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lucianocaciato/IA369Z_2017S1 | deliver/21062017_LucianoECaciato_Workflow.ipynb | 1 | 47855 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
" # Workflow - Reprodutibilidade em Pesquisa Computacional"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Objetivo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" Este workflow tem o objetivo de demonstrar a sequência de passos necessários para automatizar a busca de informações na geração de gráfico. "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 2. Funcionamento"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Utilizando os dados do banco de dados local ou diretamente da Internet será possível gerar uma quantidade de informações que serão extraídas e ajustadas para resultar no gráfico."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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/AATQ/e/szapdDmPUPiP49voz6xz+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\nw717xRrEwt9I8N6dcapfSn/llBBE0kjfgimtyvm/9u+5/wCF1XfhP4B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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"Image(filename='figures/WorkFlow_LucianoECaciato.jpg')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Resultado"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Com essas informações o administrador da nuvem poderá tomar medidas para sua melhoria."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
dedert/Brand2Vec | ParamSearch.ipynb | 1 | 56009 | {
"cells": [
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from parameter_search import util\n",
"import numpy as np\n",
"import time\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from pandas import DataFrame\n",
"import csv\n",
"import gensim\n",
"from gensim.models.doc2vec import TaggedDocument\n",
"from gensim.models import Doc2Vec\n",
"from gensim.models import Phrases\n",
"assert gensim.models.doc2vec.FAST_VERSION == 1, \"this will be painfully slow otherwise\"\n",
"import nltk, re, random\n",
"import datetime\n",
"import multiprocessing\n",
"from ast import literal_eval\n",
"import pickle\n",
"from tqdm import tqdm\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.cross_validation import KFold\n",
"from sklearn import linear_model\n",
"import datetime\n",
"from scipy.cluster.hierarchy import dendrogram, linkage\n",
"#%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_path = \"E:/dataset/Amazon/\"\n",
"save_path = \"E:/dataset/MasterThesis/FINAL/preprocess_data/\"\n",
"model_path = \"E:/dataset/MasterThesis/FINAL/doc2vec/\"\n",
"category_list = [\"Electronics\",\"Beauty\",\"Clothing_Shoes_and_Jewelry\"]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Electronics category is finished\n",
"Beauty category is finished\n",
"Clothing_Shoes_and_Jewelry category is finished\n",
"Wall time: 4min 27s\n"
]
}
],
"source": [
"%%time\n",
"documents = util.load_data(save_path, category_list, tagby=True) "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"300000"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(documents)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"start : 2016-12-12 02:19:23.873986\n",
"num of epoch 1 \n",
"epoch : 1, training time: 94.910786, modeling time: 7.684298, dimension size : 50, window size : 2, avg accuracy : 0.805577\n",
"num of epoch 2 \n",
"epoch : 2, training time: 94.926707, modeling time: 7.632461, dimension size : 50, window size : 2, avg accuracy : 0.846273\n",
"num of epoch 3 \n",
"epoch : 3, training time: 95.410011, modeling time: 7.764786, dimension size : 50, window size : 2, avg accuracy : 0.859070\n",
"num of epoch 4 \n",
"epoch : 4, training time: 95.008087, modeling time: 7.397732, dimension size : 50, window size : 2, avg accuracy : 0.864263\n",
"num of epoch 5 \n",
"epoch : 5, training time: 95.168256, modeling time: 7.753154, dimension size : 50, window size : 2, avg accuracy : 0.866477\n",
"num of epoch 6 \n",
"epoch : 6, training time: 95.471654, modeling time: 7.480951, dimension size : 50, window size : 2, avg accuracy : 0.867317\n",
"num of epoch 7 \n",
"epoch : 7, training time: 95.164119, modeling time: 7.908519, dimension size : 50, window size : 2, avg accuracy : 0.867590\n",
"num of epoch 8 \n",
"epoch : 8, training time: 94.485579, modeling time: 7.689636, dimension size : 50, window size : 2, avg accuracy : 0.867793\n",
"num of epoch 9 \n",
"epoch : 9, training time: 95.619172, modeling time: 7.760576, dimension size : 50, window size : 2, avg accuracy : 0.867597\n",
"num of epoch 10 \n",
"epoch : 10, training time: 95.648172, modeling time: 7.480480, dimension size : 50, window size : 2, avg accuracy : 0.867470\n",
"num of epoch 1 \n",
"epoch : 1, training time: 95.985193, modeling time: 7.708891, dimension size : 50, window size : 4, avg accuracy : 0.808720\n",
"num of epoch 2 \n",
"epoch : 2, training time: 95.021187, modeling time: 7.827334, dimension size : 50, window size : 4, avg accuracy : 0.840533\n",
"num of epoch 3 \n",
"epoch : 3, training time: 96.060538, modeling time: 7.751967, dimension size : 50, window size : 4, avg accuracy : 0.854417\n",
"num of epoch 4 \n",
"epoch : 4, training time: 95.407035, modeling time: 7.892474, dimension size : 50, window size : 4, avg accuracy : 0.860697\n",
"num of epoch 5 \n",
"epoch : 5, training time: 95.927204, modeling time: 7.829961, dimension size : 50, window size : 4, avg accuracy : 0.864157\n",
"num of epoch 6 \n",
"epoch : 6, training time: 101.415483, modeling time: 7.666568, dimension size : 50, window size : 4, avg accuracy : 0.865753\n",
"num of epoch 7 \n",
"epoch : 7, training time: 94.882056, modeling time: 7.766237, dimension size : 50, window size : 4, avg accuracy : 0.866363\n",
"num of epoch 8 \n",
"epoch : 8, training time: 95.370203, modeling time: 7.987982, dimension size : 50, window size : 4, avg accuracy : 0.867180\n",
"num of epoch 9 \n",
"epoch : 9, training time: 95.233908, modeling time: 7.689811, dimension size : 50, window size : 4, avg accuracy : 0.867413\n",
"num of epoch 10 \n",
"epoch : 10, training time: 94.387086, modeling time: 7.706984, dimension size : 50, window size : 4, avg accuracy : 0.867430\n",
"num of epoch 1 \n",
"epoch : 1, training time: 96.511691, modeling time: 7.680370, dimension size : 50, window size : 6, avg accuracy : 0.804593\n",
"num of epoch 2 \n",
"epoch : 2, training time: 96.043197, modeling time: 7.790175, dimension size : 50, window size : 6, avg accuracy : 0.829993\n",
"num of epoch 3 \n",
"epoch : 3, training time: 102.703386, modeling time: 7.772690, dimension size : 50, window size : 6, avg accuracy : 0.842697\n",
"num of epoch 4 \n",
"epoch : 4, training time: 101.529539, modeling time: 7.792926, dimension size : 50, window size : 6, avg accuracy : 0.849240\n",
"num of epoch 5 \n",
"epoch : 5, training time: 99.584792, modeling time: 8.167925, dimension size : 50, window size : 6, avg accuracy : 0.853343\n",
"num of epoch 6 \n",
"epoch : 6, training time: 99.338187, modeling time: 8.399225, dimension size : 50, window size : 6, avg accuracy : 0.855860\n",
"num of epoch 7 \n",
"epoch : 7, training time: 98.125195, modeling time: 7.654441, dimension size : 50, window size : 6, avg accuracy : 0.857293\n",
"num of epoch 8 \n",
"epoch : 8, training time: 99.075206, modeling time: 7.835770, dimension size : 50, window size : 6, avg accuracy : 0.858293\n",
"num of epoch 9 \n",
"epoch : 9, training time: 97.842523, modeling time: 7.637531, dimension size : 50, window size : 6, avg accuracy : 0.858657\n",
"num of epoch 10 \n",
"epoch : 10, training time: 97.481833, modeling time: 7.721786, dimension size : 50, window size : 6, avg accuracy : 0.858727\n",
"num of epoch 1 \n",
"epoch : 1, training time: 96.507440, modeling time: 7.857562, dimension size : 50, window size : 8, avg accuracy : 0.795573\n",
"num of epoch 2 \n",
"epoch : 2, training time: 96.712679, modeling time: 7.779955, dimension size : 50, window size : 8, avg accuracy : 0.816020\n",
"num of epoch 3 \n",
"epoch : 3, training time: 95.827857, modeling time: 7.901669, dimension size : 50, window size : 8, avg accuracy : 0.828317\n",
"num of epoch 4 \n",
"epoch : 4, training time: 99.162270, modeling time: 7.809842, dimension size : 50, window size : 8, avg accuracy : 0.835187\n",
"num of epoch 5 \n",
"epoch : 5, training time: 98.087966, modeling time: 7.810312, dimension size : 50, window size : 8, avg accuracy : 0.839383\n",
"num of epoch 6 \n",
"epoch : 6, training time: 98.305388, modeling time: 7.898095, dimension size : 50, window size : 8, avg accuracy : 0.841583\n",
"num of epoch 7 \n",
"epoch : 7, training time: 98.005543, modeling time: 7.796983, dimension size : 50, window size : 8, avg accuracy : 0.843080\n",
"num of epoch 8 \n",
"epoch : 8, training time: 98.287882, modeling time: 7.794000, dimension size : 50, window size : 8, avg accuracy : 0.844120\n",
"num of epoch 9 \n",
"epoch : 9, training time: 97.816772, modeling time: 7.767962, dimension size : 50, window size : 8, avg accuracy : 0.844610\n",
"num of epoch 10 \n",
"epoch : 10, training time: 98.591846, modeling time: 7.971370, dimension size : 50, window size : 8, avg accuracy : 0.844830\n",
"num of epoch 1 \n",
"epoch : 1, training time: 99.774295, modeling time: 7.768061, dimension size : 50, window size : 10, avg accuracy : 0.788323\n",
"num of epoch 2 \n",
"epoch : 2, training time: 97.926256, modeling time: 7.790662, dimension size : 50, window size : 10, avg accuracy : 0.806730\n",
"num of epoch 3 \n",
"epoch : 3, training time: 96.648530, modeling time: 7.849429, dimension size : 50, window size : 10, avg accuracy : 0.817787\n",
"num of epoch 4 \n",
"epoch : 4, training time: 97.835552, modeling time: 7.761726, dimension size : 50, window size : 10, avg accuracy : 0.824240\n",
"num of epoch 5 \n",
"epoch : 5, training time: 97.941299, modeling time: 7.771809, dimension size : 50, window size : 10, avg accuracy : 0.828310\n",
"num of epoch 6 \n",
"epoch : 6, training time: 96.320775, modeling time: 7.814663, dimension size : 50, window size : 10, avg accuracy : 0.830920\n",
"num of epoch 7 \n",
"epoch : 7, training time: 97.064499, modeling time: 7.749414, dimension size : 50, window size : 10, avg accuracy : 0.832387\n",
"num of epoch 8 \n",
"epoch : 8, training time: 97.119556, modeling time: 7.842331, dimension size : 50, window size : 10, avg accuracy : 0.833290\n",
"num of epoch 9 \n",
"epoch : 9, training time: 97.389316, modeling time: 7.668760, dimension size : 50, window size : 10, avg accuracy : 0.833907\n",
"num of epoch 10 \n",
"epoch : 10, training time: 97.477592, modeling time: 7.786455, dimension size : 50, window size : 10, avg accuracy : 0.834117\n",
"num of epoch 1 \n",
"epoch : 1, training time: 97.772316, modeling time: 11.367375, dimension size : 100, window size : 2, avg accuracy : 0.827743\n",
"num of epoch 2 \n",
"epoch : 2, training time: 97.242387, modeling time: 10.991893, dimension size : 100, window size : 2, avg accuracy : 0.853937\n",
"num of epoch 3 \n",
"epoch : 3, training time: 97.637961, modeling time: 11.230720, dimension size : 100, window size : 2, avg accuracy : 0.862703\n",
"num of epoch 4 \n",
"epoch : 4, training time: 96.849477, modeling time: 11.300398, dimension size : 100, window size : 2, avg accuracy : 0.865160\n",
"num of epoch 5 \n",
"epoch : 5, training time: 96.108051, modeling time: 11.131224, dimension size : 100, window size : 2, avg accuracy : 0.865563\n",
"num of epoch 6 \n",
"epoch : 6, training time: 96.525809, modeling time: 11.337811, dimension size : 100, window size : 2, avg accuracy : 0.864670\n",
"num of epoch 7 \n",
"epoch : 7, training time: 97.642301, modeling time: 11.337871, dimension size : 100, window size : 2, avg accuracy : 0.864150\n",
"num of epoch 8 \n",
"epoch : 8, training time: 98.295291, modeling time: 11.255976, dimension size : 100, window size : 2, avg accuracy : 0.863483\n",
"num of epoch 9 \n",
"epoch : 9, training time: 96.514372, modeling time: 11.369366, dimension size : 100, window size : 2, avg accuracy : 0.862893\n",
"num of epoch 10 \n",
"epoch : 10, training time: 97.417124, modeling time: 11.032053, dimension size : 100, window size : 2, avg accuracy : 0.862357\n",
"num of epoch 1 \n",
"epoch : 1, training time: 98.353335, modeling time: 11.522533, dimension size : 100, window size : 4, avg accuracy : 0.824893\n",
"num of epoch 2 \n",
"epoch : 2, training time: 97.102675, modeling time: 11.459244, dimension size : 100, window size : 4, avg accuracy : 0.846427\n",
"num of epoch 3 \n",
"epoch : 3, training time: 97.598952, modeling time: 11.301922, dimension size : 100, window size : 4, avg accuracy : 0.856550\n",
"num of epoch 4 \n",
"epoch : 4, training time: 97.208431, modeling time: 11.090281, dimension size : 100, window size : 4, avg accuracy : 0.860883\n",
"num of epoch 5 \n",
"epoch : 5, training time: 104.130908, modeling time: 11.467291, dimension size : 100, window size : 4, avg accuracy : 0.862403\n",
"num of epoch 6 \n",
"epoch : 6, training time: 108.637652, modeling time: 11.353493, dimension size : 100, window size : 4, avg accuracy : 0.863413\n",
"num of epoch 7 \n",
"epoch : 7, training time: 105.663459, modeling time: 11.386100, dimension size : 100, window size : 4, avg accuracy : 0.863420\n",
"num of epoch 8 \n",
"epoch : 8, training time: 105.009875, modeling time: 11.133373, dimension size : 100, window size : 4, avg accuracy : 0.863040\n",
"num of epoch 9 \n",
"epoch : 9, training time: 105.473003, modeling time: 11.507952, dimension size : 100, window size : 4, avg accuracy : 0.862883\n",
"num of epoch 10 \n",
"epoch : 10, training time: 105.659156, modeling time: 11.248288, dimension size : 100, window size : 4, avg accuracy : 0.862620\n",
"num of epoch 1 \n",
"epoch : 1, training time: 107.444453, modeling time: 11.588594, dimension size : 100, window size : 6, avg accuracy : 0.814113\n",
"num of epoch 2 \n",
"epoch : 2, training time: 107.484423, modeling time: 11.417046, dimension size : 100, window size : 6, avg accuracy : 0.833560\n",
"num of epoch 3 \n",
"epoch : 3, training time: 108.140485, modeling time: 11.440959, dimension size : 100, window size : 6, avg accuracy : 0.843570\n",
"num of epoch 4 \n",
"epoch : 4, training time: 106.278127, modeling time: 11.279302, dimension size : 100, window size : 6, avg accuracy : 0.848600\n",
"num of epoch 5 \n",
"epoch : 5, training time: 106.181874, modeling time: 11.461387, dimension size : 100, window size : 6, avg accuracy : 0.851437\n",
"num of epoch 6 \n",
"epoch : 6, training time: 108.512925, modeling time: 11.997840, dimension size : 100, window size : 6, avg accuracy : 0.852703\n",
"num of epoch 7 \n",
"epoch : 7, training time: 108.053198, modeling time: 11.360303, dimension size : 100, window size : 6, avg accuracy : 0.853153\n",
"num of epoch 8 \n",
"epoch : 8, training time: 104.034764, modeling time: 11.229464, dimension size : 100, window size : 6, avg accuracy : 0.853460\n",
"num of epoch 9 \n",
"epoch : 9, training time: 104.610960, modeling time: 10.910285, dimension size : 100, window size : 6, avg accuracy : 0.853377\n",
"num of epoch 10 \n",
"epoch : 10, training time: 104.847548, modeling time: 11.247652, dimension size : 100, window size : 6, avg accuracy : 0.853477\n",
"num of epoch 1 \n",
"epoch : 1, training time: 105.045554, modeling time: 11.107920, dimension size : 100, window size : 8, avg accuracy : 0.807180\n",
"num of epoch 2 \n",
"epoch : 2, training time: 104.211194, modeling time: 11.399123, dimension size : 100, window size : 8, avg accuracy : 0.822997\n",
"num of epoch 3 \n",
"epoch : 3, training time: 105.459237, modeling time: 11.078738, dimension size : 100, window size : 8, avg accuracy : 0.832733\n",
"num of epoch 4 \n",
"epoch : 4, training time: 104.434665, modeling time: 11.452276, dimension size : 100, window size : 8, avg accuracy : 0.837663\n",
"num of epoch 5 \n",
"epoch : 5, training time: 105.327762, modeling time: 11.612976, dimension size : 100, window size : 8, avg accuracy : 0.839730\n",
"num of epoch 6 \n",
"epoch : 6, training time: 105.590011, modeling time: 11.470301, dimension size : 100, window size : 8, avg accuracy : 0.841007\n",
"num of epoch 7 \n",
"epoch : 7, training time: 105.170676, modeling time: 11.298806, dimension size : 100, window size : 8, avg accuracy : 0.841413\n",
"num of epoch 8 \n",
"epoch : 8, training time: 105.013155, modeling time: 11.306391, dimension size : 100, window size : 8, avg accuracy : 0.841717\n",
"num of epoch 9 \n",
"epoch : 9, training time: 104.951044, modeling time: 11.322352, dimension size : 100, window size : 8, avg accuracy : 0.841660\n",
"num of epoch 10 \n",
"epoch : 10, training time: 104.690306, modeling time: 11.473388, dimension size : 100, window size : 8, avg accuracy : 0.841723\n",
"num of epoch 1 \n",
"epoch : 1, training time: 105.639504, modeling time: 11.330600, dimension size : 100, window size : 10, avg accuracy : 0.793840\n",
"num of epoch 2 \n",
"epoch : 2, training time: 106.569234, modeling time: 11.413828, dimension size : 100, window size : 10, avg accuracy : 0.806610\n",
"num of epoch 3 \n",
"epoch : 3, training time: 105.771199, modeling time: 11.539334, dimension size : 100, window size : 10, avg accuracy : 0.815650\n",
"num of epoch 4 \n",
"epoch : 4, training time: 106.860032, modeling time: 11.652927, dimension size : 100, window size : 10, avg accuracy : 0.820330\n",
"num of epoch 5 \n",
"epoch : 5, training time: 106.776093, modeling time: 11.471375, dimension size : 100, window size : 10, avg accuracy : 0.822817\n",
"num of epoch 6 \n",
"epoch : 6, training time: 107.883348, modeling time: 11.344701, dimension size : 100, window size : 10, avg accuracy : 0.823573\n",
"num of epoch 7 \n",
"epoch : 7, training time: 106.231408, modeling time: 11.130049, dimension size : 100, window size : 10, avg accuracy : 0.824097\n",
"num of epoch 8 \n",
"epoch : 8, training time: 105.778570, modeling time: 11.350393, dimension size : 100, window size : 10, avg accuracy : 0.824377\n",
"num of epoch 9 \n",
"epoch : 9, training time: 106.824792, modeling time: 11.278334, dimension size : 100, window size : 10, avg accuracy : 0.824317\n",
"num of epoch 10 \n",
"epoch : 10, training time: 108.370288, modeling time: 11.358579, dimension size : 100, window size : 10, avg accuracy : 0.824497\n",
"num of epoch 1 \n",
"epoch : 1, training time: 107.329733, modeling time: 17.237167, dimension size : 200, window size : 2, avg accuracy : 0.837277\n",
"num of epoch 2 \n",
"epoch : 2, training time: 107.028125, modeling time: 17.585290, dimension size : 200, window size : 2, avg accuracy : 0.858283\n",
"num of epoch 3 \n",
"epoch : 3, training time: 107.587175, modeling time: 17.818207, dimension size : 200, window size : 2, avg accuracy : 0.866237\n",
"num of epoch 4 \n",
"epoch : 4, training time: 109.170259, modeling time: 17.477657, dimension size : 200, window size : 2, avg accuracy : 0.868243\n",
"num of epoch 5 \n",
"epoch : 5, training time: 109.075440, modeling time: 17.333456, dimension size : 200, window size : 2, avg accuracy : 0.869230\n",
"num of epoch 6 \n",
"epoch : 6, training time: 108.200291, modeling time: 17.951704, dimension size : 200, window size : 2, avg accuracy : 0.869480\n",
"num of epoch 7 \n",
"epoch : 7, training time: 107.767636, modeling time: 17.556831, dimension size : 200, window size : 2, avg accuracy : 0.869440\n",
"num of epoch 8 \n",
"epoch : 8, training time: 107.814095, modeling time: 17.802654, dimension size : 200, window size : 2, avg accuracy : 0.869657\n",
"num of epoch 9 \n",
"epoch : 9, training time: 108.257390, modeling time: 17.917108, dimension size : 200, window size : 2, avg accuracy : 0.869553\n",
"num of epoch 10 \n",
"epoch : 10, training time: 107.581749, modeling time: 17.600928, dimension size : 200, window size : 2, avg accuracy : 0.869667\n",
"num of epoch 1 \n",
"epoch : 1, training time: 107.570666, modeling time: 17.502574, dimension size : 200, window size : 4, avg accuracy : 0.832107\n",
"num of epoch 2 \n",
"epoch : 2, training time: 108.280152, modeling time: 17.437518, dimension size : 200, window size : 4, avg accuracy : 0.852063\n",
"num of epoch 3 \n",
"epoch : 3, training time: 108.239809, modeling time: 17.662406, dimension size : 200, window size : 4, avg accuracy : 0.860030\n",
"num of epoch 4 \n",
"epoch : 4, training time: 110.264311, modeling time: 17.096394, dimension size : 200, window size : 4, avg accuracy : 0.864057\n",
"num of epoch 5 \n",
"epoch : 5, training time: 108.344003, modeling time: 16.929231, dimension size : 200, window size : 4, avg accuracy : 0.865640\n",
"num of epoch 6 \n",
"epoch : 6, training time: 109.604213, modeling time: 16.937238, dimension size : 200, window size : 4, avg accuracy : 0.866010\n",
"num of epoch 7 \n",
"epoch : 7, training time: 110.890055, modeling time: 17.538506, dimension size : 200, window size : 4, avg accuracy : 0.866527\n",
"num of epoch 8 \n",
"epoch : 8, training time: 109.344753, modeling time: 17.416225, dimension size : 200, window size : 4, avg accuracy : 0.866680\n",
"num of epoch 9 \n",
"epoch : 9, training time: 110.129771, modeling time: 17.538834, dimension size : 200, window size : 4, avg accuracy : 0.866550\n",
"num of epoch 10 \n",
"epoch : 10, training time: 107.935608, modeling time: 16.817126, dimension size : 200, window size : 4, avg accuracy : 0.866800\n",
"num of epoch 1 \n",
"epoch : 1, training time: 109.477087, modeling time: 17.253545, dimension size : 200, window size : 6, avg accuracy : 0.824073\n",
"num of epoch 2 \n",
"epoch : 2, training time: 109.356971, modeling time: 17.283575, dimension size : 200, window size : 6, avg accuracy : 0.840050\n",
"num of epoch 3 \n",
"epoch : 3, training time: 109.445042, modeling time: 17.063377, dimension size : 200, window size : 6, avg accuracy : 0.848910\n",
"num of epoch 4 \n",
"epoch : 4, training time: 109.639230, modeling time: 17.183494, dimension size : 200, window size : 6, avg accuracy : 0.853077\n",
"num of epoch 5 \n",
"epoch : 5, training time: 108.985616, modeling time: 17.147482, dimension size : 200, window size : 6, avg accuracy : 0.854420\n",
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"epoch : 7, training time: 135.594138, modeling time: 31.219965, dimension size : 500, window size : 4, avg accuracy : 0.878750\n",
"num of epoch 8 \n",
"epoch : 8, training time: 137.802260, modeling time: 31.684412, dimension size : 500, window size : 4, avg accuracy : 0.879293\n",
"num of epoch 9 \n",
"epoch : 9, training time: 139.935306, modeling time: 31.464198, dimension size : 500, window size : 4, avg accuracy : 0.879847\n",
"num of epoch 10 \n",
"epoch : 10, training time: 130.431183, modeling time: 31.115864, dimension size : 500, window size : 4, avg accuracy : 0.880103\n",
"num of epoch 1 \n",
"epoch : 1, training time: 141.398708, modeling time: 31.273015, dimension size : 500, window size : 6, avg accuracy : 0.828493\n",
"num of epoch 2 \n",
"epoch : 2, training time: 142.004292, modeling time: 31.525271, dimension size : 500, window size : 6, avg accuracy : 0.847057\n",
"num of epoch 3 \n",
"epoch : 3, training time: 141.762059, modeling time: 31.244988, dimension size : 500, window size : 6, avg accuracy : 0.856073\n",
"num of epoch 4 \n",
"epoch : 4, training time: 142.534800, modeling time: 31.607336, dimension size : 500, window size : 6, avg accuracy : 0.860433\n",
"num of epoch 5 \n",
"epoch : 5, training time: 146.282398, modeling time: 31.027780, dimension size : 500, window size : 6, avg accuracy : 0.863293\n",
"num of epoch 6 \n",
"epoch : 6, training time: 141.567868, modeling time: 31.660387, dimension size : 500, window size : 6, avg accuracy : 0.865143\n",
"num of epoch 7 \n",
"epoch : 7, training time: 141.144465, modeling time: 31.403140, dimension size : 500, window size : 6, avg accuracy : 0.866527\n",
"num of epoch 8 \n",
"epoch : 8, training time: 143.775992, modeling time: 31.604347, dimension size : 500, window size : 6, avg accuracy : 0.867617\n",
"num of epoch 9 \n",
"epoch : 9, training time: 140.464800, modeling time: 31.928659, dimension size : 500, window size : 6, avg accuracy : 0.868190\n",
"num of epoch 10 \n",
"epoch : 10, training time: 140.473823, modeling time: 31.349103, dimension size : 500, window size : 6, avg accuracy : 0.868507\n",
"num of epoch 1 \n",
"epoch : 1, training time: 149.502474, modeling time: 31.041808, dimension size : 500, window size : 8, avg accuracy : 0.817747\n",
"num of epoch 2 \n",
"epoch : 2, training time: 148.986994, modeling time: 31.173933, dimension size : 500, window size : 8, avg accuracy : 0.832313\n",
"num of epoch 3 \n",
"epoch : 3, training time: 148.243280, modeling time: 31.292033, dimension size : 500, window size : 8, avg accuracy : 0.840477\n",
"num of epoch 4 \n",
"epoch : 4, training time: 153.414240, modeling time: 31.497245, dimension size : 500, window size : 8, avg accuracy : 0.844907\n",
"num of epoch 5 \n",
"epoch : 5, training time: 143.725945, modeling time: 31.640371, dimension size : 500, window size : 8, avg accuracy : 0.847353\n",
"num of epoch 6 \n",
"epoch : 6, training time: 153.472300, modeling time: 32.157864, dimension size : 500, window size : 8, avg accuracy : 0.849563\n",
"num of epoch 7 \n",
"epoch : 7, training time: 157.800451, modeling time: 32.215935, dimension size : 500, window size : 8, avg accuracy : 0.850723\n",
"num of epoch 8 \n",
"epoch : 8, training time: 146.958047, modeling time: 32.136848, dimension size : 500, window size : 8, avg accuracy : 0.851710\n",
"num of epoch 9 \n",
"epoch : 9, training time: 148.576600, modeling time: 31.941673, dimension size : 500, window size : 8, avg accuracy : 0.852377\n",
"num of epoch 10 \n",
"epoch : 10, training time: 150.039004, modeling time: 32.105828, dimension size : 500, window size : 8, avg accuracy : 0.852957\n",
"num of epoch 1 \n",
"epoch : 1, training time: 163.485895, modeling time: 32.045758, dimension size : 500, window size : 10, avg accuracy : 0.808430\n",
"num of epoch 2 \n",
"epoch : 2, training time: 161.987472, modeling time: 31.760483, dimension size : 500, window size : 10, avg accuracy : 0.820803\n",
"num of epoch 3 \n",
"epoch : 3, training time: 166.051273, modeling time: 31.757481, dimension size : 500, window size : 10, avg accuracy : 0.828500\n",
"num of epoch 4 \n",
"epoch : 4, training time: 163.170668, modeling time: 32.540428, dimension size : 500, window size : 10, avg accuracy : 0.832063\n",
"num of epoch 5 \n",
"epoch : 5, training time: 158.504590, modeling time: 33.070312, dimension size : 500, window size : 10, avg accuracy : 0.833460\n",
"num of epoch 6 \n",
"epoch : 6, training time: 168.355695, modeling time: 32.997456, dimension size : 500, window size : 10, avg accuracy : 0.835007\n",
"num of epoch 7 \n",
"epoch : 7, training time: 158.624243, modeling time: 33.096953, dimension size : 500, window size : 10, avg accuracy : 0.835657\n",
"num of epoch 8 \n",
"epoch : 8, training time: 170.299777, modeling time: 32.679254, dimension size : 500, window size : 10, avg accuracy : 0.836347\n",
"num of epoch 9 \n",
"epoch : 9, training time: 165.297154, modeling time: 32.554044, dimension size : 500, window size : 10, avg accuracy : 0.836950\n",
"num of epoch 10 \n",
"epoch : 10, training time: 161.321805, modeling time: 32.552951, dimension size : 500, window size : 10, avg accuracy : 0.837617\n",
"end : 2016-12-12 13:46:24.136835\n"
]
}
],
"source": [
"#parameters\n",
"y = np.array([1] * 100000 + [0] * 100000 + [-1] * 100000)\n",
"tags = [doc.tags[0] for doc in documents]\n",
"tag_dict = {}\n",
"for index, tag in enumerate(tags):\n",
" tag_dict[tag] = y[index]\n",
" \n",
"window = [2,4,6,8,10]\n",
"size = [50,100,200,300,400,500]\n",
"alpha, min_alpha, passes = (0.02, 0.001, 10)\n",
"alpha_delta = (alpha-min_alpha)/passes\n",
"\n",
"df_param = pd.DataFrame(columns=[['epoch', 'training_time', 'modeling_time', 'dimension', 'window', 'avg_accuracy']])\n",
"\n",
"print(\"start : \", datetime.datetime.now())\n",
"for s in size:\n",
" for w in window:\n",
" #PV_DM w/average\n",
" model = Doc2Vec(dm=1, dm_mean=1, size=s, min_count=50, window=w, workers=12, \n",
" alpha=alpha, min_alpha=min_alpha)\n",
" model.build_vocab(documents)\n",
"\n",
" for epoch in range(passes):\n",
" print(\"num of epoch %s \"%(epoch+1))\n",
" start = time.time()\n",
" random.shuffle(documents)\n",
" model.train(documents)\n",
" model.alpha -= alpha_delta # decrease the learning rate\n",
" model.min_alpha = model.alpha # fix the learning rate, no decay\n",
" end = time.time()\n",
"\n",
" start2 = time.time()\n",
" X = np.array([model.docvecs[tag] for tag in tags])\n",
" y = [tag_dict[tag] for tag in tags]\n",
" y = np.array(y)\n",
" \n",
" accuracy = []\n",
" kf = KFold(X.shape[0], n_folds=10, shuffle=True, random_state=10)\n",
" for k, (train_index, test_index) in enumerate(kf):\n",
" X_train, X_test = X[train_index], X[test_index]\n",
" y_train, y_test = y[train_index], y[test_index]\n",
" sgdreg = linear_model.SGDClassifier(loss='log', penalty='l2', n_jobs=-1, alpha=0.0001, n_iter=5, random_state =111)\n",
" sgdreg.fit(X_train, y_train)\n",
" accuracy.append(np.mean(sgdreg.predict(X_test) == y_test))\n",
" end2 = time.time()\n",
" print(\"epoch : %i, training time: %f, modeling time: %f, dimension size : %i, window size : %i, avg accuracy : %f\" %(epoch+1, end-start, end2-start2, s, w, np.mean(accuracy)))\n",
" df_param.loc[len(df_param)] = [epoch+1, start-end, start2-end2, s, w, np.mean(accuracy)]\n",
" #model.save(model_path + \"model_\" + str(w) + '_' + str(s) + '_' + str(epoch+1))\n",
"print(\"end : \", datetime.datetime.now())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_param.to_csv('D:/Dropbox/2016-2/졸업논문/FINAL_result/ParamSearch/result.csv',index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
monicathieu/cu-psych-r-tutorial | public/tutorials/python/1_r2python-translation/3_controlFlow.ipynb | 3 | 7482 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Control Flow\n",
"\n",
"## Goals of this lesson\n",
"\n",
"#### Students will learn:\n",
"- How to use if/else and for loops in python\n",
"- How to indent code correctly in python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Indenting in python\n",
"\n",
"- Python, unlike R, is *strict* about indentation! \n",
"- Indentations in python often have bearing on the *order* in which they are executed, and switching indentation can change how code runs (or break it)\n",
"- Coming from R, indentation might seem annoying at first, but eventually this can help with code readability \n",
"- Ultimately, python is trying to help us stay organized\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# If statements\n",
"\n",
"- If statements in python are the equivalent of the following English: \"If condition X is met, then do action Y\"\n",
"- If statements in python consist of the following syntax\n",
"\n",
"`if (condition X):\n",
" actions...`"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Above 10!\n"
]
}
],
"source": [
"myVar = 25\n",
"if myVar > 10:\n",
" print('Above 10!')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Nesting If Statements\n",
"\n",
"Any conditional statements within others are called 'nested'"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Above 10!\n",
"Above 20!\n"
]
}
],
"source": [
"if myVar > 5:\n",
" print('Above 10!')\n",
" if myVar > 20:\n",
" print('Above 20!')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Else Statements\n",
"\n",
"- It is also very helpful to specify code that we want to run if a condition is NOT met\n",
"- Else statements in python always follow if statements, and consist of the following syntax\n",
"\n",
"`if (condition X):\n",
" actions...\n",
" else:\n",
" actions...`"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"woof\n"
]
}
],
"source": [
"myVar2 = 'dog'\n",
"if myVar2 == 'cat':\n",
" print('meow')\n",
"else:\n",
" print('woof')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Else If & Sequential If Statements\n",
"\n",
"- We may also want to specify a series of conditions\n",
"- Python always evaluates conditions on the same nest level in order, from top to bottom \n",
"- Elif means 'else if' -- only run this statement if the previous if statement condition was not met, and the condition following is met\n",
"- Sequential if statements on the same level will run if the statement condition is met, regardless of the previous"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3 letters long\n"
]
}
],
"source": [
"myVar2 = 'dog'\n",
"if len(myVar2) == 3:\n",
" print('3 letters long')\n",
"elif myVar2 == 'dog':\n",
" print('woof')\n",
"else:\n",
" print('unknown animal')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3 letters long\n",
"woof\n"
]
}
],
"source": [
"myVar2 = 'dog'\n",
"if len(myVar2) == 3:\n",
" print('3 letters long')\n",
"if myVar2 == 'dog':\n",
" print('woof')\n",
"else:\n",
" print('unknown animal')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loops\n",
"\n",
"- Looping is a great way to apply the same operation to many pieces of data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Looping through a list"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"3\n",
"4\n",
"-1\n",
"7\n"
]
}
],
"source": [
"nums = [2,3,4,-1,7]\n",
"for number in nums:\n",
" print(number)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Looping a certain number of times\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n"
]
}
],
"source": [
"for i in range(10):\n",
" print(i)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fancly looping with enumerate"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 banana\n",
"1 mango\n",
"2 kiwi\n",
"3 blackberry\n"
]
}
],
"source": [
"stringList = ['banana', 'mango', 'kiwi', 'blackberry']\n",
"# fancy looping with enumerate()\n",
"for index, item in enumerate(stringList):\n",
" print(index, item)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Nested loops"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"banana 0\n",
"banana 1\n",
"banana 2\n",
"banana 3\n",
"mango 0\n",
"mango 1\n",
"mango 2\n",
"mango 3\n",
"kiwi 0\n",
"kiwi 1\n",
"kiwi 2\n",
"kiwi 3\n",
"blackberry 0\n",
"blackberry 1\n",
"blackberry 2\n",
"blackberry 3\n"
]
}
],
"source": [
"for i in stringList:\n",
" for j in range(4):\n",
" print(i, j)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ivukotic/ML_platform_tests | tutorial/Simple Examples/Fitting custom function.ipynb | 1 | 13711 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### This example shows how to fit an arbitrary function to your data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"from scipy.optimize import curve_fit\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### your function"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def fit_func(x, a, b):\n",
" return a*x + b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"x = np.array([1, 2, 3, 9])\n",
"y = np.array([1, 1.5, 2, 3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### fitting"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"params = curve_fit(fit_func, x, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### results - parameters"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a: 0.229032258063 \tb: 1.01612903226\n"
]
}
],
"source": [
"[a, b] = params[0]\n",
"print ('a:',a,'\\tb:',b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### results - errors"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"err a: 0.0446980850234 \terr b: 0.21783152214\n"
]
}
],
"source": [
"pcov=params[1]\n",
"[da,db] = np.sqrt(np.diag(pcov))\n",
"print ('err a:',da,'\\terr b:',db)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### plot data and fit"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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r7djcZZs+DvmPlFdWM3fZpvYZ9CXv1fZ4P7IrXCx7/BdCj/e8i7U0I9KAJq3Rm9kQYAKw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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f20d1176320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x_new = np.linspace(x[0], x[-1], 50)\n",
"y_new = fit_func(x_new, a, b)\n",
"\n",
"plt.plot(x,y,'o')\n",
"plt.plot(x_new, y_new,'-')\n",
"plt.xlim([x[0]-1, x[-1] + 1 ])\n",
"plt.ylim([y[0]-1, y[-1] + 1 ])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
ckemere/CloudShuffles | score_bayes_parallel-dask.ipynb | 1 | 202322 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Score multi-session events using the replay score from Davidson et al."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import os\n",
"import pandas as pd\n",
"import warnings\n",
"\n",
"import nelpy as nel\n",
"\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load experimental data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"datadirs = ['/Users/ckemere/Development/Data/Buzsaki']\n",
"\n",
"fileroot = next( (dir for dir in datadirs if os.path.isdir(dir)), None)\n",
"# conda install pandas=0.19.2\n",
"if fileroot is None:\n",
" raise FileNotFoundError('datadir not found')\n",
"\n",
"load_from_nel = True\n",
"\n",
"# load from nel file:\n",
"if load_from_nel:\n",
" jar = nel.load_pkl(os.path.join(fileroot,'gor01vvp01_processed_speed.nel'))\n",
" exp_data = jar.exp_data\n",
" aux_data = jar.aux_data\n",
" del jar\n",
" \n",
" with pd.HDFStore(os.path.join(fileroot,'DibaMetadata.h5')) as store:\n",
" df = store.get('Session_Metadata')\n",
" df2 = store.get('Subset_Metadata')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Define subset of sessions to score"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Evaluating subset of 40 sessions\n"
]
},
{
"data": {
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" <td>44</td>\n",
" <td>27</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" animal month day time track segment duration n_cells \\\n",
"58 gor01 6 9 22-24-40 two short 1620.0 203 \n",
"58 gor01 6 9 22-24-40 two short 1620.0 203 \n",
"35 gor01 6 7 16-40-19 two short 1330.0 117 \n",
"35 gor01 6 7 16-40-19 two short 1330.0 117 \n",
"36 gor01 6 7 16-40-19 two long 1180.0 117 \n",
"36 gor01 6 7 16-40-19 two long 1180.0 117 \n",
"73 gor01 6 9 1-22-43 one long 1012.0 203 \n",
"73 gor01 6 9 1-22-43 one long 1012.0 203 \n",
"59 gor01 6 9 22-24-40 two long 912.0 203 \n",
"59 gor01 6 9 22-24-40 two long 912.0 203 \n",
"72 gor01 6 9 1-22-43 one short 617.0 203 \n",
"72 gor01 6 9 1-22-43 one short 617.0 203 \n",
"6 gor01 6 8 21-16-25 two long 720.0 171 \n",
"6 gor01 6 8 21-16-25 two long 720.0 171 \n",
"0 vvp01 4 9 17-29-30 one short 490.0 68 \n",
"0 vvp01 4 9 17-29-30 one short 490.0 68 \n",
"83 vvp01 4 9 16-40-54 two long 861.0 41 \n",
"83 vvp01 4 9 16-40-54 two long 861.0 41 \n",
"5 gor01 6 8 21-16-25 two short 457.0 171 \n",
"5 gor01 6 8 21-16-25 two short 457.0 171 \n",
"82 vvp01 4 9 16-40-54 two short 485.0 41 \n",
"82 vvp01 4 9 16-40-54 two short 485.0 41 \n",
"14 gor01 6 12 16-53-46 two long 470.0 81 \n",
"14 gor01 6 12 16-53-46 two long 470.0 81 \n",
"28 vvp01 4 25 14-28-51 one long 385.0 80 \n",
"28 vvp01 4 25 14-28-51 one long 385.0 80 \n",
"51 vvp01 4 16 15-12-23 one short 347.0 74 \n",
"51 vvp01 4 16 15-12-23 one short 347.0 74 \n",
"1 vvp01 4 9 17-29-30 one long 800.0 68 \n",
"1 vvp01 4 9 17-29-30 one long 800.0 68 \n",
"61 gor01 6 12 15-55-31 one long 660.0 97 \n",
"61 gor01 6 12 15-55-31 one long 660.0 97 \n",
"75 gor01 6 13 15-22-3 two long 530.0 82 \n",
"75 gor01 6 13 15-22-3 two long 530.0 82 \n",
"50 gor01 6 13 14-42-6 one long 520.0 109 \n",
"50 gor01 6 13 14-42-6 one long 520.0 109 \n",
"41 vvp01 4 17 12-52-15 two short 328.0 59 \n",
"41 vvp01 4 17 12-52-15 two short 328.0 59 \n",
"60 gor01 6 12 15-55-31 one short 410.0 97 \n",
"60 gor01 6 12 15-55-31 one short 410.0 97 \n",
"\n",
" n_placecells n_PBEs Notes prescreen_z \n",
"58 61 301 NaN NaN \n",
"58 61 301 NaN NaN \n",
"35 46 277 NaN NaN \n",
"35 46 277 NaN NaN \n",
"36 43 150 NaN NaN \n",
"36 43 150 NaN NaN \n",
"73 62 117 NaN NaN \n",
"73 62 117 NaN NaN \n",
"59 66 103 NaN NaN \n",
"59 66 103 NaN NaN \n",
"72 71 91 NaN NaN \n",
"72 71 91 NaN NaN \n",
"6 82 57 NaN NaN \n",
"6 82 57 NaN NaN \n",
"0 32 46 NaN NaN \n",
"0 32 46 NaN NaN \n",
"83 25 42 NaN NaN \n",
"83 25 42 NaN NaN \n",
"5 64 37 NaN NaN \n",
"5 64 37 NaN NaN \n",
"82 16 37 NaN NaN \n",
"82 16 37 NaN NaN \n",
"14 36 36 NaN NaN \n",
"14 36 36 NaN NaN \n",
"28 32 36 NaN NaN \n",
"28 32 36 NaN NaN \n",
"51 24 34 NaN NaN \n",
"51 24 34 NaN NaN \n",
"1 34 33 NaN NaN \n",
"1 34 33 NaN NaN \n",
"61 40 32 NaN NaN \n",
"61 40 32 NaN NaN \n",
"75 37 31 NaN NaN \n",
"75 37 31 NaN NaN \n",
"50 36 29 NaN NaN \n",
"50 36 29 NaN NaN \n",
"41 26 28 NaN NaN \n",
"41 26 28 NaN NaN \n",
"60 44 27 NaN NaN \n",
"60 44 27 NaN NaN "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# restrict sessions to explore to a smaller subset\n",
"min_n_placecells = 16\n",
"min_n_PBEs = 27 # 27 total events ==> minimum 21 events in training set\n",
"\n",
"df2_subset = df2[(df2.n_PBEs >= min_n_PBEs) & (df2.n_placecells >= min_n_placecells)]\n",
"\n",
"sessions = df2_subset['time'].values.tolist()\n",
"segments = df2_subset['segment'].values.tolist()\n",
"\n",
"print('Evaluating subset of {} sessions'.format(len(sessions)))\n",
"\n",
"df2_subset.sort_values(by=['n_PBEs', 'n_placecells'], ascending=[0,0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Parallel scoring"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTE:** it is relatively easy (syntax-wise) to score each session as a parallel task, but since the Bayesian scoring takes such a long time to compute, we can be more efficient (higher % utilization) by further parallelizing over events, and not just over sessions. This further level of parallelization makes the bookkeeping a little ugly, so I provide the code for both approaches here."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_jobs = 20 # set this equal to number of cores\n",
"n_shuffles = 100 # 5000\n",
"n_samples = 35000 # 35000\n",
"w=3 # single sided bandwidth (0 means only include bin who's center is under line, 3 means a total of 7 bins)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"distributed.client - WARNING - Client report stream closed to scheduler\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665c5bf8>, <tornado.concurrent.Future object at 0x1c665c1b00>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-6407a9716f68431588a9af8b84f20215']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665c8048>, <tornado.concurrent.Future object at 0x1c665c1cc0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-26da7ce53ab044509e59182ee3938f89']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665c8400>, <tornado.concurrent.Future object at 0x1c665c1e80>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-ddac9995d7e54cdca15818d8a136f560']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665d3378>, <tornado.concurrent.Future object at 0x1c665ca5c0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-d9d69834d4294a2a85e4cac8f277f93c']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665d3730>, <tornado.concurrent.Future object at 0x1c665ca780>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-813964dd1fb74a4aa1b6ab2bb3513628']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665d3ae8>, <tornado.concurrent.Future object at 0x1c665ca940>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-d08512594b1a4edd9cc985af54da6a1a']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665e36a8>, <tornado.concurrent.Future object at 0x1c665caf98>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-67fc4ea64ce749788fc3b7ea25445668']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665e3a60>, <tornado.concurrent.Future object at 0x1c8b899208>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-748a59748cd14f23b5bc02b6332e28f6']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665e3e18>, <tornado.concurrent.Future object at 0x1c8b899438>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-a6e6dd3837c74cbea6047e5b444bfe82']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c8b89dd90>, <tornado.concurrent.Future object at 0x1c8b899cf8>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-c4b043b3de264c159820fafe0b531c09']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c8b8a51e0>, <tornado.concurrent.Future object at 0x1c8b899f28>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-1898167894c9431c96db172454602ff1']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c8b8a5598>, <tornado.concurrent.Future object at 0x1c8b8a1198>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-1bdd41c97f33460f8cf0610c95df7809']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c8b8afb70>, <tornado.concurrent.Future object at 0x1c8b8d1940>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-d83d51c8f73f488e82b2cdebc340fc46']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c8b8aff28>, <tornado.concurrent.Future object at 0x1c8b8d1b00>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-941e5055b1bb46f99d568ba53da80007']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1caca3d378>, <tornado.concurrent.Future object at 0x1c8b8d1cc0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-e99cfbbc14d04172963adae93ad9ee63']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1caca3dea0>, <tornado.concurrent.Future object at 0x1caca3f240>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-a291416f50234eb5ba627a45da48694b']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1caca422f0>, <tornado.concurrent.Future object at 0x1caca3f400>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-87fb012e2bfa46b9851ea426dfbfbee0']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1caca426a8>, <tornado.concurrent.Future object at 0x1caca3f5c0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-cfd465e6ce7f4891946654cb5f61f624']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c96202e18>, <tornado.concurrent.Future object at 0x1c96206748>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-b5673a0c21f845a5b708af3d933771d1']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c96202510>, <tornado.concurrent.Future object at 0x1c961f9d30>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-dea4305e14444e4a917bc7a6ba91feda']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c8b8d2bf8>, <tornado.concurrent.Future object at 0x1c1d82f470>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-e3b663a420c444c889406754456a0975']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c1d8339d8>, <tornado.concurrent.Future object at 0x1c1d82f630>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-33c6ab5feecf4af596dcb04e2b27335f']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c1d8428c8>, <tornado.concurrent.Future object at 0x1c665c17b8>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-2e832b4246cc475fa32c23ffe5793d6d']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665c5620>, <tornado.concurrent.Future object at 0x1c665c1978>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-6440fa3eafdf4be3a1f3b9548b833aca']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665b1488>, <tornado.concurrent.Future object at 0x1c1d8254e0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-829131006e7a4784803d77f21e7d857e']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c9621b400>, <tornado.concurrent.Future object at 0x1c961f9630>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-d37ed62f20c842c190530c19a2073328']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c1d82cd90>, <tornado.concurrent.Future object at 0x1c1d825cf8>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-b7752b6a0de44d63bebe08f6495fb492']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c1d82c7b8>, <tornado.concurrent.Future object at 0x1c1d825b70>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-18555c569b6547719329a8372d444421']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c7160e378>, <tornado.concurrent.Future object at 0x1c1d82f5f8>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-7bf32d7bb35c4881a379a2796073b0f2']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c9621bf28>, <tornado.concurrent.Future object at 0x1c96213ac8>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-4b9bc1db0b634d24aa42a7022fb0b7e7']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c1d82c378>, <tornado.concurrent.Future object at 0x1c1d8259b0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-dca11a517c3b4acda2309690cf69d276']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c961fd0d0>, <tornado.concurrent.Future object at 0x1caf97b898>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-b65d8fdf16374118a97eda72a6a6dc26']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c961fd048>, <tornado.concurrent.Future object at 0x1c665c11d0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-d93b759350c740f9a61c4495e3797095']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c9620e268>, <tornado.concurrent.Future object at 0x1c96213fd0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-74029b057a084d15a2122b91fcee1818']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665bd7b8>, <tornado.concurrent.Future object at 0x1c665ae390>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-99f79e21b623420cb5b81e1ebd914fd2']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c71616bf8>, <tornado.concurrent.Future object at 0x1c961f9b70>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-2ea1fdb3b0854cb3a24f9ac842c71f42']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c1d8426a8>, <tornado.concurrent.Future object at 0x1c1d82fc50>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-37c2cf884e9847ccaaf427a14ea240ec']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c9621b7b8>, <tornado.concurrent.Future object at 0x1c96206208>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-d0504b86914d4eb0948d08ec7bdb2ed7']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c9620e950>, <tornado.concurrent.Future object at 0x1c665c1588>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-a6ca03df81754fd0a6f4eb8c7e4672e2']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c8b8d2ae8>, <tornado.concurrent.Future object at 0x1c1d82f048>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-0fa3c317be1f420fb7046415caed3be5']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c96216d08>, <tornado.concurrent.Future object at 0x1c961f97f0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-7c360b253392409496d96dd59a3b0209']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c96230268>, <tornado.concurrent.Future object at 0x1c71602358>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-016afbd8dcb446868b8893fab469b165']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c1d828c80>, <tornado.concurrent.Future object at 0x1c1d825630>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-f4a571d4648e49df80dbd068cf509d7c']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c715e0d08>, <tornado.concurrent.Future object at 0x1c1d82f2b0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-2c0cd671b771471fb42588c481f2b4b8']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c715e0bf8>, <tornado.concurrent.Future object at 0x1caf97bc88>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-303b0ed7475e418f99e49ef3ce51212a']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1caca45488>, <tornado.concurrent.Future object at 0x1c1d825160>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-7fd950ed94b84282b87996e8805484f6']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c9621b378>, <tornado.concurrent.Future object at 0x1c96206c18>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-a73a73e65df647b191614e9967783ea4']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c1d8332f0>, <tornado.concurrent.Future object at 0x1c1d82f0f0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-ac5ae1219d8646ccb7244b10e579a051']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c1d828f28>, <tornado.concurrent.Future object at 0x1c1d8257f0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-0ea38ad87928479ca2ade34134e36373']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c9621b730>, <tornado.concurrent.Future object at 0x1c96213668>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-803a075599f949fc934ceefac1d7d943']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c1d833950>, <tornado.concurrent.Future object at 0x1c715ec6d8>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-4b29ce28a85f40f7b7e58e8213527811']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c962162f0>, <tornado.concurrent.Future object at 0x1c71602d30>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-573eafef27174563a793bc127cbea4ad']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c7160ed90>, <tornado.concurrent.Future object at 0x1caca3f0b8>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-9bc070bc1e3741fe9da096dd84527831']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1caca3d510>, <tornado.concurrent.Future object at 0x1c8b8d1eb8>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-02ff4cd66c014a55af2930413728b5ee']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665d3c80>, <tornado.concurrent.Future object at 0x1c665cab70>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-a3b50ab5f1a24c36a51090019986e49a']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665e3378>, <tornado.concurrent.Future object at 0x1c665cada0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-0b66d906109d4799b4bf5adc58e8ea8c']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c8b89d400>, <tornado.concurrent.Future object at 0x1c8b8998d0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-021b15cddf2d43eca3611a97eac931a4']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c8b8d22f0>, <tornado.concurrent.Future object at 0x1c8b899b00>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-648f34f2cc83473580f75e2b587e8d11']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c9620e8c8>, <tornado.concurrent.Future object at 0x1caca3fb00>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-ee9553bc36bd469cac8378b7e4f797ca']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c8b8af598>, <tornado.concurrent.Future object at 0x1c8b8d13c8>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-68ead2f3c6ab406c95cb8b687e1433ee']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665ab598>, <tornado.concurrent.Future object at 0x1c8b8996a0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-59299ef9202d45ef9b2dadcf380888c3']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c8b8af1e0>, <tornado.concurrent.Future object at 0x1c8b8a1f60>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-c6d97994bcd942e1ac1db4883379c23f']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665c8950>, <tornado.concurrent.Future object at 0x1c665ca278>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-1faba7a288bb4f7d81a53a0cbf3d346e']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c665bda60>, <tornado.concurrent.Future object at 0x1caca3f978>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-620458dbd711491f9a90375bc1f4b56c']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c962302f0>, <tornado.concurrent.Future object at 0x1c1d85b080>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-ccbe4c232a6747c7b8c49ca08915a2b4']\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c1d856048>, <tornado.concurrent.Future object at 0x1c1d8472b0>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-62995942315941c6b119340a74567de0']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c1d856950>, <tornado.concurrent.Future object at 0x1c8b8a1400>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-b846d4a46046400fa9fc22053014da58']\n",
"tornado.application - ERROR - Exception in callback functools.partial(<function wrap.<locals>.null_wrapper at 0x1c1d8330d0>, <tornado.concurrent.Future object at 0x1c8b8ac198>)\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 605, in _run_callback\n",
" ret = callback()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\n",
" return fn(*args, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 626, in _discard_future_result\n",
" future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\", line 128, in callback_wrapper\n",
" result = yield _wait([future])\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1069, in run\n",
" yielded = self.gen.send(value)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 2906, in _wait\n",
" raise CancelledError(cancelled)\n",
"concurrent.futures._base.CancelledError: ['work_events-batch-d2edfb4506d34e689a551bff06f32967']\n",
"distributed.utils - ERROR - in <closed TCP>: TimeoutError: [Errno 60] Operation timed out: while trying to call remote method 'cancel'\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/comm/tcp.py\", line 170, in read\n",
" n_frames = yield stream.read_bytes(8)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
"tornado.iostream.StreamClosedError: Stream is closed\n",
"\n",
"During handling of the above exception, another exception occurred:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/core.py\", line 454, in send_recv_from_rpc\n",
" result = yield send_recv(comm=comm, op=key, **kwargs)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/core.py\", line 340, in send_recv\n",
" response = yield comm.read()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/comm/tcp.py\", line 184, in read\n",
" convert_stream_closed_error(self, e)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/comm/tcp.py\", line 120, in convert_stream_closed_error\n",
" raise CommClosedError(\"in %s: %s: %s\" % (obj, exc.__class__.__name__, exc))\n",
"distributed.comm.core.CommClosedError: in <closed TCP>: TimeoutError: [Errno 60] Operation timed out\n",
"\n",
"During handling of the above exception, another exception occurred:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/utils.py\", line 229, in f\n",
" result[0] = yield make_coro()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/client.py\", line 1602, in _cancel\n",
" yield self.scheduler.cancel(keys=keys, client=self.id, force=force)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1055, in run\n",
" value = future.result()\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\", line 238, in result\n",
" raise_exc_info(self._exc_info)\n",
" File \"<string>\", line 4, in raise_exc_info\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/tornado/gen.py\", line 1063, in run\n",
" yielded = self.gen.throw(*exc_info)\n",
" File \"/Users/ckemere/anaconda3/lib/python3.6/site-packages/distributed/core.py\", line 457, in send_recv_from_rpc\n",
" % (e, key,))\n",
"distributed.comm.core.CommClosedError: in <closed TCP>: TimeoutError: [Errno 60] Operation timed out: while trying to call remote method 'cancel'\n"
]
},
{
"ename": "CommClosedError",
"evalue": "in <closed TCP>: TimeoutError: [Errno 60] Operation timed out: while trying to call remote method 'cancel'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mCancelledError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\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 702\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'cancelled'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 169\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mCancelledError\u001b[0m: work_events-batch-6407a9716f68431588a9af8b84f20215",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mCommClosedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-b5f8ff536de8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'dask.distributed'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscheduler_host\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'35.184.42.12:8786'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;31m# Anything returned by work() can be stored:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0mparallel_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwork_events\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparallel_events\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 33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 787\u001b[0m \u001b[0;31m# consumption.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 788\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 789\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\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 790\u001b[0m \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 791\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 719\u001b[0m \u001b[0;31m# scheduling.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0mensure_ready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_managed_backend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 721\u001b[0;31m \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabort_everything\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_ready\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 722\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 723\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTransportableException\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/distributed/joblib.py\u001b[0m in \u001b[0;36mabort_everything\u001b[0;34m(self, ensure_ready)\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;31m# Tell the client to cancel any task submitted via this instance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 140\u001b[0m \u001b[0;31m# as joblib.Parallel will never access those results.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 141\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcancel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfutures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 142\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfutures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/distributed/client.py\u001b[0m in \u001b[0;36mcancel\u001b[0;34m(self, futures, asynchronous, force)\u001b[0m\n\u001b[1;32m 1621\u001b[0m \"\"\"\n\u001b[1;32m 1622\u001b[0m return self.sync(self._cancel, futures, asynchronous=asynchronous,\n\u001b[0;32m-> 1623\u001b[0;31m force=force)\n\u001b[0m\u001b[1;32m 1624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1625\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoroutine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/distributed/client.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 559\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 560\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 561\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 562\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 563\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__str__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/distributed/utils.py\u001b[0m in \u001b[0;36msync\u001b[0;34m(loop, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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 242\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m 684\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 685\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 688\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/distributed/utils.py\u001b[0m in \u001b[0;36mf\u001b[0;34m()\u001b[0m\n\u001b[1;32m 227\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmoment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m \u001b[0mthread_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masynchronous\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mmake_coro\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 230\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1053\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1054\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1055\u001b[0;31m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\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 1056\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1057\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhad_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m \u001b[0mraise_exc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 239\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/util.py\u001b[0m in \u001b[0;36mraise_exc_info\u001b[0;34m(exc_info)\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1062\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1063\u001b[0;31m \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1064\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1065\u001b[0m \u001b[0;31m# Break up a reference to itself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/distributed/client.py\u001b[0m in \u001b[0;36m_cancel\u001b[0;34m(self, futures, force)\u001b[0m\n\u001b[1;32m 1600\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_cancel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfutures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1601\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mtokey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfutures_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfutures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1602\u001b[0;31m \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcancel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1603\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1604\u001b[0m \u001b[0mst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfutures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1053\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1054\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1055\u001b[0;31m \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\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 1056\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1057\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhad_exception\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/concurrent.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 237\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 238\u001b[0;31m \u001b[0mraise_exc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 239\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/util.py\u001b[0m in \u001b[0;36mraise_exc_info\u001b[0;34m(exc_info)\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tornado/gen.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1061\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mexc_info\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1062\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1063\u001b[0;31m \u001b[0myielded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1064\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1065\u001b[0m \u001b[0;31m# Break up a reference to itself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/distributed/core.py\u001b[0m in \u001b[0;36msend_recv_from_rpc\u001b[0;34m(**kwargs)\u001b[0m\n\u001b[1;32m 455\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mRPCClosed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCommClosedError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 456\u001b[0m raise e.__class__(\"%s: while trying to call remote method %r\"\n\u001b[0;32m--> 457\u001b[0;31m % (e, key,))\n\u001b[0m\u001b[1;32m 458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 459\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m \u001b[0;31m# mark as open\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mCommClosedError\u001b[0m: in <closed TCP>: TimeoutError: [Errno 60] Operation timed out: while trying to call remote method 'cancel'"
]
}
],
"source": [
"# Parallelize by EVENT\n",
"import dask\n",
"import distributed.joblib\n",
"from joblib import Parallel, delayed \n",
"from joblib import parallel_backend\n",
"\n",
"# A function that can be called to do work:\n",
"def work_events(arg): \n",
"\n",
" # Split the list to individual variables:\n",
" session, segment, ii, bst, tc = arg\n",
" scores, shuffled_scores, percentiles = nel.analysis.replay.score_Davidson_final_bst_fast(bst=bst,\n",
" tuningcurve=tc,\n",
" w=w,\n",
" n_shuffles=n_shuffles,\n",
" n_samples=n_samples)\n",
"\n",
" return (session, segment, ii, scores, shuffled_scores, percentiles)\n",
"\n",
"# List of instances to pass to work():\n",
"# unroll all events:\n",
"parallel_events = []\n",
"for session, segment in zip(sessions, segments):\n",
" for nn in range(aux_data[session][segment]['PBEs'].n_epochs):\n",
" parallel_events.append((session, segment, nn, aux_data[session][segment]['PBEs'][nn], \n",
" aux_data[session][segment]['tc']))\n",
"\n",
"#parallel_results = list(map(work_events, parallel_events))\n",
"\n",
"with parallel_backend('dask.distributed', scheduler_host='35.184.42.12:8786'):\n",
" # Anything returned by work() can be stored:\n",
" parallel_results = Parallel(n_jobs=n_jobs, verbose=1)(map(delayed(work_events), parallel_events))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"\n",
"# standardize parallel results\n",
"bdries_ = [aux_data[session][segment]['PBEs'].n_epochs for session, segment in zip(sessions, segments) ]\n",
"bdries = np.cumsum(np.insert(bdries_,0,0))\n",
"bdries\n",
"\n",
"sessions_ = np.array([result[0] for result in parallel_results])\n",
"segments_ = np.array([result[1] for result in parallel_results])\n",
"idx = [result[2] for result in parallel_results]\n",
"\n",
"scores_bayes_evt = np.array([float(result[3]) for result in parallel_results])\n",
"scores_bayes_shuffled_evt = np.array([result[4].squeeze() for result in parallel_results])\n",
"scores_bayes_percentile_evt = np.array([float(result[5]) for result in parallel_results])\n",
"\n",
"results = {}\n",
"for nn in range(len(bdries)-1):\n",
" session = np.unique(sessions_[bdries[nn]:bdries[nn+1]])\n",
" if len(session) > 1:\n",
" raise ValueError(\"parallel results in different format / order than expected!\")\n",
" session = session[0]\n",
" segment = np.unique(segments_[bdries[nn]:bdries[nn+1]])\n",
" if len(segment) > 1:\n",
" raise ValueError(\"parallel results in different format / order than expected!\")\n",
" segment = segment[0]\n",
" try:\n",
" results[session][segment]['scores_bayes'] = scores_bayes_evt[bdries[nn]:bdries[nn+1]]\n",
" except KeyError:\n",
" try:\n",
" results[session][segment] = dict()\n",
" results[session][segment]['scores_bayes'] = scores_bayes_evt[bdries[nn]:bdries[nn+1]]\n",
" except KeyError:\n",
" results[session] = dict()\n",
" results[session][segment] = dict()\n",
" results[session][segment]['scores_bayes'] = scores_bayes_evt[bdries[nn]:bdries[nn+1]]\n",
"\n",
" results[session][segment]['scores_bayes_shuffled'] = scores_bayes_shuffled_evt[bdries[nn]:bdries[nn+1]]\n",
" results[session][segment]['scores_bayes_percentile'] = scores_bayes_percentile_evt[bdries[nn]:bdries[nn+1]]\n",
"\n",
"print('done packing results')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save results to disk"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"jar = nel.ResultsContainer(results=results, description='gor01 and vvp01 speed restricted results for best 20 candidate sessions')\n",
"jar.save_pkl('score_bayes_all_sessions.nel')"
]
}
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"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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| gpl-3.0 |
pycroscopy/pycroscopy | jupyter_notebooks/AFM_simulations/IntroductionToSimulations/IntroToAFMSimulations.ipynb | 1 | 543164 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to dynamic AFM simulations\n",
"###### Content under Creative Commons Attribution license CC-BY 4.0 version, \n",
"#### Enrique A. López-Guerra."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Purpose of the notebook: show an application of numerical methods to simulate the dynamics of a probe in atomic force microscopy.\n",
"\n",
"Requirements to take the best advantage of this notebook: knowing the fundamentals of [Harmonic Oscillators](http://en.wikipedia.org/wiki/Harmonic_oscillsator) in clasical mechanics and [Fundamentals of Vibrations](http://en.wikipedia.org/wiki/Vibration). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the atomic force microscope (AFM) was invented in 1986 it has become one of the main tools to study matter at the micro and nanoscale. This powerful tool is so versatile that it can be used to study a wide variety of materials, ranging from stiff inorganic surfaces to soft biological samples. \n",
"\n",
"In its early stages the AFM was used in permanent contact with the sample (the probe is dragged over the sample during the whole operation), which brought about important drawbacks, such as rapid probe wear and often sample damage, but these obstacles have been overcome with the development of dynamic techniques.\n",
"\n",
"In this Jupyter notebook, we will focus on the operation of the probe in dynamic mode."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import division, print_function, absolute_import, unicode_literals\n",
"import os\n",
"import numpy\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"from IPython.display import Image"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path = os.getcwd()\n",
"fig1 = path + '/Fig1.jpg'\n",
"Image(filename=fig1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Figure 1. Schematics of the setup of a atomic force microscope (Adapted from reference 6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In AFM the interacting probe is in general a rectangular cantilever (please check the image above that shows the AFM setup where you will be able to see the probe!). \n",
"Probably the most used dynamic technique in AFM is the Tapping Mode. In this method the probe taps a surface in intermittent contact fashion. The purpose of tapping the probe over the surface instead of dragging it is to reduce frictional forces that may cause damage of soft samples and wear of the tip. Besides with the tapping mode we can get more information about the sample! HOW???"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In Tapping Mode AFM the cantilever is shaken to oscillate up and down at a specific frequency (most of the time shaken at its natural frequency). Then the deflection of the tip is measured at that frequency to get information about the sample. Besides acquiring the topography of the sample, the phase lag between the excitation and the response of the cantilever can be related to compositional material properties! \n",
"In other words one can simultaneously get information about how the surface looks and also get compositional mapping of the surface! THAT SOUNDS POWERFUL!!!"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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E01dEdvax2ke2GOOJc52ooUVJRRQBHBaRWu7y4449xy21Qu4+9SUUUARx2kUUzSLHGskn3mCgM31NSUU2aZLeFpJGWOOMFmZjhVA6kmjVg3bVnF/tB+OY/Avwu1KXzAt1exm0thn5i7jBI/3Vy34V+hH/AATs+B0v7P37IPg/Q7yFodUurY6nqCOu10nuD5hRh/eRSiH/AHK+H/2Jvg1J+23+1la6/dWsk3w5+HMgnLyIRDqF4CGjjHY5YK7D/nnGAcbxX6nV/SHhvkEsDgvb1lac9fRdv663MeA8M8xzGtn7/hRTpUv7yTvUmvJySjFrdRfcKKKK/SD9aCiiigAooooAKKKKACiiigAooooAKKKKACiiigArlvjR8G9A+P3w01Twn4ms1vNJ1aLy5B0khYcrJG38Lo2GB9R3GRXU0VMoqSs9jHEYelXpSo1oqUZJpp6pp6NNdmfkT8S/hf4y/wCCe/i6Pw74xt5dW8DXkzLo/iC3QtGFJJCOP4WA5MZ5HJUstddpWrWuuafHdWdxDdW0w3JLEwZWHsRX6YeP/h9onxT8IX2g+ItMtNY0fUo/LuLW5Tckg/mCDyGGCCAQQRmvg/44/wDBIPxF8L9QuNd+B/iKQxMTJJ4b1aYbX/2Ypm+VvQCXaQP+WhNfkXFXhusTN4rL3aT1a6N/12+7qfjuM4bzXIHbL4PE4RbRT/e012jf+JFdFdTS01scPRXnPif4seLPgteGz+JHw/8AEXhuaNthuFtmNvIfVGbCsPdXanWn7VXge5i3Pqstu392SzmJ/wDHVIr8ixXDWZ4eXJUoy+Sv+VzzqXGGTyfJUrxpyW8an7uS8mp2Z6JRXnd1+1R4Ht4dyatJO39xLOYN/wCPKB+tR+G/jD4o+Ml79i+HPgDxH4muGbYJvszeREfVymVUe7OtGF4bzPES5KdGV/NW/OwVeMMng1CFeNST2jD95J+ihzM9B1LU7fR7GS6u54ra3hXc8kjBVQe5Ncd8Ovhz4z/4KA+LpfDPge3k0zwbayqms+IblCsKp1KL/eYjkRj5m43bVya9o+CH/BIrxZ8XdRt9c+OHiCSztUbzI/DekzKSvtJKuUT0Ij3sQfvqa+9fh18ONC+Eng2x8P8AhvS7TR9G02Py7e1t12qg7knqzE8lmJZiSSSTmv1zhTw2WHnHFZjrJapdE/6729Op34Ph3Nc/dsdCWFwj3i3atUXZpfw4vqr87WmlzM+BfwR8P/s7fC/S/CXhm0Frpelx7cnmS5kP35pG/idzyT+AwAAOuoor9ejFRXLHY/ZMNhqWHpRoUIqMIpJJaJJaJJeQUUUVRsFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAMuLeO6haOWNJI5BtZHXcrD0Iryj43fsy/DfVPh74k1C6+Hvge41CHSrl47mXQrV5kZYmIIcpkEHkHPFetVy/xwma2+C3jCRfvR6JesM+ogero04zqRUlfVfmcWOweHr02q8FKye6T/ADPwd+ElpDP8VfDMbxRvHJqtqrKyghgZkyCPSv6BrW0isbZIYI44YYxtREUKqj0AHAr8A/ghAtz8afCEbZ2ya3ZKcehnQV/QBX6Bx/h6NKpRjSio6S2SXbsfGcB4elSp1vZxUdVsku/YKKKK/PD9ACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK5T47/8AJD/GX/YCvv8A0neurrjv2iJmt/2f/HUi/ej8PX7D6i2krfC/xoeq/Myr/wAKXo/yPwz+BH/JcPBv/Ydsf/ShK/f2vwJ/Z2hW5/aB8CxtysniHT1b6G5jr99q/QfEX+NQ9Jfmj4zgn+FV9V+TCiiivzc+4CiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK4v9o/8A5N48ef8AYu6h/wCk0ldpXC/tQzNb/s0fESRTtZPDGpMp9CLWWujCa14LzX5mOI/hS9H+R+Iv7N//ACcR4C/7GPT/AP0pjr99K/BH9l2Jbj9pn4dxsNyv4n01WHqDdRV+91ffeIn8eivJ/mj43gn+DV9V+QUUUV+cn3AUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFfkd/wV2/4OuPAv7BvxW1n4Y/CvwzD8UvH3h6U2ms38979n0LRrkbw9uXTMlxPEwUSImxEJKmTzEdFAP1xor+Y7wH/AMHqv7SGl+M7W68SfDv4N61oIcm6sLCy1DT7iRTnAjna7mEZBxy0cnGRjJyP3e/4JRf8FKvDP/BVr9j7S/ip4c0q58PzNeS6RrOj3Fyly+k6hCsbSwiRQN6FZY3RiqMySISik4AB9J0UUUAeU/tu/HTVv2a/2YPE/jbQrfTrrVdF+y+RFfxvJbv5t3DC24I6McLIxGGHIHUcH5T/AGIv+CrfxE/aU/af8MeCdd0bwXa6VrX2rz5bC0uY7hPKtJpl2l7h1GWjUHKngnoeR7x/wVi/5MA8ff8AcO/9OVrX51/8Enf+T/8AwD/3Ef8A023VfoGQZZha2R4nEVYJzjz2fVWgmvuZ8bnGYYilm1ChTm1GXLdd7yaf4H6xax+1R8MPD2r3Wn6h8R/AdjfWMz29zbXHiC0imt5UJV0dGkBVlYEEEAggiuB/aS/ar+F+u/s7ePrGx+JHgG8vbzw5qEFvbweILSSWeRraRVRFEhLMSQABySa/NP8Aan/ZY+J/iH9p74j6hp/w48eX1jfeKNTuLa5t/D93LDcRPdysjo6xkMrKQQQSCCDXBf8ADIHxa/6Jd8Rf/CbvP/jdduD4QwFoVniNdHb3fJ23OTE8TYy86Soaarr6Gf8As2ara6F+0V4Bvr65t7Oys/EenT3FxPII4oI1uY2Z3Y4CqACSTwAK/a7/AIbA+Ev/AEVH4df+FJZ//HK/GH/hkD4tf9Eu+Iv/AITd5/8AG6P+GQPi1/0S74i/+E3ef/G69/PsnwWaThOpXUeVNaNdfmeNk+aYvL4ShCk5czvqmfr3+1f+1Mvwr/ZB8QfErwJe+G/Ex0024s5xN9s0+cvexW8nzQyLu273HyuMMvPQivmX9iL/AIKt/ET9pT9p/wAMeCdd0bwXa6VrX2rz5bC0uY7hPKtJpl2l7h1GWjUHKngnoeRX8Q+DNY+H/wDwQp1LSde0nUtE1S3lQy2d/avbXEQbXo2XcjgMMqQRkcgg181/8Enf+T//AAD/ANxH/wBNt1XzuXZLgv7MxkpRU5U5VFGXW0Yqz+/U9zHZrivr+FipOKmoNx9ZO/8Akfs9RRRX5qfdBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHhv/AAUy/aF1L9lH/gnz8ZPiLorRx654S8JahfaW8iF0jvBCy27MAQSBKyEjI4HUda/lD/4IT/sOWP8AwUw/4KjeDPB/jJrnUvDMMlz4n8UeZMzTajbWw8xoncgkiedoY3JIbZK5DBsGv6Zv+Dg74Tj4z/8ABGP9oLRybhfsfhv+3f3BAb/iXXMOoc5B+X/Rfm4zt3YIPI/nx/4NQfjzo3wQ/wCCxvhO01qT7PH4+0XUPC9pOzBY4rqVUnhDEkf6x7YRKBkl5UGMEkAH9O3ir/gnx8CvG/whXwDqnwf+G914NjgFtFpP/CPWqW1ugCgeWFQeWw2qQyEMCoIIIBrJ/wCCev8AwTt+HP8AwTI+B+ofD74X2+qW/h3UtevPEMi6hci5mE9xsXZ5m0FkjiihiQtufZEu5nbLH3asbwZ8RNA+I1vfzeH9a0rXItLvZdNvHsLpLhbW6ix5kDlCdsiZG5TyMjIoA2aKKKAPnX/grF/yYB4+/wC4d/6crWvzr/4JO/8AJ/8A4B/7iP8A6bbqv0U/4Kxf8mAePv8AuHf+nK1r86/+CTv/ACf/AOAf+4j/AOm26r9O4Z/5J3F/9xP/AE2j4HPv+R3hv+3P/S2fs9RRRX5iffBRRRQB86/8FYv+TAPH3/cO/wDTla1+df8AwSd/5P8A/AP/AHEf/TbdV+in/BWL/kwDx9/3Dv8A05WtfnX/AMEnf+T/APwD/wBxH/023Vfp3DP/ACTuL/7if+m0fA59/wAjvDf9uf8ApbP2eooor8xPvgooooAKKKKACiiigAooooAKKKKACiiigCpr+hWfinQr3S9RtorzT9Sge1ureVd0c8TqVdGHcFSQR6Gv5Sf+Cpf/AAbX/H79gv426t4m+E/hnxD8RfhkNSa+8Pal4Yjmu9Y0OLzFaGK5gjzOJYiQonjDIwjEhMZJRf6waKAP5INN/ag/4Kh/tAeCrf4XWd1+05qmn3Ah0po4dAvLa8wpQAT6gIEnUcr5kkswBUkyMVJz+7//AAba/wDBOPx9/wAEzP8AgnfceEfiYtjZ+LvFvii68V3OmW0yznRlmtLO2S2klQlHkAtN7FCVBk2gnbk/oBRQAUUUUAfOv/BWL/kwDx9/3Dv/AE5WtfnX/wAEnf8Ak/8A8A/9xH/023Vfop/wVi/5MA8ff9w7/wBOVrX51/8ABJ3/AJP/APAP/cR/9Nt1X6dwz/yTuL/7if8AptHwOff8jvDf9uf+ls/Z6iiivzE++CiiigD51/4Kxf8AJgHj7/uHf+nK1r86/wDgk7/yf/4B/wC4j/6bbqv0U/4Kxf8AJgHj7/uHf+nK1r86/wDgk7/yf/4B/wC4j/6bbqv07hn/AJJ3F/8AcT/02j4HPv8Akd4b/tz/ANLZ+z1FFFfmJ98FFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfOv8AwVi/5MA8ff8AcO/9OVrX5k/8E9viz4f+B37X/hHxT4p1D+y9B0v7Z9quvIkn8rzLK4iT5I1Zzl3UcA4zk8ZNfsZ+0L8DNJ/aU+D+seCdduNStNK1ryfPlsJEjuE8qeOZdpdHUZaNQcqeCeh5H5k/tC/8EgviT4c+MGsWfw58M6lr3g2Hyf7Pv7/WNPS4nzBGZdwLxHiUyKPkXhR16n9G4QzDA/UK2XYqfI58zu2krOMY6N6X3sj4fibBYv65TxuHhzcvLok3qm3qlrY+4P8Ah7F+z/8A9D9/5Q9S/wDkej/h7F+z/wD9D9/5Q9S/+R6/Ov8A4dO/tAf9CD/5XNN/+SK534sf8E9vjB8Dvh/qHinxT4R/svQdL8v7Vdf2rZT+V5kiRJ8kczOcu6jgHGcnjJrqp8K8PzkoQxV29ElOF2+y0OeXEOcxi5Sw9kt3yT/zP02/4exfs/8A/Q/f+UPUv/kej/h7F+z/AP8AQ/f+UPUv/kevyD+E/wAJ/EHxx+IGn+FvC2n/ANqa9qnmfZbXz44PN8uN5X+eRlQYRGPJGcYHOBXtH/Dp39oD/oQf/K5pv/yRW2I4PyPDy5K+IcXvZzgnbvrEzo8TZtWjzUaKku6jJ/kz6s/4KE/8FCfg/wDHH9kDxd4W8LeLv7U17VPsf2W1/sq9g83y723lf55IVQYRGPJGcYHOBXyn/wAEnf8Ak/8A8A/9xH/023VH/Dp39oD/AKEH/wArmm//ACRX2f8A8E9v+CYFl8Dv+ER+IXilvEWl/EfS/tn2rS/t1pPp8XmfaLdP9WjE5gdX4lOGPPda0rV8pyvKq2Fw1bn9pzJe9GTvKNvs2stN7GdKjmWYZjSxFely8nL0cVZSv1vrqfZ9FFFfkp+kBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAV86/8ABWL/AJMA8ff9w7/05WtfRVfOv/BWL/kwDx9/3Dv/AE5Wtepkn/Ixw/8Ajh/6Ujz82/3Gt/gl+TPzr/4JO/8AJ/8A4B/7iP8A6bbqv2er8Yf+CTv/ACf/AOAf+4j/AOm26r9nq+m8Qv8AkYw/wL/0qR4PBf8AuMv8b/KIUUUV8KfXBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFfOv/AAVi/wCTAPH3/cO/9OVrX0VXzr/wVi/5MA8ff9w7/wBOVrXqZJ/yMcP/AI4f+lI8/Nv9xrf4Jfkz86/+CTv/ACf/AOAf+4j/AOm26r9nq/GH/gk7/wAn/wDgH/uI/wDptuq/Z6vpvEL/AJGMP8C/9KkeDwX/ALjL/G/yiFFFFfCn1wUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBxfiz9pD4d+AvEFxpOu+PfBei6rabfPs7/W7a2uIdyh13Ru4ZcqysMjkEHoazv+GwPhL/0VH4df+FJZ/wDxyvza/wCCmv7OHxE8e/tv+NtW0LwF401rSrv7B5F5YaJc3NvNtsLZG2yIhVsMrKcHggjqK+R9S0240bUZ7O8t5rW7tZGimhmQxyQupwysp5DAggg8g1+k5bwThMVh4VVXd5RTaVna6vY+Fx3FeJw9adN0lZNpN31sz93f+GwPhL/0VH4df+FJZ/8Axyj/AIbA+Ev/AEVH4df+FJZ//HK/D7wF8IvFnxVe6Xwv4X8ReJGsgpuBpemzXhgDZ27/AC1bbnBxnrg1h6lptxo2oz2d5bzWt3ayNFNDMhjkhdThlZTyGBBBB5BrsXh/hHJwVd3W6sjlfGeJSUnSVn6n7u/8NgfCX/oqPw6/8KSz/wDjlH/DYHwl/wCio/Dr/wAKSz/+OV+H3gL4ReLPiq90vhfwv4i8SNZBTcDS9NmvDAGzt3+Wrbc4OM9cGszxP4V1TwTrtxpetabf6RqdoQs9pe27288JIBAZHAZeCDyOhpLw/wAI5ciru66aX+4b4yxKjzuird9bH7qf8NgfCX/oqPw6/wDCks//AI5R/wANgfCX/oqPw6/8KSz/APjlfhr4H+HXiD4m6u2n+G9B1nxBfxxmZrbTLKS7mVAQCxSNSdoJAzjHIpPG3w91/wCGmsDTvEmh6x4f1BoxKLXUrOS0mKHIDbJADtODzjHBo/4h/hObk9u79tL/AHB/rlieXn9ird9bH7l/8NgfCX/oqPw6/wDCks//AI5R/wANgfCX/oqPw6/8KSz/APjlfhZ4W8Jar451630vRNM1DWNUuiRDZ2Ns9xcTEAk7UQFmwATwOgNdF4v/AGdviD8PdCk1TX/AvjLQ9MhKrJd6hotzbQRljhQXdAoyTgZPJpS4BwUZKEsQ0300uOPGOKlHmVFWXXU/bH/hsD4S/wDRUfh1/wCFJZ//AByvBv8Agpr+0h8O/Hv7EHjbSdC8e+C9a1W7+weRZ2Gt21zcTbb+2dtsaOWbCqzHA4AJ6CvyVrqNV+CHjTQvBcfiS+8IeKLPw7MiSR6pPpU8dlIr42MJioQhsjBB5zxXXheB8LhcRTrus7xkmk7atNOxz4jizEYijOl7JWaabV9E1a56j/wTK8YaT4C/bf8ABOra7qmnaLpVp9v8+8v7lLa3h3WFyi7pHIVcsyqMnkkDqa/Wr/hsD4S/9FR+HX/hSWf/AMcr8HK7B/2ePiBH4Q/4SBvA3jBdA+z/AGv+0jo1z9j8nGfN83Zs2Y53ZxjvXocQcM4fMMRGvWquDsopaa6t9fU4clz6vgqMqNKnzK9+vZLp6H7Z/wDDYHwl/wCio/Dr/wAKSz/+OUf8NgfCX/oqPw6/8KSz/wDjlfg5XYeGP2efH/jbw2ms6L4G8Yato8gYrfWWjXNxbMFJDYkRCvBBB54wa8Wp4f4SCvOu162R6sOMsTN2hRT9Ln7Z/wDDYHwl/wCio/Dr/wAKSz/+OUf8NgfCX/oqPw6/8KSz/wDjlfg4Rg10GhfCjxT4o8KX2vaZ4a8Qajoembheaja6fNNaWm0Bm8yVVKJgEE7iMAg1UvD7CxV5VmvkhR40xEnaNJP7z9w/+GwPhL/0VH4df+FJZ/8Axyj/AIbA+Ev/AEVH4df+FJZ//HK/Byuo074IeNNX8EyeJrTwh4ouvDkSPI+qw6VO9iiISHYzBdgCkEE54wc0p+H+Eh8ddr1SCPGeJl8NJP7z9u/+GwPhL/0VH4df+FJZ/wDxyj/hsD4S/wDRUfh1/wCFJZ//AByvwcrsPDH7PPj/AMbeG01nRfA3jDVtHkDFb6y0a5uLZgpIbEiIV4IIPPGDSqeH+EgrzrtetkOHGWJm7Qop+lz9s/8AhsD4S/8ARUfh1/4Uln/8co/4bA+Ev/RUfh1/4Uln/wDHK/BwjBrqvAvwK8b/ABQ0yS98M+DfFXiKzhk8mSfTNJuLuKN8A7S0aEBsEHB5wRTqeH+EguaddpeaSFDjPEzdo0k35XP3a8J/GXwf48057zQ/FfhvWrSOQwvPYanBcRq4AJUsjEBgGU464I9aK+I/+CZ3wa8YeA/gRq1nrnhTxJot3Jr00yQX+mT28jIbe2AYK6glSVYZ6ZB9KK+Jx2U0KGIlShUuk99NT6zB5jVq0Y1JQs2ttT9AKKKK8A9gK+O/+ClH/BNe3/aK0658aeC7aG18dWse65tlwkeuoo6HsJwBhWPDDCt2K/YlFd2XZjXwVdYjDuzX3Ndn5HJjsDSxdJ0ayun+HmvM/A/4N/GTxb+yv8Wodd0Ka40nXNJlaC5tp0ZVlUNiS3njOCVJGCpwQQCMMAR9ofGT4N+Ev+Cpvwlm+Jfw0ht9J+KOkxKuvaCzqrXzBeATwCxAPly8BwNrYI+T1D/gqN/wT50f4v8AhbUPiJoDWmi+KtKi83UN6lYNWhXAy+0EiVR0fHzAbW7FfWv2Fv2HNB/Y38AeXGYdT8WarGp1bVdv3+/kxZ5WJT+LEbj2C/oWZcR4WphqeZ4e8a6drW0drOUZdHHXTrta2p8VgcjxEK88BWtKi1e/bs49pd+nrofkL8G/jJ4t/ZX+LUOu6FNcaTrmkytBc206MqyqGxJbzxnBKkjBU4IIBGGAI/Qbx74C8B/8Fg/gQvifww1r4f8Aij4fgEU0MrDfG+CRbzkDLwOcmOUDKnPAO9K2v+Co3/BPnR/i/wCFtQ+ImgNaaL4q0qLzdQ3qVg1aFcDL7QSJVHR8fMBtbsV9a/YW/Yc0H9jfwB5cZh1PxZqsanVtV2/f7+TFnlYlP4sRuPYLnm2f4SvhaeZ0LwxCdlZdrcyl0cbPTrr6mmW5PiaWIngK1pUWru/ns11Tuvw9D8e4ZvGH7MHxg3L/AGh4X8X+F7v/AHZbeQdj2ZWU+6urd1PP6IeD/GHgP/gsT8B20HXltfDvxT8OwGSKWNfnhbgGeEE5kt3OA8ZOUJHOdjn1b/god+wXof7V/gSbWLVrfSPGmiWzPaaiVOy5jUFjBPgElOu1gCUJyMglTgf8Ex/2CdF/Z68Baf421JrfWPGXiKyS4juVUmLTLeVQwii3AHcykb3wCfujAyWeZcQYTGYCOPV4YiDsrd97X2cWr6P/AIcwOTYnC4yWDdpUZq7v2723Ul5f8N+YHxa+Evi79ln4tzaLrUN1oviDRZlnt7iCRlDgNmO4gkGCVJGVYYIIwcEED9KP+Cf/APwUB0X9sTwZJ8O/iJHp8niyS1a3dLiNfs3iSDbhiF6CULkug4PLLxkJ7T+2V+xr4c/bF+HDaXqirY63Yqz6TqyJulspD2P96JsDcnfqMEAj57/4Ji/8E4NN+GUn/CwPFk1jrfiC3u7i20qCEM1tp5hleFp/mUFpSyNtJGFGD94/LGOzvA5plTrYpONanZKy+09rPs7apvS3ezKweU4vL8xVPDu9Oe9+y3uu6vo1v6XPmT/gov8A8E6NQ/ZT1+TxH4cjuNQ+H+oTYRzl5NHkY8QynqUJ4SQ9fut82C0P7A/7fC/AqObwH48h/t/4W6/ugubaePz/AOy/M4d0Q53RNkl4x7svzZD/AK8+JPDmn+MNAvNL1Szt9Q03UIWgubadA8c8bDBVgeCCK/OjxH/wRw09/wBsW00Oz177P4Bv7WTWmgJZr+CGOREe2ViCpy0ihZCchc5BK/NplPEeHx+DlhM13ir8y3aXXTaS79fzzzHI6+DxUcTl20na3RX6a7xfbp+XjP7fH7A7fs+yQ+NvBM39v/C/X9s9neQSef8A2d5nKRu4zujbI2Sd+FPzYLX/APgnV/wUVuv2Y9VXwn4sabUvh3qchV0ZTK+js5+aSNed0RyS8Y9SyjdkP+rOl/CXw1o3wzj8G2+i6enheOzNgNNMQa3MJGChB+9nJJJySSSSSc1+fviP/gjhp7/ti2mh2evfZ/AN/aya00BLNfwQxyIj2ysQVOWkULITkLnIJX5nlvEWEzDCVMFmd3yptStq0uul7SXfZ/mY7JMTgsTDFYCy5mk1fRN9Nd4v8Py5P/gor/wTqtfB2lN8U/hYsOpeBdSjF7eWdkwlTTlcbvPh253WxznA/wBXn+593yP9hH9u7XP2N/HO1vP1Twbqko/tTS93ToPPhzwsqj6BwNpxhWX9lPB3gHRvAHguy8O6PptrYaJp9uLWCzjT92keOmD1zkkk5JJJOSTXwB+1V/wSJ0/VP2kfDR8G6pZ+H/DvjW9ljurKRGY6Y6RtNIbcAYKMiNtRioVsDO0/LOScR4fGUZ5dmV5Rs7NrVpa62+0krpr893muR1sNVjjsBpK6ulsm9NL9G9LfptW/bt/YS0P9oPwN/wALq+Cv2fVLfVIje6nplkvF2OS80KdVmU58yLAJIJADAhvkf9lL9q3xH+yX8Rl1jRyt3p12BBq2kzt/o2qQd0cc4YZO18EqSeoLKf2s+CPwS8O/s9fDfT/CvhexWx0uwXv80txIfvSyN/E7EZJ+gGAAB8Y/8FF/+CX+n+NvGmm+MPBNxp3h+88Ratb6fqtnMrLbSTXEojW5jCKdrbmy64w33hg53LIeJKFXmyzGXlTd1Fy1duilby2a2/J5xkdanbH4a0ais5JbX6tX/FdT57/au/ZR8O/EH4dN8aPgurXngq8JfW9ERf8ASfDM/VwUGcRDOSBkICGBMZBXj/2GP26NZ/ZA8ZPDMkmseCdYcLq+kMQwII2maINwJQvBB4cDa38LL+r/AOy1+yd4X/ZR+FSeGdDt1uXuQH1S+njHnapLjBZ/ReSFTooPckk/G/7VX/BInT9U/aR8NHwbqln4f8O+Nb2WO6spEZjpjpG00htwBgoyI21GKhWwM7T8vRlvEWCxaqZdi7yp68smtXFa621ukrp7vrrvjjsjxWGcMdhrRnpzJbJvTS+ln1W3y280/bn/AGGNGn8Gp8ZPg28esfD3WEN3e2VoCx0gk/M6L1EQbIZCMxEEEbfu+a/sI/t3a5+xv452t5+qeDdUlH9qaXu6dB58OeFlUfQOBtOMKy/r78GPgX4Z+Afwxs/CPhvT47XR7RCrK4Dvduw+eSU4+d37k8dAAAAB8L/tVf8ABInT9U/aR8NHwbqln4f8O+Nb2WO6spEZjpjpG00htwBgoyI21GKhWwM7T8ueU8RYXF055dj7yhZ8smtXFa62+0kr3X571mWR4jDThjsHaMtLpbJvTS/Rvo/y2rft2/sJaH+0H4G/4XV8Ffs+qW+qRG91PTLJeLscl5oU6rMpz5kWASQSAGBDfJX7Jf7Wnib9kL4mR67oUhns7grFqmlyuVg1GEH7rf3XGSVcDKk9wWU/tT8Efgl4d/Z6+G+n+FfC9itjpdgvf5pbiQ/elkb+J2IyT9AMAAD4x/4KL/8ABL/T/G3jTTfGHgm407w/eeItWt9P1WzmVltpJriURrcxhFO1tzZdcYb7wwc7s8h4koVubK8ZeVN3UXJXduilby2a2/LTOMjrU7ZhhbRqKzkltfq1f8V1Prv4C/tMeEf2i/hpY+KPD+qQGzuvklgnkWOezmABeGRc8MuR6ggggkEElc1+z9+wp8PPgH8MbHw7FoGla7PDmW71HUrKOae8nYAM/wAwO1eAAg4AA6nJJXweKjgvbS9g5cl3a6W33n2GHlivZR9so81tbN7/AHH/2Q==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fig2 = path + '/Fig2DHO.jpg'\n",
"Image(filename=fig2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Figure 2. Schematics of a damped harmonic oscillator without tip-sample interactions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analytical Solution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The motion of the probe can be derived using [Euler-Bernoulli's](http://en.wikipedia.org/wiki/Euler%E2%80%93Bernoulli_beam_theory) equation. However that equation has partial derivatives (it depends on time and space) because it deals with finding the position of each point of the beam in a certain time, which cant make the problem too expensive computationally for our purposes. In our case, we have the advantage that we are only concerned about the position of the tip (which is the only part of the probe that will interact with the sample). As a consequence many researchers in AFM have successfully made approximations using a simple mass point model approximation [see ref. 2] like the one in figure 2 (with of course the addition of tip sample forces! We will see more about this later).\n",
"\n",
"First we will study the system of figure 2 AS IS (without addition of tip-sample force term), WHY? Because we want to get an analytical solution to get a reference of how our integration schemes are working, and the addition of tip sample forces to our equation will prevent the acquisition of straightforward analytical solutions :(\n",
"\n",
"Then, the equation of motion of the damped harmonic oscillator of figure 2, which is DRIVEN COSINUSOIDALLY (remember that we are exciting our probe during the scanning process) is:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\\begin{equation}\n",
"m \\frac{d^2z}{dt^2} = - k z - \\frac{m\\omega_0}{Q}\\frac{dz}{dt} + F_0\\cos(\\omega t)\n",
"\\end{equation}$$\n",
"where k is the stiffness of the cantilever, z is the vertical position of the tip with respect to the cantilever base position, Q is the quality factor (which is related to the damping of the system), $F_0$ is the driving force amplitude, $\\omega_0$ is the resonance frequency of the oscillator, and $\\omega$ is the frequency of the oscillating force.\n",
"\n",
"The analytical solution of the above ODE is composed by a transient term and a steady state term. We are only interested in the steady state part because during the scanning process it is assumed that the probe has achieved that state.\n",
"\n",
"The steady state solution is given by:\n",
"$$\\begin{equation}\n",
"A\\cos (\\omega t - \\phi)\n",
"\\end{equation}$$\n",
"\n",
"where A is the steady state amplitude of the oscillation response, which depends on the cantilever parameters and the driving parameters, as can be seen in the following relation:\n",
"$$\\begin{equation}\n",
"A = \\frac{F_0/m}{\\sqrt{(\\omega_0^2-\\omega^2)^2+(\\frac{\\omega\\omega_0}{Q})^2}}\n",
"\\end{equation}$$\n",
"\n",
"and $\\phi$ is given by:\n",
"$$\\begin{equation}\n",
"\\phi = \\arctan \\big( \\frac{\\omega\\omega_0/Q}{\\omega_0^2 - \\omega^2} \\big)\n",
"\\end{equation}$$\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's first name the variables that we are going to use. Because we are dealing with a damped harmonic oscillator model we have to include variables such as: spring stiffness, resonance frequency, quality factor (related to damping coefficient), target oscillation amplitude, etc."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x78a8048>]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"k = 10.\n",
"fo = 45000\n",
"wo = 2.0*numpy.pi*fo\n",
"Q = 25.\n",
"\n",
"period = 1./fo\n",
"m = k/(wo**2)\n",
"Ao = 60.e-9\n",
"Fd = k*Ao/Q\n",
"\n",
"spp = 28. # time steps per period \n",
"dt = period/spp #Intentionally chosen to be quite big\n",
"#you can decrease dt by increasing the number of steps per period\n",
"\n",
"simultime = 100.*period\n",
"N = int(simultime/dt)\n",
"\n",
"#Analytical solution\n",
"\n",
"time_an = numpy.linspace(0,simultime,N) #time array for the analytical solution\n",
"z_an = numpy.zeros(N) #position array for the analytical solution\n",
"\n",
"#Driving force amplitude this gives us 60nm of amp response (A_target*k/Q)\n",
"Fo_an = 24.0e-9 \n",
"\n",
"A_an = Fo_an*Q/k #when driven at resonance A is simply Fo*Q/k\n",
"phi = numpy.pi/2 #when driven at resonance the phase is pi/2\n",
"\n",
"z_an[:] = A_an*numpy.cos(wo*time_an[:] - phi) #this gets the analytical solution\n",
"\n",
"#slicing the array to include only steady state (only the last 10 periods)\n",
"z_an_steady = z_an[int(90.*period/dt):]\n",
"time_an_steady = time_an[int(90.*period/dt):]\n",
"\n",
"plt.title('Plot 1 Analytical Steady State Solution of Eq 1', fontsize=20)\n",
"plt.xlabel('time, ms', fontsize=18)\n",
"plt.ylabel('z_Analytical, nm', fontsize=18)\n",
"plt.plot(time_an_steady*1e3, z_an_steady*1e9, 'b--')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Approximating through Euler's method"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we perform a Taylor series expansion of $z_{n+1}$ around $z_{n}$ we get:\n",
"\n",
"$$z_{n+1} = z_{n} + \\Delta t\\frac{dz}{dt}\\big|_n + {\\mathcal O}(\\Delta t^2)$$\n",
"\n",
"The Euler formula neglects terms in the order of two or higher, ending up as:\n",
"\n",
"$$\\begin{equation}\n",
"z_{n+1} = z_{n} + \\Delta t\\frac{dz}{dt}\\big|_n\n",
"\\end{equation}$$\n",
"\n",
"It can be easily seen that the truncation error of the Euler algorithm is in the order of ${\\mathcal O}(\\Delta t^2)$.\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a second order ODE, but we can convert it to a system of two coupled 1st order differential equations. To do it we will define $\\frac{dz}{dt} = v$. Then equation (1) will be decomposed as:\n",
"$$\\begin{equation}\n",
"\\frac{dz}{dt} = v\n",
"\\end{equation}$$\n",
"\n",
"$$\\begin{equation}\n",
"\\frac{dv}{dt} = -kz-\\frac{m\\omega_0}{Q}+F_o\\cos(\\omega t)\n",
"\\end{equation}$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These coupled equations will be used during Euler's aproximation and also during our integration using Runge Kutta 4 method."
]
},
{
"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": {},
"output_type": "display_data"
}
],
"source": [
"t= numpy.linspace(0,simultime,N) #time grid for Euler method\n",
" \n",
"#Initializing variables for Euler\n",
"vdot_E = numpy.zeros(N)\n",
"v_E = numpy.zeros(N)\n",
"z_E = numpy.zeros(N)\n",
"\n",
"#Initial conditions\n",
"z_E[0]= 0.0\n",
"v_E[0]=0.0\n",
"\n",
"for i in range (N-1):\n",
" vdot_E[i] =( ( -k*z_E[i] - (m*wo/Q)*(v_E[i]) +\\\n",
" Fd*numpy.cos(wo*t[i]) ) / m) #Equation 7\n",
" v_E[i+1] = v_E[i] + dt*vdot_E[i] #Based on equation 5\n",
" z_E[i+1] = z_E[i] + v_E[i]*dt #Equation 5\n",
"\n",
"plt.title('Plot 2 Eulers approximation of Equation1', fontsize=20); \n",
"plt.plot(t*1e3,z_E*1e9);\n",
"plt.xlabel('time, s', fontsize=18);\n",
"plt.ylabel('z_Euler, nm', fontsize=18);\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This looks totally unphysical! We were expecting to have a steady state oscillation of 60 nm and we got a huge oscillation that keeps growing. Can it be due to the scheme? The timestep that we have chosen is quite big with respect to the oscillation period. We have intentionally set it to ONLY 28 time steps per period (That could be the reason why the scheme can't capture the physics of the problem). That's quite discouraging. However the timestep is quite big and it really gets better as you decrease the time step. Try it! Reduce the time step and see how the numerical solution acquires an amplitude of 60 nm as the analytical one. At this point we can't state anything about accuracy before doing an analysis of error (we will make this soon). But first, let's try to analyze if another more efficient scheme can capture the physics of our damped harmonic oscillator even with this large time step."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Let's try to get more accurate... Verlet Algorithm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a very popular algorithm widely used in molecular dynamics simulations. Its popularity has been related to high stability when compared to the simple Euler method, it is also very simple to implement and accurate as we will see soon! Verlet integration can be seen as using the central difference approximation to the second derivative. Consider the Taylor expansion of $z_{n+1}$ and $z_{n-1}$ around $z_n$:\n",
"\n",
"$$\\begin{equation}\n",
"z_{n+1} = z_n + \\Delta t \\frac{dz}{dt}\\big|_n + \\frac{\\Delta t^2}{2} \\frac{d^2 z}{d t^2}\\big|_n + \\frac{\\Delta t^3}{6} \\frac{d^3 z}{d t^3}\\big|_n + {\\mathcal O}(\\Delta t^4)\n",
"\\end{equation}$$\n",
"\n",
"$$\\begin{equation}\n",
"z_{n-1} = z_n - \\Delta t \\frac{dz}{dt}\\big|_n + \\frac{\\Delta t^2}{2} \\frac{d^2 z}{dt^2}\\big|_n - \\frac{\\Delta t^3}{6} \n",
"\\frac{d^3 z}{d t^3}\\big|_n + {\\mathcal O}(\\Delta t^4)\n",
"\\end{equation}$$\n",
"\n",
"Adding up these two expansions and solving for $z_{n+1}$ we get:\n",
"\n",
"$$z_{n+1}= 2z_{n} - z_{n-1} + \\frac{d^2 z}{d t^2} \\Delta t^2\\big|_n + {\\mathcal O}(\\Delta t^4) $$\n",
"\n",
"Verlet algorithm neglects terms on the order of 4 or higher, ending up with:\n",
"\n",
"$$\\begin{equation}\n",
"z_{n+1}= 2z_{n} - z_{n-1} + \\frac{d^2 z}{d t^2} \\Delta t^2\\big|_n\n",
"\\end{equation}$$\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This looks nice; it seems that the straightforward calculation of the second derivative will give us good results. BUT have you seen that we also need the value of the first derivative (velocity) to put it into the equation of motion that we are integrating (see equation 1). YES, that's a main drawback of this scheme and therefore it's mainly used in applications where the equation to be integrated doesn't have first derivative. But don't panic we will see what can we do...\n",
"\n",
"What about subtracting equations 8 and 9 and then solving for $\\frac{dz}{dt}\\big|_n$:\n",
"$$\n",
"\\frac{dz}{dt}\\big|_n = \\frac{z_{n+1} - z_{n-1}}{2\\Delta t} + {\\mathcal O}(\\Delta t^2)\n",
"$$\n",
"If we neglect terms on the order of 2 or higher we can calculate velocity:\n",
"$$\\begin{equation}\n",
"\\frac{dz}{dt}\\big|_n = \\frac{z_{n+1} - z_{n-1}}{2\\Delta t}\n",
"\\end{equation}$$\n",
"\n",
"This way of calculating velocity is pretty common in Verlet integration in applications where velocity is not explicit in the equation of motion. However for our purposes of solving equation 1 (where first derivative is explicitly present) it seems that we will lose accuracy because of the velocity, we will discuss more about this soon after..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Have you noticed that we need a value $z_{n-1}$? Does it sound familiar? YES! This is not a self-starting method. As a result we will have to overcome the issue by setting the initial conditions of the first step using Euler approximation. This is a bit annoying, but a couple of extra lines of code won't kill you :)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"time_V = numpy.linspace(0,simultime,N)\n",
"\n",
"#Initializing variables for Verlet\n",
"zdoubledot_V = numpy.zeros(N)\n",
"zdot_V = numpy.zeros(N)\n",
"z_V = numpy.zeros(N)\n",
"\n",
"#Initial conditions Verlet. Look how we use Euler for the first step approximation!\n",
"z_V[0] = 0.0\n",
"zdot_V[0] = 0.0\n",
"zdoubledot_V[0] = ( ( -k*z_V[0] - (m*wo/Q)*zdot_V[0] +\\\n",
" Fd*numpy.cos(wo*t[0]) ) ) / m\n",
"zdot_V[1] = zdot_V[0] + zdoubledot_V[0]*dt\n",
"z_V[1] = z_V[0] + zdot_V[0]*dt\n",
"zdoubledot_V[1] = ( ( -k*z_V[1] - (m*wo/Q)*zdot_V[1] +\\\n",
" Fd*numpy.cos(wo*t[1]) ) ) / m\n",
"\n",
"#VERLET ALGORITHM\n",
"\n",
"for i in range(2,N):\n",
" z_V[i] = 2*z_V[i-1] - z_V[i-2] + zdoubledot_V[i-1]*dt**2 #Eq 10\n",
" zdot_V[i] = (z_V[i]-z_V[i-2])/(2.0*dt) #Eq 11\n",
" zdoubledot_V[i] = ( ( -k*z_V[i] - (m*wo/Q)*zdot_V[i] +\\\n",
" Fd*numpy.cos(wo*t[i]) ) ) / m #from eq 1\n",
" \n",
"plt.title('Plot 3 Verlet approximation of Equation1', fontsize=20); \n",
"plt.xlabel('time, ms', fontsize=18);\n",
"plt.ylabel('z_Verlet, nm', fontsize=18);\n",
"plt.plot(time_V*1e3, z_V*1e9, 'g-');\n",
"plt.ylim(-65,65);\n",
" \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It WAS ABLE to capture the physics! Even with the big time step that we use with Euler scheme!\n",
"\n",
"As you can see, and as we previously discussed the harmonic response is composed of a transient and a steady part. We are only concerned about the steady-state, since it is assumed that the probe achieves steady state motion during the imaging process. Therefore, we are going to slice our array in order to show only the last 10 oscillations, and we will see if it resembles the analytical solution."
]
},
{
"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": {},
"output_type": "display_data"
}
],
"source": [
"#Slicing the full response vector to get the steady state response\n",
"z_steady_V = z_V[int(90*period/dt):]\n",
"time_steady_V = time_V[int(90*period/dt):]\n",
"\n",
"plt.title('Plot 3 Verlet approx. of steady state sol. of Eq 1', fontsize=20); \n",
"plt.xlabel('time, ms', fontsize=18);\n",
"plt.ylabel('z_Verlet, nm', fontsize=18);\n",
"plt.plot(time_steady_V*1e3, z_steady_V*1e9, 'g-');\n",
"plt.ylim(-65,65);\n",
"plt.show();\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Let's use now one of the most popular schemes... The Runge Kutta 4!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Runge Kutta 4 (RK4) method is very popular for the solution of ODEs. This method is designed to solve 1st order differential equations. We have converted our 2nd order ODE to a system of two coupled 1st order ODEs when we implemented the Euler scheme (equations 5 and 6). And we will have to use these equations for the RK4 algorithm.\n",
"\n",
"In order to clearly see the RK4 implementation we are going to put equations 5 and 6 in the following form:\n",
"$$\\begin{equation}\n",
"\\frac{dz}{dt}=v \\Rightarrow f1(t,z,v)\n",
"\\end{equation}$$\n",
"$$\\begin{equation}\n",
"\\frac{dv}{dt} = -kz-\\frac{m\\omega_0}{Q}+F_ocos(\\omega t) \\Rightarrow f2(t,z,v)\n",
"\\end{equation}$$\n",
"\n",
"It can be clearly seen that we have two coupled equations f1 and f2 and both depend in t, z, and v.\n",
"\n",
"The RK4 equations for our special case where we have two coupled equations, are the following:\n",
"$$\\begin{equation}\n",
"k_1 = f1(t_i, z_i, v_i)\n",
"\\end{equation}$$\n",
"$$\\begin{equation}\n",
"m_1 = f2(t_i, z_i, v_i)\n",
"\\end{equation}$$\n",
"$$\\begin{equation}\n",
"k_2 = f1(t_i +1/2\\Delta t, z_i + 1/2k_1\\Delta t, v_i + 1/2m_1\\Delta t)\n",
"\\end{equation}$$\n",
"$$\\begin{equation}\n",
"m_2 = f2(t_i +1/2\\Delta t, z_i + 1/2k_1\\Delta t, v_i + 1/2m_1\\Delta t)\n",
"\\end{equation}$$\n",
"$$\\begin{equation}\n",
"k_3 = f1(t_i +1/2\\Delta t, z_i + k_2\\Delta t, v_i + 1/2m_2\\Delta t)\n",
"\\end{equation}$$\n",
"$$\\begin{equation}\n",
"m_3 = f2(t_i +1/2\\Delta t, z_i + 1/2k_2\\Delta t, v_i + 1/2m_2\\Delta t)\n",
"\\end{equation}$$\n",
"$$\\begin{equation}\n",
"k_4 = f1(t_i + \\Delta t, z_i + k_3\\Delta t, v_i + m_3\\Delta t)\n",
"\\end{equation}$$\n",
"$$\\begin{equation}\n",
"k_4 = f2(t_i + \\Delta t, z_i + k_3\\Delta t, v_i + m_3\\Delta t)\n",
"\\end{equation}$$\n",
"\n",
"$$\\begin{equation}\n",
"f1_{n+1} = f1_n + \\Delta t/6(k_1+2k_2+2k_3+k_4)\n",
"\\end{equation}$$\n",
"$$\\begin{equation}\n",
"f2_{n+1} = f2_n + \\Delta t/6(m_1+2m_2+2m_3+m_4)\n",
"\\end{equation}$$\n",
"\n",
"Please notice how k values and m values are used sequentially, since it is crucial in the implementation of the method!\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Definition of v, z, vectors\n",
"vdot_RK4 = numpy.zeros(N)\n",
"v_RK4 = numpy.zeros(N)\n",
"z_RK4 = numpy.zeros(N)\n",
"k1v_RK4 = numpy.zeros(N)\n",
"k2v_RK4 = numpy.zeros(N)\n",
"k3v_RK4 = numpy.zeros(N)\n",
"k4v_RK4 = numpy.zeros(N)\n",
"\n",
"k1z_RK4 = numpy.zeros(N)\n",
"k2z_RK4 = numpy.zeros(N)\n",
"k3z_RK4 = numpy.zeros(N)\n",
"k4z_RK4 = numpy.zeros(N)\n",
" \n",
"#calculation of velocities RK4\n",
"\n",
"#INITIAL CONDITIONS\n",
"v_RK4[0] = 0\n",
"z_RK4[0] = 0\n",
"\n",
" \n",
"for i in range (1,N):\n",
" #RK4\n",
" k1z_RK4[i] = v_RK4[i-1] #k1 Equation 14 \n",
" k1v_RK4[i] = (( ( -k*z_RK4[i-1] - (m*wo/Q)*v_RK4[i-1] + \\\n",
" Fd*numpy.cos(wo*t[i-1]) ) ) / m ) #m1 Equation 15\n",
" \n",
" k2z_RK4[i] = ((v_RK4[i-1])+k1v_RK4[i]/2.*dt) #k2 Equation 16\n",
" k2v_RK4[i] = (( ( -k*(z_RK4[i-1]+ k1z_RK4[i]/2.*dt) - (m*wo/Q)*\\\n",
" (v_RK4[i-1] +k1v_RK4[i]/2.*dt) + Fd*\\\n",
" numpy.cos(wo*(t[i-1] + dt/2.)) ) ) / m ) #m2 Eq 17\n",
" \n",
" k3z_RK4[i] = ((v_RK4[i-1])+k2v_RK4[i]/2.*dt) #k3, Equation 18\n",
" k3v_RK4[i] = (( ( -k*(z_RK4[i-1]+ k2z_RK4[i]/2.*dt) - (m*wo/Q)*\\\n",
" (v_RK4[i-1] +k2v_RK4[i]/2.*dt) + Fd*\\\n",
" numpy.cos(wo*(t[i-1] + dt/2.)) ) ) / m ) #m3, Eq 19\n",
" \n",
" k4z_RK4[i] = ((v_RK4[i-1])+k3v_RK4[i]*dt) #k4, Equation 20\n",
" k4v_RK4[i] = (( ( -k*(z_RK4[i-1] + k3z_RK4[i]*dt) - (m*wo/Q)*\\\n",
" (v_RK4[i-1] + k3v_RK4[i]*dt) + Fd*\\\n",
" numpy.cos(wo*(t[i-1] + dt)) ) ) / m )#m4, Eq 21\n",
" \n",
" #Calculation of velocity, Equation 23\n",
" v_RK4[i] = v_RK4[i-1] + 1./6*dt*(k1v_RK4[i] + 2.*k2v_RK4[i] +\\\n",
" 2.*k3v_RK4[i] + k4v_RK4[i] ) \n",
" #calculation of position, Equation 22\n",
" z_RK4 [i] = z_RK4[i-1] + 1./6*dt*(k1z_RK4[i] + 2.*k2z_RK4[i] +\\\n",
" 2.*k3z_RK4[i] + k4z_RK4[i] ) \n",
"\n",
"#slicing array to get steady state\n",
"z_steady_RK4 = z_RK4[int(90.*period/dt):]\n",
"time_steady_RK4 = t[int(90.*period/dt):]\n",
" \n",
"plt.title('Plot 3 RK4 approx. of steady state sol. of Eq 1', fontsize=20); \n",
"plt.xlabel('time, ms', fontsize=18);\n",
"plt.ylabel('z_RK4, nm', fontsize=18);\n",
"plt.plot(time_steady_RK4 *1e3, z_steady_RK4*1e9, 'r-');\n",
"plt.ylim(-65,65);\n",
"plt.show();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Error Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot together our solutions using the different schemes along with our analytical reference."
]
},
{
"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": {},
"output_type": "display_data"
}
],
"source": [
"plt.title('Plot 4 Schemes comparison with analytical sol.', fontsize=20);\n",
"plt.plot(time_an_steady*1e3, z_an_steady*1e9, 'b--' );\n",
"plt.plot(time_steady_V*1e3, z_steady_V*1e9, 'g-' );\n",
"plt.plot(time_steady_RK4*1e3, z_steady_RK4*1e9, 'r-');\n",
"plt.xlim(2.0, 2.06);\n",
"plt.legend(['Analytical solution', 'Verlet method', 'Runge Kutta 4']);\n",
"plt.xlabel('time, ms', fontsize=18);\n",
"plt.ylabel('z_position, nm', fontsize=18);\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It was pointless to include Euler in the last plot because it was not following the physics at all for this given time step. REMEMBER that Euler can give fair approximations, but you MUST decrease the time step in this particular case if you want to see the sinusoidal trajectory!\n",
"It seems our different schemes are giving different quality in approximating the solution. However it's hard to conclude something strong based on this qualitative observations. In order to state something stronger we have to perform further error analysis. We will do this at the end of the notebook after the references and will choose L1 norm for this purpose (You can find more information about this [L1](http://en.wikipedia.org/wiki/Taxicab_geometry) )."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see Runge Kutta 4 converges faster than Verlet for the range of time steps studied. And the difference between both is near one order of magnitude. One additional advantage with Runge Kutta 4 is that the method is very stable, even with big time steps (eg. 10 time steps per period) the method is able to catch up the physics of the oscillation, something where Verlet is not so good at."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Let's add a sample and oscillate our probe over it"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is very common in the field of probe microscopy to model the tip sample interactions through DMT contact mechanics. \n",
"DMT stands for Derjaguin, Muller and Toporov who were the scientists that developed the model (see ref 1). This model uses Hertz contact mechanics (see ref 2) with the addition of long range tip-sample interactions. These long range tip-sample interactions are ascribed to intermolecular interactions between the atoms of the tip and the upper atoms of the surface, and include mainly the contribution of van de Waals forces and Pauli repulsion from electronic clouds when the atoms of the tip meet closely the atoms of the surface. Figure 2 displays a force vs distance curve (FD curve) where it is shown how the forces between the tip and the sample behave with respect to the separation. It can be seen that at positive distances the tip starts \"feeling\" attraction from the tip (from the contribution of van der Waals forces) where the slope of the curve is positive and at some minimum distance ($a_0$) the tip starts experiencing repulsive interactions arising from electronic cloud repulsion (area where the slope of the curve is negative and the forces are negative). At lower distances, an area known as \"contact area\" arises and it is characterized by a negative slope and an emerging positive force."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/jpeg": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fig3 = path + '/Fig3FDcurve.jpg'\n",
"Image(filename=fig3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 3. Force vs Distance profile depicting tip-sample interactions in AFM (Adapted from reference 6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In Hertz contact mechanics, one central aspect is to consider that the contact area increases as the sphere is pressed against an elastic surface, and this increase of the contact area \"modulates\" the effective stiffness of the sample. This concept is represented in figure 4 where the sample is depicted as comprised by a series of springs that are activated as the tip goes deeper into the sample. In other words, the deeper the sample goes, the larger the contact area and therefore more springs are activated (see more about this on reference 5). \n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/jpeg": 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6R/Y20K98L/sg/CrTNTs7rTtR07wfpFrd2l1E0M9rKllCrxujAMrqwIKkAggg16TX2mB4Xyu1PFOgueyfzt22v8j4zHcT5pephlXfJdr5X772+YUUUV9QfMBRRRQAUUUUAFFFFABXlv7Yn7RNv+zL8C9U8Qlo21SQfY9KhfnzrpwdnHdVALt7IR1Ir02/vodLsZrm5ljgt7dGlllkYKkaKMliTwAACSa+LvA0Vz/wUd/auHii5jkX4U/De48vTIpEO3VboENuIPXcVV2HZBGpGXJr5XirNq2HoxwOB/3mv7sP7v8ANN+UFr62XU+q4WyqjiK0sdjv92oe9P8AvfywXnN6el30PWv+Cev7O9z8E/g42ra55sni/wAayjVdXlm5mTdlo4mJ5yAzM2ed8j+le+UUV7OUZXRy7B08Fh/hgreb7t+bd2/NnjZtmdbMcZUxuI+Kbv5Lsl5JWS8kFFFFeiecFFFFABRVTXdcs/DGh3mpahcRWdhp8D3NzcSttjgiRSzux7AKCSfavnx/+Ctn7PkbkH4gDIOOND1Ij8/s9ZVK9On8ckvVnlZjnuWZe4xx+Ip0nLbnnGN/TmaufR1FfOH/AA9u/Z7/AOigf+ULUv8A5Ho/4e3fs9/9FA/8oWpf/I9Z/XMP/OvvR5n+u/Dn/Qwof+Daf/yR9H0V84f8Pbv2e/8AooH/AJQtS/8Akej/AIe3fs9/9FA/8oWpf/I9H1zD/wA6+9B/rvw5/wBDCh/4Np//ACRe/by/ZZvfjb4Z0/xR4Sc2fxB8GuLvS54ztkuUU7/Iz03bhuTPAbI4Dk1sfsW/tbWn7T3gORLyMab4x0LFvrWnMpRkkHy+ainnYxB4PKsCp7E8z/w9u/Z7/wCigf8AlC1L/wCR6+Xf2ov2vvhLofxh074sfB3x5BH4xjlCatpbaRfwwaxGcBmJeFUyQMOCw3YDAh1y3w+bp5ZjXnWXPnUrKtTTTcktFOC/nj1X2o6bpH2+UeInCmZ4JZLmOZYeEo3dGo61NKLerhN83wS6P7Mtdmz9MaK8t/ZS/ax8N/tZfD5NX0WQW9/bqq6jpsjgzWTkZH+9G3O1wMHB6EED1Kvt8FjaGMoRxOGlzQlqmv637rdPRmGKwtXDVXRrK0l/Sa6NNaprRrVaBRRRXUc4UUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUV8R/8ABRD/AIKY6b8PLif4e+DdZgt9auj9m1TW13vHpKk7XSMxqzGQc7mUEpghQX+75Oc5xQy3DuvVTk/sxiryk+0V+fRLV2R0YelRk+bFVoUKStzVKkowpwTdrylJpLXRK929Fdmt+1t8aNa/au+Jp+Bvw0lDQF/+Kp1lOYbaJWG+LcP4VPDY5ZsIP4s/T/wd+E2j/A74b6X4X0ODydP0uERgn787nl5HPdmYkn68cYFfIv7Jn7ZP7MX7Kvw0j0iw+IC3eq3e2bVdROgakHvpsf8AXvkRrkhV7DJ6kk+pf8Pbv2e/+igf+ULUv/kevn+HcI41Z5tmlSLxNVWsmmqcN1Tj6byf2peiM+IfE/hSVKGVZZj6Kw1J3u6sE6k9nUl73XaK+zH1Z9H0V84f8Pbv2e/+igf+ULUv/kej/h7d+z3/ANFA/wDKFqX/AMj19b9cw/8AOvvR8n/rvw5/0MKH/g2n/wDJH0fRXzh/w9u/Z7/6KB/5QtS/+R6P+Ht37Pf/AEUD/wAoWpf/ACPR9cw/86+9B/rvw5/0MKH/AINp/wDyR9H0V598Av2p/AP7UGnahdeBfEMOuxaS6R3gW3mt5LcvuKbklRGw21sHGDtPPFeg1vGcZLmi7o9/CYzD4ujHEYWanCW0otNPpo1o9Tzf9sb/AJNE+Kn/AGJ+rf8ApFNX8mn7XHxV8UeG/wBoXxBZad4k1+ws4fs3lwW2oSxRR5tomOFVgBkkn6k1/Wz+1B4bvvGf7NPxD0fTLaS81LVfDOpWdpbx/enmktZURB7liB+NfzlfGn/gi18Xfi/8TNS8Rf8ACJfEfT/7Q8r/AEf/AIQi9l8vZEkf3srnOzPQda9Xh3H4DBZp7bMvgdNpXi5a80Xsk+iZ+O8f1MPQ4hw+IxsG6fsZxvySmubng7e7GWtkz4T/AOF5eNf+hw8U/wDg1n/+Lo/4Xl41/wChw8U/+DWf/wCLr7D/AOHCHxd/6F/4j/8AhBXv/wAVR/w4Q+Lv/Qv/ABH/APCCvf8A4qv0H/W3hXuv/BU//kDwf7b4f/59P/wRU/8AlZ8ef8Ly8a/9Dh4p/wDBrP8A/F0f8Ly8a/8AQ4eKf/BrP/8AF19O2H/BHXxlqnxPvfBVu3i6bxbp1uLu60dPB9y17bwkIRI8Ik3quJI+SMfOvqK6b/hwh8Xf+hf+I/8A4QV7/wDFVMeL+FJfDKP/AIKn/wDIGlTNcip2U6LV0mv9nqbPZ/wz48/4Xl41/wChw8U/+DWf/wCLr0j9kf4q+KPEn7Qvh+y1HxJr9/ZzfafMgudQllikxbSsMqzEHBAP1Ar3v/hwh8Xf+hf+I/8A4QV7/wDFV1PwW/4ItfF34QfEzTfEX/CJfEfUP7P83/R/+EIvYvM3xPH97LYxvz0PSvOzribhutl9ejh2ueUJKP7qS1cWlryaa9Txs8zTJa2W4ijhqT55Qmo/uKi1cWlr7NW16n7AeFP2QvEXg74G/C74tfBqUWHjBPCOlzarpI/1GtK1lCXIXgF2xlkJG84YEOMt9A/snftuaD+0rbNpVzEfDvjewDJf6JdErJuTh2i3YLKCDlfvLzkdCez/AGXPD174R/Zl+HWk6nbS2WpaZ4Y0y0u7eUYeCWO0iR0YeoYEH6V5/wDtX/sK6T8fryLxJoN43hLx/p5WW11i1ynnsv3RNtwcjAAkX5l4+8Btr8CrZLjcrqPMMjV1LWpRbtGT6yg/sT7/AGZdddT+w+Gs3wmY5Rhctz68ZQpwjCta8oWivdqLeUF/4FHppoe90V8f/DL9uzxT+z94tg8EfHzSpdNuSdll4lgi3W14o4DybBhh0y6DIyAyKcmvrbRdbs/Emk29/p93bX1jdoJYLi3kEkUynoysMgj3FfQZLxFg80jL6u2px+KElacH2lF6r12fRnNnXD2MyyUfbpOEvhnF3hNd4yWj9N11Raooor3DwwooooAKKKKACiiigAooooAKKKKACiiigAooooAKg1LU7fRdPnu7y4htLS2QyzTTOEjiQDJZmPAAHUmvNP2kf2wPBX7L+imXX9QE2qSIWtdKtSJLu4Pb5c4Rf9pyBwcZPFfPVr8OPi5/wUXu4b3xjJP8Ovhc0izQaTBkXepIDlSwIBbth5AFHBWM9a+Uzbimlh631DAwdfE/yR2j51JbQXrq+iZ9VlPC1XEUfr+OmqGG/nlvLypx3m/TTu0X/iz+1l4u/a98V3nw8+B8Eq6bjydY8WSBoooIzwfKbGUBGcNje3OxQBuP5rf8F9/gbbfsb6DbaP4Y1LUY75PCun3l1qSTNFPcXMmqTRySLtPyAooUKD90ckkkn9z/AIXfCjw/8F/B1toPhrTLfS9MtRxHEPmkbu7sfmdzjlmJJr8xP+Dg/wDYt8e/tX/ErT7Xwz4Z8T6npt/4ZtLN9Q0rRptSW3mh1CedkZY+h2snUj73GcEUskyR4bH0c3zqp7Wspwu0nyU4cycowjq7WWrd5S/A/NvGvPKNXIaOX5bRcMJTr0ZyVnKc+WWs6lk72V7RS5VeyTPwB/4Xl41/6HDxT/4NZ/8A4uj/AIXl41/6HDxT/wCDWf8A+Lr7D/4cIfF3/oX/AIj/APhBXv8A8VR/w4Q+Lv8A0L/xH/8ACCvf/iq/oD/W3hXuv/BU/wD5A/Ff7b4f/wCfT/8ABFT/AOVnx5/wvLxr/wBDh4p/8Gs//wAXR/wvLxr/ANDh4p/8Gs//AMXX1d41/wCCJHxD+G3hi61vxFbeNNC0axCm5v8AUPBl1bW1uGYIu+R3CrlmUDJ5JA71Y8O/8EMvib4u0Gz1TStN8eanpmowpc2l3a+CLuaC5icBlkR1cqykEEEHBBqf9b+FL8vNG/8A16n/APIB/bWQWv7J/wDhPU/+VnyT/wALy8a/9Dh4p/8ABrP/APF0f8Ly8a/9Dh4p/wDBrP8A/F19h/8ADhD4u/8AQv8AxH/8IK9/+Ko/4cIfF3/oX/iP/wCEFe//ABVV/rbwr3X/AIKn/wDIB/bfD/8Az6f/AIIqf/Kz9Vv+DZW+n1T4Q+Mbm5mluLi40fw3LLLK5d5XaG8LMzHkkkkkmv1Cr89P+CAf7N3jH9m/wB4003xX4d8QaEq2mi2FpLqumy2DXxto7pZGWOQA4+dCeoG7GTiv0Lr8kw7i4uUdm5NdNOZ2P0nwupyhw1QjKLj71V2aadnWqNaOzWjTCiiitz9ACiiigD4B8HH+xv8Ag4V8V54/tbwOmP4d2IbX/vr/AFX6e1ff1fAPiP8A4k//AAcLaD2/tXwO3TnOIJ/y/wBV/nNff1eZln/L1f35foRDr6hRRRXplhRXNeP/AIz+D/hRJbr4p8V+GvDTXgJgGq6nBZmcDrt8xl3YyM4rnP8Ahsf4Rf8ARVPhx/4Utl/8cqHVgnZtHBWzXBUZunVrRjJdHJJ/c2dR8TfhX4e+MnhOfQ/E2lWur6bccmKZeUbsyMPmRhnhlII9a+VdY/Yz+KH7I+qz618EfE0+r6KXM1x4X1VwwkHUhc4Rz7jy5MADcxr6C/4bH+EX/RVPhx/4Utl/8co/4bH+EX/RVPhx/wCFLZf/AByvnc54fy7MpKtUfJVj8NSEuWcfmt15O68j3Mn8QqeXRdGniac6UvipzlGUJfJvR+as/M8p+D3/AAU+8K67qf8AYPxC0+++HXim3bybiG/if7Lv/wB8gNHnriRQB/eNfSuj6zZ+IdMhvbC6tr6zuV3w3FvKssUq+qspII+leJfFn4mfs4/HPSvsnivxn8JtYQKVjkl8Q2QnhH+xKsodP+AkV856j8M/CPwR1WXU/gl+014J8P8AmNvfSNV8UWctpIfqGZW9BviY/wC1XjrHZ9lmldRxdJfag1Cql5wb5Jf9uuLfY9d5pwZmetDFxwlR/ZnNTpP0mvfj/wBvKXqfoFRXwFov/BXDxB8ItSj07x7aeBfFUOdo1Hwz4htZncf3ysckiE+x8v6V7p8NP+Cp3wZ+JESf8VHLok7Y/dalasm0+hkj3xj8Xr0cHxtlNf3alR0pLdVU6bT7e9ZP5NnLPIq7moYOcMRfVexnGrdLd2g3Ky63SsfRNFYng34k+HviLa+foGu6PrcWNxexvI7gAe+wnFbdfU0qsKkVOm00+q1R5FSlOnJwqJpro9GFFFFWQFFFFABRUGpapbaPZvcXdxBa28fLSzSBEX6k8V5P8RP2+PhB8L45DqfjzQ5Gj4KWMhvTn0PkhgD9SK48XmOEwqviasYX/maX5nTRweIrRlOlByUdW0m0l3b6L1PX6K+I/iR/wWv8IwT/AGPwXor6tcyHYl1q19DYWyn+9jcxK/7xSuUk+OPxA/aRTb4n+Pvwh+GGh3H+ss9K8S2TXe0+hjmLdOoM49xXy9fjjCObo5fSniJ/3Y2ivWc+WK+9npUMqwigq2YY6hh4f3qsHL5Qg5Sf3I+v/jZ+1v8AD79n2Fx4l8R2VveqMrp8B+0Xj8ZH7pMsuexbC+9fPN1+038Zv21LiSx+E3h+XwV4SkYxyeJNTwssi9CUbBCnrxEHYHB3LVj4LfB/9lX4RXCX118Qvh74t1oN5jX2teJ7Gcb+pZYvM8sc8gsGYf3q98h/bA+D9vCscfxR+GyIgCqq+JLIBQOgA8yuV4LOs0/5GFeOHpP7FJ3m12lVdrefJFf4jpXF3COV/wDIvlHEVV9urKKgvONJPXy52/Q4P9nb/gnR4T+DutL4i8QXNz458Ys/nyalqfzxxy9d8cbE/N/tuWbIyCtfQ1eb/wDDY/wi/wCiqfDj/wAKWy/+OUf8Nj/CL/oqnw4/8KWy/wDjlfT5VluXZbR+r4GMYR8t2+7b1b822z5rNeNKWZVvrGOxkZy85xsl2STsl5JJHpFFch4I/aC8BfE3WP7P8N+N/CHiC/CGT7Npus293NtHVtkbk4Hriuvr1lJSV4mVDE0a8OehJSXdNNfgFFFFM2Pmz/gr5Y/2j/wTf+KUfHy2FvLz/sXkD/8AstdP/wAE37/+0v2CfhFJ83y+FrGLkf3IlT/2Wqn/AAU7sTqP/BP34tRjPy+HbiXj/Yw//stZ3/BJ2/Gpf8E6/hVIMfLpLRcf7E8qf+y15n/Mx/7c/wDbiPt/I+iKKKK9MsKKKKACiiigAooooA+Afjz/AMSf/g4P+C8nSPUvBNwjMeBkQ6wMe5+VfzFff1fL37fn/BO65/ap8V+HfHngrxfd+Afij4PhMGm6vEZDHPFl2WF9rAxgGSX51UkiRgyuMAeIaP8A8FMfjV+wzq9toH7TXw7utS0YyCCDxr4djVobjsGdVxC7HrgGFwB/qya8WFb6nVqKumoyldS3WqSs+3z0Mr8rdz9EKK4X9n79pbwN+1L4H/4SLwF4isvEOliTyZWhDRy20mM7JYnCvG2CDhlGQQRkHNd1XsQnGceaDuma77H4u/8ABz7431T4efEe31bR7o2moWXhDTzDL5aybN+q3KN8rArypI5Ffjf/AMNpfEv/AKGX/wAp9r/8ar9nv+DmX4W6p8VPifYaTYeTbyar4TsooLi63Lbl4tSuJXXcqschdvAB++ucZr8iv+HdvjX/AKCnhb/wJn/+M19NwliuHqVCtHNlT9p7R254pvl5Y23T0vf8T+YsZiuHKWc5jHOVT9p7d254pvl5IW3T0vc5b/htL4l/9DL/AOU+1/8AjVH/AA2l8S/+hl/8p9r/APGq6n/h3b41/wCgp4W/8CZ//jNWb79hHUtXggtdIvtOj1KyXbqTXdxJ5LvgD91tiJxkN94DjFfR1c44KhUhT5KL5m1dU42VlfXTTt6nZh5cHV6VWvShQcaaTk+SOibUV9nu0jjf+G0viX/0Mv8A5T7X/wCNUf8ADaXxL/6GX/yn2v8A8arqf+HdvjX/AKCnhb/wJn/+M0f8O7fGv/QU8Lf+BM//AMZro/tDgrtQ/wDAI/8AyJx/2jwN2of+AR/+ROW/4bS+Jf8A0Mv/AJT7X/41X7mf8G+vws8N/tIfB/xfceOND0zxHcSaXoFz5t1br5kUksN20pjZQDHuIGQmB8o9BX4uf8O7fGv/AEFPC3/gTP8A/Ga/dX/g208J3fg34ZeOrC6UM2n2GgWDzRhjDJLDFdq4RiBnGQemcMMgZr4njJ8OYxUKOXQpSu5cyjCOq5dL6aq538MZjkseLMsq8PuEakZVbumlFpexn1ikz5R/4K9/FHw9+wp+074g03RdOvtK8P6Zc2Npaw6exuJhLNYpcly08mcZLjhuMLx1NfOXhr/guprvg4AaX4s+Klgg/ghuEVD9V8/B/KvR/wDg6B/5Od8Wf9hvSP8A0ypX5R1nwX4W8N4zByxc8NGM+eS91KOiemyPpY53xDm+IxlTGZtjPdr1oqKxNZRUYzaSUVKysj9R7P8A4OUPiHYRbE8ZePmGc5k03T5D+bMTWon/AAc//E5FA/4SzxVwMc+GtGP/ALLX5SUV+gQ8NsngrQ51/wBvs5p5JWnrPH4p/wDcxU/+SP1Ou/8Ag5g+I960hfxh43HmZz5ej6ZHjPptxj8K5jxF/wAHAfi/xSCL3xr8VmQ9UimihRunVUmAPQdq/Niisa3hbkVZWqxlJecr/mbUctxlF81LMcXF+WJqr8pH3bq//BWfS/EFx5t+3jS9lHR7iC3kbt3aY+g/KvuLXtD0vxB/wTHtvFUmlWMfiS3+JA0R9RjDCae2GmvOFbLFfvv/AAgD5V4yM1+GFfu5b2M13/wSEnkiikkS2+LglmZVJESHR1QMx7DcyjJ7sB3r8q438PMgyOpRrZdh4xnJTu7K+nJaztdbs+b42z3P6OEqZfUzPFVqNWhW5oVcRVqRfL7Nr3ZSa0u+h+Ov/DaXxL/6GX/yn2v/AMao/wCG0viX/wBDL/5T7X/41XU/8O7fGv8A0FPC3/gTP/8AGaP+HdvjX/oKeFv/AAJn/wDjNfqv9ocFdqH/AIBH/wCROL+0eBu1D/wCP/yJy3/DaXxL/wChl/8AKfa//GqP+G0viX/0Mv8A5T7X/wCNV1lt/wAE9vFllcJNe6l4dayiYPcLDczeYYxywXMON2M4z3p9/wDsD654hu3utA1DR4tKkwIlvriXzwQMNu2xEfezjnpiud5xwUq6oclHVN39nG2jSte2+p2RlwdLCyxqhQ9nGSi3yR3abS+Hsmch/wANpfEv/oZf/Kfa/wDxqj/htL4l/wDQy/8AlPtf/jVdT/w7t8a/9BTwt/4Ez/8Axmj/AId2+Nf+gp4W/wDAmf8A+M10f2hwV2of+AR/+ROP+0eBu1D/AMAj/wDIn6Af8G4XxQ134r/tceDtR1+++33kOqarbJJ5McWIxo8jBcIqjqzHOM81/QTX4E/8G7/wO1n4Kftg+ENM1J7S8mN/ql+0lkXkjiibSpIgWLIpHzLjpj5l5ya/favzGvPCzxmIlgreyc3y8qsrWWyP0TwilhZ4HGzwNvZPES5eVWVvZ0tkFFFFI/WTyL9v2w/tL9hr4wR4Bx4M1aTn/Ys5X/8AZa86/wCCMGoHUv8Agmh8MnOcrFqEXP8AsaldKP0Fe9fG34ev8W/gx4u8KR3Isn8TaLeaStwU3i3M8DxByvfG/OPavzq+DXxs+Nv/AARp8F2vgv4kfDlPGfwk0yeV7XxJ4aYySWSyytK5ctgYLucJMsRyxw5AFeRiqioYuOIqJ8nK03a9ndPUzk7S5mfp7RXkv7Mn7cfwv/a90hJ/A/ivT9QvfL8ybSpm+z6lagY3b7dsPgE43qChPRjXrVepTqwqR56buvI0TvsFFFFWAUUUUAFIzBFJPAHJJ7VQ8V6jfaR4X1K70ywGq6jbWsstrZGcQfbJVUlIvMIITcwC7iDjOcV+VHwt+J/xV/4LX+O9U0DWvifoHwq8E2MjR3HhHSLgjVtQiAy2YyVedMcMzt5akA+VXDi8cqDjTUXKUtlt+L0IlO2h9V/tW/8ABY74cfAfWT4Y8HxXXxU8fTyfZrfR9APmwrMeAkk6hhuzkbIlkfIwQvWvH7T9i39o3/gpTdRal8evFUvw0+H8rrNF4M0T5LiZAcr5q5ZVPQhpzK6nP7tK+uP2U/2Cfhf+xpowh8FeHIIdSePy7jWbzFxqV2O+6Yj5Qf7iBU/2a9krD6lVr64yWn8sdF83u/yFyt/Eedfsy/sqeB/2Qfh63hnwHpB0nTJp/tdzvuJLiW7nKIjSu7sTuIReBhRjgCvRaKK9OEIwioQVkjTbRBRRRVAFfAP/AATv/wCJR/wVk/aoseguLiK7x93P74tnb3/1vX396+/q+Af2PR/ZH/Bcj9oux6faNDgusdc5Fg3X/tr+vtXmY/StQf8Ae/8AbWZz3R9/UUUV6ZoFFFFAHzH+0j/wSg+G/wC1B8XtR8a65qfjDT9X1VIUuU028t0gkMUSxK22SCQg7EQHBx8vTOa4T/hxB8Iv+hj+I/8A4H2X/wAiV9r0VySwGHlJylBXZ8Zi/DvhrFV54nEYOEpzbbdt29W9+rPij/hxB8Iv+hj+I/8A4H2X/wAiV8t/Hb/gnV4N+GP/AAUx+E/wls9a8XSeE/HGky3d9JLc2xvUlUXmNjiAIFzDF1Qn73PIx+vdfAP7ff8AxKv+Cv8A+y/e9BcLNa5bp/rHX65/e/yrzcywVCFOMoxt70fuujBeG3C9LWGBp66bX0fr+e66HT/8OIPhF/0MfxH/APA+y/8AkSj/AIcQfCL/AKGP4j/+B9l/8iV9r0V6H9m4b+RE/wDEMOFf+gGH3P8AzPij/hxB8Iv+hj+I/wD4H2X/AMiV9Ffso/so+Gv2O/hrceF/C9xq91Y3V/JqU02pTJLO8rpHGeURFACxIAAvb3r02itKWDo05c1ONmeplPBORZXiPrWX4aNOpZq63s9wooorpPqDj/2hdP8A7W+APji1xn7T4fv4sbd2d1tIOnfr0r5h/wCCCmofbP8Agnfo0ec/ZNZ1CIfNnGZd/wCH3+n496+ufHen/wBreCNZtev2mxnixnH3o2H9a+K/+Deq/wDtn7Bl5HnP2XxXexdOmYbZ/wD2evMraY+m+8ZfnEh/Gj7pooor0ywooooAKKK+F/8AgpL/AMFZNc/Zb+LMPwt8F+E7c+MdTgglg1rxFdRWmkxJNkLJGWkVXAYMpeR40VkbIYCubFYunh6ftKr0FKSirs+xPip8X/C/wP8AB9xr/i/XtL8O6PbD57q+nWJCcZCrnlnOOFUFj2Br4Y+IX/BVrx9+154jvfBX7Lnw+vfEAybe88WazahLC0VuCwjkwigjkeedxGR5JNWvg7/wSH1n9oTxJZfEH9pj4gXnxJ1adFuLXRNPuyulWyNhgokTaChGPkgWNM87nBr7q8C+AND+GHha10Tw5pGm6Fo9iuy3srC3WCCIeyqAOepPc9a4rYvE7/u4ffJ/ovxZHvS8kfFP7Bv/AARcs/2dvirpvxP8d+Jzr/j+0uJb2O00m3jtdItZZY3R/l2BpMeYSpURKDj5Divu2iiu3C4Slh4ezoqyLjFRVkFFFFdIwooooAK+Sv2u/wDgj58Nv2kNVfxL4cM/w08fRyfaYNa0JPKjknHIeWBSoLZ53oUcnkselfWtFYYjDUq8eSrG6E0noz84NP8A2zf2i/8Agmdew6V8evDU/wATPh5G4hg8Z6OfMuYFzhfNcgB26fLOI3Jz+8fFfbH7OH7Wnw+/az8JDWPAfiWw1uJFBuLZW8u8sSf4ZoWw6HORkjBxwSOa9B1DT4NWsZrW6giuba4QxSwyoHSVCMFWU8EEcEGvyR/4KeeAv2f/ANmr4jDXvg/401bwR8Zra4wmi+Cz9otvNLYKSqjqtoScAoj9P+WDZzXlVpVsBHn5+aHaTtL5Pr6PXzM3eGvQ/XSivl//AIJcfGD46fF74R30/wAbfDK6Jc2bQJpV9PaGyvtWjZCzvNb8KmPkwwVM7iNo25P1BXrYesq1NVIpq/fRmid1cKKKK2GFfAvwatW8Kf8ABwP8WZbnZaw+IPBVv9kEzCL7UfK0vmIEgyc28uSoOCr56GvvqvlD/goH/wAE7/g1+0G8njPxXrcXw38XRqnleK49RSzIaJQI/NWRhG4UBeQVcAABwBXn5jSnKMZ09XCSlq7X0a3+ZE03qj6vor8e/Cn/AAVs+IX7BXxHTwlrfjzwn+0V4Kh+WLUrG8b7fBGDgD7SVO5+5DmYHgCQCv1i+EfxFh+L3wu8PeKrew1HS7bxFp8GpQ2l+ipcwRyoHVZFVmAbDDIBNGCzKliW4w0kt1/wVoEZqWx0VFFFegWFFFFABXwB/wAFR/8AiU/8FH/2Q77objxC9rkcE/6XZLj/AMi/qa+/6/P3/gtJf2fg79ob9lTxXqV/ZabpfhvxlJcXdzcygLDGtxp0zMUGXKhYGywG1eAxG5c+ZnGmFb7OP/pSIqfCfoFRWb4Q8ZaR8QPDlrrGhapp+s6VfJ5lveWNwlxBOvqrqSpH0NaVemndXRYUUUUAFFFFACOgkQqwBBGCCOCK+B/+DeqB/Df7LXjnQLsrDqOmeNrozWsjgXEA+y2kX7yLO6P54nGGA5B9K++a/P79sH/gl/8ADb4U+IX+Ifw6+KFr+z14xUtPHLPrAtdLumJywwzh4wx4IQsmOPKNeZj41I1IYmmr8t7q9t7bdOnkRK91JH6A0V+XH7IX/Bb7xXonxh0v4ZfFK00Tx3Lf6lBpFn4p8MXESi4klkWON3Q7InQswyy+UVA5Qmv1HrfBY+jiouVLpv5DjNS2CiiiuwoK80/aV/ZA+Hf7XPhX+yvHnhqy1gRqVtrzb5V7Yk94p1w6c8lc7TgZBr0uionTjOPJNXQb7n5v3X7IH7SH/BMe8l1L4HeIp/ip8N4nMs3g/Vh5l3bpnLeVGCAzdfmtyjMSMxNivcf2QP8Agr38NP2nNRTw7rLTfDrx6j+RNoWuP5QkmBwyQzMFV2zxsYJIT0Q4zX1fXzt+3j+w/wDBP9ovwNf658TLfTPDc9hDlvFcdxHYXVkoGBvmb5ZFHQLKGHPAB5ry3g6uG97CS93+WW3ye6/Iz5XH4T6Jor8lf+Cc37VvxT8K/te2vwi+HXjFvjf8K7a4RZtU1Wxnt/7Jsgf3kscj5kTYBhUYtG5ACBd2R+tVdWAx0cVTc4q1nZ+vk1oyoT5lcKKKK7SgooooAK+df2v/APgqH8J/2N0nsda1n+3PFSDEfh/R8XF5u7CU52Q9v9YwbByFavVP2jvhZffG34GeKPCul6/qXhbU9bsJLe01WxneGaym6o25CG27gAwBG5SwyM1+VXwH8L6p/wAEbfi7JqHxn+Ctj4t0q6u/9E+IGll76SyJOAY/NPlKSc4BWCY5b5nAArycyxlai1GCtF7yd2l8l+uhnOTWx7b/AGd+1h/wVFObp2/Z8+Et7/yyXeNW1GE+o+SaTIPfyImU9Hxz9O/shf8ABMz4T/sZW8Nz4d0Ian4lRcSa/quLm/Ynr5ZwFhB9I1XI6luteifs+/tPeA/2pfBy654E8Sadr9kAPOSF9txZsedk0TYeNvZgM9Rkc13taYXA0bqu37SX8z1+7ovkOMVvuFFFFekWFfO//BSH9oT4r/s2fBy18QfCvwdp/i+5Wd01NbmGWc6fDsyswijdGcZBBwepXg19EUVlXpyqU3CMuVvqugnqrH4heEf+Ci3xX/ax8VTaX47/AGkbb4LWDSeUsdhotxBIjZwQJLeNWXHrJcDHPSvqr4Cf8EafgZ8aseJNX+LWvfGy6YAz3VvrsRt5M/3/ACzJMDnPBmyOe9favxg/ZZ+HHx/gZfGfgjwz4jdl2ie8sI3uUH+zNjzF/wCAsK+R/jX/AMERPgn4eu/7c8LeM/EPwd1NCWt7mHWQ1tCfUeewl4JHSYV868rrU3zVkq3q2n9zvEx9m1vqfSvwk/4J/fBX4GeU3hn4aeE7O4h/1d1PZi9u0+k8++T/AMer2BEEaBVAAAwABwBX5LeMP2gfjb+xCG/4R79qr4RfF/SrTppms6vDc6jLjkBtzNIuR2+1f0NfpH+yH8U9f+OH7M/gzxh4nsdM03W/EumpqM1vp7FrZFkJaIoSzHBjKH7x5J5r08BjKM5OjThyNdNLfetC4ST0SPR6KKK9Q0CiiuQ+Pdr4wvfg14ij+H91Y2fjQ2bnR5bxA8AuBgqHDAjBwRkjjOe1KT5Yt7gdfXj/AO1r4h+B994Kn0P4y6n4CGluu8WevXkCTAkfehVmEofHRo/m9DX5IftA/E79pzTPGkkPx+8Q/Gvw94SWQrdXPh7TRFZ3C5xhPLe3tmHQjcx7ZHNey/shfBf9gTxlNbvqPjDV9Y1uZg8kHjnUJNKAkPPJjEUDc9vNf3znn53+2XXk6MIKP+N2/DW5j7W+i/E8g+MHxu8AfsVfEj+1/wBlD4z+L5ftV0PtfhqbTprjS5STjAeZVWYAcDdG7YPEua/Uv/gnF+0z48/as/Z9HiX4geC18G6rHeNaRKqyxLqaKiN9pSGUbo0JYqBufJRiDiu9+CHwG+GHww0aC7+H3hXwbpVpOmY73RrKBTcL6mZBuf6ljXoNdeX5dVoTc3P3X9lL3fldv8LFQg073CiiivYNAr5B/wCCqX7Xvxq/ZL0LS9Q+GngvS9e0K6t3/tLVbi0mu30uUNhR5cci8FTkMQwG1sivr6isMTSlVpuEJOL7oUldWR+Jfwu/bK8eftraobX4jftax/Ce3uH2rY2OmXFnlO6mWBIIgOo/eTnr36V9c/Af/giP+z/4utl8Rah4v134wNckNNe/28jWdwcDndbEPz7ymvqn4z/sTfCX9oXzX8YfD7wxrN1NnfeNZrDenP8A08R7ZR+DV8j/ABf/AOCL3wq+FWqNrngb4v8AiT4JasBvjmfWkMEIGSCpZ4ph0PJmPT2r5/8AsyrSfNWgq3m5O/3SvH8jHka1ep9e/CT9jD4T/AkRHwn8PPCejXEONt3Hp0b3fHTM7gyn8Wr02vyN8V/tvfHb9iaXZZ/tBfBX44aRbNhbObVIbm+2jg72UpJu9vPc+3r+rngLUNT1bwNo11rVvb2msXNhBLfwQEmKG4aNTIiEkkqGJA56CvVwGMo1b06ceVx3Wn6aGkJJ6I1qKKK9IsKpeI/EuneDtCutU1e/stL02yQy3N3eTrBBboOrO7EKo9yau1+Sf/BWD4NfFnTf2lpvFHxMg8cfEX9ny2vBdW9p4fvo7RNMg2glJY1jYRlGJXzXTMigfvVY/Lw5hjJYal7SMeb8l5vy+RM5cqufQPxy/wCC0Np4h8YP4G/Z58I6l8XPGkpKLdRW8g0u2xwX4w8qqerZjjwc+YRWB4M/4JR/En9rzxLaeLf2p/iBf6qI28618H6NcCO0sc/wM6fu04+VvJBZgAfONe7f8E4vjL+z344+GEemfA9NF0VIY1kvdFMQt9WiI43XCsTJMRnHm7pF7BzX0pXJSwn1qKq4mfOuy+H/AIPz+4lR5tZO5y/wj+CnhL4CeD4dA8G+HtL8OaRB0t7GARh2xjc5+87nuzEse5rqKKK9iMVFcsVZGgUUUUwCiiigAqtrGjWniHSrixv7W2vrK7jMU9vcRLLFMh4KsrAhge4IqzRQB8MftBf8EYdPsvGJ8d/s/eJ734Q+OYCXS3tppF0q5J5KYXLwqe6gPGQAPLrnfh//AMFYfiD+yd4utPBf7VHga+0OWRvKtvGGlW3mWV8B/wAtHSPKPxyxgORkAwivbf2wP+CsXwq/ZInl0h9Qfxj40DeVH4f0NhPMkp4CzSDKQ84BUkycghDXzlqnwC/ab/4KwxRt8SbiH4K/CWaVbiLQIoC2o3qjlS8bYkJ95igBwyxGvm8R7OlVay9v2nWK1j/290XyaZi7J+5ufoT8Mfit4a+NHg+21/wnrmmeIdGux+6u7G4WaMnupI+6w7qcEHggV0FeYfsk/skeEP2LfhKng7wbFe/2ebmS9uLi9lWW5vJ3CgySMqqCdqqowoACivT6+gpObgnUVpdbGyvbUK/PH/gsZ40/aY+CniKHxT8PvFOsW/wwmt40u4NE0yKe60aRB+9mnfyd6xN8u0+aRkMDs+Xd+h1IRkVjjMM69J01JxfdEyjdWPya/ZN/Z1H/AAUK0kTTftl/EPWtR2eZdeGpBPZXtnjk/upLt1dRhCXiRkBH3ieR714b/wCDfD4KWN19p1rWviF4luXOZGvNUhjV8cD/AFcKv0x1Y9Pwrsf2sf8Agjj8N/j9q7eJvCT3Hwt8fRSfaINY0FfKheYch5IFKjdnJ3xmN8nJZuleN2P7an7Rn/BNO8h0r4+eFpviV8P43EMHjPRT5lxEvQeY+FVj0AWcROxyfMevAWFo4d2xtK6/m1kvmm21+RjyqPxo+hvBv/BHb9nDwSUaD4aWF7KpyX1G/u7zcfdZJWT8lxX0T4T8J6b4E8M2OjaNY22maTpkC21paW0YjhtolGFRFHAAHAFcN+zb+118PP2tfCv9q+A/E1jrSRqDc2gbyryyJ7SwNh054BI2nHBI5r0mvoMNSw8Y82HSSfa36G0VH7IUUUV0lBRRXzh/wU3/AGbviL+0X8CEh+GHjHXvDHifRJXuo7LTtRksF15Su37PJKsiKuDh1L7hlcfLu3DKvUlCm5xjzNdO4m7K59E6hd29jZvJdSQw24GHeVgqAHjknjvXyV+0x8LP2MfHwnbx5P8AB6wv5Bl7i11i203UGPGGzbyJI5G4HncORkYr84Pgf8IPhFe/E8+Ef2ptR+M3gTxpA+1J9Svo20ybPAZneBpY1PG1gWjIXJkxxX6O/DL/AIIx/swx6Pa6nYeE18U2t1GJba9m1+6uYZ0OcMvlyrE4IPXB6CvEp4urjotQpw81J3a9Vy6GSk57I+HfiT4N/Zt/Z81O41L4LftV+OPBGqk+Z9jsrO91GCcj+HzIEiG3HH7xn79a+0/+CMX7Qvj39o74R+MdT8Z+OLXxzaaRrCaVpV2lgLWYKkKyO0g8uNm3CWPBYE/I3Jr3Hwd+wV8FPAO06X8KfAMEifdmk0S3nmX6SSKzfrXp+h+HdP8ADFkLbTbGz0+3HSK2hWJB+CgCtcFllWjWVRtJfyx5rfi7fgOMGncuUUUV7ZqFfll/wVc+Kn7T37OvxsutQTx34l074QavOJLK/wDDukwyf2JC2F8iaQRxHzt24qHlwylcOTkL+ptQ39hBqllNbXMMVxbXCGOWKVA6SqRgqynggjqDXFjsI8RS5IzcX3X6+RM48ysfl/8As2/sKWP7ePhX+1n/AGw/iB8QIdoa902GSa2ubDdwUkhnuJGQcyAHywpz8uR19i8Jf8G+/wACdDl83VLnx14lmY75Tf6skYkY4JP7mKNuTn+Innqas/tJf8EW/C/iPxT/AMJr8F9cvfg54+tmM0EmlyPHpsz+nloQ0GeBmI7MZzG2a4bwt/wU1+MP7DHiG08LftQ+Bru80p5PItPG2hwq8N0OzOFxFIcckL5ciqBmIk14kcPh6DtjqX/b2so/O92vn95lZL40fQvg3/gkb+zn4G2fZPhdotyy/wAWo3Fzf7j6kTyOP0xX0Zb26WlukUShI4lCIqjhQOABXJ/BT4++Df2jPBkXiDwR4j0zxHpUuAZbSXLQsRnZKhw8b/7LgN7V19fQ0KVGEb0Ekn2t+hsklsFFFFbjCkdBIhVgCCMEEcEUtFAHx1+1X/wRq8A/GTXj4t+H13dfCX4g28hubfU9CzDbSTcnc8KFdjE/xxFDySQ/SvK9E/4KAfHj/gnZqtvoH7SPg+48X+EPMEFr430JRISOg8w4VJDxwsghl4JO+vt/49/tL+BP2YPCDa5478TaZ4dscHyhcPme6I6rDEuZJW9kUn8K+GfGv7e3xn/4KTjUfCH7Pfw9Gk+CbzfZaj4u8UW0bQPGcq6hHDwjryoE0mCDtSvAxsKFCfNh5ONR9Iq9/WO3z09TGVk7rc+4/gD+0/4B/ah8KrrHgTxRpfiG12K80cEuLm03ZwJoWxJEeDw6jOOM13tfEv7A/wDwRg0T9jP4kaL46u/Geta54ssbe4iuYbZBaaZIZkChQg+dlTL/AH2wx2NtUqBX21Xq4OdedJPER5ZeTuaRba94KKKK6igooooA4z9ob40Wv7O3wT8SeN77TNU1iy8M2TX09ppyK9xJGuNxAZlGFB3MSeFVjzjFfnV4E+PPx6/4LO65qmmeFPFXh/4O/DOxcR6hDp+orca3NET/ABqjLO2en/LCJhkZcg1+otzbR3tvJDNGksUqlHR13K6kYIIPBBFfF37Tf/BGjwv4x8Vf8Jv8HdZu/g98Q7ZzPBPpLvFp08h67o0IMOehMWFwTmNs15OZ0MRUadN3h1inZv5/poZzTe2x6j+yB/wTJ+FH7GUEN14f0Qat4nVf3viDVttxfFiMN5ZxthByeIwCQcEt1r6Dr4Q/ZY/b1+Mvwy/aj0D4A/HnwnbX/inXImk0rxHpM8KrewKsrefKmVRkIhk+ZAjjZjyi1fd9dGXzoSpWoR5UtGrWs/McLW0Ciiiu4sK53xv8XvCfwziL+JPE/h7w+gG4tqWpQ2gA45zIw9R+ddFXwv8A8FSv+CQkv7a/i2Dxr4P1jT9J8YrAlreR6o8gtLyKMHywpjVjG3zHJKtkBcbcHPLjKlanSc6EeaXbYmTaWh7B48/4Kxfs7/DreL34p+HrtkyNulibU9xHYG3Rx+Oce9eMeO/+C/XwPcTaXpOg+OfGrXatCILfSIlguVPBUiaRXIK548s8dRXzb4E+GPin9gllj+J37G3hHx3o1mRnX9GhfVGCdPNk8xrlP++1h5x3OT+gH/BP79rD4XftXeBdUvfhn4cm8MQ6LNHbX9jLpMNg0EjKSFHlEowwvY8cZAzXj0MbisRP2TnGEu3K7/i0mZqUm7Xsfl98V/A3if44fFK08Zfs9/s4fFr4T67FMZRqmm3M1tYSk8nbF5CJCcY4jmCYOChzk/r7+yVH43i/Zt8Hf8LIuJbnx0dORtZeWKKJ1nJJKMIv3eVBC5Xg7c969ForuwOWrDTlU57uXSyS9bLqVCHK7hRRRXqGgUUVxXxt/aN8Dfs4aHb6j458T6V4Zs7xzFbyXsu37Q4wSqAZLEAg4APFTKcYrmk7ICP49fs1+Bf2nvB7aF478Nab4isOfK+0R4mtWPBaGVcSRN7owNfD+u/8E7fjp/wT41i58Q/s0+NbnxP4Y3me68Ea86v5g6kICVjkP+0phlwAAXJ59X8d/wDBdb9nTwbvFp4l1nxJInBTS9FuOTnGA06xKfqDj3rgD/wXbb4gHHwz+AfxR8cbv9X+58rdz/07x3Pt+deHi8Rl1SXM5++tnHf8LmUnB9dTsv2Xv+Cz/gj4l+I/+EP+KOm3nwf8f2zi3uLLWg0VlJL6CZwpiJ67ZgoGQAzmvsyCdLqBJYnWSORQyOpyrA8gg9xX5YftM2X7R/8AwUV0FbG//ZS8JaaFTbb6rrtybfU9PH/TOZri3kHJB2FWU45Q9vpz/gkP+yL8SP2P/gtrmi/EbVjdz3d8j6XYRao97a6ZbqnKxqQFjZnZiwTg4XvmqwGNxEqvspxco/zcrj96f6BCUr2Z9bUUUV7hqFcl47+PngX4Xbv+Em8aeE/D2z7w1PV7e029v+WjjuDXW1+cX/BR7/giVqn7QnxkvviH8OdS0VNT1iT7Xq+la1PNHHeT/KP3UkanapVR8pwcljvGQF48dWr0qfNh4cz7EybS0Pojxz/wV+/Zz8AFlufibpd9KOiaZa3N/u+jQxsnbuw/UV4l8Sf+C73wT8f6bdeG9P8AAHjj4ixahGY5rB9Ht2tbtD/CyPIzMM46xkc14b4B1ub9hIpF8ZP2K/DtxYWZ2yeJdEsBqcUajneWna4j3Ec8zR9+ByB+jn7GP7QPgP8AaZ+CsHin4dabJpWgSXElr9mk05LF4pUxuBRMqcbuqkjOea8qhisTiZezdSMH25Xf/wAmaM1KUtLn5IN8KvizrHxrh8a/s3fAv4u/B2WRsuGvpTYXIz0VbiCJQm7OY2kkj44VQMV+2PgOHU7fwPo0etXH2vWEsYFv5/KWLzpxGvmPsX5Vy2TgcDOBWtRXfgMuWFcmpN39Evkloi4Q5Qooor0iwr88P2zf+Cs3jqP9pC++Bnwm8Pab4d8XRXv9mza94rvLe2jR2AKvbpI/l4ZWVkZyxcMAIiSK/Q+vFf2t/wDgn78MP20tFaLxloEf9rJHsttbsMW+pWvpiUA71HZJAy+2ea4cwpYipS5cPKz/ADXa+tvUiabXungnwD/4Iv6Ze+L18c/H7xRf/GDxxORI8F1NIdLtjkkJhsPMoPQEJHjjysV9t6Joll4a0i20/TrO1sLCzjEVvbW0SxQwIOAqIoAUDsAK/NnxKv7SH/BHLRpNUTV7b4zfA3TXRJItQnMN9o0TOqIoZizxcsqDaZYv9hCa/QT4E/FmD47/AAZ8MeM7SwvNMtfFGmw6nBa3TI00UcqB13FGZeQQRz0IyAcgc+WuhFulGDhNatPd+fN1FC21tTrKKKK9Y0CiiigAooooAKKKKAPgH9q7/Q/+C8P7Pc7fck8NXEIx1zs1Qf8As4r7+r4B/bTP2H/gtn+zdc/eMulTw7fTm8Gf/H/0r7+rzMv/AIldf3//AG2JEN2FFFFemWFFFFABXwD/AMEqv9F/4KGftgwv8sj+J0kVfVftmoHP/jw/Ovv6vgH/AIJnH7F/wU7/AGtLduWl1eOYEdAPtNwcf+Pj8q8vG/7zQf8Aef8A6SzOXxI+/qKKK9Q0CiiigAryX9sj9jjwp+2x8JZfC/ieN4niYzadqEIDTaZORtMiA/KSVJUhgeDkYIBHrVFRUpxqRcJq6YNXVmfmB4Z/4JcftCfsLam9/wDCHVfhl8R9ORjIdP1vQLW2vpz1A8yQFvbIuk7cenuf7Kf/AAU38ZeNf2idK+EXxd+FVz8OPGWqWstza3Ju9tpfBFY/uo5BlgdjAeXJJyrdNpx9mV8A/thf8px/2dP+wHcfyv68WrhfqSjLDyai5RXLurN266r7zJx5fhPv6iiivdNQooooAKKKKACvgH/g3l/0f9mX4hW7fLLD44ud6/3f9EtR/MH8q+/q+Af+CAJ+y/Cf4t2Z5e28cT7mHQ5hjHH/AHya8zE/77Q9J/kiH8aPv6iiivTLCiiigAooooA+ZP8Agshbtdf8E1vigq4yLazfn0W/tmP6Cu4/4J53K3X7CfwgZc4HhDTE59VtkU/qK5P/AIK5232v/gnH8U1zjGmwvnH926hb+lb/APwTVuvtf7AvwjbGMeGbNMf7qBf6V5i/5GL/AMC/9KZH2/ke4UUUV6ZYUUUUAFFFFABRRRQB8A/t7/6L/wAFhP2YJk4kkjmiY+q+ZIP/AGY19/V8A/8ABR0eR/wVV/ZTlHyF72VC/TcPPj4z/wAC6e/vX39XmYH+PXX95f8ApKIjuwooor0ywooooAK+Af8Agnn/AKF/wVq/amt15WSaKYk9QfOzj/x8/lX39XwB+wsPsv8AwWX/AGmYk4je0hkZfVt8Bz/48fzrzMf/ABqD/vf+2sie6Pv+iiivTLCiiigAooooAK+Af2tv9N/4Lr/s8W68NH4duJiT0I26kf8A2Q/nX39XwD+1H/ynu/Z//wCxVuP/AEXq1eZmn8OH+OH/AKUiJ7L1Pv6iiivTLCiiigAooooAK+Af+CDv+i6T8c7QcpbeOJdrHqeGHP8A3yK+/q+AP+CG4+z+Kv2i7dOIYfHD7F/u/PcD+QH5V5mK/wB8of8Ab35ES+JH3/RRRXplhRRRQAUUUUAfPv8AwVVgW5/4J4/FdWGQNFLfiJIyP1Aqb/glxO1x/wAE9/hOzckaDEvTsGYD9AKn/wCCm8Xn/wDBP/4tArvx4cuGxjPQA5/DGfwrL/4JMy+b/wAE6fhUd27/AIlTrnOelxKMfpXmf8zH/tz/ANuI+38j6Jooor0ywooooA/nH/4LEf8ABsBZ/sP/AArs/if8Grzx38QvBHhz994z0XUru2bWbW0Vtz3dtNDbKnlBMh8wOYv9YQ6BwnsP7Af/AAbm/sRf8FHf2ctK+I3w9+Jnx3mtLtRDqOnTa5pH2zQ7wKDJa3CDTuHXIwR8rqVZSVYGv3VliWeJkdVdHBVlYZDA9QRX4xft3fsdeP8A/gg7+03f/tXfszaW+p/CDWpAfiX8O4mZbS0hLktPCig7IAWZlYAm1djwYHZFAPz/APGv/BG74b/sAf8ABRa3+Ff7V2r+PtI+FPjZmXwX8RvDV3a2Vk+GGDeLPbXAXG5ElAKmFirndE4cfpJq3/Bmz+zZq/he5fQ/iR8aYr25tWawu59U0u7tUkZf3cjxpYxmSPJBKrIhYcBlzmvtrx58Mvg5/wAFz/8Agnfpcmq6ZqN54G+IenrqekXN1Zm01XRLgblW4i3g7JonDruXdHIu4Zkjf5vh7/gmF+1d8V/+CTf7X+jfsUftDJqnibwzrshh+FHjW2tprlbqAtiO1bG5vIA+XBybVvlYmBkkQA+D/wBin/gmp8N/gD+2j4z/AGVP2stS8efDzxH4xZE8J+JdE1Kzg0TxHCxaOJUa4tJSpmJby33gbw0LqkigN9a/tlf8GcfgHR/2ftbv/gZ40+Id/wDEXTo/tNhpvinULCax1YKCWtg0VrAYpX42SMxQEAMAG3r+j3/BVb/glp4F/wCCqH7O83hPxKq6V4m0rfdeGPEkMQa60S7I/N4HwokiyAwAIKuqMvy1/wAEXf8Agot8UvBHx51D9jP9prS9VHxf8E2jS+HvEgjluofE+mRKSJJZ8fMRGAUuGwJQCj7ZlIkiNOMW5RWr3Cx+eX/BHP8A4I1fsp/8FFtG1rwR488R/HD4eftAeBJZbbxN4SfV9NtxJ5T+W9xbRy6eZAiv8jxMzPE3DEhlZvpT9sr/AIM4/AOj/s/a3f8AwM8afEO/+IunR/abDTfFOoWE1jqwUEtbBorWAxSvxskZigIAYANvX6a/4LQ/8EgNe+N3i3Tf2kP2dbuTwf8AtJ/D8reQyWLrAPFkMS48mT+E3ATKKz/LKhMMmUKlPY/+CNX/AAVLi/4Kffs73+oat4ev/CXxI8CXa6L4z0iWzmit7a+Ab5oWcfdfYxMTHzImBVgRsd7A/G7/AII5/wDBGr9lP/goto2teCPHniP44fDz9oDwJLLbeJvCT6vptuJPKfy3uLaOXTzIEV/keJmZ4m4YkMrNi/8ABXf/AIN6NG/4Jg+OPDfxF0//AIWL8Q/2b3uYbbxQ1neWkXiXw8WIQkzfZzAUdiDHIYAu7ET7SySN+pH/AAWh/wCCQGvfG7xbpv7SH7Ot3J4P/aT+H5W8hksXWAeLIYlx5Mn8JuAmUVn+WVCYZMoVKepf8EmP+Cjmi/8ABX39k7xBZ+MvBjaV4v8ADjN4Z+IPhrUdNkOntO6OjqglUho5FV90LkyRHcjgjY7gHxZ+zd/way/sW/tb/BbQviD8P/iv8bvEPhXxFbie0u4Nb0rI7NHIp03ckiMCrowDKwII4r5J/wCCkn/BJzwl/wAE1/jp4A8MfFyH4iaz+ytq+otb2Xjjw5eW0esaNLJvIhvEe1liZ4lLMQiJ58as0eGRoh9PfEXwJ4+/4NeP2q5fHHg221rxp+xp8StURNd0QSNPceDbmQgB0JOA4HEcjECZFEMrCRY5T+u3xE+G3gH9ur9mebRPEmkReJ/AHxD0iOZrW+tZIGmt5kWSKTY4WSGVcq6nCyRuAflZeM50YTcXJbO69RNJ7n5b6F/waGfsrfFv4V2/iDwN8WPi/f2mv6cLzRNXXV9KvrCUSJuhmKx2KGWPlSVWRCRkblPI/PX9lf8A4I/fCP4bft/ax+zb+1/qnxG+Hfi/UpkbwV4k0DVrK20HxJE7FIlBuLOYgzEHy33gbw0LqkigN9z/AAA+I/xH/wCDan9r3Tfg38QJde8d/snfE/Vmj8G+IUgku7vwzdSt/qTHGD825h5sCL+8BM8K7vNiP6Jf8FVP+CWvgX/gqj+ztL4U8SKuleJtLD3XhjxJDCDdaHdEfgXgfCiSLIDAAgq6oy6DPym/4Kc/8GjWk/Bz9nO78Xfs5674/wDGPibw/uutQ8OeILi0up9VtQMt9jNvbwfv0wW8tgxlHC4cBXwP+CS//BDP9iv/AIKqfABfEGjeOvjpofjbQwlv4q8Ly6/pLXGj3ByNy500F7eQqxjkx2Kth1YD7T/4Iu/8FFvil4I+POofsZ/tNaXqo+L/AIJtGl8PeJBHLdQ+J9MiUkSSz4+YiMApcNgSgFH2zKRJz3/BWj/gmT49/ZL+P7ftnfskwtp/j7RC93458IWsRNp4rtOGuJlgTHmMwXM0Q5kIEqYmXLgH58/8Fd/+DejRv+CYPjjw38RdP/4WL8Q/2b3uYbbxQ1neWkXiXw8WIQkzfZzAUdiDHIYAu7ET7SySN9j/ALOH/BrL+xd+1v8ABXQviD8Pviv8bvEHhbxFb+fZ3cOt6VwejRyIdN3JIjAq6NhlZSCK/Rb/AIJ/ftp+Bv8Agrf+xBZ+Nbfw9J/YniWCbRvEOgaxaebBHcBAl1a5dQlzD8+A6gqythgrBkX80fiL4E8ff8GvH7Vcvjjwbba140/Y0+JWqImu6IJGnuPBtzIQA6EnAcDiORiBMiiGVhIscpAPhzxr/wAEbvhv+wB/wUWt/hX+1dq/j7SPhT42Zl8F/Ebw1d2tlZPhhg3iz21wFxuRJQCphYq53ROHH2Z/wUU/4Ngbb4G/sM63cfsz634t8c3ySf2rqei6/PbXt3q1qBG2dPktoYF81RGG8tlcygkIQwVX/V79qz9lH4Yf8FU/2QP+EY8W2EupeFPGFhDquk3/ANna21DS5JIw9veQCVA8Myq/3XUZDMjqVZlP55f8Ewv2rviv/wAEm/2v9G/Yo/aGTVPE3hnXZDD8KPGttbTXK3UBbEdq2NzeQB8uDk2rfKxMDJIkVKcZpKavZ3+aC19z5q/4JL/8EM/2K/8Agqp8AF8QaN46+Omh+NtDCW/irwvLr+ktcaPcHI3LnTQXt5CrGOTHYq2HVgPK/wDgrv8A8G9Gjf8ABMHxx4b+Iun/APCxfiH+ze9zDbeKGs7y0i8S+HixCEmb7OYCjsQY5DAF3YifaWSRv0G/4K0f8EyfHv7Jfx/b9s79kmFtP8faIXu/HPhC1iJtPFdpw1xMsCY8xmC5miHMhAlTEy5f7X/4J/ftp+Bv+Ct/7EFn41t/D0n9ieJYJtG8Q6BrFp5sEdwECXVrl1CXMPz4DqCrK2GCsGRbA/On9nD/AINZf2Lv2t/groXxB+H3xX+N3iDwt4it/Ps7uHW9K4PRo5EOm7kkRgVdGwyspBFfAnjX/gjd8N/2AP8Agotb/Cv9q7V/H2kfCnxszL4L+I3hq7tbKyfDDBvFntrgLjciSgFTCxVzuicOPuP4i+BPH3/Brx+1XL448G22teNP2NPiVqiJruiCRp7jwbcyEAOhJwHA4jkYgTIohlYSLHKf1H/as/ZR+GH/AAVT/ZA/4RjxbYS6l4U8YWEOq6Tf/Z2ttQ0uSSMPb3kAlQPDMqv911GQzI6lWZSAfnzq3/Bmz+zZq/he5fQ/iR8aYr25tWawu59U0u7tUkZf3cjxpYxmSPJBKrIhYcBlzmvzi/ZX/wCCP3wj+G37f2sfs2/tf6p8Rvh34v1KZG8FeJNA1ayttB8SROxSJQbizmIMxB8t94G8NC6pIoDfot/wTC/au+K//BJv9r/Rv2KP2hk1TxN4Z12Qw/CjxrbW01yt1AWxHatjc3kAfLg5Nq3ysTAySJ9x/wDBVb/glp4F/wCCqH7O83hPxKq6V4m0rfdeGPEkMQa60S7I/N4HwokiyAwAIKuqMoB8GfGb/gzO+Cdx8LdcX4e/EX4qWfjT7KzaPL4gv7C70z7QOVWeOGyik2NjaWRwV3bsNjafjj/gn5/wTY+DP7Z37Rusfs7ftP3/AMTPhF8cvAAFjo+kabqVjb2uvQrGGkMcs9rN5km1VkRVO14m3ozjds/RX/gi7/wUW+KXgj486h+xn+01peqj4v8Agm0aXw94kEct1D4n0yJSRJLPj5iIwClw2BKAUfbMpEnsf/BZv/gj1pf/AAUi+H1h4o8JXo8GfHjwEBd+EfFFtIbaR3jbzEtLiVPnEe8bkkHzQudy8F1fOVKEpxnJax2+Yra3Pzo/4Kc/8GjWk/Bz9nO78Xfs5674/wDGPibw/uutQ8OeILi0up9VtQMt9jNvbwfv0wW8tgxlHC4cBXwP+CS//BDP9iv/AIKqfABfEGjeOvjpofjbQwlv4q8Ly6/pLXGj3ByNy500F7eQqxjkx2Kth1YD9Fv+CIX/AAVR8W/tf2vir4M/Gnw3qvhn9oL4OqLbxIkliyW+qwqwjW6LKPLjmZiNyZ2ybhJFlGKx+N/8FaP+CZPj39kv4/t+2d+yTC2n+PtEL3fjnwhaxE2niu04a4mWBMeYzBczRDmQgSpiZcvoM/Pn/grv/wAG9Gjf8EwfHHhv4i6f/wALF+If7N73MNt4oazvLSLxL4eLEISZvs5gKOxBjkMAXdiJ9pZJG+x/2bv+DWX9i39rf4LaF8Qfh/8AFf43eIfCviK3E9pdwa3pWR2aORTpu5JEYFXRgGVgQRxX6Lf8E/v20/A3/BW/9iCz8a2/h6T+xPEsE2jeIdA1i082CO4CBLq1y6hLmH58B1BVlbDBWDIv5o/EXwJ4+/4NeP2q5fHHg221rxp+xp8StURNd0QSNPceDbmQgB0JOA4HEcjECZFEMrCRY5SAfF37fv8AwQ78G/8ABLD9tDw4/wAWbj4ka9+yz4yu/sVr4v8ADs9tFq+gzPyI7wNbSxu8YDMQkaedGGaPDI0Q+5PFP/BoX+zz8W/2d5vEXwa+LXxGv9W1zS1v/C+qapqmnahol4XUPE0gt7KJ2iccbkfK7t2G27T+sfxE+G3gH9ur9mebRPEmkReJ/AHxD0iOZrW+tZIGmt5kWSKTY4WSGVcq6nCyRuAflZePyU+AHxH+I/8AwbU/te6b8G/iBLr3jv8AZO+J+rNH4N8QpBJd3fhm6lb/AFJjjB+bcw82BF/eAmeFd3mxEA+Q/wDgkX/wSj+BPxP/AGp9a+CH7Quo/Fv4UftH+CtSaSy0yDV9Oi0vxDGg8wC2EtlIxkCYcASss0bCSMkbgvpf/BcH/g2yH7MHhb/hdHwpu/GvxB8J6UwufHGiXtzbf2taWqY33drLFbqnlCNcPmF2i/1hDoHCfqD/AMFmv+CPel/8FJPh7p/inwjejwZ8d/AYF34R8T28jW0jvG3mJaXEifOI943JIPmhc7l4Lq/O/wDBEf8A4KmeKv2x7DxZ8FvjZ4Z1Pw58fvhAgtPE0c1gy22rQBhEt0WAMccrEjcmdsgYSRbkZljj2cef2ltbWv5BbW58V/sBf8G5v7EP/BR39nPSviN8PviX8eJrO6Ah1HTptc0j7Zod4FBktbhRpvDrng/ddSrKSrA181/8Fd/+DejRv+CYPjjw38RdP/4WL8Q/2b3uYbbxQ1neWkXiXw8WIQkzfZzAUdiDHIYAu7ET7SySN9mft3fsdeP/APgg7+03f/tXfszaW+p/CDWpAfiX8O4mZbS0hLktPCig7IAWZlYAm1djwYHZF/U34B/GfwN/wUD/AGTtE8Y6bp0mreA/iTo7OdP1vTthuLeQNHLBPDICGGQ6HG5GAyrMpDGwPyt/Zu/4NZf2Lf2t/gtoXxB+H/xX+N3iHwr4itxPaXcGt6VkdmjkU6buSRGBV0YBlYEEcV+qn7GP7J3h39hn9mTwn8KfCV7rWoeHfB1vLbWVxq80Ut7Ksk8kxMjRRxoTulYDai8AfWvyT+IvgTx9/wAGvH7Vcvjjwbba140/Y0+JWqImu6IJGnuPBtzIQA6EnAcDiORiBMiiGVhIscp/afwJ410/4keCNH8RaTJNLpWvWUOo2ck1vJbyPDMiyIWjkVXQlWGVZQw6EA0AatQ6hp8GrWE1rdQRXNtcxtFNDKgeOVGGGVlPBBBIIPXNTUUAV9K0q10LS7aysraCzsrOJYLe3gjEcUEagKqIowFUAAADgAVFf+GtO1XV7DULqwsrm/0syNZXMsCvNZl12OY3Iym5flO0jI4NXaKACqR8NacfEY1j7BZf2stsbMX3kL9pEBYOYvMxu2bgG25xkA4zV2igAqlo3hrTvDjXh0+wsrA6hcteXZt4Fi+0zsAGlfaBuchVBY5JwOeKu0UAFUtG8Nad4ca8On2FlYHULlry7NvAsX2mdgA0r7QNzkKoLHJOBzxV2igCl4i8Nad4w0WfTdWsLLVNOugFmtbuBZ4ZgCCAyMCp5API6gVdoooApa34a07xKtqNRsLK/Fjcx3lsLmBZfs88ZykqbgdrqeQw5HY1doooApHw1px8RjWPsFl/ay2xsxfeQv2kQFg5i8zG7ZuAbbnGQDjNXaKKAKXh3w1p3hDSI9P0mwstMsISzR21pAsMMZZi7EIoAGWZmPHJJPejxF4a07xhos+m6tYWWqaddALNa3cCzwzAEEBkYFTyAeR1Aq7RQAVSv/DWnarq9hqF1YWVzf6WZGsrmWBXmsy67HMbkZTcvynaRkcGrtFABVLw74a07whpEen6TYWWmWEJZo7a0gWGGMsxdiEUADLMzHjkknvV2igCl4i8Nad4w0WfTdWsLLVNOugFmtbuBZ4ZgCCAyMCp5API6gVdoooApX/hrTtV1ew1C6sLK5v9LMjWVzLArzWZddjmNyMpuX5TtIyODV2iigCkfDWnHxGNY+wWX9rLbGzF95C/aRAWDmLzMbtm4BtucZAOM1doooApWXhrTtN1q+1K3sLKDUdTEa3l1HAqzXYjBEYkcDc+0MQuScAnHWrtFFAFLw74a07whpEen6TYWWmWEJZo7a0gWGGMsxdiEUADLMzHjkknvR4i8Nad4w0WfTdWsLLVNOugFmtbuBZ4ZgCCAyMCp5API6gVdooAKpa34a07xKtqNRsLK/Fjcx3lsLmBZfs88ZykqbgdrqeQw5HY1dooAKpWXhrTtN1q+1K3sLKDUdTEa3l1HAqzXYjBEYkcDc+0MQuScAnHWrtFAEOoafBq1hNa3UEVzbXMbRTQyoHjlRhhlZTwQQSCD1zTdK0q10LS7aysraCzsrOJYLe3gjEcUEagKqIowFUAAADgAVYooApeIvDWneMNFn03VrCy1TTroBZrW7gWeGYAggMjAqeQDyOoFXaKKAP/2Q==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fig4 = path + '/Fig4Hertzspring.jpg'\n",
"Image(filename= fig4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Figure 4. Conceptual representation of Hertz contact mechanics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This concept is represented mathematically by a non-linear spring whose elastic coefficient is a function of the contact area which at the same time depends on the sample indentation ( k(d) ).\n",
"$$F_{ts} = k(d)d$$\n",
"where\n",
"$$k(d) = 4/3E*\\sqrt{Rd}$$\n",
"being $\\sqrt{Rd}$ the contact area when a sphere of radius R indents a half-space to depth d.\n",
"$E*$ is the effective Young's modulus of the tip-sample interaction. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The long range attractive forces are derived using Hamaker's equation (see reference 4): $if$ $d > a_0$\n",
"$$F_{ts} = \\frac{-HR}{6d^2}$$\n",
"\n",
"where H is the Hamaker constant, R the tip radius and d the tip sample distance. $a_0$ is defined as the intermolecular distance and normally is chosen to be 0.2 nm."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In summary the equations that we will include in our code to take care of the tip sample interactions are the following:\n",
"\n",
"$$\\begin{equation}\n",
"Fts_{DMT} = \\begin{cases} \\frac{-HR}{6d^2} \\quad \\quad d \\leq{a_0}\\\\ \\\\\n",
"\\frac{-HR}{6d^2} + 4/3E*R^{1/2}d^{3/2} \\quad \\quad d> a_0 \\end{cases}\n",
"\\end{equation}$$\n",
"\n",
"where the effective Young's modulus E* is defined by:\n",
"$$\\begin{equation}\n",
"1/E* = \\frac{1-\\nu^2}{E_t}+\\frac{1-\\nu^2}{E_s}\n",
"\\end{equation}$$\n",
"where $E_t$ and $E_s$ are the tip and sample Young's modulus respectively. $\\nu_t$ and $\\nu_s$ are tip and sample Poisson ratios, respectively.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Enough theory, Let's make our code!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we will have to solve equation (1) but with the addition of tip-sample interactions which are described by equation (5). So we have a second order non-linear ODE which is no longer analytically straightforward:\n",
"\n",
"$$\\begin{equation}\n",
"m \\frac{d^2z}{dt^2} = - k z - \\frac{m\\omega_0}{Q}\\frac{dz}{dt} + F_0 cos(\\omega t) + Fts_{DMT}\n",
"\\end{equation}$$\n",
"\n",
"Therefore we have to use numerical methods to solve it. RK4 has shown to be more accurate to solve equation (1) among the methods reviewed in the previous section of the notebook, and therefore it is going to be the chosen method to solve equation (6).\n",
"\n",
"Now we have to declare all the variables related to the tip-sample forces. Since we are modeling our tip-sample forces using Hertz contact mechanics with addition of long range Van der Waals forces we have to define the Young's modulus of the tip and sample, the diameter of the tip of our probe, Poisson ratio, etc."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"#DMT parameters (Hertz contact mechanics with long range Van der Waals forces added\n",
"a=0.2e-9 #intermolecular parameter\n",
"H=6.4e-20 #hamaker constant of sample\n",
"R=20e-9 #tip radius of the cantilever\n",
"Es=70e6 #elastic modulus of sample\n",
"Et=130e9 #elastic modulus of the tip\n",
"vt=0.3 #Poisson coefficient for tip\n",
"vs=0.3 #Poisson coefficient for sample\n",
"E_star= 1/((1-pow(vt,2))/Et+(1-pow(vs,2))/Es) #Effective Young Modulus"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's declare the timestep, the simulation time and let's oscillate our probe!"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,u'Plot 7 Tip response and driving force')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#IMPORTANT distance where you place the probe above the sample\n",
"z_base = 40.e-9 \n",
"\n",
"spp = 280. # time steps per period \n",
"dt = period/spp \n",
"\n",
"simultime = 100.*period\n",
"N = int(simultime/dt)\n",
"t = numpy.linspace(0,simultime,N)\n",
"\n",
"#Initializing variables for RK4\n",
"v_RK4 = numpy.zeros(N)\n",
"z_RK4 = numpy.zeros(N)\n",
"k1v_RK4 = numpy.zeros(N) \n",
"k2v_RK4 = numpy.zeros(N)\n",
"k3v_RK4 = numpy.zeros(N)\n",
"k4v_RK4 = numpy.zeros(N)\n",
" \n",
"k1z_RK4 = numpy.zeros(N)\n",
"k2z_RK4 = numpy.zeros(N)\n",
"k3z_RK4 = numpy.zeros(N)\n",
"k4z_RK4 = numpy.zeros(N)\n",
"\n",
"TipPos = numpy.zeros(N)\n",
"Fts = numpy.zeros(N)\n",
"Fcos = numpy.zeros(N)\n",
"\n",
"for i in range(1,N):\n",
" #RK4\n",
" k1z_RK4[i] = v_RK4[i-1] #k1 Equation 14 \n",
" k1v_RK4[i] = (( ( -k*z_RK4[i-1] - (m*wo/Q)*v_RK4[i-1] + \\\n",
" Fd*numpy.cos(wo*t[i-1]) +Fts[i-1]) ) / m ) #m1 Equation 15\n",
" \n",
" k2z_RK4[i] = ((v_RK4[i-1])+k1v_RK4[i]/2.*dt) #k2 Equation 16\n",
" k2v_RK4[i] = (( ( -k*(z_RK4[i-1]+ k1z_RK4[i]/2.*dt) - (m*wo/Q)*\\\n",
" (v_RK4[i-1] +k1v_RK4[i]/2.*dt) + Fd*\\\n",
" numpy.cos(wo*(t[i-1] + dt/2.)) +Fts[i-1]) ) / m ) #m2 Eq 17\n",
" \n",
" k3z_RK4[i] = ((v_RK4[i-1])+k2v_RK4[i]/2.*dt) #k3, Equation 18\n",
" k3v_RK4[i] = (( ( -k*(z_RK4[i-1]+ k2z_RK4[i]/2.*dt) - (m*wo/Q)*\\\n",
" (v_RK4[i-1] +k2v_RK4[i]/2.*dt) + Fd*\\\n",
" numpy.cos(wo*(t[i-1] + dt/2.)) +Fts[i-1]) ) / m ) #m3, Eq19\n",
" \n",
" k4z_RK4[i] = ((v_RK4[i-1])+k3v_RK4[i]*dt) #k4, Equation 20\n",
" k4v_RK4[i] = (( ( -k*(z_RK4[i-1] + k3z_RK4[i]*dt) - (m*wo/Q)*\\\n",
" (v_RK4[i-1] + k3v_RK4[i]*dt) + Fd*\\\n",
" numpy.cos(wo*(t[i-1] + dt)) +Fts[i-1]) ) / m )#m4, Eq 21\n",
" \n",
" #Calculation of velocity, Equation 23\n",
" v_RK4[i] = v_RK4[i-1] + 1./6*dt*(k1v_RK4[i] + 2.*k2v_RK4[i] +\\\n",
" 2.*k3v_RK4[i] + k4v_RK4[i] ) \n",
" #calculation of position, Equation 22\n",
" z_RK4 [i] = z_RK4[i-1] + 1./6*dt*(k1z_RK4[i] + 2.*k2z_RK4[i] +\\\n",
" 2.*k3z_RK4[i] + k4z_RK4[i] ) \n",
" \n",
" TipPos[i] = z_base + z_RK4[i] #Adding base position to z position\n",
" \n",
" #calculation of DMT force\n",
"\n",
" if TipPos[i] > a: #this defines the attractive regime\n",
" Fts[i] = -H*R/(6*(TipPos[i])**2)\n",
" else: #this defines the repulsive regime\n",
" Fts[i] = -H*R/(6*a**2)+4./3*E_star*numpy.sqrt(R)*(a-TipPos[i])**1.5\n",
" \n",
" \n",
" Fcos[i] = Fd*numpy.cos(wo*t[i]) #Driving force (this will be helpful to plot the driving force)\n",
"\n",
"#Slicing arrays to get steady state\n",
"TipPos_steady = TipPos[int(95*period/dt):] \n",
"t_steady = t[int(95*period/dt):] \n",
"Fcos_steady = Fcos[int(95*period/dt):] \n",
"Fts_steady = Fts[int(95*period/dt):] \n",
"\n",
"plt.figure(1)\n",
"fig, ax1 = plt.subplots()\n",
"ax2 = ax1.twinx()\n",
"ax1.plot(t_steady*1e3,TipPos_steady*1e9, 'g-')\n",
"ax2.plot(t_steady*1e3, Fcos_steady*1e9, 'b-')\n",
"ax1.set_xlabel('Time,s')\n",
"ax1.set_ylabel('Tip position (nm)', color='g')\n",
"ax2.set_ylabel('Drive Force (nm)', color='b')\n",
"plt.title('Plot 7 Tip response and driving force', fontsize = 20)\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-20, 30)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(2)\n",
"plt.title('Plot 8 Force-Distance curve', fontsize=20)\n",
"plt.plot(TipPos*1e9, Fts*1e9, 'b--' )\n",
"plt.xlabel('Tip Position, nm', fontsize=18)\n",
"plt.ylabel('Force, nN', fontsize=18)\n",
"plt.xlim(-20, 30)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check that we have two sinusoidals. The one in green (the output) is the response signal of the tip (the tip trajectory in time) while the blue one (the input) is the cosinusoidal driving force that we are using to excite the tip. When the tip is excited in free air (without tip sample interactions) the phase lag between the output and the input is 90 degrees. You can test that with the previous code by only changing the position of the base to a high-enough position that it does not interact with the sample. However in the above plot the phase lag is less than 90 degrees. Interestingly the phase can give relative information about the material properties of the sample. There is a well-developed theory of this in tapping mode AFM and it's called phase spectroscopy. If you are interested in this topic you can read reference 1.\n",
"Also look at the above plot and see that the response amplitude is no longer 60 nm as we initially set (in this case is near 45 nm!). It means that we have experienced a significant amplitude reduction due to the tip sample interactions.\n",
"Besides with the data acquired we are able to plot a Force-curve as the one shown in Figure 3. It shows the attractive and repulsive interactions of our probe with the surface.\n",
"\n",
"We have arrived to the end of the notebook. I hope you have found it interesting and helpful!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"REFERENCES\n",
"\n",
"1. Garcı́a, Ricardo, and Ruben Perez. \"Dynamic atomic force microscopy methods.\" Surface science reports 47.6 (2002): 197-301.\n",
"2. B. V. Derjaguin, V. M. Muller, and Y. P. Toporov, J. Colloid\n",
"Interface Sci. 53, 314 (1975)\n",
"3. Hertz, H. R., 1882, Ueber die Beruehrung elastischer Koerper (On Contact Between Elastic Bodies), in Gesammelte Werke (Collected Works), Vol. 1, Leipzig, Germany, 1895.\n",
"4. Van Oss, Carel J., Manoj K. Chaudhury, and Robert J. Good. \"Interfacial Lifshitz-van der Waals and polar interactions in macroscopic systems.\" Chemical Reviews 88.6 (1988): 927-941.\n",
"5. Enrique A. López-Guerra, and Santiago D. Solares. \"Modeling viscoelasticity through spring–dashpot models in intermittent-contact atomic force microscopy.\" Beilstein journal of nanotechnology 5, no. 1 (2014): 2149-2163.\n",
"6. Enrique A. López-Guerra, and Santiago D. Solares, \"El microscopio de Fuerza Atómica: Metodos y Aplicaciones.\" Revista UVG (2013) No. 28, 14-23."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### OPTIONAL: Further error analysis based in norm L1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print('This cell takes a while to compute')\n",
"\n",
"\"\"\"ERROR ANALYSIS EULER, VERLET AND RK4\"\"\"\n",
"\n",
"# time-increment array\n",
"dt_values = numpy.array([8.0e-7, 2.0e-7, 0.5e-7, 1e-8, 0.1e-8])\n",
"\n",
"# array that will contain solution of each grid\n",
"z_values_E = numpy.zeros_like(dt_values, dtype=numpy.ndarray)\n",
"z_values_V = numpy.zeros_like(dt_values, dtype=numpy.ndarray)\n",
"z_values_RK4 = numpy.zeros_like(dt_values, dtype=numpy.ndarray)\n",
"z_values_an = numpy.zeros_like(dt_values, dtype=numpy.ndarray)\n",
"\n",
"for n, dt in enumerate(dt_values):\n",
" simultime = 100*period\n",
" timestep = dt\n",
" N = int(simultime/dt)\n",
" t = numpy.linspace(0.0, simultime, N)\n",
" \n",
" #Initializing variables for Verlet\n",
" zdoubledot_V = numpy.zeros(N)\n",
" zdot_V = numpy.zeros(N)\n",
" z_V = numpy.zeros(N)\n",
" \n",
" #Initializing variables for RK4\n",
" vdot_RK4 = numpy.zeros(N)\n",
" v_RK4 = numpy.zeros(N)\n",
" z_RK4 = numpy.zeros(N)\n",
" k1v_RK4 = numpy.zeros(N) \n",
" k2v_RK4 = numpy.zeros(N)\n",
" k3v_RK4 = numpy.zeros(N)\n",
" k4v_RK4 = numpy.zeros(N)\n",
" \n",
" k1z_RK4 = numpy.zeros(N)\n",
" k2z_RK4 = numpy.zeros(N)\n",
" k3z_RK4 = numpy.zeros(N)\n",
" k4z_RK4 = numpy.zeros(N)\n",
" \n",
" \n",
" #Initial conditions Verlet (started with Euler approximation)\n",
" z_V[0] = 0.0\n",
" zdot_V[0] = 0.0\n",
" zdoubledot_V[0] = ( ( -k*z_V[0] - (m*wo/Q)*zdot_V[0] + \\\n",
" Fd*numpy.cos(wo*t[0]) ) ) / m\n",
" zdot_V[1] = zdot_V[0] + zdoubledot_V[0]*timestep**2\n",
" z_V[1] = z_V[0] + zdot_V[0]*dt\n",
" zdoubledot_V[1] = ( ( -k*z_V[1] - (m*wo/Q)*zdot_V[1] + \\\n",
" Fd*numpy.cos(wo*t[1]) ) ) / m\n",
" \n",
" \n",
" #Initial conditions Runge Kutta\n",
" v_RK4[1] = 0\n",
" z_RK4[1] = 0 \n",
" \n",
" #Initialization variables for Analytical solution\n",
" z_an = numpy.zeros(N)\n",
" \n",
" # time loop \n",
" for i in range(2,N):\n",
" \n",
" #Verlet\n",
" z_V[i] = 2*z_V[i-1] - z_V[i-2] + zdoubledot_V[i-1]*dt**2 #Eq 10\n",
" zdot_V[i] = (z_V[i]-z_V[i-2])/(2.0*dt) #Eq 11\n",
" zdoubledot_V[i] = ( ( -k*z_V[i] - (m*wo/Q)*zdot_V[i] +\\\n",
" Fd*numpy.cos(wo*t[i]) ) ) / m #from eq 1\n",
" \n",
" #RK4\n",
" k1z_RK4[i] = v_RK4[i-1] #k1 Equation 14 \n",
" k1v_RK4[i] = (( ( -k*z_RK4[i-1] - (m*wo/Q)*v_RK4[i-1] + \\\n",
" Fd*numpy.cos(wo*t[i-1]) ) ) / m ) #m1 Equation 15\n",
" \n",
" k2z_RK4[i] = ((v_RK4[i-1])+k1v_RK4[i]/2.*dt) #k2 Equation 16\n",
" k2v_RK4[i] = (( ( -k*(z_RK4[i-1]+ k1z_RK4[i]/2.*dt) - (m*wo/Q)*\\\n",
" (v_RK4[i-1] +k1v_RK4[i]/2.*dt) + Fd*\\\n",
" numpy.cos(wo*(t[i-1] + dt/2.)) ) ) / m ) #m2 Eq 17\n",
" \n",
" k3z_RK4[i] = ((v_RK4[i-1])+k2v_RK4[i]/2.*dt) #k3, Equation 18\n",
" k3v_RK4[i] = (( ( -k*(z_RK4[i-1]+ k2z_RK4[i]/2.*dt) - (m*wo/Q)*\\\n",
" (v_RK4[i-1] +k2v_RK4[i]/2.*dt) + Fd*\\\n",
" numpy.cos(wo*(t[i-1] + dt/2.)) ) ) / m ) #m3, Eq 19\n",
" \n",
" k4z_RK4[i] = ((v_RK4[i-1])+k3v_RK4[i]*dt) #k4, Equation 20\n",
" k4v_RK4[i] = (( ( -k*(z_RK4[i-1] + k3z_RK4[i]*dt) - (m*wo/Q)*\\\n",
" (v_RK4[i-1] + k3v_RK4[i]*dt) + Fd*\\\n",
" numpy.cos(wo*(t[i-1] + dt)) ) ) / m )#m4, Equation 21\n",
" \n",
" #Calculation of velocity, Equation 23\n",
" v_RK4[i] = v_RK4[i-1] + 1./6*dt*(k1v_RK4[i] + 2.*k2v_RK4[i] +\\\n",
" 2.*k3v_RK4[i] + k4v_RK4[i] ) \n",
" #calculation of position, Equation 22\n",
" z_RK4 [i] = z_RK4[i-1] + 1./6*dt*(k1z_RK4[i] + 2.*k2z_RK4[i] +\\\n",
" 2.*k3z_RK4[i] + k4z_RK4[i] ) \n",
"\n",
" \n",
" #Analytical solution\n",
" A_an = Fo_an*Q/k #when driven at resonance A is simply Fo*Q/k\n",
" phi = numpy.pi/2 #when driven at resonance the phase is pi/2\n",
" z_an[i] = A_an*numpy.cos(wo*t[i] - phi) #Analytical solution eq. 1\n",
" \n",
" \n",
" #Slicing the full response vector to get the steady state response\n",
" z_steady_V = z_V[int(80*period/timestep):]\n",
" z_an_steady = z_an[int(80*period/timestep):]\n",
" z_steady_RK4 = z_RK4[int(80*period/timestep):]\n",
" time_steady = t[int(80*period/timestep):]\n",
" \n",
" z_values_V[n] = z_steady_V.copy() # error for certain value of timestep\n",
" z_values_RK4[n] = z_steady_RK4.copy() #error for certain value of timestep\n",
" z_values_an[n] = z_an_steady.copy() #error for certain value of timestep\n",
"\n",
"\n",
"def get_error(z, z_exact, dt):\n",
" #Returns the error with respect to the analytical solution using L1 norm\n",
" \n",
" return dt * numpy.sum(numpy.abs(z-z_exact))\n",
" \n",
"#NOW CALCULATE THE ERROR FOR EACH RESPECTIVE DELTA T\n",
"error_values_V = numpy.zeros_like(dt_values)\n",
"error_values_RK4 = numpy.zeros_like(dt_values)\n",
"\n",
"for i, dt in enumerate(dt_values):\n",
" ### call the function get_error() ###\n",
" error_values_V[i] = get_error(z_values_V[i], z_values_an[i], dt)\n",
" error_values_RK4[i] = get_error(z_values_RK4[i], z_values_an[i], dt)\n",
"\n",
"\n",
"plt.figure(1)\n",
"plt.title('Plot 5 Error analysis Verlet based on L1 norm', fontsize=20)\n",
"plt.tick_params(axis='both', labelsize=14)\n",
"plt.grid(True) #turn on grid lines\n",
"plt.xlabel('$\\Delta t$ Verlet', fontsize=16) #x label\n",
"plt.ylabel('Error Verlet', fontsize=16) #y label\n",
"plt.loglog(dt_values, error_values_V, 'go-') #log-log plot\n",
"plt.axis('equal') #make axes scale equally;\n",
"\n",
"plt.figure(2)\n",
"plt.title('Plot 6 Error analysis RK4 based on L1 norm', fontsize=20) \n",
"plt.tick_params(axis='both', labelsize=14) \n",
"plt.grid(True) #turn on grid lines\n",
"plt.xlabel('$\\Delta t$ RK4', fontsize=16) #x label\n",
"plt.ylabel('Error RK4', fontsize=16) #y label\n",
"plt.loglog(dt_values, error_values_RK4, 'co-') #log-log plot\n",
"plt.axis('equal') #make axes scale equally;"
]
}
],
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| mit |
mclaughlin6464/pearce | notebooks/Debug Delta Sigma h.ipynb | 1 | 57068 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"My DS emu varies much more as a function of *h* than i woud expect. I thought I removed all *h* dependence in my code but its possible is still lingering somewhere. Here I will attempt to Identify such. "
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from glob import glob\n",
"from os import path"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from matplotlib import pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns\n",
"sns.set()\n",
"import matplotlib.colors as colors"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#from pearce.mocks.kittens import TrainingBox\n",
"import pyccl as ccl"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"h1, h2 = 0.7, 0.7*0.9"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"omch2 = 0.25/(0.7**2)\n",
"ombh2 = 0.05/(0.7**2)\n",
"param_dict1 = {'Omega_c': omch2*(h1**2), 'Omega_b': ombh2*(h1**2), 'n_s': 0.96, 'sigma8': 0.8, 'h': h1}\n",
"param_dict2 = {'Omega_c': omch2*(h2**2), 'Omega_b': ombh2*(h2**2), 'n_s': 0.96, 'sigma8': 0.8, 'h': h2}"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def xi_mm(param_dict):\n",
" if 'omch2' in param_dict: # in other units, convert\n",
" new_param_dict = {}\n",
" new_param_dict['Omega_c'] = param_dict['omch2']*self.h**2\n",
" new_param_dict['Omega_b'] = param_dict['ombh2']*self.h**2\n",
" new_param_dict['n_s'] = param_dict['ns']\n",
" new_param_dict['h'] = self.h\n",
" new_param_dict['A_s'] = np.exp(param_dict['ln10As'])*(np.power(10, -10))\n",
"\n",
" param_dict = new_param_dict\n",
"\n",
" elif 'Omega_c' not in param_dict:\n",
" param_dict['Omega_c'] = param_dict['Omega_m'] - param_dict['Omega_b']\n",
" del param_dict['Omega_m']\n",
"\n",
" cosmo = ccl.Cosmology(**param_dict)\n",
"\n",
" big_rbins = np.logspace(-1, 1.6, 21)\n",
" big_rpoints = (big_rbins[1:] + big_rbins[:-1]) / 2.0\n",
" big_xi_rmax = big_rpoints[-1]\n",
" return ccl.correlation_3d(cosmo, 1.0, big_rpoints)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xi_mm_7 = xi_mm(param_dict1)\n",
"xi_mm_6h = xi_mm(param_dict2)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"param_dict1 = {'Omega_c': 0.25, 'Omega_b': 0.05, 'n_s': 0.96, 'sigma8': 0.8, 'h': h1}\n",
"param_dict2 = {'Omega_c': 0.25, 'Omega_b': 0.05, 'n_s': 0.96, 'sigma8': 0.8, 'h': h2}"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#xi_mm_7 = xi_mm(param_dict1)\n",
"xi_mm_6 = xi_mm(param_dict2)"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": true
},
"source": [
"def calc_ds(cat, bins):\n",
" x_g, y_g, z_g = [cat.model.mock.galaxy_table[c] for c in ['x', 'y', 'z']]\n",
" pos_g = return_xyz_formatted_array(x_g, y_g, z_g, period=cat.Lbox)\n",
"\n",
" x_m, y_m, z_m = [self.halocat.ptcl_table[c] for c in ['x', 'y', 'z']]\n",
" pos_m = return_xyz_formatted_array(x_m, y_m, z_m, period=cat.Lbox)\n",
"\n",
"\n",
" return mean_delta_sigma(pos_g, pos_m, cat.pmass/cat._downsample_factor,\n",
" rp_bins=rp_bins,\n",
" period=cat.Lbox, num_threads=n_cores) / (1e12)#*self.h**2)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f9acbe25190>"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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ftGlTzb+fffZZhg0bJmEuhLB6JsXE9vTd2Kh19GvXy9LVaRL1BvrcuXPZu3cv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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9acd449790>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(big_rpoints, xi_mm_6/xi_mm_7, label = 'Fixed Om, h = %0.2f/%0.2f'%(h2,h1))\n",
"plt.plot(big_rpoints, xi_mm_6h/xi_mm_7, label = 'Fixed omch2, h = %0.2f/%0.2f'%(h2,h1))\n",
"\n",
"plt.legend(loc='best')\n",
"plt.xscale('log');\n",
"plt.title('Fixed omh2')"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f9acbd35e50>"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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9A+jevSdPPjmE118fz8SJb1Ueg3j//VmcPXum8r07dmylZ88H7/rzchLn4QuV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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9acc459a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(big_rpoints, xi_mm_6, label = 'Fixed Om, h = %0.2f'%h2)\n",
"plt.plot(big_rpoints, xi_mm_6h, label = 'Fixed omch2, h = %0.2f'%h2)\n",
"plt.plot(big_rpoints, xi_mm_7, label = 'Fixed Om, h = %0.2f'%h1)\n",
"\n",
"plt.legend(loc='best')\n",
"#plt.xscale('log');\n",
"plt.loglog()\n",
"plt.title('Fixed omh2')"
]
},
{
"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 [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": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
caseyjlaw/FRB121102 | candidate_spectra.ipynb | 1 | 13699866 | null | bsd-3-clause |
maxrose61/GA_DS | FInal_Project/Quantifying_Influence_Acquire_Clean_Merge.ipynb | 1 | 1991777 | null | gpl-3.0 |
ky822/Data_Bootcamp | Code/IPython/Python_Visualization_Demos.ipynb | 1 | 204057 | {
"metadata": {
"name": "",
"signature": "sha256:3e25346d96c688010139d3614452b412910ef9dc18e573756606ebf02a7e9ac0"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"from pylab import savefig\n",
"import seaborn as sns\n",
"import plotly.plotly as py\n",
"from plotly.graph_objs import *\n",
"py.sign_in(\"sebecketthile\", \"a649z2ue29\")\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Import data into dataframe with read_csv:\n",
"df = pd.read_csv('https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/recent-grads.csv')\n",
"\n",
"\n",
"group_df = df.groupby('Major_category').mean()\n",
"group_df\n",
"\n",
"quartile_df = group_df[['Median','P25th','P75th']].transpose()\n",
"quartile_df\n",
"\n",
"sns.set_context('poster')\n",
"sns.set_style('whitegrid')\n",
"sns.boxplot(quartile_df, vert=False, linewidth=3)\n",
"sns.despine()\n",
"savefig('majors.png', bbox_inches='tight')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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KXTUmARe7+5DsOnd/3MwGE+MHLJniWczMlnb3TzL76EU82Z+et/uPgLuI8Qqy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\nHWsBlkoYAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10c0b5518>"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sns.set(font=\"monospace\")\n",
"\n",
"df = sns.load_dataset(\"brain_networks\", header=[0, 1, 2], index_col=0)\n",
"used_networks = [1, 5, 6, 7, 8, 11, 12, 13, 16, 17]\n",
"used_columns = (df.columns.get_level_values(\"network\")\n",
" .astype(int)\n",
" .isin(used_networks))\n",
"df = df.loc[:, used_columns]\n",
"\n",
"network_pal = sns.cubehelix_palette(len(used_networks),\n",
" light=.9, dark=.1, reverse=True,\n",
" start=1, rot=-2)\n",
"network_lut = dict(zip(map(str, used_networks), network_pal))\n",
"\n",
"networks = df.columns.get_level_values(\"network\")\n",
"network_colors = pd.Series(networks).map(network_lut)\n",
"\n",
"cmap = sns.diverging_palette(h_neg=210, h_pos=350, s=90, l=30, as_cmap=True)\n",
"\n",
"sns.clustermap(df.corr(), row_colors=network_colors, method=\"average\",\n",
" col_colors=network_colors, figsize=(13, 13), cmap=cmap)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"<seaborn.matrix.ClusterGrid at 0x10c62d860>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Ydp/i/+vt91iv/ZIkSeosfSpHeg+HY0mSJEmqKYdj9RIv7FjZ1V0A4KRB4951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\n",
"text": [
"<matplotlib.figure.Figure at 0x10c62d470>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"from scipy.stats import kendalltau\n",
"import seaborn as sns\n",
"sns.set(style=\"ticks\")\n",
"\n",
"rs = np.random.RandomState(11)\n",
"x = rs.gamma(2, size=1000)\n",
"y = -.5 * x + rs.normal(size=1000)\n",
"\n",
"sns.jointplot(x, y, kind=\"hex\", stat_func=kendalltau, color=\"#4CB391\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"<seaborn.axisgrid.JointGrid at 0x10c6496d8>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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6NZzAR3Uwruq5PFs4w1QH7w2g6jscKk5QdGsdjXshkmas4wq+hGGtWzlvEIWU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"text": [
"<matplotlib.figure.Figure at 0x10c649208>"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = Data([\n",
" Surface(\n",
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179.6626, 221.3898, 154.2617, 142.1604, 148.5737, 67.17937, 40.69044, 39.74512, 26.10166, 14.48469, 8.65873, 3.896037, 3.571392, 3.896037, 3.896037, 3.896037, 1.077756, 0], [0.001229679, 3.008948, 5.909858, 33.50574, 104.3341, 152.2165, 198.1988, 191.841, 228.7349, 168.1041, 144.2759, 110.7436, 57.65214, 42.63504, 27.91891, 15.41052, 8.056102, 3.90283, 3.879774, 3.936718, 3.968634, 0.1236256, 3.985531, -0.1835741], [0, 5.626141, 7.676256, 63.16226, 45.99762, 79.56688, 227.311, 203.9287, 172.5618, 177.1462, 140.4554, 123.9905, 110.346, 65.12319, 34.31887, 24.5278, 9.561069, 3.334991, 5.590495, 5.487353, 5.909499, 5.868994, 5.833817, 3.568177]]\n",
" )\n",
"])\n",
"layout = Layout(\n",
" title='Mt Bruno Elevation',\n",
" autosize=False,\n",
" width=500,\n",
" height=500,\n",
" margin=Margin(\n",
" l=65,\n",
" r=50,\n",
" b=65,\n",
" t=90\n",
" )\n",
")\n",
"fig = Figure(data=data, layout=layout)\n",
"plot_url = py.plot(fig, filename='elevations-3d-surface')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = ['day 1', 'day 1', 'day 1', 'day 1', 'day 1', 'day 1',\n",
" 'day 2', 'day 2', 'day 2', 'day 2', 'day 2', 'day 2']\n",
"\n",
"trace1 = Box(\n",
" y=[0.2, 0.2, 0.6, 1.0, 0.5, 0.4, 0.2, 0.7, 0.9, 0.1, 0.5, 0.3],\n",
" x=x,\n",
" name='kale',\n",
" marker=Marker(\n",
" color='#3D9970'\n",
" )\n",
")\n",
"trace2 = Box(\n",
" y=[0.6, 0.7, 0.3, 0.6, 0.0, 0.5, 0.7, 0.9, 0.5, 0.8, 0.7, 0.2],\n",
" x=x,\n",
" name='radishes',\n",
" marker=Marker(\n",
" color='#FF4136'\n",
" )\n",
")\n",
"trace3 = Box(\n",
" y=[0.1, 0.3, 0.1, 0.9, 0.6, 0.6, 0.9, 1.0, 0.3, 0.6, 0.8, 0.5],\n",
" x=x,\n",
" name='carrots',\n",
" marker=Marker(\n",
" color='#FF851B'\n",
" )\n",
")\n",
"data = Data([trace1, trace2, trace3])\n",
"layout = Layout(\n",
" yaxis=YAxis(\n",
" title='normalized moisture',\n",
" zeroline=False\n",
" ),\n",
" boxmode='group'\n",
")\n",
"fig = Figure(data=data, layout=layout)\n",
"plot_url = py.plot(fig, filename='box-grouped')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stats Homework"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\"\n",
"Created on Wed Oct 29 21:33:04 2014\n",
"@author: sarahbeckett-hile\n",
"\"\"\"\n",
"import pandas as pd\n",
"from math import factorial, pow\n",
"import numpy as np\n",
"import scipy as sp\n",
"#%%\n",
"'''\n",
" 1. Let r be a binomial random variable. Compute Pr(r) for each of the following situations:\n",
"'''\n",
"# There are lots of good ways to do this\n",
"# Either make a couple of lists....\n",
"Ns = [10, 4, 16]\n",
"thetas = [.2, .4, .7]\n",
"Rs = [3, 2, 12]\n",
"questions = ['1a', '1b', '1c']\n",
"#%%\n",
"#...and then write a while loop\n",
"i = 0\n",
"while i < len(questions):\n",
" n = Ns[i] #name the different parts of the equation for easy reading\n",
" theta = thetas[i]\n",
" r = Rs[i]\n",
" factorials = factorial(n) / (factorial(r)*factorial(n-r)) #this is the first chunk of the equation\n",
" probability = factorials*pow(theta, r)*pow((1-theta), (n-r))\n",
" rounded_prob = round(probability, 3)\n",
" print('{}.'.format(questions[i]), rounded_prob)# \n",
" i += 1 #add 1 to the value of i to make sure this loop ends. Doing while loops like this can be risky if you forget to add this part\n",
"#%%\n",
"###...or we could put it in dictionaries and make a DataFrame:\n",
"dict1 = {'n': Ns, 'theta': thetas, 'r': Rs} #create a dictionary from the lists\n",
"#%%\n",
"df = pd.DataFrame(data=dict1, index=questions) #create a DataFrame from the dictionary\n",
"#%%\n",
"# we'll have to use scipy.misc.factorial instead of math..factorial because math cannot work with a series, which is what we're using when applying functions to Pandas df columns\n",
"df['probability'] = ( \n",
" (sp.misc.factorial(df.n, exact = False)/\n",
" (sp.misc.factorial(df.r, exact=False)*sp.misc.factorial(df.n-df.r, exact=False)))\n",
" *np.power(df.theta, df.r)*np.power(1-df.theta, df.n-df.r)\n",
" )\n",
"#%%\n",
"# or we could write a function:\n",
"def binomial_pdf(n, theta, r):\n",
" factorials = factorial(n)/(factorial(r)*factorial(n-r))\n",
" probability = factorials*pow(theta, r)*pow((1-theta), (n-r))\n",
" return probability\n",
"#%%\n",
"binomial_pdf(10, .2, 3)\n",
"#%%\n",
"# finally, the simplest option - use a module that already does this:\n",
"# the module we'll use is scipy. List R first, then N and P (probability)\n",
"# http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.binom.html\n",
"sp.stats.distributions.binom.pmf(3, n = 10, p =.2)\n",
"#%%\n",
"\n",
"'''\n",
"2. A chain of motels has adopted a policy of giving a 3% discount to customers who pay in cash \n",
"rather than by credit cards. Its experience is that 30% of all customers take the discount. Let Y = \n",
"number of discount takers among the next 20 customers.\n",
" a. Do you think the binomial assumptions are reasonable in this situation?\n",
"'''\n",
"print('2a. Yes, binomial assumptions are reasonable in this siutation')\n",
"#%%\n",
"'''\n",
" b. Assuming that the binomial probabilities apply, find the probability that exactly 5 of \n",
"the next 20 customers take the discount.\n",
"'''\n",
"# we already have something for this!\n",
"print('2b.', binomial_pdf(20, .3, 5))\n",
"#%%\n",
"'''\n",
"c. Find P(5 or fewer customers take the discount).\n",
"'''\n",
"r_list = range(0,6)\n",
"p_5_or_fewer = 0\n",
"for r in r_list:\n",
" prob = binomial_pdf(20, .3, r)\n",
" p_5_or_fewer =+ p_5_or_fewer + prob\n",
"print('2c.','Probability that 5 or fewer customers will take the discount:', round(p_5_or_fewer, 3))\n",
"#%%\n",
"'''\n",
"d. What is the most probable number of discount takers in the next 20 customers?\n",
"'''\n",
"print('2d.', .3*20)\n",
"#%%\n",
"# or, to replicate the answers given in the assignment's answer key:\n",
"N = 20\n",
"theta = .3\n",
"binom_pdf_df = pd.DataFrame(index=range(0, N+1))\n",
"#%%\n",
"cumulative = 0\n",
"for index, i in binom_pdf_df.iterrows():\n",
" probability = binomial_pdf(N, theta, index)\n",
" binom_pdf_df.ix[index, 'P(X = x)'] = round(probability, 4)\n",
" cumulative += probability\n",
" binom_pdf_df.ix[index, 'P(X <= x)'] = round(cumulative, 4)\n",
"#%%\n",
"print('Binomial with n = {} and p = {}'.format(N, theta))\n",
"binom_pdf_df\n",
"#%%\n",
"# this looks crazy messy, but all it's doing is asking for the value of the index where 'P(X = x)' is greatest\n",
"binom_pdf_df.index[binom_pdf_df['P(X = x)']==binom_pdf_df['P(X = x)'].max()].item()\n",
"#%%\n",
"# All of this can be turned into a new and fairly comprehensive function:\n",
"def binom_pdf(n, theta, R=None, Return=None):\n",
" df = pd.DataFrame(index=range(0, n+1))\n",
" cumulative = 0\n",
" for index, i in df.iterrows():\n",
" r = index\n",
" factorials = factorial(n) / (factorial(r)*factorial(n-r)) # recreating the pdf function from so it can standalone outside this script\n",
" probability = factorials*pow(theta, r)*pow((1-theta), (n-r)) # need to use this and it can run independantly: from math import factorial, pow\n",
" df.ix[index, 'P(X = x)'] = round(probability, 4)\n",
" cumulative += probability\n",
" df.ix[index, 'P(X <= x)'] = round(cumulative, 4)\n",
" df.ix[index, 'P(X => x)'] = round(1-cumulative, 4)\n",
" if R == None:\n",
" return df\n",
" if R != None:\n",
" if Return == None:\n",
" return df[df.index == R]\n",
" #let's make some plain English options for specifying the value you're looking for with this function\n",
" if Return == 'exactly':\n",
" return df[df.index == R]['P(X = x)'].item()\n",
" if Return == 'at_most' or Return == 'or_fewer' or Return == 'not_more_than' :\n",
" return df[df.index == R]['P(X <= x)'].item()\n",
" if Return == 'at_least' or Return == 'or_more':\n",
" return df[df.index == R-1]['P(X => x)'].item()\n",
" if Return == 'less_than' or Return == 'fewer_than':\n",
" return df[df.index == R-1]['P(X <= x)'].item()\n",
" if Return == 'greater_than' or Return == 'more_than':\n",
" return df[df.index == R]['P(X => x)'].item()\n",
" return df[df.index == R]\n",
"#%%\n",
"# since it's optional to submit a number for R, you can leave it out and get a table with all variables\n",
"print(binom_pdf(20, .3))\n",
"#%%\n",
"# or, if you are only interested in a particular value for R:\n",
"binom_pdf(20, .3, 5)\n",
"#%%\n",
"#or, if you want to get really specific:\n",
"binom_pdf(20, .3, 5, 'or_fewer')\n",
"#%%\n",
"'''\n",
"3. The admissions office of a small, selective liberal-arts college will only offer admission to \n",
"applicants who have a certain mix of accomplishments, including a combined SAT score of 1,400 \n",
"or more. Based on past records, the head of admissions feels that the probability is 0.66 that an \n",
"admitted applicant will come to the college. If 500 applicants are admitted, what is the probability \n",
"that 340 or more will come? Note that \u201c340 or more\u201d means the set of values {340, 341, 342, \n",
"343, \u2026, 499, 500}.\n",
"'''\n",
"# this is a great example for when we'd want to use the new binom_pdf function\n",
"# Instead of \"R or fewer\", we want \"R or more\"\n",
"# In other words, we want 'P(X => x)' \n",
"# luckily I already threw in this column!\n",
"#print binom_pdf(500, .66, 339)['P(X => x)'].item()\n",
"print('3a.', binom_pdf(500, .66, 340, 'at_least'))\n",
"# Bam, we're done!\n",
"#%%\n",
"'''\n",
"4. Suppose that a full-repair warranty is offered with each new Power-Up foodprocessor. If the \n",
"probability that any individual food processor will be returned forneeded warranty repairs within \n",
"one year is 0.11, and if a certain store sells 83 of these,find the probabilities that...\n",
"a. at most 10 food processors will be returned for warranty repairs;\n",
"'''\n",
"print('4a.', binom_pdf(83, .11, 10, 'at_most'))\n",
"\n",
"#%%\n",
"'''\n",
"b. at least 10 food processors will be returned for warranty repairs\n",
"'''\n",
"print('4b.', binom_pdf(83, .11, 10, 'at_least'))\n",
"#%%\n",
"'''\n",
"c. exactly 10 food processors will be returned for warranty repairs;\n",
"'''\n",
"print('4c.', binom_pdf(83, .11, 10, 'exactly'))\n",
"#%%\n",
"'''\n",
"d. not more than 15 food processors will be returned for warranty repairs.\n",
"'''\n",
"print('4d.', binom_pdf(83, .11, 15, 'not_more_than'))\n",
"#%%\n",
"'''\n",
"5. The rate of home sales at a small real estate agency is 1.3 per day. We\u2019ll assume that a Poisson \n",
"phenomenon can represent these home sales.\n",
"a. Find the probability that no homes will be sold on Monday.\n",
"'''\n",
"# First, let's write how how we'd do this if there was no existing module for Poisson probability\n",
"# we'll still cheat and use scipy to do exp()\n",
"# http://docs.scipy.org/doc/numpy/reference/routines.math.html\n",
"mean = 1.3\n",
"r = 0\n",
"poisson_probability = sp.exp(-mean) * (pow(mean,r)/factorial(r))\n",
"print('5a. part 1:', poisson_probability)\n",
"#%%\n",
"# fortunately, a fuction for Poisson probability does exists in scipy \n",
"# http://stackoverflow.com/questions/280797/calculate-poisson-probability-percentage\n",
"no_sales = sp.stats.distributions.poisson.pmf(0, 1.3)\n",
"print('5a. part 2:', no_sales)\n",
"#%%\n",
"'''\n",
"b. Find the probability that one home will be sold on Monday.\n",
"'''\n",
"one_sale = sp.stats.distributions.poisson.pmf(1, 1.3)\n",
"print('5b.', one_sale)\n",
"#%%\n",
"'''\n",
"c. Find the probability that two homes will be sold on Monday.\n",
"'''\n",
"print('5c.', sp.stats.distributions.poisson.pmf(2, 1.3))\n",
"#%%\n",
"'''\n",
"d. Find the probability that more than two homes will be sold on Monday\n",
"'''\n",
"no_sales = sp.stats.distributions.poisson.pmf(0, 1.3)\n",
"one_sale = sp.stats.distributions.poisson.pmf(1, 1.3)\n",
"two_sales = sp.stats.distributions.poisson.pmf(2, 1.3)\n",
"more_than_two_sales = 1 - no_sales - one_sale - two_sales\n",
"print('5d.', more_than_two_sales)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1a. 0.201\n",
"1b. 0.346\n",
"1c. 0.204\n",
"2a. Yes, binomial assumptions are reasonable in this siutation\n",
"2b. 0.17886305056987956\n",
"2c. Probability that 5 or fewer customers will take the discount: 0.416\n",
"2d. 6.0\n",
"Binomial with n = 20 and p = 0.3\n",
" P(X = x) P(X <= x) P(X => x)\n",
"0 0.0008 0.0008 0.9992\n",
"1 0.0068 0.0076 0.9924\n",
"2 0.0278 0.0355 0.9645\n",
"3 0.0716 0.1071 0.8929\n",
"4 0.1304 0.2375 0.7625\n",
"5 0.1789 0.4164 0.5836\n",
"6 0.1916 0.6080 0.3920\n",
"7 0.1643 0.7723 0.2277\n",
"8 0.1144 0.8867 0.1133\n",
"9 0.0654 0.9520 0.0480\n",
"10 0.0308 0.9829 0.0171\n",
"11 0.0120 0.9949 0.0051\n",
"12 0.0039 0.9987 0.0013\n",
"13 0.0010 0.9997 0.0003\n",
"14 0.0002 1.0000 0.0000\n",
"15 0.0000 1.0000 0.0000\n",
"16 0.0000 1.0000 0.0000\n",
"17 0.0000 1.0000 0.0000\n",
"18 0.0000 1.0000 0.0000\n",
"19 0.0000 1.0000 0.0000\n",
"20 0.0000 1.0000 0.0000\n",
"3a."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0.1852\n",
"4a."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0.6969\n",
"4b."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0.4306\n",
"4c."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0.1275\n",
"4d."
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 0.982\n",
"5a. part 1: 0.272531793034\n",
"5a. part 2: 0.272531793034\n",
"5b. 0.354291330944\n",
"5c. 0.230289365114\n",
"5d. 0.142887510908\n"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
minxuancao/shogun | doc/ipython-notebooks/multiclass/KNN.ipynb | 1 | 16921 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# K-Nearest Neighbors (KNN)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### by Chiyuan Zhang and Sören Sonnenburg"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook illustrates the <a href=\"http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm\">K-Nearest Neighbors</a> (KNN) algorithm on the USPS digit recognition dataset in Shogun. Further, the effect of <a href=\"http://en.wikipedia.org/wiki/Cover_tree\">Cover Trees</a> on speed is illustrated by comparing KNN with and without it. Finally, a comparison with <a href=\"http://en.wikipedia.org/wiki/Support_vector_machine#Multiclass_SVM\">Multiclass Support Vector Machines</a> is shown. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The basics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The training of a KNN model basically does nothing but memorizing all the training points and the associated labels, which is very cheap in computation but costly in storage. The prediction is implemented by finding the K nearest neighbors of the query point, and voting. Here K is a hyper-parameter for the algorithm. Smaller values for K give the model low bias but high variance; while larger values for K give low variance but high bias.\n",
"\n",
"In `SHOGUN`, you can use [CKNN](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CKNN.html) to perform KNN learning. To construct a KNN machine, you must choose the hyper-parameter K and a distance function. Usually, we simply use the standard [CEuclideanDistance](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CEuclideanDistance.html), but in general, any subclass of [CDistance](http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CDistance.html) could be used. For demonstration, in this tutorial we select a random subset of 1000 samples from the USPS digit recognition dataset, and run 2-fold cross validation of KNN with varying K."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we load and init data split:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import os\nSHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')\n",
"\n",
"from scipy.io import loadmat, savemat\n",
"from numpy import random\n",
"from os import path\n",
"\n",
"mat = loadmat(os.path.join(SHOGUN_DATA_DIR, 'multiclass/usps.mat'))\n",
"Xall = mat['data']\n",
"Yall = np.array(mat['label'].squeeze(), dtype=np.double)\n",
"\n",
"# map from 1..10 to 0..9, since shogun\n",
"# requires multiclass labels to be\n",
"# 0, 1, ..., K-1\n",
"Yall = Yall - 1\n",
"\n",
"random.seed(0)\n",
"\n",
"subset = random.permutation(len(Yall))\n",
"\n",
"Xtrain = Xall[:, subset[:5000]]\n",
"Ytrain = Yall[subset[:5000]]\n",
"\n",
"Xtest = Xall[:, subset[5000:6000]]\n",
"Ytest = Yall[subset[5000:6000]]\n",
"\n",
"Nsplit = 2\n",
"all_ks = range(1, 21)\n",
"\n",
"print Xall.shape\n",
"print Xtrain.shape\n",
"print Xtest.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us plot the first five examples of the train data (first row) and test data (second row)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pylab as P\n",
"def plot_example(dat, lab):\n",
" for i in xrange(5):\n",
" ax=P.subplot(1,5,i+1)\n",
" P.title(int(lab[i]))\n",
" ax.imshow(dat[:,i].reshape((16,16)), interpolation='nearest')\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
" \n",
" \n",
"_=P.figure(figsize=(17,6))\n",
"P.gray()\n",
"plot_example(Xtrain, Ytrain)\n",
"\n",
"_=P.figure(figsize=(17,6))\n",
"P.gray()\n",
"plot_example(Xtest, Ytest)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we import shogun components and convert the data to shogun objects:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from modshogun import MulticlassLabels, RealFeatures\n",
"from modshogun import KNN, EuclideanDistance\n",
"\n",
"labels = MulticlassLabels(Ytrain)\n",
"feats = RealFeatures(Xtrain)\n",
"k=3\n",
"dist = EuclideanDistance()\n",
"knn = KNN(k, dist, labels)\n",
"labels_test = MulticlassLabels(Ytest)\n",
"feats_test = RealFeatures(Xtest)\n",
"knn.train(feats)\n",
"pred = knn.apply_multiclass(feats_test)\n",
"print \"Predictions\", pred[:5]\n",
"print \"Ground Truth\", Ytest[:5]\n",
"\n",
"from modshogun import MulticlassAccuracy\n",
"evaluator = MulticlassAccuracy()\n",
"accuracy = evaluator.evaluate(pred, labels_test)\n",
"\n",
"print \"Accuracy = %2.2f%%\" % (100*accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot a few missclassified examples - I guess we all agree that these are notably harder to detect."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"idx=np.where(pred != Ytest)[0]\n",
"Xbad=Xtest[:,idx]\n",
"Ybad=Ytest[idx]\n",
"_=P.figure(figsize=(17,6))\n",
"P.gray()\n",
"plot_example(Xbad, Ybad)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now the question is - is 97.30% accuracy the best we can do? While one would usually re-train KNN with different values for k here and likely perform Cross-validation, we just use a small trick here that saves us lots of computation time: When we have to determine the $K\\geq k$ nearest neighbors we will know the nearest neigbors for all $k=1...K$ and can thus get the predictions for multiple k's in one step:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"knn.set_k(13)\n",
"multiple_k=knn.classify_for_multiple_k()\n",
"print multiple_k.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have the prediction for each of the 13 k's now and can quickly compute the accuracies:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for k in xrange(13):\n",
" print \"Accuracy for k=%d is %2.2f%%\" % (k+1, 100*np.mean(multiple_k[:,k]==Ytest))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So k=3 seems to have been the optimal choice."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Accellerating KNN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Obviously applying KNN is very costly: for each prediction you have to compare the object against all training objects. While the implementation in `SHOGUN` will use all available CPU cores to parallelize this computation it might still be slow when you have big data sets. In `SHOGUN`, you can use *Cover Trees* to speed up the nearest neighbor searching process in KNN. Just call `set_use_covertree` on the KNN machine to enable or disable this feature. We also show the prediction time comparison with and without Cover Tree in this tutorial. So let's just have a comparison utilizing the data above:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from modshogun import Time, KNN_COVER_TREE, KNN_BRUTE\n",
"start = Time.get_curtime()\n",
"knn.set_k(3)\n",
"knn.set_knn_solver_type(KNN_BRUTE)\n",
"pred = knn.apply_multiclass(feats_test)\n",
"print \"Standard KNN took %2.1fs\" % (Time.get_curtime() - start)\n",
"\n",
"\n",
"start = Time.get_curtime()\n",
"knn.set_k(3)\n",
"knn.set_knn_solver_type(KNN_COVER_TREE)\n",
"pred = knn.apply_multiclass(feats_test)\n",
"print \"Covertree KNN took %2.1fs\" % (Time.get_curtime() - start)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we can significantly speed it up. Let's do a more systematic comparison. For that a helper function is defined to run the evaluation for KNN:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def evaluate(labels, feats, use_cover_tree=False):\n",
" from modshogun import MulticlassAccuracy, CrossValidationSplitting\n",
" import time\n",
" split = CrossValidationSplitting(labels, Nsplit)\n",
" split.build_subsets()\n",
" \n",
" accuracy = np.zeros((Nsplit, len(all_ks)))\n",
" acc_train = np.zeros(accuracy.shape)\n",
" time_test = np.zeros(accuracy.shape)\n",
" for i in range(Nsplit):\n",
" idx_train = split.generate_subset_inverse(i)\n",
" idx_test = split.generate_subset_indices(i)\n",
"\n",
" for j, k in enumerate(all_ks):\n",
" #print \"Round %d for k=%d...\" % (i, k)\n",
"\n",
" feats.add_subset(idx_train)\n",
" labels.add_subset(idx_train)\n",
"\n",
" dist = EuclideanDistance(feats, feats)\n",
" knn = KNN(k, dist, labels)\n",
" knn.set_store_model_features(True)\n",
" if use_cover_tree:\n",
" knn.set_knn_solver_type(KNN_COVER_TREE)\n",
" else:\n",
" knn.set_knn_solver_type(KNN_BRUTE)\n",
" knn.train()\n",
"\n",
" evaluator = MulticlassAccuracy()\n",
" pred = knn.apply_multiclass()\n",
" acc_train[i, j] = evaluator.evaluate(pred, labels)\n",
"\n",
" feats.remove_subset()\n",
" labels.remove_subset()\n",
" feats.add_subset(idx_test)\n",
" labels.add_subset(idx_test)\n",
"\n",
" t_start = time.clock()\n",
" pred = knn.apply_multiclass(feats)\n",
" time_test[i, j] = (time.clock() - t_start) / labels.get_num_labels()\n",
"\n",
" accuracy[i, j] = evaluator.evaluate(pred, labels)\n",
"\n",
" feats.remove_subset()\n",
" labels.remove_subset()\n",
" return {'eout': accuracy, 'ein': acc_train, 'time': time_test}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Evaluate KNN with and without Cover Tree. This takes a few seconds:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"labels = MulticlassLabels(Ytest)\n",
"feats = RealFeatures(Xtest)\n",
"print(\"Evaluating KNN...\")\n",
"wo_ct = evaluate(labels, feats, use_cover_tree=False)\n",
"wi_ct = evaluate(labels, feats, use_cover_tree=True)\n",
"print(\"Done!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate plots with the data collected in the evaluation:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib\n",
"\n",
"fig = P.figure(figsize=(8,5))\n",
"P.plot(all_ks, wo_ct['eout'].mean(axis=0), 'r-*')\n",
"P.plot(all_ks, wo_ct['ein'].mean(axis=0), 'r--*')\n",
"P.legend([\"Test Accuracy\", \"Training Accuracy\"])\n",
"P.xlabel('K')\n",
"P.ylabel('Accuracy')\n",
"P.title('KNN Accuracy')\n",
"P.tight_layout()\n",
"\n",
"fig = P.figure(figsize=(8,5))\n",
"P.plot(all_ks, wo_ct['time'].mean(axis=0), 'r-*')\n",
"P.plot(all_ks, wi_ct['time'].mean(axis=0), 'b-d')\n",
"P.xlabel(\"K\")\n",
"P.ylabel(\"time\")\n",
"P.title('KNN time')\n",
"P.legend([\"Plain KNN\", \"CoverTree KNN\"], loc='center right')\n",
"P.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Although simple and elegant, KNN is generally very resource costly. Because all the training samples are to be memorized literally, the memory cost of KNN *learning* becomes prohibitive when the dataset is huge. Even when the memory is big enough to hold all the data, the prediction will be slow, since the distances between the query point and all the training points need to be computed and ranked. The situation becomes worse if in addition the data samples are all very high-dimensional. Leaving aside computation time issues, k-NN is a very versatile and competitive algorithm. It can be applied to any kind of objects (not just numerical data) - as long as one can design a suitable distance function. In pratice k-NN used with bagging can create improved and more robust results."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comparison to Multiclass Support Vector Machines"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In contrast to KNN - multiclass Support Vector Machines (SVMs) attempt to model the decision function separating each class from one another. They compare examples utilizing similarity measures (so called Kernels) instead of distances like KNN does. When applied, they are in Big-O notation computationally as expensive as KNN but involve another (costly) training step. They do not scale very well to cases with a huge number of classes but usually lead to favorable results when applied to small number of classes cases. So for reference let us compare how a standard multiclass SVM performs wrt. KNN on the mnist data set from above."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us first train a multiclass svm using a Gaussian kernel (kind of the SVM equivalent to the euclidean distance)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from modshogun import GaussianKernel, GMNPSVM\n",
"\n",
"width=80\n",
"C=1\n",
"\n",
"gk=GaussianKernel()\n",
"gk.set_width(width)\n",
"\n",
"svm=GMNPSVM(C, gk, labels)\n",
"_=svm.train(feats)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's apply the SVM to the same test data set to compare results:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"out=svm.apply(feats_test)\n",
"evaluator = MulticlassAccuracy()\n",
"accuracy = evaluator.evaluate(out, labels_test)\n",
"\n",
"print \"Accuracy = %2.2f%%\" % (100*accuracy)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the SVM performs way better on this task - let's apply it to all data we did not use in training."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"Xrem=Xall[:,subset[6000:]]\n",
"Yrem=Yall[subset[6000:]]\n",
"\n",
"feats_rem=RealFeatures(Xrem)\n",
"labels_rem=MulticlassLabels(Yrem)\n",
"out=svm.apply(feats_rem)\n",
"\n",
"evaluator = MulticlassAccuracy()\n",
"accuracy = evaluator.evaluate(out, labels_rem)\n",
"\n",
"print \"Accuracy = %2.2f%%\" % (100*accuracy)\n",
"\n",
"idx=np.where(out.get_labels() != Yrem)[0]\n",
"Xbad=Xrem[:,idx]\n",
"Ybad=Yrem[idx]\n",
"_=P.figure(figsize=(17,6))\n",
"P.gray()\n",
"plot_example(Xbad, Ybad)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The misclassified examples are indeed much harder to label even for human beings."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
pioneers/topgear | ipython-in-depth/examples/IPython Kernel/Third Party Rich Output.ipynb | 1 | 263103 | {
"metadata": {
"name": "",
"signature": "sha256:123d82ef0551f78e5dca94db6e00f1e10ae07d930467cf44709ccc6a9216776a"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Third Party Libraries With Rich Output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A number of third party libraries defined their own custom display logic. This gives their objcts rich output by default when used in the Notebook."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import display"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Pandas](http://pandas.pydata.org/) is a data analysis library for Python. Its `DataFrame` objects have an HTML table representation in the Notebook."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is a small amount of stock data for APPL:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%writefile data.csv\n",
"Date,Open,High,Low,Close,Volume,Adj Close\n",
"2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50\n",
"2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26\n",
"2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48\n",
"2012-03-01,548.17,621.45,516.22,599.55,26486000,596.99\n",
"2012-02-01,458.41,547.61,453.98,542.44,22001000,540.12\n",
"2012-01-03,409.40,458.24,409.00,456.48,12949100,454.53"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Writing data.csv\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read this as into a `DataFrame`:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pandas.read_csv('data.csv')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And view the HTML representation:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df"
],
"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>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Adj Close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 2012-06-01</td>\n",
" <td> 569.16</td>\n",
" <td> 590.00</td>\n",
" <td> 548.50</td>\n",
" <td> 584.00</td>\n",
" <td> 14077000</td>\n",
" <td> 581.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2012-05-01</td>\n",
" <td> 584.90</td>\n",
" <td> 596.76</td>\n",
" <td> 522.18</td>\n",
" <td> 577.73</td>\n",
" <td> 18827900</td>\n",
" <td> 575.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 2012-04-02</td>\n",
" <td> 601.83</td>\n",
" <td> 644.00</td>\n",
" <td> 555.00</td>\n",
" <td> 583.98</td>\n",
" <td> 28759100</td>\n",
" <td> 581.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 2012-03-01</td>\n",
" <td> 548.17</td>\n",
" <td> 621.45</td>\n",
" <td> 516.22</td>\n",
" <td> 599.55</td>\n",
" <td> 26486000</td>\n",
" <td> 596.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 2012-02-01</td>\n",
" <td> 458.41</td>\n",
" <td> 547.61</td>\n",
" <td> 453.98</td>\n",
" <td> 542.44</td>\n",
" <td> 22001000</td>\n",
" <td> 540.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> 2012-01-03</td>\n",
" <td> 409.40</td>\n",
" <td> 458.24</td>\n",
" <td> 409.00</td>\n",
" <td> 456.48</td>\n",
" <td> 12949100</td>\n",
" <td> 454.53</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6 rows \u00d7 7 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
" Date Open High Low Close Volume Adj Close\n",
"0 2012-06-01 569.16 590.00 548.50 584.00 14077000 581.50\n",
"1 2012-05-01 584.90 596.76 522.18 577.73 18827900 575.26\n",
"2 2012-04-02 601.83 644.00 555.00 583.98 28759100 581.48\n",
"3 2012-03-01 548.17 621.45 516.22 599.55 26486000 596.99\n",
"4 2012-02-01 458.41 547.61 453.98 542.44 22001000 540.12\n",
"5 2012-01-03 409.40 458.24 409.00 456.48 12949100 454.53\n",
"\n",
"[6 rows x 7 columns]"
]
}
],
"prompt_number": 12
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"SymPy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[SymPy](http://sympy.org/) is a symbolic computing library for Python. Its equation objects have LaTeX representations that are rendered in the Notebook."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sympy.interactive.printing import init_printing\n",
"init_printing(use_latex='mathjax')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"import sympy as sym\n",
"from sympy import *\n",
"x, y, z = symbols(\"x y z\")\n",
"k, m, n = symbols(\"k m n\", integer=True)\n",
"f, g, h = map(Function, 'fgh')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Rational(3,2)*pi + exp(I*x) / (x**2 + y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\frac{3 \\pi}{2} + \\frac{e^{i x}}{x^{2} + y}$$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
" \u2148\u22c5x \n",
"3\u22c5\u03c0 \u212f \n",
"\u2500\u2500\u2500 + \u2500\u2500\u2500\u2500\u2500\u2500\n",
" 2 2 \n",
" x + y"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = 1/x + (x*sin(x) - 1)/x\n",
"a"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\frac{1}{x} \\left(x \\sin{\\left (x \\right )} - 1\\right) + \\frac{1}{x}$$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"x\u22c5sin(x) - 1 1\n",
"\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + \u2500\n",
" x x"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(1/cos(x)).series(x, 0, 6)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$1 + \\frac{x^{2}}{2} + \\frac{5 x^{4}}{24} + \\mathcal{O}\\left(x^{6}\\right)$$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
" 2 4 \n",
" x 5\u22c5x \u239b 6\u239e\n",
"1 + \u2500\u2500 + \u2500\u2500\u2500\u2500 + O\u239dx \u23a0\n",
" 2 24 "
]
}
],
"prompt_number": 17
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Vincent"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Vincent](https://vincent.readthedocs.org/en/latest/) is a visualization library that uses the [Vega](http://trifacta.github.io/vega/) visualization grammar to build [d3.js](http://d3js.org/) based visualizations in the Notebook and on http://nbviewer.ipython.org. `Visualization` objects in Vincetn have rich HTML and JavaSrcript representations."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import vincent\n",
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas.io.data as web\n",
"import datetime\n",
"all_data = {}\n",
"date_start = datetime.datetime(2010, 1, 1)\n",
"date_end = datetime.datetime(2014, 1, 1)\n",
"for ticker in ['AAPL', 'IBM', 'YHOO', 'MSFT']:\n",
" all_data[ticker] = web.DataReader(ticker, 'yahoo', date_start, date_end)\n",
"price = pd.DataFrame({tic: data['Adj Close']\n",
" for tic, data in all_data.items()})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"vincent.initialize_notebook()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
" <script>\n",
" \n",
" function vct_load_lib(url, callback){\n",
" if(typeof d3 !== 'undefined' &&\n",
" url === 'http://d3js.org/d3.v3.min.js'){\n",
" callback()\n",
" }\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = true;\n",
" s.onreadystatechange = s.onload = callback;\n",
" s.onerror = function(){\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" };\n",
" var vincent_event = new CustomEvent(\n",
" \"vincent_libs_loaded\",\n",
" {bubbles: true, cancelable: true}\n",
" );\n",
" \n",
" function load_all_libs(){\n",
" console.log('loading all libs')\n",
" vct_load_lib('http://d3js.org/d3.v3.min.js', function(){\n",
" vct_load_lib('http://d3js.org/d3.geo.projection.v0.min.js', function(){\n",
" vct_load_lib('http://wrobstory.github.io/d3-cloud/d3.layout.cloud.js', function(){\n",
" vct_load_lib('http://trifacta.github.com/vega/vega.js', function(){\n",
" window.dispatchEvent(vincent_event);\n",
" });\n",
" });\n",
" });\n",
" });\n",
" };\n",
" if(typeof define === \"function\" && define.amd){\n",
" if (window['d3'] === undefined ||\n",
" window['topojson'] === undefined){\n",
" require.config(\n",
" {paths: {\n",
" d3: 'http://d3js.org/d3.v3.min',\n",
" topojson: 'http://d3js.org/topojson.v1.min'\n",
" }\n",
" }\n",
" );\n",
" require([\"d3\"], function(d3){\n",
" console.log('Loading from require.js...')\n",
" window.d3 = d3;\n",
" require([\"topojson\"], function(topojson){\n",
" window.topojson = topojson;\n",
" load_all_libs();\n",
" });\n",
" });\n",
" };\n",
" }else{\n",
" console.log('Require.js not found, loading manually...')\n",
" load_all_libs();\n",
" };\n",
"\n",
" </script>"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML object>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"line = vincent.Line(price[['AAPL', 'IBM', 'YHOO', 'MSFT']], width=600, height=300)\n",
"line.axis_titles(x='Date', y='Price')\n",
"line.legend(title='Ticker')\n",
"display(line)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div id=\"vis47fdfca404f24684b44753131b44ed27\"></div>\n",
"<script>\n",
" ( function() {\n",
" var _do_plot = function() {\n",
" if (typeof vg === 'undefined') {\n",
" window.addEventListener('vincent_libs_loaded', _do_plot)\n",
" return;\n",
" }\n",
" vg.parse.spec({\"axes\": [{\"scale\": \"x\", \"title\": \"Date\", \"type\": \"x\"}, {\"scale\": \"y\", \"title\": \"Price\", \"type\": \"y\"}], \"data\": [{\"name\": \"table\", \"values\": [{\"col\": \"AAPL\", \"idx\": 1262592000000, \"val\": 205.7}, {\"col\": \"IBM\", \"idx\": 1262592000000, \"val\": 122.62}, {\"col\": \"YHOO\", \"idx\": 1262592000000, \"val\": 17.1}, {\"col\": \"MSFT\", \"idx\": 1262592000000, \"val\": 27.67}, {\"col\": \"AAPL\", \"idx\": 1262678400000, \"val\": 206.05}, {\"col\": \"IBM\", \"idx\": 1262678400000, \"val\": 121.14}, {\"col\": \"YHOO\", \"idx\": 1262678400000, \"val\": 17.23}, {\"col\": \"MSFT\", \"idx\": 1262678400000, \"val\": 27.68}, {\"col\": \"AAPL\", \"idx\": 1262764800000, \"val\": 202.77}, {\"col\": \"IBM\", \"idx\": 1262764800000, \"val\": 120.35}, {\"col\": \"YHOO\", \"idx\": 1262764800000, \"val\": 17.17}, {\"col\": \"MSFT\", \"idx\": 1262764800000, \"val\": 27.51}, {\"col\": \"AAPL\", \"idx\": 1262851200000, \"val\": 202.4}, {\"col\": 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\"idx\": 1386144000000, \"val\": 38.13}, {\"col\": \"MSFT\", \"idx\": 1386144000000, \"val\": 38.65}, {\"col\": \"AAPL\", \"idx\": 1386230400000, \"val\": 564.52}, {\"col\": \"IBM\", \"idx\": 1386230400000, \"val\": 175.12}, {\"col\": \"YHOO\", \"idx\": 1386230400000, \"val\": 38.87}, {\"col\": \"MSFT\", \"idx\": 1386230400000, \"val\": 37.72}, {\"col\": \"AAPL\", \"idx\": 1386316800000, \"val\": 556.69}, {\"col\": \"IBM\", \"idx\": 1386316800000, \"val\": 176.7}, {\"col\": \"YHOO\", \"idx\": 1386316800000, \"val\": 38.86}, {\"col\": \"MSFT\", \"idx\": 1386316800000, \"val\": 38.07}, {\"col\": \"AAPL\", \"idx\": 1386576000000, \"val\": 563.06}, {\"col\": \"IBM\", \"idx\": 1386576000000, \"val\": 176.49}, {\"col\": \"YHOO\", \"idx\": 1386576000000, \"val\": 38.87}, {\"col\": \"MSFT\", \"idx\": 1386576000000, \"val\": 38.42}, {\"col\": \"AAPL\", \"idx\": 1386662400000, \"val\": 562.18}, {\"col\": \"IBM\", \"idx\": 1386662400000, \"val\": 176.15}, {\"col\": \"YHOO\", \"idx\": 1386662400000, \"val\": 40.22}, {\"col\": \"MSFT\", \"idx\": 1386662400000, \"val\": 37.83}, {\"col\": \"AAPL\", \"idx\": 1386748800000, \"val\": 558.02}, {\"col\": \"IBM\", \"idx\": 1386748800000, \"val\": 174.24}, {\"col\": \"YHOO\", \"idx\": 1386748800000, \"val\": 39.16}, {\"col\": \"MSFT\", \"idx\": 1386748800000, \"val\": 37.33}, {\"col\": \"AAPL\", \"idx\": 1386835200000, \"val\": 557.2}, {\"col\": \"IBM\", \"idx\": 1386835200000, \"val\": 172.42}, {\"col\": \"YHOO\", \"idx\": 1386835200000, \"val\": 39.35}, {\"col\": \"MSFT\", \"idx\": 1386835200000, \"val\": 36.94}, {\"col\": \"AAPL\", \"idx\": 1386921600000, \"val\": 551.13}, {\"col\": \"IBM\", \"idx\": 1386921600000, \"val\": 171.86}, {\"col\": \"YHOO\", \"idx\": 1386921600000, \"val\": 39.73}, {\"col\": \"MSFT\", \"idx\": 1386921600000, \"val\": 36.42}, {\"col\": \"AAPL\", \"idx\": 1387180800000, \"val\": 554.18}, {\"col\": \"IBM\", \"idx\": 1387180800000, \"val\": 176.88}, {\"col\": \"YHOO\", \"idx\": 1387180800000, \"val\": 39.73}, {\"col\": \"MSFT\", \"idx\": 1387180800000, \"val\": 36.62}, {\"col\": \"AAPL\", \"idx\": 1387267200000, \"val\": 551.69}, {\"col\": \"IBM\", \"idx\": 1387267200000, \"val\": 174.8}, {\"col\": \"YHOO\", \"idx\": 1387267200000, \"val\": 39.51}, {\"col\": \"MSFT\", \"idx\": 1387267200000, \"val\": 36.25}, {\"col\": \"AAPL\", \"idx\": 1387353600000, \"val\": 547.49}, {\"col\": \"IBM\", \"idx\": 1387353600000, \"val\": 177.73}, {\"col\": \"YHOO\", \"idx\": 1387353600000, \"val\": 40.04}, {\"col\": \"MSFT\", \"idx\": 1387353600000, \"val\": 36.31}, {\"col\": \"AAPL\", \"idx\": 1387440000000, \"val\": 541.22}, {\"col\": \"IBM\", \"idx\": 1387440000000, \"val\": 179.24}, {\"col\": \"YHOO\", \"idx\": 1387440000000, \"val\": 40.2}, {\"col\": \"MSFT\", \"idx\": 1387440000000, \"val\": 35.98}, {\"col\": \"AAPL\", \"idx\": 1387526400000, \"val\": 545.75}, {\"col\": \"IBM\", \"idx\": 1387526400000, \"val\": 179.04}, {\"col\": \"YHOO\", \"idx\": 1387526400000, \"val\": 40.12}, {\"col\": \"MSFT\", \"idx\": 1387526400000, \"val\": 36.53}, {\"col\": \"AAPL\", \"idx\": 1387785600000, \"val\": 566.7}, {\"col\": \"IBM\", \"idx\": 1387785600000, \"val\": 181.24}, {\"col\": \"YHOO\", \"idx\": 1387785600000, \"val\": 40.77}, {\"col\": \"MSFT\", \"idx\": 1387785600000, \"val\": 36.35}, {\"col\": \"AAPL\", \"idx\": 1387872000000, \"val\": 564.29}, {\"col\": \"IBM\", \"idx\": 1387872000000, \"val\": 182.22}, {\"col\": \"YHOO\", \"idx\": 1387872000000, \"val\": 40.85}, {\"col\": \"MSFT\", \"idx\": 1387872000000, \"val\": 36.8}, {\"col\": \"AAPL\", \"idx\": 1388044800000, \"val\": 560.54}, {\"col\": \"IBM\", \"idx\": 1388044800000, \"val\": 184.34}, {\"col\": \"YHOO\", \"idx\": 1388044800000, \"val\": 40.65}, {\"col\": \"MSFT\", \"idx\": 1388044800000, \"val\": 37.16}, {\"col\": \"AAPL\", \"idx\": 1388131200000, \"val\": 556.76}, {\"col\": \"IBM\", \"idx\": 1388131200000, \"val\": 184.07}, {\"col\": \"YHOO\", \"idx\": 1388131200000, \"val\": 40.49}, {\"col\": \"MSFT\", \"idx\": 1388131200000, \"val\": 37.01}, {\"col\": \"AAPL\", \"idx\": 1388390400000, \"val\": 551.22}, {\"col\": \"IBM\", \"idx\": 1388390400000, \"val\": 185.39}, {\"col\": \"YHOO\", \"idx\": 1388390400000, \"val\": 40.2}, {\"col\": \"MSFT\", \"idx\": 1388390400000, \"val\": 37.01}, {\"col\": \"AAPL\", \"idx\": 1388476800000, \"val\": 557.68}, {\"col\": \"IBM\", \"idx\": 1388476800000, \"val\": 186.55}, {\"col\": \"YHOO\", \"idx\": 1388476800000, \"val\": 40.44}, {\"col\": \"MSFT\", \"idx\": 1388476800000, \"val\": 37.13}]}], \"height\": 300, \"legends\": [{\"fill\": \"color\", \"offset\": 0, \"properties\": {}, \"title\": \"Ticker\"}], \"marks\": [{\"from\": {\"data\": \"table\", \"transform\": [{\"keys\": [\"data.col\"], \"type\": \"facet\"}]}, \"marks\": [{\"properties\": {\"enter\": {\"stroke\": {\"field\": \"data.col\", \"scale\": \"color\"}, \"strokeWidth\": {\"value\": 2}, \"x\": {\"field\": \"data.idx\", \"scale\": \"x\"}, \"y\": {\"field\": \"data.val\", \"scale\": \"y\"}}}, 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" chart({el: \"#vis47fdfca404f24684b44753131b44ed27\"}).update();\n",
" });\n",
" };\n",
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" })();\n",
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],
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"output_type": "display_data",
"text": [
"<vincent.charts.Line at 0x10290f710>"
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} | apache-2.0 |
chengsoonong/crowdastro | notebooks/56_nonlinear_astro_features.ipynb | 1 | 14186 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Nonlinear Astro Features\n",
"\n",
"This notebook examines whether $w_1 - w_2$ and $w_2 - w_3$ are good features. There are indications that these may be correlated with whether galaxies contain AGNs. It also looks at whether the fluxes are more useful than the magnitudes, i.e., should we exponentiate the magnitudes."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import h5py, numpy, sklearn.linear_model, sklearn.cross_validation, sklearn.metrics"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with h5py.File('../data/training.h5') as f:\n",
" raw_astro_features = f['features'][:, :4]\n",
" dist_features = f['features'][:, 4]\n",
" image_features = f['features'][:, 5:]\n",
" \n",
" w1_w2 = raw_astro_features[:, 0] - raw_astro_features[:, 1]\n",
" w2_w3 = raw_astro_features[:, 1] - raw_astro_features[:, 2]\n",
" \n",
" features_linear = f['features'][:]\n",
" features_nonlinear = numpy.hstack([\n",
" raw_astro_features,\n",
" dist_features.reshape((-1, 1)),\n",
" w1_w2.reshape((-1, 1)),\n",
" w2_w3.reshape((-1, 1)),\n",
" image_features,\n",
" ])\n",
" features_exp = numpy.hstack([\n",
" numpy.power(10, -0.4 * raw_astro_features),\n",
" dist_features.reshape((-1, 1)),\n",
" image_features,\n",
" ])\n",
" features_nlexp = numpy.hstack([\n",
" numpy.power(10, -0.4 * raw_astro_features),\n",
" numpy.power(10, -0.4 * w1_w2.reshape((-1, 1))),\n",
" numpy.power(10, -0.4 * w2_w3.reshape((-1, 1))),\n",
" dist_features.reshape((-1, 1)),\n",
" image_features,\n",
" ])\n",
" labels = f['labels'].value"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x_train, x_test, t_train, t_test = sklearn.cross_validation.train_test_split(\n",
" numpy.arange(raw_astro_features.shape[0]), labels, test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear features, balanced accuracy: 88.20%\n",
"[[4114 268]\n",
" [ 78 368]]\n"
]
}
],
"source": [
"lr = sklearn.linear_model.LogisticRegression(C=100.0, class_weight='balanced')\n",
"lr.fit(features_linear[x_train], t_train)\n",
"cm = sklearn.metrics.confusion_matrix(t_test, lr.predict(features_linear[x_test]))\n",
"tp = cm[1, 1]\n",
"n, p = cm.sum(axis=1)\n",
"tn = cm[0, 0]\n",
"ba = (tp / p + tn / n) / 2\n",
"print('Linear features, balanced accuracy: {:.02%}'.format(ba))\n",
"print(cm)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nonlinear features, balanced accuracy: 88.52%\n",
"[[4103 279]\n",
" [ 74 372]]\n"
]
}
],
"source": [
"lrnl = sklearn.linear_model.LogisticRegression(C=100.0, class_weight='balanced')\n",
"lrnl.fit(features_nonlinear[x_train], t_train)\n",
"cm = sklearn.metrics.confusion_matrix(t_test, lrnl.predict(features_nonlinear[x_test]))\n",
"tp = cm[1, 1]\n",
"n, p = cm.sum(axis=1)\n",
"tn = cm[0, 0]\n",
"ba = (tp / p + tn / n) / 2\n",
"print('Nonlinear features, balanced accuracy: {:.02%}'.format(ba))\n",
"print(cm)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"So maybe they're useful features (but not very). What about the fact they're magnitudes?"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Exponentiated features, balanced accuracy: 89.10%\n",
"[[4124 258]\n",
" [ 71 375]]\n"
]
}
],
"source": [
"lrexp = sklearn.linear_model.LogisticRegression(C=100.0, class_weight='balanced')\n",
"lrexp.fit(features_exp[x_train], t_train)\n",
"cm = sklearn.metrics.confusion_matrix(t_test, lrexp.predict(features_exp[x_test]))\n",
"tp = cm[1, 1]\n",
"n, p = cm.sum(axis=1)\n",
"tn = cm[0, 0]\n",
"ba = (tp / p + tn / n) / 2\n",
"print('Exponentiated features, balanced accuracy: {:.02%}'.format(ba))\n",
"print(cm)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Exponentiated features, balanced accuracy: 89.35%\n",
"[[4107 275]\n",
" [ 67 379]]\n"
]
}
],
"source": [
"lrnlexp = sklearn.linear_model.LogisticRegression(C=100.0, class_weight='balanced')\n",
"lrnlexp.fit(features_nlexp[x_train], t_train)\n",
"cm = sklearn.metrics.confusion_matrix(t_test, lrnlexp.predict(features_nlexp[x_test]))\n",
"tp = cm[1, 1]\n",
"n, p = cm.sum(axis=1)\n",
"tn = cm[0, 0]\n",
"ba = (tp / p + tn / n) / 2\n",
"print('Exponentiated features, balanced accuracy: {:.02%}'.format(ba))\n",
"print(cm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Those are promising results, but we need to rererun this a few times with different training and testing sets to get some error bars."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.87926733193277307]\n",
"[0.8515625]\n",
"[0.88248424369747902]\n",
"[0.88453584558823528]\n",
"[0.87926733193277307, 0.88709672756497815]\n",
"[0.8515625, 0.88978171720807331]\n",
"[0.88248424369747902, 0.90201871711218939]\n",
"[0.88453584558823528, 0.90356851867109755]\n",
"[0.87926733193277307, 0.88709672756497815, 0.88338354869339275]\n",
"[0.8515625, 0.88978171720807331, 0.87759469717527616]\n",
"[0.88248424369747902, 0.90201871711218939, 0.90199786319816277]\n",
"[0.88453584558823528, 0.90356851867109755, 0.90017429356059719]\n",
"[0.87926733193277307, 0.88709672756497815, 0.88338354869339275, 0.89836929366341134]\n",
"[0.8515625, 0.88978171720807331, 0.87759469717527616, 0.89028843970020444]\n",
"[0.88248424369747902, 0.90201871711218939, 0.90199786319816277, 0.89785600726777193]\n",
"[0.88453584558823528, 0.90356851867109755, 0.90017429356059719, 0.9004042698160345]\n",
"[0.87926733193277307, 0.88709672756497815, 0.88338354869339275, 0.89836929366341134, 0.87160312684947727]\n",
"[0.8515625, 0.88978171720807331, 0.87759469717527616, 0.89028843970020444, 0.86731233971197474]\n",
"[0.88248424369747902, 0.90201871711218939, 0.90199786319816277, 0.89785600726777193, 0.90828134247386072]\n",
"[0.88453584558823528, 0.90356851867109755, 0.90017429356059719, 0.9004042698160345, 0.90953590451765631]\n",
"[0.87926733193277307, 0.88709672756497815, 0.88338354869339275, 0.89836929366341134, 0.87160312684947727, 0.87634941658782717]\n",
"[0.8515625, 0.88978171720807331, 0.87759469717527616, 0.89028843970020444, 0.86731233971197474, 0.86569082308420053]\n",
"[0.88248424369747902, 0.90201871711218939, 0.90199786319816277, 0.89785600726777193, 0.90828134247386072, 0.901931251970987]\n",
"[0.88453584558823528, 0.90356851867109755, 0.90017429356059719, 0.9004042698160345, 0.90953590451765631, 0.90642144433932514]\n",
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"[0.8515625, 0.88978171720807331, 0.87759469717527616, 0.89028843970020444, 0.86731233971197474, 0.86569082308420053, 0.87744690610655063]\n",
"[0.88248424369747902, 0.90201871711218939, 0.90199786319816277, 0.89785600726777193, 0.90828134247386072, 0.901931251970987, 0.90776139078849805]\n",
"[0.88453584558823528, 0.90356851867109755, 0.90017429356059719, 0.9004042698160345, 0.90953590451765631, 0.90642144433932514, 0.90758745067882418]\n",
"[0.87926733193277307, 0.88709672756497815, 0.88338354869339275, 0.89836929366341134, 0.87160312684947727, 0.87634941658782717, 0.87592505060404435, 0.89230593847556916]\n",
"[0.8515625, 0.88978171720807331, 0.87759469717527616, 0.89028843970020444, 0.86731233971197474, 0.86569082308420053, 0.87744690610655063, 0.8868778280542986]\n",
"[0.88248424369747902, 0.90201871711218939, 0.90199786319816277, 0.89785600726777193, 0.90828134247386072, 0.901931251970987, 0.90776139078849805, 0.894419306184012]\n",
"[0.88453584558823528, 0.90356851867109755, 0.90017429356059719, 0.9004042698160345, 0.90953590451765631, 0.90642144433932514, 0.90758745067882418, 0.89259532077589532]\n",
"[0.87926733193277307, 0.88709672756497815, 0.88338354869339275, 0.89836929366341134, 0.87160312684947727, 0.87634941658782717, 0.87592505060404435, 0.89230593847556916, 0.89844451547113535]\n",
"[0.8515625, 0.88978171720807331, 0.87759469717527616, 0.89028843970020444, 0.86731233971197474, 0.86569082308420053, 0.87744690610655063, 0.8868778280542986, 0.89511513933914744]\n",
"[0.88248424369747902, 0.90201871711218939, 0.90199786319816277, 0.89785600726777193, 0.90828134247386072, 0.901931251970987, 0.90776139078849805, 0.894419306184012, 0.90695059586320259]\n",
"[0.88453584558823528, 0.90356851867109755, 0.90017429356059719, 0.9004042698160345, 0.90953590451765631, 0.90642144433932514, 0.90758745067882418, 0.89259532077589532, 0.90993466812778712]\n",
"[0.87926733193277307, 0.88709672756497815, 0.88338354869339275, 0.89836929366341134, 0.87160312684947727, 0.87634941658782717, 0.87592505060404435, 0.89230593847556916, 0.89844451547113535, 0.86800810449011045]\n",
"[0.8515625, 0.88978171720807331, 0.87759469717527616, 0.89028843970020444, 0.86731233971197474, 0.86569082308420053, 0.87744690610655063, 0.8868778280542986, 0.89511513933914744, 0.85676049910927476]\n",
"[0.88248424369747902, 0.90201871711218939, 0.90199786319816277, 0.89785600726777193, 0.90828134247386072, 0.901931251970987, 0.90776139078849805, 0.894419306184012, 0.90695059586320259, 0.89731786414315262]\n",
"[0.88453584558823528, 0.90356851867109755, 0.90017429356059719, 0.9004042698160345, 0.90953590451765631, 0.90642144433932514, 0.90758745067882418, 0.89259532077589532, 0.90993466812778712, 0.89731786414315262]\n"
]
}
],
"source": [
"def balanced_accuracy(lr, x_test, t_test):\n",
" cm = sklearn.metrics.confusion_matrix(t_test, lr.predict(x_test))\n",
" tp = cm[1, 1]\n",
" n, p = cm.sum(axis=1)\n",
" tn = cm[0, 0]\n",
" ba = (tp / p + tn / n) / 2\n",
" return ba\n",
"\n",
"def test_feature_set(features, x_train, t_train, x_test, t_test):\n",
" lr = sklearn.linear_model.LogisticRegression(C=100.0, class_weight='balanced')\n",
" lr.fit(features[x_train], t_train)\n",
" return balanced_accuracy(lr, features[x_test], t_test)\n",
"\n",
"linear_ba = []\n",
"nonlinear_ba = []\n",
"exp_ba = []\n",
"nonlinear_exp_ba = []\n",
"\n",
"n_trials = 10\n",
"for trial in range(n_trials):\n",
" print('Trial {}/{}'.format(trial + 1, n_trials))\n",
" x_train, x_test, t_train, t_test = sklearn.cross_validation.train_test_split(\n",
" numpy.arange(raw_astro_features.shape[0]), labels, test_size=0.2)\n",
" linear_ba.append(test_feature_set(features_linear, x_train, t_train, x_test, t_test))\n",
" nonlinear_ba.append(test_feature_set(features_nonlinear, x_train, t_train, x_test, t_test))\n",
" exp_ba.append(test_feature_set(features_exp, x_train, t_train, x_test, t_test))\n",
" nonlinear_exp_ba.append(test_feature_set(features_nlexp, x_train, t_train, x_test, t_test))"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear features: (88.31 +- 1.02)%\n",
"Nonlinear features: (87.58 +- 1.43)%\n",
"Exponentiated features: (90.01 +- 0.73)%\n",
"Exponentiated nonlinear features: (90.12 +- 0.77)%\n"
]
}
],
"source": [
"print('Linear features: ({:.02f} +- {:.02f})%'.format(\n",
" numpy.mean(linear_ba) * 100, numpy.std(linear_ba) * 100))\n",
"print('Nonlinear features: ({:.02f} +- {:.02f})%'.format(\n",
" numpy.mean(nonlinear_ba) * 100, numpy.std(nonlinear_ba) * 100))\n",
"print('Exponentiated features: ({:.02f} +- {:.02f})%'.format(\n",
" numpy.mean(exp_ba) * 100, numpy.std(exp_ba) * 100))\n",
"print('Exponentiated nonlinear features: ({:.02f} +- {:.02f})%'.format(\n",
" numpy.mean(nonlinear_exp_ba) * 100, numpy.std(nonlinear_exp_ba) * 100))"
]
},
{
"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 |
nkmk/python-snippets | notebook/pathlib_file.ipynb | 1 | 10249 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pathlib\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"os.makedirs('temp', exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"p_empty = pathlib.Path('temp/empty_file.txt')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"temp/empty_file.txt\n"
]
}
],
"source": [
"print(p_empty)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pathlib.PosixPath'>\n"
]
}
],
"source": [
"print(type(p_empty))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(p_empty.exists())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"p_empty.touch()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(p_empty.exists())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"p_empty.touch()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# p_empty.touch(exist_ok=False)\n",
"# FileExistsError: [Errno 17] File exists: 'temp/empty_file.txt'"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# pathlib.Path('temp/new_dir/empty_file.txt').touch()\n",
"# FileNotFoundError: [Errno 2] No such file or directory: 'temp/new_dir/empty_file.txt'"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"p_empty_new = pathlib.Path('temp/new_dir/empty_file.txt')\n",
"p_empty_new.parent.mkdir(parents=True, exist_ok=True)\n",
"p_empty_new.touch()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"pathlib.Path('temp/empty_file2.txt').touch()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"p_new = pathlib.Path('temp/new_file.txt')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(p_new.exists())"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"with p_new.open(mode='w') as f:\n",
" f.write('line 1\\nline 2\\nline 3')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"line 1\n",
"line 2\n",
"line 3\n"
]
}
],
"source": [
"with p_new.open() as f:\n",
" print(f.read())"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"s = p_new.read_text()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"line 1\n",
"line 2\n",
"line 3\n"
]
}
],
"source": [
"print(s)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'str'>\n"
]
}
],
"source": [
"print(type(s))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"i = p_new.write_text('new text')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"8\n"
]
}
],
"source": [
"print(i)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"new text\n"
]
}
],
"source": [
"print(p_new.read_text())"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"p_new2 = pathlib.Path('temp/new_file2.txt')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(p_new2.exists())"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# print(p_new2.read_text())\n",
"# FileNotFoundError: [Errno 2] No such file or directory: 'temp/new_file2.txt'"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9\n"
]
}
],
"source": [
"print(p_new2.write_text('new text2'))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"new text2\n"
]
}
],
"source": [
"print(p_new2.read_text())"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# print(pathlib.Path('temp/new_dir2/new_file.txt').write_text('new_text'))\n",
"# FileNotFoundError: [Errno 2] No such file or directory: 'temp/new_dir2/new_file.txt'"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"8\n"
]
}
],
"source": [
"p_text_new = pathlib.Path('temp/new_dir2/new_file.txt')\n",
"p_text_new.parent.mkdir(parents=True, exist_ok=True)\n",
"print(p_text_new.write_text('new_text'))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"new_text\n"
]
}
],
"source": [
"print(p_text_new.read_text())"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9\n"
]
}
],
"source": [
"print(pathlib.Path('temp/new_file3.txt').write_text('new_text3'))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"new_text3\n"
]
}
],
"source": [
"print(pathlib.Path('temp/new_file3.txt').read_text())"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"p_empty = pathlib.Path('temp/empty_file.txt')"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print(p_empty.exists())"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"p_empty.unlink()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print(p_empty.exists())"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"# p_empty.unlink()\n",
"# FileNotFoundError: [Errno 2] No such file or directory: 'temp/empty_file.txt'"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"p_dir = pathlib.Path('temp/')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"# p_dir.unlink()\n",
"# PermissionError: [Errno 1] Operation not permitted: 'temp'"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"for p in p_dir.iterdir():\n",
" if p.is_file():\n",
" p.unlink()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[p.unlink() for p in p_dir.iterdir() if p.is_file()]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"\n",
"shutil.rmtree('temp')"
]
}
],
"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 |
wzxiong/DAVIS-Machine-Learning | labs/lab1-soln.ipynb | 1 | 128583 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lab 1: Nearest Neighbor Regression and Overfitting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is based on the notebook file [01 in Aurélien Geron's github page](https://github.com/ageron/handson-ml)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"# Import the necessary packages\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.model_selection import LeaveOneOut\n",
"from sklearn import linear_model, neighbors\n",
"%matplotlib inline\n",
"plt.style.use('ggplot')\n",
"\n",
"# Where to save the figures\n",
"PROJECT_ROOT_DIR = \"..\"\n",
"datapath = PROJECT_ROOT_DIR + \"/data/lifesat/\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load and prepare data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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LysvLfWoVFRVKTEwMaFvV1SfU0HDqrPoMlviOjffJlqTOMR109GhtkLtpfe3bt1NcXHSb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\ns98AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xb81a710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Download CSV from http://stats.oecd.org/index.aspx?DataSetCode=BLI\n",
"oecd_bli = pd.read_csv(datapath+\"oecd_bli_2015.csv\", thousands=',')\n",
"oecd_bli = oecd_bli[oecd_bli[\"INEQUALITY\"]==\"TOT\"]\n",
"oecd_bli = oecd_bli.pivot(index=\"Country\", columns=\"Indicator\", values=\"Value\")\n",
"\n",
"oecd_bli.columns\n",
"\n",
"oecd_bli[\"Life satisfaction\"].head()\n",
"\n",
"# Load and prepare GDP per capita data\n",
"\n",
"# Download data from http://goo.gl/j1MSKe (=> imf.org)\n",
"gdp_per_capita = pd.read_csv(datapath+\"gdp_per_capita.csv\", thousands=',', delimiter='\\t',\n",
" encoding='latin1', na_values=\"n/a\")\n",
"gdp_per_capita.rename(columns={\"2015\": \"GDP per capita\"}, inplace=True)\n",
"gdp_per_capita.set_index(\"Country\", inplace=True)\n",
"\n",
"full_country_stats = pd.merge(left=oecd_bli, right=gdp_per_capita, left_index=True, right_index=True)\n",
"full_country_stats.sort_values(by=\"GDP per capita\", inplace=\"True\")\n",
"\n",
"_ = full_country_stats.plot(\"GDP per capita\",'Life satisfaction',kind='scatter')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's the full dataset, and there are other columns. I will subselect a few of them by hand."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xvars = ['Self-reported health','Water quality','Quality of support network','GDP per capita']"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X = np.array(full_country_stats[xvars])\n",
"y = np.array(full_country_stats['Life satisfaction'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I will define the following functions to expedite the LOO risk and the Empirical risk."
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def loo_risk(X,y,regmod):\n",
" \"\"\"\n",
" Construct the leave-one-out square error risk for a regression model\n",
" \n",
" Input: design matrix, X, response vector, y, a regression model, regmod\n",
" Output: scalar LOO risk\n",
" \"\"\"\n",
" loo = LeaveOneOut()\n",
" loo_losses = []\n",
" for train_index, test_index in loo.split(X):\n",
" X_train, X_test = X[train_index], X[test_index]\n",
" y_train, y_test = y[train_index], y[test_index]\n",
" regmod.fit(X_train,y_train)\n",
" y_hat = regmod.predict(X_test)\n",
" loss = np.sum((y_hat - y_test)**2)\n",
" loo_losses.append(loss)\n",
" return np.mean(loo_losses)\n",
"\n",
"def emp_risk(X,y,regmod):\n",
" \"\"\"\n",
" Return the empirical risk for square error loss\n",
" \n",
" Input: design matrix, X, response vector, y, a regression model, regmod\n",
" Output: scalar empirical risk\n",
" \"\"\"\n",
" regmod.fit(X,y)\n",
" y_hat = regmod.predict(X)\n",
" return np.mean((y_hat - y)**2)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LOO Risk: 0.348022001422\n",
"Emp Risk: 0.2703035744\n"
]
}
],
"source": [
"lin1 = linear_model.LinearRegression(fit_intercept=False)\n",
"print('LOO Risk: '+ str(loo_risk(X,y,lin1)))\n",
"print('Emp Risk: ' + str(emp_risk(X,y,lin1)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, the empirical risk is much less than the leave-one-out risk! This can happen in more dimensions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Nearest neighbor regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the method described here: http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html\n",
"\n",
"I have already imported the necessary module, so you just need to use the regression object (like we used LinearRegression)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# knn = neighbors.KNeighborsRegressor(n_neighbors=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise 1** For each k from 1 to 30 compute the nearest neighbors empirical risk and LOO risk. Plot these as a function of k and reflect on the bias-variance tradeoff here. (Hint: use the previously defined functions)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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Cf/+7n2bNWl3zPdbZMwge85rndcyWXfjf/wBLly5k586tnDt3ln379pCcnJQu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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xd3e44e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"LOOs = []\n",
"MSEs = []\n",
"K=30\n",
"Ks = range(1,K+1)\n",
"for k in Ks:\n",
" knn = neighbors.KNeighborsRegressor(n_neighbors=k)\n",
" LOOs.append(loo_risk(X,y,knn))\n",
" MSEs.append(emp_risk(X,y,knn))\n",
"\n",
"plt.plot(Ks,LOOs,'r',label=\"LOO risk\")\n",
"plt.title(\"Risks for kNN Regression\")\n",
"plt.plot(Ks,MSEs,'b',label=\"Emp risk\")\n",
"plt.legend()\n",
"_ = plt.xlabel('k')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I decided to see what the performance is for k from 1 to 30. We see that the bias does not dominate until k exceeds 17, the performance is somewhat better for k around 12. This demonstrates that you can't trust the Empirical risk, since it includes the training sample. We can compare this LOO risk to that of linear regression (0.348) and see that it outperforms linear regression."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Exercise 2** Do the same but for the reduced predictor variables below..."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X1 = np.array(full_country_stats[['Self-reported health']])"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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E/ItherhKs91u5/nnn+fjjz/GZrPxwAMPcP/995fY9uOPP2bq1KkcOHCA2rVr88wzz1C7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"text/plain": [
"<matplotlib.figure.Figure at 0xef4e748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"LOOs = []\n",
"MSEs = []\n",
"K=30\n",
"Ks = range(1,K+1)\n",
"for k in Ks:\n",
" knn = neighbors.KNeighborsRegressor(n_neighbors=k)\n",
" LOOs.append(loo_risk(X1,y,knn))\n",
" MSEs.append(emp_risk(X1,y,knn))\n",
"\n",
"plt.plot(Ks,LOOs,'r',label=\"LOO risk\")\n",
"plt.title(\"Risks for kNN Regression\")\n",
"plt.plot(Ks,MSEs,'b',label=\"Emp risk\")\n",
"plt.legend()\n",
"_ = plt.xlabel('k')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For this one dimensional nearest neighbor method, we have a more significant cost of overfitting (k small) when the variance dominates. "
]
}
],
"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"
},
"nav_menu": {},
"toc": {
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 6,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": true
},
"toc_position": {
"height": "616px",
"left": "0px",
"right": "20px",
"top": "106px",
"width": "213px"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
jsaudino/75.06_tp1_acs | old/Fabi Parte TP.ipynb | 3 | 548860 | {
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# magic function para hacer que los graficos de matplotlib se renderizen en el notebook.\n",
"%matplotlib inline\n",
"\n",
"import datetime as datetime\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.style.use('default') # Make the graphs a bit prettier\n",
"plt.rcParams['figure.figsize'] = (15, 5)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Cargo los datos pero parseando las fechas a DataTime\n",
"trip = pd.read_csv('trip.csv', parse_dates=['start_date','end_date'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>duration</th>\n",
" <th>start_date</th>\n",
" <th>start_station_name</th>\n",
" <th>start_station_id</th>\n",
" <th>end_date</th>\n",
" <th>end_station_name</th>\n",
" <th>end_station_id</th>\n",
" <th>bike_id</th>\n",
" <th>subscription_type</th>\n",
" <th>zip_code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4576</td>\n",
" <td>63</td>\n",
" <td>2013-08-29 14:13:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 14:14:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>520</td>\n",
" <td>Subscriber</td>\n",
" <td>94127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4607</td>\n",
" <td>70</td>\n",
" <td>2013-08-29 14:42:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>2013-08-29 14:43:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>661</td>\n",
" <td>Subscriber</td>\n",
" <td>95138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4130</td>\n",
" <td>71</td>\n",
" <td>2013-08-29 10:16:00</td>\n",
" <td>Mountain View City Hall</td>\n",
" <td>27</td>\n",
" <td>2013-08-29 10:17:00</td>\n",
" <td>Mountain View City Hall</td>\n",
" <td>27</td>\n",
" <td>48</td>\n",
" <td>Subscriber</td>\n",
" <td>97214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4251</td>\n",
" <td>77</td>\n",
" <td>2013-08-29 11:29:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>2013-08-29 11:30:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>26</td>\n",
" <td>Subscriber</td>\n",
" <td>95060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4299</td>\n",
" <td>83</td>\n",
" <td>2013-08-29 12:02:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 12:04:00</td>\n",
" <td>Market at 10th</td>\n",
" <td>67</td>\n",
" <td>319</td>\n",
" <td>Subscriber</td>\n",
" <td>94103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>4927</td>\n",
" <td>103</td>\n",
" <td>2013-08-29 18:54:00</td>\n",
" <td>Golden Gate at Polk</td>\n",
" <td>59</td>\n",
" <td>2013-08-29 18:56:00</td>\n",
" <td>Golden Gate at Polk</td>\n",
" <td>59</td>\n",
" <td>527</td>\n",
" <td>Subscriber</td>\n",
" <td>94109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4500</td>\n",
" <td>109</td>\n",
" <td>2013-08-29 13:25:00</td>\n",
" <td>Santa Clara at Almaden</td>\n",
" <td>4</td>\n",
" <td>2013-08-29 13:27:00</td>\n",
" <td>Adobe on Almaden</td>\n",
" <td>5</td>\n",
" <td>679</td>\n",
" <td>Subscriber</td>\n",
" <td>95112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>4563</td>\n",
" <td>111</td>\n",
" <td>2013-08-29 14:02:00</td>\n",
" <td>San Salvador at 1st</td>\n",
" <td>8</td>\n",
" <td>2013-08-29 14:04:00</td>\n",
" <td>San Salvador at 1st</td>\n",
" <td>8</td>\n",
" <td>687</td>\n",
" <td>Subscriber</td>\n",
" <td>95112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>4760</td>\n",
" <td>113</td>\n",
" <td>2013-08-29 17:01:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 17:03:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>553</td>\n",
" <td>Subscriber</td>\n",
" <td>94103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4258</td>\n",
" <td>114</td>\n",
" <td>2013-08-29 11:33:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>2013-08-29 11:35:00</td>\n",
" <td>MLK Library</td>\n",
" <td>11</td>\n",
" <td>107</td>\n",
" <td>Subscriber</td>\n",
" <td>95060</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id duration start_date start_station_name \\\n",
"0 4576 63 2013-08-29 14:13:00 South Van Ness at Market \n",
"1 4607 70 2013-08-29 14:42:00 San Jose City Hall \n",
"2 4130 71 2013-08-29 10:16:00 Mountain View City Hall \n",
"3 4251 77 2013-08-29 11:29:00 San Jose City Hall \n",
"4 4299 83 2013-08-29 12:02:00 South Van Ness at Market \n",
"5 4927 103 2013-08-29 18:54:00 Golden Gate at Polk \n",
"6 4500 109 2013-08-29 13:25:00 Santa Clara at Almaden \n",
"7 4563 111 2013-08-29 14:02:00 San Salvador at 1st \n",
"8 4760 113 2013-08-29 17:01:00 South Van Ness at Market \n",
"9 4258 114 2013-08-29 11:33:00 San Jose City Hall \n",
"\n",
" start_station_id end_date end_station_name \\\n",
"0 66 2013-08-29 14:14:00 South Van Ness at Market \n",
"1 10 2013-08-29 14:43:00 San Jose City Hall \n",
"2 27 2013-08-29 10:17:00 Mountain View City Hall \n",
"3 10 2013-08-29 11:30:00 San Jose City Hall \n",
"4 66 2013-08-29 12:04:00 Market at 10th \n",
"5 59 2013-08-29 18:56:00 Golden Gate at Polk \n",
"6 4 2013-08-29 13:27:00 Adobe on Almaden \n",
"7 8 2013-08-29 14:04:00 San Salvador at 1st \n",
"8 66 2013-08-29 17:03:00 South Van Ness at Market \n",
"9 10 2013-08-29 11:35:00 MLK Library \n",
"\n",
" end_station_id bike_id subscription_type zip_code \n",
"0 66 520 Subscriber 94127 \n",
"1 10 661 Subscriber 95138 \n",
"2 27 48 Subscriber 97214 \n",
"3 10 26 Subscriber 95060 \n",
"4 67 319 Subscriber 94103 \n",
"5 59 527 Subscriber 94109 \n",
"6 5 679 Subscriber 95112 \n",
"7 8 687 Subscriber 95112 \n",
"8 66 553 Subscriber 94103 \n",
"9 11 107 Subscriber 95060 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Observacion de los tipos de datos\n",
"trip.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.text.Text at 0xbe7fd90>,\n",
" <matplotlib.text.Text at 0xbe9c2d0>,\n",
" <matplotlib.text.Text at 0xbe739d0>,\n",
" <matplotlib.text.Text at 0xcfe6510>,\n",
" <matplotlib.text.Text at 0xf1f2f90>,\n",
" <matplotlib.text.Text at 0x13f95310>,\n",
" <matplotlib.text.Text at 0x13f95690>]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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uCg0NVefOnbl/HgxVtWpV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"text/plain": [
"<matplotlib.figure.Figure at 0x2f94ab0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#A cada dato de la columna de comienzo del viaje (start_date) le aplico una funcion para saber en que dia de la semana fueron\n",
"#realizados los viajes\n",
"#Aclaracion: dayofweek nos da los dias ordenados desde 0(lunes) hasta 6(domingo)\n",
"#Realizo un plot de barras para visualizar lo calculado en el paso anterior\n",
"plt = trip['start_date'].apply(lambda x: x.dayofweek).value_counts().plot('bar')\n",
"plt.set_xlabel('Dias de la semana')\n",
"plt.set_ylabel('Cantidad')\n",
"plt.set_title('Cantidad de viajes por dia de la semana')\n",
"plt.set_xticklabels(['Martes','Miercoles','Jueves','Lunes','Viernes','Sabado','Domingo'], fontdict=None, minor=False)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x14445f50>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x13f9f690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#A cada dato de la columna de comienzo del viaje (start_date) le aplico una funcion para saber en que mes fueron\n",
"#realizados los viajes\n",
"#Realizo un plot en el cual observamos la cantidad de viajes segun el mes del año\n",
"plt = trip['start_date'].apply(lambda x: x.month).value_counts().plot('bar')\n",
"plt.set_xlabel('meses')\n",
"plt.set_ylabel('Cantidad de viajes')\n",
"plt.set_title('Cantidad de viajes por mes')"
]
},
{
"cell_type": "code",
"execution_count": 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>duration</th>\n",
" <th>start_date</th>\n",
" <th>start_station_name</th>\n",
" <th>start_station_id</th>\n",
" <th>end_date</th>\n",
" <th>end_station_name</th>\n",
" <th>end_station_id</th>\n",
" <th>bike_id</th>\n",
" <th>subscription_type</th>\n",
" <th>zip_code</th>\n",
" <th>start_date_without_time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4576</td>\n",
" <td>63</td>\n",
" <td>2013-08-29 14:13:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 14:14:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>520</td>\n",
" <td>Subscriber</td>\n",
" <td>94127</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4607</td>\n",
" <td>70</td>\n",
" <td>2013-08-29 14:42:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>2013-08-29 14:43:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>661</td>\n",
" <td>Subscriber</td>\n",
" <td>95138</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4130</td>\n",
" <td>71</td>\n",
" <td>2013-08-29 10:16:00</td>\n",
" <td>Mountain View City Hall</td>\n",
" <td>27</td>\n",
" <td>2013-08-29 10:17:00</td>\n",
" <td>Mountain View City Hall</td>\n",
" <td>27</td>\n",
" <td>48</td>\n",
" <td>Subscriber</td>\n",
" <td>97214</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4251</td>\n",
" <td>77</td>\n",
" <td>2013-08-29 11:29:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>2013-08-29 11:30:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>26</td>\n",
" <td>Subscriber</td>\n",
" <td>95060</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4299</td>\n",
" <td>83</td>\n",
" <td>2013-08-29 12:02:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 12:04:00</td>\n",
" <td>Market at 10th</td>\n",
" <td>67</td>\n",
" <td>319</td>\n",
" <td>Subscriber</td>\n",
" <td>94103</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>4927</td>\n",
" <td>103</td>\n",
" <td>2013-08-29 18:54:00</td>\n",
" <td>Golden Gate at Polk</td>\n",
" <td>59</td>\n",
" <td>2013-08-29 18:56:00</td>\n",
" <td>Golden Gate at Polk</td>\n",
" <td>59</td>\n",
" <td>527</td>\n",
" <td>Subscriber</td>\n",
" <td>94109</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4500</td>\n",
" <td>109</td>\n",
" <td>2013-08-29 13:25:00</td>\n",
" <td>Santa Clara at Almaden</td>\n",
" <td>4</td>\n",
" <td>2013-08-29 13:27:00</td>\n",
" <td>Adobe on Almaden</td>\n",
" <td>5</td>\n",
" <td>679</td>\n",
" <td>Subscriber</td>\n",
" <td>95112</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>4563</td>\n",
" <td>111</td>\n",
" <td>2013-08-29 14:02:00</td>\n",
" <td>San Salvador at 1st</td>\n",
" <td>8</td>\n",
" <td>2013-08-29 14:04:00</td>\n",
" <td>San Salvador at 1st</td>\n",
" <td>8</td>\n",
" <td>687</td>\n",
" <td>Subscriber</td>\n",
" <td>95112</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>4760</td>\n",
" <td>113</td>\n",
" <td>2013-08-29 17:01:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 17:03:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>553</td>\n",
" <td>Subscriber</td>\n",
" <td>94103</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4258</td>\n",
" <td>114</td>\n",
" <td>2013-08-29 11:33:00</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>10</td>\n",
" <td>2013-08-29 11:35:00</td>\n",
" <td>MLK Library</td>\n",
" <td>11</td>\n",
" <td>107</td>\n",
" <td>Subscriber</td>\n",
" <td>95060</td>\n",
" <td>2013-08-29</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id duration start_date start_station_name \\\n",
"0 4576 63 2013-08-29 14:13:00 South Van Ness at Market \n",
"1 4607 70 2013-08-29 14:42:00 San Jose City Hall \n",
"2 4130 71 2013-08-29 10:16:00 Mountain View City Hall \n",
"3 4251 77 2013-08-29 11:29:00 San Jose City Hall \n",
"4 4299 83 2013-08-29 12:02:00 South Van Ness at Market \n",
"5 4927 103 2013-08-29 18:54:00 Golden Gate at Polk \n",
"6 4500 109 2013-08-29 13:25:00 Santa Clara at Almaden \n",
"7 4563 111 2013-08-29 14:02:00 San Salvador at 1st \n",
"8 4760 113 2013-08-29 17:01:00 South Van Ness at Market \n",
"9 4258 114 2013-08-29 11:33:00 San Jose City Hall \n",
"\n",
" start_station_id end_date end_station_name \\\n",
"0 66 2013-08-29 14:14:00 South Van Ness at Market \n",
"1 10 2013-08-29 14:43:00 San Jose City Hall \n",
"2 27 2013-08-29 10:17:00 Mountain View City Hall \n",
"3 10 2013-08-29 11:30:00 San Jose City Hall \n",
"4 66 2013-08-29 12:04:00 Market at 10th \n",
"5 59 2013-08-29 18:56:00 Golden Gate at Polk \n",
"6 4 2013-08-29 13:27:00 Adobe on Almaden \n",
"7 8 2013-08-29 14:04:00 San Salvador at 1st \n",
"8 66 2013-08-29 17:03:00 South Van Ness at Market \n",
"9 10 2013-08-29 11:35:00 MLK Library \n",
"\n",
" end_station_id bike_id subscription_type zip_code start_date_without_time \n",
"0 66 520 Subscriber 94127 2013-08-29 \n",
"1 10 661 Subscriber 95138 2013-08-29 \n",
"2 27 48 Subscriber 97214 2013-08-29 \n",
"3 10 26 Subscriber 95060 2013-08-29 \n",
"4 67 319 Subscriber 94103 2013-08-29 \n",
"5 59 527 Subscriber 94109 2013-08-29 \n",
"6 5 679 Subscriber 95112 2013-08-29 \n",
"7 8 687 Subscriber 95112 2013-08-29 \n",
"8 66 553 Subscriber 94103 2013-08-29 \n",
"9 11 107 Subscriber 95060 2013-08-29 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Ahora para hacer un visualizacion de todos los viajes a traves del tiempo creamos una nueva columa en la cual tendremos\n",
"#la fecha pero sin la hora ni los minutos\n",
"trip['start_date_without_time']=trip.start_date.dt.date\n",
"trip.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xbb71790>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ggkGiEkEQBEEEYdq0aZg/fz42bNiA0aNHB132iy++QFNTEz7//HOT06AtpTWy6ygY/fv3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5rtX+/ehnP+/D9w+Q2PL1md2nOtaSq58LeeFIx1bJxFapzlGQdXiKm0b9IKf9OZSlOG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\nCCGEEEIIIYQQQgLz/wONZfFuv4wocgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xedaa1d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Realizamos una visualizacion de los viajes a traves del tiempo\n",
"#Quiero aclarar que se realizo una agrupacion dia a dia para realizar este plot\n",
"plt = trip.groupby('start_date_without_time').count()['id'].plot(figsize=(14,4));\n",
"plt.set_xlabel('Fecha')\n",
"plt.set_ylabel('Cantidad de viajes')\n",
"plt.set_title('Cantidad de viajes a traves del tiempo')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x148a9df0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x14c577d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Ahora añadiremos otra columna la cual tendra solo la hora en la que se realiza el viaje\n",
"trip['hora'] = trip['start_date'].apply(lambda x: x.hour)\n",
"#Realizo una visualizacion en base a la hora en que se realiza el viaje\n",
"plt = trip.groupby('hora').count()['id'].plot('bar');\n",
"plt.set_xlabel('Hora del dia')\n",
"plt.set_ylabel('Cantidad de viajes')\n",
"plt.set_title('Cantidad de viajes dependiendo de la hora')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x15bec670>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0xbe73d10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Cambiamos la duracion a minutos\n",
"trip['duration'] = trip['duration'].apply(lambda x: x/60)\n",
"#Cantidad de viajes segun la duracion (en minutos). Visualizacion de la cantidad de viajes segun la duracion del viaje \n",
"plt = trip['duration'].value_counts()[:20].plot('bar')\n",
"plt.set_xlabel('Cantidad de minutos del viajes')\n",
"plt.set_ylabel('Cantidad de viajes')\n",
"plt.set_title('Cantidad de viajes dependiendo de su duracion')"
]
},
{
"cell_type": "code",
"execution_count": 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>start_station_id</th>\n",
" <th>name</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" <th>dock_count</th>\n",
" <th>city</th>\n",
" <th>installation_date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2</td>\n",
" <td>San Jose Diridon Caltrain Station</td>\n",
" <td>37.329732</td>\n",
" <td>-121.901782</td>\n",
" <td>27</td>\n",
" <td>San Jose</td>\n",
" <td>8/6/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>San Jose Civic Center</td>\n",
" <td>37.330698</td>\n",
" <td>-121.888979</td>\n",
" <td>15</td>\n",
" <td>San Jose</td>\n",
" <td>8/5/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4</td>\n",
" <td>Santa Clara at Almaden</td>\n",
" <td>37.333988</td>\n",
" <td>-121.894902</td>\n",
" <td>11</td>\n",
" <td>San Jose</td>\n",
" <td>8/6/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5</td>\n",
" <td>Adobe on Almaden</td>\n",
" <td>37.331415</td>\n",
" <td>-121.893200</td>\n",
" <td>19</td>\n",
" <td>San Jose</td>\n",
" <td>8/5/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6</td>\n",
" <td>San Pedro Square</td>\n",
" <td>37.336721</td>\n",
" <td>-121.894074</td>\n",
" <td>15</td>\n",
" <td>San Jose</td>\n",
" <td>8/7/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>7</td>\n",
" <td>Paseo de San Antonio</td>\n",
" <td>37.333798</td>\n",
" <td>-121.886943</td>\n",
" <td>15</td>\n",
" <td>San Jose</td>\n",
" <td>8/7/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>8</td>\n",
" <td>San Salvador at 1st</td>\n",
" <td>37.330165</td>\n",
" <td>-121.885831</td>\n",
" <td>15</td>\n",
" <td>San Jose</td>\n",
" <td>8/5/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>9</td>\n",
" <td>Japantown</td>\n",
" <td>37.348742</td>\n",
" <td>-121.894715</td>\n",
" <td>15</td>\n",
" <td>San Jose</td>\n",
" <td>8/5/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>10</td>\n",
" <td>San Jose City Hall</td>\n",
" <td>37.337391</td>\n",
" <td>-121.886995</td>\n",
" <td>15</td>\n",
" <td>San Jose</td>\n",
" <td>8/6/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>11</td>\n",
" <td>MLK Library</td>\n",
" <td>37.335885</td>\n",
" <td>-121.885660</td>\n",
" <td>19</td>\n",
" <td>San Jose</td>\n",
" <td>8/6/2013</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" start_station_id name lat long \\\n",
"0 2 San Jose Diridon Caltrain Station 37.329732 -121.901782 \n",
"1 3 San Jose Civic Center 37.330698 -121.888979 \n",
"2 4 Santa Clara at Almaden 37.333988 -121.894902 \n",
"3 5 Adobe on Almaden 37.331415 -121.893200 \n",
"4 6 San Pedro Square 37.336721 -121.894074 \n",
"5 7 Paseo de San Antonio 37.333798 -121.886943 \n",
"6 8 San Salvador at 1st 37.330165 -121.885831 \n",
"7 9 Japantown 37.348742 -121.894715 \n",
"8 10 San Jose City Hall 37.337391 -121.886995 \n",
"9 11 MLK Library 37.335885 -121.885660 \n",
"\n",
" dock_count city installation_date \n",
"0 27 San Jose 8/6/2013 \n",
"1 15 San Jose 8/5/2013 \n",
"2 11 San Jose 8/6/2013 \n",
"3 19 San Jose 8/5/2013 \n",
"4 15 San Jose 8/7/2013 \n",
"5 15 San Jose 8/7/2013 \n",
"6 15 San Jose 8/5/2013 \n",
"7 15 San Jose 8/5/2013 \n",
"8 15 San Jose 8/6/2013 \n",
"9 19 San Jose 8/6/2013 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Cargamos los datos de station.csv y le cambiamos el nombre a una de sus columnas para un posterior procesamiento\n",
"station = pd.read_csv('station.csv', low_memory=False)\n",
"station.rename(columns={'id': 'start_station_id'}, inplace=True)\n",
"station.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th>id</th>\n",
" <th>duration</th>\n",
" <th>start_date</th>\n",
" <th>start_station_name</th>\n",
" <th>start_station_id</th>\n",
" <th>end_date</th>\n",
" <th>end_station_name</th>\n",
" <th>end_station_id</th>\n",
" <th>bike_id</th>\n",
" <th>subscription_type</th>\n",
" <th>zip_code</th>\n",
" <th>start_date_without_time</th>\n",
" <th>hora</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>4576</td>\n",
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" <td>94127</td>\n",
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" <td>14</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
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" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4299</td>\n",
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" <td>67</td>\n",
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" <td>12</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
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" <td>19</td>\n",
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" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>4760</td>\n",
" <td>1</td>\n",
" <td>2013-08-29 17:01:00</td>\n",
" <td>South Van Ness at Market</td>\n",
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" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>553</td>\n",
" <td>Subscriber</td>\n",
" <td>94103</td>\n",
" <td>2013-08-29</td>\n",
" <td>17</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5070</td>\n",
" <td>2</td>\n",
" <td>2013-08-29 21:43:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 21:46:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>598</td>\n",
" <td>Subscriber</td>\n",
" <td>94115</td>\n",
" <td>2013-08-29</td>\n",
" <td>21</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4765</td>\n",
" <td>3</td>\n",
" <td>2013-08-29 17:05:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 17:08:00</td>\n",
" <td>Market at 10th</td>\n",
" <td>67</td>\n",
" <td>553</td>\n",
" <td>Subscriber</td>\n",
" <td>94103</td>\n",
" <td>2013-08-29</td>\n",
" <td>17</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>4560</td>\n",
" <td>3</td>\n",
" <td>2013-08-29 13:58:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 14:02:00</td>\n",
" <td>San Francisco City Hall</td>\n",
" <td>58</td>\n",
" <td>438</td>\n",
" <td>Subscriber</td>\n",
" <td>94124</td>\n",
" <td>2013-08-29</td>\n",
" <td>13</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4559</td>\n",
" <td>4</td>\n",
" <td>2013-08-29 13:58:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 14:02:00</td>\n",
" <td>San Francisco City Hall</td>\n",
" <td>58</td>\n",
" <td>554</td>\n",
" <td>Subscriber</td>\n",
" <td>94115</td>\n",
" <td>2013-08-29</td>\n",
" <td>13</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>4584</td>\n",
" <td>4</td>\n",
" <td>2013-08-29 14:17:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 14:21:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>587</td>\n",
" <td>Subscriber</td>\n",
" <td>94612</td>\n",
" <td>2013-08-29</td>\n",
" <td>14</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>5075</td>\n",
" <td>5</td>\n",
" <td>2013-08-29 21:47:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 21:52:00</td>\n",
" <td>Civic Center BART (7th at Market)</td>\n",
" <td>72</td>\n",
" <td>598</td>\n",
" <td>Subscriber</td>\n",
" <td>94115</td>\n",
" <td>2013-08-29</td>\n",
" <td>21</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4981</td>\n",
" <td>6</td>\n",
" <td>2013-08-29 19:41:00</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>66</td>\n",
" <td>2013-08-29 19:47:00</td>\n",
" <td>Market at 10th</td>\n",
" <td>67</td>\n",
" <td>632</td>\n",
" <td>Subscriber</td>\n",
" <td>94110</td>\n",
" <td>2013-08-29</td>\n",
" <td>19</td>\n",
" <td>South Van Ness at Market</td>\n",
" <td>37.774814</td>\n",
" <td>-122.418954</td>\n",
" <td>19</td>\n",
" <td>San Francisco</td>\n",
" <td>8/23/2013</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id duration start_date start_station_name \\\n",
"0 4576 1 2013-08-29 14:13:00 South Van Ness at Market \n",
"1 4299 1 2013-08-29 12:02:00 South Van Ness at Market \n",
"2 4760 1 2013-08-29 17:01:00 South Van Ness at Market \n",
"3 5070 2 2013-08-29 21:43:00 South Van Ness at Market \n",
"4 4765 3 2013-08-29 17:05:00 South Van Ness at Market \n",
"5 4560 3 2013-08-29 13:58:00 South Van Ness at Market \n",
"6 4559 4 2013-08-29 13:58:00 South Van Ness at Market \n",
"7 4584 4 2013-08-29 14:17:00 South Van Ness at Market \n",
"8 5075 5 2013-08-29 21:47:00 South Van Ness at Market \n",
"9 4981 6 2013-08-29 19:41:00 South Van Ness at Market \n",
"\n",
" start_station_id end_date end_station_name \\\n",
"0 66 2013-08-29 14:14:00 South Van Ness at Market \n",
"1 66 2013-08-29 12:04:00 Market at 10th \n",
"2 66 2013-08-29 17:03:00 South Van Ness at Market \n",
"3 66 2013-08-29 21:46:00 South Van Ness at Market \n",
"4 66 2013-08-29 17:08:00 Market at 10th \n",
"5 66 2013-08-29 14:02:00 San Francisco City Hall \n",
"6 66 2013-08-29 14:02:00 San Francisco City Hall \n",
"7 66 2013-08-29 14:21:00 South Van Ness at Market \n",
"8 66 2013-08-29 21:52:00 Civic Center BART (7th at Market) \n",
"9 66 2013-08-29 19:47:00 Market at 10th \n",
"\n",
" end_station_id bike_id subscription_type zip_code start_date_without_time \\\n",
"0 66 520 Subscriber 94127 2013-08-29 \n",
"1 67 319 Subscriber 94103 2013-08-29 \n",
"2 66 553 Subscriber 94103 2013-08-29 \n",
"3 66 598 Subscriber 94115 2013-08-29 \n",
"4 67 553 Subscriber 94103 2013-08-29 \n",
"5 58 438 Subscriber 94124 2013-08-29 \n",
"6 58 554 Subscriber 94115 2013-08-29 \n",
"7 66 587 Subscriber 94612 2013-08-29 \n",
"8 72 598 Subscriber 94115 2013-08-29 \n",
"9 67 632 Subscriber 94110 2013-08-29 \n",
"\n",
" hora name lat long dock_count \\\n",
"0 14 South Van Ness at Market 37.774814 -122.418954 19 \n",
"1 12 South Van Ness at Market 37.774814 -122.418954 19 \n",
"2 17 South Van Ness at Market 37.774814 -122.418954 19 \n",
"3 21 South Van Ness at Market 37.774814 -122.418954 19 \n",
"4 17 South Van Ness at Market 37.774814 -122.418954 19 \n",
"5 13 South Van Ness at Market 37.774814 -122.418954 19 \n",
"6 13 South Van Ness at Market 37.774814 -122.418954 19 \n",
"7 14 South Van Ness at Market 37.774814 -122.418954 19 \n",
"8 21 South Van Ness at Market 37.774814 -122.418954 19 \n",
"9 19 South Van Ness at Market 37.774814 -122.418954 19 \n",
"\n",
" city installation_date \n",
"0 San Francisco 8/23/2013 \n",
"1 San Francisco 8/23/2013 \n",
"2 San Francisco 8/23/2013 \n",
"3 San Francisco 8/23/2013 \n",
"4 San Francisco 8/23/2013 \n",
"5 San Francisco 8/23/2013 \n",
"6 San Francisco 8/23/2013 \n",
"7 San Francisco 8/23/2013 \n",
"8 San Francisco 8/23/2013 \n",
"9 San Francisco 8/23/2013 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Realizamos un join entre trip y station en base a la columna start_station_id\n",
"arch_unidos = pd.merge(trip, station, on='start_station_id', how='inner')\n",
"arch_unidos.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xebd65f0>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x148a9e70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Visulizacion de la cantidad de viajes segun la ciudad\n",
"plt = arch_unidos['city'].value_counts().plot('bar')\n",
"plt.set_xlabel('Ciudad')\n",
"plt.set_ylabel('Cantidad de viajes')\n",
"plt.set_title('Cantidad de viajes dependiendo de la ciudad')\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0xf03ad90>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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qT58+GjJkiL7//nuDEzpG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bx9+xYVFRVQVVXlj6Wnp2Pnzp2oqKiAvb09Zs+eTaEhOVRUVHw2c+jjx4+0KntAtFKqubq6GhIS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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xcb7fd90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Visulizacion de la cantidad de viajes segun la estacion\n",
"#Solo mostramos las 20 ciudades con mas cantidad de viajes\n",
"plt = arch_unidos['start_station_name'].value_counts().tail(20).plot('bar')\n",
"plt.set_xlabel('Ciudad')\n",
"plt.set_ylabel('Cantidad de viajes')\n",
"plt.set_title('Top20 de estaciones con mas cantidad de viajes')\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": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
lowcloudnine/singularity-spark | ipython_notebooks/schiefjm/PySpark Examples/Structures of GSOD.ipynb | 1 | 7633 | {
"metadata": {
"name": "",
"signature": "sha256:e5d192f6bc81a49ae3a355be2c6352fb063c2d07084ae4f9fa35e9f78f5c0e4b"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Imports"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"\n",
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"from future.builtins import (bytes, str, open, super, range,\n",
" zip, round, input, int, pow, object)\n",
"\n",
"if sys.version_info.major == 2:\n",
" # in Python 2 cPickle is much faster than pickle but doesn't deal w/ unicode\n",
" import cPickle as pickle\n",
"else:\n",
" # Python 3 loads the faster pickle by default if it's available\n",
" import pickle\n",
"\n",
"import collections\n",
"import random\n",
"import time"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"SparkContext Info"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from utils import header\n",
"print(header.create_header(sc))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Test Name No\n",
"Machine c1-master.ec2.internal\n",
"Date 19 Feb 2015\n",
"Start Time 16:19:43\n",
"\n",
"Spark Configuration\n",
"========================================================================\n",
"spark.executor.extraLibraryPath\n",
" /opt/cloudera/parcels/CDH-5.3.1- .. 5.3.1.p0.5/lib/hadoop/lib/native\n",
"spark.executor.memory\n",
" 2g\n",
"spark.driver.extraLibraryPath\n",
" /opt/cloudera/parcels/CDH-5.3.1- .. 5.3.1.p0.5/lib/hadoop/lib/native\n",
"spark.executor.instances\n",
" 8\n",
"spark.serializer.objectStreamReset\n",
" 100\n",
"spark.eventLog.enabled\n",
" true\n",
"spark.yarn.historyServer.address\n",
" http://c1-master.ec2.internal:18088\n",
"spark.cores.max\n",
" 8\n",
"spark.rdd.compress\n",
" True\n",
"spark.app.name\n",
" PySparkShell\n",
"spark.eventLog.dir\n",
" hdfs://c1-master.ec2.internal:8020/user/spark/applicationHistory\n",
"spark.master\n",
" yarn-client\n",
"========================================================================\n",
"\n",
"\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Filter Functions for RDDs"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def is_ob(ob):\n",
" return \"STN\" not in ob\n",
"\n",
"def is_station(station_id, ob):\n",
" return station_id in ob\n",
"\n",
"def obs_by_station(obs_rdd):\n",
" \"\"\" Given an RDD of observations from GSOD returns a dictionary of the observations\n",
" the key is the station id and the value is an array of the observations for that\n",
" station.\n",
" \n",
" \"\"\"\n",
" stations = obs_rdd.map(lambda line: (line.split()[0], 1))\\\n",
" .reduceByKey(lambda x, y: x + y)\\\n",
" .collect()\n",
" \n",
" station_obs = {}\n",
" for station in stations:\n",
" station_id = str(station[0])\n",
" station_obs[station_id] = \\\n",
" obs_rdd.filter(lambda line: is_station(station_id, line))\\\n",
" .collect()\n",
" \n",
" return station_obs\n",
"\n",
"def obs_by_year(years):\n",
" \"\"\" Given a list of years returns an ordered dictionary of the years with a value of\n",
" a dictionary of station_ids with the value of observations.\n",
" \n",
" \"\"\"\n",
" years_of_obs = collections.OrderedDict()\n",
" for year in years:\n",
" obs_rdd = sc.textFile(\"/user/schiefjm/weather/gsod/\" + str(year))\\\n",
" .filter(lambda line: is_ob(line))\n",
" obs_dict = collections.OrderedDict(obs_by_station(obs_rdd))\n",
" years_of_obs[year] = obs_dict\n",
" \n",
" return years_of_obs"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"structured_obs = obs_by_year([year for year in range(1929, 1930)])\n",
"sys.getsizeof(structured_obs)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"280"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"persistent_stations = set()\n",
"\n",
"years = [key for key in structured_obs]\n",
"for year in years:\n",
" for key in structured_obs[year]:\n",
" persistent_stations.add(key)\n",
"\n",
"print(len(persistent_stations))\n",
"for station in sorted(persistent_stations):\n",
" # print(station)\n",
" pass"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"21\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for year in range(1929, 1933):\n",
" stations = sc.textFile(\"/user/schiefjm/weather/gsod/\" + str(year))\\\n",
" .filter(lambda line: \"STN\" not in line)\\\n",
" .map(lambda line: (line.split()[0], 1))\\\n",
" .reduceByKey(lambda x, y: x + y)\\\n",
" .collect()\n",
" \n",
" print(year, len(set(stations)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1929 21\n",
"1930"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 23\n",
"1931"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 31\n",
"1932"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39\n"
]
}
],
"prompt_number": 6
}
],
"metadata": {}
}
]
} | apache-2.0 |
jakerylandwilliams/partitioner | tests/partitioner_examples.ipynb | 2 | 14235 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Partitioner examples\n",
"### This is a jupyter notebook with a few vignettes that present some of the Python partitioner package's functionality.\n",
"Note: Cleaning of text and determination of clauses occurs in the partitionText method. Because of this, it is unwise to pass large, uncleaned pieces of text as 'clauses' directly through the .partition() method (regardless of the type of partition being taken), as this will simply tokenize the text by splitting on \" \", producing many long, punctuation-filled phrases, and likely run very slow. As such, best practices only use .partition() for testing and exploring the tool on case-interested clauses.\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from partitioner import partitioner\n",
"from partitioner.methods import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Process the English Wiktionary to generate the (default) partition probabilities.\n",
"Note: this step can take significant time for large dictionaries (~5 min)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Vignette 1: Build informed partition data from a dictionary, \n",
"## and store to local collection\n",
"def preprocessENwiktionary():\n",
" pa = partitioner(informed = True, dictionary = \"./dictionaries/enwiktionary.txt\")\n",
" pa.dumpqs(qsname=\"enwiktionary\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"preprocessENwiktionary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Perform a few one-off partitions."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Vignette 2: An informed, one-off partition of a single clause\n",
"def informedOneOffPartition(clause = \"How are you doing today?\"):\n",
" pa = oneoff()\n",
" print pa.partition(clause)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['How are you doing', 'today?']\n",
"['Fine,', 'thanks a bunch', 'for', 'asking!']\n"
]
}
],
"source": [
"informedOneOffPartition()\n",
"informedOneOffPartition(\"Fine, thanks a bunch for asking!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Solve for the informed stochastic expectation partition (given the informed partition probabilities)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Vignette 3: An informed, stochastic expectation partition of a single clause\n",
"def informedStochasticPartition(clause = \"How are you doing today?\"):\n",
" pa = stochastic()\n",
" print pa.partition(clause)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'are you': 1.407092428930965e-09, 'How are you': 0.00025712526951610467, 'How': 5.472370457590498e-06, 'doing': 0.000257136448270894, 'you doing': 3.79920846523168e-05, 'How are': 3.800164835444141e-05, 'are': 2.0796023583003835e-10, 'are you doing': 5.47075540492574e-06, 'today?': 1.0, 'you': 9.771662360456963e-09, 'How are you doing': 0.999699400711672}\n"
]
}
],
"source": [
"informedStochasticPartition()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Perform a pure random (uniform) one-off partition."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Vignette 4: An uniform, one-off partition of a single clause\n",
"def uniformOneOffPartition(informed = False, clause = \"How are you doing today?\", qunif = 0.25):\n",
" pa = oneoff(informed = informed, qunif = qunif)\n",
" print pa.partition(clause)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['How are', 'you doing today?']\n",
"['How', 'are', 'you doing today?']\n"
]
}
],
"source": [
"uniformOneOffPartition()\n",
"uniformOneOffPartition(qunif = 0.75)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Solve for the uniform stochastic expectation partition (given the uniform partition probabilities)."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Vignette 5: An uniform, stochastic expectation partition of a single clause\n",
"def uniformStochasticPartition(informed = False, clause = \"How are you doing today?\", qunif = 0.25):\n",
" pa = stochastic(informed = informed, qunif = qunif)\n",
" print pa.partition(clause)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'are you doing today?': 0.10546875000000001, 'How are you': 0.14062499999999997, 'How': 0.25, 'doing': 0.0625, 'How are': 0.1875, 'How are you doing today?': 0.31640625, 'doing today?': 0.1875, 'you doing': 0.046875, 'are you doing': 0.03515624999999999, 'are': 0.0625, 'you doing today?': 0.14062499999999997, 'today?': 0.25, 'are you': 0.046875, 'you': 0.0625, 'How are you doing': 0.10546875000000001}\n",
"{'a': 0.0625, 'Fine,': 0.25, 'thanks a': 0.046875, 'Fine, thanks a bunch for asking!': 0.23730468749999997, 'bunch for asking!': 0.14062499999999997, 'a bunch for': 0.03515624999999999, 'for': 0.0625, 'thanks a bunch for': 0.026367187499999993, 'Fine, thanks a bunch': 0.10546875000000001, 'Fine, thanks a bunch for': 0.0791015625, 'a bunch': 0.046875, 'Fine, thanks a': 0.14062499999999997, 'Fine, thanks': 0.1875, 'thanks': 0.0625, 'a bunch for asking!': 0.10546875000000001, 'asking!': 0.25, 'bunch for': 0.046875, 'for asking!': 0.1875, 'thanks a bunch for asking!': 0.0791015625, 'thanks a bunch': 0.03515624999999999, 'bunch': 0.0625}\n"
]
}
],
"source": [
"uniformStochasticPartition()\n",
"uniformStochasticPartition(clause = \"Fine, thanks a bunch for asking!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build a rank-frequency distribution for a text and determine its Zipf/Simon (bag-of-phrase) $R^2$."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Vignette 6: Use the default partitioning method to partition the main partitioner.py file and compute rsq\n",
"def testPartitionTextAndFit():\n",
" pa = oneoff()\n",
" pa.partitionText(textfile = pa.home+\"/../README.md\")\n",
" pa.testFit()\n",
" print \"R-squared: \",round(pa.rsq,2)\n",
" print\n",
" phrases = sorted(pa.counts, key = lambda x: pa.counts[x], reverse = True)\n",
" for j in range(25):\n",
" phrase = phrases[j]\n",
" print phrase, pa.counts[phrase]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R-squared: 0.11\n",
"\n",
"project 7.0\n",
" 5.0\n",
"the 5.0\n",
"code 4.0\n",
"to 4.0\n",
"and 4.0\n",
"of the 3.0\n",
"API 3.0\n",
"should 2.0\n",
"docs 2.0\n",
"A short 2.0\n",
"This 2.0\n",
"etc 2.0\n",
"your 2.0\n",
"size 2.0\n",
"reference 2.0\n",
"can 2.0\n",
"examples 2.0\n",
"is 2.0\n",
"how 2.0\n",
"added 2.0\n",
"description 2.0\n",
"important 2.0\n",
"Make sure 1.0\n",
"show 1.0\n"
]
}
],
"source": [
"testPartitionTextAndFit()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Process the some other Wiktionaries to generate the partition probabilities.\n",
"Note: These dictionaries are not as well curated and potentially contain phrases from other languages (a consequence of wiktionary construction). As a result, they hold many many more phrases and will take longer to process. However, since the vast majority of these dictionaries are language-correct, effects on the partitioner and its (course) partition probabilities is likely negligable."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Vignette X1: Build informed partition data from other dictionaries, \n",
"## and store to local collection\n",
"def preprocessOtherWiktionaries():\n",
" for lang in [\"ru\", \"pt\", \"pl\", \"nl\", \"it\", \"fr\", \"fi\", \"es\", \"el\", \"de\", \"en\"]:\n",
" print \"working on \"+lang+\"...\"\n",
" pa = partitioner(informed = True, dictionary = \"./dictionaries/\"+lang+\".txt\")\n",
" pa.dumpqs(qsname=lang)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"working on ru...\n",
"working on pt...\n",
"working on pl...\n",
"working on nl...\n",
"working on it...\n",
"working on fr...\n",
"working on fi...\n",
"working on es...\n",
"working on el...\n",
"working on de...\n",
"working on en...\n"
]
}
],
"source": [
"preprocessOtherWiktionaries()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test partitioner on some other languages."
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from partitioner import partitioner\n",
"from partitioner.methods import *\n",
"## Vignette X2: Use the default partitioning method to partition the main partitioner.py file and compute rsq\n",
"def testFrPartitionTextAndFit():\n",
" for lang in [\"ru\", \"pt\", \"pl\", \"nl\", \"it\", \"fr\", \"fi\", \"es\", \"el\", \"de\", \"en\"]:\n",
" pa = oneoff(qsname = lang)\n",
" pa.partitionText(textfile = \"./tests/test_\"+lang+\".txt\")\n",
" pa.testFit()\n",
" print\n",
" print lang+\" R-squared: \",round(pa.rsq,2)\n",
" print\n",
" phrases = sorted(pa.counts, key = lambda x: pa.counts[x], reverse = True)\n",
" for j in range(5):\n",
" phrase = phrases[j]\n",
" print phrase, pa.counts[phrase]"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"ru R-squared: 0.07\n",
"\n",
"и 328.0\n",
"въ 204.0\n",
"я 126.0\n",
"е 106.0\n",
"на 101.0\n",
"\n",
"pt R-squared: 0.75\n",
"\n",
"de 470.0\n",
"e 265.0\n",
"que 243.0\n",
"da 234.0\n",
"a 193.0\n",
"\n",
"pl R-squared: 0.04\n",
"\n",
"i 40.0\n",
"Illustration 26.0\n",
"się 23.0\n",
"z 20.0\n",
"w 18.0\n",
"\n",
"nl R-squared: 0.74\n",
"\n",
"ik 980.0\n",
"een 741.0\n",
"dat 705.0\n",
"van 644.0\n",
"de 634.0\n",
"\n",
"it R-squared: 0.7\n",
"\n",
"e 6646.0\n",
"che 5656.0\n",
"di 5393.0\n",
"a 3873.0\n",
"il 3692.0\n",
"\n",
"fr R-squared: 0.87\n",
"\n",
"et 2001.0\n",
"a 1486.0\n",
"de 1333.0\n",
"les 1139.0\n",
"des 1060.0\n",
"\n",
"fi R-squared: 0.31\n",
"\n",
"ja 246.0\n",
"oli 150.0\n",
"hän 147.0\n",
"Lopo 109.0\n",
"että 88.0\n",
"\n",
"es R-squared: 0.67\n",
"\n",
"de 1981.0\n",
"y 1651.0\n",
"que 1311.0\n",
"el 698.0\n",
"en 684.0\n",
"\n",
"el R-squared: 0.6\n",
"\n",
"να 332.0\n",
"του 253.0\n",
"τον 250.0\n",
"και 205.0\n",
"ΟΙΔΙΠΟΥΣ 192.0\n",
"\n",
"de R-squared: 0.77\n",
"\n",
"und 2691.0\n",
"die 2521.0\n",
"der 2282.0\n",
"zu 2145.0\n",
"sie 1702.0\n",
"\n",
"en R-squared: 0.91\n",
"\n",
"and 3691.0\n",
"the 2838.0\n",
"that 1556.0\n",
"of 1472.0\n",
"to 1358.0\n"
]
}
],
"source": [
"testFrPartitionTextAndFit()"
]
},
{
"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 |
ekaakurniawan/3nb | Equations/Dose-Response Relations/Dose-Response Relations.ipynb | 1 | 351462 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Part of [Neural Network Notebook (3nb)](http://ekaakurniawan.github.io/3nb/) project."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Copyright (C) 2014 Eka A. Kurniawan\n",
">\n",
"> eka.a.kurniawan(ta)gmail(tod)com\n",
">\n",
"> This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\n",
">\n",
"> This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\n",
">\n",
"> You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tested On"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python 3.5.2\n"
]
}
],
"source": [
"import sys\n",
"print(\"Python %d.%d.%d\" % (sys.version_info.major, \\\n",
" sys.version_info.minor, \\\n",
" sys.version_info.micro))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NumPy 1.11.1\n"
]
}
],
"source": [
"import numpy as np\n",
"print(\"NumPy %s\" % np.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"matplotlib 1.5.1\n"
]
}
],
"source": [
"# Display graph inline\n",
"%matplotlib inline\n",
"\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"print(\"matplotlib %s\" % matplotlib.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Display Settings"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Display graph in 'retina' format for Mac with retina display. Others, use PNG or SVG format.\n",
"%config InlineBackend.figure_format = 'retina'\n",
"#%config InlineBackend.figure_format = 'PNG'\n",
"#%config InlineBackend.figure_format = 'SVG'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Housekeeping Functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following function plots different concentrations used to visualize dose-response relation."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def plot_concentration(c):\n",
" fig = plt.figure()\n",
" \n",
" sp111 = fig.add_subplot(111)\n",
" # Display grid\n",
" sp111.grid(True, which = 'both')\n",
" # Plot concentration\n",
" len_c = len(c)\n",
" sp111.plot(np.linspace(0,len_c-1,len_c), c, color = 'gray', linewidth = 2)\n",
" # Label\n",
" sp111.set_ylabel('Concentration (nM)')\n",
" \n",
" # Set X axis within different concentration\n",
" plt.xlim([0, len_c-1])\n",
" \n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following function plots dose-response relation in both linear (`log_flag = False`) and logarithmic (`log_flag = True`) along X axis."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# d : Dose\n",
"# r1 : First response data\n",
"# r1_label : First response label\n",
"# r2 : Second response data\n",
"# r2_label : Second response label\n",
"# log_flag : Selection for linear or logarithmic along X axis\n",
"# - False: Plot linear (default)\n",
"# - True: Plot logarithmic\n",
"def plot_dose_response_relation(d, r1, r1_label, r2 = None, r2_label = \"\", log_flag = False):\n",
" fig = plt.figure()\n",
" \n",
" sp111 = fig.add_subplot(111)\n",
" # Handle logarithmic along X axis\n",
" if log_flag:\n",
" sp111.set_xscale('log')\n",
" # Display grid\n",
" sp111.yaxis.set_ticks([0.0, 0.5, 1.0])\n",
" sp111.grid(True, which = 'both')\n",
" # Plot dose-response\n",
" sp111.plot(d, r1, color = 'blue', label = r1_label, linewidth = 2)\n",
" if r2 is not None:\n",
" sp111.plot(d, r2, color = 'red', label = r2_label, linewidth = 2)\n",
" # Labels\n",
" sp111.set_ylabel('Response')\n",
" sp111.set_xlabel('Concentration (nM)')\n",
" # Legend\n",
" sp111.legend(loc='upper left')\n",
" \n",
" # Set Y axis in between 0 and 1\n",
" plt.ylim([0, 1])\n",
" \n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dose-Response Relations $^{[1]}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generally, dose-response relations can be written as follow. In which the dose is represented as concentration ($c$), while the formula returns the response ($r$).\n",
"\n",
"$$r = \\frac{F.c^{n_H}}{{c^{n_H} + EC_{50}^{n_H}}}$$\n",
"\n",
"Other terms like $EC_{50}$ is the effective concentration achieved at 50% of maximum response. Normally, efficacy ($F$) is normalized to one so that it is easier to make comparison among different drugs. Furthermore, if full agonist is defined to have efficacy equal to one, anything lower than one is treated to be partial agonist. Finally, Hill coefficients ($n_H$) defines the number of drug molecules needed to activate target receptor."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Drug Concentartion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Both linearly and logarithmically increased concentrations are used to study dose-response relations."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Linearly increased concentration (`c_lin`):"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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GGeYqsyZUxbpohtWkFouLi2zYMJ3f31oXKnOKlCRnmGsn1oSqWBezNWqtRa/XY2lpaepr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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bfb6160>"
]
},
"metadata": {
"image/png": {
"height": 255,
"width": 396
}
},
"output_type": "display_data"
}
],
"source": [
"c_lin = np.linspace(0,100,101) # Drug concentration in nanomolar (nM)\n",
"plot_concentration(c_lin)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Logarithmically increased concentration (`c_log`):"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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jYnfgA8A7gJfQf7vrABYD3wDOzsw/DGReSZIkSQPXzY+8wSC7vWXmTZl5FDAV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\nxAKtshKcogAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bfdfb70>"
]
},
"metadata": {
"image/png": {
"height": 255,
"width": 415
}
},
"output_type": "display_data"
}
],
"source": [
"c_log = np.logspace(0,5,101) # Drug concentration in nanomolar (nM)\n",
"plot_concentration(c_log)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Agonist Only"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To calculate dose-response relation in the case of agonist only, we use general dose-response relation equation described previously. The function is shown below."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Calculate dose-response relation (DRR) for agonist only\n",
"# c : Drug concentration(s) in nanomolar (nM)\n",
"# EC_50 : 50% effective concentration in nanomolar (nM)\n",
"# F : Efficacy (unitless)\n",
"# n_H : Hill coefficients (unitless)\n",
"def calc_drr(c, EC_50 = 20, F = 1, n_H = 1):\n",
" r = (F * (c ** n_H) / ((c ** n_H) + (EC_50 ** n_H)))\n",
" return r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following result shows drug response of agonist only to the linearly increased concentrations."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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JaNhQuvlmafp0ad066dln3Q7TJBHRxM8L+BAXiCq6NgWErk0oCf2/4VPauMjJ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c2616d8>"
]
},
"metadata": {
"image/png": {
"height": 270,
"width": 393
}
},
"output_type": "display_data"
}
],
"source": [
"c = c_lin # Drug concentration(s) in nanomolar (nM)\n",
"EC_50 = 20 # 50% effective concentration in nanomolar (nM)\n",
"F = 1 # Efficacy (unitless)\n",
"n_H = 1 # Hill coefficients (unitless)\n",
"r = calc_drr(c, EC_50, F, n_H)\n",
"plot_dose_response_relation(c, r, \"Agonist\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following result shows drug response of agonist only to the logarithmically increased concentrations."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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4q6/1vQtPucjcznUuXnsNJk5MPd62LTz/PPTrl7E7BaFcZG6nzws/6ysXhed7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\nXLKacUmSJKkzJhHqqYcfhm23hfvuG3l9s83g0kthqaWqGZc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+cnJScGDT9a80nvuNgudc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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c6440f0>"
]
},
"metadata": {
"image/png": {
"height": 272,
"width": 392
}
},
"output_type": "display_data"
}
],
"source": [
"c = c_log # Drug concentration(s) in nanomolar (nM)\n",
"EC_50 = 20 # 50% effective concentration in nanomolar (nM)\n",
"F = 1 # Efficacy (unitless)\n",
"n_H = 1 # Hill coefficients (unitless)\n",
"r = calc_drr(c, EC_50, F, n_H)\n",
"plot_dose_response_relation(c, r, \"Agonist\", log_flag = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Agonist Plus Competitive Antagonist"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compatitive antagonist, as the name sugest, competes with agonist molecules to sit in the same pocket. It makes the binding harder for agonist as well as to trigger the activation. Therefore, higher agonist concentration is required to reach both full and partial (like $EC_{50}$) activation. New $EC_{50}$ value, called $EC_{50}'$ ($EC_{50}$ prime) is calculated using following formula.\n",
"\n",
"$$EC_{50}' = EC_{50} * \\left(1 + \\frac{c_i}{K_i}\\right)$$\n",
"\n",
"It depends on inhibitor concentration ($c_i$) and dissociation constant of the inhibitor ($K_i$)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following is a new function to calculate drug response of agonist with competitive antagonist. It shows new $EC_{50}$ value (`EC_50_prime`) replacing agonist only $EC_{50}$ value (`EC_50`)."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Calculate dose-response relation (DRR) for agonist plus competitive antagonist\n",
"# - Agonist\n",
"# c : Drug concentration(s) in nanomolar (nM)\n",
"# EC_50 : 50% effective concentration in nanomolar (nM)\n",
"# F : Efficacy (unitless)\n",
"# n_H : Hill coefficients (unitless)\n",
"# - Antagonist\n",
"# K_i : Dissociation constant of inhibitor in nanomolar (nM)\n",
"# c_i : Inhibitor concentration in nanomolar (nM)\n",
"def calc_drr_agonist_cptv_antagonist(c, EC_50 = 20, F = 1, n_H = 1, K_i = 5, c_i = 25):\n",
" EC_50_prime = EC_50 * (1 + (c_i / K_i))\n",
" r = calc_drr(c, EC_50_prime, F, n_H)\n",
" return r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following result shows drug response of agonist with competitive antagonist to the linearly increased concentrations."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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RrV+/nnbt2nHLLbcwb948Nm3aRMWKFalTpw716tWjdu3aAOzcuTPPecERlMIW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\niIiIiIjkTImEiIiIiIjk7P8BVCaUlYCcKJwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c46b898>"
]
},
"metadata": {
"image/png": {
"height": 270,
"width": 393
}
},
"output_type": "display_data"
}
],
"source": [
"c = c_lin # Drug concentration(s) in nanomolar (nM)\n",
"EC_50 = 20 # 50% effective concentration in nanomolar (nM)\n",
"F = 1 # Efficacy (unitless)\n",
"n_H = 1 # Hill coefficients (unitless)\n",
"r_a = calc_drr(c, EC_50, F, n_H)\n",
"\n",
"K_i = 5 # Dissociation constant of inhibitor in nanomolar (nM)\n",
"c_i = 25 # Inhibitor concentration in nanomolar (nM)\n",
"r_aca = calc_drr_agonist_cptv_antagonist(c, EC_50, F, n_H, K_i, c_i)\n",
"\n",
"plot_dose_response_relation(c, r_a, \"Agonist Only\", r_aca, \"Plus Antagonist\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following result shows drug response of agonist with competitive antagonist to the logarithmically increased concentrations."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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9ESGxZMkS+zfW7iZ6c6d9+/ailJJu3bo5rZ88ebJERESIpmmilJKkpCR7D8Ld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\nB0cykztocbzNAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c41ccf8>"
]
},
"metadata": {
"image/png": {
"height": 272,
"width": 392
}
},
"output_type": "display_data"
}
],
"source": [
"c = c_log # Drug concentration(s) in nanomolar (nM)\n",
"EC_50 = 20 # 50% effective concentration in nanomolar (nM)\n",
"F = 1 # Efficacy (unitless)\n",
"n_H = 1 # Hill coefficients (unitless)\n",
"r_a = calc_drr(c, EC_50, F, n_H)\n",
"\n",
"K_i = 5 # Dissociation constant of inhibitor in nanomolar (nM)\n",
"c_i = 25 # Inhibitor concentration in nanomolar (nM)\n",
"r_aca = calc_drr_agonist_cptv_antagonist(c, EC_50, F, n_H, K_i, c_i)\n",
"\n",
"plot_dose_response_relation(c, r_a, \"Agonist Only\", r_aca, \"Plus Antagonist\", log_flag = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Agonist Plus Noncompetitive Antagonist"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unlike competitive antagonist, noncompetitive antagonist does not compete directly to the location where agonist binds but somewhere else in the subsequent pathway. Instead of altering effective concentration (like $EC_{50}$), noncompetitive antagonist affects efficacy. New efficacy value ($F'$) due to the existance of noncompetitive antagonist is calculated as follow.\n",
"\n",
"$$F' = \\frac{F}{\\left(1 + \\frac{c_i}{K_i}\\right)}$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following is a new function to calculate drug response of agonist with noncompetitive antagonist. It shows new efficacy value (`F_prime`) replacing agonist only efficacy value (`F`)."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Calculate dose-response relation (DRR) for agonist plus noncompetitive antagonist\n",
"# - Agonist\n",
"# c : Drug concentration(s) in nanomolar (nM)\n",
"# EC_50 : 50% effective concentration in nanomolar (nM)\n",
"# F : Efficacy (unitless)\n",
"# n_H : Hill coefficients (unitless)\n",
"# - Antagonist\n",
"# K_i : Dissociation constant of inhibitor in nanomolar (nM)\n",
"# c_i : Inhibitor concentration in nanomolar (nM)\n",
"def calc_drr_agonist_non_cptv_antagonist(c, EC_50 = 20, F = 1, n_H = 1, K_i = 5, c_i = 25):\n",
" F_prime = F / (1 + (c_i / K_i))\n",
" r = calc_drr(c, EC_50, F_prime, n_H)\n",
" return r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following result shows drug response of agonist with noncompetitive antagonist to the linearly increased concentrations."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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79u257bbbmDdvHps2baJy5crUq1ePBg0aULduXQB27tyZ77zQCEpRC7slJiaS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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10cd7bb70>"
]
},
"metadata": {
"image/png": {
"height": 270,
"width": 393
}
},
"output_type": "display_data"
}
],
"source": [
"c = c_lin # Drug concentration(s) in nanomolar (nM)\n",
"EC_50 = 20 # 50% effective concentration in nanomolar (nM)\n",
"F = 1 # Efficacy (unitless)\n",
"n_H = 1 # Hill coefficients (unitless)\n",
"r_a = calc_drr(c, EC_50, F, n_H)\n",
"\n",
"K_i = 5 # Dissociation constant of inhibitor in nanomolar (nM)\n",
"c_i = 25 # Inhibitor concentration in nanomolar (nM)\n",
"r_ana = calc_drr_agonist_non_cptv_antagonist(c, EC_50, F, n_H, K_i, c_i)\n",
"\n",
"plot_dose_response_relation(c, r_a, \"Agonist Only\", r_ana, \"Plus Antagonist\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following result shows drug response of agonist with noncompetitive antagonist to the logarithmically increased concentrations."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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w4DMRRESFxMWLwBNPAJMm5S1r0QKYPRuoXt3/7SIiosKHIxFERIXA4cNAu3bG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\nAPj777/Rs2dPx6xCub3zzjv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3b+yYF4sWAbfd5hQFs2cLevUy/7zMC2N2zAszYjAvfMO8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\niRARERERkaL8fzK6TYcDGLKAAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10cf39eb8>"
]
},
"metadata": {
"image/png": {
"height": 272,
"width": 392
}
},
"output_type": "display_data"
}
],
"source": [
"c = c_log # Drug concentration(s) in nanomolar (nM)\n",
"EC_50 = 20 # 50% effective concentration in nanomolar (nM)\n",
"F = 1 # Efficacy (unitless)\n",
"n_H = 1 # Hill coefficients (unitless)\n",
"r_a = calc_drr(c, EC_50, F, n_H)\n",
"\n",
"K_i = 5 # Dissociation constant of inhibitor in nanomolar (nM)\n",
"c_i = 25 # Inhibitor concentration in nanomolar (nM)\n",
"r_ana = calc_drr_agonist_non_cptv_antagonist(c, EC_50, F, n_H, K_i, c_i)\n",
"\n",
"plot_dose_response_relation(c, r_a, \"Agonist Only\", r_ana, \"Plus Antagonist\", log_flag = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reference"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. Henry A. Lester, 2014. Drugs and the Brain. Week 2: Dose-response Relations. California Institute of Technology. Coursera. https://www.coursera.org/course/drugsandbrain"
]
}
],
"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": 0
}
| gpl-3.0 |
atcemgil/notes | LSTM.ipynb | 1 | 5552320 | null | mit |
chrisbarnettster/cfg-analysis-on-heroku-jupyter | notebooks/notebooks/visualise_highbinders_for_sna.ipynb | 1 | 5627 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Visualise Highbinders "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%reset -f\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## import all required dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# standard imports\n",
"import urllib2\n",
"import os\n",
"import sys\n",
"import json\n",
"import StringIO\n",
"import pickle\n",
"\n",
"# dataframe and numerical\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# plotting\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"#scipy\n",
"from scipy import stats\n",
"from scipy.special import erf\n",
"from scipy import sqrt\n",
"\n",
"# glypy\n",
"import glypy\n",
"from glypy.plot import plot\n",
"from glypy.io import glycoct\n",
"\n",
"# other modules\n",
"sys.path.append('../scripts/')\n",
"import post_glycan_convert as pgc\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## variables for this project\n",
"\n",
"samples_in=\"../data/sna/SNA_4.0_plant.json\"\n",
"results_dir = \"../results/sna/\"\n",
"dataframe_out=results_dir+\"dataframes_sna.pkl\"\n",
"dataframefile=dataframe_out\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Check whether or not the dataframes exist\n",
"\n",
"subdir=\"./\"\n",
"dataframefile=dataframe_out\n",
"\n",
"if not os.path.isfile(dataframefile):\n",
" print \"calling the notebook that loads the data\"\n",
" %run download_cfg_for_sna.ipynb\n",
"with open(os.path.join(subdir, dataframefile)) as f:\n",
" dataframes = pickle.load(f)\n",
" \n",
" \n",
"dataframes[0][\"sample\"]\n",
"frame=dataframes[0][\"dataframe\"]\n",
"frame.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# create a data frame with the glycan and the spacer as separate columns\n",
"df=pd.concat([frame[\"Chart Number\"], frame[\"Structure\"]], axis=1)\n",
" \n",
"df.head()\n",
"#frame[\"Structure\"]\n",
"df[\"Structure\"].str.extract('(.*-)')\n",
"df[\"Glycan\"]=df[\"Structure\"].str.extract('(.*-)').str.strip('-')\n",
"\n",
"#df['Structure'].str.extract('(-Sp.+?$)')\n",
"df[\"Spacer\"]=df[\"Structure\"].str.split('.*-').str[1]\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# create a function that plots using glypy\n",
"\n",
"\n",
"def plotter(func):\n",
" \"\"\"\n",
" A decorator that plots the function .\n",
" (it actually just prints it, but it could be logging!)\n",
" \"\"\"\n",
" def wrapper(*args, **kwargs):\n",
" res = func(*args, **kwargs)\n",
" #print func.__name__, args, kwargs\n",
" plot(res)\n",
" return res\n",
" return wrapper\n",
"\n",
"@plotter\n",
"def get_gly_iupac(iupacstring):\n",
" kchandle2 = StringIO.StringIO(iupacstring)\n",
" abc=pgc.mechanise_glycan_convert(kchandle2, \"Glycoct\", \"text\")\n",
" return glycoct.loads(abc)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"high_binders_from_paper=[353,256,327,341,259,343,54,315,52,314,51,46,258,275,260,325,257,321,53,342,255,407,300,292,340,373]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# show all the outputs\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# show high binder glycan rows\n",
"for i in high_binders_from_paper:\n",
" #df[\"Glycan\"][i].head()\n",
" df[df[\"Chart Number\"]==i].head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# plot glycan images\n",
"for i in high_binders_from_paper:\n",
" try:\n",
" get_gly_iupac(df[\"Glycan\"][i])\n",
" except:\n",
" pass"
]
},
{
"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 |
gridley/moltres | tests/nts/gold/examine_gen_mesh_one_material.ipynb | 3 | 774823 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import yt"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"yt : [INFO ] 2016-12-22 10:33:41,113 Loading coordinates\n",
"yt : [INFO ] 2016-12-22 10:33:41,114 Loading connectivity\n",
"yt : [INFO ] 2016-12-22 10:33:41,124 Parameters: current_time = 2.0\n",
"yt : [INFO ] 2016-12-22 10:33:41,125 Parameters: domain_dimensions = [1 1 1]\n",
"yt : [INFO ] 2016-12-22 10:33:41,126 Parameters: domain_left_edge = [-0.6 -0.6 0. ]\n",
"yt : [INFO ] 2016-12-22 10:33:41,128 Parameters: domain_right_edge = [ 6.6 6.6 1. ]\n",
"yt : [INFO ] 2016-12-22 10:33:41,129 Parameters: cosmological_simulation = 0\n",
"yt : [INFO ] 2016-12-22 10:33:41,137 Loading coordinates\n",
"yt : [INFO ] 2016-12-22 10:33:41,138 Loading connectivity\n",
"yt : [INFO ] 2016-12-22 10:33:41,269 xlim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:33:41,269 ylim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:33:41,271 xlim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:33:41,272 ylim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:33:41,275 Making a fixed resolution buffer of (('connect1', 'group1')) 800 by 800\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"959.3496444727219 dimensionless\n",
"900.0 dimensionless\n",
"8.138120750714538 dimensionless\n",
"8.137864325565221 dimensionless\n",
"0.8231674445177946 dimensionless\n",
"0.8230987236017343 dimensionless\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"yt : [INFO ] 2016-12-22 10:33:41,526 xlim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:33:41,527 ylim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:33:41,528 xlim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:33:41,529 ylim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:33:41,531 Making a fixed resolution buffer of (('connect1', 'group2')) 800 by 800\n"
]
}
],
"source": [
"ds = yt.load(\"gen-mesh-one-material_out.e\", step=-1)\n",
"\n",
"ad = ds.all_data()\n",
"\n",
"temp = ad[('connect1', 'temp')]\n",
"group1 = ad['group1']\n",
"group2 = ad['group2']\n",
"\n",
"print(temp.max())\n",
"print(temp.min())\n",
"print(group1.max())\n",
"print(group1.min())\n",
"print(group2.max())\n",
"print(group2.min())\n",
"\n",
"g1slice = yt.SlicePlot(ds, 'z', 'group1')\n",
"g2slice = yt.SlicePlot(ds, 'z', 'group2')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
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m69d+W03rV2+ds1tt6zXDmjesSCkbvCdTnIxbWysUe2WSdOORZnHuGmvX9OOcRNfu81b5bh1HuPy2+VrV37bfO3Kb7tp7fK1a267Zb12QPOOBQD4wTj8YGNp7eu2R4gJRnoQQgghhBBCCCFkKKH0ICMDQ/iaC1+75sLXrrnwtWsufO2aC1+75sLXjpD6wk8nGRmaNvSC9OBr11z42jUXvnbNha9dc+Fr11z42sn46KCD6oac+OhUti3SPBjpQQghhBBCCCGEkKGE0oMQQgghhBBCCCFDCYe3EEIIIYQQQggpDB8d+BzeQmoCIz0IIYQQQgghhBAylFB6EEIIIYQQQgghZCjh8BZCCCGEEEIIIYXhY7zi1Vuq2xZpHoz0IIQQQgghhBBCyFDCSA9CCCGEEEIIIY3jp/Nfxvz5r+C11ziRKTFD6UEIIYQQQgghpDA68NGpYEWVjx/zdnz8mLdj2bK1mDHjsdK3R5oJh7cQQgghhBBCCCFkKKH0IIQQQgghhBBCyFDC4S2EEEIIIYQQQgrDh48O/Eq3R4gJRnoQQgghhBBCCCFkKKH0IIQQQgghhBBCyFDC4S0jwMK31mMTz8NOYxMwbWxs0N0hhBBCCCGEDBHLN27E4xvH8bofDjPpIKh0eEsHQWXbEvF9H1dffTU+8pGPYKeddhpIH0g6jPQYAWZM3ARHTtqUwoMQQgghhBBSONPGxnDkpE2x99veNuiuWPnVr36FWbNmYcstt8Q73vEOzJgxA3fddVeutm644QbstttuOOmkk7Bq1aqCe0qKhJEehBBCCCGEEEKGmptuuglHH300Nt10U2y99dZYuXIl7rvvPsyePRvXXnstjj/+eKd2Hn30Ufz0pz/F6tWr8dhjj8HzvJJ7TvqFkR6EEEIIIYQQQgqjgwDjFf6kDW957rnncOqpp+Kaa67BmjVr8OSTT2LlypWYNWsWAOAb3/iG877tsssuOO+883DFFVfgne98Z1/HiVQDpQchhBBCCCGEkKFl3rx5+OlPf4rjjz8+jszYZpttcPnllwMAnn766VztTpw4sbA+kvLg8BZCCCGEEEIIIUPLMcccg1133TWRPnXqVADAbrvtVnWXSIUw0oMQQgghhBBCyNCiEx4AsHTpUrRaLZx//vkV94hUCSM9CCGEEEIIIYQUhu8wz0bR28vK2rVrccEFF+Daa6/FoYceWkKvSF1gpAchhBBCCCGEkJHhzjvvxL777ov77rsPS5Yswdq1awfdJVIijPQghBBCCCGEEDL0rF27Fueccw6WLl2KZ555Bhs3bsTcuXNx//33Y9GiRRgbGxt0F0kJMNKDEEIIIYQQQkhhdAbw48KkSZMwd+5cLF68GKtXr8a3vvUttNttLF26FFdffXW/u01qCqUHIYQQQgghhJCRYtNNN8WXvvQl/O3f/i2CIMA999wz6C6RkuDwFkIIIYQQQgghjWDB/Ndw0/zXpLQ1r7nGeiQ54YQTcN555+HVV1/tt2ukplB6EEIIIYQQQggpjA48jMMrpe3Djnk7Djvm7VLaw8vW4agZK3K1N2XKFADA1KlT++4bqScc3kIIIYQQQgghZCR57LHHAABHHnnkgHtCyoLSgxBCCCGEEELI0PLUU09h2bJl2rxLLrkEBxxwAI466qg47e6778Yee+yBSy+91NruunXrAAC+7xfXWVI4lB6EEEIIIYQQQgrDR7Urt6Qph4MOOgh77bUXZs2ahcWLF8P3faxZswZf/epX8eKLL+Kmm26Syv/zP/8z/ud//gfnn3++sc37778fL7zwAgDg3nvvdTgqZFBQehBCCCGEEEIIGVq+8pWv4P3vfz8WLVqEj370o9hrr71w9tlnY+bMmbjjjjuw+eabS+WPPfZYbLHFFjjxxBMTbf3mN7/BBz/4QUyfPh2eF85b8o1vfAPvf//78c1vfrOK3SEZ4USmhBBCCCGEEEKGljlz5mDOnDnO5T/zmc/gM5/5jDZvr732wm9/+9uiukYqgNKDEEIIIYQQQkhhlLl6i2l7hJjg8BZCCCGEEEIIIYQMJZQehBBCCCGEEEIIGUo4vGUEWPjWemziedhpbAKmjY0NujuEEEIIIYSQIWL5xo14fOM4Xu8u3ToOD+MVPl+vcigNaR6UHiPAjImbYNt2e9DdIIQQQgghhAwh08bGMG1sDMs3bMTtb60fdHcIkeDwFkIIIYQQQgghhAwljPQghBBCCCGEEFIYPlroVPh83eezfGKB7w5CCCGEEEIIIYQMJZQehBBCCCGEEEIIGUo4vIUQQgghhBBCSGF04FW6okqHq7cQC4z0IIQQQgghhBBCyFDCSA9CCCGEEEIIIY3jjvmv4I75/4c3XusMuiukxlB6EEIIIYQQQghpHLOPeQdmH/MOLF+2Fn82Y/mgu0NqCqUHIYQQQgghhJDCCOf0qG4mBc7pQWxwTg9CCCGEEEIIIYQMJZQeDeFXv/oVZs2ahS233BLveMc7MGPGDNx1112D7hYhhBBCCCGEEFJbKD0awE033YQDDzwQS5YswVZbbYU33ngD9913H2bPno0f/ehHg+4eIYQQQgghhMSMo135DyEmKD1qznPPPYdTTz0V11xzDdasWYMnn3wSK1euxKxZswAA3/jGNwbcQ0IIIYQQQgghpJ5QetScefPm4ac//SmOP/54eF44Qc8222yDyy+/HADw9NNPD7J7hBBCCCGEEEJIbeHqLTXnmGOOwa677ppInzp1KgBgt912q7pLhBBCCCGEEGLEh4dOhc/Xfa7eQiww0qPm6IQHACxduhStVgvnn39+xT0ihBBCCCGEEEKaAaVHA1m7di0uuOACXHvttTj00EMH3R1CCCGEEEIIIaSWUHo0jDvvvBP77rsv7rvvPixZsgRr164ddJcIIYQQQgghJKaDFsYr/KlyKA1pHpzToyGsXbsW55xzDpYuXYpnnnkGGzduxNy5c3H//fdj0aJFGBsbG3QXCSGEEEIIIYSQWkEl1hAmTZqEuXPnYvHixVi9ejW+9a1vod1uY+nSpbj66qsH3T1CCCGEEEIIIaR2MNKjgWy66ab40pe+hLVr1+K8887DPffcg5NOOmnQ3ao9337mjxJpPjY6pQFAB+PadD/oGMrr08fha8rq0oLe717QrRvlie150v8dYfbq8a7XjP4XQ//ivCAq0xa2LaeNS/XktE7QlvLGlbpRvpo+HrTQEX6X6yT/tuWJbXTQirfZK9uS/hb/j/bV1+Sp6dHvflzHk8r0/u+lB920QCkb/Y3u/1K5OK8llUn+r+QD8JQyaX+Laaaynq9r361u8nf0MJYRZmAXyhvL2NryHcpI20vO/i71WUo3zBRvSDeVN6b72dpvNIaDHLQM6aby5hcrYzv2NgLD71IZ4bGSqYy9LZcyYnpK+e7/gfI/PE2dllrGUFeTl/q32O/oA6qU1eV73TRPqROlt7rpYrkoraWUSaSjl95WyrSV/+N0+Im86P8JUNM7aHfTJiTKdhJ/2/KkNtCJf4/z0Nummj5BuHro5cv1dWlt4RpF7cMEIS/eR+jygm6a/H+YF7Xd/Vt4M7c1K3K0Nc9sJxie47aFaxuRlqdPbxtujVpIRlTr0r603f3a+qNIOOxEf5zL2h4hJig9GswJJ5yA8847D6+++qq13A1vrkPLAzb3PGzeCk8IO41NwLQRHxJTtfDQyY6wrD49zjddxKMnOvR58g2/Ni9I5qnCQ66XlCBqnr0vsuTQYZIa1rKC8DBtWyc80tpVRYgOm/AwYRUeicIpwkPARXCY6tikRbJ9Q11L++HvQkZRwsMiQvoVHk2RHZZTRG1J7EqUoOxMdCxU+REdO1VamNKN7Rvb0fQz8Ho32oEX1xF/l8r4vY+ptB2lL+a2EAsJp+1JTXvJYwA5Ly7T3Y6uTq9MuJ1EXR1K2cTfUtlW90B5vR0Q/xeLBh48L0AQtELx0S0TpevwgxZang8/8NDyAuHvViwvVDpBC22hTPS3raz6/zha0g0/EH4/teFjPGhhgmO742hjAjrWslF7UVndtuOy3TJyWlhen9fdfrfv+u0mt9dr09YXTxIf0v57gSQ+EvnwE+JjPBYtLaVsRys+/KCjFR+d7l4nymNjQnLo0kaN5Rs34vGN4TXyG76PN4IA4w38TiLDD6VHg5kyZQoAYOrUqdZyR2+2KbZtV2dam0BZwiNLdEdY3pSe/MbQRXn08pJRHsk2zTfhsWCwCRLNRUMvikMfzSFGgST7o4/ykNu3R4DI7cmRJy5SwxTloStrivLQ0Yvm0Ed5iCSEh0VqaMklLZRIDpu08NX2lbYsfRmU8EjcG2QUHpQd5aOVCmJCXeVHDvER9hv2ciWKD1U+qPutEx+e7xmjbEztZpEmur5oUaRIQnwIRJJCLWMTHLEMQQst2EVHQoZ0RYCbFGmj7RnkgSI2bKIjFhuROEEbbcN1R7RNVYb02m/H0R6J9m3SRiNH1DL6PA9tBFrZ0UEY7TGO5I1JB4E22kMnPsJ98LXiA0hGfVB89M+0sbHEQ9TlGzbi9rfWD6hHhOhhHFCDeeyxxwAARx555IB70izqLDw6CLTDWvTtFh/l0Stji+hIDmux4RLl4SI1jGWt+5E9ukOVHDrShrWIpA1r0ZJxWItNWmSRIglpYRAeIi4iZWDCI/AKER5e4JmPl0EumaSTNt339MLD2H6zhYeIcV9M+244VlmPefbXTqkrlNf9nnjvCe9Lp/dxX5+HZHm3z79SJ/H5dxepLtFlpvOZS1Rbr89dWWU6zwpkOW/36uT/DrGF2o8nymb4ztNFYSp5rmH+6vBTebsOfbPsq+06pFfGfOzF6x/12qiXbooiMV13Ja/TzBG7huu/DNeQow5XbyF1gu+OmvPUU09h2bJl2rxLLrkEBxxwAI466qiKe9Vc6i48TBQV5WEbluISCWKr30+Uh7ZMlmEuDlEeieEtSpSHiHqhq0Z56FAvqAOnC3UHEZJ2g6Apm+VGxCgtTFEHyHYjNVDhIZQR++wqPAYiO6K2lfKRHDD1sYk/LvtnPB4DkB+297LT+2tA4iNRVv2s2wSFQXy4SFMnEWI6r1nKBvH/lnNhVCWL4DBE/lm/IxzEei8v/bs2y/deb1in5bs2JRpTas9BctiHv8rHRF/f65ZNvg7R9Y3+yisqQ/FBCMkHpUfNOeigg7DXXnth1qxZWLx4MXzfx5o1a/DVr34VL774Im666aZBd7Ex1EF4dOA7Cw+XKA/dhYPT0xXbEyOHJ0XqhZS5n9VHeZgmL9WR5Sle2uSlIqmTl0plM0gNDf08aXWTFlEZQ11r+0JiKTd2bsLD2I7hJrFM2RHdrBtlR6KNjNEnDcJ83NyjP0zHsyz5kXhPu77X4v4ivUzB4sPts6r/nOvKZpEmmSLNLPXV86LtXKqeb3UC2jgXk4OQcIkGdPkeGnS0h8sDCrskSX94YY86NV/HROiug0ziQ3dtRfExGiycvxoXHPf/8P2v/O+gu0JqDKVHzfnKV76C97///Vi0aBE++tGPYq+99sLZZ5+NmTNn4o477sDmm28+6C42lizCww86hQgP/fbsceq2KI9eG5aLBgcJknfy0l6Z6qM81MlLrds0RHnoyrg85YsoYvJSfWFVhOgv/LNJi36e7iptafqqtlt74RGnyzeHrjfE5ogFQ3pBQ1iGQXaoFCE/AP0xzvo6uciPRL8aJD5sUtQoOAuL+jKUMZzfrFEfXdRoD+1k0F2ynK9N3wPaSI5MwyjlaA+7uC832kPbnsO1gD0q1D3aQ58XtWPHdN1UhPjQX+u5iw/Sww/C1fTK/vnIx38fX/33PTHnop0GvcukxnAi05ozZ84czJkzZ9DdGDqyCg992XKEhy3CI2zf/HSk3yiPZDvpk5e6tlF0lIcOl6drLpOXRqQtUasrW/jkpWnCQ0eWp6uqrChoHo9E/1HkjVxxwkNbRiknYjruxvSCJifNut0m4Dr56EAmPRU3JqR7gTiZqFAs8OJychm3lV3KnNxU7XtcrltGqhfvf7iNuE53YlO1rq79RLuasqY6idVcpD7JeUVNappY4UWZ1FSHaVJTXZngQep2AAAgAElEQVRoUtAsq7XoME14KpUJ3FdykdvMNqlpokx3u/oy5pVcoklN01ZyAZBYzSXLBKe6yU3Dsu4ru2SZ3JQQUj8Y6UFGjiYJj0FHefT6YY6mqGuUR9VL1NrKus3ZYXjKqSurScsSPm6UFkXP49FE4RF4TsMcUtNLjuwYhoiPzMe0JpOeeob3UV0jPgYyv0fKdrKe34znSU1Zm3COcBmmGJHl+8Hle6eu0R62SU3jbQ0w2sN1qEuY3l/EB2AazsyID0KaCqUHGSnKEh7j8J2Fh3EWcoflGAYd5dErY784KirKI9NkpkH6/he9RK1p8lJtiHV8QW4po5Q1RYDkuZHIF4Yu/+0SGt944aFA2VE+jZIfQhmxP+ll6iE+nKK9DJ9/kVRp4rAd4znIRQBHRZXzqq6sWsYmrNW5PbJ8VziJgH6Ff4FzexTxXS+VKXFujwiT+HCd4NR8rUbxUQZcvYXUCb47yMhQpvDQl3VfoUX3RZ4lykP71Ea5yNA+TeozyqNXppwoD+22CoryKGKJWpHSJi9NmcdDRzFPcZW2LNsZRuFRJ9kRpdnSm/qTZT9dj2Gp8sP4fnMpUy/x4fLZLmt+j2TfDOdETdmqJjXV4TL5dVHRHhFlRXvYy+SL9lCXsNWWifPMx9k2t4fpAVG/K7tQfBAy3FB6kJGm7sJDTkt/GuISVmq9YFEjNLRl7EvfVRnloS5Rqw/ZlY9Jv0vUVj55qUCWJ67WGw5VVhQwj4cthD5x81hj4TEI2ZH9Zn94oj2y7KP5OFUsP2ouPmxSI21iU/35QqljGOYibyf7ecl2rnKL+kiXIREu5/EilrAVUaM9bMK+7GiPPEvY2h+uuCxza7suMV/fNE18EELqCaUHGVmaJDz024kuEhxCSh2eIvW7RO0gojxs7eguFotYolZHVZOXqvlZnqgapUXJT24TN2eGlVLqIjxUipAd0U25SXY4b3OIZIdKEcfCdJyLlB9Sm5r0qsWHJCo173GbmEi0afvsqnXKnN8j7fynyXOJ5LCVUcsWtYSt05xSA4j2sG+jz+GvDkNNXaI95PJJKD4IIVng6i1kJGmK8OiFeJqfgvTq9hfloZbJs0Rt3aM8dGR5iuf0dNAkN3S4jF0vUjw43ZDIf7vcxKjDWozCQ92Wrv2aCA/TfhtFh65sQSuxZOpLw+hrhRVrerd98RCJf4grsmRY7cULXFZaETZlLNPfqi5iv+RygLqyS5zfbaP3q2bVFpjqhu3a6vTa12/Xth21rrZOlBb/31JODMmy0eotTqu2xH+by6qrtuiIVnLRrdairuSiWzVFXZ1F307+lVyibbqtzhKtvGJeVSbaX7cy9tVkwm2GK7l0AOnqYBzJm5aOFyRWdQm3l1zZRbeqS9hucmWXIlZ1IejOtWFf6a/o7RFigu8OMnIMWnh0vKCvCI+w3UiCVB/lkfVLpd8ojzjPIjCSYcP5ojxcJi9Vy+aavNRS1ikCRJOW5WmtOUwdxjqpw1qsEia57cR2Ak/axqCEhzaCoBsRkJAMQp+l8gVNTpo5yqSBFDXJqDndcL+tee1Mr3OuqKQCIz6kz7ZTWzCjfG5dxKhRiKrnD00d5+gOY5q7GO5nUlMdpklNRfJEe1iHiVi+n4qK9tDWMzzIcClr60eWaA+XiUxNER+uK7sw4oOQ0YXSg4wUdRAeOrRf5HGey4VAOVEeeUJhi47y0KFGeejq26I8XJYgNE1eqhMhlU1e2iXTjUTcpiIrLDcsadtJCA9NHfVGSd2W042jL9wAOt/w9Sc8pH0Z0EosmWVH1J8m/Tju2yDkh6k/lYiP+POZlB/Z2pLTTMdEW8ZSR92Wy/wear/TtpNoK8MQwCyTmopkOd+7fH+oc3vYv6tcvmfzfZ+aVmzLM6mp27wd/c3tERFd9+iuzkx6geKDEGKD0oOMDFm+kOoiPORy0UWA1/272CiPLMvW2SY9ExlElIeOPEvU6ttRL56TZUubvDSD4Ei7idHfOLvVte1H0cLDVKZs4dEI2WERCI3A0P+6y4+yxIdr1IdLW70yclqWc4fLZ7rXx5TzTQEiROq3w364TGrqsoRtRJ4lbHUMOtojz+TkucsWFO1RV/Gh3T7Fh0QHbYxX+JM1womMFpQeZKTR2/pqhMc40oVHWVEeWZaozfMkqElRHjr6WaJWRyGTl8IeRm6+SXG5wZH/dnrC6zKPh6bf/QoP8Xdz2L9+267CI8EAZEfWITWNxiI/0oaa5E/PLj/ENnW/O4sP4bPi1FY/4kNJS54vzHXMddPrpJ2P0s4Tajt5JjXVUfYStiJ1jfbIE8mZZQnboqM9wnq6fPNwl+R2ixcfpihhQkg9ofQgI8ughYe+TT1FR3kkyjrY8SxlgcFHeahL1OaN8uhn8lJtlEeGMepGcZHl6WgXczi6nG/bjkl4aNG0W6Tw0JeRb+L6Fh4lyo4ozSQ7Ehj74jX2x3kfLfLDdFxdXoMw3V1+5BEf4mdmIEva1mB+j2TdfOc3XRmbSDZFe9gnq7aUyfGdYhPvdZrbI8ucXZnKFji3h/n6SFOW4oMQokDpQUaSJgiPYYry6NXJF+XRUfrlEuVhy7Nf0LrIDUVyaCh68lLnGwZL2SLm8bDVsYmUKoSHsYzQPyfhYRQM7rIj8015TtnRZIqQH6Z2sksRN/lhEx/SZ8f4/lXaNrSlLdOn+Mj3Wc5QJ8sytjnOa31NaqrBZVJTlyVsXSbJzhLtoRf87tEeOoYh2iOsayqn2Q7Fx8DpwEO4gks1P65LIJPRhNKDjBxNEB5yHa9bxl2CmCYv05btY4laE2qUR2bJoUR52PrkEuUh0s8StTqclqhVpYbDBb3tRkFHnpuVPPN4pA5rsUxuqu5DJcLD0I54oxbfyGaQHYVFGoyg7FApU36Y0s2vX7r8ELdvE2l1EB+9fDkv+VlPVkkVoJYokjzy1EXg5h0a6BLtEeEyvDHL90jWaI+IvNEe6gOItGiPLCu0ZbtuUK8D0m85XFZyofgghOSB0oOMFHUTHh2Yxqqav/B7Fx3pUR697aRLkLhsPxdADYjyEKlsiVpNGdvTyjT5kH4zor9RlMvIbbk9/ZX3wVZXm1cT4dHbrr581K5NdqgMSnaIN+9N+3HdxzLlhynd9Pq7Rn1oyxvet2WKD6fPpvL5djsXGM4jpve0dbtu5zddm7bzaB2WsM2eV320h7ZsxgcdUtlM1yQuw1+Sr5v52kmzLYoPQggoPcgIUUfhoUP8gq9DlEevjnuoa1o/8kZ5qE/H+o3y0FH6ErVSofQnlMYbANsNgVLXJDy0GG5Q8szj0UThMfBlZ/tsv0k0QX6E6RZJ0STxUfD8Hr26Sp0C5vewiw73iLmil7DVUVS0h1ZmFBTtYSLLkFa1TpXRHlnm+aD4qA8+2ugE1f34XL2FWKD0GAEWvrUeN61dh+UbNw66K7WibsJjHJ41wiMs03+Uh+2iI0uUR69P6aG0RUV56PtcXpRHYUvUZhiLbrvJyHMTYbsBSXvy2tcT34YKD5U6yQ5blESTMe1XHeWH1Adhu2Jd3e91Ex/5PuPp5wezaM0gQkRc5Iuapz1Pu8iQdOFdZrRHr0zx0R4uQ06T7blHeyTKFhztEebrr5HqJj5GmeUbN+Kmtevw3xs2DLorhCSYMOgOkPKZMXETbNum/RTJtu56NcLDlFZ0lEevTv8Tmpm207Qoj6KWqC178lJTfpYbi6rm8VD7qrZdd+FhOoaZ03WiQ9l24W03iKAlH/hoXwPhBRH3X0yPj6Hy4kXHxaXtrOleILx0gRdv2/O9eHte4MV1xN+l8lIZQ5su7fi9U4ipTIIAgNcrH9fr1pH6IxwLuYxaN2xTR3pdpa9KOe2+BK3uznu9A5goE6YFgQfPCxAELXie/H3uBy20PN+pTO9/Dy1lW52ghbZQRle/gxbayvVEVG8cLUyAH/8tMh60MMEz5KGNCehY86L6KnF+vO022l4nTtfRKxPWsZU19SOqa6MDD20EGIeHCcoXjC6tAySuKMaRvMnpeAHaypu7gwDtxISpPtqOz4U76KDNCAMAwLSxMUwbG8PyDRtx+1vrK9vukvnP4r9+9izWruHDXWKGkR5k5DAJD73FH6zwkNPKjfLobcdNbEh1Ko7yUJ+W9Rvloc2zyI1BTF6aKxpj0PN4NFB4lBrZEXjaKIXCokYaSCHRGabjWlLkR9MiPvJ8Zkub3yN1u+ZzaiIv55BBNdqjqCVsRbJ8N+nLlB/tYSPXdUBJ0R6u10hVRHyYo4EZ8aFjvKLVW/Y55j34q3//MD5x0QcGvcukxlB6EIL6Cw8bZUd5RLguUVtFlIcuTxfloY61donykPPkYS2DnLzUVE/NM4eXm6uabo76msejocJDpVDZUUbbQ0JRgqJs+dH7XdmmsC19+ZqJj0HN7+FwDhPJdF60bKOoSU1tQ19s3zfq3B56YWHJK3hujzxL2Ea4Rnza+uHyAEauX3/xQQipN5QeZKRwncejLsKjN6FX9guFIqM8nC6EBhTlIWKTG+oFqUuUh46BTl6q5BujP/p4wtrXk920G8UGCY8sN8TRTXXVsiMq3+SfrPvqmm6TH2r7mV5rw/un1uJDScvy+TXXzXDeSJnfw3iMbW07nEfzTGqqI0u0h8t3jy6v6mgPG6a5vcqK9kjWjQSRp+TXW3ww2oOQekPpQUaGpgkPG4mQ0AKiPHLN2l6zKA9dnv1C1XIxmyPKQ4tyAa/Lsz0FTb1RcLhRcYnGSNbV9zHbk+LmCQ/Tja5NdiTQ3HCb2ramZxQATWQQ8sPUvvF1VtIaJz5s75XUOX7MVdPODdb3aMr5zCY6rPvlsM9FR3vYBLpt7qm6RnvYyDq/l60fWYbq9tqg+GgSHbQxXuGPi9QjowulBxlZ6i48qo7y6PVl+KM8dPUGskStJs9JgGiIbx5S5vGw1lVvdAp4Ahz2SahQc+EhkvWGfBCyoxV4jf0p+ljY0rPID1M7wyA+skR1pA5zsdZV6mRZxlbE4bxYlyVsRyHaw4Uyoz3CvHqLD0JIPaH0ICNJU4SHDlOUhwtlRHm4LIVX5ygPKS9x8ZusX9oStbY8082XeiOT5cbCcBNkm8fDNXKk6cKjTrLDFH2gEwdNwyY/XKNqqpYfQyk+bGI0TXxkECHOy9g6iI5M0R7a87iLDEkX4k2N9jB9b5cd7RG3kSHag+KDEFIElB5k5GiS8Mj2dEV+qlJklEfWScfUPonUOcpDpIolavOEdGe52XCVE9kiOQzpurrRx0q40RRvIhM3pjURHsn9KE92ZJ1nYhhkh4ppn4o4XkXKj/TflfaFNtPq5hEf2s+R+Fmz3Iv19bl2PZ9Yhsak100XHfLxcxfNiUlNBdRoD1uZpkR7ZMX0UKSIaI9MYkTYHsVHM+kELYwH7cp+dJ8DQiL47iAjRZOEh1xWfhpSdZRHoo2Up0WuF2B1jvKQ87LIDfdQaxEXmWEXDI43MdabGUhlstzEJKNO5HS1/4kbvri+/FR5kMKjCtnh2kaaGGjiT9Z9TKRXLD+qFh+9z5/y2XT9TKH3OXT5/Fcxv4fLJMvaNMt5MdOQQkv9LEvYynndejWK9pC2o0ZzZIjSNFFmtIdJrlB8EEL6gdKDjDR1Fh51i/LI+tQoswCpcZSHnOk5l7E9dcz9RFOTXrd5PHRPmN1u9PTlXSJFEjepDu3axEm/ssN0c1+07GgyVcgP0+uSwFF+9Cs+xM+q7n1slxqGdk2frXi7Sp5JUGgwiY9sdZU6KcvYZhEddgliPsfmWcJWpI7RHrq28n5v1zHaI2yj3uKDEFJfKD3IyOBi4esiPOTy5Ud5ZLqQ6SPKw/bUSqSqKI/ELPwuUR4CZS1Rq8szC4b0G9+0mx3bE1zjjUvOG5NcwkOTrkoKqbywX7qoEfXG19iWpk2xTpabasoOM2XKD1N5Y8SJRX6IdXVtO0dnVDXPh8O5Ji0yQ0c/83vEbWRcxja1fYfzbL9L2Nq+LwYV7SG15TJ3Vkq0hwtFPiDJGu0RlqmP+Ei2RREi0kGr8h9CTPDdQUaWvJa+CuFRdZRHoi3DRYzrhVFWAVJVlEepS9RmGFOuvSmx5SlldPQlJwx9SG1T1w9lWEvlwiOlTeMNq+6G2SI7VAYhO8T5LZr6U/QxyXPMXeWHKVIj+lv/u9Km0FZa3SLFRyHzezidf/LLlFLPmaoUEYtkiPZwHfpSVbRH3lXSdJgmNc0zFDZus4Roj7Cd8sWHC4z2IKQ5UHqQkSTvsJaqhUeWpx8miozyiGhKlEd8MWp5+lbYErVSZvqFdpYICd1TakD3tBTWeto2SpzHo+7Cw9hOQ2THMNAY+ZFRWNRZfLhIDNf5PdzOMYbzk3r+MtbTv1bGfcggoItawlbKg/m7qM7RHlkoejisWFd3vTMI8cFhLoQMF5QeZOToZx6PZL0kVUR4JGRDhVEeaRdIeQVIEVEetgvLLFEeIrmWqLXkZQ3bNj5BNQgPLQahYVue1tQPc3/S66rbqqXw0PS9atkRlR1m2aGSZV6OgcgPoJHiQ6Wf+T3UbaUNc9HXVeqkDHOR+p5FgrgMNRSznIa+ZIv2iNAJ+X6jPXTl+4n2SFvCtsxoD/cI0vLFR6IMxQchQ8OEQXeAkCopeuLSZJnihIfL+vXp/Sk+yiNuu6AoD32obr4oD5GiojzkPOXJn+3GKIPA0NW3XvRbSJUT/TyZdbqpsWyvQcLDdJyM6QYZYRIdZbXdNALlXBvta9BS0qObUaG8eBx9IV1XNmvbxvTAi99onu/FbXmBF5cz/y68zTK2I5V3abNbJi4r5CXo5iXrAPA0bWkwbc+lrtqGNk3Tf9O2dPum3f9uWhB48LwAQdCCp9hbP2ih5flxGTnPQ8sL4jJSHlpowdfmdYIW2moaWmjD1+aNo4UJSl5UfjxoYYKh/XG0MUG5tVfzdPVdifoVtdUJ2mh7nUR6HuL967alblMu66GtXMeNw8MEh7TkPiVvjDpegLZyru0gQBtqmo82nyVLdNDKJcr62R4hJvjuICND2Su1lB3hoU5gOsgoj7QnM8Ma5SFnKk8NtWXMeTYBkiYZnIe1WAWHvB9pwkPfRvS0NqUtpS91Fh5lRnaUHaHQRKqImOm37US64X1U14iPxLlGjfbQ4HpuKWUZW8t5JIs4LmoJWymrwdEeJtImJ68y2iPLtZBI3ogPruhCyOhA6UFGljoLj34iMHptuEV59GPhVZHhesGli/LotVl9lIcawZF3GVttlIeDiLBKCvUGIEV46FBvHlKFh6UN47AWl5sQyDdJdRQeKllunKOyVcsOcbtN+SnrWBQxXMjWxxjD+6lu4iNRVvncOkkEpQ2T+Mh2HlHqGIa5SDicY7T11TRNfZflaXVlss7tEZF3bg9dedvcHnIb6d/XrpiuI4qY2yO5LdfjUQ/xQQipJ5QeZCQpemnaZL1ihIdpmdp+ZkI3kTXKw7RN3QWVS5SHtJpLyVEemZaotd3EZxw3nkWAmG4ATXlqfZPQcJrHw3EFGKd5PAw3ZnUXHnlkh8rA5p5oAIOQH6byWdpumviwnitc5/dwiuaQ28olUwyvgbG+w/k0bySeGu2RddWWsqI9emlu0R62714xP2u0Rz+4RnuIDzmKFh/Jerp+aspRfGSiE7QwXuFPWlQTGW04pwch6E94qF+og4zwSAiLEqM8IpoQ5dG7EM0XwZEpykOTl/WC3fmGwVVOZHp6a29T3w/TzVLzhIdNNiXSDcekzDk7mio5TIj7I87HEKX3Oy9H9Fr4ajua8uLrILavlvUCw1wbNZ3jI8qPywl1ExjrAIXP79Ft01Rfrafrn25frPsZpWnyEnN7uJQRiObvEOfxKGNuD11529wechvR/BvJvKzE84I4zu2h63+27SXr553jQze/RwdIXMW4zvFBBsuyn/1/eOiG32HdaxsH3RVSY6jEyMjhYuXrIDxcozyKIG+UhyodsgqQqqI8dGlOUR4CicgPsb4tTynjJEAEzOHfSr7DRVil83gMgfAoM7oga/tpbTTxp4j9dj3WRbw2TpEXNY/4SIjRqub3UNoytmmJZrMJ2EzRHtIxlPNclrAVsUV2DDLaQ8T1+7rKaI+4DxmiPcT0tDSXiI+8S9nqYLTHYNnz43+AP7/uIBx+4b6D7gqpMYz0ICNDHebwcP/CdhcapugN1yiPNOGRpQ9yu5pQW03Uhm7FFvVCT1dedxHoGuWhTl4qXuhal6hVpYbuBkTJ096wWPK00iCL8DDc5OQRHsbIkRzCo2myQ2UQUR2mdFMbTcM1skJMS6SntKGWT1vxxdS+GHkRlZH6GdX3AqmOqbz8e7RdZG5HKq+0Ge9S4BkiLbrpfnjaskVYpEZ8xHWiX83RJalRJEKki7HvcO1vd7tBKzxxCf2w5anRHuLqLWq0hxTZ4RDtIa3C0v1dtzKLLs91JRc1okNcSSXLqipRm2q0Ri+aI1+0R5Y+RPsX96n7/a6u6qKmuUZ8hPUCoV56xEd07ShGfFB8JOHqLaROUHqMAAvfWo9NPA87jU3AtLGxQXenFjRFdmRdsaVo4WGbx0OVGp2gBZPwiMZ1in3vBO2E8OgIYzJV4eFr8sQ0UXio0Rzi0zid8DDN4xEELXOEh5huEx7Gp6LJPLFM5giPNNlhKZNrDo8U4dE02WF7qi8yiKErpjZMfWkSkXhQ91G80ZfSvWR6YGkDSB7XqLx6/HzPXF6VK02QH9EHO24vRXyEB8UTaiL+JnMa6iKID7k2pDb1/VDa9L04FjnQ1RG2kBAfhjxJbnTb1OUJiiwhPiKh3oKfEB+ALy1hG6WF20qKj/Bwy3IjTtOIDwBdgdETH2FaRxIfvXJJ8YHAkqd54dvCsrZliY/e8rTyMrri0JXoOiCr/IiuxdopokNNi679xNv16BoxTX6MMss3bsTjG8fxus9VbEj9oPQYAWZM3ATbtqszrXXGRXa4DGPRpfU7jMVUpm7CQyc00v4etPBQ0woRHpY8Y0SHJU8rQTTCwykSQxUeKbJDn2cQHqa2pDx9/9TydZMduptsSo5yMYqHDAKjSgkiCpAmyA8p6kMQA9L+R784RH1IkgKAZxUbUfX0OT7CukFCfET7qooMsS1J8aTkJcSH0F814iM8/+sjPvygZRQfIXLEhx94tRIf8cEUXjSxfCQeOmgPXHwASMgPoCdAssiPsF7QLeMuP8J6URl0y/Sg/AiZNjaGaWNjWL5hI25/a/2gu0OIBKUHGRlU4VF32QFQeADlCA91jLYoNxJDXkSpkUhL5pUiPDJGd4S/w17Gsc2wL2ltDY/saILkGJbhLSKRkKi7BJFkQwPkh6Bruv/qpIh5uEuvJhLfdFmHu0h1lTZ7ERzd1wQa8SGIDKNEUfZNFwECKMNZpL7pxUfYJ18rPqJ6nio5aiw+XP6ui/gATMNX5OiPLMNewnru8iOsh269qAy6ZXroHqyNMuK1XVXbI8QEpQcZOaqUHa7zdZhkR5g3msJDnaC0scLDkmeOqhDzIOelCI9U2ZGlzQKiO5oqO7IICmPZkiWHqf0mkSYkypAgcXpOCdI4+ZEj6sPzu5LENerDKjZ6w2xcoj6iNuM4DR9a8SG9Phq54eny4gOgER+GPGkOD5348HwpKqRI8RHtyCiLDwC55IdYrmr5QQipH/yMkpEhj+zIuxpLlvk6pLRALUPhIf5fivCIbhB0wiN6PXTCQ8lziegQ87QSxDJ/h4tM6De6Q8orMLqjKbJDG51Rc8nRanDUh6+Zt6MKCWJKd2kj8IJGyY8ioz70bUUtxuNdLGIjKT56vYFGZPTaEwIqEiJD2itPL0e0ESDxd7CvFx9CXkJ8dNssW3y0o2EzIyw+ABjlB5Ac+mKb9yOL/AjLmYWISX4QQuoJ44DIyNGBfoJS8YtO/Tus52knKJXm31D+Duu1JOGhKzMuSIKwTJvCQ/jf7/5QeCQFRPjTlQbdffK020KibqIPgQfP7woPa1t24eEJ9XoSRVgCVDj2cd+VfTX+bmwnvbyYLi5hqiuvqye9FrZ0w9Ks0TbV5VNdthn32/eknyaj2xfTfhdxTHXt29Jtr4u4bTXdVD7xWc5QXv5dEZyWduznDxjSu3Wic0D3nJB2LgrTYW8riPqfcj40teen9MHSpinPNZIviP/vnf913yuB+j0VeNL3U5wGOU33XScJfyS/G/Xfq8q1g1Qu3/d61uVsM1+HqOWEv8UJ1NVrpaiutPqbcs2lK6O/VtNd47lcG+ofopHwtYhewyp+XFZv8X0fl112Gfbcc09MmjQJW265JWbOnIlbb701075t2LAB3/72tzF9+nTMmDEDH/zgB7H33nvjqquuynu4SMkw0oOMDEVFdvS7EouUZojsUNsYdeEhpvlBS7hYLFh4iBe6gCI69HlZL7S1EsQyYWnaTUbu6A5dnbzRHbb+NSSyI21CU2t6AZEcprZNYsNUvonEw0eUffVtQ1YGGAmiRlTUPfLDNJykW7v7b/fzKu6vWscyyal0rIR5OYxtaaI+zBEcASpZ2UV4+g9DXhTdIS5p22/ER9RwS43uABjxEUdohNcYpsiPsIwcxZEl8kOu53XrmYe52CI/SL355Cc/iRtvvBETJkxAp9PB+vXrsXDhQixcuBDf+c538IUvfCG1jXXr1mHmzJlotVpYsGABpkyZAgC45557cPjhh2PZsmW44ooryt4VkhFGepCRJE9khzZCw/CEwRbZET2pMEV2iO1EeVE7ur8pPFrCTy8tiH/ENK8S4SE93RTy5CeeGuERwCg8kk/Au+LAKlZgSPfkNpXoDmMdaISH2lbcZrMiO9R08e9EegmRHHEbhkgO17406Sdt37Ici6oiQYp8j0nbEN/bBUd+JPZJ+zkX6kvpyudcjfro6/wT9V3TP7Weem6UzpvKOVXYjqlNsbz2/G7JM0V8qKuBRd9BasSHr6QBUdSiJrqDER/K9ZQ+8kO+nsp+Xaavly/yg9SXq666Ck+zla4AACAASURBVI8++igWLlyIdevW4Y033sAtt9yCHXbYAQDw5S9/Gc8//3xqO5dddhkeeOABXHzxxbHwAICDDjoIc+bMwZVXXomHH364tP0g+aD0ICNF2bLDVkYflmmWHVF+1Jbu70EIj+gCKw4njP4eoPBQ09ThKpHwCBN7F6yqBElc9BryslxIJ4QHTBfu0JfXXMzrblDkmwbENwhmeRH1Ia0t+QbF2payX02UHf1KDtPNfVmSo8lUKUGi9vuRIOLfYh01vY7yIyor/q8TH7phcon+oXfesJ0LdOcibVsZzn1G8dFtz7qPmjz1XO76HRDEP7II1y2DroqP8P/Big/td3hNxUfUf5v8iPphkx/6dig/ysQX3m9V/PhBy9qf733ve7j99tvxkY98BK1WCxMnTsQhhxyCBQsWYGxsDOvXr8dtt92Wul+LFi0CAGy11VaJvN133x0A8Oijj+Y4YqRMOLyFjAxpQ1nyDGPJuhJLr4x+GIuuTJXCI/dToIYKDzEt9Slfxovp6G+zbPCkC3kpT9OmTXjI29a0pd0+ehjb0t+UpMoOTV2pvu13TRuu5aUb5IzljX0Q0A1XMZX1NGWB7MNVsvSlqeQdgpJlOIyp/SzDYaJ2E8NMumVrO+xFHA6kfNWok4iaVngxDXcJ2/QsbSVXeMk63MXTtOWVsbJLXKsVniC7eeJrEY7vSS5p6ylpPnpDXaLhMD4QD2tpdYfDQE1DC1UMdZkgtCUOZ5FnfoX270EMdYlIrtQiD3sBkkNfsqz4Ylvu1jTsRbfaC6kfjz/+OGbPno3tttsukbfrrrtin332wZIlS/Dyyy+ntjV58mQAwG233YY99thDynvmmWcAAO973/sK6DUpEkZ6kJFDNfJ1i+wQy5QpPOKwUOFpj9xGfuEhHpOobO2FR+KpXjnCQww5D/MglTcJD7ensEjUS/QXgvAwtSX1Afa2VOERtynXTX1KnSGyQ3oqL/RbfIIv7ZOhvKkMkIwIMEVyRGhfDxQXyeEaVdK0H9O+DepYO73uDu+hvO9L9T1fSOSH0kZUTvxfF/Uhbt96jnCI+gjTDa+Hcs7Q9c12rkueU+3nYLFNU55xmKMmr+4RH2F6MuJDrJdpGGt0ndK9hqgi4sP12kmkqsgPRnk0gx122AHnnXeeMf8973kPAOC9731valuHHXYYgiDAhRdeiMWLF8fpr7zyCn7wgx/g4IMPxj777NN/p0mhUHqQkSFNdoRlBis7dH0pUnh00I6Fh1ynWOERXhjJF1Xi77UUHlKaXXiY5IT+4lwWHnJ52MvHP0jkqe1oQ8iVGxZAER7atuQbJlNbqrBQ6yb3wX6TCMs21PJi20B/wwlMN7wRtb3xFtpuMkXsf5XCSSwntpX4Ped71fTelz4fLgJG6ld3R5TPa9r5IjFcBpbzhSA+rOeL7rnK2pbjedF1ZReX87Wz+DDk1Ul8iL+PQ6xTnPgQ/7ZdWxQlPiIGLT/kOpQfadRp9ZZ2u42JEyca81etWoVNNtkEs2bNSt2vT3/605g9ezZef/11zJo1C9/97nexYsUKHHLIIZg9ezZuvPHGXMeLlAuHt5CRQxUdgF52yHWSJ9K0YSzqF6+uHV2ZceULXEzLc+FQxoSlYjsuwkOK9JCePg1QeGS4oE19Omi5IVDFgCo81PLR37obFukGTWjHtG0pPU12SHnqNvR1bLJD7ad6c6lu01RPLS/d1GYsbyqjSg4d0nESKGK4SpZVXmzbbDLx0BRl32yrspjKqse+jOEw4vbrOvRFrBedKOJVWnxI9UwrqJiGu0jHpfu/53e30YKlLfNwl7gtZbiLtN+JlVoCuR3f629lF/U4xN9XPoROafMGNdQFQDx0paPJH++2E6a1nYe65F3ZJTEUpoChLoA69EQe1mIa9iKWKWLYi76O160jjOUkjWL9+vVYtmwZTjjhBO08HSqtVgs33ngjPvGJT+DWW2/F6aefjlarhSuuuAInn3xyBT0meaD0ICNDnWWHfv6Q4RUe6hOr2gsPm1DIKjwskiIhFTTCI1V2qP2Dg/BwlB1ym/a6TZcdRUgOo7QoSHKY+tgkUoVEHxIkKl+0BGmK/BD7bxQYvpcQHwASUzqo0kQvMqI2zeJD6pdpadu4fVl8xL3QiY9uO4G6T5a+GsUHklIkXrZWFR9K3iDER1SnTuIjmuejKPEByNdJE6Cf00O/TK1cpmz5QZrHDTfcgE022QQXXXSRc52JEyfiuuuuw+67747nnnsOvu/ji1/8ItrtNubMmVNib0leKD3ISNIE2SHmU3jkEx5qvlF4xL8XIDwSwiFFeBgkQTwWX5PXb3SHvo4pXb8/aj39Pih1hHrG/ijlq5QdOonQBMnRxMiPNMFQhAQxle9HgqhCou7yQ436kCY6VaI+4giJjFEfWlnSjfpQJznVCwggMclpVBxBLD5M0RpiO0bxoS1vEB+GvFhuCPtiEx/hNntpFB/5xQegTFaakBD1kR+kR0cYXlXV9rLy5ptv4m//9m/x4x//2CnKI2LFihU48sgjceaZZ2L33XfHySefjKeffhonnXQSVq9ejXPPPTdzX0i5VPdOJKQGJObfMIzttNYJ1PGhyTGkqWvLa8eLJssA9RcekdjIIzzi41Uz4eEF2ceAm4RHrzzSywfCOHpt+0A/S9H2tuHF+x5tT2wzq/CI6iZ+xLkRlP1N9Cc6XhXM2WHdVoZ5IFzmgHCZHyTr3BOm8k2iqH0u4jXIss1+3ltlv9elz1jKnB9hXfTqQRG0wv/q51Y6RyXaVOrkWNpWLS+eo5J5aprQz8SQQn0/s5zvXVf5EufwENOKnOND/F16gADNd3D8nS4u8VneHB9hu72/+41cjfJ0D45sZfTzsCnXWolruhzXhZo6pBmcdtppOOecc3DAAQc411m5ciWmT5+OP/mTP8GZZ56JWbNm4aGHHsKf/umfAgC+/vWvY8mSJSX1mOSFkR5kZFC/oFR0X2rS3xVEdqhl6io8xOgOcftZhYeYVifhIZbTXTDrbt5NwiPMQ3r57sW9Ka8WS9GKN48pw1n0dQy/1yCyQ73x1jGISA5j2YbKDh1BK9Dup58nKqOESJBou9E2fTXqouaRH2lRH4GHhKRQv6r0kRrmqI9+lraNozXUdsKxKtpoDfF1EiNHci1pa4p06eZJy9bGaV4vT4juiCI6yoj4iPKjaIssER/icrbRi1HniA/AHNURppUf+aFvQx/5Qcpn+Q2PYfmNj0tp69esz9TGhRdeiJ122inzcJSLLroIq1atwumnnx6nTZ48Gddffz0OO+ww3HnnnZg7dy7233//TO2ScqH0ICNFEbIjLJOM7LDlZ5UdQP2Eh244i7j9QQgP9alZKcLDIhm0aYmni7CXVwSEvv1eG6a+AMINi6PssLZlkBfOskPTZuL3msuOJkgOU1+agE08AHqxMQgJAvReq8bJD6Gs+X6sO1+HKDAMsiQpOKIWup91bcuI5UfQgqUtWXzEbWjEhz5P3HdI4iPaf3N5RW50t1Km+Ai75A9cfETDXXoWCrUTH0C62HApY5IfUhsFyA9SPtOO3hnTjt5ZSlv90Au47uAfO9WfN28e1qxZg3/4h3/IvO1omdodd9xRSm+1Wrjwwgtxxx134JFHHsncLikXSg8yMriEK0p/D1h2iGVdl3cL+1Sc8LDN3yFuvzHCQ7nBNy1JG/+tybOJhyJXaMkSJQJkFx7OssNSN12QNE92lLm6yqhLDpWs4mHQEiQhJJogP6LOZ436sMgSfaSGUtckSwTxoWsrak+SFuJ2hW2ZpEWvfBC3Ia7sYi4Pqe9RVIhJigCK+BCOtYv48Lpio07iI5rnQ1yIpEjxASAhOFzER9iP8uSHto0+5AcJiZasrXJ7Llx//fV44IEH8L3vfU+bf/PNN+Pwww83b6cTvk9eeOEFTJ06VcrbZZddAABjY2NOfSHVwU8nGTnS1mNXx3eGZbKtDe86Z4c6lKUf4RFGYwxWeIjHB3ATHhF1ER6xXHAUHlF5V+HhidtAuvBQy+uiOzy/t4+eZhu5hUfcJro/lvkBDMJDLVOXOTtc2ulnTg7TcSpqbgq1nSb/pO1rXY57Ee8pWztlfVYA9D6XluPkCZ918ZzpMtdH+D8S6dLrEJ1PonOWoa1eeSjte9pzmemcrW3DTyuf7TshNYqw+7tpjg9AHp7pOsdHlA7oJwTXzfGh5gO67/HkPB9FzvGhXpu4zvERlZGvkezzebiUSbuWi/pvm/NDN28IqS+33XYbFixYgH/5l39J5K1btw5///d/jxdeeAEAcPfdd2OPPfbApZdeKpU75JBDAAC33357oo0owuPQQw8tuuukT7wgCIL0YqSJ/OY3v8Hee++N4zfbFNu2qzOtdeXYZ6ZJf2ujMDSyQ6TsyA61fNOERydoZRYe4QRt9REeYjkX4QEoNwWWoSgJuWCasBRIjRIJtwt9eqI8rO3IbdrrWgWJrg9iGSW9bpEd6tP+LJEcXuBe1tp25na0yY1AE0wHoBeVkUj39OmBqbwhPdC0YyyrpIt9EPPENn1DuljepR1TXWlbGcoHvQ+zVEbN7/0Pa3ltm6a6xj6ktRNGa5jaCHR5xm0Kaa2U8qb+mvJ6FkdIi37349+9+P9kWsvzpfxWt82WkC+mteDH6UAYkSH+nkiDL6WL/0fRC730TlwHCKMdemU7Sl23vyfE/ejEv8d5kLerpotpEVHZXr6v/C3nu5Rpp+RP0Jxw1TZ/st3yRJlRY/mGjbj9rfU47u7jse0H3lnZdl94aDV+fPCP8N///d/Ya6+9Evn33XcfDjnkEGzYsEFbv9PpYPLkyXj++ecxadIkHHHEEbjlllswefJkvPbaa3G5NWvWYPr06Xj++efxk5/8JJ4EddWqVfjYxz6GDRs2YOHChdh8881L2U+SDw5vISNJ1klKq5YdYnkX4aHuRx7hYVuhRS7rJjx6+1mc8IioTHhYZIKz8DDJApPwcJEmFS5FqxUjjsKjrrLDpQ1dfRFKjv5R9yX62JqGphQxHAZIvnbiUBW1vNp2SziN+2L/hZv3Og570Q81MQ936e5N91/lvCPsa6LNwDBURmk7HtaiW9pWHR7TLa2b4LSwJW0T25TbkPZDKR/uaAvxUJc4Dd3fW3AZ6gIg0+Sm8MKhMX7QMg51AdDL7w5bEYe4uAx1iSY4jfHQ11AXAPHvrkNdAM0QlQEMe1GHvERtci4PPeJ1X1XbM/HEE0/giCOOwFtvvWUs43kejj32WEyaNAkAcOyxx+K+++7DCSecIJXbYostsGTJElxyySU444wzMD4+jq233hrj4+M44ogjcNZZZ2HixInF7BQpDEZ6DDFRpMfUdgubeB52GpuAaSM8xuzYZ6YVLjvCtGxCxFV2iHlpwkMXNlqV8NDN4aHmFSU8Mq/SkiY8NGLBlKdNyyE8dCHgmdroM7pDV6eo6A7jselShOwoI6pDHTKhYxCSwyQ4mrpMrQ1zpIV72TIjQdTyWaM2skR/FB354RSh0WfUh3TMCoz6sEZrJKIw9HnOER/G8oFmW4a8PiM+oqiMIiM+2p6Sj2ReloiPKL+fiI+2Ev2RFvEh5vX+7i/yQycq+o38mAB/pCM9lm/ciMc3juN138eLfoBP3vUZTKkw0uPFh1bjp7OuNUZ6kNGGkR4jwIyJm3B4CxSZkFF26MqUKTvE/FEQHhGDFh7WaAjNjb84FCW1vEF4ZGqjrMlKlXrasinCw0V21G0Ii012NEFymLbZJFKjMjTRIIOIBBHLixEgrlEbmaI/NBEkCQxlol/FyA9x9ZJ4u4F71Id2lRbT0rYZoz5Mk5yqE4mq0Rr67QEwLGlrbMOwpK2ujXD/Ie2DWL6IiA+X5WyBXvSGS8QHEIoHMeIjz+Sm6pK2/UR8hEvkdpwjPgCHKI2MkR+6FVfUlVyyRn6M+kSm08bGMG1sLB7eQkidoPQgI0Wa7ACyD2UpS3aIdW3CQ61XtvBIDGtJER4RNuERRnrUT3gYpYFBeNgkg6vwMG9T365cFjF5hYer7LCVif5umuzQiY4iJEfWoSrDLDlUMgsJBwkSlS9DgiQERpnyw9KOqW782RP2OZYMBrERnwMMIkPc94TAsLVpkyZq293/PT/Qig+xD+rKLq7iw9iGQXzI5YX9AQYqPoBIeJjFB4CEBLGJj4iqxAcAaSnbNPEBmGVF0fJDNywm2vcIl2EvJDkJbBXbI8QEpQcZGYqet6NM2SHWTxMe40Gr1sJDnEW+6cJDt0KLtbwqI6Q0xzZqvBRtmuwwbbfOsqMJkqM1hNfVfvcjXIQEMZUvconcJsgPac6PeCe6/9siNByiPlTxoW2z+78nbMMl6kOc50MVDr2+QhIf8fYE8aHdN1MbwnExl2+O+AAUyZEiPtpKmbLFBwJIS9mmiQ8gu6woWn7ol6iVI0N4001IfaH0ICNH3WSHtk8ZhEevDoVH1cLDLCDchYe1DY3wkKIR8goPR9mhr+MmPJouO5ogOUxDZ5pEJB7UfayzBGmK/JAmVw0896gPJV9Pd26LSC5kjPpQxUfcqtQHGMVHuO0glhbSBKTCNkzSwtiG71F8oHzxAfQmM80iPgCNaBiw/NBFhhBC6gelBxkZBi07XNpU16AXy5iEhyQxBig8IkzCQ82vm/Cwywc3WZGUB7C0n9KGb6vrJjukvvQ5nCVNdgBm4dEU2ZFYqcNwwzcIyWHqS5OjqYOWfr98L6iFBIn6Im4zaJlFRR3lhxr1YZvrI97H7v/Rscsa9aGWl9pUtiJJk0DJFcSHlCZJDnfx4dyGRXwk2lDaG7T4CF9Ws/gAEIuHQYqPqGwW8RH2SR/1EabJq6gMSn6QHj5a0jDsKrZHiAlKDzJyDHpFFl1+sn45wiNuvwThIUoOnfDwA88oPCIqER4OskKWD30ID50wMMiGooWHq+xwq2OXGcMkO+oSyTGMkkPFKCSUfR+UBFH7osqGJsgPVWxEZ9u8UR+q+Ogexe6/yvlLiDTJEvWRkBfiBKdRmiotAHlJ224XjEvaOoqPeO9MfZO21ZMdZYoPQJnIVBAfgB8ub2sQH9HvZYgPAFbR0a/4AHTiIsfkpInIkaQsySRTNMNeCCH1g9KDjBSDHsriKjvEsjbhoZZ1ESBVDGmJcBUegSA5ais8XGRFHuGREAaGuvG25DbEfc8qPLLKjsQxQbrwaKLsKFNyZB2qknWbTSRVSDhIkLCdciWISTbUXX6kRX2o+dLx6P5vmuQ0a9SHPnpCkCY6uQBAmuA0SlO2H63KkpzENIP4gNCG2H9teTlNlR1liQ+gBU8d1mIRH4Ac3SFSpPjolXWfzFRc0SVNfADILT+qWumF4oOQ+kLpQUaGjkYo9P6uj+wQy6cJj06QbRLTqoSHH7QKER4RtRQeGjEQSYXChYdBWrjKDl3dKqM7mio7miA5mhz5kRZlkUWChO3I5cuQIE2TH05RH5B/yRv1kRAf4vE0DKGBWldY3UU3wWksPhSx4CI+uhvURINY2hig+Oi+2H2LDwSI5/Qoe46PXlm3yUzVpWxt4gNIyodByw9T1AcJEa8Tq9oeISYoPchI0RTZAbgJj167zRAeEa7CI1DTGiY8em1Dk1aN8MgqO2xlskZ3NFF26CQCJUc5ZB1qMmgJErSaJz90Q0yyRH0kxId4TIz3F93IjUh+OEZ9qNIki/iItyxICzkt2g4ArRSpn/iIxIar+ADEYS/1Fx8AEnk28QGY5UPd5AchpH5QepCRQR4OUl/ZAbgLD9c5PeoiPMS02ggPzY26TlYYy/cpPMT5O8zl0mVH1He1nq5sUdEdujJq3SbKDp1wKGpllSIkx7AuWVsHCRK2E2i32ZvYs1nyQxuZoQxnMUV96Iaz5Ir60GwzbrP7v6ApJPGhbjvPkrb9iI/oeEjio9vfOomPWHI0RHyYJj41iQ8gXT7URX4QQuoHpQcZOfoRHlXJDl2adWLSlCEuQPXCI8JFeMS4CA+lbBXCQysrFLEwSOGRVXbo62QTHnWXHf2KjiZIjmGQH9GEpHohIadVIUHCdpSbckWC+L3BGWiJt+XCqcz3k8kDkx+myAzNcJayl7b1NNvURX2I4iMpNKJto2/xEffSEjUSt+HL/U6IEiC/+EBvX9PEB9CL6EgTH2FZr3LxASSjOGxzepjER0R0HVSl/OhnslMS0gk87XVtmdsjxASlBxkZqozu6Fd2uER4RH+nzukRtCoRHhHinB2uwiMQBESq8BDKpi5LK/5esvBIiAVBVmj7MCDhkVV2APmFR9Gyo+yojroMVxlmyaFiEg91lSCJCI4M0R/iWd4X/hC3G58WCpYfrlEfqviQOqVGfSA6Pr0+54n6KEJ8xP0R+x22IIkPuVzUjE6KGORJVvEBGCWIJD669VzERwDAE8SGTXwAiOf8aCnCpEzxgQDxUrauc3roxEdkodqRrChAfqStzNJv1AchpJ5QepCRow4rssR/G2SHrn1rFEcNhIePlpPwiChdeCgSoa7CQ18O5ropskMq06fw6GfuDq080UiLOgxhkbcFLUVIDnPZbOnDNK9HRE9gyOlZJEjUThXDYfqRH05DX4TttgwRJOJ8HTb5oYoPsW6vYbnNODolZ9SHSXwAyagPrUwRxEdcJyE0EIuPoAWtdAjTeuIjKSdKFh9Kf8oSH0D4/ZkmPuD1lrIdtPgA3KJBgN61URb5IcoMMXKj7CEvpHp+d+PDePI//gcb1qwfdFdIjaH0ICOFbV6PQUxSGtc1yBCXlVlswkPtWxVDWlThESEuPeskPOKK5QmPxI15w4VHVtmhK5NXeJhkR53n67DJjiZIjmGI/OhFU8jpeSSIqZ2iJUil8sNh6Iv4VSNFjcTmopvX6u1PnqgPk/gAklEfpofezlEfgCIvZPGRKDcA8RHtZ6niA73jZBUf8HtL3arDWjTiI3rRyhQfAHrzc2jkhmlODzUaBOjJDRf5Mcj5Pig+ZHz0HryVyXs+9gG852MfwCsPPY/bD/l+6dsjzYTSg4wMeYVHmfN2mGSH3H5+4dFBq3LhESGm5RIegtioSnhYZYUoGHRyQSceEoLAVFcvL8oUHnllhy4vTXg0RXboRId++ElWIZItPWs7TYz8SJMaVUqQsHzgJEF8L6it/NANffFbSWFhG+4SNtrdWTXqA9Dm5436yC0+gKRgACCt7BKlSeW6sku7bG0giQ9AjTiJ0hR54nvlig9l7g9X8RGO+tEPa6lqjg94iCc2zSs+IlS5oZMfZc73IUWdWOb74ESmhNQXSg8yUvQT3VH0JKWi8FDzkv3IMImpo/Do7UexwiOM9MgmPGKyCo8uQyc8NOXqPFmpS3RHU2RH3SVHEwWHDvPQFDmtCgkSlveU8noJIsqGussPuV60Y939jaI+LAIjT9RHEZOcRhJBLy96skQSDJFIMCxpq1uVJbl6S098mMWLJmokr/iA3E9X8QGgF9HhKD6A3oouRYoPQByikl18AObJTKM8QBASGvlR5Hwfavm8UR+EkPpB6UFGBlFKFDmUpR/ZoeYnt2OfqNQmPMQ2xH6K0R0DEx5dxPxcwsMgOUZdeOSVHbYyeaM7mig7miA5WtGynUOA31JuervUVYI0RX6oc35kjfowiQ/AHPURH0fIxzdr1IckPhIyYUjEh9Kuq/iIJIc4lCUIvIGIjyjPJD4ASEvZquLDNplplAfo5UeWIS9SXY38kCM3zENkXOUHCREfvlW1PUJMUHqQkaOsJWj7kR1q/Y5aVyM8EnmKwOgE7doIjwhxCEtm4dGlScJDnL8jThP3r9t2Mk2cA0TsV3wYChceeWWH1JZBeDRVdpQpObILEb3kaHLkR08wyPuWRYKE5auTIE2TH2rUhyo+AHPUR+/gyOn9Rn2kTnIqSIT4dRBkQlRSFB+JctF7qtUw8RG9ObKID2GyUy+OBPFqIT7CvI5RfADmyUyjvKh9QBESmqgPIN9kp0UvcUsIqR+UHmRkqGooSz+yI9yW3A+nOT1qJjwixKVpMwuPuJH4Srsv4RFhFB4aSRBLBq20gCatfOGRV3aIZYoSHsMoO7IICkqO/jALBncJEpaHUr4cCaKKikbID0VYiH2L2ugn6sMkPqRjD+VYGh7GJiYsVdpLTjDqvqRtUppE5ULxIaeF4qPba1lcAMl5QgTxAQi/5xEf3d9j8YHe/rmKDx+9pWwjCSKu8tITHoMXH7Y5PaI8AJIIcRnyApQ32WnaEreEkHpC6UFGjizRHVXO26FGd1jn7dC0qy5NK/ZXnbBULK/KDV2aaYLS6H81TZyY1Fl4RIgSoEDh4fmysBgl4ZFXdoh5eYVHE2VHEySHN0TDW4JUqVEPCWISFXWXH65RH6by8oHo/t+S/ksMd+k36qNM8ZEsh1hcJLcBQBsNohcfuZez1fwuLlkbi4/ozZJBfADyUrbRK6cTHwAk0SFKDgBJGdIVGWKeKj7CvLZWfADpc3qI4gOwD3kB9PN9RPn9zPfhGvVBCKknlB5kpChqVZaqZEfqnB4G4RHW0wuPTtAyCo9O0DIKDx9JudGX8IgQxMYoCA9VYCT63u2vKEfUdnRSomjhUWR0R1NlR7Y5NvTpZUqOYZjXw295iX0bhASJyqdJkKbJjyxRH6ZJTota2tZ1ktPESi2CSIjLAJL4CNNGQHyI6d03jTiUxUV82CI+4IVzgujEByBHc4jiI5p/Qyc+enlJ8RG1aZvTIzmUxF1+9DPkJdxWO9OQF4oPGT9oJa5xy94eISYoPcjIEH1hVTVJab+yQ0wrW3gUNaQlk/BQxMaghUcvHfp2dfXVcr6tbrHCoyjZYavbb3RHnWSHi+ig5KgencAYhAQxlTe9zoAQ3QCgJQyR8KOb8DrIj3hfion6UMVHd+fF//qe5DTah/g+P/Ak8WGeZyMpPuLtCeJDSpPKCeJCrdsVH3F6XcSHMp9HEREf8KLv3+RQF5GqxIeYD5QvP8TrvnCIjvt8H1yyxV9IggAAIABJREFUlpD6QukxAix8az028TzsNDYB08bGBt2dgVFH2eGSV4TwqGIOj7oJj9RJS6sSHhqBoY0uSUgMjYwoWHj0KztsdXRli5YdRUV1iL83QXIM09CWiEhsNEWC+MrvWlmBXj9ard77EEL/fJQvP9SoD93wFdeoj4TAKDjqI1BEBMQJS4U+mOfZECJEgKRk8NFb2SVKk0SKPEeHXC7qcu/4pokPsb91Ex9lTG4qvOULEx/xHB+Q5QeARB5QfORH+Le7/Bhllm/ciMc3juN13/BFR8gAofQYAWZM3ATbtmmfI4qSHf1OUGrKE/PrJjx60iOn8IjTKxQeuvkwTMJDJxFS6gPdGx5t3WKFRx2Xoi1CdgxqCItNdpQpOUzSIqvkGAb50RMYerHhIkGi8lVJkMzyw2XoixBFIH4bSOJEnIYzkhEO8qO3jW5VRWQUFfVRxNK26lK14T4o83b4XqKcLBSECBEI5eJtBbL4gCpIgm6abhLT3sougEV8iPWF/pYqPrrHM6/4iN9afYoP2+Sm/YgPwB7dkUd+AKEAKVJ+jDLTxsYwbWwMyzdsxO1vrYcPr9JlZMXzKCEqlB5kZBiE7LANYUnLdxUeRa7SIs7fEebZhYcawVFn4WGsi/KFh7httT9pE5YOOroDyC884r9rNl+HSXZkG95CyVEE5igOdwliKl+GBEmKjV76IOb9EL+6xIerccCGTmikiAxRfISNdNtMifooamlbdanaSHwAyL2kbUKQiOJDJyPCkJHUJW17W4NxZZjKxIfnh9+7cBMf0UsmSY7um14nPiKKEB+9tjIMdYHb0BbxukpX17baSxHygxBSTyg9yMhRN9nhOsSlCOGhzvGRJjx8pdwoCI9eWX393raQTEuUK1B4OMoOoLzhLKMgO7JIjixDVUxt2MqPkvxIkx1lSJAwPZsEMYuNXnqdJj3tyY/w/7Soj+Qkp8VEfUTiQ964cny7/4vzdwBq5IcsPlxXdkkKCSFiKOeStpH40MkLtX4dxUeUbxIf0YsViY9oglNALz4i+l3ONipfxNAWXd2y5QchpJ5QepCRoUmyQ8yn8ChHeESSoQzhoQoMrWyRREZye3mER7+yQ2qrIOHRRNlR1PKxZUsObwjGTQfdu+6ssqMICRKmq/3Rv26+IE2aJj+iev1GfYiTl2aN+ojli2PUhyo+4tdHFB9IChIAla3sIg16GULxEc3z4So+2oIAKVp8AP0NbalSfpAe4vVoVdsjxASlBxkpBiE7+hUhZQkP3ZK04d964SHO3xH+P7zCQ5UEUn1BeOjLwVw3g/DQDWdJRIgIbQ46ukNKU+o0VXY0QXJ4QXOjPgLPS+xTHSWI2E7T5IdJfIT71G27u6/pUR/d7RQU9aFbASZqPxIfUV7dVnaxLmmr1G+S+IjeEa7iI6Js8RGRJjjShrZUIT8IIfWE0oOMDKLwyCs78szJ4VLXlOcqPBJL01YgPHzh9/CX5gmPpLQoUHioskErMjRSwlF49Cs7tG0VHN3RRNkxiDk5dOmmKI4mSw4VdV90EgQIRUgdJIgYBdIU+RFPeOpLfxYW9RGIkSVZoz6i45o2VEWQBHLkR86VXTyDIPHhvLJLrwdRuXBLTRMf4SHze/mq5EgRHy1VcjiIjwhX8RGVBYqL7rANmSlCfhBC6gelBxkpmiI7xO1XITx0S9KG/4+w8EhIA2hFhi5iI4/wSERxaPpY1VK0abJDW9cgPJooO8qUHFmHqpgkxzAMbYmIozs0EgRI7qtOgoTpyZVdypgTpGnyIy3qIxIaQPaoD8TbKibqwzhUpSsJ4nRh+EokPnSCRLuyi2lJW4SvuyQ+EuV64kO3pG3TxIcXiY0M4iNsP7/4EPNcxMcEpTxQjvxQ6/YjP0iIeM1Z1fYIMUHpQUYGUTBULTsyiRCpb4MRHrolacP/HYRHRFOEhzIcJa/wELer9iXPCi06cZFVdoh5ZU9WOoyyI4vkKGo+jqySo8nyozeUxTDEJYMECdN1oiKbHEmTIE2UH/FSt8aoj+42Cor6UMvLBxTSxtWoD9iGqmiiQSLxAQhRHBpBEkmFbkovQkRox3llF9OStnUWH4A0lCWL+IheqUh8xMvcVig+ADjLj16bg5MfhJD6QelBRoqmyI6wf/URHkEizSI8FLHRZOEhDmfRbSNMK1B4aMTKoKI7pLScwqNM2VGG6NCv0FLUEBZKDhWzvChPgpjSTXIkIUHkPYh/awnrcPpSur6u7nlkmfIja9SHKj4A96gPNUok8wovtqEqghQBZGEhDV+xDZfpihJRfESkrexiXdIWqK/4UCSHTXxEB99TJccAxQcAZ/kBZB/aUrT8INXy3IJlWLlgGcbXvDXorpAaQ+lBRobecJFmyI6wnLvw8NW0MoVHdPE2JMJDKy1yCA+teBDKqu3kER4m2ZFoD/WI7ui1JeTlFB3q72XIjqZIDi8YnovrwEuRHQOQIKZ0ObKj2OgPEVmQeEK68L5tCbfrQl8j6SIJjYxRH+okp0VFfUjzcmiiPhKTnIriIxYSsviIyLOySyQ+klIikidIiA9xe0MhPrrHJRYfnt/9Ti9efIT9QWbxAaBR8oOEdAKvkuEtv3fkXvi9I/fCa//vWdx/+D+Xvj3STPjprDm+7+Oyyy7DnnvuiUmTJmHLLbfEzJkzceuttw66a42jg7YkPMaD3soo42gnpIU694a4hGw/eeIkpepQFjm6o0HCI/4ZPuHhCT+9tGKEh+d7CeER/wj72ur+2KI7xPbi8t2/o/yWH/6o5YHihEe8jagvvhCd0e1XVF6tk0yXIzui301l5PQgvvkU0z0/iG9W5TK9dPFv93Rf+onTg0D6SS1vbMeXfgCEd6xN/zHtW65jlPVYZ32N0987Lu9Bl/eyuYzhsyJ8tqK6vc+gp/2cRvXC9Gyfe9dzi+lcJG4j7VynPQcL7UV1e79DKidtS/1O6Z7LdefgOM2Xz/Xq9szzQdm2q6nrK/toqJv2O4KW8v2rpAde/N0eCN/Nuu/7QL1+CAyTnUNO6wQp1yWWlefk653wWgiQr5EA+RoqcX1luabLm6fmJ6/h2omHaoSQ+sBIj5rzyU9+EjfeeCMmTJiATqeD9evXY+HChVi4cCG+853v4Atf+MKgu9g4qo7sSOSlRHao9fIIDzWtdOEBNF549C48DfXiNGjSkv00Xvgay+mlRktXB5o2BxTdIR4zU3RHUyI7BjHxqH4ZWn1ZGNpoNOo+tVra/Q+8VuJYDSISJFAiPMqM/Chi3o9o2Isa9SG2kTXqIx6+UlDUh7p0bTzcBZDa0EdieFI0iPDq9YavxB03RE+oQ2MCTYSGD5iWtG1cxIfnh5IDkObwENOKjPiIEJer9YMWXCM+wrxuNEVK5AdQTHRHP5EfhJB6QulRY6666io8+uijWLhwIT784Q9jw4YNuPfee3HaaadhxYoV+PKXv4yjjz4a73rXuwbd1UZQJ9nRUYKsRNkh1i1KeIhL0kZpcR6FR7rwEASGrn9qP7MID528iIWD6Rgo5aWyKbLDVqffuTuGQXaUKTn+f/bePnabs6r3/c798PiUbmpttsJRU9BUam2igrxojIbCsWm3GzjJllBNCI3gMTHSk4gS8Q9LjLRJj2hAIDSQxj/8hxfTpvLSbsGXx8oBYZ9QySYtJRtNDqHIprvQ96cPv5nzx8w1s651rXXNuubtnrnvayW/PHNf7zPP73fPNZ/5rrWSXVWOwLXFmXNxkSAIEJ6zBEEANJld5oMgPthYP/xwbi/O5aUFGi74ZzPG0FgfUtBSa6wPrjXmD/0OfLgx+jK7VAxYUPAhtmvG6cAC9MwuRQeLTPDiiMGH+8/ZcciBceADgAg/AAQBT+u2+4Ef2TorsXD2luzAkC1iGXqs2G699VbcfffduPTSSwEAF1xwAa699lrceeedeOELX4hz587hrrvuwhve8IY9r3RbNgvQmBh21GPOBzyoLNWV8fgcJYEfFHjwsrUDj34JMnmAFwBFH/Dg8EFSg+jt4sBDUn7w9q6t1G4qdQftkwo8tgo7ZAXGeBVHPc40kOMQ4Ecbz0OAGgCSIAggAwwVjgj/n1VR9EKQrcEPq+qDgw8gQfXRzjmR6sPNB3gBTmOZXbSUtgBRcQjtfLCATiFCxmkBiZLSth0PFQovTww2AT7c1S5YWQkffLj/ewo+2t5N2a6o6n3HxODDlQE+/ADgqT/2BT9yutps2dZtGXqs1B544AFcc801LfCgduWVV+LFL34xPvvZz+Khhx7aw+q2aWuFHeGYO69u9cAjKFseeHT94fcfCDwKdk48fgdvN0eGFgl4WGEHMJ87yyHDDv6wmyHH/KbCixkhSLRcUIPQtjVE2Rb8aAOe9qg+giCnI1UfHHw0kzVraj4Kqg/u0sLBh2s/dWYXiioo+KDrQlX44EOEEhXqqZrx3fpqDQWZBfsDH+4iNpADReUpOmgZBR9OFULBx65RhYCXTQg+AHjwoxtjPfAjp6vNlm3dlqHHSu2yyy7D2972NrX+uc99Lj772c/iec973oKr2r5tCXaI/2bgEbYbATyoO0vYLwF40DUE6xoHPJZWd0h9hgKPLcIOCXSsDnJUB/hGsai/G9cMQSgA2Rr8GKv68NPgNpe6R/URpra1qT5aIxCDgo85MrvEUtqGYKL7v1RT2nLw0bapmiE5EAFptwD4aAHI+sGHa0vhB+ADhzXBj2ydleiC3C41X7ZsmmXosVI7deoUTp3SqfE3vvENnDlzBldfffWCq9q28QjcUvmouglhBz3OwANd+YLAgys2gvViOPCQVBw7oY80DocXtH7f6g65bVu1GdixFchxCMqPFl7wc1whBHG/I1uDHxx81A0TVR/tmNOoPmjwUgpLgtS2DHzUa4QHBNr/IwF8VAxYeO4rAiDx1RfMNUYCJPWT/zjwwZQmGXz44COm/ADWCT+yZcu2PsvQY4N27tw53Hvvvbj++utxySWX7Hs5m7ItwA76OQOPCLSYCHhwsDAUeAT15FxSgIcUv2MudYdXNrG6o/vcTjUZ6Kg/zw879gI5UgHHllUfxSnxvKpiJ5+X0H5pCDI3/ACAHYmyQN9c0rsEXaX2mNUJLprveBq8tBqv+qBuK+mqDzTXkMES0p6CD1pmyexSSOO05md2UeOEtGUh+KhHwWzgo1llBh8FD2S6DfiRLVu29VmGHhu022+/HWfOnMHNN9+876VsytxNaQuwgx6fCO33AjwI1OjKCAhpyg4KeJhARj/wkIBJ+7CwJ+AxFHbUZWBz8Las3UZUHVNAjtlVHFuGHNwS1R1SezW9rQZTRkIQqS8ANm7/214NYEyt/nD1muqDgw+6tj7VR9tuItUHbx9kZCFlU2Z2kQAJdTtJSWk7FfigfWcDH84o+HAXdI/gA6j3PVqMD2D98CNbbWW1cPaWrLTJFrEMPTZmjz/+OG688UZ88IMfzCqPAaa5uMTq9gU76Pj0xjE38OD1RwE86JhCP75GDXhw8DAUeEgAYyzsEPvOrO7YIuxYnauKBjkOwLWltYEuLpO4xEj/rzsdmvjKjmXUH2PghzNN9REEOR2p+nBAw5vTqPqgQU4p+OiuvwA+BDeYlMwuDnxogMSHC+hNabsp8OFgRltW+FCEgI/6nLuyOcGH60PBB6/fJ/yQ0tXyumzZsq3PMvTYmN1www1461vfiquuumrfS9mcuRuZFXYEdQvCDjrHMQIPZ2sAHjLIIG2j7ZRzBJIytOxL3eGPBTYHlL7+v5uFHRuBHMWGlR9VAymCc9snBAF61SBbgx9DVR9e8FKz6sNdy3GqDwo+gowsBGLsgP7MLto4DfgAME1K25nAR7PAvYIPBzb2BT4ADjGWgR8S4OBzS3XZsmVbn2XosSG76aabcPnll+ONb3xjUr/bH38SuwJ4VlHgWc3m7fLTz8AVp0/PsczV2hSwA+DuJvPBDiAEHrzPoQKP9uF/QeBB1xrMqQAPScURU4howEMaR3JnscIOYDp3linUHZuBHWtyVzlAyMGNn8smIAhRgWwBfoxSfTT1Y1UfDnw0l8+/pILqg4IP13ZUZpemSyGNAwIzBqa0nRt8dJ8PG3wAHWjg4APA7PDDjU/hhwVw8Lpjs/vPn8cD578LAHisLPFYVeG7VU+nbNn2YBl6bMRuu+02PPLII7jllluS+/6X//BMPDuSCebYbCuwg7ahkGM1wIM9qB808KDzB2uyA48hGVqWUneIbUeqO7YIOzYDOQ4IfnQwYgMQpJU2bAR+wC8bq/pw7QC76sNLWWtSfbgB4AU4HZLZJZbSVszsMiCl7abBh/sfWwH4cPUcfLTlM8MPPv5Q+HFsdsXp08FL1PufPo+7nzrn7UmXsBzTI1vMMvTYgH3kIx/B5z//edx6661i/cc+9jG88pWvXHhV27OpYUc45nSwg7abEniU5Lg+yMBDXStswGNIhhZXJ43TBzw8NcdEwGMo7PDaKsBjq7BjVZDjkMFHcUo+j+LUaiHI1uAHV31w8AHYVR8d0Bin+giCljaDc3cXDj5oOw4+pMwuWkrbYBzXh7qvCMoQCg0OAny0JzwefNSXtJwcfFD3l6nhR9duOviRH7uzZVunZeixcrvrrrtw55134i//8i+DuieffBJ/+qd/ih/6oR/aw8q2a1qQ0rXADtp278CDlgNHDTyCegPwaOGDdu6sTAMeU6s76jrIbSdSd2wRdgTgYm0qjkOAHNwU8CBnagnPvypOydc3Ie1tPY6tPc0MsxX4QVUQ9LOzsaoP148O3qv6cGtx7RmkqOcTwIeg4tBS2rqJVMUGH6c1e0rbgwYfzozgo2jAxlzgA8Dk8CNsNxx+5HS12bKt2zL0WLHdc889eM1rXoOnn34aH/rQh4L6k5MTXHTRRXjwwQf3sLrt2Zywg9eNhR30mAOPru8BAg+En6cGHlSxwc9HBhn9wEMCJn3AQwIZUsDSqYHHUNjhl/n/asDDPYBpoIO24XV7gx3Cg+/qIMchwo8I7IiVL6oGIQBkbfCjWWB7tIMPP6yqDwowzKoPFwx0ItVH67LCwEdzeUdldmnb1//pAfgYktLW3cgqzAw+wNvNDD7aMswOPupfr12vq8sU8KMDFdPCD7fH/B5kc1ZWBU44RJt5vmzZNMvQY6X2la98Ba961avw1FNPqW2KosB1112HCy+8cMGVbdus6WfXADtcOw48SuwOF3g0x2sAHl5bNn8K8OB9LMBDit8xFHb4dfyzDXgMhR20bA5VR1A3EezYDuRQ1rkpi8OIVUEQ13YG+AGABdLleozQaAtJ/eEpPwJQIc/Sfn2OVH048EFPy6z6IJBCC3A6JLPLkJS2AILMLn0pbWcDH26sAwQfKOr2GvgAZIgxBH6EKo3h8ENKc5ttefuff/3f8D8/9t9w8oj+zJQtW4YeK7XnP//5+Pa3v73vZRyUjU0/uzTsoGVDgYezSYGHEX5sBXjIIIO0jbZTzg824GHN0LK0ukPu6/9rVXeszYUl+ByDHRlyLGD8nNYNQSzwgw9f0K99jafNov5oHo4b+DFU9eEFLzWqPtoYIUNVHwxSuACnfZldqsKHIRx8uMVaU9pKmV16U9puEXw0vy1Tgg8AScFNNfDh2uwbfljT3GZb3n7g1S/GD7z6xXjsv/9/+Jf/Iz3hQ7bjsAw9sh2VzQk7groRsIOWjwEeyVlaMvDw2koqjphCRAMe0jh9wENyZ+mDHbT90uqOukwGHmuAHSbQsRXIccjuLSuHIAU5lh1LfACyb9cXrvpIivXRlncAY4zqgwYtjak+tMClHHxIgIQufh8pbTcLPljbKcDHkKwuEvgIApmOgB9SjA8r/JDS3LrxOfzIVltZFcH+d+75smXTLEOPbEdj7qZkhR2AHqR0TthB6zTg0fU7HODhbC/Ag8IWXq8AD0nFMVVK2iHAYyjskNvCbztQ3bFJ2LFE0NHJIMchwI8eeDEWgrg6Y4YYoAEhfcFRmcpjTteXUfCjW3w99chYHxx81IPZVB9tkNM+1QcI+GAP8VOntO3+z5uHfmkc18eBD4SAZCnwUQ/Bg6luG3zUv0K7XvDh2gHD4UcswKkEP/rS3AIy/MiWLdv6LEOPbEdlWtyOtcEOWicBj1rpcVjAo3CAYmXAY0hK2qkytEjuLH5g0XHAYyjs8MviwGOrsGN9Ko4DDGpanIJ8XjqkkK/bLtIedkBiVYNsDH70qT4c6AASVB80eGk1XPXh2gFd3A4HPppLZM/swpQhrp6W8ZS2IriQxnF93HM/AyRef9p2YvABFOFYGwcfgAMecfABKFBjRvghpbnt2oXw45T2vZ4tW7a9W4Ye2Y7G3A1vTtgByC4q0hhaO0/JkYHHYsAjUGQYgEcLLRKAhzVDCwUeU6k7vLXOrO7YIuwQQUeGHPOZqu7g5zizGiQBgngqkI3AD6pyaBbVlPsKjbGqDwc+6Ji9qo+mfQdJmmoHNko5cKkls4sXl4OqRap5UtrS/hl8TAs+gBBwAJ1CBIirNrx2M8IPvh88dquwC17uzT1ftmyaZeiR7WjMmpElCjgWgh3AEQIPSO3WATyCegPwiIEMDXhIAUunAh5DYYdUZgUem4Qdm4EcBwQ/eqHGeiCI+13ZGvxIVX04wEGuXmua6sMFOR2r+ig52CjlwKUUfEiZXYKMLBx8CG4wbXtABB9aStvFwEfdDccGPgBd3UHLAR1cBO164IcEQiT4wTO9ZMuWbZ2WoUe2o7MtwA6p38EDj6Z8n8CDgwoL8OB9LMDDmqHFgyOJsKMug9dnH+qO7l//QXT1sGMrkGPLyo/BUGPPEGTF8IOeX+UBjjTVR9tupOrDgQ9/7rjqYweI4EOK37HvlLaLgw/X50jAR/370sCKheFHTAUSy/SSLVu2dVqGHtmOxuZMPzs17OhTeDhbBHhE4IdY35Z3x4cAPJZKSSsFLF1K3SHVDVV3SLAjGXQAy8KOFPeWDDnG22RQY0EIQoOiLgg/xGW3yyf3BA+A+PeYsaoP95lan+qjDXKaqPrg4MPNtbaUtlsCH3W1NkfX131LF8I5zAE+6l+TnQg+3H/KrqgWgR99aW5dXSzTS7bOcvaWbGuyDD2yHZXNCTv451g7HrejKy+CNhLwqAjk4MDD2T6AR6uC2DLw4OdkAB6SGmRshhbJnUWDHV67hdUdtEwDHrOoOoB5YIcIFeYGHEP6HBD8mApqtH1mCo7KgMfU8IObVzxQ/eHcXrjqw5ThJYjL0axrpOrDC14qqD4o+AA6FUeQ2YVBCktml9aoWgQYlNJ2a+CDq1mi4EOAHWPAR7MYEXwAOxTcrUVydYEOPyTAIcEPSbXRl+bWtbOmuc2WLds6LUOPbEdjU2ZkWQJ2uONU4FHxsqWBR2MpwKO1BYGHBCr6gEdfStq+DC1Su1iGFgvwGAo76PFc6o69ubAAI2HH2lQccnl1AKqPYhCkUNoD48FJTA2iAI8k+AGHFprp2dxTu75AUX20849UfTjwQa9Yr+qjDZwqqz4o+KABTvsyu3Tgw3dZiWV26UtpW3EIIAGU5n9yKfBRNy3SwEfTZ1/gw4GNoeADQBR+xNQdsUCoKfDDmuY2W7Zs67QMPbIdlS2VfnYI7AjaCcebAB4MKliAh1OHrAV4eOUIgccSKWmpWmSpVLR9sMOvswGP1cfriMGODDlmN35OxWIuLkMhiAF+IAY20tQfU8APq+qjJLE/YFR9UDXGGNWHBD6Azj0lAB9CZpc2VkcJGXwIMKMvpS2P5cGhhp/ZZRnwUYiAo/4/U8EHVYpsBHwAiBxPDz+mSnObrbOqKhZ1OakWnCvb9ixDj2xHYxZXlrGwI1Y3DHYUqwYe/OGfggup79qAx5pT0krAYyjs8NoupO5YHewYBTrWBjkOD3446LBqCMJdYTyawcZcUdBTL8BFXSAuuwMY41QffuaX5p7Vo/roYEoVxvlg4AMgYEMJXDp3StuCXMq1g4/6nyIAH3T9awQfQFlDDU/p4UONFPghQo0IuEiBHxyEZMuWbX2WoUe2o7IpM7IMgR11XSG3Y7CDlnHg0ZoFeLC2SwCPDkrYgIezfQEPSfnRBzxiIEMDHtaUtBR47EvdUR+PAx6bgR0BXNgI5DgE5UcPdFgVBFEBiEH9sWf4MVT14cBHfZY21YezwaoPEudDBh8MbCiBS/eR0tbVzgk+3AKs4IMDEwc+krK6pIKP+mRN4APoVBwS+ABggh87N5YCP6JQowd+pGZ6yZYt2/osQ49sR2PuprRURpYxsIOWS8CjIg/6vcCDtO1NS0uPFwQeLUQwAg9vrTMDDw43LMBjqpS0FGRYYYdf5v+7ZOyOrm4rsCPiAuPZdEFKjxJycEuEFKMhSDvHmDgiDGysHH5w8AHArvpo/h2r+mjdVmBTfThAwsGHV8/Ax75T2g4FH02hGXx0Y+wBfKDrFwUfTT8L+KgAFA3UiMf4iMMP2qb+fbLDj6kzvWSrrY5Lt9w1WXKubNuzDD2yHZXNFaTUkn6Wtwv6KMAjLJsJeJCH+bHAw9lcwMOBjCmBx1pS0krxO+ZWd9CyqdQdLbAgoMMrZ8ergB0zurYMclU5QvcWOwSp+0jXNRocNWkOzcXlpKtfK/xohgLg/eoDGK36cOCjHroDFXSqQPXRzm1TfXDw4QZdc0rboeDDawdpbIwCH03jacAHPZ4RfAD1/iaM8UEUHQnwY0ia2zHBTrNly7Y+y9Aj29FYq/SYMEjpXLAD6IBHHQjKADzajvsHHhQOyGNCbDcH8PDHHgY8JDVIH/DoS0kLpAOPHT2fiYCHFXb4ZenAYzuwYyLXFgwAHclzHAL8GOp+wvt05fS6F2p7yxwRF5e1ww+g/vtrlB+a6kMDH9RCdtLcg0aqPjj4qCepRPDRKkUU8JGS0paCjclS2nJwAPc9PwP4aMeOg4+w3QrAR72YfvCBEkVReeADUKCGAX5IaW5duzmCnWbLlm19lqHHEdjZp87hTFEhz4O+AAAgAElEQVTg8tPPwBWnT+97OXuzsXE7xqafDfr0wI76WAceXaMQbESBR2OpwCPs1w88ioqsr22HoJ23hug6BJDRAzz6AMzWgEcf7KDHc7uzSMFKOfDYDuxgdWK90s41z5BjgCkgYqKYHbo7DO9DygNlB23P1rF2+BEBH/XSCCBxawWSVR9TpLZt4ceU4IM9zFvAh5bS1gI+6n860GACH22fOPhw7Sh0aMcg4KMpRNGMlwI+2jlSwQfrp4IPIfaHAxsx8AHABD92ApTogx8pmV4s8T6O2e4/fx4PnP8uHnUxharC36/ObDl7S7aYZehxBPayC87g2adO9Tc8EpsrSGlKRhapnN8YAuDB2lEIEgUejfVBjgA0sH5SWtq2vgd4iIoOad6y6xOOrZwDbMDDtTlk4KHBDr9sWuBhUXcsCzuAUeqOFBcX7A90VPTabdyKgn73KSBiEKAI249SgXDA4eo98CKVl2j1EgvDD+rywt1dpgpyqoEPMlVyatutgg8JamjgA5AgRgg+moGaax4BH5AARxr4CNLZujX2gQ963K65H3w4yNGCD9R7Gw4+6vJ++BEGOB2W5jYl2Cnvf8x2xenTuOL0adz/9Hnc/dS5fS8nWzbPMvTIdjS2xiClzri6w6/rHvRNwKPtuGvLBgEPBQCMAh5MqaEBjxCSKOsikIG301QgvN+hA48+2OHXTQM8JocdQbsZXFn6VB+u2eyQIza3tqneogKEggjy/54MQEjd3CoQk7rDoPpwYxUCICHHU8APZ32qj6Hgo5mwOTM/zsdod5eNgY/mQveDD0BVigTtOKwQwIdr58EL2mcM+CDrSgIfbm0OfDTXphd8kJgfHexo7nlcGYKIokNxean7DMv0Yg12mm15e/jjn8XDH/9nnDz6xL6Xkm3FlqFHtqOzpeN2xBQgGuwIFB4x4NEOQMBHIvBwlgw8eD/YgAeFJfUcWp/C7enCdVHgwdppwENSnADrBR7e9cH0wGOO+B0B8NBgB6vbW9wOQzyP9ak5tgg5uGkgwj/nDoJobjC8bm4ViBVyTOPyAtR/D1ULRUq0kg0PkOjwI1B9TBnnY6S7Swx8ALTNesFHff05nAjBR90OqGq6oIKPoJ0CPppCHV7QPgykyCoOG/hw8yaBjwZmtOAD6IBIBHy0UKIoPfhBVR9ARNGhwI85gp3mIKb7s0v+88/hkv/8c3jiS/+GB17zR/teTraVWoYe2Y7GlojbkRqktD7W2ijAo23QgY1U4NGNoYEGGXg4U1UXCniIqkNKrU83XgBJFOARQA0FeMTS0q4JeOzI9fFT2HbXbBXpaFOBxxpgR6RuU2oOde4NmAlErE0FormwzAs/LKoPAB3cIOsbAj4A7C3OR5u1ZRfP6rIW8EHL6/8jH3zU/y9dnQY+mgYEYijgA833OcvoEgUfcKxBUWy086MXfNBzBFl3Evig5cytRVJ8UOCgwY+oosMAP6aK95GtsxJFq/xaar5s2TTL0CPbUdmUcTumCFKq9ZeysqipaQ3AoxtEAQ9G4KG2pcCDgQcT8Gj69AEPDjU04OFBEV53IMCjD3bUZdMADxV2AL3AY1nYAYxRd8iwY2Vqji1DDm7JIGL/KpAiCjJcf0O8j7bP9PAjUH30gA8A3p8UgMnjfDRn6E3VqUV8KHIw4AMI1ByujgINgAGKPvBRdzCBDzd2ByIADXx08yMJfBTkOAZBouBDcGtxY9AyP6VtP/wYG+x0SLyPbNmyrdMy9Mh2NJbqyjJ3kFK5XQgogtS0GvBg/egxBwcW4NGNgXhbBXjU83TD9AEPuuY+4NEFJQ3XFqpACPw4EuCxlvgdkuJjPUFKNdixHTVHdQBuLsUoELG8CsQGPwzlbt6Zg51y8EGt/bhEnI9Ed5ctgw+AgYYe8CG6wijgIxwbIvioB4KYyjYFfABor4cVfMQgiHccAR8gqWy7MgSqDwAs/kc//Bgb7NQS7yNbtmzrsww9sh2VLRG3wxqktA92uL4q8ODtq50JcmjAw5kX4HMC4MHVGRrwoGuLrVUMStoDPFz5MQOPJeN3DIYdQZ+JXFmGwo4VqjkOAXRQo+ejAhAgCiKA8BrPqwKZEH4MDXba9OmDH+6bsv1r31Ccj62CDzFAqQI+moNe8NG2gzQ2OvABsDHGgY+2b3M93DpSwIcHQYSUtR0EiYCPZn0a/Jg72GlqvI9sznaLpqwFlpwr29YsQ49sR2PuprTKuB1tQQR48DYUEliBR2NcWeHKUoGHtG5LX3Gs9jhcqwQ8xHYR4OEsAw/pXx92eGVjgQd/GF1JkFIVeKS4uERT2c6t5jgk+NGAC3ZO86tAaB+DCkRwVamqExbvo+k7d7BTtzYt2CnQwQ9F9bHmOB9bBR8AAsAhgY+6qACEVLYcigTtEI7t2vXBi6bQ75MAPtr1C2N4x1bw4dYvQBAPfLB1W+BHX7DTOeN9ZMuWbX2W/zqzHZVxdYe7QZXYtTe1siramx1t4z53x3K7Gla446K9CfOxAnWH22hUjIwLYMMMPBpToUUi8ODjFRX8MRRY4c1bCmNJIIOPpwAPPq8GPIqqgxluvK0Bj6L52ZX8czVp/A7vpyonAR5FddIBj+qEtFGOUaJ9+PPKT9A+LGrldA1tXVNcnRDgoY1F6/jDsFxeVaX3I7dn66M/dCyctD+WubdvyjXVroN67eRrrf/f6HNH/8+8ev47hbBPMKY+lulvQDn2/saA7u9P+fsM/o7J33dRlcF3QV3HVWDy90r4vYO2fFdWke8y1w5tP/r9uasKVi9/v6Z8P7t2Xj27D+z4PRL+/aBtowF1DeRXTC1Z+u2gteP3vFLrE7snkz5krLAtxPOK7Se8c+BrrgpY4pK1S6J7InYt+QulUth70f7evo/s4VydNJa31/P2iv6eLru2ZHP26KOP4lOf+tS+l5GNWf4LzXY0diLd9MgNrK5LvAEqsKP+LI9Fb8Ya7GjbpAKPxrSNRirw6BYdboRU4FGFfSXgEW7IdDjjbwRl4CG6vQjAIxxvO8CDlknqjvqnQtE8WEwav4MAEA+E9ACPWWAHnaetQ7SPDXZID8AKtADSH6QNkKOyzM0f+g/hp+ec9WukX9dx/2/amsDmSv0dA4L/y2Ae3mcE/DCAD+9vuvlbp23D7wU7+Ng130cFAR2urm7rPqP9vCXwEfRTwEd9zf3+Ivhg/VTwwcbQwEf3WeljfMExCHz07E0s4EODF/7eqYjut7rjfvjR96KrPVbgRwYfvlXk/3Cpnz4ryxLvfOc78YIXvAAXXnghLr74Yrz85S/HJz7xieTz+/KXv4zdbhf8XHzxxdjt8u/C2iy7t2Q7Gltz3A5/LLbx4MCjbdj164McZuDBxtPeGlmAR7Dx0oAH28ipwCOy4dNS07rPEvDg/bcOPOp/beqO+jgBeJDPKfE7PNjR1mkPfOwB1PyQGC+3v4FH5LMbiz8kx9uHD+GkKtVtRRmr4tdtQ1bQ9y70/CzuK6R8nlgg2twn3Wc3F3NVGRfvw82hBTsFom4v5Lh1ealKLBHnQ7Kh7i61W8r6XV2Arr7tVzXjijE2Ym4sEFPZFhV3YyHtgrGrbmtQcVeYCkEq26pA0bjOhOWkL3eHYa4u/jxo16Yet64su+4GVi8CgQtM4Wdw8dxW3L1+oXgfQTYYId5HtnXba1/7Wtxxxx14xjOegZOTE5w7dw5nz57F2bNn8e53vxu//du/bR7rHe94B4oiBC1XXnklXvGKV0y57GwTWP4LzXZ0ZnFlCWm/BjL6XVk0ZYfr3x0XMvBoG8hvQpKBR2Py25fpgUdBHurDPkxiy9YkAY8AaijAg79ly8CDua9UVf1DpetTxO+oSgTqDvevBXho6o7EcvWtezC38qYdGOYWMZeawxurbH+2bOp5rEQFIrsp0c9kDqFdmrrIWg6ocW648gPo/habv02/Tv6s/v2T7wn9OyTd3cX/jPbzVhQfqfccyY1FvL9KSpG++3Bz7voLj3DOmOJD60vXzo+jig/huPfFjoMxlf8mP3ADFtpZXF5cu+7Y3+9J+0LN5SXbuu39738/7rvvPpw9exZPPvkkHnvsMXz84x/HZZddBgB4y1veggcffNA01je/+U3ccccd+Nd//Vc8/PDD3s+//Mu/zHka2QZaVnpkOxqbKwWtpuzgdTHYIbWxSD81yCGNJ25kPJlqOvDo5ukOVeBRSRs4pU+knagC4XWC3PhQgAd/SKiPh8fv8MvJoAPjdwxWd0yk7AAwQN3hgw7ZVqjmiMy9GStcEFOmuFiBCgTwfx9qFQhRaBjVGosGO3XnaVF9JAY4Bbppx6a1dYqP+gzcW/PiYBQfOyBIZRsoO5iaox2DKDkA+P0qqqoI27VqihKIpbJt09FWtI1B8UHVIuS8ZBVHtzZ3rKk/aOYWqt5olSCkLBaslM5J1SGWYKdRRUe1M6e4zdZZDZqWuy597i233nor7r77blx66aUAgAsuuADXXnst7rzzTrzwhS/EuXPncNddd+ENb3hD71zvfve78brXvQ7Pfe5zJ1l7tvkt/4VmOzpLpvhzx+1gberP/cCjaxseqxBhLPBorPfNkfssAA9J0aEpNQLlhwQ8HNSIAA9xjAMAHjx+h/vs6vx/jcBjZPwOv/xEPt5rkFI6Fn0rvxE1h6pc2KiNOf89qUD8NuzYpDwC4r/PPb+3lr8lRfVRf+5XfMwR50P/rqqndwFO62O0dVtTfLjPUbdKYQ4R+GtKEd5Oun9TxUcwBruPt22kthG1SCnMq621Z98SjV1G27b7qf3F+4ipgbOt0x544AFcc801LfCgduWVV+LFL34xAOChhx7qHeuJJ57A+973Ptxzzz34/d//fdx99914+umnJ19ztmktQ49sR2OWm1csmrfmimJxZVFhh2vXHu8Gg43JgEdj0kbILpU1bMgqfUMmZWqJAY92DGNAuUMBHnUb+eHCf6hIAB7ks/cwVJ10D0HeA1UEeJC+zQRIgx2ACAlYnzGwo+5PH6ztcCL6QJ0MTEr5QT8KRk42/6NeG+86K9cmcj1VcKHNb4IpTVPV7SUVfoz7vTfDD/KvCD6Uv+0Ud5e6PAQf4XdRv7vLVsGHG8scQDsCPqSMLuKLBfXerMwVAx/knuv1ZcdjwcdwICK4+yIdfqh7Nvj7PEu2vtj+Mdu67LLLLsPb3vY2td4pNp73vOf1jnXbbbfh4Ycfxr333os/+ZM/wS//8i/jB3/wB3HLLbegLMve/tn2Y9m9JdtRWRDMdCJXFhViRPqLyo6gnL3tmAh4dON3h9pbnaHAIzU1bQx40DEk4ME3lLHUgd7G8wCBh/+v/DDi1xmAh/A5gB1eHftXdWMBxriyjA1SGj68ElOUFFO5rNRjKZuj5Lm3a/ycVHeUgrqvkN9jzQ3G68OvW6IrjOCa4n53usCnJ5g12KlbkxbstO3jrk3nssL/dX+z7df/QHcXHuC0nra5Hs3nVHeXNsDphlxdAHhtiqrwXD+cq4vUrnNH6crrogJo3E1QuWtD3GBcP94O3Rh1vzqwqTeXG6P2j0GBIlxPfSCUU/cVoHWTaa6FPg9ZW+QYNIBpM07g7lJP6JcDQbBTz+WlaWcJdqq5vAD1PlFzeaHtstVmzagy5XyanTp1CqdOnVLrv/GNb+DMmTO4+uqre+f5+Z//eXzgAx/AV7/6Vfz93/89PvvZz+Lb3/42/uAP/gCf+cxncMcdd4gBTrPt1zL0yHY0tuq4HUE5e/sxAHhI4/pvZNAezwk8wrdTMmBp1ycADz6GBjzomzU3ntSOy5K3AjxohoRJ09G66zU2fkcAO4A54naosCM2lgV2CLAhGXIo49RjTQk5DgmAyBACICBCARrjY4F085vmDuAFhR8MUnAwIcKTRPhhhiIlvHgf3MbG+UDdpZ0KwNg4H+6qbw187MhYEvgA4GV0iWdm6UCBBj5cOxF8sDHmAh9iXwKBUBXeOY0CH+76GODH0vE+QiiSBfRbtHPnzuHee+/F9ddfj0suuaS3/Yte9CK86EUvaj9/8YtfxJve9CZ8+tOfxkc/+lHceOON+OM//uM5l5xtgOW/zmxHZauJ2xGRavYCj8b6jgPVxwzAQ1p/X2padT5P+isDDzHOh1FKfCjAg/vEy3E80oDHZPE76L9j4naYXAOAAFj0uLKoMRkibiu+nfj9+NwTuGXY5qbnXW73p+/cYtdjQjehpLnV383UeB8AnW+eTC/sb1D4d3CcD2BUnA/6477Turp6+C25ukhtgj7sXhaAfMGNxbsXs36hshP6vZf+uUl7h4iriz6f0Lfk9/z0vUu9V2Juvsp+I3AJbvdh+4v3kW17dvvtt+PMmTO4+eabB/X/qZ/6KfzjP/4jXv/616OqKvz5n/85HnnkkYlXmW2s5b/QbEdjQ9Qds8XtoOUiFCmCtqI6IgItpgIewfyIbILIxkoCHvS8ReDBN3gK8AjVI/GgcYcEPOoyLY4HSR25r/gd9eDeWLWNeJADBNjR18fyQErXDOHhWn8Ytzx0q+0VwGKHLBI42KCp59EPIWzxOIz/H+rckd8N5fdxTLDTun/i77kZfpA1W8CH8h0wJs5H/aMHWz408CGBgFhwbQ181J/R3f8U8BG042OU8j5CgxCzgA/LcTu/AD6kY9qWtkc6/Bga70Mqz9ZdwyV/Uu3xxx/HjTfeiA9+8IMmlUfM3ve+9+HSSy/FY489hi9+8Yujxso2veW/zmxHZRZ1h/UGaHZlaW/S8hsJ/bhrOwfwkM5D3vAoG5tgnq5f8AaLzss3fALwCDdr8kYreKsW2TQeMvCo/+0eMoqqAyA+CDEAD+GzezAtqpP6AckDIRHgAcD6sDfZG3D1AdQfl/b3H2gNkGPfag5lrK39+CeyDRWIN19UubGiTC8AouCj+buu/7bJ9Y+oPrxyAXx4P813kqur/z1s8OHGSL5X0XsiH0MBH20/oV17LlV33vq+IZyzb3/glU8JPoR9kGnPJLWHvy+rDIBE2/tZVB/ZtmU33HAD3vrWt+Kqq64aPdYFF1yA1772tQCAr3/966PHyzat5Zge2Y7GKOxwxvOHpwYqnSRuR6yPEXg4S5GQam9sdIlsGvBwbSTgwYGEBjw8KMLrhLdnojxY2IgeMvDwPvfADu84BjwAJMXv8B6+eBtSHtTF3njH2wcPxIa4HaGqA2I7btPF5tDn0FQchxTMNB7ElMrG6Pc0P/+EeBwApgmIeqqdr+iJs1FVJQt26urIsdLfFu/DjSsFNT3p2rdBToU4HyzIaWqcD6D77yraW5cf5HR4nI9txfgAmvgdJfzAo6SPC2xaVIUX8NS1A+CN741RARUKuBum168q2tgYrl33mQZNBbAL42zUbSrUw/Nx65gh+nxdgFPLcTOYvy7luA5IUnr92uO2DT1OjPdB+wJBvA8Xr4MHSnX9pXgf2ea373zi03jkrv/HKzt57ImkMW666SZcfvnleOMb3zjZun7sx34MAPC93/u9k42ZbRrL0OMI7OxT53CmKHD56WfgitOn972cvdpUWVlMsAOwAY9UQCIcB2+KBDAQhRYzAQ9n6nxSjA4JeDBIYpULR32tNwg8OOyoj4cBjwBuSGVD3Flak4DHVLCD1bE37H39U2FHKuQI56CmlA+BHJH5N2MehNDABbs2KgRJzMpC5jcFRB0IP/ozvWjAwvW3wA8ftnSg44S1dSfVAQz+rwl8kKGs2V008MFti+ADgAcxNPDh2kngw9V1cEIGH80BgowuPCioa4cFwQftS9ZHYQbt6wGM2HEka4vel8GSpo8FfliyvAD1XtAFSqWA5Fjt/vPn8cD57+LRNkB64e9rJ7SL/9Mv4uL/9Ite2ZP3fRX/9qt/YOp/22234ZFHHsEtt9wy6bqeeKIGL1dcccWk42Ybb9m95QjsZRecwasvfGYGHkNdWdgDvFndMYEs0yoFXQPwcOa36foG85U97QTg4col4CEFkOuTI7sx2vYbAh5cOu65sQxwZ/F894nEPR14NNL6xBgEqhuLNzavo/37g5TKMTsgtBvnsmKPC1Kiz2VFnfsQgAcQPSeTK4xn8rWOu9Vocwu/AzF3FtXtxfA7avx9Hx7slJUBSIrzQb4bYsGOLXE+9O+s8LtuC64url5TD2oBtYsqjEMl3mclWE9ibukxQFg7Ya8yxNVFdEGN9S2Fc1L6xo779kqmvRU5d8DfvwUuL03dEJeXY7YrTp/Gqy98Jl70Pd+z76VE7SMf+Qg+//nPq8DjYx/72OCx/+Zv/gYvfelL8SM/8iODx8g2jx33X2e2o7JYoFGruiMpK8sI2AEsAzy6uUCO6fnL5eIGys0vgQy+IVOAB9+4ScDDmQY8eDtr4LmtAY96raG6o36oiGRYSHBnGRa/g39OgR1kjEifVNgBQAERfrtFA5CSB/YpHsq3/GM7Xy2IqXxNY/8PYwKieu28Obqxg/FYu71keqFjWMBHReJ8ABgT56OoWGwhY3YXYP3goy9OlBtHuj9R8CG1i6ogBfDB+0ngwwMJtI9wrN//hfK5wYdb88R7LTXQKbtGlix+Qf9sq7S77roLd955J973vvcFdU8++STe/va345vf/CYA4FOf+hR+8id/En/2Z3/WtnnwwQdx00034eMf/3jQ/1Of+hTOnj2L9773vfOdQLbBlt1bsh2dLRq3I2jX36dg/bUNiQQeAHgboD5oob0Nir/pIZtEBVZMlZp2yEaRb0iPCXjU5T2wAzABD7+cPywBNuDB+/gPbr7F3nRL/Uuljd8/quxo24TXRrKpXFaiYynzqy42sflXbXJsDQCdawm/FoorjBoLBACCeBrS/NpY4fxurQWNjcHdUZrjInA1gd/Oc3vpyq0uLHa3FxrXg38uMWWcD2rtx0R3l6p1a8E2XF1KtG4c9bid28qOjOO5qjR9gCYGCJo61q4CILmx1MdV55Eh9IPUjoxRA5Oq9gIJ+rg2QIFCGBf1/0Uknke9+G597nypC4rWlx+362H9o/E+xNgfw+N9aC4vALx4H9mIUfC01HwRu+eee/Ca17wGTz/9ND70oQ8F9ScnJ7jooovw4IMPAgDe9a534Utf+hL+6I/+CG9+85sBAH/1V3+FP/zDPwQAXH311XjXu96FH//xH8ftt9+Ot7zlLfjYxz6Gn/mZn5n4xLJNYRl6ZDsamwx2sHZTxe3Q1BPus/5GRQYDvdBiDuBBx5TmK+V2EvDw6iKSYKkdlxVn4KG8pSVl49xZIJdJbcCBxxjYofdPhh3B/AMgB6CCjlTIUffRQMcWIQe3GIToztsSYHSRgKhufhYDxAw/vDZu/i7YKTAOfhQCVNFBh/v7bE+ysWFxPuhQPMjp0DgfhwA+gGZsAXy4ujYYqgI+6gtQiOADaJ7jm8CmGvgI2s0FPvr60vOqCjnWiHQsjtsPMrzj5jr2wZIp4n1kW6d95Stfwate9So89dRTapuiKHDdddfhwgsvBABcd911uOeee3D99de3bX79138dX/7yl/HRj34UZ8+exc/93M/hiiuuwLXXXovPfe5z+P7v//7ZzyXbMMvQI9vR2aJZWQbCDq98g8BDlOYKwCNQiJSFPDZ04MHbaZJjN/6WgYcGO+pzSAQeXLaOEcDDAkAwDnbU/RV1RyLsqNvJ6o59BiAdpOaIqElWb6aApIoKBPCv41IBURm8qB+7mTLFDD84vJgq04vrHwMdPaqPPvBBrSe7iwY+PNs1530A4AOAl9GFgg+gu99wEEDBB29HwUcwhgI+mgZBRpfNgQ8GMEbBD0t7IMjUUiiARMrykm2d9vznPx/f/va3k/q87nWvw+te9zqv7FnPehbe85734D3vec+Uy8u2gGXoke1obG+wI9Ln0IEHBxIa8GjPQegXy7rC26X4WB8K8OCwg5aNTkfrHRuBxySwg/cfATuCdlbYkQ4ZFlFzbBlycBvkipKmAqn7MHAxcH4NXniqD7eW2TK9NOsxwQ8L6DCAD3qJmn9T09q24INcZk31sVXwAcCHGBL4EBQbsSwwEviYIpXtJsFHPZh3jiaXl2acVPgxVPWRLVu2dVqGHtmOytbsyhIr19pYjlPnVdtQlXhsHAVcBMoPAdpI/VooUQnwgwEPWmeNpg8cDvCYNR2tV2YDHsunn421s8COfao5YmMNmH8jNs4VZUIViHl+HV7oLi9ujDg8scX78Meyw48+dxcOPuDDkgR3FymtbXuJje4uFvABgMCJ/YGPeh2yGwsFHwAGpbIN2vH4IKSOKkI08OHaSfDAnXO0TSKE0PpaxnTHmrrDpPpQxgyPp3F5yUasKpZ1+1lyrmybsww9sh2NbcKVRSoXIIKvvqDl3VgqgKigjBnp654FGKwAGTMGJKLtyvCc+HlJmVok4BFThVDgQWN87At4OJsbeEgAZIn4HVW0D8K6RNgBRIDFWGXHvgOQHjDk4DatK4oCQSZXgSQoP2LuLDjxg50K7UzxPqL9TxLAB3zQkeLuIoCOVHeXZPDRHO8bfLTBS2PxOwjEkMAHBRwS+HBjtOoHaXwXsFQDH1I7dPCkGxvArnMr8dtUqIUiHKhUDSeQQUvQl6xPVoYQSGOAHCb44ZVrx+NdXrJly7ZOy9Aj23HZGmBHrG7rwEMAF1I7N7cEPLjyQ0tN6z5LwGPH2q0NeIxReHhBSEeko21thvgd/cBjf7Aj3j+EDft2WRky/+ZstqwsXZ/JAqIKygsNXgzO9CK1S4EfTF0SBx9goENSfcTBBx0i2d2lAR8AmgfhwwEfAPw6BXy4dhL4ABAoOzj4ALo6CXy48X24sDHwURe2cCGmKqH9m8LJ4IdF9ZEtW7Z1WoYe2Y7H3M3QAjuAeYBHanmsXUnL1wU8OLiQgAc9J6lfUdlS0/I6KVPLwQGP1aWjpeBCa+O3M8EO1n9sRhYL7Fi1y8ohgQ5qs7miAJO7wijwI5amdv9pbjn4QA/oiIAPySZMazsUfDQLWRf4oBBDAB9ACDRSUkakTXUAACAASURBVNlKD/oe0BDGXxP4cOuzpLKlY8E7BwFwsP50XHOKW619M0cv/MhWW7Xz98ZLzJctm2IZemQ7Klu9Kwsr12EELYc4hn8st7H01dUnBuBRFcHcFEjwsWOpaaXxralped1BAg8OO0jZcHcWID1+x1DYwdqtAXYs4LKizj14/m3aPK4owByuMGb4weJ67C/NbbP+5rs4Oc4HGSPZ3WVAWttB4GMlwU1putohqWwd+Fgila0GPtzYGoDo2nS/F34bygf0vubApt567fBjFtUHuzaqy0u2bNlWZxl6ZDsac8BjbbBDq0sBHr5ygwGCCmJ5b9/SX4sGUTRwQYGH6y8Bj3BsOTUt/yxlapGAB83UAhwJ8GCwo65LBR4ReGEGHvPDjrqdAHqi/dnb5mjcEWEOz+aHHNH5t6z+KGQIMZkrSls5kSsMmTuIPZIIP6ZMcwsY4Ieq+rCAjjR3lynS2m4VfADwMrrI4KODGGoMEBoMtbKlsqX3UUsq2w5q+JDCH3sFGV3cAjUVhzj2zPCDlEuqj2zZsq3PMvTIdlS2NuDRV7464MFAhgYuJODRnpsCPKypaQEZeNDxJeDB220ReBQexLABjwB2eG2swEMHIP3AgwGKxdPP8v6KuiMGUjyLQIYlsrxsGXJwU4GGosLgfZZ2hfHidRAo4eYI4Iox04sbe2CaW6D+uyqE+TWwIYKPeqKur8XdhYMPMkRqWlt3GbcIPgB4mVo08MHbSTFA6msXD4aqgY9gDAI+eJ0PFwggWTP4kMaoD3QoMrfLi6D6yNZYBX/vvMR82bIplqFHtqOxFnhsAHYMOQ4UF3sAHlOmppWAhzU1bdtWAB5ujK0Cj1Wno40Ajy3Ajn2qOaIuKwfs5mIDGj0QQuxD/j4mdIXhrim6ywsdb0ya23h/Pd6H6x8HH3V7cs4WdxcprW17gWzuLlJa21b1sUXw0UAHDXwA8NUbpS2Vreuzw/ypbOcAH/U/41PZpqo49q36yJYt2/osQ49sx2MbhB0+jDAGHx0wR1s+EfAoqnC+1NS00vg8U4sEPHicDwl4eOeL7QKP1aSjFeN3uLapsCPWzgI7eP8hsGN+yCHPO6LPRi3VrWVInyldYTRI0Q8//LWkw494/3iw07iiY5VpbTcEPgAfWnCgobmxuHaxVLb04V8CH66uBRGSOgRhRpcx4KMp7IUX9bjTZnTZBPzIli3bKi1Dj2zHZ0cAPAoiKfThRaQvVYAb19oHPFybuVLT8nGkTC0S8PAhxoEAjzncWYSyFHeWDnjIsAHgD6RpsCPevx92BGNL4GIJl5UhfQ7JzWWUW8uQPkYIIqpA4nE5LMFO3RqGprmN9efww4/1gW4Mi7vLFGltQQAHva4Gd5etgA/XVgIfUirbmNvKkqlsJbDSq8gogX2ksg3WYVjrmPJRLi/ZiHV7w8Xmy5ZNsQw9sh2PbQh2UOOpab0+UwMPBiVE5UYVAoluTSHwoOclAY+iKlTgwcGIlpoWkIEH7XeUwEMCILOko9XUHSuCHbGxlf76fLxyAcgR6bN5m9StZUgfBYKIKhACFSJxOZIzvdA1GuFHrH/3t9gUaaqPudPaprq7HBD4AJr7mwA+gjrysA+EQCMAH2jqFPDhxgfIGPBT2YpKEQ5IEI49B/hof9+E9WkKjdWqPrItbo/cfQ8e+a/34OSxx/e9lGwrtgw9sh2XbQR2eAoK95m4l2hjuL1DOPb0wCNUcPhzSyoQ3s8Bj7ZOAR4OaEjAw5qatvuMoG5XgihJpLbdv/sEHtO6swDp8Tt0BYgKPMbCjqBuOdixd5eVKOQ4pLeK87i1DO/T1YkqkBj8YK4pg+HH0DS39Nw8oFEawAeFJFZ3F8llZoS7S7PutYIPAEBZRcEHwOoI3ADC+B0cfMRS2VZ0DITgoz7xmJoDnfuKphSZCHzUTW2pbNs1VggCm3Z1G4Ef2Ra37732F/G91/4inrr/f+DfXvd7+15OtpVahh7ZjscODXhwAFFBLA/UIcr8UwCPAMh4bi8hrElNTQvowMMFLpWABw1cGtRtFXhw2OHV9QEPCUgYgYcEQAzAYwnYEbQbADtE0LFiyFFF+6zbihY08HOzxOlQFB2T9xEAyED4MTbNrTy2BZ7EwAe6e8SCaW3ptIHqo1nLGsFHtWvuFwL4AOBldBHBBxBJZUuAieYKQ2OCEPABcHjQgQ9eNxX4cOPKLicEXnjj1r80KRldWleXPtcbQ5sx5WaXl2ydkf3kYvNly6ZYhh7Zjss2ADsABjxI/VDgofYtY+scDjzaNgLwmDs1LaADjxpSHCDwkNxYzMBDByDp7ixhWwvsqNuVYt1ssIONEVOHqOsMKqfrE1NybBl0UKPnURClhRmCjI7tkdbHnqZWBhRifyAZfkixRKL9GfgA4GdsMbm79IEOPoYAPqj1ubu0bTYEPlAFqWwlKCKBj7bOAQjBFQbo6lJS2UoZXZLBB0IYMGsqWzIfBR/03CD0W4XqI1u2bKu0DD2yHY/1AY8VwQ6vvLLPOxR4hG3gjavPF84hqjYE4DF3alo3Bwcebd2hAg8OO7yyPuDBYUekT5K6Y0LYAeiwIqYasbjCpEKLVag5IkBl9dZBBn6OOgTZsytMUqaWBPgxNM0tXZ8APwoFVtjcXSZOa2t1d9kg+AAc0AjBR1vnFBoEfADw1Ruln/llzlS2FHwAIdToygkgCaDKTKlsSWyQ9nwhgx0I8y4CPzTVR7Zs2VZpGXocgZ196hzOFAUuP/0MXHH69L6Xs1+TYAcrXyvwqEGGAAiqAfOSZwgNeKjzCTE6tPm085NS09J2qZlaXF1fatq2zvvcrIkBEfovPebAo6v3IQc9dpCDHnPg0Z0HV2aQ47UDD2J9wGMNQUrtWV3keYKqDDoSTYEZgFEFwgIHGhQdxdg+7QM9TC4rbqy0YKeAD08MmV7Y2C20oH1b61QfFvABIDGt7Sm2FkH1YQAf9JzaYKdA/X2pgA8AHQRpwEddVnngoy6DCj6A7tgv88FH2w4kMCitq9JT2QLwIIYUHFVKZevGlAKUSv16Y4BU1K1FByT0fDU3kWA+pGV0qbTz26fLi6b6OGK7//x5PHD+u3jU7WlYXLXZbcm5sm3OMvQ4AnvZBWfw7FN803OEtgHg4beF3NY7lttEYYnbyzPwYFk/z9RC28SgSCxwqdSO10mZWrQ6S+BSaYy6bwg8aJkPT9y/EuSgSg8HKioPeLTW1ocQoKjKRYGHbwOAR+DSkgY8wngaaeqMKdQdqeBiOdeVIX3Waz7IACwAJKUPdwtpiy0AROnTm6IWQKja6IvJIcATBXz481B4IkOVAHxIdQngo7NlwEf9AOxDkBZ+NOADoJCjLquKwgMfAFrVB4cg9IG9gyAu9gYDH21ZF7B0VxUecGhjeBBliKuTMroEY7A6r58Q32OSwKbueV0BCiGwoGCiHmTqwKY8owtXuawGfKD5paCqjyO2K06fxhWnT+P+p8/j7qfO7Xs52bJ5tutvki3bAVm163Y4ipJhqnJTm9IHAt06IZRNG7hUbgNvzd581sClUOoiIIQDjVgcD3rM43jQMdS60ndrocCDtuFlHTypAuCxE9xZ6uMOeIRlobqDQg4fgiwDPKZISRsAj+qE1JUJ7iwE9iQDD9LfjWHoLwZSJevnbUXgofSp/2/cD+tSnbQ/4/tsw+Lrl897SB/9/yPy/9jz/+61a+vk3zf+u2ju7/2Nae2U319v7ezvUlByhX/P8t+/SQWmfgaC+ELs3+77LfxuRHWCwP2PHHtqOuZGWB93389claep97hbo19GgHrlqwf5vUcC7N0Y/aBfcvf09hCx+6zh/kzVojyDmr9XgNe/NdMLH4jjFkq511+Yl++dptiPjSn39pbZsmVbpeW/0GzHYytVd4Tt/TJ1I5Awf7dRgHrztsbxSJmPn6fVrUUaf6hbi1cnbF7psby59YFHV1+pZR74IBvwAHhIcTzoMZX4bxV4uCZ8faSP1Z3Fe3CLAg/SvxkjmKdvbZGH5LBC7zMMWhwm6NBs+msh95nq/zYGLrrfUR/WRcGH9reg9jf+LWwZfHhlCiBm36f+d2z8e1lyR9x5EATtcVcflvH6Lkh24dfRe5igToy5dEpj8Lo+FeWYvYr/AqRrF+whStJ3svl08CGeZ8/Yc+z3VBVxtg4MLfmTLZti+a8z2/HZFDc6hfwPUXd4N3UJeAhr0jceEVhCN2rGzQFds/ZmRmrT1kXcWkQViRunsSncWoI6sjmzBi6tj+NxPCyBS+vz7YBHUCZs+FGVKwQexIYAj7acP0DpwEPuzx9Ujf0Na+NzWNUAtU2oVCD9wopy+z/Wc51YKTNYxUPaBWO1dRZwMYVqxAjxyNwy+GiK1gQ+It+JngpOUs6JLoXhd7UPrBGUSS6OflnRHWsP4qRdUFcxRSKHGBGVo3b/3LF7cgAThL1HfRxv4z7TsaRyen/UVKixFy7SfNoeaQ7VR9/5mcBHtmzZVmkZemQ7HlNugIPKG5ta3UHL/U2BsDkxjCGtS4IimltLuGZljgFuLbxu6vS0rq47luN4uDr6Lz2WpM+SRJoea4FLWyMbdQ48pDgetH4dwMNvqwEPXXIPAXi4iu5h2KzOGOgOo6/N4L4S2ABoMeRBPgIMNmnK+UwOiCwAhBdLvweeMiMBXKjuKnp/m2pEBnrB7zfpF4IP+n2wDvBRH3dtpO9JESAL37ccfNBj3zURpMwdd8uR7xP+Pczi5tJ3D0txc+nOZ7ibS3cMvY0GSLSXLOoeBEob+YWN9AJoTtXH6L1hhh/Zsq3WciDTbEdnc8gbBwOPPlAxYB0iLCljfbph43LTcA4PYggbK/d5CrcW15bWqW/JvLdwoVvLHIFLuzLpLSN5G9kXuLQtc+2Eh/aNAI9wbRHYAZhhhW/yW3Fzdpfo2GEb33TwMCT7SrSPAjnC4K/bsSCriDvHwn8fo2dyAbprOaQP60evvyWYaU8g0fmDnAJBalttbW3ZCeTgpmQ9lT2dbcECowbjqZ8BNbgp6u89L7Ap6u/HWGDTYgdUBUlhKwQ2Berv68kDm6ISs7kEdVUhprGt54sELa26IJrWbC5FVbQZYNp29YE3vrvnJqexbW65vE1vGtvouGG5K/PGcLfcgp3/GoKcZmutANtLLjBftmyaZaVHtqOyqcm+qU3MnUVYF79B+PNp645AEfrgHgEZ2tqHgB4tS0qfW0sLSQS3lpg/dNvO6NZS14H0C8v64niE7iwEcqQELmVl9MHLc2s5ZOCRoM7w+xuAh/nNPIMKivtDbYnqDK9PgqIh5gbCVTAbNPUcDO4v0wV97VF/sD6x3x+TqinqrpKqGnFjSO2Ecbei+GghcHg+1u9RLbCp9L3dF9+jHUdQevD6sW4u0kuAXdl/z7TedwGY7+may4i0HwnGor+ivE2PmwtXpsTXGfZZPMiptK5s2bKtzjL0yHY0pt28WjM+0Ce1SXBnkcbUFBnWsaT0tBRwxOZPTU/LNypRxYYCNIBwY2iJfC+5tXSQpN+tpZ6rK+PAoyh94NH1iQcu7coEGXZEui3G8fCOtwU8Vhus1FDX2QBoMTZOB69qzm/rsINb9LzWFP+jXW/C71Jbvqcgp3zcLYAPcizF91CzXPXE9+jK4oFNpe//WGDT+ti/p3Xtw3tTzM2FjmG9L2oKy6COQwY1dkd3XrHAqa0p+4ui7IcV4bis3LT/UsZaKsiptqfMli3bqixDj2zHZVPc4KDf7EzqDs1NRBhTWo+/uQjH4rDE6tYivjGq9Lcv6maox62FXqM53VqAEGpobi1DApd248bjeHDgIcXx8I5jcTzqgcL6lQAPz4K2rr384GcLVuq/GR8brDT2dr62AdBiqOJgCOjYR+DRqX+GnG/KdZ3y/3CIamiA6mOaWCH9f5urBB+0vBKulaL+EL9nB8T36MoQlGmBTaXA2LS9WMdAvHpvjUAMHgeLt9tJ93Q2xmJpbMW9DiLjsrGkfZZlb2VUfZjOIaE8W7Zs67MMPbIdj/UAhkEyyMamVHfQcmkDYu5PH/gDKEL6DgU+7Vjyxiomr53KrWVH+3mAg49B67pjLXApbytJoP2MLd3bxL7Apa1JbyppHA++8a9OsHbgwd8mL6HuCOYxuxzID5VTu0kMcV8ZqnzYpA0BIIPcX4DJ3JIMgGMYuACmVn3QudOyuuwJfAjfe53qjcALAYL0BTblKjwp65YU2JQeS/cMqa3FzSUp/XoVd3Oh7azupWMVDyIgCV6OwOtjmU91czH1h9ymR/VheQllLs/WmbtOS/5ky6ZYDmSa7ahsEnVHrE0P8EjeWCD2JkV+S1LQh/wBm4ah6WnpseUN0xRuLbE6ya2FblxjgUtp9P6UwKVhWbhJF6XZRMIdD1zKymcAHr5NATy0h7M4rBDXwcbQ+gdjxMbW3ja7avFBWG/f2ycCK1S3lSF9NmBqIFMAQTDT5jyT+tAHYi8wKb2eqQFQ5UCm9doMQU4BoJCCj5boDXIajM2CnLbXwJ1DGLBUDrp6Iral6+jMlTX/8jGqscFNybpIuRfYtLmGNLBpAaAqdl0Z0B57QUyrCqmBTXeoUJIgpzywKVAHN63bFih3XcBRlFVT1wQYdXVl1y5Wx4OWBv12aAOjFpUf0NMFKt1VRb1eJRhn0Yzv7s9d0NCqvs2TNsE8VSSwZ31Q30t3TbuqaP6vmgClqOCiT/rjdlsMui5SKKzXtYE85pJBTrNly7Y6y0qPbEdjKcAjlfpb3VlS1lKP07XxAYd8XrZx4Z2PptRITU9r9SVOfYNF68a6tUh1/FgLXMrLtDgeAfAgLi5JgUudtce8/TzAI3irOwPwWCwVbWxspvygD8KriNMx1AVkQzZE0bL/+B/675DJjSqoY38jqruK3r9P9TGF4kNUf82i+Ai/F734HkGZApbZ97D/3WyP7xGWgfQFqafH491cONy3KiIDpQW/D2vt1Ngd8NqoL1qsbi5iGyjjDts3uTHHuLuo+7+EtWTLlm1dlqFHtuMy5cY2xr9zSneWYF2xeVmb7qYO9Ryt6WlpuQhFIhumIW4tfA2S7Jf2i7m1tL7WVdytpS+OBwcekhRakkzX6+2AR1AmKjmEhxDvgeqwgEf34QT0QSzqzkL6TxKstLXugXfSOB1Tx604ENCh2eTXRIMmkwAt1mbk76DdXWVohpgNgw8RBMfACFljT2BTHt+DfsfHApvWx248m5tLfc+RQb31fhfUCS8JtDrV3YbufUrlYR8w7he6z8HeoyR9jXsa71yE/dmc7i7qmIY9ZLbOimq3+E+2bJpl95Zsx2ND3xjE2pTSjRhy28Q5tYwp2luRej3y3MF80hsNxa0lZe0xt5ZC2eDRt1ZTuLW4Ofw6d6zH8ZgqcGlrkcCl1MUlGsejHoiVbQh4aLBDG4etKToGe2g1qzs889UdWh23Ie4rUVgxscvL5mwKVxZyndR+ZvcXQHOB8d03yq7Oc11p/qYT3F3cOejuLgB1edHcXdrzD9xdpnF1qaoSReCG0/wbtB3j6sKubePuUgCdmwtzbXHt3PdtVezq7+HWjaV2afFdW1xZ6NqyK2u3lroMjTtLfdVaN5dmiX1uLiVxsRjr5lKrHiG6uVC3FldndXOB4uIBwc3F3bO5y0p0rOYaujauT1F1bi7euKiHcbdzNxaf2zWk5X6bZk42/mzuLtkWt+988h/wnU+excnjj+97KdlWbBmJZTs6S5YzKoBgEeARvG2xjZ8qFR2TntacIg9Q3VqCNfS4tfhZWCjgIGvvreuOaXn9ryx/tgQulaTVACC+ibTG8Tgk4BF7+61I/fvUHcODlTZVkTraZoz7ypA+KaqHTduESg7aL6mPSQHStdXqLKqPPneXMalt3RjdB/9ve1uKD3JMAZWgkOsLbErdXNy/lsCm9FgLbDrWzWUnwH5335TcXKR2QHh/jd6XFRePsWlsdZdcy8sl+OVk79Lfd8D4CPdyY/aH2fZnF199FZ77f78N/9v/9Zv7Xkq2FVtWemQ7Gpta3RG2l+fybpA9c1rcT4JNSnvzjvXRxx2bntbZroqAELKpm8KtpT1fWuc2kFXo1iKlF6RjSIFLd+oGuAMeYVm4CR8dx+PQgAcfg63H2j8YQx2b97OrO1YTkFTrF1vfVswLYDlSycH6DekDIKIA6dQHro2q+hgR5NStvU/1kRTkdJOKD9TXlJxXp+ZoApt6ZUIQ05GBTWvlRYFYYNO6D5q2fqDTtm5Xqz3qOqIwIGPwoKW+SiIStLQq6lvFrhLHcHVF1ShJegJ78nIAjXoCqGq5RKAo8ZUP3TaDqyDq69iUN41DdUeoqPA/aoqOdtLg+nTjAXMFOc3gQzAGvBaZL1s2xbLSI9vx2UDgYQ1Wqr4R6JlTAhN8HnWNdP/O2kjpaTnUSI4bUvnXIuYvbHlLJbm1aG+6hri11J/946kCl3ZlwltFAjkC4FGVIfDw3tgKUGBfwIPYIOAxQN3hj42gf3qwUtvb+rGKjrljUoTntXHTzmUl1zr8fTCqPoyqo7lT23r9Sb+o4oPNsTfFB4BYfI/aJbBkZaV/7P7tie/RlaUHNo2lsdWDbxfBvUyqk5SOhVIXjZlleEFhV2QYXsJUYTt6XbSxpL1UEDfEuD59PwZ5z6YqeNOuSbZs2dZpGXpkOx5THvpNksaJg5WGx/IaLfNI6Wmtmxfu1iJdk770tP5aZKgyxK0FCDeMMbeWTklic2up67qN69DApQWBHKMDl7YW6bsP4MEfjFKBR1tGH/BOEAAP0tcETIwuA1JmFq0usLEP3zyQ5ljQIazRnfuWflLOb9HsNz0ApDPb79RgVyzyNxYPUhr//R8EPgRAsTz4oMc935eR71otsGnoljg+sKnJlaUq9LqEmFZD3Fz6XFFbU+7lBXmJwtdgfVkCcp/W+yAs9/ZGQr0wb38bZZwJ3F2yZcu2PsvQI9vRWfJNMgF4pN4ka9Cgzx9782G7wcNrIyk1qBrEeu5e+rwqspHqcWvRgptqY9Sf4dcxqEGPY24tcwYubU3aePfG8eAb+BPoDwYrAh7ENODRGXtgmU3dAVhjMajZV5gtGWfCCjq2aHMCkCF9rKoR+++Q/ju5qOqD92V91gs+eF0pAo8h8T3aMuF7nIMPeuwrANGWpbpTdnXyfc16Pxx8T63iQcfpXHJ/EHgRe9kCsX+o7pD2T2wfxPrSvVPyS6wE8NG7dqlNttbcy7TlfvZ9xtnWbBl6ZDsq6wMewVuRRHcWPl5szr74HSY4QzdUARQhfVNBTwBF5A3SWLcWPkafWwt9IzbWraVel/u3x50lMXCpJLEGkWJ7bi3im0sJKKwceDTjSg9R+05FC+gPpMFb+gnVGYMCkh4w6NBsNAAJxpv4/4uDObNayP8dHPs7PTi1LejfZdhnleBDrCPH5Jp54EP43g3cDY2BTetje2DTsW4uvF2fm0vbrhru5iK18xUZ+kuN2L6CvtjR3Gqjbi7sWJ8XQVvLOk17uliQ0745s2XLtjrLgUyzHY0lwYmJ3VlSYId3rEYzl91aTGO68yObEnEj0DOW9KaI90/xP7a4tQR1ZdytxU9JC1Inv8UL3VkI5FgqcGlrNhiyduARnA9p3360wI5YXfBAbHj7zuuUh2DRJFjR16enn/hQD+EaGPpsyliqWHq+BX8v486Xp5el13WCAKi0n9fHtS+6YKZ+IFMA2PUHOW3OoS/IaVvHzjsIUtquyZ0rC5TajNsFJS0BFoRUDW5K1rRccFME7b3rKLSbI7ApsGsDmmqBTQF4qW1pEFOaxrauK1CiDuS5qwqgrPw6kqoWJGgpr6vHDwNw1u0Al46VBz4FurohaWzb/lUBIExjy/vHxmqDmJYAdkUbiLQga0ETdNSNQ9fgBy2lU/jrTAtIWs8XtJGCnHrz82uTLVu2NVpWemQ7KpsCeHB1h0f/hfKxwCN4O2KCEvDWpSk1om4t0rkYfYIDlxR2HJPgdm+9xru1yHV6HI+6PD1waVAmvpkUHtK9N7y0rwAwDhB4rErdwda0RIwITcUwWPmwRRuibLGoP+aMwbIx1Qftuw3FR+z7j6xJBMj6d7AW38PZkMCmMTeXrn14H/Lr5PscvT9yheQkGdGU+3KgGo25uZD1iEqRSnefjSpNhX1Q/xjhPNY9mvqiiZxfyjlkq825IS35ky2bZlnpcQR29qlzOFMUuPz0M3DF6dP7Xs7eTLpRzanuCI8Rba8BFrFPCfHmzeNzxOWnCPqHUES+PjFZ7Fi3FrFfSY/5hi5sV3/2j/VNJ93AcqWHLXBpUZEyusFmwKPQNurSw0VM7o2tAA/toQzQYIff36+LKT+sqWhj6g71YViwqRUd9ZhKv1if2Fwrt6JQlBwANAWIqv4Q+oxWgAh9ZIUFBNXHnlLbtmty5xeqNlav+KDHovpDuIbVSaMSIOfVlnXrd9/TVbGrv793tfoDJVDtOtVHUVWAOyZKDwCodoWn7tBS1wKN8sOrq1UbNI1tXdekk3V16NQdXGlQj9kA+6rw1By0zutH1B1FVbRpbIMxJFWD1B/uGHBpbHkfX13RteOKDZfGNuzjVBWAU3x080bmkdQaanuhjdtD8TmbvYdL/0vPgY53zHb/+fN44Px38Wi53ftStsO1rPQ4AnvZBWfw6gufedTAozVG+p1NATy0NwdBsFKlPZ9TfWtC9/GJb02smVh4Gyk9bQyQ9Lm10DdUY91axDq39y85/OjexhVltZLApaysx6fdf7O8PeDRp+5IzW6RmorWtqZ+tUBYka7ocOfVq+rgxVXZ/mzZoucx5noNVIBY+thVH7bfS+vveV+Q0z7Vx9oVH/7fpu27UPoeXTqwKY3voWVzofek+jO/zyl1TO2oZXPhao5YOni+73GftTS2AMx7Bs3N1aawJZ+lvVGSiqPbc+mKDss5hXPWaxXak/mP2a44fRqvvvCZeNH3fM++l5ItW2AZemQ7Hovc+IPyTo6MhwAAIABJREFUCh58sEglxbFT3FncvEB4ww/asXLvZh9uHMLzhNdfgiKxjUsMqljcWsL1xN1avDpFsWF1a6k/97izTBXHgwMPUelBFCE9Em9bAMDuoWONwMObzyTjnzgVrfAQq9XRNqkuL6NBRwR2HKL1wo8FAEhK4NnY782kqW2FuuActTW1tj/wEXwHqd9b7roJ9eL3I1sPuRbRwKaIf2/7LowSANdBOTCNm0ugWiT3zpgq0urmMuTeHoca9Jy0vQlRoVZpUCUGPvhxUpDTYH3SPq+b1/aiLIMP34ruOi/xg3z9s+mWoUe2ozL+gK5mZyHtpeMkd5YRN1bXv1szxLG8+YT1Selpg/4RtxbL2yBt8zWFW0tQV8XdWugbtphbiyVwaVGWIfCg0f8J5FgicKnc1vCWdY/AY3p1BzAoFa3xjT2tXyTzigF0yLCjPICfhPOdE4AE40VAF2sj1ll/J6MQQ64zqz5WAD7q87WDD7leayush4MPryyEIGL2Le87n94HwvuFlMbWHWuKQx/kh+6iUrugrioCxYhU1/eyIVXFGfRv+6Dbm0SVIhD7g9zTKSwJ1pD8QkoYgx2nvpDq3UNmy5ZtlZahR7ajMRulV9oLN0jthhu4s/SOF5nTW7PcTld9DJSoKhufWHpa2oaDC9on5tZCpbpj3Vrc5+5Ydmtxn+m/9TkJb/gkOXQkaJ4auLS1SF9B1m11aVkj8PDmmkTd0Yzdo+6IAQ+vPPYgy9st4IphBx2HYHsCIEG5/v+ruj+Req+uXb/195Od/5Sqj42Bj+B7TmxLj3u+ZyPf0VMGNo25udDjsW4uffdOy/23PiflZYPywiPYjxgBgqY8jbm50DaD9k1sjr24u2TLlm11lgOZZjs6U91ZeBniN3P5GPb2ljmFNUibEM+thY/H3FpEUGOM9cHfDNH+Y91aAL7hiru1eG/KWJ3fTndr2UvgUkmarUq4Dwx4uEMVaMTrTG/LeZ32cNpXx+rDOqE9ID9o9/QB+P8dN70uDNq6HSt44FHvPFkQUfowOiAAKgA/CGqkjxYANUhjW5VtvVfn+vektpXrtIClcjDTeADUWMDSZYOb1ufqApy6suZf1ra7Jif1fbLgbcH6lV07V1YtG9gUQFsO1PedEoUX4LSiwU2BJq1tl8a2q6uDiQZ1VcHS2AJiWlUSdBSo77PueFcVKEsEwThjfeixU2QE/asCQNUFEdUCotJ2VdGmqC2qLo2t3IfOAzHgaH/Q0m6bI7bvKtPnJOebjdjSCpgMnbJFLCs9sh2Nrc6dxTBnBxVia4Dch8KGSk8bp8pVFbeWaP8etxaLX7LVraWuA+nXfZbcWqTgc3Xd+gOXynUbAR4TqzsAHXiMUXcEdQPVBUl9Bio63HluGXgAfecxnwIkpY8WzDSo1+raNU+T2jZV9bF9xYc8ztoCm0ppbOvPcTcXr516X7W5uUgvH6xuLl4fYQ/BlaPRNLbCHiJUikDsH3Nz6VNgRFUc5AVR/3gD5yz9a5wtW7Z1WYYe2Y7OhkskF3BncWMwxYO2LirZ1OaOrcmHIgqEYOUSVEmR1aa4tXSQZLxbi/vcrWuGwKVtWQxy6HDCP45t8EcAD++BaAHg4aoGyvStLgFJwUpJeSyuh7eeyEP0dJlXDh90aDYrAOHFY+J/tGNE4nnEoIgCOHqDnI4Ah4uADw4tJovxYf2OFCBMC6WlMtv3ujWwKcDvL+PdXCRXFmvmM6ubqfiSBD3urEL/ts6Z5WVOpbfjbi50HyaPC7Fc3st1e7YoKOkdT54zW7Zs67Ts3pLtqGw2dYel/YA52xt/FXtjElFgeDd5+Y1NbCxrelqvT+TNEq2zuLUAPQHgom/UbG4tkwYurcoQeHgPBLS9sNEX33ZODDwaWxp4dB+UclYXdVlR35J36/DWwsr76qJuKJO7r+jjRQFH1B1mY1ZwVxYCEAe4wEzi/qL1464rzp2D1UVdYdyDt+DWEnOF8V1Xym7Nbp2aK0yv+8oEri7e+uS2qa4uVYXOzUV1bTlB5wLTXEd6PZp2RXXSuE/4ri1V066oSlRF7dpSlWjdXKpi194PnJtLVRRN2S7JzYW6tdR1PW4u7bHNzaVuB9HNhbvD0DG4m0bbp2rG63UbKZoi5uZSda4wwTxsrIL019xc3JzJrifqurvtj9i+q0yeM1ttwX5zgfmyZdMsKz2yHY3NBjzYWwj5rcHwOUe5tbB2OhSJZGWJQBXpjRFvx986aWNM4dbC6yxuLfWa1hy4VGq3DeCxOXXHxMoAm6qD94koHmKuN1u2yHnZFCC8zwIBULek+lhK8eGtQW6bpvig1yj8juxV0knfw5Hv8DGBTd1xzM2FHo91c7HeV4O6Sldoxu77smtwrD89pwncXEh533r37e6SLVu2dVpWemzU/uEf/gGPPvooXvWqV+17Kdsyi2tJrB4hbOhrL81puqHSjRDfaAxwa9GhiLzB0tLTSgqOtk/p97G4tcTqJLcWGrdjqFvL8oFLwY7jG/joRn9DwKNrozyksbrJ1R2xuh51x2oCkkbGE4OvbsxaJYQzer6KAmTvAVCtqg8pyCmvW0r1sTXFB52LXgdhjFAlQtqRf2s1wTyBTQH46o5W9QHs0H32FRzN/+zOtStQ7jolBsqqqauvbFtXdu1idTxoaT13PPBpURXNuTZ9gE4RogUn5f0BVREiqzuACqGKw12/age/PKLA6MYHEFNxkLX5HzVFhz+nPF43Z7Zl7eG//Tt8++/+HiePPb7vpWRbsWWlx8bsC1/4Aq655hq84hWvwBe+8IV9L2dbNoEPZx/wCNQdypyxMQACPKrC1p4BCsmtpW9u+iYnNT0td1dJcWvx3VUo4AAZo6+OHsfevIUxO+jxfIFLCSDx4ED4FlQOXKoBj3CMzQAPs7ojrPPmHxlgUnpzvoqApJryoVEVHALwAHrOZ5T6Y3oFSNCWrpOcj7b+NNVHVz6F6oOWrUfxIX2fNf/y/sr3pfy9ytQc5FrMFdi0Pu7uMVxZSEE8Vyfy+B6Wup3wUsHdn/lLBeuLidQXIAB69iRdGzUgamysqtsbmNrz8ZP3YPI8UnuLqjfbcnbJ//4K/OhNf4wf+u3f2vdSsq3YstJjI/ad73wHH/jAB/Dv//7v+Od//mcURcbJQ2yofHEKdYdlziB4lzgG+cw3EBG3FhmKGF1ZKuNGSYEiQTs+hgcxukBuU7i11J+50sOBCnK86sClXVn4gNC1Wyvw2IK6QwUdis2SYnaoqmOL7i5cdYHuHIeoPwDMrgCJpYc1x/Mwqz7c+kaqPlat+CDjBIqPsD9tL6o6vHYlwK5Bp+Y4qRUfXlkdvwMAvPgevKxVd5TAbhfE9wDgxfSo7z1y6tpa3dEpP4K6XR13g6ax5e2omqNep6/A2O0gx+4gx1oa21gfroKQ0tjW7bqtTKAUaRrUfUi6W0mN4W5fO7k+tlZJ9aGrOOiwlrggSptstRF4tdh82bIplqHHRuziiy/G7/3e7wEAvva1r+HDH/7wnle0PbOQe7ktgrbx8cI5U91ZRChB35xExuHuLzoU8cvbt0SRtzpD3Vq0N1BRlxc+BnlLxt1aujqbW0t9/qHqY/HApa3Rt5lQ220eeAQP/BGoEVN3EBsUrJTNFV8jqcqgYzqLgQxy3msEIMGDPanjgEMKctpbFwCOfleYPrcWqWy94KMr89bfXpcYBDlpHnLnDWxa7RwMqQObAoDVzYXDjj43F+peYnFz4a4sKW4uFEpIbi603LWjkMXrDwTuK7xPEFhUcnOhbUrM7u7SrqXg56GsSYBB2bJlW59l95YN2gUXXLDvJWzWAneWkcBjcncW0maMW0vsnMDWZk1TJwU/s7q1AAxwVHG3li5Aqs2tpa6zu7WsJnApgSGeW0sAPE4ODHiU0IFHVxdI+xOCR1qARyzQare2GV1XUt1XIv02bTNcj9lcYNp1RX53Jvg9NQfw5ecR+duXytbh6kLqqhIQv88M35N9SjtRkad/T6cENk1xc+mO/Xu/6spSFXpdQuwsy4uK2IuOWPp6895jiJsLL6P7gJ5+s7q7sD1kBh7Zsq3bstIj29HYWtxZ+mCHvw7/BjyVW0tsnRRw8Bv56IjwRreWsK7bEE7h1lJ/Ltt/RwUudSZuqAXAIIGJWDuxrOu/euCxFnUHW0sMdmxF0RGdbyMWKDLWrgDRFBWuLjG1bW/dSNXHFhUf/nFYNklgU9Tf31MFNgXcPWWcmwtPa+u5spTj3Vxo0FHejqexrc873ocrJeZwc2nnoIoOd3uRVB8Tu7vw9Ujnjaz0EI3HlVtivmzZNMtKj2zHZwKZ94/Dtq6NdKy5s/QqSqzAo4q7tbSAgbQb69YitRP7lDoU6XNroW+qLG4t9Wf/OObWUmhKD8GtBcDwwKXUnYW+XYyoOobH8fDXNQp4kDfkywAP29vq2dQdZG1qwMcNKDriCobt2bquX+z/mf1uaKoP+n3g1sLWnFw3UvUxq+KDKdDGKz7o910qQD4J21UnEAObtt/t4XmnBjat62VXSqo6rD+DqBDDe5rfTldZaErJvsDh3Xr9dpoihO8JdpXSJ7Lf8PZW5CVMVGHB9kFqW3rNehQYw19UdftCbd+YY0lky7Z+y9Aj2/GYBh/4zZa2Z23mdmeRgIfXX1pHbJ4AisgblphsVVNwBBstspmxuLUEdWXcrcXP0AJSJ7u1uM/dse/W4sXxYMBjycClalaCAHicsId3GXgED2BiW/9hi/f32pL244AH2vOdPTNL5OHUK2fAw7eNgA7ab2s/a76uyv9/AD4s6qGJfpcHZ3iha5safHjtdfARzi+0Jd+FsQxVepYrBO06E76XOPjwysJ7gBjzybuXhG4uzqxuLmHMqribCz22uLmk3MeH7Bc0qBHbr2gKVgl8iHsjCXwI84RjND+sPApjhHkk2JItW7b1WYYe2Y7OeoHH5G8JqIsJuUH3QRjvLYPljYpyYydjSXNZ3twMfUPUvY0a79Yi13VvzFLdWuo1Rja21djApWDH4RvKQYFLAQFixCDEPoGH/9C4L3WHtt56bq7u2Bjo2LJtDoC4tnHVh1e+BtXHCsCHPL8wDi0X1mB2C4wBafr/QsEH+76nLo1RIA4E95j4/ag+5gpFa3ayuk6+n1KowZWYQxSbQ5ShGtSoj+k5RV7W0P2YUN/WCXsrFY6o60VQrqt1w7bBeNlaK1C013KRH+T/h2y6ZeiR7WhMkyVS6eI+3Vm8Y+EGa3FrCedUNh+RtzC8vxrgTNkkxTZafW4tfh1IP/9Y3wzG3VpicTzqNhFoIT3kSG9Xe6XWHHgI7SKb+G0BD7Tnuk91Rwx4+GvyH4ZX9eB9KKBDs00AkBB+dB9sv3N7U31sBXzEQEbq9yhpJyrxpO/xyD1AC2zq31fsbi702OLmUpRpbi7tcRV3c6HHpthcSh/+skXvD2iKEFFZEQMfvA+9dsqebPyLrG5t2r4yW7Zs67McyDTb0Zmo7gBEEDH0phi0EYCHZYy2TrupGjYisaCnFreWoE+CWwvdQFncWsQ69/Ym2Bimu7W4z62tOHCpl8Vg48CjM61cWV9iXRAbwAQ7etalzcWrpAdrS7855tuYTRaUNNaPXK/x85XoAoo23w0u+CWAIaltvXVZ69x3VZueFWRdQgDU6gSzBjf11ufahgFLw/m1tieoqubaNm3WGNgUqO8rNI0tvACnlZ/GFhDT2tIgpjSNbV3np7FFWcl1TdBRF7Q0SHELoNpVQeBTnv6WprF1QUyLqvDS1fJ0umJw0kgQVCkAqAskWqDrQ9vSdrxPO1aJLhBsZL5wjGauAsE8sbS2wTzZsmVbpWWlR7ajsr0CD00BEnnLEVNgSOeRAkW8PhO7tVCbw62FHg92axkTuJS1T43jobaTZN09wMMzrS1tEoMYbSMZeMhzCW9GXZM9AQ+7uqNnXXQeBUCoSoK+fppyYcx8G7VVXcOe+TorWd1A1Qdb16A6698ZmT/2dxtXfLj1RBQfrfFx9Lmi6hCmIJH6ece9370xNUd4zn2BTce6udSf5eMxbi7U1P3DSDeXoC6y/9D2XyY3F2G84JjusSTFh2UMQNzv9apReJtsALr/9yV/smXTLCs9jsBuf/xJ7ArgWUWBZzVvHi4//Qxccfr0nle2rO0deCSNEV+fVG6SlXJ3EgtUKf2biLYp0lQeUh01bYz6s3YcZmvp1uu/bVFVHl4b6cE/skGPyMf9Ywlo6A8HQ8pMDx8WiMEecKRxwrmk69n03yPw8NuNAB5Z2bGoBaoGr7K5NkX4riZM32rsN2A+LYVsXVf6qW3JeqKpbckcsmrCUKesqyv31+qtiStA2DXyFR9UQeEaxdQhfBx9LnFsY1l3nkaFB70eguqDnxu1oiprpQczrvaoCnofrUR1R52ulqk9aIpaMg1Xe5Rko8DVHlS1Eag9JKUGUypwtYeYCrdqxtbUHko5gKj6Iiwn2x2lvdQ3RfERtgGktLYWxcex2f3nz+OB898FADxWlnisqvDdLHrJtkLL0OMI7L/8h2fi2aeETd2RmgkotPUQ65NdWpLGiLfVVB7mdfE3QEaVh9pnBpWHP1esbprgpQDIm76IyoO0i4IDalHAkKry4OWRt7UpECPyRjcOOPw2UgDQTQKPrcCOQ4jvMRGMAJaDH1sDH501ZdLDPV+jCAB88DEekshziS4x7XUl44muLGB11CLtYm4uIKDDa9fBD+/Yc22pr3nVQo5KPK4/k4d0chyrq11iKKAgdQyMqCCCHPN2GhiJ9bEAhvozgQURsFIDh0TwIV23EeBDOk8VzByZXXH6dPAS9f6nz+Pup87taUXZssmW3VuyHZWlSSURr4+NQZ9FBPWISeERGT8Ga+ZSecSkr1ElxwCVR5i2jx77F2mQyqMPgkRl2NRGqDzEdugvi0CIWBwPPm8MYkzn1rIc8NDWWc89HnjM4oIRmc/NOaTfpmwmwDSL2wubozNel/47Oqery2RuLoLF+/tQ1aIeG/p9CNDzDL97+90SeV1YpgJzFbDzB3CqzNDvY/zep6ap5S8ZBtyHNRfWer0G9WjFM7P17yt4nWWfU9dBrFNdXdq50+aVlL6D9m/Z0LocLfmTLZtiGXps0J588kkAQKlI9bPJlnTD9PotDzxSVR6BL+OeVR59KWr98eTjWF1fLA9/DLvKwzOryiO2WU90ZelVeQx+MDGoU2Zxa4FcttagpWLsgp4YGmPUHRMDls3aTNdiDGyKuYnJ49vAR0yNNA/44GXS73j/33cY32OG7yLvOklQIzL3GJWd8L0uxfaQxrWksK0/j4vt0V8n32+jWVgGuLFaQYbZFdcY20Nqvxj46KvP4CNbtk1Yhh4bs4ceegj/9E//hKqq8OlPfxonJ5FNXTbZJqf7ceBBLQl4zKjy0NZkydjCxxj0dqmMqzz8MeixTeURbjiHqTzE4KXdQGHRCJVHyoZ+S24t+wQe2pr61qWugbdfSYDNg7EZrs3Q/ws3p9iHjd+Z5W9lafCh/23G/lbSFBxjIQnvbwTDY9QerenffRLwTlV78M9D1B78M1dqqHUD1B68bpgrazok6dvvpIIPsX4o+EhU/WbLlm2dlqHHRqwsS7zkJS/Bj/7oj+LBBx9EURT4u7/7O/zwD/8wfuVXfmXfy9uO7QF4FMJN2xr926ry8MxwjkXk7Y/6Vof3sW5sBqg8YhHrzXE9KhbXY4DKwy+zqjysG3HoZRGVx3RuLRIM6X8Lm+LWsm/g4T8gxR5Iw4fJbWQTKTf/s9y1mvD/JgqfKFjT4cY+wMc8bi6W/hySREBsj9qDz9tblurykno/QFzt4btYxhSJ+rE9/pWyH+F1kRcd2p5hykwuYbv+dcb6JO23MvhY1OoshMv+ZMumWQ5kuhHb7Xb4/Oc/v+9lbNsSgAe1vQCPVOBilLAupfKo3z5ZlRzpKo9ww7iQyiPVF9y4CU+WbZNxUtLTyuMYFCED3VqkecS5FgYelfUBU1sHrxrhRjGo3wEpPty5FEKA0akzr9TzNf0GBDv15qrKtp0fQBTws6eU/rlVJ2iDc/JAoKRuquCmwXlUU2dzkSZx/U+E/58T1EFQw2ClUjuxr3dNm3b0ulZSJhd/bb4118K7/vVxG9TUK5MDmKIsgV0YzJR/DgOYypldwuwtNLNLl4UlVrcrC2DXZVjRsrzEssFYMrnwMWKZXLBDWtBTpbwvuKk6Romk4KatkcCm/jrYGNmyZVulZeiR7ejMAjz6YYUdeHiWADzmVHlQ24fKo/Yzlttx8KG3q6IqD28Mi8pD8uGOvv3bo8oj9ubVAjFSFCHRczdCkC0Djww7FrEtwI8tgo8Y5Ihmc+HgQ2zj5nEgIw2SqGlsvesTgyMxMEL+P9q+tH0IS/i5i/8XAhgBfPjBU9rS9LU8e0udTtZ9hpq9hUIN3m63U2ADG8OUUjYCJaIpbhUIoEGFsF23fRHXmQg+pDmGgg+/Hv3gI9vi9r/+/m/xv/7hb3Hy2GP7Xkq2FVuGHtmOylKAh2cjgIcco0OZLwIkzMFJDSqPQJK6MpUHl/QOUXkUZZmm8qBjmh/0E+XXRpVHTL1hieMhtR/s1tK20SFIVEa/VeCxFdjRp7BZswnAYe3w4xDAR7jObk4JXIRtYgoOOySRxpbHpPPZ1B58DHkuWqcDIE/t0ZbZ1B4AvPS1g9QegJ+idmVqD5RoP1vUHvyzWW2RAD5M4xvAh7TeDD4SrCqCPeoc9h+v+iX8x6t+CY9/5QHcd8P/Oft82bZpOaZHtqOxVOAhw4qBwMNbhzyfPk8/LCnKwrSGoSoPr88EKg+tXYrKw2+3lMojon5IDapnBRqKa0mKW4volrKEW8sWgcdScSFM80XiXgDbBh5AHLKtOOZH9Hc5GIPWsbm9+BV63VQxPrx2EbXWsGwusf769yWvsynMwnFGpQiXroXo3iisjRzHYLoW2wPgUB/icd0uUqe+xPDbDQkyatknxPrwfY6epaV/nfo+B4FZYm/0xfgYvG/Mli3b6ixDj2xHZ0NvXKOAR3sD1dYR9ptT5eH1Mag8eAraIVHgQyWHXuePobiwBO4tpXgMwKbycG+YUzfbyRtrUmRUeQxyaxmrCBnr1rJV4CHYPrKx9MKOrQMPZz3nEr8Oe4AfZHy1boXgY3A2F9P3Qvg9FvaPQZIYpJW+8xzU0L/HfBv/XS2lsLVkcgH0e1N4D6MqCw72QerocRGto2a9h8f2AdIx/2zJ5MI/T7Mf6qtPBB+9Y8T7ZcuWbV2WoUe2o7Jk4CHR/gWARzeXUeUBpS5yPJfKg2/Chqg8YtlbrCqPOnJ+gsqDmlXlEdk421UeEUjR+2AbgRhuiH25tfA52oLtAI9ZYIcyVzdnTOFwQLCDW+Tc+hUvsd//ieHHFsFHsCb97zWu7uJtYgoOy/9Xz+9y9DvR9r0q//3GIEiP2kNaW0Tt4WcQG6b24PdGqR0QV3uo8GICtYd1z+GNnaj2CMZYAnyYxlDWl62zqljuJ1u2iGXoke1obJ/AQ19H4o050mZOlQe1KVQefrtYna7yoDaLyiO24TXG64gBDekhLk3l0Q8x9u3WslXgMSvsGOLKYgACW/wZeq6p1xaYGH5sDXxEIZuuANmLm4tZ7eHWH1N7GOFy7PvcAxrTqz3qz+PUHrG6IWqPerz+/cBgtceE+x6xfOgYGXxky3bQlqFHtuOzBOBBbQzwcG8fUm7qc6o8qFlUHlzyGlN5eOtRNkFxSa43hKryCKTBlezqUtf1qzzEFLWtWVUehreLvRvxCCjZmFuLtE5vrULdGoCHaIaHar1yPtixZdsX/NAr4/AjaKuNuSLwwcs34eYi9PPGNKs9muZWCBK7Nk2dCMbZcRDPI6L20CBH7OVA1FU0sj+wqj2sewJqc6s90lxhEK2PjiH9WSSCj2zZsq3TMvTIdlyWCDw6AIFofXwM2xxBmwFz1Wu1vW2ZS+WRFq9DrqvHGKfyCAPJxVUenkkAJNEHPAY04ioPWqY9YPgPDqb0tNJc0Te4+sNNslvLloHHbAqC44Ud3JaGH0MVOVsEH3O4uaQpOCzqEH7tDH1pmTTOFN/XUfcWg9qD1w1UewxJ7x6N+zGx2mOIynTQ3mZh8JGuPEG2DVhZlnjnO9+JF7zgBbjwwgtx8cUX4+Uvfzk+8YlPJI3z8MMP481vfjMuu+wynDlzBs95znPwq7/6q7jvvvtmWnm2sZahR7bjsYHAwx9jGPBIHSNV5THUr3b0G52BKg9vDQNVHv6aFJUHi+uRpPIwvPVLV3kIZckqjwTVhldnUITM4dayVeCRYcfitgX4sWnwMZGbi7SeFDcXv9IHFuPUHlDLzGqP1oQ1p6o9VCCvx6UaovaoP0+n9gjGmFjtEdvHdMcwtNEBCR9jOXfkYIijNvf/veRPn732ta/F7/7u7+K+++7D008/jcceewxnz57FK1/5Srz3ve81nddDDz2En/3Zn8W73vUufP3rX8d3v/tdfOtb38KHP/xhvPSlL8XnPve5sZcu2wyWoUe2o7NkH9Kyp94APFL8Q9sb/IwqD7VP5G2OVeVBLa7k0Ov89cWyt8yo8hDLrBtmo8oj8qZUflDoV23I6zAoQuZwa9ky8FBsdbDD9d3ij3ZKa4Qf3jzbAx+8bDI3F2nuFDcXz0KAEJ+HXKPBag/jd/pMao/Y/c0bI6L2GKQEiag9hqhA51Z7pIAPDZ6kzYdofXSMbKu197///bjvvvtw9uxZPPnkk3jsscfw8Y9/HJdddhkA4C1veQsefPDB3nFuuOEG/MRP/AQeeOABPPnkk3jooYfwnve8BxdddBGeeOIJ/MZv/Mbcp5JtgGXoke2obK/Aw7COcCOh9BmQBo73n0vlwTdNQ1QeXNI7VOXh2WiVRz9cqMQxbG8iY/Jq7UHK5NaSqAiZzK1li8Bj6IPxDLDD9dUr4303YTOd/yzwg/XbGvhYws3Fr+QKDgsk4eXTfp+nX+XKAAAgAElEQVTKao/Y+OPUHvUQ49UeO68O4nH9WYcN2kuLKdQeFiAR3acY1B7xdn17LFKRwcdR26233oq7774bv/ALv4DdbocLLrgA1157Le68806cPn0a586dw1133RUd42tf+xq+8pWv4Pbbb29hyfd93/fht37rt/COd7wDVVXhS1/6Er761a8ucUrZEixDj2xHY4OBR/IYcnv5LYQCPJIhiw4a1qjy8NcQqxsQ14O9cTOpPCTwwes8S9xQD1V5iHNaIEYMRqQ92IQPRJG3x7F5WVtpLXsHHoLtC3YcVdraKZQuqX3HwI92/I2AD14mnnf/33mSm4tn7PvFAEnEeb0yo9qDr8EKUmL3AuW6W+891ngeVrVHrG6Nao90lxELICl6xiMLTYAWGXyMs6L5XVryR7MHHngA11xzDS699NKg7sorr8SLX/xiALXrSsw+8YlP4O1vfztOnToV1L3+9a/HbrczjZNtecvQI9vRWTLw6IEVcwGPrao8qOmbqrjKwxtD3RCW06k8xLZpKg95jLQNuUnl4aoNwUjncWtpmrQPAAnzrhV4CJZhxx5sS/CjHXsD4MPk5iKZBXLYFRxe+8TvN//YCJYn+N6OXYMp1B6+u4uerjam9tBS1MbTyG9M7bE0+FD2g5b9Y7Z12mWXXYa3ve1tav1zn/tcAMDznve86Di/9mu/hmuuuUasO3PmDH7gB37ANE625S1Dj2xHZUOBh22M/jbyfHrbNag8qFlUHrUyBEqdN5xaZ1V81J/1N21euySVR2SzP1Dl4Zm04Y+qLfyHiMnS09KqGdxa/AYrBh7sQXVy2AHMAjsqlJv/ST7n2a5zAvxI/H1S6xYAH7x8324u8pj8u8MGaU3fg0JZutqj535hvO8MUXvUn/uPY3V13A/5fh1zP12V2oO3i+xt4mP2t5Hng9w2g4/N2KlTp3DBBReo9d/4xjdw5swZXH311dFxLrroIrWuLEt861vfwkte8hI8+9nPHrzWbPNYhh7ZjsbGAA/XZgzwkMbQXVrk8aZQeVj68I3QPlQeRVlFVR7eeSgqD74BVVUeMfeW1DeOQ1Uewhhm3/y1u7VsCHiINhZ2DFIg9MOOQ7BZ4McoRU0cfnjtvDHXCT5W7ebSs6bhUCNR7ZH63d53H2nrlPtS5P4VpquV1R9hPA96PF7tob7giCk3lojt0dPeBlOU8frGyOAj3ao9/Aywc+fO4d5778X111+PSy65ZNggAD73uc/h5OQEv/M7vzN4jGzzWYYeR2BnnzqHv37iSdx//vy+l7IOGwg89DEgtx0JPFJjkAA2lQeXom5V5REEgtPetlUn/SoPz4QNfWQjHE2FOKXKQx0nFUb0P7BEFR2pbi1bBh4ZdixmW4EfmwYfiW4uAfiY0c0ltCnVHuF3czS1uPjdL127iNrDAyMxJci4eB7WTGhzqz1UMBIBMClqj6XAR+/aDODjmO3+8+fx1088if/36af3vZQku/3223HmzBncfPPNo8b5i7/4C/zSL/0SrrvuuolWlm1Ke8a+F5BtfnvZBWfwbCHgzlHaCOAh+4tCbNsHTSwKj3CN+qZiTpWHtu6hKg+vXUTl4c8rqzzqlH8zqDwSlRyxt4mSyqNKhiIMJszq1mJ5+0v79z9QSWteNfCIwIwo6AB6H7IH9esDHZG+q7VCvie5cy2kdzLuPIW+7toWRVq//r7NtSd1FU5Q4FRXT+uqExR0HlLv9eN1vB9KuPdSVVX6a6tO2nOpUPrXitTxtdGyYC3eGrq5g/U04wfz0rXQNQTnegKA17kxmzVJ10wasxnLP5emjF8z79rQNTTHkfH9a9ock7KiOkHF6xsrqhKVW0dZAk2QQ3ffqsjn7rhCtaP37u7zrqxQtsdASU6Rft6VBcpdfd/cVQVKsrnw6shxURaodko7MkZRFaiEYz6G1q6oCqCE3E7pX38m26WqaDdMfN1BX9rWW0c4nnpu3nxAtYvPc6x2xenTuOL0adz/9Hnc/dS5fS/HZI8//jhuvPFGfPCDHxyl8vjCF76AT37yk/jMZz4z4eqyTWlZ6ZHtKG0fwKN9C5Hq0jKxysPrY1B5RKWuTOVBLf7GydKuwhQqD8+0OtPbvMS3hUYoIgGJKBShXadya7G85fUqY0oSAYIcAPBYpbKjR8mwautTYaxR+eGtb1uKj/FuLuH4c7i5yO2Ec40q1YTv3lSVXrIKEOF1nEDtwdPXasd2haVN7aG+cDG+7JhD7VEfQ25fCmragYoP0xia4iObZwXq36XFfpD+f3HDDTfgrW99K6666qrB5/noo4/iTW96E+644w485znPGTxOtnktKz2yHY9JN7QU4EFsCeBhVXn46+pvF1OGjFV57MplVR6exQKaGn2w/TLrRliqc1BEUlaE41sCAQ7K1iI9GEVdaSKKjlS3lrZu28BjH8oOoOeBcKugQ7I+FYagKLD0nUX5wVUdW1F88PXGFCDB/HRNoQKEqzu8dUQUHHr/2BollYmk9nDrdteLKDtEtQe/ZkYlSETtUQCtwkNTewBgCo951R4oK7luArWHp9wwqD2i7YTyPoUG7WdSa2jjubUNVXxkALKYfevsJ/HQPZ/0yk4efzxpjJtuugmXX3453vjGNw5ex8nJCV7/+tfj5ptvxk//9E8PHifb/JahR7ajslHAo4UR8nhJedstipGE8edUeXh9JlB52NpFwAdLU7uMyoOaReUhlEUBCykyub7QtfowYtr0tBEIYnFrsb6NxvqAR4YdC1sMYMRcXvr6Tg0/Vg4+AATQYU43F7+jBCXYdRLcXKJuMRGoEQcRAtQQyrpzjUGQnvOPuRR5YKT0QEgNCtznClXhXo74wINCDu9BmxzX7TrgEWvnQQVSx91gVBBBjikU4WNo/XdVgRJIAhPBeAr4kNsmgA+1PtHVJdvs9v0vuxrf/zI/08rj/+PL+O+/8wZT/9tuuw2PPPIIbrnlllHr+M3f/E284Q1vwMte9rJR42Sb37J7S7ajtKHAwxsjVZbpwIABePz/7L15uFxVlf7/nrq5CQmQMMicb5hCjMwKOEA3o5LQQKIGCD9FGoVmUDQgkYAiU0cFlVEGm6ACKhhQhijSjKJgFLAJMotKR0AhEJsEyES4dX5/1K26e5+z9zpr7zNXrc/z3Cd1a49Vt1Jnn3e/a+08XR62NnaHhj3xKe3kMJdFrbj2E1qi4S2eLg+F2rs8rOPbBZu0x9Ny58NKWhiZ29CY1RE8yghjac0pXThHHX+cX2uKcJ9Mw14K+RxG56N/RvV2hJsq0neuYS6mORDfa7bwPVpQtQu/5uN4DWMZv+epMp5wHUTKrdci8vQW9bE9xDN6kottcyEWgmIpiyUmtV3niQ0dzuYJ2Yaz7rGsnzqbUpxxnTal3EJdBJ1CQ1vCwPz3NHDTTTfhkUcesQoev/jFL1j9nHTSSdhnn31w8MEHx8reeust/OY3v2H1IxSDiB5CzzAUYqI86XpxM5ys4iR4aPOJz02bp4eLhOPyCMIgN5dH62QYGOvl4fLQ6iU6Oagyk3OC6fKgFtCEKMJ1edjDWqibF7cblDzDWuomeFipqNhRZ+oofgz9UhPhw9OlxTrNhTmeU34PY12iven71/E72Zzbg+rL8FpZ1xmDEK8J9vYwT05uj1Y9c1nQJPJ+qddfwm1K5fag1humNrGyhPUOS/gwzdNpDJDlZB9NQ7lQSe644w7cdtttuPLKK2NlK1aswOzZs/Hqq68CAO655x7ssMMOuPDCC2N1zzzzTIwfPx5HHHFErGzRokU48sgjsdlmm2X/AgRvRPQQepcyBA/DxTXtLkgjtOfoqKLLQ69nd3loc7XthIVNnssjbBIuD2Kx7rpANrk8iAW/+UbBwTGh1KtqWIt5zMFf6yB4iNiRO3USP+okfMSedxQuSddXnsfYGuZnEjXo/tXvbMpZ4ihkm96TqNvDch2Kuj20MsLJyHF7tMqglKmPq+f24ITkst2s7bfB0SmStfAhVJ8HHngAhxxyCObOnYvhw4ejv79f+1lzzTXxrW99C4cffjgA4JJLLsFTTz2Fc845R+vnggsuwOzZs3HyySfH+ujv78cmm2yCJUuWYOutty7jZQoWRPQQeorOosBT8NDIUfDI0+VhbZOBy0PvW2kfecxxecSsvbm5PFQMTgnHXUDOApt2eRiei9WnbmKoGwJi9zfvsJa6CR4idhROXcSPuggftQ1zIYVirhsD2nP6+2T/Hva/Bhj6y8DtYRM5qNNbyI2GnNwe0Xp5uD3ox4Z2jsJHrC/mPIZCa2JdCY6hKalDWwgR6s9//jMOPvhgrFy5Es1m0/gTBAGmT5+OUaNGAQCmT5+O0aNH46ijjur0M3fuXJx66qkIgsDaT6PRwDHHHJP3uys4IolMhZ7BdEFyFTw6dTwFD87YQ2E4PJeHtU8PK6ptZ4cqi+frKNDlodYjXB4aSS4PYoFLHnFIhnhk6fJwEUfUOTg6OrIMazHuuupziI1rGLsQwYMb3hArtLdrzcGvbZqEqpXH9fQUYOj1lpzwVEsoGjZRdnJTbe7hAGKnuijPxeYZmaM6dtrTXPTC1nPGpKSdhKWG03o682vV0fuikqYa5tDpQ+mrPbbhtRpPb+k8Vl5/dCzlPY0mMA2V91ors5zk0vp9KLmp7SSXVhmGEpM2Iye5ILSX2RKTWsqiCUtb/dFJT4NQT/Sp9ZFwkov2nOXx0HsfdBZurJNkBuvr5cqyTe0vYWwRPqrLNttsgyVLlji1OeKII2LhK9OnT8f06dOznJpQEOL0EHqPjuPCU/CA+TmO4GEKkaHcG9zdjrxcHkEzakM1t3dxedjrEcKHzeURsQjbd9YGrGWuTg7SreHq8uDsaFot69SOLbW7Sjg68ghr6QLBo5LOjoRxa0HSe1dF50ek7tAv5To+4vMh/i9WPMxFb8v8bnR2e1DtfJ0grcfWUMqo28Mq6NvzWtncHq0y82Nftwcryain28PV5UrVSwxNYSZVjZdb+jM9JhwGgiBUAxE9hN4i4SLHEjwSRJMsBI88XR4qZbg8WvWSXR4ta28BLg9jeXwBTB9Ry3R5kK4LB5eH6QYl2rfJ5m64eYne0JiEB9MNCiuspeaCRxlih3FuDm1rSU7vRy7iRw2FD3aYC/X/nfqeiCUhJeagfS9Fx0mer9npRvXt9r1NuvmMfRFCEye3R7Qscs2jro9tGs2QCBlVHwdkmekxVRZdg6RPkO64HspS+LD2a+nPJHKI8BEnROt9Keyn7BcsVBkRPYTewXiBLFbwUEnsg+nysPVp3WWJ7uBwk4hV0OWhQbk8Euul2OVrd2VYlJtOY4mOzXd5ULu01I6qfSc20zwe1BxqJniUKXb4jtsV5OR8yVz8qJPw0enP8D1Q9fwe3k44U7u2eM0Vu6kyw9iReXDcHgAq7faIlREbK7Z1Cek2dXB7uLk1lF8KED6ScoQIglANRPQQeo60goepL4AneJjGJoUGhssjtuDI0OXRCIkM7tSCinB5qHB2sVq/u++YmW7Ih1DbMRbP7N1Coi9XlwdZP2HRT7a137DkkcejjoKHkQqKHe12df7xfc32NzM/8SNW11ZeJeGDdHwZ5kj933cUTv2PsaVFGq7bIzoWNxeT9xG2sTqAze0R/d3m9mj9zr1Omh/TGw7qY/1az10jqHDdHtR6Rq2nzzvJ5cEQKoj+Y3V9hQ9BECqJiB5CT5F8kbPUTbrAZi141NDlQVtn1cchOC6PoNlkuTyCsGndSYvttlE7bgDcXR723UNnlwclKlBhLZ3xOIt+RxEkZR6PrhA8Kix2dAN1Ej+6QfgYKvQTOIvJ78H7LnJ3ewxWJ1wlfKEjWobY54Zye0SPr+20ibg99HAXu9ujoZXB+DgWgloztwc5l5TCR9J6j+rPOGcBQOs9K/pHEGyI6CH0DMl2RnPdpDhOF8HDOh/j0bY8l4dtLlm4PLQ24vKgF7+OzgueKJJwY+tpJc/1eFrrTb5+s1Z5wUPEjkKpi/hRReHDmF8jCvWd4vl/PpcwF3a9uKjh7vbQ+9JcMeL2aP2ek9sj+nui28PBreEqfBj7chA+ksJtBEGoBiJ6CD1HUgwo78I3eLFzFDxcXCK2tqbHXJeH1jcnWWrEvUG5PFQol4dez+7y0OdKuDxUOLts0d+jC1juwtfR5cGJZ3dyeTjeaBQW1tLpU32O2J2umuAhYkep1EH8qJrwEW9LfPZBfxeVHubi6PaI9xMXtdluj2g7ruBNXF+o6xDH7RH93eb2iP5O5dPiuj2s4gU3JJdYl3DXNhoObg0X4YPlMLH1Z5urIAiVQ0QPoacwJyWly6PPpxU8tPlwQloydHlEj6DN0uXRWiTZy9THnF2rmLXXtlsWtQrbXB7hAIwuDxVfizOZeJRYvLPzY9gFj+qHtdRM8LBQSbGj3bbOP2W9Z6YijvihjGMri5VXRfigviPKDHOxCh8J32+EqGH8vnb+DifKjH01LXWAmNtDE0bsbg89xJM66QzK7+bHjSbPCeLr9qDWHabH0d9ZuT0c3BqFCB/i8hCE2iCih9AzuCQl5eTUMNc19+cmmiTtWtjnk7XLw9ama10eajVjmf6c6YbZPd9G/MYidkStBd7xtEp96kYll7CW+gselRY7uoEKvoeJf3NlDFtZrLxk4SM6x1LDXKg5aMTrubs9ou1Mfz/7dz2ZxJp5namr2yNaL0u3B1UvyR3rIlr45uRwCXEW4YMiQKFH1kLef8GOiB5C72G4CLoKHuZjZWGu6yF4xMYBvQvCdXmocFwescRnjN0kqoyd1yN6XB/h8tAgXR4qzJ1Byq3BsEdzXR50yIrZ5cGPlSccHZ67vOQcOn3lIHiEzcIEDxE7CqaC76n1M5CX8BHy/s/4CB+lhrk4fGfxRA2DOOHpzrMmL461I76jY38Py7WHFOrtgj7H7dH6PfkxVRZ1e2hlDLdHq7/k9Qd14hzpZs1J+GAdh2sUYuJ9CYJQXUT0EHqLCgge9vn4PwZ4Lo+oBTVLl0ejyXN5BM2Q5fIImk2WyyNuFbaJHbYFp+PuHikCmG+e43NhOCc4sePa2NwdWOKGJdOwFpjHSSN42NpFytMIHnUTO4o+YjbLn9q8x6bPRB7CR6Q8O+EjMg/i/3AWYS5p83voEN9vJuHZ0e0RHcfZ9Rfry/zdEr0uWUUO4hpIuT1s7o/oNdl6RG0Gbg/uGkQl1brHwa0hwocgCCJ6CL2D7aJnLE8WBkx9sS64RtGEFkd8zrHPwuWht4GxrFYuD5vjg2thbnfj6vIgRRFiDpabjUzzeHha2jlhLZmGtNjaRdqmFTzMA+ZzI26cb1Zta0Burz1H8SNW1zafNMKH1i4L4YNyTTiKHIwwF+NzDoKtu9sj2s70nU8IKmy3R0IZ57pTI7dHrCxDtwdAbLp4hJ5wRIvEZKSOwoexLxE+NNpJa4v8EQQbw8qegJA/v165CiOCABP6h2Fif3/Z0ymdpKNkre2IU1bo8WjRhDWeZ5/UDosNajFjr0f1Z1+g2YjGM1vrsW/61Hr2GwyAvtmm2pIuD2MDau70HF3GoV+PqUHCDaF1nIS/RUY3ekn9klPwEQkS3r/EG+gy2taIMGwiCIj9l3AACPoq1dbaJKlPrXITsNWNlIXhAALb68AAAihlVL9Jc6ReM9GvaX70nJsInPbcBgB4zMswTuz9Yo5DvZ542yY4e4pB2ETI+LwEzSbCBqdeiLDRui43miGaDfP1u9EEmo324wDNhvm6rJZR9bQ2YYDm4CIrCAOExh2m1rolbLjVS4LbZ4ww6CwMncZTxghC0TsA4NnVq/Hc6nfwJnMNlxWvPXgnXnvwLgwse6vQcYV6IU6PHmCvNUZgyqiRInhQuISkKLic1GLvg3BuMPu02UrZ41LCScIOjHE+zOsdlcBUg0pgqvZHujqQUOYndvBt2sl9cU6O4J4uEYeyh5vm4jnPxOSEpg75ogYtltC77VRZUu4G1nhVaFtDkgWzNGJbtm0pt0dSe2q8JNcGu8wKNbbf9wDf7dGuz/hO4dQxUsx3NDvs0FAWEPVsIS6xepYQFwru9ZgKcdHnkG69wa3nux7i9pmkibi4PYQWE/v7MWXUSOwyfHih427wL5Ow7WkXYMtPn1zouEK9ENFD6Ckq4/JIElMYjhD2IoKwl6pQoS32vpX2RBu9nn2loVt4mYtnrpPBVs9RHGGFtpi6cl2UJ7VzFTjazahcHsYGvs4PInTFxeVBkafLQwSPQqmT8EFWd/n7OH1+fcO9/L53XAVR+v+t++c98+9LAP4hLg5z8LkeUWKIJvjbr526o9I+rC1MlcIW4hKv57HWYKxjEtdDzPBfnz4S5+YgovQiEt4iVAkRPQRBIQ+3Rtp2Prsv/LAUd+cGtVCiYoj1eh4hL9wbBLYg4rc4Ng6ZxwKd+Xq9Q1uIhIXm+iluHNW6KXa0C3N5iOBRCnURPqro9qBv9iNNfT+Djt8ZtFDD/T73daFkL4zw6xvmwBRDuNc57jWUf00eesx2hnisOdK6Squ4RhMEoR6I6CH0DFaXh4vrwtcamaHLwydHSFn5PFR88nnQoSxKGblQ5OTzaO/2OS54fRf1rvk88ghtaTejEgIa6zuGtmS0m52uX5fddxE8yqQuwgdZtQS3B6/f7EJcaOEgjxAXX1eK7/85ys1CX0da8L7/udc4dp4rj5CXzDdJfFyoad2vnm4PWxJSp/HE7SEItUASmQrCIFXbQfDpl9umqHweZChLWfk8VHyt355CiGs+jyJCW4YKs3R+MC3rVXR5EFRR8HBOVFshrIkuq5rcVK0bTZJJJgHVy6hxnBKTRhOeWpJ0kkk4XZOXOiY0HaIJ6z6bZQ6m10MnQR2APeEp8Tp923GT3Sr1gnAAoaWNlti02QQsyUvVxKZq8tIoajJTNXkphZaIlEjqyU5EqiX75CUXTWrj02cZ7QQA4eBPkeMJggVxegg9h3dCKk813zcfCNlnyBMjuDG2ehvmHMrK56GScfx0C9/d4AzDX8oIbSEH8nxPusjlIYJH9pDhD1V0fHSF26PdTzavM3mcnEJcMg1fGWzn+f0em08WortC1fJ60PXyyevBwjsvh7kPp/Ek9EUQKo+IHoIAu0PCKdTE4QLIHS/rfB5a30wXR9b5PPQ2Hvk8uLv43tZlXv1OO09Bw9zO0/pd1dAWqt8auTxE8MiP2gkfar1a5vaoaIgLJ4SPmkNhwkiKEEjm58Mnrwddz/yYqkceQx+mW2/4ukttYSve6y2HkBrJ7SEI9UdED6GnqIvLw3ZRTZvPI4vY3bT5POika1nn81DhxGE7lDnvYHqKCdZQEceY/ZJDWyiq7PIQwSN/aiV81M3twWlTUPJS0/eT9bvP2yFiEkayy+uRXMZ1eWSb18MnOXjWeT24GykqnPwbxZzCYu7DaTwRQ2LI6S1ClRDRQ+h5qury4PbnOod8E6GqjzPI56G28Tm9xXex67wQbu82Ou6Geu9E0n34hrYYx8wgtKWuLg8RPIqjVsKHWqfqbo8MQ1x8T5sy/m2Zn+NMvz/JRKS+ohBT5OGGsrBdHqoYwgt5YZ/KknEi0rT5yThjittDEIQkRPQQeoa6uzxM5Xnm86AtqEOPs8jnobdRGlGCiGYHziLkBaAWw6YbhMKPqnUOY3EUOIg5pAltoaiqy0MEj+KpjfBRU7fHEI4hLq5CKTmOrxDt+13m9j089PdyFEaiYxHjspNua8IG77ORdV4PfR3BmkIueT3E7SEIQlpE9BCEQXwcELF2nuKGabw65fPQ58OtpyzOiORs2efzUGkLGtnt/GV6VK2lT9cEpsUlNWXsDtfA5SGCR3nURvhQy6vu9uiM5etYSB4vaRyXhKau4SusORV6dC3RfwZ5PfSk3+55Pbj1qpjXI1peyEl7nuMJLSS0RagKInoIvYfLhc/i8nCxRLqMl2dYShYxu3obZW7kTlG2+Tw0vPJ5OOzWkf1k085/Mc4oj9UvPrSFer6KLg8RPMqnFsJHFd0ezP93eh/FhLhw++eW+wsjeXy/R9tyxRD30M4s8npwhQ2VsvN6JJXFyr1DlZUCz/EEQagmw8qegCBUgaq5PFz7LCqfB73YUR/nl8+DFERS5fPwvVnyDWPxbGekmqEtTq+jIi4Pbj+Jc8qobaLgUWdBJOizFoXhAAJLeRg2EQTEnk04YO07TVuKEE0E6j4S1U+kjJpTiAEEUPoJmwA1f6qtRhO2fa/Ya0kY130c+9jO/VN/a3JeA4CljP6MGNqRnxnltRL1grCJcHBM9TFF0GwibAy2aYYIG+brc6MZojlY1mgCTUvXjWaAZqN1fW6EAZqMeN6gGSAcbBOEAUJLG7WMqmdr4ztuFvNIM54gCNVBnB5Cb+EtUqiP84n15OTqqFM+D5XS8nlY2mhPezs0ysnnUVhoi2PyweSbc77Lw8nWH5tHDi6PkgSPROoseACp5l+G46MstwctALr9X3EOcfHNs+Eb4lKBvB5DeHzfsROWMkNeUub1oOsp86lwXg/v3B/eoS/sZuL2SCIMiv8RBAsiegg9j9X94HBx9T71hSFI1DWfBzvkJYt8Hirs3B7Z25yLyOfBmYfRfeF5w2Aa3yu0hSKNqJG3y6NEwSPTHAxVJYWQVlaoi7VJTrk94m2z/T9mTtKcnVPMJ3lp1fN68ObCvRaZ22SR14N7Tba1qUJeD06d3E5hsazXJH+EINQPET2E3qHCLg+VpDjYbsznobXxyefBXrB2UT6PDo67rVnG5HNvjOrq8hDBoxhqJHzUze3hfroKf05akWviVO7Y3ZjXI7R/N9rgH2Vb/7wexj49XRvi9hAEoY2IHoIwSJkuD5Mo4pWno+L5PLT5MG27mefzMOJ7Y+Rmm+48Z7SRN63tfENbon0nPhcZL/E5zthJVNXlIYJHsdRI+CCrV9nt0Rkn5xAXzxw1VIjL0HenXZwkv3ONZP+9b4Yn0HOvddxrp4p6Xd5ZrvIAACAASURBVCbdGwUlIk1Tz2UdxZmnr9tDSCZoBoX/CIINET2EnsPlYlcll0c35PPgnt5C5vNQ26QMeaGPqqXEB9PimohJLzu0hSD30JY6ujxE8CiHmggfdXN7kM8R8/IOcTFScogLIRyTYjRxPQgt1xWvvB4U3A0CTdjorrwe0XqJYxfg9vBNZC8IQjnI6S2CADeXh9bOcweAvTPiESfLrVf1fB4a3AWlTww1q36Ph7YY5+N+Q+SadFFvW5DLQwSPcgkHYD95xX6iS6u8Gqe65HWSS7xtE+qJKrb3x/l0FXK++phJ49B/M8PYxo6Jv5vppJkOA7Cd0EL/PX3bJdTnfg6YYwRhiDBoXcep01uCJhA24o/JvpV65Ikvyikv6ukvFNx66okpsTL1FBaiHtWn0yksYdBRQVzGE4rh1fl34NXf/TfeWf5W2VMRKow4PYSeIguXh5MV0qEdZ7fDZ+ekrvk86FAWbj4PFVu8dTGhLUOFfu1I63feoS0E3qfDRKtWxeXhO44IHtlRVccHVbcCbo+ktvocMhSFswhx4YTwFX5CC/f7n3c90TtXBfoMwjvVNjXK68FZ03BdrtG6Lm4Pa8iyuD1qwYa7H4DtT7kEWx8xs+ypCBVGnB5Cz1NVlwenjs8FtyvzeWio9XxvnooVNPxECFMbtxuGIk9tqbvLo6qCR6ojb0vG35VRkuOjxm4PW32tyDAu5aQwOjtcXSU2t0eGbhNOn/S87c4P+v1W2/FcLUHYRDj42tTHsXrNJsJGI/aYotEM0Rx0g5DuDcWJoTo5yHkz3Q+aQ4NwWnBcGOL2qD5BGBQqBonwJFCI00PoGXwV+Sq4PFx2Ojpt2CE05jZUvcrl88gs5MVX0PAVAzz7VOuV4vzIMtbf0eWRKvmjX1sRPPIhnSujfMdHpm4Ph/+zmfwf07DnwODMx/pcu8j1O0qtV5ujayNtmf+vuzGvR9qk6WnXQFm7PZIQt4cg1AMRPXqAX69chXnLV+DZ1avLnkrlsLk8nCyNObk8fPJ5kCEv3Z7Pw9Ymdf0K5fNwfl2O4oTjjSBXXMnU5UFWzt7lIYJHvtRO+HARNlxOcuGOb6wcEUWYoRNmMcLt857pEdie/x+qd3StZ32Pa516HeWGsvhc1ym4rtDAI5zW90Q8l35dQp3lJBeaZ1evxrzlK/A/b79d9lQEIYaIHj3AXmuMwJRRIzGxv7/sqVSCIhT8rHN5ANnn89Da1Cmfhwp7kegef13JfB4d0oe2ULgf7+p/A15ll4cIHsVQO+GDqlNht4etr8TqGZ4Ixfnu6uq8Hl7CRvl5PfQw1mwTp3PXNpm4PRLIol0vM7G/H1NGjcQuw4cDAAK0RKPCfsp9+ULFEdFDEIBsXB6eZ8RzxyLn4RHyQubpqEA+D62NlriUasPL52FmIPKvQifJXlXyeQwW1yC0xcnlEduxLs/lIYJHsdRK+CjL7eH0f8fRlVHFEBfPJMmpv6eNdYjrA4lan/f34wsb7tfShhb+QtXjOkPdnQ/ctYvPOilxDpa6vm4PQRDqg4geQk/hcnEsc6fAdjEuK5+HStb5PDSavIUfO5+HilIvZLdPXjybb67bi+d883kM4XgDUmBoC4XbSRTFuTxE8CiHWgkfVHlebo9oW/ZJIVULccnSacIZ2zTnwbIMQlz41xNzeyqvh3Yd5G4SpMzrQfddfF4Pnw2jWB85tfOdmyAIxSOihyA4uDxUsnB5cEUMG1nn81Dh5+ng1dPaaHHIzAV25nHR5YS2pM/n4XgD43hDQt9gpNzdrYHLQwSPcqmN8FFTtwfdDyWUphdW6b8PQ4Tg5PUoNcQl2jC/65fm8mDmx8rzmp5nXg/OmFm4PZIQt4cHYVD8jyBYENFD6BmycHkkWyCzdXmYnssznwc3ppefCI0ZV5xrPg+VpHrpBA33ds3EOrYbeO7pB9Gx+PXd+k/cdU6Tk6Agl0dtBY9woH4/aV+vtW05jo/KuD2SPoeu4Rk5f8/Y/l6svB55hrgY8b9+WMdnvr/ca6RXPi2lu6rm9SjC7eG97hO3h1ABzj///LKnUHlE9BB6mwJcHlo7z0RcVFlZ+Ty0Nh5H02afz0NlwPKYUz8CZ3fRWJgytIV9s1HP0BaqTdkntrjWK13wqCM1FT686sHwuczL7WHtpKYhLhyHXNWOro3V4X6e3MV/7jXVL+Sl+nk9uIngY4jbQ+gybr755rKnUHmGlT2Boli5ciX+8Y9/YOnSpVixYgXWXHNNbLjhhthkk03KnppQMG4XuAx2CVIcuVblfB70jlB++Tw0tIUpbwGbZT4P13Y9G9pSZ5eHCB75EQ4AQZ+9OGwiCIi9GaJ9GA4gyKFvql2sLOH1ZdYWAwig1A2bwGBf5Pug1BuiCdt+WIgmgmiZsQ/LvJLm0x6beu3U39w0vw4DANz75LbTXhfZn/L+Mv/GQdhE2H6Pm02gkbxfGTRDhI3Wdb3RDNFsmK/xjSbQbMQf030HCBvh4GMgtLQJwgBhEMbaUDTCAM12G6V9bNxIGTlHdR5J7Rzq2sYThmidqlKcOOSyedlt/O///m/ZU6g8XSt6rFy5EnfccQduvfVWPPTQQ/jrX/+KpkEFX2ONNbDrrrtin332wWGHHYZtt922hNkKpWBxebhYGfNyeXDo1XweVNI3v5CXSBc55vNwbecV2pLFCS2Ou+hZHp9ZKZeHCB75U0Phw7lOu2r0pjyVKGIXG5Ln4ShGmOZJChOGuVHiiGVsSsQwvQbO3Mh2hMCR+Fmxoc6FmFcQDiA01aPaNJsIBwWQIAwRBsnXd1WkIAULZj0VVbBoNAM0LSKAKhBQ9bhQQglJGHQWf0mihS6GKEs/pQ9BSMM3vvENrF692qvtCy+8gH/+858Zz6j76DrRY+nSpbjwwgtx6aWXYmBgAO9///sxZcoUbLnllthkk00watQo9Pf3Y/ny5Vi6dClefPFFPP3007jxxhtx7rnnYo899sAZZ5yBSZMmlf1ShBzIy+Vhbefp8rA+L/k8WniJGza78YDhOb3vqh1VO4RjIkFHgcU5eSZDcKmNy0MEj+KomfBRN7cH+ZzLfOAuRNAig91VonfiKLBwxuYII8Y6bWFEFUhsjwmYwoaK6vjQ3B/ReorLQ31M9q0IG5TjQ3eG8AQLtZ4qUMTnoDpILE6NDNweSYjbQyiDv//977jiiisQBAFCZnJiAJ36AUP07HW6SvS4/fbbcfTRR2P33XfHDTfcgH333RfDhw9nt3/hhRdwww034Pjjj8fOO++Mq666ChtssEGOMxaqQBYuD5fQl7QuD8nnkYRHLDXZRwROjHnafB5GWmXm+PsMnR/E2Nw+2ElWC3J5OCeKFMGjeGomfFDlpChSgtsjyxAXdh+Ozo6h5xzDazpQYSiUMDLYp2+IC4mPGDI0V0rYUNEcH8rjeD1eyIsKV7BQ63Fv/n3FBeP4Dm4PrTwLt4dgJwyKTe5a4z/KZZddhqeeegprrbUWdt11V6e2L774In7wgx/kNLPuoWtEjzPPPBMPPvgg7r77buywww5efYwbNw6zZs3CKaecgssvvxz77rsv5s6dKyEvXYLTEbMFuDxsczONJ/k8DHDzeahNEhwi6UNb8sznESUDMSPv0BYHl0eSMEE6NTJNEimCR2nUSPhwCneItqXcEgluD1pQYbg9bHWRJI44hrgY+3AQWHLM65FpiIstrweJ5PWgMIkrPv3E5pmX2yMinAiCL2eccQZ+8pOf4KyzznJqF4Yh5s2bl9OsuoeuOL3lvPPOQ19fH+69915vwUNl2LBhmDFjBm655RbMmDFDksN0MS4uD40MXB4u7aJ0fT4PFW4+D0sb3o1ijrk6Ms7nwSH30BYP94jr0ZrkXHIUNZz60soGRPDIghzfp6S/kXM4FzFuXqFY8baO4WOO/3eNpzdlGFaXhOtYQ4U5hiZyXo9HCGbg00bbMHA/ojbrazzXLcp1nxayvvI+ApfdTBBY7L333vjTn/7k3C4IAmy55ZY5zKi76AqnxzbbbINp06Zl3u/48eNx00034bHHHpMPUzdRwiksZDvCncHN56FSp3weWhstlIW5UGUvEpPyeSSMEy3yDm1h1HF1WuSd1NRRfGGHtmhtUogaBbk8kgQPeqB8b+TripOrQC1OGY5C7ciTfRfk9sh03E4brguhjcGBkXPyUmenCWMc1lyovwU77IUZymLL5cF0tvBDXqqb14OegzmvB1CM28P31BdJaGomKDi8pdBQmhzo6+vDTjvt5NX2Yx/7WMaz6T66QvTIQ/Bos84662DvvffOrX+hPIpweahk5fIw1aFcHFXI56HPhxfKUuV8Hq7thm7M0+XzsP8OereWSNLKJU1oS91dHiJ45IP3TS5KFD4oshRFnNrqN/Ra+EamR8omz9vWr22uLaICS7q8HoUfXUvCFEO0efDyemiJTYlcHlqbCuT1cD1txSVHBzU/sl8H0UJye1SPV37/S7zy0C/xzvI3y55Kar7zne94tTvttNMynkn30RXhLVlw0003lT0FoQhKPIXF2I5RlyuWuIxrakORNp8HGcpSej6PQRt06nwepsJ04S/JN8zpreRFhLaQ41fc5SGCR76UGRLkFeqSZRhLiiOW04Xi5BviYuzDFPaS5v9PSeErQ++F+XrC/3/JdDYqaNdH4pqqJw0fug43iI0J7jVeH4e5zmCupTgbOVVZlxkR9aM0Nv7gv2HnGZdhwv83q+ypCBVGRI9BvvGNb5Q9BaEknJKWVsDlwc3nYR0/gxje7srnYe+jrKNqnfN5cG9iMgxt4d7E1NnlIYJHMdRO+HAYr0q5PdxFUtN3SAbhdI7va+f70HXsTvschWmKHsrroZb5JF5PcpfmdUKeS24P3wT3PU0YAM0Cf3rk77J69eqyp1BLuiK8JYmXX34Zt99+OxYtWoSBgfhF5I033sATTzxRwsyEQnFIQJXXKSwuddPm8yBDWeqaz0PFS9xICn/h7Cz65vPwDW0ZrNN5jel3WF1zVzjfZDNvaqrs8hDBo1hqFepSt9wezKNth/o15dvg5zoBkHBCiolWSAsnvKZaR9f6hK8oYyR8Nk1t6pTXQwuNySCvBxAJL3E5ZjavPCBqiEuP3HQLxfP444/j/PPPxx133IGlS5di/fXXx9SpU3H66adjq622Knt6taDrRY8HH3wQ//Zv/4Zly5YhJBTwIJAvql4kC5dHojMjh92BrPN5qFQhn4eGjyDimNvDN7SFUyf9UbXm9mmPj02cG7MPpx3Wmrg8RPAoh1oJHxRVzO2RUNf6HDWcSXzILIfIYF4PZoJP1tw6cMQJex3e3yStGML7W5SR14NbL8u8HhxccntYk5Qm5PZwSm4qCBlxzTXX4Pjjj8fbb7/deW7x4sX43ve+h5tuugk//elP8eEPf7jEGdaDrhc9vvzlL+Pd7343Pv7xj2OjjTZCX1/8wrN06VLMmiVxYL1ClV0e0XZF5fOghJMy8nmQgog1nwfzZtGQz8PYpqTjEPMWIozzyim0hZpTVV0eIniUS22Ej6q6PRJulo0uDtKVYUgqanofSXEiJ4GlU0aJO5QwMtiOU8eIImYofdBuFVUAUd5bYg6asKG6PJpNwCJyqAIIV+TQ3RuEy0MVLJjuCa7jghIubIJD5dweQgc5vSU9Dz/8MI499liMHTsWxx13HPbcc0+sv/76ePHFF/Hb3/4WV1xxBQ477DA89thjGDduXNnTrTRdL3q88sorePrppzFsGP1Sf/jDHxY0I6Eq+MZnFuHysIktVcjnYWtD1qthPg9jESO0JW0+D2MRI7TFKES4JjUtMLRFb5MgPBTk8hDBoxrURvigyNPtwQ2J0PrMOcTF1IfJ2cEQWPI4unZI4GCEuLiOS+ERyhKEAwhd26giRxgiZLiX1VAWbj2qjRYawzxFRRUWbG4SJ6EiC7eHwxji9hDy5utf/zqmTZuGa665BiNGjOg8P2HCBOy3336YMWMGDjnkEHz729/GpZdeWuJMq0/Xix7jx49PFDwA4IILLihgNkLZeF+bClCPs1SouSEqPnCFjvqR541ltn1ncRPs7CgxdlKBD0M3CALd8BqKxPcGtEpU4TU4CiHGLlxcKrY+GHk83GCGk1Su7/LgCiA+cENjOBQiMjgcX6sibg8hD5577jksWLBAEzxUxowZg7lz52KfffYpeGb1o+tFj7Fjx+KVV17BxhtvTNZbtGhRQTMqnl+vXIURQYAJ/cMwsb+/7OkIgiAIgiAIgtBFPLt6NZ5b/Q7e5Dp6hUQ23nhjq+DRZr311sM666xT0IzqS9cfWXvOOedgxowZeOGFF8h63Xxk7V5rjMCUUSNF8BAEQRAEQRAEIXMm9vdjyqiR2GX4cAAt90s7r0cxPyW/ATkwevRo48mjKmEYomHI73P//ffnNKt60vVOj0022QTnnHMO9t57b+y6666YMGEChg/+Z2wjR9YKgiAIgiAIgiAIVeHII4/E5Zdfji984QvWOv/1X/+FQw45JPb8F7/4RTz66KN5Tq9WdL3o8fDDD2Py5MlYsmQJFi5caK0nR9YKgiAIgiAIgiAIVWDZsmW45ppr8Prrr2PLLbeMlT/99NOYN28eZs2aheuuu67z/OLFi/Hkk08WOdXK0/Wix2mnnYbx48fj4x//ODbccEM5slYQBEEQBEEQBCFH5Mja9Jx00klYsmQJHnvsMeMGfTh4OuJnPvOZ2POyoa/T9aLHokWL8PjjjxvFDpUf/ehHBc1IEARBEARBEARBEOysv/76mDBhAiZNmuQkYrz++uu47LLLcpxZ/eh60WPLLbdMFDwA4PLLLy9gNoIgCIIgCIIgCIJAs/766+Oiiy7CBz/4Qee2kshUp+tPbxk/fjz+9re/JdaTRKaCIAiCIAiCIAhZUOTJLQGA7gvnOPfcc7Htttt6tT3//PMznk296XrR45xzzsEZZ5yRmL324osvLmhGgiAIgiAIgiAIgmDnIx/5CEaPHk3W+dvf/oaf/OQnsecnTZqU17RqSdeHt1x99dXYeuutcdBBB2Hs2LHGI2vffPNNPPXUUyXNUBAEQRAEQRAEQRDcWLBgAS6//HIcfvjhZU+l0nS96PHNb34TixcvRhiGeOWVV/CHP/zBWE8y3AqCIAiCIAiCIKQnaLZ+ihyv2zj33HPJ8nfeeQc33nhj5xQXwU7Xix4bbLABdt99d5x44okYNsz8cpcuXYpPfvKTBc+Mz/PPP49zzjkHTz75JNZYYw2sWrUKxx9/PI455piypyYIgiAIgiAIglAK/3jkF3j5kV9g9Yo3y55K5px99tkIgsAqarTL1llnnYJnVj+6XvTYcMMN8bnPfQ777bcfWW+77bYraEZuPPbYY9h7773x0Y9+FA8//DD6+vowf/58TJo0CX/4wx/w3e9+t+wpCoIgCIIgCIIgFM6mux2ETXc7CEtfeArzv/HxsqeTKcOGDcNXv/pVjBs3Tnt+YGAACxcuxK233opJkybh4IMPLmmG9aHrRY85c+Zgo402Sqx33XXXFTAbN9544w1MmTIF/f39uPzyyztH7+6+++740pe+hLPPPhu77747jjzyyJJnKgiCIAiCIAiC0GLoVJXixus23ve+9+GrX/2qtfycc87B7Nmz8frrrxc4q3rS9ae3bL311hg1ahSWLFkSK/vrX/+Kv/zlLwCACRMmFD21RK688kq89NJLOPTQQ7HmmmtqZZ/5zGcAAGeccQYGBgbKmJ4gCIIgCIIgCIKQA9/5znfI8iAI8NWvfhVz5swpaEb1petFjxdffBFbb701NthgA9xyyy1a2ciRI3H++efjK1/5Skmzo/ne976HIAiw1157xcrGjh2LrbbaCn//+99xzz33lDA7QRAEQRAEQRAEIQ922203Vr1Vq1blPJP60/Wix7e+9S1svvnmGBgYiFl/Nt10U8yZMwcbbLBB5cJbFi9e3HGhbLvttsY622+/PQDgrrvuKmxegiAIgiAIgiAIFO3wliJ/epG33noLb7zxRtnTqDxdn9Pj2WefxV133YWnn34aO++8s7HOjBkzMGXKlErlxnjqqac6jzfbbDNjnU033RRhGOLJJ58salqCIAiCIAiCIAhCzrTTGdhYvnw5fve732HatGkFzai+dL3oMTAwgOHDh1sFD6AVD7V8+fICZ5XM//3f/3Uer7322sY6o0ePBgC8+uqrhcxJEARBEARBEAShG7j//vvx5ptvVvb0k2uuuSaxzs4774wvf/nL+U+m5nS96PHWW28l1lm5ciUWLVpUwGz4qCJMf3+/sc6IESMAAMuWLStkToIgCIIgCIIgCEkEKPZEFZeRFixYgNNOOw133303zj77bGfRY/Xq1bjoootwyy23YPjw4Xjttdfwvve9D2effTbGjx/vNnGCkSNH4rvf/W7syFqgtWn/rne9y5oGQdDpetFj6623xvnnn49Zs2ZZ68ycORPbbbddgbNKZuTIkZ3H77zzDoYNi/+pVq9eDQAYNWpUYfMSBEEQBEEQBEGoG0uXLsWcOXOwaNEiPPTQQwgCd1FmxYoVmDx5MgYGBnDnnXdi9OjRePvtt3H00Udj5513xi9/+Uvsueeemcx35513xqc+9alM+up1ul70mDVrFj7wgQ/gwQcfxFFHHYWdd94Z6667LpYsWYKHHnoIV1xxBR5++GHMnz+/7KlqbLzxxp3Hb731FtZZZ51YnbaLZaONNipsXoIgCIIgCIIgCHVjzJgxmDlzJgDgpZdewo033ujcxymnnIIHH3wQv//97zupBoYPH445c+bgvvvuw6GHHopnnnkG6623Xur5/uAHP0jdh9Ci60WPnXbaCXPmzMF//Md/4Je//GWsvNFo4Morr8Quu+xSwuzsvOc97+mojy+//LJR9Hj55ZcRBEGiS+XmZSvQCIC1ggBrNVoH9kzoH4aJlrAZQRAEQRAEQRAEimdXr8Zzq98BALzVbOKtMMQ74WBhM0DQLPBEFcex1lhjDechXnnlFVx99dVYZ511YsfJrrHGGjj88MNx8cUX44ILLsDXvvY15/6jTJgwAQDQbDZx880342c/+xlefPFFbLDBBjjggANw5JFHer2OXqTrj6wFgE996lP43e9+hylTpqC/vx9hGKLRaOAjH/kIfvOb3+CYY44pe4ox1l13Xey4444AgKefftpYp33Cyz777EP29fE1R+LYtdfCJ9ZaE1NGjcSUUSNF8BAEQRAEQRAEwZuJ/f2de4tPrLUmjl17Ley7xoiyp5Ub9957L9555x1ss802xvIPfehDCMMQP/7xjzMb86WXXsIee+yB6dOnY+7cuZg/fz5uu+02nHDCCdhll13wl7/8JbOxupmud3q0ee9734tbbrkFYRjitddew/rrr4++vr6yp0Vy+OGH449//CMeeOCB2FFEr776Kp577jmst9562H///UuaoSAIgiAIgiAIQvfz4osvAmhFCpjYYostOvUWLVqUOgXBsmXLcMABB6DZbGLWrFkYN24c+vv78dprr+Gvf/0rfv7zn+OAAw7Ao48+aj3tU2hRe9Fj6dKlGDNmDLt+EATYcMMNc+s/S4499lhceOGFmDt3Ls477zzNvvT9738fYRhi5syZnVNcBEEQBEEQBEEQhOxpn6j5j3/8w1jezvHRrpNW9PjOd76DHXfcET/60Y+MSVebzSa+9KUv4eKLL8ZXv/rVVGN1O7UPb7n77rtx5pln5tL3E088gaOPPjqXvjmsu+66+OEPf4g333wTJ554It55pxUz98gjj+C8887DgQceiFNPPbW0+QmCIAiCIAiCIEQJwqDwn7zZYYcdALQEjVdffTVWrm5Et0/ZTMNtt92Gyy+/3HrKTKPRwDe/+U3cd999qcfqdmovehxyyCEYPXo0PvrRj2Lx4sWZ9XvVVVfhuOOOw1VXXZVZnz7sv//++P3vf49ly5bhQx/6EPbcc0+ccMIJmD17Nm677Tavo5YEQRAEQRAEQRAEPvvttx8233xzDAwMYM6cObHyl19+ufN4gw02SD3eiBEjjIdZqPT19WHYsNoHb+ROV7xDM2fOxKabboqddtoJxx9/PI4//nivD1oYhpg3bx5mz56NzTbbDHfeeWcl4qO233573HDDDWVPQxAEQRAEQRAEoSfp6+vDj370I0yePBlf//rXsf3222Pq1KkAgN/97nc4+eSTAQDDhg3Dpptumnq8lStXsuqtWrUq9VjdTu2dHm0+8YlPYP78+ViwYAHGjRuHAw88EBdffDEefPBBLF68GM1mM9Zm5cqVeOaZZ3D99dfjuOOOw9ixY/HFL34RM2fOxK233loJwUMQBEEQBEEQBKFOdGN4CwDsscceePTRR/GJT3wCZ555Jv7lX/4F06ZNw/z583HYYYcBAD74wQ9mknPx//2//4c77riDrHPvvfdi4403Tj1Wt9MVTo82m2++OW6++WY88cQTmDNnDr75zW/ilVde6YSArLXWWhgzZgyazSaWLVuGN954A0ArKc1HPvIRXHjhhTjkkEMqf6qLIAiCIAiCIAhCL/LS/8zD3x+dpz23esWbhY2/zTbbGMNbJk2ahCAIMGXKlEzG+dKXvoRJkybh/PPPx7Rp07D++ut3yhYuXIibbroJX/va13DnnXdmMl4301WiR5sddtgBl156KS699FL86U9/wh/+8Ac8//zzeO2117B8+XIMHz4cY8aMwRZbbIHtttsOu+22m5yAIgiCIAiCIAiCUHHG7jIFY3fRhYUlLz6JX19wcEkzah2Acffdd2PdddfFsccem0mf73//+3HuuefihBNOwAknnIARI0Zg+PDhWL58OQYGBgAAs2fPxgc+8IFMxutmulL0UHn3u9+Nd7/73WVPQxAEQRAEQRAEoScIwgBBs7gDF4oKb7Fx4oknIggC/Od//memKRI+//nPY5tttsHpp5+OP/7xj508H+PHj8e5556Lww8/PLOxupmuyekhCIIgRybpjgAAIABJREFUCIIgCIIgCEmsWLECAIx5HwHgnnvuwQ477IALL7wwsa+vfOUrePDBB3HUUUfhs5/9bKbzBIDJkydjwYIFeOGFFzB//nz85S9/wXPPPSeChwMiegiCIAiCIAiCIAg9wT//+U88+OCDCMMQv/3tbzuhIiqXXHIJnnrqKZxzzjnWfsIwxOmnn47zzjsPJ510Eq6++urM56rObezYsfjgBz+IrbbaKvNxuh0RPQRBEARBEARBEITMCEKgEQaF/QRh8pyazSZ22203bLnllnj55ZcRBAHuu+8+bLbZZpg2bZpWd/r06Rg9ejSOOuooY18PPPAAdt99d9x888247bbbcMEFF3QOz8iKCy+8ECNHjsTnPve5TPvtRbo+p4cgCIIgCIIgCILQ2zQaDTzyyCOsukcccQSOOOKI2POXXXYZ5s+fj3e9610466yzsP/++6PRyMdHcMkll2BgYKBz4qjgj4gegiAIgiAIgiAIgpDAiSeeiBNPPLGQsbbYYgssWLAA6667Llnv2GOPxVVXXVXInOqKhLcIgiAIgiAIgiAImRGEQeE/3caMGTNw1lln4Z133iHrPfDAAwXNqL6I00MQBEEQBEEQBEEQKsQGG2yAsWPH4r3vfS923313bL/99hgzZoyWO2TRokV47rnnSpxlPRDRQxAEQRAEQRAEQRAqxNSpU7F06VKEYYinn36687wqeoRhmHkC1W5ERA9BEARBEARBEAQhM4JmA0GzuEwKRY5VFBtttBG22247HHXUURg2zHzbvnTpUsyaNavgmdUPET0EQRAEQRAEQRAEoUJsuOGG+PKXv4xJkyaR9X74wx8WNKP6IqKHIAiCIAiCIAiCIFSI8847DxMmTEisd8EFFxQwm3ojokcP8OuVqzAiCDChfxgm9veXPZ1SCQMgCD0aBmGrcY6EQZhZ5ulmI0Sjmc98wwYQNHPpumT6AAzUou8g6EMYpusvQAMhUv4hgwYQlvxhCPqAlO9F6XTDayiSoK/sGaSnCq8hSG8FDzI4BDDI/L3I872twN8tB8IcowKaDZ9Fl5nQawHniOcYXXhwiDPPrl6N51a/gzebrXVBgGJPVAnQfX+ED33oQ9rv999/P/bee+9YvT333LOgGdWX7gt+EmLstcYITBk1sucFjyjqxdPlQurSruk7hmWRoC4ebHXIORD/49Uy7gKIXU9NuNRgNlIWwiF3UazW811IE+3IBf7gDURALYqpmwxq3E6Zvb1pXHIuxnHi/TvfkPjcSEXGiM3DYQ6ZttXKEvpJKKf6Tmqf/U1htSFfb47vc9LYiX0T42Ta1ufz4Pj/0vQ+OL0G+HwnNaxjD3Xg9/3aGZes4/f9TOJxTQp92ijX1ZCZ0NDnOs9dQzSZ6x11HWMTR7hrHUBfZyXNoYg1YK8xsb8fU0aNxC7Dh5c9la7l5JNPLnsKtUWcHkLPkeT2UB0XYSNEwHRMqHWTXBu2utF2zSBEw1LmO7apjTpOlGYDaDTjj+P1AjSa4WDfAYLO4waCpqVRowEMloVBA0EYfxxD2xVvAB23gt1NoTkjtPZEG/QhtLkzBvvg1HHt29mBYXJbUA4M07yMc1Xf2+gc4/NPdJ9E5hQEDYTqHB3cDrH3yKVtwrixcq1t0muk50H1ndQ+C3dPHaiN4JGlqOGy/5QoiNB9GcUMhhBBzcH6HDUnZwHFTfAdKqRE7D5+HSNmcYIWKdUy5TURbcKgYXwMYgNBE0EaypqiYV8bqDkgqXyQ+kYKU9xgtqFEC5vYkDQHl7pauwZf3BCXh5CGO++8E3PnzjWWHXbYYZg8eXLn9yVLluCAAw7AJptsotVbe+21cckll+Q6z7ojoofQ01CCQwwlxMWlHVe48J03JVhowgYz5IVfzyxykPNWBBBS2FBRb5bZN7eqmJEcWkLfjHJCUyjxhBAxqNfTKTOLD6abYFqEiQsheQsssfk4hMOkEkVStDW218pE+MiL2ggeSeTp8iDb6nWdXWdZCBEct4X6nPV9H+zH8zNBC0kc90Taz0Of5TGB5vDg/S00QYTpouSKICrcEBWuG9XXBUuRicsjJ3dIrxI0kVuotW28OrLnnnviuuuuww033IAgCDB8+HBMnz4dkydPxk477RSrf9ddd2m/Dxs2TBKZMuh60eOUU06R5C5CC020qK7bw1jOnIMqWFDihV6Pcm8MlXFzeeguD6YYogog3BtUtV5mYgghcHTcHZSIQd3Uewoj7TqdG167A8Pct0EIIW+s4zflzjfbTHGjqm4PYx2tTISPrKmV4FFVl4e1Xc6hLc7uEROM0JZO356CBie0he3SMD2XYximFu7pLm6ETHFDC1Gh3B5aPULcYISvxOdACyeVdnmIECJ4MHLkSPz4xz/GwoULscUWW+Ciiy7ChhtuaK2/xRZbYNy4cQCAV155BaNHj8bBBx9c1HRrS9fn9Pjud7+LV155pexpCBXGSbn3VPlddjUS84RUIK+HV/6PGub1SG2bNhYm3zw4Jwfk7to65xRJv2sbq+dwA1ZWbg9rH1pZQn+S44NNrQSPJCrs8sgttMUyB6fnCFiChKPDZKgwXfgLSU3yeWS9Jsgyn0enrsO6iTt+rF9xedSWvz12Cx647t+x4BdnlT0Vb+bMmYMpU6bgxz/+MSl4AMCvfvWrzs8zzzyDGTNm4POf/3xBM60vXS96rFixAnvttRd++tOfYmCg+3bHBEd8k1V57iYkXqgZdbmCicu4pjYU7Fhfy84SKXJoCzZ3MYT7NRZYF5ODQgM5tq/AkXJB7XHD5py81PD+0bu66W9iyLFchI3o3HMQRUT4yJfaCR51c3kY+3IMSzElOHZcPrqFtrQrJH82chGmWaEt5usJ//8lL5+HShXyeejjFJfPw7Uvqm4W6zIjIoSUxuY7fwz/euS1eO9B55Q9FS9WrlyJ22+/HbNmzUqsO2bMmI7Lo80nPvEJLF68GK+99lpeU+wKul70GDFiBE477TQsXLgQ++yzD77yla9g4cKFZU9LqACqJdH3wlaW2yNah9rZ4O6mqH3w43d97LM8kUMrYy/wPazGZB8RnK3Pg81gWChb65iIlqWPpXfdvXQWWJT51NntYexHKxPhw5faCR5JVNHlkVmODVsn/t8LQzC+3zp9J4vT/uEvft/vND7hL+7XyKLyeVBrjbzyeVTB5WFrJwlMaYIwKPynjtx999046KCDWHUnTJhgfP5jH/sY7rvvviyn1XV0fU6PH/zgBzj88MMBADNnzsR9992H008/HcuWLcOnP/1pTJkyBX199V44Co4ouT1M2JKUJuXVyCMPSNq8Htx6tcrrYcvlQeZR6IM5l4f6mBgnWuSd14NRxzGfA5njIYuTXVxziHjknKhybg9rXa2s3BwfXUdVBY8auzzcPyc5hbYQ88njqFrWXLyPqrUJGszXwQ1lqWk+D71N+nweheTnsNR1CX0WBFeee+457Ljjjqy6N954o/H5TTbZBI8//niW0+o6ut7p0RY82uy777644YYbcPXVV3c+ZGeeeSZeeOGFkmYolEkRbg/bLkQatwd7R4W5Q6ON25N5PcoJX6H6ds7rkdCv61hZh7jU3e1h7Esr6yvVudA15OyMycXhYRi3Ei4PRn3rc8RcXI+PdU9q6j/WUGE54S/Gvrs8n4cK1zHKdaAWsr4Sl4dQMsOGDUsdmvLyyy8jDEV8o+h60cPEsmXLcNttt+Gmm27CM888g9mzZ+Nf//VfMXXqVMybN6/s6Qk5oYkDOSWsymsnwicxGJ3/w9yGIm1eD5LS83q0y9Pm9fAUMbxuljPIrWHq2zGcwsXKTgovCeIEKTgUkNuDM49WuQgf3pQYCuTad14uj3T/D/jConNoi+N3hbkPTmgLo29SrOCEv/gJGkkn+VQhn4fWpqB8HqRw4rEmMq13inB5WMdzSXDvMLdeIAgDBM0Cf2qqRI0bNw6/+tWvUvVx1113YYsttshmQl1K14seJ598cufx448/js9+9rPYdNNNcfzxx+OPf/wjpk6dijvuuAMLFy7ElVdeiQULFmDffffFPffcU+KshSJxcXvYhJO8Yk6T+vJJWFpWXg89uRpz0VbhvB60iOErniTn9XC3hWcgjrjmEGE7ciKiSEk3kkl9J/YXKxfhw5kaCR5J5cUJdB5ib6zQQYhw6cM7tCVdPg9zQ0afNcvnocK9tlYhn4c2nwxPQ8nC5eGUGLWe99ZCRdl3331x6623YtGiRV7tn3/+ecybNw977rlnxjPrLro+p8f3vvc9jBs3DjfeeCMefvhhhGGIsWPH4pRTTsExxxyDTTfdtFN30003xVlnnYU333wTxxxzDJYuXYpp06aVOHsha5xybVhyezi188wDYn2e6K8ZhGiE7Rwd9cnroeby0PJ6ULBzeaj0gc7roT6njxOgD6FX7g+qnV9eDx17vg3T2NR8THlKEnNNxDpJziFCviex/vT3wWk+Dm2NZSnzcNC5VtL17ZK3pBbUTPCgw5zKCcViuTwcxc78Q1t8XH3RsnThL2Q7Yx2Tq89H3PAJf+EJIlXO58FNum7NrVHAKSzeTl9xeQiejBkzBh/+8Ifx6U9/GvPmzcOwYfzb8xUrVuDf//3fsd9++2GjjTbKcZb1p+tFj7feegszZ85EEASYPHkyjj/+eBx44IFoEGr42muvjeuuuw777befiB7djJLQNAyGBP8kgUMTHhKSomrtlH5VgcIXVbAgxRBV2GCOqwoblMjBrae1CQIEYVsMaSBoMm78lcdh0IfANcmpA/QNp0EYabfLQRixJUzNPXlpQuLMWDJVFyEjYb6phA0qwWxCW586InxkQM0ED6867aoluDzM88g5tCUD5wfgLrAMFXJCEU04hrZwYIob1nweVJsuz+fBxfU0mA5ZuDwkgSlJIwxSr3Vdx6sr3/jGN7DtttviwAMPxPe//31sttlmiW0WLlyII488Eo8//jgeeeSRAmZZb7pe9ACAGTNm4KSTToqda2xi+fLlOPXUU7HHHns4KW1CfcjL7ZHHqS8+Qgl1GoxeTxUsuCfDDDk+aGdIgIbj6S1oNIBmCscHdcqIUk+7ESVvHO0Ch287+tQXos9OGeHscBUjDEIILRoYxibEFP19rqnbw9AHu12nXIQPKzUUPOrm8sgytMUoQmSZN4QTapIyn4drn9x2SfmihpB8HhSUgFHEKSzi8hDKYosttsC1116Lww8/HBMnTsShhx6Kgw46CDvuuCM22WQTjBo1CsuWLcOiRYvw6KOP4rbbbsMtt9yCVatW4Wc/+5n1KFthiK6/q584cSIuvPBCdv3f//73uOKKK/D9738fn/3sZ3OcmVAJLG6PJIpwe5CChUfojTpnKvzFL0yGJ3Kozg7S5aG20Y6yJRwLGrZQFk79CKRLw/foWoYwkibEhRIjTIID6eyI33g7O02SqLLbg1FPhA8Paih4eNdDBVwezjk2qJxFjmEsafKGcHIopc3nQYzrPJ6xjuTziM2HKS4kCQ9FuDz0+Zj7EIQsOPTQQxGGIY4++mhce+21uPbaa611wzDEmDFjcO2112Lq1KkFzrK+dL3oceqppzrV33vvvXHllVdi/fXXxyGHHJLTrIQy8HZfZOD2SDs3yethIHVeD7fyPPN6ZB7iQozFr+92M53o3qiz28PQj1NbFCB8dBFVFTzq5vJg1U8cwLW+a96QFKEteebzMJL0Xkg+j3ibdPk8jOU55eew1c0rd0hPMHiqSpHj1Z3DDjsMu+66K8444wzceOONaBo2B4MgwNSpU3HRRRfJiS0OdL3ocdRRRznVbzQaOO644/KZjFBNCnB72EJO0ub26NW8HiTsNoTAkWNej54NcUmoWxW3R22Fjy6hNoJHFV0esdOQejC0pfB8Hsz5+riHJJ8He0ynUBpxeQgVZ6uttsL111+PSy+9FHfccQeeeeYZLF68GGuvvTa23XZbTJ48mZXzQ9DpetFDEFSycHs4jZGB26PKeT3oevnl9dCSmXqIIVnm9TDfYA+2SxviwqaeIS5VdHsklovwkRu1ETySystyeVgr1jS0pdN/2tAW05wZIkvm+TzM7UNK3Mgxn4dKlfN5FH4Ki7g8hIrwrne9C5/61KfKnkbX4BEYKghdiMP569aLeQ4XR26sK7s/5i4Ld9dGt7tyd5t4MchaG3XhR96AqGWulv++yL8KnbjyHCzTjjcYWrHxhsVxLOfxU+7uOu1UO1yiIuPQN0p028SxU95gO5+U4dB3XamV4FFTl4fz8bEZfpc4f1f5jN8uKuyoWg5qfd7fL+t8HipNTRCh6lU/n4exP0+XR3Loi7kPgaYRDp3gUsxP2a9YqDLduXoSBIKq7RRwE3Elxbya+vOJu6XrqfOx1/OKK/ZY9PHtw32Mx5GuPS3NeQgj8Rt4V2u620LdSTAAEoSopLFcRJGIsJFGFGG0FeGjGGoleCTVycvlERU1nG++7X0lVnd2ZjiKK4i+turk6uBfB3jXlTzzeWhtmNfgOuTz6JQFvP6idYte8wmCUE26a+UkCI7YnBrebg+H8VxskxTcnRb+zo06B9YUHGKF1eN4mblM0sZFp67ve3PqImIw+8wwqaDxJsDxBpDrKnFyezDGtVfO1u2ROL4IH6mpneBRlsuDrEy4PCoX2kKQNp+Hkey/v7O9vljqsQURd3HD57pOkWc+j6R1UCFrMYvLQ46pFYR6ITk9eoBfr1yFEUGACf3DMLG/v+zplEYhOTq0pKj8dmquDlPdKuT1UOtlnddDS2aq5PWgYOf1cEqG2ofKHV2bgDF5qWu+DTLBqOl1G3KIdObtkKfD1sZhPnnm9mDVkxwf3tRO8EiqV2GXR+1CW9r16nRULVe0YObz0Mj1aFq1vb1eGfk8TM95uzwq5g7pRp5dvRrPrX4Hbw6u4YKw2NNbuOt6oTcR0aMH2GuNEdiwL4UVtstxOYVFa+eQFNU2Hkd8IQULDyGHnbCUefytKoCoIgc9hyGRgzq9xesoW+2GnBAVqJvNko6udevTJDy4iRGuR+XSN/+UuGN4v5nJTo3jckUu8MWjpH6rLHx0JVUUPPJyeaQK20rO5UHVTxrXNdQkbWjLUEcl5fMw4vtZZObzUOAfTVt+Pg99PsXn8yjC5aEhLo9EJvb3Y2J/P559ezX+e+WqwsZ9/smf4X+fvBmrV75R2JhC/ejS1ZIgmHE7s92vHSx1s8jtkXVej6aH3bQSeT20xKbcG5EuyetRSvJSorrnzUmsqktuj9gc8s3twapbYqhL11FFwSOpblGhKy4uD0f3Bd1Xvs6PoWTRVRI0Ms7noXXOC0GqQj4PFe6aIct8HmW6PJxCnXvc5VEmW20/Dfsd/mPsNml22VMRKoyIHoKAyMXW19aYw06DT14Pbr2uzOvhk/+DVb9CeT00HC3lmZ4i435zkmluD5ed8wxye1jnkVVbiPABoLqCR41dHrmHthDj0H8z5mvtlnweGV+7isrnQbpCckyczk1qKi6P6hI0AzQK/CkylEaoHyJ6CD1HFm6PRDzVf3JHhLnbwXWD6G3M7al6/J0h7sJM6ZB9lK3PrtrQY/OCvC/yb7wfs63b7sSgb+49FvRZ3Eh0xnAbn77pT97xpW6CKuP2EOGjHGoieFTR5cESFHMObaFx/z+TaT4Pwknie1RtYBMq2KIF8z2sUT4PexueM5VaL3FDbWL9eOfncKkrQoggVB0RPQRhEBe3h9UK6ZlpPClxFzf2lV3PI7kYbWVVH3NzmzAXcuyQF/f46SGKCXEhF9eD41CnqpA3IXmHuKS4ISWpotsjqW/TXLJqix4VPmoieCRRlsvDBefPV96hLe0yKoSv891JCRqO7rYcvvfN8MIxuyGfR+ZrlaS1keeJeN4hy+LyEITaIqKH0Dt4Z+ZWH5fr9gDyECyqm9eDhJ3XwyHeWkJcmGMyxlbHqaPbw1DHaTwRPvjUSPCom8vDPbEof05akYS2gH2tYebzUOnGfB76fPg5zxL7KsDlkcV4vUAQFv8jCDZE9BB6HqsDomC3h7V+xufe1ymvR8gWNhTYNuPsF760iFFyiIvniQeZhLgkUVW3R1L/pvlk1RY9InzUSPBIotIuj6JCW4zjlBzawukz72uFx/WLdHt0QT4P7tqGU6dol4eEswhC/RDRQ+gtKuz24OTs6Ia8HvQclA4p2662MPQQQ9SnvV0avsKIp9PCM8TF+3SVrENc6ur2MNRzGjPtjTvZuObCR4r5lyF41M3lwaqvFWYQ2kKM4xvawhqvKkfV+uTzoNqkDGWh6ynzqXA+D27Oj1i5uDwEQVAYVvYEBKEKhEGIYPBKFzZCdgZorZ3yOKvxuH1y5+HzOptBiMZgm2YjRMPSptkAGs324wCNpkW4aQQIBsvCRgNBs5k4hzBoIAibsccxgj4gHBj8pQGAU69NH4Doc4PVgwZC25hUO/QhtJSZ58BoZ8T0Wu2vn55XA4i81gANhLb30nechHGptrG/B/FeRsvo10LNjxjDNKeM2gZBH0Kibe2FDwvkDXLFHB5Afi6PVAKgZ2iLUVj1FRyM47gJfdU9qjZ5Dlnn89DaaIKI/Vpep3wetja+42YxjzTjCYJQHcTpIfQevtZGB7eHLUzGaZeCKOvGvB7aAs4nrwcJM9Ya8Lc5l5HXw/UGzfXGxffm0eqY4Ls92H2aqubh9jDUdRo3RdtUiWJrSBUFj7JcHnS/TEHEMbRlqNDg1sgyJ1CK77fa5vPQ+nTP58E9vaWu+Ty4a6DEct+QY/Vty8lV0ks0wmKPrG242HSEnkNED0EYxPvcd++QGXq8JnM+Vc7rQdfLOK+HT/z04OKUPro2YaxoUQF5PQoLcTHWd7N8O+1UO9n688vtIcJHedRC8IiWV8Hlwfi/k2mOmDxCWyy/6/iGv2SXz2PotXiEVjL/Blnk89Da1DSfh2l9Ii4PQRB8ENFD6Bm8VXnfWE+HpKhauwxyf0THLyuvh16vpLweTmKIXfwwJ/vLwXbtfdPsZiN3nQN9I+J/Kami2yOxvQgfuVAbwaOKLg8nKhraUoF8HkN/L0chPDoWGcpSTD4PSuioSz4PbrmG9waWuQ+n8UQUEYTKIqKH0PNkcgqL58WbM55P/GueYTJkPabIodtw3S2+JNoi0neB7WqB7rO2yycene7D19mRV4hLHd0exv65Y+XYtluFj9oIHtE6VXd5VCC0xfi3ZX6OM/3+JB0anteDDPJ5qJSVz0NrU+F8Hr6nsHiv13xPfREAAEEzKPxHEGyI6CH0FHV3e1Qhr4dejzWFzPN66CEv3K8x26Iz+xhv8w21b0iMuZ25jefNsnE32++m3eemvKpuj8Q+RPjIhFoJHjV1eXiHtjh+N9Bzc3CQZZjPo9ijarlitfu1Lot8Hir6JkXGa4MMhRNxeQiCkAUieggCquv2qFNej5BZT2/DPe2GuWPGDmVxdEdkeXTt4Gsxt2Ms9J13Td2cHaSzIkWIS13dHsYxuOPl2LZbhI9aCR7RelV3eRhxDG0h+vU9HYb6DnMXNNouO7cwpGyPqs3gmsTM56Hik8+Dew2vYj4PcXkIgpAGET2EnqMubg+yz4LyenDrZZHXQ0/O5hPKwqznlddjsKl3IlI/YSQJ14Smrv0P1U8f4pI4RIXdHon9iPDhRe0Ej7q5PDIMbfEfx1dETeg/F0HD7/s9Nh+f6xKB7oDkhbJkkc9DnwO3Xvb5PNiIy6NyBCEKPb1F/gwCxbCyJyAIVSEMQgSDVz/1cVntfPrlttHqNUJrHKRa1gxC1nFgzQbQaLYfB2g0bbG5AYLBsrDRQNBsmjtsNIDBsjBoIAjN9cKgD0E40Pol6APajymoemQffQCybReggRDNyHN9CE31yTEaAMzvURD0IbS+3gYQfW9NzzHmYBsn9nrI/vUy63uB1s1KqPbj8Hc1ve82YuMQ/RbVtu7Ch4lKCh7RujVyeVQ1tCVpDmZ3iacw4RvG4p3Pw1yPSrqtOTzIUJZi8nlwHRl55vMwtcnkxLwC2gmCUC3E6SH0DOr9uovbw+bUSLr/t4atuIzn0i8BPxFpMXk96HpZ5/VQy+g47MoeXcuxflc1xKWL3B6JfZXVtouorODRFS6Pioa2ROfg/D3s97qyP6pWfZ73/Z9FPg+tTUn5PLQ2GSdCjdY1YlkXJYe+JPeROJ6nC1gQhGLpjVWUIDCp4g5C1nk9vBYxGef18Em65nV6S6ZJ6jIMX2G1KyHExTt5qcsOeQ1ye4jwUQp1ETzq5PIwNvX9DGbo/EgV2tLpI4fvXeejajPI56Hgk8+DTg5ufkzVY7tCPNYcvvk8OnUquEYT7DTCoPAfQbAh4S1CTxEGQwK9UyiKGgIShGw53xpGktAHFXJi6ps7d6pfNXwlbAABw/Gv1lPDWuh6RMhLECAIGSEvajhA2lAWxxCXoVADU6gKFb5iD88gQ0UKCHGh55ZBiItTqAoRguMyt1i/RHiJsUE54Sqp2taYuggeSZTh8qDEkkxDW2zjJY2TUWhLUpvEdoSgYfy7+bx3GeeZqlo+jyxyiLnm88jL5cHpI3Fu4vKoBH9+5qf487M/xdurlpY9FaHCdPe2kSBw8T6FRX2c/U4Ct0+fpGDcMBnO7ktsPh4hL6R9lx3ywtxZc7VPOyci9euLY8XONMTFcxc1zSku5g4r6PZIam8arwpta0idBI8qujx4ZBfaQs8jj9CWSB0jxXxHU+GHSWMXmc9Dpax8HlqbDPJ5ZL3GcgpR9k2ALxTCNu85BP/2sZ9gj32+UfZUhArTXSsnQWBgze2R1M7zFBafHBxJddmCBfOUF5Ws83o0fUJZuPHL7MU/J69HiyyPrs00rweBa4iL98kuqW4gXcSLfG4AvcQCET5ypU6CRxKZuTyi/1c8XR7G+lmGtnT6dPuOYYe2dMgun0e2R9Xa2mZ7/fLJ50GdlFZUPg8ywSk3EarnmklcHtUgaAaF/wiCje5YNQkkv165CvOWr8DN0LByAAAgAElEQVSzq1eXPZVySbpAMdwepgsix+1h7MPBXWLbgYnthFgWKdF52xYz+jiROVhssdF6trJ4PbMYErXwasnctOSlxGLf5viILVz7LM/TZUM3Dsx2HZeGSRgh5jA4TrQduSvaeY65C2sZQ5+b6eaB2pkd7DNBvNDGzOhGL8ntERCfIevNWNCXeANtvZlktLUXpmhbA3J77URb8m+V1JZwebh97rIRAJMEj4D6LqCcY57fCeR3DvFdFZ0DOYbrdymZfJQT2kJ9x0bff8Z1h/icWK910K+LVD4P/vXX/Di6gWELbaEEEe46xLa2Mm3QJLkubOsiF5cHpw/Thpf3kbddyLOrV2Pe8hX4n7ffLnsqghBDcnr0AHutMQIb9rnvWnUlSi6Ndn4PLTeGVm4+/rWTF8PQF9nOlIODMx6zPy0nh6VeMwjRaGAox4cl50izEaLRVBdW5qNr1XrtxVLDkONDzeMRNgI0Ae1309G17d2tTl4Py9G1YdBAAKCdB8F+dG0DWp6LTlkfhvJwtB6b80q066n19b5MuSo6R6Jqcxnsw5CTYugIVfs45rFNR6+2XrP+ekzPtccenL8pV0b7OWoOhnwTsXGo/B7RMpe8IEp50hG2Wnm0bPAGzHiMbfsGxZrHZLCtKecG0ZZsx2zbNeTkjknlCkkIfcpU8KDycXgJHoZ5UGIpkayUzuVhmrejWEqIz5Q4o4kPhJhDCsydPqjXqJbZ+jKLqtHQS6vAoYWvNCLhLOpj3dVhc3norg67y4MKbbEJF1HRwubyoBynPIHCRYBwFDwcRBPTRpUIHjoT+/sxsb8fz769Gv+9clXZ0xEEjS5bLQkCA8PFL+nCFntsdGvAXNd0obQeg0vsmBBuD1sCsSzcHrYwltq6PQDwd+8ydHu02xkW105uD3JRb7qhsLgu1Occb3TIOXT64t28xcagbt6CBvvGL43jo9V3o3DnRxoXQu2p4Htq/QzkIXhEP9uZCR7t//9uAgTttmB8pxhu/jnfWe4uD7vzI1uXB0cwibaPPq6Py6PZCHNzeYSNkOXyoJwhppAUETyqRxC2Nr+K+pE/g0AhoofQM7jsEDglNs1J+HDe9YA9npYSU6yLD2oxY1kAtX63PQ4i9WwLuciCT/2dSmhK7KrZ6lGLYnJ3z7RgJm3X1O5mXFwYujlI2PU29EveyJB2d2I3N2WYiz62Pl5sDFOZ5w1gWuGj1b+IH7lSwfcw8W+ujGEri5UX8HmPt6WEg+y+Ayhh1TgeNQeNeD2eKMJzecSFa4OYQokczOsMdT2iXB5aPcLloUK5PPR62bo8tLnm4PLIS/DgzIfamGKVC4JQCUT0EHoKYyyoLbGpt2hh7s90MeQ4Pti7IEy3B2fHJro44bo9OE6QsEE4PCJuj9AickTdHqHt5iK22FS/8qiFq9+uob6gte8Mmtwe0XH0mwSz20OrR9rF3W5UOLu8ZIy+1q7+wkdrjAqLH3X+Kes9MxVRf+N2W2UcW1msvGTBo6iwltjYziJIfC60qBppZxjHFPri6s7jXysaljrQXlMY6PVs17aw0WC5PMJGwHJ5NBt8l4cKd83AdXmocN2sHYg1ULQuR/BIEk3s60C6XBCE6iGih9BzGMWHgoUP6y6J506IbTclC7eHvkjqQrdHB4OgwFgkm23j8Xaubo/4vOzCx1A/hhuKDoTI4XnjQ+6s1ln4qJv40UVUVuyou+ChzcH+XZRpWAtjPLvg4To303eRXRThhCfqoXXEtYC4vtTV5RENbc3S5RFds3CdrDbBI9kdS/fhLXgoiOBhptEMCv8RBBu9sYoSBFgubAyhIknJdxU+jPMx9JGX2yPaR5ZuD6qMnc8jFqfMcHtEfyd37xPcHpEy9sLXUYxwdnuYoESHSoW56GMFQV88V4dpHFNZEcJHu1zEj8Kog9jRHi9Wx1ZeyOe6zyp4mOZZalhLp8+k7wpOvbigwXO9ZSB421wexjpAzOWhQLk8tHrs66X5MXkqSwYuD61NHi4PjgvE8FyS4GFrxxI8EsQWQRCqRXeungTBgm9OjsScGw7CR2JyVMt41OPo75Tbw7YIIXdvmG4P+0JKfRxE6tndHrq1l3B7WHbTYrtslPsDgPsRtm5uj+jYppsgJ7eHNh5nwe/oAMk0zCU+fuWFj3YdET9yoy5iR3vMWD1beUGCh060X8Z3QplhLe0iT5cH5zQWtsvDWeSIliH2ubG6PIJISKbthJZoiKfF5dFyZSS7PGKJyQmXhwpnnRBLTspYj8T64zguEpKOugoeiTlDGP0Z5ywIQuWQI2uFniMMWse0tv8FAM7xs2qdaF9A60LaPtY1qT+tna0Pokw7nrYRAk0Y61HH0NqPnlUeByHQgLmsMXQkbdgAAuW0TVtZtJ5+XK35cet35SjbIHJcrXLMp/Z70AAsx5Lqx9f2oXV8bLTOUBn/CFuir8HnzEfLttvF52yu31rMh15jtV678cja9nPqPDqFredMx/Iax2s/F0SPsVXfe+jziIzLPs42Up7ZcbbR1wgYPiPtubbuFMo46rbrSBCKvNsynVOscWsoeFQ6rIXTlhRFXF0ediFDXB6tx3m5PKKbL6xcHkRZWsFDwzVExiSaCB2Cpr7mK2I8QbDRpSsmQTCQdKFycHy4uzXi00nso4Zuj2g9Kq6Y4/ZozSnZ7QEgvuPWaR+9cUja3XN1eww+ZViQO7s9ou0MrhL3UwuIG50sw1yoOdTQ8eHjPmiNW7zzo2uomLPDOm5dBA9tHnYRgXZR2B0dmYa1EO4L/ndkG1O7weeI789UR5lH5kG5PDQIl4fWH+HyUKFcHlq9mrk8WMfKdp5TfnFwiWQieIj4IQiVRUQPobdIsiQWIHywEnhxkoxSC4gMc3tQZaR11rLgilpx6Yz0tgUhb8cstmDXFpzcxW97sUyIIOwQFvsNASVqxI6wNQkS0b7JHV7TDZThRsbzpsg4h5oJH+06ZYkf9sIuFD9yej8yFzvabWxzq5rgQX3fOIqa5PdE5P87eTyt9r0UHSd5vlyXB0cUcT6q3NhX/LXG50xco6JlkWsex+URDRmlnBxcl4dK1i4PrY3juidp/VS04KEhgocgVBoRPYTeI0G08E5GmrHwEW/LdG5wd1mYbg99UWJuX6bbI3p8relxGL25SXR7UGX2BTDnRADT2O47mUi4aaF2VOPCxxD2Hdy0+T1a/dZP+GjXK1r8SONsqA05OV9yETva7SJ1h36ppuChV0j+P1xeHg9bW+Z3I/U97uzyYAgmxPWE4/IIg0alXR7RI2g5myrRela3KeEMoVwTuQke1jEs/SWIJoIgVBPJ6SH0DNF8EgCsuTUSc3xo5Yz8HEr9pLE7eT/U3ByWftW8HFS9eG4PdOpxcntQZdH8HU1w834EaLjm87Dk9oj+bs3tAUDP4dBAKyGK8lzncR8AS84LUx4PQ/12X6Y8GJ38F9p8Bvsw5NQYypdhmFdN8nu0+m2trIfyVwz+DaLjKuPEXqepLPoa0uT4AOLvo3Hukddtadeaw2Bbx5wf5Jhq2y4iUeyxFWWZsyOhXZUED0q8q08eD0eXB0tQ4bo82qKIq8iRr8tDq0e4PPR6UMrUx/m5PMIgzN3lwd4oAtwFD+NGmKW/JNFEtpI1grDYY2QDUyy5IAwi/z2FnqJzQXLcZTB+jzqGqQxdWGGuy5oH07mRodsj1kcGbg+7FTca3qIKOha3ByILSSp+mioDQC9weU4QZ7dHu52T24O6WaFuBogbIGo31zW/R3Q8w5j6jVrFHB/t+dbJ+dEFVNLZ0Q2Ch6HfShxPa/uu5Lg8DPX5Lg9fJwc1tqE/yuWhYHN5RK+BNpdHK2QUSj0Y6wF8l4c2v4xdHta+Hdc9RpHCU/DQ5wRjeXLekVhXgiBUCPkvKvQunsKH84XZU/iItqVye7BygzAXKNGdHVtOEFv+DqosbsflZqW32YAjFmEit4fVckwtyg0LYDKzv2mBTS7gTWMzRYTOk9RNTPKua2FhLobnKy98tOcs4keu1EXsaLcb+qW6gofx+YqHteTl8uAI0fr3DUfkMAu6rVBKxjUpEpqZt8tDhePyCBsh2+Whz4EnOrjm8jD1MfR48EEKwSMxRMZBNBEEoZp018pJEAg6FzX1U1+G8BHpy9YuT7dHeiuqecxWHDCM9ah8Hja3R6usCLeH2y4ge4Fs7Ivj9uiLPxdb6FOChNuNCClyeN4wWcero/ABiPiRA3USO9pth36ptuBR27AWY12iven71/E72fuI2pxdHiqUy0OvZy4LG/m5PJqRHB15uDwSBQ+13xIEj/Z7aAy36WHaR9YW+SMINiSnh9CTaPklgrBztePk+Oj0YSlPzPGh1R26bnJyg1hzhnDbE23I3B5AJCcInduDKmvl/Rj6ncrtAUD53ZzbA0DL7WHI9RHP+9GHIJa7Q6HzXB9iOTtM9Y1lrediuSIAJbfEUP/m3B7tBg1Yc2do47fqmV6LKZcHNeZQbowGEM0/QcxnKK9IfC6xOUTG1d8rfdwycnwASM6hUbGcH11DgkDk3dYzT4ixbV0ED0PflQ5r4czHOKZdFDG6PJxFjmQxGQCix6NTLg+tns3JGA3xtLg8oiGjVXd5WNs4OjVMIoWv4KHhKXgI5fHsn2/Es3/5KVa9vbTsqQgVRv6rCj1FaLtQucRtJhxlS/ZhrGvpz6F/jlU0++PllHlGd3AKdHvYQ1rsNxTJbg9LHwB4R9ganktYMHeeMiba0+ubFvxVD3OJ7c5X2PHRnk+dnB+1p6LOjjoJHtb/Y5UMa9Hnbe2T5fIwPcf7fua5PFQSXB7E3yd6rSrS5aGStctDmwPD5UGF5FKCBCfRqLkuZx2VVJ4seIjLozwmbnMYPnrAjdh79/PLnopQYUT0EHoHk03SVfjoPAdzeUIfSUfZanU5ux7cmNfIgsVWz5bbI9oHldvDnh9E6y6y40Tl8zCHtETDW8jcHrbdOPaimRAuiBAW082WOYQleVfUHuaSXNc0RtFhLrF5mMoqJny051SW+OEzZu3I6X3IXOwAKi942Mpi4+YY1mL6/83K48GoWxuXR9CXictDr2d3eahYnRxByHZ56P0luzzCILSKIlm5PDiCR9JJL4mChzo3X8FDhA+NRthy9Rb2I2+/QCCih9CTeAsfHIdGhsIHt2+Ad/ybr9vDuvDxdHvYyqJuD5u1V90Va/1ehNvDJGpQu4luC2d1IU+6PdSmWeX3cLzRcbbK11z4aM+raPEjjcOh8uTkeMlF7ADqK3gUEdZC1rF/RyaGKxkEDZ5rzfDdy3Z5UAJ3di4PFZvLI2wELJdH9PSWtC6PaI6OrF0e2nwc3bK5Ch5GgUUcHoLQLYjoIfQW1osyo04GwsfQeMnCR3yO9gWFfYHg7gpJ6/ag+sjD7aE5PixuDwDkrly0PMvdQNNuIy+EhbhxIR0n3B1S6M8xLO3eYS6GOVde+Kij+FGnH9tLqaLYUXPBwzRm2rCWTPN4EC4PzneeMekp8T2YysVnuF5Q7kGbyyN67crb5aHXy9bloc+VI1yE5HrG2neC4KHXLUHwEPFDECqNiB5C75FC+DCXw1zX5PKwXagN/eXp9rCJF9QRt1y3B2fHiev2aP2e3u0RPb5WLYtD7eY57hayRQ37jYTV7UG1ySi/RyZhLnUVPtrldRI/akwdxI7WPOsneGQW1sJxfahFDmEtcXjfXSwh2NWlZ/zup0QUaK8xHlLp5/LQ6qV0ebSuuTyXhz5usssjbIS5uzw4godpneUieLDmJoKHE0ETaDSDwn7k9BaBojtXT4LGr1euwrzlK/Ds6tVlT6VUWBfCBOHD3gd/HBfhg6pPJ/nK1u1hXcTk7PawuT9ix9Xm4fboQC2EKeFAWZhTu5y+bg+1eiHH2LqKK10ifLTriPiRC3URO1pzrbHgoVZ1CmtxdXSY3Bdt3AQSnqBh6F97XTl8t2fg8tDqWVwe0RDPolweZEhrBi4PrQ3HzVqQ4JHYB0Pw6PVQl2dXr8a85SvwP2+/XfZUBCGGHFnbA+y1xghs2GdfAPYSYcA4ZrYB43G2yX0MXftsdUx9aMfIRupq9Yi5o4GhI2WDoaNnbcfdRsvU42WpI261Y2i1cYbes7ARoolAO6I2MDymyuL19KNs249bvytH2QbUcbXK70EDneNNgz4gjB472wBsx8iiD9FjaoeORx0sM9VXGWyrH7nafi5+xGz0aNqh42Gp+SmvMTZuumNsY8fBAsqcDOMC5vc8OrapLCj+OFsA5iOC2zcxhtfHOurWdlwt1bZ9I2U6rrZLhI8YCYJFLm2JsuSTfqopeJjG5YS1FHY8rTWsJUuXR/w5XTDlCNgGYZZyeSjYXB5x4cPP5cGpl7fLQ2vDcHlEQ3LZSeSZG0B0n8l1zOPBXFcED42J/f2Y2N+PZ99ejf9euars6bC4//778eabb+Lggw9O1c/y5cvxi1/8Avfeey9GjBiB97znPTjhhBMymqWQBV26YhIEO76OD164CT0Oy4FhsHNm7fbg7Nxwj6/zdXs0I4uyocfUcX3R42ot7o/oyS6W3biQWNzrX4+Oi2tft4dhnjb3gddpLlr75N1a6hLhdZpLlR0fEQL0Ze/8SOVAoNt2Bb7OjjRtE5wd1s9Ap+/qCh6+YS2xOeUY1sL9ftMfZ+jy0AY19W9/D6wuj2hdW+6OICBdHio2l0f8Wpre5aG1Ya4Vcnd5sOqG8bqOgoexDxE8vAmaxf9wWbBgASZNmoR9990XCxYs8H6NzWYTl112GcaNG4frrrsOJ510Ei699FIRPCqIOD2EnoHrmjA6Ppz6GLoWmurY+rA5Pqrg9mgCQw4PhtujVQ+p3B6t380ODxe3R+utMrg/Et0e0Ms0/NwemmvB5PZoD8lwe5jmONQuPgfSHUK6TKLuDt1t0WofcaCYxo3UVcc1vu6iHR8Wh0ouzg/CvZG2bS2purPD0q4WgofpubLDWhwdIbSw4iYsu7s8DM8xXB5h0Ejt8uA6PqiyaBgMx+XRbNiPoC3F5VG04KG+JkfBQ8SP6rN06VLMmTMHixYtwkMPPYQgkh/OhRUrVmDatGm46667cP755+OUU07JcKZC1ojoIfQU3sKHswhhET4S5hENJ7H1QwoZtr4j7ZtNDAkWlnqqqEHNQRNCGiEaMAswar3W70PCiP64dfsbqL/bBI8gQBC2y4bEDzQaQFO5aVRvopXHYdCHICp4dB6rN9dxoWOItqih3kS369lFEONzhnnYhANaMHERSQztMwxzqZXwAVRK/CDDZYi2tSDJbZPk7PBql17saI1RE8GjImEtLo4QX5eH8TlNFOGIHAkuj8HHWbg8VGwuj1aiUrvLQ8WebDyst8sjsZ5BrEgjeLTdtiJ4dCVjxozBzJkzAQAvvfQSbrzxRq9+Vq9ejf333x/z58/H1772NRE8aoCIHkLPUarwweijIyY4jqe1RfXdHq3+zGVctwegODwsbo/o71a3RxvS7aGKFckiiF7m6fYw9KHX4+f34I8Tfd0Gd0dU+DC+lzUUPgzl2ksqWPxg5QrpMioldhja1k3wMM3H5Xha09g8EcPPEcIfJ8Hl4SxyGMSWWH31ObvLQ8Xm8mgdW5vO5UGdhFZFl4eGkwDhUFcEj8rQCIfWeUWN58Iaa6zhPdbMmTPx29/+FnvuuSdOO+00736E4kjwjQpCF2HLkeESo+lqxTS55hh96Bd4y7wjiwBWPg/PnRwVTm6PWJllgdX63fy41YdaRiwIidwe0eNrTY87u3bGctONg33BTB6FaHou4C26h24SzLujajtWfg/HGxpyN9g2RmSetcrxQeR6aPdVZM6PxJwWXUAu702anB2GtnUUPOiwluj8XB0dERGDkcdDL4w6QpgChaPzgzxa3Pi5SufyCINGJi4PFV+Xh61eFi4P7XUw1yCuLg8XwUOfD4zlbn04Ch7+kRJCjXjqqadw2WWXIQgCfP3rXy97OgKT7l5BCUIUT+Ej6Shbug/eGLE6HmO15pq8+AmjllTmbo7NIquPYz+illrERXer7AlMowtG28KS2G3TbrYJUSNN/Dchaphu0NySjeo3GGmPsXW1rnNs8V0hfLTriPiRK3UQO1rj1VjwUOtmHNaS/nja6HvHaKs+5+3yiM/X+J2fk8tDq2e5xrWOrVXrQSnTp2Qra7k3lN8Jl4feX/K6IJqjIw+Xh0u9zpotheDRfq9E8BBsnHfeeQjDEOPHj8fuu+9e9nQEJt21chIEDo7CR6e8YOHD1e1BzSULt4d1t8XT7WFfoGldkG4PbaFoc3sgKnI4uD062Hf93N0eSTuNHLeH+iTDweHiDiGt3QwHCHUT2n5YN+GjXS9NPggRP2IULXYAaf5O9RM8THPKLKyF4dowt6ccIWlcHnaRg+3yYLj72C4PBZvLI3oN83F5xDYZUro8Mjndjbn2cHF5FC14aIjg4U2VT2/xpdls4vbbb0cQBNhmm20we/ZsTJ8+HR/4wAewyy674Atf+AIWLlyY/0QEZ+q9YhIEB2wXKKf40BTCR1K5a8hNtI7Pbktebo94PXVMROpRZencHgB0i3GC28O4oOWGqzjap2m3h+HmJ7ar6ufgYLlD8ghzUZ5PKquC8OEjfvg6CFpjphMA6vjj+1pd31sg4W+TIHbUVfDIOqwln+Npo2KIaSx7e+PnwTmUhfg+NwrjjGtKzMnh5/LQ60EpA6uMf4y8n8vD2oYKYclw3WN83rcPdS3Sft5R8JDcHt3PE088gSVLlgAAXn/9dUyfPh1z587FQw89hDPPPBPXXHMNdt55Zzz88MMlz1SIIqJHDbn//vvx85//vOxp1JIyhQ+bW4O6cOfp9rDt2njtzMQSoZnbxHaHCLeH3dJrj4eOuj3sIodlty76nFbGdXsMFrF3I4nFOCUsaG2pmz377ivtDom0zyLMJTLXKgsf7ba5iR+2Ik9BoPakcbUUJXZExqqF4KE+lzKsJV6HEjE4f6+EzzL5ncj7XiVdd0kuDzK8JdnlEQYN0uWhYnN5tJwcaj0Y6wGE0NDguzxUOC4P33xirrk8Yn1Yx0kqF8FDSMff//73zuPrr78e22yzTef3qVOn4tvf/jbeeOMNfPKTn0QYymeiSojoUSMWLFiASZMmYd9998WCBQvKnk5tcRY+TOUFCB8+OUDydHtwdohc3B58J4jd7aH/noPbo1PmunDm9WGKRXdze6idRdwVFQpzqbPw0W6fufiR5Ewgb/K7SPxIeC2JYkfG733i31rp31pWJcEjiPy/rHJYC9vl0RY0KJeHSvrv6jxcHvFrmLvLgw4VdXd5RENZM3d5qG2cQlYKFjxY86TbCUDQBBrNsLCfIsJb2i6P9dZbD5tvvnms/JOf/CSGDx+O559/Hvfff3/+ExLYiOhRA5YuXYpvf/vbuP766/HQQw8hCCR40AfXC1ei2u8qfHSeM49nH4fp9vDZbcnJ7dH63dyG6/aI96HW47k9Wr87uD1M5Ulujw7/P3tvGnNLctZ5/iPfe6nCbjDGUALKQJsSdgl3aSgbC6sZsViijRF8GBvK0sgsAiQ+sGk0BlkWmz+ABhAGD7JkgywxQkKYxQyNwRbgEiXKLDJQHqY9lMsDDZ4SBQ3VeCtquXUz50NkRD7xxPNERuR2Ms+Jv/TeN0/sed5zT0b+8lni9vnWHsKaWBm/ITqSm4s4z57Bx07gx7iFw8HBxwjsWDxI6UJ/26MBD0m7c2tR+oaVie/dMSuPLMgx8v1vhLUlrDyoplp5hO0SdQkrj2AdC1t55PSJXHFnZGwZBR4Z68kCHpKV78QHZlXnpU/+5E8GANxyyy1i/TOf+UzccccdAIC/+qu/2mxdVeO6duoFVI3rWc96Fl772tcCAB5++GH86q/+6olXdFx1poPpr470GAZAF7fxMp2/qgZjNBjIstbGHQf1w3U3XEcXXL21OvU8ovV1MK09bk2HhpSjhdgu6NN0aFry9ImMQeuCY9MBDeS6JszZrtV1DdBieN02Bk3bb0IaA9PSTcvwumsamLb1xwD8azQN4OpMA9PFx8N7eAXT3QzKYK6A7ibsxti1vwJwk9RR9XXiGEOdMQ26rg3LcIUON0fGEOYyDcDOxbU3aNAhnMc3cfOJY7s2fX+hjTFX6Mh7M7xGsKbhvFBUx+cczkWo8+8nEP6toK5LnR+I308MN1Od9F6l+vU3XB3/m47O1/TzCY+xjg4+mOa4sUzqlwnt1HkOADxO4tYy0SJEHnPMykMC1KxOnCsPaEvWfzlWHl3D3FuYlUcwnmLlwV09k5YcSl27gKWmZuXRccuQFaw8xuqkwKVLAA9tvRV47EP/5b/+Kj7wd78elD1546Orz/sFX/AFAAaLD0mf/umfDgB4/PHHV19PVb4q9DiYbr311lMv4fAqAR9Z0CEDfHgVgA8NXqj9UmvMhCQBGGF90EAEI9o6+XsTAZjGkLqhHQcj4Ri0nd1eGwGGdMbAdHQzokAOesPsjqWbbQkGCABDgiDDjbYAMBJQI7gJ5+BCauP72/VLdRH4yIEkyXMPoYLVGYEP1wbYP/w4uPYOO8S5Dgg8xDUu5NZSbhGiWKQF708h0Mi28kiMu5GVR9eY2VYeXbOslUeUgnZhKw+9z8g6C4CHNkcp8JBda7T1V+Cxtf7D8+7Bf3jePUHZI4++H2971/+46rwvfOEL8axnPQsf+9jH8PDDD+O5z31u1OZjH/sYAOCzPuuzVl1LVZkSu4yqqjNT1kU57pbngpKeRx+jbJ7UOWQFGU1tOjJie/AxUk+M1LpoM5eqo8ep7C2JdLX09Vhsj2ATK218EzcQqSeVIxtwKbYHH0Obr8TNRR6nYePQ/uNm7lluLmQeca69urrwdRzF7eVA2rMbC58vakvGDLUP4CFpTbeWoVGORcjYd1Pq+zRt5SG7FSJqN0gHGlJGr1wrD6qpVh5Ue7TyCPpkWHmkoEr5vgfJNksAj0AFwe+rrM2LcQ8AACAASURBVEyHbVPWbvD2X7t2Da985SsBAO95z3vENg8//DAA4Mu//MvXX1BVts5j11RVlasC8FF6YV4cfETj5/XJje2hbUpSsT1yNj+5sT3iuuE4lb0lfnqlPEUzJtxo5sT28GUJqDHnCWIu1JBuGnJuREoClZZCkownv6Nm9DsCH+r4OM0N9Rz4ceSf7d6rBf82SbCwH+Axya0l2SYFMUosQsYBa9Buge/S0e9o6fvd16Uht3ZtsRnEctOu68cpK49wjO2tPFJ9FrPyGOmzJvAYLE20dVTgcWQ595O2la0n/+AP/gB33XUX3vjGNwblP/ADP4BP+qRPwpvf/Oaoz3ve8x7867/+K175yld6V5iqfahCj6rL0wnAx/gYQpvSpyHI29hsZe2Rqmub1BjBEKq1B3+9mbXHMFBclNhgL2ntkbzBYOAjeYNRBEkEZZjOnxp8SGsaW5e6Bt5+J/DjbLQj2OHmFPuw8Qfl/F85DfAIlfr+YGtczK0lA7h4LW3lMe17fG0rj9T1LNfKw1pVKnW51+MNrTyoSqw8SoBHoAo8qhQ9+uijuP/++9F1Hd773vfi5s3Y3fRNb3oTPvCBD+ANb3hDUP6CF7wAP/dzP4cHHngA3/d934cbN24AsHE+Xv/61+Ouu+7CW97ylk3OoypfZ76DqqoalHOhlC6aS4CPvDHG50xZexRdxMXxxp/qRH0Sm5tW22xkWnuk6romYeERPUGbZ+0hbXzHrT0S4+Zu6BPWHtIcqRv0kjS2a7i5nBJ8xNYE5eBjtbgRFX5YrfReTInbkZyzAHhI/5ft2FsCD6lM+oyP//8ugxgTv4tGrDyK3QCT33fp73UpRa00bsrKgyp1vcq3akzVyddbnoI25yEHb5ey8tD6pPYjpVYepcBDfHA0FXgQle7jqrZNV+t+xtS2LV7ykpfgec97Hh555BEYY3Dvvffi9ttvx6te9aqg7atf/Wp86qd+Kr71W781Guc7vuM78J73vAd/+7d/i7vvvhsve9nL8IpXvAJf+7Vfiz/5kz/Bc57znKXexqqFVAOZVl2UaBDQ8WwrgJTRhR6XBDfNG4M9MFDGTwVMzQky2prOBgEV157K5DL0ycnkMlqnjmHrh+wtciYX/jrK7EICmtLMLqOZXMwV9OCdVA3Ag3364yvwAKdhEE2pHYL2YlnfLwjk6ZYIlvXFNBjP5hIrmc2F9xfa8GwuYZlrNKxtqeCm0jrt3A0L/pm3Lv6eAJgQfLTvp65Tns/NKc430u9QKrW0yOk3A3bk9psCPOIx1wYeOoic79bi1lJiETIOVPPjGs2w8khZwSTeh1Irjyh9+gQrj7ZJW3mEY8yz8uhMV2zl0Rn+gGN5K4+hDmLdqsCjyOoXVQdQ0zR43/vel9X2Na95DV7zmteo9V/+5V9e43YcSGf2uKiqalxlppEQ64stPorGSLfVrD2y17WRtUc0BrP2yPdTTtWlnp5pG9G0tUdYlmnt4TXjaWOutUdQnjJTL7Dg2MDNBRBuvha2+Eg9RS+2+NiJm0XWfEf+Ocp7zOYZlA88VDesFYFHqNT3BVvjlm4t0fuUaeWxhJVdppWH7P6oXFeitLXz4nqk6mz8K93KIxhjB1YeYR9yPGLlMdlSdwHgEa65Ao+qqqOqQo8V9da3vhVN0xT/vP71rz/10s9S04NgaWMUgI+sC2yZCWfSZDQztkcWWMncEPEnSVpMkNz4IPa1dmzYxi+xkQwCyclfeTzFIABl0y/BCmHjnnjiKN+UTS+LTdP1m5XTubkcEHy4OY5wY34m2tV7qvQ7GvA4iVuL9L6x7x75u2PG9+AcK48UyBbKxOsFpll52EClupUH1RQrD/uAIc/KI1zvOlYeua64pfshMQ7IQsBD3hfK6xMfnl2yOmyavQX17a9KqLq3rKhnP/vZuPPOO4v73XbbbYuu4x2PPY7GAP/OGPy7/qL8/OvXcOf164vOcwSFLibkuiW4i4TuJMBsVxfjB8h2dQHkdqELCx2664cZWRM7RgPRJYa6n1jTV6BpTdxHcamJxnDvRd+nhfFuK2GddT7Q6ugxMLi0UPcWu8kcXFqoe0tnGrsvb9vAvcW5RIRlDdC16MwVTMdcPvxxA3hXkSuAu4YkXF5CFw1bFrhjeHcS7rpC+yHdljZhriuim0t/zsu5uQBburq4Gyp/XlNdXeg8ADZze5k630G1ZzcWfb484LF9DI8xkJe2sgjbpCBGBmzlbi1JqKC3zbfySEGOPFBdbOXRNJOtPHJieaQymS1l5eHqWpaJZcmMLVNjeZzSpYWWV+Ch68EbN/DQjacBAJ9oW3yi6/D0Zb4VVTtXhR4r6p577sE999xz6mXglc/8ZNx2dd5PCEtUCj5slRkuhF0IF+bE+KBjDPN0wZW0M52dmrdLzUehhOnQkT5RbA9SjpasR4EaNPZG13RoW6DpTA8xEMbvgB2j7ceTYnu0DYB2PLYHByE8loetk+BHA7RtMraH27TSeB6dafr3fYiN4cEHMNxIT4Abw8210C4AH0AYx0MGHwG4UNsK8CDqT9ajgA95Lg4+NJDgdqJtfNPP1geQG/vMOn9TQuBHsNa+zt1EDfAjXK9TBCTozR8DEvSmLwISqX4LzHd22i3ocKJgYYJ1B5tzqoVTGsLoN/lyelgFYgTvjdxfBjIJiKHE8YjbXgnAI4YcY5mxJgcvNVcR8OgMgRyJWB6pjC2a1QcFHGOxPFIZWxyskKw8HGDgAU5TsTzojb+WCS4CF2QNxVCEW4OQtlljFQCPeVa9yjwXpjuvX48eoj741A28+4knT7SiqipZ+pW+qurMRC+q4TG5eM14ChA8bZAu0PR/W+YTi9RTDr/JYE9DcsxKc5/UdGwDpbXjLiqqS03TkU1dvIEK60D6hcf6k69hQ9mxzSXdiHaNsnEVnuiBb4SDstTTw6t4A26uEG/UhXaJDbvhbSRzcbWtcDMV9Y9vYJKm6eqNVHhzlvU0mrkURDdTmXX8Bisvs0sD6cZRdZ1IxKVwbhOqq1HCdWLKfGehFd6X5f8O4WckZd2RdGdZ4DOe+v90LsCDz138PUraZbu1JK4BUvDSzjTsusKtOYbrEXdroceaW4ut64Zjfn2mdQmXlBy3FrqvSGZ9Ufpku9kaqFBEhApzgQfbk80FHnTPqO0rq6xM223+U1Wl6Ux3T+erxx9/HIBNuVQ1TfrFuz9Y/AJJYQO5KGtwRAAfKShB169efBMblpb2yQxq2jZKH2WjNZbCNhXUNF03WNIsFdSUgo8IgpiGPA0kv/mGOvPJ49gTSi1F47HAR+pGrfHlEfwga9RM+1N14d9FXq+duxmFH3SNp7/xTvc7lA4BOgAJdmS5s7DP4BKf63CNef+/DgM8+HzCGsL/u+OQODwm87Pv8c5cRcCjM00EPJYIXjrAfd3FZY5biwcXiQcQc91aUjHBNAsO7q6S5dYytkcS9lbRQyhljmG9iMr1B1lx22i8qqqqXergO6bL0qOPPor7778fXdfhve99L27ePB+f7k00BhkQXtA0U8jhQgh/sdThiLZ5iOcRx+D9J0CN0CJEhhX86ZHUbswcNmVSS+dR68hTLP7Uipr6xk/Fwg2lFtSUPoWzm1KyiW3CDa24sRXNoBMbawA5PuZGfBpJn2SSmwmyWU+BD/3m5BTgA9iT1UdQrlp9uHUdDIAc7WfP76vy949gRwZYO5l1B1nHKYGH9J0ktg0sOOiY8fdjBDxGQbPwvUSBR1QWXwOkOB7htWQdtxZfZ/LcWqS6KdfxKfuFRd1a2H5IhRnkPRnbF83bw8nzSHCkqqpqf6rQ4wBq2xYveclL8LznPQ+PPPIIjDG49957cfvtt+NVr3rVqZd3PCmQYTgmF7fFnxKkwQcfw9WVurlkm5lmPrmZ8oSo5e0K3FykdvZ1eKw9MRtzcxnOhXRi4AOAuAGG3wDzzTrIhrqJN94q3IDQLiyLbshHwIetywAfozBjafCRuoFrfPmaVh9j7i4x/KDr2/uN+vG0r/cv9XdOW3cEn/k9WHeQNa4GPIL3WAcefD1jAU6nBy69ituZK0TAg/yNpgQu9WVKtpYxtxZqoZjj1gKUubVQS8yUW0uuxWbQRwEhqf0G3Vtlu7UIwENsKwEP5QHRdGvdYU+o7Rsr7JBV3Vuq9qQayPQAapoG73vf+069jMOrMxkBR4Pj/jrmLmZC8NEp2V2CMdzeSsnuwtcDl83F6MFJtWwutg0yzrvzQUxpfyDO5iL1aU2Hpj+O2pHAp/Y1irO5tA36AKlyO6Asm4sNdAobxHTHgU3DsmHsVFYXHmBUzOrSn8uqwU3d+wZACho6muHF3XBIWVwK6sT3xpWTNdKb2y7KoEJvCpcPSjo1COrZSIROfZUEOHL6Jd+nVD+hjt+gp6w7gnYT63Ksolhd2qVkQeARtZ0LPKRjCQKXwhByHIAMdi4mL3Apj+Phx5uRrcUBjyXcWuwaxy04eDsORbqMPhwSqPE9Um4tAURAWEfnmGjdEbdJzBGNIc+TM2dVVdW+pF/tq6rOUFkWGMoF7xTuLqP92MYk74I/LRq7ZB7LnyTxp0U5bi6iK4vR64bx89xcIksP5uYynKPwJE944ge+UQ7KhBuU1AY9MOdOPK2ElMVgGG+WxQc5l9UsPtiaTm714dslAk76tTWHsAA5tHZo0TEGPEaDlbL1F9fNtO5waxwr2xvwsO/7xO9J8bt25Hs68R2vBS51Srm1uGtOrluLez0cp91aqKVkjluLXZdsiZmEIoVuLf61cJyGIiP7nYnAYyt3Fn4+VVVV+9OZ7ZyqqnSNmzDq7i68bXq8eM4UbBk2HvE8vF/OWpZ0c0ltgHI2ULxOcnMJ2mW4ucQ+z0DKzWVY77ibi+iznXgKCFMS2JRvuMufYJ4F+Eg8uV491oeyDnGNFYCsqyOBjpzPC10fOY9o3Tl1E/6PqGs8HPAIy0L3M/27U3ZxIefAvqeLApfymE8Zbi0Av/6k3VrCmFU6aNAeJIy5teQ+qBjOi/VRHpSUurXwPim3lhTwyNpTjezLRtcijCeuSRuvCk3XoWk3/Onq+1+lq7q3VF2UxlxPtDa02dLuLsE8bg/WyvX9tMBCbi7cfUVbK+8fuKgofZZwc7FjGNGVZQk3l84YgLi5mN61xfRuBO5YKqOuEt7NhZQNxw0GN4grcBeUXBcYV2ZM07tckPZ9uz27ugCIyq3c7l9wayF1kStMP789t74OI3Uj7i7BGl29E3NXOZULTOT+wvsdWKr7ylauK75SHtPw8XJgR0mdBjRG6pJAhpfvHnikwe9JApfSMiE97RJuLWKdf1jA4cTQLtetJaqjDxUyLTA0K9O13Frc2Pycc61ghzZx38nWHblzVm2q/+vhX8f//fCv44kbHzv1Uqp2rPPYKVVV5WgBcr+5uwvbQJS6uaTS3WoBUelTHT+e0H+Km4v0BCrl5kLfjxw3F/o0zb5Ou7nw40UCm5J2wRNH0RQ7fkopZyVgNw/BDQK/6ZD694cntPgQy3dq9RHd3NJ2W1uA8OKUFcQBlWXVwYs3sugY1ii4suzZusPV8fJdAY/huysGHldxf+X7Uv5eZcCDgkYTn09p4FI1BfqIWwu1Vsxxa0nVSW4tmkVlrlWmZrWRsv5M70mGNpPcWiYAj3l7MHkeEcKo+0hUnUD/w3O/Aa956a/ga+/68VMvpWrHqpYeVZelMQuMhBXHcNwP5aw+RiwepDkDCwttTmq5wNp3MP3Unb3GZliSBOeSsMBw5a3prCVFb91hg30K/WmfpkPTDn2oNYetI8cA6xfXtQ2AtgvHgLX8cJvBhlp++GNrb2Do68DSAzBtZzesbQvTdVgssKkvu8KagU0Hi494jF1afPTvyzyrDyUAaoHVR06QU/c+OXXMWmNNC5CpAVDPRpOsOlJ9Es92ElYiIvjifdjYqdgdYUMNdgDp2B3yOlJxafYLPPoiCXjwuUatOmK4EQEPcxUDDzM9cCmN45Hr1mLrEByn3FqGujy3Fl5X8mAiuIEvfAAS9c+AImFssaFdBEUaua+4jhHrDm2cxaw7KvCI1XY+ePxW81VVaUrsBqqqzksSiZ9zgQvS2hbQ/5I5uya1FnJyfN5Gbhc9yZHWnRmpvSQifI75LX/CFT1pCuqG41S0+64ZIAd/8ibF9zh5YFNynN7o8yekpG5nFh/LWn00UV2whoyn6rmpbanceaxjAcL7TIv/cWjNjtPB+8yz6Ej+nemafb+rEBBMtu5ofPkS1h2HBR70PEohx0aBS4eylGUhSF14nOPWYl93rF9/vcy8rkZ1pF9yDKUPt47V+9NzSgU9Je04FJkJPJJWvSsBj6CsqqpqV6qWHlUXpcGiwxUMF6kxC4xkWlvbyV9BZSuSzDkVq42OtNesO2KrDbd+O3E038i6W1hLjZR1CE9jay0uZIsQm3bWBO2iMRqgBbX8GCw4aB1NYzvU2df2OLTuMKKlRxjfw1l82HoSy6Np0PXWHzSmh/2bkPgepunLmKUHgCC+h7mCtf6hVhhX9jPSUWuOK3RSuwNZfARzAcH7sWurDyduyTHTAmSV+B9npsXjdEyx6ND6Hcm6g9QdD3iMuPrROg48KBjyQOMqAh4lgUs58KBxPGLXyXy3Fuquku3yQh8qjLi1BHXsYYRvp1htULcW3i5yjw3gwtAmB6rwhzYp65D0OBDL9XWPtC+AHVF5VVXV7nS+u6aqKkXTTSD17C6B1cfoeCNz8ot+I7cH2VzkbBSk+fyGhG9uMtPUaU+MIvNbI9fRTRqN7xHVNfFGbziOTYaH43Q2l+FYeoInPOlLBLiT/cZHnkQmn1imnmyym4tSiw9+w7KyxYdrvxurD1KuWn3w9cy1DPDrXCH+xxlo1TgdvHii5U5JKtqg/BTWHaRudeARrG8Z4MHnHv+OTFlwSGV53+tSphYex8NpiluLfR1eb1NuLdS6MsethddpFprcQjTp1iL093VOWYBDbxdls1sQeNA9W9IaZHQ8ec6qQabrNv+pqtJUoUfVxagjN9FLuJ7Ex6TtCCjJnXNwOUmtAXKfCGooc2pp5jKf+JQ8WRogRgKyLODmIrm8jLm5bBvYFNkbfPlpJ+03AXwEdRuAjxk3bFYJwJEK+jgnwOREAFLU58IByMkDkmb0kT4PUwLmZgfn5ec8ExxuAjxG2k4BHovE8QAF0GOgui/LDFxa4tYSxu0I64J26nU1BhfDumkw8NithQYRT6WnDfoIe4iO7wFSbi0KVJnk1lIAH5IAg4KW0fEmzsksbaqqqval6t5SdXEK07ru3N2F9dXcXII0tny+4HyHdsGcxBVGXW9/7FxWeP9UGlvu5kLf/xw3F5rGNq6L3VxM0C7PzWXbwKYAiGuJ5NpCXWCiwKa031RXF4DUrezqQuYChhsmP6d7j/o6yRXGqoHu7gK5jriuZKe2daI3f9wthd5s7SAA6nlIfw6zakDSqE7uk52KlrvAaBYcqTq2vizYEdUdHXhcxeOYK6FfEwMPs27gUvc7x63F1oHUUUAfu7UEDwdoHbN2zHn4YNcuP3zQHnLwPjGU0B7q0PPVxwrcX1QoMt3SQgcYI+3nzFlhR1XV7pXYRVRVnZdynlLszt0lWLPcLppDetKRvZaJ5q6kTa5ZbYmby3A8382FPpVzr+lve057Dmyq3VS4dju1+HDtF7T6GHt6PtXqI5k+dQM3iqkWIMfUehYdJW5HyWCqkivLAtYdmqvWEm5hZwE82Pjyd+XI92ziO3pO4NIYcBBYP9OtJaoruHbmupmqlpasvBRKaLE+uAttrlvLEsBjbXcWdT9ZVVW1K1VLj6qLErdOABBZLdgn7kN7zeJi3Cqiv44yCxE/P58Tdt5gnn4ub93RwFoqiBYh5LrN1uctLUyY7jboL1nA9OUuXa2WxjbqT/s0QxpbILTm4HVR+lsYYrXB0uk2LnVt2K5t7DbYpbV1x3YMIEhjCzBLj4UDmwLW2kNNXTtYLkjBRmOLDihtXb39vUeLDwD7sPrICHJK64M2vnKwHgnLyY3VwgFQAYxYgJyHFrfoGOmXbdUhtM8PVnoC6w62niMADz7mOBBOAQ0FcjDgMTVwqfvN43jQ45Rby+CSMt+tBYgDk+a6tUjpaaM+iRt8+rBHcmsBhnLfTuifcmsJ1rC0dUfUPg08Kuwok+naTVPWmugaWVU16Px2TFVVmjKecgQX1iI/Ue2imTF/4sKafZEOnmjIT1ZSaWypiatmKZI6pv1zUuXF60n4JQcbxC7aPIbtUnVaEFPB0mOpwKY8vgd9GiuZZidNuPlTY+3GwT0xbeKbjRNafIRjIXwvgr6IytNWH33xyFP1YD2ZVh+0jWgBMjP+x1QLkHPUahYdBVYd6t+Z9iFt1boJn9FFrTvcmrxOAzzE76AkHJG/9+TvR7Ye8l4Ebi2CVccSgUvnurXIdb3FhuDWkhMPa4pbC2+nXds1txb7mp6TtjeR3Vrc69RaVgMe0fqkfd4wbxbwqPCjqmq3Os/dU1WVJg1UbOnuorTnc0pmpZ0JN0O5GwcJauSc0zBn2dMgvsEqcXOhY8x1c+EgRHNzOVpg01xXF9t+f+Ajgh++rgnnzrrZa5ATFHLM3SU3mOks95epN+68OOn+chxtnnllgb9pMmuL0V1W1GClmZ/zNAhJZSE6HfDgbVMuLUcLXAqMu7XQwNpz3VqkupRbS9aDCNKuZfuEckuIPKiScmuRHtikgIf+EGrYcyUfWo2eUzynXWsaoFRVVe1T1b2l6mJEXTac+4e7YI26uwDgrifSeLw8PCbXRKF9NGeXmKOB7OZiBveV1Lo0N5fOdGHQUSMHRA0DjnbelSXqz91VZrq52GPku7kILi/D++fqbKDTkwU2BeBdNWiZP77C4NoyHOcGN7VtnbtL2HYbVxf0czraNLiXBO4u7n0BYrcWyRWG1XF3FwDM5SUd5DRak5PiypLl/iL0y3GBmer+cnhJsMJVLRiQ1I5X3m9KsNI40GyGBUeqjo03BjuC/jsFHtKap8Tx2DJwaY5bi23H67rhmN1IB3Uz3Vqifiw+R2m8rqg/AQwaQIisLhT4EKWn5WNJlhapeaT1l0Cc0tgd2jxVMC188Pit5quq0nRGu6UqTfc98ST+8789jgdv3Dj1Uk6uvCcOaeqffHIgbgrkC3LuhXfSUxPFzSW2zoDcX3FzyY3wnuvmwp9Q0U2b5OYSjK+YCee6ufA0tkMbwfpjzcCmfCOfa+INHMTiQ27v+qSsPoLyrJvAsafrQ/mY1UeJK0uWW0SBBchka4YjaooVzEyLjpK/k9hHte4Apqaijc5Nq2PrktZ0aOAx+v0nWMhI/5eF7+AlA5e6ssGaY75bS1RHr5WCW0ubcV2WrrHasWipkexPz0kOemrHgtg/xzp1LvDI3aNV4LGMHrxxA//53x7HXzz11KmXUlUVqVp6XIC+4tZbcNvVmWyQZ0i2qOhcpWrVEFpIAFOCnIbWFEMzPv/QhqS1pfPRtr2Fg7XaQJnVSTOkqNXS2I6N1cKmqKXWHby/tcQY6mh6Wh741KadpZYfwxholLoGQGstRmgaW18Ha+3B09o66w47t7XqyA1savs0YWBTwB9LZdQiYenApsew+HDnA5RYfaQsO1IBUHWrD3dHMW714dZl58i35JhtATI1AOqZaFJA0oUtOtR+B7LuCMY4FPCA2N4qBTTSkGNKHI9U4FIJgthjIOXW4uvMfLcWuw5WR0AIf/AgPbygbi2unZfWn4KMTKiixiFrSDu2Fg48VrXuIPNFbTR3lrHxLlB3Xr+OO69fx4NP3cC7n3jy1MupqgpULT2qLkpjF8RoYyA++UBwMR6/II8/JZHaZ1286T0UfzpDNwwTNgL+Qs83O5lmsTkbMz4GreObu2DDyOv4GIp5sfbErSSwaWwG3cTR/6UAeubK32ykA5tC2fDLNw8Adm/xcTirjykBLqdaFkTlV9E5DuMlLEAOrNlxOqLxFv578bgeB7DuOC7wyIQh5D1LBi41V4iARzO4uNDv89LApVPcWoAytxaahWWuWwuvy0lPm7IiSe0rhrGGdpHVKv3IjOyZlgYeWXs6dQ9YgUe2unb7n6oqRee1c6qqylDxk4CCIKelppRRkFNhPNHNJXoikgdWRHhBn9pknnvuk6IxE9yUm4s0hn2NsE4xG6abUAl2tMLGdR+BTfkNxxWiG4K9gg/XRbhZSYIPBj/43K7/lIwWuTek0dP5lW6oS/qcMwA5dUBS3icXTOV/hqZAjXSwUn1d6bFla5cdAw9zJdQNQIOubevApUMZfFnJdccp163Frn3ha6qZlp52OIYIReIHLBD7q1YfDESkgEd2sFJlDzaMJ6+x1J2lAo+qqv3rmLulqqopyoAQapsC8BG3Gb9IatldcubxmyV+Uc+Zl8X3EMFMkzmWUZ6KoOypVE42l+jJWQOyEYzrhvHjbC6unG9sadA6Cj7408CuiQPj2fMUbh6EDbpo6ZEw4941+JDghXpTR25shL5B/wzAsVRqWxV+rBkjImOuXABylJ+S85tlnbHU363oc5MBNbI/12PWHenPv9x378AD7Hjk+3JCHI+pgUt5HA8nDjhK3FqoBeNctxY3h2/HruWp67eXci3v+AOSTLcW2YIk1QdxebA3EuqFecfbKONo1h0F+8eqqqr9qUKPqstT5kWfl09xdykxh5TcXbQnH9GY9MY+2nCQcmnczPVpliL0qZFdiwxCeF3K/3jMzSVoN9PNRQpiOjewaWRObeIsAjANOr6BD26EhJuJU4IP12cq+Di51cfYU/kR+EHXt5Ary2wAcg5aEnTQflP6SP1EoJHxOTqldccc4BHoVMBDAEj0/fRA4yoCHpJroeSCCITAYyhLBy6Ny+a7tdh+ch1/SDCWDp4eaw84tPS0uQ9LtFgfmltLNBb92PA2imXr0AZh+8T69P0Y5D0b2+Opa0zMWRXKdC1Mu+FPdW+pSqgGcYSyngAAIABJREFUMq26GLkLlA9gOhLsU20zNcip8ZXBWsLjYerUGul5dK5vA5/GNu5DrslsXDSw52M6MY1t6pyjNLZ+LR1cGlvXrm37wKe8joxHA59Kc7kx7GuQFLQdWhg0rRvDgKandUFMtdS1NoipDe6aCmxq+2wc2NQASAUq5a+BdYKbkvWNBjc1DdQgpWL6WXeTEwZGFfsD8TqSY4d1ualtaX3YxlXSu5vMYKZT+qT6nQv4cJIAj6sSXTTS/SYHMVXfVw12hHU64EvUsfGKMrOIY08EHhxWnAp4mCtsHbjUlyUCl2qukaPulB7iczgxtCty8+TgIeHWkvuAQiuXQAaHKrluLT6OVuIhjAww5DGXtu6YNqYASqo21fsf+U28/x//Tzzx9MdOvZSqHataelRdnKaaK+Y9ERDmidqMX0SDyOgTxhfNSU1ivkaYG/E5q+9FajNGXk9xc5GedKXcXIYx8txcSgKb8vgeuYFNAYgb8/H4Hqzs1BYf0g1bicWHa19o9TEW5DTbZcAr3+qDtpljATKlT1EA1CNrB/FTkn/fks9LlstK2rojCDpaaN3hxhheHBV4sDVRAEms5YayGJCUBi6VXBrpsXStyHVrSdVJbi2apeMUtxb3OqhTXDg0F9VctxZp75JvQYKwfAPgkQxWWrg/rDqdvviz/yd8693/B77+BW849VKqdqxq6VF1OWIWDiUpXsesPkLri34+ZjERWZrYwaI2knUGH78jKWaNawyE1g+p9UtzN3Ia25L3S0pj69ffv+bWHDYF7bAGas0RpqftLUnE1LWsznThGDC9RYc9hSF1LU1pa9C0XfAeutS2oaWHS2drrTtsWW/pQcrQNEBv/WHftsEyojNN/zcYrCI6c0XKBMuEMQuPvVp8AHDpbO2nSrb6EFPbAv79se1mWn2wPtzqI7TmoDd2zCojxwJkoVS2WWlzz0yqhcbSliBJS5mcPttbd6TGGOt/LOAhrM1cxcDDxK6DnVHiLC0cuJS7tVCQPtethdeNubWowU0pQEmAkBQ0kNLTRrAkZyz60YmgCJ0vhi17te5IlVcB6LptXU66+v5X6TrfXVNVlSSF3mvEPu9CXnihHRmTX/SL4200UM9xLE2uVB6sq6FjKU+flA0Xb5eyEMk18+VP0mg2FzsGrRuOw5getHyAR1MDm9r1ChvuRLA9NbApvwE4osUHOz/N6iMOcKn3Xyu17fiT/gILgS1jUpyBtorTkWfRof+dw3auct5ncHIq2nMHHvQcjXB+Iy4uY3E85gYutfWxFSE9Trm1OHCxhFuLa6vVJd1aBGCSuw+J3Fq0vUdD+s6ACicHHhl7yKqqqn2qQo+qi1HJBTzXnHHcRBLBE4yStaRNReXzyhs3vICLUAPDE5aov+bvm+nmIm3iqCtLiZvL0G66mws/nhvYtOMbbSmwKT0Wzbalm/rzAh9Z4AJXZL4mGkPrv0Rq2/k3xrRiAgCZCk0OpC2z3ywHtKZAjSnBSslnPzn2ODA5LPBgayyK48G+h8Pv5vT3+Vjg0qEvSD09znNrAYTg2wHQlx8SSG4tuluKDEKidqrLCoI24T6EjgWxfy6YkMedATzY3mt8r6bs7XibgrVUVVXtS9W9peqi1JlxN40l3F3i9sAUd5e+KHBzoWNKbi6d6QAXyNMMgU5zz01zcwnWTtpIY6XcXLSgpdRdZQk3l7YB0I67udBAp6nApnZdZpnApgC8m0tH3S56Nxce0JS4awTlK7m6DArbTnd1Abi7i+2TE+SUrAMAd3ex7W6S93CZIKf+rU65siguMHMDoE5xfzkLTXFfmRmQNL8P66eCukSdBjsAEVaMj52GHUGbGcAj1JrAg61LBMJNDDzM4OIyN3BpXBYDD24pmHJrGermu7WI/bSb/wK3ltH0tNEDkcybfw8aKIzIfYAjl+/ZnaUCj1imuznsZzaar6pKU7X0qLocaeS+oDyH+i/p7jLMm5hP60/vk9i6pTS2Y2vR2miWIlPcXFJPsHjdmJtLOAatG46lVINaYNM4in9eYFMA0wKb8o2/uYJ805V5YwFkW3yIT3VnWXyE61CtPlJPztUn4vzmT3nCnvlUfpIrS9CvoM/C1gyH1NJWMEv/jaTPQ4b10NqpaLcEHpH112rAYwAa9PeUwKW+rDBwaRuUgYyDvn4oy3Frsa/D63LKrWUI0l1mEUnb5VyHU9f+3Jt9aX8SW5Ag6JMzH92fbAU8kvu80vKqqqpd6kx2TVVVmVqI4o/CkUx3l7GLrLQe6SlJDDVyzgFBGxFeJDYgKR/k1BMm+h5N9VWe6+aipRfU4nvEZdITQW2jHW7EgyB7grm2Gt/Da9/gg68tBh/0iW0huGAm/5MyxGRCEatxABJrIjSZG7fiqD9TzrfkfV3yb5gNNWRYYetSsGIOMAn/b8QQg52H7yO1PRHw4OszwnslfGdKgUs702CpwKVOUhyP8Pox363Fjp94YKA8FIiuz4YENxXgijiXBjKSsARBfy++ZnGvg8S4bKxRONH/8DaZ7ixZ51BQXlVVtT9V6FF1MRq72M21eBDbzLT60CBF7lh+kxXBkvFzmpPGVl7L+MYNSJvvchNgbwXCQQita+IN5nBMzkvcxOqBTW15Or7HUBZuyIMyE2cfgGmQFd8jKNsH+JBu1uKbWB1czLH6WCzIacGN8C7ifxxcp4/Tke5T8nlZKxVtiXWHBDy4FdYugYcINK4i4NFJLi7AaODSoSwdx0P6/pcsATnU8Mcm7dZCM62s6dYS1fGHIyowGc4r161F2l8Ebi1RH4jl0T4na/+ljLWVO4u2p6wCug5o2+1+avaWqoQq9Ki6KI2ZNc52d5HapKw+hHXxi6b69EV5YkLPzc6vn7/k5qJBjeQ6E4AjZV67lJuLbzfTzaUksGmcyYW4tgQbbGEjnvA/l/3YyY3VQcFH2D5cS4nVR9hfhydB/9Kn87Rf8qaadZkAQKa4v9j1Hh9+zAEdJdBiisuL9vefYjWUY92RHlv/vyJadwhrC8t2DDzIe1cUuJSXKXE8pO/t0sClc91aojoz3a1Fu56m3Euz3UwUCwlpPxKNRT+ivI1qoSqUj64z7jM7WGlpubSuqqqq3alCj6qL01Lmi4tYfSTcXaauQ9ycNKk+w7Bptxp5nak0th5i8DrWbk03F5rGNq4LzZR5mRbfYyiT3F0G8MHNq6WsAgDEDX0Q34O1Ozr4CPvMBBc5ViPRGjJuXKlUALLsjfZc95ej/WSf49Lv5Zj7itCnyJ3Fl6+dinb4P3g2wKPXlDgeHHioKcVHApcOZfa35AI55tYSQHpaR2H+Am4tvK4zI24tFAyIAACZ+w6obaT0tLE1Sf5+S4YTCPZOvv+WwUor8KiqOpQq9Ki6HE0h+AVkfzL4APzFm5bLT1a6xGZDLo/hBRmfbHSk+eI1K3MofsHSWnLcXDjQoGBEMglOubkMx+NuLlpgU/7UT3s6OBbfwysR34OabifjexwEfGTdzClPsrPBBYUnBVYjqSCnowAk0jgAmdIn6pewgjiklPPZLE4HMAo6lg1WGoK++e4wGXDwSMDDsPetL5O+J8cCl8ZlY1Z7IGXueFiOfJ0Ir2FqXcE1LLJepP0Se4vkdTdjH5J0a5H2IMp+QprfK+HWIu9NuvwxFgAes/eGFX4EMl27+U9VlaYz2TVVVRVoCZqvXPSyTCkzg5zydUSbE+Xph7oJoU+wMjcEdM0SINHa+DoKMZQNWsqaQ3Jz0eqmuLnQdnMDm8Zm08pTxpH4Hl4EfETxPXYBPsL5UsFN891d5KfaeUFOw3OaG+SUzzHN/WXlG/mtgo2u+ZN7rmvF6WB9RoEXaReN5evkz6Fq3RGNneqfAB7a2sjcqQCnewEeUhyPsJ6c30gcDw486Hf11MClQ1k3HGs3300IP7KtFQXwT4+162fLrsmxFYa2b0m3ca/pWFI5d2sJ9yND+/Ahy9BFmk/bI43vqcr3aWPnl1NeVVW1T1XoUXU5MoQALwE+EuVLWH3kPEHJ9s31mwOImwAtvsfc+fh5LuHmUpTmL9iYxm4u/Dg3sKmtLw9sGsX3oCbZJfE9pN+bg4/4ZqQIfJA+uwxymgAgcUUOAGFdsgBISZ/jaj33Fd5pmb/t7oKVzgIe2v/rEwIPtn41jgf7Pg2/Y/MDl8Zl6cClqdhQKbeWnPTrS7i1uHGCdjP2Klocj2gP0ZC+i81HgIc03h7cWYLjamlQVbVXVehRdVky7XBRUp4CLFU+xepjWCeEssSTmQCQDF2KQczCaWx1SxX9adKYmws9TkW+z3Fz4U/raBteJpk+pwKb2mNpA65DDjUrAY/vcTTw4esyn25PdFcR+7sxMvrnWnNkub8UWB0sH5xz/1o86OuMOB2lf/egna9LwYo8YBL0V91hMj6/wdoPCDwUADwlcKkvSwQulVwY6bEMw4mVoMlza5HqcqwcpQcCmjVHdJ3NuD4vnp5WbANxXM2tJegvuf9uHax0pDzYW1ZVVe1S1069gKqqzWS64apt2mH3Qso708H4NsuUZ7VpOpjW+Iup6Yy90PfXU228zgzX3HAeoIN93ZnO7hn6YzTGXptNh66fi7ZJztevU5rbrdu2CcdCA39+LYBGHG9ox+vapkPTwq+jNZ0fg9dF/WDP1x4P48djWJjRAmhau9Ft+o+JK+uaYV/jjrvGwLRuI2aP3WbaHjcwbWs34u64/w3APq1sW3SmGfxRzRXQ3bRlANDdtDcCXYvOXMF0N32b6Dca2BWDlF0BuDn+OpDWxv42pkHXtWHfvq3BFTrcFOvsx7NBF61xuDHr/Fpof3v+rl1Hx+3fI3fT58dO9PfzkP5ubeEYiOYJislNbG6f8MY87ONuLLsJfY4t/RmMfn6J5zZKHxFUlfZRYR0E4CGMEcGUFDDR5skZezngEWoD4GGEVN4gwEOJ48GBh+R6SI+nBi514tYVYZBsHTjMdWvhdVPcWuxrev458EKJC9ZQSJH7IEYrTz8A2rV1By2vstfc6Fq28nxVVYqqpUfVZUkj8ytfILPaSBfyRHAv6SlN9rx0oxdtaEi5NJ8Qu0Mfa3yDJr0PKVPfqW4uwfjKkzu/PuEJXyqwqa0vD2w6Jb6HbSPfOCxh8ZH1NDdl8eGm5TdR7Jz2HOSUrq8klsce4n8cS8tavyxpmZP8W/o28mfzJMFK2dgR8AhUDjxGrcAWBh78nLQ4HlMClw5l8GWlgUs1t5aojl6zCt1aUtc6eryEW8tq6Wm1+dT4HjEs2S3wUCyHq6qq9qlq6XEBuu+JJ3GLMXj+9Wu48/r1Uy/ndDLd8GghOHaP8PsLVsctLpYpz2rTX9CdVYSz+HBWG3zt3IqkI3MZ2zGa17dpYC0UhLV1MMP1Ozmf6avZeVKLDX5+vVVGa6zlBbUCoe3alliEmK63vBDGb3rrEWdJ0rcD+idorl8DtOiGugZAa609nNVGaBlCLD6AoMyOZdC0zsTZWni0jSujlh7GW3ygbWE6Yv3RNP370dr4Hv2xt/owV/3fyx5nW3wAcFZCdgJnTdFbPiRfA84bp0tahdjfocUH/EY1tvjAME5fb5sTy4rAaqO/2eH9AeRYbYxafQBA18r9yfroGsPxEEOMmX00aw5+49+pfY6rtLWKco6JPqpFxwJ9dMsMoNiVJRojATu0dUqgkvYXIUYe8IhghzSe+joFcNPAQ3L143E84pTgeYFLuwByxMAjJ3ApILu1UIiuBd6W3FqCOtpPARKuj5SeNrLm4IFPM0BG5NYi9LewhPQV2yCYOxsw8Nhme4IdAFTrjgsHHw/euIGHbjyNj7fV4qJqf6rQ4wL0FbfegtuuUhvKCxKBDeEx9VnYB/wIwAcS8MJ0gIMUM8CHm4O6zsDBFj6fgw6j4EM+Nwc+/HkJ4APoXVBIHQUaFIxQ8AGEriwi+HB15wQ+qCR3lxHQQWHFADSuMsAHhSRt1FaFFwL8CFxe+jq9/wAubP8c+JEJT+gayBrdOoO3WgMaM/vEN/oyBIndYI6j3bmtjPSZ68oyF3akxh7vr1l3kOMx4FEEQNLWHcD5AA/n1iIBjyXT03LgEdRplhoZwEOL42HbI5jHawbwSKWnlcCFHPeswo496s7r13Hn9et48KkbePcTT9rr65YuJ9W9pSqhCj2qLkdmuLkftfpg5dzCYYnysTYB+BiDFxngY2z+sA96WAFMBR8AQON7RHVmiO8hWoc0A7RoSB2N4SGDD1ungQ8/hqs7MviA/9PqVh8q+IAIOjjYEMEHnTgV58OPxaGCDD8i64yJ8GM83sc4PBnmIuP4igygsXAfDYKcezyPU0IO20co3yJux1zYEbTTgEcMIrKBRxKApIGHFLD0KMDDgQgOPJw04GGhvQw8XJ1T0u2FWoUIkIADD6muGHgYuY1/LRzPAh60zdGAR4UfJ9ED/+2deOC//Q6eePrjp15K1Y5VoUfVZcldkCJLD3q8H6sPagEhgg9wkEGHWSawaSn4cHP7cywMbOqsQCTwAQo0BPABhFAkCl6qBDZ14AMAmsbsGnz0f87+jS4NcEo6FwQ4HYJruokLApxGFh0T4cdo/wFeqFYbBfCDzhWMRcfzlad2hTkjnchlJeyj1O0wSOlY/20ztAhrLXFnEV4fBXhIgUs58HDSgIez2JCAB7Xm8GMoYARs/BAMYBHgsXTgUg14VNhRlaO7b/s63H3b1+Hhj38AP/uXrzr1cqp2qgo9qi5HCszYM/yw0w/ZSGCAwGpDHAswlo6wcZcHH3x9tB2HMxow8eDD1Sngw80lgQ8gdIWRMro4MJICH03rxtkv+PBZXXLjfFBlu7vIFh157i43/Q1WBEn8GibAj+z+I/BDzNIi9PfvWWwBEozpxg0qwzmGPuNAY5orzMFVaplxEsjhpAAJNsf+YAftOwY8dAuQYncWcnwJwKMowLYAPHydAjw606nAQ7LCCMag0EEJeLoU8IisTtRyCg7k9R0GdtSUtZFM19o9yYbzVVVpqtCj6vKkuLDox6dzefGQgMbBSMEL134O+ChYK8j6BjcWgIOP0CJEBhousKkY94O5sWgxQSTw4QOgXgL4wMAoQmsQyepDAR90kFx3F+oyg6FvFORUgR9hitq43Rj8kPsr8CPqT2KJ8JgfQHjDqliB+LF9pQwnFo0HchZuLaFO6rLiK7XxuAWHDCVyYEfcrgx2pPpnBSsN+owBjxwAogCPXHcW4fXRgYeLzyEBj9JMLRx4+DrNUqMvF4EHG68UIqwBPKK+TXxO0Toq8KiqqipUhR5VlyN3gUpYcezR6mMJ8GGrM+KJNBjN6KLOp4APN58EPuycw3lR8OH7kfE5+JAyuvAYIHbjGYOP4XyPDT7sLL2K3V0oRHCD5Lm7dBR0SC4zkdWHDj8iiww/BrLgR7r/OPyQM71QsbH8+50BQbZyhTmotrDmWAJyiHNnwY5wrHLYkddfhh20XIEZKAEeEgApBB4cdghlRwIeTlNS07p2WpwPfpMvZWpxrznwoHWjwCMAEBjG4m3oxyoTRiwBPA4DO2h5VVXV7lShR9XFyJgOXSbM2Av8kON25AdDdeUdHVOKDRLAlWXAh1sHPZ8oWCnpt1UqWzcGzehyNPBhR+01y93FHWS6u0xMa2vbl8IPZnVRDD94/3z44cf2ou+bYgXSj+2rCuOBLGEFcngt6uai1KmQA8gCHWze/cIOWifDi0XT0S7gzuKPDwQ8XOBSCXjsJTWtfa+XAR4cRgxtEMxdCh7Gjo8CO0wFH0w3g2vWJvNVVSk6sx1TVVVaZuUL3pQLMN1Y8Ccb0QYjeAoij6GatJqCvg2gbn5IX7GN0I5vzKR+2Wn4UBb5PhVMTtrADptXOhetR1jfQNlQD9Yk8oZ8OLa/G3RmOB7K+kH74+BpKKDeSHh3F3Nlbz7cT9BGuYERb3jYGLlm8a5vr/gpNOnX9zW4Ijd5YX9+w2jo2rP6N6Q/aRec2zB2MEcwHhvXje1+aHE/T7gucj5s7uT8tP3RXVyUc9HfeyTer9R7LP9dtL/l6HsPxPMon2vtMyaOXdA/+/9VLvDwY1+pfbLcWfrvmynxO44KPJw04LFValpxDAIyxH4rAo9OLVf6Ntra5D1SBR5VVVUlqpYeVRcjY1p0XeMvTHux+rC/YosNVxZZVRALiCVS2RZbfLC3gluYSBld6GYjdoXhY5ensh0sOYbx41S2bmMsp7I9isUH2sHNJRnng6rU3WWJtLYdt7YYjkOrD4Ttciw3SBu7RBKXI6v/YPVh+wtpbsn4bg5frFqBuHNFfIO9lSvMQXVKl5XS+TdPP6u0ASTYIRyXgsqkBUgCeAAq7LB1OvCwQx8TeEhpa1OpaSXgsURqWlcugowmBQlAjncMPMga5fM4PewwNbZHVdVuVaFH1UXJgQ973Lu7AMGN+55cXkJIQdoJ4MNWa24wdKowle1YXw41hnXYFa6dytaVp1LZBilpBfDB684WfAx/TmS7u6yV1jYaA8iDH5I7yzj8ENPcjvYf4EccF4SM4ZTjinICV5iz0qlcVkrn3xx2sHazYAdpV+rOQssygUdkhUbKzgl4SNaEuRaJgYWmAjy2Sk3rXg9jaQABShutPOybBTz2BjtYXQUeI+q64Fq3yXxVVYoq9Ki6HPU37+7ClLT6ALBalpd+jiz4IfUnAUBpKttuzYwuDGoUgQ//lk5MZUvOtzSVrQcfDpLsBHwA6K1PZoAPxHuJ4rS2ktZIa+vHzYEftA3InJgOPyakufVvx6ysLIoVCBDeNM/MCnN4bWDNsViWlxOmnwVKYcdQvko62gBi5AEPDXa4sinAYygHKXPH8HVbAQ8HNCTg0ZFy15YDD6elUtOWAI+gTUPAQdQmnhtkzLg87uvW55UFWjKgRqpubesOto6qqqr9qEKPqssSgQtZ8GMNqw+3jiKrihCQeEhAbvb3Bj7s23CaVLYUbvgxdgQ+LB8zk8GH/bsVurtIaW2L3V1k0DHq7gIE8KEYfkTuLIXwQ+g/lubWaVFXlJmuMEkrkDNRuTVHyjKjzJojH7JwCw4ZOGwOO5T+u0xHCwTWHQABHWcAPJw04LFlatqo3w6ABz/fcG1xX/c6OJ+C8go7qqqqKvSouhhp7ixJlxdgPfiRWX448AGE8yngw7cTwId96wjEEMAHHV8CH3z8cwIfhoCOHPAB+L0mVKuPtdPaCvBiCfghw4tx+JGV5ta/Z8u5osxxhUlagRxUu3JZyZpfARaFsCPVfynYYfvnAg8JgBQCDw47hLJLAR6pIKRSphYJeERWIQLwCMYXgEfs9nJA4LED2AHkAY8azJTrJrbNqHL8a2LVeqrQo+qipFl0qFYfQABIJrm8ANPhx4hlSM5xtlXJWJuGnE5qHOFclkxlq4GVnFS2PAaIO49Tgg8Ak2J8aOCDioKOorS21P3F7+Ey3V3EOB858COGF+VpbtlYxfAjA0LMdEVZ0xXm8DqVy0rh/Nunn9XHGocdpLw0focR5imM3xHBDmAR4OF0auAxrEMGHrmpad3rLVLTutd0Xv5+JtsUggetb86YFIiUzJmKw7GGdUeFHVVV+1eFHlUXo6a/WLUJd5ZVXF4AFZaMQghDrB/IcWyJgdNmdDFDwFLVOgWI2oGuyUEAAZho1hsi+CB1Eviwf7oho8upwQdmBDc1BHT4e4pSd5cefNiVMOW6u2TE+QDG4Ae1+gAovCiHH2ysIK5HieUH1fJWIHQ9fk1e5fMfThu4rNg+06051HE02BGtZQ7s4GuZCzukshHgocEOoBx4MNgBTAceewha6uq1GB1SphYJSJSmppWAR2cQwQDZ4mJoF4899JXbIJgbZEy5XOmbEbg0OnYqBR4bwo6Gg5aqqqpdqUKPqotTY1q0Ixlc9uLycg7gw/4i0EEBH/atKU9ly9vJqWwJ7CCuMEcEH/ZDzCw6Ct1dlk1rmxfnA8AE+BHCC2OazdLcDmM4ZViBAMhyRSm2AimY/7DawJpjkssMK2dzni79LJtDsyxZM34HGWuJ+B1h+bGAB4/RoaWmdf0lAJCbmjYag4IMBlDOBnhsCDuACjyWkulau6fYcL6qKk0VelRdjKjlxiyrDyAAJEmXl36sqfBDgh1HAR/uPQjmE8CHXf4wRmkq28AtphmARU4q2yOCDws4jGr1sX1a2/73qLtL3wbz4MfqaW4DCKGBC8UKA0CxK8ySViCH0nLWHMtmeUmNpVh3FMMO1m4N2BH0ybTuoGUT3Vlo2SUCDwvayQ16Ks6HAjyWTk0btTsS8FgKdmT2mQM7qotLVdW+VaFH1cWJWm6s7vICxCAkw+XFFk2M29GEqWzhlxP3XRN8YEYqWwc+pH4UfLjzlVLZOvCRTGV77uADmJ/WVkpvO8ndRYYfnRTUtBh+hOOG7iyF8CMJIXKsQOg6ZCuQ5FjK/PlWIMfTKV1W0mPF5XtIPzv0L+hT6s5CjpdKRwtcDvDo+nIJeHSkjx8jAS5ElxjT+a2E1i9otzbwAKJjsPUN5XL7nGPefy+uLBV4VFXtXxV6VF2MUpYbuS4vYv8c+KEe67CEW3Fox3aZHEgMIMCyCzkrywBB6PQUpiAJTeh7FMMaQAIf9nTTqWzDGCAyMNFS2UZghQZDvQDwYd8c9/YVuruk4nyUurtkWnGEVh8YxgDmwY/InaUw04uTCiE0KxA6vluvG0uGIEu4wpyNTuGyMjK37TMOO6KxF4MdvH8hINlB/I6OApALAR7D+crAozN5qWl9Pwl48Lkk4CGMkQMewPoOx0K54WsnczbxOUXwZeyYrXl12JFop1l3RP2r7DVuQ/eWQ8e4qlpdFXpUXYwc2NDgRY7VB4B0vA9qxQHMhh9bgA/X1zYTAEaqL0llK8MawIMPIJxPAR++nQMXAvhIpbKlYEVKZUvBB9CDioOCD3sCYZyPRdLaOk2O80EGmwQ/dMuNOfCjNNOLfxs0CLKBK0xuQNTDagNrjlyXlbCPMEcKRCwFO5L9Z8Jt8Q/fAAAgAElEQVSOoCwTeKyUjpa+tsfHAx5+7Qrw4HE+NODhykWLCinIqQA8AoggtRNgwB5T02rHw9pnwA5Wt5QrC+1bY3ucRn/5z7+HB/7l9/H405849VKqdqwKPaouSqFFRwgvgHz4kWX1ASAZ74Meuz4cfoy6yEiQ4nTgg8IJ18b+mpnKloxt355OBB/RGAr48O2aAWgcBXxQzY3zYWfpNTWtrRjnQ7L6AGbBD2a5EQCLIFhqKfwQAEswT3wDHAVAJe/h0EcBF2tZgZyBTmnNURYwdQ7sCOsmZ2RJtA/rFOsOWjYxfkeQhWWB+B309xGARyrrypTUtH4+YXw+hgo8pHYiTNkH8KDrTB4ngo4uZt1RCDt4/wo8TqcXfeZ/wos+8z/h4U98EG/8q2899XKqdqoKPS5A9z3xJG4xBs+/fg13Xr9+6uWcTI3p0EZQY4AXAKJ4H6rLC5APP3KsPty4HJbQm/3EcQAXMo+3Ah/LpLKVgQYHH8lUtuT8pVS2RwEfRvw9Dj6A4R7GGRWUurvMivPh2wCahUU5/JCtPgDMy/RC1xWcX1+lBRgtdoWZZwUSjnU8bRKANNdlJatPGewAOPBYA3ZkjLVh/I6xdLRh+XkAj5bBj7HUtBLwiMCIAjw6g+DmXAcQ/PUGwIOsaewY0rqU4y3jdvB2pdYdDQcyF6YHb9zAQzeexsfbCoCq9qcKPS5AX3HrLbjt6ryeCE6VuyCl4EeW1QcQwIbVUtwWgA8OG8CggS03cMFJxSCoQvkc8OHGWSuVbcp6g4+hpbI9R/BhP+zhDd3ktLYOfABD1hc3aIm7y0hQ09giZAh2CkyDH/MyvZC10fb03BHfOJe6wiwZEPXQWjkAqW1fCjni+feTkaW0XAEeGuwI6sqAhwY7wrrzAx65qWl9nWapwV1iBOCRm5rWn8sM4BGMQ+aLyg3k89XgSwnwmAE7gDzgUWHHfN15/TruvH4dDz51A+9+4knYa9iWYP64DwGq1leFHlUXI2q54aw+ePmceB+TU9z2/aNjCj5AgMSC4ANKX2DI6CIBEdVaxIEPSOMvl8o2giJGTmXrN10Uapwp+PAnCmDRtLb973J3l2GsbPiRgCLbZ3qh8yDs4/rR98a/X0tZgfD5x61ADqedW3Mk554TtyNpGTIVaqTKC4GHAjto2Vx3Fnp8bsDDtZEAAM3U4uaSgEdk+aEAjwAQ8HZ8jBTwIOvxIhBFgxabAg8NdtC2rG4N2MHbNUr/qqqqfalCj6qLEoUXmtUHANVyg7u8AAXwI4AaxOoDYFDEAQ8CPtD4m/3J7iszwEcIU0asRRpg7VS2FMRIqWzj8bqzAh8Aolu5yWltifuLH2uqu4sU52NB+LFlphenPAixIyuQQ2pf1hzJuReDHbz/irCDls+M3xF8Vywcv8OVnQPwGM5HBh6ipYbkEmMG1xLeLwYQUIBE58/bvfbjSdAiGHdB4MHnTBxnA4+FYAcwz7qjwo6qqv2rQo+qi1Fo6TEOP3KsPoAQitCb/0XifQTtCPjoy5PHIpDoFgcfYl8NfCAEGoGlil+fO4ehHR9DAyYUfLjxpFS2RwYfAGbH+bAf4P6t9GxiWlpbDz4kUasPgAARYDn4oVtuxMFOXf8R+OHnjG98l7ICsWO1cfusuaX5z0PrBiB1mmtJkgM72OtZcTvyrERKrDuAGcBjZjpa+ruNyuHLjww8OgIx3DgSGIgtNRTgwcYTgYcwBgUeWdBiJvDwEsGKDET4cQA8loIdiXZLwo4KP5hqytqqHekcHhFVVWWrMS27eOkXMldnTOsvfsZ0/qIYthnK+YXaKBfUoJ1p5Qt91EbegGjH4hOXYGMyHGpPZ4JNVzAPSBtpHsQbH2Ez5foE65PaubkaZZ2ZG8xg40g2u1rKQddubGMsbax9vSsjG1Bbj7C+gbLRD28I9BsG/ruxP8bYn/61q7Pno9zIjJm6mwYwDTpzRW6etBusBsFNWPYN3ZXQZyg35orcNI635/MZ05CbVtbOjcFueA2u/E+oK/YjjBON1fifsGJ87nOJ5ZE8pznvm/hZ4p+FxN8zOQ7/7ADxZ2yFz27q/432f0347f/P9v+Hw7ppwCP9XXO+wMNJu850hsX50Cw1pJgfAvDw18qRa/TQbxhvKeAxjD8cLpaaNmsfJEORpHVHX5ezh6N7vnjPKAMP3q7qOPrDP/xD/PZv//apl1G1gSr0qLoYNabzFyx6gQqPO/WixuFFCfzQwIlv54/ZBV1sswz4UDc9vcJjxGNkgA+5TwJoJMCHCC7YXLmmxPzpnBv3XMAH/bFl2tPXAvBhrtCZxpbRmyzfNgE+RuGHdtMH6Dd3c24gw5vJ+OY1D1ykIYR8k51zI2+4W0ImBDnij/reBO+z8t4k3s8cyBEFJR0FJn1T8fPCjmd9VoGcz33W/y0BeNjX4/+3l4jfEX8XnRfwoJla3FgS8IiAuwI83LUuCTzoXOq1WZkrE3gEfdlxdO1fCng4Scd0b8TaaMCiFHa4/k4cdpTuH6v2rQceeAAvf/nL8bKXvQwPPPDApDHe9ra34e6778YznvEMPPe5z8U3fuM34kMf+tDCK61aStW9perixIOYAs7NpczlBcA68T4A0Mwtvtx0AEi5GCTVHndGSQ9rqAtK510/rLdKRipbvxzaVnB16dukUtmCtBPnk+J3kDHC4KcI+vFUtkYaoxlS2VK3n6O4utiPSH8D4T7b7n2YmNbWx/kAIivRWWlt+fGceB/AMD4A6sKybKYXILxhznFfCW+wR11h+I16aUDULc2G11IEK/ritQKQzhlrdxlZADFuh3I8150lqFsgHa0f8wyAhwQkkqlpE8DDSxtfaifBiyaGFuMPO6S2iYcdhcCDrjk6JtaskXWH0O+UcTtit5YKO2TtL3vLRz/6UfzCL/wC/umf/gl/9md/BkOyS5Xota99Ld74xjfiOc95Dj7t0z4NjzzyCH7jN34Dv/d7v4f3ve99eP7znz9p3Kr1VC09qi5GDVo0GLfoyKH7OS4vrm54EV5gRcsP6UIflHVBecnGotjiQ9jA8EBpwzzDYdTX34lLGzWlT6KdaPHB6wSLDzFI3EEtPtzNwRR3F7vu2OLDmqY30Y1OkbsL6I2V8LSZlxdbfmSUg99gZjwxD57EN4LrAm3DrAcyLTH0sWgHbZxMK5Aj/oydY+J9meWyolpzsLGiz4P8uUlZasSfx4mf7az/Owj/3y0EPNx3g+wiNz1+B/9Os+3g648GPFy/JPAAojm062zUb6SdCDyiayrCPr7N2Hx5wGOYJz6O2uYCD1KmWnawOXncjhJXlkl7RLLHrNqvnvWsZ+G1r30tfuqnfgqveMUrJo3xzne+E+9617vwx3/8x/jnf/5n/MM//APe//7343M/93PxiU98Aj/zMz+z8KqrllCFHlUXJw1+aCaLrt1wHLq8LBnvI2gHQHzCkQk+1E1LL/F4AvjI6ku+aeINFaI+Q7+RDRn6Tai0TslH+kzAh319wDgf2pNo6UYewJwbRNWNIJpbuSFFeMObhiBEGQDEiHMKa8yAIEfWUrE5psQFyYEcuhsLkP48jXwGZ33GgaR1B/t/d4T4HeFr+NdHBR5OKvCg1zY+Br2+svE04BFfE2XgEUGLGcCDrp0fa2AjG3j48w1dfVOuKKWuLByW5MR306BIhR3H1a233jqp39vf/nbcf//9eOlLX+rL7rrrLvzQD/0Quq7Dhz/84aWWWLWgqntL1cWIZ11p0KLtN4+aO8vUFLcAokwvOS4vrr9vwzK3hK4srd/ZdUbIhqIcdyaRypa6nIy5uliflqBcc5MB7FJTqWz9PoOtNXRjMeStI+shrjDB2H27FohS2fL+R3N1sa9jdxee2UXS1LS2UWYXYHAtcoOnsrvkurwAtq4kNa1SXu7yAoQ3t8TlhIGPwRWG31DnuK8MdcVpcRMZYQ6rCEo4yeWlWV7yXFZy+ozDs3kZWVJ1CdghHOdad9CyOfE7pN/A5QEPGk+Kt1NdVqADj/iBAGkXjY1BSwIPci58fap7TerYr3H8wQ6ANOzw5SGskMp5/1JXFu7GQmFHDWQaq0NLUsxvM9/aatsWP/iDP4hnP/vZUd3tt98OAHjhC1+4+jqqylWhR9XF6Mq0uMlBRv8FSeEHjfcxNcUtQOAFG6so3kdfF4EPoVyCFpPAB4nRwdPeloIP19fPq4EPODYSrzsAH6ydBj7ceyClso3H684GfPRveP+Ztpqa1nbJOB+27QT4kQIThVCkDH6QeTSggfCG+aSxQM5KhZADWBB0ZI4zC3ak6haGHcAo8Ngyfge9IT8H4OHfJwV4iJYaEjAh4IH3i6wmmWuKBDwkq8lS4BH0BRBYck4BHtF4DGwo5aeM25Fsp8COqwo+zl5N0+AFL3iBWPfnf/7n+IzP+Ax87/d+78arqspRhR5VFyUHPgCo8CNl0RGCkHH4oVl9ACEUSVl+qBYfffsIfPTlYxAkF3wAAzQBG08FH4j7SuDDj+XXAUjgAwCz5hja8Xk1YELBhxvPBTw9Eviwn1Or4XUP10asPnLBRyC//xyx+kiAD/vnXQF+TIAiefADUMHFHq1ADq+9WXMo5Vmwg/WdZfUxA3YAk4GHBjts3TzgkYId9PURgEdHynwbYTwJQojAg42nAg8KKHw/rY88LwcedKy47QTgQc+BrzkTePglKbDD1snAohR2xHVlsAOowOPS9aEPfQjvfOc78fu///t47nOfe+rlVAk6E7vYqqpxuQvUlWmDi1NwcTtlvA8gutBHMT7ocbBZaPUnLKmNCBmPbuDST4bi8fKfKrGNmrjOeK3BBjTVrpHXmb0x7cv2HOMjHIO/Lo/zYX9CH31bx5/uKjdGmXE+nLrUzZsY/6OBv1QtFO8D4DeqwjoyYkmUBcCU+tAO0rwoiAVy5J+xc9Xfn3nvf8bfUv08uHmkMTDzs6p85vk6UtYdCwMPHr9DSkN7qcCjM8OcfLzg2ibF/BCAhwwrSLsETBmDFm6MoE9mXw48+BjBsXbtTwEPtj67P9KtO04Zt4Nbd1yR/lVUN0/ws62eeuopvPWtb8WXfumX4u///u9x//33b76GqjxVS4+qixK1tnAXKe7yAsTxPqamuAWwQLyPhpQ7v4YOIJYNGLP4UI5td2bFQSwcNIuPrj+25X2cDVa+l1S2tFxKZXtIi4/AvSU/zof9bIea7O4yJc4HgKTLC4DAQiOw1phh+VHq8uLfHHIjGfgl0xtdrXyo28YK5Jha35pDGydRlzH3vLgdWjk7h6muLKTNlvE73HdMWIeg7pyBhysfAx4caqjAQ2onjh2DBxFaBGCjHHjQdSTXmgs82Hj04U0APBjscForbkfULsOVpcKOy9SHPvQh/MiP/Aj++q//Go899hg++tGP4nu+53vw0EMP4U1vetOpl1fFVC09qi5G2gWKU3pP+QtS3IpPBoyc4ta+lp8ySBlcIouPzKck9Dhl/SFacQQbHKTbmtRTqGGYaN4m7jO8VvrQ9bF2omUHGzvH9/pcLD5oWlv6VHZOWltbTm6USJrbIN2tdMMlWH5EWV7Gju3Ew+FCT9nlLBuJJ/5ZViC8jgy1khXIEX9yznOx93GBv+1iGVlcnRf7XI/8Xwj/7zDLjoR1R/B/tf8/TNsuEb+jI9897rvI1qFvC/H1OQAPpxzg4QKSjgEPGrg0HhtiH/t6HvDw8zVjbROQw69Fghx0D5MGHpplh6sbjsdT0MbWuiEUkaw7aJ+UtXB1cbksfeEXfiF++Zd/GQ888AD+7u/+Dt/0Td8EAHjzm9+Mhx566MSrq+Kq0KPqoqQBDlfnlAM/si+cCvzgri3iRTy4mGeAD1Ke2njkgg++ASoFH66vusFi4CMcG3KfBCARwQevOyPw4VLauuB/9LVtU+7uYn/HNzzBDwUcGe4uy8OPBrLLC5D9dH0UfoDVz7tBV106RlOk5t6gH1CFIKn4vYrqlLkz2ufBDiD6W2nlJa4sE2BHiTtLZ5ro/7itWy5+h/Qd5V7btvBtjww8+DVqDHj4dhnAw7cZAR7pBw75wIOfVw7w8BLmiFLTKsCDj5HaJ0muLBrs4P1zXFly94kchFT16jqgazf86cbXtJI++7M/G7/4i7+Il73sZei6Dvfee+/J1lIlq7q3VF2MqDuL5tpCywGEgUszgp3GgU9l15icYKc0da1B69sOwU+JqwtgdzOmA3WB6YDIJUVyjXGbljjoaOddP2AA+KmULCtKSlktlW1nusHVJerT+RS1tI9v1yBKZRv0h91wOlcYWkfP62iuLvYzaDXX3UULcNqPbn+1PaSb6u7Su2b4MmC4mct1e9GOVw526tSJwU3ZOEGH8cwvmovMrIwwh5Z8HrMzrfiBytrH8Etov5QrywJuLEO7uG4pdxZ6vEb8Dvv6TIBHCrxTkMHifIwCDzdeBvCI+iSAxzA/6ZsBPOi6xXFzgIcbggAMDjxKg5Ta1zrsENuA9x93ZblSjqvW11/+yx/igUfvAwB89Kl/wUefehTthulxNX3zN38z7r33XnzkIx859VKqmCr0qLo40QwuWjaXkngftk6HH7SNm4fG+wAS8IPE8wjAB9BDjnbY/TmYkQk+JAgiwgwGPgJ4wfu5ZY2kso0gRgM5lS0BGu4cA8gigA+3HpD1S+CDrvtQ4CNof5w4Hy67i33/Cfzw8Tsa0EwvpmNAYS34IZb3de79oDefObE96I3olrFADqWlIEeibg3QEY27LuwA8oCHBDto+d7jd9jXxwMe/j1QgIcEHiTgEQAC3i4aG4NygIchlpjCusW+CvCg84r1OcDDjzFYpmrAIwd2AOVxO6LMLRPidlTgcVq96DO+Ei/6jK8Myh5+7P/Fz/yX06aL/czP/EwAwO23337SdVTFqtCj6mKkQY2UdYcGP6jVh+2zTrBT93oUfNBUtoIlSA74oDChCHz06whAQwb46AvFVLYxnAAgpLINwAdrp4EP227odwTwASCsLwQf9rPqAIcnSP3HZFpa2zHwQZWEHzlWHwADJMrxYvADyAEgAHZoBXI87c+aQ+mzFOzgdTOsO7JhB7BK/A77G6x8mHIK8Ij77hd4iJYaAvDoOHhQgEfUTgEeMSQZBx6aW0oJ8MiBHBHwIO+N/50BPHw3Bii2DlKaqqvBTMe0dUaV0z8A+OAHP4hP+qRPwstf/vJTL6WKSdtpVFWdrTQ/zCXjfbh2Yh+TH+yUBjgdykmZ9iRF2HBImx3tWHwSxGJ86Bs2dwyxPHpN+zRDn3hs5Rz6tUntgvX0bbTNLLDfGB+tievD18NvzWd+jTgf7ncQ50MKjqjE9Vgn3gegPk3PiOtREmMCgI/1sE0sEDpvczY/c96HqbE50n8v4XMyK54H+zyuEbdDKFeDDtO4HSsBj9z4HfY1+z7rYbItOwbwcNeqUeBB2tB+YrsIpmh9UtdWrTwDeNA1CvOqx37NA9jIAh6uG9kfbR2kFMiL25HaL1YdR48//jgAoG3lv98f/MEf4K677sIb3/jGoPyBBx7Ahz/84aj9k08+iTe/+c143eteh9tuu235BVfNUoUeVRejK7S4ysizrl3MUhG71wx2GoAPYYOQBT4SMCMJPpL9ysDH0A5RO3GDJ44dr4VDDd6ObwrPAXy4YH8uwOn4TQV/XQ4+7I8DHMb+iDdP5EYL8U3YdvCjgb/EaTetrs5ruRvqOKXpyA11FmTR4ctxVXiuhe+Z/jfhfYiSf3fSJhoHeZ/BDWBHjjuL9H+ZZnpaI35HCELC7zDg2MDDKQd4yLCCtEuMLbu9cOBQDjyCORLrGN8bhGBDAx5+qQIEocBjrSClvE/O3i+ZuYXtMav2rUcffRT3338/uq7De9/7Xty8GVuJvOlNb8IHPvABvOENb/BlDz/8MF784hfjjjvuwHd+53fib/7mbwAAf//3f4977rkHr3zlK/GjP/qjW51GVYGqe8sF6L4nnsQtxuD516/hzuvXT72ck8nF73AXpZuI43XweB9AOtipFO8DQHGwU6mdc3kxprNxRJhbi+ndWpzri9/huGO3GWAuMJ0JXVJGXV1IW+4SkuPq4tYUjmlgvV/CdnaOPrCpug4AUNxqgvgdfTvBpUVzkXHnJbm6OJW4ugAI2s11dUHbb9w61mZmnI/+zPrPLvrPcqg57i72/W/6968Njme5vQRtlOPJLi8Y6oH4BjjDTWVWLJCsOc4FfADlLit6n+zYHKNzCOVTLDtGjpdwY4naAKu7s4R17jXE1znWHWG7+cAjAtoZwIPXlwAP7uqSBTz64zHgEQQujcaA2Eeba2g3tInWXwI82Bwq8HDyMKPLAh6uzCmKx5Hj7jIzSGmyHfQ+l6YHb9zAQzeexsed5UTXIoxrtbK68fe/bVt86Zd+KT74wQ/isccegzEG9957L26//XZ82Zd9GX7jN37Dt331q1+NP/qjP8K3fMu3+LLbb78d3/3d341f//Vfxy/+4i/iHe94B+6880689KUvxU/+5E/iBS94wSqnVjVfputOmN+nalX95V/+JV784hfjf37mJ+O2q3PaHE/T333gdQCG2B1ON8kGtSV1N5XjVDta3oL3MXK74Dhu03WNj+8xlBkPOWg9OjPsEIMyd9z4Y0PKDWkrHfO2LobG0BZRW/ra7z2iMUHa8Dm0Pu5YWJc7buN2Rlh/NHbf1x03fExpHteO1DdtPGbD+9B5WhOMNZQhrndlfryhjdNQFv7m5cPrLnhtHFzx5drv3gqJXEZ8ma8jmxBiQkrLgzZkg6SVI+h7U2mjHEMZk/sCqxs1pTy5sZPrukXnOBMVAotiwDFhjnyLjx3AjiYu57DD1i3vziK95uW2rAx4tARQRPWGts0HHpK13hjwkKxBJODhxloFePg+aeDBrR13ATyI1WkKePD6PQUp1WAHbffvX/i/4dL14FM38O4nnsT/8sKfxnOfecdm8z782N/gZz7wv+Iv/uIv8KIXvWizeauOoWrpUXUx8pYexFIDsBcuBz7WDnZq6/Iyvbg5qEXHUJaw+CCZW1Bq8QEMx2aexQftC5dZJRqTWXwEc6QtPvxaaR/XTrD4AF2PYvGhpbO1f8DhnMN5+nak3m3em3Zoxy1DrIUGAosPYLAMcSlqqcWH/Tz1u1UHJtzNCRyoWCa7ix3Tn7oiYs4CqFYftq4dbshY+b4sP4DA+oNKtAQZaw+hz9KpcQ+stSHHWu0PADuAGHhMhR32GKzOveb1Wvk47ODtWgFklAAPbg2SAzw8KNkAeHgdAXgg7Lcl8FgLdqTqklBEse64dEuPqqo9q0KPqouSBjKoywugQ42Ua8samV4AHXwA8GBjFHyQtknwQY83Bh92aD6HDj5citqlwIeTCD6Ec/bqyzj48DCDgQ8Avl0EPtoQkDjwYT8JQ32U0rZLu7tQkVt+9rr/TLr3qYcfc7O72LEOAj983UIuLoXtZ0OQQ2vnkENsu3/YQY+3tu4I6/KBB++TAzzG4ixJ7YKbe1amAY/gJl8YpxR4dIa/TgMPr1MADw3acODh1zgOPJw04HHqjCxRuwo7qqoOqwo9qi5GPF6HFLtjLN6HbzcCP6hFiKvT4n1E7SL4oYAPFs8jCT6AHngsDz76k4rAB4Cobwp8qLFBVgYfAEibsK/b37SACD44QHHgg74nHHxwQOLqAWeBIYCPHmJ4aw4/xYjVh09LO8/qw4EPOnes0OoDFHjsCX5wBQ8HJQAC7B+CnI92BTnE9hmgg70+GuwI6xDUbWHdQV9z4OGhRAHwSAU21YBHJ4wZ3PBLgU83AB4OXOQCDycOPKIsLUsCDzr3CPDgwdol4DEXdgB5cTumwI6xMapabJtGtr7/Vboq9Ki6KKVcVkrgR0mwU6AMfrSkDz/eI/jglhEOfAAQ++4NfMRtyF0wARwcfPR/RBl8cGsRAjYkyxAPNkwXgQ/XJwpwOtHqw4GPflh3Gux1aPUBD0+mWX3YvhvCD/9XpqsHaZdh/QGsDjWWgyDH1f4hh9NCVh1Ru/Vgh22zjXWHXDcdeHCQkQM8ovoM4FGavetowMPPfzDg4VQapHQK7Ei2q7CjqupsVKFH1cXoGlo8jbTVhquj8T6AGH6kgMlc+BFbfTTpGB97BB8YNky54MM2xqbgw0kCH1JmFwoH+DlTGELjd4TgQ7cMaQERfCQzu/RtbP88qw9/DjOtPhz4kBVafawCP9hfMQYbBdYfKvxwWhuC9H0K44jowOBoWghYTOmTAzmktiu4sPC6LWAHsJw7SynsENtmAI+xlLT2/MN2s1PSAirM2BJ4OBUBD9LnKMCjIfWnhh28XQp2XKuWBlVVu1WFHlUXJXdBGoMfUrBTIA9+SIAjFQg1BT+mgg/AbiROBj6klLQj4GNotx34MFobYNGUtt5NpgFyU9pSawwKPmwZAxozrT4c6LBjDvPa17LVxwBP8qw+7HtTCD8ANQPd6nE//AIkCNK/AxsEOy0DJwfXHqw4xtqfKF5H1G4EdtDypa07aNka7iyu3R5T0kbjbAw8BsuNAuBhTgs8vGYAj7npZ3ldhR3bqOtubmqReA7Wj1XrqUKPqosRBQ8S/BDdXAT4MSXTi9SOu81I8APQwQcwgA0OPgxadF2zHPjAAAHGwId9KWRmORT4ENY6UAARfEiZXXjg0sCKg4AiDj4ARJldqOtMDDBid5f+w9XPn7b6AJlzjtWHEa0/KNggdeyGzisASIXWH0vG/ejGoMPW1iBnmMHlCJBDKt9RvA7bJvw/p8EOery1dYetmwY89pKSNhrnQMDD/w1ygMcI/CgFHoaVUeDhpAGPrWAHkB+klL6+pvSpqqranyr0qLo4afAjGeODHguZXoB14IfrJ4IPIAAbq4IPBixS4COwijgg+Oj/CLtOaevms7840Ojfw5lWH66cjjlq9eHnliDIcPczy/pjC/gB6HBh7xDkkNo55BDK9wY7wjYx5JgKO6SyNa07gnYZwOMUKWmHMYPbvWcAACAASURBVPs36ADAIztLy8bAw5h23KWlwo6qqqoFVKFH1cXoytibhJvdVf96sO4Ygx9jwU6BleCHadKuLjgz8AHebnvwcYSUtqVWHw582LEzrT7c+S5g9REfx9Yfu4MfToeBIGegHUMOp6PBDnq8lXVH2HYZ4LGXlLRDO/h2mwEPwI93CcCjMV1W+tmlYUdqDO7GErY7sNXdampRs7dU7UXKzqBqL2rbFj/7sz+LL/7iL8YznvEMPOtZz8JXfdVX4Xd/93dPvbTDyV3grsxNf3G6Mm10Qbvm2w11vF1Qhza4ePILszaG1q6hGwHEadt4KjfnD2uCsmFTwuujzQvb5NHNS85GSN180Q2ptHljfd3GMG6X2OD5O/N4HR2ZUxq3a2j5UNcJ56D1d6/Hnhjyp5Q88F6wJtKO1vObALfBb/ty6+9uN9HD675dQ25cyGbbjR++Dn9LN0J2DoO2Mf511xh0fVnbH9OnzPJx42/aOmNAgy8OJvq0TTPcFDaNv1mk5UEbc+VvOrVymMbfvHbmyv94uba0z5RyNOxnrP0V+xlrf0Atdu6F721hefS5IJ+ZnM+Y+vnUPsPq51/7P5L+v8b/T2r/h4Fl3FnahnxHKd9N7rvLtXNjSt91OSlpS4GH/74vAB7DD3y7TYFHP/elAQ+6J8rdRyXbkT1bcm9HjuneMG5H9pT1pruqareqlh471z333IPf/M3fxLVr13Dz5k08+eSTuO+++3Dffffh537u5/Bd3/Vdp17ioURjclDLD26NoVl+zElzK42RauetOy7N4sM2Yu2wicWHYetz5wAgSmnLY3m0wGhKWwCJzC59H2YZ0g8TBzidafXh3F3s+D0wG7H68EsstPqw799xLD/sRyUECmaqxYcavFR65tAm2kuAQwmOeiitbMWRqlPK+d9+aH8syw5gfesOYFl3Fvd6jQwtvp0AUYKbfWkcCjGEdhV4rAc8gNgSQ7P6SLZb2bKjAo+qqn2rWnrsWD//8z+Pv/7rv8Z9992Hxx9/HJ/4xCfwO7/zO7jjjjsAAN///d+PRx555MSrPI6uKZYZY5YfQ7tyyw/6hCI1htTOlQOyxYc9HsrOweLD1gGjm7loDqjryLb4YG2lJ4W8Pz8///RS2HTSMr6Rl55cAnpgPwDBk1DXtsTqw7W3Y4CN6V6Hv/UbpLTVh217MMsP+gN7I8x/gr7cQkArl+q85lqDHPFn4fek8O+h/13lz8ERLDsA+n9Stu6w7dG3hfg6x7rDfueUWXfYMaYDD/7dm5uSVvpuLgYe7lpxAuDhdE7AwykXeGhWH5Jlh283wbIDSO3/bgbAg45/jfSvAqxry9Y/VVWyqqXHjvWWt7wF7373u/G5n/u5AIBbb70VX/M1X4Pf+q3fwt13340nn3wS73rXu/Bt3/ZtJ17pceQuSE8LMTk0y4/cNLeubizNbWoMHu8jZfHh2jWmOxuLD/j+OIHFh1vH0JZqzZS2UmYXLaUtAG8VwgONzrX6cO3smJlWH37utNWHXffpLT8Af1/h/45Sua101hrs+cDa1iClcUHOSoWWHGtYcSTGybHqiF6fwLKDli9l3RGUeQjhyjmMDfvmwI6gXgAQp0pJa38j2W4L4OHLzwh4NKxMAx5zLTtS/VLpZ3MtOyrsOK0eePSP8cB//xM88fS/nXopVTtWhR471UMPPYSXv/zlHnhQfdEXfRG+5Eu+BH/6p3+KRx999ASrO6aCwKUj8IMGOwXK4IeU5taNXwo/6Oup4ANAADkWAR+ACDuOCj4AsMClpG0SkITgw/5BIIKPpVLaDgFOeUBU2d3FnpoDGO61W2r/12SpbUNI4l5DzfCiubv0f81+zOXhRyQKl0iTLoBOma4vQAJS7AyCHFr7hhzDOOOwYw7osO3WgR20fsnYHbY8DTtonyWBx1Ypae1vBO14/eGBh3A+c4GH01zgsQfYYV+Hlh2+T9TuHL6Tj6e7n/Mfcfdz/iMefuy/4mf/nx869XKqdqoKPXaqO+64Az/yIz+i1n/e530e/vRP/xSf//mfv+Gqjq8ododp8bQAJ6RML67fKeDHHPDRwPZbFHwkYMcRwYecsWXAO3tJaQsgiPPh2gCx1YdvP9Pqw/Wjc5JTCtfiX1O4AW/1AYTwg/ZJi969DatYO+7HMM9RIMiBtWfIIbTdU7wOuV3YphR22LJ5wGMK7KCv7XchAxAZwMOPXwA8YtBBXEoq8CgGHtS9di7w2CPsAELgUWGHrK5r0XXbuZx0Xf07VOmq0GOnurq6wtWVbsL8j//4j7jlllvw1V/91Ruu6ti61vv6PQ3mvkKsPoB58EMMcDoDfmjgA0AAOdwYZwE+EL5eA3z0JxdZrVCLDdO68fq2SUAyaG5KW/vHBLHgEADJRKuPEGB0bvr+tWz14dtNtPqwcwTv0HC0M9eXAH4AHi7sHoKck/YEOYT2e4MdEuiI27gy+fVS1h20bGngkYrzUQI8gpt7bRwGPJLAhLddC3hAancewMMpBTyWgB389dKw41qNJ1FVtWtV6HFAPfnkk3j/+9+Pb/mWb8Gzn/3sUy/ncLqGm3gaAsRYAH4ks7sUwo8U+EAHtGi2AR9OW4EP2s73XxZ8cJihWWxw8OEkgY9ozSSWRwuMZnbhQGMsswtQbvXhCuZafTjAQaVZfQTwg7i9aNYfe4Afvp2SSUW6QU61F2/Au1a8YTedkJFl1OLjwCqAHFsDDq39ucGOsG4a8JgLO1xZ1C4DeEzJ0CKO44/h2+0GePTHmwEP9l6tCTxsoPY84LFH2GHHPIPv4qqqM1eFHgfUO97xDtxyyy348R//8VMv5VAKQcVg9RHVFcKPsTS3fPxc+LEW+ABQFtzUl+EiwIfT3JS2AIJzpOCDW4PQcfSUtny+Zaw+HNDol9t/PkasPnycjjyrj6HtMeCH/XiUWXesag2iwpQzsfjYiRXHWPujwQ5bHpYtZd0h9V3CuoP3m5uSlltxaCBjDHhEwKQCj6BsStBSIB94LA074n7zYYer61A1qMW2GVXa8SZVF6sKPQ6mxx57DD/8wz+MX/mVX6lWHhO1NPyI4oQsCD9o26XAx6SsLhr4AEZhx9HARzSnAx+2lQg+YkASu8E48IH+nZPAhxS41FuLNHGAUw18AAVWHwHAmG71wcEHEFqgHA1+OO0SgkjjnIF2DTmcDgo7aNnSwUpzYUfQJgE8uJVGbkraoH4q8BDWdbbAg0INd347Ax5Hgh1VVVX7lrIzqJqrt771rWiapvjn9a9/fXLc7/me78HrXvc6fOVXfuU2J3JGuoab0YXKXaySdaYNL34IL5juoinleHcX25Lc8DTne8Pa0f6ufggAlthcmNZuVADRDBWmi+qjpz++LN4oSRsv7TgyD6abUL7pG9uYapvBaA6MrGNkzoaeR7jGqF0/RunTSD5O22/kuY97S9s3XdS29CmrdiPjx27szc8wh23r1+Jfhz/hmGEZDbJIAy12vo3xN3lhm6FcP278zWVnjL/pDMqDNs1w89o0ww8Q1AftEuUwV+HPxPYwTfjjx7kKfo4s9VyUc1/qvS3+m7LPBa3XP1faZ2/8M6x/5sf/76T+/7kf9f9w/398aCd8n8yw7pC+q9x3Gf1uc+2l70D63efKslLSauNI1xUFeAx9EJQF87i/h3rtgzK2Vn4ZwMNJAh50T+Tq1P1TsK9rI1eWod9NFXjQPV+8rxv2iqm6qqqq/alaeqykZz/72bjzzjuL+912221q3Y/92I/h+c9/Pr7927+9aMz7nngSt5CnTQDw/OvXcOf168XrO7KctUTSukOoy01za8dY1vKDWn3QNtT95eQWH31dByxq8WGr4/Sya1p82FPMS2nbr3Cw6HBjNmUpbenY0jg8swt3d8kNcgrkW32EwUuXsfpwdXZcXr4zyw8gAB/0zdq7JcghtSdLDiD+2wvt92rZoZVvbd0R9pX7RGCEAQ/eVrrhn5uSlgIMPrYMFSCUrQc8vM4cePAHPBR4OC1t2cHHT6WfTVl28LobuCw9eOMGHrrxdFD2cb+5aIFNM6pUq5sqXRV6rKR77rkH99xzz2Ljve1tb8PHPvYx/MRP/ERx36+49RbclsgEc0kKY3AkAIeS6QXYFn6cCnxYNScFHyq88P0XBh9AUUpbB0o4+OjfOhF8LJXZBQgBgmsLIJHadlqsDw4++g9VPweDJB6ahDoi/KAKXGCo9uQOcw46sbtK1hhnDjts3TzgMQd28PYcQOwhJa0+z/LAw/U9JfAwtC22Bx57gR0ldZekO69fjx6iPvjUDbz7iSdPtKKqKlkVehxAv/Zrv4b3ve99eMtb3iLWv/Od78TXfd3Xbbyq48llbYlhRAJwnBh+UPDh6rcAH+hMv9Fh4AOAC2LqN0h2huODD782Bj6A1VLa9u+cCD5SmV3mWn24fv1f065jzOrDrX+i1QewHPxw75tT3u0/vSMkvZWbXjvnCMAQrEE0aJKd8WUqBDmiFoAca1hxBOWs/dqgw76G0o6Wa+3j47WtO4DTAA/el4MDdRwGPMYytNjfEMoODDwi0NGFIASnAR5OHHgcBXZU95aqqv2qQo+d613vehd+67d+C7/0S78U1T3++OP46Z/+aXzO53zOCVZ2TE0GHCeAHxx8wCDK6gJsDD6cZQcFH4I1yNLgw2kt8OHHl8CHtF4PE9yYAJSUtgAFJGTdBF5w8GH/YPE4NMCpkdxWsq0+hnXOsfrg4MPO4Ta4stWHE72V1ACGzyIDoKHv7wbWH0B8wzsFgqTai+VLpMk9kPbgqhKUa+0PYNWhteEQA1jenWUq7OB9pmRo8e0oEJDGoQAj0W4MeMRgZVngMby+PODRmDYJPCrsOJY6tOg2fG86dcdRVVWhx671R3/0R/iGb/gGPPXUU3j7298e1d+8eROf8imfgkceeeQEqzuest1XdgI/KvgIY3x4YEDb+f7zwEfcL47boVlsUPBh6DpVQEJGIedJecVYZhdguJdbyurDAQ0g3+ojSm2rWH3YdYZwZmnrjzXgh/9bnxKCADEAUKxBDq0dQ45hjGPBDlvOIMZM2BG0nQk8xtpz4LFYhhahXQQyCJiowOP0wOMosONSXVyqqo6gCj12qg996EP4+q//ejzxxBNqG2MMXv3qV+MZz3jGhis7thYDHBvBD1dfwYcAPgAPIeaAD8DeAEvgw8EMCj4i4OKAhm0VgA/Q9THwIaW0pQFOuRtMOE7a3QUot/oYYMMyVh+u3TBmfw4HgR/2/XY3HTuHIGekPUEOOw53PTkW7KDlp7buoGWpPpIVxxTgwesDi42ZwMO9V7OBhzh+BR7ud4UdVVVVS6lCj53qC7/wC/GRj3zk1Ms4K60COBaEH67vqOXHicEH0MMGCj6ctgQfgvUFeocYv0bfTgcfxt+kh0DDjubaDSOKliYNzdgSusbogGQ4Zy2zC3eD8VJAhtvnLWX14drTObOtPjxUOB78sK9DAGLf151CkANr75BDanspsENsOxF4lFh38PK5GVrsb4wCD9EaRAQriMtSwIOMq0GLTYEHLe/Lggwtrh32ATx42lmqPcGO6uJSVbVvVehRdXFyAU2BfVl+8PVcQ7sq+LBqAvDhyij4AAbIYQxJaduX9Xeaw0bqROBjAAwWOnAgIsYHAYEPDnzQsmAtjv9MT2kbtyPzMJcWLaVt7DZTFuQUKLD6cO/VArE+jgQ/ABlgSDfEkjuMb78xBDmy1oQcJYAj2f5gsAPY3rpDbDsBePgxCoBHDDoEiw3NQmQMeJjw/cqCFgcGHq6MAg9XT+FGI5QBywOPPcMOqb7K6Wb/s+V8VVWyKvSouhhR+CClqwW2hx+ABSAS/FgafAAADIEcrg5hGXVlGcq6Q4OPYcyRvv2NvLgWICuzSyql7VKZXQKNWH3wIKdzrT44+LBTdXQpaqwP4Djww/9NEhAEiG+cU9YgKhyZCUHOSgWQYwkrjvQ4ktXHecEOWrZlKtqg3tBzCIGHZPlBYQDvUwQ8oj4QyirwKAEeDcKypYEHhR10HwWcHnbw+auqqvalCj2qLk57gh8AAusP7vYyB3w4OfDRdo3dkFTwofYFEAQ4Dfr5dunMLlpKWycdkAzrGcvsspTVB3VfybX6iIKcZlp9AMeBH+41EN/4LglBtHI1U4sCQQ4tMWBoIcxYEXJI5UeAHbTvWtYdtGwJ645gnInAQ7TYSFmIFACPaO4KPEaBB4cfU4FHhR1VVVVzVaFH1cXoqr9I3ewBx6ngh6vPgR++7QTw4a08sBH4AEABB/p2RwIfwzzjKW37E5TXTON3MNcY0PVFgITMRs6VcgYORQL3mUKrDwpRlrD6CECGn9r4OY8CP4AU7NgegthxBGsQ5eb9aNo75JDKjwg7aJ9TW3dIbTuzboYW/zvoM7Tl45wl8PCg4xjAIxd2jLmbbAE7rqprRayuhcs2ttl8VVWKKvSouhhdM23vWnJa+MHHzbP8yAMfTpuADwAGbRAAlQMOAIcBH/Z86DzplLbUimM0pa1tFYAPsZ3iBuPBgwnjfADwQGMpqw/Xngydb/UBBw5Cqw87FvmAtsN70NAbSfIONqDlstw61oAf8GMfA4Kcg/YMOZyWhh1Lgw7bhpYPN+70dSnskPqsYd3B68eARzQmtdhIWYhMBB7xeg4OPFjZmsDDlxUCjyPBjmr1cRo98N//HO//17/AEzcfP/VSqnasCj2qLkphXI39w49S8HETIeRYEnzA1gzgo7/RPxfwUZLSFqCABKDggwOSVTK7CIAjOAcKLgqtPnz7Ja0+CPxwbi9u7kWsPzCIbjnDckPKAzwl9rVzxBDErmMKHFkXghxRe4IcWtuWle/VqsO2iWEH7Xsq646gTQbwWDNDy1AGXyaDFcRlFwA8uAXInoBHhR1Vmu7+9C/B3Z/+JXj43/4//O8P/uSpl1O1U1XoUXUxollbTgU/XH0u/Dgl+LAawIcDG1ngA3CP048BPoCilLZSZhcoKW0BBPE75mZ2sX8V3zw8YPuupaw+OPgAyq0+gA3gR2TloZWPW3/YcwxveJsE2CiHI+tAkCNqq8CjOeX8by61P1fYEZQt7M5SYt0R9RPgAP0dWWxoFiIUjkRlBcDDhPW7Bx7udCcAD5qSNvy9HfDIhR1LgRBeXwI7ahYXrpvoavaWqp2oQo+qixKHDVvDD953DH5sCT6sBvBhf3e+rAh8AD3kOAj46IzdiHLwAeSltAXizC6Kq8pgDdL3SrQjOES06iix+pDcV7KtPpqg+aQML0eEH/69nwBBtHIdjsyDIOegEsixhKsKkAc5hjK5315hBy1f27oj1WcK8IisM5YEHsI4Q3/EZUcHHqZbDHjQlLS0bm3gUWFHVVXVXFXoUXUxcqDAHs+DH67v2vBjWNM4+HCaCj5ahJAjB3wAQNvH/Dgq+HASwQfv37ttyNCki8EHQkCydGYX+/5bjVl9pNxX5lh9uH7B1Nzq44Dww9UB60IQrVxPbytDkCNKghxrxuMAyiCHLdf7Hxl2AMsBj7mwg7cZAx6ixUbKQmQF4BGt8YDAg8bv8PVYF3jwoKZjwGML2MHr5sCOa9GFt6qqai+q0KPqouQuSHPhB++7mGuLAlVywAdNV5sLPpxywQf6f6krS2PaQ4MPDhUc+HAaS2kbt0untF0qs4utW8bqI4AoxVYfw/mnrD5s2+PADyC+4T0lBLHlsTWIZh1xFO0dckjjHAF20HGWtu7I6TPFuiOYQwEZSwGPGKwgLmPAQu17JsCDuq7Y32ngQbOxAOsBjxLrjDkgpMKONTTsHbebr6pKVoUeVRejARZcnQR+2DWMW3fQvmuDj5a0zwEfPvYHzgN8AICBDD5yU9rGfeO4HRIgmZvZRXN3ASZYfUgQZYbVBwcfAA4JP4K3jFmBuLbAfiDIkbUnyKGPQduMl58adtC+W6WiHYMdqTYa8IjGpgAjZSFipmVoidtB7NuRcv+7Ao/VgMfeYQdNr1tVVbU/VehRdXHaK/yQ+q4BPpxywYfTOYIP4ywtGPgAkJXSFgBOkdklEAMbS1l9BG4rhVYfPMjpEeGHk/HnFAMM6YbZfsS3hSDnpK1icuSNIbWV688VdtCyLaw7eP1SwEMGK4jLonYKlDlz4BGVjQCPqGxB4DE1JsdY3wo7qqouSxV6VP3/7b15uBxVncf9re57ySIkJBKCBJAI8oaEKMMqogQCIYiAyhJ9HR5hZIYZFX1gwFEQgaCOooyP5nkji0R9kEEDZBQGFQEVCDIEMk8QErZhUZZEwiJkuVlubtf7R3VVnzp1zqlT1bV2fz/Pc5/b92x1qvt2LZ/6nXP6hqh4KE5+eNtJPiGqrfgAALiwEh8tKU9O89vyxYcvOQC1+PBo1EJ8AEEQR+dvhfiwXdK2rJVdbKM+GmIbCaM+QhIlo7k+xO2IGVWTH34ekEyC6MrnLUHqTNUlh1deX65s2aESHZF6KYVHt7IjVCeB8IiKjhJWaAGshId1/YoJD9WStP7fKuEhR3R0Izw6afbCI8shLnnJDk5mqsAd8X6K3B4hGig9SN+RtfwAPAFikh/edqLRHTZDW2zEx4jbzstAfARzfmjEh0d4mVvvAspSfAAQBQeA/MUHoF2SFkEbbfEB2C1pK5bzxUekrl8uu5VdgM71beSWVxIWQQRGRlEfsvgA7Of68NIkiSELGSBY6tZryxXSIaQ7wTbykB9A9EY3TwnipbtdS5A6k0RydDMfh115dXqesiOLqI5IXUvZoSzbpfDoJrpD/p1KeCja6dRHNC1UDvq6NsJDrl+E8BDkhvc7mfBQLUkLVE94ZCU7mjEihLKDkN6D0oP0DXFCI638ABCK/kgjP/x83dCWLMQHAMBF1+LDn+cjtfhQCA6vb9mLD8BrTrskrUp8eA3GL2krb7+BQlZ2kYfFJI36UK0AExAT9SFPcmob9eGXjZMfSYe+tARBAiHqoSHc5PriJKn8CN73AiSIlx4VG7YSpJeoquRQlY2THUUNYYnUdaLpeUV3KNvKQHgoIzZU7USkBRRp5QiPSORLHwgPWXRkJTy6GeJSlOzgZKaEVBtKjz7g3s1bMMpxsM/gAKYNDpbdndLwpUEa+eGXTyM//PykQ1sAtAVGNuIjECAa8eGjEh8AAKehFB/tWpUUH0FEgqX4aO9oSHwEqUL9cFrxK7uIkRaJoz6k/KyiPuTyoW3mLD9shr6Eo0ZEKaKnLhKkztRJcsh/lz1fR6SuQXaIbRS1FK2t7JDLZCU8ottT9CFUDvq62qEu9RQeqglLvd+d5ehVwkOWGXkLj7SywzSERZUfqiudFZLIjn6P+nhyeBhPD2/D+vaTExctuAW+Jy7FEzFA6dEHzBo9Cjs3m2V3oxL40sB7HS8/RtymULeVKvLDz7eJ/JDzOwIjf/EhRoXI4sOf4FQlPlrtfqeK+ACQ9xwfScSHuLKLLz7kaJCqrOwSLptN1IdKqthGfUSiRKSoD69s+fIjWldMD++TSmyobozFumJZoFgJUlfKkBxJhUjdZIdYvlvZESqTsfCItKsSGEJZtbSAIq0PhIe/2wmFh09dhEdZkR1in23K9rvwAIBpg4OYNjiIJ7cO447NW8ruDiEhKD1I3xERFgr5oYr60JUNtSXIDcA74dpMeOptKyo/On1UD3URtymLD7+/KvHh9S06x8eIJDBk8eHlqYe6eKSY48N1OuIDCG7ee1l8+FEcoviQy8Wt7JJ0uEv74xF/xS5tm2XURx3lR/D2pIjuKFqC9AJVlRyqNuogO8R2io7uUNWl8OhSePj4BwEn+aSlpggPH92QFlVaE9E8eZUWLy0qPHxMc3hkNUGpbpudv+2jOyg7CKknlB6kb4gKjI448P7WR3LYyI8kUR9ivkp+hKI+HP0cH0GeQnx4/bVfzrbbOT7kiA8AyZezRaMjPBSRIN2KD+9PzRwd7f8Dsb5/NeukXNI2L/GhRJwTpL0fIt1GfejEB2Af9eH1rx7yQ3wv6yBB6kjVJYeqnV6THWKZMicr7eybunxWwiPaN53UQCgtIiaCtHC5aDtdCA8fcZ8SCg+fuAgPIPtJSzt5auHRdFrGOTx0wqMb2SHX72Yoi5zPpWsVuC3vp8jtEaKB0oP0HeFIiPioDzFfJT+6GfIi5ocjTMJRH6qhLZG8CoiP0OSmQGQoizifh5X4ENPb+9uRFl2u6qISH6r6bTkhio/Qyi4upDRxe+39Fpa09emUA3xFoZxjpN1nX3wAvh9JH/WR5dK2tlEfQL3kB2ASEtJbk4EE8ct3K0HqSBLJkWQ+DlP55O3US3aI6VlHd4TqZCQ8IgKjUY0laYPfFsKj61VafASxkVR4BE0I+WWs0tLJ0wsPH9OkpXnN2yH209tOetmhqk8IqR6UHqRvEAWFKuoDsBvy4tdPMuQF6GK+D9cwp4ecJ4kPv40qiI9GMPRFlCAJxUc7vU7io7Oyi6quX04QGXJdoc9ihEic+AD0UR9FLW0rbquO8iN4uwqQILrythKkF6iq5FCVrZPsEOt1Kzyymqw0/BuROn0pPCSxYSU8/LfCP5Y5rcoIDx+d8FDN22E7iWle83bI5ZPKDq7gQkh1ofQgfYUsKGzkRzdDXkJtKeSH7ZAX45wecp4gPrzhL02t+PD2o1WO+AAA1+lefKBz899T4gPQL2kLIFwqSl6TnALQRn1EJzlViZh6yI/gfay4BBHL14mk0qJsyRGUq4HsEOsmlR2qst0KjySyI1LOUKfWwgPRfqYSHkJa0cLDRyc8ms6IUXj42M7pIbYTbNsQ+dGN7JDLU3akg6u3kCpB6UH6Bnnllm3oXI1GV2pJtsoLkG6y07ghL1776qEtxvk+LMWH1+9G5uIDQDgNduIDQHCDbi0+2ulZiQ8f7coqvvgApLbbN8KmeUJ88RGp65ezXdI2WdSHTnx4H1R743LUB6DM7ybqoy7yw28LUAsJ1Q2yPB+IXxbIV4KoyteJMiSH6vMzbtMJ97+HIwAAIABJREFUl6+D7BDr5h3dEXqdt/BQSgi/DRjqKmRFqFwnL7IvoTSpjbTCQ96OhfDwSSo8fLIWHmKZNMIjbk4PkaImKVWVTyI8OKlpOTzyt0fx6N8exaaRzWV3hVQYSg/SV5hkRpohL2J+HpOdmoa22IoPv295ig8AEJezBdCO+vDEB4BQRIdOfECY7DS4Cg0kSD7iw+usX9Z7z8UbbVEWIGin3R2FlIiNGmkgmOA0Wje6UktS8aEkEiXiv7XdRX0o5+3QRH0AnZv0cJ+qJz+A6I2uSYJ4+ymVL0GC1JkqSw7VNqskO7T1NLJDVbeo6A7vNeLrVFx4pF6lRSprFB4+gcxwrYSHj7hSi+2ytP7f3QoPn26ExwBGKjNJqbq+uTwpjv0nvAf7T3gPXh5ajf/v6avK7g6pKJQepO+IkxlZyI9w1Ej6yU79Oibx4W1TI0XaUkJcyjYP8dFyG96FlEZ8QFjK1o/o8OsZxYewZG1e4iOIPihRfPiI4qO9o8qIE+8NbNcJREZbLgnCJEnUh058CJuKRH1E+6Jf4SUiK/zPUrxJFNptiOl+WoHyI+hHjhKk0xfNNnswsiOOtEvIhstmLzk6bRQjO9JGdcjp3UZ3mMrkEt0h5jv2K7QE9XtJeDgtK+Hho1uatgzh0UTLKDx8TMJDJMt5Oyg7COlPKD1I3xA77MRCfuiWuJXzs5jvAwDgeGkm8REbDVKg+ADCw1oAtfjwxUbu4gOGiAmpbBbiw8dquEwXS9p2O9wFiEZ9BCiGs3SztK23rXj5YTP0JRQoIvwRiibx3+8u5IdfX25b3G4WEkRXPqkEqTNVlxxeO65ULp3syHMIi5xus3xt1tEdyrYLEB7WsiJSzpSXgfCQ3pO8hIeYbxIePlUQHnGTmALedVGek5SqypuWoI2THZzXQ4E74v0UuT1CNFB6kL7DJCtU+VkvcWsqr5rTwx+mkmgyU1U0iLCUbVFzfACdiI448QEguHGPFR9CWaP4EF93KT4AqJeUbQ9HUcuLcBsReZKh+ACiUR8KpRG0n8UkpwC0UR/BJKc5yA+roS9Cl8QIEoQEidMpI7xXsgDxtwlUW4LUmSpLDnmbVZqvQ05PIzvEtKImKzW2WZDwUG0/mia0kVR4SNvVCg8fC+HhYyM8OmX9Y2K+c3iIdcQ8m4lKVcJDpEjZoa5P4UFI3aH0IH1DkslJy5jvI9Se409E6q3wYhIf2jk9xGgQILSUbVHiw5ccVuJDms/DKD4ACCEN3YsPQBh64naiHGxWZfGMR1fiA0BoZRefzrb8HkKrM7Ka5DSEPD+HlJxoro+y5EcXQ1+Ct0EhQTrbl96yEiRIr1D2cJW4bdZNdojpmUV3AF0LD5PskOspxYQ4nEWqn7XwUL4fqv3tRngI9eOEh+O41sIjlFag8BgIldXP2xEnPJoKAVJ12cGhLoRUF0oP0lcklRVlyg9b8eG1q5/otGriAxDn86iI+JDTxBv5LsUHEJYnnTSpjUZYfES3FRYYcREa4UgRIKpJ2khzg5jaNEV9mJa2raP8EN8TvZAIp5clQepK1SWHapt1lR3i67wnK+0cc+LrZCE8jLKiT4SHT12Eh4+t8KDsqCvt67ZCt0eIGkoP0jf4N+5APeSHrfiwWeGlSuID6Ex2aiM+AO8Cr5LiAwi14T9edKCSFxbyxCQ+gNDKLgi2pjjNK0VG8qgP1RAaXdRH3NK2Xn/qIz+ANEIiXoJ45fOTIHUkC8mRZKhKqm3WTHaI5ZPKDlXdvKM7grqadrsVHn6/TLKiSsIjwFJ4NIT8OggP1ZweNsKj6rKjWegNPiEkCZQepK8QxQegns8DqIb8sBUf4n6UJT58RPEBeBdc3YoPB52lbksXH4B5VZZAFCQUHxDkiS8+xLTQttpiRWgnSdSHLD5CSLJEvi3sJuqjjvIjeFsylCBeeUjlu5cgvUBVJYeqH3WQHeLrvKI7wmmwqxMnSlRSIW6FFqB74SGXK1F4OFJaWuHhI8oNUWiI7RQpPHxshUfVZIeyTQoPQioNpQfpO/wTky7qw0tLP9mprry3DXv5YSs+bOb0iEx8GrSXjfgYEQRIS3ptKz6ATkRH7uIDHaFgEh+A14x4Ex+7HK0sPoRtWcuTBkJL2srlVAJDJT78/gOiyAjHh6SN+kiytG0d5YffFpB8qEkZEqSOVF1yqPrSK7LDVCbr6A5lm1kKD6PUMOWZhYe+z1IbMfIjS+HhYys8RMmRp/DwsRUe4pweSYRHUtmhKsPojuJwMQK3wKE/RW6L1A9KD9I3RERDjPzIOurDryPLlFCfhDbyEh9B2RzEB4BIFIgoPsT8iPgAQmIjV/EhSYaQ5BDS/PJJxIfYRtBNpbyIaUNY2UW+HROHu/ivwuliH+yjPtRDUsJtBijEhinqQ+xP8Lqi8kNuK7TbFYkE6exjJLlWZLGyile+e8mh64u8zTrIDrFMt9EdqjK5RneE2jO1YS88ErWhER7KNgoWHo7T0goPH53w8MlDeJgmJs1CeMTJDrGsrkzesoPzehBSbSg9SN9RtvyQy/tlRDniCwhb8eHViR/akrf48MvoxAeccHRH1cQHpJtyG/Hho2vDRnxo21AsaZs26iMiPhCVJjJpoz4awnsgTmZaF/kBGISEdGNclgRR9aXOVFlyqNqok+wQ28grusOqzYyERycN0fqWwkPbRo2FR8NxjcIjjzk8/PwkwsMnK+FB2UEIsYHSg/QNoiAAFKKhZPmhivqwFR9JJjOl+EBX4sPbGT8PMC5HG9BZ2SWUatOGYknbOPHhbbH9VkBCkBBJoj5SLW2rjByph/wIdq0GEqSXqKrkCMrWQHaI6VVailZZVyUOclqS1tiGLIooPKS87ITHAEZSCY+qyQ5lG060TF/jtgC3QCnk8v0neig9SF/hn5B8+aEagqKa7FQsU6T8sBUffr2ixIePrfgAhGEtNRMfABSrrLha8aEs7xmPQHxExYpFG9IEpyLycBdRYFhFfSikSYSUS9sKVWsjP4LPBSmEhIUECbaXkQSpM1lIDtv5OFJvU2qnTrJDTOs2usN7Das63UR3yOnRsp0ytsLD2IZCeBj3p+LCw8dWeMjzfOQtPHxshUfZssPra0wblB2EVB5KD9I3iPNpxMkPOerDL1P0Si+24gMOIkvZ5ik+RiS5ESc+xNc68QF4F29liw9ALS104sMr7wTSQlveID6s2xAmOFVJB13kRlyEhn+9ppImNlEfqolT5bk+xKzIH7rrRaFMq12mCPnhve2SYMhQguja6VaC1JkqSw5V+TxlR1rREemLRkyEXlsKD2vZIZbJSXjohYS98NC+NxrhoZM5RQsPH5PwaDqtSguPAaeVSnhUYUWWONmhapMQUg0oPUhfERELCeVHmslJu5EfdREfAAAXXYkPoD0Epmjx4XU9GhXRrhuREEL5UGRDAvEBoMslbaGVDnLUB6RJTlWyxGaS0xCapW11UR9+Xkg4aKREXPRHS/iXa2jEiTj8oyFegwpDe+LkR6d+dSSI105vLlcLqAUHUA3JoWpbJTvKiOqItGPol63sCKfBro6l7FD3Q0jXyhGd1JD7m0J4GISMbbRHEcKjIUd9pBAesuiouvDIWnZYtUnZkQkuWnALfG+K3BapH5QefcC9m7dglONgn8EBTBscLLs7lSAiKrqUH7rJSU1lbORHEvHh70c34sMnqfgIBIgkPrz9bGUqPgCEJEfX4sMwvCUiHBTlrcSHog1vVItGfACxK7sAiAx3STvJqddmuw0p6iOQJgmjPkwyIwv50c3QF1UECSBJFH8buqEpJUgQrx25j5Hu1YYqSQ59pEk4vUpDWCLtxJTPOrpD2WaM8LCJ7oiUN0oNRZ6NNIkTHrr+5iQ8fLIUHj5ZCY+kgkMlPHzSCI8qyg5Vm/3Mk8PDeHp4G9a3+J6Q6kHp0QfMGj0KOzeb8QV7nKi4UIgKJzrZqVemHPmRRHx4wqHZlfjw/wayER/efjUyFR8NePUKEx+y5LARH9BLC6X4gEqsqNsIyQWb4S5QCIzQX+oIDVPUh1pshNuE2HZF5YepHcAgKlRRGRWRIHWkypJD1ZdekR2qMllHd5j7AXV6nPCQthUrPORyFRQeDaeVufAIpVVEeDSFSUxthUcVZIe4D7o2VROd9hvTBgcxbXAQT24dxh2bt5TdHUJCUHqQviNOfshRH16ZcuRHHcWH//4kER9AeD4PoFzxAcA42ak6+qJzo22SFuItVSA+vIYTL2nriw8A+klOE0Z9RMQHotJExmaujzrKj+CjzUCCyNsXyxrbtpQgvUDWq6vElTW2rWinbrIj9DpGXhilRZfDWXSyQ9eWGJkRaUchPBJJk0Zc+WifdeXLEh4+SYSHT52FB2VHHWmh0NVb+FkQA5QepG+Qh7TUQX4kFR9en0YyFR8A4EdvpJ3cNEjTiA+4QAuNYsRHsE+C+ADUUR7tciZpIZbXiQ9teajFR7i81znxRkYZqZFiklNVWwHdLm0r969m8iPYd+jFQxIJAkRvyLOUIHWk6pJDVb6uskMsm3V0h7JujPAwigcgJCuiZU15BmmiER4mqWGK9ggJD/n9zlF4NNBKLDySTlo6ECnbvfDwSSo86iQ7ONSlHP705hN49M0nsXmE0SVED6UH6StUEiKt/JCXuQ23kY38SCo+/MlMsxQf/gSnYghElcQH4NWzFh/CsI9AfLTztOIDaikCVZqweolKWqja8N9YX3zIecq5QjRRH4H4QFRkdPalPSeI0KZV1EdbfEDZehulLIkXFVWTH0By8VAFCVJHqiw5VNuthewAYuVFRHZY1Ekb3eG1CWWeSnjot2vuk1KayHORxAkP0z5nJDzSTFoK5Cs8VBOWemWzER4DTmcC1DTCIw/ZoSpD2VE/3rvjvnjvjvvi5U2v4AfP3FB2d0hFofQgfUNoydoM5IdqJZes5UfVxIc/3EWc9LRs8ZFqVZek4kOI8tBJCzkqxF/S1hitERkG0pYRliu7BO1AUBDBJKfQigxd5IZOlsiTnKrqxkV91FF+iMhRIH4bQLESJOhjjw1vyWJlFSAbyWGzzTrIDrF8VtEdxro6WRAjO8J56nbE6I5oXlSa+H/bLkkb/N1nwsO0QotXtnzhUSfZQfkhU+zqLRzeQkxQepC+IiIyNPJjJCQlypMfVRYf/nCXABepxAcgrfBSEfEBRMWEKS+IClGID6+8IlpD1YYvFGJWdgEAtciIn+Q06D+gXKUlSdSHLz6UyFEfwv6K/Q9eV0h+BLtgEBJlSBBT+TpRN8mhKltn2eG9DtfLfSlaZVtRUSGXTyo8XM3+6ISHVsYo8qoiPHzSCA/T/B3hstUSHmXJDlU7lB2E1A9KD9KXxMkPWVoo6xQgP+oiPvzhLmnEB+AJjbzEh0cjXnx4WwjlqcSEKS9IA7Jb0laRJ0aORMQHoIj6CKdbR30oZIluaVtH0Z+Q/EF95IdfB0gmJKoiQepE1SWHqnwdZIdYJml0R+i1pfCwlR3qOop0aXvqPEvhYehrYuERSVMLD0eUI8heeDQkodHLwoOygxCSBZQepG9otm/5RoSxGVWTH+G+jFB8ZCA+4DrtC9AY8aHJU4mJWPEhRIUoxQfQ1couACJRH2K9JFEfYVkiRX2E/pKkjCHqIyRJxLo1kR9et+wEQxIJIm83aCMjCVJnsl5dJZTepeRQtV8n2RFOC9ctbSlaICI7gtcK4WFsSxYj3QgPzT5ReJQvPFSCoUqyoymdiwkAt4VCV29xKaGIHkoP0ncULT+8MnarvcjtZiU+ACDL5WyzEB8AIvmFiw8A/rK1EfEh5KlEhkmKKEWG5ZK2UQkQFh+IbMPbsFJkSOIDQOZL23pt+vsfHSojS5C6yI/gfclQggBqsdGtBOklqio5VP2oquwQX8fJDlXZbqM7zG2phUe0fCc/qfAwlzfsqypPEhgw5JUhPHyqJDzSLkurEx5pZEea1VjU7VB2ENIrUHqQvmGgfWLa1r5K0smPbQaxkUZ+eGWSLXUr9iUL8bGtLTvECSmKFh+AIDnQApyw5ChFfLgOgmVrZfEh5alEhmroi3j5ExEZKZa09bfkiw+VfAgu/OWoD/jpnfatoj7a4kOsLbeFUJsJ5/rwEW6SxOtRbS1hjpA85UewuYpIkLi+1Jk4sRBXFihOcqjKZS07shAd0dfhNhJHdyjz1MLDVnaYt2/um1acaISHrk/aaA//7xoIj4bTCgkPn6KFR1OSH3kJj7JkhypNlh0DlB+15J577sH69etx4oknlt0VkjON+CKE9BYDcEMnpybc0MlrAC2l3RfTmmgpT5KhSbecluJkOxI6Kcvt+Pm6C4OuLyCkpzF++OkARtJf+ERCa6PhqKqnT03FRZ1qRvqG0wqWpjVeJDqt4KLTCaWFL1gdRVoQniw87VOFLicJh9ZdbLsNV7o4R6i8LiTbuxFA0EdX+pHb0vYXwhNZXVuhPsDclnQj1WlT/bRW+7rhRtrQlQ/ew0a43y3hJ7RPmvK6Mj4tqU2xn3JZ5ecB78Zb/Ikrr2vfpi91/NHtW1nvtVVfLP6H0v5fyv/zoe+C0E+r75R0zPDb8MuJv43RHXHHiBTDWVRt+f1V9c10rIseU3MSHsFPdYWHeK4NXbNkKDyC65T2NUTewkO+LvL3Q3XtJCJfg6mu7aLt2Fz/ydeM4WtKUg9WrFiBuXPnYvbs2VixYkWqNv7whz9g7ty5OOyww3DggQfimGOOwb333ptxT0lWMNKD9A0DcIMoD9XfcuSHepLRYiI//DKqSI4sIj5083xEQwOifydZ1UVeztZPq2zEhz+XB6CO+Gi/FXHDW1TRGpG5Pxpu+pVdvA1pIjUQaUv1UaZd2lbZVtBme3+kqA9Aqm85lEX8LGzKi+lZRH8En51hQtIyIkFUfekFqhLJoSsbEibi65RRHaY2M4nsENqxlR2h+lp5YSc7wnnhv6N19PkqeeJq9ktdXvjbIk8rPBR5VRceXpq98Ohm/o5QfobCQ6RKkR1eGdf4N2mh2GVk47f11ltv4Yc//CFeeeUVLFu2DI6TLmJy0aJFOPvss3HVVVfh7LPPBgAsWLAAxxxzDH784x/j9NNPT9UuyQ9KD9JXyENc5L+BasmPMsRH3PAWrRgBaik+gl1RiQ8fKS+064rhLdp5MKTy/rAQL08jPtCRKSbxIdLt0rbhtvw+tCWJ0EedLAkNdwn2GaH6Yr+Ur0uWH/7fgL14UEkQeRty21lJkDqSp+TQtd3t0Jm6yQ6xnUTRHaryYl63S9GG6ijKGNrLUnjI5WKXpG3/HVqSVihTB+GRx4SlofwchAdlB8mK8ePH44ILLgAAvPTSS7jpppsSt7Fs2TJ89rOfxZw5cwLhAQBf+MIX8Mtf/hL//M//jEMOOQT77LNPZv0m3cPhLaQvkcMRVeGJ3Qx7MYVLBsNKQmGX+tDNIoa6iH+nvRBqCvskDnVRX4hJF2wZDHXxfjppTvAjprUvVqULVid0gWv/lM90Ia29cG//KC/cHYQv6hVtdn7aNwsxNwjqdDfcZqN9E2NsS32DEmkraFMYkhBqN9yGuB3la2078eXF9NCwEIvy4t+R9ARDUHTldW13O0SjTj9x+5bkvcjiMzG+zzn8j4W2If5vpx3Gomknsk/K77lQP5Qufc/bxwpzW7bHH7/viv7J9eRjY+i4KR1The3o2sxaePjnFlF4+OcgUW6I5ys/DaDw0JaLGcaivp5Kfl2mrudGhIfNtSOpD6NHj05V77LLLsO2bdvwD//wD5G8s846C5s2bcKll17abfdIxjDSg/QN/hSh4vOBbiI/vHLq1V68enLUhiJiRIr+UEV+jCgiPKoQ8aH729+fvCM+AABOoxPd4echnCYOZemkuUF0h+O0OlEhwScjRHwEYRXqPNexWNJWyHMd+5VdfIxRFYbhLgAyW9rWa8vfl3C4j90KL/WL/Ai9V4A+PUEkiK68ru1+WK7WpwqRHLbbDMkZR51edmSHWD4qL82yQ1mn26VoQ3Xi+6eVJ42osAgLEsM+a/KSLEnr/Y5KEFl4iGm+8IikIZxG4REWHj6q+TpkVPN1xJWxXY3FJrKjGUkhrtuCW+CStW4BS9auXr0av/3tb+E4Do488shI/qxZswAAv/zlL7F+/XrssMMOufeJ2MFID9J3NBE9OensvWnCU6+cFMWheHpg9YTBEPnBiI/OkzHx4g/wn46FLzjliA8AkYgPL80VLmSFNKD9pE8R8aHJMz09VOUpL/Ajodro5Bku8L2f9k1Ee5/kJ8Jye9o+OJ0nuea21DcukbbEujWL/LCJytCm5xAJEvRbE/1QR1T7otvvoiI5bP8XxG3L6WVHdmj3M3L8gCY9HMURie7Q1PHSYW5Liu4I16XwkM91/S48dJEdIqkmmldeq6knKI2PCqbw6CceeOABAMCOO+6IyZMnR/J32203jBs3Dlu3bsU999xTcO+ICUoP0jcMIBzaZCM/VGlZyw/Tii8UH/qnX5mKD+FCNSI+dBfEUp4sN3QX2aYbETEc3ctDJy9y8xC9aYgPUfd+IjdEir6GVnhRtqW5iVG1pbkhrKr8iBMYZUgQXfuyOKjTT1nvmS7dpg3d/4zpf6wM2eG/Fst29iMsScWykeOSKrpDeu+87z0sxGo0X3VMUx7/IsdGxXdWsV1TXpLje0h4OP45I3vh4b8Wf/ez8PDRDWHJU3aE24mXHfI1JulNVq1aBQCYMmWKtsyuu+4KAFi5cmUhfSJ28PtJ+g7/n35b+7fNsBdVmjzsxSvTapcJD2kR05JOehoMHenDoS4+I/LwlnZaMKwFLXQ11KU9PMNBKzQBanvsBYLVW0JpUh5Q3MouMcNdvHpusF0R3QovuuEuAOC0TG35dfyX0bb8sqHX7Zu0Kg578ZoI761pKItcVt6uTRtAsuEwqj7WmTyHq+jep6RDXuowjEW3XVGcqNPVskOZF6kDTbq0TWOZaJuiTJLrGNuxyEsSyRcRHkJansIjSOtj4ZHX5KRevajYiNaLpkUflhE9LVRt9ZZueeONNwDAOGxl3LhxAIC1a9fm3h9iD7+rpG9oug5GhIvBIuWHKs1WfvSz+BhBWHJkKT7g5SjFB9q5SvHhI+cJK7uoBIIpL5AjkvgIrewCwEXMMrRtki5tG6ortRk310ewP0B0rg/hvaqb/PBJI0F05fOSIL1EVSWHqmydZUc4Ty08YsUEYC084mRH8FoxnEWul4vw0OTplqQFOpNje2kUHnkKj7rIjmboS0t6kaGhIQDAdtttpy0zatQoAMDGjRsL6ROxg9KD9BX+CclGfgAdAZJEfnj1nHYZe/nh1YtOelpF8QFAuCtG8HeVxYdHR3z4ER0q8QHXCUWFqORGCIX4CN4ahfhQ5amiQkLioy0q/BZsoz4C8SF8ZAj64AQbzD3qQ9hfsf/B6wrJj2B/YBAPFZIgdabqkkNVvg6yQ0wvI7pD3G6S6I5Quig8DH3XihRFnu0KLUA64aEaaun9ro7w0OWJaVURHmkmJ1XXsxMbqiEs0TJRKDv6hzFjxgAAhoeHtWX8vLFjxxbSJ2IHpQfpS2zkBxCN/rCRH149t13PXn549cLRH4GQKFF8AIg8/tfKjQqJD4+O+PB+u9CJD690Qyk+gl2XxYd0Qy/nqUSGKU8lR1Qru2glRecjEvC2EsiPmKgPUySJddSHICsgSJo6yA+/jk5IFClBVH0T29DdpNcRlaDQyoyCJYeq7VrJDiE/bXRHOE/ejlzHnK/Ok9I183cEv00iRbddS+GhmrDU+zu8JK1YRic8xCVpxbyqCQ+d7BDTyhQelB31pBdXb9lll10AABs2bNCW8fNUE52S8qD06APu3bwFoxwH+wwOYNrgYNndKY1m+6pqRJyUVCM/tiFME3bDXsS/vXpu4mEvXr1WaNiLibzFR5I+iK9l8QFAkCmd/euUa3WiX3zhIYkPVZ6YJoqPlj+ExQmLDzEttFxt+39BFfHhBmEOvuiQxIchL5ABkCIfNHlyGQDKiA+l+BD6YYz6sBAfpv7aig+vjaioqLL88N/voA78KoIUES50xRtnMfJCV16WK7Hlpb6Z2qgbVY7m0LVfB9mha7NOwiO279b9VQgPqW1Vni7CQ3wtD2kRX4fSFMLDR560FFALD58B6MsrJ0GXhIX8Oo68hYcNWQ1j8cq5xr+9elHkmyWV7GiivsfirHhyeBhPD2/D+lb+8uHRt57FY289G0rb3Nqa+3anT58OAFizZo22jJ83Y8aM3PtD7KH06ANmjR6FnZtcUMunCSckPlTYiA+vnJtYfHjlWiHJoUrrRHOYoz2yoNs5PjrtREWHnKfarkpgqMr7skLMU6V1yrtoSRcnfpooQeT2RQnio5zcNCJDFHlBA1HRYZPXEQ0JxIdEN+JD21YK8SHWD71vUnrodXu/o+XVbSZvR13erwNExYJKRACITIYaVz5J+3Ft9AJVlh1y2Z4VHgrSCg9TW3HCw/i/kDBPO2mpIs9R1JcnLRURh69E8xQ31AopokqT89SiJCoOmoo2VMNYxL/TDmnJgrgojyoKDxUUHh7TBgcxbXAQT24dxh2bt+S6rfeM3wvvGb9XKG31ptdw9fO/zHW7RxxxBBqNBl577TW8/vrrePvb3x7KX7t2LV5//XUMDAzgiCOOyLUvJBlcspYQqK29MpRRkWZ3IlWdgO1O3klIu5RtN6ieJMl5oT466osZsV+qNlTlVe2LS9lG0hTlI0vVhjJd6zLqi239Bbw2BFuRpr2JiL0Jib8hjr0JMe2fZl/U+xTdZrSM5rXiya+pzeTthF/L75tqGVNdWQCR5VTjyqva1vbFiS6/Wmd0+5PFewUk+yxsP+deER5KtHWgTjegOx7JbdrWU6YlFiD6PN2wFmMZAZsoDxHV+UuO8hAxTeYpr3Kib0MtQNKQNsoj/faqITw4pCUBbqv4n5yZOHEi5syZAwBYunRpJP++++42MF3QAAAgAElEQVQDAMydOxc77rhj7v0h9lB6kL5EZeW7ER/RetmIj85TkPCFjWk9+7RoL1Rinv5E29GH0yov8pxonurplo8qRFiV1smLPqVTpcltOVYXzooTrEWezQW77oZPlyfX191oBP/mxhsfTV/ktgxnkDqLD/9v25vuMuSHKb0OJN2nImSHVdmaCg8V1qu0mO7xpONKGgErR3ko8wzH4URRHtZ5vtyIP1eIER2myI+iojxsz71ifpFRHrZzeYgPOrIUHiq6ER6M8qgvmzZtAgC0NMNx7r77bsycORPf/e53Q+kXXXQRAODHP/5xpM51112HZrMZlCHVgdKD9C1Zig/7Jc+Si4+0FBHtYRo37FOVaI9InnBRKkdypI0IUYdPZ/NkUhv+nSKkXBYfNk9vY8WHxc1JaNty2QqKDz8tqfyQyxclP+r0k9d7oXqvddtN8z4HaP6fqiQ8lH2Xvrep5ESkjibf0IbN0rQRLI4xyvpymqK+TQSHqoxRoFc8yiOLc79tlEc3ZC08spy0lMKjvrz++uu4//774bou/vjHP2JkJPq/+v3vfx+rVq3C/PnzQ+kf/OAHcdlll+H222/Hj370oyD9mmuuwV133YWvf/3rOOyww3LfB5IMSg/SNzQV/+5VFh+6MnHRHmmESWxYquYpkI6kYbWmp1Z5RXvI9VQXu4miPZRl9Hmmmw3dzUWQHyM+jDcOwTbC+6F7umv3hDamLakvVRcfed2U5y0/6kgRsqPbtiPpNREeWkmRwzweSiyPLZHPwnAcsZI08rHZlGeor57fQy8y8o7y6OTZRXnIeTrSLk+bhLRL1JraEqmK8FBdZ/YzLlpwMVLgT/z/T6vVwsEHH4ypU6dizZo1cBwHv//97zFlyhSccsopobIf//jHMW7cOJx55pmRdr761a/i1ltvxQ033IDDDz8chx12GBYvXozbb78dX/rSl7J6C0mGcCJT0lc00cBIZEmz6MSmTdcJregCQDm5qYxqItO0k5sGq690MWGp7Uouqdp2zJOaittX5jl2K7n4qFZykfNEVCu5RPKc6ESn/oSlqolOI6u9OG70qt9PU+S5jn71Frm+Kk9ZXsIvo9uWqY1oXUC7PK6uH6btCfWqPLmpX85Lzn5S0rgVX5K23UskmaA0i8lJjeVNN9RSe3URHkps5YSFJNFJCZv/VWN0jUWeWYDEyw2bKI9wnhsqE8rLIcojbsUWuX2TAJFlQxryjPKQh/bK2wyXTSc8VFB49D6NRgMPP/ywVdnTTz8dp59+ujb/hBNOwAknnJBV10jO8BtK+o5uIj6i9aLkHfEBVCPaI76d3oz2UJVJEu1hFZotoL2J0ER7KNHcgFjN7yH1Q9+f+Lrytqoc8SGWzSvyA8hmOEYvUMXhQhFqJjxUWM/jYUI6fiQTLFIdzbCWbgVI0vmWTEvUymXqGOURJyCqEOVh20643+mFh838bBQehPQO/JaSviSt+Ei7oosuLU58yBN6dSMz4ub2SILuAimp6DA/pTJcDBryGgoJYju3h488t4dpGUPz0Jf4C3AbCRK5+Y0RH8mezMZsM4lMMczvUSvx4f8IlCE/dOV7SX5UfmJYxf9DXYRHRCRohIeSEpentZEoxn2QJbThGGuzRG0WUR4mIZ80yiPIyyjKI+5Bhoo8ojxM1ztFCA/5+o7CIwtaKHbllvSRS6T34TeV9C224kOmaPER35/ioz3s27GTIE1JyIgkjfaQ6yVftUV/oZso2sNCZoTK2DwttbjB6GoZ2xjxYSVTaiY+xBsw5dwCNZAfdf7J+j1J854bZYemXblOXYSHCuvvu9Xxx+4YoiTPY2bwOz7KI5wXPuYnXcXF9twk5xlXOhNXeMsgysOnjCiPJG2I5C08VFB4EFJv+G0lfcNAyn/3bpay7UZ8dMqrn34kGYubZbRH3KRnVYv28Ok8fYtemCZawlYgEu1hE1Kd8KllnLyweeIfd0NiGsnVlUwpS3wI+yU+kRb7btWWok2xjlJElCA/6k4ZskP3+ak+a79tsa6q7fBr4X9aarNo4RHajwzkRDfL0wZtxAxrsT2eyHlJhhraRHmImM4XQZohqrDKUR7Jrieyi/LQLVNruiYpQnjYDG9WkfY6kxCSP/x2kr4m7xVdvHLpxEfR0R5Zh6vaSBCRoqI95Hppl6dNvYRtmzQ3JnL72jDxDJextalrM79H1+JDeWNpiNSwaNckPmzlh9wPOU0XnWHTBtDb8iNP2ZFUSplkR5bDWVSRIqE+yYIkrfAI7YOUZyMY5Pak40SyulIdjfDQbzOtANEfYyOTl9qUETCdP4qK8vDJIspDbiuTByQ5RHmUJTwY5ZGOKq7eQvoXfmNJX6Gy8FUWH52y+Ud7JGqji2gPkeDCrMRojyDP4glfWUvYRsoosJ7fw3iTglAZ3VNgJTrxYXNzFpIa6ugM27ayEh9+W93KD1161vKjjj9J9zGSnmCITLeyw28j/rXUttBeXF35/7vz/ZO+m7bfKeiFh5IylqcVMB7zDMfFbiPt5MiPpBOVlhXlkfg8a3n+tqGMKI+qCw9GeRBSbfgNJX1HncRH1aI9jHVTXoCF8ioc7WEVEWK8II9emKd5omlVJ6gb3k43T3pjZYqqrig+hPLKJ9xCv7y8aB+iZfIXH357ecuPbue3qDNp5zGJK2tKTyI7/HbiX0vtC23G1U0T3aH8HonftSzm8bCqaxYepjbijm82dbw0SWpYyA2bJWrrEuVhK0BsHpRUMcqDwoMQ0i38lpK+pE7iI5pXTrSH7RK2iUVHhaM9MlvC1kJmGPPkGz/dDYNmNQSruvLTXONNB6Q6+puR0I2XjWioiPgoWn7o2tdFRvSC/Mhiud4iZEcVhUdsO8L3zlZOmI9F4TJJ5Kv2+KSrm0KAqOqbIkDSLFFblygPGwESt0StiTyjPFRQeBBCsoDfVNK31EV8DMSEfKrII9rDvL2UoqOC0R6ZLWFrE2qtyLO60Fegv7FIUFcjPrp56uv1SahQE/Hh/10V+aEr74uDOv5k/V6Y0ruVHX6a+rW0HaHtuLqlCw8F0TrhdLu6Up0kQlbE4riY9eSlNkvU1iXKQy1cLM7pOUR5JKvryxAKj1pT5HK1wbK1hKjht5X0DU3FKbDq4kOHLtrDLmIj+2iPID+t6Cg52iOXJWylMlbRHpqbOFVZ401ApG74b7u66j7ahK7XXXzo5Idt9EEZ8qOuVEl2aD9nxxTtIW1LaF9dvjzhoaSAeTyS1TXIESsBEr/PWS9RW5coj7jzdd5RHnI/bKI8om1UW3iorjMJIdWA0oP0FXUTH0VHe8hleznaw3Shqor2kMsUuYSt7omzVohYTBbYzRPgWJmi2hfUR3z4f9veXOvmmShKftT5J+m+2qar3vss5gOpnfBQ9D3J91dfN8FxI+HytEkESJ5L1PoY5TjC55VejvJI82AkyygPCg9CSDeojgOE9B0DaGBbZGm0BkYiaQ5GZDHhOhhx5JMzsE3aRhOIXDIMwMU26SSrSjP3vYVtaGDAaWGb28AARrAt5uTrl/HrNp0RjLjmOn6ZYHt+G+3tBm0b+tF0WhiJy5PaC9fztq3Ka6KFETTQcFpotev7eX6a/7dIw3HRcp1QvU6el+Y4LbhuA47jwpUvlBwXcB2rMpHfAq7jwhHygr9V7aiQ6rkNF05LaMcBtMFEct3gd/t+wbhdr125rlhH3Be3IbggXRlx30NlOvc2+jLqdvz3w7pNdNK8Kpbp7Ru8YFv+9vyNZd12j6ATdbqbae1NtkakZNF2LYWHTjBYRXCE25NlhLquVCdmeVrVdkWSCRD9NtIsUZt0FRdZbojSIUireJSH1QOODKM8bKm68CAa3BZcN9myyWl4bN0LWLn+BWxuDee+LVJf+M0lfYfOxlc54sNLSx4SmmW0h7FOcEFXTrSH6emaKSLEJtqjyCVsTRf02TxhhX1d6cbHZrumm6PQjVvFIz6SRBQkilhwXHUEQhZt15RMIjt072tGUSNyeu2FB6Llo8cXRXuxdaX0LiLObARIkUvU2kR5iCQ5NxUd5WG3gksxUR6RobrK/a2+8GCUR7nMHLcH/t8pH8Bxk/YvuyukwlB6kL4kyQmqbPFhWsmlU7Z9wZDB3B6msrZPeIqY28M0VjrpJHM2k9QVsYStXEZ7A6B9ehsvM5KsvOKTxfwecttVFh9+ndzkh79tjfxQhfub2q7zj837qHtftMOHcpIdXpq0fWGbYj3V6zKEh5IU83jEStcsl6e1ECBlLlFrE+Whqm+ae6rIKI9IfoL5vYqK8ugM7a2O8NBB4UFIPaD0IH2L7fweXtlixEfcZKZ5R3ukmbE9bglbr93soz3k+t1Ge1RuCVvTk04F2hsLw6oJceKjq6e8Njd7FRcffr2i5YeuHe3Nf81JKnvKkB1eutQHYbtiXdXrsoRHou+pRJrjg/a4k2B7IawEiJRnENKyCFGVSbJEbbdRHiqKiPJIFblhfDCST5RHeBvqa6SihAfn8UhDq4QfQtRQevQB927egtuGNuHJ4f4e69ZwoienqokPr6wbeZ11tEdkFZYuZm3Xlq9ZtIdcppQlbE2iI+YGRIX+BkRbRXtjI0d7qOtK262p+Ki6/DCl14nE+1ai7Ogl4aFE+p7HRmOE6kp1kixPm+S4ZnG8LHKJWp86Rnl0ytlHech1bK4fsory0D0QqprwUF1n9hNPDg/jtqFN+N+tW8vuCiERKD36gFmjR+GksWMwbXCw7K6UThXFh+1wl05e99EexrIJoj3ilrANt5tNtEfSp2fdRnvIZZMsYWsqY4r6iLsBSBwuDr34SPbEN7wPVk98ayg+/HZVAqSq8qOOP7b7WNYwFqUA0/z/VF54COiOF7nN4xFznLI9vqnaNB1Hs5q81CfPKI9OXvZRHkEZ6Tyb5NxuM8FpXlEeuugOCo/qMW1wECeNHYMDt9uu7K4QEoHSg/QdVRMfXn09VYj2SHoBZBNKmzbaQ+5Tt9Ee5knsksgQwxPRIM/+CaXxQl+B7Q1FuL3kddPM71FF8SHepAU3alrhoJcfqhu2MuRHnSlDdug/P4XokPoibl9uo0rCI9z/cJ5OeIjojgem77p1XRUGQdNtxJwc+ZFkidpuzyM2UR4qsoryiFumtqsHHQVEeUTL6LanKEvhUS5uC3BHCvzh8Baih9KD9CV1EB9x83t4ZeKjPeSnLzYipJsJzeoQ7WGeiV9/UWwV7dEm70lNIzcDAtobji7Czrt5+hupX7T4EPqnaysS9WEpP1TtimnWUqQP5UeesiO5lIqXHX4fxLaU7cp1ShIe2miMRN/lBHWSzOOR4riW1eSlQVGpjNUqLoYVv+oY5ZFmAnNjWYvrDDnKw4R8HUThQQhJC6UH6VvqID465d12vv8UxOJJTMxY3lDZFON447BZHi9JtIf6QtA+2sN0sZnXEra5TWqa6Y1JON+0nc7vcL6SksSHGKKf6MYQiN5QZyA/dOnam/KU8qOOP9b7qHhPkkuN7mSH3w+xPdVr+f9M/M6UKTyUSN9nnfAQ0YqUDObxiPYr/hhpEsa6yUuzWqLW5pySNMqjKZ2/u5UccVEecptplrU3lrWJaAke3vjXN+r/IQoPQkg3UHqQvqZs8RE3wWkvRXvIF2pJoz3kbdo8TTNdcHYb3myzhG2nbPwFuk2Itu5mTixvc+MSrRv+2+5JcLjfprp5iQ/9k3X1tlOJD0X74nbymvRUu6yrpi+1RiMYTLJDJk/Z4fdHbFP12jq6Q/iu5Co8xP5rjgtZzOOhJOZ4FHeckNvpdvJSn6yWqJXLmsR7kigPFUmiPFTnzl6I8tDN30HhUW1ctAr/IUQHpQfpG5ra+ApV2WLEh9euqk1VObed5z8N6S7aQ74AMouQ+LLqp1blRnv4dBvt0WknLDhSL2EbFJZliL3UsJEZWiGivLG3q2vaD93NkVy/W/Fh15Z627bioyryQ9sfvy91FSCa/he+EovcH7m81J+uhYemnVyER4qVWOIlqqJOzLCWJMcOmyExxig5v6iFCLFZotYnTZSHirKjPFTbzfpaQO5Ht1EethOWAsUJDx1JrjMJIcVC6UH6CtUJSWfrqyI+8or2MJVJ8yQo0kaNoj3UK7uEBUduS9haPLlUhfAr85OIiQzm9zBNfpiV+Ahu+qSb0rzFh7f9GsgPsT91+rHct6rIDr9t1etuhYeqL6H/fevtiWXCabr3RFnGQlZojyMZCI5IWwki47JeolZ1vLc5f1Q1yqOz7fSRnMqyOUV5VFV4qKOEKTwIqTKUHqTvqIL4SDLPR7iMfyHgtv/ONtpDRZKZ3ZX1S4j2MC1PW4klbA1lrS70FWlJnrLqb1igrRMrPow3O9FtR7Yj3vBJN65lrezS2X6yoSalyY8aUiXZYTukJjfhEfRDs22pz3HCQ4n0vbWJAtEdH0ziNFakmI6BoTRJDhuPs+Hjraps3kvU2pSp0lwenbbD52CziCkuyiNaJ4rumorCowK4LrwVXIr66Y3zIskHSg/Sl5QtPgD1Uwm5V1WI9jBtSzfuN+toD/VTMrkdw8WmcchK/EWr7ulfkklNlSS4oE/yFFYfmh7/5Da3+T0M4sM26iO2TEbiQ9mvoB/2Q02Kkh91/sntvVG997rPKeGqMKH2FOlFTFhqIzz04kFRTyJaN75Od8edBFIk+G0Q8JIISTJ5adolan3kKA+RqKBPd/7LYi6PNOd6FUVFedjO3+HVpfAghISh9CB9S13Eh44yoz1MZdR97S7aI+irhcToNtojKGuxhK2PzaSm2miPBKHbcn6acPTIjYkhYiBJ2Lu1+NCsrlJF8aHqZ6cv9kNf/Btw+eue1Q1+L5DFe6F7n7McUhNqU5Fe9AotISmRQHiIaI8NpuNJkuVpY7ajO76lmbzUNDGpzTK2NkvU+qSJ8lC2k+A83W2UR5ohq+Ey+UR5xEHhQQjpFkqPmnDfffdhzpw5GD9+PCZOnIhZs2bhrrvuKrtbtadO4mPAIgS022iPSKRGiknPqh7tkeQpXpIlbAud1FSVlkSEaMSH/FTXZju2T4ttoz7kPkbKFCA+8pIffh908sM62qSH5EcyqaF7n9Q387nNH1IR4dEpo+5rskgO0/FCqmMxj0ea45LpWGWMhvOLWs3vkSBqTzN5qfEcUvEoD7kNm8nJTdcDWUV5mK5vKDzqi4sRuG6BPwmHdZH+gtKjBtx2222YPXs2HnzwQUyYMAEbNmzA0qVLMXfuXNx4441ld682NDCoTC9afCRZ0taro8cU7SFfaKjkQ5olbNVl4p8Q5RXtYbpwtJtELj5c2Sb82aewSU1TPFFN9uRWasuwnc5vv8/Jb9TUZcoRH35e0fJD136cFKnzT5L9tH0Pc5MdfttCvtiX+DL5Cw9TO9p5PBTojgNZRYZF+6Y5JirKZjV5qU83S9RaTaZtEvYlRHkkmbzU2B/VQxHpAYRxDi7DA5zO0F5Fnmb+jioID911JiGkfCg9Ks7LL7+Mc845B9dffz3WrVuHP//5z1i9ejXmzJkDAPja175Wcg/rRZ7iQ30S1QkR+5VdwtsxPQ2xeJpiIxVSLFsX10ae0R7RsvFhxGmWsFVdCOuiPRJNaqoi5gYg7imqqq0kNyTaGx7DdnpRfPj5VZEfpvQ6k/g9LkN2+O0LZcT+xJeplvAIoT1G6OvojiNJjkXaY5DNXEd+Uem4qiqrm7zUeExPca4wR1jq25HbyzvKQ+5TN+f6UBmjuNGfn22iWH1sJyz10qPXX/prNQoPQnodSo+Ks2jRItx000345Cc/CcfxDuw77bQTFi5cCAB44YUXyuxeLclLfAC6pwfpxUeSaA91nr0ISBvtkWTW9zTRHsoLv4yiPeSypgtcnzRPBe1WbbG40O/mRkJAe8OS0/wecrnKiw/Kj0KolezoAeEhEistFOQ6j0dcmu44qShrmlvJJ0n0XpLzQ1ZRHkmEf5ooD/VQ0O6iPDpl4iVQmiiPLISHuiyFR25w9RZSITggreKceuqpmD59eiR9ypQpAIAZM2YU3aWeoIFBtDAcSW9iACPYFi7rNNFyFTfVaGJEOQ62gW3Syb6JBkaUQ1AcjEgnf//EPqK5kByAi21wgt/hvBa2oRH8VtZ3WtjmNtp7qpM3fjvRMk1nBCNu01hf1Ya/3XAZL6/ptDAS26dwWRWddrxtq8o20cIIGmg4LbRcdRkfv0wDLbTQQMNx0XKdIF1V1nFacN0GHMeFqykrl4HjencmTku6k0c0r/2367hwdNFBchlTHSnPbbhwWmJdQL4+1bVraj9UT3otbkNfRtNOQwiEsdqWWKZz7xTpu/j9E9L9mzZ5P7Xp7RtFp6V4X6S2xT/lr3/cdnsB3b5og+B05TXyTt++5Q24VLYOwkPVdytpkcM8HioiZRIM9UszeanVKi6G+Z98bKI85OjDJFEeKrKM8oi2qbqWqUaUR5L5O7x0Co9+Y+WGNVi5YQ22tLbFFyZ9CyM9Ko5KeADA8uXL0Wg0MH/+/IJ71DskjfhQnwTzifgIt+WX05NntEc3S9iqt1VctIfcnmkJW93TPPPTQuli2WKuD3mYi7qwfCOgviGwi7rI4omu1Jair3K7yW8IFX2OlNG002XEhxj6b/OkXywrl9em5xz5UWfyjOxI+jnZRPpE+lVh4aGdx8N4rJDqZBYBFiNFdNFuhv93m8lLfZIcr3XnAWXkRmSCUtO5rZpRHkF7FtcCZUR5RMvlIzz013oUHlVmv+3fgU/scgCOffu0srtCKgylRw0ZGhrC5ZdfjhtuuAEf+tCHyu5ObVCdnJKID0Bn/5OJD9uVXUxzfOQ5t0dWS9iG+9Ld3B7assYLxvgL0CyWsLVZnjazSU0VdPN0NdmNiqausX0hsYriQ3Fz6JezlR+m8ib5EbmR7EP5kYXs0L2fST4TXduq8pH/6RoJDxXRY4G+ThqBmkTQmup3M3mpKRIk6yVq5bKm89CA1F6Vojw6ZSyuFQqO8tBNWJqF8FCXtRcelCCEVBdKj5px55134uCDD8bSpUvx4IMPYmhoqOwu1YoqiA+vvP2Stl47fhlVntvOMz09MV142Dy9Sb6EbRHRHp32wtEeVjPqZ7SEbaeOfMEdH8kRmdRUdzMW+m0K/465wUhyI5P1/B7S9osWH2Kf424SVfVCZCA/vP4a5Id8c++oZYAYsVC3H5v9074fOc3XYSpv+l+2+v8qQXiE+59cWiSaxyPF8Sc2ykNR1mby0qBKkjmYMlii1nzuiT/XFhXlYbNErbI9i/o2UR4q0WET5dHtCi1eeQqPonAxUvgPITooPWrC0NAQvvCFL+Cyyy7Diy++iOHhYSxYsABHHXUUhoejc1MQPVUXH+JJ3SbaQ51nkhz6KIlOGZPkiB9Oou5LcdEe0b7ahB0nGdYSFhymC2pttIfh4js2zFs3zEXZVvqbFt0wFxGbG6lSxYfV9uJvFguXH8b2NZEQNaQqk5PGtyPVFcqrXkf+90oSHkkEZWXm8dDlq5BEiCmqLslxu1Mn/TnEJtqyrCiPTp/jz/U2S8ib65vO0fr3Xrz+yWJJWq88hQch/QqlR00YO3YsFixYgAceeACvvPIKvv3tb6PZbGL58uX4yU9+Unb3akee4kO3pK26bft5PrKK9lBhivYwXfjEPUVKEu2RZMlaZVmLaA+53ayXsPXRLWFrMxTG6kI/1ID9TU02T3qltgx9KVN8ZDXPhwjlR3bUSXZ0O39H1YSHiO57Xdg8HnFohrV0O3lpp0772NzFErV2IsR0jlWf30xL1mYR5ZF08lKbld7yiPJIMn+H14Z6SVoKD0L6G0qPHLnmmmvQaDQS/1x00UXGdseMGYPzzz8fl1xyCVzXxe9+97uC9qi3yEt8eOXV4iPJBKdBfsHRHmknNbVZ8tZmDHCiYS1StIdJ2hS9hG2aSU2Vk/AlePrZzVPXZDcwmrqG9r3XQkaWN3xCP4xRHzZthfpeL/lRpx/rfauK7PDbEOqpXpuiO0LfsYKEh4rod15fJ40oTRZdFhPlIRaVhYckQlTojst5L1FrlAYJ2k0yzCXJaivhtPhzvvLcWlCUhzJfM3+HTniooPAoAC5ZSyoEl6zNkQkTJmDatOQzCe+8885W5c444wxceumlePPNN43l7t28BaOc8Alkn8EBTBvsr4P1+bv/T9ldIBngX9JsV2ovCCGEkOzwb9eG2z+E1IEnh4fx9HB4qdj1LctIVUIKhNIjR+bNm4d58+bl1v6kSZMAAFOmTDGWmzV6FHZumhY8JYQQQgghhBB7pg0ORh6iPrl1GHds3lJSjwhRQ+lRY5566ikAwEknnVRyTwghhBBCCCHEw3VH4LrFrahS5LZI/eCcHhXnL3/5Cx555BFl3pVXXokjjzwSH/nIRwruFSGEEEIIIYQQUn0oPSrO0UcfjQMOOABz5szBAw88gFarhXXr1uErX/kKXn31Vdx2221ld5EQQgghhBBCCKkklB4V58ILL8Tee++N+++/H8cffzwOOOAAXHDBBTjqqKPw29/+Fttvv33ZXSSEEEIIIYQQARdAq8Afrt5C9HBOj4pz1lln4ayzziq7G4QQQgghhBBCSO1gpAchhBBCCCGEEEJ6EkZ6EEIIIYQQQgjJjoJXbwFXbyEGGOlBCCGEEEIIIYSQnoTSgxBCCCGEEEIIIT0Jh7cQQgghhBBCCMkQF3BbxW6PEA2UHoQQQgghhBBCaseqja/h8aHXsbm1reyukArD4S2EEEIIIYQQQmrHjLfthNMm/T+YM2HPsrtCKgwjPQghhBBCCCGEZIbrtuAWOLylyG2R+sFID0IIIYQQQgghhPQklB6EEEIIIYQQQgjpSTi8hRBCCCGEEEJIdrgtwB0pdnuEaGCkByGEEEIIIYQQQnoSSg9CCCGEEEIIIYT0JJQehBBCCCGEEEII6Uk4pwchhBBCCCGEkMxw0YKLApesLXBbpH4w0oMQQgghhBBCCGKAzxMAABnLSURBVCE9CSM9+oB7N2/BKMfBPoMDmDY4WHZ3CCGEEEIIIT3Ek8PDeHp4G9a3GHFBqgelRx8wa/Qo7Nxslt0NQgghhBBCSA8ybXAQ0wYH8eTWYdyxeYu3XG2hS9YWuC1SOzi8hRBCCCGEEEIIIT0JpQchhBBCCCGEEEJ6Eg5vIYQQQgghhBCSGa7rwnULXL3FdQvbFqkfjPQghBBCCCGEEEJIT0LpQQghhBBCCCGEkJ6Ew1sIIYQQQgghhGRIwau3gKu3ED2UHoQQQgghhBBCasfjm97CE5vWYTOXrCUGOLyFEEIIIYQQQkjtmD5mPE6ZuDuOHje57K6QCsNID0IIIYQQQgghmcHVW0iVYKQHIYQQQgghhBBCehJKD0IIIYQQQgghhPQkHN5CCCGEEEIIISQ73IJXb+FEpsQAIz0IIYQQQgghhBDSk1B6EEIIIYQQQgjpeZ577jmcccYZOPDAA3H44YfjoIMOwnXXXWddf+HChWg0GsafHXfcMcc9IGng8BZCCCGEEEIIIdlR8OotsFi95ZFHHsGRRx6Jj370o3jooYfQbDbxwAMPYO7cuVi+fDmuvvrq2Dauu+46OI6DcePGYXBwMJL/+uuv44QTTki1CyQ/GOlBCCGEEEIIIaRnWbduHU466SQMDg5i4cKFaDabAID3v//9+OIXv4hrr70W119/vbGNZcuWYWBgAH/+85/xt7/9DWvXrg39LF26FK7r4uSTTy5il0gCKD0IIYQQQgghhPQsV111FV566SWcdtppeNvb3hbK+/SnPw0AuPjiizEyop8Q9a677sIvfvEL7L777sr8m266CWPGjMFxxx2XXcdJJlB6EEIIIYQQQgjJkJESfvQsWrQIjuNg1qxZkbzddtsN73rXu/Dyyy/j7rvv1rZx8cUXY7fddtPmL1myBHPmzMHYsWONfSHFQ+lBCCGEEEIIIaQnee211/DMM88AAKZPn64ss99++wEA7rzzzlTbeOaZZ/Doo4/ilFNOSddJkiuUHoQQQgghhBBCepJVq1YFr6dMmaIss+uuu8J1XaxcuTLVNm6++WYMDg7ixBNPTFWf5AtXb+kD7t28BaMcB/sMDmCaYpZhQgghhBBCCEnLk8PDeHp4G9a3ClyxxZI33ngjeL3DDjsoy4wbNw4AsHbt2lTbuOWWWzBr1iwuV1tRKD36gFmjR2Hn9gzFhBBCCCGEEJIl0wYHMW1wEE9uHcYdm7fALXjJWtewZO3Q0FDwWrXMLACMGjUKALBx48bE237++eexYsUK/OAHP0hclxQDh7cQQgghhBBCCOlJxowZE7zetm2bsszw8DAApJqE9Oabb0aj0cBHP/rRdB0kuUPpQQghhBBCCCGkJ9lll12C1xs2bFCW8dMnT56cuP0lS5bg0EMPDW2HVAsObyGEEEIIIYQQkhkuRuC65mVk0/LEliE8uWVTKG1LSz+8Zd9994XjOACANWvWKOfdWLNmDRzHwYwZMxL15cUXX8TDDz+MK6+8MlE9UiyUHoQQQgghhBBCasG+o8Zi31HhYSivbNuKG958TVl+woQJeM973oNHH30Ujz/+OPbdd99IGX+Fl6OOOipRX26++WY4joOPfexjieqRYuHwFkIIIYQQQgghPcsnPvEJuK6LpUuXRvLWrl2Lp59+GhMnTsSxxx6bqN0lS5Zg5syZmDp1alZdJTlA6UEIIYQQQgghJDtcF3BbBf7oh7cAwNlnn41JkyZh8eLF2Lx5cyjvRz/6EVzXxQUXXBCs4nL33Xdj5syZ+O53v6ttc/Xq1XjwwQdxyimndP9+kVyh9CCEEEIIIYQQ0rNMmDABP/3pT7F+/Xqcc845wSouDz/8ML71rW/hwx/+MP7t3/4tKP/9738fq1atwvz587Vt3nLLLQDAoS01gNKDEEIIIYQQQkhPc+yxx+LBBx/Exo0bcdhhh+GII47AZz7zGXz961/HrbfeGkx2CgAf//jHMW7cOJx55pna9pYsWYK9994b++23XwG9J93AiUwJIYQQQgghhGSG647AdYt7vm67Usx+++2Hn/3sZ7HlTj/9dJx++unGMvfee6/VNkn5MNKDEEIIIYQQQgghPQmlByGEEEIIIYQQQnoSDm8hhBBCCCGEEJIdbguwHHKS2fYI0cBID0IIIYQQQgghhPQklB6EEEIIIYQQQgjpSSg9SN/w5PBw2V0gKeFnV1/42dUXfnb1hZ9dfeFnV1/42YVx3RbcVoE/HN5CDFB6kL7h6eFtZXeBpISfXX3hZ1df+NnVF3529YWfXX3hZ0dIdaH0IIQQQgghhBBCSE9C6UFSkVcIXx1DA+v2XuT5Htft86vbe1zHz25DK59wU77H+bfLzy7/tvnZ5d923drlZ1ffdvP67ID6vRcAOqu3FPbD4S1ED6UHSUVeIXx1DA2s23uR53tct8+vbu9xHT+7Da6bS7t8j/Nvl59d/m3zs8u/7bq1y8+uvu3m9dkB9XsvCKkaA2V3gOTHpk2bAABvjGRvPre4LtaOZL/2dl7t5tk2282/bbabb7t5tt1yUav3oo7vMT+7fNvNs21+dvm3Xbd2+dnVt928PjugXu/FW+2IlzdyjHxRUfT2SL1wXDdHLUlK5T//8z9x+umnl90NQgghhBBCCMmVsWPH4oknnsAee+xRdldIxaD06GFee+01/Pa3v8Wee+6JMWPGlN0dQgghhBBCSA+zadMmLF++HPvttx8mTJhQ6LZ32mknCg+ihNKDEEIIIYQQQgghPQknMiWEEEIIIYQQQkhPQulBCCGEEEIIIYSQnoTSgxBCCCGEEEIIIT0JpQchhJBSueeee/Df//3fZXeDkJ6C3ytCCCHEg9KD9D333Xcf5syZg/Hjx2PixImYNWsW7rrrrrK7RQy0Wi1873vfw/7774+xY8di/PjxOOqoo/DrX/+67K6RBKxYsQJz587F7NmzsWLFirK7QwA899xzOOOMM3DggQfi8MMPx0EHHYTrrruu7G6RBPB7VS94Pqs/vI4kpPpQepC+5rbbbsPs2bPx4IMPYsKECdiwYQOWLl2KuXPn4sYbbyy7e0TDvHnzcP755+OJJ57A1q1bsWHDBtx777044YQTsHDhwrK7R2J46623cOWVV+LGG2/EsmXL4DhO2V0iAB555BEccMABcBwHDz30EP74xz9iwYIFOO+88/Av//IvZXePxMDvVT3h+aze8DqSkHpA6UH6lpdffhnnnHMOrr/+eqxbtw5//vOfsXr1asyZMwcA8LWvfa3kHhIV1157LZ544gnce++92LRpEzZs2IBf/epX2GuvvQAAX/ziF7FmzZqSe0lMjB8/HhdccAG+853v4EMf+lDZ3SEA1q1bh5NOOgmDg4NYuHAhms0mAOD9738/vvjFL+Laa6/F9ddfX3IviQl+r+oHz2f1hteRhNQHSg/StyxatAg33XQTPvnJTwZPxHbaaafgycoLL7xQZveIhquvvhp33HEHPvCBD6DRaGD06NE47rjjcOutt2JwcBBbtmzBb37zm7K7SSwZPXp02V0gAK666iq89NJLOO200/C2t70tlPfpT38aAHDxxRdjZGSkjO6RhPB7VQ94Pqs3vI4kpD5QepC+5dRTT8X73ve+SPqUKVMAADNmzCi6SySGp59+GnPnzsXuu+8eyZs+fToOOuggAMDrr79edNcIqTWLFi2C4ziYNWtWJG+33XbDu971Lrz88su4++67S+gdIb0Hz2f1h9eRhNQHSg/St0yfPl2Zvnz5cjQaDcyfP7/gHpE49tprL1x66aXa/D322AMA8M53vrOoLhFSe1577TU888wzAPTHxf322w8AcOeddxbWL0J6GZ7P6g+vIwmpDwNld4CQKjE0NITLL78cN9xwA8dEV5BmsxnMNaDir3/9K0aNGhWMpyWExLNq1argtf+EUmbXXXeF67pYuXJlUd0ipKfh+aw34XUkIdWEkR6EtLnzzjtx8MEHY+nSpXjwwQcxNDRUdpdIArZs2YJHHnkEZ5xxBiZMmFB2dwipDW+88UbweocddlCWGTduHABg7dq1hfSJkH6G57N6wutIQqoLIz1I3zM0NIQvf/nLWL58OV588UUMDw9jwYIF+J//+R/cf//9GBwcLLuLxIL/+q//wqhRo/Dv//7vZXeFkFohXpjrjnejRo0CAGzcuLGQPhHSz/B8Vi94HUlI9WGkB+l7xo4diwULFuCBBx7AK6+8gm9/+9toNptYvnw5fvKTn5TdPWLBxo0bcckll+DnP/85n4oRkpAxY8YEr7dt26YsMzw8DMA7XhJC8oPns/rB60hCqg+lB6kl11xzDRqNRuKfiy66yNjumDFjcP755+OSSy6B67r43e9+V9Ae9Qd5fW6f//zn8eUvfxlHHnlkMTvSp+T1+ZFy2WWXXYLXGzZsUJbx0ydPnlxInwjpV3g+qze8jiSkmnB4C6klEyZMwLRp0xLX23nnna3KnXHGGbj00kvx5ptvJt4G0ZPH5/aNb3wD++yzD84666xuukYsyPt7R8ph3333heM4AIA1a9Zgxx13jJRZs2YNHMfhEoyE5AjPZ70DryMJqRaUHqSWzJs3D/Pmzcut/UmTJgHQr2RA0pH157Zo0SKsW7cOV1xxRWZtEj15f+9IOUyYMAHvec978Oijj+Lxxx/HvvvuGynjr/By1FFHFd09QvoCns96C15HElItOLyFEAVPPfUUAOCkk04quSdEx80334yHH35Ye4F4++23F9wjQurLJz7xCbiui6VLl0by1q5di6effhoTJ07EscceW0LvCOlteD7rPXgdSUi1oPQgfctf/vIXPPLII8q8K6+8EkceeSQ+8pGPFNwrYsNvfvMb3HrrrbjqqqsieZs2bcLXv/51Lq1ZEzZt2gQAaLVaJfekvzn77LMxadIkLF68GJs3bw7l/ehHP4LrurjggguCVVxIteH3qj7wfFZfeB1JSH1wXNd1y+4EIWWw995747nnnsPRRx+N+fPn433vex82bNiAK664AsuXL8eSJUuw/fbbl91NIrF06VIcd9xx2Lp1qzJ/ZGQEO+ywA9asWcOVJirO66+/jve+971YvXo1jj76aNxxxx1oNptld6tvufPOO3HyySfjE5/4BK6++moMDAzg4Ycfxpw5c3DEEUfg1ltvDeb+INWF36v6wPNZveF1JCH1gdKD9C2LFi3CFVdcgRdffBGjRo3CnnvuiUMOOQTz5s3DMcccU3b3iIL/+7//w8EHH4z169cby5111lm49tprC+oVSUqr1cKhhx6Kp556Chs3bgzSJ02ahMMPPxxLliwpsXf9zcqVK/GNb3wDzzzzDMaMGYOhoSGceeaZ+NznPkfhUXH4vaoXPJ/VH15HElIfKD0IIYQQQgghhBDSk3BOD0IIIYQQQgghhPQklB6EEEIIIYQQQgjpSSg9CCGEEEIIIYQQ0pNQehBCCCGEEEIIIaQnofQghBBCCCGEEEJIT0LpQQghhBBCCCGEkJ6E0oMQQgghhBBCCCE9CaUHIYQQQgghhBBCehJKD0IIIYQQQgghhPQklB6EEEIIIYQQQgjpSSg9CCGEEEIIIYQQ0pNQehBCCCGEEEIIIaQnofQghBBCCCGEEEJIT0LpQQghhBDccssteOutt8ruBiGEEEJIplB6EEIIIX3OlVdeiUcffRTjx48vuysAgCVLluBb3/pW2d0ghBBCSA9A6UEIIYT0MTfeeCPuv/9+XH755WV3JeCUU07Bli1bcMkll5TdFUIIIYTUHMd1XbfsThBCCCGkeP7yl7/g/e9/P/70pz9hp512Krs7IVzXxdFHH43zzz8fH/7wh8vuDiGEEEJqCqUHIYQQ0qecfPLJ+Lu/+zt89atfLbsrSh577DHMmTMHTz31VGWG3hBCCCGkXlB6EEIIIX3IY489hkMOOQQvvPACJk2aVHZ3tJx88smYOXMm5s+fX3ZXCCGEEFJDOKcHIYQQ0of88Ic/xOzZsystPADg7LPPxoIFC7B58+ayu0IIIYSQGkLpQQghhPQht9xyC+bMmVN2N2KZPXs2RkZG8Jvf/KbsrhBCCCGkhlB6EEIIISXyzW9+E9OnT0ej0UCj0cDAwACuuOIKAMDzzz+PsWPHBnlTp07FLbfc0vU2n3rqKfz1r3/FQQcdZCz3pz/9CZ/85Cdx6KGH4uijj8axxx6LL3zhC3jxxRcBAM8++yxmz56N/fbbD5MnT8Z//Md/4K233sK5556LE044ATNmzMDs2bPx0EMPAQDWrFmDs88+G8cffzz22WcfHHHEEbj//vuNfdhuu+1wyCGH4Je//GXX+00IIYSQ/oPSgxBCCCmRCy+8EI8//jgWL14Mx3Fw8MEH40tf+hIAYOrUqbjgggvwrne9C/fddx+ef/55nHrqqV1vc/ny5XAcB9OmTdOW+elPf4pDDz0U733ve7Fs2TL87ne/w+23345HH30URxxxBFqtFiZPnoyLLroI7373u/Haa69h/fr1OOecc3DeeecFZTds2IDjjjsOTz75JM4991xcfvnl+PWvf43HH38cmzZtwvHHH481a9YY+3vAAQdg2bJlXe83IYQQQvoPSg9CCCGkApx22mk477zzsGzZMlx33XUAgKGhIfz+97/HH/7wB3zgAx/IbFvPPfccAGDixInK/BUrVuCf/umfMGfOnEDAAMDjjz+O++67Dy+99BLWrVuH7bffHscccwzOOeccuK6La6+9Fueddx7e+c53AgCazSZOPvlkvPnmmzj++OPxne98B7vssgsAYGBgACeffDI2btyIX/3qV8b+Tp06Fc8++yzn9SCEEEJIYig9CCGEkIrwzW9+EwceeCDOPfdcPPHEE/j85z+PCy+8ELvvvnum23n11Vex/fbbo9FQXwZcfvnlGB4explnnhlK33///XHdddfhpptuwo477hikDwwMAAB22203HHDAAaE6O++8MwDgoIMOwh577BHKmzx5MlzXxSuvvGLs7zve8Q60Wi2sXr3aav8IIYQQQnwoPQghhJCKMDg4iJ///OdoNps46qijMHHiRHz4wx/OfDtDQ0MYP368Ms913WDS0P333z+S/+lPfxof+9jHlHX32WefSFqz2YzNGxkZMfZ37NixAIC33nrLWI4QQgghRIbSgxBCCKkQe+21Fy699FK8+uqruQ3n2G677dBqtZR5r776KrZu3QoAmDBhQuJ20+TFMTg4CADYtGlT6jYIIYQQ0p9QehBCCCEVYmhoCL/4xS9w4oknYuHChbj99tsz38b48eOxceNGZd7b3/72IALjzTffzHzbaRgaGgIAvO1tbyu5J4QQQgipG5QehBBCSIX47Gc/i6985Su44YYbMHXqVJx11ln461//muk29txzT6xbt04ZSdJsNnH00UcDgHbFlNWrV+PZZ5/NtE8m/GEt/vwghBBCCCG2UHoQQgghFWHRokWYNGkSjjvuOGy//fa48cYb8be//Q2f+tSnMt3OjBkzAADPP/+8Mv/SSy9Fo9HAD37wA22+67qZ9snEiy++iNGjR+Md73hHYdskhBBCSG9A6UEIIYSUzPDwMBYsWIDPfOYz+Pu///sg/dBDD8U//uM/4u6778bFF1+c2fYOPvhgDA4OYsWKFcr8ww47DFdddRUeeughnHPOOUFEiOu6+N73voedd94Ze++9d1Dej8RYv359pC0/b926dYnyRB5//HEcdNBBFntGCCGEEBLGcYt8VEMIIYSQEJ/97Gfx85//PBAAH/zgB3HPPfcAAO6++2585CMfCaTDHnvsgcMOOww33nhj19s98cQTseuuu+Kaa67Rlvnf//1ffPvb38aqVaswefJkjB07FqeddloQefL000/jU5/6FFatWhXMuzFjxgyce+65mDlzJj73uc/hsccew9atW9FoNDBz5kxceOGFmDx5Ms4//3ysXLkyknfaaadF+jF9+nTMmzcPl112Wdf7TQghhJD+gtKDEEII6UMWL16Mf/3Xf8VLL70Ex3HK7o6WF154AXvuuSdWrlyJ6dOnl90dQgghhNQMDm8hhBBC+pBTTz0Vo0ePxm233VZ2V4z87Gc/w+GHH07hQQghhJBUMNKDEEII6VMWL16MK6+8Eg8//HDZXVGybds2vPvd78bVV1+NuXPnlt0dQgghhNQQRnoQQgghfcrHP/5xTJkyBddee23ZXVGycOFC7L///hQehBBCCEkNIz0IIYSQPmb9+vWYO3currnmGsycObPs7gQ888wz+NjHPobf//73mDRpUtndIYQQQkhNYaQHIYQQ0sfssMMOuP322zF//vxgBZmyefPNN/GZz3wGixcvpvAghBBCSFcw0oMQQgghleKee+7BO9/5TkydOrXsrhBCCCGk5lB6EEIIIYQQQgghpCfh8BZCCCGEEEIIIYT0JJQehBBCCCGEEEII6UkoPQghhBBCCCGEENKTUHoQQgghhBBCCCGkJ6H0IIQQQgghhBBCSE9C6UEIIYQQQgghhJCehNKDEEIIIYQQQgghPQmlByGEEEIIIYQQQnoSSg9CCCGEEEIIIYT0JJQehBBCCCGEEEII6UkoPQghhBBCCCGEENKTUHoQQgghhBBCCCGkJ6H0IIQQQgghhBBCSE9C6UEIIYQQQgghhJCehNKDEEIIIYQQQgghPQmlByGEEEIIIYQQQnoSSg9CCCGEEEIIIYT0JJQehBBCCCGEEEII6UkoPQghhBBCCCGEENKTUHoQQgghhBBCCCGkJ6H0IIQQQgghhBBCSE9C6UEIIYQQQgghhJCehNKDEEIIIYQQQgghPQmlByGEEEIIIYQQQnoSSg9CCCGEEEIIIYT0JP8/VQQkQx/25wEAAAAASUVORK5CYII=\"><br>"
],
"text/plain": [
"<yt.visualization.plot_window.AxisAlignedSlicePlot at 0x7fb3c0dd3cc0>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g1slice.set_log('group1', False)\n",
"g1slice.set_zlim('group1', group1.min(), group1.max())"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<img style=\"max-width:100%%;max-height:100%%;\" 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],
"text/plain": [
"<yt.visualization.plot_window.AxisAlignedSlicePlot at 0x7fb3c0dd3780>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g2slice.set_log('group2', False)\n",
"g2slice.set_zlim('group2', group2.min(), group2.max())"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"yt : [INFO ] 2016-12-22 10:35:23,543 Loading coordinates\n",
"yt : [INFO ] 2016-12-22 10:35:23,544 Loading connectivity\n",
"yt : [INFO ] 2016-12-22 10:35:23,551 Parameters: current_time = 2.0\n",
"yt : [INFO ] 2016-12-22 10:35:23,552 Parameters: domain_dimensions = [1 1 1]\n",
"yt : [INFO ] 2016-12-22 10:35:23,554 Parameters: domain_left_edge = [-0.6 -0.6 0. ]\n",
"yt : [INFO ] 2016-12-22 10:35:23,555 Parameters: domain_right_edge = [ 6.6 6.6 1. ]\n",
"yt : [INFO ] 2016-12-22 10:35:23,557 Parameters: cosmological_simulation = 0\n",
"yt : [INFO ] 2016-12-22 10:35:23,558 Loading coordinates\n",
"yt : [INFO ] 2016-12-22 10:35:23,560 Loading connectivity\n",
"yt : [INFO ] 2016-12-22 10:35:23,697 xlim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:35:23,698 ylim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:35:23,699 xlim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:35:23,700 ylim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:35:23,702 Making a fixed resolution buffer of (('connect1', 'group1')) 800 by 800\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"900.4045415306368 dimensionless\n",
"900.0 dimensionless\n",
"8.097011032290823 dimensionless\n",
"8.097006592146112 dimensionless\n",
"0.8138626175116652 dimensionless\n",
"0.8138613746801414 dimensionless\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"yt : [INFO ] 2016-12-22 10:35:23,956 xlim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:35:23,957 ylim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:35:23,958 xlim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:35:23,959 ylim = -0.600000 6.600000\n",
"yt : [INFO ] 2016-12-22 10:35:23,961 Making a fixed resolution buffer of (('connect1', 'group2')) 800 by 800\n"
]
}
],
"source": [
"ds = yt.load(\"../gen-mesh-one-material_out.e\", step=-1)\n",
"\n",
"ad = ds.all_data()\n",
"\n",
"temp = ad[('connect1', 'temp')]\n",
"group1 = ad['group1']\n",
"group2 = ad['group2']\n",
"\n",
"print(temp.max())\n",
"print(temp.min())\n",
"print(group1.max())\n",
"print(group1.min())\n",
"print(group2.max())\n",
"print(group2.min())\n",
"\n",
"g1slice = yt.SlicePlot(ds, 'z', 'group1')\n",
"g2slice = yt.SlicePlot(ds, 'z', 'group2')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<yt.visualization.plot_window.AxisAlignedSlicePlot at 0x7fb3c43b01d0>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g1slice.set_log('group1', False)\n",
"g1slice.set_zlim('group1', group1.min(), group1.max())"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\"><br>"
],
"text/plain": [
"<yt.visualization.plot_window.AxisAlignedSlicePlot at 0x7fb3c43812b0>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"g2slice.set_log('group2', False)\n",
"g2slice.set_zlim('group2', group2.min(), group2.max())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| lgpl-2.1 |
aqeel13932/DM | HW12/Q3&Q4&Q5.ipynb | 1 | 61485 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Q3"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = pd.read_csv('extract_medium.csv',sep=';')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"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>State</th>\n",
" <th>House_id</th>\n",
" <th>Weight</th>\n",
" <th>House_relation</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>Race</th>\n",
" <th>Marriage</th>\n",
" <th>Education</th>\n",
" <th>Ancestry</th>\n",
" <th>Language</th>\n",
" <th>Employment_status</th>\n",
" <th>Traveltime</th>\n",
" <th>Industry</th>\n",
" <th>Occupation</th>\n",
" <th>Hours</th>\n",
" <th>Weeks</th>\n",
" <th>Salary</th>\n",
" <th>Income</th>\n",
" <th>Earnings</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Arizona</td>\n",
" <td> 3399818</td>\n",
" <td> 18</td>\n",
" <td> 3</td>\n",
" <td> 2</td>\n",
" <td> 14</td>\n",
" <td> 8</td>\n",
" <td> 5</td>\n",
" <td> 4</td>\n",
" <td> 210</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State House_id Weight House_relation Sex Age Race Marriage \\\n",
"0 Arizona 3399818 18 3 2 14 8 5 \n",
"\n",
" Education Ancestry Language Employment_status Traveltime Industry \\\n",
"0 4 210 0 0 0 0 \n",
"\n",
" Occupation Hours Weeks Salary Income Earnings \n",
"0 0 0 0 0 0 0 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Genders = ['Male','Female']\n",
"Education = ['Not in universe (Under 3 years)','No schooling completed','Nursery school to 4th grade',\\\n",
" '5th grade or 6th grade','7th,8th grade','9th grade','10th grade','11th grade','12th grade,no diploma',\\\n",
"'High school graduate','college,less than 1 year','college 1+ years, no degree','Associate degree','Bachelor,s degree',\\\n",
"'Master.s degree','Professional degree','Doctorate degree']\n",
"MarriageState=['married','Widowed','Divorced','Separated','Never married']"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"table = pd.pivot_table(data,values='Earnings',index=['Sex', 'Education'],aggfunc=np.mean)\n",
"##OR###\n",
"temp1 = data.groupby(['Sex', 'Education']).Earnings.mean()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Sex Education\n",
"1 0 0.000000\n",
" 1 3504.784689\n",
" 2 690.859232\n",
" 3 5083.671988\n",
" 4 4073.961606\n",
" 5 9596.498516\n",
" 6 11185.848485\n",
" 7 11167.638889\n",
" 8 19404.356436\n",
" 9 24012.275826\n",
" 10 28201.210614\n",
" 11 28488.347335\n",
" 12 35081.001821\n",
" 13 53294.264282\n",
" 14 70755.173611\n",
" 15 94245.204918\n",
" 16 61467.676768\n",
"2 0 0.000000\n",
" 1 947.790323\n",
" 2 387.342995\n",
" 3 2771.055195\n",
" 4 2256.137405\n",
" 5 3326.657609\n",
" 6 4281.338798\n",
" 7 5003.164557\n",
" 8 9200.885781\n",
" 9 11515.832571\n",
" 10 16305.045514\n",
" 11 16949.317489\n",
" 12 21991.480263\n",
" 13 33193.713496\n",
" 14 44412.231834\n",
" 15 46821.961290\n",
" 16 41476.065574\n",
"Name: Earnings, dtype: float64"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp1"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"temp1 = temp1.values\n",
"test=temp1.reshape(2,17)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2, 17)"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.shape"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5c973e6b90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.pcolor(test,cmap=plt.cm.Reds,vmin=np.min(test), vmax=np.max(test))\n",
"ax.set_yticks([1,2])\n",
"ax.set_yticklabels(Genders)\n",
"ax.set_xticks(range(1,18))\n",
"ax.set_xticklabels(Education)\n",
"for tick in ax.get_xticklabels():\n",
" tick.set_rotation(90)\n",
"plt.gcf().subplots_adjust(bottom=0.20)\n",
"ax.set_title('Heat map of average Earnings Gender Vs Education')\n",
"plt.show()\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Q4"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"table = pd.pivot_table(data,values='Earnings',index=['Sex', 'Marriage'],aggfunc=np.mean)\n",
"table = table.values\n",
"test=table.reshape(2,5)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5c9657fc50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.pcolor(test,cmap=plt.cm.Reds,vmin=np.min(test), vmax=np.max(test))\n",
"ax.set_yticks([1,2])\n",
"ax.set_yticklabels(Genders)\n",
"ax.set_xticks(range(6))\n",
"ax.set_xticklabels(MarriageState)\n",
"for tick in ax.get_xticklabels():\n",
" tick.set_rotation(45)\n",
"plt.gcf().subplots_adjust(bottom=0.20)\n",
"ax.set_title('Heat map of average Earnings Gender Vs Mariage')\n",
"plt.show()\n",
"plt.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Q5"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"table = pd.pivot_table(data,values='Earnings',index=['Sex', 'Marriage','Hours'],aggfunc=np.mean)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.series.Series"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(table)"
]
}
],
"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 |
irzaip/chippy | speech/.ipynb_checkpoints/Cipi_recog-checkpoint.ipynb | 1 | 1928 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import snowboydecoder\n",
"import sys\n",
"import signal\n",
"\n",
"# Demo code for listening two hotwords at the same time\n",
"\n",
"interrupted = False\n",
"\n",
"\n",
"def signal_handler(signal, frame):\n",
" global interrupted\n",
" interrupted = True\n",
"\n",
"def interrupt_callback():\n",
" global interrupted\n",
" return interrupted\n",
"\n",
"'''if len(sys.argv) != 4:\n",
" print(\"Error: need to specify 2 model names\")\n",
" print(\"Usage: python demo.py 1st.model 2nd.model\")\n",
" sys.exit(-1)'''\n",
"\n",
"models = ['cipi.pdml','ya.pdml','enggak.pdml']\n",
"\n",
"# capture SIGINT signal, e.g., Ctrl+C\n",
"signal.signal(signal.SIGINT, signal_handler)\n",
"\n",
"sensitivity = [0.5]*len(models)\n",
"detector = snowboydecoder.HotwordDetector(models, sensitivity=sensitivity)\n",
"callbacks = [lambda: snowboydecoder.play_audio_file(snowboydecoder.DETECT_DING),\n",
" lambda: snowboydecoder.play_audio_file(snowboydecoder.DETECT_DONG),\n",
" lambda: snowboydecoder.play_audio_file(snowboydecoder.DETECT_DING)]\n",
"print('Listening... Press Ctrl+C to exit')\n",
"\n",
"# main loop\n",
"# make sure you have the same numbers of callbacks and models\n",
"detector.start(detected_callback=callbacks,\n",
" interrupt_check=interrupt_callback,\n",
" sleep_time=0.03)\n",
"\n",
"detector.terminate()"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | lgpl-3.0 |
peterfig/keras-deep-learning-course | .ipynb_checkpoints/TextModels-checkpoint.ipynb | 1 | 318618 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Text Models with Keras\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dense vector word embeddings\n",
"\n",
"A _dense vector_ word embedding means we represent words with number-full numerical vectors-- most components are nonzero. This is in contrast to _sparse vector_, or bag-of-word embeddings, which have very high-dimensional vectors (the size of the vocabulary) yet with most components zero.\n",
"\n",
"Dense vector models also capture word meaning, such that similar words (car and automobile) have similar numerical vectors. In a sparse vector representation, similar words probably have completely different numerical vectors. **Dense vectors are formed as a by-product of some prediction task.** The quality of the embedding depends on both the prediction task and the data set upon which the prediction task was trained. \n",
"\n",
"When we use word embeddings in our deep learning models, we refer to their birthplace as the _embedding layer_. Sometimes, we don't actually care about the trained predictor (skip-gram and cbow models); we're just interested in the embeddings by-product for use elsewhere. Other times, we need an embedding layer to represent words in a larger model such as a sentiment classifier; there, we may opt for pre-trained dense vectors. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we don't care about the trained model and just want to create meaningful, dense word vectors, there are two popular prediction models: **skip-gram** and **CBOW** (continuous bag of words). Word embeddings constructed in this manner are termed **word2vec** or **w2v**. We will also look at another more recent method, **fastText**. In any case, we've first got to construct training data from our corpus. The exact procedure depends on the model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Keras Models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's have a look at the Keras models we'll use in this section. (I'm keeping the code as markup since we haven't defined any of the parameters yet. We'll run this code after we develop input data and parameters.) "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from IPython.display import Image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### skip-gram"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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Hqk6dLun3YX9YaXtIYsn9LUl7S3pKUl9JXcKxIkqxrCImaT+S1Cccb5wmY8M6nNkQ2liB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\nxP5Dhw4dcvnll3/RulLYPK2qrc8555z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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
" Image('diagrams/skip-gram.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"word1 = Input(shape=(1,), dtype='int64', name='word1')\n",
"word2 = Input(shape=(1,), dtype='int64', name='word2')\n",
"\n",
"shared_embedding = Embedding(\n",
" input_dim=VOCAB_SIZE+1, \n",
" output_dim=DENSEVEC_DIM, \n",
" input_length=1, \n",
" embeddings_constraint = unit_norm(),\n",
" name='shared_embedding')\n",
"\n",
"embedded_w1 = shared_embedding(word1)\n",
"embedded_w2 = shared_embedding(word2)\n",
"\n",
"w1 = Flatten()(embedded_w1)\n",
"w2 = Flatten()(embedded_w2)\n",
"\n",
"dotted = Dot(axes=1, name='dot_product')([w1, w2])\n",
"\n",
"prediction = Dense(1, activation='sigmoid', name='output_layer')(dotted)\n",
"\n",
"sg_model = Model(inputs=[word1, word2], outputs=prediction)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### fastText"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"ft_model = Sequential()\n",
"\n",
"ft_model.add(Embedding(\n",
" input_dim = MAX_FEATURES,\n",
" output_dim = EMBEDDING_DIMS,\n",
" input_length= MAXLEN))\n",
"\n",
"ft_model.add(GlobalAveragePooling1D())\n",
"\n",
"ft_model.add(Dense(1, activation='sigmoid'))\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Models and Training data construction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first step for **CBOW** and **skip-gram**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our training corpus is a collection of sentences, Tweets, emails, comments, or even longer documents. It is something composed of words. Each word takes is turn being the \"target\" word, and we collect the _n_ words behind it and _n_ words which follow it. This _n_ is referred to as **window size**. If our example document is the sentence \"I love deep learning\" and the window size is 1, we'd get:\n",
" * **I**, love\n",
" * I, **love**, deep\n",
" * love, **deep**, learning\n",
" * deep, **learning**\n",
" \n",
"The target word is bold."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Skip-gram model training data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Skip-gram means form word pairs with a target word and all words in the window. These become the \"positive\" (1) samples for the skip-gram algorithm. In our \"I love deep learning\" example we'd get (eliminating repeated pairs):\n",
"\n",
" * (I, love) = 1\n",
" * (love, deep) = 1\n",
" * (deep, learning) = 1\n",
" \n",
"To create negative samples (0), we pair random vocabulary words with the target word. Yes, it's possible to unluckily pick a negative sample that usually appears around the target word.\n",
"\n",
"For our prediction task, we'll take the dot product of the words in each pair (a small step away from the cosine similarity). The training will keep tweaking the word vectors to make this product as close to unity as possible for our positive samples, and zero for our negative samples."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Happily, Keras include a function for creating skipgrams from text. It even does the negative sampling for us."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from keras.preprocessing.sequence import skipgrams\n",
"from keras.preprocessing.text import Tokenizer, text_to_word_sequence"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"text1 = \"I love deep learning.\"\n",
"text2 = \"Read Douglas Adams as much as possible.\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tokenizer = Tokenizer()\n",
"tokenizer.fit_on_texts([text1, text2])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('love', 2),\n",
" ('adams', 3),\n",
" ('i', 4),\n",
" ('possible', 5),\n",
" ('deep', 6),\n",
" ('read', 7),\n",
" ('as', 1),\n",
" ('much', 8),\n",
" ('douglas', 9),\n",
" ('learning', 10)]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"word2id = tokenizer.word_index\n",
"word2id.items()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note word id's are numbered from 1, not zero"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{1: 'as',\n",
" 2: 'love',\n",
" 3: 'adams',\n",
" 4: 'i',\n",
" 5: 'possible',\n",
" 6: 'deep',\n",
" 7: 'read',\n",
" 8: 'much',\n",
" 9: 'douglas',\n",
" 10: 'learning'}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"id2word = { wordid: word for word, wordid in word2id.items()}\n",
"id2word"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[4, 2, 6, 10]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"encoded_text = [word2id[word] for word in text_to_word_sequence(text1)]\n",
"encoded_text"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[7, 9, 3, 1, 8, 1, 5]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[word2id[word] for word in text_to_word_sequence(text2)]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"([[2, 6],\n",
" [2, 6],\n",
" [2, 9],\n",
" [6, 2],\n",
" [6, 3],\n",
" [6, 10],\n",
" [6, 1],\n",
" [2, 4],\n",
" [4, 2],\n",
" [10, 6],\n",
" [10, 8],\n",
" [4, 2]],\n",
" [1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sg = skipgrams(encoded_text, vocabulary_size=len(word2id.keys()), window_size=1)\n",
"sg"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(love,deep)=1\n",
"(love,deep)=0\n",
"(love,douglas)=0\n",
"(deep,love)=1\n",
"(deep,adams)=0\n",
"(deep,learning)=1\n",
"(deep,as)=0\n",
"(love,i)=1\n",
"(i,love)=1\n",
"(learning,deep)=1\n",
"(learning,much)=0\n",
"(i,love)=0\n"
]
}
],
"source": [
"for i in range(len(sg[0])):\n",
" print \"({0},{1})={2}\".format(id2word[sg[0][i][0]], id2word[sg[0][i][1]], sg[1][i])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Model parameters"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"VOCAB_SIZE = len(word2id.keys())\n",
"VOCAB_SIZE"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"DENSEVEC_DIM = 50"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Model build"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import keras"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from keras.layers.embeddings import Embedding\n",
"from keras.constraints import unit_norm\n",
"from keras.layers.merge import Dot\n",
"from keras.layers.core import Activation\n",
"from keras.layers.core import Flatten\n",
"\n",
"from keras.layers import Input, Dense\n",
"from keras.models import Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a dense vector for each word in the pair. The output of `Embedding` has shape `(batch_size, sequence_length, output_dim)` which in our case is `(batch_size, 1, DENSEVEC_DIM)`. We'll use `Flatten` to get rid of that pesky middle dimension (1), so going into the dot product we'll have shape `(batch_size, DENSEVEC_DIM)`."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"word1 = Input(shape=(1,), dtype='int64', name='word1')\n",
"word2 = Input(shape=(1,), dtype='int64', name='word2')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"shared_embedding = Embedding(\n",
" input_dim=VOCAB_SIZE+1, \n",
" output_dim=DENSEVEC_DIM, \n",
" input_length=1, \n",
" embeddings_constraint = unit_norm(),\n",
" name='shared_embedding')\n",
"\n",
"embedded_w1 = shared_embedding(word1)\n",
"embedded_w2 = shared_embedding(word2)\n",
"\n",
"w1 = Flatten()(embedded_w1)\n",
"w2 = Flatten()(embedded_w2)\n",
"\n",
"dotted = Dot(axes=1, name='dot_product')([w1, w2])\n",
"\n",
"prediction = Dense(1, activation='sigmoid', name='output_layer')(dotted)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sg_model = Model(inputs=[word1, word2], outputs=prediction)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sg_model.compile(optimizer='adam', loss='mean_squared_error')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At this point you can check out how the data flows through your compiled model."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<keras.engine.topology.InputLayer at 0x116536310>,\n",
" <keras.engine.topology.InputLayer at 0x11650ebd0>,\n",
" <keras.layers.embeddings.Embedding at 0x1165d7e50>,\n",
" <keras.layers.core.Flatten at 0x1165a7310>,\n",
" <keras.layers.core.Flatten at 0x1165a73d0>,\n",
" <keras.layers.merge.Dot at 0x1165d7f90>,\n",
" <keras.layers.core.Dense at 0x1115a1810>]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sg_model.layers"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def print_layer(model, num):\n",
" print model.layers[num]\n",
" print model.layers[num].input_shape\n",
" print model.layers[num].output_shape"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<keras.layers.core.Flatten object at 0x1165a7310>\n",
"(None, 1, 50)\n",
"(None, 50)\n"
]
}
],
"source": [
"print_layer(sg_model,3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's try training it with our toy data set!"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pairs = np.array(sg[0])\n",
"targets = np.array(sg[1])"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0])"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"targets"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 2, 6],\n",
" [ 2, 6],\n",
" [ 2, 9],\n",
" [ 6, 2],\n",
" [ 6, 3],\n",
" [ 6, 10],\n",
" [ 6, 1],\n",
" [ 2, 4],\n",
" [ 4, 2],\n",
" [10, 6],\n",
" [10, 8],\n",
" [ 4, 2]])"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 2],\n",
" [ 2],\n",
" [ 2],\n",
" [ 6],\n",
" [ 6],\n",
" [ 6],\n",
" [ 6],\n",
" [ 2],\n",
" [ 4],\n",
" [10],\n",
" [10],\n",
" [ 4]])"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w1_list = np.reshape(pairs[:, 0], (len(pairs), 1))\n",
"w1_list"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 6],\n",
" [ 6],\n",
" [ 9],\n",
" [ 2],\n",
" [ 3],\n",
" [10],\n",
" [ 1],\n",
" [ 4],\n",
" [ 2],\n",
" [ 6],\n",
" [ 8],\n",
" [ 2]])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w2_list = np.reshape(pairs[:, 1], (len(pairs), 1))\n",
"w2_list"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(12, 1)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w2_list.shape"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('int64')"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w2_list.dtype"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"12/12 [==============================] - 0s - loss: 0.1396\n",
"Epoch 2/10\n",
"12/12 [==============================] - 0s - loss: 0.1390\n",
"Epoch 3/10\n",
"12/12 [==============================] - 0s - loss: 0.1383\n",
"Epoch 4/10\n",
"12/12 [==============================] - 0s - loss: 0.1377\n",
"Epoch 5/10\n",
"12/12 [==============================] - 0s - loss: 0.1371\n",
"Epoch 6/10\n",
"12/12 [==============================] - 0s - loss: 0.1365\n",
"Epoch 7/10\n",
"12/12 [==============================] - 0s - loss: 0.1360\n",
"Epoch 8/10\n",
"12/12 [==============================] - 0s - loss: 0.1354\n",
"Epoch 9/10\n",
"12/12 [==============================] - 0s - loss: 0.1349\n",
"Epoch 10/10\n",
"12/12 [==============================] - 0s - loss: 0.1344\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x116b8f850>"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sg_model.fit(x=[w1_list, w2_list], y=targets, epochs=10)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<tf.Variable 'shared_embedding_2/embeddings:0' shape=(11, 50) dtype=float32_ref>]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sg_model.layers[2].weights"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Continuous Bag of Words (CBOW) model "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"CBOW means we take _all_ the words in the window and use them to predict the target word. Note we are trying to predict an actual word (or a probability distribution over words) with CBOW, whereas in skip-gram we are trying to predict a similarity score. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### FastText Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"FastText is creating dense document vectors using the words in the document enhanced with n-grams. These are embedded, averaged, and fed through a hidden dense layer, with a sigmoid activation. The prediction task is some binary classification of the documents. As usual, after training we can extract the dense vectors from the model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### FastText Model Data Prep"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"MAX_FEATURES = 20000 # number of unique words in the dataset\n",
"MAXLEN = 400 # max word (feature) length of a review \n",
"EMBEDDING_DIMS = 50\n",
"NGRAM_RANGE = 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some data prep functions lifted from the example"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_ngram_set(input_list, ngram_value=2):\n",
" \"\"\"\n",
" Extract a set of n-grams from a list of integers.\n",
" \"\"\"\n",
" return set(zip(*[input_list[i:] for i in range(ngram_value)]))"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{(1, 2), (2, 3), (3, 4), (4, 5)}"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"create_ngram_set([1, 2, 3, 4, 5], ngram_value=2)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{(1, 2, 3), (2, 3, 4), (3, 4, 5)}"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"create_ngram_set([1, 2, 3, 4, 5], ngram_value=3)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def add_ngram(sequences, token_indice, ngram_range=2):\n",
" \"\"\"\n",
" Augment the input list of list (sequences) by appending n-grams values.\n",
" \"\"\"\n",
" new_sequences = []\n",
" for input_list in sequences:\n",
" new_list = input_list[:]\n",
" for i in range(len(new_list) - ngram_range + 1):\n",
" for ngram_value in range(2, ngram_range + 1):\n",
" ngram = tuple(new_list[i:i + ngram_value])\n",
" if ngram in token_indice:\n",
" new_list.append(token_indice[ngram])\n",
" new_sequences.append(new_list)\n",
"\n",
" return new_sequences"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sequences = [[1,2,3,4,5, 6], [6,7,8]]\n",
"token_indice = {(1,2): 20000, (4,5): 20001, (6,7,8): 20002}"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[1, 2, 3, 4, 5, 6, 20000, 20001], [6, 7, 8]]"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"add_ngram(sequences, token_indice, ngram_range=2)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[1, 2, 3, 4, 5, 6, 20000, 20001], [6, 7, 8, 20002]]"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"add_ngram(sequences, token_indice, ngram_range=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"load canned training data"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from keras.datasets import imdb"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=MAX_FEATURES)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ list([1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 19193, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 10311, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 12118, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]),\n",
" list([1, 194, 1153, 194, 8255, 78, 228, 5, 6, 1463, 4369, 5012, 134, 26, 4, 715, 8, 118, 1634, 14, 394, 20, 13, 119, 954, 189, 102, 5, 207, 110, 3103, 21, 14, 69, 188, 8, 30, 23, 7, 4, 249, 126, 93, 4, 114, 9, 2300, 1523, 5, 647, 4, 116, 9, 35, 8163, 4, 229, 9, 340, 1322, 4, 118, 9, 4, 130, 4901, 19, 4, 1002, 5, 89, 29, 952, 46, 37, 4, 455, 9, 45, 43, 38, 1543, 1905, 398, 4, 1649, 26, 6853, 5, 163, 11, 3215, 10156, 4, 1153, 9, 194, 775, 7, 8255, 11596, 349, 2637, 148, 605, 15358, 8003, 15, 123, 125, 68, 2, 6853, 15, 349, 165, 4362, 98, 5, 4, 228, 9, 43, 2, 1157, 15, 299, 120, 5, 120, 174, 11, 220, 175, 136, 50, 9, 4373, 228, 8255, 5, 2, 656, 245, 2350, 5, 4, 9837, 131, 152, 491, 18, 2, 32, 7464, 1212, 14, 9, 6, 371, 78, 22, 625, 64, 1382, 9, 8, 168, 145, 23, 4, 1690, 15, 16, 4, 1355, 5, 28, 6, 52, 154, 462, 33, 89, 78, 285, 16, 145, 95])], dtype=object)"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train[0:2]"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0])"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train[0:2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Add n-gram features"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ngram_set = set()\n",
"for input_list in x_train:\n",
" for i in range(2, NGRAM_RANGE + 1):\n",
" set_of_ngram = create_ngram_set(input_list, ngram_value=i)\n",
" ngram_set.update(set_of_ngram)"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1185229"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(ngram_set)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assign id's to the new features"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2561, 3221)"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ngram_set.pop()"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"start_index = MAX_FEATURES + 1\n",
"token_indice = {v: k + start_index for k, v in enumerate(ngram_set)}\n",
"indice_token = {token_indice[k]: k for k in token_indice}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Update MAX_FEATURES"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1205229"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"MAX_FEATURES = np.max(list(indice_token.keys())) + 1\n",
"MAX_FEATURES"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Add n-grams to the input data"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_train = add_ngram(x_train, token_indice, NGRAM_RANGE)\n",
"x_test = add_ngram(x_test, token_indice, NGRAM_RANGE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make all input sequences the same length by padding with zeros"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from keras.preprocessing import sequence"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0, 0, 0, 0, 0, 1, 2, 3, 4, 5],\n",
" [0, 0, 0, 0, 0, 0, 0, 6, 7, 8]], dtype=int32)"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sequence.pad_sequences([[1,2,3,4,5], [6,7,8]], maxlen=10)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_train = sequence.pad_sequences(x_train, maxlen=MAXLEN)\n",
"x_test = sequence.pad_sequences(x_test, maxlen=MAXLEN)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(25000, 400)"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(25000, 400)"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_test.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### FastText Model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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I71Ys/nqfom4Ak4OKWhe/03r+9y6q2HLLTAG1nkPzqJZL96/z83obFC059X9XQZbZcQsz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\nyJlwmpBNyJlTqK7sJ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5ZMIuITUak4nkCtvX2dzl6sss6ccFZZFwM4jI3UhBFA5szrO7sJ\nuRwyzioh448HSaxvcJ1h6AgQf3jZQK6DVhajQUG27ZGfUFWT/jVo4VXkMyFnomhCNiFnTqG6spuQ\n7480V2IgYqQp1JZIRE5GYGQImJAzoTchm5Azp1Bd2U3I90UatTSOHyBi7pw6GYGRI2BCzhwCE7IJ\nOXMK1ZXdhHwP0lhOIxUvCWT8p7oGwPUYgW4ImJC7IdTlexOyCTlzCtWV3YR8j6UuXpqQjvHq5GQE\nGoWACTlzOEzIJuTMKVRX9mkmZNwkcpUJaRgV9dV1gT5F9QwaUQnXlVcFT1tTBFfJukn0i6qvp0xb\neSOy/p26cRsRzpOySFQ+X8ZgPLCqRT3NvVWkYu6xOg0HgUEjKuFvGpeZvxpOs8arVEvImeNlCdkS\ncuYUqiv7tBEyDiUgY+4TQ8acGTsND4FBIiq9NxjU4aryFZKItMSRwjXh/vAakj4qCb/UBJrYN3z3\nEUkfClGb8LR1UejWmuEe8yvD3WKIfrGkskhS7XOCsrAxwBMVbeAq1AGSLu4jIhTSPrGxcf3JJuMt\nwcFIT8ibkHuCqfwhE7IJOXMK1ZV9WgiZkHwQMY4fUE9/ry6Ap7weCBnnG/1EVMLt5deCVzQcf+Ci\nFKI9MwR0wEnLEyStGpx8UAeetgjJuLukrSRBzo8P94oXSjo0uNOEjFcPMZAh+6JIUjgESRPuOedK\nujlsFIj0tJYkXKn2EhEKaZ8+EOqRO+2QMpsI2gfBd00m5K4QdX7AhGxCzpxCdWWfBkLGqxJkfGyQ\niu+sC1zX04o53G9EJe6BHx3O9wkg8cHgT5p4xFF6hRAhNLx2QbLYATxK0pXhWcgb8sPj1v4h3CLj\njtcvbAUeK4nITUWRpCBqgk20J9TvSMUQPiSLAWAvEaE4Dyd0JO1l0wC/nirpG2HT0XWamJC7QmRC\nHgQie+oaBLWh5plkQkYSgohZSJGKfzxUJF14EQLtRl04W+kWUYnxwnf0wUG9+/SgckaqRO0MCV4e\nojn9UtIf2lxgxnbg9hICRwpuT5SJlP3wxC1njCSFsV87ISMh48MaSRt1NQkVdC8RofDHjb1CSvSQ\nOeW1S+OFs8iEnPlyWUK2hJw5herKPqmE/PZAxiyaqC+dRoMAhNxvRKWfBW1GJOSHhNjHD5b0tqDm\njZsrpGniGGMTgC/rfwcyfYqkyyS9UNI7wnkxZ84Y9D01RHKCgHshZPKdEaRvJPK7ApRIuOcFdXyn\niFCcVUPkMRwk/PrNICV/qZdhMSH3glKHZ0zIJuTMKVRX9kkjZNSCSMVE5kEq5ozPaXQIxDPkfiIq\nIQUfE875OWYgHRkMuh4XrkLhW/qccBaMP2vOiTEG43lU0b8OampU1pA158C/l/SGIHkTSnGbHggZ\nLkT1HSM//V3SCkFKR/LtJSIUpP8PSRgUnhTsGCB4zsL/1svQmJB7QcmE3DdKVln3DdmwM0wSIX8g\nkPHeYREdNnYuvzsCkZDjk71EVMIC+v3BgIrwh0RaIsoTxLi+pFsl/Sic434yFPzsYDQV64F4MQwj\nIVVD6KS0/k6RpKLKGukbtTiGXGmiXZxv9xoRikhRkHFMbAYwROspmZB7gqn8IUvIxdiYkDMnVvXZ\nJ4GQNwxETKQerjJxNufULAQGiaiEdNsefalTr5BcZwW1NaSdJqRovifkYtVGfb1GhJqZtO8//QyP\nCbkftAqeNSGbkDOnUF3Zx52Q54UrMagOP1sXaK7HCNSJgAk5E20Tsgk5cwrVlX1cCXnTIBX/NUjF\nV9QFmOsxAnUjYELORNyEbELOnEJ1ZR9HQuYs8c3BoObLdQHleozAqBAwIWcib0I2IWdOobqyjxMh\nvzRIxRjZoKJeVBdIrscIjBIBE3Im+iZkE3LmFKor+zgQMsYwXGV6SSDi4+sCx/WMPQJbh+tSZd63\nxqKDJuTMYTIhm5Azp1Bd2ZtOyK8OZHxiION269m6cHI944kAhExwCHyYF7nDHItemZAzh8mEbELO\nnEJ1ZW8qIRM4AKkYr0pcZcIfstN0IYC/auJV4yiECEnvDneCifbEnXPuAX9FElGhIFz8TJNeG/xE\no0nByxebuCIJmbvAUdtCRCms9Lmexd3i04Kfa+4fUz/exkhlkaOGOjIm5Ex4Tcgm5MwpVFf2JhIy\nBluQMc4c9qoLCNfTKASWDQ5B8Mb1KUn4lybkIgSJ5ysiSBFBab4k3KTi/xpShoyJb00ACMgVIiVi\nFE5AUgkZr1lE/Npc0nWS0MAQ0AJ3nfjaniMJ7cy6gZgpn1QWOarqu833GQwTcubcNCGbkDOnUF3Z\nm0TIROAZb9mAAAAgAElEQVSBiFcLUjHh6pymEwF8Q3O1DReYhD3EHSakCTFClpAmbjQhUyTpn4bv\nCPOINiVa3+PeEk9dacCI6Eua8iBvUozIhJMZIkW9M7jmxI81JIw0jdexsshRBLsYWjIhZ0JrQjYh\nZ06hurI3hZCJmgMZIwWx6DkZgah6jkigmo6ETMxkjjOQeiFKNm8ElyDoBOE2LwiZiiI44Q4Tv9aQ\nbHwuLQPf188IPrOJTnVRiNaE/+uyyFFsHIaWTMiZ0JqQTciZU6iu7KMmZCLgQMSoKLnK9Lu6Ou56\nGo0AUu8lISgEAUIIFoJL1G6E/MMQEvHs0DtIl81eKiEjfRNRCmMvVOIkokYhWSMhQ+5FhEy4xrLI\nUbcPE00Tcia6JmQTcuYUqiv7KAmZ82ECxEPEhKdzMgIRAYgXIy2OMYiw9OFwVowUjF/qIgmZPJAv\naupnhU0eQSjw5tZu1IXLVa7RvVjSHeE8GsMx/o/UXETIhF0sixx1yzCHzoScia4J2YScOYXqyj4K\nQiZSDlIx53+Q8aV1ddb1jA0CBJb4giTiD5MOCQRKJKidgsFfvMoU1c38Jn1fEtGfSMQbRu0MSadG\nXVhfE5MYUiYRUYq/schuJ2TOq6MavCxy1FCBNSFnwmtCNiFnTqG6stdNyJwP7xyMtlhwnYxAJwSI\npIQ6mOhIRGviaKOXu+hcm7sp/HQqn+fgu3/2MQzDjBxV2AwTch+jU/SoCdmEnDmF6speFyFvEaTi\nPwSp+Jq6Ouh6jMC4I2BCzhxBE7IJOXMK1ZV92ISMVIN6GucKOPhATehkBIxAHwiYkPsAyxJy72Cx\nUVm6dGnvGbo8OcKNT2V9GHFBwyRkSBgyPj2Q8Y0j7qurNwJjiYAJOXPYRkgUlS+wVRNo1eWFM6DM\nEZva7JXPF0mzg8tDDGsw2jp5atF1x41ABQiYkDNBNCFbZZ05herKPgxCxkDmq0Eqrk4dUhcirscI\nNAwBE3LmgJiQTciZU6iu7JUQ8pVXXqk99thDxx57LO3GPzDuB52MgBGoAAETciaIJmQTcuYUqit7\nNiEffvjhLTLefffdtd9++PloXSNxMgJGoCIETMiZQJqQTciZU6iu7AMT8sUXX9wi4ptuukkHHXSQ\nNtpoI41w3teFl+sxArUj0CLk2mudvApHISlM47iNAudJma3DmC8ej0mZHe5HIxDwC9WIYXAjjIAR\nMAJGYNoRMCFP+wxw/42AETACRqARCJiQGzEMboQRMAJGwAhMOwIm5GmfAe6/ETACRsAINAIBE3Ij\nhsGNMAJGwAgYgWlHwIQ87TPA/TcCRsAIGIFGIGBCbsQwuBFGwAgYASMw7QiYkKd9Brj/RsAIGAEj\n0AgETMiNGAY3wggYASNgBKYdARPytM8A998IGAEjYAQagYAJuRHD4EYYASNgBIzAtCNgQp72GeD+\nGwEjYASMQCMQMCE3YhjcCCNgBIyAEZh2BEzI0z4D3H8jYASMgBFoBAIm5EYMgxthBIyAETAC046A\nCXnaZ4D7bwSMgBEwAo1AwITciGFwI4yAETACRmDaETAhT/sMcP+NgBEwAkagEQiYkBsxDG6EETAC\nRsAITDsCJuRpnwHuvxEwAkbACDQCARNyI4bBjTACRsAIGIFpR8CEPO0zwP03AkbACBiBRiBgQm7E\nMLgRRsAIGAEjMO0ImJCnfQa4/5OGwAqS7pZ056R1zP0xApOOgAl50kfY/ZsmBF4h6TuSdpd0SI8d\n307StpJ2kPQpSb+R9KUe81bx2NaS3iqJtrORGEZaQ9Lyki4fRuEu0whUhYAJuSokXY4RGD0C35D0\nB0kH9NGUV0l6j6TNJX1O0gWSPt1H/txHIeTdJG02REJ+o6TV+8Qlt1/ObwT6RsCE3DdkzjAhCEBA\nX5D0WEnfkvR+SXdI+qqknRNpCunt9vD/TYL0SJ73SfqMpAdIOlDS6eHzayQtkfQJSa+U9CtJ75b0\ny4Db/0g6WtKtkj4v6XGS5oXPReXfVYD3NpKOD/+nHZ8NhHqapBslvU7SKW35ivr7d0kQ8i6Snh/a\n89tQXsz+pND+d4V/fFzSuUESR/KE/N8haV1JxwU8j5K0p6TFodzHS1pV0tOCJPxqSWwe6MNDQt9T\nCXnuAOMATjMkfVDSfpKulfT6MKZnhLa/VtKxkorwQ8UPlldIepukUyX9UNJhkrYIf9PPv07I/Hc3\nGojAuBDyAyVdImkDSdcPgCP5z5e0fZAABiii0iwnh4XsZ5WW2rmwNwUpYf8a62xqVatJgowgrvPC\nQvzwsID/SNIxko6UFOcNEuR/JP0iLOYs2t+W9EVJ/xvm1hMDkaMyPknSOUEFDNF9VBLfPyzM47dL\nukjSTwNxrCNp7ZLy26Xdl0v6XiDg6ySdGAgeNTMkDMnz/VUJ+GX95X1AZd2JkJEsKesRgdyo85vh\nXaItbw4S7qWByMDv/0li47FeIGCIGvI9VNIjJX1d0laSlpP03bAhYiyiynpZST/ucxzA6f8kvSz8\nPCN8frqkHUP7F0hig1GEH21mYwMWjBebNDQFtJvnIesXhzWoaJPU1Lnudo0RAuNCyEghz5L08yCt\n9AsxCysLINLLr/vNPITnN5R0pSQWt7rSYyStKOniuipscD1IZRDGT8LZ4hvCoo0UifQ0Pyy8bAAh\nPCQ8zlf/GaRZuhYlTqQ+iBWp+uwgMW8a5tnNQSqkDEgX9SzPQ/AkyAEC47uPlZQPqSFNk3hfIUPK\ng9RiGd8PhA8ZI5kj5aapW387EXIkx30CISPp/yVIxAeFDQ3z6gXhHJp6qY8N9PPCc0isTw6GZkjG\nP5D05dBAzq6RSNm4pGfISO79jAPjSdsYP9TuYEW9X5G0kaQ5gXA74ffJ0DfIPW7GkJApAxJeM6wj\nDZ7abto4I1AnIa/VQY2XYshLzEuLiovFhZeKlwEVGC8MO18WMtRTqArZfSNxoCbkfzzHTpi8t0g6\nKywaKSH3ohpkgVk/LC604QOSfhfUnKjUXhIWYtpepAJ7qqT3StopLKhsKthxI3mxa4cMWLRmSvqI\npA8FtRjnabQ1TbGvqVr0jwX5UNOVqVz/JmmVIJEw7kgNSFXkeUuQrlgE6RuLEL9Z9HcNJA4eLGyo\nLJcmjSsb15XCOCBZUAfq2yKNQK/zosr3LFVtxnKRalGbIsUiPSMxgxH9QKWM9BWJNOahXxANhJxu\n9lCNMofT5yBdcGXcmQMkpGLUv3zHHCsqn++iVuhBkn6fkE57GUdIOjioydvnT1TltvcXg65OhMzz\nzAGst1Hp855xJst7hkSJZIt0mfYrkhlaGTaCsXzKaNdUpSrzlJCjVN/rOCBxM4bMJ9T2aYL0KQd8\nOuHHBiNV2TPneedIjDXrDJuhdP5XOS9d1pQjUBchs8tGui1S46UvD7tYpEZI+R/hvAfVERajUWXN\nThoShnSRDJAKOAuE7KLKCjUcLyiLHOd3qBYjIdOWItVju2qQMqgHi1VeYs7neCl5uXnpkRhQQyIp\nFanAMJDh+aeEHTuLL4sR0hbtpW1I60hIbByohzZDzjxzTTI34wIX1aInhAW/PR+LNyRapHJlgUPt\nyKKDNIf0saUk/k/7nxsWXRZZpDI0EmxmUElC3PSH9kYyoXmdxpVFGE3AHqGsuMlKtQK9zouqX1PG\nlo0Fcwn187PD5g7DIhZbpCjOfpGcIRw2P7Qf9TakyubqoYFssEpON3vMDebqc8I8YxN0YSBdFnTy\nxmMDxguMI1kXlZ9uYsjLZzZtvEskxgqJk7nBvCoi5E797YWQ2QDzDvCu8jcbl9gX6t87bJpjv9g4\nIEXzHtOuSMhYOiPFsvFAm0BiM8vndgk5agN6HQcsqNlIMZ95d8jP+T/vF+PLeB3eBT82/JGQwfrR\n4cwYMn9hmCPpBqnqeenyphyBugiZyV2mxkvPhNlN82K9KOy4IWheYhZx1FBIx7y4kBnqRCRndtgs\nnBAl51gshCyI8YXmxU8JGYm1SPWYqgaZFixiaT0syBAXizXthNQxZOElL1IhUh7niyxMkP07JSGZ\nY2hCGSycSBt/lvSooMIGpzMDWbDbjykSclSLQu5l+Xi2SNXHwsfCAuGyMUB655yOhAT44KBVAGtw\n53yRDQ4ETL0syEi51BtTp3EFO6RLFms2V0iSSOnpePc6L6p+TZkr9Jk20UbOXlnEwQjjnigZQcRR\n1cr4sbl4pqQbgrQF2ULYEG6UkFmwORbA8AuC+HAgL8pB48JmCoK4OhApiz55yF9UPpinEhntRjvD\neSYSK22HtJBYMVgqIuRO/aV8CJHNCJvbIitrCBYiZW7RVjQlbAzYlDK32cwxXzcOfUebhBEYz0Le\nsXwkYNpPe9jwsSGDMDGUKrr21M84sLFgc4OkztxjveFvCJo1g7WDjQyaqDL82NDE/tNn7AYoh80P\nGy00ISbkqt9Gl3cvAnURMhWWqfHSBZr2sJvGepWEgQwGFixwkZBfGogl3rOMKi8WAKwpIat/h/xI\ndRBJSsioIbupBsnOQsuiGnf9EDIEy+IXVY1IgBB+PLciX6qGZBGmDfxm4WEBY9GIC2ck5PYpyaaB\nxSEl5FQKi4RclI8FukjlGtV2EDKbCTY0aUITAQmjCeAogHGg7ZAyZIFxC4sRJJCmsnGdFdTjsR5U\n12xesFjuJf8gxnu9vtpYBzMOjC+JDQw/bFLQtHAGiqTEeLF5IqHm5jvGhoTGBw3DTW1qWJ5j7Nh4\nkZinEAAWx5AexwNRy0AZSG5oUSCrovLJlybaRpsoM7aDv5lL6RxN83TqL5sl2socZnyY3+nci+Uw\nD0hgxIaN+ciZOH3gvWXjheqahGYIMuUKFhvbWD59pP1ottiUkNC+QPTMz/Z7yP2MAzhxxst7Fsc1\nWlWzYWCjixYK7MvwY76n/UfCTrFgo4EtgVXWbZPSH6tBoC5C7qTGSxdezlM5u0R64DoI6lJ20iw4\nqAbZ5ZYRMi8fkjHS678CPBBfu4TMLrmbapDskcAi8afSRyRdJB4IsEyFCL4s7JA6u3bUd6j9Yln8\nDTFA2GwiWJBYnC9rM/hqN0qLqs6ifItKVK7t52gskkjKtBGsWWTY+HDux+LLQo0BEloByBXL8H3b\npl2ncSUPUidtZ9xYhDG8YcGLqdd5Uc1sv28pWPhChuB+WzB4Y7PRzcMVeUhxjpW1jefYfCBlcXaK\nNIiFMaTBuSR4s6HjChT2BpGMei0fGwvGDm1PL2nQ/vZSdnwGjQHvMG3qhiPtZzPDzyCpDCf6uXIo\nl3GNqd2DWa/4oUGhTxjWoRlxMgJDQ6AuQu6kxkvPSiE41KgsVJAyVxhQiaEmhNg6ETJqbgxkuDtI\nHj4jYbefIaNu60U12Ash0y+k7TIVGAOH5AWRRfUe/4uEjIp9YZA6+B8EyDkt6m7UpTG1EzILX6d8\nRaq+2B+kfKQIDNT2CosX0gNny0grcayQ1pCGMAbCAhi1HyrANJWNK5sKiAYShtxZ0NgEYU2bEnKv\n82JoL0DNBXPEwbhi/8DYY72LJiZK4TU3x9UZASPQJATqIuRuarwoHXCmiNFMVAuCFYYhLOZRZc3Z\nGcZJUXJNVWL8H4kVNSmqan5zVxKJJFp3QmS9qAZZKNN6UnVgPEOGUDjHLlOB0X6kH6R7NhvRejot\nC9UdG4mYOA//WtskKbpH3Slfkaov7Q/napAyEjsJPFDHIdVQFyptJHrOO5FqOR9kk4D0naZO4wqR\npwZJqMSxzo1XeCin13nRpHcmty1oV3A0wf1e5mqdd9Fz2+78RsAIDBGBugg5dqFIjZcu0OlzEANq\nwaLviyCBzJFUIV7OUOkbpIMRRyqVpXXwdzfVY6/w96oCKyoPdRoqXtSnvfaXcgbNF9tAm1Hr5ari\nysYVwqVfEH37WWiKQ6/zotex8HNGwAgYgbFDoG5CHiZA0aoawxnU1BipIN2hBsbgxckIGAEjYASM\nQGMRmCRCBuToMQhVNdIm3oCixXVjB8ENmxgEOCPGpWa8kpfTsVxXp2kUp2FFUcrpn/MaASPQhsCk\nEbIH2AiMEgHO7jlrx+YhN+W6Ok2jONn3cu5oOL8RqAEBE3INIFdUBY4KBr0iUlETXEwXBLjvy51t\nPD5hjFjm4jUtpszFKHdnsaYn6hBGd9wPj1fncOZBUAZSWfQovL5FD1nYY3Rzz0pZRW5r8cfdyb0r\n9WOkiGMZLPjZSHDfF4cabFC4PUDCviN+LnPdCn4YF3KjAgt07jNzBBX9r6d33vE7wL1hNhu9uML1\n5DUCjUfAhNz4IWotYjEAAV6dhuUwA/Igqs+wym8+0vktRGWNxT3X9iDNMhevaU1Yoxe5GMVVI1b+\nkDGGijjjIGwhdUDMWPjP7hA9iqtvEDK/IfNu7lnL3NZyhbDMvStEj2c9bkXQRm4TQKK0DX8B0VUr\n/eXaXTfXrdx9xxMc1udcF8Q9J2VBytyW4Bojrmq5w81VPTYtGEH24go3f3RdghEYMgIm5CEDXEHx\nOM9goeOKUhpSr4Ki71MEjj9Y6Iss0quua1LL40oTZ8hcdcOosMjFa7uLSO5pF7kYjQ5woqtTXHfi\nwhFXslzdo3zqKYseFQmZfDzfzT1rmdtaSLfMTSubRdoJ2ZK4f44fgEjIuGqN1xNTT3FlrlvxosV3\nOKrBhWt0Vxld1HJ9MXrOwrENm0eIuhdXuJM659yvCUJgnAi5U/SgMpUV127wnYtbv+iOE0f87PhZ\n6AhKj7csnks/l5WHOg1SRP2G0Q2ONXCogdqMu73Ee+XsjkUJV5EshL1Ec2JKsXixOJMfUsTFIp7H\nkAqoj3u8uPLD8xOJiFFl0aRQ+RVFcyJfUTspH3VjdHnIAozzFNyWpgHvi6JOsSi3R+dCzTmNKSVk\n7n2nhFQW1QjPXaif212M8nz0PR7v4ENAqZMYPMSVRY9qJ+T28Wh3z1rmtpb5lvovj+WQPzqLKYpe\n1e5RL96D7+S6lXeVeR49l3VyURvbURaFyz6np/ENHPM+jxMhl6n2WNDKVFZ4mGJhw+KUxQPyjJ67\nYvSouMjFz50CxccA5iwu3N3FExXESHAB2oCDDyQCvHehcsS1JeTcTV0YnXGQnyhEtBnvXk8ITiT4\nG4ch1BENdHAxWhZNCnVpUTQnAgQUtRN8sEjnh0hRYE0b8JQGvvQT/HBigQQTo07hO5xNR3t0ruj/\ne8xfj76bPyghF7kYJeJXN0JmTMqiR0VCjj7eu7lnLXNbS2ARSL8oP99h1xCl4NSPOw58Ul/wbPCw\n9o4hEItct3JfPWoYeLbdRS0+BaK3OJyrcIeeNvTiCrfvwXQGI1A3AuNEyGWqvbLA7jFgOTv1PwVH\nIZzpQSIEm2iXOuLnToHikbJj9CbGCvUZJIcqkTOtGIsVYxwIMdbTTV3I+S2kxyIMeTMuGPMgeeKL\nGwkKdWDq1J5nkEzao0nhnhL3m0XRnHBnWdRONAj0hRjSSEOUyVlddN+JT3E2GEjtlB2jTpWpOa+s\neyI3pL5+CTmOc5GL0V4IGacrZdGjIGQ2hmzOGEfmQyf3rGVua9lsleXnTJj6ifLEnGE+4rI2qqxx\nEcp3bB4p4x3hmTLXrURTKiJkyuM8mflHeTiSwd87Z8uos3txhduQKeJmGIFyBMaJkMtUe4SbK4re\nFAOWl0V/KiPkToHicesZIz5FQkY9jOMRrGvbzwc7RWVKo8hwvtaeHwtSJG9U7OkilY4mC1J7NCkk\n4LJoTrjAxDCoKNRde8CLFDckaBZ0VNu4JI2hBsvUnGAyjQnM2IQxLmBU5uI1vRcMwRAxKaboYhQi\nJT9amfa5Gt3A8n82YEXRo9BuxChL+ILv5p61zG0tG9gyN62MP6RLiMmYolEXn9EOoUHi2IMzYd4d\n+lPmupV3POKXSsjgivQMNvSLhLaLjSTBJHpxhTuN89F9HjMExo2Qi1R7nOsWqayw/kQyTi2TkThZ\nmKLkSuxkFooYrKFboPj2eLOQKuRDPSw03EFl4eBclTNmpFsWpW7qQqQZzp+jQ4nodQwpuV1qSKdY\n9FmdRpNiIYM0i1SC+E8uaif9QHpmc8MZOf2JhjXUFyVkzrAJqRcJuUzNiXTtu6+9Lwa9uhhtLxGi\n6hY9Kubp1c0q0meR29pO+ZmHzDskYTYX8fw2Rl7COU9R9KdBXLfyrhKVK9pSxP71GiWr91Hxk0ag\nZgTGhZA7qfawMi1SWXEGCplhmckdzCglcIZMVCNIi93918POHZLpFiieKy1pAPgY0Byih9ix+OTK\nBsZiqNggfKTcbupCzgq58kF+pFgkLCQTNhMs1mUSMtOlPZpU9OFdFM0JS+qidrKR+GoItYhaPw30\njpSGmhpJDCzTuMxlas40nGDNU3qqqmta9Cjag3ZmmNfzpmqA3dnpQmBcCJlRKVPtca5aprJigeDM\nlJ14TNG6FMJGBUaCaJBiIWQshMvKaw8AnwY0x8iE+54k1HYYtSA99xLNiTwEu4eMY+JaCxbWqRq0\nyAViUTSpTtGcytrJJgKVIhsXVPCQcsSNjQs/XLlJ1aWd1JzT9SaNrrdNih6FpMx854wYKdbJCBiB\nPhAYJ0KmW51Ue90ClmNNjVoYa2oIl8RVKgLHF0U7GkQFhgoXdR91pQZYvaoLyR+jPrWr5PoY1nsf\nLVMJlrUTfGNQd9SNOItgYe0WEatMzTlIm53HCBgBIzCVCIwbIecOEipbJD8MQpyMQBkCnFPybpSd\nfRo5I2AEjEDlCEwbIaP2Rh2L0ZKTEWhHgGtc30ucdPA9qnyu3HFc0EsEpW5HDGWoUwce2bBviGn7\ncG93oxLnHE0ZQaz2OV5JEwaJHA9dPWAjwZE7x9x531YSBom599vRhuHs5sDEKIyrftigpAnjRcbB\nUbIGHDxnGwyBaSPkwVByrmlAgMAG3J9Ge0JwCBxeYFeApTyGgFin9xJBCTuEw4OzlPTYohuG0UAw\nHqdwPo/9A2ey3CSgTU1NbFQgS64aQmKEQd01BH3A8G+Q8+TUycjKocwYZGIQHPAFwMZhn8QKnHLA\nnbv/0bkJdifcC+czRN3PGA7SLucxAvciYEL2ZDAC9yCAtTpBHtrvaG8d/DXjDAVJLUZQwsCtyL1o\nJBIWeu7Jcu0Nf9JXBDU41uqfD6Bz5x1pjXP7eIUO4idFZzE8g4vWSGx4cqPMaEDIPV8kaSIsQToQ\nCXeYIXbyLg5uXKmfupFciaQEMeGRjTvC9I3bBiQMAmkTRoW4smSjQpu4RsgVq6Lyo1tQnIhEAkPb\nwE0GLK4xlCzCivqKXMZyMyElZK4D5kS+op54Nz/ek45BVNpx51mc8GCQiXMWbE6cjEAtCJiQa4HZ\nlTQcAVSZPw7+zo8PbUVCxbCN+7PxTnXqj5ooSEXuRZGy8XaGExl+kBRxcQmhcg0OIkXqxZAQN6VY\ntuOytJ0YIEPaAAHjBYsrcZAKJIolPISJNIqlP3VCmrgxxXCRa3KQMWpeSJL/sZFAXcvVPRzREMow\n+ptGEqd9eL+iDq65kYf201ZIn+/KymcTw9U+LPhJtJt+vzHczf9/HVyxRleuqctYiBCDS3wGpJGj\nBo18Fck3BqtI/VwXETKGmdwmwJeAg600/OWdpOaZkCdpNN2XQREg1jTSHCSH9yveC66gRa9QUaqK\n/qEhx0tL3IvidxwplEUfVS1lQ6iUtSi4feSsmPjBEAzkB5mmxBDz4K6U9vAdlu57hnJx14rjFiRr\nJDiCoSBFojaOzmm4P875Lf6kkYZTl6/YUnDvne8fFly/cg8fNT2Gj5Azaty07Ui8ZeXjC4D76+0J\nN5r4AihzxYokzr39Ipex+E1PCTn69YYo+418FQk5lbo7SciRkNlQMD5ORqAWBEzItcDsShqOQDyv\n5Zw2Ssh4nUJyhuC4fw7BtgdsKHIvikMYVNI4d0GCTRd3PM0hESNZxxTPp1NCJs4vUmua8KAG6SJZ\nc66NBE0sYDys0Uak1CJSRLqFwFDBRnU4hIx0DlnHxHMEM0lduKZth5DLyo9kjYe2uKZgoY46HNy4\n2VCEFS4/wSI9JkhdxhYR8iCRr/ol5LhBQ4tgCbnhL+8kNc+EPEmj6b7kIEBQESx6OX9N3X6i1oX0\nUkLGLSkGRkXuRTmLJuACZ8CRkKN3M6yOIX/OU5F48SCH1T+SciRk1MSolVF7c9aL2hwjKdTQRG7C\ntzmqVEgbCRwJFJU3amiCN7Bp4HybfDiNiW5lIyFHkmXzQT2c70LwhAvlHjqbiejCNRITJA1Zl5XP\nOXs8W2+3TOZMuswV61GSPtXBZWw/hNwp8lU3QkZtHv2BM4fAkDFEcmcT5WQEakHAhFwLzK5kDBCI\nbig5y+QqDOpgVMGon1OVNYFMOJdFguVaDme1qXtRzlkhUwzAuArEFZpolAXxoipGukUtjJqcmMZI\nzdHKGuL9eyDANA4x6l0kUYgXkkNiRgKNpAH5IolyTk25hOvEzSt5qBfJkt+RZCO5o6ZHK7BpUMNT\nN/+j33hnY/OA9Mwmpax8vMyVETJaBgi/CCukz04uY/sh5E6Rr7oRMmPNBoXNEhspPPih5sdOwMkI\n1IaACbk2qF3RGCAAAUOI0WUoRAyxcRUKFTRSWIygBBEWuRdFDZwSKaRJXs6No49yoKDsr4UrTYQK\nxQIagkJihuS5cpUGZIAUOd9FUucZpOLbg8/0KJWmkZ/SelMXr9TNNap4zYdNA+fFSIWUjZYgRoai\nzVieowngHLisfK497VgSRYz6otq63RUrJN/JZSztwBCLZ3IiX9E/EpsTpOHUqIv732m0OHBDWwC+\nDpAyBi/tJDXRhDxJo+m+VIEAqt7ZYTFGrdzpHmon96Ix0hGGUtEdKe3DbSkq6Fg2EivPVJWwTsZV\nK2fNRRGWYj1cI4LIo9tY2kE+Nh0YtNFm7v9iwZ0Gi+i1/Pb+dMKqCpex/US+qgprl2MEKkXAhFwp\nnC7MCIw1AtGq+pxg9IVmACcZSIxNlxabFvlqrCeCGz8aBEzIo8HdtRqBpiKAdTnXlTDiIoQnKu1x\ncQGaFP0AAADPSURBVCHZpMhXTR1ft6vBCJiQGzw4bpoRMAJGwAhMDwIm5OkZa/fUCBgBI2AEGoyA\nCbnBg+OmGQEjYASMwPQgYEKenrF2T42AETACRqDBCJiQGzw4bpoRMAJGwAhMDwIm5OkZa/fUCBgB\nI2AEGoyACbnBg+OmGQEjYASMwPQgYEKenrF2T42AETACRqDBCJiQGzw4bpoRMAJGwAhMDwIm5OkZ\na/fUCBgBI2AEGoyACbnBg+OmGQEjYASMwPQgYEKenrF2T42AETACRqDBCPx/85l3mGRqgDYAAAAA\nSUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
" Image('diagrams/fasttext.png')"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from keras.models import Sequential\n",
"from keras.layers.embeddings import Embedding\n",
"from keras.layers.pooling import GlobalAveragePooling1D\n",
"from keras.layers import Dense"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"ft_model = Sequential()\n",
"\n",
"ft_model.add(Embedding(\n",
" input_dim = MAX_FEATURES,\n",
" output_dim = EMBEDDING_DIMS,\n",
" input_length= MAXLEN))\n",
"\n",
"ft_model.add(GlobalAveragePooling1D())\n",
"\n",
"ft_model.add(Dense(1, activation='sigmoid'))"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ft_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 302,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<keras.layers.embeddings.Embedding at 0x117f12650>,\n",
" <keras.layers.pooling.GlobalAveragePooling1D at 0x117bc51d0>,\n",
" <keras.layers.core.Dense at 0x1190c3dd0>]"
]
},
"execution_count": 302,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ft_model.layers"
]
},
{
"cell_type": "code",
"execution_count": 306,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<keras.layers.embeddings.Embedding object at 0x117f12650>\n",
"(None, 400)\n",
"(None, 400, 50)\n"
]
}
],
"source": [
"print_layer(ft_model, 0)"
]
},
{
"cell_type": "code",
"execution_count": 307,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<keras.layers.pooling.GlobalAveragePooling1D object at 0x117bc51d0>\n",
"(None, 400, 50)\n",
"(None, 50)\n"
]
}
],
"source": [
"print_layer(ft_model, 1)"
]
},
{
"cell_type": "code",
"execution_count": 308,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<keras.layers.core.Dense object at 0x1190c3dd0>\n",
"(None, 50)\n",
"(None, 1)\n"
]
}
],
"source": [
"print_layer(ft_model, 2)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/3\n",
"25000/25000 [==============================] - 187s - loss: 0.6599 - acc: 0.7460 - val_loss: 0.6032 - val_acc: 0.8109\n",
"Epoch 2/3\n",
"25000/25000 [==============================] - 183s - loss: 0.4989 - acc: 0.8840 - val_loss: 0.4610 - val_acc: 0.8578\n",
"Epoch 3/3\n",
"25000/25000 [==============================] - 184s - loss: 0.3338 - acc: 0.9333 - val_loss: 0.3725 - val_acc: 0.8797\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1167c5b10>"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ft_model.fit(x_train, y_train, batch_size=100, epochs=3, validation_data=(x_test, y_test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### fastText classifier vs. convolutional neural network (CNN) vs. long short-term memory (LSTM) classifier: Fight!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A CNN takes the dot product of various \"filters\" (some new vector) with each word window down the sentence. For each convolutional layer in your model, you can choose the size of the filter (for example, 3 word vectors long) and the number of filters in the layer (for example, ten 3-word filters, or five 3-word filters). \n",
"\n",
"Add a bias to each dot product of the filter and word window, and run it through an activation function. This produces a number.\n",
"\n",
"Running a single filter down a sentence produces a series of numbers. Generally the maximum value is taken to represent the alignment of the sentence with that particular filter. All of this is just another way of extracting features from a sentence. In fastText, we extracted features in a human-readable way (n-grams) and tacked them onto the input data. With a CNN we take a different approach, letting the algorithm figure out what makes good features for the dataset."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"insert filter operating on sentence image here"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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1yAgYASNgBIyAETACRsAIGAFTVOwaMAJGwAgYASNgBIyAETACRiDoCJiiEnQfiXXICBgB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i8rqzZqPYpARUHgzz/FYc8c09sbbgAmTwauvx5gDCbSSy8B7qqe5qx+KgKKQBkhEFBG9ZSqmnVT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\nTuB/ATWyNM1mp+a+AAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image('diagrams/text-cnn-classifier.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Diagram from _Convolutional Neural Networks for Sentence Classification_, Kim Yoon (2014)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### A CNN sentence classifier"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"embedding_dim = 50 # we'll get a vector representation of words as a by-product\n",
"filter_sizes = (2, 3, 4) # we'll make one convolutional layer for each filter we specify here\n",
"num_filters = 10 # each layer will contain this many filters"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dropout_prob = (0.2, 0.2)\n",
"hidden_dims = 50\n",
"\n",
"# Prepossessing parameters\n",
"sequence_length = 400\n",
"max_words = 5000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Canned input data"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
"from keras.datasets import imdb"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
"(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_words) # limits vocab to num_words"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"?imdb.load_data"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from keras.preprocessing import sequence"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_train = sequence.pad_sequences(x_train, maxlen=sequence_length, padding=\"post\", truncating=\"post\")\n",
"x_test = sequence.pad_sequences(x_test, maxlen=sequence_length, padding=\"post\", truncating=\"post\")"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458,\n",
" 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43,\n",
" 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150,\n",
" 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536,\n",
" 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6,\n",
" 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22,\n",
" 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247,\n",
" 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5,\n",
" 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 2,\n",
" 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619,\n",
" 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6,\n",
" 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82,\n",
" 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7,\n",
" 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317,\n",
" 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15,\n",
" 297, 98, 32, 2071, 56, 26, 141, 6, 194, 2, 18,\n",
" 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30,\n",
" 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226,\n",
" 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472,\n",
" 113, 103, 32, 15, 16, 2, 19, 178, 32, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0], dtype=int32)"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train[0]"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"vocabulary = imdb.get_word_index() # word to integer map"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"49"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vocabulary['good']"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"88584"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(vocabulary)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Model build"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from keras.models import Model\n",
"from keras.layers import Input\n",
"from keras.layers import Embedding\n",
"from keras.layers import Dropout\n",
"from keras.layers import Conv1D\n",
"from keras.layers import MaxPooling1D\n",
"from keras.layers import Flatten\n",
"from keras.layers import Dense\n",
"from keras.layers.merge import Concatenate"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Input, embedding, and dropout layers\n",
"input_shape = (sequence_length,)\n",
"model_input = Input(shape=input_shape)\n",
"z = Embedding(\n",
" input_dim=len(vocabulary) + 1, \n",
" output_dim=embedding_dim, \n",
" input_length=sequence_length, \n",
" name=\"embedding\")(model_input)\n",
"z = Dropout(dropout_prob[0])(z)\n",
"\n",
"# Convolutional block\n",
"# parallel set of n convolutions; output of all n are\n",
"# concatenated into one vector\n",
"conv_blocks = []\n",
"for sz in filter_sizes:\n",
" conv = Conv1D(filters=num_filters, kernel_size=sz, activation=\"relu\" )(z)\n",
" conv = MaxPooling1D(pool_size=2)(conv)\n",
" conv = Flatten()(conv)\n",
" conv_blocks.append(conv)\n",
" \n",
"z = Concatenate()(conv_blocks) if len(conv_blocks) > 1 else conv_blocks[0]\n",
"z = Dropout(dropout_prob[1])(z)\n",
"\n",
"# Hidden dense layer and output layer\n",
"z = Dense(hidden_dims, activation=\"relu\")(z)\n",
"model_output = Dense(1, activation=\"sigmoid\")(z)\n",
"\n",
"cnn_model = Model(model_input, model_output)\n",
"cnn_model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<keras.engine.topology.InputLayer at 0x126639c10>,\n",
" <keras.layers.embeddings.Embedding at 0x126639d10>,\n",
" <keras.layers.core.Dropout at 0x11f1039d0>,\n",
" <keras.layers.convolutional.Conv1D at 0x10defda50>,\n",
" <keras.layers.convolutional.Conv1D at 0x126639c90>,\n",
" <keras.layers.convolutional.Conv1D at 0x1121d3d90>,\n",
" <keras.layers.pooling.MaxPooling1D at 0x126abe790>,\n",
" <keras.layers.pooling.MaxPooling1D at 0x112185dd0>,\n",
" <keras.layers.pooling.MaxPooling1D at 0x1121c6cd0>,\n",
" <keras.layers.core.Flatten at 0x126676850>,\n",
" <keras.layers.core.Flatten at 0x112197610>,\n",
" <keras.layers.core.Flatten at 0x11cc280d0>,\n",
" <keras.layers.merge.Concatenate at 0x10b926e90>,\n",
" <keras.layers.core.Dropout at 0x126aaf9d0>,\n",
" <keras.layers.core.Dense at 0x11cc51e50>,\n",
" <keras.layers.core.Dense at 0x11cd6bf50>]"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cnn_model.layers"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<keras.layers.merge.Concatenate object at 0x10b926e90>\n",
"[(None, 1990), (None, 1990), (None, 1980)]\n",
"(None, 5960)\n"
]
}
],
"source": [
"print_layer(cnn_model, 12)"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<keras.layers.core.Dense object at 0x112d2d990>\n",
"(None, 50)\n",
"(None, 1)\n"
]
}
],
"source": [
"print_layer(cnn_model, 12)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/3\n",
"25000/25000 [==============================] - 50s - loss: 0.4335 - acc: 0.7704 - val_loss: 0.2938 - val_acc: 0.8758\n",
"Epoch 2/3\n",
"25000/25000 [==============================] - 50s - loss: 0.2309 - acc: 0.9071 - val_loss: 0.2859 - val_acc: 0.8802\n",
"Epoch 3/3\n",
"25000/25000 [==============================] - 50s - loss: 0.1893 - acc: 0.9257 - val_loss: 0.2995 - val_acc: 0.8766\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x126751c50>"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cnn_model.fit(x_train, y_train, batch_size=64, epochs=3, validation_data=(x_test, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<tf.Variable 'embedding_5/embeddings:0' shape=(88585, 50) dtype=float32_ref>]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cnn_model.layers[1].weights"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[array([[-0.00537731, 0.01004505, -0.01243093, ..., -0.000989 ,\n",
" 0.00684546, -0.00744937],\n",
" [-0.05866501, -0.00139329, 0.01262602, ..., 0.01466062,\n",
" 0.01777977, -0.04964167],\n",
" [ 0.0226872 , -0.00739046, -0.01942088, ..., 0.00778489,\n",
" 0.02367541, -0.02095466],\n",
" ..., \n",
" [ 0.00730095, 0.03500965, -0.02484518, ..., -0.04528098,\n",
" 0.03952632, -0.00274396],\n",
" [ 0.00923758, -0.03889309, -0.00641484, ..., -0.00164293,\n",
" -0.02593929, 0.01862602],\n",
" [-0.04924661, 0.02040339, 0.00640279, ..., 0.01267619,\n",
" 0.04790827, 0.00526398]], dtype=float32)]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cnn_model.layers[1].get_weights()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<tf.Variable 'conv1d_11/kernel:0' shape=(3, 50, 100) dtype=float32_ref>,\n",
" <tf.Variable 'conv1d_11/bias:0' shape=(100,) dtype=float32_ref>]"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cnn_model.layers[3].weights"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### An LSTM sentence classifier"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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85zWrrrpq89KXvrSZM2dOs+CCCzaPe9zjmkc96lElH/jzLl95VegvDqg39a5egTolJ4Cs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\nxDlQ19gnzsNpxRAlTslTohR6W5EPRZw0Ae/t//G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05bh4YISOf9oD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1z03yT2sVDKsDYhEEqY1fA0apo4WYywH1hE7Ejc69HIPXjC62ycd9n1dRzhOmWQdfhTn/pU+dKX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\njUASe7cRzfwSgUQgEUgEEoEeIvD/CTcbAyhTbPwAAAAASUVORK5CYII=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image('diagrams/LSTM.png')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Embedding\n",
"from keras.layers.core import SpatialDropout1D\n",
"from keras.layers.core import Dropout\n",
"from keras.layers.recurrent import LSTM\n",
"from keras.layers.core import Dense"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hidden_dims = 50\n",
"embedding_dim = 50"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"lstm_model = Sequential()\n",
"lstm_model.add(Embedding(len(vocabulary) + 1, embedding_dim, input_length=sequence_length, name=\"embedding\"))\n",
"lstm_model.add(SpatialDropout1D(Dropout(0.2)))\n",
"lstm_model.add(LSTM(hidden_dims, dropout=0.2, recurrent_dropout=0.2)) # first arg, like Dense, is dim of output\n",
"lstm_model.add(Dense(1, activation='sigmoid'))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lstm_model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 25000 samples, validate on 25000 samples\n",
"Epoch 1/3\n",
"25000/25000 [==============================] - 482s - loss: 0.6939 - acc: 0.5040 - val_loss: 0.6946 - val_acc: 0.5006\n",
"Epoch 2/3\n",
"25000/25000 [==============================] - 462s - loss: 0.6896 - acc: 0.5157 - val_loss: 0.6911 - val_acc: 0.5069\n",
"Epoch 3/3\n",
"25000/25000 [==============================] - 463s - loss: 0.6758 - acc: 0.5354 - val_loss: 0.6948 - val_acc: 0.5100\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x12016a510>"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lstm_model.fit(x_train, y_train, batch_size=64, epochs=3, validation_data=(x_test, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<keras.layers.embeddings.Embedding at 0x11e953950>,\n",
" <keras.layers.core.SpatialDropout1D at 0x111fed590>,\n",
" <keras.layers.recurrent.LSTM at 0x11e975690>,\n",
" <keras.layers.core.Dense at 0x10ef82590>]"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lstm_model.layers"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(None, 400, 50)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lstm_model.layers[2].input_shape"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(None, 50)"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lstm_model.layers[2].output_shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Appendix: Our own data download and preparation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll use the [Large Movie Review Dataset v1.0](http://ai.stanford.edu/~amaas/data/sentiment/) for our corpus. While Keras has its own data samples you can import for modeling (including this one), I think it's very important to get and process your own data. Otherwise, the results appear to materialize out of thin air and it's more difficult to get on with your own research."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import glob"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"datapath = \"/Users/pfigliozzi/aclImdb/train/unsup\"\n",
"files = glob.glob(datapath+\"/*.txt\")[:1000] #first 1000 (there are 50k)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.concat([pd.read_table(filename, header=None, names=['raw']) for filename in files], ignore_index=True) "
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x107ee6710>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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S1MmAkCR1GsvtvpNsAr4D7ACeq6qVSQ4DrgOWAZuAt1bVt8dRnyRpvHsQv1pVK6pqZZte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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x105edc890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.raw.map(lambda x: len(x)).plot.hist()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"100.0"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"50000. * 2000. / 10**6"
]
},
{
"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
}
| mit |
quantopian/alphalens | alphalens/examples/alphalens_tutorial_on_quantopian.ipynb | 1 | 7108380 | null | apache-2.0 |
weichetaru/weichetaru.github.com | notebook/machine-learning/deep_learning-rnn-with-poetry-data.ipynb | 1 | 6520 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use RNN to produce poetry\n",
"\n",
"In this note, I am going to use RNN to build a model that produce poetry. This is a code practice from Udemy deep learning course. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The function below will process robert_frost.txt and transform it into format required for the model training. `remove_punctuation` removes punctuation as it's not required in our model. `get_robert_frost` parse on each line and collect each word into a word-index dictionary. The ourput will be a list with all sentences where each word has been transformed into index. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import string\n",
"import theano\n",
"import theano.tensor as T\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.utils import shuffle\n",
"\n",
"def init_weight(Mi, Mo):\n",
" return np.random.randn(Mi, Mo) / np.sqrt(Mi + Mo)\n",
"\n",
"def remove_punctuation(s):\n",
" translator = str.maketrans({key: None for key in string.punctuation})\n",
" return s.translate(translator)\n",
"\n",
"def get_robert_frost():\n",
" word2idx = {'START': 0, 'END': 1}\n",
" current_idx = 2\n",
" sentences = []\n",
" for line in open('../data/robert_frost.txt'):\n",
" line = line.strip()\n",
" if line:\n",
" tokens = remove_punctuation(line.lower()).split()\n",
" sentence = []\n",
" for t in tokens:\n",
" if t not in word2idx:\n",
" # true means it's a new word for our word2idx dictionary.\n",
" # add to dictionary if not exists and assign word index for it.\n",
" word2idx[t] = current_idx\n",
" current_idx += 1\n",
" idx = word2idx[t]\n",
" sentence.append(idx) # transform word into index.\n",
" sentences.append(sentence)\n",
" return sentences, word2idx"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class SimpleRNN:\n",
" def __init__(self, D, M, V):\n",
" self.D = D # dim of wrod embedding\n",
" self.M = M # hidden layer size\n",
" self.V = V # vocabulary size\n",
" \n",
" def fit(self, X, learning_rate=10e-1, mu=0.99, reg=1.0, activation=T.tanh, epochs=500, show_fig=False):\n",
" N = len(X) # numbers of sentences\n",
" D = self.D\n",
" M = self.M\n",
" V = self.V\n",
" self.f = activation # for hidden layer\n",
"\n",
" # initial weights\n",
" We = init_weight(V, D)\n",
" Wx = init_weight(D, M)\n",
" Wh = init_weight(M, M)\n",
" bh = np.zeros(M)\n",
" h0 = np.zeros(M)\n",
" Wo = init_weight(M, V)\n",
" bo = np.zeros(V)\n",
"\n",
" # make them theano shared\n",
" self.We = theano.shared(We)\n",
" self.Wx = theano.shared(Wx)\n",
" self.Wh = theano.shared(Wh)\n",
" self.bh = theano.shared(bh)\n",
" self.h0 = theano.shared(h0)\n",
" self.Wo = theano.shared(Wo)\n",
" self.bo = theano.shared(bo)\n",
" self.params = [self.We, self.Wx, self.Wh, self.bh, self.h0, self.Wo, self.bo]\n",
" \n",
" # sentence input:\n",
" # [START, w1, w2, ..., wn]\n",
" # sentence target:\n",
" # [w1, w2, w3, ..., END]\n",
" thX = T.ivector('X') # the sequence. will have length T. Note each sequence will have different T\n",
" Ei = self.We[thX] # this will be a TxD matrix\n",
" thY = T.ivector('Y')\n",
" \n",
" def recurrence(x_t, h_t1):\n",
" # return h(t), y(t)\n",
" h_t = self.f(x_t.dot(Wx) + h_t1.dot(self.Wh) + self.bh)\n",
" y_t = T.nnet.softmax(h_t.dot(self.Wo) + self.bo)\n",
" return h_t, y_t\n",
" \n",
" [h, y], _ = theano.scan(\n",
" fn=recurrence,\n",
" output_info=[self.h0, None],\n",
" sequences=Ei,\n",
" n_steps=Ei.shape[0],\n",
" )\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"output: [ 2. 3. 5. 8. 13. 21. 34. 55.]\n"
]
}
],
"source": [
"import numpy as np\n",
"import theano\n",
"import theano.tensor as T\n",
"\n",
"\n",
"N = T.iscalar('N')\n",
"\n",
"def recurrence(n, fn_1, fn_2): # Theano will know there're 2 recursive parameters.\n",
" fn_t = fn_1 + fn_2\n",
" # return current and last\n",
" return fn_t, fn_1 # As Theano knows there're 2 recursive parameters, both will be used for next iteration.\n",
"\n",
"outputs, _ = theano.scan(\n",
" fn=recurrence,\n",
" n_steps=N,\n",
" sequences=T.arange(N), # if remove this or set as sequences=[], n argument in recurrence() needs to be removed.\n",
" outputs_info=[1., 1.] # must be a list and has the same lenght as output of fn.\n",
")\n",
"\n",
"fibonacci = theano.function(\n",
" inputs=[N],\n",
" outputs=outputs[0],\n",
")\n",
"\n",
"o_val = fibonacci(8)\n",
"\n",
"print(\"output:\", o_val)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
wehlutyk/brainscopypaste | data/notebooks/Model(time=Time.discrete, source=Source.majority, past=Past.all, durl=Durl.all, max_distance=2) - susceptibility.ipynb | 1 | 1417320 | null | gpl-3.0 |
GD-park/python_basic | Practice/07-21.ipynb | 1 | 29659 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'My name is Tom'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"My name is %s\" % \"Tom\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"Jane's score is 100, 100, 100, 100, 100, 100, 100\""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"{1}'s score is {0}, {0}, {0}, {0}, {0}, {0}, {0}\".format(100, \"Jane\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"짝수\n"
]
}
],
"source": [
"a = 100\n",
"\n",
"if a %2 == 0:\n",
" print('짝수')\n",
"else:\n",
" print('홀수')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fail\n"
]
}
],
"source": [
"sex = \"boy\"\n",
"pushup = 5\n",
"\n",
"if sex == \"boy\":\n",
" if pushup >=10:\n",
" grade = \"Pass\"\n",
" else:\n",
" grade = \"Fail\"\n",
"\n",
" \n",
"print(grade)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def twotimes(x):\n",
" y = 2 * x\n",
" return y"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"twotimes(2)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"*\n",
"*\n",
"*\n",
"*\n"
]
}
],
"source": [
"for i in range(4):\n",
" print(\"*\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 1)\n",
"(1, 2)\n",
"(1, 3)\n",
"(1, 4)\n",
"(1, 5)\n",
"(1, 6)\n",
"(2, 1)\n",
"(2, 2)\n",
"(2, 3)\n",
"(2, 4)\n",
"(2, 5)\n",
"(2, 6)\n",
"(3, 1)\n",
"(3, 2)\n",
"(3, 3)\n",
"(3, 4)\n",
"(3, 5)\n",
"(3, 6)\n",
"(4, 1)\n",
"(4, 2)\n",
"(4, 3)\n",
"(4, 4)\n",
"(4, 5)\n",
"(4, 6)\n",
"(5, 1)\n",
"(5, 2)\n",
"(5, 3)\n",
"(5, 4)\n",
"(5, 5)\n",
"(5, 6)\n",
"(6, 1)\n",
"(6, 2)\n",
"(6, 3)\n",
"(6, 4)\n",
"(6, 5)\n",
"(6, 6)\n"
]
}
],
"source": [
"for i in range(6):\n",
" n1 = i + 1\n",
" for j in range(6):\n",
" n2 = j + 1\n",
" print(n1, n2)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tips = sns.load_dataset('tips')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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>total_bill</th>\n",
" <th>tip</th>\n",
" <th>sex</th>\n",
" <th>smoker</th>\n",
" <th>day</th>\n",
" <th>time</th>\n",
" <th>size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>239</th>\n",
" <td>29.03</td>\n",
" <td>5.92</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>240</th>\n",
" <td>27.18</td>\n",
" <td>2.00</td>\n",
" <td>Female</td>\n",
" <td>Yes</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>241</th>\n",
" <td>22.67</td>\n",
" <td>2.00</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>242</th>\n",
" <td>17.82</td>\n",
" <td>1.75</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>243</th>\n",
" <td>18.78</td>\n",
" <td>3.00</td>\n",
" <td>Female</td>\n",
" <td>No</td>\n",
" <td>Thur</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill tip sex smoker day time size\n",
"239 29.03 5.92 Male No Sat Dinner 3\n",
"240 27.18 2.00 Female Yes Sat Dinner 2\n",
"241 22.67 2.00 Male Yes Sat Dinner 2\n",
"242 17.82 1.75 Male No Sat Dinner 2\n",
"243 18.78 3.00 Female No Thur Dinner 2"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tips.tail()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tips['tip_pct'] = tips['tip'] / tips['total_bill']"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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>total_bill</th>\n",
" <th>tip</th>\n",
" <th>sex</th>\n",
" <th>smoker</th>\n",
" <th>day</th>\n",
" <th>time</th>\n",
" <th>size</th>\n",
" <th>tip_pct</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>239</th>\n",
" <td>29.03</td>\n",
" <td>5.92</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>3</td>\n",
" <td>0.203927</td>\n",
" </tr>\n",
" <tr>\n",
" <th>240</th>\n",
" <td>27.18</td>\n",
" <td>2.00</td>\n",
" <td>Female</td>\n",
" <td>Yes</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" <td>0.073584</td>\n",
" </tr>\n",
" <tr>\n",
" <th>241</th>\n",
" <td>22.67</td>\n",
" <td>2.00</td>\n",
" <td>Male</td>\n",
" <td>Yes</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" <td>0.088222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>242</th>\n",
" <td>17.82</td>\n",
" <td>1.75</td>\n",
" <td>Male</td>\n",
" <td>No</td>\n",
" <td>Sat</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" <td>0.098204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>243</th>\n",
" <td>18.78</td>\n",
" <td>3.00</td>\n",
" <td>Female</td>\n",
" <td>No</td>\n",
" <td>Thur</td>\n",
" <td>Dinner</td>\n",
" <td>2</td>\n",
" <td>0.159744</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill tip sex smoker day time size tip_pct\n",
"239 29.03 5.92 Male No Sat Dinner 3 0.203927\n",
"240 27.18 2.00 Female Yes Sat Dinner 2 0.073584\n",
"241 22.67 2.00 Male Yes Sat Dinner 2 0.088222\n",
"242 17.82 1.75 Male No Sat Dinner 2 0.098204\n",
"243 18.78 3.00 Female No Thur Dinner 2 0.159744"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tips.tail()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>total_bill</th>\n",
" <th>tip</th>\n",
" <th>size</th>\n",
" <th>tip_pct</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>244.000000</td>\n",
" <td>244.000000</td>\n",
" <td>244.000000</td>\n",
" <td>244.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>19.785943</td>\n",
" <td>2.998279</td>\n",
" <td>2.569672</td>\n",
" <td>0.160803</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>8.902412</td>\n",
" <td>1.383638</td>\n",
" <td>0.951100</td>\n",
" <td>0.061072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>3.070000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.035638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>13.347500</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.129127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>17.795000</td>\n",
" <td>2.900000</td>\n",
" <td>2.000000</td>\n",
" <td>0.154770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>24.127500</td>\n",
" <td>3.562500</td>\n",
" <td>3.000000</td>\n",
" <td>0.191475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>50.810000</td>\n",
" <td>10.000000</td>\n",
" <td>6.000000</td>\n",
" <td>0.710345</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill tip size tip_pct\n",
"count 244.000000 244.000000 244.000000 244.000000\n",
"mean 19.785943 2.998279 2.569672 0.160803\n",
"std 8.902412 1.383638 0.951100 0.061072\n",
"min 3.070000 1.000000 1.000000 0.035638\n",
"25% 13.347500 2.000000 2.000000 0.129127\n",
"50% 17.795000 2.900000 2.000000 0.154770\n",
"75% 24.127500 3.562500 3.000000 0.191475\n",
"max 50.810000 10.000000 6.000000 0.710345"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tips.describe()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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>total_bill</th>\n",
" <th>tip</th>\n",
" <th>smoker</th>\n",
" <th>day</th>\n",
" <th>time</th>\n",
" <th>size</th>\n",
" <th>tip_pct</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sex</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>Male</th>\n",
" <td>157</td>\n",
" <td>157</td>\n",
" <td>157</td>\n",
" <td>157</td>\n",
" <td>157</td>\n",
" <td>157</td>\n",
" <td>157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Female</th>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill tip smoker day time size tip_pct\n",
"sex \n",
"Male 157 157 157 157 157 157 157\n",
"Female 87 87 87 87 87 87 87"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tips.groupby('sex').count()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"sex smoker\n",
"Male Yes 60\n",
" No 97\n",
"Female Yes 33\n",
" No 54\n",
"dtype: int64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tips.groupby(['sex', 'smoker']).size()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>smoker</th>\n",
" <th>Yes</th>\n",
" <th>No</th>\n",
" <th>All</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sex</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Male</th>\n",
" <td>60.0</td>\n",
" <td>97.0</td>\n",
" <td>157.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Female</th>\n",
" <td>33.0</td>\n",
" <td>54.0</td>\n",
" <td>87.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All</th>\n",
" <td>93.0</td>\n",
" <td>151.0</td>\n",
" <td>244.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"smoker Yes No All\n",
"sex \n",
"Male 60.0 97.0 157.0\n",
"Female 33.0 54.0 87.0\n",
"All 93.0 151.0 244.0"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tips.pivot_table(\"tip_pct\", \"sex\", \"smoker\", aggfunc=\"count\", margins=True)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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></th>\n",
" <th></th>\n",
" <th>tip</th>\n",
" <th>tip_pct</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sex</th>\n",
" <th>smoker</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"16\" valign=\"top\">Male</th>\n",
" <th rowspan=\"8\" valign=\"top\">Yes</th>\n",
" <th>count</th>\n",
" <td>60.000000</td>\n",
" <td>60.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3.051167</td>\n",
" <td>0.152771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.500120</td>\n",
" <td>0.090588</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.035638</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.000000</td>\n",
" <td>0.101845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.000000</td>\n",
" <td>0.141015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3.820000</td>\n",
" <td>0.191697</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>10.000000</td>\n",
" <td>0.710345</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"8\" valign=\"top\">No</th>\n",
" <th>count</th>\n",
" <td>97.000000</td>\n",
" <td>97.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3.113402</td>\n",
" <td>0.160669</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.489559</td>\n",
" <td>0.041849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.250000</td>\n",
" <td>0.071804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.000000</td>\n",
" <td>0.131810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2.740000</td>\n",
" <td>0.157604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3.710000</td>\n",
" <td>0.186220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>9.000000</td>\n",
" <td>0.291990</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"16\" valign=\"top\">Female</th>\n",
" <th rowspan=\"8\" valign=\"top\">Yes</th>\n",
" <th>count</th>\n",
" <td>33.000000</td>\n",
" <td>33.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2.931515</td>\n",
" <td>0.182150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.219916</td>\n",
" <td>0.071595</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.056433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.000000</td>\n",
" <td>0.152439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2.880000</td>\n",
" <td>0.173913</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3.500000</td>\n",
" <td>0.198216</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>6.500000</td>\n",
" <td>0.416667</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"8\" valign=\"top\">No</th>\n",
" <th>count</th>\n",
" <td>54.000000</td>\n",
" <td>54.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2.773519</td>\n",
" <td>0.156921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.128425</td>\n",
" <td>0.036421</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.056797</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.000000</td>\n",
" <td>0.139708</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2.680000</td>\n",
" <td>0.149691</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3.437500</td>\n",
" <td>0.181630</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>5.200000</td>\n",
" <td>0.252672</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" tip tip_pct\n",
"sex smoker \n",
"Male Yes count 60.000000 60.000000\n",
" mean 3.051167 0.152771\n",
" std 1.500120 0.090588\n",
" min 1.000000 0.035638\n",
" 25% 2.000000 0.101845\n",
" 50% 3.000000 0.141015\n",
" 75% 3.820000 0.191697\n",
" max 10.000000 0.710345\n",
" No count 97.000000 97.000000\n",
" mean 3.113402 0.160669\n",
" std 1.489559 0.041849\n",
" min 1.250000 0.071804\n",
" 25% 2.000000 0.131810\n",
" 50% 2.740000 0.157604\n",
" 75% 3.710000 0.186220\n",
" max 9.000000 0.291990\n",
"Female Yes count 33.000000 33.000000\n",
" mean 2.931515 0.182150\n",
" std 1.219916 0.071595\n",
" min 1.000000 0.056433\n",
" 25% 2.000000 0.152439\n",
" 50% 2.880000 0.173913\n",
" 75% 3.500000 0.198216\n",
" max 6.500000 0.416667\n",
" No count 54.000000 54.000000\n",
" mean 2.773519 0.156921\n",
" std 1.128425 0.036421\n",
" min 1.000000 0.056797\n",
" 25% 2.000000 0.139708\n",
" 50% 2.680000 0.149691\n",
" 75% 3.437500 0.181630\n",
" max 5.200000 0.252672"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tips.groupby([\"sex\", \"smoker\"])[[\"tip\", \"tip_pct\"]].describe()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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></th>\n",
" <th>tip</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sex</th>\n",
" <th>smoker</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Male</th>\n",
" <th>Yes</th>\n",
" <td>9.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>No</th>\n",
" <td>7.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">Female</th>\n",
" <th>Yes</th>\n",
" <td>5.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>No</th>\n",
" <td>4.20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" tip\n",
"sex smoker \n",
"Male Yes 9.00\n",
" No 7.75\n",
"Female Yes 5.50\n",
" No 4.20"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def peak_to_peak(x):\n",
" return x.max() - x.min()\n",
"\n",
"tips.groupby(['sex', 'smoker'])[['tip']].agg(peak_to_peak)"
]
},
{
"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>total_bill</th>\n",
" <th>tip</th>\n",
" <th>smoker</th>\n",
" <th>day</th>\n",
" <th>time</th>\n",
" <th>size</th>\n",
" <th>tip_pct</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sex</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>Male</th>\n",
" <td>157</td>\n",
" <td>157</td>\n",
" <td>157</td>\n",
" <td>157</td>\n",
" <td>157</td>\n",
" <td>157</td>\n",
" <td>157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Female</th>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" <td>87</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_bill tip smoker day time size tip_pct\n",
"sex \n",
"Male 157 157 157 157 157 157 157\n",
"Female 87 87 87 87 87 87 87"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tips.groupby('sex').count()"
]
},
{
"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": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
AlbertoAlfredo/exercicios-cursos | ExerciciosGerais/slides/7.Prática em Python/scripts/8.Facavcmesmo.ipynb | 1 | 2125 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Formação Cientista de Dados - Prof. Fernando Amaral\n",
"Faça você mesmo"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#1. Faça um programa que tenha uma função chamada amplitude. A função deve receber uma lista e imprimir a amplitude.\n",
"#Crie também um código para testar sua função\n",
"def amplitude(vet):\n",
" print(\"Amplitude:\", max(vet) - min(vet))\n",
"\n",
"vetor = [12,23,45,2,100] \n",
"amplitude(vetor)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#2. Faça uma função que receba uma string e imprima esta string na forma vertical\n",
"def imprime(texto):\n",
" for n in range(0, len(texto)):\n",
" print(texto[n])\n",
"\n",
"imprime(\"Cientista\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#3. Crie um programa que leia o peso de uma carga em números inteiros. \n",
"#Se o peso for até 10 kg, informe que o valor será de R$ 50,00. \n",
"#Entre 11 e 20 kg, informe que o valor será de R$ 80. \n",
"# Se for maior que 20 informe que o transporte não é aceito. Teste vários pesos.\n",
"\n",
"peso = 10\n",
"if peso <= 10:\n",
"\tprint(\"Valor da carga é de R$ 50,00\")\n",
"elif peso >= 11 and peso <=20:\n",
"\tprint(\"Valor da carga é de R$ 80,00\")\n",
"else:\n",
"\tprint(\"O transporte não é aceito\")\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 |
google/brax | notebooks/biggym/biggym_rl.ipynb | 1 | 8958 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "ssCOanHc8JH_"
},
"source": [
"# [BIG-Gym](https://github.com/google/brax/blob/main/brax/experimental/biggym) RL training\n",
"\n",
"[](https://colab.research.google.com/github/google/brax/blob/main/notebooks/biggym/biggym_rl.ipynb)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rlVNS8JstMRr"
},
"outputs": [],
"source": [
"#@title Colab setup and imports\n",
"#@markdown ## ⚠️ PLEASE NOTE:\n",
"#@markdown This colab runs best using a TPU runtime. From the Colab menu, choose Runtime \u003e Change Runtime Type, then select **'TPU'** in the dropdown.\n",
"from datetime import datetime\n",
"import functools\n",
"import os\n",
"import pprint\n",
"import jax\n",
"import jax.numpy as jnp\n",
"# from jax.config import config\n",
"# config.update(\"jax_debug_nans\", True)\n",
"from IPython.display import HTML, clear_output\n",
"import matplotlib.pyplot as plt\n",
"\n",
"try:\n",
" import brax\n",
"except ImportError:\n",
" !pip install git+https://github.com/google/brax.git@main\n",
" clear_output()\n",
" import brax\n",
"\n",
"from brax.io import html\n",
"from brax.experimental import biggym\n",
"from brax.experimental.composer import composer\n",
"from brax.experimental.composer.training import mappo\n",
"from brax.experimental.braxlines import experiments\n",
"from brax.experimental.braxlines.common import evaluators\n",
"from brax.experimental.braxlines.common import logger_utils\n",
"from brax.experimental.braxlines.training import ppo\n",
"\n",
"if \"COLAB_TPU_ADDR\" in os.environ:\n",
" from jax.tools import colab_tpu\n",
" colab_tpu.setup_tpu()\n",
"\n",
"def show_env(env):\n",
" jit_env_reset = jax.jit(env.reset)\n",
" state = jit_env_reset(rng=jax.random.PRNGKey(seed=0))\n",
" clear_output(wait=True)\n",
" return HTML(html.render(env.sys, [state.qp]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "j-HZevAii3z-"
},
"outputs": [],
"source": [
"# @title Register a BIG-Gym registry\n",
"registry_name = 'proant' # @param {type: 'string'}\n",
"register_all = True # @param {type: 'boolean'}\n",
"\n",
"if register_all:\n",
" biggym.register_all(verbose=True)\n",
" pprint.pprint(biggym.ENVS_BY_TRACKS)\n",
"env_names, comp_names, task_env_names, _ = biggym.register(registry_name)\n",
"print(f'env_names: {env_names}')\n",
"print(f'comp_names: {comp_names}')\n",
"print(f'task_env_names: {task_env_names}')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "riA5oBKFK5B7"
},
"outputs": [],
"source": [
"#@title Specify an environment\n",
"env_name = 'sumo__proant__ant' # @param {type: 'string'}\n",
"output_path = '' # @param {type: 'string'}\n",
"show_params = True # @param {'type':'boolean'}\n",
"\n",
"if output_path:\n",
" output_path = f'{output_path}/{datetime.now().strftime(\"%Y%m%d\")}' \n",
" output_path = f'{output_path}/{env_name}'\n",
" print(f'Saving outputs to {output_path}')\n",
"\n",
"if show_params:\n",
" supported_params, support_kwargs = biggym.inspect_env(env_name=env_name)\n",
" print(f'supported_params for \"{env_name}\" =')\n",
" pprint.pprint(supported_params)\n",
" print(f'support variable-length kwargs? (i.e. **kwargs): {support_kwargs}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "T1ZJ2jZDKH8Y"
},
"outputs": [],
"source": [
"#@title Create a custom env\n",
"env_params = {'num_legs': 2}# @param{'type': 'raw'}\n",
"mode = 'viewer'# @param ['print_step', 'print_obs', 'print_sys', 'viewer']\n",
"ignore_kwargs = True # @param {'type':'boolean'}\n",
"\n",
"# check supported params\n",
"env_params = env_params or {}\n",
"biggym.assert_env_params(env_name, env_params, ignore_kwargs)\n",
"\n",
"# create env\n",
"env_fn = composer.create_fn(env_name=env_name, **env_params)\n",
"# env_fn = biggym.create_fn(env_name=env_name, **env_params)\n",
"env = env_fn()\n",
"show_env(env)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WGRizNxK3MtF"
},
"outputs": [],
"source": [
"#@title Training the custom env\n",
"num_timesteps_multiplier = 2# @param {type: 'number'}\n",
"seed = 0 # @param{type: 'integer'}\n",
"skip_training = False # @param {type: 'boolean'}\n",
"\n",
"log_path = output_path\n",
"if log_path:\n",
" log_path = f'{log_path}/training_curves.csv'\n",
"tab = logger_utils.Tabulator(output_path=log_path,\n",
" append=False)\n",
"\n",
"ppo_lib = mappo if biggym.is_multiagent(env) else ppo\n",
"ppo_params = experiments.defaults.get_ppo_params(\n",
" 'ant', num_timesteps_multiplier)\n",
"train_fn = functools.partial(ppo_lib.train, **ppo_params)\n",
"\n",
"times = [datetime.now()]\n",
"plotpatterns = ['eval/episode_reward', 'eval/episode_score']\n",
"\n",
"progress, _, _, _ = experiments.get_progress_fn(\n",
" plotpatterns, times, tab=tab, max_ncols=5,\n",
" xlim=[0, train_fn.keywords['num_timesteps']],\n",
" pre_plot_fn = lambda : clear_output(wait=True),\n",
" post_plot_fn = plt.show)\n",
"\n",
"if skip_training:\n",
" action_size = (env.group_action_shapes if \n",
" biggym.is_multiagent(env) else env.action_size)\n",
" params, inference_fn = ppo_lib.make_params_and_inference_fn(\n",
" env.observation_size, action_size,\n",
" normalize_observations=True)\n",
" inference_fn = jax.jit(inference_fn)\n",
"else:\n",
" inference_fn, params, _ = train_fn(\n",
" environment_fn=env_fn, seed=seed,\n",
" extra_step_kwargs=False, progress_fn=progress)\n",
" print(f'time to jit: {times[1] - times[0]}')\n",
" print(f'time to train: {times[-1] - times[1]}')\n",
" print(f'Saved logs to {log_path}')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "P-0VYySqOEk0"
},
"outputs": [],
"source": [
"#@title Visualizing a trajectory of the learned inference function\n",
"eval_seed = 0 # @param {'type': 'integer'}\n",
"batch_size = 0# @param {type: 'integer'}\n",
"\n",
"env, states = evaluators.visualize_env(\n",
" env_fn=env_fn, inference_fn=inference_fn,\n",
" params=params, batch_size=batch_size,\n",
" seed = eval_seed, output_path=output_path,\n",
" verbose=True,\n",
")\n",
"HTML(html.render(env.sys, [state.qp for state in states]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-48ybSUcyMJu"
},
"outputs": [],
"source": [
"#@title Plot information of the trajectory\n",
"experiments.plot_states(states[1:], max_ncols=5)\n",
"plt.show()"
]
}
],
"metadata": {
"accelerator": "TPU",
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "//learning/deepmind/public/tools/ml_python:ml_notebook",
"kind": "private"
},
"name": "biggym_rl.ipynb",
"private_outputs": true,
"provenance": [
{
"file_id": "1PWXVD5BforifYfej0R-PQaW8T6hlFuJ6",
"timestamp": 1639457023559
},
{
"file_id": "1BCqjiaBc13bQK1gQiEMUQGrxjPTov2EN",
"timestamp": 1639058480819
},
{
"file_id": "1ZaAO4BS2tJ_03CIXdBCFibZR2yLl6dtv",
"timestamp": 1630801484981
}
]
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
luwei0917/awsemmd_script | notebook/Optimization/frag_memory_use_cbd.ipynb | 1 | 26227 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import random\n",
"import time\n",
"from random import seed, randint\n",
"import argparse\n",
"import platform\n",
"from datetime import datetime\n",
"import imp\n",
"import numpy as np\n",
"import fileinput\n",
"from itertools import product\n",
"import pandas as pd\n",
"from scipy.interpolate import griddata\n",
"from scipy.interpolate import interp2d\n",
"import seaborn as sns\n",
"from os import listdir\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from scipy.interpolate import griddata\n",
"import matplotlib as mpl\n",
"# sys.path.insert(0,'..')\n",
"# from notebookFunctions import *\n",
"# from .. import notebookFunctions\n",
"from Bio.PDB.Polypeptide import one_to_three\n",
"from Bio.PDB.Polypeptide import three_to_one\n",
"from Bio.PDB.PDBParser import PDBParser\n",
"from pyCodeLib import *\n",
"from small_script.myFunctions import *\n",
"sys.path.insert(0, \"/Users/weilu/openmmawsem\")\n",
"from helperFunctions.myFunctions import *\n",
"from collections import defaultdict\n",
"%matplotlib inline\n",
"# plt.rcParams['figure.figsize'] = (10,6.180) #golden ratio\n",
"# %matplotlib notebook\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"frag_name = \"/Users/weilu/Research/server/mar_2020/mass_iterative_run/setups/1a1x/1a1x.gro\"\n",
"frag = pd.read_csv(frag_name, skiprows=2, sep=\"\\s+\", header=None, names=[\"Res_id\", \"Res\", \"Type\", \"i\", \"x\", \"y\", \"z\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_side_chain_center_of_mass(atoms):\n",
" # ensure complete first\n",
" total = np.array([0., 0., 0.])\n",
" total_mass = 0\n",
" for atom in atoms:\n",
" if atom.get_name() in [\"N\", \"CA\", \"C\", \"O\", \"OXT\"]:\n",
" continue\n",
" if atom.element == \"H\":\n",
" continue\n",
" total += atom.mass * atom.get_coord()\n",
" total_mass += atom.mass\n",
" # print(atom.get_name(), atom.get_coord())\n",
" x_com = total / total_mass\n",
" return x_com"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Res_id</th>\n",
" <th>Res</th>\n",
" <th>Type</th>\n",
" <th>i</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>z</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>ALA</td>\n",
" <td>N</td>\n",
" <td>1</td>\n",
" <td>0.943</td>\n",
" <td>2.721</td>\n",
" <td>3.690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>ALA</td>\n",
" <td>H</td>\n",
" <td>2</td>\n",
" <td>1.019</td>\n",
" <td>2.802</td>\n",
" <td>3.646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>ALA</td>\n",
" <td>H2</td>\n",
" <td>3</td>\n",
" <td>0.840</td>\n",
" <td>2.775</td>\n",
" <td>3.664</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>ALA</td>\n",
" <td>H3</td>\n",
" <td>4</td>\n",
" <td>0.971</td>\n",
" <td>2.717</td>\n",
" <td>3.806</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>ALA</td>\n",
" <td>CA</td>\n",
" <td>5</td>\n",
" <td>0.956</td>\n",
" <td>2.591</td>\n",
" <td>3.617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1</td>\n",
" <td>ALA</td>\n",
" <td>HA</td>\n",
" <td>6</td>\n",
" <td>0.924</td>\n",
" <td>2.604</td>\n",
" <td>3.503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1</td>\n",
" <td>ALA</td>\n",
" <td>C</td>\n",
" <td>7</td>\n",
" <td>0.877</td>\n",
" <td>2.485</td>\n",
" <td>3.693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1</td>\n",
" <td>ALA</td>\n",
" <td>O</td>\n",
" <td>8</td>\n",
" <td>0.927</td>\n",
" <td>2.426</td>\n",
" <td>3.789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1</td>\n",
" <td>ALA</td>\n",
" <td>CB</td>\n",
" <td>9</td>\n",
" <td>1.104</td>\n",
" <td>2.550</td>\n",
" <td>3.606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1</td>\n",
" <td>ALA</td>\n",
" <td>HB1</td>\n",
" <td>10</td>\n",
" <td>1.163</td>\n",
" <td>2.608</td>\n",
" <td>3.519</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1</td>\n",
" <td>ALA</td>\n",
" <td>HB2</td>\n",
" <td>11</td>\n",
" <td>1.114</td>\n",
" <td>2.435</td>\n",
" <td>3.571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1</td>\n",
" <td>ALA</td>\n",
" <td>HB3</td>\n",
" <td>12</td>\n",
" <td>1.176</td>\n",
" <td>2.558</td>\n",
" <td>3.701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2</td>\n",
" <td>GLY</td>\n",
" <td>N</td>\n",
" <td>13</td>\n",
" <td>0.754</td>\n",
" <td>2.461</td>\n",
" <td>3.649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2</td>\n",
" <td>GLY</td>\n",
" <td>H</td>\n",
" <td>14</td>\n",
" <td>0.730</td>\n",
" <td>2.460</td>\n",
" <td>3.532</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2</td>\n",
" <td>GLY</td>\n",
" <td>CA</td>\n",
" <td>15</td>\n",
" <td>0.668</td>\n",
" <td>2.365</td>\n",
" <td>3.715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2</td>\n",
" <td>GLY</td>\n",
" <td>HA2</td>\n",
" <td>16</td>\n",
" <td>0.587</td>\n",
" <td>2.314</td>\n",
" <td>3.644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2</td>\n",
" <td>GLY</td>\n",
" <td>HA3</td>\n",
" <td>17</td>\n",
" <td>0.723</td>\n",
" <td>2.271</td>\n",
" <td>3.764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2</td>\n",
" <td>GLY</td>\n",
" <td>C</td>\n",
" <td>18</td>\n",
" <td>0.585</td>\n",
" <td>2.439</td>\n",
" <td>3.818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2</td>\n",
" <td>GLY</td>\n",
" <td>O</td>\n",
" <td>19</td>\n",
" <td>0.518</td>\n",
" <td>2.376</td>\n",
" <td>3.899</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>3</td>\n",
" <td>GLU</td>\n",
" <td>N</td>\n",
" <td>20</td>\n",
" <td>0.591</td>\n",
" <td>2.572</td>\n",
" <td>3.814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>3</td>\n",
" <td>GLU</td>\n",
" <td>H</td>\n",
" <td>21</td>\n",
" <td>0.592</td>\n",
" <td>2.617</td>\n",
" <td>3.704</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>3</td>\n",
" <td>GLU</td>\n",
" <td>CA</td>\n",
" <td>22</td>\n",
" <td>0.518</td>\n",
" <td>2.659</td>\n",
" <td>3.906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>3</td>\n",
" <td>GLU</td>\n",
" <td>HA</td>\n",
" <td>23</td>\n",
" <td>0.461</td>\n",
" <td>2.587</td>\n",
" <td>3.982</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>3</td>\n",
" <td>GLU</td>\n",
" <td>C</td>\n",
" <td>24</td>\n",
" <td>0.409</td>\n",
" <td>2.738</td>\n",
" <td>3.835</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>3</td>\n",
" <td>GLU</td>\n",
" <td>O</td>\n",
" <td>25</td>\n",
" <td>0.407</td>\n",
" <td>2.748</td>\n",
" <td>3.712</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>3</td>\n",
" <td>GLU</td>\n",
" <td>CB</td>\n",
" <td>26</td>\n",
" <td>0.612</td>\n",
" <td>2.762</td>\n",
" <td>3.970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>3</td>\n",
" <td>GLU</td>\n",
" <td>HB2</td>\n",
" <td>27</td>\n",
" <td>0.664</td>\n",
" <td>2.822</td>\n",
" <td>3.881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>3</td>\n",
" <td>GLU</td>\n",
" <td>HB3</td>\n",
" <td>28</td>\n",
" <td>0.549</td>\n",
" <td>2.834</td>\n",
" <td>4.041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>3</td>\n",
" <td>GLU</td>\n",
" <td>CG</td>\n",
" <td>29</td>\n",
" <td>0.714</td>\n",
" <td>2.708</td>\n",
" <td>4.069</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>3</td>\n",
" <td>GLU</td>\n",
" <td>HG2</td>\n",
" <td>30</td>\n",
" <td>0.809</td>\n",
" <td>2.781</td>\n",
" <td>4.060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1705</th>\n",
" <td>104</td>\n",
" <td>PRO</td>\n",
" <td>HG2</td>\n",
" <td>1706</td>\n",
" <td>3.084</td>\n",
" <td>3.651</td>\n",
" <td>3.611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1706</th>\n",
" <td>104</td>\n",
" <td>PRO</td>\n",
" <td>HG3</td>\n",
" <td>1707</td>\n",
" <td>3.165</td>\n",
" <td>3.543</td>\n",
" <td>3.735</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1707</th>\n",
" <td>104</td>\n",
" <td>PRO</td>\n",
" <td>CD</td>\n",
" <td>1708</td>\n",
" <td>2.974</td>\n",
" <td>3.632</td>\n",
" <td>3.814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1708</th>\n",
" <td>104</td>\n",
" <td>PRO</td>\n",
" <td>HD2</td>\n",
" <td>1709</td>\n",
" <td>3.000</td>\n",
" <td>3.576</td>\n",
" <td>3.916</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1709</th>\n",
" <td>104</td>\n",
" <td>PRO</td>\n",
" <td>HD3</td>\n",
" <td>1710</td>\n",
" <td>2.918</td>\n",
" <td>3.547</td>\n",
" <td>3.751</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1710</th>\n",
" <td>105</td>\n",
" <td>ASP</td>\n",
" <td>N</td>\n",
" <td>1711</td>\n",
" <td>3.094</td>\n",
" <td>4.076</td>\n",
" <td>3.730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1711</th>\n",
" <td>105</td>\n",
" <td>ASP</td>\n",
" <td>H</td>\n",
" <td>1712</td>\n",
" <td>3.179</td>\n",
" <td>4.100</td>\n",
" <td>3.810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1712</th>\n",
" <td>105</td>\n",
" <td>ASP</td>\n",
" <td>CA</td>\n",
" <td>1713</td>\n",
" <td>3.074</td>\n",
" <td>4.175</td>\n",
" <td>3.624</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1713</th>\n",
" <td>105</td>\n",
" <td>ASP</td>\n",
" <td>HA</td>\n",
" <td>1714</td>\n",
" <td>2.956</td>\n",
" <td>4.187</td>\n",
" <td>3.617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1714</th>\n",
" <td>105</td>\n",
" <td>ASP</td>\n",
" <td>C</td>\n",
" <td>1715</td>\n",
" <td>3.147</td>\n",
" <td>4.134</td>\n",
" <td>3.497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1715</th>\n",
" <td>105</td>\n",
" <td>ASP</td>\n",
" <td>O</td>\n",
" <td>1716</td>\n",
" <td>3.265</td>\n",
" <td>4.102</td>\n",
" <td>3.501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1716</th>\n",
" <td>105</td>\n",
" <td>ASP</td>\n",
" <td>CB</td>\n",
" <td>1717</td>\n",
" <td>3.120</td>\n",
" <td>4.314</td>\n",
" <td>3.667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1717</th>\n",
" <td>105</td>\n",
" <td>ASP</td>\n",
" <td>HB2</td>\n",
" <td>1718</td>\n",
" <td>3.082</td>\n",
" <td>4.354</td>\n",
" <td>3.773</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1718</th>\n",
" <td>105</td>\n",
" <td>ASP</td>\n",
" <td>HB3</td>\n",
" <td>1719</td>\n",
" <td>3.238</td>\n",
" <td>4.329</td>\n",
" <td>3.675</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1719</th>\n",
" <td>105</td>\n",
" <td>ASP</td>\n",
" <td>CG</td>\n",
" <td>1720</td>\n",
" <td>3.082</td>\n",
" <td>4.422</td>\n",
" <td>3.565</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1720</th>\n",
" <td>105</td>\n",
" <td>ASP</td>\n",
" <td>OD1</td>\n",
" <td>1721</td>\n",
" <td>3.152</td>\n",
" <td>4.435</td>\n",
" <td>3.462</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1721</th>\n",
" <td>105</td>\n",
" <td>ASP</td>\n",
" <td>OD2</td>\n",
" <td>1722</td>\n",
" <td>2.982</td>\n",
" <td>4.493</td>\n",
" <td>3.589</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1722</th>\n",
" <td>106</td>\n",
" <td>ASP</td>\n",
" <td>N</td>\n",
" <td>1723</td>\n",
" <td>3.075</td>\n",
" <td>4.133</td>\n",
" <td>3.385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1723</th>\n",
" <td>106</td>\n",
" <td>ASP</td>\n",
" <td>H</td>\n",
" <td>1724</td>\n",
" <td>2.964</td>\n",
" <td>4.171</td>\n",
" <td>3.369</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1724</th>\n",
" <td>106</td>\n",
" <td>ASP</td>\n",
" <td>CA</td>\n",
" <td>1725</td>\n",
" <td>3.135</td>\n",
" <td>4.096</td>\n",
" <td>3.258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1725</th>\n",
" <td>106</td>\n",
" <td>ASP</td>\n",
" <td>HA</td>\n",
" <td>1726</td>\n",
" <td>3.253</td>\n",
" <td>4.078</td>\n",
" <td>3.263</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1726</th>\n",
" <td>106</td>\n",
" <td>ASP</td>\n",
" <td>C</td>\n",
" <td>1727</td>\n",
" <td>3.128</td>\n",
" <td>4.214</td>\n",
" <td>3.161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1727</th>\n",
" <td>106</td>\n",
" <td>ASP</td>\n",
" <td>O</td>\n",
" <td>1728</td>\n",
" <td>3.187</td>\n",
" <td>4.318</td>\n",
" <td>3.196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1728</th>\n",
" <td>106</td>\n",
" <td>ASP</td>\n",
" <td>CB</td>\n",
" <td>1729</td>\n",
" <td>3.066</td>\n",
" <td>3.973</td>\n",
" <td>3.198</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1729</th>\n",
" <td>106</td>\n",
" <td>ASP</td>\n",
" <td>HB2</td>\n",
" <td>1730</td>\n",
" <td>2.954</td>\n",
" <td>3.989</td>\n",
" <td>3.159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1730</th>\n",
" <td>106</td>\n",
" <td>ASP</td>\n",
" <td>HB3</td>\n",
" <td>1731</td>\n",
" <td>3.070</td>\n",
" <td>3.878</td>\n",
" <td>3.270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1731</th>\n",
" <td>106</td>\n",
" <td>ASP</td>\n",
" <td>CG</td>\n",
" <td>1732</td>\n",
" <td>3.140</td>\n",
" <td>3.917</td>\n",
" <td>3.076</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1732</th>\n",
" <td>106</td>\n",
" <td>ASP</td>\n",
" <td>OD1</td>\n",
" <td>1733</td>\n",
" <td>3.255</td>\n",
" <td>3.960</td>\n",
" <td>3.048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1733</th>\n",
" <td>106</td>\n",
" <td>ASP</td>\n",
" <td>OD2</td>\n",
" <td>1734</td>\n",
" <td>3.084</td>\n",
" <td>3.827</td>\n",
" <td>3.009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1734</th>\n",
" <td>106</td>\n",
" <td>ASP</td>\n",
" <td>OXT</td>\n",
" <td>1735</td>\n",
" <td>3.086</td>\n",
" <td>4.199</td>\n",
" <td>3.047</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1735 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Res_id Res Type i x y z\n",
"0 1 ALA N 1 0.943 2.721 3.690\n",
"1 1 ALA H 2 1.019 2.802 3.646\n",
"2 1 ALA H2 3 0.840 2.775 3.664\n",
"3 1 ALA H3 4 0.971 2.717 3.806\n",
"4 1 ALA CA 5 0.956 2.591 3.617\n",
"5 1 ALA HA 6 0.924 2.604 3.503\n",
"6 1 ALA C 7 0.877 2.485 3.693\n",
"7 1 ALA O 8 0.927 2.426 3.789\n",
"8 1 ALA CB 9 1.104 2.550 3.606\n",
"9 1 ALA HB1 10 1.163 2.608 3.519\n",
"10 1 ALA HB2 11 1.114 2.435 3.571\n",
"11 1 ALA HB3 12 1.176 2.558 3.701\n",
"12 2 GLY N 13 0.754 2.461 3.649\n",
"13 2 GLY H 14 0.730 2.460 3.532\n",
"14 2 GLY CA 15 0.668 2.365 3.715\n",
"15 2 GLY HA2 16 0.587 2.314 3.644\n",
"16 2 GLY HA3 17 0.723 2.271 3.764\n",
"17 2 GLY C 18 0.585 2.439 3.818\n",
"18 2 GLY O 19 0.518 2.376 3.899\n",
"19 3 GLU N 20 0.591 2.572 3.814\n",
"20 3 GLU H 21 0.592 2.617 3.704\n",
"21 3 GLU CA 22 0.518 2.659 3.906\n",
"22 3 GLU HA 23 0.461 2.587 3.982\n",
"23 3 GLU C 24 0.409 2.738 3.835\n",
"24 3 GLU O 25 0.407 2.748 3.712\n",
"25 3 GLU CB 26 0.612 2.762 3.970\n",
"26 3 GLU HB2 27 0.664 2.822 3.881\n",
"27 3 GLU HB3 28 0.549 2.834 4.041\n",
"28 3 GLU CG 29 0.714 2.708 4.069\n",
"29 3 GLU HG2 30 0.809 2.781 4.060\n",
"... ... ... ... ... ... ... ...\n",
"1705 104 PRO HG2 1706 3.084 3.651 3.611\n",
"1706 104 PRO HG3 1707 3.165 3.543 3.735\n",
"1707 104 PRO CD 1708 2.974 3.632 3.814\n",
"1708 104 PRO HD2 1709 3.000 3.576 3.916\n",
"1709 104 PRO HD3 1710 2.918 3.547 3.751\n",
"1710 105 ASP N 1711 3.094 4.076 3.730\n",
"1711 105 ASP H 1712 3.179 4.100 3.810\n",
"1712 105 ASP CA 1713 3.074 4.175 3.624\n",
"1713 105 ASP HA 1714 2.956 4.187 3.617\n",
"1714 105 ASP C 1715 3.147 4.134 3.497\n",
"1715 105 ASP O 1716 3.265 4.102 3.501\n",
"1716 105 ASP CB 1717 3.120 4.314 3.667\n",
"1717 105 ASP HB2 1718 3.082 4.354 3.773\n",
"1718 105 ASP HB3 1719 3.238 4.329 3.675\n",
"1719 105 ASP CG 1720 3.082 4.422 3.565\n",
"1720 105 ASP OD1 1721 3.152 4.435 3.462\n",
"1721 105 ASP OD2 1722 2.982 4.493 3.589\n",
"1722 106 ASP N 1723 3.075 4.133 3.385\n",
"1723 106 ASP H 1724 2.964 4.171 3.369\n",
"1724 106 ASP CA 1725 3.135 4.096 3.258\n",
"1725 106 ASP HA 1726 3.253 4.078 3.263\n",
"1726 106 ASP C 1727 3.128 4.214 3.161\n",
"1727 106 ASP O 1728 3.187 4.318 3.196\n",
"1728 106 ASP CB 1729 3.066 3.973 3.198\n",
"1729 106 ASP HB2 1730 2.954 3.989 3.159\n",
"1730 106 ASP HB3 1731 3.070 3.878 3.270\n",
"1731 106 ASP CG 1732 3.140 3.917 3.076\n",
"1732 106 ASP OD1 1733 3.255 3.960 3.048\n",
"1733 106 ASP OD2 1734 3.084 3.827 3.009\n",
"1734 106 ASP OXT 1735 3.086 4.199 3.047\n",
"\n",
"[1735 rows x 7 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"frag"
]
},
{
"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",
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| mit |
pedrosiracusa/pedrosiracusa.github.io | _notebooks/construindo-redes-sociais-com-dados-de-colecoes-biologicas.ipynb | 1 | 35306 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Construindo redes sociais com dados de coleções biológicas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Em um artigo anterior fiz uma breve caracterização das [redes sociais por trás do Herbário da UnB](https://medium.com/@pedrosiracusa/a-redes-sociais-por-tr%C3%A1s-do-herb%C3%A1rio-da-unb-e08980f049dc), mostrando uma nova perspectiva de aplicação para dados de coleções biológicas. Tal abordagem consiste em derivar interações sociais de colaboração entre coletores e caracterizar seus interesses taxonômicos a partir de registros de ocorrências de espécies, e incorpora conceitos e ferramentas vindos do campo de analítica de redes sociais.\n",
"Tive a oportunidade de desenvolver estas ideias durante minha [pesquisa de mestrado](https://tede.lncc.br/handle/tede/279), que resultou na síntese de dois modelos baseados em redes: as **Redes Espécie-Coletor (SCN)**; e as **Redes de Colaboração de Coletores (CWNs)**. Caso você ainda não tenha ouvido falar nestes modelos, recomendo a leitura do meu [artigo de divulgação](https://medium.com/@pedrosiracusa/a-redes-sociais-por-tr%C3%A1s-do-herb%C3%A1rio-da-unb-e08980f049dc) antes de continuar."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Neste artigo demonstrarei o processo de construção destes modelos em 3 etapas, a partir de um conjunto de dados de ocorrência de espécies e usando a biblioteca [**Caryocar**](https://github.com/pedrosiracusa/caryocar/) (escrita na linguagem *Python*). Aqui usarei novamente o conjunto de [dados do Herbário da UnB](https://www.gbif.org/dataset/d82d5a18-0428-4e52-be16-f509153e8126) (sigla UB), que podem ser baixados através da plataforma GBIF."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vamos começar importando as classes que implementam os modelos SCN e CWN:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": [
"remove_cell"
]
},
"outputs": [],
"source": [
"# este pedaço de código só é necessário para atualizar o PATH do Python\n",
"import sys,os\n",
"sys.path.insert(0,os.path.expanduser('~/Documents/caryocar'))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from caryocar.models import CWN, SCN"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"O pacote Caryocar também fornece algumas funções e classes auxiliares para realizar a limpeza dos dados."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from caryocar.cleaning import NamesAtomizer, namesFromString\n",
"from caryocar.cleaning import normalize, read_NamesMap_fromJson\n",
"from caryocar.cleaning import getNamesIndexes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Etapa 1. Leitura do conjunto de dados"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"O primeiro passo é ler o conjunto de dados de ocorrência de espécies.\n",
"Para isso vamos extender as funcionalidades da linguagem *Python* usando uma biblioteca muito útil para a análise de dados: a [Pandas](https://pandas.pydata.org/).\n",
"Com esta biblioteca, podemos carregar, transformar e analisar nosso conjunto de dados no ambiente de programação."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Com a função `read_csv` do *Pandas*, carregaremos nossos dados que estão no arquivo CSV e os colocaremos na estrutura de um *Data Frame*, que é basicamente uma tabela.\n",
"Esta função espera receber o nome do arquivo CSV que contém os dados, bem como uma lista com os nomes das colunas que estamos interessados em carregar.\n",
"Especificarei o caminho para o arquivo na variável `dsetPath` e a lista das colunas de interesse em `cols`.\n",
"O dataframe ficará armazenado na variável `occs_df`.\n",
"Para deixar este artigo o mais simples possível usarei apenas os campos essenciais:\n",
"* `recordedBy`: Armazena os nomes dos coletores responsáveis pelo registro. Caso haja mais que 1 coletor, os nomes são separados por ponto-e-vírgula;\n",
"* `species`: Armazena o nome científico, a nível de espécie, determinado para o espécime em questão."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"dsetPath = '/home/pedro/datasets/ub_herbarium/occurrence.csv'\n",
"cols = ['recordedBy','species']\n",
"\n",
"occs_df = pd.read_csv(dsetPath, sep='\\t', usecols=cols)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vamos dar uma olhada no jeitão do dataframe. Para isso, vamos pedir as 10 primeira linhas apenas."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>species</th>\n",
" <th>recordedBy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Lychnophora pinaster</td>\n",
" <td>Ferreira, VF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Lychnophora pinaster</td>\n",
" <td>Ferreira, VF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Lychnophora pinaster</td>\n",
" <td>Ferreira, VF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Lychnophora pinaster</td>\n",
" <td>Irwin, HS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Arundo donax</td>\n",
" <td>Gomes, SM; Silva, ALH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Arundo donax</td>\n",
" <td>Gomes, SM; Silva, ALH</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Terminalia fagifolia</td>\n",
" <td>Viana, G</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Dimorphandra mollis</td>\n",
" <td>Heringer, EP</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Dimorphandra mollis</td>\n",
" <td>Heringer, EP</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Caesalpinia echinata</td>\n",
" <td>Heringer, EP</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" species recordedBy\n",
"0 Lychnophora pinaster Ferreira, VF\n",
"1 Lychnophora pinaster Ferreira, VF\n",
"2 Lychnophora pinaster Ferreira, VF\n",
"3 Lychnophora pinaster Irwin, HS\n",
"4 Arundo donax Gomes, SM; Silva, ALH\n",
"5 Arundo donax Gomes, SM; Silva, ALH\n",
"6 Terminalia fagifolia Viana, G\n",
"7 Dimorphandra mollis Heringer, EP\n",
"8 Dimorphandra mollis Heringer, EP\n",
"9 Caesalpinia echinata Heringer, EP"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"occs_df.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Etapa 2: Limpeza dos dados"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Antes de construir o modelo, precisamos fazer uma limpeza de dados para garantir que eles estejam no formato adequado para a construção dos modelos. \n",
"O primeiro passo é filtrar os registros com elementos nulos (`NaN`) para cada um dos campos do dataframe. Um elemento nulo significa ausência de informação, e portanto não ajudará muito na construção dos nossos modelos.\n",
"Vejamos o número de nulos em cada campo:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"species 32711\n",
"recordedBy 9\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"occs_df.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A informação de coletor está ausente em apenas 9 dos registros. Vamos simplesmente eliminá-los. Um outro ponto é que para simplificar nossa modelagem, vou apenas usar registros que tenham sido identificados ao nível de espécie. Isso significa que teremos que descartar 32711 registros, nos quais a informação sobre a identidade de espécie está ausente."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"occs_df.dropna(how='any', inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Agora não temos mais nulos em nenhuma das colunas, e podemos prosseguir:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"species 0\n",
"recordedBy 0\n",
"dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"occs_df.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Atomização dos nomes de coletores"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"O campo de coletores (`recordedBy`) é fundamental para nossa modelagem, mas infelizmente costuma ser um pouco problemático.\n",
"O primeiro problema é que os nomes dos coletores não são **atômicos**. Isso significa múltiplos nomes podem ser codificados em um mesmo valor (no caso, a lista de nomes é codificada como uma única *string*, sendo cada nome separado por um ponto-e-vígula). \n",
"\n",
"Segundo as recomendações do [*Biodiversity Information Standards (TDWG)*](https://www.tdwg.org/), nomes de coletores devem ser incluídos, em geral, usando a seguinte regra: sobrenome com a primeira letra maiúscula, seguido por vírgula e espaço e iniciais do nome em letras maiúsculas, separadas por pontos (ex. Proença, C.E.B.).\n",
"Além disso, o TDWG recomenda que o separador utilizado para delimitar nomes de coletore deva ser o caractere pipe ( `|` ).\n",
"No entanto, o caractere usado no dataset do UB é o ponto-e-vírgula.\n",
"Isso não será um grande problema no nosso caso, já que neste dataset o ponto-e-vírgula é usado de forma consistente, em quase todos os registros."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Para proceder com a atomização dos nomes utilizaremos uma classe auxiliar, chamada `NamesAtomizer`. Criaremos o objeto atomizador e atribuiremos à variável `na`. Passaremos a função `namesFromString` que especifica as regras usadas para separar os nomes."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"na = NamesAtomizer(atomizeOp=namesFromString)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"O atomizador de nomes resolve a grande maioria dos casos. Mas existem alguns poucos registros com erros na delimitação dos nomes. Neste caso a correção deve ser feita fazendo a substituição em cada registro pela sua forma correta.\n",
"Para o dataset do UB, estas substituições estão especificadas no arquivo armazenado na variável `names_replaces_file`, abaixo:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"names_replaces_file = '/home/pedro/data/ub_collectors_replaces.json'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Só por curiosidade, vejamos o conteúdo deste arquivo:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\r\n",
" \"_replaces\": {\r\n",
" \"Barbosa; M.G.\": \"Barbosa, M.G.\",\r\n",
" \"Bueno; S.B.\": \"Bueno, S.B.\",\r\n",
" \"Carboni, M; Faraco, AG; Soares; P.G.; Sampaio, D; Breier, TB\": \"Carboni, M; Faraco, AG; Soares, P.G.; Sampaio, D; Breier, TB\",\r\n",
" \"Hatschbach, G; M.\": \"Hatschbach, G; Hatschbach, M\",\r\n",
" \"Hällström; E.\": \"Hällström, E.\",\r\n",
" \"Irwin, HS; Souza, R; Santos; RR\": \"Irwin, HS; Souza, R; Santos, RR\",\r\n",
" \"Kirkbride Junior, JH; Ono; E.K.M; et al.\": \"Kirkbride Junior, JH; Ono, E.K.M; et al.\",\r\n",
" \"Quintiliano; F.J.; Colvéquia; L.P.T; Silva; D.R.\": \"Quintiliano, F.J.; Colvéquia, L.P.T; Silva, D.R.\",\r\n",
" \"Silva; D.R.; Colvéquia; L.P.T\": \"Silva, D.R.; Colvéquia, L.P.T\",\r\n",
" \"Sr. Air, Sr. Milton, Rodrigo\": \"Sr. Air; Sr. Milton; Rodrigo\",\r\n",
" \"Sônia / Josefina\": \"Sônia; Josefina\",\r\n",
" \"Yushun.; K.\": \"Yushun., K.\"\r\n",
" }\r\n",
"}"
]
}
],
"source": [
"! cat {names_replaces_file}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Prosseguindo com a substituição:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"na.read_replaces(names_replaces_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Agora, com o auxílio do atomizador de nomes, vamos adicionar uma nova coluna ao dataframe, contendo os nomes dos coletores atomizados. Ela se chamará `recordedBy_atomized`:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"occs_df['recordedBy_atomized'] = na.atomize(occs_df['recordedBy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Normalização e mapeamento de nomes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Um segundo problema é que nomes de coletores podem ter sido escritos de algumas formas diferentes, seja por conta de erros ou omissão de partes do nome.\n",
"Por exemplo, o nome 'Proença, C.E.B.' pode ter alguns variantes, incluindo 'Proenca, C.E.B,', 'Proença, C.E.', Proença, C.'.\n",
"Precisamos pensar em uma forma para ligar todas essas variantes a um nome principal.\n",
"\n",
"A solução para este problema até o momento é armazenar um mapa ligando cada variante a uma forma normal do nome. O processo de **normalização** inclui a transformação do nome para uma forma simplificada. Isso significa que só usaremos caracteres em caixa-baixo, omitiremos acentos e pontuações, e removeremos caracteres não-alfanuméricos. \n",
"No exemplo citado acima, todos os nomes seriam mapeados para 'proenca,ceb'."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Para o conjunto de dados do UB, já tenho um mapa de nomes pronto, guardado no seguinte arquivo:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"namesMap_file = '/home/pedro/data/ub_namesmap.json'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Este arquivo é grande, mas vamos ver as 20 primeiras linhas para termos uma ideia:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\r\n",
" \"_map_prim_norm\": {\r\n",
" \".\": \"\",\r\n",
" \"1980 Sino-Amer Exped.\": \"sinoamerexped\",\r\n",
" \"?\": \"\",\r\n",
" \"A.J.N.V.\": \"ajnv\",\r\n",
" \"A.M.\": \"am\",\r\n",
" \"Abbas, B\": \"abbas,b\",\r\n",
" \"Abdala, GC\": \"abdala,gc\",\r\n",
" \"Abdo, MSA\": \"abdo,msa\",\r\n",
" \"Abdon\": \"abdon\",\r\n",
" \"Abe, LB\": \"abe,lb\",\r\n",
" \"Abe, LM\": \"abe,lm\",\r\n",
" \"Abrahim, MA\": \"abrahim,ma\",\r\n",
" \"Abreu, CG\": \"abreu,cg\",\r\n",
" \"Abreu, GX\": \"abreu,gx\",\r\n",
" \"Abreu, I\": \"abreu,i\",\r\n",
" \"Abreu, LC\": \"abreu,lc\",\r\n",
" \"Abreu, LCR\": \"abreu,lcr\",\r\n",
" \"Abreu, M\": \"abreu,m\",\r\n"
]
}
],
"source": [
"! head {namesMap_file} -n 20"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note que alguns nomes de coletores que não eram nulos porêm remetem à falta da informação (por exemplo '.', '?') são mapeados para uma *string* vazia. Mais tarde iremos filtrar estes nomes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vamos agora ler o mapa de nomes do arquivo e armazená-lo na variável `nm`."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"nm = read_NamesMap_fromJson(namesMap_file, normalizationFunc=normalize)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Caso haja nomes de coletores que não estão no arquivo, vamos nos assegurar de que eles serão inseridos:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"collectors_names = list(set( n for n,st,num in na.getCachedNames() ))\n",
"nm.addNames(collectors_names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assim, este mapa nos permite buscar, para cada variante do nome, sua forma normal:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'proenca,ceb'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nm.getMap()['Proença, CEB']"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'proenca,ceb'"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nm.getMap()['Proença, C']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A figura abaixo ilustra as etapas envolvidas no preprocessamento do campo dos coletores, conforme descrito."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"{:width=\"700px\"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### O índice de nomes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finalmente, vamos construir um índice de nomes, apenas para mantermos a referência de quais linhas do dataframe cada coletor aparece. Para isso usaremos a função `getNamesIndexes`. Precisamos informar o nome do dataframe, o nome da coluna que armazena os nomes atomizados e o mapa de nomes. Mas enfatizo que este passo não é necessário para a construção dos modelos (apesar de ser útil para algumas análises)."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"ni = getNamesIndexes(occs_df,'recordedBy_atomized', namesMap=nm.getMap())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Etapa 3: Construindo os modelos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Chegamos na etapa que realmente interessa. Já temos um dataframe com os dados minimamente limpos e estruturados, e podemos então construir os modelos!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rede Espécie-Coletor (SCN)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Redes espécie-coletor modelam relações de interesse, envolvendo necessariamente um **coletor** e uma **espécie**. A semântica destas relações pode ser descrita como `coletor -[registra]-> espécie` ou, por outro lado, `espécie-[é registrada por]-> coletor`. A figura abaixo exemplifica esta estrutura (a).\n",
"\n",
"Como o modelo envolve duas classes de entidades (coletores e espécies), existem duas perspectivas adicionais que podem ser exploradas: Podemos investigar o quão fortemente dois coletores estão associados entre si em termos de seus interesses em comum (b); bem como quão fortemente duas espécies estão associadas entre si em termos do conjunto de coletores que as registram (c).\n",
"\n",
"Nos referimos às perspectivas (b) e (c) como **projeções** da rede (a). Estas projeções são obtidas simplesmente ligando entidades da mesma classe tomando como base o número de entidades da classe oposta que eles compartilham, na estrutura (a)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"{:width=\"500px\"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vamos então ao código. Construiremos a rede espécie-coletor usando a classe `SCN`, disponível no pacote Caryocar. Para sua construção, devemos fornecer:\n",
"* Uma lista de espécies, neste caso a coluna do dataframe `occs_df['species']`;\n",
"* Uma lista contendo listas de coletores, neste caso a coluna do dataframe `occs_df['recordedBy_atomized']`;\n",
"* Um mapa de nomes."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"scn = SCN(species=occs_df['species'], collectors=occs_df['recordedBy_atomized'], namesMap=nm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Após a construção do modelo, vamos remover nomes de coletores indevidos, como 'etal', 'ilegivel', 'incognito'."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"cols_to_filter = ['','ignorado','ilegivel','incognito','etal']\n",
"scn.remove_nodes_from(cols_to_filter)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vejamos então um pequeno resumo sobre esta rede. Este pedaço de código pode ser um pouco feio, mas o que importa mesmo aqui são as informações imprimidas abaixo dele."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rede Espécie-Coletor (SCN)\n",
"==========================\n",
"Número total de coletores:6795\n",
"Número total de espécies: 15374\n",
"Em média, um coletor registra 21 espécies distintas\n",
"Em média, uma espécie é registrada por 9 coletores distintos\n",
"Número total de arestas: 143861\n",
"\n",
"Top-10 coletores mais produtivos:\n",
" irwin,hs (4557 especies distintas)\n",
" heringer,ep (2595 especies distintas)\n",
" anderson,wr (2163 especies distintas)\n",
" proenca,ceb (1906 especies distintas)\n",
" ratter,ja (1805 especies distintas)\n",
" faria,jeq (1764 especies distintas)\n",
" eiten,g (1598 especies distintas)\n",
" souza,rr (1555 especies distintas)\n",
" harley,rm (1514 especies distintas)\n",
" santos,rrb (1510 especies distintas)\n",
"\n",
"Top-10 espécies coletadas:\n",
" Myrcia splendens (388 coletores distintos)\n",
" Myrcia guianensis (318 coletores distintos)\n",
" Eugenia punicifolia (264 coletores distintos)\n",
" Casearia sylvestris (258 coletores distintos)\n",
" Palicourea rigida (241 coletores distintos)\n",
" Myrcia tomentosa (239 coletores distintos)\n",
" Qualea parviflora (232 coletores distintos)\n",
" Solanum lycocarpum (228 coletores distintos)\n",
" Piper aduncum (211 coletores distintos)\n",
" Miconia albicans (201 coletores distintos)\n"
]
}
],
"source": [
"n_cols = len(scn.listCollectorsNodes())\n",
"cols_degrees = scn.degree(scn.listCollectorsNodes())\n",
"n_spp = len(scn.listSpeciesNodes())\n",
"spp_degrees = scn.degree(scn.listSpeciesNodes())\n",
"\n",
"print(\n",
"f\"\"\"Rede Espécie-Coletor (SCN)\n",
"==========================\n",
"Número total de coletores:{n_cols}\n",
"Número total de espécies: {n_spp}\n",
"Em média, um coletor registra {round( sum( k for n,k in cols_degrees)/n_cols)} espécies distintas\n",
"Em média, uma espécie é registrada por {round( sum( k for n,k in spp_degrees)/n_spp)} coletores distintos\n",
"Número total de arestas: {len(scn.edges)}\\n\"\"\")\n",
"print(\"Top-10 coletores mais produtivos:\")\n",
"for n,k in sorted(cols_degrees,key=lambda x:x[1],reverse=True)[:10]:\n",
" print(f\" {n} ({k} especies distintas)\")\n",
"print(\"\\nTop-10 espécies coletadas:\")\n",
"for n,k in sorted(spp_degrees,key=lambda x:x[1],reverse=True)[:10]:\n",
" print(f\" {n} ({k} coletores distintos)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Um aspecto interessante a ser notado é a distribuição de grau (número de conexões de um vértice) nesta rede.\n",
"Embora em média um coletor registre 21 espécies diferentes, os coletores mais produtivos registraram mais de 1000!\n",
"De forma simlar, embora em média uma espécie seja registrada por 9 coletores distintos, as primeiras 10 foram registradas por mais de 200 coletores cada.\n",
"Embora esteja fora do escopo deste artigo, é fácil mostrar que a distribuição desta rede está longe de ser normal. Na verdade, é aproximada por uma lei de potência."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Isso significa que enquanto uma grande maioria de coletores registra pouquíssimas espécies diferentes, alguns poucos (chamados *hubs*, ou coletores-chave) registram um número muito acima da média.\n",
"De forma análoga enquanto uma grande maioria de espécies foi coletadas por apenas um ou poucos coletores diferentes, algumas poucas foram coletadas por um grande número de coletores distintos."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rede de Colaboração de Coletores (CWN)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Redes de colaboração de coletores (CWNs), como o nome sugere, modelam relações de colaboração que se estabelecem entre coletores enquanto registram espécies em campo. Uma ligação entre pares de coletores é criada ou fortalecida cada vez que eles co-autoram um registro de espécie. Sendo assim, a semântica destas relações é descrita como `coletor -[coleta espécime com]-> coletor`. A figura abaixo ilustra a estrutura destas redes. É importante notar que, diferente das SCNs, nas CWNs a identidade taxonômica de cada registro não é representada em sua estrutura. Coletores que nunca colaboraram aparecem como vértices isolados na rede."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"{:width=\"300px\"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"O pacote Caryocar também fornece a classe `SCN`, que facilita a construção de redes de colaboração de coletores. Para sua construção, devemos fornecer:\n",
"\n",
"* Uma lista contendo listas de coletores (cliques), neste caso a coluna do dataframe `occs_df['recordedBy_atomized']`;\n",
"* Um mapa de nomes."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"cwn = CWN(cliques=occs_df['recordedBy_atomized'],namesMap=nm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assim como fizemos com a SCN, vamos remover nomes de coletores indevidos"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"cols_to_filter = ['','ignorado','ilegivel','incognito','etal']\n",
"cwn.remove_nodes_from(cols_to_filter)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vejamos um resumo sobre a rede:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rede de Colaboração de Coletores (CWN)\n",
"======================================\n",
"Número total de coletores:6795\n",
"Número total de arestas: 10435\n",
"Em média, um coletor colabora com 3 pares ao longo de sua carreira\n",
"No total 2684 coletores nunca colaboraram\n",
"No total, 1578 coletores colaboraram com mais que 3 colegas\n",
"\n",
"Top-10 coletores mais colaborativos:\n",
" proenca,ceb (219 colegas)\n",
" faria,jeq (119 colegas)\n",
" ratter,ja (111 colegas)\n",
" munhoz,cbr (109 colegas)\n",
" silva,ma (93 colegas)\n",
" oliveira,rc (92 colegas)\n",
" harley,rm (90 colegas)\n",
" mendes,vc (89 colegas)\n",
" souza,vc (89 colegas)\n",
" carvalho,avm (88 colegas)\n",
"\n",
"Top-10 coletores sem colaborações com maior número de registros:\n",
" leite,alta (2094 registros, 0 colaborações)\n",
" touw,a (375 registros, 0 colaborações)\n",
" schiffner,v (283 registros, 0 colaborações)\n",
" oliveira,e (258 registros, 0 colaborações)\n",
" coelho,gsf (227 registros, 0 colaborações)\n",
" rocha,mal (162 registros, 0 colaborações)\n",
" aquino,ppu (155 registros, 0 colaborações)\n",
" pinto,rnm (144 registros, 0 colaborações)\n",
" silva,nf (133 registros, 0 colaborações)\n",
" rios,mns (133 registros, 0 colaborações)\n"
]
}
],
"source": [
"n_cols = len(cwn.nodes)\n",
"cols_degrees = cwn.degree()\n",
"\n",
"print(\n",
"f\"\"\"Rede de Colaboração de Coletores (CWN)\n",
"======================================\n",
"Número total de coletores:{n_cols}\n",
"Número total de arestas: {len(cwn.edges)}\n",
"Em média, um coletor colabora com {round( sum(k for n,k in cols_degrees)/n_cols )} pares ao longo de sua carreira\n",
"No total {len([ n for n,k in cols_degrees if k==0 ])} coletores nunca colaboraram\n",
"No total, {len([ n for n,k in cols_degrees if k>3 ])} coletores colaboraram com mais que 3 colegas\\n\"\"\")\n",
"print(\"Top-10 coletores mais colaborativos:\")\n",
"for n,k in sorted(cols_degrees,key=lambda x:x[1],reverse=True)[:10]:\n",
" print(f\" {n} ({k} colegas)\")\n",
" \n",
"print(\"\\nTop-10 coletores sem colaborações com maior número de registros:\") \n",
"for n,k, in sorted([ (n,d['count']) for n,d in cwn.nodes(data=True) if cwn.degree(n)==0 ],key=lambda x: x[1], reverse=True)[:10]:\n",
" print(f\" {n} ({cwn.nodes[n]['count']} registros, 0 colaborações)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Considerações finais"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Meu objetivo neste artigo foi demonstrar o processo de construção dos modelos SCN e CWN a partir de um conjunto de dados de ocorrência de espécies, usando o pacote Caryocar.\n",
"Mostrei também como proceder com a limpeza dos dados de ocorrência de espécies, fazendo todas as transformações necessárias para que o conjunto de dados torne-se adequado para a modelagem.\n",
"\n",
"Um ponto a ser ressaltado é sobre a importância em se verificar a qualidade do campo do coletor, normalmente subutilizado na maioria das aplicações de dados de coleções biológicas.\n",
"Dentre os problemas mais comuns estão a inclusão apenas do coletor principal (sendo os coletores auxiliares omitidos ou agrupados no nome 'et. al'), o não-cumprimento dos padrões recomendados para a escrita dos nomes, e a ausência de identificadores únicos para a distinção dos coletores.\n",
"De forma geral, estes fatores são a maior limitação para a construção dos modelos.\n",
"A alta qualidade deste campo no conjunto de dados do herbário da UnB foi um dos fatores decisivos para que fosse escolhido como prova de conceito.\n",
"\n",
"Os modelos SCN e CWN permitem investigar o aspecto humano envolvido na formação de coleções biológicas, e portanto abrem portas para novos tipos de análises e aplicações para dados de ocorrência de espécies.\n",
"Pretendo explorar e demonstrar algumas dessas possibilidades em artigos futuros."
]
}
],
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| mit |
jepayne/NYU | Projects/MacroFin/.ipynb_checkpoints/Ode exampls-checkpoint.ipynb | 1 | 10757 | {
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{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Examples of python ode solvers"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Event locations"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def odelay(func, y0, xspan, events, TOLERANCE = 1e-6, fsolve_args=None, **kwargs):\n",
" '''Solve an ODE with events.\n",
" func is callable, with signature func(Y, x)\n",
" y0 are the initial conditions xspan is what you want to integrate\n",
" over\n",
" events is a list of callable functions with signature event(Y, x).\n",
" These functions return zero when an event has happened.\n",
" TOLERANCE is what is used to identify when an event has occurred.\n",
" \n",
" [value, isterminal, direction] = event(Y, x)\n",
" value is the value of the event function. When value = 0, an event\n",
" is triggered\n",
" isterminal = True if the integration is to terminate at a zero of\n",
" this event function, otherwise, False.\n",
" direction = 0 if all zeros are to be located (the default), +1\n",
" if only zeros where the event function is increasing, and -1 if\n",
" only zeros where the event function is decreasing.\n",
" fsolve_args is a dictionary of options for fsolve\n",
" \n",
" kwargs are any additional options you want to send to odeint.\n",
" Returns [x, y, te, ye, ie]\n",
" x is the independent variable array\n",
" y is the solution\n",
" te is an array of independent variable values where events occurred\n",
" ye is an array of the solution at the points where events occurred\n",
" ie is an array of indices indicating which event function occurred.\n",
" '''\n",
" if 'full_output' in kwargs:\n",
" raise Exception('full_output not supported as an option')\n",
"\n",
" if fsolve_args is None:\n",
" fsolve_args = {}\n",
"\n",
" x0 = xspan[0] # initial point\n",
"\n",
" X = [x0]\n",
" sol = [y0]\n",
" TE, YE, IE = [], [], [] # to store where events occur\n",
" \n",
" # initial value of events\n",
" e = np.zeros((len(events), len(xspan)))\n",
" for i,event in enumerate(events):\n",
" e[i,0], isterminal, direction = event(y0, x0)\n",
"\n",
" # now we step through the integration\n",
" for i, x1 in enumerate(xspan[0:-1]):\n",
" x2 = xspan[i + 1]\n",
" f1 = sol[i]\n",
"\n",
" f2 = odeint(func, f1, [x1, x2], **kwargs)\n",
" \n",
" X += [x2]\n",
" sol += [f2[-1,:]]\n",
"\n",
" # check event functions. At each step we compute the event\n",
" # functions, and check if they have changed sign since the\n",
" # last step. If they changed sign, it implies a zero was\n",
" # crossed. \n",
" for j, event in enumerate(events):\n",
" e[j, i + 1], isterminal, direction = event(sol[i + 1], X[i + 1])\n",
" \n",
" if ((e[j, i + 1] * e[j, i] < 0) # sign change in\n",
" # event means zero\n",
" # crossing\n",
" or np.abs(e[j, i + 1]) < TOLERANCE # this point is\n",
" # practically 0\n",
" or np.abs(e[j, i]) < TOLERANCE):\n",
"\n",
" xLt = X[-1] # Last point\n",
" fLt = sol[-1]\n",
" eLt = e[j, i+1]\n",
"\n",
" # we need to find a value of x that makes the event zero\n",
" def objective(x):\n",
" # evaluate ode from xLT to x\n",
" txspan = [xLt, x]\n",
" tempsol = odeint(func, fLt, txspan, **kwargs)\n",
" sol = tempsol[-1, :]\n",
" val, isterminal, direction = event(sol, x)\n",
" return val\n",
"\n",
" from scipy.optimize import fsolve\n",
" xZ, = fsolve(objective, xLt, **fsolve_args) # this should be the\n",
" # value of x that makes\n",
" # the event zero\n",
"\n",
" # now evaluate solution at this point, so we can\n",
" # record the function values here.\n",
" txspan = [xLt, xZ]\n",
" tempsol = odeint(func, fLt, txspan, **kwargs)\n",
" fZ = tempsol[-1,:]\n",
"\n",
" vZ, isterminal, direction = event(fZ, xZ)\n",
"\n",
" COLLECTEVENT = False\n",
" if direction == 0:\n",
" COLLECTEVENT = True\n",
" elif (e[j, i + 1] > e[j, i] ) and direction == 1:\n",
" COLLECTEVENT = True\n",
" elif (e[j, i + 1] < e[j, i] ) and direction == -1:\n",
" COLLECTEVENT = True\n",
" \n",
" if COLLECTEVENT:\n",
" TE.append(xZ)\n",
" YE.append(fZ)\n",
" IE.append(j)\n",
"\n",
" if isterminal:\n",
" X[-1] = xZ\n",
" sol[-1] = fZ\n",
" return (np.array(X), \n",
" np.array(sol), \n",
" np.array(TE), \n",
" np.array(YE), \n",
" np.array(IE))\n",
"\n",
" # at the end, return what we have\n",
" return (np.array(X), \n",
" np.array(sol), \n",
" np.array(TE), \n",
" np.array(YE), \n",
" np.array(IE))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pycse import *\n",
"import numpy as np\n",
"\n",
"def myode(f, x):\n",
" return 3*x**2 + 12*x -4\n",
"\n",
"def event1(f, x):\n",
" 'an event is when f = 0 and event is decreasing'\n",
" isterminal = True\n",
" direction = -1\n",
" return f, isterminal, direction\n",
"\n",
"def event2(f, x):\n",
" 'an event is when f = 0 and increasing'\n",
" isterminal = False\n",
" direction = 1\n",
" return f, isterminal, direction\n",
"\n",
"f0 = -120\n",
"\n",
"xspan = np.linspace(-8, 4)\n",
"X, F, TE, YE, IE = odelay(myode, f0, xspan, events=[event1, event2])\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.plot(X, F, '.-')\n",
"\n",
"# plot the event locations.use a different color for each event\n",
"colors = 'rg'\n",
"\n",
"for x,y,i in zip(TE, YE, IE):\n",
" plt.plot([x], [y], 'o', color=colors[i])\n",
"\n",
"plt.savefig('images/event-ode-2.png')\n",
"plt.show()\n",
"print TE, YE, IE"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-5-74ebeff03583>, line 35)",
"output_type": "pyerr",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-5-74ebeff03583>\"\u001b[0;36m, line \u001b[0;32m35\u001b[0m\n\u001b[0;31m print TE, YE, IE\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"from scipy.integrate import odeint\n",
"\n",
"def dCadt(Ca, t):\n",
" \"the ode function\"\n",
" k = 0.23\n",
" return -k * Ca**2\n",
"\n",
"Ca0 = 2.3\n",
"\n",
"# create lists to store time span and solution\n",
"tspan = [0, ]\n",
"sol = [Ca0,]\n",
"i = 0\n",
"\n",
"while i < 100: # take max of 100 steps\n",
" t1 = tspan[i]\n",
" Ca = sol[i]\n",
"\n",
" # pick the next time using a Newton-Raphson method\n",
" # we want f(t, Ca) = (Ca(t) - 1)**2 = 0\n",
" # df/dt = df/dCa dCa/dt\n",
" # = 2*(Ca - 1) * dCadt\n",
" t2 = t1 - (Ca - 1.0)**2 / (2 * (Ca - 1) *dCadt(Ca, t1))\n",
"\n",
" f = odeint(dCadt, Ca, [t1, t2])\n",
"\n",
" if np.abs(Ca - 1.0) <= 1e-4:\n",
" print 'Solution reached at i = {0}'.format(i)\n",
" break\n",
"\n",
" tspan += [t2]\n",
" sol.append(f[-1][0])\n",
" i += 1\n",
"\n",
"print 'At t={0:1.2f} Ca = {1:1.3f}'.format(tspan[-1], sol[-1])\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.plot(tspan, sol, 'bo')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-6-0d9915ffb09e>, line 29)",
"output_type": "pyerr",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-6-0d9915ffb09e>\"\u001b[0;36m, line \u001b[0;32m29\u001b[0m\n\u001b[0;31m print 'Solution reached at i = {0}'.format(i)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
cloudera/ibis | docs/source/tutorial/03-Expressions-Lazy-Mode-Logging.ipynb | 1 | 177179 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Expressions, lazy mode and logging queries\n",
"\n",
"So far, we have seen Ibis in interactive mode. Interactive mode (also known as eager mode) makes Ibis return the\n",
"results of an operation immediately.\n",
"\n",
"In most cases, instead of using interactive mode, it makes more sense to use the default lazy mode.\n",
"In lazy mode, Ibis won't be executing the operations automatically, but instead, will generate an\n",
"expression to be executed at a later time.\n",
"\n",
"Let's see this in practice, starting with the same example as in previous tutorials - the geography database."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import ibis\n",
"\n",
"\n",
"connection = ibis.sqlite.connect(os.path.join('data', 'geography.db'))\n",
"countries = connection.table('countries')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In previous tutorials, we set interactive mode to `True`, and we obtained the result\n",
"of every operation."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
" name continent population\n",
"0 Andorra EU 84000\n",
"1 United Arab Emirates AS 4975593\n",
"2 Afghanistan AS 29121286"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ibis.options.interactive = True\n",
"\n",
"countries['name', 'continent', 'population'].limit(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But now let's see what happens if we leave the `interactive` option to `False` (the default),\n",
"and we operate in lazy mode."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"ref_0\n",
"SQLiteTable[table]\n",
" name: countries\n",
" schema:\n",
" iso_alpha2 : string\n",
" iso_alpha3 : string\n",
" iso_numeric : int32\n",
" fips : string\n",
" name : string\n",
" capital : string\n",
" area_km2 : float64\n",
" population : int32\n",
" continent : string\n",
"\n",
"ref_1\n",
"Selection[table]\n",
" table:\n",
" Table: ref_0\n",
" selections:\n",
" name = Column[string*] 'name' from table\n",
" ref_0\n",
" continent = Column[string*] 'continent' from table\n",
" ref_0\n",
" population = Column[int32*] 'population' from table\n",
" ref_0\n",
"\n",
"Limit[table]\n",
" table:\n",
" Table: ref_1\n",
" n:\n",
" 3\n",
" offset:\n",
" 0"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ibis.options.interactive = False\n",
"\n",
"countries['name', 'continent', 'population'].limit(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What we find is the graph of the expressions that would return the desired result instead of the result itself.\n",
"\n",
"Let's analyze the expressions in the graph:\n",
"\n",
"- We query the `countries` table (all rows and all columns)\n",
"- We select the `name`, `continent` and `population` columns\n",
"- We limit the results to only the first `3` rows\n",
"\n",
"Now consider that the data is in a database, possibly in a different host than the one executing Ibis.\n",
"Also consider that the results returned to the user need to be moved to the memory of the host executing Ibis.\n",
"\n",
"When using interactive (or eager) mode, if we perform one operation at a time, we would do:\n",
"\n",
"- We would move all the rows and columns from the backend (database, big data system, etc.) into memory\n",
"- Once in memory, we would discard all the columns but `name`, `continent` and `population`\n",
"- After that, we would discard all the rows, except the first `3`\n",
"\n",
"This is not very efficient. If you consider that the table can have millions of rows, backed by a\n",
"big data system like Spark or Impala, this may not even be possible (not enough memory to load all the data).\n",
"\n",
"The solution is to use lazy mode. In lazy mode, instead of obtaining the results after each operation,\n",
"we build an expression (a graph) of all the operations that need to be done. After all the operations\n",
"are recorded, the graph is sent to the backend which will perform the operation in an efficient way - only\n",
"moving to memory the required data.\n",
"\n",
"You can think of this as writing a shopping list and requesting someone to go to the supermarket and buy\n",
"everything you need once the list is complete. As opposed as getting someone to bring all the products of\n",
"the supermarket to your home and then return everything you don't want.\n",
"\n",
"Let's continue with our example, save the expression in a variable `countries_expression`, and check its type."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ibis.expr.types.TableExpr"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"countries_expression = countries['name', 'continent', 'population'].limit(3)\n",
"type(countries_expression)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The type is an Ibis `TableExpr`, since the result is a table (in a broad way, you can consider it a dataframe).\n",
"\n",
"Now we have our query instructions (our expression, fetching only 3 columns and 3 rows) in the variable `countries_expression`.\n",
"\n",
"At this point, nothing has been requested from the database. We have defined what we want to extract, but we didn't\n",
"request it from the database yet. We can continue building our expression if we haven't finished yet. Or once we\n",
"are done, we can simply request it from the database using the method `.execute()`."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>continent</th>\n",
" <th>population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Andorra</td>\n",
" <td>EU</td>\n",
" <td>84000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>United Arab Emirates</td>\n",
" <td>AS</td>\n",
" <td>4975593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Afghanistan</td>\n",
" <td>AS</td>\n",
" <td>29121286</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name continent population\n",
"0 Andorra EU 84000\n",
"1 United Arab Emirates AS 4975593\n",
"2 Afghanistan AS 29121286"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"countries_expression.execute()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can build other types of expressions, for example, one that instead of returning a table,\n",
"returns a columns."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"ref_0\n",
"SQLiteTable[table]\n",
" name: countries\n",
" schema:\n",
" iso_alpha2 : string\n",
" iso_alpha3 : string\n",
" iso_numeric : int32\n",
" fips : string\n",
" name : string\n",
" capital : string\n",
" area_km2 : float64\n",
" population : int32\n",
" continent : string\n",
"\n",
"population_in_millions = Divide[float64*]\n",
" left:\n",
" population = Column[int32*] 'population' from table\n",
" ref_0\n",
" right:\n",
" Literal[int32]\n",
" 1000000"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"population_in_millions = (countries['population'] / 1_000_000).name('population_in_millions')\n",
"population_in_millions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we check its type, we can see how it is a `FloatingColumn` expression."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ibis.expr.types.FloatingColumn"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(population_in_millions)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can combine the previous expression to be a column of a table expression."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"ref_0\n",
"SQLiteTable[table]\n",
" name: countries\n",
" schema:\n",
" iso_alpha2 : string\n",
" iso_alpha3 : string\n",
" iso_numeric : int32\n",
" fips : string\n",
" name : string\n",
" capital : string\n",
" area_km2 : float64\n",
" population : int32\n",
" continent : string\n",
"\n",
"ref_1\n",
"Selection[table]\n",
" table:\n",
" Table: ref_0\n",
" selections:\n",
" name = Column[string*] 'name' from table\n",
" ref_0\n",
" continent = Column[string*] 'continent' from table\n",
" ref_0\n",
" population_in_millions = Divide[float64*]\n",
" left:\n",
" population = Column[int32*] 'population' from table\n",
" ref_0\n",
" right:\n",
" Literal[int32]\n",
" 1000000\n",
"\n",
"Limit[table]\n",
" table:\n",
" Table: ref_1\n",
" n:\n",
" 3\n",
" offset:\n",
" 0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"countries['name', 'continent', population_in_millions].limit(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we are in lazy mode (not interactive), those expressions don't request any data from the database\n",
"unless explicitly requested with `.execute()`.\n",
"\n",
"## Logging queries\n",
"\n",
"For SQL backends (and for others when it makes sense), the query sent to the database can be logged.\n",
"This can be done by setting the `verbose` option to `True`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SELECT t0.name, t0.continent, t0.population / CAST(? AS REAL) AS population_in_millions \n",
"FROM base.countries AS t0\n",
" LIMIT ? OFFSET ?\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>continent</th>\n",
" <th>population_in_millions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Andorra</td>\n",
" <td>EU</td>\n",
" <td>0.084000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>United Arab Emirates</td>\n",
" <td>AS</td>\n",
" <td>4.975593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Afghanistan</td>\n",
" <td>AS</td>\n",
" <td>29.121286</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name continent population_in_millions\n",
"0 Andorra EU 0.084000\n",
"1 United Arab Emirates AS 4.975593\n",
"2 Afghanistan AS 29.121286"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ibis.options.verbose = True\n",
"\n",
"countries['name', 'continent', population_in_millions].limit(3).execute()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, the logging is done to the terminal, but we can process the query with a custom function.\n",
"This allows us to save executed queries to a file, save to a database, send them to a web service, etc.\n",
"\n",
"For example, to save queries to a file, we can write a custom function that given a query, saves it to a\n",
"log file."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import datetime\n",
"\n",
"def log_query_to_file(query):\n",
" \"\"\"\n",
" Log queries to `data/tutorial_queries.log`.\n",
" \n",
" Each file is a query. Line breaks in the query are represented with the string '\\n'.\n",
" \n",
" A timestamp of when the query is executed is added.\n",
" \"\"\"\n",
" fname = os.path.join('data', 'tutorial_queries.log')\n",
" query_in_a_single_line = query.replace('\\n', r'\\n')\n",
" with open(fname, 'a') as f:\n",
" f.write(f'{datetime.datetime.now()} - {query_in_a_single_line}\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we can set the `verbose_log` option to the custom function, execute one query,\n",
"wait one second, and execute another query."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>continent</th>\n",
" <th>population_in_millions</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Andorra</td>\n",
" <td>EU</td>\n",
" <td>0.084000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>United Arab Emirates</td>\n",
" <td>AS</td>\n",
" <td>4.975593</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Afghanistan</td>\n",
" <td>AS</td>\n",
" <td>29.121286</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name continent population_in_millions\n",
"0 Andorra EU 0.084000\n",
"1 United Arab Emirates AS 4.975593\n",
"2 Afghanistan AS 29.121286"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import time\n",
"\n",
"ibis.options.verbose_log = log_query_to_file\n",
"\n",
"countries.execute()\n",
"time.sleep(1.)\n",
"countries['name', 'continent', population_in_millions].limit(3).execute()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This has created a log file in `data/tutorial_queries.log` where the executed queries have been logged."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2020-07-02 17:51:02.760861 - SELECT t0.iso_alpha2, t0.iso_alpha3, t0.iso_numeric, t0.fips, t0.name, t0.capital, t0.area_km2, t0.population, t0.continent \\nFROM base.countries AS t0\\n LIMIT ? OFFSET ?\n",
"2020-07-02 17:51:03.779023 - SELECT t0.name, t0.continent, t0.population / CAST(? AS REAL) AS population_in_millions \\nFROM base.countries AS t0\\n LIMIT ? OFFSET ?\n"
]
}
],
"source": [
"!cat data/tutorial_queries.log"
]
}
],
"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
}
| apache-2.0 |
28ideas/quant-econ | solutions/finite_mc_solutions.ipynb | 2 | 44281 | {
"metadata": {
"name": "",
"signature": "sha256:ad347f154a4476ca892ae2e0bd2a377cfaa70b4b3a4ce07ac450c08f861925e9"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"quant-econ Solutions: Finite Markov Chains"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Solutions for http://quant-econ.net/finite_markov.html"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import print_function, division # Omit for Python 3.x\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from quantecon import mc_compute_stationary, mc_sample_path\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Exercise 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Compute the fraction of time that the worker spends unemployed,\n",
"and compare it to the stationary probability.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"alpha = beta = 0.1\n",
"N = 10000\n",
"p = beta / (alpha + beta)\n",
"\n",
"P = ((1 - alpha, alpha), # Careful: P and p are distinct\n",
" (beta, 1 - beta))\n",
"P = np.array(P)\n",
"\n",
"fig, ax = plt.subplots(figsize=(9, 6))\n",
"ax.set_ylim(-0.25, 0.25)\n",
"ax.grid()\n",
"ax.hlines(0, 0, N, lw=2, alpha=0.6) # Horizonal line at zero\n",
"\n",
"for x0, col in ((0, 'blue'), (1, 'green')):\n",
" # == Generate time series for worker that starts at x0 == #\n",
" X = mc_sample_path(P, x0, N)\n",
" # == Compute fraction of time spent unemployed, for each n == #\n",
" X_bar = (X == 0).cumsum() / (1 + np.arange(N, dtype=float)) \n",
" # == Plot == #\n",
" ax.fill_between(range(N), np.zeros(N), X_bar - p, color=col, alpha=0.1)\n",
" ax.plot(X_bar - p, color=col, label=r'$X_0 = \\, {} $'.format(x0))\n",
" ax.plot(X_bar - p, 'k-', alpha=0.6) # Overlay in black--make lines clearer\n",
"\n",
"ax.legend(loc='upper right')\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"<matplotlib.legend.Legend at 0x54ac490>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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LxKNwkKRFCCGEEG8F6WkRQgghRK6TnhYhhBBCFFqStIgsSX1YWSQeyiMxURaJR+EgSYsQ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\nDwi+MQaRVV7HAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x54b14d0>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Exercise 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First save the data into a file called `web_graph_data.txt` by executing the next cell"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%file web_graph_data.txt\n",
"a -> d;\n",
"a -> f;\n",
"b -> j;\n",
"b -> k;\n",
"b -> m;\n",
"c -> c;\n",
"c -> g;\n",
"c -> j;\n",
"c -> m;\n",
"d -> f;\n",
"d -> h;\n",
"d -> k;\n",
"e -> d;\n",
"e -> h;\n",
"e -> l;\n",
"f -> a;\n",
"f -> b;\n",
"f -> j;\n",
"f -> l;\n",
"g -> b;\n",
"g -> j;\n",
"h -> d;\n",
"h -> g;\n",
"h -> l;\n",
"h -> m;\n",
"i -> g;\n",
"i -> h;\n",
"i -> n;\n",
"j -> e;\n",
"j -> i;\n",
"j -> k;\n",
"k -> n;\n",
"l -> m;\n",
"m -> g;\n",
"n -> c;\n",
"n -> j;\n",
"n -> m;\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Overwriting web_graph_data.txt\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\"\"\"\n",
"Return list of pages, ordered by rank\n",
"\"\"\"\n",
"import numpy as np\n",
"from operator import itemgetter\n",
"import re\n",
"\n",
"infile = 'web_graph_data.txt'\n",
"alphabet = 'abcdefghijklmnopqrstuvwxyz'\n",
"\n",
"n = 14 # Total number of web pages (nodes)\n",
"\n",
"# == Create a matrix Q indicating existence of links == #\n",
"# * Q[i, j] = 1 if there is a link from i to j\n",
"# * Q[i, j] = 0 otherwise\n",
"Q = np.zeros((n, n), dtype=int)\n",
"f = open(infile, 'r')\n",
"edges = f.readlines()\n",
"f.close()\n",
"for edge in edges:\n",
" from_node, to_node = re.findall('\\w', edge)\n",
" i, j = alphabet.index(from_node), alphabet.index(to_node)\n",
" Q[i, j] = 1\n",
"# == Create the corresponding Markov matrix P == #\n",
"P = np.empty((n, n))\n",
"for i in range(n):\n",
" P[i,:] = Q[i,:] / Q[i,:].sum()\n",
"# == Compute the stationary distribution r == #\n",
"r = mc_compute_stationary(P)\n",
"ranked_pages = {alphabet[i] : r[i] for i in range(n)}\n",
"# == Print solution, sorted from highest to lowest rank == #\n",
"print('Rankings\\n ***')\n",
"for name, rank in sorted(ranked_pages.iteritems(), key=itemgetter(1), reverse=1):\n",
" print('{0}: {1:.4}'.format(name, rank))\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Rankings\n",
" ***\n",
"g: 0.1607\n",
"j: 0.1594\n",
"m: 0.1195\n",
"n: 0.1088\n",
"k: 0.09106\n",
"b: 0.08326\n",
"i: 0.05312\n",
"e: 0.05312\n",
"c: 0.04834\n",
"h: 0.0456\n",
"l: 0.03202\n",
"d: 0.03056\n",
"f: 0.01164\n",
"a: 0.002911\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Exercise 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A solution from the [quantecon library](https://github.com/jstac/quant-econ/tree/master/quantecon) can be found [here](https://github.com/jstac/quant-econ/blob/master/quantecon/tauchen.py)\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | bsd-3-clause |
RaspberryJamBe/ipython-notebooks | notebooks/en-gb/Output - Speach synthesis.ipynb | 2 | 1117 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from espeak import espeak\n",
"from datetime import datetime"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"espeak.set_voice('en')\n",
"t = datetime.now().strftime(\"%k %M\")\n",
"espeak.synth(\"Good evening, this is your Raspberry Pi speaking. The time is now %s.\"%t)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| cc0-1.0 |
transcranial/keras-js | notebooks/layers/convolutional/ZeroPadding2D.ipynb | 1 | 32251 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import numpy as np\n",
"from keras.models import Model\n",
"from keras.layers import Input\n",
"from keras.layers.convolutional import ZeroPadding2D\n",
"from keras import backend as K\n",
"import json\n",
"from collections import OrderedDict"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def format_decimal(arr, places=6):\n",
" return [round(x * 10**places) / 10**places for x in arr]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"DATA = OrderedDict()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ZeroPadding2D"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[convolutional.ZeroPadding2D.0] padding (1,1) on 3x5x2 input, data_format='channels_last'**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (3, 5, 2)\n",
"in: [-0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.170854, -0.139788, -0.710674]\n",
"out shape: (5, 7, 2)\n",
"out: [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, -0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.0, 0.0, 0.0, 0.0, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.0, 0.0, 0.0, 0.0, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.170854, -0.139788, -0.710674, 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"
]
}
],
"source": [
"data_in_shape = (3, 5, 2)\n",
"L = ZeroPadding2D(padding=(1, 1), data_format='channels_last')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(250)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['convolutional.ZeroPadding2D.0'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[convolutional.ZeroPadding2D.1] padding (1,1) on 3x5x2 input, data_format='channels_first'**"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (3, 5, 2)\n",
"in: [0.275222, -0.793967, -0.468107, -0.841484, -0.295362, 0.78175, 0.068787, -0.261747, -0.625733, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.99869, -0.963008, 0.013277, -0.180306, 0.832137, -0.385252, -0.524308, 0.659706, -0.905127, 0.526292, 0.832569, 0.084455, 0.23838, -0.046178, -0.735871, 0.776883]\n",
"out shape: (3, 7, 4)\n",
"out: [0.0, 0.0, 0.0, 0.0, 0.0, 0.275222, -0.793967, 0.0, 0.0, -0.468107, -0.841484, 0.0, 0.0, -0.295362, 0.78175, 0.0, 0.0, 0.068787, -0.261747, 0.0, 0.0, -0.625733, -0.042907, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.861141, 0.85267, 0.0, 0.0, 0.956439, 0.717838, 0.0, 0.0, -0.99869, -0.963008, 0.0, 0.0, 0.013277, -0.180306, 0.0, 0.0, 0.832137, -0.385252, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.524308, 0.659706, 0.0, 0.0, -0.905127, 0.526292, 0.0, 0.0, 0.832569, 0.084455, 0.0, 0.0, 0.23838, -0.046178, 0.0, 0.0, -0.735871, 0.776883, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
]
}
],
"source": [
"data_in_shape = (3, 5, 2)\n",
"L = ZeroPadding2D(padding=(1, 1), data_format='channels_first')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(251)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['convolutional.ZeroPadding2D.1'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[convolutional.ZeroPadding2D.2] padding (3,2) on 2x6x4 input, data_format='channels_last'**"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (2, 6, 4)\n",
"in: [-0.989173, -0.133618, -0.505338, 0.023259, 0.503982, -0.303769, -0.436321, 0.793911, 0.416102, 0.806405, -0.098342, -0.738022, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772, 0.40423, 0.213794, 0.645662, -0.044634, -0.552595, 0.771242, -0.131944, -0.172725, 0.700856, -0.001994, 0.606737, -0.593306]\n",
"out shape: (8, 10, 4)\n",
"out: [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, 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, 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, 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, 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, 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, 0.0, 0.0, -0.989173, -0.133618, -0.505338, 0.023259, 0.503982, -0.303769, -0.436321, 0.793911, 0.416102, 0.806405, -0.098342, -0.738022, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111, 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, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772, 0.40423, 0.213794, 0.645662, -0.044634, -0.552595, 0.771242, -0.131944, -0.172725, 0.700856, -0.001994, 0.606737, -0.593306, 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, 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, 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, 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, 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, 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, 0.0, 0.0]\n"
]
}
],
"source": [
"data_in_shape = (2, 6, 4)\n",
"L = ZeroPadding2D(padding=(3, 2), data_format='channels_last')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(252)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['convolutional.ZeroPadding2D.2'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[convolutional.ZeroPadding2D.3] padding (3,2) on 2x6x4 input, data_format='channels_first'**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (2, 6, 4)\n",
"in: [-0.47588, 0.366985, 0.040173, 0.015578, -0.906159, 0.241982, -0.771299, -0.443554, -0.56404, -0.17751, 0.541277, -0.233327, 0.024369, 0.858275, 0.496191, 0.980574, -0.59522, 0.480899, 0.392553, -0.191718, 0.055121, 0.289836, -0.498339, 0.800408, 0.132679, -0.716649, 0.840092, -0.088837, -0.538209, -0.580887, -0.370128, -0.924933, -0.161736, -0.205619, 0.793729, -0.354472, 0.687519, 0.272041, -0.943352, -0.730959, -0.330419, -0.479307, 0.520387, 0.137906, 0.897598, 0.869815, 0.978562, 0.731387]\n",
"out shape: (2, 12, 8)\n",
"out: [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, 0.0, 0.0, 0.0, 0.0, 0.0, -0.47588, 0.366985, 0.040173, 0.015578, 0.0, 0.0, 0.0, 0.0, -0.906159, 0.241982, -0.771299, -0.443554, 0.0, 0.0, 0.0, 0.0, -0.56404, -0.17751, 0.541277, -0.233327, 0.0, 0.0, 0.0, 0.0, 0.024369, 0.858275, 0.496191, 0.980574, 0.0, 0.0, 0.0, 0.0, -0.59522, 0.480899, 0.392553, -0.191718, 0.0, 0.0, 0.0, 0.0, 0.055121, 0.289836, -0.498339, 0.800408, 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, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.132679, -0.716649, 0.840092, -0.088837, 0.0, 0.0, 0.0, 0.0, -0.538209, -0.580887, -0.370128, -0.924933, 0.0, 0.0, 0.0, 0.0, -0.161736, -0.205619, 0.793729, -0.354472, 0.0, 0.0, 0.0, 0.0, 0.687519, 0.272041, -0.943352, -0.730959, 0.0, 0.0, 0.0, 0.0, -0.330419, -0.479307, 0.520387, 0.137906, 0.0, 0.0, 0.0, 0.0, 0.897598, 0.869815, 0.978562, 0.731387, 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, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
]
}
],
"source": [
"data_in_shape = (2, 6, 4)\n",
"L = ZeroPadding2D(padding=(3, 2), data_format='channels_first')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(253)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['convolutional.ZeroPadding2D.3'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[convolutional.ZeroPadding2D.4] padding ((1,2),(3,4)) on 2x6x4 input, data_format='channels_last'**"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (2, 6, 4)\n",
"in: [0.024124, 0.280236, -0.680013, -0.042458, -0.164273, 0.358409, 0.511014, -0.585272, -0.481578, 0.692702, 0.64189, -0.400252, -0.922248, -0.735105, -0.533918, 0.071402, 0.310474, 0.369868, 0.767931, -0.842066, -0.091189, 0.835301, -0.480484, 0.950819, -0.002131, 0.086491, -0.480947, 0.405572, -0.083803, -0.921447, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549, -0.185686, -0.89067, 0.709257, -0.256164, -0.873627, 0.330906, -0.583426, -0.51286, 0.751485, 0.030077, -0.998662, 0.175588]\n",
"out shape: (5, 13, 4)\n",
"out: [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, 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, 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, 0.0, 0.024124, 0.280236, -0.680013, -0.042458, -0.164273, 0.358409, 0.511014, -0.585272, -0.481578, 0.692702, 0.64189, -0.400252, -0.922248, -0.735105, -0.533918, 0.071402, 0.310474, 0.369868, 0.767931, -0.842066, -0.091189, 0.835301, -0.480484, 0.950819, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.002131, 0.086491, -0.480947, 0.405572, -0.083803, -0.921447, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549, -0.185686, -0.89067, 0.709257, -0.256164, -0.873627, 0.330906, -0.583426, -0.51286, 0.751485, 0.030077, -0.998662, 0.175588, 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, 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, 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, 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, 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, 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"
]
}
],
"source": [
"data_in_shape = (2, 6, 4)\n",
"L = ZeroPadding2D(padding=((1,2),(3,4)), data_format='channels_last')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(254)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['convolutional.ZeroPadding2D.4'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[convolutional.ZeroPadding2D.5] padding 2 on 2x6x4 input, data_format='channels_last'**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"in shape: (2, 6, 4)\n",
"in: [-0.072127, -0.553929, -0.355552, -0.936405, 0.556627, -0.482815, -0.225337, -0.640315, 0.023246, -0.638412, -0.797304, 0.284959, -0.569771, -0.685286, 0.002481, 0.398436, 0.11345, 0.416629, -0.526713, 0.962183, 0.021732, 0.922994, 0.07991, -0.164385, 0.461494, -0.982877, -0.142158, 0.175741, -0.124041, -0.875609, -0.528708, -0.911127, 0.782257, -0.509403, 0.573973, -0.151309, -0.895619, -0.721042, 0.483952, -0.745814, -0.588825, -0.154089, 0.423904, -0.262707, -0.517175, -0.535505, -0.266104, -0.46314]\n",
"out shape: (6, 10, 4)\n",
"out: [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, 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, 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, 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, 0.0, 0.0, 0.0, 0.0, -0.072127, -0.553929, -0.355552, -0.936405, 0.556627, -0.482815, -0.225337, -0.640315, 0.023246, -0.638412, -0.797304, 0.284959, -0.569771, -0.685286, 0.002481, 0.398436, 0.11345, 0.416629, -0.526713, 0.962183, 0.021732, 0.922994, 0.07991, -0.164385, 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, 0.461494, -0.982877, -0.142158, 0.175741, -0.124041, -0.875609, -0.528708, -0.911127, 0.782257, -0.509403, 0.573973, -0.151309, -0.895619, -0.721042, 0.483952, -0.745814, -0.588825, -0.154089, 0.423904, -0.262707, -0.517175, -0.535505, -0.266104, -0.46314, 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, 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, 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, 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, 0.0, 0.0, 0.0, 0.0]\n"
]
}
],
"source": [
"data_in_shape = (2, 6, 4)\n",
"L = ZeroPadding2D(padding=2, data_format='channels_last')\n",
"\n",
"layer_0 = Input(shape=data_in_shape)\n",
"layer_1 = L(layer_0)\n",
"model = Model(inputs=layer_0, outputs=layer_1)\n",
"\n",
"# set weights to random (use seed for reproducibility)\n",
"np.random.seed(255)\n",
"data_in = 2 * np.random.random(data_in_shape) - 1\n",
"result = model.predict(np.array([data_in]))\n",
"data_out_shape = result[0].shape\n",
"data_in_formatted = format_decimal(data_in.ravel().tolist())\n",
"data_out_formatted = format_decimal(result[0].ravel().tolist())\n",
"print('')\n",
"print('in shape:', data_in_shape)\n",
"print('in:', data_in_formatted)\n",
"print('out shape:', data_out_shape)\n",
"print('out:', data_out_formatted)\n",
"\n",
"DATA['convolutional.ZeroPadding2D.5'] = {\n",
" 'input': {'data': data_in_formatted, 'shape': data_in_shape},\n",
" 'expected': {'data': data_out_formatted, 'shape': data_out_shape}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### export for Keras.js tests"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"\n",
"filename = '../../../test/data/layers/convolutional/ZeroPadding2D.json'\n",
"if not os.path.exists(os.path.dirname(filename)):\n",
" os.makedirs(os.path.dirname(filename))\n",
"with open(filename, 'w') as f:\n",
" json.dump(DATA, f)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\"convolutional.ZeroPadding2D.0\": {\"input\": {\"data\": [-0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.170854, -0.139788, -0.710674], \"shape\": [3, 5, 2]}, \"expected\": {\"data\": [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, -0.570441, -0.454673, -0.285321, 0.237249, 0.282682, 0.428035, 0.160547, -0.332203, 0.546391, 0.272735, 0.0, 0.0, 0.0, 0.0, 0.010827, -0.763164, -0.442696, 0.381948, -0.676994, 0.753553, -0.031788, 0.915329, -0.738844, 0.269075, 0.0, 0.0, 0.0, 0.0, 0.434091, 0.991585, -0.944288, 0.258834, 0.162138, 0.565201, -0.492094, 0.170854, -0.139788, -0.710674, 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], \"shape\": [5, 7, 2]}}, \"convolutional.ZeroPadding2D.1\": {\"input\": {\"data\": [0.275222, -0.793967, -0.468107, -0.841484, -0.295362, 0.78175, 0.068787, -0.261747, -0.625733, -0.042907, 0.861141, 0.85267, 0.956439, 0.717838, -0.99869, -0.963008, 0.013277, -0.180306, 0.832137, -0.385252, -0.524308, 0.659706, -0.905127, 0.526292, 0.832569, 0.084455, 0.23838, -0.046178, -0.735871, 0.776883], \"shape\": [3, 5, 2]}, \"expected\": {\"data\": [0.0, 0.0, 0.0, 0.0, 0.0, 0.275222, -0.793967, 0.0, 0.0, -0.468107, -0.841484, 0.0, 0.0, -0.295362, 0.78175, 0.0, 0.0, 0.068787, -0.261747, 0.0, 0.0, -0.625733, -0.042907, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.861141, 0.85267, 0.0, 0.0, 0.956439, 0.717838, 0.0, 0.0, -0.99869, -0.963008, 0.0, 0.0, 0.013277, -0.180306, 0.0, 0.0, 0.832137, -0.385252, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.524308, 0.659706, 0.0, 0.0, -0.905127, 0.526292, 0.0, 0.0, 0.832569, 0.084455, 0.0, 0.0, 0.23838, -0.046178, 0.0, 0.0, -0.735871, 0.776883, 0.0, 0.0, 0.0, 0.0, 0.0], \"shape\": [3, 7, 4]}}, \"convolutional.ZeroPadding2D.2\": {\"input\": {\"data\": [-0.989173, -0.133618, -0.505338, 0.023259, 0.503982, -0.303769, -0.436321, 0.793911, 0.416102, 0.806405, -0.098342, -0.738022, -0.982676, 0.805073, 0.741244, -0.941634, -0.253526, -0.136544, -0.295772, 0.207565, -0.517246, -0.686963, -0.176235, -0.354111, -0.862411, -0.969822, 0.200074, 0.290718, -0.038623, 0.294839, 0.247968, 0.557946, -0.455596, 0.6624, 0.879529, -0.466772, 0.40423, 0.213794, 0.645662, -0.044634, -0.552595, 0.771242, -0.131944, -0.172725, 0.700856, -0.001994, 0.606737, -0.593306], \"shape\": [2, 6, 4]}, \"expected\": {\"data\": [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, 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, 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, 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, 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-0.479307, 0.520387, 0.137906, 0.897598, 0.869815, 0.978562, 0.731387], \"shape\": [2, 6, 4]}, \"expected\": {\"data\": [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, 0.0, 0.0, 0.0, 0.0, 0.0, -0.47588, 0.366985, 0.040173, 0.015578, 0.0, 0.0, 0.0, 0.0, -0.906159, 0.241982, -0.771299, -0.443554, 0.0, 0.0, 0.0, 0.0, -0.56404, -0.17751, 0.541277, -0.233327, 0.0, 0.0, 0.0, 0.0, 0.024369, 0.858275, 0.496191, 0.980574, 0.0, 0.0, 0.0, 0.0, -0.59522, 0.480899, 0.392553, -0.191718, 0.0, 0.0, 0.0, 0.0, 0.055121, 0.289836, -0.498339, 0.800408, 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, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.132679, -0.716649, 0.840092, -0.088837, 0.0, 0.0, 0.0, 0.0, -0.538209, -0.580887, -0.370128, -0.924933, 0.0, 0.0, 0.0, 0.0, -0.161736, -0.205619, 0.793729, -0.354472, 0.0, 0.0, 0.0, 0.0, 0.687519, 0.272041, -0.943352, -0.730959, 0.0, 0.0, 0.0, 0.0, -0.330419, -0.479307, 0.520387, 0.137906, 0.0, 0.0, 0.0, 0.0, 0.897598, 0.869815, 0.978562, 0.731387, 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, 0.0, 0.0, 0.0, 0.0, 0.0], \"shape\": [2, 12, 8]}}, \"convolutional.ZeroPadding2D.4\": {\"input\": {\"data\": [0.024124, 0.280236, -0.680013, -0.042458, -0.164273, 0.358409, 0.511014, -0.585272, -0.481578, 0.692702, 0.64189, -0.400252, -0.922248, -0.735105, -0.533918, 0.071402, 0.310474, 0.369868, 0.767931, -0.842066, -0.091189, 0.835301, -0.480484, 0.950819, -0.002131, 0.086491, -0.480947, 0.405572, -0.083803, -0.921447, -0.291545, 0.674087, -0.560444, 0.881432, 0.076544, 0.63549, -0.185686, -0.89067, 0.709257, -0.256164, -0.873627, 0.330906, -0.583426, -0.51286, 0.751485, 0.030077, -0.998662, 0.175588], \"shape\": [2, 6, 4]}, \"expected\": {\"data\": [0.0, 0.0, 0.0, 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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, 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, 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, 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, 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, 0.0, 0.0, 0.0, 0.0], \"shape\": [5, 13, 4]}}, \"convolutional.ZeroPadding2D.5\": {\"input\": {\"data\": [-0.072127, -0.553929, -0.355552, -0.936405, 0.556627, -0.482815, -0.225337, -0.640315, 0.023246, -0.638412, -0.797304, 0.284959, -0.569771, -0.685286, 0.002481, 0.398436, 0.11345, 0.416629, -0.526713, 0.962183, 0.021732, 0.922994, 0.07991, -0.164385, 0.461494, -0.982877, -0.142158, 0.175741, -0.124041, -0.875609, -0.528708, -0.911127, 0.782257, -0.509403, 0.573973, -0.151309, -0.895619, -0.721042, 0.483952, -0.745814, -0.588825, -0.154089, 0.423904, -0.262707, -0.517175, -0.535505, -0.266104, -0.46314], \"shape\": [2, 6, 4]}, \"expected\": {\"data\": [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, 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, 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, 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, 0.0, 0.0, 0.0, 0.0, -0.072127, -0.553929, -0.355552, -0.936405, 0.556627, -0.482815, -0.225337, -0.640315, 0.023246, -0.638412, -0.797304, 0.284959, -0.569771, -0.685286, 0.002481, 0.398436, 0.11345, 0.416629, -0.526713, 0.962183, 0.021732, 0.922994, 0.07991, -0.164385, 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, 0.461494, -0.982877, -0.142158, 0.175741, -0.124041, -0.875609, -0.528708, -0.911127, 0.782257, -0.509403, 0.573973, -0.151309, -0.895619, -0.721042, 0.483952, -0.745814, -0.588825, -0.154089, 0.423904, -0.262707, -0.517175, -0.535505, -0.266104, -0.46314, 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, 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, 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, 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, 0.0, 0.0, 0.0, 0.0], \"shape\": [6, 10, 4]}}}\n"
]
}
],
"source": [
"print(json.dumps(DATA))"
]
},
{
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
steve-federowicz/om | examples/heatmap.ipynb | 2 | 197216 | {
"metadata": {
"name": "",
"signature": "sha256:1434b4363d90325faea6f4f9fe48a9b304b02d775a0ded14de52e47ccad2d479"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from om import base, settings\n",
"from om.components import *\n",
"from om.data import *\n",
"from om.util import *\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import math,cobra\n",
"\n",
"ome = base.Session()\n",
"#model = cobra.io.read_sbml_model('/Users/dbuser/Dropbox/om_data/annotation/iJO1366.xml')"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ome.query(GeneGroup).all()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.display import display\n",
"from IPython.html import widgets as W\n",
"from IPython.utils import traitlets as T\n",
"\n",
"class HeatmapWidget(W.DOMWidget):\n",
" _view_name = T.Unicode('HeatmapView', sync=True)\n",
" heatmap_data = T.Unicode(sync=True)\n",
" row_labels = T.Unicode(sync=True)\n",
" col_labels = T.Unicode(sync=True)\n",
" html_style = T.Unicode(sync=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ged = GeneExpressionData\n",
"x = ome.query(ged).filter(and_(ged.gene_name.in_(['sucA','sucB','sucC','sucD','frdA','frdB','frdC','frdD','gapA']),\n",
" ged.dataset_type == 'array_experiment')).all()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"[Gene: (b0726, sucA), Value: 8.87, std: 0.20, Condition: glucose, NH4Cl, anaerobic, Strain: wt, array_experiment,\n",
" Gene: (b0726, sucA), Value: 13.67, std: 0.02, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcA, array_experiment,\n",
" Gene: (b0726, sucA), Value: 13.28, std: 0.14, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b0726, sucA), Value: 9.97, std: 0.76, Condition: glucose, NH4Cl, anaerobic, Strain: delta-fnr, array_experiment,\n",
" Gene: (b0726, sucA), Value: 8.49, std: 0.09, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narL, array_experiment,\n",
" Gene: (b0726, sucA), Value: 8.69, std: 0.16, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b0726, sucA), Value: 8.74, std: 0.23, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narP, array_experiment,\n",
" Gene: (b0727, sucB), Value: 9.84, std: 0.26, Condition: glucose, NH4Cl, anaerobic, Strain: wt, array_experiment,\n",
" Gene: (b0727, sucB), Value: 13.90, std: 0.04, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcA, array_experiment,\n",
" Gene: (b0727, sucB), Value: 13.64, std: 0.20, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b0727, sucB), Value: 11.08, std: 0.54, Condition: glucose, NH4Cl, anaerobic, Strain: delta-fnr, array_experiment,\n",
" Gene: (b0727, sucB), Value: 9.69, std: 0.12, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narL, array_experiment,\n",
" Gene: (b0727, sucB), Value: 9.72, std: 0.10, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b0727, sucB), Value: 9.78, std: 0.18, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narP, array_experiment,\n",
" Gene: (b0728, sucC), Value: 10.77, std: 0.15, Condition: glucose, NH4Cl, anaerobic, Strain: wt, array_experiment,\n",
" Gene: (b0728, sucC), Value: 14.50, std: 0.05, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcA, array_experiment,\n",
" Gene: (b0728, sucC), Value: 14.50, std: 0.16, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b0728, sucC), Value: 12.07, std: 0.41, Condition: glucose, NH4Cl, anaerobic, Strain: delta-fnr, array_experiment,\n",
" Gene: (b0728, sucC), Value: 10.72, std: 0.04, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narL, array_experiment,\n",
" Gene: (b0728, sucC), Value: 10.68, std: 0.03, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b0728, sucC), Value: 10.78, std: 0.08, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narP, array_experiment,\n",
" Gene: (b0729, sucD), Value: 5.88, std: 0.13, Condition: glucose, NH4Cl, anaerobic, Strain: wt, array_experiment,\n",
" Gene: (b0729, sucD), Value: 11.41, std: 0.12, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcA, array_experiment,\n",
" Gene: (b0729, sucD), Value: 11.16, std: 0.40, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b0729, sucD), Value: 7.56, std: 0.73, Condition: glucose, NH4Cl, anaerobic, Strain: delta-fnr, array_experiment,\n",
" Gene: (b0729, sucD), Value: 5.79, std: 0.05, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narL, array_experiment,\n",
" Gene: (b0729, sucD), Value: 5.73, std: 0.07, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b0729, sucD), Value: 5.79, std: 0.05, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narP, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.26, std: 0.05, Condition: glucose, NH4Cl, anaerobic, Strain: wt, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.20, std: 0.06, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcA, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.07, std: 0.07, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.07, std: 0.06, Condition: glucose, NH4Cl, anaerobic, Strain: delta-fnr, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.31, std: 0.02, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narL, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.27, std: 0.02, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.31, std: 0.02, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narP, array_experiment,\n",
" Gene: (b4151, frdD), Value: 7.53, std: 1.33, Condition: glucose, NH4Cl, anaerobic, Strain: wt, array_experiment,\n",
" Gene: (b4151, frdD), Value: 5.54, std: 0.71, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcA, array_experiment,\n",
" Gene: (b4151, frdD), Value: 4.73, std: 0.12, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b4151, frdD), Value: 4.02, std: 0.62, Condition: glucose, NH4Cl, anaerobic, Strain: delta-fnr, array_experiment,\n",
" Gene: (b4151, frdD), Value: 9.76, std: 0.32, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narL, array_experiment,\n",
" Gene: (b4151, frdD), Value: 9.07, std: 0.23, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b4151, frdD), Value: 8.50, std: 0.36, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narP, array_experiment,\n",
" Gene: (b4152, frdC), Value: 4.63, std: 0.45, Condition: glucose, NH4Cl, anaerobic, Strain: wt, array_experiment,\n",
" Gene: (b4152, frdC), Value: 3.66, std: 0.66, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcA, array_experiment,\n",
" Gene: (b4152, frdC), Value: 2.58, std: 0.06, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b4152, frdC), Value: 2.36, std: 0.09, Condition: glucose, NH4Cl, anaerobic, Strain: delta-fnr, array_experiment,\n",
" Gene: (b4152, frdC), Value: 5.23, std: 0.28, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narL, array_experiment,\n",
" Gene: (b4152, frdC), Value: 4.75, std: 0.21, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b4152, frdC), Value: 5.11, std: 0.26, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narP, array_experiment,\n",
" Gene: (b4153, frdB), Value: 9.59, std: 0.56, Condition: glucose, NH4Cl, anaerobic, Strain: wt, array_experiment,\n",
" Gene: (b4153, frdB), Value: 8.70, std: 0.58, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcA, array_experiment,\n",
" Gene: (b4153, frdB), Value: 7.41, std: 0.07, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b4153, frdB), Value: 5.66, std: 0.66, Condition: glucose, NH4Cl, anaerobic, Strain: delta-fnr, array_experiment,\n",
" Gene: (b4153, frdB), Value: 9.84, std: 0.20, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narL, array_experiment,\n",
" Gene: (b4153, frdB), Value: 9.92, std: 0.05, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b4153, frdB), Value: 10.19, std: 0.24, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narP, array_experiment,\n",
" Gene: (b4154, frdA), Value: 11.04, std: 0.57, Condition: glucose, NH4Cl, anaerobic, Strain: wt, array_experiment,\n",
" Gene: (b4154, frdA), Value: 10.09, std: 0.48, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcA, array_experiment,\n",
" Gene: (b4154, frdA), Value: 9.40, std: 0.06, Condition: glucose, NH4Cl, anaerobic, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b4154, frdA), Value: 8.06, std: 0.84, Condition: glucose, NH4Cl, anaerobic, Strain: delta-fnr, array_experiment,\n",
" Gene: (b4154, frdA), Value: 11.52, std: 0.33, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narL, array_experiment,\n",
" Gene: (b4154, frdA), Value: 11.50, std: 0.10, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b4154, frdA), Value: 11.77, std: 0.17, Condition: glucose, NH4Cl, anaerobic, Strain: delta-narP, array_experiment,\n",
" Gene: (b0726, sucA), Value: 11.86, std: 0.10, Condition: fructose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b0726, sucA), Value: 13.67, std: 0.08, Condition: fructose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b0727, sucB), Value: 12.23, std: 0.06, Condition: fructose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b0727, sucB), Value: 13.92, std: 0.04, Condition: fructose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b0728, sucC), Value: 11.91, std: 0.03, Condition: fructose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b0728, sucC), Value: 14.21, std: 0.03, Condition: fructose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b0729, sucD), Value: 12.22, std: 0.03, Condition: fructose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b0729, sucD), Value: 14.49, std: 0.03, Condition: fructose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b1779, gapA), Value: 14.93, std: 0.01, Condition: fructose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.05, std: 0.04, Condition: fructose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b4151, frdD), Value: 4.60, std: 0.27, Condition: fructose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b4151, frdD), Value: 7.23, std: 1.11, Condition: fructose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b4152, frdC), Value: 3.98, std: 0.17, Condition: fructose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b4152, frdC), Value: 6.66, std: 1.32, Condition: fructose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b4153, frdB), Value: 7.90, std: 0.29, Condition: fructose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b4153, frdB), Value: 11.45, std: 0.91, Condition: fructose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b4154, frdA), Value: 7.42, std: 0.19, Condition: fructose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b4154, frdA), Value: 12.05, std: 0.45, Condition: fructose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b0726, sucA), Value: 12.88, std: 0.06, Condition: glucose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b0726, sucA), Value: 13.23, std: 0.18, Condition: glucose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b0726, sucA), Value: 13.54, std: 0.12, Condition: glucose, NH4Cl, O2, Strain: delta-arcA, array_experiment,\n",
" Gene: (b0726, sucA), Value: 13.23, std: 0.03, Condition: glucose, NH4Cl, O2, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b0726, sucA), Value: 12.78, std: 0.09, Condition: glucose, NH4Cl, O2, Strain: delta-fnr, array_experiment,\n",
" Gene: (b0726, sucA), Value: 12.89, std: 0.05, Condition: glucose, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b0726, sucA), Value: 13.49, std: 0.06, Condition: glucose, NH4Cl, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b0726, sucA), Value: 12.17, std: 0.26, Condition: glucose, NH4Cl, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b0727, sucB), Value: 13.10, std: 0.02, Condition: glucose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b0727, sucB), Value: 13.54, std: 0.10, Condition: glucose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b0727, sucB), Value: 13.71, std: 0.14, Condition: glucose, NH4Cl, O2, Strain: delta-arcA, array_experiment,\n",
" Gene: (b0727, sucB), Value: 13.56, std: 0.07, Condition: glucose, NH4Cl, O2, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b0727, sucB), Value: 13.30, std: 0.12, Condition: glucose, NH4Cl, O2, Strain: delta-fnr, array_experiment,\n",
" Gene: (b0727, sucB), Value: 13.35, std: 0.03, Condition: glucose, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b0727, sucB), Value: 13.64, std: 0.02, Condition: glucose, NH4Cl, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b0727, sucB), Value: 13.08, std: 0.08, Condition: glucose, NH4Cl, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b0728, sucC), Value: 12.99, std: 0.02, Condition: glucose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b0728, sucC), Value: 14.01, std: 0.25, Condition: glucose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b0728, sucC), Value: 14.45, std: 0.08, Condition: glucose, NH4Cl, O2, Strain: delta-arcA, array_experiment,\n",
" Gene: (b0728, sucC), Value: 14.47, std: 0.08, Condition: glucose, NH4Cl, O2, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b0728, sucC), Value: 13.96, std: 0.16, Condition: glucose, NH4Cl, O2, Strain: delta-fnr, array_experiment,\n",
" Gene: (b0728, sucC), Value: 13.18, std: 0.05, Condition: glucose, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b0728, sucC), Value: 13.72, std: 0.03, Condition: glucose, NH4Cl, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b0728, sucC), Value: 12.97, std: 0.02, Condition: glucose, NH4Cl, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b0729, sucD), Value: 13.26, std: 0.06, Condition: glucose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b0729, sucD), Value: 11.71, std: 1.73, Condition: glucose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b0729, sucD), Value: 11.57, std: 0.13, Condition: glucose, NH4Cl, O2, Strain: delta-arcA, array_experiment,\n",
" Gene: (b0729, sucD), Value: 11.21, std: 0.24, Condition: glucose, NH4Cl, O2, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b0729, sucD), Value: 10.60, std: 0.39, Condition: glucose, NH4Cl, O2, Strain: delta-fnr, array_experiment,\n",
" Gene: (b0729, sucD), Value: 13.35, std: 0.00, Condition: glucose, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b0729, sucD), Value: 13.88, std: 0.06, Condition: glucose, NH4Cl, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b0729, sucD), Value: 13.35, std: 0.00, Condition: glucose, NH4Cl, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b1779, gapA), Value: 14.66, std: 0.02, Condition: glucose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b1779, gapA), Value: 14.74, std: 0.22, Condition: glucose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b1779, gapA), Value: 14.75, std: 0.11, Condition: glucose, NH4Cl, O2, Strain: delta-arcA, array_experiment,\n",
" Gene: (b1779, gapA), Value: 14.95, std: 0.03, Condition: glucose, NH4Cl, O2, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b1779, gapA), Value: 14.78, std: 0.04, Condition: glucose, NH4Cl, O2, Strain: delta-fnr, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.12, std: 0.02, Condition: glucose, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.04, std: 0.09, Condition: glucose, NH4Cl, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.12, std: 0.01, Condition: glucose, NH4Cl, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b4151, frdD), Value: 4.41, std: 0.37, Condition: glucose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b4151, frdD), Value: 4.11, std: 0.38, Condition: glucose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b4151, frdD), Value: 3.27, std: 0.00, Condition: glucose, NH4Cl, O2, Strain: delta-arcA, array_experiment,\n",
" Gene: (b4151, frdD), Value: 2.86, std: 0.34, Condition: glucose, NH4Cl, O2, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b4151, frdD), Value: 3.15, std: 0.00, Condition: glucose, NH4Cl, O2, Strain: delta-fnr, array_experiment,\n",
" Gene: (b4151, frdD), Value: 11.15, std: 0.13, Condition: glucose, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b4151, frdD), Value: 7.80, std: 1.34, Condition: glucose, NH4Cl, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b4151, frdD), Value: 11.39, std: 0.72, Condition: glucose, NH4Cl, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b4152, frdC), Value: 4.12, std: 0.08, Condition: glucose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b4152, frdC), Value: 2.54, std: 0.36, Condition: glucose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b4152, frdC), Value: 2.26, std: 0.01, Condition: glucose, NH4Cl, O2, Strain: delta-arcA, array_experiment,\n",
" Gene: (b4152, frdC), Value: 2.25, std: 0.00, Condition: glucose, NH4Cl, O2, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b4152, frdC), Value: 2.25, std: 0.00, Condition: glucose, NH4Cl, O2, Strain: delta-fnr, array_experiment,\n",
" Gene: (b4152, frdC), Value: 11.38, std: 0.08, Condition: glucose, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b4152, frdC), Value: 8.11, std: 1.75, Condition: glucose, NH4Cl, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b4152, frdC), Value: 11.52, std: 0.46, Condition: glucose, NH4Cl, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b4153, frdB), Value: 9.12, std: 0.26, Condition: glucose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b4153, frdB), Value: 4.88, std: 0.30, Condition: glucose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b4153, frdB), Value: 4.68, std: 0.05, Condition: glucose, NH4Cl, O2, Strain: delta-arcA, array_experiment,\n",
" Gene: (b4153, frdB), Value: 4.40, std: 0.00, Condition: glucose, NH4Cl, O2, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b4153, frdB), Value: 4.61, std: 0.00, Condition: glucose, NH4Cl, O2, Strain: delta-fnr, array_experiment,\n",
" Gene: (b4153, frdB), Value: 13.79, std: 0.11, Condition: glucose, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b4153, frdB), Value: 12.77, std: 0.72, Condition: glucose, NH4Cl, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b4153, frdB), Value: 13.94, std: 0.25, Condition: glucose, NH4Cl, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b4154, frdA), Value: 8.82, std: 0.19, Condition: glucose, NH4Cl, O2, Strain: delta-crp, array_experiment,\n",
" Gene: (b4154, frdA), Value: 6.32, std: 0.97, Condition: glucose, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b4154, frdA), Value: 7.24, std: 0.21, Condition: glucose, NH4Cl, O2, Strain: delta-arcA, array_experiment,\n",
" Gene: (b4154, frdA), Value: 6.47, std: 0.70, Condition: glucose, NH4Cl, O2, Strain: delta-arcAfnr, array_experiment,\n",
" Gene: (b4154, frdA), Value: 6.96, std: 0.29, Condition: glucose, NH4Cl, O2, Strain: delta-fnr, array_experiment,\n",
" Gene: (b4154, frdA), Value: 13.60, std: 0.08, Condition: glucose, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b4154, frdA), Value: 13.08, std: 0.45, Condition: glucose, NH4Cl, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b4154, frdA), Value: 13.85, std: 0.04, Condition: glucose, NH4Cl, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b0726, sucA), Value: 9.26, std: 0.22, Condition: glucose, NH4Cl, NO3, Strain: wt, array_experiment,\n",
" Gene: (b0726, sucA), Value: 9.11, std: 0.08, Condition: glucose, NH4Cl, NO3, Strain: delta-narL, array_experiment,\n",
" Gene: (b0726, sucA), Value: 8.46, std: 0.35, Condition: glucose, NH4Cl, NO3, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b0726, sucA), Value: 9.29, std: 0.14, Condition: glucose, NH4Cl, NO3, Strain: delta-narP, array_experiment,\n",
" Gene: (b0727, sucB), Value: 10.81, std: 0.14, Condition: glucose, NH4Cl, NO3, Strain: wt, array_experiment,\n",
" Gene: (b0727, sucB), Value: 10.06, std: 0.12, Condition: glucose, NH4Cl, NO3, Strain: delta-narL, array_experiment,\n",
" Gene: (b0727, sucB), Value: 9.83, std: 0.20, Condition: glucose, NH4Cl, NO3, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b0727, sucB), Value: 10.31, std: 0.13, Condition: glucose, NH4Cl, NO3, Strain: delta-narP, array_experiment,\n",
" Gene: (b0728, sucC), Value: 11.91, std: 0.16, Condition: glucose, NH4Cl, NO3, Strain: wt, array_experiment,\n",
" Gene: (b0728, sucC), Value: 11.50, std: 0.11, Condition: glucose, NH4Cl, NO3, Strain: delta-narL, array_experiment,\n",
" Gene: (b0728, sucC), Value: 11.05, std: 0.13, Condition: glucose, NH4Cl, NO3, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b0728, sucC), Value: 11.67, std: 0.05, Condition: glucose, NH4Cl, NO3, Strain: delta-narP, array_experiment,\n",
" Gene: (b0729, sucD), Value: 6.23, std: 0.25, Condition: glucose, NH4Cl, NO3, Strain: wt, array_experiment,\n",
" Gene: (b0729, sucD), Value: 6.14, std: 0.15, Condition: glucose, NH4Cl, NO3, Strain: delta-narL, array_experiment,\n",
" Gene: (b0729, sucD), Value: 5.75, std: 0.04, Condition: glucose, NH4Cl, NO3, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b0729, sucD), Value: 6.43, std: 0.00, Condition: glucose, NH4Cl, NO3, Strain: delta-narP, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.15, std: 0.04, Condition: glucose, NH4Cl, NO3, Strain: wt, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.19, std: 0.01, Condition: glucose, NH4Cl, NO3, Strain: delta-narL, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.33, std: 0.04, Condition: glucose, NH4Cl, NO3, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.14, std: 0.04, Condition: glucose, NH4Cl, NO3, Strain: delta-narP, array_experiment,\n",
" Gene: (b4151, frdD), Value: 3.77, std: 0.43, Condition: glucose, NH4Cl, NO3, Strain: wt, array_experiment,\n",
" Gene: (b4151, frdD), Value: 9.26, std: 0.26, Condition: glucose, NH4Cl, NO3, Strain: delta-narL, array_experiment,\n",
" Gene: (b4151, frdD), Value: 8.16, std: 0.76, Condition: glucose, NH4Cl, NO3, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b4151, frdD), Value: 3.21, std: 0.06, Condition: glucose, NH4Cl, NO3, Strain: delta-narP, array_experiment,\n",
" Gene: (b4152, frdC), Value: 2.25, std: 0.00, Condition: glucose, NH4Cl, NO3, Strain: wt, array_experiment,\n",
" Gene: (b4152, frdC), Value: 4.98, std: 0.23, Condition: glucose, NH4Cl, NO3, Strain: delta-narL, array_experiment,\n",
" Gene: (b4152, frdC), Value: 4.64, std: 0.30, Condition: glucose, NH4Cl, NO3, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b4152, frdC), Value: 2.25, std: 0.00, Condition: glucose, NH4Cl, NO3, Strain: delta-narP, array_experiment,\n",
" Gene: (b4153, frdB), Value: 4.50, std: 0.04, Condition: glucose, NH4Cl, NO3, Strain: wt, array_experiment,\n",
" Gene: (b4153, frdB), Value: 9.78, std: 0.07, Condition: glucose, NH4Cl, NO3, Strain: delta-narL, array_experiment,\n",
" Gene: (b4153, frdB), Value: 9.49, std: 0.23, Condition: glucose, NH4Cl, NO3, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b4153, frdB), Value: 4.62, std: 0.01, Condition: glucose, NH4Cl, NO3, Strain: delta-narP, array_experiment,\n",
" Gene: (b4154, frdA), Value: 6.48, std: 0.46, Condition: glucose, NH4Cl, NO3, Strain: wt, array_experiment,\n",
" Gene: (b4154, frdA), Value: 11.32, std: 0.33, Condition: glucose, NH4Cl, NO3, Strain: delta-narL, array_experiment,\n",
" Gene: (b4154, frdA), Value: 11.31, std: 0.15, Condition: glucose, NH4Cl, NO3, Strain: delta-narLnarP, array_experiment,\n",
" Gene: (b4154, frdA), Value: 7.09, std: 0.00, Condition: glucose, NH4Cl, NO3, Strain: delta-narP, array_experiment,\n",
" Gene: (b0726, sucA), Value: 14.35, std: 0.05, Condition: acetate, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b0726, sucA), Value: 14.53, std: 0.02, Condition: acetate, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b0727, sucB), Value: 14.50, std: 0.02, Condition: acetate, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b0727, sucB), Value: 14.64, std: 0.01, Condition: acetate, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b0728, sucC), Value: 14.75, std: 0.01, Condition: acetate, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b0728, sucC), Value: 14.84, std: 0.03, Condition: acetate, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b0729, sucD), Value: 15.04, std: 0.02, Condition: acetate, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b0729, sucD), Value: 15.01, std: 0.07, Condition: acetate, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b1779, gapA), Value: 14.38, std: 0.01, Condition: acetate, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.18, std: 0.02, Condition: acetate, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b4151, frdD), Value: 9.18, std: 0.18, Condition: acetate, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b4151, frdD), Value: 7.75, std: 0.18, Condition: acetate, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b4152, frdC), Value: 8.98, std: 0.28, Condition: acetate, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b4152, frdC), Value: 6.74, std: 0.11, Condition: acetate, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b4153, frdB), Value: 12.79, std: 0.04, Condition: acetate, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b4153, frdB), Value: 11.74, std: 0.16, Condition: acetate, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b4154, frdA), Value: 13.01, std: 0.14, Condition: acetate, NH4Cl, O2, Strain: wt, array_experiment,\n",
" Gene: (b4154, frdA), Value: 11.87, std: 0.30, Condition: acetate, NH4Cl, O2, Strain: delta-cra, array_experiment,\n",
" Gene: (b0726, sucA), Value: 13.54, std: 0.03, Condition: glucose, leucine, O2, Strain: wt, array_experiment,\n",
" Gene: (b0726, sucA), Value: 13.23, std: 0.07, Condition: glucose, leucine, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b0727, sucB), Value: 13.65, std: 0.03, Condition: glucose, leucine, O2, Strain: wt, array_experiment,\n",
" Gene: (b0727, sucB), Value: 13.39, std: 0.02, Condition: glucose, leucine, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b0728, sucC), Value: 13.51, std: 0.02, Condition: glucose, leucine, O2, Strain: wt, array_experiment,\n",
" Gene: (b0728, sucC), Value: 13.46, std: 0.01, Condition: glucose, leucine, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b0729, sucD), Value: 13.68, std: 0.02, Condition: glucose, leucine, O2, Strain: wt, array_experiment,\n",
" Gene: (b0729, sucD), Value: 13.64, std: 0.07, Condition: glucose, leucine, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b1779, gapA), Value: 14.65, std: 0.03, Condition: glucose, leucine, O2, Strain: wt, array_experiment,\n",
" Gene: (b1779, gapA), Value: 15.02, std: 0.02, Condition: glucose, leucine, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b4151, frdD), Value: 6.33, std: 0.04, Condition: glucose, leucine, O2, Strain: wt, array_experiment,\n",
" Gene: (b4151, frdD), Value: 6.90, std: 1.02, Condition: glucose, leucine, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b4152, frdC), Value: 5.72, std: 0.23, Condition: glucose, leucine, O2, Strain: wt, array_experiment,\n",
" Gene: (b4152, frdC), Value: 6.87, std: 1.02, Condition: glucose, leucine, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b4153, frdB), Value: 10.71, std: 0.22, Condition: glucose, leucine, O2, Strain: wt, array_experiment,\n",
" Gene: (b4153, frdB), Value: 12.55, std: 0.38, Condition: glucose, leucine, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b4154, frdA), Value: 10.63, std: 0.43, Condition: glucose, leucine, O2, Strain: wt, array_experiment,\n",
" Gene: (b4154, frdA), Value: 12.57, std: 0.24, Condition: glucose, leucine, O2, Strain: delta-lrp, array_experiment,\n",
" Gene: (b0726, sucA), Value: 11.36, std: 0.17, Condition: glucose, adenine, O2, Strain: wt, array_experiment,\n",
" Gene: (b0726, sucA), Value: 11.45, std: 0.74, Condition: glucose, adenine, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b0727, sucB), Value: 12.31, std: 0.06, Condition: glucose, adenine, O2, Strain: wt, array_experiment,\n",
" Gene: (b0727, sucB), Value: 12.69, std: 0.33, Condition: glucose, adenine, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b0728, sucC), Value: 11.71, std: 0.04, Condition: glucose, adenine, O2, Strain: wt, array_experiment,\n",
" Gene: (b0728, sucC), Value: 12.13, std: 0.36, Condition: glucose, adenine, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b0729, sucD), Value: 12.26, std: 0.26, Condition: glucose, adenine, O2, Strain: wt, array_experiment,\n",
" Gene: (b0729, sucD), Value: 12.23, std: 0.38, Condition: glucose, adenine, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b1779, gapA), Value: 14.40, std: 0.09, Condition: glucose, adenine, O2, Strain: wt, array_experiment,\n",
" Gene: (b1779, gapA), Value: 14.43, std: 0.15, Condition: glucose, adenine, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b4151, frdD), Value: 4.34, std: 0.62, Condition: glucose, adenine, O2, Strain: wt, array_experiment,\n",
" Gene: (b4151, frdD), Value: 3.73, std: 0.51, Condition: glucose, adenine, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b4152, frdC), Value: 2.95, std: 0.07, Condition: glucose, adenine, O2, Strain: wt, array_experiment,\n",
" Gene: (b4152, frdC), Value: 2.98, std: 0.12, Condition: glucose, adenine, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b4153, frdB), Value: 5.56, std: 0.20, Condition: glucose, adenine, O2, Strain: wt, array_experiment,\n",
" Gene: (b4153, frdB), Value: 5.74, std: 0.50, Condition: glucose, adenine, O2, Strain: delta-purR, array_experiment,\n",
" Gene: (b4154, frdA), Value: 5.98, std: 0.35, Condition: glucose, adenine, O2, Strain: wt, array_experiment,\n",
" Gene: (b4154, frdA), Value: 7.71, std: 2.86, Condition: glucose, adenine, O2, Strain: delta-purR, array_experiment]"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"genes = [ome.query(Gene).filter(Gene.name == x).one() for x in ['gapA','sucA','sucB','sucC']]\n",
"genes_data = {gene: gene.data for gene in genes}\n",
"\n",
"col_labels = [x.data_set.name for x in genes_data.itervalues().next()]\n",
"row_labels = [x.name for x in genes_data.keys()]\n",
"\n",
"heatmap_data = []\n",
"for i,gene in enumerate(genes_data):\n",
" for j,data in enumerate(genes_data[gene]):\n",
" if data.value < 0: value = 1.\n",
" else: value = data.value\n",
" heatmap_data.append({\"row\": i+1, \"col\": j+1, \"value\": int(np.log(value))})\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%javascript\n",
"\n",
"require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
"\n",
"require([\"widgets/js/widget\", \"d3\"], function(WidgetManager, d3){\n",
" var HeatmapView = IPython.DOMWidgetView.extend({\n",
"\n",
" render: function(){\n",
" \n",
" this.$el.append(this.model.get(\"html_style\"));\n",
" this.svg = d3.select(this.el).append(\"svg\")\n",
" .attr({\n",
" width: 600,\n",
" height: 600\n",
" });\n",
" \n",
" \n",
" this.update();\n",
" },\n",
" update: function(){\n",
" var margin = { top: 150, right: 10, bottom: 50, left: 100 },\n",
" cellSize=12,\n",
" width = cellSize*col_number, // - margin.left - margin.right,\n",
" height = cellSize*row_number , // - margin.top - margin.bottom,\n",
" //gridSize = Math.floor(width / 24),\n",
" legendElementWidth = cellSize*2.5,\n",
" colorBuckets = 21,\n",
" colors = ['#005824','#1A693B','#347B53','#4F8D6B','#699F83','#83B09B','#9EC2B3','#B8D4CB','#D2E6E3','#EDF8FB','#FFFFFF','#F1EEF6','#E6D3E1','#DBB9CD','#D19EB9','#C684A4','#BB6990','#B14F7C','#A63467','#9B1A53','#91003F'],\n",
" hcrow = [1,2,4,3], //,18,6,12,20,19,33,32,26,44,35,38,3,23,41,22,10,2,15,16,36,8,25,29,7,27,34,48,31,45,43,14,9,39,1,37,47,42,21,40,5,28,46,50,17,24,13], // change to gene name or probe id\n",
" hccol = [2,1,5,3,6,4]; //,42,21,58,56,14,16,43,15,17,46,47,48,54,49,37,38,25,22,7,8,2,45,9,20,24,44,23,19,13,40,11,1,39,53,10,52,3,26,27,60,50,51,59,18,31,32,30,4,55,28,29,57,36,34,33,35], // change to gene name or probe id\n",
" //rowLabel = ['1','2','1759080_s_at','1759302_s_at','1759502_s_at','1759540_s_at','1759781_s_at','1759828_s_at','1759829_s_at','1759906_s_at','1760088_s_at','1760164_s_at','1760453_s_at','1760516_s_at','1760594_s_at','1760894_s_at','1760951_s_at','1761030_s_at','1761128_at','1761145_s_at','1761160_s_at','1761189_s_at','1761222_s_at','1761245_s_at','1761277_s_at','1761434_s_at','1761553_s_at','1761620_s_at','1761873_s_at','1761884_s_at','1761944_s_at','1762105_s_at','1762118_s_at','1762151_s_at','1762388_s_at','1762401_s_at','1762633_s_at','1762701_s_at','1762787_s_at','1762819_s_at','1762880_s_at','1762945_s_at','1762983_s_at','1763132_s_at','1763138_s_at','1763146_s_at','1763198_s_at','1763383_at','1763410_s_at','1763426_s_at','1763490_s_at','1763491_s_at'], // change to gene name or probe id\n",
" //colLabel = ['3','4','con1027','con1028','con1029','con103','con1030','con1031','con1032','con1033','con1034','con1035','con1036','con1037','con1038','con1039','con1040','con1041','con108','con109','con110','con111','con112','con125','con126','con127','con128','con129','con130','con131','con132','con133','con134','con135','con136','con137','con138','con139','con14','con15','con150','con151','con152','con153','con16','con17','con174','con184','con185','con186','con187','con188','con189','con191','con192','con193','con194','con199','con2','con200','con201','con21']; // change to contrast name\n",
" \n",
" var rowLabel = JSON.parse(this.model.get(\"row_labels\"));\n",
" var colLabel = JSON.parse(this.model.get(\"col_labels\"));\n",
" var data = JSON.parse(this.model.get(\"heatmap_data\"));\n",
" \n",
" \n",
" var col_number = colLabel.length,\n",
" row_number = rowLabel.length;\n",
" \n",
" var colorScale = d3.scale.quantile()\n",
" .domain([ -10 , 0, 10])\n",
" .range(colors);\n",
" \n",
" var svg = this.svg;\n",
" \n",
" var rowSortOrder=false;\n",
" var colSortOrder=false;\n",
" var rowLabels = svg.append(\"g\")\n",
" .selectAll(\".rowLabelg\")\n",
" .data(rowLabel)\n",
" .enter()\n",
" .append(\"text\")\n",
" .text(function (d) { return d; })\n",
" .attr(\"x\", 0)\n",
" .attr(\"y\", function (d, i) { return hcrow.indexOf(i+1) * cellSize; })\n",
" .style(\"text-anchor\", \"end\")\n",
" .attr(\"transform\", \"translate(-6,\" + cellSize / 1.5 + \")\")\n",
" .attr(\"class\", function (d,i) { return \"rowLabel mono r\"+i;} ) \n",
" .on(\"mouseover\", function(d) {d3.select(this).classed(\"text-hover\",true);})\n",
" .on(\"mouseout\" , function(d) {d3.select(this).classed(\"text-hover\",false);})\n",
" .on(\"click\", function(d,i) {rowSortOrder=!rowSortOrder; sortbylabel(\"r\",i,rowSortOrder);d3.select(\"#order\").property(\"selectedIndex\", 4).node().focus();;})\n",
" ;\n",
"\n",
" var colLabels = svg.append(\"g\")\n",
" .selectAll(\".colLabelg\")\n",
" .data(colLabel)\n",
" .enter()\n",
" .append(\"text\")\n",
" .text(function (d) { return d; })\n",
" .attr(\"x\", 0)\n",
" .attr(\"y\", function (d, i) { return hccol.indexOf(i+1) * cellSize; })\n",
" .style(\"text-anchor\", \"left\")\n",
" .attr(\"transform\", \"translate(\"+cellSize/2 + \",-6) rotate (-90)\")\n",
" .attr(\"class\", function (d,i) { return \"colLabel mono c\"+i;} )\n",
" .on(\"mouseover\", function(d) {d3.select(this).classed(\"text-hover\",true);})\n",
" .on(\"mouseout\" , function(d) {d3.select(this).classed(\"text-hover\",false);})\n",
" .on(\"click\", function(d,i) {colSortOrder=!colSortOrder; sortbylabel(\"c\",i,colSortOrder);d3.select(\"#order\").property(\"selectedIndex\", 4).node().focus();;})\n",
" ;\n",
"\n",
" var heatMap = svg.append(\"g\").attr(\"class\",\"g3\")\n",
" .selectAll(\".cellg\")\n",
" .data(data,function(d){return d.row+\":\"+d.col;})\n",
" .enter()\n",
" .append(\"rect\")\n",
" .attr(\"x\", function(d) { return hccol.indexOf(d.col) * cellSize; })\n",
" .attr(\"y\", function(d) { return hcrow.indexOf(d.row) * cellSize; })\n",
" .attr(\"class\", function(d){return \"cell cell-border cr\"+(d.row-1)+\" cc\"+(d.col-1);})\n",
" .attr(\"width\", cellSize)\n",
" .attr(\"height\", cellSize)\n",
" .style(\"fill\", function(d) { return colorScale(d.value); })\n",
" /* .on(\"click\", function(d) {\n",
" var rowtext=d3.select(\".r\"+(d.row-1));\n",
" if(rowtext.classed(\"text-selected\")==false){\n",
" rowtext.classed(\"text-selected\",true);\n",
" }else{\n",
" rowtext.classed(\"text-selected\",false);\n",
" }\n",
" })*/\n",
" .on(\"mouseover\", function(d){\n",
" //highlight text\n",
" d3.select(this).classed(\"cell-hover\",true);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-highlight\",function(r,ri){ return ri==(d.row-1);});\n",
" d3.selectAll(\".colLabel\").classed(\"text-highlight\",function(c,ci){ return ci==(d.col-1);});\n",
" \n",
" //Update the tooltip position and value\n",
" d3.select(\"#tooltip\")\n",
" .style(\"left\", (d3.event.pageX+10) + \"px\")\n",
" .style(\"top\", (d3.event.pageY-10) + \"px\")\n",
" .select(\"#value\")\n",
" .text(\"lables:\"+rowLabel[d.row-1]+\",\"+colLabel[d.col-1]+\"\\ndata:\"+d.value+\"\\nrow-col-idx:\"+d.col+\",\"+d.row+\"\\ncell-xy \"+this.x.baseVal.value+\", \"+this.y.baseVal.value); \n",
" //Show the tooltip\n",
" d3.select(\"#tooltip\").classed(\"hidden\", false);\n",
" })\n",
" .on(\"mouseout\", function(){\n",
" d3.select(this).classed(\"cell-hover\",false);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-highlight\",false);\n",
" d3.selectAll(\".colLabel\").classed(\"text-highlight\",false);\n",
" d3.select(\"#tooltip\").classed(\"hidden\", true);\n",
" })\n",
" ;\n",
"\n",
" var legend = svg.selectAll(\".legend\")\n",
" .data([-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10])\n",
" .enter().append(\"g\")\n",
" .attr(\"class\", \"legend\");\n",
" \n",
" legend.append(\"rect\")\n",
" .attr(\"x\", function(d, i) { return legendElementWidth * i; })\n",
" .attr(\"y\", height+(cellSize*2))\n",
" .attr(\"width\", legendElementWidth)\n",
" .attr(\"height\", cellSize)\n",
" .style(\"fill\", function(d, i) { return colors[i]; });\n",
" \n",
" legend.append(\"text\")\n",
" .attr(\"class\", \"mono\")\n",
" .text(function(d) { return d; })\n",
" .attr(\"width\", legendElementWidth)\n",
" .attr(\"x\", function(d, i) { return legendElementWidth * i; })\n",
" .attr(\"y\", height + (cellSize*4));\n",
"\n",
"// Change ordering of cells\n",
"\n",
" function sortbylabel(rORc,i,sortOrder){\n",
" var t = svg.transition().duration(3000);\n",
" var log2r=[];\n",
" var sorted; // sorted is zero-based index\n",
" d3.selectAll(\".c\"+rORc+i) \n",
" .filter(function(ce){\n",
" log2r.push(ce.value);\n",
" })\n",
" ;\n",
" if(rORc==\"r\"){ // sort log2ratio of a gene\n",
" sorted=d3.range(col_number).sort(function(a,b){ if(sortOrder){ return log2r[b]-log2r[a];}else{ return log2r[a]-log2r[b];}});\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return sorted.indexOf(d.col-1) * cellSize; })\n",
" ;\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return sorted.indexOf(i) * cellSize; })\n",
" ;\n",
" }else{ // sort log2ratio of a contrast\n",
" sorted=d3.range(row_number).sort(function(a,b){if(sortOrder){ return log2r[b]-log2r[a];}else{ return log2r[a]-log2r[b];}});\n",
" t.selectAll(\".cell\")\n",
" .attr(\"y\", function(d) { return sorted.indexOf(d.row-1) * cellSize; })\n",
" ;\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return sorted.indexOf(i) * cellSize; })\n",
" ;\n",
" }\n",
" }\n",
"\n",
" d3.select(\"#order\").on(\"change\",function(){\n",
" order(this.value);\n",
" });\n",
" \n",
" function order(value){\n",
" if(value==\"hclust\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return hccol.indexOf(d.col) * cellSize; })\n",
" .attr(\"y\", function(d) { return hcrow.indexOf(d.row) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return hcrow.indexOf(i+1) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return hccol.indexOf(i+1) * cellSize; })\n",
" ;\n",
"\n",
" }else if (value==\"probecontrast\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return (d.col - 1) * cellSize; })\n",
" .attr(\"y\", function(d) { return (d.row - 1) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
"\n",
" }else if (value==\"probe\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"y\", function(d) { return (d.row - 1) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
" }else if (value==\"contrast\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return (d.col - 1) * cellSize; })\n",
" ;\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
" }\n",
" }\n",
" // \n",
" var sa=d3.select(\".g3\")\n",
" .on(\"mousedown\", function() {\n",
" if( !d3.event.altKey) {\n",
" d3.selectAll(\".cell-selected\").classed(\"cell-selected\",false);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-selected\",false);\n",
" d3.selectAll(\".colLabel\").classed(\"text-selected\",false);\n",
" }\n",
" var p = d3.mouse(this);\n",
" sa.append(\"rect\")\n",
" .attr({\n",
" rx : 0,\n",
" ry : 0,\n",
" class : \"selection\",\n",
" x : p[0],\n",
" y : p[1],\n",
" width : 1,\n",
" height : 1\n",
" })\n",
" })\n",
" .on(\"mousemove\", function() {\n",
" var s = sa.select(\"rect.selection\");\n",
" \n",
" if(!s.empty()) {\n",
" var p = d3.mouse(this),\n",
" d = {\n",
" x : parseInt(s.attr(\"x\"), 10),\n",
" y : parseInt(s.attr(\"y\"), 10),\n",
" width : parseInt(s.attr(\"width\"), 10),\n",
" height : parseInt(s.attr(\"height\"), 10)\n",
" },\n",
" move = {\n",
" x : p[0] - d.x,\n",
" y : p[1] - d.y\n",
" }\n",
" ;\n",
" \n",
" if(move.x < 1 || (move.x*2<d.width)) {\n",
" d.x = p[0];\n",
" d.width -= move.x;\n",
" } else {\n",
" d.width = move.x; \n",
" }\n",
" \n",
" if(move.y < 1 || (move.y*2<d.height)) {\n",
" d.y = p[1];\n",
" d.height -= move.y;\n",
" } else {\n",
" d.height = move.y; \n",
" }\n",
" s.attr(d);\n",
" \n",
" // deselect all temporary selected state objects\n",
" d3.selectAll('.cell-selection.cell-selected').classed(\"cell-selected\", false);\n",
" d3.selectAll(\".text-selection.text-selected\").classed(\"text-selected\",false);\n",
"\n",
" d3.selectAll('.cell').filter(function(cell_d, i) {\n",
" if(\n",
" !d3.select(this).classed(\"cell-selected\") && \n",
" // inner circle inside selection frame\n",
" (this.x.baseVal.value)+cellSize >= d.x && (this.x.baseVal.value)<=d.x+d.width && \n",
" (this.y.baseVal.value)+cellSize >= d.y && (this.y.baseVal.value)<=d.y+d.height\n",
" ) {\n",
" \n",
" d3.select(this)\n",
" .classed(\"cell-selection\", true)\n",
" .classed(\"cell-selected\", true);\n",
"\n",
" d3.select(\".r\"+(cell_d.row-1))\n",
" .classed(\"text-selection\",true)\n",
" .classed(\"text-selected\",true);\n",
"\n",
" d3.select(\".c\"+(cell_d.col-1))\n",
" .classed(\"text-selection\",true)\n",
" .classed(\"text-selected\",true);\n",
" }\n",
" });\n",
" }\n",
" })\n",
" .on(\"mouseup\", function() {\n",
" // remove selection frame\n",
" sa.selectAll(\"rect.selection\").remove();\n",
" \n",
" // remove temporary selection marker class\n",
" d3.selectAll('.cell-selection').classed(\"cell-selection\", false);\n",
" d3.selectAll(\".text-selection\").classed(\"text-selection\",false);\n",
" })\n",
" .on(\"mouseout\", function() {\n",
" if(d3.event.relatedTarget.tagName=='html') {\n",
" // remove selection frame\n",
" sa.selectAll(\"rect.selection\").remove();\n",
" // remove temporary selection marker class\n",
" d3.selectAll('.cell-selection').classed(\"cell-selection\", false);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-selected\",false);\n",
" d3.selectAll(\".colLabel\").classed(\"text-selected\",false);\n",
" }\n",
" });\n",
"\n",
" \n",
" }\n",
" });\n",
" WidgetManager.register_widget_view(\"HeatmapView\", HeatmapView);\n",
"});"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"\n",
"require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
"\n",
"require([\"widgets/js/widget\", \"d3\"], function(WidgetManager, d3){\n",
" var HeatmapView = IPython.DOMWidgetView.extend({\n",
"\n",
" render: function(){\n",
" \n",
" this.$el.append(this.model.get(\"html_style\"));\n",
" this.svg = d3.select(this.el).append(\"svg\")\n",
" .attr({\n",
" width: 600,\n",
" height: 600\n",
" });\n",
" \n",
" \n",
" this.update();\n",
" },\n",
" update: function(){\n",
" var margin = { top: 150, right: 10, bottom: 50, left: 100 },\n",
" cellSize=12,\n",
" width = cellSize*col_number, // - margin.left - margin.right,\n",
" height = cellSize*row_number , // - margin.top - margin.bottom,\n",
" //gridSize = Math.floor(width / 24),\n",
" legendElementWidth = cellSize*2.5,\n",
" colorBuckets = 21,\n",
" colors = ['#005824','#1A693B','#347B53','#4F8D6B','#699F83','#83B09B','#9EC2B3','#B8D4CB','#D2E6E3','#EDF8FB','#FFFFFF','#F1EEF6','#E6D3E1','#DBB9CD','#D19EB9','#C684A4','#BB6990','#B14F7C','#A63467','#9B1A53','#91003F'],\n",
" hcrow = [1,2,4,3], //,18,6,12,20,19,33,32,26,44,35,38,3,23,41,22,10,2,15,16,36,8,25,29,7,27,34,48,31,45,43,14,9,39,1,37,47,42,21,40,5,28,46,50,17,24,13], // change to gene name or probe id\n",
" hccol = [2,1,5,3,6,4]; //,42,21,58,56,14,16,43,15,17,46,47,48,54,49,37,38,25,22,7,8,2,45,9,20,24,44,23,19,13,40,11,1,39,53,10,52,3,26,27,60,50,51,59,18,31,32,30,4,55,28,29,57,36,34,33,35], // change to gene name or probe id\n",
" //rowLabel = ['1','2','1759080_s_at','1759302_s_at','1759502_s_at','1759540_s_at','1759781_s_at','1759828_s_at','1759829_s_at','1759906_s_at','1760088_s_at','1760164_s_at','1760453_s_at','1760516_s_at','1760594_s_at','1760894_s_at','1760951_s_at','1761030_s_at','1761128_at','1761145_s_at','1761160_s_at','1761189_s_at','1761222_s_at','1761245_s_at','1761277_s_at','1761434_s_at','1761553_s_at','1761620_s_at','1761873_s_at','1761884_s_at','1761944_s_at','1762105_s_at','1762118_s_at','1762151_s_at','1762388_s_at','1762401_s_at','1762633_s_at','1762701_s_at','1762787_s_at','1762819_s_at','1762880_s_at','1762945_s_at','1762983_s_at','1763132_s_at','1763138_s_at','1763146_s_at','1763198_s_at','1763383_at','1763410_s_at','1763426_s_at','1763490_s_at','1763491_s_at'], // change to gene name or probe id\n",
" //colLabel = ['3','4','con1027','con1028','con1029','con103','con1030','con1031','con1032','con1033','con1034','con1035','con1036','con1037','con1038','con1039','con1040','con1041','con108','con109','con110','con111','con112','con125','con126','con127','con128','con129','con130','con131','con132','con133','con134','con135','con136','con137','con138','con139','con14','con15','con150','con151','con152','con153','con16','con17','con174','con184','con185','con186','con187','con188','con189','con191','con192','con193','con194','con199','con2','con200','con201','con21']; // change to contrast name\n",
" \n",
" var rowLabel = JSON.parse(this.model.get(\"row_labels\"));\n",
" var colLabel = JSON.parse(this.model.get(\"col_labels\"));\n",
" var data = JSON.parse(this.model.get(\"heatmap_data\"));\n",
" \n",
" \n",
" var col_number = colLabel.length,\n",
" row_number = rowLabel.length;\n",
" \n",
" var colorScale = d3.scale.quantile()\n",
" .domain([ -10 , 0, 10])\n",
" .range(colors);\n",
" \n",
" var svg = this.svg;\n",
" \n",
" var rowSortOrder=false;\n",
" var colSortOrder=false;\n",
" var rowLabels = svg.append(\"g\")\n",
" .selectAll(\".rowLabelg\")\n",
" .data(rowLabel)\n",
" .enter()\n",
" .append(\"text\")\n",
" .text(function (d) { return d; })\n",
" .attr(\"x\", 0)\n",
" .attr(\"y\", function (d, i) { return hcrow.indexOf(i+1) * cellSize; })\n",
" .style(\"text-anchor\", \"end\")\n",
" .attr(\"transform\", \"translate(-6,\" + cellSize / 1.5 + \")\")\n",
" .attr(\"class\", function (d,i) { return \"rowLabel mono r\"+i;} ) \n",
" .on(\"mouseover\", function(d) {d3.select(this).classed(\"text-hover\",true);})\n",
" .on(\"mouseout\" , function(d) {d3.select(this).classed(\"text-hover\",false);})\n",
" .on(\"click\", function(d,i) {rowSortOrder=!rowSortOrder; sortbylabel(\"r\",i,rowSortOrder);d3.select(\"#order\").property(\"selectedIndex\", 4).node().focus();;})\n",
" ;\n",
"\n",
" var colLabels = svg.append(\"g\")\n",
" .selectAll(\".colLabelg\")\n",
" .data(colLabel)\n",
" .enter()\n",
" .append(\"text\")\n",
" .text(function (d) { return d; })\n",
" .attr(\"x\", 0)\n",
" .attr(\"y\", function (d, i) { return hccol.indexOf(i+1) * cellSize; })\n",
" .style(\"text-anchor\", \"left\")\n",
" .attr(\"transform\", \"translate(\"+cellSize/2 + \",-6) rotate (-90)\")\n",
" .attr(\"class\", function (d,i) { return \"colLabel mono c\"+i;} )\n",
" .on(\"mouseover\", function(d) {d3.select(this).classed(\"text-hover\",true);})\n",
" .on(\"mouseout\" , function(d) {d3.select(this).classed(\"text-hover\",false);})\n",
" .on(\"click\", function(d,i) {colSortOrder=!colSortOrder; sortbylabel(\"c\",i,colSortOrder);d3.select(\"#order\").property(\"selectedIndex\", 4).node().focus();;})\n",
" ;\n",
"\n",
" var heatMap = svg.append(\"g\").attr(\"class\",\"g3\")\n",
" .selectAll(\".cellg\")\n",
" .data(data,function(d){return d.row+\":\"+d.col;})\n",
" .enter()\n",
" .append(\"rect\")\n",
" .attr(\"x\", function(d) { return hccol.indexOf(d.col) * cellSize; })\n",
" .attr(\"y\", function(d) { return hcrow.indexOf(d.row) * cellSize; })\n",
" .attr(\"class\", function(d){return \"cell cell-border cr\"+(d.row-1)+\" cc\"+(d.col-1);})\n",
" .attr(\"width\", cellSize)\n",
" .attr(\"height\", cellSize)\n",
" .style(\"fill\", function(d) { return colorScale(d.value); })\n",
" /* .on(\"click\", function(d) {\n",
" var rowtext=d3.select(\".r\"+(d.row-1));\n",
" if(rowtext.classed(\"text-selected\")==false){\n",
" rowtext.classed(\"text-selected\",true);\n",
" }else{\n",
" rowtext.classed(\"text-selected\",false);\n",
" }\n",
" })*/\n",
" .on(\"mouseover\", function(d){\n",
" //highlight text\n",
" d3.select(this).classed(\"cell-hover\",true);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-highlight\",function(r,ri){ return ri==(d.row-1);});\n",
" d3.selectAll(\".colLabel\").classed(\"text-highlight\",function(c,ci){ return ci==(d.col-1);});\n",
" \n",
" //Update the tooltip position and value\n",
" d3.select(\"#tooltip\")\n",
" .style(\"left\", (d3.event.pageX+10) + \"px\")\n",
" .style(\"top\", (d3.event.pageY-10) + \"px\")\n",
" .select(\"#value\")\n",
" .text(\"lables:\"+rowLabel[d.row-1]+\",\"+colLabel[d.col-1]+\"\\ndata:\"+d.value+\"\\nrow-col-idx:\"+d.col+\",\"+d.row+\"\\ncell-xy \"+this.x.baseVal.value+\", \"+this.y.baseVal.value); \n",
" //Show the tooltip\n",
" d3.select(\"#tooltip\").classed(\"hidden\", false);\n",
" })\n",
" .on(\"mouseout\", function(){\n",
" d3.select(this).classed(\"cell-hover\",false);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-highlight\",false);\n",
" d3.selectAll(\".colLabel\").classed(\"text-highlight\",false);\n",
" d3.select(\"#tooltip\").classed(\"hidden\", true);\n",
" })\n",
" ;\n",
"\n",
" var legend = svg.selectAll(\".legend\")\n",
" .data([-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10])\n",
" .enter().append(\"g\")\n",
" .attr(\"class\", \"legend\");\n",
" \n",
" legend.append(\"rect\")\n",
" .attr(\"x\", function(d, i) { return legendElementWidth * i; })\n",
" .attr(\"y\", height+(cellSize*2))\n",
" .attr(\"width\", legendElementWidth)\n",
" .attr(\"height\", cellSize)\n",
" .style(\"fill\", function(d, i) { return colors[i]; });\n",
" \n",
" legend.append(\"text\")\n",
" .attr(\"class\", \"mono\")\n",
" .text(function(d) { return d; })\n",
" .attr(\"width\", legendElementWidth)\n",
" .attr(\"x\", function(d, i) { return legendElementWidth * i; })\n",
" .attr(\"y\", height + (cellSize*4));\n",
"\n",
"// Change ordering of cells\n",
"\n",
" function sortbylabel(rORc,i,sortOrder){\n",
" var t = svg.transition().duration(3000);\n",
" var log2r=[];\n",
" var sorted; // sorted is zero-based index\n",
" d3.selectAll(\".c\"+rORc+i) \n",
" .filter(function(ce){\n",
" log2r.push(ce.value);\n",
" })\n",
" ;\n",
" if(rORc==\"r\"){ // sort log2ratio of a gene\n",
" sorted=d3.range(col_number).sort(function(a,b){ if(sortOrder){ return log2r[b]-log2r[a];}else{ return log2r[a]-log2r[b];}});\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return sorted.indexOf(d.col-1) * cellSize; })\n",
" ;\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return sorted.indexOf(i) * cellSize; })\n",
" ;\n",
" }else{ // sort log2ratio of a contrast\n",
" sorted=d3.range(row_number).sort(function(a,b){if(sortOrder){ return log2r[b]-log2r[a];}else{ return log2r[a]-log2r[b];}});\n",
" t.selectAll(\".cell\")\n",
" .attr(\"y\", function(d) { return sorted.indexOf(d.row-1) * cellSize; })\n",
" ;\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return sorted.indexOf(i) * cellSize; })\n",
" ;\n",
" }\n",
" }\n",
"\n",
" d3.select(\"#order\").on(\"change\",function(){\n",
" order(this.value);\n",
" });\n",
" \n",
" function order(value){\n",
" if(value==\"hclust\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return hccol.indexOf(d.col) * cellSize; })\n",
" .attr(\"y\", function(d) { return hcrow.indexOf(d.row) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return hcrow.indexOf(i+1) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return hccol.indexOf(i+1) * cellSize; })\n",
" ;\n",
"\n",
" }else if (value==\"probecontrast\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return (d.col - 1) * cellSize; })\n",
" .attr(\"y\", function(d) { return (d.row - 1) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
"\n",
" }else if (value==\"probe\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"y\", function(d) { return (d.row - 1) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
" }else if (value==\"contrast\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return (d.col - 1) * cellSize; })\n",
" ;\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
" }\n",
" }\n",
" // \n",
" var sa=d3.select(\".g3\")\n",
" .on(\"mousedown\", function() {\n",
" if( !d3.event.altKey) {\n",
" d3.selectAll(\".cell-selected\").classed(\"cell-selected\",false);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-selected\",false);\n",
" d3.selectAll(\".colLabel\").classed(\"text-selected\",false);\n",
" }\n",
" var p = d3.mouse(this);\n",
" sa.append(\"rect\")\n",
" .attr({\n",
" rx : 0,\n",
" ry : 0,\n",
" class : \"selection\",\n",
" x : p[0],\n",
" y : p[1],\n",
" width : 1,\n",
" height : 1\n",
" })\n",
" })\n",
" .on(\"mousemove\", function() {\n",
" var s = sa.select(\"rect.selection\");\n",
" \n",
" if(!s.empty()) {\n",
" var p = d3.mouse(this),\n",
" d = {\n",
" x : parseInt(s.attr(\"x\"), 10),\n",
" y : parseInt(s.attr(\"y\"), 10),\n",
" width : parseInt(s.attr(\"width\"), 10),\n",
" height : parseInt(s.attr(\"height\"), 10)\n",
" },\n",
" move = {\n",
" x : p[0] - d.x,\n",
" y : p[1] - d.y\n",
" }\n",
" ;\n",
" \n",
" if(move.x < 1 || (move.x*2<d.width)) {\n",
" d.x = p[0];\n",
" d.width -= move.x;\n",
" } else {\n",
" d.width = move.x; \n",
" }\n",
" \n",
" if(move.y < 1 || (move.y*2<d.height)) {\n",
" d.y = p[1];\n",
" d.height -= move.y;\n",
" } else {\n",
" d.height = move.y; \n",
" }\n",
" s.attr(d);\n",
" \n",
" // deselect all temporary selected state objects\n",
" d3.selectAll('.cell-selection.cell-selected').classed(\"cell-selected\", false);\n",
" d3.selectAll(\".text-selection.text-selected\").classed(\"text-selected\",false);\n",
"\n",
" d3.selectAll('.cell').filter(function(cell_d, i) {\n",
" if(\n",
" !d3.select(this).classed(\"cell-selected\") && \n",
" // inner circle inside selection frame\n",
" (this.x.baseVal.value)+cellSize >= d.x && (this.x.baseVal.value)<=d.x+d.width && \n",
" (this.y.baseVal.value)+cellSize >= d.y && (this.y.baseVal.value)<=d.y+d.height\n",
" ) {\n",
" \n",
" d3.select(this)\n",
" .classed(\"cell-selection\", true)\n",
" .classed(\"cell-selected\", true);\n",
"\n",
" d3.select(\".r\"+(cell_d.row-1))\n",
" .classed(\"text-selection\",true)\n",
" .classed(\"text-selected\",true);\n",
"\n",
" d3.select(\".c\"+(cell_d.col-1))\n",
" .classed(\"text-selection\",true)\n",
" .classed(\"text-selected\",true);\n",
" }\n",
" });\n",
" }\n",
" })\n",
" .on(\"mouseup\", function() {\n",
" // remove selection frame\n",
" sa.selectAll(\"rect.selection\").remove();\n",
" \n",
" // remove temporary selection marker class\n",
" d3.selectAll('.cell-selection').classed(\"cell-selection\", false);\n",
" d3.selectAll(\".text-selection\").classed(\"text-selection\",false);\n",
" })\n",
" .on(\"mouseout\", function() {\n",
" if(d3.event.relatedTarget.tagName=='html') {\n",
" // remove selection frame\n",
" sa.selectAll(\"rect.selection\").remove();\n",
" // remove temporary selection marker class\n",
" d3.selectAll('.cell-selection').classed(\"cell-selection\", false);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-selected\",false);\n",
" d3.selectAll(\".colLabel\").classed(\"text-selected\",false);\n",
" }\n",
" });\n",
"\n",
" \n",
" }\n",
" });\n",
" WidgetManager.register_widget_view(\"HeatmapView\", HeatmapView);\n",
"});"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x1160b7dd0>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#circles = CirclesWidget(radii=[1,2,3], sets=json.dumps(sets), overlaps=json.dumps(overlaps))\n",
"heatmap_diagram = HeatmapWidget(row_labels=json.dumps(row_labels), \n",
" col_labels=json.dumps(col_labels), \n",
" heatmap_data=json.dumps(heatmap_data), html_style=html_style)\n",
"display(heatmap_diagram)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"html_style = \"\"\"\n",
" <style>\n",
" /* disable text selection */\n",
" svg *::selection {\n",
" background : transparent;\n",
" }\n",
" \n",
" svg *::-moz-selection {\n",
" background:transparent;\n",
" } \n",
" \n",
" svg *::-webkit-selection {\n",
" background:transparent;\n",
" }\n",
" rect.selection {\n",
" stroke : #333;\n",
" stroke-dasharray: 4px;\n",
" stroke-opacity : 0.5;\n",
" fill : transparent;\n",
" }\n",
"\n",
" rect.cell-border {\n",
" stroke: #eee;\n",
" stroke-width:0.3px; \n",
" }\n",
"\n",
" rect.cell-selected {\n",
" stroke: rgb(51,102,153);\n",
" stroke-width:0.5px; \n",
" }\n",
"\n",
" rect.cell-hover {\n",
" stroke: #F00;\n",
" stroke-width:0.3px; \n",
" }\n",
"\n",
" text.mono {\n",
" font-size: 9pt;\n",
" font-family: Consolas, courier;\n",
" fill: #aaa;\n",
" }\n",
"\n",
" text.text-selected {\n",
" fill: #000;\n",
" }\n",
"\n",
" text.text-highlight {\n",
" fill: #c00;\n",
" }\n",
" text.text-hover {\n",
" fill: #00C;\n",
" }\n",
" #tooltip {\n",
" position: fixed;\n",
" width: 200px;\n",
" height: auto;\n",
" padding: 10px;\n",
" background-color: white;\n",
" -webkit-border-radius: 10px;\n",
" -moz-border-radius: 10px;\n",
" border-radius: 10px;\n",
" -webkit-box-shadow: 4px 4px 10px rgba(0, 0, 0, 0.4);\n",
" -moz-box-shadow: 4px 4px 10px rgba(0, 0, 0, 0.4);\n",
" box-shadow: 4px 4px 10px rgba(0, 0, 0, 0.4);\n",
" pointer-events: none;\n",
" }\n",
"\n",
" #tooltip.hidden {\n",
" display: none;\n",
" }\n",
"\n",
" #tooltip p {\n",
" margin: 0;\n",
" font-family: sans-serif;\n",
" font-size: 12px;\n",
" line-height: 20px;\n",
" }\n",
" </style>\n",
"\n",
"\n",
"<div id=\"tooltip\" class=\"hidden\">\n",
" <p><span id=\"value\"></p>\n",
"</div>\n",
"\n",
" \n",
" <select id=\"order\">\n",
" <option value=\"hclust\">by cluster</option>\n",
" <option value=\"probecontrast\">by probe name and contrast name</option>\n",
" <option value=\"probe\">by probe name</option>\n",
" <option value=\"contrast\">by contrast name</option>\n",
" <option value=\"custom\">by log2 ratio</option>\n",
" </select>\n",
" </select>\n",
"<div id=\"chart\" style='overflow:auto; width:960px; height:auto;'></div>\n",
"\"\"\""
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%html\n",
"\n",
" <style>\n",
" /* disable text selection */\n",
" svg *::selection {\n",
" background : transparent;\n",
" }\n",
" \n",
" svg *::-moz-selection {\n",
" background:transparent;\n",
" } \n",
" \n",
" svg *::-webkit-selection {\n",
" background:transparent;\n",
" }\n",
" rect.selection {\n",
" stroke : #333;\n",
" stroke-dasharray: 4px;\n",
" stroke-opacity : 0.5;\n",
" fill : transparent;\n",
" }\n",
"\n",
" rect.cell-border {\n",
" stroke: #eee;\n",
" stroke-width:0.3px; \n",
" }\n",
"\n",
" rect.cell-selected {\n",
" stroke: rgb(51,102,153);\n",
" stroke-width:0.5px; \n",
" }\n",
"\n",
" rect.cell-hover {\n",
" stroke: #F00;\n",
" stroke-width:0.3px; \n",
" }\n",
"\n",
" text.mono {\n",
" font-size: 9pt;\n",
" font-family: Consolas, courier;\n",
" fill: #aaa;\n",
" }\n",
"\n",
" text.text-selected {\n",
" fill: #000;\n",
" }\n",
"\n",
" text.text-highlight {\n",
" fill: #c00;\n",
" }\n",
" text.text-hover {\n",
" fill: #00C;\n",
" }\n",
" #tooltip {\n",
" position: fixed;\n",
" width: 200px;\n",
" height: auto;\n",
" padding: 10px;\n",
" background-color: white;\n",
" -webkit-border-radius: 10px;\n",
" -moz-border-radius: 10px;\n",
" border-radius: 10px;\n",
" -webkit-box-shadow: 4px 4px 10px rgba(0, 0, 0, 0.4);\n",
" -moz-box-shadow: 4px 4px 10px rgba(0, 0, 0, 0.4);\n",
" box-shadow: 4px 4px 10px rgba(0, 0, 0, 0.4);\n",
" pointer-events: none;\n",
" }\n",
"\n",
" #tooltip.hidden {\n",
" display: none;\n",
" }\n",
"\n",
" #tooltip p {\n",
" margin: 0;\n",
" font-family: sans-serif;\n",
" font-size: 12px;\n",
" line-height: 20px;\n",
" }\n",
" </style>\n",
"\n",
"\n",
"<div id=\"tooltip\" class=\"hidden\">\n",
" <p><span id=\"value\"></p>\n",
"</div>\n",
"\n",
" \n",
" <select id=\"order\">\n",
" <option value=\"hclust\">by cluster</option>\n",
" <option value=\"probecontrast\">by probe name and contrast name</option>\n",
" <option value=\"probe\">by probe name</option>\n",
" <option value=\"contrast\">by contrast name</option>\n",
" <option value=\"custom\">by log2 ratio</option>\n",
" </select>\n",
" </select>\n",
"<div id=\"chart\" style='overflow:auto; width:960px; height:auto;'></div>"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"\n",
" <style>\n",
" /* disable text selection */\n",
" svg *::selection {\n",
" background : transparent;\n",
" }\n",
" \n",
" svg *::-moz-selection {\n",
" background:transparent;\n",
" } \n",
" \n",
" svg *::-webkit-selection {\n",
" background:transparent;\n",
" }\n",
" rect.selection {\n",
" stroke : #333;\n",
" stroke-dasharray: 4px;\n",
" stroke-opacity : 0.5;\n",
" fill : transparent;\n",
" }\n",
"\n",
" rect.cell-border {\n",
" stroke: #eee;\n",
" stroke-width:0.3px; \n",
" }\n",
"\n",
" rect.cell-selected {\n",
" stroke: rgb(51,102,153);\n",
" stroke-width:0.5px; \n",
" }\n",
"\n",
" rect.cell-hover {\n",
" stroke: #F00;\n",
" stroke-width:0.3px; \n",
" }\n",
"\n",
" text.mono {\n",
" font-size: 9pt;\n",
" font-family: Consolas, courier;\n",
" fill: #aaa;\n",
" }\n",
"\n",
" text.text-selected {\n",
" fill: #000;\n",
" }\n",
"\n",
" text.text-highlight {\n",
" fill: #c00;\n",
" }\n",
" text.text-hover {\n",
" fill: #00C;\n",
" }\n",
" #tooltip {\n",
" position: fixed;\n",
" width: 200px;\n",
" height: auto;\n",
" padding: 10px;\n",
" background-color: white;\n",
" -webkit-border-radius: 10px;\n",
" -moz-border-radius: 10px;\n",
" border-radius: 10px;\n",
" -webkit-box-shadow: 4px 4px 10px rgba(0, 0, 0, 0.4);\n",
" -moz-box-shadow: 4px 4px 10px rgba(0, 0, 0, 0.4);\n",
" box-shadow: 4px 4px 10px rgba(0, 0, 0, 0.4);\n",
" pointer-events: none;\n",
" }\n",
"\n",
" #tooltip.hidden {\n",
" display: none;\n",
" }\n",
"\n",
" #tooltip p {\n",
" margin: 0;\n",
" font-family: sans-serif;\n",
" font-size: 12px;\n",
" line-height: 20px;\n",
" }\n",
" </style>\n",
"\n",
"\n",
"<div id=\"tooltip\" class=\"hidden\">\n",
" <p><span id=\"value\"></p>\n",
"</div>\n",
"\n",
" \n",
" <select id=\"order\">\n",
" <option value=\"hclust\">by cluster</option>\n",
" <option value=\"probecontrast\">by probe name and contrast name</option>\n",
" <option value=\"probe\">by probe name</option>\n",
" <option value=\"contrast\">by contrast name</option>\n",
" <option value=\"custom\">by log2 ratio</option>\n",
" </select>\n",
" </select>\n",
"<div id=\"chart\" style='overflow:auto; width:960px; height:auto;'></div>"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x10c53c910>"
]
}
],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%javascript\n",
"\n",
"require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
"\n",
"require([\"d3\"], function(d3){\n",
"\n",
"var margin = { top: 150, right: 10, bottom: 50, left: 100 },\n",
" cellSize=12,\n",
" col_number=6,\n",
" row_number=4,\n",
" width = cellSize*col_number, // - margin.left - margin.right,\n",
" height = cellSize*row_number , // - margin.top - margin.bottom,\n",
" //gridSize = Math.floor(width / 24),\n",
" legendElementWidth = cellSize*2.5,\n",
" colorBuckets = 21,\n",
" colors = ['#005824','#1A693B','#347B53','#4F8D6B','#699F83','#83B09B','#9EC2B3','#B8D4CB','#D2E6E3','#EDF8FB','#FFFFFF','#F1EEF6','#E6D3E1','#DBB9CD','#D19EB9','#C684A4','#BB6990','#B14F7C','#A63467','#9B1A53','#91003F'],\n",
" hcrow = [1, 2, 3, 4],\n",
" hccol = [6, 2, 1, 3, 5, 4],\n",
" rowLabel = ['1759302_s_at','1759080_s_at','1759502_s_at','1759540_s_at'],\n",
" colLabel = ['con1027','con1028','con1029','con103','con1030','con1031'];\n",
"\n",
"//d3.tsv(\"data/heatmap_data.tsv\",\n",
"//function(d) {\n",
" //return {\n",
" //row: +d.row_idx,\n",
" //col: +d.col_idx,\n",
" //value: +d.log2ratio\n",
" //};\n",
"//},\n",
"\n",
" var data = [{\"col\": 1, \"value\": 6, \"row\": 1}, {\"col\": 2, \"value\": 6, \"row\": 1}, {\"col\": 3, \"value\": 7, \"row\": 1}, {\"col\": 4, \"value\": 6, \"row\": 1}, {\"col\": 5, \"value\": 6, \"row\": 1}, {\"col\": 6, \"value\": 6, \"row\": 1}, {\"col\": 1, \"value\": 6, \"row\": 2}, {\"col\": 2, \"value\": 6, \"row\": 2}, {\"col\": 3, \"value\": 6, \"row\": 2}, {\"col\": 4, \"value\": 6, \"row\": 2}, {\"col\": 5, \"value\": 6, \"row\": 2}, {\"col\": 6, \"value\": 6, \"row\": 2}, {\"col\": 1, \"value\": 9, \"row\": 3}, {\"col\": 2, \"value\": 9, \"row\": 3}, {\"col\": 3, \"value\": 9, \"row\": 3}, {\"col\": 4, \"value\": 9, \"row\": 3}, {\"col\": 5, \"value\": 9, \"row\": 3}, {\"col\": 6, \"value\": 9, \"row\": 3}, {\"col\": 1, \"value\": 6, \"row\": 4}, {\"col\": 2, \"value\": 6, \"row\": 4}, {\"col\": 3, \"value\": 6, \"row\": 4}, {\"col\": 4, \"value\": 6, \"row\": 4}, {\"col\": 5, \"value\": 6, \"row\": 4}, {\"col\": 6, \"value\": 5, \"row\": 4}];\n",
" console.log(data.slice(1,10));\n",
" var colorScale = d3.scale.quantile()\n",
" .domain([ -10 , 0, 10])\n",
" .range(colors);\n",
" \n",
" var svg = d3.select(\"#chart\").append(\"svg\")\n",
" .attr(\"width\", width + margin.left + margin.right)\n",
" .attr(\"height\", height + margin.top + margin.bottom)\n",
" .append(\"g\")\n",
" .attr(\"transform\", \"translate(\" + margin.left + \",\" + margin.top + \")\")\n",
" ;\n",
" var rowSortOrder=false;\n",
" var colSortOrder=false;\n",
" var rowLabels = svg.append(\"g\")\n",
" .selectAll(\".rowLabelg\")\n",
" .data(rowLabel)\n",
" .enter()\n",
" .append(\"text\")\n",
" .text(function (d) { return d; })\n",
" .attr(\"x\", 0)\n",
" .attr(\"y\", function (d, i) { return hcrow.indexOf(i+1) * cellSize; })\n",
" .style(\"text-anchor\", \"end\")\n",
" .attr(\"transform\", \"translate(-6,\" + cellSize / 1.5 + \")\")\n",
" .attr(\"class\", function (d,i) { return \"rowLabel mono r\"+i;} ) \n",
" .on(\"mouseover\", function(d) {d3.select(this).classed(\"text-hover\",true);})\n",
" .on(\"mouseout\" , function(d) {d3.select(this).classed(\"text-hover\",false);})\n",
" .on(\"click\", function(d,i) {rowSortOrder=!rowSortOrder; sortbylabel(\"r\",i,rowSortOrder);d3.select(\"#order\").property(\"selectedIndex\", 4).node().focus();;})\n",
" ;\n",
"\n",
" var colLabels = svg.append(\"g\")\n",
" .selectAll(\".colLabelg\")\n",
" .data(colLabel)\n",
" .enter()\n",
" .append(\"text\")\n",
" .text(function (d) { return d; })\n",
" .attr(\"x\", 0)\n",
" .attr(\"y\", function (d, i) { return hccol.indexOf(i+1) * cellSize; })\n",
" .style(\"text-anchor\", \"left\")\n",
" .attr(\"transform\", \"translate(\"+cellSize/2 + \",-6) rotate (-90)\")\n",
" .attr(\"class\", function (d,i) { return \"colLabel mono c\"+i;} )\n",
" .on(\"mouseover\", function(d) {d3.select(this).classed(\"text-hover\",true);})\n",
" .on(\"mouseout\" , function(d) {d3.select(this).classed(\"text-hover\",false);})\n",
" .on(\"click\", function(d,i) {colSortOrder=!colSortOrder; sortbylabel(\"c\",i,colSortOrder);d3.select(\"#order\").property(\"selectedIndex\", 4).node().focus();;})\n",
" ;\n",
"\n",
" var heatMap = svg.append(\"g\").attr(\"class\",\"g3\")\n",
" .selectAll(\".cellg\")\n",
" .data(data,function(d){return d.row+\":\"+d.col;})\n",
" .enter()\n",
" .append(\"rect\")\n",
" .attr(\"x\", function(d) { return hccol.indexOf(d.col) * cellSize; })\n",
" .attr(\"y\", function(d) { return hcrow.indexOf(d.row) * cellSize; })\n",
" .attr(\"class\", function(d){return \"cell cell-border cr\"+(d.row-1)+\" cc\"+(d.col-1);})\n",
" .attr(\"width\", cellSize)\n",
" .attr(\"height\", cellSize)\n",
" .style(\"fill\", function(d) { return colorScale(d.value); })\n",
" /* .on(\"click\", function(d) {\n",
" var rowtext=d3.select(\".r\"+(d.row-1));\n",
" if(rowtext.classed(\"text-selected\")==false){\n",
" rowtext.classed(\"text-selected\",true);\n",
" }else{\n",
" rowtext.classed(\"text-selected\",false);\n",
" }\n",
" })*/\n",
" .on(\"mouseover\", function(d){\n",
" //highlight text\n",
" d3.select(this).classed(\"cell-hover\",true);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-highlight\",function(r,ri){ return ri==(d.row-1);});\n",
" d3.selectAll(\".colLabel\").classed(\"text-highlight\",function(c,ci){ return ci==(d.col-1);});\n",
" \n",
" //Update the tooltip position and value\n",
" d3.select(\"#tooltip\")\n",
" .style(\"left\", (d3.event.pageX+10) + \"px\")\n",
" .style(\"top\", (d3.event.pageY-10) + \"px\")\n",
" .select(\"#value\")\n",
" .text(\"lables:\"+rowLabel[d.row-1]+\",\"+colLabel[d.col-1]+\"\\ndata:\"+d.value+\"\\nrow-col-idx:\"+d.col+\",\"+d.row+\"\\ncell-xy \"+this.x.baseVal.value+\", \"+this.y.baseVal.value); \n",
" //Show the tooltip\n",
" d3.select(\"#tooltip\").classed(\"hidden\", false);\n",
" })\n",
" .on(\"mouseout\", function(){\n",
" d3.select(this).classed(\"cell-hover\",false);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-highlight\",false);\n",
" d3.selectAll(\".colLabel\").classed(\"text-highlight\",false);\n",
" d3.select(\"#tooltip\").classed(\"hidden\", true);\n",
" })\n",
" ;\n",
"\n",
" var legend = svg.selectAll(\".legend\")\n",
" .data([-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10])\n",
" .enter().append(\"g\")\n",
" .attr(\"class\", \"legend\");\n",
" \n",
" legend.append(\"rect\")\n",
" .attr(\"x\", function(d, i) { return legendElementWidth * i; })\n",
" .attr(\"y\", height+(cellSize*2))\n",
" .attr(\"width\", legendElementWidth)\n",
" .attr(\"height\", cellSize)\n",
" .style(\"fill\", function(d, i) { return colors[i]; });\n",
" \n",
" legend.append(\"text\")\n",
" .attr(\"class\", \"mono\")\n",
" .text(function(d) { return d; })\n",
" .attr(\"width\", legendElementWidth)\n",
" .attr(\"x\", function(d, i) { return legendElementWidth * i; })\n",
" .attr(\"y\", height + (cellSize*4));\n",
"\n",
"// Change ordering of cells\n",
"\n",
" function sortbylabel(rORc,i,sortOrder){\n",
" var t = svg.transition().duration(3000);\n",
" var log2r=[];\n",
" var sorted; // sorted is zero-based index\n",
" d3.selectAll(\".c\"+rORc+i) \n",
" .filter(function(ce){\n",
" log2r.push(ce.value);\n",
" })\n",
" ;\n",
" if(rORc==\"r\"){ // sort log2ratio of a gene\n",
" sorted=d3.range(col_number).sort(function(a,b){ if(sortOrder){ return log2r[b]-log2r[a];}else{ return log2r[a]-log2r[b];}});\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return sorted.indexOf(d.col-1) * cellSize; })\n",
" ;\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return sorted.indexOf(i) * cellSize; })\n",
" ;\n",
" }else{ // sort log2ratio of a contrast\n",
" sorted=d3.range(row_number).sort(function(a,b){if(sortOrder){ return log2r[b]-log2r[a];}else{ return log2r[a]-log2r[b];}});\n",
" t.selectAll(\".cell\")\n",
" .attr(\"y\", function(d) { return sorted.indexOf(d.row-1) * cellSize; })\n",
" ;\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return sorted.indexOf(i) * cellSize; })\n",
" ;\n",
" }\n",
" }\n",
"\n",
" d3.select(\"#order\").on(\"change\",function(){\n",
" order(this.value);\n",
" });\n",
" \n",
" function order(value){\n",
" if(value==\"hclust\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return hccol.indexOf(d.col) * cellSize; })\n",
" .attr(\"y\", function(d) { return hcrow.indexOf(d.row) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return hcrow.indexOf(i+1) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return hccol.indexOf(i+1) * cellSize; })\n",
" ;\n",
"\n",
" }else if (value==\"probecontrast\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return (d.col - 1) * cellSize; })\n",
" .attr(\"y\", function(d) { return (d.row - 1) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
"\n",
" }else if (value==\"probe\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"y\", function(d) { return (d.row - 1) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
" }else if (value==\"contrast\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return (d.col - 1) * cellSize; })\n",
" ;\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
" }\n",
" }\n",
" // \n",
" var sa=d3.select(\".g3\")\n",
" .on(\"mousedown\", function() {\n",
" if( !d3.event.altKey) {\n",
" d3.selectAll(\".cell-selected\").classed(\"cell-selected\",false);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-selected\",false);\n",
" d3.selectAll(\".colLabel\").classed(\"text-selected\",false);\n",
" }\n",
" var p = d3.mouse(this);\n",
" sa.append(\"rect\")\n",
" .attr({\n",
" rx : 0,\n",
" ry : 0,\n",
" class : \"selection\",\n",
" x : p[0],\n",
" y : p[1],\n",
" width : 1,\n",
" height : 1\n",
" })\n",
" })\n",
" .on(\"mousemove\", function() {\n",
" var s = sa.select(\"rect.selection\");\n",
" \n",
" if(!s.empty()) {\n",
" var p = d3.mouse(this),\n",
" d = {\n",
" x : parseInt(s.attr(\"x\"), 10),\n",
" y : parseInt(s.attr(\"y\"), 10),\n",
" width : parseInt(s.attr(\"width\"), 10),\n",
" height : parseInt(s.attr(\"height\"), 10)\n",
" },\n",
" move = {\n",
" x : p[0] - d.x,\n",
" y : p[1] - d.y\n",
" }\n",
" ;\n",
" \n",
" if(move.x < 1 || (move.x*2<d.width)) {\n",
" d.x = p[0];\n",
" d.width -= move.x;\n",
" } else {\n",
" d.width = move.x; \n",
" }\n",
" \n",
" if(move.y < 1 || (move.y*2<d.height)) {\n",
" d.y = p[1];\n",
" d.height -= move.y;\n",
" } else {\n",
" d.height = move.y; \n",
" }\n",
" s.attr(d);\n",
" \n",
" // deselect all temporary selected state objects\n",
" d3.selectAll('.cell-selection.cell-selected').classed(\"cell-selected\", false);\n",
" d3.selectAll(\".text-selection.text-selected\").classed(\"text-selected\",false);\n",
"\n",
" d3.selectAll('.cell').filter(function(cell_d, i) {\n",
" if(\n",
" !d3.select(this).classed(\"cell-selected\") && \n",
" // inner circle inside selection frame\n",
" (this.x.baseVal.value)+cellSize >= d.x && (this.x.baseVal.value)<=d.x+d.width && \n",
" (this.y.baseVal.value)+cellSize >= d.y && (this.y.baseVal.value)<=d.y+d.height\n",
" ) {\n",
" \n",
" d3.select(this)\n",
" .classed(\"cell-selection\", true)\n",
" .classed(\"cell-selected\", true);\n",
"\n",
" d3.select(\".r\"+(cell_d.row-1))\n",
" .classed(\"text-selection\",true)\n",
" .classed(\"text-selected\",true);\n",
"\n",
" d3.select(\".c\"+(cell_d.col-1))\n",
" .classed(\"text-selection\",true)\n",
" .classed(\"text-selected\",true);\n",
" }\n",
" });\n",
" }\n",
" })\n",
" .on(\"mouseup\", function() {\n",
" // remove selection frame\n",
" sa.selectAll(\"rect.selection\").remove();\n",
" \n",
" // remove temporary selection marker class\n",
" d3.selectAll('.cell-selection').classed(\"cell-selection\", false);\n",
" d3.selectAll(\".text-selection\").classed(\"text-selection\",false);\n",
" })\n",
" .on(\"mouseout\", function() {\n",
" if(d3.event.relatedTarget.tagName=='html') {\n",
" // remove selection frame\n",
" sa.selectAll(\"rect.selection\").remove();\n",
" // remove temporary selection marker class\n",
" d3.selectAll('.cell-selection').classed(\"cell-selection\", false);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-selected\",false);\n",
" d3.selectAll(\".colLabel\").classed(\"text-selected\",false);\n",
" }\n",
" })\n",
" ;\n",
"//});\n",
"\n",
"});"
],
"language": "python",
"metadata": {},
"outputs": [
{
"javascript": [
"\n",
"require.config({paths: {d3: \"https://mpld3.github.io/js/d3.v3.min\"}});\n",
"\n",
"require([\"d3\"], function(d3){\n",
"\n",
"var margin = { top: 150, right: 10, bottom: 50, left: 100 },\n",
" cellSize=12,\n",
" col_number=6,\n",
" row_number=4,\n",
" width = cellSize*col_number, // - margin.left - margin.right,\n",
" height = cellSize*row_number , // - margin.top - margin.bottom,\n",
" //gridSize = Math.floor(width / 24),\n",
" legendElementWidth = cellSize*2.5,\n",
" colorBuckets = 21,\n",
" colors = ['#005824','#1A693B','#347B53','#4F8D6B','#699F83','#83B09B','#9EC2B3','#B8D4CB','#D2E6E3','#EDF8FB','#FFFFFF','#F1EEF6','#E6D3E1','#DBB9CD','#D19EB9','#C684A4','#BB6990','#B14F7C','#A63467','#9B1A53','#91003F'],\n",
" hcrow = [1, 2, 3, 4],\n",
" hccol = [6, 2, 1, 3, 5, 4],\n",
" rowLabel = ['1759302_s_at','1759080_s_at','1759502_s_at','1759540_s_at'],\n",
" colLabel = ['con1027','con1028','con1029','con103','con1030','con1031'];\n",
"\n",
"//d3.tsv(\"data/heatmap_data.tsv\",\n",
"//function(d) {\n",
" //return {\n",
" //row: +d.row_idx,\n",
" //col: +d.col_idx,\n",
" //value: +d.log2ratio\n",
" //};\n",
"//},\n",
"\n",
" var data = [{\"col\": 1, \"value\": 6, \"row\": 1}, {\"col\": 2, \"value\": 6, \"row\": 1}, {\"col\": 3, \"value\": 7, \"row\": 1}, {\"col\": 4, \"value\": 6, \"row\": 1}, {\"col\": 5, \"value\": 6, \"row\": 1}, {\"col\": 6, \"value\": 6, \"row\": 1}, {\"col\": 1, \"value\": 6, \"row\": 2}, {\"col\": 2, \"value\": 6, \"row\": 2}, {\"col\": 3, \"value\": 6, \"row\": 2}, {\"col\": 4, \"value\": 6, \"row\": 2}, {\"col\": 5, \"value\": 6, \"row\": 2}, {\"col\": 6, \"value\": 6, \"row\": 2}, {\"col\": 1, \"value\": 9, \"row\": 3}, {\"col\": 2, \"value\": 9, \"row\": 3}, {\"col\": 3, \"value\": 9, \"row\": 3}, {\"col\": 4, \"value\": 9, \"row\": 3}, {\"col\": 5, \"value\": 9, \"row\": 3}, {\"col\": 6, \"value\": 9, \"row\": 3}, {\"col\": 1, \"value\": 6, \"row\": 4}, {\"col\": 2, \"value\": 6, \"row\": 4}, {\"col\": 3, \"value\": 6, \"row\": 4}, {\"col\": 4, \"value\": 6, \"row\": 4}, {\"col\": 5, \"value\": 6, \"row\": 4}, {\"col\": 6, \"value\": 5, \"row\": 4}];\n",
" console.log(data.slice(1,10));\n",
" var colorScale = d3.scale.quantile()\n",
" .domain([ -10 , 0, 10])\n",
" .range(colors);\n",
" \n",
" var svg = d3.select(\"#chart\").append(\"svg\")\n",
" .attr(\"width\", width + margin.left + margin.right)\n",
" .attr(\"height\", height + margin.top + margin.bottom)\n",
" .append(\"g\")\n",
" .attr(\"transform\", \"translate(\" + margin.left + \",\" + margin.top + \")\")\n",
" ;\n",
" var rowSortOrder=false;\n",
" var colSortOrder=false;\n",
" var rowLabels = svg.append(\"g\")\n",
" .selectAll(\".rowLabelg\")\n",
" .data(rowLabel)\n",
" .enter()\n",
" .append(\"text\")\n",
" .text(function (d) { return d; })\n",
" .attr(\"x\", 0)\n",
" .attr(\"y\", function (d, i) { return hcrow.indexOf(i+1) * cellSize; })\n",
" .style(\"text-anchor\", \"end\")\n",
" .attr(\"transform\", \"translate(-6,\" + cellSize / 1.5 + \")\")\n",
" .attr(\"class\", function (d,i) { return \"rowLabel mono r\"+i;} ) \n",
" .on(\"mouseover\", function(d) {d3.select(this).classed(\"text-hover\",true);})\n",
" .on(\"mouseout\" , function(d) {d3.select(this).classed(\"text-hover\",false);})\n",
" .on(\"click\", function(d,i) {rowSortOrder=!rowSortOrder; sortbylabel(\"r\",i,rowSortOrder);d3.select(\"#order\").property(\"selectedIndex\", 4).node().focus();;})\n",
" ;\n",
"\n",
" var colLabels = svg.append(\"g\")\n",
" .selectAll(\".colLabelg\")\n",
" .data(colLabel)\n",
" .enter()\n",
" .append(\"text\")\n",
" .text(function (d) { return d; })\n",
" .attr(\"x\", 0)\n",
" .attr(\"y\", function (d, i) { return hccol.indexOf(i+1) * cellSize; })\n",
" .style(\"text-anchor\", \"left\")\n",
" .attr(\"transform\", \"translate(\"+cellSize/2 + \",-6) rotate (-90)\")\n",
" .attr(\"class\", function (d,i) { return \"colLabel mono c\"+i;} )\n",
" .on(\"mouseover\", function(d) {d3.select(this).classed(\"text-hover\",true);})\n",
" .on(\"mouseout\" , function(d) {d3.select(this).classed(\"text-hover\",false);})\n",
" .on(\"click\", function(d,i) {colSortOrder=!colSortOrder; sortbylabel(\"c\",i,colSortOrder);d3.select(\"#order\").property(\"selectedIndex\", 4).node().focus();;})\n",
" ;\n",
"\n",
" var heatMap = svg.append(\"g\").attr(\"class\",\"g3\")\n",
" .selectAll(\".cellg\")\n",
" .data(data,function(d){return d.row+\":\"+d.col;})\n",
" .enter()\n",
" .append(\"rect\")\n",
" .attr(\"x\", function(d) { return hccol.indexOf(d.col) * cellSize; })\n",
" .attr(\"y\", function(d) { return hcrow.indexOf(d.row) * cellSize; })\n",
" .attr(\"class\", function(d){return \"cell cell-border cr\"+(d.row-1)+\" cc\"+(d.col-1);})\n",
" .attr(\"width\", cellSize)\n",
" .attr(\"height\", cellSize)\n",
" .style(\"fill\", function(d) { return colorScale(d.value); })\n",
" /* .on(\"click\", function(d) {\n",
" var rowtext=d3.select(\".r\"+(d.row-1));\n",
" if(rowtext.classed(\"text-selected\")==false){\n",
" rowtext.classed(\"text-selected\",true);\n",
" }else{\n",
" rowtext.classed(\"text-selected\",false);\n",
" }\n",
" })*/\n",
" .on(\"mouseover\", function(d){\n",
" //highlight text\n",
" d3.select(this).classed(\"cell-hover\",true);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-highlight\",function(r,ri){ return ri==(d.row-1);});\n",
" d3.selectAll(\".colLabel\").classed(\"text-highlight\",function(c,ci){ return ci==(d.col-1);});\n",
" \n",
" //Update the tooltip position and value\n",
" d3.select(\"#tooltip\")\n",
" .style(\"left\", (d3.event.pageX+10) + \"px\")\n",
" .style(\"top\", (d3.event.pageY-10) + \"px\")\n",
" .select(\"#value\")\n",
" .text(\"lables:\"+rowLabel[d.row-1]+\",\"+colLabel[d.col-1]+\"\\ndata:\"+d.value+\"\\nrow-col-idx:\"+d.col+\",\"+d.row+\"\\ncell-xy \"+this.x.baseVal.value+\", \"+this.y.baseVal.value); \n",
" //Show the tooltip\n",
" d3.select(\"#tooltip\").classed(\"hidden\", false);\n",
" })\n",
" .on(\"mouseout\", function(){\n",
" d3.select(this).classed(\"cell-hover\",false);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-highlight\",false);\n",
" d3.selectAll(\".colLabel\").classed(\"text-highlight\",false);\n",
" d3.select(\"#tooltip\").classed(\"hidden\", true);\n",
" })\n",
" ;\n",
"\n",
" var legend = svg.selectAll(\".legend\")\n",
" .data([-10,-9,-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10])\n",
" .enter().append(\"g\")\n",
" .attr(\"class\", \"legend\");\n",
" \n",
" legend.append(\"rect\")\n",
" .attr(\"x\", function(d, i) { return legendElementWidth * i; })\n",
" .attr(\"y\", height+(cellSize*2))\n",
" .attr(\"width\", legendElementWidth)\n",
" .attr(\"height\", cellSize)\n",
" .style(\"fill\", function(d, i) { return colors[i]; });\n",
" \n",
" legend.append(\"text\")\n",
" .attr(\"class\", \"mono\")\n",
" .text(function(d) { return d; })\n",
" .attr(\"width\", legendElementWidth)\n",
" .attr(\"x\", function(d, i) { return legendElementWidth * i; })\n",
" .attr(\"y\", height + (cellSize*4));\n",
"\n",
"// Change ordering of cells\n",
"\n",
" function sortbylabel(rORc,i,sortOrder){\n",
" var t = svg.transition().duration(3000);\n",
" var log2r=[];\n",
" var sorted; // sorted is zero-based index\n",
" d3.selectAll(\".c\"+rORc+i) \n",
" .filter(function(ce){\n",
" log2r.push(ce.value);\n",
" })\n",
" ;\n",
" if(rORc==\"r\"){ // sort log2ratio of a gene\n",
" sorted=d3.range(col_number).sort(function(a,b){ if(sortOrder){ return log2r[b]-log2r[a];}else{ return log2r[a]-log2r[b];}});\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return sorted.indexOf(d.col-1) * cellSize; })\n",
" ;\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return sorted.indexOf(i) * cellSize; })\n",
" ;\n",
" }else{ // sort log2ratio of a contrast\n",
" sorted=d3.range(row_number).sort(function(a,b){if(sortOrder){ return log2r[b]-log2r[a];}else{ return log2r[a]-log2r[b];}});\n",
" t.selectAll(\".cell\")\n",
" .attr(\"y\", function(d) { return sorted.indexOf(d.row-1) * cellSize; })\n",
" ;\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return sorted.indexOf(i) * cellSize; })\n",
" ;\n",
" }\n",
" }\n",
"\n",
" d3.select(\"#order\").on(\"change\",function(){\n",
" order(this.value);\n",
" });\n",
" \n",
" function order(value){\n",
" if(value==\"hclust\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return hccol.indexOf(d.col) * cellSize; })\n",
" .attr(\"y\", function(d) { return hcrow.indexOf(d.row) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return hcrow.indexOf(i+1) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return hccol.indexOf(i+1) * cellSize; })\n",
" ;\n",
"\n",
" }else if (value==\"probecontrast\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return (d.col - 1) * cellSize; })\n",
" .attr(\"y\", function(d) { return (d.row - 1) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
"\n",
" }else if (value==\"probe\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"y\", function(d) { return (d.row - 1) * cellSize; })\n",
" ;\n",
"\n",
" t.selectAll(\".rowLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
" }else if (value==\"contrast\"){\n",
" var t = svg.transition().duration(3000);\n",
" t.selectAll(\".cell\")\n",
" .attr(\"x\", function(d) { return (d.col - 1) * cellSize; })\n",
" ;\n",
" t.selectAll(\".colLabel\")\n",
" .attr(\"y\", function (d, i) { return i * cellSize; })\n",
" ;\n",
" }\n",
" }\n",
" // \n",
" var sa=d3.select(\".g3\")\n",
" .on(\"mousedown\", function() {\n",
" if( !d3.event.altKey) {\n",
" d3.selectAll(\".cell-selected\").classed(\"cell-selected\",false);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-selected\",false);\n",
" d3.selectAll(\".colLabel\").classed(\"text-selected\",false);\n",
" }\n",
" var p = d3.mouse(this);\n",
" sa.append(\"rect\")\n",
" .attr({\n",
" rx : 0,\n",
" ry : 0,\n",
" class : \"selection\",\n",
" x : p[0],\n",
" y : p[1],\n",
" width : 1,\n",
" height : 1\n",
" })\n",
" })\n",
" .on(\"mousemove\", function() {\n",
" var s = sa.select(\"rect.selection\");\n",
" \n",
" if(!s.empty()) {\n",
" var p = d3.mouse(this),\n",
" d = {\n",
" x : parseInt(s.attr(\"x\"), 10),\n",
" y : parseInt(s.attr(\"y\"), 10),\n",
" width : parseInt(s.attr(\"width\"), 10),\n",
" height : parseInt(s.attr(\"height\"), 10)\n",
" },\n",
" move = {\n",
" x : p[0] - d.x,\n",
" y : p[1] - d.y\n",
" }\n",
" ;\n",
" \n",
" if(move.x < 1 || (move.x*2<d.width)) {\n",
" d.x = p[0];\n",
" d.width -= move.x;\n",
" } else {\n",
" d.width = move.x; \n",
" }\n",
" \n",
" if(move.y < 1 || (move.y*2<d.height)) {\n",
" d.y = p[1];\n",
" d.height -= move.y;\n",
" } else {\n",
" d.height = move.y; \n",
" }\n",
" s.attr(d);\n",
" \n",
" // deselect all temporary selected state objects\n",
" d3.selectAll('.cell-selection.cell-selected').classed(\"cell-selected\", false);\n",
" d3.selectAll(\".text-selection.text-selected\").classed(\"text-selected\",false);\n",
"\n",
" d3.selectAll('.cell').filter(function(cell_d, i) {\n",
" if(\n",
" !d3.select(this).classed(\"cell-selected\") && \n",
" // inner circle inside selection frame\n",
" (this.x.baseVal.value)+cellSize >= d.x && (this.x.baseVal.value)<=d.x+d.width && \n",
" (this.y.baseVal.value)+cellSize >= d.y && (this.y.baseVal.value)<=d.y+d.height\n",
" ) {\n",
" \n",
" d3.select(this)\n",
" .classed(\"cell-selection\", true)\n",
" .classed(\"cell-selected\", true);\n",
"\n",
" d3.select(\".r\"+(cell_d.row-1))\n",
" .classed(\"text-selection\",true)\n",
" .classed(\"text-selected\",true);\n",
"\n",
" d3.select(\".c\"+(cell_d.col-1))\n",
" .classed(\"text-selection\",true)\n",
" .classed(\"text-selected\",true);\n",
" }\n",
" });\n",
" }\n",
" })\n",
" .on(\"mouseup\", function() {\n",
" // remove selection frame\n",
" sa.selectAll(\"rect.selection\").remove();\n",
" \n",
" // remove temporary selection marker class\n",
" d3.selectAll('.cell-selection').classed(\"cell-selection\", false);\n",
" d3.selectAll(\".text-selection\").classed(\"text-selection\",false);\n",
" })\n",
" .on(\"mouseout\", function() {\n",
" if(d3.event.relatedTarget.tagName=='html') {\n",
" // remove selection frame\n",
" sa.selectAll(\"rect.selection\").remove();\n",
" // remove temporary selection marker class\n",
" d3.selectAll('.cell-selection').classed(\"cell-selection\", false);\n",
" d3.selectAll(\".rowLabel\").classed(\"text-selected\",false);\n",
" d3.selectAll(\".colLabel\").classed(\"text-selected\",false);\n",
" }\n",
" })\n",
" ;\n",
"//});\n",
"\n",
"});"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.Javascript at 0x108c0c1d0>"
]
}
],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import simplejson as json\n",
"json.dumps([{'row': 1, 'col': 1, 'value':1},{'row': 1, 'col': 2, 'value':2},{'row': 2, 'col': 1, 'value':3},{'row': 2, 'col': 2, 'value':4}])\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"'[{\"col\": 1, \"value\": 1, \"row\": 1}, {\"col\": 2, \"value\": 2, \"row\": 1}, {\"col\": 1, \"value\": 3, \"row\": 2}, {\"col\": 2, \"value\": 4, \"row\": 2}]'"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"genes = [ome.query(Gene).filter(Gene.name == x).one() for x in ['gapA','sucA','sucB','sucC','sucD']]\n",
"genes_data = {gene: gene.data for gene in genes}\n",
"json.dumps({\n",
" \"rowlabels\" : [x.data_set.name for x in genes_data.itervalues().next()],\n",
" \"collabels\" : [x.name for x in genes_data.keys()],\n",
" \"data\" : [[{\"row\": i, \"col\": j, \"value\": data.value} for j,data in enumerate(genes_data[gene])] for i,gene in enumerate(genes_data)]})"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"'{\"rowlabels\": [\"RNAseq_Ar3_glycerol_NH4Cl_O2_1_1.0\", \"RNAseq_Ar3_glycerol_NH4Cl_O2_3_1.0\", \"RNAseq_delAr1_glycerol_NH4Cl_O2_2_1.0\", \"RNAseq_delAr1delAr2_glycerol_NH4Cl_O2_1_1.0\", \"RNAseq_delAr1delAr2_glycerol_NH4Cl_O2_3_1.0\", \"RNAseq_delAr2_glycerol_NH4Cl_O2_2_1.0\", \"RNAseq_delta-crp_glycerol_NH4Cl_O2_1_1.0\", \"RNAseq_delta-crp_glycerol_NH4Cl_O2_3_1.0\", \"RNAseq_delta-crp_fructose_NH4Cl_O2_2_1.0\", \"RNAseq_delta-crp_glucose_NH4Cl_O2_1_1.0\", \"RNAseq_delta-crp_glucose_NH4Cl_O2_3_1.0\", \"RNAseq_wt_glycerol_NH4Cl_O2_2_1.0\", \"RNAseq_wt_fructose_NH4Cl_O2_2_1.0\", \"RNAseq_wt_glucose_NH4Cl_O2_2_1.0\", \"RNAseq_Ar3_glycerol_NH4Cl_O2_2_1.0\", \"RNAseq_delAr1_glycerol_NH4Cl_O2_1_1.0\", \"RNAseq_delAr1_glycerol_NH4Cl_O2_3_1.0\", \"RNAseq_delAr1delAr2_glycerol_NH4Cl_O2_2_1.0\", \"RNAseq_delAr2_glycerol_NH4Cl_O2_1_1.0\", \"RNAseq_delAr2_glycerol_NH4Cl_O2_3_1.0\", \"RNAseq_delta-crp_glycerol_NH4Cl_O2_2_1.0\", \"RNAseq_delta-crp_fructose_NH4Cl_O2_1_1.0\", \"RNAseq_delta-crp_fructose_NH4Cl_O2_3_1.0\", \"RNAseq_delta-crp_glucose_NH4Cl_O2_2_1.0\", \"RNAseq_wt_glycerol_NH4Cl_O2_1_1.0\", \"RNAseq_wt_fructose_NH4Cl_O2_1_1.0\", \"RNAseq_wt_glucose_NH4Cl_O2_1_1.0\", \"RNAseq_wt_glucose_NH4Cl_O2_3_1.0\", \"RNAseq_delta-crp_fructose/glucose_NH4Cl_O2\", \"RNAseq_delta-crp_glucose/glycerol_NH4Cl_O2\", \"RNAseq_delAr2/wt_glycerol_NH4Cl_O2\", \"RNAseq_delAr1/wt_glycerol_NH4Cl_O2\", \"RNAseq_delta-crp/wt_glycerol_NH4Cl_O2\", \"RNAseq_delAr1delAr2/wt_glycerol_NH4Cl_O2\", \"RNAseq_Ar3/wt_glycerol_NH4Cl_O2\", \"RNAseq_wt_fructose/glycerol_NH4Cl_O2\", \"RNAseq_wt_glucose/glycerol_NH4Cl_O2\", \"RNAseq_delta-crp/wt_fructose_NH4Cl_O2\", \"RNAseq_delta-crp/wt_glucose_NH4Cl_O2\", \"RNAseq_wt_fructose/glucose_NH4Cl_O2\", \"affyexp_delta-arcA_glucose_NH4Cl_O2_1_asv2\", \"affyexp_delta-arcA_glucose_NH4Cl_O2_3_asv2\", \"affyexp_delta-arcA_glucose_NH4Cl_anaerobic_2_asv2\", \"affyexp_delta-arcAfnr_glucose_NH4Cl_O2_1_asv2\", \"affyexp_delta-arcAfnr_glucose_NH4Cl_O2_3_asv2\", \"affyexp_delta-arcAfnr_glucose_NH4Cl_anaerobic_2_asv2\", \"affyexp_delta-fnr_glucose_NH4Cl_O2_1_asv2\", \"affyexp_delta-fnr_glucose_NH4Cl_O2_3_asv2\", \"affyexp_delta-fnr_glucose_NH4Cl_anaerobic_2_asv2\", \"affyexp_delta-narL_glucose_NH4Cl_anaerobic_1_asv2\", \"affyexp_delta-narL_glucose_NH4Cl_anaerobic_3_asv2\", \"affyexp_delta-narL_glucose_NH4Cl_NO3_2_asv2\", \"affyexp_delta-narLnarP_glucose_NH4Cl_anaerobic_1_asv2\", \"affyexp_delta-narLnarP_glucose_NH4Cl_anaerobic_3_asv2\", \"affyexp_delta-narLnarP_glucose_NH4Cl_NO3_2_asv2\", \"affyexp_delta-narP_glucose_NH4Cl_anaerobic_1_asv2\", \"affyexp_delta-narP_glucose_NH4Cl_anaerobic_3_asv2\", \"affyexp_delta-narP_glucose_NH4Cl_NO3_3_asv2\", \"affyexp_wt_glucose_NH4Cl_NO3_2_asv2\", \"affyexp_wt_glucose_NH4Cl_O2_1_asv2\", \"affyexp_wt_glucose_NH4Cl_O2_3_asv2\", \"affyexp_wt_glucose_NH4Cl_anaerobic_2_asv2\", \"affyexp_wt_glucose_NH4Cl_anaerobic_4_asv2\", 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"[Gene: (b0727, sucB) 761521-762739 (+),\n",
" Gene: (b0728, sucC) 763013-764180 (+),\n",
" Gene: (b1779, gapA) 1862770-1863766 (+),\n",
" Gene: (b0729, sucD) 764179-765049 (+),\n",
" Gene: (b0726, sucA) 758705-761507 (+)]"
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]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
DavidVargasMora/SimulationLabs | WichmannHillGen27Feb2016.ipynb | 2 | 6425 | {
"cells": [
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Please input the amount of random numbers you want to generate:100\n",
"Randy: 0.947888364713\n",
"Randy: 0.877120129184\n",
"Randy: 0.624955306442\n",
"Randy: 0.968891207746\n",
"Randy: 0.96856849619\n",
"Randy: 0.570253880009\n",
"Randy: 0.941520970303\n",
"Randy: 0.19496404814\n",
"Randy: 0.69833777018\n",
"Randy: 0.00954458884121\n",
"Randy: 0.356905987735\n",
"Randy: 0.577726480094\n",
"Randy: 0.857372594472\n",
"Randy: 0.0555948738174\n",
"Randy: 0.804639431295\n",
"Randy: 0.0199100011056\n",
"Randy: 0.020717043878\n",
"Randy: 0.279656492201\n",
"Randy: 0.658851114737\n",
"Randy: 0.636086122565\n",
"Randy: 0.876602196137\n",
"Randy: 0.134293174971\n",
"Randy: 0.84214920367\n",
"Randy: 0.493405626096\n",
"Randy: 0.645142640297\n",
"Randy: 0.147829768436\n",
"Randy: 0.196971201649\n",
"Randy: 0.173583206641\n",
"Randy: 0.927913002239\n",
"Randy: 0.233140080316\n",
"Randy: 0.458004310889\n",
"Randy: 0.370709417034\n",
"Randy: 0.50184472531\n",
"Randy: 0.28283507594\n",
"Randy: 0.740599417479\n",
"Randy: 0.51191277957\n",
"Randy: 0.281639895806\n",
"Randy: 0.0960282311676\n",
"Randy: 0.694767663561\n",
"Randy: 0.885519328191\n",
"Randy: 0.862349193814\n",
"Randy: 0.565679245962\n",
"Randy: 0.8963467836\n",
"Randy: 0.329540452577\n",
"Randy: 0.180385163541\n",
"Randy: 0.344865143146\n",
"Randy: 0.874882810387\n",
"Randy: 0.0452016240604\n",
"Randy: 0.735735918614\n",
"Randy: 0.543502507742\n",
"Randy: 0.906762417475\n",
"Randy: 0.544185495593\n",
"Randy: 0.297349119365\n",
"Randy: 0.784226240796\n",
"Randy: 0.145120829551\n",
"Randy: 0.23478251141\n",
"Randy: 0.977684234811\n",
"Randy: 0.86614027379\n",
"Randy: 0.259651059252\n",
"Randy: 0.972667482244\n",
"Randy: 0.67153977128\n",
"Randy: 0.0104980462282\n",
"Randy: 0.172663140992\n",
"Randy: 0.422112016007\n",
"Randy: 0.217270155173\n",
"Randy: 0.0621747374893\n",
"Randy: 0.642800352666\n",
"Randy: 0.217870012286\n",
"Randy: 0.0168172317896\n",
"Randy: 0.134787299044\n",
"Randy: 0.716275284996\n",
"Randy: 0.801322218793\n",
"Randy: 0.885975806489\n",
"Randy: 0.576782224957\n",
"Randy: 0.733019452255\n",
"Randy: 0.579139662505\n",
"Randy: 0.904377695036\n",
"Randy: 0.835791901208\n",
"Randy: 0.549452280176\n",
"Randy: 0.393542527581\n",
"Randy: 0.375404584557\n",
"Randy: 0.403432980107\n",
"Randy: 0.0666218762862\n",
"Randy: 0.963948015263\n",
"Randy: 0.0410211811706\n",
"Randy: 0.0345907952332\n",
"Randy: 0.746977924425\n",
"Randy: 0.972666025647\n",
"Randy: 0.367085515682\n",
"Randy: 0.774232436093\n",
"Randy: 0.0771193543474\n",
"Randy: 0.886557374192\n",
"Randy: 0.978152306959\n",
"Randy: 0.982565602243\n",
"Randy: 0.98330446378\n",
"Randy: 0.763962138651\n",
"Randy: 0.132633175853\n",
"Randy: 0.428013787454\n",
"Randy: 0.795380155837\n",
"Randy: 0.556011789597\n"
]
}
],
"source": [
"import time\n",
"import datetime\n",
"\n",
"def generadorS1(n):\n",
" a = int(time.clock())\n",
" m = int(900217*float(time.clock()))\n",
" lastXn = 7\n",
" for i in range(n):\n",
" Xn = float((a*lastXn)% m)\n",
" lastXn = Xn\n",
" random = float(lastXn/m)\n",
" return random\n",
"\n",
"def generadorS2(n):\n",
" a = int(time.clock())\n",
" m = int(10007*float(time.clock()))\n",
" lastXn = 29\n",
" for i in range(n):\n",
" Xn = float((a*lastXn)% m)\n",
" lastXn = Xn\n",
" random = float(lastXn/m)\n",
" return random\n",
"\n",
"def generadorS3(n):\n",
" a = int(time.clock())\n",
" m = int(9067*float(time.clock()))\n",
" lastXn = 997\n",
" for i in range(n):\n",
" Xn = float((a*lastXn)% m)\n",
" lastXn = Xn\n",
" random = float(lastXn/m)\n",
" return random\n",
"\n",
"def generadorS4(n):\n",
" a = int(time.clock())\n",
" m = int(900233*float(time.clock()))\n",
" lastXn = 787\n",
" for i in range(n):\n",
" Xn = float((a*lastXn)% m)\n",
" lastXn = Xn\n",
" random = float(lastXn/m)\n",
" return random\n",
"\n",
"\n",
" \n",
"def generador(n):\n",
" for i in range(n):\n",
" w = generadorS1(int(str(time.clock())[-2:]))\n",
" x = generadorS2(int(str(time.clock())[-2:]))\n",
" y = generadorS3(int(str(time.clock())[-2:]))\n",
" z = generadorS4(int(str(time.clock())[-2:]))\n",
" random = float((w+x+y+z)%1)\n",
" print \"Randy: \"+str(random)\n",
" \n",
"def main():\n",
" n=int(raw_input('Please input the amount of random numbers you want to generate:'))\n",
" generador(n)\n",
"\n",
"main()"
]
},
{
"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 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
}
| cc0-1.0 |
wcmckee/wcmckee | whaiout.ipynb | 2 | 24379 | {
"metadata": {
"name": "",
"signature": "sha256:f786b2f825b80130cc678aca052b33673abe01974fa8373cc65f5fc264d6f4ca"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3>WhaiOut</h3>\n",
"\n",
"This is the signout script that opens the login file and fills in signout info.\n",
"\n",
"Opens up list of signin data. This is date of sign in, time, name, and reason.\n",
"This is .meta. This script appends sign out data. This is signout date, signout time, and comments.\n",
"\n",
"8 urandom 128 keys are generated. Used these in saving the achieve, as .html, and .meta files.\n",
"Why not save as yr-month-day-hr-min.meta /html\n",
"\n",
"Turn it into a Nikola blog. Three blog posts. login (whaxlu.py), logout (whaiout.py), and the result of both of them. Perhaps it\n",
"\n",
"creates date and time mark and asks for comment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This needs rewriten to remove xl stuff opening. keep it to json and dict. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#import xlrd\n",
"import os\n",
"import time\n",
"#from xlutils.copy import copy\n",
"#from xlrd import *\n",
"import dominate\n",
"import json\n",
"from dominate.tags import *\n",
"from time import strftime, gmtime"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#wrkbook = xlrd.open_workbook('/home/wcmckee/whai/index.xls')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"jsopn = open('/home/wcmckee/visignsys/index.json', 'r')\n",
"jsrdv = jsopn.read()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"jsrdv"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"'{\"signin hrmin\": \"18:14:35\", \"reason\": \"ESW\", \"firstname\": \"William\", \"signin comment\": \"testing locally\", \"lastname\": \"Mckee\", \"signin date\": \"07-Jan-2015\"}'"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#print wrkbook.sheet_names()\n",
"\n",
"#worksheet = wrkbook.sheet_by_name('visitor sign database')\n",
"#swlis = []\n",
"#num_rows = worksheet.nrows - 1\n",
"#curr_row = -1\n",
"#while curr_row < num_rows:\n",
"# curr_row += 1\n",
"# row = worksheet.row(curr_row)\n",
" #print row\n",
"# swlis.append(row)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#valis = []"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#for swl in swlis[1]:\n",
"# print swl.value\n",
"# valis.append(swl.value)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tiran = os.urandom(128).encode('hex')\n",
"reran = os.urandom(128).encode('hex')\n",
"comran = os.urandom(128).encode('hex')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"inpcom = raw_input('comment: ')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"comment: chexk\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"endate = time.strftime(\"%d\" + \"-\" + \"%b\" + \"-\" + \"%Y\")\n",
" \n",
"entim = time.strftime(\"%H:%M\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"snoutm = {'out-date': endate}\n",
"snoutm.update({'out-time': entim})\n",
"snoutm.update({'out-comment': inpcom})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"signoutdic = {endate: tiran}\n",
"timoutdic = {entim: reran}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"signoutdic.update({entim:reran})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"signoutdic.update({inpcom: comran})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"signkeys = signoutdic.keys()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"wha = open('/home/wcmckee/visignsys/index.json', 'a')\n",
"\n",
"#w = copy(open_workbook('/home/wcmckee/whai/index.xls'))\n",
"#w.get_sheet(0).write(1,5, time.strftime(\"%d\" + \"-\" + \"%b\" + \"-\" + \"%Y\"))\n",
"#w.get_sheet(0).write(1,6, time.strftime(\"%H:%M\"))\n",
"#w.get_sheet(0).write(1,7, tiran)\n",
"\n",
"#w.save('/home/wcmckee/whai/index.xls')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"indsav = ('/home/wcmckee/whai/index.html')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"opind = open(indsav, 'w')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"doc = dominate.document(title=wrkbook.sheet_names())\n",
"\n",
"with doc.head:\n",
" link(rel='stylesheet', href='style.css')\n",
" script(type='text/javascript', src='script.js')\n",
"\n",
"with doc:\n",
" with div(id='header').add(ol()):\n",
" for i in valis:\n",
" li(a(i))\n",
"\n",
" with div():\n",
" attr(cls='body')\n",
" p('visitor sign database is open source. Visit https://github.com/wcmckee/wcmckee ')\n",
"\n",
"#print doc"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"opind.write(str(doc))\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"opind.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"liop = open('/home/wcmckee/visignsys/index.meta', 'a+')\n",
"liop.write(str(signkeys))\n",
"liop.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 80
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"oplis = open('/home/wcmckee/visignsys/index.meta', 'r')\n",
"oplsav = oplis.read()\n",
"oplis.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 81
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"trsor = tiran[0:12]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"yearz = strftime(\"%y\", gmtime())\n",
"monthz = strftime(\"%m\", gmtime())\n",
"dayz = strftime(\"%d\", gmtime())\n",
"\n",
"hurz = strftime(\"%H\", gmtime())\n",
"minz = strftime(\"%M\", gmtime())\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dform = (yearz + monthz + dayz +hurz + minz)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"optrd = open('/home/wcmckee/visignsys/posts/' + dform + '.meta', 'w')\n",
"optrd.write(oplsav)\n",
"optrd.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 84
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"jsnrd = open('/home/wcmckee/visignsys/posts/' + dform + '.json', 'w')\n",
"jsnrd.write(oplsav)\n",
"jsnrd.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 85
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"savpos = open('/home/wcmckee/visignsys/index.json', 'r')\n",
"signindi = savpos.read()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"jsnaccept = signindi.replace(\"'\", \"\\\"\")\n",
"snoutm = json.loads(jsnaccept)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"snct = dict(d.items() + snoutm.items())"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"savpos.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"os.chdir('/home/wcmckee/visignsys/posts')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lismet = os.listdir('/home/wcmckee/visignsys/posts')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lismet"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"text": [
"['b9b5a2864917.meta',\n",
" '2ce7b7e78b2d.json',\n",
" 'f63a51c5660b.json',\n",
" 'ec335a74fef8.json',\n",
" 'd4da1b21b2ac.meta',\n",
" '06fd12b77af5.html',\n",
" 'bd18983ba81c.json',\n",
" 'bc3f7ce010a0.meta',\n",
" '4300bad9866e.meta',\n",
" '8d73b8f33da2.json',\n",
" 'cbbcc7a60aa1.meta',\n",
" '624bf3800e42.meta',\n",
" 'ec335a74fef8.meta',\n",
" '8d73b8f33da2.meta',\n",
" '417ca4249670.meta',\n",
" '891149fcb0cc.meta',\n",
" '086ef98a8bea.meta',\n",
" 'a10ef30465fb.html',\n",
" 'be7f3bba40a9.meta',\n",
" 'ad81cda1984a.meta',\n",
" '9479b52fcb96.json',\n",
" '3342b7e37622.json',\n",
" 'f51c0885211f.json',\n",
" '8fb418bed294.meta',\n",
" 'be7f3bba40a9.json',\n",
" '20923a94e619.json',\n",
" '91ece852eb61.meta',\n",
" 'fab0f7427796.html',\n",
" 'cbbcc7a60aa1.html',\n",
" '8bf3b3c045b2.json',\n",
" '1bca31594654.meta',\n",
" 'd7bfad4d84af.json',\n",
" '4be47b95b184.html',\n",
" 'e51d650f3bd8.meta',\n",
" 'c173782cc4ba.html',\n",
" 'bc3f7ce010a0.json',\n",
" '9cce0605ff95.meta',\n",
" '891149fcb0cc.json',\n",
" '4611a6b53369.json',\n",
" '8fb418bed294.json',\n",
" '68dc32243aee.html',\n",
" '1e1b72c3728f.meta',\n",
" '1ca6f0c7d074.json',\n",
" '710d4ea76685.meta',\n",
" '9479b52fcb96.meta',\n",
" '50028413d505.meta',\n",
" '231f0cbc6422.json',\n",
" '710d4ea76685.json',\n",
" 'f30478d49490.html',\n",
" '1e1b72c3728f.json',\n",
" '81ae6564478e.meta',\n",
" 'e51d650f3bd8.html',\n",
" 'd948bc2cb2d5.meta',\n",
" '788ab4a4a085.html',\n",
" '2f125041a9ff.html',\n",
" '69ac885a08d4.html',\n",
" '20923a94e619.meta',\n",
" '046ddc95c545.meta',\n",
" '175dea65c5bd.meta',\n",
" 'd7bfad4d84af.meta',\n",
" '50028413d505.html',\n",
" '39848c9e2306.meta',\n",
" 'a5fd59588711.json',\n",
" '9c5044cb8c12.meta',\n",
" '6ac58d43a79e.html',\n",
" 'a10ef30465fb.meta',\n",
" 'fab0f7427796.meta',\n",
" 'faab1e3ed989.json',\n",
" 'c02c3d6eb062.json',\n",
" 'f31b6e50e61d.json',\n",
" '2a107fb19ec7.json',\n",
" '6e8165886873.json',\n",
" 'd4da1b21b2ac.html',\n",
" '4611a6b53369.meta',\n",
" '1ca6f0c7d074.html',\n",
" 'g\\xe9~\\xaf\"\\x80\\xa8\\x88\\xc7X\\xce\\x83\\x98\\xe6\\x15t.html',\n",
" '1537f9b58c99.meta',\n",
" '6ac58d43a79e.meta',\n",
" '1bca31594654.json',\n",
" 'faab1e3ed989.meta',\n",
" 'a5fd59588711.meta',\n",
" '06fd12b77af5.meta',\n",
" 'df5aed94d944.html',\n",
" '2a107fb19ec7.meta',\n",
" '9c5044cb8c12.json',\n",
" '1537f9b58c99.html',\n",
" '5e98dfab4326.json',\n",
" '2f6d172a9d5d.json',\n",
" 'e8e05f2bf0c0.meta',\n",
" '1ef070dc6543.meta',\n",
" '125540ca4363.meta',\n",
" '8457b8a9b2c0.json',\n",
" 'b9b5a2864917.json',\n",
" 'f63a51c5660b.meta',\n",
" '046ddc95c545.html',\n",
" '231f0cbc6422.html',\n",
" 'be7f3bba40a9.html',\n",
" 'g\\xe9~\\xaf\"\\x80\\xa8\\x88\\xc7X\\xce\\x83\\x98\\xe6\\x15t.json',\n",
" '8f05ce526d45.meta',\n",
" 'fde78ce1d94a.json',\n",
" '0c4a69aa64c3.meta',\n",
" 'd948bc2cb2d5.html',\n",
" '2f125041a9ff.meta',\n",
" 'ed90e8d91209.meta',\n",
" 'f51c0885211f.meta',\n",
" '\\xff\\xceX\\xa6\\x1b\\xc7\\xdf\\x051\\x81\\x9d+\\x95h\\xf16.html',\n",
" '4300bad9866e.html',\n",
" '03ba33c75d39.html',\n",
" '5832e9b60262.html',\n",
" '6e8165886873.meta',\n",
" '086ef98a8bea.json',\n",
" '4be47b95b184.meta',\n",
" '68c7478ad623.meta',\n",
" '81ae6564478e.html',\n",
" '68dc32243aee.meta',\n",
" '231f0cbc6422.meta',\n",
" 'ce4b76e41145.meta',\n",
" 'ce4b76e41145.html',\n",
" 'bd18983ba81c.meta',\n",
" '469b6be62e65.json',\n",
" '6dbfbbb9c12e.meta',\n",
" 'e8e05f2bf0c0.json',\n",
" '1bca31594654.html',\n",
" '175dea65c5bd.json',\n",
" 'f30478d49490.meta',\n",
" '0c4a69aa64c3.json',\n",
" '9f223146a0a7.html',\n",
" '39848c9e2306.html',\n",
" 'f63a51c5660b.html',\n",
" '3b572fbccfff.html',\n",
" '5f866c12e733.html',\n",
" 'df5aed94d944.meta',\n",
" '3342b7e37622.meta',\n",
" '08eed5e7841d.json',\n",
" '9cce0605ff95.html',\n",
" '69ac885a08d4.meta',\n",
" '1ca6f0c7d074.meta',\n",
" '63e5d8747f8f.html',\n",
" 'ad81cda1984a.json',\n",
" '63e5d8747f8f.meta',\n",
" '5e98dfab4326.meta',\n",
" '49f5eeb93b58.json',\n",
" '4a1d7fbd4af7.html',\n",
" 'a5fd59588711.html',\n",
" '81ae6564478e.json',\n",
" 'c9739e8e0ad8.meta',\n",
" 'f78d6acee067.meta',\n",
" '417ca4249670.json',\n",
" '953205620074.meta',\n",
" 'c173782cc4ba.meta',\n",
" 'c81a54c3aa37.json',\n",
" '8f05ce526d45.html',\n",
" '49f5eeb93b58.meta',\n",
" 'de3a5ee926b8.meta',\n",
" '788ab4a4a085.meta',\n",
" '4994b1705e91.meta',\n",
" '5832e9b60262.meta',\n",
" '469b6be62e65.meta',\n",
" '8bf3b3c045b2.meta',\n",
" '1ef070dc6543.json',\n",
" 'f31b6e50e61d.meta',\n",
" '5e98dfab4326.html',\n",
" 'df5aed94d944.json',\n",
" '624bf3800e42.html',\n",
" '03ba33c75d39.meta',\n",
" '68c7478ad623.json',\n",
" '5f866c12e733.meta',\n",
" 'c8840e55dd2b.json',\n",
" '4a1d7fbd4af7.json',\n",
" 'c02c3d6eb062.meta',\n",
" '6dbfbbb9c12e.html',\n",
" 'd948bc2cb2d5.json',\n",
" 'ff09eb4188ad.html',\n",
" '953205620074.json',\n",
" 'cee5c35e8560.meta',\n",
" 'dce45c4cde99.meta',\n",
" '08eed5e7841d.meta',\n",
" '2ce7b7e78b2d.meta',\n",
" 'c9739e8e0ad8.json',\n",
" '\\xff\\xceX\\xa6\\x1b\\xc7\\xdf\\x051\\x81\\x9d+\\x95h\\xf16.json',\n",
" 'ff09eb4188ad.meta',\n",
" '125540ca4363.json',\n",
" '2f6d172a9d5d.meta',\n",
" 'c81a54c3aa37.meta',\n",
" '3b572fbccfff.meta',\n",
" 'dce45c4cde99.json',\n",
" '4994b1705e91.json',\n",
" 'f78d6acee067.html',\n",
" 'ed90e8d91209.json',\n",
" '4a1d7fbd4af7.meta',\n",
" 'fde78ce1d94a.meta',\n",
" '8457b8a9b2c0.meta',\n",
" '9f223146a0a7.meta',\n",
" 'cee5c35e8560.json',\n",
" 'de3a5ee926b8.html',\n",
" 'c8840e55dd2b.meta']"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"opjsnz = []"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for beca in lismet:\n",
" if '.json' in beca:\n",
" print beca\n",
" opjsnz.append(beca)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2ce7b7e78b2d.json\n",
"f63a51c5660b.json\n",
"ec335a74fef8.json\n",
"bd18983ba81c.json\n",
"8d73b8f33da2.json\n",
"9479b52fcb96.json\n",
"3342b7e37622.json\n",
"f51c0885211f.json\n",
"be7f3bba40a9.json\n",
"20923a94e619.json\n",
"8bf3b3c045b2.json\n",
"d7bfad4d84af.json\n",
"bc3f7ce010a0.json\n",
"891149fcb0cc.json\n",
"4611a6b53369.json\n",
"8fb418bed294.json\n",
"1ca6f0c7d074.json\n",
"231f0cbc6422.json\n",
"710d4ea76685.json\n",
"1e1b72c3728f.json\n",
"a5fd59588711.json\n",
"faab1e3ed989.json\n",
"c02c3d6eb062.json\n",
"f31b6e50e61d.json\n",
"2a107fb19ec7.json\n",
"6e8165886873.json\n",
"1bca31594654.json\n",
"9c5044cb8c12.json\n",
"5e98dfab4326.json\n",
"2f6d172a9d5d.json\n",
"8457b8a9b2c0.json\n",
"b9b5a2864917.json\n",
"g\ufffd~\ufffd\"\ufffd\ufffd\ufffd\ufffdX\u0383\ufffd\ufffd\u0015t.json\n",
"fde78ce1d94a.json\n",
"086ef98a8bea.json\n",
"469b6be62e65.json\n",
"e8e05f2bf0c0.json\n",
"175dea65c5bd.json\n",
"0c4a69aa64c3.json\n",
"08eed5e7841d.json\n",
"ad81cda1984a.json\n",
"49f5eeb93b58.json\n",
"81ae6564478e.json\n",
"417ca4249670.json\n",
"c81a54c3aa37.json\n",
"1ef070dc6543.json\n",
"df5aed94d944.json\n",
"68c7478ad623.json\n",
"c8840e55dd2b.json\n",
"4a1d7fbd4af7.json\n",
"d948bc2cb2d5.json\n",
"953205620074.json\n",
"c9739e8e0ad8.json\n",
"\ufffd\ufffdX\ufffd\u001b\ufffd\ufffd\u00051\ufffd\ufffd+\ufffdh\ufffd6.json\n",
"125540ca4363.json\n",
"dce45c4cde99.json\n",
"4994b1705e91.json\n",
"ed90e8d91209.json\n",
"cee5c35e8560.json\n"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"optjz = []\n",
"optjsz = []"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"apgpls = []"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#for opjw in opjsnz:\n",
"# print opjw\n",
"# optjsz.append(objw)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#for filop in opjsnz:\n",
" #print filop\n",
"# opt = open(opj, 'r')\n",
"# thedict = str(opt.read())\n",
"# thedict\n",
"# opt.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#opt = open(opj, 'r')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#thedict = opt.read()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#thedict"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#convgpj = json.loads(thedict)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#convgpj.values()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
ioos/comt_notebooks | admin/.ipynb_checkpoints/check_catalog-Copy0-checkpoint.ipynb | 1 | 4350 | {
"metadata": {
"gist_id": "3a639c25f29ebdcae92a",
"name": "",
"signature": "sha256:5ed27051015d19abe4617c0d0c4e519476967d3a16b52e5270e790ca5761f83b"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import iris\n",
"iris.FUTURE.netcdf_promote = True\n",
"\n",
"url='http://comt.sura.org/thredds/dodsC/data/comt_1_archive/inundation_tropical/MDL_SLOSH/Hurricane_Ike_final_run_egm3'\n",
"\n",
"name_list=['water_surface_height_above_reference_datum',\n",
" 'sea_surface_height_above_geoid','sea_surface_elevation',\n",
" 'sea_surface_height_above_reference_ellipsoid','sea_surface_height_above_sea_level',\n",
" 'sea_surface_height','water level']\n",
"\n",
"def name_in_list(cube):\n",
" return cube.standard_name in name_list"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def my_func(cube):\n",
" if cube.standard_name in name_list:\n",
" if cube.b2 = not any(m.method == 'maximum' for m in cube.cell_methods)\n",
" b = b1 and b2\n",
" return b"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def my_func(cube):\n",
" return(cube.standard_name in name_list) and (not any(m.method == 'maximum' for m in cube.cell_methods))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_func(cube)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 53,
"text": [
"True"
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_constraint = iris.Constraint(cube_func=my_func)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cube = iris.load_cube(url,my_constraint)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print cube"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"water_surface_height_above_reference_datum / (m) (time: 511; -- : 567; -- : 327)\n",
" Dimension coordinates:\n",
" time x - -\n",
" Auxiliary coordinates:\n",
" latitude - x x\n",
" longitude - x x\n",
" Attributes:\n",
" Conventions: CF-1.0\n",
" Source: SLOSH\n",
" VerticalDatum: urn:ogc:def:datum:epsg::5103\n",
" _ChunkSize: [ 1 567 327]\n",
" cdm_data_type: grid\n",
" id: inundation_tropical.MDL_SLOSH.Hurricane_Ike_final_run_egm3\n",
" institution: MDL - NOAA/NWS/MDL, inundation\n",
" summary: A hindcast of Hurricane Ike (2008) using SLOSH on the egm3 mesh with SLOSH's...\n",
" title: Inundation Tropical : MDL : SLOSH : Hurricane Ike final run egm3\n",
" Cell methods:\n",
" point: time\n"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cube.attrs()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
ocefpaf/folium | examples/TilesExample.ipynb | 2 | 37730 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import folium\n",
"\n",
"\n",
"lon, lat = -38.625, -12.875\n",
"\n",
"zoom_start = 8"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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],
"text/plain": [
"<folium.folium.Map at 0x7f24b00bac70>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map(location=[lat, lon], tiles=\"OpenStreetMap\", zoom_start=zoom_start)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=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 onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f24560a0730>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map(location=[lat, lon], tiles=\"Stamen Terrain\", zoom_start=zoom_start)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=PCFET0NUWVBFIGh0bWw+CjxoZWFkPiAgICAKICAgIDxtZXRhIGh0dHAtZXF1aXY9ImNvbnRlbnQtdHlwZSIgY29udGVudD0idGV4dC9odG1sOyBjaGFyc2V0PVVURi04IiAvPgogICAgCiAgICAgICAgPHNjcmlwdD4KICAgICAgICAgICAgTF9OT19UT1VDSCA9IGZhbHNlOwogICAgICAgICAgICBMX0RJU0FCTEVfM0QgPSBmYWxzZTsKICAgICAgICA8L3NjcmlwdD4KICAgIAogICAgPHN0eWxlPmh0bWwsIGJvZHkge3dpZHRoOiAxMDAlO2hlaWdodDogMTAwJTttYXJnaW46IDA7cGFkZGluZzogMDt9PC9zdHlsZT4KICAgIDxzdHlsZT4jbWFwIHtwb3NpdGlvbjphYnNvbHV0ZTt0b3A6MDtib3R0b206MDtyaWdodDowO2xlZnQ6MDt9PC9zdHlsZT4KICAgIDxzY3JpcHQgc3JjPSJodHRwczovL2Nkbi5qc2RlbGl2ci5uZXQvbnBtL2xlYWZsZXRAMS42LjAvZGlzdC9sZWFmbGV0LmpzIj48L3NjcmlwdD4KICAgIDxzY3JpcHQgc3JjPSJodHRwczovL2NvZGUuanF1ZXJ5LmNvbS9qcXVlcnktMS4xMi40Lm1pbi5qcyI+PC9zY3JpcHQ+CiAgICA8c2NyaXB0IHNyYz0iaHR0cHM6Ly9tYXhjZG4uYm9vdHN0cmFwY2RuLmNvbS9ib290c3RyYXAvMy4yLjAvanMvYm9vdHN0cmFwLm1pbi5qcyI+PC9zY3JpcHQ+CiAgICA8c2NyaXB0IHNyYz0iaHR0cHM6Ly9jZG5qcy5jbG91ZGZsYXJlLmNvbS9hamF4L2xpYnMvTGVhZmxldC5hd2Vzb21lLW1hcmtlcnMvMi4wLjIvbGVhZmxldC5hd2Vzb21lLW1hcmtlcnMuanMiPjwvc2NyaXB0PgogICAgPGxpbmsgcmVsPSJzdHlsZXNoZWV0IiBocmVmPSJodHRwczovL2Nkbi5qc2RlbGl2ci5uZXQvbnBtL2xlYWZsZXRAMS42LjAvZGlzdC9sZWFmbGV0LmNzcyIvPgogICAgPGxpbmsgcmVsPSJzdHlsZXNoZWV0IiBocmVmPSJodHRwczovL21heGNkbi5ib290c3RyYXBjZG4uY29tL2Jvb3RzdHJhcC8zLjIuMC9jc3MvYm9vdHN0cmFwLm1pbi5jc3MiLz4KICAgIDxsaW5rIHJlbD0ic3R5bGVzaGVldCIgaHJlZj0iaHR0cHM6Ly9tYXhjZG4uYm9vdHN0cmFwY2RuLmNvbS9ib290c3RyYXAvMy4yLjAvY3NzL2Jvb3RzdHJhcC10aGVtZS5taW4uY3NzIi8+CiAgICA8bGluayByZWw9InN0eWxlc2hlZXQiIGhyZWY9Imh0dHBzOi8vbWF4Y2RuLmJvb3RzdHJhcGNkbi5jb20vZm9udC1hd2Vzb21lLzQuNi4zL2Nzcy9mb250LWF3ZXNvbWUubWluLmNzcyIvPgogICAgPGxpbmsgcmVsPSJzdHlsZXNoZWV0IiBocmVmPSJodHRwczovL2NkbmpzLmNsb3VkZmxhcmUuY29tL2FqYXgvbGlicy9MZWFmbGV0LmF3ZXNvbWUtbWFya2Vycy8yLjAuMi9sZWFmbGV0LmF3ZXNvbWUtbWFya2Vycy5jc3MiLz4KICAgIDxsaW5rIHJlbD0ic3R5bGVzaGVldCIgaHJlZj0iaHR0cHM6Ly9jZG4uanNkZWxpdnIubmV0L2doL3B5dGhvbi12aXN1YWxpemF0aW9uL2ZvbGl1bS9mb2xpdW0vdGVtcGxhdGVzL2xlYWZsZXQuYXdlc29tZS5yb3RhdGUubWluLmNzcyIvPgogICAgCiAgICAgICAgICAgIDxtZXRhIG5hbWU9InZpZXdwb3J0IiBjb250ZW50PSJ3aWR0aD1kZXZpY2Utd2lkdGgsCiAgICAgICAgICAgICAgICBpbml0aWFsLXNjYWxlPTEuMCwgbWF4aW11bS1zY2FsZT0xLjAsIHVzZXItc2NhbGFibGU9bm8iIC8+CiAgICAgICAgICAgIDxzdHlsZT4KICAgICAgICAgICAgICAgICNtYXBfNmM3MmUyNWUyMWZlNDcyMzg5NzQ0ZDk2MGIyMTY4MTUgewogICAgICAgICAgICAgICAgICAgIHBvc2l0aW9uOiByZWxhdGl2ZTsKICAgICAgICAgICAgICAgICAgICB3aWR0aDogMTAwLjAlOwogICAgICAgICAgICAgICAgICAgIGhlaWdodDogMTAwLjAlOwogICAgICAgICAgICAgICAgICAgIGxlZnQ6IDAuMCU7CiAgICAgICAgICAgICAgICAgICAgdG9wOiAwLjAlOwogICAgICAgICAgICAgICAgfQogICAgICAgICAgICA8L3N0eWxlPgogICAgICAgIAo8L2hlYWQ+Cjxib2R5PiAgICAKICAgIAogICAgICAgICAgICA8ZGl2IGNsYXNzPSJmb2xpdW0tbWFwIiBpZD0ibWFwXzZjNzJlMjVlMjFmZTQ3MjM4OTc0NGQ5NjBiMjE2ODE1IiA+PC9kaXY+CiAgICAgICAgCjwvYm9keT4KPHNjcmlwdD4gICAgCiAgICAKICAgICAgICAgICAgdmFyIG1hcF82YzcyZTI1ZTIxZmU0NzIzODk3NDRkOTYwYjIxNjgxNSA9IEwubWFwKAogICAgICAgICAgICAgICAgIm1hcF82YzcyZTI1ZTIxZmU0NzIzODk3NDRkOTYwYjIxNjgxNSIsCiAgICAgICAgICAgICAgICB7CiAgICAgICAgICAgICAgICAgICAgY2VudGVyOiBbLTEyLjg3NSwgLTM4LjYyNV0sCiAgICAgICAgICAgICAgICAgICAgY3JzOiBMLkNSUy5FUFNHMzg1NywKICAgICAgICAgICAgICAgICAgICB6b29tOiA4LAogICAgICAgICAgICAgICAgICAgIHpvb21Db250cm9sOiB0cnVlLAogICAgICAgICAgICAgICAgICAgIHByZWZlckNhbnZhczogZmFsc2UsCiAgICAgICAgICAgICAgICB9CiAgICAgICAgICAgICk7CgogICAgICAgICAgICAKCiAgICAgICAgCiAgICAKICAgICAgICAgICAgdmFyIHRpbGVfbGF5ZXJfNWQ0YTkwMTEyNGNjNGViOGE3NjI5MTVhOGI5ZDk3OTkgPSBMLnRpbGVMYXllcigKICAgICAgICAgICAgICAgICJodHRwczovL3N0YW1lbi10aWxlcy17c30uYS5zc2wuZmFzdGx5Lm5ldC90b25lci97en0ve3h9L3t5fS5wbmciLAogICAgICAgICAgICAgICAgeyJhdHRyaWJ1dGlvbiI6ICJNYXAgdGlsZXMgYnkgXHUwMDNjYSBocmVmPVwiaHR0cDovL3N0YW1lbi5jb21cIlx1MDAzZVN0YW1lbiBEZXNpZ25cdTAwM2MvYVx1MDAzZSwgdW5kZXIgXHUwMDNjYSBocmVmPVwiaHR0cDovL2NyZWF0aXZlY29tbW9ucy5vcmcvbGljZW5zZXMvYnkvMy4wXCJcdTAwM2VDQyBCWSAzLjBcdTAwM2MvYVx1MDAzZS4gRGF0YSBieSBcdTAwMjZjb3B5OyBcdTAwM2NhIGhyZWY9XCJodHRwOi8vb3BlbnN0cmVldG1hcC5vcmdcIlx1MDAzZU9wZW5TdHJlZXRNYXBcdTAwM2MvYVx1MDAzZSwgdW5kZXIgXHUwMDNjYSBocmVmPVwiaHR0cDovL3d3dy5vcGVuc3RyZWV0bWFwLm9yZy9jb3B5cmlnaHRcIlx1MDAzZU9EYkxcdTAwM2MvYVx1MDAzZS4iLCAiZGV0ZWN0UmV0aW5hIjogZmFsc2UsICJtYXhOYXRpdmVab29tIjogMTgsICJtYXhab29tIjogMTgsICJtaW5ab29tIjogMCwgIm5vV3JhcCI6IGZhbHNlLCAib3BhY2l0eSI6IDEsICJzdWJkb21haW5zIjogImFiYyIsICJ0bXMiOiBmYWxzZX0KICAgICAgICAgICAgKS5hZGRUbyhtYXBfNmM3MmUyNWUyMWZlNDcyMzg5NzQ0ZDk2MGIyMTY4MTUpOwogICAgICAgIAo8L3NjcmlwdD4= onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f24560cc8b0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map(location=[lat, lon], tiles=\"Stamen Toner\", zoom_start=zoom_start)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=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 onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f24560a3160>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map(location=[lat, lon], tiles=\"Stamen Watercolor\", zoom_start=zoom_start)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=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 onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f24560a9820>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map(location=[lat, lon], tiles=\"Cartodb Positron\", zoom_start=zoom_start)\n",
"\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=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 onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f24560a01f0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map(location=[lat, lon], tiles=\"Cartodb dark_matter\", zoom_start=zoom_start)\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Custom tiles"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" data-html=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 onload=\"this.contentDocument.open();this.contentDocument.write(atob(this.getAttribute('data-html')));this.contentDocument.close();\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7f24560bd6d0>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"attr = (\n",
" '© <a href=\"http://www.openstreetmap.org/copyright\">OpenStreetMap</a> '\n",
" 'contributors, © <a href=\"http://cartodb.com/attributions\">CartoDB</a>'\n",
")\n",
"tiles = \"http://{s}.basemaps.cartocdn.com/light_nolabels/{z}/{x}/{y}.png\"\n",
"\n",
"m = folium.Map(location=[lat, lon], tiles=tiles, attr=attr, zoom_start=zoom_start)\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### There are plenty tile sources to choose from"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For a list of many more tile providers go to http://leaflet-extras.github.io/leaflet-providers/preview/"
]
}
],
"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": "3.9.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
matousc89/PPSI | podklady/notebooks/skriptovani_v_pythonu.ipynb | 1 | 19091 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Skriptování v Pythonu\n",
"V tomto tutoriálu jsou představeny základní funkce a objekty potřebné pro nejzákladnější skriptování v Pythonu.\n",
"\n",
"Poznámky:\n",
"* Komentáře v Pythonu se se dělají pomocí *#*. Cokoliv za tímto znakem až do konce řádku je komentář"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = 1 # tohle je komentar"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* V Jupyter notebook (nástroj v kterém je tento tutoriál vytvořen) každý blok kódu ukončení proměnnou nebo výrazem vytiskne obsah této proměnné. Toho je v tomto tutoriálu využívano. Následuje příklad:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = 1 # tady si vytvorim promennou\n",
"a # tohle vytiskne hodnotu bez uziti prikazu print"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"## List\n",
"List je pravděpodobně nejznámější kontejner na data v jazyce Python. Položky v listu se můžou opakovat a mají pořadí položek dané při vytvoření listu. Položky v listu je možné měnit, mazat a přidávat. Následují příklady."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 1, 2]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[1, 1, 2] # list celych cisel, polozka 1 se opakukuje"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"['abc', 1, 0.5]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[\"abc\", 1, 0.5] # list obsahujici ruzne datove typy"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[] # prazdny list"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"[[1, 2], 'abc', {'0', 1, 3}]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[[1,2], \"abc\", {1, \"0\", 3}] # list obsahujici take dalsi listy"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 'a', 2, 5, 3, 5]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[1, \"a\", 2] + [5, 3, 5] # spojeni dvou listu"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[1, 2, 3]*5 # opakovani listu"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Indexování a porcování\n",
"V Pythonu se pro indexování užívají hranaté závorky `[]`. Symbol `:` zastupuje všechny položky v daném rozsahu. Indexuje se od 0. Indexování a porcování listu je ukázáno na nasledujících příkladech."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"a = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\"] # ukázkový list"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'d'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[3] # vrati objekt z indexem 3 (ctvrty objekt)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'b']"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[:2] # vrati prvni dva objekty"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"['d', 'e', 'f', 'g', 'h']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[3:] # vrati vse od objektu 3 dal"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"['c', 'd', 'e']"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[2:5] # vse mezi objekty s indexy 2 a 5"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'f'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[-3] # treti objekt od konce"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'c', 'e', 'g']"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[0:-1:2] # kazdy druhy objekt od zacatku do konce"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'c', 'e', 'g']"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[::2] # kratsi ekvivalent predchoziho prikladu"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"b = [[1, 2, 3], [4, 5, 6]] # priklad vnorenych listu"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"[4, 5, 6]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b[1] # vraci druhy list"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b[0][0:2] # vraci prvni dve polozky z druheho listu"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Přepisování, přidávání, vkládání a mazání položek z listu\n",
"Ukázáno na následujících příkladech."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"a = [\"a\", \"b\", \"c\", \"d\"]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['a', 'b', 'x', 'd']\n"
]
}
],
"source": [
"a[2] = \"x\" # prepsani objektu s indexem 2\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['a', 'b', 'x', 'd', 'h']\n"
]
}
],
"source": [
"a.append(\"h\") # pridani objektu h na konec\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['a', 'b', 'y', 'x', 'd', 'h']\n"
]
}
],
"source": [
"a.insert(2, \"y\") # pridani objektu y na pozici 2\n",
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['a', 'b', 'x', 'd', 'h']\n"
]
}
],
"source": [
"del a[2] # odebere objekt na pozici 2\n",
"print (a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***\n",
"## Podmínka If, else, elif\n",
"Podmínky slouží k implementaci logiky. Logika operuje s proměnnou *bool*, která nabývá pouze hodnot *True* nebo *False*.\n",
"\n",
"### Výrazy a jejich vyhodnocení\n",
"Následuje ukázka vyhodnocení pravdivosti několika výrazů."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = 1\n",
"a == 1"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a == 1"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"not a == 1"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a > 1"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a >= 1"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 in [1, 2]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"not (1 in [1, 2]) == (not 1 > 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Podmínky a jejich vyhodnocení\n",
"Podmínka *if* testuje, zda výraz pravdivé hodnoty - pokud ano, podmínka vykoná svůj kód. Následuje příklad."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'green'"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fruit = \"apple\"\n",
"color = \"No color\"\n",
"\n",
"if fruit == \"apple\":\n",
" color = \"green\"\n",
" \n",
"color"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podmínka *else* umožňuje nastavit alternativní kód pro případ kdy podmínka *if* není splněna. Příklad následuje."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'orange'"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fruit = \"orange\"\n",
"\n",
"if fruit == \"apple\":\n",
" color = \"red\"\n",
"else:\n",
" color = \"orange\"\n",
"\n",
"color"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podmínka *elif* umožňuje zadat více podmínek pro případ nesplnění podmínky *if*. Podmínek *elif* je možné umístit více za jednu podmínku *if*. Příklad následuje."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'red'"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fruit = \"apple\"\n",
"\n",
"if fruit == \"apple\":\n",
" color = \"red\"\n",
"elif fruit == \"orange\":\n",
" color = \"orange\"\n",
"elif fruit == \"pear\":\n",
" color = \"green\"\n",
"else:\n",
" color = \"yellow\"\n",
" \n",
"color"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"***\n",
"## Smyčky\n",
"Iterace je jedna z nejčastější operací v programování. Následující ukázky se vztahují k rovnici\n",
"\n",
"$\\forall i \\in \\{2,\\ldots,9\\}.\\ a_i = a_{i-1} + a_{i-2}$.\n",
"\n",
"### For smyčka\n",
"For smyčka je navržena pro iterování přes předem daný iterovatelný objekt. Příklad následuje."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 1, 2, 3, 5, 8, 13, 21, 34]\n"
]
}
],
"source": [
"a = [] # list na vysledky\n",
"a.append(1) # prvni pocatecni podminka\n",
"a.append(1) # druha pocatecni podminka\n",
"for i in [2, 3, 4, 5, 6, 7, 8]: # rozsah pres ktery iterovat\n",
" a.append(a[i-1] + a[i-2]) # pridavani vypoctenych polozek do listu\n",
"print(a)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Vylepšení předchozího příkladu následuje."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 1, 2, 3, 5, 8, 13, 21, 34]\n"
]
}
],
"source": [
"a = [0]*9 # list na vysledky\n",
"a[0:2] = [1, 1] # pocatecni podminky\n",
"for i in range(2,9): # fukce range\n",
" a[i] = a[i-1] + a[i-2] # realizace vypoctu\n",
"print(a)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"V případě že je potřeba přerušit smyčku před koncem, je možné použít příkaz **break**.\n",
"\n",
"### While smyčka\n",
"Tato smyčka iteruje dokud není splněna podmínka. Příklad následuje."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 1, 2, 3, 5, 8, 13, 21, 34]\n"
]
}
],
"source": [
"a = [0]*9 \n",
"a[0:2] = [1, 1]\n",
"i = 2 # nastaveni pomocne promenne\n",
"while i < 9: # iteruj dokud pomocna promenna nesplni podminku\n",
" a[i] = a[i-1] + a[i-2]\n",
" i += 1 # pridej 1 k pomocne promenne\n",
"print(a)"
]
}
],
"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": 2
}
| mit |
GoogleCloudPlatform/asl-ml-immersion | notebooks/launching_into_ml/labs/supplemental/decision_trees_and_random_Forests_in_Python.ipynb | 1 | 11556 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "vDnaW6s9oGSY"
},
"source": [
"# Decision Trees and Random Forests in Python\n",
"\n",
"\n",
"**Learning Objectives**\n",
"\n",
"\n",
"1. Explore and analyze data using a Pairplot\n",
"2. Train a single Decision Tree\n",
"3. Predict and evaluate the Decision Tree\n",
"4. Compare the Decision Tree model to a Random Forest\n",
"\n",
"\n",
"## Introduction \n",
"\n",
"In this lab, you explore and analyze data using a Pairplot, train a single Decision Tree, predict and evaluate the Decision Tree, and compare the Decision Tree model to a Random Forest. Recall that the [Decision Tree](https://en.wikipedia.org/wiki/Decision_tree_learning) algorithm belongs to the family of supervised learning algorithms. Unlike other supervised learning algorithms, the decision tree algorithm can be used for solving both regression and classification problems too. Simply, the goal of using a Decision Tree is to create a training model that can use to predict the class or value of the target variable by learning simple decision rules inferred from prior data(training data).\n",
"\n",
" Each learning objective will correspond to a _#TODO_ in this student lab notebook -- try to complete this notebook first and then review the [solution notebook](https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive2/launching_into_ml/solutions/decision_trees_and_random_Forests_in_Python.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8sulisrOoGSa"
},
"source": [
"## Load necessary libraries \n",
"We will start by importing the necessary libraries for this lab."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "1k_uHvb0oGSb"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "lv5dJSNmoGSe"
},
"source": [
"## Get the Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "jUxkkwtyoGSf"
},
"outputs": [],
"source": [
"df = pd.read_csv(\"../kyphosis.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"colab_type": "code",
"id": "bQp5k7jUoGSh",
"outputId": "8f029320-43d6-449c-893c-0e36c69078e1"
},
"outputs": [],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ECImqktYoGSk"
},
"source": [
"## Exploratory Data Analysis\n",
"\n",
"**Lab Task #1:** Check a pairplot for this small dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 562
},
"colab_type": "code",
"id": "gG5VVJuGoGSk",
"outputId": "b9204f57-d66a-4ed6-e9b5-e1c822d21d6f"
},
"outputs": [],
"source": [
"# TODO 1\n",
"# TODO -- Your code here."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "jq1bXb5yoGSm"
},
"source": [
"## Train Test Split\n",
"\n",
"Let's split up the data into a training set and a test set!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "yqOsAvyNoGSn"
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "0lWbTfXDoGSp"
},
"outputs": [],
"source": [
"X = df.drop(\"Kyphosis\", axis=1)\n",
"y = df[\"Kyphosis\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "N9XE_wWToGSr"
},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gkhZpiNIoGSt"
},
"source": [
"## Decision Trees\n",
"\n",
"**Lab Task #2:** Train a single decision tree."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "LIT289hKoGSt"
},
"outputs": [],
"source": [
"from sklearn.tree import DecisionTreeClassifier"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "z8jarMdhoGSv"
},
"outputs": [],
"source": [
"dtree = DecisionTreeClassifier()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
},
"colab_type": "code",
"id": "5sQoHceMoGSx",
"outputId": "c7afcec0-99f5-4ec1-950c-2c03a270c451"
},
"outputs": [],
"source": [
"# TODO 2\n",
"# TODO -- Your code here."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "YLwbeai1oGSz"
},
"source": [
"## Prediction and Evaluation \n",
"\n",
"Let's evaluate our decision tree."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "3t6k16iSoGS0"
},
"outputs": [],
"source": [
"predictions = dtree.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ESl5l5HcoGS3"
},
"outputs": [],
"source": [
"from sklearn.metrics import classification_report, confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 170
},
"colab_type": "code",
"id": "mGKDt0PkoGS5",
"outputId": "c4524dbc-bf11-48e0-eea9-5ad872a702e6"
},
"outputs": [],
"source": [
"# TODO 3a\n",
"# TODO -- Your code here."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"colab_type": "code",
"id": "Tp3iEQ7DoGS7",
"outputId": "7017be66-cb58-419b-af5f-db832cb89d03"
},
"outputs": [],
"source": [
"# TODO 3b\n",
"print(confusion_matrix(y_test, predictions))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "-cOflfnkoGS8"
},
"source": [
"## Tree Visualization\n",
"\n",
"Scikit learn actually has some built-in visualization capabilities for decision trees, you won't use this often and it requires you to install the pydot library, but here is an example of what it looks like and the code to execute this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 88
},
"colab_type": "code",
"id": "EFVGxz9uoGS9",
"outputId": "9f9785b9-e971-4c14-abaa-a2cdece6949f"
},
"outputs": [],
"source": [
"import pydot\n",
"from IPython.display import Image\n",
"from six import StringIO\n",
"from sklearn.tree import export_graphviz\n",
"\n",
"features = list(df.columns[1:])\n",
"features"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"colab_type": "code",
"id": "KQz0TWtwoGS_",
"outputId": "26e0b809-9cfc-4bf9-fd3e-930748f8509c"
},
"outputs": [],
"source": [
"dot_data = StringIO()\n",
"export_graphviz(\n",
" dtree, out_file=dot_data, feature_names=features, filled=True, rounded=True\n",
")\n",
"\n",
"graph = pydot.graph_from_dot_data(dot_data.getvalue())\n",
"Image(graph[0].create_png())"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5m-x2yZpoGTA"
},
"source": [
"## Random Forests\n",
"\n",
"**Lab Task #4:** Compare the decision tree model to a random forest."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
},
"colab_type": "code",
"id": "BmgJz4SxoGTB",
"outputId": "ec141c46-605c-461e-aa4a-c92f91b6d8ce"
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"rfc = RandomForestClassifier(n_estimators=100)\n",
"rfc.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "edhzvBCDoGTD"
},
"outputs": [],
"source": [
"rfc_pred = rfc.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "muBlj4B3oGTG"
},
"outputs": [],
"source": [
"# TODO 4a\n",
"# TODO -- Your code here."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 170
},
"colab_type": "code",
"id": "fhKc4sz3oGTH",
"outputId": "ea3c9732-7840-47c9-fcc7-61f8c0271ff6"
},
"outputs": [],
"source": [
"# TODO 4b\n",
"# TODO -- Your code here."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "-sYHmikioGTJ"
},
"source": [
"Copyright 2021 Google Inc.\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
"http://www.apache.org/licenses/LICENSE-2.0\n",
"Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
]
}
],
"metadata": {
"colab": {
"name": "Conchita_Decision_Trees_and_Random_Forests_in_Python.ipynb",
"provenance": [],
"toc_visible": true
},
"environment": {
"name": "tf2-gpu.2-5.m76",
"type": "gcloud",
"uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-5:m76"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
plipp/informatica-pfr-2017 | nbs/6/1-2-Pandas-Text-Support-Primer.ipynb | 1 | 3927 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Working with Text Data in pandas"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"time_sentences = [\"Monday: Soccer is at 2:45pm.\", \n",
" \"Tuesday: Appointment is at 11:30 am.\",\n",
" \"Wednesday: At 7:00pm, there is a python MOOC!\",\n",
" \"Thursday: Beer at 11:15 pm at the latest.\",\n",
" \"Friday: Starts at 08:10 am, ends at 09:00am.\"]\n",
"\n",
"df = pd.DataFrame(time_sentences, columns=['text'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# find the number of chars for each string in df['text']\n",
"df['text'].str.len()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# find the number of tokens for each string in df['text']\n",
"df['text'].str.split().str.len()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['text'].str.contains('Appointment')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df['text'].str.count(r'\\d')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# find all digits\n",
"df['text'].str.findall(r'\\d')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# group and find the hours and minutes\n",
"df['text'].str.findall(r'(\\d?\\d):(\\d\\d)')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# replace weekdays with '???'\n",
"df['text'].str.replace(r'\\w+day\\b', '???')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# replace weekdays with 3 letter abbrevations\n",
"df['text'].str.replace(r'(\\w+day\\b)', lambda x: x.groups()[0][:3])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# create new columns from first match of extracted groups\n",
"df['text'].str.extract(r'(\\d?\\d):(\\d\\d)', expand=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# extract the entire time, the hours, the minutes, and the period\n",
"df['text'].str.extractall(r'((\\d?\\d):(\\d\\d) ?([ap]m))')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# extract the entire time, the hours, the minutes, and the period with group names\n",
"df['text'].str.extractall(r'(?P<time>(?P<hour>\\d?\\d):(?P<minute>\\d\\d) ?(?P<period>[ap]m))')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Links\n",
"\n",
"- [Coursera: Applied Text Mining in Python](https://www.coursera.org/learn/python-text-mining/home/welcome)\n",
"- [Working with Text Data in Pandas](https://pandas.pydata.org/pandas-docs/stable/text.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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
arve0/TFY4500 | comparison objective.ipynb | 1 | 80148 | {
"metadata": {
"name": "",
"signature": "sha256:0d5991ffa19ec71e2f96714cb6223484001802c1527766d4369b3d214e953230"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"from skimage import io\n",
"\n",
"plt.rcParams['image.interpolation'] = 'none'\n",
"plt.rcParams['image.cmap'] = 'gray_r'\n",
"plt.rcParams['font.size'] = 14"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"img20 = io.imread('/Users/arve/Dokumenter/TFY4500/comparison/20x oppl\u00f8sning/12_13_35_23ch0z6.tif')\n",
"img63 = io.imread('/Users/arve/Dokumenter/TFY4500/comparison/63x oppl\u00f8sning/12_20_25_23ch0z4.tif')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 89
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"img20.shape, img63.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 90,
"text": [
"((4096, 4096), (5120, 5120))"
]
}
],
"prompt_number": 90
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"img0 = 255-img20[80:190, 1150:1260]\n",
"img1 = 255-img63[320:450, 1515:1645]\n",
"\n",
"figure(figsize=(9.7,4))\n",
"\n",
"subplot(121)\n",
"imshow(img0)\n",
"xticks([])\n",
"yticks([])\n",
"colorbar()\n",
"title('(a) 20x objective')\n",
"\n",
"subplot(122)\n",
"imshow(img1)\n",
"xticks([])\n",
"yticks([])\n",
"title('(b) 63x water objective')\n",
"colorbar()\n",
"\n",
"tight_layout();"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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sfzag/HALpFb1jx8/vsGEoCwQunPuu8C7CYvIf5ifTwV+ERcHLgN+DpyWHbsF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JAAAg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\nH0g6juM4juM4vfCBpOM4juM4jtOL/wcaurHAAvNuFAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x11389e470>"
]
}
],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"img0.shape, img1.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 50,
"text": [
"((110, 110), (130, 130))"
]
}
],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"img0.min(), img1.min()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 94,
"text": [
"(0, 1)"
]
}
],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"img0.max(), img1.max()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 95,
"text": [
"(255, 117)"
]
}
],
"prompt_number": 95
}
],
"metadata": {}
}
]
} | mit |
JoseGuzman/myIPythonNotebooks | Optimization/Maximum_likelihood_estimation.ipynb | 1 | 51001 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<H2>Parameter estimation by maximum likelihood method<H2>"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from scipy.stats import norm\n",
"from lmfit import minimize, Parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <H2> Create a normally distributed random variable</H2>"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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p3ffnn382W4FkPlzTkbimI3EdQ2IW6jO66YSGhmL9+vUAgOzsbPzyyy+qFUVE\nRNbJ6Om1devWwdnZGf/5z3+QlZWFoqIigz9qWrx4MTQajd6fUaNGqfra1R3XMSRmIXEdQ2IW6jP6\nSOfGjRuYMmUK+vXrp2Y9jxQfHw87OzssWLCg1Nj//M//VEJFRERkLKObTps2bXDr1i01azFKQkIC\nfHx8yn1D0pqMazoS13QkrmNIzEJ9Rk+vLVq0CLt27UJ4eLia9ZTp7t27uHLlCtq3b19pNRARUfkZ\nfaTzyy+/wNvbG0OGDIGHhwc8PT1Rt25dvfvu27fPbAX+VUJCAgCw6ZQT1zEkbWIij3b+xO+QkZiF\n+oxuOosXL9b97+TkZCQnJ6tSUFmKm87NmzfRr18//PTTT1AUBU8//TTefvtteHl5WbwmIiIyntFN\n59KlS2rWYZTipvP+++/j+eefR0hICOLj47F37158++230Gq18PX1reQqqy6u6Ug8ypH4yV5iFuoz\nuum4u7urWIZxbGxs4O7ujm3btqFnz5667f/6178QHByMCRMm4PTp05VYIRERlcXoEwnu3r1r9I9a\nPvroI1y6dKlEwwGA0aNHo2fPnoiNjUVSUlKp5ymKYvCnJuGajsQsJF6bItWkLCrr76LRRzqOjo6P\nLEYIAUVRUFhYWOHCTOXn54eoqCikpKSYtLZT/I+s+LC6uj9OTEpC/YwM3fRR8R/X4sfJN26UWER/\neNzQ42L6xpNv3Cj3eHnredTjhORkICwMiX9+CGn357+JxKQkpPz6K0Z3727w+fuPHdPV+/Dzf0lL\nQ+fevavM/9/meBwXF1el6qnMx3FxcVWqHrUfVwZFGHn3TkNF3r9/H5cuXUJGRga6dOkCf39/hIaG\nmrNGAH98c2l8fDwKCwvRqVOnUuNTpkzBpk2boNVqdUdCxU2yJt2g9GBYGIJcXQ2Or/zySywycOcG\nNcas7fcevHYNQVOmGHwukTVQ82+n0Uc6ZR12CiGwceNGzJkzR5WGAwD5+fno0qULGjRogN9//x0a\njZwZFEIgJiYGtWvXRocOHVR5fSIiqjij13TKoigKpk6dij59+ui9PY051KlTB4MGDUJmZibeeeed\nEmMffPABzpw5g9GjRxv8Gm3iOsZfMQupJq1jPAqzUJ/RRzrGaN++PT766CNz/soSPvjgA8TExGDR\nokXQarVo3749Tp8+jaNHj8LHxwcffvihaq9NREQVZ5YjHQAoKipCVFQU7O3tzfUrS2nZsiV++ukn\njBs3DmeTXH0SAAAQw0lEQVTOnMH69etx5coVzJ07FzExMXByclLtta0Br02RmIXEa1MkZqE+o490\n1q5dq/fstaKiIty7dw+RkZE4ceIExo0bZ9YCH9aiRQt8+umnqr4GERGpw+imM3v27Efu07Fjx1Lr\nLVR1cB1D4r3XJC3vN6bDLNRndNPZunWr3u2KosDW1hZt27blmWNERFQmo5vO+PHjVSyDLIH3XpN4\nlCPxk73ELNRnthMJiIiIHsXgkU6fPn3KfQ+eI0eOlLsgerT/7N6NvPR0vWNnT582eEcCrulIXNOR\nuI4hMQv1GWw6R48etWQdZIK89HSDjSU+KsrC1RARGc9g08nIyHjkkxVFQVJSEqZMmYLY2FjY2tpi\n/vz5Zi2QzIdrOhKPciR+speYhfoMNh1HR8cyn1hQUID33nsPK1euRE5ODrp164bNmzejbdu2Zi+S\niIisQ7lOJDh16hQ6duyIRYsWwdbWFhs2bEB0dDQbThXHNR2JWUi835jELNRnUtO5d+8eZs6ciW7d\nuiExMRHPP/88zp07h1deeUWt+oiIyIoY3XQiIiLg7e2N9evXw8XFBXv27MFXX32F5s2bq1kfmRHX\nMSRmIXEdQ2IW6ntk07l58yZGjhyJoKAgXL16FZMnT8a5c+cwbNgwS9RHRERWpMyms3XrVrRt2xa7\ndu1C69atodVqERYWBgcHB0vVR2bEdQyJWUhcx5CYhfoMnr3Wt29f3f8Bfn5+mDdvHjIzMxEeHv7I\nXzp48GCzFUhERNbDYNP5a8ePjY3FyJEjjfqFiqKgsLCwwoVVBw8ePEB+fr7B8fr165f7rg5q4HU6\nEtd0JK5jSMxCfQabzpIlS8r1C6vSH1m1afftg312NvS94wdFRWj//PM80YKI6C8MNp1ly5ZZsIzq\nSTx4gJ5Nm0KjKb00FpuaCiFEJVRlGNcxJN57TeL9xiRmoT7eZZqIiCyGTacG4Sd7iVlI/GQvMQv1\nsekQEZHFsOnUIFzTkZiFxGtTJGahPjYdIiKyGDadGoTrGBKzkLiOITEL9bHpEBGRxbDp1CBcx5CY\nhcR1DIlZqI9Nh4iILMbgHQnI+vDea1J513QSTp8GwsL0jtk6OyNwxIiKlFUpuI4hMQv1sekQmUDc\nu4cgV1e9YwevXbNwNUTVD6fXahCuY0jMQuI6hsQs1MemQ0REFsOmU4Pw2hSJWUhcx5CYhfrYdIiI\nyGLYdGoQrmNIzELiOobELNTHpkNERBbDplODcB1DYhYS1zEkZqE+Nh0iIrIYNp0ahOsYErOQuI4h\nMQv1sekQEZHF8DY4Koo6dAj1hNA7di4pCd5eXgafW9b42dOnDd6KpSy895pUGWs6/9m9G3np6XrH\nKvO+bVzHkJiF+qpl0ykoKMD69euxefNmpKSkoFmzZnj55Zcxf/582NhUnbdUkJmJIB8fvWPxUVEI\n6tPH4HPLGo+PijJLfWRZeenpvG8b1XjVcnpt2rRpmDNnDho3boxZs2bB1dUVS5YswahRoyq7tCqN\n6xgSs5C4jiExC/VVncMCI8XExGDz5s0YMWIEdu7cqds+fvx47NixAxERERg4cGAlVlg1KYMHAwDe\nYmPG4i+/BACI8PBKrqTyKYoCABAGpoFrEmZhGdXuSGfDhg0AgKVLl5bYvnr1aiiKgi1btlRGWURE\nZIRq13SioqLQuHFjeHt7l9jerFkzPPnkk4jiegcRUZVVrZpObm4url27hlatWukdd3d3R2ZmJtIN\nnCFERESVq1o1nYyMDACAo6Oj3nEHBwcAwJ07dyxWExERGa9aNZ38/HwAgJ2dnd7x4u05OTkWq4mI\niIxXrZqOvb09ACAvL0/veG5uLgCgbt26FquJiIiMp4hqdH5gXl4e7O3t0bVrV0RHR5ca79+/P775\n5hukp6fD0dFRdwokERGZTo32UK2OdGxtbfHEE08gOTlZ73hycjIaN26sW/OpRv2UiKhKUevvZ7W7\nODQgIACfffYZLly4gCeffFK3PTU1FRcuXMDgPy+CLMbGQ0RUdVSrIx0AGDt2LABg4cKFuoYihMCC\nBQsAAJMnT6602oiIqGzVak2n2KhRo7Bz50507twZvXv3RkxMDKKjo0vdGoeIiKqWanekAwCfffYZ\nVqxYgVu3bmHt2rW4efMm3nrrLXz++ecA/rgLdWhoKLy9vfHYY4+hVatWWLlyJQoKCiq5cnWlpaVh\nypQpaNGiBezs7NCsWTOMGTNG7xrYjh074Ofnh3r16qFFixaYM2cO7t27VwlVq2/u3LnQaDR671ZR\nU3L44osv0LlzZ9StWxfNmzfH8OHD8euvv5baz5rzuHXrFkJCQuDq6go7Ozt4eHhg3rx5ePDgQal9\nrTGH1NRUODg4YO3atXrHTXnPERER6Nq1Kxo0aAAXFxf8/e9/x++//25cIcIKTZ48WSiKInr27CkW\nLFggAgIChKIoYvjw4ZVdmmquX78uWrRoIRRFEYGBgeKNN94QgwcPFhqNRjg7O4sLFy7o9l21apVQ\nFEV06NBBLFiwQAwcOFAoiiK6desm8vLyKvFdmN/JkydFrVq1hEajEUePHi0xVlNyePPNN4WiKKJ1\n69bi9ddfF6NGjRI2NjbCyclJXLp0SbefNedx584d4eXlJRRFEU8//bR44403RLdu3YSiKKJ79+6i\noKBAt6815pCVlSW6dOkiFEURa9euLTVuynv+17/+JRRFEZ6enmLevHli5MiRolatWsLT01Pcvn37\nkbVYXdM5duyYUBRFvPDCCyW2jxs3TiiKIg4dOlRJlakrJCREKIoiQkNDS2z//PPPhaIoYvDgwUII\nIVJSUoSNjU2p/9CWLFkiFEURH330kUXrVlNubq7w8fERiqIIRVFKNJ2aksPJkyeFoiiiT58+Iicn\nR7d9z549QlEUMX78eCGE9efx7rvvCkVRxOzZs0tsDw4OFoqiiO3btwshrDOHlJQU8dRTT+n+O3i4\n6ZjynrOysoSTk5Pw9PQUWVlZuu1bt24ViqKIuXPnPrIeq2s6o0ePFoqiiLNnz5bYnpqaKjQajRgy\nZEglVaauJk2aCBcXF71jrVq1EnXq1BFFRUVi4cKFQlEUERERUWKfnJwc4eDgIDp06GCJci1iyZIl\nws7OTvTr169U06kpOYwdO1bUqlWrxJFusZCQELFq1SohhPXn8eKLLwpFUURiYmKJ7VqtViiKIqZO\nnSqEsL4cQkNDRf369UXt2rXF008/rbfpmPKeN23aJBRFERs2bCj1Wm3atBGNGjUShYWFZdZkdU3H\nzc1NNGnSRO9Y69atRcOGDS1ckfoKCwvF2rVrxcaNG/WOe3t7C41GI3Jzc0WPHj2ERqMR9+7dK7Vf\nYGCgUBRF3L17V+2SVRcfHy9sbW3F8uXLxaxZs0o1nZqSQ+PGjY36Q2ntecyYMUMoiiIOHjxYYnvx\nTMDSpUuFENaXg7u7u2jfvr04fvy42LZtm96mY8p7Lj4yPHfuXKl9i2db4uPjy6ypWp5IYEhNvQu1\nRqPBjBkzMGXKlFJj58+fx/nz59GqVSvY2tri4sWLcHFxwWOPPVZqX3d3dwBAUlKS2iWrqrCwEBMn\nToSXlxcWLFig91qtmpDDzZs3cevWLfj4+OD8+fMYNmwYHB0d4ejoiBdeeAEpKSm6fa09j5CQENSt\nWxezZ89GTEwM7t+/D61Wi3nz5sHR0RETJkwAYH05bNq0CXFxcfD39zd4zaIp7/nixYtQFAUtW7Y0\nuO+FCxfKrMmqmg7vQl1SUVERpk+fDiGE7vql4lsE6WMt+bz//vuIjY3Fli1bULt2bb371IQcUlNT\nAQBXr15Fly5dcOXKFfz9739H9+7dsWfPHvj7++PKlSsArD8Pb29vREdHIycnBz169EC9evXQt29f\n2NjY4NixY3j88ccBWF8O/fr1e+TtwEx5z+np6bCzs9N702Vj87GqpsO7UEtCCISEhODIkSPo1KkT\nZs2aBeCPjKw5n6SkJCxbtgzTpk1Dly5dDO5n7TkA0J3uGhUVhWHDhuHHH3/E+++/j4iICKxbtw43\nb96sMf8urly5guDgYKSmpmLw4MGYO3cuevfujStXrmDy5Mm6P5TWnoM+prxnc+RjVU2Hd6H+Q0FB\nASZMmIBPPvkErVq1woEDB2Bj88cdj+zt7a02HyEEJk6ciKZNm2L16tVl7mvNORTTaP74z9vGxgah\noaElPvFOmzYNHh4eiIyMxIMHD6w+j9GjR+Ps2bPYuXMn9u/fj/feew9HjhzBhx9+iGPHjulmAqw9\nB31Mec/myMeqmo6DgwMURTF4eHfnzh0oiqI7DLRG9+/fx/PPP4/t27fDy8sL33//PZo2baobd3Jy\nKjMfANU2nw0bNuDYsWPYuHGj3vnpv85pW3MOxYrrd3d3LzV9oigK2rdvj/z8fFy5csWq87hy5Qpi\nYmLQq1cvDB8+vMTYrFmz0LZtW+zbtw/Z2dlWnYMhprxnJycn5OTk6GaVytrXEKtqOqbehdraZGZm\nom/fvjh8+DCeeuopREdHw83NrcQ+Xl5euHHjhu5TyV8lJyejVq1aJW6kWp3s2bMHADBgwABoNBrd\nz7p16wAAffr0gUajweXLl+Hl5YW0tDSrzKFYy5YtodFoDH4yLf7D8dhjj1l1HteuXQMAtG3bVu+4\nt7c3ioqKcO3aNavOwRBT/iZ4eXlBCFHiJJS/7gsArVu3LvP1rKrpAH/chfr69eulzqAovgu1v79/\nJVWmrpycHAwaNAinTp1C7969odVq0ahRo1L7BQQEoLCwsNQtYXJycnDixAn4+PhU2+mDl19+GcuW\nLSv1U7y2M378eCxbtgyOjo4ICAhAUVGRVeZQrE6dOujUqROuXLmCixcvlhgrKChAfHw8nJ2d4erq\nih49elhtHs2aNQMAvbf9Af4420pRFLi4uNSIfxcPM+VvQkBAAABAq9WW+j1arRaOjo4Gm7tOec79\nrsq+/fZb3S1vioqKhBBCFBUVibFjx+q9AMpazJ49W3dLj79eef6w8+fPCxsbG9GtWzeRm5ur2754\n8WKDF31VdzNnzix1nU5NyaH4SvEBAwaI/Px83fZ33nlHKIoi5syZI4Sw/jy6dOkiNBqNOHDgQInt\nW7Zs0eUjhHXn8Omnn+q9TseU95yRkSEaNGggPD09RUZGhm77J598IhRFEa+//voj67C6piOEECNH\njhSKooguXbqIefPm6e699vCtcazF9evXha2trVAURUycOFEsXbpU709xM5o/f75QFEV4e3uLN954\nQ3efpYCAgGp7b6my6Gs6QtScHIYOHSoURRE+Pj5izpw5YsCAAUJRFNGmTZsSFzpacx5nzpwRDRs2\nFLVq1RJDhgwRb7zxhujfv79QFEW4urqKlJQU3b7WmoOhpiOEae85LCxMKIoiHn/8cTFnzhzdvdfa\ntGkjMjMzH1mHVTad/Px88dZbb+lu/9K6dWuxcuXKav0PpixfffWVUBRFaDQa3f2VHv7RaDTizp07\nuuds2LBB+Pj4iDp16ggPDw8xZ86canWltSlmzZql94afQtSMHAoKCkRoaKjufbq5uYnp06eX+KRa\nzJrzSE5OFuPHjxfNmjUTtWvXFi1atBBTpkwRaWlppfa1xhy2bdsmNBqN3qYjhGnveefOnaJjx47C\n3t5euLm5iYkTJ+rNUZ9q+X06RERUPVndiQRERFR1sekQEZHFsOkQEZHFsOkQEZHFsOkQEZHFsOkQ\nEZHFsOkQEZHFsOkQEZHFsOkQEZHFsOkQEZHF/D9Q/65DSSe9wAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb92f4ffe10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# create some data\n",
"mymean = 28.74\n",
"mysigma = 8.33 # standard deviation!\n",
"rv_norm = norm(loc = mymean, scale = mysigma)\n",
"data = rv_norm.rvs(size = 150)\n",
"plt.hist(data, bins=20, facecolor='red', alpha=.3);\n",
"\n",
"plt.ylabel('Number of events');\n",
"plt.xlim(0,100);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <H2> Define a model function</H2>"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.3989422804014327"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def mynorm(x, params):\n",
" mu, sigma = params\n",
" # scipy implementation\n",
" mynorm = norm(loc = mu, scale = sigma)\n",
" return mynorm.pdf(x)\n",
"\n",
"mynorm(0, [0,1]) # 0.39"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<H2> Loglikelihood function to be minimize </H2>"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"794.4880315495127"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def loglikelihood(params, data):\n",
" mu = params['mean'].value\n",
" sigma = params['std'].value\n",
" \n",
" l1 = np.log( mynorm(data, [mu, sigma]) ).sum()\n",
" return(-l1) # return negative loglikelihood to minimize\n",
"\n",
"myfoo = Parameters()\n",
"myfoo.add('mean', value = 20)\n",
"myfoo.add('std', value = 5.0)\n",
"loglikelihood(myfoo, data)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"myparams = Parameters()\n",
"myparams.add('mean', value = 20.3)\n",
"myparams.add('std', value = 5.0)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'MinimizerResult' object has no attribute 'userfcn'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-15-92b2e991c59d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mminimize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfcn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mloglikelihood\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmyparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'nelder'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muserfcn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmyparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# ~523.631337424\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m: 'MinimizerResult' object has no attribute 'userfcn'"
]
}
],
"source": [
"out = minimize(fcn = loglikelihood, params=myparams, method='nelder', args=(data,))\n",
"print(out.userfcn(myparams, data)) # ~523.631337424"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[Variables]]\n",
" mean: 20.3000000 (init= 20.3)\n",
" std: 5 (init= 5)\n",
"[[Correlations]] (unreported correlations are < 0.100)\n"
]
}
],
"source": [
"from lmfit import report_errors\n",
"report_errors(myparams)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The estimated mean and standard deviation should be identical to the mean\n",
"and the standard deviation of the sample population"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(28.812147384304652, 7.7946507327263665)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(data), np.std(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <H2> Plot histogram and model together </H2>"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Compute binwidth\n",
"counts, binedge = np.histogram(data, bins=20);\n",
"\n",
"bincenter = [0.5 * (binedge[i] + binedge[i+1]) for i in xrange(len(binedge)-1)]\n",
"binwidth = (max(bincenter) - min(bincenter)) / len(bincenter) "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Adjust PDF function to data\n",
"ivar = np.linspace(0, 100, 100)\n",
"params = [ myparams['mean'].value, myparams['std'].value ]\n",
"mynormpdf = mynorm(ivar, params)*binwidth*len(data)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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6UqkUn3zyiVYBlJSUYMiQITh//jx69uyJAwcOoGHDhjX2CwkJQXR0NGJiYtC3b99qnz97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\nRQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb92f686590>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot everything together\n",
"plt.hist(data, bins=20, facecolor='white', histtype='stepfilled');\n",
"\n",
"plt.plot(ivar, mynormpdf);\n",
"plt.ylabel('Number of events');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"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
}
| gpl-2.0 |
chengsoonong/crowdastro | notebooks/51_thesis_galaxy_classification.ipynb | 1 | 5503 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Classifying Galaxies\n",
"\n",
"We want to classify each galaxy as either containing an AGN or not containing an AGN, assuming that all galaxies are independent."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sys\n",
"\n",
"from astropy.coordinates import SkyCoord\n",
"import h5py\n",
"import matplotlib.pyplot as plt\n",
"import numpy\n",
"import sklearn.linear_model\n",
"import sklearn.ensemble\n",
"import sklearn.metrics\n",
"import sklearn.neighbors\n",
"\n",
"sys.path.insert(1, '..')\n",
"import crowdastro\n",
"\n",
"NORRIS_DAT_PATH = '../data/norris_2006_atlas_classifications_ra_dec_only.dat'\n",
"TRAINING_H5_PATH = '../data/training.h5'\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How many infrared objects are there?"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total: 24140\n",
"Testing: 5922 (24.53%)\n",
"Training: 18218 (75.47%)\n",
"Testing: 452 (19.97%)\n",
"Testing: 1811 (80.03%)\n"
]
}
],
"source": [
"with h5py.File(TRAINING_H5_PATH) as training_f:\n",
" print('Total:', training_f['features'].shape[0])\n",
" print('Testing:', training_f['is_ir_test'].value.sum(),\n",
" '({:.02%})'.format(training_f['is_ir_test'].value.sum() / training_f['features'].shape[0]))\n",
" print('Training:', training_f['is_ir_train'].value.sum(),\n",
" '({:.02%})'.format(training_f['is_ir_train'].value.sum() / training_f['features'].shape[0]))\n",
"\n",
" atlas_test = training_f['is_atlas_test'].value.sum()\n",
" atlas_train = training_f['is_atlas_train'].value.sum()\n",
" atlas_total = atlas_test + atlas_train\n",
" print('Testing:', atlas_test, '({:.02%})'.format(atlas_test / atlas_total))\n",
" print('Testing:', atlas_train, '({:.02%})'.format(atlas_train / atlas_total))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training the classifier"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with h5py.File(TRAINING_H5_PATH) as training_f:\n",
" lr = sklearn.linear_model.LogisticRegression(n_jobs=-1, class_weight='balanced', C=100.0, penalty='l1')\n",
" x = training_f['features'][training_f['is_ir_train'].value, :]\n",
" y = training_f['labels'][training_f['is_ir_train'].value]\n",
" lr.fit(x, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Testing the classifier"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Load Norris labels.\n",
"with h5py.File(TRAINING_H5_PATH, 'r') as training_f:\n",
" ir_positions = training_f['positions'].value\n",
"ir_tree = sklearn.neighbors.KDTree(ir_positions)\n",
"\n",
"with open(NORRIS_DAT_PATH, 'r') as norris_dat:\n",
" norris_coords = [r.strip().split('|') for r in norris_dat]\n",
"\n",
"norris_labels = numpy.zeros((len(ir_positions)))\n",
"for ra, dec in norris_coords:\n",
" # Find a neighbour.\n",
" skycoord = SkyCoord(ra=ra, dec=dec, unit=('hourangle', 'deg'))\n",
" ra = skycoord.ra.degree\n",
" dec = skycoord.dec.degree\n",
" ((dist,),), ((ir,),) = ir_tree.query([(ra, dec)])\n",
" if dist < 0.1:\n",
" norris_labels[ir] = 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Predict.\n",
"with h5py.File(TRAINING_H5_PATH) as training_f:\n",
" test_indices = training_f['is_ir_test'].value\n",
" x = training_f['features'][test_indices, :]\n",
" t = norris_labels[test_indices]\n",
" y = lr.predict(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Accuracy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# Raw accuracy.\n",
"sklearn.metrics.accuracy_score(t, y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Balanced accuracy.\n",
"cm = sklearn.metrics.confusion_matrix(t, y).astype(float)\n",
"cm /= cm.sum(axis=1).reshape((-1, 1))\n",
"cm.trace() / 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
cmfeng/class_project | Analysis/maps/ee_debug.ipynb | 1 | 1309 | {
"metadata": {
"name": "",
"signature": "sha256:9f1ab0f8e8a20eeaef194468452c8e5aa250c0197074136e491005a85567c9aa"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"''' Unit tests for ee_utils.py '''\n",
"\n",
"import unittest\n",
"import os\n",
"\n",
"\n",
"% run 'ee_utils.ipynb'\n",
"initialize_api()\n",
"\n",
"\n",
"\n",
"class TestProntoUtils(unittest.TestCase):\n",
"\n",
" # Did authetication work? \n",
" def testAuthentication(self):\n",
" try:\n",
" ee.Initialize()\n",
" print(\"Authentication successful!\")\n",
" except:\n",
" print('Authentication failed')\n",
"\n",
" #Did the metadata actually export and save? \n",
" def testMetadatasave(self):\n",
" self.assertTrue(os.path.exists('missco2_metadata.txt '), \"Failed to save image data.\")\n",
" \n",
"\n",
" \n",
"if __name__ == '__main__':\n",
" unittest.main()"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
goddoe/CADL | session-5/session-5-part-1-new.ipynb | 1 | 108546 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Session 5: Generative Networks\n",
"## Assignment: Generative Adversarial Networks and Recurrent Neural Networks\n",
"\n",
"<p class=\"lead\">\n",
"<a href=\"https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info\">Creative Applications of Deep Learning with Google's Tensorflow</a><br />\n",
"<a href=\"http://pkmital.com\">Parag K. Mital</a><br />\n",
"<a href=\"https://www.kadenze.com\">Kadenze, Inc.</a>\n",
"</p>\n",
"\n",
"# Table of Contents\n",
"\n",
"<!-- MarkdownTOC autolink=\"true\" autoanchor=\"true\" bracket=\"round\" -->\n",
"\n",
"- [Overview](#overview)\n",
"- [Learning Goals](#learning-goals)\n",
"- [Part 1 - Generative Adversarial Networks \\(GAN\\) / Deep Convolutional GAN \\(DCGAN\\)](#part-1---generative-adversarial-networks-gan--deep-convolutional-gan-dcgan)\n",
" - [Introduction](#introduction)\n",
" - [Building the Encoder](#building-the-encoder)\n",
" - [Building the Discriminator for the Training Samples](#building-the-discriminator-for-the-training-samples)\n",
" - [Building the Decoder](#building-the-decoder)\n",
" - [Building the Generator](#building-the-generator)\n",
" - [Building the Discriminator for the Generated Samples](#building-the-discriminator-for-the-generated-samples)\n",
" - [GAN Loss Functions](#gan-loss-functions)\n",
" - [Building the Optimizers w/ Regularization](#building-the-optimizers-w-regularization)\n",
" - [Loading a Dataset](#loading-a-dataset)\n",
" - [Training](#training)\n",
" - [Equilibrium](#equilibrium)\n",
"- [Part 2 - Variational Auto-Encoding Generative Adversarial Network \\(VAEGAN\\)](#part-2---variational-auto-encoding-generative-adversarial-network-vaegan)\n",
" - [Batch Normalization](#batch-normalization)\n",
" - [Building the Encoder](#building-the-encoder-1)\n",
" - [Building the Variational Layer](#building-the-variational-layer)\n",
" - [Building the Decoder](#building-the-decoder-1)\n",
" - [Building VAE/GAN Loss Functions](#building-vaegan-loss-functions)\n",
" - [Creating the Optimizers](#creating-the-optimizers)\n",
" - [Loading the Dataset](#loading-the-dataset)\n",
" - [Training](#training-1)\n",
"- [Part 3 - Latent-Space Arithmetic](#part-3---latent-space-arithmetic)\n",
" - [Loading the Pre-Trained Model](#loading-the-pre-trained-model)\n",
" - [Exploring the Celeb Net Attributes](#exploring-the-celeb-net-attributes)\n",
" - [Find the Latent Encoding for an Attribute](#find-the-latent-encoding-for-an-attribute)\n",
" - [Latent Feature Arithmetic](#latent-feature-arithmetic)\n",
" - [Extensions](#extensions)\n",
"- [Part 4 - Character-Level Language Model](session-5-part-2.ipynb#part-4---character-level-language-model)\n",
"- [Part 5 - Pretrained Char-RNN of Donald Trump](session-5-part-2.ipynb#part-5---pretrained-char-rnn-of-donald-trump)\n",
" - [Getting the Trump Data](session-5-part-2.ipynb#getting-the-trump-data)\n",
" - [Basic Text Analysis](session-5-part-2.ipynb#basic-text-analysis)\n",
" - [Loading the Pre-trained Trump Model](session-5-part-2.ipynb#loading-the-pre-trained-trump-model)\n",
" - [Inference: Keeping Track of the State](session-5-part-2.ipynb#inference-keeping-track-of-the-state)\n",
" - [Probabilistic Sampling](session-5-part-2.ipynb#probabilistic-sampling)\n",
" - [Inference: Temperature](session-5-part-2.ipynb#inference-temperature)\n",
" - [Inference: Priming](session-5-part-2.ipynb#inference-priming)\n",
"- [Assignment Submission](session-5-part-2.ipynb#assignment-submission)\n",
"<!-- /MarkdownTOC -->\n",
"\n",
"\n",
"<a name=\"overview\"></a>\n",
"# Overview\n",
"\n",
"This is certainly the hardest session and will require a lot of time and patience to complete. Also, many elements of this session may require further investigation, including reading of the original papers and additional resources in order to fully grasp their understanding. The models we cover are state of the art and I've aimed to give you something between a practical and mathematical understanding of the material, though it is a tricky balance. I hope for those interested, that you delve deeper into the papers for more understanding. And for those of you seeking just a practical understanding, that these notebooks will suffice.\n",
"\n",
"This session covered two of the most advanced generative networks: generative adversarial networks and recurrent neural networks. During the homework, we'll see how these work in more details and try building our own. I am not asking you train anything in this session as both GANs and RNNs take many days to train. However, I have provided pre-trained networks which we'll be exploring. We'll also see how a Variational Autoencoder can be combined with a Generative Adversarial Network to allow you to also encode input data, and I've provided a pre-trained model of this type of model trained on the Celeb Faces dataset. We'll see what this means in more details below.\n",
"\n",
"After this session, you are also required to submit your final project which can combine any of the materials you have learned so far to produce a short 1 minute clip demonstrating any aspect of the course you want to invesitgate further or combine with anything else you feel like doing. This is completely open to you and to encourage your peers to share something that demonstrates creative thinking. Be sure to keep the final project in mind while browsing through this notebook!\n",
"\n",
"<a name=\"learning-goals\"></a>\n",
"# Learning Goals\n",
"\n",
"* Learn to build the components of a Generative Adversarial Network and how it is trained\n",
"* Learn to combine the Variational Autoencoder with a Generative Adversarial Network\n",
"* Learn to use latent space arithmetic with a pre-trained VAE/GAN network\n",
"* Learn to build the components of a Character Recurrent Neural Network and how it is trained\n",
"* Learn to sample from a pre-trained CharRNN model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# First check the Python version\n",
"import sys\n",
"if sys.version_info < (3,4):\n",
" print('You are running an older version of Python!\\n\\n',\n",
" 'You should consider updating to Python 3.4.0 or',\n",
" 'higher as the libraries built for this course',\n",
" 'have only been tested in Python 3.4 and higher.\\n')\n",
" print('Try installing the Python 3.5 version of anaconda'\n",
" 'and then restart `jupyter notebook`:\\n',\n",
" 'https://www.continuum.io/downloads\\n\\n')\n",
"\n",
"# Now get necessary libraries\n",
"try:\n",
" import os\n",
" import numpy as np\n",
" import matplotlib.pyplot as plt\n",
" from skimage.transform import resize\n",
" from skimage import data\n",
" from scipy.misc import imresize\n",
" from scipy.ndimage.filters import gaussian_filter\n",
" import IPython.display as ipyd\n",
" import tensorflow as tf\n",
" from libs import utils, gif, datasets, dataset_utils, nb_utils\n",
"except ImportError as e:\n",
" print(\"Make sure you have started notebook in the same directory\",\n",
" \"as the provided zip file which includes the 'libs' folder\",\n",
" \"and the file 'utils.py' inside of it. You will NOT be able\",\n",
" \"to complete this assignment unless you restart jupyter\",\n",
" \"notebook inside the directory created by extracting\",\n",
" \"the zip file or cloning the github repo.\")\n",
" print(e)\n",
"\n",
"# We'll tell matplotlib to inline any drawn figures like so:\n",
"%matplotlib inline\n",
"plt.style.use('ggplot')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Bit of formatting because I don't like the default inline code style:\n",
"from IPython.core.display import HTML\n",
"HTML(\"\"\"<style> .rendered_html code { \n",
" padding: 2px 4px;\n",
" color: #c7254e;\n",
" background-color: #f9f2f4;\n",
" border-radius: 4px;\n",
"} </style>\"\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"part-1---generative-adversarial-networks-gan--deep-convolutional-gan-dcgan\"></a>\n",
"# Part 1 - Generative Adversarial Networks (GAN) / Deep Convolutional GAN (DCGAN)\n",
"\n",
"<a name=\"introduction\"></a>\n",
"## Introduction\n",
"\n",
"Recall from the lecture that a Generative Adversarial Network is two networks, a generator and a discriminator. The \"generator\" takes a feature vector and decodes this feature vector to become an image, exactly like the decoder we built in Session 3's Autoencoder. The discriminator is exactly like the encoder of the Autoencoder, except it can only have 1 value in the final layer. We use a sigmoid to squash this value between 0 and 1, and then interpret the meaning of it as: 1, the image you gave me was real, or 0, the image you gave me was generated by the generator, it's a FAKE! So the discriminator is like an encoder which takes an image and then perfoms lie detection. Are you feeding me lies? Or is the image real? \n",
"\n",
"Consider the AE and VAE we trained in Session 3. The loss function operated partly on the input space. It said, per pixel, what is the difference between my reconstruction and the input image? The l2-loss per pixel. Recall at that time we suggested that this wasn't the best idea because per-pixel differences aren't representative of our own perception of the image. One way to consider this is if we had the same image, and translated it by a few pixels. We would not be able to tell the difference, but the per-pixel difference between the two images could be enormously high.\n",
"\n",
"The GAN does not use per-pixel difference. Instead, it trains a distance function: the discriminator. The discriminator takes in two images, the real image and the generated one, and learns what a similar image should look like! That is really the amazing part of this network and has opened up some very exciting potential future directions for unsupervised learning. Another network that also learns a distance function is known as the siamese network. We didn't get into this network in this course, but it is commonly used in facial verification, or asserting whether two faces are the same or not.\n",
"\n",
"The GAN network is notoriously a huge pain to train! For that reason, we won't actually be training it. Instead, we'll discuss an extension to this basic network called the VAEGAN which uses the VAE we created in Session 3 along with the GAN. We'll then train that network in Part 2. For now, let's stick with creating the GAN.\n",
"\n",
"Let's first create the two networks: the discriminator and the generator. We'll first begin by building a general purpose encoder which we'll use for our discriminator. Recall that we've already done this in Session 3. What we want is for the input placeholder to be encoded using a list of dimensions for each of our encoder's layers. In the case of a convolutional network, our list of dimensions should correspond to the number of output filters. We also need to specify the kernel heights and widths for each layer's convolutional network.\n",
"\n",
"We'll first need a placeholder. This will be the \"real\" image input to the discriminator and the discrimintator will encode this image into a single value, 0 or 1, saying, yes this is real, or no, this is not real.\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# We'll keep a variable for the size of our image.\n",
"n_pixels = 32\n",
"n_channels = 3\n",
"input_shape = [None, n_pixels, n_pixels, n_channels]\n",
"\n",
"# And then create the input image placeholder\n",
"X = tf.placeholder(dtype=tf.float32, name='X', shape=[None, n_pixels, n_pixels,n_channels])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"building-the-encoder\"></a>\n",
"## Building the Encoder\n",
"\n",
"Let's build our encoder just like in Session 3. We'll create a function which accepts the input placeholder, a list of dimensions describing the number of convolutional filters in each layer, and a list of filter sizes to use for the kernel sizes in each convolutional layer. We'll also pass in a parameter for which activation function to apply.\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def encoder(x, channels, filter_sizes, activation=tf.nn.tanh, reuse=None):\n",
" # Set the input to a common variable name, h, for hidden layer\n",
" h = x\n",
"\n",
" # Now we'll loop over the list of dimensions defining the number\n",
" # of output filters in each layer, and collect each hidden layer\n",
" hs = []\n",
" for layer_i in range(len(channels)):\n",
" \n",
" with tf.variable_scope('layer{}'.format(layer_i+1), reuse=reuse):\n",
" # Convolve using the utility convolution function\n",
" # This requirs the number of output filter,\n",
" # and the size of the kernel in `k_h` and `k_w`.\n",
" # By default, this will use a stride of 2, meaning\n",
" # each new layer will be downsampled by 2.\n",
" h, W = utils.conv2d(h, channels[layer_i], k_h = filter_sizes[layer_i], k_w=filter_sizes[layer_i],reuse=reuse)\n",
"\n",
"\n",
" # Now apply the activation function\n",
" h = activation(h)\n",
" \n",
" # Store each hidden layer\n",
" hs.append(h)\n",
"\n",
" # Finally, return the encoding.\n",
" return h, hs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"building-the-discriminator-for-the-training-samples\"></a>\n",
"## Building the Discriminator for the Training Samples\n",
"\n",
"Finally, let's take the output of our encoder, and make sure it has just 1 value by using a fully connected layer. We can use the `libs/utils` module's, `linear` layer to do this, which will also reshape our 4-dimensional tensor to a 2-dimensional one prior to using the fully connected layer.\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def discriminator(X,\n",
" channels=[50, 50, 50, 50],\n",
" filter_sizes=[4, 4, 4, 4],\n",
" activation=utils.lrelu,\n",
" reuse=None):\n",
"\n",
" # We'll scope these variables to \"discriminator_real\"\n",
" with tf.variable_scope('discriminator', reuse=reuse):\n",
" # Encode X:\n",
" H, Hs = encoder(X, channels, filter_sizes, activation, reuse)\n",
" \n",
" # Now make one last layer with just 1 output. We'll\n",
" # have to reshape to 2-d so that we can create a fully\n",
" # connected layer:\n",
" shape = H.get_shape().as_list()\n",
" H = tf.reshape(H, [-1, shape[1] * shape[2] * shape[3]])\n",
" \n",
" # Now we can connect our 2D layer to a single neuron output w/\n",
" # a sigmoid activation:\n",
" D, W = utils.linear(H, 1, activation=tf.nn.sigmoid, reuse=reuse, name='FCNN')\n",
" return D"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's create the discriminator for the real training data coming from `X`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"D_real = discriminator(X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And we can see what the network looks like now:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"graph = tf.get_default_graph()\n",
"nb_utils.show_graph(graph.as_graph_def())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"building-the-decoder\"></a>\n",
"## Building the Decoder\n",
"\n",
"Now we're ready to build the Generator, or decoding network. This network takes as input a vector of features and will try to produce an image that looks like our training data. We'll send this synthesized image to our discriminator which we've just built above.\n",
"\n",
"Let's start by building the input to this network. We'll need a placeholder for the input features to this network. We have to be mindful of how many features we have. The feature vector for the Generator will eventually need to form an image. What we can do is create a 1-dimensional vector of values for each element in our batch, giving us `[None, n_features]`. We can then reshape this to a 4-dimensional Tensor so that we can build a decoder network just like in Session 3.\n",
"\n",
"But how do we assign the values from our 1-d feature vector (or 2-d tensor with Batch number of them) to the 3-d shape of an image (or 4-d tensor with Batch number of them)? We have to go from the number of features in our 1-d feature vector, let's say `n_latent` to `height x width x channels` through a series of convolutional transpose layers. One way to approach this is think of the reverse process. Starting from the final decoding of `height x width x channels`, I will use convolution with a stride of 2, so downsample by 2 with each new layer. So the second to last decoder layer would be, `height // 2 x width // 2 x ?`. If I look at it like this, I can use the variable `n_pixels` denoting the `height` and `width` to build my decoder, and set the channels to whatever I want.\n",
"\n",
"Let's start with just our 2-d placeholder which will have `None x n_features`, then convert it to a 4-d tensor ready for the decoder part of the network (a.k.a. the generator)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# We'll need some variables first. This will be how many\n",
"# channels our generator's feature vector has. Experiment w/\n",
"# this if you are training your own network.\n",
"n_code = 16\n",
"\n",
"# And in total how many feature it has, including the spatial dimensions.\n",
"n_latent = (n_pixels // 16) * (n_pixels // 16) * n_code\n",
"\n",
"# Let's build the 2-D placeholder, which is the 1-d feature vector for every\n",
"# element in our batch. We'll then reshape this to 4-D for the decoder.\n",
"Z = tf.placeholder(name='Z', shape=[None, n_latent], dtype=tf.float32)\n",
"\n",
"# Now we can reshape it to input to the decoder. Here we have to\n",
"# be mindful of the height and width as described before. We need\n",
"# to make the height and width a factor of the final height and width\n",
"# that we want. Since we are using strided convolutions of 2, then\n",
"# we can say with 4 layers, that first decoder's layer should be:\n",
"# n_pixels / 2 / 2 / 2 / 2, or n_pixels / 16:\n",
"Z_tensor = tf.reshape(Z, [-1, n_pixels // 16, n_pixels // 16, n_code])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll build the decoder in much the same way as we built our encoder. And exactly as we've done in Session 3! This requires one additional parameter \"channels\" which is how many output filters we want for each net layer. We'll interpret the `dimensions` as the height and width of the tensor in each new layer, the `channels` is how many output filters we want for each net layer, and the `filter_sizes` is the size of the filters used for convolution. We'll default to using a stride of two which will downsample each layer. We're also going to collect each hidden layer `h` in a list. We'll end up needing this for Part 2 when we combine the variational autoencoder w/ the generative adversarial network."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def decoder(z, dimensions, channels, filter_sizes,\n",
" activation=tf.nn.relu, reuse=None):\n",
" h = z\n",
" hs = []\n",
" for layer_i in range(len(dimensions)):\n",
" with tf.variable_scope('layer{}'.format(layer_i+1), reuse=reuse):\n",
" h, W = utils.deconv2d(x=h,\n",
" n_output_h=dimensions[layer_i],\n",
" n_output_w=dimensions[layer_i],\n",
" n_output_ch=channels[layer_i],\n",
" k_h=filter_sizes[layer_i],\n",
" k_w=filter_sizes[layer_i],\n",
" reuse=reuse)\n",
" h = activation(h)\n",
" hs.append(h)\n",
" return h, hs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"building-the-generator\"></a>\n",
"## Building the Generator\n",
"\n",
"Now we're ready to use our decoder to take in a vector of features and generate something that looks like our training images. We have to ensure that the last layer produces the same output shape as the discriminator's input. E.g. we used a `[None, 64, 64, 3]` input to the discriminator, so our generator needs to also output `[None, 64, 64, 3]` tensors. In other words, we have to ensure the last element in our `dimensions` list is 64, and the last element in our `channels` list is 3."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Explore these parameters.\n",
"def generator(Z,\n",
" dimensions=[n_pixels//8, n_pixels//4, n_pixels//2, n_pixels],\n",
" channels=[50, 50, 50, n_channels],\n",
" filter_sizes=[4, 4, 4, 4],\n",
" activation=utils.lrelu):\n",
"\n",
" with tf.variable_scope('generator'):\n",
" G, Hs = decoder(Z_tensor, dimensions, channels, filter_sizes, activation)\n",
"\n",
" return G"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's call the `generator` function with our input placeholder `Z`. This will take our feature vector and generate something in the shape of an image."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"G = generator(Z)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"graph = tf.get_default_graph()\n",
"nb_utils.show_graph(graph.as_graph_def())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"building-the-discriminator-for-the-generated-samples\"></a>\n",
"## Building the Discriminator for the Generated Samples\n",
"\n",
"Lastly, we need *another* discriminator which takes as input our generated images. Recall the discriminator that we have made only takes as input our placeholder `X` which is for our actual training samples. We'll use the same function for creating our discriminator and **reuse** the variables we already have. This is the crucial part! We aren't making *new* trainable variables, but reusing the ones we have. We just create a new set of operations that takes as input our generated image. So we'll have a whole new set of operations exactly like the ones we have created for our first discriminator. But we are going to use the exact same variables as our first discriminator, so that we optimize the same values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"D_fake = discriminator(G, reuse=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can look at the graph and see the new discriminator inside the node for the discriminator. You should see the original discriminator and a new graph of a discriminator within it, but all the weights are shared with the original discriminator."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nb_utils.show_graph(graph.as_graph_def())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"gan-loss-functions\"></a>\n",
"## GAN Loss Functions\n",
"\n",
"We now have all the components to our network. We just have to train it. This is the notoriously tricky bit. We will have 3 different loss measures instead of our typical network with just a single loss. We'll later connect each of these loss measures to two optimizers, one for the generator and another for the discriminator, and then pin them against each other and see which one wins! Exciting times!\n",
"\n",
"Recall from Session 3's Supervised Network, we created a binary classification task: music or speech. We again have a binary classification task: real or fake. So our loss metric will again use the binary cross entropy to measure the loss of our three different modules: the generator, the discriminator for our real images, and the discriminator for our generated images.\n",
"\n",
"To find out the loss function for our generator network, answer the question, what makes the generator successful? Successfully fooling the discriminator. When does that happen? When the discriminator for the fake samples produces all ones. So our binary cross entropy measure will measure the cross entropy with our predicted distribution and the true distribution which has all ones."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with tf.variable_scope('loss/generator'):\n",
" loss_G = tf.reduce_mean(utils.binary_cross_entropy(D_fake, tf.ones_like(D_fake)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What we've just written is a loss function for our generator. The generator is optimized when the discriminator for the generated samples produces all ones. In contrast to the generator, the discriminator will have 2 measures to optimize. One which is the opposite of what we have just written above, as well as 1 more measure for the real samples. Try writing these two losses and we'll combine them using their average. We want to optimize the Discriminator for the real samples producing all 1s, and the Discriminator for the fake samples producing all 0s:\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with tf.variable_scope('loss/discriminator/real'):\n",
" loss_D_real = utils.binary_cross_entropy(D_real, tf.zeros_like(D_real))\n",
"with tf.variable_scope('loss/discriminator/fake'):\n",
" loss_D_fake = utils.binary_cross_entropy(D_fake, tf.ones_like(D_fake))\n",
"with tf.variable_scope('loss/discriminator'):\n",
" loss_D = tf.reduce_mean((loss_D_real + loss_D_fake) / 2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nb_utils.show_graph(graph.as_graph_def())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With our loss functions, we can create an optimizer for the discriminator and generator:\n",
"\n",
"<a name=\"building-the-optimizers-w-regularization\"></a>\n",
"## Building the Optimizers w/ Regularization\n",
"\n",
"We're almost ready to create our optimizers. We just need to do one extra thing. Recall that our loss for our generator has a flow from the generator through the discriminator. If we are training both the generator and the discriminator, we have two measures which both try to optimize the discriminator, but in opposite ways: the generator's loss would try to optimize the discriminator to be bad at its job, and the discriminator's loss would try to optimize it to be good at its job. This would be counter-productive, trying to optimize opposing losses. What we want is for the generator to get better, and the discriminator to get better. Not for the discriminator to get better, then get worse, then get better, etc... The way we do this is when we optimize our generator, we let the gradient flow through the discriminator, but we do not update the variables in the discriminator. Let's try and grab just the discriminator variables and just the generator variables below:\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Grab just the variables corresponding to the discriminator\n",
"# and just the generator:\n",
"vars_d = [v for v in tf.trainable_variables()\n",
" if v.name.startswith('discriminator')]\n",
"print('Training discriminator variables:')\n",
"[print(v.name) for v in tf.trainable_variables()\n",
" if v.name.startswith('discriminator')]\n",
"\n",
"vars_g = [v for v in tf.trainable_variables()\n",
" if v.name.startswith('generator')\n",
"print('Training generator variables:')\n",
"[print(v.name) for v in tf.trainable_variables()\n",
" if v.name.startswith('generator')]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also apply regularization to our network. This will penalize weights in the network for growing too large."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"d_reg = tf.contrib.layers.apply_regularization(\n",
" tf.contrib.layers.l2_regularizer(1e-6), vars_d)\n",
"g_reg = tf.contrib.layers.apply_regularization(\n",
" tf.contrib.layers.l2_regularizer(1e-6), vars_g)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The last thing you may want to try is creating a separate learning rate for each of your generator and discriminator optimizers like so:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"learning_rate = 0.0001\n",
"\n",
"lr_g = tf.placeholder(tf.float32, shape=[], name='learning_rate_g')\n",
"lr_d = tf.placeholder(tf.float32, shape=[], name='learning_rate_d')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you can feed the placeholders to your optimizers. If you run into errors creating these, then you likely have a problem with your graph's definition! Be sure to go back and reset the default graph and check the sizes of your different operations/placeholders.\n",
"\n",
"With your optimizers, you can now train the network by \"running\" the optimizer variables with your session. You'll need to set the `var_list` parameter of the `minimize` function to only train the variables for the discriminator and same for the generator's optimizer:\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"opt_g = tf.train.AdamOptimizer(learning_rate=lr_g).minimize(loss_G + g_reg, var_list=vars_g)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"opt_d = tf.train.AdamOptimizer(learning_rate=lr_d).minimize(loss_D + d_reg, var_list=vars_d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"loading-a-dataset\"></a>\n",
"## Loading a Dataset\n",
"\n",
"Let's use the Celeb Dataset just for demonstration purposes. In Part 2, you can explore using your own dataset. This code is exactly the same as we did in Session 3's homework with the VAE.\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# You'll want to change this to your own data if you end up training your own GAN.\n",
"batch_size = 64\n",
"n_epochs = 1\n",
"crop_shape = [n_pixels, n_pixels, 3]\n",
"crop_factor = 0.8\n",
"input_shape = [218, 178, 3]\n",
"\n",
"files = datasets.CELEB()\n",
"batch = dataset_utils.create_input_pipeline(\n",
" files=files,\n",
" batch_size=batch_size,\n",
" n_epochs=n_epochs,\n",
" crop_shape=crop_shape,\n",
" crop_factor=crop_factor,\n",
" shape=input_shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"training\"></a>\n",
"## Training\n",
"\n",
"We'll now go through the setup of training the network. We won't actually spend the time to train the network but just see how it would be done. This is because in Part 2, we'll see an extension to this network which makes it much easier to train."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ckpt_name = './gan.ckpt'\n",
"\n",
"sess = tf.Session()\n",
"saver = tf.train.Saver()\n",
"sess.run(tf.global_variables_initializer())\n",
"coord = tf.train.Coordinator()\n",
"tf.get_default_graph().finalize()\n",
"threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n",
"\n",
"if os.path.exists(ckpt_name + '.index') or os.path.exists(ckpt_name):\n",
" saver.restore(sess, ckpt_name)\n",
" print(\"VAE model restored.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_examples = 10\n",
"\n",
"zs = np.random.uniform(0.0, 1.0, [4, n_latent]).astype(np.float32)\n",
"zs = utils.make_latent_manifold(zs, n_examples)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"equilibrium\"></a>\n",
"## Equilibrium\n",
"\n",
" Equilibrium is at 0.693. Why? Consider what the cost is measuring, the binary cross entropy. If we have random guesses, then we have as many 0s as we have 1s. And on average, we'll be 50% correct. The binary cross entropy is:\n",
"\n",
"\\begin{align}\n",
"\\sum_i \\text{X}_i * \\text{log}(\\tilde{\\text{X}}_i) + (1 - \\text{X}_i) * \\text{log}(1 - \\tilde{\\text{X}}_i)\n",
"\\end{align}\n",
"\n",
"Which is written out in tensorflow as:"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"```python\n",
"(-(x * tf.log(z) + (1. - x) * tf.log(1. - z)))\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Where `x` is the discriminator's prediction of the true distribution, in the case of GANs, the input images, and `z` is the discriminator's prediction of the generated images corresponding to the mathematical notation of $\\tilde{\\text{X}}$. We sum over all features, but in the case of the discriminator, we have just 1 feature, the guess of whether it is a true image or not. If our discriminator guesses at chance, i.e. 0.5, then we'd have something like:\n",
"\n",
"\\begin{align}\n",
"0.5 * \\text{log}(0.5) + (1 - 0.5) * \\text{log}(1 - 0.5) = -0.693\n",
"\\end{align}\n",
"\n",
"So this is what we'd expect at the start of learning and from a game theoretic point of view, where we want things to remain. So unlike our previous networks, where our loss continues to drop closer and closer to 0, we want our loss to waver around this value as much as possible, and hope for the best."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"equilibrium = 0.693\n",
"margin = 0.2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we go to train the network, we switch back and forth between each optimizer, feeding in the appropriate values for each optimizer. The `opt_g` optimizer only requires the `Z` and `lr_g` placeholders, while the `opt_d` optimizer requires the `X`, `Z`, and `lr_d` placeholders.\n",
"\n",
"Don't train this network for very long because GANs are a huge pain to train and require a lot of fiddling. They very easily get stuck in their adversarial process, or get overtaken by one or the other, resulting in a useless model. What you need to develop is a steady equilibrium that optimizes both. That will likely take two weeks just trying to get the GAN to train and not have enough time for the rest of the assignment. They require a lot of memory/cpu and can take many days to train once you have settled on an architecture/training process/dataset. Just let it run for a short time and then interrupt the kernel (don't restart!), then continue to the next cell.\n",
"\n",
"From there, we'll go over an extension to the GAN which uses a VAE like we used in Session 3. By using this extra network, we can actually train a better model in a fraction of the time and with much more ease! But the network's definition is a bit more complicated. Let's see how the GAN is trained first and then we'll train the VAE/GAN network instead. While training, the \"real\" and \"fake\" cost will be printed out. See how this cost wavers around the equilibrium and how we enforce it to try and stay around there by including a margin and some simple logic for updates. This is highly experimental and the research does not have a good answer for the best practice on how to train a GAN. I.e., some people will set the learning rate to some ratio of the performance between fake/real networks, others will have a fixed update schedule but train the generator twice and the discriminator only once."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"t_i = 0\n",
"batch_i = 0\n",
"epoch_i = 0\n",
"n_files = len(files)\n",
"\n",
"if not os.path.exists('imgs'):\n",
" os.makedirs('imgs')\n",
" \n",
"while epoch_i < n_epochs:\n",
"\n",
" batch_i += 1\n",
" batch_xs = sess.run(batch) / 255.0\n",
" batch_zs = np.random.uniform(\n",
" 0.0, 1.0, [batch_size, n_latent]).astype(np.float32)\n",
"\n",
" real_cost, fake_cost = sess.run([\n",
" loss_D_real, loss_D_fake],\n",
" feed_dict={\n",
" X: batch_xs,\n",
" Z: batch_zs})\n",
" real_cost = np.mean(real_cost)\n",
" fake_cost = np.mean(fake_cost)\n",
" \n",
" if (batch_i % 20) == 0:\n",
" print(batch_i, 'real:', real_cost, '/ fake:', fake_cost)\n",
"\n",
" gen_update = True\n",
" dis_update = True\n",
"\n",
" if real_cost > (equilibrium + margin) or \\\n",
" fake_cost > (equilibrium + margin):\n",
" gen_update = False\n",
"\n",
" if real_cost < (equilibrium - margin) or \\\n",
" fake_cost < (equilibrium - margin):\n",
" dis_update = False\n",
"\n",
" if not (gen_update or dis_update):\n",
" gen_update = True\n",
" dis_update = True\n",
"\n",
" if gen_update:\n",
" sess.run(opt_g,\n",
" feed_dict={\n",
" Z: batch_zs,\n",
" lr_g: learning_rate})\n",
" if dis_update:\n",
" sess.run(opt_d,\n",
" feed_dict={\n",
" X: batch_xs,\n",
" Z: batch_zs,\n",
" lr_d: learning_rate})\n",
"\n",
" if batch_i % (n_files // batch_size) == 0:\n",
" batch_i = 0\n",
" epoch_i += 1\n",
" print('---------- EPOCH:', epoch_i)\n",
" \n",
" # Plot example reconstructions from latent layer\n",
" recon = sess.run(G, feed_dict={Z: zs})\n",
"\n",
" recon = np.clip(recon, 0, 1)\n",
" m1 = utils.montage(recon.reshape([-1] + crop_shape),\n",
" 'imgs/manifold_%08d.png' % t_i)\n",
"\n",
" recon = sess.run(G, feed_dict={Z: batch_zs})\n",
"\n",
" recon = np.clip(recon, 0, 1)\n",
" m2 = utils.montage(recon.reshape([-1] + crop_shape),\n",
" 'imgs/reconstructions_%08d.png' % t_i)\n",
" \n",
" fig, axs = plt.subplots(1, 2, figsize=(15, 10))\n",
" axs[0].imshow(m1)\n",
" axs[1].imshow(m2)\n",
" plt.show()\n",
" t_i += 1\n",
"\n",
" # Save the variables to disk.\n",
" save_path = saver.save(sess, \"./\" + ckpt_name,\n",
" global_step=batch_i,\n",
" write_meta_graph=False)\n",
" print(\"Model saved in file: %s\" % save_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Tell all the threads to shutdown.\n",
"coord.request_stop()\n",
"\n",
"# Wait until all threads have finished.\n",
"coord.join(threads)\n",
"\n",
"# Clean up the session.\n",
"sess.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"part-2---variational-auto-encoding-generative-adversarial-network-vaegan\"></a>\n",
"# Part 2 - Variational Auto-Encoding Generative Adversarial Network (VAEGAN)\n",
"\n",
"In our definition of the generator, we started with a feature vector, `Z`. This feature vector was not connected to anything before it. Instead, we had to randomly create its values using a random number generator of its `n_latent` values from -1 to 1, and this range was chosen arbitrarily. It could have been 0 to 1, or -3 to 3, or 0 to 100. In any case, the network would have had to learn to transform those values into something that looked like an image. There was no way for us to take an image, and find the feature vector that created it. In other words, it was not possible for us to *encode* an image.\n",
"\n",
"The closest thing to an encoding we had was taking an image and feeding it to the discriminator, which would output a 0 or 1. But what if we had another network that allowed us to encode an image, and then we used this network for both the discriminator and generative parts of the network? That's the basic idea behind the VAEGAN: https://arxiv.org/abs/1512.09300. It is just like the regular GAN, except we also use an encoder to create our feature vector `Z`.\n",
"\n",
"We then get the best of both worlds: a GAN that looks more or less the same, but uses the encoding from an encoder instead of an arbitrary feature vector; and an autoencoder that can model an input distribution using a trained distance function, the discriminator, leading to nicer encodings/decodings.\n",
"\n",
"Let's try to build it! Refer to the paper for the intricacies and a great read. Luckily, by building the `encoder` and `decoder` functions, we're almost there. We just need a few more components and will change these slightly.\n",
"\n",
"Let's reset our graph and recompose our network as a VAEGAN:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tf.reset_default_graph()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"batch-normalization\"></a>\n",
"## Batch Normalization\n",
"\n",
"You may have noticed from the `VAE` code that I've used something called \"batch normalization\". This is a pretty effective technique for regularizing the training of networks by \"reducing internal covariate shift\". The basic idea is that given a minibatch, we optimize the gradient for this small sample of the greater population. But this small sample may have different characteristics than the entire population's gradient. Consider the most extreme case, a minibatch of 1. In this case, we overfit our gradient to optimize the gradient of the single observation. If our minibatch is too large, say the size of the entire population, we aren't able to manuvuer the loss manifold at all and the entire loss is averaged in a way that doesn't let us optimize anything. What we want to do is find a happy medium between a too-smooth loss surface (i.e. every observation), and a very peaky loss surface (i.e. a single observation). Up until now we only used mini-batches to help with this. But we can also approach it by \"smoothing\" our updates between each mini-batch. That would effectively smooth the manifold of the loss space. Those of you familiar with signal processing will see this as a sort of low-pass filter on the gradient updates.\n",
"\n",
"In order for us to use batch normalization, we need another placeholder which is a simple boolean: True or False, denoting when we are training. We'll use this placeholder to conditionally update batch normalization's statistics required for normalizing our minibatches. Let's create the placeholder and then I'll get into how to use this."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# placeholder for batch normalization\n",
"is_training = tf.placeholder(tf.bool, name='istraining')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The original paper that introduced the idea suggests to use batch normalization \"pre-activation\", meaning after the weight multipllication or convolution, and before the nonlinearity. We can use the `tensorflow.contrib.layers.batch_norm` module to apply batch normalization to any input tensor give the tensor and the placeholder defining whether or not we are training. Let's use this module and you can inspect the code inside the module in your own time if it interests you."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function batch_norm in module tensorflow.contrib.layers.python.layers.layers:\n",
"\n",
"batch_norm(inputs, decay=0.999, center=True, scale=False, epsilon=0.001, activation_fn=None, param_initializers=None, updates_collections='update_ops', is_training=True, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, batch_weights=None, fused=False, data_format='NHWC', scope=None)\n",
" Adds a Batch Normalization layer from http://arxiv.org/abs/1502.03167.\n",
" \n",
" \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
" Internal Covariate Shift\"\n",
" \n",
" Sergey Ioffe, Christian Szegedy\n",
" \n",
" Can be used as a normalizer function for conv2d and fully_connected.\n",
" \n",
" Note: When is_training is True the moving_mean and moving_variance need to be\n",
" updated, by default the update_ops are placed in `tf.GraphKeys.UPDATE_OPS` so\n",
" they need to be added as a dependency to the `train_op`, example:\n",
" \n",
" update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
" if update_ops:\n",
" updates = tf.group(*update_ops)\n",
" total_loss = control_flow_ops.with_dependencies([updates], total_loss)\n",
" \n",
" One can set updates_collections=None to force the updates in place, but that\n",
" can have speed penalty, specially in distributed settings.\n",
" \n",
" Args:\n",
" inputs: a tensor with 2 or more dimensions, where the first dimension has\n",
" `batch_size`. The normalization is over all but the last dimension if\n",
" `data_format` is `NHWC` and the second dimension if `data_format` is\n",
" `NCHW`.\n",
" decay: decay for the moving average. Reasonable values for `decay` are close \n",
" to 1.0, typically in the multiple-nines range: 0.999, 0.99, 0.9, etc. Lower \n",
" `decay` value (recommend trying `decay`=0.9) if model experiences reasonably \n",
" good training performance but poor validation and/or test performance.\n",
" center: If True, subtract `beta`. If False, `beta` is ignored.\n",
" scale: If True, multiply by `gamma`. If False, `gamma` is\n",
" not used. When the next layer is linear (also e.g. `nn.relu`), this can be\n",
" disabled since the scaling can be done by the next layer.\n",
" epsilon: small float added to variance to avoid dividing by zero.\n",
" activation_fn: activation function, default set to None to skip it and\n",
" maintain a linear activation.\n",
" param_initializers: optional initializers for beta, gamma, moving mean and\n",
" moving variance.\n",
" updates_collections: collections to collect the update ops for computation.\n",
" The updates_ops need to be executed with the train_op.\n",
" If None, a control dependency would be added to make sure the updates are\n",
" computed in place.\n",
" is_training: whether or not the layer is in training mode. In training mode\n",
" it would accumulate the statistics of the moments into `moving_mean` and\n",
" `moving_variance` using an exponential moving average with the given\n",
" `decay`. When it is not in training mode then it would use the values of\n",
" the `moving_mean` and the `moving_variance`.\n",
" reuse: whether or not the layer and its variables should be reused. To be\n",
" able to reuse the layer scope must be given.\n",
" variables_collections: optional collections for the variables.\n",
" outputs_collections: collections to add the outputs.\n",
" trainable: If `True` also add variables to the graph collection\n",
" `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`).\n",
" batch_weights: An optional tensor of shape `[batch_size]`,\n",
" containing a frequency weight for each batch item. If present,\n",
" then the batch normalization uses weighted mean and\n",
" variance. (This can be used to correct for bias in training\n",
" example selection.)\n",
" fused: Use nn.fused_batch_norm if True, nn.batch_normalization otherwise.\n",
" data_format: A string. `NHWC` (default) and `NCHW` are supported.\n",
" scope: Optional scope for `variable_scope`.\n",
" \n",
" Returns:\n",
" A `Tensor` representing the output of the operation.\n",
" \n",
" Raises:\n",
" ValueError: if `batch_weights` is not None and `fused` is True.\n",
" ValueError: if `data_format` is neither `NHWC` nor `NCHW`.\n",
" ValueError: if the rank of `inputs` is undefined.\n",
" ValueError: if rank or channels dimension of `inputs` is undefined.\n",
"\n"
]
}
],
"source": [
"from tensorflow.contrib.layers import batch_norm\n",
"help(batch_norm)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"building-the-encoder-1\"></a>\n",
"## Building the Encoder\n",
"\n",
"We can now change our encoder to accept the `is_training` placeholder and apply `batch_norm` just before the activation function is applied:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def encoder(x, is_training, channels, filter_sizes, activation=tf.nn.tanh, reuse=None):\n",
" # Set the input to a common variable name, h, for hidden layer\n",
" h = x\n",
"\n",
" print('encoder/input:', h.get_shape().as_list())\n",
" # Now we'll loop over the list of dimensions defining the number\n",
" # of output filters in each layer, and collect each hidden layer\n",
" hs = []\n",
" for layer_i in range(len(channels)):\n",
" \n",
" with tf.variable_scope('layer{}'.format(layer_i+1), reuse=reuse):\n",
" # Convolve using the utility convolution function\n",
" # This requirs the number of output filter,\n",
" # and the size of the kernel in `k_h` and `k_w`.\n",
" # By default, this will use a stride of 2, meaning\n",
" # each new layer will be downsampled by 2.\n",
" h, W = utils.conv2d(h, channels[layer_i],\n",
" k_h=filter_sizes[layer_i],\n",
" k_w=filter_sizes[layer_i],\n",
" d_h=2,\n",
" d_w=2,\n",
" reuse=reuse)\n",
" \n",
" h = batch_norm(h, is_training=is_training)\n",
"\n",
" # Now apply the activation function\n",
" h = activation(h)\n",
" print('layer:', layer_i, ', shape:', h.get_shape().as_list())\n",
" \n",
" # Store each hidden layer\n",
" hs.append(h)\n",
"\n",
" # Finally, return the encoding.\n",
" return h, hs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's now create the input to the network using a placeholder. We can try a slightly larger image this time. But be careful experimenting with much larger images as this is a big network.\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_pixels = 64\n",
"n_channels = 3\n",
"input_shape = [None, n_pixels, n_pixels, n_channels]\n",
"\n",
"# placeholder for the input to the network\n",
"X = tf.placeholder(...)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now we'll connect the input to an encoder network. We'll also use the `tf.nn.elu` activation instead. Explore other activations but I've found this to make the training much faster (e.g. 10x faster at least!). See the paper for more details: [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)\n",
"](http://arxiv.org/abs/1511.07289)\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"channels = [64, 64, 64]\n",
"filter_sizes = [5, 5, 5]\n",
"activation = tf.nn.elu\n",
"n_hidden = 128\n",
"\n",
"with tf.variable_scope('encoder'):\n",
" H, Hs = encoder(...\n",
" Z = utils.linear(H, n_hidden)[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"building-the-variational-layer\"></a>\n",
"## Building the Variational Layer\n",
"\n",
"In Session 3, we introduced the idea of Variational Bayes when we used the Variational Auto Encoder. The variational bayesian approach requires a richer understanding of probabilistic graphical models and bayesian methods which we we're not able to go over in this course (it requires a few courses all by itself!). For that reason, please treat this as a \"black box\" in this course.\n",
"\n",
"For those of you that are more familiar with graphical models, Variational Bayesian methods attempt to model an approximate joint distribution of $Q(Z)$ using some distance function to the true distribution $P(X)$. Kingma and Welling show how this approach can be used in a graphical model resembling an autoencoder and can be trained using KL-Divergence, or $KL(Q(Z) || P(X))$. The distribution Q(Z) is the variational distribution, and attempts to model the lower-bound of the true distribution $P(X)$ through the minimization of the KL-divergence. Another way to look at this is the encoder of the network is trying to model the parameters of a known distribution, the Gaussian Distribution, through a minimization of this lower bound. We assume that this distribution resembles the true distribution, but it is merely a simplification of the true distribution. To learn more about this, I highly recommend picking up the book by Christopher Bishop called \"Pattern Recognition and Machine Learning\" and reading the original Kingma and Welling paper on Variational Bayes.\n",
"\n",
"Now back to coding, we'll create a general variational layer that does exactly the same thing as our VAE in session 3. Treat this as a black box if you are unfamiliar with the math. It takes an input encoding, `h`, and an integer, `n_code` defining how many latent Gaussians to use to model the latent distribution. In return, we get the latent encoding from sampling the Gaussian layer, `z`, the mean and log standard deviation, as well as the prior loss, `loss_z`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def variational_bayes(h, n_code):\n",
" # Model mu and log(\\sigma)\n",
" z_mu = tf.nn.tanh(utils.linear(h, n_code, name='mu')[0])\n",
" z_log_sigma = 0.5 * tf.nn.tanh(utils.linear(h, n_code, name='log_sigma')[0])\n",
"\n",
" # Sample from noise distribution p(eps) ~ N(0, 1)\n",
" epsilon = tf.random_normal(tf.stack([tf.shape(h)[0], n_code]))\n",
"\n",
" # Sample from posterior\n",
" z = z_mu + tf.multiply(epsilon, tf.exp(z_log_sigma))\n",
"\n",
" # Measure loss\n",
" loss_z = -0.5 * tf.reduce_sum(\n",
" 1.0 + 2.0 * z_log_sigma - tf.square(z_mu) - tf.exp(2.0 * z_log_sigma),\n",
" 1)\n",
"\n",
" return z, z_mu, z_log_sigma, loss_z"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's connect this layer to our encoding, and keep all the variables it returns. Treat this as a black box if you are unfamiliar with variational bayes!\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Experiment w/ values between 2 - 100\n",
"# depending on how difficult the dataset is\n",
"n_code = 32\n",
"\n",
"with tf.variable_scope('encoder/variational'):\n",
" Z, Z_mu, Z_log_sigma, loss_Z = variational_bayes(h=Z, n_code=n_code)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"building-the-decoder-1\"></a>\n",
"## Building the Decoder\n",
"\n",
"In the GAN network, we built a decoder and called it the generator network. Same idea here. We can use these terms interchangeably. Before we connect our latent encoding, `Z` to the decoder, we'll implement batch norm in our decoder just like we did with the encoder. This is a simple fix: add a second argument for `is_training` and then apply batch normalization just after the `deconv2d` operation and just before the nonlinear `activation`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def decoder(z, is_training, dimensions, channels, filter_sizes,\n",
" activation=tf.nn.elu, reuse=None):\n",
" h = z\n",
" for layer_i in range(len(dimensions)):\n",
" with tf.variable_scope('layer{}'.format(layer_i+1), reuse=reuse):\n",
" h, W = utils.deconv2d(x=h,\n",
" n_output_h=dimensions[layer_i],\n",
" n_output_w=dimensions[layer_i],\n",
" n_output_ch=channels[layer_i],\n",
" k_h=filter_sizes[layer_i],\n",
" k_w=filter_sizes[layer_i],\n",
" reuse=reuse)\n",
" h = batch_norm(h, is_training=is_training)\n",
" h = activation(h)\n",
" return h"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll build a decoder just like in Session 3, and just like our Generator network in Part 1. In Part 1, we created `Z` as a placeholder which we would have had to feed in as random values. However, now we have an explicit coding of an input image in `X` stored in `Z` by having created the encoder network."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dimensions = [n_pixels // 8, n_pixels // 4, n_pixels // 2, n_pixels]\n",
"channels = [30, 30, 30, n_channels]\n",
"filter_sizes = [4, 4, 4, 4]\n",
"activation = tf.nn.elu\n",
"n_latent = n_code * (n_pixels // 16)**2\n",
"\n",
"with tf.variable_scope('generator'):\n",
" Z_decode = utils.linear(\n",
" Z, n_output=n_latent, name='fc', activation=activation)[0]\n",
" Z_decode_tensor = tf.reshape(\n",
" Z_decode, [-1, n_pixels//16, n_pixels//16, n_code], name='reshape')\n",
" G = decoder(\n",
" Z_decode_tensor, is_training, dimensions,\n",
" channels, filter_sizes, activation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we need to build our discriminators. We'll need to add a parameter for the `is_training` placeholder. We're also going to keep track of every hidden layer in the discriminator. Our encoder already returns the `Hs` of each layer. Alternatively, we could poll the graph for each layer in the discriminator and ask for the correspond layer names. We're going to need these layers when building our costs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def discriminator(X,\n",
" is_training,\n",
" channels=[50, 50, 50, 50],\n",
" filter_sizes=[4, 4, 4, 4],\n",
" activation=tf.nn.elu,\n",
" reuse=None):\n",
"\n",
" # We'll scope these variables to \"discriminator_real\"\n",
" with tf.variable_scope('discriminator', reuse=reuse):\n",
" H, Hs = encoder(\n",
" X, is_training, channels, filter_sizes, activation, reuse)\n",
" shape = H.get_shape().as_list()\n",
" H = tf.reshape(\n",
" H, [-1, shape[1] * shape[2] * shape[3]])\n",
" D, W = utils.linear(\n",
" x=H, n_output=1, activation=tf.nn.sigmoid, name='fc', reuse=reuse)\n",
" return D, Hs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recall the regular GAN and DCGAN required 2 discriminators: one for the generated samples in `Z`, and one for the input samples in `X`. We'll do the same thing here. One discriminator for the real input data, `X`, which the discriminator will try to predict as 1s, and another discriminator for the generated samples that go from `X` through the encoder to `Z`, and finally through the decoder to `G`. The discriminator will be trained to try and predict these as 0s, whereas the generator will be trained to try and predict these as 1s."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"D_real, Hs_real = discriminator(X, is_training)\n",
"D_fake, Hs_fake = discriminator(G, is_training, reuse=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"building-vaegan-loss-functions\"></a>\n",
"## Building VAE/GAN Loss Functions\n",
"\n",
"Let's now see how we can compose our loss. We have 3 losses for our discriminator. Along with measuring the binary cross entropy between each of them, we're going to also measure each layer's loss from our two discriminators using an l2-loss, and this will form our loss for the log likelihood measure. The details of how these are constructed are explained in more details in the paper: https://arxiv.org/abs/1512.09300 - please refer to this paper for more details that are way beyond the scope of this course! One parameter within this to pay attention to is `gamma`, which the authors of the paper suggest control the weighting between content and style, just like in Session 4's Style Net implementation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with tf.variable_scope('loss'):\n",
" # Loss functions\n",
" loss_D_llike = 0\n",
" for h_real, h_fake in zip(Hs_real, Hs_fake):\n",
" loss_D_llike += tf.reduce_sum(tf.squared_difference(\n",
" utils.flatten(h_fake), utils.flatten(h_real)), 1)\n",
"\n",
" eps = 1e-12\n",
" loss_real = tf.log(D_real + eps)\n",
" loss_fake = tf.log(1 - D_fake + eps)\n",
" loss_GAN = tf.reduce_sum(loss_real + loss_fake, 1)\n",
" \n",
" gamma = 0.75\n",
" loss_enc = tf.reduce_mean(loss_Z + loss_D_llike)\n",
" loss_dec = tf.reduce_mean(gamma * loss_D_llike - loss_GAN)\n",
" loss_dis = -tf.reduce_mean(loss_GAN)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nb_utils.show_graph(tf.get_default_graph().as_graph_def())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"creating-the-optimizers\"></a>\n",
"## Creating the Optimizers\n",
"\n",
"We now have losses for our encoder, decoder, and discriminator networks. We can connect each of these to their own optimizer and start training! Just like with Part 1's GAN, we'll ensure each network's optimizer only trains its part of the network: the encoder's optimizer will only update the encoder variables, the generator's optimizer will only update the generator variables, and the discriminator's optimizer will only update the discriminator variables.\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"learning_rate = 0.0001\n",
"\n",
"opt_enc = tf.train.AdamOptimizer(\n",
" learning_rate=learning_rate).minimize(\n",
" loss_enc,\n",
" var_list=[var_i for var_i in tf.trainable_variables()\n",
" if ...])\n",
"\n",
"opt_gen = tf.train.AdamOptimizer(\n",
" learning_rate=learning_rate).minimize(\n",
" loss_dec,\n",
" var_list=[var_i for var_i in tf.trainable_variables()\n",
" if ...])\n",
"\n",
"opt_dis = tf.train.AdamOptimizer(\n",
" learning_rate=learning_rate).minimize(\n",
" loss_dis,\n",
" var_list=[var_i for var_i in tf.trainable_variables()\n",
" if var_i.name.startswith('discriminator')])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"loading-the-dataset\"></a>\n",
"## Loading the Dataset\n",
"\n",
"We'll now load our dataset just like in Part 1. Here is where you should explore with your own data!\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from libs import datasets, dataset_utils\n",
"\n",
"batch_size = 64\n",
"n_epochs = 100\n",
"crop_shape = [n_pixels, n_pixels, n_channels]\n",
"crop_factor = 0.8\n",
"input_shape = [218, 178, 3]\n",
"\n",
"# Try w/ CELEB first to make sure it works, then explore w/ your own dataset.\n",
"files = datasets.CELEB()\n",
"batch = dataset_utils.create_input_pipeline(\n",
" files=files,\n",
" batch_size=batch_size,\n",
" n_epochs=n_epochs,\n",
" crop_shape=crop_shape,\n",
" crop_factor=crop_factor,\n",
" shape=input_shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll also create a latent manifold just like we've done in Session 3 and Part 1. This is a random sampling of 4 points in the latent space of `Z`. We then interpolate between them to create a \"hyper-plane\" and show the decoding of 10 x 10 points on that hyperplane."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_samples = 10\n",
"zs = np.random.uniform(\n",
" -1.0, 1.0, [4, n_code]).astype(np.float32)\n",
"zs = utils.make_latent_manifold(zs, n_samples)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now create a session and create a coordinator to manage our queues for fetching data from the input pipeline and start our queue runners:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# We create a session to use the graph\n",
"sess = tf.Session()\n",
"init_op = tf.global_variables_initializer()\n",
"\n",
"saver = tf.train.Saver()\n",
"coord = tf.train.Coordinator()\n",
"threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n",
"sess.run(init_op)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load an existing checkpoint if it exists to continue training."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"if os.path.exists(\"vaegan.ckpt\"):\n",
" saver.restore(sess, \"vaegan.ckpt\")\n",
" print(\"GAN model restored.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll also try resynthesizing a test set of images. This will help us understand how well the encoder/decoder network is doing:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_files = len(files)\n",
"test_xs = sess.run(batch) / 255.0\n",
"if not os.path.exists('imgs'):\n",
" os.mkdir('imgs')\n",
"m = utils.montage(test_xs, 'imgs/test_xs.png')\n",
"plt.imshow(m)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"training-1\"></a>\n",
"## Training\n",
"\n",
"Almost ready for training. Let's get some variables which we'll need. These are the same as Part 1's training process. We'll keep track of `t_i` which we'll use to create images of the current manifold and reconstruction every so many iterations. And we'll keep track of the current batch number within the epoch and the current epoch number."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"t_i = 0\n",
"batch_i = 0\n",
"epoch_i = 0\n",
"ckpt_name = './vaegan.ckpt'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Just like in Part 1, we'll train trying to maintain an equilibrium between our Generator and Discriminator networks. You should experiment with the margin depending on how the training proceeds."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"equilibrium = 0.693\n",
"margin = 0.4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll train! Just like Part 1, we measure the `real_cost` and `fake_cost`. But this time, we'll always update the encoder. Based on the performance of the real/fake costs, then we'll update generator and discriminator networks. This will take a long time to produce something nice, but not nearly as long as the regular GAN network despite the additional parameters of the encoder and variational networks. Be sure to monitor the reconstructions to understand when your network has reached the capacity of its learning! For reference, on Celeb Net, I would use about 5 layers in each of the Encoder, Generator, and Discriminator networks using as input a 100 x 100 image, and a minimum of 200 channels per layer. This network would take about 1-2 days to train on an Nvidia TITAN X GPU."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"while epoch_i < n_epochs:\n",
" if batch_i % (n_files // batch_size) == 0:\n",
" batch_i = 0\n",
" epoch_i += 1\n",
" print('---------- EPOCH:', epoch_i)\n",
"\n",
" batch_i += 1\n",
" batch_xs = sess.run(batch) / 255.0\n",
" real_cost, fake_cost, _ = sess.run([\n",
" loss_real, loss_fake, opt_enc],\n",
" feed_dict={\n",
" X: batch_xs,\n",
" is_training: True})\n",
" real_cost = -np.mean(real_cost)\n",
" fake_cost = -np.mean(fake_cost)\n",
"\n",
" gen_update = True\n",
" dis_update = True\n",
"\n",
" if real_cost > (equilibrium + margin) or \\\n",
" fake_cost > (equilibrium + margin):\n",
" gen_update = False\n",
"\n",
" if real_cost < (equilibrium - margin) or \\\n",
" fake_cost < (equilibrium - margin):\n",
" dis_update = False\n",
"\n",
" if not (gen_update or dis_update):\n",
" gen_update = True\n",
" dis_update = True\n",
"\n",
" if gen_update:\n",
" sess.run(opt_gen, feed_dict={\n",
" X: batch_xs,\n",
" is_training: True})\n",
" if dis_update:\n",
" sess.run(opt_dis, feed_dict={\n",
" X: batch_xs,\n",
" is_training: True})\n",
"\n",
" if batch_i % 50 == 0:\n",
" print('real:', real_cost, '/ fake:', fake_cost)\n",
"\n",
" # Plot example reconstructions from latent layer\n",
" recon = sess.run(G, feed_dict={\n",
" Z: zs,\n",
" is_training: False})\n",
"\n",
" recon = np.clip(recon, 0, 1)\n",
" m1 = utils.montage(recon.reshape([-1] + crop_shape),\n",
" 'imgs/manifold_%08d.png' % t_i)\n",
"\n",
" # Plot example reconstructions\n",
" recon = sess.run(G, feed_dict={\n",
" X: test_xs,\n",
" is_training: False})\n",
" recon = np.clip(recon, 0, 1)\n",
" m2 = utils.montage(recon.reshape([-1] + crop_shape),\n",
" 'imgs/reconstruction_%08d.png' % t_i)\n",
" \n",
" fig, axs = plt.subplots(1, 2, figsize=(15, 10))\n",
" axs[0].imshow(m1)\n",
" axs[1].imshow(m2)\n",
" plt.show()\n",
" \n",
" t_i += 1\n",
" \n",
"\n",
" if batch_i % 200 == 0:\n",
" # Save the variables to disk.\n",
" save_path = saver.save(sess, \"./\" + ckpt_name,\n",
" global_step=batch_i,\n",
" write_meta_graph=False)\n",
" print(\"Model saved in file: %s\" % save_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# One of the threads has issued an exception. So let's tell all the\n",
"# threads to shutdown.\n",
"coord.request_stop()\n",
"\n",
"# Wait until all threads have finished.\n",
"coord.join(threads)\n",
"\n",
"# Clean up the session.\n",
"sess.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"part-3---latent-space-arithmetic\"></a>\n",
"# Part 3 - Latent-Space Arithmetic\n",
"\n",
"<a name=\"loading-the-pre-trained-model\"></a>\n",
"## Loading the Pre-Trained Model\n",
"\n",
"We're now going to work with a pre-trained VAEGAN model on the Celeb Net dataset. Let's load this model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tf.reset_default_graph()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from libs import celeb_vaegan as CV"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"net = CV.get_celeb_vaegan_model()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll load the graph_def contained inside this dictionary. It follows the same idea as the `inception`, `vgg16`, and `i2v` pretrained networks. It is a dictionary with the key `graph_def` defined, with the graph's pretrained network. It also includes `labels` and a `preprocess` key. We'll have to do one additional thing which is to turn off the random sampling from variational layer. This isn't really necessary but will ensure we get the same results each time we use the network. We'll use the `input_map` argument to do this. Don't worry if this doesn't make any sense, as we didn't cover the variational layer in any depth. Just know that this is removing a random process from the network so that it is completely deterministic. If we hadn't done this, we'd get slightly different results each time we used the network (which may even be desirable for your purposes)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sess = tf.Session()\n",
"g = tf.get_default_graph()\n",
"tf.import_graph_def(net['graph_def'], name='net', input_map={\n",
" 'encoder/variational/random_normal:0': np.zeros(512, dtype=np.float32)})\n",
"names = [op.name for op in g.get_operations()]\n",
"print(names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's get the relevant parts of the network: `X`, the input image to the network, `Z`, the input image's encoding, and `G`, the decoded image. In many ways, this is just like the Autoencoders we learned about in Session 3, except instead of `Y` being the output, we have `G` from our generator! And the way we train it is very different: we use an adversarial process between the generator and discriminator, and use the discriminator's own distance measure to help train the network, rather than pixel-to-pixel differences."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X = g.get_tensor_by_name('net/x:0')\n",
"Z = g.get_tensor_by_name('net/encoder/variational/z:0')\n",
"G = g.get_tensor_by_name('net/generator/x_tilde:0')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's get some data to play with:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"files = datasets.CELEB()\n",
"img_i = 50\n",
"img = plt.imread(files[img_i])\n",
"plt.imshow(img)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now preprocess the image, and see what the generated image looks like (i.e. the lossy version of the image through the network's encoding and decoding)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p = CV.preprocess(img)\n",
"synth = sess.run(G, feed_dict={X: p[np.newaxis]})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
"axs[0].imshow(p)\n",
"axs[1].imshow(synth[0] / synth.max())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we lost a lot of details but it seems to be able to express quite a bit about the image. Our inner most layer, `Z`, is only 512 values yet our dataset was 200k images of 64 x 64 x 3 pixels (about 2.3 GB of information). That means we're able to express our nearly 2.3 GB of information with only 512 values! Having some loss of detail is certainly expected!\n",
"\n",
"<a name=\"exploring-the-celeb-net-attributes\"></a>\n",
"## Exploring the Celeb Net Attributes\n",
"\n",
"Let's now try and explore the attributes of our dataset. We didn't train the network with any supervised labels, but the Celeb Net dataset has 40 attributes for each of its 200k images. These are already parsed and stored for you in the `net` dictionary:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"net.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"len(net['labels'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"net['labels']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see what attributes exist for one of the celeb images:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.imshow(img)\n",
"[net['labels'][i] for i, attr_i in enumerate(net['attributes'][img_i]) if attr_i]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"find-the-latent-encoding-for-an-attribute\"></a>\n",
"## Find the Latent Encoding for an Attribute\n",
"\n",
"The Celeb Dataset includes attributes for each of its 200k+ images. This allows us to feed into the encoder some images that we know have a *specific* attribute, e.g. \"smiling\". We store what their encoding is and retain this distribution of encoded values. We can then look at any other image and see how it is encoded, and slightly change the encoding by adding the encoded of our smiling images to it! The result should be our image but with more smiling. That is just insane and we're going to see how to do it. First lets inspect our latent space:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Z.get_shape()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have 512 features that we can encode any image with. Assuming our network is doing an okay job, let's try to find the `Z` of the first 100 images with the 'Bald' attribute:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bald_label = net['labels'].index('Bald')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bald_label"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's get all the bald image indexes:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bald_img_idxs = np.where(net['attributes'][:, bald_label])[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bald_img_idxs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's just load 100 of their images:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bald_imgs = [plt.imread(files[bald_img_i])[..., :3]\n",
" for bald_img_i in bald_img_idxs[:100]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see if the mean image looks like a good bald person or not:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.imshow(np.mean(bald_imgs, 0).astype(np.uint8))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Yes that is definitely a bald person. Now we're going to try to find the encoding of a bald person. One method is to try and find every other possible image and subtract the \"bald\" person's latent encoding. Then we could add this encoding back to any new image and hopefully it makes the image look more bald. Or we can find a bunch of bald people's encodings and then average their encodings together. This should reduce the noise from having many different attributes, but keep the signal pertaining to the baldness.\n",
"\n",
"Let's first preprocess the images:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bald_p = np.array([CV.preprocess(bald_img_i) for bald_img_i in bald_imgs])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can find the latent encoding of the images by calculating `Z` and feeding `X` with our `bald_p` images:\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bald_zs = sess.run(Z, feed_dict=..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's calculate the mean encoding:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bald_feature = np.mean(bald_zs, 0, keepdims=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bald_feature.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's try and synthesize from the mean bald feature now and see how it looks:\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bald_generated = sess.run(G, feed_dict=..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.imshow(bald_generated[0] / bald_generated.max())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"latent-feature-arithmetic\"></a>\n",
"## Latent Feature Arithmetic\n",
"\n",
"Let's now try to write a general function for performing everything we've just done so that we can do this with many different features. We'll then try to combine them and synthesize people with the features we want them to have..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_features_for(label='Bald', has_label=True, n_imgs=50):\n",
" label_i = net['labels'].index(label)\n",
" label_idxs = np.where(net['attributes'][:, label_i] == has_label)[0]\n",
" label_idxs = np.random.permutation(label_idxs)[:n_imgs]\n",
" imgs = [plt.imread(files[img_i])[..., :3]\n",
" for img_i in label_idxs]\n",
" preprocessed = np.array([CV.preprocess(img_i) for img_i in imgs])\n",
" zs = sess.run(Z, feed_dict={X: preprocessed})\n",
" return np.mean(zs, 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's try getting some attributes positive and negative features. Be sure to explore different attributes! Also try different values of `n_imgs`, e.g. 2, 3, 5, 10, 50, 100. What happens with different values?\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Explore different attributes\n",
"z1 = get_features_for('Male', True, n_imgs=10)\n",
"z2 = get_features_for('Male', False, n_imgs=10)\n",
"z3 = get_features_for('Smiling', True, n_imgs=10)\n",
"z4 = get_features_for('Smiling', False, n_imgs=10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"b1 = sess.run(G, feed_dict={Z: z1[np.newaxis]})\n",
"b2 = sess.run(G, feed_dict={Z: z2[np.newaxis]})\n",
"b3 = sess.run(G, feed_dict={Z: z3[np.newaxis]})\n",
"b4 = sess.run(G, feed_dict={Z: z4[np.newaxis]})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fig, axs = plt.subplots(1, 4, figsize=(15, 6))\n",
"axs[0].imshow(b1[0] / b1.max()), axs[0].set_title('Male'), axs[0].grid('off'), axs[0].axis('off')\n",
"axs[1].imshow(b2[0] / b2.max()), axs[1].set_title('Not Male'), axs[1].grid('off'), axs[1].axis('off')\n",
"axs[2].imshow(b3[0] / b3.max()), axs[2].set_title('Smiling'), axs[2].grid('off'), axs[2].axis('off')\n",
"axs[3].imshow(b4[0] / b4.max()), axs[3].set_title('Not Smiling'), axs[3].grid('off'), axs[3].axis('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's interpolate between the \"Male\" and \"Not Male\" categories:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notmale_vector = z2 - z1\n",
"n_imgs = 5\n",
"amt = np.linspace(0, 1, n_imgs)\n",
"zs = np.array([z1 + notmale_vector*amt_i for amt_i in amt])\n",
"g = sess.run(G, feed_dict={Z: zs})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fig, axs = plt.subplots(1, n_imgs, figsize=(20, 4))\n",
"for i, ax_i in enumerate(axs):\n",
" ax_i.imshow(np.clip(g[i], 0, 1))\n",
" ax_i.grid('off')\n",
" ax_i.axis('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And the same for smiling:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"smiling_vector = z3 - z4\n",
"amt = np.linspace(0, 1, n_imgs)\n",
"zs = np.array([z4 + smiling_vector*amt_i for amt_i in amt])\n",
"g = sess.run(G, feed_dict={Z: zs})\n",
"fig, axs = plt.subplots(1, n_imgs, figsize=(20, 4))\n",
"for i, ax_i in enumerate(axs):\n",
" ax_i.imshow(np.clip(g[i] / g[i].max(), 0, 1))\n",
" ax_i.grid('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There's also no reason why we have to be within the boundaries of 0-1. We can extrapolate beyond, in, and around the space."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_imgs = 5\n",
"amt = np.linspace(-1.5, 2.5, n_imgs)\n",
"zs = np.array([z4 + smiling_vector*amt_i for amt_i in amt])\n",
"g = sess.run(G, feed_dict={Z: zs})\n",
"fig, axs = plt.subplots(1, n_imgs, figsize=(20, 4))\n",
"for i, ax_i in enumerate(axs):\n",
" ax_i.imshow(np.clip(g[i], 0, 1))\n",
" ax_i.grid('off')\n",
" ax_i.axis('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"extensions\"></a>\n",
"## Extensions\n",
"\n",
"[Tom White](https://twitter.com/dribnet), Lecturer at Victoria University School of Design, also recently demonstrated an alternative way of interpolating using a sinusoidal interpolation. He's created some of the most impressive generative images out there and luckily for us he has detailed his process in the arxiv preprint: https://arxiv.org/abs/1609.04468 - as well, be sure to check out his twitter bot, https://twitter.com/smilevector - which adds smiles to people :) - Note that the network we're using is only trained on aligned faces that are frontally facing, though this twitter bot is capable of adding smiles to any face. I suspect that he is running a face detection algorithm such as AAM, CLM, or ASM, cropping the face, aligning it, and then running a similar algorithm to what we've done above. Or else, perhaps he has trained a new model on faces that are not aligned. In any case, it is well worth checking out!\n",
"\n",
"Let's now try and use sinusoidal interpolation using his implementation in [plat](https://github.com/dribnet/plat/blob/master/plat/interpolate.py#L16-L24) which I've copied below:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def slerp(val, low, high):\n",
" \"\"\"Spherical interpolation. val has a range of 0 to 1.\"\"\"\n",
" if val <= 0:\n",
" return low\n",
" elif val >= 1:\n",
" return high\n",
" omega = np.arccos(np.dot(low/np.linalg.norm(low), high/np.linalg.norm(high)))\n",
" so = np.sin(omega)\n",
" return np.sin((1.0-val)*omega) / so * low + np.sin(val*omega)/so * high"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"amt = np.linspace(0, 1, n_imgs)\n",
"zs = np.array([slerp(amt_i, z1, z2) for amt_i in amt])\n",
"g = sess.run(G, feed_dict={Z: zs})\n",
"fig, axs = plt.subplots(1, n_imgs, figsize=(20, 4))\n",
"for i, ax_i in enumerate(axs):\n",
" ax_i.imshow(np.clip(g[i], 0, 1))\n",
" ax_i.grid('off')\n",
" ax_i.axis('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's certainly worth trying especially if you are looking to explore your own model's latent space in new and interesting ways.\n",
"\n",
"Let's try and load an image that we want to play with. We need an image as similar to the Celeb Dataset as possible. Unfortunately, we don't have access to the algorithm they used to \"align\" the faces, so we'll need to try and get as close as possible to an aligned face image. One way you can do this is to load up one of the celeb images and try and align an image to it using e.g. Photoshop or another photo editing software that lets you blend and move the images around. That's what I did for my own face..."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"img = plt.imread('parag.png')[..., :3]\n",
"img = CV.preprocess(img, crop_factor=1.0)[np.newaxis]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see how the network encodes it:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"img_ = sess.run(G, feed_dict={X: img})\n",
"fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
"axs[0].imshow(img[0]), axs[0].grid('off')\n",
"axs[1].imshow(np.clip(img_[0] / np.max(img_), 0, 1)), axs[1].grid('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice how blurry the image is. Tom White's preprint suggests one way to sharpen the image is to find the \"Blurry\" attribute vector:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"z1 = get_features_for('Blurry', True, n_imgs=25)\n",
"z2 = get_features_for('Blurry', False, n_imgs=25)\n",
"unblur_vector = z2 - z1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"z = sess.run(Z, feed_dict={X: img})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_imgs = 5\n",
"amt = np.linspace(0, 1, n_imgs)\n",
"zs = np.array([z[0] + unblur_vector * amt_i for amt_i in amt])\n",
"g = sess.run(G, feed_dict={Z: zs})\n",
"fig, axs = plt.subplots(1, n_imgs, figsize=(20, 4))\n",
"for i, ax_i in enumerate(axs):\n",
" ax_i.imshow(np.clip(g[i] / g[i].max(), 0, 1))\n",
" ax_i.grid('off')\n",
" ax_i.axis('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that the image also gets brighter and perhaps other features than simply the bluriness of the image changes. Tom's preprint suggests that this is due to the correlation that blurred images have with other things such as the brightness of the image, possibly due biases in labeling or how photographs are taken. He suggests that another way to unblur would be to synthetically blur a set of images and find the difference in the encoding between the real and blurred images. We can try it like so:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy.ndimage import gaussian_filter\n",
"\n",
"idxs = np.random.permutation(range(len(files)))\n",
"imgs = [plt.imread(files[idx_i]) for idx_i in idxs[:100]]\n",
"blurred = []\n",
"for img_i in imgs:\n",
" img_copy = np.zeros_like(img_i)\n",
" for ch_i in range(3):\n",
" img_copy[..., ch_i] = gaussian_filter(img_i[..., ch_i], sigma=3.0)\n",
" blurred.append(img_copy)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Now let's preprocess the original images and the blurred ones\n",
"imgs_p = np.array([CV.preprocess(img_i) for img_i in imgs])\n",
"blur_p = np.array([CV.preprocess(img_i) for img_i in blurred])\n",
"\n",
"# And then compute each of their latent features\n",
"noblur = sess.run(Z, feed_dict={X: imgs_p})\n",
"blur = sess.run(Z, feed_dict={X: blur_p})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"synthetic_unblur_vector = np.mean(noblur - blur, 0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_imgs = 5\n",
"amt = np.linspace(0, 1, n_imgs)\n",
"zs = np.array([z[0] + synthetic_unblur_vector * amt_i for amt_i in amt])\n",
"g = sess.run(G, feed_dict={Z: zs})\n",
"fig, axs = plt.subplots(1, n_imgs, figsize=(20, 4))\n",
"for i, ax_i in enumerate(axs):\n",
" ax_i.imshow(np.clip(g[i], 0, 1))\n",
" ax_i.grid('off')\n",
" ax_i.axis('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For some reason, it also doesn't like my glasses very much. Let's try and add them back."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"z1 = get_features_for('Eyeglasses', True)\n",
"z2 = get_features_for('Eyeglasses', False)\n",
"glass_vector = z1 - z2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"z = sess.run(Z, feed_dict={X: img})"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_imgs = 5\n",
"amt = np.linspace(0, 1, n_imgs)\n",
"zs = np.array([z[0] + glass_vector * amt_i + unblur_vector * amt_i for amt_i in amt])\n",
"g = sess.run(G, feed_dict={Z: zs})\n",
"fig, axs = plt.subplots(1, n_imgs, figsize=(20, 4))\n",
"for i, ax_i in enumerate(axs):\n",
" ax_i.imshow(np.clip(g[i], 0, 1))\n",
" ax_i.grid('off')\n",
" ax_i.axis('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Well, more like sunglasses then. Let's try adding everything in there now!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_imgs = 5\n",
"amt = np.linspace(0, 1.0, n_imgs)\n",
"zs = np.array([z[0] + glass_vector * amt_i + unblur_vector * amt_i + amt_i * smiling_vector for amt_i in amt])\n",
"g = sess.run(G, feed_dict={Z: zs})\n",
"fig, axs = plt.subplots(1, n_imgs, figsize=(20, 4))\n",
"for i, ax_i in enumerate(axs):\n",
" ax_i.imshow(np.clip(g[i], 0, 1))\n",
" ax_i.grid('off')\n",
" ax_i.axis('off')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Well it was worth a try anyway. We can also try with a lot of images and create a gif montage of the result:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_imgs = 5\n",
"amt = np.linspace(0, 1.5, n_imgs)\n",
"z = sess.run(Z, feed_dict={X: imgs_p})\n",
"imgs = []\n",
"for amt_i in amt:\n",
" zs = z + synthetic_unblur_vector * amt_i + amt_i * smiling_vector\n",
" g = sess.run(G, feed_dict={Z: zs})\n",
" m = utils.montage(np.clip(g, 0, 1))\n",
" imgs.append(m)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gif.build_gif(imgs, saveto='celeb.gif')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ipyd.Image(url='celeb.gif?i={}'.format(\n",
" np.random.rand()), height=1000, width=1000)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Exploring multiple feature vectors and applying them to images from the celeb dataset to produce animations of a face, saving it as a GIF. Recall you can store each image frame in a list and then use the `gif.build_gif` function to create a gif. Explore your own syntheses and then include a gif of the different images you create as \"celeb.gif\" in the final submission. Perhaps try finding unexpected synthetic latent attributes in the same way that we created a blur attribute. You can check the documentation in scipy.ndimage for some other image processing techniques, for instance: http://www.scipy-lectures.org/advanced/image_processing/ - and see if you can find the encoding of another attribute that you then apply to your own images. You can even try it with many images and use the `utils.montage` function to create a large grid of images that evolves over your attributes. Or create a set of expressions perhaps. Up to you just explore!\n",
"\n",
"<h3><font color='red'>TODO! COMPLETE THIS SECTION!</font></h3>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"imgs = []\n",
"\n",
"... DO SOMETHING AWESOME ! ...\n",
"\n",
"gif.build_gif(imgs=imgs, saveto='vaegan.gif')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a name=\"part-4---character-level-recurrent-neural-network\"></a>\n",
"# Part 4 - Character Level Recurrent Neural Network\n",
"\n",
"Please visit [session-5-part2.ipynb](session-5-part2.ipynb) for the rest of the homework!"
]
}
],
"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
}
| apache-2.0 |
dereneaton/ipyrad | tests/ipyrad-manuscript-horserace.ipynb | 1 | 18767757 | null | gpl-3.0 |
jserenson/Python_Bootcamp | Object Oriented Programming Homework -Solution.ipynb | 1 | 3948 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Object Oriented Programming\n",
"## Homework Assignment\n",
"\n",
"####Problem 1\n",
"Fill in the Line class methods to accept coordinate as a pair of tuples and return the slope and distance of the line."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Line(object):\n",
" \n",
" def __init__(self,coor1,coor2):\n",
" self.coor1 = coor1\n",
" self.coor2 = coor2\n",
" \n",
" def distance(self):\n",
" x1,y1 = self.coor1\n",
" x2,y2 = self.coor2\n",
" return ( (x2-x1)**2 + (y2-y1)**2 )**0.5\n",
" \n",
" def slope(self):\n",
" x1,y1 = self.coor1\n",
" x2,y2 = self.coor2\n",
" return float((y2-y1))/(x2-x1)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"coordinate1 = (3,2)\n",
"coordinate2 = (8,10)\n",
"\n",
"li = Line(coordinate1,coordinate2)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9.433981132056603"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"li.distance()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.6"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"li.slope()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"________\n",
"####Problem 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fill in the class "
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"class Cylinder(object):\n",
" \n",
" def __init__(self,height=1,radius=1):\n",
" self.height = height\n",
" self.radius = radius\n",
" \n",
" def volume(self):\n",
" return self.height * (3.14)* (self.radius)**2\n",
" \n",
" def surface_area(self):\n",
" top = (3.14)* (self.radius)**2\n",
" return 2*top + 2*3.14*self.radius*self.height"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"c = Cylinder(2,3)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"56.52"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c.volume()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"94.2"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c.surface_area()"
]
},
{
"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": 1
}
| gpl-3.0 |
adiultra/pysick | math/.ipynb_checkpoints/Tests-checkpoint.ipynb | 1 | 1031 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.00000000e+00, 5.70919174e-15, -8.14710295e-15])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"x = [1, 3, 4, 5, 6, 7, 8]\n",
"\n",
"y = [i*i for i in x]\n",
"\n",
"np.polyfit(x, y, 2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
evanmiltenburg/python-for-text-analysis | Chapters/Chapter 19 - More about Natural Language Processing Tools (spaCy).ipynb | 1 | 36016 | {
"cells": [
{
"cell_type": "markdown",
"source": [
"# Chapter 19 - More about Natural Language Processing Tools (spaCy)\n",
"\n",
"Text data is unstructured. But if you want to extract information from text, then you often need to process that data into a more structured representation. The common idea for all Natural Language Processing (NLP) tools is that they try to structure or transform text in some meaningful way. You have already learned about four basic NLP steps: sentence splitting, tokenization, POS-tagging and lemmatization. For all of these, we have used the NLTK library, which is widely used in the field of NLP. However, there are some competitors out there that are worthwhile to have a look at. One of them is spaCy, which is fast and accurate and supports multiple languages. \n",
"\n",
"**At the end of this chapter, you will be able to:**\n",
"- work with spaCy\n",
"- find some additional NLP tools"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## 1. The NLP pipeline\n",
"\n",
"There are many tools and libraries designed to solve NLP problems. In Chapter 15, we have already seen the NLTK library for tokenization, sentence splitting, part-of-speech tagging and lemmatization. However, there are many more NLP tasks and off-the-shelf tools to perform them. These tasks often depend on each other and are therefore put into a sequence; such a sequence of NLP tasks is called an NLP pipeline. Some of the most common NLP tasks are:\n",
"\n",
"* **Tokenization:** splitting texts into individual words\n",
"* **Sentence splitting:** splitting texts into sentences\n",
"* **Part-of-speech (POS) tagging:** identifying the parts of speech of words in context (verbs, nouns, adjectives, etc.)\n",
"* **Morphological analysis:** separating words into morphemes and identifying their classes (e.g. tense/aspect of verbs)\n",
"* **Stemming:** identifying the stems of words in context by removing inflectional/derivational affixes, such as 'troubl' for 'trouble/troubling/troubled'\n",
"* **Lemmatization:** identifying the lemmas (dictionary forms) of words in context, such as 'go' for 'go/goes/going/went'\n",
"* **Word Sense Disambiguation (WSD):** assigning the correct meaning to words in context\n",
"* **Stop words recognition:** identifying commonly used words (such as 'the', 'a(n)', 'in', etc.) in text, possibly to ignore them in other tasks\n",
"* **Named Entity Recognition (NER):** identifying people, locations, organizations, etc. in text\n",
"* **Constituency/dependency parsing:** analyzing the grammatical structure of a sentence\n",
"* **Semantic Role Labeling (SRL):** analyzing the semantic structure of a sentence (*who* does *what* to *whom*, *where* and *when*)\n",
"* **Sentiment Analysis:** determining whether a text is mostly positive or negative\n",
"* **Word Vectors (or Word Embeddings) and Semantic Similarity:** representating the meaning of words as rows of real valued numbers where each point captures a dimension of the word's meaning and where semantically similar words have similar vectors (very popular these days)\n",
"\n",
"You don't always need all these modules. But it's important to know that they are\n",
"there, so that you can use them when the need arises.\n",
"\n",
"### 1.1 How can you use these modules?\n",
"\n",
"Let's be clear about this: **you don't always need to use Python for this**. There are\n",
"some very strong NLP programs out there that don't rely on Python. You can typically\n",
"call these programs from the command line. Some examples are:\n",
"\n",
"* [Treetagger](http://www.cis.uni-muenchen.de/~schmid/tools/TreeTagger/) is a POS-tagger\n",
" and lemmatizer in one. It provides support for many different languages. If you want to\n",
" call Treetagger from Python, use [treetaggerwrapper](http://treetaggerwrapper.readthedocs.io/).\n",
" [Treetagger-python](https://github.com/miotto/treetagger-python) also works, but is much slower.\n",
"\n",
"* [Stanford's CoreNLP](http://stanfordnlp.github.io/CoreNLP/) is a very powerful system\n",
" that is able to process English, German, Spanish, French, Chinese and Arabic. (Each to\n",
" a different extent, though. The pipeline for English is most complete.) There are also\n",
" Python wrappers available, such as [py-corenlp](https://github.com/smilli/py-corenlp).\n",
"\n",
"* [The Maltparser](http://www.maltparser.org/) has models for English, Swedish, French, and Spanish.\n",
"\n",
"\n",
"Having said that, there are many **NLP-tools that have been developed for Python**:\n",
"\n",
"* [Natural Language ToolKit (NLTK)](http://www.nltk.org/): Incredibly versatile library with a bit of everything.\n",
" The only downside is that it's not the fastest library out there, and it lags behind the\n",
" state-of-the-art.\n",
" * Access to several corpora.\n",
" * Create a POS-tagger. (Some of these are actually state-of-the-art if you have enough training data.)\n",
" * Perform corpus analyses.\n",
" * Interface with [WordNet](https://wordnet.princeton.edu/).\n",
"* [Pattern](http://www.clips.ua.ac.be/pattern): A module that describes itself as a 'web mining module'. Implements a\n",
" tokenizer, tagger, parser, and sentiment analyzer for multiple different languages.\n",
" Also provides an API for Google, Twitter, Wikipedia and Bing.\n",
"* [Textblob](http://textblob.readthedocs.io/en/dev/): Another general NLP library that builds on the NLTK and Pattern.\n",
"* [Gensim](http://radimrehurek.com/gensim/): For building vector spaces and topic models.\n",
"* [Corpkit](http://corpkit.readthedocs.io/en/latest/) is a module for corpus building and corpus management. Includes an interface to the Stanford CoreNLP parser.\n",
"* [SpaCy](https://spacy.io/): Tokenizer, POS-tagger, parser and named entity recogniser for English, German, Spanish, Portugese, French, Italian and Dutch (more languages in progress). It can also predict similarity using word embeddings."
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## 2. spaCy\n",
"\n",
"[spaCy](https://spacy.io/) provides a rather complete NLP pipeline: it takes a raw document and performs tokenization, POS-tagging, stop word recognition, morphological analysis, lemmatization, sentence splitting, dependency parsing and Named Entity Recognition (NER). It also supports similarity prediction, but that is outside of the scope of this notebook. The advantage of SpaCy is that it is really fast, and it has a good accuracy. In addition, it currently supports multiple languages, among which: English, German, Spanish, Portugese, French, Italian and Dutch. \n",
"\n",
"In this notebook, we will show you the basic usage. If you want to learn more, please visit spaCy's website; it has extensive documentation and provides excellent user guides. \n",
"\n",
"### 2.1 Installing and loading spaCy\n",
"\n",
"To install spaCy, check out the instructions [here](https://spacy.io/usage). On this page, it is explained exactly how to install spaCy for your operating system, package manager and desired language model(s). Simply run the suggested commands in your terminal or cmd. Alternatively, you can probably also just run the following cells in this notebook:"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"pip install -U spacy"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"%%bash\n",
"python -m spacy download en_core_web_sm"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Now, let's first load spaCy. We import the spaCy module and load the English tokenizer, tagger, parser, NER and word vectors."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"import spacy\n",
"nlp = spacy.load('en_core_web_sm') # other languages: de, es, pt, fr, it, nl"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"`nlp` is now a Python object representing the English NLP pipeline that we can use to process a text. "
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### EXTRA: Larger models\n",
"\n",
"For English, there are three [models](https://spacy.io/usage/models) ranging from 'small' to 'large':\n",
"\n",
"- en_core_web_sm\n",
"- en_core_web_md\n",
"- en_core_web_lg\n",
"\n",
"By default, the smallest one is loaded. Larger models should have a better accuracy, but take longer to load. If you like, you can use them instead. You will first need to download them."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"#%%bash\n",
"#python -m spacy download en_core_web_md"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"#%%bash\n",
"#python -m spacy download en_core_web_lg"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# uncomment one of the lines below if you want to load the medium or large model instead of the small one\n",
"# nlp = spacy.load('en_core_web_md') \n",
"# nlp = spacy.load('en_core_web_lg') "
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### 2.2 Using spaCy\n",
"\n",
"Parsing a text with spaCy after loading a language model is as easy as follows:"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"doc = nlp(\"I have an awesome cat. It's sitting on the mat that I bought yesterday.\")"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"`doc` is now a Python object of the class `Doc`. It is a container for accessing linguistic annotations and a sequence of `Token` objects."
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Doc, Token and Span objects\n",
"\n",
"At this point, there are three important types of objects to remember:\n",
"\n",
"* A `Doc` is a sequence of `Token` objects.\n",
"* A `Token` object represents an individual token — i.e. a word, punctuation symbol, whitespace, etc. It has attributes representing linguistic annotations. \n",
"* A `Span` object is a slice from a `Doc` object and a sequence of `Token` objects.\n",
"\n",
"Since `Doc` is a sequence of `Token` objects, we can iterate over all of the tokens in the text as shown below, or select a single token from the sequence: "
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# Iterate over the tokens\n",
"for token in doc:\n",
" print(token)\n",
"print()\n",
"\n",
"# Select one single token by index\n",
"first_token = doc[0]\n",
"print(\"First token:\", first_token)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Please note that even though these look like strings, they are not:"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"for token in doc:\n",
" print(token, \"\\t\", type(token))"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"These `Token` objects have many useful methods and *attributes*, which we can list by using `dir()`. We haven't really talked about attributes during this course, but while methods are operations or activities performed by that object, attributes are 'static' features of the objects. Methods are called using parantheses (as we have seen with `str.upper()`, for instance), while attributes are indicated without parantheses. We will see some examples below.\n",
"\n",
"You can find more detailed information about the token methods and attributes in the [documentation](https://spacy.io/api/token)."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"dir(first_token)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Let's inspect some of the attributes of the tokens. Can you figure out what they mean? Feel free to try out a few more."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# Print attributes of tokens\n",
"for token in doc:\n",
" print(token.text, token.lemma_, token.pos_, token.tag_, token.dep_, token.shape_)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Notice that some of the attributes end with an underscore. For example, tokens have both `lemma` and `lemma_` attributes. The `lemma` attribute represents the id of the lemma (integer), while the `lemma_` attribute represents the unicode string representation of the lemma. In practice, you will mostly use the `lemma_` attribute."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"for token in doc:\n",
" print(token.lemma, token.lemma_)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"You can also use spacy.explain to find out more about certain labels:"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# try out some more, such as NN, ADP, PRP, VBD, VBP, VBZ, WDT, aux, nsubj, pobj, dobj, npadvmod\n",
"spacy.explain(\"VBZ\")"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"You can create a `Span` object from the slice doc[start : end]. For instance, doc[2:5] produces a span consisting of tokens 2, 3 and 4. Stepped slices (e.g. doc[start : end : step]) are not supported, as `Span` objects must be contiguous (cannot have gaps). You can use negative indices and open-ended ranges, which have their normal Python semantics."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# Create a Span\n",
"a_slice = doc[2:5]\n",
"print(a_slice, type(a_slice))\n",
"\n",
"# Iterate over Span\n",
"for token in a_slice:\n",
" print(token.lemma_, token.pos_)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Text, sentences and noun_chunks\n",
"\n",
"If you call the `dir()` function on a `Doc` object, you will see that it has a range of methods and attributes. You can read more about them in the [documentation](https://spacy.io/api/doc). Below, we highlight three of them: `text`, `sents` and `noun_chunks`."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"dir(doc)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"First of all, `text` simply gives you the whole document as a string:"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"print(doc.text)\n",
"print(type(doc.text))"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"`sents` can be used to get all the sentences. Notice that it will create a so-called 'generator'. For now, you don't have to understand exactly what a generator is (if you like, you can read more about them online). Just remember that we can use generators to iterate over an object in a fast and efficient way."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# Get all the sentences as a generator \n",
"print(doc.sents, type(doc.sents))\n",
"\n",
"# We can use the generator to loop over the sentences; each sentence is a span of tokens\n",
"for sentence in doc.sents:\n",
" print(sentence, type(sentence))"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"If you find this difficult to comprehend, you can also simply convert it to a list and then loop over the list. Remember that this is less efficient, though."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# You can also store the sentences in a list and then loop over the list \n",
"sentences = list(doc.sents)\n",
"for sentence in sentences:\n",
" print(sentence, type(sentence))"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"The benefit of converting it to a list is that we can use indices to select certain sentences. For example, in the following we only print some information about the tokens in the second sentence."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# Print some information about the tokens in the second sentence.\n",
"sentences = list(doc.sents)\n",
"for token in sentences[1]:\n",
" data = '\\t'.join([token.orth_,\n",
" token.lemma_,\n",
" token.pos_,\n",
" token.tag_,\n",
" str(token.i), # Turn index into string\n",
" str(token.idx)]) # Turn index into string\n",
" print(data)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Similarly, `noun_chunks` can be used to create a generator for all noun chunks in the text. "
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# Get all the noun chunks as a generator \n",
"print(doc.noun_chunks, type(doc.noun_chunks))\n",
"\n",
"# You can loop over a generator; each noun chunk is a span of tokens\n",
"for chunk in doc.noun_chunks:\n",
" print(chunk, type(chunk))\n",
" print()"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"#### Named Entities\n",
"\n",
"Finally, we can also very easily access the Named Entities in a text using `ents`. As you can see below, it will create a tuple of the entities recognized in the text. Each entity is again a span of tokens, and you can access the type of the entity with the `label_` attribute of `Span`."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# Here's a slightly longer text, from the Wikipedia page about Harry Potter.\n",
"harry_potter = \"Harry Potter is a series of fantasy novels written by British author J. K. Rowling.\\\n",
"The novels chronicle the life of a young wizard, Harry Potter, and his friends Hermione Granger and Ron Weasley,\\\n",
"all of whom are students at Hogwarts School of Witchcraft and Wizardry.\\\n",
"The main story arc concerns Harry's struggle against Lord Voldemort, a dark wizard who intends to become immortal,\\\n",
"overthrow the wizard governing body known as the Ministry of Magic, and subjugate all wizards and Muggles.\"\n",
"\n",
"doc = nlp(harry_potter)\n",
"print(doc.ents)\n",
"print(type(doc.ents))"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# Each entity is a span of tokens and is labeled with the type of entity\n",
"for entity in doc.ents:\n",
" print(entity, \"\\t\", entity.label_, \"\\t\", type(entity))"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Pretty cool, but what does NORP mean? Again, you can use spacy.explain() to find out:"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## 3. EXTRA: Stanford CoreNLP\n",
"\n",
"Another very popular NLP pipeline is [Stanford CoreNLP](https://stanfordnlp.github.io/CoreNLP/index.html). You can use the tool from the command line, but there are also some useful Python wrappers that make use of the Stanford CoreNLP API, such as pycorenlp. As you might want to use this in the future, we will provide you with a quick start guide. To use the code below, you will have to do the following:\n",
"\n",
"1. Download Stanford CoreNLP [here](https://stanfordnlp.github.io/CoreNLP/download.html).\n",
"2. Install pycorenlp (run `pip install pycorenlp` in your terminal, or simply run the cell below).\n",
"3. Open a terminal and run the following commands (replace with the correct directory names): \n",
" `cd LOCATION_OF_CORENLP/stanford-corenlp-full-2018-02-27` \n",
" `java -mx4g -cp \"*\" edu.stanford.nlp.pipeline.StanfordCoreNLPServer` \n",
" This step you will always have to do if you want to use the Stanford CoreNLP API.\n"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"%%bash\n",
"pip install pycorenlp"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"from pycorenlp import StanfordCoreNLP\n",
"nlp = StanfordCoreNLP('http://localhost:9000')"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Next, you will want to define which [annotators](https://stanfordnlp.github.io/CoreNLP/annotators.html) to use and which [output format](https://stanfordnlp.github.io/CoreNLP/cmdline.html#output-options) should be produced (text, json, xml, conll, conllu, serialized). Annotating the document then is very easy. Note that Stanford CoreNLP uses some large models that can take [a long time](https://stackoverflow.com/questions/11219392/stanford-corenlp-very-slow) to load. You can read more about it [here](https://stanfordnlp.github.io/CoreNLP/memory-time.html)."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"harry_potter = \"Harry Potter is a series of fantasy novels written by British author J. K. Rowling.\\\n",
"The novels chronicle the life of a young wizard, Harry Potter, and his friends Hermione Granger and Ron Weasley,\\\n",
"all of whom are students at Hogwarts School of Witchcraft and Wizardry.\\\n",
"The main story arc concerns Harry's struggle against Lord Voldemort, a dark wizard who intends to become immortal,\\\n",
"overthrow the wizard governing body known as the Ministry of Magic, and subjugate all wizards and Muggles.\"\n",
"\n",
"# Define annotators and output format\n",
"properties= {'annotators': 'tokenize, ssplit, pos, lemma, parse',\n",
" 'outputFormat': 'json'}\n",
"\n",
"# Annotate the string with CoreNLP\n",
"doc = nlp.annotate(harry_potter, properties=properties)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"In the next cells, we will simply show some examples of how to access the linguistic annotations if you use the properties as shown above. If you'd like to continue working with Stanford CoreNLP in the future, you will likely have to experiment a bit more."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"doc.keys()"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"sentences = doc[\"sentences\"]\n",
"first_sentence = sentences[0]\n",
"first_sentence.keys()"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"first_sentence[\"parse\"]"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"first_sentence[\"basicDependencies\"]"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"first_sentence[\"tokens\"]"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"for sent in doc[\"sentences\"]:\n",
" for token in sent[\"tokens\"]:\n",
" word = token[\"word\"]\n",
" lemma = token[\"lemma\"]\n",
" pos = token[\"pos\"]\n",
" print(word, lemma, pos)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# find out what the entity label 'NORP' means\n",
"spacy.explain(\"NORP\")"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## 4. NLTK vs. spaCy vs. CoreNLP\n",
"\n",
"There might be different reasons why you want to use NLTK, spaCy or Stanford CoreNLP. There are differences in efficiency, quality, user friendliness, functionalities, output formats, etc. At this moment, we advise you to go with spaCy because of its ease in use and high quality performance.\n",
"\n",
"Here's an example of both NLTK and spaCy in action. \n",
"\n",
"* The example text is a case in point. What goes wrong here?\n",
"* Try experimenting with the text to see what the differences are."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"import nltk\n",
"import spacy\n",
"\n",
"nlp = spacy.load('en_core_web_sm')"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"text = \"I like cheese very much\"\n",
"\n",
"print(\"NLTK results:\")\n",
"nltk_tagged = nltk.pos_tag(text.split())\n",
"print(nltk_tagged)\n",
"\n",
"print()\n",
"\n",
"print(\"spaCy results:\")\n",
"doc = nlp(text)\n",
"spacy_tagged = []\n",
"for token in doc:\n",
" tag_data = (token.orth_, token.tag_,)\n",
" spacy_tagged.append(tag_data)\n",
"print(spacy_tagged)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Do you want to learn more about the differences between NLTK, spaCy and CoreNLP? Here are some links:\n",
"- [Facts & Figures (spaCy)](https://spacy.io/usage/facts-figures)\n",
"- [About speed (CoreNLP vs. spaCy)](https://nlp.stanford.edu/software/tokenizer.html#Speed)\n",
"- [NLTK vs. spaCy: Natural Language Processing in Python](https://blog.thedataincubator.com/2016/04/nltk-vs-spacy-natural-language-processing-in-python/) \n",
"- [What are the advantages of Spacy vs NLTK?](https://www.quora.com/What-are-the-advantages-of-Spacy-vs-NLTK) \n",
"- [5 Heroic Python NLP Libraries](https://elitedatascience.com/python-nlp-libraries)\n"
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"## 5. Some other useful modules for cleaning and preprocessing\n",
"\n",
"Data is often messy, noisy or includes irrelevant information. Therefore, chances are big that you will need to do some cleaning before you can start with your analysis. This is especially true for social media texts, such as tweets, chats, and emails. Typically, these texts are informal and notoriously noisy. Normalising them to be able to process them with NLP tools is a NLP challenge in itself and fully discussing it goes beyond the scope of this course. However, you may find the following modules useful in your project:\n",
"\n",
"- [tweet-preprocessor](https://pypi.python.org/pypi/tweet-preprocessor/0.4.0): This library makes it easy to clean, parse or tokenize the tweets. It supports cleaning, tokenizing and parsing of URLs, hashtags, reserved words, mentions, emojis and smileys.\n",
"- [emot](https://pypi.python.org/pypi/emot/1.0): Emot is a python library to extract the emojis and emoticons from a text (string). All the emojis and emoticons are taken from a reliable source, i.e. Wikipedia.org.\n",
"- [autocorrect](https://pypi.python.org/pypi/autocorrect/0.1.0): Spelling corrector (Python 3).\n",
"- [html](https://docs.python.org/3/library/html.html#module-html): Can be used to remove HTML tags.\n",
"- [chardet](https://pypi.python.org/pypi/chardet): Universal encoding detector for Python 2 and 3.\n",
"- [ftfy](https://pypi.python.org/pypi/ftfy): Fixes broken unicode strings.\n",
"\n",
"If you are interested in reading more about these topic, these papers discuss preprocessing and normalization:\n",
"\n",
"* [Assessing the Consequences of Text Preprocessing Decisions](http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2849145) (Denny & Spirling 2016). This paper is a bit long, but it provides a nice discussion of common preprocessing steps and their potential effects.\n",
"* [What to do about bad language on the internet](http://www.cc.gatech.edu/~jeisenst/papers/naacl2013-badlanguage.pdf) (Eisenstein 2013). This is a quick read that we recommend everyone to at least look through.\n",
"\n",
"And [here](https://www.kaggle.com/rtatman/character-encodings-tips-tricks/) is a nice blog about character encoding."
],
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"source": [
"## Exercises"
],
"metadata": {}
},
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"cell_type": "code",
"execution_count": null,
"source": [
"import spacy\n",
"nlp = spacy.load('en_core_web_sm')"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Exercise 1:\n",
"1. What is the difference between token.pos\\_ and token.tag\\_? Read [the docs](https://spacy.io/api/annotation#pos-tagging) to find out.\n",
"\n",
"2. What do the different labels mean? Use `space.explain` to inspect some of them. You can also refer to [this page](http://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html) for a complete overview. "
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"doc = nlp(\"I have an awesome cat. It's sitting on the mat that I bought yesterday.\")\n",
"for token in doc:\n",
" print(token.pos_, token.tag_)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"spacy.explain(\"PRON\")"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Exercise 2:\n",
"\n",
"Let's practice a bit with processing files. Open the file `charlie.txt` for reading and use `read()` to read its content as a string. Then use spaCy to annotate this string and print the information below. Remember: you can use `dir()` to remind yourself of the attributes.\n",
"\n",
"For each **token** in the text:\n",
"1. Text \n",
"2. Lemma\n",
"3. POS tag\n",
"4. Whether it's a stopword or not\n",
"5. Whether it's a punctuation mark or not\n",
"\n",
"For each **sentence** in the text:\n",
"1. The complete text\n",
"2. The number of tokens\n",
"3. The complete text in lowercase letters\n",
"4. The text, lemma and POS of the first word\n",
"\n",
"For each **noun chunk** in the text:\n",
"1. The complete text\n",
"2. The number of tokens\n",
"3. The complete text in lowercase letters\n",
"4. The text, lemma and POS of the first word\n",
"\n",
"For each **named entity** in the text:\n",
"1. The complete text\n",
"2. The number of tokens\n",
"3. The complete text in lowercase letters\n",
"4. The text, lemma and POS of the first word"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"filename = \"../Data/Charlie/charlie.txt\"\n",
"\n",
"# read the file and process with spaCy"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# print all information about the tokens"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# print all information about the sentences"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# print all information about the noun chunks"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"# print all information about the entities"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"### Exercise 3:\n",
"\n",
"Remember how we can use the `os` and `glob` modules to process multiple files? For example, we can read all `.txt` files in the `dreams` folder like this:"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"import glob\n",
"filenames = glob.glob(\"../Data/dreams/*.txt\")\n",
"print(filenames)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Now create a function called `get_vocabulary` that takes one positional parameter `filenames`. It should read in all `filenames` and return a set called `unique_words`, that contains all unique words in the files."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"def get_vocabulary(filenames):\n",
" # your code here\n",
"\n",
"# test your function here\n",
"unique_words = get_vocabulary(filenames)\n",
"print(unique_words, len(unique_words))\n",
"assert len(unique_words) == 415 # if your code is correct, this should not raise an error"
],
"outputs": [],
"metadata": {
"scrolled": true
}
},
{
"cell_type": "markdown",
"source": [
"### Exercise 4:\n",
"Create a function called `get_sentences_with_keyword` that takes one positional parameter `filenames` and one keyword parameter `filenames` with default value `None`. It should read in all `filenames` and return a list called `sentences` that contains all sentences (the complete texts) with the keyword. \n",
"\n",
"Hints:\n",
"- It's best to check for the *lemmas* of each token\n",
"- Lowercase both your keyword and the lemma"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"import glob\n",
"filenames = glob.glob(\"../Data/dreams/*.txt\")\n",
"print(filenames)"
],
"outputs": [],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": null,
"source": [
"def get_sentences_with_keyword(filenames, keyword=None):\n",
" #your code here\n",
"\n",
"# test your function here\n",
"sentences = get_sentences_with_keyword(filenames, keyword=\"toy\")\n",
"print(sentences)\n",
"assert len(sentences) == 4 # if your code is correct, this should not raise an error"
],
"outputs": [],
"metadata": {}
}
],
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} | apache-2.0 |
Bio204-class/bio204-notebooks | inclass-2016-03-09-Introduction-to-Statistical-Power.ipynb | 1 | 389447 | {
"cells": [
{
"cell_type": "markdown",
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"source": [
"# Introduction to Statistical Power\n",
"**Author**: Paul M. Magwene"
]
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"If viewing this as a live notebook, or using the nbviewer, use the \"Show Code\" button below to toggle the display of the Python code that generated the figures.\n",
"\n",
"If viewing this notebook via GitHub you might want to use [this nbviewer link](http://nbviewer.jupyter.org/github/Bio204-class/bio204-notebooks/blob/master/inclass-2016-03-09-Introduction-to-Statistical-Power.ipynb) to view this notebook."
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"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import scipy.stats as stats\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
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"np.random.seed(20160309)"
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"cell_type": "markdown",
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"source": [
"## Outcomes of hypothesis tests\n",
"\n",
"In standard statistical hypothesis testing there are two real possibilities -- the null hypothesis is true or the alternative hypothesis is true. When you carry out a hypothesis test, there are two possible test outcomes -- you reject the null hypothesis or you fail to reject the null hypothesis. It is typical to represent the different combinations of the reality / statistical tests in a table like the following:\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| | do not reject $H_0$ | reject $H_0$ |\n",
"|------------|:-----------------------------:|:------------------------------:|\n",
"|$H_0$ true | okay | Type 1 error (false positive), $\\alpha$ |\n",
"|$H_A$ true | Type 2 error (false negative), $\\beta$ | okay |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we specify a significance threshold, $\\alpha$, for hypothesis testing, this controls the Type I error (false positive rate) of our test. The false negative rate is often referred to as $\\beta$. In general, there is a tradeoff between the false positive and false negative rate -- the lower the false positive rate the higher the false negative rate, and vice versa.\n",
"\n",
"### Statistical Power\n",
"\n",
"The power of a statistical test is defined as:\n",
"$$\n",
"\\mbox{Power}\\ = P(\\mbox{reject}\\ H_0\\ |\\ H_A \\mbox{is true})\n",
"$$\n",
"\n",
"In words, this is the probability that the null hypothesis is rejected, conditional on the alternative hypothesis being true.\n",
"\n",
"If $\\beta$ is the false negative rate, then \n",
"$$\n",
"\\mbox{Power}\\ = 1 - \\beta\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exploring statistical power for one-sample t-tests\n",
"\n",
"We'll use a one-sample t-tests to illustrate the concept of statistical power. For this example, we'll explore the power of the t-test to distinguish between the null hypothesis that the data of interest are $H_0 \\sim N(\\mu=0,\\sigma=1)$ and a true underlying distribution $H_A \\sim N(\\mu \\neq 0, \\sigma=1)$.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Effect of sample size on power\n",
"\n",
"First we'll hold the effect size ( $|\\mu_{H_A} - \\mu_{H_0}|$ ) constant and vary sample size.\n",
"\n",
"Consider the population distributions below for the null hypothesis (red) and true underlying distribution (black). Both have the same standard deviation ($\\sigma = 1$) and differ only in their means ($\\mu_{H_0} = 0$ and $\\mu_{H_A} = 0.5$)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Population Distributions"
]
},
{
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"execution_count": 4,
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{
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aeXp+fidZ2fE8EDxXK9N3ePDwiBYtWtCiRYt8C0pERGKEc76HCmD1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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7b85c4ac50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"muH0, muHA = 0, 0.5\n",
"sigma = 1\n",
"\n",
"fig, ax1 = plt.subplots(figsize=(12,5))\n",
"x = np.linspace(-4, 4, 500)\n",
"\n",
"ax1.plot(x, stats.norm.pdf(x, loc=muH0, scale=sigma), color='red', \n",
" linestyle='dotted', linewidth=2, label='Distn under $H_0$')\n",
"ax1.plot(x, stats.norm.pdf(x, loc=muHA, scale=sigma), color='black', \n",
" linewidth=2, label='True Distn, $H_A$')\n",
"\n",
"offset = 0.2\n",
"ax1.text(muH0 - offset, 0.45, \"$\\mu_{H_0}$\", \n",
" horizontalalignment='center', color='red', fontsize=18)\n",
"ax1.text(muHA + offset, 0.45, \"$\\mu_{H_A}$\", \n",
" horizontalalignment='center', color='black', fontsize=18)\n",
"\n",
"ax1.set_ylim(0, 0.5)\n",
"ax1.vlines(muH0,0,0.5,color='red', linestyle='dashed')\n",
"ax1.vlines(muHA,0,0.5,color='black', linestyle='dashed')\n",
"ax1.legend(loc='best')\n",
"ax1.set_xlabel(\"Random Variable X\")\n",
"ax1.set_ylabel(\"Density\")\n",
"ax1.set_title(\"Population Distributions\\nfor the Null and True Distributions\",fontsize=18)\n",
"fig.tight_layout()\n",
"#fig.savefig(\"fig-H0distn-vs-TrueDistn.pdf\")\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Sampling distributions of the mean\n",
"\n",
"Now imagine taking samples of size n=10, n=25, and n=50. What will happen to the **sampling distributions of the mean**? We've encountered this repeatedly before -- the sampling distribution of the mean should be centered around the true population mean, and should have standard deviations given by $\\frac{\\sigma}{\\sqrt{n}}$ (remember that the standard deviation of a sampling distribution is called a \"standard error\").\n",
"\n",
"Here is what the expected sampling distributions of the mean look like for the null and true distributions with samples of size 10, 25, and 50."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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0J1mpXNJ7TimllCo+tCdZKaWUUkqpHCoZSbLzA2253S5A0dHRNGvWDB8fH6ZN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99zBtGuTm+h1Jypk1axYbNmygcePGXHnlld481V99BY88Anl5foeXUvr27QvA6NGjfY5ExH/x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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7b834ca550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n1, n2, n3 = 10, 25, 50\n",
"\n",
"fig, (ax1,ax2,ax3) = plt.subplots(3, 1, figsize=(10,12), sharex=True)\n",
"\n",
"x = np.linspace(-2, 2, 500)\n",
"ax1.plot(x, stats.norm.pdf(x, scale=1/np.sqrt(n1)), color='red', linestyle='dotted', linewidth=2,\n",
" label='Sampling Distn\\n of mean under $H_0$')\n",
"ax1.plot(x, stats.norm.pdf(x, loc=0.5, scale=1/np.sqrt(n1)), color='black', linewidth=2,\n",
" label='Sampling Distn\\n of mean under $H_A$')\n",
"\n",
"offset = 0.1\n",
"\n",
"ax1.text(muH0 - offset, 1.4, \"$\\mu_{H_0}$\", \n",
" horizontalalignment='center', color='red', fontsize=18)\n",
"ax1.text(muHA + offset, 1.4, \"$\\mu_{H_A}$\", \n",
" horizontalalignment='center', color='black', fontsize=18)\n",
"ax1.set_ylim(0, 1.5)\n",
"ax1.set_xlim(-1.75,1.75)\n",
"ax1.vlines(0,0,1.5,color='red', linestyle='dashed')\n",
"ax1.vlines(0.5,0,1.5,color='black', linestyle='dashed')\n",
"ax1.legend(loc='upper left')\n",
"ax1.set_xlabel(\"Random Variable X\")\n",
"ax1.set_ylabel(\"Density\")\n",
"ax1.set_title(\"\"\"Sampling Distributions of the Mean\n",
"For Samples of Size n = {}\"\"\".format(n1))\n",
"\n",
"ax2.plot(x, stats.norm.pdf(x, scale=1/np.sqrt(n2)), color='red', linestyle='dotted', linewidth=2,\n",
" label='Sampling Distn\\n of mean under $H_0$')\n",
"ax2.plot(x, stats.norm.pdf(x, loc=0.5, scale=1/np.sqrt(n2)), color='black', linewidth=2,\n",
" label='Sampling Distn\\n of mean under $H_A$')\n",
"\n",
"ax2.set_ylim(0, 2.25)\n",
"ax2.vlines(0,0,2.25,color='red', linestyle='dashed')\n",
"ax2.vlines(0.5,0,2.25,color='black', linestyle='dashed')\n",
"ax2.legend(loc='upper left')\n",
"ax2.set_xlabel(\"Random Variable X\")\n",
"ax2.set_ylabel(\"Density\")\n",
"ax2.set_title(\"\"\"n = {}\"\"\".format(n2))\n",
"\n",
"\n",
"ax3.plot(x, stats.norm.pdf(x, scale=1/np.sqrt(n3)), color='red', linestyle='dotted', linewidth=2,\n",
" label='Sampling Distn\\n of mean under $H_0$')\n",
"ax3.plot(x, stats.norm.pdf(x, loc=0.5, scale=1/np.sqrt(n3)), color='black', linewidth=2,\n",
" label='Sampling Distn\\n of mean under $H_A$')\n",
"\n",
"ax3.set_ylim(0, 3)\n",
"ax3.vlines(0,0,3,color='red', linestyle='dashed')\n",
"ax3.vlines(0.5,0,3,color='black', linestyle='dashed')\n",
"ax3.legend(loc='upper left')\n",
"ax3.set_xlabel(\"Random Variable X\")\n",
"ax3.set_ylabel(\"Density\")\n",
"ax3.set_title(\"\"\"n = {}\"\"\".format(n3))\n",
"\n",
"fig.tight_layout(h_pad=2.5)\n",
"#fig.savefig(\"fig-H0sampling-vs-Truesampling.pdf\")\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the figures above we see that as sample size increases, the sampling distributions of the mean for the null and true distributions have less and less overlap, suggesting that as sample size increases the probability that we corrected reject the null hypothesis will increase as well."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exploring power with simulations: varying sample size\n",
"\n",
"In general one doesn't know either $\\mu_{H_A}$ or $\\sigma$ so these parameters need to be estimated from the data itself. Because of this, in the simulations below we use the t-distribution to model the sampling distribution of the mean under the null hypothesis.\n",
"\n",
"Each set of simulations below estimates the probability that you reject that null hypothesis, given the alternative hypothesis is true for a fixed effect size. The significance threshold, $\\alpha = 0.5$, for all the simulations."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For sample size n = 10\n",
"the percent of simulations where we failed to reject H0 is: 71.3\n",
"and hence, the percent of simulations where we correctly rejected H0 is: 28.700000000000003\n"
]
},
{
"data": {
"image/png": 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O1K1bl1WrVhX4z6AgPv30U1xcXAgLC2Pp0qXFOrYQ1mzJyk9Yve1HPos+aFqO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\n9etDKjHzeDx8++237Nq1q8Ji98BsWKJj47j1/ie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"text/plain": [
"<matplotlib.figure.Figure at 0x7f7b83372668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n = 10\n",
"mu_null = 0\n",
"mu_alt = 0.5\n",
"sigma = 1\n",
"SE = sigma/np.sqrt(n)\n",
"samplingdist_null = stats.t(df=n-1,scale=SE)\n",
"dist_alt = stats.norm(loc=mu_alt, scale=sigma)\n",
"\n",
"nsims = 1000\n",
"alpha = 0.05\n",
"null_left_cutoff = mu_null + samplingdist_null.ppf(alpha/2)\n",
"null_right_cutoff = mu_null + samplingdist_null.ppf(1-(alpha/2))\n",
"\n",
"sample_means = []\n",
"sample_zscores = []\n",
"\n",
"for i in range(nsims):\n",
" sample = dist_alt.rvs(size=n)\n",
" mean = np.mean(sample)\n",
" sample_means.append(mean)\n",
" zscore = mean/np.std(sample,ddof=1)\n",
" sample_zscores.append(zscore)\n",
" \n",
"sample_means = np.asarray(sample_means)\n",
"sample_zscores = np.asarray(sample_zscores)\n",
"\n",
"fig, ax = plt.subplots(figsize=(10,4))\n",
"x = np.linspace(-2,2,250)\n",
"ax.plot(x, samplingdist_null.pdf(x), color='black', label=\"Expected distn of\\nsample means, H0\")\n",
"ax.hist(sample_means, bins=50, normed=True, label=\"Observed distn of\\nsample means, HA\",\n",
" color='steelblue', histtype='stepfilled', alpha=0.5)\n",
"ax.set_xlabel(\"mean X\")\n",
"ax.set_ylabel(\"density\")\n",
"\n",
"min_y, max_y = ax.get_ylim()\n",
"\n",
"# draw left_cutoff\n",
"ax.vlines(null_left_cutoff, 0, max_y, linestyle='dotted')\n",
"# draw right cutoff\n",
"ax.vlines(null_right_cutoff, 0, max_y, linestyle='dotted')\n",
"\n",
"ax.legend(loc='upper left')\n",
"\n",
"failed_to_reject_H0 = np.logical_and(sample_zscores > null_left_cutoff, \n",
" sample_zscores < null_right_cutoff)\n",
"\n",
"How_often_failed_to_reject_H0 = np.count_nonzero(failed_to_reject_H0)/nsims\n",
"print(\"For sample size n =\", n)\n",
"print(\"the percent of simulations where we failed to reject H0 is:\", \n",
" How_often_failed_to_reject_H0 * 100)\n",
"print(\"and hence, the percent of simulations where we correctly rejected H0 is:\", \n",
" (1.0 - How_often_failed_to_reject_H0) * 100)\n",
"\n",
"#fig.savefig(\"fig-powersim-n10.pdf\")\n",
"\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For sample size n = 25\n",
"the percent of simulations where we failed to reject H0 is: 29.9\n",
"and hence, the percent of simulations where we correctly rejected H0 is: 70.10000000000001\n"
]
},
{
"data": {
"image/png": 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XaDSws7PT7gcEBCAxMbFETPHx8fj666+1iYpGo8Hly5dLbfuocZTV96JFi7RDnhqNBrGx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\nqlWrGDduXKLDSDrpWC9Hjhxh06ZNrFu3jpUrV7J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"text/plain": [
"<matplotlib.figure.Figure at 0x7f7b829edbe0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n = 25\n",
"mu_null = 0\n",
"mu_alt = 0.5\n",
"sigma = 1\n",
"SE = sigma/np.sqrt(n)\n",
"samplingdist_null = stats.t(df=n-1,scale=SE)\n",
"dist_alt = stats.norm(loc=mu_alt, scale=sigma)\n",
"\n",
"nsims = 1000\n",
"alpha = 0.05\n",
"null_left_cutoff = mu_null + samplingdist_null.ppf(alpha/2)\n",
"null_right_cutoff = mu_null + samplingdist_null.ppf(1-(alpha/2))\n",
"\n",
"sample_means = []\n",
"sample_zscores = []\n",
"\n",
"for i in range(nsims):\n",
" sample = dist_alt.rvs(size=n)\n",
" mean = np.mean(sample)\n",
" sample_means.append(mean)\n",
" zscore = mean/np.std(sample,ddof=1)\n",
" sample_zscores.append(zscore)\n",
" \n",
"sample_means = np.asarray(sample_means)\n",
"sample_zscores = np.asarray(sample_zscores)\n",
"\n",
"fig, ax = plt.subplots(figsize=(10,4))\n",
"x = np.linspace(-2,2,250)\n",
"ax.plot(x, samplingdist_null.pdf(x), color='black', label=\"Expected distn of\\nsample means, H0\")\n",
"ax.hist(sample_means, bins=50, normed=True, label=\"Observed distn of\\nsample means, HA\",\n",
" color='steelblue', histtype='stepfilled', alpha=0.5)\n",
"ax.set_xlabel(\"mean X\")\n",
"ax.set_ylabel(\"density\")\n",
"\n",
"min_y, max_y = ax.get_ylim()\n",
"\n",
"# draw left_cutoff\n",
"ax.vlines(null_left_cutoff, 0, max_y, linestyle='dotted')\n",
"# draw right cutoff\n",
"ax.vlines(null_right_cutoff, 0, max_y, linestyle='dotted')\n",
"\n",
"ax.legend(loc='upper left')\n",
"\n",
"failed_to_reject_H0 = np.logical_and(sample_zscores > null_left_cutoff, \n",
" sample_zscores < null_right_cutoff)\n",
"\n",
"How_often_failed_to_reject_H0 = np.count_nonzero(failed_to_reject_H0)/nsims\n",
"print(\"For sample size n =\", n)\n",
"print(\"the percent of simulations where we failed to reject H0 is:\", \n",
" How_often_failed_to_reject_H0 * 100)\n",
"print(\"and hence, the percent of simulations where we correctly rejected H0 is:\", \n",
" (1.0 - How_often_failed_to_reject_H0) * 100)\n",
"\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For sample size n = 50\n",
"the percent of simulations where we failed to reject H0 is: 5.7\n",
"and hence, the percent of simulations where we correctly rejected H0 is: 94.3\n"
]
},
{
"data": {
"image/png": 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kjrLaLpg419nZmdatW7Nu3TrTvhUrVtCzZ0/eeOMN3NzcCAwMZPPmzab9y5cvJzAwEGdn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\nqn7VCW1XAytXMr5cT6VJy/7Wrl3LokWLUpWnNOx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"text/plain": [
"<matplotlib.figure.Figure at 0x7f7b829ed080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n = 50\n",
"mu_null = 0\n",
"mu_alt = 0.5\n",
"sigma = 1\n",
"SE = sigma/np.sqrt(n)\n",
"samplingdist_null = stats.t(df=n-1,scale=SE)\n",
"dist_alt = stats.norm(loc=mu_alt, scale=sigma)\n",
"\n",
"nsims = 1000\n",
"alpha = 0.05\n",
"null_left_cutoff = mu_null + samplingdist_null.ppf(alpha/2)\n",
"null_right_cutoff = mu_null + samplingdist_null.ppf(1-(alpha/2))\n",
"\n",
"sample_means = []\n",
"sample_zscores = []\n",
"\n",
"for i in range(nsims):\n",
" sample = dist_alt.rvs(size=n)\n",
" mean = np.mean(sample)\n",
" sample_means.append(mean)\n",
" zscore = mean/np.std(sample,ddof=1)\n",
" sample_zscores.append(zscore)\n",
" \n",
"sample_means = np.asarray(sample_means)\n",
"sample_zscores = np.asarray(sample_zscores)\n",
"\n",
"fig, ax = plt.subplots(figsize=(10,4))\n",
"x = np.linspace(-2,2,250)\n",
"ax.plot(x, samplingdist_null.pdf(x), color='black', label=\"Expected distn of\\nsample means, H0\")\n",
"ax.hist(sample_means, bins=50, normed=True, label=\"Observed distn of\\nsample means, HA\",\n",
" color='steelblue', histtype='stepfilled', alpha=0.5)\n",
"ax.set_xlabel(\"mean X\")\n",
"ax.set_ylabel(\"density\")\n",
"\n",
"min_y, max_y = ax.get_ylim()\n",
"\n",
"# draw left_cutoff\n",
"ax.vlines(null_left_cutoff, 0, max_y, linestyle='dotted')\n",
"# draw right cutoff\n",
"ax.vlines(null_right_cutoff, 0, max_y, linestyle='dotted')\n",
"\n",
"ax.legend(loc='upper left')\n",
"\n",
"failed_to_reject_H0 = np.logical_and(sample_zscores > null_left_cutoff, \n",
" sample_zscores < null_right_cutoff)\n",
"\n",
"How_often_failed_to_reject_H0 = np.count_nonzero(failed_to_reject_H0)/nsims\n",
"print(\"For sample size n =\", n)\n",
"print(\"the percent of simulations where we failed to reject H0 is:\", \n",
" How_often_failed_to_reject_H0 * 100)\n",
"print(\"and hence, the percent of simulations where we correctly rejected H0 is:\", \n",
" (1.0 - How_often_failed_to_reject_H0) * 100)\n",
"\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can repeat this exercise for multiple sample sizes as shown below."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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0BD529+0xxdcmzSWEsrIynnnmGU499dQEohIRyW+xJYR8ki4h7Ny5k2OPPZaG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\nFCAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7b829edc18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mu_null = 0\n",
"mu_alt = 0.5\n",
"sigma = 1\n",
"\n",
"nsims = 2000\n",
"sizes = [5, 10, 20, 40, 60, 80, 100, 150, 300]\n",
"\n",
"alpha = 0.05\n",
"SEs = [sigma/np.sqrt(i) for i in sizes]\n",
"left_cutoffs = [mu_null + stats.t.ppf(alpha/2, df=i-1, loc=mu_null, scale=j)\n",
" for (i,j) in zip(sizes, SEs)]\n",
"right_cutoffs = [mu_null + stats.t.ppf((1-alpha/2), df=i-1, loc=mu_null, scale=j)\n",
" for (i,j) in zip(sizes,SEs)]\n",
"\n",
"dist_alt = stats.norm(loc=mu_alt, scale=sigma)\n",
"samples = [dist_alt.rvs(size=(i, nsims)) for i in sizes]\n",
"sample_means = np.asarray([np.mean(i, axis=0) for i in samples])\n",
"sample_stds = np.asarray([np.std(i, ddof=1, axis=0) for i in samples])\n",
"sample_zs = (sample_means-mu_null)/sample_stds\n",
"\n",
"failed_to_reject_H0 = [np.logical_and(i > left, i < right) for (i, left, right) \n",
" in zip(sample_zs, left_cutoffs, right_cutoffs)]\n",
"\n",
"frac_failed_to_reject_H0 = [np.count_nonzero(i)/nsims for i in failed_to_reject_H0]\n",
"\n",
"correctly_rejected_H0 = [(1-i) for i in frac_failed_to_reject_H0]\n",
"\n",
"fig, ax = plt.subplots(figsize=(6,4))\n",
"ax.plot(sizes, correctly_rejected_H0, marker='o', color='black')\n",
"ax.set_xlabel(\"sample size, n\")\n",
"ax.set_ylabel(\"Power ( = Probability $H_0$ correctly rejected)\")\n",
"ax.set_ylim(0,1.1)\n",
"ax.set_xlim(0, max(sizes)*1.05)\n",
"\n",
"#fig.savefig('fig-powercurve-diff05.pdf')\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Varying both sample size and effect size\n",
"\n",
"In the simulations above we kept the effect size (the difference between the mean of the null and true distributions in our example) constant. Now let's explore how both sample size and effect size influence power. We'll consider three different scenarios -- $\\mu_{H_A} = 1$, $\\mu_{H_A} = 0.5$, and $\\mu_{H_A} = 0.25$ ($\\mu_{H_0} = 0$ in all three cases). As before, the significance threshold, $\\alpha = 0.5$, for all the simulations."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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V1dWA81zc2trKtm3biIuL89WWnpFmzyRv2rSJrq4uj6NR/aVFsvIl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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7b828e8278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"def power_sim(mu_null, mu_alt, sigma, alpha=0.05,\n",
" ssizes = [5, 10, 20, 40, 60, 80, 100, 150],\n",
" nsims = 2000):\n",
"\n",
" SEs = [sigma/np.sqrt(i) for i in ssizes]\n",
" left_cutoffs = [mu_null + stats.norm.ppf(alpha/2, loc=mu_null, scale=i)\n",
" for i in SEs]\n",
" right_cutoffs = [mu_null + stats.norm.ppf((1-alpha/2), loc=mu_null, scale=i)\n",
" for i in SEs]\n",
"\n",
" dist_alt = stats.norm(loc=mu_alt, scale=sigma)\n",
" samples = [dist_alt.rvs(size=(i, nsims)) for i in ssizes]\n",
" sample_means = np.asarray([np.mean(i, axis=0) for i in samples])\n",
" sample_stds = np.asarray([np.std(i, ddof=1, axis=0) for i in samples])\n",
" sample_zs = (sample_means-mu_null)/sample_stds\n",
"\n",
" failed_to_reject_H0 = [np.logical_and(i > left, i < right) for (i, left, right) \n",
" in zip(sample_zs, left_cutoffs, right_cutoffs)]\n",
"\n",
" frac_failed_to_reject_H0 = [np.count_nonzero(i)/nsims for i in failed_to_reject_H0]\n",
"\n",
" correctly_rejected_H0 = [(1-i) for i in frac_failed_to_reject_H0]\n",
" return ssizes, correctly_rejected_H0\n",
"\n",
"\n",
"\n",
"ssizes, power025 = power_sim(0, 0.25, 1)\n",
"ssizes, power05 = power_sim(0, 0.5, 1)\n",
"ssizes, power1 = power_sim(0, 1, 1)\n",
"\n",
"\n",
"fig, ((ax1,ax2),(ax3,ax4)) = plt.subplots(2, 2, figsize=(10,10))\n",
"\n",
"x = np.linspace(-4,4,250)\n",
"ax1.plot(x, stats.norm.pdf(x), linestyle='dashed', color='gray', linewidth=2)\n",
"ax1.plot(x, stats.norm.pdf(x, loc=1), color='blue', linewidth=2)\n",
"ax1.set_xlabel(\"X\")\n",
"ax1.set_ylabel(\"Density\")\n",
"ax1.set_title(\"True diff in means Zscore = 1\")\n",
"\n",
"ax2.plot(x, stats.norm.pdf(x), linestyle='dashed', color='gray', linewidth=2)\n",
"ax2.plot(x, stats.norm.pdf(x, loc=0.5), color='black', linewidth=2)\n",
"ax2.set_xlabel(\"X\")\n",
"ax2.set_ylabel(\"Density\")\n",
"ax2.set_title(\"True diff in means Zscore = 0.5\")\n",
"\n",
"ax3.plot(x, stats.norm.pdf(x), linestyle='dashed', color='gray', linewidth=2)\n",
"ax3.plot(x, stats.norm.pdf(x, loc=0.25), color='red', linewidth=2)\n",
"ax3.set_xlabel(\"X\")\n",
"ax3.set_ylabel(\"Density\")\n",
"ax3.set_title(\"True diff in means Zscore = 0.25\")\n",
"\n",
"ax4.plot(ssizes, power025, marker='o', color='red', label=\"true diff = 0.25\")\n",
"ax4.plot(ssizes, power05, marker='o', color='black', label=\"true diff = 0.5\")\n",
"ax4.plot(ssizes, power1, marker='o', color='blue', label=\"true diff = 1\")\n",
"ax4.set_xlabel(\"sample size, n\")\n",
"ax4.set_ylabel(\"Power\")\n",
"ax4.set_ylim(0,1.1)\n",
"ax4.set_xlim(0, max(ssizes)*1.05)\n",
"ax4.legend(loc='best')\n",
"ax4.set_title(\"Power for alpha = 0.5\\n for variable effect sizes\")\n",
"\n",
"fig.tight_layout()\n",
"#fig.savefig('fig-powercurves-variable-diffs.pdf')\n",
"\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As intuition would suggest, we need to consider sample size and effect size when considering the power of a test. We can get away with fewer samples of the effect size is very large."
]
}
],
"metadata": {
"anaconda-cloud": {},
"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
}
| cc0-1.0 |
jobovy/misc-notebooks | apogee/lsf-deconvolution-example.ipynb | 1 | 1306585 | null | bsd-3-clause |
opesci/devito | examples/seismic/abc_methods/02_damping.ipynb | 1 | 316378 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# 2 - Damping"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2.1 - Introduction\n",
"\n",
"In this notebook we describe a simple method for reduction of reflections at the computational boundaries of the domain $\\Omega$ when we simulate the acoustic wave equation. This method, called *Damping*, has been proposed by Sochaki. It adds a damping term, modifying the original wave equation at a boundary layer. We saw in the notebook <a href=\"01_introduction.ipynb\">Introduction to Acoustic Problem</a> that the (artificial) wave reflections on the computational boundaries lead to a very noisy solution of the acoustic problem. \n",
"\n",
"We describe this method in the next Sections, omitting information already discussed in the notebook <a href=\"01_introduction.ipynb\">Introduction to Acoustic Problem</a>, highlighting only the new elements necessary to apply Damping."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2.2 - Acoustic Problem with Damping\n",
"\n",
"We define an extension of the spatial domain $\\Omega=\\left[x_{I}-L_{x},x_{F}+L_{x}\\right] \\times\\left[z_{I},z_{F}+L_{z}\\right]$, in which we added an *absorption region* to the previous spatial domain\n",
"$\\Omega_{0}=\\left[x_{I},x_{F}\\right]\\times\\left[z_{I},z_{F}\\right]$. \n",
"The *absorption region* is composed by two bands of length $L_{x}$ at the beginning and end of the domain in the direction $x$ and of a band of length $L_{z}$ at the end of the domain in the $z$ direction. Again, $\\partial\\Omega$ denotes the boundary of $\\Omega$. The figure below shows the extended domain $\\Omega$, with the absorption region highlighted in blue.\n",
"\n",
"<img src='domain2.png' width=500>\n",
"\n",
"The damping acoustic problem equation is given by:\n",
"\n",
"\\begin{equation}\n",
"u_{tt}(x,z,t)+c^2(x,z)\\zeta(x,z)u_t(x,z,t)-c^2(x,z)\\Delta(u(x,z,t))=c^2(x,z)f(x,z,t),\n",
"\\end{equation}\n",
"\n",
"where $u(x,z,t)$, $f(x,z,t)$ and $c(x,z)$ are as before. The wave equation has been modified by the introduction of the damping term $c^2(x,z)\\zeta(x,z)u_t(x,z,t)$, where $\\zeta$ is different from zero only in the absorption region, growing smoothly along the absorption bands from zero to its maximum at the outer boundary. The actual form of\n",
"$\\zeta$ used in this notebook will be given ahead. We still use the same initial conditions\n",
"\n",
"\\begin{equation}\n",
"u(x,z,0) = 0.0 \\hspace{.5cm} \\mbox{ and } \\hspace{.5cm} u_t(x,z,0)= 0.0.\n",
"\\end{equation}\n",
"\n",
"and Dirichlet null boundary conditions at the (outer) bottom and lateral boundaries. At the surface we\n",
"use a zero Neumman boundary condition.\n",
"\n",
"The source term and the velocity field are defined as before."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2.3 - Finite Difference Operators and Discretization of Spatial and Temporal Domains\n",
"\n",
"The only difference with respect to the discretization used in the notebook <a href=\"01_introduction.ipynb\">Introduction to Acoustic Problem</a> is the extra damping term. The temporal derivative of $u$ is approximated by a centered difference:\n",
"$$ u_t(x_i,z_j,t_k) = \\frac{u_{i,j,k+1}-u_{i,j,k-1}}{2\\Delta t} $$. All the other terms are discretized as before.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2.4 - Standard Problem\n",
"\n",
"Redeeming the Standard Problem definitions discussed on the notebook <a href=\"01_introduction.ipynb\">Introduction to Acoustic Problem</a> we have that:\n",
"\n",
"- $x_{I}$ = 0.0 Km;\n",
"- $x_{F}$ = 1.0 Km = 1000 m;\n",
"- $z_{I}$ = 0.0 Km;\n",
"- $z_{F}$ = 1.0 Km = 1000 m;\n",
"- $L_x$ and $L_z$ will be defined ahead;\n",
"\n",
"The spatial discretization parameters are given by:\n",
"- $\\Delta x$ = 0.01 km = 10m;\n",
"- $\\Delta z$ = 0.01 km = 10m;\n",
"\n",
"Let's consider a $I$ the time domain with the following limitations:\n",
"\n",
"- $t_{I}$ = 0 s = 0 ms;\n",
"- $t_{F}$ = 1 s = 1000 ms;\n",
"\n",
"The temporal discretization parameters are given by:\n",
"\n",
"- $\\Delta t$ $\\approx$ 0.0016 s = 1.6 ms;\n",
"- $NT$ = 626.\n",
"\n",
"With respect to the $f(x,z,t)$ external force term, we will consider a Ricker source with the following properties:\n",
"\n",
"- Position at $x:$ $\\bar{x} = 500 m = 0.5 Km$;\n",
"- Position at $z:$ $\\bar{z} = 10 m = 0.01 Km$;\n",
"- Peak frequency: $f_{0} = 10 Hz = 0.01 Khz$;\n",
"\n",
"The graph of $f(\\bar{x},\\bar{z},t)$ will be generated when building the code. We will use a velocity profile $c(x, z)$ with the following properties:\n",
"\n",
"- Minimum propagation velocity: $v_{min} = 1500 m/s = 1,5 Km/s$;\n",
"- Maximum propagation velocity: $v_{max} = 2500 m/s = 2,5 Km/s$;\n",
"\n",
"The figure of the velocity profile will be generated when building the code. We introduce receivers along the $x$ direction, that is, at all discrete points between $0.0$ Km and $1.0$ Km , at depth $z=0.01$ Km to generate the seismogram."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2.5 - Damping Functions\n",
"\n",
"Sochaki proposed various forms for the damping function $\\zeta$, including linear, cubic or exponential functions. In general, the damping functions have a similar characteristic: they are zero in the \"interior\" domain $\\Omega_{0}$ and increase toward the outer boundary $\\partial\\Omega$. \n",
"\n",
"Our particular damping function will be chosen as follows.\n",
" We define the pair of functions $\\zeta_{1}(x,z)$ and $\\zeta_{2}(x,z)$ given, respectively, by:\n",
"\n",
"\\begin{equation}\n",
"\\zeta_{1}(x,z)=\\left\\{ \\begin{array}{ll}\n",
"0, & \\textrm{if $x\\in \\left(x_{I},x_{F}\\right)$,}\\\\ \\bar{\\zeta}_{1}(x,z)\\left(\\displaystyle\\frac{\\vert x-x_{I} \\vert}{L_{x}}-\\displaystyle\\frac{1}{2\\pi}\\sin\\left(\\displaystyle\\frac{2\\pi\\vert x-x_{I} \\vert}{L_{x}}\\right)\\right) , & \\textrm{if $x_{I}-L_{x}\\leq x \\leq x_{I}$,}\\\\ \\bar{\\zeta}_{1}(x,z)\\left(\\displaystyle\\frac{\\vert x-x_{F} \\vert}{L_{x}}-\\displaystyle\\frac{1}{2\\pi}\\sin\\left(\\displaystyle\\frac{2\\pi\\vert x-x_{F} \\vert}{L_{x}}\\right)\\right) , & \\textrm{if $x_{F}\\leq x \\leq x_{F}+L_{x}$.}\\end{array}\\right.\n",
"\\end{equation} \n",
"\n",
"\\begin{equation}\n",
"\\zeta_{2}(x,z)=\\left\\{ \\begin{array}{ll}\n",
"0, & \\textrm{if $z\\in \\left(z_{I},z_{F}\\right)$,} \\\\ \\bar{\\zeta}_{2}(x,z)\\left(\\displaystyle\\frac{\\vert z-z_{F} \\vert}{L_{z}}-\\displaystyle\\frac{1}{2\\pi}\\sin\\left(\\displaystyle\\frac{2\\pi\\vert z-z_{F} \\vert}{L_{z}}\\right)\\right) , & \\textrm{if $z_{F}\\leq z \\leq z_{F}+L_{z}$.}\\end{array}\\right.\n",
"\\end{equation} \n",
"\n",
"Thus, we define the function $\\zeta(x,z)$ as being the following function:\n",
"\n",
"\\begin{equation}\n",
"\\zeta(x,z) = \\displaystyle\\frac{1}{v_{max}}\\left(\\displaystyle\\frac{\\zeta_{1}(x,z)}{\\Delta x}+\\displaystyle\\frac{\\zeta_{2}(x,z)}{\\Delta z} \\right) ,\n",
"\\end{equation}\n",
"\n",
"where $v_{max}$denotes the maximum velocity of propagation of $c(x,z)$. Below we display the shape of the function $\\zeta_1(x,z)$ with $\\bar{\\zeta_{1}}(x,z)=0.26$ at the left band of the domain. It is similar at the other ones. The figures of the damping profiles will be generated when building the code."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2.6 - Numerical Simulations\n",
"\n",
"In the numerical simulations we import the following Python and Devito packages:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# NBVAL_IGNORE_OUTPUT\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plot\n",
"import math as mt\n",
"import matplotlib.ticker as mticker \n",
"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
"from matplotlib import cm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From Devito's library of examples we import the following structures:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# NBVAL_IGNORE_OUTPUT\n",
"\n",
"%matplotlib inline\n",
"from examples.seismic import TimeAxis\n",
"from examples.seismic import RickerSource\n",
"from examples.seismic import Receiver\n",
"from devito import SubDomain, Grid, NODE, TimeFunction, Function, Eq, solve, Operator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The mesh parameters define the domain $\\Omega_{0}$. The absorption region will be included bellow."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"nptx = 101\n",
"nptz = 101\n",
"x0 = 0.\n",
"x1 = 1000. \n",
"compx = x1-x0\n",
"z0 = 0.\n",
"z1 = 1000.\n",
"compz = z1-z0;\n",
"hxv = (x1-x0)/(nptx-1)\n",
"hzv = (z1-z0)/(nptz-1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Observation:** In this code we need to work with symbolic values and the real values of $\\Delta x$ and $\\Delta z$, then the numerica values of $\\Delta x$ and $\\Delta z$ are represented by *hxv* and *hzv*, respectively. The symbolic values of $\\Delta x$ and $\\Delta z$ will be given after.\n",
"\n",
"In this case, we need to define the size of the bands $L_{x}$ and $L_{z}$ that extend the domain $\\Omega_{0}$ for $\\Omega$. The code that we will implement will build the values $L_{x}$ and $L_{z}$ from choosing a certain amount of points in each direction. Without loss of generality, we say that the size $L_{x}$ is such that:\n",
"\n",
"- $L_{x}$ = npmlx*$\\Delta x$;\n",
"- *0<npmlx<nptx;*\n",
"\n",
"Similarly, we have $L_{z}$ such that:\n",
"\n",
"- $L_{z}$ = npmlz*$\\Delta z$;\n",
"- *0<npmlz<nptz*; \n",
"\n",
"So, we can explicitly define the lengths $L_{x}$ and $L_{z}$ depending on the number of points *npmlx* and *npmlz*. Thus, we choose these values as being:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"npmlx = 20\n",
"npmlz = 20"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And we define $L_{x}$ and $L_{z}$ as beeing:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"lx = npmlx*hxv\n",
"lz = npmlz*hzv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Thus, from the *nptx* points, the first and the last *npmlx* points are in the absorption region of the *x* direction. Similarly, from the *nptz* points, the last *npmlz* points are in the absorption region of the *z* direction. Considering the construction of *grid*, we also have the following elements:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"nptx = nptx + 2*npmlx\n",
"nptz = nptz + 1*npmlz\n",
"x0 = x0 - hxv*npmlx\n",
"x1 = x1 + hxv*npmlx\n",
"compx = x1-x0\n",
"z0 = z0\n",
"z1 = z1 + hzv*npmlz\n",
"compz = z1-z0\n",
"origin = (x0,z0)\n",
"extent = (compx,compz)\n",
"shape = (nptx,nptz)\n",
"spacing = (hxv,hzv)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The $\\zeta(x,z)$ function is non zero only in the blue region in the figure that represents the domain. In this way, the wave equation can be divided into 2 situations:\n",
"\n",
"- In the region in blue:\n",
"\n",
"\\begin{equation}\n",
"u_{tt}(x,z,t)+c^2(x,z)\\zeta(x,z)u_t(x,z,t)-c^2(x,z)^\\Delta(u(x,z,t))=c^2(x,z)f(x,z,t),\n",
"\\end{equation}\n",
"\n",
"- In the white region:\n",
"\n",
"\\begin{equation}\n",
"u_{tt}(x,z,t)-c^2(x,z)^\\Delta(u(x,z,t))=c^2(x,z)f(x,z,t),\n",
"\\end{equation}\n",
"\n",
"For this reason, we use the structure of the *subdomains* to represent the white region and the blue region.\n",
"\n",
"**Observation:** Note that we can describe the blue region in different ways, that is, the way we choose here is not the only possible discretization for that region.\n",
"\n",
"First, we define the white region, naming this region as *d0*, which is defined by the following pairs of points $(x,z)$:\n",
"\n",
"- $x\\in\\{npmlx,nptx-npmlx\\}$ and $z\\in\\{0,nptz-npmlz\\}$.\n",
"\n",
"In the language of *subdomains* *d0 it is written as:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"class d0domain(SubDomain):\n",
" name = 'd0'\n",
" def define(self, dimensions):\n",
" x, z = dimensions\n",
" return {x: ('middle', npmlx, npmlx), z: ('middle', 0, npmlz)}\n",
"d0_domain = d0domain()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The blue region will be the union of the following regions:\n",
"\n",
"- *d1* represents the left range in the direction *x*, where the pairs $(x,z)$ satisfy: $x\\in\\{0,npmlx\\}$ and $z\\in\\{0,nptz\\}$;\n",
"- *d2* represents the rigth range in the direction *x*, where the pairs $(x,z)$ satisfy: $x\\in\\{nptx-npmlx,nptx\\}$ and $z\\in\\{0,nptz\\}$;\n",
"- *d3* represents the left range in the direction *y*, where the pairs $(x,z)$ satisfy: $x\\in\\{npmlx,nptx-npmlx\\}$ and $z\\in\\{nptz-npmlz,nptz\\}$;\n",
"\n",
"Thus, the regions *d1*, *d2* and *d3* are described as follows in the language of *subdomains*:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"class d1domain(SubDomain):\n",
" name = 'd1'\n",
" def define(self, dimensions):\n",
" x, z = dimensions\n",
" return {x: ('left',npmlx), z: z}\n",
"d1_domain = d1domain()\n",
"\n",
"class d2domain(SubDomain):\n",
" name = 'd2'\n",
" def define(self, dimensions):\n",
" x, z = dimensions\n",
" return {x: ('right',npmlx), z: z}\n",
"d2_domain = d2domain()\n",
"\n",
"class d3domain(SubDomain):\n",
" name = 'd3'\n",
" def define(self, dimensions):\n",
" x, z = dimensions\n",
" return {x: ('middle', npmlx, npmlx), z: ('right',npmlz)}\n",
"d3_domain = d3domain()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The figure below represents the division of domains that we did previously:\n",
"\n",
"<img src='domain2.png' width=500>\n",
"\n",
"The advantage of dividing into regions is that the equations will be calculated where they actually operate and thus we gain computational efficiency, as we decrease the number of operations to be done. After defining the spatial parameters and constructing the *subdomains*, we set the *spatial grid* with the following command:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"grid = Grid(origin=origin, extent=extent, shape=shape, subdomains=(d0_domain,d1_domain,d2_domain,d3_domain), dtype=np.float64)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, we use a velocity field given by a binary file. The reading and scaling of the velocity field for the Devito work units is done with the following commands:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"v0 = np.zeros((nptx,nptz)) \n",
"X0 = np.linspace(x0,x1,nptx)\n",
"Z0 = np.linspace(z0,z1,nptz)\n",
" \n",
"x10 = x0+lx\n",
"x11 = x1-lx\n",
" \n",
"z10 = z0\n",
"z11 = z1 - lz\n",
"\n",
"xm = 0.5*(x10+x11)\n",
"zm = 0.5*(z10+z11)\n",
" \n",
"pxm = 0\n",
"pzm = 0\n",
" \n",
"for i in range(0,nptx):\n",
" if(X0[i]==xm): pxm = i\n",
" \n",
"for j in range(0,nptz):\n",
" if(Z0[j]==zm): pzm = j\n",
" \n",
"p0 = 0 \n",
"p1 = pzm\n",
"p2 = nptz\n",
" \n",
"v0[0:nptx,p0:p1] = 1.5\n",
"v0[0:nptx,p1:p2] = 2.5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Previously we introduce the local variables *x10,x11,z10,z11,xm,zm,pxm* and *pzm* that help us to create a specific velocity field, where we consider the whole domain (including the absorpion region). Below we include a routine to plot the velocity field."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def graph2dvel(vel):\n",
" plot.figure()\n",
" plot.figure(figsize=(16,8))\n",
" fscale = 1/10**(3)\n",
" scale = np.amax(vel[npmlx:-npmlx,0:-npmlz])\n",
" extent = [fscale*(x0+lx),fscale*(x1-lx), fscale*(z1-lz), fscale*(z0)]\n",
" fig = plot.imshow(np.transpose(vel[npmlx:-npmlx,0:-npmlz]), vmin=0.,vmax=scale, cmap=cm.seismic, extent=extent)\n",
" plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n",
" plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n",
" plot.title('Velocity Profile')\n",
" plot.grid()\n",
" ax = plot.gca()\n",
" divider = make_axes_locatable(ax)\n",
" cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
" cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n",
" cbar.set_label('Velocity [km/s]')\n",
" plot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below we include the plot of velocity field."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 576x432 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# NBVAL_IGNORE_OUTPUT\n",
"\n",
"graph2dvel(v0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Time parameters are defined and constructed by the following sequence of commands:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"t0 = 0.\n",
"tn = 1000. \n",
"CFL = 0.4\n",
"vmax = np.amax(v0) \n",
"dtmax = np.float64((min(hxv,hzv)*CFL)/(vmax))\n",
"ntmax = int((tn-t0)/dtmax)+1\n",
"dt0 = np.float64((tn-t0)/ntmax)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the temporal parameters, we generate the time informations with *TimeAxis* as follows:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"time_range = TimeAxis(start=t0,stop=tn,num=ntmax+1)\n",
"nt = time_range.num - 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The symbolic values associated with the spatial and temporal grids that are used in the composition of the equations are given by:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"(hx,hz) = grid.spacing_map \n",
"(x, z) = grid.dimensions \n",
"t = grid.stepping_dim\n",
"dt = grid.stepping_dim.spacing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We chose a single Ricker source, whose frequency is $ 0.005Khz $. This source is positioned at $\\bar{x}$ = 35150m and $\\bar{z}$ = 32m. We then defined the following variables that represents our choice:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"f0 = 0.01\n",
"nsource = 1\n",
"xposf = 0.5*(compx-2*npmlx*hxv)\n",
"zposf = hzv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we know, Ricker's source is generated by the *RickerSource* command. Using the parameters listed above, we generate and position the Ricker source with the following sequence of commands:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"src = RickerSource(name='src',grid=grid,f0=f0,npoint=nsource,time_range=time_range,staggered=NODE,dtype=np.float64)\n",
"src.coordinates.data[:, 0] = xposf\n",
"src.coordinates.data[:, 1] = zposf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below we include the plot of Ricker source."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# NBVAL_IGNORE_OUTPUT\n",
"\n",
"src.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With respect to receivers, the number of receivers is the same number of discrete points in the $x$ direction. So, we position these receivers along the direction $x$, at height $\\bar{z}$ = 10m. In this way, our variables are chosen as:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"nrec = nptx\n",
"nxpos = np.linspace(x0,x1,nrec)\n",
"nzpos = hzv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we know, receivers are generated by the command *Receiver*. Thus, we use the parameters listed above and using the *Receiver* command, we create and position the receivers:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"rec = Receiver(name='rec',grid=grid,npoint=nrec,time_range=time_range,staggered=NODE,dtype=np.float64)\n",
"rec.coordinates.data[:, 0] = nxpos\n",
"rec.coordinates.data[:, 1] = nzpos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The displacement field *u* is a second order field in time and space, which uses points of type *non-staggered*. In this way, we construct the displacement field *u* with the command *TimeFunction*:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"u = TimeFunction(name=\"u\",grid=grid,time_order=2,space_order=2,staggered=NODE,dtype=np.float64)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The velocity field, the source term and receivers are defined as in the previous notebook: "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"vel0 = Function(name=\"vel0\",grid=grid,space_order=2,staggered=NODE,dtype=np.float64)\n",
"vel0.data[:,:] = v0[:,:]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"src_term = src.inject(field=u.forward,expr=src*dt**2*vel0**2)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"rec_term = rec.interpolate(expr=u)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next step is to create the sequence of structures that reproduce the function $\\zeta(x,z)$. Initially, we define the region $\\Omega_{0}$, since the damping function uses the limits of that region. We previously defined the limits of the $\\Omega$ region to be *x0*, *x1*, *z0* and *z1*. Now, we define the limits of the region $\\Omega_{0}$ as: *x0pml* and *x1pml* in the direction $x$ and *z0pml* and *z1pml* in the direction $z$. These points satisfy the following relationships with the lengths $L_{x}$ and $L_{z}$:\n",
"\n",
"- x0pml = x0 + $L_{x}$;\n",
"- x1pml = x1 - $L_{x}$;\n",
"- z0pml = z0;\n",
"- z1pml = z1 - $L_{z}$;\n",
"\n",
"In terms of program variables, we have the following definitions:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"x0pml = x0 + npmlx*hxv \n",
"x1pml = x1 - npmlx*hxv \n",
"z0pml = z0 \n",
"z1pml = z1 - npmlz*hzv "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Having built the $\\Omega$ limits, we then create a function, which we will call *fdamp*, which computationally represents the $\\zeta(x,z)$ function. In the *fdamp* function, we highlight the following elements:\n",
"\n",
"- *quibar* represents a constant choice for $\\bar{\\zeta_{1}}(x,z)$ and $\\bar{\\zeta_{2}}(x,z)$, satisfying $\\bar{\\zeta_{1}}(x,z)=\\bar{\\zeta_{2}}(x,z)$;\n",
"- *adamp* denotes the function $\\zeta_{1}(x,z)$;\n",
"- *bdamp* denotes the function $\\zeta_{2}(x,z)$;\n",
"- The terms *a* and *b* locate the $(x,z)$ points that are passed as an argument to the *fdamp* function.\n",
"\n",
"The *fdamp* function is defined using the following structure:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"def fdamp(x,z):\n",
"\n",
" quibar = 1.5*np.log(1.0/0.001)/(40)\n",
" cte = 1./vmax\n",
" \n",
" a = np.where(x<=x0pml,(np.abs(x-x0pml)/lx),np.where(x>=x1pml,(np.abs(x-x1pml)/lx),0.))\n",
" b = np.where(z<=z0pml,(np.abs(z-z0pml)/lz),np.where(z>=z1pml,(np.abs(z-z1pml)/lz),0.))\n",
" adamp = quibar*(a-(1./(2.*np.pi))*np.sin(2.*np.pi*a))/hxv\n",
" bdamp = quibar*(b-(1./(2.*np.pi))*np.sin(2.*np.pi*b))/hzv\n",
" fdamp = cte*(adamp+bdamp)\n",
"\n",
" return fdamp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Created the damping function, we define an array that loads the damping information in the entire domain $\\Omega$. The objective is to assign this array to a *Function* and use it in the composition of the equations. To generate this array, we will use the function *generatemdamp*. In summary, this function generates a *non-staggered* grid and evaluates that grid in the *fdamp* function. At the end, we generate an array that we call *D0* and which will be responsible for providing the damping value at each of the $\\Omega$ points. The *generatemdamp* function is expressed as follows:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"def generatemdamp():\n",
" \n",
" X0 = np.linspace(x0,x1,nptx) \n",
" Z0 = np.linspace(z0,z1,nptz) \n",
" X0grid,Z0grid = np.meshgrid(X0,Z0) \n",
" D0 = np.zeros((nptx,nptz)) \n",
" D0 = np.transpose(fdamp(X0grid,Z0grid))\n",
" \n",
" return D0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Built the function *generatemdamp* we will execute it using the command:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"D0 = generatemdamp();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below we include a routine to plot the damping field."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"def graph2damp(D): \n",
" plot.figure()\n",
" plot.figure(figsize=(16,8))\n",
" fscale = 1/10**(-3)\n",
" fscale = 10**(-3)\n",
" scale = np.amax(D)\n",
" extent = [fscale*x0,fscale*x1, fscale*z1, fscale*z0]\n",
" fig = plot.imshow(np.transpose(D), vmin=0.,vmax=scale, cmap=cm.seismic, extent=extent)\n",
" plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n",
" plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n",
" plot.title('Absorbing Layer Function')\n",
" plot.grid()\n",
" ax = plot.gca()\n",
" divider = make_axes_locatable(ax)\n",
" cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
" cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n",
" cbar.set_label('Damping')\n",
" plot.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below we include the plot of damping field."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 576x432 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# NBVAL_IGNORE_OUTPUT\n",
"\n",
"graph2damp(D0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Like the velocity function $c(x,z)$, the damping function $\\zeta(x,z)$ is constant in time. Therefore, the damping function will be a second-order *Function* in space, which uses points of the non-staggered type and which we will evaluate with the D0 array. The symbolic name *damp* will be assigned to this field."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"damp = Function(name=\"damp\",grid=grid,space_order=2,staggered=NODE,dtype=np.float64)\n",
"damp.data[:,:] = D0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The expressions for the acoustic equation with damping can be separeted between the white and blue regions.\n",
"\n",
"Translating these expressions in terms of an *eq* that can be inserted in a Devito code, we have that in the white region the equation takes the form:\n",
"\n",
"- eq1 = u.dt2 - vel0 * vel0 * u.laplace,\n",
"\n",
"and in the blue region we have the following equation:\n",
"\n",
"- eq2 = u.dt2 + vel0 * vel0 * damp * u.dtc - vel0 * vel0 * u.laplace.\n",
"\n",
"Here *u.dtc* represents the centered derivative with respect to the variable $t$ for the field *u*. Then, we set the two pdes for the two regions\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"pde0 = Eq(u.dt2 - u.laplace*vel0**2)\n",
"pde1 = Eq(u.dt2 - u.laplace*vel0**2 + vel0**2*damp*u.dtc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we did on the notebook <a href=\"introduction.ipynb\">Introduction to Acoustic Problem</a>, we define the *stencils* for each of the *pdes* that we created previously. In the case of *pde0* it is defined only in the white region, which is represented by *subdomain* *d0*. Then, we define the *stencil0* which resolves *pde0* in *d0* and it is defined as follows:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"stencil0 = Eq(u.forward, solve(pde0,u.forward),subdomain = grid.subdomains['d0'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The *pde1* will be applied in the blue region, the union of the subdomains *d1*, *d2* and *d3*. In this way, we create a vector called *subds* that comprises these three *subdomains*, and we are ready to set the corresponding stencil"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"subds = ['d1','d2','d3']"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"stencil1 = [Eq(u.forward, solve(pde1,u.forward),subdomain = grid.subdomains[subds[i]]) for i in range(0,len(subds))]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The boundary conditions of the problem are kept the same as the notebook <a href=\"1_introduction.ipynb\">Introduction to Acoustic Problem</a>. So these are placed in the term *bc* and have the following form:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"bc = [Eq(u[t+1,0,z],0.),Eq(u[t+1,nptx-1,z],0.),Eq(u[t+1,x,nptz-1],0.),Eq(u[t+1,x,0],u[t+1,x,1])]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then define the operator (*op*) that join the acoustic equation, source term, boundary conditions and receivers.\n",
"\n",
"- 1. The acoustic wave equation in the *d0* region: *[stencil0];*\n",
"- 2. The acoustic wave equation in the *d1*, *d2* and *d3* region: *[stencil1];*\n",
"- 3. Source term: *src_term;*\n",
"- 4. Boundary conditions: *bc;*\n",
"- 5. Receivers: *rec_term;*\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"# NBVAL_IGNORE_OUTPUT\n",
"\n",
"op = Operator([stencil0,stencil1] + src_term + bc + rec_term,subs=grid.spacing_map)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We reset the field *u*:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"u.data[:] = 0."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We assign in *op* the number of time steps it must execute and the size of the time step in the local variables *time* and *dt*, respectively. "
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Operator `Kernel` run in 0.02 s\n"
]
},
{
"data": {
"text/plain": [
"PerformanceSummary([(PerfKey(name='section0', rank=None),\n",
" PerfEntry(time=0.013632000000000042, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
" (PerfKey(name='section1', rank=None),\n",
" PerfEntry(time=3.799999999999999e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
" (PerfKey(name='section2', rank=None),\n",
" PerfEntry(time=3.3e-05, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
" (PerfKey(name='section3', rank=None),\n",
" PerfEntry(time=0.00027800000000000025, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[])),\n",
" (PerfKey(name='section4', rank=None),\n",
" PerfEntry(time=0.0010460000000000092, gflopss=0.0, gpointss=0.0, oi=0.0, ops=0, itershapes=[]))])"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# NBVAL_IGNORE_OUTPUT\n",
"\n",
"op(time=nt,dt=dt0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To view the result of the displacement field at the end time, we use the *graph2d* routine given by:"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"def graph2d(U): \n",
" plot.figure()\n",
" plot.figure(figsize=(16,8))\n",
" fscale = 1/10**(3)\n",
" scale = np.amax(U[npmlx:-npmlx,0:-npmlz])/10.\n",
" extent = [fscale*x0pml,fscale*x1pml,fscale*z1pml,fscale*z0pml]\n",
" fig = plot.imshow(np.transpose(U[npmlx:-npmlx,0:-npmlz]),vmin=-scale, vmax=scale, cmap=cm.seismic, extent=extent)\n",
" plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n",
" plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n",
" plot.axis('equal')\n",
" plot.title('Map - Acoustic Problem with Devito')\n",
" plot.grid()\n",
" ax = plot.gca()\n",
" divider = make_axes_locatable(ax)\n",
" cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
" cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n",
" cbar.set_label('Displacement [km]')\n",
" plot.draw()\n",
" plot.show()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 576x432 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# NBVAL_IGNORE_OUTPUT\n",
"\n",
"graph2d(u.data[0,:,:])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the solution obtained here has a reduction in noise when compared to the results displayed on the notebook <a href=\"01_introduction.ipynb\">Introduction to Acoustic Problem</a>. To plot the result of the Receivers we use the *graph2drec* routine."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"def graph2drec(rec): \n",
" plot.figure()\n",
" plot.figure(figsize=(16,8))\n",
" fscaled = 1/10**(3)\n",
" fscalet = 1/10**(3)\n",
" scale = np.amax(rec[:,npmlx:-npmlx])/10.\n",
" extent = [fscaled*x0pml,fscaled*x1pml, fscalet*tn, fscalet*t0]\n",
" fig = plot.imshow(rec[:,npmlx:-npmlx], vmin=-scale, vmax=scale, cmap=cm.seismic, extent=extent)\n",
" plot.gca().xaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f km'))\n",
" plot.gca().yaxis.set_major_formatter(mticker.FormatStrFormatter('%.1f s'))\n",
" plot.axis('equal')\n",
" plot.title('Receivers Signal Profile with Damping - Devito')\n",
" ax = plot.gca()\n",
" divider = make_axes_locatable(ax)\n",
" cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
" cbar = plot.colorbar(fig, cax=cax, format='%.2e')\n",
" plot.show()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Figure size 576x432 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# NBVAL_IGNORE_OUTPUT\n",
"\n",
"graph2drec(rec.data)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"assert np.isclose(np.linalg.norm(rec.data), 990, rtol=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2.6 - Conclusions\n",
"\n",
"\n",
"- The damping strategy is a simple way to reduce artificial wave reflections coming from the computational boundaries, leading to a solution with less noise at the end of the simulation, when compared to the results of the notebook <a href=\"01_introduction.ipynb\">Introduction to Acoustic Problem</a>. However, the level of artificial reflections on the boundaries is still high. In the following notebooks we present methods which are more effective."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2.7 - Reference\n",
"\n",
"- Sochaki, J., Kubichek, R., George, J., Fletcher, W.R. and Smithson, S. (1987). \"Absorbing boundary conditions and surface waves,\" Geophysics, 52(1), 60-71. DOI: 10.1190/1.1442241. <a href=\"https://library.seg.org/doi/abs/10.1190/1.1442241\">Reference Link.</a>"
]
}
],
"metadata": {
"hide_input": false,
"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": "3.8.5"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
subhankarb/Machine-Learning-PlayGround | Machine-Learning-Specialization/case_study_approach/week2/predicting_house_prices.ipynb | 1 | 95757 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fireup Graph Lab Create"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"A newer version of GraphLab Create (v2.0.1) is available! Your current version is v1.10.1.\n",
"\n",
"You can use pip to upgrade the graphlab-create package. For more information see https://dato.com/products/create/upgrade.\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import graphlab"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO] graphlab.cython.cy_server: GraphLab Create v1.10.1 started. Logging: /tmp/graphlab_server_1468334178.log\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"This non-commercial license of GraphLab Create is assigned to [email protected] and will expire on July 05, 2017. For commercial licensing options, visit https://turi.com/buy/.\n"
]
}
],
"source": [
"sales = graphlab.SFrame('home_data.gl/')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1180</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1955</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98178</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.51123398</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1951</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.72102274</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1933</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98028</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.73792661</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1965</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98136</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.52082</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1680</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1987</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98074</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.61681228</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">11</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3890</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1530</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2001</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98053</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.65611835</td>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1715</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1995</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98003</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.30972002</td>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1060</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1963</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98198</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.40949984</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1050</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">730</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1960</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98146</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.51229381</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1890</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2003</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98038</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.36840673</td>\n",
" </tr>\n",
"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">long</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_living15</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_lot15</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.25677536</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1340.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5650.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.3188624</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1690.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7639.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.23319601</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2720.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8062.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.39318505</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1360.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.04490059</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1800.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7503.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.00528655</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4760.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">101930.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.32704857</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2238.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">6819.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.31457273</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1650.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">9711.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.33659507</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1780.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8113.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.0308176</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2390.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7570.0</td>\n",
" </tr>\n",
"</table>\n",
"[21613 rows x 21 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tid\tstr\n",
"\tdate\tdatetime\n",
"\tprice\tint\n",
"\tbedrooms\tstr\n",
"\tbathrooms\tstr\n",
"\tsqft_living\tint\n",
"\tsqft_lot\tint\n",
"\tfloors\tstr\n",
"\twaterfront\tint\n",
"\tview\tint\n",
"\tcondition\tint\n",
"\tgrade\tint\n",
"\tsqft_above\tint\n",
"\tsqft_basement\tint\n",
"\tyr_built\tint\n",
"\tyr_renovated\tint\n",
"\tzipcode\tstr\n",
"\tlat\tfloat\n",
"\tlong\tfloat\n",
"\tsqft_living15\tfloat\n",
"\tsqft_lot15\tfloat\n",
"\n",
"Rows: 21613\n",
"\n",
"Data:\n",
"+------------+---------------------------+---------+----------+-----------+-------------+\n",
"| id | date | price | bedrooms | bathrooms | sqft_living |\n",
"+------------+---------------------------+---------+----------+-----------+-------------+\n",
"| 7129300520 | 2014-10-13 00:00:00+00:00 | 221900 | 3 | 1 | 1180 |\n",
"| 6414100192 | 2014-12-09 00:00:00+00:00 | 538000 | 3 | 2.25 | 2570 |\n",
"| 5631500400 | 2015-02-25 00:00:00+00:00 | 180000 | 2 | 1 | 770 |\n",
"| 2487200875 | 2014-12-09 00:00:00+00:00 | 604000 | 4 | 3 | 1960 |\n",
"| 1954400510 | 2015-02-18 00:00:00+00:00 | 510000 | 3 | 2 | 1680 |\n",
"| 7237550310 | 2014-05-12 00:00:00+00:00 | 1225000 | 4 | 4.5 | 5420 |\n",
"| 1321400060 | 2014-06-27 00:00:00+00:00 | 257500 | 3 | 2.25 | 1715 |\n",
"| 2008000270 | 2015-01-15 00:00:00+00:00 | 291850 | 3 | 1.5 | 1060 |\n",
"| 2414600126 | 2015-04-15 00:00:00+00:00 | 229500 | 3 | 1 | 1780 |\n",
"| 3793500160 | 2015-03-12 00:00:00+00:00 | 323000 | 3 | 2.5 | 1890 |\n",
"+------------+---------------------------+---------+----------+-----------+-------------+\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"| sqft_lot | floors | waterfront | view | condition | grade | sqft_above | sqft_basement |\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"| 5650 | 1 | 0 | 0 | 3 | 7 | 1180 | 0 |\n",
"| 7242 | 2 | 0 | 0 | 3 | 7 | 2170 | 400 |\n",
"| 10000 | 1 | 0 | 0 | 3 | 6 | 770 | 0 |\n",
"| 5000 | 1 | 0 | 0 | 5 | 7 | 1050 | 910 |\n",
"| 8080 | 1 | 0 | 0 | 3 | 8 | 1680 | 0 |\n",
"| 101930 | 1 | 0 | 0 | 3 | 11 | 3890 | 1530 |\n",
"| 6819 | 2 | 0 | 0 | 3 | 7 | 1715 | 0 |\n",
"| 9711 | 1 | 0 | 0 | 3 | 7 | 1060 | 0 |\n",
"| 7470 | 1 | 0 | 0 | 3 | 7 | 1050 | 730 |\n",
"| 6560 | 2 | 0 | 0 | 3 | 7 | 1890 | 0 |\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"| yr_built | yr_renovated | zipcode | lat | long | sqft_living15 | ... |\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"| 1955 | 0 | 98178 | 47.51123398 | -122.25677536 | 1340.0 | ... |\n",
"| 1951 | 1991 | 98125 | 47.72102274 | -122.3188624 | 1690.0 | ... |\n",
"| 1933 | 0 | 98028 | 47.73792661 | -122.23319601 | 2720.0 | ... |\n",
"| 1965 | 0 | 98136 | 47.52082 | -122.39318505 | 1360.0 | ... |\n",
"| 1987 | 0 | 98074 | 47.61681228 | -122.04490059 | 1800.0 | ... |\n",
"| 2001 | 0 | 98053 | 47.65611835 | -122.00528655 | 4760.0 | ... |\n",
"| 1995 | 0 | 98003 | 47.30972002 | -122.32704857 | 2238.0 | ... |\n",
"| 1963 | 0 | 98198 | 47.40949984 | -122.31457273 | 1650.0 | ... |\n",
"| 1960 | 0 | 98146 | 47.51229381 | -122.33659507 | 1780.0 | ... |\n",
"| 2003 | 0 | 98038 | 47.36840673 | -122.0308176 | 2390.0 | ... |\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"[21613 rows x 21 columns]\n",
"Note: Only the head of the SFrame is printed.\n",
"You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exploring the data for house sales"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"graphlab.canvas.set_target('ipynb')\n",
"sales.show(view= 'Scatter Plot', x = 'sqft_living', y='price')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create a simple model of sqf of living and price"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_data,test_data = sales.random_split(.8, seed=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build the regrassion Model"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.\n",
" You can set ``validation_set=None`` to disable validation tracking.\n",
"\n"
]
},
{
"data": {
"text/html": [
"<pre>Linear regression:</pre>"
],
"text/plain": [
"Linear regression:"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>--------------------------------------------------------</pre>"
],
"text/plain": [
"--------------------------------------------------------"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>Number of examples : 16438</pre>"
],
"text/plain": [
"Number of examples : 16438"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>Number of features : 1</pre>"
],
"text/plain": [
"Number of features : 1"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>Number of unpacked features : 1</pre>"
],
"text/plain": [
"Number of unpacked features : 1"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>Number of coefficients : 2</pre>"
],
"text/plain": [
"Number of coefficients : 2"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>Starting Newton Method</pre>"
],
"text/plain": [
"Starting Newton Method"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>--------------------------------------------------------</pre>"
],
"text/plain": [
"--------------------------------------------------------"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+</pre>"
],
"text/plain": [
"+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>| Iteration | Passes | Elapsed Time | Training-max_error | Validation-max_error | Training-rmse | Validation-rmse |</pre>"
],
"text/plain": [
"| Iteration | Passes | Elapsed Time | Training-max_error | Validation-max_error | Training-rmse | Validation-rmse |"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+</pre>"
],
"text/plain": [
"+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>| 1 | 2 | 1.024188 | 4323580.713062 | 1192229.405122 | 265193.676923 | 220442.872231 |</pre>"
],
"text/plain": [
"| 1 | 2 | 1.024188 | 4323580.713062 | 1192229.405122 | 265193.676923 | 220442.872231 |"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+</pre>"
],
"text/plain": [
"+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>SUCCESS: Optimal solution found.</pre>"
],
"text/plain": [
"SUCCESS: Optimal solution found."
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre></pre>"
],
"text/plain": []
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sqft_model = graphlab.linear_regression.create(train_data, target= 'price',features=['sqft_living'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluate The Model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'max_error': 4123192.3891082285, 'rmse': 255257.47237577607}\n"
]
}
],
"source": [
"print sqft_model.evaluate(test_data)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x117c86250>,\n",
" <matplotlib.lines.Line2D at 0x10dfb7a50>]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x115029fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(test_data['sqft_living'],test_data['price'],'.', \n",
" test_data['sqft_living'], sqft_model.predict(test_data), '-')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">stderr</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-52317.1465609</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5097.79447557</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">sqft_living</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">284.542442614</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.23804843986</td>\n",
" </tr>\n",
"</table>\n",
"[2 rows x 4 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tname\tstr\n",
"\tindex\tstr\n",
"\tvalue\tfloat\n",
"\tstderr\tfloat\n",
"\n",
"Rows: 2\n",
"\n",
"Data:\n",
"+-------------+-------+----------------+---------------+\n",
"| name | index | value | stderr |\n",
"+-------------+-------+----------------+---------------+\n",
"| (intercept) | None | -52317.1465609 | 5097.79447557 |\n",
"| sqft_living | None | 284.542442614 | 2.23804843986 |\n",
"+-------------+-------+----------------+---------------+\n",
"[2 rows x 4 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sqft_model.get('coefficients')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore other features"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"my_features = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'zipcode']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sales[my_features].show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sales.show(view= 'BoxWhisker Plot', x='zipcode', y='price')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build a regression model with more freatures"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Creating a validation set from 5 percent of training data. This may take a while.\n",
" You can set ``validation_set=None`` to disable validation tracking.\n",
"\n"
]
},
{
"data": {
"text/html": [
"<pre>Linear regression:</pre>"
],
"text/plain": [
"Linear regression:"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>--------------------------------------------------------</pre>"
],
"text/plain": [
"--------------------------------------------------------"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>Number of examples : 16560</pre>"
],
"text/plain": [
"Number of examples : 16560"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>Number of features : 6</pre>"
],
"text/plain": [
"Number of features : 6"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>Number of unpacked features : 6</pre>"
],
"text/plain": [
"Number of unpacked features : 6"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>Number of coefficients : 115</pre>"
],
"text/plain": [
"Number of coefficients : 115"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>Starting Newton Method</pre>"
],
"text/plain": [
"Starting Newton Method"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>--------------------------------------------------------</pre>"
],
"text/plain": [
"--------------------------------------------------------"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+</pre>"
],
"text/plain": [
"+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>| Iteration | Passes | Elapsed Time | Training-max_error | Validation-max_error | Training-rmse | Validation-rmse |</pre>"
],
"text/plain": [
"| Iteration | Passes | Elapsed Time | Training-max_error | Validation-max_error | Training-rmse | Validation-rmse |"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+</pre>"
],
"text/plain": [
"+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>| 1 | 2 | 0.036191 | 3753305.895604 | 1331207.860498 | 182452.613274 | 171672.186156 |</pre>"
],
"text/plain": [
"| 1 | 2 | 0.036191 | 3753305.895604 | 1331207.860498 | 182452.613274 | 171672.186156 |"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+</pre>"
],
"text/plain": [
"+-----------+----------+--------------+--------------------+----------------------+---------------+-----------------+"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre>SUCCESS: Optimal solution found.</pre>"
],
"text/plain": [
"SUCCESS: Optimal solution found."
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<pre></pre>"
],
"text/plain": []
},
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_features_model = graphlab.linear_regression.create(train_data, target='price',features=my_features)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Class : LinearRegression\n",
"\n",
"Schema\n",
"------\n",
"Number of coefficients : 115\n",
"Number of examples : 16560\n",
"Number of feature columns : 6\n",
"Number of unpacked features : 6\n",
"\n",
"Hyperparameters\n",
"---------------\n",
"L1 penalty : 0.0\n",
"L2 penalty : 0.01\n",
"\n",
"Training Summary\n",
"----------------\n",
"Solver : newton\n",
"Solver iterations : 1\n",
"Solver status : SUCCESS: Optimal solution found.\n",
"Training time (sec) : 0.0555\n",
"\n",
"Settings\n",
"--------\n",
"Residual sum of squares : 5.51265112856e+14\n",
"Training RMSE : 182452.6133\n",
"\n",
"Highest Positive Coefficients\n",
"-----------------------------\n",
"bathrooms[8] : 1736324.4701\n",
"zipcode[98039] : 1189313.641\n",
"bathrooms[5.75] : 1132408.3907\n",
"bathrooms[6] : 1038888.6791\n",
"bathrooms[6.25] : 921271.8796\n",
"\n",
"Lowest Negative Coefficients\n",
"----------------------------\n",
"bedrooms[10] : -634621.7503\n",
"bedrooms[9] : -433390.2447\n",
"bedrooms[8] : -374095.1289\n",
"bedrooms[7] : -370887.5765\n",
"bathrooms[7.5] : -289789.3143\n",
"\n"
]
}
],
"source": [
"print my_features_model"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'max_error': 4123192.3891082285, 'rmse': 255257.47237577607}\n",
"{'max_error': 3459083.769148753, 'rmse': 179550.7974297731}\n"
]
}
],
"source": [
"print sqft_model.evaluate(test_data)\n",
"print my_features_model.evaluate(test_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Apply learned model to predict 3 houses price"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"house1 = sales[sales['id'] =='5309101200']"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">date</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">price</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bedrooms</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bathrooms</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_living</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_lot</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">floors</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">waterfront</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5309101200</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2014-06-05 00:00:00+00:00</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">620000</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2.25</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2400</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5350</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.5</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" </tr>\n",
"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">view</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">condition</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">grade</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_above</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_basement</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">yr_built</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">yr_renovated</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">zipcode</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">lat</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">7</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1460</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">940</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1929</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98117</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.67632376</td>\n",
" </tr>\n",
"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">long</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_living15</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_lot15</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.37010126</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1250.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4880.0</td>\n",
" </tr>\n",
"</table>\n",
"[? rows x 21 columns]<br/>Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.<br/>You can use sf.materialize() to force materialization.\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tid\tstr\n",
"\tdate\tdatetime\n",
"\tprice\tint\n",
"\tbedrooms\tstr\n",
"\tbathrooms\tstr\n",
"\tsqft_living\tint\n",
"\tsqft_lot\tint\n",
"\tfloors\tstr\n",
"\twaterfront\tint\n",
"\tview\tint\n",
"\tcondition\tint\n",
"\tgrade\tint\n",
"\tsqft_above\tint\n",
"\tsqft_basement\tint\n",
"\tyr_built\tint\n",
"\tyr_renovated\tint\n",
"\tzipcode\tstr\n",
"\tlat\tfloat\n",
"\tlong\tfloat\n",
"\tsqft_living15\tfloat\n",
"\tsqft_lot15\tfloat\n",
"\n",
"Rows: Unknown\n",
"\n",
"Data:\n",
"+------------+---------------------------+--------+----------+-----------+-------------+\n",
"| id | date | price | bedrooms | bathrooms | sqft_living |\n",
"+------------+---------------------------+--------+----------+-----------+-------------+\n",
"| 5309101200 | 2014-06-05 00:00:00+00:00 | 620000 | 4 | 2.25 | 2400 |\n",
"+------------+---------------------------+--------+----------+-----------+-------------+\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"| sqft_lot | floors | waterfront | view | condition | grade | sqft_above | sqft_basement |\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"| 5350 | 1.5 | 0 | 0 | 4 | 7 | 1460 | 940 |\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"| yr_built | yr_renovated | zipcode | lat | long | sqft_living15 | ... |\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"| 1929 | 0 | 98117 | 47.67632376 | -122.37010126 | 1250.0 | ... |\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"[? rows x 21 columns]\n",
"Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n",
"You can use sf.materialize() to force materialization."
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"house1"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[620000, ... ]\n"
]
}
],
"source": [
"print house1['price']"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[630584.71571273]\n"
]
}
],
"source": [
"print sqft_model.predict(house1)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[722162.9299354439]\n"
]
}
],
"source": [
"print my_features_model.predict(house1)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"house2 = sales[sales['id'] == '1925069082']"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">id</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">date</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">price</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bedrooms</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">bathrooms</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_living</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_lot</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">floors</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">waterfront</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1925069082</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2015-05-11 00:00:00+00:00</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2200000</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.25</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4640</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22703</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">view</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">condition</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">grade</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_above</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_basement</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">yr_built</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">yr_renovated</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">zipcode</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">lat</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">8</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">2860</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1780</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1952</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">98052</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">47.63925783</td>\n",
" </tr>\n",
"</table>\n",
"<table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">long</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_living15</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sqft_lot15</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-122.09722322</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3140.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">14200.0</td>\n",
" </tr>\n",
"</table>\n",
"[? rows x 21 columns]<br/>Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.<br/>You can use sf.materialize() to force materialization.\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tid\tstr\n",
"\tdate\tdatetime\n",
"\tprice\tint\n",
"\tbedrooms\tstr\n",
"\tbathrooms\tstr\n",
"\tsqft_living\tint\n",
"\tsqft_lot\tint\n",
"\tfloors\tstr\n",
"\twaterfront\tint\n",
"\tview\tint\n",
"\tcondition\tint\n",
"\tgrade\tint\n",
"\tsqft_above\tint\n",
"\tsqft_basement\tint\n",
"\tyr_built\tint\n",
"\tyr_renovated\tint\n",
"\tzipcode\tstr\n",
"\tlat\tfloat\n",
"\tlong\tfloat\n",
"\tsqft_living15\tfloat\n",
"\tsqft_lot15\tfloat\n",
"\n",
"Rows: Unknown\n",
"\n",
"Data:\n",
"+------------+---------------------------+---------+----------+-----------+-------------+\n",
"| id | date | price | bedrooms | bathrooms | sqft_living |\n",
"+------------+---------------------------+---------+----------+-----------+-------------+\n",
"| 1925069082 | 2015-05-11 00:00:00+00:00 | 2200000 | 5 | 4.25 | 4640 |\n",
"+------------+---------------------------+---------+----------+-----------+-------------+\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"| sqft_lot | floors | waterfront | view | condition | grade | sqft_above | sqft_basement |\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"| 22703 | 2 | 1 | 4 | 5 | 8 | 2860 | 1780 |\n",
"+----------+--------+------------+------+-----------+-------+------------+---------------+\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"| yr_built | yr_renovated | zipcode | lat | long | sqft_living15 | ... |\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"| 1952 | 0 | 98052 | 47.63925783 | -122.09722322 | 3140.0 | ... |\n",
"+----------+--------------+---------+-------------+---------------+---------------+-----+\n",
"[? rows x 21 columns]\n",
"Note: Only the head of the SFrame is printed. This SFrame is lazily evaluated.\n",
"You can use sf.materialize() to force materialization."
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"house2"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1267959.7871681442]\n"
]
}
],
"source": [
"print sqft_model.predict(house2)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1450774.2551164567]\n"
]
}
],
"source": [
"print my_features_model.predict(house2)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9221\n"
]
}
],
"source": [
"sqft_living = sales['sqft_living']\n",
"count = len([i for i in sqft_living if i>=2000 and i<=4000])\n",
"print count"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.426641373248\n"
]
}
],
"source": [
"from __future__ import division\n",
"print count/len(sqft_living)"
]
},
{
"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": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
tien-le/uranus | linear_algebra.ipynb | 1 | 18987 | {
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import re, math, random # regexes, math functions, random numbers\n",
"import matplotlib.pyplot as plt # pyplot\n",
"from collections import defaultdict, Counter\n",
"from functools import partial"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# \n",
"# functions for working with vectors\n",
"#\n",
"\n",
"def vector_add(v, w):\n",
" \"\"\"adds two vectors componentwise\"\"\"\n",
" return [v_i + w_i for v_i, w_i in zip(v,w)]\n",
"#--------------------------------------------------------------------------------\n",
"def vector_subtract(v, w):\n",
" \"\"\"subtracts two vectors componentwise\"\"\"\n",
" return [v_i - w_i for v_i, w_i in zip(v,w)]\n",
"#--------------------------------------------------------------------------------\n",
"def vector_sum(vectors):\n",
" return reduce(vector_add, vectors)\n",
"#--------------------------------------------------------------------------------\n",
"def scalar_multiply(c, v):\n",
" return [c * v_i for v_i in v]\n",
"#--------------------------------------------------------------------------------\n",
"# this isn't right if you don't from __future__ import division\n",
"def vector_mean(vectors):\n",
" \"\"\"compute the vector whose i-th element is the mean of the\n",
" i-th elements of the input vectors\"\"\"\n",
" n = len(vectors)\n",
" return scalar_multiply(1/n, vector_sum(vectors))\n",
"#--------------------------------------------------------------------------------\n",
"def dot(v, w):\n",
" \"\"\"v_1 * w_1 + ... + v_n * w_n\"\"\"\n",
" return sum(v_i * w_i for v_i, w_i in zip(v, w))\n",
"#--------------------------------------------------------------------------------\n",
"def sum_of_squares(v):\n",
" \"\"\"v_1 * v_1 + ... + v_n * v_n\"\"\"\n",
" return dot(v, v)\n",
"#--------------------------------------------------------------------------------\n",
"def magnitude(v):\n",
" return math.sqrt(sum_of_squares(v))\n",
"#--------------------------------------------------------------------------------\n",
"def squared_distance(v, w):\n",
" return sum_of_squares(vector_subtract(v, w))\n",
"#--------------------------------------------------------------------------------\n",
"def distance(v, w):\n",
" return math.sqrt(squared_distance(v, w))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#\n",
"# functions for working with matrices\n",
"#\n",
"\n",
"def shape(A):\n",
" num_rows = len(A)\n",
" num_cols = len(A[0]) if A else 0\n",
" return num_rows, num_cols\n",
"#--------------------------------------------------------------------------------\n",
"def get_row(A, i):\n",
" return A[i]\n",
"#--------------------------------------------------------------------------------\n",
"def get_column(A, j):\n",
" return [A_i[j] for A_i in A]\n",
"#--------------------------------------------------------------------------------\n",
"def make_matrix(num_rows, num_cols, entry_fn):\n",
" \"\"\"returns a num_rows x num_cols matrix \n",
" whose (i,j)-th entry is entry_fn(i, j)\"\"\"\n",
" return [[entry_fn(i, j) for j in range(num_cols)]\n",
" for i in range(num_rows)] \n",
"#--------------------------------------------------------------------------------\n",
"def is_diagonal(i, j):\n",
" \"\"\"1's on the 'diagonal', 0's everywhere else\"\"\"\n",
" return 1 if i == j else 0\n",
"#--------------------------------------------------------------------------------\n",
"identity_matrix = make_matrix(5, 5, is_diagonal)\n",
"\n",
"# user 0 1 2 3 4 5 6 7 8 9\n",
"#\n",
"friendships = [[0, 1, 1, 0, 0, 0, 0, 0, 0, 0], # user 0\n",
" [1, 0, 1, 1, 0, 0, 0, 0, 0, 0], # user 1\n",
" [1, 1, 0, 1, 0, 0, 0, 0, 0, 0], # user 2\n",
" [0, 1, 1, 0, 1, 0, 0, 0, 0, 0], # user 3\n",
" [0, 0, 0, 1, 0, 1, 0, 0, 0, 0], # user 4\n",
" [0, 0, 0, 0, 1, 0, 1, 1, 0, 0], # user 5\n",
" [0, 0, 0, 0, 0, 1, 0, 0, 1, 0], # user 6\n",
" [0, 0, 0, 0, 0, 1, 0, 0, 1, 0], # user 7\n",
" [0, 0, 0, 0, 0, 0, 1, 1, 0, 1], # user 8\n",
" [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]] # user 9\n",
"#--------------------------------------------------------------------------------\n",
"#####\n",
"# DELETE DOWN\n",
"#\n",
"\n",
"def matrix_add(A, B):\n",
" if shape(A) != shape(B):\n",
" raise ArithmeticError(\"cannot add matrices with different shapes\")\n",
" \n",
" num_rows, num_cols = shape(A)\n",
" def entry_fn(i, j): return A[i][j] + B[i][j]\n",
" \n",
" return make_matrix(num_rows, num_cols, entry_fn)\n",
"#--------------------------------------------------------------------------------\n",
"def make_graph_dot_product_as_vector_projection(plt):\n",
" v = [2, 1]\n",
" w = [math.sqrt(.25), math.sqrt(.75)]\n",
" c = dot(v, w)\n",
" vonw = scalar_multiply(c, w)\n",
" o = [0,0]\n",
"\n",
" plt.arrow(0, 0, v[0], v[1], \n",
" width=0.002, head_width=.1, length_includes_head=True)\n",
" plt.annotate(\"v\", v, xytext=[v[0] + 0.1, v[1]])\n",
" plt.arrow(0 ,0, w[0], w[1], \n",
" width=0.002, head_width=.1, length_includes_head=True)\n",
" plt.annotate(\"w\", w, xytext=[w[0] - 0.1, w[1]])\n",
" plt.arrow(0, 0, vonw[0], vonw[1], length_includes_head=True)\n",
" plt.annotate(u\"(v•w)w\", vonw, xytext=[vonw[0] - 0.1, vonw[1] + 0.1])\n",
" plt.arrow(v[0], v[1], vonw[0] - v[0], vonw[1] - v[1], \n",
" linestyle='dotted', length_includes_head=True)\n",
" plt.scatter(*zip(v,w,o),marker='.')\n",
" plt.axis('equal')\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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Gu8DlwIPAZcA7Bw4ws8OAQufcPjNrCZwaHX+Quv5FiYhI3YSho2kBvAZ0AFYD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\nEREJlIJGREQCpaAREZFA/R/QDXzPy8cnPgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3ed2cdeac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"make_graph_dot_product_as_vector_projection(plt)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
kazuhirokomoda/deep_learning | fast.ai/lesson1/dogscats_run.ipynb | 1 | 20918 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# lesson1: Convolutional Neural Networks with dogscats"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's classify images using deep learning and submit the result to Kaggle!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisite"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook assumes Keras with Theano backend.\n",
"- TODO: make TensorFlow version as another notebook\n",
"\n",
"It also assumes that you will run it on either one of these two cases:\n",
"- Floydhub (--env theano:py2 -> Theano rel-0.8.2 + Keras 1.2.2 on Python2)\n",
"- local conda virtual environment (Theano 0.9.0 + Keras 2.0.4 on Python3)\n",
"\n",
"Refer to [this FloydHub document](http://docs.floydhub.com/home/environments/) for available FloydHub environments."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make sure to have these files in the parent directory of the directory where you execute this notebook.\n",
"\n",
"- available [in the official repo](https://github.com/fastai/courses/tree/master/deeplearning1/nbs) for Keras1 on Python2 (rename from original files)\n",
" - utils_keras1.py\n",
" - vgg16_keras1.py\n",
" - vgg16bn_keras1.py\n",
"- available [in the unofficial repo](https://github.com/roebius/deeplearning1_keras2/tree/master/nbs) for Keras2 on Python3\n",
" - utils.py\n",
" - vgg16.py\n",
" - vgg16bn.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The directory structure looks like this. Please modifiy the symlinks according to your environment.\n",
"\n",
"- (*) only for FloydHub\n",
"- (**) only for local\n",
"\n",
"```\n",
"floyd_requirements.txt (*)\n",
"floydhub.data.unzip/ (*)\n",
"floydhub.data.zipped/ (*)\n",
" dogscats.zip\n",
"lesson1/\n",
" data/ (**)\n",
" redux/\n",
" train/\n",
" cat.437.jpg\n",
" dog.9924.jpg\n",
" ...\n",
" test/\n",
" 231.jpg\n",
" 325.jpg\n",
" ...\n",
" dogscats_run.ipynb\n",
" floyd_requirements.txt -> ../floyd_requirements.txt (*)\n",
" utils.py -> ../utils(_keras1).py\n",
" vgg16.py -> ../vgg16(_keras1).py\n",
" vgg16bn.py -> ../vgg16bn(_keras1).py\n",
"utils.py\n",
"utils_keras1.py\n",
"vgg16.py\n",
"vgg16_keras1.py\n",
"vgg16bn.py\n",
"vgg16bn_keras1.py\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The details of data preparation largely depends on which dataset you use. In this section, we will use a pre-organized dataset from http://files.fast.ai/files/dogscats.zip\n",
"\n",
"For another example of data preparation, please refer to [this notebook](../lesson1_17flowers/17flowers_data.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How the dataset looks like"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After extracting the dogscats.zip file, the directory structure look like this."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"dogscats/\n",
" models/\n",
" sample/\n",
" train/\n",
" cats/\n",
" cat.394.jpg\n",
" ... (8 items)\n",
" dogs/\n",
" dog.1402.jpg\n",
" ... (8 items)\n",
" valid/\n",
" cats/\n",
" cat.10435.jpg\n",
" ... (4 items)\n",
" dogs/\n",
" dog.10459.jpg\n",
" ... (4 items)\n",
" features.npy\n",
" labels.npy\n",
" test1/\n",
" 1.jpg\n",
" 10.jpg\n",
" 100.jpg\n",
" ... (12500 items)\n",
" train/\n",
" cats/\n",
" cat.0.jpg\n",
" cat.1.jpg\n",
" cat.3.jpg\n",
" ... (11500 items)\n",
" dogs/\n",
" cat.0.jpg\n",
" cat.1.jpg\n",
" cat.2.jpg\n",
" cat.4.jpg\n",
" ... (11500 items)\n",
" valid/\n",
" cats/\n",
" cat.2.jpg\n",
" cat.5.jpg\n",
" ... (1000 item. these are copied from train/cats/ directory)\n",
" dogs/\n",
" dog.3.jpg\n",
" dog.9.jpg\n",
" ... (1000 item. these are copied from train/dogs/ directory)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### FloydHub"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The cell below shows how to update data to FloydHub."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"# from the directory which this notebook is executed\n",
"cd ../floydhub.data.zipped/; pwd\n",
"\n",
"# expected: empty\n",
"ls -l\n",
"\n",
"wget http://files.fast.ai/files/dogscats.zip\n",
"\n",
"# upload the zipped dataset to floydnet, and create a floydnet dataset\n",
"floyd data init dogscats.zipped\n",
"floyd data upload\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Using the data we have just uploaded to FloydHub, let's unzip it on FloydHub.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"```\n",
"# from the directory which this notebook is executed\n",
"cd ../floydhub.fast.ai.data.unzip/; pwd\n",
"\n",
"# expected: empty\n",
"ls -l\n",
"\n",
"floyd init dogscats.unzip\n",
"floyd run --gpu --data [data ID of the uploaded zip] \"unzip /input/dogscats.zip -d /output\"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Please note:\n",
"- the data ID should be the one you see from the above step\n",
"- the mounted data is available in /input/ directory, and you need to direct the unzipped files to /output/ directory"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### local"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"TODO"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run the notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's run the notebook in the environment of your choice."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"# from the directory which this notebook is executed\n",
"cd ./; pwd\n",
"\n",
"# FloydHub\n",
"floyd init dogscats\n",
"floyd run --mode jupyter --data [data ID of unzipped data] --env theano:py2 --gpu\n",
"\n",
"# alternatively, for local\n",
"#jupyter notebook\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and check ~/.keras/keras.json"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"mkdir ~/.keras\n",
"\n",
"# FloydHub (Keras1)\n",
"echo '{\n",
" \"image_dim_ordering\": \"th\",\n",
" \"epsilon\": 1e-07,\n",
" \"floatx\": \"float32\",\n",
" \"backend\": \"theano\"\n",
"}' > ~/.keras/keras.json\n",
"\n",
"# alternatively, for local (Keras2)\n",
"#echo '{\n",
"# \"image_data_format\": \"channels_first\",\n",
"# \"backend\": \"theano\",\n",
"# \"floatx\": \"float32\",\n",
"# \"epsilon\": 1e-07\n",
"#}' > ~/.keras/keras.json\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, let's start running the notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# make some Python3 functions available on Python2\n",
"from __future__ import division, print_function\n",
"\n",
"import sys\n",
"print(sys.version_info)\n",
"\n",
"import theano\n",
"print(theano.__version__)\n",
"\n",
"import keras\n",
"print(keras.__version__)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# FloydHub: check data\n",
"%ls /input/dogscats/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# check current directory\n",
"%pwd\n",
"%ls\n",
"\n",
"# see some files are loaded fine\n",
"%cat floyd_requirements.txt\n",
"\n",
"# check no Keras2 specific function is used (when Keras1 is used)\n",
"%cat utils.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Create references to important directories we will use over and over\n",
"import os, sys\n",
"current_dir = os.getcwd()\n",
"\n",
"LESSON_HOME_DIR = current_dir\n",
"\n",
"# FloydHub\n",
"DATA_HOME_DIR = \"/input/dogscats/\"\n",
"OUTPUT_HOME_DIR = \"/output/\"\n",
"\n",
"# alternatively, for local\n",
"#DATA_HOME_DIR = current_dir+'/data/redux'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#import modules\n",
"from utils import *\n",
"from vgg16 import Vgg16\n",
"\n",
"#Instantiate plotting tool\n",
"#In Jupyter notebooks, you will need to run this command before doing any plotting\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Finetuning and Training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%cd $DATA_HOME_DIR\n",
"\n",
"#Set path to sample/ path if desired\n",
"path = DATA_HOME_DIR + '/' #'/sample/'\n",
"test_path = DATA_HOME_DIR + '/test1/' #We use all the test data\n",
"\n",
"# FloydHub\n",
"# data needs to be output under /output\n",
"# if results_path cannot be created, execute mkdir directly in the terminal\n",
"results_path = OUTPUT_HOME_DIR + '/results/'\n",
"%mkdir results_path\n",
"\n",
"train_path = path + '/train/'\n",
"valid_path = path + '/valid/'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use a pretrained VGG model with our Vgg16 class"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# As large as you can, but no larger than 64 is recommended.\n",
"#batch_size = 8\n",
"batch_size = 64\n",
"\n",
"no_of_epochs=3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The original pre-trained Vgg16 class classifies images into one of the 1000 categories. This number of categories depends on the dataset which Vgg16 was trained with. (http://image-net.org/challenges/LSVRC/2014/browse-synsets)\n",
"\n",
"In order to classify images into the categories which we prepare (2 categories of dogs/cats, in this notebook), *fine-tuning* technology is useful. It:\n",
"- keeps the most weights from the pre-trained Vgg16 model, but modifies only a few parts of the weights\n",
"- changes the dimension of the output layer (from 1000 to 2, in this notebook) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"vgg = Vgg16()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Grab a few images at a time for training and validation.\n",
"batches = vgg.get_batches(train_path, batch_size=batch_size)\n",
"val_batches = vgg.get_batches(valid_path, batch_size=batch_size*2)\n",
"\n",
"# Finetune: note that the vgg model is compiled inside the finetune method.\n",
"vgg.finetune(batches)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Fit: note that we are passing in the validation dataset to the fit() method\n",
"# For each epoch we test our model against the validation set\n",
"latest_weights_filename = None\n",
"\n",
"# FloydHub (Keras1)\n",
"for epoch in range(no_of_epochs):\n",
" print(\"Running epoch: %d\" % epoch)\n",
" vgg.fit(batches, val_batches, nb_epoch=1)\n",
" latest_weights_filename = 'ft%d.h5' % epoch\n",
" vgg.model.save_weights(results_path+latest_weights_filename)\n",
"print(\"Completed %s fit operations\" % no_of_epochs)\n",
"\n",
"# alternatively, for local (Keras2)\n",
"\"\"\"\n",
"for epoch in range(no_of_epochs):\n",
" print(\"Running epoch: %d\" % epoch)\n",
" vgg.fit(batches, val_batches, batch_size, nb_epoch=1)\n",
" latest_weights_filename = 'ft%d.h5' % epoch\n",
" vgg.model.save_weights(results_path+latest_weights_filename)\n",
"print(\"Completed %s fit operations\" % no_of_epochs)\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate Predictions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# OUTPUT_HOME_DIR, not DATA_HOME_DIR due to FloydHub restriction\n",
"%cd $OUTPUT_HOME_DIR\n",
"%mkdir -p test1/unknown\n",
"\n",
"%cd $OUTPUT_HOME_DIR/test1\n",
"%cp $test_path/*.jpg unknown/\n",
"\n",
"# rewrite test_path\n",
"test_path = OUTPUT_HOME_DIR + '/test1/' #We use all the test data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"batches, preds = vgg.test(test_path, batch_size = batch_size*2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print(preds[:5])\n",
"\n",
"filenames = batches.filenames\n",
"print(filenames[:5])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# You can verify the column ordering by viewing some images\n",
"from PIL import Image\n",
"Image.open(test_path + filenames[2])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Save our test results arrays so we can use them again later\n",
"save_array(results_path + 'test_preds.dat', preds)\n",
"save_array(results_path + 'filenames.dat', filenames)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Validate Predictions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate predictions on validation set, so we can find correct and incorrect examples:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"vgg.model.load_weights(results_path+latest_weights_filename)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"val_batches, probs = vgg.test(valid_path, batch_size = batch_size)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"filenames = val_batches.filenames\n",
"expected_labels = val_batches.classes #0 or 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Round our predictions to 0/1 to generate labels\n",
"our_predictions = probs[:,0]\n",
"our_labels = np.round(1-our_predictions)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(TODO) look at data to improve model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"confusion matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"cm = confusion_matrix(expected_labels, our_labels)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plot_confusion_matrix(cm, val_batches.class_indices)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Submit Predictions to Kaggle!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This section also depends on which dataset you use (and which Kaggle competition you are participating)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Load our test predictions from file\n",
"preds = load_array(results_path + 'test_preds.dat')\n",
"filenames = load_array(results_path + 'filenames.dat')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Grab the dog prediction column\n",
"isdog = preds[:,1]\n",
"print(\"Raw Predictions: \" + str(isdog[:5]))\n",
"print(\"Mid Predictions: \" + str(isdog[(isdog < .6) & (isdog > .4)]))\n",
"print(\"Edge Predictions: \" + str(isdog[(isdog == 1) | (isdog == 0)]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# sneaky trick to round down our edge predictions\n",
"# Swap all ones with .95 and all zeros with .05\n",
"isdog = isdog.clip(min=0.05, max=0.95)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Extract imageIds from the filenames in our test/unknown directory \n",
"filenames = batches.filenames\n",
"ids = np.array([int(f[8:f.find('.')]) for f in filenames])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"subm = np.stack([ids,isdog], axis=1)\n",
"subm[:5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# FloydHub\n",
"%cd $OUTPUT_HOME_DIR\n",
"\n",
"# alternatively, for local\n",
"#%cd $DATA_HOME_DIR\n",
"\n",
"submission_file_name = 'submission1.csv'\n",
"np.savetxt(submission_file_name, subm, fmt='%d,%.5f', header='id,label', comments='')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from IPython.display import FileLink\n",
"\n",
"# FloydHub\n",
"%cd $OUTPUT_HOME_DIR\n",
"FileLink(submission_file_name)\n",
"\n",
"# alternatively, for local\n",
"#%cd $LESSON_HOME_DIR\n",
"#FileLink('data/redux/'+submission_file_name)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Environment (conda_py3tf)",
"language": "python",
"name": "conda_py3tf"
},
"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 |
GoogleCloudPlatform/tf-estimator-tutorials | 01_Regression/04.0 - TF Regression Model - Dataset Input.ipynb | 1 | 69002 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/khalidsalama/anaconda/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
" return f(*args, **kwds)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.4.0\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow import data\n",
"import shutil\n",
"import math\n",
"from datetime import datetime\n",
"from tensorflow.python.feature_column import feature_column\n",
"\n",
"from tensorflow.contrib.learn import learn_runner\n",
"from tensorflow.contrib.learn import make_export_strategy\n",
"\n",
"print(tf.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Steps to use the TF Experiment APIs\n",
"1. Define dataset **metadata**\n",
"2. Define **data input function** to read the data from csv files + **feature processing**\n",
"3. Create TF **feature columns** based on metadata + **extended feature columns**\n",
"4. Define an **estimator** (DNNRegressor) creation function with the required **feature columns & parameters**\n",
"5. Define a **serving function** to export the model\n",
"7. Run an **Experiment** with **learn_runner** to train, evaluate, and export the model\n",
"8. **Evaluate** the model using test data\n",
"9. Perform **predictions**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"MODEL_NAME = 'reg-model-03'\n",
"\n",
"TRAIN_DATA_FILES_PATTERN = 'data/train-*.csv'\n",
"VALID_DATA_FILES_PATTERN = 'data/valid-*.csv'\n",
"TEST_DATA_FILES_PATTERN = 'data/test-*.csv'\n",
"\n",
"RESUME_TRAINING = False\n",
"PROCESS_FEATURES = True\n",
"EXTEND_FEATURE_COLUMNS = True\n",
"MULTI_THREADING = True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Define Dataset Metadata\n",
"* CSV file header and defaults\n",
"* Numeric and categorical feature names\n",
"* Target feature name\n",
"* Unused columns"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Header: ['key', 'x', 'y', 'alpha', 'beta', 'target']\n",
"Numeric Features: ['x', 'y']\n",
"Categorical Features: ['alpha', 'beta']\n",
"Target: target\n",
"Unused Features: ['key']\n"
]
}
],
"source": [
"HEADER = ['key','x','y','alpha','beta','target']\n",
"HEADER_DEFAULTS = [[0], [0.0], [0.0], ['NA'], ['NA'], [0.0]]\n",
"\n",
"NUMERIC_FEATURE_NAMES = ['x', 'y'] \n",
"\n",
"CATEGORICAL_FEATURE_NAMES_WITH_VOCABULARY = {'alpha':['ax01', 'ax02'], 'beta':['bx01', 'bx02']}\n",
"CATEGORICAL_FEATURE_NAMES = list(CATEGORICAL_FEATURE_NAMES_WITH_VOCABULARY.keys())\n",
"\n",
"FEATURE_NAMES = NUMERIC_FEATURE_NAMES + CATEGORICAL_FEATURE_NAMES\n",
"\n",
"TARGET_NAME = 'target'\n",
"\n",
"UNUSED_FEATURE_NAMES = list(set(HEADER) - set(FEATURE_NAMES) - {TARGET_NAME})\n",
"\n",
"print(\"Header: {}\".format(HEADER))\n",
"print(\"Numeric Features: {}\".format(NUMERIC_FEATURE_NAMES))\n",
"print(\"Categorical Features: {}\".format(CATEGORICAL_FEATURE_NAMES))\n",
"print(\"Target: {}\".format(TARGET_NAME))\n",
"print(\"Unused Features: {}\".format(UNUSED_FEATURE_NAMES))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Define Data Input Function\n",
"* Input csv files name pattern\n",
"* Use TF Dataset APIs to read and process the data\n",
"* Parse CSV lines to feature tensors\n",
"* Apply feature processing\n",
"* Return (features, target) tensors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. parsing and preprocessing logic"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def parse_csv_row(csv_row):\n",
" \n",
" columns = tf.decode_csv(csv_row, record_defaults=HEADER_DEFAULTS)\n",
" features = dict(zip(HEADER, columns))\n",
" \n",
" for column in UNUSED_FEATURE_NAMES:\n",
" features.pop(column)\n",
" \n",
" target = features.pop(TARGET_NAME)\n",
"\n",
" return features, target\n",
"\n",
"def process_features(features):\n",
"\n",
" features[\"x_2\"] = tf.square(features['x'])\n",
" features[\"y_2\"] = tf.square(features['y'])\n",
" features[\"xy\"] = tf.multiply(features['x'], features['y']) # features['x'] * features['y']\n",
" features['dist_xy'] = tf.sqrt(tf.squared_difference(features['x'],features['y']))\n",
" \n",
" return features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### b. data pipeline input function"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def csv_input_fn(files_name_pattern, mode=tf.estimator.ModeKeys.EVAL, \n",
" skip_header_lines=0, \n",
" num_epochs=None, \n",
" batch_size=200):\n",
" \n",
" shuffle = True if mode == tf.estimator.ModeKeys.TRAIN else False\n",
" \n",
" print(\"\")\n",
" print(\"* data input_fn:\")\n",
" print(\"================\")\n",
" print(\"Input file(s): {}\".format(files_name_pattern))\n",
" print(\"Batch size: {}\".format(batch_size))\n",
" print(\"Epoch Count: {}\".format(num_epochs))\n",
" print(\"Mode: {}\".format(mode))\n",
" print(\"Shuffle: {}\".format(shuffle))\n",
" print(\"================\")\n",
" print(\"\")\n",
" \n",
" file_names = tf.matching_files(files_name_pattern)\n",
"\n",
" dataset = data.TextLineDataset(filenames=file_names)\n",
" dataset = dataset.skip(skip_header_lines)\n",
" \n",
" if shuffle:\n",
" dataset = dataset.shuffle(buffer_size=2 * batch_size + 1)\n",
"\n",
" #useful for distributed training when training on 1 data file, so it can be shareded\n",
" #dataset = dataset.shard(num_workers, worker_index)\n",
" \n",
" dataset = dataset.batch(batch_size)\n",
" dataset = dataset.map(lambda csv_row: parse_csv_row(csv_row))\n",
" \n",
" if PROCESS_FEATURES:\n",
" dataset = dataset.map(lambda features, target: (process_features(features), target))\n",
" \n",
" #dataset = dataset.batch(batch_size) #??? very long time\n",
" dataset = dataset.repeat(num_epochs)\n",
" iterator = dataset.make_one_shot_iterator()\n",
" \n",
" features, target = iterator.get_next()\n",
" return features, target"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): \n",
"Batch size: 200\n",
"Epoch Count: None\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"Feature read from CSV: ['x', 'y', 'alpha', 'beta', 'x_2', 'y_2', 'xy', 'dist_xy']\n",
"Target read from CSV: Tensor(\"IteratorGetNext:8\", shape=(?,), dtype=float32)\n"
]
}
],
"source": [
"features, target = csv_input_fn(files_name_pattern=\"\")\n",
"print(\"Feature read from CSV: {}\".format(list(features.keys())))\n",
"print(\"Target read from CSV: {}\".format(target))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Define Feature Columns\n",
"The input numeric columns are assumed to be normalized (or have the same scale). Otherise, a normlizer_fn, along with the normlisation params (mean, stdv) should be passed to tf.feature_column.numeric_column() constructor."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature Columns: {'x': _NumericColumn(key='x', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), 'y': _NumericColumn(key='y', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), 'x_2': _NumericColumn(key='x_2', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), 'y_2': _NumericColumn(key='y_2', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), 'xy': _NumericColumn(key='xy', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), 'dist_xy': _NumericColumn(key='dist_xy', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), 'alpha': _VocabularyListCategoricalColumn(key='alpha', vocabulary_list=('ax01', 'ax02'), dtype=tf.string, default_value=-1, num_oov_buckets=0), 'beta': _VocabularyListCategoricalColumn(key='beta', vocabulary_list=('bx01', 'bx02'), dtype=tf.string, default_value=-1, num_oov_buckets=0), 'alpha_X_beta': _CrossedColumn(keys=(_VocabularyListCategoricalColumn(key='alpha', vocabulary_list=('ax01', 'ax02'), dtype=tf.string, default_value=-1, num_oov_buckets=0), _VocabularyListCategoricalColumn(key='beta', vocabulary_list=('bx01', 'bx02'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), hash_bucket_size=4, hash_key=None)}\n"
]
}
],
"source": [
"def extend_feature_columns(feature_columns):\n",
" \n",
" # crossing, bucketizing, and embedding can be applied here\n",
" \n",
" feature_columns['alpha_X_beta'] = tf.feature_column.crossed_column(\n",
" [feature_columns['alpha'], feature_columns['beta']], 4)\n",
" \n",
" return feature_columns\n",
"\n",
"def get_feature_columns():\n",
" \n",
" CONSTRUCTED_NUMERIC_FEATURES_NAMES = ['x_2', 'y_2', 'xy', 'dist_xy']\n",
" all_numeric_feature_names = NUMERIC_FEATURE_NAMES.copy() \n",
" \n",
" if PROCESS_FEATURES:\n",
" all_numeric_feature_names += CONSTRUCTED_NUMERIC_FEATURES_NAMES\n",
"\n",
" numeric_columns = {feature_name: tf.feature_column.numeric_column(feature_name)\n",
" for feature_name in all_numeric_feature_names}\n",
"\n",
" categorical_column_with_vocabulary = \\\n",
" {item[0]: tf.feature_column.categorical_column_with_vocabulary_list(item[0], item[1])\n",
" for item in CATEGORICAL_FEATURE_NAMES_WITH_VOCABULARY.items()}\n",
" \n",
" feature_columns = {}\n",
"\n",
" if numeric_columns is not None:\n",
" feature_columns.update(numeric_columns)\n",
"\n",
" if categorical_column_with_vocabulary is not None:\n",
" feature_columns.update(categorical_column_with_vocabulary)\n",
" \n",
" if EXTEND_FEATURE_COLUMNS:\n",
" feature_columns = extend_feature_columns(feature_columns)\n",
" \n",
" return feature_columns\n",
"\n",
"feature_columns = get_feature_columns()\n",
"print(\"Feature Columns: {}\".format(feature_columns))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Define an Estimator Creation Function\n",
"\n",
"* Get dense (numeric) columns from the feature columns\n",
"* Convert categorical columns to indicator columns\n",
"* Create Instantiate a DNNRegressor estimator given **dense + indicator** feature columns + params"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_estimator(run_config, hparams):\n",
" \n",
" feature_columns = list(get_feature_columns().values())\n",
" \n",
" dense_columns = list(\n",
" filter(lambda column: isinstance(column, feature_column._NumericColumn),\n",
" feature_columns\n",
" )\n",
" )\n",
"\n",
" categorical_columns = list(\n",
" filter(lambda column: isinstance(column, feature_column._VocabularyListCategoricalColumn) |\n",
" isinstance(column, feature_column._BucketizedColumn),\n",
" feature_columns)\n",
" )\n",
"\n",
" indicator_columns = list(\n",
" map(lambda column: tf.feature_column.indicator_column(column),\n",
" categorical_columns)\n",
" )\n",
" \n",
" \n",
" estimator = tf.estimator.DNNRegressor(\n",
" \n",
" feature_columns= dense_columns + indicator_columns ,\n",
" hidden_units= hparams.hidden_units,\n",
" \n",
" optimizer= tf.train.AdamOptimizer(),\n",
" activation_fn= tf.nn.elu,\n",
" dropout= hparams.dropout_prob,\n",
" \n",
" config= run_config\n",
" )\n",
"\n",
" print(\"\")\n",
" print(\"Estimator Type: {}\".format(type(estimator)))\n",
" print(\"\")\n",
" \n",
" return estimator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Define Serving Funcion"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def csv_serving_input_fn():\n",
" \n",
" SERVING_HEADER = ['x','y','alpha','beta']\n",
" SERVING_HEADER_DEFAULTS = [[0.0], [0.0], ['NA'], ['NA']]\n",
"\n",
" rows_string_tensor = tf.placeholder(dtype=tf.string,\n",
" shape=[None],\n",
" name='csv_rows')\n",
" \n",
" receiver_tensor = {'csv_rows': rows_string_tensor}\n",
"\n",
" row_columns = tf.expand_dims(rows_string_tensor, -1)\n",
" columns = tf.decode_csv(row_columns, record_defaults=SERVING_HEADER_DEFAULTS)\n",
" features = dict(zip(SERVING_HEADER, columns))\n",
"\n",
" return tf.estimator.export.ServingInputReceiver(\n",
" process_features(features), receiver_tensor)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Run Experiment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. Define Experiment Function"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def generate_experiment_fn(**experiment_args):\n",
"\n",
" def _experiment_fn(run_config, hparams):\n",
"\n",
" train_input_fn = lambda: csv_input_fn(\n",
" files_name_pattern=TRAIN_DATA_FILES_PATTERN,\n",
" mode = tf.contrib.learn.ModeKeys.TRAIN,\n",
" num_epochs=hparams.num_epochs,\n",
" batch_size=hparams.batch_size\n",
" )\n",
"\n",
" eval_input_fn = lambda: csv_input_fn(\n",
" files_name_pattern=VALID_DATA_FILES_PATTERN,\n",
" mode=tf.contrib.learn.ModeKeys.EVAL,\n",
" num_epochs=1,\n",
" batch_size=hparams.batch_size\n",
" )\n",
"\n",
" estimator = create_estimator(run_config, hparams)\n",
"\n",
" return tf.contrib.learn.Experiment(\n",
" estimator,\n",
" train_input_fn=train_input_fn,\n",
" eval_input_fn=eval_input_fn,\n",
" eval_steps=None,\n",
" **experiment_args\n",
" )\n",
"\n",
" return _experiment_fn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### b. Set HParam and RunConfig"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('batch_size', 500), ('dropout_prob', 0.0), ('hidden_units', [8, 4]), ('num_epochs', 1000)]\n",
"Model Directory: trained_models/reg-model-04\n",
"\n",
"Dataset Size: 12000\n",
"Batch Size: 500\n",
"Steps per Epoch: 24.0\n",
"Total Steps: 24000.0\n",
"Required Evaluation Steps: 10\n",
"That is 1 evaluation step after each 100.0 epochs\n",
"Save Checkpoint After 2400 steps\n"
]
}
],
"source": [
"TRAIN_SIZE = 12000\n",
"NUM_EPOCHS = 1000\n",
"BATCH_SIZE = 500\n",
"NUM_EVAL = 10\n",
"CHECKPOINT_STEPS = int((TRAIN_SIZE/BATCH_SIZE) * (NUM_EPOCHS/NUM_EVAL))\n",
"\n",
"hparams = tf.contrib.training.HParams(\n",
" num_epochs = NUM_EPOCHS,\n",
" batch_size = BATCH_SIZE,\n",
" hidden_units=[8, 4], \n",
" dropout_prob = 0.0)\n",
"\n",
"model_dir = 'trained_models/{}'.format(MODEL_NAME)\n",
"\n",
"run_config = tf.contrib.learn.RunConfig(\n",
" save_checkpoints_steps=CHECKPOINT_STEPS,\n",
" tf_random_seed=19830610,\n",
" model_dir=model_dir\n",
")\n",
"\n",
"print(hparams)\n",
"print(\"Model Directory:\", run_config.model_dir)\n",
"print(\"\")\n",
"print(\"Dataset Size:\", TRAIN_SIZE)\n",
"print(\"Batch Size:\", BATCH_SIZE)\n",
"print(\"Steps per Epoch:\",TRAIN_SIZE/BATCH_SIZE)\n",
"print(\"Total Steps:\", (TRAIN_SIZE/BATCH_SIZE)*NUM_EPOCHS)\n",
"print(\"Required Evaluation Steps:\", NUM_EVAL) \n",
"print(\"That is 1 evaluation step after each\",NUM_EPOCHS/NUM_EVAL,\" epochs\")\n",
"print(\"Save Checkpoint After\",CHECKPOINT_STEPS,\"steps\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### c. Run Experiment via learn_runner"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Removing previous artifacts...\n",
"Experiment started at 14:31:43\n",
".......................................\n",
"WARNING:tensorflow:RunConfig.uid (from tensorflow.contrib.learn.python.learn.estimators.run_config) is experimental and may change or be removed at any time, and without warning.\n",
"INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x11e860a58>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_tf_config': gpu_options {\n",
" per_process_gpu_memory_fraction: 1\n",
"}\n",
", '_tf_random_seed': 19830610, '_save_summary_steps': 100, '_save_checkpoints_secs': None, '_log_step_count_steps': 100, '_session_config': None, '_save_checkpoints_steps': 2400, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': 'trained_models/reg-model-04'}\n",
"\n",
"Estimator Type: <class 'tensorflow.python.estimator.canned.dnn.DNNRegressor'>\n",
"\n",
"WARNING:tensorflow:RunConfig.uid (from tensorflow.contrib.learn.python.learn.estimators.run_config) is experimental and may change or be removed at any time, and without warning.\n",
"WARNING:tensorflow:From /Users/khalidsalama/anaconda/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:267: BaseMonitor.__init__ (from tensorflow.contrib.learn.python.learn.monitors) is deprecated and will be removed after 2016-12-05.\n",
"Instructions for updating:\n",
"Monitors are deprecated. Please use tf.train.SessionRunHook.\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/train-*.csv\n",
"Batch size: 500\n",
"Epoch Count: 1000\n",
"Mode: train\n",
"Shuffle: True\n",
"================\n",
"\n",
"INFO:tensorflow:Create CheckpointSaverHook.\n",
"INFO:tensorflow:Saving checkpoints for 1 into trained_models/reg-model-04/model.ckpt.\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/valid-*.csv\n",
"Batch size: 500\n",
"Epoch Count: 1\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"INFO:tensorflow:Starting evaluation at 2017-11-11-14:31:48\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-1\n",
"INFO:tensorflow:Finished evaluation at 2017-11-11-14:31:49\n",
"INFO:tensorflow:Saving dict for global step 1: average_loss = 328.465, global_step = 1, loss = 164232.0\n",
"INFO:tensorflow:Validation (step 1): average_loss = 328.465, loss = 164232.0, global_step = 1\n",
"INFO:tensorflow:loss = 155660.0, step = 1\n",
"INFO:tensorflow:global_step/sec: 37.5745\n",
"INFO:tensorflow:loss = 185020.0, step = 101 (0.809 sec)\n",
"INFO:tensorflow:global_step/sec: 150.261\n",
"INFO:tensorflow:loss = 137635.0, step = 201 (0.665 sec)\n",
"INFO:tensorflow:global_step/sec: 142.137\n",
"INFO:tensorflow:loss = 171515.0, step = 301 (0.705 sec)\n",
"INFO:tensorflow:global_step/sec: 148.363\n",
"INFO:tensorflow:loss = 140163.0, step = 401 (0.675 sec)\n",
"INFO:tensorflow:global_step/sec: 131.501\n",
"INFO:tensorflow:loss = 141613.0, step = 501 (0.763 sec)\n",
"INFO:tensorflow:global_step/sec: 148.133\n",
"INFO:tensorflow:loss = 136004.0, step = 601 (0.671 sec)\n",
"INFO:tensorflow:global_step/sec: 164.039\n",
"INFO:tensorflow:loss = 130641.0, step = 701 (0.609 sec)\n",
"INFO:tensorflow:global_step/sec: 154.876\n",
"INFO:tensorflow:loss = 111425.0, step = 801 (0.647 sec)\n",
"INFO:tensorflow:global_step/sec: 161.257\n",
"INFO:tensorflow:loss = 113015.0, step = 901 (0.619 sec)\n",
"INFO:tensorflow:global_step/sec: 164.882\n",
"INFO:tensorflow:loss = 93464.3, step = 1001 (0.607 sec)\n",
"INFO:tensorflow:global_step/sec: 155.051\n",
"INFO:tensorflow:loss = 87641.5, step = 1101 (0.645 sec)\n",
"INFO:tensorflow:global_step/sec: 132.637\n",
"INFO:tensorflow:loss = 88079.6, step = 1201 (0.760 sec)\n",
"INFO:tensorflow:global_step/sec: 133.662\n",
"INFO:tensorflow:loss = 85597.5, step = 1301 (0.742 sec)\n",
"INFO:tensorflow:global_step/sec: 131.989\n",
"INFO:tensorflow:loss = 63857.3, step = 1401 (0.762 sec)\n",
"INFO:tensorflow:global_step/sec: 105.007\n",
"INFO:tensorflow:loss = 70258.2, step = 1501 (0.949 sec)\n",
"INFO:tensorflow:global_step/sec: 131.114\n",
"INFO:tensorflow:loss = 55564.2, step = 1601 (0.765 sec)\n",
"INFO:tensorflow:global_step/sec: 128.256\n",
"INFO:tensorflow:loss = 63768.5, step = 1701 (0.777 sec)\n",
"INFO:tensorflow:global_step/sec: 144.842\n",
"INFO:tensorflow:loss = 54877.1, step = 1801 (0.691 sec)\n",
"INFO:tensorflow:global_step/sec: 126.825\n",
"INFO:tensorflow:loss = 60213.6, step = 1901 (0.792 sec)\n",
"INFO:tensorflow:global_step/sec: 143.734\n",
"INFO:tensorflow:loss = 54891.5, step = 2001 (0.692 sec)\n",
"INFO:tensorflow:global_step/sec: 146.572\n",
"INFO:tensorflow:loss = 47515.1, step = 2101 (0.685 sec)\n",
"INFO:tensorflow:global_step/sec: 117.477\n",
"INFO:tensorflow:loss = 51576.9, step = 2201 (0.849 sec)\n",
"INFO:tensorflow:global_step/sec: 144.71\n",
"INFO:tensorflow:loss = 50460.4, step = 2301 (0.693 sec)\n",
"INFO:tensorflow:Saving checkpoints for 2401 into trained_models/reg-model-04/model.ckpt.\n",
"INFO:tensorflow:global_step/sec: 93.9596\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/valid-*.csv\n",
"Batch size: 500\n",
"Epoch Count: 1\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"INFO:tensorflow:Starting evaluation at 2017-11-11-14:32:07\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-2401\n",
"INFO:tensorflow:Finished evaluation at 2017-11-11-14:32:08\n",
"INFO:tensorflow:Saving dict for global step 2401: average_loss = 104.677, global_step = 2401, loss = 52338.7\n",
"INFO:tensorflow:Validation (step 2401): average_loss = 104.677, loss = 52338.7, global_step = 2401\n",
"INFO:tensorflow:loss = 54241.1, step = 2401 (1.982 sec)\n",
"INFO:tensorflow:global_step/sec: 64.2617\n",
"INFO:tensorflow:loss = 42089.1, step = 2501 (0.636 sec)\n",
"INFO:tensorflow:global_step/sec: 156.711\n",
"INFO:tensorflow:loss = 43037.2, step = 2601 (0.639 sec)\n",
"INFO:tensorflow:global_step/sec: 149.293\n",
"INFO:tensorflow:loss = 46601.3, step = 2701 (0.670 sec)\n",
"INFO:tensorflow:global_step/sec: 147.748\n",
"INFO:tensorflow:loss = 42919.7, step = 2801 (0.676 sec)\n",
"INFO:tensorflow:global_step/sec: 152.002\n",
"INFO:tensorflow:loss = 44527.5, step = 2901 (0.659 sec)\n",
"INFO:tensorflow:global_step/sec: 144.838\n",
"INFO:tensorflow:loss = 47868.0, step = 3001 (0.689 sec)\n",
"INFO:tensorflow:global_step/sec: 160.648\n",
"INFO:tensorflow:loss = 47298.0, step = 3101 (0.622 sec)\n",
"INFO:tensorflow:global_step/sec: 161.367\n",
"INFO:tensorflow:loss = 49777.9, step = 3201 (0.620 sec)\n",
"INFO:tensorflow:global_step/sec: 153.168\n",
"INFO:tensorflow:loss = 44413.0, step = 3301 (0.653 sec)\n",
"INFO:tensorflow:global_step/sec: 158.438\n",
"INFO:tensorflow:loss = 47032.9, step = 3401 (0.631 sec)\n",
"INFO:tensorflow:global_step/sec: 119.087\n",
"INFO:tensorflow:loss = 36708.7, step = 3501 (0.845 sec)\n",
"INFO:tensorflow:global_step/sec: 147.487\n",
"INFO:tensorflow:loss = 48602.3, step = 3601 (0.673 sec)\n",
"INFO:tensorflow:global_step/sec: 151.377\n",
"INFO:tensorflow:loss = 46881.4, step = 3701 (0.662 sec)\n",
"INFO:tensorflow:global_step/sec: 158.061\n",
"INFO:tensorflow:loss = 44374.4, step = 3801 (0.632 sec)\n",
"INFO:tensorflow:global_step/sec: 160.813\n",
"INFO:tensorflow:loss = 45259.9, step = 3901 (0.622 sec)\n",
"INFO:tensorflow:global_step/sec: 162.422\n",
"INFO:tensorflow:loss = 50385.1, step = 4001 (0.615 sec)\n",
"INFO:tensorflow:global_step/sec: 161.446\n",
"INFO:tensorflow:loss = 46569.6, step = 4101 (0.620 sec)\n",
"INFO:tensorflow:global_step/sec: 154.099\n",
"INFO:tensorflow:loss = 47248.7, step = 4201 (0.649 sec)\n",
"INFO:tensorflow:global_step/sec: 157.475\n",
"INFO:tensorflow:loss = 40043.4, step = 4301 (0.635 sec)\n",
"INFO:tensorflow:global_step/sec: 132.105\n",
"INFO:tensorflow:loss = 45197.2, step = 4401 (0.757 sec)\n",
"INFO:tensorflow:global_step/sec: 154.426\n",
"INFO:tensorflow:loss = 47951.4, step = 4501 (0.647 sec)\n",
"INFO:tensorflow:global_step/sec: 136.858\n",
"INFO:tensorflow:loss = 44457.1, step = 4601 (0.734 sec)\n",
"INFO:tensorflow:global_step/sec: 119.193\n",
"INFO:tensorflow:loss = 37337.1, step = 4701 (0.839 sec)\n",
"INFO:tensorflow:Saving checkpoints for 4801 into trained_models/reg-model-04/model.ckpt.\n",
"INFO:tensorflow:global_step/sec: 72.1377\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/valid-*.csv\n",
"Batch size: 500\n",
"Epoch Count: 1\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"INFO:tensorflow:Starting evaluation at 2017-11-11-14:32:25\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-4801\n",
"INFO:tensorflow:Finished evaluation at 2017-11-11-14:32:26\n",
"INFO:tensorflow:Saving dict for global step 4801: average_loss = 96.7373, global_step = 4801, loss = 48368.7\n",
"INFO:tensorflow:Validation (step 4801): average_loss = 96.7373, loss = 48368.7, global_step = 4801\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:loss = 46016.4, step = 4801 (2.349 sec)\n",
"INFO:tensorflow:global_step/sec: 60.8736\n",
"INFO:tensorflow:loss = 46275.1, step = 4901 (0.679 sec)\n",
"INFO:tensorflow:global_step/sec: 151.574\n",
"INFO:tensorflow:loss = 33856.8, step = 5001 (0.658 sec)\n",
"INFO:tensorflow:global_step/sec: 162.539\n",
"INFO:tensorflow:loss = 58067.3, step = 5101 (0.616 sec)\n",
"INFO:tensorflow:global_step/sec: 161.898\n",
"INFO:tensorflow:loss = 36591.6, step = 5201 (0.617 sec)\n",
"INFO:tensorflow:global_step/sec: 161.238\n",
"INFO:tensorflow:loss = 49899.2, step = 5301 (0.621 sec)\n",
"INFO:tensorflow:global_step/sec: 161.159\n",
"INFO:tensorflow:loss = 59058.9, step = 5401 (0.620 sec)\n",
"INFO:tensorflow:global_step/sec: 162.119\n",
"INFO:tensorflow:loss = 39515.4, step = 5501 (0.617 sec)\n",
"INFO:tensorflow:global_step/sec: 160.662\n",
"INFO:tensorflow:loss = 37766.9, step = 5601 (0.622 sec)\n",
"INFO:tensorflow:global_step/sec: 160.995\n",
"INFO:tensorflow:loss = 45288.4, step = 5701 (0.621 sec)\n",
"INFO:tensorflow:global_step/sec: 159.086\n",
"INFO:tensorflow:loss = 41809.8, step = 5801 (0.629 sec)\n",
"INFO:tensorflow:global_step/sec: 153.174\n",
"INFO:tensorflow:loss = 42418.6, step = 5901 (0.652 sec)\n",
"INFO:tensorflow:global_step/sec: 136.012\n",
"INFO:tensorflow:loss = 49231.9, step = 6001 (0.736 sec)\n",
"INFO:tensorflow:global_step/sec: 147.14\n",
"INFO:tensorflow:loss = 37564.2, step = 6101 (0.679 sec)\n",
"INFO:tensorflow:global_step/sec: 158.619\n",
"INFO:tensorflow:loss = 42841.4, step = 6201 (0.630 sec)\n",
"INFO:tensorflow:global_step/sec: 146.474\n",
"INFO:tensorflow:loss = 47508.5, step = 6301 (0.683 sec)\n",
"INFO:tensorflow:global_step/sec: 160.396\n",
"INFO:tensorflow:loss = 44208.4, step = 6401 (0.624 sec)\n",
"INFO:tensorflow:global_step/sec: 148.18\n",
"INFO:tensorflow:loss = 40910.1, step = 6501 (0.675 sec)\n",
"INFO:tensorflow:global_step/sec: 138.929\n",
"INFO:tensorflow:loss = 50287.4, step = 6601 (0.720 sec)\n",
"INFO:tensorflow:global_step/sec: 151.384\n",
"INFO:tensorflow:loss = 39495.4, step = 6701 (0.660 sec)\n",
"INFO:tensorflow:global_step/sec: 149.712\n",
"INFO:tensorflow:loss = 43742.4, step = 6801 (0.668 sec)\n",
"INFO:tensorflow:global_step/sec: 121.472\n",
"INFO:tensorflow:loss = 44626.8, step = 6901 (0.827 sec)\n",
"INFO:tensorflow:global_step/sec: 134.407\n",
"INFO:tensorflow:loss = 40497.6, step = 7001 (0.742 sec)\n",
"INFO:tensorflow:global_step/sec: 135.498\n",
"INFO:tensorflow:loss = 37368.0, step = 7101 (0.740 sec)\n",
"INFO:tensorflow:Saving checkpoints for 7201 into trained_models/reg-model-04/model.ckpt.\n",
"INFO:tensorflow:global_step/sec: 76.9883\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/valid-*.csv\n",
"Batch size: 500\n",
"Epoch Count: 1\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"INFO:tensorflow:Starting evaluation at 2017-11-11-14:32:43\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-7201\n",
"INFO:tensorflow:Finished evaluation at 2017-11-11-14:32:43\n",
"INFO:tensorflow:Saving dict for global step 7201: average_loss = 95.7069, global_step = 7201, loss = 47853.4\n",
"INFO:tensorflow:Validation (step 7201): average_loss = 95.7069, loss = 47853.4, global_step = 7201\n",
"INFO:tensorflow:loss = 51782.6, step = 7201 (2.226 sec)\n",
"INFO:tensorflow:global_step/sec: 60.5837\n",
"INFO:tensorflow:loss = 42918.2, step = 7301 (0.720 sec)\n",
"INFO:tensorflow:global_step/sec: 148.242\n",
"INFO:tensorflow:loss = 40595.4, step = 7401 (0.675 sec)\n",
"INFO:tensorflow:global_step/sec: 141.722\n",
"INFO:tensorflow:loss = 29395.8, step = 7501 (0.706 sec)\n",
"INFO:tensorflow:global_step/sec: 145.886\n",
"INFO:tensorflow:loss = 51844.5, step = 7601 (0.687 sec)\n",
"INFO:tensorflow:global_step/sec: 141.609\n",
"INFO:tensorflow:loss = 43390.5, step = 7701 (0.704 sec)\n",
"INFO:tensorflow:global_step/sec: 126.915\n",
"INFO:tensorflow:loss = 47932.0, step = 7801 (0.787 sec)\n",
"INFO:tensorflow:global_step/sec: 144.669\n",
"INFO:tensorflow:loss = 50953.5, step = 7901 (0.692 sec)\n",
"INFO:tensorflow:global_step/sec: 121.705\n",
"INFO:tensorflow:loss = 45364.8, step = 8001 (0.821 sec)\n",
"INFO:tensorflow:global_step/sec: 143.123\n",
"INFO:tensorflow:loss = 37158.0, step = 8101 (0.700 sec)\n",
"INFO:tensorflow:global_step/sec: 129.806\n",
"INFO:tensorflow:loss = 51231.4, step = 8201 (0.772 sec)\n",
"INFO:tensorflow:global_step/sec: 135.586\n",
"INFO:tensorflow:loss = 45554.2, step = 8301 (0.737 sec)\n",
"INFO:tensorflow:global_step/sec: 139.29\n",
"INFO:tensorflow:loss = 38198.0, step = 8401 (0.715 sec)\n",
"INFO:tensorflow:global_step/sec: 126.346\n",
"INFO:tensorflow:loss = 37683.4, step = 8501 (0.793 sec)\n",
"INFO:tensorflow:global_step/sec: 142.809\n",
"INFO:tensorflow:loss = 48373.8, step = 8601 (0.701 sec)\n",
"INFO:tensorflow:global_step/sec: 133.683\n",
"INFO:tensorflow:loss = 39778.3, step = 8701 (0.748 sec)\n",
"INFO:tensorflow:global_step/sec: 135.688\n",
"INFO:tensorflow:loss = 46058.1, step = 8801 (0.737 sec)\n",
"INFO:tensorflow:global_step/sec: 135.275\n",
"INFO:tensorflow:loss = 46736.0, step = 8901 (0.738 sec)\n",
"INFO:tensorflow:global_step/sec: 129.718\n",
"INFO:tensorflow:loss = 40734.2, step = 9001 (0.771 sec)\n",
"INFO:tensorflow:global_step/sec: 143.523\n",
"INFO:tensorflow:loss = 43216.0, step = 9101 (0.698 sec)\n",
"INFO:tensorflow:global_step/sec: 134.956\n",
"INFO:tensorflow:loss = 51448.4, step = 9201 (0.740 sec)\n",
"INFO:tensorflow:global_step/sec: 144.751\n",
"INFO:tensorflow:loss = 46285.5, step = 9301 (0.690 sec)\n",
"INFO:tensorflow:global_step/sec: 129.764\n",
"INFO:tensorflow:loss = 45954.6, step = 9401 (0.771 sec)\n",
"INFO:tensorflow:global_step/sec: 145.96\n",
"INFO:tensorflow:loss = 48068.8, step = 9501 (0.685 sec)\n",
"INFO:tensorflow:Saving checkpoints for 9601 into trained_models/reg-model-04/model.ckpt.\n",
"INFO:tensorflow:global_step/sec: 75.1645\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/valid-*.csv\n",
"Batch size: 500\n",
"Epoch Count: 1\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"INFO:tensorflow:Starting evaluation at 2017-11-11-14:33:02\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-9601\n",
"INFO:tensorflow:Finished evaluation at 2017-11-11-14:33:02\n",
"INFO:tensorflow:Saving dict for global step 9601: average_loss = 95.0352, global_step = 9601, loss = 47517.6\n",
"INFO:tensorflow:Validation (step 9601): average_loss = 95.0352, loss = 47517.6, global_step = 9601\n",
"INFO:tensorflow:loss = 53438.2, step = 9601 (2.298 sec)\n",
"INFO:tensorflow:global_step/sec: 59.2006\n",
"INFO:tensorflow:loss = 53259.5, step = 9701 (0.721 sec)\n",
"INFO:tensorflow:global_step/sec: 141.779\n",
"INFO:tensorflow:loss = 34251.2, step = 9801 (0.706 sec)\n",
"INFO:tensorflow:global_step/sec: 131.811\n",
"INFO:tensorflow:loss = 42877.2, step = 9901 (0.763 sec)\n",
"INFO:tensorflow:global_step/sec: 115.783\n",
"INFO:tensorflow:loss = 49685.0, step = 10001 (0.862 sec)\n",
"INFO:tensorflow:global_step/sec: 127.013\n",
"INFO:tensorflow:loss = 47262.9, step = 10101 (0.784 sec)\n",
"INFO:tensorflow:global_step/sec: 151.498\n",
"INFO:tensorflow:loss = 49317.5, step = 10201 (0.661 sec)\n",
"INFO:tensorflow:global_step/sec: 143.846\n",
"INFO:tensorflow:loss = 41488.7, step = 10301 (0.694 sec)\n",
"INFO:tensorflow:global_step/sec: 140.025\n",
"INFO:tensorflow:loss = 40965.7, step = 10401 (0.714 sec)\n",
"INFO:tensorflow:global_step/sec: 151.54\n",
"INFO:tensorflow:loss = 34246.2, step = 10501 (0.660 sec)\n",
"INFO:tensorflow:global_step/sec: 141.593\n",
"INFO:tensorflow:loss = 41920.2, step = 10601 (0.707 sec)\n",
"INFO:tensorflow:global_step/sec: 142.479\n",
"INFO:tensorflow:loss = 40829.2, step = 10701 (0.702 sec)\n",
"INFO:tensorflow:global_step/sec: 146.329\n",
"INFO:tensorflow:loss = 53730.8, step = 10801 (0.683 sec)\n",
"INFO:tensorflow:global_step/sec: 143.41\n",
"INFO:tensorflow:loss = 39976.0, step = 10901 (0.698 sec)\n",
"INFO:tensorflow:global_step/sec: 141.968\n",
"INFO:tensorflow:loss = 42867.9, step = 11001 (0.704 sec)\n",
"INFO:tensorflow:global_step/sec: 145.57\n",
"INFO:tensorflow:loss = 44854.7, step = 11101 (0.687 sec)\n",
"INFO:tensorflow:global_step/sec: 135.972\n",
"INFO:tensorflow:loss = 43357.3, step = 11201 (0.735 sec)\n",
"INFO:tensorflow:global_step/sec: 145.339\n",
"INFO:tensorflow:loss = 44547.3, step = 11301 (0.688 sec)\n",
"INFO:tensorflow:global_step/sec: 142.515\n",
"INFO:tensorflow:loss = 41676.9, step = 11401 (0.703 sec)\n",
"INFO:tensorflow:global_step/sec: 145.965\n",
"INFO:tensorflow:loss = 30990.5, step = 11501 (0.685 sec)\n",
"INFO:tensorflow:global_step/sec: 144.552\n",
"INFO:tensorflow:loss = 50680.9, step = 11601 (0.691 sec)\n",
"INFO:tensorflow:global_step/sec: 138.256\n",
"INFO:tensorflow:loss = 41594.9, step = 11701 (0.726 sec)\n",
"INFO:tensorflow:global_step/sec: 129.769\n",
"INFO:tensorflow:loss = 42287.2, step = 11801 (0.769 sec)\n",
"INFO:tensorflow:global_step/sec: 131.017\n",
"INFO:tensorflow:loss = 49489.5, step = 11901 (0.763 sec)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Saving checkpoints for 12001 into trained_models/reg-model-04/model.ckpt.\n",
"INFO:tensorflow:global_step/sec: 74.3397\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/valid-*.csv\n",
"Batch size: 500\n",
"Epoch Count: 1\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"INFO:tensorflow:Starting evaluation at 2017-11-11-14:33:21\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-12001\n",
"INFO:tensorflow:Finished evaluation at 2017-11-11-14:33:21\n",
"INFO:tensorflow:Saving dict for global step 12001: average_loss = 94.4759, global_step = 12001, loss = 47237.9\n",
"INFO:tensorflow:Validation (step 12001): average_loss = 94.4759, loss = 47237.9, global_step = 12001\n",
"INFO:tensorflow:loss = 48240.1, step = 12001 (2.347 sec)\n",
"INFO:tensorflow:global_step/sec: 54.1707\n",
"INFO:tensorflow:loss = 33612.9, step = 12101 (0.847 sec)\n",
"INFO:tensorflow:global_step/sec: 124.694\n",
"INFO:tensorflow:loss = 38861.4, step = 12201 (0.802 sec)\n",
"INFO:tensorflow:global_step/sec: 118.321\n",
"INFO:tensorflow:loss = 42655.6, step = 12301 (0.845 sec)\n",
"INFO:tensorflow:global_step/sec: 124.359\n",
"INFO:tensorflow:loss = 35384.6, step = 12401 (0.803 sec)\n",
"INFO:tensorflow:global_step/sec: 127.487\n",
"INFO:tensorflow:loss = 43430.5, step = 12501 (0.785 sec)\n",
"INFO:tensorflow:global_step/sec: 140.518\n",
"INFO:tensorflow:loss = 48275.4, step = 12601 (0.710 sec)\n",
"INFO:tensorflow:global_step/sec: 142.918\n",
"INFO:tensorflow:loss = 37730.8, step = 12701 (0.700 sec)\n",
"INFO:tensorflow:global_step/sec: 143.119\n",
"INFO:tensorflow:loss = 48645.6, step = 12801 (0.701 sec)\n",
"INFO:tensorflow:global_step/sec: 138.138\n",
"INFO:tensorflow:loss = 30703.5, step = 12901 (0.722 sec)\n",
"INFO:tensorflow:global_step/sec: 145.613\n",
"INFO:tensorflow:loss = 43611.7, step = 13001 (0.687 sec)\n",
"INFO:tensorflow:global_step/sec: 144.863\n",
"INFO:tensorflow:loss = 38099.9, step = 13101 (0.691 sec)\n",
"INFO:tensorflow:global_step/sec: 137.656\n",
"INFO:tensorflow:loss = 45194.5, step = 13201 (0.728 sec)\n",
"INFO:tensorflow:global_step/sec: 122.444\n",
"INFO:tensorflow:loss = 38871.8, step = 13301 (0.815 sec)\n",
"INFO:tensorflow:global_step/sec: 138.867\n",
"INFO:tensorflow:loss = 36956.7, step = 13401 (0.720 sec)\n",
"INFO:tensorflow:global_step/sec: 124.395\n",
"INFO:tensorflow:loss = 39388.8, step = 13501 (0.803 sec)\n",
"INFO:tensorflow:global_step/sec: 157.459\n",
"INFO:tensorflow:loss = 46446.1, step = 13601 (0.635 sec)\n",
"INFO:tensorflow:global_step/sec: 148.935\n",
"INFO:tensorflow:loss = 44792.6, step = 13701 (0.672 sec)\n",
"INFO:tensorflow:global_step/sec: 138.586\n",
"INFO:tensorflow:loss = 55869.1, step = 13801 (0.721 sec)\n",
"INFO:tensorflow:global_step/sec: 152.045\n",
"INFO:tensorflow:loss = 42912.5, step = 13901 (0.658 sec)\n",
"INFO:tensorflow:global_step/sec: 127.431\n",
"INFO:tensorflow:loss = 44276.9, step = 14001 (0.786 sec)\n",
"INFO:tensorflow:global_step/sec: 128.268\n",
"INFO:tensorflow:loss = 32231.6, step = 14101 (0.780 sec)\n",
"INFO:tensorflow:global_step/sec: 136.64\n",
"INFO:tensorflow:loss = 33367.5, step = 14201 (0.730 sec)\n",
"INFO:tensorflow:global_step/sec: 146.577\n",
"INFO:tensorflow:loss = 43804.7, step = 14301 (0.682 sec)\n",
"INFO:tensorflow:Saving checkpoints for 14401 into trained_models/reg-model-04/model.ckpt.\n",
"INFO:tensorflow:global_step/sec: 77.9368\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/valid-*.csv\n",
"Batch size: 500\n",
"Epoch Count: 1\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"INFO:tensorflow:Starting evaluation at 2017-11-11-14:33:40\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-14401\n",
"INFO:tensorflow:Finished evaluation at 2017-11-11-14:33:40\n",
"INFO:tensorflow:Saving dict for global step 14401: average_loss = 93.9907, global_step = 14401, loss = 46995.4\n",
"INFO:tensorflow:Validation (step 14401): average_loss = 93.9907, loss = 46995.4, global_step = 14401\n",
"INFO:tensorflow:loss = 57803.0, step = 14401 (2.139 sec)\n",
"INFO:tensorflow:global_step/sec: 61.7375\n",
"INFO:tensorflow:loss = 46377.5, step = 14501 (0.764 sec)\n",
"INFO:tensorflow:global_step/sec: 124.959\n",
"INFO:tensorflow:loss = 34797.7, step = 14601 (0.801 sec)\n",
"INFO:tensorflow:global_step/sec: 144.906\n",
"INFO:tensorflow:loss = 44715.1, step = 14701 (0.690 sec)\n",
"INFO:tensorflow:global_step/sec: 143.668\n",
"INFO:tensorflow:loss = 39205.6, step = 14801 (0.695 sec)\n",
"INFO:tensorflow:global_step/sec: 147.122\n",
"INFO:tensorflow:loss = 40149.7, step = 14901 (0.679 sec)\n",
"INFO:tensorflow:global_step/sec: 147.896\n",
"INFO:tensorflow:loss = 43184.4, step = 15001 (0.677 sec)\n",
"INFO:tensorflow:global_step/sec: 146.5\n",
"INFO:tensorflow:loss = 38481.5, step = 15101 (0.682 sec)\n",
"INFO:tensorflow:global_step/sec: 144.174\n",
"INFO:tensorflow:loss = 40600.9, step = 15201 (0.694 sec)\n",
"INFO:tensorflow:global_step/sec: 141.158\n",
"INFO:tensorflow:loss = 49492.6, step = 15301 (0.709 sec)\n",
"INFO:tensorflow:global_step/sec: 132.475\n",
"INFO:tensorflow:loss = 29930.4, step = 15401 (0.754 sec)\n",
"INFO:tensorflow:global_step/sec: 147.538\n",
"INFO:tensorflow:loss = 46129.1, step = 15501 (0.678 sec)\n",
"INFO:tensorflow:global_step/sec: 143.941\n",
"INFO:tensorflow:loss = 47175.0, step = 15601 (0.696 sec)\n",
"INFO:tensorflow:global_step/sec: 144.395\n",
"INFO:tensorflow:loss = 39116.7, step = 15701 (0.692 sec)\n",
"INFO:tensorflow:global_step/sec: 140.103\n",
"INFO:tensorflow:loss = 35493.4, step = 15801 (0.713 sec)\n",
"INFO:tensorflow:global_step/sec: 148.264\n",
"INFO:tensorflow:loss = 41123.8, step = 15901 (0.674 sec)\n",
"INFO:tensorflow:global_step/sec: 141.081\n",
"INFO:tensorflow:loss = 28964.1, step = 16001 (0.709 sec)\n",
"INFO:tensorflow:global_step/sec: 129.559\n",
"INFO:tensorflow:loss = 42644.8, step = 16101 (0.773 sec)\n",
"INFO:tensorflow:global_step/sec: 117.856\n",
"INFO:tensorflow:loss = 41032.1, step = 16201 (0.849 sec)\n",
"INFO:tensorflow:global_step/sec: 126.27\n",
"INFO:tensorflow:loss = 38422.3, step = 16301 (0.793 sec)\n",
"INFO:tensorflow:global_step/sec: 133.53\n",
"INFO:tensorflow:loss = 40377.9, step = 16401 (0.746 sec)\n",
"INFO:tensorflow:global_step/sec: 146.292\n",
"INFO:tensorflow:loss = 45031.1, step = 16501 (0.685 sec)\n",
"INFO:tensorflow:global_step/sec: 128.616\n",
"INFO:tensorflow:loss = 44225.1, step = 16601 (0.776 sec)\n",
"INFO:tensorflow:global_step/sec: 119.769\n",
"INFO:tensorflow:loss = 44445.1, step = 16701 (0.836 sec)\n",
"INFO:tensorflow:Saving checkpoints for 16801 into trained_models/reg-model-04/model.ckpt.\n",
"INFO:tensorflow:global_step/sec: 76.8777\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/valid-*.csv\n",
"Batch size: 500\n",
"Epoch Count: 1\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"INFO:tensorflow:Starting evaluation at 2017-11-11-14:33:59\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-16801\n",
"INFO:tensorflow:Finished evaluation at 2017-11-11-14:33:59\n",
"INFO:tensorflow:Saving dict for global step 16801: average_loss = 93.6571, global_step = 16801, loss = 46828.6\n",
"INFO:tensorflow:Validation (step 16801): average_loss = 93.6571, loss = 46828.6, global_step = 16801\n",
"INFO:tensorflow:loss = 43306.4, step = 16801 (2.293 sec)\n",
"INFO:tensorflow:global_step/sec: 55.6313\n",
"INFO:tensorflow:loss = 54241.1, step = 16901 (0.808 sec)\n",
"INFO:tensorflow:global_step/sec: 139.604\n",
"INFO:tensorflow:loss = 34022.9, step = 17001 (0.715 sec)\n",
"INFO:tensorflow:global_step/sec: 139.468\n",
"INFO:tensorflow:loss = 45510.8, step = 17101 (0.716 sec)\n",
"INFO:tensorflow:global_step/sec: 142.418\n",
"INFO:tensorflow:loss = 43210.0, step = 17201 (0.702 sec)\n",
"INFO:tensorflow:global_step/sec: 143.537\n",
"INFO:tensorflow:loss = 50877.1, step = 17301 (0.697 sec)\n",
"INFO:tensorflow:global_step/sec: 135.489\n",
"INFO:tensorflow:loss = 38702.2, step = 17401 (0.739 sec)\n",
"INFO:tensorflow:global_step/sec: 145.958\n",
"INFO:tensorflow:loss = 34215.1, step = 17501 (0.684 sec)\n",
"INFO:tensorflow:global_step/sec: 153.087\n",
"INFO:tensorflow:loss = 39389.1, step = 17601 (0.654 sec)\n",
"INFO:tensorflow:global_step/sec: 133.414\n",
"INFO:tensorflow:loss = 43825.1, step = 17701 (0.750 sec)\n",
"INFO:tensorflow:global_step/sec: 132.724\n",
"INFO:tensorflow:loss = 43972.8, step = 17801 (0.754 sec)\n",
"INFO:tensorflow:global_step/sec: 138.88\n",
"INFO:tensorflow:loss = 48032.7, step = 17901 (0.721 sec)\n",
"INFO:tensorflow:global_step/sec: 131.104\n",
"INFO:tensorflow:loss = 43332.0, step = 18001 (0.761 sec)\n",
"INFO:tensorflow:global_step/sec: 129.382\n",
"INFO:tensorflow:loss = 39407.1, step = 18101 (0.772 sec)\n",
"INFO:tensorflow:global_step/sec: 146.652\n",
"INFO:tensorflow:loss = 46066.7, step = 18201 (0.681 sec)\n",
"INFO:tensorflow:global_step/sec: 157.141\n",
"INFO:tensorflow:loss = 43000.3, step = 18301 (0.637 sec)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:global_step/sec: 134.653\n",
"INFO:tensorflow:loss = 43819.3, step = 18401 (0.743 sec)\n",
"INFO:tensorflow:global_step/sec: 146.891\n",
"INFO:tensorflow:loss = 43357.4, step = 18501 (0.680 sec)\n",
"INFO:tensorflow:global_step/sec: 155.775\n",
"INFO:tensorflow:loss = 50256.3, step = 18601 (0.642 sec)\n",
"INFO:tensorflow:global_step/sec: 135.905\n",
"INFO:tensorflow:loss = 42203.0, step = 18701 (0.736 sec)\n",
"INFO:tensorflow:global_step/sec: 150.761\n",
"INFO:tensorflow:loss = 39352.0, step = 18801 (0.663 sec)\n",
"INFO:tensorflow:global_step/sec: 156.62\n",
"INFO:tensorflow:loss = 44110.5, step = 18901 (0.638 sec)\n",
"INFO:tensorflow:global_step/sec: 148.473\n",
"INFO:tensorflow:loss = 52128.4, step = 19001 (0.674 sec)\n",
"INFO:tensorflow:global_step/sec: 131.937\n",
"INFO:tensorflow:loss = 44822.1, step = 19101 (0.758 sec)\n",
"INFO:tensorflow:Saving checkpoints for 19201 into trained_models/reg-model-04/model.ckpt.\n",
"INFO:tensorflow:global_step/sec: 75.6735\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/valid-*.csv\n",
"Batch size: 500\n",
"Epoch Count: 1\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"INFO:tensorflow:Starting evaluation at 2017-11-11-14:34:18\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-19201\n",
"INFO:tensorflow:Finished evaluation at 2017-11-11-14:34:18\n",
"INFO:tensorflow:Saving dict for global step 19201: average_loss = 93.4035, global_step = 19201, loss = 46701.7\n",
"INFO:tensorflow:Validation (step 19201): average_loss = 93.4035, loss = 46701.7, global_step = 19201\n",
"INFO:tensorflow:loss = 41447.1, step = 19201 (2.250 sec)\n",
"INFO:tensorflow:global_step/sec: 60.5112\n",
"INFO:tensorflow:loss = 39285.7, step = 19301 (0.724 sec)\n",
"INFO:tensorflow:global_step/sec: 142.25\n",
"INFO:tensorflow:loss = 47136.1, step = 19401 (0.704 sec)\n",
"INFO:tensorflow:global_step/sec: 143.985\n",
"INFO:tensorflow:loss = 48500.1, step = 19501 (0.693 sec)\n",
"INFO:tensorflow:global_step/sec: 145.566\n",
"INFO:tensorflow:loss = 41969.5, step = 19601 (0.689 sec)\n",
"INFO:tensorflow:global_step/sec: 126.264\n",
"INFO:tensorflow:loss = 45881.1, step = 19701 (0.789 sec)\n",
"INFO:tensorflow:global_step/sec: 149.807\n",
"INFO:tensorflow:loss = 38483.6, step = 19801 (0.668 sec)\n",
"INFO:tensorflow:global_step/sec: 147.313\n",
"INFO:tensorflow:loss = 38991.4, step = 19901 (0.679 sec)\n",
"INFO:tensorflow:global_step/sec: 154.983\n",
"INFO:tensorflow:loss = 33769.5, step = 20001 (0.645 sec)\n",
"INFO:tensorflow:global_step/sec: 135.423\n",
"INFO:tensorflow:loss = 37037.8, step = 20101 (0.738 sec)\n",
"INFO:tensorflow:global_step/sec: 143.824\n",
"INFO:tensorflow:loss = 44271.2, step = 20201 (0.698 sec)\n",
"INFO:tensorflow:global_step/sec: 142.841\n",
"INFO:tensorflow:loss = 47297.9, step = 20301 (0.699 sec)\n",
"INFO:tensorflow:global_step/sec: 136.822\n",
"INFO:tensorflow:loss = 49067.2, step = 20401 (0.730 sec)\n",
"INFO:tensorflow:global_step/sec: 148.37\n",
"INFO:tensorflow:loss = 29920.0, step = 20501 (0.673 sec)\n",
"INFO:tensorflow:global_step/sec: 155.2\n",
"INFO:tensorflow:loss = 41268.2, step = 20601 (0.645 sec)\n",
"INFO:tensorflow:global_step/sec: 150.811\n",
"INFO:tensorflow:loss = 48263.5, step = 20701 (0.664 sec)\n",
"INFO:tensorflow:global_step/sec: 152.439\n",
"INFO:tensorflow:loss = 43600.9, step = 20801 (0.655 sec)\n",
"INFO:tensorflow:global_step/sec: 154.507\n",
"INFO:tensorflow:loss = 53666.0, step = 20901 (0.647 sec)\n",
"INFO:tensorflow:global_step/sec: 140.796\n",
"INFO:tensorflow:loss = 43610.4, step = 21001 (0.711 sec)\n",
"INFO:tensorflow:global_step/sec: 147.657\n",
"INFO:tensorflow:loss = 39353.9, step = 21101 (0.677 sec)\n",
"INFO:tensorflow:global_step/sec: 150.085\n",
"INFO:tensorflow:loss = 41081.1, step = 21201 (0.667 sec)\n",
"INFO:tensorflow:global_step/sec: 127.016\n",
"INFO:tensorflow:loss = 43726.5, step = 21301 (0.787 sec)\n",
"INFO:tensorflow:global_step/sec: 135.764\n",
"INFO:tensorflow:loss = 46147.2, step = 21401 (0.737 sec)\n",
"INFO:tensorflow:global_step/sec: 136.951\n",
"INFO:tensorflow:loss = 42668.0, step = 21501 (0.731 sec)\n",
"INFO:tensorflow:Saving checkpoints for 21601 into trained_models/reg-model-04/model.ckpt.\n",
"INFO:tensorflow:global_step/sec: 73.8537\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/valid-*.csv\n",
"Batch size: 500\n",
"Epoch Count: 1\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"INFO:tensorflow:Starting evaluation at 2017-11-11-14:34:36\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-21601\n",
"INFO:tensorflow:Finished evaluation at 2017-11-11-14:34:36\n",
"INFO:tensorflow:Saving dict for global step 21601: average_loss = 93.2296, global_step = 21601, loss = 46614.8\n",
"INFO:tensorflow:Validation (step 21601): average_loss = 93.2296, loss = 46614.8, global_step = 21601\n",
"INFO:tensorflow:loss = 39012.0, step = 21601 (2.317 sec)\n",
"INFO:tensorflow:global_step/sec: 57.3267\n",
"INFO:tensorflow:loss = 42405.3, step = 21701 (0.783 sec)\n",
"INFO:tensorflow:global_step/sec: 141.019\n",
"INFO:tensorflow:loss = 47157.8, step = 21801 (0.707 sec)\n",
"INFO:tensorflow:global_step/sec: 143.884\n",
"INFO:tensorflow:loss = 40285.0, step = 21901 (0.695 sec)\n",
"INFO:tensorflow:global_step/sec: 130.032\n",
"INFO:tensorflow:loss = 51357.3, step = 22001 (0.769 sec)\n",
"INFO:tensorflow:global_step/sec: 150.628\n",
"INFO:tensorflow:loss = 43420.6, step = 22101 (0.663 sec)\n",
"INFO:tensorflow:global_step/sec: 134.978\n",
"INFO:tensorflow:loss = 50706.4, step = 22201 (0.741 sec)\n",
"INFO:tensorflow:global_step/sec: 138.679\n",
"INFO:tensorflow:loss = 47902.4, step = 22301 (0.721 sec)\n",
"INFO:tensorflow:global_step/sec: 137.255\n",
"INFO:tensorflow:loss = 34552.7, step = 22401 (0.730 sec)\n",
"INFO:tensorflow:global_step/sec: 139.58\n",
"INFO:tensorflow:loss = 40107.3, step = 22501 (0.715 sec)\n",
"INFO:tensorflow:global_step/sec: 122.743\n",
"INFO:tensorflow:loss = 43026.8, step = 22601 (0.817 sec)\n",
"INFO:tensorflow:global_step/sec: 118.511\n",
"INFO:tensorflow:loss = 38352.9, step = 22701 (0.846 sec)\n",
"INFO:tensorflow:global_step/sec: 127.974\n",
"INFO:tensorflow:loss = 47005.3, step = 22801 (0.781 sec)\n",
"INFO:tensorflow:global_step/sec: 132.907\n",
"INFO:tensorflow:loss = 34692.8, step = 22901 (0.751 sec)\n",
"INFO:tensorflow:global_step/sec: 145.023\n",
"INFO:tensorflow:loss = 45891.2, step = 23001 (0.689 sec)\n",
"INFO:tensorflow:global_step/sec: 143.874\n",
"INFO:tensorflow:loss = 44916.1, step = 23101 (0.695 sec)\n",
"INFO:tensorflow:global_step/sec: 132.35\n",
"INFO:tensorflow:loss = 42158.5, step = 23201 (0.755 sec)\n",
"INFO:tensorflow:global_step/sec: 119.94\n",
"INFO:tensorflow:loss = 34986.8, step = 23301 (0.838 sec)\n",
"INFO:tensorflow:global_step/sec: 124.828\n",
"INFO:tensorflow:loss = 49937.9, step = 23401 (0.798 sec)\n",
"INFO:tensorflow:global_step/sec: 129.237\n",
"INFO:tensorflow:loss = 51601.8, step = 23501 (0.773 sec)\n",
"INFO:tensorflow:global_step/sec: 135.224\n",
"INFO:tensorflow:loss = 44515.1, step = 23601 (0.740 sec)\n",
"INFO:tensorflow:global_step/sec: 140.371\n",
"INFO:tensorflow:loss = 43017.3, step = 23701 (0.713 sec)\n",
"INFO:tensorflow:global_step/sec: 122.712\n",
"INFO:tensorflow:loss = 34417.2, step = 23801 (0.817 sec)\n",
"INFO:tensorflow:global_step/sec: 133.027\n",
"INFO:tensorflow:loss = 45028.1, step = 23901 (0.750 sec)\n",
"INFO:tensorflow:Saving checkpoints for 24000 into trained_models/reg-model-04/model.ckpt.\n",
"INFO:tensorflow:Loss for final step: 41759.8.\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/valid-*.csv\n",
"Batch size: 500\n",
"Epoch Count: 1\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"INFO:tensorflow:Starting evaluation at 2017-11-11-14:34:56\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-24000\n",
"INFO:tensorflow:Finished evaluation at 2017-11-11-14:34:56\n",
"INFO:tensorflow:Saving dict for global step 24000: average_loss = 93.1534, global_step = 24000, loss = 46576.7\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-24000\n",
"INFO:tensorflow:Assets added to graph.\n",
"INFO:tensorflow:No assets to write.\n",
"INFO:tensorflow:SavedModel written to: b\"trained_models/reg-model-04/export/Servo/temp-b'1510410896'/saved_model.pb\"\n",
".......................................\n",
"Experiment finished at 14:34:57\n",
"\n",
"Experiment elapsed time: 193.639007 seconds\n"
]
}
],
"source": [
"if not RESUME_TRAINING:\n",
" print(\"Removing previous artifacts...\")\n",
" shutil.rmtree(model_dir, ignore_errors=True)\n",
"else:\n",
" print(\"Resuming training...\") \n",
"\n",
"\n",
"tf.logging.set_verbosity(tf.logging.INFO)\n",
"\n",
"time_start = datetime.utcnow() \n",
"print(\"Experiment started at {}\".format(time_start.strftime(\"%H:%M:%S\")))\n",
"print(\".......................................\") \n",
"\n",
"\n",
"learn_runner.run(\n",
" experiment_fn=generate_experiment_fn(\n",
"\n",
" export_strategies=[make_export_strategy(\n",
" csv_serving_input_fn,\n",
" exports_to_keep=1\n",
" )]\n",
" ),\n",
" run_config=run_config,\n",
" schedule=\"train_and_evaluate\",\n",
" hparams=hparams\n",
")\n",
"\n",
"time_end = datetime.utcnow() \n",
"print(\".......................................\")\n",
"print(\"Experiment finished at {}\".format(time_end.strftime(\"%H:%M:%S\")))\n",
"print(\"\")\n",
"time_elapsed = time_end - time_start\n",
"print(\"Experiment elapsed time: {} seconds\".format(time_elapsed.total_seconds()))\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Evaluate the Model"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x11e860a58>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_tf_config': gpu_options {\n",
" per_process_gpu_memory_fraction: 1\n",
"}\n",
", '_tf_random_seed': 19830610, '_save_summary_steps': 100, '_save_checkpoints_secs': None, '_log_step_count_steps': 100, '_session_config': None, '_save_checkpoints_steps': 2400, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': 'trained_models/reg-model-04'}\n",
"\n",
"Estimator Type: <class 'tensorflow.python.estimator.canned.dnn.DNNRegressor'>\n",
"\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/train-*.csv\n",
"Batch size: 12000\n",
"Epoch Count: None\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"INFO:tensorflow:Starting evaluation at 2017-11-11-14:34:58\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-24000\n",
"INFO:tensorflow:Evaluation [1/1]\n",
"INFO:tensorflow:Finished evaluation at 2017-11-11-14:34:58\n",
"INFO:tensorflow:Saving dict for global step 24000: average_loss = 84.9214, global_step = 24000, loss = 1.01906e+06\n",
"\n",
"############################################################################################\n",
"# Train RMSE: 9.21528 - {'average_loss': 84.921417, 'loss': 1019057.0, 'global_step': 24000}\n",
"############################################################################################\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/valid-*.csv\n",
"Batch size: 3000\n",
"Epoch Count: None\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"INFO:tensorflow:Starting evaluation at 2017-11-11-14:34:59\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-24000\n",
"INFO:tensorflow:Evaluation [1/1]\n",
"INFO:tensorflow:Finished evaluation at 2017-11-11-14:34:59\n",
"INFO:tensorflow:Saving dict for global step 24000: average_loss = 93.1534, global_step = 24000, loss = 279460.0\n",
"\n",
"############################################################################################\n",
"# Valid RMSE: 9.6516 - {'average_loss': 93.153397, 'loss': 279460.19, 'global_step': 24000}\n",
"############################################################################################\n",
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/test-*.csv\n",
"Batch size: 5000\n",
"Epoch Count: None\n",
"Mode: eval\n",
"Shuffle: False\n",
"================\n",
"\n",
"INFO:tensorflow:Starting evaluation at 2017-11-11-14:34:59\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-24000\n",
"INFO:tensorflow:Evaluation [1/1]\n",
"INFO:tensorflow:Finished evaluation at 2017-11-11-14:35:00\n",
"INFO:tensorflow:Saving dict for global step 24000: average_loss = 93.2213, global_step = 24000, loss = 466107.0\n",
"\n",
"############################################################################################\n",
"# Test RMSE: 9.65512 - {'average_loss': 93.221344, 'loss': 466106.72, 'global_step': 24000}\n",
"############################################################################################\n"
]
}
],
"source": [
"TRAIN_SIZE = 12000\n",
"VALID_SIZE = 3000\n",
"TEST_SIZE = 5000\n",
"\n",
"train_input_fn = lambda: csv_input_fn(files_name_pattern= TRAIN_DATA_FILES_PATTERN, \n",
" mode= tf.estimator.ModeKeys.EVAL,\n",
" batch_size= TRAIN_SIZE)\n",
"\n",
"valid_input_fn = lambda: csv_input_fn(files_name_pattern= VALID_DATA_FILES_PATTERN, \n",
" mode= tf.estimator.ModeKeys.EVAL,\n",
" batch_size= VALID_SIZE)\n",
"\n",
"test_input_fn = lambda: csv_input_fn(files_name_pattern= TEST_DATA_FILES_PATTERN, \n",
" mode= tf.estimator.ModeKeys.EVAL,\n",
" batch_size= TEST_SIZE)\n",
"\n",
"estimator = create_estimator(run_config, hparams)\n",
"\n",
"train_results = estimator.evaluate(input_fn=train_input_fn, steps=1)\n",
"train_rmse = round(math.sqrt(train_results[\"average_loss\"]),5)\n",
"print()\n",
"print(\"############################################################################################\")\n",
"print(\"# Train RMSE: {} - {}\".format(train_rmse, train_results))\n",
"print(\"############################################################################################\")\n",
"\n",
"valid_results = estimator.evaluate(input_fn=valid_input_fn, steps=1)\n",
"valid_rmse = round(math.sqrt(valid_results[\"average_loss\"]),5)\n",
"print()\n",
"print(\"############################################################################################\")\n",
"print(\"# Valid RMSE: {} - {}\".format(valid_rmse,valid_results))\n",
"print(\"############################################################################################\")\n",
"\n",
"test_results = estimator.evaluate(input_fn=test_input_fn, steps=1)\n",
"test_rmse = round(math.sqrt(test_results[\"average_loss\"]),5)\n",
"print()\n",
"print(\"############################################################################################\")\n",
"print(\"# Test RMSE: {} - {}\".format(test_rmse, test_results))\n",
"print(\"############################################################################################\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 8. Prediction"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"* data input_fn:\n",
"================\n",
"Input file(s): data/test-*.csv\n",
"Batch size: 5\n",
"Epoch Count: None\n",
"Mode: infer\n",
"Shuffle: False\n",
"================\n",
"\n",
"WARNING:tensorflow:Input graph does not contain a QueueRunner. That means predict yields forever. This is probably a mistake.\n",
"INFO:tensorflow:Restoring parameters from trained_models/reg-model-04/model.ckpt-24000\n",
"\n",
"Predicted Values: [51.019321, -5.8079214, 19.57333, 2.9324729, 1.5238302]\n"
]
}
],
"source": [
"import itertools\n",
"\n",
"predict_input_fn = lambda: csv_input_fn(files_name_pattern=TEST_DATA_FILES_PATTERN, \n",
" mode= tf.estimator.ModeKeys.PREDICT,\n",
" batch_size= 5)\n",
"\n",
"predictions = estimator.predict(input_fn=predict_input_fn)\n",
"values = list(map(lambda item: item[\"predictions\"][0],list(itertools.islice(predictions, 5))))\n",
"print()\n",
"print(\"Predicted Values: {}\".format(values))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What can we improve?\n",
"\n",
"* **Use .tfrecords files instead of CSV** - TFRecord files are optimised for tensorflow.\n",
"\n",
"\n",
"* **Build a Custom Estimator** - Custom Estimator APIs give you the flexibility to build custom models in a simple and standard way\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
wenleicao/wenleicao.github.io | Files/regex_test.ipynb | 1 | 11953 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"query = '''\n",
"\"proc sql;\n",
"\tcreate table Wenlei.DW_Example_latest AS\n",
"\tSELECT DISTINCT \n",
"\t\t\tT2.POLICY_ID,\n",
"\t\t\tT2.POLICY,\n",
"\t\t\tstrip(left(T2.ROLE)),\n",
" strip(left(T2.type)),\t\t\t\n",
"\t\t\tT2.SEQ_ID, \n",
"\t\t\tINTCK('day', T1.LastPayDate, today()) as DayPaid,\n",
"\t\t\t(today()-T1.date_created) as DaysBtw\t\t\t\n",
"\tFROM \tWORK.DW_Table1 t1 \n",
"\t\t\tINNER JOIN\n",
"\t\t\tWORK.DW_Table2 t2 ON (t1.ID = t2.ID AND T1.POLICY_ID=T2.POLICY_ID \n",
"\t\t\t\t\t\t\t\t\t\t AND t1.MAX_PROCESS_Time=t2.PROCESS_Time)\n",
"\tWHERE t2.ID<>-1 \n",
"\t;\n",
"quit;\"\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'strip(left(b.type_rw))'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#goal strip(left(b.type_rw)) -> trim(b.type_rw) \n",
"import re\n",
"text = '''strip(left(b.type_rw)) abc'''\n",
"pattern = re.compile(\"(strip\\(left\\().+\\)\\)\", re.IGNORECASE)\n",
"match = pattern.search(text)\n",
"match.group(0)\n",
"# for match in pattern.finditer(text):\n",
"# print (match.group(1), match.group(2))\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#replace prefix\n",
"def remove_prefix(text, prefix):\n",
" return text[len(prefix):] if text.startswith(prefix) else text\n",
"#remove appendix\n",
"def remove_appendix(text, appendix):\n",
" return text[:len(text)-len(appendix)] if text.endswith(appendix) else text"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'trim(b.type_rw) abc'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stringX = match.group(0)\n",
"string_kept = remove_appendix (stringX, '))')\n",
"#print(string_kept)\n",
"string_kept = remove_prefix (string_kept, 'strip(left(')\n",
"#print(string_kept)\n",
"string_kept = 'trim(' + string_kept + ')'\n",
"text1 = text.replace(stringX, string_kept )\n",
"text1\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"https://stackoverflow.com/questions/6930982/how-to-use-a-variable-inside-a-regular-expression\n",
"https://docs.python.org/3/howto/regex.html#the-backslash-plague\n",
"'''\n",
"#define function to replace both side of string\n",
"def replace_both_sides (text, prefix, appendix, new_prefix, new_appendix):\n",
" import re\n",
" pattern = re.compile((re.escape(prefix)) + r'.+' + re.escape(appendix) , re.IGNORECASE) #left side, keep group, use escape to remove all \\\n",
" if pattern.search(text) != None:\n",
" match = pattern.search(text) #use search for any place in a row\n",
" # return match.group(0)\n",
" stringX = match.group(0) # get matched\n",
" string_kept = remove_appendix (stringX, appendix) #remove appendix\n",
" #print(string_kept) \n",
" string_kept = remove_prefix (string_kept, prefix) #remove prefix\n",
" #print(string_kept)\n",
" string_kept = new_prefix + string_kept + new_appendix #form replacement str\n",
" text1 = text.replace(stringX, string_kept ) #replace string\n",
" return text1\n",
" else:\n",
" return text\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trim(b.type_rw) abc\n",
"trim(c.type_rw) abc\n"
]
}
],
"source": [
"#test run\n",
"print(replace_both_sides (r'strip(left(b.type_rw)) abc', r\"strip(left(\", r\"))\", r'trim(', r')'))\n",
"print(replace_both_sides (r'strip(left(c.type_rw)) abc', r\"strip(left(\", r\"))\", r'trim(', r')'))\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\"proc sql;\n",
"\tcreate table Wenlei.DW_Example_latest AS\n",
"\tSELECT DISTINCT \n",
"\t\t\tT2.POLICY_ID,\n",
"\t\t\tT2.POLICY,\n",
"\t\t\ttrim(T2.ROLE),\n",
" strip(left(T2.type)),\t\t\t\n",
"\t\t\tT2.SEQ_ID, \n",
"\t\t\tINTCK('day', T1.LastPayDate, today()) as DayPaid,\n",
"\t\t\t(today()-T1.date_created) as DaysBtw\t\t\t\n",
"\tFROM \tWORK.DW_Table1 t1 \n",
"\t\t\tINNER JOIN\n",
"\t\t\tWORK.DW_Table2 t2 ON (t1.ID = t2.ID AND T1.POLICY_ID=T2.POLICY_ID \n",
"\t\t\t\t\t\t\t\t\t\t AND t1.MAX_PROCESS_Time=t2.PROCESS_Time)\n",
"\tWHERE t2.ID<>-1 \n",
"\t;\n",
"quit;\"\n",
"\n"
]
}
],
"source": [
"query_m= replace_both_sides (query, r\"strip(left(\", r\"))\", r'trim(', r')')\n",
"print(query_m)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"strip(left(T2.ROLE))\n",
"strip(left(T2.type))\n"
]
}
],
"source": [
"#https://stackoverflow.com/questions/12870178/looping-through-python-regex-matches\n",
"import re\n",
"text = query\n",
"prefix = r\"strip(left(\"\n",
"appendix = r\"))\"\n",
"new_prefix = r'trim('\n",
"new_appendix =r')'\n",
"pattern = re.compile((re.escape(prefix)) + r'.+' + re.escape(appendix) , re.IGNORECASE) #left side, keep group, use escape to remove all \\\n",
"matches = re.findall(pattern, text) #use findall instead of search use loop to get the value\n",
"for g1 in matches:\n",
" print (g1)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def replace_both_sides_All (text, prefix, appendix, new_prefix, new_appendix):\n",
" import re\n",
" pattern = re.compile((re.escape(prefix)) + r'.+' + re.escape(appendix) , re.IGNORECASE) #left side, keep group, use escape to remove all \\\n",
" if pattern.search(text) != None:\n",
" matches = re.findall(pattern, text) #use search for any place in a row\n",
" for match in matches: #loop through all match\n",
" # return match.group(0)\n",
" stringX = match # get matched\n",
" string_kept = remove_appendix (stringX, appendix) #remove appendix\n",
" #print(string_kept) \n",
" string_kept = remove_prefix (string_kept, prefix) #remove prefix\n",
" #print(string_kept)\n",
" string_kept = new_prefix + string_kept + new_appendix #form replacement str\n",
" text = text.replace(stringX, string_kept ) #replace string\n",
" return text \n",
" else:\n",
" return text"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\"proc sql;\n",
"\tcreate table Wenlei.DW_Example_latest AS\n",
"\tSELECT DISTINCT \n",
"\t\t\tT2.POLICY_ID,\n",
"\t\t\tT2.POLICY,\n",
"\t\t\ttrim(T2.ROLE),\n",
" trim(T2.type),\t\t\t\n",
"\t\t\tT2.SEQ_ID, \n",
"\t\t\tINTCK('day', T1.LastPayDate, today()) as DayPaid,\n",
"\t\t\t(today()-T1.date_created) as DaysBtw\t\t\t\n",
"\tFROM \tWORK.DW_Table1 t1 \n",
"\t\t\tINNER JOIN\n",
"\t\t\tWORK.DW_Table2 t2 ON (t1.ID = t2.ID AND T1.POLICY_ID=T2.POLICY_ID \n",
"\t\t\t\t\t\t\t\t\t\t AND t1.MAX_PROCESS_Time=t2.PROCESS_Time)\n",
"\tWHERE t2.ID<>-1 \n",
"\t;\n",
"quit;\"\n",
"\n"
]
}
],
"source": [
"print(replace_both_sides_All (query, r\"strip(left(\", r\"))\", r'trim(', r')'))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\"proc sql;\n",
"\tcreate table Wenlei.DW_Example_latest AS\n",
"\tSELECT DISTINCT \n",
"\t\t\tT2.POLICY_ID,\n",
"\t\t\tT2.POLICY,\n",
"\t\t\ttrim(T2.ROLE),\n",
" trim(T2.type),\t\t\t\n",
"\t\t\tT2.SEQ_ID, \n",
"\t\t\tINTCK('day', T1.LastPayDate, today()) as DayPaid,\n",
"\t\t\t(today()-T1.date_created) as DaysBtw\t\t\t\n",
"\tFROM \tWORK.DW_Table1 t1 \n",
"\t\t\tINNER JOIN\n",
"\t\t\tWORK.DW_Table2 t2 ON (t1.ID = t2.ID AND T1.POLICY_ID=T2.POLICY_ID \n",
"\t\t\t\t\t\t\t\t\t\t AND t1.MAX_PROCESS_Time=t2.PROCESS_Time)\n",
"\tWHERE t2.ID<>-1 \n",
"\t;\n",
"quit;\"\n",
"\n"
]
}
],
"source": [
"#use Re to solve this issue more elegantly\n",
"prefix= r\"strip(left(\"\n",
"appendix =r\"))\"\n",
"pattern = re.compile((re.escape(prefix)) + r'(.+)' + re.escape(appendix) , re.IGNORECASE) \n",
"print(re.sub(pattern, r'trim(\\1)', query))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trim(T2.type)\n"
]
}
],
"source": [
"print(re.sub(pattern, r'trim(\\1)', 'strip(left(T2.type))'))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\"proc sql;\n",
"\tcreate table Wenlei.DW_Example_latest AS\n",
"\tSELECT DISTINCT \n",
"\t\t\tT2.POLICY_ID,\n",
"\t\t\tT2.POLICY,\n",
"\t\t\ttrim(T2.ROLE),\n",
" trim(T2.type),\t\t\t\n",
"\t\t\tT2.SEQ_ID, \n",
"\t\t\tINTCK('day', T1.LastPayDate, today()) as DayPaid,\n",
"\t\t\t(today()-T1.date_created) as DaysBtw\t\t\t\n",
"\tFROM \tWORK.DW_Table1 t1 \n",
"\t\t\tINNER JOIN\n",
"\t\t\tWORK.DW_Table2 t2 ON (t1.ID = t2.ID AND T1.POLICY_ID=T2.POLICY_ID \n",
"\t\t\t\t\t\t\t\t\t\t AND t1.MAX_PROCESS_Time=t2.PROCESS_Time)\n",
"\tWHERE t2.ID<>-1 \n",
"\t;\n",
"quit;\"\n",
"\n"
]
}
],
"source": [
"#trouble shooting in regex101\n",
"# https://stackoverflow.com/questions/47966350/regex-to-include-all-characters-with-at-least-one-letter-and-at-least-6-characte\n",
"# https://stackoverflow.com/questions/3028642/regular-expression-for-letters-numbers-and\n",
"#test another pattern\n",
"prefix= r\"strip(left(\"\n",
"appendix =r\"))\"\n",
"pattern = re.compile((re.escape(prefix)) + r'([a-zA-Z0-9_.-]*)' + re.escape(appendix) , re.IGNORECASE) \n",
"print(re.sub(pattern, r'trim(\\1)', query))"
]
},
{
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
thundergolfer/Insults | insults/exploration/model/does_quoting_insults_lower_the_score.ipynb | 1 | 1258 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Does quoting insulting content lower the model's output score?"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Ugly Python PATH hack to import insults from notebook\n",
"import os\n",
"import sys\n",
"nb_dir = os.path.split(os.getcwd())[0]\n",
"if nb_dir not in sys.path:\n",
" sys.path.append(nb_dir)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import insults"
]
},
{
"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 |
ctralie/TUMTopoTimeSeries2016 | SlidingWindow1-Basics.ipynb | 1 | 24504 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Geometry of Sliding Window Embeddings\n",
"In this module, you will interactively explore how various parameters of sliding window embeddings of 1 dimensional periodic time series data affect the geometry of their embeddings. In each code box, press *ctrl-enter* (or *cmd-enter* on a Mac) to execute the code. Progress through the lab sequentially. As you examine the plots in each experiment, answer the questions that follow.\n",
"\n",
"This first box imports all of the necessary Python packages to run the code in this module. There will be a similar box at the beginning of every module in this series."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"#Do all of the imports and setup inline plotting\n",
"import numpy as np\n",
"from ripser import ripser\n",
"\n",
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import gridspec\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"\n",
"from sklearn.decomposition import PCA\n",
"\n",
"from scipy.interpolate import InterpolatedUnivariateSpline\n",
"\n",
"import ipywidgets as widgets\n",
"from IPython.display import display\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"from IPython.display import clear_output"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pure Sinusoid Sliding Window\n",
"In this first experiment, you will alter the extent of the sliding window of a pure sinusoid and examine how the geometry of a 2-D embedding changes. \n",
"\n",
"First, setup and plot a pure sinusoid in NumPy:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# Step 1: Setup the signal\n",
"T = 40 # The period in number of samples\n",
"NPeriods = 4 # How many periods to go through\n",
"N = T*NPeriods #The total number of samples\n",
"t = np.linspace(0, 2*np.pi*NPeriods, N+1)[:N] # Sampling indices in time\n",
"x = np.cos(t) # The final signal\n",
"plt.plot(x);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sliding Window Code\n",
"The code below performs a sliding window embedding on a 1D signal. The parameters are as follows:\n",
"\n",
"| | |\n",
"|:-:|---|\n",
"|$x$ | The 1-D signal (numpy array) |\n",
"|dim|The dimension of the embedding|\n",
"|$\\tau$ | The skip between samples in a given window |\n",
"|$dT$ | The distance to slide from one window to the next |\n",
"\n",
"That is, along the signal given by the array $x$, the first three windows will will be $$\\begin{bmatrix} x(\\tau)\\\\ x(2\\tau) \\\\ \\ldots \\\\ x((\\mbox{dim}-1)\\cdot\\tau)\\end{bmatrix}, \\begin{bmatrix} x(dT + \\tau)\\\\ x(dT +2\\tau) \\\\ \\ldots \\\\ x(dT +(\\mbox{dim}-1)\\cdot\\tau)\\end{bmatrix}, \\begin{bmatrix} x(2dT + \\tau)\\\\ x(2dT +2\\tau) \\\\ \\ldots \\\\ x(2dT +(\\mbox{dim}-1)\\cdot\\tau)\\end{bmatrix}$$\n",
"\n",
"Spline interpolation is used to fill in information between signal samples, which is necessary for certain combinations of parameters, such as a non-integer $\\tau$ or $dT$.\n",
"\n",
"The function *getSlidingWindow* below creates an array $X$ containing the windows as its columns."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"def getSlidingWindow(x, dim, Tau, dT):\n",
" \"\"\"\n",
" Return a sliding window of a time series,\n",
" using arbitrary sampling. Use linear interpolation\n",
" to fill in values in windows not on the original grid\n",
" Parameters\n",
" ----------\n",
" x: ndarray(N)\n",
" The original time series\n",
" dim: int\n",
" Dimension of sliding window (number of lags+1)\n",
" Tau: float\n",
" Length between lags, in units of time series\n",
" dT: float\n",
" Length between windows, in units of time series\n",
" Returns\n",
" -------\n",
" X: ndarray(N, dim)\n",
" All sliding windows stacked up\n",
" \"\"\"\n",
" N = len(x)\n",
" NWindows = int(np.floor((N-dim*Tau)/dT))\n",
" if NWindows <= 0:\n",
" print(\"Error: Tau too large for signal extent\")\n",
" return np.zeros((3, dim))\n",
" X = np.zeros((NWindows, dim))\n",
" spl = InterpolatedUnivariateSpline(np.arange(N), x)\n",
" for i in range(NWindows):\n",
" idxx = dT*i + Tau*np.arange(dim)\n",
" start = int(np.floor(idxx[0]))\n",
" end = int(np.ceil(idxx[-1]))+2\n",
" # Only take windows that are within range\n",
" if end >= len(x):\n",
" X = X[0:i, :]\n",
" break\n",
" X[i, :] = spl(idxx)\n",
" return X"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sliding Window Result\n",
"\n",
"We will now perform a sliding window embedding with various choices of parameters. [Principal component analysis](https://en.wikipedia.org/wiki/Principal_component_analysis) will be performed to project the result down to 2D for visualization. \n",
"\n",
"The first two eigenvalues computed by PCA will be printed. The closer these eigenvalues are to each other, the rounder and more close to a circle the 2D projection of the embedding is. A red vertical line will be drawn to show the product of $\\tau$ and the dimension, or \"extent\" (window length).\n",
"\n",
"**An important note:** we choose to project the results to 2D (or later, to 3D). Nothing in particular tells us that this is the best choice of dimension. We merely make this choice to enable visualization. In general, when doing PCA, we want to choose enough eigenvalues to account for a significant portion of [explained variance](https://stats.stackexchange.com/questions/22569/pca-and-proportion-of-variance-explained).\n",
"\n",
"** Exercise: ** Execute the code. Using the sliders, play around with the parameters of the sliding window embedding and examine the results. Then answer the questions below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"def on_value_change(change):\n",
" execute_computation1()\n",
" \n",
"dimslider = widgets.IntSlider(min=1,max=40,value=20,description='Dimension:',continuous_update=False)\n",
"dimslider.observe(on_value_change, names='value')\n",
"\n",
"Tauslider = widgets.FloatSlider(min=0.1,max=5,step=0.1,value=1,description=r'\\(\\tau :\\)' ,continuous_update=False)\n",
"Tauslider.observe(on_value_change, names='value')\n",
"\n",
"dTslider = widgets.FloatSlider(min=0.1,max=5,step=0.1,value=0.5,description='dT: ',continuous_update=False)\n",
"dTslider.observe(on_value_change, names='value')\n",
"\n",
"display(widgets.HBox(( dimslider,Tauslider,dTslider)))\n",
"\n",
"plt.figure(figsize=(9.5, 3))\n",
"def execute_computation1(): \n",
" plt.clf()\n",
" # Step 1: Setup the signal again in case x was lost\n",
" T = 40 # The period in number of samples\n",
" NPeriods = 4 # How many periods to go through\n",
" N = T*NPeriods # The total number of samples\n",
" t = np.linspace(0, 2*np.pi*NPeriods, N+1)[0:N] # Sampling indices in time\n",
" x = np.cos(t) # The final signal\n",
" \n",
" # Get slider values\n",
" dim = dimslider.value\n",
" Tau = Tauslider.value\n",
" dT = dTslider.value\n",
" \n",
" #Step 2: Do a sliding window embedding\n",
" X = getSlidingWindow(x, dim, Tau, dT)\n",
" extent = Tau*dim\n",
"\n",
" #Step 3: Perform PCA down to 2D for visualization\n",
" pca = PCA(n_components = 2)\n",
" Y = pca.fit_transform(X)\n",
" eigs = pca.explained_variance_\n",
" print(\"lambda1 = %g, lambda2 = %g\"%(eigs[0], eigs[1]))\n",
"\n",
" #Step 4: Plot original signal and PCA of the embedding\n",
" ax = plt.subplot(121)\n",
" ax.plot(x)\n",
" ax.set_ylim((-2*max(x), 2*max(x)))\n",
" ax.set_title(\"Original Signal\")\n",
" ax.set_xlabel(\"Sample Number\")\n",
" yr = np.max(x)-np.min(x)\n",
" yr = [np.min(x)-0.1*yr, np.max(x)+0.1*yr]\n",
" ax.plot([extent, extent], yr, 'r')\n",
" ax.plot([0, 0], yr, 'r') \n",
" ax.plot([0, extent], [yr[0]]*2, 'r')\n",
" ax.plot([0, extent], [yr[1]]*2, 'r')\n",
" ax2 = plt.subplot(122)\n",
" ax2.set_title(\"PCA of Sliding Window Embedding\")\n",
" ax2.scatter(Y[:, 0], Y[:, 1])\n",
" ax2.set_aspect('equal', 'datalim')\n",
" plt.tight_layout()\n",
" \n",
"execute_computation1()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Questions\n",
"1. For fixed $\\tau$:\n",
" 1. What does varying the dimension do to the *extent* (the length of the window)?\n",
" 1. what dimensions give eigenvalues nearest each other? (Note: dimensions! Plural!) Explain why this is the case. Explain how you might use this information to deduce the period of a signal.\n",
"<br><br>\n",
"1. What does varying $dT$ do to the PCA embedding? Explain this in terms of the definition of sliding windows above.\n",
"<br><br>\n",
"1. The command \n",
"```python\n",
"np.random.randn(pts)\n",
"```\n",
"generates an array of length *pts* filled with random values drawn from a standard normal distribution ($\\mu=0$, $\\sigma=1$). Modify the code above to add random noise to signal. \n",
" 1. Can you still detect the period visually by inspecting the plot of the signal?\n",
" 1. Does your method of detecting the period from the first question still work?\n",
" 1. How does adding noise change the geometry of the PCA embedding? \n",
" 1. Modify the amplitude of the noise (for example, by multiplying the noise-generating command by a constant) and examine the 2D projection. What feature of the 2D projection appears to imply that the signal is periodic? At what noise amplitude does this feature appear to vanish?\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Non-Periodic Signal Sliding Window\n",
"For a contrasting example, we will now examine the sliding window embedding of a non-periodic signal which is a linear function plus Gaussian noise. The code below sets up the signal and then does the sliding window embedding, as before. \n",
"\n",
"** Exercise: ** Execute the code. Using the sliders, play around with the parameters of the sliding window embedding and examine the results. Then answer the questions below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"noise = 0.05*np.random.randn(400)\n",
"\n",
"def on_value_change(change):\n",
" execute_computation2()\n",
" \n",
"dimslider = widgets.IntSlider(min=1,max=40,value=20,description='Dimension:',continuous_update=False)\n",
"dimslider.observe(on_value_change, names='value')\n",
"\n",
"Tauslider = widgets.FloatSlider(min=0.1,max=5,step=0.1,value=1,description='Tau: ',continuous_update=False)\n",
"Tauslider.observe(on_value_change, names='value')\n",
"\n",
"dTslider = widgets.FloatSlider(min=0.1,max=5,step=0.1,value=0.5,description='dT: ',continuous_update=False)\n",
"dTslider.observe(on_value_change, names='value')\n",
"\n",
"display(widgets.HBox(( dimslider,Tauslider,dTslider)))\n",
"\n",
"plt.figure(figsize=(9.5, 3))\n",
"\n",
"def execute_computation2(): \n",
" plt.clf()\n",
" # Step 1: Set up the signal\n",
" x = np.arange(400)\n",
" x = x/float(len(x))\n",
" x = x + noise # Add some noise\n",
" \n",
" # Get slider values\n",
" dim = dimslider.value\n",
" Tau = Tauslider.value\n",
" dT = dTslider.value\n",
" \n",
" #Step 2: Do a sliding window embedding\n",
" X = getSlidingWindow(x, dim, Tau, dT)\n",
" extent = Tau*dim\n",
"\n",
" #Step 3: Perform PCA down to 2D for visualization\n",
" pca = PCA(n_components = 2)\n",
" Y = pca.fit_transform(X)\n",
" eigs = pca.explained_variance_\n",
" print(\"lambda1 = %g, lambda2 = %g\"%(eigs[0], eigs[1]))\n",
"\n",
" #Step 4: Plot original signal and PCA of the embedding\n",
" gs = gridspec.GridSpec(1, 2)\n",
" ax = plt.subplot(gs[0,0])\n",
" ax.plot(x)\n",
" ax.set_ylim((-2, 2))\n",
" ax.set_title(\"Original Signal\")\n",
" ax.set_xlabel(\"Sample Number\")\n",
" yr = np.max(x)-np.min(x)\n",
" yr = [np.min(x)-0.1*yr, np.max(x)+0.1*yr]\n",
" ax.plot([extent, extent], yr, 'r')\n",
" ax.plot([0, 0], yr, 'r') \n",
" ax.plot([0, extent], [yr[0]]*2, 'r')\n",
" ax.plot([0, extent], [yr[1]]*2, 'r') \n",
" ax2 = plt.subplot(gs[0, 1])\n",
" ax2.set_title(\"PCA of Sliding Window Embedding\")\n",
" ax2.scatter(Y[:, 0], Y[:, 1])\n",
" ax2.set_aspect('equal', 'datalim')\n",
"\n",
"execute_computation2()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Questions\n",
"\n",
"1. Notice how changing the window extent doesn't have the same impact as it did in the periodic example above. Why might this be?\n",
"<br><br>\n",
"1. Why is the second eigenvalue always tiny?\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Multiple Sines Sliding Window\n",
"We will now go back to periodic signals, but this time we will increase the complexity by adding two sines together. If the ratio between the two sinusoids is a rational number, then they are called *harmonics* of each other. For example, $\\sin(t)$ and $\\sin(3t)$ are harmonics of each other. By contrast, if the ratio of the two frequencies is irrational, then the sinusoids are called *incommensurate*. For example, $\\sin(t)$ and $\\sin(\\pi t)$ are incommensurate.\n",
"\n",
"The plots below are initialized with \n",
"\n",
"$$f(t) = \\sin(\\omega t) + \\sin(3\\omega t),$$\n",
"\n",
"a sum of two harmonics. \n",
"\n",
"This time, the eigenvalues of PCA will be plotted (up to the first 10), in addition to the red line showing the extent of the window. Also, 3D PCA will be displayed instead of 2D PCA, and you can click and drag your mouse to view it from different angles. Colors will be drawn to indicate the position of the window in time, with cool colors (greens and yellows) indicating earlier windows and hot colors (oranges and reds) indicating later windows (using the \"jet\" colormap).\n",
"\n",
"** Exercise: ** Execute the code. Then play with the sliders, as well as the embedding dimension (note that for the 3-D projection, you can change the view by dragging around. Do!). Then, try changing the second sinusoid to be another multiple of the first. Try both harmonic and incommensurate values. Once you have gotten a feel for the geometries and the eigenvalues, answer the questions below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"source": [
"def on_value_change(change):\n",
" execute_computation3()\n",
"\n",
"embeddingdimbox = widgets.Dropdown(options=[2, 3],value=3,description='Embedding Dimension:',disabled=False)\n",
"embeddingdimbox.observe(on_value_change,names='value')\n",
"\n",
"secondfreq = widgets.Dropdown(options=[2, 3, np.pi],value=3,description='Second Frequency:',disabled=False)\n",
"secondfreq.observe(on_value_change,names='value')\n",
"\n",
"noiseampslider = widgets.FloatSlider(min=0,max=6,step=0.5,value=0,description='Noise Amplitude',continuous_update=False)\n",
"noiseampslider.observe(on_value_change, names='value')\n",
"\n",
"dimslider = widgets.IntSlider(min=1,max=100,value=30,description='Dimension:',continuous_update=False)\n",
"dimslider.observe(on_value_change, names='value')\n",
"\n",
"Tauslider = widgets.FloatSlider(min=0.1,max=5,step=0.1,value=1,description='Tau: ',continuous_update=False)\n",
"Tauslider.observe(on_value_change, names='value')\n",
"\n",
"dTslider = widgets.FloatSlider(min=0.1,max=5,step=0.1,value=0.5,description='dT: ',continuous_update=False)\n",
"dTslider.observe(on_value_change, names='value')\n",
"\n",
"display(widgets.HBox(( secondfreq,embeddingdimbox,noiseampslider)))\n",
"display(widgets.HBox((dimslider,Tauslider,dTslider)))\n",
"\n",
"noise = np.random.randn(10000)\n",
"\n",
"fig = plt.figure(figsize=(9.5, 4))\n",
"def execute_computation3():\n",
" plt.clf()\n",
" \n",
" # Step 1: Setup the signal\n",
" T1 = 20 # The period of the first sine in number of samples\n",
" T2 = T1*secondfreq.value # The period of the second sine in number of samples\n",
" NPeriods = 10 # How many periods to go through, relative to the second sinusoid\n",
" N = T2*NPeriods # The total number of samples\n",
" t = np.arange(N) # Time indices\n",
" x = np.cos(2*np.pi*(1.0/T1)*t) # The first sinusoid\n",
" x += np.cos(2*np.pi*(1.0/T2)*t) # Add the second sinusoid\n",
" x += noiseampslider.value*noise[:len(x)]\n",
" \n",
" # Get widget values\n",
" dim = dimslider.value\n",
" Tau = Tauslider.value\n",
" dT = dTslider.value\n",
" embeddingdim = embeddingdimbox.value\n",
" \n",
" # Step 2: Do a sliding window embedding\n",
" X = getSlidingWindow(x, dim, Tau, dT)\n",
" extent = Tau*dim\n",
"\n",
" # Step 3: Perform PCA down to dimension chosen for visualization\n",
" pca = PCA(n_components = 10)\n",
" Y = pca.fit_transform(X)\n",
" eigs = pca.explained_variance_\n",
"\n",
" # Step 4: Plot original signal and PCA of the embedding \n",
" gs = gridspec.GridSpec(2, 2,width_ratios=[1, 2])\n",
" \n",
" # Plot the signal\n",
" ax = plt.subplot(gs[0,0])\n",
" ax.plot(x)\n",
" yr = np.max(x)-np.min(x)\n",
" yr = [np.min(x)-0.1*yr, np.max(x)+0.1*yr]\n",
" ax.plot([extent, extent], yr, 'r')\n",
" ax.plot([0, 0], yr, 'r') \n",
" ax.plot([0, extent], [yr[0]]*2, 'r')\n",
" ax.plot([0, extent], [yr[1]]*2, 'r')\n",
" ax.set_title(\"Original Signal\")\n",
" ax.set_xlabel(\"Sample Number\")\n",
"\n",
" c = plt.get_cmap('jet')\n",
" C = c(np.array(np.round(np.linspace(0, 255, Y.shape[0])), dtype=np.int32))\n",
" C = C[:, 0:3]\n",
"\n",
" # Plot the PCA embedding\n",
" if embeddingdim == 3:\n",
" ax2 = plt.subplot(gs[:,1],projection='3d')\n",
" ax2.scatter(Y[:, 0], Y[:, 1], Y[:, 2], c=C)\n",
" ax2.set_aspect('equal', 'datalim')\n",
" else:\n",
" ax2 = plt.subplot(gs[:,1])\n",
" ax2.scatter(Y[:, 0], Y[:, 1],c=C)\n",
"\n",
" ax2.set_title(\"PCA of Sliding Window Embedding\")\n",
" ax2.set_aspect('equal', 'datalim')\n",
"\n",
" # Plot the eigenvalues as bars\n",
" ax3 = plt.subplot(gs[1,0])\n",
" eigs = eigs[0:min(len(eigs), 10)]\n",
" ax3.bar(np.arange(len(eigs)), eigs)\n",
" ax3.set_xlabel(\"Eigenvalue Number\")\n",
" ax3.set_ylabel(\"Eigenvalue\")\n",
" ax3.set_title(\"PCA Eigenvalues\")\n",
"\n",
" plt.tight_layout()\n",
"\n",
" plt.show();\n",
"\n",
"execute_computation3()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Questions\n",
"\n",
"1. Comment on the relationship between the eigenvalues and the extent (width) of the window.\n",
"<br><br>\n",
"1. When are the eigenvalues near each other? When are they not?\n",
"<br><br>\n",
"1. Comment on the change in geometry when the second sinusoid is incommensurate to the first. Specifically, comment on the *intrinsic dimension* of the object in the projection. Can you name the shape in the 3-D projection in the incommensurate case?\n",
"<br><br>\n",
"1. Try adding noise in like you did in the single frequency case. \n",
" 1. Can you distinguish in the projection between the incommensurate case and the noisy, but harmonic one with second frequency 3? Explain.\n",
" 1. What can you say about the eigenvalues in the two cases? Explain your answer.\n",
"\n",
"It seems reasonable to ask what the ideal dimension to embed into is. While that question may be answerable, it would be better to bypass the question altogether. Similarly, it seems that beyond detecting the largest period, these tools are limited in detecting the secondary ones. Topological tools that we will see beginning in the next lab will allow us to make some progress toward that goal."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Power Spectrum\n",
"\n",
"We saw above that for a rather subtle change in frequency changing the second sinusoid from harmonic to noncommensurate, there is a marked change in the geometry. By contrast, the power spectral density functions are very close between the two, as shown below. Hence, it appears that geometric tools are more appropriate for telling the difference between these two types of signals"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"T = 20 #The period of the first sine in number of samples\n",
"NPeriods = 10 #How many periods to go through, relative to the faster sinusoid\n",
"N = T*NPeriods*3 #The total number of samples\n",
"t = np.arange(N) #Time indices\n",
"\n",
"#Make the harmonic signal cos(t) + cos(3t)\n",
"xH = np.cos(2*np.pi*(1.0/T)*t) + np.cos(2*np.pi*(1.0/(3*T)*t))\n",
" \n",
"#Make the incommensurate signal cos(t) + cos(pi*t)\n",
"xNC = np.cos(2*np.pi*(1.0/T)*t) + np.cos(2*np.pi*(1.0/(np.pi*T)*t))\n",
"\n",
"plt.figure()\n",
"P1 = np.abs(np.fft.fft(xH))**2\n",
"P2 = np.abs(np.fft.fft(xNC))**2\n",
"plt.plot(np.arange(len(P1)), P1)\n",
"plt.plot(np.arange(len(P2)), P2)\n",
"plt.xlabel(\"Frequency Index\")\n",
"plt.legend({\"Harmonic\", \"Noncommensurate\"})\n",
"plt.xlim([0, 50])\n",
"plt.show();"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Summary\n",
"\n",
"* Signals can be transformed into geometric objects via embeddings.\n",
"<br><br>\n",
"* Signal properties are captured by the geometry of the sliding window embedding. Periodicity corresponds to circularity, period length over window size corresponds to roundness, number of incommensurate frequencies corresponds to intrinsic dimension.\n",
"<br><br>\n",
"* The window extent is one of the most important parameters for determining roundness.\n",
"<br><br>\n",
"* Adding noise makes things a little trickier to see what's going on by inspection of projections."
]
}
],
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| apache-2.0 |
nickedes/Baby-science | Linear-Reg/Linear-reg-Overview.ipynb | 1 | 74950 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
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HXH458NxzwKOPMpnVQw9xANJ4vZjAmRtuyLi++uPyyyM7P9gYQaFC7tq5+27OmzalUDf+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\nhLogCEIWQoS6IAhCFkKEuiAIQhbi/wDU7X2KDKyV2wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd16c327f98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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PRh5bBgpuNoRnUU7g2cARKeVCIcRE4AdCiG1SykdNbS29iznQLdvQIsdQnoE5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\nMRDg5Z1iVg3ES0PhNua2tc3GnMYzDbP/hVdfeo8mFZOQ6NGeyWDqdb1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EOn6h9PTX+P73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\neSAeDmkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd14f9e3b70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%pylab inline\n",
"import pandas as pan\n",
"import matplotlib.pyplot as pl\n",
"df = pan.read_csv('../datasets/loanf.csv')\n",
"intrate = df['Interest.Rate']\n",
"fico = df['FICO.Score']\n",
"p = pl.plot(fico,intrate)\n",
"pl.show()\n",
"p = plot(fico,intrate,'o')\n",
"ax = gca()\n",
"xt = ax.set_xlabel('FICO Score')\n",
"yt = ax.set_ylabel('Interest Rate %')"
]
},
{
"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": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
pulinagrawal/nupic | src/nupic/frameworks/viz/examples/Demo.ipynb | 1 | 7961 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Visualizing Networks\n",
"\n",
"The following demonstrates basic use of `nupic.frameworks.viz.NetworkVisualizer` to visualize a network.\n",
"\n",
"Before you begin, you will need to install the otherwise optional dependencies. From the root of nupic repository:\n",
"\n",
"```\n",
"pip install --user .[viz]\n",
"```\n",
"\n",
"Setup a simple network so we have something to work with:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from nupic.engine import Network, Dimensions\n",
"\n",
"# Create Network instance\n",
"network = Network()\n",
"\n",
"# Add three TestNode regions to network\n",
"network.addRegion(\"region1\", \"TestNode\", \"\")\n",
"network.addRegion(\"region2\", \"TestNode\", \"\")\n",
"network.addRegion(\"region3\", \"TestNode\", \"\")\n",
"\n",
"# Set dimensions on first region\n",
"region1 = network.getRegions().getByName(\"region1\")\n",
"region1.setDimensions(Dimensions([1, 1]))\n",
"\n",
"# Link regions\n",
"network.link(\"region1\", \"region2\", \"UniformLink\", \"\")\n",
"network.link(\"region2\", \"region1\", \"UniformLink\", \"\")\n",
"network.link(\"region1\", \"region3\", \"UniformLink\", \"\")\n",
"network.link(\"region2\", \"region3\", \"UniformLink\", \"\")\n",
"\n",
"# Initialize network\n",
"network.initialize()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Render with `nupic.frameworks.viz.NetworkVisualizer`, which takes as input any `nupic.engine.Network` instance:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from nupic.frameworks.viz import NetworkVisualizer\n",
"\n",
"# Initialize Network Visualizer\n",
"viz = NetworkVisualizer(network)\n",
"\n",
"# Render to dot (stdout)\n",
"viz.render()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"That's interesting, but not necessarily useful if you don't understand [dot](http://www.graphviz.org/doc/info/lang.html). Let's capture that output and do something else:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from nupic.frameworks.viz import DotRenderer\n",
"from io import StringIO\n",
"\n",
"outp = StringIO()\n",
"viz.render(renderer=lambda: DotRenderer(outp))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"`outp` now contains the rendered output, render to an image with `graphviz`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Render dot to image\n",
"from graphviz import Source\n",
"from IPython.display import Image\n",
"\n",
"Image(Source(outp.getvalue()).pipe(\"png\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the example above, each three-columned rectangle is a discrete region, the user-defined name for which is in the middle column. The left-hand and right-hand columns are respective inputs and outputs, the names for which, e.g. \"bottumUpIn\" and \"bottomUpOut\", are specific to the region type. The arrows indicate links between outputs from one region to the input of another.\n",
"\n",
"I know what you're thinking. _That's a cool trick, but nobody cares about your contrived example. I want to see something real!_\n",
"\n",
"Continuing below, I'll instantiate a CLA model and visualize it. In this case, I'll use one of the \"hotgym\" examples."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from nupic.frameworks.opf.modelfactory import ModelFactory\n",
"\n",
"# Note: parameters copied from examples/opf/clients/hotgym/simple/model_params.py\n",
"model = ModelFactory.create({'aggregationInfo': {'hours': 1, 'microseconds': 0, 'seconds': 0, 'fields': [('consumption', 'sum')], 'weeks': 0, 'months': 0, 'minutes': 0, 'days': 0, 'milliseconds': 0, 'years': 0}, 'model': 'CLA', 'version': 1, 'predictAheadTime': None, 'modelParams': {'sensorParams': {'verbosity': 0, 'encoders': {'timestamp_timeOfDay': {'type': 'DateEncoder', 'timeOfDay': (21, 1), 'fieldname': u'timestamp', 'name': u'timestamp_timeOfDay'}, u'consumption': {'resolution': 0.88, 'seed': 1, 'fieldname': u'consumption', 'name': u'consumption', 'type': 'RandomDistributedScalarEncoder'}, 'timestamp_weekend': {'type': 'DateEncoder', 'fieldname': u'timestamp', 'name': u'timestamp_weekend', 'weekend': 21}}, 'sensorAutoReset': None}, 'spParams': {'columnCount': 2048, 'spVerbosity': 0, 'spatialImp': 'cpp', 'synPermConnected': 0.1, 'seed': 1956, 'numActiveColumnsPerInhArea': 40, 'globalInhibition': 1, 'inputWidth': 0, 'synPermInactiveDec': 0.005, 'synPermActiveInc': 0.04, 'potentialPct': 0.85, 'boostStrength': 3.0}, 'spEnable': True, 'clParams': {'implementation': 'cpp', 'alpha': 0.1, 'verbosity': 0, 'steps': '1,5', 'regionName': 'SDRClassifierRegion'}, 'inferenceType': 'TemporalMultiStep', 'tmEnable': True, 'tmParams': {'columnCount': 2048, 'activationThreshold': 16, 'pamLength': 1, 'cellsPerColumn': 32, 'permanenceInc': 0.1, 'minThreshold': 12, 'verbosity': 0, 'maxSynapsesPerSegment': 32, 'outputType': 'normal', 'initialPerm': 0.21, 'globalDecay': 0.0, 'maxAge': 0, 'permanenceDec': 0.1, 'seed': 1960, 'newSynapseCount': 20, 'maxSegmentsPerCell': 128, 'temporalImp': 'cpp', 'inputWidth': 2048}, 'trainSPNetOnlyIfRequested': False}})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Same deal as before, create a `NetworkVisualizer` instance, render to a buffer, then to an image, and finally display it inline."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# New network, new NetworkVisualizer instance\n",
"viz = NetworkVisualizer(model._netInfo.net)\n",
"\n",
"# Render to Dot output to buffer\n",
"outp = StringIO()\n",
"viz.render(renderer=lambda: DotRenderer(outp))\n",
"\n",
"# Render Dot to image, display inline\n",
"Image(Source(outp.getvalue()).pipe(\"png\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In these examples, I'm using `graphviz` to render an image from the `dot` document in Python, but you may want to do something else. `dot` is a generic and flexible graph description language and there are many tools for working with `dot` files."
]
},
{
"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": 2
}
| agpl-3.0 |
RittmanResearch/maybrain | docs/01 - Simple Usage.ipynb | 1 | 25285 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simple Usage\n",
"\n",
"## Basic Principles\n",
"The purpose of Maybrain is to allow easy visualisation of brain connectome and related data and perform various analyses.\n",
"\n",
"The code is built around the class **Brain**. It contains all the information about the brain and numerous functions to change, measure and highlight those. At its heart is a *Networkx* object, Brain.G, via which all *Networkx* functions are available.\n",
"\n",
"Besides this main class, you have four packages with other modules, **algorithms**, **plotting**, **utils** and **resources**, which will be explained throughout these notebooks.\n",
"\n",
"## Data Input\n",
"Several types of data can be input. The basic connectome is made up of two files: a coordinate file and an adjacency matrix. In fact only the second of these is strictly required.\n",
"\n",
"The coordinate file defines the position of each node. It is a text file where each line has four entries: the node index, x, y and z coordinates. e.g.:\n",
"\n",
"```\n",
"0 0.0 3.1 4.4\n",
"1 5.3 7.6 8.4\n",
"2 3.2 4.4 3.1\n",
"```\n",
"\n",
"The adjacency matrix defines the strength of connection between each pair of nodes. For n nodes it is an n × n text matrix. Nodes in maybrain are labelled 0,1,... and the order of the rows and columns in the adjacency matrix is assumed to correspond to this. Entering an adjacency matrix with the wrong dimensions will lead to certain doom."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing Maybrain\n",
"\n",
"The main class of maybrain is contained in the `brain` module and can easily be achieved with a normal import."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from maybrain import brain as mbt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The `constants` module\n",
"\n",
"Before going further in explaining Maybrain's functionalities, it is important to briefly refer the `constants` module. This module has some constants which can be used elsewhere, rather than writing the values by hand everywhere being prone to typos. \n",
"\n",
"In further notebooks you will see this module being used in practice, but for now, also just a normal import is required:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"weight\n",
"anatlabel\n"
]
}
],
"source": [
"from maybrain import constants as ct\n",
"# Printing some of the constants\n",
"print(ct.WEIGHT)\n",
"print(ct.ANAT_LABEL)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The `resources` package\n",
"\n",
"Maybrain also have another package that can be useful for different things. In its essence, it is just a package with access to files like matrices, properties, etc. When importing this package, you will have access to different variables in the path for the file in your system.\n",
"\n",
"Farther in the documentation you will see this package being used in practice, but for now, also just a normal import is required:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from maybrain import resources as rr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing an Adjacency Matrix\n",
"Firstly, create a `Brain` object:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nodes: []\n",
"Edges: []\n",
"Adjacency matrix: None\n"
]
}
],
"source": [
"a = mbt.Brain()\n",
"print(\"Nodes: \", a.G.nodes())\n",
"print(\"Edges: \", a.G.edges())\n",
"print(\"Adjacency matrix: \", a.adjMat)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This creates a brain object, where a graph (from the package NetworkX) is stored as `a.G`, initially empty. \n",
"\n",
"Then import the adjacency matrix. The *import_adj_file()* function imports the adjacency matrix to form the nodes of your graph, but does not create any edges (connections), as you can check from the following outputs.\n",
"\n",
"Note the use of the `resources` package. In maybrain you can access a dummy adjacency matrix (500x500) for various reasons; in this case, just for testing purposes."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of nodes:\n",
" 500\n",
"First 5 nodes (notice labelling starting with 0):\n",
" [0, 1, 2, 3, 4]\n",
"Edges:\n",
" []\n",
"Size of Adjacency matrix:\n",
" (500, 500)\n"
]
}
],
"source": [
"a.import_adj_file(rr.DUMMY_ADJ_FILE_500)\n",
"\n",
"print(\"Number of nodes:\\n\", a.G.number_of_nodes())\n",
"print(\"First 5 nodes (notice labelling starting with 0):\\n\", list(a.G.nodes())[0:5])\n",
"print(\"Edges:\\n\", a.G.edges())\n",
"print(\"Size of Adjacency matrix:\\n\", a.adjMat.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you wish to create a fully connected graph with all the available values in the adjacency matrix, it is necessary to threshold it, which is explained in the next section."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Thresholding\n",
"There are a few ways to apply a threshold, either using an absolute threshold across the whole graph to preserve a specified number of edges or percentage of total possible edges; or to apply a local thresholding that begins with the minimum spanning tree and adds successive n-nearest neighbour graphs. The advantage of local thresholding is that the graph will always be fully connected, which is necessary to collect some graph measures.\n",
"\n",
"For an absolute threshold you have several possibilities. Note that our adjacency matrix (`a.adjMat`) always stays the same so we can apply all the thresholds we want to create our graph (`a.G`) accordingly. Also notice that in this specific case of an undirected graph, are dealing with a symmetric adjacency matrix, so although `a.adjMat` will always have the size of 500x500, the `a.G` will not."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of edges (notice it corresponds to the upper half edges of adjacency matrix):\n",
" 124750\n",
"Size of Adjacency matrix after 1st threshold:\n",
" (500, 500)\n",
"\n",
"Number of edges after 2nd threshold:\n",
" 1000\n",
"Size of Adjacency matrix after 2nd threshold:\n",
" (500, 500)\n",
"\n",
"Number of edges after 3rd threshold:\n",
" 6237\n",
"Size of Adjacency matrix after 3rd threshold:\n",
" (500, 500)\n",
"\n",
"Number of edges after 4th threshold:\n",
" 34848\n",
"Size of Adjacency matrix after 4th threshold:\n",
" (500, 500)\n"
]
}
],
"source": [
"# Bring everything from the adjacency matrix to a.G\n",
"a.apply_threshold()\n",
"print(\"Number of edges (notice it corresponds to the upper half edges of adjacency matrix):\\n\", a.G.number_of_edges())\n",
"print(\"Size of Adjacency matrix after 1st threshold:\\n\", a.adjMat.shape)\n",
"\n",
"# Retain the most strongly connected 1000 edges\n",
"a.apply_threshold(threshold_type= \"totalEdges\", value = 1000) \n",
"print(\"\\nNumber of edges after 2nd threshold:\\n\", a.G.number_of_edges())\n",
"print(\"Size of Adjacency matrix after 2nd threshold:\\n\", a.adjMat.shape)\n",
"\n",
"# Retain the 5% most connected edges as a percentage of the total possible number of edges\n",
"a.apply_threshold(threshold_type = \"edgePC\", value = 5) \n",
"print(\"\\nNumber of edges after 3rd threshold:\\n\", a.G.number_of_edges())\n",
"print(\"Size of Adjacency matrix after 3rd threshold:\\n\", a.adjMat.shape)\n",
"\n",
"# Retain edges with a weight greater than 0.3\n",
"a.apply_threshold(threshold_type= \"tVal\", value = 0.3) \n",
"print(\"\\nNumber of edges after 4th threshold:\\n\", a.G.number_of_edges())\n",
"print(\"Size of Adjacency matrix after 4th threshold:\\n\", a.adjMat.shape)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The options for local thresholding are similar. Note that a local thresholding always yield a connected graph, and in the case where no arguments are passed, the graph will be the Minimum Spanning Tree. Local thresholding can be very slow for bigger matrices because in each step it is adding successive N-nearest neighbour degree graphs."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Is the graph connected? True\n",
"Is the graph connected? True\n",
"Is the graph connected? True\n"
]
}
],
"source": [
"a.local_thresholding()\n",
"print(\"Is the graph connected? \", mbt.nx.is_connected(a.G))\n",
"\n",
"a.local_thresholding(threshold_type=\"edgePC\", value = 5)\n",
"print(\"Is the graph connected? \", mbt.nx.is_connected(a.G))\n",
"\n",
"a.local_thresholding(threshold_type=\"totalEdges\", value = 10000)\n",
"print(\"Is the graph connected? \", mbt.nx.is_connected(a.G))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Absolute Thresholding\n",
"\n",
"In a real brain network, an edge with high negative value is as strong as an edge with a high positive value. So, if you want to threshold in order to get the most strongly connected edges (both negative and positive), you just have to pass an argument `use_absolute=True` to `apply_threshold()`.\n",
"\n",
"In the case of the brain that we are using in this notebook there are not many negative edges. Thus, we have to threshold the 80% most strongly connected edges in order to see a difference (notice the use of the module `constants` (`ct`) to access the weight property of each edge):"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Edges with negative weight which belong to the 70% strongest ones:\n",
"(28, 64, {'weight': -0.17897503260377354})\n",
"(64, 408, {'weight': -0.16721546026415093})\n",
"(64, 454, {'weight': -0.17173085994339626})\n",
"\n",
"Edges with negative weight which belong to the 80% strongest ones:\n",
"(28, 64, {'weight': -0.17897503260377354})\n",
"(28, 217, {'weight': -0.11984709196226416})\n",
"(64, 213, {'weight': -0.14727152767924526})\n",
"(64, 408, {'weight': -0.16721546026415093})\n",
"(64, 454, {'weight': -0.17173085994339626})\n",
"(217, 479, {'weight': -0.11798177724339623})\n"
]
}
],
"source": [
"# Thresholding the 80% most strongly connected edges\n",
"a.apply_threshold(threshold_type=\"edgePC\", value=80)\n",
"for e in a.G.edges(data=True):\n",
" # Printing the edges with negative weight\n",
" if e[2][ct.WEIGHT] < 0:\n",
" print(e) # This line is never executed because a negative weighted edge is not strong enough\n",
"\n",
"# Absolute thresholding of the 70% most strongly connected edges \n",
"print(\"Edges with negative weight which belong to the 70% strongest ones:\")\n",
"a.apply_threshold(threshold_type=\"edgePC\", value=70, use_absolute=True)\n",
"for e in a.G.edges(data=True):\n",
" if e[2][ct.WEIGHT] < 0:\n",
" print(e)\n",
" \n",
"# Absolute thresholding of the 80% most strongly connected edges \n",
"print(\"\\nEdges with negative weight which belong to the 80% strongest ones:\")\n",
"a.apply_threshold(threshold_type=\"edgePC\", value=80, use_absolute=True)\n",
"for e in a.G.edges(data=True):\n",
" if e[2][ct.WEIGHT] < 0:\n",
" print(e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Binary and Absolute Graphs\n",
"\n",
"If necessary the graph can be binarised so that weights are removed. You can see that essentially this means that each edge will have a weight of 1."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Do all the edges have weight of 1? True\n"
]
}
],
"source": [
"a.binarise()\n",
"print(\"Do all the edges have weight of 1?\", all(e[2][ct.WEIGHT] == 1 for e in a.G.edges(data=True)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also, you can make all the weights to have an absolute value, instead of negative and positive values:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Do all the edges have a positive weight before? False\n",
"Do all the edges have a positive weight? True\n"
]
}
],
"source": [
"# Applying threshold again because of last changes\n",
"a.apply_threshold()\n",
"print(\"Do all the edges have a positive weight before?\", all(e[2][ct.WEIGHT] >= 0 for e in a.G.edges(data=True)))\n",
"a.make_edges_absolute()\n",
"print(\"Do all the edges have a positive weight?\", all(e[2][ct.WEIGHT] >= 0 for e in a.G.edges(data=True)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing 3D Spatial Information\n",
"\n",
"You can add spatial info to each node of your graph. You need this information if you want to use the visualisation tools of Maybrain.\n",
"\n",
"To do so, provide Maybrain with a file that has 4 columns: an anatomical label, and x, y and z coordinates. e.g.:\n",
"```\n",
"0 70.800000 30.600000 53.320000\n",
"1 32.064909 62.154158 69.707911\n",
"2 59.870968 92.230014 41.552595\n",
"3 19.703504 66.398922 52.878706\n",
"```\n",
"\n",
"Ideally these values would be in MNI space (this makes it easier to import background images for plotting and for some other functions), but this is not absolutely necessary.\n",
"\n",
"We are using the `resources` package again to get an already prepated text file with spatial information for a brain with 500 regions in the MNI template:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Attributes: {} / {}\n",
"Attributes of one node: 0 / (-53.6, -62.8, 36.64)\n"
]
}
],
"source": [
"# Initially, you don't have anatomical/spatial attributes in each node:\n",
"print(\"Attributes: \", mbt.nx.get_node_attributes(a.G, ct.ANAT_LABEL), \"/\", mbt.nx.get_node_attributes(a.G, ct.XYZ))\n",
"\n",
"#After calling import_spatial_info(), you can see the node's attributes\n",
"a.import_spatial_info(rr.MNI_SPACE_COORDINATES_500)\n",
"print(\"Attributes of one node: \", \n",
" mbt.nx.get_node_attributes(a.G, ct.ANAT_LABEL)[0], \n",
" \"/\", \n",
" mbt.nx.get_node_attributes(a.G, ct.XYZ)[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Properties in Nodes and Edges\n",
"\n",
"We have seen already that nodes can have properties about spatial information after calling `import_spatial_info()`, and edges can have properties about weight after calling applying thresholds. \n",
"\n",
"You can add properties\n",
"to nodes or edges from a text file. The format should be as follows:\n",
"\n",
"```\n",
"property\n",
"node1 value\n",
"node2 value2\n",
"(...)\n",
"node1 node2 value1\n",
"node3 node4 value2\n",
"(...)\n",
"```\n",
"Let's give a specific example. Imagine that you want to add properties about colours. You can use [this file](https://github.com/RittmanResearch/maybrain/blob/master/docs/data/3d_grid_properties.txt), which is transcribed here:\n",
"\n",
"```\n",
"colour\n",
"1 red\n",
"3 red\n",
"6 green\n",
"0 blue\n",
"1 3 green\n",
"1 2 green\n",
"1 0 grey\n",
"2 3 green\n",
"2 0 red\n",
"3 0 green\n",
"```\n",
"\n",
"Note that the first line contains the property name. Subsequent lines refer to edges if they contain 3 terms and nodes if they contain 2. The above will give node 1 the property `'colour'` with value `'red'` and node 6 the property `'colour'` with value `'green'`. Nodes 0 and 3 will also have the property `'colour'` but with value `'blue'` and `'red'`, respectively.\n",
"\n",
"The edge connecting nodes 1 and 3 will have the same property with value `'green'`. All the other 5 edges will have the same property but with different values. These properties are stored in the `G` object from networkx.\n",
"\n",
"\n",
"In order to be easier to see the properties features, we will be importing a shorter matrix with just 4 nodes ([link here](https://github.com/RittmanResearch/maybrain/blob/master/docs/data/3d_grid_adj.txt)).\n",
"\n",
"From the following code you can see that a warning is printed because we tried to add a property to a node `6`, which doesn't exist. However, the other properties are added. \n",
"\n",
"Note the fact that as the brain is not directed, adding the property to the edge `(1,0)` is considered as adding to the edge `(0,1)`. The same thing happens with edges `(2,0)` and `(3,0)`. No property was imported to node `2` because it is not specified in the properties file."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Edges and nodes information:\n",
"(0, 1, {'weight': 0.60080034391699999})\n",
"(0, 2, {'weight': 0.203602458588})\n",
"(0, 3, {'weight': 0.16390494700200001})\n",
"(1, 2, {'weight': 0.84379894778099995})\n",
"(1, 3, {'weight': 0.242747996199})\n",
"(2, 3, {'weight': 0.63727884848299998})\n",
"(0, {})\n",
"(1, {})\n",
"(2, {})\n",
"(3, {})\n",
"\n",
"Importing properties...\n",
"Warning! Unable to process property ['colour', 6, 'green']\n",
"\n",
"Edges and nodes information after importing properties:\n",
"(0, 1, {'weight': 0.60080034391699999, 'colour': 'grey'})\n",
"(0, 2, {'weight': 0.203602458588, 'colour': 'red'})\n",
"(0, 3, {'weight': 0.16390494700200001, 'colour': 'green'})\n",
"(1, 2, {'weight': 0.84379894778099995, 'colour': 'green'})\n",
"(1, 3, {'weight': 0.242747996199, 'colour': 'green'})\n",
"(2, 3, {'weight': 0.63727884848299998, 'colour': 'green'})\n",
"(0, {'colour': 'blue'})\n",
"(1, {'colour': 'red'})\n",
"(2, {})\n",
"(3, {'colour': 'red'})\n"
]
}
],
"source": [
"# Creating a new Brain and importing the shorter adjacency matrix\n",
"b = mbt.Brain()\n",
"b.import_adj_file(\"data/3d_grid_adj.txt\")\n",
"b.apply_threshold()\n",
"\n",
"print(\"Edges and nodes information:\")\n",
"for e in b.G.edges(data=True):\n",
" print(e)\n",
"for n in b.G.nodes(data=True):\n",
" print(n)\n",
"\n",
"# Importing properties and showing again edges and nodes\n",
"print(\"\\nImporting properties...\")\n",
"b.import_properties(\"data/3d_grid_properties.txt\")\n",
"\n",
"print(\"\\nEdges and nodes information after importing properties:\")\n",
"for e in b.G.edges(data=True):\n",
" print(e)\n",
"for n in b.G.nodes(data=True):\n",
" print(n)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can notice that if we threshold our brain again, edges are created from scratch and thus properties are lost. The same doesn't happen with nodes as they are always present in our `G` object.\n",
"\n",
"By default, properties **of the edges** are not imported everytime you threshold the brain. However, you can change that behaviour by setting the field `update_props_after_threshold` to True."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Edges information:\n",
"(0, 1, {'weight': 0.60080034391699999})\n",
"(0, 2, {'weight': 0.203602458588})\n",
"(0, 3, {'weight': 0.16390494700200001})\n",
"(1, 2, {'weight': 0.84379894778099995})\n",
"(1, 3, {'weight': 0.242747996199})\n",
"(2, 3, {'weight': 0.63727884848299998})\n",
"\n",
"Setting b.update_properties_after_threshold and rethresholding again...\n",
"Warning! Unable to process property ['colour', 6, 'green']\n",
"\n",
"Edges information again:\n",
"(0, 1, {'weight': 0.60080034391699999, 'colour': 'grey'})\n",
"(0, 2, {'weight': 0.203602458588, 'colour': 'red'})\n",
"(0, 3, {'weight': 0.16390494700200001, 'colour': 'green'})\n",
"(1, 2, {'weight': 0.84379894778099995, 'colour': 'green'})\n",
"(1, 3, {'weight': 0.242747996199, 'colour': 'green'})\n",
"(2, 3, {'weight': 0.63727884848299998, 'colour': 'green'})\n"
]
}
],
"source": [
"# Rethresholding the brain, thus loosing information\n",
"b.apply_threshold(threshold_type=\"totalEdges\", value=0)\n",
"b.apply_threshold()\n",
"\n",
"print(\"Edges information:\")\n",
"for e in b.G.edges(data=True):\n",
" print(e)\n",
"\n",
"# Setting field to allow automatic importing of properties after a threshold\n",
"print(\"\\nSetting b.update_properties_after_threshold and rethresholding again...\")\n",
"b.apply_threshold(threshold_type=\"totalEdges\", value=0)\n",
"b.update_props_after_threshold = True\n",
"b.apply_threshold() # Now, warning is thrown just like before\n",
"\n",
"print(\"\\nEdges information again:\")\n",
"for e in b.G.edges(data=True):\n",
" print(e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"You can also import the properties from a dictionary, both for nodes and edges. In the following example there are two dictionaries being created with the values of a certain property, named `own_property`, that will be added to brain:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Edges information:\n",
"(0, 1, {'own_property': 'edge_val1', 'weight': 0.60080034391699999, 'colour': 'grey'})\n",
"(0, 2, {'weight': 0.203602458588, 'colour': 'red'})\n",
"(0, 3, {'weight': 0.16390494700200001, 'colour': 'green'})\n",
"(1, 2, {'weight': 0.84379894778099995, 'colour': 'green'})\n",
"(1, 3, {'weight': 0.242747996199, 'colour': 'green'})\n",
"(2, 3, {'own_property': 'edge_val2', 'weight': 0.63727884848299998, 'colour': 'green'})\n",
"\n",
"Nodes information:\n",
"(0, {'own_property': 'val1', 'colour': 'blue'})\n",
"(1, {'own_property': 'val2', 'colour': 'red'})\n",
"(2, {})\n",
"(3, {'colour': 'red'})\n"
]
}
],
"source": [
"nodes_props = {0: \"val1\", 1: \"val2\"}\n",
"edges_props = {(0, 1): \"edge_val1\", (2,3): \"edge_val2\"}\n",
"\n",
"b.import_edge_props_from_dict(\"own_property\", edges_props)\n",
"b.import_node_props_from_dict(\"own_property\", nodes_props)\n",
"\n",
"print(\"\\nEdges information:\")\n",
"for e in b.G.edges(data=True):\n",
" print(e)\n",
" \n",
"print(\"\\nNodes information:\")\n",
"for n in b.G.nodes(data=True):\n",
" print(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": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
makeyourownneuralnetwork/complex_valued_neuralnetwork | single_neuron-periodic.ipynb | 1 | 6935 | {
"cells": [
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# simple neural network of one complex valued neuron\n",
"# extended to use a periodic activation function\n",
"import numpy"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class neuralNetwork:\n",
" \n",
" def __init__(self, inputs, cats, periods):\n",
" # link weights matrix\n",
" self.w = numpy.random.normal(0.0, pow(1.0, -0.5), (inputs + 1))\n",
" self.w = numpy.array(self.w, ndmin=2, dtype='complex128')\n",
" self.w += 1j * numpy.random.normal(0.0, pow(1.0, -0.5), (inputs + 1))\n",
" \n",
" # testing overrride\n",
" #self.w = numpy.array([1.0 + 0.0j, 1.0 + 0.0j], ndmin=2, dtype='complex128')\n",
" \n",
" # number of output class categories\n",
" self.categories = cats\n",
" \n",
" # todo periodicity\n",
" self.periodicity = periods\n",
" \n",
" pass\n",
" \n",
" def z_to_class(self, z):\n",
" # first work out the angle, but shift angle from [-pi/2, +pi.2] to [0,2pi]\n",
" angle = numpy.mod(numpy.angle(z) + 2*numpy.pi, 2*numpy.pi)\n",
" # from angle to category\n",
" p = int(numpy.floor (self.categories * self.periodicity * angle / (2*numpy.pi)))\n",
" p = numpy.mod(p, self.categories)\n",
" return p\n",
"\n",
" def class_to_angles(self, c):\n",
" # class to several angles due to periodicity, using bisector\n",
" angles = (c + 0.5 + (self.categories * numpy.arange(self.periodicity))) / (self.categories * self.periodicity) * 2 * numpy.pi\n",
" return angles\n",
" \n",
" def status(self):\n",
" print (\"w = \", self.w)\n",
" print (\"categories = \", self.categories)\n",
" print (\"periodicity = \", self.periodicity)\n",
" pass\n",
"\n",
" def query(self, inputs_list):\n",
" # add bias input\n",
" inputs_list.append(1.0)\n",
" \n",
" # convert input to complex\n",
" inputs = numpy.array(inputs_list, ndmin=2, dtype='complex128').T\n",
" print(\"inputs = \\n\", inputs)\n",
" \n",
" # combine inputs, weighted\n",
" z = numpy.dot(self.w, inputs)\n",
" print(\"z = \", z)\n",
" \n",
" # map to output classes\n",
" o = self.z_to_class(z)\n",
" print(\"output = \", o)\n",
" print (\"\")\n",
" return o\n",
" \n",
" def train(self, inputs_list, target):\n",
" # add bias input\n",
" inputs_list.append(1.0)\n",
" \n",
" # convert inputs and outputs list to 2d array\n",
" inputs = numpy.array(inputs_list, ndmin=2, dtype='complex128').T\n",
"\n",
" # combine inputs, weighted\n",
" z = numpy.dot(self.w, inputs)[0]\n",
" \n",
" # desired angle from trainging set\n",
" # first get all possible angles\n",
" desired_angles = self.class_to_angles(target)\n",
" \n",
" # potential errors errors\n",
" errors = numpy.exp(1j*desired_angles) - z\n",
" # select smallest error\n",
" e = errors[numpy.argmin(numpy.abs(errors))]\n",
" \n",
" # dw = e * x.T / (x.x.T)\n",
" dw = (e * numpy.conj(inputs.T)) / (3)\n",
" #print(\"dw = \", dw)\n",
" self.w += dw\n",
" #print(\"new self.w = \", self.w )\n",
" #print(\"test new self.w with query = \", self.query(inputs.T))\n",
" #print(\"--\")\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"w = [[-1.56572466+0.70528849j -2.71967065+0.00440014j 1.31235568-0.46018874j]]\n",
"categories = 2\n",
"periodicity = 2\n"
]
}
],
"source": [
"# create instance of neural network\n",
"number_of_inputs = 2\n",
"categories = 2\n",
"periods = 2\n",
"\n",
"n = neuralNetwork(number_of_inputs, categories, periods)\n",
"n.status()"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# train neural network - XOR\n",
"for i in range(10):\n",
" n.train([-1.0, -1.0], 0)\n",
" n.train([-1.0, 1.0], 1)\n",
" n.train([1.0, -1.0], 1)\n",
" n.train([1.0, 1.0], 0)\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"inputs = \n",
" [[-1.+0.j]\n",
" [-1.+0.j]\n",
" [ 1.+0.j]]\n",
"z = [[ 0.70710678+0.70710678j]]\n",
"output = 0\n",
"\n",
"inputs = \n",
" [[-1.+0.j]\n",
" [ 1.+0.j]\n",
" [ 1.+0.j]]\n",
"z = [[-0.70710678+0.70710678j]]\n",
"output = 1\n",
"\n",
"inputs = \n",
" [[ 1.+0.j]\n",
" [-1.+0.j]\n",
" [ 1.+0.j]]\n",
"z = [[ 0.70710678-0.70710678j]]\n",
"output = 1\n",
"\n",
"inputs = \n",
" [[ 1.+0.j]\n",
" [ 1.+0.j]\n",
" [ 1.+0.j]]\n",
"z = [[-0.70710678-0.70710678j]]\n",
"output = 0\n",
"\n"
]
},
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# query after training\n",
"n.query( [-1.0, -1.0] )\n",
"n.query( [-1.0, 1.0] )\n",
"n.query( [1.0, -1.0] )\n",
"n.query( [1.0, 1.0] )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"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-2.0 |
luwei0917/awsemmd_script | notebook/Optimization/ER_rnative_visual.ipynb | 1 | 1443349 | null | mit |
GoogleCloudPlatform/asl-ml-immersion | notebooks/launching_into_ml/labs/supplemental/python.BQ_explore_data.ipynb | 1 | 377387 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "DGPlYumZnO1t"
},
"source": [
"# Exploratory Data Analysis Using Python and BigQuery\n",
"\n",
"\n",
"\n",
"## Learning Objectives\n",
"\n",
"1. Analyze a Pandas Dataframe\n",
"2. Create Seaborn plots for Exploratory Data Analysis in Python \n",
"3. Write a SQL query to pick up specific fields from a BigQuery dataset\n",
"4. Exploratory Analysis in BigQuery\n",
"\n",
"\n",
"## Introduction \n",
"This lab is an introduction to linear regression using Python and Scikit-Learn. This lab serves as a foundation for more complex algorithms and machine learning models that you will encounter in the course. We will train a linear regression model to predict housing price.\n",
"\n",
"Each learning objective will correspond to a __#TODO__ in this student lab notebook -- try to complete this notebook first and then review the [solution notebook](../solutions/python.BQ_explore_data.ipynb). "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "AsHg6SD2nO1v"
},
"source": [
"### Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "gEXV-RxPnO1w"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# Seaborn is a Python data visualization library based on matplotlib.\n",
"import seaborn as sns\n",
"from google.cloud import bigquery\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "dr2TkzKRnO1z"
},
"source": [
"### Load the Dataset\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, we create a directory called usahousing. This directory will hold the dataset that we copy from Google Cloud Storage."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.isdir(\"../data/explore\"):\n",
" os.makedirs(\"../data/explore\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we copy the Usahousing dataset from Google Cloud Storage."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": [
"flake8-noqa-cell"
]
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Copying gs://cloud-training-demos/feat_eng/housing/housing_pre-proc.csv...\n",
"/ [1 files][ 1.4 MiB/ 1.4 MiB] \n",
"Operation completed over 1 objects/1.4 MiB. \n"
]
}
],
"source": [
"!gsutil cp gs://cloud-training-demos/feat_eng/housing/housing_pre-proc.csv ../data/explore"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we use the \"ls\" command to list files in the directory. This ensures that the dataset was copied."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total 1404\n",
"-rw-r--r-- 1 jupyter jupyter 1435069 Aug 28 03:31 housing_pre-proc.csv\n"
]
}
],
"source": [
"!ls -l ../data/explore"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we read the dataset into a Pandas dataframe."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "CzrXJI8VnO10"
},
"outputs": [],
"source": [
"df_USAhousing = # TODO 1: Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Inspect the Data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 272
},
"colab_type": "code",
"id": "Y6VJQ1tdnO12",
"outputId": "7a1d4eed-3e83-44a8-f495-a9b74444d3ec"
},
"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>longitude</th>\n",
" <th>latitude</th>\n",
" <th>housing_median_age</th>\n",
" <th>total_rooms</th>\n",
" <th>total_bedrooms</th>\n",
" <th>population</th>\n",
" <th>households</th>\n",
" <th>median_income</th>\n",
" <th>median_house_value</th>\n",
" <th>ocean_proximity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-122.23</td>\n",
" <td>37.88</td>\n",
" <td>41.0</td>\n",
" <td>880.0</td>\n",
" <td>129.0</td>\n",
" <td>322.0</td>\n",
" <td>126.0</td>\n",
" <td>8.3252</td>\n",
" <td>452600.0</td>\n",
" <td>NEAR BAY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-122.22</td>\n",
" <td>37.86</td>\n",
" <td>21.0</td>\n",
" <td>7099.0</td>\n",
" <td>1106.0</td>\n",
" <td>2401.0</td>\n",
" <td>1138.0</td>\n",
" <td>8.3014</td>\n",
" <td>358500.0</td>\n",
" <td>NEAR BAY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-122.24</td>\n",
" <td>37.85</td>\n",
" <td>52.0</td>\n",
" <td>1467.0</td>\n",
" <td>190.0</td>\n",
" <td>496.0</td>\n",
" <td>177.0</td>\n",
" <td>7.2574</td>\n",
" <td>352100.0</td>\n",
" <td>NEAR BAY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-122.25</td>\n",
" <td>37.85</td>\n",
" <td>52.0</td>\n",
" <td>1274.0</td>\n",
" <td>235.0</td>\n",
" <td>558.0</td>\n",
" <td>219.0</td>\n",
" <td>5.6431</td>\n",
" <td>341300.0</td>\n",
" <td>NEAR BAY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-122.25</td>\n",
" <td>37.85</td>\n",
" <td>52.0</td>\n",
" <td>1627.0</td>\n",
" <td>280.0</td>\n",
" <td>565.0</td>\n",
" <td>259.0</td>\n",
" <td>3.8462</td>\n",
" <td>342200.0</td>\n",
" <td>NEAR BAY</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" longitude latitude housing_median_age total_rooms total_bedrooms \\\n",
"0 -122.23 37.88 41.0 880.0 129.0 \n",
"1 -122.22 37.86 21.0 7099.0 1106.0 \n",
"2 -122.24 37.85 52.0 1467.0 190.0 \n",
"3 -122.25 37.85 52.0 1274.0 235.0 \n",
"4 -122.25 37.85 52.0 1627.0 280.0 \n",
"\n",
" population households median_income median_house_value ocean_proximity \n",
"0 322.0 126.0 8.3252 452600.0 NEAR BAY \n",
"1 2401.0 1138.0 8.3014 358500.0 NEAR BAY \n",
"2 496.0 177.0 7.2574 352100.0 NEAR BAY \n",
"3 558.0 219.0 5.6431 341300.0 NEAR BAY \n",
"4 565.0 259.0 3.8462 342200.0 NEAR BAY "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Show the first five row.\n",
"\n",
"df_USAhousing.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check for any null values."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"longitude 0\n",
"latitude 0\n",
"housing_median_age 0\n",
"total_rooms 0\n",
"total_bedrooms 0\n",
"population 0\n",
"households 0\n",
"median_income 0\n",
"median_house_value 0\n",
"ocean_proximity 0\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_USAhousing.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"colab_type": "code",
"id": "nRTsvSzqnO17",
"outputId": "f44ad14e-5fb4-4c70-e71c-9d149bca4869"
},
"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>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>longitude</th>\n",
" <td>20433.0</td>\n",
" <td>-119.570689</td>\n",
" <td>2.003578</td>\n",
" <td>-124.3500</td>\n",
" <td>-121.8000</td>\n",
" <td>-118.4900</td>\n",
" <td>-118.010</td>\n",
" <td>-114.3100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>latitude</th>\n",
" <td>20433.0</td>\n",
" <td>35.633221</td>\n",
" <td>2.136348</td>\n",
" <td>32.5400</td>\n",
" <td>33.9300</td>\n",
" <td>34.2600</td>\n",
" <td>37.720</td>\n",
" <td>41.9500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>housing_median_age</th>\n",
" <td>20433.0</td>\n",
" <td>28.633094</td>\n",
" <td>12.591805</td>\n",
" <td>1.0000</td>\n",
" <td>18.0000</td>\n",
" <td>29.0000</td>\n",
" <td>37.000</td>\n",
" <td>52.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>total_rooms</th>\n",
" <td>20433.0</td>\n",
" <td>2636.504233</td>\n",
" <td>2185.269567</td>\n",
" <td>2.0000</td>\n",
" <td>1450.0000</td>\n",
" <td>2127.0000</td>\n",
" <td>3143.000</td>\n",
" <td>39320.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>total_bedrooms</th>\n",
" <td>20433.0</td>\n",
" <td>537.870553</td>\n",
" <td>421.385070</td>\n",
" <td>1.0000</td>\n",
" <td>296.0000</td>\n",
" <td>435.0000</td>\n",
" <td>647.000</td>\n",
" <td>6445.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>population</th>\n",
" <td>20433.0</td>\n",
" <td>1424.946949</td>\n",
" <td>1133.208490</td>\n",
" <td>3.0000</td>\n",
" <td>787.0000</td>\n",
" <td>1166.0000</td>\n",
" <td>1722.000</td>\n",
" <td>35682.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>households</th>\n",
" <td>20433.0</td>\n",
" <td>499.433465</td>\n",
" <td>382.299226</td>\n",
" <td>1.0000</td>\n",
" <td>280.0000</td>\n",
" <td>409.0000</td>\n",
" <td>604.000</td>\n",
" <td>6082.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>median_income</th>\n",
" <td>20433.0</td>\n",
" <td>3.871162</td>\n",
" <td>1.899291</td>\n",
" <td>0.4999</td>\n",
" <td>2.5637</td>\n",
" <td>3.5365</td>\n",
" <td>4.744</td>\n",
" <td>15.0001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>median_house_value</th>\n",
" <td>20433.0</td>\n",
" <td>206864.413155</td>\n",
" <td>115435.667099</td>\n",
" <td>14999.0000</td>\n",
" <td>119500.0000</td>\n",
" <td>179700.0000</td>\n",
" <td>264700.000</td>\n",
" <td>500001.0000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min \\\n",
"longitude 20433.0 -119.570689 2.003578 -124.3500 \n",
"latitude 20433.0 35.633221 2.136348 32.5400 \n",
"housing_median_age 20433.0 28.633094 12.591805 1.0000 \n",
"total_rooms 20433.0 2636.504233 2185.269567 2.0000 \n",
"total_bedrooms 20433.0 537.870553 421.385070 1.0000 \n",
"population 20433.0 1424.946949 1133.208490 3.0000 \n",
"households 20433.0 499.433465 382.299226 1.0000 \n",
"median_income 20433.0 3.871162 1.899291 0.4999 \n",
"median_house_value 20433.0 206864.413155 115435.667099 14999.0000 \n",
"\n",
" 25% 50% 75% max \n",
"longitude -121.8000 -118.4900 -118.010 -114.3100 \n",
"latitude 33.9300 34.2600 37.720 41.9500 \n",
"housing_median_age 18.0000 29.0000 37.000 52.0000 \n",
"total_rooms 1450.0000 2127.0000 3143.000 39320.0000 \n",
"total_bedrooms 296.0000 435.0000 647.000 6445.0000 \n",
"population 787.0000 1166.0000 1722.000 35682.0000 \n",
"households 280.0000 409.0000 604.000 6082.0000 \n",
"median_income 2.5637 3.5365 4.744 15.0001 \n",
"median_house_value 119500.0000 179700.0000 264700.000 500001.0000 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_stats = df_USAhousing.describe()\n",
"df_stats = df_stats.transpose()\n",
"df_stats"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 20433 entries, 0 to 20432\n",
"Data columns (total 10 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 longitude 20433 non-null float64\n",
" 1 latitude 20433 non-null float64\n",
" 2 housing_median_age 20433 non-null float64\n",
" 3 total_rooms 20433 non-null float64\n",
" 4 total_bedrooms 20433 non-null float64\n",
" 5 population 20433 non-null float64\n",
" 6 households 20433 non-null float64\n",
" 7 median_income 20433 non-null float64\n",
" 8 median_house_value 20433 non-null float64\n",
" 9 ocean_proximity 20433 non-null object \n",
"dtypes: float64(9), object(1)\n",
"memory usage: 1.6+ MB\n"
]
}
],
"source": [
"df_USAhousing.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a peek at the first and last five rows of the data for all columns."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rows : 20433\n",
"Columns : 10\n",
"\n",
"Features : \n",
" ['longitude', 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income', 'median_house_value', 'ocean_proximity']\n",
"\n",
"Missing values : 0\n",
"\n",
"Unique values : \n",
" longitude 844\n",
"latitude 861\n",
"housing_median_age 52\n",
"total_rooms 5911\n",
"total_bedrooms 1923\n",
"population 3879\n",
"households 1809\n",
"median_income 12825\n",
"median_house_value 3833\n",
"ocean_proximity 5\n",
"dtype: int64\n"
]
}
],
"source": [
"print(\"Rows : \", df_USAhousing.shape[0])\n",
"print(\"Columns : \", df_USAhousing.shape[1])\n",
"print(\"\\nFeatures : \\n\", df_USAhousing.columns.tolist())\n",
"print(\"\\nMissing values : \", df_USAhousing.isnull().sum().values.sum())\n",
"print(\"\\nUnique values : \\n\", df_USAhousing.nunique())"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "QWVdsrmgnO1_"
},
"source": [
"## Explore the Data\n",
"\n",
"Let's create some simple plots to check out the data! "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"_ = sns.heatmap(df_USAhousing.corr())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a distplot showing \"median_house_value\"."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
},
"colab_type": "code",
"id": "SOsTLClWnO2B",
"outputId": "b8a78674-5ddb-4706-90b4-37d7d83e8092"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# TODO 2a: Your code goes here"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 0, 'median_house_value')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_style(\"whitegrid\")\n",
"df_USAhousing[\"median_house_value\"].hist(bins=30)\n",
"plt.xlabel(\"median_house_value\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = df_USAhousing[\"median_income\"]\n",
"y = df_USAhousing[\"median_house_value\"]\n",
"\n",
"plt.scatter(x, y)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a jointplot showing \"median_income\" versus \"median_house_value\"."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.JointGrid at 0x7f09c783ea50>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# TODO 2b: Your code goes here"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='ocean_proximity', ylabel='count'>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x=\"ocean_proximity\", data=df_USAhousing)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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oPvIDNA/ZAZqH7Hg3rrzwIEuXLtWzzz6rkpISBQcH6/nnn5ckTZ06VbNmzdK6des0dOhQc/6//OUvWrdunfz8/PSv//qveuSRR3T11Vdr1qxZmjp1qqqqqtS+fXulpKSoR48e9b7v008/rfnz52vlypUKDAw03/dSzz//vObPn69OnTrpjjvuaHAMQGsjO0DzkR+gecgO0Dxkx3v5GNXX1gAAAAAAAFgQt40AAAAAAABLo3kBAAAAAAAsjeYFAAAAAACwNJoXAAAAAADA0mheAAAAAAAAS6N5AQAAAAAALI3mBQAAAAAAsLT/B8QmuoitnpEUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1080x216 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# takes numeric only?\n",
"# plt.figure(figsize=(20,20))\n",
"g = sns.FacetGrid(df_USAhousing, col=\"ocean_proximity\")\n",
"_ = g.map(plt.hist, \"households\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1080x216 with 5 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# takes numeric only?\n",
"# plt.figure(figsize=(20,20))\n",
"g = sns.FacetGrid(df_USAhousing, col=\"ocean_proximity\")\n",
"_ = g.map(plt.hist, \"median_income\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can see below that this is the state of California!"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = df_USAhousing[\"latitude\"]\n",
"y = df_USAhousing[\"longitude\"]\n",
"\n",
"plt.scatter(x, y)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore and create ML datasets\n",
"\n",
"In this notebook, we will explore data corresponding to taxi rides in New York City to build a Machine Learning model in support of a fare-estimation tool. The idea is to suggest a likely fare to taxi riders so that they are not surprised, and so that they can protest if the charge is much higher than expected.\n",
"\n",
"## Learning Objectives\n",
"* Access and explore a public BigQuery dataset on NYC Taxi Cab rides\n",
"* Visualize your dataset using the Seaborn library"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3> Extract sample data from BigQuery </h3>\n",
"\n",
"The dataset that we will use is <a href=\"https://bigquery.cloud.google.com/table/nyc-tlc:yellow.trips\">a BigQuery public dataset</a>. Click on the link, and look at the column names. Switch to the Details tab to verify that the number of records is one billion, and then switch to the Preview tab to look at a few rows.\n",
"\n",
"Let's write a SQL query to pick up interesting fields from the dataset. It's a good idea to get the timestamp in a predictable format."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"tags": [
"flake8-noqa-cell"
]
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Query complete after 0.00s: 100%|██████████| 1/1 [00:00<00:00, 845.28query/s] \n",
"Downloading: 100%|██████████| 10/10 [00:01<00:00, 7.92rows/s]\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pickup_datetime</th>\n",
" <th>pickup_longitude</th>\n",
" <th>pickup_latitude</th>\n",
" <th>dropoff_longitude</th>\n",
" <th>dropoff_latitude</th>\n",
" <th>passenger_count</th>\n",
" <th>trip_distance</th>\n",
" <th>tolls_amount</th>\n",
" <th>fare_amount</th>\n",
" <th>total_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" <td>2010-02-08 09:17:00 UTC</td>\n",
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" <td>2010-02-23 13:00:19 UTC</td>\n",
" <td>-73.974440</td>\n",
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" <td>-73.782493</td>\n",
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" <td>0.000000</td>\n",
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" <td>2010-02-16 13:17:44 UTC</td>\n",
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" <td>2010-03-16 01:04:50 UTC</td>\n",
" <td>-74.008316</td>\n",
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" <td>-74.008352</td>\n",
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" <td>2015-02-25 03:38:41 UTC</td>\n",
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" <th>6</th>\n",
" <td>2015-04-17 16:43:09 UTC</td>\n",
" <td>-73.795265</td>\n",
" <td>40.666615</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <tr>\n",
" <th>7</th>\n",
" <td>2015-03-19 08:57:55 UTC</td>\n",
" <td>-73.902100</td>\n",
" <td>40.764107</td>\n",
" <td>-73.902206</td>\n",
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" <th>8</th>\n",
" <td>2010-02-12 16:11:52 UTC</td>\n",
" <td>-73.955030</td>\n",
" <td>40.783350</td>\n",
" <td>-73.956183</td>\n",
" <td>40.782330</td>\n",
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" <th>9</th>\n",
" <td>2015-05-08 07:59:51 UTC</td>\n",
" <td>-73.848007</td>\n",
" <td>40.723724</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pickup_datetime pickup_longitude pickup_latitude \\\n",
"0 2010-02-08 09:17:00 UTC 0.000000 0.000000 \n",
"1 2010-02-23 13:00:19 UTC -73.974440 40.754140 \n",
"2 2010-03-28 11:40:29 UTC 0.000000 0.000000 \n",
"3 2010-02-16 13:17:44 UTC -73.995773 40.761368 \n",
"4 2010-03-16 01:04:50 UTC -74.008316 40.736286 \n",
"5 2015-02-25 03:38:41 UTC -73.937500 40.758198 \n",
"6 2015-04-17 16:43:09 UTC -73.795265 40.666615 \n",
"7 2015-03-19 08:57:55 UTC -73.902100 40.764107 \n",
"8 2010-02-12 16:11:52 UTC -73.955030 40.783350 \n",
"9 2015-05-08 07:59:51 UTC -73.848007 40.723724 \n",
"\n",
" dropoff_longitude dropoff_latitude passenger_count trip_distance \\\n",
"0 0.000000 0.000000 1 0.0 \n",
"1 -73.782493 40.648832 1 17.6 \n",
"2 0.000000 0.000000 1 0.0 \n",
"3 -73.995773 40.761368 1 0.0 \n",
"4 -74.008352 40.736250 1 0.0 \n",
"5 -73.937523 40.758190 1 0.0 \n",
"6 0.000000 0.000000 4 0.0 \n",
"7 -73.902206 40.764126 6 0.0 \n",
"8 -73.956183 40.782330 4 0.1 \n",
"9 0.000000 0.000000 1 0.0 \n",
"\n",
" tolls_amount fare_amount total_amount \n",
"0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 \n",
"5 0.0 0.0 0.0 \n",
"6 0.0 0.0 0.0 \n",
"7 0.0 0.0 0.0 \n",
"8 0.0 0.0 0.0 \n",
"9 0.0 0.0 0.0 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%bigquery\n",
"SELECT\n",
" FORMAT_TIMESTAMP(\n",
" \"%Y-%m-%d %H:%M:%S %Z\", pickup_datetime) AS pickup_datetime,\n",
" pickup_longitude, pickup_latitude, dropoff_longitude,\n",
" dropoff_latitude, passenger_count, trip_distance, tolls_amount, \n",
" fare_amount, total_amount \n",
"# TODO 3: Set correct BigQuery public dataset for nyc-tlc yellow taxi cab trips\n",
"# Tip: For projects with hyphens '-' be sure to escape with backticks ``\n",
"FROM \n",
"LIMIT 10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's increase the number of records so that we can do some neat graphs. There is no guarantee about the order in which records are returned, and so no guarantee about which records get returned if we simply increase the LIMIT. To properly sample the dataset, let's use the HASH of the pickup time and return 1 in 100,000 records -- because there are 1 billion records in the data, we should get back approximately 10,000 records if we do this.\n",
"\n",
"We will also store the BigQuery result in a Pandas dataframe named \"trips\""
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"tags": [
"flake8-noqa-cell"
]
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Query complete after 0.00s: 100%|██████████| 1/1 [00:00<00:00, 613.92query/s] \n",
"Downloading: 100%|██████████| 10789/10789 [00:01<00:00, 10616.51rows/s]\n"
]
}
],
"source": [
"%%bigquery trips\n",
"SELECT\n",
" FORMAT_TIMESTAMP(\n",
" \"%Y-%m-%d %H:%M:%S %Z\", pickup_datetime) AS pickup_datetime,\n",
" pickup_longitude, pickup_latitude, \n",
" dropoff_longitude, dropoff_latitude,\n",
" passenger_count,\n",
" trip_distance,\n",
" tolls_amount,\n",
" fare_amount,\n",
" total_amount\n",
"FROM\n",
" `nyc-tlc.yellow.trips`\n",
"WHERE\n",
" ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), 100000)) = 1"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10789\n"
]
}
],
"source": [
"print(len(trips))"
]
},
{
"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>pickup_datetime</th>\n",
" <th>pickup_longitude</th>\n",
" <th>pickup_latitude</th>\n",
" <th>dropoff_longitude</th>\n",
" <th>dropoff_latitude</th>\n",
" <th>passenger_count</th>\n",
" <th>trip_distance</th>\n",
" <th>tolls_amount</th>\n",
" <th>fare_amount</th>\n",
" <th>total_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2014-12-08 21:50:00 UTC</td>\n",
" <td>-73.994802</td>\n",
" <td>40.720612</td>\n",
" <td>-73.949125</td>\n",
" <td>40.668893</td>\n",
" <td>1</td>\n",
" <td>5.33</td>\n",
" <td>0.00</td>\n",
" <td>22.0</td>\n",
" <td>27.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2012-03-04 00:57:00 UTC</td>\n",
" <td>-74.005625</td>\n",
" <td>40.734517</td>\n",
" <td>-73.952492</td>\n",
" <td>40.725197</td>\n",
" <td>1</td>\n",
" <td>7.33</td>\n",
" <td>0.00</td>\n",
" <td>20.5</td>\n",
" <td>23.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013-12-09 15:03:00 UTC</td>\n",
" <td>-73.990950</td>\n",
" <td>40.749772</td>\n",
" <td>-73.870807</td>\n",
" <td>40.774070</td>\n",
" <td>1</td>\n",
" <td>9.44</td>\n",
" <td>5.33</td>\n",
" <td>29.0</td>\n",
" <td>34.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2011-12-03 10:28:00 UTC</td>\n",
" <td>-73.998822</td>\n",
" <td>40.680933</td>\n",
" <td>-73.968960</td>\n",
" <td>40.757878</td>\n",
" <td>1</td>\n",
" <td>8.28</td>\n",
" <td>0.00</td>\n",
" <td>20.9</td>\n",
" <td>25.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2009-03-28 20:30:35 UTC</td>\n",
" <td>-73.973926</td>\n",
" <td>40.757725</td>\n",
" <td>-73.981695</td>\n",
" <td>40.761591</td>\n",
" <td>1</td>\n",
" <td>0.50</td>\n",
" <td>0.00</td>\n",
" <td>4.6</td>\n",
" <td>4.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2009-08-23 23:59:22 UTC</td>\n",
" <td>-73.783319</td>\n",
" <td>40.648480</td>\n",
" <td>-73.893649</td>\n",
" <td>40.646566</td>\n",
" <td>1</td>\n",
" <td>8.30</td>\n",
" <td>0.00</td>\n",
" <td>18.9</td>\n",
" <td>18.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2014-05-17 15:15:00 UTC</td>\n",
" <td>-73.999550</td>\n",
" <td>40.760600</td>\n",
" <td>-73.999650</td>\n",
" <td>40.725220</td>\n",
" <td>1</td>\n",
" <td>5.57</td>\n",
" <td>0.00</td>\n",
" <td>31.0</td>\n",
" <td>33.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2014-04-30 16:45:10 UTC</td>\n",
" <td>-73.989434</td>\n",
" <td>40.756601</td>\n",
" <td>-73.949989</td>\n",
" <td>40.826892</td>\n",
" <td>1</td>\n",
" <td>6.20</td>\n",
" <td>0.00</td>\n",
" <td>24.5</td>\n",
" <td>31.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2013-04-09 09:39:13 UTC</td>\n",
" <td>-73.981443</td>\n",
" <td>40.763466</td>\n",
" <td>-74.010072</td>\n",
" <td>40.704927</td>\n",
" <td>1</td>\n",
" <td>6.50</td>\n",
" <td>0.00</td>\n",
" <td>24.5</td>\n",
" <td>30.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2014-04-19 14:08:46 UTC</td>\n",
" <td>-73.964716</td>\n",
" <td>40.773071</td>\n",
" <td>-73.997511</td>\n",
" <td>40.697289</td>\n",
" <td>1</td>\n",
" <td>8.70</td>\n",
" <td>0.00</td>\n",
" <td>26.5</td>\n",
" <td>33.75</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pickup_datetime pickup_longitude pickup_latitude \\\n",
"0 2014-12-08 21:50:00 UTC -73.994802 40.720612 \n",
"1 2012-03-04 00:57:00 UTC -74.005625 40.734517 \n",
"2 2013-12-09 15:03:00 UTC -73.990950 40.749772 \n",
"3 2011-12-03 10:28:00 UTC -73.998822 40.680933 \n",
"4 2009-03-28 20:30:35 UTC -73.973926 40.757725 \n",
"5 2009-08-23 23:59:22 UTC -73.783319 40.648480 \n",
"6 2014-05-17 15:15:00 UTC -73.999550 40.760600 \n",
"7 2014-04-30 16:45:10 UTC -73.989434 40.756601 \n",
"8 2013-04-09 09:39:13 UTC -73.981443 40.763466 \n",
"9 2014-04-19 14:08:46 UTC -73.964716 40.773071 \n",
"\n",
" dropoff_longitude dropoff_latitude passenger_count trip_distance \\\n",
"0 -73.949125 40.668893 1 5.33 \n",
"1 -73.952492 40.725197 1 7.33 \n",
"2 -73.870807 40.774070 1 9.44 \n",
"3 -73.968960 40.757878 1 8.28 \n",
"4 -73.981695 40.761591 1 0.50 \n",
"5 -73.893649 40.646566 1 8.30 \n",
"6 -73.999650 40.725220 1 5.57 \n",
"7 -73.949989 40.826892 1 6.20 \n",
"8 -74.010072 40.704927 1 6.50 \n",
"9 -73.997511 40.697289 1 8.70 \n",
"\n",
" tolls_amount fare_amount total_amount \n",
"0 0.00 22.0 27.00 \n",
"1 0.00 20.5 23.50 \n",
"2 5.33 29.0 34.83 \n",
"3 0.00 20.9 25.58 \n",
"4 0.00 4.6 4.60 \n",
"5 0.00 18.9 18.90 \n",
"6 0.00 31.0 33.50 \n",
"7 0.00 24.5 31.20 \n",
"8 0.00 24.5 30.00 \n",
"9 0.00 26.5 33.75 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We can slice Pandas dataframes as if they were arrays\n",
"trips[:10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3> Exploring data </h3>\n",
"\n",
"Let's explore this dataset and clean it up as necessary. We'll use the Python Seaborn package to visualize graphs and Pandas to do the slicing and filtering."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# TODO 4: Visualize your dataset using the Seaborn library.\n",
"# Plot the distance of the trip as X and the fare amount as Y.\n",
"ax = sns.regplot(\n",
" x=\"\",\n",
" y=\"\",\n",
" fit_reg=False,\n",
" ci=None,\n",
" truncate=True,\n",
" data=trips,\n",
")\n",
"ax.figure.set_size_inches(10, 8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hmm ... do you see something wrong with the data that needs addressing?\n",
"\n",
"It appears that we have a lot of invalid data that is being coded as zero distance and some fare amounts that are definitely illegitimate. Let's remove them from our analysis. We can do this by modifying the BigQuery query to keep only trips longer than zero miles and fare amounts that are at least the minimum cab fare ($2.50).\n",
"\n",
"Note the extra WHERE clauses."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Query complete after 0.00s: 100%|██████████| 1/1 [00:00<00:00, 825.49query/s] \n",
"Downloading: 100%|██████████| 10716/10716 [00:01<00:00, 8829.94rows/s]\n"
]
}
],
"source": [
"%%bigquery trips\n",
"SELECT\n",
" FORMAT_TIMESTAMP(\n",
" \"%Y-%m-%d %H:%M:%S %Z\", pickup_datetime) AS pickup_datetime,\n",
" pickup_longitude, pickup_latitude, \n",
" dropoff_longitude, dropoff_latitude,\n",
" passenger_count,\n",
" trip_distance,\n",
" tolls_amount,\n",
" fare_amount,\n",
" total_amount\n",
"FROM\n",
" `nyc-tlc.yellow.trips`\n",
"WHERE\n",
" ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), 100000)) = 1\n",
" # TODO 4a: Filter the data to only include non-zero distance trips and fares above $2.50\n",
" AND "
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10716\n"
]
}
],
"source": [
"print(len(trips))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = sns.regplot(\n",
" x=\"trip_distance\",\n",
" y=\"fare_amount\",\n",
" fit_reg=False,\n",
" ci=None,\n",
" truncate=True,\n",
" data=trips,\n",
")\n",
"ax.figure.set_size_inches(10, 8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What's up with the streaks around 45 dollars and 50 dollars? Those are fixed-amount rides from JFK and La Guardia airports into anywhere in Manhattan, i.e. to be expected. Let's list the data to make sure the values look reasonable.\n",
"\n",
"Let's also examine whether the toll amount is captured in the total amount."
]
},
{
"cell_type": "code",
"execution_count": 28,
"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>pickup_datetime</th>\n",
" <th>pickup_longitude</th>\n",
" <th>pickup_latitude</th>\n",
" <th>dropoff_longitude</th>\n",
" <th>dropoff_latitude</th>\n",
" <th>passenger_count</th>\n",
" <th>trip_distance</th>\n",
" <th>tolls_amount</th>\n",
" <th>fare_amount</th>\n",
" <th>total_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>2012-02-27 09:19:10 UTC</td>\n",
" <td>-73.874431</td>\n",
" <td>40.774011</td>\n",
" <td>-73.983967</td>\n",
" <td>40.744082</td>\n",
" <td>1</td>\n",
" <td>11.6</td>\n",
" <td>4.8</td>\n",
" <td>27.7</td>\n",
" <td>38.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pickup_datetime pickup_longitude pickup_latitude \\\n",
"31 2012-02-27 09:19:10 UTC -73.874431 40.774011 \n",
"\n",
" dropoff_longitude dropoff_latitude passenger_count trip_distance \\\n",
"31 -73.983967 40.744082 1 11.6 \n",
"\n",
" tolls_amount fare_amount total_amount \n",
"31 4.8 27.7 38.0 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tollrides = trips[trips[\"tolls_amount\"] > 0]\n",
"tollrides[tollrides[\"pickup_datetime\"] == \"2012-02-27 09:19:10 UTC\"]"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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>pickup_datetime</th>\n",
" <th>pickup_longitude</th>\n",
" <th>pickup_latitude</th>\n",
" <th>dropoff_longitude</th>\n",
" <th>dropoff_latitude</th>\n",
" <th>passenger_count</th>\n",
" <th>trip_distance</th>\n",
" <th>tolls_amount</th>\n",
" <th>fare_amount</th>\n",
" <th>total_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>2012-02-27 09:19:10 UTC</td>\n",
" <td>-73.972311</td>\n",
" <td>40.753067</td>\n",
" <td>-73.957389</td>\n",
" <td>40.817824</td>\n",
" <td>1</td>\n",
" <td>5.6</td>\n",
" <td>0.0</td>\n",
" <td>16.9</td>\n",
" <td>22.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7795</th>\n",
" <td>2012-02-27 09:19:10 UTC</td>\n",
" <td>-73.987582</td>\n",
" <td>40.725468</td>\n",
" <td>-74.016628</td>\n",
" <td>40.715534</td>\n",
" <td>1</td>\n",
" <td>2.8</td>\n",
" <td>0.0</td>\n",
" <td>12.1</td>\n",
" <td>15.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10537</th>\n",
" <td>2012-02-27 09:19:10 UTC</td>\n",
" <td>-74.015483</td>\n",
" <td>40.715279</td>\n",
" <td>-73.998045</td>\n",
" <td>40.756273</td>\n",
" <td>1</td>\n",
" <td>3.3</td>\n",
" <td>0.0</td>\n",
" <td>10.9</td>\n",
" <td>13.40</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pickup_datetime pickup_longitude pickup_latitude \\\n",
"45 2012-02-27 09:19:10 UTC -73.972311 40.753067 \n",
"7795 2012-02-27 09:19:10 UTC -73.987582 40.725468 \n",
"10537 2012-02-27 09:19:10 UTC -74.015483 40.715279 \n",
"\n",
" dropoff_longitude dropoff_latitude passenger_count trip_distance \\\n",
"45 -73.957389 40.817824 1 5.6 \n",
"7795 -74.016628 40.715534 1 2.8 \n",
"10537 -73.998045 40.756273 1 3.3 \n",
"\n",
" tolls_amount fare_amount total_amount \n",
"45 0.0 16.9 22.62 \n",
"7795 0.0 12.1 15.75 \n",
"10537 0.0 10.9 13.40 "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notollrides = trips[trips[\"tolls_amount\"] == 0]\n",
"notollrides[notollrides[\"pickup_datetime\"] == \"2012-02-27 09:19:10 UTC\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking at a few samples above, it should be clear that the total amount reflects fare amount, toll and tip somewhat arbitrarily -- this is because when customers pay cash, the tip is not known. So, we'll use the sum of fare_amount + tolls_amount as what needs to be predicted. Tips are discretionary and do not have to be included in our fare estimation tool.\n",
"\n",
"Let's also look at the distribution of values within the columns."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>pickup_longitude</th>\n",
" <th>pickup_latitude</th>\n",
" <th>dropoff_longitude</th>\n",
" <th>dropoff_latitude</th>\n",
" <th>passenger_count</th>\n",
" <th>trip_distance</th>\n",
" <th>tolls_amount</th>\n",
" <th>fare_amount</th>\n",
" <th>total_amount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>10716.000000</td>\n",
" <td>10716.000000</td>\n",
" <td>10716.000000</td>\n",
" <td>10716.000000</td>\n",
" <td>10716.000000</td>\n",
" <td>10716.000000</td>\n",
" <td>10716.000000</td>\n",
" <td>10716.000000</td>\n",
" <td>10716.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-72.602192</td>\n",
" <td>40.002372</td>\n",
" <td>-72.594838</td>\n",
" <td>40.002052</td>\n",
" <td>1.650056</td>\n",
" <td>2.856395</td>\n",
" <td>0.226428</td>\n",
" <td>11.109446</td>\n",
" <td>13.217078</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>9.982373</td>\n",
" <td>5.474670</td>\n",
" <td>10.004324</td>\n",
" <td>5.474648</td>\n",
" <td>1.283577</td>\n",
" <td>3.322024</td>\n",
" <td>1.135934</td>\n",
" <td>9.137710</td>\n",
" <td>10.953156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-74.258183</td>\n",
" <td>0.000000</td>\n",
" <td>-74.260472</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.000000</td>\n",
" <td>2.500000</td>\n",
" <td>2.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-73.992153</td>\n",
" <td>40.735936</td>\n",
" <td>-73.991566</td>\n",
" <td>40.734310</td>\n",
" <td>1.000000</td>\n",
" <td>1.040000</td>\n",
" <td>0.000000</td>\n",
" <td>6.000000</td>\n",
" <td>7.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>-73.981851</td>\n",
" <td>40.753264</td>\n",
" <td>-73.980373</td>\n",
" <td>40.752956</td>\n",
" <td>1.000000</td>\n",
" <td>1.770000</td>\n",
" <td>0.000000</td>\n",
" <td>8.500000</td>\n",
" <td>10.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>-73.967400</td>\n",
" <td>40.767340</td>\n",
" <td>-73.964142</td>\n",
" <td>40.767510</td>\n",
" <td>2.000000</td>\n",
" <td>3.160000</td>\n",
" <td>0.000000</td>\n",
" <td>12.500000</td>\n",
" <td>14.600000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.000000</td>\n",
" <td>41.366138</td>\n",
" <td>0.000000</td>\n",
" <td>41.366138</td>\n",
" <td>6.000000</td>\n",
" <td>42.800000</td>\n",
" <td>16.000000</td>\n",
" <td>179.000000</td>\n",
" <td>179.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pickup_longitude pickup_latitude dropoff_longitude dropoff_latitude \\\n",
"count 10716.000000 10716.000000 10716.000000 10716.000000 \n",
"mean -72.602192 40.002372 -72.594838 40.002052 \n",
"std 9.982373 5.474670 10.004324 5.474648 \n",
"min -74.258183 0.000000 -74.260472 0.000000 \n",
"25% -73.992153 40.735936 -73.991566 40.734310 \n",
"50% -73.981851 40.753264 -73.980373 40.752956 \n",
"75% -73.967400 40.767340 -73.964142 40.767510 \n",
"max 0.000000 41.366138 0.000000 41.366138 \n",
"\n",
" passenger_count trip_distance tolls_amount fare_amount \\\n",
"count 10716.000000 10716.000000 10716.000000 10716.000000 \n",
"mean 1.650056 2.856395 0.226428 11.109446 \n",
"std 1.283577 3.322024 1.135934 9.137710 \n",
"min 0.000000 0.010000 0.000000 2.500000 \n",
"25% 1.000000 1.040000 0.000000 6.000000 \n",
"50% 1.000000 1.770000 0.000000 8.500000 \n",
"75% 2.000000 3.160000 0.000000 12.500000 \n",
"max 6.000000 42.800000 16.000000 179.000000 \n",
"\n",
" total_amount \n",
"count 10716.000000 \n",
"mean 13.217078 \n",
"std 10.953156 \n",
"min 2.500000 \n",
"25% 7.300000 \n",
"50% 10.000000 \n",
"75% 14.600000 \n",
"max 179.000000 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trips.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright 2020 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n",
"http://www.apache.org/licenses/LICENSE-2.0\n",
"Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."
]
}
],
"metadata": {
"colab": {
"name": "Conchita_Linear_Regression_with_Python.ipynb",
"provenance": [],
"toc_visible": true
},
"environment": {
"name": "tf2-gpu.2-5.m76",
"type": "gcloud",
"uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-5:m76"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
rsterbentz/phys202-2015-work | assignments/assignment02/ProjectEuler2.ipynb | 1 | 2945 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Project Euler: Problem 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Each new term in the Fibonacci sequence is generated by adding the previous two terms. By starting with 0 and 1, the first 12 terms will be:\n",
"\n",
"0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ...\n",
"\n",
"By considering the terms in the Fibonacci sequence whose values do not exceed four million, find the sum of the even-valued terms."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811, 514229, 832040, 1346269, 2178309, 3524578]\n"
]
}
],
"source": [
"a = 0\n",
"b = 1\n",
"fib_max = 0\n",
"fib_list = [a, b]\n",
"while fib_max <= 4000000:\n",
" fib_max = a + b\n",
" if fib_max < 4000000:\n",
" fib_list.append(fib_max)\n",
" a = b\n",
" b = fib_max\n",
"print(fib_list)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 2, 8, 34, 144, 610, 2584, 10946, 46368, 196418, 832040, 3524578]\n"
]
}
],
"source": [
"even_fib = [x for x in fib_list if x % 2 == 0]\n",
"print(even_fib)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4613732\n"
]
}
],
"source": [
"print(sum(even_fib))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "e8afe8a5735f0fff949b706895f8583d",
"grade": true,
"grade_id": "projecteuler2",
"points": 10
}
},
"outputs": [],
"source": [
"# This cell will be used for grading, leave it at the end of the notebook."
]
}
],
"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 |
bbfamily/abu | abupy_ui/数据下载界面操作.ipynb | 1 | 1740 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 点击上方的运行按钮，启动回测操作界面，如下图所示：\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "54b6210ad8eb4103acf7880e2bb687f0"
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import widget_update_ui\n",
"widget_update_ui.show_ui()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[abu量化系统github地址](https://github.com/bbfamily/abu) (您的star是我的动力！)\n",
"\n",
"[更多阿布量化量化技术文章](http://www.abuquant.com/article)\n",
"\n",
"更多关于量化交易相关请阅读[《量化交易之路》](http://www.abuquant.com/books/quantify-trading-road.html)\n",
"\n",
"更多关于量化交易与机器学习相关请阅读[《机器学习之路》](http://www.abuquant.com/books/machine-learning-road.html)\n",
"\n",
"更多关于abu量化系统请关注微信公众号: abu_quant\n",
"\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
}
| gpl-3.0 |
namco1992/algorithms_in_python | algorithms/tree.ipynb | 1 | 9949 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tree\n",
"### 定义\n",
"一棵二叉树的定义如下。`key`可以存储任意的对象，亦即每棵树也可以是其他树的子树。"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a\n",
"None\n",
"b\n"
]
}
],
"source": [
"class BinaryTree():\n",
" def __init__(self, root_obj):\n",
" self.key = root_obj\n",
" self.left_child = None\n",
" self.right_child = None\n",
" \n",
" def insert_left(self, new_node):\n",
" # if the tree do not have a left child\n",
" # then create a node: one tree without children\n",
" if self.left_child is None:\n",
" self.left_child = BinaryTree(new_node)\n",
" # if there is a child, then concat the child\n",
" # under the node we inserted\n",
" else:\n",
" t = BinaryTree(new_node)\n",
" t.left_child = self.left_child\n",
" self.left_child = t\n",
"\n",
" def insert_right(self, new_node):\n",
" # if the tree do not have a right child\n",
" # then create a node: one tree without children\n",
" if self.right_child is None:\n",
" self.right_child = BinaryTree(new_node)\n",
" # if there is a child, then concat the child\n",
" # under the node we inserted\n",
" else:\n",
" t = BinaryTree(new_node)\n",
" t.right_child = self.right_child\n",
" self.right_child = t\n",
" \n",
" def get_right_child(self):\n",
" return self.right_child\n",
" \n",
" def get_left_child(self):\n",
" return self.left_child\n",
" \n",
" def set_root(self, obj):\n",
" self.key = obj\n",
" \n",
" def get_root(self):\n",
" return self.key\n",
"\n",
"r = BinaryTree('a')\n",
"print r.get_root()\n",
"print r.get_left_child()\n",
"r.insert_left('b')\n",
"print r.get_left_child().get_root()\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 遍历\n",
"1. 前序\n",
"2. 中序\n",
"3. 后序"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"root\n",
"l2\n",
"l1\n",
"r3\n",
"r2\n",
"r1\n"
]
}
],
"source": [
"def preorder(tree):\n",
" if tree:\n",
" print tree.get_root()\n",
" preorder(tree.get_left_child())\n",
" preorder(tree.get_right_child())\n",
"\n",
"def postorder(tree):\n",
" if tree:\n",
" postorder(tree.get_left_child())\n",
" postorder(tree.get_right_child())\n",
" print tree.get_root()\n",
"\n",
"def inorder(tree):\n",
" if tree:\n",
" inorder(tree.get_left_child())\n",
" print tree.get_root()\n",
" inorder(tree.get_right_child())\n",
"\n",
"r = BinaryTree('root')\n",
"r.insert_left('l1')\n",
"r.insert_left('l2')\n",
"r.insert_right('r1')\n",
"r.insert_right('r2')\n",
"r.get_left_child().insert_right('r3')\n",
"\n",
"\n",
"preorder(r)\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 二叉堆实现优先队列\n",
"\n",
"二叉堆是队列的一种实现方式。\n",
"\n",
"二叉堆可以用完全二叉树来实现。所谓完全二叉树（complete binary tree），有定义如下：\n",
"> A complete binary tree is a binary tree in which every level, except possibly the last, is completely filled, and all nodes are as far left as possible.\n",
"除叶节点外，所有层都是填满的，叶节点则按照从左至右的顺序填满。\n",
"\n",
"完全二叉树的一个重要性质：\n",
"\n",
"当以列表表示完全二叉树时，位置 p 的父节点，其 left child 位于 2p 位置，其 right child 位于 2p+1 的位置。\n",
"\n",
"为了满足使用列表表示的性质，列表中第一个位置`list[0]`由 0 填充，树从`list[1]`开始。\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"### Operations\n",
"- BinaryHeap() creates a new, empty, binary heap.\n",
"- insert(k) adds a new item to the heap.\n",
"- findMin() returns the item with the minimum key value, leaving item in the heap.\n",
"- delMin() returns the item with the minimum key value, removing the item from the heap.\n",
"- isEmpty() returns true if the heap is empty, false otherwise.\n",
"- size() returns the number of items in the heap.\n",
"- buildHeap(list) builds a new heap from a list of keys.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class BinHeap(object):\n",
" def __init__(self):\n",
" self.heap_list = [0]\n",
" self.current_size = 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 二叉搜索树 Binary Search Trees\n",
"\n",
"其性质与字典非常相近。\n",
"\n",
"### Operations\n",
"- Map() Create a new, empty map.\n",
"- put(key,val) Add a new key-value pair to the map. If the key is already in the map then replace the old value with the new value.\n",
"- get(key) Given a key, return the value stored in the map or None otherwise.\n",
"- del Delete the key-value pair from the map using a statement of the form del map[key].\n",
"- len() Return the number of key-value pairs stored in the map.\n",
"- in Return True for a statement of the form key in map, if the given key is in the map."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class BinarySearchTree(object):\n",
" def __init__(self):\n",
" self.root = None\n",
" self.size = 0\n",
" \n",
" def length(self):\n",
" return self.size\n",
" \n",
" def __len__(self):\n",
" return self.size\n",
" \n",
" def __iter__(self):\n",
" return self.root.__iter__()\n",
" \n",
" def put(self, key, val):\n",
" if self.root:\n",
" self._put(key, val, self.root)\n",
" else:\n",
" self.root = TreeNode(key, val)\n",
" self.size += 1\n",
" \n",
" def _put(key, val, current_node):\n",
" if key < current_node:\n",
" if current_node.has_left_child():\n",
" _put(key, val, current_node.left_child)\n",
" else:\n",
" current_node.left_child = TreeNode(key, val, parent=current_node)\n",
" else:\n",
" if current_node.has_right_child():\n",
" _put(key, val, current_node.right_child)\n",
" else:\n",
" current_node.right_child = TreeNode(key, val, parent=current_node)\n",
" \n",
" def __setitem__(self, k, v):\n",
" self.put(k, v)\n",
"\n",
"class TreeNode(object):\n",
" def __init__(self, key, val, left=None, right=None, parent=None):\n",
" self.key = key\n",
" self.payload = val\n",
" self.left_child = left\n",
" self.right_child = right\n",
" self.parent = parent\n",
" \n",
" def has_left_child(self):\n",
" return self.left_child\n",
" \n",
" def has_right_child(self):\n",
" return self.right_child\n",
" \n",
" def is_root(self):\n",
" return not self.parent\n",
" \n",
" def is_leaf(self):\n",
" return not (self.right_child or self.left_child)\n",
" \n",
" def has_any_children(self):\n",
" return self.right_child or self.left_child\n",
" \n",
" def has_both_children(self):\n",
" return self.right_child and self.right_child\n",
" \n",
" def replace_node_data(self, key, value, lc, rc):\n",
" self.key = key\n",
" self.payload = value\n",
" self.left_child = lc\n",
" self.right_child = rc\n",
" if self.has_left_child():\n",
" self.left_child.parent = self\n",
" if self.has_right_child():\n",
" self.right_child.parent = self\n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 平衡二叉搜索树 Balanced Binary Search Tree\n",
"\n",
"又名 AVL 树。避免出现最坏情况下 O(n) 的复杂度。AVL 的搜索复杂度稳定在 O(logN)。\n",
"```\n",
"balanceFactor=height(leftSubTree)−height(rightSubTree)\n",
"```\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
opalytics/opalytics-ticdat | examples/amplpy/netflow/netflow_other_data_sources.ipynb | 1 | 21190 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using ticdat to build modular engines\n",
"\n",
"The goal of the `ticdat` package is to facilitate solve engines that are modular and robust. For example, the multicommodity `netflow.py` engine can read and write from a variety of file types when run from the the command line. It can also be run from a Python script that contains embedded static data, or from a script that reads and writes from a system-of-record data source such as an ERP system. \n",
"\n",
"With regards to the latter, we should note that Python is one of the most popular \"glue\" [languages](https://en.wikipedia.org/wiki/Scripting_language#Glue_languages). The market has recognized that Python scripts are easy to write, manage data with intuitive programming syntax, and can be connected to nearly any data source.\n",
"\n",
"The `ticdat` package can easily be used in any Python glue script. One way to do this is to exploit `ticdat`'s ability to recognize data tables as list-of-lists. The inner lists contain data values in the field order defined by by the `PanDatFactory` (i.e. `netflow.input_schema`).\n",
"\n",
"For example, suppose the `netflow` engine needs to connect to an Oracle database for a daily automated solve. The integration engineer can use the `cx_Oracle` [package](https://oracle.github.io/python-cx_Oracle/) (or something equivalent) to turn system data into a list-of-lists for each input table. These data structures can then be used to create a `PanDat` object that can be passed as input data to `netflow.solve`. The solution `PanDat` object returned by `netflow.solve` can then be converted back into a list-of-lists representation of each solution report table. (The list-of-lists strategy is just one approach. It might make sense to convert system-of-record data into `pandas.DataFrame` objects, and then use these `DataFrame`s to build the `PanDat` object.)\n",
"\n",
"We demonstrate this approach without explicit references to `cx_Oracle`. By demonstrating that `ticdat` is compatible with list-of-list/`DataFrame` table representations we thus show that `ticdat` is compatible with any data source that can be connected to Python, and also with human readable static data."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"commodities = [['Pencils', 0.5], ['Pens', 0.2125]]\n",
"\n",
"# a one column table can just be a simple list \n",
"nodes = ['Boston', 'Denver', 'Detroit', 'New York', 'Seattle']\n",
"\n",
"cost = [['Pencils', 'Denver', 'Boston', 10.0],\n",
" ['Pencils', 'Denver', 'New York', 10.0],\n",
" ['Pencils', 'Denver', 'Seattle', 7.5],\n",
" ['Pencils', 'Detroit', 'Boston', 2.5],\n",
" ['Pencils', 'Detroit', 'New York', 5.0],\n",
" ['Pencils', 'Detroit', 'Seattle', 15.0],\n",
" ['Pens', 'Denver', 'Boston', 15.0],\n",
" ['Pens', 'Denver', 'New York', 17.5],\n",
" ['Pens', 'Denver', 'Seattle', 7.5],\n",
" ['Pens', 'Detroit', 'Boston', 5.0],\n",
" ['Pens', 'Detroit', 'New York', 5.0],\n",
" ['Pens', 'Detroit', 'Seattle', 20.0]]\n",
"\n",
"inflow = [['Pencils', 'Boston', -200],\n",
" ['Pencils', 'Denver', 240],\n",
" ['Pencils', 'Detroit', 200],\n",
" ['Pencils', 'New York', -200],\n",
" ['Pencils', 'Seattle', -40],\n",
" ['Pens', 'Boston', -160],\n",
" ['Pens', 'Denver', 160],\n",
" ['Pens', 'Detroit', 240],\n",
" ['Pens', 'New York', -120],\n",
" ['Pens', 'Seattle', -120]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An integration engineer might prefer to copy system-of-records data into `pandas.DataFrame` objects. Note that `pandas` is itself [capable](https://stackoverflow.com/questions/35781580/cx-oracle-import-data-from-oracle-to-pandas-dataframe) of reading directly from various SQL databases, although it usually needs a supporting package like `cx_Oracle`."
]
},
{
"cell_type": "code",
"execution_count": 2,
"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>Destination</th>\n",
" <th>Source</th>\n",
" <th>Capacity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Boston</td>\n",
" <td>Denver</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>New York</td>\n",
" <td>Denver</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Seattle</td>\n",
" <td>Denver</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Boston</td>\n",
" <td>Detroit</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>New York</td>\n",
" <td>Detroit</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Seattle</td>\n",
" <td>Detroit</td>\n",
" <td>120</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Destination Source Capacity\n",
"0 Boston Denver 120\n",
"1 New York Denver 120\n",
"2 Seattle Denver 120\n",
"3 Boston Detroit 100\n",
"4 New York Detroit 80\n",
"5 Seattle Detroit 120"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pandas import DataFrame\n",
"arcs = DataFrame({\"Source\": [\"Denver\", \"Denver\", \"Denver\", \"Detroit\", \"Detroit\", \"Detroit\",], \n",
" \"Destination\": [\"Boston\", \"New York\", \"Seattle\", \"Boston\", \"New York\", \n",
" \"Seattle\"], \n",
" \"Capacity\": [120, 120, 120, 100, 80, 120]})\n",
"# PanDatFactory doesn't require the fields to be in order so long as the field names are supplied\n",
"arcs = arcs[[\"Destination\", \"Source\", \"Capacity\"]]\n",
"arcs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we create a `PanDat` input data object from the list-of-lists/`DataFrame` representations."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"env: PATH=PATH:/Users/petercacioppi/ampl/ampl\n"
]
}
],
"source": [
"%env PATH = PATH:/Users/petercacioppi/ampl/ampl\n",
"from netflow import input_schema, solve, solution_schema\n",
"dat = input_schema.PanDat(commodities=commodities, nodes=nodes, cost=cost, arcs=arcs, \n",
" inflow=inflow)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now create a `PanDat` solution data object by calling `solve`."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gurobi 8.0.0: optimal solution; objective 5627.5\n",
"3 simplex iterations\n"
]
}
],
"source": [
"sln = solve(dat)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now create a list-of-lists representation of the solution data object."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"sln_lists = {t: list(map(list, getattr(sln, t).itertuples(index=False))) \n",
" for t in solution_schema.all_tables}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we demonstrate that `sln_lists` is a dictionary mapping table name to list-of-lists of solution report data."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"**\n",
"Solution Table flow\n",
"**\n",
"[['Pencils', 'Denver', 'Boston', 51.0],\n",
" ['Pencils', 'Denver', 'New York', 149.0],\n",
" ['Pencils', 'Denver', 'Seattle', 40.0],\n",
" ['Pencils', 'Detroit', 'Boston', 149.0],\n",
" ['Pencils', 'Detroit', 'New York', 51.0],\n",
" ['Pens', 'Denver', 'Boston', 40.0],\n",
" ['Pens', 'Denver', 'Seattle', 120.0],\n",
" ['Pens', 'Detroit', 'Boston', 120.0],\n",
" ['Pens', 'Detroit', 'New York', 120.0]]\n",
"\n",
"\n",
"**\n",
"Solution Table parameters\n",
"**\n",
"[['Total Cost', 5627.5]]\n"
]
}
],
"source": [
"import pprint\n",
"for sln_table_name, sln_table_data in sln_lists.items():\n",
" print \"\\n\\n**\\nSolution Table %s\\n**\"%sln_table_name\n",
" pprint.pprint(sln_table_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Of course the solution data object itself contains `DataFrames`, if that representation is preferred."
]
},
{
"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>Commodity</th>\n",
" <th>Source</th>\n",
" <th>Destination</th>\n",
" <th>Quantity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Pencils</td>\n",
" <td>Denver</td>\n",
" <td>Boston</td>\n",
" <td>51.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Pencils</td>\n",
" <td>Denver</td>\n",
" <td>New York</td>\n",
" <td>149.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Pencils</td>\n",
" <td>Denver</td>\n",
" <td>Seattle</td>\n",
" <td>40.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Pencils</td>\n",
" <td>Detroit</td>\n",
" <td>Boston</td>\n",
" <td>149.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Pencils</td>\n",
" <td>Detroit</td>\n",
" <td>New York</td>\n",
" <td>51.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Pens</td>\n",
" <td>Denver</td>\n",
" <td>Boston</td>\n",
" <td>40.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Pens</td>\n",
" <td>Denver</td>\n",
" <td>Seattle</td>\n",
" <td>120.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Pens</td>\n",
" <td>Detroit</td>\n",
" <td>Boston</td>\n",
" <td>120.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Pens</td>\n",
" <td>Detroit</td>\n",
" <td>New York</td>\n",
" <td>120.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Commodity Source Destination Quantity\n",
"0 Pencils Denver Boston 51.0\n",
"1 Pencils Denver New York 149.0\n",
"2 Pencils Denver Seattle 40.0\n",
"3 Pencils Detroit Boston 149.0\n",
"4 Pencils Detroit New York 51.0\n",
"5 Pens Denver Boston 40.0\n",
"6 Pens Denver Seattle 120.0\n",
"7 Pens Detroit Boston 120.0\n",
"8 Pens Detroit New York 120.0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sln.flow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using ticdat to build robust engines\n",
"\n",
"The preceding section demonstrated how we can use `ticdat` to build modular engines. We now demonstrate how we can use `ticdat` to build engines that check `solve` pre-conditions, and are thus robust with respect to data integrity problems.\n",
"\n",
"First, lets violate our (somewhat artificial) rule that the commodity volume must be positive."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Pencils</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Pens</td>\n",
" <td>0.2125</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Volume\n",
"0 Pencils 0.0000\n",
"1 Pens 0.2125"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dat.commodities.loc[dat.commodities[\"Name\"] == \"Pencils\", \"Volume\"] = 0\n",
"dat.commodities"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `input_schema` can not only flag this problem, but give us a useful data structure to examine."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{TableField(table='commodities', field='Volume'): Name Volume\n",
" 0 Pencils 0.0}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_type_failures = input_schema.find_data_type_failures(dat)\n",
"data_type_failures"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Pencils</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Volume\n",
"0 Pencils 0.0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_type_failures['commodities', 'Volume']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, lets add a Cost record for a non-existent commodity and see how `input_schema` flags this problem."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{('cost',\n",
" 'commodities',\n",
" ('Commodity', 'Name')): Commodity Source Destination Cost\n",
" 12 Crayons Detroit Seattle 10.0}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dat.cost = dat.cost.append({'Commodity':'Crayons', 'Source': 'Detroit', \n",
" 'Destination': 'Seattle', 'Cost': 10}, \n",
" ignore_index=True)\n",
"fk_failures = input_schema.find_foreign_key_failures(dat, verbosity=\"Low\")\n",
"fk_failures"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>Commodity</th>\n",
" <th>Source</th>\n",
" <th>Destination</th>\n",
" <th>Cost</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Crayons</td>\n",
" <td>Detroit</td>\n",
" <td>Seattle</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Commodity Source Destination Cost\n",
"12 Crayons Detroit Seattle 10.0"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fk_failures['cost', 'commodities', ('Commodity', 'Name')]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In real life, data integrity failures can typically be grouped into a small number of categories. However, the number of failures in each category might be quite large. `ticdat` creates data structures for each of these categories that can themselves be examined programmatically. As a result, an analyst can leverage the power of Python and `pandas` to detect patterns in the data integrity problems."
]
}
],
"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": 1
}
| bsd-2-clause |
keras-team/keras-io | examples/generative/ipynb/vae.ipynb | 1 | 9879 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"# Variational AutoEncoder\n",
"\n",
"**Author:** [fchollet](https://twitter.com/fchollet)<br>\n",
"**Date created:** 2020/05/03<br>\n",
"**Last modified:** 2020/05/03<br>\n",
"**Description:** Convolutional Variational AutoEncoder (VAE) trained on MNIST digits."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Create a sampling layer"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"\n",
"class Sampling(layers.Layer):\n",
" \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n",
"\n",
" def call(self, inputs):\n",
" z_mean, z_log_var = inputs\n",
" batch = tf.shape(z_mean)[0]\n",
" dim = tf.shape(z_mean)[1]\n",
" epsilon = tf.keras.backend.random_normal(shape=(batch, dim))\n",
" return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Build the encoder"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"latent_dim = 2\n",
"\n",
"encoder_inputs = keras.Input(shape=(28, 28, 1))\n",
"x = layers.Conv2D(32, 3, activation=\"relu\", strides=2, padding=\"same\")(encoder_inputs)\n",
"x = layers.Conv2D(64, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n",
"x = layers.Flatten()(x)\n",
"x = layers.Dense(16, activation=\"relu\")(x)\n",
"z_mean = layers.Dense(latent_dim, name=\"z_mean\")(x)\n",
"z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n",
"z = Sampling()([z_mean, z_log_var])\n",
"encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name=\"encoder\")\n",
"encoder.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Build the decoder"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"latent_inputs = keras.Input(shape=(latent_dim,))\n",
"x = layers.Dense(7 * 7 * 64, activation=\"relu\")(latent_inputs)\n",
"x = layers.Reshape((7, 7, 64))(x)\n",
"x = layers.Conv2DTranspose(64, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n",
"x = layers.Conv2DTranspose(32, 3, activation=\"relu\", strides=2, padding=\"same\")(x)\n",
"decoder_outputs = layers.Conv2DTranspose(1, 3, activation=\"sigmoid\", padding=\"same\")(x)\n",
"decoder = keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n",
"decoder.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Define the VAE as a `Model` with a custom `train_step`"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"\n",
"class VAE(keras.Model):\n",
" def __init__(self, encoder, decoder, **kwargs):\n",
" super(VAE, self).__init__(**kwargs)\n",
" self.encoder = encoder\n",
" self.decoder = decoder\n",
" self.total_loss_tracker = keras.metrics.Mean(name=\"total_loss\")\n",
" self.reconstruction_loss_tracker = keras.metrics.Mean(\n",
" name=\"reconstruction_loss\"\n",
" )\n",
" self.kl_loss_tracker = keras.metrics.Mean(name=\"kl_loss\")\n",
"\n",
" @property\n",
" def metrics(self):\n",
" return [\n",
" self.total_loss_tracker,\n",
" self.reconstruction_loss_tracker,\n",
" self.kl_loss_tracker,\n",
" ]\n",
"\n",
" def train_step(self, data):\n",
" with tf.GradientTape() as tape:\n",
" z_mean, z_log_var, z = self.encoder(data)\n",
" reconstruction = self.decoder(z)\n",
" reconstruction_loss = tf.reduce_mean(\n",
" tf.reduce_sum(\n",
" keras.losses.binary_crossentropy(data, reconstruction), axis=(1, 2)\n",
" )\n",
" )\n",
" kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n",
" kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))\n",
" total_loss = reconstruction_loss + kl_loss\n",
" grads = tape.gradient(total_loss, self.trainable_weights)\n",
" self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n",
" self.total_loss_tracker.update_state(total_loss)\n",
" self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n",
" self.kl_loss_tracker.update_state(kl_loss)\n",
" return {\n",
" \"loss\": self.total_loss_tracker.result(),\n",
" \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n",
" \"kl_loss\": self.kl_loss_tracker.result(),\n",
" }\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Train the VAE"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"(x_train, _), (x_test, _) = keras.datasets.mnist.load_data()\n",
"mnist_digits = np.concatenate([x_train, x_test], axis=0)\n",
"mnist_digits = np.expand_dims(mnist_digits, -1).astype(\"float32\") / 255\n",
"\n",
"vae = VAE(encoder, decoder)\n",
"vae.compile(optimizer=keras.optimizers.Adam())\n",
"vae.fit(mnist_digits, epochs=30, batch_size=128)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Display a grid of sampled digits"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"def plot_latent_space(vae, n=30, figsize=15):\n",
" # display a n*n 2D manifold of digits\n",
" digit_size = 28\n",
" scale = 1.0\n",
" figure = np.zeros((digit_size * n, digit_size * n))\n",
" # linearly spaced coordinates corresponding to the 2D plot\n",
" # of digit classes in the latent space\n",
" grid_x = np.linspace(-scale, scale, n)\n",
" grid_y = np.linspace(-scale, scale, n)[::-1]\n",
"\n",
" for i, yi in enumerate(grid_y):\n",
" for j, xi in enumerate(grid_x):\n",
" z_sample = np.array([[xi, yi]])\n",
" x_decoded = vae.decoder.predict(z_sample)\n",
" digit = x_decoded[0].reshape(digit_size, digit_size)\n",
" figure[\n",
" i * digit_size : (i + 1) * digit_size,\n",
" j * digit_size : (j + 1) * digit_size,\n",
" ] = digit\n",
"\n",
" plt.figure(figsize=(figsize, figsize))\n",
" start_range = digit_size // 2\n",
" end_range = n * digit_size + start_range\n",
" pixel_range = np.arange(start_range, end_range, digit_size)\n",
" sample_range_x = np.round(grid_x, 1)\n",
" sample_range_y = np.round(grid_y, 1)\n",
" plt.xticks(pixel_range, sample_range_x)\n",
" plt.yticks(pixel_range, sample_range_y)\n",
" plt.xlabel(\"z[0]\")\n",
" plt.ylabel(\"z[1]\")\n",
" plt.imshow(figure, cmap=\"Greys_r\")\n",
" plt.show()\n",
"\n",
"\n",
"plot_latent_space(vae)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Display how the latent space clusters different digit classes"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"\n",
"def plot_label_clusters(vae, data, labels):\n",
" # display a 2D plot of the digit classes in the latent space\n",
" z_mean, _, _ = vae.encoder.predict(data)\n",
" plt.figure(figsize=(12, 10))\n",
" plt.scatter(z_mean[:, 0], z_mean[:, 1], c=labels)\n",
" plt.colorbar()\n",
" plt.xlabel(\"z[0]\")\n",
" plt.ylabel(\"z[1]\")\n",
" plt.show()\n",
"\n",
"\n",
"(x_train, y_train), _ = keras.datasets.mnist.load_data()\n",
"x_train = np.expand_dims(x_train, -1).astype(\"float32\") / 255\n",
"\n",
"plot_label_clusters(vae, x_train, y_train)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "vae",
"private_outputs": false,
"provenance": [],
"toc_visible": true
},
"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": 0
} | apache-2.0 |
MathiasRiechert/BigDataPapers | 1-number of papers over time/Creating overview bar-plots.ipynb | 2 | 38677 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Number of papers over time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## data source\n",
"We load the data from the Competence Centre for Bibliometrics: http://www.bibliometrie.info/.\n",
"They licence access to the Web of Science and Scopus bibliometric databases, spanning a high proportion of all peer-reviewed research literature. The Competence Centre for Bibliometrics further processes both databases' data, so that it can be queried with SQL."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## load libraries:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import cx_Oracle #ensure that OS, InstantClient (Basic, ODBC, SDK) and cx_Oracle are all 64 bit. Install with \"pip install cx_Oracle\". Add link to InstantClient in Path variable!\n",
"import pandas as pd\n",
"import re\n",
"import plotly.plotly as py\n",
"import plotly.graph_objs as go"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## set parameter"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#parameter:\n",
"searchterm=\"big data\" #lowecase!\n",
"colorlist=[\"#01be70\",\"#586bd0\",\"#c0aa12\",\"#0183e6\",\"#f69234\",\"#0095e9\",\"#bd8600\",\"#007bbe\",\"#bb7300\",\"#63bcfc\",\"#a84a00\",\"#01bedb\",\"#82170e\",\"#00c586\",\"#a22f1f\",\"#3fbe57\",\"#3e4681\",\"#9bc246\",\"#9a9eec\",\"#778f00\",\"#00aad9\",\"#fc9e5e\",\"#01aec1\",\"#832c1e\",\"#55c99a\",\"#dd715b\",\"#017c1c\",\"#ff9b74\",\"#009556\",\"#83392a\",\"#00b39b\",\"#8e5500\",\"#50a7c6\",\"#f4a268\",\"#02aca7\",\"#532b00\",\"#67c4bd\",\"#5e5500\",\"#f0a18f\",\"#007229\",\"#d2b073\",\"#005d3f\",\"#a5be6b\",\"#2a4100\",\"#8cb88c\",\"#2f5c00\",\"#007463\",\"#5b7200\",\"#787c48\",\"#3b7600\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## load data from SQL database:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dsn_tns=cx_Oracle.makedsn('127.0.0.1','6025',service_name='bibliodb01.fiz.karlsruhe') #due to licence requirements,\n",
"# access is only allowed for members of the competence center of bibliometric and cooperation partners. You can still \n",
"# continue with the resulting csv below.\n",
" #open connection:\n",
"db=cx_Oracle.connect(<username>, <password>, dsn_tns)\n",
"print(db.version)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#%% define sql-query function:\n",
"def read_query(connection, query):\n",
" cursor = connection.cursor()\n",
" try:\n",
" cursor.execute( query )\n",
" names = [ x[0] for x in cursor.description]\n",
" rows = cursor.fetchall()\n",
" return pd.DataFrame( rows, columns=names)\n",
" finally:\n",
" if cursor is not None:\n",
" cursor.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#%% load paper titles from WOSdb:\n",
"database=\"wos_B_2016\" \n",
" \n",
"command=\"\"\"SELECT DISTINCT(ARTICLE_TITLE), PUBYEAR \n",
" FROM \"\"\"+database+\"\"\".KEYWORDS, \"\"\"+database+\"\"\".ITEMS_KEYWORDS, \"\"\"+database+\"\"\".ITEMS \n",
" WHERE\n",
" \"\"\"+database+\"\"\".ITEMS_KEYWORDS.FK_KEYWORDS=\"\"\"+database+\"\"\".KEYWORDS.PK_KEYWORDS\n",
" AND \"\"\"+database+\"\"\".ITEMS.PK_ITEMS=\"\"\"+database+\"\"\".ITEMS_KEYWORDS.FK_ITEMS \n",
" AND (lower(\"\"\"+database+\"\"\".KEYWORDS.KEYWORD) LIKE '%\"\"\"+searchterm+\"\"\"%' OR lower(ARTICLE_TITLE) LIKE '%\"\"\"+searchterm+\"\"\"%')\n",
"\"\"\"\n",
"\n",
"dfWOS=read_query(db,command)\n",
"dfWOS['wos']=True #to make the source identifyable\n",
"dfWOS.to_csv(\"all_big_data_titles_year_wos.csv\", sep=';')\n",
"\n",
"\n",
"#%% load paper titles from SCOPUSdb:\n",
"database=\"SCOPUS_B_2016\" \n",
" \n",
"command=\"\"\"SELECT DISTINCT(ARTICLE_TITLE), PUBYEAR \n",
" FROM \"\"\"+database+\"\"\".KEYWORDS, \"\"\"+database+\"\"\".ITEMS_KEYWORDS, \"\"\"+database+\"\"\".ITEMS \n",
" WHERE\n",
" \"\"\"+database+\"\"\".ITEMS_KEYWORDS.FK_KEYWORDS=\"\"\"+database+\"\"\".KEYWORDS.PK_KEYWORDS\n",
" AND \"\"\"+database+\"\"\".ITEMS.PK_ITEMS=\"\"\"+database+\"\"\".ITEMS_KEYWORDS.FK_ITEMS \n",
" AND (lower(\"\"\"+database+\"\"\".KEYWORDS.KEYWORD) LIKE '%\"\"\"+searchterm+\"\"\"%' OR lower(ARTICLE_TITLE) LIKE '%\"\"\"+searchterm+\"\"\"%')\n",
"\"\"\"\n",
"\n",
"dfSCOPUS=read_query(db,command)\n",
"dfSCOPUS['scopus']=True #to make the source identifyable\n",
"dfSCOPUS.to_csv(\"all_big_data_titles_year_scopus.csv\", sep=';')\n",
"\n",
"#this takes some time, we will work with the exported CSV from here on"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## merging data"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ARTICLE_TITLE</th>\n",
" <th>PUBYEAR_y</th>\n",
" <th>wos</th>\n",
" <th>scopus</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Big Data with Cloud Computing: an insight on t...</td>\n",
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" </tr>\n",
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" <td>Understanding Democracy and Development Traps ...</td>\n",
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" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Psycho-Informatics: Big Data shaping modern ps...</td>\n",
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" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
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" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Context-aware Task Allocation for Fast Paralle...</td>\n",
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" <td>True</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>8</th>\n",
" <td>Improving China's Corporate Governance Within ...</td>\n",
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" <td>True</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>9</th>\n",
" <td>Big Data and Predictive Analytics in ERP Syste...</td>\n",
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" <td>True</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>10</th>\n",
" <td>Re-Stream: Real-time and energy-efficient reso...</td>\n",
" <td>2015.0</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>BIG DATA IN SURVEY RESEARCH</td>\n",
" <td>2015.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Models and Data Sources Used in Systems Medici...</td>\n",
" <td>2016.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Big data and precision</td>\n",
" <td>2015.0</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>IoT-Security approach analysis for the novel n...</td>\n",
" <td>2014.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>A meeting report from the 2013 GARNet workshop...</td>\n",
" <td>2015.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Learning methodologies for wireless big data n...</td>\n",
" <td>2016.0</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Reducing Data Dimensions for Systems Engineeri...</td>\n",
" <td>2014.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Twitter Streams Fuel Big Data Approaches to He...</td>\n",
" <td>2015.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>THE LATENT STATE HAZARD MODEL, WITH APPLICATIO...</td>\n",
" <td>2015.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Deploying and Managing a Network of Autonomous...</td>\n",
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" <td>True</td>\n",
" <td>NaN</td>\n",
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" <th>21</th>\n",
" <td>The Person-Event Data Environment: leveraging ...</td>\n",
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" <td>True</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>22</th>\n",
" <td>A secure and scalable storage system for aggre...</td>\n",
" <td>2015.0</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>MaRDiGraS: Simplified Building of Reachability...</td>\n",
" <td>2013.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Power System Disaster-Mitigating Dispatch Plat...</td>\n",
" <td>2014.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>A k-anonymity Method based on SEM (Search Engi...</td>\n",
" <td>2013.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Philosophical Reflections on Data</td>\n",
" <td>2014.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>A Risk and Benefits Behavioral Model to Assess...</td>\n",
" <td>2013.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Complications of Laryngeal Masks in Children B...</td>\n",
" <td>2013.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>From social data mining to forecasting socio-e...</td>\n",
" <td>2011.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9449</th>\n",
" <td>Big data analysis and data velocity</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9450</th>\n",
" <td>I/O characteristics and implications of big da...</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9451</th>\n",
" <td>Secure distribution of big data based on bitto...</td>\n",
" <td>2013.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9452</th>\n",
" <td>Modern aspects in development of branch applic...</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9453</th>\n",
" <td>Multi-strategy based sina microblog data acqui...</td>\n",
" <td>2014.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9454</th>\n",
" <td>A novel Cp-Tree-based co-located classifier fo...</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9455</th>\n",
" <td>Application of big data technology in support ...</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9456</th>\n",
" <td>Real-time effective framework for unstructured...</td>\n",
" <td>2013.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9457</th>\n",
" <td>Big Data-Security and Privacy</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9458</th>\n",
" <td>Research on public opinion based on Big Data</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9459</th>\n",
" <td>Locally refined splines representation for geo...</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9460</th>\n",
" <td>Big data study for coping with stress</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9461</th>\n",
" <td>Digital Data Grows into Big Data</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9462</th>\n",
" <td>SAW classification algorithm for Chinese text ...</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9463</th>\n",
" <td>Interactive e-science cyberinfrastructure for ...</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9464</th>\n",
" <td>Public policy considerations for data-driven i...</td>\n",
" <td>2013.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9465</th>\n",
" <td>The performance of MapReduce over the varying ...</td>\n",
" <td>2013.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9466</th>\n",
" <td>Understanding library user engagement strategi...</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9467</th>\n",
" <td>Twitter Mining for Discovery, Prediction and C...</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9468</th>\n",
" <td>Process optimization and monitoring along big ...</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9469</th>\n",
" <td>RSenter: Terms mining tool from unstructured d...</td>\n",
" <td>2013.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9470</th>\n",
" <td>Resource management in cloud federation using ...</td>\n",
" <td>2014.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9471</th>\n",
" <td>Design and implementation of a dynamic educati...</td>\n",
" <td>2014.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9472</th>\n",
" <td>Big data for cyber physical systems an analysi...</td>\n",
" <td>2014.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9473</th>\n",
" <td>Designing a big data processing platform for a...</td>\n",
" <td>2013.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9474</th>\n",
" <td>Potential and Pitfalls for Big Data in Health ...</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9475</th>\n",
" <td>GridKa school - Teaching information technolog...</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9476</th>\n",
" <td>A survey on PCM-based big data storage and man...</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9477</th>\n",
" <td>A distributed file system over heterogeneous s...</td>\n",
" <td>2015.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9478</th>\n",
" <td>Adaptive collaborative filtering based on scal...</td>\n",
" <td>2016.0</td>\n",
" <td>NaN</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>9479 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" ARTICLE_TITLE PUBYEAR_y wos \\\n",
"0 Big Data with Cloud Computing: an insight on t... 2014.0 True \n",
"1 Understanding Democracy and Development Traps ... 2015.0 True \n",
"2 Psycho-Informatics: Big Data shaping modern ps... 2014.0 True \n",
"3 Keywords co-occurrence mapping knowledge domai... 2015.0 True \n",
"4 Introducing TPCx-HS: The First Industry Standa... 2015.0 True \n",
"5 Application and Exploration of Big Data Mining... 2016.0 True \n",
"6 Performance Evaluation of a Natural Language P... 2014.0 True \n",
"7 Context-aware Task Allocation for Fast Paralle... 2014.0 True \n",
"8 Improving China's Corporate Governance Within ... 2015.0 True \n",
"9 Big Data and Predictive Analytics in ERP Syste... 2014.0 True \n",
"10 Re-Stream: Real-time and energy-efficient reso... 2015.0 True \n",
"11 BIG DATA IN SURVEY RESEARCH 2015.0 True \n",
"12 Models and Data Sources Used in Systems Medici... 2016.0 True \n",
"13 Big data and precision 2015.0 True \n",
"14 IoT-Security approach analysis for the novel n... 2014.0 True \n",
"15 A meeting report from the 2013 GARNet workshop... 2015.0 True \n",
"16 Learning methodologies for wireless big data n... 2016.0 True \n",
"17 Reducing Data Dimensions for Systems Engineeri... 2014.0 True \n",
"18 Twitter Streams Fuel Big Data Approaches to He... 2015.0 True \n",
"19 THE LATENT STATE HAZARD MODEL, WITH APPLICATIO... 2015.0 True \n",
"20 Deploying and Managing a Network of Autonomous... 2015.0 True \n",
"21 The Person-Event Data Environment: leveraging ... 2013.0 True \n",
"22 A secure and scalable storage system for aggre... 2015.0 True \n",
"23 MaRDiGraS: Simplified Building of Reachability... 2013.0 True \n",
"24 Power System Disaster-Mitigating Dispatch Plat... 2014.0 True \n",
"25 A k-anonymity Method based on SEM (Search Engi... 2013.0 True \n",
"26 Philosophical Reflections on Data 2014.0 True \n",
"27 A Risk and Benefits Behavioral Model to Assess... 2013.0 True \n",
"28 Complications of Laryngeal Masks in Children B... 2013.0 True \n",
"29 From social data mining to forecasting socio-e... 2011.0 True \n",
"... ... ... ... \n",
"9449 Big data analysis and data velocity 2015.0 NaN \n",
"9450 I/O characteristics and implications of big da... 2015.0 NaN \n",
"9451 Secure distribution of big data based on bitto... 2013.0 NaN \n",
"9452 Modern aspects in development of branch applic... 2015.0 NaN \n",
"9453 Multi-strategy based sina microblog data acqui... 2014.0 NaN \n",
"9454 A novel Cp-Tree-based co-located classifier fo... 2015.0 NaN \n",
"9455 Application of big data technology in support ... 2015.0 NaN \n",
"9456 Real-time effective framework for unstructured... 2013.0 NaN \n",
"9457 Big Data-Security and Privacy 2015.0 NaN \n",
"9458 Research on public opinion based on Big Data 2015.0 NaN \n",
"9459 Locally refined splines representation for geo... 2015.0 NaN \n",
"9460 Big data study for coping with stress 2015.0 NaN \n",
"9461 Digital Data Grows into Big Data 2015.0 NaN \n",
"9462 SAW classification algorithm for Chinese text ... 2015.0 NaN \n",
"9463 Interactive e-science cyberinfrastructure for ... 2015.0 NaN \n",
"9464 Public policy considerations for data-driven i... 2013.0 NaN \n",
"9465 The performance of MapReduce over the varying ... 2013.0 NaN \n",
"9466 Understanding library user engagement strategi... 2015.0 NaN \n",
"9467 Twitter Mining for Discovery, Prediction and C... 2015.0 NaN \n",
"9468 Process optimization and monitoring along big ... 2015.0 NaN \n",
"9469 RSenter: Terms mining tool from unstructured d... 2013.0 NaN \n",
"9470 Resource management in cloud federation using ... 2014.0 NaN \n",
"9471 Design and implementation of a dynamic educati... 2014.0 NaN \n",
"9472 Big data for cyber physical systems an analysi... 2014.0 NaN \n",
"9473 Designing a big data processing platform for a... 2013.0 NaN \n",
"9474 Potential and Pitfalls for Big Data in Health ... 2015.0 NaN \n",
"9475 GridKa school - Teaching information technolog... 2015.0 NaN \n",
"9476 A survey on PCM-based big data storage and man... 2015.0 NaN \n",
"9477 A distributed file system over heterogeneous s... 2015.0 NaN \n",
"9478 Adaptive collaborative filtering based on scal... 2016.0 NaN \n",
"\n",
" scopus \n",
"0 NaN \n",
"1 NaN \n",
"2 NaN \n",
"3 NaN \n",
"4 NaN \n",
"5 NaN \n",
"6 NaN \n",
"7 NaN \n",
"8 NaN \n",
"9 NaN \n",
"10 True \n",
"11 NaN \n",
"12 NaN \n",
"13 True \n",
"14 NaN \n",
"15 NaN \n",
"16 True \n",
"17 NaN \n",
"18 NaN \n",
"19 NaN \n",
"20 NaN \n",
"21 NaN \n",
"22 True \n",
"23 NaN \n",
"24 NaN \n",
"25 NaN \n",
"26 NaN \n",
"27 NaN \n",
"28 NaN \n",
"29 NaN \n",
"... ... \n",
"9449 True \n",
"9450 True \n",
"9451 True \n",
"9452 True \n",
"9453 True \n",
"9454 True \n",
"9455 True \n",
"9456 True \n",
"9457 True \n",
"9458 True \n",
"9459 True \n",
"9460 True \n",
"9461 True \n",
"9462 True \n",
"9463 True \n",
"9464 True \n",
"9465 True \n",
"9466 True \n",
"9467 True \n",
"9468 True \n",
"9469 True \n",
"9470 True \n",
"9471 True \n",
"9472 True \n",
"9473 True \n",
"9474 True \n",
"9475 True \n",
"9476 True \n",
"9477 True \n",
"9478 True \n",
"\n",
"[9479 rows x 4 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfWOS=pd.read_csv(\"all_big_data_titles_year_wos.csv\",sep=\";\")\n",
"dfSCOPUS=pd.read_csv(\"all_big_data_titles_year_scopus.csv\",sep=\";\")\n",
"\n",
"df=pd.merge(dfWOS,dfSCOPUS,on='ARTICLE_TITLE',how='outer')\n",
"#get PUBYEAR in one column:\n",
"df.loc[df['wos'] == 1, 'PUBYEAR_y'] = df['PUBYEAR_x']\n",
"#save resulting csv again:\n",
"df=df[['ARTICLE_TITLE','PUBYEAR_y','wos','scopus']]\n",
"df.to_csv(\"all_big_data_titles_with_year.csv\", sep=';')\n",
"df\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## grouping data"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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>PUBYEAR_y</th>\n",
" <th>ARTICLE_TITLE</th>\n",
" <th>wos</th>\n",
" <th>scopus</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1995.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2003.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2004.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2005.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2006.0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2007.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2008.0</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2009.0</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2010.0</td>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2011.0</td>\n",
" <td>31</td>\n",
" <td>10</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2012.0</td>\n",
" <td>323</td>\n",
" <td>106</td>\n",
" <td>228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2013.0</td>\n",
" <td>1421</td>\n",
" <td>570</td>\n",
" <td>904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2014.0</td>\n",
" <td>2652</td>\n",
" <td>1048</td>\n",
" <td>1789</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2015.0</td>\n",
" <td>4111</td>\n",
" <td>1452</td>\n",
" <td>3127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2016.0</td>\n",
" <td>919</td>\n",
" <td>322</td>\n",
" <td>750</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PUBYEAR_y ARTICLE_TITLE wos scopus\n",
"0 1995.0 1 1 0\n",
"1 2003.0 1 0 1\n",
"2 2004.0 1 1 0\n",
"3 2005.0 1 0 1\n",
"4 2006.0 2 0 2\n",
"5 2007.0 1 1 0\n",
"6 2008.0 4 3 1\n",
"7 2009.0 4 4 1\n",
"8 2010.0 7 4 3\n",
"9 2011.0 31 10 22\n",
"10 2012.0 323 106 228\n",
"11 2013.0 1421 570 904\n",
"12 2014.0 2652 1048 1789\n",
"13 2015.0 4111 1452 3127\n",
"14 2016.0 919 322 750"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grouped=df.groupby(['PUBYEAR_y']) \n",
"df2=grouped.agg('count').reset_index()\n",
"df2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## visualize with plotly:\n",
"\n",
"we make three diagrams:\n",
"1) a horizontal bar plot comparing the overall papers per db\n",
"2) a vertical bar plot differentiating time and db\n",
"3) a vertical bar plot differentiating tima and db with a logarithmic y-scale (allows for better\n",
"inspection of smaller numbers)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'https://plot.ly/~mathias.riechert/131'"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#set data for horizontal bar plot:\n",
"data = [go.Bar(\n",
" x=[pd.DataFrame.sum(df2)['wos'],pd.DataFrame.sum(df2)['scopus'],pd.DataFrame.sum(df2)['ARTICLE_TITLE']],\n",
" y=['Web of Science', 'Scopus', 'Total'],\n",
" orientation = 'h',\n",
" marker=dict(\n",
" color=colorlist\n",
" )\n",
")]\n",
"#py.plot(data, filename='big_data_papers_horizontal') #for uploading to plotly\n",
"py.iplot(data, filename='horizontal-bar')"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#set data for stacked bar plot:\n",
"trace1 = go.Bar(\n",
" x=df2['PUBYEAR_y'],\n",
" y=df2['wos'],\n",
" name='Web of Science',\n",
" marker=dict(\n",
" color=colorlist[0]\n",
" )\n",
")\n",
"trace2 = go.Bar(\n",
" x=df2['PUBYEAR_y'],\n",
" y=df2['scopus'],\n",
" name='Scopus',\n",
" marker=dict(\n",
" color=colorlist[1]\n",
" )\n",
"\n",
")\n",
"trace3 = go.Bar(\n",
" x=df2['PUBYEAR_y'],\n",
" y=df2['ARTICLE_TITLE'],\n",
" name='All Papers',\n",
" marker=dict(\n",
" color=colorlist[2]\n",
" )\n",
")\n",
"data = [trace1, trace2,trace3]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#set layout for stacked bar chart with logarithmic y scale:\n",
"\n",
"#set layout for stacked bar chart with normal y scale:\n",
"layout_no_log = go.Layout(\n",
" title='Big data papers over time',\n",
" barmode='group',\n",
" xaxis=dict(\n",
" title='year',\n",
" titlefont=dict(\n",
" family='Arial, sans-serif',\n",
" size=14,\n",
" color='lightgrey'\n",
" ),\n",
" tickfont=dict(\n",
" family='Arial, sans-serif',\n",
" size=10,\n",
" color='black'\n",
" ),\n",
" showticklabels=True,\n",
" dtick=1,\n",
" tickangle=45,\n",
" )\n",
")\n",
"#plot:\n",
"fig1 = go.Figure(data=data, layout=layout_no_log)\n",
"py.iplot(fig1, filename='big_data_papers_no_log')\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~mathias.riechert/125.embed\" height=\"525px\" width=\"100%\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"layout_log = go.Layout(\n",
" title='Big data papers over time (log y-scale)',\n",
" barmode='group',\n",
" xaxis=dict(\n",
" title='year',\n",
" titlefont=dict(\n",
" family='Arial, sans-serif',\n",
" size=14,\n",
" color='lightgrey'\n",
" ),\n",
" tickfont=dict(\n",
" family='Arial, sans-serif',\n",
" size=10,\n",
" color='black'\n",
" ),\n",
" showticklabels=True,\n",
" dtick=1,\n",
" tickangle=45,\n",
" ),\n",
" yaxis=dict(\n",
" type='log'\n",
" )\n",
" )\n",
"fig2 = go.Figure(data=data, layout=layout_log)\n",
"py.iplot(fig2, filename='big_data_papers_log')"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
automayt/pharmakarma | .ipynb_checkpoints/PharmaKarma2-checkpoint.ipynb | 1 | 544465 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Drug</th>\n",
" <th>Stage</th>\n",
" <th>Catalyst</th>\n",
" <th>Date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>OCUL</th>\n",
" <td>DEXTENZA Allergic conjunctivitis</td>\n",
" <td>Phase 3</td>\n",
" <td>Phase 3 trial did not meet primary endpoint ...</td>\n",
" <td>06/06/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OCUL</th>\n",
" <td>DEXTENZA Ocular inflammation and pain followi...</td>\n",
" <td>Approved</td>\n",
" <td>CRL July 25 2016 - due to manufacturing defi...</td>\n",
" <td>07/25/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OCUL</th>\n",
" <td>DEXTENZA Ocular inflammation and pain followi...</td>\n",
" <td>Approved</td>\n",
" <td>CRL July 25 2016 - due to manufacturing defi...</td>\n",
" <td>07/11/2017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OCUL</th>\n",
" <td>DEXTENZA Ocular inflammation and pain followi...</td>\n",
" <td>Approved</td>\n",
" <td>FDA approval announced December 3, 2018.</td>\n",
" <td>12/03/2018</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Drug Stage \\\n",
"OCUL DEXTENZA Allergic conjunctivitis Phase 3 \n",
"OCUL DEXTENZA Ocular inflammation and pain followi... Approved \n",
"OCUL DEXTENZA Ocular inflammation and pain followi... Approved \n",
"OCUL DEXTENZA Ocular inflammation and pain followi... Approved \n",
"\n",
" Catalyst Date \n",
"OCUL Phase 3 trial did not meet primary endpoint ... 06/06/2016 \n",
"OCUL CRL July 25 2016 - due to manufacturing defi... 07/25/2016 \n",
"OCUL CRL July 25 2016 - due to manufacturing defi... 07/11/2017 \n",
"OCUL FDA approval announced December 3, 2018. 12/03/2018 "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# You may need to install htmllib5,lxml, and BeautifulSoup4. In your terminal/command prompt run:\n",
"\n",
"# conda install lxml\n",
"# conda install html5lib\n",
"# conda install BeautifulSoup4\n",
"# Then restart Jupyter Notebook. (or use pip install if you aren't using the Anaconda Distribution)\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os.path\n",
"from datetime import datetime, timedelta\n",
"\n",
"one_hour_ago = datetime.now() - timedelta(hours=1)\n",
"if os.path.exists(\"history.csv\"):\n",
" filetime = datetime.fromtimestamp(os.path.getctime(\"history.csv\"))\n",
" if filetime < one_hour_ago:\n",
" histdata = pd.read_html(\"https://www.biopharmcatalyst.com/calendars/historical-catalyst-calendar\")\n",
" histdata[0].to_csv('history.csv',index=False)\n",
"else:\n",
" histdata = pd.read_html(\"https://www.biopharmcatalyst.com/calendars/historical-catalyst-calendar\")\n",
" histdata[0].to_csv('history.csv',index=False)\n",
"\n",
"df = pd.read_csv('history.csv').set_index('Ticker')\n",
"df.index.name=None\n",
"\n",
"df[[\"Date\",\"Catalyst\"]] = df.Catalyst.str.extract('(?P<Date>[0-9]{2}\\/[0-9]{2}\\/[0-9]{4})(?P<Catalyst>.*)', expand=True)\n",
"\n",
"df.loc[\"OCUL\"]\n"
]
},
{
"cell_type": "code",
"execution_count": 469,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1152x1296 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# You may need to install htmllib5,lxml, and BeautifulSoup4. In your terminal/command prompt run:\n",
"\n",
"# conda install lxml\n",
"# conda install html5lib\n",
"# conda install BeautifulSoup4\n",
"# Then restart Jupyter Notebook. (or use pip install if you aren't using the Anaconda Distribution)\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os.path\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime, timedelta\n",
"from dateutil.parser import parse\n",
"from dateutil.relativedelta import relativedelta\n",
"from pandas.tseries.offsets import *\n",
"from pylab import text\n",
"from mpl_toolkits.axes_grid.anchored_artists import AnchoredText\n",
"import quandl\n",
"\n",
"\n",
"# Quandl API key\n",
"quandl.ApiConfig.api_key = \"UsYsv7dKGxHHQ5oURP4B\"\n",
"\n",
"# Some formatting\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.set_option('display.max_seq_items', 2)\n",
"\n",
"# Only pull fresh PDUFA data\n",
"three_weeks_ago = relativedelta(weeks=3)\n",
"one_week_ahead = relativedelta(weeks=1)\n",
"one_hour_ago = datetime.now() - timedelta(hours=1)\n",
"if os.path.exists(\"history.csv\"):\n",
" filetime = datetime.fromtimestamp(os.path.getctime(\"history.csv\"))\n",
" if filetime < one_hour_ago:\n",
" histdata = pd.read_html(\"https://www.biopharmcatalyst.com/calendars/historical-catalyst-calendar\")\n",
" histdata[0].to_csv('history.csv',index=False)\n",
"else:\n",
" histdata = pd.read_html(\"https://www.biopharmcatalyst.com/calendars/historical-catalyst-calendar\")\n",
" histdata[0].to_csv('history.csv',index=False)\n",
"\n",
"# Create dataframe\n",
"df = pd.read_csv('history.csv').set_index('Ticker')\n",
"df.index.name=None\n",
"df[[\"Date\",\"Catalyst\"]] = df.Catalyst.str.extract('(?P<Date>[0-9]{2}\\/[0-9]{2}\\/[0-9]{4})(?P<Catalyst>.*)', expand=True)\n",
"df['Date'] = pd.to_datetime(df['Date'])\n",
"df[\"Past\"] = df[\"Date\"] - DateOffset(weeks=3)\n",
"df[\"Future\"] = df[\"Date\"] + DateOffset(weeks=1)\n",
"\n",
"df\n",
"# Set stock ticker\n",
"stockpick=\"HALO\"\n",
"dataset=str(f\"WIKI/{stockpick}\")\n",
"# Set variables for plot creation\n",
"length = len(df.loc[stockpick].index)\n",
"count = 0\n",
"fig, axes = plt.subplots(nrows=length, ncols=1,figsize=(16,length * 3))\n",
"fig.subplots_adjust(hspace=0, wspace=0)\n",
"allplots=[]\n",
"\n",
"# Combine all data together into list of dataframes, iterate through each part of the list, plot each frame.\n",
"while (count < length):\n",
" pasttime= df.loc[stockpick].iloc[count][\"Past\"]\n",
" futuretime= df.loc[stockpick].iloc[count][\"Future\"]\n",
" pdufa=df.loc[stockpick].iloc[count][\"Date\"]\n",
" annotate = df.loc[stockpick].iloc[count][\"Date\"] + timedelta(days=1)\n",
" stage = df.loc[stockpick].iloc[count][\"Stage\"]\n",
" catalyst = df.loc[stockpick].iloc[count][\"Catalyst\"]\n",
" drug = df.loc[stockpick].iloc[count][\"Drug\"]\n",
" \n",
" #Annotation\n",
" tooltip = f\"stage:{stage} -{catalyst}\\n{drug}\"\n",
" at = AnchoredText(tooltip,\n",
" prop=dict(size=10), frameon=True,\n",
" loc=2, \n",
" )\n",
" at.patch.set_boxstyle(\"round,pad=0.2,rounding_size=0.2\")\n",
" axes[count].add_artist(at)\n",
" axes[count].margins(0.0, 0.5)\n",
" #Get quandl data\n",
" mydata = quandl.get(dataset,start_date=pasttime,end_date=futuretime)\n",
" allplots.append(mydata)\n",
"# axes[count].annotate('local max', xy=(pdufa, allplots[count][\"Close\"].max()), xytext=(annotate, (allplots[count][\"Close\"].max() - allplots[count][\"Close\"].min()) * 0.9 + allplots[count][\"Close\"].min()),\n",
"# arrowprops=dict(facecolor='black', shrink=0.05, width=1, headwidth=5),)\n",
" # Set y limit for notes\n",
" axes[count].set_ylim(allplots[count][\"Close\"].min() * .99 , (allplots[count][\"Close\"].max()-allplots[count][\"Close\"].min()) * 0.5 + allplots[count][\"Close\"].max())\n",
" axes[count].hlines(allplots[count][\"Close\"].max() * 1.01, allplots[count].head(1).index, allplots[count].tail(1).index, linestyle=\"-\", lw=1, color='black')\n",
" if count % 2 == 0:\n",
" axes[count].set_facecolor((0.91, 0.91, 0.91)) \n",
"\n",
" axes[count].vlines(pdufa, allplots[count][\"Close\"].min() * .99, allplots[count][\"Close\"].max() * 1.01, linestyle=\"--\", color='black')\n",
" axes[count].plot(allplots[count].index,allplots[count][\"Close\"],c=np.random.rand(3,), lw=2, label=pdufa) \n",
" axes[count].axes.get_xaxis().set_visible(False) # remove x axis\n",
" count = count + 1\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"13.65\n",
"1 - 0 = 0.11999999999999922\n",
"2 - 0 = -0.14000000000000057\n",
"3 - 0 = -0.33999999999999986\n",
"4 - 0 = 0.20999999999999908\n",
"5 - 0 = 1.0299999999999994\n",
"6 - 0 = 0.8899999999999988\n",
"7 - 0 = 0.7599999999999998\n",
"8 - 0 = -0.3600000000000012\n",
"9 - 0 = -0.16000000000000014\n",
"10 - 0 = -0.1899999999999995\n",
"11 - 0 = -0.120000000000001\n",
"12 - 0 = 0.6199999999999992\n",
"13 - 0 = 0.5600000000000005\n",
"14 - 0 = 0.6399999999999988\n",
"15 - 0 = -0.620000000000001\n",
"16 - 0 = -0.6899999999999995\n",
"17 - 0 = -0.6600000000000001\n",
"18 - 0 = -0.5700000000000003\n",
"19 - 0 = -0.75\n",
"13.77\n",
"2 - 1 = -0.2599999999999998\n",
"3 - 1 = -0.4599999999999991\n",
"4 - 1 = 0.08999999999999986\n",
"5 - 1 = 0.9100000000000001\n",
"6 - 1 = 0.7699999999999996\n",
"7 - 1 = 0.6400000000000006\n",
"8 - 1 = -0.4800000000000004\n",
"9 - 1 = -0.27999999999999936\n",
"10 - 1 = -0.3099999999999987\n",
"11 - 1 = -0.2400000000000002\n",
"12 - 1 = 0.5\n",
"13 - 1 = 0.4400000000000013\n",
"14 - 1 = 0.5199999999999996\n",
"15 - 1 = -0.7400000000000002\n",
"16 - 1 = -0.8099999999999987\n",
"17 - 1 = -0.7799999999999994\n",
"18 - 1 = -0.6899999999999995\n",
"19 - 1 = -0.8699999999999992\n",
"13.51\n",
"3 - 2 = -0.1999999999999993\n",
"4 - 2 = 0.34999999999999964\n",
"5 - 2 = 1.17\n",
"6 - 2 = 1.0299999999999994\n",
"7 - 2 = 0.9000000000000004\n",
"8 - 2 = -0.22000000000000064\n",
"9 - 2 = -0.019999999999999574\n",
"10 - 2 = -0.049999999999998934\n",
"11 - 2 = 0.019999999999999574\n",
"12 - 2 = 0.7599999999999998\n",
"13 - 2 = 0.7000000000000011\n",
"14 - 2 = 0.7799999999999994\n",
"15 - 2 = -0.4800000000000004\n",
"16 - 2 = -0.5499999999999989\n",
"17 - 2 = -0.5199999999999996\n",
"18 - 2 = -0.4299999999999997\n",
"19 - 2 = -0.6099999999999994\n",
"13.31\n",
"4 - 3 = 0.5499999999999989\n",
"5 - 3 = 1.3699999999999992\n",
"6 - 3 = 1.2299999999999986\n",
"7 - 3 = 1.0999999999999996\n",
"8 - 3 = -0.02000000000000135\n",
"9 - 3 = 0.17999999999999972\n",
"10 - 3 = 0.15000000000000036\n",
"11 - 3 = 0.21999999999999886\n",
"12 - 3 = 0.9599999999999991\n",
"13 - 3 = 0.9000000000000004\n",
"14 - 3 = 0.9799999999999986\n",
"15 - 3 = -0.28000000000000114\n",
"16 - 3 = -0.34999999999999964\n",
"17 - 3 = -0.3200000000000003\n",
"18 - 3 = -0.23000000000000043\n",
"19 - 3 = -0.41000000000000014\n",
"13.86\n",
"5 - 4 = 0.8200000000000003\n",
"6 - 4 = 0.6799999999999997\n",
"7 - 4 = 0.5500000000000007\n",
"8 - 4 = -0.5700000000000003\n",
"9 - 4 = -0.3699999999999992\n",
"10 - 4 = -0.3999999999999986\n",
"11 - 4 = -0.33000000000000007\n",
"12 - 4 = 0.41000000000000014\n",
"13 - 4 = 0.3500000000000014\n",
"14 - 4 = 0.4299999999999997\n",
"15 - 4 = -0.8300000000000001\n",
"16 - 4 = -0.8999999999999986\n",
"17 - 4 = -0.8699999999999992\n",
"18 - 4 = -0.7799999999999994\n",
"19 - 4 = -0.9599999999999991\n",
"14.68\n",
"6 - 5 = -0.14000000000000057\n",
"7 - 5 = -0.2699999999999996\n",
"8 - 5 = -1.3900000000000006\n",
"9 - 5 = -1.1899999999999995\n",
"10 - 5 = -1.2199999999999989\n",
"11 - 5 = -1.1500000000000004\n",
"12 - 5 = -0.41000000000000014\n",
"13 - 5 = -0.46999999999999886\n",
"14 - 5 = -0.39000000000000057\n",
"15 - 5 = -1.6500000000000004\n",
"16 - 5 = -1.7199999999999989\n",
"17 - 5 = -1.6899999999999995\n",
"18 - 5 = -1.5999999999999996\n",
"19 - 5 = -1.7799999999999994\n",
"14.54\n",
"7 - 6 = -0.129999999999999\n",
"8 - 6 = -1.25\n",
"9 - 6 = -1.049999999999999\n",
"10 - 6 = -1.0799999999999983\n",
"11 - 6 = -1.0099999999999998\n",
"12 - 6 = -0.2699999999999996\n",
"13 - 6 = -0.3299999999999983\n",
"14 - 6 = -0.25\n",
"15 - 6 = -1.5099999999999998\n",
"16 - 6 = -1.5799999999999983\n",
"17 - 6 = -1.549999999999999\n",
"18 - 6 = -1.459999999999999\n",
"19 - 6 = -1.6399999999999988\n",
"14.41\n",
"8 - 7 = -1.120000000000001\n",
"9 - 7 = -0.9199999999999999\n",
"10 - 7 = -0.9499999999999993\n",
"11 - 7 = -0.8800000000000008\n",
"12 - 7 = -0.14000000000000057\n",
"13 - 7 = -0.1999999999999993\n",
"14 - 7 = -0.120000000000001\n",
"15 - 7 = -1.3800000000000008\n",
"16 - 7 = -1.4499999999999993\n",
"17 - 7 = -1.42\n",
"18 - 7 = -1.33\n",
"19 - 7 = -1.5099999999999998\n",
"13.29\n",
"9 - 8 = 0.20000000000000107\n",
"10 - 8 = 0.1700000000000017\n",
"11 - 8 = 0.2400000000000002\n",
"12 - 8 = 0.9800000000000004\n",
"13 - 8 = 0.9200000000000017\n",
"14 - 8 = 1.0\n",
"15 - 8 = -0.2599999999999998\n",
"16 - 8 = -0.3299999999999983\n",
"17 - 8 = -0.29999999999999893\n",
"18 - 8 = -0.20999999999999908\n",
"19 - 8 = -0.3899999999999988\n",
"13.49\n",
"10 - 9 = -0.02999999999999936\n",
"11 - 9 = 0.03999999999999915\n",
"12 - 9 = 0.7799999999999994\n",
"13 - 9 = 0.7200000000000006\n",
"14 - 9 = 0.7999999999999989\n",
"15 - 9 = -0.46000000000000085\n",
"16 - 9 = -0.5299999999999994\n",
"17 - 9 = -0.5\n",
"18 - 9 = -0.41000000000000014\n",
"19 - 9 = -0.5899999999999999\n",
"13.46\n",
"11 - 10 = 0.06999999999999851\n",
"12 - 10 = 0.8099999999999987\n",
"13 - 10 = 0.75\n",
"14 - 10 = 0.8299999999999983\n",
"15 - 10 = -0.4300000000000015\n",
"16 - 10 = -0.5\n",
"17 - 10 = -0.47000000000000064\n",
"18 - 10 = -0.3800000000000008\n",
"19 - 10 = -0.5600000000000005\n",
"13.53\n",
"12 - 11 = 0.7400000000000002\n",
"13 - 11 = 0.6800000000000015\n",
"14 - 11 = 0.7599999999999998\n",
"15 - 11 = -0.5\n",
"16 - 11 = -0.5699999999999985\n",
"17 - 11 = -0.5399999999999991\n",
"18 - 11 = -0.4499999999999993\n",
"19 - 11 = -0.629999999999999\n",
"14.27\n",
"13 - 12 = -0.05999999999999872\n",
"14 - 12 = 0.019999999999999574\n",
"15 - 12 = -1.2400000000000002\n",
"16 - 12 = -1.3099999999999987\n",
"17 - 12 = -1.2799999999999994\n",
"18 - 12 = -1.1899999999999995\n",
"19 - 12 = -1.3699999999999992\n",
"14.21\n",
"14 - 13 = 0.0799999999999983\n",
"15 - 13 = -1.1800000000000015\n",
"16 - 13 = -1.25\n",
"17 - 13 = -1.2200000000000006\n",
"18 - 13 = -1.1300000000000008\n",
"19 - 13 = -1.3100000000000005\n",
"14.29\n",
"15 - 14 = -1.2599999999999998\n",
"16 - 14 = -1.3299999999999983\n",
"17 - 14 = -1.299999999999999\n",
"18 - 14 = -1.209999999999999\n",
"19 - 14 = -1.3899999999999988\n",
"13.03\n",
"16 - 15 = -0.06999999999999851\n",
"17 - 15 = -0.03999999999999915\n",
"18 - 15 = 0.05000000000000071\n",
"19 - 15 = -0.129999999999999\n",
"12.96\n",
"17 - 16 = 0.02999999999999936\n",
"18 - 16 = 0.11999999999999922\n",
"19 - 16 = -0.0600000000000005\n",
"12.99\n",
"18 - 17 = 0.08999999999999986\n",
"19 - 17 = -0.08999999999999986\n",
"13.08\n",
"19 - 18 = -0.17999999999999972\n",
"12.9\n"
]
}
],
"source": [
"# You may need to install htmllib5,lxml, and BeautifulSoup4. In your terminal/command prompt run:\n",
"\n",
"# conda install lxml\n",
"# conda install html5lib\n",
"# conda install BeautifulSoup4\n",
"# Then restart Jupyter Notebook. (or use pip install if you aren't using the Anaconda Distribution)\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os.path\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime, timedelta\n",
"from dateutil.parser import parse\n",
"from dateutil.relativedelta import relativedelta\n",
"from pandas.tseries.offsets import *\n",
"from pylab import text\n",
"from mpl_toolkits.axes_grid.anchored_artists import AnchoredText\n",
"import quandl\n",
"\n",
"\n",
"# Quandl API key\n",
"quandl.ApiConfig.api_key = \"UsYsv7dKGxHHQ5oURP4B\"\n",
"\n",
"# Some formatting\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.set_option('display.max_seq_items', 2)\n",
"\n",
"# Only pull fresh PDUFA data\n",
"three_weeks_ago = relativedelta(weeks=3)\n",
"one_week_ahead = relativedelta(weeks=1)\n",
"one_hour_ago = datetime.now() - timedelta(hours=1)\n",
"if os.path.exists(\"history.csv\"):\n",
" filetime = datetime.fromtimestamp(os.path.getctime(\"history.csv\"))\n",
" if filetime < one_hour_ago:\n",
" histdata = pd.read_html(\"https://www.biopharmcatalyst.com/calendars/historical-catalyst-calendar\")\n",
" histdata[0].to_csv('history.csv',index=False)\n",
"else:\n",
" histdata = pd.read_html(\"https://www.biopharmcatalyst.com/calendars/historical-catalyst-calendar\")\n",
" histdata[0].to_csv('history.csv',index=False)\n",
"\n",
"# Create dataframe\n",
"df = pd.read_csv('history.csv').set_index('Ticker')\n",
"df.index.name=None\n",
"df[[\"Date\",\"Catalyst\"]] = df.Catalyst.str.extract('(?P<Date>[0-9]{2}\\/[0-9]{2}\\/[0-9]{4})(?P<Catalyst>.*)', expand=True)\n",
"df['Date'] = pd.to_datetime(df['Date'])\n",
"df[\"Past\"] = df[\"Date\"] - DateOffset(weeks=3)\n",
"df[\"Future\"] = df[\"Date\"] + DateOffset(weeks=1)\n",
"\n",
"df\n",
"# Set stock ticker\n",
"stockpick=\"HALO\"\n",
"dataset=str(f\"WIKI/{stockpick}\")\n",
"# Set variables for plot creation\n",
"length = len(df.loc[stockpick].index)\n",
"count = 0\n",
"allplots=[]\n",
"\n",
"# Combine all data together into list of dataframes, iterate through each part of the list, plot each frame.\n",
"while (count < length):\n",
" pasttime= df.loc[stockpick].iloc[count][\"Past\"]\n",
" futuretime= df.loc[stockpick].iloc[count][\"Future\"]\n",
" pdufa=df.loc[stockpick].iloc[count][\"Date\"]\n",
" annotate = df.loc[stockpick].iloc[count][\"Date\"] + timedelta(days=1)\n",
" stage = df.loc[stockpick].iloc[count][\"Stage\"]\n",
" catalyst = df.loc[stockpick].iloc[count][\"Catalyst\"]\n",
" drug = df.loc[stockpick].iloc[count][\"Drug\"]\n",
" \n",
" #Annotation\n",
" #Get quandl data\n",
" mydata = quandl.get(dataset,start_date=pasttime,end_date=futuretime)\n",
" allplots.append(mydata)\n",
" count = count + 1\n",
"# len(allplots[3].index) # 21\n",
"i=0\n",
"total = len(allplots[3].index) - 1\n",
"\n",
"while (i < total):\n",
" print(allplots[3].iloc[i][\"Close\"])\n",
" n = i + 1\n",
" while (n < total): # see 14 - 8 = 1.0 !!!\n",
" rangevalue=(allplots[3].iloc[n][\"Close\"] - allplots[3].iloc[i][\"Close\"])\n",
"\n",
" print(f\"{n} - {i} = {rangevalue}\")\n",
" n=n + 1\n",
"\n",
"\n",
"# print(allplots[3].iloc[i + 1][\"Close\"] - allplots[3].iloc[i][\"Close\"] )\n",
" i = i + 1\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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s8/HxUYLq7NmzUVRUBL1ej9dff10JdMOGDcOMGTMQGxuLWbNmISEhQXltLVq0CK+//jpGjRrlMp3NBx98gKioKOj1emRmZuLOO+9Ely5dMHr0aERFRdU52NO8efOQmJiIn3/+GcHBwVi1ytH4ITMzs84a0meffRaFhYV48MEHodfrlR8qNBoNVq5cicmTJ2Pw4MG4+eabERkZCcDxOgwODkZubi5iYmJwzz33NLgvALjhhhtw9uzZOp9TIiIiIqLLJZpxwOAWl5CQIJ2D5AghGmyueaUMBgN8fX0hpcRDDz2E/v3747e//e2v3m9KSgqWLVuGzz//vNZ9vr6+yqi5dPVyvraMRiPGjh2LN998UwnXLW3atGnYsGEDtFptqxyPqCNqqc8lIiKijkQIcUBKWX8z0CpXXY3sr/XWW29Br9cjMjISJSUluO+++9xdJLpKLFy4EHq9HvHx8Zg9e3arhVgA+PzzzxliiYiIiOiqwRpZIiKiZsDPJSIiol+vQ9fI+vr6utxes2YNHn74YWzZsgWJiYnKFw2bzQa9Xo8ffvgBAPDmm29i0KBBGDRoEBISEpCSkgIAWLJkCZ566imXfaalpWHw4MEAHIP1VO/buHHjRgghkJmZ2aTyvvPOO4iOjlb63zr7stbHeT6/VnZ2ttJ/tz144YUXmnV/a9asaZV+mw0dJzMzE3q9HnFxccogUy0tJSVFec0Djvlq//Of/1zRvlriNXTPPffg6NGjAFyf8+zsbERFRTXbcc6ePYs5c+Y02/4u16effoqlS5e67fhXoub/1rq8+uqrGDx4MG6//fYmneOoUaOaq3iX5dy5c5g2bRoAx3siICAAcXFxGDx4sDJKe0pKirLO559/jmeeecYtZSUiIqLarsogW5/rrrsOoaGhygA4//znPzFs2DCMGjUKn3/+Of79739j586dyMzMxJtvvok77rgDZ86cwbx581xGMAYc87U653Staf369RgzZowyMnBDcnNz8fzzz2Pnzp04fPgw9uzZc0Xzq16JqyXISildprxpqrYQZDdt2oQbb7wRhw4dQt++fRvd15Wea3U1g+z999+PO++884r21RKvobfffhtDhgwB0Pw/XjhZrVb06tWrwemifs2+m2LGjBlYvHhxsx/f3V577TVs3rwZ69ata9I5Vn8ttqZ//OMfuPfee5XbSUlJOHToEFJTU/Hee+8pI4k7TZ06FZ9++imMRmNrF5WIiIjq0KGCLAAsX74cf/vb35CRkYGVK1fixRdfBAC8+OKLePnll9G1a1cAQHx8PO6++27861//wsCBAxEYGIi9e/cq+/nwww9x66231tq/wWDArl27sGrVqiYF2by8PPj5+Sk1Hb6+vggPDwcAjB8/Hs6m1AUFBQgLC1O2y8nJwZQpUzBw4ECl9gAA/vOf/yAmJgaxsbGYP38+ACA5OdnlC7vzWIsXL8aOHTug1+uxfPlyZGdnIykpCfHx8YiPj1e+YKakpGD8+PGYM2cOBg0ahNtvv12p1T5w4ADGjRuHoUOHYvLkyTh37hwAR431yJEjERMTg5kzZ+LixYsNnlNGRoYy2nRMTAyOHz/u8jgtXrwYFRUV0Ov1uP3225GdnY3BgwfjwQcfRHx8PHJycpQa9/j4eMydO1cZQOvZZ5/FsGHDEBUVhYULF0JKiY8//hipqam4/fbbodfrUVFRgbCwMPzhD39AYmIiEhIScPDgQUyePBl9+/bFG2+8oZTl5ZdfxrBhwxATE6PU0DjLc++99yIyMhLXXXcdKioq6jyO0+bNm/HKK6/g7bffxoQJEwA4vlxHRUUhKioKr7zyisu+q59rdWFhYXjyyScxfPhwDB8+XJlW6LPPPsOIESMQFxeHa6+9FhcuXEB2djbeeOMNLF++HHq9Hjt27MCSJUuwbNkyAI5pjKZMmYKhQ4ciKSlJaVWQnJyMRx55BKNGjUJERITyeqr5Gmrsefzwww/x+OOPA3CMnu2cF/fEiRPKdFLO10jN5xxwtKKo+RjXlJycjPvvvx9JSUkYMGCAMsDamjVrMHfuXEyfPh3XXXedSw3vmjVrcNNNN2H69OkIDw/HypUr8Y9//ANxcXEYOXIkioqKADj60A8bNgyxsbGYPXu2EmqSk5Px+OOPY8KECXjiiSfQv39/5OfnA3BMr9WvXz+XVhvOYzpbViQnJ+OBBx7AhAkTEBERge3bt2PBggUYPHgwkpOTlW18fX3x5JNPYujQobj22muxb98+jB8/HhEREfj0009r7RdwDPTlbF3i6+uLP/7xj4iNjcXIkSNx4cIFAMCpU6cwceJExMTEYOLEiTh9+jQAx5zbiYmJGDZsmDJSu1Nd74P7778fJ0+exIwZM7B8+XKXsly4cAEzZ85EbGwsYmNjlf8v1Wt5L+e9BQC//PILrr32WsTGxiI+Ph4nTpzA/PnzXVq1OGuGa/rkk08wZcqUWst9fHwwdOjQWi0khBAYP358nQP2ERERkRtIKdvN39ChQ6WTo+h1U6lUMjY2VvkLCQmRDz30kHL/q6++Kv38/OTq1auVZZ06dZLFxcUu+9m0aZO88cYbpZRSvvTSS/Kxxx6TUkq5e/dumZCQoKwXGhoq8/PzpZRSrl27Vi5YsEBKKWViYqI8cOBAveWUUkqr1Sqvu+46GRISIpOTk+Wnn36q3Ddu3Di5f/9+KaWU+fn5MjQ0VEop5erVq2WPHj1kQUGBNBqNMjIyUu7fv1+mp6fLAQMGKGUpLCyUUkp51113yY8++kjZr4+Pj5RSym3btsmpU6cqy8vLy2VFRYWUUspjx45J5+O9bds26e/vL3NycqTNZpMjR46UO3bskGazWSYmJsq8vDwppZTvv/++vPvuu6WUUkZHR8uUlBQppZRPP/20fPTRRxs8p4cffli+9957UkopKysrpdForPVYOcstpZRZWVlSCCF3796t7CspKUkaDAYppZRLly6Vf/nLX1weBymlvOOOO5THuHpZpHQ8j6+99pqUUsrHHntMRkdHy9LSUpmXlyeDgoKklFJ+/fXX8t5775V2u13abDY5depUuX37dpmVlSXVarU8dOiQlFLKuXPnyrVr19Z5nOqeeeYZ+fLLL0sppUxNTZVRUVHSYDDIsrIyOWTIEHnw4MFa51pTaGiofO6556SUUr777rvKc1pUVCTtdruUUsq33npLPv7447WOWfP2NddcI48dOyallHLPnj1ywoQJUkrHa2jOnDnSZrPJjIwM2bdvXyll7ddQY8/juXPnlPfO7NmzZUJCgszNzZVr1qyRixcvrvV41XzO63uMq7vrrrvk5MmTpc1mk8eOHZO9e/eWFRUVcvXq1bJ3797K6yErK0tGRkZKKR3vqb59+yrPt7+/v3z99dellI7XwvLly6WUUhYUFCjH+eMf/yhfffVV5ZhTp06VVqtVSinlkiVLlG2+/vprOWvWrFrlXL16tfJ/6a677pK33HKLtNvtctOmTdLPz08ePnxY2mw2GR8fr5wzALl582YppZQ33XSTnDRpkjSbzTItLU3GxsbW2q+UUk6dOlVu27ZN2d75+n/iiSfkX//6VymllNOmTZNr1qyRUkq5atUq5f/e9OnT5bvvviullHLlypXK81Hf+0BK1/+H1cty8803K4+J1WpV/t82ts+Gnvfhw4fLDRs2SCmlrKiokOXl5TIlJUUpf3FxsQwLC5MWi8XlsT958qSMj49Xbld/HRcUFMjQ0FCZnp5e6/X93nvvyYcffrjWc+nU0OcSERERNQ2AVNmEbKhxa4puIV5eXkhLS1Nur1mzRqkFBICHHnoIixcvdqnpqIusNmjHrbfeilGjRuHvf/873n//fcybN6/ObdavX4/HHntM2Wb9+vUNjk6rVqvx1VdfYf/+/fj222/x29/+FgcOHMCSJUsaLNukSZOUeUFnzZqFnTt3Qq1WY86cOUqtcufOnRvcR00WiwUPP/ww0tLSoFarcezYMeW+4cOHIzg4GACg1+uRnZ2NwMBApKenY9KkSQActWU9e/ZESUkJiouLMW7cOADAXXfdhblz5zZ47MTERDz//PPIzc3FrFmz0L9//0bLGxoaipEjRwIA9uzZg6NHjypzApvNZiQmJgIAtm3bhpdeeglGoxFFRUWIjIzE9OnT69znjBkzAADR0dEwGAzw8/ODn58fPD09UVxcjC1btmDLli2Ii4sD4KiBP378OPr06YPw8HDo9XoAwNChQ5Gdnd3oOVS3c+dOzJw5Ez4+PgAcz+uOHTswY8YMl3Oti/P1OG/ePGW6qNzcXNxyyy04d+4czGazUtNfH4PBgB9++MHluaqsrFSu33TTTVCpVBgyZIhSk1dTY89jjx49YDAYUFZWhpycHNx22234/vvvsWPHDsyaNavB8gFo8mN88803Q6VSoX///oiIiFBqlidNmlTv+2LChAnK8x0QEKC8RqKjo3H48GEAQHp6Ov70pz+huLgYBoMBkydPVrafO3cu1Go1AGDBggW48cYb8dhjj+Gdd97B3Xff3ei5TZ8+HUIIREdHo3v37oiOjgYAREZGIjs7G3q9HlqtVqlFjI6Ohk6ng4eHB6Kjo5v0etNqtUqfz6FDh+Kbb74BAOzevRsbNmwAAMyfPx+///3vAQC7du3CJ598oix/8sknAaDe98HYsWPrPfZ3332n9MVWq9XKPMpOl/veKisrw5kzZ5T5uD09PQEA48aNw0MPPYS8vDxs2LABs2fPhkbj+lF37tw5BAUFuSzbsWMH4uLioFKpsHjxYkRGRio12U7dunXjfMhERERtxFUZZBujUqkghHBZNmTIEBw4cADXXHONsuzgwYNISHAMmBUSEoKwsDBs374dn3zyCXbv3l1rv4WFhfjuu++Qnp4OIQRsNhuEEHjppZdqHa86IYTSLHTSpEm4++67sWTJEmg0GqU/pMlkqrVNzdtSyjqPU30/UkqYzeY6y7F8+XJ0794dP/74I+x2u/LFEAB0Op1yXa1Ww2q1QkqJyMjIWo9FSUlJveda3znddtttGDFiBL744gtMnjwZb7/9tstzURdn4HOe16RJk7B+/XqXdUwmEx588EGkpqYiJCQES5YsqfVYVuc8T5VK5XLOKpVKOeennnqq1rRM2dnZtR6jupq9NqT6Dyc1VT/XulR/3p3X/+///g+PP/44ZsyYgZSUlEZ/HLHb7QgMDHT5Eai66udXX1mb8jwmJiZi9erVGDhwIJKSkvDOO+9g9+7d+Pvf/95g+WqWoaHHuK73B9Dw41jz+a7+WnD2e01OTsamTZsQGxuLNWvWuASd6vsOCQlB9+7d8d1332Hv3r1Yt25dk8+tvtceAHh4eCjnUl8Zq7/HANf3WfXtne/jutT1eqquvvfBr3G5762G3i/z58/HunXr8P777+Odd96pdb+Xl1et/wNJSUmNNhs2mUzw8vJqyukQERFRC+twfWTr8/vf/x5PPvkkCgsLATj6eG7cuNHlS5Wztqtv375K7WR1H3/8Me68806cOnUK2dnZyMnJQXh4OHbu3Fnvcc+ePYuDBw8qt9PS0hAaGgrA0ffROeBIzUFpvvnmGxQVFaGiogKbNm3C6NGjMXHiRHz44YfKOTj79VXfz//+9z9YLBYAgJ+fH8rKypR9lpSUoGfPnlCpVFi7di1sNluDj9nAgQORn5+vBFmLxYKMjAwEBASgU6dO2LFjBwBg7dq1Su1sfed08uRJRERE4JFHHsGMGTOUGrDqPDw8lLLXNHLkSOzatUvpH2o0GnHs2DHly2rXrl1hMBhcjlnz/Jti8uTJeOedd5T+t2fOnEFeXl6D2zT1OGPHjsWmTZtgNBpRXl6OjRs3IikpqUnlcg5G9sEHHyg10SUlJejduzcA4N133220PP7+/ggPD8dHH30EwBEsfvzxx8s6t6Y8j2PHjsWyZcswduxYxMXFYdu2bdDpdLVq6ICGn/OGfPTRR7Db7Thx4gROnjyJgQMHXvY+6lJWVoaePXvCYrE0Gk7vuece3HHHHbj55puVmtrWEBYWhrS0NNjtduTk5GDfvn2NbjNq1CilT/+6deuU/sqjR492We50Je+DiRMn4vXXXwfgaL1RWlrqcv/l7tPf3x/BwcHYtGkTAEfrgep9lp19zCMOLRHkAAAgAElEQVQjI2ttO2DAgMtuMQEAx44da9aRs4mIiOjKMchWmTFjBn7zm99g9OjR6NevH8aMGYNNmza5ND+bO3cuMjIy6hzkCXA0K3Y2c3OaPXt2g6O6WiwWLFq0CIMGDYJer8cHH3yAFStWAAAWLVqE119/HaNGjao1UMyYMWMwf/586PV6zJ49GwkJCYiMjMQf//hHjBs3DrGxscqgOvfeey+2b9+O4cOHY+/evUrNUUxMDDQaDWJjY7F8+XI8+OCDePfddzFy5EgcO3as0VpArVaLjz/+GE8++SRiY2NdpjJ699138cQTTyAmJgZpaWn485//3OA5ffDBB4iKioJer0dmZmado+guXLgQMTExysA/1QUFBWHNmjWYN28eYmJiMHLkSGRmZiIwMBD33nsvoqOjcdNNN2HYsGHKNs5BgWoOwtSQ6667DrfddhsSExMRHR2NOXPmNBpSm3qc+Ph4JCcnY/jw4RgxYgTuuecepZllYyorKzFixAisWLECy5cvB+CYNmru3LlISkpSmpsDjiasGzduVAZ7qm7dunVYtWoVYmNjERkZ2ehUUDVfQ015HpOSkpCTk4OxY8dCrVYjJCRECU41NfScN2TgwIEYN24crr/+erzxxhsurQt+jb/+9a8YMWIEJk2ahEGDBjW47owZM2AwGJrUrLg5jR49GuHh4YiOjsaiRYsa7Nrg9Oqrr2L16tWIiYnB2rVrlf9BK1aswL/+9S8MGzbMpaXFlbwPVqxYgW3btiE6OhpDhw5FRkaGy/1Xss+1a9fi1VdfRUxMDEaNGoXz588DALp3747BgwfX+9j7+Pigb9++yg9fTbVt2zZMnTr1srYhIiKiliEaap7V1iQkJEhnX9eWnHjearXi7rvvht1ux3vvvddgs2AidwsLC0NqaqpLWO3IkpOTMW3aNLfOEQsAqamp+O1vf1vrxwJqeUajEdHR0Th48GCdNf2AY77vAwcO4LnnnmvSPi9cuIDbbrsN3377bb3rtOTnEhERUUchhDggpUxobL0O2Ue2MRqNBmvXrnV3MYionVq6dClef/31JvWNpea1detWLFiwAI8//ni9IRYAZs6cqXTDaIrTp083qR83ERERtQ7WyBIRETUDfi4RERH9ek2tkWUfWSIiIiIiImpXGGSJiIiIiIioXWm3QValUtU7HyoREVFrMpvNUKna7UcqERFRu9NuP3Xj4+OxbNkyhlkiInIrs9mMZcuWNWmqIyIiImoe7XbU4o0bN2LmzJl4+umnYbfb3V0cIiLqoFQqFeLj47Fx40Z3F4WIiKjDaLdBNjg4GPv373d3MYiIiIiIiKiVtdumxURERERERNQxMcgSERERERFRu8IgS0RERERERO0KgywRERERERG1KwyyRERERERE1K4wyBIREREREVG7wiBLRERERERE7QqDLBEREREREbUrLR5khRDvCCHyhBDp1ZZ1FkJ8I4Q4XnXZqaXLQURERERERFeH1qiRXQNgSo1liwF8K6XsD+DbqttEREREREREjWrxICul/B5AUY3FNwJ4t+r6uwBuaulyEBERERER0dXBXX1ku0spzwFA1WW3+lYUQiwUQqQKIVLz8/NbrYBERERERETUNrX5wZ6klG9KKROklAlBQUHuLg4RERERERG5mbuC7AUhRE8AqLrMc1M5iIiIiIiIqJ3RuOm4nwK4C8DSqsv/NWWjs2fPQgjRkuUiIiIiqpOXnw4mgxlSSncXhYiow2vxICuEWA9gPICuQohcAM/AEWA/FEL8BsBpAHObsq9evXrh7NmzLVVUagFSSv74QERE7ZaUEmcrT+Bw6Q7km3OgFhr4aTrDX90ZfhrHn3/Vpbfaj595RES/UlP/j7Z4kJVSzqvnroktfWxqXTZpQ5H5HPLNucgz5yC/MgcVdgM0wgMaoYWHSue4FFp4qLTKdY2q6rJqnerLLl2v2lalhQpqflEgIqIWJaVEjulnHCndgULLOQCAgAo2aUWxJQ/Fltq9otRCAz/1pWDLkHvlZs+eDQD45JNP3FwSImqr3NW0mK4CJlu5S2gtNJ+DDdZa61mlBVZpgcle3izHFVC5BFwP4QFNVQCuHZAvBWCPqqDcRdsLGuHRLGUhIqKri13akV2RgfTSnSi2OmZL8FT5YIhfIgb6DIWERJm1CKXWIpfLMmsRTHYjiq15KLY2FnI7wU/ThSG3AYWFhe4uAhG1cQyy1CRSSpRY85FXmYt8cw7yzbkotdb+kAnQdEWQNgRBumB004bAT9MZNmmBRZphtZthkWZYZKVy3SrNsNidl5WwSAusshKWeu83Q8IOszTBbDNd0bl4CC36eA1BhHcUuuvCoBJtfvBuIiJqYTZpw0njj0gv3YUy20UAgLfaH1F+o9DPJ87lB9Au2l7oou1Vax9mu6kZQ67jkiGXiKhuDLJUJ4vdjELzGUdtqzkX+ZW5MEvX4KgWGnTV9kY3bQiCtMHoqg2Gp9q71r5UQgcP6AB185TNJq2Xwq00w1oVcKuHZcdlJazSogRgqzTDaCvDRcsFnDCm4YQxDV4qP4R7RyHCOxqdPLrzywIRUQdjlRb8Un4I6WU/wGgrBQD4qTshyn8MIrxjoBZN//DSqjybEHIvosxaiDLrRZRaCxlyiYiuEIMsAQCMtjLkV+Ygz5yDvMrTKLKch4TrqIzean8ltAbpQtDZoztUl/EB31zUQgO1WgOgdmhuihJLAbKMR3DSeAQGWzGOGnbjqGE3AjVBCPeORrh3FHw1gc1baCIialMs9kr8XH4AR8t2K11fAjVBiPIfgzCvyGZvrXNlIfciTPbyRkOun6ZTVdBlyCWijoNBtgOSUqLUWog882nkVTqCq7MZlZOAQBePngjShSBIG4Ju2mD4aALcVOLmFeDRFfqACYj1H48Ccy5OGo8guyIDxdZ8HCr9DodKv0N3bR+Ee0cj1HsIdCovdxeZiIiaSaW9ApmGffipbK/S0qiLR09E+ychxHOgW8Jf4yH3oksz5SsLuZ2VsNseQu7EiRwTlIgaJtrTXGgJCQkyNTXV3cVod5yjCTtrW/PMOai0G13W0QgtgrTB6KYLQTdtH3TV9oaHSuumErc+u7ThrOkEThqPIKfiZ2XQKhXU6O3ZDxHeMQj26g+14G8/RETtUYWtHEcNu3HMkAqLNAMAgrQhiPFPQi9d3zYf7OpSd8h19smtf4DFukJuZ21PdPHo2S4fByK6ugghDkgpExpdj0H26mO2V6LAnIu8ytO4YD6NAvMZ2KTraMJeKl8ltHbT9UEnj+4c9KiK2V6JnIpMnDQewfnKLKWJtYfQIdRrCCK8o9FdF8oPe6IWJqVEpb0CWpUn/z/RFTPaypBetgvHyw8qn4U9dRGI9h+D7tqr93+52V5ZK9w2FnIDNUHo5xOHCO+YOse8ICJqDQyyHYjRVqbUtOZVnsZFy4Va/VsDNF0RpAtBd20fdNOFwFfd6ar98G5ORlsZso0ZOGk8gqKqeQQBR3/hzh49lCl9Ls2V6zr9T91z5XIuXKKGmO0mnDQewfHyg7houQAVVPBRB8BXEwhfTSf4qh2XfppA+Ko7Qafy4vuJ6nTG9At2Fm1SWiEFew5AjH8Sump7u7lk7lUz5JZaC3HWdEIJuCqo0cdrEPr5xKGnLtwt76/rr78eAPDll1+2+rGJyL2aGmTZTrKdcUyDU+Do21rVx9VQq3+rCl21vdBNG4Juuj7opg2Bp9rHTSVu37zVfhjiNxJD/Eai2JKPLOMRZBnTYbAVK6NbXgkBUTv41hF4Lycga4QHv8xTuyWlRKHlLI4ZDiK7Ih1WaQEAqKGBDVaU2S46+vJXZtXaViO08FMCblXIdV5Xd4JGxXmjOxq7tOPH0u04UrYDANBTF46hAZPQWdvDzSVrG7QqHbpoe6KLtqeyzC5tyDUdx/HygzhrOoHsigxkV2TAVx2Ifj569PXWw0fj32plrKioaLVjEVH7xBrZNu5S/9bTuFB5GvnmHFTaXf+5ewitY0Cmav1b+cWt5Ti/cBttBlirz49rr3TcrjFvrrXGdYvdDDtsLVK2S8FXVxV2XYNw/aHYQwnFHkIHb7U/X0PUKsz2SmQZj+BY+UFctJxXlvfQhaG/Tzz6eA2ChITBWuz4sxXDYL0Ig7UYZVXXLbKywWN4qnzgp+mELtpe6KkLR3ddKLQqz5Y+NXKTCpsBO4o24HxlNgQEYv3HIdoviT/0XYZyawlOGH/E8fJDKLeVAHD8ANvLsx/6+8Qh2LN/i89aMH78eABASkpKix6HiNoe1si2U2a7CflV/VvzKnMc/VtRV//WPkptK/u3ti4hxK9ulmaXtqo5bqvPh1vtelXgvXSfxWW9ugKyVVqUv4YG+Wgqncrb0ZxTHQAfTQB81FV/Vdc9Vd7t4othubXUUbNgzIAdNgzxHYlw72i+Z9yswHwWx8oPINt4qfZVp/JCX+9YDPAZCn+PLi7rB3oEIdAjqNZ+pJQwS1O1cHtRCb1ltosot5bAZC+HyVyOfHMuMg37lFHZe3iGo6cuHEG6EGgEf7i5Gpw3ZWNH0QZU2A3wVPkgqfMs9PQMd3ex2h0fTQBi/Mci2i8J5ypP4nj5IeRUZOKM6TjOmI7DU+WDvt6x6O8TV+u9SkTUWlgj62ZGWykuVF4aTbi4nv6tztDaTdcHvurAdhEgqHVJKauCbI0aYrsZFmmpOzDXCsUWmO0mGG2ljdYaq6FxCbi+NcKut9ofajfMMwwAJls5TlX8hGxjOi6YT9e6P0DTFbH+4xHqNZjvpVZksVciy5iOY+UHXfqcd9eGYoDvUPTxGtTsI4NLKWG0laHUWogLladwvjIL+eYzkLAr66igRjddCHrowtBTF44u2t78oaOdkVIivWwn0kpTICHRXRuKpC6z4K32c3fRrhommxEnjYdxvPwQSqz5yvLu2j7o5xOPUK/BzdqShzWyRB0XB3tqgy71bz1drX9rscs6KqjQpVr/1iBtCEcOpFYnpYTJXg6DtRjlthKU20pgsJYo18utJcr8iw0J9OiG7tpQdNP1QXddnxb9UukcbTrLmI5zlSeVH4RUUCPYawDCvaJglWb8WLpded919ugBvf8E9Pbsx0DbggrNZ3Gs/CCyjOmwVk17olV5oZ93LPr7xCPAo2urlsdiNyPPfBrnTFk4X5mFompNmgFHd41uulD01DlqbAM9uvH10YaZbEbsurgJZ0y/AACi/cYg1n88f4xoIVJKFJhzcbz8ELIrMpQWFR5Ch3DvaPT3iXPpe3ulli1bBgBYtGjRr94XEbUvDLJtgE1aUVjVv9UxOFMOzLX6t+qq5m91jCbcVdubTdyoXTDbK2GsEXCrX6+wldVqXeCr7oTuVc3iu2v7wE/T+VcFBKu04EzFcWRVpCO34rhSi+zoy9UXYV5RCPEaCK1Kp2xjkzb8Un4Ih0t3oMJeBgAI0gYjzv8a9PAMu+KykCtn7evx8oModKl97YP+vkMR6jW4zczLbLIZcaHyFM5VOoJtqbXQ5X5PlTe6V9XW9vAMhx9HfW8z8itzsb3oYxhtpdCqvJDU6Sb09urv7mJ1GGZ7JbKNGfil/CAKLGeV5b10fRHrPxZBuhA3lo6I2qurMsgOjusv9+zb3eq/3jeV2W5CflVgzas8jQLz2Vr9W73Vfsrcrd20IQj06MZfjemqZJUWFJrP4kLlaeRVDVRmqaqNc/JU+SihtqnzGdulDedMJ5FVkYGcikyXfXbXhiLMOxKhXkMabclglRb8bEhFetlOZQC1nrpwxAVc0+Gn5rhSZdaLOF+ZhXOmLOSajl+qfRWe6OvjqH2tq59rW1NuLcX5yiycr8zGucqsWiOUa4Un/DSd4afpDH/lshP8NJ2hayd9x9s7KSV+MuzFgZKtkLCjq7Y3xnaeA19NgLuL1mFdtFzA8fJD+KU8TXnvM9AS0ZW4KoNseExv+ZfN9yHEcyCG+CWimzbErV8Yyq2lVbWtjv6tFy0Xaq0TqAly6d/qow7glxzqkOzSjouWC8irPI0L5tPIqzwFU9Xcjk7OFgrOWtuu2t5QCw2klLhgPoVsYwZOVRx1Gbm7i0cvhHtHItQr8oqmhrDYK/GTYS8yynYro9+GeA6EPmA8Onl0/3UnfZWrsBkctZgmR+ir2VWimzYEA3yGItR7SJupfb1cUkqUWYuU2trzldm1Ro6vzkPoqkJuJyXk+lWFXC+VL///NwOz3YQfLn6K0xWZAIDBviMQH3Ct2/rkkyuTzYifDHvwk2GfEmh76iIQ6z8O3S4j0LKPLFHHdVUG2cFx/eUfP0tWmg921fZGpN8ohHgObLVazXJrCbIq0pFlTK8VXC/1b3X0BwzShUCn8mqVchG1N1JKlFoLlaml8ipP19FnXI0u2l4otxXDaCtTlgdoghDuHYkwr8hmGzGz0l6B9LJdyDTsg006WlKEeUVB7z+Oo3JWMdtNOF81YNI5U5bLgC+Ao6ayh2cYeujC0UsXcVU+bs7+46XWIpQpfxdRZi1CqbWowamANMJDqclVgq7acdtb7ceQ2wSF5nPYXvgxDLaL8BA6jO58I/p4DXJ3sagOzkCbadintJy5nEDLIEvUcV2VQTYhIUHu2JuCTMN+/FyeqvQ39dN0xhDfkejrHdsic1+a7SacqvgJJ42HcaHylLLcQ+iUuVu76ULQRduL/VuJfgXHKN6nlVrbYkuecp+vOhBh3pEI94pq0cF3KmwGHCnbiWOGA7DDBgGBvj56xPiN7XDNFq12C/LMpx1NbE1ZKLKcc+n3rBEe6Kbto0xj09GnApNSotJeoQTcUmsRymyXQm7NMRKqU0Oj1NxeunQ0XfZW+3foxxVwPLbHyw9iX/FXsMOGzh49MK7LHPhpOru7aNSISnsFjpbtQaZhb41AOxbddH3q3Y5BlqjjumqDrHOwJ4vdjBPGNBwt26PU4uhU3hjok4BBvsPgqfb5VceySSvOmH7BSeMR5FYcU2qBVVAjxGsgwr2j0duzH5syEbWgSnsFCsxnoBWe6Krt3ao1VgZrCQ6Xfo8TxjRISKigxgDfoYj2GwMvtW+rlaOlSCkhYYeEdLlebMl31LhWZiG/MtdlGiYVVOiqDUZPz3D00IWhqzaY/wMvw6WQeyncOm83NPezCuqqcNupVo2ujzrwqg+5FrsZey5+jqyKdADAAJ+hGBY4ud02V++oKu0V+KlsD35yCbThiPEfh+51BFoGWaKO66oPsk52acfpikxklP2AwqoR89TQoK9PLIb4jryspm1SSuSZT+Ok8QhOGY+6TC/SQxeGCO8Y9PEaBK3Ks3lOiIjavFJLIdJKtyO76ku0Rnign08cPFXesMuqIFhHIHRcr7pdfTkkpKzrur2ObRra92Xuo8Y2TdXZoyd66sLQwzMc3bR94KHSttRD3aGZ7ZUuIbd60K2wG+rdTkAFX00g/NSdqg0+1Qn+mi7w0QS2+x8aii152F74MUqsBdAID4zsNA0R3tHuLhb9CpcC7T6lKX4PXThiawRaBlmijqvDBFknZwjNKPsBuabjyvI+XoMQ6ZvY4Ih5xZY8nDQeQZYxHeW2EmV5J4/uiPCORphX1BUNIkNEV4+L5gs4VLoNuaZj7i5KsxFQQUBACKFc91b7oUdVcO2hC2M//zbAYjfDYL2IUlvtPrk1R1SuTkDARx1QrZmya41uW6zRtEs7ym0lKLUWosh8DofLdsAmrQjUBGFslzntYtRrahpHoN1bVUNbPdCORXddKF577TUAwIMPPujOYhKRG3S4IFtdsSUfR8v24KTxsNIsLkgbrAwMJYSA0VaKLGM6ThrTcdFyXtnWW+2PCO9ohHtHo5NHtxY7FyJqn/Irc6t+LJOO8FctBLper7qEqLaecNlGBQFUuy5q3q6+D+V6I8eqvl6tY7puT+2fVVpgsBZXq8W9VKNbbitpsPbdW+1fx8jKPvBU+UCn9oZWeLbI60RKCaOtDGXWQpRai1Badekstx12l/UjvGMwIvAGtga4StUdaMOqamhD3Vw6InKHDh1knYy2Mvxs2I+fDalKM2E/TWf4qP1xvjJbWU8rPBHqPQQR3tHopu3DL3hERNTu2aS1Wsi9eCks2i7CYL3YaBNzARU8Vd7Qqbzhqa66VPlUu+7406l9lPWc/XUdozsb6wyrpdZCZWTwunir/eCv6QI/TWf00vVFH69B/FzuAOoKtCN8pmFgp3g3l4yIWhuDbDUWuxm/lB/CUcMepemwCmoEe/VHhHdM1aBNba+JFRERUUuwS1tVE97q/XIdg06ZbEZU2suVAXkuh1Z4Qqf2hslW3uBURJ4q76rmzl3gX3XpbPbMmteOzWw34WDJtzhWfgCnDuTj6Rv/6e4iEVEra2qQ7RDpzUOlxWC/ERjoOwy5pmOw2M0I9urPvl9ERNQhqYRaCY71sUkrKu1GmGxGR8C1G1FpMzou7cZqoffSMrM0wWx1tIDyEDoloPprOsPf41Jo5aCJVB+tyhND/BJxrPwAgvr6Q0rJGnkiqlOHCLJOKqHixOlERERNoBYaeKv94a1u2mCHdmmH2V4Bk92oND1mAKEr4afuBFOZGd6BOpTbSuCrCXR3kYioDbq6J58jIiKiVqESKniqfRDoEQQvtQ9DLF0xIQTyTzhG5M43n3FzaYiorWKQJSIiIqI2Je8XR5AtMOe6uSRE1FYxyBIRERFRmxIXMQIAUMAaWSKqB4MsEREREbUpt027GwBQaD4Hm7S5uTRE1BYxyBIRERFRm1JaZICPCIQdNly0nHd3cYioDXJrkBVC/FYIkSGESBdCrBdCcDx+IiIiog5uzpw5SNv+EwA2LyaiurktyAohegN4BECClDIKgBrAre4qDxERERG1HXm/lADgyMVEVDd3Ny3WAPASQmgAeAM46+byEBEREVEb4JyChzWyRFQXtwVZKeUZAMsAnAZwDkCJlHJLzfWEEAuFEKlCiNT8/PzWLiYRERERucHFnHKohQZl1iKYbEZ3F4eI2hh3Ni3uBOBGAOEAegHwEULcUXM9KeWbUsoEKWVCUFBQaxeTiIiIiNxA2iW6ePQCwFpZIqpN48ZjXwsgS0qZDwBCiA0ARgF4z41lIiIiIiI3e+CBBwAAXbWdkWc+jQLzGQR79XdzqYioLXFnkD0NYKQQwhtABYCJAFLdWB4iIiIiagNuueUWAMApo2Pk4nxzrjuLQ0RtkNuCrJRyrxDiYwAHAVgBHALwprvKQ0RERERtQ05ODgCga8/eAIBC81lIKSGEcGexiKgNcWeNLKSUzwB4xp1lICIiIqK2Zf78+QCAlJQUeKv9YLSVodRaiACPrm4uGRG1Fe6efoeIiIiIqF5dtY5aWQ74RETVMcgSERERUZvlDLLsJ0tE1THIEhEREVGbFaQNBsAaWSJyxSBLRERERG1WZ4+eEBC4aLkAq93i7uIQURvh1sGeiIiIiIhq+t3vfqdc91BpEejRHRct51FoOYvuulA3loyI2goGWSIiIiJqU6ZPn+5yO0jbGxct51FgPsMgS0QA2LSYiIiIiNqYn3/+GT///LNy+9KAT+wnS0QOrJElIiIiojblvvvuA+CYRxbgFDxEVBtrZImIiIioTQvQdIWH0MFoK4XRVuru4hBRG8AgS0RERERtmhCCzYuJyAWDLBERERG1eUrz4koGWSJikCUiIiKidiBI6Seb6+aSEFFbwMGeiIiIiKhN+dOf/lRrmbNGttByDnZph0qwPoaoI2OQJSIiIqI25dprr621zFPtAz91J5TZLqLYkofO2h5uKBkRtRX8KYuIiIiI2pS0tDSkpaXVWs5peIjIiUGWiIiIiNqUxx57DI899lit5ZdGLmY/WaKOjkGWiIiIiNqFIF0wANbIEhGDLBERERG1E508ukMFNUqsBTDbTe4uDhG5EYMsEREREbULaqFRBnlirSxRx8YgS0RERETtRpCWzYuJiNPvEBEREVEb88ILL9R736UBnxhkiToyBlkiIiIialNGjRpV733Vp+CRUkII0VrFIqI2hE2LiYiIiKhN+eGHH/DDDz/UeZ+vOhCeKm9U2o0w2C62csmIqK1gjSwRERERtSl/+MMfAAApKSm17hNCoKs2GLmmY8g3n4GfpnMrl46I2gLWyBIRERFRu1K9eTERdUwMskRERETUrgQ5g2xlrptLQkTuwiBLRERERO1Kl6ogW2S5AJu0urk0ROQODLJERERE1K5oVToEaIJghw1F5vPuLg4RuQEHeyIiIiKiNuWVV15pdJ0gbW+UWPNRYD6DIF1wK5SKiNoS1sgSERERUZui1+uh1+sbXMc54FO+mf1kiToitwZZIUSgEOJjIUSmEOInIUSiO8tDRERERO63detWbN26tcF1grSOWliOXEzUMbm7afEKAF9JKecIIbQAvN1cHiIiIiJys+eeew4AcO2119a7ToBHEDTCAwZbMSps5fBS+7RW8agGm7QhtXgLTHYDPFU+8FT7uFx6VV33EDoIIdxdXLpKuC3ICiH8AYwFkAwAUkozALO7ykNERERE7YdKqNBF2wsXKk+hwJyLEK+B7i5Sh3Wo5Fv8XL6/0fVUUF8KuSpveKovhVyX4Kv2gafKFyrBXpAdiU1acdJ4pMnru7NGNgJAPoDVQohYAAcAPCqlLK++khBiIYCFANCnT59WLyQRERERtU1dtb2rguwZBlk3ya04hqOGPRAQGBY4GVJKmOzlMNmNMNnKYbKXo6Lq0irNMNpKYbSVNmnfjlDrCy+VL7zUfvBW+1bd9nNcqn3hrfaDWri7kSn9GhZ7JY6VH8DRsr2osJc1eTt3PusaAPEA/k9KuVcIsQLAYgBPV19JSvkmgDcBICEhQbZ6KYmIiIioTXL2k81nP1m3KLeWYOfF/wEA4gKuwSDf4Q2ub7VbqkJuOUy2clRUXdYOvoZL69nLcREXGtyvVngqwdZL7QcvlS+81b7w03RGb8/+rNltoypsBmQa9uFnQyrM0gKuZ3kAACAASURBVAQACNR0a/L27gyyuQBypZR7q25/DEeQJSIiIiJqlHPk4kLzGdilnYGlFdmlDd8XbYDZXoHenv0Q6Tuq0W00Kg/4qgLhi8Am7N8Ok90Ao82AClsZKmwGVNgNqLAZYKxx2yxNMFtNKLEW1NqPv6YL4vyvQR+vQeyf20aUWS8io2w3TpSnwQYrAKCbtg+i/Eajt2c/AA80aT9uC7JSyvNCiBwhxEAp5c8AJgI46q7yEBEREVHb8O9//7tJ63mr/eCt9ofRVopSawECPZpem0O/TlppCvLNOfBS+WF0pxubPSSqhArean94q/0bXE9KiUp7BSrsVeHWGXTtBuRWHEeptRDbiz5CF49eiA+4Bj09I5q1nNR0RebzSC/bhVMVRyHhaGgb7DkAUX6j0U0Xctn7c3eD8v8DsK5qxOKTAO52c3mIiIiIyM0GDmx6f9cgbTBOVRxFvvkMg2wrOWP6BelluyAgMLbLLHi6ccRoIQQ81d7wVHujk0d3l/uGBkzCL+WHcLj0exRazuKbgvfQUxeOuIBrlNp8allSSlyozMaRsl04V3kSACCgQl/vGET6jUKgR9AV79utQVZKmQYgwZ1lICIiIqK25bPPPgMATJ8+vdF1u2p74//Zu/O4qOr1geOfM+ybirnkCrihsgqI4oa7loqamrnl0qbWLetW2q1+Wdlt817TLKvrgpmZZrncrrcyl0RzRdHU6y4KSgjiwj4w8/39MXACZRPRAXzerxevYc7yPc85cwbmme92LvMoycYLtHRpd6dDu+dlmFLZkbIWgIAa4dR38LByRMWz0Wzwdg2hmbM/x9L2cDh1BwnZZ0m4tAgPpzYE1uhBTbs61g6zWjIrM3FZxzl8fQeXcy4CYKvZ0dIliLauHXGxrXnbx7B2jawQQgghhBCF/OMf/wDKlsjWzatZS5YBn+44szITlfI9WeYMGjh44evWxdohlYmdwR6/Gl1o5RrM4es7OJa2h3OZ/+N85jGauwQS4BaOi23JTZirE7Myk23OIMucgVnlYm9wwtHgjK1mf9tNxC1T6BziSOpOrudeBsDB4Ewb11C8XdvjYHCqiFMAJJEVQgghhBBVWG37BmgYuJpziRyzETuDvbVDqrYOXd9GYvY5HA0udKk9tMoNruVgcCK4Vm/auIVy6HoUJ9P3cyr9AGfSD9HaNRRft8442jhbO8xb9mdimk6WyZKgZpnTyTbljQadNyJ0/jbZ5swiyzFgwMHgbPmxccr73ZLkOhj+fG5Zb/ndXnNE0zSMeVPo/C91F5nmNABcbWrR1q0jLZzbYWuwq/DzlkRWCCGEEEJUWbaaHe529UnJSeCy8SL3O3paO6RqKSHrLIdStwHQtfZQnGxcrRxR+Tnb1KCj+wDaunYk5vpWYjOPcDRtJyfT9+Pj1ok2rh2s+oVI4cT0z2T0z8Q0P2G1PDcWk5iWxJKgumDQbDGaM8k2Z5CrciwjQZvTyBtMuFQaGg4GJ3JVLrnKCEAtu3r4unXG08nnjn7ZIYmsEEIIIYSo0uraNyIlJ4Fk4wVJZO+ATFMa21PWAODv1rXajPxbw+4+ut03DB9jJw5c28zF7NPEXN/CsbQ9+NfoRkuXIGw0m9s+jlmZ8hLRjBvmzM2wJKwF59Itd2LqjKPBGUeDC442zjgYXCzPbfIeDc442LjgaHDBweBUZIKZq3LINlmS2mxz0Y9ZpsLLclQ2WeYMAOrbe+Dr1omGji3uylRHksgKIYQQQogqrY59I46n7yPJGG/tUKodpRTbU9aSaU6jvn1T/GuEWzukCneffQN61x3DH1ln2X99M8nGC+y5+l+Opu4ksGYPvJx8CyVm+YlpcclotimDTHO6vtyosm4pnvxazhuTUQf99/zllsTVvpjE9FbZanbY2trhQtn7C5uUCaM5E7My3/V+xpLICiGEEEKISmXZsmW3tH1d+8aAZcAnpdRdqQ26VxxO3U5C9hkcDM50ve+hKtcv9lbc7+jFAw6TiMs6zoFrm7mWm8z2lDX8bhuFvcHpNhPT/FrRvFpTveb0z1rU/AS1ohLTu8FGs7FaM3NJZIUQQgghRKXSpEmTW9rezbY29pojmeY00k3Xca2AqT0EJGafI+b6VgC6uA/G2ab6j+yraRpNnVrT2LEVZzIOEXN9K9dykwtvoyemxSejBRNWe4NjlUlMqxJJZIUQQgghRKWycuVKAEaOHFmm7TVNo459Iy5mnybZeEES2QqQZcogKuV7FAof1040cmpp7ZDuKoNmoIVLIF7OviRmn8NGs9UTVgeDk9T6VwKSyAohhBBCiEplwYIFQNkTWaBAIhuPp3PbOxXaPUEpxY4r68gwpVLXvjHtavawdkhWY6PZ0tCxubXDEEWQOm4hhBBCCFHl1bVvBECS8YKVI6n6jqbt4kLWSew1R7rWHoahAkbuFaKiSSIrhBBCCCGqvPvyEtkUYwJmZbJyNFVXUnY8+69tAqBz7cHSTFtUWpLICiGEEEKIKs/Rxhk329qYyOVKTqK1w6mSss2ZbEv5DoWZNq4daOLkbe2QhCiWJLJCCCGEEKJayJ+GR5oX3zqlFL+lrCfddI377BoSVLO3tUMSokQy2JMQQgghhKhUVq9eXa796tg34kzGIZKNF4D2FRtUNXcsfS9xWcex0xzodt8wbKRfrKjkJJEVQgghhBCVSp06dcq3X14/2WSpkb0ll40Xib66EYBO7oNws3W3ckRClE6aFgshhBBCiEolMjKSyMjIW97P3a4+NthyPfcy2ebMig+sGjKas/j18neYMeHtEoKHTF0kqghJZIUQQgghRKVS3kTWRrOhtv39gNTKlkQpRbY5k+u5Key88gNppiu4291PSK2+1g5NiDKTpsVCCCGEEKLaqGvfmCRjPMnGCzRybGHtcO44szJjNGeRbc7I+8kkK+8x25T3WGBd/qNC6WXYavaE1x6GjSapgag65G4VQgghhBDVRn4/2aTseCtHcuvMylQo2cwyZ5BtKjoRzU9WjeVsQm2n2eNgcMbRxoWAGuHUsLuvgs9GiDtLElkhhBBCCFFt1MmbgifZeAGlFJqmWSUOk8q1JJ2mAjWkRSWkBWpNc1R2uY5lrzniYOOMg8EJB4Pl0dFQ8Hne7wW2kVGJRVUniawQQgghhKg2XGxq4GRwJdOcRmpuSoXUNJqViQxTWqmJaMFkNVfl3PJxNDTs85JQ+0LJaH4SWiApLbCdQZNhb8S9RxJZIYQQQghRqWzYsKHc+2qaRh37RsRlHSfJeKHciaxJ5ZKQdYbYzKPEZR6/5dpSDUPhWlEbpwJJqPMNNaaWRNVec7RaDbIQVY0kskIIIYQQolJxdna+rf3zE9lk4wWau/iXeT+TMpGQdYZzmUc5n3msUPLqZHDD0aZwc13HGxLRguvsNHtJSoW4gySRFUIIIYQQlcqnn34KwNSpU8u1f928AZ+SjaUP+GRWJhKyzxKbcZS4zGMYVZa+zt2uPp5ObfFwaiuDIQlRyUgiK4QQQgghKpVVq1YB5U9k77NviIZGSk4iuSoHW82u0HqzMvFHdiyxGUc5n3Ws0Mi/tezq6clrTbs65T8JIcQdJYmsEEIIIYSoVuwMDtS0q8vVnEukGBOo59AUszLzR3ZsXrPh/5FdIHmtaVsXT2dL8lrLrq4VIxdClJUkskIIIYQQotqpa9+YqzmXOJV+kDMZv3Mu839kmzP09TVt6+Dh1BZP57bUsqtnxUiFEOUhiawQQgghhKh26tg34mT6fk5lHNCXudnWxtPJx5K82taTwZiEqMIkkRVCCCGEENVOI8cWOBlcsdXs8HT2wcOpLe529SV5FaKa0JRS1o6hzOrUqaM8PT2tHYYQQgghhBBCiDsgOjpaKaUMpW1XpWpkPT092bdvn7XDEEIIIYQQQghxB2iatr8s25Wa6QohhBBCCCGEEJVJlaqRLSg+Pp6hQ4eyf/9+zGaztcMRQghxjzIYDAQFBbFmzRoaN25s7XCEEEKIe0KVTWSHDh3KQw89xI4dO7C3t7d2OEIIIe5RRqOR2bNnM3ToUPbu3WvtcIQQQoh7QpUa7CkkJETl95G1sbEhMzNTklghhBBWZzQacXJywmQyWTsUIYQQokrTNC1aKRVS2nZVto+s2WyWJFYIIUSlYG9vL91chBBCiLuoyiayQgghhBBCCCHuTZLICiGEEEIIIYSoUkpNZDVNW6xp2iVN0w4XWFZb07SNmqadzHt0L2Zfk6ZpMXk/6wss99I0bXfe/is1TZM2wkIIIYQQQgghyqQsNbKRQP8bls0ANimlWgKb8p4XJVMpFZj3E1Fg+fvAnLz9rwCP3VrYt+6jjz4iIyPjTh8GgDVr1qBpGseOHbsrx7tdrq6uFVpedTn/d955Bx8fH/z9/QkMDGT37t3lKn/r1q389ttvtxNiIZ6eniQnJ1dYef3796dWrVoMHDiw0PIxY8bg7e2Nr68vkyZNIicnp8RyYmJiCAsL06/ZypUr9XVnz56lQ4cOtGzZkpEjR2I0GgHYtm0bQUFB2Nrasnr16pvKvH79Oo0aNeKZZ56pgDMVQgghhBDVRamJrFJqG5Byw+LBwNK835cCQ8p6QE3TNKAnkP+p9Zb2L6+7mciuWLGCLl268M0331RYmbm5uRVW1p1WHc5/586d/PDDD+zfv59Dhw7xyy+/0KRJk3KVVdGJ7O0o6jq+9NJLLFu27KblY8aM4dixY/z+++9kZmaycOHCEst2dnbmyy+/5MiRI/z4449MmzaNq1evAjB9+nSef/55Tp48ibu7O4sWLQKgadOmREZGMnr06CLLfP311wkPD7/V0xRCCCGEENVcefvI1ldKJQDkPdYrZjtHTdP2aZq2S9O0/GT1PuCqUir/E3U80Ki4A2ma9mReGfuSkpJKDSw9PZ0BAwYQEBCAr68vK1euZN68eVy8eJEePXrQo0cPAKZMmUJISAg+Pj688cYb+v4bNmygdevWdOnShWeffVavpUpPT2fSpEm0b9+edu3asW7duiKPn5aWxo4dO1i0aFGhRG7r1q1069aNoUOH0rZtWyZPnqyPcOnq6spf//pXgoKC6NWrF/nn2b17d/72t78RHh7O3LlzOXfuHL169cLf359evXpx/vx5rl27hqenp15WRkYGTZo0IScnh9OnT9O/f3+Cg4Pp2rWrXkN69uxZwsLCaN++Pa+//nqp1/RWVJfzT0hIoE6dOjg4OABQp04dGjZsCEB0dDTh4eEEBwfTr18/EhIS9HinTZtGp06d8PX1Zc+ePcTGxvLZZ58xZ84cAgMDiYqKIikpiWHDhtG+fXvat2/Pjh07AJg5cybjx4+nb9++eHp68v333/Pyyy/j5+dH//79C9WIfvjhh4SGhhIaGsqpU6cASiz3ySefpG/fvjz66KM3nWuvXr1wc3O7afmDDz6IpmlomkZoaCjx8fElvvatWrWiZcuWADRs2JB69eqRlJSEUorNmzczfPhwAMaPH8/atWsBS+2yv78/BsPNf4qio6NJTEykb9++JR5XCCGEEELcg5RSpf4AnsDhAs+v3rD+SjH7Ncx7bAbEAs2BusCpAts0AX4vSxzBwcEqnyX0m61evVo9/vjj+vOrV68qpZTy8PBQSUlJ+vLLly8rpZTKzc1V4eHh6uDBgyozM1M1btxYnTlzRiml1COPPKIGDBiglFLqlVdeUcuWLVNKKXXlyhXVsmVLlZaWpi5cuKAeeOABvdxly5apSZMmKaWUCgsLU9HR0UoppbZs2aIcHBzU6dOnVW5ururdu7f69ttv9XP56quvlFJKvfnmm+rpp59WSikVHh6upkyZopc9cOBAFRkZqZRSatGiRWrw4MFKKaUiIiLU5s2blVJKffPNN+qxxx5TSinVs2dPdeLECaWUUrt27VI9evRQSik1aNAgtXTpUqWUUvPnz1cuLi5FXsvyqC7nn5qaqgICAlTLli3VlClT1NatW5VSShmNRhUWFqYuXbqkH2/ixIl6vPn33q+//qp8fHyUUkq98cYb6sMPP9TLHjVqlIqKilJKKXXu3DnVunVrfbvOnTsro9GoYmJilJOTk9qwYYNSSqkhQ4aoNWvWKKUs9/KsWbOUUkotXbpUv0dLKjcoKEhlZGQU+7pt2bJFL+dGRqNRtWvXTm3btq3Y/W+0e/du1bp1a2UymVRSUpJq3ry5vu78+fP6tck3fvx4/X5QSimTyaTCw8PV+fPn1ZIlS/R7QojKrLj/S0IIIYQoO2CfKkNuWN4a2URN0xoA5D1eKiZJvpj3eAbYCrQDkoFamqbZ5m3WGLhYzjhu4ufnxy+//ML06dOJioqiZs2aRW63atUqgoKCaNeuHUeOHOHo0aMcO3aMZs2a4eXlBcCoUaP07X/++Wfee+89AgMD6d69O1lZWZw/f56GDRuyYcMGfbsVK1bwyCOPAPDII4+wYsUKfV1oaCjNmjXDxsaGUaNGsX37dgAMBgMjR44EYOzYsfpyQF8Oluau+U0wx40bp283cuRIvT/iN998w8iRI0lLS+O3335jxIgRBAYG8tRTT+k1hzt27NDPbdy4cbd8jUtSXc7f1dWV6OhovvjiC+rWrcvIkSOJjIzk+PHjHD58mD59+hAYGMisWbMK1VTml9utWzeuX7+uN60t6JdffuGZZ54hMDCQiIgIrl+/TmpqKgAPPPAAdnZ2+Pn5YTKZ6N/f0j3dz8+P2NjYm44zatQodu7cWWq5ERERODk5FXmupZk6dSrdunWja9euZdo+ISGBcePGsWTJEgwGQ/4XVoVYehgU79NPP+XBBx8sd3NuIYQQQghRvdmWvkmR1gPjgffyHm9qZ5s3knGGUipb07Q6QGfgA6WU0jRtCzAc+Ka4/curVatWREdHs2HDBl555RX69u3L//3f/xXa5uzZs8yePZu9e/fi7u7OhAkTyMrKKvIDdz6lFN999x3e3t7FbnP58mU2b97M4cOH0TQNk8mEpml88MEHwM0f3ov7MF9wuYuLS7HHy98uIiKCV155hZSUFKKjo+nZsyfp6enUqlWLmJiYUo9RmjVr1vDmm28CsHDhQkJCQorcrrqdv42NDd27d6d79+74+fmxdOlSgoOD8fHx0ZPH0sot6jhms5mdO3cWmVjmN2U2GAzY2dnp+xsMhkL9WwuWm/97SeWWdB1L8uabb5KUlMTnn39epu2vX7/OgAEDmDVrFh07dgQszbKvXr1Kbm4utra2xMfH6820i7Nz506ioqL49NNPSUtLw2g04urqynvvvVeu8xBCCCGEENVLWabfWQHsBLw1TYvXNO0xLAlsH03TTgJ98p6jaVqIpmn5I8K0AfZpmnYQ2AK8p5Q6mrduOvCCpmmnsPSZXVRRJ3Tx4kWcnZ0ZO3YsL774Ivv37wfAzc1Nr526fv06Li4u1KxZk8TERP773/8C0Lp1a86cOaPXfBUcdbVfv358/PHHerJ74MCBm469evVqHn30Uc6dO0dsbCxxcXF4eXnpNYd79uzh7NmzmM1mVq5cSZcuXQBLApI/YuvXX3+tL79Rp06d9H6ny5cv17dzdXUlNDSU5557joEDB2JjY0ONGjXw8vLi22+/BSyJ+MGDBwHo3LlzoXJKM3ToUGJiYoiJiSk2ia1u53/8+HFOnjypP4+JicHDwwNvb2+SkpL0RDYnJ4cjR47o2+XfM9u3b6dmzZrUrFmz0L0H0LdvX+bPn1+o7FuVf5yVK1cSFhZWYeUWtHDhQn766SdWrFhRqA/rnj17iuxrazQaGTp0KI8++igjRozQl2uaRo8ePfTXeOnSpQwePLjEYy9fvpzz588TGxvL7NmzefTRRyWJFUIIIYQQurKMWjxKKdVAKWWnlGqslFqklLqslOqllGqZ95iSt+0+pdTjeb//ppTyU0oF5D0uKlDmGaVUqFKqhVJqhFIqu6JO6Pfffyc0NJTAwEDeeecdXnvtNQCefPJJHnjgAXr06EFAQADt2rXDx8eHSZMm0blzZwCcnJz49NNP6d+/P126dKF+/fp60+TXX3+dnJwc/P398fX11QcJunjxIg8++CBgaVY7dOjQQvEMGzaMr7/+GoCwsDBmzJiBr68vXl5e+rYuLi4cOXKE4OBgNm/efFMNcr558+axZMkS/P39WbZsGXPnztXXjRw5kq+++qpQU9zly5ezaNEiAgIC8PHx0Qeomjt3Lp988gnt27fn2rVrt3fBC6hO55+Wlsb48eNp27Yt/v7+HD16lJkzZ2Jvb8/q1auZPn06AQEBBAYGFhqR2N3dnU6dOjF58mR9ZN5BgwaxZs0afbCnefPmsW/fPvz9/Wnbti2fffbZrV5qsrOz6dChA3PnzmXOnDn69SlPuV27dmXEiBFs2rSJxo0b89NPPwEwefJkEhMTCQsLIzAwkLfeeguA8+fPF1nru2rVKrZt20ZkZCSBgYEEBgbqyfT777/PP//5T1q0aMHly5d57DHLjFt79+6lcePGfPvttzz11FP4+PiUGu/jjz/Ovn37ynRuQgghhBCietJKak5b2YSEhKj8D7CappXYFLi80tLScHV1RSnF008/TcuWLXn++edvu9ytW7cye/Zsfvjhh5vWubq6kpaWdtvHqMzuhfPv3r07s2fPLrHWujp46aWXGDduHP7+/tYORYhK5U79XxJCCCHuJZqmRSulSv1AXd4+stXWv/71L5YuXYrRaKRdu3Y89dRT1g5JiErlww8/tHYIQgghhBDiHic1skIIIUQFkP9LQgghxO0ra41seaffqdRcXV0LPY+MjOSZZ57h559/JiwsTP+gYTKZCvVx/OKLL2jdujWtW7cmJCSErVu3AjBz5kxeeeWVQmXGxMTQpk0bADw9PUlOTtbXrVmzBk3TOHbsWJniXbx4MX5+fnr/2/y+nMXJP5/bFRsbq/dfrQr+/ve/V2h5kZGRXLxYYTM/les4x44dIzAwkHbt2nH69Ok7HgtYmnkX7Nf72Wef8eWXX5arrDtxDz3++OMcPWoZF67gax4bG4uvr2+FHefixYsMHz68wsq7VevXr69yA1jd+Le1KPPmzaNNmzaMGTOmTOfYqVOnigrvliQkJDBw4EDA8p6oWbMm7dq1o02bNvoo7Vu3btW3+eGHH3jjjTesEqsQQgghblYtE9ni9O3bFw8PD30Qno8//pj27dvTqVMnfvjhBz7//HO2b9/OsWPH+OKLLxg7diwXLlxg1KhRhUYwBst8pflzmt5oxYoVdOnSRR8ZtyTx8fG88847bN++nUOHDrFr16671vewuiSySinMZvMtl1cZEtm1a9cyePBgDhw4QPPmzUstq7znWtCNiezkyZOLHIW4LO7EPbRw4ULatm0LVPyXF/lyc3Np2LChPpJyRZddFhEREcyYMaPCj29tn376KRs2bGD58uVlOseC9+Ld9M9//pMnnnhCf961a1cOHDjAvn37+Oqrr4iOji60/YABA1i/fj0ZGRl3O1QhhBBCFOGeSmQB5syZw7vvvsuRI0eYP38+77//PmAZVfXDDz+kTp06AAQFBTFx4kQ++eQTvL29qVWrFrt379bLWbVqFY888shN5aelpbFjxw4WLVpUpkT20qVLuLm56TUdrq6ueHl5AZbBg/KbUicnJ+Pp6anvFxcXR//+/fH29tZrDwC+/PJL/P39CQgIYNy4cQBMmDCh0Af2/GPNmDGDqKgoAgMDmTNnDrGxsXTt2pWgoCCCgoL0D5hbt26le/fuDB8+nNatWzNmzBi9Vjs6Oprw8HCCg4Pp168fCQkJgKXGumPHjvj7+zN06FCuXLlS4jkdOXJEH23a39+/0NQ3+bFmZmYSGBjImDFjiI2NpU2bNkydOpWgoCDi4uL0GvegoCBGjBihDyD11ltv0b59e3x9fXnyySdRSrF69Wr27dvHmDFjCAwMJDMzE09PT/72t78RFhZGSEgI+/fvp1+/fjRv3rzQCMAffvgh7du3x9/fX6+hyY/niSeewMfHh759+5KZmVnkcfJt2LCBjz76iIULF9KjRw/A8uHa19cXX19fPvroo0JlFzzXgjw9PZk+fTqhoaGEhoZy6tQpAP7973/ToUMH2rVrR+/evUlMTCQ2NpbPPvuMOXPm6KMoz5w5k9mzZwNw+vRp+vfvT3BwMF27dtVbFUyYMIFnn32WTp060axZM/1+uvEeKu11XLVqFS+88AJgGT26WbNm+nHzp1PKv0dufM3B0orixmt8owkTJjB58mS6du1Kq1at9AHGIiMjGTFiBIMGDaJv376FangjIyMZMmQIgwYNwsvLi/nz5/PPf/6Tdu3a0bFjR1JSUgBLH/r27dsTEBDAsGHD9KRmwoQJvPDCC/To0YOXXnqJli1bkpSUBFiml2rRokWhVhv5x8xvWTFhwgSmTJlCjx49aNasGb/++iuTJk2iTZs2TJgwQd/H1dWV6dOnExwcTO/evdmzZw/du3enWbNmrF+//qZyAQYOHKi3LnF1deXVV18lICCAjh07kpiYCMC5c+fo1asX/v7+9OrVi/PnzwOWObfDwsJo3769PlJ7vqLeB5MnT+bMmTNEREQwZ86cQrEkJiYydOhQAgICCAgI0P++FKzlvZX3FsCpU6fo3bs3AQEBBAUFcfr0acaNG1eoVUt+zfCNvvvuO/r373/TchcXF4KDg29qIaFpGt27dy9ywDohhBBCWIFSqsr8BAcHq3yW0ItmMBhUQECA/tOkSRP19NNP6+vnzZun3Nzc1JIlS/Rl7u7u6urVq4XKWbt2rRo8eLBSSqkPPvhATZs2TSml1M6dO1VISIi+nYeHh0pKSlJKKbVs2TI1adIkpZRSYWFhKjo6utg4lVIqNzdX9e3bVzVp0kRNmDBBrV+/Xl8XHh6u9u7dq5RSKikpSXl4eCillFqyZIm6//77VXJyssrIyFA+Pj5q79696vDhw6pVq1Z6LJcvX1ZKKTV+/Hj17bff6uW6uLgopZTasmWLTcELzAAAIABJREFUGjBggL48PT1dZWZmKqWUOnHihMq/3lu2bFE1atRQcXFxymQyqY4dO6qoqChlNBpVWFiYunTpklJKqW+++UZNnDhRKaWUn5+f2rp1q1JKqddff10999xzJZ7TM888o7766iullFLZ2dkqIyPjpmuVH7dSSp09e1ZpmqZ27typl9W1a1eVlpamlFLqvffeU2+++Wah66CUUmPHjtWvccFYlLK8jp9++qlSSqlp06YpPz8/df36dXXp0iVVt25dpZRSP/30k3riiSeU2WxWJpNJDRgwQP3666/q7NmzysbGRh04cEAppdSIESPUsmXLijxOQW+88Yb68MMPlVJK7du3T/n6+qq0tDSVmpqq2rZtq/bv33/Tud7Iw8NDzZo1Syml1NKlS/XXNCUlRZnNZqWUUv/617/UCy+8cNMxb3zes2dPdeLECaWUUrt27VI9evRQSlnuoeHDhyuTyaSOHDmimjdvrpS6+R4q7XVMSEjQ3zvDhg1TISEhKj4+XkVGRqoZM2bcdL1ufM2Lu8YFjR8/XvXr10+ZTCZ14sQJ1ahRI5WZmamWLFmiGjVqpN8PZ8+eVT4+Pkopy3uqefPm+utdo0YNtWDBAqWU5V6YM2eOUkqp5ORk/Tivvvqqmjdvnn7MAQMGqNzcXKWUUjNnztT3+emnn9RDDz10U5xLlizR/y6NHz9ejRw5UpnNZrV27Vrl5uamDh06pEwmkwoKCtLPGVAbNmxQSik1ZMgQ1adPH2U0GlVMTIwKCAi4qVyllBowYIDasmWLvn/+/f/SSy+pt99+Wyml1MCBA1VkZKRSSqlFixbpf/cGDRqkli5dqpRSav78+frrUdz7QKnCfw8LxvLwww/r1yQ3N1f/e1tamSW97qGhoer7779XSimVmZmp0tPT1datW/X4r169qjw9PVVOTk6ha3/mzBkVFBSkPy94HycnJysPDw91+PDhm+7vr776Sj3zzDM3vZb5Svq/JIQQQoiyAfapMuSG1XLUYicnJ33+SrDUUBScd/Lpp59mxowZhWo6iqIKDNrxyCOP0KlTJ/7xj3/wzTffMGrUqCL3WbFiBdOmTdP3WbFiBUFBQcUew8bGhh9//JG9e/eyadMmnn/+eaKjo5k5c2aJsfXp04f77rsPgIceeojt27djY2PD8OHD9Vrl2rVrl1jGjXJycnjmmWeIiYnBxsaGEydO6OtCQ0Np3LgxAIGBgcTGxlKrVi0OHz5Mnz59AEttWYMGDbh27RpXr14lPDwcgPHjxzNixIgSjx0WFsY777xDfHw8Dz30EC1btiw1Xg8PDzp27AjArl27OHr0qD4nsNFoJCwsDIAtW7bwwQcfkJGRQUpKCj4+PgwaNKjIMiMiIgDw8/MjLS0NNzc33NzccHR05OrVq/z888/8/PPPtGvXDrDUwJ88eZKmTZvi5eVFYGAgAMHBwcTGxpZ6DgVt376doUOH4uLiAlhe16ioKCIiIgqda1Hy78dRo0bp00XFx8czcuRIEhISMBqNek1/cdLS0vjtt98KvVbZ2X9O8TxkyBAMBgNt27bVa/JuVNrreP/995OWlkZqaipxcXGMHj2abdu2ERUVxUMPPVRifECZr/HDDz+MwWCgZcuWNGvWTK9Z7tOnT7Hvix49euivd82aNfV7xM/Pj0OHDgFw+PBhXnvtNa5evUpaWhr9+vXT9x8xYgQ2NjYATJo0icGDBzNt2jQWL17MxIkTSz23QYMGoWkafn5+1K9fHz8/PwB8fHyIjY0lMDAQe3t7vRbRz88PBwcH7Ozs8PPzK9P9Zm9vr/f5DA4OZuPGjQDs3LmT77//HoBx48bx8ssvA7Bjxw6+++47ffn06dMBin0fdOvWrdhjb968We+LbWNjo8/Rne9W31upqalcuHBBn4/a0dERgPDwcJ5++mkuXbrE999/z7Bhw7C1LfyvLiEhgbp16xZaFhUVRbt27TAYDMyYMQMfHx+9JjtfvXr17kp3BCGEEEKUrlomsqUxGAxomlZoWdu2bYmOjqZnz576sv379+tzgjZp0gRPT09+/fVXvvvuO3bu3HlTuZcvX2bz5s0cPnwYTdMwmUxomsYHH3xw0/EK0jRNbxbap08fJk6cyMyZM7G1tdX7Q2ZlZd20z43PlVJFHqdgOUopjEZjkXHMmTOH+vXrc/DgQcxms/7BEMDBwUH/3cbGhtzcXJRS+Pj43HQtrl27Vuy5FndOo0ePpkOHDvznP/+hX79+LFy4sNBrUZT8hC//vPr06cOKFSsKbZOVlcXUqVPZt28fTZo0YebMmTddy4Lyz9NgMBQ6Z4PBoJ/zK6+8ctO0TLGxsTddo6KavZak4BcnNyp4rkUp+Lrn//6Xv/yFF154gYiICLZu3VrqlyNms5latWoV+hKooILnV1ysZXkdw8LCWLJkCd7e3nTt2pXFixezc+dO/vGPf5QY340xlHSNi3p/QMnX8cbXu+C9kN/vdcKECaxdu5aAgAAiIyMLJToFy27SpAn169dn8+bN7N69m+XLl5f53Iq79wDs7Oz0cykuxoLvMSj8Piu4f/77uChF3U8FFfc+uB23+t4q6f0ybtw4li9fzjfffMPixYtvWu/k5HTT34GuXbuW2mw4KysLJyenspyOEEIIIe6we66PbHFefvllpk+fzuXLlwFLH881a9YU+lCVX9vVvHlzvXayoNWrV/Poo49y7tw5YmNjiYuLw8vLi+3btxd73IsXL7J//379eUxMDB4eHoCl72P+gCM3DkqzceNGUlJSyMzMZO3atXTu3JlevXqxatUq/Rzy+/UVLGfdunXk5OQA4ObmRmpqql7mtWvXaNCgAQaDgWXLlmEymUq8Zt7e3iQlJemJbE5ODkeOHKFmzZq4u7sTFRUFwLJly/Ta2eLO6cyZMzRr1oxnn32WiIgIvQasIDs7Oz32G3Xs2JEdO3bo/UMzMjI4ceKE/mG1Tp06pKWlFTrmjedfFv369WPx4sV6/9sLFy5w6dKlEvcp63G6devG2rVrycjIID09nTVr1tC1a9cyxZU/GNnKlSv1muhr167RqFEjAJYuXVpqPDVq1MDLy4tvv/0WsCQWBw8evKVzK8vr2K1bN2bPnk23bt1o164dW7ZswcHB4aYaOij5NS/Jt99+i9ls5vTp05w5cwZvb+9bLqMoqampNGjQgJycnFKT08cff5yxY8fy8MMP6zW1d4OnpycxMTGYzWbi4uLYs2dPqft06tRJ79O/fPlyvb9y586dCy3PV573Qa9evViwYAFgab1x/fr1QutvtcwaNWrQuHFj1q5dC1haDxTss5zfx9zHx+emfVu1anXLLSYATpw4UaEjZwshhBCi/CSRzRMREcFjjz1G586dadGiBV26dGHt2rWFmp+NGDGCI0eOFDnIE1iaFec3c8s3bNiwEkd1zcnJ4cUXX6R169YEBgaycuVK5s6dC8CLL77IggUL6NSp000DxXTp0oVx48YRGBjIsGHDCAkJwcfHh1dffZXw8HACAgL0QXWeeOIJfv31V0JDQ9m9e7dec+Tv74+trS0BAQHMmTOHqVOnsnTpUjp27MiJEydKrQW0t7dn9erVTJ8+nYCAgEJTGS1dupSXXnoJf39/YmJi+L//+78Sz2nlypX4+voSGBjIsWPHihxF98knn8Tf318f+KegunXrEhkZyahRo/D396djx44cO3aMWrVq8cQTT+Dn58eQIUNo3769vk/+oEA3DsJUkr59+zJ69GjCwsLw8/Nj+PDhpSapZT1OUFAQEyZMIDQ0lA4dOvD444/rzSxLk52dTYcOHZg7dy5z5swBLNNGjRgxgq5du+rNzcHShHXNmjX6YE8FLV++nEWLFhEQEICPj0+pU0HdeA+V5XXs2rUrcXFxdOvWDRsbG5o0aaInTjcq6TUvibe3N+Hh4TzwwAN89tlnhVoX3I63336bDh060KdPH1q3bl3ithEREaSlpZWpWXFF6ty5M15eXvj5+fHiiy+W2LUh37x581iyZAn+/v4sW7ZM/xs0d+5cPvnkE9q3b1+opUV53gdz585ly5Yt+Pn5ERwczJEjRwqtL0+Zy5YtY968efj7+9OpUyf++OMPAOrXr0+bNm2KvfYuLi40b95c/+KrrLZs2cKAAQNuaR8hhBBC3BlaSc2zKpuQkBCV39f1Tk48n5uby8SJEzGbzXz11VclNgsWwto8PT3Zt29foWT1XjZhwgQGDhxo1TliAfbt28fzzz9/05cF4s7LyMjAz8+P/fv3F1nTD5b5vqOjo5k1a1aZykxMTGT06NFs2rSp2G3u5P8lIYQQ4l6haVq0UiqktO3uyT6ypbG1tWXZsmXWDkMIUUW99957LFiwoEx9Y0XF+uWXX5g0aRIvvPBCsUkswNChQ/VuGGVx/vz5MvXjFkIIIcTdITWyQgghRAWQ/0tCCCHE7StrjWypfWQ1TVusadolTdMOF1hWW9O0jZqmncx7dC9iv0BN03ZqmnZE07RDmqaNLLAuUtO0s5qmxeT9BN7KyQkhhBBCCCGEuHeVZbCnSKD/DctmAJuUUi2BTXnPb5QBPKqU8snb/yNN02oVWP+SUiow76fo+T6EEEIIIYQQQogblJrIKqW2ASk3LB4M5M/nsRQYUsR+J5RSJ/N+vwhcAureuJ0QQgghhBBCCHEryjv9Tn2lVAJA3mO9kjbWNC0UsAdOF1j8Tl6T4zmapjkUsyuapj2pado+TdP2JSUl/Rm4wYDRaCxn+EIIIUTFMRqNGAwyo50QQghxt9zx/7qapjUAlgETlVLmvMWvAK2B9kBtYHpx+yulvlBKhSilQgrO6RoUFMTs2bMlmRVCCGFVRqOR2bNnl2nOXiGEEEJUjPJOv5OoaVoDpVRCXqJ6qaiNNE2rAfwHeE0ptSt/eX5tLpCtadoS4MVbDWDNmjUMHTqU119/HbPZXPoOQgghxB1gMBgICgpizZo11g5FCCGEuGeUN5FdD4wH3st7XHfjBpqm2QNrgC+VUt/esC4/Cdaw9K89fOP+pWncuDF79+4tT+xCCCGEEEIIIaqwsky/swLYCXhrmhavadpjWBLYPpqmnQT65D1H07QQTdMW5u36MNANmFDENDvLNU37HfgdqAPMqtCzEkIIIYQQQghRbWlVafL2kJAQtW/fPmuHIYQQQgghhBDiDtA0LVopFVLadjLEohBCCCGEEEKIKqW8fWSt4uLFi1i61QohhBBCCCGEuFdVqUS2YcOGXLx40dphCCGEEEKIO2jatGkAfPTRR1aORAhxt5W14rJKJbJCCCGEEKL6kwRWCFEa6SMrhBBCCCGEEKJKkURWCCGEEEJUKmPHjmXs2LHWDkMIUYlJ02IhhBBCCFGpxMfHWzsEIUQlJzWyQgghhBBCCCGqFElkhRBCCCGEEEJUKdK0WAgruZx9iV//+BGjOYeOdcPxcGku8yQLIYQQQghRBpLICnEXZeZmsP3SL2xMWMf+yzsxYwbgi5Mf0tjZg051e9GlXm/a1ArARrOxcrRCCCGEdYSFhVk7BCFEJacppawdQ5mFhISoffv2WTuMCrU3eTu/Jv7I0KZjae7W2trhiDvApEwcSNnFxovribq0kSxTBgC2mh0d63bHxdaVnUlbuJ5zVd/H3f4+wur2oHO9XgTVDsPBxtFa4QshhBBCCHHXaJoWrZQKKXU7SWSt43z6GT47/j67kn8FwNHgxAy/9+lWv6+VIxMV5XTqcTZeXMemP37gcvYlfblPrXb0aTCY7vf3p4ZdLQBM5lwOXz3Ab0mb2H5pEwmZcfr2jjbOtL+vM53r9aZj3XB9HyGEEEIIIaobSWQrqes5V/ny9Kesi/sak8rF2caF1jX92Z+yE4BJLaYxxusp6StZRSVnJbLpjx/YeHE9Z9KO68sbOjWlT8MIejeIoJFz0xLLUEpxNu0EOy5tYkfSJk5cP6KvM2g2BLi3p3PdnnSq14v7nRrdsXMRQgghrGXYsGEAfPfdd1aORAhxt0kiW8mYzLn8O34lkafncT3nGhoaDzYazsQWz+Fufx/fxC5k4cl/olD0vH8AL/m8I81Jq4jM3HSi8vq9Hri8S+/3WsOuJt3rP0ifhhG0rRlY7i8nEjMv8lvSZn67tJmYK3swqVx9XQu3tnSu15Mu9XrTzNVbvgARQghRLXTv3h2ArVu3WjUOIcTdV6GJrKZpi4GBwCWllG/estrASsATiAUeVkpdKWLf8cBreU9nKaWW5i0PBiIBJ2AD8JwqJZiqmsjuTY7i0+Pvcy79FACB7qFM9X6FFjXaFNpux6XN/P33F8k0ZeBdw4+3A+dTx7G+NUIWpTApE/sv72Rjwnq2J24ky5wJWPq9htXtTp+Gg+lQpxt2BvsKPW5qzjV2J29jx6VN7EneRmZef1sAT5eWvBf8BfUcG1ToMYUQQoi7TRJZIe5dFZ3IdgPSgC8LJLIfAClKqfc0TZsBuCulpt+wX21gHxACKCAaCFZKXdE0bQ/wHLALSyI7Tyn135LiqGqJ7Pn0Myw4/j678/rBNnRqymTvl+lct1exNWdnUo/z2oGp/JF1gToO9ZnV7hNa1fC9m2GLEpxOPcbPF9ex+Y8fuJydpC/3rRVEnwYRhBfo93qnGU3Z7E/ZxW9Jm9hxaRNXjJfxdw/hHyFLZcRjIYQQVZokskLcuyq8abGmaZ7ADwUS2eNAd6VUgqZpDYCtSinvG/YZlbfNU3nPPwe25v1sUUq1Lmq74lSVRNbSD/YT1sWtwKRycbF1ZWyzKQxtOg77MtTQXTWm8EbMX/j9ajQOBkem+75L9/sfuAuRi6IkZSWyKeEHfklYx5m0E/ryRs4e9Glg6ffa0LmJFSO03DOP/zaYFGMSj7WYxphmk60ajxBCCHE7JJEV4t5V1kT2duaRra+USgDIS2brFbFNIyCuwPP4vGWN8n6/cflNNE17EngSoGnTkgfJsbZccw7r479h6an5pOZew4CBgY0fZmLz53B3uK/M5dSyr83skCV89L83+e+F73jr0PPEpp3i0eZPY9AMd/AMRD5Lv9eNbLy4nv0pO1FYvvCpYVeTHvcPoE+DCNrUDKg0fVJr2ddmuu+7TN//OJGn5xN0Xyfa1PS3dlhCCCFEufTq1cvaIQhxTzCajWWqaKuMbieRLYuiPuWrEpbfvFCpL4AvwFIjW3GhVaw9yVEsOP4e59JPAxDo3oGnW79S7rlh7Qz2vNh2Fp4uLfj8xId8eeYTzqWfZrrvuzjaOFVk6HdMtinLMjKzrau1QykTkzmX/Sn5/V5/0fu92ml2hNXtQe+GEXek32tFaV+nC8M9xrP63FL+/vtLfNHxe5xsXawdlhBCCHHLXn/9dWuHIES1lpmbzqsHpnIu/TTzQr8udVaNyuh2EtlETdMaFGhafKmIbeKB7gWeN8bSrDg+7/eCyy/eRixWcy7tNAtOvM+e5G2ApR/sFO/pdKrb87Zr6zRNY4TnRJq4NOOd3//Kr4k/cjHjPLPafUpdx/srIvw7IjbtFOvjVvDzxbVkmbN4oOFDjGs+tVIOQqSU4lTq/9iYsI7NCRtIMf7Z79WvVjB9GkYQXr8/bnY1rRhl2T3e8q8cSNnN6dRjfHzsHV72/bu1QxJCCCGEEJVIjtnIGwefJebKbgAWnvwnbwR8ZOWobt3t9JH9ELhcYLCn2kqpl2/YpzaWAZ6C8hbtxzLYU4qmaXuBvwC7sQz29LFSakNJMVSmPrLXjFf48oylH6xZmXCxdWVcs6kMaTr2jlTPx6ad4tUDU0jIjKO2fV3eDpxPm1oBFX6c8so157Dj0ibWxX1NzJU9+nINDYXCTrNjUJNHGO31FLUd6lgxUoukrD/4JeHfbLy4ntj0k/ryRs4e9G0wmF4NBlm932t5xaadYvKuYRjN2fyf/0d0v7+/tUMSQgghbskDD1jGBvnvf0scB1QIcYvMysw7v7/Ilj82UMuuNpmmDLLNWcwP/Ya2tQKtHR5Q8aMWr8BSs1oHSATeANYCq4CmwHlgRF6CGgJMVko9nrfvJOBveUW9o5Rakrc8hD+n3/kv8JeqMP1OrjmH9XErWHr6E70f7IDGI5jQ/Nlb6gdbHteMV3jz4HPEXNmDncGel33eoVeDQXf0mKVJykrkP/Gr+M+FVfoovo42zvRpEEFEk1HYGexYeno+W/6wfEfhaHBiqMdYRno+dtdG982XkZtGVOJGfk5YR0zK7gL9XmvR4/4HK12/19uxLm4Fc//3Jq62NVjYaV2lrA0XQgghiiODPQlR8ZRSzDv2NuvivsbZxoU57ZexLfEnlp/9HN9aQcxtv7xSfA6u8FGLKwNrJ7K7k7ax4MR7nE8/A0BQ7TCmeM+guZt3KXtWnByzkY+PzeKH+FUAjPGazMQWz97VQaCUUsRc2c2681+zPWkTZmUCwMOlORFNRtGnwWBc7dwK7XM69RiLT81lZ9IWAFxsXXnYYxLDPB69o31oTeZc9l3+jV8S1rP90i9km7OAvH6v9XrSp8FgQut0qbT9XstLKcVrMVPZmbSFAPf2zA6JlCl5hBBCVBmSyApR8Zaens/S0/OxM9jzftC/CKzdgfTcNMZF9eVqTgpvBnxM1/p9rB2mJLIVKTbtFJ8df589l6MAS/PTya2m06luD6t8a6GUYk3cV3x67F3MmOlSrw+v+L53xwf2SctJ5eeEtayPW6En8wbNhi71ejOkyWgC3ENLvR7/u3qQxafmEp3yGwA17dwZ5fUEg5uMxsHGsULiVEpxMvUoGy+uZ/Mf/+GKMVlfZ+n3Opju9fvjalejQo5XWRWckufxFs8zulmJs1sJIYQQlYYkskJUrHXnv2busbcwYGBm4Dy61Outr1t7fjnzjr1NY2cPFnf6AVuDnRUjlUS2QlwzXmHp6fmsj/8mrx+sG482m8qQpmMqRQ3e3uTtvHXoedJzU2nu1ppZgZ9S36lhhR/ndOpx1sd9zcaEf5NlygDgPod6DGz8MAMajaCOY/1bLjMmZTeLTn3EkasH8sqry1ivKTzYeHi5r+2lrAS93+u59FP68sbOHvRpMJjeDSJo4Ny4hBKqn73JUUzf/wQ2mi0fh66gdU0/a4ckhBBClEoSWSEqzuaE//DO7y+iULzYdhYPNh5eaH2uOYdJvw0iPiOWZ1u/zpCmY6wUqYUksrch15zDurgVLD09n7Tc63n9YB9mYotnqWVf+44f/1acTz/DawemEJ9xDnf7+3iw0XCcbV1xtnHBydYFF1tXnGxccLbN+8n73dHGucTmyEazkajEn1kX9zWHr+7Xlwe6d2BI09F0qtvztr+tUUqxO3kbi0/N5VTqUQDud2zEo82fpk+DCGwMpQ+qnZ6bxrbEn9h4cT0Hr+zR+73WtHO39HttOJjWNfwqRXt/a/nk2Lt8d34pjZ09+Fym5BFCCFEFzJ49G4AXX3zRypEIUbXtTd7OqwemkKtyeKLlXxnl9USR20UlbuSNg3+hll1tlnX9GRcrTp8piWw5WBKrX1lw/H3iMs4CEFy7E1O8p9PsLvaDvVWpOdd46+DzenPdsnKycdaTXmdbF5xsLImvvY0DMSm7uWK8DFj6s/ZtOISIxqPwcG1e4fGblZmoxJ+JPP2xPg9vE2cvJrZ4lm71+92UcJvMuey9vIONCev47dLmP/u9GuzpVLcnfRpEEFqnq9WbRVQWRlM2U3c/zJm04zzQaBgv+bxj7ZBEBTKasll1bgkt3FrTsW53a4cjhBBCiErif1cP8td9E8gyZzLCYyKTW71cbOWOUorn9o7h8NX9jPF6isdaPn+Xo/2TJLK36GzaSRYcf499l3cAluaok1tNJ8xK/WBvVa45h81/bCAx8wIZpnQyctPIyE0v9HumKZ2M3AwyctPIMmeWWmYzV28GNxlF7waD7kotnkmZ2JTwA0tPzychMw6A5m6tmdTiOTrW6c7J1CP8fHE9W/74j55kA/i7h9CnwWDC6/er9v1ey+ts2kmm7BqO0ZzNG/4fES5T8lQLGblpvHZgqj7l1QiPCTzR8q/yJY4QQogySctJ5V8nZ3M951qFlNfCrTUPNBpeKaZavNfFpp1i2t4xXM+5Rr+GQ3jJ5++lDg579GoMz+x5BHuDA8u6/ERdx/vvUrSFSSJbRteMV4g8/TH/jl/5Zz/Y5k8zpMnoStEP9k4xKROZuRlkmtJJz00jMy/pzf+9sYsnbWsGWiWJzzXn8N8L37HszAKSsxMBS1PhazlX9G2aOHvRp+FgejcYyP1O91a/1/LK78gvU/JUD9eMV5ix/wmOXz9MTTt30nJTMalc/GqF8EbAHGo71LV2iEIIUW7SR/buWHP+Kz4+NqtCy7TV7Ohavw+Dm4zGr1ZwlagQqm4SMy/y7J7RJGX/Qcc63Xkr8OMyf8n95sFp/Jr4I/0aDmW677t3ONKiSSJbihyzkXVxK/jy9Cd6P9hBTR5hQvO/UNPevUKOIW6P0ZTN+vhv+PrM51zNSaGmnTs9GwygT4PBeNfwlT+Mt0gpxasHprAreSuB7qF8GLJEpuSpopKyEnk5+jHOpZ+igVNjPgxeQkp2Em8eeo7L2UnUtq/LGwEf4ecebO1QhRCiXCSRvTvePvQCW/7YwOAmowlwb39bZeWYc9h26Wd2XtqMGTMAzVxbEdFkNL0bDLyj0y2KP101pvDcnjHEZZzFr1YwHwQvuqWZQS5knGfijgGYVC5fhK2huVvrOxht0SSRLYZSil3JW1lw/H3iM2IBCL6vE1NbzcDLrVUFRCkqWmZuOvEZ5/BybSlNJm/TlezLPL5zMFeMyTze8gVGez1p7ZDELbqQcZ6X9k3kj6wLeLq05IPghfrI4SnZSbx96AUOXtmLjWbLU61eYljTR+VLHyFElSOJ7N3xyLYeXMpKYFGnf+Pl2rJCyryUlcAP8av4T/y3+hSIzjYulvFWmozC07VFhRxH3CwjN42/7pvA8euHaebqzUftl5Wr2938Y3/n+/NfEnJUkl14AAAgAElEQVRfZz4IXnQHIi2ZJLJFOJt6gk9PvEf0ZcugSI2dPZniPZ2OdbrLBz1xz9iTHMWMvCl55oeuwFum5KkyzqQe5+Xox0kxJtG6hj/vBn1+UwsSkzmXf538J6vOLQagx/0P8mLbt2W0aiFElSKJ7J13KSuBR7b1wNW2Bmt77Cq1/+StyjEbiUrcyLq4Ffx+9c/P74HuoUQ0GU2Xer2kgqICGc1G/rb/Kfan7KSBUxPmhS7nPod65SrrmvEKY7f3JT03lfeDFtK+TpcKjrZkZU1kK/aOraSuGa/w0dE3eWLnEKIv/4arbQ2mer/Cok7rq8xgTkJUlNA6XXmo6aOYVC7v/P4imbnp1g5JlMHRqzE8v/dRUoxJtKvdkdkhi4vsBmFjsGWy98u84f8RTjbObPljA1N3P8z59DNWiFoIIURldfiKZXrFtrUCKzyJBctsEj0bDGBu6FcsDFvHoMaP4GjjTMyVPbx1aBqjonoSeepjkrISK/zY9xqTMvH3319if8pO3O3r8EHwonInsQA17d0Z4/UUAJ+f+BCTMlVUqBWqWieyOWYj356LZOz2vqyPXwGaxuAmo1nW5SeGe4yv1oM5CVGSJ1v+lWau3sRnnOOT49bpyC/KLvryb7wYPYnU3Gt0rtuLd9t9Xmpfo/D7+7Og42o8XJpzLv00U3YN59c/frxLEQshxO15+OGHefjhh60dRrV25NoBAHxrBd3xYzVz8+b5tjP5Nnwbz7Z+HQ+X5lzOTuLLM58wKqonMw8+x4GUXVSllqKVhVKKef97i22JP+Fi68r7Qf+ikXPT2y73oabjqOfYkDNpx9l4cV0FRFrxqmXTYqUUO5O28NmJ94nPOGfZ977OTPGeUWHt/4Wo6gpOyTMzYC7d6vezdkiiCFGJG5l16AVyVA59GwzmJZ93sDHYlnn/zNx0Zh99nS1/bABghMdEnmz511sqQwghRPXz1M6HOJl6lH+ERNKudse7emylFAev7GFd3Aq2X/oFk8oFwMOlOYOaPELfBkNwtXO7qzFVVYtPzeWrMwuwNzjwftBCAmrf3qBdBW28uJ53D79MHYf6fNnlRxxtnCqs7JLcs31kz6QeZ8Hx94lO+bMf7FTvGXSoEy5NiIW4Qf6w+262NVnYaZ3V5gsTRfvxwvfMPvIaZswMbTqWp73/Vq7mX0opvj+/jM9OfIBJ5eLvHsL/+csUPUKIyisjIwMAZ2dnK0dSPWXmpv8/e/cdV3X1P3D8dbjsKShOQFERlamiYpZalqM0d+Y3S/uWq73MrLRSM00r03I2NKufK9Mc2VYrxVyooCBucSJ7r3t+f4D3q7mBywV8Px8PHnE/n/P5nPcHMHjfc8770POPNgCsuXs7DtaW+zpfyDnHulPLWRu/jMTc8wDYKBuCPVoTXqMT4Z4dqedY32LxVWQrj3/FJ7GTsVIG3gmZRfua95Tp/Y3ayKiI/sSl7+eJxi/wSMORZXr/a7ntEtmUvCS+PDSTdfHLMGLE2dqVIY2eoZf3IFlILsQ1FG3JM5KIC5sIdW/LtLAvZEueCuK741/xaexkAB5r+BRDGj1b6jfj9iXvZMLeF0jMTaC6nSfjg2WLHiFExSTFnsxrV2IEr+wcir9rIHPCV1g6HAAKjPlsSfid1Sf/j8ikbWj+l6N4O/oS7tmRtjU6EeTeUpYHAr+eWcPkfaMBeDVgMt3q9TVLPxd/VhwNTiy+82fc7aqbpZ9L3TbFnvKNeSw79gWP/tWVNfFLQCl6ez/C4jt/ol/9xySJFeI6lFKMDpiMu211IpO3sezYF5YO6bantWbhoVmmJPYp/7EMbfxcmcwoCXJvxbzwlQS7h5GYm8BLO4bw3fGvZE2SEELcZqJTigo9BZTD+tibZW1lQ4daXfkgbCErO21hbOD73F37fpytXTmZdZTlxxfyys6h9N14B2/veZ4Np1aSlHvB0mFbxLaEzUyNGgvAiCajzZbEArSsHk6bGh3IKszkqyOfmq2fkijViKxS6nlgGKCABVrrGf86Pxp4pPilNdAM8NRaJymljgHpQCFQcDNZ96UjslprthSvgz1VvA62dfU7GeX/muxPJcQt2pawmbG7h8uWPBZm1EZmx77HyhOLscKKlwMm0r1evzLvp8CYz4K4D1h+fCEgW/SIot+p0Sm78XcLlJEOUSHIiKx5jdn5JNsT/2J88Ed0qt3d0uFcV6GxgKiU3Wy7sImIhE0cy4wznVMo/F2DCPfsSLhnRxq7NDdLBeaKwqiNfHfiKxYc/IACnc/DDZ5keJNXzN7v0fSDDNvaG6Ws+OKONXg7+Zq1P7NPLVZKBQJLgDZAHrABGKW1jrtG+57Ai1rre4pfHwPCtNY3/VbKxUT2cHosc2KnsCtpKwA+Tg0Z1eQ12np2KNGzCCHgk5h3WXliMV6O9ZkX/r1F18vcjgqNBUzf/yY/nV6FjbLhjeAP6FCri1n73Hh2A9OiXye7MIv6To15J3QmPk4NzdqnqJgWHprJV0dmM7DBE4xoMtrS4QghiawZFepCev/RlsyCDJZ22ISnfS1Lh3RLzmbHE5GwiYgLm9idFEG+Mc90zsPWs3gKckdaVW93wwr/lUlybiJTo8fyz4XNAPSvP4RRTV4rtxpA06PfZP2pFdxZ8z4mhM4ya1/lkcgOALpqrZ8sfj0OyNVav3+N9t8Cf2itFxS/PsYtJrItWoXqxxb3Yl38cowYcbF2Y0ijp3lQ1sEKUWp5hbmM2tafoxlx9PMZwtNNx1o6pNtGXmEuE/e9zN/nf8Xe4MjE0E9oVf2Ocun7eMZh3t7zHMczD+NocGJM4BTuqnVfufQtKobjGYcZtrU3BTofF2s3lnXchJ3B3tJhiducJLLmcyQ9lie39qKWfV3+r8Pvlg6nVLILstidFGEarU3IPWs6V5UKRu1I/Jv39o0hOe8CrjZujA6YTPuancs1hgs553jsr27kGLP5uPU3Zq2xUR6JbDNgNdAOyAZ+A3ZorZ+9SltHIB5orLVOKj52FEgGNDBPaz3/Gv0MB4YDuPo5tGr1iQ9WykAv70E81vBp3GzdSxS/EOJKcWn7GRXRH5RiXvhKGrn4WzqkKi+7IJNxkc+wK2krztauTGk5n+bVQss1hqyCDKZFv8mmcxswKGs+bbuEJq6B5RqDsAytNS/ueJS9yf8rpPha4FS61O1lwaiEgIULFwIwdOhQi8ZRFf1wcgkzDrxN59o9eCN4uqXDKTNaa45kHCQi4Q8iLmziQMoejBhN570cG9DOs1OlKhiVb8zjy0MzWXLsMwBC3FvzetA0i+0y8eWhmSw+MpvmbiHMarPEbKPB5VK1WCn1BPA0kAHsB7K11i9epd1AYLDWuuclx+pqrU8rpWoCvwDPaq03X68/1yb2+qkljzGqyRjqOzcqcdxCiGubFTOJ7098TVC1Vsxo/bVsW2VGafkpjN01ggOpe/Cw9eT9Vp/R0EJvHmitmRkzkdUnv6W+UyPmhn8no3K3gR9Pfce06DeoZuPBQw3+y/y46QRWa8nMNt9aOjQhhJlM3vcqv575geeajqO3zyM3vqCSSs1LZnviX2xN+IPtF/4ioyDNdM7R4ERY9faEe3aiTY0OeNjVsGCkV3cq6wST9r5MbNo+rJSBIY2e4T++wy26u0RWQQaP/tWV5LxExgfPoFPtbmbpp9y331FKTQbitdazr3Lue2C51vqqvxmVUm8DGVrr674t1Cy0qT4QGVMW4QohriEjP40hf3cnOS9RRmbMRGvN3uQdzIyZwNGMOGrb12Na2BcWn/aUW5jDiIi+nMg8woD6Qxnl/5pF4xHmlZqXzJC/uxe9oRL4PnfW7MyATUWVKT+/Yw2+zn6WDlHcxi5cKFp5VqNGxUswKrtH/ryXM9nxzA//nsauzSwdTrkoNBYQnRpJRMLGKwpGAfi7BtHOs1OFKRj165k1zNj/NlmFmdS0r8ubQdMJdK8YFaYvjujXdfDhy/ZrzTKyXV4jsjW11ueVUj7Az0A7rXXyv9q4AUcBb611ZvExJ8BKa51e/PkvwASt9Ybr9Xe9fWSFEGXn59OrmBL1Gu621VnU/kecbVwtHVKVkJqXzM+nV7E2fhkns44CUN+pEe+3+qLCFNuISd3HM/88jNZGPghbSKhHW0uHJMxkatRYfjr9PS092jGt1Rcopfho/9usiV9CX59HeabpG5YOUdzGZI2seSTmnmfApg44GpxYfc8/t+3e8Wez49mWsJmtFzYSmbSNPGOu6ZyHrSdtPTsQXqNTuReMyirIYOaBifx8ZjUAHWt14+XmEyrU32GFxgKe2PogJzKP8LT/6/Sr/1iZ91Fe+8h+p5TaD6wBntZaJyulRiqlRl7Spg/w88Uktlgt4C+l1B7gH2DdjZJYIUT5ua9OL4KqtSI5L5GFh81bma6q01qzOymCiXtf4qFNHZhzcCons45S3a4mgxuOYmabbytMEgvQ1C2Iwb4j0WimRI0lsyDD0iEJM9iT9A8/nf4eG2XD883Gm5YQ9PQaCMDPp1eTW5hjyRCFEGYQnbIbgGbVQm7bJBagtoMXvXz+w5SW81l1dwTvtphDT6+HqWlfh6S8BH489R1v7XmW3n+0Y/SO/7Li+CLTdp/mcjAtihER/fj5zGrsrOx5uflExgd/VKGSWACDlTXD/Iq2/Fl85FMy8tNucIX5WJfmYq31XVc5NvdfrxcCC/917AgQUpq+hRDmo5TiuWbjGRHRl1UnvqFb3b63zfSjspKcm8hPp1ex/tQy4ot/+VlhRXiNjjzgNZDwGh0wWJXqf8FmM7jhSLZd2ERsWhSfxkzm1cDJlg5JlKE8Yx4fHXgbgP80HHHZfoCNXZvh7xpEbNo+Np79ka71+lgoSiGEOUSl7AIgwK2FhSOpOOwNDrTzvJt2nnebCkZtS9hIxIVN7E+JZGfSFnYmbWF27Ht4OTYo2rO2xt1lVjDKqI2sOL6Qz+I+okDn09DZn3HBH1boekB3eN5NsHsYe5N38O3RBQxv8rJF4qiYf0UJISyukYs/vb0fYeWJr/g4ZgIft/7G4mtGKjqjNrI7KYK18cv4+/xvFOh8ADztatO9Xj+61+tHLYe6Fo7yxqytbHgtcCojIvqy4fRK2tfsXO5l/oX5LD36GScyj+Dl2IBBvsOvON/TayCx+/exJn6pJLJCVDFRyUUjshVlvWVFo5SikYs/jVz8+U/DEaaCUREJG9l+4S/is46x4vgxVhxfZCoY1bZ439qSFIxKyr3A1KjX2J74FwB9fAYzwm80tga7sn60MqWUYmSTMTy1bQDfnVhEL+9BFvn7psyKPZUHWSMrRPnKyE9n6N/3k5SXwKsBk+lWr6+lQ6qQknIT2HD6e9bFL+dM9kmgePTVsxM9vB6idY27KuUUru+Of8WnsZOpZuPB53eswd2uuqVDEqUUn3mMJ7Y+SL4xjw/CFtLCI/yKNtkFWTy0uQOZBRl81m61xSppi9ubrJEte7mFOfT4PQytjay+5x+cynHtZ1Xwv4JRm9h2YSNHM64sGFU0WtsRP9eAG775v/3Cn0yJeo3kvERcbarxasBk7qh5jzkfocxN3PsSf5xdz311ejE2aGqZ3bfcqxaXB0lkhSh/v55Zw+R9o6lm48GiO3/ExcbN0iFVCEZtZGfi36yLX87fCb9TqAsAqGlflwfq9adbvb4W2+etrBi1kVd2PE5k8jba17yXCSGzZDumSkxrzas7n2Bn0ha61OnFa9f5o+PjAxNYffJbens/wnPNxpVjlEIUWbp0KQADBw60cCRVx56k7by441EauTRlQbtVlg6n0jubfYptCZuIuLCJ3UkRVy0Y1bZGR8Kq33FZwah8Yx6fx81g2fEvAAh1b8vYoPcrVL2Mm3UmK56hf3enQBcwN/w7/Fybl8l9JZEVQpQJrTUv7niUvck76OX9H55vNt7SIVnUhZxzbDj9Pevjl3M25xQAVsrAHZ730MPrIVpVv6NSjr5ey7ns0zy59UEyCzIYE/CeTDWtxH47s4Z3943G1caNhe1/pJqtxzXbHk6PYdjW3jhZu7C842bsDQ7lGKkQwhy+PTKPzw59JL/LzSCnMJvIpG1sTdjItgubOJ9zxnTOWtkQ4t6atp4d8XNpxtyD7xObFoWVMvB4o2d52HdYpf67YU7sFJYfX0hLj3ZMbjmvTO5pZ7C7qURW1sgKIa5LKcXzTcczLKIPa04uoXu9fjRxDbB0WOWqUBey48LfrD21jK0Jf2DUhQDUtq/HA14D6FavL9Xtalo4SvOo5VCXZ/zfYGr0WD6JfZcQjzbUdqhn6bDELUrPT2V27BQAhvuNvm4SC9DIpSnN3EI4kLqHP86up3u9fuURphAmJ08WLdPw9va2cCRVx8VCT4HVpNBTWbM3OBDu2Ylwz05orTmacbBoz9p/FYy6qJZ9Xd4M/oCAKvC9eKThSH48tZJdSVvp9mtwufYtiawQ4oZ8XZrQz+dRlh9fyMcHJjCrzf/dFoWfEnLO8uOp71h/6jvO55wGwKCs6VCzCw8Uj77eDl+HLnV783fC7/x1/hfej3qd6WFf3hbPXZV8FvchyXmJBFVrddNr3Xt4PcSB1D2sjV8miawod48++igga2TLilEb2Z8aCUBANSn0ZE5KKRq6+NPwkoJROxL/YmvCRqJTdhPi3ppnmr5R4bbVKSlXm2qM8h/DrJh3KTTml2vfksgKIW7KY42e4fez6ziQuocNp1Zyv1d/S4dkFoXGAv5J/JO18cvYlrAJI0YA6jr48IDXALrW7VOiyoSVmVKKl5q/Q3TKLiKTt/H9ia/NsgG6MI+olF2siV+KQVnzQvO3b/pNiLtr38/s2CkcSN3D4fQYGrk0NXOkQghzOZl5lLT8VKrb1aSWfcWvnl+VuNm607lOTzrX6WnpUMzm4s4MZUVxc/U45C11IcRNcbJ2ZmSTMQDMj5tOWn6KhSMqW+eyT7Pw0Cz+8+e9vLF7FFsT/sBKGehUqzvTW33JV3duYJDvsNsuib2omq0HLzWfAMCCuA84nnHYwhGJm1FgzOej/W8B8HCDJ/B19rvpa+0NDtxX50EA1sYvM0t8Qojy8b9pxS2laJ+oMiSRFULctHtqP0Coe1vS8lP4PG6GpcMptUJjAX+f/42xu0bwyJ/38tWRT0nIPYuXY31GNBnN0g4bGR/yES2rt5OptED7mp3pVrcvecZc3ot6lYJynkIkbt2K44s4mhFHHQdvBjccdcvX9/B6CIBfz/xAdkFWWYcnhCgnlyayQlQV8peZEOKmKaV4rtk4DMqatfFLiU3dZ+mQSuRsdjxfHPqYh/+8h3GRT7PtwiYMysA9tR/gw7BFLGq/gYENnpB9U6/i6aavU8u+LgfTovn6yFxLhyOu42x2PIsOfwLAC83GY2ewv+V7NHTxp7lbKJkFGfxxdn1ZhyiEKCfRKbsBKfQkqhZZIyuEuCUNnBvTv/4Qlh77nBkHJvBJ2yWVomx8gTGfLQl/sC5+GTsS/0ZTtPWYt6MvPbweokvd3rjZuls4yorPydqZMYHv8fKOoXx9dC7hnp1o6hZk6bDEv2itmXlgIrnGHO6ufT+ta9xV4nv19BrI/tRI1sYvq7Jr40XF8/LLL1s6hCojJS+J+Kxj2Fs5yFp3UaVIIiuEuGWPNhzFb2fWEpu2jx9PraCHV8XdsP501knWn1rOj6dWkpx3AQAbK1s61urGA/UGEOweJuuFblGoR1v61x/C8uMLeW/fq8xv932JRvuE+fx5/mciLmzCydqZp/xfK9W9OtXuzqex7xGTtpdDaQdo7NqsjKIU4tp69qy6hXHK28XR2KZuQVhb2Vg4GiHKjkwtFkLcMsdL/jj+LO5DUvOSLRzR5fKNeWw8u4HRO//L4L/u49uj80nOu0B9p8Y87f86yztu5vWg9wnxaC1JbAk90fhF6js15mTWURbEfWDpcMQlMgsymBXzLgBPNn6p1Hsc2xns6VK3FwBr4peWOj4hbkZsbCyxsbGWDqNKuJjIyrY7oqqRRFYIUSIda3WjlccdpOWn8lnch5YOB4D4zGPMPzidgZs6MWHvC+xM3IKtlR1d6vRiZutv+eKONfSr/xiuNtUsHWqlZ2uwY2zQFAzKmpUnFrMrcaulQxLFvjg0g8Tc8zRzC6GHd9nMlniguOjTb2fWkF2QWSb3FOJ6RowYwYgRIywdRpUghZ5EVVWqRFYp9bxSKkopFa2UeuEq5zsppVKVUpHFH+MvOddNKRWrlDqklCrdvCchRLlTSvFsszexVjasP7WCAyl7LBJHnjGPP86u5+UdQ3ns724sOfYZKflJ+Dr78WzTN1necTOvBU0l0F22HChrTVwDeazhUwBMjRpLRn6ahSMSsan7WHXiG6yUgZeav1Nm69d9nf0IrNaSrMJMfju7rkzuKYQwvzxjHrFpUQA0rxZi4WiEKFslTmSVUoHAMKANEAL0UEpdbYO6P7XWocUfE4qvNQCfAt2B5sAgpVTzksYihLAMH6eGDGgwFI3m45gJFOrCcuv7ROYR5sROZeCmjkzc+xK7kyKws7KnW92+fNJmCZ+1+4E+PoNxsXErt5huR//xHU5T12AScs/ySfF0VmEZhcYCPtz/FhpNP5/Hyryoy8W18OtkT1khKo2DaVHkG/No4OQnvw9FlVOaYk/NgAitdRaAUmoT0Ad4/yaubQMc0lofKb52CdAL2F+KeIQQFjDYt6jw08G0aNbGL6OX9yCz9ZVXmMuf539mbfwy9iRvNx1v5NKUHl4D6Vy7B842LmbrX1zJYGXNa0FTGLG1Lz+fWc0dNTvToVaXEt/PqI2cyjpObFoUMan7iM88ip3BHhcbN5ytXXG2ccWl+L/O1q642Fz+uY2VbRk+XeWy6uS3xKXvp6Z9HYY2eqbM79+xVlc+jZlMbFoUB9OiaeIaUOZ9CCHKlmnbHXfZdkdUPaVJZKOAd5VS1YFs4H5gx1XatVNK7QFOA69oraOBesDJS9rEA22v1olSajgwHMDHx6cU4QohzMHB2pGn/F/j7T3P83ncR3Ss1ZVqth5l2sexjEOsi1/OL2dWkZafCoC9wZF7aj9AD6+H8HcNlGnDFuTj1JDhTV5hVswkPtr/FoHVWuJhV+OG12mtScg9S0zqPmLT9hGbGkVsWhSZBekljsXOyv7yJPdi8nvp59dIiB0MTpX25ygh5yxfHJoBwLNNx+Fg7VTmfdgZ7Lmvbi9WnviKtfFLean5hDLvQwhRtqJM+8fK+lhR9ZQ4kdVaH1BKTQV+ATKAPUDBv5rtAuprrTOUUvcDqwA/4Gp/Kehr9DMfmA8QFhZ21TZCCMu6q2YXwqq3Z0fi3yyI+4DRAaWbYppXmEt06m52JUawM3ELMWl7Tef8XJrTw+sh7qnTAydr59KGLspIL+//sOX87+xM2sIH+8cxKXT2FUlhal4ysWlRxKbuI6Y4cU3KS7jiXtXtPGnqGoy/WxC+zk3IN+aRUZBKRn466cX/zShIIz3/4ueppOenkVGQTq4xh9zcHBJzz9/yM1gpA87WLlcd6b3aaPDF1y42bjhZO1t0W4tPYt4luzCL9jXvpX3Ne8zWT0+vgaw88RW/nVnLyCav4ij/BoWZvPnmm5YOodLTWhOdXFToKaCajMiKqqdU+8hqrT8HPgdQSk2maGT10vNpl3y+Xik1WylVo7id9yVNvSgasRVCVEJKKZ5t+iZPbHmQH099x/31+t/SL81CYwGxaVHsTopgV1IEUSm7yDfmmc47GBy5t05PHvAaQBPXQHM8giglK2XF6MB3eWLLg2xN+IPVJ7/F19nPlLDGpu3jTHb8Fdc5W7vi7xZIU9cg/N2C8HcNwtO+Voli0FqTU5hFRkE66fn/S24z8lNNxzIK0sjITyO9IO2KJDinMIu0/BTS8lOK5hndIgeD41WTYKfi/14tCXa2ccPZ2gV7g0OJR4O3nP+dP8//goPBkWebvlGie9ys+s6NCKoWxr6UHfx+dl2F3kNaVG733nuvpUOo9E5lHSclPwl32+rUdZBZjaLqKVUiq5SqqbU+r5TyAfoC7f51vjZwTmutlVJtKCoulQikAH5KKV/gFPAw8J/SxCKEsCxvJ18GNvgv3xydx8cHJjAnfMU1K6YatZGjGXHsStrK7sQI9iZvJ6vw8i09Grs0o4VHW1p4tCPEPcwsUyVF2appX4fnmo7jvahXmRkz8Yrzdlb2+Lk2x981iKZuRR91HXzKbDqvUgoHayccrJ3wtK99y9fnG/PILMgoHulNKx71/V8ynH5JEpxZPDqcnp9GZnFSnF2YRXZhFgm5Z2+5b2tlUzQabONavB7Y5V8jwm4427j8b1p08TFbK1tmxkwC4PHGz1PTvs4t932renoPZF/KDtacXCqJrDCbyMhIAEJDQy0cSeV16f6xlXXZhBDXU6pEFviueI1sPvC01jpZKTUSQGs9F+gPjFJKFVD0/vbDWmsNFCilngF+AgzAF8VrZ4UQldgjDUfyy5k1HEo/wJqTS+jt8whQNFJ2Kus4u5Mi2J20jd1JEaTmJ192rZdjA1p6tKOFR1tCPdriZutuiUcQpXRvnZ7sStrCb2fW4evsR9PiUVZ/tyAaODXCYFXaXzvmY2NlSzVbjxKt8TZqI9mFWVdJgi8dAb7y+MXXecZcUvKTSMlPKlHsjV2a08f7kRJde6s61OzCJzaTiEvfT2zqPvzdgsqlX1GxZeSnMTNmEv9c2EQjl2a09AinhUc4/q6BJfp3/8ILRbs6bty4sYwjvX1c3D82oJq8GSCqptJOLb7rKsfmXvL5J8An17h2PbC+NP0LISoWe4MDzzR9nfGRz/D5oRnYWtmxL2UHu5O2cT7nzGVtPe1q07J60R86LTzCSzSCJioepRRjAqcwOmAyVqpUW5VXKlbKCidr56J12w71bvn6vMLcy5Lc6ybB/zpnrax5JWBiub1JYGuwo0vd3qw4vog18cskkRVEp+zm3b2vcDbnFE9SGuYAACAASURBVEDxm5YRADganAh2b02L6uG09GiHr7PfbfX/BkuSQk+iqqu4b40LISql9p6daVOjA/9c2Mz0/f8r1uFm406oR1vTu/T1HOvLVKcqTP5QvTW2Bjs8DJ542HlaOpSb0sNrICuOL+L3s+sY5T9GCq/dpgp1IUuOLuDLw7Mw6kL8XJrzYvN3OJt9qjiZ3Up81nEiLmwk4sJGQH4XlJe0/BSOZx7CxsoWP9kqS1RRksgKIcqUUornm45n0r6XcbOpRovq7WjpEY6vcxNJboSoInycGhLi3po9ydv57cxaHvR+2NIhiXKWkHOO9/a9SmTyNgAG1H+cJ/1exMbKlqZuQXSq3Q2A8zlniEzaxq7ErexKiuBC7jk2ndvApnMbgKK19RfrIRTNzilZsTdxuf0pewBo6hqE7W28v7ao2iSRFUKUuTqOXnzadqmlwxBCmFEPr4HsSd7O2vil9PQaKKNqt5Et53/n/ejXSctPwd22Oq8FTqF1jStWmwFFiWqXur3pUrc3Wmvis46ZaiXsTorgfM4Zfjq9ip9OrwL+Vy8B/xyIsyvPx6pSolNk2x1R9UkiK4QQQohbdletLrjGVONQ+gFi0vbRzC3Y0iEJM8srzGVe3DS+P/E1AK2r38mYwCl42NW4qeuVUng7+eLt5MuD3g8XV7A/yK7ErexOimBP8nbis44Rn3UMq35Qx8qH4xmHqe/cyJyPVSX9r9CTrI8VVZckskIIIYS4ZbZWtnSt24flx79kbfxSSWSruOMZh5m49yWOZMRirWx40u9F+tcfWqolI1bKikYuTWnk0pQBDR6nwJhfvKf4NtbHL+dMzglGRvRjpP8YHvR6WEb9b1KBMZ+Y1H0ABMqIrKjCZMGaEEIIIUqkh9dDAPxxZj0Z+ekWjkaYg9aadfHLGRnRjyMZsdRzrM+sNt/yUIP/lnndA2srGwKqtWBww5E8aXidUOs7yDXm8PGBd3hj9yiScxPLtL+q6lB6DLnGHLwdfWUrO1GlSSIrhBBCiBLxdvIl1L0tOcZsfjuzxtLhiDKWkZ/GhL0v8sH+ceQac+hSpxfzwr8rly2XJrwxkV0TjjAu+EOcrV2JuLCRJ7c+yLaEzWbvu7KLkvWx4jYhiawQQgghSqyn90AA1sQvRWtt4WhEWYlK2cWwrb3ZdG4DDgZHxga+z2tBU3Es562W7q59P5/dsZoQ99Yk5yUydvdwZh6YSG5hTrnGUZlcTGRl/1hR1UkiK4QQQogSa1/zXtxs3DmSEcuB1D2WDkeUUqEuZPGRObyw/VHO5ZzG3zWI+e1WcV/dBy0WU037OkwPW8gwv5cxKGtWnfyGkRH9OZweY7GYKiqtNdEpuwEIdJdEVlRtksgKIYQQosRsrWzpVq8vAGvjl1k4GlEaCTlneWXHUL489DFGXcjDDZ5kZptvqOfoY+nQMCgDg3yH8WnbJXg7+nI88xBPRQxg+bEvMWqjpcOrMM7lnCIx9zyuNm54O/paOhwhzEoSWSGEEEKUiqno09n1ZOSnWTgaURJ/n/+NYVt7sSd5O+62NZja8jOGN3kFGytbS4d2mSaugcwN/46eXg+Tr/OZc3AqY3Y+SULOOUuHViFEFY/GNndrIVWeRZUniawQQgghSqWeY31aerQj15jDL2d+sHQ44hYtiPuAcZFPk5afSpvqd/FZu9W0rnGnRWOaMWMGM2bMuOo5B2tHXmz+NhNDZ+Nm487OpC0M2/ogm8/9XM5RVjxRycXrY2VasbgNyD6yQgghhCi1Hl4D2ZW0lbXxS+nt/YiMBhUzaiPnc05zNCOOo+kHOZoRx+nsk/T1eZTOdXpYOjx+PbOG/zu6AGtlw7AmL9PP57Ey31anJEJDQ2/Ypn3Ne2jm9gNTo8ayPfEv3t7zHPfX68/T/mNxsHYqhygrnujU4vWxUuhJ3AYkkRVCCCFEqbWveQ/uttU5mhFHdOru2/IP6aTcCxzLiCtKWjPiOJpxkGMZcWQXZl3R9v2o/Xg51i+XrWyuJT7zGB/tfwuA55qNM00Rrwh+/fVXAO69997rtvOw8+S9lvNZdeJr5sVNZ/2pFexJ3s7rQdNo5hZcHqFWGJkFGRxNP4i1ssHfNdDS4QhhdpLICiGEEKLUbKxs6Vq3L0uOLWDmgUkMafQM4Z4dMSiDpUMrc5kFGZckrAc5mh7HsYw4UvKTrtrew9YTX2c/fF388HVuwt7kHfx0+nve3vM889qtxNWmWjk/AeQV5jJh74tkF2Zxd+37eaDegHKP4XomTZoE3DiRBbBSVvSt/xgtPMJ5d98rHMk4yLP/DGJoo2cZ5DusSv4MXs3+lEiMGPF3DcLOYG/pcIQwu1Ilskqp54FhgAIWaK1n/Ov8I8CY4pcZwCit9Z7ic8eAdKAQKNBah5UmFiGEEEJYVi/vQfx4agWH0vczLvIpatrXpafXQ3Sv1x8PuxqWDu+W5RXmciLziGmE9VjGQY5kxHE+5/RV2ztZO+Pr3ARfZz8aOPsVJa/OTXCzdb+sXec6PTmecZiYtL1M3vcqk1vMLffpvHMPvs+h9APUdfDhpeYTqsRUcF+XJsxuu5zPDn3IiuOL+OLQDLZf+JOxQVOp7eBl6fDMLtq0f2wLC0ciRPkocSKrlAqkKIltA+QBG5RS67TWcZc0Owp01FonK6W6A/OBtpecv1trfaGkMQghhBCi4qjlUJdFd/7IT6dX8cPJ/+NU1nE+PzSDRYc/pWOtrvTy/g8B1SpeNdVCXcjprBP/G2HNKBphjc86jlEXXtHexsqW+k6NixPVomTV18UPT7vaN/Vstla2vBUygxERffnnwma+OTqPRxuOMsejXdXmcz+z6uQ3WCsbxod8iJO1c7n1bW62Bjue8h9L6+p38X70WPal7GTY1t483+wt7q3T09LhmVV0SiQAAbfhtH5xe1Ja65JdqNQAoKvW+sni1+OAXK31+9do7w5Eaa3rFb8+BoTdSiIbFhamd+zYUaJ4hRBCCFF+jNrIzsQt/HDy/9ia8AdGivb6bOjsTy/vQdxbp2e5F+TRWnMh9xxH0g+aRliPZsRxPPMwecbcK9pbYUU9x/rF04L/N9Jaz8EHg1XpV2dtv/Anr+0aDsDUVp8RVr19qe95I2ez4xm2tQ+ZBek84/86fes/ZvY+S6JTp04AbNy4scT3SM1LZvr+cfx9vmi9befaPXi+2XicbVzLIMKKpdBYwIN/tCG7MIsVHf/Ew87T0iEJUWJKqZ03M1u3NIlsM2A10A7IBn4Ddmitn71G+1eAppckvkeBZEAD87TW869x3XBgOICPj0+r48ePlyheIYQQQljG2exTrItfxrr45aZ1pE7WznSp25sHvQZR37lRmfeZmpd8+TrW4s8zC9Kv2r6mfR0aOPvR0LmJaVpwfadG2Brsyjy2Sy08NIuvjnyKm40789qtpKZ9HbP1VWDM5/ntgzmQuof2np2ZEPpJhRsdv6gsElkoevNi/akVfBozmRxjNjXt6/J60PsEu1etFW1xafsZEdGXOg7efHPXL5YOR4hSMXsiW9zJE8DTFK1/3Q9ka61fvEq7u4HZwJ1a68TiY3W11qeVUjWBX4Bntdabr9efjMgKIYQQlVeeMY8/z/3E6pP/R1Txej6AUPe29PIeRPuanbG2srmle2YXZHEs81Bx0lqcsKbHkZSXcNX2rjbVLktWfV2a0MDJD2cbl1I9W0kV6kJe3zWC7Yl/0dwthI9aL8bGytYsfc07OI2lxz6npn0d5rf73iJFpm5WbGwsAP7+/mVyv5OZR5m8bzSxaVFYYcUg32EMafTMLf+8VVTfn/iaWTGTuK9OL8YGTbV0OEKUSrkksv/qcDIQr7We/a/jwcD3QHet9cFrXPs2kKG1nn69PiSRFUIIIaqGw+kxrD75Lb+eXkOOMRuA6nY16eH1EA/UG0AN+1qXtc835hGfeeyyrW2OZsRxJvvkVe9vb3CkgVNjfF38TCOtvs5+uNvWqHCjkKl5yYyI6Mv5nDP08RnMs03fLPM+IhI28fruEVgpAzNaL74tt0cqMOaz6PAnfHt0PhqNv2sQbwRNw8upgaVDK7VJe1/m97PreLHZ2/T0ftjS4QhRKuU1IltTa31eKeUD/Ay001onX3LeB/gdeExrveWS406AldY6vfjzX4AJWusN1+tPElkhhBCiasnIT+fnM0XFoU5kHgHAShm4s+a9NHJpyrHiwksnM49RoPOvuN6grPFx8r1kWnBRwlrboV65VwIujQOpe3n+n0co0Pm8GfQB99R5oMzunZBzjmFbe5GWn8Iwv5cZ5DuszO5tLmvWrAGgZ8+yL9C0J2k770WN4XzOaeytHHi66evcX69/hXuD41Y8vPkezuec5rN2q2noUjaj2EJYSnklsn8C1YF84CWt9W9KqZEAWuu5SqnPgH7AxYWtBVrrMKVUQ4pGaaGocvK3Wut3b9SfJLJCCCFE1aS1ZnfSNn44+S1/Jfx21WrBdRy8r5gW7OVY32xTccvb6hPf8nHMBOwNjsxpu7xM1g4XGgt4eedQ9ibvoHX1O3mv5fxKkeCX1RrZa8nIT2PGgXf4/ew6AO6seR8vN59wxVZJlUFCzlkGbu6Ek7ULq+/eVim+v0Jcz80msqUquae1vusqx+Ze8vmTwJNXaXMECClN30IIIYSoOpRStKweTsvq4STknOOn09+Tlp9cvC9rE+o7NSz3Ksfl7UHvQUSl7OK3s2t5e89zzG67rNTPvPjIbPYm78DD1pPXAqdKklPM2caVN4M/INyzEx8feIe/zv/CgdRIxgROKZfq0WXp4nrzALdQ+f6K24r8tAshhBCiQvG0r8XghiN5yn8s3ev1o6lbUJVPYqEomX+p+QTqOzXmeOZhpu8fR2lmzu1KjGDxkTkoFG8ET8PdrnoZRls13FunJwvarSKwWksScxN4decTzI59j7zCK7djqqiiU3YDEOB++617Frc3SWSFEEIIISoIB2tH3gmdiYPBkT/OrmfVyW9KdJ+k3AtM3jcajebRhk/RwiO8jCOtOmo7ePFR2Fc83vh5rJSBFccX8dS2hziaEWfp0G7K/0ZkW1g4EiHKlySyQgghhBAViI9TQ0YHFJUOmRM7lf0pkbd0vVEbmRI1hqS8BELd2/Boo6fMEWaVYrCy5tGGo5jV+lvqOvhwJCOWkRH9WHlicalGxc0tuyCTQ+kxWCkDzdyCLR2OEOVKElkhhBBCiAqmU+3u9PV5jAKdzzt7XiAlL+mmr11y7DN2JP6Nm407rwdNw6AMZozUPBYvXszixYvLvd9m1UJY0O577q/Xn3xjHp/EvMvYXcNJyr36vsSWdiB1H0ZdSGOXpjhYO1o6HCHKlSSyQgghhBAV0Igmr9DcLZSE3LO8u/cVCq9SyfnfopJ38cWhjwF4LXDqFfvxVhbe3t54e3tbpG8HaydeCZjE2yEf42Ltxj+Jf/LElgfZcv53i8RzPdGpRetjb8d9gYWQRFYIIYQQogKysbLlrZAZVLPxYGfSFr46/Ol126fmJTNp38sYdSEPN3iStp4dyinSsrd06VKWLl1q0Rg61OrKZ3f8QEuPdqTmJ/Nm5FN8tP9tcgqzLRrXpaKTi9fHVpP1seL2I4msEEIIIUQF5WlfmzeDP8AKKxYfmU1EwqarttNaMy36Dc7nnKG5Wwj/bfx8OUdatubMmcOcOXMsHQae9rV4v9XnjGzyKjbKhjXxSxixtS8H06ItHRpGbSQ6tWj9tIzIituRJLJCCCGEEBVYy+rtGNr4OQDe2/cqZ7Pjr2iz8sRitiT8jrO1K28Gf4i1lU15h1llWSkrHmrwXz5tu4z6To04mXWUZ7Y9zJKjn93UdG9zOZ5xiMyCdGra18XTvrbF4hDCUiSRFUIIIYSo4P7jO5zwGh1JL0jlnT0vkGfMM52LTd3HvIPTABgd8C61HepZKswqrbFrM+aGf0dv70co0PnMj5vO6B2Pcz7njEXiMW27Uy3UIv0LYWmSyAohhBBCVHBWyoqxQe9T274esWlRfBozGYCM/HQm7H2JAp1PH5/B3FXrPgtHWrXZGex5rtk4JreYh7ttdSKT/+HJLb3YePbHco8lKkUKPYnbmySyQgghhBCVgIuNG2+Hfmxaq/nz6VV8uH88Z7JP4ufSnBFNXrV0iLeNcM+OfNbuB8JrdCSjII0Je19katRrZBZklFsMF0dkJZEVtytVkTd5/rewsDC9Y8cOS4chhBBCCGExa+OX8eH+8VgpA0ZdiIPBkXnhK/FyamDp0MrMhQsXAKhRo4aFI7k+rTU/xC9hTuwU8oy51HHw4vWgaWavIpyUm0D/TXfhYHDkh7v/wWBlbdb+hChPSqmdWuuwG7WTEVkhhBBCiErkgXoD6Fq3N8biQkMvNZ9QpZJYKEpgK3oSC6CUopf3IOaFr6SxSzPOZMfz/PbBLDw0i0Jjgdn63Ze8E4BmbiGSxIrblvzkCyGEEEJUIkopnm/2FlbKgI9TQzrX6WHpkMrcwoULARg6dKhF47hZ9Z0b8UnbpXx56GOWHfuCr458yo7Ev3k9aBp1Hb1Lff+cwmyiU3azK2kruxO3cTAtCoBA2T9W3MZKNbVYKfU8MAxQwAKt9Yx/nVfAx8D9QBYwVGu9q/jcEODN4qaTtNaLbtSfTC0WQgghhKj6OnXqBMDGjRstGkdJ7EqMYErUGC7knsPB4MhzTcfRpW5viv4svjkFxnxiUvexOymC3UkRRKfsJl/nm84blDWB1VowJvA9ajt4meMxhLCYm51aXOIRWaVUIEVJbBsgD9iglFqntY67pFl3wK/4oy0wB2irlPIA3gLCAA3sVEr9oLVOLmk8QgghhBBCWFrL6uF8dsdqPtz/FpvP/cTU6LFEXNjES83fwcXG7arXGLWRw+kx7E6KYFdSBHuTd5BTmGU6r1A0dmlOS49wWniEE+zeCgdrp/J6JCEqpNJMLW4GRGitswCUUpuAPsD7l7TpBXyli4Z9I5RS1ZRSdYBOwC9a66Tia38BugH/V4p4hBBCCCGEsDhXm2q8FTyDn05/z6yYSWw6t4H9KZG8FjSFFh7haK05mXWU3YlFI66RydtIy0+97B4+Tg1pUZy4hri3xs3W3UJPI0TFVJpENgp4VylVHcimaPrwv+f91gNOXvI6vvjYtY4LIYQQQghR6Sml6FavL0HuYUzeN5oDqXt4ZcfjtK5xF4fTY0jMPX9Z+5r2dWjhEU5Lj3aEerTF076WhSIXonIocSKrtT6glJoK/AJkAHuAf5dnu9piAH2d41dQSg0HhgP4+PiUNFwhhBBCCCHKXT1HHz5u/TVfH5nD10fm8s+FzQBUs/GgRfVwWni0pYVHO+o6eN/SOlohbnelqlqstf4c+BxAKTWZopHVS8UDl5Zq8wJOFx/v9K/jG6/Rx3xgPhQVeypNvEIIIYQQouJbv369pUMoU9ZWNgxt/Bzta97LwbRomrkF08DZDyslO2EKUVKlSmSVUjW11ueVUj5AX6Ddv5r8ADyjlFpCUbGnVK31GaXUT8BkpdTFyf5dgLGliUUIIYQQQlQNjo6Olg7BLPxcm+Pn2tzSYQhRJZR2H9nvitfI5gNPa62TlVIjAbTWc4H1FK2dPUTR9juPF59LUkpNBLYX32fCxcJPQgghhBDi9jZ79mwAnnrqKQtHIoSoqEq1j2x5k31khRBCCCGqvsq8j6wQonRudh9ZmZgvhBBCCCGEEKJSkURWCCGEEEIIIUSlIomsEEIIIYQQQohKRRJZIYQQQgghhBCVSqUq9lSjRg3doEEDS4chhBBCCCGEEMIMdu7cqbXWNxxwLe32O+WqQYMGSNViIYQQQgghhKialFK7bqadTC0WQgghhBBCCFGpVKoR2UvFx8fTp08fdu3ahdFotHQ4QlRqVlZWtGzZku+//x4vLy9LhyOEEEIIIcR1VdpEtk+fPvTt25e///4bW1tbS4cjRKWWl5fH9OnT6dOnD9u3b7d0OEIIIYQQQlxXpSr2FBYWpi+ukTUYDGRnZ0sSK0QZycvLw8HBgcLCQkuHIoQQQgghblNKqZ1a67Abtau0a2SNRqMksUKUIVtbW5mmL4QQQgghKgWzJ7JKqS+UUueVUlH/Ov6sUipWKRWtlHrf3HEIIYQQQgghhKgaymNEdiHQ7dIDSqm7gV5AsNY6AJheDnEIIYQQQgghhKgCzJ7Iaq03A0n/OjwKmKK1zi1uc97ccQghhBBCCCGEqBostUa2CXCXUmqbUmqTUqr1tRoqpYYrpXYopXYkJCSUuMMZM2aQlZVV4utvlsFgIDQ0lMDAQAYMGEBWVhbHjh0jMDDQ7H0D5OTk0KZNG0JCQggICOCtt94qs3sPHToUX19fQkNDadmyJVu3br1qu7lz5/LVV1+VWb/lrVOnTlwsKnarxo8fz6+//nrdNhs3bmTLli0luv+NLFy4kNOnT5vl3kIIIYQQQlQUlkpkrQF3IBwYDSxTSqmrNdRaz9dah2mtwzw9PUvcYXklsg4ODkRGRhIVFYWtrS1z5841e5+XsrOz4/fff2fPnj1ERkayYcMGIiIiyuz+06ZNIzIykilTpjBixIgrzhcUFDBy5Egee+yxMuvzospQTXfChAnce++9120jiawQQgghhBClY6lENh5YqYv8AxiBGmVx48zMTB544AFCQkIIDAxk6dKlzJw5k9OnT3P33Xdz9913AzBq1CjCwsKuGLVcv349TZs25c477+S5556jR48epvv+97//pXXr1rRo0YLVq1ffMJa77rqLQ4cOAUVJ2LBhwwgICKBLly5kZ2cDsGDBAlq3bk1ISAj9+vUzJdvLly8nMDCQkJAQOnToYLrH6NGjad26NcHBwcybN++KPpVSODs7A5Cfn09+fj7XeI+gVDp06GB6tk6dOvH666/TsWNHPv74Y95++22mT59uOvfiiy/SoUMHmjVrxvbt2+nbty9+fn68+eabpvv17t2bVq1aERAQwPz5803HnZ2dGT9+PG3btmXSpEn06dPHdO6XX36hb9++V8S2c+dOOnbsSKtWrejatStnzpwxxTJmzBjatGlDkyZN+PPPPwHIzs7m4YcfJjg4mIEDB5q+Nxf7f/nll2nZsiWdO3fm4qyAyMhIwsPDCQ4Opk+fPiQnJwNFo9YrVqwAoEGDBrz11lu0bNmSoKAgYmJiOHbsGHPnzuWjjz4iNDTUFMNFb7/9NkOGDKFLly40aNCAlStX8uqrrxIUFES3bt3Iz8+/5jOuWLGCHTt28MgjjxAaGnrZcwghhBBCCFGlaK3N/gE0AKIueT0SmFD8eRPgJMV72l7vo1WrVvqiotCvtGLFCv3kk0+aXqekpGitta5fv75OSEgwHU9MTNRaa11QUKA7duyo9+zZo7Ozs7WXl5c+cuSI1lrrhx9+WD/wwANaa63Hjh2rFy9erLXWOjk5Wfv5+emMjAx96tQp3b17d9N9nZyctNZa5+fn6wcffFDPnj1bHz16VBsMBr17926ttdYDBgww3evChQuma9944w09c+ZMrbXWgYGBOj4+3tSf1lrPmzdPT5w4UWutdU5Ojm7VqpUp1ksVFBTokJAQ7eTkpF999dWrfp1KYsiQIXr58uVaa62XLVum27Rpo7XWumPHjnrUqFGmdm+99ZaeNm2a6dzFGGbMmKHr1KmjT58+rXNycnS9evVMz3/x+5GVlaUDAgJMxwG9dOlSrbXWRqNR+/v76/Pnz2uttR40aJD+4YcfLosxLy9Pt2vXztRmyZIl+vHHHzfF8tJLL2mttV63bp3u3Lmz1lrrDz74wNRmz5492mAw6O3bt5v6//rrr7XWWr/zzjv66aef1lprHRQUpDdu3Ki11nrcuHH6+eefv+JrVL9+fdP389NPP9VPPPHEFV+ff3vrrbd0+/btdV5eno6MjNQODg56/fr1Wmute/furb///vsbPuPF2EviWv+uhBBCCCGEKA/ADn0TOWZ5bL/zf8BWwF8pFa+UegL4AmhYvCXPEmBIcdClFhQUxK+//sqYMWP4888/cXNzu2q7ZcuW0bJlS1q0aEF0dDT79+8nJiaGhg0b4uvrC8CgQYNM7X/++WemTJlCaGgonTp1IicnhxMnTlC3bl3Wr19vapednU1oaChhYWH4+PjwxBNPAJjWlgK0atWKY8eOARAVFcVdd91FUFAQ33zzDdHR0QC0b9+eoUOHsmDBAtOU2p9//pmvvvqK0NBQ2rZtS2JiInFxcVc8m8FgIDIykvj4eP755x+ioqKuaFNSo0ePJjQ0lPnz5/P555+bjg8cOPCa1zz44INA0fcmICCAOnXqYGdnR8OGDTl58iQAM2fOJCQkhPDwcE6ePGl6LoPBQL9+/YCi0eZHH32Ur7/+mpSUFLZu3Ur37t0v6ys2NpaoqCjuu+8+QkNDmTRpEvHx8abzF0dwL/0ebN68mcGDBwMQHBxMcHCwqb2VlZXp2QYPHsxff/1FamoqKSkpdOzYEYAhQ4awefPmqz771fq7ke7du2NjY0NQUBCFhYV069bN9PU7duzYDZ9RCCGEEEKIqs7a3B1orQdd49Rgc/TXpEkTdu7cyfr16xk7dixdunRh/Pjxl7U5evQo06dPZ/v27bi7uzN06FBycnK4Xi6ttea7777D39//uv1fXCP7b3Z2dqbPDQaDadrn0KFDWbVqFSEhISxcuJCNGzcCRQWTtm3bxrp16wgNDSUyMhKtNbNmzaJr16439bWoVq0anTp1YsOGDdcsNlVYWEirVq2AooRzwoQJ173ntGnT6N+//xXHnZycrnnNxWe3srK67OtgZWVFQUEBGzdu5Ndff2Xr1q04Ojqa3igAsLe3x2AwmK55/PHH6dmzJ/b29gwYMABr68t/hLXWBAQEXLMQ1cX+DQYDBQUFpuM3O/36VqdpX6u/m7nGysoKGxsbU58Xv143ekYhhBBCCCGqOkutkTWb06dP4+joyODBg3nllVfYtWsXAC4uLqSnV0bkzAAAIABJREFUpwOQlpaGk5MTbm5unDt3jh9//BGApk2bcuTIEdPI2dKlS0337dq1K7NmzTIlu7t37y6TeNPT06lTpw75+fl88803puOHDx+mbdu2TJgwgRo1anDy5Em6du3KnDlzTOskDx48SGZm5mX3S0hIICUlBSgaHf71119p2rTpNfu/OHobGRl5wyTWXFJTU3F3d8fR0ZGYmJjrFqeqW7cudevWZdKkSQwdOvSK8/7+/iQkJJiSvPz8fNMo97V06NDB9LWPiopi7969pnNGo9G05vXbb7/lzjvvxM3NDXd3d9P61sWLF5tGZ2/GpT+LJXG9ZyztvYUQQgghhKgMzD4iW9727dvH6NGjTaNZc+bMAWD48OF0796dOnXq8Mcff9CiRQsCAgJo2LAh7du3B4pGU2fPnk23bt2oUaMGbdq0Md133LhxvPDCCwQHB6O1pkGDBqxdu5bTp0/z5JNPXja9+FZMnDiRtm3bUr9+fYKCgkxJyOjRo4mLi0NrTefOnQkJCSE4OJhjx47RsmVLtNZ4enqyatWqy+535swZhgwZQmFhIUajkYceeshUsKqi6tatG3PnziU4OBh/f3/Cw8Ov2/6RRx4hISGB5s2bX3HO1taWFStW8Nxzz5GamkpBQQEvvPACAQEB17zfqFGjePzxxwkODiY0NPSy77uTkxPR0dG0atUKNzc305sbixYtYuTIkWRlZdGwYUO+/PLLm37enj170r9/f1avXs2sWbO46667bvraGz3j0KFDGTlyJA4ODmzdupX33nuPsLAw0/RuIYQQQgghqgJVRktTy0VYWJi+uL+nUuq6U4FLKiMjA2dnZ7TWPP300/j5+fHiiy+WeT+i5J555hlatGhhWn9sTs7OzmRkZJi9n4rCXP+uhBBCCCGEuBlKqZ1a67AbtatyU4tLa8GCBYSGhhIQEEBqaupV90oVltOqVSv27t1rKs4khBBCCCGEuP3IiKwQwkT+XQkhhBBCCEu6rUdkDQYDoaGhBAYGMmDAALKysi47fvFjypQpABQUFPD666/j5+dnOvfuu+/e8H7Ozs6X9btw4UKeeeYZAD788EOaN29OcHAwnTt35vjx46Z2ixYtws/PDz8/PxYtWnTLz3e9fi8KCQm5bPsgKKqQfLFw0aWio6O55557aNKkCX5+fkycOPGaycwTTzxhWq/bv39/07Tb3Nz/Z+/O47Iq8/+Pvw77jiDgigLuCoKKeyqmqa1Tqe2lOVPNtDi/ZqZ9n9bvt/qWZU0zpanVmO1mi5UVWW5JifuuCLgBsu9w3+f3xw23oOAKHMD38/HgwX1f59znfA6i3u/7us51lXH11VfTvXt3hg4d6pww68iRI4wdOxY/P7/jaqzP9OnTncsVDRw4sNnNzpucnFzrnujPP//c+bskIiIiIiKNr1UG2eolcDZt2oSHhwdvvPFGrfbqr/vvvx+Ahx9+mAMHDrBx40aSk5P5+eefnTMDn+h4JzJgwACSkpLYsGEDU6ZM4d577wUgOzubJ554gjVr1vDrr7/yxBNPkJOT06DXv3XrVux2O8uXLz9uVuNjlZSUcNlll3H//fezY8cO1q9fz8qVK3n99dfr3P+ll15i/fr1bNiwgS5dujB79mwA5syZQ1BQELt27eLuu+/mvvvuAxzL5zz55JO88MILp3UNzz//PMnJyTz33HONOrz7VJfEqenYIFv982spzuSaRURERESak1YZZGsaNWoUu3btqnd7cXExb775Jq+++ipeXl6AYwmTxx9//IyOV23s2LH4+PgAMGzYMNLT0wH45ptvuOCCCwgODiYoKIgLLriApUuXnuZVndh///tfbrzxRiZMmMDnn39+0n1HjhzJhAkTAPDx8WH27Nn19jAGBAQAjvVaS0pKnGucLl68mGnTpgEwZcoUvv/+e0zTxNfXl/POO8/5sz1do0ePdv6833zzTQYPHkxsbCyTJ0929oxPnz6dmTNnMmLECKKiomr1Ov/v//4vMTExxMbGOsNmQkICDz74IGPGjGHWrFlkZmYyefJkBg8ezODBg1mxYgUAv/76KyNGjGDAgAGMGDGC7du3U15ezqOPPsqiRYuIi4tj0aJFtXrEDx8+zBVXXEFsbCyxsbGsXLnyuGtaunQpAwcOJDY2lnHjxtV7LnD0tl955ZVMmjSJHj16OD8Qqe84RUVFzJgxg8GDBzNgwAAWL17sPM7UqVO59NJLnX/WIiIiIiItVatbfqemyspKvv76ayZNmgQ4eh/j4uKc2x944AH69OlDly5d8Pf3P+vjZWdn17nMyZw5c7jwwgsB2L9/P+Hh4c5tnTt3Zv/+/ad1XSc776JFi/juu+/Yvn07s2fPPm6IcU3VS8vU1K1bNwoLC8nPz3cG15puvvlmvvrqK/r27cuLL7543HW5ubkRGBjIkSNHCAkJOa1rO9aSJUuIiYkB4Morr+SWW24BHL3oc+bM4a677gIcyw798ssvbNu2jcsuu4wpU6bw9ddf89lnn7FmzRp8fHzIzs52Hjc3N5effvoJgOuuu467776b8847j9TUVCZOnMjWrVvp3bs3y5cvx83NjWXLlvHggw/y8ccf889//pOkpCRnb/S8efOcx505cyZjxozh008/xWazHTfjcWZmJrfccgvLly8nMjLSWVN95wJHD/C6devw9PSkV69e3HXXXXh5edV5nKeffprzzz+fuXPnkpuby5AhQxg/fjwAq1atYsOGDQQHB5/Vn4mIiIiIiNVaZZCtGfRGjRrlXKaleohwTRs2bKj1/O2332bWrFkcOXKElStXEh4efsrHmzdvHtWTUVV79913SUpKcoamuu49re7VPFUnOu/atWsJDQ2la9eudO7cmRkzZpCTk0NQUFCdxzJNs97z19f+9ttvY7PZuOuuu1i0aBE333xzg1xXTffccw9PPfUUoaGhzJkzB4BNmzbx8MMPk5ubS2FhIRMnTnTuf/nll+Pi4kLfvn05fPgwAMuWLePmm2929ozXDHBXX3218/GyZcvYsmWL83l+fj4FBQXk5eUxbdo0du7ciWEYtYab1+eHH35gwYIFgOPe6sDAwFrbV69ezejRo4mMjKxV04nONW7cOOdx+vbty759+8jJyanzON9++y2ff/65cyh3aWkpqampAM6RACIiIiIiLV2rHFpc817YV199FQ8Pj3r37d69O6mpqRQUFACO3sbk5GQCAwOx2Wynfbyali1bxtNPP83nn3+Op6cn4OiBTUtLc+6Tnp5Ox44da71uzZo1zkmnTjY0+FgLFy5k27ZtRERE0K1bN/Lz8509e3Xp16/fceF7z549+Pn54e/vz8SJE4mLi+NPf/pTrX1cXV25+uqrnceueV2VlZXk5eWdVWiqvkf2u+++Izo6GnAMIZ49ezYbN27kscceo7S01Ll/9c8Xjn5YcKKQ7uvr63xst9tZtWqV8894//79+Pv788gjjzB27Fg2bdrEkiVLap3vTNVX04nOVfPaXF1dqaysrPc4pmny8ccfO68lNTWVPn36HHfNIiIiIiItWasMsqfDx8eHP/7xj9x5553O8GCz2SgvLz+r465bt47bbruNzz//nLCwMGf7xIkT+fbbb8nJySEnJ4dvv/22Vs8iwNChQ51BpK6hyvWx2+18+OGHbNiwgZSUFFJSUli8eDELFy6s9zXXX389v/zyC8uWLQMcvdkzZ8503ov5zTffkJyczFtvvYVpms77VU3TZMmSJfTu3RtwTHhUPQPzRx99xPnnn39WPbJ1KSgooEOHDlRUVPDee++ddP8JEyYwd+5c5720NYcWH7tf9TBhwNnbnZeXR6dOnYDaw4f9/f2dH3wca9y4cfzrX/8CHL9H+fn5tbYPHz6cn376ib1799aqqb5z1ae+40ycOJFXX33VGebXrVt30mOJiIiIiLQ051SQrR4iXP1VPfnP008/TYcOHYiOjmbAgAGMGjWKadOmHddTejruueceCgsLmTp1KnFxcc5AGhwczCOPPOKcWOjRRx9tsOGey5cvp1OnTs5ABI7JkrZs2cLBgwcBuO222+jcuTOdO3dm+PDheHt7s3jxYp566il69epFTEwMgwcPrnOpHNM0mTZtGjExMcTExHDw4EEeffRRwLEsz5EjR+jevTv/93//V2uyqIiICP72t78xb948OnfuXGsY7+l48sknGTp0KBdccIEzQJ/IpEmTuOyyy4iPjycuLq7emZNfeeUVkpKS6N+/P3379nXOSn3vvffywAMPMHLkSGfvPDgm8tqyZYtzsqeaZs2axY8//khMTAyDBg1i8+bNtbaHhobyn//8hyuvvJLY2FjnEOf6zlWf+o7zyCOPUFFRQf/+/YmOjuaRRx456bFERERERFoao771Qpuj+Ph4s3oYrGEY9a51KiJnRn+vRERERMRKhmH8Zppm/Mn2O6d6ZEVERERERKTlU5AVERERERGRFqXRg6xhGHMNw8gwDGNTjbbHDcPYbxhGctXXRY1dh4iIiIiIiLQOTdEjOw+YVEf7S6ZpxlV9fdUEdYiIiIiIiEgr0OhB1jTN5UDd656IiIiIiIiInCYr75G90zCMDVVDj4Pq28kwjFsNw0gyDCMpMzPT2e7i4nLWa72KyFHl5eW4uOi2eRERERFp/qx61/ovoBsQBxwEXqxvR9M0/2OaZrxpmvGhoaHO9oEDB/LCCy8ozIo0gPLycl544QUGDhxodSkiIiIiIiflZsVJTdM8XP3YMIw3gS9O9xiffvopV1xxBY888gh2u71B6xM517i4uDBw4EA+/fRTq0sRERERETkpS4KsYRgdTNM8WPX0CmDTifavS+fOnVm7dm3DFiYiIiIiIiLNXqMHWcMwFgIJQIhhGOnAY0CCYRhxgAmkALc1dh0iIiIiIiLSOjR6kDVN89o6muc09nlFRERERESkddIUpSIiIiIiItKiKMiKiIiIiIhIi6IgKyIiIiIiIi2KgqyIiIiIiIi0KAqyIiIiIiIi0qIoyIqIiIiIiEiLoiArIiIiIiIiLYqCrIiIiIiIiLQoCrIiIiIiIiLSorhZXcDpOHDgAIZhWF2GiIiIiIiIWKhFBdmOHTty4MABq8sQERERkUb0wAMPAPDss89aXImINLVT7bhsUUFWRERERFo/BdgTy0lJJidlPV1HXoOru6fV5YhYQkFWRERERKSZKy/MYd/KRexJnE9uynoA8vdvJ+76ZyyuTMQaCrIiIiIi0qxMnjwZgI8//tjiSqxl2u1kbF3Onh/ns3/tYmwVpQB4+AZRXpTDzm9ep8eE2/AN7WpxpSJNT0FWRERERJqVI0eOWF2CpYqPpLN3+bvsTVxAUWaKs71d9PlEJUyjU/ylrH3zdvateJ+NHzzBsDvmWlesiEUaPcgahjEXuATIME0z+pht/wCeB0JN08xq7FpERERERJojW2U5B377kr2J8zm0YRmmaQfAp21nIsfcSOSYm2r1vMZc9Rhpaz5h34r36XnhnQRHDbSqdBFLNEWP7DxgNrCgZqNhGOHABUBqE9QgIiIiItLs5KVvYc+P89n3y0LKChz9Oi5uHnSOv5SohGmERY/FxcX1uNf5hnalx8Tb2f7ly6z/70MkPPSVlqmUc0qjB1nTNJcbhhFRx6aXgHuBxY1dg4iIiIhIc1FRUkDqqg/Zkzif7F1rne2B4dFEJUyj68ir8QwIOelx+v7hHvYmzidjy08cTP6GjgMmNWbZIs2KJffIGoZxGbDfNM31J/vkyDCMW4FbAbp06dIE1YmIiIiIlcaNG2d1CQ3ONE2ytq9iT+I80tZ8gq2sGAB37wC6jJhKZMI0gqMGnVavqodfEH0vv4/k9+5n/cKHad9/PC6umgJHzg2GaZqNfxJHj+wXpmlGG4bhA/wITDBNM88wjBQg/lTukY2PjzeTkpIatVYRERERkYZSknuIlJ//y97EBRQc3OFsD+19HpEJ0wgfegVunj5nfHxbRRlf/2MARZkpxN/yGt3G3twQZYtYxjCM30zTjD/ZflZ8ZNMNiASqe2M7A78bhjHENM1DFtQjIiIiItJg7LZKDiZ/w97E+RxY9zWm3QaAV5v2RI6+gcgxN+HfoXuDnMvV3ZP+Vz/BqtnT2PThk3QdfhVuXr4NcmyR5qzJg6xpmhuBsOrnp9MjKyIiIiKt34UXXgjA119/bXElp6fg4C72JM4n5ed3Kc09DIDh4kqnQZcQmTCNDnETG2Xob/iwyWz7ahY5e35n+1ev0O/KBxr8HCLNTVMsv7MQSABCDMNIBx4zTXNOY59XRERERFqmkpISq0s4ZZWlRaT9+il7E+eTuW2Fs92/Qw8iE6YRMeo6vNu0b9QaDBcX4q5/lh+fnMi2L16i27gZeAW2a9RzilitKWYtvvYk2yMauwYRERERkYZimibZu5PYkzif1FUfUllSAICrpw9dhk0mMmE6IT2HNelyOGF9RtFx4EUc+P0rNn38DPEzZjXZuUWsoGnNREREREROQVl+Fikr3mdv4nzy0jY729t2H0Lk2Gl0GTYFd29/y+rrf82THFy3lD0/zKXnxNsJ6NTLslpEGpuCrIiIiIhIPex2G4c3/sDexPns/+0L7JXlAHj6h9B11HVEJdxEYOe+FlfpENi5D5Fjp7Pnh7lseP8Rzvv7B1aXJNJoFGRFREREpFm55JJLrC6Bosx97P1pAXt/eofiI+kAGIYLHWInEDl2Oh0HXoSrm4fFVR4vesrDpK5YxP7fviBz2wpCe4+0uiSRRtEk68g2FK0jKyIiIiKNxVZeyv6kJexJnM/hzT9C1ftk39AIIhNuInL0Dfi07WxxlSe36aOn2PzJMwR3H8z4JxKb9F5dkbPVnNeRFRERERFpNnJS1rP3pwXs++V9yotyAHBx96Tz4MuJGjuNsD6jMVxcLK7y1PW65P+x+4c5ZO9aS9qaT+gybLLVJYk0OAVZEREREWlWEhISAEhMTGy0c5QX5bJv5SL2/jifnJRkZ3tQRByRY6fRdfhVePgFNdr5G5O7lx/Rkx8mac5dbHj/UTrFX9osh0GLnA0FWRERERE5J5h2Oxlbf2Zv4nzSf/0MW0UpAO4+beh63jVEJUwjKCLW4iobRmTCNHYsfY38/dvY/d2b9LzwDqtLEmlQCrIiIiIi0qoVZ+8nZfm77ElcQFHGXmd7u35jiRw7jc7xl+Hq4WVhhQ3PxdWN/tc8yS8vTmXzp88SMfp6PHzbWF2WSINRkBURERGRVunA71+xa9mbHFr/HaZpB8A7uBORY24kcsxN+IVFWFtgI+s48CJC+4wic+vPbP38BWKvfcrqkkQajIKsiIiIiLQ6mz99jk0f/hMAF1d3Og36A1Fjp9EuZhwuLq4WV9c0DMMg9rqnWfbIaHYsfY3uF9yGb0i41WWJNAgFWRERERFpVq666qqzev22L2c5QqxhEDP1MaLOvxmvgNAGqq5ladstni7Dp5K66kM2fvAEw25/y+qSRBqE1pEVERERkVZj53f/5ve37wZg8K1vEJVwk8UVWa8wI4Wv/x6L3V7JhKdXEBQRZ3VJIvU61XVkW86CWCIiIiJyTiguLqa4uPi0X7cncYEzxA68+SWF2Cp+YRF0n/BnME3Wv/cQLakjS6Q+CrIiIiIi0qxcdNFFXHTRRaf1mn0rP2Dtm38BIO765+hxwW2NUVqL1feK+3D3acPhzT9yaMN3VpcjctYUZEVERESkRUtfu5g1r/8RTJOYqx6j18UzrS6p2fH0C6bv5fcCsP6/D2G32yyuSOTsKMiKiIiISIt1YN1SVr1yE6bdRp8/3EPfy++zuqRmq8eEP+MT0oW8tM2kLH/P6nJEzkqjB1nDMOYahpFhGMamGm1PGoaxwTCMZMMwvjUMo2Nj1yEiIiIircvhTT+y4uVrsdsq6HnhXcRc9bjVJTVrrh5ezp/Rpg+foLLs9O9DFmkumqJHdh4w6Zi2503T7G+aZhzwBfBoE9QhIiIiIq1E5raV/PziVOwVZXQb9yfibngOwzCsLqvZ6zriKoIi4ijJOciOr1+1uhyRM9boQdY0zeVA9jFt+TWe+gKaOk1EREREAJg+fTrTp0+vd/uRXWtZ/vwV2MqKiRh9A4Nuflkh9hQZLi7EXv8MAFuX/B+leRkWVyRyZiy7R9YwjKcNw0gDrucEPbKGYdxqGEaSYRhJmZmZTVegiIiIiFjiREE2J2U9Pz33BypLCggfNoXBt/4Lw0XTvpyOdv0S6BA3kcqSAjZ/8qzV5YicEcv+1pum+ZBpmuHAe8CdJ9jvP6ZpxpumGR8aGtp0BYqIiIiIJbKyssjKyjquPS99Kz89eykVxbl0ir+UYbfPwcXF1YIKW77+1z6FYbiw+4c5FBzcaXU5IqetOXx89V9gstVFiIiIiEjzMGXKFKZMmVKrreDgLhKfuYiygizax17A8LsW4OLmblGFLV+b8H5EjLkR01ZJ0pyZWo5HWhxLgqxhGD1qPL0M2GZFHSIiIiLS/BVl7iPxmYsozT1MWN8xjLz7fVzdPa0uq8Xrf/XjeAWGkbHlJ7Z88ozV5YiclqZYfmchsAroZRhGumEYfwSeMwxjk2EYG4AJwF8buw4RERERaXmKs/fz49MXUXwknZCewznvHx/i5uFtdVmtgldgO4bd8TYYBps/fY5DG5ZZXZLIKWuKWYuvNU2zg2ma7qZpdjZNc45pmpNN04yuWoLnUtM09zd2HSIiIiLSspTmHSbx6YspythLUNRARt37Ce5eflaX1aq0ix5L9OSHwDRZ/doMirP1tlxahuZwj6yIiIiISC2eRgWJz1xKwcEdBHaJYcz9n+PhE2h1Wa1Sn8vvo13MOMoKslj16jTstkqrSxI5KQVZEREREWlW/vKnaczonUNe2ib8O/Yi4YElePoFW11Wq+Xi4sqw2+fgHdSBrO0r2fjBE1aXJHJSCrIiIiIi0mxUlBbSducCyEvBr10UYx/6Eq/AMKvLavW8AsMYftcCDBdXti15kQO/f2V1SSInpCArIiIiIs3G9i9e4sjONXi06UjCg1/hHdTR6pLOGaG9RxJz1eMArPnXLRRlplpbkMgJKMiKiIiISLNgt9vYk7gAgI92BeAb2sXiis49vS+5mw4DJlFelMPKV27AVlludUkidVKQFREREZFm4fDG7ynJ3k9epSf7y/2tLuecZLi4MPTPb+ITEk727iQ2LHzY6pJE6qQgKyIiIiLNwp4f5wOwrTgUMKwt5hzm6d+W4Xe9g+Hqxo6vZ5P+62dWlyRyHAVZEREREbFcaX4mB377AsNwYXtxiNXlnPNCegwh9rpnAPj133+m8PAeiysSqU1BVkREREQst++X97HbKmgfewFFdg+ryxGg56Q76DT4MipK8lk56wZs5aVWlyTi5GZ1ASIiIiJybjNNkz2JjmHFUQnT+HsfV4srEgDDMBhy6xt8u28jOSnJrHv3PuJnzLK6LBFAQVZERERELJa9ey356VvwDAilw8CL6OymHtnmwsO3DSP++i7fPzaW3cveJLT3SLqOuMrqskQ0tFhERERErFU9yVPEqOtwdfNg+/btbN++3eKqpFpw5AAG3Pi/ACS9dSf5B3ZYXJGIgqyIiIiIWKiytIjU1R8BjmHFALfddhu33XablWXJMbqNv4Uuw6dSWVrIylk3UFlWbHVJco5TkBURERERy6T9+imVJQW07TGUgE69rS5H6mEYBvF/mo1/hx7kpW3i9/l/s7okOccpyIqIiIiIZfbWmORJmjd3b39G/PVdXN292Ju4gL3L37W6JDmHNXqQNQxjrmEYGYZhbKrR9rxhGNsMw9hgGManhmG0aew6RERERKR5KTi4k8xtK3Dz9CV82GSry5FT0KZLDANvfgmA3+b+ldy0zRZXJOeqpuiRnQdMOqbtOyDaNM3+wA7ggSaoQ0RERESakT2JCwAIH3Yl7t7+FlcjpypyzE1EjL4BW3kJK2fdQEVpodUlyTmo0ZffMU1zuWEYEce0fVvj6WpgSmPXISIiIiLNh91WScrPjqGpkQnTa217+OGHLahITpVhGAya/hLZu38jf/9Wkt66i2F3zMUwDKtLk3NIc7hHdgbwtdVFiIiIiEjTOZi8lNLcw/h36ElIz2G1to0fP57x48dbVJmcCjcvX0b+v/dw9fQhdeUi9vww1+qS5BxjaZA1DOMhoBJ47wT73GoYRpJhGEmZmZlNV5yIiIiINJo9NSZ5OrYnLzk5meTkZCvKktMQ0Kk38X98FYDfF/yDg8nfUJiRQnlxHqbdbnF10toZpmk2/kkcQ4u/ME0zukbbNODPwDjTNE9pIar4+HgzKSmpUWo8F5mmSUVxHmX5WZQVVH3lZ1FWmE1w1CDa9RtjdYkiIiLSCpXkHGTJXT3BMLhs9k68AtvV2p6QkABAYmJi0xcnp23tW3ce1yNrGC64+7bBw7cN7r5t8PQNdj738G2Dh1+w87G7b1CtNjcvPw1TPocZhvGbaZrxJ9uv0e+RrYthGJOA+4Axpxpi5eTstsqjYbTgSNXjTMfj/KxjtjnaTVtlvcfredFMYq95Ehc39ya8ChEREWntUn5ZiGm30Sn+0uNCrLQ8A296AYDs3WspL8qlvDCHytJCyguzKS/MPu3j+YZFMnDaC3QccGFDlyqtSKMHWcMwFgIJQIhhGOnAYzhmKfYEvqv6tGW1aZp/buxaWhNbZTl5aZvJ2fM72Xt+J2fvOvLSNmO3VZzWcdy8/fH0D8EzIARP/7Z4+ofg4urO3uXvsOOrV8jencSImQvwDurYSFciIiIi5xLTNLV2bCvj6uHF4D/NrtVmr6ygvDiXiqJcygpzqCjKcYTcohzKC48+rqhuq7GtKGMvPz8/mfBhkxlw0/N4t2lv0ZVJc9YUsxZfW0fznMY+b2tir6wgb/9WZ2jN3vs7eambsFeW197RMPAMCHUG0prh1DMg9JjnIXj6tcXVw6vOc0aOuZGVr9xA1vaVfPPACIZeFHMcAAAgAElEQVTfNV9DjUVEROSsZW1fRcHBnXi1aU/72AlWlyONxMXNHa+AULwCQjmdhZXstkp2Ln2djR/9k7TVH3Now/fEXvcUUQnTMVyawzy10lxYMrRY6me3VZJ/YHut0Jq7bwP2irLj9vXv0IOgyIEERzm+2nTt32BrsIX0HMaEp1eyevbNHN78Iz89czHRUx+lz2X/0D8iIiIicsb2JM4DIHL0Dbi46q2o1Obi6kavi2fSecgfSJo7k0PrvyPprTvZ98v7xP/xVQI69bK6RGkmmmSyp4bS2iZ7stttFB7c6Qis1aE1ZT228pLj9vVr142gyAFHQ2tELB4+gU1S4+aPnmLLZ/8DQMcBFzL0L2/h4RfU6OcWaUimaWIrK8bNy9fqUkREzlkVxfksviMKW1kxF724Af8O3evcb+XKlQCMGDGiKcuTZsY0TdJWfcTvC+6hLD8DFzcP+l5+L70v/Tuu7p5WlyeN5FQne1KQbSKm3U7BoV3k7F1XdU/r7+TsTaayrOi4fX1DIwiOGugMrkERcZYHx4PJ37D69T9SXpiNb2hXRvz1PYKjBjbJuUtyDnJk1694B3XANywST/8QzWQnp8w0TdLWfMKG9x+lKGMvngFh+Lfvhl/7bvi3745/++74tY/Cr3133L38rC5XRKRV2/3DXJLeupPQ3udx/qPfWl2OtBBlhdms/++D7E1cAFQv+zOb0N76oKM1UpC1kGmaFB7eUyu0Zu9dR2VJwXH7+oSEExxZI7RGDsDTv60FVZ9cUWYqK2ZdR86e33Fx82DgtBeJOn9Go4XKI7vWsvObf5G2+uNak1i5efvjFxaBX1gUfu2i8A2LwK9dFH5hkfi0Ddcsy+KUuW0lyf99gOxdawHHUgCmWf+6dl5t2lUF2274tevmfOzfrpt6ckVEGsB3j44he9dahvz5TSJHX1/vfuqRlboc3vwTSXPuovDQLgC6jfsT/a/5Jx6+bSyuTBqSgmwTMU2Tosx9jnta965zfq8ozj1uX+/gjo7QGjWQ4MgBBEUOwCswzIKqz5ytoozkd+9j13f/AaDredcRP2NWg73Jt1WWk/7rZ+xc+jpHdv0KOMJHaJ/zKC/Oo+jwXipK8ut9veHiik/b8OMCrl+7SHzDIptkOLZYr+DgTta//wj7134OgFdgGNFTHyVy9I2U5h2m4NAuCg7tovDQ7qrveyjM2FPnvejVvIM64NeuZk9uN/zad8evXRRunj5NdWkiIi1WXvoWlt4bj7t3AJe9vueE/3ZqHVmpj628lC2f/Q9bl7yIaavEq017Bk7/PzoP/oNG7LUSCrKNwDRNio+kk7Pnt6rQuo7svb/XuT6WV5t2R0NrVU9ra5o6fN+KRax96w5sZcUEhvdjxF/fI6BjzzM+XmleBrt/mMuu7/5Dae4hADx8g4g6/2a6j78V39AugOPPoLwwm8LDeyjKSKEwYw+FGXspPLyXooy9FGfvhxP8Tnv4tcU3JBzf0K74hHTBN6QLPiHh+IZ2wSekCx6+QfpHsAUrzc9k8yfPsvv7tzBtlbh6+tD74r/S65K7Tzps2G63UXJkf1Ww3UXBod0UHt5NwaHdFB3ec8KlrbyDOzp6b529uFFHQ66Hd0NfpohIi7TunfvY8fWrdBv3R+L/+OoJ91WQlZPJTdtM0pt3ODs+Og66mEHTX8KnbWeLK5OzpSB7lkzTpCTnwHE9rWX5mcft6xkQ6uhhrRlagzq2+kCUl76FFS9dR8HBHbh5+zPk1jcIH3rFaR0jJyWZHUtfJ3XVh87esIDOfek58S90HXnNaff02spLKcpKrQq6e6tC7h4KM1Ioythb50RaNbl5+VUF3HBn0HWE3S74hobjFdheszY3Q5XlJexc+hpbF79ARUk+huFCxJgbiZn6SIOsgWy32yjOSnME3MO7a/Tk7qYwYy+mrbLuFxoGPsGd8KvuwW3X7WhPblhkvctfiYi0NrbKcpbc0Z2ygizGP7mctt1O/B5VQVZOhWm3s2vZf9iw6DEqSwpw8/Kj/9VP0O2CW3FxcbW6PDlDCrKnqST3kHPJm5y9jp7W0tzDx+3n4de2KrRWzyA8CO/gTq0+tNanoqSAtW/eQdrqjwDoeeGdxF779AnvU7XbKtmftISd37xO5rYVjkbDoOOAC+k56Q7C+iU0ys/TNE1Kcw9RlJVGcVYqRVmpju+Z+5xtlaWFJzyGi5sHPm3DHeE2NPxoyK367hPcSffoNiHTbmffivfZ+MHjFB9JB6B97AXEXvs0bbpEN0kNdlslxVlptXtyq74XZaZg2m11v9Aw8Gkbjn+N4crV333DIjQbo4i0Kmm/fsbKl68jMDyaic+tOen/8wqycjqKj6Tz+7y/sf+3LwAI7j6YwX96rcneC0jDUpA9gdK8jBoTMTlCa0n2geP2c/dpU2v24OCogfiEdDlnQ2t9TNNk57dvsP7d+7HbKmjbcxgj7lpw3NCOsoIj7PlxHru++7czdLh7BxCZMI0eE27Dr12UFeU7maZJeVFOVbhNpTgr7WjYzXI8LyvIOuExDMMFr6AONQJuOD6htXt2dT9lwzi8OZH17z1ITkoyAG26xhB73TO0jxlncWVH2SsrHCMEqoJtwaFdFFb16BZl7qs35BqGCz4h4Ud7ctt3x7+dY7iyb1gErm4eTXwlIiJnZ/n/XM7B9d8y4Mbn6XnhHSfdX0FWzkT62sX89vbdlOYewnB1o8fE22kfM47Azn3O6Y6nlkZBlqMTMeWmJJOzbwO5+zaQk7Kekuz9x+3r7h1AUNUETNWh1TcsUr/wpyFr56+snHU9Jdn78QwIZdgdb9M+5nxyUzex85t/sW/F+86hvf4detBj4l+IGHU97t7+Fld+6ipLiyg+4gi4RVmpFGemHn2clUpJzsET3qMLjqHoPiG1e3NrDmV2922j37sTyEvfyvqFD3Fw3VLAcX9qzFWP0/W8a1vUMCJbZTnFmfuqJp5yhNvqIcvFWan1zq5suLjiE9KlxlBlR0+uZ0AIbp4+uHn64urhjaunD24ePhoKLyKWKz6Szhcze2O4unHZa7tPaXWG5GTHh5RxcXGNXZ60MuXFeWx4/1F2L3uzVru7TyABnfsQ2KkPgZ37Oh537oNXm/Z639XMnHNB1lZZTn76VkdY3beB3JT15KZupKI477h93bz8CIqIc84eHBw1EL923fSGrwGU5mey+rUZHN74PRgGwZEDyd7zm3N7h9gJ9Jh0O+1jxrfKn7etspySI/uP6clNdQ5dLs5KO+GkQeBYXsg5CVXNsFs1IZVXQFir/NmdTEnuITZ99BR7f5yHadpx8/Kjz2X/oOeFd7a6Xm5bRZljMrOqyaZqzrBcfCTtpB+W1OTq7oWrpy9unt64evjg5unjCLnV3z2OPnfz9CEociCd4i/Vf+oi0mA2f/ocmz78J+HDJjNi5jtWlyPniKwda0j5+T3y928lL31rnZOzgmNyUUeo7Utg5z4EVH1vaSuLtCatOsiWF+eRu29jVQ9rMrn7NpCfvrXOgOAVGEabiFiCusbSpmt/giJi8W0X1aJ6bloau93Glk+eZfOnz4Jp4ubpS8SYG+kx4c9nNbNxa2Da7ZTmHaIo8+h9ukWZ+2qF3cqyohMew8Xd8+h9ujXCbkDHHgRHxbe6kFtZWsT2r2axbclLVJYVYbi4EnX+DKInP4hXYDury2tyjpC7t0ZPrmP5oPLiXCrLirGVF2MrK6GyrOikk5vVJ3zolcT/8VU8/IIauHoROdeYdjtf3h1NUWYKo+9bTIfYC07pdcuWLQNg/PjxjVmenCNM06Q07zD56Y5Qm5e+hbz0LeSnb62z0wvA0z/E2Wvr6MF1BNxTGVFwrslN3cjhjT8S2CWakJ7DzrqDoVUG2d7hweYLkztQlLH3+I2GgV+7bgRFHA2sbbr2b1VL3rQ0mdtWkL9/G+HDp2j91lNUvbzQsT25R4cxp1FeeKTe13u1aU/40CsJHzaZkB5DW2yoNU2T7N1JpPyykNSVHzqvueOgi4m95ikCOvWyuMKWwbTbsVWUOkJtWQm28mIqy4qodD4uxlZWTGV5MbbSYsoKstj57RtUlhbi07YzQ2+fQ1ifUVZfhoi0YIc3J5L49EX4tO3MxbO2nnJHgu6RlaZQvUpJzYCbn76VvP1bqSwpqPM1XoFhzlDrCLr9COzcBw/fNk1cvfXy0rey+eOnSVvzibPNxc2Dtt2HENZvDGF9x9C2++DTnsCyVQbZbm1dzGcmeuHi7klgeD+CuvavCq1xBIb3a1H3WoqcqYrSQoqrA26NGZezdq6hOCvVuZ93cCdnqG3bfXCLGCpalLmPlF8Wsu+XhRQc3OlsD44aROz1zyhUNYHCw3tY9drNZO9aC4ZBn8v+QfTkhzUbt4ickVWzbyZ15SL6XfkA0VMeOeXXKciKlUzTpPhIOvnpW2oF3Pz92+odOecd1KF2wO3keOzuE9DE1Te+goO72PzJM+xbuQhMExd3TzrHX0bBwZ3k7Ftf6xYoVw9vQnoOdwbb4KiBuLi6nfD4rTLI9u8dZS7/bgkBHXrqTZXIMap7MdPWfELa6o+dM0MD+ISEEz50MuHDriQ4alCzCrXlxXmkrfmEfT8vJHPbL852r8Awuoy4mohR19Kma2yzqrm1s1dWsPmTZ9i6+HlM005wt3iG3fE2/u27WV2aiLQg5YU5LL4jCntlOZe8vAXf0K6n/FoFWWmOTLudoqzUGgF3K/npW8g/sL3e23l82nZ23oMb0KmPM+i6e/k1cfVnryhzH5s/eYaUn/+Labfh4upO5Njp9P3DPc7VSsoKs8nc+gsZW34iY8ty8tI21zqGm7c/ob1HEtZ3DO36jaFNl/7HjSBslUG2MdeRFWlNTLudI7vWkrbmY9LWfFJreSnf0AjChzlCbVBEnCUB0V5ZwaEN35Hyy0L2//YF9ooywPGpXaf4S4kYdR3tos8/6Sd20rgyt61g9eszKM5Kw83Lj4HTXiRi9A36UEFETsnO7/7N72/fTbvo80l48IvTeq2CrLQkdruN4sx95KVtPhpw928h/8AO53ucY/mGdj0+4Hbq3SwnsCw+ks6Wz/6XvYnzsdsqMFxciRh9A/2uuP+kH1CV5h0mY8vPZGz5icObf6Lw0K5a2z18gwjtO8oZbAM69cHFxaV5BFnDMOYClwAZpmlGV7VNBR4H+gBDTNM8pXSqICty+ky7nawdq52htjT3sHObX7tuhA9zDD9u0yWmUQOKaZpk7/mNfb8sJHXVR5TlZzo2GAZhfccQcd61dB78h1Y5BKclKy/KJWnOTNJWfwRA+LDJxM94RRNBichJffvgCHJSkhl+53y6jJh6Wq9VkJXWwG6rpChjb9XkUlvJS9tC/v6tFBzYUfcqFoaBX1ikc5kg51Dljr1w9fBq8vpLcg+xdfEL7P5hjiOQGwZdR15LvysfOONRWsVH0snYsrwq2CZSnJVWa7tnQBhX/Htfswmyo4FCYEGNINsHsAP/Bv6hICvSNOx2G1nbV5G2+iPS1nxGWX6Gc5t/h56ED7uSzoMvwzuoY4OtQ1qUmcq+FQtJ+XkhBQd3ONsDOvUhYtS1dB15jXM4ijRPpmmS8vN7/D7vbzUmgppLWJ/zrC5NRJqpnJT1fPvgcDx8g7jstd2n/SZ8+/btAPTqpcn9pPWxV1ZQeHh37Qmm0rdQcGgXpq3yuP0NwwW/9t2OWwfXv2NPXN08Gry+0vxMti15iV3f/ds5ZDp82GSiJz9EQKfeDXYe0zQpykhx9tZmbPmJ0txDXLOwpHkEWQDDMCKAL6qDbI32RBRkRSxht9vI3PoLaas/Iv3XxZQVZNW5n2Md0qp1Rz2OWX/Uq+q7hzduXr6O754+uHr6gmmy/7cvyNz6s/NYngFhdB15FV3Pu9ayYc1y5goO7Wb1azeTvTtJE0GJyAn9Nu9v7Pr2DXpM/AsDp71odTkiLYKtspyCgzudwbZ6NuXCQ7swTftx+xsurvi17+5cA9cZcNt3P6P/m8sLc9j+1Sx2LH2dytJCADrFX0r0lIdp0yXmrK/vZEzTpODADgI7924dQdYwjFuBWwG6dOkyaN++fY1XqMg5ym6rJGPLctJWf8zhzYlUlhQ4lmQpKz7rY7u6ex297zVmnO57beEcE0E9zZbFz4NpEtwtnuF3zsOvXZTVpYlIM2ErL+XzO7pRXpTDhGdXE9S1/2kfY8mSJQBceumlDV2eSItjKy8l/+CO4wNuxp5aMwRXc3F1x69DD2e4dUww1Re/dlF1vg+rKM5nx9LZbP/qVee6uh3iJhI95RGCowY2+vUdq1lN9qQeWZGWyTRNbOUlVWuNlmArK3KsPVpeUrU2qaO99jqlji97RSnB3QYTPuRy3ffaCmVs/YU1r8+g+Ei6JoISkVr2rfyA1bOnExQ5gAlPrzijY+geWZGTqywvoeDAdvLStjhnUM5L30pRZkqd+7u4eeDfsVdVwO1DQKc+FBzcwbYvXqa8MBuAdv3GEj31UUJ6Dm3CK6ntVIOsukZEpF6GYeBWNaz49JayltYurM95THx2DUlzZ5K2+mN+/fdtHFz/rSaCEhH2/jgfgMiEmyyuRKR1c/PwJigijqCIuFrtlaVF5B/YdnSCqfQt5O3fSnFWGnmpG8lL3XjcsUJ7jyR66qOE9RnVVOWfNQVZERE5Ix5+QQy/awEdYifw+7y/kbb6Y47sXKOJoETOYYUZKRze/COu7l50HXG11eWInJPcvHwJjhpEcNSgWu0VJQXk7996dImgtC0YLi70vOgu2kWf3+JGVTV6kDUMYyGQAIQYhpEOPAZkA68CocCXhmEkm6Y5sbFrERGRhmUYBpFjbiSk1whWz55O9p7fSHxqEn3+8A/6TX5Y90SLnGP2/rQAgM5DLsfDt43F1YhITe7e/rTtPoS23YdYXUqDOLt1NU6BaZrXmqbZwTRNd9M0O5umOcc0zU+rHnuaptlOIVZEpGXzb9+NcY//QN/L78XEZMtn/0vi0xdSnL3f6tJEpInY7TZSlr8LQGTCNIurEZHWrtGDrIiInBtc3NyJuepxxj68FK827cnctoJvHxzBoQ3LrC5NRJrA4Y0/UHwkHd+wyLO+z+6dd97hnXfeaaDKRKQ1UpAVEZEGFdZnFBOfXU276PMpy8/kp//5Axs//Cd2u83q0kSkkZTmZbB+4cMARCXchOFydm8xw8PDCQ8Pb4jSRKSVUpAVEZEG5xUYxuj7FxM9xfHGdsunz/HTM5dQknvI4spEpKEVH0nnh39OIC91I/4detBt3J/O+piLFi1i0aJFDVCdiLRWTbKObEPROrIiIi3P4U0/smr2zZTlZ+AVGMawO+fTrt+YJq3BVlGG4eKqyadEGljBod0kPnMxxVmptOkaw5j7P8crsN1ZH1fryIqcu7SOrIiINAvtoscy8dlVrH7tZjK2LOenZy6m3+SH6Hv5fWc9/PBkclM3su2Ll0ld9SGm3YaHbzCeASF4BYTiGRBa43EIXgFheAaE4BkQildAKB5+wY1en0hLlpu2mZ+evYTS3MO07T6E0fd+qnWkRaTJKMiKiEij8w7qwJgHv2Tzx0+z5bP/YdNHT5K1fRVD75iDV0Bog57LNE0yNiey7YuXjk40VbU2XnnhEcoLj1BwYPtJj2MYLnj4h9QKu9Uht3YYdjz38A1qcWvwiZypI7uTWP4/l1NemE1YvwTO+/sHuHv5WV2WiJxDFGRFRKRJuLi4EjP1UUJ7jWD1azM4tHEZ3z4wjOF3LSC098izPr7dVkn6mk/Z9sVL5KQkA+Dq6UNUwnR6XXQX3sGdKC/MpjQ/k7L8LMryM6seV39l1dpWXpRDWX4GZfkZ5J/C+Q1XNzz9a4ddx+OabUeDr7t3gIKvtEgZW3/m5xemUFlSQMeBFzFi5ru4enhZXZaInGMUZEVEpEm17z+eCc+uYtWr08javpIfn5pEzFWP0/uSu89oKG9laRF7Euez4+tXKcrcB4BnQBg9Jv6Z7uNvwdO/rXNfr8AwvALDTum49soKygpqh1tH2M2oM/hWlORTmnuI0txD5J3C8V3cPI7p5Q2tFXw9/UPwCgzFs2rIs5unr4KvWO7AuqWsfPk6bBWldBlxNUP//B9c3NytLktEzkGa7ElERCxht1Wy8YMn2LbkRQA6xE1k6F/eqhU8T6Q0L4Od3/yLXcvepLwwGwC/9t3pdfFMIkZdj5uHd6PVXhdbRVmNXt6s2mG3IJOyvNrbKsuKTuv4rh7eR4OvfwiegWGOsBsQimdgjeDr73je1NcvrV/a6k9Y9dp0TFslUefPYNCMWbi4uDbKubKysgAICQlplOOLSPN1qpM9KciKiIilDqz7mjX/uoXywmx82nZm+F3vENJzaL37FxzcxfavZrF3+bvYK8oAaNt9CL0vvZuOgy5ptDfWDa2yrNgRagsyKc07ZnhzQRZleRmUFlQF4rxMbBWlp3V8N09fR89uYHXwdYTcWsOeqwKwZ0Aoru6ejXSl0hrsSZxP0pt3YJp2el38/4i97mmNEBCRRqEgKyIiLUZRVhqrXr2JIzvXYLi6EXvNU/S86K5ab5Szdv7K9i9eIj3pc6j6v6vjoIvpffHdhPQa3qrfVJumSWVZEWV5mUeDb1XIdT7Oy6S04GggtleWn9Y5PPyCiRh1HX3/cC+eAeoFk6N2fP0a6965B4DoqY86Zhxv5L9v8+bNA2D69OmNeh4RaX4UZEVEpEWxV1awYdGjbP9yFgCdBl3C4NveIGv7KrZ98RJZ21cCjntLu553Lb0v/isBnXpbWXKzZZomFSX5VcE3i9K8jKPBN/9oL6+jxzeDsvwsTLsNAHfvAHpf+jd6TroDNy9fi69ErGSaJls+fY5NHz0JwICbXqDnpNub5NxaR1bk3KUgKyIiLdL+pCWseeM2KopzcXHzcPYsuvsE0n38LfSY+Be8gzpYXGXrYtrt5Oxbz8YPHufQ+u8A8GrTnujJDxGZMA0XV80Nea4xTZP1/32I7V++jGG4EH/L60Ql3NRk51eQFTl3KciKiEiLVZiRwqpXbiR7z2/4tO1MzwvvImrsdNy9/a0urdU7vDmR9QsfJmfP7wD4d+hB/6ufoNPgP7Tq4dtylN1u47e5f2XPD3NxcXVn2B1vEz7syiatQUFW5NylICsiIi2arbKc3JRkgiIGaHmPJmaaJmlrPmHjoscoPLwHgODug4m99mnC+pxncXXSmOyVFax54xZSV36Aq7sXI+9eSIe4iU1eh4KsyLnrVIPs6S/YJyIi0gRc3Txo232IQqwFDMOgy7DJXPj8Ogbe/BKeAWFk71rLj09OYPnzV5KbusnqEqUR2MpLWfHytaSu/AA3b39G3/+5JSFWRORUNHqPrGEYc4FLgAzTNKOr2oKBRUAEkAJcZZpmzsmOpR5ZERGRpldRWsiOr15h2xcvU1laCIZBxKjriZ7yCL4h4VaXJw2gorSQX168iozNiXj4BTPm/sUERw2yrJ7i4mIAfHx8LKtBRKzRnHpk5wGTjmm7H/jeNM0ewPdVz0VERKQZcvfyo9+VD3LxS5voMfEvuLi4kbL8Xb76e3+S33uAsoIjVpcoZ8huq6Qk9xA/PXMJGZsT8WrTjrGPfGNpiAVHgFWIFZETaZJ7ZA3DiAC+qNEjux1IME3zoGEYHYBE0zR7new46pEVERGxXuHhPWz88J+krvwAcMwo3eeyv9Nj4u24eSp8nC273YatrBhbeYnzq7KsGFt5KbZyR3tl9baq9sryY7aXlWCrqGN7WXFVu+P1dluF87y+oV1JePBL/NpFWXj1Dq+//joAt9/eNMv9iEjz0awme6ojyOaaptmmxvYc0zSD6nntrcCtAF26dBm0b9++Rq9XRERETi577zo2vP8ohzd+D4B3UAf6TXmYdn3HUFlWXBW+irFVPa5+XllW7AxqlWVFVe2Ox7ayqu/lJRiubnj5h+Lh3xbPgBC8AkLw8G+Ll38IngGhePqH4OnfFg+/YAyXxh1kZrfbagVHW3mxI0zWGRiPtleWVYfROrbXDKQ1gmbNcNnYDMMFVw9v2kTEMvzOefi07dxk5z4RTfYkcu461SDb7BeGM03zP8B/wNEja3E5IiIiUiU4cgAJDyzh0Mbv2bDwEXJSkkl6844mr8MwXBxh17+tI9wGhNR4HIqbl29VWCw9GiiP7fGs1WN5/Pbq9Yyb6IJw8/DB1cMbV09vXN2rvnt4V7V74Vr13c2zaj+PY7ZXtbvV2Obq4V21f/XrvXFx89CySiLSIjV6kK2a7OkywK9G82HDMB4CqlfWVkAVERFpodrHjKNdv7Gkrf6YrV/8HxVFebh5+R4NTp4+joDl6VMVpHxqtHvj6unrDGSO1/ng5umNvbKcsoIjlOVnOb4Kqr7ysygtyKK8alt5UQ5l+ZmU5Wc23kXWFS6rAqNbjaBZKzC61xEoq9qPDZRuzsDqo3ApInIKmqJHdh7wcdVXtXXAdCAauBtoHuNYRERE5IwYLi50GTGVLiOmNvm57ZUVlBdlU1pH4C3Lz6KyrEjhUkSklWmKIPsXYBzgaRhGOvAY4AUUAJuAVKDp/9cTERGRVsHFzR2vwHZ4BbazuhQREWkiVk32lAwsxrEsTynwD9M019bzWudkTz4+PoO6d+/e6PWKiIiIiIhI09uwYYNpmuZJZ/CzarInNyAIGAYMBj4wDCPKrCNV15zsKTY21vz222+btFARERERERFpGu3bt//9VPZr3Lnq65cOfGI6/ArYgRCLahEREREREZEWxKoe2c+A84FEwzB6Ah5A1ukc4MCBA8yYMYMNGzZgt9sbo0YREWkgLi4u9O/fn7lz59KxY0eryxEREZEWrimW31kIJAAhNSZ7mgvMNQxjE1AOTKtrWPGJzJgxg6lTp7JmzRo8PDwaumwREWlA5eXlPP/888yYMYOlS5daXY6IiIi0cE0y2VNDqXmPbMeOHSkpKVGIFRFpIcrLy9wzQ0kAACAASURBVPH29ubAgQNWlyIiIiLNVPv27X8zTTP+ZPtZdY/sWbPb7QqxIiItiIeHh24FERERkQbRYoOsiIiIiIiInJsUZEVERERERKRFUZAVERERERGRFuWcCbIvv/wyxcXFjX4eV1dX4uLiiI6OZurUqRQXF5OSkkJ0dHSjn7vajBkzCAsLq/Ocq1at4pZbbuG7775j0KBBxMTEMGjQIH744YcGO39CQgK9evUiNjaWkSNHsn37dgAiIiLIyjqtVZbO2OzZs+nevTuGYRx3zoqKCgYNGkRaWhpjx46lT58+9OvXj1mzZjVJbTXV/Jn4+fk16rlyc3N5/fXXT+s1jz/+OC+88EIjVeT4XUlKSjrhPomJiVxyySUNcr4z+XfgVGqsy7x587jzzjtP+3UAZWVljB8/nri4OBYtWnRKr8nMzGTo0KEMGDCAn3/+mQ8//JA+ffowduxYkpKSmDlz5hnVIiIiItIcKcg2MG9vb5KTk9m0aRMeHh688cYbjX7OY02fPr3e5S2WLl3KpEmTCAkJYcmSJWzcuJH58+dz4403NmgN7733HuvXr2fatGncc889DXrsUzFy5EiWLVtG165dj9v2yy+/MGLECNzc3HjxxRfZunUrq1ev5rXXXmPLli0NWodpms1mcpszCbKtTVP9O3C21q1bR0VFBcnJyVx99dWn9Jrvv/+e3r17s27dOkaNGsWcOXN4/fXX+fHHH4mPj+eVV15p5KpFREREmk6rC7JFRUVcfPHFxMbGEh0dzaJFi3jllVc4cOAAY8eOZezYsQD85S9/IT4+nn79+vHYY485X//VV1/Ru3dvzjvvPGbOnOnsCSoqKmLGjBkMHjyYAQMGsHjx4pPWMmrUKHbt2gWAzWbjlltuoV+/fkyYMIGSkhIA3nzzTQYPHkxsbCyTJ092vsn+8MMPiY6OJjY2ltGjRzuPcc899zB48GD69+/Pv//97zrPO3r0aIKDg+vc9v333zN+/HgGDBhAx44dAejXrx+lpaWUlZWd9JpO1+jRo50/A4BXX32VgQMHEhMTw7Zt2wD49ddfGTFiBAMGDGDEiBHOHtzNmzczZMgQ4uLi6N+/Pzt37gTg3Xffdbbfdttt2Gy24847YMAAIiIi6qxp6dKlXHjhhXTo0IGBAwcC4O/vT58+fdi/f/9x+2dmZjJ58mQGDx7M4MGDWbFiBeDorZwxYwYJCQlERUU5g0JKSgp9+vTh9ttvZ+DAgaSlpbFw4UJiYmKIjo7mvvvuO+HPLDExkTFjxnDVVVfRs2dP7r//ft577z2GDBlCTEwMu3fvPqO67r//fnbv3k1cXFydHy4sWLCA/v37ExsbW+cHG8nJyQwbNoz+/ftzxRVXkJOTA8Arr7xC37596d+/P9dccw1Q/9+XkpISrrnmGvr378/VV1/t/HtQ159R9d/DTz75xNl+bA9xdHQ0KSkpwMl/L+r6d+B0/lyg/n831q5dy4gRI4iNjWXIkCEUFBTUet2XX37J8OHDjxsdkJ2dzeWXX07//v0ZNmwYGzZsICMjgxtuuIHk5GTi4uKcf97Vdu/ezaRJkxg0aBCjRo1i27ZtJCcnc+//Z+/Ow6oo+z+Ov+ecw1lANvddQXEBBdxzKbdwybTcMtOUcre0tMW9RfNXT9mqlWWaaT6laZrmkpmauVSKIokbijtqirJz4Czz+wOdRwR34AB+X9fFJcyZueczcwD5zj33Pa++ypo1awgNDeXNN99k69atDB8+nFdeeSVbr3ZKSgrPPPMM9evXJzg4mGXLlt3xsT344INERkZq67Rs2ZKoqKhbnj8hhBBCiLxS7ArZdevWUbFiRfbu3cu+ffvo1KkTo0ePpmLFimzatIlNmzYBMH36dHbt2kVUVBS///47UVFRWK1Whg0bxtq1a9m6dSsXLlzQ2p0+fTrt2rVj586dbNq0iVdeeYXU1FTi4uJ45JFHcuSw2+2sXbuW+vXrAxATE8Nzzz1HdHQ0Pj4+2h+PPXr0YOfOnezdu5e6desyd+5cAKZOncovv/zC3r17WblyJQBz587F29ubnTt3snPnTubMmcOxY8du+9xcvHgRNzc3vL29sy1ftmwZDRo0wGQy3cGZvj2rVq3SzgFA6dKl2b17NyNGjNAKkjp16rBlyxb27NnD1KlTmThxIgCzZ8/mhRdeIDIykl27dlG5cmUOHDjA4sWL2bZtG5GRkej1ehYtWnRHmTZt2kSbNm2yLTt+/Dh79uyhWbNmOdZ/4YUXGDNmDDt37mTZsmUMHjxYe+3gwYP88ssv/P3337z55pvYbDYADh06xIABA9izZw9ubm6MGzeOjRs3EhkZyc6dO1mxYsVNM+7du5ePP/6Yf/75h4ULF3L48GH+/vtvBg8ezMyZM+8q1zvvvEONGjWIjIzkvffey7a/6Ohopk+fzsaNG7V9X2/AgAH85z//ISoqivr16/Pmm28C8M4777Bnzx6ioqK0OxBu9PPy+eef4+7uTlRUFJMmTSIiIiLHfqxWK0OGDGHVqlX88ccfnDt37qbnCrit74vrfw/ExcXd8fuS2++NzMxM+vTpw8cff8zevXvZsGEDFotF22b58uW88847rFmzhtKlS2dr7/XXX6dBgwZERUXxf//3fwwYMICyZcvy1VdfacVijRo1sm0zdOhQZs6cSUREBDNmzGDkyJGEhoYydepU+vTpQ2RkJK+//jqNGzdm0aJFOd7radOm4e3tzT///ENUVBTt2rW742MbPHgw8+fPB+Dw4cNkZGQQHBx8y/dJCCGEECKvGFwdIK/Vr1+fl19+mXHjxvHoo4/y4IMP5rrekiVL+PLLL7Hb7Zw9e5b9+/fjdDrx9/fHz88PgL59+/Lll18CsH79elauXKkVX1arlZMnT1K3bl3WrFmjtZuenk5oaCiQ1SM7aNAg4uLi8PPz05Y3atRI60Xat28fkydPJiEhgZSUFDp27Ahk9XCEh4fzxBNP0KNHDy1DVFQUS5cuBSAxMZGYmBgt762sX7+eDh06ZFsWHR3NuHHjWL9+/W21cbv69euHxWKhevXqWuEFaMfSqFEjractMTGRgQMHEhMTg6IoWjHYvHlzpk+fzunTp+nRowcBAQH89ttvRERE0KRJEyDrfJctW/a2c8XFxVGyZEnc3d21ZSkpKfTs2ZOPPvoILy+vHNts2LAh2y3HSUlJWo9bly5dMJlMmEwmypYty/nz5wGoVq0aDzzwAJDVo9WmTRvKlCmjnZstW7bw+OOP3zBnkyZNqFChAgA1atTQ3rf69etrF2PuJteNbNy4kV69emmF1vU9+omJiSQkJNC6dWsABg4cSO/evQEIDg6mX79+PP7449ox3ejnZcuWLdpYzeDg4FyLn4MHD+Ln50dAQAAA/fv3134Ob+Ruvi/u5n3J7feGoihUqFBB2/e130ObNm1i165drF+/Ptfvra1bt2oXtdq1a0d8fDyJiYk33H9KSgrbt2/Xzj1wx3dSbNiwge+//1772tfX946PrXfv3kybNo333nuPefPmER4efkcZhBBCCCHuVbErZGvVqkVERARr1qxhwoQJdOjQgddeey3bOseOHWPGjBns3LkTX19fwsPDsVqtqKp6w3ZVVWXZsmXUrl37pvu/Okb2etf2dur1eu2WyvDwcFasWEFISAjz589n8+bNQFZv5F9//cXq1asJDQ0lMjISVVWZOXOmVuzeqbVr1zJ27Fjt69OnT9O9e3cWLFiQo9fneqdOnaJr164ADB8+nOHDh990/UWLFtG4ceMcy6+eB71ej91uB2DKlCm0bduW5cuXc/z4ca239KmnnqJZs2asXr2ajh078tVXX6GqKgMHDuTtt9++7eO+1tq1a7OdP5vNRs+ePenXr59WZF/P6XSyY8eObL1s1x/P9cfk4eGhLb/Z99WNXNuuTqfTvtbpdNo+7ibXjaiqiqIod5wTsm6b3bJlCytXrmTatGlER0ff9OfldvZzo3UMBkO2McdWq1XLf6ffF3f6vtzs98aN8vr7+xMbG8vhw4dz/XnILcPNzo/T6cTHxyfX3zG3K7e8d3ps7u7uhIWF8dNPP7FkyZK7mgxLCCGEEOJeFLtbi+Pi4nB3d6d///68/PLL7N69G8gaA3m1tyopKQkPDw+8vb05f/48a9euBbJucY2NjdV6S6+dLbRjx47MnDlT+8Nzz549eZI3OTmZChUqYLPZst0KefToUZo1a8bUqVMpXbo0p06domPHjnz++edaj+Xhw4dJTU29rf2oqkpUVJTWK5yQkECXLl14++23admy5S23r1KlCpGRkURGRt6yiL1TiYmJVKpUCUC7XREgNjYWf39/Ro8eTbdu3YiKiqJ9+/YsXbqUf//9F8gaY3jixInb3tfV8bGQdU4GDRpE3bp1sxX41+vQoQOzZs3Svr7TIqJZs2b8/vvvXLx4EYfDwXfffaf1bN6LO8117c/A9dq3b8+SJUuIj48Hss7rtby9vfH19eWPP/4AYOHChbRu3Rqn06nN/vzuu+9mu7Mgt5+Xhx56SPs+37dvX67jKuvUqcOxY8e0saHfffed9lr16tW1n+ndu3drt9bf7vfFtefgTt+Xm/3eiIuLY+fOnUDWz/TVCwfVqlXjxx9/ZMCAAURHR+do89rzsXnzZkqXLp1rz+1VXl5e+Pn58cMPPwBZ38N79+694fq5uf775vLly3d1bIMHD2b06NE0adLkhmPyhRBCCCHyS7ErZP/55x9twpfp06czefJkIGtcWefOnWnbti0hISE0aNCAoKAgnn32Wa2Qs1gsfPbZZ3Tq1IlWrVpRrlw5bTzplClTsNlsBAcHU69ePaZMmQJwwzGyt2vatGk0a9aMsLAw6tSpoy1/5ZVXtEloHnroIUJCQhg8eDCBgYE0bNiQevXqMWzYsFx72vr27Uvz5s05dOgQlStXZu7cuURERNCgQQOtd2XWrFkcOXKEadOmERoaSmhoqFYEFLRXX32VCRMm0LJly2wT9CxevJh69eoRGhrKwYMHGTBgAIGBgbz11lt06NCB4OBgwsLCOHv2bI42P/nkEypXrszp06cJDg5m8ODBOBwOYmJitPO8bds2Fi5cyMaNG7VzcO1t4te2tWvXLoKDgwkMDLzjmagrVKjA22+/rX3vNWzYkMcee+wOz1JOd5qrVKlStGzZknr16uWY7CkoKIhJkybRunVrQkJCci3sv/nmG1555RWCg4OJjIzktddew+Fw0L9/f+rXr0+DBg0YM2YMPj4+N/x5GTFiBCkpKQQHB/Puu+/StGnTHPsxm818+eWXdOnShVatWmWbebpnz55cunSJ0NBQPv/8c2rVqgVw298X1/4euNP35Ua/N4xGI4sXL2bUqFGEhIQQFham9RQD1K5dm0WLFtG7d+8cEze98cYb2ns4fvx4vvnmmxvu/6pFixYxd+5cQkJCCAoKuq2J5641efJkLl++rE0mt2nTprs6tkaNGuHl5cUzzzyjtf3XX3/l+YUuIYQQQojcKHdz26OrhISEqFfHcpYvX/6ubtm8lZSUFEqUKIGqqjz33HMEBAQwZsyYPN9PQXvrrbeoWbOmNqvs/Wjr1q18++23LnkkkhDFTVxcHG3atOHgwYPodLd/TVRRlNuawEsIIYQQ96fy5ctHqKqac0zWdYrdGNl7NWfOHL755hsyMzNp0KABw4YNc3WkPHG1Z/p+1qpVK1q1auXqGEIUeQsWLGDSpEl88MEHd1TECiGEEELkFemRFUIIUWCkR1YIIYQQN3O7PbJyKV0IIYQQQgghRJFSLAtZvV6vTd4TGhqqzUK8detWmjZtSp06dahTp062Z1O+8cYb2jMvb9XW5s2befTRR4GsWXbLlClDaGgoderU4cMPP8y1zfDwcCpVqqQ98/HixYtUr15dWzc6Opp27dpRq1YtAgICmDZtmtbj/MEHHzBo0CBt3UWLFtGlS5e8OVnXmD9/Ps8//3yuy68eY2BgIHPmzMmx/OrH1eeaxsTE8Oijj1KjRg0aNWpE27Zt2bJlyy3bu37/bdq0YdeuXaSlpdGlSxfq1KlDUFAQ48eP19bJyMigT58+1KxZk2bNmmnv97WOHz+OxWLR9jl8+PBsj3EpDD766CPS0tK0rx955BESEhIKZN83eu/zsv24uLgbvv7aa6+xYcOGfNu/EEIIIYQoXoplIXv1Wa5XP6pXr865c+d46qmnmD17NgcPHmTr1q188cUXrF69+o7bul6fPn2IjIxk27ZtTJ8+nVOnTuXall6vZ968eTmWp6en061bN8aPH8/hw4fZu3cv27dv57PPPgNg9OjRREREsG3bNhISEpg8eTIzZ8688xNzD64e4+bNm5k4cSLnz5/PtvzqR2BgIFarlS5dujB06FCOHj1KREQEM2fOJDY29pbt3czLL7/MwYMH2bNnD9u2bdMeETJ37lx8fX05cuQIY8aMYdy4cbluX6NGDSIjI4mKimL//v2sWLEiD85MTtfOvHwnri9k16xZg4+PT17FcqmbFbIOh4OpU6fy8MMPF3Cqu3er5/IKIYQQQoj8VSwL2dx8+umnhIeH07BhQwBKly7Nu+++yzvvvJNn+yhVqhQ1a9bM9bEfAC+++CIffvhhjj+C//vf/9KyZUs6dOgAgLu7O7NmzdKyGQwGPvvsM5577jleffVVnn32Wfz9/XO0//fff9OiRQsaNGhAixYtOHToEJBVRPTo0YNOnToREBDAq6++qm3z9ddfU6tWLVq3bs22bdtueYxly5alRo0aN31266JFi2jevDndunXTltWrV4/w8PC7ag+yzknbtm2BrEeCNGzYkNOnTwPw008/MXDgQAB69erFb7/9dtPx0waDgRYtWnDkyBFSUlJo3749DRs2pH79+tqjTI4fP07dunUZMmQIQUFBdOjQgfT0dACOHDnCww8/rD2y5ejRo2zevJm2bdvy1FNPUb9+fQC+/fZb7VFQw4YN0wrcESNG0LhxY4KCgnj99deBrEfpxMXF0bZtW+04q1evzsWLF4GsyXWCg4MJCQnh6aefznFM+fXeX7hwgbCwMBo2bMiwYcOoVq2alunxxx+nUaNGBAUFaXc3OBwOwsPDqVevHvXr1+fDDz9k6dKl7Nq1i379+hEaGkp6ejrVq1dn6tSptGrVih9++IHw8HCWLl0KwM6dO2nRogUhISE0bdo012ffvvvuu9SvX5+QkBCtd37OnDk0adKEkJAQevbsqV0UCA8PZ/To0bRo0QJ/f39tPzdq5+jRo3Tq1IlGjRrx4IMPcvDgQa2dsWPH0rZtW8aNG8fvv/+u3YnQoEGDGz6jVwghhBBC5L1iOWtxeno6oaGhAPj5+bF8+XKio6O1Yueqxo0bEx0dfcdt3cjJkyexWq0EBwfn+nrVqlVp1aoVCxcupGvXrtry6OhoGjVqlG3dGjVqkJKSQlJSEl5eXrRo0YK6deuyYcMGDhw4kGv7derUYcuWLRgMBjZs2MDEiRNZtmwZAJGRkezZsweTyUTt2rUZNWoUBoOB119/nYiICLy9vWnbti0NGjS46fmIjY0lNjaWmjVrsn//fhYvXszWrVu113fs2EF0dLR2weBWbtXekSNHcmyTkJDAqlWreOGFFwA4c+YMVapUAbKKVG9vb+Lj4yldunSu+0xLS+O3335j6tSpmM1mli9fjpeXFxcvXuSBBx7QCvCYmBi+++475syZwxNPPMGyZcvo378//fr1Y/z48XTv3h2r1YrT6eTUqVP8/fff7Nu3Dz8/Pw4cOMDixYvZtm0bbm5ujBw5kkWLFjFgwACmT59OyZIlcTgctG/fnqioKEaPHs0HH3zApk2bcuSOjo5m+vTpbNu2jdKlS3Pp0qUcx5Rf7/2bb75Ju3btmDBhAuvWrct2O/68efMoWbIk6enpNGnShJ49e3L8+HHOnDnDvn37tPfKx8eHWbNmMWPGDBo3/t+4fbPZrL3X69atAyAzM5M+ffqwePFimjRpQlJSEhaLJVumtWvXsmLFCv766y/c3d2189GjRw+GDBkCZM3SPXfuXEaNGgXA2bNn2bp1KwcPHqRbt2706tXrhu0MHTqU2bNnExAQwF9//cXIkSPZuHEjAIcPH2bDhg3o9Xq6du3Kp59+SsuWLUlJScFsNuf6/SaEEEIIIfJesSxkr94OfC1VVVEUJce6uS27VVvXW7x4MZs2beLQoUPMmTPnpn/QTpw4kW7dumUb43qjbNfmS0lJYdeuXdhsNi5cuEDlypVzrJuYmMjAgQOJiYlBURRsNpv2Wvv27fH29gYgMDCQEydOcPHiRdq0aUOZMmWArNt9Dx8+fMNj3Lp1KyaTiS+++IKSJUtq28yaNetmp4fu3bsTExNDrVq1+PHHH++ovTZt2mRry26307dvX0aPHq31SufW+5rb+Tx69CihoaEoisJjjz1G586dsdlsTJw4kS1btqDT6Thz5ox2m7Ofn592EaNRo0YcP36c5ORkzpw5Q/fu3QGyvddNmzbFz88PgN9++42IiAiaNGkCZF0QKVu2LABLlizhyy+/xG63c/bsWfbv33/Dix8AGzdupFevXlqBe/VcXSu/3vutW7dqF286deqEr6+v9tonn3yivXbq1CliYmKoXbs2sbGxjBo1ii5dumh3GeSmT58+OZYdOnSIChUqaOfNy8srxzobNmzgmWeewd3dPdv52LdvH5MnTyYhIYGUlBQ6duyobfP444+j0+kIDAzU3t/c2klJSWH79u307t1b2/bquHaA3r17o9frAWjZsiVjx46lX79+9OjRI9efSSGEEEIIkT+KZSGbm6CgIHbt2pXtdteIiAgCAwPvue2rxdeOHTvo0qULnTt3pnz58rmuW7NmTUJDQ1myZEm2bFcnQroqNjaWEiVK4OnpCcDrr79O//79KVeuHGPGjOGHH37I0faUKVNo27Yty5cv5/jx49mKQJPJpH2u1+u125tvVchff4y34/rjWb58Obt27eLll1++q/auNXToUAICAnjxxRe1ZZUrV+bUqVNUrlwZu91OYmJirsXe1TGy11q0aBEXLlwgIiICNzc3qlevjtVqBXKes/T09Jvesuzh4aF9rqoqAwcO5O233862zrFjx5gxYwY7d+7E19eX8PBwbX83crMLHVfl13t/o+PdvHkzGzZsYMeOHbi7u9OmTRusViu+vr7s3buXX375hU8//ZQlS5bkOi4csp+va/d3q1w3Wic8PJwVK1YQEhLC/Pnz2bx5s/batefg6jHl1o7T6cTHx+eGF6+uzTx+/Hi6dOnCmjVreOCBB9iwYQN16tS5aXYhhBBCCJE37psxss899xzz58/X/kCNj49n3Lhx2cYM3qvmzZvz9NNP8/HHH990vUmTJmWbIblfv35s3bpVm7U1PT2d0aNHa9n++ecfVq9ezbhx4xg6dCgnTpzg119/zdFuYmIilSpVArLGRt5Ks2bN2Lx5M/Hx8dhstlyL47vx1FNPsW3bNlauXKktu3YSo7s1efJkEhMT+eijj7It79atG9988w0AS5cupV27drddoCcmJlK2bFnc3NzYtGnTLcfqenl5UblyZW2iqIyMjFyPrX379ixdupR///0XgEuXLnHixAmSkpLw8PDA29ub8+fPaxNWAXh6euY6zrJ9+/YsWbKE+Ph4ra3cjiM/3vtWrVppF13Wr1/P5cuXtf35+vri7u7OwYMH+fPPP4Gs2bidTic9e/Zk2rRp7N69+6bHdr06deoQFxfHzp07AUhOTs4xprxDhw7MmzdPO+9Xz0dycjIVKlTAZrOxaNGiW+4rt3a8vLzw8/PTzoeqquzduzfX7Y8ePUr9+vUZN24cjRs31sbSCiGEEEKI/HffFLIVKlTg22+/ZciQIdSpU4cWLVrw7LPPZhur+tZbb1G5cmXt426MGzeOr7/++qZ/tAcFBWUbQ2qxWPjpp5946623qF27NvXr16dJkyY8//zzqKrKiBEj+PDDDzGbzeh0Oj777DNeeOEFMjMzs7X76quvMmHCBFq2bHlbM+dWqFCBN954g+bNm/Pwww/f9rjWay1evDjb43e2b9+OxWLh559/Zvbs2fj7+9O8eXPeeustJk+efMftX3X69GmmT5/O/v37adiwIaGhoXz11VcADBo0iPj4eGrWrMkHH3xwRxN49evXj127dtG4cWMWLVp0Wz1qCxcu5JNPPiE4OJgWLVpw7ty5HOsEBgby1ltv0aFDB4KDgwkLC+Ps2bOEhITQoEEDgoKCePbZZ2nZsqW2zdChQ+ncubM22dNVQUFBTJo0idatWxMSEsLYsWNz7C+/3vvXX3+d9evX07BhQ9auXUuFChXw9PSkU6dO2O12goODmTJlCg888ACQNV65TZs2hIaGEh4ervVIh4eHM3z4cG2ypxsxGo0sXryYUaNGERISQlhYWI4e606dOtGtWzcaN25MaGiodlFo2rRpNGvWjLCwsNt6H2/UzqJFi5g7dy4hISEEBQVpE4Bd76OPPqJevXqEhIRgsVjo3LnzLfcphBBCCCHyhnKzWyULm5CQEHX9+vUAlC9f/qa3eQoh7l1GRgZ6vR6DwcCOHTsYMWLELceMC3EziqLkevFHCCGEEAKgfPnyEaqqNr7VevfNGFkhxJ07efIkTzzxBE6nE6PRyJw5c1wdSQghhBBCCClkhRA3FhAQwJ49e1wdQwghhBBCiGzumzGyQgghhBBCCCGKBylkhRBCCCGEEEIUKUW2kNXpdDlm7RVCCFF4ZWZmotMV2f92hBBCCFGIFNm/KIKDg3nvvfekmBVCiCIgMzOT9957j+DgYFdHEUIIIUQxkO+TPSmKMg94FPhXVdV6V5a9AQwBLlxZbaKqqmvupN158+bx7LPP8tprr+F0OvMyshBCiDym0+kIDg5m3rx5ro4ihBBCiGKgIGYtng/MAhZct/xDVVVn3G2jFStWZN26dfeSSwghhBBCCCFEEZTveNf0eQAAIABJREFUtxarqroFuJTf+xFCCCGEEEIIcX9w5RjZ5xVFiVIUZZ6iKL43WklRlKGKouxSFGXXpUtSDwshhBBCCCHE/c5VheznQA0gFDgLvH+jFVVV/VJV1caqqjYuWbJkQeUTQgghhBBCCFFIuaSQVVX1vKqqDlVVncAcoKkrcgghhBBCCCGEKHpcUsgqilLhmi+7A/tckUMIIYQQQgghRNFTEI/f+Q5oA5RWFOU08DrQRlGUUEAFjgPD8juHEEIIIYQQQojiId8LWVVV++ayeG5+71cIIYQQQgghRPHkylmLhRBCCCGEEEKIO5bvPbJ56fz585QvX97VMYQQQgghhBBCuFCRKmTLlSvH3r17XR1DCCGEEELko+7duwOwfPlyFycRQhS02+24lFuLhRBCCCGEEEIUKVLICiGEEEIIIYQoUorUrcVCCCHuH6qq8q/zLGV05dEpct1ViPtJ48aNXR1BCFHISSErhBCiUFqYNpuv02bhq5SihakNLY3taGh8AJNidnU0IUQ+mzRpkqsjCCEKOSlkhRBCFDr/Os6yKG0OAJfVeFZbl7HaugwzFhobW9DS1JYHjK3x0ZV0cVIhhBBCuIIUskIIIQqduamfkEkGbUyd6O8+lG0ZG9mWuYnD9mi2Zv7G1szf0KEjyBBKS1M7WhjbUsVQ3dWxhRB5ZNCgQQDMnTvXxUmEEIWVFLJCCCEKlcO2/azPWIkbbgz1GEMFfWVqGGozwGMEFxzn2J65mW0ZG9lj+4t/7Lv5x76b2akzqKr3p6WxLS1MbalrCEav6F19KEKIu3Tp0iVXRxBCFHJSyAohhCg0VFXl89R3Aehu6UcFfeVsr5fRl+cxy5M8ZnmSVGcKO23b2JaxkT8zt3DSEcvJ9Fi+S5+Lr1KKB0ytaWlsSyNjc8yKxRWHI4QQQoh8IoWsEEKIQuPPzN+JtO3ES/Gmv/vQm67roStBG1NH2pg6Yldt/GPbzbbMjWzL2MQ55xnWWn9krfVHTJhpZGyOvyGAkrrSVz7KaJ9bFPcCOjohhBBC5BUpZIUQQhQKdtXG7NT3AXjafQSeOu/b3taguNHA2IwGxmY85zGeY44YtmdsYlvmRg7a97E9cxPbMzfluq274pFrgVtKW5a13FvxlduVhRBCiEJCClkhhBCFwmrrMk46Yqmoq8Jjlj533Y6iKPgbauFvqEV/j2FccJwnwrad846zXHJe1D7inRe45LxImppKmiOV044TN21Xhw4fXUlKacXujYte6eUV4t48+OCDro4ghCjkpJAVQgjhcqnOFOanfgrA0BJjcVOMedZ2GX05Oum75/qaqqqkqslaUXttgXvJGc+la5Ynqpe1z2/ForhfKXDL4KsVu1cKX6U0pfRZy7yVktLLK0Quxo4d6+oIQohCTgpZIYQQLvfftK9IUC9Rz9CAh4xhBbZfRVEooXhRQudFNWrcdF2bmkmC89J1xW7W55ed8drn8c4LpKtpnHGc5Izj5E3bvNrLe7XALakvk/WvrjSlsn1eGovikZeHLoQQQhRpUsgKIYRwqfOOOJamLwBgRIlXURTFxYly56YYKaMvTxl9+Zuul9XLm5KtN/dq4RvvvMjla5bl6OW13bhdM5bsPbtXbmOupK9Ka1MHdIouD49WCNfq27cvAN99952LkwghCispZIUQQrjU3NSZZJJBW1NnAt2CXR3nnmX18npSQudJVfxvuq5dtXHZeSnXovf68bxW0olzniLOeSpHOxN4mw7mbvl1SEIUOKvV6uoIQohCTgpZIYQQLnPIFs2vGStxw40hHi+6Ok6BMyhulNGXo4y+3E3X+18v79XiNqvYPWCPYmPGWn5K/14KWSGEEPcVKWSFEEK4hKqqzE59D4Aelv5U0Fd2caLCK3svr5+2PF1N46/Mrey37+WI/QA1DXVdmFIIIYQoODKgRgghhEvsyNxMpG0nXoo3/dyHuDpOkWRR3LWe2FXpP7g4jRBCCFFwpJAVQghR4Oyqjdmp7wMwwH0knjpvFycqurqanwDg14xVpDlTXZxG3G9UVeWcI44dGZtZlDaH+amfkqlm3nO7YWFhhIUV3AzmQoiiR24tFkIIUeB+ti7llOMYlfRV6WZ5wtVxijQ/Q02C3RoRZYtgQ8bPdLP0cXUkUQAO2aL5Lu0rUtUUKugrU0FfiQr6KlTQVaaivnK+XBxKdiYSa48h1nGYY/YYYu2HOe44Qqqakm09H11JHrf0vad9jRw58p62F0IUf1LICiGEKFApzmTmp34KwFCPsbgpRhcnKvq6mfsQZYtgZfpiupqfKLSPMBL3Ls5xirmpn7AxY83/Fuby2CYPxZOK+spU0FW+Uuhe+dBVppy+Isab/NxlqhmcsMdmK1iPOWK46Pw31/V9lVL4GQKwKO5sy9zIr9ZV91zICiHErUghK4QQokB9l/YViepl6hsa8qDxYVfHKRYeNIXhneLLUcchDtijCHQLcXUkkccSnJdYmPYFK9O/x44dN4z0sPQjxK0J55yniXOc5pzjDHGOU5x1niZVTSbGfoAYDuRoS0GhjK7clR7cStpEa7GOGI7ZYzjtOIETR47tzFiobqiBn6EW/vqArH8NAfjqSgFgVdPpEf8Q++17OW0/QWVDtbs+3u7duwOwfPnyu25DCFG8SSErhBCiwJx3xPFD+gIAhpd4RXoO84hRMdLZ3J3v0+exMn2xFLLFSLqaxg9p37A4/WvS1FQUFDqZHifc4znK6Svmuo2qqiSpCVlFreM0Z51nOOs4pRW7551n+dd5jn+d59jLzhzb69BRRe93pVgNwP9KwVpBVwWdcuPpVcyKhYeMYfyS8RMbMn4m3PBcnp0HIYS4nhSyQgghCszc1E+wkUk7U2cC3YJdHadYedTSm+/T57EpYx0jna/ipfNxdSRxD+yqjTXWH/km7TMuOS8C0Mz4EEM9xuBvqHXTbRVFwVvxxVvnS91cfs7sqo1/neeyitwrHw4cVDfUxF9fi2oGf0yK+a5yh5m78kvGT/xqXcVA95FysUoIkW+kkBVCCFEgDtn28WvGKtxwY4jHGFfHKXYq6avS2K0Fu2zb+cX6E73dB7o6krgLqqqyJfNX5qZ+zCnHcQDqGOozzGMsocamebIPg+JGRX0VKuqr5El71wp1a0ppXVninKfYb99LkFtonu9DCCFAHr8jhBCiAKiqyuepMwDoYelPeX0lFycqnh6zPAnAKusSVFV1cRpxp6IyI3g+oR9vJI3hlOM4lfXVeMPrAz7z+S7Pitj8plf0tDd1AWC9daWL0wghijPpkRVCCJHvtmduYq9tJ16KD/3dh7o6TrHV3Nia0rqynHIcJ9L2Nw2MzVwdSdyGY/YjzEn9gB2ZvwNZswAP9BhJF3NPDIqbi9PduTBzVxanf83mjHU8X2L8Xc1M3q1bt3xIJoQoTqSQFUIIka/sqo0vUt8HYKDHSErovFycqPjSKwa6mHvxTdpnrLQukUK2kLvgOMe8tFmst/6EEycWxZ0+lmd4wn0gFsXD1fHuWg1Dbfz1tYh1HOavzK20MrW74zaeeeaZfEgmhChO8v3WYkVR5imK8q+iKPtyee1lRVFURVFK53cOIYQQrrHK+oN2m2RXc29Xxyn2uph7okPHHxkbtEmCROGS7Ezki5T36X/pEdZZl6Ogo7v5KRaVXMdAj5FFuoi9KszcFYBf7/L24rS0NNLS0vIykhCimCmIMbLzgU7XL1QUpQoQBpwsgAxCCCFcIMWZzDepnwEw1GPsXd1iKO5MGX15mhvb4MDOmvQfXR1HXCNTzWBx2tf0u9SJ79PnkUkGbU2d+abkSkZ7TtKex1octDd1QUFhR+Zmkp2Jd7x9v3796NevXz4kE0IUF/leyKqqugW4lMtLHwKvAjIbhRBCFFP/TZtDonqZYLdGtDK2d3Wc+0Y3Sx8Afrb+gEN1uDiNcKgO1llX8PSlR5idOoNkNYkGbk353Od7XvOaQSV9NVdHzHNl9OVo4NYMGzZ+z1jv6jhCiGLIJbMWK4rSDTijqure21h3qKIouxRF2XXpUm71sBBCiMLonCOOpekLARju8bI8T7IANXZrQQVdZc4749iZuc3Vce5bqqryZ8YWhl7uxX+SJ/Gv8xz++lr8x3s273vPo45bfVdHzFfa7cUZq1ycRAhRHBV4IasoijswCXjtdtZXVfVLVVUbq6rauGTJkvkbTgghRJ6Zm/oxNjJpZ3qEum7Bro5zX9EpOrpassYjr7IudnGa+9MBWxRjE59hQtIIYh2HKaerwATPt5nju4ymxgfviws7DxnDMGEmyhbBOccZV8cRQhQzruiRrQH4AXsVRTkOVAZ2K4pS3gVZhBBC5IODtn/YkPEzbhgZ4vGiq+PclzqZu2PAwJ+ZWzjviHN1nPvGafsJ3kgay8iEvkTaduKleDPC4xUWlFxNB3M3dIpLboZzCXedBy2vzFi8wfqzi9MIIYqbAv9tqqrqP6qqllVVtbqqqtWB00BDVVXPFXQWIYQQeU9VVT5PnQFAT0t/yusruTjR/clXV4qHTGE4cbLautTVcYq9S86LfJQ8jfDL3fg94xeMmHjKMphFJdfxhHs4RsXk6ogu0cGU9TzY9RmrUNXbnxalT58+9OnTJ79iCSGKgXx/jqyiKN8BbYDSiqKcBl5XVXVufu9XCCGEa2zL3ESUbRdeig/93Ie4Os59rZv5STZmrGW1dRkD3EdgUNxcHanYSXOmsiR9PovTvsZKOjp0PGLuSbj7c5TRl3N1PJdrbGyOr1KKU45jHLZHU9ut3m1t9+STT+ZzMiFEUZfvhayqqn1v8Xr1/M4ghBCiYFxwnOfjlGkAhHuMpITOy8WJ7m/Bbo2opvfnhCOWbZmbaG3q4OpIxYZNzeRn61IWps7mshoPQAtjW4Z4vEh1Q00Xpys89IqBdubOLEv/lvUZq267kI2PzzqnpUoVn0cSCSHy1v0zUEMIIUS+ylCtTEkaxUXnvwS7Naar+QlXR7rvKYpCV3PW7Zmr0pe4OE3xoKoqm6zrCL/UjU9SpnNZjSfIEMonPguY7j1LithchF25vXijdQ121XZb2wwePJjBgwfnZywhRBEnhawQQoh7pqoq7yZP5pA9mvK6Skz1+khuYy0kOpq7YcJMhG0Hp+0nXB2nSNuT+RcjEvowNfkl4pynqKL3Y5rXJ8z0+Zb6bo1cHa/QqmUIpKrenwT1Ersyd7g6jhCimMj3W4uFEMJVVFXlpCOW3bY/2Z35F0lqIjX0tahhqEOAoS7VDTUxKkZXxywWFqV9ycaMtVgUd/7P+1O8db6ujiSuKKHzop35EdZaf2SVdQkjSrzi6khFzlH7Ib5M+YC/bVsBKKUrw0D3kTxi7oFekT+lbkVRFMJMXZmb9jG/ZqzkAdNDro4khCgG5LevEKJYueA4R8SVwnWP7U8uOv/N9nqUbZf2uR4D1fT+BBjqUtNQl5qGOtQ01KGEzrOgYxdpWzJ+ZW7aJygoTPZ8Fz9DgKsjiet0Mz/BWuuPrLMuZ5DH6Pt2Bt07dc4Rx7zUmWzIWIWKiodSgictg+jp3h+L4u7qeEXKw+ZHmZv2MVszNpLqTMFDV8LVkYQQRZwUskKIIi3ZmUikbSe7M/8kwvYnpxzHsr3uq5SigbEZDd0eoIy+LEfthzhiP0iM/SCnHceJdRwm1nGYXzJ+0rapqKtCDUPtbAVuaV1ZFEUp6MMr9I7YD/B20gQAhniMoYWprYsTidzUNtQjwBBIjH0/v2esJ8zc1dWRCrVEZwL/TZvD8vRF2LBhwMDjlr70dx8mdxvcpfL6igS7NSbKtos/Mn+lk7m7qyMJIYo4KWSFEEVKhmplny2S3Zk72G37k8P2/Thxaq9bFHdC3JrQyO0BGhofwE8fkK0AbWp8UPs8XU0l1h5DjP0AR+wHOGI/SKw9hjjnKeIyT/FH5gZtXR+lpNZje7XAraSvil7RF8yBF0KXnBeZlPg8VtLpYOrGk5ZnXR1J3ICiKHQzP8H7KW+wMn2xFLI3kKFa+TH9WxalfUWqmgzAw6ZHedZjFBX0lV2crujrYOpKlG0X662rblnIDhw4sIBSCSGKKuVOHk7taiEhIer69etdHUMIUYAcqoPD9mh22/4iInMH+2x7sJGpvW7AQKBbKA3dmtHI2Jw6hnr3NMmQXbVx0nHsSq/t1QL3EClqUo51zVioYaidrcD1MwTcF7dtZqqZjE14hmh7JEGGUD7wmXdfHHdRlq6m0iu+LWlqKnN9l+NvqOXqSIWGQ7WzzvoT89M+5aLzPACN3Vow1GMsAW51XZyu+EhxJtEjvjV2bCwuuYEy+vKujiSEKITKly8foapq41utJz2yQohC65T9OOMThxPnPJVteU1DHRq5NaehsRn13Rrl6Vg1g+KGv6EW/oZadCDrkRGqqnLeGXelsD3IEfsBYuwHueA8R7Q9kmh7pLa9Dj3V9P7UNNQl4EqBW9NQB0+dd55ldDVVVXk/+XWi7ZGU1ZVnqvdHUsQWARbFgw6mbqywfsfK9MW86DnF1ZFcTlVVtmduYk7qR5xwHAUgwFCXYR4v0cjY3MXpip8SOi9aGNvwe+Z6NmSspq/7oBuue+bMGQAqVapUUPGEEEWM9MgKIQqlY/YYXkoYxGU1nrK68jQ1PkgjY3MauDUtNGPUEp2Xr+m5zSpwTzmOZ7vV+aryukrX3ZpchzK68kVy3O33afP4IvV9zFiY6buQmgbpsSoqYu2HGXS5O+6KB0tLbcKieLg6kstE2yL5IuV9/rHvBqCCrjKDPEbT1tQZnSJPJ8wv2zI2MjlpFH76AOb6Lr/h78Du3bNuPV6+fHlBxhNCFALSIyvEdQ7ZolmfsRI33CipK619lNKVoaSuNCUUryJZVBRHMbYDvJw4mCQ1gYZuD/CW98xCOUOot86XRsbm2XpurGo6sfaYK722V8fdHuac8wznMs+wNfM3bV0vxUcraq8WuJX11Qv1uNsdGZv5MvUDACZ4vS1FbBHjb6hFPUMD9tn38Jt1DY9aers6UoE7aT/GV6kfaWPgvRVfnnYfTlfLE/I4rgLQ1NgKL8WHY44YjjoOUdNQx9WRhBBFlBSyotiLsR1gftqnbM/cdNP13DDmWuD66kpT6prlJXWlcZM/dvLNflsU4xKHkaIm0cz4EG96fYhJMbs61m0zKxYC3YIJdAvWljlUOycdx6+5LTlr7G2SmkCEbQcRth3auibM+BtqEWCoe+V5t3XwN9QqFOfgmD2GacmvoKLyrPsoHjKFuTqSuAvdLH3Yl7yHldbFdDH3um8u4MU7LjA/7VPWWH/EiQMzFnq7D6CP5Vl5FEwBclOMtDV14ifr9/xqXUXNElLICiHujhSyotg6Yj/IN6mfaT1gJsx0tfTGV1eKeOdFLmkfF7jkvEiamsp5ZxznnXG3bNtL8b5S4JbJVvj6ap9n/eupeN83fyTmhX9sEYxPHEGamkorY3umeM0oFj0kesWAn6EmfoaahPEokDU271/nWWLsBzl6zcRS551nOWCP4oA9Stteh44qej8CDHWzFbheOp8CO4ZE52UmJT5PuppGO1Nn+rsPK7B9i7zV2tSBWSnvEGM/wCH7Puq41Xd1pHzjVJ2cd8axxrqMpWkLsZKODj1dzU8w0H0kpfRlXB3xvtTB3I2frN/zW8ZqhnqMLdR3oQghCi8pZEWxE2s/zDepn7El81cAjJh4zPIkT7o/S0ld6Rtul66mXVPc/q/AveSM55LzIvHOC1y+8nWSmkiSI1GbHORGrt7G7HtdL2/WR5lsvbzFoWC7FxGZfzL5yqNc2pk6M8Hz7XuafbiwUxSFcvqKlNNXpJWpnbY80ZnAUfvBK723WQXuSUcsJxxHOeE4yoaMn7V1y+kqXClq6/KA8aF8K0hsaiavJ43hrPM0tQ1BvOr5llygKcKMionO5u4sTv+aldYlxaKQTXWmcMpxjFOO45xyHOOkPevz044TZJKhrfeg8WEGe7xIVYOfC9OKuoZgKumrcsZxkj22v2hsbOHqSEKIIkgmexLFxjH7Eb5J+4zfM34Bsm4V7mZ5gr6WwXl61d2pOklUL2sF7/8K3Is5enpT1ZTbbtdT8cpR4OZW+HoVw17ePzO28FrSC9jIpJPpcV72nCpX6K+RoVo5pj3vNqvAPWo/RAbWbOsFGULp5T6AB43t0St5c51SVVXeT3mD1dallNKV4XOfxZTRl8uTtoXrnLaf4OnLj2DCzA+lNubZrNrH7UdYZ12BVU3HU+eFp+JNCcULT50XJRTPK/9646l44a543NHvMofq4LwzjlP241rRetIRyynHceKdF264XSldGWoZAunnPpQgt9C8OEyRB75J/Yz5aZ8SZurGRK+3c7x+9e+9Dh06FHQ0IYSL3e5kT1LIiiLvhP0oC9I+Z1PGOlRU3DDyqKU3T1kGU1pf1qXZrGo6l53xxGu9u9f29P6v8L3sjMeB/bbaNGDI1pN7tcANMATS0ti2yBW5f2RsYGrSS9ix083chxdKTJYZQ2+DQ3Vw+sq422j7XtZbV5KqJgNZPbXdLf3oYu5JCZ3XPe1nWdq3zEp9GyMmPvb5plj03oksLycMJsK2g+c9JtDTvf9dt+NUneyybWNp2kJ22rbd9nY69JRQPLUC11PxpoTOC08l66OEzpM0NTVb7+q1z5C+lhETlfXVqGLwo6q+OlX0flTV+1FZX13GvxZSZxwn6H/pEcxY+LH0lkI5oZ8QwjWkkBXF3kn7MRakfc7GjDVXClg3uph78ZT7kCLXY+RUnSSpCdl6eXO7xTneeUErVnLznMd4erk/XYDJ781G6xqmJ4/HiYNelqcZ6TGuyBXihUW6mso660/8mP4tpx0nADBjobO5Oz0s/alsqHbHbe7M3Mb4xOE4cTLF8z3amR/J69jChX7PWM8bSWOopvfna9+Vd/yzZ1XTWW9dxbL0hZx0xAJZcxF0ND9GNUMNkp2JpKhJJDuTSFaTSFGTSbnyebIzESvpd5y5tK4sVa4UqlX0flQ1VKeq3p+yugpyAawIev5yP6LtkUz0/A9h5kezvXbkyBEAatas6YpoQggXkkJWFFun7SdYkPY5v2WsxokTAwYeMfekn/sQyuoruDpevstQrTl6eU85jrEs/VsMGJjl819quwW5OuYtrbOu4L3kKThx8pRlCIM9XpAiNg84VSd/Zf7B0vQF7Lb9CYCCwgPGh+hlGUADt2a3dZ5P2o8xMqEvqWoyT7sP41mP0fkdXRQwu2rjyUthxDsv8JH3N4QYb/k3AwAXHOdZkf5ffrb+QJKaCEAZXXm6W/rSxdzrtichs6mZpKjJJDuvFLlq4v+K3iv/mhSz1rtaRV8dd939+9zb4uin9O/5KGUaTdxa8q7Pl9lek+fICnH/kufIimLnjOMEC1JnsyHjZ5w40WPgUXNP+rkPpby+oqvjFRiTYqa8vhLl9ZWyLXeoDlZYv2Na8st86bO0UP/BtzJ9MR+mTAXgGffnedp9uBSxeUSn6Ghuak1zU2ti7YdZlv4tv1pXsSPzd3Zk/o6/vha93AfQ3vQIRsWUaxvJzkQmJT1HqprMg8aHCXd/voCPQhQEg+LGI+aeLEybzUrr4lsWsgdsUSxNX8jvGeu1oRB1DPXpbRnAQ6awO56czU0x4quUwldX6q6PQRRtbUydmJXyNhG2HVxyXqCkTmaRFkLcPumRFYVevOMCX6V9zHrrSpw40GOgk/kx+rsPy1HM3c8y1QxGXu7LUcchHjY9ykTPdwplcbg0bQGfpv4HgOEeL9PH/RkXJyr+LjvjWZW+hBXp33FZjQfARylJN0sfHrP0yfbHo121MT5xBBG2HdTQ12am77cydq0YO++I46lLHdGhY0mpjTmKSodq54/M31iatoBoeySQNba1takDvSxPE+gW4orYohiZnDiKbZkbGenxKr3dB2rLpUdWiPvX7fbIyoASUaj96zjL6ISnWWfN+o+ss7kHC0r+zMueU6WIvY5RMfGa1/uYsbAh42d+yVjh6kg5LEqboxWxo0tMlCK2gPjqSjHAYwTfl9rABM+3CTDUJUG9xIK0z3kyPox3kiZyxH4AgM9S3iXCtgNfpRTTvWdJEVvMldNX5AHjQ9ixa79nAVKcSSxO+5p+lzrxZtJYou2RlFC8eNLyLP8tuY7XvGZIESvyxNWxsb9mrHJxEiFEUSO3FotC64LjHGMSniHOeYpahiBe83qPSvo7n7DmflLV4McLnpP5T/IkPk6eTqAhhKoGf1fHQlVV5qd9yoK0z1FQeKnEG3Sx9HJ1rPuOUTHSwdyNMFNXomwRLEtfyNbM3/gl4yd+yfiJGvraHHUcwg03pnp/TLn76Jb9+1lXcx+2Z25mVfoPtDS2Z3n6oqxH6FyZjKmKvjo9LP3paH5MLmyIPNfc2AYPxZMY+wGO2Y/gZ5DJnYQQt0cKWVEoXXCcZ0xiVhEbYAhkhvecPHvOYXHX0fQYuzP/5NeMVbyZ9BKf+35/w7GQBUFVVb5IfZ/F6V+jQ8d4z/8jzNzVZXkEKIpCiLExIcbGxDlOsTx9EWusP3LUcQiAlzzfpJ5bAxenFAWlibEl5XWVOOs8zcDL/5s5trFbC3panqapsZXMCCzyjVEx0cbUkdXWpWywrmJIiTEAjBkzxsXJhBCFnYyRFYXORce/jE18hlOO4wQY6jLD+6vbngVTZElzpjIsoTenHSd43NyXFzwnuySHU3UyK+Vtllv/ix4DU7zepbWpo0uyiJtLdaawIeNnPJQSPHzdYzBE8bc47Wtmp87ADSMdzF3paXkaP0OAq2OJ+8TezF28mDiQsrryfFfyV7lwIsR9TmYtFkVSvOMCYxOf5ZTjODX0tXlPiti74q7zYIrnDJ5PeIoV1u9oYGzGQ6awAs3gUB18mPImq63LcMONN7w+pIWpbYFmELfPQ1eCxyxPujqGcJHeloH4GQKoZQjER1fS1XHEfaa+W0PK6Spy3hlHlG2r+NCuAAAgAElEQVQXocam7Nu3D4B69eq5OJ0QorCSS16i0LjkvFrEHsNfX4v3febiLUXsXavlFsgwj5cBeC/5Nc454gps33bVxn+SJ7HaugwjJt7yniVFrBCFmE7R0dTYSopY4RI6RadN+rT+yqRPU6ZMYcqUKa6MJYQo5KSQFYXCJedFxiYM4qQjFn99LT7wmYe3ztfVsYq8HpZ+tDC2IUVN4q2kV7CrtnzfZ6LzMq8mDuXXjFWYsfAf79k0NbbK9/0KIYQousJMWXMnbMlYT4ZqdXEaIURRIIWscLnLznheShjECcdRqutrXumJlSI2LyiKwque0ymjK0+0PZL5aZ/l6/6O2A8y/HIf9tj+xlcpxfs+8wg1Ns3XfQohhCj6qhr8qW0IIlVNYXvGZlfHEUIUAVLICpdKcF7ipYRBHHccoZq+Bh/4zJNb2/KYt86HyZ7vokPHf9PmEJG5I1/2sznjF0Zd7s855xlqG4L4wncJgW7B+bIvIYQQxc/DV3plf81Y6eIkQoiiQApZ4TKJzsu8lDCIY44Yqun9+cBnHr66Uq6OVSwFGxsx0H0kKirTk8ZxyXkxz9p2qk7mpX7Cm0ljsZJOmKkbH/ssoIy+fJ7tQwghRPHX3vwIOvT8nbkNZwm7q+MIIQo5KWSFSyQ6E3gpYRCxjsNU0fvxgc88SupKuzpWsdbPfSihbk24rMbzdtIEnKrznttMdaYwJWkUC9O+QIeOkR6vMsHz/zAp5jxILIQQ4n7iqytFE2NLHNhpO6kFEydOdHWkYi9dTeWI/QBWNd3VUYS4Y/L4HVHgkpwJvJw4iKOOQ1TRV+dD768pqSvj6ljFnl7RM8nzXQZf7sEu23YWp39NX/dBd93eafsJJic9zwlHLJ6KF695vU9jY4s8TCyEEOJ+E2Z6lL8yt3Co3F5erDPB1XGKlVRnCjH2A8TY93PYvp8Y+35OOo6houKhePKwqQuPWnpR01DX1VGFuC2Kqqr5uwNFmQc8Cvyrqmq9K8umAY8BTuBfIFxV1Vs+GyQkJERdv359fsYV+SzZmchLiYOJse+nsr4aH3rPp7S+rKtj3Vf+zNjChKQR6DHwic8CAt1C7riNvzO3Mi3pFVLUJKrpazDdeyaV9NXyIa0QQoj7iVVNp0f8Q6SraYyKe43uwU+gKIqrYxU5Kc6kK8XqAQ7bo4mxH+CU43iO9fQYKKMrxznnGW1ZbUMQXcy9aG/qgrvOowBTC5GlfPnyEaqqNr7VegVRyD4EpAALrilkvVRVTbry+WggUFXV4bdqSwrZoi3FmcRLiYM5bI+mkr4qH3rPp4y+nKtj3Zc+T3mPJenzKa+rxBzfpZTQed3Wdqqqsjj9a+akfogTJy2N7Zjo+Y78RyeEECLPvJM0kV8yfgLAorhTRe9HFX11quirU1XvTxVDdSrrq2FWLC5OWjgkOhOu9LJGc9h+gBjbfuKcp3Ks54YbfoZa1DIEEmAIpLYhED9DAEbFxFH7IVanL+XXjJ9JyfoTHTMW2psfoYu5F3UM9eWCgigwhaaQBVAUpTrw89VC9rrXJgBVVVUdcat2pJAtulKcSbySOISD9n1U1FXhI5/5MhmQC9nUTEYl9OeQPZqHjGG84fXhLf+DylCtvJf8Gr9lrAZggPsIBrqPRKfIUHshhBB556Q9lkF/PoGjUgZqiRvP51BOVzGrwDX4UVXvRxW9H1X11SmtK1fgRZdTdWLHhl21Y8OGAxs21ZZ92ZV/7aoNO/Zsr9u5fln212xXtrGrNq2tFDWZGPsBzjtz3tRoxEQNQ20CDHWpZQiiliGQ6oYauCnGmx5Hhvr/7N13eBTVGsDh32xJo0lvARIglPQEQlFaRJogEoqIIoSqXhFFQQRpClwVUBAs2BDUK6AgRUFEFIgoAgkEpNdQQgsljSRky7l/hIwJqUjCUr73eXjIzpw9883s7Ox+e86ck8bGq2tZlbaEXZYofXltYz26uPbkIeculDKUKfLjJ0RWt30iq2naVKAfkACEKqXi8njuUGAogLu7e6PIyMhij1cUrWR70rUk9m+qGtyZed8XVDZWc3RY97xY2wmGXu5JirrCiJIT6er6WJ5lz9vOMD7xBQ5a9+CCK2NKv0kr53a3MFohhBD3krCwMADmL/2Ck7ZjnLAe46TtGCdtMZy0xRBrO4GN3Ec2dsGVGibPay24njhrLnoCqCeG2RJLa7bkMf8E05prgmnHdisPT479rWOqTz2zD/WuJa41jZ6YNPNN1XvCepRVaUv5OW0FCeoykJEgt3buQBeXnviZg6WVVhSL2z6RzbJuDOCilJpYUD3SInvnuWJP5pWEoey17qSKoToz75tPFUlibxu/pa1mctIonHDmo7KLqG2ql6PM35YoJiaM4LK6SFWDO1PKzMm1nBBCCFFUMhPZZcuW5breqiycscVmJLmZCa414+/MpOtWM2PGpJkxY8aICbNmxnRtmQmTvi7r42zLc1ummTBhxqz9U6fx2mNnXKht8qKG0ROjZiy2/UpX6fxx9TdWpS0hyvLPXPQ1jbXp7NKDDi6PUsZQtti2L+49d1IiWwtYldu660kie2dJsV/hlYSh7LFGU9lQlVn3LaCKsbqjwxLXmZE0gVVpS6llrM1HZRfjqrnp635I/ZbZyVOxYiXY3IwJpd+hjOE+B0YrhBDiXlBQIpufRHv8tZbbY5y0HseGNUeCmT3RNOWaYOrrs6zL+pzMBNOECQPGe6J18rTtJKvTvuentO/1OelNmGjh/BBdXHoSZG4qtxyJm3ZbJ7KapnkppQ5d+/t5oLVSqmdB9Ugie/tKtMdz1HqIY7aDGf9bD3HMdogUdYVKhirMum8BVY3ujg5T5CJNpfLM5cc4bjvKwy49GFXqDSwqnfeT32Jl2mIAero+xTMlRmLUZMYuIYQQxW/37t0A+PoW2M4hHMCqLPyVHsGPaUvYlr4JOxn3MnubAphUeqYM5iluym2TyGqathBoA1QAzgETgYeB+mRMv3MceEYpFZtXHZkkkc1dqkphbdpK1qatxICBisYqVDRUzvhnrExFQ8bjcoYKN52IpKurxFiPcNR2MCNZvZawXrCfz7W8h7EuU8u8TzVjjZvariheR60Hefby46RzleElx7Lh6lp2WSIxY+alUhPp6BLm6BCFEEIIcRs6bzvDT2nLWJm2mEv2C5QzVOCN0u/hYw50dGjiDnXbJLJFSRLZ7M7bzrAs9Rt+TFuiD5WeHwNGyhsq5khyK2T7uyImzYxd2TljP8lR6yGOWjOS1qO2Q8Tajuu/umXlgisepjp4mupR2+iV8b/Ji7KG8sWx66IYrEz9lpnJr+uPyxsq8kbp2Xib/R0YlRBCiHtRREQEAK1atXJwJKKw4u2XmJT4Ejst2zBjZkSpiXSSH8LFv1DYRFb6Cd6B9lp2siT1KzZeXauPkudtCqCHa1/KGSoSZz9HnP0McbZz1/4+R5ztLJfVReLsZ4mzn82zbg2NsobypNivkEZqjvUGDNQ01sbT5EVtoxe1TfXwNHlR1eAu90Tc4R5x6cV2y19svPozDU3+vFH6PSoYKzk6LCGEEPegmTNnApLI3knuM5RjRplP+TB5GsvSvmFa0jgOW/fzbImRNz2Csrg3pKlUPr0yq9DlJZG9Q1iVhYir61iS+iX7rLuAjBbWB5070cP1KbzNAQXWYVHpXLTHZSSzepKb/e+L9jj95v0Khkp4ZklWaxvrUctUGyfNuVj3VTiGpmmMK/U2j7r0xscchFMBc80JIYQQQmRl0swML/UadUz1mZU8me9Tv+aY9SATS78rIxuLfB2w7GZq0quctB0r9HMkkb3NJdrjWZW2hGWpC/WW1FJaabq49KKbax8qGasWui6z5kQVY/WMkYPz+GHMqixcsl/AWXOV0WnvQSbNTJBTU0eHIYQQQog7WGfXntQy1WFCwgvssGzlmcu9mVJmDnVM9R0dmrjN2JSNhSmfMT/lQ2xYqWWswz4uFuq5ksjepk5Yj7I09WvWpq3Uu/jWMHrS0/Up2rk8km2KlKJk0sw3lBwLIYQQQghxPV9zEB+X/ZbxicM5YN3DsMtP8mrpqbR27uDo0MRt4oztFP9NfJXd1h0A9HDty5ASI6iFR6GeL4nsbUQpRZRlM0tSv2RL+u/68sbm++np2o8QpwfkPlQhhBBCCHFHqGiswnv3fck7Sa/zy9WVTEp8iafcnibcbZh8p72HKaX4+eoK5iT/lxR1hQqGSowuNZXGTvffUD2SyN4Grqo0fkn7gaWpXxNjOwyAE860c3mEHq5P4Wmq6+AIhRBCCCFunenTpzs6BFFEnDUXxpT6L16mBsy9MoOvUj7miPUgY0u9RQlDSUeHJ26xBHs87yZNIiL9FwBaO7VnRKmJ/+qWRklkHeiC7Twr0hayMvVbElU8kDHlSTeXJ3jEtZfcFC+EEEKIe1LduvIj/t1E0zR6ufXH0+TFG4kv82f6ep6L78OU0u/jbqrl6PDyZFM2bFixKAtWLFiVFQsWbFhyLLMqC1as+v9Z11uvlbeRtWyWZVmea8GC7fo6c9lOCa0kNY2e1DB6UsPkQU1jbaoba97Wg3VuS/+Dt5Ne46I9DjetBMNLvkZ7565omvav6pN5ZB3ggGUPS1O/Yv3Vn7BiBaCeyYeerv1o49we8218AgohhBBCFLfM73vt27d3cCSiqMXajvNawvMctx2hpFaa8aWn08SpRZHVf1Wlsc+yi52WSI7bjmJR6VkSTEuOBDNrAnl90mnHXmRx3QoGDFQxVKeGyZMaRg9qGD2pafSkpsmDslqFf50w3qyrKo1Prszk+9SvAfA1BTG29FtUNbrnWr6w88hKInuL2JSNP9J/Y0nKl/xt3Q5knGwtnNrS0+0pfE3BDju5hBBCCCFuJ2FhYQAsW7bMwZGI4pBiv8KbSWPYlP4rBgwMLfESj7mG/6vvwsn2JPZYotlliWSXJYr91r/1hqKbpaFhwoxJM2HGjEkzY8J0bVnG32bNnO2xSTNjxowx8zm5LdMy6jBrZozX6jBee2zin3qN2eo36XWYMJFgj+eE7SgnbTGctMVwwnqUs/bYPJPvElrJa8ltbWoaPbK04tYq1lbcw9Z9TEkczXHbEYyYCHd7jj5ugzBqxjyfU9hEVroWF7NkexKr05ayLPUbztpjgYwT6WGXHnR3fTJjKhwhhBBCCCHuEW6GErxeehZfpnzEgpQPmXtlBoet+xlZ6nWcNZd8nxtvv8QuSxR/W6LYaYniiHV/tuRNQ6OuqQH+5sbUN/ngorlel0RmTRTNuSz7J3nML9m6HQSQPddLV+mctp3ghO1YRoJrPab/nawS2W/dzX7r7mzPMWGigckPf3Mj/M2N8TEHUtJQ6qZjsykb36bOZ96V2VixUsPoyWul3qa+2eem684kiWwxibWd4PvU//FT2vekqhQAqhlq0MPtKTo6d8PNUMLBEQohhBBCCOEYBs1AeInnqG2qx5uJY1h39UdO2I4xufR72aaCPG87wy5L1LV/Gd2FszJiwsfkrydivuZAShpK3+rduS04aU54mOricd1AsUop4tUlTlpjsrTiZiS5p20n2W3dwW7rDr5J/QwDBuqYGhBw7Xj6mYO5z1DuhuI4ZzvNm0lj2WnZBsCjLo/zTMmRuGiuRbavIIlskVJKsdOyjSWpX/Fn+noUGd22g8xN6OH6FM2cWt/2v+wIIYQQQghxq7Rybod72VqMS3ieg9Y9PHO5N4+7DeSI9QC7LFF6j8ZMzrjgbQ4gwNwYf3MjGpr9izxButtomkZZrTxlncrjT6Ns65Ltifxt2cEuSyR/W7az37qbQ9a9HLLuZUnqVwDUMtbG/9rx9jc3yvZDw/XWpf3IrOQpXFFJlNXK80qpKTRzblU8+yX3yN68dJXOb1dXsSTlK47YDgBgxkxb5870cHuKuqYGDo5QCCGEEOLOIffI3nsS7PG8kfgy2y1/ZVteQiuFnzlYT6LqmbxlYNRilKpS2GfZpbeA77Xs4ipp2cpUMVTXW8D9zY1wN9YiWSUyK3kyv139CYAHnB5kZKnXb7g1F2Swp1vikv0CP6R+y4rURVxWFwEoq5XnUdfHecT1McoZKjg4QiGEEEKIO09sbEYrXPXqMpbIvcSmrCxO/YIj1oP4mAPxNwfjaawnPRodyKLSOWjdyy5LJDstUey27OCKSspWpqxWHk3TuGS/gAuuDCs5hodduv/rgWwlkS1Gh637WZr6Nb+m/YgFCwC1jfXo5daPB50739bzNwkhhBBCCCHEv2FTNo7ZDrIzPUrvjpzZoNfQ5M9rpd+iuvHm5gaWUYuLmF3Z+St9I0tSv2SHZSuQMSra/U6h9HLtR4A5RKbPEUIIIYQoAsuXLwegW7duDo5ECJGVUTNS19SQuqaG9KAvSilO2mK4ZI/DzxyMUbt16aUksgVIVVf4KW0536d+TaztBACumhudXLrT3fWJm/7FQQghhBBCZLdgwQJAElkhbneaplHT5ElNPG/5tiWRzcNZWyzLUr9hVdpSvR94ZUM1urs+ycMuPYpkfiUhhBBCCCGEEDdOEtkslFLstu5gScqXbEr/VZ9c2c8UTE+3fjzgFHpLm8uFEEIIIYQQQuQkWRkZo3FtvLqWJalfcsC6B8iYXLmdc2d6uPalvtnXwREKIYQQQgghhMh0TyeyCfb4jOlz0hZywX4egNLafXR1fYxHXfpQwVjJwREKIYQQQgghhLjePZnIxlgPszT1a9amrSSdqwB4GOvS0/UpHnLpgrPm4uAIhRBCCCHuXZ999pmjQxBC3ObuqUR2n2UXC1I+ZEv67/qypk6t6On6FI3MzWX6HCGEEEKI20D58uUdHYIQ4jZ3TySyByy7+SLlA7akRwDggivtXbrSw7UvNU21HRydEEIIIYTIatGiRQA8/vjjDo5ECHG7uqsT2YOWvSxI+YA/0zcAGQlsd9cnecwtnDKGso4NTgghhBBC5Grx4sWAJLJCiLzdlYnsYes+5l/5kD/SfwMyEthHXR/ncbeB3Gco5+DohBBCCCGEEELcjLsqkT1iPcCCKx/ye/o6AJxx0RPYsga510IIIYQQQggh7gZ3RSJ7zHqIBVc+ZGP6WgCccKara2/6uA2knKGig6MTQgghhBBCCFGU7uhENsZ6mAUpH7Hx6s8oFGaceMT1MZ5wHUx5oySwQgghhBBCCHE3uiMT2RPWoyxI+Yj1V3+6lsCa6eLSiz5ug6lorOzo8IQQQgghxE343//+5+gQhBC3uTsqkbWQztTE0fx2dTV27Jgw0dmlJ0+6DaGisYqjwxNCCCGEEEXAzc3N0SEIIW5zxZ7Iapo2D+gCnFdK+V5bNh14BEgHjgADlFLxBdV1zHqYdVfjsiWwlY3VijN8IYQQQghxi33xxRcADBgwwMGRCCFuV4ZbsI35QMfrlv0C+Cql/IGDwJjCVtbZpSdflfuJl0pNlCRWCCGEEOIutHLlSlauXOnoMIQQt7FiT2SVUhHApeuWrVVKWa89/AtwL0xdniYvRpZ6nSqSwAohhBBCCCHEPetWtMgWZCDwU14rNU0bqmlapKZpkUmXkm5hWEIIIYQQQgghbkcOTWQ1TXsNsAJ5Dk2nlPpEKdVYKdW4XLlyty44IYQQQgghhBC3JYeNWqxpWn8yBoFqq5RSjopDCCGEEEIIIcSdRbsVOaSmaR7Aj1lGLe4IvAu0VkrFFbaeChUqKA8Pj+IIUQghhBBCCCGEg0VFRSmlVIE9h2/F9DsLgTZABU3TTgETyRil2Bn4RdM0gL+UUs8UVJeHhweRkZHFGK0QQgghhBBCCEfRNG17YcoVeyKrlOqTy+LPi3u7QgghhBBCCCHuTg67R/ZmnTp1irCwMLZv347dbnd0OEIIIYQQQmAwGAgODmbZsmW4uxdqhkkhxL9wxyayYWFhdO/enT/++AMnJydHhyOEEEIIIQTp6enMmDGDsLAwtm3b5uhwhLhr3ZLBnopK48aNVeY9skajkdTUVElihRBCCCHEbSU9PR1XV1dsNpujQxHijqNpWpRSqnFB5Rw6j+zNsNvtksQKIYQQQojbjpOTk9z6JkQxu2MTWSGEEEIIIYQQ9yZJZIUQQgghhBBC3FEkkRVCCCGEEEIIcUe5ZxLZWbNmkZKSUuzbMRqNBAYG4uvrS69evUhJSSEmJgZfX99i3zZAWloaTZo0ISAgAB8fHyZOnFhkdYeHh+Pp6UlgYCDBwcFs3rwZgDZt2pA5CFdxGzVqFA0aNMDf35+wsDDi4+MLfE54eDhLlizJt8z8+fM5ffp0UYWZzYYNG+jSpUux1P1vtuuoeG5ETEwMrq6uBAYGEhgYyDPPPFPgc/I7N958803q1q1L/fr1+fnnn/XlAwcOpFKlSjnen71799a37eHhQWBgYIHbT0hIoF+/ftSpU4c6derQr18/EhISbmCvM3h4eHDhwoUbft6tUtj3yvXlBg8ezN69e4strqtXr/LQQw8RGBjI4sWLs62Ta1fhbNiwgT///DPfMo8++ijNmzfPtuzAgQO0adOGwMBAGjZsyNChQ/V1W7dupVWrVtSvX58GDRowePBg/bN4+fLl+Pv706BBA/z8/Fi+fHmu25w0aRIzZsy4yb0rnBkzZqBpWoHvwejoaJo3b46Pjw/+/v7Zzrljx47RtGlTvLy86N27N+np6QBEREQQHByMyWTK9rquX79ev94EBgbi4uKS57G43q08Nlnl9zmyadMmmjRpQoMGDWjQoAGffPKJvm7SpElUr16dwMBAvL29Wbhwob4u6/s0ICCAX3/99YZi2r9/P4GBgQQFBXHkyJFcy3Tt2jXb9T5rPIGBgaxevRqAP/74A39/f0JCQjh8+DAA8fHxdOjQgTtpkFQh7naSyBYxV1dXoqOj2b17N05OTsydO7fYt5mVs7Mzv/32Gzt37iQ6Opo1a9bw119/FVn906dPJzo6mrfeeounn366yOotrHbt2rF792527dpFvXr1ePPNN4uk3pv9Mmi1WoskjrvdjRynOnXqEB0dTXR0dKHeR3mdG3v37mXRokXs2bOHNWvW8J///EcfRTI8PJw1a9bkqGvx4sX6tnv06EH37t0L3P6gQYOoXbs2R44c4ciRI3h6ejJ48OAc5YpzBMtbcR7+20T2s88+w9vbu9ji2rFjBxaLhejoaHr37p1jvVy7ClZQIhsfH8/27duJj4/n2LFj+vLhw4czYsQIoqOj2bdvH88//zwA586do1evXrz99tscOHCAffv20bFjR5KSkti5cycjR45kxYoV7N+/n5UrVzJy5Eh27dpVJPvyb5w8eZJffvmFmjVrFljWzc2NL7/8Ur+uvPjii/qPE6NHj2bEiBEcOnSIsmXL8vnnnwNQs2ZN5s+fzxNPPJGtrtDQUP1689tvv+Hm5kb79u2LfgdvgbNnz/LEE08wd+5c9u/fz6ZNm/j4449ZtWqVXibzXFmxYgVPP/00FotFX5f5Pp01a1ahfsDMavny5Tz66KPs2LGDOnXq5Fj//fffU7JkyRzLM+OJjo7m4YcfBuCdd95h6dKl/Pe//+Wjjz4CYPLkyYwdOxZN024oLiFE8bnrEtkrV67QuXNnAgIC8PX1ZfHixcyePZvTp08TGhpKaGgoAM8++yyNGzfO0Wq5evVqGjRoQIsWLRg+fLj+i+OVK1cYOHAgISEhBAUFsWLFigJjadmypf5Lns1mY8iQIfj4+NC+fXtSU1MB+PTTTwkJCSEgIIAePXroyfZ3332Hr68vAQEBtGrVSq9j1KhRhISE4O/vz8cff5xjm5qm6Rdqi8WCxWIplotuq1at9H3LjLdJkybUq1eP33//HchoVWvZsiXBwcEEBwfrX5DOnDlDq1at9JbrzPJr166lefPmBAcH06tXL5KTk3Nst3379phMGdMfN2vWjFOnTuUoo5Ri2LBheHt707lzZ86fP6+ve+ONNwgJCcHX15ehQ4eilGLJkiVERkby5JNPEhgYSGpqaq7lrhceHs5LL71EaGgoo0ePLtQ5kleZojpWa9as0c/f77//vqCXka1bt3L//fcTFBTE/fffz4EDB4CML8fdu3enY8eOeHl58corr+jPyfpFYMmSJYSHhwPwww8/0LRpU4KCgnjooYc4d+4ckPGL99ChQ2nfvj39+vWjZcuWREdH63U88MADRfLlNa9zY8WKFTz++OM4Ozvj6elJ3bp12bp1K5BxHpcrVy7POpVSfPvtt/Tp0yffbR8+fJioqCjGjx+vL5swYQKRkZEcOXKEDRs2EBoayhNPPIGfnx8A3bp1o1GjRvj4+GRrschLyZIlefnllwkODqZt27bExcUBGa2KY8eOpXXr1rz33nvExcXRo0cPQkJCCAkJ4Y8//gBg48aNeqtDUFAQSUlJQMYXx8xrSua1MCYmhoYNG+a4ZhX2vZJbuaytnwsXLsTPzw9fX19Gjx6dbR9fe+01AgICaNasmX4OZXXp0iW6deuGv78/zZo1Y9euXZw/f56+ffsSHR1NYGBgnq0xINcuyP29GhMTw9y5c5k5cyaBgYH6vmW1dOlSHnnkER5//HEWLVqkLz9z5gzu7u7648xz/IMPPqB///56C66mafTs2ZPKlSszY8YMxo4di6enJwCenp6MGTOG6dOn5/q6Zcp6Hl24cAEPDw8g/2tWYV4fyEhopk2bVqjPzHr16uHl5QVAtWrVqFSpEnFxcSil+O233+jZsycA/fv311tXPTw88Pf3x2DI+6vXkiVL6NSpE25ubjnWzZ49G29vb/z9/Xn88cf15Xv37qVNmzbUrl2b2bNn68vfffddfH198fX1ZdasWQBMmzZNLzNixAgefPBBAH799Vf69u2b7/EqzOfLBx98QHh4OMHBwQBUqFCBadOm8dZbb+Uo6+XlhZubG5cvX86xrnnz5sTGxua6jejoaJo1a6b3brh8+TKrV69m1qxZfPbZZ/r3vKySk5N59913GTduXAUoE+sAACAASURBVK51Xs9sNpOamkpKSgpms5kjR44QGxtL69atC/V8IcQtopS6Y/41atRIZcoIPaclS5aowYMH64/j4+OVUkrVqlVLxcXF6csvXryolFLKarWq1q1bq507d6rU1FTl7u6ujh49qpRS6vHHH1edO3dWSik1ZswY9dVXXymllLp8+bLy8vJSycnJKjY2VnXq1Emvt0SJEkoppSwWi+ratav68MMP1bFjx5TRaFQ7duxQSinVq1cvva4LFy7oz33ttdfU7NmzlVJK+fr6qlOnTunbU0qpjz/+WE2ePFkppVRaWppq1KiRHmtWVqtVBQQEqBIlSqhXXnkl1+P0b/Tv31999913Simlvv32W9WkSROllFKtW7dWL730klJKqVWrVqm2bdsqpZS6cuWKSk1NVUopdfDgQZX5+s2YMUNNmTJFjzUxMVHFxcWpli1bquTkZKWUUm+99ZZ6/fXX842nS5cu+nHMaunSpeqhhx5SVqtVxcbGqjJlyuhxZ77uSinVt29ftXLlSn0ftm3bpq/Lq9z1x6Nz587KarUqpfI+R9avX1/geVQUxyrz/D148KCy2+2qV69e+nazyhpPQkKCslgsSimlfvnlF9W9e3ellFJffPGF8vT0VPHx8So1NVXVrFlTnThxQin1zzmulFLfffed6t+/v1JKqUuXLim73a6UUurTTz/Vz4mJEyeq4OBglZKSopRSav78+eqFF15QSil14MABlfV9nenYsWPKzc1NBQYGqlatWqmIiIgcZfKT9dx47rnnsp0nAwcO1M+HzG35+PjkWs/GjRtzje96K1asUN26dcuxvFu3bmrFihVq/fr1ys3NLdv7NfMcS0lJUT4+Pvq14PprVSZAff3110oppV5//XX13HPPKaUyzt1nn31WL9enTx/1+++/K6WUOn78uGrQoIFSKuOYbNq0SSmlVFJSkrJYLOrnn39WQ4YMUXa7XdlsNtW5c2e1cePGfK9ZhX2vXF8u83FsbKyqUaOGOn/+vLJYLCo0NFQtW7ZM38fM548aNUq/3mU1bNgwNWnSJKWUUr/++qsKCAhQSmU/r68n167s8nuvTp8+Pc+427ZtqyIiItSBAweUn5+fvnzevHmqdOnSqmPHjurdd9/VP7PCwsLU8uXLc60rKChIRUdHZ1sWHR2tgoKCcpTNGlfW/Y2Li1O1atVSSuV9zSrs67NixQo1fPhwpVTe78G8bNmyRTVo0EDZbDYVFxen6tSpo687ceJEjutL1vPxeqGhoeqHH37IdV3VqlVVWlqaUuqf7wUTJ05UzZs3V2lpaSouLk6VK1dOpaenq8jISOXr66uSk5NVUlKS8vb2Vtu3b1ebN29WPXv2VEop1aJFCxUSEqLS09PVpEmT1Ny5c2/68yW31zw+Pl6VLVtWjzfztYyKilItWrTI9bgsW7ZM9enTJ9fj4OfnpzZs2KCUUmr8+PH650l+5++LL76ovv/++xzX+4kTJ6patWopPz8/NWDAAHXp0iWllFI7duxQTZs2VW3atFEnT55UvXv3VgcPHsy17vzk9V1VCJE/IFIVIjc0OTSLLgZ+fn6MHDmS0aNH06VLF1q2bJlruW+//ZZPPvkEq9XKmTNn2Lt3L3a7ndq1a+u/EPfp00dvKVm7di0rV67U70VJS0vjxIkTNGzYUL+nAiA1NVW/n65ly5YMGjSI06dP6/d9ADRq1IiYmBgAdu/ezbhx44iPjyc5OZkOHToAGa1U4eHhPPbYY3q3xrVr17Jr1y793pqEhAQOHTqkx5vJaDQSHR1NfHw8YWFh7N69u8ju0R01ahRTpkyhYsWKencpQI8x675ZLBaGDRtGdHQ0RqORgwcPAhASEsLAgQOxWCx069aNwMBANm7cyN69e3nggQeAjInEr78PK6upU6diMpl48sknc6yLiIigT58+GI1GqlWrpv/iDBn3Ik2bNo2UlBQuXbqEj48PjzzySI46CluuV69eGI1GIO9zJKu8ylSrVu2mj9X+/fvx9PTUWwn69u1bYEtfQkIC/fv359ChQ2ialq2LV9u2bSlTpgwA3t7eHD9+nBo1auRZ16lTp+jduzdnzpwhPT0923nZtWtXXF1d9WM2efJkpk+fzrx58/QW3ayqVq3KiRMnKF++PFFRUXTr1o09e/ZQunTpfPcHcp4bKpcWqcL2Uli4cGGBrbGZ28itzqzLmzRpku2YzJ49m2XLlgEZXRoPHTpE+fLl89yGwWDQu8z27ds3W3fnrF1p161bl+1e1MTERJKSknjggQd46aWXePLJJ+nevTvu7u6sXbuWtWvXEhQUBGS0Whw6dIiaNWvmec26XmHfK5m2bdtGmzZtqFixIgBPPvkkERERdOvWDScnJ70XTKNGjfjll19yPH/Tpk0sXboUgAcffJCLFy8W6l5kuXb9I7/3al7OnTvH4cOHadGiBZqmYTKZ9M+WAQMG0KFDB9asWcOKFSv4+OOP2blzZ7715faeyet9VFi5XbPi4+MLfH1SUlKYOnUqa9euveFtnjlzhqeeeooFCxZgMBhu6npz5swZ/v77b/17wPX8/f158skn6datG926ddOXd+7cGWdnZ5ydnalUqRLnzp1j06ZNhIWFUaJECSDjPP/999959tlniYqKIikpCWdnZ4KDg4mMjOT3339n9uzZ/PXXXzf1+ZLXa5h12cyZM/n00085evRojls7Ro0axSuvvML58+dzvS0qISGB+Ph4vWW0f//+9OrVK9/jGh0dzeHDh5k5c2aO69izzz7L+PHj0TSN8ePH8/LLLzNv3jwCAwP17UdERFCtWjWUUvTu3Ruz2cw777xD5cqV892uEKL43XWJbL169YiKimL16tWMGTOG9u3bM2HChGxljh07xowZM9i2bRtly5YlPDyctLS0fG/gV0qxdOlS6tevn+/2M++RvZ6zs7P+t9Fo1LsWh4eHs3z5cgICApg/fz4bNmwAYO7cuWzZsoVVq1YRGBhIdHQ0SinmzJmT54fc9e677z7atGnDmjVr8kxkbTYbjRo1AjKSjTfeeCPfOqdPn653mcpt/4xGo36f3syZM6lcuTI7d+7Ebrfj4uICZHTti4iIYNWqVTz11FOMGjWKsmXL0q5du2wDP+RlwYIF/Pjjj/z66695fkHIbXlaWhr/+c9/iIyMpEaNGkyaNIm0tLR/XQ7QvyRA3udI1u6ReZWZNGnSTR+r6OjoG/4SOH78eEJDQ1m2bBkxMTG0adNGX3f9OZv5umbdRtbj8vzzz/PSSy/RtWtXNmzYwKRJk/R1WY+Tm5sb7dq1Y8WKFXz77be5DraT+aUMMhKMOnXqcPDgQRo3bpzv/uR2bri7u3Py5Em9zKlTp6hWrVq+9UDG/abff/89UVFRBZb18fFhx44d2O12vdug3W5n586dNGzYkFOnTmU7Bhs2bGDdunVs3rwZNzc32rRpk+c5lpesr0PWuu12O5s3b9Z/OMj06quv0rlzZ1avXk2zZs1Yt24dSinGjBmT457RmJiYPK9ZWd3IeyVTftdZs9ms71fWc66g5xfmvJdr1z/ye6/mZfHixVy+fFlPehMTE1m0aBFTpkwBMrrXDhw4kIEDB+Lr68vu3bvx8fEhKiqKRx99NEd9Pj4+REZG4u/vry/bvn17gfdRm0wm7Ha7vr9Z5XbNUkoV+PocOXKEY8eOERAQAGRcI4KDg9m6dStVqlTJ83mJiYl07tyZKVOm0KxZMyCjK218fDxWqxWTyVTo6w1k/MAeFhaG2WzOdf2qVauIiIhg5cqVTJ48mT179uS737kxm814eHjwxRdfcP/99+Pv78/69es5cuQIDRs25MiRIzf1+ZL5unbt2lVfFhUVle11HTFiBCNHjuT777+nX79+HDlyRH+PTZ8+ne7duzN79mz69+9fqOtvQTZv3kxUVBQeHh5YrVbOnz9PmzZt2LBhQ7ZkdMiQITkGsFJKMWXKFBYvXsywYcN4/fXXiYmJYfbs2UydOvWmYxNC3Jy77h7Z06dP4+bmRt++fRk5ciTbt28HoFSpUvo9YYmJiZQoUYIyZcpw7tw5fvrpJwAaNGjA0aNH9V/sso5C2KFDB+bMmaN/OOzYsaNI4k1KSqJq1apYLBb+97//6cuPHDlC06ZNeeONN6hQoQInT56kQ4cOfPTRR3qr2cGDB7ly5Uq2+uLi4vQBJ1JTU1m3bh0NGjTIc/uZrbfR0dEFJrE3KiEhgapVq2IwGPjqq6/0QW6OHz9OpUqVGDJkCIMGDWL79u00a9aMP/74Q793LSUlRW8FyWrNmjW8/fbbrFy5Mtd7iCDjy+aiRYuw2WycOXOG9evXA/986alQoQLJycnZRo3Men7kVy4/hTlH8ipTFMeqQYMGHDt2TL8/sDBfrBMSEqhevTqQcY9ZYVSuXJl9+/Zht9v1FsXr61qwYEG+dQwePJjhw4cTEhKS6z2qcXFx+jE4evQohw4donbt2gD069dPv8c1q7zOja5du7Jo0SKuXr3KsWPHOHToEE2aNClwPzPfO1nv/ctL3bp1CQoK0r/UA0yZMoXg4GDq1q2bo3xCQgJly5bFzc2N/fv3F2pANrvdrp+L33zzDS1atMi1XPv27Xn//ff1x5k/rB05cgQ/Pz9Gjx5N48aN2b9/Px06dGDevHn6PXCxsbHZ7svMTWHfK1nLZdW0aVM2btzIhQsXsNlsLFy48IbuO2vVqpV+rdywYQMVKlQoVEv9jbjbr115vVfzes0g43qyZs0aYmJiiImJISoqSr9Pds2aNfrn0tmzZ7l48SLVq1dn2LBhLFiwgC1btuj1fP3115w9e5aRI0fy5ptv6p+3MTEx/Pe//+Xll1/OdfuZPDw89OSmMNfmwrw+fn5+nD9/Xt83d3d3tm/fTpUqVdi6dSv9+vXLUW96ejphYWH069cvW4ugpmmEhobqsS1YsCDXRD43+fUAsdvtnDx5ktDQUKZNm6b34spLq1atWL58OSkpKVy5coVly5bpPdRatWrFjBkzaNWqFS1btmTu3LkEBgaiadpNf74899xzzJ8/X7/uXLx4kdGjR2e7ZzlT9+7dady4cY7PC4PBwAsvvIDdbs82yjxAmTJlKFu2rH4P91dffVXg9ePZZ5/l9OnTxMTEsGnTJurVq6c3Gpw5c0Yvt2zZshw/+i9YsIDOnTtTtmxZUlJSMBgMGAyGWzJ4qBCiYHddi+zff//NqFGjMBgMmM1mfbS5oUOH0qlTJ6pWrcr69esJCgrCx8eH2rVr611oXF1d+fDDD+nYsSMVKlTI9mV3/PjxvPjii/j7+6OUwsPDgx9//JHTp08zePDgbN2Lb8TkyZNp2rQptWrVws/PT/8SMWrUKA4dOoRSirZt2xIQEIC/vz8xMTEEBwejlKJixYo5hug/c+YM/fv3x2azYbfbeeyxxxw21cp//vMfevTowXfffUdoaKjearRhwwamT5+O2WymZMmSfPnll1SsWJH58+fTp08frl69CmQkAvXq1ctW57Bhw7h69Srt2rUDMr6kXD+ibVhYGL/99ht+fn7Uq1dP/5C77777GDJkCH5+fnh4eBASEqI/Jzw8nGeeeQZXV1c2b96cZ7n85HWOFKZMUR2rTz75hM6dO1OhQgVatGjB7t27c8RptVr1X/BfeeUV+vfvz7vvvputG2N+3nrrLbp06UKNGjXw9fXVv0xNmjSJXr16Ub16dZo1a5ZtVNPrNWrUiNKlSzNgwIBc10dERDBhwgRMJhNGo5G5c+fqCe+uXbuoWrVqjufkdW74+Pjw2GOP4e3tjclk4oMPPtC7g/fp04cNGzZw4cIF3N3def311xk0aBAAixYtyvGlMr/3++eff87zzz9P3bp1UUrRvHnzbF1Ys+rYsSNz587F39+f+vXr6605+SlRogR79uyhUaNGlClTJscUM5lmz57Nc889h7+/P1arlVatWjF37lxmzZrF+vXrMRqNeHt706lTJ5ydndm3b5/e1bJkyZJ8/fXX+vHJTWHfK9eXy1S1alXefPNNQkNDUUrx8MMPF/qLPmScZwMGDMDf3x83N7cCfzT5N+72a1de79VHHnmEnj17smLFCubMmaMnPjExMZw4cSLbeerp6Unp0qXZsmULa9eu5YUXXsjWqpbZkrlo0SJGjhzJ+fPnMRgMtGrViu7du1OlShXefvttHnnkESwWC2azmWnTpuU61VXWa9bIkSN57LHH+Oqrrwp1zSrs65OXEydO5OjdABmtpxEREVy8eFH/EXD+/PkEBgby9ttv8/jjjzNu3DiCgoL0a8q2bdv0wYl++OEHJk6cqLeqxsTEcPLkyTyTMpvNRt++fUlISEApxYgRI7jvvvvyjDs4OJjw8HD9e8zgwYP1WwhatmzJ1KlTad68OSVKlMDFxUV/rW/286Vq1ap8/fXXDBkyhKSkJJRSvPjii3nebjBhwgSeeOIJhgwZkm25pmmMGzeOadOm5eiFtmDBAp555hlSUlKoXbs2X3zxRZ7HoSCvvPKK3trs4eGRbRDNlJQUFixYoHc5f+mll+jRowdOTk56Ij948GCeeeaZAnsLCSGKh5ZfN6/bTePGjVVmN0RN04plLq/k5GRKliyJUornnnsOLy8vRowYUeTbEcJR3nvvPWJjY5k2bZrDYjh9+jRt2rRh//79+Y7geb3ExEQGDRrEd999V4zR3Z5KliyZbwuMEHersLAwhgwZok+NciuNGjWKp556KlsXaCEKq7i+qwpxt9M0LUopVeAvRHdd1+Kb9emnnxIYGIiPjw8JCQkOmW9QiOIyaNAgvvnmG5577jmHxfDll1/StGlTpk6dekNJLEDp0qXvySRWiHuVn58fBoPBYfOqTp8+XZJYIYS4TUmLrBBCCCGEEEVMvqsK8e9Ii6wQQgghhBBCiLvSXZnIGo1GAgMD8fX1pVevXvrocpnLM/+99dZbQMZAEmPHjsXLy0tfl3VY9bzqK1myZLbtzp8/n2HDhgHw7rvv4u3tjb+/P23btuX48eN6uQULFuDl5YWXl9e/Gqgkv+1mCggIyDFQTXh4eK6jPO7Zs4cHH3yQevXq4eXlxeTJk/P8BXHQoEH6wFM9e/bU79m7evUqvXv3pm7dujRt2lQfifLixYuEhoZSsmTJHDHmJTw8XJ/DMjg4ONtAMbeD6OjobIP9rFy5Uj+XhBBCCCGEEMXvrkxkM+dy3b17N05OTvrIkJnLM/+9+uqrAIwbN47Tp0/z999/Ex0dze+//65PJZBfffkJCgoiMjKSXbt20bNnT33o+UuXLvH666+zZcsWtm7dyuuvv87ly5eLdP8zp0aJiIjIMT3P9VJTU+natSuvvvoqBw8eZOfOnfz55598+OGHuZafOXMmO3fuZNeuXdSsWVOf5uPzzz+nbNmyHD58mBEjRjB69GgAXFxcmDx5MjNmzLihfZg+fTrR0dG89dZbxXqfcm7zVBbk+kQ28/jdKf7NPgshhBBCCHE7uSsT2axatmypz4eWm5SUFD799FPmzJmjTx1QqlSpPCeIL6i+TKGhofpcgc2aNePUqVMA/Pzzz7Rr145y5cpRtmxZ2rVrx5o1a25wr/L3zTff8NRTT9G+fXtWrlxZYNkHHnhAH0jDzc2N999/P88Wxsz5GpVSpKam6hOkr1ixgv79+wPQs2dPfv31V5RSlChRghYtWujH9ka1atVKP96ffvopISEhBAQE0KNHD71lPDw8nOHDh3P//fdTu3btbK3O06ZNw8/Pj4CAAD3ZbNOmDWPHjqV169a89957xMXF0aNHD0JCQggJCeGPP/4AYOvWrdx///0EBQVx//33c+DAAdLT05kwYQKLFy8mMDCQxYsXZ2sRP3fuHGFhYQQEBBAQEMCff/6ZY5/WrFlDcHAwAQEBtG3bNs9tQUZre/fu3enYsSNeXl7Z5uLLrZ4rV64wcOBAQkJCCAoKYsWKFXo9vXr14pFHHnHYoClCCCGEEEIUlbtuHtmsrFYrP/30Ex07dgQyWh+zzlE3ZswYGjZsSM2aNSlVqtRN13fp0iW6du2a43mff/45nTp1AiA2NpYaNWro69zd3YmNjb2h/Spou4sXL+aXX37hwIEDvP/++3lOsA7o81JmVadOHZKTk0lMTNQT16wGDBjA6tWr8fb25p133smxXyaTiTJlynDx4kUqVKhwQ/t2vR9++AE/Pz8gY/L0zLnmxo0bp8/bCRnz527atIn9+/fTtWtXevbsyU8//cTy5cvZsmULbm5uXLp0Sa83Pj6ejRs3AvDEE08wYsQIWrRowYkTJ+jQoQP79u2jQYMGREREYDKZWLduHWPHjmXp0qW88cYbREZG6q3RmXMIAgwfPpzWrVuzbNkybDZbjulS4uLiGDJkCBEREXh6euox5bUtyGgB3rFjB87OztSvX5/nn38eFxeXXOuZOnUqDz74IPPmzSM+Pp4mTZrw0EMPAbB582Z27dqlz8cqhBBCCCHEnequTGSzJnotW7bUJyPP7CKc1a5du7I9/uKLL3jvvfe4ePEif/75JzVq1Ch0ffPnzydzVOVMX3/9NZGRkXrSlNu9p5mtmoWV33a3bdtGxYoVqVWrFu7u7gwcOJDLly9TtmzZXOtSSuW5/byWf/HFF9hsNp5//nkWL17MgAEDimS/sho1ahRTpkyhYsWKfP755wDs3r2bcePGER8fT3JycrZJ0rt164bBYMDb25tz584BsG7dOgYMGKC3jGdN4Hr37q3/vW7dOvbu3as/TkxMJCkpiYSEBPr378+hQ4fQNC1bd/O8/Pbbb3z55ZdAxr3VZcqUybb+r7/+olWrVnh6emaLKb9ttW3bVq/H29ub48ePc/ny5VzrWbt2LStXrtS7cqelpXHixAkAvSeAEEIIIYQQd7q7smtx1nth58yZg5OTU55l69aty4kTJ0hKSgIyWhujo6MpU6YMNpvthuvLat26dUydOpWVK1fi7OwMZLTAnjx5Ui9z6tQpqlWrlu15W7Zs0QedKqhr8PUWLlzI/v378fDwoE6dOiQmJuote7nx8fHJkXwfPXqUkiVLUqpUKTp06EBgYCCDBw/OVsZoNNK7d2+97qz7ZbVaSUhIuKmkKfMe2V9++QVfX18gowvx+++/z99//83EiRNJS0vTy2ceX/jnx4L8kvQSJUrof9vtdjZv3qy/xrGxsZQqVYrx48cTGhrK7t27+eGHH7Jt79/KK6b8tpV134xGI1arNc96lFIsXbpU35cTJ07QsGHDHPsshBBCCCHEneyuTGRvhJubG4MGDWLYsGF68mCz2UhPT7+penfs2MHTTz/NypUrqVSpkr68Q4cOrF27lsuXL3P58mXWrl2brWURoGnTpnoikltX5bzY7Xa+++47du3aRUxMDDExMaxYsYKFCxfm+Zwnn3ySTZs2sW7dOiCjNXv48OH6vZg///wz0dHRfPbZZyil9PtVlVL88MMPNGjQAMgY8ChzBOYlS5bw4IMP3lSLbG6SkpKoWrUqFouF//3vfwWWb9++PfPmzdPvpc3atfj6cpndhAG9tTshIYHq1asD2bsPlypVSv/h43pt27blo48+AjLOo8TExGzrmzdvzsaNGzl27Fi2mPLaVl7yqqdDhw7MmTNHT+Z37NhRYF1CCCGEEELcae6pRDazi3Dmv8zBf6ZOnUrVqlXx9fUlKCiIli1b0r9//xwtpTdi1KhRJCcn06tXLwIDA/WEtFy5cowfP14fWGjChAlF1t0zIiKC6tWr6wkRZAyWtHfvXs6cOQPA008/jbu7O+7u7jRv3hxXV1dWrFjBlClTqF+/Pn5+foSEhOQ6VY5Siv79++Pn54efnx9nzpxhwoQJQMa0PBcvXqRu3bq8++672QaL8vDw4KWXXmL+/Pm4u7tn68Z7IyZPnkzTpk1p166dnkDnp2PHjnTt2pXGjRsTGBiY58jJs2fPJjIyEn9/f7y9vfVRqV955RXGjBnDAw88oLfOQ8ZAXnv37tUHe8rqvffeY/369fj5+dGoUSP27NmTbX3FihX55JNP6N69OwEBAXoX57y2lZe86hk/fjwWiwV/f398fX0ZP358gXUJIYQQQghxp9Hymi/0dtS4cWOV2Q1W07Q85zoVQgghhBDCkeS7qhD/jqZpUUqpxgWVu6daZIUQQgghhBBC3PkkkRVCCCGEEEIIcUeRRFYIIYQQQgghxB2l2BNZTdPmaZp2XtO03VmWTdI0LVbTtOhr/x4u7jiEEEIIIYQQQtwdbkWL7HygYy7LZyqlAq/9W32jlRoMhpueIkcIIYQQQoiilp6ejsEgHR+FKE7F/g5TSkUAuU/geROCg4OZMWOGJLNCCCGEEOK2kZ6ezowZMwgODnZ0KELc1UwO3PYwTdP6AZHAy0qpy7kV0jRtKDAUoGbNmvryZcuWERYWxvjx47Hb7bciXiGEEEIIIfJlMBgIDg5m2bJljg5FiLvaLZlHVtM0D+BHpZTvtceVgQuAAiYDVZVSAwuqJ+s8skIIIYQQQggh7i639TyySqlzSimbUsoOfAo0cUQcQgghhBBCCCHuPA5JZDVNq5rlYRiwO6+yQgghhBBCCCFEVsV+j6ymaQuBNkAFTdNOAROBNpqmBZLRtTgGeLq44xBCCCGEEEIIcXco9kRWKdUnl8WfF/d2hRBCCCGEEELcnWSCKyGEEEIIIYQQdxRJZIUQQgghhBBC3FEkkRVCCCGEEEIIcUeRRFYIIYQQQgghxB2l2Ad7KkqnT59G0zRHhyGEEEIIIYQQwoHuqES2WrVqnD592tFhCCGEEEKIO1CbNm0A2LBhg0PjEELkrbANl9K1WAghhBBCCCHEHUUSWSGEEEIIIYQQd5Q7qmuxEEIIIYQQ/1bz5s0dHYIQoohIIiuEEIWglOLs6h84OONNXKpVJ2TBIgwmuYQKIcSd5M0333R0CEKIIiJdi4UQogAJu3ayuUcXoob0J+nAfuLW/0rMvE8cHZYQQggh6jPFNQAAIABJREFUxD1LElkhhMhD2tkzRL/4HL93CuXSX39iLluOWuGDATgw7b+kxp5ycIRCCCFuRI8ePejRo4ejwxBCFAHpFyeEENexpaZy9OMPOPz+LGwpV9DMZjwHPo3XiyMxlynD1bjznF21kt3jRhPyxf8cHa4QQohCunjxoqNDEEIUEWmRFUKIa5RSxC5fyoZWTTgwbSq2lCtU7tiZNhv+wnviZMxlygDg88abmEqW4tzPqzm7ZrWDoxZCCCGEuPdIIiuEEMDl7ZH82bUDO/4zmNTYU5T29qXZdysJmfc1JTxrZyvrWrUa9V95DYDdr43CmpzkiJCFEEIIIe5ZksgKIe5pqbGn2DFsKH90acflqG04V6yE/4zZtPx5AxUeaJnn8zwGDKaMfyBpZ05zYMZbtzBiIYQQQggh98gKIe5J1pQrHPlgNkc+moM9LRWDszO1h/6Hus+PwFSyVIHP14xG/KfN4veHH+TYZ3Nx79GbMn7+tyByIYQQ/1bbtm0dHYIQoohoSilHx1BojRs3VpGRkY4OQwhxB1N2O7FLv2Xfm29w9ewZAKo+EkbDcZNwq1HzhuvbM2EMxz6bS5nAYFr8sBbNaCzqkIUQQggh7hmapkUppRoXVE66Fgsh7hmXtv7Fps4PEf3Cs1w9e4YyAUHcv2w1jT6e96+SWID6r4zFpWo1EqK3E7Pg8yKOWAghhBBC5EYSWSHEXS/l5Aminh7In906kbBzB85VqhL43ke0WLWOck2b31TdppKl8JnyNgAH3ppC2rVWXiGEELefTp060alTJ0eHIYQoAnKPrBD3qMuRW4ldtgS7JR1URpdb7HaUUqDsGY+VyrKcPJZnLZ+lHlTG/7mWv+6x3Y5SmeVUAXFkeX6ecWSvx341DZTC4OJKnWefp85zwzG5lSiyY1mlY2cqt+vIuV/WsGfiWBp9/EWR1S2EEKLopKamOjoEIUQRkURWiHuMsts5POddDkx/E+x2R4dzaxgMVA/rSYMxE3Ct7l7k1Wuahu/UaVz443fO/LCcc4/1oXLb9kW+HSGEEEIIkeGeS2StyUkkHzlcpN3/NE3DuUpVStTyxFymTJHVK0RRuxp3nh3PP8OFiPUAeAwcSqn6DUDT0DQDGAxompbxv0HLY7khy3Itj+XXlydjuaZlPDb88/c/y7Vcl2erO7PeXJdnLa9lW66ZTBicnIr12Lq616DeyFfZ98Z4do8dRYX1LTC6uRXrNoUQQggh7lV3ZSKrlCLt7BmSDx0k+fBBkg8f4srhQyQfOUTamdPFum1z2bK41fKkRC0P3Dw8KVHLEzfPjP+dK1fJ+BIuhANc2BTBjmFDuXr+HE7lKxA4Zy6V2sg0BEXJc/AzxC5ZTOLe3RycOY2Gr01ydEhCCCGEEHelOzqRtaencyXmaLaENfOf7Upyrs8xODtTonYdXKu7Z7TgFAFlt5F6OpaUmBgsly+TcPkyCdHbc27bxZUStWrhVsvzWpJ7Ldn1qI2rew0MZnORxCNEVspm4+C70zg0azooRfn7WxD0/ie4VKnq6NDuOgaTCb9pM/njkfYc/fgDqnfvRemGPo4OSwghxDVdunRxdAhCiCJyR80j61+7tlo0oK/euppyPAZls+Va1qlceUrW9aJE3XqUrOuV8c+rPm7uNYptnkelFFfPnyMl5hhXjsdc+/8YKdf+Tr90Mc/nakYjrtXdcavlSWlvXyp37ES5xk1lTkpxU9LOnmHHsKFc/HMTaBpeL46i3kuvyHlVzP4eM5LjCz6nbKMQ7l+xJqMLdDE7+/Nq9ox/lWrdetBw7MRi354QQgghRHEo7Dyyd1Qi6+VkUjMrlPpngcGAW81aGUlqnXqU9LqWtNbxwql8eccFmgdLYgIpMTEZyW1msnvt79TTsXDda+FUvgKV23WkSqfOVGjRGqOrq4MiF3ei8xt+Jfr5Z0i/eAHnSpUJev8TKrRo5eiw7gmWxAQ2tGrK1fPn8Js2k1p9w4ttW8pm48CMNzn83jsAaCYTD0XtwblipWLbphBCCCFEcbkrE1nvShXVirGjKXGthbWER22MLi6ODqtI2K5eJfXkCa7EHOXin5s4u2YVKTHH9PVGtxJUDG1LlY4PU6ltB5zuu8+B0Yrbmd1q5eD0/3J4zkwAKrRsQ9D7H0tic4udXrmM7c8MxFymDG0ithbL8U+/dIkdzw0hbuNvGT/sudcg5cRx6r86Dq/hLxf59oQQ4k7Xpk0bADZs2ODQOIQQebsrE9nGjRuryMhIR4dxSyilSDqwj3NrVnP2px9J+Hunvk4zmSjfvAVVOj5M5Q4P41qtugMjFbeT1NhTbP/PYC5v2wIGA/VHjaHusBHSldgBlFJs7duLuPW/Ui2sJ8EffFqk9cfviiZqcD9ST53EqVx5gufOw25JZ+uTvXB1r8GDm3fI6y6EENeRRFaI299tk8hqmjYP6AKcV0r5XrduJDAdqKiUulBQXfdSInu91FMnOfvzT5z9eRWXNv+R7d7gMoHBVOn4MFU6daFk3XoyMvI96ty6n4l+4Vksly/jXKUqwR9+SvlmD/yfvfsOi+rK/zj+vkMHFVGwgqKJXRAQjb1EoyYmZnVj1FhjdmOM6d1fiqZtmrspm7ZJrEmWaIrp1WzUaIqKYkNNURCsgILSy5zfH+BElGYEhvJ5PQ/PzJx77jnfexlxvnPOPdfZYdVrGfFxrBnaF3t2NhdFfUDA4KGV0u7+qLfY8X93Yc/JoXF4T3q+tgSv1oEYu53v+kWQuT+eXsveofnwkZXSn4hIXaFEVqTmq2giW/UrkMASYNSZhZZlBQGXAPurIYZazyswiHbXXU/fFR9xydZfCHv+FVpcejk2Ty/SYjaz58nHWDO4D6sH9mbXY/M4Hr0RY7c7O2ypBvbcXGIffpCN0yaSd/w4ARcPZ9A3a5XE1gA+bYPpePs9AGyfeycFWVnn1V5BTg7b7rmNbXfejD0nhzZTZ9D3g8/wah0IgGWz0aboetz4ZYvPqy8RERGRmqxaphZblhUMfHr6iKxlWe8BjwIfAZEakf1zCjIzSVq7msNffsaRb74g7/hxxzaPZs1pNmwEPsHt8AhohkezZnj4Fz66N/XX7X7qgMyE/WyefR2pmzdhubjQee5DtL/hpmpZJVcqxp6Xx/cjBnFyz2463HYXne65/0+1k3UgkejrZ5C6JRqbhwchT/yToImTz6qXk5LMqoiumPx8Lv45Bu/ANud7CCIidYZGZEVqvoqOyDrlPrKWZY0BDhhjtpY3DdayrOuB6wHatNEHsjO5eHsXTisedRn2/HyObfiJI19+xuEvPiXrQCIJUW+Wuq97k6Z4BATgHtCsMNEt9hOAR0Dzwu1N/bG51upbDtdJh7/4jK13zCEvLQ3PVq3p+eoi/CJ7OzssOYPNzY2Qp57lh79cym8vPU+rsVfRsEOnc2ojed1aNt8wk9xjKXgFBtHzjWU0Dg0rsa5HU39ajh7DwQ/fZ//by+h87wOVcRgiInXC1Vdf7ewQRKSSVPuIrGVZ3sB3wAhjTJplWXFoRLbSGWM4sWM7KT+uI/vIYXKSjpKblER20hFyk5LISUmGik49tixH0nsquXU8nhrlDQjAo1lz3Js01QIzVawgJ4ddj80jbuF/AGg+4lJ6PPsS7n5+To5MyrLt7tvY//ZSmvTpR9/3P63QtezGGH5/+QV2P/EI2O0EDL6Y8Jdex71JkzL3S/n5B34cOxqPgGYM27gdm7t7ZR2GiIiISJWqMYs9FQUTzB+JbAjwLZBZtDkQOAj0NsYcLqsdJbKVxxQUkHsshZyko+QcPUpO8lFykpL+eH7aY+6xlLPucVsqmw2Ppv64BwQ4pjF7+BcmucUT4WaFSa+mwJ6TjLh9bL5hJmnbYrDc3OjywMO0+9sNWuCrFsg9fpzVg3qTm5JMj3+9WOK04NPlnTzB1ttv4vDnnwDQ4ba76HjnfRX6osgYw9qL+3Fyz24i/rOYVlf8pVKOQUSktsvMLPz46e3t7eRIRKQ0NTaRLWFbHBqRrdHs+fmFSe/pSW7SUXKTk8g+WjTCm3SEnKSkwqS3giwXF9yb+p8xnbn4FGf3gAA8A5rj5udXr5PevLQ0fn/lBfa9/ioFWZl4t2lLxKuLaBwW4ezQ5Bwkvr+cmJtvwM2vCUPXbsC9adMS6538dQ/R100j/bdfcG3YkLAXXqXFyMvOqa99i15j5wP30rT/QPq++3FlhC8iUuvpGlmRmq/GXCNrWVYUMATwtywrEZhnjFlY1f1K5bG5uuLZrDmezZqXW9eel0duSnLh6G5Rcptz9Ag5yUmFo79JRx3b8o4fL9x29Ei57Vqurrj7BxSO7hab2ty8qKwZLl5eWG5u2Fxdix7dsNxcix7Pfl0bRjELMjPZt+g1fn/5efJSUwFodeU4Qp78F26+vk6OTs5V63FXk7g8iuR1a4h99EHCnnv5rDoHP/mQrXfcTEFGOg07d6HnG2/SoP0F59xX4FUT2P34w6Ss/570X3+hQYeOlXEIIiIiIjVClSeyxphJ5WwPruoYpPrY3NzwbNESzxYty61rz80lJyX5jwT36FFyHVOci5LfolHgvNRUcg4fIufwoUqL1XJxOTvBdXXF5uaGV2AQQZOm0HL0lbh4elZanxVlz8sjIepNfnn2GXKOFM64b9pvAJ3nPoRfz17VHo9UDsuy6P7kP1k7rD+JK6IIvPoa/PsNAApnPuz+x8PsffVFAFr95a+ELngeV2+fP9WXWyNfWo29ioT/LiP+rSV0e/gflXYcIiIiIs5WLVOLK4umFtdfBTk55CYnFSW3R4qP+CYlkZN8FHtODiYvD3t+HiYvH3teLva8PEx+ftFjHva8fEx+YVlFuDdpStDEybSZei0+bYOr9iABY7dz8KMP2PPMP8iM2weAb2gYne97EP/BQ2vFKLKU75d/Pc0vC57A54IODFr1PfknT7D5hpmk/LAOy9WVLg89SrvrZp337zt1WwzrRg3FzdeX4Zt34eLlVUlHICJSO2lqsUjNV6Ouka0sSmSlshhjMPn5xRPf/LzC13l5JP/wPfFLFnJi5/bCHSyLgKHDCJ42k2bDRlT6yszGGI7+7xv2PPEoJ2J3AODT/kI63fcALUePUQJbxxTk5LB2+EAyfv+VwPETSV63luxDB/Fo1pyI/yyi6UX9Kq2v7y8bRlrMZno8+xJBE66ptHZFRGojJbIiNZ8SWZHzZIwhdfMm4pYu5NAnH2LPyQHAq3UgbabOoM2kqXgENDvvfo79/CO7n3iEYxt+AsCzZSs63nkfgVdP0v1767DkH9bx01VXOF779bqInq8twbN5i0rtZ3/UW2y782YaR0Qy4NNvKrVtEZHaZsmSJQDMmDHDqXGISOmUyIpUotyUFBJW/Jf4ZYvIjI8DwHJzo+Wll9N2xnU0uajfOY+anti5nd1PPcbRVV8D4ObXhAtvvp3g6ddpCmg9sf2+O4hftpjg62bR9aFHsbm5VXofBZmZfBPRhfwTJxj41Rp8Q0IrvQ8RERGRyqJEVqQKGLudpLXfEb90EUe++RLsdgAadupM22kzaX3VBNwaNiqzjYy4fex55h8c/PB9MAYXbx/az5pD+xvmlLuv1C3GGHJTkvHwD6jSfnY8eB9xC/9DmynTCX36uSrtS0SkJktOLrzbo7+/v5MjEZHSKJEVqWJZiQns/+8y9r+9jJykowC4ePvQetxVtJ12Hb7dQ4rVzz5ymF+fe4b9by/D5Odjc3en7bSZXHjLHVWeyEj9dvLXPawZ3AcXbx+Gb4nVFyYiUm/pGlmRmq+iiaytOoIRqYu8AoPodM/9DNu4nYhXF9G03wAKMjPY/9ZSvh8xiPVXjCDxvXfIPnqEXf94mP/1jSB+6SKM3U7g1dcwZN1Guj3yhJJYqXINO3SiSd/+FGRmcOD9Fc4OR0REROS8aSUZkfNkc3en1ZixtBozlpO/7CZ+2WIS343iePRGjkdvLFa3xaWX0+ne+2nYsbOTopX6KnjaTI79uJ74ZYtoO/06rYQtIiIitZpGZEUqUcOOnen+2FMM37KL0Geep1H3woV1mvYfSP9PvyFy4ZtKYsUpWlx6Oe7+AZzcvYvjG392djgiIiIi50UjsiJVwNXbhzaTpxF0zVTsWVnYvLw0AiZOZXN3p82kKfz272eJX7aIJr37ODskERERkT9NI7IiVciyLFy8vZXESo3QZsoMsCwOffoRuSkpzg5HRKTazZ49m9mzZzs7DBGpBEpkRUTqCe+gNjS7eDj23FwSlr/t7HBERKrdhAkTmDBhgrPDEJFKoERWRKQeaTv1WgDi31qCKboPsohIfZGQkEBCQoKzwxCRSqBEVkSkHmk2bASerVqTGbeP5LWrnR2OiEi1mjp1KlOnTnV2GCJSCmNMhesqkRURqUcsFxfaTpkBQPybi50bjIiIiEiRzMT9/HDlqArXVyIrIlLPBE2aguXqypGvvyDr0EFnhyMiIiL13NH/fcP3I4dwfNOGCu+jRFZEpJ7xbN6CFqNGYwoK2P/2MmeHIyIiIvWUKShg91OPsWHK1eQdP07AxcMrvK8SWRGReqjttJkA7P/vMuz5+U6ORkREROqbnKSj/DRpHL89/0+w2eh03wP0Xra8wvu7VmFsIiJSQzXtPxCf9heSsfc3jnzzJS0vvdzZIYmIVLk777zT2SGICJDy03o2z/4bOUcO4+4fQMQrC/HvP/Cc2tCIrIhIPWRZFm2nFd6KZ/8yLfokIvXDFVdcwRVXXOHsMETqLWMMv730PD+Nv5KcI4dpclFfBn295pyTWFAiKyJSbwWOn4TN05OkNf8jY99eZ4cjIlLl9uzZw549e5wdhki9lJuayqZrJ7P78fmYggIumHMrfd79GM8WLf9Ue0pkRUTqKXc/P1qNGQtA/FtLnBuMiEg1mDVrFrNmzXJ2GCL1Tuq2GNaNGsKRr7/AzdeXyMVv0+X++dhc//yVrkpkRUTqsbZTC6cXJ7zzNgXZ2U6ORkREROoSYwzxby7mhzEjydwfj29IDwZ+tYYWIy8777a12JOISD3WOCKSRt1COLFzO4c+/5jAcVc7OyQRERE5Tfre3zm48j3yMzMw+fnY8/Iw+XmYvHzs+XmYvDzs+flFj6WV52Py84r2zadBh060GHUZzUdehlfLVlUSd35mBtvvvYMD768AoO20a+k6/x+4eHpWSvtKZEVE6rFTiz5tv/cO4pcuUiIrIiJSQxi7nbjFb7Dr8fnYs7Mqte3sQwdJXvsdO/7vbhqH96TFqMtoMepyGnToWCntn/x1D9F/n0H6L7tx8fIm5JlnK/0zhhJZEZF6rvXYq9j16DyOb/yZE7t20qhLN2eHJCIiUq9lHUhk6+03kbxuDQCtxoylUfdQbG5uWK5u2Nxcix7dsFxdsdzcsLm6Ybm5Fj26YSulHODYzz9y+MvPSFr9P1K3RJO6JZrdTzyKzwUdipLa0TQO74llO/crUQ+sfI9td99GQWYGDS7sSM83ltKwY+dKPT8AljGm0hutKpGRkWbTpk3ODkNEpM7ZPvdO4pcuou306wh5YoGzwxERqRKrVq0CYPjw4U6ORKRkxhgS332HnQ/eS/7Jk7g39Sfk6Wer7H7vBZmZJK35jsNffcaRb74k7/hxxzaP5i1oMfJSmo8ajX+/gdjc3ctuKyeH2Pn3E790IQCt/vJXQp95DlefBucUk2VZ0caYyHLrKZEVEZETsTtYO3wgrg0aMnxL7Dn/pyMiIiLnJyc5iW333M6RLz8DoPnIywh95jk8/AOqpX97fr5jpPbIl5+RdSDRsc21YUOaDRtBi1GjCRg6DLeGjYrtm5mwn+jrZ5C2dQs2d3e6PvwP2k6biWVZ5xyHElkRETkn68eM5PimDYQ8/Sxtp8xwdjgiIpUuJiYGgLCwMCdHIlLc4S8+Y9s9t5Gbkoxrw4Z0e/QpAsdP/FOJYGUwxnBi+zYOf/kZh7/6jJO7Yh3bbO7uNB0wiBajRtN8xKWkbd1CzK2zyUtNxSswiJ6vL6Vxj/A/3XeNSWQty1oEXA4cNcZ0Lyp7FLgSsANHgRnGmIPltaVEVkSk6iS+v5yYm2+gUfdQBn612mn/eYqIVJUhQ4YAsHr1aqfGIXJK3ok0dj40l8QVUQA0HTCIsH+9iFdgkJMjKy4jbp9jpPbYxp/hVA5pWY7nzYaPIOz5V3H38zuvviqayFbHfWSXAKPOKHvGGBNqjAkDPgUeqoY4RESkDC1HX4mbXxNO7NhG6pZoZ4cjIiJSpyWvW8uai/uTuCIKm6cn3R55kj7vrKxxSSyAT3A7LrjhJvp9+AWXxOwm9JnnaTZ8BDY3N7DZ6Dz3IXotiTrvJPZcVPmqxcaYtZZlBZ9RduK0lz5A7ZnfLCJSR7l4ehI04Rr2vvoi8csW4xdR7pehIiIico4KMjPZ9cQjxC38DwC+YRGEP/9Kpd36pqp5BDSjzeRptJk8jfz0k+Snp+PZomW1x1EdI7IlsizrccuyEoDJlDEia1nW9ZZlbbIsa1NSUlL1BSgiUg+1Kbo29uDHH5B72sqFIiIicv6Ob4lm7YjBxC38D5arKx3v/j/6f/xVrUliz+TaoKFTklhwYiJrjLnfGBMEvA3cVEa914wxkcaYyICA6lmxS0SkvmrQ/gL8Bw3Fnp1N4nvvODscERGROsGem8uep//BD2NGkrH3Nxp07MyAT1fR8fa7sblW+STZOqkmnLX/Ap8B85wdiIiIQNvpM0le+x1xi17D1acB7k2a4t6kSdFjU9waN/5TN0gXEXG2f/zjH84OoV4zxpB37BgGg4unJzYPz3qRxJ3YHUvMLbM5sWMbWBbtZ91Ep3vvx8XT09mh1WpOeedYltXBGPNr0csxwG5nxCEiImdrfskoPFu2IjM+jm133XJ2BZsNd78mZyW4xZLdM8pdGzTUKsgi4nT9+vVzdgh1WkF2NtmHDpJ1IPGPn8SEP54fTMSenV1sH8vFBZunFy4eHtg8PHDx8sLm4YHNwxMXT09cPDyxeXoWbvf0/CMB9vDAxbOwrouHJzavojoef9Rx8Tx9X68ztntU+ZeypqCAva+9zJ6nHsOem4t3m7b0eO4lmvbpX6X91hdVnshalhUFDAH8LctKpHDk9TLLsjpRePudeOCGqo5DREQqxubqSuTCNzn40Upyj6eQeyyF3GPHih5TyD9xgtyUZHJTkiveprs7bqcnu36lJ8Gnnrt4eVXhUYpIffTDDz8ASmj/DGMMucdSiiWp2UWPmYkJZB9IJCfpaLntuDZqhM3VjYLsbAqyszAFBRRkpFOQkV4NR1Gczd29KNH1/OPRw8ORAJ+VLDvqehRPlj2KkmWvP5JlgF8WPMGxn38EoM3k6XSd9yiuDRpW+3HWVVV+H9nKpPvIiog4nz03l9zU48US3Lwzkt3clJQ/nh87RkFW5jn34+LtU2ay63ZmuV+TwtsA1FIFOTlk/PYLJ3bHcnJXLCd2x2LyC2gzeRotL7sCy8XF2SGK1Hq6j2zFZe6PJ3n996T88D2pMVvIOpCIPTurzH0sFxc8W7bCKzAIr9aBZ/14tmqNW8NGxfax5+Vhz8mmICcHe1ZW4WNONgXZ2dhzcijIycaelV34mF30mJNDQXYW9uyc08pP3y+7cFt2VlHd7D/6yC7q44yR4ari0aw5oQuep/nwkdXSX11Q0fvI1v1J6SIiUqls7u54NmuOZ7PmFd6nIDOT3OPHiie7Zz4/ffQ3JZmCzAyyMjPISkyocD9uvr5/JLglTn92/vW+xhiyEhM4uWvnH0nrrlgy9v6Gyc8/q37y2u/waX8BF8y+hdZXTcDFw6Na4xWR+iHr4AFSfvie5HWFyWtJf3vdfH3xal2YpHqWlKg2b3HOX7rZ3NywublV+0ilMQZ7Ts4fyfKpJLlYApx1djJ9WmJ8ZkLtSJizC9tr1LU7nec+hHuTJtV6bPWFRmRFRKTGMcaQn36yWLKbV1YSfCyF3OPHwG4/t46q+Hrf3NRUTu7eyYnYnZwsSlpP7tlNfvrJsytbFj7t2tOwc1cadelGwy5dyTlymN9ffZGshP0AeLRoSfvrb6TtlOmanibyJ9S0EdlTX2ylbt7EyT27cff3xye4Hd5t2+Ed1Aabu3uV9Z199Agp678n5Yd1JP/wPZn79hbb7ta4MU369Me/30CaXNQX7+Dgs0ZTRapCRUdklciKiEidYOx28tLSSpneXDzhPTUdOi8t7Zz7sdzcSk58/Zri5udH9uFDhUnr7l1kHzpYYhseAc1o2LkrDbt0pVHRY8MOnXDx9j6rrj0/n4Mff8DvLz7Hyd27gMIPmMEz/k7wddfj0dT/nI9BpL5ydiKbdyKN1JgtpG7ZROrmaI5viSY3OankyjYbXq0D8WnbDu/gYLzbtnMkuT7Bwef8ZVZuSgrJP65zJK/pv+4ptt21QUOa9OlL034D8e8/iEZdu+mSBnEKJbIiIiLlsOflkXv8GHlnTXsuYdQ3Jbnwet/MjAq37+LlTYNOnR3J6qlHD/9zvy+6MYajq77it38/y/FNGwCweXrRZvI0Lpg1B6/AoHNuU6S+qc5E1p6fT/qeXRzf/EfSmv7rHjjjs7ebXxP8InrSqGt3co8dIyN+H5n79pJ18MBZdU/n7h+AT9tgvE9Lbk8lu+7+AeSfOEHKj+sLpwv/8D0nY3cW29/Fy5smvfvQtP9AmvYfiG9Ij3pxKxyp+ZTIioiIVIGCrKwSrvf9I/H1aOpfmLR26YZ3m7ZVMqJx7Ocf+e2l5zi66msALFdXWo+9igvm3ErDjp0rvT+RuiImJgaAsLCwSm876+ABUrdEFyauW6JJ2xpz1kJ3lpsbvt1DaRzek8YRPfGL6IV32+ASL1coyMkhK2F/YWIbF0dm/D4y4vaRGR9H5v447Dk5pcbi4u1T2Pdpn/NtHh74RfbGv/8gmvYbQOOwiCqduizyZymRFRERqeNOxO7gt5ee5+BHHziuD26yDlz/AAAgAElEQVQ+8jIuvPl2/CLK/QwgUunyM9LZ/cSjpP+yB2y2wsXULIoebYUJm6PcOq2cUsqtEts5Vad4fauU8jPrl94ONhuW9Ueds8uLt5N95AipW6JJ3RJd4qUE3m2DaRxemLA2juhJo24hlbJgm7HbyT58qDCxjdtXlOQWJruZ8fvIS0vDcnPDL7wnTfsPomn/gfhFROLi6XnefYtUNSWyIiIi9URGfBx7X/03Ce+87RiladpvABfcdBsBgy+u8OJUIucj+/AhNkybyIkd25wdilO4NmqEX3ikY7S1cXhPp13Dnpuaiou7e4nX3YvUdEpkRURE6pnso0fY98arxC9dSP7JwpWRG3UP5cKbbqPl6DFauEWqzImd29kwbSLZhw7i3a493eY9hs3dA4MBux1jt4MxhY92U0q5HWNOvS7abswZ5fbTyk0p5aW3s2zZUixg8jXXlN2O3Q6cFq8xJZTbcW3YqHDENbwnPhdcWO238xKpi5TIioiI1FN5J9KIX7qIva+/4lgR1btde4KnX0frsVfhEdDMyRFKXXL0u1VEX38tBRnp+PW6iF6L3sa9aVNnh1UiZ69aLCLlq2giq6+NRERE6hi3Rr5cePPtDPt5K92fWIB3m7Zk7ttL7Pz7WRXRlQ3TJnLos48pKGOxGJGKiH9zMRunTaQgI51WV46jz/IPa2wSKyJ1ixJZERGROsrFy4vg6dcxZN0mer6+lOaXjALg6KqviP77dFZFdGH73LtIjdlMbZqhJc5n7HZiH32I7ffegSko4MJb7iD8pde1mJCIVBtNLRYREalHcpKOcmDleySuiOJE7A5HeYMOnQgcP5HWf70ar5atnBih1HQFWVlsueUGDn/2MZarKyFPPUubSVOcHVaFaGqxSM2na2RFRESkTGk7tpP4bhQHPniX3JTkwkKbjYBBQwm8ehItRl6Gi5eXc4OUGiUnOYmNM64hdfMmXBs2pOfrywgYNMTZYVXYnj17AOjUqZOTIxGR0iiRFRERkQqx5+WR9N0qElZEceSbLzF5eUDh7URaXTGWwPET8et1kW7jU8+d/HUPG6dOIHN/PF6BQfR+czkNO3VxdlgiUscokRUREZFzlnvsGAc/ep+EFVGkbd3iKPdpfwGBV00k8KoJeAUGOTFCcYbk9d8T/bep5KWl4RsWQa8l/8WzWXNnh3XOPvnkEwCuuOIKJ0ciIqVRIisiIiLn5eQvu0lcEUXi+yvIOXK4sNCyaNp/IC1GXIZ3mzZ4BbXBKzAIt4aNnBusVJmEFVFsu/tWTF4ezUeNJuLF13Dx9nZ2WH+KrpEVqfmUyIqIiEilsOfnk/z9ahJXRHH4q8+xZ2efVcetcWO8WgfhFRiEd1Fy6xUYhHdgUaLr56epybWMMYZfFjzJr88+DUD7WXPo8sDDWC4uTo7sz1MiK1LzVTSRda2OYERERKT2srm60mzocJoNHU5eWhqHPv2I1K2byUpMJDNhP1kHEshLTSUvNZUTO7eX2IaLtw/eQUGOBNcrsA3ep54HtcEjoJkS3RqkICeHbXfezIEP3gWbje6PPUXwjL85OywREQclsiIiIlJhbr6+tJk8jTaTpznKjDHkpiQXJrWJCWQlJpz2fD9ZiYnkp5/k5J7dnNyzu8R2bR4eeLUOxKtoBNc7MAivoD9GdT1btKzVI4G1Se7x42y6bgrHfvoBF28fIl5dSPPhI50dlohIMUpkRURE5LxYloWHfwAe/gH4hfc8a7sxhry0NLKKktvMAwmO51mJCWQm7ifv+HEy9v5Oxt7fS+7D1RXPlq3+mLbcuvgUZq9WrbG5u1f1odZ5Gfv2smHqBDL2/oZHi5b0XrYc3+4hzg5LROQsukZWREREnC4//SRZBxLPGNVNIOtA4fOco0fKbsCy8GzRsnBUN6j4tGXvomt3dU/csh3b+DObrp1M7rEUGnXtTq9l7+DVqrWzw6pUCQkJAAQFaeVtkZpKiz2JiIhInVGQnU3WgUSyEvYXjugm/jGqm5mYQPbhQ2C3l9mGu3/AH9OWA89emKo+r7x88KMPiLntRuw5OQRcPJyery7CtUFDZ4clIvWQFnsSERGROsPF05MGF1xIgwsuLHG7PS+P7EMHyUw8e9pyVmICWQcPkJucRG5yEmkxm0tsoz6uvGyM4fcXn2P3E48A0Hb6TLo9+hQ217r5EXH58uUATJgwwcmRiMj50oisiIiI1HmmoIDsI4fPTnATCkd0sw4klHhbodPVtZWX7Xl5bL/vThKi3gTLosuDj9B+1pxadQznSrffEan5NCIrIiIiUsRyccGrVevCaz579zlruzGG3OSkwqS2aFTX8bwOrrycdyKN6OuvJXntd9g8vQh/8T+0vOwKZ4clIlJhSmRFRESk3rMsC4+AZngENDvnlZcd99Itb+VlNzeCJlxD13mP4erToKoPqVRZiQlsmDaBk7t34e4fQK+lUSUes4hITaZEVkRERKQclmXh3rgx7o0b4xsSWmKd/PSTp43inrqXbmLhyssJ+8lJOsr+t5aSvP57wl98zSnJY+q2GDZOm0jO0SM06NCJ3m8ux7tN22qPQ0TkfCmRFREREakErg0a0qhzVxp17lri9hO7drLlpus5uSuWH8aMpMMd93LhzbdX28JKh7/8nC1z/k5BViZN+w+k5+vLcG/cuFr6FhGpbFrsSURERKSaFGRns/vJR9n32ssA+EX2Juzf/8GnbXCV9rv3jVeJnfd/YAyBV19D6NPPYnN3r9I+a6Lk5GQA/P39nRyJiJSmoos92aohkEWWZR21LGvHaWXPWJa127KsbZZlrbQsS18HioiISJ3n4ulJt/mPc9E7K/Fo0ZLjmzbw/SWDSFgRRVUMLpiCAnY8cC+xD80FY+h0z/30ePbFepnEQmECqyRWpG6o8kQWWAKMOqPsG6C7MSYU+AWYWw1xiIiIiNQIAYOGMHjVOlqMHkN++km23nYjm2ddS+7x45XWR35GOhuvnUzcotewubsT/uJrdLjtrjp9e53yLFmyhCVLljg7DBGpBFWeyBpj1gLHzij72hiTX/TyJyCwquMQERERqUncmzSh52tL6PHsS7j4NODQpx+xZlh/ktauPu+2sw8f4oexozm66ivc/Py4aPlKWo8bf/5B13JKZEXqjuoYkS3PTOCL0jZalnW9ZVmbLMvalJSUVI1hiYiIiFQty7IImnANg75Zi1/PXuQcPsTPE8cS+/ADFGRn/6k2T8TuYN3o4ZzYsQ3vdu3p//HXNL2oXyVHLiLiXE5NZC3Luh/IB94urY4x5jVjTKQxJjIgIKD6ghMRERGpJj7B7ei78nM63jUXy8WFvf95iXWjh3Fid+w5tXN09bf88JfLyD50EL9eFzHg469pcMGFVRS1iIjzOC2RtSxrOnA5MNnUpqWTRURERKqAzdWVjnfcQ78Pv8A7uB0nd8Wy7tKL2fvGqxi7vdz9499czMapE8hPP0mrK8fRZ/mHuDdtWg2Ri4hUP6ckspZljQLuBcYYYzKdEYOIiIhITeTXsxeDvllL0KSp2HNyiH1oLhsmjyf78KES6xu7ndhHH2L7vXdgCgq48JY7CH/pdVw8Pas5chGR6lPl95G1LCsKGAL4A0eAeRSuUuwBpBRV+8kYc0N5bek+siIiIlKfHPriU7bddSt5x4/h5teE0AXP0/LSyx3bC7Ky2HLLDRz+7GMsV1dCnnqWNpOmODHimi0zs3D8xNvb28mRiEhpKnof2SpPZCuTElkRERGpb7IPH2Lr7TeRtOZ/AARNnEK3R5+gICuLjddOJjV6I64NG9Lz9WUEDBri3GBFRM6TElkRERGROsLY7cQtep1dj8/DnpODd3A7sNvJ3B+PV2AQvd9cTsNOXZwdZo338ssvA3DjjTc6ORIRKU1FE9macPsdERERESmDZbPR7m+zGPjldzTs2o3MuH1k7o/HNyyC/p9+oyS2glasWMGKFSucHYaIVAJXZwcgIiIiIhXTsFMXBnz2Lb+/+Bx5aal0vu9BXHS9p4jUQ0pkRURERGoRFw8POt55r7PDEBFxKk0tFhERERERkVpFiayIiIiIiIjUKrVq1WJ/f38THBzs7DBERERERESkCkRHRxtjTLkDrrXqGtng4GB0+x0REREREZG6ybKszRWpp6nFIiIiIiIiUqvUqhHZ0yUmJjJ27Fg2b96M3W53djgiUofZbDYiIiJYuXIlgYGBzg5HREREpN6rtYns2LFjGTduHOvXr8fd3d3Z4YhIHZabm8uCBQsYO3YsGzdudHY4IiIiIvVerVrsKTIy0py6RtbFxYWsrCwlsSJSLXJzc/Hy8qKgoMDZoYiIiIjUWZZlRRtjIsurV2uvkbXb7UpiRaTauLu76zIGERERkRqi1iayIiIiIiIiUj8pkRUREREREZFaRYmsiIiIiIiI1Cr1JpF97rnnyMzMrJa+Vq5ciWVZ7N69u1r6O18NGjSo1PZq+vHPnz+fBQsWODuMajFkyBBOLZBWkfLKkpmZyejRo+ncuTPdunXjvvvuc2z717/+RdeuXQkNDWXYsGHEx8f/6bZycnKYMGECF154IRdddBFxcXEApKSkMHToUBo0aMBNN93kqH/y5EnCwsIcP/7+/tx2222Ve/AiIiIiUuWUyFaBqKgoBgwYwDvvvFNpbebn51daW1Wtvh+/FLrrrrvYvXs3W7ZsYf369XzxxRcAhIeHs2nTJrZt28ZVV13FPffc86fbWrhwIX5+fvz222/cfvvt3HvvvQB4enry6KOPnvWFRcOGDYmJiXH8tG3blnHjxlXykYuIiIhIVavyRNayrEWWZR21LGvHGeU3W5a1x7KsnZZlPV1Z/WVkZDB69Gh69OhB9+7dWb58OS+88AIHDx5k6NChDB06FIDZs2cTGRlJt27dmDdvnmP/zz//nM6dOzNgwABuueUWLr/8cke7M2fOpFevXoSHh/PRRx+V2H96ejrr169n4cKFxRK51atXM2jQIMaOHUvXrl254YYbHCugNmjQgDvvvJOIiAiGDRtGUlISUDhq9n//938MHjyY559/nvj4eIYNG+YYydq/fz9paWkEBwc72srMzCQoKIi8vDx+//13Ro0aRc+ePRk4cKBjhHTfvn307duXXr168eCDD1bWqa9xx//666/Tq1cvevTowV//+tdyv8j45JNPuOiiiwgPD2f48OEcOXIEKBzBnTlzJkOGDKF9+/a88MILAMTFxdGlSxf+/ve/061bN0aMGEFWVhYAMTEx9OnTh9DQUMaOHcvx48cdx3RqJDQ5OZng4GAAlixZwrhx4xg1ahQdOnQoltx9+eWXRERE0KNHD4YNGwaU/n7Myspi4sSJhIaGMmHCBEc8ZTl9RP69995jxowZAMyYMYNbbrmFfv360b59e9577z1HvWeeeYZevXoRGhpa7N/PKd7e3o5/a+7u7kRERJCYmAjA0KFD8fb2BqBPnz6O8tKU1dZHH33E9OnTAbjqqqv49ttvMcbg4+PDgAED8PT0LLXdX3/9laNHjzJw4MAy+xcRERGRmqc6RmSXAKNOL7AsayhwJRBqjOkGVNo8zy+//JJWrVqxdetWduzYwahRo7jlllto1aoV3333Hd999x0Ajz/+uGNUaM2aNWzbto3s7GxmzZrFF198wbp16xwJ1an6F198MRs3buS7777j7rvvJiMjg4MHD3LZZZc56n344YeMGjWKjh070qRJEzZv3uzYtmHDBv75z3+yfft2fv/9dz744AOgMCmJiIhg8+bNDB48mIcfftixT2pqKmvWrOHOO+/kpptuYtq0aWzbto3Jkydzyy234OvrS48ePVizZg1QmIyNHDkSNzc3rr/+ev79738THR3NggULuPHGGwG49dZbmT17Nhs3bqRFixaVdepr3PGPGzeOjRs3snXrVrp06cLChQvLjH3AgAH89NNPbNmyhYkTJ/L00398v7J7926++uorNmzYwMMPP0xeXh5QmAzNmTOHnTt30rhxY95//30Apk2bxlNPPcW2bdsICQkpdkyliYmJYfny5Wzfvp3ly5eTkJBAUlISf//733n//ffZunUr7777LlD6+/GVV17B29ubbdu2cf/99xMdHV2RX1upDh06xLp16/j0008dU3q//vprfv31VzZs2EBMTAzR0dGsXbu21DZSU1P55JNPHEn46RYuXMill15a4XjObOvAgQMEBQUB4Orqiq+vLykpKRVqKyoqigkTJmBZVoX7FxEREZGaocoTWWPMWuDYGcWzgSeNMTlFdY5WVn8hISGsWrWKe++9l++//x5fX98S661YsYKIiAjCw8PZuXMnsbGx7N69m/bt29OuXTsAJk2a5Kj/9ddf8+STTxIWFsaQIUPIzs5m//79tGrVis8//9xRLyoqiokTJwIwceJEoqKiHNt69+5N+/btcXFxYdKkSaxbtw4Am83GhAkTAJgyZYqjHHCUA/z4449cc801AEydOtVRb8KECSxfvhyAd955hwkTJpCens4PP/zA+PHjCQsLY9asWRw6dAiA9evXO45t6tSp53yOy1JTjh9gx44dDBw4kJCQEN5++2127txZZuyJiYmMHDmSkJAQnnnmmWL1R48ejYeHB/7+/jRr1swxWtuuXTvCwsIA6NmzJ3FxcaSlpZGamsrgwYMBmD59epmJ3inDhg3D19cXT09PunbtSnx8PD/99BODBg1yvCebNGkClP5+XLt2LVOmTAEgNDSU0NDQcvsty1/+8hdsNhtdu3Z1HPPXX3/N119/TXh4OBEREezevZtff/21xP3z8/OZNGkSt9xyC+3bty+27a233mLTpk3cfffdFYqlpLaMMWfVq2hi+s477xT7Ny4iIiIitYerk/rtCAy0LOtxIBu4yxizsaSKlmVdD1wP0KZNm/Ib7tiR6OhoPv/8c+bOncuIESN46KGHitXZt28fCxYsYOPGjfj5+TFjxgyys7NL/FB8ijGG999/n06dOpVaJyUlhf/973/s2LEDy7IoKCjAsizHyN6ZH7BL+8B9ermPj0+p/Z2qN2bMGObOncuxY8eIjo7m4osvJiMjg8aNGxMTE1NuH+VZuXKlY0TxjTfeIDIyssR6Nen4oXBq7IcffkiPHj1YsmQJq1evLvM4b775Zu644w7GjBnD6tWrmT9/vmObh4eH47mLi4vjmt0zy8ubyuvq6uqYBp2dnV1sW0l9GGNKPE9lvR/PdYTx9PplxXTq34cxhrlz5zJr1qxy277++uvp0KHDWQsqrVq1iscff5w1a9YU6+Nc2woMDCQhIYHAwEDy8/NJS0tzJPtl2bp1K/n5+fTs2bNCfYuIiIhIzeKsxZ5cAT+gD3A3sMIq5dO3MeY1Y0ykMSYyICCg3IYPHjyIt7c3U6ZM4a677nJMbW3YsCEnT54E4MSJE/j4+ODr68uRI0ccC8d07tyZvXv3OlY+PTXKBzBy5Ej+/e9/Oz7Mb9my5ay+33vvPaZNm0Z8fDxxcXEkJCTQrl07x8jhhg0b2LdvH3a7neXLlzNgwAAA7Ha74/rD//73v47yM/Xr189x3enbb7/tqNegQQN69+7NrbfeyuWXX46LiwuNGjWiXbt2jqmoxhi2bt0KQP/+/Yu1U56xY8c6FscpLYmtaccPhSvUtmzZkry8vAodZ1paGq1btwZg6dKl5dYvja+vL35+fnz//fcAvPnmm47R2eDgYMd039OvOS1N3759WbNmDfv27QPg2LHCyQ2lvR8HDRrkONYdO3awbdu2cvto3rw5u3btwm63s3LlynLrjxw5kkWLFpGeng4UTu89evTsSRUPPPAAaWlpPPfcc8XKt2zZwqxZs/j4449p1qxZsW2dO3cusc/S2hozZozjd/Xee+9x8cUXVyiRj4qK0misiIiISC3mrEQ2EfjAFNoA2AH/ymh4+/bt9O7dm7CwMB5//HEeeOABoHA059JLL2Xo0KH06NGD8PBwunXrxsyZM+nfvz8AXl5evPzyy4waNYoBAwbQvHlzx9TkBx98kLy8PEJDQ+nevbtjkaTTr5GNiopi7NixxeL561//yn//+1+gMCm577776N69O+3atXPU9fHxYefOnfTs2ZP//e9/Z40gn/LCCy+wePFiQkNDefPNN3n++ecd2yZMmMBbb71VbCru22+/zcKFC+nRowfdunVzLAj0/PPP89JLL9GrVy/S0tLO74SfpqYd/6OPPspFF13EJZdcUmqCdLr58+czfvx4Bg4ciL//+b0dly5dyt13301oaCgxMTGOY7rrrrt45ZVX6NevH8nJyeW2ExAQwGuvvca4cePo0aOH4/hKez/Onj2b9PR0QkNDefrpp+ndu3eJ7ebn5ztGQp988kkuv/xyLr74Ylq2bFluTCNGjOCaa66hb9++hISEcNVVVzm+JDolMTGRxx9/nNjYWCIiIggLC+ONN94A4O677yY9Pd0x7X3MmDFA4eJXJc2KKKut6667jpSUFC688EL+9a9/8eSTTzr2Cw4O5o477mDJkiUEBgYSGxvr2LZixYqzEtmPP/641PeeiIiIiNQsVlnTaSutE8sKBj41xnQven0D0MoY85BlWR2Bb4E2ppxgIiMjzakVXy3LKnMq8J+Vnp5OgwYNMMYwZ84cOnTowO23337e7a5evZoFCxbw6aefnrWtQYMGjtGtuqq+H39NkpOTw4UXXsiOHTtKvYbcGT799FP27t3LLbfc4uxQSlVVf3dEREREpJBlWdHGmNKngRap8mtkLcuKAoYA/pZlJQLzgEXAoqJb8uQC08tLYqvL66+/ztKlS8nNzSU8PLxC1wGK1BabNm1i6tSp3HjjjTUqiQUct7oSERERESlPtYzIVpbqGJEVESmN/u6IiIiIVK2Kjsg66xpZERERERERkT+lTiayjz/+ON26dSM0NJSwsDB+/vnnMuvPnz+fBQsWnHe/MTExxe4pW9tcdtllpKamkpqayssvv1wpbZ2vJUuWcNNNN1W4/vz585k7d26xspiYGLp06XJO/T700EOsWrXqnPapqLi4OLp3717itiFDhnBq1sH5ONfzJiIiIiJSm9S5RPbHH3/k008/ZfPmzWzbto1Vq1YRFBRULX3X1kTWGIPdbufzzz+ncePGlZLInmqruk2aNKnYbZMA3nnnHa655poKt1FQUMAjjzzC8OHDKzs8ofD8ioiIiIicjzqXyB46dAh/f3/HrUX8/f1p1aoVUHg7jlO3PNm0aRNDhgxx7Ld161YuvvhiOnTowOuvv+4of/rppwkJCaFHjx7cd999QPFRs+TkZIKDg8nNzeWhhx5i+fLlhIWFsXz5cjZs2EC/fv0IDw+nX79+7NmzBygcLRs3bhyjRo2iQ4cO3HPPPY7+vv76a/r27UtERATjx493rOb77bffEh4eTkhICDNnziQnJ6fMY1qzZg1hYWGEhYURHh5+1u1R4uLi6NKlCzfeeCMREREkJCQ42rrvvvv4/fffCQsL4+6772b16tXFFuK56aabWLJkCWlpaXTq1MlxXJMmTXKcu1NtxcXF0blzZ/72t7/RvXt3Jk+ezKpVq+jfvz8dOnRgw4YNAKWeK4CEhARGjRpFp06dePjhh8v8/Xfq1InGjRsXG4VfsWIFEydOBApvTxMZGUm3bt2YN2+eo05wcDCPPPIIAwYM4N1332XGjBmO+7xW9rmHwtvfTJ8+ndDQUK666ioyMzPPqhMVFUVISAjdu3fn3nvvLbd88eLFdOzYkcGDB7N+/foSz096ejrXXnstISEhhIaG8v7775d7XubNm0dERAQhISHs3r27zHZKe/+eeX5feOEFunbtSmhoqON3IyIiIiJSYcaYWvPTs2dPc0ph6Gc7efKk6dGjh+nQoYOZPXu2Wb16tWNb27ZtTVJSkjHGmI0bN5rBgwcbY4yZN2+eCQ0NNZmZmSYpKckEBgaaAwcOmM8//9z07dvXZGRkGGOMSUlJMcYYM3jwYLNx40ZjjDFJSUmmbdu2xhhjFi9ebObMmePoLy0tzeTl5RljjPnmm2/MuHHjHPXatWtnUlNTTVZWlmnTpo3Zv3+/SUpKMgMHDjTp6enGGGOefPJJ8/DDD5usrCwTGBho9uzZY4wxZurUqebZZ58t85guv/xys27dOsc5ORXHKfv27TOWZZkff/zxrPOzb98+061bN0f5d999Z0aPHu14PWfOHLN48WJjjDFff/216dOnj4mKijIjR44ssS0XFxezbds2U1BQYCIiIsy1115r7Ha7+fDDD82VV15Z7rlq0aKFSU5ONpmZmaZbt26Oc1+ap59+2tx2223GGGN+/PFHExkZ6dh26neYn59vBg8ebLZu3eqI96mnnnLUmz59unn33Xer7NwDjjrXXnuteeaZZ4wxf7y3Dhw4YIKCgszRo0dNXl6eGTp0qFm5cmWp5QcPHnSU5+TkmH79+hV7L55yzz33mFtvvdXx+tixY+WelxdeeMEYY8xLL71krrvuulLbKe39W9L5bdmypcnOzjbGGHP8+PGz4qypSvu7IyIiIiKVA9hkKpAb1rkR2QYNGhAdHc1rr71GQEAAEyZMYMmSJeXud+WVV+Ll5YW/vz9Dhw5lw4YNrFq1imuvvRZvb28AmjRpck6xpKWlMX78eLp3787tt9/Ozp07HduGDRuGr68vnp6edO3alfj4eH766SdiY2Pp378/YWFhLF26lPj4ePbs2UO7du3o2LEjANOnT2ft2rVl9t2/f3/uuOMOXnjhBVJTU3F1PftOS23btqVPnz7ndExnuuSSSwgJCWHOnDm88cYbJdZp164dISEh2Gw2unXrxrBhw7Asi5CQEOLi4oCyz9Ull1xC06ZN8fLyYty4caxbt67MmCZOnMh7772H3W7nnXfeYdKkSY5tK1asICIigvDwcHbu3ElsbKxj24QJE85qq6rOfVBQEP379wdgypQpZx3Txo0bGTJkCAEBAbi6ujJ58mTWrl1bakXaZvUAACAASURBVPnPP//sKHd3dy/xWABWrVrFnDlzHK/9/PzKPS/jxo0DoGfPno7fV0ntlPb+PeX0mEJDQ5k8eTJvvfVWiedHRERERKQsdS6RBXBxcWHIkCE8/PDDvPjii45pj66urtjtdgCys7OL7WNZ1lmvjTFnlZfXzukefPBBhg4dyo4dO/jkk0+K1T019flUvPn5+RhjuOSSS4iJiSEmJobY2FgWLlxY5u0+Sovlvvvu44033iArK4s+ffo4poSezsfHp9R2S+vjzH7sdju7du3Cy8uLY8eOlbj/6cdqs9kcr202G/n5+UDZ56qk301ZgoKCCA4OZs2aNbz//vtcffXVAOzbt48FCxbw7bffsm3bNkaPHl2sn5LOR1Wd+/KOqbR+y4qnvPNyav8z65V3Xk79vk69T0trp7T37ymnn9/PPvuMOXPmEB0dTc+ePR3tioiIiIhURJ1LZPfs2cOvv/7qeB0TE0Pbtm2Bwuv0oqOjARzJ7SkfffQR2dnZpKSksHr1anr16sWIESNYtGiR4/rFU4na6e2cuo4SoGHDhsWuh0xLS6N169YAFRoV7tOnD+vXr+e3334DIDMzk19++YXOnTsTFxfnKH/zzTcZPHhwmcf0+++/ExISwr333ktkZGSJyVRpzjyOtm3bEhsbS05ODmlpaXz77beObc8++yxdunQhKiqKmTNnkpeXV+F+TlfWufrmm284duwYWVlZfPjhh46RzLJMmjSJ22+/nQsuuIDAwEAATpw4gY+PD76+vhw5coQvvvii3Haq6tzv37+fH3/8ESi85nXAgAHFtl900UWsWbOG5ORkCgoKiIqKYvDgwWWWr169mpSUFPLy8nj33XdLPJ4RI0bw4osvOl4fP378T52Xktop7f17JrvdTkJCAkOHDuXpp58mNTXVcS2tiIiIiEhF1LlENj09nenTpzsWkomNjWX+/PkAzJs3j1tvvZWBAwfi4uJSbL/evXszevRo+vTpw4MPPkirVq0YNWoUY8aMITIykrCwMMcteu666y5eeeUV+vXr51jsB2Do0KHExsY6Fnu65557mDt3Lv3796/QSq0BAQEsWbKESZMmERoa6hjN8/T0ZPHixYwfP94xRfeGG24o85iee+45unfvTo8ePfDy8uLSSy+t8Dls2rQp/fv3p3v37tx9990EBQVx9dVXO6aDhoeHA/DLL7/wxhtv8M9//pOBAwcyaNAgHnvssQr3c7qyztWAAQOYOnUqYWFh/PWvfyUystz7IzN+/Hh27txZbCGhHj16EB4eTrdu3Zg5c2aFEuKqOvddunRh6dKlhIaGcuzYMWbPnl1se8uWLXniiScYOnQoPXr0ICIigiuvvLLM8vnz59O3b1+GDx9OREREicfzwAMPcPz4cUd833333Z86LyW1U9r790wFBQVMmTKFkJAQwsPDuf32252ywrWIiIiI1F5WWVMVa5rIyEhzarXgU1N/RUSqi/7uiIiIiFQty7KijTHljlzVuRFZERERERERqduUyIqIiIiIiEitokRWREREREREahUlsiIiIiIiIlKr1NpE1mazkZub6+wwRKSeyM3NxWartX8yRUREROqUWvupLCIiggULFiiZFZEql5uby4IFC0q9rZGIiIiIVC9XZwfwZ61cuZKxY8fy4IMPYrfbnR2OiNRhNpuNiIgIVq5c6exQRERERIRanMgGBgayceNGZ4chIiIiIiIi1azWTi0WERERERGR+kmJrIiIiIiIiNQqSmRFRERERESkVlEiKyIiIiIiIrWKElkRERERERGpVZTIioiIiIiISK2iRFZERERERERqlSpPZC3LWmRZ1lHLsnacVjbfsqwDlmXFFP1cVtVxiIiIiIiISN1QHSOyS4BRJZQ/a4wJK/r5vBriEBERERERkTrAtao7MMastSwruDLaOnjwIJZlVUZTIiIiIiIiUktVeSJbhpssy5oGbALuNMYcL6mSZVnXA9cDtGnTBmNMNYYoIiIiItVtyJAhAKxevdqpcYhI9avowKWzFnt6BbgACAMOAf8sraIx5jVjTKQxJjIgIKC64hMREREREZEayikjssaYI6eeW5b1OvCpM+IQERERkZqnb9++zg5BRGo4pySylmW1NMYcKno5FthRVn0RERERqT+eeOKJCtc1xpBnzycrP5esghyy8nPIzs8tfCzIxcPFjUbuPvi6++Dr4YOHi3sVRi4i1aXKE1nLsqKAIYC/ZVmJwDxgiGVZYYAB4oBZVR2HiIiIiNQ+p5LU7PzCJLUwWf0jUc3Kz6HA2Mts40BGsuO5h4sbvu4NHIltI3cffFw9taCoSC1THasWTyqheGFV9ysiIiIitYvdGA5lJPPhmi9o0MSXgJbNyTcF5e7narng5eqOp6sHXi4eeLm64+XqgaeLO1kFuZzIzSAtJ5203AxyCvI4mnWco1l/rDPqYtmKjdr6uvvQ0M0HF5uzlpMRkfI4c9ViERERERHy7fnEnzzK3hMHycrPIajrBYXlpgAXy4anq3tRglqYpHqe9tzL1QM3W8U+0hpjyMjPLkpsMwofczPILsjleM5JjuechJOFdS2ggZs3vu4+NCpKbn3dfXB3cauisyAi50KJrIiIiIg4RVZ+DvtOHCLu5GHy7YUjrz6unny8aAUHf9vP20uW4WZzrbRpv5Zl0cDNiwZuXrTy8XeU5xTkkpabyYmiUdu03AzS87I4mZfJybxMyEhy1PV0cXeM2jYqmqLs7eqhqcki1UyJrIiIiIhUq7TcDH5PO8CB9OT/b+9eY+NK8zu/f59T93sV71dJFNW6dbe6Z0bdc/G23Y57F2tjZveNYSMvnBgJMI7nRWDAQYLFGtgXi7wKjLxZ2LGBGIM1EsOAgSDwxgZiBzue+/Sop6WWWt26UKIkXou3ut/rPHlRVImkqBYlkSxW8fdBE8U659Spv9As8vzOc8NiAegLxDmTGGM43Me//89/CHBorZ8Bj5+hkJ+hULK9reE2yddL21puc5utt5VSjeXSk67JXuNpj7d93HIb9YfxGHVNFjkoCrIiIiIicuCstayUM8xkF1ipZNrbxyL9TCfGSQViHazuaV7HQyoQ21aXtZZivdxutc1udlGuuXXWKjnWKrn2sQZDzL/ZNXlLwPV5dPktsh/0SRIRERGRA+Nal7nCKjPZ+VY3XVqTK52IDXM6PkbEF3zqNb/2a7922GXuiTGGqD9M1B9mnMH29kqj1m61zdZa3ZOL9dZY3FytuO0cYW+gHWwfP4bUNVnkhRlrbadr2LPLly/bK1eudLoMEREREXmOWrPBg/wS93OLVJo1oLX0zen4GCdjI/h7vGWy4Ta3dUnOVovk6iXcXZYK8jne7S23gQhRXwhHXZPlGDLGfGStvfy843r7N4iIiIiIHKpSvcK93AIP8svt9V1jvjDTiXHGowPHZtyo1/HQF4zTF4y3t7nbuiYX2uNva26D1UqW1Uq2fayzs2tyIErcH97zDM0ivU6fBBERERF5ZdlqgTvZORaKa+1tA8EEZxLjDIaSL9R19td//dcB+Pu///t9r7OTHNMKpzF/mInNrsnWWirttW6fjL0tNSrt77cKe4Mk/BFSwRinYiN4HU8n/ikiHacgKyIiIiIvLVcrcmvjEYulVoA1GMajA0zHx0kEIi91znK5vJ8lHmnGmM01cQMMh/va2+tug1ytRLZaaHdRztdKlBoVSo0Ki6U1VsoZvjp8QV2Q5VhSkBURERGRF5avlbiVedhugXWMw6nYCNOJMULeQIer634+x0t/ME7/tq7JLoV6mUy1yM31WVbKGa6tzvD2wBlNFiXHjoKsiIiIiOxZoVbiVuYR88VVoDWW82R8hNcSEwS9/g5X19sc4xDfHDMb84f48eINHhXShLwBzqdOdLo8kUOlICsiIiIiz1Wsl7mdmeNRIQ20uhCfjA3zWnJCLbAdkArE+MrQOT5c/ozbmUeEPH5Oxkc6XZbIoVGQFREREZFnKtUr3M484lEhjaUVYE/EhngtMUF4lzVg98M3v/nNAzlvrxkJ93Gpf5pP1mb4ZG2GoNe/bZytSC/TOrIiIiIi8pRyo8rtzBwP88tYLAaYiA5xNjlJ5IACrLyczzcecDszh8c4fGP0DVKBWKdLEnlpWkdWRERERF5YpVHlTnaeB7klXFoNHhORQc6mJon6Qh2uTnZzLnmCcqPGo0Kany19xntjbxLR/yvpcQqyIiIiIkKlUeNudo7Z/DKudQEYjwxwNjlJzB8+1Fref/99AL73ve8d6vt2K2MMbw1MU2nUWKlk+OnSTf7Z2CUCHl+nSxM5MAqyIiIiIsdYpVFjJjfPbG6J5maAHQ33cy41Sdz/cuvAyuFzjMM7w+f44eINcrUiHy7f5Osjb+B1PJ0uTeRAKMiKiIiIHCOuddmo5kmXMqTLG2Rrxfa+kXAf55InSAQUYLuR1/HyteGL/GDhEzaqBX6xcpt3hs5rjVnpSQqyIiIiIj2u3KiSLm+QLmVYqWRouM32Psc4DIWSnE1OkgxEO1il7Ieg18/XRi7yw8XrLJXWub52jzf7TyvMSs9RkBURERHpMU3rsl7JtcNrvl7atj/qCzEUSjIUStEfjONR99OeEvOHeXf4Aj9ZusFsfomQN8BryYlOlyWyrxRkRURERHpAsV5pBdfyBqvlbHu8K4DHOAyGkgyFkgyGUkd++Zzf+q3f6nQJXa8/GOfLg2e5kr7FZxsPCHr8TMaGOl2WyL5RkBURERHpQk23yVolx/JmeC3WK9v2x/1hhkIphkJJ+oJxHON0qNIX953vfKfTJfSEscgAb/TVuLF+n6urdwl6/QyGkp0uS2RfdFWQLTeqnS5BREREpKPKjSr3cos8yC3RsE/GunodT7u78GAoScgb6GCVr6ZUanWFDocPd9mfXnQ6MUa5UWUmt8DPlz/nl0bf1GRe0hO6KsjW3AZ1t4HP6aqyRURERF5ZrlZkJrvAXGEFiwUg4Y8wHE4xGEqRCsRwemRCn9/4jd8AtI7sfrnYd4pys8ZCcZWfLbfWmA138Y0OEeiyIGut5VE+zenEWKdLERERETlw1lrWKjnuZudJlzfa28ci/UwnxkkFYh2sTrqFMYYvDb5GtVljrZLjZ0uf8kujl/B7uioKiGzTdT+993OLTMVHNYW4iIiI9CxrLYulNe5m5snUCkBrwqbJ6BDTiTEivlCHK5Ru4zEO7wxd4EeL18nXS/w8/RlfG34dj9M9Y6dFtjrwn1xjzF8YY9LGmBu77PsfjDHWGDOwl3M5xlBsVFgpZ/a/UBEREZEOa7hN7ucW+f/mfsGV9C0ytQJ+x8u55CQfTF7m0sC0Qqy8NL/Hy1dHLhL0+Fmr5Ph49TbW2k6XJfJSDqNF9rvAfwD+49aNxphJ4J8DD/d6ooDHB8C93CJD4dT+VSgiIiLSQdVmnfu5RWZzi9TcBgBhb5DpxBiT0SG8WudV9knYG+Crwxf50eJ1FoprBD2zvNE/1emyRF7YgQdZa+33jTGndtn1vwL/I/B/7/Vcfo8PxxjS5Q0K9TJR3ZEUERGRLlasl5nJLvCokG6v+5r0RzmTHGc03H9sh1L97u/+bqdL6GmJQIR3hs/z06Wb3Mst4HM8nE1OHtufN+lOHRkja4z5V8C8tfba8z4wxphvA98GOHHiBOORQR4V0szmlnT3SERERLrSRjXP3cw8i6W19rbhUIrpxDj9wfixDxQKsgdvMJTk7cEzfLxyh1uZRxTqZd4eOINHrf/SJQ49yBpjwsC/Bf7FXo631v458OcAly9ftlPxUR4V0jwsLHM+dUJdbURERKQrWGtJlze4m51nrZIDwGCYiA4ynRgn7teaqY+trq4CMDCwp2lU5CVNRofwOV4+St9ivrhKsV7h3eHzBLU0j3SBTrTITgNTwOPW2AngF8aYd621S897cTIQJRWIsVHNM1dIcyo+esDlioiIiLw817rMFVaYyS6Qr5cA8BoPp+IjTMVHCSk0POU3f/M3Aa0jexhGwn28N3aJD5c/I1Mr8P2FT3h3+DxJLe0kR9yhB1lr7XVg6PFzY8wscNlau7rXc0zFR9lYyXM/t8TJ2Mix734jIiIiR0/dbTCbW+J+bpFKswZA0OPndGKMk7FhfE7XrYIoPSruj/De2FtcSX/OWiXHDxdv8KWBM4xHBztdmsgzHfhvUGPMXwHvAwPGmDng31lr//dXOedYpJ9P133k6yXWKlkGQsn9KFVERETklZUbVe7lFniQW6ZhmwDEfGGmE+NMRAdwjNbtlKMn4PHx9ZHX+WT1Hg8Ly3y0cpt8vcS55Ak1GsmRdBizFv+Xz9l/6kXP6RiHU7ERbmUecS+3qCArIiIiHZerFZnJLjBXWMHSWpuzPxjnTGKCoVBSYUCOPMc4vDUwTdwf5sb6fW5n5sjXynxp8DXNSyNHTtf2aTkZG+F2Zo6l0jqleoWwL9jpkkREROSYsdayVslyN7tAurzR3j4W6edMYlzjDKXrGGM4nRgj6gtxZeUWi6U1iosV3h2+QFjjueUI6dogG/T6GYv0M19cZTa/xMW+U50uSURERI4J11oWi2vMZOfJ1AoAeIzDZHSI6cQ4Ed1gfyW///u/3+kSjr2hcIr3RluTQOVqRX6wcI13hs7TF4x3ujQRAIy1ttM17Nnly5ftlStX2s/XKzl+uHgdv+Pln09e1rpXIiIicqAabpNHhTQz2QVKjQoAfsfLVHyUU/FRAh5fhysU2V+1Zp0r6VusVrI4GN4aOMNkbOj5LxR5ScaYj6y1l593XNe2yAKkAjES/gjZWpH54ionYsOdLklERER6ULVZ535ukdncIjW3AUDYG2Q6McZkdEjjB/fZo0ePAJicnOxwJeL3+PjayEVurN1nNr/Ex6t3yNdLXEid1Lhv6aiuDrLGGKbio1xdvcv93CKT0SF9oERERGTfFOpl7mUXeFhI41oXaK1pfyYxzmi4X9cdB+R3fud3AK0je1Q4xuHSwDQxf5gba/e4m50nXy/xlcGzeLWMlHRI1//kjUcGubk+S7ZWZKOaV799EREReWUb1Tx3M/Msltba24ZDKc4kxukLxhVg5Viaio+2JoFK32K5tMEPFq7z7vAFjQmXjuj6IOtxHE7GRriTneN+blFBVkRERF6KtZbl8gYz2XnWKjkADIaJ6CDTiXHi/nCHKxTpvMFQkvfGWpNA5eul9iRQ/aFEp0uTY6brgyzAqXgryC4U13i9USWoqcFFRERkj5rWZb6wwkx2nny9DIDXeDgVH2EqPkpI1xUi20R9Id4bu8RH6dukyxv8eOlTLg2c5mRspNOlyTHSE0E25A0wGu5nsbTGbH6Z86kTnS5JREREjri622A2t8S93ALVZh2AoMfP6cQYJ2PD+DT2T+SZfI6Xrw5f4Ob6LDO5Ba6tzlCoV7ioSaDkkPTMb+ip+CiLpTUe5Jc4m5zAMU6nSxIREZEjqNyoci+7wIP8Mg3bBCDmC3MmMc54dEDXEEfAH/7hH3a6BNkDYwyv908R84e5tjrDTHYer3E4p0YlOQQ9E2T7g3FivjD5eomF4hoT0cFOlyQiIiJHSK5W5G52nvnCKhYLwEAwwXRinKFQUq1IR8i3vvWtTpcgL+BEbBiv4+VK+nNuZR7h9/iYio92uizpcT0TZB8vxfPJ2gz3c4sKsiIiIoK1lrVKlrvZBdLljfb2sUg/ZxLjJAOxDlYnz3Lr1i0Azp071+FKZK/GIv28NXCGa6t3ub52D5/j1fW4HKieCbIAE9FBPtuYZaOaJ1PN64+TiIjIMVZp1Ph49Q4r5QwAHuNwIjbM6fiYlgs54n7v934P0Dqy3eZkbJhas85nGw/4eOUOPsfDcLiv02VJj+qpQSBex8NkdBiA+7mlDlcjIiIinZIubfC9+auslDP4HC/nkpN8MHmZN/tPK8SKHKDXkhOcSYxjsVxJ32ovZSWy33oqyALt/vjzxZX2DIQiIiJyPLjW5eb6LD9dvknNrTMQTPCr429zLnWCgMfX6fJEjoULqZOciA7RtC4fLt8kWyt2uiTpQT0XZCO+IMOhFK61PMirVVZEROS4KNYr/GjxOnez8xjgfOoEXx95XevLixwyYwyXBs4wEu6j7jb56dKnFDfXaBbZLz0XZOFJq+xsbgnX2g5XIyIiIgdtobjKPy1cZaNaIOTx843RNzmbnNRMxCId4hjDVwbPMRBMUG3W+cnSTSqNWqfLkh7SU5M9PTYYShLxBSnWKyyV1hiLDHS6JBERETkATbfJjfX7PMgvAzAS7uPtgTP41Y24q/3RH/1Rp0uQfeBxHN4dPs+PFz8lUyvwk6VP+aXRN/F7ejKCyCHryRZZYwxTsVar7P3cYoerERERkYOQq5X4/sInPMgv42B4s/807wydV4jtAR988AEffPBBp8uQfeB1vHx15CJRX4h8vcTPlm/ScJudLkt6QE8GWYDJ2BAe47BWyWmAuYiIyD6y1uJaS9Nt0nAb1Jp1Ks0a5UaVUr2Ca90Df/8H+SV+sHCNfL1E1BfivbFLTMVH1ZW4R1y9epWrV692ugzZJwGPj6+PvE7I42ejmudK+taB/56Q3tez7fo+x8tkdIjZ/BKzuUXeGjjT6ZJERESOpOXSOnez89TdJtZaLK2gaq2Li20F18ePm/u/iNfxMBRKMRJOMRRK7WsLad1tcG11hoXiKgCT0SHe7D+N1/Hs23tI5/3BH/wBoHVke0nIG+BrI6/zo8XrpMsbfLxyhy8PntXNJ3lpPRtkoTXp02x+ibnCChdSp9QfX0REZItas86N9fvMFVZe6vUOBmMMjjEYHBxjsFiqzToLxVUWiqsYoC8YZzjcx0i4j6gv9NL1blTzfJS+RalRxWMcLg1MMxkdeunzicjhivnDfG3kIj9avMF8cRW/x8cbfVMKs13IWkuxXmajWmCjmqfarHMyNsxQOHVoNfR0sov5wwwGk6xUMjwsLHMmMd7pkkRERI6ExeIan6zNUG3WcYzD+eQkA6FkK5g+Dqi7BNWt25+lWC+zVNpgubTOWiXX/rq5PkvEF2Qk1MdwuI++YBxnDxew1lpmcgt8tv4AiyXhj/CVoXOvFIpFpDOSgRjvDl/gZ0s3uZ9bxOd4OZ860emy5DlqzXo7tG5U82SqBepuY9sxi6U1RsJ9vN53isgh/H7u6SALMJUYZaWSYTa3yHR8THd8RETkWKs269xYu8f8ZtfcvkCctwfP7GsojPhCTCdCTCfGqDcbpMsbLJU2SJc3KNYrzNQXmMkt4HO8DIWSjIT7GAqn8DlPX5ZUmzU+XrlDupwB4HR8lAt9p/CYnp3mQ6TnDYaSfGXoHD9Pf87tzCP8jpfTibFOlyWbXOuSqxVbwbXSCq7FRuWp4wIeH6lAjFQghrWWO9k5lkrrpEsbTCfGeS05caDDPno+yA6HUoS9AUqNKsvlDUbCfZ0uSURE5NBZa1kornF97R41t47HOFxInTzwCZJ8Hi/j0UHGo4O41rJeybFcWmepvE6xXmG+uMp8cRWDob/dBTlFxBdipZzhFyu3qTbr+B0vbw++pr/jIj1iNNLP2wNnuLp6lxvr9/F7vExoqMChs9ZSblS3tbZma8WnJuPyGIeEP0oqECUVbIXXoMe/7e/HidgwNzdmmSuscCc7x6NCmot9JxmPDB7I3xlj7RdP2PDKb2DMXwDfBNLW2jc2t/174F8DLpAGftdau/C8c12+fNleuXLlhWu4m53n5vosg6EkXx95/YVfLyIi0s0qzRrXV++xWFoDYCCY4K2BM0R8wY7WVaiXWSqts1xaZ72S2zaFVMQbbLcA9AfjfHnwLCFvoDOFyqH78Y9/DMA3vvGNDlciB+3xdbrB8O7weYZ1s+pANdwGmWphW3CtNutPHRf1hUgGou0W17g/jLPHnjDrlTw31u6RqRUASAVivNl/mmQguqfXG2M+stZefu5xhxBkfxkoAP9xS5CNW2tzm9//98BFa+1/97xzvWyQrTXr/MOjKzSty38x/iWi/vALn0NERKTbWGuZK65wY+0+dbeB13i42HeKk7HhIzfUptasb+uC/HidyXPJSc4mJ49cvSKyf26uz3I3O49jHL4+8jr9wXinS+oJ1lry9dK2LsL5eump43yOdzOwtoJrMhB75UlyrbU8KqS5uf6AmtsKyieiw1zoO0HA4//C1+41yB5412Jr7feNMad2bMtteRqB58zj/4r8Hh8T0UEe5Je5n1/izf7TB/l2IiIiHVduVPlkdYbl8gbQGpP21sAZwke0VbP1t3qIiegQrnXZqOYJePya0OmYUovs8XIhdZJas8HDwjI/W77JL428SSIQ6XRZXafSrJGp5NutrZlqgYZtbjvGYEj4I63W1s0uwhFvcN9vFhpjOBEbZjTSz+2NR9zLLfKwsMxCaZVzyRNMxUf23ML7zPc46BZZgM0g+58et8hubvufgf8KyAK/aq3dde5/Y8y3gW8DnDhx4isPHjx4qRqytSL/NH8Vj3H4Fyfe2XVCCRERkW73+C74jfX7NNwmXsfDG31TTEaH1KopXeP9998HtI7scWKt5Ur6FoulNTzGYSCUYDjUx3A4pWEFu2i6LtlaYUtozVNqVJ86LuQNtFtaU4EYCX8ETwfW3c7XSny6fr89cV/UF+KN/tMMhZJPHXtkuhZvFnOKHUF2y75/AwSttf/ueed52a7Fj/1o8TprlRxv9p9mKj760ucRERE5ikqNKtdW77KyeaEwHEpxaWBaF4HSdRRkj6emdflF+nZ7PP9jMV+Y4XCK4XAfqUBsT8t2dRtrLTW3Qa1Zbz/WdzxvPTaouXWK9Qp2R6dWj3E2uwY/Ca5B7xd34z1M1lqWyxvcWLtPaXMOhNZyPVPb5mw4Ml2L9+D/BP4f4LlB9lVNxUdZq+T4fOMhi8U1/B4ffse75dGL3/Hh93jxOT4CHi8e49EdbBGRLtN0XcrNKpVGlXKjRqVZA8AxDh5jNh+dipiu3gAAELJJREFUbY9fvO+L103tNGstD/LL3FyfpWGb+Bwvb/RPMXFAM0WKiBwEj3F4Z/g85UaVdHmD5dIGK+UM+XqJfLbE3ex8e9muoXCKoVCKgMfX6bKf0rQu9WadqtugviOAbn2st583nlqTdS9ivvC2WYRjvvCR/p1vjGEk3MdgKMm97AK3M49eabmejgRZY8xr1to7m0//FfD5YbzvSLi/PQviaiW7p9cYzLaAu/XR5/HuCMKtR5/jPdI/RCIi3cy1LpVGjXKzFVLLjSrlRpVK88n3tZe4IHie3YLuk+/NCwbjXfY5zo5jn+w3PDtIF+sVrq3ebf9dGw3382b/6SN1F15E5EWEvAFOxkY4GRuhad32sl3LpQ2KjSfLdkFrRtzhUIrhcIq4P7Kv1+DWWprW3aVVtN4On7XmlnC6+by5Y+mavfI5T2cLn+MlsPm4NYuEvIGuHSrpMQ6vJSeYjA5yc+PBjuV6Tu35PAf+rzfG/BXwPjBgjJmj1fL6G8aYc7SW33kAPHfG4v3gGMOvjL9NoV7acVfk2c34TetSbdZb01I/PTP1M/kd72bQ3RKAt4bdXVqDX3XA81HhWpdCvUy+VmrfeemVf5uIHI7WZD8FMtX8Zjh9HFyruy4TsJPBEPT6CXkChLx+gl4/BkPTurjWbT+6mxcpW7c1rYvr2i3HuLjYze8Bms97+wOxtXV4azAu1Ms0rYvf8fJm/zRjkX7dTBWRnuExDoOhJIOhJG/0t5btSpdarbVrlWx7CZnPMw8JeHybobaPwVAC75agZ61tXePvaB2t72gl3fpYbzZwX2JO2p0NYa2AupkJdmaALbnguP3uDnoDfHnwLKdiI1xfu0e2VuQXK7f3/PpDGSO7X151jOzLaLru9h9ot0G12frBfnLnZfuHoe6+3EWO13h2tPY+DsG7/NBv3pl5keb3/eZaS6leJlcvka+VWt0+aiUKO/rsBz1+puKjnIyNvPJU3iLSm5puk41qnrVKjrVKjvVq/qnF2LcKevyEvJsh1RNof/84uAZ2LNL+qqy1myHX7gjCWwOwfToQ77L/i0Lz9vM+Od/OcVA7jUcGeKP/9JHsYifyMq5evQrA22+/3eFK5ChruE1Wypl2N+THw0igFSbj/vC2FtWX4THOjkaoL+qp2Xru1dDEF7Z1uZ5fP/XVozPZ037pRJB9Ga61u9zd2dINYZe+8nW3/lJrEDmbH65njfNt79sSiF/0w2WtpdSokNsSVvP1EoVa+Zl3qcLeIHF/mGK90l6vymMcTsaGOR0fI7xlQLeIdF61WSNTLeBaS9gbIOwLHmiXpYbbZH0ztK5VcmSq+ad+n8R8IfqCcSLeICFvgKC3FViDHt+x6+VhrX1myPUaR+uji8ixZ60lVyuyvBlqN6r5p47xOp4tvSR3XDM/oxGpEzP8Hmd1t4Hf4+uayZ56jmMMAY//uYv9btXq7tBsd2PYrXtDK/Bu3+Zal0qztu0O1PMYzFP97He29lab9S0trOVn9vUPeQLE/OHWly9M3B8m6gu1W4qttayUM8xkF1ipZLiXW+RebpGxSD/TiXFSgdie6xaR/VF3G2SqhW1f5ebTU/b7HS9hb5CwL9B69AaJ+FqPIa//hcJkvdlgvZpjtZJjrZIlWy08dRss7o8wEIzTF0zQH4yrdXELYwxe4wF0QSXHwz/+4z8C8MEHH3S4EukWxhgSgSiJQJSzyUlqzTr5ennLcL/eGcbXy17kJrpaZLtYewD6rq29Tz8+7hb9MgPQgx5/O6zG/GHivjAxf2jb2IPnyVaLzOTmmS+strvJ9QfjTMfHGA73qQuGyAFouk2yteKT0ForUKiXnzrOYxySgShe46HUqFBqVL/wd4WhNRnH44Ab9gWIeIOEN4MusNnimmWtkiNbKz71+oQ/Sn8oTn8wQV8grqEHItKm5XdEjq9uWn5HXtLjO/Rex0P4BdYIbE0J/sWtvl7H0w6tMV94Xy4wE4EIXx48y4XUSe7nFpnNLbW7FUZ8Qabj40xGB9WFQ+QludYlXyu1A+tGtUC+VnpqfKWDIe6PkAxEN79ixHyhbTeTrLVUm3VKjQrFRoVSvboZcCuU6hXKzRqlRnVz8fXnzwJvMCQDUQY2W1v7grEXuhEmIiIispWuIo4hj3HweP0E6czSDCFvgIt9p3gtOcHD/DL3sosU6xU+WZvh842HTMVHOBUfVbdC2Td1t8G11bukyxmgFaqeRDbD4/z2eKvBsPnfk61mx37YfN2Tc+36ul3O33rtzqPYFiSf/x5PXmdpzeKYrRV3nSAp5gu3Q2sqECXmj+B5TvcqY1qz/ga9fvqIP7W/aV3KjSql+mbQ3Qy7xc2g6+KSCsTo3wyuqUCso5PTiYiISG9RkJWO8TlephPjTMXHWCyucjc7T7ZW5FbmEXey80xGB5lOjBP1hTpdqnSxQr3Mh8uf7dqdtheFvUFS7ZbWKAl/9EACpMc4RH2hXT+fj4esaLiAiIiIHBQFWek4xxjGo4OMRQZYq+SYyc6zXN7gQX6ZB/llRsJ9TCfG6AvEdWEsL2S1nOHn6VvU3QYxX5jLQ+cIevzYLZ1tLbbVpLl1m92xv7Vx27bNl2w54sm2J1u2vm77+dv72697+j3tzvPbHfu3vC7sDZAMRPEfgZ4M+pyKiIjIQVOQlSPDGMNAKMFAKEG+VmImu8BcMc1SaZ2l0joRb5Dx6CAT0UG10spzzeYWub52H4tlOJTiy0NnD3Q5GRER2T9/9md/1ukSROSI06zFcqRVmjVmc4s8yC9Tbdbb21OBKOORQcajAy+0zJH0Pte63Fi7z2x+CYAziXEupE6qlVBERESkC2jWYukJQY+f86mTnEueYKWSZa6QZrG4xka1NSPrp+v3GQylmIgOMhLu02Qyx1ytWedK+harlSwOhrcGzjAZG+p0WSIi8oL+9m//FoBvfetbHa5ERI4qBVnpCsYYhkJJhkJJGv1NlkvrzBVWSJczpMsbpMsbeIzDaLifieggA6EkjlrgjpV8rcSHy59RbFQIeHy8M3SevuDTs+2KiMjR98d//MeAgqyIPJuCrHQdr+NhPDrIeHSQarPOQnGVucIKG9U8c8UV5oor+B0fE9EBxqODJP1RdSvtcenSBldWbtFwm8T9Ed4dvvBCayuLiIiISHdRkJWuFvD4mIqPMhUfpVgvM1dYZa6YplivcC+3yL3cIhFfkIlIa5KoiCaJ6inWWu7lFvl0/T4Ao+F+vjT4mrqYi4iIiPQ4BVnpGRFfiHOpSc4mJ8jWCswVVpgvrlKsV7iVecStzCNSm+tqhryB9lfYGyDo8avVtsu41uWT1Xs8LCwDcDY5wbnkCf1/FBERETkGFGSl5xhjSAZiJAMxLvZNsVrOMFdYYbH0ZJKop14DBL0BQp4n4Tbk9RPyBjef+/Fq6ZYjo9qs8/Plz1mv5nCMw5cGzjAeHex0WSIiIiJySHRlLj3NMYahcIqhcIqG22S1nKHYqFLe+tWsUm3W28+p7n4un+NpteJ6trfmPv5erbqHI1cr8uHyZ5QaVYIeP+8OnycZiHW6LBER2Ud/+Zd/2ekSROSIU5CVY8PreBiJ9O+6r+m6lJs7Am6jSmkz6JYbNepuk3qtRI7SrucwGIJePyHP9oD7JPSqVfdVLRXX+GjlNk3rkvRHeXf4PEFN6iQi0nMmJyc7XYKIHHG6qhYBPI5D1AkRfcZkUNZaam7jSbh9Tqvu+vNadZ/qxqxW3S9ireVudp7PNh4AMB4Z4O2BM3g0qZOISE/667/+awB++7d/u8OViMhRpSArsgfGGAIeHwGPj2QguusxL9SqW3t2q25rbO72LszHuVW36bpcW73LXHEFgPOpE7yWmFDgFxHpYX/6p38KKMiKyLMdrytikQO0X626pc39z+JzvJvB1t/zrbqVRo2fpz9no5rHYxy+PHiW0Wd0DxcRERGR40NBVuSQ7F+rboN6rUGuVtz9fZ7Rqrs17HbDOquZaoEPlz+j0qwR8vh5d/giiUCk02WJiIiIyBGgICtyhOy9VbdCqVE7kFbdsC9I0OM/qH/iniwUV/l45Q5N65IKxHhn+HzHaxIRERGRo0NBVqSLbG/V3f2Y/WjVPREd4vX+KXyHPB7XWsvtzCNuZR4BMBkd4tLANB7jHGodIiIiInK0KciK9Ji9terWN8Pt0626uVqRh4U0K+Usbw+eYTCUPJS6G26Tq6t3WCiuAXCx7xTT8bGeGe8rIiJ79zd/8zedLkFEjjgFWZFjptWq6yfg8e/aqpuvlfh45Q6ZWoGfLH3KqdgIF/tOHei42nKjyofLn5GtFfEaD18ZOstwuO/A3k9ERI62gYGBTpcgIkfcgffXM8b8hTEmbYy5sWXb/2KM+dwY84kx5v8yxhxOk4+IPFfMH+afjV3ifPIEBsNsfonvzV9lrZI7kPfbqOT5/sI1srUiYW+Q98YuKcSKiBxz3/3ud/nud7/b6TJE5Ag7jIFn3wX+5Y5t/wC8Ya29BNwG/s0h1CEie+QYw9nUJL88dom4P0ypUeFHi9f5dP0+Tdfdt/eZK6T50dJ1qs06/cE4vzx2iZg/vG/nFxGR7qQgKyLPc+BB1lr7fWB9x7b/11rb2Hz6U2DioOsQkReXCER5b+wtXku0PqIz2QX+aeEqG9X8K53XWstn6w/4xcodXGs5GRvm6yOv4/f49qNsEREREelxR2Eq0P8G+Ptn7TTGfNsYc8UYc2VlZeUQyxIRAI9xuNB3kvdGLxH1hSjUy/xw4RM+33iAa1+8dbbhNvh5+nPuZOcwwJv9p7nUP42jmYlFREREZI86euVojPm3QAP4P551jLX2z621l621lwcHBw+vOBHZJhWM8Stjb3E6PoYFbmfm+P7CJ2SfsYTPbkr1Cj9YuM5SaR2f4+FrI68zFR/VzMQiIiIi8kI6NmuxMea/Br4J/Jq11naqDhHZO4/j4Y3+KUYjfXy8cpdcrcj3569xLjXJmcQEzhcE0rVKjp8vf07NrRP1hXh3+MIzlwgSEREREfkiHQmyxph/CfxPwK9Ya0udqEFEXl5/MMH7429zc32W2fwSn288ZKm0zpcGXtt1sqaH+WWurc5gsQyGklwePIfPo9W/RERkd3/3d3/X6RJE5Ig7jOV3/gr4CXDOGDNnjPlvgf8AxIB/MMZcNcb8bwddh4jsL6/j4dLANF8beZ2gx0+mWuCfFq4xk53ncScLay031u5zdfUuFsvp+ChfHb6oECsiIl8oHA4TDmsWexF5NtNNvXovX75sr1y50ukyRGSHutvgxtp9HhXSAPQH47zRN8VnGw9JlzcwGC71n+ZkfKTDlYqISDf4kz/5EwC+853vdLgSETlsxpiPrLWXn3ucgqyI7Jel0jrXVu9Sbdbb2/yOl8tD5xkIJTpYmYiIdJP3338fgO9973sdrUNEDt9eg6zWuxCRfTMS7uNXx7/EeGQAgJgvzHtjbynEioiIiMi+0kA1EdlXfo+Prwyd42xtkrAviEfrw4qIiIjIPlOQFZEDsdvsxSIiIiIi+0FNJSIiIiIiItJVumqyJ2PMCvCg03WIiIiIiIjIgThprR183kFdFWRFRERERERE1LVYREREREREuoqCrIiIiIiIiHQVBVkRERERERHpKgqyIiIiIiIi0lUUZEVERERERKSrKMiKiIiIiIhIV1GQFRERERERka6iICsiIiIiIiJdRUFWREREREREusr/D2+wMyBZCFlGAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 1152x1296 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# You may need to install htmllib5,lxml, and BeautifulSoup4. In your terminal/command prompt run:\n",
"\n",
"# conda install lxml\n",
"# conda install html5lib\n",
"# conda install BeautifulSoup4\n",
"# Then restart Jupyter Notebook. (or use pip install if you aren't using the Anaconda Distribution)\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os.path\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime, timedelta\n",
"from dateutil.parser import parse\n",
"from dateutil.relativedelta import relativedelta\n",
"from pandas.tseries.offsets import *\n",
"from pylab import text\n",
"from mpl_toolkits.axes_grid.anchored_artists import AnchoredText\n",
"import quandl\n",
"\n",
"\n",
"# Quandl API key\n",
"quandl.ApiConfig.api_key = \"UsYsv7dKGxHHQ5oURP4B\"\n",
"\n",
"# Some formatting\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.set_option('display.max_seq_items', 2)\n",
"\n",
"# Only pull fresh PDUFA data\n",
"three_weeks_ago = relativedelta(weeks=3)\n",
"one_week_ahead = relativedelta(weeks=1)\n",
"one_hour_ago = datetime.now() - timedelta(hours=1)\n",
"if os.path.exists(\"history.csv\"):\n",
" filetime = datetime.fromtimestamp(os.path.getctime(\"history.csv\"))\n",
" if filetime < one_hour_ago:\n",
" histdata = pd.read_html(\"https://www.biopharmcatalyst.com/calendars/historical-catalyst-calendar\")\n",
" histdata[0].to_csv('history.csv',index=False)\n",
"else:\n",
" histdata = pd.read_html(\"https://www.biopharmcatalyst.com/calendars/historical-catalyst-calendar\")\n",
" histdata[0].to_csv('history.csv',index=False)\n",
"\n",
"# Create dataframe\n",
"df = pd.read_csv('history.csv').set_index('Ticker')\n",
"df.index.name=None\n",
"df[[\"Date\",\"Catalyst\"]] = df.Catalyst.str.extract('(?P<Date>[0-9]{2}\\/[0-9]{2}\\/[0-9]{4})(?P<Catalyst>.*)', expand=True)\n",
"df['Date'] = pd.to_datetime(df['Date'])\n",
"df[\"Past\"] = df[\"Date\"] - DateOffset(weeks=5)\n",
"df[\"Future\"] = df[\"Date\"] + DateOffset(weeks=2)\n",
"\n",
"df\n",
"# Set stock ticker\n",
"stockpick=\"HALO\"\n",
"dataset=str(f\"WIKI/{stockpick}\")\n",
"# Set variables for plot creation\n",
"length = len(df.loc[stockpick].index)\n",
"count = 0\n",
"fig, axes = plt.subplots(nrows=length, ncols=1,figsize=(16,length * 3))\n",
"fig.subplots_adjust(hspace=0, wspace=0)\n",
"allplots=[]\n",
"\n",
"# Combine all data together into list of dataframes, iterate through each part of the list, plot each frame.\n",
"while (count < length):\n",
" pasttime= df.loc[stockpick].iloc[count][\"Past\"]\n",
" futuretime= df.loc[stockpick].iloc[count][\"Future\"]\n",
" pdufa=df.loc[stockpick].iloc[count][\"Date\"]\n",
" annotate = df.loc[stockpick].iloc[count][\"Date\"] + timedelta(days=1)\n",
" stage = df.loc[stockpick].iloc[count][\"Stage\"]\n",
" catalyst = df.loc[stockpick].iloc[count][\"Catalyst\"]\n",
" drug = df.loc[stockpick].iloc[count][\"Drug\"]\n",
" \n",
" #Annotation\n",
" tooltip = f\"stage:{stage} -{catalyst}\\n{drug}\"\n",
" at = AnchoredText(tooltip,\n",
" prop=dict(size=10), frameon=True,\n",
" loc=2, \n",
" )\n",
" at.patch.set_boxstyle(\"round,pad=0.2,rounding_size=0.2\")\n",
" axes[count].add_artist(at)\n",
" axes[count].margins(0.0, 0.5)\n",
" #Get quandl data\n",
" mydata = quandl.get(dataset,start_date=pasttime,end_date=futuretime)\n",
" allplots.append(mydata)\n",
"# axes[count].annotate('local max', xy=(pdufa, allplots[count][\"Close\"].max()), xytext=(annotate, (allplots[count][\"Close\"].max() - allplots[count][\"Close\"].min()) * 0.9 + allplots[count][\"Close\"].min()),\n",
"# arrowprops=dict(facecolor='black', shrink=0.05, width=1, headwidth=5),)\n",
" # Set y limit for notes\n",
" axes[count].set_ylim(allplots[count][\"Close\"].min() * .99 , (allplots[count][\"Close\"].max()-allplots[count][\"Close\"].min()) * 0.5 + allplots[count][\"Close\"].max())\n",
" axes[count].hlines(allplots[count][\"Close\"].max() * 1.01, allplots[count].head(1).index, allplots[count].tail(1).index, linestyle=\"-\", lw=1, color='black')\n",
" if count == 3:\n",
" axes[count].set_facecolor((0.91, 0.91, 0.91)) \n",
"\n",
" axes[count].vlines(pdufa, allplots[count][\"Close\"].min() * .99, allplots[count][\"Close\"].max() * 1.01, linestyle=\"--\", color='black')\n",
" axes[count].plot(allplots[count].index,allplots[count][\"Close\"],c=np.random.rand(3,), lw=2, label=pdufa) \n",
" axes[count].axes.get_xaxis().set_visible(False) # remove x axis\n",
" count = count + 1\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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" <td>-0.090</td>\n",
" <td>0.17</td>\n",
" <td>0.59</td>\n",
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" <td>0.83</td>\n",
" <td>0.08</td>\n",
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" <td>0.78</td>\n",
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" <td>-1.410</td>\n",
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" <th>3</th>\n",
" <td>0.770</td>\n",
" <td>0.24</td>\n",
" <td>0.08</td>\n",
" <td>0.37</td>\n",
" <td>0.86</td>\n",
" <td>-0.15</td>\n",
" <td>-0.15</td>\n",
" <td>-1.320</td>\n",
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" <th>4</th>\n",
" <td>0.680</td>\n",
" <td>0.14</td>\n",
" <td>0.40</td>\n",
" <td>0.36</td>\n",
" <td>1.32</td>\n",
" <td>-0.29</td>\n",
" <td>-0.29</td>\n",
" <td>-1.560</td>\n",
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" <th>5</th>\n",
" <td>0.350</td>\n",
" <td>0.35</td>\n",
" <td>0.30</td>\n",
" <td>0.64</td>\n",
" <td>1.55</td>\n",
" <td>-0.30</td>\n",
" <td>-0.30</td>\n",
" <td>-1.750</td>\n",
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" <th>6</th>\n",
" <td>0.220</td>\n",
" <td>0.29</td>\n",
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" <td>0.67</td>\n",
" <td>1.42</td>\n",
" <td>-0.51</td>\n",
" <td>-0.51</td>\n",
" <td>-2.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.110</td>\n",
" <td>0.02</td>\n",
" <td>-0.22</td>\n",
" <td>0.61</td>\n",
" <td>0.80</td>\n",
" <td>-0.24</td>\n",
" <td>-0.24</td>\n",
" <td>-2.360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-0.110</td>\n",
" <td>-0.30</td>\n",
" <td>-0.25</td>\n",
" <td>0.48</td>\n",
" <td>1.17</td>\n",
" <td>0.14</td>\n",
" <td>0.14</td>\n",
" <td>-1.920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.210</td>\n",
" <td>-0.25</td>\n",
" <td>0.02</td>\n",
" <td>1.15</td>\n",
" <td>1.61</td>\n",
" <td>0.15</td>\n",
" <td>0.15</td>\n",
" <td>-1.480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>-0.270</td>\n",
" <td>-0.02</td>\n",
" <td>0.43</td>\n",
" <td>0.85</td>\n",
" <td>1.73</td>\n",
" <td>0.44</td>\n",
" <td>0.44</td>\n",
" <td>-1.490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>-0.240</td>\n",
" <td>0.16</td>\n",
" <td>0.11</td>\n",
" <td>0.69</td>\n",
" <td>1.47</td>\n",
" <td>-0.38</td>\n",
" <td>-0.38</td>\n",
" <td>-1.380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.000</td>\n",
" <td>0.01</td>\n",
" <td>0.08</td>\n",
" <td>0.98</td>\n",
" <td>1.27</td>\n",
" <td>0.02</td>\n",
" <td>0.02</td>\n",
" <td>-1.450</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.370</td>\n",
" <td>-0.27</td>\n",
" <td>-0.45</td>\n",
" <td>1.04</td>\n",
" <td>1.82</td>\n",
" <td>-1.23</td>\n",
" <td>-1.23</td>\n",
" <td>-1.320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.250</td>\n",
" <td>-0.23</td>\n",
" <td>-1.01</td>\n",
" <td>0.94</td>\n",
" <td>2.64</td>\n",
" <td>-1.35</td>\n",
" <td>-1.35</td>\n",
" <td>-1.260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.010</td>\n",
" <td>-0.28</td>\n",
" <td>-0.67</td>\n",
" <td>0.99</td>\n",
" <td>2.50</td>\n",
" <td>-1.39</td>\n",
" <td>-1.39</td>\n",
" <td>-1.310</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>-0.380</td>\n",
" <td>0.03</td>\n",
" <td>-0.92</td>\n",
" <td>0.89</td>\n",
" <td>2.37</td>\n",
" <td>-1.30</td>\n",
" <td>-1.30</td>\n",
" <td>-0.960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>-0.455</td>\n",
" <td>-0.45</td>\n",
" <td>-1.32</td>\n",
" <td>0.45</td>\n",
" <td>1.25</td>\n",
" <td>-1.54</td>\n",
" <td>-1.54</td>\n",
" <td>-0.950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>-0.320</td>\n",
" <td>-0.53</td>\n",
" <td>-1.29</td>\n",
" <td>0.42</td>\n",
" <td>1.45</td>\n",
" <td>-1.73</td>\n",
" <td>-1.73</td>\n",
" <td>-1.110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.380</td>\n",
" <td>-0.43</td>\n",
" <td>-1.35</td>\n",
" <td>0.65</td>\n",
" <td>1.42</td>\n",
" <td>-1.98</td>\n",
" <td>-1.98</td>\n",
" <td>-0.720</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.825</td>\n",
" <td>-0.68</td>\n",
" <td>-1.24</td>\n",
" <td>1.25</td>\n",
" <td>1.49</td>\n",
" <td>-2.34</td>\n",
" <td>-2.34</td>\n",
" <td>0.010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.430</td>\n",
" <td>-0.42</td>\n",
" <td>-0.54</td>\n",
" <td>1.20</td>\n",
" <td>2.23</td>\n",
" <td>-1.90</td>\n",
" <td>-1.90</td>\n",
" <td>0.160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.070</td>\n",
" <td>-0.28</td>\n",
" <td>1.38</td>\n",
" <td>1.28</td>\n",
" <td>2.17</td>\n",
" <td>-1.46</td>\n",
" <td>-1.46</td>\n",
" <td>0.430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>-0.390</td>\n",
" <td>-0.33</td>\n",
" <td>2.15</td>\n",
" <td>1.74</td>\n",
" <td>2.25</td>\n",
" <td>-1.47</td>\n",
" <td>-1.47</td>\n",
" <td>0.670</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>-4.650</td>\n",
" <td>-0.74</td>\n",
" <td>3.01</td>\n",
" <td>1.97</td>\n",
" <td>0.99</td>\n",
" <td>-1.36</td>\n",
" <td>-1.36</td>\n",
" <td>0.730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>-4.340</td>\n",
" <td>-0.69</td>\n",
" <td>1.66</td>\n",
" <td>1.84</td>\n",
" <td>0.92</td>\n",
" <td>-1.43</td>\n",
" <td>-1.43</td>\n",
" <td>0.430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>-4.130</td>\n",
" <td>0.15</td>\n",
" <td>0.68</td>\n",
" <td>1.22</td>\n",
" <td>0.95</td>\n",
" <td>-1.30</td>\n",
" <td>-1.30</td>\n",
" <td>-0.170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>-2.890</td>\n",
" <td>0.15</td>\n",
" <td>0.63</td>\n",
" <td>1.59</td>\n",
" <td>1.04</td>\n",
" <td>-1.24</td>\n",
" <td>-1.24</td>\n",
" <td>-0.610</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>-2.970</td>\n",
" <td>0.07</td>\n",
" <td>0.16</td>\n",
" <td>2.03</td>\n",
" <td>0.86</td>\n",
" <td>-1.29</td>\n",
" <td>-1.29</td>\n",
" <td>-1.185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-3.060</td>\n",
" <td>-0.10</td>\n",
" <td>-0.34</td>\n",
" <td>2.15</td>\n",
" <td>0.49</td>\n",
" <td>-0.94</td>\n",
" <td>-0.94</td>\n",
" <td>-1.340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.36</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.47</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.09</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>568</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.09</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.35</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <th>571</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>-0.32</td>\n",
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" <tr>\n",
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" <th>573</th>\n",
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" <th>577</th>\n",
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" <th>578</th>\n",
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" <tr>\n",
" <th>579</th>\n",
" <td>NaN</td>\n",
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" <tr>\n",
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" <tr>\n",
" <th>581</th>\n",
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" <tr>\n",
" <th>582</th>\n",
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" <td>-0.23</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>583</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.30</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>584</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.41</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>585</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.11</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>586</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.14</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>587</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.07</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>588</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.04</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>589</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.03</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>590</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.04</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>591</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.15</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>592</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.07</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>593</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.18</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>594</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-0.11</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>595 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7\n",
"0 -0.420 0.33 0.14 -0.11 0.23 0.08 0.08 -1.250\n",
"1 -0.090 0.17 0.59 -0.39 0.83 0.08 0.08 -1.370\n",
"2 0.560 0.16 0.65 -0.07 0.78 0.01 0.01 -1.410\n",
"3 0.770 0.24 0.08 0.37 0.86 -0.15 -0.15 -1.320\n",
"4 0.680 0.14 0.40 0.36 1.32 -0.29 -0.29 -1.560\n",
"5 0.350 0.35 0.30 0.64 1.55 -0.30 -0.30 -1.750\n",
"6 0.220 0.29 0.03 0.67 1.42 -0.51 -0.51 -2.000\n",
"7 0.110 0.02 -0.22 0.61 0.80 -0.24 -0.24 -2.360\n",
"8 -0.110 -0.30 -0.25 0.48 1.17 0.14 0.14 -1.920\n",
"9 0.210 -0.25 0.02 1.15 1.61 0.15 0.15 -1.480\n",
"10 -0.270 -0.02 0.43 0.85 1.73 0.44 0.44 -1.490\n",
"11 -0.240 0.16 0.11 0.69 1.47 -0.38 -0.38 -1.380\n",
"12 0.000 0.01 0.08 0.98 1.27 0.02 0.02 -1.450\n",
"13 0.370 -0.27 -0.45 1.04 1.82 -1.23 -1.23 -1.320\n",
"14 0.250 -0.23 -1.01 0.94 2.64 -1.35 -1.35 -1.260\n",
"15 0.010 -0.28 -0.67 0.99 2.50 -1.39 -1.39 -1.310\n",
"16 -0.380 0.03 -0.92 0.89 2.37 -1.30 -1.30 -0.960\n",
"17 -0.455 -0.45 -1.32 0.45 1.25 -1.54 -1.54 -0.950\n",
"18 -0.320 -0.53 -1.29 0.42 1.45 -1.73 -1.73 -1.110\n",
"19 0.380 -0.43 -1.35 0.65 1.42 -1.98 -1.98 -0.720\n",
"20 0.825 -0.68 -1.24 1.25 1.49 -2.34 -2.34 0.010\n",
"21 0.430 -0.42 -0.54 1.20 2.23 -1.90 -1.90 0.160\n",
"22 0.070 -0.28 1.38 1.28 2.17 -1.46 -1.46 0.430\n",
"23 -0.390 -0.33 2.15 1.74 2.25 -1.47 -1.47 0.670\n",
"24 -4.650 -0.74 3.01 1.97 0.99 -1.36 -1.36 0.730\n",
"25 -4.340 -0.69 1.66 1.84 0.92 -1.43 -1.43 0.430\n",
"26 -4.130 0.15 0.68 1.22 0.95 -1.30 -1.30 -0.170\n",
"27 -2.890 0.15 0.63 1.59 1.04 -1.24 -1.24 -0.610\n",
"28 -2.970 0.07 0.16 2.03 0.86 -1.29 -1.29 -1.185\n",
"29 -3.060 -0.10 -0.34 2.15 0.49 -0.94 -0.94 -1.340\n",
".. ... ... ... ... ... ... ... ...\n",
"565 NaN NaN NaN NaN -0.36 NaN NaN NaN \n",
"566 NaN NaN NaN NaN -0.47 NaN NaN NaN \n",
"567 NaN NaN NaN NaN 0.09 NaN NaN NaN \n",
"568 NaN NaN NaN NaN -0.09 NaN NaN NaN \n",
"569 NaN NaN NaN NaN -0.46 NaN NaN NaN \n",
"570 NaN NaN NaN NaN -0.35 NaN NaN NaN \n",
"571 NaN NaN NaN NaN -0.32 NaN NaN NaN \n",
"572 NaN NaN NaN NaN -0.39 NaN NaN NaN \n",
"573 NaN NaN NaN NaN -0.50 NaN NaN NaN \n",
"574 NaN NaN NaN NaN -0.18 NaN NaN NaN \n",
"575 NaN NaN NaN NaN -0.55 NaN NaN NaN \n",
"576 NaN NaN NaN NaN -0.44 NaN NaN NaN \n",
"577 NaN NaN NaN NaN -0.41 NaN NaN NaN \n",
"578 NaN NaN NaN NaN -0.48 NaN NaN NaN \n",
"579 NaN NaN NaN NaN -0.59 NaN NaN NaN \n",
"580 NaN NaN NaN NaN -0.37 NaN NaN NaN \n",
"581 NaN NaN NaN NaN -0.26 NaN NaN NaN \n",
"582 NaN NaN NaN NaN -0.23 NaN NaN NaN \n",
"583 NaN NaN NaN NaN -0.30 NaN NaN NaN \n",
"584 NaN NaN NaN NaN -0.41 NaN NaN NaN \n",
"585 NaN NaN NaN NaN 0.11 NaN NaN NaN \n",
"586 NaN NaN NaN NaN 0.14 NaN NaN NaN \n",
"587 NaN NaN NaN NaN 0.07 NaN NaN NaN \n",
"588 NaN NaN NaN NaN -0.04 NaN NaN NaN \n",
"589 NaN NaN NaN NaN 0.03 NaN NaN NaN \n",
"590 NaN NaN NaN NaN -0.04 NaN NaN NaN \n",
"591 NaN NaN NaN NaN -0.15 NaN NaN NaN \n",
"592 NaN NaN NaN NaN -0.07 NaN NaN NaN \n",
"593 NaN NaN NaN NaN -0.18 NaN NaN NaN \n",
"594 NaN NaN NaN NaN -0.11 NaN NaN NaN \n",
"\n",
"[595 rows x 8 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# You may need to install htmllib5,lxml, and BeautifulSoup4. In your terminal/command prompt run:\n",
"\n",
"# conda install lxml\n",
"# conda install html5lib\n",
"# conda install BeautifulSoup4\n",
"# Then restart Jupyter Notebook. (or use pip install if you aren't using the Anaconda Distribution)\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os.path\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime, timedelta\n",
"from dateutil.parser import parse\n",
"from dateutil.relativedelta import relativedelta\n",
"from pandas.tseries.offsets import *\n",
"from pylab import text\n",
"from mpl_toolkits.axes_grid.anchored_artists import AnchoredText\n",
"import quandl\n",
"\n",
"\n",
"# Quandl API key\n",
"quandl.ApiConfig.api_key = \"UsYsv7dKGxHHQ5oURP4B\"\n",
"\n",
"# Some formatting\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.set_option('display.max_seq_items', 2)\n",
"\n",
"# Only pull fresh PDUFA data\n",
"three_weeks_ago = relativedelta(weeks=3)\n",
"one_week_ahead = relativedelta(weeks=1)\n",
"one_hour_ago = datetime.now() - timedelta(hours=1)\n",
"if os.path.exists(\"history.csv\"):\n",
" filetime = datetime.fromtimestamp(os.path.getctime(\"history.csv\"))\n",
" if filetime < one_hour_ago:\n",
" histdata = pd.read_html(\"https://www.biopharmcatalyst.com/calendars/historical-catalyst-calendar\")\n",
" histdata[0].to_csv('history.csv',index=False)\n",
"else:\n",
" histdata = pd.read_html(\"https://www.biopharmcatalyst.com/calendars/historical-catalyst-calendar\")\n",
" histdata[0].to_csv('history.csv',index=False)\n",
"\n",
"# Create dataframe\n",
"df = pd.read_csv('history.csv').set_index('Ticker')\n",
"df.index.name=None\n",
"df[[\"Date\",\"Catalyst\"]] = df.Catalyst.str.extract('(?P<Date>[0-9]{2}\\/[0-9]{2}\\/[0-9]{4})(?P<Catalyst>.*)', expand=True)\n",
"df['Date'] = pd.to_datetime(df['Date'])\n",
"df[\"Past\"] = df[\"Date\"] - DateOffset(weeks=5)\n",
"df[\"Future\"] = df[\"Date\"] + DateOffset(weeks=2)\n",
"\n",
"df\n",
"# Set stock ticker\n",
"stockpick=\"HALO\"\n",
"dataset=str(f\"WIKI/{stockpick}\")\n",
"# Set variables for plot creation\n",
"length = len(df.loc[stockpick].index)\n",
"count = 0\n",
"allplots=[]\n",
"e = []\n",
"# Combine all data together into list of dataframes, iterate through each part of the list, plot each frame.\n",
"while (count < length):\n",
" pasttime= df.loc[stockpick].iloc[count][\"Past\"]\n",
" futuretime= df.loc[stockpick].iloc[count][\"Future\"]\n",
" pdufa=df.loc[stockpick].iloc[count][\"Date\"]\n",
" annotate = df.loc[stockpick].iloc[count][\"Date\"] + timedelta(days=1)\n",
" stage = df.loc[stockpick].iloc[count][\"Stage\"]\n",
" catalyst = df.loc[stockpick].iloc[count][\"Catalyst\"]\n",
" drug = df.loc[stockpick].iloc[count][\"Drug\"]\n",
" d = []\n",
" #Annotation\n",
" #Get quandl data\n",
" mydata = quandl.get(dataset,start_date=pasttime,end_date=futuretime)\n",
" allplots.append(mydata)\n",
" i=0\n",
"# print(len(allplots[count].index))\n",
" total = len(allplots[count].index) - 1\n",
" while (i < total):\n",
" n = i + 1\n",
" while (n < total): # see 14 - 8 = 1.0 !!!\n",
" rangevalue=(allplots[count].iloc[n][\"Close\"] - allplots[count].iloc[i][\"Close\"])\n",
" d.append(rangevalue)\n",
" range = f\"{n} - {i}\"\n",
" n=n + 1\n",
" i = i + 1\n",
" e.append(d)\n",
" count = count + 1\n",
"#need to add row names to see what the ranges are specifically.\n",
"finaldf=pd.DataFrame(e).transpose()\n",
"finaldf\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas._libs.tslibs.timestamps.Timestamp'>\n",
"<class 'pandas._libs.tslibs.timestamps.Timestamp'>\n",
"<class 'pandas._libs.tslibs.timestamps.Timestamp'>\n",
"<class 'pandas._libs.tslibs.timestamps.Timestamp'>\n",
"<class 'pandas._libs.tslibs.timestamps.Timestamp'>\n",
"<class 'pandas._libs.tslibs.timestamps.Timestamp'>\n",
"<class 'pandas._libs.tslibs.timestamps.Timestamp'>\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Ex-Dividend</th>\n",
" <th>Split Ratio</th>\n",
" <th>Adj. Open</th>\n",
" <th>Adj. High</th>\n",
" <th>Adj. Low</th>\n",
" <th>Adj. Close</th>\n",
" <th>Adj. Volume</th>\n",
" <th>PDUFA</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012-07-11</td>\n",
" <td>9.06</td>\n",
" <td>9.0600</td>\n",
" <td>8.7600</td>\n",
" <td>8.840</td>\n",
" <td>697000.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>9.06</td>\n",
" <td>9.0600</td>\n",
" <td>8.7600</td>\n",
" <td>8.840</td>\n",
" <td>697000.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2012-07-12</td>\n",
" <td>8.80</td>\n",
" <td>9.1600</td>\n",
" <td>8.5600</td>\n",
" <td>9.160</td>\n",
" <td>867200.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>8.80</td>\n",
" <td>9.1600</td>\n",
" <td>8.5600</td>\n",
" <td>9.160</td>\n",
" <td>867200.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2012-07-13</td>\n",
" <td>8.86</td>\n",
" <td>9.0200</td>\n",
" <td>8.6300</td>\n",
" <td>8.680</td>\n",
" <td>858800.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>8.86</td>\n",
" <td>9.0200</td>\n",
" <td>8.6300</td>\n",
" <td>8.680</td>\n",
" <td>858800.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2012-07-16</td>\n",
" <td>8.69</td>\n",
" <td>8.8100</td>\n",
" <td>8.4200</td>\n",
" <td>8.710</td>\n",
" <td>459200.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>8.69</td>\n",
" <td>8.8100</td>\n",
" <td>8.4200</td>\n",
" <td>8.710</td>\n",
" <td>459200.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2012-07-17</td>\n",
" <td>8.79</td>\n",
" <td>9.0100</td>\n",
" <td>8.6800</td>\n",
" <td>8.950</td>\n",
" <td>859100.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>8.79</td>\n",
" <td>9.0100</td>\n",
" <td>8.6800</td>\n",
" <td>8.950</td>\n",
" <td>859100.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2012-07-18</td>\n",
" <td>8.92</td>\n",
" <td>9.3450</td>\n",
" <td>8.9200</td>\n",
" <td>9.320</td>\n",
" <td>634700.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>8.92</td>\n",
" <td>9.3450</td>\n",
" <td>8.9200</td>\n",
" <td>9.320</td>\n",
" <td>634700.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2012-07-19</td>\n",
" <td>9.40</td>\n",
" <td>9.5400</td>\n",
" <td>9.1800</td>\n",
" <td>9.200</td>\n",
" <td>441500.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>9.40</td>\n",
" <td>9.5400</td>\n",
" <td>9.1800</td>\n",
" <td>9.200</td>\n",
" <td>441500.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2012-07-20</td>\n",
" <td>9.15</td>\n",
" <td>9.1801</td>\n",
" <td>8.8901</td>\n",
" <td>8.960</td>\n",
" <td>627600.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>9.15</td>\n",
" <td>9.1801</td>\n",
" <td>8.8901</td>\n",
" <td>8.960</td>\n",
" <td>627600.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2012-07-23</td>\n",
" <td>8.72</td>\n",
" <td>8.7300</td>\n",
" <td>8.4000</td>\n",
" <td>8.570</td>\n",
" <td>692000.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>8.72</td>\n",
" <td>8.7300</td>\n",
" <td>8.4000</td>\n",
" <td>8.570</td>\n",
" <td>692000.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2012-07-24</td>\n",
" <td>8.64</td>\n",
" <td>8.6400</td>\n",
" <td>8.4000</td>\n",
" <td>8.495</td>\n",
" <td>553600.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>8.64</td>\n",
" <td>8.6400</td>\n",
" <td>8.4000</td>\n",
" <td>8.495</td>\n",
" <td>553600.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>2012-07-25</td>\n",
" <td>8.60</td>\n",
" <td>8.8600</td>\n",
" <td>8.5206</td>\n",
" <td>8.630</td>\n",
" <td>302700.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>8.60</td>\n",
" <td>8.8600</td>\n",
" <td>8.5206</td>\n",
" <td>8.630</td>\n",
" <td>302700.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2012-07-26</td>\n",
" <td>8.80</td>\n",
" <td>9.3800</td>\n",
" <td>8.8000</td>\n",
" <td>9.330</td>\n",
" <td>743000.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>8.80</td>\n",
" <td>9.3800</td>\n",
" <td>8.8000</td>\n",
" <td>9.330</td>\n",
" <td>743000.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>2012-07-27</td>\n",
" <td>9.35</td>\n",
" <td>9.9200</td>\n",
" <td>9.2750</td>\n",
" <td>9.775</td>\n",
" <td>737400.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>9.35</td>\n",
" <td>9.9200</td>\n",
" <td>9.2750</td>\n",
" <td>9.775</td>\n",
" <td>737400.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>2012-07-30</td>\n",
" <td>9.77</td>\n",
" <td>9.7900</td>\n",
" <td>9.2400</td>\n",
" <td>9.380</td>\n",
" <td>397700.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>9.77</td>\n",
" <td>9.7900</td>\n",
" <td>9.2400</td>\n",
" <td>9.380</td>\n",
" <td>397700.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>2012-07-31</td>\n",
" <td>9.32</td>\n",
" <td>9.4500</td>\n",
" <td>8.9900</td>\n",
" <td>9.020</td>\n",
" <td>451100.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>9.32</td>\n",
" <td>9.4500</td>\n",
" <td>8.9900</td>\n",
" <td>9.020</td>\n",
" <td>451100.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>2012-08-01</td>\n",
" <td>9.10</td>\n",
" <td>9.1000</td>\n",
" <td>8.5500</td>\n",
" <td>8.560</td>\n",
" <td>1390500.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>9.10</td>\n",
" <td>9.1000</td>\n",
" <td>8.5500</td>\n",
" <td>8.560</td>\n",
" <td>1390500.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>2012-08-02</td>\n",
" <td>3.91</td>\n",
" <td>4.7900</td>\n",
" <td>3.8600</td>\n",
" <td>4.300</td>\n",
" <td>10958600.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.91</td>\n",
" <td>4.7900</td>\n",
" <td>3.8600</td>\n",
" <td>4.300</td>\n",
" <td>10958600.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>2012-08-03</td>\n",
" <td>4.57</td>\n",
" <td>5.0500</td>\n",
" <td>4.5000</td>\n",
" <td>4.610</td>\n",
" <td>3767900.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>4.57</td>\n",
" <td>5.0500</td>\n",
" <td>4.5000</td>\n",
" <td>4.610</td>\n",
" <td>3767900.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>2012-08-06</td>\n",
" <td>4.69</td>\n",
" <td>5.0100</td>\n",
" <td>4.6500</td>\n",
" <td>4.820</td>\n",
" <td>2346800.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>4.69</td>\n",
" <td>5.0100</td>\n",
" <td>4.6500</td>\n",
" <td>4.820</td>\n",
" <td>2346800.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2012-08-07</td>\n",
" <td>5.02</td>\n",
" <td>6.1280</td>\n",
" <td>4.8500</td>\n",
" <td>6.060</td>\n",
" <td>4417200.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>5.02</td>\n",
" <td>6.1280</td>\n",
" <td>4.8500</td>\n",
" <td>6.060</td>\n",
" <td>4417200.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2012-08-08</td>\n",
" <td>5.98</td>\n",
" <td>6.0500</td>\n",
" <td>5.7900</td>\n",
" <td>5.980</td>\n",
" <td>2933200.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>5.98</td>\n",
" <td>6.0500</td>\n",
" <td>5.7900</td>\n",
" <td>5.980</td>\n",
" <td>2933200.0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volume Ex-Dividend \\\n",
"0 2012-07-11 9.06 9.0600 8.7600 8.840 697000.0 0.0 \n",
"1 2012-07-12 8.80 9.1600 8.5600 9.160 867200.0 0.0 \n",
"2 2012-07-13 8.86 9.0200 8.6300 8.680 858800.0 0.0 \n",
"3 2012-07-16 8.69 8.8100 8.4200 8.710 459200.0 0.0 \n",
"4 2012-07-17 8.79 9.0100 8.6800 8.950 859100.0 0.0 \n",
"5 2012-07-18 8.92 9.3450 8.9200 9.320 634700.0 0.0 \n",
"6 2012-07-19 9.40 9.5400 9.1800 9.200 441500.0 0.0 \n",
"7 2012-07-20 9.15 9.1801 8.8901 8.960 627600.0 0.0 \n",
"8 2012-07-23 8.72 8.7300 8.4000 8.570 692000.0 0.0 \n",
"9 2012-07-24 8.64 8.6400 8.4000 8.495 553600.0 0.0 \n",
"10 2012-07-25 8.60 8.8600 8.5206 8.630 302700.0 0.0 \n",
"11 2012-07-26 8.80 9.3800 8.8000 9.330 743000.0 0.0 \n",
"12 2012-07-27 9.35 9.9200 9.2750 9.775 737400.0 0.0 \n",
"13 2012-07-30 9.77 9.7900 9.2400 9.380 397700.0 0.0 \n",
"14 2012-07-31 9.32 9.4500 8.9900 9.020 451100.0 0.0 \n",
"15 2012-08-01 9.10 9.1000 8.5500 8.560 1390500.0 0.0 \n",
"16 2012-08-02 3.91 4.7900 3.8600 4.300 10958600.0 0.0 \n",
"17 2012-08-03 4.57 5.0500 4.5000 4.610 3767900.0 0.0 \n",
"18 2012-08-06 4.69 5.0100 4.6500 4.820 2346800.0 0.0 \n",
"19 2012-08-07 5.02 6.1280 4.8500 6.060 4417200.0 0.0 \n",
"20 2012-08-08 5.98 6.0500 5.7900 5.980 2933200.0 0.0 \n",
"\n",
" Split Ratio Adj. Open Adj. High Adj. Low Adj. Close Adj. Volume \\\n",
"0 1.0 9.06 9.0600 8.7600 8.840 697000.0 \n",
"1 1.0 8.80 9.1600 8.5600 9.160 867200.0 \n",
"2 1.0 8.86 9.0200 8.6300 8.680 858800.0 \n",
"3 1.0 8.69 8.8100 8.4200 8.710 459200.0 \n",
"4 1.0 8.79 9.0100 8.6800 8.950 859100.0 \n",
"5 1.0 8.92 9.3450 8.9200 9.320 634700.0 \n",
"6 1.0 9.40 9.5400 9.1800 9.200 441500.0 \n",
"7 1.0 9.15 9.1801 8.8901 8.960 627600.0 \n",
"8 1.0 8.72 8.7300 8.4000 8.570 692000.0 \n",
"9 1.0 8.64 8.6400 8.4000 8.495 553600.0 \n",
"10 1.0 8.60 8.8600 8.5206 8.630 302700.0 \n",
"11 1.0 8.80 9.3800 8.8000 9.330 743000.0 \n",
"12 1.0 9.35 9.9200 9.2750 9.775 737400.0 \n",
"13 1.0 9.77 9.7900 9.2400 9.380 397700.0 \n",
"14 1.0 9.32 9.4500 8.9900 9.020 451100.0 \n",
"15 1.0 9.10 9.1000 8.5500 8.560 1390500.0 \n",
"16 1.0 3.91 4.7900 3.8600 4.300 10958600.0 \n",
"17 1.0 4.57 5.0500 4.5000 4.610 3767900.0 \n",
"18 1.0 4.69 5.0100 4.6500 4.820 2346800.0 \n",
"19 1.0 5.02 6.1280 4.8500 6.060 4417200.0 \n",
"20 1.0 5.98 6.0500 5.7900 5.980 2933200.0 \n",
"\n",
" PDUFA \n",
"0 False \n",
"1 False \n",
"2 False \n",
"3 False \n",
"4 False \n",
"5 False \n",
"6 False \n",
"7 False \n",
"8 False \n",
"9 False \n",
"10 False \n",
"11 False \n",
"12 False \n",
"13 False \n",
"14 False \n",
"15 True \n",
"16 False \n",
"17 False \n",
"18 False \n",
"19 False \n",
"20 False "
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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Lj3/84/ziF7/IFVdc0fX1LFy4MBdccMHQ8uOPPz6h49JtP73G70jr1q3LypUrc9hhh+Wv/uqv8pOf/GRoLl0AAAAAXphf2iD3ySefzAknnDD0QUrLly/P2WefnSRZvHhx3v3ud+e1r31ttt566+dsd9BBB+VNb3pTDj744Hz4wx/OrrvumiOOOCJHHXVUBgYGMm/evKH5Qd/znvfkwgsvzG//9m8PfdhVkhx22GFZvnz50Iedve9978sHPvCBLFiwIM8+++yYfd9ll11yySWXZNGiRZkzZ87Q3ZzbbLNNPvvZz+aYY44ZmqLg1FNPHfU1/c3f/E1mzZqVuXPnZtttt80b3/jGcR/DnXbaKQsWLMisWbPy3ve+NzNnzszb3va2obfDH3jggUmSBx54IBdffHE+8YlP5LWvfW1e97rX5eMf//i4n2e40Y7VIYcckj/6oz/KvHnz8ta3vjUDAwNj7u+YY47Jvffe+5z5bufOnZsDDzwwBxxwQE466aRxBcKb69jvv//++dznPpc5c+ZkzZo1Oe20056z/hWveEX+8i//Mocddljmzp2b+fPn5+ijjx61/eyzz85v/dZv5Xd/93czf/78rq/nz//8z/P4448P9e9b3/rWhI5Lt/30Gr8jPfvss3n729+e2bNn58ADD8yZZ56ZHXbYYcznBAAAAOD5arS3cCdJVS1J8uYkj7bWZnXazk7yJ0n+d6fsg621r3fZdkWSnyV5Nsna1trYyVySuXPntquvvjpJMmPGjFHfZg788quq/OhHP5rsbgAAAABscjNmzLh9vLnoaMbz6UOXJLkgyd+PaP9ka+38cWx/WGvtsbHLAAAAAADoZsypFVprNyTp/ulWAAAAAABsdhszR+4ZVbWsqpZU1Y49alqSq6vq9qo6ZbSdVdUpVbW0qpZu+LAxAAAAAAAmHuRemGSvJPOSPJzkEz3qFrTW5id5Y5LTq+p1vXbYWruotTbQWhuYPn36BLsFAAAAAPDiM6Egt7X2SGvt2dbauiR/l+SgHnWrO98fTfKVXnWjdnCrrfLMM89MpJvAL4FnnnkmW221MW8OAAAAAHjxm1B6UlWvGLb4B0nu6VKzXVW9bMPjJAu71Y1lzpw5Oe+884S58CL0zDPP5LzzzsucOXMmuysAAAAAfW3aWAVVdWmSQ5PsXFWrkixOcmhVzcv6OXBXJPnTTu2uSS5urR2Z5OVJvlJVG57nC621f3mhHVyyZElOOumkfOQjH8m6dete6OZAH9tqq60yZ86cLFmyZLK7AgAAANDXqrU22X14nrlz57arr756srsBAAAAALBRZsyYcXtrbWBj92NiSgAAAACAPifIBQAAAADoc4JcAAAAAIA+J8gFAAAAAOhzglwAAAAAgD4nyAUAAAAA6HOCXAAAAACAPifIBQAAAADoc4JcAAAAAIA+J8gFAAAAAOhzglwAAAAAgD4nyAUAAAAA6HOCXAAAAACAPjdmkFtVS6rq0aq6Z1jb2VX1w6oa7Hwd2WPbI6rq/qp6sKrevyk7DgAAAAAwVVRrbfSCqtcleTLJ37fWZnXazk7yZGvt/FG22zrJA0kOT7IqyW1JFrXWlo/VqRkzZrRHHnlkvK8BAAAAAKBf3d5aG9jYnUwbq6C1dkNV7TGBfR+U5MHW2veTpKouS3J0kjGD3Je//OW56667JvCUAAAAAAD9Y8aMGZtkPxszR+4ZVbWsM/XCjl3WvzLJymHLqzptXVXVKVW1tKqWrlmzZiO6BQAAAADw4jLRIPfCJHslmZfk4SSf6FJTXdp6zuPQWruotTbQWhuYPn36BLsFAAAAAPDiM6Egt7X2SGvt2dbauiR/l/XTKIy0KsnMYcu7JVk9kecDAAAAAJjKJhTkVtUrhi3+QZJ7upTdlmTvqtqzql6S5LgkX53I8wEAAAAATGVjfthZVV2a5NAkO1fVqiSLkxxaVfOyfqqEFUn+tFO7a5KLW2tHttbWVtUZSf41ydZJlrTW7t0srwIAAAAA4EVszCC3tbaoS/NnetSuTnLksOWvJ/n6hHsHAAAAAMCEP+wMAAAAAIAtRJALAAAAANDnBLkAAAAAAH1OkAsAAAAA0OcEuQAAAAAAfU6QCwAAAADQ5wS5AAAAAAB9TpALAAAAANDnBLkAAAAAAH1OkAsAAAAA0OcEuQAAAAAAfU6QCwAAAADQ5wS5AAAAAAB9TpALAAAAANDnxgxyq2pJVT1aVfd0WfeeqmpVtXOPbZ+tqsHO11c3RYcBAAAAAKaaaeOouSTJBUn+fnhjVc1McniSH4yy7dOttXkT7h0AAAAAAGPfkdtauyHJmi6rPpnkfUnapu4UAAAAAAD/14TmyK2qo5L8sLV21xil21TV0qq6pap+f4x9ntKpXbpmTbfcGAAAAABgahrP1ArPUVUvTfKhJAvHUf4brbXVVfX/JPlmVd3dWvtet8LW2kVJLkqSuXPnussXAAAAAKBjInfk7pVkzyR3VdWKJLsluaOqZowsbK2t7nz/fpLrkhw44Z4CAAAAAExRLzjIba3d3Vr79dbaHq21PZKsSjK/tfaj4XVVtWNV/Urn8c5JFiRZvgn6DAAAAAAwpYwZ5FbVpUluTrJvVa2qqj8epXagqi7uLO6fZGlV3ZXkW0nOba0JcgEAAAAAXqAx58htrS0aY/0ewx4vTXJy5/H/SjJ7I/sHAAAAADDlTWSOXAAAAAAAtiBBLgAAAABAnxPkAgAAAAD0OUEuAAAAAECfE+QCAAAAAPQ5QS4AAAAAQJ8T5AIAAAAA9DlBLgAAAABAnxPkAgAAAAD0OUEuAAAAAECfE+QCAAAAAPQ5QS4AAAAAQJ8T5AIAAAAA9DlBLgAAAABAnxtXkFtVS6rq0aq6p8u691RVq6qde2x7QlV9t/N1wsZ2GAAAAABgqhnvHbmXJDliZGNVzUxyeJIfdNuoqqYnWZzkNUkOSrK4qnacUE8BAAAAAKaocQW5rbUbkqzpsuqTSd6XpPXY9PeSXNNaW9NaezzJNekSCAMAAAAA0NuE58itqqOS/LC1dtcoZa9MsnLY8qpOW7f9nVJVS6tq6Zo13TJjAAAAAICpaUJBblW9NMmHknxkrNIubV3v3m2tXdRaG2itDUyfPn0i3QIAAAAAeFGa6B25eyXZM8ldVbUiyW5J7qiqGSPqViWZOWx5tySrJ/icAAAAAABT0oSC3Nba3a21X2+t7dFa2yPrA9v5rbUfjSj91yQLq2rHzoecLey0AQAAAAAwTuMKcqvq0iQ3J9m3qlZV1R+PUjtQVRcnSWttTZKPJbmt83VOpw0AAAAAgHGaNp6i1tqiMdbvMezx0iQnD1tekmTJBPsHAAAAADDlTXSOXAAAAAAAthBBLgAAAABAnxPkAgAAAAD0OUEuAAAAAECfE+QCAAAAAPQ5QS4AAAAAQJ8T5AIAAAAA9DlBLgAAAABAnxPkAgAAAAD0OUEuAAAAAECfE+QCAAAAAPQ5QS4AAAAAQJ8T5AIAAAAA9Lkxg9yqWlJVj1bVPcPaPlZVy6pqsKqurqpde2z7bKdmsKq+uik7DgAAAAAwVYznjtxLkhwxou281tqc1tq8JP8zyUd6bPt0a21e5+uojegnAAAAAMCUNWaQ21q7IcmaEW0/Hba4evMB3gAADCdJREFUXZK2ifsFAAAAAEDHhOfIrar/UlUrkxyf3nfkblNVS6vqlqr6/TH2d0qndumaNWtGKwUAAAAAmFImHOS21j7UWpuZ5PNJzuhR9huttYEk/2+Sv6mqvUbZ30WttYHW2sD06dMn2i0AAAAAgBedCQe5w3whyVu7rWitre58/36S65IcuAmeDwAAAABgSplQkFtVew9bPCrJfV1qdqyqX+k83jnJgiTLJ/J8AAAAAABT2bSxCqrq0iSHJtm5qlYlWZzkyKraN8m6JA8lObVTO5Dk1NbayUn2T/Lfq2pd1gfG57bWBLkAAAAAAC/QmEFua21Rl+bP9KhdmuTkzuP/lWT2RvUOAAAAAIBNMkcuAAAAAACbkSAXAAAAAKDPCXIBAAAAAPqcIBcAAAAAoM8JcgEAAAAA+pwgFwAAAACgzwlyAQAAAAD6nCAXAAAAAKDPCXIBAAAAAPqcIBcAAAAAoM8JcgEAAAAA+pwgFwAAAACgzwlyAQAAAAD6nCAXAAAAAKDPjSvIraolVfVoVd0zrO1jVbWsqgar6uqq2rXHtidU1Xc7Xydsqo4DAAAAAEwV470j95IkR4xoO6+1Nqe1Ni/J/0zykZEbVdX0JIuTvCbJQUkWV9WOE+8uAAAAAMDUM64gt7V2Q5I1I9p+OmxxuySty6a/l+Sa1tqa1trjSa7J8wNhAAAAAABGMW1jNq6q/5LkHUmeSHJYl5JXJlk5bHlVp63bvk5JckqS7LbbbhvTLQAAAACAF5WN+rCz1tqHWmszk3w+yRldSqrbZj32dVFrbaC1NjB9+vSN6RYAAAAAwIvKRgW5w3whyVu7tK9KMnPY8m5JVm+i5wQAAAAAmBImHORW1d7DFo9Kcl+Xsn9NsrCqdux8yNnCThsAAAAAAOM0rjlyq+rSJIcm2bmqViVZnOTIqto3ybokDyU5tVM7kOTU1trJrbU1VfWxJLd1dnVOa23N854AAAAAAICeqrWuU9ZOqrlz57arr756srsBAAAAALBRZsyYcXtrbWBj97Op5sgFAAAAAGAzEeQCAAAAAPQ5QS4AAAAAQJ8T5AIAAAAA9DlBLgAAAABAnxPkAgAAAAD0OUEuAAAAAECfE+QCAAAAAPQ5QS4AAAAAQJ8T5AIAAAAA9DlBLgAAAABAnxPkAgAAAAD0OUEuAAAAAECfGzPIraolVfVoVd0zrO28qrqvqpZV1Veqaoce266oqrurarCqlm7KjgMAAAAATBXjuSP3kiRHjGi7Jsms1tqcJA8k+cAo2x/WWpvXWhuYWBcBAAAAAKa2MYPc1toNSdaMaLu6tba2s3hLkt02Q98AAAAAAMimmSP3pCRX9VjXklxdVbdX1Smj7aSqTqmqpVW1dM2aNaOVAgAAAABMKRsV5FbVh5KsTfL5HiULWmvzk7wxyelV9bpe+2qtXdRaG2itDUyfPn1jugUAAAAA8KIy4SC3qk5I8uYkx7fWWrea1trqzvdHk3wlyUETfT4AAAAAgKlqQkFuVR2R5KwkR7XWnupRs11VvWzD4yQLk9wz0Y4CAAAAAExVYwa5VXVpkpuT7FtVq6rqj5NckORlSa6pqsGq+nSndteq+npn05cnubGq7kpya5Kvtdb+ZbO8CgAAAACAF7FpYxW01hZ1af5Mj9rVSY7sPP5+krkb1TsAAAAAADbuw84AAAAAANj8BLkAAAAAAH1OkAsAAAAA0OcEuQAAAAAAfU6QCwAAAADQ5wS5AAAAAAB9TpALAAAAANDnBLkAAAAAAH1OkAsAAAAA0OcEuQAAAAAAfU6QCwAAAADQ5wS5AAAAAAB9rlprk92H56mqnyW5f7L7AV3snOSxye4EdGFs0s+MT/qVsUk/Mz7pV8Ym/crYpJ/t21p72cbuZNqm6MlmcH9rbWCyOwEjVdVSY5N+ZGzSz4xP+pWxST8zPulXxib9ytikn1XV0k2xH1MrAAAAAAD0OUEuAAAAAECf69cg96LJ7gD0YGzSr4xN+pnxSb8yNulnxif9ytikXxmb9LNNMj778sPOAAAAAAD4v/r1jlwAAAAAADoEuQAAAAAAfW7SgtyqOqKq7q+qB6vq/V3W/0pVXd5Z/+2q2mPL95KpqKpmVtW3quo7VXVvVb27S82hVfVEVQ12vj4yGX1l6qmqFVV1d2fcLe2yvqrqU51z57Kqmj8Z/WTqqap9h50TB6vqp1X1n0fUOHeyRVTVkqp6tKruGdY2vaquqarvdr7v2GPbEzo1362qE7Zcr5kqeozP86rqvs7v7q9U1Q49th31OgA2Ro+xeXZV/XDY7+4je2w76t/3sDF6jM3Lh43LFVU12GNb5002m1750ea87pyUOXKrauskDyQ5PMmqJLclWdRaWz6s5l1J5rTWTq2q45L8QWvt2C3eWaacqnpFkle01u6oqpcluT3J748Yn4cmeU9r7c2T1E2mqKpakWSgtfZYj/VHJvn/khyZ5DVJ/ltr7TVbrocw9Hv+h0le01p7aFj7oXHuZAuoqtcleTLJ37fWZnXa/irJmtbauZ2QYcfW2lkjtpueZGmSgSQt668BXt1ae3yLvgBe1HqMz4VJvtlaW1tV/zVJRo7PTt2KjHIdABujx9g8O8mTrbXzR9luzL/vYWN0G5sj1n8iyROttXO6rFsR5002k175UZITs5muOyfrjtyDkjzYWvt+a+2ZJJclOXpEzdFJPtd5/KUkb6iq2oJ9ZIpqrT3cWruj8/hnSb6T5JWT2ysYt6Oz/gKntdZuSbJD55cLbElvSPK94SEubEmttRuSrBnRPPza8nNZf5E90u8luaa1tqZzEX1NkiM2W0eZkrqNz9ba1a21tZ3FW5LstsU7xpTX49w5HuP5+x4mbLSx2cmJ3pbk0i3aKcio+dFmu+6crCD3lUlWDltelecHZUM1nYuaJ5LstEV6Bx21fkqPA5N8u8vq36qqu6rqqqo6YIt2jKmsJbm6qm6vqlO6rB/P+RU2t+PS+2LauZPJ8vLW2sPJ+ovuJL/epcY5lH5wUpKreqwb6zoANoczOtN+LOnx9mDnTibTa5M80lr7bo/1zptsESPyo8123TlZQW63O2tHzvEwnhrYbKrqV5N8Ocl/bq39dMTqO5Ls3lqbm+T/T3Lllu4fU9aC1tr8JG9McnrnbUbDOXcyqarqJUmOSnJFl9XOnfQ751AmVVV9KMnaJJ/vUTLWdQBsahcm2SvJvCQPJ/lElxrnTibToox+N67zJpvdGPlRz826tI157pysIHdVkpnDlndLsrpXTVVNS7J9JvY2D3jBquo/Zf0/ws+31v7HyPWttZ+21p7sPP56kv9UVTtv4W4yBbXWVne+P5rkK1n/VrbhxnN+hc3pjUnuaK09MnKFcyeT7JENU810vj/apcY5lEnT+ZCTNyc5vvX4IJNxXAfAJtVae6S19mxrbV2Sv0v3MefcyaToZEVvSXJ5rxrnTTa3HvnRZrvunKwg97Yke1fVnp07d45L8tURNV9NsuET2/4w6yf/9796bHadOXY+k+Q7rbW/7lEzY8OczVV1UNb/W/rxluslU1FVbdeZQD1VtV2ShUnuGVH21STvqPUOzvpJ/x/ewl1laut5V4RzJ5Ns+LXlCUn+qUvNvyZZWFU7dt4+vLDTBptVVR2R5KwkR7XWnupRM57rANikRnzWwh+k+5gbz9/3sDn8bpL7Wmuruq103mRzGyU/2mzXndM2rssT0/k01jOyvoNbJ1nSWru3qs5JsrS19tWsPxD/UFUPZv2duMdNRl+ZkhYk+aMkd1fVYKftg0l+I0laa5/O+v9cOK2q1iZ5Oslx/qOBLeDlSb7SycGmJflCa+1fqurUZGhsfj3JkUkeTPJUkndOUl+ZgqrqpVn/idV/Oqxt+Ph07mSLqKpLkxyaZOeqWpVkcZJzk3yxqv44yQ+SHNOpHUhyamvt5Nbamqr6WNaHEklyTmvNO8LYpHqMzw8k+ZUk13R+z9/SWju1qnZNcnFr7cj0uA6YhJfAi1SPsXloVc3L+rf7rkjnd/zwsdnr7/tJeAm8SHUbm621z6TL5zI4b7KF9cqPNtt1Z/n7CQAAAACgv03W1AoAAAAAAIyTIBcAAAAAoM8JcgEAAAAA+pwgFwAAAACgzwlyAQAAAAD6nCAXAAAAAKDPCXIBAAAAAPrc/wGFQpgBkvbQggAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1728x2016 with 7 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# You may need to install htmllib5,lxml, and BeautifulSoup4. In your terminal/command prompt run:\n",
"\n",
"# conda install lxml\n",
"# conda install html5lib\n",
"# conda install BeautifulSoup4\n",
"# Then restart Jupyter Notebook. (or use pip install if you aren't using the Anaconda Distribution)\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os.path\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime, timedelta\n",
"from dateutil.parser import parse\n",
"from dateutil.relativedelta import relativedelta\n",
"from pandas.tseries.offsets import *\n",
"from pandas.tseries.holiday import USFederalHolidayCalendar\n",
"from pylab import text\n",
"from mpl_toolkits.axes_grid.anchored_artists import AnchoredText\n",
"import quandl\n",
"\n",
"\n",
"# Quandl API key\n",
"quandl.ApiConfig.api_key = \"UsYsv7dKGxHHQ5oURP4B\"\n",
"\n",
"# Some formatting\n",
"pd.set_option('display.max_colwidth', -1)\n",
"pd.set_option('display.max_seq_items', 2)\n",
"\n",
"# Only pull fresh PDUFA data\n",
"three_weeks_ago = relativedelta(weeks=3)\n",
"one_week_ahead = relativedelta(weeks=1)\n",
"one_hour_ago = datetime.now() - timedelta(hours=1)\n",
"if os.path.exists(\"history.csv\"):\n",
" filetime = datetime.fromtimestamp(os.path.getctime(\"history.csv\"))\n",
" if filetime < one_hour_ago:\n",
" histdata = pd.read_html(\"https://www.biopharmcatalyst.com/calendars/historical-catalyst-calendar\")\n",
" histdata[0].to_csv('history.csv',index=False)\n",
"else:\n",
" histdata = pd.read_html(\"https://www.biopharmcatalyst.com/calendars/historical-catalyst-calendar\")\n",
" histdata[0].to_csv('history.csv',index=False)\n",
"\n",
"# Create dataframe\n",
"us_bd = CustomBusinessDay(calendar=USFederalHolidayCalendar())\n",
"df = pd.read_csv('history.csv').set_index('Ticker')\n",
"df.index.name=None\n",
"df[[\"Date\",\"Catalyst\"]] = df.Catalyst.str.extract('(?P<Date>[0-9]{2}\\/[0-9]{2}\\/[0-9]{4})(?P<Catalyst>.*)', expand=True)\n",
"df['Date'] = pd.to_datetime(df['Date'])\n",
"df['day_of_week'] = df['Date'].dt.day_name()\n",
"df[\"Original_PDUFA\"] = df[\"Date\"]\n",
"df[\"Date\"] = df[\"Date\"].map(lambda x : x + 0*us_bd)\n",
"\n",
"df[\"Past\"] = df[\"Date\"] - DateOffset(weeks=3)\n",
"df[\"Future\"] = df[\"Date\"] + DateOffset(weeks=1)\n",
"# Set stock ticker\n",
"stockpick=\"HALO\"\n",
"dataset=str(f\"WIKI/{stockpick}\")\n",
"# Set variables for plot creation\n",
"length = len(df.loc[stockpick].index)\n",
"count = 0\n",
"fig, axes = plt.subplots(nrows=length, ncols=1,figsize=(24,length * 4))\n",
"#set hspace and wspace to 0 for stacked \"sparklines\" of sorts\n",
"fig.subplots_adjust(hspace=.2, wspace=0)\n",
"allplots=[]\n",
"\n",
"# Combine all data together into list of dataframes, iterate through each part of the list, plot each frame.\n",
"while (count < length):\n",
" pasttime= df.loc[stockpick].iloc[count][\"Past\"]\n",
" futuretime= df.loc[stockpick].iloc[count][\"Future\"]\n",
" pdufa=df.loc[stockpick].iloc[count][\"Date\"]\n",
" annotate = df.loc[stockpick].iloc[count][\"Date\"] + timedelta(days=1)\n",
" stage = df.loc[stockpick].iloc[count][\"Stage\"]\n",
" catalyst = df.loc[stockpick].iloc[count][\"Catalyst\"]\n",
" drug = df.loc[stockpick].iloc[count][\"Drug\"]\n",
" print(type(pdufa))\n",
" #Annotation\n",
" tooltip = f\"stage:{stage}\\n{drug}\"\n",
" at = AnchoredText(tooltip,\n",
" prop=dict(size=10), frameon=True,\n",
" loc=2, \n",
" )\n",
" at.patch.set_boxstyle(\"round,pad=0.2,rounding_size=0.2\")\n",
" axes[count].add_artist(at)\n",
" axes[count].margins(0.0, 0.5)\n",
" #Get quandl data\n",
" mydata = quandl.get(dataset,start_date=pasttime,end_date=futuretime)\n",
" allplots.append(mydata)\n",
" allplots[count].reset_index(inplace=True)\n",
" allplots[count][\"PDUFA\"] = allplots[count][\"Date\"] == pdufa\n",
"# axes[count].annotate('local max', xy=(pdufa, allplots[count][\"Close\"].max()), xytext=(annotate, (allplots[count][\"Close\"].max() - allplots[count][\"Close\"].min()) * 0.9 + allplots[count][\"Close\"].min()),\n",
"# arrowprops=dict(facecolor='black', shrink=0.05, width=1, headwidth=5),)\n",
" # Set y limit for notes\n",
" axes[count].set_ylim(allplots[count][\"Close\"].min() * .99 , (allplots[count][\"Close\"].max()-allplots[count][\"Close\"].min()) * 0.5 + allplots[count][\"Close\"].max())\n",
" axes[count].hlines(allplots[count][\"Close\"].max() * 1.01, allplots[count].head(1).index, allplots[count].tail(1).index, linestyle=\"-\", lw=1, color='black')\n",
" if count % 2 == 0:\n",
" axes[count].set_facecolor((0.91, 0.91, 0.91)) \n",
"\n",
"# axes[count].vlines(15, allplots[count][\"Close\"].min() * .99, allplots[count][\"Close\"].max() * 1.01, linestyle=\"--\", color='black')\n",
"# axes[count].plot(allplots[count].index,allplots[count][\"Close\"],c=np.random.rand(3,), lw=2, label=pdufa) \n",
"# axes[count].axes.get_xaxis().set_visible(False) # remove x axis\n",
" count = count + 1\n",
"# plt.show() # need to show x axis temporarily to ensure null times aren't being added\n",
"# allplots[0].reset_index(inplace=True)\n",
"allplots[0]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"\"\"\"\n",
"=====================================\n",
"Custom tick formatter for time series\n",
"=====================================\n",
"\n",
"When plotting time series, e.g., financial time series, one often wants\n",
"to leave out days on which there is no data, i.e. weekends. The example\n",
"below shows how to use an 'index formatter' to achieve the desired plot\n",
"\"\"\"\n",
"from __future__ import print_function\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.mlab as mlab\n",
"import matplotlib.cbook as cbook\n",
"import matplotlib.ticker as ticker\n",
"r = quandl.get(\"wiki/aapl\",start_date=\"2018-01-01\",end_date=\"2018-02-14\")\n",
"\n",
"\n",
"# first we'll do it the default way, with gaps on weekends\n",
"fig, axes = plt.subplots(ncols=2, figsize=(8, 4))\n",
"ax = axes[0]\n",
"ax.plot(r.index, r['Adj. Close'], 'o-')\n",
"ax.set_title(\"Default\")\n",
"fig.autofmt_xdate()\n",
"\n",
"# next we'll write a custom formatter\n",
"N = len(r)\n",
"ind = np.arange(N) # the evenly spaced plot indices\n",
"\n",
"\n",
"def format_date(x, pos=None):\n",
" thisind = np.clip(int(x + 0.5), 0, N - 1)\n",
" return r.index.strftime('%Y-%m-%d')\n",
"\n",
"ax = axes[1]\n",
"ax.plot(ind, r['Adj. Close'], 'o-')\n",
"ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))\n",
"ax.set_title(\"Custom tick formatter\")\n",
"fig.autofmt_xdate()\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| agpl-3.0 |
juliusf/ipython-notebooks | probabilistic_programming/.ipynb_checkpoints/Erlang_1,k-checkpoint.ipynb | 1 | 6212 | {
"metadata": {
"name": "",
"signature": "sha256:dd8cf2ea3523ff88f56f45eab5f5de0597186f187bb049652f5dbab4f1bdd2eb"
},
"nbformat": 3,
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Erlang_1,k\n",
"This notebook computes the CDF of an Erlang_1,k distribution"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sympy import *\n",
"from mpmath import *\n",
"%matplotlib inline\n",
"init_printing()\n",
"p, mu, t, k = symbols('p, mu, t, k')\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pdf = p * mu * exp(-mu*t) + (1-p)* (mu**k) * ((t**(k-1))/fac(k-1)) *exp(-mu*t)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "cannot create mpf from -mu*t",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-16-93bcaec81069>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mp\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mmu\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mexp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mmu\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/home/jules/ipython-notebooks/env/lib/python2.7/site-packages/mpmath/ctx_mp_python.pyc\u001b[0m in \u001b[0;36mf\u001b[1;34m(x, **kwargs)\u001b[0m\n\u001b[0;32m 982\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 983\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtypes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 984\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 985\u001b[0m \u001b[0mprec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrounding\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_prec_rounding\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 986\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/jules/ipython-notebooks/env/lib/python2.7/site-packages/mpmath/ctx_mp_python.pyc\u001b[0m in \u001b[0;36mconvert\u001b[1;34m(ctx, x, strings)\u001b[0m\n\u001b[0;32m 660\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_mpmath_'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 661\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mpmath_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrounding\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 662\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_fallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstrings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 663\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 664\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0misnan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/jules/ipython-notebooks/env/lib/python2.7/site-packages/mpmath/ctx_mp.pyc\u001b[0m in \u001b[0;36m_convert_fallback\u001b[1;34m(ctx, x, strings)\u001b[0m\n\u001b[0;32m 612\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 613\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"can only create mpf from zero-width interval\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 614\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"cannot create mpf from \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mrepr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\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 615\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 616\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmpmathify\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\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[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: cannot create mpf from -mu*t"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | apache-2.0 |
danielfrg/datasciencebox | examples/spark-mesos-df.ipynb | 1 | 8197 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pyspark import SparkContext"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"SparkContext.setSystemProperty(\"spark.executor.uri\", os.environ[\"SPARK_EXECUTOR_URI\"])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc = SparkContext(os.environ.get(\"CLUSTER_URL\"), 'pyspark-demo')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pyspark.sql import SQLContext"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sqlContext = SQLContext(sc)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2015-08-24 19:46:19-- http://files.figshare.com/1315364/iris.json\n",
"Resolving files.figshare.com (files.figshare.com)... 54.231.129.44\n",
"Connecting to files.figshare.com (files.figshare.com)|54.231.129.44|:80... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 15802 (15K) [application/json]\n",
"Saving to: 'iris.json'\n",
"\n",
"100%[======================================>] 15,802 --.-K/s in 0.08s \n",
"\n",
"2015-08-24 19:46:19 (204 KB/s) - 'iris.json' saved [15802/15802]\n",
"\n"
]
}
],
"source": [
"!wget http://files.figshare.com/1315364/iris.json"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SLF4J: Failed to load class \"org.slf4j.impl.StaticLoggerBinder\".\r\n",
"SLF4J: Defaulting to no-operation (NOP) logger implementation\r\n",
"SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.\r\n"
]
}
],
"source": [
"!hadoop fs -put iris.json /tmp"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"lsr: DEPRECATED: Please use 'ls -R' instead.\n",
"SLF4J: Failed to load class \"org.slf4j.impl.StaticLoggerBinder\".\n",
"SLF4J: Defaulting to no-operation (NOP) logger implementation\n",
"SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.\n",
"-rw-r--r-- 3 dsb supergroup 15802 2015-08-24 19:46 /tmp/iris.json\n"
]
}
],
"source": [
"!hadoop fs -lsr /tmp"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iris = sqlContext.read.load('hdfs://54.159.244.205:8020/tmp/iris.json', 'json')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---------------+-----------+----------+-----------+----------+-------+\n",
"|_corrupt_record|petalLength|petalWidth|sepalLength|sepalWidth|species|\n",
"+---------------+-----------+----------+-----------+----------+-------+\n",
"| [| null| null| null| null| null|\n",
"| null| 1.4| 0.2| 5.1| 3.5| setosa|\n",
"| null| 1.4| 0.2| 4.9| 3.0| setosa|\n",
"| null| 1.3| 0.2| 4.7| 3.2| setosa|\n",
"| null| 1.5| 0.2| 4.6| 3.1| setosa|\n",
"| null| 1.4| 0.2| 5.0| 3.6| setosa|\n",
"| null| 1.7| 0.4| 5.4| 3.9| setosa|\n",
"| null| 1.4| 0.3| 4.6| 3.4| setosa|\n",
"| null| 1.5| 0.2| 5.0| 3.4| setosa|\n",
"| null| 1.4| 0.2| 4.4| 2.9| setosa|\n",
"| null| 1.5| 0.1| 4.9| 3.1| setosa|\n",
"| null| 1.5| 0.2| 5.4| 3.7| setosa|\n",
"| null| 1.6| 0.2| 4.8| 3.4| setosa|\n",
"| null| 1.4| 0.1| 4.8| 3.0| setosa|\n",
"| null| 1.1| 0.1| 4.3| 3.0| setosa|\n",
"| null| 1.2| 0.2| 5.8| 4.0| setosa|\n",
"| null| 1.5| 0.4| 5.7| 4.4| setosa|\n",
"| null| 1.3| 0.4| 5.4| 3.9| setosa|\n",
"| null| 1.4| 0.3| 5.1| 3.5| setosa|\n",
"| null| 1.7| 0.3| 5.7| 3.8| setosa|\n",
"+---------------+-----------+----------+-----------+----------+-------+\n",
"\n"
]
}
],
"source": [
"iris.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"root\n",
" |-- _corrupt_record: string (nullable = true)\n",
" |-- petalLength: double (nullable = true)\n",
" |-- petalWidth: double (nullable = true)\n",
" |-- sepalLength: double (nullable = true)\n",
" |-- sepalWidth: double (nullable = true)\n",
" |-- species: string (nullable = true)\n",
"\n"
]
}
],
"source": [
"iris.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---------------+-----------+----------+-----------+----------+-------+\n",
"|_corrupt_record|petalLength|petalWidth|sepalLength|sepalWidth|species|\n",
"+---------------+-----------+----------+-----------+----------+-------+\n",
"| null| 1.1| 0.1| 4.3| 3.0| setosa|\n",
"| null| 1.0| 0.2| 4.6| 3.6| setosa|\n",
"+---------------+-----------+----------+-----------+----------+-------+\n",
"\n"
]
}
],
"source": [
"iris.filter(iris.petalLength < 1.2).show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+----------+-----+\n",
"| species|count|\n",
"+----------+-----+\n",
"|versicolor| 50|\n",
"| setosa| 50|\n",
"| virginica| 50|\n",
"| null| 2|\n",
"+----------+-----+\n",
"\n"
]
}
],
"source": [
"iris.groupBy(\"species\").count().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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
haraldurt/UCSBDataScienceBootcamp2015 | Day02_EverythingData/notebooks/Data Preparation.ipynb | 1 | 1122 | {
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"nbformat": 3,
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Wrangling with Python and Pandas (tutorial)\n",
"\n",
"Pandas Tutorial:\n",
"\n",
"http://pandas.pydata.org/pandas-docs/stable/10min.html\n",
"\n",
"[Run through it]"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A More involved example:\n",
"\n",
"http://nbviewer.ipython.org/github/herrfz/dataanalysis/blob/master/week4/clustering_example.ipynb\n",
"\n",
"(replace \"%load_ext rmagic\" with \"%load_ext rpy2.ipython\")\n",
"\n",
"An 11-lesson tutorial:\n",
"\n",
"https://bitbucket.org/hrojas/learn-pandas"
]
}
],
"metadata": {}
}
]
} | cc0-1.0 |
abeelen/GeometryInMotion | circle-wave.ipynb | 1 | 4977 | {
"metadata": {
"name": "",
"signature": "sha256:f30402e115bf43412f8f35d1e83168891153789d05f29cd60a0f5791c623e081"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Circle-wave\n",
"\n",
"Inspired by https://dribbble.com/shots/1696376-Circle-wave\n",
"\n",
"They are several ways to produce such a plot : \n",
"* fixed rotating curve\n",
"* moving filter to a fixed curve\n",
"\n",
"I tried the second approach here"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib \n",
" \n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.animation as manimation\n",
"\n",
"plt.ion()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nwave = 8\n",
"amp_wave = 0.1\n",
"rad_wave = 0.8\n",
"frames = 100\n",
"\n",
"npoints=500"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# We want a square figure with a black background\n",
"\n",
"fig = plt.figure(facecolor='k', figsize=(5,5))\n",
"\n",
"# Obviously a polar plot and enough space to see all the curves\n",
"ax = fig.add_subplot(111, polar=True)\n",
"ax.set_xlim(0,rad_wave+amp_wave*1.1)\n",
"ax.set_ylim(0,rad_wave+amp_wave*1.1)\n",
"\n",
"# Let's put everything in black and remove the axis \n",
"ax.set_axis_bgcolor('black')\n",
"ax.xaxis.set_visible(False)\n",
"ax.yaxis.set_visible(False)\n",
"for spine in ax.spines.itervalues():\n",
" spine.set_visible(False)\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We basically need two cosine plots"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.linspace(0,2*np.pi,npoints)\n",
"\n",
"plots = [ ax.plot(x, rad_wave+amp_wave*np.cos(nwave*x), color='w', linewidth=4)[0], \\\n",
" ax.plot(x, rad_wave-amp_wave*np.cos(nwave*x), color='w', linewidth=4)[0] ]\n",
"\n",
"fig.tight_layout()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can define an atenuation filter on those plots, let's try a simple gaussian"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tau = np.exp(-(x-np.pi)**2/(2*(np.pi/4)**2))\n",
"\n",
"plots[0].set_ydata(rad_wave+amp_wave*np.cos(nwave*x)*tau)\n",
"plots[1].set_ydata(rad_wave-amp_wave*np.cos(nwave*x)*tau)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks good enough, we can try to animate it by shifting the filter"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def init():\n",
" return plots\n",
"\n",
"def update_image(iframe):\n",
" global x\n",
" tau = np.roll( np.exp(-(x-np.pi)**2/(2*(np.pi/4)**2)), -int(iframe*1.0/frames*npoints))\n",
" plots[0].set_ydata( rad_wave+amp_wave*np.cos(nwave*x)*tau )\n",
" plots[1].set_ydata( rad_wave-amp_wave*np.cos(nwave*x)*tau )\n",
"\n",
" return plots\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"map_animation = manimation.FuncAnimation(fig, update_image, init_func=init, frames=frames, interval=50, repeat=True)\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And we can simply save this animation as a gif"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"map_animation.save('circle-wave.gif', writer='imagemagic', dpi=40, fps=25, savefig_kwargs=dict(facecolor=fig.get_facecolor(), edgecolor='none'))"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img file='circle-wave.gif'>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And if everything worked properly you should get a tidy gif : \n",
"<img src='circle-wave.gif'/>"
]
}
],
"metadata": {}
}
]
} | gpl-2.0 |
opensyllabus/osp-pipeline | osp_pipeline/corpus/notebooks/01-token-counts.ipynb | 1 | 72698 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Word counts"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"mpl.style.use('bmh')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_csv('data/token-counts.csv').sort_values('token_count')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"x = df['token_count']\n",
"y = df['count']"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
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aQQx6gRetIDdFL0j19RU9PnbQ/v37U4+AgtALvFK2sthocFp6Yfi9BV60ghj0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Fuhcudx9Sqe75aRXm4bPncOw/PzSL7ySlvmLevr63joC19wzYVXu70UwLW3pVIgPcgiu15t6xRg\nD7sIsCo/++n1PuQ8B/VaAF+VKcfei0rOet1HPy8i0C+3vPTTP9Ofd1X9fJYIUpDHv9Nn3F63u1fy\nshexqHkXXLJHqjg9lXTs5byoFtsMqqDxoHQqnAy4Fx13e+yrX/yiZ1Z0ivkC4RexVhVUv/0obN+t\nzaAzefD4cYhKBTKbxf3bt3fdfuvWLZx+5hmtPvTb57Bfq360HeVziZ2f/ez0PjTIc4cpx74fcdhH\nvz+3xOGYmCzI4696btHZLvMSXyxq3sXQ0FDYXSBDjI+Ph90FMshezotqsc2gChoPilfh5E5Fx90e\n2ykrvRTzjeKxcgqq370WRdZpM+hMbk1PY2h5GVvT0663j4+Pa/eh3z6H/Vr1o+0on0vs/Oyn7rkl\nqGNjyrHvRxz20e/PLXE4JiYL8vj387nF78cRARGdISWEOAngfwdwQEr5E1734wwpUrWXZ7yQPuYl\n2nSLkgdZlNTvrDgLc6sW6B60oI5p0O2qHE+dIrcAPIvPu7UziHOLzutDtxB8p231clEAv9gvAlCd\nnXUtqA/A9T5+bV+3CL7X82T9+9s/93M47rLss5c+9COORZ0tYe+bnwW1gzi3hH18dJjUV139XIjF\nDT/jko5YzZASQnwBwMcBFKWUp22/nwHw7wEkAfyGlPKSlPINAD8jhPi9Tm0mEkbXZKcBSqfTYXeB\nDMK8RJtVZNRi/dvrw5n9/n5/UPU7K1ZfZTbbWvolKhUA/ve9H0Ed06DbVTme3frgzF+yUAAAJO7f\nb2vbrZ1BnFt0Xh86x6XbtlQeF+Tzaz0P6aWl9463x3Nlv49f2/faL6/bvJ4n69/7X3kF0BiQMu01\nGQVh75vOuaZb/4I4t4R9fHSY1Fddup95urbHz7gUMYNcsvdFAL8K4DetXwghkgB+DcCPAngbwNeF\nEL8vpfxzlQY5IEWqRkdHw+4CGYR5iTbd5SpBTiX3OytWH91mSEVJUMc06HZVjqfuUktrmad9hpRX\nO4M4t+i8PnSOi+q2/Ly/Tj/cltt6PVeDzK3OUlT7v/d/4AO+9aEfcV6KE/a+9bKs20sQ55awj48O\nk/qqy+8luvyMS1Ez0CV7QohHAfyBNUNKCPG/ALgopfw7Oz//IgBIKf/PnZ9/j0v2yA+cnko6mJd2\nfk+FV1neEvRSNb/2aVBZicNyBLd90H2+nUuzgPblC9btYmMDcmSk69KsQR9Xt7w4++C2PEP39eC2\nhK3T8e+0zDAqhs+da9Xn2rxyxZc2VZdMOY9/L0v0dLZXy+dx8xOfiPwSLD/b81p22U9fopRnnfzq\n9Nu6r7XEs9d97vf46/Z70Prtmx/7Nug+eN2/188tvfQ/Cse9H2FvPwpitWTPw3EAb9l+fhvAGSHE\nYQD/B4C/KoT4RWuAyun+/fv4kR/5EaRSKTQaDeTzeXz2s5/FysoKhoeHkUwmsb6+jqNHj+Kdd96B\nlBJHjx7FnTt3MDIyAgDY2NjAsWPHsLq6CiEEHnroIayurmJsbAyNRgObm5sYHx/HysoKhoaGcODA\nAdy9excHDhxArVZDpVJp3Z5OpzE6Oop79+7h0KFDqFQqePDgQev2ffv2IZvNolQq4fDhwyiXy6jV\naq3bs9ks0uk01tbWcOTIEaytrWFra6t1O/ep932qVquoVqux2qc4Pk9R2adqtYp79+7Fap/6eZ4e\nfvllvHXqFA5eu4b7xWLf+yRKJTz21lu4VSohUSy27dO927fROHUKP/Dii3jzb/5NDH/726iVSpHd\np0wmg0KhEPjzlF1ZwcTdu3irVEKmVDIye9kbN7Bx6hQm/viP8daP/ziEEHjkpZe2n4c330Rj/36s\nlUo4tHOudtunZqmEx6pV3Po7fwdD3/seDnz/+3jr1Ck89PWv496dO5DW7R//OPbfuYN9X/86bt+6\n5blPB69dw/1Tp5B7+WV8/x/+w8BfT9bPu/Zp5/UgCwUc+7M/a+3T5pEjqNpeD5k334RYX+/6PD0o\nlXA8nca906fxwDqmpRIO7N8P8a1v4c6tWzh69CjurqwA738/jv3xH+PWJz6BozduIJlOo1Qq4eCD\nB5E6790ZH8fDuRzujoxg/dYtX56nlVIJhxMJ1L79bdy1tWnt00qphKONBh5Iic1Tp3D8j/4Itz7x\nCYx973t4aGMDhVIJ+9fX2/epVMLI4cOu2Xv8j/4I3zl1CtnXX0fj/v1dryfr9uFvfxupfB63dp6n\nqJ33VM7luueIozduIPm+92Ht8cfbzhGq2Zu4dg1bBw6gbDuHdHyeBvye+0OvvYZbH/84BIDsu+/6\n9jzJN99E9dQpHPof/wO3PvxhHHzzTYxVq7hTKmFkY0N5nx7/oz/C6x/9KEa/+922c4Sfr6cw359G\nvvUtrJ86hWMvvYS3/8E/0H49pb/97bZzQC/7JG2vF3HnjvY+JWyPT92929N77kMPPYRqtYpSD58j\nenk99XuOSN+9i0cKBdwqlTAUwufyXl9Pcfr+NAhhz5D6CQAzUsqf3fn5PIAzUsqfV2nvpZdekh/8\n4AcD6i3FyZ07d3Ds2LGwu0GGYF7acYaUt0FlJQ5/peMMKfe8cIaUmr02Q+p7/+gf+X5u4Qyp8AQ9\nQ+q7P/uzOPzTP80ZUh6iMFMnKjOkev3cwhlS0cr0oAxihlTYA1Idl+x1wyV7pIpLsEgH8+KNb87t\ndLLixwezoK4S1q9evkB5fWH3GnCy/u02OOPHYIuf2R4+dw5DV69CJhKQhw+36ks5r5zW6epwvQwo\nqep2hbdeBlNUt+d1pTk/BjTcnm+/Brd7yXgzl0Py1VcB7L4KY7dtqCzZ08m4Sv+jdH5XHQzUHcD1\nex+dA02DGOR1azMqn1tUz1NRH0Ds1yDOeb22GeWsqNwWJf2cV03Zx0EMSIVdFfzrAH5ICPGYECIN\n4CcB/L7qgyuVCubn5/H8888H1kGKh/Hx8bC7QAZhXrxZV3tJLy2F3ZVI0MlKv8cuvbR9JbFkoRC5\n46+zb53u69xH+32tfw8tL+96vFubzt9166Of2R5aXoao15Go1ZAsFFp9fvRLX+q4v51+r7KPqtyO\nq/PfnR6jy+uxfh5zr+fbLS9+tK9y36Hl5dbzqNIP+zZUzi06GVfpf5TO717PX6d9VnnO/d7HoeVl\niEoFQ8vLXfvnF7c2o/K5RfU85ef5LIoGcc7rtc0oZ0Xltijp57xqyj4OwsBqSAkhfhvARwEcEUK8\nDeBfSSn/oxDi5wF8BUASwBeklDdU28xms7h8+XIg/aV4WVlZicRfA8gMzIu3OF/Jphc6Wen32NXy\n7lcSiwKdfet0X699tP/b7Ypwbm06f9etj35me2t62nWG1HfPn8dxxza99tf5e5V9VNWpLa/2+jk+\nXo/185h7HSe/rlTZS8a9ZkipbEPl3KKTcZX+R+n87vX8qexzp+fc733cmp5uzZBS7V+/3NqMyucW\n1fOUn+ezKBrEOa/XNqOcFZXboqSf86op+zgIA12y5zfWkCJVrAlEOpgXUsWskA7mhVQxK6SDeSFV\nzArp2AtL9vqSSBjdfRqgbDYbdhfIIMwLqXLLSmZhAaNnzyKzsNDxdyp6fVxUZRYWMDY5ibHJyV37\n1M8x8mqzl/7Z++Bn2wAw9t//u9Y+Wv0ZPneu7f+ZhYVdt+n0L7OwgAMnTuBgLofhc+d63Z1dx8ft\n+HXqm/12r8f2sn9e2/D7cZ36328/vN6HumXSftzc7tcpU7q6vV68+qqSa7+Oo9fj+82V6naCOuc7\nj60zL1776fc5jd6je/4LYpsqVD/jerWt+/uwdepXkOcbP49HVI+tXwa2ZC8I9+/fx/z8PGZmZjAz\nMxN2dyjCSqUSxsbGwu4GGYJ5IVVuWbHqAgBoFap0+52KXh8XVVZtJOvf9n3q5xh5tdlL/+x98LNt\nAFh7+208rLGPVn9kNgtRqbT+b7Hfptqm1W6iXAaAVu2bXjiPj9Unqy/dnlP77V6P7WX/vLbh92uv\nU//77YfX+1C3TLodN/v9OmVK9/h2e70AcO2rSq69+tPvOdGvXKluR7d91cc5j3XpE59oy4vXfvp9\nTqP3uL0egn7/7mUbqp9xvdrW/X3YOvVL5z2q3/NjP6J6bP1i9IDUwYMHWUOKlBw+fDjsLpBBmBdS\n5ZaVoGv9mKxTHax+jpFftbXc6r74Wbfr0MMPoz41pdyWs46OW22cXmok1fJ5JN54A6JSadW+6UW3\nml+91O3yuwZUkK+9brW4+umH1/tQt0y61a9y61+nTKlSeb10ykenPvh1HL0e71dtsW7bCeqc7zzW\nzrx0qsEV1VqEphtE/bBu21Sh+hlXt/ZRVD+v9FOrqp998vN4RPXY+oU1pGhPKBQKmJiYCLsbZAjm\nhVQxK6SDeSFVzArpYF5IFbNCOlhDqotmsxl2F8gQtVot7C6QQZiXvcGPNfm9ZiXu9QAA//fRz9pc\nzna71VRRqbvTrS/D584hcekShs+d66kGh05tmV7a0KmZ1Kn+Tre6QV51guyP69a+ar0h1X51q8k0\nNjmJAydODLTuTr/nliBrQ3W7fy/b6JVqDRi/akbp1Jzx4/Ws+rharaZ9jlA5JqqvD+fr1rQ6Q1EU\n1LHaK59xdY+fzntgWPn183NQ2PtiZ/QMqeeee07+zu/8DmtIUVfVahWZTCbsbpAhmJe9YfTsWaSu\nX0d9agrlq1d7aqPXrPix7ajzex/d2vNjG1YbADzbse7jrHmj05eDx4+jms0iU6mg8f73u963Uxuq\n+9prGyrtO49Dp3YAeB4fAJ7PJQCl9r3a6XZcvPrl/L3b47zuE4R+zy2djqFqG9ZjVY+Rzrb8Oj90\na8eP49HL9rrls9f993pctVrFkZkZrXOEyjHReX3Y2/Pa973w3ueXoI7VXvmMq3v8dN4Dw8qvn5+D\nVB83iBlSRteQymazrCFFSlZWVnDixImwu0GGYF72Bj/W5PealbjXAwD830c/a3M52+1WU0Wn5o1X\nG1vT0/ju+DhOrqygvlO3SacGh05tmV7a0KmZ1Kn+jmpdKbd/W49rnD7dsf1O7XTqu1e/utVkEsUi\nxMYG5MjIwF6z/Z5bgq4N1en+vWyjV6o1YPyqGdVPXTSddnS3v7KygjHNc4TKMVF9fQDur1tT6gxF\nUVDHaq98xtU9fjrvgWHl18/PQWHvi53RM6RYQ4pUFYtF5HK5sLtBhmBeSBWzQjqYF1LFrJAO5oVU\nMSukgzWkuhBChN0FMkQ6nQ67C2QQ5oVUMSukg3khVcwK6WBeSBWzQlFj9IBUo9EIuwtkiLW1tbC7\nQAZhXkgVsxJMYUyvwsCdivH6WURXpYi5Sn+dvPLSS6FSP4qc6zymUzv/f3v3HmTHeZd5/PnN1aO5\nSaPb2GNnrIQ41EjYJCRKYJVgo61k4iV20AbGCRUCZJNiN6TWxbIQilrI1l4o2FpWhaDWVSwhhEui\nwAo2EJPYYX0bLw6OhW2U4MiSbSGPNBqPNNJcNNZc9O4f02fSOu7u875nzpw+LX0/VafmTJ/ut3/v\n28/pGbXO9Ns5MqKN27apd3AwuJ6ssUtrN2156M2aQ27c7buOr0rHpTwrIftOG+Na3ejc53hXe/P5\nJJ0jI9o4MKDu3buvaNPnJvhZ7cUnGPC5UX/v4ODqIyQDWeeTeD+6d+9erStp+6yxnHvwwZrc7Dgt\nZ+V9SKu12v1WI+tcsp77yVqe942iffZfOrfkUWst9rnW417L8/h6W8t45Z3FEIX+k73777/fHTx4\nkJuao6K5uTl1dnbmXQYKgrzAF1lZn5t8pt0YuPQ85Kbia7nhctZNzH3qLV83LS/V3Ki0Fjc5D9km\nq52NAwOy+XlJlW/6nXbD7KSxS2s3bXk1N2v2vXF3fBufflZS6biUZyVk32l9q9WNzpuPHq14vKu9\n+XyS0vF2ZjLnrmhTyr4JfmZ7HR2rEwz43qg/LvRmyUnnkyv6EfXPdXTo/NjYa7bPHMt77tGmBx5Y\n882OK938f7UPKbVWu99qZJ1LarnfkJ8tjXjT63Klc0setdZin2s97rU8j6+3tYVH1m4AACAASURB\nVIxXrY4vNzWvoLOzk5uaw8uFCxeu+X80wh95gS+ysj43xqx0Y+CQm4qv5YbLWTcxD6m3JC0v1dyo\ntBY3OQ/ZJqudxT171PrQQ3IdHcH1ZO0rrd205aE3ay7/6nNz+0rr+Kp0XMqzErLvtLGt1Y3OW0ZH\nKx7vam8+n2Rxzx61jo5qeWBA6uxcbcfnJvhZ7S3u2bM6wYDPjfptdnZ1WcgN7iudT0r9cJ2dah4b\n02JUU/n2WWM5cffd6j5zZs03O866+X+8D5qbS6y12v1Ww+dcsh77yVqe942iffZfOrfkUWst9rnW\n417L8/h6W8t45Z3FEIX+hNTDDz/sbov+twDIcuLEiWtiRgnUBnmBL7KCEOQFvsgKQpAX+CIrCMFN\nzStobW3NuwQURH9/f94loEDIC3yRFYQgL/BFVhCCvMAXWUGjKfQFqcXFxbxLQEGMj4/nXQIKhLzA\nF1lBCPICX2QFIcgLfJEVNJpCX5Bqbm7OuwQUxLV+jxeEIS/XhlrMQEJWvqNIM7rEredsdeU2PvLI\nmtqIz/YVnyEoZBao+AxjafsInaEu6bVKs5bVYgbB8nXbDxxQ7+CgNm7bdsWMbJXaD5mxLWvGs0oz\nlGUdn9fMMBhwbqlm1iifmduqmZ2vfEa6pJoqtVvtey2t3ZBZIKupYa3nhlrMSNjZ2ZmZv/Llaxlj\nn6xVO8Nf0jZJsyKm9a9zZMTrvVzvmQDXMsNlLetoP3BgNStrnaGwlufxaoX87PPdfj1n4Qttu54z\nAqaNWdvk5PZ13bEKfkFqZmZG9957r77yla/kXQoaHBcvEYK8XBvaDh1SyzPPqO3QoarbICvfUYvx\nzINv3bXoX/tjj62pjbZDh9R8+rSaZmbUfPr0ajtptSUtbx0dlc3Pq3V0NHUflWrMWqf0WuvoaMV1\n0vYRMtbxddsOHVLTzIxsaUnNx4977780rs2nT2fWnbS/tHbi2/scn/J1Qs4tafuttE3avsvXqTQm\nSbU0zcy8Jqe+7Vb7Xktrt3V0VLa0pKaZmarGp5brVtq+2raam5sz81e+fC1j7JO1atpP2yZ+zkpa\np/z96/NertfPq2reQ+tZR9uhQ6tZKT931ep4+b5eCyE/+3y3r+Z8GlJvSNvrWUvSvhLff7Ozfeu6\nYxV8lr0NGzYwyx68TE9Pa9OmTXmXgYIgL9eGWsxAQla+o0gzusSt52x15c6++93aeuxY1W3EZ/uK\nz/IVMgtUfIaxtH0kteW7js+MbrWaQTBp3aYXXpDNz2v55puvmJEta5v4rEuVZmyrNLNW1gxlIbN0\nhZxbqpk1ymfmtmpm5yufkS5pNrpK7Vb7XktrN2QWyGpqWOu5oRYzEk5PT2fmr3z5WsbYJ2vVzvCX\ntE3SrIhp/VvetUvNR46sPq/2HFQr1byH1rOOhX37VrNSvjz+NbTNal6vhZBzq+/26zkLX2jb9ZwR\nMPX919V1bl13rILPsvf444+7oaGhvMtAAVy8eFEbNmzIuwwUBHmBL7KCEOQFvsgKQpAX+CIrCMEs\nexUsLS3lXQIK4ty5db+4i6sIeYEvsoIQ5AW+yApCkBf4IitoNIW+IAX4KvInAVF/5AW+yApCkBf4\nIisIQV7gi6yg0RT6glRLS6FvgYU62rp1a94loEDIC3yRFYQgL/BFVhCCvMAXWUGjKfQFqcXFxbxL\nQEGcOXMm7xJQIOQFvq7lrOQ5pXORxPtw5syZ1Gmmq532vRZTxie12bNzp3oHB18zfXqex2Q9+pq0\nj7Rptq+YOj02FX35a5Xqjreftr+zf/mXmdPXZ41F1lTzaet2796tjQMD6t69O7P/5a/5TkvuOz17\nWnu+x76a9bLqTRrD0L75vH/SjkvW8Y23sdafRZXGKn4+CHnP+WTRd4yT3g8h54FKfewdHNTGbdtW\n39PVtONbQ+fIiNf7Zj3aTMpK0nkp7VwXUlOts5L1HljL+73S+kn5K50zQ8bFt56s/pX2X/51LefD\nvBX6I0Zzc3O69957NTw8rOHh4bzLQQPr6urKuwQUCHmBr2s5K6UpgiXp0ic/Wdh9rLd4H7o+9KHE\nPsWXSQrq81q2zWqz+fTplW9mZtR0/rxsfr6m+6i2rlr3NWkfpb63HTp0Rdvx/TcfPbo6FX35a+X1\nlNcdb7/8+9K2m/7mb1aXx8ffJzOl11xHR+Z2V6xrJnNOzcePy6I/6Unqf3mtWeOVNgZpY5rVnu+x\nr2a9pJqzxjC0b/E20mpKOy5J2yTtb60/iyrlN34+yDrOae1mZdF3jJPeD5L/eaBSH5tmZiRp9T1d\nTTuVJPU5ZDxr0WZSVsrfd5JSz3UhNUn+Y1TNeStrnbXUk3VeKuWvdM4MGRfferL6Vxqf8q+V+i3l\n97O7kkJfkOrp6dH+/fvzLgMAgGtOnlM6F4nP9Nprmfa9FlPGJ7VpExOy2Vm5rq7E6dPzOCbr0dek\nfaRNsx3ff8vo6OpU9Gm1pdVd3n7S/pa+//u1/NhjkpKnr/fJUdJU86k5nJtT89iYlgcGZHNzqf0v\nr9V3WnLf6dnT2vM99mtZL+n1pDEM7VvWcUhrw+f41jL7lfIbPx+E7NdnDHzHuJSLas9HlfrY9MIL\nsvn51fd0Ne341nB52zY1HzlSdTu1bjPrfVd+rgupqdZZCf05Wm09WeelUv5K58yQcfGtJ6t/pfEp\n/7qW82HerMg3Nnv44YfdbbfdlncZKIATJ05ocHAw7zJQEOQFvsgKQpAX+CIrCEFe4IusIMThw4ef\n2rt371vXcx+FvodUa2tr3iWgILZv3553CSgQ8gJfZAUhyAt8kRWEIC/wRVbQaAp9QWppaSnvElAQ\nr7zySt4loEDIC3yRFYQgL/BFVhCCvMAXWUGjKfQFKcCXmeVdAgqEvMAXWUEI8gJfZAUhyAt8kRU0\nmkJfkGppKfQ92VFHfX19eZeAAiEv8EVWEIK8wBdZQQjyAl9kBY2m0BekFhcX8y4BBcHHUxGCvMAX\nWUEI8gJfZAUhyAt8kRU0mkJfkGpubs67BBRET09P3iWgQMgLfJGVa1f7gQPqvuMOtR844L1NrfJS\n2nfnyEhwDXloP3BAPTt3qmfnztVa08av0rjGX/cdh6RtssYsqd7Q/mbV5VN3T0/Pa+r2HUPfvoSM\n9Vr49Ld8X759q1X+Q9uqVG8txjYkt2nnlkrvs/U+h/iMQ8/OneodHHxNtsuXx2uu5v0Zfy9Uem9m\nZbS0bfxrUh9CxqJzZEQbN2/Wxi1b1Dky4t2P8vHq2blT3bt3a+O2beodHEzMTikrSfVUk2vfrJcf\nt5D3XKX3Qtqx9am/c2TkivHy6Vf8tc6REW0cGFg9buW1pOU7KUNJY1PLc6Hvz6Z6/05hzrm67rCW\nvvzlL7svfvGLGh4e1vDwcN7loIFNTk5qy5YteZeBgiAv8EVWrl3dd9yhlmee0dJtt2nmoYe8tqlV\nXkr7dh0dsvn5oBryUKpX0mqtaeNXaVzjr0vyGoekbbLGLKneavqbVpfP8ZucnNSOH/3R19Qdr8kn\ng1l9CRnrteTLp7/l+wrpWy3yH9pWpXprMbYhuU07t1R6n633OcR3HErKxy++XLry/R5fP6QWSRXf\nm1kZLW1b/rW8DyFjsXFgYLUN19Gh82NjXv1IGi9nJov+fZ+UnVJWkuqpJte+x7j8uJXXlaXSeyHt\n2Javm1RrfOx9+xV/rfno0dUcnB8bS6ylpLyOpAyV1+0zTr7nL9+fTfHlhw8ffmrv3r1vzTg8a1bo\nmzB1dnZq//79eZeBApibm+MfjfBGXuCLrFy7Fvbtu+Krj1rlpbTPy9u2qWliIqiGPCzs2yebmFh9\nnvQ1vm7S8qzXK41D0jZZY5ZUb4hKx8fn+M3Nzb2mbt8x9O1LNWNdDZ/+pvWpUt9qUV81bVWqtxZj\nG5LbtHNLpffZep9DfMbBJiZks7NyXV1XrJ+0vFRz85Ejme1m7UuSlnftynxvZmW0NGbxr81HjiTW\n6tv24p49av3a1yQzLe7Z492P8vGSJNfZqeaXXpLr6EjMTikrWfkKybVv1tOOm88xrPReqHRss+pf\n3LNHrQ89lDpelc5XLaOjah0dXT1u5bUkZSMpS5XGphbnQt+fTfX+naLQn5B6/PHH3dDQUN5loAAu\nXbqk9vb2vMtAQZAX+CIrCEFe4IusIAR5gS+yghD1+IRUoe8hxU3N4Wt8fDzvElAg5AW+yApCkBf4\nIisIQV7gi6yg0RT6gpSZ5V0CCqK1tTXvElAg5AW+yApCkBf4IisIQV7gi6yg0RT6ghSz7MFXb29v\n3iWgQMgLfJEVhCAv8EVWEIK8wBdZQaMp9AWppaWlvEtAQUxOTuZdAgqEvMAXWUEI8gJfZAUhyAt8\nkRU0mkJfkOITUvDF/wYgBHmBL7KCEOQFvsgKQpAX+CIraDSFviBV5BkCUV8LCwt5l4ACIS/wRVYQ\ngrzAF1lBCPICX2QFjabQF6QuX76cdwkoiPn5+bxLQIGQF/giKwhBXuCLrCAEeYEvsoJGU+gLUswS\nAF/9/f15l4ACIS/wRVYQgrzAF1lBCPICX2QFjabQF6QWFxfzLgEFMT4+nncJKBDyAl9kBSHIC3yR\nFYQgL/BFVtBoCn1Bqqmp0OWjjtra2vIuAQVCXuCLrBRX+4ED6r7jDrUfOFC3fa5nXrL6037ggHp2\n7lTPzp2p/U3a3meMqt2umnVrzWdcfNoIqd93THt/7de86qpm/ErbdI6MZGam2nZrlRffGta6Xml5\n9+7d2jgwoM6RkSteK2WkNF5p41apjrXmLd5+/HnnyIg2/sqvvKZu3xo7R0ZW+522jySdIyPauG2b\negcHE49n2nhljYPvGMX3kbV+Vi3xfq9Fec0+76+48jrKj0FS/0KyVH4c29rago5zUlvl4xnv61p+\nJpT61Ts4mNnn0nEvXy+pX+W1+eSlfPusmnxymDUGoccibf1qjmn7gQPqHRzUxs2b1Ts4mHg82yYn\nt2c2UgNW5BuD33///e7gwYMaHh7W8PBw3uWggc3OzqqrqyvvMlAQ5AW+yEpxdd9xh1qeeUZLt92m\nmYceqss+1zMvWf0pvSYptb9J2/uMUbXb+dS93nzGxbcN3+19x3R2dlYbjx+v2G4141faxnV0yObn\nMzNTTbu1yotvDWtdb3U8zGTOyXV06PzY2BWvSVodr7Rxq1THWvMWb1/S6vPmo0d14YYb1Hvq1Gvq\n9qlx48DAar+Wb7klcR9JtZa2K+9Peb7KxytrHHzHqHwfaetn1dJ89OjqstK4VaO85tK4Zb2/4uLj\nf35sLPE4l/cvJEvlx3x2dlbXv+993sc5qa3y8Yz3Nam90PdoSVqf48c9vl7S2CXVljZuWdtn1VQp\nh1ljkPa+9jmHxtdPex4y3knH8xuf/OTFd3z0o52pjdRAy3o2vt42bNig/fv3510GCuDs2bP8oxHe\nyAt8kZXiWti374qv9bCeecnqz8K+fbKJidTX07b3GaNqt6tm3VrzGRefNkK29x3T0y+/rO6LFyu2\nW834lda9vG2bmiYmUjNTbbu1yotvDWtdb/X7uTk1j41pcc+eK14rZWR51y41TUykjlulOtaat7Sx\naxkd1Xh/vzbE/hQrZEwX9+xR6+ioFvfs0VLU96TjU25xzx61PvSQXEdH4vqlcSofr6xx8B2j+D6a\njxxJXT+rlpbR0dV+r0VazVnvr7j4+MfbKH1NajskS+XtnT17Vpsz3oc+bZWPZ1Jfq/mZUOqXzc7K\ndXWl9nl51y41HznymvWS9lNeW9a4pW2ftK/y8cjKYdYYZJ0TfdvxeZ7WVtMLL6z2bekd73jN8Vzs\n6jqX2UgNFPoTUqOjo27nzp15l4ECmJ6eVk9PT95loCDIC3yRFYQgL/BFVhCCvMAXWUGIw4cPP7V3\n7963ruc+Cn0TpsuXL+ddAgqCKU4RgrzAF1lBCPICX2QFIcgLfJEVNBouSOGa8Oqrr+ZdAgqEvMAX\nWUEI8gJfZAUhyAt8kRU0mkJfkGptbc27BBREf39/3iWgQMgLfJEVhCAv8EVWEIK8wBdZQaMp9AWp\nxcXFvEtAQYzHbvQIVEJe4IusIAR5gS+yghDkBb7IChpNoS9INTUVunzU0XXXXZd3CSgQ8gJfZAUh\nyAt8kRWEIC/wRVbQaAp9RYcLUvDV0dGRdwkoEPICX2QFIcgLfJEVhCAv8EVW0GgKfUVnaWkp7xJQ\nEFNTU3mXgAIhL/BFVhCCvMAXWUEI8gJfZAWNptAXpFpaWvIuAQWxefPmvEtAgZAX+CIrCEFe4Ius\nIAR5gS+ygkZT6AtSly9fzrsEFMTMzEzeJaBAyAt8kRWEIC/wRVYQgrzAF1lBo+GCFK4JCwsLeZeA\nAiEv8EVWEIK8wBdZQQjyAl9kBY2m0BekWltb8y4BBdHf3593CSgQ8gJfZAUhyAt8kRWEIC/wRVbQ\naAp9QWpxcTHvElAQ4+PjeZeAAiEv8EVWEIK8wBdZQQjyAl9kBY2m0BekmpoKXT7qiClOEYK8wBdZ\nQQjyAl9kBSHIC3yRFTSaQl/RMbO8S0BBtLW15V0CCoS8wBdZQQjyAl9kBSHIC3yRFTSaQl+QWl5e\nzrsEFMSFCxfyLgEFQl7gi6wgBHmBL7KCEOQFvsgKGk2hL0i1tLTkXQIKYsuWLXmXgAIhL/BFVhCC\nvMAXWUEI8gJfZAWNpiEvSJlZp5n9gZn9rpn9eNp6fEIKvvjfAIQgL/BFVhCCvMAXWUEI8gJfZAWN\npm4XpMzsM2Y2YWZHypYPm9m3zeyYmX0qWrxP0p855z4m6a60Np1z61gxribMyIgQ5AW+yApCkBf4\nIisIQV7gi6yg0dTzE1KflTQcX2BmzZJ+R9J7JQ1J+qCZDUm6UdLJaLXUj0G1trauS6G4+vT39+dd\nAgqEvMAXWUGIIual/cABdd9xh9oPHMi7lJqoV39C91O+fq2z4lNP+4ED6tm5Uz07dwaPj2/71Y69\n77al9TpHRqqqJ76sc2REGwcG1DkyElyvr0o1+Bq8//6ajn+t140fl6SMlR+38q+ldX3r8j12oZkv\n33/a9/H6y9uv5r1Y6k/37t1B2Yhv17Nzp3oHB/XGD37wilrS6ovXnnXc4suzjrNPu/GxKT3v3r07\nse9Z45j0WjwTWe+7Wpw70vKcNnZJ41We4fL1s2pN6ktp+97BQa+8l9bvOn781swVa6BuN2Fyzj1q\nZjeXLd4t6Zhz7gVJMrMvSLpb0stauSj1tDIumk1MTOhjH/uYWlpatLy8rH379ukTn/iExsfH1dnZ\nqebmZk1PT2vr1q06d+6cnHPaunWrzpw5o66uLknS7Oystm/frldeeUVmpr6+Pr3yyivq6enR8vKy\n5ubm1N/fr/HxcbW2tqq3t1eTk5Pq7e3VwsKC5ufnV19va2tTd3e3zp49q02bNml+fl6vvvrq6uvX\nXXedOjo6NDU1pc2bN2tmZkYLCwurr3d0dKitrU0XLlzQli1bdOHCBS0uLq6+Tp+q79P4+Lje9KY3\nXVV9uhqPU6P06ejRoxoYGLiq+nQ1HqdG6NPCwoK6urquqj5djcepUfo0MzOjHTt2FKpPHePjun5y\nUienptQ+NVX44zQxOamWHTu0+dFHdeKuu9Yte5uffFInh4a05YkndOYDH6jYp6apKe04eVInpqbU\nMjmpyclJdXR01Cx7XWNj2nb+vMampnTd+fOJfdr+xBM6efvt2vr3f6/pF1/U+RMnvI9T37Fj2rC8\nrFemptQ9N5d4nLqefVbTQ0Pa/uijevlHfiSoT9uffVZuwwZdmJrSpkuXUo/T5WgcX7r1Vl3X1KTu\nJ57QybvuSjxOG198UT2XLunM1JS6Zmc1MzMjix2Hbud03RveoMn+fnWl9Gmt54i2557T3NCQBh58\nUCfuukutra264fHHdXJoSBsPH9b5iQmv7DVfuKC2oSG1v/iibHo6+ThNTam7t1ethw/r9IkTme+n\nS1NTumlyUqfHxzV/8uSazxGlPvUdPapLb3yj5gYGVvu8mr2hIW351rd0cfNmzfX36+anntJLt96q\nzuVlXffkkxo7cUI3PfGEJvr7NR/LQdL7qbm/Xzd3dOil/n41T06mHqfxqSltuvFGSdLU1JQ2vvpq\n5vvpux58UMeHhtQ8Nqbr5uY0PjWlrcvLunjsmM6dOLH6evvsrDbOzmr81lu17eRJzd1wg2ZLNU9N\nqWvz5tU+pb6fNm/W2e/5Hm17/HGNd3Vp8f3v1+seeED/9La3qfO557QwNVUxe2f6+/W67m69/OY3\nq3lxUduffFLPfPjDev3hw5qdnNTS1JR2XLqkE+95z2v69Kpzmrv9dg1+9at6qb9f1zU1aeOxYzo9\nNaUN09Oan5+Xi23f/uKL6pyc1MmhIW175hlduPVWXerrWz3OpXPEZH+/bjh6VGd37dKlvj7d/NRT\nOvGe96jzuefUvLCweo545ZZbpFtu0Q2PPqqX3/9+9R4/Ljm3epyyzuXlx2lyclID587p0tveptn+\nft0Yvd76T/+k1tlZnT17VgNPPqmzseydmJqSnTmTeN4rtd9x7JiWz5/XhQsXNPj44zpz4416dWpK\nN33jGzo+NKTuU6fU0tSkc/39u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"text/plain": [
"<matplotlib.figure.Figure at 0x7f55f8853198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20, 10))\n",
"\n",
"plt.title('Document lengths')\n",
"plt.xlabel('Word count')\n",
"plt.ylabel('Number of documents')\n",
"plt.yscale('log')\n",
"plt.xlim((0, 200000))\n",
"plt.scatter(x, y, s=5, c='r')\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
YufeiZhang/Principles-of-Programming-Python-3 | Lectures/Lecture_6/permutation_efficiency.ipynb | 1 | 21934 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1 align=\"center\">Comparison of 3 permutation algorithms</h1>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inserting elements at the end of a list"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
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+G2OMF4puKArIHOivvgqDB0Pbtv63VYzd+m+MMV4IWLmlaKjinDn+t1WK9dCN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\nhCV0Y4ypJf4fvKAS2vu0/7QAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107c37cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"from matplotlib.pyplot import plot, legend\n",
"\n",
"import itertools\n",
"from time import time\n",
"import sys\n",
"sys.setrecursionlimit(10000)\n",
"\n",
"# Recursive implementation of Heap's algorithm\n",
"def recursive_permute(L):\n",
" yield from recursive_heap_permute(L, len(L))\n",
" \n",
"def recursive_heap_permute(L, length):\n",
" if length <= 1:\n",
" yield L\n",
" else:\n",
" length -= 1\n",
" for i in range(length):\n",
" yield from recursive_heap_permute(L, length)\n",
" if length % 2:\n",
" L[i], L[length] = L[length], L[i]\n",
" else:\n",
" L[0], L[length] = L[length], L[0]\n",
" yield from recursive_heap_permute(L, length)\n",
"\n",
"# Iterative implementation of Heap's algorithm\n",
"def iterative_permute(L):\n",
" yield L\n",
" stack = [(0, i) for i in range(len(L) - 1, 0, -1)]\n",
" while stack:\n",
" low, high = stack.pop()\n",
" if high % 2:\n",
" L[low], L[high] = L[high], L[low]\n",
" else:\n",
" L[0], L[high] = L[high], L[0]\n",
" yield L\n",
" if low + 1 != high:\n",
" stack.append((low + 1, high))\n",
" for i in range(high - 1, 0, -1):\n",
" stack.append((0, i))\n",
"\n",
"# As documentated in the itertools module for its permutations() function.\n",
"def permutations(iterable, r=None):\n",
" pool = tuple(iterable)\n",
" n = len(pool)\n",
" r = n if r is None else r\n",
" indices = list(range(n))\n",
" cycles = list(range(n-r+1, n+1)[::-1])\n",
" yield tuple(pool[i] for i in indices[:r])\n",
" while n:\n",
" for i in reversed(range(r)):\n",
" cycles[i] -= 1\n",
" if cycles[i] == 0:\n",
" indices[i:] = indices[i+1:] + indices[i:i+1]\n",
" cycles[i] = n - i\n",
" else:\n",
" j = cycles[i]\n",
" indices[i], indices[-j] = indices[-j], indices[i]\n",
" yield tuple(pool[i] for i in indices[:r])\n",
" break\n",
" else:\n",
" return\n",
"\n",
"\n",
"data = [], [], [], []\n",
"for i in range(1, 11):\n",
" L = list(range(i))\n",
" before = time()\n",
" for _ in itertools.permutations(L):\n",
" pass\n",
" after = time()\n",
" data[0].append((i, after - before))\n",
"\n",
" L = list(range(i))\n",
" before = time()\n",
" for _ in permutations(L):\n",
" pass\n",
" after = time()\n",
" data[1].append((i, after - before))\n",
" \n",
" L = list(range(i))\n",
" before = time()\n",
" for _ in recursive_permute(L):\n",
" list(_)\n",
" after = time()\n",
" data[2].append((i, after - before))\n",
"\n",
" L = list(range(i))\n",
" before = time()\n",
" for _ in iterative_permute(L):\n",
" list(_)\n",
" after = time()\n",
" data[3].append((i, after - before))\n",
"\n",
"labels = 'Itertools', 'Itertools doc', 'Heaps Recursive', 'Heaps Iterative'\n",
"for i in range(4):\n",
" plot(*tuple(zip(*data[i])), label = labels[i])\n",
"legend()\n",
"print()"
]
}
],
"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": 0
}
| gpl-3.0 |
mne-tools/mne-tools.github.io | 0.19/_downloads/ff83425ee773d1d588a6994e5560c06c/plot_mne_dspm_source_localization.ipynb | 1 | 10087 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\nSource localization with MNE/dSPM/sLORETA/eLORETA\n=================================================\n\nThe aim of this tutorial is to teach you how to compute and apply a linear\ninverse method such as MNE/dSPM/sLORETA/eLORETA on evoked/raw/epochs data.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.minimum_norm import make_inverse_operator, apply_inverse"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Process MEG data\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'\n\nraw = mne.io.read_raw_fif(raw_fname) # already has an average reference\nevents = mne.find_events(raw, stim_channel='STI 014')\n\nevent_id = dict(aud_l=1) # event trigger and conditions\ntmin = -0.2 # start of each epoch (200ms before the trigger)\ntmax = 0.5 # end of each epoch (500ms after the trigger)\nraw.info['bads'] = ['MEG 2443', 'EEG 053']\nbaseline = (None, 0) # means from the first instant to t = 0\nreject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)\n\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,\n picks=('meg', 'eog'), baseline=baseline, reject=reject)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute regularized noise covariance\n------------------------------------\n\nFor more details see `tut_compute_covariance`.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"noise_cov = mne.compute_covariance(\n epochs, tmax=0., method=['shrunk', 'empirical'], rank=None, verbose=True)\n\nfig_cov, fig_spectra = mne.viz.plot_cov(noise_cov, raw.info)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute the evoked response\n---------------------------\nLet's just use MEG channels for simplicity.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"evoked = epochs.average().pick('meg')\nevoked.plot(time_unit='s')\nevoked.plot_topomap(times=np.linspace(0.05, 0.15, 5), ch_type='mag',\n time_unit='s')\n\n# Show whitening\nevoked.plot_white(noise_cov, time_unit='s')\n\ndel epochs # to save memory"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inverse modeling: MNE/dSPM on evoked and raw data\n-------------------------------------------------\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Read the forward solution and compute the inverse operator\nfname_fwd = data_path + '/MEG/sample/sample_audvis-meg-oct-6-fwd.fif'\nfwd = mne.read_forward_solution(fname_fwd)\n\n# make an MEG inverse operator\ninfo = evoked.info\ninverse_operator = make_inverse_operator(info, fwd, noise_cov,\n loose=0.2, depth=0.8)\ndel fwd\n\n# You can write it to disk with::\n#\n# >>> from mne.minimum_norm import write_inverse_operator\n# >>> write_inverse_operator('sample_audvis-meg-oct-6-inv.fif',\n# inverse_operator)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute inverse solution\n------------------------\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"method = \"dSPM\"\nsnr = 3.\nlambda2 = 1. / snr ** 2\nstc, residual = apply_inverse(evoked, inverse_operator, lambda2,\n method=method, pick_ori=None,\n return_residual=True, verbose=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualization\n-------------\nView activation time-series\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.figure()\nplt.plot(1e3 * stc.times, stc.data[::100, :].T)\nplt.xlabel('time (ms)')\nplt.ylabel('%s value' % method)\nplt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Examine the original data and the residual after fitting:\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, axes = plt.subplots(2, 1)\nevoked.plot(axes=axes)\nfor ax in axes:\n ax.texts = []\n for line in ax.lines:\n line.set_color('#98df81')\nresidual.plot(axes=axes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we use peak getter to move visualization to the time point of the peak\nand draw a marker at the maximum peak vertex.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"vertno_max, time_max = stc.get_peak(hemi='rh')\n\nsubjects_dir = data_path + '/subjects'\nsurfer_kwargs = dict(\n hemi='rh', subjects_dir=subjects_dir,\n clim=dict(kind='value', lims=[8, 12, 15]), views='lateral',\n initial_time=time_max, time_unit='s', size=(800, 800), smoothing_steps=5)\nbrain = stc.plot(**surfer_kwargs)\nbrain.add_foci(vertno_max, coords_as_verts=True, hemi='rh', color='blue',\n scale_factor=0.6, alpha=0.5)\nbrain.add_text(0.1, 0.9, 'dSPM (plus location of maximal activation)', 'title',\n font_size=14)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Morph data to average brain\n---------------------------\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# setup source morph\nmorph = mne.compute_source_morph(\n src=inverse_operator['src'], subject_from=stc.subject,\n subject_to='fsaverage', spacing=5, # to ico-5\n subjects_dir=subjects_dir)\n# morph data\nstc_fsaverage = morph.apply(stc)\n\nbrain = stc_fsaverage.plot(**surfer_kwargs)\nbrain.add_text(0.1, 0.9, 'Morphed to fsaverage', 'title', font_size=20)\ndel stc_fsaverage"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dipole orientations\n-------------------\nThe ``pick_ori`` parameter of the\n:func:`mne.minimum_norm.apply_inverse` function controls\nthe orientation of the dipoles. One useful setting is ``pick_ori='vector'``,\nwhich will return an estimate that does not only contain the source power at\neach dipole, but also the orientation of the dipoles.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"stc_vec = apply_inverse(evoked, inverse_operator, lambda2,\n method=method, pick_ori='vector')\nbrain = stc_vec.plot(**surfer_kwargs)\nbrain.add_text(0.1, 0.9, 'Vector solution', 'title', font_size=20)\ndel stc_vec"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that there is a relationship between the orientation of the dipoles and\nthe surface of the cortex. For this reason, we do not use an inflated\ncortical surface for visualization, but the original surface used to define\nthe source space.\n\nFor more information about dipole orientations, see\n`tut-dipole-orientations`.\n\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's look at each solver:\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for mi, (method, lims) in enumerate((('dSPM', [8, 12, 15]),\n ('sLORETA', [3, 5, 7]),\n ('eLORETA', [0.75, 1.25, 1.75]),)):\n surfer_kwargs['clim']['lims'] = lims\n stc = apply_inverse(evoked, inverse_operator, lambda2,\n method=method, pick_ori=None)\n brain = stc.plot(figure=mi, **surfer_kwargs)\n brain.add_text(0.1, 0.9, method, 'title', font_size=20)\n del stc"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
tensorflow/kfac | kfac/examples/keras/KFAC_vs_Adam_on_CIFAR10.ipynb | 1 | 21591 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "_DDaAex5Q7u-"
},
"source": [
"##### Copyright 2019 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "both",
"colab": {},
"colab_type": "code",
"id": "W1dWWdNHQ9L0"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "_C170SDp6jBt"
},
"source": [
"# KFAC vs Adam on CIFAR10 on a GPU\n",
"\n",
"This notebook contains the code used to run the experiment comparing KFAC and Adam on CIFAR 10 with a Resnet-20. This was run on a NVIDIA Tesla P100 for the experiment. It can be run on a public GPU colab instance.\n",
"\n",
"[](https://colab.research.google.com/github/tensorflow/kfac/blob/master/kfac/examples/keras/KFAC_vs_Adam_on_CIFAR10.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "rw0qz2RWkLeJ"
},
"outputs": [],
"source": [
"!pip install kfac"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "LfGyhnaOsgYu"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import tensorflow_datasets as tfds\n",
"import math\n",
"import kfac"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "DYWIY0C380ye"
},
"outputs": [],
"source": [
"TRAINING_SIZE = 40000\n",
"VALIDATION_SIZE = 10000\n",
"TEST_SIZE = 10000\n",
"SEED = 20190524\n",
"\n",
"num_training_steps = 7500\n",
"batch_size = 1000\n",
"layers = tf.keras.layers\n",
"\n",
"# We take the ceiling because we do not drop the remainder of the batch\n",
"compute_steps_per_epoch = lambda x: int(math.ceil(1. * x / batch_size))\n",
"steps_per_epoch = compute_steps_per_epoch(TRAINING_SIZE)\n",
"val_steps = compute_steps_per_epoch(VALIDATION_SIZE)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "GfeTgsbh5G4g"
},
"outputs": [],
"source": [
"optimizer_name = 'kfac' # 'kfac' or 'adam'\n",
"\n",
"# Best Hyperparameters from the Random Search\n",
"if optimizer_name == 'kfac':\n",
" init_learning_rate = 0.22721400059936694\n",
" final_learning_rate = 1e-04\n",
" init_damping = 0.28872127217018184\n",
" final_damping = 1e-6\n",
" momentum = 1 - 0.018580394981260295\n",
" lr_decay_rate = 1 - 0.001090107322908028\n",
" damping_decay_rate = 1 - 0.0002870880729016523\n",
"elif optimizer_name == 'adam':\n",
" init_learning_rate = 2.24266320779\n",
" final_learning_rate = 1e-4\n",
" init_epsilon = 0.183230038808\n",
" final_epsilon = 1e-8\n",
" momentum = 1 - 0.0296561513388\n",
" lr_decay_rate = 1 - 0.000610416031571\n",
" epsilon_decay_rate = 1 - 0.000212682338199\n",
"else:\n",
" raise ValueError('Ensure optimizer_name is kfac or adam')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "v3vSki-usp9k"
},
"source": [
"## Input Pipeline"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "D2U3i5kgssy_"
},
"outputs": [],
"source": [
"def _parse_fn(x):\n",
" image, label = x['image'], x['label']\n",
" image = tf.cast(image, tf.float32)\n",
" label = tf.cast(label, tf.int32)\n",
" image = image / 127.5 - 1\n",
" return image, label\n",
"\n",
"\n",
"def _augment_image(image, crop_amount, seed=None):\n",
" # Random Brightness, Contrast, Jpeg Quality, Hue, and Saturation did not\n",
" # seem to work well as augmentations for our training specifications\n",
" input_shape = image.shape.as_list()\n",
" cropped_size = [input_shape[0] - crop_amount,\n",
" input_shape[1] - crop_amount,\n",
" input_shape[2]]\n",
" flipped = tf.image.random_flip_left_right(image, seed)\n",
" cropped = tf.image.random_crop(flipped, cropped_size, seed)\n",
" return tf.image.pad_to_bounding_box(image=cropped,\n",
" offset_height=crop_amount // 2,\n",
" offset_width=crop_amount // 2,\n",
" target_height=input_shape[0],\n",
" target_width=input_shape[1])\n",
"\n",
"\n",
"def _get_raw_data():\n",
" # We split the training data into training and validation ourselves for\n",
" # hyperparameter tuning.\n",
" training_pct = int(100.0 * TRAINING_SIZE / (TRAINING_SIZE + VALIDATION_SIZE))\n",
" train_split = tfds.Split.TRAIN.subsplit(tfds.percent[:training_pct])\n",
" validation_split = tfds.Split.TRAIN.subsplit(tfds.percent[training_pct:])\n",
"\n",
" train_data, info = tfds.load('cifar10:3.*.*', with_info=True, split=train_split)\n",
" val_data = tfds.load('cifar10:3.*.*', split=validation_split)\n",
" test_data = tfds.load('cifar10:3.*.*', split='test')\n",
"\n",
" input_shape = info.features['image'].shape\n",
" num_classes = info.features['label'].num_classes\n",
" info = {'input_shape': input_shape, 'num_classes': num_classes}\n",
" return info, train_data, val_data, test_data\n",
"\n",
"\n",
"def get_input_pipeline(batch_size=None,\n",
" use_augmentation=True,\n",
" seed=None,\n",
" crop_amount=6,\n",
" drop_remainder=False,\n",
" repeat_validation=True):\n",
" \"\"\"Creates CIFAR10 Data Pipeline.\n",
"\n",
" Args:\n",
" batch_size (int): Batch size used for training.\n",
" use_augmentation (bool): If true, applies random horizontal flips and crops\n",
" then pads to images.\n",
" seed (int): Random seed used for augmentation operations.\n",
" crop_amount (int): Number of pixels to crop from the height and width of the\n",
" image. So, the cropped image will be [height - crop_amount, width -\n",
" crop_amount, channels] before it is padded to restore its original size.\n",
" drop_remainder (bool): Whether to drop the remainder of the batch. Needs to\n",
" be true to work on TPUs.\n",
" repeat_validation (bool): Whether to repeat the validation set. Test set is\n",
" never repeated.\n",
"\n",
" Returns:\n",
" A tuple with an info dict (with input_shape (tuple) and number of classes\n",
" (int)) and data dict (train_data (tf.DatasetAdapter), validation_data,\n",
" (tf.DatasetAdapter) and test_data (tf.DatasetAdapter))\n",
" \"\"\"\n",
" info, train_data, val_data, test_data = _get_raw_data()\n",
"\n",
" if not batch_size:\n",
" batch_size = max(TRAINING_SIZE, VALIDATION_SIZE, TEST_SIZE)\n",
"\n",
" train_data = train_data.map(_parse_fn).shuffle(8192, seed=seed).repeat()\n",
" if use_augmentation:\n",
" train_data = train_data.map(\n",
" lambda x, y: (_augment_image(x, crop_amount, seed), y))\n",
" train_data = train_data.batch(\n",
" min(batch_size, TRAINING_SIZE), drop_remainder=drop_remainder)\n",
" train_data = train_data.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n",
"\n",
" val_data = val_data.map(_parse_fn)\n",
" if repeat_validation:\n",
" val_data = val_data.repeat()\n",
" val_data = val_data.batch(\n",
" min(batch_size, VALIDATION_SIZE), drop_remainder=drop_remainder)\n",
" val_data = val_data.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n",
"\n",
" # Don't repeat test data because it is only used once to evaluate at the end.\n",
" test_data = test_data.map(_parse_fn)\n",
" if repeat_validation:\n",
" test_data = test_data.repeat()\n",
" test_data = test_data.batch(\n",
" min(batch_size, TEST_SIZE), drop_remainder=drop_remainder)\n",
" test_data = test_data.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\n",
"\n",
" data = {'train': train_data, 'validation': val_data, 'test': test_data}\n",
" return data, info"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "SLvlpsups2aR"
},
"source": [
"## Model - Resnet V2\n",
"\n",
"Based on https://keras.io/examples/cifar10_resnet/. The only difference is that tf.keras layer implementations are used."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Cch3Ld5Ds4i2"
},
"outputs": [],
"source": [
"def resnet_layer(inputs,\n",
" num_filters=16,\n",
" kernel_size=3,\n",
" strides=1,\n",
" activation='relu',\n",
" batch_normalization=True,\n",
" conv_first=True):\n",
" \"\"\"2D Convolution-Batch Normalization-Activation stack builder.\n",
"\n",
" Based on https://keras.io/examples/cifar10_resnet/.\n",
"\n",
" Args:\n",
" inputs (tensor): input tensor from input image or previous layer\n",
" num_filters (int): Conv2D number of filters\n",
" kernel_size (int): Conv2D square kernel dimensions\n",
" strides (int): Conv2D square stride dimensions\n",
" activation (string): activation name\n",
" batch_normalization (bool): whether to include batch normalization\n",
" conv_first (bool): conv-bn-activation (True) or bn-activation-conv (False)\n",
"\n",
" Returns:\n",
" x (tensor): tensor as input to the next layer\n",
" \"\"\"\n",
" conv = layers.Conv2D(num_filters,\n",
" kernel_size=kernel_size,\n",
" strides=strides,\n",
" padding='same',\n",
" kernel_initializer='he_normal',\n",
" kernel_regularizer=tf.keras.regularizers.l2(1e-4))\n",
"\n",
" x = inputs\n",
" if conv_first:\n",
" x = conv(x)\n",
" if batch_normalization:\n",
" x = layers.BatchNormalization()(x)\n",
" if activation is not None:\n",
" x = layers.Activation(activation)(x)\n",
" else:\n",
" if batch_normalization:\n",
" x = layers.BatchNormalization()(x)\n",
" if activation is not None:\n",
" x = layers.Activation(activation)(x)\n",
" x = conv(x)\n",
" return x\n",
"\n",
"\n",
"def resnet_v2(input_shape, depth, num_classes=10):\n",
" \"\"\"ResNet Version 2 Model builder [b].\n",
"\n",
" Based on https://keras.io/examples/cifar10_resnet/.\n",
"\n",
" Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or also known as\n",
" bottleneck layer\n",
" First shortcut connection per layer is 1 x 1 Conv2D.\n",
" Second and onwards shortcut connection is identity.\n",
" At the beginning of each stage, the feature map size is halved (downsampled)\n",
" by a convolutional layer with strides=2, while the number of filter maps is\n",
" doubled. Within each stage, the layers have the same number filters and the\n",
" same filter map sizes.\n",
" Features maps sizes:\n",
" conv1 : 32x32, 16\n",
" stage 0: 32x32, 64\n",
" stage 1: 16x16, 128\n",
" stage 2: 8x8, 256\n",
"\n",
" Args:\n",
" input_shape (tuple/list): shape of input image tensor\n",
" depth (int): number of core convolutional layers\n",
" num_classes (int): number of classes (CIFAR10 has 10)\n",
"\n",
" Returns:\n",
" model (Model): Keras model instance\n",
" \"\"\"\n",
" if (depth - 2) % 9 != 0:\n",
" raise ValueError('depth should be 9n+2 (eg 56 or 110 in [b])')\n",
" # Start model definition.\n",
" num_filters_in = 16\n",
" num_res_blocks = int((depth - 2) / 9)\n",
"\n",
" inputs = tf.keras.Input(shape=input_shape)\n",
" # v2 performs Conv2D with BN-ReLU on input before splitting into 2 paths\n",
" x = resnet_layer(inputs=inputs, num_filters=num_filters_in, conv_first=True)\n",
"\n",
" # Instantiate the stack of residual units\n",
" for stage in range(3):\n",
" for res_block in range(num_res_blocks):\n",
" activation = 'relu'\n",
" batch_normalization = True\n",
" strides = 1\n",
" if stage == 0:\n",
" num_filters_out = num_filters_in * 4\n",
" if res_block == 0: # first layer and first stage\n",
" activation = None\n",
" batch_normalization = False\n",
" else:\n",
" num_filters_out = num_filters_in * 2\n",
" if res_block == 0: # first layer but not first stage\n",
" strides = 2 # downsample\n",
"\n",
" # bottleneck residual unit\n",
" y = resnet_layer(inputs=x,\n",
" num_filters=num_filters_in,\n",
" kernel_size=1,\n",
" strides=strides,\n",
" activation=activation,\n",
" batch_normalization=batch_normalization,\n",
" conv_first=False)\n",
" y = resnet_layer(inputs=y, num_filters=num_filters_in, conv_first=False)\n",
" y = resnet_layer(inputs=y,\n",
" num_filters=num_filters_out,\n",
" kernel_size=1,\n",
" conv_first=False)\n",
" if res_block == 0:\n",
" # linear projection residual shortcut connection to match\n",
" # changed dims\n",
" x = resnet_layer(inputs=x,\n",
" num_filters=num_filters_out,\n",
" kernel_size=1,\n",
" strides=strides,\n",
" activation=None,\n",
" batch_normalization=False)\n",
" x = layers.Add()([x, y])\n",
"\n",
" num_filters_in = num_filters_out\n",
"\n",
" # Add classifier on top.\n",
" # v2 has BN-ReLU before Pooling\n",
" x = layers.BatchNormalization()(x)\n",
" x = layers.Activation('relu')(x)\n",
" x = layers.AveragePooling2D(pool_size=8)(x)\n",
" y = layers.Flatten()(x)\n",
" outputs = layers.Dense(num_classes,\n",
" activation='softmax',\n",
" kernel_initializer='he_normal')(y)\n",
"\n",
" # Instantiate model.\n",
" model = tf.keras.Model(inputs=inputs, outputs=outputs)\n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "dAUaN-i9tHMY"
},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Hf5WFHYP8tT9"
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"tf.set_random_seed(SEED)\n",
"\n",
"data, info = get_input_pipeline(batch_size=batch_size,\n",
" seed=SEED,\n",
" repeat_validation=True,\n",
" use_augmentation=True)\n",
"\n",
"model = resnet_v2(input_shape=info['input_shape'],\n",
" depth=20,\n",
" num_classes=info['num_classes'])\n",
"\n",
"loss = 'sparse_categorical_crossentropy'\n",
"\n",
"training_callbacks = [\n",
" kfac.keras.callbacks.ExponentialDecay(hyperparameter='learning_rate',\n",
" init_value=init_learning_rate,\n",
" final_value=final_learning_rate,\n",
" decay_rate=lr_decay_rate)\n",
"]\n",
"\n",
"if optimizer_name == 'kfac':\n",
" opt = kfac.keras.optimizers.Kfac(learning_rate=init_learning_rate,\n",
" damping=init_damping,\n",
" model=model,\n",
" loss=loss,\n",
" momentum=momentum,\n",
" seed=SEED)\n",
" training_callbacks.append(kfac.keras.callbacks.ExponentialDecay(\n",
" hyperparameter='damping',\n",
" init_value=init_damping,\n",
" final_value=final_damping,\n",
" decay_rate=damping_decay_rate))\n",
"\n",
"elif optimizer_name == 'adam':\n",
" opt = tf.keras.optimizers.Adam(learning_rate=init_learning_rate,\n",
" beta_1=momentum,\n",
" epsilon=init_epsilon)\n",
" training_callbacks.append(kfac.keras.callbacks.ExponentialDecay(\n",
" hyperparameter='epsilon',\n",
" init_value=init_epsilon,\n",
" final_value=final_epsilon,\n",
" decay_rate=epsilon_decay_rate))\n",
"\n",
"else:\n",
" raise ValueError('optimizer_name must be \"adam\" or \"kfac\"')\n",
"\n",
"model.compile(loss=loss, optimizer=opt, metrics=['acc'])"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "dD8b27hLy6lO"
},
"outputs": [],
"source": [
"history = model.fit(x=data['train'],\n",
" epochs=num_training_steps//steps_per_epoch,\n",
" steps_per_epoch=steps_per_epoch,\n",
" validation_data=data['validation'],\n",
" validation_steps=val_steps,\n",
" callbacks=training_callbacks)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"_DDaAex5Q7u-"
],
"last_runtime": {
"build_target": "",
"kind": "local"
},
"name": "KFAC vs Adam on CIFAR10.ipynb",
"provenance": [
{
"file_id": "1pqtoYduODZyJKt4-kwVkt_KtNQCnaNDp",
"timestamp": 1565229994386
}
],
"version": "0.3.2"
},
"kernelspec": {
"display_name": "Python 2",
"name": "python2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
zzsza/Datascience_School | 12. 추정 및 검정/02. 검정과 유의 확률.ipynb | 2 | 202380 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "e366c994b84e45ce81ca615e2f61250f"
},
"source": [
"# 검정과 유의 확률"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "08e6e95c92d146b79af0c0facba90c9e"
},
"source": [
"**검정(testing)**은 데이터 뒤에 숨어있는 확률 변수의 분포와 모수에 대한 가설의 진위를 정량적(quantitatively)으로 증명하는 작업을 말한다."
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "d2762322694e4f608412735e6feffd60"
},
"source": [
"[[school_notebook:87b67dafd7544e3380af278ff4f22d77]]"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "1abace5a14db49d19d992128fb862448"
},
"source": [
"예를 들어 다음과 같은 문제가 주어졌다면 어떻게 풀겠는가?"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "b1ebd502176b4836b5c71ff5fb5e8f10"
},
"source": [
"* 문제1\n",
"\n",
"<blockquote> \n",
"어떤 동전을 15번 던졌더니 12번이 앞면이 나왔다. 이 동전은 휘어지지 않은 공정한 동전(fair coin)인가?\n",
"</blockquote>"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "88c6d96390ce4a4797c46dd173665b38"
},
"source": [
"* 문제2\n",
"\n",
"<blockquote> \n",
"어떤 트레이더의 일주일 수익률은 다음과 같다.:<br>\n",
"-2.5%, -5%, 4.3%, -3.7% -5.6% <br>\n",
"이 트레이더는 돈을 벌어다 줄 사람인가, 아니면 돈을 잃을 사람인가? \n",
"</blockquote>"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "cf3289eed06a4906bf5b739d5476591a"
},
"source": [
"이러한 문제들을 데이터 분석의 방법론으로 푼다면 다음과 같이 풀 수 있다.\n",
"\n",
"1. 데이터가 어떤 고정된(fixed) 확률 분포를 가지는 확률 변수라고 가정한다. 동전은 베르누이 분포를 따르는 확률 변수의 표본이며 트레이더의 수익률은 정규 분포를 따르는 확률 변수의 표본이라고 가정한다.\n",
"\n",
"2. 이 확률 분포의 모수값이 특정한 값을 가지는지 혹은 특정한 값보다 크거나 같은지 알고자 한다. 동전이 공정한 동전이라고 주정하는 것은 그 뒤의 베르누이 확률 분포의 모수 $\\theta$의 값이 0.5 이라고 주장하는 것과 같다. 트레이더가 장기적으로 돈을 벌어다 줄 것이라고 주장하는 것은 그 뒤의 정규 분포의 기댓값 모수 $\\mu$ 가 0보다 크거나 같다고 주장하는 것이다.\n",
"\n",
"3. 모수 값이 이러한 주장을 따른다고 가정하면 실제로 현실에 나타난 데이터가 나올 확률을 계산할 수 있다. 동전의 경우에는 공정한 동전임에도 불구하고 15번 중 12번이나 앞면이 나올 확률을 계산할 수 있으며 트레이더의 경우에는 정규 분포에서 해당 데이터가 나올 확률을 계산할 수 있다.\n",
"\n",
"4. 이렇게 구한 확률의 값이 판단자가 정한 특정한 기준에 미치지 못한다면 이러한 주장이 틀렸다고 생각할 수 밖에 없다. 반대로 값이 기준보다 높다면 그 주장이 틀렸다고 판단할 증거가 부족한 것이다."
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "01c507456a5645a2a6867965fbcb6b80"
},
"source": [
"## 가설"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "65d026dd0fa3417dbb6913887667db15"
},
"source": [
"이렇게 확률 분포에 대한 어떤 주장을 가설(hypothesis)이라고 하며 $H$로 표기하는 경우가 많다. 이 가설을 증명하는 행위를 통계적 가설 검정(statistical hypothesis testing) 줄여서 검정(testing)이라고 한다. 특히 확률 분포의 모수 값이 특정한 값을 가진다는 주장을 모수 검정 (parameter testing)이라고 한다. \n",
"\n",
"가장 일반적으로 사용되는 가설은 모수의 값이 0 이라는 가설이다. \n",
"\n",
"$$ H: \\theta = 0 $$\n",
"\n",
"이 가설은 회귀 분석(regression)에서 흔하게 사용되는데 회귀 계수의 값이 0 이면 종속 변수(target)가 해당 독립 변수(feature)의 영향을 받지 않는 다는 의미가 된다."
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "3ee48965237d4473990f3cd798ee6163"
},
"source": [
"## 검정 방법론"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "68bc265c4cd34feeb66816fd021cb783"
},
"source": [
"가설 증명, 즉 검정의 기본적인 논리는 다음과 같다.\n",
"\n",
"1. 만약 가설이 맞다면 즉, 모수 값이 특정한 조건을 만족한다면 해당 확률 변수로부터 만들어진 표본(sample) 데이터들은 어떤 규칙을 따르게 된다.\n",
"\n",
"2. 해당 규칙에 따라 표본 데이터 집합에서 어떤 숫자를 계산하면 계산된 숫자는 특정한 확률 분포를 따르게 된다. 이 숫자를 **검정 통계치(test statistics)**라고 하며 확률 분포를 **검정 통계 분포(test statistics distribution)**라고 한다. 검정 통계 분포의 종류 및 모수의 값은 처음에 정한 가설에 의해 결정된다. 이렇게 검정 통계 분포를 결정하는 최초의 가설을 **귀무 가설(Null hypothesis)**이라고 한다.\n",
"\n",
"3. 데이터에 의해서 실제로 계산된 숫자, 즉, 검정 통계치가 해당 검정 통계 분포에서 나올 수 있는 확률을 계산한다. 이를 **유의 확률(p-value)라**고 한다.\n",
"\n",
"4. 만약 유의 확률이 미리 정한 특정한 기준값보다 작은 경우를 생각하자. 이 기준값을 **유의 수준(significance level)**이라고 하는 데 보통 1% 혹은 5% 정도의 작은 값을 지정한다. 유의 확률이 유의 수준으로 정한 값(예 1%)보다도 작다는 말은 해당 검정 통계 분포에서 이 검정 통계치가 나올 수 있는 확률이 아주 작다는 의미이므로 가장 근본이 되는 가설 즉, 귀무 가설이 틀렸다는 의미이다. 따라서 이 경우에는 귀무 가설을 **기각(reject)**한다.\n",
"\n",
"5. 만약 유의 확률이 유의 수준보다 크다면 해당 검정 통계 분포에서 이 검정 통계치가 나오는 것이 불가능하지만은 않다는 의미이므로 귀무 가설을 기각할 수 없다. 따라서 이 경우에는 귀무 가설을 **채택(accept)**한다."
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "e9908998a20040f2b5c1c160a0b6d64b"
},
"source": [
"## 귀무 가설과 대립 가설"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "161f109dc2094bc080209a947d377d61"
},
"source": [
"검정 작업을 하기 위해서는 기각 혹은 채택하고자 하는 가설을 만들어야 한다. 이러한 가설을 귀무 가설(Null Hypothesis)이라고 하며 $H_0$ 로 표기한다. 일반적으로 검정에서 그냥 가설이라고 하면 귀무가설을 가리킨다. 귀무 가설이 사실이라고 증명되면 채택(accept)하고 거짓이라고 증명되면 기각(reject)한다."
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "59e0a587b3bd41d5ab40f8b65219cca6"
},
"source": [
"귀무 가설이 기각되면 채택할 수도 있는 가설을 대립 가설(Alternative Hypothesis)이라고 하며 보통 $H_a$ 로 표기한다. \n",
"\n",
"예를 들어 귀무 가설 $H_0$가 다음과 같다고 가정하면,\n",
"$$ H_0: \\theta = 0 $$\n",
"\n",
"다음 가설들은 이 귀무 가설에 대한 대립 가설이 될 수 있다.\n",
"$$ H_a: \\theta \\neq 0 $$\n",
"$$ H_a: \\theta > 0 $$\n",
"$$ H_a: \\theta < 0 $$\n",
"\n",
"첫번째와 같은 형태의 대립 가설을 가지는 경우를 양측 검정(two-tailed testing), 두번째나 세번째와 같은 형태의 대립 가설을 가지는 경우를 단측 검정(one-tailed testing)이라고 한다."
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "f62b5d3bf0da4550a04371dde2cb5beb"
},
"source": [
"## 검정 통계량"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "e8c3cc775ac440ceb7c30c80aff0f049"
},
"source": [
"검정을 하려면 즉, 귀무 가설이 맞거나 틀린 것을 증명하려면 어떤 증거가 있어야 한다. 이 증거에 해당하는 숫자를 검정 통계량(test statistics)라고 한다. \n",
"\n",
"비유를 들어보자.\n",
"\n",
"\"어떤 병에 걸렸다\"라는 가설을 증명하려면 환자의 혈액을 채취하여 혈액 내의 특정한 성분의 수치를 측정해야 한다고 가정하자. 이 때 해당 수치가 바로 검정 통계량이 된다.\n",
"\n",
"\"어떤 학생이 우등 상장을 받을 수 있는 우등생이다\"라는 가설을 증명하려면 시험(test)에 대한 성적을 측정하면 된다. 이 시험 성적을 검정 통계량이라고 부를 수 있다."
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "66a0f080efdb4ec988641f4c1926cdb6"
},
"source": [
"데이터 분석의 경우 검정 통계량은 데이터로부터 계산되는 일종의 함수이다.\n",
"\n",
"$$\n",
"\\text{test statistics } t = f(x_1, x_2, \\ldots, x_n)\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "8212fb27fcd241319ec7c6a5fd9a2941"
},
"source": [
"예를 들어 동전을 $N$번 던질 경우 앞면이 나온 횟수가 $n$ 자체가 검정 통계량이 될 수 있다. \n",
"\n",
"\n",
"정규 분포를 따르는 수익률의 경우라면 $N$개의 수익률 데이터 $x_1, \\ldots, x_N$에서 다음 수식으로 계산한 값도 검정 통계량이 된다. \n",
"\n",
"\n",
"$$\n",
"t = \\dfrac{m}{\\frac{s}{\\sqrt{N}}}\n",
"$$\n",
"\n",
"여기에서\n",
"\n",
"$$\n",
"m = \\dfrac{1}{N}\\sum_{i=1}^{N} x_i\n",
"$$\n",
"\n",
"$$\n",
"s^2 = \\dfrac{1}{N}\\sum_{i=1}^{N} (x_i-m)^2\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "918080d17c9a4da8a5849d9d8b64da4b"
},
"source": [
"검정 통계량은 표본 자료에서 계산된 함수값이므로 표본처럼 확률적(random)이다. 즉, 경우에 따라 표본 값이 달라질 수 있는 것처럼 달라진 표본값에 의해 검정 통계량도 달라진다. 따라서 검정 통계량 $t$ 도 검정 통계량 확률 변수 $T$ 라는 확률 변수의 표본으로 볼 수 있다."
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "a996812dd14448e5a1ec592b1f888b2a"
},
"source": [
"데이터에 대한 아무 함수나 검정 통계량이 될 수 있는 것이 아닌다. 어떤 함수가 검정 통계량이 되려면 **귀무 가설이 사실일 경우 표본에서 계산된 검정 통계량이 따르는 검정 통계량 확률 변수 $T$의 확률 분포를 귀무 가설로부터 알 수 있어야만 한다.**\n",
"\n",
"예를 들어 \"어떤 병에 걸렸다\"는 가설을 혈액 성분 수치로부터 판단하려면 병에 걸린 환자의 성분 수치가 어떤 분포를 따르는지 알 수 있어야 한다. 현실에서는 실제로 병에 걸린 다수의 환자의 혈액 성분 수치를 사용하여 검정 통계량 분포를 구한다. 또한 \"어떤 학생이 우등생이다\"라는 가설을 시험 성적으로부터 판단하라면 우등생인 모든 학생의 시험 성적에 대한 분포를 구해야 한다.\n",
"\n",
"데이터 분석에서는 어떤 귀무 가설을 만족하는 표본을 입력 변수로 놓고 특정한 함수로 계산한 검정 통계량이 특정한 분포를 따른다는 것을 수학적인 증명을 통해 보이는 것이 일반적이다. 통계학자들의 중요한 업적 중의 하나가 특정한 귀무 가설에 대해 어떤 검정 통계량 함수가 어떤 검정 통계량 분포를 따른 다는 것을 증명해 준 것이다. "
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "86a7a166bf3d4cc388f23d0dde88d294"
},
"source": [
"## 검정 통계량의 예"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "f2469373c46649648ad4309d74425dbf"
},
"source": [
"일반적으로 많이 사용되는 검정 통계량에는 다음과 같은 것들이 있다."
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "a364125cf5654b4da87801b24bb6eed8"
},
"source": [
"### 1. 베르누이 분포 확률 변수"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "487776c92c8b45c28519d2590a051983"
},
"source": [
"모수 $\\theta$를 가지는 베르누이 분포 확률 변수에 대해서는 전체 시도 횟수 $N$ 번 중 성공한 횟수 $n$ 자체를 검정 통계량으로 쓸 수 있다. 이 검정 통계량은 자유도 $N$과 모수 $\\theta$를 가지는 이항 분포를 따른다.\n",
"\n",
"$$ x \\sim \\text{Ber} \\;\\; \\rightarrow \\;\\; t = \\sum x \\sim \\text{Bin} $$"
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"source": [
"### 2. 카테고리 분포 확률 변수"
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"모수 벡터 $\\alpha$를 가지는 카테고리 분포 확률 변수에 대해서는 전체 시도 횟수 $N$ 번 중 성공한 횟수 벡터 $x$ 자체를 검정 통계량으로 쓸 수 있다. 이 검정 통계량은 자유도 $N$과 모수 벡터 $\\alpha$를 가지는 다항 분포를 따른다.\n",
"\n",
"$$ x \\sim \\text{Cat} \\;\\; \\rightarrow \\;\\; t = \\sum x \\sim \\text{Mul} $$"
]
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"source": [
"### 3. 분산 $\\sigma^2$ 값을 알고 있는 정규 분포 확률 변수"
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"분산 모수 $\\sigma^2$의 값을 알고 있는 정규 분포 확률 변수에 대해서는 다음과 같이 샘플 평균을 정규화(nomarlize)한 값을 검정 통계량으로 쓴다. 이 검정 통계량은 표준 정규 분포를 따른다. 이 검정 통계량은 특별히 $z$라고 부른다.\n",
"\n",
"\n",
"$$\n",
"x \\sim \\mathcal{N}(\\mu, \\sigma^2) \\;\\; \\rightarrow \\;\\; z = \\dfrac{m-\\mu}{\\frac{\\sigma}{\\sqrt{N}}} \\sim \\mathcal{N}(z;0,1)\n",
"$$\n",
"\n",
"여기에서 $m$은 샘플 평균\n",
"\n",
"$$\n",
"m = \\dfrac{1}{N}\\sum_{i=1}^{N} x_i\n",
"$$"
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"source": [
"### 4. 분산 $\\sigma^2$ 값을 모르는 정규 분포 확률 변수"
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"이번에는 분산 모수 $\\sigma^2$의 값을 모르는 정규 분포 확률 변수를 고려하자.\n",
"\n",
"평균 모수 $\\mu$ 에 대한 검정을 할 때는 다음과 같이 샘플 평균을 샘플 분산으로 정규화(nomarlize)한 값을 검정 통계량으로 쓴다. 이 검정 통계량은 자유도가 $N-1$인 표준 student-t 분포를 따른다. $N$은 데이터의 수이다.\n",
"\n",
"\n",
"$$\n",
"x \\sim \\mathcal{N}(\\mu, \\sigma^2) \\;\\; \\rightarrow \\;\\; t = \\dfrac{m-\\mu}{\\frac{s}{\\sqrt{N}}} \\sim t(t;0,1,N-1)\n",
"$$\n",
"\n",
"여기에서 $m$은 샘플 평균\n",
"\n",
"$$\n",
"m = \\dfrac{1}{N}\\sum_{i=1}^{N} x_i\n",
"$$\n",
"\n",
"$s^2$은 샘플 분산이다.\n",
"$$\n",
"s^2 = \\dfrac{1}{N-1}\\sum_{i=1}^{N} (x_i-m)^2\n",
"$$"
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"분산 모수 $\\sigma^2$에 대한 검정을 할 때는 다음과 같이 샘플 분산을 정규화(normalize)한 값을 검정 통계량으로 쓴다. 이 검정 통계량은 자유도가 $N-1$인 카이 제곱 분포를 따른다. $N$은 데이터의 수이다.\n",
"\n",
"$$\n",
"x \\sim \\mathcal{N}(\\mu, \\sigma^2) \\;\\; \\rightarrow \\;\\; t = (N-1)\\dfrac{s^2}{\\sigma^2} \\sim \\chi^2 (t;N-1)\n",
"$$"
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"source": [
"## 유의 확률 p-value"
]
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"귀무 가설이 사실이라는 가정하에 검정 통계량이 따르는 검정 통계량 분포를 알고 있다면 실제 데이터에서 계산한 검정 통계량 숫자가 분포에서 어느 부분쯤에 위치해 있는지를 알 수 있다. 이 위치를 나타내는 값이 바로 **유의 확률(p-value)** 이다.\n",
"\n",
"검정 통계량의 유의 확률은 **검정 통계량 숫자보다 더 희귀한(rare) 값이면서 대립 가설을 따르는 값이 나올 수 있는 확률**을 말한다. 이 확률은 검정 통계 확률 분포 밀도 함수(pdf)에서 양 끝의 꼬리(tail)부분에 해당하는 영역의 면적으로 계산한다. 실제로는 누적 확률 분포 함수를 사용한다.\n",
"\n",
"유의 확률은 같은 귀무 가설에 대해서도 대립 가설이 어떤 것인가에 따라 달라질 수 있다. "
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"source": [
"xx1 = np.linspace(-4, 4, 100)\n",
"xx2 = np.linspace(-4, -2, 100)\n",
"xx3 = np.linspace(2, 4, 100)\n",
"\n",
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"\n",
"plt.subplot(3, 1, 2)\n",
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"plt.title(r\"Test statistics = 2. One-tailed test. $H_a: \\mu > 0$\")\n",
"\n",
"plt.subplot(3, 1, 3)\n",
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"plt.fill_between(xx2, sp.stats.norm.pdf(xx2), facecolor='blue', alpha=0.35)\n",
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"plt.title(r\"Test statistics = -2. One-tailed test. $H_a: \\mu < 0$\")\n",
"\n",
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},
{
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"metadata": {
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"source": [
"유의 확률의 값이 아주 작으면 귀무 가설이 맞다는 가정하에 계산된 검정 통계량이 나올 가능성이 희귀하다는 의미이다. \n",
"\n",
"다시 예를 들자면 \"어떤 병에 걸렸다\"는 귀무 가설을 증명하기 위한 검정에서 혈액 검사를 사용하여 계산한 유의확률이 0.02%라는 의미는 실제로 병에 걸린 환자들 중 혈액 검사 수치가 해당 환자의 혈액 검사 수치보다 낮은 사람은 0.02% 뿐이었다는 뜻이고 \"어떤 학생이 우등생이다.\"라는 귀무사설을 증명하기 위한 검정에서 시험 성적을 사용하여 계산한 유의확률이 0.3%라는 의미는 실제로 우등생의 성적을 분석해 보면 실수로 시험을 잘 못치른 경우를 포함해도 해당 점수보다 나쁜 경우는 0.3%에 지나지 않는다는 뜻이다.\n",
"\n",
"따라서 이렇게 유의 확률의 값이 아주 작은 숫자가 나오면 해당 귀무 가설을 기각할 수 있다."
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "8446d8ad9ffe40bcae14b551c23ef0bc"
},
"source": [
"## 유의 수준과 기각역"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "dcdc65c683c24764a33d1cec41589915"
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"source": [
"계산된 유의 확률 값에 대해 귀무 가설을 기각하는지 채택하는지를 결정할 수 있는 기준 값을 유의 수준(level of significance)라고 한다. 일반적으로 사용되는 유의 수준은 1%, 5%, 10% 등이다.\n",
"\n",
"검정 통계량이 나오면 확률 밀도 함수(또는 누적 확률 함수)를 사용하여 유의 확률을 계산할 수 있는 것처럼 반대로 특정한 유의 확률 값에 대해 해당하는 검정 통계량을 계산할 수도 있다. 유의 수준에 대해 계산된 검정 통계량을 기각역(critical value)라고 한다.\n",
"\n",
"기각역 값을 알고 있다면 유의 확률을 유의 수준과 비교하는 것이 아니라 검정 통계량을 직접 기각역과 비교하여 기각/채택 여부를 판단할 수도 있다."
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "b60eca4086e4454591f2ce749cbc3da0"
},
"source": [
"## 검정의 예"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "21eb88f74605471d93189344242eef92"
},
"source": [
"이제 서두에서 제기한 문제를 다시 풀어보자."
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "24301de086ca4c159e0a551ab2f69c0f"
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"source": [
"* 문제1\n",
"\n",
"<blockquote> \n",
"어떤 동전을 15번 던졌더니 12번이 앞면이 나왔다. 이 동전은 휘어지지 않은 공정한 동전(fair coin)인가?\n",
"</blockquote>"
]
},
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"cell_type": "markdown",
"metadata": {
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"source": [
"동전의 앞면이 나오는 것을 숫자 1, 뒷면이 나오는 것을 숫자 0으로 나타낸다면 이 문제는 베르누이 확률 변수의 모수 검정 문제로 생각할 수 있다. 판단하고자하는 귀무 가설은 베르누이 확률 분포 모수 $\\theta = 0.5$이다. \n",
"\n",
"이 문제에 대한 검정 통계량은 15번 던져 앞면이 나온 횟수가 12이고 이 값은 자유도가 15인 이항 분포를 따른다. 이 경우의 유의 확률을 계산하면 \n",
"1.76% 이다.\n",
"$$ \\text{Bin}(n \\geq 12;N=15) = 0.017578125 $$"
]
},
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"execution_count": 2,
"metadata": {
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"source": [
"1 - sp.stats.binom(15, 0.5).cdf(12-1)"
]
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"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "811c0d31a35e4c308fb43dd8a100a2f9"
},
"source": [
"이 값은 5% 보다는 작고 1% 보다는 크기 때문에 유의 수준이 5% 라면 기각할 수 있으며(즉 공정한 동전이 아니라고 말할 수 있다.) 유의 수준이 1% 라면 기각할 수 없다.(즉, 공정한 동전이 아니라고 말할 수 없다.)"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "719566b3f8c941bc9df784887de97953"
},
"source": [
"* 문제2\n",
"\n",
"<blockquote> \n",
"어떤 트레이더의 일주일 수익률은 다음과 같다.:<br>\n",
"-2.5%, -5%, 4.3%, -3.7% -5.6% <br>\n",
"이 트레이더는 돈을 벌어다 줄 사람인가, 아니면 돈을 잃을 사람인가? \n",
"</blockquote>"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "241b569358004affb1f29aad847f9f9d"
},
"source": [
"수익률이 정규 분포를 따른 다고 가정하면 이 트레이더의 검정통계량은 다음과 같이 계산된다.\n",
"\n",
"$$ t = \\dfrac{m}{\\frac{s}{\\sqrt{N}}} = -1.4025 $$\n",
"\n",
"이 검정 통계량에 대한 유의 확률은 11.67%이다.\n",
"\n",
"$$ F(t=-1.4025;4) = 0.1167 $$"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"school_cell_uuid": "47c7632b55bb4f62974155cef74007ec"
},
"outputs": [
{
"data": {
"text/plain": [
"(-1.4025921414082105, 0.11669216509589829)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = np.array([-0.025, -0.05, 0.043, -0.037, -0.056])\n",
"t = x.mean()/x.std(ddof=1)*np.sqrt(len(x))\n",
"t, sp.stats.t(df=4).cdf(t)"
]
},
{
"cell_type": "markdown",
"metadata": {
"school_cell_uuid": "5573c7a910d04305aab626d155bb12c2"
},
"source": [
"만약 유의 수준이 10%라면 유의 확률이 이보다 크기 때문에 귀무 가설을 기각할 수 없다. 즉, 정규 분포의 기댓값이 0 보다 작다고 말할수 없다. 따라서 해당 트레이더가 장기적으로 손실을 보는 트레이더라고 말할 수 있는 증거가 부족하다는 의미이다."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | mit |
limiear/soyprice | soyprice/notes/Soy maximum prices.ipynb | 2 | 3396 | {
"metadata": {
"name": "",
"signature": "sha256:f8ca1583ad39f712aeccc4875ae2f7f21b357adcc45421bae6264951f3cddba2"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sys\n",
"sys.path.append('../')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from variables.core import get_var\n",
"from datetime import datetime\n",
"soy = get_var(name=u'soy/afascl')\n",
"to_date = lambda m: datetime.strptime(m, '%Y-%m-%d').date()\n",
"data = map(lambda c: (to_date(c['moment']), float(c['value'])), soy['changes'])\n",
"years = sorted(list(set(map(lambda d: d[0].year, data))))\n",
"print years\n",
"print '-' * 80\n",
"rank = {}\n",
"for y in years:\n",
" values = filter(lambda d: d[0].year == y, data)\n",
" m = max(map(lambda v: v[1], values))\n",
" elems = filter(lambda v: v[1] > m * 0.96, values)\n",
" months = map(lambda e: e[0].month, elems)\n",
" print y, m, m * 0.96, months\n",
" for m in months:\n",
" rank[m] = rank.get(m, 0) - 1 / (y - 2017)\n",
"print '-' * 80\n",
"print rank\n",
"{k:v for k, v in rank.items() if v > 13}"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016]\n",
"--------------------------------------------------------------------------------\n",
"2007 910.0 873.6 [12, 12, 12]\n",
"2008 1173.0 1126.08 [2, 2, 2, 3]\n",
"2009 1100.0 1056.0 [6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7]\n",
"2010 1470.0 1411.2 [12, 12, 12, 12, 12]\n",
"2011 1500.0 1440.0 [1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2]\n",
"2012 2050.0 1968.0 [8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12]\n",
"2013 2300.0 2208.0 [11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12]\n",
"2014 3000.0 2880.0 [3]\n",
"2015 3000.0 2880.0 [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12]\n",
"2016 3650.0 3504.0 [2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3]\n",
"--------------------------------------------------------------------------------\n",
"{1: 8, 2: 20, 3: 5, 6: 11, 7: 1, 8: 4, 9: 15, 10: 3, 11: 23, 12: 59}\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"{2: 20, 9: 15, 11: 23, 12: 59}"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"3000 * 1.3043"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"3912.9"
]
}
],
"prompt_number": 8
}
],
"metadata": {}
}
]
} | gpl-3.0 |
maxalbert/tohu | notebooks/v6/Primitive_generators.ipynb | 1 | 21404 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Primitive generators"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook contains tests for tohu's primitive generators."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import tohu\n",
"from tohu.v6.primitive_generators import *\n",
"from tohu.v6.generator_dispatch import *\n",
"from tohu.v6.utils import print_generated_sequence"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tohu version: v0.5.2+316.g62e480a\n"
]
}
],
"source": [
"print(f'Tohu version: {tohu.__version__}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Constant"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`Constant` simply returns the same, constant value every time."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"g = Constant('quux')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence: quux, quux, quux, quux, quux, quux, quux, quux, quux, quux\n"
]
}
],
"source": [
"print_generated_sequence(g, num=10, seed=12345)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Boolean"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`Boolean` returns either `True` or `False`, optionally with different probabilities."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"g1 = Boolean()\n",
"g2 = Boolean(p=0.8)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence: True, True, False, True, True, True, False, True, True, True, False, True, False, True, False, True, False, True, False, True\n",
"Generated sequence: True, True, False, True, True, True, True, False, True, False, True, True, True, True, True, True, True, True, False, True\n"
]
}
],
"source": [
"print_generated_sequence(g1, num=20, seed=12345)\n",
"print_generated_sequence(g2, num=20, seed=99999)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Incremental"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`Incremental` returns a sequence of numbers that increase in regular steps."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"g = Incremental(start=200, step=4)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence: 200, 204, 208, 212, 216, 220, 224, 228, 232, 236, 240, 244, 248, 252, 256, 260, 264, 268, 272, 276\n"
]
}
],
"source": [
"print_generated_sequence(g, num=20, seed=12345)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Integer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`Integer` returns a random integer between `low` and `high` (both inclusive)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"g = Integer(low=100, high=200)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence: 102, 164, 118, 185, 182, 124, 149, 158, 100, 160, 162, 179, 145, 109, 122, 196, 197, 141, 147, 106\n"
]
}
],
"source": [
"print_generated_sequence(g, num=20, seed=12345)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Float"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`Float` returns a random float between `low` and `high` (both inclusive)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"g = Float(low=2.3, high=4.2)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence:\n",
"\n",
"3.091577757836\n",
"2.319321421968\n",
"3.867892367582\n",
"2.867415724879\n",
"2.999982210028\n",
"2.667956563186\n",
"3.375415520585\n",
"2.607206865466\n",
"2.536107080139\n",
"3.122578909219\n"
]
}
],
"source": [
"print_generated_sequence(g, num=10, sep='\\n', fmt='.12f', seed=12345)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CharString"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence: bFj7lCDM5eUVwz8, QG5ThX0t5TMklKn, Qule67xq5QaV597, SA4TteJc6OZuDxy, HxzQkefvT0jmCgC\n",
"Generated sequence: Ylx3SYjPqrPO0vC, udVUmJ5f2xi6RRv, 8ZYmUYrEgjY5INZ, B9cgzt0nNwfbstm, h84ObqDckapVKgd\n"
]
}
],
"source": [
"g = CharString(length=15)\n",
"print_generated_sequence(g, num=5, seed=12345)\n",
"print_generated_sequence(g, num=5, seed=99999)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is possible to explicitly specify the character set."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence:\n",
"\n",
"ADBGBDDEGAFF\n",
"CCGEDGFAFFCG\n",
"FEBBEBECBAGG\n",
"CBGEAFGGGFDG\n",
"FCAEAGEFCDCC\n"
]
}
],
"source": [
"g = CharString(length=12, charset=\"ABCDEFG\")\n",
"print_generated_sequence(g, num=5, sep='\\n', seed=12345)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are also a few pre-defined character sets."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence:\n",
"\n",
"andyelmqybtt\n",
"jkzrnytduvhy\n",
"tqeepfrifbyz\n",
"jgyratyzzslx\n",
"sibpayqvimjk\n",
"\n",
"Generated sequence:\n",
"\n",
"ASF8GQRW7C11\n",
"NO9YS70E24L7\n",
"0WGGVHYMGC78\n",
"NJ7YA1798ZP6\n",
"0LCUB8X4MRNN\n"
]
}
],
"source": [
"g1 = CharString(length=12, charset=\"<lowercase>\")\n",
"g2 = CharString(length=12, charset=\"<alphanumeric_uppercase>\")\n",
"print_generated_sequence(g1, num=5, sep='\\n', seed=12345); print()\n",
"print_generated_sequence(g2, num=5, sep='\\n', seed=12345)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DigitString"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`DigitString` is the same as `CharString` with `charset='0123456789'`."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence: 051914469077349, 659717839761152, 631099329607999, 749730509683433, 534610037812414\n",
"Generated sequence: 813878162266834, 307715908319673, 988278241189568, 490143826300232, 199602401027500\n"
]
}
],
"source": [
"g = DigitString(length=15)\n",
"print_generated_sequence(g, num=5, seed=12345)\n",
"print_generated_sequence(g, num=5, seed=99999)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sequential"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generates a sequence of sequentially numbered strings with a given prefix."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"g = Sequential(prefix='Foo_', digits=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calling `reset()` on the generator makes the numbering start from 1 again."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence: Foo_001, Foo_002, Foo_003, Foo_004, Foo_005\n",
"Generated sequence: Foo_006, Foo_007, Foo_008, Foo_009, Foo_010\n",
"\n",
"Generated sequence: Foo_001, Foo_002, Foo_003, Foo_004, Foo_005\n"
]
}
],
"source": [
"g.reset()\n",
"print_generated_sequence(g, num=5)\n",
"print_generated_sequence(g, num=5)\n",
"print()\n",
"g.reset()\n",
"print_generated_sequence(g, num=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the method `Sequential.reset()` supports the `seed` argument for consistency with other generators, but its value is ignored - the generator is simply reset to its initial value. This is illustrated here:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence: Foo_001, Foo_002, Foo_003, Foo_004, Foo_005\n",
"Generated sequence: Foo_001, Foo_002, Foo_003, Foo_004, Foo_005\n"
]
}
],
"source": [
"g.reset(seed=12345); print_generated_sequence(g, num=5)\n",
"g.reset(seed=99999); print_generated_sequence(g, num=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## HashDigest"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`HashDigest` returns hex strings representing hash digest values (or alternatively raw bytes)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### HashDigest hex strings (uppercase)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"g = HashDigest(length=6)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence: E251FB, E52DE1, 1DFDFD, 810876, A44D15, A9AD2D, FE0F5E, 7E5191, 656D56, 224236\n"
]
}
],
"source": [
"print_generated_sequence(g, num=10, seed=12345)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### HashDigest hex strings (lowercase)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"g = HashDigest(length=6, uppercase=False)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence: e251fb, e52de1, 1dfdfd, 810876, a44d15, a9ad2d, fe0f5e, 7e5191, 656d56, 224236\n"
]
}
],
"source": [
"print_generated_sequence(g, num=10, seed=12345)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### HashDigest byte strings"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"g = HashDigest(length=10, as_bytes=True)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence:\n",
"\n",
"b'\\xe2Q\\xfb\\xed\\xe5-\\xe1\\xe3\\x1d\\xfd'\n",
"b'\\x81\\x08v!\\xa4M\\x15/\\xa9\\xad'\n",
"b'\\xfe\\x0f^4~Q\\x91\\xd3em'\n",
"b'\"B6\\x88\\x1d\\x9eu\\x98\\x01\\xbb'\n",
"b'vl\\xea\\xf6q\\xcd@v;\\x9d'\n"
]
}
],
"source": [
"print_generated_sequence(g, num=5, seed=12345, sep='\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NumpyRandomGenerator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This generator can produce random numbers using any of the random number generators [supported](https://docs.scipy.org/doc/numpy/reference/routines.random.html) by numpy."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"g1 = NumpyRandomGenerator(method=\"normal\", loc=3.0, scale=5.0)\n",
"g2 = NumpyRandomGenerator(method=\"poisson\", lam=30)\n",
"g3 = NumpyRandomGenerator(method=\"exponential\", scale=0.3)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence: 1.9764617025764353, 5.394716690287741, 0.40280642471630923, 0.22134847826254989\n",
"Generated sequence: 40, 24, 31, 34, 27, 32, 29, 29, 35, 38, 30, 32, 38, 36, 36\n",
"Generated sequence: 0.7961371899305246, 0.11410397056571128, 0.060972430042086474, 0.06865806254932436\n"
]
}
],
"source": [
"g1.reset(seed=12345); print_generated_sequence(g1, num=4)\n",
"g2.reset(seed=12345); print_generated_sequence(g2, num=15)\n",
"g3.reset(seed=12345); print_generated_sequence(g3, num=4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## FakerGenerator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`FakerGenerator` gives access to any of the methods supported by the [faker](https://faker.readthedocs.io/) module. Here are a couple of examples."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Example: random names"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"g = FakerGenerator(method='name')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence: Adam Bryan, Jacob Lee, Candice Martinez, Justin Thompson, Heather Rubio, William Jenkins, Brittany Ball, Glenn Johnson\n"
]
}
],
"source": [
"print_generated_sequence(g, num=8, seed=12345)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Example: random addresses"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"g = FakerGenerator(method='address')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence:\n",
"\n",
"453 Ryan Islands\n",
"Greenstad, FL 97251\n",
"---\n",
"USS Irwin\n",
"FPO AA 66552\n",
"---\n",
"55075 William Rest\n",
"North Elizabeth, NH 38062\n",
"---\n",
"926 Alexandra Road\n",
"Romanberg, HI 99597\n",
"---\n",
"8202 Michelle Branch\n",
"Baileyborough, AL 08481\n",
"---\n",
"205 William Coves\n",
"Alexanderport, WI 72565\n",
"---\n",
"821 Patricia Hill Apt. 242\n",
"Apriltown, MO 24730\n",
"---\n",
"486 Karen Lodge Apt. 205\n",
"West Gregory, MT 33130\n"
]
}
],
"source": [
"print_generated_sequence(g, num=8, seed=12345, sep='\\n---\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Timestamp"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"g = Timestamp(start=\"2018-01-01 11:22:33\", end=\"2018-02-13 12:23:34\")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"datetime.datetime"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(next(g))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence:\n",
"\n",
"2018-01-02 13:07:20\n",
"2018-01-26 00:51:38\n",
"2018-02-10 08:57:07\n",
"2018-01-08 16:01:04\n",
"2018-02-02 22:03:09\n",
"2018-02-01 18:16:12\n",
"2018-01-10 19:21:16\n",
"2018-01-20 02:51:48\n",
"2018-01-23 19:07:20\n",
"2018-01-01 17:56:48\n"
]
}
],
"source": [
"print_generated_sequence(g, num=10, seed=12345, sep='\\n')"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"g = Timestamp(start=\"2018-01-01 11:22:33\", end=\"2018-02-13 12:23:34\").strftime(\"%-d %b %Y, %H:%M (%a)\")"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"str"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(next(g))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence:\n",
"\n",
"2 Jan 2018, 13:07 (Tue)\n",
"26 Jan 2018, 00:51 (Fri)\n",
"10 Feb 2018, 08:57 (Sat)\n",
"8 Jan 2018, 16:01 (Mon)\n",
"2 Feb 2018, 22:03 (Fri)\n",
"1 Feb 2018, 18:16 (Thu)\n",
"10 Jan 2018, 19:21 (Wed)\n",
"20 Jan 2018, 02:51 (Sat)\n",
"23 Jan 2018, 19:07 (Tue)\n",
"1 Jan 2018, 17:56 (Mon)\n"
]
}
],
"source": [
"print_generated_sequence(g, num=10, seed=12345, sep='\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Date"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"g = Date(start=\"2018-01-01\", end=\"2018-02-13\")"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"datetime.date"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(next(g))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence:\n",
"\n",
"2018-01-02\n",
"2018-02-02\n",
"2018-01-10\n",
"2018-02-12\n",
"2018-02-11\n",
"2018-01-13\n",
"2018-01-25\n",
"2018-01-30\n",
"2018-01-01\n",
"2018-01-31\n"
]
}
],
"source": [
"print_generated_sequence(g, num=10, seed=12345, sep='\\n')"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"g = Date(start=\"2018-01-01\", end=\"2018-02-13\").strftime(\"%-d %b %Y\")"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"str"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(next(g))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generated sequence:\n",
"\n",
"2 Jan 2018\n",
"25 Jan 2018\n",
"9 Feb 2018\n",
"8 Jan 2018\n",
"2 Feb 2018\n",
"1 Feb 2018\n",
"10 Jan 2018\n",
"19 Jan 2018\n",
"23 Jan 2018\n",
"1 Jan 2018\n"
]
}
],
"source": [
"print_generated_sequence(g, num=10, seed=12345, sep='\\n')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
rysard/cursos | julia/data-science/Machine learning.ipynb | 1 | 3453 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Machine learning"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mCloning cache of IterTools from https://github.com/JuliaCollections/IterTools.jl.git\n",
"\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mCloning cache of MLBase from https://github.com/JuliaStats/MLBase.jl.git\n",
"\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mInstalling IterTools v0.1.0\n",
"\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mInstalling MLBase v0.7.0\n",
"\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mBuilding SpecialFunctions\n",
"\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mPackage database updated\n",
"\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mMETADATA is out-of-date — you may not have the latest version of MLBase\n",
"\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mUse `Pkg.update()` to get the latest versions of your packages\n",
"\u001b[39m"
]
}
],
"source": [
"Pkg.add(\"MLBase\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mPrecompiling module IterTools.\n",
"\u001b[39m\u001b[1m\u001b[36mINFO: \u001b[39m\u001b[22m\u001b[36mRecompiling stale cache file /home/ricardo/.julia/lib/v0.6/StatsBase.ji for module StatsBase.\n",
"\u001b[39m\u001b[91mERROR (unhandled task failure): \u001b[91mInterruptException:\u001b[39m\n",
"Stacktrace:\n",
" [1] \u001b[1mprocess_events\u001b[22m\u001b[22m at \u001b[1m./libuv.jl:82\u001b[22m\u001b[22m [inlined]\n",
" [2] \u001b[1mwait\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./event.jl:216\u001b[22m\u001b[22m\n",
" [3] \u001b[1mwait\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Condition\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./event.jl:27\u001b[22m\u001b[22m\n",
" [4] \u001b[1mstream_wait\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Timer, ::Condition, ::Vararg{Condition,N} where N\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./stream.jl:42\u001b[22m\u001b[22m\n",
" [5] \u001b[1mwait\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m::Timer\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./event.jl:357\u001b[22m\u001b[22m\n",
" [6] \u001b[1m(::Base.##300#301{IJulia.#send_stderr,Timer})\u001b[22m\u001b[22m\u001b[1m(\u001b[22m\u001b[22m\u001b[1m)\u001b[22m\u001b[22m at \u001b[1m./event.jl:430\u001b[22m\u001b[22m\n",
"\u001b[39m"
]
}
],
"source": [
"using MLBase"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.6.1",
"language": "julia",
"name": "julia-0.6"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
ProfessorKazarinoff/staticsite | content/code/matplotlib_plots/stress_strain_curves/stress_strain_curve_with_python.ipynb | 1 | 87571 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this post, we'll use data from a tensile test to build a stress strain curve with Python and Matplotlib."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A tensile test is a type of mechanical test performed by engineers used to determine the mechanical properties of a material. Engineering metal alloys such as steel and aluminum alloys are tensile tested in order to determine their strength and stiffness. Tensile tests are performed in a piece of equipment called a mechanical test frame.\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After a tensile test is complete, a set of data is produced by the mechanical test frame. Using the data acquired during a tensile test, a stress-strain curve can be produced. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this post, we will create a stress-strain curve (a plot) from a set of tensile test data of a steel 1045 sample and an aluminum 6061 sample. The stress strain curve we construct will have the following features:\n",
"\n",
" * A descriptive title\n",
" * Axes labels with units\n",
" * Two lines on the same plot. One line for steel 1045 and one line for aluminum 6061\n",
" * A legend"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Install Python\n",
"\n",
"We are going to build our stress strain curve with Python and a Jupyter notebook. I suggest engineers and problem-solvers download and install the [Anaconda distribution of Python](https://anaconda.com). See [this post]({filename}/posts/installation/installing_anaconda_on_windows.md) to learn how to install Anaconda on your computer. Alternatively, you can download Python form [Python.org](https://python.org) or download Python the Microsoft Store."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Install Jupyter, NumPy, Pandas, and Matplotlib\n",
"\n",
"Once Python is installed, the next thing we need to do is install a couple of Python packages. If you are using the Anaconda distribution of Python, the packages we are going to use to build the plot: Jupyter, NumPy, Pandas, and Matplotlib come pre-installed and no additional installation steps are necessary. \n",
"\n",
"However, if you downloaded Python from Python.org or installed Python using the Microsoft Store, you will need to install install Jupyter, NumPy, Pandas, and Matplotlib separately. You can install Jupyter, NumPy, Pandas, and Matplotlib with pip (the Python package manager) or install theses four packages with the Anaconda Prompt.\n",
"\n",
"If you are using a terminal and pip, type:\n",
"\n",
"```text\n",
"> pip install jupyter numpy pandas matplotlib \n",
"```\n",
"\n",
"If you have Anaconda installed and use the Anaconda Prompt, type:\n",
"\n",
"```text\n",
"> conda install jupyter numpy pandas matplotlib \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Open a Jupyter notebook\n",
"\n",
"We will construct our stress strain curve using a Jupyter notebook. See [this post]({filename}/posts/jupyter/opening_jupyter_notebook.md) to see how to open a Jupyter notebook. \n",
"\n",
"Make sure to save your Jupyter notebook with a recognizable name."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download the data and move the data into the same folder as the Jupyter notebook\n",
"\n",
"Next, we need to download the two data files that we will use to build our stress-strain curve. You can download sample data using the links below:\n",
"\n",
"[steel1045.xls](https://github.com/ProfessorKazarinoff/staticsite/raw/master/content/code/matplotlib_plots/stress_strain_curves/steel1045.xls)\n",
"\n",
"[aluminum6061.xls](https://github.com/ProfessorKazarinoff/staticsite/raw/master/content/code/matplotlib_plots/stress_strain_curves/aluminum6061.xls)\n",
"\n",
"After these .xls files are downloaded, both .xls files need to be moved into the same folder as our Jupyter notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import NumPy, Pandas, and Matplotlib\n",
"\n",
"Now that our Jupyter notebook is open and the two .xls data files are in the same folder as the Jupyter notebook, we can start coding and build our plot.\n",
"\n",
"At the top of the Jupyter notebook, import NumPy, Pandas and Matplotlib. The command ```%matplotlib inline``` is included so that our plot will display directly inside our Jupyter notebook. If you are using a .py file instead of a Jupyter notebook, make sure to comment out ```%matplotlib inline``` as this line is not valid Python code.\n",
" \n",
"We will also print out the versions of our NumPy and Pandas packages using the ```.__version__``` attribute. If the versions of NumPy and Pandas prints out, that means that NumPy and Pandas are installed and we can use these packages in our code."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NumPy version: 1.16.5\n",
"Pandas version: 0.25.1\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"print(\"NumPy version:\",np.__version__)\n",
"print(\"Pandas version:\",pd.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ensure the two .xls data files are in the same folder as the Jupyter notebook\n",
"\n",
"Before we proceed, let's make sure the two .xls data files are in the same folder as our running Jupyter notebook. We'll use a Jupyter notebook magic command to print out the contents of the folder that our notebook is in. The ```%ls``` command lists the contents of the current folder."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Volume in drive C is Windows\n",
" Volume Serial Number is A048-4C53\n",
"\n",
" Directory of C:\\Users\\peter.kazarinoff\\Documents\\staticsite\\content\\code\\matplotlib_plots\\stress_strain_curves\n",
"\n",
"02/13/2020 02:50 PM <DIR> .\n",
"02/13/2020 02:50 PM <DIR> ..\n",
"05/20/2019 10:37 AM <DIR> .ipynb_checkpoints\n",
"05/13/2019 12:59 PM 36,864 aluminum6061.xls\n",
"05/20/2019 10:20 AM <DIR> images\n",
"05/13/2019 01:09 PM 38,912 steel1045.xls\n",
"10/21/2019 11:22 AM 44,972 stress_strain_curve_with_inset.png\n",
"10/21/2019 11:22 AM 148,175 stress_strain_curve_with_inset_elastic_region.ipynb\n",
"02/13/2020 02:50 PM 87,568 stress_strain_curve_with_python.ipynb\n",
"02/13/2020 02:40 PM 113,352 stress-strain_curve.png\n",
" 6 File(s) 469,843 bytes\n",
" 4 Dir(s) 119,576,158,208 bytes free\n"
]
}
],
"source": [
"%ls"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see our Jupyter notebook ```stress_strain_curve_with_python.ipynb``` as well as the two .xls data files ```aluminum6061.xls``` and ```steel1045.xls``` are in our current folder. \n",
"\n",
"Now that we are sure the two .xls data files are in the same folder as our notebook, we can import the data in the two two .xls files using Panda's ```pd.read_excel()``` function. The data from the two excel files will be stored in two Pandas dataframes called ```steel_df``` and ```al_df```."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
"WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n"
]
}
],
"source": [
"steel_df = pd.read_excel(\"steel1045.xls\")\n",
"al_df = pd.read_excel(\"aluminum6061.xls\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use Pandas ```.head()``` method to view the first five rows of each dataframe. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\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>TESTNUM</th>\n",
" <th>POINTNUM</th>\n",
" <th>TIME</th>\n",
" <th>POSIT</th>\n",
" <th>FORCE</th>\n",
" <th>EXT</th>\n",
" <th>CH5</th>\n",
" <th>CH6</th>\n",
" <th>CH7</th>\n",
" <th>CH8</th>\n",
" </tr>\n",
" </thead>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
" <td>5.969</td>\n",
" <td>0.01284</td>\n",
" <td>201.030792</td>\n",
" <td>0.001572</td>\n",
" <td>-0.007133</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>762</td>\n",
" <td>2</td>\n",
" <td>6.242</td>\n",
" <td>0.01392</td>\n",
" <td>215.235886</td>\n",
" <td>0.000009</td>\n",
" <td>-0.014581</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>762</td>\n",
" <td>3</td>\n",
" <td>6.936</td>\n",
" <td>0.01646</td>\n",
" <td>246.816742</td>\n",
" <td>-0.000832</td>\n",
" <td>0.006942</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>762</td>\n",
" <td>4</td>\n",
" <td>8.632</td>\n",
" <td>0.02340</td>\n",
" <td>371.870361</td>\n",
" <td>0.002203</td>\n",
" <td>0.000776</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>762</td>\n",
" <td>5</td>\n",
" <td>10.533</td>\n",
" <td>0.03110</td>\n",
" <td>502.501862</td>\n",
" <td>0.001481</td>\n",
" <td>0.018102</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TESTNUM POINTNUM TIME POSIT FORCE EXT CH5 CH6 \\\n",
"0 762 1 5.969 0.01284 201.030792 0.001572 -0.007133 NaN \n",
"1 762 2 6.242 0.01392 215.235886 0.000009 -0.014581 NaN \n",
"2 762 3 6.936 0.01646 246.816742 -0.000832 0.006942 NaN \n",
"3 762 4 8.632 0.02340 371.870361 0.002203 0.000776 NaN \n",
"4 762 5 10.533 0.03110 502.501862 0.001481 0.018102 NaN \n",
"\n",
" CH7 CH8 \n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"steel_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>TESTNUM</th>\n",
" <th>POINTNUM</th>\n",
" <th>TIME</th>\n",
" <th>POSIT</th>\n",
" <th>FORCE</th>\n",
" <th>EXT</th>\n",
" <th>CH5</th>\n",
" <th>CH6</th>\n",
" <th>CH7</th>\n",
" <th>CH8</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>761</td>\n",
" <td>1</td>\n",
" <td>6.532</td>\n",
" <td>0.01524</td>\n",
" <td>201.158508</td>\n",
" <td>0.018893</td>\n",
" <td>-0.023081</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>761</td>\n",
" <td>2</td>\n",
" <td>6.702</td>\n",
" <td>0.01600</td>\n",
" <td>205.978119</td>\n",
" <td>0.000265</td>\n",
" <td>-0.013024</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>761</td>\n",
" <td>3</td>\n",
" <td>7.098</td>\n",
" <td>0.01720</td>\n",
" <td>219.295441</td>\n",
" <td>-0.000877</td>\n",
" <td>-0.024879</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>761</td>\n",
" <td>4</td>\n",
" <td>8.697</td>\n",
" <td>0.02350</td>\n",
" <td>268.505890</td>\n",
" <td>0.001453</td>\n",
" <td>-0.006798</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>761</td>\n",
" <td>5</td>\n",
" <td>10.196</td>\n",
" <td>0.03004</td>\n",
" <td>322.028168</td>\n",
" <td>0.001865</td>\n",
" <td>0.012563</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TESTNUM POINTNUM TIME POSIT FORCE EXT CH5 CH6 \\\n",
"0 761 1 6.532 0.01524 201.158508 0.018893 -0.023081 NaN \n",
"1 761 2 6.702 0.01600 205.978119 0.000265 -0.013024 NaN \n",
"2 761 3 7.098 0.01720 219.295441 -0.000877 -0.024879 NaN \n",
"3 761 4 8.697 0.02350 268.505890 0.001453 -0.006798 NaN \n",
"4 761 5 10.196 0.03004 322.028168 0.001865 0.012563 NaN \n",
"\n",
" CH7 CH8 \n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"al_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see a number of columns in each dataframe. The columns we are interested in are **FORCE**, **EXT**, and **CH5**. Below is a description of what these columns mean.\n",
"\n",
" * **FORCE** Force measurements from the load cell in pounds (lb), force in pounds\n",
" * **EXT** Extension measurements from the mechanical extensometer in percent (%), strain in percent\n",
" * **CH5** Extension readings from the laser extensometer in percent (%), strain in percent"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create stress and strain series from the **FORCE**, **EXT**, and **CH5** columns\n",
"\n",
"Next we'll create a four Pandas series from the ```['CH5']``` and ```['FORCE']``` columns of our ```al_df``` and ```steel_df``` dataframes. The equations below show how to calculate stress, $\\sigma$, and strain, $\\epsilon$, from force $F$ and cross-sectional area $A$. Cross-sectional area $A$ is the formula for the area of a circle. For the steel and aluminum samples we tested, the diameter $d$ was $0.506 \\ in$.\n",
"\n",
"$$ \\sigma = \\frac{F}{A_0} $$\n",
"\n",
"$$ F \\ (kip) = F \\ (lb) \\times 0.001 $$\n",
"\n",
"$$ A_0 = \\pi (d/2)^2 $$\n",
"\n",
"$$ d = 0.506 \\ in $$\n",
"\n",
"$$ \\epsilon \\ (unitless) = \\epsilon \\ (\\%) \\times 0.01 $$"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"strain_steel = steel_df['CH5']*0.01\n",
"d_steel = 0.506 # test bar diameter = 0.506 inches\n",
"stress_steel = (steel_df['FORCE']*0.001)/(np.pi*((d_steel/2)**2))\n",
"\n",
"strain_al = al_df['CH5']*0.01\n",
"d_al = 0.506 # test bar diameter = 0.506 inches\n",
"stress_al = (al_df['FORCE']*0.001)/(np.pi*((d_al/2)**2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build a quick plot\n",
"\n",
"Now that we have the data from the tensile test in four series, we can build a quick plot using Matplotlib's ```plt.plot()``` method. The first x,y pair we pass to ```plt.plot()``` is ```strain_steel,stress_steel``` and the second x,y pair we pass in is ```strain_al,stress_al```. The command ```plt.show()``` shows the plot."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(strain_steel,stress_steel,strain_al,stress_al)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see a plot with two lines. One line represents the steel sample and one line represents the aluminum sample. We can improve our plot by adding axis labels with units, a title and a legend."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add axis labels, title and a legend\n",
"\n",
"Axis labels, titles and a legend are added to our plot with three Matplotlib methods. The methods are summarized in the table below.\n",
"\n",
"| Matplotlib method | description | example |\n",
"| --- | --- | --- |\n",
"| ```plt.xlabel()``` | x-axis label | ```plt.xlabel('strain (in/in)')``` |\n",
"| ```plt.ylabel()``` | y-axis label | ```plt.ylabel('stress (ksi)')``` |\n",
"| ```plt.title()``` | plot title | ```plt.title('Stress Strain Curve')``` |\n",
"| ```plt.legend()``` | legend | ```plt.legend(['steel','aluminum'])``` |\n",
"\n",
"The code cell below shows these four methods in action and produces a plot."
]
},
{
"cell_type": "code",
"execution_count": 8,
"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": [
"plt.plot(strain_steel,stress_steel,strain_al,stress_al)\n",
"plt.xlabel('strain (in/in)')\n",
"plt.ylabel('stress (ksi)')\n",
"plt.title('Stress Strain Curve of Steel 1045 and Aluminum 6061 in tension')\n",
"plt.legend(['Steel 1045','Aluminum 6061'])\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The plot we see has two lines, axis labels, a title and a legend. Next we'll save the plot to a .png image file."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save the plot as a .png image\n",
" \n",
"Now we can save the plot as a .png image using Matplotlib's ```plt.savefig()``` method. The code cell below builds the plot and saves an image file called ```stress-strain_curve.png```. The argument ```dpi=300``` inside of Matplotlib's ```plt.savefig()``` method specifies the resolution of our saved image. The image ```stress-strain_curve.png``` will be saved in the same folder as our running Jupyter notebook."
]
},
{
"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": [
"plt.plot(strain_steel,stress_steel,strain_al,stress_al)\n",
"plt.xlabel('strain (in/in)')\n",
"plt.ylabel('stress (ksi)')\n",
"plt.title('Stress Strain Curve of Steel 1045 and Aluminum 6061 in tension')\n",
"plt.legend(['Steel 1045','Aluminum 6061'])\n",
"\n",
"plt.savefig('stress-strain_curve.png', dpi=300, bbox_inches='tight')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our complete stress strain curve contains two lines, one for steel and one for aluminum. The plot has axis labels with units, a title and a legend. A copy of the plot is now saved as ```stress-strain_curve.png``` in the same folder as our Jupyter notebook."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary\n",
"\n",
"In this post, we built a stress strain curve using Python. First we installed Python and made sure that NumPy, Pandas, Matplotlib and Jupyter were installed. Next we opened a Jupyter notebook and moved our .xls data files into the same folder as the Jupyter notebook. Inside the Jupyter notebook we entered code into a couple different code cells. \n",
"\n",
"In the first Jupyter notebook code cell, we imported NumPy, Pandas, and Matplotlib and printed our their versions. In the next code cell, we saved the data from two .xls data files into two Pandas dataframes. In the third code cell, we created Pandas series for stress and strain from the columns in the dataframes. In the final code cell we built our stress strain curve with Matplotlib and saved the plot to a .png image file."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
lesley2958/lesley2958.github.io | blog/2018/gis.ipynb | 1 | 6614 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import folium"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "ImportError",
"evalue": "dlopen(/Users/nipun/anaconda3/lib/python3.6/site-packages/fiona/ogrext.cpython-36m-darwin.so, 2): Library not loaded: @rpath/libjson-c.2.dylib\n Referenced from: /Users/nipun/anaconda3/lib/libgdal.20.dylib\n Reason: image not found",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-6de75afd7bba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgeopandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_rows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/geopandas/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgeopandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeodataframe\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGeoDataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mgeopandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mread_file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgeopandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msql\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mread_postgis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgeopandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msjoin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/geopandas/io/file.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mfiona\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/fiona/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msix\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mfiona\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCollection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBytesCollection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvsi_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 70\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mfiona\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drivers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdriver_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGDALEnv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mfiona\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrvsupport\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msupported_drivers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/fiona/collection.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mfiona\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mfiona\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mogrext\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mIterator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mItemsIterator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mKeysIterator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mfiona\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mogrext\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mWritingSession\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m from fiona.ogrext import (\n",
"\u001b[0;31mImportError\u001b[0m: dlopen(/Users/nipun/anaconda3/lib/python3.6/site-packages/fiona/ogrext.cpython-36m-darwin.so, 2): Library not loaded: @rpath/libjson-c.2.dylib\n Referenced from: /Users/nipun/anaconda3/lib/libgdal.20.dylib\n Reason: image not found"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import pandas as pd\n",
"import geopandas\n",
"\n",
"pd.options.display.max_rows = 10"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
JaeGyu/PythonEx_1 | kimsh_multi Variable 선형 회귀 lab1_2.ipynb | 1 | 90167 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.2.0\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"print(tf.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_data = [[73., 80., 75.],[93., 88., 93.],[89., 91., 90.],[96., 98., 100.],[73., 66., 70.]]\n",
"y_data = [[152.],[185.],[180.],[196.],[142.]]\n",
"\n",
"X = tf.placeholder(tf.float32, shape=[None,3]) #shape에서 None은 지금은 5개지만 n개까지 가능하도록 None로 설정 그리고 3은 각 인스턴스(데이터)안에 포함된 피쳐의 개수 3개 \n",
"Y = tf.placeholder(tf.float32, shape=[None,1]) #마찬가지로 None로 설정 그리고 y의 피쳐의 개수는 1개\n",
"\n",
"W = tf.Variable(tf.random_normal([3,1]), name=\"weight\")\n",
"b = tf.Variable(tf.random_normal([1]), name=\"bias\")\n",
"\n",
"hypothesis = tf.matmul(X, W) + b\n",
"\n",
"cost = tf.reduce_mean(tf.square(hypothesis - Y))\n",
"\n",
"\n",
"optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)\n",
"train = optimizer.minimize(cost)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sess = tf.Session()\n",
"sess.run(tf.global_variables_initializer())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 Cost : 29726.2 \n",
"Prediction:\n",
" [[ 294.35977173]\n",
" [ 374.81066895]\n",
" [ 358.01467896]\n",
" [ 395.2791748 ]\n",
" [ 286.68954468]]\n",
"================================================================================\n",
"10 Cost : 36.4391 \n",
"Prediction:\n",
" [[ 141.95579529]\n",
" [ 191.59107971]\n",
" [ 177.50691223]\n",
" [ 198.70704651]\n",
" [ 146.93186951]]\n",
"================================================================================\n",
"20 Cost : 36.0007 \n",
"Prediction:\n",
" [[ 141.51651001]\n",
" [ 191.02185059]\n",
" [ 176.96759033]\n",
" [ 198.11610413]\n",
" [ 146.49043274]]\n",
"================================================================================\n",
"30 Cost : 35.835 \n",
"Prediction:\n",
" [[ 141.53700256]\n",
" [ 191.00531006]\n",
" [ 176.97279358]\n",
" [ 198.11813354]\n",
" [ 146.47061157]]\n",
"================================================================================\n",
"40 Cost : 35.6702 \n",
"Prediction:\n",
" [[ 141.55879211]\n",
" [ 190.99046326]\n",
" [ 176.97962952]\n",
" [ 198.12193298]\n",
" [ 146.4520874 ]]\n",
"================================================================================\n",
"50 Cost : 35.5061 \n",
"Prediction:\n",
" [[ 141.58056641]\n",
" [ 190.97566223]\n",
" [ 176.98643494]\n",
" [ 198.12574768]\n",
" [ 146.43362427]]\n",
"================================================================================\n",
"60 Cost : 35.343 \n",
"Prediction:\n",
" [[ 141.60224915]\n",
" [ 190.96089172]\n",
" [ 176.99324036]\n",
" [ 198.12953186]\n",
" [ 146.41520691]]\n",
"================================================================================\n",
"70 Cost : 35.1808 \n",
"Prediction:\n",
" [[ 141.62388611]\n",
" [ 190.94616699]\n",
" [ 177. ]\n",
" [ 198.13330078]\n",
" [ 146.39685059]]\n",
"================================================================================\n",
"80 Cost : 35.0194 \n",
"Prediction:\n",
" [[ 141.64546204]\n",
" [ 190.9315033 ]\n",
" [ 177.00675964]\n",
" [ 198.13703918]\n",
" [ 146.37854004]]\n",
"================================================================================\n",
"90 Cost : 34.8589 \n",
"Prediction:\n",
" [[ 141.66700745]\n",
" [ 190.91687012]\n",
" [ 177.01351929]\n",
" [ 198.14080811]\n",
" [ 146.36029053]]\n",
"================================================================================\n",
"100 Cost : 34.6992 \n",
"Prediction:\n",
" [[ 141.68849182]\n",
" [ 190.90228271]\n",
" [ 177.02024841]\n",
" [ 198.14456177]\n",
" [ 146.34208679]]\n",
"================================================================================\n",
"110 Cost : 34.5403 \n",
"Prediction:\n",
" [[ 141.70988464]\n",
" [ 190.88768005]\n",
" [ 177.02693176]\n",
" [ 198.14823914]\n",
" [ 146.32389832]]\n",
"================================================================================\n",
"120 Cost : 34.3824 \n",
"Prediction:\n",
" [[ 141.73123169]\n",
" [ 190.87316895]\n",
" [ 177.03363037]\n",
" [ 198.15194702]\n",
" [ 146.30580139]]\n",
"================================================================================\n",
"130 Cost : 34.2252 \n",
"Prediction:\n",
" [[ 141.75253296]\n",
" [ 190.8586731 ]\n",
" [ 177.04031372]\n",
" [ 198.15565491]\n",
" [ 146.28773499]]\n",
"================================================================================\n",
"140 Cost : 34.0689 \n",
"Prediction:\n",
" [[ 141.77377319]\n",
" [ 190.84422302]\n",
" [ 177.04695129]\n",
" [ 198.15933228]\n",
" [ 146.26974487]]\n",
"================================================================================\n",
"150 Cost : 33.9136 \n",
"Prediction:\n",
" [[ 141.79495239]\n",
" [ 190.82984924]\n",
" [ 177.05360413]\n",
" [ 198.1630249 ]\n",
" [ 146.25178528]]\n",
"================================================================================\n",
"160 Cost : 33.7589 \n",
"Prediction:\n",
" [[ 141.81608582]\n",
" [ 190.81546021]\n",
" [ 177.06019592]\n",
" [ 198.16667175]\n",
" [ 146.23387146]]\n",
"================================================================================\n",
"170 Cost : 33.605 \n",
"Prediction:\n",
" [[ 141.8371582 ]\n",
" [ 190.8011322 ]\n",
" [ 177.06681824]\n",
" [ 198.17030334]\n",
" [ 146.21601868]]\n",
"================================================================================\n",
"180 Cost : 33.452 \n",
"Prediction:\n",
" [[ 141.85818481]\n",
" [ 190.78683472]\n",
" [ 177.07339478]\n",
" [ 198.1739502 ]\n",
" [ 146.19821167]]\n",
"================================================================================\n",
"190 Cost : 33.2998 \n",
"Prediction:\n",
" [[ 141.87911987]\n",
" [ 190.77255249]\n",
" [ 177.07995605]\n",
" [ 198.17755127]\n",
" [ 146.18045044]]\n",
"================================================================================\n",
"200 Cost : 33.1484 \n",
"Prediction:\n",
" [[ 141.90005493]\n",
" [ 190.75836182]\n",
" [ 177.08651733]\n",
" [ 198.18119812]\n",
" [ 146.1627655 ]]\n",
"================================================================================\n",
"210 Cost : 32.9978 \n",
"Prediction:\n",
" [[ 141.92089844]\n",
" [ 190.74417114]\n",
" [ 177.0930481 ]\n",
" [ 198.18476868]\n",
" [ 146.14509583]]\n",
"================================================================================\n",
"220 Cost : 32.8481 \n",
"Prediction:\n",
" [[ 141.94168091]\n",
" [ 190.73002625]\n",
" [ 177.0995636 ]\n",
" [ 198.18836975]\n",
" [ 146.12748718]]\n",
"================================================================================\n",
"230 Cost : 32.6991 \n",
"Prediction:\n",
" [[ 141.96243286]\n",
" [ 190.71592712]\n",
" [ 177.10606384]\n",
" [ 198.19194031]\n",
" [ 146.10992432]]\n",
"================================================================================\n",
"240 Cost : 32.551 \n",
"Prediction:\n",
" [[ 141.98312378]\n",
" [ 190.70187378]\n",
" [ 177.11254883]\n",
" [ 198.19552612]\n",
" [ 146.09243774]]\n",
"================================================================================\n",
"250 Cost : 32.4036 \n",
"Prediction:\n",
" [[ 142.00376892]\n",
" [ 190.68786621]\n",
" [ 177.11903381]\n",
" [ 198.19908142]\n",
" [ 146.07499695]]\n",
"================================================================================\n",
"260 Cost : 32.2569 \n",
"Prediction:\n",
" [[ 142.02430725]\n",
" [ 190.67381287]\n",
" [ 177.1254425 ]\n",
" [ 198.20256042]\n",
" [ 146.05754089]]\n",
"================================================================================\n",
"270 Cost : 32.1111 \n",
"Prediction:\n",
" [[ 142.04484558]\n",
" [ 190.65988159]\n",
" [ 177.13188171]\n",
" [ 198.20613098]\n",
" [ 146.04019165]]\n",
"================================================================================\n",
"280 Cost : 31.9661 \n",
"Prediction:\n",
" [[ 142.06530762]\n",
" [ 190.64596558]\n",
" [ 177.13832092]\n",
" [ 198.20962524]\n",
" [ 146.02288818]]\n",
"================================================================================\n",
"290 Cost : 31.8218 \n",
"Prediction:\n",
" [[ 142.08572388]\n",
" [ 190.63209534]\n",
" [ 177.14471436]\n",
" [ 198.21313477]\n",
" [ 146.00561523]]\n",
"================================================================================\n",
"300 Cost : 31.6783 \n",
"Prediction:\n",
" [[ 142.1060791 ]\n",
" [ 190.61824036]\n",
" [ 177.15109253]\n",
" [ 198.21662903]\n",
" [ 145.98840332]]\n",
"================================================================================\n",
"310 Cost : 31.5355 \n",
"Prediction:\n",
" [[ 142.12638855]\n",
" [ 190.60444641]\n",
" [ 177.1574707 ]\n",
" [ 198.22010803]\n",
" [ 145.97123718]]\n",
"================================================================================\n",
"320 Cost : 31.3935 \n",
"Prediction:\n",
" [[ 142.14665222]\n",
" [ 190.59068298]\n",
" [ 177.16381836]\n",
" [ 198.22358704]\n",
" [ 145.95411682]]\n",
"================================================================================\n",
"330 Cost : 31.2522 \n",
"Prediction:\n",
" [[ 142.1668396 ]\n",
" [ 190.57693481]\n",
" [ 177.1701355 ]\n",
" [ 198.22702026]\n",
" [ 145.93702698]]\n",
"================================================================================\n",
"340 Cost : 31.1118 \n",
"Prediction:\n",
" [[ 142.1869812 ]\n",
" [ 190.56323242]\n",
" [ 177.17648315]\n",
" [ 198.23048401]\n",
" [ 145.92002869]]\n",
"================================================================================\n",
"350 Cost : 30.972 \n",
"Prediction:\n",
" [[ 142.20707703]\n",
" [ 190.54959106]\n",
" [ 177.18278503]\n",
" [ 198.2339325 ]\n",
" [ 145.90304565]]\n",
"================================================================================\n",
"360 Cost : 30.833 \n",
"Prediction:\n",
" [[ 142.22709656]\n",
" [ 190.53593445]\n",
" [ 177.18904114]\n",
" [ 198.23731995]\n",
" [ 145.88609314]]\n",
"================================================================================\n",
"370 Cost : 30.6948 \n",
"Prediction:\n",
" [[ 142.24710083]\n",
" [ 190.52238464]\n",
" [ 177.19532776]\n",
" [ 198.24075317]\n",
" [ 145.86924744]]\n",
"================================================================================\n",
"380 Cost : 30.5572 \n",
"Prediction:\n",
" [[ 142.26705933]\n",
" [ 190.50883484]\n",
" [ 177.20158386]\n",
" [ 198.24417114]\n",
" [ 145.85241699]]\n",
"================================================================================\n",
"390 Cost : 30.4204 \n",
"Prediction:\n",
" [[ 142.28691101]\n",
" [ 190.49528503]\n",
" [ 177.20777893]\n",
" [ 198.24749756]\n",
" [ 145.83560181]]\n",
"================================================================================\n",
"400 Cost : 30.2843 \n",
"Prediction:\n",
" [[ 142.30674744]\n",
" [ 190.48182678]\n",
" [ 177.21403503]\n",
" [ 198.25091553]\n",
" [ 145.81886292]]\n",
"================================================================================\n",
"410 Cost : 30.1489 \n",
"Prediction:\n",
" [[ 142.32652283]\n",
" [ 190.46838379]\n",
" [ 177.22024536]\n",
" [ 198.25428772]\n",
" [ 145.8021698 ]]\n",
"================================================================================\n",
"420 Cost : 30.0143 \n",
"Prediction:\n",
" [[ 142.34625244]\n",
" [ 190.45497131]\n",
" [ 177.22640991]\n",
" [ 198.25762939]\n",
" [ 145.78552246]]\n",
"================================================================================\n",
"430 Cost : 29.8804 \n",
"Prediction:\n",
" [[ 142.36592102]\n",
" [ 190.44161987]\n",
" [ 177.23258972]\n",
" [ 198.26098633]\n",
" [ 145.76893616]]\n",
"================================================================================\n",
"440 Cost : 29.7472 \n",
"Prediction:\n",
" [[ 142.38554382]\n",
" [ 190.42828369]\n",
" [ 177.23875427]\n",
" [ 198.264328 ]\n",
" [ 145.75238037]]\n",
"================================================================================\n",
"450 Cost : 29.6146 \n",
"Prediction:\n",
" [[ 142.40509033]\n",
" [ 190.41493225]\n",
" [ 177.24487305]\n",
" [ 198.26760864]\n",
" [ 145.7358551 ]]\n",
"================================================================================\n",
"460 Cost : 29.4829 \n",
"Prediction:\n",
" [[ 142.42460632]\n",
" [ 190.40168762]\n",
" [ 177.25100708]\n",
" [ 198.2709198 ]\n",
" [ 145.71939087]]\n",
"================================================================================\n",
"470 Cost : 29.3518 \n",
"Prediction:\n",
" [[ 142.4440918 ]\n",
" [ 190.38848877]\n",
" [ 177.25714111]\n",
" [ 198.27424622]\n",
" [ 145.70300293]]\n",
"================================================================================\n",
"480 Cost : 29.2213 \n",
"Prediction:\n",
" [[ 142.46348572]\n",
" [ 190.37527466]\n",
" [ 177.26321411]\n",
" [ 198.27749634]\n",
" [ 145.68659973]]\n",
"================================================================================\n",
"490 Cost : 29.0916 \n",
"Prediction:\n",
" [[ 142.48284912]\n",
" [ 190.36210632]\n",
" [ 177.26930237]\n",
" [ 198.28077698]\n",
" [ 145.67030334]]\n",
"================================================================================\n",
"500 Cost : 28.9626 \n",
"Prediction:\n",
" [[ 142.50215149]\n",
" [ 190.34898376]\n",
" [ 177.27534485]\n",
" [ 198.28404236]\n",
" [ 145.65400696]]\n",
"================================================================================\n",
"510 Cost : 28.8342 \n",
"Prediction:\n",
" [[ 142.5214386 ]\n",
" [ 190.33592224]\n",
" [ 177.28141785]\n",
" [ 198.287323 ]\n",
" [ 145.63778687]]\n",
"================================================================================\n",
"520 Cost : 28.7066 \n",
"Prediction:\n",
" [[ 142.5406189 ]\n",
" [ 190.3228302 ]\n",
" [ 177.28741455]\n",
" [ 198.2905426 ]\n",
" [ 145.62158203]]\n",
"================================================================================\n",
"530 Cost : 28.5797 \n",
"Prediction:\n",
" [[ 142.55979919]\n",
" [ 190.30986023]\n",
" [ 177.29347229]\n",
" [ 198.29380798]\n",
" [ 145.60548401]]\n",
"================================================================================\n",
"540 Cost : 28.4533 \n",
"Prediction:\n",
" [[ 142.57888794]\n",
" [ 190.29682922]\n",
" [ 177.29942322]\n",
" [ 198.29698181]\n",
" [ 145.58935547]]\n",
"================================================================================\n",
"550 Cost : 28.3276 \n",
"Prediction:\n",
" [[ 142.59794617]\n",
" [ 190.28387451]\n",
" [ 177.30540466]\n",
" [ 198.30020142]\n",
" [ 145.57330322]]\n",
"================================================================================\n",
"560 Cost : 28.2027 \n",
"Prediction:\n",
" [[ 142.61697388]\n",
" [ 190.27099609]\n",
" [ 177.31140137]\n",
" [ 198.30340576]\n",
" [ 145.55731201]]\n",
"================================================================================\n",
"570 Cost : 28.0784 \n",
"Prediction:\n",
" [[ 142.63589478]\n",
" [ 190.25808716]\n",
" [ 177.31733704]\n",
" [ 198.30659485]\n",
" [ 145.54133606]]\n",
"================================================================================\n",
"580 Cost : 27.9548 \n",
"Prediction:\n",
" [[ 142.65480042]\n",
" [ 190.24525452]\n",
" [ 177.32327271]\n",
" [ 198.30976868]\n",
" [ 145.52542114]]\n",
"================================================================================\n",
"590 Cost : 27.8316 \n",
"Prediction:\n",
" [[ 142.6736908 ]\n",
" [ 190.23243713]\n",
" [ 177.32920837]\n",
" [ 198.3129425 ]\n",
" [ 145.509552 ]]\n",
"================================================================================\n",
"600 Cost : 27.7093 \n",
"Prediction:\n",
" [[ 142.69247437]\n",
" [ 190.21963501]\n",
" [ 177.33508301]\n",
" [ 198.31607056]\n",
" [ 145.49371338]]\n",
"================================================================================\n",
"610 Cost : 27.5877 \n",
"Prediction:\n",
" [[ 142.71122742]\n",
" [ 190.20690918]\n",
" [ 177.34100342]\n",
" [ 198.31922913]\n",
" [ 145.47793579]]\n",
"================================================================================\n",
"620 Cost : 27.4666 \n",
"Prediction:\n",
" [[ 142.72994995]\n",
" [ 190.19419861]\n",
" [ 177.34689331]\n",
" [ 198.32237244]\n",
" [ 145.46220398]]\n",
"================================================================================\n",
"630 Cost : 27.3461 \n",
"Prediction:\n",
" [[ 142.74861145]\n",
" [ 190.1815033 ]\n",
" [ 177.35273743]\n",
" [ 198.32548523]\n",
" [ 145.44651794]]\n",
"================================================================================\n",
"640 Cost : 27.2264 \n",
"Prediction:\n",
" [[ 142.76721191]\n",
" [ 190.16886902]\n",
" [ 177.35858154]\n",
" [ 198.32859802]\n",
" [ 145.43086243]]\n",
"================================================================================\n",
"650 Cost : 27.1073 \n",
"Prediction:\n",
" [[ 142.7857666 ]\n",
" [ 190.15626526]\n",
" [ 177.3644104 ]\n",
" [ 198.33171082]\n",
" [ 145.41525269]]\n",
"================================================================================\n",
"660 Cost : 26.9887 \n",
"Prediction:\n",
" [[ 142.80429077]\n",
" [ 190.14367676]\n",
" [ 177.370224 ]\n",
" [ 198.33479309]\n",
" [ 145.39968872]]\n",
"================================================================================\n",
"670 Cost : 26.8709 \n",
"Prediction:\n",
" [[ 142.82273865]\n",
" [ 190.13114929]\n",
" [ 177.37602234]\n",
" [ 198.33787537]\n",
" [ 145.38417053]]\n",
"================================================================================\n",
"680 Cost : 26.7535 \n",
"Prediction:\n",
" [[ 142.84118652]\n",
" [ 190.11863708]\n",
" [ 177.38183594]\n",
" [ 198.3409729 ]\n",
" [ 145.36869812]]\n",
"================================================================================\n",
"690 Cost : 26.6369 \n",
"Prediction:\n",
" [[ 142.85951233]\n",
" [ 190.10614014]\n",
" [ 177.38757324]\n",
" [ 198.34399414]\n",
" [ 145.35325623]]\n",
"================================================================================\n",
"700 Cost : 26.5208 \n",
"Prediction:\n",
" [[ 142.87785339]\n",
" [ 190.09371948]\n",
" [ 177.39335632]\n",
" [ 198.34706116]\n",
" [ 145.33787537]]\n",
"================================================================================\n",
"710 Cost : 26.4055 \n",
"Prediction:\n",
" [[ 142.89610291]\n",
" [ 190.08131409]\n",
" [ 177.39907837]\n",
" [ 198.35009766]\n",
" [ 145.32252502]]\n",
"================================================================================\n",
"720 Cost : 26.2907 \n",
"Prediction:\n",
" [[ 142.91433716]\n",
" [ 190.06893921]\n",
" [ 177.40481567]\n",
" [ 198.35313416]\n",
" [ 145.30723572]]\n",
"================================================================================\n",
"730 Cost : 26.1764 \n",
"Prediction:\n",
" [[ 142.93251038]\n",
" [ 190.05657959]\n",
" [ 177.41052246]\n",
" [ 198.35614014]\n",
" [ 145.29196167]]\n",
"================================================================================\n",
"740 Cost : 26.0629 \n",
"Prediction:\n",
" [[ 142.9506073 ]\n",
" [ 190.04425049]\n",
" [ 177.41621399]\n",
" [ 198.35914612]\n",
" [ 145.27674866]]\n",
"================================================================================\n",
"750 Cost : 25.9499 \n",
"Prediction:\n",
" [[ 142.96870422]\n",
" [ 190.03199768]\n",
" [ 177.42190552]\n",
" [ 198.3621521 ]\n",
" [ 145.26158142]]\n",
"================================================================================\n",
"760 Cost : 25.8375 \n",
"Prediction:\n",
" [[ 142.98675537]\n",
" [ 190.01976013]\n",
" [ 177.42758179]\n",
" [ 198.36514282]\n",
" [ 145.24645996]]\n",
"================================================================================\n",
"770 Cost : 25.7257 \n",
"Prediction:\n",
" [[ 143.00469971]\n",
" [ 190.00750732]\n",
" [ 177.43319702]\n",
" [ 198.36807251]\n",
" [ 145.23132324]]\n",
"================================================================================\n",
"780 Cost : 25.6145 \n",
"Prediction:\n",
" [[ 143.0226593 ]\n",
" [ 189.99536133]\n",
" [ 177.43887329]\n",
" [ 198.37107849]\n",
" [ 145.21632385]]\n",
"================================================================================\n",
"790 Cost : 25.5039 \n",
"Prediction:\n",
" [[ 143.04052734]\n",
" [ 189.98320007]\n",
" [ 177.44447327]\n",
" [ 198.37402344]\n",
" [ 145.2013092 ]]\n",
"================================================================================\n",
"800 Cost : 25.3938 \n",
"Prediction:\n",
" [[ 143.05836487]\n",
" [ 189.97106934]\n",
" [ 177.45010376]\n",
" [ 198.37696838]\n",
" [ 145.18634033]]\n",
"================================================================================\n",
"810 Cost : 25.2844 \n",
"Prediction:\n",
" [[ 143.07617188]\n",
" [ 189.95901489]\n",
" [ 177.45570374]\n",
" [ 198.37992859]\n",
" [ 145.1714325 ]]\n",
"================================================================================\n",
"820 Cost : 25.1756 \n",
"Prediction:\n",
" [[ 143.09390259]\n",
" [ 189.94696045]\n",
" [ 177.46127319]\n",
" [ 198.38284302]\n",
" [ 145.15653992]]\n",
"================================================================================\n",
"830 Cost : 25.0672 \n",
"Prediction:\n",
" [[ 143.11160278]\n",
" [ 189.93492126]\n",
" [ 177.46684265]\n",
" [ 198.38575745]\n",
" [ 145.14170837]]\n",
"================================================================================\n",
"840 Cost : 24.9595 \n",
"Prediction:\n",
" [[ 143.12927246]\n",
" [ 189.92294312]\n",
" [ 177.47239685]\n",
" [ 198.38865662]\n",
" [ 145.12692261]]\n",
"================================================================================\n",
"850 Cost : 24.8523 \n",
"Prediction:\n",
" [[ 143.14685059]\n",
" [ 189.91094971]\n",
" [ 177.47790527]\n",
" [ 198.39154053]\n",
" [ 145.11213684]]\n",
"================================================================================\n",
"860 Cost : 24.7458 \n",
"Prediction:\n",
" [[ 143.16442871]\n",
" [ 189.89906311]\n",
" [ 177.48344421]\n",
" [ 198.3944397 ]\n",
" [ 145.09745789]]\n",
"================================================================================\n",
"870 Cost : 24.6398 \n",
"Prediction:\n",
" [[ 143.18190002]\n",
" [ 189.88716125]\n",
" [ 177.48893738]\n",
" [ 198.39729309]\n",
" [ 145.08277893]]\n",
"================================================================================\n",
"880 Cost : 24.5343 \n",
"Prediction:\n",
" [[ 143.1993866 ]\n",
" [ 189.87528992]\n",
" [ 177.4944458 ]\n",
" [ 198.40016174]\n",
" [ 145.06814575]]\n",
"================================================================================\n",
"890 Cost : 24.4293 \n",
"Prediction:\n",
" [[ 143.21682739]\n",
" [ 189.86346436]\n",
" [ 177.49992371]\n",
" [ 198.4030304 ]\n",
" [ 145.05357361]]\n",
"================================================================================\n",
"900 Cost : 24.3251 \n",
"Prediction:\n",
" [[ 143.23417664]\n",
" [ 189.85166931]\n",
" [ 177.50538635]\n",
" [ 198.40586853]\n",
" [ 145.03901672]]\n",
"================================================================================\n",
"910 Cost : 24.2212 \n",
"Prediction:\n",
" [[ 143.25152588]\n",
" [ 189.83992004]\n",
" [ 177.510849 ]\n",
" [ 198.40870667]\n",
" [ 145.02452087]]\n",
"================================================================================\n",
"920 Cost : 24.1179 \n",
"Prediction:\n",
" [[ 143.26879883]\n",
" [ 189.82815552]\n",
" [ 177.51628113]\n",
" [ 198.41152954]\n",
" [ 145.01004028]]\n",
"================================================================================\n",
"930 Cost : 24.0152 \n",
"Prediction:\n",
" [[ 143.28604126]\n",
" [ 189.81645203]\n",
" [ 177.52171326]\n",
" [ 198.41433716]\n",
" [ 144.99562073]]\n",
"================================================================================\n",
"940 Cost : 23.9131 \n",
"Prediction:\n",
" [[ 143.30323792]\n",
" [ 189.80480957]\n",
" [ 177.52713013]\n",
" [ 198.41716003]\n",
" [ 144.98124695]]\n",
"================================================================================\n",
"950 Cost : 23.8114 \n",
"Prediction:\n",
" [[ 143.32037354]\n",
" [ 189.7931366 ]\n",
" [ 177.53251648]\n",
" [ 198.41995239]\n",
" [ 144.96688843]]\n",
"================================================================================\n",
"960 Cost : 23.7103 \n",
"Prediction:\n",
" [[ 143.33747864]\n",
" [ 189.78152466]\n",
" [ 177.53790283]\n",
" [ 198.42272949]\n",
" [ 144.95259094]]\n",
"================================================================================\n",
"970 Cost : 23.6098 \n",
"Prediction:\n",
" [[ 143.35455322]\n",
" [ 189.76997375]\n",
" [ 177.54328918]\n",
" [ 198.42552185]\n",
" [ 144.93833923]]\n",
"================================================================================\n",
"980 Cost : 23.5097 \n",
"Prediction:\n",
" [[ 143.37155151]\n",
" [ 189.75840759]\n",
" [ 177.54862976]\n",
" [ 198.42826843]\n",
" [ 144.92410278]]\n",
"================================================================================\n",
"990 Cost : 23.4102 \n",
"Prediction:\n",
" [[ 143.38853455]\n",
" [ 189.74690247]\n",
" [ 177.5539856 ]\n",
" [ 198.43104553]\n",
" [ 144.90994263]]\n",
"================================================================================\n",
"1000 Cost : 23.3113 \n",
"Prediction:\n",
" [[ 143.40542603]\n",
" [ 189.73538208]\n",
" [ 177.5592804 ]\n",
" [ 198.43377686]\n",
" [ 144.89576721]]\n",
"================================================================================\n",
"1010 Cost : 23.2128 \n",
"Prediction:\n",
" [[ 143.42233276]\n",
" [ 189.72393799]\n",
" [ 177.56462097]\n",
" [ 198.4365387 ]\n",
" [ 144.88168335]]\n",
"================================================================================\n",
"1020 Cost : 23.1149 \n",
"Prediction:\n",
" [[ 143.43913269]\n",
" [ 189.7124939 ]\n",
" [ 177.56990051]\n",
" [ 198.4392395 ]\n",
" [ 144.86763 ]]\n",
"================================================================================\n",
"1030 Cost : 23.0175 \n",
"Prediction:\n",
" [[ 143.45593262]\n",
" [ 189.70111084]\n",
" [ 177.57519531]\n",
" [ 198.44197083]\n",
" [ 144.85360718]]\n",
"================================================================================\n",
"1040 Cost : 22.9205 \n",
"Prediction:\n",
" [[ 143.47267151]\n",
" [ 189.68972778]\n",
" [ 177.58047485]\n",
" [ 198.44465637]\n",
" [ 144.83959961]]\n",
"================================================================================\n",
"1050 Cost : 22.8241 \n",
"Prediction:\n",
" [[ 143.48937988]\n",
" [ 189.6783905 ]\n",
" [ 177.58570862]\n",
" [ 198.44737244]\n",
" [ 144.82565308]]\n",
"================================================================================\n",
"1060 Cost : 22.7283 \n",
"Prediction:\n",
" [[ 143.50605774]\n",
" [ 189.66711426]\n",
" [ 177.59098816]\n",
" [ 198.45007324]\n",
" [ 144.81178284]]\n",
"================================================================================\n",
"1070 Cost : 22.6328 \n",
"Prediction:\n",
" [[ 143.52264404]\n",
" [ 189.65579224]\n",
" [ 177.59619141]\n",
" [ 198.45269775]\n",
" [ 144.79788208]]\n",
"================================================================================\n",
"1080 Cost : 22.538 \n",
"Prediction:\n",
" [[ 143.53924561]\n",
" [ 189.64459229]\n",
" [ 177.60144043]\n",
" [ 198.45542908]\n",
" [ 144.78408813]]\n",
"================================================================================\n",
"1090 Cost : 22.4437 \n",
"Prediction:\n",
" [[ 143.55574036]\n",
" [ 189.63336182]\n",
" [ 177.60662842]\n",
" [ 198.45805359]\n",
" [ 144.77027893]]\n",
"================================================================================\n",
"1100 Cost : 22.3498 \n",
"Prediction:\n",
" [[ 143.57223511]\n",
" [ 189.62217712]\n",
" [ 177.61183167]\n",
" [ 198.46072388]\n",
" [ 144.75654602]]\n",
"================================================================================\n",
"1110 Cost : 22.2564 \n",
"Prediction:\n",
" [[ 143.58865356]\n",
" [ 189.61100769]\n",
" [ 177.61700439]\n",
" [ 198.46334839]\n",
" [ 144.74282837]]\n",
"================================================================================\n",
"1120 Cost : 22.1635 \n",
"Prediction:\n",
" [[ 143.60505676]\n",
" [ 189.59986877]\n",
" [ 177.62216187]\n",
" [ 198.46598816]\n",
" [ 144.72914124]]\n",
"================================================================================\n",
"1130 Cost : 22.071 \n",
"Prediction:\n",
" [[ 143.62141418]\n",
" [ 189.58877563]\n",
" [ 177.62731934]\n",
" [ 198.46861267]\n",
" [ 144.71551514]]\n",
"================================================================================\n",
"1140 Cost : 21.9791 \n",
"Prediction:\n",
" [[ 143.63772583]\n",
" [ 189.57769775]\n",
" [ 177.63244629]\n",
" [ 198.47119141]\n",
" [ 144.7019043 ]]\n",
"================================================================================\n",
"1150 Cost : 21.8877 \n",
"Prediction:\n",
" [[ 143.65400696]\n",
" [ 189.56668091]\n",
" [ 177.6375885 ]\n",
" [ 198.47383118]\n",
" [ 144.68836975]]\n",
"================================================================================\n",
"1160 Cost : 21.7967 \n",
"Prediction:\n",
" [[ 143.67021179]\n",
" [ 189.5556488 ]\n",
" [ 177.6427002 ]\n",
" [ 198.47642517]\n",
" [ 144.67485046]]\n",
"================================================================================\n",
"1170 Cost : 21.7062 \n",
"Prediction:\n",
" [[ 143.68641663]\n",
" [ 189.54469299]\n",
" [ 177.64781189]\n",
" [ 198.47903442]\n",
" [ 144.66137695]]\n",
"================================================================================\n",
"1180 Cost : 21.6162 \n",
"Prediction:\n",
" [[ 143.70254517]\n",
" [ 189.53372192]\n",
" [ 177.65287781]\n",
" [ 198.48158264]\n",
" [ 144.6479187 ]]\n",
"================================================================================\n",
"1190 Cost : 21.5268 \n",
"Prediction:\n",
" [[ 143.71862793]\n",
" [ 189.52281189]\n",
" [ 177.65795898]\n",
" [ 198.48416138]\n",
" [ 144.63452148]]\n",
"================================================================================\n",
"1200 Cost : 21.4376 \n",
"Prediction:\n",
" [[ 143.73469543]\n",
" [ 189.51190186]\n",
" [ 177.6630249 ]\n",
" [ 198.48672485]\n",
" [ 144.62115479]]\n",
"================================================================================\n",
"1210 Cost : 21.3491 \n",
"Prediction:\n",
" [[ 143.75068665]\n",
" [ 189.5010376 ]\n",
" [ 177.6680603 ]\n",
" [ 198.48925781]\n",
" [ 144.6078186 ]]\n",
"================================================================================\n",
"1220 Cost : 21.261 \n",
"Prediction:\n",
" [[ 143.76669312]\n",
" [ 189.49023438]\n",
" [ 177.67312622]\n",
" [ 198.49183655]\n",
" [ 144.59455872]]\n",
"================================================================================\n",
"1230 Cost : 21.1733 \n",
"Prediction:\n",
" [[ 143.78257751]\n",
" [ 189.47937012]\n",
" [ 177.67810059]\n",
" [ 198.49430847]\n",
" [ 144.58125305]]\n",
"================================================================================\n",
"1240 Cost : 21.0862 \n",
"Prediction:\n",
" [[ 143.79849243]\n",
" [ 189.46864319]\n",
" [ 177.68315125]\n",
" [ 198.49688721]\n",
" [ 144.56808472]]\n",
"================================================================================\n",
"1250 Cost : 20.9994 \n",
"Prediction:\n",
" [[ 143.8143158 ]\n",
" [ 189.45783997]\n",
" [ 177.68811035]\n",
" [ 198.49935913]\n",
" [ 144.55487061]]\n",
"================================================================================\n",
"1260 Cost : 20.9131 \n",
"Prediction:\n",
" [[ 143.83013916]\n",
" [ 189.44715881]\n",
" [ 177.69311523]\n",
" [ 198.50187683]\n",
" [ 144.54176331]]\n",
"================================================================================\n",
"1270 Cost : 20.8272 \n",
"Prediction:\n",
" [[ 143.84590149]\n",
" [ 189.43644714]\n",
" [ 177.6980896 ]\n",
" [ 198.50437927]\n",
" [ 144.52864075]]\n",
"================================================================================\n",
"1280 Cost : 20.7419 \n",
"Prediction:\n",
" [[ 143.86160278]\n",
" [ 189.42578125]\n",
" [ 177.70303345]\n",
" [ 198.50686646]\n",
" [ 144.51556396]]\n",
"================================================================================\n",
"1290 Cost : 20.6569 \n",
"Prediction:\n",
" [[ 143.87731934]\n",
" [ 189.41517639]\n",
" [ 177.70802307]\n",
" [ 198.50938416]\n",
" [ 144.50254822]]\n",
"================================================================================\n",
"1300 Cost : 20.5725 \n",
"Prediction:\n",
" [[ 143.89291382]\n",
" [ 189.40452576]\n",
" [ 177.71290588]\n",
" [ 198.51179504]\n",
" [ 144.48951721]]\n",
"================================================================================\n",
"1310 Cost : 20.4884 \n",
"Prediction:\n",
" [[ 143.90853882]\n",
" [ 189.39396667]\n",
" [ 177.71786499]\n",
" [ 198.51428223]\n",
" [ 144.47659302]]\n",
"================================================================================\n",
"1320 Cost : 20.4048 \n",
"Prediction:\n",
" [[ 143.92407227]\n",
" [ 189.38339233]\n",
" [ 177.72273254]\n",
" [ 198.51669312]\n",
" [ 144.46363831]]\n",
"================================================================================\n",
"1330 Cost : 20.3216 \n",
"Prediction:\n",
" [[ 143.93960571]\n",
" [ 189.37287903]\n",
" [ 177.72766113]\n",
" [ 198.5191803 ]\n",
" [ 144.45079041]]\n",
"================================================================================\n",
"1340 Cost : 20.2389 \n",
"Prediction:\n",
" [[ 143.95506287]\n",
" [ 189.36238098]\n",
" [ 177.73252869]\n",
" [ 198.52157593]\n",
" [ 144.43792725]]\n",
"================================================================================\n",
"1350 Cost : 20.1567 \n",
"Prediction:\n",
" [[ 143.97050476]\n",
" [ 189.35192871]\n",
" [ 177.73739624]\n",
" [ 198.52401733]\n",
" [ 144.42512512]]\n",
"================================================================================\n",
"1360 Cost : 20.0748 \n",
"Prediction:\n",
" [[ 143.98588562]\n",
" [ 189.34146118]\n",
" [ 177.74224854]\n",
" [ 198.52642822]\n",
" [ 144.41235352]]\n",
"================================================================================\n",
"1370 Cost : 19.9934 \n",
"Prediction:\n",
" [[ 144.00125122]\n",
" [ 189.33106995]\n",
" [ 177.74710083]\n",
" [ 198.52883911]\n",
" [ 144.39959717]]\n",
"================================================================================\n",
"1380 Cost : 19.9124 \n",
"Prediction:\n",
" [[ 144.01655579]\n",
" [ 189.32067871]\n",
" [ 177.75193787]\n",
" [ 198.53125 ]\n",
" [ 144.38691711]]\n",
"================================================================================\n",
"1390 Cost : 19.8318 \n",
"Prediction:\n",
" [[ 144.03184509]\n",
" [ 189.31031799]\n",
" [ 177.7567749 ]\n",
" [ 198.53364563]\n",
" [ 144.37425232]]\n",
"================================================================================\n",
"1400 Cost : 19.7517 \n",
"Prediction:\n",
" [[ 144.04707336]\n",
" [ 189.30000305]\n",
" [ 177.76158142]\n",
" [ 198.536026 ]\n",
" [ 144.36161804]]\n",
"================================================================================\n",
"1410 Cost : 19.672 \n",
"Prediction:\n",
" [[ 144.0622406 ]\n",
" [ 189.28968811]\n",
" [ 177.76635742]\n",
" [ 198.53839111]\n",
" [ 144.34901428]]\n",
"================================================================================\n",
"1420 Cost : 19.5927 \n",
"Prediction:\n",
" [[ 144.07739258]\n",
" [ 189.27941895]\n",
" [ 177.77114868]\n",
" [ 198.54075623]\n",
" [ 144.3364563 ]]\n",
"================================================================================\n",
"1430 Cost : 19.5137 \n",
"Prediction:\n",
" [[ 144.09251404]\n",
" [ 189.26914978]\n",
" [ 177.77589417]\n",
" [ 198.54309082]\n",
" [ 144.32392883]]\n",
"================================================================================\n",
"1440 Cost : 19.4353 \n",
"Prediction:\n",
" [[ 144.10760498]\n",
" [ 189.25897217]\n",
" [ 177.78070068]\n",
" [ 198.54547119]\n",
" [ 144.3114624 ]]\n",
"================================================================================\n",
"1450 Cost : 19.3572 \n",
"Prediction:\n",
" [[ 144.12260437]\n",
" [ 189.24873352]\n",
" [ 177.78541565]\n",
" [ 198.54776001]\n",
" [ 144.29898071]]\n",
"================================================================================\n",
"1460 Cost : 19.2795 \n",
"Prediction:\n",
" [[ 144.13761902]\n",
" [ 189.23858643]\n",
" [ 177.79017639]\n",
" [ 198.55012512]\n",
" [ 144.28657532]]\n",
"================================================================================\n",
"1470 Cost : 19.2023 \n",
"Prediction:\n",
" [[ 144.15254211]\n",
" [ 189.22840881]\n",
" [ 177.79486084]\n",
" [ 198.55241394]\n",
" [ 144.27416992]]\n",
"================================================================================\n",
"1480 Cost : 19.1254 \n",
"Prediction:\n",
" [[ 144.16749573]\n",
" [ 189.21833801]\n",
" [ 177.79962158]\n",
" [ 198.55477905]\n",
" [ 144.26184082]]\n",
"================================================================================\n",
"1490 Cost : 19.0489 \n",
"Prediction:\n",
" [[ 144.18235779]\n",
" [ 189.20822144]\n",
" [ 177.80427551]\n",
" [ 198.55703735]\n",
" [ 144.24952698]]\n",
"================================================================================\n",
"1500 Cost : 18.9729 \n",
"Prediction:\n",
" [[ 144.19720459]\n",
" [ 189.19818115]\n",
" [ 177.80899048]\n",
" [ 198.55934143]\n",
" [ 144.23724365]]\n",
"================================================================================\n",
"1510 Cost : 18.8973 \n",
"Prediction:\n",
" [[ 144.2119751 ]\n",
" [ 189.18812561]\n",
" [ 177.81362915]\n",
" [ 198.56161499]\n",
" [ 144.22499084]]\n",
"================================================================================\n",
"1520 Cost : 18.822 \n",
"Prediction:\n",
" [[ 144.22676086]\n",
" [ 189.17814636]\n",
" [ 177.81832886]\n",
" [ 198.56394958]\n",
" [ 144.21281433]]\n",
"================================================================================\n",
"1530 Cost : 18.7472 \n",
"Prediction:\n",
" [[ 144.24143982]\n",
" [ 189.1681366 ]\n",
" [ 177.82293701]\n",
" [ 198.56616211]\n",
" [ 144.2006073 ]]\n",
"================================================================================\n",
"1540 Cost : 18.6727 \n",
"Prediction:\n",
" [[ 144.25616455]\n",
" [ 189.15821838]\n",
" [ 177.8276062 ]\n",
" [ 198.56846619]\n",
" [ 144.18849182]]\n",
"================================================================================\n",
"1550 Cost : 18.5986 \n",
"Prediction:\n",
" [[ 144.27078247]\n",
" [ 189.14825439]\n",
" [ 177.83222961]\n",
" [ 198.57069397]\n",
" [ 144.17636108]]\n",
"================================================================================\n",
"1560 Cost : 18.525 \n",
"Prediction:\n",
" [[ 144.28540039]\n",
" [ 189.13838196]\n",
" [ 177.83685303]\n",
" [ 198.57295227]\n",
" [ 144.16430664]]\n",
"================================================================================\n",
"1570 Cost : 18.4517 \n",
"Prediction:\n",
" [[ 144.29995728]\n",
" [ 189.12849426]\n",
" [ 177.84144592]\n",
" [ 198.57519531]\n",
" [ 144.1522522 ]]\n",
"================================================================================\n",
"1580 Cost : 18.3787 \n",
"Prediction:\n",
" [[ 144.31448364]\n",
" [ 189.11863708]\n",
" [ 177.84605408]\n",
" [ 198.5774231 ]\n",
" [ 144.14025879]]\n",
"================================================================================\n",
"1590 Cost : 18.3062 \n",
"Prediction:\n",
" [[ 144.32896423]\n",
" [ 189.10882568]\n",
" [ 177.85061646]\n",
" [ 198.57963562]\n",
" [ 144.12828064]]\n",
"================================================================================\n",
"1600 Cost : 18.234 \n",
"Prediction:\n",
" [[ 144.34344482]\n",
" [ 189.0990448 ]\n",
" [ 177.85520935]\n",
" [ 198.58187866]\n",
" [ 144.11636353]]\n",
"================================================================================\n",
"1610 Cost : 18.1623 \n",
"Prediction:\n",
" [[ 144.35784912]\n",
" [ 189.08926392]\n",
" [ 177.85975647]\n",
" [ 198.58406067]\n",
" [ 144.10444641]]\n",
"================================================================================\n",
"1620 Cost : 18.0908 \n",
"Prediction:\n",
" [[ 144.37223816]\n",
" [ 189.07951355]\n",
" [ 177.86430359]\n",
" [ 198.58627319]\n",
" [ 144.09259033]]\n",
"================================================================================\n",
"1630 Cost : 18.0198 \n",
"Prediction:\n",
" [[ 144.38658142]\n",
" [ 189.06980896]\n",
" [ 177.86885071]\n",
" [ 198.58847046]\n",
" [ 144.08076477]]\n",
"================================================================================\n",
"1640 Cost : 17.9492 \n",
"Prediction:\n",
" [[ 144.40084839]\n",
" [ 189.06008911]\n",
" [ 177.87335205]\n",
" [ 198.59062195]\n",
" [ 144.06893921]]\n",
"================================================================================\n",
"1650 Cost : 17.8789 \n",
"Prediction:\n",
" [[ 144.41511536]\n",
" [ 189.05041504]\n",
" [ 177.87785339]\n",
" [ 198.5927887 ]\n",
" [ 144.05717468]]\n",
"================================================================================\n",
"1660 Cost : 17.809 \n",
"Prediction:\n",
" [[ 144.42935181]\n",
" [ 189.04078674]\n",
" [ 177.88237 ]\n",
" [ 198.59498596]\n",
" [ 144.04545593]]\n",
"================================================================================\n",
"1670 Cost : 17.7394 \n",
"Prediction:\n",
" [[ 144.44354248]\n",
" [ 189.03117371]\n",
" [ 177.88685608]\n",
" [ 198.59710693]\n",
" [ 144.03373718]]\n",
"================================================================================\n",
"1680 Cost : 17.6702 \n",
"Prediction:\n",
" [[ 144.45770264]\n",
" [ 189.02159119]\n",
" [ 177.89134216]\n",
" [ 198.59928894]\n",
" [ 144.02207947]]\n",
"================================================================================\n",
"1690 Cost : 17.6014 \n",
"Prediction:\n",
" [[ 144.47180176]\n",
" [ 189.01200867]\n",
" [ 177.89576721]\n",
" [ 198.60139465]\n",
" [ 144.01042175]]\n",
"================================================================================\n",
"1700 Cost : 17.5328 \n",
"Prediction:\n",
" [[ 144.48590088]\n",
" [ 189.00247192]\n",
" [ 177.90026855]\n",
" [ 198.6035614 ]\n",
" [ 143.99884033]]\n",
"================================================================================\n",
"1710 Cost : 17.4648 \n",
"Prediction:\n",
" [[ 144.49992371]\n",
" [ 188.99295044]\n",
" [ 177.90467834]\n",
" [ 198.60566711]\n",
" [ 143.98727417]]\n",
"================================================================================\n",
"1720 Cost : 17.397 \n",
"Prediction:\n",
" [[ 144.51393127]\n",
" [ 188.98347473]\n",
" [ 177.90913391]\n",
" [ 198.60778809]\n",
" [ 143.97573853]]\n",
"================================================================================\n",
"1730 Cost : 17.3296 \n",
"Prediction:\n",
" [[ 144.52789307]\n",
" [ 188.97399902]\n",
" [ 177.91352844]\n",
" [ 198.6098938 ]\n",
" [ 143.9642334 ]]\n",
"================================================================================\n",
"1740 Cost : 17.2626 \n",
"Prediction:\n",
" [[ 144.5418396 ]\n",
" [ 188.96458435]\n",
" [ 177.91796875]\n",
" [ 198.61201477]\n",
" [ 143.95278931]]\n",
"================================================================================\n",
"1750 Cost : 17.1958 \n",
"Prediction:\n",
" [[ 144.55570984]\n",
" [ 188.9551239 ]\n",
" [ 177.9223175 ]\n",
" [ 198.61405945]\n",
" [ 143.9413147 ]]\n",
"================================================================================\n",
"1760 Cost : 17.1295 \n",
"Prediction:\n",
" [[ 144.56959534]\n",
" [ 188.94577026]\n",
" [ 177.92674255]\n",
" [ 198.61618042]\n",
" [ 143.92993164]]\n",
"================================================================================\n",
"1770 Cost : 17.0635 \n",
"Prediction:\n",
" [[ 144.5834198 ]\n",
" [ 188.93640137]\n",
" [ 177.93112183]\n",
" [ 198.61825562]\n",
" [ 143.91856384]]\n",
"================================================================================\n",
"1780 Cost : 16.9978 \n",
"Prediction:\n",
" [[ 144.59721375]\n",
" [ 188.92704773]\n",
" [ 177.93547058]\n",
" [ 198.62033081]\n",
" [ 143.90722656]]\n",
"================================================================================\n",
"1790 Cost : 16.9325 \n",
"Prediction:\n",
" [[ 144.6109314 ]\n",
" [ 188.91770935]\n",
" [ 177.93981934]\n",
" [ 198.62237549]\n",
" [ 143.89588928]]\n",
"================================================================================\n",
"1800 Cost : 16.8675 \n",
"Prediction:\n",
" [[ 144.62466431]\n",
" [ 188.90844727]\n",
" [ 177.94416809]\n",
" [ 198.62445068]\n",
" [ 143.88464355]]\n",
"================================================================================\n",
"1810 Cost : 16.8029 \n",
"Prediction:\n",
" [[ 144.63832092]\n",
" [ 188.89915466]\n",
" [ 177.94848633]\n",
" [ 198.6264801 ]\n",
" [ 143.87336731]]\n",
"================================================================================\n",
"1820 Cost : 16.7386 \n",
"Prediction:\n",
" [[ 144.6519928 ]\n",
" [ 188.88995361]\n",
" [ 177.95283508]\n",
" [ 198.62854004]\n",
" [ 143.86218262]]\n",
"================================================================================\n",
"1830 Cost : 16.6746 \n",
"Prediction:\n",
" [[ 144.66555786]\n",
" [ 188.88069153]\n",
" [ 177.95710754]\n",
" [ 198.63053894]\n",
" [ 143.85098267]]\n",
"================================================================================\n",
"1840 Cost : 16.611 \n",
"Prediction:\n",
" [[ 144.67915344]\n",
" [ 188.87153625]\n",
" [ 177.96144104]\n",
" [ 198.63259888]\n",
" [ 143.83985901]]\n",
"================================================================================\n",
"1850 Cost : 16.5477 \n",
"Prediction:\n",
" [[ 144.69268799]\n",
" [ 188.86235046]\n",
" [ 177.9657135 ]\n",
" [ 198.63459778]\n",
" [ 143.82873535]]\n",
"================================================================================\n",
"1860 Cost : 16.4847 \n",
"Prediction:\n",
" [[ 144.70617676]\n",
" [ 188.85321045]\n",
" [ 177.96998596]\n",
" [ 198.63659668]\n",
" [ 143.81764221]]\n",
"================================================================================\n",
"1870 Cost : 16.422 \n",
"Prediction:\n",
" [[ 144.71963501]\n",
" [ 188.84407043]\n",
" [ 177.97424316]\n",
" [ 198.63859558]\n",
" [ 143.80657959]]\n",
"================================================================================\n",
"1880 Cost : 16.3597 \n",
"Prediction:\n",
" [[ 144.73309326]\n",
" [ 188.83499146]\n",
" [ 177.97851562]\n",
" [ 198.64060974]\n",
" [ 143.795578 ]]\n",
"================================================================================\n",
"1890 Cost : 16.2977 \n",
"Prediction:\n",
" [[ 144.74647522]\n",
" [ 188.82591248]\n",
" [ 177.98275757]\n",
" [ 198.64257812]\n",
" [ 143.78456116]]\n",
"================================================================================\n",
"1900 Cost : 16.236 \n",
"Prediction:\n",
" [[ 144.75984192]\n",
" [ 188.81686401]\n",
" [ 177.98698425]\n",
" [ 198.64454651]\n",
" [ 143.77362061]]\n",
"================================================================================\n",
"1910 Cost : 16.1747 \n",
"Prediction:\n",
" [[ 144.77316284]\n",
" [ 188.80784607]\n",
" [ 177.99121094]\n",
" [ 198.64654541]\n",
" [ 143.76269531]]\n",
"================================================================================\n",
"1920 Cost : 16.1136 \n",
"Prediction:\n",
" [[ 144.78643799]\n",
" [ 188.79884338]\n",
" [ 177.99542236]\n",
" [ 198.64848328]\n",
" [ 143.75178528]]\n",
"================================================================================\n",
"1930 Cost : 16.0529 \n",
"Prediction:\n",
" [[ 144.79969788]\n",
" [ 188.78987122]\n",
" [ 177.99961853]\n",
" [ 198.65045166]\n",
" [ 143.74092102]]\n",
"================================================================================\n",
"1940 Cost : 15.9925 \n",
"Prediction:\n",
" [[ 144.81291199]\n",
" [ 188.78091431]\n",
" [ 178.0038147 ]\n",
" [ 198.65240479]\n",
" [ 143.73007202]]\n",
"================================================================================\n",
"1950 Cost : 15.9324 \n",
"Prediction:\n",
" [[ 144.82611084]\n",
" [ 188.77198792]\n",
" [ 178.00799561]\n",
" [ 198.65434265]\n",
" [ 143.71929932]]\n",
"================================================================================\n",
"1960 Cost : 15.8726 \n",
"Prediction:\n",
" [[ 144.83924866]\n",
" [ 188.76304626]\n",
" [ 178.012146 ]\n",
" [ 198.65623474]\n",
" [ 143.70849609]]\n",
"================================================================================\n",
"1970 Cost : 15.8132 \n",
"Prediction:\n",
" [[ 144.85238647]\n",
" [ 188.75421143]\n",
" [ 178.0163269 ]\n",
" [ 198.65820312]\n",
" [ 143.69778442]]\n",
"================================================================================\n",
"1980 Cost : 15.754 \n",
"Prediction:\n",
" [[ 144.86546326]\n",
" [ 188.74533081]\n",
" [ 178.02046204]\n",
" [ 198.66011047]\n",
" [ 143.6870575 ]]\n",
"================================================================================\n",
"1990 Cost : 15.6952 \n",
"Prediction:\n",
" [[ 144.87852478]\n",
" [ 188.73651123]\n",
" [ 178.02461243]\n",
" [ 198.66203308]\n",
" [ 143.6763916 ]]\n",
"================================================================================\n",
"2000 Cost : 15.6367 \n",
"Prediction:\n",
" [[ 144.89151001]\n",
" [ 188.72769165]\n",
" [ 178.02871704]\n",
" [ 198.66392517]\n",
" [ 143.66572571]]\n",
"================================================================================\n",
"2010 Cost : 15.5784 \n",
"Prediction:\n",
" [[ 144.90452576]\n",
" [ 188.71891785]\n",
" [ 178.03286743]\n",
" [ 198.66584778]\n",
" [ 143.65512085]]\n",
"================================================================================\n",
"2020 Cost : 15.5205 \n",
"Prediction:\n",
" [[ 144.91744995]\n",
" [ 188.71012878]\n",
" [ 178.03694153]\n",
" [ 198.66770935]\n",
" [ 143.64451599]]\n",
"================================================================================\n",
"2030 Cost : 15.4629 \n",
"Prediction:\n",
" [[ 144.93035889]\n",
" [ 188.70140076]\n",
" [ 178.0410614 ]\n",
" [ 198.6696167 ]\n",
" [ 143.63395691]]\n",
"================================================================================\n",
"2040 Cost : 15.4055 \n",
"Prediction:\n",
" [[ 144.9432373 ]\n",
" [ 188.69267273]\n",
" [ 178.04515076]\n",
" [ 198.67147827]\n",
" [ 143.62342834]]\n",
"================================================================================\n",
"2050 Cost : 15.3484 \n",
"Prediction:\n",
" [[ 144.95608521]\n",
" [ 188.68397522]\n",
" [ 178.04922485]\n",
" [ 198.67333984]\n",
" [ 143.61291504]]\n",
"================================================================================\n",
"2060 Cost : 15.2917 \n",
"Prediction:\n",
" [[ 144.96890259]\n",
" [ 188.67530823]\n",
" [ 178.05328369]\n",
" [ 198.67521667]\n",
" [ 143.60244751]]\n",
"================================================================================\n",
"2070 Cost : 15.2353 \n",
"Prediction:\n",
" [[ 144.98167419]\n",
" [ 188.66665649]\n",
" [ 178.05732727]\n",
" [ 198.67706299]\n",
" [ 143.5920105 ]]\n",
"================================================================================\n",
"2080 Cost : 15.1791 \n",
"Prediction:\n",
" [[ 144.99443054]\n",
" [ 188.65803528]\n",
" [ 178.06138611]\n",
" [ 198.6789093 ]\n",
" [ 143.581604 ]]\n",
"================================================================================\n",
"2090 Cost : 15.1233 \n",
"Prediction:\n",
" [[ 145.0071106 ]\n",
" [ 188.6493988 ]\n",
" [ 178.06538391]\n",
" [ 198.6807251 ]\n",
" [ 143.57121277]]\n",
"================================================================================\n",
"2100 Cost : 15.0676 \n",
"Prediction:\n",
" [[ 145.01982117]\n",
" [ 188.64083862]\n",
" [ 178.06944275]\n",
" [ 198.68257141]\n",
" [ 143.56086731]]\n",
"================================================================================\n",
"2110 Cost : 15.0124 \n",
"Prediction:\n",
" [[ 145.03244019]\n",
" [ 188.63227844]\n",
" [ 178.07342529]\n",
" [ 198.68438721]\n",
" [ 143.55053711]]\n",
"================================================================================\n",
"2120 Cost : 14.9574 \n",
"Prediction:\n",
" [[ 145.04507446]\n",
" [ 188.62373352]\n",
" [ 178.07745361]\n",
" [ 198.68621826]\n",
" [ 143.54025269]]\n",
"================================================================================\n",
"2130 Cost : 14.9027 \n",
"Prediction:\n",
" [[ 145.05763245]\n",
" [ 188.61521912]\n",
" [ 178.08143616]\n",
" [ 198.6880188 ]\n",
" [ 143.52998352]]\n",
"================================================================================\n",
"2140 Cost : 14.8482 \n",
"Prediction:\n",
" [[ 145.07020569]\n",
" [ 188.60675049]\n",
" [ 178.08544922]\n",
" [ 198.68984985]\n",
" [ 143.51976013]]\n",
"================================================================================\n",
"2150 Cost : 14.7941 \n",
"Prediction:\n",
" [[ 145.08270264]\n",
" [ 188.5982666 ]\n",
" [ 178.08938599]\n",
" [ 198.69161987]\n",
" [ 143.509552 ]]\n",
"================================================================================\n",
"2160 Cost : 14.7402 \n",
"Prediction:\n",
" [[ 145.09516907]\n",
" [ 188.58979797]\n",
" [ 178.09333801]\n",
" [ 198.69338989]\n",
" [ 143.49935913]]\n",
"================================================================================\n",
"2170 Cost : 14.6867 \n",
"Prediction:\n",
" [[ 145.1076355 ]\n",
" [ 188.58140564]\n",
" [ 178.09732056]\n",
" [ 198.69520569]\n",
" [ 143.48924255]]\n",
"================================================================================\n",
"2180 Cost : 14.6333 \n",
"Prediction:\n",
" [[ 145.12002563]\n",
" [ 188.57296753]\n",
" [ 178.10124207]\n",
" [ 198.69696045]\n",
" [ 143.47909546]]\n",
"================================================================================\n",
"2190 Cost : 14.5803 \n",
"Prediction:\n",
" [[ 145.13243103]\n",
" [ 188.56462097]\n",
" [ 178.10520935]\n",
" [ 198.69874573]\n",
" [ 143.46903992]]\n",
"================================================================================\n",
"2200 Cost : 14.5275 \n",
"Prediction:\n",
" [[ 145.14477539]\n",
" [ 188.5562439 ]\n",
" [ 178.10910034]\n",
" [ 198.70050049]\n",
" [ 143.45895386]]\n",
"================================================================================\n",
"2210 Cost : 14.475 \n",
"Prediction:\n",
" [[ 145.15710449]\n",
" [ 188.54789734]\n",
" [ 178.11302185]\n",
" [ 198.70225525]\n",
" [ 143.44892883]]\n",
"================================================================================\n",
"2220 Cost : 14.4228 \n",
"Prediction:\n",
" [[ 145.16938782]\n",
" [ 188.53959656]\n",
" [ 178.1169281 ]\n",
" [ 198.70401001]\n",
" [ 143.43891907]]\n",
"================================================================================\n",
"2230 Cost : 14.3709 \n",
"Prediction:\n",
" [[ 145.18164062]\n",
" [ 188.53129578]\n",
" [ 178.12081909]\n",
" [ 198.70573425]\n",
" [ 143.42893982]]\n",
"================================================================================\n",
"2240 Cost : 14.3192 \n",
"Prediction:\n",
" [[ 145.19389343]\n",
" [ 188.52304077]\n",
" [ 178.12472534]\n",
" [ 198.70750427]\n",
" [ 143.41902161]]\n",
"================================================================================\n",
"2250 Cost : 14.2678 \n",
"Prediction:\n",
" [[ 145.20605469]\n",
" [ 188.51478577]\n",
" [ 178.1285553 ]\n",
" [ 198.709198 ]\n",
" [ 143.40907288]]\n",
"================================================================================\n",
"2260 Cost : 14.2166 \n",
"Prediction:\n",
" [[ 145.2182312 ]\n",
" [ 188.50657654]\n",
" [ 178.13244629]\n",
" [ 198.7109375 ]\n",
" [ 143.3992157 ]]\n",
"================================================================================\n",
"2270 Cost : 14.1658 \n",
"Prediction:\n",
" [[ 145.23034668]\n",
" [ 188.49836731]\n",
" [ 178.1362915 ]\n",
" [ 198.71263123]\n",
" [ 143.38934326]]\n",
"================================================================================\n",
"2280 Cost : 14.1151 \n",
"Prediction:\n",
" [[ 145.24246216]\n",
" [ 188.49017334]\n",
" [ 178.14013672]\n",
" [ 198.71434021]\n",
" [ 143.3795166 ]]\n",
"================================================================================\n",
"2290 Cost : 14.0648 \n",
"Prediction:\n",
" [[ 145.25450134]\n",
" [ 188.48200989]\n",
" [ 178.14398193]\n",
" [ 198.71604919]\n",
" [ 143.3697052 ]]\n",
"================================================================================\n",
"2300 Cost : 14.0147 \n",
"Prediction:\n",
" [[ 145.26654053]\n",
" [ 188.47386169]\n",
" [ 178.14779663]\n",
" [ 198.71772766]\n",
" [ 143.35992432]]\n",
"================================================================================\n",
"2310 Cost : 13.9648 \n",
"Prediction:\n",
" [[ 145.27854919]\n",
" [ 188.46574402]\n",
" [ 178.15162659]\n",
" [ 198.71942139]\n",
" [ 143.35017395]]\n",
"================================================================================\n",
"2320 Cost : 13.9153 \n",
"Prediction:\n",
" [[ 145.29049683]\n",
" [ 188.4576416 ]\n",
" [ 178.15541077]\n",
" [ 198.72109985]\n",
" [ 143.34043884]]\n",
"================================================================================\n",
"2330 Cost : 13.8659 \n",
"Prediction:\n",
" [[ 145.30245972]\n",
" [ 188.44960022]\n",
" [ 178.15924072]\n",
" [ 198.72279358]\n",
" [ 143.33076477]]\n",
"================================================================================\n",
"2340 Cost : 13.8168 \n",
"Prediction:\n",
" [[ 145.31436157]\n",
" [ 188.44151306]\n",
" [ 178.16300964]\n",
" [ 198.72444153]\n",
" [ 143.3210907 ]]\n",
"================================================================================\n",
"2350 Cost : 13.768 \n",
"Prediction:\n",
" [[ 145.32621765]\n",
" [ 188.43345642]\n",
" [ 178.16677856]\n",
" [ 198.72608948]\n",
" [ 143.31143188]]\n",
"================================================================================\n",
"2360 Cost : 13.7195 \n",
"Prediction:\n",
" [[ 145.33808899]\n",
" [ 188.42547607]\n",
" [ 178.17054749]\n",
" [ 198.72776794]\n",
" [ 143.30186462]]\n",
"================================================================================\n",
"2370 Cost : 13.6711 \n",
"Prediction:\n",
" [[ 145.34991455]\n",
" [ 188.41749573]\n",
" [ 178.17431641]\n",
" [ 198.72941589]\n",
" [ 143.2922821 ]]\n",
"================================================================================\n",
"2380 Cost : 13.6231 \n",
"Prediction:\n",
" [[ 145.36169434]\n",
" [ 188.40951538]\n",
" [ 178.17807007]\n",
" [ 198.73104858]\n",
" [ 143.28269958]]\n",
"================================================================================\n",
"2390 Cost : 13.5752 \n",
"Prediction:\n",
" [[ 145.37342834]\n",
" [ 188.40153503]\n",
" [ 178.18179321]\n",
" [ 198.73265076]\n",
" [ 143.27316284]]\n",
"================================================================================\n",
"2400 Cost : 13.5277 \n",
"Prediction:\n",
" [[ 145.38514709]\n",
" [ 188.39363098]\n",
" [ 178.18551636]\n",
" [ 198.73428345]\n",
" [ 143.26368713]]\n",
"================================================================================\n",
"2410 Cost : 13.4803 \n",
"Prediction:\n",
" [[ 145.39686584]\n",
" [ 188.38572693]\n",
" [ 178.18925476]\n",
" [ 198.7359314 ]\n",
" [ 143.25421143]]\n",
"================================================================================\n",
"2420 Cost : 13.4333 \n",
"Prediction:\n",
" [[ 145.4085083 ]\n",
" [ 188.37783813]\n",
" [ 178.19296265]\n",
" [ 198.73753357]\n",
" [ 143.24476624]]\n",
"================================================================================\n",
"2430 Cost : 13.3865 \n",
"Prediction:\n",
" [[ 145.42015076]\n",
" [ 188.36997986]\n",
" [ 178.19667053]\n",
" [ 198.73916626]\n",
" [ 143.23535156]]\n",
"================================================================================\n",
"2440 Cost : 13.3398 \n",
"Prediction:\n",
" [[ 145.43174744]\n",
" [ 188.36210632]\n",
" [ 178.20033264]\n",
" [ 198.74073792]\n",
" [ 143.22593689]]\n",
"================================================================================\n",
"2450 Cost : 13.2935 \n",
"Prediction:\n",
" [[ 145.44332886]\n",
" [ 188.35432434]\n",
" [ 178.20405579]\n",
" [ 198.74235535]\n",
" [ 143.21659851]]\n",
"================================================================================\n",
"2460 Cost : 13.2474 \n",
"Prediction:\n",
" [[ 145.45484924]\n",
" [ 188.34649658]\n",
" [ 178.20770264]\n",
" [ 198.743927 ]\n",
" [ 143.20724487]]\n",
"================================================================================\n",
"2470 Cost : 13.2015 \n",
"Prediction:\n",
" [[ 145.46635437]\n",
" [ 188.33869934]\n",
" [ 178.21138 ]\n",
" [ 198.74549866]\n",
" [ 143.19793701]]\n",
"================================================================================\n",
"2480 Cost : 13.1558 \n",
"Prediction:\n",
" [[ 145.47787476]\n",
" [ 188.33096313]\n",
" [ 178.21505737]\n",
" [ 198.74711609]\n",
" [ 143.18865967]]\n",
"================================================================================\n",
"2490 Cost : 13.1104 \n",
"Prediction:\n",
" [[ 145.48927307]\n",
" [ 188.32318115]\n",
" [ 178.21864319]\n",
" [ 198.74862671]\n",
" [ 143.17936707]]\n",
"================================================================================\n",
"2500 Cost : 13.0652 \n",
"Prediction:\n",
" [[ 145.50074768]\n",
" [ 188.31550598]\n",
" [ 178.22232056]\n",
" [ 198.75022888]\n",
" [ 143.17018127]]\n",
"================================================================================\n",
"2510 Cost : 13.0203 \n",
"Prediction:\n",
" [[ 145.51211548]\n",
" [ 188.30778503]\n",
" [ 178.22595215]\n",
" [ 198.75177002]\n",
" [ 143.16094971]]\n",
"================================================================================\n",
"2520 Cost : 12.9756 \n",
"Prediction:\n",
" [[ 145.52349854]\n",
" [ 188.30012512]\n",
" [ 178.22956848]\n",
" [ 198.75334167]\n",
" [ 143.15177917]]\n",
"================================================================================\n",
"2530 Cost : 12.9311 \n",
"Prediction:\n",
" [[ 145.5348053 ]\n",
" [ 188.29244995]\n",
" [ 178.23316956]\n",
" [ 198.75486755]\n",
" [ 143.1426239 ]]\n",
"================================================================================\n",
"2540 Cost : 12.8868 \n",
"Prediction:\n",
" [[ 145.5460968 ]\n",
" [ 188.28477478]\n",
" [ 178.23675537]\n",
" [ 198.75637817]\n",
" [ 143.13346863]]\n",
"================================================================================\n",
"2550 Cost : 12.8428 \n",
"Prediction:\n",
" [[ 145.55740356]\n",
" [ 188.27719116]\n",
" [ 178.24038696]\n",
" [ 198.75794983]\n",
" [ 143.12442017]]\n",
"================================================================================\n",
"2560 Cost : 12.7989 \n",
"Prediction:\n",
" [[ 145.56864929]\n",
" [ 188.26957703]\n",
" [ 178.24395752]\n",
" [ 198.75946045]\n",
" [ 143.11532593]]\n",
"================================================================================\n",
"2570 Cost : 12.7554 \n",
"Prediction:\n",
" [[ 145.5798645 ]\n",
" [ 188.26200867]\n",
" [ 178.24752808]\n",
" [ 198.76098633]\n",
" [ 143.10627747]]\n",
"================================================================================\n",
"2580 Cost : 12.712 \n",
"Prediction:\n",
" [[ 145.59104919]\n",
" [ 188.25444031]\n",
" [ 178.25109863]\n",
" [ 198.76249695]\n",
" [ 143.09727478]]\n",
"================================================================================\n",
"2590 Cost : 12.6689 \n",
"Prediction:\n",
" [[ 145.60218811]\n",
" [ 188.24688721]\n",
" [ 178.25462341]\n",
" [ 198.76397705]\n",
" [ 143.08825684]]\n",
"================================================================================\n",
"2600 Cost : 12.626 \n",
"Prediction:\n",
" [[ 145.61332703]\n",
" [ 188.23937988]\n",
" [ 178.25819397]\n",
" [ 198.76550293]\n",
" [ 143.07928467]]\n",
"================================================================================\n",
"2610 Cost : 12.5833 \n",
"Prediction:\n",
" [[ 145.62442017]\n",
" [ 188.23188782]\n",
" [ 178.26173401]\n",
" [ 198.76698303]\n",
" [ 143.07035828]]\n",
"================================================================================\n",
"2620 Cost : 12.5408 \n",
"Prediction:\n",
" [[ 145.63548279]\n",
" [ 188.22438049]\n",
" [ 178.26524353]\n",
" [ 198.76846313]\n",
" [ 143.06141663]]\n",
"================================================================================\n",
"2630 Cost : 12.4985 \n",
"Prediction:\n",
" [[ 145.64656067]\n",
" [ 188.21694946]\n",
" [ 178.26881409]\n",
" [ 198.76998901]\n",
" [ 143.05255127]]\n",
"================================================================================\n",
"2640 Cost : 12.4565 \n",
"Prediction:\n",
" [[ 145.657547 ]\n",
" [ 188.2094574 ]\n",
" [ 178.27226257]\n",
" [ 198.77140808]\n",
" [ 143.0436554 ]]\n",
"================================================================================\n",
"2650 Cost : 12.4147 \n",
"Prediction:\n",
" [[ 145.66853333]\n",
" [ 188.20205688]\n",
" [ 178.27578735]\n",
" [ 198.77290344]\n",
" [ 143.03482056]]\n",
"================================================================================\n",
"2660 Cost : 12.373 \n",
"Prediction:\n",
" [[ 145.67950439]\n",
" [ 188.19467163]\n",
" [ 178.27929688]\n",
" [ 198.77436829]\n",
" [ 143.02601624]]\n",
"================================================================================\n",
"2670 Cost : 12.3317 \n",
"Prediction:\n",
" [[ 145.69041443]\n",
" [ 188.18727112]\n",
" [ 178.28277588]\n",
" [ 198.77581787]\n",
" [ 143.01721191]]\n",
"================================================================================\n",
"2680 Cost : 12.2905 \n",
"Prediction:\n",
" [[ 145.70132446]\n",
" [ 188.17991638]\n",
" [ 178.28625488]\n",
" [ 198.77728271]\n",
" [ 143.00843811]]\n",
"================================================================================\n",
"2690 Cost : 12.2495 \n",
"Prediction:\n",
" [[ 145.71217346]\n",
" [ 188.17256165]\n",
" [ 178.28970337]\n",
" [ 198.77870178]\n",
" [ 142.99969482]]\n",
"================================================================================\n",
"2700 Cost : 12.2087 \n",
"Prediction:\n",
" [[ 145.72303772]\n",
" [ 188.16525269]\n",
" [ 178.29318237]\n",
" [ 198.78015137]\n",
" [ 142.99099731]]\n",
"================================================================================\n",
"2710 Cost : 12.1682 \n",
"Prediction:\n",
" [[ 145.7338562 ]\n",
" [ 188.15794373]\n",
" [ 178.29663086]\n",
" [ 198.78160095]\n",
" [ 142.98231506]]\n",
"================================================================================\n",
"2720 Cost : 12.1278 \n",
"Prediction:\n",
" [[ 145.74461365]\n",
" [ 188.15061951]\n",
" [ 178.30003357]\n",
" [ 198.78297424]\n",
" [ 142.97361755]]\n",
"================================================================================\n",
"2730 Cost : 12.0877 \n",
"Prediction:\n",
" [[ 145.75540161]\n",
" [ 188.14338684]\n",
" [ 178.30351257]\n",
" [ 198.78443909]\n",
" [ 142.9650116 ]]\n",
"================================================================================\n",
"2740 Cost : 12.0477 \n",
"Prediction:\n",
" [[ 145.76611328]\n",
" [ 188.13612366]\n",
" [ 178.30693054]\n",
" [ 198.7858429 ]\n",
" [ 142.95639038]]\n",
"================================================================================\n",
"2750 Cost : 12.008 \n",
"Prediction:\n",
" [[ 145.77684021]\n",
" [ 188.12890625]\n",
" [ 178.31036377]\n",
" [ 198.78727722]\n",
" [ 142.94779968]]\n",
"================================================================================\n",
"2760 Cost : 11.9685 \n",
"Prediction:\n",
" [[ 145.7875061 ]\n",
" [ 188.1217041 ]\n",
" [ 178.31375122]\n",
" [ 198.78866577]\n",
" [ 142.93922424]]\n",
"================================================================================\n",
"2770 Cost : 11.9291 \n",
"Prediction:\n",
" [[ 145.79814148]\n",
" [ 188.11448669]\n",
" [ 178.31713867]\n",
" [ 198.79005432]\n",
" [ 142.93067932]]\n",
"================================================================================\n",
"2780 Cost : 11.89 \n",
"Prediction:\n",
" [[ 145.80877686]\n",
" [ 188.10733032]\n",
" [ 178.32052612]\n",
" [ 198.79144287]\n",
" [ 142.92216492]]\n",
"================================================================================\n",
"2790 Cost : 11.851 \n",
"Prediction:\n",
" [[ 145.81938171]\n",
" [ 188.10018921]\n",
" [ 178.32392883]\n",
" [ 198.79284668]\n",
" [ 142.91368103]]\n",
"================================================================================\n",
"2800 Cost : 11.8123 \n",
"Prediction:\n",
" [[ 145.82992554]\n",
" [ 188.09303284]\n",
" [ 178.32728577]\n",
" [ 198.79420471]\n",
" [ 142.90519714]]\n",
"================================================================================\n",
"2810 Cost : 11.7738 \n",
"Prediction:\n",
" [[ 145.84046936]\n",
" [ 188.0859375 ]\n",
" [ 178.33068848]\n",
" [ 198.79560852]\n",
" [ 142.89675903]]\n",
"================================================================================\n",
"2820 Cost : 11.7354 \n",
"Prediction:\n",
" [[ 145.85096741]\n",
" [ 188.07881165]\n",
" [ 178.33401489]\n",
" [ 198.79693604]\n",
" [ 142.88833618]]\n",
"================================================================================\n",
"2830 Cost : 11.6973 \n",
"Prediction:\n",
" [[ 145.86146545]\n",
" [ 188.07174683]\n",
" [ 178.33737183]\n",
" [ 198.79832458]\n",
" [ 142.87995911]]\n",
"================================================================================\n",
"2840 Cost : 11.6594 \n",
"Prediction:\n",
" [[ 145.87191772]\n",
" [ 188.06469727]\n",
" [ 178.3407135 ]\n",
" [ 198.79968262]\n",
" [ 142.87156677]]\n",
"================================================================================\n",
"2850 Cost : 11.6216 \n",
"Prediction:\n",
" [[ 145.88232422]\n",
" [ 188.05763245]\n",
" [ 178.34402466]\n",
" [ 198.80101013]\n",
" [ 142.86320496]]\n",
"================================================================================\n",
"2860 Cost : 11.584 \n",
"Prediction:\n",
" [[ 145.89274597]\n",
" [ 188.0506134 ]\n",
" [ 178.34736633]\n",
" [ 198.80236816]\n",
" [ 142.85491943]]\n",
"================================================================================\n",
"2870 Cost : 11.5467 \n",
"Prediction:\n",
" [[ 145.90312195]\n",
" [ 188.04362488]\n",
" [ 178.35069275]\n",
" [ 198.8037262 ]\n",
" [ 142.84661865]]\n",
"================================================================================\n",
"2880 Cost : 11.5094 \n",
"Prediction:\n",
" [[ 145.91345215]\n",
" [ 188.03659058]\n",
" [ 178.35395813]\n",
" [ 198.80500793]\n",
" [ 142.83830261]]\n",
"================================================================================\n",
"2890 Cost : 11.4725 \n",
"Prediction:\n",
" [[ 145.92379761]\n",
" [ 188.02967834]\n",
" [ 178.3572998 ]\n",
" [ 198.80638123]\n",
" [ 142.83009338]]\n",
"================================================================================\n",
"2900 Cost : 11.4357 \n",
"Prediction:\n",
" [[ 145.93408203]\n",
" [ 188.02270508]\n",
" [ 178.36056519]\n",
" [ 198.80767822]\n",
" [ 142.82185364]]\n",
"================================================================================\n",
"2910 Cost : 11.399 \n",
"Prediction:\n",
" [[ 145.9443512 ]\n",
" [ 188.01577759]\n",
" [ 178.36387634]\n",
" [ 198.80900574]\n",
" [ 142.81364441]]\n",
"================================================================================\n",
"2920 Cost : 11.3626 \n",
"Prediction:\n",
" [[ 145.9546051 ]\n",
" [ 188.00888062]\n",
" [ 178.36714172]\n",
" [ 198.81033325]\n",
" [ 142.80548096]]\n",
"================================================================================\n",
"2930 Cost : 11.3264 \n",
"Prediction:\n",
" [[ 145.96478271]\n",
" [ 188.00195312]\n",
" [ 178.37039185]\n",
" [ 198.81161499]\n",
" [ 142.79730225]]\n",
"================================================================================\n",
"2940 Cost : 11.2903 \n",
"Prediction:\n",
" [[ 145.9750061 ]\n",
" [ 187.99508667]\n",
" [ 178.37367249]\n",
" [ 198.81292725]\n",
" [ 142.78918457]]\n",
"================================================================================\n",
"2950 Cost : 11.2544 \n",
"Prediction:\n",
" [[ 145.98516846]\n",
" [ 187.98823547]\n",
" [ 178.37692261]\n",
" [ 198.81422424]\n",
" [ 142.78108215]]\n",
"================================================================================\n",
"2960 Cost : 11.2187 \n",
"Prediction:\n",
" [[ 145.99528503]\n",
" [ 187.98138428]\n",
" [ 178.38014221]\n",
" [ 198.81549072]\n",
" [ 142.772995 ]]\n",
"================================================================================\n",
"2970 Cost : 11.1832 \n",
"Prediction:\n",
" [[ 146.00540161]\n",
" [ 187.97457886]\n",
" [ 178.38340759]\n",
" [ 198.81680298]\n",
" [ 142.76493835]]\n",
"================================================================================\n",
"2980 Cost : 11.1479 \n",
"Prediction:\n",
" [[ 146.01547241]\n",
" [ 187.96775818]\n",
" [ 178.38661194]\n",
" [ 198.8180542 ]\n",
" [ 142.75689697]]\n",
"================================================================================\n",
"2990 Cost : 11.1127 \n",
"Prediction:\n",
" [[ 146.0255127 ]\n",
" [ 187.96095276]\n",
" [ 178.38980103]\n",
" [ 198.81930542]\n",
" [ 142.74885559]]\n",
"================================================================================\n",
"3000 Cost : 11.0777 \n",
"Prediction:\n",
" [[ 146.03559875]\n",
" [ 187.95422363]\n",
" [ 178.39305115]\n",
" [ 198.82061768]\n",
" [ 142.74090576]]\n",
"================================================================================\n"
]
}
],
"source": [
"for step in range(3001):\n",
" cost_val, hy_val, _ = sess.run([cost, hypothesis, train], feed_dict = {X: x_data, Y: y_data})\n",
" if step % 10 == 0:\n",
" print(step,\" Cost : \", cost_val, \"\\nPrediction:\\n\", hy_val)\n",
" print(\"=\"*80)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
zipeiyang/liupengyuan.github.io | chapter2/homework/computer/4-5/201611680969.task5.ipynb | 27 | 5560 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"请输入一个整数，以回车结束0\n",
"请输入一个整数，以回车结束100\n",
"请输入一个整数，以回车结束3\n",
"12\n",
"70\n",
"55\n",
"11.704699910719626\n"
]
}
],
"source": [
"import random,math\n",
"m=int(input('请输入一个整数，以回车结束'))\n",
"k=int(input('请输入一个整数，以回车结束'))\n",
"n=int(input('请输入一个整数，以回车结束'))\n",
"i=0\n",
"total=0\n",
"while i<n:\n",
" i=i+1\n",
" number = random.randint(m,k)\n",
" print(number)\n",
" total=total+number\n",
"print(math.sqrt(total))\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"请输入一个整数，以回车结束0\n",
"请输入一个整数，以回车结束100\n",
"请输入一个整数，以回车结束3\n",
"99\n",
"24\n",
"56\n",
"17.021674042858372\n",
"0.5411439264025237\n"
]
}
],
"source": [
"import random,math\n",
"m=int(input('请输入一个整数，以回车结束'))\n",
"k=int(input('请输入一个整数，以回车结束'))\n",
"n=int(input('请输入一个整数，以回车结束'))\n",
"i=0\n",
"total=0\n",
"sum=0\n",
"while i<n:\n",
" i=i+1\n",
" num = random.randint(m,k)\n",
" print(num)\n",
" x=(math.log2(num))\n",
" total=total+x\n",
" sum=sum+1/x\n",
"print(total)\n",
"print(sum)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"请输入一个整数，以回车结束3\n",
"5\n",
"615\n"
]
}
],
"source": [
"import random\n",
"n=int(input('请输入一个整数，以回车结束'))\n",
"a=random.randint(1,9)\n",
"print(a)\n",
"i=0\n",
"total=0\n",
"while i<n+2:\n",
" s=a*n\n",
" i=i+1\n",
" a=a*10\n",
" n=n-1\n",
" total=total+s\n",
"print(total) "
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"请输入一个整数，以回车结束29\n",
"请输入一个整数，以回车结束1000\n",
"请输入要猜整数，以回车结束479\n",
"376\n",
"一共可以猜 9 次\n",
"你已经猜了 1 次\n",
"你猜小了\n",
"692\n",
"一共可以猜 9 次\n",
"你已经猜了 2 次\n",
"你猜大了\n",
"426\n",
"一共可以猜 9 次\n",
"你已经猜了 3 次\n",
"你猜小了\n",
"688\n",
"一共可以猜 9 次\n",
"你已经猜了 4 次\n",
"你猜大了\n",
"524\n",
"一共可以猜 9 次\n",
"你已经猜了 5 次\n",
"你猜大了\n",
"522\n",
"一共可以猜 9 次\n",
"你已经猜了 6 次\n",
"你猜大了\n",
"441\n",
"一共可以猜 9 次\n",
"你已经猜了 7 次\n",
"你猜小了\n",
"462\n",
"一共可以猜 9 次\n",
"你已经猜了 8 次\n",
"你猜小了\n",
"494\n",
"一共可以猜 9 次\n",
"你已经猜了 9 次\n",
"你猜大了\n",
"487\n",
"一共可以猜 9 次\n",
"你已经猜了 10 次\n",
"你猜大了\n",
"484\n",
"You Lose!\n"
]
}
],
"source": [
"import random,math\n",
"m=int(input('请输入一个整数，以回车结束'))\n",
"k=int(input('请输入一个整数，以回车结束'))\n",
"n=int(input('请输入要猜整数，以回车结束'))\n",
"max_times = math.ceil(math.log(n, 2))\n",
"guess=random.randint(m,k)\n",
"print(guess)\n",
"guess_times=0\n",
"while guess_times<=max_times:\n",
" guess_times+=1\n",
" print('一共可以猜', max_times, '次')\n",
" print('你已经猜了', guess_times, '次')\n",
" if(guess==n):\n",
" print('You Win!')\n",
" elif(guess>n):\n",
" print('你猜大了')\n",
" k=guess\n",
" guess=random.randint(m,k)\n",
" else:\n",
" print('你猜小了')\n",
" m=guess\n",
" guess=random.randint(m,k)\n",
" print(guess)\n",
"if(guess_times>max_times):\n",
" print('You Lose!')\n",
"\n",
" \n",
" \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
jakobkolb/MayaSim | Evaluation_Notebooks/X2_agriculture_vs_ecosystem.ipynb | 1 | 1344360 | null | gpl-3.0 |
scikit-multilearn/scikit-multilearn | docs/source/multilabelembeddings.ipynb | 1 | 8931 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multi-label embedding-based classification "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Multi-label embedding techniques emerged as a response the need to cope with a large label space, but with the rise of computing power they became a method of improving classification quality. Typically the embedding-based multi-label classification starts with embedding the label matrix of the training set in some way, training a regressor for unseen samples to predict their embeddings, and a classifier (sometimes very simple ones) to correct the regression error. Scikit-multilearn provides several multi-label embedders alongisde a general regressor-classifier classification class. \n",
"\n",
"Currently available embedding strategies include: \n",
"\n",
"- Label Network Embeddings via OpenNE network embedding library, as in the [LNEMLC paper](https://arxiv.org/abs/1812.02956)\n",
"- Cost-Sensitive Label Embedding with Multidimensional Scaling, as in the [CLEMS paper](https://github.com/ej0cl6/csmlc)\n",
"- scikit-learn based embeddings such as PCA or [manifold learning approaches](https://scikit-learn.org/stable/modules/manifold.html)\n",
"\n",
"Let's start with loading some data:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"emotions:train - exists, not redownloading\n",
"emotions:test - exists, not redownloading\n"
]
}
],
"source": [
"import numpy\n",
"import sklearn.metrics as metrics\n",
"from skmultilearn.dataset import load_dataset\n",
"\n",
"X_train, y_train, feature_names, label_names = load_dataset('emotions', 'train')\n",
"X_test, y_test, _, _ = load_dataset('emotions', 'test')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Label Network Embeddings\n",
"\n",
"The label network embeddings approaches require a working tensorflow installation and the OpenNE library. To install them, run the following code:\n",
"\n",
"```bash\n",
"pip install networkx tensorflow\n",
"git clone https://github.com/thunlp/OpenNE/\n",
"pip install -e OpenNE/src\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"For an example we will use the LINE embedding method, one of the most efficient and well-performing state of the art approaches, for the meaning of parameters consult the [OpenNE documentation](). We select `order = 3` which means that the method will take both first and second order proximities between labels for embedding. We select a dimension of 5 times the number of labels, as the linear embeddings tend to need more dimensions for best performance, normalize the label weights to maintain normalized distances in the network and agregate label embedings per sample by summation which is a classical approach."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from skmultilearn.embedding import OpenNetworkEmbedder \n",
"from skmultilearn.cluster import LabelCooccurrenceGraphBuilder"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"graph_builder = LabelCooccurrenceGraphBuilder(weighted=True, include_self_edges=False)\n",
"openne_line_params = dict(batch_size=1000, order=3)\n",
"embedder = OpenNetworkEmbedder(\n",
" graph_builder, \n",
" 'LINE', \n",
" dimension = 5*y_train.shape[1], \n",
" aggregation_function = 'add', \n",
" normalize_weights=True, \n",
" param_dict = openne_line_params\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now need to select a regressor and a classifier, we use random forest regressors with MLkNN which is a well working combination often used for multi-label embedding:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from skmultilearn.embedding import EmbeddingClassifier\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from skmultilearn.adapt import MLkNN"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pre-procesing for non-uniform negative sampling!\n",
"Pre-procesing for non-uniform negative sampling!\n",
"epoch:0 sum of loss:4.97153359652\n",
"epoch:0 sum of loss:5.11869335175\n",
"epoch:1 sum of loss:4.98133981228\n",
"epoch:1 sum of loss:4.97720247507\n",
"epoch:2 sum of loss:4.81723511219\n",
"epoch:2 sum of loss:5.05428689718\n",
"epoch:3 sum of loss:5.09079384804\n",
"epoch:3 sum of loss:4.72988516092\n",
"epoch:4 sum of loss:5.0347994566\n",
"epoch:4 sum of loss:4.95063251257\n",
"epoch:5 sum of loss:4.68008613586\n",
"epoch:5 sum of loss:4.9329983592\n",
"epoch:6 sum of loss:4.74205821753\n",
"epoch:6 sum of loss:4.68989795446\n",
"epoch:7 sum of loss:4.62912601233\n",
"epoch:7 sum of loss:4.81548637152\n",
"epoch:8 sum of loss:4.40033769608\n",
"epoch:8 sum of loss:4.73801320791\n",
"epoch:9 sum of loss:4.61178982258\n",
"epoch:9 sum of loss:4.91443294287\n"
]
}
],
"source": [
"clf = EmbeddingClassifier(\n",
" embedder,\n",
" RandomForestRegressor(n_estimators=10),\n",
" MLkNN(k=5)\n",
")\n",
"\n",
"clf.fit(X_train, y_train)\n",
"\n",
"predictions = clf.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cost-Sensitive Label Embedding with Multidimensional Scaling\n",
"\n",
"CLEMS is another well-perfoming method in multi-label embeddings. It uses weighted multi-dimensional scaling to embedd a cost-matrix of unique label combinations. The cost-matrix contains the cost of mistaking a given label combination for another, thus real-valued functions are better ideas than discrete ones. Also, the `is_score` parameter is used to tell the embedder if the cost function is a score (the higher the better) or a loss (the lower the better). Additional params can be also assigned to the weighted scaler. The most efficient parameter for the number of dimensions is equal to number of labels, and is thus enforced here."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from skmultilearn.embedding import CLEMS, EmbeddingClassifier\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from skmultilearn.adapt import MLkNN\n",
"\n",
"dimensional_scaler_params = {'n_jobs': -1}\n",
"\n",
"clf = EmbeddingClassifier(\n",
" CLEMS(metrics.jaccard_similarity_score, is_score=True, params=dimensional_scaler_params),\n",
" RandomForestRegressor(n_estimators=10, n_jobs=-1),\n",
" MLkNN(k=1),\n",
" regressor_per_dimension= True\n",
")\n",
"\n",
"clf.fit(X_train, y_train)\n",
"\n",
"predictions = clf.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scikit-learn based embedders\n",
"\n",
"Any scikit-learn embedder can be used for multi-label classification embeddings with scikit-multilearn, just select one and try, here's a spectral embedding approach with 10 dimensions of the embedding space:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from skmultilearn.embedding import SKLearnEmbedder, EmbeddingClassifier\n",
"from sklearn.manifold import SpectralEmbedding\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from skmultilearn.adapt import MLkNN\n",
"\n",
"clf = EmbeddingClassifier(\n",
" SKLearnEmbedder(SpectralEmbedding(n_components = 10)),\n",
" RandomForestRegressor(n_estimators=10),\n",
" MLkNN(k=5)\n",
")\n",
"\n",
"clf.fit(X_train, y_train)\n",
"\n",
"predictions = clf.predict(X_test)"
]
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-2-clause |
rsignell-usgs/notebook | CSW/CSW_ISO_Queryables-IOOS.ipynb | 2 | 18947 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#CSW access with OWSLib using ISO queryables \n",
"\n",
"Demonstration of how to use the OGC Catalog Services for the Web (CSW) to search for find all datasets containing a specified variable that fall withing a specified date range and geospatial bounding box, and then use the data access service contained in the returned metadata to retrieve and visualize the data. <P> Here we are accessing a Geoportal Server CSW, but in the future we should be able to point it toward any another CSW service, such as the one provided by catalog.data.gov."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from pylab import *\n",
"from owslib.csw import CatalogueServiceWeb\n",
"from owslib.sos import SensorObservationService\n",
"from owslib import fes\n",
"import netCDF4\n",
"import pandas as pd\n",
"import datetime as dt\n",
"from IPython.core.display import HTML"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe src=http://www.nodc.noaa.gov/geoportal/ width=950 height=400></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML at 0x3baf610>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML('<iframe src=http://www.nodc.noaa.gov/geoportal/ width=950 height=400></iframe>')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'2.0.2'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# connect to CSW, explore it's properties\n",
"\n",
"endpoint = 'http://www.ngdc.noaa.gov/geoportal/csw' # NGDC Geoportal\n",
"\n",
"#endpoint = 'http://www.nodc.noaa.gov/geoportal/csw' # NODC Geoportal: granule level\n",
"#endpoint = 'http://data.nodc.noaa.gov/geoportal/csw' # NODC Geoportal: collection level \n",
"#endpoint = 'http://geodiscover.cgdi.ca/wes/serviceManagerCSW/csw' # NRCAN CUSTOM\n",
"#endpoint = 'http://geoport.whoi.edu/gi-cat/services/cswiso' # USGS Woods Hole GI_CAT\n",
"#endpoint = 'http://cida.usgs.gov/gdp/geonetwork/srv/en/csw' # USGS CIDA Geonetwork\n",
"#endpoint = 'http://cmgds.marine.usgs.gov/geonetwork/srv/en/csw' # USGS Coastal and Marine Program\n",
"#endpoint = 'http://geoport.whoi.edu/geoportal/csw' # USGS Woods Hole Geoportal \n",
"#endpoint = 'http://geo.gov.ckan.org/csw' # CKAN testing site for new Data.gov\n",
"#endpoint = 'https://edg.epa.gov/metadata/csw' # EPA\n",
"#endpoint = 'http://cwic.csiss.gmu.edu/cwicv1/discovery' # CWIC\n",
"\n",
"csw = CatalogueServiceWeb(endpoint,timeout=60)\n",
"csw.version"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<owslib.fes.FilterCapabilities at 0x3babdd0>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On the GeoIDE Wiki they give some example CSW examples to illustrate the range possibilities. Here's one where to search for PACIOOS WMS services:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe src=https://geo-ide.noaa.gov/wiki/index.php?title=ESRI_Geoportal#PacIOOS_WAF width=950 height=350></iframe>"
],
"text/plain": [
"<IPython.core.display.HTML at 0x3baf790>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML('<iframe src=https://geo-ide.noaa.gov/wiki/index.php?title=ESRI_Geoportal#PacIOOS_WAF width=950 height=350></iframe>')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also on the GEO-IDE Wiki we find the list of UUIDs for each region/provider, which we turn into a dictionary here:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"regionids = {'AOOS':\t'{1E96581F-6B73-45AD-9F9F-2CC3FED76EE6}',\n",
"'CENCOOS':\t'{BE483F24-52E7-4DDE-909F-EE8D4FF118EA}',\n",
"'CARICOOS':\t'{0C4CA8A6-5967-4590-BFE0-B8A21CD8BB01}',\n",
"'GCOOS':\t'{E77E250D-2D65-463C-B201-535775D222C9}',\n",
"'GLOS':\t'{E4A9E4F4-78A4-4BA0-B653-F548D74F68FA}',\n",
"'MARACOOS':\t'{A26F8553-798B-4B1C-8755-1031D752F7C2}',\n",
"'NANOOS':\t'{C6F4754B-30DC-459E-883A-2AC79DA977AB}',\n",
"'NAVY':\t'{FB160233-7C3B-4841-AD4B-EB5AD843E743}',\n",
"'NDBC':\t'{B3F50F38-3DE4-4EC9-ABF8-955887829FCC}',\n",
"'NERACOOS':\t'{E13C88D9-3FF3-4232-A379-84B6A1D7083E}',\n",
"'NOS/CO-OPS':\t'{2F58127E-A139-4A45-83F2-9695FB704306}',\n",
"'PacIOOS':\t'{78C0463E-2FCE-4AB2-A9C9-6A34BF261F52}',\n",
"'SCCOOS':\t'{20A3408F-9EC4-4B36-8E10-BBCDB1E81BDF}',\n",
"'SECOORA':\t'{E796C954-B248-4118-896C-42E6FAA6EDE9}',\n",
"'USACE':\t'{4C080A33-F3C3-4F27-AF16-F85BF3095C41}',\n",
"'USGS/CMGP': '{275DFB94-E58A-4157-8C31-C72F372E72E}'}"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['GetCapabilities',\n",
" 'DescribeRecord',\n",
" 'GetRecords',\n",
" 'GetRecordById',\n",
" 'Transaction']"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[op.name for op in csw.operations]"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def dateRange(start_date='1900-01-01',stop_date='2100-01-01',constraint='overlaps'):\n",
" if constraint == 'overlaps':\n",
" start = fes.PropertyIsLessThanOrEqualTo(propertyname='startDate', literal=stop_date)\n",
" stop = fes.PropertyIsGreaterThanOrEqualTo(propertyname='endDate', literal=start_date)\n",
" elif constraint == 'within':\n",
" start = fes.PropertyIsGreaterThanOrEqualTo(propertyname='startDate', literal=start_date)\n",
" stop = fes.PropertyIsLessThanOrEqualTo(propertyname='endDate', literal=stop_date)\n",
" return start,stop"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# get specific ServiceType URL from records\n",
"def service_urls(records,service_string='urn:x-esri:specification:ServiceType:odp:url'):\n",
" urls=[]\n",
" for key,rec in records.iteritems():\n",
" #create a generator object, and iterate through it until the match is found\n",
" #if not found, gets the default value (here \"none\")\n",
" url = next((d['url'] for d in rec.references if d['scheme'] == service_string), None)\n",
" if url is not None:\n",
" urls.append(url)\n",
" return urls"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Perform the CSW query, using Kyle's cool new filters on ISO queryables\n",
"# find all datasets in a bounding box and temporal extent that have \n",
"# specific keywords and also can be accessed via OPeNDAP \n",
"\n",
"box=[-89.0, 30.0, -87.0, 31.0]\n",
"start_date='2013-08-21'\n",
"stop_date='2013-08-30'\n",
"std_name = 'temperature'\n",
"service_type='SOS'\n",
"region_id = regionids['GCOOS']\n",
"\n",
"# convert User Input into FES filters\n",
"start,stop = dateRange(start_date,stop_date,constraint='overlaps')\n",
"bbox = fes.BBox(box)\n",
"keywords = fes.PropertyIsLike(propertyname='anyText', literal=std_name)\n",
"serviceType = fes.PropertyIsLike(propertyname='apiso:ServiceType', literal=('*%s*' % service_type))\n",
"siteid = fes.PropertyIsEqualTo(propertyname='sys.siteuuid', literal=region_id)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"carocoops:cap2:buoy\n",
"Weatherflow, Inc. SOS\n",
"Central & Northern California Ocean Observing System -- SOS\n",
"carocoops:cap2:buoy\n",
"carocoops:cap2:buoy\n",
"Louisiana Universities Marine Consortium (LUMCON) SOS\n",
"NOAA.NOS.CO-OPS SOS\n",
"carocoops:cap2:buoy\n",
"NERRS SOS\n",
"carocoops:cap2:buoy\n",
"carocoops:cap2:buoy\n",
"carocoops:cap2:buoy\n",
"Central and Northern California Ocean Observing System SOS\n",
"Central Gulf of Mexico Ocean Observing System SOS\n",
"carocoops:cap2:buoy\n"
]
}
],
"source": [
"# try simple query with serviceType and keyword first\n",
"csw.getrecords2(constraints=[[serviceType,keywords]],maxrecords=15,esn='full')\n",
"for rec,item in csw.records.iteritems():\n",
" print item.title"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The filters can be passed as a list to getrecords2, with AND or OR implied by syntax: \n",
"<pre>\n",
"[a,b,c] --> a || b || c\n",
"\n",
"[[a,b,c]] --> a && b && c\n",
"\n",
"[[a,b],[c],[d],[e]] or [[a,b],c,d,e] --> (a && b) || c || d || e\n",
"</pre>"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"carocoops:cap2:buoy\n",
"Weatherflow, Inc. SOS\n",
"Central & Northern California Ocean Observing System -- SOS\n",
"carocoops:cap2:buoy\n",
"carocoops:cap2:buoy\n",
"Louisiana Universities Marine Consortium (LUMCON) SOS\n",
"NOAA.NOS.CO-OPS SOS\n",
"carocoops:cap2:buoy\n",
"NERRS SOS\n",
"carocoops:cap2:buoy\n",
"carocoops:cap2:buoy\n",
"carocoops:cap2:buoy\n",
"Central and Northern California Ocean Observing System SOS\n",
"Central Gulf of Mexico Ocean Observing System SOS\n",
"carocoops:cap2:buoy\n"
]
}
],
"source": [
"# try simple query with serviceType and keyword first\n",
"csw.getrecords2(constraints=[[serviceType,keywords]],maxrecords=15,esn='full')\n",
"for rec,item in csw.records.iteritems():\n",
" print item.title"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[{'scheme': 'urn:x-esri:specification:ServiceType:ArcIMS:Metadata:Onlink',\n",
" 'url': 'http://opendap.co-ops.nos.noaa.gov/ioos-dif-sos/SOS?service=SOS&request=GetCapabilities'},\n",
" {'scheme': 'urn:x-esri:specification:ServiceType:ArcIMS:Metadata:Document',\n",
" 'url': 'http://www.ngdc.noaa.gov/geoportal/csw?getxml=%7BA0CDFAA4-1667-4538-A26C-225E2AAE119A%7D'},\n",
" {'scheme': 'urn:x-esri:specification:ServiceType:sos:url',\n",
" 'url': 'http://opendap.co-ops.nos.noaa.gov/ioos-dif-sos/SOS?service=SOS&request=GetCapabilities'},\n",
" {'scheme': 'urn:x-esri:specification:ServiceType:sos:url',\n",
" 'url': 'http://opendap.co-ops.nos.noaa.gov/ioos-dif-sos/SOS?service=SOS&request=GetCapabilities'},\n",
" {'scheme': 'urn:x-esri:specification:ServiceType:download:url',\n",
" 'url': 'http://opendap.co-ops.nos.noaa.gov/ioos-dif-sos/SOS?service=SOS&request=GetCapabilities'}]"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check out references for one of the returned records\n",
"csw.records['NOAA.NOS.CO-OPS SOS'].references"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dauphin Island Sea Lab SOS\n",
"Louisiana Universities Marine Consortium (LUMCON) SOS\n",
"Conrad Blucher Institute for Surveying and Science at Texas A&M University-Corpus Christi SOS\n",
"WAVCIS SOS DATA SERVICE\n",
"Central Gulf of Mexico Ocean Observing System SOS\n",
"University of South Florida COMPS SOS\n"
]
}
],
"source": [
"# filter for GCOOS SOS data\n",
"csw.getrecords2(constraints=[[keywords,serviceType,siteid]],maxrecords=15,esn='full')\n",
"for rec,item in csw.records.iteritems():\n",
" print item.title"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"National Data Buoy Center SOS\n",
"Weatherflow, Inc. SOS\n",
"Central Gulf of Mexico Ocean Observing System SOS\n",
"NOAA.NOS.CO-OPS SOS\n",
"Dauphin Island Sea Lab SOS\n"
]
}
],
"source": [
"# filter for SOS data in BBOX\n",
"csw.getrecords2(constraints=[[keywords,serviceType,bbox]],maxrecords=15,esn='full')\n",
"for rec,item in csw.records.iteritems():\n",
" print item.title"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://gcoos.disl.org/cgi-bin/oostethys_sos.cgi?service=SOS&request=GetCapabilities\n",
"http://weather.lumcon.edu/sos/oostethys_server.asp?service=SOS&request=GetCapabilities\n",
"http://lighthouse.tamucc.edu/sos?service=SOS&request=GetCapabilities\n",
"http://www.wavcis.lsu.edu/gcoos/oostethys_server.asp?service=SOS&request=GetCapabilities\n",
"http://www.cengoos.org/cgi-bin/oostethys_sos.cgi?service=SOS&request=GetCapabilities\n",
"http://comps.marine.usf.edu/cgi-bin/sos/v1.0/oostethys_sos.cgi?service=SOS&request=GetCapabilities\n"
]
}
],
"source": [
"urls = service_urls(csw.records,service_string='urn:x-esri:specification:ServiceType:sos:url')\n",
"print \"\\n\".join(urls)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://lighthouse.tamucc.edu/sos?service=SOS&request=GetCapabilities\n"
]
}
],
"source": [
"urls = [url for url in urls if 'oostethys' not in url]\n",
"print \"\\n\".join(urls)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sos = SensorObservationService(urls[0])"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"getob = sos.get_operation_by_name('getobservation')"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'eventTime': {'values': []}, 'observedProperty': {'values': ['air_pressure', 'air_temperature', 'chl_concentration', 'conductivity', 'current_speed', 'depth', 'eastward_current', 'northward_current', 'oxygen_concentration', 'oxygen_saturation', 'photosynthetically_available_radiation', 'relative_humidity', 'salinity', 'sea_surface_elevation', 'signifcant_wave_height', 'signifcant_wave_to_direction', 'significant_wave_period', 'specific_conductance', 'turbidity', 'vertical_current', 'voltage', 'water_pressure', 'water_temperature', 'wind_from_direction', 'wind_gust', 'wind_speed']}, 'offering': {'values': ['001', '002', '003', '004', '005', '006', '007', '008', '009', '010', '011', '012', '013', '017', '023', '024', '025', '026', '028', '029', '030', '031', '032', '033', '034', '035', '036', '037', '038', '039', '041', '042', '043', '046', '047', '048', '049', '050', '051', '054', '057', '058', '059', '060', '061', '064', '065', '068', '069', '072', '074', '075', '076', '077', '078', '079', '080', '082', '083', '084', '085', '086', '087', '088', '089', '090', '094', '095', '096', '098', '100', '106', '108', '109', '110', '111', '113', '114', '115', '116', '117', '118', '119', '121', '122', '123', '124', '125', '126', '127', '128', '129', '130', '131', '132', '133', '134', '135', '136', '137', '138', '139', '140', '141', '146', '147', '148', '149', '150', '151', '153', '168', '170', '171', '181', '185', '198', '200', '201', '202', '205', '206', '207', '208', '209', '210', '211', '212', '213', '214', '215', '216', '217', '218', '219', '220', '248', '255', '501', '502', '503', '504', '505', '506', '507', '508', '509', '513', '514', '515', '517', '518', '519', '520', '521', '522', '523', '524', '525', '526', '527', '528', '529', '532', '533', '920']}}\n"
]
}
],
"source": [
"print getob.parameters"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"off = sos.offerings[1]\n",
"offerings = [off.name]\n",
"responseFormat = off.response_formats[0]\n",
"observedProperties = [off.observed_properties[0]]"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Offering id: 001, name: 001\n"
]
}
],
"source": [
"print sos.offerings[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"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 |
keflavich/APEX_CMZ_H2CO | reduction/BaselineFlagging_TestonData_HighFreq.ipynb | 2 | 1509263 | null | bsd-3-clause |
mu4farooqi/deep-learning-projects | language-translation/dlnd_language_translation.ipynb | 2 | 62981 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Language Translation\n",
"In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.\n",
"## Get the Data\n",
"Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"import helper\n",
"import problem_unittests as tests\n",
"\n",
"source_path = 'data/small_vocab_en'\n",
"target_path = 'data/small_vocab_fr'\n",
"source_text = helper.load_data(source_path)\n",
"target_text = helper.load_data(target_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the Data\n",
"Play around with view_sentence_range to view different parts of the data."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset Stats\n",
"Roughly the number of unique words: 227\n",
"Number of sentences: 137861\n",
"Average number of words in a sentence: 13.225277634719028\n",
"\n",
"English sentences 0 to 10:\n",
"new jersey is sometimes quiet during autumn , and it is snowy in april .\n",
"the united states is usually chilly during july , and it is usually freezing in november .\n",
"california is usually quiet during march , and it is usually hot in june .\n",
"the united states is sometimes mild during june , and it is cold in september .\n",
"your least liked fruit is the grape , but my least liked is the apple .\n",
"his favorite fruit is the orange , but my favorite is the grape .\n",
"paris is relaxing during december , but it is usually chilly in july .\n",
"new jersey is busy during spring , and it is never hot in march .\n",
"our least liked fruit is the lemon , but my least liked is the grape .\n",
"the united states is sometimes busy during january , and it is sometimes warm in november .\n",
"\n",
"French sentences 0 to 10:\n",
"new jersey est parfois calme pendant l' automne , et il est neigeux en avril .\n",
"les états-unis est généralement froid en juillet , et il gèle habituellement en novembre .\n",
"california est généralement calme en mars , et il est généralement chaud en juin .\n",
"les états-unis est parfois légère en juin , et il fait froid en septembre .\n",
"votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme .\n",
"son fruit préféré est l'orange , mais mon préféré est le raisin .\n",
"paris est relaxant en décembre , mais il est généralement froid en juillet .\n",
"new jersey est occupé au printemps , et il est jamais chaude en mars .\n",
"notre fruit est moins aimé le citron , mais mon moins aimé est le raisin .\n",
"les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre .\n"
]
}
],
"source": [
"view_sentence_range = (0, 10)\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"import numpy as np\n",
"\n",
"print('Dataset Stats')\n",
"print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()})))\n",
"\n",
"sentences = source_text.split('\\n')\n",
"word_counts = [len(sentence.split()) for sentence in sentences]\n",
"print('Number of sentences: {}'.format(len(sentences)))\n",
"print('Average number of words in a sentence: {}'.format(np.average(word_counts)))\n",
"\n",
"print()\n",
"print('English sentences {} to {}:'.format(*view_sentence_range))\n",
"print('\\n'.join(source_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))\n",
"print()\n",
"print('French sentences {} to {}:'.format(*view_sentence_range))\n",
"print('\\n'.join(target_text.split('\\n')[view_sentence_range[0]:view_sentence_range[1]]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implement Preprocessing Function\n",
"### Text to Word Ids\n",
"As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function `text_to_ids()`, you'll turn `source_text` and `target_text` from words to ids. However, you need to add the `<EOS>` word id at the end of `target_text`. This will help the neural network predict when the sentence should end.\n",
"\n",
"You can get the `<EOS>` word id by doing:\n",
"```python\n",
"target_vocab_to_int['<EOS>']\n",
"```\n",
"You can get other word ids using `source_vocab_to_int` and `target_vocab_to_int`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int):\n",
" \"\"\"\n",
" Convert source and target text to proper word ids\n",
" :param source_text: String that contains all the source text.\n",
" :param target_text: String that contains all the target text.\n",
" :param source_vocab_to_int: Dictionary to go from the source words to an id\n",
" :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
" :return: A tuple of lists (source_id_text, target_id_text)\n",
" \"\"\"\n",
" source_id_text = [[source_vocab_to_int[word] for word in line.split()] for line in source_text.split('\\n')]\n",
" target_id_text = [[target_vocab_to_int[word] for word in line.split()] + [target_vocab_to_int['<EOS>']] for line in target_text.split('\\n')]\n",
" return source_id_text, target_id_text\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_text_to_ids(text_to_ids)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Preprocess all the data and save it\n",
"Running the code cell below will preprocess all the data and save it to file."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"helper.preprocess_and_save_data(source_path, target_path, text_to_ids)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check Point\n",
"This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"import numpy as np\n",
"import helper\n",
"import problem_unittests as tests\n",
"\n",
"(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check the Version of TensorFlow and Access to GPU\n",
"This will check to make sure you have the correct version of TensorFlow and access to a GPU"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TensorFlow Version: 1.3.0\n",
"Default GPU Device: /gpu:0\n"
]
}
],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"from distutils.version import LooseVersion\n",
"import warnings\n",
"import tensorflow as tf\n",
"from tensorflow.python.layers.core import Dense\n",
"\n",
"# Check TensorFlow Version\n",
"assert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer'\n",
"print('TensorFlow Version: {}'.format(tf.__version__))\n",
"\n",
"# Check for a GPU\n",
"if not tf.test.gpu_device_name():\n",
" warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n",
"else:\n",
" print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build the Neural Network\n",
"You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below:\n",
"- `model_inputs`\n",
"- `process_decoder_input`\n",
"- `encoding_layer`\n",
"- `decoding_layer_train`\n",
"- `decoding_layer_infer`\n",
"- `decoding_layer`\n",
"- `seq2seq_model`\n",
"\n",
"### Input\n",
"Implement the `model_inputs()` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n",
"\n",
"- Input text placeholder named \"input\" using the TF Placeholder name parameter with rank 2.\n",
"- Targets placeholder with rank 2.\n",
"- Learning rate placeholder with rank 0.\n",
"- Keep probability placeholder named \"keep_prob\" using the TF Placeholder name parameter with rank 0.\n",
"- Target sequence length placeholder named \"target_sequence_length\" with rank 1\n",
"- Max target sequence length tensor named \"max_target_len\" getting its value from applying tf.reduce_max on the target_sequence_length placeholder. Rank 0.\n",
"- Source sequence length placeholder named \"source_sequence_length\" with rank 1\n",
"\n",
"Return the placeholders in the following the tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Process Decoder Input\n",
"Implement `process_decoder_input` by removing the last word id from each batch in `target_data` and concat the GO ID to the begining of each batch."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ERROR:tensorflow:==================================\n",
"Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):\n",
"<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>\n",
"If you want to mark it as used call its \"mark_used()\" method.\n",
"It was originally created here:\n",
"['File \"/home/ufarooqi/anaconda3/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\\n \"__main__\", mod_spec)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/runpy.py\", line 85, in _run_code\\n exec(code, run_globals)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py\", line 3, in <module>\\n app.launch_new_instance()', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\\n app.start()', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 477, in start\\n ioloop.IOLoop.instance().start()', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\\n super(ZMQIOLoop, self).start()', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 888, in start\\n handler_func(fd_obj, events)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\\n return fn(*args, **kwargs)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\\n self._handle_recv()', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\\n self._run_callback(callback, msg)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\\n callback(*args, **kwargs)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\\n return fn(*args, **kwargs)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\\n return self.dispatch_shell(stream, msg)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\\n handler(stream, idents, msg)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\\n user_expressions, allow_stdin)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\\n res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2698, in run_cell\\n interactivity=interactivity, compiler=compiler, result=result)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2808, in run_ast_nodes\\n if self.run_code(code, result):', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2862, in run_code\\n exec(code_obj, self.user_global_ns, self.user_ns)', 'File \"<ipython-input-5-4de6f2622d71>\", line 22, in <module>\\n tests.test_model_inputs(model_inputs)', 'File \"/home/ufarooqi/workspace/deep-learning/language-translation/problem_unittests.py\", line 106, in test_model_inputs\\n assert tf.assert_rank(lr, 0, message=\\'Learning Rate has wrong rank\\')', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py\", line 617, in assert_rank\\n dynamic_condition, data, summarize)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py\", line 571, in _assert_rank_condition\\n return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py\", line 175, in wrapped\\n return _add_should_use_warning(fn(*args, **kwargs))', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py\", line 144, in _add_should_use_warning\\n wrapped = TFShouldUseWarningWrapper(x)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py\", line 101, in __init__\\n stack = [s.strip() for s in traceback.format_stack()]']\n",
"==================================\n",
"ERROR:tensorflow:==================================\n",
"Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):\n",
"<tf.Operation 'assert_rank_3/Assert/Assert' type=Assert>\n",
"If you want to mark it as used call its \"mark_used()\" method.\n",
"It was originally created here:\n",
"['File \"/home/ufarooqi/anaconda3/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\\n \"__main__\", mod_spec)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/runpy.py\", line 85, in _run_code\\n exec(code, run_globals)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py\", line 3, in <module>\\n app.launch_new_instance()', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\\n app.start()', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 477, in start\\n ioloop.IOLoop.instance().start()', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\\n super(ZMQIOLoop, self).start()', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py\", line 888, in start\\n handler_func(fd_obj, events)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\\n return fn(*args, **kwargs)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\\n self._handle_recv()', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\\n self._run_callback(callback, msg)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\\n callback(*args, **kwargs)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\\n return fn(*args, **kwargs)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\\n return self.dispatch_shell(stream, msg)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\\n handler(stream, idents, msg)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\\n user_expressions, allow_stdin)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\\n res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2698, in run_cell\\n interactivity=interactivity, compiler=compiler, result=result)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2808, in run_ast_nodes\\n if self.run_code(code, result):', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2862, in run_code\\n exec(code_obj, self.user_global_ns, self.user_ns)', 'File \"<ipython-input-5-4de6f2622d71>\", line 22, in <module>\\n tests.test_model_inputs(model_inputs)', 'File \"/home/ufarooqi/workspace/deep-learning/language-translation/problem_unittests.py\", line 107, in test_model_inputs\\n assert tf.assert_rank(keep_prob, 0, message=\\'Keep Probability has wrong rank\\')', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py\", line 617, in assert_rank\\n dynamic_condition, data, summarize)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py\", line 571, in _assert_rank_condition\\n return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py\", line 175, in wrapped\\n return _add_should_use_warning(fn(*args, **kwargs))', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py\", line 144, in _add_should_use_warning\\n wrapped = TFShouldUseWarningWrapper(x)', 'File \"/home/ufarooqi/anaconda3/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py\", line 101, in __init__\\n stack = [s.strip() for s in traceback.format_stack()]']\n",
"==================================\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def model_inputs():\n",
" \"\"\"\n",
" Create TF Placeholders for input, targets, learning rate, and lengths of source and target sequences.\n",
" :return: Tuple (input, targets, learning rate, keep probability, target sequence length,\n",
" max target sequence length, source sequence length)\n",
" \"\"\"\n",
" input_data = tf.placeholder(tf.int32, [None, None], name='input')\n",
" targets = tf.placeholder(tf.int32, [None, None], name='targets')\n",
" lr = tf.placeholder(tf.float32, name='learning_rate')\n",
" keep_prob = tf.placeholder(tf.float32, name='keep_prob')\n",
"\n",
" target_sequence_length = tf.placeholder(tf.int32, (None,), name='target_sequence_length')\n",
" max_target_sequence_length = tf.reduce_max(target_sequence_length, name='max_target_len')\n",
" source_sequence_length = tf.placeholder(tf.int32, (None,), name='source_sequence_length')\n",
" \n",
" return input_data, targets, lr, keep_prob, target_sequence_length, max_target_sequence_length, source_sequence_length\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_model_inputs(model_inputs)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def process_decoder_input(target_data, target_vocab_to_int, batch_size):\n",
" \"\"\"\n",
" Preprocess target data for encoding\n",
" :param target_data: Target Placehoder\n",
" :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
" :param batch_size: Batch Size\n",
" :return: Preprocessed target data\n",
" \"\"\"\n",
" ending = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1])\n",
" return tf.concat([tf.fill([batch_size, 1], target_vocab_to_int['<GO>']), ending], 1)\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_process_encoding_input(process_decoder_input)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Encoding\n",
"Implement `encoding_layer()` to create a Encoder RNN layer:\n",
" * Embed the encoder input using [`tf.contrib.layers.embed_sequence`](https://www.tensorflow.org/api_docs/python/tf/contrib/layers/embed_sequence)\n",
" * Construct a [stacked](https://github.com/tensorflow/tensorflow/blob/6947f65a374ebf29e74bb71e36fd82760056d82c/tensorflow/docs_src/tutorials/recurrent.md#stacking-multiple-lstms) [`tf.contrib.rnn.LSTMCell`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/LSTMCell) wrapped in a [`tf.contrib.rnn.DropoutWrapper`](https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/DropoutWrapper)\n",
" * Pass cell and embedded input to [`tf.nn.dynamic_rnn()`](https://www.tensorflow.org/api_docs/python/tf/nn/dynamic_rnn)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"from imp import reload\n",
"reload(tests)\n",
"\n",
"def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, \n",
" source_sequence_length, source_vocab_size, \n",
" encoding_embedding_size):\n",
" \"\"\"\n",
" Create encoding layer\n",
" :param rnn_inputs: Inputs for the RNN\n",
" :param rnn_size: RNN Size\n",
" :param num_layers: Number of layers\n",
" :param keep_prob: Dropout keep probability\n",
" :param source_sequence_length: a list of the lengths of each sequence in the batch\n",
" :param source_vocab_size: vocabulary size of source data\n",
" :param encoding_embedding_size: embedding size of source data\n",
" :return: tuple (RNN output, RNN state)\n",
" \"\"\"\n",
" rnn_inputs = tf.contrib.layers.embed_sequence(rnn_inputs, source_vocab_size, encoding_embedding_size)\n",
" lstm = lambda: tf.contrib.rnn.DropoutWrapper(tf.contrib.rnn.LSTMCell(rnn_size), output_keep_prob=keep_prob)\n",
" return tf.nn.dynamic_rnn(tf.contrib.rnn.MultiRNNCell([lstm() for _ in range(num_layers)]), rnn_inputs, source_sequence_length, dtype=tf.float32)\n",
" \n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_encoding_layer(encoding_layer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Decoding - Training\n",
"Create a training decoding layer:\n",
"* Create a [`tf.contrib.seq2seq.TrainingHelper`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/TrainingHelper) \n",
"* Create a [`tf.contrib.seq2seq.BasicDecoder`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/BasicDecoder)\n",
"* Obtain the decoder outputs from [`tf.contrib.seq2seq.dynamic_decode`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_decode)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"\n",
"def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, \n",
" target_sequence_length, max_summary_length, \n",
" output_layer, keep_prob):\n",
" \"\"\"\n",
" Create a decoding layer for training\n",
" :param encoder_state: Encoder State\n",
" :param dec_cell: Decoder RNN Cell\n",
" :param dec_embed_input: Decoder embedded input\n",
" :param target_sequence_length: The lengths of each sequence in the target batch\n",
" :param max_summary_length: The length of the longest sequence in the batch\n",
" :param output_layer: Function to apply the output layer\n",
" :param keep_prob: Dropout keep probability\n",
" :return: BasicDecoderOutput containing training logits and sample_id\n",
" \"\"\"\n",
" taining_helper = tf.contrib.seq2seq.TrainingHelper(dec_embed_input, target_sequence_length)\n",
" decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, taining_helper, encoder_state, output_layer)\n",
" output = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_summary_length)\n",
" return output[0]\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_decoding_layer_train(decoding_layer_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Decoding - Inference\n",
"Create inference decoder:\n",
"* Create a [`tf.contrib.seq2seq.GreedyEmbeddingHelper`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/GreedyEmbeddingHelper)\n",
"* Create a [`tf.contrib.seq2seq.BasicDecoder`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/BasicDecoder)\n",
"* Obtain the decoder outputs from [`tf.contrib.seq2seq.dynamic_decode`](https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/dynamic_decode)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id,\n",
" end_of_sequence_id, max_target_sequence_length,\n",
" vocab_size, output_layer, batch_size, keep_prob):\n",
" \"\"\"\n",
" Create a decoding layer for inference\n",
" :param encoder_state: Encoder state\n",
" :param dec_cell: Decoder RNN Cell\n",
" :param dec_embeddings: Decoder embeddings\n",
" :param start_of_sequence_id: GO ID\n",
" :param end_of_sequence_id: EOS Id\n",
" :param max_target_sequence_length: Maximum length of target sequences\n",
" :param vocab_size: Size of decoder/target vocabulary\n",
" :param decoding_scope: TenorFlow Variable Scope for decoding\n",
" :param output_layer: Function to apply the output layer\n",
" :param batch_size: Batch size\n",
" :param keep_prob: Dropout keep probability\n",
" :return: BasicDecoderOutput containing inference logits and sample_id\n",
" \"\"\"\n",
" \n",
" start_tokens = tf.tile(tf.constant([start_of_sequence_id], dtype=tf.int32), [batch_size], name='start_tokens')\n",
" taining_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(dec_embeddings, start_tokens, end_of_sequence_id)\n",
" decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, taining_helper, encoder_state, output_layer)\n",
" output = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_target_sequence_length)\n",
" return output[0]\n",
"\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_decoding_layer_infer(decoding_layer_infer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build the Decoding Layer\n",
"Implement `decoding_layer()` to create a Decoder RNN layer.\n",
"\n",
"* Embed the target sequences\n",
"* Construct the decoder LSTM cell (just like you constructed the encoder cell above)\n",
"* Create an output layer to map the outputs of the decoder to the elements of our vocabulary\n",
"* Use the your `decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_target_sequence_length, output_layer, keep_prob)` function to get the training logits.\n",
"* Use your `decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob)` function to get the inference logits.\n",
"\n",
"Note: You'll need to use [tf.variable_scope](https://www.tensorflow.org/api_docs/python/tf/variable_scope) to share variables between training and inference."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def decoding_layer(dec_input, encoder_state,\n",
" target_sequence_length, max_target_sequence_length,\n",
" rnn_size,\n",
" num_layers, target_vocab_to_int, target_vocab_size,\n",
" batch_size, keep_prob, decoding_embedding_size):\n",
" \"\"\"\n",
" Create decoding layer\n",
" :param dec_input: Decoder input\n",
" :param encoder_state: Encoder state\n",
" :param target_sequence_length: The lengths of each sequence in the target batch\n",
" :param max_target_sequence_length: Maximum length of target sequences\n",
" :param rnn_size: RNN Size\n",
" :param num_layers: Number of layers\n",
" :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
" :param target_vocab_size: Size of target vocabulary\n",
" :param batch_size: The size of the batch\n",
" :param keep_prob: Dropout keep probability\n",
" :param decoding_embedding_size: Decoding embedding size\n",
" :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)\n",
" \"\"\"\n",
" dec_embeddings = tf.Variable(tf.random_uniform([target_vocab_size, decoding_embedding_size]))\n",
" dec_embed_input = tf.nn.embedding_lookup(dec_embeddings, dec_input)\n",
" lstm = lambda: tf.contrib.rnn.DropoutWrapper(tf.contrib.rnn.LSTMCell(rnn_size), output_keep_prob=keep_prob)\n",
" dec_cell = tf.contrib.rnn.MultiRNNCell([lstm() for _ in range(num_layers)])\n",
" output_layer = Dense(target_vocab_size,\n",
" kernel_initializer = tf.truncated_normal_initializer(mean = 0.0, stddev=0.1))\n",
" \n",
" with tf.variable_scope('decoder'):\n",
" dec_train = decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, \n",
" max_target_sequence_length, output_layer, keep_prob)\n",
" \n",
" with tf.variable_scope('decoder', reuse=True):\n",
" dec_infer = decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, target_vocab_to_int['<GO>'],\n",
" target_vocab_to_int['<EOS>'], max_target_sequence_length,\n",
" target_vocab_size, output_layer, batch_size, keep_prob)\n",
" \n",
" return dec_train, dec_infer\n",
"\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_decoding_layer(decoding_layer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build the Neural Network\n",
"Apply the functions you implemented above to:\n",
"\n",
"- Encode the input using your `encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, encoding_embedding_size)`.\n",
"- Process target data using your `process_decoder_input(target_data, target_vocab_to_int, batch_size)` function.\n",
"- Decode the encoded input using your `decoding_layer(dec_input, enc_state, target_sequence_length, max_target_sentence_length, rnn_size, num_layers, target_vocab_to_int, target_vocab_size, batch_size, keep_prob, dec_embedding_size)` function."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def seq2seq_model(input_data, target_data, keep_prob, batch_size,\n",
" source_sequence_length, target_sequence_length,\n",
" max_target_sentence_length,\n",
" source_vocab_size, target_vocab_size,\n",
" enc_embedding_size, dec_embedding_size,\n",
" rnn_size, num_layers, target_vocab_to_int):\n",
" \"\"\"\n",
" Build the Sequence-to-Sequence part of the neural network\n",
" :param input_data: Input placeholder\n",
" :param target_data: Target placeholder\n",
" :param keep_prob: Dropout keep probability placeholder\n",
" :param batch_size: Batch Size\n",
" :param source_sequence_length: Sequence Lengths of source sequences in the batch\n",
" :param target_sequence_length: Sequence Lengths of target sequences in the batch\n",
" :param source_vocab_size: Source vocabulary size\n",
" :param target_vocab_size: Target vocabulary size\n",
" :param enc_embedding_size: Decoder embedding size\n",
" :param dec_embedding_size: Encoder embedding size\n",
" :param rnn_size: RNN Size\n",
" :param num_layers: Number of layers\n",
" :param target_vocab_to_int: Dictionary to go from the target words to an id\n",
" :return: Tuple of (Training BasicDecoderOutput, Inference BasicDecoderOutput)\n",
" \"\"\"\n",
" _, encoder_state = encoding_layer(input_data, rnn_size, num_layers, keep_prob, source_sequence_length, \n",
" source_vocab_size, enc_embedding_size)\n",
" dec_input = process_decoder_input(target_data, target_vocab_to_int, batch_size)\n",
" return decoding_layer(dec_input, encoder_state, target_sequence_length, max_target_sentence_length, \n",
" rnn_size, num_layers, target_vocab_to_int, target_vocab_size, \n",
" batch_size, keep_prob, dec_embedding_size)\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_seq2seq_model(seq2seq_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Neural Network Training\n",
"### Hyperparameters\n",
"Tune the following parameters:\n",
"\n",
"- Set `epochs` to the number of epochs.\n",
"- Set `batch_size` to the batch size.\n",
"- Set `rnn_size` to the size of the RNNs.\n",
"- Set `num_layers` to the number of layers.\n",
"- Set `encoding_embedding_size` to the size of the embedding for the encoder.\n",
"- Set `decoding_embedding_size` to the size of the embedding for the decoder.\n",
"- Set `learning_rate` to the learning rate.\n",
"- Set `keep_probability` to the Dropout keep probability\n",
"- Set `display_step` to state how many steps between each debug output statement"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Number of Epochs\n",
"epochs = 3\n",
"# Batch Size\n",
"batch_size = 256\n",
"# RNN Size\n",
"rnn_size = 256\n",
"# Number of Layers\n",
"num_layers = 2\n",
"# Embedding Size\n",
"encoding_embedding_size = 300\n",
"decoding_embedding_size = 300\n",
"# Learning Rate\n",
"learning_rate = 0.01\n",
"# Dropout Keep Probability\n",
"keep_probability = 0.75\n",
"display_step = 20"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build the Graph\n",
"Build the graph using the neural network you implemented."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"save_path = 'checkpoints/dev'\n",
"(source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess()\n",
"max_target_sentence_length = max([len(sentence) for sentence in source_int_text])\n",
"\n",
"train_graph = tf.Graph()\n",
"with train_graph.as_default():\n",
" input_data, targets, lr, keep_prob, target_sequence_length, max_target_sequence_length, source_sequence_length = model_inputs()\n",
"\n",
" #sequence_length = tf.placeholder_with_default(max_target_sentence_length, None, name='sequence_length')\n",
" input_shape = tf.shape(input_data)\n",
"\n",
" train_logits, inference_logits = seq2seq_model(tf.reverse(input_data, [-1]),\n",
" targets,\n",
" keep_prob,\n",
" batch_size,\n",
" source_sequence_length,\n",
" target_sequence_length,\n",
" max_target_sequence_length,\n",
" len(source_vocab_to_int),\n",
" len(target_vocab_to_int),\n",
" encoding_embedding_size,\n",
" decoding_embedding_size,\n",
" rnn_size,\n",
" num_layers,\n",
" target_vocab_to_int)\n",
"\n",
"\n",
" training_logits = tf.identity(train_logits.rnn_output, name='logits')\n",
" inference_logits = tf.identity(inference_logits.sample_id, name='predictions')\n",
"\n",
" masks = tf.sequence_mask(target_sequence_length, max_target_sequence_length, dtype=tf.float32, name='masks')\n",
"\n",
" with tf.name_scope(\"optimization\"):\n",
" # Loss function\n",
" cost = tf.contrib.seq2seq.sequence_loss(\n",
" training_logits,\n",
" targets,\n",
" masks)\n",
"\n",
" # Optimizer\n",
" optimizer = tf.train.AdamOptimizer(lr)\n",
"\n",
" # Gradient Clipping\n",
" gradients = optimizer.compute_gradients(cost)\n",
" capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients if grad is not None]\n",
" train_op = optimizer.apply_gradients(capped_gradients)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Batch and pad the source and target sequences"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"def pad_sentence_batch(sentence_batch, pad_int):\n",
" \"\"\"Pad sentences with <PAD> so that each sentence of a batch has the same length\"\"\"\n",
" max_sentence = max([len(sentence) for sentence in sentence_batch])\n",
" return [sentence + [pad_int] * (max_sentence - len(sentence)) for sentence in sentence_batch]\n",
"\n",
"\n",
"def get_batches(sources, targets, batch_size, source_pad_int, target_pad_int):\n",
" \"\"\"Batch targets, sources, and the lengths of their sentences together\"\"\"\n",
" for batch_i in range(0, len(sources)//batch_size):\n",
" start_i = batch_i * batch_size\n",
"\n",
" # Slice the right amount for the batch\n",
" sources_batch = sources[start_i:start_i + batch_size]\n",
" targets_batch = targets[start_i:start_i + batch_size]\n",
"\n",
" # Pad\n",
" pad_sources_batch = np.array(pad_sentence_batch(sources_batch, source_pad_int))\n",
" pad_targets_batch = np.array(pad_sentence_batch(targets_batch, target_pad_int))\n",
"\n",
" # Need the lengths for the _lengths parameters\n",
" pad_targets_lengths = []\n",
" for target in pad_targets_batch:\n",
" pad_targets_lengths.append(len(target))\n",
"\n",
" pad_source_lengths = []\n",
" for source in pad_sources_batch:\n",
" pad_source_lengths.append(len(source))\n",
"\n",
" yield pad_sources_batch, pad_targets_batch, pad_source_lengths, pad_targets_lengths\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train\n",
"Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forms to see if anyone is having the same problem."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 0 Batch 20/538 - Train Accuracy: 0.3679, Validation Accuracy: 0.4071, Loss: 2.8275\n",
"Epoch 0 Batch 40/538 - Train Accuracy: 0.5179, Validation Accuracy: 0.5167, Loss: 2.1477\n",
"Epoch 0 Batch 60/538 - Train Accuracy: 0.5229, Validation Accuracy: 0.5579, Loss: 1.7172\n",
"Epoch 0 Batch 80/538 - Train Accuracy: 0.5439, Validation Accuracy: 0.5788, Loss: 1.2155\n",
"Epoch 0 Batch 100/538 - Train Accuracy: 0.5875, Validation Accuracy: 0.5914, Loss: 0.8572\n",
"Epoch 0 Batch 120/538 - Train Accuracy: 0.6160, Validation Accuracy: 0.6108, Loss: 0.7206\n",
"Epoch 0 Batch 140/538 - Train Accuracy: 0.5998, Validation Accuracy: 0.6301, Loss: 0.7124\n",
"Epoch 0 Batch 160/538 - Train Accuracy: 0.6347, Validation Accuracy: 0.6312, Loss: 0.5701\n",
"Epoch 0 Batch 180/538 - Train Accuracy: 0.6881, Validation Accuracy: 0.6610, Loss: 0.5387\n",
"Epoch 0 Batch 200/538 - Train Accuracy: 0.6904, Validation Accuracy: 0.6770, Loss: 0.5034\n",
"Epoch 0 Batch 220/538 - Train Accuracy: 0.6559, Validation Accuracy: 0.6667, Loss: 0.4566\n",
"Epoch 0 Batch 240/538 - Train Accuracy: 0.7180, Validation Accuracy: 0.6886, Loss: 0.4349\n",
"Epoch 0 Batch 260/538 - Train Accuracy: 0.7156, Validation Accuracy: 0.7143, Loss: 0.4067\n",
"Epoch 0 Batch 280/538 - Train Accuracy: 0.7548, Validation Accuracy: 0.7225, Loss: 0.3641\n",
"Epoch 0 Batch 300/538 - Train Accuracy: 0.7442, Validation Accuracy: 0.7427, Loss: 0.3624\n",
"Epoch 0 Batch 320/538 - Train Accuracy: 0.7455, Validation Accuracy: 0.7530, Loss: 0.3213\n",
"Epoch 0 Batch 340/538 - Train Accuracy: 0.8113, Validation Accuracy: 0.7736, Loss: 0.3211\n",
"Epoch 0 Batch 360/538 - Train Accuracy: 0.7957, Validation Accuracy: 0.8010, Loss: 0.2826\n",
"Epoch 0 Batch 380/538 - Train Accuracy: 0.8203, Validation Accuracy: 0.8118, Loss: 0.2615\n",
"Epoch 0 Batch 400/538 - Train Accuracy: 0.8369, Validation Accuracy: 0.8184, Loss: 0.2396\n",
"Epoch 0 Batch 420/538 - Train Accuracy: 0.8615, Validation Accuracy: 0.8629, Loss: 0.2175\n",
"Epoch 0 Batch 440/538 - Train Accuracy: 0.8566, Validation Accuracy: 0.8686, Loss: 0.2054\n",
"Epoch 0 Batch 460/538 - Train Accuracy: 0.8482, Validation Accuracy: 0.8677, Loss: 0.1855\n",
"Epoch 0 Batch 480/538 - Train Accuracy: 0.9038, Validation Accuracy: 0.8656, Loss: 0.1584\n",
"Epoch 0 Batch 500/538 - Train Accuracy: 0.9261, Validation Accuracy: 0.8706, Loss: 0.1261\n",
"Epoch 0 Batch 520/538 - Train Accuracy: 0.8996, Validation Accuracy: 0.8887, Loss: 0.1401\n",
"Epoch 1 Batch 20/538 - Train Accuracy: 0.9040, Validation Accuracy: 0.8981, Loss: 0.1180\n",
"Epoch 1 Batch 40/538 - Train Accuracy: 0.9180, Validation Accuracy: 0.9048, Loss: 0.0926\n",
"Epoch 1 Batch 60/538 - Train Accuracy: 0.9148, Validation Accuracy: 0.9171, Loss: 0.0963\n",
"Epoch 1 Batch 80/538 - Train Accuracy: 0.9176, Validation Accuracy: 0.9176, Loss: 0.0943\n",
"Epoch 1 Batch 100/538 - Train Accuracy: 0.9396, Validation Accuracy: 0.9139, Loss: 0.0730\n",
"Epoch 1 Batch 120/538 - Train Accuracy: 0.9361, Validation Accuracy: 0.9023, Loss: 0.0700\n",
"Epoch 1 Batch 140/538 - Train Accuracy: 0.9219, Validation Accuracy: 0.9142, Loss: 0.0916\n",
"Epoch 1 Batch 160/538 - Train Accuracy: 0.9020, Validation Accuracy: 0.9286, Loss: 0.0694\n",
"Epoch 1 Batch 180/538 - Train Accuracy: 0.9373, Validation Accuracy: 0.9249, Loss: 0.0667\n",
"Epoch 1 Batch 200/538 - Train Accuracy: 0.9447, Validation Accuracy: 0.9324, Loss: 0.0567\n",
"Epoch 1 Batch 220/538 - Train Accuracy: 0.9381, Validation Accuracy: 0.9263, Loss: 0.0672\n",
"Epoch 1 Batch 240/538 - Train Accuracy: 0.9324, Validation Accuracy: 0.9498, Loss: 0.0582\n",
"Epoch 1 Batch 260/538 - Train Accuracy: 0.9055, Validation Accuracy: 0.9286, Loss: 0.0654\n",
"Epoch 1 Batch 280/538 - Train Accuracy: 0.9554, Validation Accuracy: 0.9144, Loss: 0.0505\n",
"Epoch 1 Batch 300/538 - Train Accuracy: 0.9379, Validation Accuracy: 0.9320, Loss: 0.0624\n",
"Epoch 1 Batch 320/538 - Train Accuracy: 0.9544, Validation Accuracy: 0.9391, Loss: 0.0507\n",
"Epoch 1 Batch 340/538 - Train Accuracy: 0.9301, Validation Accuracy: 0.9391, Loss: 0.0576\n",
"Epoch 1 Batch 360/538 - Train Accuracy: 0.9301, Validation Accuracy: 0.9457, Loss: 0.0515\n",
"Epoch 1 Batch 380/538 - Train Accuracy: 0.9375, Validation Accuracy: 0.9467, Loss: 0.0472\n",
"Epoch 1 Batch 400/538 - Train Accuracy: 0.9509, Validation Accuracy: 0.9524, Loss: 0.0548\n",
"Epoch 1 Batch 420/538 - Train Accuracy: 0.9531, Validation Accuracy: 0.9478, Loss: 0.0446\n",
"Epoch 1 Batch 440/538 - Train Accuracy: 0.9359, Validation Accuracy: 0.9423, Loss: 0.0547\n",
"Epoch 1 Batch 460/538 - Train Accuracy: 0.9356, Validation Accuracy: 0.9341, Loss: 0.0515\n",
"Epoch 1 Batch 480/538 - Train Accuracy: 0.9580, Validation Accuracy: 0.9334, Loss: 0.0452\n",
"Epoch 1 Batch 500/538 - Train Accuracy: 0.9627, Validation Accuracy: 0.9350, Loss: 0.0377\n",
"Epoch 1 Batch 520/538 - Train Accuracy: 0.9531, Validation Accuracy: 0.9384, Loss: 0.0464\n",
"Epoch 2 Batch 20/538 - Train Accuracy: 0.9410, Validation Accuracy: 0.9524, Loss: 0.0528\n",
"Epoch 2 Batch 40/538 - Train Accuracy: 0.9293, Validation Accuracy: 0.9538, Loss: 0.0377\n",
"Epoch 2 Batch 60/538 - Train Accuracy: 0.9420, Validation Accuracy: 0.9467, Loss: 0.0469\n",
"Epoch 2 Batch 80/538 - Train Accuracy: 0.9539, Validation Accuracy: 0.9537, Loss: 0.0442\n",
"Epoch 2 Batch 100/538 - Train Accuracy: 0.9484, Validation Accuracy: 0.9304, Loss: 0.0431\n",
"Epoch 2 Batch 120/538 - Train Accuracy: 0.9695, Validation Accuracy: 0.9522, Loss: 0.0313\n",
"Epoch 2 Batch 140/538 - Train Accuracy: 0.9369, Validation Accuracy: 0.9339, Loss: 0.0519\n",
"Epoch 2 Batch 160/538 - Train Accuracy: 0.9338, Validation Accuracy: 0.9402, Loss: 0.0484\n",
"Epoch 2 Batch 180/538 - Train Accuracy: 0.9442, Validation Accuracy: 0.9432, Loss: 0.0470\n",
"Epoch 2 Batch 200/538 - Train Accuracy: 0.9652, Validation Accuracy: 0.9309, Loss: 0.0365\n",
"Epoch 2 Batch 220/538 - Train Accuracy: 0.9457, Validation Accuracy: 0.9492, Loss: 0.0395\n",
"Epoch 2 Batch 240/538 - Train Accuracy: 0.9670, Validation Accuracy: 0.9490, Loss: 0.0420\n",
"Epoch 2 Batch 260/538 - Train Accuracy: 0.9232, Validation Accuracy: 0.9498, Loss: 0.0501\n",
"Epoch 2 Batch 280/538 - Train Accuracy: 0.9580, Validation Accuracy: 0.9549, Loss: 0.0333\n",
"Epoch 2 Batch 300/538 - Train Accuracy: 0.9496, Validation Accuracy: 0.9693, Loss: 0.0450\n",
"Epoch 2 Batch 320/538 - Train Accuracy: 0.9552, Validation Accuracy: 0.9519, Loss: 0.0366\n",
"Epoch 2 Batch 340/538 - Train Accuracy: 0.9523, Validation Accuracy: 0.9576, Loss: 0.0444\n",
"Epoch 2 Batch 360/538 - Train Accuracy: 0.9549, Validation Accuracy: 0.9538, Loss: 0.0408\n",
"Epoch 2 Batch 380/538 - Train Accuracy: 0.9570, Validation Accuracy: 0.9471, Loss: 0.0371\n",
"Epoch 2 Batch 400/538 - Train Accuracy: 0.9550, Validation Accuracy: 0.9634, Loss: 0.0416\n",
"Epoch 2 Batch 420/538 - Train Accuracy: 0.9568, Validation Accuracy: 0.9510, Loss: 0.0379\n",
"Epoch 2 Batch 440/538 - Train Accuracy: 0.9459, Validation Accuracy: 0.9572, Loss: 0.0429\n",
"Epoch 2 Batch 460/538 - Train Accuracy: 0.9440, Validation Accuracy: 0.9583, Loss: 0.0419\n",
"Epoch 2 Batch 480/538 - Train Accuracy: 0.9606, Validation Accuracy: 0.9510, Loss: 0.0380\n",
"Epoch 2 Batch 500/538 - Train Accuracy: 0.9810, Validation Accuracy: 0.9474, Loss: 0.0242\n",
"Epoch 2 Batch 520/538 - Train Accuracy: 0.9488, Validation Accuracy: 0.9474, Loss: 0.0452\n",
"Model Trained and Saved\n"
]
}
],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"def get_accuracy(target, logits):\n",
" \"\"\"\n",
" Calculate accuracy\n",
" \"\"\"\n",
" max_seq = max(target.shape[1], logits.shape[1])\n",
" if max_seq - target.shape[1]:\n",
" target = np.pad(\n",
" target,\n",
" [(0,0),(0,max_seq - target.shape[1])],\n",
" 'constant')\n",
" if max_seq - logits.shape[1]:\n",
" logits = np.pad(\n",
" logits,\n",
" [(0,0),(0,max_seq - logits.shape[1])],\n",
" 'constant')\n",
"\n",
" return np.mean(np.equal(target, logits))\n",
"\n",
"# Split data to training and validation sets\n",
"train_source = source_int_text[batch_size:]\n",
"train_target = target_int_text[batch_size:]\n",
"valid_source = source_int_text[:batch_size]\n",
"valid_target = target_int_text[:batch_size]\n",
"(valid_sources_batch, valid_targets_batch, valid_sources_lengths, valid_targets_lengths ) = next(get_batches(valid_source,\n",
" valid_target,\n",
" batch_size,\n",
" source_vocab_to_int['<PAD>'],\n",
" target_vocab_to_int['<PAD>'])) \n",
"with tf.Session(graph=train_graph) as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
"\n",
" for epoch_i in range(epochs):\n",
" for batch_i, (source_batch, target_batch, sources_lengths, targets_lengths) in enumerate(\n",
" get_batches(train_source, train_target, batch_size,\n",
" source_vocab_to_int['<PAD>'],\n",
" target_vocab_to_int['<PAD>'])):\n",
"\n",
" _, loss = sess.run(\n",
" [train_op, cost],\n",
" {input_data: source_batch,\n",
" targets: target_batch,\n",
" lr: learning_rate,\n",
" target_sequence_length: targets_lengths,\n",
" source_sequence_length: sources_lengths,\n",
" keep_prob: keep_probability})\n",
"\n",
"\n",
" if batch_i % display_step == 0 and batch_i > 0:\n",
"\n",
"\n",
" batch_train_logits = sess.run(\n",
" inference_logits,\n",
" {input_data: source_batch,\n",
" source_sequence_length: sources_lengths,\n",
" target_sequence_length: targets_lengths,\n",
" keep_prob: 1.0})\n",
"\n",
"\n",
" batch_valid_logits = sess.run(\n",
" inference_logits,\n",
" {input_data: valid_sources_batch,\n",
" source_sequence_length: valid_sources_lengths,\n",
" target_sequence_length: valid_targets_lengths,\n",
" keep_prob: 1.0})\n",
"\n",
" train_acc = get_accuracy(target_batch, batch_train_logits)\n",
"\n",
" valid_acc = get_accuracy(valid_targets_batch, batch_valid_logits)\n",
"\n",
" print('Epoch {:>3} Batch {:>4}/{} - Train Accuracy: {:>6.4f}, Validation Accuracy: {:>6.4f}, Loss: {:>6.4f}'\n",
" .format(epoch_i, batch_i, len(source_int_text) // batch_size, train_acc, valid_acc, loss))\n",
"\n",
" # Save Model\n",
" saver = tf.train.Saver()\n",
" saver.save(sess, save_path)\n",
" print('Model Trained and Saved')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save Parameters\n",
"Save the `batch_size` and `save_path` parameters for inference."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"# Save parameters for checkpoint\n",
"helper.save_params(save_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Checkpoint"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import helper\n",
"import problem_unittests as tests\n",
"\n",
"_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = helper.load_preprocess()\n",
"load_path = helper.load_params()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sentence to Sequence\n",
"To feed a sentence into the model for translation, you first need to preprocess it. Implement the function `sentence_to_seq()` to preprocess new sentences.\n",
"\n",
"- Convert the sentence to lowercase\n",
"- Convert words into ids using `vocab_to_int`\n",
" - Convert words not in the vocabulary, to the `<UNK>` word id."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed\n"
]
}
],
"source": [
"def sentence_to_seq(sentence, vocab_to_int):\n",
" \"\"\"\n",
" Convert a sentence to a sequence of ids\n",
" :param sentence: String\n",
" :param vocab_to_int: Dictionary to go from the words to an id\n",
" :return: List of word ids\n",
" \"\"\"\n",
" return [vocab_to_int.get(word, vocab_to_int['<UNK>']) for word in sentence.lower().split()]\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
"\"\"\"\n",
"tests.test_sentence_to_seq(sentence_to_seq)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Translate\n",
"This will translate `translate_sentence` from English to French."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Restoring parameters from checkpoints/dev\n",
"Input\n",
" Word Ids: [112, 71, 35, 100, 14, 138, 199]\n",
" English Words: ['he', 'saw', 'a', 'old', 'yellow', 'truck', '.']\n",
"\n",
"Prediction\n",
" Word Ids: [280, 100, 30, 199, 23, 122, 135, 76, 1]\n",
" French Words: il a vu un vieux camion jaune . <EOS>\n"
]
}
],
"source": [
"translate_sentence = 'he saw a old yellow truck .'\n",
"\n",
"\n",
"\"\"\"\n",
"DON'T MODIFY ANYTHING IN THIS CELL\n",
"\"\"\"\n",
"translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)\n",
"\n",
"loaded_graph = tf.Graph()\n",
"with tf.Session(graph=loaded_graph) as sess:\n",
" # Load saved model\n",
" loader = tf.train.import_meta_graph(load_path + '.meta')\n",
" loader.restore(sess, load_path)\n",
"\n",
" input_data = loaded_graph.get_tensor_by_name('input:0')\n",
" logits = loaded_graph.get_tensor_by_name('predictions:0')\n",
" target_sequence_length = loaded_graph.get_tensor_by_name('target_sequence_length:0')\n",
" source_sequence_length = loaded_graph.get_tensor_by_name('source_sequence_length:0')\n",
" keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n",
"\n",
" translate_logits = sess.run(logits, {input_data: [translate_sentence]*batch_size,\n",
" target_sequence_length: [len(translate_sentence)*2]*batch_size,\n",
" source_sequence_length: [len(translate_sentence)]*batch_size,\n",
" keep_prob: 1.0})[0]\n",
"\n",
"print('Input')\n",
"print(' Word Ids: {}'.format([i for i in translate_sentence]))\n",
"print(' English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))\n",
"\n",
"print('\\nPrediction')\n",
"print(' Word Ids: {}'.format([i for i in translate_logits]))\n",
"print(' French Words: {}'.format(\" \".join([target_int_to_vocab[i] for i in translate_logits])))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imperfect Translation\n",
"You might notice that some sentences translate better than others. Since the dataset you're using only has a vocabulary of 227 English words of the thousands that you use, you're only going to see good results using these words. For this project, you don't need a perfect translation. However, if you want to create a better translation model, you'll need better data.\n",
"\n",
"You can train on the [WMT10 French-English corpus](http://www.statmt.org/wmt10/training-giga-fren.tar). This dataset has more vocabulary and richer in topics discussed. However, this will take you days to train, so make sure you've a GPU and the neural network is performing well on dataset we provided. Just make sure you play with the WMT10 corpus after you've submitted this project.\n",
"## Submitting This Project\n",
"When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_language_translation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission."
]
}
],
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
zzsza/Kaggle_Expedia-hotel-recommendations | notebook/07. Feature Engineering(2).ipynb | 1 | 45739 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from __future__ import print_function\n",
"import sklearn\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn import preprocessing\n",
"from datetime import datetime\n",
"import os\n",
"\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'png'\n",
"pd.set_option(\"max_columns\",50)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 41.9 s\n"
]
}
],
"source": [
"%%time\n",
"train = pd.read_csv(\"../data/train_2013.csv\", index_col=0)\n",
"train = train.reset_index(drop=True)\n",
"train = train[train[\"is_booking\"] == 1]\n",
"np.random.seed(402)\n",
"train = train.ix[np.random.choice(train.index, 50000)]\n",
"train = train.reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"preprocessing train_data\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:8: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"C:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:9: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"C:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:10: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Wall time: 617 ms\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:11: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
}
],
"source": [
"%%time\n",
"\n",
"print('preprocessing train_data')\n",
"use_col = [\"srch_co\",\"srch_ci\",\"srch_destination_id\",\"hotel_country\",\"srch_adults_cnt\",\"srch_children_cnt\",\"hotel_cluster\"]\n",
"\n",
"train_y = train[[\"hotel_cluster\"]]\n",
"\n",
"train_x = train[use_col]\n",
"train_x[\"srch_ci\"] = pd.to_datetime(train_x[\"srch_ci\"], errors=\"coerce\")\n",
"train_x[\"srch_co\"] = pd.to_datetime(train_x[\"srch_co\"], errors=\"coerce\")\n",
"train_x[\"period\"] = train_x[\"srch_co\"] - train_x[\"srch_ci\"]\n",
"train_x[\"period\"] = (train_x[\"period\"] / np.timedelta64(1, 'D')).astype(int)\n",
"train_x = train_x.drop([\"srch_co\",\"srch_ci\"], axis=1)\n",
"train_x[\"srch_adults_cnt\"] = train_x[\"srch_adults_cnt\"].apply(lambda x: 3 if x>=3 else x)\n",
"train_x = train_x.drop([\"srch_children_cnt\"], axis=1)\n",
"train_x = train_x[[\"srch_destination_id\",\"hotel_country\",\"srch_adults_cnt\",\"period\"]]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"source": [
"train_x.head()"
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{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"source": [
"train[\"hotel_cluster\"].unique()"
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{
"cell_type": "code",
"execution_count": 13,
"metadata": {
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" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62487</th>\n",
" <th>6</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62508</th>\n",
" <th>32</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62824</th>\n",
" <th>21</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>22857 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" sum count\n",
"srch_destination_id hotel_cluster \n",
"2 20 1 1\n",
"4 67 1 1\n",
" 78 1 1\n",
" 81 1 1\n",
"8 7 1 1\n",
" 32 1 1\n",
" 42 1 1\n",
" 48 1 1\n",
" 76 1 1\n",
"11 91 1 1\n",
"14 20 1 1\n",
" 61 1 1\n",
"16 15 1 1\n",
" 85 1 1\n",
"19 64 1 1\n",
"21 62 1 1\n",
" 67 3 3\n",
" 82 1 1\n",
" 89 1 1\n",
"24 3 1 1\n",
" 23 1 1\n",
" 42 1 1\n",
" 47 2 2\n",
" 60 1 1\n",
" 76 2 2\n",
" 91 3 3\n",
"25 5 1 1\n",
" 10 1 1\n",
" 13 2 2\n",
" 32 1 1\n",
"... ... ...\n",
"60988 41 1 1\n",
" 68 1 1\n",
"61097 28 1 1\n",
" 72 1 1\n",
"61102 95 1 1\n",
"61128 12 1 1\n",
"61193 30 1 1\n",
" 36 2 2\n",
"61306 60 1 1\n",
"61413 29 1 1\n",
" 62 1 1\n",
"61418 58 1 1\n",
"61442 5 1 1\n",
"61528 32 1 1\n",
" 49 1 1\n",
" 72 1 1\n",
"61531 10 1 1\n",
"61533 11 1 1\n",
" 41 2 2\n",
" 83 1 1\n",
"61702 33 1 1\n",
" 47 1 1\n",
" 48 1 1\n",
" 91 2 2\n",
"61756 56 2 2\n",
" 72 1 1\n",
" 77 1 1\n",
"62487 6 1 1\n",
"62508 32 1 1\n",
"62824 21 1 1\n",
"\n",
"[22857 rows x 2 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.groupby(['srch_destination_id',\n",
" 'hotel_cluster'])['is_booking'].agg(['sum','count'])\n",
"\n",
"# srch_destination_id = ex) 도쿄라고 했을때 나오는 그룹 => hotel_cluster 고로, srch_destination_id 와 hotel_country는 유사할것임"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# 이게 baseline 앞으론 남들과 다르게 예약한 사람을 찾아서 그들을 지켜보자"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"preprocessing train_data\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:14: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"C:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:15: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"C:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\ipykernel\\__main__.py:16: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
},
{
"ename": "ValueError",
"evalue": "Cannot convert NA to integer",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-fb4819ba599b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mu'time'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mu''\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34mu'%%time\\ntrain = pd.read_csv(\"../data/train_2013.csv\", index_col=0)\\ntrain = train.reset_index(drop=True)\\nnp.random.seed(402)\\ntrain = train.ix[np.random.choice(train.index, 50000)]\\ntrain = train.reset_index(drop=True)\\n\\n\\nprint(\\'preprocessing train_data\\')\\nuse_col = [\"srch_co\",\"srch_ci\",\"user_location_region\",\\\\\\n \"hotel_market\",\"srch_destination_id\",\"hotel_country\",\"srch_adults_cnt\",\"srch_children_cnt\",\"hotel_cluster\"]\\n\\ntrain_y = train[[\"hotel_cluster\"]]\\n\\ntrain_x = train[use_col]\\ntrain_x[\"srch_ci\"] = pd.to_datetime(train_x[\"srch_ci\"], errors=\"coerce\")\\ntrain_x[\"srch_co\"] = pd.to_datetime(train_x[\"srch_co\"], errors=\"coerce\")\\ntrain_x[\"period\"] = train_x[\"srch_co\"] - train_x[\"srch_ci\"]\\ntrain_x[\"period\"] = (train_x[\"period\"] / np.timedelta64(1, \\'D\\')).astype(int)\\ntrain_x = train_x.drop([\"srch_co\",\"srch_ci\"], axis=1)\\ntrain_x[\"srch_adults_cnt\"] = train_x[\"srch_adults_cnt\"].apply(lambda x: 3 if x>=3 else x)\\ntrain_x = train_x.drop([\"srch_children_cnt\"], axis=1)\\ntrain_x = train_x[[\"hotel_market\",\"srch_destination_id\",\"hotel_country\",\"srch_adults_cnt\",\"period\",\"user_location_region\"]]\\n\\n\\n\\nuse_col = [\"srch_co\",\"srch_ci\",\"user_location_region\",\\\\\\n \"hotel_market\",\"srch_destination_id\",\"hotel_country\",\"srch_adults_cnt\",\"srch_children_cnt\"]\\nprint(\"read the test.csv\")\\ntest = pd.read_csv(\"../data/test.csv\")\\ntest = test[use_col]\\n\\nprint(\"preprocessing test_data\")\\n\\ntest[\"srch_ci\"] = pd.to_datetime(test[\"srch_ci\"], errors=\"coerce\")\\ntest[\"srch_co\"] = pd.to_datetime(test[\"srch_co\"], errors=\"coerce\")\\ntest[\"period\"] = test[\"srch_co\"] - test[\"srch_ci\"]\\ntest[\"period\"] = (test[\"period\"] / np.timedelta64(1, \\'D\\')).fillna(0.0).astype(int)\\ntest = test.drop([\"srch_co\",\"srch_ci\"], axis=1)\\ntest[\"num\"] = 1\\ntest[\"srch_adults_cnt\"] = test[\"srch_adults_cnt\"].apply(lambda x: 3 if x>=3 else x)\\ntest = test.drop([\"num\",\"srch_children_cnt\"], axis=1)\\n\\ntest = test[[\"hotel_market\",\"srch_destination_id\",\"hotel_country\",\"srch_adults_cnt\",\"period\",\"user_location_region\"]]\\n\\nprint(\"modeling strart\")\\nmodel = RandomForestClassifier(n_estimators=10, max_depth=7, n_jobs=-1, random_state=777)\\nprint(\\'=\\'*50)\\nprint(\\'# Test shape : {}\\'.format(test.shape))\\n\\nmodel.fit(train_x,train_y)\\n\\npreds = model.predict_proba(test)\\npreds = np.fliplr(np.argsort(preds, axis=1))\\n\\nprint(\"save file\")\\n\\nresult_df = pd.DataFrame([ \" \".join(row) for row in preds[:,:5].astype(str)], columns=[\"hotel_cluster\"])\\nresult_df.index.names = [\"id\"]\\nfile_name = datetime.now().strftime(\"result_%Y%m%d%H%M%S\") + \\'.csv\\'\\nresult_df.to_csv(os.path.join(\\'../output\\',file_name), index=True)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32mC:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\IPython\\core\\interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2113\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2114\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2115\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2116\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<decorator-gen-60>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\IPython\\core\\magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\IPython\\core\\magics\\execution.pyc\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 1174\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'eval'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1175\u001b[0m \u001b[0mst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1176\u001b[0;31m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1177\u001b[0m \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1178\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\IPython\\core\\interactiveshell.pyc\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2113\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvar_expand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2114\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2115\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2116\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<decorator-gen-60>\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\IPython\\core\\magic.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[1;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 188\u001b[0;31m \u001b[0mcall\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 189\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\IPython\\core\\magics\\execution.pyc\u001b[0m in \u001b[0;36mtime\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m 1178\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1179\u001b[0m \u001b[0mst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1180\u001b[0;31m \u001b[1;32mexec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mglob\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlocal_ns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1181\u001b[0m \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclock2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1182\u001b[0m \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, raise_on_error, **kwargs)\u001b[0m\n\u001b[1;32m 2948\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2949\u001b[0m mgr = self._data.astype(dtype=dtype, copy=copy,\n\u001b[0;32m-> 2950\u001b[0;31m raise_on_error=raise_on_error, **kwargs)\n\u001b[0m\u001b[1;32m 2951\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2952\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m 2936\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2937\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\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[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2938\u001b[0;31m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'astype'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\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[1;32m 2939\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2940\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, consolidate, raw, **kwargs)\u001b[0m\n\u001b[1;32m 2888\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2889\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mgr'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2890\u001b[0;31m \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\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[1;32m 2891\u001b[0m \u001b[0mresult_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 2892\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, raise_on_error, values, **kwargs)\u001b[0m\n\u001b[1;32m 432\u001b[0m **kwargs):\n\u001b[1;32m 433\u001b[0m return self._astype(dtype, copy=copy, raise_on_error=raise_on_error,\n\u001b[0;32m--> 434\u001b[0;31m values=values, **kwargs)\n\u001b[0m\u001b[1;32m 435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 436\u001b[0m def _astype(self, dtype, copy=False, raise_on_error=True, values=None,\n",
"\u001b[0;32mC:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36m_astype\u001b[0;34m(self, dtype, copy, raise_on_error, values, klass, mgr, **kwargs)\u001b[0m\n\u001b[1;32m 475\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 476\u001b[0m \u001b[1;31m# _astype_nansafe works fine with 1-d only\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_astype_nansafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 478\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Byeon\\Anaconda3\\envs\\py27\\lib\\site-packages\\pandas\\core\\common.pyc\u001b[0m in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy)\u001b[0m\n\u001b[1;32m 1912\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1913\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1914\u001b[0;31m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Cannot convert NA to integer'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1915\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobject_\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 1916\u001b[0m \u001b[1;31m# work around NumPy brokenness, #1987\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Cannot convert NA to integer"
]
}
],
"source": [
"%%time\n",
"train = pd.read_csv(\"../data/train_2013.csv\", index_col=0)\n",
"train = train.reset_index(drop=True)\n",
"np.random.seed(402)\n",
"train = train.ix[np.random.choice(train.index, 50000)]\n",
"train = train.reset_index(drop=True)\n",
"\n",
"\n",
"print('preprocessing train_data')\n",
"use_col = [\"srch_co\",\"srch_ci\",\"user_location_region\",\\\n",
" \"hotel_market\",\"srch_destination_id\",\"hotel_country\",\"srch_adults_cnt\",\"srch_children_cnt\",\"hotel_cluster\"]\n",
"\n",
"train_y = train[[\"hotel_cluster\"]]\n",
"\n",
"train_x = train[use_col]\n",
"train_x[\"srch_ci\"] = pd.to_datetime(train_x[\"srch_ci\"], errors=\"coerce\")\n",
"train_x[\"srch_co\"] = pd.to_datetime(train_x[\"srch_co\"], errors=\"coerce\")\n",
"train_x[\"period\"] = train_x[\"srch_co\"] - train_x[\"srch_ci\"]\n",
"train_x[\"period\"] = (train_x[\"period\"] / np.timedelta64(1, 'D')).fillna(0.0).astype(int)\n",
"train_x = train_x.drop([\"srch_co\",\"srch_ci\"], axis=1)\n",
"train_x[\"srch_adults_cnt\"] = train_x[\"srch_adults_cnt\"].apply(lambda x: 3 if x>=3 else x)\n",
"train_x = train_x.drop([\"srch_children_cnt\"], axis=1)\n",
"train_x = train_x[[\"hotel_market\",\"srch_destination_id\",\"hotel_country\",\"srch_adults_cnt\",\"period\",\"user_location_region\"]]\n",
"\n",
"\n",
"\n",
"use_col = [\"srch_co\",\"srch_ci\",\"user_location_region\",\\\n",
" \"hotel_market\",\"srch_destination_id\",\"hotel_country\",\"srch_adults_cnt\",\"srch_children_cnt\"]\n",
"print(\"read the test.csv\")\n",
"test = pd.read_csv(\"../data/test.csv\")\n",
"test = test[use_col]\n",
"\n",
"print(\"preprocessing test_data\")\n",
"\n",
"test[\"srch_ci\"] = pd.to_datetime(test[\"srch_ci\"], errors=\"coerce\")\n",
"test[\"srch_co\"] = pd.to_datetime(test[\"srch_co\"], errors=\"coerce\")\n",
"test[\"period\"] = test[\"srch_co\"] - test[\"srch_ci\"]\n",
"test[\"period\"] = (test[\"period\"] / np.timedelta64(1, 'D')).fillna(0.0).astype(int)\n",
"test = test.drop([\"srch_co\",\"srch_ci\"], axis=1)\n",
"test[\"num\"] = 1\n",
"test[\"srch_adults_cnt\"] = test[\"srch_adults_cnt\"].apply(lambda x: 3 if x>=3 else x)\n",
"test = test.drop([\"num\",\"srch_children_cnt\"], axis=1)\n",
"\n",
"test = test[[\"hotel_market\",\"srch_destination_id\",\"hotel_country\",\"srch_adults_cnt\",\"period\",\"user_location_region\"]]\n",
"\n",
"print(\"modeling strart\")\n",
"model = RandomForestClassifier(n_estimators=10, max_depth=7, n_jobs=-1, random_state=777)\n",
"print('='*50)\n",
"print('# Test shape : {}'.format(test.shape))\n",
"\n",
"model.fit(train_x,train_y)\n",
"\n",
"preds = model.predict_proba(test)\n",
"preds = np.fliplr(np.argsort(preds, axis=1))\n",
"\n",
"print(\"save file\")\n",
"\n",
"result_df = pd.DataFrame([ \" \".join(row) for row in preds[:,:5].astype(str)], columns=[\"hotel_cluster\"])\n",
"result_df.index.names = [\"id\"]\n",
"file_name = datetime.now().strftime(\"result_%Y%m%d%H%M%S\") + '.csv'\n",
"result_df.to_csv(os.path.join('../output',file_name), index=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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
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