources above that threshold. In a second step, the mask of each source is then grown by adding neighbouring pixels above a second, slightly lower threshold (e.g. 2σ). This ensures that low-level emission is included in the source mask without increasing the number of false detections in the catalogue (as would be case if a lower flux threshold had been applied right from the start)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While applying a growth threshold can improve the source mask and decrease the resulting parameterisation errors, a small systematic error will remain, as the growth threshold, too, will need to be finite. An improved method of source **mask optimisation** has therefore been developed for the [SoFiA](https://github.com/SoFiA-Admin/SoFiA/) source finder. The basic concept of mask optimisation is to iteratively expand the initial source mask produced by a threshold finder until the integrated flux within the mask does not increase any further. This ensures that the final mask encompasses the entire source, thereby minimising parameterisation biases. Different methods can be used to grow masks, including the use of simple elliptical masks of increasing size or the dilation of initial source masks with a specific shape."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Impact of noise"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition to a finite detection threshold, the presence of noise in a data cube can also result in systematic errors in the measurement of certain source parameters. One of the most strongly affected parameters is the **peak flux density** of a source. It is often determined by simply picking the largest value encountered within the source mask. In this case, however, there is a high probability that the value is systematically elevated by the presence of noise peaks superposed on the source emission. The effect is clearly visible in the figure below from [<cite data-cite=\"2012PASA...29..276W\">Westmeier et al. (2012)</cite>](http://adsabs.harvard.edu/abs/2012PASA...29..276W), which shows the ratio of measured versus true peak flux density of a set of point source models with Gaussian spectral line profiles. The average peak flux density measurement is too high in all bins of the plot and increases significantly for faint sources below a signal-to-noise ratio of about 5."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<figure>\n",
" <img src=\"01_figures/peak_flux_bias.png\" width=\"640\" height=\"442\" alt=\"Peak flux bias\" \\>\n",
" <figcaption>*Ratio of measured versus true peak flux density as a function of peak signal-to-noise ratio for a set of point source models with Gaussian spectral line profiles. A systematic bias is clearly visible, increasing drastically below about 5σ. The image was taken from [<cite data-cite=\"2012PASA...29..276W\">Westmeier et al. (2012)</cite>](http://adsabs.harvard.edu/abs/2012PASA...29..276W).*</figcaption>\n",
"</figure>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The bias in the peak flux density measurement is further elevated by an additional selection effect: sources just below the detection threshold might get pushed above the threshold by superposed noise peaks. Hence, sources at the faint end of the flux density range are even more likely to yield a peak flux density measurement that is systematically too high."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition to peak flux density, other parameters might be affected by the noise as well, although to a lesser degree. This is the case, e.g., for line width measurements, in particular close to the noise level. A general solution to the issue of noise-related biases is the **fitting of source models** and extraction of source parameters from the fit. This will ensure that statistical noise does not enter into the measurement of observational parameters in the first place, but instead solely controls the statistical uncertainty of the measurements."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Correction factors at higher redshift"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In all of our calculations so far we have made the fundamental assumption that the source is located in the nearby universe at redshift zero. For sources at higher redshift, corrections will need to be applied to all source parameters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Velocity width"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One of the most fundamental corrections applies to the velocity resolution of the data. HI data cubes usually have a channel width, $\\Delta f$, that is constant in frequency. However, a constant frequency width does not translate into a constant velocity width, and the velocity resolution of a data cube therefore deteriorates with increasing redshift, with $\\Delta v = (1 + Z) \\, \\Delta v_{0}$ (see [<cite data-cite=\"2014MNRAS.442.1117D\">Davis & Scrimgeour 2014</cite>](http://adsabs.harvard.edu/abs/2014MNRAS.442.1117D)), where $\\Delta v_{0}$ is the velocity resolution at $Z = 0$. Therefore, a $(1 + Z)$ scaling factor will need to be applied to all velocity widths, e.g. $w_{50}$ and $w_{20}$ line widths, of sources beyond redshift zero. Velocity correction factors will also need to be applied to any other source parameters that have a dependence on velocity, for example integrated flux in units of Jy km s<sup>−1</sup>."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The algorithm below calculates the velocity resolution of a telescope for a given frequency resolution and observing frequency. It also plots velocity resolution as a function of frequency in the range of $f = 700 \\ldots 1400~\\mathrm{MHz}$ (redshift range of $Z = 0 \\ldots 1$ for HI observations). In addition to the correct $(1 + Z)$ scaling factor (blue curve), the solution for the relativistic Doppler effect (red curve) is shown in order to illustrate that the treatment of redshifts as caused by relativistic recession velocities will lead to wrong and non-sensical results beyond $Z \\approx 0.03$."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Calculating velocity resolution as a function of redshift\n",
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt\n",
"from numpy import linspace\n",
"\n",
"# Function to convert frequency to redshift\n",
"def f2z(frequency):\n",
" return (f0 / frequency) - 1.0\n",
"\n",
"# Function to convert redshift to frequency\n",
"def z2f(redshift):\n",
" return f0 / (redshift + 1.0)\n",
"\n",
"# Function to convert redshift resolution to velocity resolution at given redshift\n",
"def dz2dv(delta_z, redshift):\n",
" return c * delta_z / (1 + redshift)\n",
"\n",
"# Input parameters\n",
"c = 299792.458 # km/s, speed of light\n",
"f0 = 1420.40575 # MHz, HI rest frequency\n",
"f = 1200.0 # MHz, observing frequency\n",
"df = 0.0185 # MHz, frequency resolution of telescope\n",
"\n",
"# Calculate redshift and velocity resolution\n",
"z = f2z(f)\n",
"dv = dz2dv(f2z(f) - f2z(f + df), z)\n",
"\n",
"# Print result\n",
"print(\"Velocity resolution: dv = %.2f km/s at Z = %.3f\" % (dv, z))\n",
"\n",
"# Plot velocity resolution curve\n",
"f = linspace(f0 / 2.0, f0, 201)\n",
"# Correct cosmological transformation (where f0 / f = 1 + z):\n",
"dv = dz2dv(f2z(f0) - f2z(f0 + df), 0.0) * f0 / f\n",
"plt.plot(f, dv, color=\"blue\")\n",
"plt.text(850.0, 6.6, \"1 + Z\", color=\"blue\")\n",
"# Wrong transformation using special relativity:\n",
"dv_sr = c * (((f0 * f0 - (f - df) * (f - df)) / (f0 * f0 + (f - df) * (f - df))) - ((f0 * f0 - f * f) / (f0 * f0 + f * f)))\n",
"plt.plot(f, dv_sr, color=\"red\", linestyle=\"dashed\")\n",
"plt.text(800.0, 5.15, \"SR\", color=\"red\")\n",
"# Add redshift markers:\n",
"plt.axvline(z2f(0.0), color=\"grey\", linestyle=\"dotted\")\n",
"plt.axvline(z2f(0.1), color=\"grey\", linestyle=\"dotted\")\n",
"plt.axvline(z2f(0.2), color=\"grey\", linestyle=\"dotted\")\n",
"plt.axvline(z2f(0.5), color=\"grey\", linestyle=\"dotted\")\n",
"plt.axvline(z2f(1.0), color=\"grey\", linestyle=\"dotted\")\n",
"plt.text(z2f(0.0), 3.6, \" 0.0\", color=\"grey\")\n",
"plt.text(z2f(0.1), 3.6, \" 0.1\", color=\"grey\")\n",
"plt.text(z2f(0.2), 3.6, \" 0.2\", color=\"grey\")\n",
"plt.text(z2f(0.5), 3.6, \" 0.5\", color=\"grey\")\n",
"plt.text(z2f(1.0), 3.6, \" Z = 1.0\", color=\"grey\")\n",
"# Add axis labels:\n",
"plt.xlabel(\"Frequency (MHz)\")\n",
"plt.ylabel(\"dv (km/s)\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Further information"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A detailed introduction to the correct handling of HI source parameters at higher redshift will be published by <span style=\"background-color:red;\">Meyer et al. (in prep.)</span>. The correction factors derived in that paper have been used as the basis for a publicly available online calculator (<span style=\"background-color:red;\">http://TBD</span>) that can be used to convert between a large range of source parameters in different reference systems and units."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Determination of uncertanties"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Statistical uncertainties"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So far, we have only been dealing with the calculation of observational and physical parameters of HI sources. However, for a meaningful interpretation of our results we additional require knowledge of the statistical uncertainties of our measurements. Unfortunately, determination of statistical uncertainties is often hampered by several issues. One of these issues is the inherent problem that the error of a measurement based on HI data is usually by far dominated by systematic effects rather than statistical errors. Such systematic effects include\n",
"\n",
"* flux calibration issues,\n",
"* continuum subtraction issues,\n",
"* errors in spectral bandpass subtraction,\n",
"* radio frequency interference,\n",
"* missing diffuse flux (due to lack of short spacings),\n",
"* parameterisation errors due to insufficient source mask,\n",
"* systematic errors in source distance measurements,\n",
"* etc.\n",
"\n",
"The result of these effects is that the actual error of a measurement, e.g. integrated flux of a galaxy, is substantially larger than the calculated statistical uncertainty would suggest. As an example, let us consider a simple point source that extends across $N = 50$ spectral channels and has a constant flux density of $S = 1~\\mathrm{Jy}$ in each channel with a statistical rms noise level of $\\sigma = 0.1~\\mathrm{Jy}$. Let us further assume that there is a systematic flux calibration error of 5% ($e_{\\rm cal} = 1.05$) and a constant flux offset of $e_{\\rm bp} = 1 \\sigma = 0.1~\\mathrm{Jy}$ in each channel due to inaccurate bandpass subtraction. The measured total flux of the source is then given by"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ S_{\\rm tot} = N \\times (S + e_{\\rm bp}) \\times e_{\\rm cal} = 57.75~\\mathrm{Jy}$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"while the actual total flux of the source is $N \\times S = 50~\\mathrm{Jy}$. Hence, the measured flux is $7.75~\\mathrm{Jy}$, or $15.5\\%$, too high. Furthermore, the statistical uncertainty of the measurement is only $\\sqrt{N} \\times \\sigma = 0.7~\\mathrm{Jy}$, and hence the actual measurement error is almost eleven times as large as the derived statistical uncertainty! This basic example illustrates how summation over a large number of pixels minimises the relative statistical uncertainty of a measurement, but at the same time drastically elevates the impact of any systematic errors on the result, as their contribution always acts in the same direction. As a result, the derived statistical uncertainty of the measurement is not representative of the actual measurement error, and quoting the statistical uncertainty in a publication would therefore be grossly misleading."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Determination of meaningful uncertainties"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In order to determine meaningful uncertainties that incorporate both statistical and systematic components, we need to apply numerical methods such as <a href=\"https://en.wikipedia.org/wiki/Monte_Carlo_method\">Monte Carlo experiments</a>, <a href=\"https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29\">bootstrapping</a> or <a href=\"https://en.wikipedia.org/wiki/Jackknife_resampling\">jackknifing</a>. An approach often followed in previous HI surveys is the injection of a large number of **artificial sources** into empty sections of the data at an early stage of the data reduction process. These sources will then be calibrated and analysed in the same way as genuine astronomical sources. Comparison of the measured parameters of the artificial sources with their original ones will inform on the combined statistical and systematic errors introduced by the data reduction procedure."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instead of injecting artificial sources into the data, a somewhat simpler approach would be to randomly **shift the source mask** to a large number of empty regions of the data cube and repeat the measurement in each case. As in the previous case, the variation of the resulting measurements will provide information on the combined statistical and systematic errors. While this method avoids the complexity of generating fake sources, it can only be used for a limited number of source parameters, in particular integrated flux measurements which simply require summation of all pixels in the mask. The uncertainties of more complex parameters, such as spectral line widths, can not be recovered with this method, as there is no analysable signal in the shifted masks."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another complication arises from the fact that the distribution of errors derived through one of the numerical methods discussed above may not be Gaussian any more, in particular in the presence of strong systematic errors. Hence, the standard deviation of the derived values may no longer be a meaningful parameter to represent the uncertainty of the measurement, as the exact shape of the distribution can no longer be recovered from the standard deviation alone. In such cases, full multi-dimensional error distribution plots (or low-dimensional projections thereof) will need to be disseminated in order to characterise measurement uncertainties. An example of a non-Gaussian error distribution, here of the Busy Function parameter $b_{1}$, can be seen in the right-hand panel of the figure below."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<figure>\n",
" <img src=\"01_figures/error_analysis.png\" with=\"1200\" height=\"406\" alt=\"Example of error analysis for the Busy Function\" />\n",
" <figcaption style=\"font-style:italic;\">Example of the distribution of the Busy Function parameters $a$ and $b_{1}$ (black data points) derived from fitting the Busy Function to 10,000 realisations of the integrated spectrum of the galaxy NGC 3351 with artificial Gaussian noise added to the data (from [<cite data-cite=\"2014MNRAS.438.1176W\">Westmeier et al. 2014</cite>](http://adsabs.harvard.edu/abs/2014MNRAS.438.1176W)). The red curve shows a Gaussian fit to the distribution. Note that the error distribution for the parameter $b_{1}$ is non-Gaussian and can no longer be expressed in a meaningful way by simply stating the standard deviation.</figcaption>\n",
"</figure>"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### List of commonly used symbols"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* $a$ – major axis\n",
"* $b$ – minor axis / Galactic latitude\n",
"* $\\mathrm{c}$ – speed of light\n",
"* $d$ – distance\n",
"* $f$ – frequency\n",
"* $\\mathrm{k_{B}}$ – Boltzmann constant\n",
"* $l$ – Galactic longitude\n",
"* $M$ – mass / moment\n",
"* $N$ – column density\n",
"* $\\vec{p} = (x, y, z)$ – position in three-dimensional data cube in pixels\n",
"* $S$ – flux density\n",
"* $T$ – temperature\n",
"* $v$ – velocity\n",
"* $w$ – line width\n",
"* $Z$ – redshift\n",
"* $\\alpha$ – right ascension / longitude\n",
"* $\\beta$ – latitude\n",
"* $\\delta$ – declination\n",
"* $\\theta$ – beam angular size\n",
"* $\\lambda$ – wavelength\n",
"* $\\pi$ – Archimedes’ constant\n",
"* $\\sigma$ – standard deviation\n",
"* $\\phi$ – position angle\n",
"* $\\Omega$ – solid angle"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Clean up\n",
"if os.path.exists('figures/UGCA105_m0.fits'): os.remove('figures/UGCA105_m0.fits') \n",
"if os.path.exists('figures/UGCA105_m1.fits'): os.remove('figures/UGCA105_m1.fits') \n",
"if os.path.exists('figures/UGCA105_m2.fits'): os.remove('figures/UGCA105_m2.fits') \n",
"if os.path.exists('figures/UGCA105_mask.fits'): os.remove('figures/UGCA105_mask.fits') \n",
"if os.path.exists('figures/UGCA105_peak.fits'): os.remove('figures/UGCA105_peak.fits') \n",
"if os.path.exists('figures/UGCA_105_pvorig_pvmaj.fits'): os.remove('figures/UGCA_105_pvorig_pvmaj.fits') \n",
"if os.path.exists('figures/UGCA_105_pvorig_pvmin_cent.fits'): os.remove('figures/UGCA_105_pvorig_pvmin_cent.fits') \n",
"if os.path.exists('figures/UGCA_105_pvorig_pvmin_left.fits'): os.remove('figures/UGCA_105_pvorig_pvmin_left.fits') \n",
"if os.path.exists('figures/UGCA_105_pvorig_pvmin_right.fits'): os.remove('figures/UGCA_105_pvorig_pvmin_right.fits')"
]
}
],
"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": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
dereneaton/ipyrad | testdocs/analysis/cookbook-pca-empirical.ipynb | 1 | 740813 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1><span style=\"color:gray\">ipyrad-analysis toolkit:</span> Dimensionality reduction</h1>\n",
"\n",
"The `pca` tool can be used to implement a number of dimensionality reduction methods on SNP data (PCA, t-SNE, UMAP) and to filter and/or impute missing data in genotype matrices to reduce the effects of missing data. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load libraries"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# conda install ipyrad -c conda-forge -c bioconda\n",
"# conda install ipcoal -c conda-forge\n",
"# conda install scikit-learn -c conda-forge"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import ipyrad.analysis as ipa\n",
"import toyplot"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9.61\n",
"0.19.0\n"
]
}
],
"source": [
"print(ipa.__version__)\n",
"print(toyplot.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### The input data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# the simulated SNP database file\n",
"SNPS = \"/tmp/oaks.snps.hdf5\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"file already exists\n"
]
}
],
"source": [
"# download example hdf5 dataset (158Mb, takes ~2-3 minutes)\n",
"URL = \"https://www.dropbox.com/s/x6a4i47xqum27fo/virentes_ref.snps.hdf5?raw=1\"\n",
"ipa.download(url=URL, path=SNPS);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Make an IMAP dictionary (map popnames to list of samplenames)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [],
"source": [
"IMAP = {\n",
" \"virg\": [\"LALC2\", \"TXWV2\", \"FLBA140\", \"FLSF33\", \"SCCU3\"],\n",
" \"mini\": [\"FLSF47\", \"FLMO62\", \"FLSA185\", \"FLCK216\"],\n",
" \"gemi\": [\"FLCK18\", \"FLSF54\", \"FLWO6\", \"FLAB109\"],\n",
" \"bran\": [\"BJSL25\", \"BJSB3\", \"BJVL19\"],\n",
" \"fusi\": [\"MXED8\", \"MXGT4\", \"TXMD3\", \"TXGR3\"],\n",
" \"sagr\": [\"CUCA4\", \"CUSV6\", \"CUVN10\"],\n",
" \"oleo\": [\"MXSA3017\", \"BZBB1\", \"HNDA09\", \"CRL0030\", \"CRL0001\"],\n",
"}\n",
"MINMAP = {\n",
" \"virg\": 3,\n",
" \"mini\": 3,\n",
" \"gemi\": 3,\n",
" \"bran\": 2,\n",
" \"fusi\": 2,\n",
" \"sagr\": 2,\n",
" \"oleo\": 3,\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initiate tool with filtering options"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Samples: 26\n",
"Sites before filtering: 1182005\n",
"Filtered (indels): 0\n",
"Filtered (bi-allel): 26249\n",
"Filtered (mincov): 142749\n",
"Filtered (minmap): 876036\n",
"Filtered (subsample invariant): 600226\n",
"Filtered (minor allele frequency): 494278\n",
"Filtered (combined): 1068892\n",
"Sites after filtering: 74034\n",
"Sites containing missing values: 61935 (83.66%)\n",
"Missing values in SNP matrix: 140810 (7.32%)\n",
"Imputation: 'sampled'; (0, 1, 2) = 61.2%, 17.0%, 21.8%\n"
]
}
],
"source": [
"tool = ipa.pca(data=SNPS, minmaf=0.05, imap=IMAP, minmap=MINMAP, impute_method=\"sample\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run PCA\n",
"Unlinked SNPs are automatically sampled from each locus. By setting `nreplicates=N` the subsampling procedure is repeated N times to show variation over the subsampled SNPs. The imap dictionary is used in the `.draw()` function to color points, and can be overriden to color points differently from the IMAP used in the tool above."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Subsampling SNPs: 25092/100093\n"
]
},
{
"data": {
"text/html": [
"<div class=\"toyplot\" id=\"t3f18da96356647a3a8df29ee3e4dc27a\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"300.0px\" id=\"t0dc89e52ea374f74a50264c448e1bb2d\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 400.0 300.0\" width=\"400.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"ta68d8f0e7c874d96b5a1a89d25ff0477\"><clipPath id=\"t0f9e30df18c745d091d382db89242bdb\"><rect height=\"220.0\" width=\"240.0\" x=\"40.0\" y=\"40.0\"></rect></clipPath><g clip-path=\"url(#t0f9e30df18c745d091d382db89242bdb)\"><g class=\"toyplot-mark-Point\" id=\"tb76eeef703fb428e86bbc66bf30fba88\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(145.1833571982674, 50.02337729257344)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(144.89332414674377, 58.99551981420947)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(143.4592615026294, 59.05864677183459)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(254.6061204433573, 223.8742884155866)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(229.94821498758188, 228.56016881598975)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(260.8595515930679, 223.5255831504285)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(189.02810810578382, 233.00800913235395)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(179.87518891131975, 231.14850073372307)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(201.73426642783997, 234.5425900554306)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.43005216724703, 219.7997797672387)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.78803787879448, 215.23210813292428)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.15393006754822, 220.17970522304194)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(68.4522173585304, 212.52189539237347)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(73.91639161236989, 208.90117521107067)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(75.46707721628849, 207.61852000658584)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.085417787770055, 217.68505088433588)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.39714548371342, 215.97445613310254)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.65072999793026, 220.54212181059944)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.267994468362645, 223.05580658377332)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(260.5790907728815, 222.85047500415405)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.76902790860451, 217.01928440233175)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(138.18750867495518, 114.9478076312717)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(130.87514660497627, 103.20595503416061)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(242.18248453822903, 217.94418905015954)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.983669466160855, 217.07587437229358)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(115.56716411097871, 122.9819880055006)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(113.26724278672377, 127.14526803904296)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.715491518071524, 215.5801917105541)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"tcb65bb059f86464b9a28561440a86e66\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(141.9856418827068, 50.082342049611746)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(142.89244853601576, 59.8915687295402)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(141.12154812621574, 59.385090596818735)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(255.01398505269424, 222.4346842501002)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(228.72507428302734, 228.2081474565722)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(259.6873215484494, 222.25061104616148)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(192.5907687483068, 231.52930673716173)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(182.10823728646074, 231.67366626382534)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(201.618620643055, 234.2941667065652)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.08884309205877, 220.83437078303348)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.302338238189805, 215.00277601755386)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.43103942372068, 220.68191228817332)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(71.88294835266487, 214.33966622023618)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(73.30303867305534, 210.1565109426646)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(75.99880180288854, 209.4038789470721)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.350257699212406, 217.4830415527532)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.24387388041475, 216.31369770095532)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.76705844340839, 220.34969871743021)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.766984216209366, 223.01642376426355)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(259.07776008414396, 221.30219759993457)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.141460791327304, 216.81863380727668)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(138.84405073750418, 114.01243975544071)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(130.74183493935382, 102.08744809218946)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(240.2984269099913, 216.33210495972162)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.85456908303454, 218.5980340343931)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(114.92079842759128, 124.32256178621019)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(112.08037621336712, 124.97830359506983)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.48510662165947, 217.21505217591593)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t29b053b10b684abc80310cca476e924f\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(153.49610517234603, 50.79737830144795)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(151.89882977372, 59.128754313431806)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(150.54119864301822, 58.23366046939687)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(252.37618648257646, 228.32751788262257)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(226.51400199716545, 232.95386830604656)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(258.2219521766367, 228.67982007118587)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(189.07766495330281, 235.94138546936762)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(180.30036879731992, 232.71142079469993)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(197.44404375704403, 236.78781952744993)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.44586756647981, 217.83592110474498)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.630015248015425, 211.4185132354792)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.940123308588845, 216.95704211490354)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(68.19844709726081, 213.24714402102197)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(70.47352999091574, 206.52869405446643)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(72.94495538959362, 204.5178201785953)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.87744997556367, 214.45201608806062)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.50117867434163, 212.5576469119289)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.56011302294496, 218.6752582729442)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.23881273330783, 219.7528986308426)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(257.0884193767537, 228.9848731127003)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.18048423377144, 215.14766511382896)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(142.23613251544157, 116.32425504823527)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(136.20590328730933, 103.64243003613565)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(239.48301605213206, 222.13923129827091)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.02042006596996, 213.91707003599976)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(120.95271694845269, 124.28893348609218)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(117.14971823836383, 126.0571925196998)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(50.32555825839058, 212.9921061770452)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t7be236a3eb734a53b1b66948ec9bec39\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(140.56091330353593, 50.0)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(140.11125983363624, 58.869385279536445)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(138.47172541584948, 58.519496288267135)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(254.68288664162023, 219.95022287500896)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(229.23818339114055, 227.08196392048959)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(261.22364882585805, 220.1632683020478)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(193.50109930918137, 232.32084889572502)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(185.18440969792888, 230.54222561759715)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(201.92383438934877, 234.0087206830872)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.00179838921461, 220.89172122449173)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.40034533826117, 214.302354814566)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.26858111964499, 222.16275628279382)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(69.63755993475903, 216.4732603663931)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(73.23415971582543, 209.8905211477661)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(75.34866612653755, 209.54108192198993)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.47127767355153, 217.61961852165945)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.96322802606069, 217.18625336375527)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.43947040280291, 220.5926484549639)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.80948001729926, 224.51936871415325)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(260.1430372990982, 220.90722727973935)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.897740738159676, 217.51620177062966)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(137.28899046801203, 115.92842620438213)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(130.72381258135383, 103.64771848266976)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(242.80945330486912, 214.61180237871918)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.097228428267684, 218.080348310625)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(115.32861709064662, 124.11292979716626)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(111.50686158841157, 126.43556439777603)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.05494468585193, 217.12240128064568)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t3101a7c16bec453d91df74fafae63bcb\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(145.30998830384917, 50.29744539533074)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(144.2677479395996, 58.743881895384604)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(144.434587989798, 58.00448661259785)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(254.7493225643759, 223.7432920820001)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(228.79036656726717, 228.63564390661247)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(261.5329397391598, 223.21337735793577)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(189.6612405637088, 234.01423323335166)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(181.03661542852842, 231.47235560354147)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(200.45672221364765, 234.97487407259501)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.64732573073618, 219.07037963943912)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.901322317946416, 213.84802435423836)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.74667905618485, 220.79680679304926)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(69.58841546688826, 213.11651297351358)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(72.71219997782045, 207.70213381687208)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(73.99705089375962, 207.50200839713858)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.72933587793937, 217.4729251110806)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(69.22345815664465, 215.75361960091868)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.4922435777026, 221.27229596645972)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.7382091648998, 220.95654985386736)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(260.14125872637555, 223.81824276494203)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.86309630142763, 217.67241832564648)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(137.8213462807639, 115.37133970982174)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(132.730539499121, 103.55426521216023)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(242.34642256522434, 218.38876598221495)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.54432182479334, 217.13958982345466)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(116.83377683589362, 123.76773195832752)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(113.36292933984035, 125.97303456138629)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(51.66375083283104, 216.7221015727638)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t935caa7e44614ff28f615f8ee38eff9f\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(146.1785729530077, 50.36182246651693)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(145.28521329707416, 58.98060133710017)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(144.53195805286995, 58.57228764311315)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(255.5900440294028, 224.40973984476588)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(229.29382265223003, 229.94813694944204)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(260.6877421399021, 224.6948043312785)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(189.73010056521017, 232.6975862984109)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(182.91994568069123, 231.13721625359727)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(200.57189082236175, 235.24643638511148)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.73564636416433, 220.3233912089352)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.95799216615724, 213.37985041215876)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.821070354113175, 219.45361548335583)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(68.5134353761979, 214.03749780774288)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(73.37470832604697, 208.67786654398117)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(72.97066071873044, 207.64974416707338)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.03754516170046, 217.42187026611228)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.84433531623196, 215.77865778261798)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.56592330872476, 219.33325210333993)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.760906709772485, 222.64202361390014)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(261.116343245467, 223.43990111370016)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.99861819105718, 217.66757346777965)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(138.1462470530316, 115.19543431162808)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(132.3798828766922, 104.24111574563761)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(242.71136650473633, 217.7971136258936)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.77478180025357, 215.72489435220965)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(116.29404125091301, 123.19903151795171)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(115.13987964038265, 125.59277773161378)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(51.39053917960426, 215.39409381167673)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"te639e72596f640f28206ddd3cfd67b70\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(153.20329951511115, 52.817870588418046)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(153.04583012556193, 60.115415009072066)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(151.75682692181556, 60.22298981660481)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(251.81117463503855, 227.73546603357303)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(224.8284046498819, 232.6537473412303)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(256.28573697297855, 229.4582386221203)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(189.06467751011888, 237.36197410179074)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(180.36101342484258, 234.96937485043125)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(197.92563003462013, 237.74134264400047)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.694997064402216, 217.68123329652465)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.13998750134946, 210.62760208569128)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.249628541915186, 216.39451613570478)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.94440621062645, 211.9276268513869)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(70.89666146301246, 206.03315929691436)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(74.1110901328206, 204.46860272734617)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.313452986147865, 214.1924284023725)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.71819984980857, 212.50416111232133)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.03549655224727, 217.1452611220527)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.128603252328354, 219.78485681753708)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(256.51649159570724, 229.40450114479904)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.17161460901625, 214.23590073262758)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(143.07172032059742, 115.33354904925122)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(138.00723368691007, 103.70020958834127)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(238.63508167918775, 221.90121705451403)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(51.58610300503607, 213.79534121603882)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(121.67492762479695, 122.27856892888325)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(118.14492387084778, 125.47850762220156)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(50.0, 213.0346743848952)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t5ac5fe389fbe4cdaa33cfd2de32e07e1\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(151.39152802537487, 51.824355067875906)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(150.28415752621854, 60.35159202966269)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(148.18656982266256, 59.964620557167265)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(254.81085553339446, 226.62164703056519)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(226.51367329573014, 231.62663087081964)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(259.4183537114125, 227.1431313803199)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(188.19442072088907, 235.56260407080867)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(178.82666010398208, 232.57065278825797)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(199.06671055012623, 236.73734054996984)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.251540831075665, 217.88347798417288)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.30434285302884, 212.29787813744528)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.278402561257415, 217.7783886075378)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.30053313396706, 213.28266527244062)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(71.47194437043126, 207.39617555709194)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(72.94327059255502, 206.89827823469807)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.426735458289656, 216.26473524275508)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.23571806839969, 212.97096037859453)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.04413981367897, 218.2421259291879)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.914210885040134, 219.84277055333425)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(258.885737741215, 228.35723374392865)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.5010355637498, 215.42390883408254)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(142.08036733944093, 113.68829918372548)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(136.59805823051553, 103.84693358518736)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(242.08709919419866, 221.8032939663568)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.5028191696218, 214.83643422071685)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(120.44691939615117, 122.20812699436306)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(116.15666003627206, 124.9680053932346)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.20074920804829, 212.60607041234408)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t0b2643f171b644c38c4ce8b4551e1ece\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(141.67509251719912, 50.59829355880332)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(141.07811752661408, 59.354960596824256)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(141.12783346748932, 58.250648973114764)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(256.3119906124995, 221.72552783605366)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(229.7690996026497, 226.10912321944585)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(261.50137156383556, 221.8245946782313)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(192.80095879503742, 232.59556595393036)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(182.71433596501188, 230.1204333127965)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(201.15087925190554, 231.9438910226191)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.31365736192042, 221.35035001498176)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.370077695257194, 213.96186368536848)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.36829766537276, 221.90197538243405)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(69.5538127887452, 216.21516023799458)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(72.91865485311509, 209.64118816205286)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(73.88463849101117, 207.81281962828)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.223662846281904, 218.90521290817287)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(68.52739134889059, 216.83034014892294)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.22329221241402, 221.38881656989568)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.70151825261879, 224.020885405749)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(260.83849636159, 220.9617184535788)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.96673356708895, 218.92274212176727)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(136.40319210845485, 113.37826631819104)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(129.3113099011781, 103.72497985047515)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(243.4978099836404, 217.56147051734627)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.13508815414192, 219.39981650808403)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(114.56586729404985, 122.58761325711416)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(111.771575343894, 126.1079661525906)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.6184582048201, 215.80211210182605)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t4594ef3e4e044b3889f555014d369e9f\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(151.37426552537588, 52.133701106452435)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(150.29809026390154, 61.19965323994616)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(150.05515019490934, 60.31194135058586)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(252.61980759026576, 227.3022978038714)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(227.2672633182209, 232.51712776170035)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(258.25593585531243, 227.80666100059852)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(188.09847001541686, 232.6846874980727)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(178.75055820741102, 232.4522926495164)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(198.762628495856, 236.68306417877628)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.3733024471326, 216.35188144323345)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.5381658881623, 211.94038338631978)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.567878108743514, 217.76994569998544)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.97517083861784, 211.50078576513778)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(73.09488016175818, 206.95607796182674)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(73.58858026292297, 205.30100583659265)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.98307731251651, 214.92310822354472)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.85395372239765, 215.18442151483467)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.29720927952644, 218.13236944427246)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.71413320360708, 219.36632600873983)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(257.81238167282436, 228.7224334804014)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.83365872908435, 215.100046231902)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(142.93646363328997, 116.60809897889288)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(136.045527717493, 103.32016735761778)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(239.29998811669122, 220.63934640793323)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.35070111949354, 215.1785170239763)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(119.36955166637938, 122.89540316389021)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(116.6832464076089, 126.84632633547133)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(50.523173981807915, 213.1702657225523)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t1942fb888dbe4d56aa0ea8f0854c45a5\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(147.0358764396774, 50.893658582703075)\"><title>BJSB3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(146.40550189690856, 59.563133224470775)\"><title>BJSL25</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(145.36866601372577, 59.05238690795012)\"><title>BJVL19</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(254.2572373585225, 224.61246840541475)\"><title>BZBB1</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(228.08881047448952, 229.82945585483486)\"><title>CRL0001</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(259.7674554126613, 224.8760089940308)\"><title>CRL0030</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(190.1747509286956, 233.77162013909734)\"><title>CUCA4</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(181.20773335034968, 231.87981388679864)\"><title>CUSV6</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(200.0655226585805, 235.2960245825605)\"><title>CUVN10</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(63.79830310144317, 219.2022506466796)\"><title>FLAB109</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(60.03326251251626, 213.20113542617452)\"><title>FLBA140</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(62.18256302070896, 219.40766640109797)\"><title>FLCK18</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(68.70469465582579, 213.6662214908241)\"><title>FLCK216</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(72.53961691443507, 208.1883502694707)\"><title>FLMO62</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(74.1254791627108, 207.0713760045372)\"><title>FLSA185</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(54.24982127789734, 216.64200072008472)\"><title>FLSF33</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(66.95084825269038, 215.1054214647952)\"><title>FLSF47</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(65.80756766113807, 219.5673848391146)\"><title>FLSF54</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(61.304085290344574, 221.69579099461606)\"><title>FLWO6</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(259.21990168760567, 224.87488036978783)\"><title>HNDA09</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(57.7323470633287, 216.5524374807873)\"><title>LALC2</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(139.70160191314918, 115.07879161908403)\"><title>MXED8</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(133.36192493249033, 103.49712229845747)\"><title>MXGT4</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(241.33511488489006, 218.91185352411304)\"><title>MXSA3017</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(53.984970211677336, 216.37459198977916)\"><title>SCCU3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(117.5954380645853, 123.26428888954993)\"><title>TXGR3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(114.52634134657119, 125.95829463480868)\"><title>TXMD3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(51.797777249108506, 214.9639069350219)\"><title>TXWV2</title><circle r=\"5.0\"></circle></g></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t7bfff0b5da2f4d20a43881ea2b26d448\" transform=\"translate(50.0,250.0)translate(0,10.0)\"><line style=\"stroke-width:2.25\" x1=\"0\" x2=\"211.53293973915981\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(6.320886450181827,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.67\" y=\"10.265999999999998\">-40</text></g><g transform=\"translate(77.54725763345455,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"10.265999999999998\">0</text></g><g transform=\"translate(148.77362881672727,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"10.265999999999998\">40</text></g><g transform=\"translate(220.0,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"10.265999999999998\">80</text></g></g><g transform=\"translate(110.0,22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-74.30499999999999\" y=\"11.977\">PC0 (16.8%) explained</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t29f47ab5d303475794cd90e1d4bdd8dc\" transform=\"translate(50.0,250.0)rotate(-90.0)translate(0,-10.0)\"><line style=\"stroke-width:2.25\" x1=\"12.258657355999564\" x2=\"200.0\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(0.0,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.67\" y=\"0.0\">-40</text></g><g transform=\"translate(62.035773693691254,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"0.0\">0</text></g><g transform=\"translate(124.07154738738251,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"0.0\">40</text></g><g transform=\"translate(186.10732108107376,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"0.0\">80</text></g></g><g transform=\"translate(100.0,-22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-74.30499999999999\" y=\"0.0\">PC1 (14.8%) explained</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g><g class=\"toyplot-coordinates-Table\" id=\"t88f6f1e5322348398e0b87aceab47fcf\"><g transform=\"translate(310.0,75.0)\"><g style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,75.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">virg</text></g><g transform=\"translate(310.0,100.0)\"><g style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,100.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">mini</text></g><g transform=\"translate(310.0,125.0)\"><g style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,125.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">gemi</text></g><g transform=\"translate(310.0,150.0)\"><g style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,150.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">bran</text></g><g transform=\"translate(310.0,175.0)\"><g style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,175.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">fusi</text></g><g transform=\"translate(310.0,200.0)\"><g style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,200.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">sagr</text></g><g transform=\"translate(310.0,225.0)\"><g style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,225.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">oleo</text></g></g></svg><div class=\"toyplot-behavior\"><script>(function()\n",
"{\n",
"var modules={};\n",
"modules[\"toyplot/tables\"] = (function()\n",
" {\n",
" var tables = [];\n",
"\n",
" var module = {};\n",
"\n",
" module.set = function(owner, key, names, columns)\n",
" {\n",
" tables.push({owner: owner, key: key, names: names, columns: columns});\n",
" }\n",
"\n",
" module.get = function(owner, key)\n",
" {\n",
" for(var i = 0; i != tables.length; ++i)\n",
" {\n",
" var table = tables[i];\n",
" if(table.owner != owner)\n",
" continue;\n",
" if(table.key != key)\n",
" continue;\n",
" return {names: table.names, columns: table.columns};\n",
" }\n",
" }\n",
"\n",
" module.get_csv = function(owner, key)\n",
" {\n",
" var table = module.get(owner, key);\n",
" if(table != undefined)\n",
" {\n",
" var csv = \"\";\n",
" csv += table.names.join(\",\") + \"\\n\";\n",
" for(var i = 0; i != table.columns[0].length; ++i)\n",
" {\n",
" for(var j = 0; j != table.columns.length; ++j)\n",
" {\n",
" if(j)\n",
" csv += \",\";\n",
" csv += table.columns[j][i];\n",
" }\n",
" csv += \"\\n\";\n",
" }\n",
" return csv;\n",
" }\n",
" }\n",
"\n",
" return module;\n",
" })();\n",
"modules[\"toyplot/root/id\"] = \"t3f18da96356647a3a8df29ee3e4dc27a\";\n",
"modules[\"toyplot/root\"] = (function(root_id)\n",
" {\n",
" return document.querySelector(\"#\" + root_id);\n",
" })(modules[\"toyplot/root/id\"]);\n",
"modules[\"toyplot/canvas/id\"] = \"t0dc89e52ea374f74a50264c448e1bb2d\";\n",
"modules[\"toyplot/canvas\"] = (function(canvas_id)\n",
" {\n",
" return document.querySelector(\"#\" + canvas_id);\n",
" })(modules[\"toyplot/canvas/id\"]);\n",
"modules[\"toyplot/menus/context\"] = (function(root, canvas)\n",
" {\n",
" var wrapper = document.createElement(\"div\");\n",
" wrapper.innerHTML = \"<ul class='toyplot-context-menu' style='background:#eee; border:1px solid #b8b8b8; border-radius:5px; box-shadow: 0px 0px 8px rgba(0%,0%,0%,0.25); margin:0; padding:3px 0; position:fixed; visibility:hidden;'></ul>\"\n",
" var menu = wrapper.firstChild;\n",
"\n",
" root.appendChild(menu);\n",
"\n",
" var items = [];\n",
"\n",
" var ignore_mouseup = null;\n",
" function open_menu(e)\n",
" {\n",
" var show_menu = false;\n",
" for(var index=0; index != items.length; ++index)\n",
" {\n",
" var item = items[index];\n",
" if(item.show(e))\n",
" {\n",
" item.item.style.display = \"block\";\n",
" show_menu = true;\n",
" }\n",
" else\n",
" {\n",
" item.item.style.display = \"none\";\n",
" }\n",
" }\n",
"\n",
" if(show_menu)\n",
" {\n",
" ignore_mouseup = true;\n",
" menu.style.left = (e.clientX + 1) + \"px\";\n",
" menu.style.top = (e.clientY - 5) + \"px\";\n",
" menu.style.visibility = \"visible\";\n",
" e.stopPropagation();\n",
" e.preventDefault();\n",
" }\n",
" }\n",
"\n",
" function close_menu()\n",
" {\n",
" menu.style.visibility = \"hidden\";\n",
" }\n",
"\n",
" function contextmenu(e)\n",
" {\n",
" open_menu(e);\n",
" }\n",
"\n",
" function mousemove(e)\n",
" {\n",
" ignore_mouseup = false;\n",
" }\n",
"\n",
" function mouseup(e)\n",
" {\n",
" if(ignore_mouseup)\n",
" {\n",
" ignore_mouseup = false;\n",
" return;\n",
" }\n",
" close_menu();\n",
" }\n",
"\n",
" function keydown(e)\n",
" {\n",
" if(e.key == \"Escape\" || e.key == \"Esc\" || e.keyCode == 27)\n",
" {\n",
" close_menu();\n",
" }\n",
" }\n",
"\n",
" canvas.addEventListener(\"contextmenu\", contextmenu);\n",
" canvas.addEventListener(\"mousemove\", mousemove);\n",
" document.addEventListener(\"mouseup\", mouseup);\n",
" document.addEventListener(\"keydown\", keydown);\n",
"\n",
" var module = {};\n",
" module.add_item = function(label, show, activate)\n",
" {\n",
" var wrapper = document.createElement(\"div\");\n",
" wrapper.innerHTML = \"<li class='toyplot-context-menu-item' style='background:#eee; color:#333; padding:2px 20px; list-style:none; margin:0; text-align:left;'>\" + label + \"</li>\"\n",
" var item = wrapper.firstChild;\n",
"\n",
" items.push({item: item, show: show});\n",
"\n",
" function mouseover()\n",
" {\n",
" this.style.background = \"steelblue\";\n",
" this.style.color = \"white\";\n",
" }\n",
"\n",
" function mouseout()\n",
" {\n",
" this.style.background = \"#eee\";\n",
" this.style.color = \"#333\";\n",
" }\n",
"\n",
" function choose_item(e)\n",
" {\n",
" close_menu();\n",
" activate();\n",
"\n",
" e.stopPropagation();\n",
" e.preventDefault();\n",
" }\n",
"\n",
" item.addEventListener(\"mouseover\", mouseover);\n",
" item.addEventListener(\"mouseout\", mouseout);\n",
" item.addEventListener(\"mouseup\", choose_item);\n",
" item.addEventListener(\"contextmenu\", choose_item);\n",
"\n",
" menu.appendChild(item);\n",
" };\n",
" return module;\n",
" })(modules[\"toyplot/root\"],modules[\"toyplot/canvas\"]);\n",
"modules[\"toyplot/io\"] = (function()\n",
" {\n",
" var module = {};\n",
" module.save_file = function(mime_type, charset, data, filename)\n",
" {\n",
" var uri = \"data:\" + mime_type + \";charset=\" + charset + \",\" + data;\n",
" uri = encodeURI(uri);\n",
"\n",
" var link = document.createElement(\"a\");\n",
" if(typeof link.download != \"undefined\")\n",
" {\n",
" link.href = uri;\n",
" link.style = \"visibility:hidden\";\n",
" link.download = filename;\n",
"\n",
" document.body.appendChild(link);\n",
" link.click();\n",
" document.body.removeChild(link);\n",
" }\n",
" else\n",
" {\n",
" window.open(uri);\n",
" }\n",
" };\n",
" return module;\n",
" })();\n",
"modules[\"toyplot.coordinates.Axis\"] = (\n",
" function(canvas)\n",
" {\n",
" function sign(x)\n",
" {\n",
" return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
" }\n",
"\n",
" function mix(a, b, amount)\n",
" {\n",
" return ((1.0 - amount) * a) + (amount * b);\n",
" }\n",
"\n",
" function log(x, base)\n",
" {\n",
" return Math.log(Math.abs(x)) / Math.log(base);\n",
" }\n",
"\n",
" function in_range(a, x, b)\n",
" {\n",
" var left = Math.min(a, b);\n",
" var right = Math.max(a, b);\n",
" return left <= x && x <= right;\n",
" }\n",
"\n",
" function inside(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(in_range(segment.range.min, range, segment.range.max))\n",
" return true;\n",
" }\n",
" return false;\n",
" }\n",
"\n",
" function to_domain(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n",
" {\n",
" if(segment.scale == \"linear\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" return mix(segment.domain.min, segment.domain.max, amount)\n",
" }\n",
" else if(segment.scale[0] == \"log\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" var base = segment.scale[1];\n",
" return sign(segment.domain.min) * Math.pow(base, mix(log(segment.domain.min, base), log(segment.domain.max, base), amount));\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" var axes = {};\n",
"\n",
" function display_coordinates(e)\n",
" {\n",
" var current = canvas.createSVGPoint();\n",
" current.x = e.clientX;\n",
" current.y = e.clientY;\n",
"\n",
" for(var axis_id in axes)\n",
" {\n",
" var axis = document.querySelector(\"#\" + axis_id);\n",
" var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
" if(coordinates)\n",
" {\n",
" var projection = axes[axis_id];\n",
" var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
" if(inside(local.x, projection))\n",
" {\n",
" var domain = to_domain(local.x, projection);\n",
" coordinates.style.visibility = \"visible\";\n",
" coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
" var text = coordinates.querySelector(\"text\");\n",
" text.textContent = domain.toFixed(2);\n",
" }\n",
" else\n",
" {\n",
" coordinates.style.visibility= \"hidden\";\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" canvas.addEventListener(\"click\", display_coordinates);\n",
"\n",
" var module = {};\n",
" module.show_coordinates = function(axis_id, projection)\n",
" {\n",
" axes[axis_id] = projection;\n",
" }\n",
"\n",
" return module;\n",
" })(modules[\"toyplot/canvas\"]);\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tb76eeef703fb428e86bbc66bf30fba88\",\"data\",\"point\",[\"x\", \"y0\"],[[9.90425274899005, 9.741373160037037, 8.936018277967092, 71.35495502527698, 57.50732806007437, 74.86681786249491, 34.52701545843618, 29.386829854476293, 41.66266373643811, -35.44597565067607, -36.92970379493566, -36.16263274186227, -33.187168905666454, -30.118544651439265, -29.247695510506038, -40.69382653749566, -34.34127620647451, -34.760455492675995, -37.221754843889826, 74.70931394054736, -38.625148849926184, 5.975455924391921, 1.86890833618842, 64.37796844082165, -41.312557102204224, -6.727897728581371, -8.019509970534282, -42.02475283927255], [88.94277659521687, 83.15763554680372, 83.11693196313485, -23.154422018874403, -26.175827328359365, -22.929580612443416, -29.04374695056051, -27.84475592463253, -30.033228233185504, -20.5272226429361, -17.582037075094018, -20.772194492681482, -15.83452103447345, -13.4999195839101, -12.672877296459575, -19.163668192341472, -18.060695085450217, -21.005876812400388, -22.62667373231019, -22.494278137063397, -18.734389121661113, 47.08020184325513, 54.651222174258244, -19.33075769595802, -18.770877725956595, 41.89984870450099, 39.21541049367185, -17.806477624089833]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tcb65bb059f86464b9a28561440a86e66\",\"data\",\"point\",[\"x\", \"y0\"],[[8.108448603734598, 8.617701925640139, 7.623182406883075, 71.58400760934725, 56.820424777352166, 74.2085054845683, 36.527769158694696, 30.640886933641596, 41.59771824904981, -35.07600541978134, -37.76405748496534, -35.445421217559655, -31.260505543689558, -30.462997375409344, -28.949084432745607, -39.983505407594045, -33.86576221768931, -34.1335368798461, -36.94152731604749, 73.86618201410148, -38.41599436035406, 6.344163217290311, 1.794041871193625, 63.31990098802954, -41.38505855411401, -7.09089007124854, -8.686042072978603, -41.59254488550349], [88.90475675374316, 82.579872838625, 82.90644449402001, -22.226180728554006, -25.948847739997518, -22.107492305420884, -28.09029554202744, -28.18337701297122, -29.87304752836056, -21.1943158081818, -17.434166192397992, -21.09601220960789, -17.006600123444375, -14.309346568921857, -13.82405754887435, -19.033414746914893, -18.279434401592432, -20.881804470451808, -22.60128011365124, -21.49596551063319, -18.605011774941264, 47.68331699448995, 55.372423426615576, -18.29130320416895, -19.752349913030685, 41.03546114171901, 40.61264587251809, -18.860618077586313]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t29b053b10b684abc80310cca476e924f\",\"data\",\"point\",[\"x\", \"y0\"],[[14.572606807173964, 13.675593315069426, 12.913161587523769, 70.10264696929418, 55.57870924467533, 73.38556906511093, 34.554846076054744, 29.62560652044239, 39.25331865847156, -36.56027338495862, -38.70321693526013, -36.844294176409385, -33.32968368329937, -32.05202044938232, -30.66409327711706, -41.93379862958782, -35.40603173332468, -35.372934807214726, -37.23814300718947, 72.74898866318853, -39.51725869544707, 8.249121575597911, 4.862606649761902, 62.86197460805935, -41.85350809223142, -3.7034264559307393, -5.839151551515544, -43.36691486155579], [88.44370906511963, 83.07172737396125, 83.64887426243536, -26.025816507495954, -29.008837527765387, -26.25297717147209, -30.935156479196397, -28.852509978732968, -31.48092806077556, -19.260947688623123, -15.123072080943938, -18.694255963825103, -16.302153553883635, -11.970169238041187, -10.67357937955078, -17.079042110468954, -15.85757322995793, -19.80214327189642, -20.496994190154904, -26.44967209335421, -17.527589124134455, 46.192683345454206, 54.36978778504263, -22.03567584130095, -16.73411464671087, 41.05714430813461, 39.91698989185306, -16.137707893715962]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t7be236a3eb734a53b1b66948ec9bec39\",\"data\",\"point\",[\"x\", \"y0\"],[[7.308335636864564, 7.055814857029974, 6.135069132911557, 71.39806613538276, 57.10858159319944, 75.07129113650365, 37.0390014709697, 32.36843382974997, 41.76912316058653, -35.12488883270725, -37.14742795192563, -35.536656135962374, -32.52149266438857, -30.50167909179367, -29.314193964819403, -41.03872133082342, -33.461780302735384, -33.75591721554264, -36.91766210972915, 74.46443077913443, -37.991275293936155, 5.4708573090104515, 1.7839206996665118, 64.73006767385814, -40.125604052655525, -6.861863290139059, -9.008122007939683, -42.39570916976983], [88.95785002216492, 83.23896573882861, 83.46457039913119, -20.624226741579616, -25.22269670227966, -20.76159614271953, -28.600673416878585, -27.453836247144856, -29.688995000935066, -21.231294756323184, -16.982542130161733, -22.050844498430376, -18.382318692985756, -14.137839208532126, -13.91252455218462, -19.121478108278353, -18.842048913089158, -21.038455849530777, -23.570362860848448, -21.241292894058482, -19.054796098932975, 46.447909528854524, 54.366377851567655, -17.18207059300068, -19.418551723409173, 41.1706295947339, 39.67301977232527, -18.800877776302922]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t3101a7c16bec453d91df74fafae63bcb\",\"data\",\"point\",[\"x\", \"y0\"],[[9.975367479940425, 9.390055974139981, 9.483751636253158, 71.43537586864699, 56.857092254947965, 75.24498574324188, 34.88257615732163, 30.039075082143558, 40.94520800032876, -35.32395704443256, -37.98926391543842, -36.952930485793296, -32.5490916938235, -30.794806330670927, -30.073247225752823, -41.45538823847933, -32.75404797851448, -34.287870091627255, -36.39609733973079, 74.4634319509234, -39.133910754892995, 5.769822876907803, 2.9108779681218557, 64.47003435644916, -41.559290234367886, -6.016581004804562, -7.965773383072672, -42.61539962796504], [88.76606042876067, 83.31988897178226, 83.7966431017058, -23.06995699117389, -26.224492855444026, -22.72827367362177, -29.692549434086278, -28.053574063287478, -30.31196032044783, -20.05691328150146, -16.68959473334457, -21.170094951248714, -16.217924058719504, -12.726790582492946, -12.597751863174876, -19.02689177407503, -17.918302063466314, -21.476685258807855, -21.273095559644023, -23.11828438582333, -19.155522854297157, 46.80711291192898, 54.42663551577989, -19.617416122594587, -18.81196076393122, 41.393209450378606, 39.97125403868488, -18.54276882783817]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t935caa7e44614ff28f615f8ee38eff9f\",\"data\",\"point\",[\"x\", \"y0\"],[[10.463155716083223, 9.961454090074602, 9.538433665650068, 71.90751642617371, 57.139828031935636, 74.77033143461065, 34.92124722836311, 31.096734047987425, 41.00988551052663, -35.835946832162165, -37.9574387095382, -36.911153095373166, -33.15278949441723, -30.422748432889318, -30.649657427748465, -40.72071131361119, -34.65172873033482, -34.808082060081645, -37.506530131562634, 75.01102942241748, -39.619392800943636, 5.952283820443281, 2.7139528031283753, 64.67498313227428, -40.86827652412614, -6.319690977144326, -6.967856307684956, -42.768832492050606], [88.7245507853062, 83.16725482047183, 83.43053110102768, -23.49967534436568, -27.070774260305782, -23.68348186085738, -28.843589644245114, -27.837479813154335, -30.487060780293678, -20.864841671777015, -16.387721208309628, -20.30401963391612, -16.811765179345645, -13.355932555914201, -12.69301029948567, -18.99397215887339, -17.93444641386834, -20.22641062037484, -22.359870921457027, -22.87433375623339, -19.152398942026313, 46.92053482172071, 53.983761675360796, -19.235925043435834, -17.899780331891968, 41.759901380866204, 40.21643955480343, -17.686483699425494]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"te639e72596f640f28206ddd3cfd67b70\",\"data\",\"point\",[\"x\", \"y0\"],[[14.408170151272198, 14.319736956132136, 13.595845968941655, 69.78534210697904, 54.63209505148762, 72.29821045256777, 34.54755246108144, 29.659663921663412, 39.523772575792094, -35.85877506226111, -38.97841149509813, -37.232068959935525, -34.59552994175018, -31.814394151668523, -30.009203957976318, -41.68894380779025, -34.72256511541406, -35.10596428974777, -36.73844577188824, 72.42779988350198, -40.64541928618454, 8.718379122360082, 5.87421533888954, 62.38578335537712, -42.65900585217948, -3.2978403426155145, -5.280254268977764, -43.54974504255877], [87.14091735854949, 82.43553916390555, 82.36617608442569, -25.644067839720574, -28.81532275591899, -26.75489308520145, -31.851136758212466, -30.308414481112653, -32.0957495160595, -19.16120665275945, -14.613101073767883, -18.33154525952966, -15.451343067566265, -11.650653753315337, -10.6418444960675, -16.9116627612761, -15.823086161337377, -18.8156175563014, -20.51760048538854, -26.720243737496627, -16.939693252501844, 46.83147992361945, 54.33253215090422, -21.882206815553293, -16.65562521217139, 42.35340576342673, 40.290119692961404, -16.165155416534134]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t5ac5fe389fbe4cdaa33cfd2de32e07e1\",\"data\",\"point\",[\"x\", \"y0\"],[[13.390697852943571, 12.76880993095078, 11.590826176501938, 71.46993215334679, 55.578524649318396, 74.05745590415671, 34.05882516821371, 28.797986823492888, 40.164591697445786, -37.23099503794359, -38.88611121420555, -37.21590976560106, -33.83394296164072, -31.49132116740065, -30.66503944186509, -42.186915282752295, -33.87034243812147, -35.10111032271948, -38.543615578457356, 73.75834423450446, -39.89883010431379, 8.161645449318883, 5.082836846354194, 64.32440100929551, -41.582597700118036, -3.9874771769762014, -6.39684285915582, -42.313826844572496], [87.78152548601332, 82.2832547599055, 82.53276980547011, -24.925889320011965, -28.153049097186436, -25.262136822834833, -30.690922305263655, -28.76174428141966, -31.44837975873982, -19.29161185324704, -15.690077116656427, -19.223851352891895, -16.32505727494886, -12.529511985604096, -12.208473144465565, -18.24786393488593, -16.124073310189953, -19.522864192767052, -20.554942639664322, -26.04497697542389, -17.70570810536457, 47.89231935065671, 54.23792609500459, -21.81906706097184, -17.326910789953356, 42.39882596556237, 40.619286042356926, -15.88879618247837]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t0b2643f171b644c38c4ce8b4551e1ece\",\"data\",\"point\",[\"x\", \"y0\"],[[7.934047263136408, 7.598792227302015, 7.626712190118778, 72.31295422742802, 57.40673869579451, 75.2272573795475, 36.64580973453128, 30.98126573912171, 41.33503947803902, -35.51134178032326, -37.16442594999918, -36.60383584634386, -32.56852420319788, -30.678863388827992, -30.13637687893097, -40.054599779427065, -33.144951963199034, -34.438910421673846, -36.9782923301865, 74.85499346030893, -38.51411937856607, 4.973402029544977, 0.9906736723590488, 65.11664172913329, -39.54275266846597, -7.290215757869937, -8.859461476125526, -41.51765600322841], [88.57207676703798, 82.92587199412228, 83.63792025786259, -21.76892429612965, -24.59541947617638, -21.83280153100822, -28.777808022831444, -27.18186910322994, -28.35761503255525, -21.527013670222505, -16.76299711029707, -21.882695777244177, -18.215898504742157, -13.977071979646182, -12.798159603828585, -19.95041554870504, -18.612559898192888, -21.551816491319812, -23.248946182229727, -21.276428217176093, -19.96171819719361, 48.092225209534384, 54.31656055215788, -19.08398490018183, -20.269330632993857, 42.15413730277572, 39.88425159917599, -17.949569506762376]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t4594ef3e4e044b3889f555014d369e9f\",\"data\",\"point\",[\"x\", \"y0\"],[[13.381003409881428, 12.77663441362569, 12.64020176096838, 70.23946208630328, 56.001733081797234, 73.40465395072906, 34.004940235496676, 28.755248778408298, 39.99382233254985, -36.039435462012136, -37.631619093928585, -38.17652276558813, -33.455073341614806, -30.57989706176935, -30.302640145285736, -41.87447939981488, -34.08473738182552, -34.958989104612606, -37.532797653205726, 73.15555846818833, -39.71203234398501, 8.642420353123095, 4.772541373571611, 62.75918799551672, -42.22961538808293, -4.5925158512052215, -6.101117350421484, -43.25593589680755], [87.5820624857748, 81.73643400807876, 82.30882109153384, -25.364765621719418, -28.72723191968343, -25.689973589120598, -28.835272636447705, -28.68542693631748, -31.413382938059176, -18.304054868142376, -15.459568344159642, -19.218407456862458, -15.176120523647189, -12.245741787177458, -11.17856907266845, -17.382797255240856, -17.551289256375686, -19.45209439116363, -20.247736319035898, -26.280453839644828, -17.49688498096565, 46.009663830256976, 54.57757929586288, -21.068566187607587, -17.547482104142833, 41.95567767959392, 39.40816489054465, -16.252583253463555]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t1942fb888dbe4d56aa0ea8f0854c45a5\",\"data\",\"point\",[\"x\", \"y0\"],[[10.944608567002042, 10.590596685000177, 10.008320280371947, 71.15902586081789, 56.46310554405827, 74.25350784135314, 35.17095831491632, 30.13517315311276, 40.72551433992281, -35.800759450725806, -37.91516765452947, -36.70814251904287, -33.04538024334882, -30.89172721012514, -30.00112322627475, -41.1630889727376, -34.03032240676332, -34.6723770685742, -37.201486608188716, 73.94600728168163, -39.207338186854955, 6.825755167798872, 3.2654575559235086, 63.90209432888148, -41.31182661685456, -5.588839865651547, -7.312413124840634, -42.54013176632846], [88.3816285747687, 82.79164452164848, 83.1209682560747, -23.630392540962518, -26.994249966311695, -23.800320679470016, -29.53611511897496, -28.316298784200313, -30.519034716941196, -20.141942289371407, -16.27248770651329, -20.274392159623787, -16.572370201375684, -13.040297724355549, -12.320084725675997, -18.491120659106002, -17.50035087335203, -20.3773768915014, -21.749750300438432, -23.79959295469074, -18.433371245201897, 46.995744775977094, 54.46348065225544, -19.954697346477356, -18.318698384419196, 41.71782412916921, 39.98075818488955, -17.40910382581971]],\"toyplot\");\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"t7bfff0b5da2f4d20a43881ea2b26d448\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 80.0, \"min\": -43.54974504255877}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 220.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"t29f47ab5d303475794cd90e1d4bdd8dc\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 88.95785002216492, \"min\": -40.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 200.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"})();</script></div></div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tool.run(nreplicates=10)\n",
"tool.draw(imap=IMAP);"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"toyplot\" id=\"t5db1355fc05143eaa1674bca9eb71ed6\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"300.0px\" id=\"t6ac9e47c2d484d1d9ae644409d013f23\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 1000.0 300.0\" width=\"1000.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"t1c796d33144146ca9a5ef7538bbd8301\"><clipPath id=\"t35959c9744684cf3986aa8657eb5078a\"><rect height=\"220.0\" width=\"220.0\" x=\"40.0\" y=\"40.0\"></rect></clipPath><g clip-path=\"url(#t35959c9744684cf3986aa8657eb5078a)\"><g class=\"toyplot-mark-Point\" id=\"t06bbc1e400ab43fdb8f88d077a687d98\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(136.53032472569765, 50.02337729257344)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(136.2666583152216, 58.99551981420947)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(134.96296500239035, 59.05864677183459)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(236.00556403941573, 223.8742884155866)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(213.58928635234716, 228.56016881598975)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(241.69050144824357, 223.5255831504285)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(176.3891891870762, 233.00800913235395)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(168.06835355574523, 231.14850073372307)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(187.94024220712726, 234.5425900554306)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.11822924295185, 219.7997797672387)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.716398071631346, 215.23210813292428)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.95811824322565, 220.17970522304194)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.77474305320946, 212.52189539237347)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(71.74217419306353, 208.90117521107067)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(73.15188837844408, 207.61852000658584)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.62310707979096, 217.68505088433588)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.90649589428493, 215.97445613310254)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.22793636175479, 220.54212181059944)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.24363133487513, 223.05580658377332)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(241.4355370662559, 222.85047500415405)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.97184355327683, 217.01928440233175)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(130.17046243177742, 114.9478076312717)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(123.52286054997843, 103.20595503416061)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(224.71134958020824, 217.94418905015954)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.62151769650987, 217.07587437229358)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(109.60651282816247, 122.9819880055006)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(107.51567526065797, 127.14526803904296)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.4686286527923, 215.5801917105541)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"tffcc5cece66c4ae2996aa5919d238831\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(133.62331080246074, 50.082342049611746)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(134.44768048728707, 59.8915687295402)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(132.83777102383252, 59.385090596818735)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(236.37635004790383, 222.4346842501002)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(212.47734025729758, 228.2081474565722)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(240.6248377713176, 222.25061104616148)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(179.62797158936982, 231.52930673716173)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(170.09839753314614, 231.67366626382534)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(187.83510967550455, 234.2941667065652)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.71713008368979, 220.83437078303348)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.365762034718, 215.00277601755386)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.11912674883698, 220.68191228817332)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(69.89358941151352, 214.33966622023618)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(71.18458061186848, 210.1565109426646)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(73.63527436626231, 209.4038789470721)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.77296154473855, 217.4830415527532)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.67624898219523, 216.31369770095532)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.24278040309852, 220.34969871743021)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.697258378372155, 223.01642376426355)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(240.0706909855854, 221.30219759993457)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.31041890120664, 216.81863380727668)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(130.76731885227653, 114.01243975544071)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(123.4016681266853, 102.08744809218946)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(222.99856991817393, 216.33210495972162)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.50415371184958, 218.5980340343931)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(109.0189076614466, 124.32256178621019)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(106.43670564851557, 124.97830359506983)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.16827874696315, 217.21505217591593)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t5778884db1bb4446b9c34730146e5619\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(144.0873683384964, 50.79737830144795)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(142.63529979429092, 59.128754313431806)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(141.4010896754711, 58.23366046939687)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(233.9783513477968, 228.32751788262257)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(210.46727454287768, 232.95386830604656)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(239.2926837969425, 228.67982007118587)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(176.43424086663893, 235.94138546936762)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(168.45488072483627, 232.71142079469993)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(184.04003977913095, 236.78781952744993)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.31442506043619, 217.83592110474498)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.84546840728675, 211.4185132354792)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.854657553262584, 216.95704211490354)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.54404281569165, 213.24714402102197)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(68.61229999174158, 206.52869405446643)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(70.859050354176, 204.5178201785953)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.61586361414879, 214.45201608806062)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.18288970394694, 212.5576469119289)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.236466384495415, 218.6752582729442)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.217102484825304, 219.7528986308426)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(238.26219943341246, 228.9848731127003)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.52771293979221, 215.14766511382896)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(133.85102955949233, 116.32425504823527)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(128.36900298846302, 103.64243003613565)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(222.25728732012007, 222.13923129827091)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.74583642360905, 213.91707003599976)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(114.50246995313881, 124.28893348609218)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(111.04519839851258, 126.0571925196998)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(50.29596205308235, 212.9921061770452)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"tb62059420eb545bf9849ed7c2f27184e\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(132.3281030032145, 50.0)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(131.91932712148747, 58.869385279536445)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(130.4288412871359, 58.519496288267135)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(236.07535149238203, 219.95022287500896)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(212.94380308285506, 227.08196392048959)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(242.02149893259823, 220.1632683020478)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(180.45554482652852, 232.32084889572502)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(172.89491790720808, 230.54222561759715)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(188.1125767175898, 234.0087206830872)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.63799853564965, 220.89172122449173)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.36395030751015, 214.302354814566)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.97143738149545, 222.16275628279382)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.8523272134173, 216.4732603663931)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(71.12196337802312, 209.8905211477661)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(73.04424193321596, 209.54108192198993)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.06479788504684, 217.61961852165945)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.3302072964188, 217.18625336375527)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.8540640025481, 220.5926484549639)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.73589092481751, 224.51936871415325)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(241.03912481736202, 220.90722727973935)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.997946125599704, 217.51620177062966)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(129.35362769819275, 115.92842620438213)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(123.38528416486712, 103.64771848266976)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(225.28132118624467, 214.61180237871918)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.5429349347888, 218.080348310625)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(109.38965190058784, 124.11292979716626)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(105.91532871673778, 126.43556439777603)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(51.86813153259266, 217.12240128064568)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t7a9654ac115141cdb95248e83d830daf\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(136.64544391259014, 50.29744539533074)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(135.69795267236327, 58.743881895384604)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(135.8496254452709, 58.00448661259785)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(236.13574778579627, 223.7432920820001)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(212.5366968793338, 228.63564390661247)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(242.3026724901453, 223.21337735793577)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(176.96476414882616, 234.01423323335166)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(169.12419584411674, 231.47235560354147)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(186.77883837604332, 234.97487407259501)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.31575066430561, 219.07037963943912)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.00120210722402, 213.84802435423836)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.67879914198623, 220.79680679304926)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.80765042444388, 213.11651297351358)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(70.64745452529132, 207.70213381687208)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(71.81550081250874, 207.50200839713858)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.39030534358125, 217.4729251110806)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.47587105149513, 215.75361960091868)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.99294870700236, 221.27229596645972)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.58019014990891, 220.95654985386736)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(241.03750793306867, 223.81824276494203)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.14826936493421, 217.67241832564648)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(129.83758752796717, 115.37133970982174)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(125.20958136283727, 103.55426521216023)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(224.86038415020397, 218.38876598221495)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.222110749812124, 217.13958982345466)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(110.75797894172146, 123.76773195832752)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(107.6026630362185, 125.97303456138629)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(51.51250075711913, 216.7221015727638)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t2d6450ada76b41599f8c10fc9f288fd2\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(137.43506632091606, 50.36182246651693)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(136.6229211791583, 58.98060133710017)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(135.93814368442725, 58.57228764311315)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(236.9000400267298, 224.40973984476588)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(212.99438422930004, 229.94813694944204)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(241.53431103627466, 224.6948043312785)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(177.02736415019106, 232.6975862984109)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(170.83631425517385, 231.13721625359727)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(186.88353711123796, 235.24643638511148)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.486951240149395, 220.3233912089352)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.05272015105204, 213.37985041215876)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.74642759464834, 219.45361548335583)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.83039579654354, 214.03749780774288)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(71.24973484186089, 208.67786654398117)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(70.88241883520949, 207.64974416707338)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.57958651063677, 217.42187026611228)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.40394119657452, 215.77865778261798)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.15083937156797, 219.33325210333993)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.782642463429525, 222.64202361390014)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(241.92394840497002, 223.43990111370016)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.36238017368835, 217.66757346777965)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(130.13295186639237, 115.19543431162808)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(124.8908026151747, 104.24111574563761)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(225.1921513679421, 217.7971136258936)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.34071072750325, 215.72489435220965)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(110.26731022810274, 123.19903151795171)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(109.21807240034786, 125.59277773161378)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(51.26412652691297, 215.39409381167673)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"td7c8ed9dca6442e6b0041e3af6205896\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(143.8211813773738, 52.817870588418046)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(143.67802738687448, 60.115415009072066)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(142.5062062925596, 60.22298981660481)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(233.4647042136714, 227.73546603357303)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(208.93491331807445, 232.6537473412303)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(237.53248815725323, 229.4582386221203)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(176.4224341001081, 237.36197410179074)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(168.51001220440236, 234.96937485043125)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(184.47784548601828, 237.74134264400047)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.449997331274744, 217.68123329652465)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.39998863759042, 210.62760208569128)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.22693503810471, 216.39451613570478)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.49491473693314, 211.9276268513869)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(68.99696496637497, 206.03315929691436)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(71.91917284801873, 204.46860272734617)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.012229987407146, 214.1924284023725)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.28927259073507, 212.50416111232133)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.668633229315695, 217.1452611220527)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.02600295666214, 219.78485681753708)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(237.74226508700661, 229.40450114479904)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.70146782637841, 214.23590073262758)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(134.61065483690675, 115.33354904925122)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(130.00657607900916, 103.70020958834127)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(221.4864378901707, 221.90121705451403)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(51.44191182276006, 213.79534121603882)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(115.15902511345178, 122.27856892888325)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(111.9499307916798, 125.47850762220156)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(50.0, 213.0346743848952)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"tae79a9e5fcb14dcba836191c87d8dcd3\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(142.1741163867044, 51.824355067875906)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(141.16741593292593, 60.35159202966269)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(139.26051802060232, 59.964620557167265)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(236.19168684854043, 226.62164703056519)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(210.46697572339104, 231.62663087081964)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(240.38032155582954, 227.1431313803199)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(175.6312915644446, 235.56260407080867)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(167.1151455490746, 232.57065278825797)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(185.51519140920567, 236.73734054996984)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.22867348279606, 217.88347798417288)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.54940259366258, 212.29787813744528)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.25309323750675, 217.7783886075378)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.7277573945155, 213.28266527244062)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(69.51994942766477, 207.39617555709194)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(70.85751872050456, 206.89827823469807)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.20612314389968, 216.26473524275508)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.66883460763609, 212.97096037859453)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.67649073970815, 218.2421259291879)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.10382807730922, 219.84277055333425)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(239.89612521928638, 228.35723374392865)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.91003233068164, 215.42390883408254)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(133.70942485403722, 113.68829918372548)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(128.72550748228684, 103.84693358518736)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(224.6246356310897, 221.8032939663568)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.18438106329254, 214.83643422071685)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(114.04265399650106, 122.20812699436306)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(110.14241821479278, 124.9680053932346)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.00068109822572, 212.60607041234408)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"td25ebf94f88f494fbfcb408cc81e1b2f\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(133.34099319745374, 50.59829355880332)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(132.79828866055826, 59.354960596824256)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(132.84348497044485, 58.250648973114764)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(237.55635510227228, 221.72552783605366)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(213.426454184227, 226.10912321944585)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(242.27397414894145, 221.8245946782313)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(179.819053450034, 232.59556595393036)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(170.649396331829, 230.1204333127965)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(187.40989022900504, 231.9438910226191)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.01241578356402, 221.35035001498176)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.33643426841563, 213.96186368536848)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.24390696852069, 221.90197538243405)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.77619344431382, 216.21516023799458)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(70.83514077555917, 209.64118816205286)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(71.71330771910107, 207.81281962828)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.65787531480173, 218.90521290817287)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.84308304444599, 216.83034014892294)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.74844746583094, 221.38881656989568)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.63774386601708, 224.020885405749)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(241.67136032871815, 220.9617184535788)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.15157597008086, 218.92274212176727)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(128.5483564622317, 113.37826631819104)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(122.10119081925282, 103.72497985047515)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(225.90709998512764, 217.56147051734627)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.48644377649265, 219.39981650808403)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(108.69624299459078, 122.58761325711416)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(106.15597758535819, 126.1079661525906)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.28950745892737, 215.80211210182605)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t95093cccb1b944e5ab1a2aafeac0f78d\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(142.15842320488716, 52.133701106452435)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(141.1800820580923, 61.19965323994616)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(140.9592274499176, 60.31194135058586)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(234.1998250820598, 227.3022978038714)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(211.15205756201902, 232.51712776170035)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(239.323578050284, 227.80666100059852)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(175.54406365037897, 232.6846874980727)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(167.04596200673728, 232.4522926495164)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(185.2387531780509, 236.68306417877628)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.157547679211454, 216.35188144323345)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.58015080742027, 211.94038338631978)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.69807100794865, 217.76994569998544)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.3410643987435, 211.50078576513778)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(70.99534560159834, 206.95607796182674)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(71.44416387538452, 205.30100583659265)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.7118884659241, 214.92310822354472)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.3217761112706, 215.18442151483467)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.90655389047858, 218.13236944427246)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.740121094188254, 219.36632600873983)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(238.92034697529488, 228.7224334804014)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.21241702644032, 215.100046231902)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(134.48769421208178, 116.60809897889288)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(128.22320701590274, 103.32016735761778)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(222.0908982879011, 220.63934640793323)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.1370010177214, 215.1785170239763)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(113.06322878761762, 122.89540316389021)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(110.62113309782627, 126.84632633547133)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(50.475612710734474, 213.1702657225523)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"tfdc872d5b22045568a5675e50197cae8\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(138.21443312697946, 50.893658582703075)\"><title>BJSB3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(137.64136536082597, 59.563133224470775)\"><title>BJSL25</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(136.69878728520524, 59.05238690795012)\"><title>BJVL19</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(235.68839759865682, 224.61246840541475)\"><title>BZBB1</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(211.8989186131723, 229.82945585483486)\"><title>CRL0001</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(240.69768673878303, 224.8760089940308)\"><title>CRL0030</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(177.43159175335964, 233.77162013909734)\"><title>CUCA4</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(169.279757591227, 231.87981388679864)\"><title>CUSV6</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(186.42320241689134, 235.2960245825605)\"><title>CUVN10</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(62.54391191040288, 219.2022506466796)\"><title>FLAB109</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(59.121147738651146, 213.20113542617452)\"><title>FLBA140</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(61.075057291553605, 219.40766640109797)\"><title>FLCK18</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(67.00426786893254, 213.6662214908241)\"><title>FLCK216</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(70.4905608313046, 208.1883502694707)\"><title>FLMO62</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(71.93225378428255, 207.0713760045372)\"><title>FLSA185</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(53.86347388899758, 216.64200072008472)\"><title>FLSF33</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(65.40986204790035, 215.1054214647952)\"><title>FLSF47</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(64.37051605558005, 219.5673848391146)\"><title>FLSF54</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(60.276441173040524, 221.69579099461606)\"><title>FLWO6</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(240.19991062509604, 224.87488036978783)\"><title>HNDA09</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(57.02940642120791, 216.5524374807873)\"><title>LALC2</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(131.5469108301356, 115.07879161908403)\"><title>MXED8</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(125.78356812044574, 103.49712229845747)\"><title>MXGT4</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(223.94101353171823, 218.91185352411304)\"><title>MXSA3017</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(53.62270019243394, 216.37459198977916)\"><title>SCCU3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(111.45039824053211, 123.26428888954993)\"><title>TXGR3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(108.66031031506472, 125.95829463480868)\"><title>TXMD3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(51.63434295373501, 214.9639069350219)\"><title>TXWV2</title><circle r=\"5.0\"></circle></g></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t0d403b2cd957452d8810d665487ffc1a\" transform=\"translate(50.0,250.0)translate(0,10.0)\"><line style=\"stroke-width:2.25\" x1=\"0\" x2=\"192.3026724901453\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(5.746260409256206,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.67\" y=\"10.265999999999998\">-40</text></g><g transform=\"translate(70.49750693950413,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"10.265999999999998\">0</text></g><g transform=\"translate(135.24875346975207,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"10.265999999999998\">40</text></g><g transform=\"translate(200.0,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"10.265999999999998\">80</text></g></g><g transform=\"translate(100.0,22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-74.30499999999999\" y=\"11.977\">PC0 (16.8%) explained</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"tdae5bf5c33314481992b43f43b6793b1\" transform=\"translate(50.0,250.0)rotate(-90.0)translate(0,-10.0)\"><line style=\"stroke-width:2.25\" x1=\"12.258657355999564\" x2=\"200.0\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(0.0,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.67\" y=\"0.0\">-40</text></g><g transform=\"translate(62.035773693691254,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"0.0\">0</text></g><g transform=\"translate(124.07154738738251,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"0.0\">40</text></g><g transform=\"translate(186.10732108107376,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"0.0\">80</text></g></g><g transform=\"translate(100.0,-22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-74.30499999999999\" y=\"0.0\">PC1 (14.8%) explained</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g><g class=\"toyplot-coordinates-Cartesian\" id=\"t80bea47f042c4a5da4c32835d347171e\"><clipPath id=\"tdf0d44628407462295bc358f9ef68770\"><rect height=\"220.0\" width=\"220.0\" x=\"340.0\" y=\"40.0\"></rect></clipPath><g clip-path=\"url(#tdf0d44628407462295bc358f9ef68770)\"><g class=\"toyplot-mark-Point\" id=\"taefdd3db57154609981629c66e9821f2\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(436.5303247256976, 199.3723466519079)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(436.2666583152216, 196.73058715458933)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(434.9629650023904, 198.6367550713185)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(536.0055640394157, 158.93878020331115)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(513.5892863523471, 174.60445830375204)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(541.6905014482436, 162.89551952990027)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(476.3891891870762, 184.33558075338277)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(468.06835355574526, 188.4071505829901)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(487.9402422071273, 179.68932731493572)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(363.1182292429518, 231.89512686587454)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(360.7163980716314, 109.53839818105531)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(361.9581182432256, 234.99424838846147)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(366.7747430532094, 241.64673506232458)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(371.74217419306353, 231.61322853424645)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(373.1518883784441, 246.40306180196666)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(354.62310707979094, 90.72256161083602)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(364.90649589428494, 233.72955895716328)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(364.22793636175476, 216.06149648189435)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(360.24363133487515, 233.46581290900565)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(541.4355370662558, 163.10193450562988)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(357.9718435532768, 99.56163523880461)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(430.1704624317774, 146.38513758801886)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(423.5228605499784, 150.55894511432427)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(524.7113495802082, 163.34408939526503)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(353.6215176965099, 72.64835659528019)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(409.6065128281624, 135.7548045328968)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(407.515675260658, 133.29087539659744)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(352.4686286527923, 66.03818226891424)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t9db40600b4f34940a9e5748c51300b5f\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(433.6233108024607, 198.33168139589318)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(434.447680487287, 195.92801822840207)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(432.8377710238325, 198.70578588474868)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(536.3763500479039, 158.74679295265062)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(512.4773402572976, 173.75434803654653)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(540.6248377713176, 162.01073460479876)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(479.6279715893698, 187.30607851032175)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(470.0983975331461, 184.98336513725474)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(487.83510967550455, 182.22157772567255)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(363.71713008368977, 233.17462957271476)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(359.36576203471805, 106.71162750857945)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(363.11912674883695, 233.45040644983038)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(369.8935894115136, 241.21654896996517)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(371.1845806118685, 231.92218253813743)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(373.6352743662623, 250.0)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(355.77296154473856, 90.37503975253487)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(365.6762489821952, 231.14549809339513)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(365.24278040309855, 217.91553734665922)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(360.69725837837217, 233.28729835766396)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(540.0706909855854, 162.10189096710184)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(358.3104189012066, 96.03404853073205)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(430.7673188522765, 150.94150674918313)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(423.4016681266853, 152.12331779881555)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(522.9985699181739, 160.76328375642493)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(353.5041537118496, 75.53859484383563)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(409.01890766144663, 134.50396386333423)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(406.4367056485156, 134.1998427039322)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(353.16827874696315, 66.97109471551863)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t7ec9b0e0eaec440b9a07f4ccab40a7c8\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(444.0873683384964, 198.53249571851444)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(442.63529979429086, 196.65581623891276)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(441.4010896754711, 198.5690112862179)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(533.9783513477968, 161.02438762338554)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(510.4672745428777, 172.37161162310971)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(539.2926837969426, 162.94039581908743)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(476.4342408666389, 186.07256045258856)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(468.45488072483624, 184.50129194191192)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(484.04003977913095, 180.5318726979911)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(361.3144250604362, 232.44196967163353)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(357.8454684072868, 107.63287301591143)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(360.8546575532626, 236.61331187477518)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(366.5440428156916, 242.75260530223676)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(368.61229999174157, 234.27155669476716)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(370.859050354176, 245.48971081103087)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(352.6158636141488, 90.87361201920854)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(363.1828897039469, 228.73761569293526)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(363.23646638449543, 217.15369315699297)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(360.21710248482526, 229.00212575352356)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(538.2621994334124, 163.9999870659538)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(356.5277129397922, 99.23193750205328)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(433.8510295594923, 147.07214180269108)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(428.369002988463, 150.0400033779744)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(522.2572873201201, 162.50403123680923)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(352.7458364236091, 75.39676634282077)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(414.5024699531388, 134.60351272780778)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(411.0451983985126, 134.95729836059905)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(350.29596205308235, 70.39049918320337)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t7314bf36e35e4a17982c4e2a72038131\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(432.32810300321444, 198.93230841284804)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(431.9193271214875, 195.38634594514141)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(430.4288412871359, 198.08071129844228)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(536.075351492382, 160.18688122896933)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(512.9438030828551, 173.36756135482227)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(542.0214989325983, 161.70935786626856)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(480.4555448265285, 185.38588993986411)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(472.8949179072081, 185.44263034405037)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(488.1125767175898, 180.54887880597042)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(363.6379985356496, 232.591345457341)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(360.36395030751015, 109.91331929174149)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(362.9714373814955, 238.24524805868012)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(367.85232721341725, 243.52794376482458)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(371.1219633780231, 233.43179027328534)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(373.0442419332159, 243.5663775005149)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(354.06479788504686, 89.34280430317436)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(366.3302072964188, 230.58069266057007)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(365.8540640025481, 213.94563297639738)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(360.7358909248175, 233.76257862426243)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(541.0391248173621, 163.74363448226418)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(358.9979461255997, 97.86662816618133)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(429.3536276981927, 149.51753714227834)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(423.38528416486713, 151.67877081991622)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(525.2813211862447, 161.51344762262687)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(355.5429349347888, 72.15684951825155)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(409.38965190058786, 138.32208327547352)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(405.9153287167378, 136.91846634013513)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(351.86813153259266, 64.69897952035181)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t81406817f5644d2592ec28d00367a795\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(436.64544391259017, 199.17943983125926)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(435.6979526723633, 193.87382840848528)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(435.84962544527093, 197.7626206793712)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(536.1357477857963, 161.40594212900822)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(512.5366968793338, 171.97669039345368)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(542.3026724901454, 162.11427294265158)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(476.96476414882613, 183.89203681419517)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(469.12419584411674, 185.1155971395054)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(486.7788383760433, 179.19199696423829)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(363.3157506643056, 233.79974235395267)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(359.001202107224, 108.60842023960683)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(360.6787991419862, 236.31960516800888)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(367.80765042444386, 240.02537423422888)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(370.6474545252913, 232.9023782640483)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(371.81550081250873, 246.85268803738353)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(353.39030534358125, 90.04132797235631)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(367.4758710514951, 234.99942863801124)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(364.99294870700237, 215.9013279941758)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(361.5801901499089, 232.76009366696397)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(541.0375079330686, 163.85127957558234)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(357.1482693649342, 98.02876835169857)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(429.8375875279671, 149.91375267541875)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(425.2095813628373, 150.3284323271622)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(524.8603841502039, 163.75089957034064)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(353.2221107498121, 75.01616662292147)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(410.7579789417215, 133.38233312420522)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(407.6026630362185, 135.38021781125946)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(351.5125007571191, 67.99003306515432)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"tc8e3794ff1a64fe7aac22a3b10d9b924\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(437.4350663209161, 202.28038646195978)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(436.6229211791583, 198.0492035047587)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(435.93814368442725, 200.80992980210775)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(536.9000400267298, 158.70999432065935)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(512.9943842293001, 174.23499497014063)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(541.5343110362747, 163.2217536483408)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(477.02736415019103, 187.086929910028)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(470.8363142551739, 190.3119190222256)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(486.88353711123796, 181.61006395267373)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(362.4869512401494, 230.29893332273517)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(359.05272015105203, 109.21878010270302)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(360.74642759464837, 233.58016885714935)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(366.83039579654354, 241.6881063539241)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(371.2497348418609, 235.34884196418375)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(370.8824188352095, 249.1795717636187)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(354.5795865106367, 89.81217443737899)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(364.4039411965745, 231.39463297682371)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(364.150839371568, 215.38783836281507)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(359.78264246342957, 228.49782174262148)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(541.92394840497, 162.75108123625634)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(356.36238017368834, 97.6230800175782)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(430.1329518663923, 144.3572375604769)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(424.8908026151747, 148.80248332004464)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(525.1921513679421, 159.74897953599162)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(354.3407107275033, 78.2678821566392)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(410.2673102281027, 132.2404695727314)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(409.21807240034786, 132.49199020150436)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(351.264126526913, 67.35944591657714)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t21c9aaa15a1548599c233ddd0b0daae4\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(443.8211813773738, 200.43074822938686)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(443.67802738687453, 197.93615278102595)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(442.5062062925596, 200.82050531076223)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(533.4647042136714, 160.82197997836752)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(508.93491331807445, 173.5977423815036)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(537.5324881572533, 163.8868900003363)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(476.42243410010803, 181.7396304133271)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(468.51001220440236, 182.48650802879064)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(484.4778454860183, 179.32621683490746)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(362.44999733127474, 234.68455505930788)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(357.39998863759047, 108.94093041982606)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(360.2269350381047, 235.42796486679345)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(364.4949147369332, 242.23301042978048)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(368.99696496637495, 231.75365864133872)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(371.9191728480187, 246.85659629453374)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(353.0122299874071, 92.94202037069921)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(364.28927259073504, 233.8553392511983)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(363.66863322931573, 216.65160961731686)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(361.0260029566621, 232.11676348303803)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(537.7422650870066, 163.59474406610005)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(354.7014678263784, 99.01617263622089)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(434.61065483690675, 144.34001231581001)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(430.00657607900916, 149.1104326321648)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(521.4864378901707, 164.1408681562675)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(351.4419118227601, 74.6434952908103)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(415.1590251134518, 132.72907468067922)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(411.9499307916798, 132.61322650343226)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(350.0, 67.66784632092188)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"tc6af9d76a50f468a966ed03874283ed4\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(442.17411638670444, 199.98830278864094)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(441.16741593292596, 196.3563316865166)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(439.2605180206024, 199.81196603829505)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(536.1916868485404, 159.42206358105847)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(510.46697572339104, 173.42703657168116)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(540.3803215558296, 163.25844863625616)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(475.6312915644446, 186.1180947869378)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(467.1151455490746, 187.27986474824883)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(485.51519140920567, 181.01668127794653)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(360.22867348279607, 232.89556763698005)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(357.54940259366253, 106.60224729970989)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(360.25309323750673, 235.28994490163996)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(365.72775739451555, 241.0340493888902)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(369.51994942766476, 234.15351167371702)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(370.8575187205046, 245.90077098664023)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(352.2061231438997, 91.15949745908654)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(365.6688346076361, 227.75316818583863)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(363.6764907397082, 217.3179844872135)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(358.10382807730923, 232.52467625949078)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(539.8961252192863, 163.2060659423952)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(355.9100323306817, 96.79954080978037)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(433.7094248540372, 146.89690147251824)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(428.72550748228684, 150.52061961782246)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(524.6246356310896, 162.47988903700485)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(353.18438106329256, 73.98836070808473)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(414.04265399650103, 133.75948314991572)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(410.14241821479277, 135.7522449206469)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(352.0006810982257, 69.65138094169079)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"tc39f43c12439466c9a6bbc6ee6ac70b3\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(433.3409931974537, 199.33983039308455)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(432.7982886605582, 196.93743540421954)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(432.8434849704448, 198.29027941038186)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(537.5563551022723, 160.2538639790256)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(513.4264541842269, 174.92113750843788)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(542.2739741489415, 164.34629100831532)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(479.819053450034, 183.016987570074)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(470.64939633182894, 181.77920287837517)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(487.40989022900504, 180.1792981081654)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(363.012415783564, 234.82404083756572)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(360.33643426841564, 109.14197441768651)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(361.24390696852066, 237.1751621039092)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(367.7761934443138, 242.5215588414631)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(370.8351407755592, 230.12499970288457)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(371.71330771910107, 245.74627136244467)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(355.65787531480174, 89.32777655022443)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(366.84308304444596, 232.69573476526742)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(364.7484474658309, 215.85402105442685)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(360.6377438660171, 230.70592285574787)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(541.6713603287182, 164.55956734144266)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(358.15157597008084, 96.37315865458478)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(428.5483564622317, 148.23378545643968)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(422.1011908192528, 151.65588329513082)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(525.9070999851276, 165.14502811336476)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(356.48644377649265, 75.330429107513)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(408.69624299459076, 136.70665175026048)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(406.1559775853582, 132.3982342807037)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(353.28950745892735, 66.78016824350786)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"tf7d6de1f5d514a34a73aeac40820dfd8\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(442.1584232048872, 196.13049537464673)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(441.18008205809224, 191.44626843811304)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(440.95922744991753, 194.55574106284544)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(534.1998250820598, 161.00344303695186)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(511.15205756201897, 172.03629647386265)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(539.323578050284, 162.515304309832)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(475.5440636503789, 183.49529084578808)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(467.0459620067373, 184.5113545257356)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(485.2387531780509, 177.16266982983274)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(362.15754767921146, 234.58377711622097)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(359.58015080742024, 107.85578438521549)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(358.6980710079486, 237.75990660046597)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(366.3410643987435, 239.37865627918683)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(370.9953456015984, 233.2093062414431)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(371.44416387538456, 247.27842520955693)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(352.71188846592406, 89.47531951972051)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(365.3217761112706, 231.30112154211636)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(363.90655389047856, 216.11978333707606)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(359.7401210941883, 236.393283171098)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(538.9203469752948, 164.08227149791603)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(356.21241702644033, 96.1874787420896)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(434.48769421208175, 154.6358515650303)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(428.22320701590274, 154.7270870337701)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(522.0908982879012, 165.3829688889769)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(352.13700101772145, 73.0842764318033)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(413.0632287876176, 138.651056232532)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(410.62113309782626, 135.79410958699444)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(350.4756127107345, 65.60736771582643)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t811a8e370dfa4faa802fb2142ee8e933\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(438.21443312697943, 199.25180352581418)\"><title>BJSB3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(437.64136536082594, 195.92999877901647)\"><title>BJSL25</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(436.69878728520524, 198.6043305844491)\"><title>BJVL19</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(535.6883975986568, 160.05141290333876)\"><title>BZBB1</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(511.89891861317227, 173.42918776173101)\"><title>CRL0001</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(540.697686738783, 162.8898968365787)\"><title>CRL0030</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(477.4315917533596, 184.84490799965073)\"><title>CUCA4</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(469.27975759122694, 185.48188843490885)\"><title>CUSV6</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(486.42320241689134, 180.1478583512334)\"><title>CUVN10</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(362.5439119104028, 233.1189687894326)\"><title>FLAB109</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(359.1211477386512, 108.41643548620354)\"><title>FLBA140</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(361.0750572915536, 235.88559672697141)\"><title>FLCK18</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(367.0042678689326, 241.60245886268248)\"><title>FLCK216</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(370.49056083130466, 232.8731454528052)\"><title>FLMO62</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(371.9322537842826, 246.72734737676907)\"><title>FLSA185</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(353.86347388899753, 90.40721339952198)\"><title>FLSF33</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(365.40986204790033, 231.61927907633194)\"><title>FLSF47</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(364.3705160555801, 216.23089248149682)\"><title>FLSF54</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(360.27644117304055, 232.25163768234157)\"><title>FLWO6</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(540.1999106250961, 163.49924566806422)\"><title>HNDA09</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(357.02940642120797, 97.67224486497237)\"><title>LALC2</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(431.54691083013563, 148.22938643278653)\"><title>MXED8</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(425.7835681204458, 150.95459753371256)\"><title>MXGT4</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(523.9410135317182, 162.87734853130723)\"><title>MXSA3017</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(353.62270019243397, 74.60711776179603)\"><title>SCCU3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(411.4503982405321, 135.06534329098363)\"><title>TXGR3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(408.66031031506475, 134.3796506105805)\"><title>TXMD3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(351.634342953735, 67.31549978916664)\"><title>TXWV2</title><circle r=\"5.0\"></circle></g></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"tc8d9171faf7a45219bef22941075ad81\" transform=\"translate(350.0,250.0)translate(0,10.0)\"><line style=\"stroke-width:2.25\" x1=\"0\" x2=\"192.3026724901453\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(5.746260409256206,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.67\" y=\"10.265999999999998\">-40</text></g><g transform=\"translate(70.49750693950413,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"10.265999999999998\">0</text></g><g transform=\"translate(135.24875346975207,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"10.265999999999998\">40</text></g><g transform=\"translate(200.0,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"10.265999999999998\">80</text></g></g><g transform=\"translate(100.0,22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-74.30499999999999\" y=\"11.977\">PC0 (16.8%) explained</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"tc1a40ba8cb784357b912adc8e08bf4b4\" transform=\"translate(350.0,250.0)rotate(-90.0)translate(0,-10.0)\"><line style=\"stroke-width:2.25\" x1=\"0\" x2=\"185.30102047964817\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(15.480462768143873,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.67\" y=\"0.0\">-30</text></g><g transform=\"translate(76.98697517876258,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"0.0\">0</text></g><g transform=\"translate(138.4934875893813,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"0.0\">30</text></g><g transform=\"translate(200.0,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"0.0\">60</text></g></g><g transform=\"translate(100.0,-22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-70.41300000000001\" y=\"0.0\">PC2 (6.6%) explained</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g><g class=\"toyplot-coordinates-Cartesian\" id=\"tcf13704b3c974a78b94a1ac2798fd661\"><clipPath id=\"tfee4109a946c4b19be10f14d8af7f536\"><rect height=\"220.0\" width=\"220.0\" x=\"640.0\" y=\"40.0\"></rect></clipPath><g clip-path=\"url(#tfee4109a946c4b19be10f14d8af7f536)\"><g class=\"toyplot-mark-Point\" id=\"tb517165936404312ba52e8eaf19efda7\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(849.9766227074266, 199.3723466519079)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(841.0044801857906, 196.73058715458933)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(840.9413532281654, 198.6367550713185)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(676.1257115844135, 158.93878020331115)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(671.4398311840102, 174.60445830375204)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(676.4744168495714, 162.89551952990027)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(666.9919908676461, 184.33558075338277)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(668.851499266277, 188.4071505829901)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(665.4574099445693, 179.68932731493572)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(680.2002202327614, 231.89512686587454)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(684.7678918670756, 109.53839818105531)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(679.820294776958, 234.99424838846147)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(687.4781046076265, 241.64673506232458)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(691.0988247889293, 231.61322853424645)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(692.3814799934141, 246.40306180196666)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.3149491156641, 90.72256161083602)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(684.0255438668975, 233.72955895716328)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(679.4578781894006, 216.06149648189435)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(676.9441934162267, 233.46581290900565)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(677.149524995846, 163.10193450562988)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.9807155976682, 99.56163523880461)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(785.0521923687284, 146.38513758801886)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(796.7940449658395, 150.55894511432427)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.0558109498404, 163.34408939526503)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.9241256277064, 72.64835659528019)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(777.0180119944994, 135.7548045328968)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(772.8547319609571, 133.29087539659744)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(684.4198082894459, 66.03818226891424)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t836928b9517344b2bbe1467c91242fe7\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(849.9176579503883, 198.33168139589318)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(840.1084312704597, 195.92801822840207)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(840.6149094031812, 198.70578588474868)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(677.5653157498998, 158.74679295265062)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(671.7918525434278, 173.75434803654653)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(677.7493889538384, 162.01073460479876)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(668.4706932628384, 187.30607851032175)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(668.3263337361747, 184.98336513725474)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(665.7058332934348, 182.22157772567255)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(679.1656292169665, 233.17462957271476)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(684.9972239824461, 106.71162750857945)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(679.3180877118267, 233.45040644983038)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(685.6603337797638, 241.21654896996517)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(689.8434890573353, 231.92218253813743)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(690.596121052928, 250.0)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.5169584472468, 90.37503975253487)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(683.6863022990447, 231.14549809339513)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(679.6503012825698, 217.91553734665922)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(676.9835762357365, 233.28729835766396)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(678.6978024000655, 162.10189096710184)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(683.1813661927234, 96.03404853073205)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(785.9875602445593, 150.94150674918313)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(797.9125519078104, 152.12331779881555)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(683.6678950402784, 160.76328375642493)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(681.4019659656069, 75.53859484383563)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(775.6774382137899, 134.50396386333423)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(775.0216964049301, 134.1998427039322)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.7849478240842, 66.97109471551863)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"tde81aa71354642ce9907417995d33884\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(849.2026216985521, 198.53249571851444)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(840.8712456865682, 196.65581623891276)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(841.7663395306032, 198.5690112862179)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(671.6724821173775, 161.02438762338554)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(667.0461316939534, 172.37161162310971)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(671.3201799288141, 162.94039581908743)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(664.0586145306323, 186.07256045258856)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(667.2885792053002, 184.50129194191192)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(663.21218047255, 180.5318726979911)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.164078895255, 232.44196967163353)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(688.5814867645208, 107.63287301591143)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(683.0429578850964, 236.61331187477518)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(686.752855978978, 242.75260530223676)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(693.4713059455336, 234.27155669476716)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(695.4821798214047, 245.48971081103087)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(685.5479839119394, 90.87361201920854)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(687.4423530880712, 228.73761569293526)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(681.3247417270559, 217.15369315699297)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(680.2471013691575, 229.00212575352356)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(671.0151268872996, 163.9999870659538)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(684.852334886171, 99.23193750205328)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(783.6757449517647, 147.07214180269108)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(796.3575699638643, 150.0400033779744)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(677.8607687017292, 162.50403123680923)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(686.0829299640002, 75.39676634282077)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(775.7110665139078, 134.60351272780778)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(773.9428074803002, 134.95729836059905)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(687.0078938229549, 70.39049918320337)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t55474ba2fd044086a9d8046c8c9e1683\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(850.0, 198.93230841284804)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(841.1306147204635, 195.38634594514141)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(841.4805037117329, 198.08071129844228)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(680.049777124991, 160.18688122896933)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(672.9180360795103, 173.36756135482227)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(679.8367316979522, 161.70935786626856)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(667.6791511042749, 185.38588993986411)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(669.4577743824029, 185.44263034405037)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(665.9912793169128, 180.54887880597042)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(679.1082787755083, 232.591345457341)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(685.697645185434, 109.91331929174149)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(677.8372437172062, 238.24524805868012)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(683.5267396336069, 243.52794376482458)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(690.1094788522339, 233.43179027328534)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(690.4589180780101, 243.5663775005149)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.3803814783406, 89.34280430317436)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.8137466362448, 230.58069266057007)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(679.407351545036, 213.94563297639738)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(675.4806312858467, 233.76257862426243)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(679.0927727202607, 163.74363448226418)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.4837982293703, 97.86662816618133)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(784.0715737956178, 149.51753714227834)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(796.3522815173303, 151.67877081991622)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(685.3881976212807, 161.51344762262687)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(681.9196516893751, 72.15684951825155)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(775.8870702028338, 138.32208327547352)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(773.564435602224, 136.91846634013513)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.8775987193544, 64.69897952035181)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t79713b13772c457ab1bdf3501189d280\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(849.7025546046692, 199.17943983125926)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(841.2561181046154, 193.87382840848528)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(841.9955133874021, 197.7626206793712)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(676.2567079179998, 161.40594212900822)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(671.3643560933874, 171.97669039345368)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(676.7866226420642, 162.11427294265158)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(665.9857667666483, 183.89203681419517)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(668.5276443964585, 185.1155971395054)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(665.0251259274049, 179.19199696423829)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(680.9296203605609, 233.79974235395267)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(686.1519756457617, 108.60842023960683)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(679.2031932069507, 236.31960516800888)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(686.8834870264864, 240.02537423422888)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(692.297866183128, 232.9023782640483)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(692.4979916028614, 246.85268803738353)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.5270748889194, 90.04132797235631)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(684.2463803990813, 234.99942863801124)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(678.7277040335402, 215.9013279941758)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(679.0434501461327, 232.76009366696397)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(676.1817572350578, 163.85127957558234)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.3275816743535, 98.02876835169857)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(784.6286602901782, 149.91375267541875)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(796.4457347878397, 150.3284323271622)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(681.611234017785, 163.75089957034064)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.8604101765454, 75.01616662292147)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(776.2322680416726, 133.38233312420522)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(774.0269654386137, 135.38021781125946)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(683.2778984272362, 67.99003306515432)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t2090b8a59f0c4e2bb917f88ba55cb371\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(849.6381775334831, 202.28038646195978)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(841.0193986628999, 198.0492035047587)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(841.4277123568868, 200.80992980210775)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(675.5902601552341, 158.70999432065935)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(670.051863050558, 174.23499497014063)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(675.3051956687215, 163.2217536483408)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(667.3024137015891, 187.086929910028)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(668.8627837464027, 190.3119190222256)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(664.7535636148885, 181.61006395267373)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(679.6766087910648, 230.29893332273517)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(686.6201495878413, 109.21878010270302)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(680.5463845166441, 233.58016885714935)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(685.9625021922571, 241.6881063539241)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(691.3221334560188, 235.34884196418375)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(692.3502558329266, 249.1795717636187)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.5781297338877, 89.81217443737899)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(684.221342217382, 231.39463297682371)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(680.6667478966601, 215.38783836281507)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(677.3579763860998, 228.49782174262148)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(676.5600988862999, 162.75108123625634)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.3324265322203, 97.6230800175782)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(784.8045656883719, 144.3572375604769)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(795.7588842543623, 148.80248332004464)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.2028863741065, 159.74897953599162)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(684.2751056477904, 78.2678821566392)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(776.8009684820482, 132.2404695727314)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(774.4072222683862, 132.49199020150436)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(684.6059061883234, 67.35944591657714)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"te3f5e059a8024415beaace94979195f3\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(847.182129411582, 200.43074822938686)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(839.8845849909279, 197.93615278102595)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(839.7770101833952, 200.82050531076223)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(672.2645339664269, 160.82197997836752)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(667.3462526587697, 173.5977423815036)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(670.5417613778797, 163.8868900003363)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(662.6380258982092, 181.7396304133271)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(665.0306251495688, 182.48650802879064)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(662.2586573559996, 179.32621683490746)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.3187667034754, 234.68455505930788)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(689.3723979143086, 108.94093041982606)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(683.6054838642953, 235.42796486679345)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(688.0723731486131, 242.23301042978048)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(693.9668407030856, 231.75365864133872)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(695.5313972726537, 246.85659629453374)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(685.8075715976275, 92.94202037069921)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(687.4958388876786, 233.8553392511983)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.8547388779473, 216.65160961731686)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(680.2151431824628, 232.11676348303803)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(670.5954988552011, 163.59474406610005)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(685.7640992673724, 99.01617263622089)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(784.6664509507488, 144.34001231581001)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(796.2997904116587, 149.1104326321648)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(678.0987829454859, 164.1408681562675)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(686.2046587839611, 74.6434952908103)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(777.7214310711167, 132.72907468067922)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(774.5214923777985, 132.61322650343226)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(686.9653256151048, 67.66784632092188)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t5c2d7e15946e432e9e148b66d6f54565\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(848.1756449321241, 199.98830278864094)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(839.6484079703373, 196.3563316865166)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(840.0353794428328, 199.81196603829505)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(673.3783529694348, 159.42206358105847)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(668.3733691291803, 173.42703657168116)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(672.8568686196801, 163.25844863625616)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(664.4373959291912, 186.1180947869378)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(667.4293472117421, 187.27986474824883)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(663.2626594500301, 181.01668127794653)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.1165220158271, 232.89556763698005)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(687.7021218625547, 106.60224729970989)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.2216113924623, 235.28994490163996)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(686.7173347275594, 241.0340493888902)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(692.603824442908, 234.15351167371702)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(693.1017217653019, 245.90077098664023)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(683.7352647572449, 91.15949745908654)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(687.0290396214054, 227.75316818583863)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(681.7578740708121, 217.3179844872135)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(680.1572294466657, 232.52467625949078)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(671.6427662560714, 163.2060659423952)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(684.5760911659175, 96.79954080978037)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(786.3117008162745, 146.89690147251824)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(796.1530664148127, 150.52061961782246)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(678.1967060336432, 162.47988903700485)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(685.1635657792832, 73.98836070808473)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(777.791873005637, 133.75948314991572)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(775.0319946067655, 135.7522449206469)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(687.3939295876559, 69.65138094169079)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t203ecf277ee34632a66005d633bcc9f3\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(849.4017064411967, 199.33983039308455)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(840.6450394031757, 196.93743540421954)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(841.7493510268853, 198.29027941038186)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(678.2744721639464, 160.2538639790256)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(673.8908767805542, 174.92113750843788)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(678.1754053217687, 164.34629100831532)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(667.4044340460697, 183.016987570074)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(669.8795666872035, 181.77920287837517)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(668.056108977381, 180.1792981081654)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(678.6496499850182, 234.82404083756572)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(686.0381363146315, 109.14197441768651)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(678.0980246175659, 237.1751621039092)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(683.7848397620055, 242.5215588414631)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(690.3588118379472, 230.12499970288457)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(692.1871803717199, 245.74627136244467)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(681.0947870918271, 89.32777655022443)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(683.1696598510771, 232.69573476526742)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(678.6111834301042, 215.85402105442685)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(675.979114594251, 230.70592285574787)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(679.0382815464211, 164.55956734144266)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(681.0772578782327, 96.37315865458478)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(786.621733681809, 148.23378545643968)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(796.2750201495248, 151.65588329513082)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.4385294826536, 165.14502811336476)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(680.600183491916, 75.330429107513)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(777.4123867428858, 136.70665175026048)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(773.8920338474094, 132.3982342807037)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(684.1978878981739, 66.78016824350786)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"tb9720dfe8417481fab834da0edbbfc6c\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(847.8662988935475, 196.13049537464673)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(838.8003467600538, 191.44626843811304)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(839.6880586494142, 194.55574106284544)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(672.6977021961286, 161.00344303695186)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(667.4828722382997, 172.03629647386265)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(672.1933389994015, 162.515304309832)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(667.3153125019273, 183.49529084578808)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(667.5477073504836, 184.5113545257356)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(663.3169358212238, 177.16266982983274)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(683.6481185567666, 234.58377711622097)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(688.0596166136803, 107.85578438521549)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(682.2300543000146, 237.75990660046597)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(688.4992142348623, 239.37865627918683)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(693.0439220381733, 233.2093062414431)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(694.6989941634074, 247.27842520955693)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(685.0768917764553, 89.47531951972051)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(684.8155784851654, 231.30112154211636)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(681.8676305557276, 216.11978333707606)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(680.6336739912601, 236.393283171098)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(671.2775665195986, 164.08227149791603)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(684.8999537680979, 96.1874787420896)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(783.3919010211071, 154.6358515650303)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(796.6798326423823, 154.7270870337701)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(679.3606535920668, 165.3829688889769)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(684.8214829760237, 73.0842764318033)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(777.1045968361097, 138.651056232532)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(773.1536736645287, 135.79410958699444)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(686.8297342774478, 65.60736771582643)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t58497e4b5f704d9195ea257ea34517f6\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(849.1063414172969, 199.25180352581418)\"><title>BJSB3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(840.4368667755292, 195.92999877901647)\"><title>BJSL25</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(840.9476130920499, 198.6043305844491)\"><title>BJVL19</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(675.3875315945852, 160.05141290333876)\"><title>BZBB1</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(670.1705441451651, 173.42918776173101)\"><title>CRL0001</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(675.1239910059693, 162.8898968365787)\"><title>CRL0030</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(666.2283798609027, 184.84490799965073)\"><title>CUCA4</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(668.1201861132014, 185.48188843490885)\"><title>CUSV6</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(664.7039754174394, 180.1478583512334)\"><title>CUVN10</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(680.7977493533203, 233.1189687894326)\"><title>FLAB109</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(686.7988645738254, 108.41643548620354)\"><title>FLBA140</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(680.5923335989021, 235.88559672697141)\"><title>FLCK18</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(686.3337785091758, 241.60245886268248)\"><title>FLCK216</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(691.8116497305293, 232.8731454528052)\"><title>FLMO62</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(692.9286239954628, 246.72734737676907)\"><title>FLSA185</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(683.3579992799154, 90.40721339952198)\"><title>FLSF33</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(684.8945785352048, 231.61927907633194)\"><title>FLSF47</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(680.4326151608853, 216.23089248149682)\"><title>FLSF54</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(678.304209005384, 232.25163768234157)\"><title>FLWO6</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(675.1251196302122, 163.49924566806422)\"><title>HNDA09</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(683.4475625192127, 97.67224486497237)\"><title>LALC2</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(784.921208380916, 148.22938643278653)\"><title>MXED8</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(796.5028777015425, 150.95459753371256)\"><title>MXGT4</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(681.088146475887, 162.87734853130723)\"><title>MXSA3017</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(683.6254080102209, 74.60711776179603)\"><title>SCCU3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(776.7357111104501, 135.06534329098363)\"><title>TXGR3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(774.0417053651913, 134.3796506105805)\"><title>TXMD3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(685.0360930649781, 67.31549978916664)\"><title>TXWV2</title><circle r=\"5.0\"></circle></g></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t639c8e70020d4211817398ff1e0ae313\" transform=\"translate(650.0,250.0)translate(0,10.0)\"><line style=\"stroke-width:2.25\" x1=\"12.258657355999564\" x2=\"200.0\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(0.0,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.67\" y=\"10.265999999999998\">-40</text></g><g transform=\"translate(62.035773693691254,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"10.265999999999998\">0</text></g><g transform=\"translate(124.07154738738251,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"10.265999999999998\">40</text></g><g transform=\"translate(186.10732108107376,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"10.265999999999998\">80</text></g></g><g transform=\"translate(100.0,22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-74.30499999999999\" y=\"11.977\">PC1 (14.8%) explained</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"td7eb81187dde4b98ad3a1bfc99e8990b\" transform=\"translate(650.0,250.0)rotate(-90.0)translate(0,-10.0)\"><line style=\"stroke-width:2.25\" x1=\"0\" x2=\"185.30102047964817\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(15.480462768143873,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.67\" y=\"0.0\">-30</text></g><g transform=\"translate(76.98697517876258,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"0.0\">0</text></g><g transform=\"translate(138.4934875893813,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"0.0\">30</text></g><g transform=\"translate(200.0,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"0.0\">60</text></g></g><g transform=\"translate(100.0,-22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-70.41300000000001\" y=\"0.0\">PC2 (6.6%) explained</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g><g class=\"toyplot-coordinates-Cartesian\" id=\"t31c7d94e3bc34fa29fd75c0bd1868d9d\"><clipPath id=\"tacaca4cfea284b97ba8f1a03135e2c3d\"><rect height=\"220.0\" width=\"95.0\" x=\"865.0\" y=\"40.0\"></rect></clipPath><g clip-path=\"url(#tacaca4cfea284b97ba8f1a03135e2c3d)\"><g class=\"toyplot-mark-Point\" id=\"t61cdd923b09c4103adc7bdf55f560034\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(908.9518095560009, 242.2509225092251)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(908.9518095560009, 211.50061500615007)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(908.9518095560009, 180.75030750307505)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(908.9518095560009, 150.0)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(908.9518095560009, 119.249692496925)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(908.9518095560009, 88.49938499384997)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(908.9518095560009, 57.74907749077494)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Text\" id=\"t72c5dfd765764187bd744e5a5b43f947\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" transform=\"translate(908.9518095560009,242.2509225092251)\"><text style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:normal;opacity:1;stroke:none;vertical-align:baseline;white-space:pre\" x=\"15.0\" y=\"3.577\">bran</text></g><g class=\"toyplot-Datum\" transform=\"translate(908.9518095560009,211.50061500615007)\"><text style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:normal;opacity:1;stroke:none;vertical-align:baseline;white-space:pre\" x=\"15.0\" y=\"3.577\">fusi</text></g><g class=\"toyplot-Datum\" transform=\"translate(908.9518095560009,180.75030750307505)\"><text style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:normal;opacity:1;stroke:none;vertical-align:baseline;white-space:pre\" x=\"15.0\" y=\"3.577\">gemi</text></g><g class=\"toyplot-Datum\" transform=\"translate(908.9518095560009,150.0)\"><text style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:normal;opacity:1;stroke:none;vertical-align:baseline;white-space:pre\" x=\"15.0\" y=\"3.577\">mini</text></g><g class=\"toyplot-Datum\" transform=\"translate(908.9518095560009,119.249692496925)\"><text style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:normal;opacity:1;stroke:none;vertical-align:baseline;white-space:pre\" x=\"15.0\" y=\"3.577\">oleo</text></g><g class=\"toyplot-Datum\" transform=\"translate(908.9518095560009,88.49938499384997)\"><text style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:normal;opacity:1;stroke:none;vertical-align:baseline;white-space:pre\" x=\"15.0\" y=\"3.577\">sagr</text></g><g class=\"toyplot-Datum\" transform=\"translate(908.9518095560009,57.74907749077494)\"><text style=\"fill:rgb(14.9%,14.9%,14.9%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:normal;opacity:1;stroke:none;vertical-align:baseline;white-space:pre\" x=\"15.0\" y=\"3.577\">virg</text></g></g></g></g></g></svg><div class=\"toyplot-behavior\"><script>(function()\n",
"{\n",
"var modules={};\n",
"modules[\"toyplot/tables\"] = (function()\n",
" {\n",
" var tables = [];\n",
"\n",
" var module = {};\n",
"\n",
" module.set = function(owner, key, names, columns)\n",
" {\n",
" tables.push({owner: owner, key: key, names: names, columns: columns});\n",
" }\n",
"\n",
" module.get = function(owner, key)\n",
" {\n",
" for(var i = 0; i != tables.length; ++i)\n",
" {\n",
" var table = tables[i];\n",
" if(table.owner != owner)\n",
" continue;\n",
" if(table.key != key)\n",
" continue;\n",
" return {names: table.names, columns: table.columns};\n",
" }\n",
" }\n",
"\n",
" module.get_csv = function(owner, key)\n",
" {\n",
" var table = module.get(owner, key);\n",
" if(table != undefined)\n",
" {\n",
" var csv = \"\";\n",
" csv += table.names.join(\",\") + \"\\n\";\n",
" for(var i = 0; i != table.columns[0].length; ++i)\n",
" {\n",
" for(var j = 0; j != table.columns.length; ++j)\n",
" {\n",
" if(j)\n",
" csv += \",\";\n",
" csv += table.columns[j][i];\n",
" }\n",
" csv += \"\\n\";\n",
" }\n",
" return csv;\n",
" }\n",
" }\n",
"\n",
" return module;\n",
" })();\n",
"modules[\"toyplot/root/id\"] = \"t5db1355fc05143eaa1674bca9eb71ed6\";\n",
"modules[\"toyplot/root\"] = (function(root_id)\n",
" {\n",
" return document.querySelector(\"#\" + root_id);\n",
" })(modules[\"toyplot/root/id\"]);\n",
"modules[\"toyplot/canvas/id\"] = \"t6ac9e47c2d484d1d9ae644409d013f23\";\n",
"modules[\"toyplot/canvas\"] = (function(canvas_id)\n",
" {\n",
" return document.querySelector(\"#\" + canvas_id);\n",
" })(modules[\"toyplot/canvas/id\"]);\n",
"modules[\"toyplot/menus/context\"] = (function(root, canvas)\n",
" {\n",
" var wrapper = document.createElement(\"div\");\n",
" wrapper.innerHTML = \"<ul class='toyplot-context-menu' style='background:#eee; border:1px solid #b8b8b8; border-radius:5px; box-shadow: 0px 0px 8px rgba(0%,0%,0%,0.25); margin:0; padding:3px 0; position:fixed; visibility:hidden;'></ul>\"\n",
" var menu = wrapper.firstChild;\n",
"\n",
" root.appendChild(menu);\n",
"\n",
" var items = [];\n",
"\n",
" var ignore_mouseup = null;\n",
" function open_menu(e)\n",
" {\n",
" var show_menu = false;\n",
" for(var index=0; index != items.length; ++index)\n",
" {\n",
" var item = items[index];\n",
" if(item.show(e))\n",
" {\n",
" item.item.style.display = \"block\";\n",
" show_menu = true;\n",
" }\n",
" else\n",
" {\n",
" item.item.style.display = \"none\";\n",
" }\n",
" }\n",
"\n",
" if(show_menu)\n",
" {\n",
" ignore_mouseup = true;\n",
" menu.style.left = (e.clientX + 1) + \"px\";\n",
" menu.style.top = (e.clientY - 5) + \"px\";\n",
" menu.style.visibility = \"visible\";\n",
" e.stopPropagation();\n",
" e.preventDefault();\n",
" }\n",
" }\n",
"\n",
" function close_menu()\n",
" {\n",
" menu.style.visibility = \"hidden\";\n",
" }\n",
"\n",
" function contextmenu(e)\n",
" {\n",
" open_menu(e);\n",
" }\n",
"\n",
" function mousemove(e)\n",
" {\n",
" ignore_mouseup = false;\n",
" }\n",
"\n",
" function mouseup(e)\n",
" {\n",
" if(ignore_mouseup)\n",
" {\n",
" ignore_mouseup = false;\n",
" return;\n",
" }\n",
" close_menu();\n",
" }\n",
"\n",
" function keydown(e)\n",
" {\n",
" if(e.key == \"Escape\" || e.key == \"Esc\" || e.keyCode == 27)\n",
" {\n",
" close_menu();\n",
" }\n",
" }\n",
"\n",
" canvas.addEventListener(\"contextmenu\", contextmenu);\n",
" canvas.addEventListener(\"mousemove\", mousemove);\n",
" document.addEventListener(\"mouseup\", mouseup);\n",
" document.addEventListener(\"keydown\", keydown);\n",
"\n",
" var module = {};\n",
" module.add_item = function(label, show, activate)\n",
" {\n",
" var wrapper = document.createElement(\"div\");\n",
" wrapper.innerHTML = \"<li class='toyplot-context-menu-item' style='background:#eee; color:#333; padding:2px 20px; list-style:none; margin:0; text-align:left;'>\" + label + \"</li>\"\n",
" var item = wrapper.firstChild;\n",
"\n",
" items.push({item: item, show: show});\n",
"\n",
" function mouseover()\n",
" {\n",
" this.style.background = \"steelblue\";\n",
" this.style.color = \"white\";\n",
" }\n",
"\n",
" function mouseout()\n",
" {\n",
" this.style.background = \"#eee\";\n",
" this.style.color = \"#333\";\n",
" }\n",
"\n",
" function choose_item(e)\n",
" {\n",
" close_menu();\n",
" activate();\n",
"\n",
" e.stopPropagation();\n",
" e.preventDefault();\n",
" }\n",
"\n",
" item.addEventListener(\"mouseover\", mouseover);\n",
" item.addEventListener(\"mouseout\", mouseout);\n",
" item.addEventListener(\"mouseup\", choose_item);\n",
" item.addEventListener(\"contextmenu\", choose_item);\n",
"\n",
" menu.appendChild(item);\n",
" };\n",
" return module;\n",
" })(modules[\"toyplot/root\"],modules[\"toyplot/canvas\"]);\n",
"modules[\"toyplot/io\"] = (function()\n",
" {\n",
" var module = {};\n",
" module.save_file = function(mime_type, charset, data, filename)\n",
" {\n",
" var uri = \"data:\" + mime_type + \";charset=\" + charset + \",\" + data;\n",
" uri = encodeURI(uri);\n",
"\n",
" var link = document.createElement(\"a\");\n",
" if(typeof link.download != \"undefined\")\n",
" {\n",
" link.href = uri;\n",
" link.style = \"visibility:hidden\";\n",
" link.download = filename;\n",
"\n",
" document.body.appendChild(link);\n",
" link.click();\n",
" document.body.removeChild(link);\n",
" }\n",
" else\n",
" {\n",
" window.open(uri);\n",
" }\n",
" };\n",
" return module;\n",
" })();\n",
"modules[\"toyplot.coordinates.Axis\"] = (\n",
" function(canvas)\n",
" {\n",
" function sign(x)\n",
" {\n",
" return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
" }\n",
"\n",
" function mix(a, b, amount)\n",
" {\n",
" return ((1.0 - amount) * a) + (amount * b);\n",
" }\n",
"\n",
" function log(x, base)\n",
" {\n",
" return Math.log(Math.abs(x)) / Math.log(base);\n",
" }\n",
"\n",
" function in_range(a, x, b)\n",
" {\n",
" var left = Math.min(a, b);\n",
" var right = Math.max(a, b);\n",
" return left <= x && x <= right;\n",
" }\n",
"\n",
" function inside(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(in_range(segment.range.min, range, segment.range.max))\n",
" return true;\n",
" }\n",
" return false;\n",
" }\n",
"\n",
" function to_domain(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n",
" {\n",
" if(segment.scale == \"linear\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" return mix(segment.domain.min, segment.domain.max, amount)\n",
" }\n",
" else if(segment.scale[0] == \"log\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" var base = segment.scale[1];\n",
" return sign(segment.domain.min) * Math.pow(base, mix(log(segment.domain.min, base), log(segment.domain.max, base), amount));\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" var axes = {};\n",
"\n",
" function display_coordinates(e)\n",
" {\n",
" var current = canvas.createSVGPoint();\n",
" current.x = e.clientX;\n",
" current.y = e.clientY;\n",
"\n",
" for(var axis_id in axes)\n",
" {\n",
" var axis = document.querySelector(\"#\" + axis_id);\n",
" var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
" if(coordinates)\n",
" {\n",
" var projection = axes[axis_id];\n",
" var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
" if(inside(local.x, projection))\n",
" {\n",
" var domain = to_domain(local.x, projection);\n",
" coordinates.style.visibility = \"visible\";\n",
" coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
" var text = coordinates.querySelector(\"text\");\n",
" text.textContent = domain.toFixed(2);\n",
" }\n",
" else\n",
" {\n",
" coordinates.style.visibility= \"hidden\";\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" canvas.addEventListener(\"click\", display_coordinates);\n",
"\n",
" var module = {};\n",
" module.show_coordinates = function(axis_id, projection)\n",
" {\n",
" axes[axis_id] = projection;\n",
" }\n",
"\n",
" return module;\n",
" })(modules[\"toyplot/canvas\"]);\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t06bbc1e400ab43fdb8f88d077a687d98\",\"data\",\"point\",[\"x\", \"y0\"],[[9.90425274899005, 9.741373160037037, 8.936018277967092, 71.35495502527698, 57.50732806007437, 74.86681786249491, 34.52701545843618, 29.386829854476293, 41.66266373643811, -35.44597565067607, -36.92970379493566, -36.16263274186227, -33.187168905666454, -30.118544651439265, -29.247695510506038, -40.69382653749566, -34.34127620647451, -34.760455492675995, -37.221754843889826, 74.70931394054736, -38.625148849926184, 5.975455924391921, 1.86890833618842, 64.37796844082165, -41.312557102204224, -6.727897728581371, -8.019509970534282, -42.02475283927255], [88.94277659521687, 83.15763554680372, 83.11693196313485, -23.154422018874403, -26.175827328359365, -22.929580612443416, -29.04374695056051, -27.84475592463253, -30.033228233185504, -20.5272226429361, -17.582037075094018, -20.772194492681482, -15.83452103447345, -13.4999195839101, -12.672877296459575, -19.163668192341472, -18.060695085450217, -21.005876812400388, -22.62667373231019, -22.494278137063397, -18.734389121661113, 47.08020184325513, 54.651222174258244, -19.33075769595802, -18.770877725956595, 41.89984870450099, 39.21541049367185, -17.806477624089833]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tffcc5cece66c4ae2996aa5919d238831\",\"data\",\"point\",[\"x\", \"y0\"],[[8.108448603734598, 8.617701925640139, 7.623182406883075, 71.58400760934725, 56.820424777352166, 74.2085054845683, 36.527769158694696, 30.640886933641596, 41.59771824904981, -35.07600541978134, -37.76405748496534, -35.445421217559655, -31.260505543689558, -30.462997375409344, -28.949084432745607, -39.983505407594045, -33.86576221768931, -34.1335368798461, -36.94152731604749, 73.86618201410148, -38.41599436035406, 6.344163217290311, 1.794041871193625, 63.31990098802954, -41.38505855411401, -7.09089007124854, -8.686042072978603, -41.59254488550349], [88.90475675374316, 82.579872838625, 82.90644449402001, -22.226180728554006, -25.948847739997518, -22.107492305420884, -28.09029554202744, -28.18337701297122, -29.87304752836056, -21.1943158081818, -17.434166192397992, -21.09601220960789, -17.006600123444375, -14.309346568921857, -13.82405754887435, -19.033414746914893, -18.279434401592432, -20.881804470451808, -22.60128011365124, -21.49596551063319, -18.605011774941264, 47.68331699448995, 55.372423426615576, -18.29130320416895, -19.752349913030685, 41.03546114171901, 40.61264587251809, -18.860618077586313]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t5778884db1bb4446b9c34730146e5619\",\"data\",\"point\",[\"x\", \"y0\"],[[14.572606807173964, 13.675593315069426, 12.913161587523769, 70.10264696929418, 55.57870924467533, 73.38556906511093, 34.554846076054744, 29.62560652044239, 39.25331865847156, -36.56027338495862, -38.70321693526013, -36.844294176409385, -33.32968368329937, -32.05202044938232, -30.66409327711706, -41.93379862958782, -35.40603173332468, -35.372934807214726, -37.23814300718947, 72.74898866318853, -39.51725869544707, 8.249121575597911, 4.862606649761902, 62.86197460805935, -41.85350809223142, -3.7034264559307393, -5.839151551515544, -43.36691486155579], [88.44370906511963, 83.07172737396125, 83.64887426243536, -26.025816507495954, -29.008837527765387, -26.25297717147209, -30.935156479196397, -28.852509978732968, -31.48092806077556, -19.260947688623123, -15.123072080943938, -18.694255963825103, -16.302153553883635, -11.970169238041187, -10.67357937955078, -17.079042110468954, -15.85757322995793, -19.80214327189642, -20.496994190154904, -26.44967209335421, -17.527589124134455, 46.192683345454206, 54.36978778504263, -22.03567584130095, -16.73411464671087, 41.05714430813461, 39.91698989185306, -16.137707893715962]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tb62059420eb545bf9849ed7c2f27184e\",\"data\",\"point\",[\"x\", \"y0\"],[[7.308335636864564, 7.055814857029974, 6.135069132911557, 71.39806613538276, 57.10858159319944, 75.07129113650365, 37.0390014709697, 32.36843382974997, 41.76912316058653, -35.12488883270725, -37.14742795192563, -35.536656135962374, -32.52149266438857, -30.50167909179367, -29.314193964819403, -41.03872133082342, -33.461780302735384, -33.75591721554264, -36.91766210972915, 74.46443077913443, -37.991275293936155, 5.4708573090104515, 1.7839206996665118, 64.73006767385814, -40.125604052655525, -6.861863290139059, -9.008122007939683, -42.39570916976983], [88.95785002216492, 83.23896573882861, 83.46457039913119, -20.624226741579616, -25.22269670227966, -20.76159614271953, -28.600673416878585, -27.453836247144856, -29.688995000935066, -21.231294756323184, -16.982542130161733, -22.050844498430376, -18.382318692985756, -14.137839208532126, -13.91252455218462, -19.121478108278353, -18.842048913089158, -21.038455849530777, -23.570362860848448, -21.241292894058482, -19.054796098932975, 46.447909528854524, 54.366377851567655, -17.18207059300068, -19.418551723409173, 41.1706295947339, 39.67301977232527, -18.800877776302922]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t7a9654ac115141cdb95248e83d830daf\",\"data\",\"point\",[\"x\", \"y0\"],[[9.975367479940425, 9.390055974139981, 9.483751636253158, 71.43537586864699, 56.857092254947965, 75.24498574324188, 34.88257615732163, 30.039075082143558, 40.94520800032876, -35.32395704443256, -37.98926391543842, -36.952930485793296, -32.5490916938235, -30.794806330670927, -30.073247225752823, -41.45538823847933, -32.75404797851448, -34.287870091627255, -36.39609733973079, 74.4634319509234, -39.133910754892995, 5.769822876907803, 2.9108779681218557, 64.47003435644916, -41.559290234367886, -6.016581004804562, -7.965773383072672, -42.61539962796504], [88.76606042876067, 83.31988897178226, 83.7966431017058, -23.06995699117389, -26.224492855444026, -22.72827367362177, -29.692549434086278, -28.053574063287478, -30.31196032044783, -20.05691328150146, -16.68959473334457, -21.170094951248714, -16.217924058719504, -12.726790582492946, -12.597751863174876, -19.02689177407503, -17.918302063466314, -21.476685258807855, -21.273095559644023, -23.11828438582333, -19.155522854297157, 46.80711291192898, 54.42663551577989, -19.617416122594587, -18.81196076393122, 41.393209450378606, 39.97125403868488, -18.54276882783817]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t2d6450ada76b41599f8c10fc9f288fd2\",\"data\",\"point\",[\"x\", \"y0\"],[[10.463155716083223, 9.961454090074602, 9.538433665650068, 71.90751642617371, 57.139828031935636, 74.77033143461065, 34.92124722836311, 31.096734047987425, 41.00988551052663, -35.835946832162165, -37.9574387095382, -36.911153095373166, -33.15278949441723, -30.422748432889318, -30.649657427748465, -40.72071131361119, -34.65172873033482, -34.808082060081645, -37.506530131562634, 75.01102942241748, -39.619392800943636, 5.952283820443281, 2.7139528031283753, 64.67498313227428, -40.86827652412614, -6.319690977144326, -6.967856307684956, -42.768832492050606], [88.7245507853062, 83.16725482047183, 83.43053110102768, -23.49967534436568, -27.070774260305782, -23.68348186085738, -28.843589644245114, -27.837479813154335, -30.487060780293678, -20.864841671777015, -16.387721208309628, -20.30401963391612, -16.811765179345645, -13.355932555914201, -12.69301029948567, -18.99397215887339, -17.93444641386834, -20.22641062037484, -22.359870921457027, -22.87433375623339, -19.152398942026313, 46.92053482172071, 53.983761675360796, -19.235925043435834, -17.899780331891968, 41.759901380866204, 40.21643955480343, -17.686483699425494]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"td7c8ed9dca6442e6b0041e3af6205896\",\"data\",\"point\",[\"x\", \"y0\"],[[14.408170151272198, 14.319736956132136, 13.595845968941655, 69.78534210697904, 54.63209505148762, 72.29821045256777, 34.54755246108144, 29.659663921663412, 39.523772575792094, -35.85877506226111, -38.97841149509813, -37.232068959935525, -34.59552994175018, -31.814394151668523, -30.009203957976318, -41.68894380779025, -34.72256511541406, -35.10596428974777, -36.73844577188824, 72.42779988350198, -40.64541928618454, 8.718379122360082, 5.87421533888954, 62.38578335537712, -42.65900585217948, -3.2978403426155145, -5.280254268977764, -43.54974504255877], [87.14091735854949, 82.43553916390555, 82.36617608442569, -25.644067839720574, -28.81532275591899, -26.75489308520145, -31.851136758212466, -30.308414481112653, -32.0957495160595, -19.16120665275945, -14.613101073767883, -18.33154525952966, -15.451343067566265, -11.650653753315337, -10.6418444960675, -16.9116627612761, -15.823086161337377, -18.8156175563014, -20.51760048538854, -26.720243737496627, -16.939693252501844, 46.83147992361945, 54.33253215090422, -21.882206815553293, -16.65562521217139, 42.35340576342673, 40.290119692961404, -16.165155416534134]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tae79a9e5fcb14dcba836191c87d8dcd3\",\"data\",\"point\",[\"x\", \"y0\"],[[13.390697852943571, 12.76880993095078, 11.590826176501938, 71.46993215334679, 55.578524649318396, 74.05745590415671, 34.05882516821371, 28.797986823492888, 40.164591697445786, -37.23099503794359, -38.88611121420555, -37.21590976560106, -33.83394296164072, -31.49132116740065, -30.66503944186509, -42.186915282752295, -33.87034243812147, -35.10111032271948, -38.543615578457356, 73.75834423450446, -39.89883010431379, 8.161645449318883, 5.082836846354194, 64.32440100929551, -41.582597700118036, -3.9874771769762014, -6.39684285915582, -42.313826844572496], [87.78152548601332, 82.2832547599055, 82.53276980547011, -24.925889320011965, -28.153049097186436, -25.262136822834833, -30.690922305263655, -28.76174428141966, -31.44837975873982, -19.29161185324704, -15.690077116656427, -19.223851352891895, -16.32505727494886, -12.529511985604096, -12.208473144465565, -18.24786393488593, -16.124073310189953, -19.522864192767052, -20.554942639664322, -26.04497697542389, -17.70570810536457, 47.89231935065671, 54.23792609500459, -21.81906706097184, -17.326910789953356, 42.39882596556237, 40.619286042356926, -15.88879618247837]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"td25ebf94f88f494fbfcb408cc81e1b2f\",\"data\",\"point\",[\"x\", \"y0\"],[[7.934047263136408, 7.598792227302015, 7.626712190118778, 72.31295422742802, 57.40673869579451, 75.2272573795475, 36.64580973453128, 30.98126573912171, 41.33503947803902, -35.51134178032326, -37.16442594999918, -36.60383584634386, -32.56852420319788, -30.678863388827992, -30.13637687893097, -40.054599779427065, -33.144951963199034, -34.438910421673846, -36.9782923301865, 74.85499346030893, -38.51411937856607, 4.973402029544977, 0.9906736723590488, 65.11664172913329, -39.54275266846597, -7.290215757869937, -8.859461476125526, -41.51765600322841], [88.57207676703798, 82.92587199412228, 83.63792025786259, -21.76892429612965, -24.59541947617638, -21.83280153100822, -28.777808022831444, -27.18186910322994, -28.35761503255525, -21.527013670222505, -16.76299711029707, -21.882695777244177, -18.215898504742157, -13.977071979646182, -12.798159603828585, -19.95041554870504, -18.612559898192888, -21.551816491319812, -23.248946182229727, -21.276428217176093, -19.96171819719361, 48.092225209534384, 54.31656055215788, -19.08398490018183, -20.269330632993857, 42.15413730277572, 39.88425159917599, -17.949569506762376]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t95093cccb1b944e5ab1a2aafeac0f78d\",\"data\",\"point\",[\"x\", \"y0\"],[[13.381003409881428, 12.77663441362569, 12.64020176096838, 70.23946208630328, 56.001733081797234, 73.40465395072906, 34.004940235496676, 28.755248778408298, 39.99382233254985, -36.039435462012136, -37.631619093928585, -38.17652276558813, -33.455073341614806, -30.57989706176935, -30.302640145285736, -41.87447939981488, -34.08473738182552, -34.958989104612606, -37.532797653205726, 73.15555846818833, -39.71203234398501, 8.642420353123095, 4.772541373571611, 62.75918799551672, -42.22961538808293, -4.5925158512052215, -6.101117350421484, -43.25593589680755], [87.5820624857748, 81.73643400807876, 82.30882109153384, -25.364765621719418, -28.72723191968343, -25.689973589120598, -28.835272636447705, -28.68542693631748, -31.413382938059176, -18.304054868142376, -15.459568344159642, -19.218407456862458, -15.176120523647189, -12.245741787177458, -11.17856907266845, -17.382797255240856, -17.551289256375686, -19.45209439116363, -20.247736319035898, -26.280453839644828, -17.49688498096565, 46.009663830256976, 54.57757929586288, -21.068566187607587, -17.547482104142833, 41.95567767959392, 39.40816489054465, -16.252583253463555]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tfdc872d5b22045568a5675e50197cae8\",\"data\",\"point\",[\"x\", \"y0\"],[[10.944608567002042, 10.590596685000177, 10.008320280371947, 71.15902586081789, 56.46310554405827, 74.25350784135314, 35.17095831491632, 30.13517315311276, 40.72551433992281, -35.800759450725806, -37.91516765452947, -36.70814251904287, -33.04538024334882, -30.89172721012514, -30.00112322627475, -41.1630889727376, -34.03032240676332, -34.6723770685742, -37.201486608188716, 73.94600728168163, -39.207338186854955, 6.825755167798872, 3.2654575559235086, 63.90209432888148, -41.31182661685456, -5.588839865651547, -7.312413124840634, -42.54013176632846], [88.3816285747687, 82.79164452164848, 83.1209682560747, -23.630392540962518, -26.994249966311695, -23.800320679470016, -29.53611511897496, -28.316298784200313, -30.519034716941196, -20.141942289371407, -16.27248770651329, -20.274392159623787, -16.572370201375684, -13.040297724355549, -12.320084725675997, -18.491120659106002, -17.50035087335203, -20.3773768915014, -21.749750300438432, -23.79959295469074, -18.433371245201897, 46.995744775977094, 54.46348065225544, -19.954697346477356, -18.318698384419196, 41.71782412916921, 39.98075818488955, -17.40910382581971]],\"toyplot\");\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"t0d403b2cd957452d8810d665487ffc1a\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 80.0, \"min\": -43.54974504255877}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 200.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"tdae5bf5c33314481992b43f43b6793b1\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 88.95785002216492, \"min\": -40.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 200.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"taefdd3db57154609981629c66e9821f2\",\"data\",\"point\",[\"x\", \"y0\"],[[9.90425274899005, 9.741373160037037, 8.936018277967092, 71.35495502527698, 57.50732806007437, 74.86681786249491, 34.52701545843618, 29.386829854476293, 41.66266373643811, -35.44597565067607, -36.92970379493566, -36.16263274186227, -33.187168905666454, -30.118544651439265, -29.247695510506038, -40.69382653749566, -34.34127620647451, -34.760455492675995, -37.221754843889826, 74.70931394054736, -38.625148849926184, 5.975455924391921, 1.86890833618842, 64.37796844082165, -41.312557102204224, -6.727897728581371, -8.019509970534282, -42.02475283927255], [-12.85684432309953, -11.568317599450118, -12.498057154834218, 6.864758250622117, -0.7762268189863434, 4.93484586987755, -5.522613210397503, -7.508534539715664, -3.2563880955209332, -28.71993537117129, 30.959954069460583, -30.231541899223373, -33.476313751731105, -28.58243854982033, -35.79622747463353, 40.13743910288488, -29.614685546100095, -20.997031033038162, -29.486042559615907, 4.8341662990615735, 35.826152404189244, 12.987837965246799, 10.952049869293143, 4.7160544698530735, 48.95319095118936, 18.172817232557744, 19.374606623500654, 52.17732481960136]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t9db40600b4f34940a9e5748c51300b5f\",\"data\",\"point\",[\"x\", \"y0\"],[[8.108448603734598, 8.617701925640139, 7.623182406883075, 71.58400760934725, 56.820424777352166, 74.2085054845683, 36.527769158694696, 30.640886933641596, 41.59771824904981, -35.07600541978134, -37.76405748496534, -35.445421217559655, -31.260505543689558, -30.462997375409344, -28.949084432745607, -39.983505407594045, -33.86576221768931, -34.1335368798461, -36.94152731604749, 73.86618201410148, -38.41599436035406, 6.344163217290311, 1.794041871193625, 63.31990098802954, -41.38505855411401, -7.09089007124854, -8.686042072978603, -41.59254488550349], [-12.34925648472535, -11.17686201463531, -12.531727156948454, 6.9584006519562385, -0.36158279160425094, 5.366402573594398, -6.971483081497269, -5.838572134981293, -4.491501408643695, -29.34401694726435, 32.33872059109538, -29.478528009413935, -33.266489096341374, -28.733131862665847, -37.550645693319105, 40.30694401123398, -28.354301517242998, -21.901345450536233, -29.398971510870723, 5.321940763585793, 37.546744209747494, 10.765454196803288, 10.189022041907577, 5.974850752242126, 47.54346791441882, 18.782918807434207, 18.931254884777534, 51.7222937618934]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t7ec9b0e0eaec440b9a07f4ccab40a7c8\",\"data\",\"point\",[\"x\", \"y0\"],[[14.572606807173964, 13.675593315069426, 12.913161587523769, 70.10264696929418, 55.57870924467533, 73.38556906511093, 34.554846076054744, 29.62560652044239, 39.25331865847156, -36.56027338495862, -38.70321693526013, -36.844294176409385, -33.32968368329937, -32.05202044938232, -30.66409327711706, -41.93379862958782, -35.40603173332468, -35.372934807214726, -37.23814300718947, 72.74898866318853, -39.51725869544707, 8.249121575597911, 4.862606649761902, 62.86197460805935, -41.85350809223142, -3.7034264559307393, -5.839151551515544, -43.36691486155579], [-12.44720431890619, -11.531847843933472, -12.465014904942844, 5.847496498166977, 0.3128513581678587, 4.912957314944916, -6.369830666466064, -5.6034393775956275, -3.6673423262358313, -28.98665971514396, 31.889380120683857, -31.02124534176526, -34.015705531513404, -29.87904669243798, -35.35073758008142, 40.06376377853999, -27.179849102650806, -21.529753487438008, -27.308864738664646, 4.396138263430899, 35.98696353970785, 12.65274944157025, 11.205165376583864, 5.125795548739592, 47.61264522367738, 18.73436352739705, 18.561803442817993, 50.05446819334706]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t7314bf36e35e4a17982c4e2a72038131\",\"data\",\"point\",[\"x\", \"y0\"],[[7.308335636864564, 7.055814857029974, 6.135069132911557, 71.39806613538276, 57.10858159319944, 75.07129113650365, 37.0390014709697, 32.36843382974997, 41.76912316058653, -35.12488883270725, -37.14742795192563, -35.536656135962374, -32.52149266438857, -30.50167909179367, -29.314193964819403, -41.03872133082342, -33.461780302735384, -33.75591721554264, -36.91766210972915, 74.46443077913443, -37.991275293936155, 5.4708573090104515, 1.7839206996665118, 64.73006767385814, -40.125604052655525, -6.861863290139059, -9.008122007939683, -42.39570916976983], [-12.642214251349348, -10.912659609704058, -12.226845009445087, 6.255992945904887, -0.17292633886535663, 5.513400050797212, -6.034904906991896, -6.062580222318249, -3.6756371103063854, -29.059518236877498, 30.77708508730312, -31.817227484116348, -34.39387936979494, -29.46944790920239, -34.41262554845993, 40.810420184197234, -28.078815843927085, -19.965011777237375, -29.6307909953306, 4.521175063751257, 36.652897576134976, 11.459999969809399, 10.405851266070796, 5.608955904623135, 49.19292511482428, 16.920618737492287, 17.605237429233977, 52.83052528378401]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t81406817f5644d2592ec28d00367a795\",\"data\",\"point\",[\"x\", \"y0\"],[[9.975367479940425, 9.390055974139981, 9.483751636253158, 71.43537586864699, 56.857092254947965, 75.24498574324188, 34.88257615732163, 30.039075082143558, 40.94520800032876, -35.32395704443256, -37.98926391543842, -36.952930485793296, -32.5490916938235, -30.794806330670927, -30.073247225752823, -41.45538823847933, -32.75404797851448, -34.287870091627255, -36.39609733973079, 74.4634319509234, -39.133910754892995, 5.769822876907803, 2.9108779681218557, 64.47003435644916, -41.559290234367886, -6.016581004804562, -7.965773383072672, -42.61539962796504], [-12.762753398534953, -10.174924298087673, -12.07169528304822, 5.661392056213527, 0.5054754629225972, 5.315901415036599, -5.30627322209196, -5.903068720985674, -3.0138136113538425, -29.648917724468873, 31.4135538128048, -30.877988947317718, -32.68548977331816, -29.21122549250802, -36.01553412256139, 40.46971300939341, -30.234068582665532, -20.91890833605482, -29.14182572091907, 4.468670821956735, 36.57381317718477, 11.26674456435155, 11.064483225434289, 4.517631493586876, 47.798283965811756, 19.329997821591757, 18.355523115377068, 51.225303292250175]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tc8e3794ff1a64fe7aac22a3b10d9b924\",\"data\",\"point\",[\"x\", \"y0\"],[[10.463155716083223, 9.961454090074602, 9.538433665650068, 71.90751642617371, 57.139828031935636, 74.77033143461065, 34.92124722836311, 31.096734047987425, 41.00988551052663, -35.835946832162165, -37.9574387095382, -36.911153095373166, -33.15278949441723, -30.422748432889318, -30.649657427748465, -40.72071131361119, -34.65172873033482, -34.808082060081645, -37.506530131562634, 75.01102942241748, -39.619392800943636, 5.952283820443281, 2.7139528031283753, 64.67498313227428, -40.86827652412614, -6.319690977144326, -6.967856307684956, -42.768832492050606], [-14.275250128961728, -12.211476981354064, -13.558030145798709, 6.9763493035131345, -0.5960198852173536, 4.775724125372224, -6.8645926441900516, -8.437591495434047, -4.193233591611714, -27.941386817247515, 31.115848819053223, -29.541820042517333, -33.496492732774605, -30.40449606057543, -37.150479172298965, 40.58148338589313, -28.475817860958934, -20.668452110574513, -27.062888829218497, 5.005296113916586, 36.77168897190321, 13.976952750687877, 11.808769780131273, 6.469580910405754, 46.21224921618595, 19.88694545529143, 19.764265456581477, 51.53287420979823]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t21c9aaa15a1548599c233ddd0b0daae4\",\"data\",\"point\",[\"x\", \"y0\"],[[14.408170151272198, 14.319736956132136, 13.595845968941655, 69.78534210697904, 54.63209505148762, 72.29821045256777, 34.54755246108144, 29.659663921663412, 39.523772575792094, -35.85877506226111, -38.97841149509813, -37.232068959935525, -34.59552994175018, -31.814394151668523, -30.009203957976318, -41.68894380779025, -34.72256511541406, -35.10596428974777, -36.73844577188824, 72.42779988350198, -40.64541928618454, 8.718379122360082, 5.87421533888954, 62.38578335537712, -42.65900585217948, -3.2978403426155145, -5.280254268977764, -43.54974504255877], [-13.373083109529048, -12.156336125872933, -13.563188384287614, 5.9462214804908315, -0.2851978777609475, 4.451301722315936, -4.256430051096379, -4.62072201930816, -3.0792797865970924, -30.08048797809414, 31.251370898904856, -30.443088511768956, -33.76227145497817, -28.650933787928505, -36.01744038760408, 39.054890927313146, -29.676035290593152, -21.28486062000104, -28.828039346739196, 4.593796844923054, 36.09220354960725, 13.985354419385276, 11.658566508940595, 4.327423056800501, 47.98005561120593, 19.64862673644462, 19.7051320589088, 51.38245091691867]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tc6af9d76a50f468a966ed03874283ed4\",\"data\",\"point\",[\"x\", \"y0\"],[[13.390697852943571, 12.76880993095078, 11.590826176501938, 71.46993215334679, 55.578524649318396, 74.05745590415671, 34.05882516821371, 28.797986823492888, 40.164591697445786, -37.23099503794359, -38.88611121420555, -37.21590976560106, -33.83394296164072, -31.49132116740065, -30.66503944186509, -42.186915282752295, -33.87034243812147, -35.10111032271948, -38.543615578457356, 73.75834423450446, -39.89883010431379, 8.161645449318883, 5.082836846354194, 64.32440100929551, -41.582597700118036, -3.9874771769762014, -6.39684285915582, -42.313826844572496], [-13.157278917384886, -11.385773286626359, -13.071270098106373, 6.629035222861643, -0.20193556790203107, 4.757826026547973, -6.392040185050996, -6.958697234415956, -3.9038092762973764, -29.207903587167593, 32.39207114110186, -30.375768828172482, -33.17747433647826, -29.821469852316365, -35.55123350782893, 39.9243222322679, -26.69968165280852, -21.609887114162127, -29.027000120386788, 4.783375854594424, 37.17337287927543, 12.738223478369612, 10.970743253944493, 5.137570984636447, 48.299599619010436, 19.1460416789352, 18.174065691696267, 50.41497550186339]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tc39f43c12439466c9a6bbc6ee6ac70b3\",\"data\",\"point\",[\"x\", \"y0\"],[[7.934047263136408, 7.598792227302015, 7.626712190118778, 72.31295422742802, 57.40673869579451, 75.2272573795475, 36.64580973453128, 30.98126573912171, 41.33503947803902, -35.51134178032326, -37.16442594999918, -36.60383584634386, -32.56852420319788, -30.678863388827992, -30.13637687893097, -40.054599779427065, -33.144951963199034, -34.438910421673846, -36.9782923301865, 74.85499346030893, -38.51411937856607, 4.973402029544977, 0.9906736723590488, 65.11664172913329, -39.54275266846597, -7.290215757869937, -8.859461476125526, -41.51765600322841], [-12.840984412880788, -11.669208501009903, -12.329062532627274, 6.223321893313373, -0.930688123460104, 4.227227397513356, -4.879465128204608, -4.275731648681976, -3.4953729317730486, -30.148522616764588, 31.153311039884596, -31.295289604940077, -33.903011874345275, -27.8565501326265, -35.47587581733068, 40.817750019215154, -29.110434458836018, -20.895834223463414, -28.139897275926575, 4.123201177474982, 37.38134215203262, 12.086153999126644, 10.417014730176474, 3.8376407958294374, 47.6450014243663, 17.70855067928167, 19.809995209639855, 51.815418765016396]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tf7d6de1f5d514a34a73aeac40820dfd8\",\"data\",\"point\",[\"x\", \"y0\"],[[13.381003409881428, 12.77663441362569, 12.64020176096838, 70.23946208630328, 56.001733081797234, 73.40465395072906, 34.004940235496676, 28.755248778408298, 39.99382233254985, -36.039435462012136, -37.631619093928585, -38.17652276558813, -33.455073341614806, -30.57989706176935, -30.302640145285736, -41.87447939981488, -34.08473738182552, -34.958989104612606, -37.532797653205726, 73.15555846818833, -39.71203234398501, 8.642420353123095, 4.772541373571611, 62.75918799551672, -42.22961538808293, -4.5925158512052215, -6.101117350421484, -43.25593589680755], [-11.27562089640685, -8.990874085242359, -10.507529396784074, 5.857712287818898, 0.47640240476687756, 5.120297070977981, -5.112759095120297, -5.608347435342352, -2.024002749932669, -30.03133321099528, 31.78065438064391, -31.580500620963722, -32.37005100283908, -29.360930604389054, -36.223188802767204, 40.745785459505896, -28.430207356781874, -21.025460634830207, -30.9139256311914, 4.356003766088775, 37.471908128813546, 8.963525585804987, 8.919025191376495, 3.721584414340746, 48.74056964357259, 16.76016111560881, 18.153646065526562, 52.38745600874034]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t811a8e370dfa4faa802fb2142ee8e933\",\"data\",\"point\",[\"x\", \"y0\"],[[10.944608567002042, 10.590596685000177, 10.008320280371947, 71.15902586081789, 56.46310554405827, 74.25350784135314, 35.17095831491632, 30.13517315311276, 40.72551433992281, -35.800759450725806, -37.91516765452947, -36.70814251904287, -33.04538024334882, -30.89172721012514, -30.00112322627475, -41.1630889727376, -34.03032240676332, -34.6723770685742, -37.201486608188716, 73.94600728168163, -39.207338186854955, 6.825755167798872, 3.2654575559235086, 63.90209432888148, -41.31182661685456, -5.588839865651547, -7.312413124840634, -42.54013176632846], [-12.798049024177867, -11.177828034591625, -12.482242006682286, 6.322068059086163, -0.2029848177939054, 4.937588356697814, -5.771039219110703, -6.0817284828779, -3.4800380888272593, -29.316868220519506, 31.50719499609362, -30.66629992901992, -33.45471789241144, -29.196967094447047, -35.95439881068853, 40.291251211044475, -28.5853897212565, -21.07965447873359, -28.893824672886335, 4.6403764968784085, 36.74770865885964, 12.08829963711557, 10.7590691243859, 4.943708833105768, 47.99779886842628, 18.50910417920348, 18.843552997806018, 51.55430907532131]],\"toyplot\");\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"tc8d9171faf7a45219bef22941075ad81\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 80.0, \"min\": -43.54974504255877}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 200.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"tc1a40ba8cb784357b912adc8e08bf4b4\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 60.0, \"min\": -37.550645693319105}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 200.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tb517165936404312ba52e8eaf19efda7\",\"data\",\"point\",[\"x\", \"y0\"],[[88.94277659521687, 83.15763554680372, 83.11693196313485, -23.154422018874403, -26.175827328359365, -22.929580612443416, -29.04374695056051, -27.84475592463253, -30.033228233185504, -20.5272226429361, -17.582037075094018, -20.772194492681482, -15.83452103447345, -13.4999195839101, -12.672877296459575, -19.163668192341472, -18.060695085450217, -21.005876812400388, -22.62667373231019, -22.494278137063397, -18.734389121661113, 47.08020184325513, 54.651222174258244, -19.33075769595802, -18.770877725956595, 41.89984870450099, 39.21541049367185, -17.806477624089833], [-12.85684432309953, -11.568317599450118, -12.498057154834218, 6.864758250622117, -0.7762268189863434, 4.93484586987755, -5.522613210397503, -7.508534539715664, -3.2563880955209332, -28.71993537117129, 30.959954069460583, -30.231541899223373, -33.476313751731105, -28.58243854982033, -35.79622747463353, 40.13743910288488, -29.614685546100095, -20.997031033038162, -29.486042559615907, 4.8341662990615735, 35.826152404189244, 12.987837965246799, 10.952049869293143, 4.7160544698530735, 48.95319095118936, 18.172817232557744, 19.374606623500654, 52.17732481960136]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t836928b9517344b2bbe1467c91242fe7\",\"data\",\"point\",[\"x\", \"y0\"],[[88.90475675374316, 82.579872838625, 82.90644449402001, -22.226180728554006, -25.948847739997518, -22.107492305420884, -28.09029554202744, -28.18337701297122, -29.87304752836056, -21.1943158081818, -17.434166192397992, -21.09601220960789, -17.006600123444375, -14.309346568921857, -13.82405754887435, -19.033414746914893, -18.279434401592432, -20.881804470451808, -22.60128011365124, -21.49596551063319, -18.605011774941264, 47.68331699448995, 55.372423426615576, -18.29130320416895, -19.752349913030685, 41.03546114171901, 40.61264587251809, -18.860618077586313], [-12.34925648472535, -11.17686201463531, -12.531727156948454, 6.9584006519562385, -0.36158279160425094, 5.366402573594398, -6.971483081497269, -5.838572134981293, -4.491501408643695, -29.34401694726435, 32.33872059109538, -29.478528009413935, -33.266489096341374, -28.733131862665847, -37.550645693319105, 40.30694401123398, -28.354301517242998, -21.901345450536233, -29.398971510870723, 5.321940763585793, 37.546744209747494, 10.765454196803288, 10.189022041907577, 5.974850752242126, 47.54346791441882, 18.782918807434207, 18.931254884777534, 51.7222937618934]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tde81aa71354642ce9907417995d33884\",\"data\",\"point\",[\"x\", \"y0\"],[[88.44370906511963, 83.07172737396125, 83.64887426243536, -26.025816507495954, -29.008837527765387, -26.25297717147209, -30.935156479196397, -28.852509978732968, -31.48092806077556, -19.260947688623123, -15.123072080943938, -18.694255963825103, -16.302153553883635, -11.970169238041187, -10.67357937955078, -17.079042110468954, -15.85757322995793, -19.80214327189642, -20.496994190154904, -26.44967209335421, -17.527589124134455, 46.192683345454206, 54.36978778504263, -22.03567584130095, -16.73411464671087, 41.05714430813461, 39.91698989185306, -16.137707893715962], [-12.44720431890619, -11.531847843933472, -12.465014904942844, 5.847496498166977, 0.3128513581678587, 4.912957314944916, -6.369830666466064, -5.6034393775956275, -3.6673423262358313, -28.98665971514396, 31.889380120683857, -31.02124534176526, -34.015705531513404, -29.87904669243798, -35.35073758008142, 40.06376377853999, -27.179849102650806, -21.529753487438008, -27.308864738664646, 4.396138263430899, 35.98696353970785, 12.65274944157025, 11.205165376583864, 5.125795548739592, 47.61264522367738, 18.73436352739705, 18.561803442817993, 50.05446819334706]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t55474ba2fd044086a9d8046c8c9e1683\",\"data\",\"point\",[\"x\", \"y0\"],[[88.95785002216492, 83.23896573882861, 83.46457039913119, -20.624226741579616, -25.22269670227966, -20.76159614271953, -28.600673416878585, -27.453836247144856, -29.688995000935066, -21.231294756323184, -16.982542130161733, -22.050844498430376, -18.382318692985756, -14.137839208532126, -13.91252455218462, -19.121478108278353, -18.842048913089158, -21.038455849530777, -23.570362860848448, -21.241292894058482, -19.054796098932975, 46.447909528854524, 54.366377851567655, -17.18207059300068, -19.418551723409173, 41.1706295947339, 39.67301977232527, -18.800877776302922], [-12.642214251349348, -10.912659609704058, -12.226845009445087, 6.255992945904887, -0.17292633886535663, 5.513400050797212, -6.034904906991896, -6.062580222318249, -3.6756371103063854, -29.059518236877498, 30.77708508730312, -31.817227484116348, -34.39387936979494, -29.46944790920239, -34.41262554845993, 40.810420184197234, -28.078815843927085, -19.965011777237375, -29.6307909953306, 4.521175063751257, 36.652897576134976, 11.459999969809399, 10.405851266070796, 5.608955904623135, 49.19292511482428, 16.920618737492287, 17.605237429233977, 52.83052528378401]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t79713b13772c457ab1bdf3501189d280\",\"data\",\"point\",[\"x\", \"y0\"],[[88.76606042876067, 83.31988897178226, 83.7966431017058, -23.06995699117389, -26.224492855444026, -22.72827367362177, -29.692549434086278, -28.053574063287478, -30.31196032044783, -20.05691328150146, -16.68959473334457, -21.170094951248714, -16.217924058719504, -12.726790582492946, -12.597751863174876, -19.02689177407503, -17.918302063466314, -21.476685258807855, -21.273095559644023, -23.11828438582333, -19.155522854297157, 46.80711291192898, 54.42663551577989, -19.617416122594587, -18.81196076393122, 41.393209450378606, 39.97125403868488, -18.54276882783817], [-12.762753398534953, -10.174924298087673, -12.07169528304822, 5.661392056213527, 0.5054754629225972, 5.315901415036599, -5.30627322209196, -5.903068720985674, -3.0138136113538425, -29.648917724468873, 31.4135538128048, -30.877988947317718, -32.68548977331816, -29.21122549250802, -36.01553412256139, 40.46971300939341, -30.234068582665532, -20.91890833605482, -29.14182572091907, 4.468670821956735, 36.57381317718477, 11.26674456435155, 11.064483225434289, 4.517631493586876, 47.798283965811756, 19.329997821591757, 18.355523115377068, 51.225303292250175]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t2090b8a59f0c4e2bb917f88ba55cb371\",\"data\",\"point\",[\"x\", \"y0\"],[[88.7245507853062, 83.16725482047183, 83.43053110102768, -23.49967534436568, -27.070774260305782, -23.68348186085738, -28.843589644245114, -27.837479813154335, -30.487060780293678, -20.864841671777015, -16.387721208309628, -20.30401963391612, -16.811765179345645, -13.355932555914201, -12.69301029948567, -18.99397215887339, -17.93444641386834, -20.22641062037484, -22.359870921457027, -22.87433375623339, -19.152398942026313, 46.92053482172071, 53.983761675360796, -19.235925043435834, -17.899780331891968, 41.759901380866204, 40.21643955480343, -17.686483699425494], [-14.275250128961728, -12.211476981354064, -13.558030145798709, 6.9763493035131345, -0.5960198852173536, 4.775724125372224, -6.8645926441900516, -8.437591495434047, -4.193233591611714, -27.941386817247515, 31.115848819053223, -29.541820042517333, -33.496492732774605, -30.40449606057543, -37.150479172298965, 40.58148338589313, -28.475817860958934, -20.668452110574513, -27.062888829218497, 5.005296113916586, 36.77168897190321, 13.976952750687877, 11.808769780131273, 6.469580910405754, 46.21224921618595, 19.88694545529143, 19.764265456581477, 51.53287420979823]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"te3f5e059a8024415beaace94979195f3\",\"data\",\"point\",[\"x\", \"y0\"],[[87.14091735854949, 82.43553916390555, 82.36617608442569, -25.644067839720574, -28.81532275591899, -26.75489308520145, -31.851136758212466, -30.308414481112653, -32.0957495160595, -19.16120665275945, -14.613101073767883, -18.33154525952966, -15.451343067566265, -11.650653753315337, -10.6418444960675, -16.9116627612761, -15.823086161337377, -18.8156175563014, -20.51760048538854, -26.720243737496627, -16.939693252501844, 46.83147992361945, 54.33253215090422, -21.882206815553293, -16.65562521217139, 42.35340576342673, 40.290119692961404, -16.165155416534134], [-13.373083109529048, -12.156336125872933, -13.563188384287614, 5.9462214804908315, -0.2851978777609475, 4.451301722315936, -4.256430051096379, -4.62072201930816, -3.0792797865970924, -30.08048797809414, 31.251370898904856, -30.443088511768956, -33.76227145497817, -28.650933787928505, -36.01744038760408, 39.054890927313146, -29.676035290593152, -21.28486062000104, -28.828039346739196, 4.593796844923054, 36.09220354960725, 13.985354419385276, 11.658566508940595, 4.327423056800501, 47.98005561120593, 19.64862673644462, 19.7051320589088, 51.38245091691867]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t5c2d7e15946e432e9e148b66d6f54565\",\"data\",\"point\",[\"x\", \"y0\"],[[87.78152548601332, 82.2832547599055, 82.53276980547011, -24.925889320011965, -28.153049097186436, -25.262136822834833, -30.690922305263655, -28.76174428141966, -31.44837975873982, -19.29161185324704, -15.690077116656427, -19.223851352891895, -16.32505727494886, -12.529511985604096, -12.208473144465565, -18.24786393488593, -16.124073310189953, -19.522864192767052, -20.554942639664322, -26.04497697542389, -17.70570810536457, 47.89231935065671, 54.23792609500459, -21.81906706097184, -17.326910789953356, 42.39882596556237, 40.619286042356926, -15.88879618247837], [-13.157278917384886, -11.385773286626359, -13.071270098106373, 6.629035222861643, -0.20193556790203107, 4.757826026547973, -6.392040185050996, -6.958697234415956, -3.9038092762973764, -29.207903587167593, 32.39207114110186, -30.375768828172482, -33.17747433647826, -29.821469852316365, -35.55123350782893, 39.9243222322679, -26.69968165280852, -21.609887114162127, -29.027000120386788, 4.783375854594424, 37.17337287927543, 12.738223478369612, 10.970743253944493, 5.137570984636447, 48.299599619010436, 19.1460416789352, 18.174065691696267, 50.41497550186339]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t203ecf277ee34632a66005d633bcc9f3\",\"data\",\"point\",[\"x\", \"y0\"],[[88.57207676703798, 82.92587199412228, 83.63792025786259, -21.76892429612965, -24.59541947617638, -21.83280153100822, -28.777808022831444, -27.18186910322994, -28.35761503255525, -21.527013670222505, -16.76299711029707, -21.882695777244177, -18.215898504742157, -13.977071979646182, -12.798159603828585, -19.95041554870504, -18.612559898192888, -21.551816491319812, -23.248946182229727, -21.276428217176093, -19.96171819719361, 48.092225209534384, 54.31656055215788, -19.08398490018183, -20.269330632993857, 42.15413730277572, 39.88425159917599, -17.949569506762376], [-12.840984412880788, -11.669208501009903, -12.329062532627274, 6.223321893313373, -0.930688123460104, 4.227227397513356, -4.879465128204608, -4.275731648681976, -3.4953729317730486, -30.148522616764588, 31.153311039884596, -31.295289604940077, -33.903011874345275, -27.8565501326265, -35.47587581733068, 40.817750019215154, -29.110434458836018, -20.895834223463414, -28.139897275926575, 4.123201177474982, 37.38134215203262, 12.086153999126644, 10.417014730176474, 3.8376407958294374, 47.6450014243663, 17.70855067928167, 19.809995209639855, 51.815418765016396]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tb9720dfe8417481fab834da0edbbfc6c\",\"data\",\"point\",[\"x\", \"y0\"],[[87.5820624857748, 81.73643400807876, 82.30882109153384, -25.364765621719418, -28.72723191968343, -25.689973589120598, -28.835272636447705, -28.68542693631748, -31.413382938059176, -18.304054868142376, -15.459568344159642, -19.218407456862458, -15.176120523647189, -12.245741787177458, -11.17856907266845, -17.382797255240856, -17.551289256375686, -19.45209439116363, -20.247736319035898, -26.280453839644828, -17.49688498096565, 46.009663830256976, 54.57757929586288, -21.068566187607587, -17.547482104142833, 41.95567767959392, 39.40816489054465, -16.252583253463555], [-11.27562089640685, -8.990874085242359, -10.507529396784074, 5.857712287818898, 0.47640240476687756, 5.120297070977981, -5.112759095120297, -5.608347435342352, -2.024002749932669, -30.03133321099528, 31.78065438064391, -31.580500620963722, -32.37005100283908, -29.360930604389054, -36.223188802767204, 40.745785459505896, -28.430207356781874, -21.025460634830207, -30.9139256311914, 4.356003766088775, 37.471908128813546, 8.963525585804987, 8.919025191376495, 3.721584414340746, 48.74056964357259, 16.76016111560881, 18.153646065526562, 52.38745600874034]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t58497e4b5f704d9195ea257ea34517f6\",\"data\",\"point\",[\"x\", \"y0\"],[[88.3816285747687, 82.79164452164848, 83.1209682560747, -23.630392540962518, -26.994249966311695, -23.800320679470016, -29.53611511897496, -28.316298784200313, -30.519034716941196, -20.141942289371407, -16.27248770651329, -20.274392159623787, -16.572370201375684, -13.040297724355549, -12.320084725675997, -18.491120659106002, -17.50035087335203, -20.3773768915014, -21.749750300438432, -23.79959295469074, -18.433371245201897, 46.995744775977094, 54.46348065225544, -19.954697346477356, -18.318698384419196, 41.71782412916921, 39.98075818488955, -17.40910382581971], [-12.798049024177867, -11.177828034591625, -12.482242006682286, 6.322068059086163, -0.2029848177939054, 4.937588356697814, -5.771039219110703, -6.0817284828779, -3.4800380888272593, -29.316868220519506, 31.50719499609362, -30.66629992901992, -33.45471789241144, -29.196967094447047, -35.95439881068853, 40.291251211044475, -28.5853897212565, -21.07965447873359, -28.893824672886335, 4.6403764968784085, 36.74770865885964, 12.08829963711557, 10.7590691243859, 4.943708833105768, 47.99779886842628, 18.50910417920348, 18.843552997806018, 51.55430907532131]],\"toyplot\");\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"t639c8e70020d4211817398ff1e0ae313\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 88.95785002216492, \"min\": -40.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 200.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"td7eb81187dde4b98ad3a1bfc99e8990b\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 60.0, \"min\": -37.550645693319105}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 200.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t61cdd923b09c4103adc7bdf55f560034\",\"data\",\"point\",[\"x\", \"y0\"],[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0]],\"toyplot\");\n",
"})();</script></div></div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# a convenience function for plotting across three axes\n",
"tool.draw_panels(0, 1, 2, imap=IMAP);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run TSNE\n",
"t-SNE is a manifold learning algorithm that can sometimes better project data into a 2-dimensional plane. The distances between points in this space are harder to interpret. "
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Subsampling SNPs: 25092/100093\n"
]
},
{
"data": {
"text/html": [
"<div class=\"toyplot\" id=\"t7db7c323b68b40d2a47f6383da96756c\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"300.0px\" id=\"t47e08daecc3542faabf35723c45dda76\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 400.0 300.0\" width=\"400.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"t27f1da806fee4b1e98b0cc593a2374e5\"><clipPath id=\"tfa41d02d11af4860aa4c8ed800deabeb\"><rect height=\"220.0\" width=\"240.0\" x=\"40.0\" y=\"40.0\"></rect></clipPath><g clip-path=\"url(#tfa41d02d11af4860aa4c8ed800deabeb)\"><g class=\"toyplot-mark-Point\" id=\"ta5e04866dffb4d2d82ddecbc9e1e2935\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(122.13626605408874, 104.7101125486281)\"><title>BJSB3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(126.02623645235201, 97.76735835884236)\"><title>BJSL25</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(116.46982376629006, 98.31993618741683)\"><title>BJVL19</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(254.6697750948508, 214.75416346896412)\"><title>BZBB1</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(233.2701279182316, 207.46056609574742)\"><title>CRL0001</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(243.6428079612274, 212.65236616398565)\"><title>CRL0030</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(230.78114611578303, 196.65319199132807)\"><title>CUCA4</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(223.80168142516987, 215.73868087425757)\"><title>CUSV6</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(224.2150294149628, 205.57038148738252)\"><title>CUVN10</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(68.17737559875825, 226.97091868287953)\"><title>FLAB109</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(60.15568834080081, 196.3143562929767)\"><title>FLBA140</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(55.93156738425461, 226.9832134283677)\"><title>FLCK18</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(96.16022817891083, 240.99446402860298)\"><title>FLCK216</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(84.51133286417468, 242.53274907655046)\"><title>FLMO62</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(89.61028190485419, 250.0)\"><title>FLSA185</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(70.97863828915717, 193.7710119405812)\"><title>FLSF33</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(83.16011559865461, 233.41375080363417)\"><title>FLSF47</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(69.87146575436873, 233.2202262462978)\"><title>FLSF54</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(60.211955957553236, 237.2362008902849)\"><title>FLWO6</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(248.00608383610853, 205.59068616503833)\"><title>HNDA09</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(62.209087494228235, 187.9178550135559)\"><title>LALC2</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(140.9001762287856, 71.08558859009071)\"><title>MXED8</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(137.73312663252804, 74.94286946460775)\"><title>MXGT4</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(260.41767697148464, 206.19066010773932)\"><title>MXSA3017</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(50.0, 183.42818239590375)\"><title>SCCU3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(122.30379466749466, 73.60528121774692)\"><title>TXGR3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(126.8407420607646, 65.9868608942398)\"><title>TXMD3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(52.19643868856031, 191.49721051610317)\"><title>TXWV2</title><circle r=\"5.0\"></circle></g></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t7309f269115145c68bf230ca05ecb4ce\" transform=\"translate(50.0,250.0)translate(0,10.0)\"><line style=\"stroke-width:2.25\" x1=\"0\" x2=\"210.41767697148467\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(17.145620345468263,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-12.006\" y=\"10.265999999999998\">-200</text></g><g transform=\"translate(84.76374689697884,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"10.265999999999998\">0</text></g><g transform=\"translate(152.38187344848942,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-10.008\" y=\"10.265999999999998\">200</text></g><g transform=\"translate(220.0,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-10.008\" y=\"10.265999999999998\">400</text></g></g><g transform=\"translate(110.0,22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-64.176\" y=\"11.977\">TSNE component 1</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"te5d1128e2425412cb42790d7b654dd9b\" transform=\"translate(50.0,250.0)rotate(-90.0)translate(0,-10.0)\"><line style=\"stroke-width:2.25\" x1=\"0\" x2=\"184.01313910576022\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(17.81388898515716,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-12.006\" y=\"0.0\">-250</text></g><g transform=\"translate(78.54259265677143,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"0.0\">0</text></g><g transform=\"translate(139.27129632838572,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-10.008\" y=\"0.0\">250</text></g><g transform=\"translate(200.0,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-10.008\" y=\"0.0\">500</text></g></g><g transform=\"translate(100.0,-22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-64.176\" y=\"0.0\">TSNE component 2</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g><g class=\"toyplot-coordinates-Table\" id=\"t3fb20f086fbb4f74914d495234fe5e08\"><g transform=\"translate(310.0,75.0)\"><g style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,75.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">virg</text></g><g transform=\"translate(310.0,100.0)\"><g style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,100.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">mini</text></g><g transform=\"translate(310.0,125.0)\"><g style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,125.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">gemi</text></g><g transform=\"translate(310.0,150.0)\"><g style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,150.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">bran</text></g><g transform=\"translate(310.0,175.0)\"><g style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,175.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">fusi</text></g><g transform=\"translate(310.0,200.0)\"><g style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,200.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">sagr</text></g><g transform=\"translate(310.0,225.0)\"><g style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,225.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">oleo</text></g></g></svg><div class=\"toyplot-behavior\"><script>(function()\n",
"{\n",
"var modules={};\n",
"modules[\"toyplot/tables\"] = (function()\n",
" {\n",
" var tables = [];\n",
"\n",
" var module = {};\n",
"\n",
" module.set = function(owner, key, names, columns)\n",
" {\n",
" tables.push({owner: owner, key: key, names: names, columns: columns});\n",
" }\n",
"\n",
" module.get = function(owner, key)\n",
" {\n",
" for(var i = 0; i != tables.length; ++i)\n",
" {\n",
" var table = tables[i];\n",
" if(table.owner != owner)\n",
" continue;\n",
" if(table.key != key)\n",
" continue;\n",
" return {names: table.names, columns: table.columns};\n",
" }\n",
" }\n",
"\n",
" module.get_csv = function(owner, key)\n",
" {\n",
" var table = module.get(owner, key);\n",
" if(table != undefined)\n",
" {\n",
" var csv = \"\";\n",
" csv += table.names.join(\",\") + \"\\n\";\n",
" for(var i = 0; i != table.columns[0].length; ++i)\n",
" {\n",
" for(var j = 0; j != table.columns.length; ++j)\n",
" {\n",
" if(j)\n",
" csv += \",\";\n",
" csv += table.columns[j][i];\n",
" }\n",
" csv += \"\\n\";\n",
" }\n",
" return csv;\n",
" }\n",
" }\n",
"\n",
" return module;\n",
" })();\n",
"modules[\"toyplot/root/id\"] = \"t7db7c323b68b40d2a47f6383da96756c\";\n",
"modules[\"toyplot/root\"] = (function(root_id)\n",
" {\n",
" return document.querySelector(\"#\" + root_id);\n",
" })(modules[\"toyplot/root/id\"]);\n",
"modules[\"toyplot/canvas/id\"] = \"t47e08daecc3542faabf35723c45dda76\";\n",
"modules[\"toyplot/canvas\"] = (function(canvas_id)\n",
" {\n",
" return document.querySelector(\"#\" + canvas_id);\n",
" })(modules[\"toyplot/canvas/id\"]);\n",
"modules[\"toyplot/menus/context\"] = (function(root, canvas)\n",
" {\n",
" var wrapper = document.createElement(\"div\");\n",
" wrapper.innerHTML = \"<ul class='toyplot-context-menu' style='background:#eee; border:1px solid #b8b8b8; border-radius:5px; box-shadow: 0px 0px 8px rgba(0%,0%,0%,0.25); margin:0; padding:3px 0; position:fixed; visibility:hidden;'></ul>\"\n",
" var menu = wrapper.firstChild;\n",
"\n",
" root.appendChild(menu);\n",
"\n",
" var items = [];\n",
"\n",
" var ignore_mouseup = null;\n",
" function open_menu(e)\n",
" {\n",
" var show_menu = false;\n",
" for(var index=0; index != items.length; ++index)\n",
" {\n",
" var item = items[index];\n",
" if(item.show(e))\n",
" {\n",
" item.item.style.display = \"block\";\n",
" show_menu = true;\n",
" }\n",
" else\n",
" {\n",
" item.item.style.display = \"none\";\n",
" }\n",
" }\n",
"\n",
" if(show_menu)\n",
" {\n",
" ignore_mouseup = true;\n",
" menu.style.left = (e.clientX + 1) + \"px\";\n",
" menu.style.top = (e.clientY - 5) + \"px\";\n",
" menu.style.visibility = \"visible\";\n",
" e.stopPropagation();\n",
" e.preventDefault();\n",
" }\n",
" }\n",
"\n",
" function close_menu()\n",
" {\n",
" menu.style.visibility = \"hidden\";\n",
" }\n",
"\n",
" function contextmenu(e)\n",
" {\n",
" open_menu(e);\n",
" }\n",
"\n",
" function mousemove(e)\n",
" {\n",
" ignore_mouseup = false;\n",
" }\n",
"\n",
" function mouseup(e)\n",
" {\n",
" if(ignore_mouseup)\n",
" {\n",
" ignore_mouseup = false;\n",
" return;\n",
" }\n",
" close_menu();\n",
" }\n",
"\n",
" function keydown(e)\n",
" {\n",
" if(e.key == \"Escape\" || e.key == \"Esc\" || e.keyCode == 27)\n",
" {\n",
" close_menu();\n",
" }\n",
" }\n",
"\n",
" canvas.addEventListener(\"contextmenu\", contextmenu);\n",
" canvas.addEventListener(\"mousemove\", mousemove);\n",
" document.addEventListener(\"mouseup\", mouseup);\n",
" document.addEventListener(\"keydown\", keydown);\n",
"\n",
" var module = {};\n",
" module.add_item = function(label, show, activate)\n",
" {\n",
" var wrapper = document.createElement(\"div\");\n",
" wrapper.innerHTML = \"<li class='toyplot-context-menu-item' style='background:#eee; color:#333; padding:2px 20px; list-style:none; margin:0; text-align:left;'>\" + label + \"</li>\"\n",
" var item = wrapper.firstChild;\n",
"\n",
" items.push({item: item, show: show});\n",
"\n",
" function mouseover()\n",
" {\n",
" this.style.background = \"steelblue\";\n",
" this.style.color = \"white\";\n",
" }\n",
"\n",
" function mouseout()\n",
" {\n",
" this.style.background = \"#eee\";\n",
" this.style.color = \"#333\";\n",
" }\n",
"\n",
" function choose_item(e)\n",
" {\n",
" close_menu();\n",
" activate();\n",
"\n",
" e.stopPropagation();\n",
" e.preventDefault();\n",
" }\n",
"\n",
" item.addEventListener(\"mouseover\", mouseover);\n",
" item.addEventListener(\"mouseout\", mouseout);\n",
" item.addEventListener(\"mouseup\", choose_item);\n",
" item.addEventListener(\"contextmenu\", choose_item);\n",
"\n",
" menu.appendChild(item);\n",
" };\n",
" return module;\n",
" })(modules[\"toyplot/root\"],modules[\"toyplot/canvas\"]);\n",
"modules[\"toyplot/io\"] = (function()\n",
" {\n",
" var module = {};\n",
" module.save_file = function(mime_type, charset, data, filename)\n",
" {\n",
" var uri = \"data:\" + mime_type + \";charset=\" + charset + \",\" + data;\n",
" uri = encodeURI(uri);\n",
"\n",
" var link = document.createElement(\"a\");\n",
" if(typeof link.download != \"undefined\")\n",
" {\n",
" link.href = uri;\n",
" link.style = \"visibility:hidden\";\n",
" link.download = filename;\n",
"\n",
" document.body.appendChild(link);\n",
" link.click();\n",
" document.body.removeChild(link);\n",
" }\n",
" else\n",
" {\n",
" window.open(uri);\n",
" }\n",
" };\n",
" return module;\n",
" })();\n",
"modules[\"toyplot.coordinates.Axis\"] = (\n",
" function(canvas)\n",
" {\n",
" function sign(x)\n",
" {\n",
" return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
" }\n",
"\n",
" function mix(a, b, amount)\n",
" {\n",
" return ((1.0 - amount) * a) + (amount * b);\n",
" }\n",
"\n",
" function log(x, base)\n",
" {\n",
" return Math.log(Math.abs(x)) / Math.log(base);\n",
" }\n",
"\n",
" function in_range(a, x, b)\n",
" {\n",
" var left = Math.min(a, b);\n",
" var right = Math.max(a, b);\n",
" return left <= x && x <= right;\n",
" }\n",
"\n",
" function inside(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(in_range(segment.range.min, range, segment.range.max))\n",
" return true;\n",
" }\n",
" return false;\n",
" }\n",
"\n",
" function to_domain(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n",
" {\n",
" if(segment.scale == \"linear\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" return mix(segment.domain.min, segment.domain.max, amount)\n",
" }\n",
" else if(segment.scale[0] == \"log\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" var base = segment.scale[1];\n",
" return sign(segment.domain.min) * Math.pow(base, mix(log(segment.domain.min, base), log(segment.domain.max, base), amount));\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" var axes = {};\n",
"\n",
" function display_coordinates(e)\n",
" {\n",
" var current = canvas.createSVGPoint();\n",
" current.x = e.clientX;\n",
" current.y = e.clientY;\n",
"\n",
" for(var axis_id in axes)\n",
" {\n",
" var axis = document.querySelector(\"#\" + axis_id);\n",
" var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
" if(coordinates)\n",
" {\n",
" var projection = axes[axis_id];\n",
" var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
" if(inside(local.x, projection))\n",
" {\n",
" var domain = to_domain(local.x, projection);\n",
" coordinates.style.visibility = \"visible\";\n",
" coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
" var text = coordinates.querySelector(\"text\");\n",
" text.textContent = domain.toFixed(2);\n",
" }\n",
" else\n",
" {\n",
" coordinates.style.visibility= \"hidden\";\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" canvas.addEventListener(\"click\", display_coordinates);\n",
"\n",
" var module = {};\n",
" module.show_coordinates = function(axis_id, projection)\n",
" {\n",
" axes[axis_id] = projection;\n",
" }\n",
"\n",
" return module;\n",
" })(modules[\"toyplot/canvas\"]);\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"ta5e04866dffb4d2d82ddecbc9e1e2935\",\"data\",\"point\",[\"x\", \"y0\"],[[-37.349395751953125, -25.843692779541016, -54.10952377319336, 354.6564636230469, 291.3608703613281, 322.0410461425781, 283.9989929199219, 263.355224609375, 264.57781982421875, -196.9482879638672, -220.6747283935547, -233.1687774658203, -114.18097686767578, -148.63592529296875, -133.5543212890625, -188.66275024414062, -152.63253784179688, -191.93753051757812, -220.50830078125, 334.9466857910156, -214.60121154785156, 18.150249481201172, 8.782792091369629, 371.65753173828125, -250.7130889892578, -36.8538818359375, -23.434558868408203, -244.21649169921875], [274.77655029296875, 303.3575744628906, 301.0827941894531, -178.23843383789062, -148.21310424804688, -169.58602905273438, -103.72271728515625, -182.29136657714844, -140.4318389892578, -228.53077697753906, -102.32784271240234, -228.58139038085938, -286.2610778808594, -292.59368896484375, -323.3338928222656, -91.85773468017578, -255.0537872314453, -254.25711059570312, -270.78955078125, -140.5154266357422, -67.76222229003906, 413.1976013183594, 397.3184509277344, -142.98532104492188, -49.27972412109375, 402.8248596191406, 434.1873779296875, -82.49724578857422]],\"toyplot\");\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"t7309f269115145c68bf230ca05ecb4ce\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 400.0, \"min\": -250.7130889892578}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 220.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"te5d1128e2425412cb42790d7b654dd9b\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 500.0, \"min\": -323.3338928222656}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 200.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"})();</script></div></div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tool.run_tsne(perplexity=5, seed=333)\n",
"tool.draw(imap=IMAP);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run UMAP\n",
"UMAP is similar to t-SNE but the distances between clusters are more representative of the differences betwen groups. This requires another package that if it is not yet installed it will ask you to install. "
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Subsampling SNPs: 25092/100093\n"
]
},
{
"data": {
"text/html": [
"<div class=\"toyplot\" id=\"tca41158dcc6d4afcb41c35d5aff66e8f\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"300.0px\" id=\"t72b86d135a9f41329cd1be0d08fc7c61\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 400.0 300.0\" width=\"400.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"tf318b0f4c30b4dd589d7bf8cbe2ce6d2\"><clipPath id=\"tfa06803b6a464d99890c9e93ac6e4ed8\"><rect height=\"220.0\" width=\"240.0\" x=\"40.0\" y=\"40.0\"></rect></clipPath><g clip-path=\"url(#tfa06803b6a464d99890c9e93ac6e4ed8)\"><g class=\"toyplot-mark-Point\" id=\"tb34701a88e2b48f69f3dfe80d879f0f9\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(187.05575987523568, 203.2477337108807)\"><title>BJSB3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(156.79852657061053, 210.49160249342043)\"><title>BJSL25</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(179.97587812201766, 215.4282199297412)\"><title>BJVL19</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(102.14057866437001, 50.0)\"><title>BZBB1</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(103.29427154861386, 65.22981932519333)\"><title>CRL0001</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(68.66883917972115, 68.3592916361131)\"><title>CRL0030</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(114.35723330462513, 79.86112245625085)\"><title>CUCA4</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(125.30059588929494, 67.92775232475678)\"><title>CUSV6</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(93.37688601289543, 72.70988053045)\"><title>CUVN10</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(196.94206140452968, 168.15819800269702)\"><title>FLAB109</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(228.22396592258096, 136.68766724019946)\"><title>FLBA140</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(184.09271664998488, 146.21163278749464)\"><title>FLCK18</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(136.3837852936384, 164.33700372304799)\"><title>FLCK216</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(149.1430786061777, 168.29268274201323)\"><title>FLMO62</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(164.21183166127418, 171.70560960186486)\"><title>FLSA185</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(270.0, 147.54191327380136)\"><title>FLSF33</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(147.11731803263427, 154.31374425852155)\"><title>FLSF47</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(188.48239798887366, 158.45410110409333)\"><title>FLSF54</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(168.52554409813553, 152.51719242056498)\"><title>FLWO6</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(63.95712855846, 61.55136921498527)\"><title>HNDA09</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(251.18150182723866, 157.78908513126478)\"><title>LALC2</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(175.1251600288634, 232.39230135032437)\"><title>MXED8</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(159.56986801983194, 224.93029874495156)\"><title>MXGT4</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(82.44258746304789, 55.24919670601196)\"><title>MXSA3017</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(241.9160866832709, 148.910576620066)\"><title>SCCU3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(201.19660156886476, 223.97759393919682)\"><title>TXGR3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(189.9494401899397, 231.44610103522942)\"><title>TXMD3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(251.6914946113917, 140.82829535879736)\"><title>TXWV2</title><circle r=\"5.0\"></circle></g></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t9f2152b9da544a5dbb5b2c96a21f02a8\" transform=\"translate(50.0,250.0)translate(0,10.0)\"><line style=\"stroke-width:2.25\" x1=\"13.95712855846\" x2=\"220.0\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(0.0,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-5.334\" y=\"10.265999999999998\">-9</text></g><g transform=\"translate(53.696776145806666,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-5.334\" y=\"10.265999999999998\">-8</text></g><g transform=\"translate(107.39355229161333,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-5.334\" y=\"10.265999999999998\">-7</text></g><g transform=\"translate(161.09032843742,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-5.334\" y=\"10.265999999999998\">-6</text></g><g transform=\"translate(214.78710458322666,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-5.334\" y=\"10.265999999999998\">-5</text></g></g><g transform=\"translate(110.0,22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-66.115\" y=\"11.977\">UMAP component 1</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"te0a5d1ff97a6404fbea5d325d50aa198\" transform=\"translate(50.0,250.0)rotate(-90.0)translate(0,-10.0)\"><line style=\"stroke-width:2.25\" x1=\"17.60769864967562\" x2=\"200.0\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(0.0,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-5.334\" y=\"0.0\">-5</text></g><g transform=\"translate(63.56433176163703,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-5.334\" y=\"0.0\">-2</text></g><g transform=\"translate(127.12866352327406,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"0.0\">0</text></g><g transform=\"translate(190.6929952849111,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"0.0\">2</text></g></g><g transform=\"translate(100.0,-22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-66.115\" y=\"0.0\">UMAP component 2</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g><g class=\"toyplot-coordinates-Table\" id=\"t61c76ede16714786ab9fcbb9a44d7ae3\"><g transform=\"translate(310.0,75.0)\"><g style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,75.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">virg</text></g><g transform=\"translate(310.0,100.0)\"><g style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,100.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">mini</text></g><g transform=\"translate(310.0,125.0)\"><g style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,125.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">gemi</text></g><g transform=\"translate(310.0,150.0)\"><g style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,150.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">bran</text></g><g transform=\"translate(310.0,175.0)\"><g style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,175.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">fusi</text></g><g transform=\"translate(310.0,200.0)\"><g style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,200.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">sagr</text></g><g transform=\"translate(310.0,225.0)\"><g style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,225.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">oleo</text></g></g></svg><div class=\"toyplot-behavior\"><script>(function()\n",
"{\n",
"var modules={};\n",
"modules[\"toyplot/tables\"] = (function()\n",
" {\n",
" var tables = [];\n",
"\n",
" var module = {};\n",
"\n",
" module.set = function(owner, key, names, columns)\n",
" {\n",
" tables.push({owner: owner, key: key, names: names, columns: columns});\n",
" }\n",
"\n",
" module.get = function(owner, key)\n",
" {\n",
" for(var i = 0; i != tables.length; ++i)\n",
" {\n",
" var table = tables[i];\n",
" if(table.owner != owner)\n",
" continue;\n",
" if(table.key != key)\n",
" continue;\n",
" return {names: table.names, columns: table.columns};\n",
" }\n",
" }\n",
"\n",
" module.get_csv = function(owner, key)\n",
" {\n",
" var table = module.get(owner, key);\n",
" if(table != undefined)\n",
" {\n",
" var csv = \"\";\n",
" csv += table.names.join(\",\") + \"\\n\";\n",
" for(var i = 0; i != table.columns[0].length; ++i)\n",
" {\n",
" for(var j = 0; j != table.columns.length; ++j)\n",
" {\n",
" if(j)\n",
" csv += \",\";\n",
" csv += table.columns[j][i];\n",
" }\n",
" csv += \"\\n\";\n",
" }\n",
" return csv;\n",
" }\n",
" }\n",
"\n",
" return module;\n",
" })();\n",
"modules[\"toyplot/root/id\"] = \"tca41158dcc6d4afcb41c35d5aff66e8f\";\n",
"modules[\"toyplot/root\"] = (function(root_id)\n",
" {\n",
" return document.querySelector(\"#\" + root_id);\n",
" })(modules[\"toyplot/root/id\"]);\n",
"modules[\"toyplot/canvas/id\"] = \"t72b86d135a9f41329cd1be0d08fc7c61\";\n",
"modules[\"toyplot/canvas\"] = (function(canvas_id)\n",
" {\n",
" return document.querySelector(\"#\" + canvas_id);\n",
" })(modules[\"toyplot/canvas/id\"]);\n",
"modules[\"toyplot/menus/context\"] = (function(root, canvas)\n",
" {\n",
" var wrapper = document.createElement(\"div\");\n",
" wrapper.innerHTML = \"<ul class='toyplot-context-menu' style='background:#eee; border:1px solid #b8b8b8; border-radius:5px; box-shadow: 0px 0px 8px rgba(0%,0%,0%,0.25); margin:0; padding:3px 0; position:fixed; visibility:hidden;'></ul>\"\n",
" var menu = wrapper.firstChild;\n",
"\n",
" root.appendChild(menu);\n",
"\n",
" var items = [];\n",
"\n",
" var ignore_mouseup = null;\n",
" function open_menu(e)\n",
" {\n",
" var show_menu = false;\n",
" for(var index=0; index != items.length; ++index)\n",
" {\n",
" var item = items[index];\n",
" if(item.show(e))\n",
" {\n",
" item.item.style.display = \"block\";\n",
" show_menu = true;\n",
" }\n",
" else\n",
" {\n",
" item.item.style.display = \"none\";\n",
" }\n",
" }\n",
"\n",
" if(show_menu)\n",
" {\n",
" ignore_mouseup = true;\n",
" menu.style.left = (e.clientX + 1) + \"px\";\n",
" menu.style.top = (e.clientY - 5) + \"px\";\n",
" menu.style.visibility = \"visible\";\n",
" e.stopPropagation();\n",
" e.preventDefault();\n",
" }\n",
" }\n",
"\n",
" function close_menu()\n",
" {\n",
" menu.style.visibility = \"hidden\";\n",
" }\n",
"\n",
" function contextmenu(e)\n",
" {\n",
" open_menu(e);\n",
" }\n",
"\n",
" function mousemove(e)\n",
" {\n",
" ignore_mouseup = false;\n",
" }\n",
"\n",
" function mouseup(e)\n",
" {\n",
" if(ignore_mouseup)\n",
" {\n",
" ignore_mouseup = false;\n",
" return;\n",
" }\n",
" close_menu();\n",
" }\n",
"\n",
" function keydown(e)\n",
" {\n",
" if(e.key == \"Escape\" || e.key == \"Esc\" || e.keyCode == 27)\n",
" {\n",
" close_menu();\n",
" }\n",
" }\n",
"\n",
" canvas.addEventListener(\"contextmenu\", contextmenu);\n",
" canvas.addEventListener(\"mousemove\", mousemove);\n",
" document.addEventListener(\"mouseup\", mouseup);\n",
" document.addEventListener(\"keydown\", keydown);\n",
"\n",
" var module = {};\n",
" module.add_item = function(label, show, activate)\n",
" {\n",
" var wrapper = document.createElement(\"div\");\n",
" wrapper.innerHTML = \"<li class='toyplot-context-menu-item' style='background:#eee; color:#333; padding:2px 20px; list-style:none; margin:0; text-align:left;'>\" + label + \"</li>\"\n",
" var item = wrapper.firstChild;\n",
"\n",
" items.push({item: item, show: show});\n",
"\n",
" function mouseover()\n",
" {\n",
" this.style.background = \"steelblue\";\n",
" this.style.color = \"white\";\n",
" }\n",
"\n",
" function mouseout()\n",
" {\n",
" this.style.background = \"#eee\";\n",
" this.style.color = \"#333\";\n",
" }\n",
"\n",
" function choose_item(e)\n",
" {\n",
" close_menu();\n",
" activate();\n",
"\n",
" e.stopPropagation();\n",
" e.preventDefault();\n",
" }\n",
"\n",
" item.addEventListener(\"mouseover\", mouseover);\n",
" item.addEventListener(\"mouseout\", mouseout);\n",
" item.addEventListener(\"mouseup\", choose_item);\n",
" item.addEventListener(\"contextmenu\", choose_item);\n",
"\n",
" menu.appendChild(item);\n",
" };\n",
" return module;\n",
" })(modules[\"toyplot/root\"],modules[\"toyplot/canvas\"]);\n",
"modules[\"toyplot/io\"] = (function()\n",
" {\n",
" var module = {};\n",
" module.save_file = function(mime_type, charset, data, filename)\n",
" {\n",
" var uri = \"data:\" + mime_type + \";charset=\" + charset + \",\" + data;\n",
" uri = encodeURI(uri);\n",
"\n",
" var link = document.createElement(\"a\");\n",
" if(typeof link.download != \"undefined\")\n",
" {\n",
" link.href = uri;\n",
" link.style = \"visibility:hidden\";\n",
" link.download = filename;\n",
"\n",
" document.body.appendChild(link);\n",
" link.click();\n",
" document.body.removeChild(link);\n",
" }\n",
" else\n",
" {\n",
" window.open(uri);\n",
" }\n",
" };\n",
" return module;\n",
" })();\n",
"modules[\"toyplot.coordinates.Axis\"] = (\n",
" function(canvas)\n",
" {\n",
" function sign(x)\n",
" {\n",
" return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
" }\n",
"\n",
" function mix(a, b, amount)\n",
" {\n",
" return ((1.0 - amount) * a) + (amount * b);\n",
" }\n",
"\n",
" function log(x, base)\n",
" {\n",
" return Math.log(Math.abs(x)) / Math.log(base);\n",
" }\n",
"\n",
" function in_range(a, x, b)\n",
" {\n",
" var left = Math.min(a, b);\n",
" var right = Math.max(a, b);\n",
" return left <= x && x <= right;\n",
" }\n",
"\n",
" function inside(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(in_range(segment.range.min, range, segment.range.max))\n",
" return true;\n",
" }\n",
" return false;\n",
" }\n",
"\n",
" function to_domain(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n",
" {\n",
" if(segment.scale == \"linear\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" return mix(segment.domain.min, segment.domain.max, amount)\n",
" }\n",
" else if(segment.scale[0] == \"log\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" var base = segment.scale[1];\n",
" return sign(segment.domain.min) * Math.pow(base, mix(log(segment.domain.min, base), log(segment.domain.max, base), amount));\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" var axes = {};\n",
"\n",
" function display_coordinates(e)\n",
" {\n",
" var current = canvas.createSVGPoint();\n",
" current.x = e.clientX;\n",
" current.y = e.clientY;\n",
"\n",
" for(var axis_id in axes)\n",
" {\n",
" var axis = document.querySelector(\"#\" + axis_id);\n",
" var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
" if(coordinates)\n",
" {\n",
" var projection = axes[axis_id];\n",
" var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
" if(inside(local.x, projection))\n",
" {\n",
" var domain = to_domain(local.x, projection);\n",
" coordinates.style.visibility = \"visible\";\n",
" coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
" var text = coordinates.querySelector(\"text\");\n",
" text.textContent = domain.toFixed(2);\n",
" }\n",
" else\n",
" {\n",
" coordinates.style.visibility= \"hidden\";\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" canvas.addEventListener(\"click\", display_coordinates);\n",
"\n",
" var module = {};\n",
" module.show_coordinates = function(axis_id, projection)\n",
" {\n",
" axes[axis_id] = projection;\n",
" }\n",
"\n",
" return module;\n",
" })(modules[\"toyplot/canvas\"]);\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tb34701a88e2b48f69f3dfe80d879f0f9\",\"data\",\"point\",[\"x\", \"y0\"],[[-6.447597980499268, -7.011081218719482, -6.579447269439697, -8.02898120880127, -8.007495880126953, -8.652328491210938, -7.801469326019287, -7.597670078277588, -8.192188262939453, -6.263484477996826, -5.6809186935424805, -6.502779006958008, -7.391266822814941, -7.15364933013916, -6.873022556304932, -4.902919769287109, -7.191375255584717, -6.421029567718506, -6.792687892913818, -8.74007511138916, -5.253378391265869, -6.669782638549805, -6.959470272064209, -8.395818710327148, -5.425929069519043, -6.18425178527832, -6.3937087059021, -5.243880748748779], [-3.161222457885742, -3.4461255073547363, -3.6402838230133057, 2.866046667098999, 2.267054319381714, 2.1439714431762695, 1.6916017532348633, 2.1609439849853516, 1.9728617668151855, -1.7811428308486938, -0.5433995127677917, -0.9179793000221252, -1.6308543682098389, -1.786432147026062, -1.9206633567810059, -0.9702995419502258, -1.2366372346878052, -1.3994784355163574, -1.1659784317016602, 2.411728620529175, -1.3733232021331787, -4.307485103607178, -4.014002799987793, 2.6595945358276367, -1.0241293907165527, -3.9765326976776123, -4.270270824432373, -0.7062513828277588]],\"toyplot\");\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"t9f2152b9da544a5dbb5b2c96a21f02a8\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": -4.902919769287109, \"min\": -9.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 220.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"te0a5d1ff97a6404fbea5d325d50aa198\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 2.866046667098999, \"min\": -5.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 200.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"})();</script></div></div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tool.run_umap(n_neighbors=13, seed=333)\n",
"tool.draw(imap=IMAP);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Missing data with imputation\n",
"Missing data has large effects on dimensionality reduction methods, and it is best to (1) minimize the amount of missing data in your input data set by using filtering, and (2) impute missing data values. In the examples above data is imputed using the 'sample' method, which probabilistically samples alleles for based on the allele frequency in the group that a taxon is assigned to in IMAP. It is good to compare this to a case where imputation is performed without IMAP assignments, to assess the impact of the *a priori* assignments. Although this comparison is useful, assigning taxa to groups with IMAP dictionaries for imputation is expected to yield more accurate imputation. "
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"toyplot\" id=\"teaa9fe37f32c46f5b8df1437cda87ffb\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"300.0px\" id=\"t8bc852b918df4fdca9f36660339934c3\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 400.0 300.0\" width=\"400.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"ta7d5a7ee0d554b80a02d664f6a1d7a69\"><clipPath id=\"t301d246545694a778e5093bcb8b2139b\"><rect height=\"220.0\" width=\"240.0\" x=\"40.0\" y=\"40.0\"></rect></clipPath><g clip-path=\"url(#t301d246545694a778e5093bcb8b2139b)\"><g class=\"toyplot-mark-Point\" id=\"tbbdcf008e94f4c71a18735c239fe0486\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(111.54152688736139, 51.684767357256604)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(110.89966928422399, 58.300888092318395)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(110.19804362757685, 58.70559809718476)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(253.86732237615524, 214.40112188786497)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(231.19411046167554, 227.13747682602067)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(268.11897082815017, 218.89733150808124)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(186.03805349661323, 233.96672029695983)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(178.9199878720106, 235.98180501522668)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(202.55190225466202, 235.31474664161593)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.67517283054278, 245.182307552124)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.881076962947624, 235.49119870933868)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.462929403204235, 243.46965302695554)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.43567422926812, 237.80996089391866)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.40333220724868, 232.4752911135649)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(70.39515005767471, 228.10060592443207)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.75842488793348, 238.55067608616707)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.63316587999665, 240.95085664316724)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.05727003299762, 244.88805886576355)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.110624628156884, 245.07099487727345)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(263.3085326867038, 217.37836244439518)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.2173058493253, 239.3721984813182)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(119.91934201252242, 124.44948978766548)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(108.18254005083654, 110.2033347877302)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(240.99894406202287, 214.04789601272984)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.08931429934485, 233.99912118419417)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(95.9651800242674, 139.84762125420588)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(91.15687386907032, 142.10863568176495)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(86.96583827184261, 217.23855609066143)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t0256f91f74384fc1940f940836ea4a30\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(97.04668801812939, 53.62518279327868)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(97.1111957481994, 60.63380807152146)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(96.06647952460997, 60.998221612275195)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(258.1697393739452, 205.33273535201243)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(233.27449583124383, 218.80307459808685)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(268.16493793810395, 207.0839786118842)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(188.61828145043674, 230.18666843980785)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(181.27514597428672, 231.25100122009025)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(206.90396902799526, 229.842180607763)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.79027629048258, 249.49774702883772)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.40659671226075, 240.8648608801976)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.10601880779275, 248.7246309333229)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(69.45223176620176, 241.32889968051157)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(70.30933269346055, 235.67590073883113)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(72.51128617416433, 231.658276374526)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.332622826366936, 243.68828706958786)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.4024201714281, 243.88787860387896)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.38726756531958, 248.7269338321422)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.71289596634153, 250.0)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(265.29887333765106, 207.3563668733549)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.28397788233586, 243.3445037824668)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(111.95585932979209, 123.55878998575918)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(99.2448914970607, 110.27395777380889)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(240.8137885839803, 204.29426621685917)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.03879199011288, 237.90551818799509)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(91.29699243849359, 143.14109435423512)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(85.09803395667397, 145.46396619751312)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(88.87318845746628, 217.87654531935146)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t8f5b2a14c5e14c79a653c88e2827e5d6\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(109.54717388830373, 52.00549001663815)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(111.12266550711138, 57.74996538484875)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(110.74888061481174, 59.060111463828946)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(254.24388540957543, 214.68710834990813)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(231.81387732998965, 227.0053096240753)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(266.6672462552236, 216.78693109420382)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(185.92124625699861, 234.02714822940982)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(176.7194412599052, 236.19590270008536)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(204.22502902132788, 235.55158019361232)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.320143026854936, 246.20254287272596)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.30560178241377, 237.55904987200452)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.12708727697898, 243.45199427351324)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.53127397064921, 239.05756769782934)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(68.77832537342746, 231.16567717650807)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(72.12177370898539, 228.53651165721527)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.316733030244166, 240.87012162111222)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.95323060381384, 240.49224846401265)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.11012350586557, 245.28752876989853)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.81251950389195, 244.10854081599626)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(263.11698321641364, 216.40863840873214)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.345620402038875, 240.06007412369343)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(116.77537323697722, 124.13892258073615)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(107.55229554109633, 109.07939392907664)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(239.5366496259407, 213.50716439728254)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.36035105913967, 235.82271966510567)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(95.05524702916527, 140.26380253555362)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(92.14961528375187, 142.5166897070823)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(84.66788661343985, 213.42653951521032)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"ta78164078a2e4ebdab408e7a75b8bb9a\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(130.30174833347732, 50.56719893801842)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(131.53287612775023, 57.753822074665464)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(129.25527188116894, 59.1441167785503)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(253.5076647152082, 228.28393329892478)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(225.7963877324412, 238.5462946572713)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(263.7464528247674, 232.39524343117927)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(180.04916428213673, 242.41900195893885)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(170.19259768471153, 241.0591708589516)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(197.3357077988918, 243.42384364271817)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.897805932566584, 238.29142945329025)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.82983191021832, 230.49456574326857)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.40940015740424, 236.94221856402254)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.87822295316536, 231.49810104443208)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.24163817430876, 225.5750154212029)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.39465210316123, 223.48990158790255)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.319677281065076, 230.75039973499355)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.9209059587125, 233.01547787756365)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.93267032847383, 238.31523603656643)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.19171883669055, 237.98446349960517)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(259.6823519717338, 232.22563130647043)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.634633605358545, 231.69629718036816)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(130.05206995409358, 125.13108014679743)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(119.80412173744588, 110.79651733424132)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(237.77722998948457, 224.7479501552854)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.47114039377013, 228.62979112953946)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(105.85190300406953, 136.11739056406586)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(99.9134809605765, 142.65064626633617)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(86.02495270148351, 213.0805364547293)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t5cfcbbc8bfe74750ba38048aa902c80a\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(112.23074791987251, 53.08870509766675)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(111.38623301848635, 59.35886581148671)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(111.64058810556902, 57.84511225194511)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(253.62917690481197, 216.49997389377774)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(230.72728329909472, 228.7769584701084)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(266.4940933750853, 217.89659068710222)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(186.85290165431053, 235.09980426809298)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(176.83115395665354, 235.7233154019068)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(202.04373986403328, 237.51535808028976)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.98173944897975, 245.43677930435294)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.29637962552866, 236.16064211271956)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.38163423288093, 243.66858860904077)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.09711197290018, 236.94474295932932)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.73806891572542, 231.5034967962856)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(69.41243921490562, 229.58544831425797)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.31661055189344, 238.68266316844972)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.84120721476192, 239.49774052925858)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.35660382312447, 244.74495394875152)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.630310749583614, 243.53250500911702)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(263.4815881389102, 218.3642441447724)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.60498962476207, 237.7950191324911)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(120.6880938943712, 122.83313632011809)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(107.88784647690555, 108.31100739709993)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(239.3676148119949, 213.21253845965214)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.22959690941114, 234.66375349014763)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(97.24658818501494, 139.2708962947662)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(94.07427223855201, 142.99825512716413)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(86.47766520621249, 216.01418005974824)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t81febb91f31a49a1bf04405b053cb65c\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(124.14380843352947, 51.51503132429005)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(124.7754374691379, 58.742280900090904)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(123.72482120976346, 57.72782757728774)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(254.87505876739476, 225.03060074991956)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(228.61939161639438, 235.31032108755528)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(265.2672096446183, 227.0178198441023)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(180.92057301882596, 239.3344950732141)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(173.10029681718814, 239.74810325037274)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(199.26704538294035, 240.69763888992642)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.338557116272284, 241.17204932556777)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.78902692871945, 231.27721583080933)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.61587965681652, 239.0901753863348)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.89767737560166, 233.013439402432)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.57092337077879, 227.6676834934512)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.87487674447041, 225.799562573312)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.916124629666214, 232.04165157946423)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.89597598753963, 236.188953432638)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.72237886990388, 240.6864692280574)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.52975159346557, 241.97867434722983)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(261.0715171583832, 226.92538514482163)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.60665135304665, 234.58097407876753)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(126.55171388324919, 125.67458199604525)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(117.25741956253248, 108.76826895692737)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(237.6702405150293, 221.98444110446957)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.99827774683913, 230.4491096408473)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(101.2918821294628, 139.48576298919832)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(96.77579400697036, 140.67649733923054)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(86.8779683457957, 212.44026059353615)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t93c3f7be34aa4a62acf0284be0a11f20\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(123.30028167544384, 50.91893928773783)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(121.97375087252638, 58.4089292365406)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(122.53645915651624, 58.08004117309615)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(252.68521204337287, 224.859772287066)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(228.13409939858434, 234.62142987178711)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(263.27764036092697, 227.54552389588628)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(181.9038501234176, 238.07124326340153)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(173.6964960362821, 238.00079280413905)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(198.48663098903225, 240.6958983875348)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.1737310855125, 241.1099163498878)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.585852697606455, 231.15311416175223)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.0654372361209, 239.2678685843493)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.220905286377146, 233.51267538367432)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.72043913491761, 227.19958468581152)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(69.29639670841567, 224.537471405582)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.71538067738889, 236.45939900556172)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.468139359715494, 236.06800690544517)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.80678801534183, 240.47063047029354)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.197604334889654, 241.65853418821834)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(259.9872705233087, 226.17237402364535)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.76077545247051, 234.14373512368132)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(126.54651192449877, 124.24417755855049)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(117.55763272902038, 109.0608127616576)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(238.47968095506982, 221.34062990019453)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.54331067530224, 231.3316438217402)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(101.35861584995465, 139.61489822958765)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(99.03880911090108, 142.6696999665824)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(84.42857692142077, 213.80753240649474)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t9884668cdbe5446bb60e965b6d9cf2e4\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(108.89179080610937, 50.274384114574545)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(109.16624886262755, 59.28827883549876)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(107.64130124509896, 57.937785637358346)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(256.89813597651664, 213.40402444492216)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(232.53780469993683, 226.47040761907266)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(270.0, 215.7978359837613)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(184.14974245027753, 233.671684912996)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(177.8039398374253, 234.6424084675361)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(202.9681071258036, 234.8373295261345)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.58577993195202, 246.42470928082645)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.623732263548845, 235.95670280436048)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.08952212852186, 242.03017934754357)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.2076477504289, 238.73649806971056)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(68.86089225593082, 231.50974503049258)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(71.4827065151092, 229.13486792967308)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.90528247943515, 241.4421351483374)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.882341877883306, 241.85110601637808)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.22169834757425, 245.79510186819687)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.84213289901121, 244.90444411823228)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(263.9535871552282, 215.71970056275615)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.51199663595577, 239.8581871591006)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(117.28831324413392, 125.27433443156883)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(107.67916128109783, 110.6680900091931)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(240.66301068061765, 213.60581229942437)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.53217028903525, 235.59353381516598)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(95.99661933070473, 140.29732671249275)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(90.41355788423651, 143.29088025148616)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(87.14905538013474, 216.6077807431059)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"taae1549c5c964a649f7bce5ae3b12b29\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(125.03585473654582, 50.49233144264189)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(123.67211233420747, 58.171396082840985)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(123.72076778572136, 57.37802382831127)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(253.97711206744697, 223.7723511172799)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(227.7801408031851, 235.88646131075126)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(264.9005607076118, 226.64417976540403)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(181.63869588232444, 238.76181629307825)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(175.58214214225148, 239.82431618638844)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(200.4146982618473, 242.33560199167724)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.40356221433255, 241.25476351349727)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.553984976409716, 232.2264355279452)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.51489292277363, 240.55880101967534)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.85986307518597, 235.05142473332813)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.7983102062521, 228.25247993726154)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(70.56785352933241, 225.79938759927748)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.284940448135245, 234.70773705175282)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.458599992689386, 236.61718870263445)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.06998279513417, 240.26163571742254)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.768918957249994, 239.8097063578703)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(259.2019136053276, 226.07604389529712)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.37493047086088, 234.5403771675532)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(125.5655382765286, 123.84475909290379)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(114.34052823466256, 106.81693746876411)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(238.93290146536316, 220.65257207388566)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.28893719571905, 231.7635237255352)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(100.36384561715587, 140.3542750693236)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(95.59838578696329, 141.41900881892784)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(85.27630484311817, 211.75173964867074)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t62e58d74640d4916a6ea617c7b3c0200\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(145.08936512200017, 50.77402272165319)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(141.90523606462722, 59.69294494801625)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(142.83944168077463, 59.56930551405142)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(248.48588950192817, 237.3876390091811)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(223.71492384453026, 245.45985898901276)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(259.87031289905246, 240.0776364982376)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(175.11569203274675, 244.35033636730776)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(166.4583182172563, 244.85658990567703)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(195.21829453566104, 248.42220592476548)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(50.44802065383449, 234.55372150579598)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.696866858070976, 225.16134195542537)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(50.32377276520699, 231.60303534213278)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.05185730129753, 226.95007187449056)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.29373622521101, 221.64588139451917)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.90760079611907, 218.18377909342615)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(50.0, 227.6737469806992)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.454784434143406, 229.18366953620009)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.672370856781924, 233.69809214077313)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.976945975048785, 236.07621808525786)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(254.35015122843154, 240.2048808952828)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.67663336897598, 226.94266042044478)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(135.17977981082828, 125.24534237804403)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(127.51607545325484, 110.19685275935828)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(234.18206942960393, 233.0399159851825)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.92006253391561, 225.05997275250314)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(108.39705293869042, 138.13540026841446)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(104.58605693585689, 140.1910888343662)\"><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(83.61496787048706, 210.6890630596804)\"><circle r=\"5.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"tf88abbb291484abb9f86cad96aaa5627\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(118.71289858207732, 51.49460530937557)\"><title>BJSB3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(118.3545425288898, 58.810117943782835)\"><title>BJSL25</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(117.83720548316111, 58.644614393388906)\"><title>BJVL19</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(254.03391971363556, 220.36592603908565)\"><title>BZBB1</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(229.35925150170758, 231.80175930537416)\"><title>CRL0001</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(265.650742483354, 223.01430713198422)\"><title>CRL0030</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(183.1208200648088, 236.9888919103207)\"><title>CUCA4</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(175.0579519797971, 237.7283405810374)\"><title>CUSV6</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(200.94151242621945, 238.86363838860376)\"><title>CUVN10</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(58.06147885313305, 242.91259661869063)\"><title>FLAB109</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(58.49689507177247, 233.63451275978215)\"><title>FLBA140</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(58.4096574587701, 240.8807145086891)\"><title>FLCK18</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(64.16324656810758, 235.39033817396566)\"><title>FLCK216</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(65.97149985572612, 229.26707557879288)\"><title>FLMO62</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(69.6964735552338, 226.48258124596046)\"><title>FLSA185</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(55.786579681212864, 236.48668174461258)\"><title>FLSF33</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(62.291077148068425, 237.77531267111766)\"><title>FLSF47</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(59.933715414051704, 242.2874640877866)\"><title>FLSF54</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(59.27734234443297, 242.51240812988001)\"><title>FLWO6</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(261.3452769022091, 222.68316276995282)\"><title>HNDA09</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(58.50175146451305, 236.2334026649885)\"><title>LALC2</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(123.05225955669954, 124.43946142781888)\"><title>MXED8</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(112.7022512563913, 109.41751731778572)\"><title>MXGT4</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(238.84221301191073, 218.04331866049657)\"><title>MXSA3017</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(62.147195309259004, 232.5218687412774)\"><title>SCCU3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(99.28239265469793, 139.65284682718433)\"><title>TXGR3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(94.88048800335528, 142.39853681904538)\"><title>TXMD3</title><circle r=\"5.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(86.03564046114012, 214.29327338911887)\"><title>TXWV2</title><circle r=\"5.0\"></circle></g></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t7f848f05ae2a4779b7f4d9d78193b5fb\" transform=\"translate(50.0,250.0)translate(0,10.0)\"><line style=\"stroke-width:2.25\" x1=\"0\" x2=\"220.0\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(4.73974764298157,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.67\" y=\"10.265999999999998\">-30</text></g><g transform=\"translate(71.85522426194056,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"10.265999999999998\">0</text></g><g transform=\"translate(138.97070088089956,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"10.265999999999998\">30</text></g><g transform=\"translate(206.08617749985856,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"10.265999999999998\">60</text></g></g><g transform=\"translate(110.0,22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-74.30499999999999\" y=\"11.977\">PC0 (15.9%) explained</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"tae34104ace784031a4506b08fe5597f1\" transform=\"translate(50.0,250.0)rotate(-90.0)translate(0,-10.0)\"><line style=\"stroke-width:2.25\" x1=\"0\" x2=\"199.72561588542547\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(1.6654630885762154,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.67\" y=\"0.0\">-25</text></g><g transform=\"translate(51.24909731643217,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"0.0\">0</text></g><g transform=\"translate(100.83273154428811,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"0.0\">25</text></g><g transform=\"translate(150.41636577214405,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"0.0\">50</text></g><g transform=\"translate(200.0,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"0.0\">75</text></g></g><g transform=\"translate(100.0,-22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-74.30499999999999\" y=\"0.0\">PC1 (15.1%) explained</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g><g class=\"toyplot-coordinates-Table\" id=\"t5e82b9e0ca0d4ae693801192096a0d0a\"><g transform=\"translate(310.0,75.0)\"><g style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,75.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">virg</text></g><g transform=\"translate(310.0,100.0)\"><g style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,100.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">mini</text></g><g transform=\"translate(310.0,125.0)\"><g style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,125.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">gemi</text></g><g transform=\"translate(310.0,150.0)\"><g style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,150.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">bran</text></g><g transform=\"translate(310.0,175.0)\"><g style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,175.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">fusi</text></g><g transform=\"translate(310.0,200.0)\"><g style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,200.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">sagr</text></g><g transform=\"translate(310.0,225.0)\"><g style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"5.0\"></circle></g></g><g transform=\"translate(320.0,225.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">oleo</text></g></g></svg><div class=\"toyplot-behavior\"><script>(function()\n",
"{\n",
"var modules={};\n",
"modules[\"toyplot/tables\"] = (function()\n",
" {\n",
" var tables = [];\n",
"\n",
" var module = {};\n",
"\n",
" module.set = function(owner, key, names, columns)\n",
" {\n",
" tables.push({owner: owner, key: key, names: names, columns: columns});\n",
" }\n",
"\n",
" module.get = function(owner, key)\n",
" {\n",
" for(var i = 0; i != tables.length; ++i)\n",
" {\n",
" var table = tables[i];\n",
" if(table.owner != owner)\n",
" continue;\n",
" if(table.key != key)\n",
" continue;\n",
" return {names: table.names, columns: table.columns};\n",
" }\n",
" }\n",
"\n",
" module.get_csv = function(owner, key)\n",
" {\n",
" var table = module.get(owner, key);\n",
" if(table != undefined)\n",
" {\n",
" var csv = \"\";\n",
" csv += table.names.join(\",\") + \"\\n\";\n",
" for(var i = 0; i != table.columns[0].length; ++i)\n",
" {\n",
" for(var j = 0; j != table.columns.length; ++j)\n",
" {\n",
" if(j)\n",
" csv += \",\";\n",
" csv += table.columns[j][i];\n",
" }\n",
" csv += \"\\n\";\n",
" }\n",
" return csv;\n",
" }\n",
" }\n",
"\n",
" return module;\n",
" })();\n",
"modules[\"toyplot/root/id\"] = \"teaa9fe37f32c46f5b8df1437cda87ffb\";\n",
"modules[\"toyplot/root\"] = (function(root_id)\n",
" {\n",
" return document.querySelector(\"#\" + root_id);\n",
" })(modules[\"toyplot/root/id\"]);\n",
"modules[\"toyplot/canvas/id\"] = \"t8bc852b918df4fdca9f36660339934c3\";\n",
"modules[\"toyplot/canvas\"] = (function(canvas_id)\n",
" {\n",
" return document.querySelector(\"#\" + canvas_id);\n",
" })(modules[\"toyplot/canvas/id\"]);\n",
"modules[\"toyplot/menus/context\"] = (function(root, canvas)\n",
" {\n",
" var wrapper = document.createElement(\"div\");\n",
" wrapper.innerHTML = \"<ul class='toyplot-context-menu' style='background:#eee; border:1px solid #b8b8b8; border-radius:5px; box-shadow: 0px 0px 8px rgba(0%,0%,0%,0.25); margin:0; padding:3px 0; position:fixed; visibility:hidden;'></ul>\"\n",
" var menu = wrapper.firstChild;\n",
"\n",
" root.appendChild(menu);\n",
"\n",
" var items = [];\n",
"\n",
" var ignore_mouseup = null;\n",
" function open_menu(e)\n",
" {\n",
" var show_menu = false;\n",
" for(var index=0; index != items.length; ++index)\n",
" {\n",
" var item = items[index];\n",
" if(item.show(e))\n",
" {\n",
" item.item.style.display = \"block\";\n",
" show_menu = true;\n",
" }\n",
" else\n",
" {\n",
" item.item.style.display = \"none\";\n",
" }\n",
" }\n",
"\n",
" if(show_menu)\n",
" {\n",
" ignore_mouseup = true;\n",
" menu.style.left = (e.clientX + 1) + \"px\";\n",
" menu.style.top = (e.clientY - 5) + \"px\";\n",
" menu.style.visibility = \"visible\";\n",
" e.stopPropagation();\n",
" e.preventDefault();\n",
" }\n",
" }\n",
"\n",
" function close_menu()\n",
" {\n",
" menu.style.visibility = \"hidden\";\n",
" }\n",
"\n",
" function contextmenu(e)\n",
" {\n",
" open_menu(e);\n",
" }\n",
"\n",
" function mousemove(e)\n",
" {\n",
" ignore_mouseup = false;\n",
" }\n",
"\n",
" function mouseup(e)\n",
" {\n",
" if(ignore_mouseup)\n",
" {\n",
" ignore_mouseup = false;\n",
" return;\n",
" }\n",
" close_menu();\n",
" }\n",
"\n",
" function keydown(e)\n",
" {\n",
" if(e.key == \"Escape\" || e.key == \"Esc\" || e.keyCode == 27)\n",
" {\n",
" close_menu();\n",
" }\n",
" }\n",
"\n",
" canvas.addEventListener(\"contextmenu\", contextmenu);\n",
" canvas.addEventListener(\"mousemove\", mousemove);\n",
" document.addEventListener(\"mouseup\", mouseup);\n",
" document.addEventListener(\"keydown\", keydown);\n",
"\n",
" var module = {};\n",
" module.add_item = function(label, show, activate)\n",
" {\n",
" var wrapper = document.createElement(\"div\");\n",
" wrapper.innerHTML = \"<li class='toyplot-context-menu-item' style='background:#eee; color:#333; padding:2px 20px; list-style:none; margin:0; text-align:left;'>\" + label + \"</li>\"\n",
" var item = wrapper.firstChild;\n",
"\n",
" items.push({item: item, show: show});\n",
"\n",
" function mouseover()\n",
" {\n",
" this.style.background = \"steelblue\";\n",
" this.style.color = \"white\";\n",
" }\n",
"\n",
" function mouseout()\n",
" {\n",
" this.style.background = \"#eee\";\n",
" this.style.color = \"#333\";\n",
" }\n",
"\n",
" function choose_item(e)\n",
" {\n",
" close_menu();\n",
" activate();\n",
"\n",
" e.stopPropagation();\n",
" e.preventDefault();\n",
" }\n",
"\n",
" item.addEventListener(\"mouseover\", mouseover);\n",
" item.addEventListener(\"mouseout\", mouseout);\n",
" item.addEventListener(\"mouseup\", choose_item);\n",
" item.addEventListener(\"contextmenu\", choose_item);\n",
"\n",
" menu.appendChild(item);\n",
" };\n",
" return module;\n",
" })(modules[\"toyplot/root\"],modules[\"toyplot/canvas\"]);\n",
"modules[\"toyplot/io\"] = (function()\n",
" {\n",
" var module = {};\n",
" module.save_file = function(mime_type, charset, data, filename)\n",
" {\n",
" var uri = \"data:\" + mime_type + \";charset=\" + charset + \",\" + data;\n",
" uri = encodeURI(uri);\n",
"\n",
" var link = document.createElement(\"a\");\n",
" if(typeof link.download != \"undefined\")\n",
" {\n",
" link.href = uri;\n",
" link.style = \"visibility:hidden\";\n",
" link.download = filename;\n",
"\n",
" document.body.appendChild(link);\n",
" link.click();\n",
" document.body.removeChild(link);\n",
" }\n",
" else\n",
" {\n",
" window.open(uri);\n",
" }\n",
" };\n",
" return module;\n",
" })();\n",
"modules[\"toyplot.coordinates.Axis\"] = (\n",
" function(canvas)\n",
" {\n",
" function sign(x)\n",
" {\n",
" return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
" }\n",
"\n",
" function mix(a, b, amount)\n",
" {\n",
" return ((1.0 - amount) * a) + (amount * b);\n",
" }\n",
"\n",
" function log(x, base)\n",
" {\n",
" return Math.log(Math.abs(x)) / Math.log(base);\n",
" }\n",
"\n",
" function in_range(a, x, b)\n",
" {\n",
" var left = Math.min(a, b);\n",
" var right = Math.max(a, b);\n",
" return left <= x && x <= right;\n",
" }\n",
"\n",
" function inside(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(in_range(segment.range.min, range, segment.range.max))\n",
" return true;\n",
" }\n",
" return false;\n",
" }\n",
"\n",
" function to_domain(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n",
" {\n",
" if(segment.scale == \"linear\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" return mix(segment.domain.min, segment.domain.max, amount)\n",
" }\n",
" else if(segment.scale[0] == \"log\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" var base = segment.scale[1];\n",
" return sign(segment.domain.min) * Math.pow(base, mix(log(segment.domain.min, base), log(segment.domain.max, base), amount));\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" var axes = {};\n",
"\n",
" function display_coordinates(e)\n",
" {\n",
" var current = canvas.createSVGPoint();\n",
" current.x = e.clientX;\n",
" current.y = e.clientY;\n",
"\n",
" for(var axis_id in axes)\n",
" {\n",
" var axis = document.querySelector(\"#\" + axis_id);\n",
" var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
" if(coordinates)\n",
" {\n",
" var projection = axes[axis_id];\n",
" var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
" if(inside(local.x, projection))\n",
" {\n",
" var domain = to_domain(local.x, projection);\n",
" coordinates.style.visibility = \"visible\";\n",
" coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
" var text = coordinates.querySelector(\"text\");\n",
" text.textContent = domain.toFixed(2);\n",
" }\n",
" else\n",
" {\n",
" coordinates.style.visibility= \"hidden\";\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" canvas.addEventListener(\"click\", display_coordinates);\n",
"\n",
" var module = {};\n",
" module.show_coordinates = function(axis_id, projection)\n",
" {\n",
" axes[axis_id] = projection;\n",
" }\n",
"\n",
" return module;\n",
" })(modules[\"toyplot/canvas\"]);\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tbbdcf008e94f4c71a18735c239fe0486\",\"data\",\"point\",[\"x\", \"y0\"],[[-4.610127750325358, -4.897032188230854, -5.210652395667008, 59.008192192554645, 48.87347525839454, 65.3785478109348, 28.689133625198195, 25.50742383938469, 36.07067194838007, -27.346919598920994, -27.254882347852732, -27.44179045644773, -25.219019311889177, -24.339494315373816, -23.00217928709274, -29.097669860971383, -25.577733153926363, -26.729134876791306, -27.152276655347933, 63.228326259760934, -27.998572714413733, -0.8653215384622438, -6.111563934230512, 53.25614558763018, -26.267820593556806, -11.572611359669466, -13.721879932622791, -15.595234250445131], [74.15054262182838, 70.81470367108803, 70.61064944474462, -7.890818940569352, -14.31247153647793, -10.157801630641256, -17.75576667674344, -18.771769612816435, -18.435439700740318, -23.410650304083166, -18.52440658995212, -22.547132254307826, -19.69352328575764, -17.003790139212306, -14.798079899705932, -20.066990864215352, -21.277158591116127, -23.26229052220017, -23.354526606927855, -9.391939523445874, -20.481201322940453, 37.4626699176883, 44.64556162267543, -7.712722941437888, -17.77210315940494, 29.69895326685749, 28.55895291050561, -9.321449352691499]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t0256f91f74384fc1940f940836ea4a30\",\"data\",\"point\",[\"x\", \"y0\"],[[-11.089187245734252, -11.060352884427575, -11.527331415856638, 60.93133297074647, 49.803388360872866, 65.39909468578519, 29.842471759920446, 26.560157823073997, 38.01600571902808, -25.060515455962914, -25.67900748548877, -24.919381458314607, -23.42365508029597, -23.040538858627063, -22.056285929959742, -26.606054713806277, -24.339901986986916, -24.34667506238108, -25.542094539543488, 64.11799020221366, -25.286826182036357, -4.424924964036719, -10.106610533326446, 53.17338279399298, -24.502440435479063, -13.659248222405436, -16.430125579209534, -14.742666281754786], [73.17218783489146, 69.6384486347577, 69.45471183004955, -3.3185509548364935, -10.110277426605894, -4.201525391433913, -15.849869743200207, -16.386504863263905, -15.676179453344798, -25.58648893708987, -21.23379964601822, -25.196684867887285, -21.46776736920966, -18.617533098511235, -16.59185243448275, -22.657367237098534, -22.758001013443916, -25.197845986296212, -25.839724192524276, -4.33886317723385, -22.484032177822094, 37.91175952950113, 44.60995361048628, -2.7949562489799593, -19.74170314165389, 28.038388671645254, 26.867199891591582, -9.643122641986015]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t8f5b2a14c5e14c79a653c88e2827e5d6\",\"data\",\"point\",[\"x\", \"y0\"],[[-5.501585175434793, -4.797354930113427, -4.9644333348851495, 59.17651240082409, 49.15050534126175, 64.72964029538429, 28.636921864737438, 24.52379976802538, 36.81854420569781, -27.505614651793888, -27.95910524541708, -27.144918002924637, -24.7292963165801, -23.724885032040333, -22.230394414977443, -28.848111262670535, -24.987676378507018, -26.258519069868523, -26.391545318194183, 63.14270541046964, -27.49422649970108, -2.2706466291539593, -6.393277426329369, 52.602513440584175, -25.6996788665699, -11.979343029147808, -13.278133662153282, -16.622397480522064], [73.98883469925674, 71.0924781404512, 70.43190429416981, -8.0350129203455, -14.24583301552057, -9.093740652285335, -17.786234356952345, -18.87971737027347, -18.55485085105459, -23.925051545788023, -19.567014294524203, -22.538228735174304, -20.322564915792984, -16.343484598154802, -15.017862969044929, -21.23645210433261, -21.045928979624207, -23.46370269698109, -22.869258596492028, -8.903006001950258, -20.82802727321983, 37.619257475138284, 45.212251053652096, -7.440086806618477, -18.69155899867006, 29.48911523873148, 28.353212633662363, -7.3994358522623855]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"ta78164078a2e4ebdab408e7a75b8bb9a\",\"data\",\"point\",[\"x\", \"y0\"],[[3.7755184781705395, 4.325821265080253, 3.3077531407135896, 58.84742853010361, 46.46074290462791, 63.42407252878464, 26.012155296424144, 21.60636079389018, 33.73908106120604, -30.823330982601412, -29.512742370842904, -30.5946530752349, -27.25615806394358, -26.19973322415822, -24.343374241968117, -29.74077679219534, -29.025042318578368, -29.91376533615895, -30.691954621024205, 61.60745687270855, -29.15300342432119, 3.663914541808177, -0.9168239403884195, 51.81606906511842, -27.438120219277618, -7.153337231915168, -9.807757199998434, -16.015801436029257], [74.71401907763965, 71.09053360276414, 70.38954893033377, -14.890513309109846, -20.064781753808248, -16.963430208142217, -22.017395434700916, -21.331770469345248, -22.524035225948193, -19.93627906942967, -16.005111139003215, -19.256008799673122, -16.511092253937157, -13.524680650861486, -12.473369131561357, -16.134102284826323, -17.27615156068273, -19.948282315887344, -19.781507258899158, -16.87791200876548, -16.611022472356257, 37.11901299855254, 44.34647980074543, -13.107675484339795, -15.064894350354901, 31.57973043669853, 28.285671921217656, -7.224981586319024]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t5cfcbbc8bfe74750ba38048aa902c80a\",\"data\",\"point\",[\"x\", \"y0\"],[[-4.302052295647097, -4.679542679652327, -4.565848298015097, 58.90174335988286, 48.664807815608796, 64.65224255248158, 29.053363247967702, 24.573734314739205, 35.843528039302115, -28.103868726099506, -28.410218255884782, -27.478128648956528, -24.9233627314909, -24.636860880451, -23.44144198428626, -28.84816600936544, -25.484740593083924, -26.595335429090316, -26.472990953457778, 63.30568045327531, -27.37829084560639, -0.5216965265086178, -6.2432892480224735, 52.52695643534732, -26.205115558682632, -10.999833711964182, -12.417829727014515, -15.813443115325137], [73.44267914919621, 70.28127276407417, 71.04450522127232, -8.949057227555201, -15.139095920520502, -9.653229488762632, -18.32706605242362, -18.641439506247398, -19.544984953394238, -23.538954610630938, -18.861939030749287, -22.647435300455605, -19.257281596305564, -16.513812784580765, -15.54673538500299, -20.13353856908715, -20.544499450384826, -23.190137220389733, -22.578822137038408, -9.88901971720836, -19.685989670251107, 38.277633107013834, 45.599670483440995, -7.291536815165409, -18.1072098515139, 29.989737196081705, 28.110408012953815, -8.704120646365379]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t81febb91f31a49a1bf04405b053cb65c\",\"data\",\"point\",[\"x\", \"y0\"],[[1.0229760497338496, 1.3053084121460712, 0.8356926190529742, 59.458641079460804, 47.72259964445879, 64.10383682301061, 26.40166697715163, 22.906075529874904, 34.6023711760987, -29.285346888452665, -29.08399103054615, -29.60837705788379, -26.353480533736263, -25.605554982371952, -24.12871824956464, -29.92116111116099, -27.695213412325746, -28.666791307827904, -29.199884717806643, 62.22840091868602, -29.612502024687913, 2.0992876119940735, -2.0551763606678866, 51.768245755274684, -27.202494676724253, -9.191624570838083, -11.210274374131032, -15.634511298217188], [74.23612329557778, 70.5921540261063, 71.1036399924946, -13.250187524368602, -18.433208342485948, -14.252140651206137, -20.462191316568777, -20.670731989111033, -21.149486549128866, -21.388683636549505, -16.399722233837547, -20.339005667370227, -17.275123764332488, -14.579801007021596, -13.63789701529596, -16.785149684123866, -18.876213559209802, -21.143854821017886, -21.79538286010545, -14.205535203299728, -18.06547258645195, 36.84498011567118, 45.36911983555679, -11.714318031900728, -15.982192234848064, 29.881401705058217, 29.281035087839154, -6.902155380069777]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t93c3f7be34aa4a62acf0284be0a11f20\",\"data\",\"point\",[\"x\", \"y0\"],[[0.6459273566844087, 0.052980303451650515, 0.30450572456334624, 58.47979975954235, 47.50567849202539, 63.214517674617966, 26.841182788165284, 23.17257109056895, 34.25353312865155, -28.9120318150985, -28.727817249611437, -29.407428959791588, -26.20900063413997, -25.53872281265302, -23.49331191608251, -30.010891809230984, -26.992470862599852, -29.0760519883831, -28.454369901204892, 61.74375265734825, -28.649628389004594, 2.0969623843361047, -1.9209838398314487, 52.13005817804988, -26.958869976765342, -9.161795212311421, -10.198727462181601, -16.72936670911493], [74.53667207837421, 70.76022947516397, 70.92605438320372, -13.164056048976672, -18.08587034150237, -14.518208306391996, -19.825261496132754, -19.789740471726414, -21.148608990223227, -21.357356275895366, -16.337150343439795, -20.42859833276357, -17.52683785760979, -14.343786233735408, -13.001552388997409, -19.0125718441234, -18.81523248698896, -21.035029217003068, -21.63396863341711, -13.825868034433135, -17.845017308266343, 37.566188060474836, 45.221619652640655, -11.389709310544967, -16.42716475180664, 29.81629189493625, 28.27606507182927, -7.5915319426445595]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t9884668cdbe5446bb60e965b6d9cf2e4\",\"data\",\"point\",[\"x\", \"y0\"],[[-5.7945353779261906, -5.671855154074227, -6.35349269627018, 60.3629379619553, 49.47409420917244, 66.2193505288515, 27.845075976443173, 25.008560645316265, 36.256711693059785, -26.93988661013104, -27.81690384992917, -26.71471848709299, -24.873954256830302, -23.687978395897918, -22.516051565639323, -29.479016661207318, -25.91334456873136, -26.208646143087957, -26.378308403277796, 63.51665966705537, -27.866848646519426, -2.0413671694837827, -6.336569608821745, 53.105986459663704, -26.06986804430889, -11.558558279208233, -14.054135332843174, -15.513337890236446], [74.86165590781746, 70.31686262002522, 70.99777942814703, -7.388083784872224, -13.976136162248114, -8.595040261599328, -17.607010243013697, -18.09644772054873, -18.19472664949063, -24.03706754237652, -18.759113112714576, -21.821351610236317, -20.16068205206253, -16.516963135651373, -15.319553376462476, -21.52486053593151, -21.731063083611495, -23.719620353180833, -23.270551943899015, -8.555644490080194, -20.72623614408179, 37.046784385723825, 44.41123268092866, -7.489824942839255, -18.576003809186336, 29.47221239494987, 27.962866828811627, -9.003413292316784]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"taae1549c5c964a649f7bce5ae3b12b29\",\"data\",\"point\",[\"x\", \"y0\"],[[1.4217125325636706, 0.8121322370615376, 0.8338807758330716, 59.05726717353782, 47.34746225939301, 63.9399481245418, 26.722661284131902, 24.01543753552095, 35.11536144453825, -29.25629020823448, -28.742061827524964, -28.312544824247446, -26.817373969064644, -25.50391516086061, -22.924982425642344, -30.65028541916159, -27.443725663079473, -28.511415554242582, -28.645988317359546, 61.39270534715487, -29.269088333904165, 1.658476197220326, -3.3589999233449093, 52.33264357256316, -27.519563370945384, -9.606448345796478, -11.736565006033272, -16.350440134618427], [74.75176716556342, 70.87999336369221, 71.2800105603361, -12.615779794764961, -18.723697448502442, -14.063751838790097, -20.173447465369726, -20.70915844634967, -21.97534528621927, -21.43038801603759, -16.878317496123998, -21.07948468641088, -18.302673157712377, -14.874654567534602, -13.637808793629068, -18.129386302620247, -19.092129191789567, -20.929654350817067, -20.70179219095831, -13.777298516562794, -18.045003679802345, 37.76757430004092, 46.35297848092965, -11.042791503377492, -16.64491800372157, 29.443499111969683, 28.906661823726857, -6.555004069164752]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t62e58d74640d4916a6ea617c7b3c0200\",\"data\",\"point\",[\"x\", \"y0\"],[[10.385447007388025, 8.962170640544715, 9.37975194811015, 56.6027412539673, 45.53034771438218, 61.691473676337544, 23.806938631988046, 19.93717225992973, 32.79261683124066, -31.9183624427699, -30.019167315977228, -31.973900104820444, -28.072526691810218, -27.517418248955472, -25.00807248235375, -32.1186235493302, -28.786403556406444, -30.4771075942407, -30.78795611239159, 59.22401224327893, -30.475202290542303, 5.955953628044605, 2.530348353235943, 50.209065401734584, -29.0254193216957, -6.01567864875227, -7.7191587675645446, -17.09304246257171], [74.60973881114873, 70.1128305241436, 70.17516935624526, -19.480589173870626, -23.550591355808674, -20.836882198245828, -22.991171580018168, -23.24642391592142, -25.044202595628043, -18.051731876742014, -13.316107059887619, -16.56399998212954, -14.217982218354884, -11.543616693030241, -9.798029487187627, -14.582858208930254, -15.344159079174139, -17.62032473084237, -18.819372552527003, -20.901038647761244, -14.214245373445664, 37.0614021391299, 44.64882985244157, -17.288473220843084, -13.26499684756616, 30.56225272667679, 29.52577738659528, -6.019203998466563]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tf88abbb291484abb9f86cad96aaa5627\",\"data\",\"point\",[\"x\", \"y0\"],[[-1.4045906420527197, -1.5647724978214177, -1.7960173932420944, 59.082659668257534, 48.053310200019766, 64.27527247007299, 27.385157145212794, 23.781129360032427, 35.350842524720306, -28.51521673800653, -28.32058969790752, -28.35958410757147, -25.787782758978114, -24.97951019113894, -23.31448124975669, -29.532075718910004, -26.624625249422547, -27.678344236207245, -27.971736953960807, 62.35076900319515, -28.318418935073716, 0.5350637535757964, -4.091294646172726, 52.292106668995906, -26.688939106400557, -10.089847861200855, -12.05745870437522, -16.011024105883507], [74.24642206412942, 70.55795068222665, 70.64139734409969, -10.898264967926947, -16.66419633034807, -12.233575062749875, -19.279541436512364, -19.652370436560368, -20.224786025517215, -22.26626518146227, -17.588268094625057, -21.241793023640867, -18.473552847107506, -15.38621229082938, -13.98227408813705, -19.026327763528926, -19.676053699602576, -21.951074221461575, -22.064490697278863, -12.066612532074092, -18.89862480086378, 37.46772620289347, 45.04176970734976, -9.727209530604705, -17.027274514872644, 29.797158264360526, 28.41278515687332, -7.836441876228673]],\"toyplot\");\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"t7f848f05ae2a4779b7f4d9d78193b5fb\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 66.2193505288515, \"min\": -32.1186235493302}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 220.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"tae34104ace784031a4506b08fe5597f1\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 75.0, \"min\": -25.839724192524276}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 200.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"})();</script></div></div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# allow very little missing data\n",
"import itertools\n",
"tool = ipa.pca(\n",
" data=SNPS, \n",
" imap={'samples': list(itertools.chain(*[i for i in IMAP.values()]))},\n",
" minmaf=0.05, \n",
" mincov=0.9, \n",
" impute_method=\"sample\", \n",
" quiet=True,\n",
")\n",
"tool.run(nreplicates=10, seed=123)\n",
"tool.draw(imap=IMAP);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Statistics"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([0.16, 0.15, 0.06, 0.05, 0.04, 0.04, 0.03, 0.03, 0.03, 0.03, 0.03,\n",
" 0.03, 0.03, 0.03, 0.03, 0.03, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02,\n",
" 0.02, 0.02, 0.02, 0.01, 0.01, 0. ])"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# variance explained by each PC axes in the first replicate run\n",
"tool.variances[0].round(2)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"scrolled": true
},
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>...</th>\n",
" <th>18</th>\n",
" <th>19</th>\n",
" <th>20</th>\n",
" <th>21</th>\n",
" <th>22</th>\n",
" <th>23</th>\n",
" <th>24</th>\n",
" <th>25</th>\n",
" <th>26</th>\n",
" <th>27</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>BJSB3</th>\n",
" <td>-4.610</td>\n",
" <td>74.151</td>\n",
" <td>-18.954</td>\n",
" <td>-26.073</td>\n",
" <td>0.587</td>\n",
" <td>0.241</td>\n",
" <td>-1.056</td>\n",
" <td>-2.311</td>\n",
" <td>-1.129</td>\n",
" <td>0.826</td>\n",
" <td>...</td>\n",
" <td>1.739</td>\n",
" <td>1.861</td>\n",
" <td>-0.803</td>\n",
" <td>-2.532</td>\n",
" <td>0.292</td>\n",
" <td>45.451</td>\n",
" <td>-3.763</td>\n",
" <td>-6.647</td>\n",
" <td>0.511</td>\n",
" <td>5.150e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BJSL25</th>\n",
" <td>-4.897</td>\n",
" <td>70.815</td>\n",
" <td>-17.631</td>\n",
" <td>-22.660</td>\n",
" <td>-0.514</td>\n",
" <td>-0.194</td>\n",
" <td>-3.377</td>\n",
" <td>-1.688</td>\n",
" <td>-0.420</td>\n",
" <td>2.316</td>\n",
" <td>...</td>\n",
" <td>0.284</td>\n",
" <td>-1.698</td>\n",
" <td>2.079</td>\n",
" <td>0.757</td>\n",
" <td>0.322</td>\n",
" <td>-17.017</td>\n",
" <td>4.234</td>\n",
" <td>39.465</td>\n",
" <td>-0.777</td>\n",
" <td>-3.872e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BJVL19</th>\n",
" <td>-5.211</td>\n",
" <td>70.611</td>\n",
" <td>-19.109</td>\n",
" <td>-23.022</td>\n",
" <td>0.183</td>\n",
" <td>-0.428</td>\n",
" <td>-2.558</td>\n",
" <td>-1.428</td>\n",
" <td>-0.932</td>\n",
" <td>1.365</td>\n",
" <td>...</td>\n",
" <td>-0.139</td>\n",
" <td>-0.828</td>\n",
" <td>-0.356</td>\n",
" <td>0.900</td>\n",
" <td>-0.154</td>\n",
" <td>-31.057</td>\n",
" <td>0.534</td>\n",
" <td>-32.138</td>\n",
" <td>0.011</td>\n",
" <td>5.343e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BZBB1</th>\n",
" <td>59.008</td>\n",
" <td>-7.891</td>\n",
" <td>3.739</td>\n",
" <td>1.181</td>\n",
" <td>9.771</td>\n",
" <td>-21.487</td>\n",
" <td>-5.505</td>\n",
" <td>-10.850</td>\n",
" <td>4.033</td>\n",
" <td>2.517</td>\n",
" <td>...</td>\n",
" <td>-23.674</td>\n",
" <td>-7.355</td>\n",
" <td>44.886</td>\n",
" <td>-0.201</td>\n",
" <td>0.726</td>\n",
" <td>0.436</td>\n",
" <td>-12.788</td>\n",
" <td>-0.580</td>\n",
" <td>-1.093</td>\n",
" <td>1.349e-14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CRL0001</th>\n",
" <td>48.873</td>\n",
" <td>-14.312</td>\n",
" <td>-3.244</td>\n",
" <td>-1.591</td>\n",
" <td>-4.756</td>\n",
" <td>24.637</td>\n",
" <td>7.354</td>\n",
" <td>-10.712</td>\n",
" <td>-12.492</td>\n",
" <td>-3.943</td>\n",
" <td>...</td>\n",
" <td>-2.002</td>\n",
" <td>-0.992</td>\n",
" <td>-1.080</td>\n",
" <td>-1.470</td>\n",
" <td>0.523</td>\n",
" <td>-0.487</td>\n",
" <td>7.434</td>\n",
" <td>0.335</td>\n",
" <td>35.415</td>\n",
" <td>1.801e-14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CRL0030</th>\n",
" <td>65.379</td>\n",
" <td>-10.158</td>\n",
" <td>1.379</td>\n",
" <td>0.016</td>\n",
" <td>7.581</td>\n",
" <td>-12.391</td>\n",
" <td>-2.594</td>\n",
" <td>-8.762</td>\n",
" <td>0.429</td>\n",
" <td>1.645</td>\n",
" <td>...</td>\n",
" <td>-16.870</td>\n",
" <td>-1.965</td>\n",
" <td>-15.548</td>\n",
" <td>-1.647</td>\n",
" <td>2.670</td>\n",
" <td>3.929</td>\n",
" <td>41.397</td>\n",
" <td>-2.348</td>\n",
" <td>-11.342</td>\n",
" <td>1.248e-14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CUCA4</th>\n",
" <td>28.689</td>\n",
" <td>-17.756</td>\n",
" <td>-5.968</td>\n",
" <td>-4.747</td>\n",
" <td>-25.342</td>\n",
" <td>-13.972</td>\n",
" <td>-15.904</td>\n",
" <td>60.494</td>\n",
" <td>-27.650</td>\n",
" <td>-2.318</td>\n",
" <td>...</td>\n",
" <td>3.390</td>\n",
" <td>0.035</td>\n",
" <td>1.886</td>\n",
" <td>1.354</td>\n",
" <td>-1.875</td>\n",
" <td>0.528</td>\n",
" <td>0.130</td>\n",
" <td>-0.005</td>\n",
" <td>0.234</td>\n",
" <td>-5.325e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CUSV6</th>\n",
" <td>25.507</td>\n",
" <td>-18.772</td>\n",
" <td>-5.325</td>\n",
" <td>-5.905</td>\n",
" <td>-13.657</td>\n",
" <td>25.755</td>\n",
" <td>-3.049</td>\n",
" <td>23.135</td>\n",
" <td>60.079</td>\n",
" <td>13.485</td>\n",
" <td>...</td>\n",
" <td>-0.083</td>\n",
" <td>1.558</td>\n",
" <td>0.302</td>\n",
" <td>-0.601</td>\n",
" <td>-1.812</td>\n",
" <td>0.539</td>\n",
" <td>-1.068</td>\n",
" <td>-0.345</td>\n",
" <td>-0.248</td>\n",
" <td>7.175e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CUVN10</th>\n",
" <td>36.071</td>\n",
" <td>-18.435</td>\n",
" <td>-5.100</td>\n",
" <td>-3.779</td>\n",
" <td>-13.754</td>\n",
" <td>53.098</td>\n",
" <td>14.693</td>\n",
" <td>-10.917</td>\n",
" <td>-24.551</td>\n",
" <td>-6.453</td>\n",
" <td>...</td>\n",
" <td>4.404</td>\n",
" <td>0.415</td>\n",
" <td>7.293</td>\n",
" <td>-0.120</td>\n",
" <td>-0.499</td>\n",
" <td>-0.757</td>\n",
" <td>-6.956</td>\n",
" <td>-0.045</td>\n",
" <td>-20.352</td>\n",
" <td>1.455e-14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FLAB109</th>\n",
" <td>-27.347</td>\n",
" <td>-23.411</td>\n",
" <td>-22.541</td>\n",
" <td>6.585</td>\n",
" <td>-17.245</td>\n",
" <td>-6.095</td>\n",
" <td>3.553</td>\n",
" <td>-13.351</td>\n",
" <td>0.285</td>\n",
" <td>-0.432</td>\n",
" <td>...</td>\n",
" <td>-9.764</td>\n",
" <td>2.632</td>\n",
" <td>-4.347</td>\n",
" <td>-0.406</td>\n",
" <td>-20.536</td>\n",
" <td>-0.271</td>\n",
" <td>1.277</td>\n",
" <td>1.406</td>\n",
" <td>-0.214</td>\n",
" <td>5.870e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FLBA140</th>\n",
" <td>-27.255</td>\n",
" <td>-18.524</td>\n",
" <td>24.450</td>\n",
" <td>-17.597</td>\n",
" <td>1.065</td>\n",
" <td>1.099</td>\n",
" <td>-6.548</td>\n",
" <td>-4.178</td>\n",
" <td>-0.478</td>\n",
" <td>-2.337</td>\n",
" <td>...</td>\n",
" <td>0.620</td>\n",
" <td>-20.521</td>\n",
" <td>-2.257</td>\n",
" <td>49.330</td>\n",
" <td>-7.602</td>\n",
" <td>2.767</td>\n",
" <td>1.443</td>\n",
" <td>-0.570</td>\n",
" <td>0.496</td>\n",
" <td>6.641e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FLCK18</th>\n",
" <td>-27.442</td>\n",
" <td>-22.547</td>\n",
" <td>-22.679</td>\n",
" <td>6.941</td>\n",
" <td>-19.645</td>\n",
" <td>-12.994</td>\n",
" <td>0.340</td>\n",
" <td>-9.506</td>\n",
" <td>-0.153</td>\n",
" <td>1.499</td>\n",
" <td>...</td>\n",
" <td>0.083</td>\n",
" <td>3.186</td>\n",
" <td>1.081</td>\n",
" <td>-0.757</td>\n",
" <td>-10.247</td>\n",
" <td>-0.010</td>\n",
" <td>-0.548</td>\n",
" <td>-0.900</td>\n",
" <td>-0.054</td>\n",
" <td>-1.586e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FLCK216</th>\n",
" <td>-25.219</td>\n",
" <td>-19.694</td>\n",
" <td>-21.351</td>\n",
" <td>8.456</td>\n",
" <td>20.746</td>\n",
" <td>3.235</td>\n",
" <td>-6.158</td>\n",
" <td>2.458</td>\n",
" <td>6.057</td>\n",
" <td>-11.410</td>\n",
" <td>...</td>\n",
" <td>-1.026</td>\n",
" <td>-4.151</td>\n",
" <td>0.246</td>\n",
" <td>-0.832</td>\n",
" <td>-1.569</td>\n",
" <td>-0.220</td>\n",
" <td>-0.991</td>\n",
" <td>0.159</td>\n",
" <td>-0.406</td>\n",
" <td>8.143e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FLMO62</th>\n",
" <td>-24.339</td>\n",
" <td>-17.004</td>\n",
" <td>-18.577</td>\n",
" <td>10.045</td>\n",
" <td>21.703</td>\n",
" <td>2.052</td>\n",
" <td>-2.756</td>\n",
" <td>7.499</td>\n",
" <td>-2.931</td>\n",
" <td>6.725</td>\n",
" <td>...</td>\n",
" <td>-2.491</td>\n",
" <td>3.808</td>\n",
" <td>0.269</td>\n",
" <td>0.365</td>\n",
" <td>-0.727</td>\n",
" <td>-0.144</td>\n",
" <td>0.970</td>\n",
" <td>0.575</td>\n",
" <td>0.632</td>\n",
" <td>1.325e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FLSA185</th>\n",
" <td>-23.002</td>\n",
" <td>-14.798</td>\n",
" <td>-21.469</td>\n",
" <td>14.032</td>\n",
" <td>55.138</td>\n",
" <td>13.869</td>\n",
" <td>-1.326</td>\n",
" <td>15.814</td>\n",
" <td>-4.724</td>\n",
" <td>4.430</td>\n",
" <td>...</td>\n",
" <td>0.218</td>\n",
" <td>0.192</td>\n",
" <td>-0.342</td>\n",
" <td>-0.226</td>\n",
" <td>1.249</td>\n",
" <td>-0.003</td>\n",
" <td>0.303</td>\n",
" <td>0.127</td>\n",
" <td>-0.266</td>\n",
" <td>5.993e-16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FLSF33</th>\n",
" <td>-29.098</td>\n",
" <td>-20.067</td>\n",
" <td>28.700</td>\n",
" <td>-18.672</td>\n",
" <td>1.083</td>\n",
" <td>-1.706</td>\n",
" <td>-6.625</td>\n",
" <td>-2.339</td>\n",
" <td>-4.928</td>\n",
" <td>-1.446</td>\n",
" <td>...</td>\n",
" <td>-9.273</td>\n",
" <td>40.859</td>\n",
" <td>1.740</td>\n",
" <td>-7.499</td>\n",
" <td>-2.054</td>\n",
" <td>-1.543</td>\n",
" <td>0.060</td>\n",
" <td>0.526</td>\n",
" <td>0.382</td>\n",
" <td>1.520e-14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FLSF47</th>\n",
" <td>-25.578</td>\n",
" <td>-21.277</td>\n",
" <td>-17.202</td>\n",
" <td>6.783</td>\n",
" <td>0.406</td>\n",
" <td>-5.679</td>\n",
" <td>0.162</td>\n",
" <td>-5.437</td>\n",
" <td>-10.473</td>\n",
" <td>4.616</td>\n",
" <td>...</td>\n",
" <td>1.728</td>\n",
" <td>-2.037</td>\n",
" <td>0.717</td>\n",
" <td>-1.482</td>\n",
" <td>-4.525</td>\n",
" <td>2.779</td>\n",
" <td>-1.255</td>\n",
" <td>-0.620</td>\n",
" <td>0.816</td>\n",
" <td>3.046e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FLSF54</th>\n",
" <td>-26.729</td>\n",
" <td>-23.262</td>\n",
" <td>-15.358</td>\n",
" <td>3.753</td>\n",
" <td>-16.081</td>\n",
" <td>-7.160</td>\n",
" <td>2.704</td>\n",
" <td>-7.128</td>\n",
" <td>1.987</td>\n",
" <td>-1.853</td>\n",
" <td>...</td>\n",
" <td>2.786</td>\n",
" <td>2.569</td>\n",
" <td>1.066</td>\n",
" <td>7.329</td>\n",
" <td>53.344</td>\n",
" <td>0.025</td>\n",
" <td>0.218</td>\n",
" <td>0.002</td>\n",
" <td>-0.061</td>\n",
" <td>4.989e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FLWO6</th>\n",
" <td>-27.152</td>\n",
" <td>-23.355</td>\n",
" <td>-17.863</td>\n",
" <td>5.182</td>\n",
" <td>-25.241</td>\n",
" <td>-11.059</td>\n",
" <td>3.364</td>\n",
" <td>-13.860</td>\n",
" <td>8.296</td>\n",
" <td>-4.614</td>\n",
" <td>...</td>\n",
" <td>4.424</td>\n",
" <td>-1.943</td>\n",
" <td>-0.669</td>\n",
" <td>-6.099</td>\n",
" <td>-8.897</td>\n",
" <td>-0.303</td>\n",
" <td>0.600</td>\n",
" <td>-0.089</td>\n",
" <td>-0.744</td>\n",
" <td>5.412e-16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HNDA09</th>\n",
" <td>63.228</td>\n",
" <td>-9.392</td>\n",
" <td>1.370</td>\n",
" <td>-0.739</td>\n",
" <td>8.133</td>\n",
" <td>-14.948</td>\n",
" <td>-3.471</td>\n",
" <td>-9.066</td>\n",
" <td>-0.608</td>\n",
" <td>3.730</td>\n",
" <td>...</td>\n",
" <td>-9.506</td>\n",
" <td>-0.481</td>\n",
" <td>-37.978</td>\n",
" <td>0.261</td>\n",
" <td>2.431</td>\n",
" <td>-3.157</td>\n",
" <td>-33.027</td>\n",
" <td>2.726</td>\n",
" <td>-1.791</td>\n",
" <td>6.453e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LALC2</th>\n",
" <td>-27.999</td>\n",
" <td>-20.481</td>\n",
" <td>26.148</td>\n",
" <td>-17.820</td>\n",
" <td>1.279</td>\n",
" <td>-2.240</td>\n",
" <td>-1.612</td>\n",
" <td>-0.461</td>\n",
" <td>-1.580</td>\n",
" <td>0.043</td>\n",
" <td>...</td>\n",
" <td>8.728</td>\n",
" <td>-39.895</td>\n",
" <td>-2.325</td>\n",
" <td>-32.678</td>\n",
" <td>2.609</td>\n",
" <td>-0.370</td>\n",
" <td>0.287</td>\n",
" <td>-0.300</td>\n",
" <td>-0.307</td>\n",
" <td>6.276e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MXED8</th>\n",
" <td>-0.865</td>\n",
" <td>37.463</td>\n",
" <td>17.114</td>\n",
" <td>34.215</td>\n",
" <td>-2.978</td>\n",
" <td>-1.335</td>\n",
" <td>10.300</td>\n",
" <td>8.005</td>\n",
" <td>3.227</td>\n",
" <td>-28.786</td>\n",
" <td>...</td>\n",
" <td>-5.141</td>\n",
" <td>-3.268</td>\n",
" <td>-1.844</td>\n",
" <td>-2.208</td>\n",
" <td>-1.518</td>\n",
" <td>-0.215</td>\n",
" <td>-0.886</td>\n",
" <td>0.676</td>\n",
" <td>-0.322</td>\n",
" <td>1.025e-14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MXGT4</th>\n",
" <td>-6.112</td>\n",
" <td>44.646</td>\n",
" <td>17.533</td>\n",
" <td>29.862</td>\n",
" <td>-4.557</td>\n",
" <td>2.026</td>\n",
" <td>6.206</td>\n",
" <td>6.791</td>\n",
" <td>8.412</td>\n",
" <td>-22.120</td>\n",
" <td>...</td>\n",
" <td>-6.112</td>\n",
" <td>0.630</td>\n",
" <td>-2.181</td>\n",
" <td>2.240</td>\n",
" <td>1.766</td>\n",
" <td>1.183</td>\n",
" <td>-1.157</td>\n",
" <td>-0.528</td>\n",
" <td>0.432</td>\n",
" <td>1.382e-14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MXSA3017</th>\n",
" <td>53.256</td>\n",
" <td>-7.713</td>\n",
" <td>3.466</td>\n",
" <td>2.372</td>\n",
" <td>12.366</td>\n",
" <td>-18.793</td>\n",
" <td>-1.507</td>\n",
" <td>-10.907</td>\n",
" <td>6.243</td>\n",
" <td>-4.876</td>\n",
" <td>...</td>\n",
" <td>52.928</td>\n",
" <td>10.552</td>\n",
" <td>6.829</td>\n",
" <td>2.753</td>\n",
" <td>-4.458</td>\n",
" <td>-0.931</td>\n",
" <td>2.271</td>\n",
" <td>-0.021</td>\n",
" <td>-1.213</td>\n",
" <td>5.627e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SCCU3</th>\n",
" <td>-26.268</td>\n",
" <td>-17.772</td>\n",
" <td>34.307</td>\n",
" <td>-26.482</td>\n",
" <td>6.734</td>\n",
" <td>8.784</td>\n",
" <td>-32.502</td>\n",
" <td>-6.384</td>\n",
" <td>2.695</td>\n",
" <td>-13.612</td>\n",
" <td>...</td>\n",
" <td>-2.683</td>\n",
" <td>8.748</td>\n",
" <td>-0.858</td>\n",
" <td>-6.687</td>\n",
" <td>1.802</td>\n",
" <td>-0.599</td>\n",
" <td>-0.454</td>\n",
" <td>-0.106</td>\n",
" <td>0.195</td>\n",
" <td>6.228e-16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TXGR3</th>\n",
" <td>-11.573</td>\n",
" <td>29.699</td>\n",
" <td>25.049</td>\n",
" <td>30.186</td>\n",
" <td>-8.234</td>\n",
" <td>5.636</td>\n",
" <td>-9.980</td>\n",
" <td>-4.686</td>\n",
" <td>-9.879</td>\n",
" <td>56.818</td>\n",
" <td>...</td>\n",
" <td>2.577</td>\n",
" <td>-0.045</td>\n",
" <td>0.119</td>\n",
" <td>-0.108</td>\n",
" <td>0.866</td>\n",
" <td>0.746</td>\n",
" <td>0.604</td>\n",
" <td>-0.698</td>\n",
" <td>0.366</td>\n",
" <td>-1.291e-15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TXMD3</th>\n",
" <td>-13.722</td>\n",
" <td>28.559</td>\n",
" <td>25.621</td>\n",
" <td>30.245</td>\n",
" <td>-3.552</td>\n",
" <td>3.446</td>\n",
" <td>-5.903</td>\n",
" <td>-3.708</td>\n",
" <td>-0.026</td>\n",
" <td>-5.823</td>\n",
" <td>...</td>\n",
" <td>6.395</td>\n",
" <td>2.715</td>\n",
" <td>1.365</td>\n",
" <td>-0.384</td>\n",
" <td>-1.193</td>\n",
" <td>-0.126</td>\n",
" <td>1.150</td>\n",
" <td>-0.409</td>\n",
" <td>-0.478</td>\n",
" <td>-3.965e-14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TXWV2</th>\n",
" <td>-15.595</td>\n",
" <td>-9.321</td>\n",
" <td>23.495</td>\n",
" <td>-20.767</td>\n",
" <td>8.781</td>\n",
" <td>-13.396</td>\n",
" <td>63.757</td>\n",
" <td>13.482</td>\n",
" <td>1.209</td>\n",
" <td>10.009</td>\n",
" <td>...</td>\n",
" <td>-1.541</td>\n",
" <td>5.421</td>\n",
" <td>0.710</td>\n",
" <td>0.646</td>\n",
" <td>-0.936</td>\n",
" <td>-1.173</td>\n",
" <td>-0.021</td>\n",
" <td>0.352</td>\n",
" <td>0.178</td>\n",
" <td>-4.576e-14</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>28 rows × 28 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 \\\n",
"BJSB3 -4.610 74.151 -18.954 -26.073 0.587 0.241 -1.056 -2.311 \n",
"BJSL25 -4.897 70.815 -17.631 -22.660 -0.514 -0.194 -3.377 -1.688 \n",
"BJVL19 -5.211 70.611 -19.109 -23.022 0.183 -0.428 -2.558 -1.428 \n",
"BZBB1 59.008 -7.891 3.739 1.181 9.771 -21.487 -5.505 -10.850 \n",
"CRL0001 48.873 -14.312 -3.244 -1.591 -4.756 24.637 7.354 -10.712 \n",
"CRL0030 65.379 -10.158 1.379 0.016 7.581 -12.391 -2.594 -8.762 \n",
"CUCA4 28.689 -17.756 -5.968 -4.747 -25.342 -13.972 -15.904 60.494 \n",
"CUSV6 25.507 -18.772 -5.325 -5.905 -13.657 25.755 -3.049 23.135 \n",
"CUVN10 36.071 -18.435 -5.100 -3.779 -13.754 53.098 14.693 -10.917 \n",
"FLAB109 -27.347 -23.411 -22.541 6.585 -17.245 -6.095 3.553 -13.351 \n",
"FLBA140 -27.255 -18.524 24.450 -17.597 1.065 1.099 -6.548 -4.178 \n",
"FLCK18 -27.442 -22.547 -22.679 6.941 -19.645 -12.994 0.340 -9.506 \n",
"FLCK216 -25.219 -19.694 -21.351 8.456 20.746 3.235 -6.158 2.458 \n",
"FLMO62 -24.339 -17.004 -18.577 10.045 21.703 2.052 -2.756 7.499 \n",
"FLSA185 -23.002 -14.798 -21.469 14.032 55.138 13.869 -1.326 15.814 \n",
"FLSF33 -29.098 -20.067 28.700 -18.672 1.083 -1.706 -6.625 -2.339 \n",
"FLSF47 -25.578 -21.277 -17.202 6.783 0.406 -5.679 0.162 -5.437 \n",
"FLSF54 -26.729 -23.262 -15.358 3.753 -16.081 -7.160 2.704 -7.128 \n",
"FLWO6 -27.152 -23.355 -17.863 5.182 -25.241 -11.059 3.364 -13.860 \n",
"HNDA09 63.228 -9.392 1.370 -0.739 8.133 -14.948 -3.471 -9.066 \n",
"LALC2 -27.999 -20.481 26.148 -17.820 1.279 -2.240 -1.612 -0.461 \n",
"MXED8 -0.865 37.463 17.114 34.215 -2.978 -1.335 10.300 8.005 \n",
"MXGT4 -6.112 44.646 17.533 29.862 -4.557 2.026 6.206 6.791 \n",
"MXSA3017 53.256 -7.713 3.466 2.372 12.366 -18.793 -1.507 -10.907 \n",
"SCCU3 -26.268 -17.772 34.307 -26.482 6.734 8.784 -32.502 -6.384 \n",
"TXGR3 -11.573 29.699 25.049 30.186 -8.234 5.636 -9.980 -4.686 \n",
"TXMD3 -13.722 28.559 25.621 30.245 -3.552 3.446 -5.903 -3.708 \n",
"TXWV2 -15.595 -9.321 23.495 -20.767 8.781 -13.396 63.757 13.482 \n",
"\n",
" 8 9 ... 18 19 20 21 22 23 \\\n",
"BJSB3 -1.129 0.826 ... 1.739 1.861 -0.803 -2.532 0.292 45.451 \n",
"BJSL25 -0.420 2.316 ... 0.284 -1.698 2.079 0.757 0.322 -17.017 \n",
"BJVL19 -0.932 1.365 ... -0.139 -0.828 -0.356 0.900 -0.154 -31.057 \n",
"BZBB1 4.033 2.517 ... -23.674 -7.355 44.886 -0.201 0.726 0.436 \n",
"CRL0001 -12.492 -3.943 ... -2.002 -0.992 -1.080 -1.470 0.523 -0.487 \n",
"CRL0030 0.429 1.645 ... -16.870 -1.965 -15.548 -1.647 2.670 3.929 \n",
"CUCA4 -27.650 -2.318 ... 3.390 0.035 1.886 1.354 -1.875 0.528 \n",
"CUSV6 60.079 13.485 ... -0.083 1.558 0.302 -0.601 -1.812 0.539 \n",
"CUVN10 -24.551 -6.453 ... 4.404 0.415 7.293 -0.120 -0.499 -0.757 \n",
"FLAB109 0.285 -0.432 ... -9.764 2.632 -4.347 -0.406 -20.536 -0.271 \n",
"FLBA140 -0.478 -2.337 ... 0.620 -20.521 -2.257 49.330 -7.602 2.767 \n",
"FLCK18 -0.153 1.499 ... 0.083 3.186 1.081 -0.757 -10.247 -0.010 \n",
"FLCK216 6.057 -11.410 ... -1.026 -4.151 0.246 -0.832 -1.569 -0.220 \n",
"FLMO62 -2.931 6.725 ... -2.491 3.808 0.269 0.365 -0.727 -0.144 \n",
"FLSA185 -4.724 4.430 ... 0.218 0.192 -0.342 -0.226 1.249 -0.003 \n",
"FLSF33 -4.928 -1.446 ... -9.273 40.859 1.740 -7.499 -2.054 -1.543 \n",
"FLSF47 -10.473 4.616 ... 1.728 -2.037 0.717 -1.482 -4.525 2.779 \n",
"FLSF54 1.987 -1.853 ... 2.786 2.569 1.066 7.329 53.344 0.025 \n",
"FLWO6 8.296 -4.614 ... 4.424 -1.943 -0.669 -6.099 -8.897 -0.303 \n",
"HNDA09 -0.608 3.730 ... -9.506 -0.481 -37.978 0.261 2.431 -3.157 \n",
"LALC2 -1.580 0.043 ... 8.728 -39.895 -2.325 -32.678 2.609 -0.370 \n",
"MXED8 3.227 -28.786 ... -5.141 -3.268 -1.844 -2.208 -1.518 -0.215 \n",
"MXGT4 8.412 -22.120 ... -6.112 0.630 -2.181 2.240 1.766 1.183 \n",
"MXSA3017 6.243 -4.876 ... 52.928 10.552 6.829 2.753 -4.458 -0.931 \n",
"SCCU3 2.695 -13.612 ... -2.683 8.748 -0.858 -6.687 1.802 -0.599 \n",
"TXGR3 -9.879 56.818 ... 2.577 -0.045 0.119 -0.108 0.866 0.746 \n",
"TXMD3 -0.026 -5.823 ... 6.395 2.715 1.365 -0.384 -1.193 -0.126 \n",
"TXWV2 1.209 10.009 ... -1.541 5.421 0.710 0.646 -0.936 -1.173 \n",
"\n",
" 24 25 26 27 \n",
"BJSB3 -3.763 -6.647 0.511 5.150e-15 \n",
"BJSL25 4.234 39.465 -0.777 -3.872e-15 \n",
"BJVL19 0.534 -32.138 0.011 5.343e-15 \n",
"BZBB1 -12.788 -0.580 -1.093 1.349e-14 \n",
"CRL0001 7.434 0.335 35.415 1.801e-14 \n",
"CRL0030 41.397 -2.348 -11.342 1.248e-14 \n",
"CUCA4 0.130 -0.005 0.234 -5.325e-15 \n",
"CUSV6 -1.068 -0.345 -0.248 7.175e-15 \n",
"CUVN10 -6.956 -0.045 -20.352 1.455e-14 \n",
"FLAB109 1.277 1.406 -0.214 5.870e-15 \n",
"FLBA140 1.443 -0.570 0.496 6.641e-15 \n",
"FLCK18 -0.548 -0.900 -0.054 -1.586e-15 \n",
"FLCK216 -0.991 0.159 -0.406 8.143e-15 \n",
"FLMO62 0.970 0.575 0.632 1.325e-15 \n",
"FLSA185 0.303 0.127 -0.266 5.993e-16 \n",
"FLSF33 0.060 0.526 0.382 1.520e-14 \n",
"FLSF47 -1.255 -0.620 0.816 3.046e-15 \n",
"FLSF54 0.218 0.002 -0.061 4.989e-15 \n",
"FLWO6 0.600 -0.089 -0.744 5.412e-16 \n",
"HNDA09 -33.027 2.726 -1.791 6.453e-15 \n",
"LALC2 0.287 -0.300 -0.307 6.276e-15 \n",
"MXED8 -0.886 0.676 -0.322 1.025e-14 \n",
"MXGT4 -1.157 -0.528 0.432 1.382e-14 \n",
"MXSA3017 2.271 -0.021 -1.213 5.627e-15 \n",
"SCCU3 -0.454 -0.106 0.195 6.228e-16 \n",
"TXGR3 0.604 -0.698 0.366 -1.291e-15 \n",
"TXMD3 1.150 -0.409 -0.478 -3.965e-14 \n",
"TXWV2 -0.021 0.352 0.178 -4.576e-14 \n",
"\n",
"[28 rows x 28 columns]"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# PC loadings in the first replicate\n",
"tool.pcs(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Styling plots (see toyplot documentation)\n",
"The `.draw()` function returns a canvas and axes object from toyplot which can be further modified and styled."
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"toyplot\" id=\"t22221e628f4d439aa9df567ea6428071\" style=\"text-align:center\"><svg class=\"toyplot-canvas-Canvas\" height=\"300.0px\" id=\"te70e45b27e95410f948a41f42edc4a76\" preserveAspectRatio=\"xMidYMid meet\" style=\"background-color:transparent;border-color:#292724;border-style:none;border-width:1.0;fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:Helvetica;font-size:12px;opacity:1.0;stroke:rgb(16.1%,15.3%,14.1%);stroke-opacity:1.0;stroke-width:1.0\" viewBox=\"0 0 400.0 300.0\" width=\"400.0px\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:toyplot=\"http://www.sandia.gov/toyplot\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"><g class=\"toyplot-coordinates-Cartesian\" id=\"t742774db13cf40b6afd4ca34e1a3315f\"><clipPath id=\"tfc604f6f6d2a4be1869108696d8f2d1b\"><rect height=\"220.0\" width=\"240.0\" x=\"40.0\" y=\"40.0\"></rect></clipPath><g clip-path=\"url(#tfc604f6f6d2a4be1869108696d8f2d1b)\"><g class=\"toyplot-mark-Point\" id=\"t90eef0f95d54411d9ed5eeae78434e21\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(111.54152688736139, 51.684767357256604)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(110.89966928422399, 58.300888092318395)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(110.19804362757685, 58.70559809718476)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(253.86732237615524, 214.40112188786497)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(231.19411046167554, 227.13747682602067)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(268.11897082815017, 218.89733150808124)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(186.03805349661323, 233.96672029695983)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(178.9199878720106, 235.98180501522668)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(202.55190225466202, 235.31474664161593)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.67517283054278, 245.182307552124)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.881076962947624, 235.49119870933868)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.462929403204235, 243.46965302695554)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.43567422926812, 237.80996089391866)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.40333220724868, 232.4752911135649)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(70.39515005767471, 228.10060592443207)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.75842488793348, 238.55067608616707)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.63316587999665, 240.95085664316724)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.05727003299762, 244.88805886576355)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.110624628156884, 245.07099487727345)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(263.3085326867038, 217.37836244439518)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.2173058493253, 239.3721984813182)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(119.91934201252242, 124.44948978766548)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(108.18254005083654, 110.2033347877302)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(240.99894406202287, 214.04789601272984)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.08931429934485, 233.99912118419417)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(95.9651800242674, 139.84762125420588)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(91.15687386907032, 142.10863568176495)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(86.96583827184261, 217.23855609066143)\"><circle r=\"4.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t0f6446f78275444d8ecd1badb043b64b\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(97.04668801812939, 53.62518279327868)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(97.1111957481994, 60.63380807152146)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(96.06647952460997, 60.998221612275195)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(258.1697393739452, 205.33273535201243)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(233.27449583124383, 218.80307459808685)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(268.16493793810395, 207.0839786118842)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(188.61828145043674, 230.18666843980785)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(181.27514597428672, 231.25100122009025)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(206.90396902799526, 229.842180607763)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.79027629048258, 249.49774702883772)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.40659671226075, 240.8648608801976)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.10601880779275, 248.7246309333229)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(69.45223176620176, 241.32889968051157)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(70.30933269346055, 235.67590073883113)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(72.51128617416433, 231.658276374526)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.332622826366936, 243.68828706958786)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.4024201714281, 243.88787860387896)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.38726756531958, 248.7269338321422)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.71289596634153, 250.0)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(265.29887333765106, 207.3563668733549)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.28397788233586, 243.3445037824668)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(111.95585932979209, 123.55878998575918)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(99.2448914970607, 110.27395777380889)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(240.8137885839803, 204.29426621685917)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.03879199011288, 237.90551818799509)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(91.29699243849359, 143.14109435423512)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(85.09803395667397, 145.46396619751312)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(88.87318845746628, 217.87654531935146)\"><circle r=\"4.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t02bad34e70b4459fbb5853a0127d6c9e\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(109.54717388830373, 52.00549001663815)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(111.12266550711138, 57.74996538484875)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(110.74888061481174, 59.060111463828946)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(254.24388540957543, 214.68710834990813)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(231.81387732998965, 227.0053096240753)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(266.6672462552236, 216.78693109420382)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(185.92124625699861, 234.02714822940982)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(176.7194412599052, 236.19590270008536)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(204.22502902132788, 235.55158019361232)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.320143026854936, 246.20254287272596)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.30560178241377, 237.55904987200452)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.12708727697898, 243.45199427351324)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.53127397064921, 239.05756769782934)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(68.77832537342746, 231.16567717650807)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(72.12177370898539, 228.53651165721527)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.316733030244166, 240.87012162111222)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.95323060381384, 240.49224846401265)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.11012350586557, 245.28752876989853)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.81251950389195, 244.10854081599626)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(263.11698321641364, 216.40863840873214)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.345620402038875, 240.06007412369343)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(116.77537323697722, 124.13892258073615)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(107.55229554109633, 109.07939392907664)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(239.5366496259407, 213.50716439728254)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.36035105913967, 235.82271966510567)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(95.05524702916527, 140.26380253555362)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(92.14961528375187, 142.5166897070823)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(84.66788661343985, 213.42653951521032)\"><circle r=\"4.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t34cb7a17726a47a493546d4c774724d1\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(130.30174833347732, 50.56719893801842)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(131.53287612775023, 57.753822074665464)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(129.25527188116894, 59.1441167785503)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(253.5076647152082, 228.28393329892478)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(225.7963877324412, 238.5462946572713)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(263.7464528247674, 232.39524343117927)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(180.04916428213673, 242.41900195893885)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(170.19259768471153, 241.0591708589516)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(197.3357077988918, 243.42384364271817)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.897805932566584, 238.29142945329025)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.82983191021832, 230.49456574326857)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.40940015740424, 236.94221856402254)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.87822295316536, 231.49810104443208)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.24163817430876, 225.5750154212029)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.39465210316123, 223.48990158790255)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.319677281065076, 230.75039973499355)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.9209059587125, 233.01547787756365)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.93267032847383, 238.31523603656643)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.19171883669055, 237.98446349960517)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(259.6823519717338, 232.22563130647043)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.634633605358545, 231.69629718036816)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(130.05206995409358, 125.13108014679743)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(119.80412173744588, 110.79651733424132)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(237.77722998948457, 224.7479501552854)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.47114039377013, 228.62979112953946)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(105.85190300406953, 136.11739056406586)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(99.9134809605765, 142.65064626633617)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(86.02495270148351, 213.0805364547293)\"><circle r=\"4.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"tc637d22e84f748e89c8461a8162db262\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(112.23074791987251, 53.08870509766675)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(111.38623301848635, 59.35886581148671)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(111.64058810556902, 57.84511225194511)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(253.62917690481197, 216.49997389377774)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(230.72728329909472, 228.7769584701084)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(266.4940933750853, 217.89659068710222)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(186.85290165431053, 235.09980426809298)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(176.83115395665354, 235.7233154019068)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(202.04373986403328, 237.51535808028976)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.98173944897975, 245.43677930435294)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.29637962552866, 236.16064211271956)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.38163423288093, 243.66858860904077)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.09711197290018, 236.94474295932932)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.73806891572542, 231.5034967962856)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(69.41243921490562, 229.58544831425797)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.31661055189344, 238.68266316844972)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.84120721476192, 239.49774052925858)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.35660382312447, 244.74495394875152)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.630310749583614, 243.53250500911702)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(263.4815881389102, 218.3642441447724)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.60498962476207, 237.7950191324911)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(120.6880938943712, 122.83313632011809)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(107.88784647690555, 108.31100739709993)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(239.3676148119949, 213.21253845965214)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.22959690941114, 234.66375349014763)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(97.24658818501494, 139.2708962947662)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(94.07427223855201, 142.99825512716413)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(86.47766520621249, 216.01418005974824)\"><circle r=\"4.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t5211e3cb15374803ad7ec184b787b3a9\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(124.14380843352947, 51.51503132429005)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(124.7754374691379, 58.742280900090904)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(123.72482120976346, 57.72782757728774)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(254.87505876739476, 225.03060074991956)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(228.61939161639438, 235.31032108755528)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(265.2672096446183, 227.0178198441023)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(180.92057301882596, 239.3344950732141)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(173.10029681718814, 239.74810325037274)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(199.26704538294035, 240.69763888992642)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.338557116272284, 241.17204932556777)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.78902692871945, 231.27721583080933)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.61587965681652, 239.0901753863348)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.89767737560166, 233.013439402432)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.57092337077879, 227.6676834934512)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(67.87487674447041, 225.799562573312)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.916124629666214, 232.04165157946423)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.89597598753963, 236.188953432638)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.72237886990388, 240.6864692280574)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.52975159346557, 241.97867434722983)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(261.0715171583832, 226.92538514482163)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.60665135304665, 234.58097407876753)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(126.55171388324919, 125.67458199604525)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(117.25741956253248, 108.76826895692737)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(237.6702405150293, 221.98444110446957)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.99827774683913, 230.4491096408473)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(101.2918821294628, 139.48576298919832)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(96.77579400697036, 140.67649733923054)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(86.8779683457957, 212.44026059353615)\"><circle r=\"4.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t3413f80ebe8342e086022f8a585d1499\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(123.30028167544384, 50.91893928773783)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(121.97375087252638, 58.4089292365406)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(122.53645915651624, 58.08004117309615)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(252.68521204337287, 224.859772287066)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(228.13409939858434, 234.62142987178711)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(263.27764036092697, 227.54552389588628)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(181.9038501234176, 238.07124326340153)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(173.6964960362821, 238.00079280413905)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(198.48663098903225, 240.6958983875348)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.1737310855125, 241.1099163498878)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.585852697606455, 231.15311416175223)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.0654372361209, 239.2678685843493)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.220905286377146, 233.51267538367432)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.72043913491761, 227.19958468581152)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(69.29639670841567, 224.537471405582)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.71538067738889, 236.45939900556172)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.468139359715494, 236.06800690544517)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.80678801534183, 240.47063047029354)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.197604334889654, 241.65853418821834)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(259.9872705233087, 226.17237402364535)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.76077545247051, 234.14373512368132)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(126.54651192449877, 124.24417755855049)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(117.55763272902038, 109.0608127616576)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(238.47968095506982, 221.34062990019453)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.54331067530224, 231.3316438217402)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(101.35861584995465, 139.61489822958765)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(99.03880911090108, 142.6696999665824)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(84.42857692142077, 213.80753240649474)\"><circle r=\"4.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"tdcc535d6c0054399a1397ebc5c255391\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(108.89179080610937, 50.274384114574545)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(109.16624886262755, 59.28827883549876)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(107.64130124509896, 57.937785637358346)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(256.89813597651664, 213.40402444492216)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(232.53780469993683, 226.47040761907266)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(270.0, 215.7978359837613)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(184.14974245027753, 233.671684912996)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(177.8039398374253, 234.6424084675361)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(202.9681071258036, 234.8373295261345)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.58577993195202, 246.42470928082645)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.623732263548845, 235.95670280436048)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.08952212852186, 242.03017934754357)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(66.2076477504289, 238.73649806971056)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(68.86089225593082, 231.50974503049258)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(71.4827065151092, 229.13486792967308)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(55.90528247943515, 241.4421351483374)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.882341877883306, 241.85110601637808)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.22169834757425, 245.79510186819687)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(62.84213289901121, 244.90444411823228)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(263.9535871552282, 215.71970056275615)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.51199663595577, 239.8581871591006)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(117.28831324413392, 125.27433443156883)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(107.67916128109783, 110.6680900091931)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(240.66301068061765, 213.60581229942437)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(63.53217028903525, 235.59353381516598)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(95.99661933070473, 140.29732671249275)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(90.41355788423651, 143.29088025148616)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(87.14905538013474, 216.6077807431059)\"><circle r=\"4.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t21a5dd5c238f4952bd165892793fa19f\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(125.03585473654582, 50.49233144264189)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(123.67211233420747, 58.171396082840985)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(123.72076778572136, 57.37802382831127)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(253.97711206744697, 223.7723511172799)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(227.7801408031851, 235.88646131075126)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(264.9005607076118, 226.64417976540403)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(181.63869588232444, 238.76181629307825)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(175.58214214225148, 239.82431618638844)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(200.4146982618473, 242.33560199167724)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.40356221433255, 241.25476351349727)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.553984976409716, 232.2264355279452)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.51489292277363, 240.55880101967534)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(61.85986307518597, 235.05142473332813)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(64.7983102062521, 228.25247993726154)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(70.56785352933241, 225.79938759927748)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.284940448135245, 234.70773705175282)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.458599992689386, 236.61718870263445)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(58.06998279513417, 240.26163571742254)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.768918957249994, 239.8097063578703)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(259.2019136053276, 226.07604389529712)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.37493047086088, 234.5403771675532)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(125.5655382765286, 123.84475909290379)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(114.34052823466256, 106.81693746876411)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(238.93290146536316, 220.65257207388566)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.28893719571905, 231.7635237255352)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(100.36384561715587, 140.3542750693236)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(95.59838578696329, 141.41900881892784)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(85.27630484311817, 211.75173964867074)\"><circle r=\"4.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"taac229afbe6341fcb2057d83feef2efe\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(145.08936512200017, 50.77402272165319)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(141.90523606462722, 59.69294494801625)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(142.83944168077463, 59.56930551405142)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(248.48588950192817, 237.3876390091811)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(223.71492384453026, 245.45985898901276)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(259.87031289905246, 240.0776364982376)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(175.11569203274675, 244.35033636730776)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(166.4583182172563, 244.85658990567703)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(195.21829453566104, 248.42220592476548)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(50.44802065383449, 234.55372150579598)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(54.696866858070976, 225.16134195542537)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(50.32377276520699, 231.60303534213278)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(59.05185730129753, 226.95007187449056)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(60.29373622521101, 221.64588139451917)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(65.90760079611907, 218.18377909342615)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(50.0, 227.6737469806992)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(57.454784434143406, 229.18366953620009)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.672370856781924, 233.69809214077313)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(52.976945975048785, 236.07621808525786)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(254.35015122843154, 240.2048808952828)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(53.67663336897598, 226.94266042044478)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(135.17977981082828, 125.24534237804403)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(127.51607545325484, 110.19685275935828)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(234.18206942960393, 233.0399159851825)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(56.92006253391561, 225.05997275250314)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(108.39705293869042, 138.13540026841446)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(104.58605693585689, 140.1910888343662)\"><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.09;opacity:1.0;stroke:none\" transform=\"translate(83.61496787048706, 210.6890630596804)\"><circle r=\"4.0\"></circle></g></g></g><g class=\"toyplot-mark-Point\" id=\"t69ace96a3a344205a908fba132548f6d\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(118.71289858207732, 51.49460530937557)\"><title>BJSB3</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(118.3545425288898, 58.810117943782835)\"><title>BJSL25</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(117.83720548316111, 58.644614393388906)\"><title>BJVL19</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(254.03391971363556, 220.36592603908565)\"><title>BZBB1</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(229.35925150170758, 231.80175930537416)\"><title>CRL0001</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(265.650742483354, 223.01430713198422)\"><title>CRL0030</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(183.1208200648088, 236.9888919103207)\"><title>CUCA4</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(175.0579519797971, 237.7283405810374)\"><title>CUSV6</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(200.94151242621945, 238.86363838860376)\"><title>CUVN10</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(58.06147885313305, 242.91259661869063)\"><title>FLAB109</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(58.49689507177247, 233.63451275978215)\"><title>FLBA140</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(58.4096574587701, 240.8807145086891)\"><title>FLCK18</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(64.16324656810758, 235.39033817396566)\"><title>FLCK216</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(65.97149985572612, 229.26707557879288)\"><title>FLMO62</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(69.6964735552338, 226.48258124596046)\"><title>FLSA185</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(55.786579681212864, 236.48668174461258)\"><title>FLSF33</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(62.291077148068425, 237.77531267111766)\"><title>FLSF47</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(59.933715414051704, 242.2874640877866)\"><title>FLSF54</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(59.27734234443297, 242.51240812988001)\"><title>FLWO6</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(261.3452769022091, 222.68316276995282)\"><title>HNDA09</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(58.50175146451305, 236.2334026649885)\"><title>LALC2</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(123.05225955669954, 124.43946142781888)\"><title>MXED8</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(112.7022512563913, 109.41751731778572)\"><title>MXGT4</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(238.84221301191073, 218.04331866049657)\"><title>MXSA3017</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(62.147195309259004, 232.5218687412774)\"><title>SCCU3</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(99.28239265469793, 139.65284682718433)\"><title>TXGR3</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(94.88048800335528, 142.39853681904538)\"><title>TXMD3</title><circle r=\"4.0\"></circle></g><g class=\"toyplot-Datum\" style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;opacity:1.0;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(86.03564046114012, 214.29327338911887)\"><title>TXWV2</title><circle r=\"4.0\"></circle></g></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t3015b920a291445f9afecc2f2fd4b16c\" transform=\"translate(50.0,250.0)translate(0,10.0)\"><line style=\"stroke-width:1.5\" x1=\"0\" x2=\"220.0\" y1=\"0\" y2=\"0\"></line><g><line style=\"\" x1=\"4.73974764298157\" x2=\"4.73974764298157\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"71.85522426194056\" x2=\"71.85522426194056\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"138.97070088089956\" x2=\"138.97070088089956\" y1=\"0\" y2=\"-5\"></line><line style=\"\" x1=\"206.08617749985856\" x2=\"206.08617749985856\" y1=\"0\" y2=\"-5\"></line></g><g><g transform=\"translate(4.73974764298157,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:13.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.3925\" y=\"11.1215\">-30</text></g><g transform=\"translate(71.85522426194056,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:13.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.6140000000000003\" y=\"11.1215\">0</text></g><g transform=\"translate(138.97070088089956,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:13.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-7.228000000000001\" y=\"11.1215\">30</text></g><g transform=\"translate(206.08617749985856,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:13.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-7.228000000000001\" y=\"11.1215\">60</text></g></g><g transform=\"translate(110.0,22.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:15.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-79.6125\" y=\"12.8325\">PC0 (15.9%) explained</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t7b3026a74a3348288ec887ad6caf0231\" transform=\"translate(50.0,250.0)rotate(-90.0)translate(0,-10.0)\"><line style=\"stroke-width:2.25\" x1=\"0\" x2=\"199.72561588542547\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(1.6654630885762154,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.67\" y=\"0.0\">-25</text></g><g transform=\"translate(51.24909731643217,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-3.3360000000000003\" y=\"0.0\">0</text></g><g transform=\"translate(100.83273154428811,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"0.0\">25</text></g><g transform=\"translate(150.41636577214405,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"0.0\">50</text></g><g transform=\"translate(200.0,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-6.672000000000001\" y=\"0.0\">75</text></g></g><g transform=\"translate(100.0,-22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:14.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-74.30499999999999\" y=\"0.0\">PC1 (15.1%) explained</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g><g class=\"toyplot-coordinates-Table\" id=\"t17f4199ce28e4f60bc5bdc2851db4eda\"><g transform=\"translate(310.0,75.0)\"><g style=\"fill:rgb(36.9%,31%,63.5%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"4.0\"></circle></g></g><g transform=\"translate(320.0,75.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">virg</text></g><g transform=\"translate(310.0,100.0)\"><g style=\"fill:rgb(33.2%,68.5%,67.8%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"4.0\"></circle></g></g><g transform=\"translate(320.0,100.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">mini</text></g><g transform=\"translate(310.0,125.0)\"><g style=\"fill:rgb(74.8%,89.8%,62.7%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"4.0\"></circle></g></g><g transform=\"translate(320.0,125.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">gemi</text></g><g transform=\"translate(310.0,150.0)\"><g style=\"fill:rgb(100%,100%,74.9%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"4.0\"></circle></g></g><g transform=\"translate(320.0,150.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">bran</text></g><g transform=\"translate(310.0,175.0)\"><g style=\"fill:rgb(99.3%,74.8%,43.5%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"4.0\"></circle></g></g><g transform=\"translate(320.0,175.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">fusi</text></g><g transform=\"translate(310.0,200.0)\"><g style=\"fill:rgb(91.6%,36.6%,27.8%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"4.0\"></circle></g></g><g transform=\"translate(320.0,200.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">sagr</text></g><g transform=\"translate(310.0,225.0)\"><g style=\"fill:rgb(62%,0.4%,25.9%);fill-opacity:0.75;stroke:rgb(14.9%,14.9%,14.9%);stroke-opacity:1.0;stroke-width:1.5\" transform=\"translate(-5.55, -4.440892098500626e-16)\"><circle r=\"4.0\"></circle></g></g><g transform=\"translate(320.0,225.0)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"0\" y=\"3.066\">oleo</text></g></g></svg><div class=\"toyplot-behavior\"><script>(function()\n",
"{\n",
"var modules={};\n",
"modules[\"toyplot/tables\"] = (function()\n",
" {\n",
" var tables = [];\n",
"\n",
" var module = {};\n",
"\n",
" module.set = function(owner, key, names, columns)\n",
" {\n",
" tables.push({owner: owner, key: key, names: names, columns: columns});\n",
" }\n",
"\n",
" module.get = function(owner, key)\n",
" {\n",
" for(var i = 0; i != tables.length; ++i)\n",
" {\n",
" var table = tables[i];\n",
" if(table.owner != owner)\n",
" continue;\n",
" if(table.key != key)\n",
" continue;\n",
" return {names: table.names, columns: table.columns};\n",
" }\n",
" }\n",
"\n",
" module.get_csv = function(owner, key)\n",
" {\n",
" var table = module.get(owner, key);\n",
" if(table != undefined)\n",
" {\n",
" var csv = \"\";\n",
" csv += table.names.join(\",\") + \"\\n\";\n",
" for(var i = 0; i != table.columns[0].length; ++i)\n",
" {\n",
" for(var j = 0; j != table.columns.length; ++j)\n",
" {\n",
" if(j)\n",
" csv += \",\";\n",
" csv += table.columns[j][i];\n",
" }\n",
" csv += \"\\n\";\n",
" }\n",
" return csv;\n",
" }\n",
" }\n",
"\n",
" return module;\n",
" })();\n",
"modules[\"toyplot/root/id\"] = \"t22221e628f4d439aa9df567ea6428071\";\n",
"modules[\"toyplot/root\"] = (function(root_id)\n",
" {\n",
" return document.querySelector(\"#\" + root_id);\n",
" })(modules[\"toyplot/root/id\"]);\n",
"modules[\"toyplot/canvas/id\"] = \"te70e45b27e95410f948a41f42edc4a76\";\n",
"modules[\"toyplot/canvas\"] = (function(canvas_id)\n",
" {\n",
" return document.querySelector(\"#\" + canvas_id);\n",
" })(modules[\"toyplot/canvas/id\"]);\n",
"modules[\"toyplot/menus/context\"] = (function(root, canvas)\n",
" {\n",
" var wrapper = document.createElement(\"div\");\n",
" wrapper.innerHTML = \"<ul class='toyplot-context-menu' style='background:#eee; border:1px solid #b8b8b8; border-radius:5px; box-shadow: 0px 0px 8px rgba(0%,0%,0%,0.25); margin:0; padding:3px 0; position:fixed; visibility:hidden;'></ul>\"\n",
" var menu = wrapper.firstChild;\n",
"\n",
" root.appendChild(menu);\n",
"\n",
" var items = [];\n",
"\n",
" var ignore_mouseup = null;\n",
" function open_menu(e)\n",
" {\n",
" var show_menu = false;\n",
" for(var index=0; index != items.length; ++index)\n",
" {\n",
" var item = items[index];\n",
" if(item.show(e))\n",
" {\n",
" item.item.style.display = \"block\";\n",
" show_menu = true;\n",
" }\n",
" else\n",
" {\n",
" item.item.style.display = \"none\";\n",
" }\n",
" }\n",
"\n",
" if(show_menu)\n",
" {\n",
" ignore_mouseup = true;\n",
" menu.style.left = (e.clientX + 1) + \"px\";\n",
" menu.style.top = (e.clientY - 5) + \"px\";\n",
" menu.style.visibility = \"visible\";\n",
" e.stopPropagation();\n",
" e.preventDefault();\n",
" }\n",
" }\n",
"\n",
" function close_menu()\n",
" {\n",
" menu.style.visibility = \"hidden\";\n",
" }\n",
"\n",
" function contextmenu(e)\n",
" {\n",
" open_menu(e);\n",
" }\n",
"\n",
" function mousemove(e)\n",
" {\n",
" ignore_mouseup = false;\n",
" }\n",
"\n",
" function mouseup(e)\n",
" {\n",
" if(ignore_mouseup)\n",
" {\n",
" ignore_mouseup = false;\n",
" return;\n",
" }\n",
" close_menu();\n",
" }\n",
"\n",
" function keydown(e)\n",
" {\n",
" if(e.key == \"Escape\" || e.key == \"Esc\" || e.keyCode == 27)\n",
" {\n",
" close_menu();\n",
" }\n",
" }\n",
"\n",
" canvas.addEventListener(\"contextmenu\", contextmenu);\n",
" canvas.addEventListener(\"mousemove\", mousemove);\n",
" document.addEventListener(\"mouseup\", mouseup);\n",
" document.addEventListener(\"keydown\", keydown);\n",
"\n",
" var module = {};\n",
" module.add_item = function(label, show, activate)\n",
" {\n",
" var wrapper = document.createElement(\"div\");\n",
" wrapper.innerHTML = \"<li class='toyplot-context-menu-item' style='background:#eee; color:#333; padding:2px 20px; list-style:none; margin:0; text-align:left;'>\" + label + \"</li>\"\n",
" var item = wrapper.firstChild;\n",
"\n",
" items.push({item: item, show: show});\n",
"\n",
" function mouseover()\n",
" {\n",
" this.style.background = \"steelblue\";\n",
" this.style.color = \"white\";\n",
" }\n",
"\n",
" function mouseout()\n",
" {\n",
" this.style.background = \"#eee\";\n",
" this.style.color = \"#333\";\n",
" }\n",
"\n",
" function choose_item(e)\n",
" {\n",
" close_menu();\n",
" activate();\n",
"\n",
" e.stopPropagation();\n",
" e.preventDefault();\n",
" }\n",
"\n",
" item.addEventListener(\"mouseover\", mouseover);\n",
" item.addEventListener(\"mouseout\", mouseout);\n",
" item.addEventListener(\"mouseup\", choose_item);\n",
" item.addEventListener(\"contextmenu\", choose_item);\n",
"\n",
" menu.appendChild(item);\n",
" };\n",
" return module;\n",
" })(modules[\"toyplot/root\"],modules[\"toyplot/canvas\"]);\n",
"modules[\"toyplot/io\"] = (function()\n",
" {\n",
" var module = {};\n",
" module.save_file = function(mime_type, charset, data, filename)\n",
" {\n",
" var uri = \"data:\" + mime_type + \";charset=\" + charset + \",\" + data;\n",
" uri = encodeURI(uri);\n",
"\n",
" var link = document.createElement(\"a\");\n",
" if(typeof link.download != \"undefined\")\n",
" {\n",
" link.href = uri;\n",
" link.style = \"visibility:hidden\";\n",
" link.download = filename;\n",
"\n",
" document.body.appendChild(link);\n",
" link.click();\n",
" document.body.removeChild(link);\n",
" }\n",
" else\n",
" {\n",
" window.open(uri);\n",
" }\n",
" };\n",
" return module;\n",
" })();\n",
"modules[\"toyplot.coordinates.Axis\"] = (\n",
" function(canvas)\n",
" {\n",
" function sign(x)\n",
" {\n",
" return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
" }\n",
"\n",
" function mix(a, b, amount)\n",
" {\n",
" return ((1.0 - amount) * a) + (amount * b);\n",
" }\n",
"\n",
" function log(x, base)\n",
" {\n",
" return Math.log(Math.abs(x)) / Math.log(base);\n",
" }\n",
"\n",
" function in_range(a, x, b)\n",
" {\n",
" var left = Math.min(a, b);\n",
" var right = Math.max(a, b);\n",
" return left <= x && x <= right;\n",
" }\n",
"\n",
" function inside(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(in_range(segment.range.min, range, segment.range.max))\n",
" return true;\n",
" }\n",
" return false;\n",
" }\n",
"\n",
" function to_domain(range, projection)\n",
" {\n",
" for(var i = 0; i != projection.length; ++i)\n",
" {\n",
" var segment = projection[i];\n",
" if(in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n",
" {\n",
" if(segment.scale == \"linear\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" return mix(segment.domain.min, segment.domain.max, amount)\n",
" }\n",
" else if(segment.scale[0] == \"log\")\n",
" {\n",
" var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
" var base = segment.scale[1];\n",
" return sign(segment.domain.min) * Math.pow(base, mix(log(segment.domain.min, base), log(segment.domain.max, base), amount));\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" var axes = {};\n",
"\n",
" function display_coordinates(e)\n",
" {\n",
" var current = canvas.createSVGPoint();\n",
" current.x = e.clientX;\n",
" current.y = e.clientY;\n",
"\n",
" for(var axis_id in axes)\n",
" {\n",
" var axis = document.querySelector(\"#\" + axis_id);\n",
" var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
" if(coordinates)\n",
" {\n",
" var projection = axes[axis_id];\n",
" var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
" if(inside(local.x, projection))\n",
" {\n",
" var domain = to_domain(local.x, projection);\n",
" coordinates.style.visibility = \"visible\";\n",
" coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
" var text = coordinates.querySelector(\"text\");\n",
" text.textContent = domain.toFixed(2);\n",
" }\n",
" else\n",
" {\n",
" coordinates.style.visibility= \"hidden\";\n",
" }\n",
" }\n",
" }\n",
" }\n",
"\n",
" canvas.addEventListener(\"click\", display_coordinates);\n",
"\n",
" var module = {};\n",
" module.show_coordinates = function(axis_id, projection)\n",
" {\n",
" axes[axis_id] = projection;\n",
" }\n",
"\n",
" return module;\n",
" })(modules[\"toyplot/canvas\"]);\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t90eef0f95d54411d9ed5eeae78434e21\",\"data\",\"point\",[\"x\", \"y0\"],[[-4.610127750325358, -4.897032188230854, -5.210652395667008, 59.008192192554645, 48.87347525839454, 65.3785478109348, 28.689133625198195, 25.50742383938469, 36.07067194838007, -27.346919598920994, -27.254882347852732, -27.44179045644773, -25.219019311889177, -24.339494315373816, -23.00217928709274, -29.097669860971383, -25.577733153926363, -26.729134876791306, -27.152276655347933, 63.228326259760934, -27.998572714413733, -0.8653215384622438, -6.111563934230512, 53.25614558763018, -26.267820593556806, -11.572611359669466, -13.721879932622791, -15.595234250445131], [74.15054262182838, 70.81470367108803, 70.61064944474462, -7.890818940569352, -14.31247153647793, -10.157801630641256, -17.75576667674344, -18.771769612816435, -18.435439700740318, -23.410650304083166, -18.52440658995212, -22.547132254307826, -19.69352328575764, -17.003790139212306, -14.798079899705932, -20.066990864215352, -21.277158591116127, -23.26229052220017, -23.354526606927855, -9.391939523445874, -20.481201322940453, 37.4626699176883, 44.64556162267543, -7.712722941437888, -17.77210315940494, 29.69895326685749, 28.55895291050561, -9.321449352691499]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t0f6446f78275444d8ecd1badb043b64b\",\"data\",\"point\",[\"x\", \"y0\"],[[-11.089187245734252, -11.060352884427575, -11.527331415856638, 60.93133297074647, 49.803388360872866, 65.39909468578519, 29.842471759920446, 26.560157823073997, 38.01600571902808, -25.060515455962914, -25.67900748548877, -24.919381458314607, -23.42365508029597, -23.040538858627063, -22.056285929959742, -26.606054713806277, -24.339901986986916, -24.34667506238108, -25.542094539543488, 64.11799020221366, -25.286826182036357, -4.424924964036719, -10.106610533326446, 53.17338279399298, -24.502440435479063, -13.659248222405436, -16.430125579209534, -14.742666281754786], [73.17218783489146, 69.6384486347577, 69.45471183004955, -3.3185509548364935, -10.110277426605894, -4.201525391433913, -15.849869743200207, -16.386504863263905, -15.676179453344798, -25.58648893708987, -21.23379964601822, -25.196684867887285, -21.46776736920966, -18.617533098511235, -16.59185243448275, -22.657367237098534, -22.758001013443916, -25.197845986296212, -25.839724192524276, -4.33886317723385, -22.484032177822094, 37.91175952950113, 44.60995361048628, -2.7949562489799593, -19.74170314165389, 28.038388671645254, 26.867199891591582, -9.643122641986015]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t02bad34e70b4459fbb5853a0127d6c9e\",\"data\",\"point\",[\"x\", \"y0\"],[[-5.501585175434793, -4.797354930113427, -4.9644333348851495, 59.17651240082409, 49.15050534126175, 64.72964029538429, 28.636921864737438, 24.52379976802538, 36.81854420569781, -27.505614651793888, -27.95910524541708, -27.144918002924637, -24.7292963165801, -23.724885032040333, -22.230394414977443, -28.848111262670535, -24.987676378507018, -26.258519069868523, -26.391545318194183, 63.14270541046964, -27.49422649970108, -2.2706466291539593, -6.393277426329369, 52.602513440584175, -25.6996788665699, -11.979343029147808, -13.278133662153282, -16.622397480522064], [73.98883469925674, 71.0924781404512, 70.43190429416981, -8.0350129203455, -14.24583301552057, -9.093740652285335, -17.786234356952345, -18.87971737027347, -18.55485085105459, -23.925051545788023, -19.567014294524203, -22.538228735174304, -20.322564915792984, -16.343484598154802, -15.017862969044929, -21.23645210433261, -21.045928979624207, -23.46370269698109, -22.869258596492028, -8.903006001950258, -20.82802727321983, 37.619257475138284, 45.212251053652096, -7.440086806618477, -18.69155899867006, 29.48911523873148, 28.353212633662363, -7.3994358522623855]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t34cb7a17726a47a493546d4c774724d1\",\"data\",\"point\",[\"x\", \"y0\"],[[3.7755184781705395, 4.325821265080253, 3.3077531407135896, 58.84742853010361, 46.46074290462791, 63.42407252878464, 26.012155296424144, 21.60636079389018, 33.73908106120604, -30.823330982601412, -29.512742370842904, -30.5946530752349, -27.25615806394358, -26.19973322415822, -24.343374241968117, -29.74077679219534, -29.025042318578368, -29.91376533615895, -30.691954621024205, 61.60745687270855, -29.15300342432119, 3.663914541808177, -0.9168239403884195, 51.81606906511842, -27.438120219277618, -7.153337231915168, -9.807757199998434, -16.015801436029257], [74.71401907763965, 71.09053360276414, 70.38954893033377, -14.890513309109846, -20.064781753808248, -16.963430208142217, -22.017395434700916, -21.331770469345248, -22.524035225948193, -19.93627906942967, -16.005111139003215, -19.256008799673122, -16.511092253937157, -13.524680650861486, -12.473369131561357, -16.134102284826323, -17.27615156068273, -19.948282315887344, -19.781507258899158, -16.87791200876548, -16.611022472356257, 37.11901299855254, 44.34647980074543, -13.107675484339795, -15.064894350354901, 31.57973043669853, 28.285671921217656, -7.224981586319024]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tc637d22e84f748e89c8461a8162db262\",\"data\",\"point\",[\"x\", \"y0\"],[[-4.302052295647097, -4.679542679652327, -4.565848298015097, 58.90174335988286, 48.664807815608796, 64.65224255248158, 29.053363247967702, 24.573734314739205, 35.843528039302115, -28.103868726099506, -28.410218255884782, -27.478128648956528, -24.9233627314909, -24.636860880451, -23.44144198428626, -28.84816600936544, -25.484740593083924, -26.595335429090316, -26.472990953457778, 63.30568045327531, -27.37829084560639, -0.5216965265086178, -6.2432892480224735, 52.52695643534732, -26.205115558682632, -10.999833711964182, -12.417829727014515, -15.813443115325137], [73.44267914919621, 70.28127276407417, 71.04450522127232, -8.949057227555201, -15.139095920520502, -9.653229488762632, -18.32706605242362, -18.641439506247398, -19.544984953394238, -23.538954610630938, -18.861939030749287, -22.647435300455605, -19.257281596305564, -16.513812784580765, -15.54673538500299, -20.13353856908715, -20.544499450384826, -23.190137220389733, -22.578822137038408, -9.88901971720836, -19.685989670251107, 38.277633107013834, 45.599670483440995, -7.291536815165409, -18.1072098515139, 29.989737196081705, 28.110408012953815, -8.704120646365379]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t5211e3cb15374803ad7ec184b787b3a9\",\"data\",\"point\",[\"x\", \"y0\"],[[1.0229760497338496, 1.3053084121460712, 0.8356926190529742, 59.458641079460804, 47.72259964445879, 64.10383682301061, 26.40166697715163, 22.906075529874904, 34.6023711760987, -29.285346888452665, -29.08399103054615, -29.60837705788379, -26.353480533736263, -25.605554982371952, -24.12871824956464, -29.92116111116099, -27.695213412325746, -28.666791307827904, -29.199884717806643, 62.22840091868602, -29.612502024687913, 2.0992876119940735, -2.0551763606678866, 51.768245755274684, -27.202494676724253, -9.191624570838083, -11.210274374131032, -15.634511298217188], [74.23612329557778, 70.5921540261063, 71.1036399924946, -13.250187524368602, -18.433208342485948, -14.252140651206137, -20.462191316568777, -20.670731989111033, -21.149486549128866, -21.388683636549505, -16.399722233837547, -20.339005667370227, -17.275123764332488, -14.579801007021596, -13.63789701529596, -16.785149684123866, -18.876213559209802, -21.143854821017886, -21.79538286010545, -14.205535203299728, -18.06547258645195, 36.84498011567118, 45.36911983555679, -11.714318031900728, -15.982192234848064, 29.881401705058217, 29.281035087839154, -6.902155380069777]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t3413f80ebe8342e086022f8a585d1499\",\"data\",\"point\",[\"x\", \"y0\"],[[0.6459273566844087, 0.052980303451650515, 0.30450572456334624, 58.47979975954235, 47.50567849202539, 63.214517674617966, 26.841182788165284, 23.17257109056895, 34.25353312865155, -28.9120318150985, -28.727817249611437, -29.407428959791588, -26.20900063413997, -25.53872281265302, -23.49331191608251, -30.010891809230984, -26.992470862599852, -29.0760519883831, -28.454369901204892, 61.74375265734825, -28.649628389004594, 2.0969623843361047, -1.9209838398314487, 52.13005817804988, -26.958869976765342, -9.161795212311421, -10.198727462181601, -16.72936670911493], [74.53667207837421, 70.76022947516397, 70.92605438320372, -13.164056048976672, -18.08587034150237, -14.518208306391996, -19.825261496132754, -19.789740471726414, -21.148608990223227, -21.357356275895366, -16.337150343439795, -20.42859833276357, -17.52683785760979, -14.343786233735408, -13.001552388997409, -19.0125718441234, -18.81523248698896, -21.035029217003068, -21.63396863341711, -13.825868034433135, -17.845017308266343, 37.566188060474836, 45.221619652640655, -11.389709310544967, -16.42716475180664, 29.81629189493625, 28.27606507182927, -7.5915319426445595]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tdcc535d6c0054399a1397ebc5c255391\",\"data\",\"point\",[\"x\", \"y0\"],[[-5.7945353779261906, -5.671855154074227, -6.35349269627018, 60.3629379619553, 49.47409420917244, 66.2193505288515, 27.845075976443173, 25.008560645316265, 36.256711693059785, -26.93988661013104, -27.81690384992917, -26.71471848709299, -24.873954256830302, -23.687978395897918, -22.516051565639323, -29.479016661207318, -25.91334456873136, -26.208646143087957, -26.378308403277796, 63.51665966705537, -27.866848646519426, -2.0413671694837827, -6.336569608821745, 53.105986459663704, -26.06986804430889, -11.558558279208233, -14.054135332843174, -15.513337890236446], [74.86165590781746, 70.31686262002522, 70.99777942814703, -7.388083784872224, -13.976136162248114, -8.595040261599328, -17.607010243013697, -18.09644772054873, -18.19472664949063, -24.03706754237652, -18.759113112714576, -21.821351610236317, -20.16068205206253, -16.516963135651373, -15.319553376462476, -21.52486053593151, -21.731063083611495, -23.719620353180833, -23.270551943899015, -8.555644490080194, -20.72623614408179, 37.046784385723825, 44.41123268092866, -7.489824942839255, -18.576003809186336, 29.47221239494987, 27.962866828811627, -9.003413292316784]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t21a5dd5c238f4952bd165892793fa19f\",\"data\",\"point\",[\"x\", \"y0\"],[[1.4217125325636706, 0.8121322370615376, 0.8338807758330716, 59.05726717353782, 47.34746225939301, 63.9399481245418, 26.722661284131902, 24.01543753552095, 35.11536144453825, -29.25629020823448, -28.742061827524964, -28.312544824247446, -26.817373969064644, -25.50391516086061, -22.924982425642344, -30.65028541916159, -27.443725663079473, -28.511415554242582, -28.645988317359546, 61.39270534715487, -29.269088333904165, 1.658476197220326, -3.3589999233449093, 52.33264357256316, -27.519563370945384, -9.606448345796478, -11.736565006033272, -16.350440134618427], [74.75176716556342, 70.87999336369221, 71.2800105603361, -12.615779794764961, -18.723697448502442, -14.063751838790097, -20.173447465369726, -20.70915844634967, -21.97534528621927, -21.43038801603759, -16.878317496123998, -21.07948468641088, -18.302673157712377, -14.874654567534602, -13.637808793629068, -18.129386302620247, -19.092129191789567, -20.929654350817067, -20.70179219095831, -13.777298516562794, -18.045003679802345, 37.76757430004092, 46.35297848092965, -11.042791503377492, -16.64491800372157, 29.443499111969683, 28.906661823726857, -6.555004069164752]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"taac229afbe6341fcb2057d83feef2efe\",\"data\",\"point\",[\"x\", \"y0\"],[[10.385447007388025, 8.962170640544715, 9.37975194811015, 56.6027412539673, 45.53034771438218, 61.691473676337544, 23.806938631988046, 19.93717225992973, 32.79261683124066, -31.9183624427699, -30.019167315977228, -31.973900104820444, -28.072526691810218, -27.517418248955472, -25.00807248235375, -32.1186235493302, -28.786403556406444, -30.4771075942407, -30.78795611239159, 59.22401224327893, -30.475202290542303, 5.955953628044605, 2.530348353235943, 50.209065401734584, -29.0254193216957, -6.01567864875227, -7.7191587675645446, -17.09304246257171], [74.60973881114873, 70.1128305241436, 70.17516935624526, -19.480589173870626, -23.550591355808674, -20.836882198245828, -22.991171580018168, -23.24642391592142, -25.044202595628043, -18.051731876742014, -13.316107059887619, -16.56399998212954, -14.217982218354884, -11.543616693030241, -9.798029487187627, -14.582858208930254, -15.344159079174139, -17.62032473084237, -18.819372552527003, -20.901038647761244, -14.214245373445664, 37.0614021391299, 44.64882985244157, -17.288473220843084, -13.26499684756616, 30.56225272667679, 29.52577738659528, -6.019203998466563]],\"toyplot\");\n",
"(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
" {\n",
" tables.set(owner_id, key, names, columns);\n",
"\n",
" var owner = document.querySelector(\"#\" + owner_id);\n",
" function show_item(e)\n",
" {\n",
" return owner.contains(e.target);\n",
" }\n",
"\n",
" function choose_item()\n",
" {\n",
" io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
" }\n",
"\n",
" context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
" })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t69ace96a3a344205a908fba132548f6d\",\"data\",\"point\",[\"x\", \"y0\"],[[-1.4045906420527197, -1.5647724978214177, -1.7960173932420944, 59.082659668257534, 48.053310200019766, 64.27527247007299, 27.385157145212794, 23.781129360032427, 35.350842524720306, -28.51521673800653, -28.32058969790752, -28.35958410757147, -25.787782758978114, -24.97951019113894, -23.31448124975669, -29.532075718910004, -26.624625249422547, -27.678344236207245, -27.971736953960807, 62.35076900319515, -28.318418935073716, 0.5350637535757964, -4.091294646172726, 52.292106668995906, -26.688939106400557, -10.089847861200855, -12.05745870437522, -16.011024105883507], [74.24642206412942, 70.55795068222665, 70.64139734409969, -10.898264967926947, -16.66419633034807, -12.233575062749875, -19.279541436512364, -19.652370436560368, -20.224786025517215, -22.26626518146227, -17.588268094625057, -21.241793023640867, -18.473552847107506, -15.38621229082938, -13.98227408813705, -19.026327763528926, -19.676053699602576, -21.951074221461575, -22.064490697278863, -12.066612532074092, -18.89862480086378, 37.46772620289347, 45.04176970734976, -9.727209530604705, -17.027274514872644, 29.797158264360526, 28.41278515687332, -7.836441876228673]],\"toyplot\");\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"t3015b920a291445f9afecc2f2fd4b16c\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 66.2193505288515, \"min\": -32.1186235493302}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 220.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"(function(axis, axis_id, projection)\n",
" {\n",
" axis.show_coordinates(axis_id, projection);\n",
" })(modules[\"toyplot.coordinates.Axis\"],\"t7b3026a74a3348288ec887ad6caf0231\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 75.0, \"min\": -25.839724192524276}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 200.0, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
"})();</script></div></div>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# get plot objects, several styling options to draw\n",
"canvas, axes = tool.draw(imap=IMAP, size=8, width=400);\n",
"\n",
"# various axes styling options shown for x axis\n",
"axes.x.ticks.show = True\n",
"axes.x.spine.style['stroke-width'] = 1.5\n",
"axes.x.ticks.labels.style['font-size'] = '13px'\n",
"axes.x.label.style['font-size'] = \"15px\"\n",
"axes.x.label.offset = \"22px\""
]
}
],
"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": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
patrick-kidger/diffrax | examples/symbolic_regression.ipynb | 1 | 59208 | {
"cells": [
{
"cell_type": "markdown",
"id": "2ce28f2b-008b-4beb-ba51-5c478e4bdec7",
"metadata": {},
"source": [
"# Symbolic Regression"
]
},
{
"cell_type": "markdown",
"id": "d01a09d3-f78c-4b46-acd9-3a4250bb34c9",
"metadata": {},
"source": [
"This example combines neural differential equations with regularised evolution to discover the equations\n",
"\n",
"$\\frac{\\mathrm{d} x}{\\mathrm{d} t}(t) = \\frac{y(t)}{1 + y(t)}$\n",
"\n",
"$\\frac{\\mathrm{d} y}{\\mathrm{d} t}(t) = \\frac{-x(t)}{1 + x(t)}$\n",
"\n",
"directly from data.\n",
"\n",
"**References:**\n",
"\n",
"This example appears as an example in:\n",
"\n",
"```bibtex\n",
"@phdthesis{kidger2021on,\n",
" title={{O}n {N}eural {D}ifferential {E}quations},\n",
" author={Patrick Kidger},\n",
" year={2021},\n",
" school={University of Oxford},\n",
"}\n",
"```\n",
"\n",
"Whilst drawing heavy inspiration from:\n",
"\n",
"```bibtex\n",
"@inproceedings{cranmer2020discovering,\n",
" title={{D}iscovering {S}ymbolic {M}odels from {D}eep {L}earning with {I}nductive\n",
" {B}iases},\n",
" author={Cranmer, Miles and Sanchez Gonzalez, Alvaro and Battaglia, Peter and\n",
" Xu, Rui and Cranmer, Kyle and Spergel, David and Ho, Shirley},\n",
" booktitle={Advances in Neural Information Processing Systems},\n",
" publisher={Curran Associates, Inc.},\n",
" year={2020},\n",
"}\n",
"\n",
"@software{cranmer2020pysr,\n",
" title={PySR: Fast \\& Parallelized Symbolic Regression in Python/Julia},\n",
" author={Miles Cranmer},\n",
" publisher={Zenodo},\n",
" url={http://doi.org/10.5281/zenodo.4041459},\n",
" year={2020},\n",
"}\n",
"```\n",
"\n",
"This example is available as a Jupyter notebook [here](https://github.com/patrick-kidger/diffrax/blob/main/examples/symbolic_regression.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "dea04fa4-a95b-47f8-b0eb-be297037bc7d",
"metadata": {},
"outputs": [],
"source": [
"import tempfile\n",
"from typing import List\n",
"\n",
"import equinox as eqx # https://github.com/patrick-kidger/equinox\n",
"import jax\n",
"import jax.numpy as jnp\n",
"import optax # https://github.com/deepmind/optax\n",
"import pysr # https://github.com/MilesCranmer/PySR\n",
"import sympy\n",
"\n",
"\n",
"# Note that PySR, which we use for symbolic regression, uses Julia as a backend.\n",
"# You'll need to install a recent version of Julia if you don't have one.\n",
"# (And can get funny errors if you have a too-old version of Julia already.)\n",
"# You may also need to restart Python after running `pysr.install()` the first time.\n",
"pysr.silence_julia_warning()\n",
"pysr.install(quiet=True)"
]
},
{
"cell_type": "markdown",
"id": "4d26c41f-7682-4ad0-aa33-77e22b2768f8",
"metadata": {},
"source": [
"Now for a bunch of helpers. We'll use these in a moment; skip over them for now."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0d294688-3ea9-43ce-855f-cc587de908a5",
"metadata": {},
"outputs": [],
"source": [
"def quantise(expr, quantise_to):\n",
" if isinstance(expr, sympy.Float):\n",
" return expr.func(round(float(expr) / quantise_to) * quantise_to)\n",
" elif isinstance(expr, sympy.Symbol):\n",
" return expr\n",
" else:\n",
" return expr.func(*[quantise(arg, quantise_to) for arg in expr.args])\n",
"\n",
"\n",
"class SymbolicFn(eqx.Module):\n",
" fn: callable\n",
" parameters: jnp.ndarray\n",
"\n",
" def __call__(self, x):\n",
" # Dummy batch/unbatching. PySR assumes its JAX'd symbolic functions act on\n",
" # tensors with a single batch dimension.\n",
" return jnp.squeeze(self.fn(x[None], self.parameters))\n",
"\n",
"\n",
"class Stack(eqx.Module):\n",
" modules: List[eqx.Module]\n",
"\n",
" def __call__(self, x):\n",
" return jnp.stack([module(x) for module in self.modules], axis=-1)\n",
"\n",
"\n",
"def expr_size(expr):\n",
" return sum(expr_size(v) for v in expr.args) + 1\n",
"\n",
"\n",
"def _replace_parameters(expr, parameters, i_ref):\n",
" if isinstance(expr, sympy.Float):\n",
" i_ref[0] += 1\n",
" return expr.func(parameters[i_ref[0]])\n",
" elif isinstance(expr, sympy.Symbol):\n",
" return expr\n",
" else:\n",
" return expr.func(\n",
" *[_replace_parameters(arg, parameters, i_ref) for arg in expr.args]\n",
" )\n",
"\n",
"\n",
"def replace_parameters(expr, parameters):\n",
" i_ref = [-1] # Distinctly sketchy approach to making this conversion.\n",
" return _replace_parameters(expr, parameters, i_ref)"
]
},
{
"cell_type": "markdown",
"id": "35245e23-17e0-4c7e-bb60-3e462b9b3b3c",
"metadata": {},
"source": [
"Okay, let's get started.\n",
"\n",
"We start by running the [Neural ODE example](./neural_ode.ipynb).\n",
"Then we extract the learnt neural vector field, and symbolically regress across this.\n",
"Finally we fine-tune the resulting symbolic expression.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b0525ef3-979f-4cf5-b15f-530eae7b8ae0",
"metadata": {},
"outputs": [],
"source": [
"def main(\n",
" symbolic_dataset_size=2000,\n",
" symbolic_num_populations=100,\n",
" symbolic_population_size=20,\n",
" symbolic_migration_steps=4,\n",
" symbolic_mutation_steps=30,\n",
" symbolic_descent_steps=50,\n",
" pareto_coefficient=2,\n",
" fine_tuning_steps=500,\n",
" fine_tuning_lr=3e-3,\n",
" quantise_to=0.01,\n",
"):\n",
" #\n",
" # First obtain a neural approximation to the dynamics.\n",
" # We begin by running the previous example.\n",
" #\n",
"\n",
" # Runs the Neural ODE example.\n",
" # This defines the variables `ts`, `ys`, `model`.\n",
" print(\"Training neural differential equation.\")\n",
" %run neural_ode.ipynb\n",
"\n",
" #\n",
" # Now symbolically regress across the learnt vector field, to obtain a Pareto\n",
" # frontier of symbolic equations, that trades loss against complexity of the\n",
" # equation. Select the \"best\" from this frontier.\n",
" #\n",
"\n",
" print(\"Symbolically regressing across the vector field.\")\n",
" vector_field = model.func.mlp # noqa: F821\n",
" dataset_size, length_size, data_size = ys.shape # noqa: F821\n",
" in_ = ys.reshape(dataset_size * length_size, data_size) # noqa: F821\n",
" in_ = in_[:symbolic_dataset_size]\n",
" out = jax.vmap(vector_field)(in_)\n",
" with tempfile.TemporaryDirectory() as tempdir:\n",
" symbolic_regressor = pysr.PySRRegressor(\n",
" niterations=symbolic_migration_steps,\n",
" ncyclesperiteration=symbolic_mutation_steps,\n",
" populations=symbolic_num_populations,\n",
" npop=symbolic_population_size,\n",
" optimizer_iterations=symbolic_descent_steps,\n",
" optimizer_nrestarts=1,\n",
" procs=1,\n",
" verbosity=0,\n",
" tempdir=tempdir,\n",
" temp_equation_file=True,\n",
" output_jax_format=True,\n",
" )\n",
" symbolic_regressor.fit(in_, out)\n",
" best_equations = symbolic_regressor.get_best()\n",
" expressions = [b.sympy_format for b in best_equations]\n",
" symbolic_fns = [\n",
" SymbolicFn(b.jax_format[\"callable\"], b.jax_format[\"parameters\"])\n",
" for b in best_equations\n",
" ]\n",
"\n",
" #\n",
" # Now the constants in this expression have been optimised for regressing across\n",
" # the neural vector field. This was good enough to obtain the symbolic expression,\n",
" # but won't quite be perfect -- some of the constants will be slightly off.\n",
" #\n",
" # To fix this we now plug our symbolic function back into the original dataset\n",
" # and apply gradient descent.\n",
" #\n",
"\n",
" print(\"Optimising symbolic expression.\")\n",
"\n",
" symbolic_fn = Stack(symbolic_fns)\n",
" flat, treedef = jax.tree_flatten(\n",
" model, is_leaf=lambda x: x is model.func.mlp # noqa: F821\n",
" )\n",
" flat = [symbolic_fn if f is model.func.mlp else f for f in flat] # noqa: F821\n",
" symbolic_model = jax.tree_unflatten(treedef, flat)\n",
"\n",
" @eqx.filter_grad\n",
" def grad_loss(symbolic_model):\n",
" vmap_model = jax.vmap(symbolic_model, in_axes=(None, 0))\n",
" pred_ys = vmap_model(ts, ys[:, 0]) # noqa: F821\n",
" return jnp.mean((ys - pred_ys) ** 2) # noqa: F821\n",
"\n",
" optim = optax.adam(fine_tuning_lr)\n",
" opt_state = optim.init(eqx.filter(symbolic_model, eqx.is_inexact_array))\n",
"\n",
" @eqx.filter_jit\n",
" def make_step(symbolic_model, opt_state):\n",
" grads = grad_loss(symbolic_model)\n",
" updates, opt_state = optim.update(grads, opt_state)\n",
" symbolic_model = eqx.apply_updates(symbolic_model, updates)\n",
" return symbolic_model, opt_state\n",
"\n",
" for _ in range(fine_tuning_steps):\n",
" symbolic_model, opt_state = make_step(symbolic_model, opt_state)\n",
"\n",
" #\n",
" # Finally we round each constant to the nearest multiple of `quantise_to`.\n",
" #\n",
"\n",
" trained_expressions = []\n",
" for module, expression in zip(symbolic_model.func.mlp.modules, expressions):\n",
" expression = replace_parameters(expression, module.parameters.tolist())\n",
" expression = quantise(expression, quantise_to)\n",
" trained_expressions.append(expression)\n",
"\n",
" print(f\"Expressions found: {trained_expressions}\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "042fd565-825a-40fb-a4da-25e3e0da106a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training neural differential equation.\n",
"Step: 0, Loss: 0.1665748506784439, Computation time: 24.18653130531311\n",
"Step: 100, Loss: 0.011155527085065842, Computation time: 0.09058809280395508\n",
"Step: 200, Loss: 0.006481727119535208, Computation time: 0.0928184986114502\n",
"Step: 300, Loss: 0.001382559770718217, Computation time: 0.09850335121154785\n",
"Step: 400, Loss: 0.001073717838153243, Computation time: 0.09830045700073242\n",
"Step: 499, Loss: 0.0007992316968739033, Computation time: 0.09975647926330566\n",
"Step: 0, Loss: 0.02832634374499321, Computation time: 24.61294913291931\n",
"Step: 100, Loss: 0.005440382286906242, Computation time: 0.40324854850769043\n",
"Step: 200, Loss: 0.004360489547252655, Computation time: 0.43680524826049805\n",
"Step: 300, Loss: 0.001799552352167666, Computation time: 0.4346010684967041\n",
"Step: 400, Loss: 0.0017023109830915928, Computation time: 0.437793493270874\n",
"Step: 499, Loss: 0.0011540694395080209, Computation time: 0.42920470237731934\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Symbolically regressing across the vector field.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Optimising symbolic expression.\n",
"Expressions found: [x1/(x1 + 1.0), x0/(-x0 - 1.0)]\n"
]
}
],
"source": [
"main()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "jax0227",
"language": "python",
"name": "jax0227"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| apache-2.0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.