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{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "1b1048b2-96fc-7920-299a-484e9c3d3dfd" }, "source": [ "This is a markdown cell. Click here to edit..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "073a0d92-96b1-c5ac-9111-1a479cd2412c" }, "outputs": [], "source": [ "from time import sleep" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "6f17a155-ce7a-b7fa-89c9-cf7e80c2e455" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n" ] } ], "source": [ "for i in range(3):\n", " print(i)\n", " sleep(0.1)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "a5df577b-2b0c-61ba-fffb-ac5ee82a7cc5" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "abbb74de-3093-5759-9d95-1ca4a4a46816" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 180, "_is_fork": 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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/299/299160.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "008957cf-6ddf-bd50-6d69-bf64d2a4fe49" }, "source": [ "This is a markdown cell. Click here to edit..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "3d4eb4b2-5e0e-08a6-9358-a431691c1c93" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>location</th>\n", " <th>count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Argentina-Buenos_Aires</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Argentina-CABA</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Argentina-Catamarca</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Argentina-Chaco</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Argentina-Chubut</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Argentina-Cordoba</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Argentina-Corrientes</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Argentina-Entre_Rios</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Argentina-Formosa</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Argentina-Jujuy</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Argentina-La_Pampa</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Argentina-La_Rioja</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>Argentina-Mendoza</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>Argentina-Misiones</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>Argentina-Neuquen</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>Argentina-Rio_Negro</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>Argentina-Salta</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>Argentina-San_Juan</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>Argentina-San_Luis</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>Argentina-Santa_Cruz</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>Argentina-Santa_Fe</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>Argentina-Sgo_Del_Estero</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Argentina-Tierra_Del_Fuego</td>\n", " <td>54</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>Argentina-Tierra_del_Fuego</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Argentina-Tucuman</td>\n", " <td>84</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>Brazil</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>Brazil-Acre</td>\n", " <td>170</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>Brazil-Alagoas</td>\n", " <td>171</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>Brazil-Amapa</td>\n", " <td>143</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>Brazil-Amazonas</td>\n", " <td>113</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>1643</th>\n", " <td>United_States-Missouri</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1644</th>\n", " <td>United_States-Montana</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1645</th>\n", " <td>United_States-Nebraska</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1646</th>\n", " <td>United_States-Nevada</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>1647</th>\n", " <td>United_States-New_Hampshire</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1648</th>\n", " <td>United_States-New_Jersey</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1649</th>\n", " <td>United_States-New_Mexico</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>1650</th>\n", " <td>United_States-New_York</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1651</th>\n", " <td>United_States-North_Carolina</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1652</th>\n", " <td>United_States-Ohio</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1653</th>\n", " <td>United_States-Oklahoma</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1654</th>\n", " <td>United_States-Oregon</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1655</th>\n", " <td>United_States-Pennsylvania</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1656</th>\n", " <td>United_States-Pennsylvania††</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1657</th>\n", " <td>United_States-Puerto_Rico</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1658</th>\n", " <td>United_States-Rhode_Island</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>1659</th>\n", " <td>United_States-South_Carolina</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>1660</th>\n", " <td>United_States-Tennessee</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1661</th>\n", " <td>United_States-Texas</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1662</th>\n", " <td>United_States-US_Virgin_Islands</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1663</th>\n", " <td>United_States-Utah</td>\n", " <td>20</td>\n", " </tr>\n", " <tr>\n", " <th>1664</th>\n", " <td>United_States-Vermont</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>1665</th>\n", " <td>United_States-Virginia</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1666</th>\n", " <td>United_States-Washington</td>\n", " <td>34</td>\n", " </tr>\n", " <tr>\n", " <th>1667</th>\n", " <td>United_States-West_Virginia</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>1668</th>\n", " <td>United_States-Wisconsin</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>1669</th>\n", " <td>United_States_Virgin_Islands</td>\n", " <td>413</td>\n", " </tr>\n", " <tr>\n", " <th>1670</th>\n", " <td>United_States_Virgin_Islands-Saint_Croix</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1671</th>\n", " <td>United_States_Virgin_Islands-Saint_John</td>\n", " <td>32</td>\n", " </tr>\n", " <tr>\n", " <th>1672</th>\n", " <td>United_States_Virgin_Islands-Saint_Thomas</td>\n", " <td>32</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1673 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " location count\n", "0 Argentina-Buenos_Aires 84\n", "1 Argentina-CABA 84\n", "2 Argentina-Catamarca 84\n", "3 Argentina-Chaco 84\n", "4 Argentina-Chubut 84\n", "5 Argentina-Cordoba 84\n", "6 Argentina-Corrientes 84\n", "7 Argentina-Entre_Rios 84\n", "8 Argentina-Formosa 84\n", "9 Argentina-Jujuy 84\n", "10 Argentina-La_Pampa 84\n", "11 Argentina-La_Rioja 84\n", "12 Argentina-Mendoza 84\n", "13 Argentina-Misiones 84\n", "14 Argentina-Neuquen 84\n", "15 Argentina-Rio_Negro 84\n", "16 Argentina-Salta 84\n", "17 Argentina-San_Juan 84\n", "18 Argentina-San_Luis 84\n", "19 Argentina-Santa_Cruz 84\n", "20 Argentina-Santa_Fe 84\n", "21 Argentina-Sgo_Del_Estero 84\n", "22 Argentina-Tierra_Del_Fuego 54\n", "23 Argentina-Tierra_del_Fuego 30\n", "24 Argentina-Tucuman 84\n", "25 Brazil 7\n", "26 Brazil-Acre 170\n", "27 Brazil-Alagoas 171\n", "28 Brazil-Amapa 143\n", "29 Brazil-Amazonas 113\n", "... ... ...\n", "1643 United_States-Missouri 32\n", "1644 United_States-Montana 32\n", "1645 United_States-Nebraska 34\n", "1646 United_States-Nevada 20\n", "1647 United_States-New_Hampshire 32\n", "1648 United_States-New_Jersey 34\n", "1649 United_States-New_Mexico 14\n", "1650 United_States-New_York 34\n", "1651 United_States-North_Carolina 32\n", "1652 United_States-Ohio 34\n", "1653 United_States-Oklahoma 32\n", "1654 United_States-Oregon 34\n", "1655 United_States-Pennsylvania 30\n", "1656 United_States-Pennsylvania†† 4\n", "1657 United_States-Puerto_Rico 32\n", "1658 United_States-Rhode_Island 6\n", "1659 United_States-South_Carolina 6\n", "1660 United_States-Tennessee 34\n", "1661 United_States-Texas 34\n", "1662 United_States-US_Virgin_Islands 34\n", "1663 United_States-Utah 20\n", "1664 United_States-Vermont 6\n", "1665 United_States-Virginia 34\n", "1666 United_States-Washington 34\n", "1667 United_States-West_Virginia 30\n", "1668 United_States-Wisconsin 6\n", "1669 United_States_Virgin_Islands 413\n", "1670 United_States_Virgin_Islands-Saint_Croix 32\n", "1671 United_States_Virgin_Islands-Saint_John 32\n", "1672 United_States_Virgin_Islands-Saint_Thomas 32\n", "\n", "[1673 rows x 2 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "sns.set_style(\"whitegrid\")\n", "\n", "zika = pd.read_csv(\"../input/cdc_zika.csv\")\n", "\n", "zika.groupby(\"location\").size().reset_index().rename(columns={0: \"count\"})" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "6475edf2-6315-e6d3-0b0a-f07b4851e78d" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "90bf2062-9643-ac25-b4dc-08de22b3782a" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 167, "_is_fork": 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": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/303/303338.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "061ccc04-2038-8bed-0d61-a50d12943e2a" }, "source": [ "# This is currently a work-in-progress" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "6aee2813-1742-ccc2-c592-1b73364cc53f" }, "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>Title</th>\n", " <th>NumVotes</th>\n", " <th>NumNonSelfVotes</th>\n", " <th>HasNonSelfVotes</th>\n", " <th>NumVersions</th>\n", " <th>NumSuccessfulRuns</th>\n", " <th>NumErroredRuns</th>\n", " <th>NumChangedVersions</th>\n", " <th>Lines</th>\n", " <th>LinesAddedOrChanged</th>\n", " <th>Name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>80455</td>\n", " <td>Initial loan book analysis</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " 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<tr>\n", " <th>35283</th>\n", " <td>105</td>\n", " <td>Random Forest Benchmark</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>35284</th>\n", " <td>104</td>\n", " <td>Random Forest Benchmark</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>35285</th>\n", " <td>103</td>\n", " <td>by time</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>35286</th>\n", " <td>94</td>\n", " <td>Rentals By Time/Temp/Workingday</td>\n", " <td>5</td>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>10</td>\n", " <td>14</td>\n", " <td>0</td>\n", " <td>8</td>\n", " <td>0.0</td>\n", " <td>8.0</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>35287</th>\n", " <td>93</td>\n", " <td>Bike Rentals By Time And Temperature</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>35288</th>\n", " <td>92</td>\n", " <td>Installed R Packages</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>35289</th>\n", " <td>90</td>\n", " <td>Bike Rentals By Time</td>\n", " <td>11</td>\n", " <td>10</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>11</td>\n", " <td>0</td>\n", " <td>11</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>R</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>35290 rows × 12 columns</p>\n", "</div>" ], "text/plain": [ " Id Title NumVotes \\\n", "0 80455 Initial loan book analysis 0 \n", "1 80443 Classification using scikit learn 0 \n", "2 80443 Classification using scikit learn 0 \n", "3 80443 Classification using scikit learn 0 \n", "4 80444 face recognition test 0 \n", "5 80444 face recognition test 0 \n", "6 80124 Calories, carbs, fats, and proteins 0 \n", "7 80124 Calories, carbs, fats, and proteins 1 \n", "8 80124 Calories, carbs, fats, and proteins 0 \n", "9 79939 Extracting Goal Times 0 \n", "10 80266 New Coder Age vs Job Role Interest 0 \n", "11 80266 New Coder Age vs Job Role Interest 0 \n", "12 80430 Plane Crash Analysis in Python 0 \n", "13 80430 Plane Crash Analysis in Python 0 \n", "14 80430 Plane Crash Analysis in Python 0 \n", "15 80408 testone 0 \n", "16 66097 List Files 0 \n", "17 66097 List Files 0 \n", "18 74216 List Files 2 0 \n", "19 80378 Looking at the Zika data 0 \n", "20 80377 Exploring the Zika data 0 \n", "21 80377 Exploring the Zika data 0 \n", "22 80376 Hello Kaggle 0 \n", "23 80073 Test Football 0 \n", "24 80361 Exploring Airplane Crashes 0 \n", "25 80361 Exploring Airplane Crashes 0 \n", "26 80361 Exploring Airplane Crashes 0 \n", "27 80348 kNN example 0 \n", "28 80343 'Clinton: Champion of the Primaries 0 \n", "29 78540 Visualizing Iris datasets with R ggplot2 0 \n", "... ... ... ... \n", "35260 167 Example 0 \n", "35261 166 Histogram Open/Closed by Length 0 \n", "35262 152 broken 0 \n", "35263 164 Random Forest Benchmark 0 \n", "35264 162 Random Forest Benchmark 0 \n", "35265 156 fiddling2 0 \n", "35266 159 Testing 0 \n", "35267 157 Random Forest Benchmark 0 \n", "35268 153 fiddling 0 \n", "35269 155 Random Forest Benchmark 0 \n", "35270 151 head of train 0 \n", "35271 150 Default R Script 0 \n", "35272 148 Random Forest Benchmark 0 \n", "35273 146 Random Forest Benchmark 0 \n", "35274 145 Random Forest Benchmark 0 \n", "35275 144 Input Files 0 \n", "35276 142 Random Forest Benchmark 0 \n", "35277 141 Installed R Packages 0 \n", "35278 126 Prueba 0 \n", "35279 125 by time 3 \n", "35280 122 Random Forest Benchmark 0 \n", "35281 121 Random Forest Benchmark 0 \n", "35282 120 Random Forest Benchmark 0 \n", "35283 105 Random Forest Benchmark 1 \n", "35284 104 Random Forest Benchmark 1 \n", "35285 103 by time 3 \n", "35286 94 Rentals By Time/Temp/Workingday 5 \n", "35287 93 Bike Rentals By Time And Temperature 1 \n", "35288 92 Installed R Packages 0 \n", "35289 90 Bike Rentals By Time 11 \n", "\n", " NumNonSelfVotes HasNonSelfVotes NumVersions NumSuccessfulRuns \\\n", "0 0 0 1 1 \n", "1 0 0 1 0 \n", "2 0 0 1 1 \n", "3 0 0 5 1 \n", "4 0 0 1 0 \n", "5 0 0 3 2 \n", "6 0 0 8 0 \n", "7 1 1 5 5 \n", "8 0 0 2 2 \n", "9 0 0 4 0 \n", "10 0 0 1 0 \n", "11 0 0 3 3 \n", "12 0 0 2 0 \n", "13 0 0 1 1 \n", "14 0 0 1 1 \n", "15 0 0 1 1 \n", "16 0 0 60 11 \n", "17 0 0 1 0 \n", "18 0 0 11 11 \n", "19 0 0 1 1 \n", "20 0 0 1 0 \n", "21 0 0 6 5 \n", "22 0 0 4 2 \n", "23 0 0 6 1 \n", "24 0 0 3 0 \n", "25 0 0 1 1 \n", "26 0 0 1 0 \n", "27 0 0 1 1 \n", "28 0 0 1 1 \n", "29 0 0 1 0 \n", "... ... ... ... ... \n", "35260 0 0 4 1 \n", "35261 0 0 9 7 \n", "35262 0 0 12 5 \n", "35263 0 0 1 0 \n", "35264 0 0 1 1 \n", "35265 0 0 13 3 \n", "35266 0 0 1 1 \n", "35267 0 0 1 0 \n", "35268 0 0 2 0 \n", "35269 0 0 1 1 \n", "35270 0 0 2 2 \n", "35271 0 0 1 1 \n", "35272 0 0 1 1 \n", "35273 0 0 1 1 \n", "35274 0 0 1 1 \n", "35275 0 0 2 1 \n", "35276 0 0 1 1 \n", "35277 0 0 2 2 \n", "35278 0 0 1 1 \n", "35279 3 1 20 17 \n", "35280 0 0 1 1 \n", "35281 0 0 1 1 \n", "35282 0 0 1 1 \n", "35283 1 1 1 1 \n", "35284 0 0 1 1 \n", "35285 3 1 1 3 \n", "35286 5 1 10 14 \n", "35287 1 1 1 1 \n", "35288 0 0 1 1 \n", "35289 10 1 1 11 \n", "\n", " NumErroredRuns NumChangedVersions Lines LinesAddedOrChanged \\\n", "0 0 0 0.0 0.0 \n", "1 1 0 NaN NaN \n", "2 0 0 0.0 0.0 \n", "3 4 5 -3.0 8.0 \n", "4 1 0 NaN NaN \n", "5 1 2 2.0 3.0 \n", "6 8 0 NaN NaN \n", "7 0 4 0.0 3.0 \n", "8 0 1 2.0 13.0 \n", "9 4 4 -3.0 15.0 \n", "10 1 0 0.0 0.0 \n", "11 0 1 1.0 1.0 \n", "12 2 0 NaN NaN \n", "13 0 1 2.0 2.0 \n", "14 0 0 0.0 0.0 \n", "15 0 0 0.0 0.0 \n", "16 49 8 2.0 21.0 \n", "17 1 0 0.0 0.0 \n", "18 0 1 0.0 1.0 \n", "19 0 0 0.0 0.0 \n", "20 1 1 0.0 0.0 \n", "21 1 5 -101.0 4.0 \n", "22 2 4 0.0 43.0 \n", "23 5 4 3.0 5.0 \n", "24 3 0 NaN NaN \n", "25 0 0 NaN NaN \n", "26 1 0 NaN NaN \n", "27 0 0 0.0 0.0 \n", "28 0 0 0.0 0.0 \n", "29 1 0 NaN NaN \n", "... ... ... ... ... \n", "35260 3 2 -18.0 3.0 \n", "35261 2 8 8.0 23.0 \n", "35262 7 7 -12.0 6.0 \n", "35263 1 0 NaN NaN \n", "35264 0 0 NaN NaN \n", "35265 10 12 15.0 23.0 \n", "35266 0 1 NaN NaN \n", "35267 1 0 NaN NaN \n", "35268 2 1 0.0 0.0 \n", "35269 0 0 NaN NaN \n", "35270 0 1 0.0 1.0 \n", "35271 0 1 NaN NaN \n", "35272 0 0 NaN NaN \n", "35273 0 0 NaN NaN \n", "35274 0 0 NaN NaN \n", "35275 1 1 0.0 1.0 \n", "35276 0 0 NaN NaN \n", "35277 0 0 0.0 0.0 \n", "35278 0 1 NaN NaN \n", "35279 4 18 0.0 25.0 \n", "35280 0 0 NaN NaN \n", "35281 0 0 NaN NaN \n", "35282 0 0 NaN NaN \n", "35283 0 0 NaN NaN \n", "35284 0 0 NaN NaN \n", "35285 0 3 NaN NaN \n", "35286 0 8 0.0 8.0 \n", "35287 0 0 NaN NaN \n", "35288 0 1 NaN NaN \n", "35289 0 11 NaN NaN \n", "\n", " Name \n", "0 RMarkdown \n", "1 IPython Notebook \n", "2 IPython Notebook HTML \n", "3 Python \n", "4 IPython Notebook \n", "5 Python \n", "6 IPython Notebook \n", "7 IPython Notebook HTML \n", "8 Python \n", "9 R \n", "10 Python \n", "11 R \n", "12 IPython Notebook \n", "13 IPython Notebook HTML \n", "14 Python \n", "15 Python \n", "16 Python \n", "17 R \n", "18 R \n", "19 RMarkdown \n", "20 R \n", "21 RMarkdown \n", "22 Python \n", "23 R \n", "24 IPython Notebook \n", "25 IPython Notebook HTML \n", "26 R Notebook \n", "27 R \n", "28 RMarkdown \n", "29 IPython Notebook \n", "... ... \n", "35260 R \n", "35261 Python \n", "35262 Python \n", "35263 Python \n", "35264 R \n", "35265 Python \n", "35266 R \n", "35267 R \n", "35268 Python \n", "35269 R \n", "35270 Python \n", "35271 R \n", "35272 R \n", "35273 R \n", "35274 R \n", "35275 Python \n", "35276 R \n", "35277 R \n", "35278 R \n", "35279 R \n", "35280 R \n", "35281 R \n", "35282 R \n", "35283 R \n", "35284 R \n", "35285 R \n", "35286 R \n", "35287 R \n", "35288 R \n", "35289 R \n", "\n", "[35290 rows x 12 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "import sqlite3\n", "\n", "\n", "\n", "con = sqlite3.connect('../input/database.sqlite')\n", "\n", "\n", "\n", "scripts = pd.read_sql_query(\"\"\"\n", "\n", "SELECT s.Id,\n", "\n", " cv.Title,\n", "\n", " COUNT(DISTINCT vo.Id) NumVotes,\n", "\n", " COUNT(DISTINCT CASE WHEN vo.UserId!=s.AuthorUserId THEN vo.Id ELSE NULL END) NumNonSelfVotes,\n", "\n", " CASE WHEN COUNT(DISTINCT CASE WHEN vo.UserId!=s.AuthorUserId THEN vo.Id ELSE NULL END)>0 THEN 1 ELSE 0 END HasNonSelfVotes,\n", "\n", " COUNT(DISTINCT v.Id) NumVersions,\n", "\n", " SUM(CASE WHEN r.WorkerStatus=2 THEN 1 ELSE 0 END) NumSuccessfulRuns,\n", "\n", " SUM(CASE WHEN r.WorkerStatus=3 THEN 1 ELSE 0 END) NumErroredRuns,\n", "\n", " SUM(CASE WHEN v.IsChange=1 THEN 1 ELSE 0 END) NumChangedVersions,\n", "\n", " SUM(v.LinesInsertedFromPrevious-v.LinesDeletedFromPrevious) Lines,\n", "\n", " SUM(v.LinesInsertedFromPrevious+v.LinesChangedFromPrevious) LinesAddedOrChanged,\n", "\n", " l.Name\n", "\n", "FROM Scripts s\n", "\n", "INNER JOIN ScriptVersions v ON v.ScriptId=s.Id\n", "\n", "INNER JOIN ScriptVersions cv ON s.CurrentScriptVersionId=cv.Id\n", "\n", "INNER JOIN ScriptRuns r ON r.ScriptVersionId=v.Id\n", "\n", "INNER JOIN ScriptLanguages l ON v.ScriptLanguageId=l.Id\n", "\n", "LEFT OUTER JOIN ScriptVotes vo ON vo.ScriptVersionId=v.Id\n", "\n", "WHERE r.WorkerStatus != 4\n", "\n", " AND r.WorkerStatus != 5\n", "\n", "GROUP BY s.Id,\n", "\n", " cv.Title,\n", "\n", " cv.Id,\n", "\n", " l.Name\n", "\n", "ORDER BY cv.Id DESC\n", "\n", "\"\"\", con)\n", "\n", "\n", "\n", "scripts" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "9b694c67-0f2e-177f-3d16-6835e7235856" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Score 0.921933\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module has been deprecated in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "from sklearn.pipeline import Pipeline, FeatureUnion\n", "\n", "from sklearn.cross_validation import train_test_split\n", "\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "\n", "\n", "class RawColumnExtractor:\n", "\n", " def __init__(self, column):\n", "\n", " self.column=column\n", "\n", "\n", "\n", " def fit(self, *_):\n", "\n", " return self\n", "\n", " \n", "\n", " def transform(self, data):\n", "\n", " return data[[self.column]]\n", "\n", "\n", "\n", "features = FeatureUnion([(\"NumSuccessfulRuns\", RawColumnExtractor(\"NumSuccessfulRuns\")),\n", "\n", " (\"NumChangedVersions\", RawColumnExtractor(\"NumChangedVersions\"))\n", "\n", " ])\n", "\n", "\n", "\n", "pipeline = Pipeline([('feature_union', features),\n", "\n", " ('predictor', RandomForestClassifier())\n", "\n", " ])\n", "\n", "\n", "\n", "train = scripts\n", "\n", "target_name = \"HasNonSelfVotes\"\n", "\n", "\n", "\n", "x_train, x_test, y_train, y_test = train_test_split(train, train[target_name], test_size=0.4, random_state=0)\n", "\n", "\n", "\n", "pipeline.fit(x_train, y_train)\n", "\n", "score = pipeline.score(x_test, y_test)\n", "\n", "print(\"Score %f\" % score)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "e313b544-9883-c965-02e4-bd81c6ea6c47" }, "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>Name</th>\n", " <th>AceLanguageName</th>\n", " <th>DefaultScriptFileName</th>\n", " <th>DockerImageName</th>\n", " <th>AllowWrite</th>\n", " <th>AllowView</th>\n", " <th>BaseScriptLanguageId</th>\n", " <th>RenderedScriptLanguageId</th>\n", " <th>NotebookKernelMetadata</th>\n", " <th>IsNotebook</th>\n", " <th>GlobalDefaultScriptId</th>\n", " <th>DisplayName</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>R</td>\n", " <td>r</td>\n", " <td>script.R</td>\n", " <td>kaggle/rstats</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>33153.0</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>Python</td>\n", " <td>python</td>\n", " <td>script.py</td>\n", " <td>kaggle/python</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>33156.0</td>\n", " <td>Python</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>5</td>\n", " <td>RMarkdown</td>\n", " <td>markdown</td>\n", " <td>script.Rmd</td>\n", " <td>kaggle/rstats</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>33158.0</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>6</td>\n", " <td>Julia</td>\n", " <td>julia</td>\n", " <td>script.jl</td>\n", " <td>kaggle/julia</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>33157.0</td>\n", " <td>Julia</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>7</td>\n", " <td>SQLite</td>\n", " <td>sql</td>\n", " <td>script.sql</td>\n", " <td>kaggle/python</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>33147.0</td>\n", " <td>SQLite</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>8</td>\n", " <td>IPython Notebook</td>\n", " <td>python</td>\n", " <td>script.xpynb</td>\n", " <td>kaggle/python</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>2.0</td>\n", " <td>9.0</td>\n", " <td>{\"display_name\":\"Python 3\",\"language\":\"python\"...</td>\n", " <td>1</td>\n", " <td>33156.0</td>\n", " <td>Python</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>9</td>\n", " <td>IPython Notebook HTML</td>\n", " <td>python</td>\n", " <td>script.ipynb</td>\n", " <td>kaggle/python</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>Python</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>10</td>\n", " <td>IJulia Notebook HTML</td>\n", " <td>julia</td>\n", " <td>script.ijlnb</td>\n", " <td>kaggle/julia</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>Julia</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>12</td>\n", " <td>R Notebook HTML</td>\n", " <td>r</td>\n", " <td>script.irnb</td>\n", " <td>kaggle/rstats</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>None</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>R</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>13</td>\n", " <td>R Notebook</td>\n", " <td>r</td>\n", " <td>script.xrnb</td>\n", " <td>kaggle/rstats</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>12.0</td>\n", " <td>{\"display_name\":\"R\",\"language\":\"R\",\"name\":\"ir\"}</td>\n", " <td>1</td>\n", " <td>33153.0</td>\n", " <td>R</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Id Name AceLanguageName DefaultScriptFileName \\\n", "0 1 R r script.R \n", "1 2 Python python script.py \n", "2 5 RMarkdown markdown script.Rmd \n", "3 6 Julia julia script.jl \n", "4 7 SQLite sql script.sql \n", "5 8 IPython Notebook python script.xpynb \n", "6 9 IPython Notebook HTML python script.ipynb \n", "7 10 IJulia Notebook HTML julia script.ijlnb \n", "8 12 R Notebook HTML r script.irnb \n", "9 13 R Notebook r script.xrnb \n", "\n", " DockerImageName AllowWrite AllowView BaseScriptLanguageId \\\n", "0 kaggle/rstats 1 1 NaN \n", "1 kaggle/python 1 1 NaN \n", "2 kaggle/rstats 1 1 NaN \n", "3 kaggle/julia 1 1 NaN \n", "4 kaggle/python 1 1 NaN \n", "5 kaggle/python 1 0 2.0 \n", "6 kaggle/python 0 1 NaN \n", "7 kaggle/julia 0 1 NaN \n", "8 kaggle/rstats 0 1 NaN \n", "9 kaggle/rstats 1 0 1.0 \n", "\n", " RenderedScriptLanguageId \\\n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "5 9.0 \n", "6 NaN \n", "7 NaN \n", "8 NaN \n", "9 12.0 \n", "\n", " NotebookKernelMetadata IsNotebook \\\n", "0 None 0 \n", "1 None 0 \n", "2 None 0 \n", "3 None 0 \n", "4 None 0 \n", "5 {\"display_name\":\"Python 3\",\"language\":\"python\"... 1 \n", "6 None 0 \n", "7 None 0 \n", "8 None 0 \n", "9 {\"display_name\":\"R\",\"language\":\"R\",\"name\":\"ir\"} 1 \n", "\n", " GlobalDefaultScriptId DisplayName \n", "0 33153.0 R \n", "1 33156.0 Python \n", "2 33158.0 R \n", "3 33157.0 Julia \n", "4 33147.0 SQLite \n", "5 33156.0 Python \n", "6 NaN Python \n", "7 NaN Julia \n", "8 NaN R \n", "9 33153.0 R " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_sql_query(\"\"\"\n", "\n", "SELECT *\n", "\n", "FROM ScriptLanguages\n", "\n", "LIMIT 100\n", "\n", "\"\"\", con)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "155b61fb-b2ce-afd7-7cd1-3c373dab449f" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 26, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/306/306027.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "97d902b5-5755-3ed4-5744-0957a4cb3174" }, "source": [ "This is a markdown cell. Click here to edit..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "1abf315d-a154-49bd-bb0f-26a6f83f1fa2" }, "outputs": [], "source": [ "# Importing Libraries \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "from subprocess import check_output\n", "\n", "# Read the input files\n", "comments=pd.read_csv(\"../input/comment.csv\")\n", "likes=pd.read_csv(\"../input/like.csv\")\n", "members=pd.read_csv(\"../input/member.csv\")\n", "posts=pd.read_csv(\"../input/post.csv\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "b68a1cc3-6673-bf49-e065-cbd367708346" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fce3daa4f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## ANALYSIS 1 \n", "\n", "## We would like to see whether there are few people who are very active on the Group ( LIKES/COMMENTS)\n", "\n", "## Or there is a separate group which is very active on LIKEs but not very active on COMMENTS\n", "\n", "## We can see this quantitatively, but let us take a more graphical loop using networkx in the way\n", "\n", "\n", "\n", "# Let us first analyse the LIKES\n", "\n", "#like1=likes.loc[likes['gid']==117291968282998]\n", "\n", "#post1=posts.loc[posts['gid']==117291968282998,['pid','name']]\n", "\n", "likeResponse=pd.merge(likes.loc[likes['gid']==117291968282998],posts.loc[posts['gid']==117291968282998,['pid','name']],left_on='pid',right_on='pid')\n", "\n", "result=likeResponse.groupby(['name_y','name_x'])['response'].count()\n", "\n", "\n", "\n", "# We will create another clean dataframe from this with the appropriate results\n", "\n", "finalResult=pd.DataFrame(result.index.values,columns=['NameCombo'])\n", "\n", "finalResult['Weight']=result.values\n", "\n", "finalResult['From']=finalResult['NameCombo'].map(lambda x:x[0])\n", "\n", "finalResult['To']=finalResult['NameCombo'].map(lambda x:x[1])\n", "\n", "del(finalResult['NameCombo'])\n", "\n", "\n", "\n", "# Creating the networkx graph\n", "\n", "g = nx.Graph()\n", "\n", "plt.figure()\n", "\n", "g.add_edges_from([(row['From'],row['To']) for index,row in finalResult.iterrows()])\n", "\n", "d = nx.degree(g)\n", "\n", "spring_pos=nx.spring_layout(g)\n", "\n", "plt.axis(\"off\")\n", "\n", "nx.draw_networkx(g,spring_pos, with_labels=False,nodelist=d.keys(), node_size=[v * 10 for v in d.values()])\n", "\n", "plt.savefig('LIKE_PLOT_GROUP1.png')\n", "\n", "plt.clf()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "87f3d34a-12e4-7eea-963a-e783b1541ee8" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 37, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/309/309674.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "97d902b5-5755-3ed4-5744-0957a4cb3174" }, "source": [ "This is a markdown cell. Click here to edit..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "1abf315d-a154-49bd-bb0f-26a6f83f1fa2" }, "outputs": [], "source": [ "# Importing Libraries \n", "\n", "\n", "\n", "import numpy as np # linear algebra\n", "\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "import networkx as nx\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "\n", "\n", "\n", "# Read the input files\n", "\n", "comments=pd.read_csv(\"../input/comment.csv\")\n", "\n", "likes=pd.read_csv(\"../input/like.csv\")\n", "\n", "members=pd.read_csv(\"../input/member.csv\")\n", "\n", "posts=pd.read_csv(\"../input/post.csv\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "b68a1cc3-6673-bf49-e065-cbd367708346" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7f43b4155eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## ANALYSIS 1 \n", "## We would like to see whether there are few people who are very active on the Group ( LIKES/COMMENTS)\n", "## Or there is a separate group which is very active on LIKEs but not very active on COMMENTS\n", "## We can see this quantitatively, but let us take a more graphical loop using networkx in the way\n", "\n", "# Let us first analyse the LIKES\n", "#like1=likes.loc[likes['gid']==117291968282998]\n", "#post1=posts.loc[posts['gid']==117291968282998,['pid','name']]\n", "likeResponse=pd.merge(likes.loc[likes['gid']==117291968282998],posts.loc[posts['gid']==117291968282998,['pid','name']],left_on='pid',right_on='pid')\n", "result=likeResponse.groupby(['name_y','name_x'])['response'].count()\n", "\n", "# We will create another clean dataframe from this with the appropriate results\n", "finalResult=pd.DataFrame(result.index.values,columns=['NameCombo'])\n", "finalResult['Weight']=result.values\n", "finalResult['From']=finalResult['NameCombo'].map(lambda x:x[0])\n", "finalResult['To']=finalResult['NameCombo'].map(lambda x:x[1])\n", "del(finalResult['NameCombo'])\n", "\n", "# Creating the networkx graph\n", "g = nx.Graph()\n", "plt.figure()\n", "g.add_edges_from([(row['From'],row['To']) for index,row in finalResult.iterrows()])\n", "d = nx.degree(g)\n", "spring_pos=nx.spring_layout(g)\n", "plt.axis(\"off\")\n", "nx.draw_networkx(g,spring_pos, with_labels=False,nodelist=d.keys(), node_size=[v * 10 for v in d.values()])\n", "plt.savefig('LIKE_PLOT_GROUP1.png')\n", "plt.clf()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "87f3d34a-12e4-7eea-963a-e783b1541ee8" }, "outputs": [ { "data": { "image/png": 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4g+ii/gERrG8TW5i6EzcwnURMRd9KhOo+xJrwQGLqfAYR0nOINxmlucdumHuN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\nEEuSlND/B3bhfc6Upi6YAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4393c730b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g.number_of_nodes()\n", "spring_pos=nx.spring_layout(g,scale=2)\n", "nx.draw(g, spring_pos,with_labels=False,nodelist=d.keys(), node_size=[v * 5 for v in d.values()])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "5aadf03c-1613-8e51-e44c-0fafe2415949" }, "outputs": [ { "data": { "text/plain": [ "<function TextIOWrapper.close>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f = open(\"g.json\",\"w\")\n", "f.write(\"{\\\"nodes\\\":[\")\n", "str1=\"\"\n", "\n", "for i in finalResult['From'].unique():\n", " str1+=\"{\\\"name\\\":\\\"\"+ str(i) + \"\\\",\\\"group\\\":\" + str(1) +\"},\"\n", "f.write(str1[:-1])\n", "f.write(\"],\\\"links\\\":[\")\n", "\n", "str1=\"\"\n", "for i in range(len(finalResult)):\n", " str1+=\"{\\\"source\\\":\" + str(finalResult['From'][i]) + \",\\\"target\\\":\" + str(finalResult['To'][i]) + \",\\\"value\\\":\" + str(finalResult['Weight'][i]) + \"},\"\n", "f.write(str1[:-1])\n", "f.write(\"]}\")\n", "f.close\n", "\n", "h1 = \"\"\"\n", "<!DOCTYPE html>\n", "<meta charset=\"utf-8\">\n", "<style>\n", ".link {stroke: #ccc;}\n", ".node text {pointer-events: none; font: 10px sans-serif;}\n", "</style>\n", "<body>\n", "<script src=\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\"></script>\n", "<script>\n", "var width = 800, height = 800;\n", "var color = d3.scale.category20();\n", "var force = d3.layout.force()\n", " .charge(-120)\n", " .linkDistance(80)\n", " .size([width, height]);\n", "var svg = d3.select(\"body\").append(\"svg\")\n", " .attr(\"width\", width)\n", " .attr(\"height\", height);\n", "d3.json(\"g.json\", function(error, graph) {\n", " if (error) throw error;\n", "\tforce.nodes(graph.nodes)\n", "\t .links(graph.links)\n", "\t .start();\n", "\tvar link = svg.selectAll(\".link\")\n", "\t .data(graph.links)\n", "\t .enter().append(\"line\")\n", "\t .attr(\"class\", \"link\")\n", "\t .style(\"stroke-width\", function (d) {return Math.sqrt(d.value);});\n", "\tvar node = svg.selectAll(\".node\")\n", "\t .data(graph.nodes)\n", "\t .enter().append(\"g\")\n", "\t .attr(\"class\", \"node\")\n", "\t .call(force.drag);\n", "\tnode.append(\"circle\")\n", "\t .attr(\"r\", 8)\n", "\t .style(\"fill\", function (d) {return color(d.group);})\n", "\tnode.append(\"text\")\n", "\t .attr(\"dx\", 10)\n", "\t .attr(\"dy\", \".35em\")\n", "\t .text(function(d) { return d.name });\n", "\tforce.on(\"tick\", function () {\n", "\t link.attr(\"x1\", function (d) {return d.source.x;})\n", "\t\t.attr(\"y1\", function (d) {return d.source.y;})\n", "\t\t.attr(\"x2\", function (d) {return d.target.x;})\n", "\t\t.attr(\"y2\", function (d) {return d.target.y;});\n", "\t d3.selectAll(\"circle\").attr(\"cx\", function (d) {return d.x;})\n", "\t\t.attr(\"cy\", function (d) {return d.y;});\n", "\t d3.selectAll(\"text\").attr(\"x\", function (d) {return d.x;})\n", "\t\t.attr(\"y\", function (d) {return d.y;});\n", " });\n", "});\n", "</script>\n", "\"\"\"\n", "\n", "f = open(\"output.html\",\"w\")\n", "f.write(h1)\n", "f.close" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "273b1168-d165-13ec-fb48-64e67563c2c5" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 270, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/309/309683.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "82f2c04a-8a1d-40ea-1c45-06291caa378f" }, "source": [ "This is a markdown cell. Click here to edit..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "b36938b5-81d7-96ac-f9a8-d2c954be2b4a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cdc_zika.csv\n", "\n" ] } ], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "import seaborn as sbn\n", "%matplotlib inline\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "e15d0f56-8408-0c2b-c74b-d2f641cba05b" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>location</th>\n", " <th>location_type</th>\n", " <th>data_field</th>\n", " <th>data_field_code</th>\n", " <th>time_period</th>\n", " <th>time_period_type</th>\n", " <th>value</th>\n", " <th>unit</th>\n", " </tr>\n", " <tr>\n", " <th>report_date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2016-03-19</th>\n", " <td>Argentina-Buenos_Aires</td>\n", " <td>province</td>\n", " <td>cumulative_confirmed_local_cases</td>\n", " <td>AR0001</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>cases</td>\n", " </tr>\n", " <tr>\n", " <th>2016-03-19</th>\n", " <td>Argentina-Buenos_Aires</td>\n", " <td>province</td>\n", " <td>cumulative_probable_local_cases</td>\n", " <td>AR0002</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>cases</td>\n", " </tr>\n", " <tr>\n", " <th>2016-03-19</th>\n", " <td>Argentina-Buenos_Aires</td>\n", " <td>province</td>\n", " <td>cumulative_confirmed_imported_cases</td>\n", " <td>AR0003</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>cases</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " location location_type \\\n", "report_date \n", "2016-03-19 Argentina-Buenos_Aires province \n", "2016-03-19 Argentina-Buenos_Aires province \n", "2016-03-19 Argentina-Buenos_Aires province \n", "\n", " data_field data_field_code time_period \\\n", "report_date \n", "2016-03-19 cumulative_confirmed_local_cases AR0001 NaN \n", "2016-03-19 cumulative_probable_local_cases AR0002 NaN \n", "2016-03-19 cumulative_confirmed_imported_cases AR0003 NaN \n", "\n", " time_period_type value unit \n", "report_date \n", "2016-03-19 NaN 0 cases \n", "2016-03-19 NaN 0 cases \n", "2016-03-19 NaN 2 cases " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"../input/cdc_zika.csv\",parse_dates=['report_date'],\n", " infer_datetime_format=True,\n", " index_col=0)\n", "df.head(3)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "9dedb55c-21ac-1ba1-3d58-769c0fd201e3" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f49d4062c50>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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03EFs81R5LTxIGD97Z3kttuVgyddi/GwPSgUWxtI9uii4u8ztMnvQcrvMHrTc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c1SCx2KLHKomBKgrYonJrRqZQ0DvLDQFLc2Ai8UqFt6kHTlUvnQmfnMLMS1aQpUTI9wgETSVMnM\nNe334gsdulg3rS2KJyZA1axhaJr5dTSDffPAicB9C59ejPFpRelB0s0jvva6bIzVlVrpEQ6QSJpK\n7fzEqSw+lzlO89rFumlRJzHhVRTJGm6tAv5B86kFNAvV3kjz6cU4n1aUHiRdyKzf3tiZ9TYQ6kqt\n9AgHSCRNq3XAvcBDbLvVcWQdrZueaTUTE9paRbOGB9VaO116kLRmZr2NsbpSKz3CARJJ02pVZp5X\no3DlddOzrlpiQqt01vAW7T8Xa4G3Zub7IuL9wCcy8+IR63UxSFqUjbE6USs9wgESSVPs6og4HbiZ\n8gs+qq2bVr3EhLZW0azh7fyE5rjDj9p7PRgRPwNGaozpYJC0NBtjdaJ0eoQDJJJmwGbgEuACtv53\nrtSCj2rrplUvMQHqZQ23dsnM32930vE/oxarOUhai42xulI6PcIBEknT7nxgn6WarAJqrpueddUS\nE1q1soYBnm3rzbe1TwD+Pm7RSoOkVdgYqytF0yMcIJE0Ax4DXqxUu+a66VlXLTGhVStrGJp4uR8D\n+0XEU8DjwMkF6hYfJK1lbn5+0afQUnERcT3wG7ZNjzgqM48fs64DJJKmUkTcAOwP3MG2T3Un7imb\nuhMRa4F3sG3W8F+B26DMQFtE7AqsyMwiDXdEbMjMXuwX8ImxulIrPcIBEknT6sb2q5g+pgRokSpZ\nwxHxBmD3zHw2MzdFxE7t8Od5mTluTnLNQdKibIzViYrpEQ6QSJpKA+kD+269lK+MWbZ3KQHaVqWN\niJ+mGejbFBGPAt8CfgrcR7PKelw1B0mL8iiFqqqdHhERFwJP4wCJpCkTEUcCVwFP0JxPfTtwWmbe\nuYxvS8usdNZwW/OPwPGZ+VhEHAjcA5ywMM9T4D1vBD5YaZC0KJ8Yq7ba6REOkEiaVpcCx2ZmwpaG\n6FrgoHEL9yklQIuUzhoG2Lww0JeZGyLi0VJNcavmIGlRNsaqqnZ6ROk1k5I0QVYuNMUAmflIiRzc\nVm9SArRI0azh1l4RMbhl8S2DP2fmZWPW3wQ80C74muhBUhtjdaJ0eoQDJJJmwP0RcSVwTfvzSZT7\n9K3aumlVVyNr+CfAbq/x87iKD5LWYmOsrpROj3CARNK0O4MmV3bhqdpdwBWFavcmJUCLFM8azsxv\nDvPnIuJrmfmdEerXGCStwuE7dSIi1mfm2OfiJGmWRMSe0KwTLlz3LJrkgX8xkBKQmc5n9ETprOEh\n7zlSHnGfBkl9YqyuVFk/6gCJpGkTEXPAGuBsYEV77b/A5Zl5UaHb1Fw3rUoqZw0PY27E36s2SFra\niuV+A5oZp9FM0d4NrG+/SpyVW0fTFD80UHd9gbqStFxWA4cBB2fmHpm5B3AIcFhErC50j96kBKjR\nZg0/BzwYEb+NiGNp1kEfR5ms4WGMesxg0SApUGqQtCifGKsTFdMjHCCRNG1OAY4ZfJqbmRsj4mTg\nVuB7Be7Rm5QAbfEN4KBaWcNDGvWJcc1B0qJsjFVVB+kRDpBImjYrlzrikJnPFIxr601KgLaonTU8\njOtG/L2ag6RF2RirttrpEb1ZMylJQ9o84mtD61NKgLaonTW8MOx5Oovndj7Xfv/2KHUz8+WIuBq4\nuvQgaWmmUqjX+rRmUpKG0Q7abVripTma42NjPzXuU0qAGhGx5rVeHzZy7XXucTfN09z1DESrZubP\nR6y3aJC0rVtykLQonxirExXTIxwgkTRVMvMNHdymNykBatTOGm7tkplfHfF3lzI4SPo4QETsDayN\niNWZWeK8fFE2xupKrfWjDpBI0o6ruW5ay+tEYNTG+OaIOC4z1xV6L10MkhZlY6yu1EqPcIBEknZc\nb1ICtMNGTY4AOBf4ekS8DLzS1prPzDeNWK+LQdKibIzVlSrpEQ6QSNJIepMSoB028vBYZu5W8o3Q\nwSBpaTbG6kqV9IilBkgiwgESSXoNfUoJ0A4b54kxEbE78B5g1cK1Mf6fekBEPL/E9bnB+pPExlhd\nqbV+1AESSRpSR+umtbxGzRomIj5Pc5zibcADwKE0i0SOHqVeR4OkRdkYqyu10iMcIJGk4fUuJUDb\nqpU13DoXOBi4NzOPioj9gHHq9Y6NsbpSKz3CARJJGl7vUgK0yC9ozoTfxkDWcCEvZeZLEUFE7JyZ\nf4mIKHyPiWZjrK7USo9wgESShte7lAAtUjpreNDf2r0DNwK/ioh/Ak9WutdEcvOdOlMrPaL9WAkH\nSCTptUXEhsw8cEdf0+SIiIuBuwtmDb/afY4A3gzckpkTmSBRg42xOlF6/Wgf10xK0nLrYt206oqI\nF4BdaY4llsgaHqx9KPBwZr7Q/vwm4L2Z+btxa/eFRynUldLpEQ6QSNIO6mNKgLZVIWt40Fpg8FOD\nfy9xbarZGKsrpdMjHCCRJM2kwlnDg+Yyc8tRgsz8X0TMVK84U3+zWlal0yMcIJEkzZzSWcPb2RgR\n59A8JQY4E9hYoG5vrHj9PyIVcQbwJ5r0iHPavz5jjHq9WzMpSVIBC1nDT2bmUcAHgH8Vqv1F4EPA\nU8DfgEOALxSq3QsO36kzJdMjHCCRJM2iiLgvMw+OiAeAQ9r13g9n5v7L/d6mgY2xqjI9QpKkciLi\nBuCzwJdojk/8k+Z44XFj1PxKZn43Ii4HFjWGBZZx9YZnjFWb6RGSJBWSmZ9s//LCdpvsm4Fbxiz7\n5/b7zG+O9YmxqoqIP7BdekR7fU/g1sz8wPK8M0mS+ses4bp8YqzaTI+QJKmcalnD7Y6BLwPvZKBH\nzMwSiRe9YGOs2kyPkCSpnJpZw9cBPwSupJkHmjk2xqrtgIh4fonrcwwEk0uSpKHUzBr+T2auff0/\nNr08YyxJktQTEbEX8H2aRIp54NfAlzLz6QK1LwSeBm4AXl64npnPjVu7L2yMJUmSREQ8vsTl+czc\nu/M3s0xsjCVJkiacWcPd8IyxJEnS5KuWNRwRR2fm7RHxqaVez8zrS99zUtkYS5IkTbjMvKn9flWF\n8kcAtwMfX+K1eWBmGmOPUkiSJPWEWcN1+cRYkiSpP6plDUfEW4BTWdx0z8z5ZRtjSZKk/qiZNbwO\nuBd4CPhfpXtMNBtjSZKk/rgpIs6kTtbwqsw8r0Cd3vKMsSRJUk/UzBqOiNXAv4GbmdEFHz4xliRJ\n6onMfFfF8puBS4AL2JqVPA/MzIIPG2NJkqQJ11HW8PnAPpn5bIFavWRjLEmSNPm6yBp+DHixQJ3e\n8oyxJEmSiIgbgP2BO9j2jLFxbZIkSZoslbOGb2y/ZpaNsSRJUn9UyxrOzKsiYidg362X8pWS95h0\nHqWQJEnqiYjYkJkHVqp9JHAV8AQwB7wdOC0z76xxv0nkE2NJkqT+uDoiTqdO1vClwLGZmQARsS9w\nLXBQgdq9sGK534AkSZKGtpA1fA+wvv26v1DtlQtNMUBmPgKsLFS7F3xiLEmS1B81s4bvj4grgWva\nn0+iXNPdCzbGkiRJ/VEza/gM4CxgIeHiLuCKSveaSA7fSZIk9UTtrOGI2LOt90yJen3jE2NJkqT+\nKJ41HBFzwBrgbNr5s4j4L3B5Zl5U8l6TzifGkiRJPVI6azgizgM+CnwhMx9vr+0NrAVuyczvjVO/\nT0ylkCRJ6ok2a/hR4Ac0538fiYjDxyx7CvCZhaYYIDM3AifTbNmbGTbGkiRJ/bGQNXxEZh4OfAQY\n94nuyqVSLtpzxjMV12ZjLEmS1B81soY3j/ja1HH4TpIkqT9qZA0fEBHPL3F9Dlg1Zu1ecfhOkiSp\nJyJiZ5qs4Q+3l+4CrsjMl1/9tzQsG2NJkqQemfWs4ZpsjCVJkibcUlnDwExmDdfk8J0kSdLkWw0c\nBhycmXtk5h7AIcBhEbF6ed/a9LAxliRJmnxmDXfAxliSJGnymTXcARtjSZKkyWfWcAfMMZYkSZp8\nZg13wFQKSZIkCY9SSJIkSYCNsSRJkgTYGEuSJEmAjbEkSZIE2BhLkiRJAPwf4w65L/3Y0jIAAAAA\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f49d444f080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.location.value_counts()[:30].plot(kind='bar', figsize=(12, 7))\n", "plt.title(\"Number of locations reported - Top 30\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "f14d675d-dc0e-eb3e-8905-b32d1b106004" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f49d40c7a58>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Gl4zoY1iYNpeUnkm+LkWpDmFZFnuOnmHV5gyyC8o81u5L4UeZO2kgV00eSPcI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\nsd1iuGfCd+gd3r7ZGt2WxY7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"text/plain": [ "<matplotlib.figure.Figure at 0x7f49d423fb00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[df.data_field == \"confirmed_male\"].value.plot()\n", "df[df.data_field == \"confirmed_female\"].value.plot().legend((\"Male\",\"Female\"),loc=\"best\")\n", "plt.title(\"Confirmed Male vs Female cases\")" ] } ], "metadata": { "_change_revision": 250, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/311/311174.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "8cedc3a2-c08e-3c88-12b8-fa5cc511f14d" }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1+1" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "673f259e-7319-54d8-74c6-31c35ab0d6b0" }, "source": [ "This is a Markdown cell.\n", "\n", "# Does header styling work?" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "ce821a5f-34ce-a6f4-dcb2-a356bb4d0dba" }, "outputs": [ { "data": { "text/plain": [ "50" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "20+30" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "710af929-5d0b-ef8d-8b7d-eab7be610feb" }, "outputs": [ { "data": { "text/plain": [ "150" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "50+100" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "1aedfa55-ad11-63b2-701b-fbf3b4348e87" }, "outputs": [ { "data": { "image/png": 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OfvuZa3XBgq9e8/YZD7IZgQwN/ianL1EgljOxliyBL76wDWmc7DnuOHj5ZduA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\nRfo7AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f51d6828080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "t = np.arange(0.0, 2.0, 0.01)\n", "s = np.sin(2*np.pi*t)\n", "plt.plot(t, s)\n", "\n", "plt.xlabel('time (s)')\n", "plt.ylabel('voltage (mV)')\n", "plt.title('About as simple as it gets, folks')\n", "plt.grid(True)\n", "plt.savefig(\"test.png\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "2760b0d8-bf6e-1d0c-c60d-46551372942d" }, "outputs": [ { "data": { "text/plain": [ "300" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "100+200" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "ad05999e-fa27-875c-a1eb-e1f29c70c740" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 65, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/311/311188.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "b36938b5-81d7-96ac-f9a8-d2c954be2b4a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cdc_zika.csv\n", "\n" ] } ], "source": [ "import numpy as np # linear algebra\n", "\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "import seaborn as sbn\n", "\n", "%matplotlib inline\n", "\n", "\n", "\n", "from subprocess import check_output\n", "\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "e15d0f56-8408-0c2b-c74b-d2f641cba05b" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>location</th>\n", " <th>location_type</th>\n", " <th>data_field</th>\n", " <th>data_field_code</th>\n", " <th>time_period</th>\n", " <th>time_period_type</th>\n", " <th>value</th>\n", " <th>unit</th>\n", " </tr>\n", " <tr>\n", " <th>report_date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2016-03-19</th>\n", " <td>Argentina-Buenos_Aires</td>\n", " <td>province</td>\n", " <td>cumulative_confirmed_local_cases</td>\n", " <td>AR0001</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>cases</td>\n", " </tr>\n", " <tr>\n", " <th>2016-03-19</th>\n", " <td>Argentina-Buenos_Aires</td>\n", " <td>province</td>\n", " <td>cumulative_probable_local_cases</td>\n", " <td>AR0002</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>cases</td>\n", " </tr>\n", " <tr>\n", " <th>2016-03-19</th>\n", " <td>Argentina-Buenos_Aires</td>\n", " <td>province</td>\n", " <td>cumulative_confirmed_imported_cases</td>\n", " <td>AR0003</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>cases</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " location location_type \\\n", "report_date \n", "2016-03-19 Argentina-Buenos_Aires province \n", "2016-03-19 Argentina-Buenos_Aires province \n", "2016-03-19 Argentina-Buenos_Aires province \n", "\n", " data_field data_field_code time_period \\\n", "report_date \n", "2016-03-19 cumulative_confirmed_local_cases AR0001 NaN \n", "2016-03-19 cumulative_probable_local_cases AR0002 NaN \n", "2016-03-19 cumulative_confirmed_imported_cases AR0003 NaN \n", "\n", " time_period_type value unit \n", "report_date \n", "2016-03-19 NaN 0 cases \n", "2016-03-19 NaN 0 cases \n", "2016-03-19 NaN 2 cases " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"../input/cdc_zika.csv\",parse_dates=['report_date'],\n", "\n", " infer_datetime_format=True,\n", "\n", " index_col=0)\n", "\n", "df.head(3)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "9dedb55c-21ac-1ba1-3d58-769c0fd201e3" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f99a55b5320>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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VqjUTaNrm+69ceec2PWak+fNnN87YGXK7zB603C6zBy23y+xBy+0ye9Byu8wetNwuswct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5vvAi4Etk2CDSLidGBPROzKzJvmWbR0wR0RFwOfBv6bpq44LSJ+a5G5F0tfGAO/dNTjdZppUweH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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f99a59c7080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.location.value_counts()[:30].plot(kind='bar', figsize=(12, 7))\n", "\n", "plt.title(\"Number of locations reported - Top 30\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "f14d675d-dc0e-eb3e-8905-b32d1b106004" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f99a5625e48>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Gl4zoY1iYNpeUnkm+LkWpDmFZFnuOnmHV5gyyC8o81u5L4UeZO2kgV00eSPcI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\nsd1iuGfCd+gd3r7ZGt2WxY7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"text/plain": [ "<matplotlib.figure.Figure at 0x7f99a579ec18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[df.data_field == \"confirmed_male\"].value.plot()\n", "\n", "df[df.data_field == \"confirmed_female\"].value.plot().legend((\"Male\",\"Female\"),loc=\"best\")\n", "\n", "plt.title(\"Confirmed Male vs Female cases\")" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9298c822-ccae-0d1a-a52e-4741df6fb19c" }, "source": [ "Introductory analysis to Zika virus dataset" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "e2e1577d-818a-2d00-9c76-37ca978059e4" }, "outputs": [ { "data": { "text/plain": [ "array(['cumulative_confirmed_local_cases',\n", " 'cumulative_probable_local_cases',\n", " 'cumulative_confirmed_imported_cases',\n", " 'cumulative_probable_imported_cases',\n", " 'cumulative_cases_under_study', 'cumulative_cases_discarded',\n", " 'microcephaly_under_investigation', 'microcephaly_confirmed',\n", " 'microcephaly_not', 'municipality_microcephaly',\n", " 'microcephaly_fatal_under_investigation',\n", " 'microcephaly_fatal_confirmed', 'microcephaly_fatal_not',\n", " 'municipality_microcephaly_suspected', 'zika_reported',\n", " 'zika_confirmed_laboratory', 'zika_confirmed_clinic',\n", " 'zika_suspected', 'zika_suspected_clinic', nan,\n", " 'total_zika_new_suspected', 'total_zika_new_confirmed_pcr',\n", " 'zika_new_confirmed_pcr_f', 'zika_new_confirmed_pcr_m',\n", " 'efe_reported', 'zika_suspected_cumulative',\n", " 'zika_suspected_pregnant_cumulative',\n", " 'zika_confirmed_pcr_cumulative', 'zika_suspected_pregnant',\n", " 'gbs_reported', 'gbs_zika_confirmed', 'gbs_zika_confirmed_pregnant',\n", " 'gbs_confirmed_cumulative', 'gbs_reported_cumulative',\n", " 'microcephaly_confirmed_cumulative', 'microcephaly_suspected',\n", " 'microcephaly_suspected_cumulative',\n", " 'zika_confirmed_pregnant_cumulative', 'zika_suspected_4weeks',\n", " 'gbs_reported_4weeks', 'microcephaly_suspected_4weeks',\n", " 'total_zika_suspected_cumulative',\n", " 'total_zika_confirmed_cumulative',\n", " 'total_zika_confirmed_autochthonous',\n", " 'total_zika_confirmed_imported', 'total_zika_confirmed_pregnant',\n", " 'total_zika_confirmed_ages_0-11mo_F',\n", " 'total_zika_confirmed_ages_0-11mo_M',\n", " 'total_zika_confirmed_ages_1-4yrs_F',\n", " 'total_zika_confirmed_ages_1-4yrs_M',\n", " 'total_zika_confirmed_ages_5-9yrs_F',\n", " 'total_zika_confirmed_ages_5-9yrs_M',\n", " 'total_zika_confirmed_ages_10-14yrs_F',\n", " 'total_zika_confirmed_ages_10-14yrs_M',\n", " 'total_zika_confirmed_ages_15-19yrs_F',\n", " 'total_zika_confirmed_ages_15-19yrs_M',\n", " 'total_zika_confirmed_ages_20-49yrs_F',\n", " 'total_zika_confirmed_ages_20-49yrs_M',\n", " 'total_zika_confirmed_ages_50-64yrs_F',\n", " 'total_zika_confirmed_ages_50-64yrs_M',\n", " 'total_zika_confirmed_ages_over65_F',\n", " 'total_zika_confirmed_ages_over65_M',\n", " 'total_zika_confirmed_Not-Aplicable', 'cumulative_suspected_total',\n", " 'cumulative_suspected_pregnant', 'cumulative_suspected_age_under_1',\n", " 'cumulative_suspected_age_1-4', 'cumulative_suspected_age_5-9',\n", " 'cumulative_suspected_age_10-19', 'cumulative_suspected_age_20-29',\n", " 'cumulative_suspected_age_30-39', 'cumulative_suspected_age_40-49',\n", " 'cumulative_suspected_age_50-59',\n", " 'cumulative_suspected_age_60_plus', 'weekly_hospitalized',\n", " 'cumulative_confirmed', 'total_zika_suspected',\n", " 'total_zika_suspected_M', 'total_zika_suspected_F',\n", " 'total_zika_confirmed', 'total_zika_confirmed_F',\n", " 'total_zika_confirmed_M',\n", " 'total_zika_confirmed_pregnant_cumulative',\n", " 'total_zika_new_suspected_cumulative', 'weekly_zika_confirmed',\n", " 'yearly_cumulative_female', 'yearly_cumulative_male',\n", " 'normal_birth_confirmed_zika', 'Zika_confirmed_laboratory_2015',\n", " 'Zika_confirmed_laboratory_2016', 'Zika_confirmed_F',\n", " 'Zika_confirmed_M', 'confirmed_age_under_1', 'confirmed_age_1-4',\n", " 'confirmed_age_5-9', 'confirmed_age_10-14', 'confirmed_age_15-19',\n", " 'confirmed_age_20-24', 'confirmed_age_25-34', 'confirmed_age_35-49',\n", " 'confirmed_age_50-59', 'confirmed_age_60-64',\n", " 'confirmed_age_60_plus', 'weekly_Zika_confirmed_asymptomatic',\n", " 'weekly_Zika_confirmed_pending', 'weekly_Zika_confirmed_local',\n", " 'weekly_Zika_confirmed_imported', 'Zika_positive_pregnant',\n", " 'Zika_negative_pregnant', 'zika_confirmed_2weeks',\n", " 'zika_confirmed_cumulative_2016', 'flavi_confirmed_cumulative_2016',\n", " 'zika_confirmed_cumulative_2015-2016',\n", " 'zika_confirmed_pregnant_cumulative_2015-2016',\n", " 'zika_confirmed_pregnant_symptomatic_cumulative_2015-2016',\n", " 'zika_confirmed_pregnant_asymptomatic_cumulative_2015-2016',\n", " 'GBS_reported_cumulative_2015-2016', 'arbovirus_suspected_2weeks',\n", " 'arbovirus_suspected_cumulative_2015', 'zika_confirmed_4weeks',\n", " 'arbovirus_suspected_cumulative_2016', 'arbovirus_suspected_4weeks',\n", " 'GBS_reported_cumulative_2015-2016_zika',\n", " 'GBS_reported_cumulative_2015-2016_flavi',\n", " 'congenital_developmental_defects_reported_cummulative_2015-2016',\n", " 'congenital_developmental_defects_reported_cumulative_2015-2016',\n", " 'zika_reported_travel', 'zika_reported_local',\n", " 'yearly_reported_travel_cases', 'zika_lab_positive', 'zika_not',\n", " 'zika_pending', 'confirmed_age_under20', 'confirmed_age_20to39',\n", " 'confirmed_age_40to59', 'confirmed_age_over59', 'confirmed_age_unk',\n", " 'confirmed_male', 'confirmed_female', 'confirmed_fever',\n", " 'confirmed_acute_fever', 'confirmed_arthralgia',\n", " 'confirmed_arthritis', 'confirmed_rash', 'confirmed_conjunctivitis',\n", " 'confirmed_eyepain', 'confirmed_headache', 'confirmed_malaise',\n", " 'zika_no_specimen'], dtype=object)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.data_field.unique()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "b06c82c7-ad5f-c561-312f-3d1329ac004d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "confirmed_age_under_1\n", "report_date\n", "2016-04-11 6\n", "Name: value, dtype: object\n", "\n", "confirmed_age_1-4\n", "report_date\n", "2016-04-11 10\n", "Name: value, dtype: object\n", "\n", "confirmed_age_5-9\n", "report_date\n", "2016-04-11 8\n", "Name: value, dtype: object\n", "\n", "confirmed_age_10-14\n", "report_date\n", "2016-04-11 15\n", "Name: value, dtype: object\n", "\n", "confirmed_age_15-19\n", "report_date\n", "2016-04-11 6\n", "Name: value, dtype: object\n", "\n", "confirmed_age_20-24\n", "report_date\n", "2016-04-11 18\n", "Name: value, dtype: object\n", "\n", "confirmed_age_25-34\n", "report_date\n", "2016-04-11 38\n", "Name: value, dtype: object\n", "\n", "confirmed_age_35-49\n", "report_date\n", "2016-04-11 50\n", "Name: value, dtype: object\n", "\n", "confirmed_age_50-59\n", "report_date\n", "2016-04-11 20\n", "Name: value, dtype: object\n", "\n", "confirmed_age_60-64\n", "report_date\n", "2016-04-11 7\n", "Name: value, dtype: object\n", "\n", "confirmed_age_60_plus\n", "report_date\n", "2016-04-11 4\n", "Name: value, dtype: object\n", "\n" ] } ], "source": [ "age_groups = ('confirmed_age_under_1', 'confirmed_age_1-4',\n", " 'confirmed_age_5-9', 'confirmed_age_10-14', 'confirmed_age_15-19',\n", " 'confirmed_age_20-24', 'confirmed_age_25-34', 'confirmed_age_35-49',\n", " 'confirmed_age_50-59', 'confirmed_age_60-64',\n", " 'confirmed_age_60_plus')\n", "\n", "for i,age_group in enumerate(age_groups):\n", " print (age_group)\n", " print (df[df.data_field==age_group].value)\n", " print (\"\")" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2ddd89d9-29ca-3bd6-e6de-1411a7b5eaf5" }, "source": [ "Looking at the confirmed cases based on age group doesn't show us anything useful other than the fact the categorical report of cases based on age group does not contain enough data and the report was done at single place on a single date :/\n", "\n", "Though looking closely in the reported data we see the number of confirmed cases is more in the working age group (20 - 60) with peak in 35-49 ages" ] } ], "metadata": { "_change_revision": 693, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/311/311500.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "9298c822-ccae-0d1a-a52e-4741df6fb19c" }, "source": [ "Introductory analysis to Zika virus dataset" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "b36938b5-81d7-96ac-f9a8-d2c954be2b4a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cdc_zika.csv\n", "\n" ] } ], "source": [ "import numpy as np # linear algebra\n", "\n", "\n", "\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "\n", "import seaborn as sbn\n", "\n", "\n", "\n", "%matplotlib inline\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "from subprocess import check_output\n", "\n", "\n", "\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "e15d0f56-8408-0c2b-c74b-d2f641cba05b" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>location</th>\n", " <th>location_type</th>\n", " <th>data_field</th>\n", " <th>data_field_code</th>\n", " <th>time_period</th>\n", " <th>time_period_type</th>\n", " <th>value</th>\n", " <th>unit</th>\n", " </tr>\n", " <tr>\n", " <th>report_date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2016-03-19</th>\n", " <td>Argentina-Buenos_Aires</td>\n", " <td>province</td>\n", " <td>cumulative_confirmed_local_cases</td>\n", " <td>AR0001</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>cases</td>\n", " </tr>\n", " <tr>\n", " <th>2016-03-19</th>\n", " <td>Argentina-Buenos_Aires</td>\n", " <td>province</td>\n", " <td>cumulative_probable_local_cases</td>\n", " <td>AR0002</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>cases</td>\n", " </tr>\n", " <tr>\n", " <th>2016-03-19</th>\n", " <td>Argentina-Buenos_Aires</td>\n", " <td>province</td>\n", " <td>cumulative_confirmed_imported_cases</td>\n", " <td>AR0003</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2</td>\n", " <td>cases</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " location location_type \\\n", "report_date \n", "2016-03-19 Argentina-Buenos_Aires province \n", "2016-03-19 Argentina-Buenos_Aires province \n", "2016-03-19 Argentina-Buenos_Aires province \n", "\n", " data_field data_field_code time_period \\\n", "report_date \n", "2016-03-19 cumulative_confirmed_local_cases AR0001 NaN \n", "2016-03-19 cumulative_probable_local_cases AR0002 NaN \n", "2016-03-19 cumulative_confirmed_imported_cases AR0003 NaN \n", "\n", " time_period_type value unit \n", "report_date \n", "2016-03-19 NaN 0 cases \n", "2016-03-19 NaN 0 cases \n", "2016-03-19 NaN 2 cases " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"../input/cdc_zika.csv\",parse_dates=['report_date'],\n", "\n", "\n", "\n", " infer_datetime_format=True,\n", "\n", "\n", "\n", " index_col=0)\n", "\n", "\n", "\n", "df.head(3)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "9dedb55c-21ac-1ba1-3d58-769c0fd201e3" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f3e7e93f358>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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m0bSt91+9+s7tesxICxfObZyxK+R2mT1ouV1mD1pul9mDlttl9qDldpk9aLldZg9abpfZ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uboVoCklkTERuC9wC+Z/EfpdOBfwB0wzkBbRDyQmVWcL+CKsYbS1/QIwylSGbdOH+pYjUl+DWoZ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\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3e7e73c0f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.location.value_counts()[:30].plot(kind='bar', figsize=(12, 7))\n", "\n", "plt.title(\"Number of locations reported - Top 30\")\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "f14d675d-dc0e-eb3e-8905-b32d1b106004" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f3e7e824940>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Gl4zoY1iYNpeUnkm+LkWpDmFZFnuOnmHV5gyyC8o81u5L4UeZO2kgV00eSPcI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\nsd1iuGfCd+gd3r7ZGt2WxY7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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3e7e918978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df[df.data_field == \"confirmed_male\"].value.plot()\n", "\n", "df[df.data_field == \"confirmed_female\"].value.plot().legend((\"Male\",\"Female\"),loc=\"best\")\n", "\n", "plt.title(\"Confirmed Male vs Female cases\")\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "b06c82c7-ad5f-c561-312f-3d1329ac004d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "confirmed_age_under_1\n", "report_date\n", "2016-04-11 6\n", "Name: value, dtype: object\n", "\n", "confirmed_age_1-4\n", "report_date\n", "2016-04-11 10\n", "Name: value, dtype: object\n", "\n", "confirmed_age_5-9\n", "report_date\n", "2016-04-11 8\n", "Name: value, dtype: object\n", "\n", "confirmed_age_10-14\n", "report_date\n", "2016-04-11 15\n", "Name: value, dtype: object\n", "\n", "confirmed_age_15-19\n", "report_date\n", "2016-04-11 6\n", "Name: value, dtype: object\n", "\n", "confirmed_age_20-24\n", "report_date\n", "2016-04-11 18\n", "Name: value, dtype: object\n", "\n", "confirmed_age_25-34\n", "report_date\n", "2016-04-11 38\n", "Name: value, dtype: object\n", "\n", "confirmed_age_35-49\n", "report_date\n", "2016-04-11 50\n", "Name: value, dtype: object\n", "\n", "confirmed_age_50-59\n", "report_date\n", "2016-04-11 20\n", "Name: value, dtype: object\n", "\n", "confirmed_age_60-64\n", "report_date\n", "2016-04-11 7\n", "Name: value, dtype: object\n", "\n", "confirmed_age_60_plus\n", "report_date\n", "2016-04-11 4\n", "Name: value, dtype: object\n", "\n" ] } ], "source": [ "age_groups = ('confirmed_age_under_1', 'confirmed_age_1-4',\n", " 'confirmed_age_5-9', 'confirmed_age_10-14', 'confirmed_age_15-19',\n", " 'confirmed_age_20-24', 'confirmed_age_25-34', 'confirmed_age_35-49',\n", " 'confirmed_age_50-59', 'confirmed_age_60-64',\n", " 'confirmed_age_60_plus')\n", "\n", "for i,age_group in enumerate(age_groups):\n", " print (age_group)\n", " print (df[df.data_field==age_group].value)\n", " print (\"\")" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2ddd89d9-29ca-3bd6-e6de-1411a7b5eaf5" }, "source": [ "Looking at the confirmed cases based on age group doesn't show us anything useful other than the fact the categorical report of cases based on age group does not contain enough data and the report was done at single place on a single date :/\n", "\n", "Though looking closely in the reported data we see the number of confirmed cases is more in the working age group (20 - 60) with peak in 35-49 ages" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "70293e07-0c02-04ed-1539-0424f24007e5" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f3e7e76c710>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Aq1ZeXLqUVq42evToXex7585dAfD2bkODBg3x8vIGwMvLG4MhhdTUcyQmJjBq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s97PNv2CnaTjbtr72SBo0jrSCJB7f6o+49M/xHJprVxBJ0OKpbmah1FJAkpAMS84HEeAZ0Ba/qS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\nJdfVkJlj0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dFxiqJwSOnHFIJ1m2K3Yc/Tb7UC8LXVJVy4ZXHMrnOicnMdjI66tI4hRMwF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\n9ntPDvHzl0+gRBTWXl7MV69eeOGXRFzG9+0FnQ7bysuSHcq0eLe2i+feb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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3e7e86efd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "symptoms = ['confirmed_fever',\n", " 'confirmed_acute_fever', 'confirmed_arthralgia',\n", " 'confirmed_arthritis', 'confirmed_rash', 'confirmed_conjunctivitis',\n", " 'confirmed_eyepain', 'confirmed_headache', 'confirmed_malaise']\n", "fig = plt.figure(figsize=(13,13))\n", "for symptom in symptoms:\n", " df[df.data_field == symptom].value.plot()\n", "plt.legend(symptoms, loc='best')\n", "plt.title('Understanding symptoms of zika virus')" ] } ], "metadata": { "_change_revision": 243, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/312/312349.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "b97da595-27b0-80c0-e8f6-76fa158e43de" }, "source": [ "This is analysis of \"Cheltenham's Facebook Groups\" dataset. We'll discover if there is any difference in these local FB groups and what posts are bound to be most liked, most shared or most commented. We will use some statistical methods, such as [confidence intervals](https://en.wikipedia.org/wiki/Confidence_interval) and [Mann–Whitney test](https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test).\n", "\n", "*Note: for lazy ones, look at the plots in the middle and conclusions at the end.*" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "24c48678-1c12-0094-ae9f-951619341323" }, "source": [ "## Data obtaining" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "bffc719d-e68b-37dd-eb72-ad692a455468" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "from statsmodels.stats.weightstats import zconfint\n", "from scipy.stats import mannwhitneyu\n", "from statsmodels.sandbox.stats.multicomp import multipletests \n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "11b41f84-8d91-7ccc-ca09-dd885643c3af" }, "source": [ "First, let's read file \"post.csv\" to get information about posts (message, number of likes and shares)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "713afc73-f4a2-2ac6-58ef-f8625c046a06" }, "outputs": [], "source": [ "posts = pd.read_csv('../input/post.csv', parse_dates=['timeStamp'])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "7414ccaf-32fb-75a7-5291-684a29b3921e" }, "source": [ "Now we are reading \"comments.csv\" to calculate number of comments for each post." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "de19617e-fa13-6ac1-c14a-26b4579b1b38" }, "outputs": [], "source": [ "comments = pd.read_csv('../input/comment.csv')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "bc61b719-986b-8543-4336-fd833599c937" }, "source": [ "Let's make new dataframe with the folowing columns: msg (message text), likes (number of likes for this message), shares (number of shares), comments (number of comments), msg_len (message length), and gid (group id). " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "564e3c45-30ea-6c68-141c-6d23156ee865" }, "outputs": [], "source": [ "com_count = comments.groupby('pid').count()['cid']\n", "data = posts.join(com_count,on='pid', rsuffix='c')[['msg', 'likes', 'shares', 'cid', 'gid']]\n", "data.columns = ['msg', 'likes', 'shares', 'comments', 'gid']\n", "data['msg_len'] = data.msg.apply(len)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "87184de7-e979-fcc6-e7b2-4ba8d2d8888d" }, "source": [ "Group IDs are too long, so we'll replace them with 1 (Elkins Park Happenings, EPH), 2 (Unofficial Cheltenham Township, UCT), 3 (Free Speech Zone, FSZ)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "c4bb893e-1d02-3338-2e6d-daa5feb4d6ef" }, "outputs": [], "source": [ "#117291968282998 Elkins Park Happenings\n", "#25160801076 Unofficial Cheltenham Township\n", "#1443890352589739 Free Speech Zone\n", "data.gid = data.gid.map({117291968282998: 1, 25160801076: 2, 1443890352589739: 3})" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "cc63998d-7c7d-fb7a-2ae9-f37b53e1fc94" }, "source": [ "Finally, let's replace NaN values with zero and look at the resulting dataframe." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "67e9655e-b27b-6e5d-789c-58e510e95708" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>msg</th>\n", " <th>likes</th>\n", " <th>shares</th>\n", " <th>comments</th>\n", " <th>gid</th>\n", " <th>msg_len</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>O.K whats the deal with this Poison Ivy Rash t...</td>\n", " <td>0.0</td>\n", " <td>1</td>\n", " <td>51.0</td>\n", " <td>1</td>\n", " <td>615</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>We are looking forward someone to tear down an...</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>9.0</td>\n", " <td>1</td>\n", " <td>183</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Need a small section of stucco replaced on the...</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>3.0</td>\n", " <td>1</td>\n", " <td>100</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Can anybody recommend a good cheap plumber or ...</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>9.0</td>\n", " <td>1</td>\n", " <td>56</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>I am looking for a reliable part time helper f...</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>2.0</td>\n", " <td>1</td>\n", " <td>139</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " msg likes shares comments \\\n", "0 O.K whats the deal with this Poison Ivy Rash t... 0.0 1 51.0 \n", "1 We are looking forward someone to tear down an... 0.0 0 9.0 \n", "2 Need a small section of stucco replaced on the... 0.0 0 3.0 \n", "3 Can anybody recommend a good cheap plumber or ... 0.0 0 9.0 \n", "4 I am looking for a reliable part time helper f... 0.0 0 2.0 \n", "\n", " gid msg_len \n", "0 1 615 \n", "1 1 183 \n", "2 1 100 \n", "3 1 56 \n", "4 1 139 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.fillna(0,inplace=True)\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "5aa8d797-eb91-fecf-1a6e-37f1794f47c2" }, "source": [ "## Preliminary conclusions" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9c17cfcb-fd91-3a94-8c1e-86acefe9f666" }, "source": [ "Now, let's visualize data obtained and see if we can say something interesting about our data." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "a4217fb4-7671-9bfa-e637-6ab6f219f90d" }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.PairGrid at 0x7f6a179f7780>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { 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NOay1jiqlbgf+Smv9baXU5xMQL0A18OT0uF0z8KzW+kdKqV8CzymlHgUuEJuBGa11i1Lq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eC2jZ/lheMvp+cFQEzjBdJz0LnORcfmsp5gLya9kbAnzHA0qLgmE8xKpUa/9iJhuiw4Kwqp7H4o\nSdKFsiwfLGAdi4o6u4tD3l6MWqX3h91ozWvY9eyVr7tu34lDtawoozoKFje+qH9Sf7YZxaKKQCAQ\nnA3B8MiMcjFgNyjHAJtBjAlLnU5/l0LOVkovqltO3TWl1Fdb5zwXHZvLbmloYc/RtGn0XFIZCQS5\nUkhl94fAO5IkdaBMPbSpgHUuKC3OtcTVIZ6Xf5MJy19rWE4opnToP6uw61n3TySh89HsKi4zKEFx\nUGq08/7JvZn+7LLXEM7qzwKBQCCYG1J9KS9NkC+oL75Fag0aReohLZqFbpKgwNTZXQq5xljHIy0P\n4x7uGbdYdOZmsTg2l43Eo5ljB7oPcXfzLcTjCZGGU5A3Cqns/gz4n6T9ds8Ln101ai6rvJRV7iiR\nwx2oaxsI25pJ2JSR/851pSofYdwFgrnQYK3lEfNWEoe7SdRU8MTQR+xs3rHQzRIIBIKiZE1DCV++\nrZmO3gD1VVaaGksWukk5YzPYINCTka36ItvZHc3Z2t7jRut0iZytc6DFuRa1+gu0e93U2WspTdTR\ncTzAxVgxHDpJvD6a83Mcm7tOtB4LjYSpNlUJVylBXimkshuRZfkfz6UASZJ+DNwMeGRZvmj0WBnw\nNNAItAJ3y7LsGz33LeBhIA78uSzLr59L/WfD4McH8P5w3MTY/idfRFan89ANRwK4bLXnvFKVjzDu\nAsFcKD3uZeD//jwj/+mffpEq0d8EAoHgrDja7uPxFw5nZLu5+LIoXFx2IZF4hK5AD7VWJ5eUF5cf\npcjZmjtq1NzUfDl9fcMcbhvi7546wBdXJxl85WeZa8ae43QRlbMZm8t6gr08ePFdhONhqk3VYk4r\nyDuFXMp6VZKkG8+xjJ8AN2Qd+ybwO1mWJdJ+8t8CkCRpLXA30ATcBPyLJEnzHhM/0q7MBdZ/+jC/\nPPoSP/3DM7hstayxSzkFp0qR5Jhf5tnDv+GYX86kfVljl7jWdXXO5QkEuRDtOqOQE11dvOl+J9MX\nBQKBQDB3psqiUGycHD5Nq68DfzRAq6+TU8NnZr9pETFVzlbB7CST6fmoHP2I7dt1lAT7FefHnuOY\nL+5/yS/zg30/RvafmLK8sbnsVTXb2FSxkc+v+5yY0woKQiF3dr8MfFOSpGEgymieJVmWq+ZagCzL\n70mSlJ0A7TbgqtG/fwrsJq0A3wr8QpblONAqSdIJYBPw4Tl9ihxIpVJoXUq/hkiVPWPEfTa+utOF\nYRcI5oNETblC7rIl+C/5ZUD0RYFAIMiV5TVWdmxfxYAvQkWJkRWuIjMBBvrC/VlyHxfYimc3TuRs\nzZ1UKsVrRz/kafkZ1tc0M1LSS1lzHcF3xq8Ze47ZcWXONU6NQDCGJEl1wH8C1aRjgT8uy/L3Z7uv\nkMpuoZKuVcmy7AGQZblHkqQx5dkFfDDhOvfosXnjWKeXLmeC8i/cSF1/Aq3Nhq20hBtK7LzbsReb\nJvfY/OKlIVhIBhoraXzoXhJuDxpXNUOr7JhPyIRGwqIvCgQCQY4MBmI899bJjPzl25rneaYi0K1p\npuFLDxPt6MRYX4d+TfNCN2lxk0oysP9jKjpk/qxhG9/veodQPMJR7Qm+8tAX0PT2oqqvoK3OxCqS\neY0rM1eTaMF5Qxx4VJblTyRJsgL7JUl6XZblYzPdVDBlV5blNkmStIA0fkiOF6Cqc05W7HDY8tEO\n3m47QFv4JI3GcoZefgaNxUzZhg1ssVu4sO5yPIkUFZUW1Dk48K+I1oM8Qa6sz1t781WOKHP+ma/2\nVx0YJHWqk1QkQio6gsuygvU1zexp35fXvjgdC/E9LVTfKPY+ORv5+nwazczvT7VaPae68vm8F9s7\neamWM98Uot0db59Syr0Bbr1yVd7rgcI992hPlD3t+zJy9drKgtVViHIHPvyIkHycRCRCIhzCVF1F\nxaaNs99YBBTkef3+QwZ/+M8AjAA7dl3LExwkFI+wvy7AO7GPYRjYB1/f8hW2rNiA0aCj3ddFIBbA\nYNDOee6b3f6POj9RWDd+fctX2FR3SU7tTyUSDO7dR7CtjYHGZVRuakGlLpzCvBTnRWfDmaGO5R93\nHfwbfzRgX1Xe+Oa2ZZu/d65lyrLcA/SM/h2QJOko6eXChVF2JUlqAZ5j3IRZK0nSDlmWPz7Hoj2S\nJFXLsuyRJMkJjIU6dgMTbVHqRo/NSr6SikdTXq70l1PW6cN45+3EfD5SIyN4d79LIhiCXdeyx2bI\naTdsmWF52oE/4qHaWM0yw/K8tNfhyH8y9fO9zPkk3+2fDtvwCOrKSmKDg+grK9AFEphKjTzS8sW8\n9cXpKMT3tBjrXKh6i7HPOhw2EonkjANXMpmcta58Pu98lSXKmVtZ80khfpP1VbYs2VqQegr5ThmO\nKv2M/dHhovoM4a5uDBPGtWB3D8kCPaul0GeHT57JbN4kk0lqk5X8t8GVDFda8BuVqbNO93ew3LCS\nSHSEpw+9CMBvjr85J7enqb7v0/0dk+TlhpU5tT925NN5C0hW6LF8PuYKeeqzmvfaPnryJfl3lwFU\nmEqvV6s1gS0NLY/no3AASZKWAZcwB3fVQpox/xPwsCzLb4w26hrgB8CWHMtRjf4b40XgIeDvgQeB\nFyYc/7kkSd8jreWvAj4628afDZf69Az8209RbduK++XxQNCV27bS/+57WPuCyEMn6TltparMTFNj\nKSpmjqE15sC/bWXLgkzCBec32kSSjud/lZHr77+XpooLhPmyQCAQnAVGvZr7rpfwDIaoLjdj0hdf\njtpSgz1LLrL0SSMx3FnjmmB6jPX1lG3YQP+771G5bStdTz6NFigDar/8kOLaMZPlfLng5cMkeqqA\nZCL6dsFxHuk7cfGYMBD26t3+7suAvCi7oybMz5LOvDNrlL9CKruWMUUXQJblNyVJ+u5MN2QjSdKT\nwNVAhSRJ7cBfAX8HPCNJ0sNAG+kIzMiyfESSpF8CR0hbWvypLMvnbOKcC/oeLwCJSERxfEwOOCwE\nR4IYSs7wj0/FeGxn8aUcEJxfRDy9k2S1KrdVVYFAIBCkGQpEiY4kSCRTREcSeAPRhW5Szph1FrY0\ntBCJRzFqDVh05oVuUk5EpxjXTAvUlmJA17SO1NGjwOT5rU/uYbN0M9ayMGudyzJpg7KVUs1ICUfa\nhua0yTORfKTaFAHJFoSBWpvTfWqwbRWATq2l0lw+J2vb2Rh1kX0WeEKW5Rdmux4Kq+yGJEm6Wpbl\n3aONuwoI5VKALMv3TXPqummu/1vgb3OpI5+M/aA0JqPiuH7lMlIXN9JZFeeA+xMudeqwGCvoGQzR\n4QnQUG3N+QWwkKRSKY60e4uy7YLcMDirFbK+uooOv7uoIm8KBALBYkGjVtHnDROOxkmlUthqi8P/\nbiK9gd4suQ9yj7+5YBhrlIqYyelcoJYUCSo1fkcDMHl+67NUsvutOI/t3MQa+/jmzZiSemKgg6Fe\nPc/9aphg5EBmk2eqeeSUVY9aN2bvCucyD9U1rWPZo48S7eigZNVykivWnMvTEMyNyCbXxX9h1Br+\nOjQSKl1VvmzPNSu2fDtPZf87cESW5X+a6w2FVHb/DHhOkqSxZUs9sKOA9S042jXrcDzwBRKnT+K6\n43ZCXW50ayQOrzbRGnATiUVZX7OOSp2TS5vK+flr45GnimmX90i7l+88dSAjF1PbBbkxXFGK647b\n075N5eUMlhvOKbKiQCAQnM+EIgneOTC+wVFVXnwLhzV2J/6BsYjSKmqLbEzQ1LoU45raVbfQTVr0\nxFeuwflHu4gdP52Z3xrWSpxwqvmTLVU0OZWm7GNKausxM7vfGs+z2+EJ0NxYNuU8ssqhNI+fiZzm\noSo1+rUXoV97ERULFJfjfGRz/fpfb65f/2tGU8/mo0xJkrYA9wMHJUk6MFruX8qy/OpM9xVS2S0F\nNgJjqYF6gXUFrG/BOdLh45RuGZesVRPo7kK3oYl/jv2etUFJEbnwnlWrKLHoFfd29wfQlPXOGF59\nsYRg7/AEJslC2V2atFfpKQvr0MS1JBw6/LWlrD8LMyKBQCAQQCA0kiXHFqglZ0/2DppaVVyWXbpV\na0iNxFGbDGicLvSrxE7fbCTtvex2hblQX0/E40XbspZvD79FqC1t1mwzPwyoMvNTfFW0dgcosRmw\nGLUEI+lkLPXV6bzSU80jc0HMQ4uKvLmUyrK8B8g50EEhld3/DWyQZbkXQJIkNfCPwIYC1rmgDPpC\ntOg7GDxzjKDDyrPB3WyuW48/pvxRjmi8SPXLeGnCMbvLyw/2PZGR71qxkyrVMoVphuw/oQjB/uDa\nB9joXDfvJsQNoy+rMeqzZMHSIZYK0h8axBoLEQypiSdKQZisCwQCwVlRXaH0Dq0uLy5/VwB/bFix\ngF9rq57h6sWNGM3mRk+ol9dOv8t7GiN3WldT7/ZwX0kzp5bp8ccCuIPdvHpyN6GRMACbLTez+620\ngvvl25rxDceor7aydtRc+VznkWIeKsiFQiq7qokBomRZTkqSVHxhB3NgVaiT4R/9BD1pm+0vfWkH\nnSYrOrVOcZ0xVU5TYymP7VxPhydAfbWVnsQfFNec7O/gP383pDDNyI5u92nnGSzRunlfzcpuu0YN\nr37UweqGMlY4LcJ/dwlR3xki8rN0nDk9YPzqfRyJesUKqkAgEJwFIyMJrlzvIhyNYzJoGYknFrpJ\nOROKhScEqDISioUXukk5ETt6aN5S0SwVfOEAZp2JPzZsIvlvzxAELEDdrmt5InmQ/V0H2dLQklkE\niWm8QFoBdfcF2XHlcsXcMHseuXYan93pmO3+xWIJKVgcFFLZHZYkabMsyx8CSJK0GQgWsL4FJ9nb\nlslFlohEUPnj/OjEW9yy5no+33QzfQEf6kAlkWicN91v4yqr4YbG9A9QO1yrGDzU3gogqjDNyPaV\n1CdKC266MV0QgObGMpobyzjcNsQ//Lw4/HfHPkvPATc15XNL/XS+ox8MYN22lUQkgsZkJDY4zEH/\nACoQz08gEAhyxD/BjFmVJRcLdoOV35zIJNvg3nW3LmBrcifW3U3lxHGtu1sou7NQYS5lfU0zkQMd\nmCbMc8tjNsxGI6F4hEh8PLJ4baWV7dt1fPRBCp1Ww5E25SL5xHnk2TDb/dmWkHPJ8ytYuhRS2f0G\n8CtJkg6PymuBOwtY34KTrC3P5CIDYO8+duy6ltbhHt5tS6f8vUu6g2fkpzP3jP0AU6mkwixoiz09\neEw0zZDsq3lw7QN82nkGfaKUvR/AV+8orOnGbEEAislvQgTWyh2j2ULvuy9m5KqHdvL6e+28/lG7\neH4CgUCQI6VWAy+9eyYjP3BT8fmL9gT7FLIn2L9ALTk7dBYTXWPzNKDhSw8vYGuKg6HwEEaNgUiV\nndop5rlPcJDltlWUNJYTTQV5q+NtQiNhbv7sXQx1xOZ9bpivPL+CpUHB9vRlWf6AtIL73dF/zbIs\n/75Q9S0GdHo9Kq1y/aCxP0WVuTIj94R6FOfHfpDuYeVxrTXAl29bh06bNhH+/cFuQMVG5zoud2yj\nSrWcr95x4ZxMP1IkOeaXecO9m2N+mRTJOX+m2YIIFJPfxLkGRDgfSfYPTSuL5ycQCAS5MeCNzCgX\nA1UWZZ4hh6V8gVpydmTn2Y1lyQIlqWSSS/r1bDoUpF5bhkqnnOfW+jR8tvYu4p56EiMa9rTvy/ju\n+uL9fHCwe9q54cT56Uedn+Q0P52JbEtIkUXi/KaQO7vIsjwEvFzIOhYTI20d6BNxxTFDMEbpiXFF\ntkzrUJwf+wFm/xAjPjOPv3WIK9e7MmkK/vv961GV9NKj7WbZmhoku2tOZqSTzDkufZhTPXWcbB+a\nNT/ZbMrsRL+JVQ1lrHRaZm3PQlFMivliwViqXIk1lpRmTJPE8xMIBILcqCgzsmP7KgZ8ESpKjFhM\nxRfKRIuaW9dcz1DYS5mpFF3uwVEXFH2JPUsuvlzH88ngxx8T+uHPMrLrzjsU59V11ejtwwwbenCa\nyzH3mTLKrtPs5Kt3LMtszGS7xqnLPPxg37+nC5LzZ248lud3os+u4PyloMru+fS85PsAACAASURB\nVIauoQ5VoA3XnXcQGxqCVIrBvXtxVlzFZtfVELPyxmsqHrz9AYYTA4of4AW2tIlyq9eNXV3Bm2/G\ngDjh6Ljy7Em08sy+pzLyXF8K2eYcJwY7ef7prow8kznqbEEAJvpNOBZ5/rKxz9IzGMJZbs45IML5\nSCwaGc9HWFFOLBbl96k3ufuOneL5CQQCQa6k4Lm3TmbEYjRj1qp1QFqZUWXk4iE+ElOMa/GR4vOb\nnk+i3Wmz+7GYNCPDw7juvJ2QO51rd3dFiNeOvZu5/tY11zMSH2FV6YpJgaGy3cnuuEe5k5svc+Ox\nPL/CdHlpIUmSAXiHdMxULfCsLMv/Y7b7hLKbR2IjEfxvvJmRK7dtJREM0W6NscxlI5VM0diYIhFP\nUa+6mCQe3nS/k8lJ9sOf95H+/oa5bmM9sXgSrUbNVetd7DvqYTg1oKhvri+F7F1jm6oCUJqjTqfs\nnmsQgcXE2Ge5uqVhUSvliwmN1YL7J+MrujV/tIst1S30JdqR/WYR4VAgEAhywB+MKXZ2/cHiy7M7\nkoozEBokEo+STKWw6oorfZLOZqftR/+ekYXP7syMONPzP0VMGsC+awffTrzDNdotmHUm1tc0k0wm\n0Wt0BCKhKcvKdn9Kz0fHEebGgpmQZTkqSdJ2WZZDoxl+9kiS9Iosyx/NdF/RKruSJLUCPiAJjMiy\nvEmSpDLgaaARaAXulmXZN19t0g8GFZFrExYjg7uu5Tn1CZoGwaIzk0glgC58Gj8v7RsP/HPXip2K\nssrtRn75xomM/JU7LqSkYpBXx+NaZF4Ks4VYzzbnUPmqmKjsCnNUwXQMDw8polYGAz72RNOB1N5q\n3SMiHAoEAkEOlFj0/OcrxzLyF4pwZzeSiCgCalavrZjh6sXHSDCsGNfiweJKnTTfxKRGbF97CF2P\nj7KNLWhMRob2f0x/n5tQRYThaJD1Nc3sad/HloYWnj38GwDebH1v0hwh253MqVmWmZ+uqKxnmWH5\nrO0RaYWKg8DpM8uH9u3/mxGf325dverNqquv/F4+ypVleWwlxUBaj03NcDlQxMouaSX36lG/4DG+\nCfxOluV/kCTp/wG+NXpsXrBaSumaELnWvmsHTyTfhSQYtQZUqDKh2b0pZTTD/mQ727eX89EHKYIR\npfkyQCAUY9M0PgizhVjPNudI2VP85UObONk+pDBNFi8QQTYWWymeZ3+TkR0P7YQJGxEiwqFAIBDM\nnX5fJJNn12zQ0u8rvgBV/khgRnmxI6Ix58Yq+0rOjJxk+NnnM8cqt22lz2GAJNTaqokkolxaeyFa\nlVKtyJ4jZLvGSfWlqChjjV2asyucSCtUFGj63n7nya5fvXgZgL6i4nqVRh1wbNv6+LkWLEmSGtgP\nrAT+P1mW9852TzEruyomR5O+Dbhq9O+fAruZR2U36E2H3x/za0h5hvjzZdv41BHnQ/cnXLP8CtQq\nFTajDXVKGdAhlozy++BL3H7LPaj9TqwmpQ9MY00JpFQkhqpI9lsY8ut44WAbNrOBcGmH4trZFBAV\nKi6/sIZVTuUKm3iBCLKJBoOKFfBoMAgTuqYwORIIBIK5U1Fi5OX3WzNyMfrsVluVgTarLZXTXLk4\nETu7uaFCjbnXj3/CsZTdwqlaFVvULQA8f/RVALY0bFTcm7FATCYZ+PhjIu0dVDfUs3bjBlSquW+m\nTAxsFXfMfc6bHRBrW4WwZJwnnP5Dhy8eE2IDA/pwR+dlwDkru7IsJ4H1kiTZSae4XSvL8pGZ7ilm\nZTcF/FaSpATwr7Is/wiolmXZAyDLco8kSVXz2SBTaTk+Jvs1XPvQTnBdgt1gw6K38PShFzHrTGxp\naEGv0RNLxDjQnU5H3Dbk5v3f+bAYtXz5tmZ8wzFKbHrcvX76hkI8+doxLm2qzkRoBrj7TmWgoLNV\nQKbKSybZLlC8KGaK3CxYeliNFjrf/VVGdn3hPrbUtqBXG1hXuUZEOBQIBIIc6PeGZ5SLgZHECFsa\nWojEoxi1BkaS8dlvWkRo9Vrlzu6DDyxga4oDc2mFQtnVOMpZ2dWLtS+IaZkVs9ZIKB7hQPchbl9z\nA53+bqTy1Zk5wsDHHzP4w38GIATw1a9R2dIy5/onBrbavt2gODfTnDc7IJbeoJu00SMoCAOmujp3\n4OSpVQAqnQ6Dw+Ge7aZckGXZL0nSW8CNwJJVdrfIstwtSZIDeF2SJJnJdtuz2nEDOBz5CTvfOTxA\n5batqNTK1ar44RNstazlldhpamxVmHXpsOx72vdx77pb+cWhcdNnM+VAjGAkTigSZ1VDGf/rP8b9\nrq9c7yKRTCrMoPra4e7VO8E0TEOJixbXRajnsGKW/blXROtBniBX1nPKE1S8KP7yoU1cfuH0L5Z8\nPctiLHM+ma/2H+9W5n+OdfewJ36Ie9fuYMuKDXPqZ2dDMpnko85PaPe5c+rT+WCh+kax98nZyNfn\n02hm7gdqtXpOdeXzeeerLFHO4qIgY0+ZaZJcqOdTqHK9bX6Fz+71K68sqs9wolu5sB/p7qa+SPto\nNoX6Hsbmt4lIBI3RSHzIR/kLbwCQ4Pfs2HUtT3CQ0EiYvtAA+7o+xaZyUNlkR61W4e5Q7sZGOzpw\n3LRdcSyZTHImemrKcb9nwgbPRx+kuPuOuc15J94H0Nbtm3EOmw8K/b4rkvdppHzzpr/QGI1/HQ8F\nS22rV++p/sy13z7XQiVJqiQdp8knSZIJ+Azwd7PdV7TKrizL3aP/75Mk6VfAJsAjSVK1LMseSZKc\nwJwyhecrMq+qupKhV97GecMNiuMao5Fgu5t9toMAbGloyQwUWpWWKxs3ExwJYdQaUHvHd02d5WZO\ntg8pygpH4zRU2xSpC3ZsX0UlVTRXpSPmDfQHZ23rVL4RywzLFT7BywzLea1d+aI42T407apYIVIP\nFVOZ88l8RZM2OquVcnU1W+qMvHjiZRzmEtbYC2OCd8wvL4hJ/UKlz1qIeouxzzocNhKJ5IwDVzKZ\nnLWufD7vfJUlyplbWfNJIX6Teq06s1htMmjRa9UFqaeQ75RKc7lCrjCXFdVnMFY7s+Tqgj2rpdBn\nATTVDvqffSUj19y9I3181G2v3JPi6yuu4aBjBIvRxrbGTVRbbXzwaQcXuMow1NczcWZqrK/n3VP7\nFDFiWqNn+Mc9/5q5ZuK4X1M+HvE7GIlTybI5zXkn3gdpl8BCjrWFHsvnY66Qrz5becVlv6684rJf\nk3Y7ndPm4xyoAX466rerBp6WZfnl2W4qSmVXkiQzoJZlOSBJkgW4HvgfwIvAQ8DfAw8CL8xnu7rr\n7azdcTvdz/0qswJmXbmCSH8/CZeDMRsQrUrLpbUXYtQaODXUilqtZn9XWhH+TEMpd1/TlAkclW0w\n3FBtm5SqYCSeyEvO06nykmVHzhORm88zDHrFai5GQ2ah5qT3zCRlN19BzqYyqRf+4wKBoNjp7A0q\n3JDMBi2sXcAGnQX9oUGFGfNAaGj2mxYTU4xrgulJkaSjzsrqh+4lfvQU5loXIz4/lVduRaXW0Lf7\n7cy1LX/yBf6nb1z3uLs5SdLv4IJL18NXv0akvQNjQz2DF9j5wb4fZa57pOWLeCIeRb0Tx/3swFZz\nnfNm37e52cnAQHEFVFsC5EvRRZblg8CGXO8rSmUXqAaelyQpRfoz/FyW5dclSdoH/FKSpIeBNuDu\n+WxUV8BDUwTKN21Ca7ejCgYIdnQyuOd9zKt3Za6Lp+IZ5XZswBijXO9gIBanZzCMVg1rGkv48m3N\nfHKiH5NBy6sftHLfDUoFY3XdzH602Q76TTkoxmfzghFRnZcOIwNDGCoriQ0Ooq+sYGRgCEat8GyG\nyQsf+Qpylu2Dc74GwhK/JYFgaVFiVSpWdqt+gVpy9lSZKznjax+VVFSZiyz10OBg1rg2iHGhG7WI\nkf3HafV3UjLgp67WRaizE43JiO/IUaq2X61IR5Ts8oAFLBojd6ZWU/PhGXrsrZzaoGZ1SwuM+un+\nwb1bUcdY6qGJTBz3VahobiyjubEsp7Zn36dWi5gz5yNFqezKsnwGuGSK44PAdfPfojSbfDai3SdI\nRCKkEnFUOj2mqnSMrGRXD9vXXQGpFGXmsszO7oHuw1y3chsAy22reOaZYYKR9CrpletdxJNQZtWz\nprEM73CUr95xIU2NJdjNc1dAsx30H9u5niqHfcprp1KMc33BiKjOSwe93UbodGu6T/clMa9YzqWV\n6b5bY3ZOuj5fO7KSfTVf3/IVTvd3KNJsnW+I35JAsLTQalUKM2adpvgm3/FUXOGzW7+uuBYjdTbr\npHFNMD0nvWcoM5firLDh/o+fZ4677rgd9zPPZeTKbVvxO8tgGO5Mrab8Z28Qt5hZvWEDycG9DKzq\nZbBiLafcAcoblBG8XbYaWlwXTZleUyA4V4pS2V2sWAaCdE+I8Fd7x22EutLmSupaJzYDjMSiLO8I\ns7pbRbzGRtWKK/FGhjnadxI7VQQj43ldwtE4R9uGFGkKHtu5HjVq1jakFdwOTwAVzBglucMTmFGe\nyFSK8XSK7nQh3YUJ6tIhGYtnySNUGMupNTWw2rZy0vX52pFVoWZT3SUsN0yu43xC/JYEgqWF2ajF\nUWpiwBehosSIJSvNYDEwGPbOKC92phrXBNNj1plQo4JIMLOL6ztyFFQqxa5u1KKnt7Gce1K3UfPh\nGaJMzk5S+YX7+f5+A6aPNdx38/14Qj3U2Wu5wL4atWrclU5YNQnyiVB280hy0KeQE6EwutIS7A/s\n4JTLyItHXuQB9YXEf/YGY6/aZX98F/8aPcD6mmYarLVYjENcvlWNwRamwuZnKOTmM9eZObBPxbr1\nCeToR2j8y8BXNWeltKHaisWoZePlENN4Ka/zkUwlp7x2KsV4unKnC+kuTFCXEPG4YqCqu+vzaFRa\nwpHElJdL9tViZTaPiN+SQLC0CEfj9HnDhKNxUqkUZqNmoZuUM6XGEoVcYpzaUmzRMsW4Jpgeo8ZA\nXbsfzy/Gd3Fdd9yO+7+ez8iV27YSW91IXWcA7+mTGBw1xC1mEpGIoqzEUZk/a2li70gNP/5ZB6AD\n+nhsp49qx3i/kn3H+cH+f8/Ij7Q8XLCAmIKlj1B284jOroxgpjYa6H7hJQBqvnIPANY+ZdS4SFsH\n69c3Y9fbKE3UceftcZ49/RRbSlt45thvM9fdcsOtvHTm1xCENzrhrhU7FeXMpJQ2NZbywL1l/PTI\nEwAcOPIejjLTlLtmuQSkylaM27p9rHJahcKzhBjx+SbJNlMDJwcPYhqIYwutpLV7Yg7myUHOBGeP\n+C0JBEuL2EhSEaDq7uuK7zcdiAUUAaqCsdkzQCwmphrXBNMzFPWyzKN8RrGsZ6Yym+j0urE/8Tpa\n0vFY7bt2YEioYO+4ybvGaCTacwL7OuV8ubXLR/8HHzJ86gzG+nraLIOK8ycGOgsWEFOw9BHKbh5R\nabXKPGSB8QHA2OsDEwQdViaGowg4LETiUeosDRwO7CWlTpvTTAxaBeBP9ivkQGoAi1Gf2a211Qyx\n+xMVZucQw4kBxQ9fhYrhxIDi/nafm+VVk5XdXAJSZSvGjTXpVTmh8Cwd9FVKvxq9o4I3Tr3LQNjL\n/q6DbLbczO630nYKM1kXCM4O8VsSCJYW/kBsRrkYKDHYefnEWxl557rbFrA1uTPVuCaYnnK9HUup\nlonbG/pS5dxQU1NF1aCHifu43oEeDmyqZ/2uHWjkNjRGI0Mff4xu502YSyNYjFqCkfT84YKIG/nv\nHs/cu+4rf8SLE8qyqSZ/R4shpkW2wl1RmXOgYME8IJTdfKLRZCL8GWtr6Pnt7zKnemzw+Qs+R38s\niOtPdpHs6iFWXcZzkQ+5pewGXjr+CiatgWtWbGVb4yZqrFUc7TtJaCQMQIla+XK2UM5dd6p5+uST\nABzwv8cty2/lmSPjr4dHLn2YhLeaDs/kYACEbRxpG5rk65tLxDsR0n3pozJbqN95D5EeD0ZnNSqr\nlesbr+IF+XVCI2FiGi+QXvSYybpAIBAIBODMyvtZnSUXA0atkVvXXM9Q2Eu5qRSTprhiGU81rgmm\nZ0XXCMn2bup33kPg5Cm0djsxv4/q669DN6r09lrVxHRK83b7ipUYdGq+o36fHdJqnD7ou+Nynovt\nJXQ0wl133cGpgw40KjW6/kNMXPbR93jZXHUzMY0XfaIUp2bZpHYthpgW2Qq3waA972ONLEaEsptH\nVKkU7ud/lZFdn7+TUFs7LHPxaR34ve3s7zqIee3nSNbWkkpquS5wG0N+L6GRMNet3MYzh3+duf/e\ndbfRM9xPiaaCPW9p2bz2ZswlYWIBC91nbKiqTyrq98b7FPKJwU6ef7oLAItRy4P3PoAn7GGoV8+z\nzw8TjBzgv9+/HlVJb2ZVSq1S0+F3K3aGp4rQrBr9T4R0X9qkAgE6nno6I9ffdy/tfj/XrdzG7069\niz5RCqMe6CIHs0AgEMxMny+siMbc7wsvdJNyxhfz8eKx1zPynWtvXMDW5M5U45pgehJdHuJ+P0mH\nA+vKFSRjI5P8dQO+OD+t7mLHrmupG9bQaUvw+PBubnBezeek6zg61E5Xo403zuyB0ZAxp4dP4lqx\nhjffiHH1OpeiznBZNavtKwiGYtQ4LUj1k60MF0NMi2yFezqrScHCIpTdPBIdVPoYxAMBtCV22qxx\n/LEIRm06v95AaJAP3Z8QGgnzuRU3YkwZqTCVogIurb0Qu95GIpWgO+DBZrTij/bTdKGD138X5+5r\n15JIpnj101au2Kr0eSjXORRy2uwjncYoGIkz2F6KjlJ2v3Uic40n0coz+57KyFsaWjIpBe5uvoVq\nU1VOwbAES4tsv5yYz0ckHsPt7+Y26QYqo01UXZNbkneBQCA4X6mrtjAciOMZDFFdYcZuLr5pWDgW\nmeCzayQUi8x+0yJiqnHNtEBtKQYSNRVYRlRotBraf/YkZZs3Ks6rDHocNS4e7IagQ8WRiysoPzPA\ng55a9IYgP08e5IrGTZMCoxq1Bjq93Vy14RK+834r9960i9qEjy5NCb/YGyMYPTTjfHNiTIsGWy2N\nnWGGO36Dsb4eXdM6UBXefzdbwW4ocU1zpWAhKb637CLGUKH0KUgEQwDUJMpx2cv43al3AXCYK7h+\n5TY6/T1oDSn84T5uWLmdJw+lV8omKpwAt665nlDFAF/c1Ugk2Y432suOu534+7WKIBEWtZ0H1z6Q\n8dlV+aoYU3YhvfOWvfc6nFL68k70FT411MovD7+UUzAsETBgaWEoL1fKZWUYtek0E75giK3Ly1hT\nLxY+BAKBYC6EwgmefF3OyA/cVHwRZsvNZbxycndG3nXRHQvXmLNgqnFNMD0HHXG2R6tIhcI4rroS\nU30dQx/uzZw3OhyougaoDWtJyF7M6ircT4zv/N+061raI36qTNXc07SDk77jGLUGDnQf5saGG0nF\n+7h4m5fTiTKODa1lzyfdjFmMzTTfnBjTInbkU1q/+73MuWWPPop+7UWAMk3m6oYyVjgt06bqzJXs\nIJItrosY6C+ugG3nA0LZzSOxIS81t91CpKs744hf3tKCJZyiQlfC1oYWjDoTvrCPqjYvG/uCBB3d\nBBvt9IbGA1BlB6dy+7vZ33WQLQ0RhRL8+bU389qRcdm+vBxtu0RDdR2SvRTsTBls6rGd6+kZDOEs\nN6Mp6+XVM+N1je0+T/w7kBqACQrrTOaqiyFggCB/RPr7FUHXYkNerMtMvNuxj5vrb1/o5gkEAkFR\n0ZU1Ec6Wi4G+oHKRvDc4AJXTXLwIifYPKMa16MAgxeV1PL84TOWool7cL75E2YYNBM600nD/ToZP\nnkSjNxAd8mKoqKTrxRczmzwTsfYFKbmghi53ig/ei/K525bTFT3D+ppm+uNd7Ov6lPU1zUTiraxu\n0PPJsXTgKotRS3mDlzfcp7BpKvG7S6mttNDUWAopFO519R0dijqjHR0ZZTc7TWY+rROzg0iq52E3\nWZA7QtnNI1qbhVR01MV+bNFIpaLrmefAeD1Bl5bXT73L123XMPKzNwDQA86v7qS1btyIxqhVvnaX\nlzZg1VnIXogaCA9yz7pb6Q32k0wmMCTKeerNtIny2I95qmBTzY1lXN3SQF/fMClKJqxKOVGrNNj1\nNvyxYQ50HwZgVUUdj+2snlOE5sUQMECQPwyVlYTHooqrIBGJcFG/Hdvqa7CrjBzzH8M93CN28QUC\ngWAOuBwWhVxbaZnmysVLqSkrz66puPLsGqochAKjwTRVaVkwPcvaQ4S7uynbsIH+d99DYzGjVqvR\nmi2kEnGMzirczzw3ft6knMOaGhuwasxoa6Ncf3uYCweMLGuN4KtQcbJWy/qa5sxGzv6ug+z8/P30\ntdopr/dmUmYCbLHfSucxB3KHF2eFmcdfOJw5978/U62o01Bfl/k7O03mjNaJ08SoWQimspQUnB1L\nTtmVJOlG4P+Q3or8sSzLfz9fdauNJtqf+mVGbnjg/vTO2JVbqfbqaHWlFQG9x8vIhPt0PUMk60zc\ntuZ6egP96NVa7my6EV9kGIvBwssn3iQ0EmZLg9JPwmaw8vSh8ejLn1+xIhPKfa6RcadKbbLathLZ\nf4JqU9W4EmNXz6m8xRAwQJA/VAYD/e++l5Ed111DqLWdpGsVAfUAP9o3Ifq32MUXCASCGQlFRrjv\neints1tuJhwdmf2mRUYgmpVnN1pcu9OpkRHFuFY/QTESTMbQ0Ye+poZIXx+V27aiKy+j+4WXMudd\n1dU4b7iBSH/aQnFo/8fYd+2gv89NxaoLOOpU0x/oIZ5KcIF7hOATr6MGyoCr/mQX75f5FfV1B3p4\n5b0BrrhOqaQay3x88F6SYCTOxrVp5dZi1LLxcvh95QDSH99FrN2Nv9KEqs7MmGqYnSZzJuvEQu4C\n58pUlpJVjpaMLNwG586SUnYlSVID/wxcC3QBeyVJekGW5WPzUX+0p0chDx+T8R85QtmGDRCNcnOX\nk/XxS7GUlTFxaBiutBCJh/n18Te4Z90t6FVanjv2Kk2OVXij/kz6oQPdh7j5gmvpDvTistfQF1Dm\n3j0z2MFnP1uD299NRZ2P/R4/bb5O6uwuWpxrUc/xR3AuuT2z/RfESlRxE/P0jpt7mYyoNBqMDfW0\n+dyTrhW7+AKBQDAzZqOO/3x5fEryhSL02bXprYo8u/c037KArcmdkeGAYlwb8QdEgKoZ0NltdL/6\nGjU33sCwfBwdKjQWc8ZkOdTaxtDefbg+fyf1991LSqMiYFAxUl3KSGcXFh+8oj4OKhVXDzcp8vUm\n3T2U1dawfdkVfOg+QGgkTE1pKVdc56G+ogI5bMrMgS0mA1s/6yU2VIY2qAfg8q1qPgm+zp1nVhPp\nC2JsrOe5yIfcOFzP6tH558Q0masayljpnN6aIpddYChsnt2pLCUnItwG586SUnaBTcAJWZbbACRJ\n+gVwGzAvyq6hqkqpGOj0GbOOMRru30ngeCv1D9xHW38b3gojnVVxNHG4e+3NjCRGGIgOcfuaG0gm\nk3RM6NyhkTBDER/7uw4Ck82da8vKeenMMwAcOPJeOtBV55hP7wNscl5Y2AfAuSnKgsWH3m6j56Xx\ndFgN9+/ktEuLMWwg265eM1IyZe5mgUAgEKSpLDHxwE1r6OoP4nJYqC0vPjXLaa3k3nW30h3opdZa\njcviXOgm5cSkcW3XzhmuFowEgtTceAPtP3syc8x1x+2EOjvRmIzoq6rQmIyEPR4G391D/X33Yh2O\nY3n6VSKkd3C/ves+on4/equVsCWdW7pswwaisSRlxz2stFZwmVciUl1CDzGWuaxEoj4eMV2BpX8Q\nnc1GsE9Hd20tzmEvet9ePtdiZ1ir4lrN5aiPnsbsqiN82M1jKzYybKuFVJLY0UNEOzq4oL6e5o3r\nqKi08X7rAXpCHrxRP+XGUqpNVay2rUSFOrMLPLZjHC47ykc9PsWGUSqV4lSXF1P3pyS6OtE6y3gn\n8Qn9Ye/c8uxOaNfEyNHZinO9XRnZOdtScja3QbHzO85SU3ZdwEQv9U7SCvD8EI8rTT6vvopERBmS\nP9zVzeCe9xnc8z6OB+/l4xo/B9yfpJ3zkyZF7rp71t06atJ8E76In0giOu5HW76coZCXe9bdisfn\nZcRXSq9XaQoyMdBVp79rXpRdwdIiOjA4SR6OWzL98P4L72AgEGSoV89zv0rnbhapqQQCgWBq+rxh\nnnhlfP39gZvWcEFdcb0vPcEBfjHBheredbey3FI8uUUnjWv9IkDVTOgsFqJ9SkvCUGcnQ3vTmymO\nq6+i/533qN95D4NA4MRJtFalqfCwfByN0UjHi7/GdcftoFbhfi6dgcQOlG7bmpk/O760g++H3uUv\nrFcR+benGJtFV27bysrhBjz/Ma50V27bCkD/h3sZYi+V27bif+I5KixfI2Y+ROt3v5u5dtmjj7Jv\npYX9nk8UwV63NLSQTCVZY5cyu8A9idP8V+vTjJthjm8YHWn3Yuv5A8HH/yNTxgN/fBff4+055dmN\nHZ3cLv3ai6bYqX14RktJm6YyS1ZmhBE7v+MsNWX3rHA4bLNfNAeOezzKAyoV5saGzAsBwOic4ETf\n1UvEoWJ9TTMHug9zaa1SGfUE+hgZdPDaPhW3fW4ZIcMAl9ddSqWlnD63lt1v24Ekd27fQFKTotTl\n5cPxTEOKyMoNpa5JnzNfn1uUOf/MV/uDNqW5j9ZqwWGuoMmxisYSF59bcw3P/u4kz781nkqjZzDE\n1S0Neal/Ib6nheobxd4nZyNfn0+jmXllWq1Wz6mufD7vfJUlyllcFKLdU0VjLtTzKVS5Paf7lHKg\nD0dz8XyGKce1Iu2j2RTic5wcGEBrV5arMY4vD8SD6T4d6fFkzmnt9knXj23+hDo70ZrNivMTN4YM\nHi/YQNM9QDLrmpS7Z9r7JsqRjg7MJcoljESPm/ZKy6SMJ5F4FE/Ew7aVaX/YKoedf3t/r+Ia93A3\njguvAKDngBtLd5fy83UPgC2dZ3e276C9R+kGluhx47hqC2/3KnUIT6SXwORVDAAAIABJREFUzzd/\nbtL9Y+WHPy1ns+VmYhov+kQp4d4KHBeO1z25vPHPeL6x1JRdNzBxll03emxG+vqG81K5qbZWIafi\ncTQWC5XbtqIxmzE4KvHsfjtzXltXw/6u32bkMpMyynGNtYpAfwMP3mTFrNfx7Z8MAFY+s7GaaDzJ\n2hVxTAYtNeUm1tSXkaIGy+gqUK3NSSicwFhXQp29lg1VaxWf0+Gw5e1zizLnfzKX7/ZPh8ZmV6Ro\n0FitGNUG9ncd5EJHE0MDYWrKlYOWs9ycl/YV4ntajHUuVL3F2GcdDhuJRHLGgSuZTM5aVz6fd77K\nEuXMraz5pBC/SZdDuePlqrQWpJ5CvlNq7VUKucZWVVSfQTvFuFaoZ7UU+qzB6SSVSmWembm+jp7X\nxq0QxxRfY42Thvt34v7VC5RffhmuO28n1NGZScVZtmFD5np9hXIXcqLy3F+qgQQkaiZfo6mrmfa+\nibKhvh6tWa8853TRUGLB7VcqgUatgWpjteLZuWzK+bzLVpM5X1NuRhNTmhjr6lw8snozLa6LZv0O\ntE7lvRqni76+YaqNyojS2W0C5W/CUWLipy/HASsQ57GdJsX1cylvKpbKws9EVKlUaqHbkDckSdIA\nMukAVd3AR8BOWZaPznBbKl8vh3B4CNW+/US7e9CVlKC22Ugk4mhSgF4POg2JIS+xIS9al5MOyYE3\nGaA70IfTUoUaFaFkCF8kgNNSySbHRrToAKiosPLOxx10eAIsq7GSSKJIBZSrj2QxKZFFUuZ8Oqnm\nrc/ORjg8hOrDvYS7uzHV1JDavJH3Bw9gM9poqdiAGg0pUhxp855Tf5wKoewWvM6i67MOh42bHvw2\n2sqLpr0m2vsHfv7dv5i1HKHsFlc5o2UVXZ/NJkySD//gwd0fwFVpZfPF1ZgK4EdXyHdKmCD7+z6h\ne7iXGlsVlzouwUT+UygV7DOEh4hMGNeMmzeCqTCm5Euiz4aHUJ04BUND6WfmcpGKxYgNeTFUVRLt\nH8BQUUGqxI5qJE5ycIh4KIi+qopkMMiIz5++zudHYzKS0muJVtpJxGJEOzvRuWpRabXE2zpR1znZ\nVxaiwlKGNqnD2e7F2jmA1mYlWGmjz1WKo30QfVsPOrudWFUp0UQMtbsXS6mDsC+MrtpJ+aUbUEHG\nN9ZQX4++aR0VDhsfnPmE7lAP3qifMmMJTlN1xmd3jCRJ9vUcptPfRZ29lhZn87jPLilOd3kxdn1K\nwu1GX1dP1YYrUKs0c+uzE3x2x9o1lc/uVD62E8ufbe51tj6789xn54UlpexCJvXQPzGeeujvZrkl\n7y+HYlDQiqGNRVZm0Q9oM3G+KJ5C2S0YQtkV5eSjrKLrs9NR6N/8fLxTiv0zzNMzEn12kZQ/H3UU\ne/mjdSw5ZXepmTEjy/KrwPnpgS0QCAQCgUAgEAgEAoDzNAa1QCAQCAQCgUAgEAiWNELZFQgEAoFA\nIBAIBALBkkMouwKBQCAQCAQCgUAgWHIIZVcgEAgEAoFAIBAIBEsOoewKBAKBQCAQCAQCgWDJIZRd\ngUAgEAgEAoFAIBAsOYSyKxAIBAKBQCAQCASCJYdQdgUCgUAgEAgEAoFAsOQQyq5AIPj/2bvz6Diu\n+8D338a+AwQJAiS4aOXVYjmWzdHYUbyNM441ycjOi+Mljh3Hejke23GcsV/eiTMzJy9nMpOZvBc7\n8ZLFiZJYjncnY8nrKE4cy9ZkJFGWbFmUrmRLFEkQAEGCxL6j3x/ohhoNgADJRndX8/s5h4ddt27d\nulX49UX/0HWrJEmSpIpTU+oOSJJUaAsLCxw58tSa686caWF4eByAyy67gurq6mJ2TZIkFUnikt0Q\nwm8DvwKczBT9Vozx65l17wfeBswD74kx3l2aXkqSSulHP/ohX3znr9DV0LhunaHpKV7zx3/OgQOh\niD2TJEnFkrhkN+MDMcYP5BaEEK4FXgdcC+wBvhFCuDrGmC5FByVJpbOwsMBXQw0NbbXr1pkenePf\nLiwUsVeSJKmYkprsptYoezXwmRjjPHAkhPAkcBNwX1F7JkkquerqKqrPvpCahc7164wNU11dxcLC\nAvfc881ztveSl7zcy50lSUqYpCa7vxpCeDNwCHhfjHEE6AX+OadOX6ZMknSJqa6uprF1O03tO89R\nK0V1dTVHjjzF73zhv9LQ1rRmrenRSW7ft58rr7wagE9+8o7lda2tDYyNTa+o/6Y3vWVVvfVk695+\n++2r2lmv7rnazfbnQvqw2ePaTLu5dSVJKpVUOl1+V/mGEP4e6M4pSgFp4D8A/xs4FWNMhxB+F+iJ\nMf6fIYQPA/8cY/xUpo2/AL4aY/y7IndfkiRJklRiZfnNbozxX2+y6p8DX8q87gP25qzbkymTJEmS\nJF1iEvec3RBCT87i/wH8IPP6LuANIYS6EMLlwFXA/cXunyRJkiSp9Mrym90N/H4I4XnAInAEeDtA\njPFwCOFzwGFgDnind2KWJEmSpEtTWc7ZlSRJkiTpYiTuMmZJkiRJkjZisitJkiRJqjgmu5IkSZKk\nimOyK0mSJEmqOCa7kiRJkqSKY7IrSZIkSao4JruSJEmSpIpjsitJkiRJqjgmu5IkSZKkimOyK0mS\nJEmqOCa7kiRJkqSKU1PqDmxWCKEeuAeoY6nfX4gx/k5enZcCdwJPZYr+Lsb4u0XtqCRJkiSp5FLp\ndLrUfdi0EEJTjHEyhFAN3Av8Wozx/pz1LwXeF2O8tWSdlCRJkiSVXKIuY44xTmZe1rP07e5amXqq\neD2SJEmSJJWjxFzGDBBCqAIeBK4EPhpjfGCNai8KITwM9AG/EWM8XMw+SpIkSZJKL1GXMWeFENqA\nLwK/mpvMhhBagMXMpc63AH8UYzxwrrbS6XQ6lfLLYF20ogWRMasCMWaVNMasksaYVdJUXBAlMtkF\nCCH8J2AixviBc9R5GnhBjHH4HE2lh4bGCtq3rq5Wyr3NJPQxYW0Wc3AoeMxuZCvOmfss7X6TGLOF\nOk+FPN/l1qdKbSfTVuJidj1b/Z4vxpiS9GMo0jkyZsuk/WLsI+ntZ/ZRccluYubshhB2hBDaM68b\ngX8NPJ5Xpzvn9U1AaoNEV5IkSZJUgZI0Z3cX8PHMvN0q4LMxxq+GEN4OpGOMHwNeG0J4BzAHTAGv\nL113JUmSJEmlkphkN8b4CPD8Ncr/LOf1R4GPFrNfkiRJkqTyk5jLmCVJkiRJ2iyTXUmSJElSxTHZ\nlSRJkiRVHJNdSZIkSVLFMdmVJEmSJFUck11JkiRJUsUx2ZUkSZIkVRyTXUmSJElSxTHZlSRJkiRV\nHJNdSZIkSVLFMdmVJEmSJFUck11JkiRJUsUx2ZUkSZIkVRyTXUmSJElSxTHZlSRJkiRVHJNdSZIk\nSVLFqSl1BzYrhFAP3APUsdTvL8QYf2eNeh8CbgEmgLfGGB8uakclSZIkSSWXmG92Y4wzwMtjjDcC\nzwNuCSHclFsnhHALcGWM8Wrg7cCfFr+nkiRJkqRSS0yyCxBjnMy8rGfp2910XpVXA3dk6t4HtIcQ\nuovXQ0mSJElSOUjMZcwAIYQq4EHgSuCjMcYH8qr0AsdylvsyZYNF6eDUGabve4An+vtp3L0bWluZ\nGxiktqWJ6YFBGnp6SNfVwcQE6YUF5qemaLw6UHvtcyCVqL876FKRF9PVV11B7e7LjVcV19QZpg99\nlyMTE8yPT1C/YzuzIyPUtrSSqq9j9vQwta0tzJ49S11nJ6mmBtITk8wOnaa2ox1qaqiqq2NudJS6\nzk6Ozc4yMzhI7bZt1O7dRxqYOfIMde1tzI2NUFNbz+zwaWo7tlHT20vtgesAmH3iMIv9J5gaGKBh\n506qOrax+IqXQXqR2cd+wMyxYzTs3bt6TF9cYPr+e5l+5hkaurup7t1D7VXXXNj7KLOv2f5+apsb\nmR0ZW3ufklQIU2eYfixybHycmYEB6nbsIFVXx9SxYzTu2kW6toZUqor5qSlqqquZm5iktrmJmVOn\nqW1rJdXQwPz4BDWN9cyeHqZ+507mZ6apbW1bHr/SVSmeuitS09K6csw9/AjTP3yC2o5tVDXUM3t2\nlLr2NmaHT1PT0EhNby/pdJqZp35EbVMT0ydP0rhvP/U3/TikUqvGyqqrroArwsWPlRuN+Vu1rS5I\nopLdGOMicGMIoQ34Ygjhuhjj4VL3K2v6vgc4+jefWl7e96Y3UpVOc/SOT64omzzex6lvfydT8hUu\ne+97qbvuuUXurbSxVTH9i79AemTCeFVRTd/3AJPPHM0ZN2HHi3+CgS99hR0v/gkABr705eV1e9/4\neo59+rMr6gLU79jBRHxiVTvAclnvz76Go5/9wor1LQuLAMw9/RR9/+OLy+t6f/Y1DPzPvyfd0saR\nD3xguTx/TJ++/16O/sVfrtguPTd/Qe+j2cd+wJEPfIAdL/4JTuQch79HJG2F6fuWvlfK/Syw48U/\nsTxm7n3j65k83kfjnl6O/s2nlsbQvLr1O3Zw7JOfWS7r/dnX8EzOmJjbXu6Ye+SDH1y1Prvc/+3v\nLI/fAP056/eRpqqtY9VY2U9hxsrsOJx1Pm1ezLa6MIlKdrNijKMhhG8CrwJyk90+YG/O8p5M2Tl1\ndbUWpF9P9PevWJ7qH4CFhVVlC9PTK8oWBvroeunNG7ZfqH5uVXuXepvFVKz+r4rpE/00LC5sKl4L\noRQ/p1LFRtJjciMXc3xP9PevHjczy/nlANMDg2vWnR0eXredrNnh4VXrFwb61lw3OzzM/OQkddu3\nrdwmb0z/4bHjq7araqxf9T7azDk6munLuX6PFCqWyq2dYtvqfie9/WLsI+ntF9tWHM8T/f2rJg3m\njj/TA4MsTE8vj7trjaFrjZ3rtZc75q61Pnd5rfEfYObYceq2T6y97SY/c5/L0fz+ncf4e65tN6PS\nYrYYEpPshhB2AHMxxpEQQiPwr4H/llftLuBdwGdDCC8EzsYYN7yEeWhorCB9bOrtXbHcuKuHhanp\nVWWT83Mryqp7ejfsQ1dXa8H6uRXt2WZxB59C9389q2J69y6qdvYUZf9b8XMqx32War9Jitmm3l4m\n5o6sKKtuaHj2/9TK+g093WvWrevsJJ1eXHNdVt327avWV/f0kgLqZmZX1u3spK5rB7S0rdwmb0xv\n2Lt31Xb5dTYbAzU9S+/J6saV/c62V6hYKrd2sm0V01a+J7f6PV+MMSXpx1Csc1RMW3E8TZlLhXPl\njpsNPd1MHZ9fHnfXGkPzy+q2d67bXu6Yu1yWP97ljv+w6ndA/d49VLd1rL3tJj5zbyQ7Due3uZmY\nWm/bzajEmC2GVH4Al6sQwg3Ax1m6qVYV8NkY438JIbwdSMcYP5ap9xGWvvGdAH45xvjdDZpOFyxw\nMvMbp/r7ady1C9pamRs4SW1zE9ODgzR0d5NuqIfxzJzdySkarj5A3XU3bHi9vslu2beZ2rhWwRQu\nZjeSF9NVV19JXZHm7Jrsbvk+kxOzU2eYfvAhFsbH8+bstpCqr392zu6Zs9R1biPV0kx6fHxpzm57\nO9TWUFVXy9zoGLXbO2EmM2e3o4OavfsglWLmyDPUtrcyPzZKTV09M6dPU9fRQXXvHuqy88d++DiL\nfceX5+ymOjrofcXLOT08sTwHq37vXurOOWd3J1W9e6nLm7O76RjImbNb09zI3MjYin2WW5Ja4GQ3\nOTG7gUpJ5JJ8DEU6R8mP2akzTP/oKRg+s8ac3R7SNbWkgPmZmdVzdltbSDU2Mj8+Tk1jQ2bObtdS\n3da2pfFrzx6ormLmiUhNS8vKMTc7Z3fbNlL19cydHaW2vZW54WFqGhqo3r2UOObO2W3Yt4+Gm25e\nMWc3O1a2X3U5i1dc4P0ScuXMuz3v8XedbTejAmO2KBKT7G6hgg8OSUjQktDHhLWZ/F9o53CpJJ4m\nu1umIDFbpglYWfWpUtvJtJW4mF1PhSRyiT6GCkwcjNkS7yPp7Wf2UXHJrrf/kiRJkiRVHJNdSZIk\nSVLFMdmVJEmSJFUck11JkiRJUsUx2ZUkSZIkVRyTXUmSJElSxTHZlSRJkiRVHJNdSZIkSVLFMdmV\nJEmSJFUck11JkiRJUsUx2ZUkSZIkVRyTXUmSJElSxTHZlSRJkiRVHJNdSZIkSVLFMdmVJEmSJFUc\nk11JkiRJUsUx2ZUkSZIkVZyaUndgs0IIe4A7gG5gEfjzGOOH8uq8FLgTeCpT9Hcxxt8takclSZIk\nSSWXmGQXmAfeG2N8OITQAjwYQrg7xvh4Xr17Yoy3lqB/kiRJkqQykZjLmGOMAzHGhzOvx4HHgN41\nqqaK2jFJkiRJUtlJ0je7y0IIlwHPA+5bY/WLQggPA33Ab8QYDxezb5IkSZKk0kul0+lS9+G8ZC5h\n/ifgP8cY71xj3WKMcTKEcAvwRzHGAxs0mawToHJVzCsKjFkVgjGrpDFmlTTGrJKm4q6QTVSyG0Ko\nAb4MfC3G+EebqP808IIY4/A5qqWHhsYK1UUAurpaKfc2k9DHhLVZ1F9ohe7/RrbinLnP0u43iTFb\nqPNUyPNdbn2q1HYybSUuZtez1e/5YowpST+GIp0jY7ZM2i/GPpLefmYfFZfsJmbObsZfAofXS3RD\nCN05r28CUhskupIkSZKkCpSYObshhJuBNwGPhBAeYulyjd8C9gPpGOPHgNeGEN4BzAFTwOtL1V9J\nkiRJUukkJtmNMd4LVG9Q56PAR4vTI0mSJElSuUraZcySJEmSJG3IZFeSJEmSVHFMdiVJkiRJFcdk\nV5IkSZJUcUx2JUmSJEkVx2RXkiRJklRxTHYlSZIkSRXHZFeSJEmSVHFMdiVJkiRJFcdkV5IkSZJU\ncUx2JUmSJEkVx2RXkiRJklRxTHYlSZIkSRXHZFeSJEmSVHFMdiVJkiRJFaemWDsKIVx3rvUxxsPF\n6oskSZIkqbIVLdkFvgKkgRSwDxjNLLcDR4HLz7VxCGEPcAfQDSwCfx5j/NAa9T4E3AJMAG+NMT5c\nwGOQJEmSJCVA0S5jjjFeHmO8Avgy8IYY47YYYyfweuBLm2hiHnhvjPF64EXAu0II1+RWCCHcAlwZ\nY7waeDvwpwU9CEmSJElSIhTzm92sl8QY351diDF+IYTwHzfaKMY4AAxkXo+HEB4DeoHHc6q9mqVv\nf4kx3hdCaA8hdMcYBwt6BOuY4gwPn4qMn5hgYm6S7uYd1KXqWFich6oqFhcXOD4+QG9LD1WpKsbn\nxhmdGaeraTtVQHVVDdPzM4zNjrOtsZ3hqRE6GtpYTC8yfnSczsZOJuYnGZ+doLt5B2PT4zTVNTEw\nMcSu1p3ULdYyl5pncHyIPe272Va3jWdGjrGnbTeQZmDyJGMz41zVcQXbdzwfgDSLxNEn6RvrZ0/b\nbkZnRzk+eoKe1i4W59Nsb9pOaLua1Cb/LrLU3hP88OzTtNW30NPUw9WtV256e5WXKc7w4NBh+p86\nya6WnYTW63hy/DGGJofpaemis347R0eO09u6i9B2NcByPGXLLuRnn2aR+48/zFOnjl1UO0mX+/68\nlM9DNg4HnjrJzpYdtNe1MTE3wcnJ03TUt1NbXc2pqbO01DZRW1VNc20LU/NTnJ46Q2djB2emR2iv\nb6W+qo6Tk6foHNrG0MRpdjRuo7qqmsGJU7Q3tNJS00R1qpaxhXFOTwzT3dzF6Mw4zXWNtNe1s5he\nZGhiiLbGNqbmpxidGadtoJW6VC1VqRRdTTuZX5zn6OgxWutbmF6YZnRmnN7WXdzY+WN89/TDnBwf\norN5G1Mz0/S27qYqVUX/+AAtZ5roHztJc10j9VV17Gjs2nDsXCs+ztdWxFhum1fM7OWy+ssvybg9\nl5mZGX7251/HYnrt9TW11Xz+k5+kqsrzptKa4gyDU8N85/AA/WNLnwWaqxsZmjpFc30zpybPsL1p\nGyNTo3Q0ttFQ1cCR0ePsat3J4uIip6aGaatroammkQUW6B8fWvpcmk7TPz5Ie0MrTdWNDBwZoqel\ni+mFWc5Oj7CzeTtnp0ZprW9heOoM2xs7qa+uZ2J+kpHpUVrqmmmrb2V8dpyz06N0t3QxNzdHd3M3\nB9qu4onRH64a1xYXF4ljTzAwOcjZmVG6GrdTW1XD6PQYva27l+ttNC5m1w9ODFJbW8vg+Cn2tO3i\nldtffMHnOX+fax3Dueqv18dL/fMDlCbZTYUQXhxj/DZACOFmzvMb5hDCZcDzgPvyVvUCx3KW+zJl\nRUl2Hxw6zNGRPu49emi57NZrXgnA6clhAO49eoib9x1cfp1bL1sH4Gs//Kfl19n6I7PjK7b5+et/\nhk//4M7l5dc/51Y++8hdK9q8K969xv6+SX19DZfXX0kcfZIPH7p9eT/5ffrUoS/y7oO3cU1b2NQ5\nWGrvL1f0fTG9uOntVV4eHDrMpx95Nsbe8Jw0n/nB6hgDePfB2wCW4ylbdiE/+9y4vJh2ks7zsCQ/\nDrPjZXY8zR23bt53kO1Nndz1+NLYd/eP7lmxDuB/PnLP8nL+trtbe/j8o19eUfaVJ/9hedvtTZ08\nffboqu0AxuYnl/d7evrMijozN8zw6Ufu5OZ9B/na9/9p1bb3PrayvSOjxzccO9eKj51dB9etv9k2\nLjbGVrQZL924PZfp6Wka97+chh1rn5fpkz9gcXHRZFcl9+DQYdLplb/7f/76nyFdVcWnMmPa3Y/c\ns/SZ8ZE7ufWaV/KPT98LrBxjcz+LrjX2AjyVN7bees0rV+03d3y+9ZpXctfjd69o53OPfZlf+rGf\n5+Pf+/xyeXYMOnTi+zw4+PCKPuXuL1tvo3Exuz5/+6pUihdse8HmTmye/H2udQy54/tm+7je+ktJ\nKZLddwGfDiFMZJYbgTduduMQQgvwBeA9McbxQnSoq6u1EM3Q/9RJpudnVpSdmToLsKI8v0623lrl\n69UHODlxasXywPjJDfeddXSkj5uufx7fOvns3wHW6/vg9CAvvnJzH6AGp1f+XWF6fua8tl9LoX4+\nW91mMRWr//1PrYyp/nViDFb/7LNlF/Kzz43Li2nnQpQqNtbabynPQ6FdzHnNj8Pc8TJ/3Jqen1l3\n7NvMcv64mr+ftcbq3HVrtQvQP3ZyU33ILdvo571WfMD5netzxdiF/swqJW63ciwYGRmhKrV+Iltd\nlaKrq5Wamgv/mFaMsWyr95H09ottK46n/6mTpFl5CcLJiVMspheB1eNf7ueC9T77bmYczG8ru99z\nrc+20zfev6I8OwZ969G+c/Zjud4GY1h2ff72R0f7eNWBl615LBvJ3+daxwDP/ow328f11l9Kip7s\nxhi/HUK4AgjPFsXZzWwbQqhhKdH9RIzxzjWq9AF7c5b3ZMrOaWhobDO739Dutp08c3Z+Rdm2xg6A\n5UEBoKGmYdW22xo7cuqkVqxrqKlfVQaws3nHiuWelp1r7nut/e1r72VoaIzuhu51+5Xdvruhe1Pn\nqKurdUV72b5vdvv12izUz2er2yymQvd/PbvbVsbU7paVP99sjACrfvbZsgvpa35bFxND52MrYuNi\n9ruV5yFJMZsfh7njZf641VBTv+7Ylz+WrrU+f1xd2ubZ/zsbO1hMp9es03mOMXd3685N9Sm3vY1+\n3mvFB5zfuV4vxi7mvbBVcZukmN1IXd3KzwX5FhbTDA2NXXCyW4yxbKv3kfT2s/sopq04nt1tO8kP\n1Z3NO5hZWPronh2vsuNu7ueC7LrVr9caByF/LOzMaSu731zb8tZn29nTuntFeXYM2tfeS9/oYE79\nhjXrbTSGZdfnb7+vrbdg42Zvy64112fb32wf11u/nkr7AxBAKp1eZ8JIgYUQ6mOMMyGEprXWxxgn\nN9HGHcCpGON711n/b4B3xRh/OoTwQuAPY4wv3KDZdKEGhykm+N6pRxibXZqzu7NpB/VVtcwvzpNK\nVbGYXszM2e2mOlXNWGbO7o6mTqpIUV1Vzcz8LGOz43Q0tnN2apS2hlbS6UXGZsbZ3riNifkpxmcn\n2Nm0g/GZcRrrGhmcOMWuli7q03XMZubs9rbtorO+k2dGjrG3rRdI0z85mJmzezk3X/ECTp+aWHFN\n/9623Yxk5ux2t3SRXji/ObtdXa2cHBpZnrPbWt/Croucs5ugZHf1XyO2TsFidiNTTPDg0MP0j52k\np6WLa1qvX56z293cxfaGrZuze2Tm6aLP2S23ZHcr59wkKWazcTgwdpKulu101LYzMb80Z7c9M2f3\n9NRZWmobqamqoaWmmamFaU5PnWHbijm7tQxNnqazafWc3baGFlprmqhO1TG2MM6piWF6MnN2m+oa\n6ahrYzGdZmhiiPbGNiazc3brl+bsplIpupu6mVucWz1nt2UXN27PmbPbtI2p2Wl6W3dRlapemrPb\n8Oyc3bqqOroucM7uzq7284rh9WLsYt4LK+bs7ijcnN0kxexG6uoWee2v/dk5L2O+4/ffabKb4PYz\n+0h8zE4xwdjUWZ4YP0L/2Em6W7poqW7i1NQpmuqbOZ0zZ7e9sY3G7Jzdli4W02lOTQ3TWtdMc03T\n8pzdva09pEktz9ltrG5gcOLUyjm7TZ2cnR6jtb6F01Nn2N64jcbqesbnpzJzdptoq2tlfG5iac5u\n8w7m5ufPOWd3+45m/vnph+mfHMjM2e2ktqr24ubs1tQyOHGK3tYefurASzhzeuqCzvNm5uzmju9b\nNWe3yDFbFMVMdr8bY3x+CGGRZx9BlJWOMVZvsP3NwD3AI5nt08BvAfsz238sU+8jwKtYevTQL8cY\nv7tB1wo+OCQhQUtCHxPWZuJ/oZ1LKZLAS2WfpdpvEmO2UOepkOe73PpUqe1k2kpczK7HZLfy28/s\no2JitkJ+Hok+hgqM2aIo2mXMMcbnZ16u9f34ht/qxhjvBc6ZEGfq/ep5dk2SJEmSVGFKcZu/MWA0\n799UCOGeEMKleZswSZIkSVJBleJuzP8BmAL+kqVLmX8J2AE8BfwZ8LIS9EmSJEmSVEFKkey+NsaY\n+xCqD4UQHowxviCE8L4S9EeSJEmSVGFKcRlzU+bRQwCEEC4HmjMbCvp2AAAgAElEQVSL82tvIkmS\nJEnS5pXim93/CNwfQngws/x84N+FEFqAz5egP5IkSZKkClP0ZDfG+LchhG8D/zJTdF+M8WTm9X8t\ndn8kSZIkSZWnFN/skkluv1SKfUuSJEmSKl8p5uxKkiRJkrSlTHYlSZIkSRXHZFeSJEmSVHFMdiVJ\nkiRJFcdkV5IkSZJUcUx2JUmSJEkVx2RXkiRJklRxTHYlSZIkSRXHZFeSJEmSVHFqSt2BzQoh3A78\nDDAYY3zuGutfCtwJPJUp+rsY4+8WsYuSJEmSpDKRmGQX+Cvgw8Ad56hzT4zx1iL1R5IkSZJUphJz\nGXOM8TvAmQ2qpYrRF0mSJElSeUvSN7ub8aIQwsNAH/AbMcbDpe6QJEmSJKn4EvPN7iY8COyLMT4P\n+AjwxRL3R5IkSZJUIql0Ol3qPmxaCGE/8KW1blC1Rt2ngRfEGIc3qJqcE6ByVsxL6I1ZFYIxq6Sp\nmJgdGRnhde/5c+q2X73m+rlTj/Ll2/9vamoq7QK8S07FxKwuGRU3JTRpo2iKdX4IIYTuGONg5vVN\nQGoTiS4AQ0Njhesh0NXVWvZtJqGPSWuzmArd/41sxTlzn6XdbxJjtlDnqZDnu9z6VKntZNsqpq18\nT9bVwWJ6cd31C4tphobGLjjZLcaY0tnZxN/+7V3rrn/JS15OdXX1Bbe/1cdQjHNUSTFbKT+PJB9D\nJcZsMSQm2Q0hfAp4GbA9hHAU+G2gDkjHGD8GvDaE8A5gDpgCXl+qvkqSJFWyH/3oR/z+P/wRTZ3N\nq9ZNDk+wb99+rrxy7W+uJalYEpPsxhh/YYP1HwU+WqTuSJIkXdK6rtlF6+6OVeVjJ86WoDeStFol\n3aBKkiRJkiTAZFeSJEmSVIFMdiVJkiRJFcdkV5IkSZJUcUx2JUmSJEkVx2RXkiRJklRxTHYlSZIk\nSRXHZFeSJEmSVHFMdiVJkiRJFcdkV5IkSZJUcUx2JUmSJEkVx2RXkiRJklRxTHYlSZIkSRXHZFeS\nJEmSVHFMdiVJkiRJFcdkV5IkSZJUcUx2JUmSJEkVp6bUHdisEMLtwM8AgzHG565T50PALcAE8NYY\n48NF7KIkSZIkqUwk6ZvdvwJ+ar2VIYRbgCtjjFcDbwf+tFgdkyRJkiSVl8R8sxtj/E4IYf85qrwa\nuCNT974QQnsIoTvGOFicHsLU1CL3PTnIiaEfsmtHE1PTc7S31FFVU8XI2CyjE7N0tNTT0lDD0Mg0\n7c11nDw7SVdHE2fHZ9i5rZGJqXnOjs2ws7OJkbEZGhtqaKqv5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WeyK0mSJEmqOCa7kiRJ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Bc5KXusS4fS7W9ISJb2BQ0O2mYE0tT/w0yNb1K3n57Tbuvt34FnSZdQX5/oljaU01Vcb4ymqh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\nX1Jm2M5is9E0Ws6LvzF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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6a179f77f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.pairplot(data, hue='gid')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "62f9810d-3757-0da9-f5a0-ccaa10e15e7f" }, "source": [ "Preliminary conclusions are the following:\n", " - In groups 1 (EPH) and 2 (UCT) posts get more likes and repost than in group 3 (FSZ). \n", " - In group 2 (UCT) posts get more comments than in groups 1 (EPH) and 3 (FSZ)\n", " - Message length is equal in all three groups." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "fe34146c-35cc-3299-34b4-027894824d38" }, "source": [ "## Hypotheses testing\n", "\n", "Now let's test these hypotheses. We will calculate [95% confidence interval](https://en.wikipedia.org/wiki/Confidence_interval) for mean values of likes, shares, comments and message length in each group and compare them. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "ebc94fc8-7d03-138a-c7e9-d316145d04b0" }, "outputs": [], "source": [ "park = data[data.gid == 1]\n", "town = data[data.gid == 2]\n", "free = data[data.gid == 3]\n", "\n", "def conf_interval(field):\n", " \"\"\"\"\n", " Calculate confidence interval for given field\n", " \"\"\"\n", " # I've rounded numbers to integers because estimated values (likes, shares, ...) are integers themselves.\n", " print(\"95% confidence interval for the EPH posts mean number of {:s}: ({z[0]:.0f}, {z[1]:.0f})\".format(field, z=zconfint(park[field])))\n", " print(\"95% confidence interval for the UCT posts mean number of {:s}: ({z[0]:.0f}, {z[1]:.0f})\".format(field, z=zconfint(town[field])))\n", " print(\"95% confidence interval for the FSZ posts mean number of {:s}: ({z[0]:.0f}, {z[1]:.0f})\".format(field, z=zconfint(free[field])))\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "c59c2008-0bc0-55bd-99c9-4e29873b66da" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "95% confidence interval for the EPH posts mean number of likes: (5, 6)\n", "95% confidence interval for the UCT posts mean number of likes: (6, 7)\n", "95% confidence interval for the FSZ posts mean number of likes: (3, 5)\n" ] } ], "source": [ "conf_interval('likes')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "93b2875a-68be-d54e-925c-b7363277b7aa" }, "source": [ "Confidence intervals for mean number of likes in groups 1 and 2 intersect, but both lower bounds are greater than upper bound in group 3. But to be sure that mean number of likes (shares, comments, ...) is different for each group (or at least in groups 2 and 3) let's apply [Mann–Whitney test](https://en.wikipedia.org/wiki/Mann%E2%80%93Whitney_U_test) (significance level 0.05, two-sided alternative). We will compare 3 pairs of samples: EPH group vs UCT group, EPH vs FSZ, and UCT vs FSZ. This is multiple test and we will use [holm multiple test correction](https://en.wikipedia.org/wiki/Holm%E2%80%93Bonferroni_method). \n", "\n", "**Null hypothesis**: mean number of likes is equal in the particular pair of groups, **alternative**: null hypothesis is wrong (mean number of likes is not equal for the pair of groups). If p-value is less than 0.05 we reject null hypothesis." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "a24a9aa1-75c9-929f-fc2a-82c6e7195396" }, "outputs": [], "source": [ "def compare_means(field):\n", " \"\"\"\n", " Mann–Whitney test to compare mean values level\n", " \"\"\"\n", " mapping = {1: 'EPH', 2: 'UCT', 3: 'FSZ'}\n", " \n", " comparison = pd.DataFrame(columns=['group1', 'group2', 'p_value'])\n", " # compare number of <field> in each group \n", " for i in range(1,4):\n", " for j in range(1,4):\n", " if i >= j:\n", " continue\n", " # obtaining p-value after Mann–Whitney U test\n", " p = mannwhitneyu(data[data.gid == i][field], data[data.gid == j][field])[1]\n", " comparison = comparison.append({'group1': mapping[i], 'group2': mapping[j], 'p_value': p},ignore_index=True)\n", " # holm correction\n", " rejected, p_corrected, a1, a2 = multipletests(comparison.p_value, \n", " alpha = 0.05, \n", " method = 'holm') \n", " comparison['p_value_corrected'] = p_corrected\n", " comparison['rejected'] = rejected\n", " return comparison " ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "76e5ac7e-9eff-3276-dcef-cea900604a76" }, "source": [ "Let's compare likes distribution in groups, if it equal or not. **p_value_corrected** here is new **p_value** after holm correction." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "6a9a06cf-7cd4-eb5f-b7e7-971dffa0d2b2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "95% confidence interval for the EPH posts mean number of likes: (5, 6)\n", "95% confidence interval for the UCT posts mean number of likes: (6, 7)\n", "95% confidence interval for the FSZ posts mean number of likes: (3, 5)\n", " group1 group2 p_value p_value_corrected rejected\n", "0 EPH UCT 0.004151 0.008303 True\n", "1 EPH FSZ 0.000513 0.001539 True\n", "2 UCT FSZ 0.032475 0.032475 True\n" ] } ], "source": [ "conf_interval('likes')\n", "print(compare_means('likes'))\n", "# compare number of likes in group1 with number of likes in group2, \n", "# and if rejected field is True make a conclusion that \n", "# mean number of likes in the first group is different from mean number of likes in the second one. " ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "6cd555e6-ddae-1104-ac6d-4ccee131e75a" }, "source": [ "Great, we see that mean number of likes in each group is statistically different. But practically speaking there is no sense because the difference is only 1-2 likes.\n", "\n", "**So, our conclusion #1: posts get about equal number of likes in all three groups .**" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b7aca06b-b7d1-3a48-94a0-ee170ce47e5c" }, "source": [ "Now, let's do the same test for number of shares." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "dcd82555-b84e-0e03-a31a-08d224ae86a5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "95% confidence interval for the EPH posts mean number of shares: (1, 1)\n", "95% confidence interval for the UCT posts mean number of shares: (1, 2)\n", "95% confidence interval for the FSZ posts mean number of shares: (0, 0)\n", " group1 group2 p_value p_value_corrected rejected\n", "0 EPH UCT 0.001386 0.002772 True\n", "1 EPH FSZ 0.019395 0.019395 True\n", "2 UCT FSZ 0.000397 0.001190 True\n" ] } ], "source": [ "conf_interval('shares')\n", "print(compare_means('shares'))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "21d0ca8f-da1e-4210-a663-983baef065a1" }, "source": [ "Confidence intervals for groups 1 and 3, 2 and 3 do not intersect, and Mann–Whitney test rejects all null hypotheses, so we can make another conclusion.\n", "\n", "**Conclusion #2: posts from group 3 are almost never shared, and it has practical significance: if you want your message to be shared, you should choose one of the first two groups.**" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "08fc85d5-da21-079a-d3cb-b8d3d1030e0d" }, "source": [ "Let's do the same test for number of comments." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "b848530e-5eca-57f9-071f-0d58e55ed23e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "95% confidence interval for the EPH posts mean number of comments: (6, 6)\n", "95% confidence interval for the UCT posts mean number of comments: (6, 7)\n", "95% confidence interval for the FSZ posts mean number of comments: (6, 10)\n", " group1 group2 p_value p_value_corrected rejected\n", "0 EPH UCT 0.000226 0.000679 True\n", "1 EPH FSZ 0.004089 0.008178 True\n", "2 UCT FSZ 0.155542 0.155542 False\n" ] } ], "source": [ "conf_interval('comments')\n", "print(compare_means('comments'))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "f2ec3649-78d0-ab3c-464b-de93942206fe" }, "source": [ "In average, posts in each group have about equal number of comments. Yes, statistical test has shown significant difference in mean number of comments, but again there is no practical sense.\n", "\n", "**Conclusion #3: hypothesis that posts in group 2 (UCT) get more comments is wrong. Posts in each group have about equal number of comments**" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "528197fa-c151-aa5b-4f95-83d905cc5bd5" }, "source": [ "Finally, let's do the same test for message length." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "6956c365-2c41-bc92-ab20-ae737a21091a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "95% confidence interval for the EPH posts mean number of msg_len: (243, 261)\n", "95% confidence interval for the UCT posts mean number of msg_len: (285, 316)\n", "95% confidence interval for the FSZ posts mean number of msg_len: (280, 396)\n", " group1 group2 p_value p_value_corrected rejected\n", "0 EPH UCT 0.000032 0.000096 True\n", "1 EPH FSZ 0.292997 0.292997 False\n", "2 UCT FSZ 0.135572 0.271144 False\n" ] } ], "source": [ "conf_interval('msg_len')\n", "print(compare_means('msg_len'))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "0fcd22c0-7e14-6c19-e289-a7f9b55eb51d" }, "source": [ "Confidence intervals for groups 1 and 2, 1 and 3 do not intersect, and Mann–Whitney test rejects only first null hypothesis. It means that message length in group 2 is significantly longer than in group 1. Probably, messages in the first group have more photos instead.\n", "\n", "**Conclusion #4: message length in group 2 (UCT) is longer than in group 1 (EPH).**\n" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "4b902dc7-1f46-00ae-ef33-7443c6d3934f" }, "source": [ "## What do people like?\n", "\n", "Some posts in this dataset have exceptional characteristics: some of them have enormous number of shares while having very little likes, and some posts got huge number of likes despite just few people decided to share them, and so on. Let's investigate why this happens, and what such posts contain." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "3564653d-8532-2fa1-8ae6-d5f26564391c" }, "source": [ "### Many shares, few likes\n", "First of all, we'll look at the posts that have number of shares larger than other 98% posts, and that was shared much more often than liked. I think it's interesting to know why this happened." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "f38cddc5-8d45-7b82-bdb7-b26dfb5fb250" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "top 10 out of 24\n", "shares: 1642 \n", " message: Irish Setter puppy just found around Tookany Creek Parkway and Jenkintown Rds. No collar{COMMA} coat was just groomed or bathed because it still has a shampoo smell. Currently at the Garza residence. \n", "\n", "shares: 508 \n", " message: Anyone lose a great dog? 215-913-6659 \n", "\n", "shares: 201 \n", " message: Who does she belong to \n", "\n", "shares: 125 \n", " message: Found dog on Asbourne Rd in Elkins Park..he was running on the Road..message me if you know his owner ...please share \n", "\n", "shares: 74 \n", " message: Please repost & help find this missing 5-month old female puppy who ran away on this frigid day in Wyncote! \n", "\n", "shares: 61 \n", " message: Lost dog - last seen near High School & Marvin Roads. Please let me know if you{APOST}ve seen him. Thank you! \n", "\n", "shares: 48 \n", " message: My friends family lost their dog yesterday. Please see the attached picture of her post in case you have seen Buster \n", "\n", "shares: 48 \n", " message: This dog was lost in the Abington area{COMMA} on Old York Road. The owner has cancer and is having a difficult time. \n", "\n", "shares: 44 \n", " message: I am posting this for my neighbor who is not on FB. His dog{COMMA} PRECIOUS{COMMA} is missing. Brown and white beagle mix. Very friendly. \n", "\n", "shares: 44 \n", " message: Milo was found at Washington La & Church Rd. If you know his people please have them contact Cheltenham Police \n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " if __name__ == '__main__':\n" ] } ], "source": [ "shared = data[data.shares > data.shares.quantile(0.98)][data.shares > data.likes*10][['msg','shares']]\n", "\n", "top = 10\n", "print(\"top %d out of %d\" % (top, shared.shape[0]))\n", "sorted_data = shared.sort_values(by='shares', ascending=False)[:top]\n", "for i in sorted_data.index.values:\n", " print('shares:',sorted_data.shares[i], '\\n','message:', sorted_data.msg[i][:200], '\\n')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "ee0329a7-3c4d-cc2c-99a3-173b55d16479" }, "source": [ "OK, this was obvious. People try to propagate on the internet information about lost and found pets. There is no need to like such posts, but reposts are very useful." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "066bc0c9-9085-86cc-4e6e-3bb4c9f5b4f6" }, "source": [ "### Many likes, few shares\n", "Let's go further and consider opposite situation when post has a lot of likes and virtually none shares." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "2b8cc01c-ff84-94a5-b182-c801b8854d12" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "top 10 out of 94\n", "likes: 294.0 \n", " message: Please help me in welcoming Jason and Julia to Elkins Park! They fell in love with EP from the start and are so excited about their new home. \n", "\n", "likes: 290.0 \n", " message: I am so late posting this. But{COMMA} better late than never{COMMA} right? {RET}{RET}Mid June I walked my girls to the co op for a rotisserie chicken for dinner. We were late{COMMA} they were out. Having not laid anything out{COMMA} we ventured around the corner to Alexander{APOST}s. {RET}{RET}I ask \n", "\n", "likes: 255.0 \n", " message: Please help me in welcoming new Elkins Park homeowners Sarah and Sean Stone! \n", "\n", "likes: 238.0 \n", " message: Hi{COMMA} everyone! My daughter Guinevere will compete on tonight{APOST}s Chopped Junior on Food Network at 8pm. Local girl making good! She is in sixth grade at EP. \n", "\n", "likes: 233.0 \n", " message: A big thanks to Cheltenham Police{COMMA} they caught the person responsible for the Mulford Road car break-in{APOST}s/theft. Excellent work! \n", "\n", "likes: 204.0 \n", " message: I am finally your neighbor. Closed on our home today in Melrose Park. My family and I look forward to meeting our new neighbors. \n", "\n", "likes: 187.0 \n", " message: Hi{COMMA} everyone! My daughter Guinevere will compete on tonight{APOST}s Chopped Junior on Food Network at 8pm. Local girl making good! She is in sixth grade at EP. \n", "\n", "likes: 185.0 \n", " message: A few thoughts after reading the last few days{APOST} posts:{RET}1) There is no right to free speech on a FB group. It is controlled by the moderator{COMMA} not the First Amendment.{RET}2) People have begun posting photos of people who have done things they don{APOST}t like{COMMA} such as not shovel \n", "\n", "likes: 154.0 \n", " message: At the urging of a psychologist{COMMA} who expressed great concern over the potential impact the posts could have{COMMA} I have removed all posts regarding the individual that has recently been in the national and local news. For the sake of well-being{COMMA} please respect this decision and do not \n", "\n", "likes: 149.0 \n", " message: We did it!! Thank you to this wonderful community in all lending a hand to install this fantastic garden. I can{APOST}t wait to see it bloom this spring. But let{APOST}s face it -- it looks a 1000 better than it did{COMMA} and I couldn{APOST}t be prouder of our community. You make Elkins Park bloo \n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " if __name__ == '__main__':\n" ] } ], "source": [ "likes = data[data.likes > data.likes.quantile(0.98)][data.likes > data.shares*100][['msg', 'likes']]\n", "print(\"top %d out of %d\" % (top, likes.shape[0]))\n", "sorted_data = likes.sort_values(by='likes', ascending=False)[:top]\n", "for i in sorted_data.index.values:\n", " print('likes:',sorted_data.likes[i], '\\n','message:', sorted_data.msg[i][:300], '\\n')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "45dca0ef-1d97-9aae-44de-024612e19c39" }, "source": [ "In this cluster we see messages of gratitude or messages from new neighbours wanting to get to know their new town. People try to show that they like such messages or to welcome the newcomers, and there is no need to share such posts outside the groups. This messages do not ask help, just post information about events. Posts have a positive tone mostly." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "40370feb-50f1-14f1-f06f-1c9ba71df734" }, "source": [ "### Most commented\n", "\n", "In this section let's discover what Cheltenham people discuss most willingly. Let's look at most commented posts." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "2b988bae-4748-1cb2-09df-d81005b9ad12" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "top 10 out of 209\n", "\n", "comments: 297.0 \n", " message: I REALLY think we need to re-register ALL of Cheltenham. Our schools have more kids then ever since Cheltenham has finished building the new schools. I speak to parents and we all see what is happening to us and our kids that now have 29 kids in there class rooms. I love walking my son to school and \n", "\n", "comments: 239.0 \n", " message: I{APOST}ve been reading up the impending lateral sewer repair requirements. Can someone explain how that relates to the township{APOST}s problem of excess sewage entering Philly{APOST}s system. I figure i should understand the situation before deciding on whether to be upset or not. \n", "\n", "comments: 205.0 \n", " message: Question about Alverthrope Park. I don{APOST}t know what rock I live under but I just discovered it yesterday and loved running there. However{COMMA} I hear it{APOST}s only open to Abington residents. How strict are they? The security guard let me come in but I wasn{APOST}t sure if this was a \n", "\n", "comments: 192.0 \n", " message: Friends and neighbors- we received this email from our commissioner Ann Rappaport about the impending sewer costs. Wondering why we aren{APOST}t hearing more about this from the township directly but perhaps I missed it. Bottom line: Property owners will receive a copy of the inspection report for t \n", "\n", "comments: 190.0 \n", " message: This post will get ridiculed by some as haters gonna hate and others as left wing radicals or media. but read closely. This letter writer built his career on being non-political. \n", "\n", "comments: 183.0 \n", " message: Can someone please explain to me in a civil manner without personal attacks how anyone can consider this man to be presidential? A president is a diplomat and one whom we must trust. From the article{COMMA} With his enormous online platform{COMMA} Mr. Trump has badgered and humiliated those who have \n", "\n", "comments: 168.0 \n", " message: My son got jumped at the Elkins Park train station lat night around 11 PM-2 men with hammers beat him up and took his cell phone. He is bruised-arms{COMMA} legs{COMMA} and ego. Last year{COMMA} my neighbor{APOST}s house was robbed. The police told me they take the train{COMMA} rob people{COMMA} ge \n", "\n", "comments: 159.0 \n", " message: My partner and I are here in Philadelphia this weekend and decided to visit Elkins Park today. It was very quiet... is it always this quiet{COMMA} or is it because everyone has gone out of town this weekend? We are very much looking forward to meeting you all when we move here in late July! Also{COM \n", "\n", "comments: 158.0 \n", " message: Oh My.....Just read the Inquirer from Sunday........... :-( our taxes are going up :-( ........why can{APOST}t we get some industry in Cheltenham to offset any tax issues ?? \n", "\n", "comments: 157.0 \n", " message: Silly question. I{APOST}ve read lots of positive things about Avelthorpe Park. We live in the Cheltenham Township part of EP (just moved here in April). Can we not use this park because we are not Abington Twp residents? The wading pool sounds great since I have a 3 yr old and an 8 week old! Thanks \n", "\n" ] } ], "source": [ "discussed = data[data.comments > data.comments.quantile(0.98)][['msg', 'comments']]\n", "\n", "print(\"top %d out of %d\\n\" % (top, discussed.shape[0]))\n", "sorted_data = discussed.sort_values(by='comments', ascending=False)[:top]\n", "for i in sorted_data.index.values:\n", " print('comments:',sorted_data.comments[i], '\\n','message:', sorted_data.msg[i][:300], '\\n')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "7d025dd7-da69-991b-f20a-bc5ab2f94d22" }, "source": [ "We can see that words such as \"please, explain\" or \"do you know\" appeare quite often in these messages. In this cluster we can find posts about local problems (school, sewer, traffic) causing discussions, or direct questions about local events. And, of course, political posts. People here do not ask help, do not thank. Messages have neutral or negative tone." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "f5d42c8b-358e-9709-966f-197e39807678" }, "source": [ "## Conclusions\n", "\n", "At the beginning we have made some preliminary conclusions and none of them was right. I'm reminding you:\n", "\n", " * In groups 1 (EPH) and 2 (UCT) posts get more likes and repost than in group 3 (FSZ). \n", " * In group 2 (UCT) posts get more comments than in groups 1 (EPH) and 3 (FSZ)\n", " * Message length is equal in all three groups.\n", "\n", "**What we see after testing:**\n", "\n", "*Comparing Groups*\n", "\n", " * Posts get about equal number of likes in all three groups\n", " * Posts get about equal number of comments in all three groups\n", " * Posts from group 3 (FSZ) are almost never shared, and it has practical significance: if you want your message to be shared, you should choose one of the first two groups.\n", " * Messages in group 2 (UCT) are longer (~300 symbols) than in group 1 (EPH) (~250 symbols). Probably, messages in the first group have more photos instead.\n", " \n", "*Likes, shares, and coments*\n", "\n", " * Posts having large number of shares contain information about lost and found pets or ask for help.\n", " * Posts having large number of likes are messages of gratitude or messages from new neighbours wanting to get to know their new town. Such posts have a positive tone mostly.\n", " * Posts causing long discussions are posts about local problems such as school, sewer, traffic, messages with direct questions about local events, and political posts." ] } ], "metadata": { "_change_revision": 365, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/316/316827.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "4bfe22b4-3ef6-0a8e-3b17-b644653f7b93" }, "source": [ "This notebook is an analysis of religious attacks using the dataset provided by Kaggle. This is inspired by the notebooks created by Keyshin: https://www.kaggle.com/keyshin/d/argolof/predicting-terrorism/religious-attacks-a-start and ArjonnSharma: https://www.kaggle.com/arjoonn/d/argolof/predicting-terrorism/scoutscript . I used some parts of the code from ScoutScript and added my own." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "feb05b5a-9ce0-9258-28a1-cd82d95b7ead" }, "outputs": [], "source": [ "# import libraries\n", "import pandas as pd\n", "import numpy as np\n", "\n", "import matplotlib.pylab as plt\n", "import datetime\n", "from mpl_toolkits.basemap import Basemap" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "9166370a-88c3-be7a-e3a4-dacbda052326" }, "outputs": [], "source": [ "%matplotlib inline\n", "from matplotlib.pylab import rcParams" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "dc3ba47c-50ce-1626-1245-fb89a6893956" }, "source": [ "Change the country here to your liking. I'm using my country for this notebook as an example" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "e644b0b0-f4de-57ac-bbfd-ef8b3ce0eef6" }, "outputs": [], "source": [ "country = \"Philippines\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "f29a1507-d735-185a-90ed-962a8e73baaa" }, "outputs": [], "source": [ "# load dataset\n", "df = pd.read_csv('../input/attacks_data_UTF8.csv',\n", " encoding='latin1', parse_dates=['Date'],\n", " infer_datetime_format=True,\n", " index_col=1,\n", " )" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "6d6ac7d0-05e2-a654-fe70-4e969e599484" }, "source": [ "I created a new 'Victims' column that just add the counts of the affected people (Killed and Injured)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "370327e3-5093-004d-d717-be964978ca31" }, "outputs": [], "source": [ "df['Victims'] = df['Killed'] + df['Injured']" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2b44ca7b-a374-0102-73d5-0f2fcc99dad3" }, "source": [ "Here we create a new dataframe for the specific country specified. If not specified, use all the data instead." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "f6e854b4-041c-bcc2-c340-916db72ada1a" }, "outputs": [], "source": [ "if country is not None:\n", " dfc=df.loc[df['Country']==country]\n", "else:\n", " dfc = df" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "e654c884-b7c2-dba9-9e63-90f691ca6f4a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Philippines is ranked 13 with 413 attacks resulting to 1604 deaths and 3222 injuries\n" ] } ], "source": [ "# Just a quick count of the data\n", "\n", "country_rank = df.Country.value_counts().rank(numeric_only=True,ascending=False).loc[country]\n", "country_attacks = df.Country.value_counts()[country]\n", "country_killed = dfc.Killed.sum()\n", "country_injured = dfc.Injured.sum()\n", "print(\"%s is ranked %.0f with %d attacks resulting to %d deaths and %d injuries\" % (country, country_rank, country_attacks, country_killed, country_injured))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b57e5201-a1b8-f361-f3ab-89153f1a0c89" }, "source": [ "From the code above, we can see the ranking of the country based on the number of attacks and a quick summary of the number of deaths and injured people. Our country is ranked 13 in number of attacks." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "92e6c8a1-5b0d-a231-3a7f-bacf91bb978c" }, "source": [ "Now we get the number of attacks by city for the country." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "451fce18-2bab-e830-cf1b-e61df966849d" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f921f831048>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TFm/2rqprJPmKJK9L8rQk951Gu0+S84+6tAAAAHAS2Uv37xslOa+qTskIwp/a3X9RVS9J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\nrZ7aaRe01346BOqV6ELS8v17iEY+00ZToD2FfrhSes5Ps33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j5/j2/9tCtVVSrNBZwXrF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ynklpj7Qf3kvgidQPlJBNDUdGWyQiJx67uBTAnjhedO7CJmZn9Groe1eIT9GO8GdwBiFr8T59+A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\nAlekGtdMZgKp3vfk0uObeiLRw1w/xwDnSPoJ0b7oP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"text/plain": [ "<matplotlib.figure.Figure at 0x7f921f6b4b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dfc.City.value_counts().plot(kind='bar', figsize=(17, 7))\n", "plt.title('Number of attacks by city')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "3f5941db-f19f-5a68-a1ed-7301a1453e82" }, "source": [ "The plot above shows that most of the attacks happened on the areas on the southern part of our country." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "0f542d8f-8497-0ae7-9e93-5bfd1f0d4c5d" }, "source": [ "Next we get the graphs of the attack on cities with the most victims. It also shows the graph of those Killed and Injured for comparison" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "481f156a-ea1c-61ba-9eea-26fa314bac7e" }, "outputs": [ { "data": { "text/plain": [ "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f921f795e48>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f921f441cf8>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f921f2c2d68>], dtype=object)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Ysy1JkiRJ6hi33XYbr33ta/dJtBv1+c9/nsWLF/O2t72NiOD444/nzDPPZP36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AAAAg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\nE/tekAHEzO4HsqWmfAhlqJ6vUgcLA2JY2FREBhYzld9nZtuiS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0S50P4K8AXggwONOGIa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\nYpb9P1ak2L4g9d16AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f921f7d3c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dfc.groupby('City').sum()[['Victims','Killed', 'Injured']].sort_values(by='Victims',ascending=0).plot(kind='bar', figsize=(17, 7), subplots=True)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d0cd72bc-c537-d117-b4d7-b13820e9902e" }, "source": [ "The graphs above shows that the city with the most victims in total is not necessarily those with the most deaths. It can also be seen that the city with most death has a very low Injured people count. " ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "64d48553-8362-52c8-bc1d-ef7ef8e0a108" }, "source": [ "The code below gets the attacks with the most victims, killed and injuries and the corresponding description." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "60b6d165-4ec4-0a34-17ac-1a81c30c03a9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Attack with most victims happened on Mariveles on February 27,2004 with 186 killed, 12 injuries with a total of 198 victims with the following article: \n", "'Abu Sayyaf, the Muslim extremist group, claims responsibility for an explosion on a ferry that kills nearly two-hundred people.' \n", "\n", "Attack with the most deaths happened on Mariveles on February 27,2004 with 186 killed, 12 injuries with a total of 198 victims with the following article: \n", "'Abu Sayyaf, the Muslim extremist group, claims responsibility for an explosion on a ferry that kills nearly two-hundred people.' \n", "\n", "Attack with the most injuries happened on Zamboanga on October 17,2002 with 7 killed, 152 injuries with a total of 159 victims with the following article: \n", "'Moro Islamic Front bombings at two department stores kill seven and injure one-hundred and fifty-two.' \n", "\n" ] } ], "source": [ "# Attack with most victims\n", "most_victim = dfc.sort_values(by='Victims',ascending=False).head(1)\n", "# most_victim.index.strftime(\"%Y-%m-%d\")\n", "print(\"Attack with most victims happened on %s on %s with %d killed, %d injuries with a total of %d victims with the following article: \\n'%s' \\n\" % (most_victim.City.values[0], most_victim.index.strftime(\"%B %d,%Y\")[0], most_victim.Killed, most_victim.Injured, most_victim.Victims, \"%s\" % most_victim.Description.values[0]))\n", "# Attack with most killed\n", "most_killed = dfc.sort_values(by='Killed',ascending=False).head(1)\n", "print(\"Attack with the most deaths happened on %s on %s with %d killed, %d injuries with a total of %d victims with the following article: \\n'%s' \\n\" % (most_killed.City.values[0], most_killed.index.strftime(\"%B %d,%Y\")[0], most_killed.Killed, most_killed.Injured, most_killed.Victims, \"%s\" % most_killed.Description.values[0]))\n", "#Attack with most injuries\n", "most_injuries = dfc.sort_values(by='Injured',ascending=False).head(1)\n", "print(\"Attack with the most injuries happened on %s on %s with %d killed, %d injuries with a total of %d victims with the following article: \\n'%s' \\n\" % (most_injuries.City.values[0], most_injuries.index.strftime(\"%B %d,%Y\")[0], most_injuries.Killed, most_injuries.Injured, most_injuries.Victims, \"%s\" % most_injuries.Description.values[0]))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "33c782f2-939d-af65-ea7d-e763a6b4f41a" }, "source": [ "The attack with most deaths came from a ferry explosion and explains why there are very few injured people compared to those killed." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "51cdf5c4-b0fb-e9d1-048c-f8bf652cbc18" }, "source": [ "Next we get the plot of deaths and injuries by year" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "62ff1b5c-c496-ddb7-06f8-e8180cc12a46" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f921f1edc18>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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v5ad+6qdy1lln5Y1vfGN+/ud/Pve///1zySWXLHpMex8cftWrXrXPdUc96/iLM1ueP5vZ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\nAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f921f7bbe48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Over the years\n", "dfc.groupby(dfc.index.year).sum()[['Victims','Killed', 'Injured']].sort_values(by='Victims',ascending=0).plot(kind='bar', figsize=(17, 7), subplots=False)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "acd28628-7c38-3fdc-beed-af839489fc08" }, "source": [ "From 2003 to 2015, the number of victims generally goes down." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "719aea73-df0b-2530-2bf4-fe2ecf06ad22" }, "source": [ "Next we get if there are differences between the number of killed and injured by day or by month. I grouped them by weekday and month to see if there are patterns." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "46253d8c-730c-678e-b945-287ad33e9e83" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f921c261c50>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Ptb2J9J45c+Zk4cKFeeihh7rtXt4EAgAAAHq1TZs25Y477jirfvTo0UybNi2V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\n3jlrAej32o8fP55p06Z1ODX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Ts1hsjkpXVRYL49IAAABw0jSBODWr1fBY9O2OAwAAAMdH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\nsuNVN1n1kVM/WfWRUx859duWrOa8E8FPJHn5+uJ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f91fe59ca20>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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q7e3tzJo1i+joaJqbm0lLS2Pnzp2OumEYPfvBuCAlJYWQkBC8vb0JCwsjJyfH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\npKSEoKAgR729vZ1Zs2YRHR1Nc3MzaWl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"text/plain": [ "<matplotlib.figure.Figure at 0x7f921c0d0f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "killedbyday = dfc.groupby([dfc.index.map(lambda x: x.weekday),dfc.index.year], sort=True).agg({'Killed': 'sum'})\n", "rcParams['figure.figsize'] = 20, 10\n", "killedbyday.unstack(level=0).plot(kind='bar', subplots=False)\n", "killedbyday.unstack(level=1).plot(kind='bar', subplots=False)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "d415c2b1-4478-446b-d800-23d10d240594" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f91fdfa3fd0>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f921c1d4668>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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SEhI+901mrf3WT7W3+lo1hUDS7RQCiYiIiIiIPB9tBQPy5dOZEEjbwUREREREREREegCF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\nM0uXLgXA4XBgsVh4+PAhADExMaSnp3d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kAAAG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\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f91fdf879b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Check if there is a difference in attack victims by month\n", "killedbymonth = dfc.groupby([dfc.index.map(lambda x: x.month),dfc.index.year], sort=True).agg({'Killed': 'sum'})\n", "rcParams['figure.figsize'] = 20, 10\n", "killedbymonth.unstack(level=0).plot(kind='bar', subplots=False)\n", "killedbymonth.unstack(level=1).plot(kind='bar', subplots=False)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e3667a14-586a-3e36-ce12-668d57de8196" }, "source": [ "I'll be modifying this notebook to show better visualization on the day and month (probably via heatmaps) like in KeyShin's notebook and get more analysis on the terrorist groups taken from the descriptions." ] } ], "metadata": { "_change_revision": 109, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/318/318069.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "38a4e813-8c17-7346-14a2-6a872398273c" }, "source": [ "Simple example of reading this dataset into Pandas from the SQLite (database.sqlite) file and adding the response id (rid) and response name (rname) to *comment.*\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "4ae32fc1-b94f-6291-a8ee-d2cd54ab439d" }, "outputs": [], "source": [ "import pandas as pd\n", "import sqlite3\n", "\n", "\n", "import warnings # current version of seaborn generates a bunch of warnings that we'll ignore\n", "warnings.filterwarnings(\"ignore\")\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.set(style=\"white\", color_codes=True)\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "82bfdff9-0547-d329-6a19-0485056491dd" }, "outputs": [], "source": [ "con = sqlite3.connect('../input/database.sqlite')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "15ac29db-215c-dde3-cfde-e23c323d881c" }, "outputs": [], "source": [ "# There are 4 tables\n", "post = pd.read_sql_query(\"SELECT * FROM post\", con)\n", "comment = pd.read_sql_query(\"SELECT * FROM comment\", con)\n", "like = pd.read_sql_query(\"SELECT * FROM like\", con)\n", "# We don't want url. That just display the image for the person\n", "rmember = pd.read_sql_query(\"SELECT distinct id as rid, name rname FROM member\", con)\n", "\n", "# We'll update comment to add the response name (rname) to comment\n", "comment=pd.merge(comment, rmember, left_on='rid', right_on='rid',how='left')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "94c567e3-95e6-2ed5-ead8-01444d545ab5" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>rid</th>\n", " <th>rname</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1000011160054538</td>\n", " <td>Liz Belmonte</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1000834876651930</td>\n", " <td>Krifka Steffey</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1001043723265231</td>\n", " <td>Owen Zieger</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " rid rname\n", "0 1000011160054538 Liz Belmonte\n", "1 1000834876651930 Krifka Steffey\n", "2 1001043723265231 Owen Zieger" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Quick look at the data\n", "rmember.head(3)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "cd487548-fa04-3931-f2a9-165627d51a8c" }, "outputs": [ { "data": { "text/plain": [ "117291968282998 37953\n", "25160801076 25747\n", "1443890352589739 4138\n", "Name: gid, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This Dataset has 4 Facebook Groups\n", "# 117291968282998 \"Unofficial Cheltenham\"\n", "# 25160801076 \"Elkins Park Happenings\"\n", "# 1443890352589739 \"Free Speech\"\n", "comment[\"gid\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "29db4060-c54f-fb46-bbd4-40de8f7ca2cb" }, "outputs": [ { "data": { "text/plain": [ "Heidi Morein 413\n", "Rhonda Genzink Isser 410\n", "Marsha Shuter 295\n", "Nora Goldberg-Allen 281\n", "Sara Lefton Koval 261\n", "Gerry Flynn-Austin 254\n", "Jason Weston 240\n", "David Cohen 236\n", "Betsy Conway 223\n", "Katie Bruton 199\n", "Name: name, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's see how many comments for just one group\n", "# Note: comment.rid == '' Otherwise, you'll pick up a reply to a comment\n", "comment[( comment.gid == '117291968282998') & (comment.rid=='')][\"name\"].value_counts().head(10)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "14798f42-b282-403b-3831-b852fdb7bdbf" }, "outputs": [ { "data": { "text/plain": [ "Rhonda Genzink Isser 131\n", "Jason Weston 99\n", "Mark Tomlinson 95\n", "Katie Bruton 94\n", "Erica Loney 80\n", "Christina Asch 73\n", "Andrea Adler Hershman 72\n", "Paula Gates 69\n", "Karen McGoran 68\n", "Mandy Levine 66\n", "Name: rname, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's see how many response to comments\n", "# Note: comment.rid != '' comments go 2 levels deep.\n", "comment[( comment.gid == '117291968282998') & (comment.rid != '')][\"rname\"].value_counts().head(10)" ] } ], "metadata": { "_change_revision": 120, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/318/318221.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "dc41185d-57d1-7f08-af66-305027a78393" }, "source": [ "Simple example of reading this dataset into Pandas from the SQLite (database.sqlite) file and adding the response id (rid) and response name (rname) to comment." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "de2f0297-4aa3-018a-624d-f5df1ec16623" }, "outputs": [], "source": [ "import pandas as pd\n", "import sqlite3\n", "\n", "\n", "import warnings # current version of seaborn generates a bunch of warnings that we'll ignore\n", "warnings.filterwarnings(\"ignore\")\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "sns.set(style=\"white\", color_codes=True)\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "617b697f-5a21-b58c-cefb-caee06639946" }, "outputs": [], "source": [ "con = sqlite3.connect('../input/database.sqlite')\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "19b67b93-85c0-81fc-a2ff-8e3c9c509fc8" }, "outputs": [], "source": [ "# There are 4 tables\n", "post = pd.read_sql_query(\"SELECT * FROM post\", con)\n", "comment = pd.read_sql_query(\"SELECT * FROM comment\", con)\n", "like = pd.read_sql_query(\"SELECT * FROM like\", con)\n", "\n", "# We don't want url. That just displays the image for the person.\n", "# Also need a column name change.\n", "rmember = pd.read_sql_query(\"SELECT distinct id as rid, name rname FROM member\", con)\n", "\n", "# We'll update comment to add the response name (rname) to comment\n", "comment=pd.merge(comment, rmember, left_on='rid', right_on='rid',how='left')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "c620750c-18a7-d078-f139-cd7bf92f155b" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>gid</th>\n", " <th>pid</th>\n", " <th>cid</th>\n", " <th>timeStamp</th>\n", " <th>id</th>\n", " <th>name</th>\n", " <th>rid</th>\n", " <th>msg</th>\n", " <th>rname</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>117291968282998</td>\n", " <td>117291968282998_1219433751402142</td>\n", " <td>1219468848065299</td>\n", " <td>2016-06-03 18:08:56</td>\n", " <td>10154835446763222</td>\n", " <td>Franca Mascia</td>\n", " <td></td>\n", " <td>Lisa Barra</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>117291968282998</td>\n", " <td>117291968282998_1219433751402142</td>\n", " <td>1219543508057833</td>\n", " <td>2016-06-03 20:06:42</td>\n", " <td>1008184819228730</td>\n", " <td>Julie Brissett</td>\n", " <td></td>\n", " <td>Michelle Bernstein - maybe try posting on the ...</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>117291968282998</td>\n", " <td>117291968282998_1219433751402142</td>\n", " <td>1219543508057833</td>\n", " <td>2016-06-03 21:31:56</td>\n", " <td>1008184819228730</td>\n", " <td>Julie Brissett</td>\n", " <td>10209640895842518</td>\n", " <td>Thank you! I didn{APOST}t know about that page.</td>\n", " <td>Michelle Bernstein</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>117291968282998</td>\n", " <td>117291968282998_1219433751402142</td>\n", " <td>1219565831388934</td>\n", " <td>2016-06-03 21:00:11</td>\n", " <td>10209749549634208</td>\n", " <td>Tracy Karfunkle Werner</td>\n", " <td></td>\n", " <td>Emma Werner</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " gid pid cid \\\n", "0 117291968282998 117291968282998_1219433751402142 1219468848065299 \n", "1 117291968282998 117291968282998_1219433751402142 1219543508057833 \n", "2 117291968282998 117291968282998_1219433751402142 1219543508057833 \n", "3 117291968282998 117291968282998_1219433751402142 1219565831388934 \n", "\n", " timeStamp id name \\\n", "0 2016-06-03 18:08:56 10154835446763222 Franca Mascia \n", "1 2016-06-03 20:06:42 1008184819228730 Julie Brissett \n", "2 2016-06-03 21:31:56 1008184819228730 Julie Brissett \n", "3 2016-06-03 21:00:11 10209749549634208 Tracy Karfunkle Werner \n", "\n", " rid msg \\\n", "0 Lisa Barra \n", "1 Michelle Bernstein - maybe try posting on the ... \n", "2 10209640895842518 Thank you! I didn{APOST}t know about that page. \n", "3 Emma Werner \n", "\n", " rname \n", "0 NaN \n", "1 NaN \n", "2 Michelle Bernstein \n", "3 NaN " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Quick look at new comment\n", "comment.head(4)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "f03121b7-d0d1-aece-4b5e-83fe8442322b" }, "outputs": [ { "data": { "text/plain": [ "EPH 37953\n", "UCT 25747\n", "FSZ 4138\n", "Name: gid, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This Dataset has 3 Facebook Groups\n", "# 117291968282998 Elkins Park Happenings EPH\n", "# 25160801076 Unofficial Cheltenham Township UCT\n", "# 1443890352589739 Free Speech Zone FSZ\n", "#\n", "\n", "# Took the idea below from (Eugenia Uchaeva)\n", " \n", "comment.gid = comment.gid.map({'117291968282998': 'EPH', '25160801076': 'UCT', '1443890352589739': 'FSZ'})\n", "comment[\"gid\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "3d71d8f4-efa9-8b85-f04a-152be790f963" }, "outputs": [ { "data": { "text/plain": [ "Heidi Morein 413\n", "Rhonda Genzink Isser 410\n", "Marsha Shuter 295\n", "Nora Goldberg-Allen 281\n", "Sara Lefton Koval 261\n", "Gerry Flynn-Austin 254\n", "Jason Weston 240\n", "David Cohen 236\n", "Betsy Conway 223\n", "Katie Bruton 199\n", "Name: name, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's see how many comments for just one group\n", "# Note: comment.rid == '' Otherwise, you'll pick up a reply to a comment\n", "comment[( comment.gid == 'EPH') & (comment.rid=='')][\"name\"].value_counts().head(10)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "95bca954-87d6-f063-f401-2a41bf709b44" }, "outputs": [ { "data": { "text/plain": [ "Rhonda Genzink Isser 131\n", "Jason Weston 99\n", "Mark Tomlinson 95\n", "Katie Bruton 94\n", "Erica Loney 80\n", "Christina Asch 73\n", "Andrea Adler Hershman 72\n", "Paula Gates 69\n", "Karen McGoran 68\n", "Mandy Levine 66\n", "Name: rname, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's see how many response to comments\n", "# Note: comment.rid != '' comments go 2 levels deep.\n", "comment[( comment.gid == 'EPH') & (comment.rid != '')][\"rname\"].value_counts().head(10)" ] } ], "metadata": { "_change_revision": 100, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/318/318372.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "052c5e16-a99c-4f2f-b446-d3320aa2ae70" }, "outputs": [], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "train_data=pd.read_csv(\"../input/train.csv\",usecols=['Producto_ID','Demanda_uni_equil'])\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "b2596823-8fce-43f0-b39c-aaa938421345" }, "outputs": [], "source": [ "train_data['log_Dem']=np.log(np.array(train_data['Demanda_uni_equil'].tolist())+1)\n", "#print(train_data)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "af745793-0efe-49d6-8a52-cbabb4ce4914" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Demanda_uni_equil log_Dem\n", "Producto_ID \n", "41 367.442623 4.428392\n", "53 291.096491 4.082771\n", "72 4.896193 1.639394\n", "73 3.193657 1.100521\n", "100 8.814516 1.464221\n", "106 5.427142 1.516158\n", "107 12.090909 1.301968\n", "108 73.500000 3.740038\n", "122 0.000000 0.000000\n", "123 10.941723 1.878661\n", "131 107.447581 4.146631\n", "132 121.349359 4.206682\n", "134 34.135593 3.358712\n", "135 44.388514 3.620671\n", "141 9.786837 1.700691\n", "145 22.579085 2.303989\n", "151 15.029197 2.572085\n", "157 58.568966 3.059626\n", "160 6.283094 1.577697\n", "162 1.584270 0.897141\n", "163 1.837975 0.945482\n", "183 7.694301 1.720188\n", "202 11.675917 1.925195\n", "205 12.822159 1.936086\n", "214 122.930000 4.456339\n", "217 7.213487 1.817460\n", "303 5.185751 1.660997\n", "306 8.482412 1.629137\n", "323 2.362937 1.098444\n", "325 2.092734 1.018717\n", "... ... ...\n", "49552 0.041667 0.028881\n", "49734 40.794271 3.216572\n", "49735 29.777213 3.026848\n", "49736 30.298179 3.049497\n", "49737 21.889503 2.792722\n", "49738 23.681416 2.949897\n", "49739 27.282828 2.969783\n", "49740 21.104762 2.709547\n", "49754 1.250000 0.577129\n", "49765 48.941435 3.553510\n", "49769 85.450085 4.080632\n", "49779 9.092421 1.818284\n", "49781 20.416667 0.900720\n", "49782 11.084842 1.962843\n", "49810 15.741935 2.443427\n", "49860 15.431229 2.481041\n", "49920 1.722730 0.848614\n", "49928 2.325444 0.089002\n", "49944 1.673592 0.809658\n", "49972 1.531056 0.854059\n", "49973 2.265366 1.039387\n", "49986 5.596895 1.515876\n", "49988 19.446237 2.620715\n", "49989 9.071429 1.695482\n", "49990 4.114345 1.336663\n", "49992 26.375000 3.026487\n", "49993 9.200000 2.109858\n", "49994 3.561983 1.257660\n", "49996 15.375000 2.174669\n", "49997 14.714286 2.394995\n", "\n", "[1799 rows x 2 columns]\n" ] } ], "source": [ "mean_data=train_data.groupby(train_data['Producto_ID']).mean()\n", "print(mean_data)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "fedba4ed-161e-4298-8609-9dedf72c77d4" }, "outputs": [], "source": [ "test_data=pd.read_csv(\"../input/test.csv\",usecols=['id','Producto_ID'])\n", "target=np.zeros(test_data.shape[0])\n", "log_target=np.zeros(test_data.shape[0])\n", "for pid in mean_data.index:\n", " target[test_data[test_data['Producto_ID']==pid]['id'].values]=mean_data.ix[pid]['Demanda_uni_equil']\n", " log_target[test_data[test_data['Producto_ID']==pid]['id'].values]=mean_data.ix[pid]['log_Dem']\n", "#print (log_target)\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "ef4ba418-0f3e-4b49-82d9-5060cf0c9eb2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " id Producto_ID Demanda_uni_equil\n", "0 0 35305 7.027703\n", "1 1 1238 2.422317\n", "2 2 32940 3.242884\n", "3 3 43066 1.944106\n", "4 4 1277 2.708953\n", "5 5 972 1.920119\n", "6 6 1232 2.271056\n", "7 7 35305 7.027703\n", "8 8 1240 3.978284\n", "9 9 43203 4.686996\n", "10 10 1278 9.879244\n", "11 11 2233 3.314518\n", "12 12 4270 3.598855\n", "13 13 1240 3.978284\n", "14 14 43274 2.484730\n", "15 15 37361 4.411942\n", "16 16 43200 2.721536\n", "17 17 1150 3.293155\n", "18 18 4270 3.598855\n", "19 19 35456 4.113465\n", "20 20 30552 2.758117\n", "21 21 1125 5.260759\n", "22 22 35525 22.048024\n", "23 23 1150 3.293155\n", "24 24 1232 2.271056\n", "25 25 43285 9.337252\n", "26 26 36920 2.162311\n", "27 27 37361 4.411942\n", "28 28 2233 3.314518\n", "29 29 41938 2.566003\n", "... ... ... ...\n", "6999221 6999221 1109 2.307755\n", "6999222 6999222 43084 1.701330\n", "6999223 6999223 31393 3.705696\n", "6999224 6999224 43316 1.907136\n", "6999225 6999225 35651 4.642866\n", "6999226 6999226 35305 7.027703\n", "6999227 6999227 43069 3.916939\n", "6999228 6999228 31719 3.700165\n", "6999229 6999229 43202 3.688614\n", "6999230 6999230 1146 2.729903\n", "6999231 6999231 1230 1.705339\n", "6999232 6999232 1242 3.829979\n", "6999233 6999233 1220 2.382717\n", "6999234 6999234 37403 1.275970\n", "6999235 6999235 35651 4.642866\n", "6999236 6999236 2233 3.314518\n", "6999237 6999237 43215 2.839566\n", "6999238 6999238 37361 4.411942\n", "6999239 6999239 43307 8.018249\n", "6999240 6999240 1230 1.705339\n", "6999241 6999241 1212 2.181863\n", "6999242 6999242 1146 2.729903\n", "6999243 6999243 43285 9.337252\n", "6999244 6999244 43206 13.787818\n", "6999245 6999245 35305 7.027703\n", "6999246 6999246 1232 2.271056\n", "6999247 6999247 43069 3.916939\n", "6999248 6999248 30532 5.396822\n", "6999249 6999249 35107 3.331531\n", "6999250 6999250 1232 2.271056\n", "\n", "[6999251 rows x 3 columns]\n" ] } ], "source": [ "test_data['Demanda_uni_equil']=np.exp(log_target)-1\n", "print(test_data)\n", "test_data.to_csv('result_groupmean_log.csv',index=False,columns=['id','Demanda_uni_equil'])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "3db17ac5-0869-4461-a923-af9844a4510a" }, "outputs": [ { "data": { "text/plain": [ "1678163 1678163\n", "3458287 3458287\n", "6236398 6236398\n", "Name: id, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_data[test_data['Producto_ID']==41]['id']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "a2f98d9e-250a-487d-9843-c856f907972f" }, "outputs": [ { "data": { "text/plain": [ "(6999251, 3)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#mean_data.index\n", "#mean_data.ix[41]\n", "test_data.shape" ] } ], "metadata": { "_change_revision": 18, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/320/320335.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "bf6d6733-3a02-4507-b9d1-f1308c35fa58" }, "source": [ "VARIABLE DESCRIPTIONS:\n", "survival Survival\n", " (0 = No; 1 = Yes)\n", "pclass Passenger Class\n", " (1 = 1st; 2 = 2nd; 3 = 3rd)\n", "name Name\n", "sex Sex\n", "age Age\n", "sibsp Number of Siblings/Spouses Aboard\n", "parch Number of Parents/Children Aboard\n", "ticket Ticket Number\n", "fare Passenger Fare\n", "cabin Cabin\n", "embarked Port of Embarkation\n", " (C = Cherbourg; Q = Queenstown; S = Southampton)\n", "\n", "SPECIAL NOTES:\n", "Pclass is a proxy for socio-economic status (SES)\n", " 1st ~ Upper; 2nd ~ Middle; 3rd ~ Lower\n", "\n", "Age is in Years; Fractional if Age less than One (1)\n", " If the Age is Estimated, it is in the form xx.5\n", "\n", "With respect to the family relation variables (i.e. sibsp and parch)\n", "some relations were ignored. The following are the definitions used\n", "for sibsp and parch.\n", "\n", "Sibling: Brother, Sister, Stepbrother, or Stepsister of Passenger Aboard Titanic\n", "Spouse: Husband or Wife of Passenger Aboard Titanic (Mistresses and Fiances Ignored)\n", "Parent: Mother or Father of Passenger Aboard Titanic\n", "Child: Son, Daughter, Stepson, or Stepdaughter of Passenger Aboard Titanic\n", "\n", "Other family relatives excluded from this study include cousins,\n", "nephews/nieces, aunts/uncles, and in-laws. Some children travelled\n", "only with a nanny, therefore parch=0 for them. As well, some\n", "travelled with very close friends or neighbors in a village, however,\n", "the definitions do not support such relations." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "adb23194-250e-44d9-8dad-524de9c54bbf" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "genderclassmodel.csv\n", "gendermodel.csv\n", "gendermodel.py\n", "myfirstforest.py\n", "test.csv\n", "train.csv\n", "\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "from IPython.display import display\n", "from sklearn.ensemble import RandomForestClassifier\n", "from matplotlib import pyplot as plt\n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import sklearn\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "e703e996-ca79-4c6a-ace1-bed7e5010918" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "test set has 418 rows.\n", "train set has 891 rows.\n" ] } ], "source": [ "Y_train = pd.read_csv('../input/genderclassmodel.csv')\n", "test = pd.read_csv('../input/test.csv')\n", "train = pd.read_csv('../input/train.csv')\n", "\n", "print('test set has %s rows.' % test.shape[0])\n", "print('train set has %s rows.' % train.shape[0])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "421c50c7-35b0-4668-8526-7a7289ebacc0" }, "outputs": [], "source": [ "# clear data\n", "_train = train.copy()\n", "Y_train = _train['Survived'].copy()\n", "_train.drop([\n", " 'Name', 'Ticket', 'Cabin', 'Survived'\n", "], axis=1, inplace=True)\n", "_train.fillna('-1', inplace=True)\n", "_train.replace('male', 1, inplace=True)\n", "_train.replace('female', 2, inplace=True)\n", "_train.replace('C', 1, inplace=True)\n", "_train.replace('Q', 2, inplace=True)\n", "_train.replace('S', 3, inplace=True)\n", "\n", "_test = test.copy()\n", "_test.drop([\n", " 'Name', 'Ticket', 'Cabin'\n", "], axis=1, inplace=True)\n", "_test.fillna('-1', inplace=True)\n", "_test.replace('male', 1, inplace=True)\n", "_test.replace('female', 2, inplace=True)\n", "_test.replace('C', 1, inplace=True)\n", "_test.replace('Q', 2, inplace=True)\n", "_test.replace('S', 3, inplace=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "002412dd-d193-c476-bd43-cc831ba99918" }, "outputs": [], "source": [ "passenger_survived = train[train.Survived==1]['PassengerId'].copy()\n", "passenger_died = train[train.Survived==0]['PassengerId'].copy()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "893285dd-0950-5045-5a0d-542b56bb6633" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f10ddd68518>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/B8uMS1NIucGyk5aftN4UeZL52aQJi/0RB5+/zkINacmSJT128FEHRzk7+KQH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\nI0xXLwe2u3rabR3tdJcvqs/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th8g9mfMI8Cut9dokFYmDFwRByCj9cRJDEARBKALi4AVB\nEDKKOHhBEISMIg5eEAQho/x/8ekjNGsc+zgAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f10c61fda20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# survived\n", "_train_survived = _train[\n", " _train.PassengerId.isin(passenger_survived)\n", "].drop('PassengerId', axis=1)\n", "_train_survived.plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "3388814d-e451-48aa-9500-cb152dae2556" }, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n", " oob_score=False, random_state=None, verbose=0,\n", " warm_start=False)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = RandomForestClassifier()\n", "model.fit(_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "3569d13e-2808-4f84-aa20-d68a4efcda7e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PassengerId,Survived\n", "892,0\n", "893,0\n", "894,0\n", "895,1\n", "896,1\n", "897,0\n", "898,0\n", "899,0\n", "900,1\n", "901,0\n", "902,0\n", "903,0\n", "904,1\n", "905,0\n", "906,1\n", "907,1\n", "908,0\n", "909,0\n", "910,0\n", "911,0\n", "912,0\n", "913,0\n", "914,1\n", "915,1\n", "916,1\n", "917,0\n", "918,1\n", "919,0\n", "920,1\n", "921,0\n", "922,0\n", "923,0\n", "924,0\n", "925,0\n", "926,1\n", "927,0\n", "928,0\n", "929,0\n", "930,0\n", "931,0\n", "932,0\n", "933,0\n", "934,0\n", "935,1\n", "936,1\n", "937,0\n", "938,0\n", "939,0\n", "940,1\n", "941,1\n", "942,0\n", "943,0\n", "944,1\n", "945,1\n", "946,1\n", "947,0\n", "948,0\n", "949,0\n", "950,0\n", "951,1\n", "952,0\n", "953,0\n", "954,0\n", "955,0\n", "956,1\n", "957,1\n", "958,0\n", "959,1\n", "960,0\n", "961,1\n", "962,0\n", "963,0\n", "964,0\n", "965,1\n", "966,1\n", "967,1\n", "968,0\n", "969,1\n", "970,0\n", "971,0\n", "972,1\n", "973,0\n", "974,1\n", "975,0\n", "976,0\n", "977,0\n", "978,0\n", "979,0\n", "980,1\n", "981,1\n", "982,0\n", "983,0\n", "984,1\n", "985,0\n", "986,1\n", "987,0\n", "988,1\n", "989,0\n", "990,0\n", "991,0\n", "992,1\n", "993,0\n", "994,0\n", "995,0\n", "996,1\n", "997,0\n", "998,0\n", "999,0\n", "1000,0\n", "1001,0\n", "1002,0\n", "1003,1\n", "1004,1\n", "1005,0\n", "1006,1\n", "1007,0\n", "1008,0\n", "1009,1\n", "1010,1\n", "1011,1\n", "1012,1\n", "1013,0\n", "1014,1\n", "1015,0\n", "1016,0\n", "1017,1\n", "1018,0\n", "1019,1\n", "1020,0\n", "1021,0\n", "1022,0\n", "1023,0\n", "1024,0\n", "1025,0\n", "1026,0\n", "1027,0\n", "1028,0\n", "1029,0\n", "1030,0\n", "1031,0\n", "1032,0\n", "1033,1\n", "1034,0\n", "1035,0\n", "1036,1\n", "1037,0\n", "1038,0\n", "1039,0\n", "1040,0\n", "1041,0\n", "1042,1\n", "1043,0\n", "1044,0\n", "1045,1\n", "1046,0\n", "1047,0\n", "1048,1\n", "1049,0\n", "1050,1\n", "1051,1\n", "1052,1\n", "1053,1\n", "1054,1\n", "1055,0\n", "1056,0\n", "1057,0\n", "1058,0\n", "1059,0\n", "1060,1\n", "1061,0\n", "1062,0\n", "1063,0\n", "1064,0\n", "1065,0\n", "1066,0\n", "1067,1\n", "1068,1\n", "1069,0\n", "1070,1\n", "1071,1\n", "1072,0\n", "1073,1\n", "1074,1\n", "1075,0\n", "1076,1\n", "1077,0\n", "1078,1\n", "1079,0\n", "1080,0\n", "1081,0\n", "1082,0\n", "1083,1\n", "1084,0\n", "1085,0\n", "1086,0\n", "1087,0\n", "1088,1\n", "1089,0\n", "1090,0\n", "1091,0\n", "1092,1\n", "1093,0\n", "1094,0\n", "1095,1\n", "1096,0\n", "1097,1\n", "1098,0\n", "1099,0\n", "1100,1\n", "1101,0\n", "1102,0\n", "1103,0\n", "1104,0\n", "1105,0\n", "1106,1\n", "1107,1\n", "1108,1\n", "1109,0\n", "1110,1\n", "1111,0\n", "1112,1\n", "1113,0\n", "1114,1\n", "1115,0\n", "1116,0\n", "1117,1\n", "1118,0\n", "1119,1\n", "1120,0\n", "1121,0\n", "1122,0\n", "1123,1\n", "1124,0\n", "1125,0\n", "1126,0\n", "1127,0\n", "1128,0\n", "1129,0\n", "1130,1\n", "1131,1\n", "1132,0\n", "1133,1\n", "1134,1\n", "1135,0\n", "1136,0\n", "1137,0\n", "1138,1\n", "1139,0\n", "1140,1\n", "1141,0\n", "1142,1\n", "1143,0\n", "1144,1\n", "1145,0\n", "1146,0\n", "1147,0\n", "1148,0\n", "1149,0\n", "1150,1\n", "1151,0\n", "1152,0\n", "1153,0\n", "1154,1\n", "1155,1\n", "1156,0\n", "1157,0\n", "1158,0\n", "1159,0\n", "1160,0\n", "1161,0\n", "1162,0\n", "1163,0\n", "1164,1\n", "1165,1\n", "1166,0\n", "1167,1\n", "1168,0\n", "1169,0\n", "1170,0\n", "1171,0\n", "1172,0\n", "1173,1\n", "1174,1\n", "1175,0\n", "1176,1\n", "1177,0\n", "1178,0\n", "1179,0\n", "1180,0\n", "1181,0\n", "1182,0\n", "1183,0\n", "1184,0\n", "1185,0\n", "1186,0\n", "1187,0\n", "1188,1\n", "1189,0\n", "1190,0\n", "1191,0\n", "1192,0\n", "1193,1\n", "1194,0\n", "1195,0\n", "1196,1\n", "1197,1\n", "1198,1\n", "1199,0\n", "1200,0\n", "1201,0\n", "1202,0\n", "1203,0\n", "1204,0\n", "1205,0\n", "1206,1\n", "1207,1\n", "1208,0\n", "1209,0\n", "1210,1\n", "1211,0\n", "1212,0\n", "1213,0\n", "1214,0\n", "1215,0\n", "1216,1\n", "1217,0\n", "1218,1\n", "1219,0\n", "1220,0\n", "1221,0\n", "1222,1\n", "1223,0\n", "1224,0\n", "1225,0\n", "1226,1\n", "1227,0\n", "1228,0\n", "1229,0\n", "1230,0\n", "1231,0\n", "1232,0\n", "1233,0\n", "1234,0\n", "1235,1\n", "1236,0\n", "1237,1\n", "1238,0\n", "1239,0\n", "1240,0\n", "1241,1\n", "1242,1\n", "1243,0\n", "1244,0\n", "1245,0\n", "1246,1\n", "1247,1\n", "1248,1\n", "1249,0\n", "1250,0\n", "1251,1\n", "1252,0\n", "1253,1\n", "1254,1\n", "1255,1\n", "1256,1\n", "1257,0\n", "1258,0\n", "1259,0\n", "1260,1\n", "1261,0\n", "1262,0\n", "1263,1\n", "1264,0\n", "1265,0\n", "1266,1\n", "1267,1\n", "1268,0\n", "1269,0\n", "1270,0\n", "1271,0\n", "1272,0\n", "1273,0\n", "1274,0\n", "1275,1\n", "1276,0\n", "1277,1\n", "1278,0\n", "1279,0\n", "1280,0\n", "1281,0\n", "1282,0\n", "1283,1\n", "1284,0\n", "1285,0\n", "1286,0\n", "1287,1\n", "1288,0\n", "1289,1\n", "1290,0\n", "1291,0\n", "1292,1\n", "1293,0\n", "1294,1\n", "1295,0\n", "1296,0\n", "1297,0\n", "1298,0\n", "1299,0\n", "1300,1\n", "1301,1\n", "1302,1\n", "1303,1\n", "1304,0\n", "1305,0\n", "1306,1\n", "1307,0\n", "1308,0\n", "1309,1\n", "\n" ] } ], "source": [ "predicted = test[['PassengerId']].copy()\n", "predicted['Survived'] = model.predict(_test)\n", "\n", "print(predicted.to_csv(index=False))" ] } ], "metadata": { "_change_revision": 257, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/320/320410.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "e3230216-1ca6-480e-9367-401bbbf87c01" }, "source": [ "# Titanic passenger survival analysis\n", "Here is my first kernel written on kaggle.com. **This is work in progress.** Please upvote and comment\n", "of you like it. Here is a little outline of the notebook.\n", "\n", "1. Munging the data\n", "2. Feature engineering\n", "3. Feature preparation\n", "4. Evaluating classifiers\n", "5. Submitting\n", "\n", "*Note:* I'm writing this from my parents residence and the internet connection here is really unstable.\n", "I loose my connection in the funniest moments.\n", "I'm also trying to make a nice vacation for my two daughters. That has a higher priority than kaggling.\n", "So this notebook will probably progress slowly towards something useful." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "773a4fd6-68fa-436a-b86a-1d8faef58bb1" }, "source": [ "## Munging the data\n", "I will start by munging the data a bit. Munging data or data wrangling is the process\n", "of handling the raw data such that it works for the later analysis. The munging is\n", "often an important part of the data scientist's work." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e96566e8-28c0-4ee9-9e52-ba297cc8a68b" }, "source": [ "### Loading the data.\n", "I will load the data into two pandas dataframes. The training dataset will be loaded into titanic,\n", "the test dataset will be loaded into a dataframe called test. I will also merge the two into a\n", "dataframe called full, as the statistics\n", "based on the both sets, will be used to make some estimations for missing data. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "b171cb91-e944-496b-8623-4f00c81cec64" }, "outputs": [], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "0c2e90dc-12e8-448a-b7ca-3bb160e898e8" }, "outputs": [], "source": [ "titanic = pd.read_csv(\"../input/train.csv\")\n", "test = pd.read_csv(\"../input/test.csv\")\n", "full = pd.concat([titanic, test])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "13f5d87b-8ba6-4dd6-8f79-78c0e9bd1758" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 12 columns):\nPassengerId 891 non-null int64\nSurvived 891 non-null int64\nPclass 891 non-null int64\nName 891 non-null object\nSex 891 non-null object\nAge 714 non-null float64\nSibSp 891 non-null int64\nParch 891 non-null int64\nTicket 891 non-null object\nFare 891 non-null float64\nCabin 204 non-null object\nEmbarked 889 non-null object\ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.6+ KB\n----------------------------------------\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 418 entries, 0 to 417\nData columns (total 11 columns):\nPassengerId 418 non-null int64\nPclass 418 non-null int64\nName 418 non-null object\nSex 418 non-null object\nAge 332 non-null float64\nSibSp 418 non-null int64\nParch 418 non-null int64\nTicket 418 non-null object\nFare 417 non-null float64\nCabin 91 non-null object\nEmbarked 418 non-null object\ndtypes: float64(2), int64(4), object(5)\nmemory usage: 36.0+ KB\n" } ], "source": [ "titanic.info()\n", "print(\"-\"*40)\n", "test.info()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "37cdb577-4704-4e38-9192-6491b15dc944" }, "source": [ "### Filling out some missing values\n", "#### Filling out the missing embarkment\n", "I looks like we miss a embarkment port for two passengers in titanic dataframe. Let's not make\n", "a big thing about this, and assume that the the passengers embarked in Southampton. That is natural\n", "enough as most passengers embarked in Southampton. I also don't believe that the embarkment port\n", "is a real big indicator to predict survival. **Southampton it is!**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "8653247f-fbb4-4875-9845-94af4b6caf99" }, "outputs": [ { "data": { "text/plain": "S 914\nC 270\nQ 123\nName: Embarked, dtype: int64" }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# just to show the numbers\n", "full.Embarked.value_counts()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "27939c67-35de-466c-80ef-400903ca1a69" }, "outputs": [ { "data": { "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f0bb4c470>" }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD+CAYAAAA09s7qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f7f0bb95588>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# or maybe even more convincing: A plot!\n", "full.Embarked.value_counts().plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "54cac140-347d-4829-b48d-10637dc7fe16" }, "outputs": [], "source": [ "# See? It's pretty safe two assume they came for Southampton\n", "titanic.Embarked.fillna(value='S', inplace=True)\n", "# Let's update the full dataframe as well:\n", "full.Embarked.fillna(value='S', inplace=True)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "3c84a01a-7fc8-41ec-b3c7-9b0630c818ab" }, "source": [ "### Filling out the missing fare\n", "There is only one passenger where the fare is missing:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "0d3b09ea-9b5e-4c36-b164-95d9a00c39ab" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Pclass</th>\n <th>Name</th>\n <th>Sex</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Ticket</th>\n <th>Fare</th>\n <th>Cabin</th>\n <th>Embarked</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>152</th>\n <td>1044</td>\n <td>3</td>\n <td>Storey, Mr. Thomas</td>\n <td>male</td>\n <td>60.5</td>\n <td>0</td>\n <td>0</td>\n <td>3701</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>S</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " PassengerId Pclass Name Sex Age SibSp Parch Ticket \\\n152 1044 3 Storey, Mr. Thomas male 60.5 0 0 3701 \n\n Fare Cabin Embarked \n152 NaN NaN S " }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test[np.isnan(test[\"Fare\"])]" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d8219397-07f1-4140-b20c-728e3d0d52e8" }, "source": [ "I will fill this in with the median fare of the passengers for that\n", "embarkment port (S) on that passenger class (Third)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "1a4d77bd-212c-47b9-93ac-e1822174f6b3" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Pclass</th>\n <th>Name</th>\n <th>Sex</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Ticket</th>\n <th>Fare</th>\n <th>Cabin</th>\n <th>Embarked</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>152</th>\n <td>1044</td>\n <td>3</td>\n <td>Storey, Mr. Thomas</td>\n <td>male</td>\n <td>60.5</td>\n <td>0</td>\n <td>0</td>\n <td>3701</td>\n <td>8.05</td>\n <td>NaN</td>\n <td>S</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " PassengerId Pclass Name Sex Age SibSp Parch Ticket \\\n152 1044 3 Storey, Mr. Thomas male 60.5 0 0 3701 \n\n Fare Cabin Embarked \n152 8.05 NaN S " }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Mr. Storey from Southampton in third class.\n", "test.loc[test[\"PassengerId\"] == 1044, \"Fare\"] = full[(full[\"Embarked\"]=='S') & (full[\"Pclass\"]==3)].Fare.median()\n", "test.loc[test[\"PassengerId\"]==1044,:]" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "472155ae-d7e1-4632-855c-d6bb8ff9cab5" }, "source": [ "8.05 GBP from Mr. Storey! Sounds good enough to me!\n", "### Filling out the missing ages\n", "I must admit that I have read Megan's notebook. She uses a MICE regression to fill in the missing ages.\n", "I really think that is a great idea, but I will settle for a simpler method in this notebook, as I don't\n", "believe the age is really the strongest indicator. I also want to keep it simple as this is my very\n", "first kernel on kaggle.\n", "\n", "(I'm at vacation, visiting my parents and the internet connection is a bit unstable here.... I also get a lot of\n", "interruptions of different kinds, so I may use a few days for this notebook.)\n", "\n", "#### Age filling strategy\n", "There was a \"Women and children first\" policy for filling up the lifeboats, so the important thing\n", "to consider is if a passenger with missing age was considered a child or adult. So to fill in the\n", "missing age data we will use the title of the person, and fill in the median age for the given title.\n", "\n", "It is important to understand that someone with title **Master** is typically a young boy who will be\n", "considered a child. **Master** was used address politely a boy who was too young to be called **Mister**.\n", "Also, a **Miss** is typically unmarried and younger than a **Mrs**, so hopefully the strategy will help\n", "us get good values for the missing ages.\n", "\n", "So the first step will be to make an additional column with the *Title*, in all three dataframes. Megan\n", "has already showed us that the title of a passenger is actually an important predictor. So we will add the\n", "column and keep it there for later." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "10f8689e-3cf2-4ffa-a5c0-7558c5d3fee2" }, "outputs": [], "source": [ "# So let's add a title column to each DataFrame\n", "# we also make a list of all our frames, such that we can easily loop over them.\n", "frames = [titanic, test, full]\n", "for df in frames:\n", " df[\"Title\"] = df.Name.str.replace('(.*, )|(\\\\..*)', '')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "10c46e64-1e80-4f28-b4d3-f5965c21264a" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Age</th>\n <th>Cabin</th>\n <th>Embarked</th>\n <th>Fare</th>\n <th>Name</th>\n <th>Parch</th>\n <th>PassengerId</th>\n <th>Pclass</th>\n <th>Sex</th>\n <th>SibSp</th>\n <th>Survived</th>\n <th>Ticket</th>\n <th>Title</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>22.0</td>\n <td>NaN</td>\n <td>S</td>\n <td>7.2500</td>\n <td>Braund, Mr. Owen Harris</td>\n <td>0</td>\n <td>1</td>\n <td>3</td>\n <td>male</td>\n <td>1</td>\n <td>0.0</td>\n <td>A/5 21171</td>\n <td>Mr</td>\n </tr>\n <tr>\n <th>1</th>\n <td>38.0</td>\n <td>C85</td>\n <td>C</td>\n <td>71.2833</td>\n <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n <td>0</td>\n <td>2</td>\n <td>1</td>\n <td>female</td>\n <td>1</td>\n <td>1.0</td>\n <td>PC 17599</td>\n <td>Mrs</td>\n </tr>\n <tr>\n <th>2</th>\n <td>26.0</td>\n <td>NaN</td>\n <td>S</td>\n <td>7.9250</td>\n <td>Heikkinen, Miss. Laina</td>\n <td>0</td>\n <td>3</td>\n <td>3</td>\n <td>female</td>\n <td>0</td>\n <td>1.0</td>\n <td>STON/O2. 3101282</td>\n <td>Miss</td>\n </tr>\n <tr>\n <th>3</th>\n <td>35.0</td>\n <td>C123</td>\n <td>S</td>\n <td>53.1000</td>\n <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n <td>0</td>\n <td>4</td>\n <td>1</td>\n <td>female</td>\n <td>1</td>\n <td>1.0</td>\n <td>113803</td>\n <td>Mrs</td>\n </tr>\n <tr>\n <th>4</th>\n <td>35.0</td>\n <td>NaN</td>\n <td>S</td>\n <td>8.0500</td>\n <td>Allen, Mr. William Henry</td>\n <td>0</td>\n <td>5</td>\n <td>3</td>\n <td>male</td>\n <td>0</td>\n <td>0.0</td>\n <td>373450</td>\n <td>Mr</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " Age Cabin Embarked Fare \\\n0 22.0 NaN S 7.2500 \n1 38.0 C85 C 71.2833 \n2 26.0 NaN S 7.9250 \n3 35.0 C123 S 53.1000 \n4 35.0 NaN S 8.0500 \n\n Name Parch PassengerId \\\n0 Braund, Mr. Owen Harris 0 1 \n1 Cumings, Mrs. John Bradley (Florence Briggs Th... 0 2 \n2 Heikkinen, Miss. Laina 0 3 \n3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0 4 \n4 Allen, Mr. William Henry 0 5 \n\n Pclass Sex SibSp Survived Ticket Title \n0 3 male 1 0.0 A/5 21171 Mr \n1 1 female 1 1.0 PC 17599 Mrs \n2 3 female 0 1.0 STON/O2. 3101282 Miss \n3 1 female 1 1.0 113803 Mrs \n4 3 male 0 0.0 373450 Mr " }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "full.head()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "a3ad8f5e-c816-4235-bd18-aed8f0fe3333" }, "source": [ "Looks like that worked fine! Let's see how many unique titles there are, and how many\n", "unique titles we need to fill in the age." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "ec64e9fd-258b-4b63-9dab-ac8703dc0feb" }, "outputs": [ { "data": { "text/plain": "Mr 757\nMiss 260\nMrs 197\nMaster 61\nRev 8\nDr 8\nCol 4\nMs 2\nMlle 2\nMajor 2\nLady 1\nDona 1\nJonkheer 1\nthe Countess 1\nCapt 1\nDon 1\nSir 1\nMme 1\nName: Title, dtype: int64" }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# That went good. There are 18 unique titles.\n", "full[\"Title\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "19c8c2ec-02e2-40ce-9d1b-35237d8a15e0" }, "outputs": [ { "data": { "text/plain": "array(['Mr', 'Mrs', 'Miss', 'Master', 'Dr', 'Ms'], dtype=object)" }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's check which titles that are missing age data\n", "full[np.isnan(full[\"Age\"])].Title.unique()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e56209a6-3d6e-486e-8c5f-bf0879361f12" }, "source": [ "OK. There are only 6 types of titles that is missing age data. The thing that comes to attraction is that \n", "there is both a **Miss** and a **Ms** title. Without much considerations, I'm joining the **Ms** titled\n", "with **Miss** titled. Then it's only 5 titles to fill with age." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "f465b873-8e21-4ee1-a1ef-183c949cdb9a" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Age</th>\n <th>Cabin</th>\n <th>Embarked</th>\n <th>Fare</th>\n <th>Name</th>\n <th>Parch</th>\n <th>PassengerId</th>\n <th>Pclass</th>\n <th>Sex</th>\n <th>SibSp</th>\n <th>Survived</th>\n <th>Ticket</th>\n <th>Title</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>443</th>\n <td>28.0</td>\n <td>NaN</td>\n <td>S</td>\n <td>13.00</td>\n <td>Reynaldo, Ms. Encarnacion</td>\n <td>0</td>\n <td>444</td>\n <td>2</td>\n <td>female</td>\n <td>0</td>\n <td>1.0</td>\n <td>230434</td>\n <td>Ms</td>\n </tr>\n <tr>\n <th>88</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>Q</td>\n <td>7.75</td>\n <td>O'Donoghue, Ms. Bridget</td>\n <td>0</td>\n <td>980</td>\n <td>3</td>\n <td>female</td>\n <td>0</td>\n <td>NaN</td>\n <td>364856</td>\n <td>Ms</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " Age Cabin Embarked Fare Name Parch \\\n443 28.0 NaN S 13.00 Reynaldo, Ms. Encarnacion 0 \n88 NaN NaN Q 7.75 O'Donoghue, Ms. Bridget 0 \n\n PassengerId Pclass Sex SibSp Survived Ticket Title \n443 444 2 female 0 1.0 230434 Ms \n88 980 3 female 0 NaN 364856 Ms " }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Hmmm what's the difference of Miss and Ms?\n", "full[full[\"Title\"]==\"Ms\"]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "a1030423-ce6a-4b34-b7ea-edf8955f87f4" }, "outputs": [], "source": [ "# Let's just set the two Ms to Miss. Can't be that bad.\n", "for df in frames:\n", " df.loc[df[\"Title\"]==\"Ms\", \"Title\"] = \"Miss\"" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "3048fa0a-8c4f-48cc-95b6-454a722c410f" }, "source": [ "Yes! Then we are ready for the real filling of all missing ages." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "8419b07d-77bb-4efc-a200-7d0a7e9db634" }, "outputs": [], "source": [ "# So here is the main juice. Assign the missing age to the median age with the given title.\n", "for t in full[np.isnan(full[\"Age\"])].Title.unique():\n", " for df in frames:\n", " df.loc[(df[\"Title\"]==t) & np.isnan(df[\"Age\"]), \"Age\" ] = full[full[\"Title\"]==t].Age.median()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "5233597f-8653-40a1-ab0c-b7b5ecdbb2b7" }, "source": [ "### Filling out the missing cabin\n", "There are so many missing cabins in the dataset that we will not even try to fill in the missing elements.\n", "We will however try to extract some information from this later in this notebook." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e8ea5990-e264-4996-bcb2-b515bc945b7b" }, "source": [ "## Feature engineering\n", "We do indeed already have some features, but I think we should try to extract some more. We have the\n", "passenger class, the sex, the age, #siblings_and_spouses, and #childern_and_parents, the embarkment port\n", "and the fare. We've also extracted a *Title* from the name column. However, I think we can gain some more.\n", "Let's consider the cabin first.\n", "### Engineering a cabin feature\n", "Important to note: The way to survive is to get onboard a lifeboat. At the RMS Titanic there were\n", "20 lifeboats, however only 18 were used. There were less capacity of the lifeboats than the number of\n", "passengers and crew onboard. Numbers from wikipedia says there was a capacity of 1178 passengers in the\n", "lifeboats. There were 2224 on board (crew included). Since there was not enough capacity of the lifeboats\n", "to evacuate everyone, the \"Women and children first\" policy were applied. \n", "\n", "#### A side note on the low capacity of the lifeboats\n", "We may think today that it was really strange to have less capacity of lifeboats than number of\n", "passengers and crew. However, the number of lifeboats was well within the maritime laws and regulations\n", "of the time. The next question that then comes up is: Why was the maritime regulation not requiring\n", "any liner to have lifeboat capacity for every passenger and crew member? The answer to this is that\n", "there never were any concern that all passengers had to be evacuated over a relatively short period of\n", "time. A ship in distress would probably stay afloat for many hours. First of all there was a lot of\n", "maritime traffic of those days, and a liner in distress would always be able to call for assistance\n", "from a nearby vessel and evacuate the personnel to that vessel. The vessels were equipped with\n", "wireless telegraphs. That was the common philosophy of the time, and that was the reason why\n", "this was not considered a problem. After all, Titanc was the ship that could not sink. It was called\n", "the ship that could not sink due to the double bottom and the watertight bulkheads. However the\n", "bulkheads was not sealed with a ceiling, so when each bulkhead was filled with water, the water\n", "simply flooded over to the next bulkhead. She was hence not so unsinkable after all.\n", "\n", "#### A feature for the cabin side\n", "This is indeed a bit interesting. The \"Women and children first\" policy was indeed enforced differently on\n", "the port and starboard side of the ship, so the side of the cabin could be a useful predictor in our\n", "analysis. Cabin numbers ending with an odd number indicates that the cabin was on starboard side, while\n", "cabins ending on an even number were located on the port side. Let's create a feature called\n", "**CabinSide** that takes the values **unknown**, **starboard** or **port**." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "729ff2da-9098-4032-8a1f-c5e27177c105" }, "outputs": [], "source": [ "# Let's look at the cabin a bit. This might be important. The \"Women and Children\" policy was enforced differently on\n", "# starboard and port side. Odd numbered cabins are starboard side, and even numbers are port side.\n", "for df in [titanic, test]:\n", " df[\"CabinSide\"] = \"Unknown\"\n", " df.loc[pd.notnull(df[\"Cabin\"]) & df[\"Cabin\"].str[-1].isin([\"1\", \"3\", \"5\", \"7\", \"9\"]),\"CabinSide\"] = \"Starboard\"\n", " df.loc[pd.notnull(df[\"Cabin\"]) & df[\"Cabin\"].str[-1].isin([\"0\", \"2\", \"4\", \"6\", \"8\"]),\"CabinSide\"] = \"Port\"" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "78535e9e-3959-48df-a891-5f66bdcbe65a" }, "source": [ "We need some cleanup. The Ryersons had four cabins, three on starboard and one on port. It is natural\n", "to set them all on starboard, as they probably gathered. They traveled with ticket **PC 17608**, so\n", "we use that to index the rows of Ryersons & co." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "fd7d82db-c408-4104-955d-7ff3bd805714" }, "outputs": [], "source": [ "for df in [titanic, test]:\n", " df.loc[df[\"Ticket\"]==\"PC 17608\", \"CabinSide\"] = \"Starboard\"" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e7f6f6f1-c027-45fb-84b7-e937e50153e0" }, "source": [ "It is also natural to assume that Bowen, Miss. Grace Scott was in cabin B68. According to sources\n", "she was the maid for the Ryersons and the deck plan drawing shows this cabin as a maids/servant cabin." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "ec7fbd33-58c9-4213-a2e8-c2bdff5d00ea" }, "outputs": [], "source": [ "test.loc[test[\"Name\"].str.contains(\"Bowen,\"),\"Cabin\"] = \"B68\"" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "bc4b5918-7f08-4fb3-9eea-861962d9b5b4" }, "outputs": [ { "data": { "text/plain": "Unknown 691\nPort 108\nStarboard 92\nName: CabinSide, dtype: int64" }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.CabinSide.value_counts()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "956caad2-b876-4a4c-98fa-d38a02f8fa94" }, "source": [ "#### A feature for the cabin deck\n", "Lower deck cabins where flooded with water before the higher level deck. Is it natural to think that\n", "passengers on low decks gathered to the lifeboats earlier than the passengers at the higher decks?\n", "At least I will try out a feature based on the deck. The deck is labeld as the first letter in the\n", "cabin number. A-G, where A is the highest and G is the lowest." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "_cell_guid": "b027b5eb-0223-43ac-84ef-5370e8ac6bb9" }, "outputs": [], "source": [ "# Maybe the Deck is important? who knows?\n", "for df in [titanic, test]:\n", " df[\"Deck\"] = \"Unknown\"\n", " df.loc[pd.notnull(df[\"Cabin\"]), \"Deck\"] = df[\"Cabin\"].str[0]" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "0aa73503-574d-46d7-b5bf-8640be3da3f3" }, "source": [ "We need some cleanup as some cabins are numbered \"F Gxx\". I am not sure what this means, but\n", "I guess it means \"Fore\" deck \"G\" cabin \"xx\". I have asked this on the forum, but I have got no replies\n", "yet." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "_cell_guid": "619a44dc-2a53-44e7-9030-86dab232c011" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Survived</th>\n <th>Pclass</th>\n <th>Name</th>\n <th>Sex</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Ticket</th>\n <th>Fare</th>\n <th>Cabin</th>\n <th>Embarked</th>\n <th>Title</th>\n <th>CabinSide</th>\n <th>Deck</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>75</th>\n <td>76</td>\n <td>0</td>\n <td>3</td>\n <td>Moen, Mr. Sigurd Hansen</td>\n <td>male</td>\n <td>25.0</td>\n <td>0</td>\n <td>0</td>\n <td>348123</td>\n <td>7.6500</td>\n <td>F G73</td>\n <td>S</td>\n <td>Mr</td>\n <td>Starboard</td>\n <td>F</td>\n </tr>\n <tr>\n <th>128</th>\n <td>129</td>\n <td>1</td>\n <td>3</td>\n <td>Peter, Miss. Anna</td>\n <td>female</td>\n <td>22.0</td>\n <td>1</td>\n <td>1</td>\n <td>2668</td>\n <td>22.3583</td>\n <td>F E69</td>\n <td>C</td>\n <td>Miss</td>\n <td>Starboard</td>\n <td>F</td>\n </tr>\n <tr>\n <th>699</th>\n <td>700</td>\n <td>0</td>\n <td>3</td>\n <td>Humblen, Mr. Adolf Mathias Nicolai Olsen</td>\n <td>male</td>\n <td>42.0</td>\n <td>0</td>\n <td>0</td>\n <td>348121</td>\n <td>7.6500</td>\n <td>F G63</td>\n <td>S</td>\n <td>Mr</td>\n <td>Starboard</td>\n <td>F</td>\n </tr>\n <tr>\n <th>715</th>\n <td>716</td>\n <td>0</td>\n <td>3</td>\n <td>Soholt, Mr. Peter Andreas Lauritz Andersen</td>\n <td>male</td>\n <td>19.0</td>\n <td>0</td>\n <td>0</td>\n <td>348124</td>\n <td>7.6500</td>\n <td>F G73</td>\n <td>S</td>\n <td>Mr</td>\n <td>Starboard</td>\n <td>F</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " PassengerId Survived Pclass \\\n75 76 0 3 \n128 129 1 3 \n699 700 0 3 \n715 716 0 3 \n\n Name Sex Age SibSp Parch \\\n75 Moen, Mr. Sigurd Hansen male 25.0 0 0 \n128 Peter, Miss. Anna female 22.0 1 1 \n699 Humblen, Mr. Adolf Mathias Nicolai Olsen male 42.0 0 0 \n715 Soholt, Mr. Peter Andreas Lauritz Andersen male 19.0 0 0 \n\n Ticket Fare Cabin Embarked Title CabinSide Deck \n75 348123 7.6500 F G73 S Mr Starboard F \n128 2668 22.3583 F E69 C Miss Starboard F \n699 348121 7.6500 F G63 S Mr Starboard F \n715 348124 7.6500 F G73 S Mr Starboard F " }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.loc[titanic.Cabin.str.len() == 5,:]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "_cell_guid": "a1e227fa-2a4d-4fff-9bc5-bab3e5540b27" }, "outputs": [], "source": [ "for df in [titanic, test]:\n", " df.loc[pd.notnull(df[\"Cabin\"]) & (df.Cabin.str.len() == 5), \"Deck\"] = df[\"Cabin\"].str[2]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "_cell_guid": "190160f9-e157-4460-816d-af57e65331eb" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Pclass</th>\n <th>Name</th>\n <th>Sex</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Ticket</th>\n <th>Fare</th>\n <th>Cabin</th>\n <th>Embarked</th>\n <th>Title</th>\n <th>CabinSide</th>\n <th>Deck</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>57</th>\n <td>949</td>\n <td>3</td>\n <td>Abelseth, Mr. Olaus Jorgensen</td>\n <td>male</td>\n <td>25.0</td>\n <td>0</td>\n <td>0</td>\n <td>348122</td>\n <td>7.6500</td>\n <td>F G63</td>\n <td>S</td>\n <td>Mr</td>\n <td>Starboard</td>\n <td>G</td>\n </tr>\n <tr>\n <th>288</th>\n <td>1180</td>\n <td>3</td>\n <td>Mardirosian, Mr. Sarkis</td>\n <td>male</td>\n <td>29.0</td>\n <td>0</td>\n <td>0</td>\n <td>2655</td>\n <td>7.2292</td>\n <td>F E46</td>\n <td>C</td>\n <td>Mr</td>\n <td>Port</td>\n <td>E</td>\n </tr>\n <tr>\n <th>321</th>\n <td>1213</td>\n <td>3</td>\n <td>Krekorian, Mr. Neshan</td>\n <td>male</td>\n <td>25.0</td>\n <td>0</td>\n <td>0</td>\n <td>2654</td>\n <td>7.2292</td>\n <td>F E57</td>\n <td>C</td>\n <td>Mr</td>\n <td>Starboard</td>\n <td>E</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " PassengerId Pclass Name Sex Age SibSp \\\n57 949 3 Abelseth, Mr. Olaus Jorgensen male 25.0 0 \n288 1180 3 Mardirosian, Mr. Sarkis male 29.0 0 \n321 1213 3 Krekorian, Mr. Neshan male 25.0 0 \n\n Parch Ticket Fare Cabin Embarked Title CabinSide Deck \n57 0 348122 7.6500 F G63 S Mr Starboard G \n288 0 2655 7.2292 F E46 C Mr Port E \n321 0 2654 7.2292 F E57 C Mr Starboard E " }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.loc[test.Cabin.str.len() == 5,:]" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9aefe948-9a01-43f3-9f16-3935198ae9a2" }, "source": [ "Yes! That looks better." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "_cell_guid": "649400eb-ed7c-428f-8e4b-6771f5595f0d" }, "outputs": [ { "data": { "text/plain": "Unknown 687\nC 59\nB 47\nD 33\nE 33\nA 15\nF 9\nG 7\nT 1\nName: Deck, dtype: int64" }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Test if there is some strange decks as well. Deck T ??\n", "titanic.Deck.value_counts()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "7b4140bc-561c-4172-b25c-c7295ca286d0" }, "source": [ "Yes, there is a deck T? What is that?" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "_cell_guid": "051cc743-118e-4180-bbc8-51380dbda4d6" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Survived</th>\n <th>Pclass</th>\n <th>Name</th>\n <th>Sex</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Ticket</th>\n <th>Fare</th>\n <th>Cabin</th>\n <th>Embarked</th>\n <th>Title</th>\n <th>CabinSide</th>\n <th>Deck</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>339</th>\n <td>340</td>\n <td>0</td>\n <td>1</td>\n <td>Blackwell, Mr. Stephen Weart</td>\n <td>male</td>\n <td>45.0</td>\n <td>0</td>\n <td>0</td>\n <td>113784</td>\n <td>35.5</td>\n <td>T</td>\n <td>S</td>\n <td>Mr</td>\n <td>Unknown</td>\n <td>T</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " PassengerId Survived Pclass Name Sex Age \\\n339 340 0 1 Blackwell, Mr. Stephen Weart male 45.0 \n\n SibSp Parch Ticket Fare Cabin Embarked Title CabinSide Deck \n339 0 0 113784 35.5 T S Mr Unknown T " }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.loc[titanic[\"Deck\"] == 'T',:]" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "0be45ff8-9c44-477e-b724-1811ba6229b2" }, "source": [ "I have no idea what cabin T is supposed to mean, so I set this to unknown. (I've also checked test set, but\n", "there is no passenger with cabin T in the test part.)\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "_cell_guid": "74f38c04-3cec-4f10-9f40-84222ca88d5b" }, "outputs": [], "source": [ "titanic.loc[titanic[\"Deck\"] == 'T',\"Deck\"] = \"Unknown\"" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "f5878d2e-48c1-4600-9e61-4209a63354d1" }, "source": [ "### A family size feature.\n", "I really don't believe that this gains better classification than the *SibSp* and *ParCh* features\n", "separated, since they are linear correlated. I see that a lot of other scripts has this featere, so\n", "I'm adding this for the fun of it." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "_cell_guid": "79d81923-9f9d-44b3-bb67-49b76b4ce453" }, "outputs": [], "source": [ "# Let's define another feature. FamilySize = Parch + SibSp + 1\n", "for df in frames:\n", " df[\"FamilySize\"] = df.Parch + df.SibSp + 1" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "400ef588-21c9-4901-90db-62d870530cc7" }, "source": [ "(Let me continue. I've had a beautiful day with my family at Oscarsborg yesterday, and Friday we also\n", "fantastic day fishing. We caught three atlantic mackerel and had them for dinner.) \n", "### Ticket group size feature\n", "The ticket looks like they were sold in groups. Can the size of the ticket group be used as an indicator?\n", "That will catch other relations than the family relations on ParCh and SibSp, like valet, servant, maid etc.\n", "This may or may not improve the predictions. Such a feature will of course be strongly related to\n", "family size, and may therefore not improve the predictions as much as I hope. Also, there are some tickets\n", "that are issues with sequential numbers even for groups traveling together.\n", "\n", "There is possibly a simple way to do this in Pandas, but as a long time Python hacker, I will do this the\n", "Python way. I will create a python dictionary with the ticket number as the key, and the number of\n", "tickets with that key." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "_cell_guid": "e1776833-48d5-435d-8219-37bdfa599c22" }, "outputs": [], "source": [ "# Ticket group size\n", "# first we make a dictionary\n", "ticket_dict = {}\n", "for t in full.Ticket.unique():\n", " ticket_dict[t] = 0\n", "for t in full.Ticket:\n", " ticket_dict[t] += 1\n", "\n", "# Then we apply it to the dataframes\n", "for df in frames:\n", " df[\"TicketGroupSize\"] = df[\"Ticket\"].apply( lambda x: ticket_dict[x])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "bdcf29a6-6334-4191-9f6f-96051ac9fcf2" }, "source": [ "### Ticket group survivors feature\n", "Here is my last predictor for today. For each ticket group, we will count the other survivors in that\n", "group. If there is a *everybody or nobody* connection in the traveling group, this may be a good predictor.\n", "Also, a non-linear classifier may also be able to find relations like *everybody except the adult male*\n", "connections in the data. That is why I hope this feature can gain something.\n", "\n", "I will reuse and overwrite the same dictionary and use the same Python method to add this feature. Again,\n", "those who know Pandas well can probably do this simpler. I'm a Pandas beginner myself. \n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "_cell_guid": "e1fc4073-9795-44a7-90f6-c5254c41bf66" }, "outputs": [], "source": [ "for t in full.Ticket.unique():\n", " ticket_dict[t] = 0\n", "for row in full.iterrows():\n", " t = row[1][\"Ticket\"]\n", " if row[1][\"Survived\"] > 0.1:\n", " ticket_dict[t] += 1\n", " \n", "# Then we apply this to the dataframes\n", "for df in [titanic, test]:\n", " df[\"TicketGroupSurvivors\"] = df[\"Ticket\"].apply( lambda x: ticket_dict[x])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "698fe901-0775-40e1-ad89-e120e028e984" }, "source": [ "I really hope that this feature can gain something, however it may be biasing a prediction towards\n", "death to singleton traveling passengers. A singleton passenger (in this case a passenger with a unique\n", "ticket number) will have either 0 ot 1 in this feature. The passenger will always have 0 as \n", "TicketGroupSurvivors in the test set, and a classifier will train to predicting this as a non-survivor,\n", "This feature can therefore may be really bad, rather than smart. I don't know, I'll try it out.\n", "\n", "### Other features\n", "At the top of my head I can think of a few other features to add. First of all, if the TicketGroupSize and\n", "the TicketGroupSurvivors features are fruitful, we might add a two similar features based on the surname\n", "of the passengers.\n", "\n", "Another feature to consider from the cabin column is to see if the cabin is closer to the bow or the\n", "stern of the ship. From the schematic plan on [wikipedia](https://en.wikipedia.org/wiki/Lifeboats_of_the_RMS_Titanic),\n", "we see that for each side there is four lifeboats by the bow and five by the stern. Also the ship sank\n", "slowly for the first hour with the bow first, so where the cabin was (by the bow or stern) may be a useful\n", "predictor. I'll see if I can add this later." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "f6720ec5-52d7-4e04-831f-cd5551a9116e" }, "source": [ "## Feature preparation\n", "To be able to use the features, we have to convert them into numeric values. Some of our features are\n", "already numerical. It is often a good idea to normalize those features. Some of the features\n", "are categorical. For categorical features, we simply make indicator (dummy) features.\n", "\n", "For noramlization, I will create a helper function. (I guess scikit learn already has one, but I'm not\n", "familiar with that one yet.) We will normalize by the function $$\\frac{X - \\mu}{\\sigma}$$ The\n", "implementation goes like this:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "_cell_guid": "c81b81f5-09c3-4d0e-856c-3456db9f9ec5" }, "outputs": [], "source": [ "# Normalizer\n", "def normalize(feat):\n", " mean = full[feat].mean()\n", " stdv = full[feat].std()\n", " for df in [titanic,test]:\n", " df[feat + \"_norm\"] = (df[feat] - mean) / stdv" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "f99e2f47-44d6-4d79-b0da-95bb7bcf73ea" }, "source": [ "Note that we use the full set (combined titanic and test) to estimate the mean and standard deviation.\n", "### Features to normalize\n", "Some features should be normalized, others not. Let's discuss.\n", "#### Age\n", "Age is already a numeric value. This makes more sense to normalize, at the values are much higher\n", "than other numeric features.\n", "**(Note to myself: Check out sklearn preprocessing\n", "#### SibSp, ParCh and Family size.\n", "Just plain normalize all of these. An alternative to be to group then into categories, but let's wait\n", "with that.\n", "#### Fare\n", "This is interesting. Is the fare of the ticket based on the ticket group size? Maybe the fare\n", "should be divided by the size of the ticket group size and then normalized? Maybe even this should\n", "be adjusted and normalized to the fare based on the given class. Let's investigate that later,\n", "and just do a plain normalization for now.\n", "#### Passenger Class (Pclass)\n", "This is indeed a numeric value, but we would rather consider this categorical feature than numerical.\n", "Let's *not* normalize this feature.\n", "#### TicketGroupSize\n", "Just normalize this." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "_cell_guid": "ff84f89e-3a37-4d31-9ba2-57bff47a1c9c" }, "outputs": [ { "data": { "text/plain": "[None, None, None, None, None, None]" }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Age, SibSp, ParCh, FamilySize, Fare and TicketGroupSize. Those are the ones.\n", "[normalize(x) for x in [\"Age\", \"SibSp\", \"Parch\", \"FamilySize\", \"Fare\", \"TicketGroupSize\"]]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "_cell_guid": "bc5b8e36-4475-4293-8f3b-5ff25c6f70d6" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Survived</th>\n <th>Pclass</th>\n <th>Name</th>\n <th>Sex</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Ticket</th>\n <th>Fare</th>\n <th>...</th>\n <th>Deck</th>\n <th>FamilySize</th>\n <th>TicketGroupSize</th>\n <th>TicketGroupSurvivors</th>\n <th>Age_norm</th>\n <th>SibSp_norm</th>\n <th>Parch_norm</th>\n <th>FamilySize_norm</th>\n <th>Fare_norm</th>\n <th>TicketGroupSize_norm</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>Braund, Mr. Owen Harris</td>\n <td>male</td>\n <td>22.0</td>\n <td>1</td>\n <td>0</td>\n <td>A/5 21171</td>\n <td>7.2500</td>\n <td>...</td>\n <td>Unknown</td>\n <td>2</td>\n <td>1</td>\n <td>0</td>\n <td>-0.564205</td>\n <td>0.481104</td>\n <td>-0.444829</td>\n <td>0.073324</td>\n <td>-0.503210</td>\n <td>-0.618937</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n <td>female</td>\n <td>38.0</td>\n <td>1</td>\n <td>0</td>\n <td>PC 17599</td>\n <td>71.2833</td>\n <td>...</td>\n <td>C</td>\n <td>2</td>\n <td>2</td>\n <td>1</td>\n <td>0.651110</td>\n <td>0.481104</td>\n <td>-0.444829</td>\n <td>0.073324</td>\n <td>0.733941</td>\n <td>-0.057086</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>1</td>\n <td>3</td>\n <td>Heikkinen, Miss. Laina</td>\n <td>female</td>\n <td>26.0</td>\n <td>0</td>\n <td>0</td>\n <td>STON/O2. 3101282</td>\n <td>7.9250</td>\n <td>...</td>\n <td>Unknown</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>-0.260376</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.490169</td>\n <td>-0.618937</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>1</td>\n <td>1</td>\n <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n <td>female</td>\n <td>35.0</td>\n <td>1</td>\n <td>0</td>\n <td>113803</td>\n <td>53.1000</td>\n <td>...</td>\n <td>C</td>\n <td>2</td>\n <td>2</td>\n <td>1</td>\n <td>0.423238</td>\n <td>0.481104</td>\n <td>-0.444829</td>\n <td>0.073324</td>\n <td>0.382632</td>\n <td>-0.057086</td>\n </tr>\n <tr>\n <th>4</th>\n <td>5</td>\n <td>0</td>\n <td>3</td>\n <td>Allen, Mr. William Henry</td>\n <td>male</td>\n <td>35.0</td>\n <td>0</td>\n <td>0</td>\n <td>373450</td>\n <td>8.0500</td>\n <td>...</td>\n <td>Unknown</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0.423238</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.487754</td>\n <td>-0.618937</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows \u00d7 24 columns</p>\n</div>", "text/plain": " PassengerId Survived Pclass \\\n0 1 0 3 \n1 2 1 1 \n2 3 1 3 \n3 4 1 1 \n4 5 0 3 \n\n Name Sex Age SibSp \\\n0 Braund, Mr. Owen Harris male 22.0 1 \n1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n2 Heikkinen, Miss. Laina female 26.0 0 \n3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n4 Allen, Mr. William Henry male 35.0 0 \n\n Parch Ticket Fare ... Deck FamilySize \\\n0 0 A/5 21171 7.2500 ... Unknown 2 \n1 0 PC 17599 71.2833 ... C 2 \n2 0 STON/O2. 3101282 7.9250 ... Unknown 1 \n3 0 113803 53.1000 ... C 2 \n4 0 373450 8.0500 ... Unknown 1 \n\n TicketGroupSize TicketGroupSurvivors Age_norm SibSp_norm Parch_norm \\\n0 1 0 -0.564205 0.481104 -0.444829 \n1 2 1 0.651110 0.481104 -0.444829 \n2 1 1 -0.260376 -0.478904 -0.444829 \n3 2 1 0.423238 0.481104 -0.444829 \n4 1 0 0.423238 -0.478904 -0.444829 \n\n FamilySize_norm Fare_norm TicketGroupSize_norm \n0 0.073324 -0.503210 -0.618937 \n1 0.073324 0.733941 -0.057086 \n2 -0.558133 -0.490169 -0.618937 \n3 0.073324 0.382632 -0.057086 \n4 -0.558133 -0.487754 -0.618937 \n\n[5 rows x 24 columns]" }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.head()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "dcb03cbf-20be-474d-99a6-e9514736af98" }, "source": [ "Looks like it works!\n", "\n", "### Categorical features. \n", "#### Sex\n", "Let's begin with **Sex** since it's the first thing that comes to my mind (no pun intended). Sex is a\n", "categorical value, but it can only take two different values: **female** or **male**. Note that is\n", "should not be necessary to have two features, with one **Sex_male** and another **Sex_female**, as\n", "that will just make to directly linear correlated values. This actually applies to all categorical\n", "features. I will therefore drop the most populated category. For sex, the baseline category will\n", "be male, and hence sex_male will be dropped.\n", "#### Passenger class (Pclass)\n", "I will we can treat this as categorical data. I think I'll handle this as categorical data. I will drop\n", "Pclass_3 as the baseline category.\n", "#### Embarkment port\n", "Let's just make a categorical inputs for each port. I'll drop Embarked_S as baseline.\n", "#### CabinSide\n", "Plain categorical. Starboard and Port, I will drop CabinSide_unknown as the baseline case.\n", "#### CabinDeck\n", "I will also do categories on the deck even though a plain numerical value could be considered.\n", "Deck_Unknown will be dropped as baseline.\n", "#### Title\n", "Magan have already showed us that title is an important feature. However we need to reduce number of\n", "titles. Some of the titles are rare and should not have their own category. I suggest these title\n", "categories: **Mr**, **Mrs**, **Miss**, **Master**, **Rev**, **Officer**, **Royal**." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "_cell_guid": "78559040-4259-45e3-bd50-ac0b35d2d527" }, "outputs": [ { "data": { "text/plain": "Mr 757\nMiss 262\nMrs 197\nMaster 61\nDr 8\nRev 8\nCol 4\nMajor 2\nMlle 2\nCapt 1\nSir 1\nDona 1\nthe Countess 1\nLady 1\nJonkheer 1\nDon 1\nMme 1\nName: Title, dtype: int64" }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "full[\"Title\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "_cell_guid": "b05809b0-a90c-4b99-a371-f1a9e120b557" }, "outputs": [], "source": [ "# These can be discussed, of course.\n", "titledict = {\"Dr\" : \"Mr\",\n", " \"Col\" : \"Officer\",\n", " \"Mlle\" : \"Miss\",\n", " \"Major\": \"Officer\",\n", " \"Lady\" : \"Royal\",\n", " \"Dona\" : \"Royal\",\n", " \"Don\" : \"Royal\",\n", " \"Mme\" : \"Mrs\",\n", " \"the Countess\": \"Royal\",\n", " \"Jonkheer\": \"Royal\",\n", " \"Capt\" : \"Officer\",\n", " \"Sir\" : \"Mr\"\n", " }\n", "#There is probably a pandas way to do this, however I do it the python way today.\n", "for df in frames:\n", " for key,val in titledict.items():\n", " df.loc[df[\"Title\"]==key, \"Title\"] = val" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "_cell_guid": "1d10828f-4db1-42f4-a803-d2c8c2eb78a7" }, "outputs": [ { "data": { "text/plain": "Mr 766\nMiss 264\nMrs 198\nMaster 61\nRev 8\nOfficer 7\nRoyal 5\nName: Title, dtype: int64" }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "full[\"Title\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "ecd614c5-e727-459e-8a45-3699b973357c" }, "source": [ "Then we have only seven titles. Let's create the indicator variables." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "_cell_guid": "f2e96a54-e0cf-4005-8815-c2ff9b9a9ce3" }, "outputs": [], "source": [ "category_list = [\"Pclass\", \"Sex\", \"Embarked\", \"Title\", \"CabinSide\", \"Deck\"]\n", "titanic = pd.get_dummies(titanic, columns=category_list)\n", "test = pd.get_dummies(test, columns=category_list)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "075dd861-ddbc-4bb2-9820-6c4c40c91915" }, "source": [ "#### TicketGroupSurvivors\n", "Maybe this can be divided by the size of the group?" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "_cell_guid": "f04bbd33-b825-472a-a8d8-a1886941864e" }, "outputs": [], "source": [ "for df in [titanic, test]:\n", " df[\"TGS_norm\"] = df[\"TicketGroupSurvivors\"] / df[\"TicketGroupSize\"]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "_cell_guid": "3a009976-cbce-43a7-8d9d-f69f226f38ff" }, "outputs": [ { "data": { "text/plain": "Index(['PassengerId', 'Survived', 'Name', 'Age', 'SibSp', 'Parch', 'Ticket',\n 'Fare', 'Cabin', 'FamilySize', 'TicketGroupSize',\n 'TicketGroupSurvivors', 'Age_norm', 'SibSp_norm', 'Parch_norm',\n 'FamilySize_norm', 'Fare_norm', 'TicketGroupSize_norm', 'Pclass_1',\n 'Pclass_2', 'Pclass_3', 'Sex_female', 'Sex_male', 'Embarked_C',\n 'Embarked_Q', 'Embarked_S', 'Title_Master', 'Title_Miss', 'Title_Mr',\n 'Title_Mrs', 'Title_Officer', 'Title_Rev', 'Title_Royal',\n 'CabinSide_Port', 'CabinSide_Starboard', 'CabinSide_Unknown', 'Deck_A',\n 'Deck_B', 'Deck_C', 'Deck_D', 'Deck_E', 'Deck_F', 'Deck_G',\n 'Deck_Unknown', 'TGS_norm'],\n dtype='object')" }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.columns" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "_cell_guid": "a768ea30-b452-48e6-a936-f2d1dafb46d5" }, "outputs": [ { "data": { "text/plain": "Index(['PassengerId', 'Name', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare',\n 'Cabin', 'FamilySize', 'TicketGroupSize', 'TicketGroupSurvivors',\n 'Age_norm', 'SibSp_norm', 'Parch_norm', 'FamilySize_norm', 'Fare_norm',\n 'TicketGroupSize_norm', 'Pclass_1', 'Pclass_2', 'Pclass_3',\n 'Sex_female', 'Sex_male', 'Embarked_C', 'Embarked_Q', 'Embarked_S',\n 'Title_Master', 'Title_Miss', 'Title_Mr', 'Title_Mrs', 'Title_Officer',\n 'Title_Rev', 'Title_Royal', 'CabinSide_Port', 'CabinSide_Starboard',\n 'CabinSide_Unknown', 'Deck_A', 'Deck_B', 'Deck_C', 'Deck_D', 'Deck_E',\n 'Deck_F', 'Deck_G', 'Deck_Unknown', 'TGS_norm'],\n dtype='object')" }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.columns" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e966a880-c209-4ddb-a58e-a21e82b2820c" }, "source": [ "Enough features for today! Instead of dropping the unwanted columns that will not be used in the\n", "classifier, I will rather make a new dataframe for training and testing." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "a0816ce4-482f-45b6-8ae1-5bd4bdfa3e7c" }, "source": [ "## Evaluating classifiers\n", "Let's try a few different classifier and use cross validation to find the one we like the most. I suggest\n", "we try Naive Bayes, KNN, Logistic Regression and Random Forest. It would be \u00fcbercool if we also could try\n", "a simple neural network to classify, but let's try the other first.\n", "\n", "### The dead simple classifier\n", "Before we do anything at all, we have to just make a dead simple classifier. I call this dead simple,\n", "because it predicts everyone dead. It can't be much simpler than that. Such preductor will of course\n", "make the wrong prediction for every passenger that actually survived. It is nice to have such a reference\n", "submission to know if we are doing good. " ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "_cell_guid": "c0c0c744-2079-4cea-8407-e5a114e9fa0c" }, "outputs": [ { "data": { "text/plain": "0.6161616161616161" }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# What should we expect if a predictor predicting all dead.\n", "1 - titanic.Survived.mean()" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "_cell_guid": "b051ded0-a8cf-4020-9587-aebcb36da3a8" }, "outputs": [], "source": [ "# Let's see the real.\n", "ds_submission = pd.DataFrame(test[\"PassengerId\"])\n", "ds_submission[\"Survived\"] = 0 # All dead" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "_cell_guid": "41e32959-813f-4386-8f99-d4af558935ac" }, "outputs": [], "source": [ "# This is actually the simplest predictor I can imagine so for the fun of it, let's submit this\n", "# and see how it scores\n", "ds_submission.to_csv(\"all_dead.csv\", index=False)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "4a5b9124-f19f-4039-9cfc-cea4ab6ec1cd" }, "source": [ "Submission says: *Your submission scored **0.62679**, which is not an improvement of your best score.* Keep trying!\n", "It's actuallly better than the **0.616** estimated.\n", "\n", "We can actually use this result to say how many survivors there should be in the test set. That can be\n", "useful information in the fine tuning of the model." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "_cell_guid": "64f528b5-1b8c-4efe-b984-b090f4c63453" }, "outputs": [ { "data": { "text/plain": "262" }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "round(len(test) * 0.62679)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "ffac1589-4cbe-4215-bfa7-321d3984216f" }, "source": [ "So there should be about 262 dead passengers in the test set. We do not know which passengers are\n", "creating the 0.62679 score, but at least we know the ballpark." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "0517769c-5860-4995-8ffe-99effeea1161" }, "source": [ "### Gaussian Naive Bayes\n", "(To be done later)\n", "### K Nearest Neighbors classifier" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "_cell_guid": "ecc75304-25bd-4fb6-a5b7-1bfb1a5baaae" }, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import GridSearchCV" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "_cell_guid": "9013b2d3-6cd2-4e83-b572-c241f55ce1f7" }, "outputs": [], "source": [ "k_range = range(1, 31)\n", "param_grid = dict(n_neighbors=list(k_range),weights = [\"uniform\", \"distance\"])" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "_cell_guid": "9306c44b-4ec5-49c7-8704-62f384e4b7f1" }, "outputs": [], "source": [ "knn = KNeighborsClassifier(n_neighbors=5)\n", "grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy')" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "_cell_guid": "f6838bdf-704a-4064-bab1-e4261a3e3ad3" }, "outputs": [ { "data": { "text/plain": "Index(['PassengerId', 'Survived', 'Name', 'Age', 'SibSp', 'Parch', 'Ticket',\n 'Fare', 'Cabin', 'FamilySize', 'TicketGroupSize',\n 'TicketGroupSurvivors', 'Age_norm', 'SibSp_norm', 'Parch_norm',\n 'FamilySize_norm', 'Fare_norm', 'TicketGroupSize_norm', 'Pclass_1',\n 'Pclass_2', 'Pclass_3', 'Sex_female', 'Sex_male', 'Embarked_C',\n 'Embarked_Q', 'Embarked_S', 'Title_Master', 'Title_Miss', 'Title_Mr',\n 'Title_Mrs', 'Title_Officer', 'Title_Rev', 'Title_Royal',\n 'CabinSide_Port', 'CabinSide_Starboard', 'CabinSide_Unknown', 'Deck_A',\n 'Deck_B', 'Deck_C', 'Deck_D', 'Deck_E', 'Deck_F', 'Deck_G',\n 'Deck_Unknown', 'TGS_norm'],\n dtype='object')" }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.columns" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "_cell_guid": "3c1a440d-6269-4842-8a8b-72be71740e89" }, "outputs": [], "source": [ "features = ['Age_norm', 'SibSp_norm', 'Parch_norm',\n", " 'FamilySize_norm', 'Fare_norm', 'TicketGroupSize_norm', 'Pclass_1',\n", " 'Pclass_2', 'Sex_female', 'Embarked_C',\n", " 'Embarked_Q', 'Title_Master', 'Title_Miss', \n", " 'Title_Mrs', 'Title_Officer', 'Title_Rev', 'Title_Royal',\n", " 'CabinSide_Port', 'CabinSide_Starboard', 'Deck_A',\n", " 'Deck_B', 'Deck_C', 'Deck_D', 'Deck_E', 'Deck_F', 'Deck_G',\n", " 'TGS_norm']" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "_cell_guid": "14874388-f732-47c3-acb4-b561c7a07867" }, "outputs": [ { "data": { "text/plain": "27" }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(features)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "_cell_guid": "2e911d3e-f5b6-4cf2-9ab7-85e3f4dcb316" }, "outputs": [ { "data": { "text/plain": "GridSearchCV(cv=10, error_score='raise',\n estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n weights='uniform'),\n fit_params={}, iid=True, n_jobs=1,\n param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30], 'weights': ['uniform', 'distance']},\n pre_dispatch='2*n_jobs', refit=True, scoring='accuracy', verbose=0)" }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.fit(titanic[features], titanic.Survived)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "_cell_guid": "169ada64-16d6-4751-9fa0-e6f772978a56" }, "outputs": [ { "data": { "text/plain": "0.93378226711560042" }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.best_score_" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "_cell_guid": "e3684e48-0e0d-4ccf-9f85-eda0bbc487dd" }, "outputs": [ { "data": { "text/plain": "{'n_neighbors': 7, 'weights': 'distance'}" }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.best_params_" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "43646bd4-7012-4e5b-aae5-7749526a5cf9" }, "source": [ "No! 0.934 is way better than I expected. This is *too good*! I don't believe this. I believe that this is\n", "a matter of the TGS_norm feature being highly correlated to survived in the train set. Let's try to submit\n", "this and see and check this theory." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "_cell_guid": "5a5ca31c-0e77-4f2c-8fa0-962f34e2c71e" }, "outputs": [ { "data": { "text/plain": "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n metric_params=None, n_jobs=1, n_neighbors=7, p=2,\n weights='distance')" }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# rerun fit() with the best parameters with the entire set (no CV)\n", "knn = KNeighborsClassifier(n_neighbors=7, weights=\"distance\")\n", "knn.fit(titanic[features], titanic.Survived)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "_cell_guid": "66601965-f36f-4d43-b8ed-21f5027777a9" }, "outputs": [], "source": [ "knn_predictions_with_TGS = knn.predict(test[features])" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "_cell_guid": "78cc80a1-e15e-4f38-873b-8eeee549ca24" }, "outputs": [ { "data": { "text/plain": "112" }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn_predictions_with_TGS.sum()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "cb7c59e7-b545-4809-9ac1-8bf336c274b1" }, "source": [ "Only 112. Far from the 262 we estimated. Makes me believe the TGS theory is right. But let's submit\n", "anyway." ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "_cell_guid": "1fe574ec-6161-4256-80ba-f76ab4ccb43a" }, "outputs": [], "source": [ "knn_submission1 = pd.DataFrame({\n", " \"PassengerId\": test[\"PassengerId\"],\n", " \"Survived\": knn_predictions_with_TGS\n", " })\n", "knn_submission1.to_csv(\"knn_predictions_with_TGS.csv\", index=False)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b8e7e018-f8b8-4214-9b35-85c6db0035a1" }, "source": [ "On the leaderboard this scores: 0.72727. Not really impressive and far far from the 0.934 we found\n", "in cross validation. If the TGS_norm feature is bad for the singleton passengers, let's try to\n", "redo the above steps w/o that feature. " ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "_cell_guid": "9daea4b5-14b9-4195-b8b8-898a5ab2461f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "0.814814814815 {'n_neighbors': 29, 'weights': 'uniform'}\n" } ], "source": [ "knn = KNeighborsClassifier(n_neighbors=5)\n", "# Fit once more without TGS_norm\n", "grid.fit(titanic[features[:-1]], titanic.Survived)\n", "print(grid.best_score_ , grid.best_params_)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c25e0466-1255-4d98-8216-08a35735bf2e" }, "source": [ "This number looks more like something I could believe. Let's try to submit this." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "_cell_guid": "a306762f-39fa-40e9-a5e5-8a851693e131" }, "outputs": [ { "data": { "text/plain": "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n metric_params=None, n_jobs=1, n_neighbors=29, p=2,\n weights='uniform')" }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# rerun fit() with the best parameters with the entire set (no CV)\n", "knn = KNeighborsClassifier(n_neighbors=29, weights='uniform')\n", "knn.fit(titanic[features[:-1]], titanic.Survived)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "_cell_guid": "5ae5d09e-34a7-43a4-9306-04be9530194e" }, "outputs": [ { "data": { "text/plain": "155" }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn_predictions_wo_TGS = knn.predict(test[features[:-1]])\n", "knn_predictions_wo_TGS.sum()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "07fc7354-7aec-434b-9d62-d399118b0a57" }, "source": [ "Hmmmm... only 155.... that's still a bit off. Let's submit anyway." ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "_cell_guid": "06e69776-5e9b-4d1d-a35d-30616f4b6745" }, "outputs": [], "source": [ "knn_submission2 = pd.DataFrame({\n", " \"PassengerId\": test[\"PassengerId\"],\n", " \"Survived\": knn_predictions_wo_TGS\n", " })\n", "knn_submission2.to_csv(\"knn_predictions_wo_TGS.csv\", index=False)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "703ff9d5-54a5-4606-84a3-bd1e6293d90c" }, "source": [ "Well ... 0.77990. Not very impressive, but still an improvement. Can we improve from here?\n", "\n", "I wonder if the two submissions can be combined? We could either do a OR-operation of the two submissions, or we could check the ticket group size to see if we should use the one or the other. Let's try both these. First the OR-operation:" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "_cell_guid": "70a864f9-5ec7-d891-c3be-e04fce7213ad" }, "outputs": [], "source": [ "knn_submission3 = pd.DataFrame({\n", " \"PassengerId\": test[\"PassengerId\"],\n", " \"Survived\": np.logical_or(knn_predictions_with_TGS, knn_predictions_wo_TGS).astype('int') \n", "})\n", "knn_submission3.to_csv(\"knn_predictions_or_TGS.csv\", index=False)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "fb80ca3b-e6a1-b2fd-eee9-22c8285025ba" }, "source": [ "*Your submission scored 0.76077*. Worse than the previous. Let's try the other strategy:" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "_cell_guid": "c8b930a3-42c3-6554-e157-86a80f1139d4" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Age_norm</th>\n <th>SibSp_norm</th>\n <th>Parch_norm</th>\n <th>FamilySize_norm</th>\n <th>Fare_norm</th>\n <th>TicketGroupSize_norm</th>\n <th>Pclass_1</th>\n <th>Pclass_2</th>\n <th>Sex_female</th>\n <th>Embarked_C</th>\n <th>...</th>\n <th>Title_Royal</th>\n <th>CabinSide_Port</th>\n <th>CabinSide_Starboard</th>\n <th>Deck_A</th>\n <th>Deck_B</th>\n <th>Deck_C</th>\n <th>Deck_D</th>\n <th>Deck_E</th>\n <th>Deck_F</th>\n <th>Deck_G</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0.385260</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.492020</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1.334724</td>\n <td>0.481104</td>\n <td>-0.444829</td>\n <td>0.073324</td>\n <td>-0.508040</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2.474082</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.456116</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>-0.184419</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.475920</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>-1.171863</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.465052</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>0.043452</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.495884</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>-0.868034</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.503612</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>10</th>\n <td>-0.032505</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.490733</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>11</th>\n <td>1.258767</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.140952</td>\n <td>-0.618937</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>16</th>\n <td>0.423238</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.404676</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>17</th>\n <td>-0.640162</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.503693</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>18</th>\n <td>-0.184419</td>\n <td>0.481104</td>\n <td>-0.444829</td>\n <td>0.073324</td>\n <td>-0.490169</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>19</th>\n <td>1.182810</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.503693</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>21</th>\n <td>-1.551649</td>\n <td>-0.478904</td>\n <td>0.710492</td>\n <td>0.073324</td>\n <td>-0.582022</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>22</th>\n <td>0.461217</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.031148</td>\n <td>-0.618937</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>27</th>\n <td>-0.526227</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.503693</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>28</th>\n <td>0.878981</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.054010</td>\n <td>-0.618937</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>35</th>\n <td>-0.830055</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.503612</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>36</th>\n <td>-0.564205</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.487754</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>37</th>\n <td>-0.640162</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.475920</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>38</th>\n <td>-0.336334</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.459739</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>41</th>\n <td>-0.032505</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.130326</td>\n <td>-0.618937</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>42</th>\n <td>0.878981</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.491618</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>43</th>\n <td>0.043452</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.392117</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>45</th>\n <td>-0.336334</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.490169</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>46</th>\n <td>1.182810</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.069466</td>\n <td>-0.618937</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>47</th>\n <td>-0.032505</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.493550</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>51</th>\n <td>-0.184419</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.352833</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>54</th>\n <td>-0.032505</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.342286</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>56</th>\n <td>0.423238</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.490733</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>351</th>\n <td>-0.336334</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.440419</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>355</th>\n <td>1.562596</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.140952</td>\n <td>-0.618937</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>357</th>\n <td>-0.032505</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.491054</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>358</th>\n <td>-0.032505</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.493550</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>363</th>\n <td>-0.184419</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.475920</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>369</th>\n <td>-0.032505</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.375535</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>370</th>\n <td>-0.640162</td>\n <td>0.481104</td>\n <td>-0.444829</td>\n <td>0.073324</td>\n <td>-0.421098</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>373</th>\n <td>1.106853</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.392117</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>376</th>\n <td>-0.564205</td>\n <td>1.441112</td>\n <td>-0.444829</td>\n <td>0.704781</td>\n <td>-0.475920</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>377</th>\n <td>-0.640162</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.421098</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>378</th>\n <td>1.942382</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>0.322739</td>\n <td>-0.618937</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>380</th>\n <td>-0.032505</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.493550</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>381</th>\n <td>-0.260376</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.491054</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>384</th>\n <td>-0.032505</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.394533</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>386</th>\n <td>-0.412291</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.493067</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>387</th>\n <td>2.094296</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.392117</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>388</th>\n <td>-0.640162</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.493550</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>393</th>\n <td>1.334724</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.440419</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>396</th>\n <td>-0.412291</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.503210</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>398</th>\n <td>-0.564205</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.493067</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>399</th>\n <td>0.119409</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.493872</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>404</th>\n <td>1.030896</td>\n <td>0.481104</td>\n <td>-0.444829</td>\n <td>0.073324</td>\n <td>-0.107705</td>\n <td>-0.618937</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>405</th>\n <td>-0.716120</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.375454</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>406</th>\n <td>-0.488248</td>\n <td>0.481104</td>\n <td>-0.444829</td>\n <td>0.073324</td>\n <td>-0.440419</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>408</th>\n <td>-0.564205</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.494114</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>410</th>\n <td>-0.564205</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.493550</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>412</th>\n <td>-0.108462</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.493067</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>413</th>\n <td>-0.032505</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.487754</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>415</th>\n <td>0.689088</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.503210</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>416</th>\n <td>-0.032505</td>\n <td>-0.478904</td>\n <td>-0.444829</td>\n <td>-0.558133</td>\n <td>-0.487754</td>\n <td>-0.618937</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>...</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n<p>232 rows \u00d7 26 columns</p>\n</div>", "text/plain": " Age_norm SibSp_norm Parch_norm FamilySize_norm Fare_norm \\\n0 0.385260 -0.478904 -0.444829 -0.558133 -0.492020 \n1 1.334724 0.481104 -0.444829 0.073324 -0.508040 \n2 2.474082 -0.478904 -0.444829 -0.558133 -0.456116 \n3 -0.184419 -0.478904 -0.444829 -0.558133 -0.475920 \n5 -1.171863 -0.478904 -0.444829 -0.558133 -0.465052 \n6 0.043452 -0.478904 -0.444829 -0.558133 -0.495884 \n8 -0.868034 -0.478904 -0.444829 -0.558133 -0.503612 \n10 -0.032505 -0.478904 -0.444829 -0.558133 -0.490733 \n11 1.258767 -0.478904 -0.444829 -0.558133 -0.140952 \n16 0.423238 -0.478904 -0.444829 -0.558133 -0.404676 \n17 -0.640162 -0.478904 -0.444829 -0.558133 -0.503693 \n18 -0.184419 0.481104 -0.444829 0.073324 -0.490169 \n19 1.182810 -0.478904 -0.444829 -0.558133 -0.503693 \n21 -1.551649 -0.478904 0.710492 0.073324 -0.582022 \n22 0.461217 -0.478904 -0.444829 -0.558133 -0.031148 \n27 -0.526227 -0.478904 -0.444829 -0.558133 -0.503693 \n28 0.878981 -0.478904 -0.444829 -0.558133 -0.054010 \n35 -0.830055 -0.478904 -0.444829 -0.558133 -0.503612 \n36 -0.564205 -0.478904 -0.444829 -0.558133 -0.487754 \n37 -0.640162 -0.478904 -0.444829 -0.558133 -0.475920 \n38 -0.336334 -0.478904 -0.444829 -0.558133 -0.459739 \n41 -0.032505 -0.478904 -0.444829 -0.558133 -0.130326 \n42 0.878981 -0.478904 -0.444829 -0.558133 -0.491618 \n43 0.043452 -0.478904 -0.444829 -0.558133 -0.392117 \n45 -0.336334 -0.478904 -0.444829 -0.558133 -0.490169 \n46 1.182810 -0.478904 -0.444829 -0.558133 -0.069466 \n47 -0.032505 -0.478904 -0.444829 -0.558133 -0.493550 \n51 -0.184419 -0.478904 -0.444829 -0.558133 -0.352833 \n54 -0.032505 -0.478904 -0.444829 -0.558133 -0.342286 \n56 0.423238 -0.478904 -0.444829 -0.558133 -0.490733 \n.. ... ... ... ... ... \n351 -0.336334 -0.478904 -0.444829 -0.558133 -0.440419 \n355 1.562596 -0.478904 -0.444829 -0.558133 -0.140952 \n357 -0.032505 -0.478904 -0.444829 -0.558133 -0.491054 \n358 -0.032505 -0.478904 -0.444829 -0.558133 -0.493550 \n363 -0.184419 -0.478904 -0.444829 -0.558133 -0.475920 \n369 -0.032505 -0.478904 -0.444829 -0.558133 -0.375535 \n370 -0.640162 0.481104 -0.444829 0.073324 -0.421098 \n373 1.106853 -0.478904 -0.444829 -0.558133 -0.392117 \n376 -0.564205 1.441112 -0.444829 0.704781 -0.475920 \n377 -0.640162 -0.478904 -0.444829 -0.558133 -0.421098 \n378 1.942382 -0.478904 -0.444829 -0.558133 0.322739 \n380 -0.032505 -0.478904 -0.444829 -0.558133 -0.493550 \n381 -0.260376 -0.478904 -0.444829 -0.558133 -0.491054 \n384 -0.032505 -0.478904 -0.444829 -0.558133 -0.394533 \n386 -0.412291 -0.478904 -0.444829 -0.558133 -0.493067 \n387 2.094296 -0.478904 -0.444829 -0.558133 -0.392117 \n388 -0.640162 -0.478904 -0.444829 -0.558133 -0.493550 \n393 1.334724 -0.478904 -0.444829 -0.558133 -0.440419 \n396 -0.412291 -0.478904 -0.444829 -0.558133 -0.503210 \n398 -0.564205 -0.478904 -0.444829 -0.558133 -0.493067 \n399 0.119409 -0.478904 -0.444829 -0.558133 -0.493872 \n404 1.030896 0.481104 -0.444829 0.073324 -0.107705 \n405 -0.716120 -0.478904 -0.444829 -0.558133 -0.375454 \n406 -0.488248 0.481104 -0.444829 0.073324 -0.440419 \n408 -0.564205 -0.478904 -0.444829 -0.558133 -0.494114 \n410 -0.564205 -0.478904 -0.444829 -0.558133 -0.493550 \n412 -0.108462 -0.478904 -0.444829 -0.558133 -0.493067 \n413 -0.032505 -0.478904 -0.444829 -0.558133 -0.487754 \n415 0.689088 -0.478904 -0.444829 -0.558133 -0.503210 \n416 -0.032505 -0.478904 -0.444829 -0.558133 -0.487754 \n\n TicketGroupSize_norm Pclass_1 Pclass_2 Sex_female Embarked_C ... \\\n0 -0.618937 0.0 0.0 0.0 0.0 ... \n1 -0.618937 0.0 0.0 1.0 0.0 ... \n2 -0.618937 0.0 1.0 0.0 0.0 ... \n3 -0.618937 0.0 0.0 0.0 0.0 ... \n5 -0.618937 0.0 0.0 0.0 0.0 ... \n6 -0.618937 0.0 0.0 1.0 0.0 ... \n8 -0.618937 0.0 0.0 1.0 1.0 ... \n10 -0.618937 0.0 0.0 0.0 0.0 ... \n11 -0.618937 1.0 0.0 0.0 0.0 ... \n16 -0.618937 0.0 1.0 0.0 0.0 ... \n17 -0.618937 0.0 0.0 0.0 1.0 ... \n18 -0.618937 0.0 0.0 1.0 0.0 ... \n19 -0.618937 0.0 0.0 1.0 1.0 ... \n21 -0.618937 0.0 0.0 0.0 0.0 ... \n22 -0.618937 1.0 0.0 1.0 0.0 ... \n27 -0.618937 0.0 0.0 0.0 1.0 ... \n28 -0.618937 1.0 0.0 0.0 0.0 ... \n35 -0.618937 0.0 0.0 0.0 1.0 ... \n36 -0.618937 0.0 0.0 1.0 0.0 ... \n37 -0.618937 0.0 0.0 1.0 0.0 ... \n38 -0.618937 0.0 0.0 0.0 0.0 ... \n41 -0.618937 1.0 0.0 0.0 0.0 ... \n42 -0.618937 0.0 0.0 0.0 0.0 ... \n43 -0.618937 0.0 1.0 1.0 0.0 ... \n45 -0.618937 0.0 0.0 0.0 0.0 ... \n46 -0.618937 1.0 0.0 0.0 1.0 ... \n47 -0.618937 0.0 0.0 0.0 0.0 ... \n51 -0.618937 0.0 1.0 0.0 1.0 ... \n54 -0.618937 0.0 1.0 0.0 1.0 ... \n56 -0.618937 0.0 0.0 0.0 0.0 ... \n.. ... ... ... ... ... ... \n351 -0.618937 0.0 1.0 0.0 0.0 ... \n355 -0.618937 1.0 0.0 0.0 0.0 ... \n357 -0.618937 0.0 0.0 0.0 0.0 ... \n358 -0.618937 0.0 0.0 0.0 0.0 ... \n363 -0.618937 0.0 0.0 0.0 0.0 ... \n369 -0.618937 0.0 1.0 0.0 1.0 ... \n370 -0.618937 0.0 1.0 0.0 0.0 ... \n373 -0.618937 0.0 1.0 0.0 0.0 ... \n376 -0.618937 0.0 0.0 1.0 0.0 ... \n377 -0.618937 0.0 1.0 0.0 0.0 ... \n378 -0.618937 1.0 0.0 0.0 0.0 ... \n380 -0.618937 0.0 0.0 0.0 0.0 ... \n381 -0.618937 0.0 0.0 0.0 0.0 ... \n384 -0.618937 0.0 1.0 0.0 0.0 ... \n386 -0.618937 0.0 0.0 0.0 0.0 ... \n387 -0.618937 0.0 1.0 0.0 0.0 ... \n388 -0.618937 0.0 0.0 0.0 0.0 ... \n393 -0.618937 0.0 1.0 0.0 0.0 ... \n396 -0.618937 0.0 0.0 0.0 0.0 ... \n398 -0.618937 0.0 0.0 0.0 0.0 ... \n399 -0.618937 0.0 0.0 0.0 0.0 ... \n404 -0.618937 1.0 0.0 0.0 1.0 ... \n405 -0.618937 0.0 1.0 0.0 1.0 ... \n406 -0.618937 0.0 1.0 0.0 0.0 ... \n408 -0.618937 0.0 0.0 1.0 0.0 ... \n410 -0.618937 0.0 0.0 1.0 0.0 ... \n412 -0.618937 0.0 0.0 1.0 0.0 ... \n413 -0.618937 0.0 0.0 0.0 0.0 ... \n415 -0.618937 0.0 0.0 0.0 0.0 ... \n416 -0.618937 0.0 0.0 0.0 0.0 ... \n\n Title_Royal CabinSide_Port CabinSide_Starboard Deck_A Deck_B Deck_C \\\n0 0.0 0.0 0.0 0.0 0.0 0.0 \n1 0.0 0.0 0.0 0.0 0.0 0.0 \n2 0.0 0.0 0.0 0.0 0.0 0.0 \n3 0.0 0.0 0.0 0.0 0.0 0.0 \n5 0.0 0.0 0.0 0.0 0.0 0.0 \n6 0.0 0.0 0.0 0.0 0.0 0.0 \n8 0.0 0.0 0.0 0.0 0.0 0.0 \n10 0.0 0.0 0.0 0.0 0.0 0.0 \n11 0.0 0.0 0.0 0.0 0.0 0.0 \n16 0.0 0.0 0.0 0.0 0.0 0.0 \n17 0.0 0.0 0.0 0.0 0.0 0.0 \n18 0.0 0.0 0.0 0.0 0.0 0.0 \n19 0.0 0.0 0.0 0.0 0.0 0.0 \n21 0.0 0.0 0.0 0.0 0.0 0.0 \n22 0.0 0.0 0.0 0.0 0.0 0.0 \n27 0.0 0.0 0.0 0.0 0.0 0.0 \n28 0.0 0.0 1.0 1.0 0.0 0.0 \n35 0.0 0.0 0.0 0.0 0.0 0.0 \n36 0.0 0.0 0.0 0.0 0.0 0.0 \n37 0.0 0.0 0.0 0.0 0.0 0.0 \n38 0.0 0.0 0.0 0.0 0.0 0.0 \n41 0.0 1.0 0.0 0.0 0.0 0.0 \n42 0.0 0.0 0.0 0.0 0.0 0.0 \n43 0.0 0.0 0.0 0.0 0.0 0.0 \n45 0.0 0.0 0.0 0.0 0.0 0.0 \n46 0.0 0.0 1.0 1.0 0.0 0.0 \n47 0.0 0.0 0.0 0.0 0.0 0.0 \n51 0.0 0.0 0.0 0.0 0.0 0.0 \n54 0.0 0.0 0.0 0.0 0.0 0.0 \n56 0.0 0.0 0.0 0.0 0.0 0.0 \n.. ... ... ... ... ... ... \n351 0.0 0.0 0.0 0.0 0.0 0.0 \n355 0.0 1.0 0.0 0.0 0.0 0.0 \n357 0.0 0.0 0.0 0.0 0.0 0.0 \n358 0.0 0.0 0.0 0.0 0.0 0.0 \n363 0.0 0.0 0.0 0.0 0.0 0.0 \n369 0.0 0.0 0.0 0.0 0.0 0.0 \n370 0.0 0.0 0.0 0.0 0.0 0.0 \n373 0.0 0.0 0.0 0.0 0.0 0.0 \n376 0.0 0.0 0.0 0.0 0.0 0.0 \n377 0.0 0.0 0.0 0.0 0.0 0.0 \n378 0.0 0.0 1.0 0.0 0.0 1.0 \n380 0.0 0.0 0.0 0.0 0.0 0.0 \n381 0.0 0.0 0.0 0.0 0.0 0.0 \n384 0.0 0.0 0.0 0.0 0.0 0.0 \n386 0.0 0.0 0.0 0.0 0.0 0.0 \n387 0.0 0.0 0.0 0.0 0.0 0.0 \n388 0.0 0.0 0.0 0.0 0.0 0.0 \n393 0.0 0.0 0.0 0.0 0.0 0.0 \n396 0.0 0.0 0.0 0.0 0.0 0.0 \n398 0.0 0.0 0.0 0.0 0.0 0.0 \n399 0.0 0.0 0.0 0.0 0.0 0.0 \n404 0.0 1.0 0.0 0.0 0.0 0.0 \n405 0.0 1.0 0.0 0.0 0.0 0.0 \n406 0.0 0.0 0.0 0.0 0.0 0.0 \n408 0.0 0.0 0.0 0.0 0.0 0.0 \n410 0.0 0.0 0.0 0.0 0.0 0.0 \n412 0.0 0.0 0.0 0.0 0.0 0.0 \n413 0.0 0.0 0.0 0.0 0.0 0.0 \n415 0.0 0.0 0.0 0.0 0.0 0.0 \n416 0.0 0.0 0.0 0.0 0.0 0.0 \n\n Deck_D Deck_E Deck_F Deck_G \n0 0.0 0.0 0.0 0.0 \n1 0.0 0.0 0.0 0.0 \n2 0.0 0.0 0.0 0.0 \n3 0.0 0.0 0.0 0.0 \n5 0.0 0.0 0.0 0.0 \n6 0.0 0.0 0.0 0.0 \n8 0.0 0.0 0.0 0.0 \n10 0.0 0.0 0.0 0.0 \n11 0.0 0.0 0.0 0.0 \n16 0.0 0.0 0.0 0.0 \n17 0.0 0.0 0.0 0.0 \n18 0.0 0.0 0.0 0.0 \n19 0.0 0.0 0.0 0.0 \n21 0.0 0.0 0.0 0.0 \n22 0.0 0.0 0.0 0.0 \n27 0.0 0.0 0.0 0.0 \n28 0.0 0.0 0.0 0.0 \n35 0.0 0.0 0.0 0.0 \n36 0.0 0.0 0.0 0.0 \n37 0.0 0.0 0.0 0.0 \n38 0.0 0.0 0.0 0.0 \n41 1.0 0.0 0.0 0.0 \n42 0.0 0.0 0.0 0.0 \n43 0.0 0.0 0.0 0.0 \n45 0.0 0.0 0.0 0.0 \n46 0.0 0.0 0.0 0.0 \n47 0.0 0.0 0.0 0.0 \n51 0.0 0.0 0.0 0.0 \n54 0.0 0.0 0.0 0.0 \n56 0.0 0.0 0.0 0.0 \n.. ... ... ... ... \n351 0.0 0.0 0.0 0.0 \n355 0.0 1.0 0.0 0.0 \n357 0.0 0.0 0.0 0.0 \n358 0.0 0.0 0.0 0.0 \n363 0.0 0.0 0.0 0.0 \n369 0.0 0.0 0.0 0.0 \n370 0.0 0.0 0.0 0.0 \n373 0.0 0.0 0.0 0.0 \n376 0.0 0.0 0.0 0.0 \n377 0.0 0.0 0.0 0.0 \n378 0.0 0.0 0.0 0.0 \n380 0.0 0.0 0.0 0.0 \n381 0.0 0.0 0.0 0.0 \n384 0.0 0.0 0.0 0.0 \n386 0.0 0.0 0.0 0.0 \n387 0.0 0.0 0.0 0.0 \n388 0.0 0.0 0.0 0.0 \n393 0.0 0.0 0.0 0.0 \n396 0.0 0.0 0.0 0.0 \n398 0.0 0.0 0.0 0.0 \n399 0.0 0.0 0.0 0.0 \n404 1.0 0.0 0.0 0.0 \n405 1.0 0.0 0.0 0.0 \n406 0.0 0.0 0.0 0.0 \n408 0.0 0.0 0.0 0.0 \n410 0.0 0.0 0.0 0.0 \n412 0.0 0.0 0.0 0.0 \n413 0.0 0.0 0.0 0.0 \n415 0.0 0.0 0.0 0.0 \n416 0.0 0.0 0.0 0.0 \n\n[232 rows x 26 columns]" }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test.loc[test[\"TicketGroupSize\"]==1, features[:-1]]" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "_cell_guid": "fd2f8876-fae3-4b21-86da-45d929193b55" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "Help on GridSearchCV in module sklearn.model_selection._search object:\n\nclass GridSearchCV(BaseSearchCV)\n | Exhaustive search over specified parameter values for an estimator.\n | \n | Important members are fit, predict.\n | \n | GridSearchCV implements a \"fit\" and a \"score\" method.\n | It also implements \"predict\", \"predict_proba\", \"decision_function\",\n | \"transform\" and \"inverse_transform\" if they are implemented in the\n | estimator used.\n | \n | The parameters of the estimator used to apply these methods are optimized\n | by cross-validated grid-search over a parameter grid.\n | \n | Read more in the :ref:`User Guide <grid_search>`.\n | \n | Parameters\n | ----------\n | estimator : estimator object.\n | This is assumed to implement the scikit-learn estimator interface.\n | Either estimator needs to provide a ``score`` function,\n | or ``scoring`` must be passed.\n | \n | param_grid : dict or list of dictionaries\n | Dictionary with parameters names (string) as keys and lists of\n | parameter settings to try as values, or a list of such\n | dictionaries, in which case the grids spanned by each dictionary\n | in the list are explored. This enables searching over any sequence\n | of parameter settings.\n | \n | scoring : string, callable or None, default=None\n | A string (see model evaluation documentation) or\n | a scorer callable object / function with signature\n | ``scorer(estimator, X, y)``.\n | If ``None``, the ``score`` method of the estimator is used.\n | \n | fit_params : dict, optional\n | Parameters to pass to the fit method.\n | \n | n_jobs : int, default=1\n | Number of jobs to run in parallel.\n | \n | pre_dispatch : int, or string, optional\n | Controls the number of jobs that get dispatched during parallel\n | execution. Reducing this number can be useful to avoid an\n | explosion of memory consumption when more jobs get dispatched\n | than CPUs can process. This parameter can be:\n | \n | - None, in which case all the jobs are immediately\n | created and spawned. Use this for lightweight and\n | fast-running jobs, to avoid delays due to on-demand\n | spawning of the jobs\n | \n | - An int, giving the exact number of total jobs that are\n | spawned\n | \n | - A string, giving an expression as a function of n_jobs,\n | as in '2*n_jobs'\n | \n | iid : boolean, default=True\n | If True, the data is assumed to be identically distributed across\n | the folds, and the loss minimized is the total loss per sample,\n | and not the mean loss across the folds.\n | \n | cv : int, cross-validation generator or an iterable, optional\n | Determines the cross-validation splitting strategy.\n | Possible inputs for cv are:\n | - None, to use the default 3-fold cross validation,\n | - integer, to specify the number of folds in a `(Stratified)KFold`,\n | - An object to be used as a cross-validation generator.\n | - An iterable yielding train, test splits.\n | \n | For integer/None inputs, if the estimator is a classifier and ``y`` is\n | either binary or multiclass, :class:`StratifiedKFold` is used. In all\n | other cases, :class:`KFold` is used.\n | \n | Refer :ref:`User Guide <cross_validation>` for the various\n | cross-validation strategies that can be used here.\n | \n | refit : boolean, default=True\n | Refit the best estimator with the entire dataset.\n | If \"False\", it is impossible to make predictions using\n | this GridSearchCV instance after fitting.\n | \n | verbose : integer\n | Controls the verbosity: the higher, the more messages.\n | \n | error_score : 'raise' (default) or numeric\n | Value to assign to the score if an error occurs in estimator fitting.\n | If set to 'raise', the error is raised. If a numeric value is given,\n | FitFailedWarning is raised. This parameter does not affect the refit\n | step, which will always raise the error.\n | \n | \n | Examples\n | --------\n | >>> from sklearn import svm, datasets\n | >>> from sklearn.model_selection import GridSearchCV\n | >>> iris = datasets.load_iris()\n | >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}\n | >>> svr = svm.SVC()\n | >>> clf = GridSearchCV(svr, parameters)\n | >>> clf.fit(iris.data, iris.target)\n | ... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS\n | GridSearchCV(cv=None, error_score=...,\n | estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=...,\n | decision_function_shape=None, degree=..., gamma=...,\n | kernel='rbf', max_iter=-1, probability=False,\n | random_state=None, shrinking=True, tol=...,\n | verbose=False),\n | fit_params={}, iid=..., n_jobs=1,\n | param_grid=..., pre_dispatch=..., refit=...,\n | scoring=..., verbose=...)\n | >>> sorted(clf.results_.keys())\n | ... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS\n | ['param_C', 'param_kernel', 'params', 'test_mean_score',...\n | 'test_rank_score', 'test_split0_score', 'test_split1_score',...\n | 'test_split2_score', 'test_std_score']\n | \n | Attributes\n | ----------\n | results_ : dict of numpy (masked) ndarrays\n | A dict with keys as column headers and values as columns, that can be\n | imported into a pandas ``DataFrame``.\n | \n | For instance the below given table\n | \n | +------------+-----------+------------+-----------------+---+---------+\n | |param_kernel|param_gamma|param_degree|test_split0_score|...|...rank..|\n | +============+===========+============+=================+===+=========+\n | | 'poly' | -- | 2 | 0.8 |...| 2 |\n | +------------+-----------+------------+-----------------+---+---------+\n | | 'poly' | -- | 3 | 0.7 |...| 4 |\n | +------------+-----------+------------+-----------------+---+---------+\n | | 'rbf' | 0.1 | -- | 0.8 |...| 3 |\n | +------------+-----------+------------+-----------------+---+---------+\n | | 'rbf' | 0.2 | -- | 0.9 |...| 1 |\n | +------------+-----------+------------+-----------------+---+---------+\n | \n | will be represented by a ``results_`` dict of::\n | \n | {\n | 'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'],\n | mask = [False False False False]...)\n | 'param_gamma': masked_array(data = [-- -- 0.1 0.2],\n | mask = [ True True False False]...),\n | 'param_degree': masked_array(data = [2.0 3.0 -- --],\n | mask = [False False True True]...),\n | 'test_split0_score' : [0.8, 0.7, 0.8, 0.9],\n | 'test_split1_score' : [0.82, 0.5, 0.7, 0.78],\n | 'test_mean_score' : [0.81, 0.60, 0.75, 0.82],\n | 'test_std_score' : [0.02, 0.01, 0.03, 0.03],\n | 'test_rank_score' : [2, 4, 3, 1],\n | 'params' : [{'kernel': 'poly', 'degree': 2}, ...],\n | }\n | \n | NOTE that the key ``'params'`` is used to store a list of parameter\n | settings dict for all the parameter candidates.\n | \n | best_estimator_ : estimator\n | Estimator that was chosen by the search, i.e. estimator\n | which gave highest score (or smallest loss if specified)\n | on the left out data. Not available if refit=False.\n | \n | best_score_ : float\n | Score of best_estimator on the left out data.\n | \n | best_params_ : dict\n | Parameter setting that gave the best results on the hold out data.\n | \n | best_index_ : int\n | The index (of the ``results_`` arrays) which corresponds to the best\n | candidate parameter setting.\n | \n | The dict at ``search.results_['params'][search.best_index_]`` gives\n | the parameter setting for the best model, that gives the highest\n | mean score (``search.best_score_``).\n | \n | scorer_ : function\n | Scorer function used on the held out data to choose the best\n | parameters for the model.\n | \n | n_splits_ : int\n | The number of cross-validation splits (folds/iterations).\n | \n | Notes\n | ------\n | The parameters selected are those that maximize the score of the left out\n | data, unless an explicit score is passed in which case it is used instead.\n | \n | If `n_jobs` was set to a value higher than one, the data is copied for each\n | point in the grid (and not `n_jobs` times). This is done for efficiency\n | reasons if individual jobs take very little time, but may raise errors if\n | the dataset is large and not enough memory is available. A workaround in\n | this case is to set `pre_dispatch`. Then, the memory is copied only\n | `pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *\n | n_jobs`.\n | \n | See Also\n | ---------\n | :class:`ParameterGrid`:\n | generates all the combinations of a hyperparameter grid.\n | \n | :func:`sklearn.model_selection.train_test_split`:\n | utility function to split the data into a development set usable\n | for fitting a GridSearchCV instance and an evaluation set for\n | its final evaluation.\n | \n | :func:`sklearn.metrics.make_scorer`:\n | Make a scorer from a performance metric or loss function.\n | \n | Method resolution order:\n | GridSearchCV\n | BaseSearchCV\n | abc.NewBase\n | sklearn.base.BaseEstimator\n | sklearn.base.MetaEstimatorMixin\n | builtins.object\n | \n | Methods defined here:\n | \n | __init__(self, estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score='raise')\n | Initialize self. See help(type(self)) for accurate signature.\n | \n | fit(self, X, y=None, labels=None)\n | Run fit with all sets of parameters.\n | \n | Parameters\n | ----------\n | \n | X : array-like, shape = [n_samples, n_features]\n | Training vector, where n_samples is the number of samples and\n | n_features is the number of features.\n | \n | y : array-like, shape = [n_samples] or [n_samples, n_output], optional\n | Target relative to X for classification or regression;\n | None for unsupervised learning.\n | \n | labels : array-like, with shape (n_samples,), optional\n | Group labels for the samples used while splitting the dataset into\n | train/test set.\n | \n | ----------------------------------------------------------------------\n | Data and other attributes defined here:\n | \n | __abstractmethods__ = frozenset()\n | \n | ----------------------------------------------------------------------\n | Methods inherited from BaseSearchCV:\n | \n | decision_function(self, X)\n | Call decision_function on the estimator with the best found parameters.\n | \n | Only available if ``refit=True`` and the underlying estimator supports\n | ``decision_function``.\n | \n | Parameters\n | -----------\n | X : indexable, length n_samples\n | Must fulfill the input assumptions of the\n | underlying estimator.\n | \n | inverse_transform(self, Xt)\n | Call inverse_transform on the estimator with the best found parameters.\n | \n | Only available if the underlying estimator implements\n | ``inverse_transform`` and ``refit=True``.\n | \n | Parameters\n | -----------\n | Xt : indexable, length n_samples\n | Must fulfill the input assumptions of the\n | underlying estimator.\n | \n | predict(self, X)\n | Call predict on the estimator with the best found parameters.\n | \n | Only available if ``refit=True`` and the underlying estimator supports\n | ``predict``.\n | \n | Parameters\n | -----------\n | X : indexable, length n_samples\n | Must fulfill the input assumptions of the\n | underlying estimator.\n | \n | predict_log_proba(self, X)\n | Call predict_log_proba on the estimator with the best found parameters.\n | \n | Only available if ``refit=True`` and the underlying estimator supports\n | ``predict_log_proba``.\n | \n | Parameters\n | -----------\n | X : indexable, length n_samples\n | Must fulfill the input assumptions of the\n | underlying estimator.\n | \n | predict_proba(self, X)\n | Call predict_proba on the estimator with the best found parameters.\n | \n | Only available if ``refit=True`` and the underlying estimator supports\n | ``predict_proba``.\n | \n | Parameters\n | -----------\n | X : indexable, length n_samples\n | Must fulfill the input assumptions of the\n | underlying estimator.\n | \n | score(self, X, y=None)\n | Returns the score on the given data, if the estimator has been refit.\n | \n | This uses the score defined by ``scoring`` where provided, and the\n | ``best_estimator_.score`` method otherwise.\n | \n | Parameters\n | ----------\n | X : array-like, shape = [n_samples, n_features]\n | Input data, where n_samples is the number of samples and\n | n_features is the number of features.\n | \n | y : array-like, shape = [n_samples] or [n_samples, n_output], optional\n | Target relative to X for classification or regression;\n | None for unsupervised learning.\n | \n | Returns\n | -------\n | score : float\n | \n | transform(self, X)\n | Call transform on the estimator with the best found parameters.\n | \n | Only available if the underlying estimator supports ``transform`` and\n | ``refit=True``.\n | \n | Parameters\n | -----------\n | X : indexable, length n_samples\n | Must fulfill the input assumptions of the\n | underlying estimator.\n | \n | ----------------------------------------------------------------------\n | Data descriptors inherited from BaseSearchCV:\n | \n | best_params_\n | \n | best_score_\n | \n | grid_scores_\n | \n | ----------------------------------------------------------------------\n | Methods inherited from sklearn.base.BaseEstimator:\n | \n | __repr__(self)\n | Return repr(self).\n | \n | get_params(self, deep=True)\n | Get parameters for this estimator.\n | \n | Parameters\n | ----------\n | deep: boolean, optional\n | If True, will return the parameters for this estimator and\n | contained subobjects that are estimators.\n | \n | Returns\n | -------\n | params : mapping of string to any\n | Parameter names mapped to their values.\n | \n | set_params(self, **params)\n | Set the parameters of this estimator.\n | \n | The method works on simple estimators as well as on nested objects\n | (such as pipelines). The latter have parameters of the form\n | ``<component>__<parameter>`` so that it's possible to update each\n | component of a nested object.\n | \n | Returns\n | -------\n | self\n | \n | ----------------------------------------------------------------------\n | Data descriptors inherited from sklearn.base.BaseEstimator:\n | \n | __dict__\n | dictionary for instance variables (if defined)\n | \n | __weakref__\n | list of weak references to the object (if defined)\n\n" } ], "source": [ "help(grid)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "_cell_guid": "a060ccee-285b-45c3-a524-2af7c481d8a2" }, "outputs": [], "source": "" } ], "metadata": { "_change_revision": 555, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/320/320432.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "007a6ef8-de5a-4619-a4a8-485025ab1727" }, "source": [ "# Predicting Loan Status with Python\n", "This notebook uses Python, NumPy, and Matplotlib to explore the relationship between several data fields in the Lending Club Loan Data SQLite database. SQL queries are used to obtain the loan data records that contain specific strings in the **title** field, which is the loan title provided by the borrower. The search strings investigated are:\n", "\n", "* \"credit card\"\n", "* \"medical\"\n", "* \"debt\"\n", "\n", "Finally, a decision tree classifier (scikit-learn) is used to predict the **loan_status**, which is the current status of the loan. A binary classification system is used, in which the values for the **loan_status** field are classified into two categories:\n", "\n", "* 0: \"Fully Paid\" or \"Current\"\n", "* 1: \"Late\" (for any time period) or \"Charged Off\"\n", "\n", "The following features are used to predict the loan status category (descriptions are from the \"LCDataDictionary.xlsx\" file):\n", "\n", "* **loan_amnt**: The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value.\n", "* **int_rate**: Interest Rate on the loan.\n", "* **annual_inc**: The self-reported annual income provided by the borrower during registration.\n", "* **delinq_2yrs**: The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years.\n", "* **open_acc**: The number of open credit lines in the borrower's credit file.\n", "* **dti**: A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.\n", "* **emp_length**: Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years.\n", "* **funded_amnt**: The total amount committed to that loan at that point in time.\n", "* **tot_cur_bal**: Total current balance of all accounts.\n", "* **home_ownership**: The home ownership status provided by the borrower during registration. Our values are: RENT, OWN, MORTGAGE, OTHER.\n", "\n", "A loan status category of 0 is considered to be **good** because the loan status is either \"Fully Paid\" or \"Current\". A loan status category of 1 is considered to be **poor** because the loan status is either \"Late\" (for any time period) or \"Charged Off\".\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "13213df4-d2a1-4e2c-a7d1-9e311537d406" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Names of tables in SQLite database: loan\n", "Number of records in table: 887383\n", "Column names:\n", "1\tindex\n", "2\tid\n", "3\tmember_id\n", "4\tloan_amnt\n", "5\tfunded_amnt\n", "6\tfunded_amnt_inv\n", "7\tterm\n", "8\tint_rate\n", "9\tinstallment\n", "10\tgrade\n", "11\tsub_grade\n", "12\temp_title\n", "13\temp_length\n", "14\thome_ownership\n", "15\tannual_inc\n", "16\tverification_status\n", "17\tissue_d\n", "18\tloan_status\n", "19\tpymnt_plan\n", "20\turl\n", "21\tdesc\n", "22\tpurpose\n", "23\ttitle\n", "24\tzip_code\n", "25\taddr_state\n", "26\tdti\n", "27\tdelinq_2yrs\n", "28\tearliest_cr_line\n", "29\tinq_last_6mths\n", "30\tmths_since_last_delinq\n", "31\tmths_since_last_record\n", "32\topen_acc\n", "33\tpub_rec\n", "34\trevol_bal\n", "35\trevol_util\n", "36\ttotal_acc\n", "37\tinitial_list_status\n", "38\tout_prncp\n", "39\tout_prncp_inv\n", "40\ttotal_pymnt\n", "41\ttotal_pymnt_inv\n", "42\ttotal_rec_prncp\n", "43\ttotal_rec_int\n", "44\ttotal_rec_late_fee\n", "45\trecoveries\n", "46\tcollection_recovery_fee\n", "47\tlast_pymnt_d\n", "48\tlast_pymnt_amnt\n", "49\tnext_pymnt_d\n", "50\tlast_credit_pull_d\n", "51\tcollections_12_mths_ex_med\n", "52\tmths_since_last_major_derog\n", "53\tpolicy_code\n", "54\tapplication_type\n", "55\tannual_inc_joint\n", "56\tdti_joint\n", "57\tverification_status_joint\n", "58\tacc_now_delinq\n", "59\ttot_coll_amt\n", "60\ttot_cur_bal\n", "61\topen_acc_6m\n", "62\topen_il_6m\n", "63\topen_il_12m\n", "64\topen_il_24m\n", "65\tmths_since_rcnt_il\n", "66\ttotal_bal_il\n", "67\til_util\n", "68\topen_rv_12m\n", "69\topen_rv_24m\n", "70\tmax_bal_bc\n", "71\tall_util\n", "72\ttotal_rev_hi_lim\n", "73\tinq_fi\n", "74\ttotal_cu_tl\n", "75\tinq_last_12m\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import sqlite3\n", "from sklearn import tree\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "#from subprocess import check_output\n", "#print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "def sql_query(s):\n", " \"\"\"Return results for a SQL query.\n", "\n", " Arguments:\n", " s (str) -- SQL query string\n", "\n", " Returns:\n", " (list) -- SQL query results\n", " \"\"\"\n", " conn = sqlite3.connect(\"../input/database.sqlite\")\n", " c = conn.cursor()\n", " c.execute(s)\n", " result = c.fetchall()\n", " conn.close()\n", " return result\n", "\n", "def print_details():\n", " \"\"\"Print database details including table names and the number of rows.\n", " \"\"\"\n", " table_names = sql_query(\"SELECT name FROM sqlite_master \" +\n", " \"WHERE type='table' \" +\n", " \"ORDER BY name;\")[0][0]\n", " print(\"Names of tables in SQLite database: {0}\".format(table_names))\n", " num_rows = sql_query(\"SELECT COUNT(*) FROM loan;\")[0][0]\n", " print(\"Number of records in table: {0}\".format(num_rows))\n", "\n", "def print_column_names():\n", " \"\"\"Print the column names in the 'loan' table.\n", " Note that the \"index\" column name is specific to Python and is not part of\n", " the original SQLite database.\n", " \"\"\"\n", " conn = sqlite3.connect(\"../input/database.sqlite\")\n", " conn.row_factory = sqlite3.Row\n", " c = conn.cursor()\n", " c.execute(\"SELECT * FROM loan LIMIT 2;\")\n", " r = c.fetchone()\n", " i = 1\n", " print(\"Column names:\")\n", " for k in r.keys():\n", " print(\"{0:d}\\t{1}\".format(i, k))\n", " i += 1\n", " conn.close()\n", "\n", "print_details()\n", "print_column_names()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "86ffcf4a-7fb3-43aa-8132-9061f2d7d3e4" }, "source": [ "# Data exploration\n", "Explore loan data records that contain specific strings in the **title** field. The search strings investigated are:\n", "\n", "* \"credit card\"\n", "* \"medical\"\n", "* \"debt\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "5c214cb4-2e68-4d94-ad7a-f5f3e5cfb22d" }, "outputs": [], "source": [ "emp_length_dict = {'n/a':0,\n", " '< 1 year':0,\n", " '1 year':1,\n", " '2 years':2,\n", " '3 years':3,\n", " '4 years':4,\n", " '5 years':5,\n", " '6 years':6,\n", " '7 years':7,\n", " '8 years':8,\n", " '9 years':9,\n", " '10+ years':10}\n", "\n", "home_ownership_dict = {'MORTGAGE':0,\n", " 'OWN':1,\n", " 'RENT':2,\n", " 'OTHER':3,\n", " 'NONE':4,\n", " 'ANY':5}\n", "\n", "features_dict = {'loan_amnt':0,\n", " 'int_rate':1,\n", " 'annual_inc':2,\n", " 'delinq_2yrs':3,\n", " 'open_acc':4,\n", " 'dti':5,\n", " 'emp_length':6,\n", " 'funded_amnt':7,\n", " 'tot_cur_bal':8,\n", " 'home_ownership':9}\n", "\n", "def get_data(s):\n", " \"\"\"Return features and targets for a specific search term.\n", "\n", " Arguments:\n", " s (str) -- string to search for in loan \"title\" field\n", "\n", " Returns:\n", " (list of lists) -- [list of feature tuples, list of targets]\n", " (features) -- [(sample1 features), (sample2 features),...]\n", " (target) -- [sample1 target, sample2 target,...]\n", " \"\"\"\n", " data = sql_query(\"SELECT \" +\n", " \"loan_amnt,int_rate,annual_inc,\" +\n", " \"loan_status,title,delinq_2yrs,\" +\n", " \"open_acc,dti,emp_length,\" +\n", " \"funded_amnt,tot_cur_bal,home_ownership \" +\n", " \"FROM loan \" +\n", " \"WHERE application_type='INDIVIDUAL';\")\n", " features_list = []\n", " target_list = []\n", " n = 0 # counter, number of total samples\n", " n0 = 0 # counter, number of samples with target=0\n", " n1 = 0 # counter, number of samples with target=1\n", " for d in data:\n", " # d[0] (loan_amnt) -- must have type 'float'\n", " # d[1] (int_rate) -- must have type 'str'\n", " # d[2] (annual_inc) -- must have type 'float'\n", " # d[3] (loan_status) -- must have type 'str'\n", " # d[4] (title) -- must have type 'str'\n", " # d[5] (delinq_2yrs) -- must have type 'float'\n", " # d[6] (open_acc) -- must have type 'float'\n", " # d[7] (dti) -- must have type 'float'\n", " # d[8] (emp_length) -- must have type 'str'\n", " # d[9] (funded_amnt) -- must have type 'float'\n", " # d[10] (tot_cur_bal) -- must have type 'float'\n", " # d[11] (home_ownership) -- must have type 'str'\n", " test0 = isinstance(d[0], float)\n", " test1 = isinstance(d[1], str)\n", " test2 = isinstance(d[2], float)\n", " test3 = isinstance(d[3], str)\n", " test4 = isinstance(d[4], str)\n", " test5 = isinstance(d[5], float)\n", " test6 = isinstance(d[6], float)\n", " test7 = isinstance(d[7], float)\n", " test8 = isinstance(d[8], str)\n", " test9 = isinstance(d[9], float)\n", " test10 = isinstance(d[10], float)\n", " if (test0 and test1 and test2 and test3 and test4 and test5 and\n", " test6 and test7 and test8 and test9 and test10):\n", " # Ensure that \"int_rate\" string value can be converted to float\n", " try:\n", " d1_float = float(d[1].replace(\"%\", \"\"))\n", " except:\n", " continue\n", " # Ensure that \"emp_length\" string value is in dict\n", " try:\n", " e = emp_length_dict[d[8]]\n", " except:\n", " print(\"Error e\")\n", " continue\n", " # Ensure that \"home_ownership\" string value is in dict\n", " try:\n", " h = home_ownership_dict[d[11]]\n", " except:\n", " print(\"Error h\")\n", " continue\n", " # Set \"title\" string to lowercase for search purposes\n", " if s.lower() in d[4].lower():\n", " if d[3] == 'Fully Paid' or d[3] == 'Current':\n", " target = 0 # Define target value as 0\n", " n += 1\n", " n0 += 1\n", " elif 'Late' in d[3] or d[3] == 'Charged Off':\n", " target = 1 # Define target value as 1\n", " n += 1\n", " n1 += 1\n", " else:\n", " continue\n", " # Define features tuple:\n", " # (loan_amnt, int_rate, annual_inc)\n", " features = (d[0],\n", " float(d[1].replace(\"%\", \"\")),\n", " d[2],\n", " d[5],\n", " d[6],\n", " d[7],\n", " emp_length_dict[d[8]],\n", " d[9],\n", " d[10],\n", " home_ownership_dict[d[11]])\n", " features_list.append(features)\n", " target_list.append(target)\n", " else:\n", " pass\n", " print(\"----------------------------------------\")\n", " print(s)\n", " print(\"----------------------------------------\")\n", " print(\"Total number of samples: {0}\".format(n))\n", " print(\"% of all samples with target=0: {0:3.4f}%\".format(100*n0/(n0+n1)))\n", " print(\"% of all samples with target=1: {0:3.4f}%\".format(100*n1/(n0+n1)))\n", " print(\"\")\n", " result = [features_list, target_list]\n", " return result\n", "\n", "def create_scatter_plot(x0_data, y0_data,\n", " x1_data, y1_data,\n", " pt, pa,\n", " x_label, y_label,\n", " axis_type):\n", " plt.figure(num=2, figsize=(8, 8))\n", " ax = plt.gca()\n", " ax.set_axis_bgcolor(\"#BBBBBB\")\n", " ax.set_axisbelow(True)\n", " plt.subplots_adjust(bottom=0.1, left=0.15, right=0.95, top=0.95)\n", " plt.title(pt, fontsize=16)\n", " plt.axis(pa)\n", " plt.xlabel(x_label, fontsize=16)\n", " plt.ylabel(y_label, fontsize=16)\n", " plt.xticks(fontsize=16)\n", " plt.yticks(fontsize=16)\n", " if axis_type == 'semilogx':\n", " plt.semilogx(x0_data, y0_data, label='0: \"Fully Paid\" or \"Current\"',\n", " linestyle='None', marker='.', markersize=8,\n", " alpha=0.5, color='b')\n", " plt.semilogx(x1_data, y1_data, label='1: \"Late\" or \"Charged Off\"',\n", " linestyle='None', marker='.', markersize=8,\n", " alpha=0.5, color='r')\n", " elif axis_type == 'semilogy':\n", " plt.semilogy(x0_data, y0_data, label='0: \"Fully Paid\" or \"Current\"',\n", " linestyle='None', marker='.', markersize=8,\n", " alpha=0.5, color='b')\n", " plt.semilogy(x1_data, y1_data, label='1: \"Late\" or \"Charged Off\"',\n", " linestyle='None', marker='.', markersize=8,\n", " alpha=0.5, color='r')\n", " elif axis_type == \"loglog\":\n", " plt.loglog(x0_data, y0_data, label='0: \"Fully Paid\" or \"Current\"',\n", " linestyle='None', marker='.', markersize=8,\n", " alpha=0.5, color='b')\n", " plt.loglog(x1_data, y1_data, label='1: \"Late\" or \"Charged Off\"',\n", " linestyle='None', marker='.', markersize=8,\n", " alpha=0.5, color='r')\n", " else:\n", " plt.plot(x0_data, y0_data, label='0: \"Fully Paid\" or \"Current\"',\n", " linestyle='None', marker='.', markersize=8,\n", " alpha=0.5, color='b')\n", " plt.plot(x1_data, y1_data, label='1: \"Late\" or \"Charged Off\"',\n", " linestyle='None', marker='.', markersize=8,\n", " alpha=0.5, color='r')\n", " plt.grid(b=True, which='major', axis='both',\n", " linestyle=\"-\", color=\"white\")\n", " plt.legend(loc='upper right', numpoints=1, fontsize=12)\n", " plt.show()\n", " plt.clf()\n", "\n", "def plot_two_fields(data, s, f1, f2,\n", " pa, x_label, y_label,\n", " axis_type):\n", " # d (list of lists) -- data from \"get_data\" function\n", " # s (string) -- search string\n", " # f1 (string) -- database field 1\n", " # f2 (string) -- database field 2\n", " # pa (list) -- plot axis\n", " # x_label (string) -- x-axis label\n", " # y_label (string) -- y-axis label\n", " # fn (string) -- figure name\n", " x0_list = [] # Fully Paid or Current\n", " y0_list = [] # Fully Paid or Current\n", " x1_list = [] # Late or Charged Off\n", " y1_list = [] # Late or Charged Off\n", " features_list = data[0]\n", " target_list = data[1]\n", " for i in range(len(features_list)):\n", " x = features_list[i][features_dict[f1]]\n", " y = features_list[i][features_dict[f2]]\n", " if target_list[i] == 0:\n", " x0_list.append(x)\n", " y0_list.append(y)\n", " elif target_list[i] == 1:\n", " x1_list.append(x)\n", " y1_list.append(y)\n", " else:\n", " pass\n", " create_scatter_plot(\n", " x0_list, y0_list,\n", " x1_list, y1_list,\n", " \"Loan title search term: \" + s, pa,\n", " x_label, y_label,\n", " axis_type)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "872f9c61-2654-4353-9d47-8d86486fb4c0" }, "source": [ "### Search string: \"credit card\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "64edef10-3442-4a9c-a8b1-a5f4147234f1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------------------------------------\n", "credit card\n", "----------------------------------------\n", "Total number of samples: 179301\n", "% of all samples with target=0: 95.6464%\n", "% of all samples with target=1: 4.3536%\n", "\n" ] } ], "source": [ "cc_data = get_data('credit card')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "3a289600-ce3d-4cbf-84e4-dc7ec615a8f1" }, "outputs": [ { "data": { "image/png": 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qaps39uzs7G7VIzk5GZ1O12ZKJRqpqanodMceWzabjaamJgC2bNnCd77zHRwO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wQjVTUyql++eHH25kelpj374wuq7S0JDj85/fy86dUYaHfQwP+ygUZpikpikIhQwmJz2k0x4S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oOPcTdzOUCQGKAQJMZKdnMJfyIoIzU2JsvYRzlj9FFDNb1YqAxRyXJecdpxYXE1T7OTT5/h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\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K+f7T/2mIhtqorryZrf5ZxPFBcWFRRBEjxC23zKSjowRJEujoKOGWW2byyCObC5Z/7bVa\nEgk3ggDptMhrr9XmXViY/AhJgnjcxebNNbzwwmjsbp3Ll7dyyy3rrDp/+EMTXq9GW1uAcNhLV1cA\nl0ulvj6Bpgm88UaNpc4ZCpVgP0dPJnWeBuhZLrt2BXG7YdcuD9/73lS2b68gGvUY7qJHHOfyaluY\np59upbfXhyjC4cMlSJKe4aqfa2f7ZfrYscMkdIKqSvzXf51MZWWKVMq+KtMfCm1tldh5Ewt5njq6\nkHFTRzdeUmxnehbfIlPfvD6HlwmQQEXEi8o5vEzumby9b4GreJIyomgGP+AqnuANziwgVpWpa/I2\nZHQCjp8Ed3AfdfSQxsNYDlFJmGf4nMUxWMxKi0g5n/WMYz8DVNFMO5PZSYAEMhJuZBbyHJVEmMxO\nRtGHmxSj6OU2foiKxDmst8ZaQZTZvE6YoCV01UdNlshWBvY4GjnKXF6xxiuhESRi3fsW2riXOy0y\nZDPruIg1xChFxk05ERbynGNhkd0+ZBaDX+HntNCOjIsgEb7Cz7MWFnqsp7HNcFXVdyROY5vR7uu2\ndgXbIlOwyi5mRVY5gHKKOL4oLt+KKGKECIV8FudAFPXr4ZF9zFj42DGbH5F9QpnLmzCFpCTrb16v\nRjQqjUgV1CyTj5Aaj0uWFEYbrTnn8rGY21pMAKTTguGECrkP72y1yExfuchP/jSvk3iRUByx5NbX\nMUQZprupgMpQAd6BIybS1j6DalyP5Ize5G24SONCJm4QO03CYRIv1fQ64rYTKf3EUXDjQkbGjddY\nVIhoqAiYtucVhBGMb+ECGkHCtNDGkCFqBeAiTRwvfVQNI3SVQTahc4gya7wKAmHjIWzGnU2G1Bce\n2fercPt2smcpQwZlU3dTLWUob4xhgtYOkIhKmOAI1Dj1eEZWrogPG8WFRRFFjBBVVQnLIlxV9evh\ncNZZPfh8Mi6Xhs8nc9ZZPXnLzZzZS0WF7sxZUZFi5sxe3G4Z+5GCfp3B7Nm9TJ4cpqYmSUVFyiJ7\nlpQoeVRBc5UizTL5CKmjRsUBDUGAVSxmE2fSRxWbOJNVLCIQSKMo+mLH61UIBGQkSUUU1Tx95Spk\nCoJq9ZlfgTJTfw0L6aKWGH52cyJruMiIRT9OyO1L//1BbqadJqIEaKeJB7kJ03m0EDYwhzRuZETS\nuNnAHO7iPl5kAT1U8yILuIv7cuqtYgmPcQ27OIldnMRjXMtWZliEQxmJ/YxzxG0nUsbxc4gW2mki\njYvDtJDAY1mVH6OeOH4GCKKhG41pCIQJ0kaLMdZmogTopI53mMY6zqWdZrqpKRg35BI6H+Rma7y/\n57P8O7c54s5eaG1jhnV/uqhlDQuHbd++GFzPOSTwk8ZNAj/rOSdvjN/lTo7SQBwfR2ngu9xZkMjq\nRH4F1iKOP4rkzSKKGCFiMf04ZGAgQEVFjJ/9bDOBYew//jfkzUgErrlmLsmkC69X5vHHN1BRUbhu\nV5fPsiSvq3PqXOzdC7feOh9ze/iKK/YzZUrCskjP5ljIsj5OPS01wcBAwKr705+up7OzxuJYnHxy\nP5MmRdiyRY+/vz/BoUP1VvnFi3cQjdbw8st1KIpISUmaX/3qVf7v/53K/v1+urrsxzQaZ5zRyfbt\no0inJQRBRZHjLOZF2xn9hWgOcaQMaa+xMUYsJtHf70V34VxlqzeP8mAJQ0MiipL/BNgUtcoQPp8w\nhJiyka2f8MHbspM122kCNJo5ShstrOECnuBaJrCfKAGe5bMcYiwiCjfzS4tj8SBfYYWx/e9sixwx\nqkKwxzGS8rpQ1t0Wx0K3oH+uYP3h2s9u6y7uQc1zOp+vDfuY7URW533REJBZbIih6VyMMv60tvh9\n2kSRvFlEER8zXKLKNWW/ozQVYqisCpfYyAfZ9FNVnaiZ1/48yyW1vBxuv323Vdbj+f/ZO/MwOcr7\nzn/eqr7nvkcaja4RIIQEGIOwuIkvEJLAeBObGGwSb7xJbGInWceO43Bl13GWPMnGrJ2113aIsbHj\nIxhEwMY4yIAkEBjQgUBCGmlGmrtnpntm+q6qd/+oo6u6e0YtMZIA1+d5BtTd71U1NV1vLNB7JQAA\nIABJREFUve/39/2ZRlejo1Ha2swokUSi2I49Qdixo4WFCzNcdJEZXbF1aytbtrTi/sJdv/4ofX2m\nc2Z7e5bbbzdNvp5+upVPfeoikskAMzMBVBUiIYMbeJBFDNBPN/d9ex1NLeYKxOLFKTQN/umfVpJO\nB4jFNC699Kg1sQAQ/Od/LiYSCnKdbkUjpLq55SPXEYnBggUJa2JR5BOfeI1vfzvAiy82k88rSGKu\n9NjgvakXU5ED1MaybNB+RpAJBmnnz/gnKxLBvFknk3MobTFvdKvZQycjRMiioKETsiJJHmI9jwHw\nGO9HorCYfjoYdUWFjCJReJRreYz3etq6gX9nAWPOTbDUTdI98QgzxdU8ST3TZAnzENdyEc/TzggA\nz3AZj3INEsEn+Spj1HIff0gQnQIqLQyQpuhjYt+Yl3CYDkYZoYM+lrCZjdQyzI/5oCN5/G98iS/z\nvxwXz0/yj3SQcCYs9lbCM1xKO2P8Mf+XPpbwVT5JmCkmaSRGhjQh9rKKLobpZzFX80t0Qp7JwC+4\nnE9xr1U+ytU8ziJX+aKjp8Fal3vmI1wHUPLeeiSl+2ulmh+fU4W/YuHjUyVP3HaEzkO70YM1qIUU\nw8vW8J57Z19aLXXPbG3N0tWVqZj+vJTSVOlbt7YQj5tGV/m8oL4+z3XXDTvtuEWYhQKsWpXkve8d\n5vHHO9m2rY3iBMh2wzziGQfAN76xgomJMNmsLYKslAb8XTxZdx2hkOlJMTkZREqv06Y3e2glx8dS\n90zhGVsgAJpm59EovSnMtlogrTTc5lg/zjdoZpKCpctwO0TOxiucRQ+HLA2DwUGWcQ77HLdOO+pD\nQWOIheQJsYzD5C1hqYbCJC2M0M5FPEsHcSQKChpJ6vkqf+JEhbjTlXvdJGGUZsdJEyCHSj/LiZJC\nAZLUkaSBIRaym3P5Ane7gm5Np84ozl6Tk568klPoj7nRufXavz3hep0lxD18njXsAnAdc4AQmqet\n73CzxzEUYIp65/zfw+c818Ft/BNR8p6+Z1zl7d9XpTTywCyp5b2Up2b306a78Z03fXxOM9HRUQqq\nKQQrqFGiFVKUuyl1zxwcjB7T5MqmNB35xETEcc8UQljpwecWYZqJxsrdLUGUjWN0NEI6HbC2T4pL\nypXSgOfzKqpq3vyLbpvuet7+ZnOMrFQWhDVRgcoTiNkjO9xjNd0ozbKlDpGz0cmIs0wvUei0Vgjc\nUR92BEOUjOO8aYeXhtDQCBAlQwuTTlsCQS0pz/HP7iZJmZNmCB0Fgwg5NOuGbo8ByuNwgpaQ08Y+\n/5WcQhVm/+0JIGzpRGz3zOIxT5S1VeoYWlxXUis6h0asSYVdXikpb1MpjfzsqeW9+GnTTw/+xMLH\np0oy7e0EdfPLPKhnyFRIUe6m1D1z4UJvuvK50q6XpiNvbs467plSSis9+NwiTHMLpTztOMiycbS3\nZ4nFNEucWhRCVkoDHgrp6LqZ0tx02ywVUHr7m11oVzl9uBCG63Ups62wSs9YTTdKs2ypk+NsDNPh\niSQZpsM6B96ojyT1ZIg6zpvjtGCgkCfgRIWM0+S0JZHMUOM5/rncJEudNPOoGChkCRNAI0/AGQOY\nG0KlTp1u7PNfySnUYPbfngRy1uTHds8sHnNzWVuljqH2v+3zX3odZAl5yhsl5W0qReZU66jpizdP\nD77GwsenSi752y4e+niYpukRJuvO5vq/bZ2z/Ec/2svRozFHGPn5z+/h179udUyu3CZWpdif2WVv\nuukgn/qUKahsb8/wsY/1kkwW23nnO+OOU2dHh9lXKGTqOlRV4+mnF1gtS771rS0cOVI+Dk2D731v\nGclkgMnJEEIo/Fy9BgoGizlKP+cy9M53sa5ljLGxCG1tWWpq8jzxxEJHY/HOd+yl/qlDjjvkI/wW\nW5veA5OG9d65PKqspzaWp6MjwcGDbv2H5Otf38I//P35rDrwK7pkv8ep0S5TWWMheW3FZXQmp3jn\n2NP8jHdzA/9BjDTjNPNufg6uSUMlQeFafsU4XQTR0BCs5VeAwSOs52K208kQM9TyDX6fd/IyZ/A6\nR1ho3eRNl8gjLKKBKXZwAe9lCwE0pqnnJdbwXn7OU1yGQoHtrGM1e6hlmkMs4SPc74xvOXsYZxGq\n9c73uYEuJq1oENXSjFyDRKGbo9zKP3Mff4RqnZE2+lDIO8LI11nBc1zIEO2oFAiSR0fhEdbTyWuM\nsNLZjvgT/op7uIcIeTQEW7mATfw7WWIMsJDnOY9u+qhjijQxUkRoZhyBzkJ6GWWxVRcOsYwaMvSx\nxNFYgLTO+3n8Tz5NnG4CSDSEtQU0wzhNnt/X7dwF4BF5quT5AP/OmYwwTAd38Vd4o49MNrPB1ee5\nbKaO2+b8q/WZD3yNhY9PldiaiYaGMMlkjvPPn5gzLXqpTmIuTcWxOFZbc31+vONwa0Pi8SB1dQXW\nrp2squ59H4izeuZFZ0/714GLWP3FMyrWsbPFlupCYo8/x8rki2RlhIN7Q1Y2zA9gPwOb2zgGNTV5\n2toKznGdf/4EFx59nIa9r3HW5PP00MsEzaSo5ZdczV+LLyGlrcUoz5jaR1fFTJ+lGhE7u2mWqKM9\nsDN3LucAixmghhmamSBBIyoaBioTNKOge/QZlTKjurUelfQZpRlRq9Ug7ODisuymf8vnPLqSKWqZ\noI0mJoiQtVZAggyxkBS1aAgCSLJE6GSQCZp5nGuc87KSfY4WZYT2WTO4gjcDbDPj5Amyh3Pn1EzY\nE0t33VKNCkhUVdLRkamYbfcXv9gy6/X7m4YfFeLjc5o5ciTGxESYkZEgwaD5ei5GRiKMjERJpVRq\nanRaWnIn3Hep5qJUnzHX58eqW4pbG2IYgoGBGFu2BKmvz9PcXDwGw4DtW5vp3LGNbvpZsLaG1pla\nz552pzbAz352OV//+nLi8QiGIaiv11iyJEVfX5REImRpKiQ7dzZyzjkJVo2McWiiGZBkCbn2xe3t\nG1BVhUQijKIo5HKm0DOXa2F1apL8TC2XMEY9CVoYJ0kdZ9Lp6EFsN1GBZAl99HAAAbQTR8G2gi5m\n+lxCH0voo4EkSRoIUmAPawAcnYN9vCs4wBRNtBAnRopmJqwtC0krcQSSJI1EyZAlwhnsp4EkzUw4\ndtedDKKiObfCWitbqdvgyW2VfTW/ZDW7CKGRJ8gAHRio1DBDC+NkCXMm+xih03McQyygm8OevupJ\nUCBMjDRgGm5pBImQY5xWlrOfJM20ME4NM+jWilGOCO/lUS5gJ0ErudtezmYph9nET62IlBFG6LQi\nUjZxJgeoIU2QJEHyBCnQxQBZwpzFfucYl3KIS3kGBck+zuJ2/mZOjQoIdB0SCTsseXYDL5+Tgz+x\n8PGpkomJEFNTAYJBQSYTYGJi7vDFiYkQw8PmTX16Okhn59zl56K9PUs8Hnaeznt6slV/fqy6pXR1\npRkbs8cdQNPMm/fgYJTduxv54AePAqZ/RuyJ51iY3E1KRkhNjdJGByrSeSru51z27GliZiaAncdh\ncjJAJqO6ok9MZmYCPPVUO4WJHs7PPk+WWMm+eHF1VdcFQkgSiSCKYtqJh0Ih9sys4B2FHbQRJ0oO\nA5VGpjiHV502+llMFwOeKIlbuN9p37zJStLWBKmDYZZxmCwRmpmkny5LI1DM2gnmHv4BVrCYAepJ\nELMEn/bEIE0NCjpLOcTLvIPlHMRApZ4pWhhnIw/zMDcQpuB5xrZdJ90GU26r7PPY7QghVXJcwdM8\nydU0M0GBEDHLprz0OFQKZX1JIEzOaquAbk21soSJkGWGOqfdEDlnbKvZzXnsJmRNUkLorOJVLuVp\nFjDkOdcLGQQgZGUJMVdmdCQGQQrESGG4jvEStjqrT4sYBP6aA6zwrFiUZzwV1vU1W4Iyn5OJP7Hw\n8amS5uY88XgBTQsRiRRobs7PWb6xMU8gIJmcDFJfX6Cxce7ybmzPix07TB3HRRfFWbkySTxeWZ9R\nqsm4+OK445nR2JjlmWdamJw0s5F+5CMH5+z71lt7ue8+c+Wiri5IIhFifDxEMKgzOhrmwQdN/4vh\n4QgrR8boHW+iUFDMFRo66WOpS7+wgUDOvD2amBMJM1ql+Np+mhwbC/Pj/A3M6EF6gn3sZRmbHR8L\nARgIAcGggaLoZLMBp41gUOex4HVkciof4bvkrKgBU1UQwb7BmI6d0MMBDrGU/ZzFRezgIItZQR8B\nDAqo/B1/BsAIHRxiKQ0kGaKTZ1nLYZazmH7u5xZMcyvTkOqL/A13cQe30EeGCHnC1KNZE5UY4zQx\nRT3bWUcz4+ioJGlkP2c5qxEJmogwir1yMk0D4zQ7SbzAG+2gOCJR80dFYyuX08koLYwzTjdbuRwJ\nnuMIkEcnSNAKTRXWuXqFVSzlMEE0xxFUQbCPMxmjjRt5kBbGOcIi9nEm4zQzTrMn8ZrZnkRYk8xK\nESmvcDYtjBMjSx7FyuuSZZwmtrHOOcYWayJjr5qcyX5u4oGy7LWlqKrEMHQg6Jyda67ZMue17zM/\n+BMLH58q6e5OMz4eoaFBkEzm6e5Oz1k+kQihaYKmpgK5nLCWZqtj+3Yz42kyGUZKmJoK8r73DfGB\nDxytWL7UZMutq/j+9xczPR0iFJLE41E+97kLuPfeF2ftOxDA0Y7ceuta8nkVRcFatYgxPR0iHg8z\nMBAlN9HDmtQLZI0YQT1LH0ssQ6vinrYp43I/NQoMo9w509zOCICAR9TrUYVkxhNMaT5ttrbmMQwY\nHw/i3j+Px00B5cPcwEf4rueJdj8rKK5FKM6+/zq2IxFkiKAQYJQOJCppIjRZIaJ9LGUhQ84qzGGW\nz6obAPgiX+IM9jv9R8iSJ8h+ziJCli1czb18mj6WeDQP9mrEIZZSb20zqOi8xln8H/7E04e96pIl\nSt6ZHJhP6DPUcpilbOMS15iXAniOQ0dhhi0ESCKsGJY4LWzjUv6T91TQdJieJO527c838VM+xA+c\nFQtzlSNiHbMZkdLMJEN0Ose6n7PpZpgsEVZY4aIHrGiPNsbZwVq6GGCcZhpJME2tEwGiE/ZoKtzX\nln0tmVtfXpfWn/3sKv78z7fM+rvzmR/8iYWPT5XYT/KTk20sWzbBrbfOLtwEc4WjszNLKqXS1KQf\nc4XDje1BYXtX5PPqMbURpfVtnUQmE0AI8wtXVWF0NDp3ZRfBoCQSMdA0xVopcAso4emma8lkA3TL\nIxyMnsPm7CZKVyHq6gokk6oVymquOtTUmCGr5oqDSSAgaWwsoCgwPR1EUQwMQzqeHQDRqE59vfmE\nPTkZQkqsiYvtz6FjGCq/a3yfB7jJ9UT7fYSQFMXqwvP0fz+3sIFHeAcvA4LXOYMRK9zUXc69ajAX\n7hWDIywiTYQpGqyohr+Zs92reZInudp5/2qeLGvfXfdH/BeuZzO1pJihln/gz+Ycs/3eI2zgJc7l\nXj5NLSmOsojz+TXX8MQsxyocO+3SzzeziR/zQX6HHxMhR5YwP+aD3M7fsIFHGGQBvSzzaCweYQNY\nKdhznMMkzdQzxRCdjNBprVQJhugs01h4Ma9tIXB+v4oCkYjOzIxaUs7XWJwK/ImFj0+V2E/yy5dD\nb+/ckwqAjo4s4+NFp82Ojrm1DW5sD4pMJoCUUFurz+l7Uam+rauIRjWmp60skjq0t2eOUbtIV1eG\nZDJEbS2kUgqxmDk5yuUEixalkVLwTP4aMpkA0aiOMgW6XnxyrK0tsGpVkr17GzAMlWxWUFubp7W1\nQDodYGJCoigQChmEwwVqanQCAaitNbeOJiaCjIyY5l+plEJDQ4FLLomTywkmJ1USiYi15A0dHWnq\n6jQOHaojlwvxIX5M8QkWFnWlMQxTvKppqrVycT0gCYcNZE4Qp815Gu9jCaUrHF63B/dNqnjMoZDG\n4fySik/2eGqUtmu2pxHhcrZRfhMslnHX3cSDxOlw+jpEj+vY3D6Yoqy/n/BhfsJNnve89bz/l+D6\nHKdts78PMEanJ9rGIFChvN1WgC/yJUyHV69DZh9LrL42AQpf4U+ZHXMMoZBOOGygqgYdHRmkFOzb\nFyzp29dYnArUO++883SPYd6466677vzoRz96uofh8zanqamJycnJY5ZbtChNLqeiaYKlS1OsWxd3\nUo1XUzcYNJiZCdLQUODyy0e55JLjq2/3fcUVIwwPmysgixaluOeeFysmQ6vEJZeM8tprDeRyCitW\nTHPjjUcwDPN4NmwYIBQyyOdVAgHJsmUz3HDDYXbubHISjv3rvz7D1VebbWgatLZmufbaIZqa8qxe\nPYmimCsVra05PvShPlavTnqO+dZbD7Jvn7v/fqf/T3xiP08/3U4+r9LenuFrX9vB+98/xPPPtzA9\nLdD1ovfFwoUTrF5tjrmlJUd/fw26DkIYtLdnWbUqSWphDZMDYULkeYVVjF18NjOpGrJZ215c0tmZ\noqMjyeRk1Gnb/gkEDDo60vzxH+/n8UM9aNOK09Zm1mNvVZg/uqe+ohSc6Jhymyr7xyh5z8wou08u\nIUbO6uscy7tBeNqorc2Szysl9bHG4RY2ZnF7g+Bx8ZTceOMeXn21zXldV5dC182EcfvkcmJkZxlH\ncSyRiEYwaNDYmHJWo/ZzBjHSrrrrOeOMGVasmGZgwN7ymu08SN73vtdRlCDBoEFPzwx3372TxsYC\nr78eZGYm4pT77Ge30NNT8VL/jeT+++/nzjvvvGu+2/V9LHx8qsTOJKrrXajqgCeD6Hz3cfRohH//\n924ymSDNzdlZM6na5YeHI+zebaY/7epKc9ZZU4yPm0nKzjsv7phrNTVlufXWXiYnzWyoDQ159uxp\nZHw8TCyms2HDUdaujfPlL5sZTzs6Mhw+HCOZjFQcR2lm1o6OOH/0R1dh31C+/vUtLF5sZnk9ejSG\nlFBToxGPh6mry/HUU3YmVGhvT/Hxj/fy0kumYHXt2jhtbXFuu63Y3j/+4xZ+9KPVHDhQh6aZOhbD\nUAgEdDZuPEpdXZ7vfrcHw7BvRO4bWyWTLQCd1tYC8bgG1Ljq5IDy7adjZwSdbTXDjcZ8LRgfb4bS\n6ikf9/Ede6Xjnrv9ExuXBIaBYlbdxYunGR+Pkk7hZzedg5PlY+FPLHx8qsQWRHZ2NjI8nHhDhlfH\n6uPRRxcyMxO0Ji6SBQtS3HffjlnLv/JKA4ODUerrdQoFPKZWTzzRTiIRQVHMrZCaGo13vnOS4eGI\ntR0RdLYjWluzCCGd7YexMVPHEAiYk4jScZSab/3wh92UGhJ96EP9vPxyM5mMyvh4CCFMjcb4uC1m\nLd6IwmGNnp4UQkBDQ45t21rL2qup0SkUVNcTeOlTsXC95/7/bLjrHftmV57Yqnybo7z9k7e3f/zj\nqZbycR+7r+OZWMwX7t+f973yBHh+EjI3vkGWj89pxja8Gh4OIUT0mIZXmmY+qQ8MxOjqSnPrrb3H\n3IKwRZfZrOpseygKTExEPCnXL744znPPmZEjqmpGjRiGyuSkiqoajugzHJZMTYUxDMUST0I2q5JK\nqWSzAcbGwui6gpRQKNimWJDLBUilFCchmKaZdYeGarjzztXOqsiTT5r9L1qUZmQkSiVDoiNHYmSz\nARKJkNOXmeuk9PtMUCiozvFMTQUrtpfJBK1X0jGJ8j49C4+BlC0ynP0p3mxfQeNuS0xoiyyNCl+R\nlZOqzYVw/evY46p2HDZLOcwlbLNCS1sYYsEJ93Und3And83a91zHfrzjdpc/YOlCzuD1qup6sX/f\nD5Uda+UkZP7E4mTjTyx8fKrENrxqaFBIJiPHNLy6776iNfbYWIT77mNOC3Aoii5NRbsZiWEYEI0W\nnJWBeDzMq6/WI6VAVbFWHkxhZCQiyecFhmFOEnI5+9/Fp3ddh0xGZWpKRQhpJS8TSAnZrIIQkmxW\ncVZLTMy6hiE5cqSGQ4dq2bmzkc7OPMPDEeLxMJrmXjko3kz7+mKk06ZOQdNMTYWuC8qfNM32JyeD\nZDIqsZhWoT3hRJe4n0a7GACk9fQsPQZS5mfM8RRvtn83f+3YYHfzJPDXFW2l3aGe7jDRaqhmXNWO\nw+ZSnqaHgxQI0UiCS3mar/CZE+rrSrY4lt2V+p7r2I933O7y66wU6Qc4s6q6Xmb7fV9fYbznVtmm\nzxvBn1j4+FSJHT4qZZBoNHvM8NHStOkDA3NbgEPR6Kq+PufRWKxfP0g2G3LaOnQoxrJlacdLI5sV\n1o3YDO9ctChNXV2enp4ssVgnMzPF7YFQSHfMuhKJIGNjEXTdDNVsb8+iaSCEIJ9XrMkCTl0hBJqm\nIKWZur27OwFAf3+UxYsz7NtXh3clQiEQkCxcmCGZDFJfL4nFNGpqdMbHGyn9CgqFdGprC4TDOl1d\nGY4cqSlpT6CqOoYhWCz7rNTZ5o1wmTgM0gAqpWrvoyj6qxR2aFSdivtEwk9tqlntqHYcNgLJBM1E\nyDFNLcKasJ1IX8t5nV7OmLXvuY79eMftLh+yTLqqq1s6Ia30++6nNKTYNmy7jV/NOS6fN44/sfDx\nqZK2tiyFgqk3KBTM13PhtsbO5QRdXXMbakHR6MowYNGirLP1YRjw2mthT1u5nCAcNpMttben2bWr\niXQ6iK4LLr44zhVXmJOUBx5YzIEDRS+IJUtSvPe9w+zd28DISBQpBYoCDQ0FGhpyGAZMTkYwb+IS\nwxDWyokgGDQIBAw0TRCLFRACOjoydHamLUOi0hUG0yBrcDCClAqxWIHf+Z1+rrwyzic/eQH79zd6\nyi5dmqKlJc+hQ7WWBsPOYFpc1ZBSEAzq9Oe76GLQehpNk+s4gwUizdBQDUdYxFVsIUqGDFHu5yP8\nzu/0s3NnA/v2NVA6sVAUyetGD2vYjYpER7CNiynXCRS1HAKDtTzHEvocbwZ7q6G4NH/EtU2jVrXa\nsd96arcTiJkpwWfXK+znTM7lFcC8QdspxOfuy2yvtC8zzXh21r69Yax2O5XGnUECn+Irs27DuMvn\nKV6fpWnQy7dY7sbA7dwqZzlWSXnYbXkGVJ/5x59Y+PhUiabBa6/Vk82GiUQEV101PGd5tzW2rbGo\nRGlkxbp1cbZvb/VsfaxcmWTVqiSjoxGWLcsiJTz/fDF64tVX65meDiKlYHo6yGuv1VtmWBGamvIu\ncyrJOecknJWRlpYcHR1pkklTVLl2bZzHH+8km1WRUlhbFsW6UprbJD0907z73cOMj0fI50M0NuZJ\nJELU1mrMzNhbRBJVNdB1QSIRBgQzMyq//GUnV14ZZ+XKBPv312PfpGKxHGvWJNizp9kSjoZnOV9m\nKKn5NKpaN+/zeF7+Fq1teYaGapz+3f//xS86XS6g3hu1YcDzXMRVPEUDUySp53neiffp2Py3uez+\nrJP/4jCL+TA/4C/4O7ZwFbdzN5t4iM/y905bCgV+ym+7nqD76Of8iqsdpgGUV6swu0BR8AIXcjW/\ncpKLvcCFCAwEBs1MAJJHWe+YW7m5g7s4g/2OkdjN3M8d/E1J38VJhKlrKZ0wmbqW27kbuJ0z2Y8E\nellOCxMl2zDF834Hdzp9P8vF7OdMzuCAx0QMvFsma/gJF/ICX+OTzmQN3OnR7eiPDSVHav/Op8rO\ngc/8408sfHyq5IEHljE9HUIIwfR0iAceWMbVV88eFeK2xp6LrVtbeeKJBeTzKqGQjmGYQtFXXmlk\nasrOM5Jj4UJzhWTfvnp03UyElkwG2LevnoGBGMEg5PMQDMLu3U0IYa5ovPRSM0VvApUnn1zAeedN\nMToaoaMjyw03HPWEzf7zP5+BYSi4A8Zsd0spVVpbU0xMhHnhhVbq6vI880w74+MRDANr66Q4GdF1\nhbGxmEsEGuC551q5556VPP54J+6Ij3Q6zJYtHczMhFEUSU2N5vocp00AXTdXMcx06hYjBiNjAArd\nHGU35zkfdXOUyckwxQlCaZsKixjica5x3l3EEHiess069rK7nf/iQn5NhBwZorybJ4HbuZAX6OYI\nOkHqSfKHfIOf8tt4TbEqYxCoUl8grHEO8jjvd417kI08zLt4jkErYZpEcW7C7rrX8Si9rGAva4iQ\nYT0/ZwcXM8wC+llcssog2MhDrOPZiroNg6Az7k/xFVqYAEq3YYrn/Toec/q2k5E9xZVl/Z7FfmqY\nYTF9BNHQUR1Nhtm3QBLwXgtWTpmiK6v9f1+4eSrwJxY+PlUyMRHGMMxtA8MQTExUfqI+XnbsaCWR\nCBEIQDqtsmNHK9PTAQYHIwSDMDio8tRT7Zx7bpJwWLJvXz2FguqEge7bV082qzA1pRIMwtQUKIru\n6Dvyee+feSIR8qyGgDfPyMxMwLXC4UXTJKlUkGxWEI9rbNvWyvR0yPUFXhq+LpxoExvDELzwQjPl\nER8K4+NhwBSOFgrHioIrjyqxI1/Kl8bPL+mrvK3KdcrpZwldDDr5L0Lk0VHIEnb0AbXMODdyiUot\nM8c4lhOn0jZAtZErpeXW8ygTNM8q+Ky23Wq2fNxtLaafVuJs5bIKExZoZoIweYLkkcfo20Th7WSl\n8FbDn1j4+FRJc3Peyrth7skfT+6PY2GHlrqdNevqNDRNIRIxyGZVZ6LQ0KBx4ECElpYCug5NTRqx\nGFaYZoi2trxHg1EJt6i0NAdJTY1GLhdwbtJuVNUUioZCpp+EpomSSUXpTdt0pCwU3CsPAk1TKcec\naKiqgZSCcNiYpRyUT2C8nIjAsto69vt2/osLeYGl9DNOi6MPeJ0zuZGfOHqNX3HVMfs/USqNeyMP\nVxW5UjoBMIWws08cqo2IqeZcuttqJU6c1or9buVSOhlhKYeBiKUDqTYa51R5afi48ScWPj5VcvPN\nvXz728vJ5aKEwxluvvnY2xzVsHZtnKmpgLMVsnZtnH376onHI4TDGrmcoLU160wU2tszaBqkUgGa\nmjTa2zMoimTRomJekpUrkygK1qTBwGsyJZ22cjlBT49XhHrmmdPs3GmmI0+nzSXpaNRA00yB58KF\nGSIRje7uNK+/blpem9lKS0NIzb4uvHCC555rdZUxCARsUWapoZIkGJQEgwZ1dXnGKMQEAAAgAElE\nQVRSqQjlNwfD1Y/XPMsrMiw1bpotKkRa/xUlwsRKlAsCFfQy/wa71dL35mr3+G+AZh1z3N7tlfIb\ne7m+wiznTSomMHgXz806cah28jVbHpTZ2trBRaiWfbi3X8lhlrONS3mSd7Oa3YzTwnbWufquZIQm\nXT/ea9/n5OM7b/r4VEk+D1/60mri8UZaWxN84Qt7CFWfCX1WKok3DcNrrvXhD/fyl395AaOjUdrb\nM/zd373ISy+VG2a527B1E4OD8LGPXYX95fov/7KFvr7KZQGyWfjsZ82+mpoyDA3FyGaDRKMFPvnJ\n15ieNu3Am5vztLRkeeKJTvbubUTXBW1t0xw+3Oj0de+9W1i6FP78zy/g8OFaABYsyNDYWKCmJsu2\nbUVLb5BcdtkwAwP1hMM65547ydln93LXXcWxq6qGqprJyH77t1/na19b7Xx2zTX9jI7GePHFVspv\nZDpCqASDGl1dKQ4d8u61d3SkGRsbwzAWV6jrxYxSuMOaNJxhRSl4n9HeiO338Vp0z6el9xtpK0CW\nJ3m3KyvrL9GoNDEs73MTD7GexwB4lGt5mOuR1gTQO6buChEm9qTRvSomiUZ1Fi0a4/XXi1bfn/70\nFjaU6jp/g/GdN318TjPf+c5y4vEIDQ2SeDzCd76zfE5xZqUJw7FyixiGKeaMxyOcffYUv//7vSgK\n3HHHao4cqUEIhSNHavjyl1fz/vcXo1LsMNVK/f/LvywnEDDLKIrkG9/w1i1lxw7zxlxTo3PkSA35\nfAAhFFKpIP/v/53Fhg1DSGlma7300jhXXlns95prrsD9hPiZz1zBX/3VXlpbC+h6lkxGYcmSDJ2d\nGR55ZAHem4FBPB7jG9/Y4Yz/Yx9b62kvHBasWjVFMKjz/e+fYaWDN8Nhd+5spVBQnfJF58k+M024\n3ISmqQwN1Vgp1IvtTk6GrUmF++nWxOvw2c06tnMz3yVMgat5kovYwVe5jUfYyEYeYj2PspTD1JEm\nQQNpYgjgIZe4UKBbN9NHMaM2ruVhbkCisomHuIXvECVLhqhV9wYEkk08aN2ABY+ynoe5nk08zC3c\nz1L6USlwMc/yRf7WdfOtvEVlnyN3mz/jfVzIrx0HzEdKJhalYbSPcB0b+A8W088f81WWcwgBdDDG\nLt7B1/gkR1gEmALaSqGndpTNSvbRxiiNTLKZ650ypjDzBtfvpdKxWGG+lmBTVSWxmOTgQVMgbL4v\nuP/+dWzYsL3smveZX/yJhY9PlRyv4VVpyCiU3/xLy738chMg6elJe+ocOFCHYdhfkAqvvNJId3dm\nzrbtdoeGoui6aemtKCovv9w8Z91HHulibCyKqkryedNW21zYVEgmiyZdpdoMKEZrmAh0XXXEqZmM\nmd9jYCDGkiVpK7Ol10xrdDTqvDKTq9V42kung+TzKsPDEctrw7r5SMHISI1HdOp1YxwEBA8bN5DN\nlkeamMfpdfi02Vji8Pm7fI96ZlDQCaLxDnayjmdZyw5Wso8ORujhAEE0hlhAkkbW86hnYrGRzdzC\n/XQwCkhamECi8jA3sJ5H6WAMjQD1TDt1N/IQt/A9OhgBBC2MIxGs5zHO5jVqSQGwnp/xnCeHR2Wx\nqn2O3G1ezlMIJMMsrOiAaU8C7HOxludQMcgSZRmHCKAjEQgkyzhECxNcxRYAdnPurILQdTzLcg6h\nEeAifs3dZc6bcwtvTfM2HC8Vw1AsDxbXpEjA1NT8CK595sZP8+bjUyW2IBKoyvDKzvsBs9+IS8vl\n8yr5vFpWp6ZGd8I/zWgQ/Zht2+0KUXz6Nrc+5Zx1MxkVRXF7QBT/bW+d5nKC9vZKBmHe8o6ZlIBA\nwHCOwTyPpXvepn7EPf7ZMCNGvH0piuF5r3IEw1xbv5VXhEvbCVKwnpGlpfYQZIlyJvuJkkEjiERB\nwSBCrsJxmm2aZQNoBImScQkWK5/DYp0gGgFXHUmYHAYKEtBQq8hfUjoOs81GkqhWf5UcMEvPheme\nab7WUJGOXkWiWVExUTJEyTh1KglC2xhFI4DAYIbaY7p2erH6lDjXuqKY/inmNYHzeX393Pl9fOYH\nf8XCx6dKbr65l507Gzl0SKWlxTimeNPO+zGbSNKmtTXLyy83ks+b+TtAsndvHUNDYerqdMbHQ9x0\nUy/33becdDpELJbnkkvicwow3f13daXo7a13zKFWrJjk4MEY+bxKMKjT3Q0PPrjI2a5ZsybBE09E\n0XUVIQzrKdC8kbe2puntNVdqmptzbN3a6tF3BJQM641fOPvsPw/+FhddFOfQoRoMQ1jZS6cwJwIa\nhhGieBM1WLgwzR13rHZFx7jNrMy99F276hHCoLExQyIRw54QaJp7+8KgkyHW8jxxWuljMf2cR1HA\n6d2P9+J9vzQSYivreB+/JGCFPiaJ8T5+xjS11DGDgUqCOpYwyUKO0sg4aUJ8nf9qbV/cwBG6aSRB\nByNkCfMaKx3B4mNcw2r20MYkSep5jGsAST/dZIhSb5k8NZJjDbuI00KeIK3ESROlj8UM0Mm/8UHH\n+OojfJf1/JzF9FtbE4JujtDJUdawk0bLkyNOI10M08kQOUJ8k4+7zn/5udjPGaxhF1EyHGEhyzmE\nioGO4AidrOU5Gpl0EqO5hZn2VtUSDjNOM10MMk09KWpcjp/u34f7erDftz/TndexWIGurjSapnL+\n+XGefbaVXC5AfX2Ob33L3wY5FfgTCx+fKvnud5cjpcKyZTrJpMJ3vzu3xsJ2txwdjdDTk3VeV6ao\nFZBSMDBQw/S0SiCQ5+WXm5ESPvGJ3opizdnatt8zU5kXJwe7d7dyySXjgO3FIQiHcbZFVq6cYteu\nRtLpINlsmHxeQQgFKQ00LUggYCY+e/XVRiYmMk5CtHBYst74hWupfBAKEiF6aGnJUVenOXlKjh6N\nYRgBSrckduxoQ0poackTjeoVzpE5IZBSkEhES+oHsG86G3mYADpxWmklTi/LrCgCd6p1N5X27s3X\npZEQBvAunkeiECBPLVl0JuhjCTPUUcsMC8ijY26v1DHDO9hFjLy15aEAkmE6CZEjgMZrnOX0I1EZ\nYiEJmsgQRVqW5pu5HoFkPY+yhD5S1DJEF6vZRYImUtRYba3kJv6NS9hOgRCXsp0n+S2e4QqyRK2t\nCcluzuODPEQj04BClCytllOnQBIiTw8HPeel9Fwo6Kx0VhdUpmnEQEVBB8ume4gF7OMsxmn2RJK4\nt6q2chnL6EVABcfPUhTKJ4MKsZiBYUiamgpccEHCuSavu26IVauSFbchfU4O/sTCx6dKjh41039n\nMipCBDh6dG6NRSVBZSXi8Qg9Peb++Kuv1mO6UAZRVZVEIgTkGRiI8Qd/4J3EeNo2DFq3bicyOkq2\nvZ34unUoimKVKTWiEq7+6izBo3dbZO1aM7nYD35gCu+kNJ/0Z2ZUUqkA4bAklVIJhSQvvNBEba1O\nTY3Gmgppqp977mIrpTp0dWU4ejRmvS7diVXIZlUiEWn5XhTHW0SUvO9+LZ33FtNPhhj7WMk+YJxm\nJKXaCjx1yyMiTJFhaejqzdzPAc6kiwGCFAiTZZiFNDDFDi5mnGZ+j2+hAnVME7C2REq3PHZxHrss\nd9Di+Ey77zwhomTIE7ISqJkhnA9xIw9xo8fZMkqWSZrYwcVOW9fyGAVMPUyBkGcLw96WAGgggQQK\n1lZILVOM0el8bk4sipSei0/xFXZbGUOXcBg7AZrAIGZpPvKEGaaT/8OfeNrybqvESNDEbs51VjQ2\n8dOy30VlrYW9YmVOOCcnQxw9GmP5cnOrcq5tSJ+Tg6+x8PE5DqamVDRNWFsW80N7e9bRboRCOqGQ\njhCSXM7885yePvb8v3X7dhr27iU0PU3D3r20bncv+Zbv2Zf2B0XdhHs8xW0Q80vdMIRloGVGjfT2\nxpDS1GWMjYUd8yKwl7276eurIZEIMzUV4sCBWiYmTCFnORLDMCcxhYKwjtsboeE9jtL/F1dlysfh\nDiOtpLNQnCfoFiZYx3Y28nDFc72fM61EXWGC5K3MolmSNDh9HWAFQfKO7iBLmAAFMkTpZ/Es4zPp\nYIRlHCZGhmUctoSVXtz1M5aKwd2W3T9AkHyF8uaNVkNFRUPFIEaGNDFPvQOsqHgOKo2jniRRMoQo\nUEOaJpJVH8NqdtPChHPu7+avZ/ldVPrdmde0YZjOq/Y2mlsPVVkP5HOy8FcsfHyqZM2aBIlEiGy2\nhqamDGvWJOalXfeWyXveY+6fP/FE0d+hvr7A6tVz9xUZHUWGza0MGQ4TGR11PrvwwjgvvNDueW0n\nNLP7M1dNvFsqdgKzyckIum6G8DU15Vm1KkFnp+ljcehQLcuWpTl6NEYqpfKoch0YwqWxuJY1tTNk\nMhr5vEI0qtPcnKe9PU8mo7ryd5g3hGhU57zzJhkaihKLGUxOhvA+/9jiU51rrz3Ko48ussSuwtKD\nmBOguQ2iKq1ayApiz76K57qYJGwfEsE2LqWNMUbooI+lbGYT/8F6HuAmVnCADD28ykoMVCsZWNFU\nqpLR1AidHGIpDSQZopMR1wqCjfv47ucWQHrCOd39mxqL77Gex1jMEe7nZmyNxc95P+t4jmYmmKCZ\nb/J7rONZp97v8v2K56DSOI6wGMMSlRrUMkOMNNGqjmGcFoZY6Jz7c9jNK6xx/S7Kc424CYd1VBVC\nIYM1axKsWpVgwYJslduQPvONP7Hw8amS9vYsDQ0FWlo0NK1Q8SnoRLwr3CiKOdGQEn70oyWkUkF0\nHVpasnzzm8ud7ZfVq80vTrv9TGs78ZdTJPO1NIRmqHlPu+OJMTYWc6I8gkGNJUvSzjaKPV43mmZm\nAh0cjKJpYBjmaoC5f51DSnNbyNRJQG9vDePjEVTVIBLTeHim6EoZUfP09sbI5wPEYgUmJkJMTgYI\nBKCjI0siEXQmA1LCypWTLFqUpq+vhlQqYPlNuMWbZnIyIQTbt7dbSc+gGG5oRyWUOj9Kqy37KMv1\nFKYwcZCi6+R5VNJemEnCvlShDRuJTogP8RPPe6UixErJyAQGCxjkYp4lgM4AXfyQ3y5rY/ZkZuYB\nVurfG35qtrWJB4nT7hxzL2fwv/nvnjKV+ygfRydDThbSTgaZoJkdXEyEDH0sqdBK0TF0Ew862hxT\nFHomETJkiRIlTSc5KwV7t8fjwqaxMUsiESGRCPH0023s3FlPTY1GOCyJRHSGhiKevxefk4s/sfDx\nOS5Kl+C9VOtdcaw6+/bVMzVl+jxMTQX5yU9M86ZMRmV6OkAiEeaccxJO+w+zkRjP08kAe1lDmotg\nu8ITTyxgcDBi2WlDLqewe3fjnH3/4hed7N3bQDAIk5NB53ilNEWbTz/dzuBglPp6nakplVTKTH4m\nhMLMjPcrJZsNUlubJ5czvSKkhFBIIZ832y5utZj8+tetDA1lyeVUcjmFSqaA2WwAKYXVV3H7w5wA\nzYZwJaWqfGfZzPUAFFOCb+JYN9bqqSQMLW9nIw9zJVuoYxoVg276uZDn+SkfrLLP2crMJUwVJSsn\ns3lGuLebynGne9/Gu3ieC1nE4BzW325R6PWecTzCBjbwCIvpo5McKrorBXu5hfnISK2rTcHUVJip\nqTChkDmhTKUCrFs3DlSne/J5Y/gTCx+fKjG3C9I0NYWZnEwTj8/uHQHVi8Yq1RkcjNHaWnDKHD0a\nZdGiLNPTipXBNOhpfzQeY7rnvey2ytfFzX3yfF7FMFSEMLcaVFUQj0eP0XeUoDWfMKNBJLGYmUgs\nnw8wNRW0UrQrVkiqsFKcw8xMqaASgkFJU5PG1JSKqpptxmIa2WwI703KrCulgqpCLGYwM0NJe4qz\n+uI2Pzq2gZLpOmpPsCphPn1/YNbPy/s60c9nL7eYfhqYJkUdYIoqz+T1Kts7fmxTruqY+7iqT/de\naRzlKzD269lTsLvHVTpJM/+t64JwWGd6OuSLOE8h/qKQj0+VuEWNswnCqilTTZ1SM6729gy5nLAy\nhZq6C3f7ldpob88SCukEAm5zLa8JVaV6CxdmKFhzGiGKCb+klESjGvX1BQoFcz87ENAd4ytdLzet\nAkkoZI65pqaAuSVhWF/4BbwrP9L53DBA16nQnjsBmUFpX5XMqOz3VLXSZ6Uc6/NqylTTRuVy/Swm\nST0qGorlB2F6Opwsqh3r8ZY9kfKV25hL6Ortp/xaUlUDXYe6urwv4jyF+CsWPj5VYgvAdL2W5ubk\nnN4RxyMaq1TnooviHD0aY3AwysKFGf7iL/bwwAOVNRZz9WsY5g39mWfa0DSFtjYzgdmx+v7Sl1Yz\nOBhl2bIku3Y1OknIPvGJ/SSTEWc7ZeHCNIYBzz7bBgiuvnqQJ5+0IzAkV17Zj6aZkSPRqMauXU1k\nsyqKovOud02ybVsTmYz5FCmEwXe+8ys2b17Orl1NRKM6119/lG9/2040Bq2tU+RyMQIBybJlSVfC\nMYmi6EhDstHyeehghBHa6WMJm9mEVoBNrjwXsyfYmvuGWF2iLjlnecB57wiLEEiu5WeA5Gku5g94\nlSgZxmjlbv4Sgcb1PMgf8g1qmeEpLmcHF7GIQQbo5Cb+jR4OWoLL72EQYCObWcJhOhhlhA766QZg\nCX1cylYUDA7QQw8H6aGXA6zgo3yLJ7i2LJGYO1GYwGCMNobptMSqGy1Pj2ISsiN08w/8CQsZq5Ar\nxCzvTeZ2JrdzV1kyN5BsZgO2uLaf86zXhqfM+vUH2L69m2QybF0nGVRVOhqLK68c9fy9+Jxc/Oym\nPj7HyfLly+ntnZ+U6bOxdWtR+5DLiTdk8PNG2rrzztWO3qJQgFWrktx5555Z2//lL1tJJsPU1Bie\n8lu3tvL4450kk2GSySCGAapqMDERRlEgFJK0tWX48If7PGP7wAcuY2bG3jKRBAI6N910hFxOsGtX\nAyMjUQxDZWbGdAndqD/MWvkcS+hjGYc5xFL6WMJ21gE4hkwRMmz35NOoXjexiZ/O0U515d1jWcMu\nFjCEYaWRP4tXiZGhQBgVnZ2s5h4+x+f4Mt0cQaKioNHPYh7nGj7E92ljjBnqCZJnK+v4Hrewju2e\n8xCywkjbGKWHXiZoZgGDhMgzQStB8oTIEqGAjur0fTnb2cRP+SjfoYMRmplAQeclLnDO7cPcwNOs\n4zz2oKMSJMcETXyLT7CGXYCZK8R9vv4Hf+mIPSNk+SVXW1sp1f8uVNVgxYopbrqpz9dOnAB+dlMf\nn98gTkSrcTLacustgkHz9Vztp1IhNE0llRKoqmRgIOqUyedNjYWum86J09PmhMHMRmmQSgXLxpZK\nuROVCTRNcR2HObZ02jRGklKhSx4hS5QGy6La/H9xX748dwhO29VSOQeJ3YrhyoRqihZnK+82rGog\nyRhmSHAtKcttE3Qr74epvZiy3DwhQoEGx9o7ibDGXyDECg44fbrPg00LExQIESFHmLxjalUgRCtj\nZKjx9G0fs51TJIippyk9t4vpR7fGLRDUM20dX5p2RomSIUkDg5a9t5lnJGKdC3dekup/F7quMDBQ\nw9CQr514M+FrLHx83oSciFbjZLTl1lsUCubrudpXFB1dN421cjmFSER3yoRCuuWHYSCEpK4ujx0G\nquuCmpryEN5QSMO9h24LN23dSaFg+msIYSYhO0I3EesGVmpaNfdeffUrt3O1U8lkq1L5UsOqJA0E\n0AhQYIYa7GReKrpT3tReFFAwyBMkST0ACRqc8rapld2++zzYRlrjNBMkT5YwOUIYtsiWPFPUoaJ7\n+raPOUOUAAUKBNARnnNrl7HrSiRTlgC1kYTlauE1y7KNxszzkj1hLUk2q7BnT+OxC/qcMvwVCx+f\nNyEnotU4GW194Qt7HL3FwoUZvvCFPWVl3O1fdNEke/c2MjMToK5O44orRp0yhgE7drSyaBE0NORp\nasqze3cjExMholGdDRsGysb2kY8c4v77V1AoKASDBldcMUxdXZ6eniy33HKQL3/ZHFtHh04korNt\n4H0EpwwGCwvoZZnLtGoj9naKN5zUjUE1z1rl5ltew6vS1Ymv8qk5y9/PzR6Nxf/mk/wp99Lt6Bye\nRCeEgsYf8nVLY3GFo7H4K/4HN/EDelymVrZWYRD7PHRaGgvBEg5zKc+gIPkPrrU0FgctjcW/8ATv\nd2ksnnSOWWCwnkcRwBitLo2FeTxX8yRPcrWlsTibf+AzLGSEV1hNHdPUM+Uxy3KHpxbzgxwvBrW1\n2gnU8zmZ+BoLH5/j5FRoLN6qzKc25ETas8v/8Ie2IVPRErypKYeuK0xNuf0vAAy6utLWtk151lO3\n8ZailIa5us27RJnRU1F/IUvanc2AqtJns5U3HUhzObVC2dI23czW57Fwt3OscRfbLNeYvMsK6610\njJX6EyXvSVQVDEMQieg0NeW4/PLRORMC+lTG11j4+Pi86ZnPlZYTac/+fMOG/TzyyArsm9x11x0g\nkWhEShgdDdHfX2tZgUu6u1N87GOHaGmJ86d/ehX2jWzRoiSaFiCdDqGqBs3NWdLpINPTAWZmgk7b\nZkijOSF5VFnPgrYUsfgoL+nnWhEMOqBhZ/s0+5zmyJE6py9F0TEMczzFlRP7+14nGBSEQnlSqaIF\nemfnDJddNs6uXSH271/gtK0oOQwjjPum3dWVIhg0OHy4jvIbNa73Cpi3BfvYdHRddV6vWBHnwIFW\nSicEoZBGW9skAwPFKJ2iCZcZxWGuFJ3HI6wnGNRQ1TzZbMTTjpva2gLNzTmmplQrk61Zrr09QSAQ\nplBQaG7Ocd55CW691Z9UvJnwVyx8fI4Tf8XC51TgX2c+J5uTtWLhizd9fHx8fHx85g1/YuHj4+Pj\n4+Mzb/gTCx8fHx8fH595w59Y+Pj4+Pj4+Mwb/sTCx8fHx8fHZ97wJxY+Pj4+Pj4+84bvY+HjUyWa\nBvfdt5zJyTaamuDWW3sJzPEXZBiwfXsro6MR2ttNDwalyqn8bHVPpM1sFj772QsYHY3S3p7hnnte\nJDJHagV3Hy0tWfbtq2dwMMbChWnOOmuKeDzCxESI5uY8ra3Fz7u60tx4Yy8f//hlpNNBYrEC//qv\nz1Bf721769ZWduxoxTCgtzdGf38dhmFmpNy4cZAFC0yb53g8Qk1Nlr//+3OQ0swvsnJlgtHRGqJR\njbVrx3j22VYSiQhNTVk+9rFeXnihlZdfbmJsLETxuUlieknM59dd9Ymyqik/e8bUJbPWqYbydjc4\neUjmm+qyvs5vXSF01q6d4Pbb9xAKvdEj8Jkv3tQ+FkKIK8HylPWSkFI2Vyjv+1j4nDS++c3lvPxy\nMw0NYZLJHOefPzGn298bcaGcre6JtHnbbRdw6FCdlQAMli2b5t57X5y1vLuPHTuamJ4O0tpaIB4P\nUleXp7Mzz/BwhM7OLMPDIaanQ7S2FsjlBAcPxtC0osFSbW2eBx98xtP2448vIJEI0d8ftYym7BuI\nQUtLjrPOmgIEPT0p7r9/MUWzqKLroqJIK4eJIBAwjysY1AmFpMu8yuZ4JwHVML8Ti+PNmFotJ6vd\n+e7rjdRVFJ116+JlWXd9js1vsvOmBG4DXnC955vD+5xyBgZiniyhAwOxOcu/kayis9U9kTZHR6Oo\n1kOqqpqvq+07nQ5if+9IqZBOB0mlDMJhSSqlWp8XM45qmtteWpBOB8vazudVAgEoFAJ4b7aCVCpg\nOWIW2/D+G4SQJe9LhBBomur6zM28f2+eQJtzl58rY+ob4WS1O999vZG6hiEqZt31OX28VTQWr0kp\nd7h+Zn/c8vE5SXR1pT1ZQru60nOWfyNZRWereyJttrdn0M2kk+i6+bravmOxgnWzBiEMYrECNTUa\nuZygpka3Pjec8QQCOu5spLFYoaztUEhH0yAYdGcuNcvX1GiEQjqhkO68524PJOYiq219bX4mpSQQ\n0AkG9ZI2qfB6PjjeNucuP3fm1RPnZLU73329kbqKIitm3fU5fbxVtkLeI6X8zyrK+1shPicNW2Nx\n8GCAnh7N11j4Got5K/9G9Alz4WssfObiZG2FvFUmFiNAG5AAfg58Xkp5pEJ5f2Lhc9LZvHkzGzdu\nPN3D8Hmb419nPieb39RcIUng74H/ClwN3A28B9gmhGg9nQObb3bu3PmW7OuNtHW8dastX025Y5U5\nlb+PU8Vb9Rp7I+2dSD3/OntjvFWvM/+7bP54U08spJQvSyn/Qkr5H1LKp6WUXwGuAToxBZ1vG/w/\nxvkr/1b9YzzZvFWvsTfSnj+xOPW8Va8z/7ts/nhTb4XMhhDiFaBfSnltyftvvYPx8fHx8fE5Tfym\nhptWzck4QT4+Pj4+Pj7V86beCqmEEOJC4Czg2dM9Fh8fHx8fHx8vb+qtECHE/cBB4CVgCrgA+Dww\nA7xTSjlxGofn4+Pj4+PjU8KbfWLxeeDDmIb5MWAYeBS4U0o5cjrH5uPj4+Pj41POm3pi4ePj4+Pj\n4/PW4i2nsXgjCCEahRCbhRCvCSFeEkL8TAjRc7rH5fP2QgjxAyHEy0KIF4UQzwohfut0j8nn7YsQ\n4veEEIYQYtPpHovP2w8hxBYhRK/1ffaiEOKLx6rztooKqQIJ/KNtDy6EuA34Jqb5lo/PfPEJKeUU\ngBDifOCXQMvpHZLP2xEhxBJMA8Htp3ssPm9bJPBpKeXmaiv8Rq1YSCmTJTlHtmHqN3x85g17UmHR\nyMnJguXzG44QQmA+GH0KyJ/m4fi8vTmuucKbemIhhOgSQtwrhNgmhEhZy30V094JIRYJIX4shEgI\nIZJCiJ8IIbqP0cVngJ/O/8h93iqcrGtMCPEPQoiDwI+AD57MY/B583OSrrM/A56WUr50ckfv81bh\nJN4z/04IsVMI8UMhxJnHGsebemIBrAD+CzABPMUsT35CiChmsrIzgVuAm4EzgP+0PqtU5w5gGfCF\n+R+2z1uIk3KNSSn/TErZA3wEuEcI8Zu27ejjZV6vMyHEOZgT1v95coft8xbjZHyf3SKlXCmlPA94\nDHjcWi2bHSnlW+IH+Dhm7uPFFT77NFAAlrneW2q995kK5b+IuSdZe7qPyw4a4/4AAAb/SURBVP95\n8/zM5zVWUvd14B2n+/j8nzfHz3xcZ8AfAgNAL3AIyGCG4//x6T4+/+fN8XMSv8/iwNK5yrzZVyyq\nZSPwrJTykP2GlPIwsBW43l3QWqm4DniflHLmVA7S5y1NVdeYECIihFjqer0OaMa8Afj4HIuqrjMp\n5f+VUnZJKZdLKZdhOhF/Qkr5tVM9YJ+3JNV+n4WFEC2u1+sBDTgyV+Nvl+XZc6islXgFc1kIACHE\nKuAO4ADwK2s5pyClXHtKRunzVqaqawyIAg8IIWoxnxZmgBullMmTP0SftwHVXmel+AJhn+Oh2uus\nHnhMCBHEvMYmgOuklPpcjb9dJhbNwGSF9yeAJvuFlHIvb35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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc020c8b128>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fbfeb8c1b00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(cc_data, 'credit card', 'loan_amnt', 'int_rate',\n", " [1e2, 1e5, 5.0, 30.0], 'loan amount', 'interest rate',\n", " 'semilogx')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "8ee15a48-d752-4496-87f5-11478b6fbb7e" }, "outputs": [ { "data": { "image/png": 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K6urqKC8vjyqz2+3ms88+47rrrjvi3g033MCKFSsYO3Ys27dvB6CpqYmVK1ey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\nZ+Fs4blYIaXzrLzHosbmqOGdRo0VUsMF43x+B2/XfKXX29rqGRubXfB97r9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VGWJe9iVU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UJk560KoXgWwTAkmuZhmrJoaFiWCok0NORwXcGaNVM0NanJ/Ve/ai0eZTRqc+aZKh+mEHpr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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc00ae7aba8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fc00ace0978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(cc_data, 'credit card', 'annual_inc', 'int_rate',\n", " [1e3, 1e7, 5.0, 30.0], 'annual income', 'interest rate',\n", " 'semilogx')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "5df827c1-f955-4d6d-a1ee-c61813544bea" }, "outputs": [ { "data": { "image/png": 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o6xrJeToKjculKzOgf7e1eURdXR2JiYmN0hITE6mtrQ1vV1aemmHzuHHjsFj0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\n1wyXHDOvV1XGmGMq8v7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jj532Om7Mk64yZEKHRTVZWRVZtXZFooUzRgw71W4HETllxJBJ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\nHpARiwRWFC2kHZ6swKSN5vXbaNqnfMw37bXFMcNmTBo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ilSD8dGgVHBs3Vp8TiXett8t7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+8jgYyasmy9IHg5d5bz4Xzp2xdaHydiR+csW/kapxyttr9bKojO3VeoJi2jpiInlRRtS57M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\n73A9r7CEQbLYZAgwQBPf4PMobMaoK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QD5cGa97x1lw78rmPZrOjnUMKHPd1F/jcJmBx86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\nLDNmKarM9lXAI8D9wH3ADuDvReTXF3WEJx8f4JeA3wX+CPgVwAZ2i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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc00acc6128>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fbfeb7d8fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(cc_data, 'credit card', 'annual_inc', 'loan_amnt',\n", " [1e3, 1e7, 0.0, 35000.0], 'annual income', 'loan amount',\n", " 'semilogx')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "1e82df4c-30e7-4d3d-8e79-457ffc267fcd" }, "outputs": [ { "data": { "image/png": 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y5Mls2rSJHTt25CWOAC+88AIzZszgiy++ICUlhaSkJG699VYAli9fzq233srE\niRNJSUlh27ZtbNy4MWzMu3fvZtGiRfTs2fOA166++mrmzJnDCSecwI8//gjAjh07+PTTT1m1ahWN\nGjXigw8+ID09ndjYWObNm0fbtm0ZMmQIQ4YMKbSuygMlPyIipWH9eoiL836Pi/Oel7Lt27fTunXr\nEtlWcnIy3bp1o0qVKlSrVo3777+fBQsWFFh+1qxZNG3alH79+mFmnH766XTv3j1f68/BTJo0iRtu\nuIHTTz+d2NhYnnjiCRYvXpyv784DDzxAzZo1qVKlSpG2uX37dgKBAPXq1Su0XOXKlXn44YepVKkS\nl112GdWrV+fnn38GoG3btpxyyikAnHrqqfTu3Zv58+fnrWtmDBs2jLi4OKpUqcK0adPo0qULrVq1\n4qijjjqgD82oUaN47LHHqFevHrGxsQwZMoRp06YRCASYPn06nTt3pk2bNsTGxjJ8+PACW2NSU1ML\nPLZ69eqxdetWwGtpCv6ZK/R5RaLkR0SkNDRqBLt3e7/v3u09L8d2797NoEGDaNKkCYmJiVxwwQWk\npaUV+IG5bt06lixZQnJyMsnJySQlJTFp0iQ2b95c5H2mpKTQuHHjvOfVqlWjVq1a+Vo+GjZsWKzj\nSEpKIiYmJt/ls3Bq1apFTMz+j8z4+HgyMjIA+Oqrr7jwwgupU6cOiYmJjBo1Ki+xCBdXSkpKvtFj\ncXFx1KpVK+/5unXr6NatW15dnXzyycTGxvL7778fsG58fHy+dYt6bJs2baJ27dpAxbmUVRxKfkRE\nSkOXLtCqFSQnez+7dIl0RIfl2Wef5ZdffuHrr78mLS0tr9UnN/kJ/UA99thjadeuHampqaSmprJ9\n+3bS09N5+eWXi7zP+vXrs27durznu3btYtu2bfkSi+J+kMfFxdGqVSumT59erPWCXXvttXTt2pWN\nGzeSlpbGoEGDDkgCg+OqV68ev/32W97z3bt3s23btrznjRo14qOPPspXV7t27aJevXrUq1ePDRs2\n5JXNzMzMt26w+Ph4WrVqFbZ17a233srrfxSNlPyIiJSGmBjo2hXuuMP7GVPy/35zcnLYs2cPOTk5\nZGdns3fv3hIZXZSVlcXevXvzHjk5OezcuZO4uDgSEhJITU3lkUceybdO3bp18/UduuKKK1i5ciUT\nJkwgOzubffv28c033+T1+SmKPn368MYbb/D999+zd+9eHnjgAVq2bHnYc/D885//5M033+TZZ58l\nNTUVgO90Naq8AAAgAElEQVS++44+ffoUaf2MjAySkpKIjY1l6dKlTJo0Kd/roYlQz549mTlzJkuW\nLGHfvn0H1N2gQYN44IEH8i7nbdmyJa8PUc+ePZk1axaLFi1i3759DBkypNDLU08++SRjx47lpZde\nIiMjg+3bt/PQQw+xZMkShg4dWmCMFZ2SHxGRCmLEiBHEx8fz1FNPMXHiROLj43nsscfyXq9RowYL\nFy4s9nY7depEfHw8cXFxxMfHM2zYMO666y4yMzOpXbs2rVu35vLLL8+3zp133snbb79NrVq1+Otf\n/0r16tX55JNPmDJlCvXr16d+/frcd999ZGVlFTmODh06MHz4cLp3706DBg1Ys2YNU6ZMyXv9UC/f\ntGrVinnz5jF37lyOO+44ateuzc0330ynTp0KXCd4XyNHjuThhx+mZs2ajBgxgl69ehVYFuDkk0/m\nxRdfpFevXtSvX5+EhATq1KmT10/pzjvv5Morr+Tiiy+mZs2atG7dmqVLl+at+/LLL9OnTx/q169P\nrVq1Cr3U16ZNG2bPns306dOpV68eTZs25bvvvmPhwoUcd9xxBcZY0S+FWbRle0eKmbk5c+ZEOoyI\na9asWZFGilRkqgNPRayHjh07Rt03ZDnydu3aRWJiIqtWrcrXp6kiMDPCfTb6f0sRy7DU8iMiIlLK\nZs2axe7du9m1axd33303p512WoVLfMoyJT8iIiKl7P3336d+/fo0bNiQX3/9Nd/lOznyjop0ACIi\nItFm9OjRjB49OtJhRC21/IiIiEhUUfIjIiIiUUXJj4iIiEQV9fkRESmievXqVfj5T0RK0sHumRYp\nSn5ERIpo3LhxxV6nIs53dChUD57DrYc//oCp1+7gba7mKAIAGOCAaXQl4aPBHKVP9oPSZS8REZFy\nYMHHmZxx7a1M4lpi/MQHvMRnN5XJHHWTEp8iUvIjIiJSxk2bks3tz/bnT/xCZfYCEACyiWEbiXRt\nOZWGzSpHNshyRDmiiIhIGfbDt9lc9/ojJJOG4bVaBIAsqnANE+n0Sg3+cbzaMopDyY+IiEgZ9dtv\nsOvezziRn8nt3ZObAK2jEb0m1qROncjGWB4p+RERESmDbr4mncVbbiCZNALALqpSjb2AI5VEptz9\nDG2V+BwSJT8iIiJlTHpagP9u6UsNMgGoBMSxh+0ksoI/MeGGR+h5qT7CD5VqTkREpAzJzAjw01Vz\nuZLMvGHs4F3qGsVfqPZ0e3qeoY/vw6HaExERKSM+nxcg7om59OBdAni9fHIToFQSafBGRxo2jGyM\nFYG6h4uIiJQBGekBqj0xm9t5maasJotYcvBGdu0knmN5X4lPCVHyIyIiEmGrVwX4scdcbuVV4smk\nEgEqk00m1XiSf5DEdmbMzop0mBWGkh8REZEI2rMHPrhlJ+fwDbFk+4PZDUcMu4njYR5l3PgFxOgT\nu8Soz4+IiEiErFoZ4KPBO7mOiVRhD7uoRk124DD2UJUFnMesDxZQWZM3lyglPyIiIhGQlQUfDd5J\nK74im1gqs4+f+BPNWQXAAs4j45WbSFbiU+KU/IiIiJSyQAAGDzqNV7mGhmxkG0msoTFHkcOz3MtM\nLufj2V+SrEtdR0SpV6uZXWxmc81sk5ntMbMNZjbVzE4KKZdoZq+Z2RYzyzCzOWZ2apjtVTGzp80s\nxcwyzWyRmZ0fppyZ2f1mtsbMdpvZt2bWvYAYbzKzn/z4VpjZoJKrARERiXazZ9fmxt+eozHrqEEG\nTVlLY9YzgeuYQRfeevtL9fE5giLR8pMMfAO8DGwBGgH3A4vN7M/OuQ1+uVn+a4OBNOAB4DMzO905\nlxK0vTHAZcA9wBrgNmC2mbV0zn0fVG4E8Dd/O8uA3sDbZtbJOfdxbiEzuwl4FXgMmAt0AEaaGc65\nUSVYDyIiEmX27IEBVzVnYdq51ON3cjiKXzmOyuzDYXzbsC0fjPpcfXyOsFJPfpxzU4ApwcvM7Gtg\nBdATeN7MrgRaAe2dcwv8Mkvwkpu/A3/1l50O9AGud86N85ctAH4EHgW6+suOBu4GHnfOPe/vdr6Z\nnQA8CXzsl6uElySNdc4NCSrXABhuZq8553JKuEpERCQKZGTAqac05HfqkkwaDqjMPo7jV77lTD7n\nAka//q1afEpBWaniVP/nPv9nFyAlN/EBcM6lAzOBK4PW6wJkAW8FlcvBS64uMbNYf/GlQCwwMWS/\nE4A/m1lj/3kroHaYcuOBWsB5xT4yERGJeoEA9OrRkin0Jpm0vJmbA0AlsplLO5L+3V6JTymJWDWb\nWYyZxfqtL6OAFPa3CJ0M/BBmtR+BRmYWH1RujXNuT5hylYHjg8rtdc79Gqac+a8DnOL/DN13aDkR\nEZEiGTtqD/Uu6UhmoAY9eZcY9t+2woBN1KXpxI40P1ljkEpLJHPMr4C9wM/AqUAH59xW/7VkYHuY\ndXJbiJKKWC456GdaEcsRZpuh5URERIpk0LThnAJ5SU+u3Pt1vfGPV6hTJzKxRatIppnXAQlAM7zO\nyp+aWRvn3PoIxnRYZs6cmff7ueeeS8uWLSMYTWQkJSXRrFmzSIcRUaoDj+rBo3rwRGs9DL07gzEs\nzZf0gJf4TKcbTRb/k751Kv61riVLlvDVV19FOow8EUt+nHM/+79+bWYfA2uB+4Bb8VpeksKsFtoy\nsx1vRFhB5VKDyiUWsRz+vn8vpFxYnTt3zvd89erVhRWvkJo1axaVxx1MdeBRPXhUD55orIfVq+Gx\n9/pSKWS5w+vrkzHqL6RnrCU9IwLBlbI6derk+4x84YUXIhhNGenw7JzbAaxifx+dH9nf/ybYycB6\n51xmULmmZlY1pNwpeB2hVwWVq2JmoV87TsE7D5cHlbMw+87t67McERGRg/jg3SwSB42kAZsPuNQV\nwJtLpVEz9fGJlDKR/JhZXeBE9icrM4AGwZMVmlkC0Bl4P2jVmXgdm68KKlcJuBqY7ZzLHT32MZAN\nXBuy6+uAH5xz6/zni4GtYcr1BbYBCw/l+EREJHqkp0Orka/RhsXkYDh/eQDYRiLTJs6mzZw5kQwx\n6pV62mlm7+BNMvg9kA78CW/enizgOb/YDGAJMMHM/o7XWfl+/7Wnc7flnPvWzKYC/zKzynjzAN0K\nNMGb/ye33BYzew6438wy2D/JYTu8hCq3XLaZPQy8bGYpwKd4kxxeD9zmnMsusYoQEZEKZ3NKgLf7\n7+RFlnAU2aRwDPXZTCUcGzmGB7vOpn+dPyIdZtSLRJvbYryWmb/htdpsAD4Dnszt7Oycc2bWCXgG\nbyboqsAioJ1zbmPI9q7Ha0Ecjtev5zvgEufcdyHlHgB2AncAx+CNMrvKOfdRcCHn3CgzC+BNingP\nsB4YrNmdRUSkMDNmwLoXvRuVplOTxng3LNhCXRbSil5M5ddnN7B6tZKfSIvEDM9PE9R6U0i5NOBG\n/1FYub14Sco9BynngMf9x8H2PRoYfbByIiIi4M3e/PKL5/EhV9CQjaSSBDgSSGcJLbmGCUyePB9v\ngLNEmnpbiYiIHIbMTOjerS1T6M0ZfIcRoCY7WE8jHuRxZtCF6dM/JyEh0pFKrjLR4VlERKQ8ys6G\nq3u2ZjhDaMsX/lIjhgAOmMkVTJ6sxKesUcuPiIjIIcjOhlsGncHD+x6lB+8Qg6Mqe9hNHHupwudc\nwDvvLqB69UhHKqGU/IiIiBRTair073UGW2lAVbIIAL9TF6iKA77mf6j1YnslPmWUkh8REZFiSE2F\n63udxk6OzneT0rr8zq+cwHS6seX2DlxxonqWlFVKfkRERIpo2TcBFt+fxg7q5rtRqeF1op1ON6o8\n1YErWijxKcuU/IiIiBTRwvt38g+eO+DD0wF7qEy90R1o1ESJT1mnd0hERKQI1q4O0IkPacqavFtW\nwP77dT1+60QlPuWEWn5EREQO4rKOe/iOv9GYdVQihwyqUI29GLAP45UhU2h/fmKkw5QiUooqIiJS\niJUr4TNG0JS1VAJicFTCsZbGPMG9xPMhp56fHOkwpRjU8iMiIlKAhZ9ncdJjr3E231AJRzaVCHAU\n2cTyN57lppk1mV010lFKcSn5ERERCWPpF3u49bG+JLIDw2F4H5r7qMRG6tN5VA2qKvEpl5T8iIiI\nhPj3Yxm8/XlPKpOD4XVodkAOxn9pQXse4qNm6jlSXin5ERERCbJ2VTYTPr82L/EBr4NsNsb7dGHr\ny7fyUfNIRiiHS2mriIiIb9Uq+P2Wz6hGZl7iA16rTxo1+enBG2muxKfcU/IjIiICZGTArbe0pR3z\nycHy5vJxQBaVGD1kPG3aVY5kiFJClPyIiEjUW7IowJvdtvMRl3Mcv7KT6mTj9fXZSTxXtZvGOeer\nd3NFoeRHRESiWmoqfD10J32ZxPH8ShZVCBDLJhrwKR04Ni6FOx/U7dkrEnV4FhGRqLV1K/Tvcxab\naEQCGQSIYTPHsIMk1tKYkdzCuElfRzpMKWFq+RERkag0p+Nt9OzTkUySSGQnhuMocjiGzewijqWc\nRd+pNaiuRp8KRy0/IiISdfbsgcf4mRjIG9WVO58POB5hCF1fr0listoIKiK9qyIiElVWr4Yundti\nkG84O4DD2Ewd/vJBEg0b6SOyotI7KyIiUeW2QeewlsYHzOMTAFKoyxv3v0JljWiv0HTZS0REosai\nRfAtZ1GP3wkAlfASHwc8QAfaf3QfbfTJWOGp5UdERKLCD98H+O/QHTRmPd4FrxhyiGEvsXRnGufP\nvI+jlPhEBb3NIiJS4d1w7R4++uNvDCCFo9iHEcARAzi2UIs+E3WH9mii5EdERCq0zh3T2MFVHIXX\n3rPXb/HJJpZ1NGLRv5+hSR1dCIkmSn5ERKTCmv1BNtvpnZf4AFQhQDrV6MdY/mdYTVqfHMkIJRKU\n/IiISIWUmgqJ//qMWHIOGNm1kfq0f7YGp54WqegkktTOJyIiFc7nH2Zyaq+b+Tv/BMh3h/Zs4LI6\nz3HqafoIjFZq+RERkQrlxacymfpp97wWn9yZmx2wj0okMYWZE9W7OZop7RURkQpj7Yo9TPr0KiqT\nc8CtK1bSnGqkM3NOYgQjlLJAyY+IiFQIy5ZkceXtfYkj64DbVuymCu/QlclTF0UkNilbdNlLRETK\nve4dU0il/wH368q9bcWL3MKxr7cnOTky8UnZopYfEREp11JTYRv9iQF/2kKPA3ZTmXh20GxqJ+o3\n0vd98Sj5ERGRcuvbZQEm99p2QP+eALCNRBLZxpTpS9XiI/koDRYRkXIpLQ2+/McOnuDhfMtzb1Ra\njw288/4S4uMjEp6UYUp+RESk3MnIgN5XteZnTuJYfiOH/S0/DkhmLe/NXKL7dUlYSn5ERKRc2fxb\nNusGzGMTV5NIGobDgGwqsZ0kXmMg4979RYmPFEjJj4iIlBupqbBhwKcM4nWSSMvr3xPAcDjmcx4N\nRrenevVIRyplmZIfEREpFzIzoV+vFuzkYo7yx3Q5vMtdWcSyjDNZNfwWzmmisTxSOCU/IiJSLgzq\nfxo7qE2lvMHs+Je7jGWcyfIXh3DOiUp85OCU/IiISJnXs+N6ttIx35B28C55vU9ntr8ymGbHK/GR\notGZIiIiZdoPP8BWbjgg8XHAHiqz5rFblPhIsehsERGRMmvJogCf3bUj7G0r9mFcetpkWpyjixhS\nPDpjRESkTFq7Fr4eupNWfJU3caGx/35db4+ewdAmGs8uxaeWHxERKXO+X5bN7zfNYSjDaM1CnuJ2\nf0i796jN6xyjxEcOkVp+RESkTLmu4/f8xt15fXw2492Y6xnuZTHn0v75mkw7NaIhSjmnlh8RESkz\n5nyQlS/xATiGVKqzk8Wcy9nDanCqEh85TGr5ERGRMmH599n0/9dfw47q+ojL6Dy6Jk2aRCY2qVjU\n8iMiImXCzrs/oxEbDkh8AkDcU+2V+EiJUfIjIiIRtX51Nl91nM1tvEwV9pDtL89NfBryLKe10IUK\nKTk6m0REJGIyMmDzoLkM4jXi2E0MXtKTRQy/chxj//ovJnSqHOkwpYJR8iMiIhGRlQVXdTuHrXSl\nOrv8O7THEMBYzXH8me+Y3enLSIcpFZAue4mISKlb/n02/9tpDik0oRq78pbHEGAX8bxDV14ZpcRH\njgy1/IiISKnKyIDMuz/hdv5DdT/xCfivOWAu7ak0vD3NmkUsRKnglPyIiEipuu6ac9noX+oC8u7b\nlUUsa2jCHy8O5qwTdWFCjpxSP7vMrKeZvWtm680s08xWmNnjZlY9qExjMwuEeeSYWULI9qqY2dNm\nluJvb5GZnR9mv2Zm95vZGjPbbWbfmln3AmK8ycx+MrM9fnyDSr4mRESiz5zZAe7f/RhxZObdryv3\n8V9a0DL2OZor8ZEjLBItP3cDvwH3+T/PAIYB7YDWIWUfA2aGLNsZ8nwMcBlwD7AGuA2YbWYtnXPf\nB5UbAfwNeABYBvQG3jazTs65j3MLmdlNwKv+vucCHYCRZoZzbtShHLCIiEC3jptJpS+V/OcBvBaf\nbIylnMXYG0fwTq8IBihRIxLJzxXOuW1BzxeY2XbgTTNr55z7POi1Nc65pQVtyMxOB/oA1zvnxvnL\nFgA/Ao8CXf1lR+MlXY875573V59vZicATwIf++Uq4SVJY51zQ4LKNQCGm9lrzrmcwzl4EZFo9O3S\nLLbTN9/szTHAHiqzlsbMe2gIvS6IYIASVUq9bTEk8cn1Nd7fQ4Nibq4LkAW8FbT9HGAKcImZxfqL\nLwVigYkh608A/mxmjf3nrYDaYcqNB2oB5xUzPhGRqLdsaTYn9ulywG0rAJ7nr8z790u0vkB3aJfS\nU1YurLbDu+T7U8jyJ8xsn5mlmdn7ZhZ6O7uT8VqH9oQs/xGoDBwfVG6vc+7XMOXMfx3gFP/nDwcp\nJyIiRbCwY0f+9uBlnMgvB9y2IgdIv6cDJ55cVj6KJFpEfLSXf0lpGDDHObfMX7wXr9/NJ8AW4ETg\nQWChmZ3tnFvpl0sGtofZbGrQ67k/04pYjjDbDC0nIiIHce8NqXwNYW9UGgCSGc+7lyjxkdIX0eTH\nzKoB7+NduhqYu9w5txm4NajoQjObjdcC8yDQvzTjLKqZM/f3zT733HNp2bJlBKOJjKSkJJpF+eQc\nqgOP6sETrfWweTN8ub5T2MRnBSewYvIMvjsn+m5bEa3nw5IlS/jqq68iHUaeiCU/ZlYVmAU0Ado6\n51IKK++c+83MvgTOCVq8HWgUpnhuC01qULnEIpYDSAJ+L6RcWJ07d873fPXq1YUVr5CaNWsWlccd\nTHXgUT14orEeXnshk6Ez76EqWWFbfGY99gItav9GlFULEJ3nA0CdOnXyfUa+8MILEYwmQn1+zOwo\nYDrQArjMObf8EDf1I9DUT6SCnYLXmrQqqFwVMwtNt0/B+3tcHlTO2N/3J1duX59DjVNEJCp8szTA\nP2cOojm/5N2kFPYnPo8BLc6JeI8LiXKRmOTQgEl4nZyvdM59XcT1GuGNtloStHgmXsfmq4LKVQKu\nBmY75/b5iz8GsoFrQzZ7HfCDc26d/3wxsDVMub7ANmBhUWIVEYlGC2alc8uD3WjA5rwPF+9mpZBF\nJc5uNJU2c+ZEMEIRTyTS75FAT7z5dHab2blBr/3mnNtoZs/g/b0swbvUdCLepIjZwOO5hZ1z35rZ\nVOBfZlYZb5LDW/EupfUJKrfFzJ4D7jezDPZPctgO6BxULtvMHgZeNrMU4FO8SQ6vB25zzmWXYD2I\niFQYsyen8sSYXmH7+OyiKn2ZwNOv14xQdCL5RSL5uRTv7+FB/xFsGN7khD8CNwM3ANXxWl3mAo86\n534JWed6vJbU4Xj9er4DLnHOfRdS7gG82aHvAI4Bfgaucs59FFzIOTfKzAJ4kyLeA6wHBmt2ZxGR\n8C7vmEkm4ROfPcQykkHc/dnpZGWvjUyAIiFKPflxzjUtQpk3gDeKuL29eEnKPQcp5/BajR4vrJxf\ndjQwuij7FxGJZj98H+BX/hJ2AsMc4BomcvX4mjRsFBOVHZylbFKvMxEROSTLfwiQdfdMGvD7AYlP\nAHiO27lxZhJVNXmzlDFKfkREpNj++APm3rWT6bwUNvF5ltvIGXa5Eh8pk5T8iIhIsaxaGWDu4N+Z\nysADhgw74D06kTjqSqJwLj8pJ5T8iIhIkX35cQaDnx3AX0g7oMXH4Q3J/e4ft9JWiY+UYbqpioiI\nFElaGgx89haSwyQ+AWA3sTx0w1TaXhR9t62Q8kXJj4iIHNTUidkEruqYbwLDXA5IJZFXnv6Qi3vr\n/s9S9in5ERGRQs18P8DFb75CT/J/aOTesmIncdzX83XOOCMy8YkUl/r8iIhIoTa9tJFuzAjbx6c3\nb3Lm8Hr0aanv0lJ+KPkREZECjfr3HiYVMKrLAZe93oBGjSIQmMhhUKouIiJhLVuazXOzbqQSB962\nIvcO7Up8pDxSy4+IiBwgJQUCD35M/ZDZmx3eHdofv38WbS/UR4iUTzpzRUQkn5UrAnxy+w7e4t8H\nXB4IAH/rM4XuSnykHNPZKyIiefp1SeOX3dcyiKywHZzf5Qq6D0yMRGgiJUZ9fkREBIDJb+xh7e6r\niCPrgLu0O2A3ldnwxC0Rik6k5KjlR0RE+OYbGDBpBEdxYNKTg5FGTSYOf4PTztLszVL+KfkREYly\nKWuzaHb/q7Tiq7Cdm3szmT8/UJMLNJePVBBKfkREotg7U7K47/UbaMDmAxKfbCCJzYwa+y3160co\nQJEjQGm8iEiUWr8e2r3+GnXZkq+Pj5f4GNXYyfCnlfhIxaOWHxGRKLTy+z2cdvcIzuYbKpGDY3/y\nEwBWcTxTpy8hISGCQYocIWr5ERGJMhkZcObd99Gar6hMDjF4CU8AyAF+4TheGfiMEh+psJT8iIhE\nkZQU6NPtDM7hx7yWHvMf0+lGVXbx9rBXubJPfASjFDmydNlLRCSKXN+/LTupccAEhgC9eIunn/6c\nM84o9bBESpVafkREosRdN6SznSTiQmZvzr1D+2OPKfGR6KDkR0QkCkwZk8l/1/egBplh79DeiKc4\n55wIBSdSypT8iIhUcMuWZvPE5P5hZ2/eTWVu6PI24+a0iFB0IqVPfX5ERCqw2TOzGPDCX0km7YDE\nZyfxDLntXfpeqe/BEl2U/IiIVFDdO6aQSv+wNynNBs5rNJ7nlfhIFNJZLyJSAXXpmBo28QkA20jk\n1j7v8/zrmshHopNafkREKpjvv4ct9A3b4vMzJzDrsRfofY7+/Uv0UsuPiEgFsnYt/OPullQNM5w9\nALxxx79oocRHopz+AkREKoipkwOkjtlKBolhE59kxvJO58oRik6k7FDyIyJSAfywLIv+Y4bRkqVU\nClqem/gkMJUZc5IjFJ1I2aLkR0SknPtkxh7uffE6ktkRti/DEs5V4iMSRMmPiEg59lDHt/mS/xzQ\nuRm8Fp89VGbVKw/RMAKxiZRV6vAsIlJOrVoZKDTx2cgxDL/9bRoeXzUC0YmUXWr5EREph6a/nsYz\nU64pMPH5irNJmTqCi5L1HVcklJIfEZFy6Mkp/YhjX9i7s79LZzJH30x9JT4iYekvQ0SknJn6SirV\n2X1Ai48D2vAsgal3UL+JhrSLFKRIyY+ZtTWz6gW8Vt3M2pZsWCIiEs68T7J56Z3eByQ+OUAP3uLq\nZ08jWQO7RApV1MtenwGtgKVhXvuT/3qlMK+JiEgJuabjclK4M+xtK57lDvq9XZOExAgFJ1KOFPWy\nV+iXjGBV8L50iIjIEXL7gIwCE58sKlFrVCcSEtWTQaQoCmz5MbMmQLOgRWeFufQVBwwE1pd4ZCIi\nAsCHswIs+G1A2MRnH8bt3SfRq5kSH5GiKuyyV39gKPsHELzIgX93BmQDg49UgCIi0ezDGdm0fPE/\nJJMW9n5dw+79kF4Xa+CuSHEU9hfzJvA5XoIzDy/BWR5SZi+w0jmXeiSCExGJZsu+CdDyxf9wIZ/l\nfQs19ic+9fk3k5T4iBRbgX81zrl1wDoAM2sPLHPO7SytwEREotnbo9N47q1riSMLA/b5yw1IJZG2\nDd9g0hthB+GKyEEU6SuDc27+kQ5EREQ8Cz7N4oW3riYWl3epKxbYy1HMoDNL7ryZF69QHx+RQ1XU\neX4qm9lQM1thZplmlhPyyD7SgYqIRIOP38nk7qeuypf4gHepK5M4ltz+Fy5X4iNyWIp6sfhpvD4/\nHwHv4PX1ERGREvSfjs8yiY/DjuoKAJ/Sgcu7qI+PyOEq6l9RT2Coc+6xIxmMiEi0Wr0qEDbxAS/5\neYfLWXbXTVwUgdhEKpqiJj/VgcVHMhARkWj12+osTrllWIF3aF/M2cS8fRcXafZmkRJR1AvHMwHd\nv0tEpISlrN5Dp0HX0IqlYVt8fqYZs+8ZQqISH5ESU9SWnxeBcWYWAD4EDpjXxzm3uiQDExGp6KZN\nyebB12+iFjvCJj4ZwPKpr3BBsjo4i5SkoiY/uZe8HsGb9Tkc3dhURKSIVi7Ppt/rD9GAzWEvdWUQ\nz997j+cqJT4iJa6oyc9AvC8iIiJymP74A3bcOYdz+W/YFp8lnMWGiY9xVR0lPiJHQlEnOXzzCMch\nIhIVfliWReN//Ju/88kBnS4dsJE6tGMYHynxETliNGGEiEgp6drxD9K4tsDh7Ls5ivFD3+Cj8ypH\nIDqR6FGk5MfMxhykiHPO3VAC8YiIVEi//QbbuK6QxKcyQwdN5VIlPiJHXFHbVS8E2oc8egDXA139\n50ViZj3N7F0zW+/fKmOFmT1uZtVDyiWa2WtmtsXMMsxsjpmdGmZ7VczsaTNL8be3yMzOD1POzOx+\nM1tjZrvN7Fsz615AjDeZ2U9mtsePb1BRj09EJJwbB7TmqJBbVsD+2ZtH/O1dLu2pG5WKlIYiJT/O\nuSbOuaYhj5pAO2AzXiJUVHcD2cB9wKXASOAW4JOQcrOAi/Fuq9Ed775+n5lZ/ZByY4AbgIeATsAm\nYLaZnRZSbgQwBHjB3+9i4G0zuzS4kJndBLwKvA1cArwFjFQCJCKH6uWnMthBUoGJT5fTx9LhMrX4\niNnm3mYAACAASURBVJSWw+rz45xbYGbP480DdF4RV7vCObct6PkCM9sOvGlm7Zxzn5vZlUAroL1z\nbgGAmS0B1gB/B/7qLzsd6ANc75wb5y9bAPwIPIrXKoWZHY2XdD3unHve3+98MzsBeBL42C9XCS9J\nGuucGxJUrgEw3Mxec87lFKeORCS6/aXLZj7+sdsBl7sCwNPcQuWnuvC3Fup+KVKaSmI4wWrgzKIW\nDkl8cn3N/7d373FWzfsfx18fMZKkQvrJJSOOg+N2UDgIZ5RLIiekUlGIyiW3JEoKx72QJEkliZKR\nyjiHUCbncOIoIUUnSZcplWmapvn+/lhr127P2tOuZmbty/v5eOzHzKz93Xt91mq15z3f9V3f5X0u\nNPB/bgEsiQQf/3Vr8Gaabhn1uouBYrzemUi7TcBrQDMz281f3Byv52hMzHpHA38ys0P8n08F9g1o\nNwrYh8QDnogILw1ax9Q5pwcGnze5lOJ+rThWwUekyu1U+DGzXfHG/SzeyTqa4vUAz/V/Phr4OqDd\nHOBgM6vh/3wUsNA5VxTQLgtoFNVug3Puh4B25j8fWS8B645tJyJSrhk5ObySW7bHxwFFZPHvmztz\n2mkhFSeS4RK92uufAYuzgCPwekRu2NEC/FNK/YA859x//MV18U5xxYrcVqMOUOi3W1VOu7pRX1cn\n2I6A94xtJyIS14vPFDEaAoNPKdDur+O46SKN8REJS6L9rbtQdobntcAE4DXn3Ic7snIz2xOYhHfq\n6podeY9kkpubu/n7xo0b06RJkxCrCUedOnXIzs4Ou4xQaR94MnU/jHqxiKGTTo4bfJofPYMXhtYP\np7gQZerxECtT90N+fj6zZs0Ku4zNEp3huWlFr9jMquNd0dUQONM5tyTq6VV4vTuxYntmVgEHl9Ou\nIKpd0D2Rg9rhr/vXctoFatGixVY/L1iQefd6zc7OzsjtjqZ94MnE/fDvz0pp/VBX9qIwMPhc3WIi\nd/cozLj9Apl5PATJ1P1Qr169rX5HDho0KMRqKmbA83bzxwq9CZwInO+cmxvTZA5bxt9EOwpY5Jwr\njGp3qB+koh2N15s0P6rd7mYWG7ePZuuxRpGxPbHrjoz1ia1TRASAubOLObP37RzJgsDgMwC4pofm\n8RFJBgmHHzP7k5m94U86WOJ/fd3M/rQ9KzQzA17FG+Tc0jn3r4BmbwMNoicrNLNaeFeBTYpql4s3\n9qh1VLtqwOXANOfcRn/xVLy5hdrGrKcd8LVz7if/50+BFQHt2gMrgRmJbaWIZJJly6D+HUNpwn8D\n5/Jp1zKX0/PywihNRAIkOuD5ZGA6sB4vmCwF6uOFkQvN7Ezn3OcJrvM54G948+msN7PGUc8tds79\n7K8jHxhtZnfiDVbu5bd5NNLYOTfbzMYBT5lZFt4g6RvxTqW1iWq33MyeAHqZ2TrgC+BKvADWIqpd\niZn1AZ41syXA+8C5eFe0dXPOlSS4jSKSIZYtKWFJh4ncytuBwaeAWnTuFts5LSJhSnTA80N4l3+f\n65xbG1loZnvhBYSH8GZjTkRzvM+E3v4jWj/gAeecM7MLgceAZ4HqwEygqR+OonXE61Hujzeu50ug\nmXPuy5h29+AN0u6BF9y+BVo756ZEN3LODTWzUrxJEW8HFgE3OeeGJrh9IpIh5n5Vwkk97+UKPg+8\nQ3spcNUp07mLFSFUJyLxJBp+mgDto4MPgHNurZk9AoxMdIXOuUMTbLca6Ow/ymu3AS+k3L6Ndg4Y\n6D+2te5hwLBE6hSRzPTR+8W0eaQ7f4gZ4wNbgk+TOkN5fWwtFixQ+BFJJomGn9jL3Lf3eRGRtDFv\nHhz7yLPlBp8+t03mYd2vSyQpJTrgeRZwj3+aazN/np678MbniIhkhO7dm3IxU+IGn3OzR+hGpSJJ\nLNGen3uAD4GfzOwdvDun1wcuAGrgDRwWEUl7nXLy2UhOYPDZBOzJJN4dWiPglSKSLBKd5PAzM2sC\n3Ac0w5v0rwD4AOjvnPtv5ZUoIpIcXhxUyEL6bJ692UV9LQUe6Daed1sq+Igku4RvJ+yc+wrvEnUR\nkYwzbkgBo3Ov2Oq2FYYXegqozQNXD6dly1rhFSgiCUs4/IiIZKp2OV+xmJ6B9+tyQMcLxnNr+3Bq\nE5Htl3D4MbPmeDMpH4Q3704055w7qyILExFJBrdfUxA3+JQCh9KfEbeGU5uI7JiErvbyZ1l+F7gI\n2BNvXF/0o7SyChQRCctjN8zn3/+7Im7waddiEiPymoRTnIjssER7froBQ/Fu8bCpEusREUkKY0eV\n8M4PXeMGn66txtG5qwY3i6SiROf5qQWMV/ARkUzw+suFPPxKG4zg4HMgj3NF17rhFCciOy3R8DMN\n7xYXIiJpbeq7pdw75nrqsnqrD8hI8DnpoHGMzjs2pOpEpCJsz2mviWbmgPeAVbENnHMLKrIwEZGq\nNnl8Ibe9cD0HsLRM8HHARYcN4bHn1eMjkuq2595ea/Hunv5gnDbVKqQiEZEQnJ9TxGpaswfFZSYw\nXE8W7RjF7Qo+Imkh0fDzMnAa8CQwDyiurIJERKpai5zV/E5rdqXsBIZFZNGBl2j2bO3wChSRCpVo\n+DkbuMk593Il1iIiUuWmTSrit5jgA16PzwZ2oy2jOfPxOjQ6IqQCRaTCJTrgeTnwa2UWIiJS1VYs\nK6XTM7eWCT7g9foM4kYO6bY3x2p8s0haSbTnZxBwo5lNc85pQkMRSXm3dV7DJz+1pSZFgcFnIi04\nYMQFnHRgon8jikiqSDT81AGOAeaaWR5lr/Zyzrn7K7QyEZFKMn8+/OOnTnGDz1ucz1uX30gnBR+R\ntJRo+Okd9X3QmW8HKPyISNJbswa6dT2NLqwpE3wc8BYt+PnJbnQ6RsFHJF0l9L/bObfLNh66zF1E\nkt70yev4y2U5bGDPwOBTCnzd50aOUvARSWv6Hy4iGWHu16X0eKoT+0Lc+3VdeMQwTj8z0Q5xEUlV\nCj8ikhE+vnUp+7A6sMdnLTV4oOck7ni2YQiViUhVSzj8mNl1ZvYfMys0s02xj8osUkRkZ0x+s4ix\ndIh7qmvAzeM5s7nu0C6SKRIKP2Z2NTAY+BdQHRgBjAbWAD8AD1RWgSIiO2PqW0X0ff6KMh92m4MP\ncN5FWVVfmIiEJtGT27cADwH9gc7Ac865L8ysDvAhsLJyyhMR2XGj7p/P8JldA8f4FLMLD941maZ/\n1RgfkUyT6Gmvw4GP8P5QKgWyAJxzq/D+cLq5UqoTEdlBb7xeGjf4lAAdzhun4COSoRL9n78e2NU5\n58xsKZAN5PvPrQMOqIziRER2xDU5M1nA/YHBZy012JdfmXJHfpxXi0i6SzT8/BdvcsP3gI+Be8xs\nId4fUH3x7vQuIhK6F58rDgw+4IWfffmVRx5X8BHJZImGnxeAw/zv+wDvA5/4P68FLqngukREtlvu\nxBLum3hL3OBTCowYlU/9+lVfm4gkj4TCj3NuXNT3883saOBUoAYw0zm3opLqExFJyOQ3i+j5fBca\nsLTMqa5I8NmHkUxQ8BHJeDs02s859zte74+ISOjee9u7nL0mhRhe2Il8LQVu5h5OHXW2go+IAJrh\nWURSXP7MUjoOvo29KNx8usvwQs+v7MvlvMoJQ8/SqS4R2UzhR0RS1juvFHD7/RdwJN+XOdX1G3tx\nIP/jnMH70TBbH3UisoUmuRCRlDR3LgwYdQVZBE1gWI0uDGXI0E/Izg6pQBFJWvpzSERSTv5HRZx0\ncy+qU/aqro3sQltGcdaT+yj4iEgg9fyISEqZP6+EDv27cABLA+/XNYmLafzIfhx1TBjViUgqUPgR\nkZTx2ovreGLcpdQk/jw+s7p14fwTq742EUkdccOPmZXifZ4kxDlXrUIqEhGJ4/5xN5QbfJpwIw+3\n1B3aRaR85fX8PMCW8GPANcAeQC7wK1AfuAjvvl/DK7FGERHGjynmOX4NDD4zOYncW/rx8IUKPiKy\nbXHDj3Oub+R7M7sX+Alo5pwrjFq+JzAN7x5fIiKV4rkHC3ht+hVxe3wm3zmAnBxdvyEiiUn00+J6\n4NHo4AObZ3p+DLihogsTEQFolbOE16dfwa6UvaS9FGjIAM5V8BGR7ZDoJ8a+QLz+5Cxgn4opR0Rk\niyduWUQBHcrcqNQBi6nP9e0m83LeKSFVJyKpKtHw82+gn5kdEL3QzBoAfYF/VXBdIpLhxo9Yx9tz\nrg0MPiXAgI7DadNBY3xEZPsleql7D+CfwAIzy8cb8Lw/0AQoBK6qnPJEJBONGVbI86+3Cgw+pcCV\nZ43jxrYKPiKyYxIKP865/5hZI+A2vMDzJ+AXvPE+TzrnVlZeiSKSSW64cjXfrGxNNYKDT11GMuHe\nuuEUJyJpIeFJDv2A07sSaxGRDPfPqcXMWXl53OBz8dHDmfDUAcEvFhFJ0HbN8Gxm++L1/OwD5Drn\nCsysOlDsnCutjAJFJDN88VkJ7R/vwW64MsFnI0aPqyZwW6eaYZUnImkkofBjZgb8HeiOd3WXA04G\nCoBJwCdA/0qqUUTS3IycHHpDmTE+4PX43HD5W7TtVKPqCxORtJTo1V69gG54sz43ZuvPp1y8mZ5F\nRLbbW68VlRt8ruJ52nZR8BGRipPoaa/OwAPOuYfMLPYeXvOBwyq2LBHJBDM+KGLg8MvjBp9vOYx2\nEw8NoTIRSWeJ9vw0APLjPFcM7Fkx5YhIppj6TgnXDLyGmqwPDD75nMzTVz9FjZqavVlEKlaiPT8/\nA8cAHwQ8dxywsMIqEpGMUPfp9/k/lgfer2sGsGDEQFofGEJhIpL2Ev2Tajxwn5mdHrXMmdkRQE/g\ntQqvTETS1pjhRdzM4DIfQN6VXZB39xQOVPARkUqSaM9PX+A04CO8u7uDF4gOAmYCD1d4ZSKSli7J\nWcZq2sadvfma5uPodO52zcIhIrJdEp3heb2ZNcW7jUUzvEHOK/Eubx/jnCuptApFJG20ylnC6jg3\nKl1JbXq3HUmnjrqyS0Qq1/bM8LwJGOU/RES2y4U56/i9nDu097t6BG3aq8dHRCpfKJdRmFkDMxts\nZjPN7HczKzWzg2PaHOIvj31sMrNaMW13N7NHzWyJmRX673tGwHrNzHqZ2UIzW29ms82sVZwau5jZ\nN2ZWZGbzzOz6it0LIpnjyX5rWMelcU913X/lMAUfEakycT9tzGwh3mdTQpxz2dux3kbA34DP8cYR\nnVdO2wF4EylGWxvz80vA+cDteFeedQOmmVkT59xXUe0exLs56z3AF8CVwHgzu9A5NzXSyMy6AM/7\n6/4HcC7wnJnhnBu6HdspkvEe6rmCaV+1iRt8ajOGt66tHk5xIpKRyvtTazpbh59zgf3xrkL91f/+\ndGApXkBImHNuOvB/AGZ2LeWHn4XOuc/iPWlmxwFtgI7OuVf8ZR8Bc/BmpL7EX7Yf3pVpA51zT0a2\n0cwOxxuwPdVvVw0vJI10zt0X1a4B0N/MXvRPAYrINtx53Wo+Wxg/+NRlJG/l1QunOBHJWHFPeznn\nOjrnOjnnOgGfAuuAw5xz5zjn2jjnzsHrwVnnPx+Wi/EmWnw9ssAPJ68BzcxsN39xc2A3YEzM60cD\nfzKzQ/yfTwX2DWg3Cu+Grn+p0OpF0tTIoUXkL2wdGHw2AXsykQl5ukO7iFS9RMf83AHc75xbHL3Q\nOfc/oB9wV0UXFuUhM9toZqvNbJKZHRPz/FF4vUNFMcvn4N2EtVFUuw3OuR8C2pn/PMDR/tevt9FO\nROJ466UC7v370exKcI/PJX95k8l5ukO7iIQj0RGGBwKx4SJiA97tLyraBrxxN+8By4Ejgd7ADDM7\n2Tn3nd+uLrAq4PUFUc9Hvq5OsB0B7xnbTkQCzMjJ4SmCb1TqgGbHjqXX/bXKvlBEpIok2vMzF7jD\nzLYalWhme+D1Cs2t6MKcc0udczc6595yzs1wzg0HzvSf7l3R6xORnTd+THG5d2h/nG70enzfqi9M\nRCRKoj0/dwKTgUVm9i5bBjxfAOyNd6VVpXPOLTazT4BTohavAg4OaB7poSmIalc7wXYAdfC2M167\nMnJzt1yU1rhxY5o0aRKvadqqU6cO2dnbc+Ff+snUfTDowTU8/fJJcYPPd2Rz/OTuZGdn1iXtmXo8\nxNJ+8GTqfsjPz2fWrFlhl7FZojM8/8PMTgDuBc7Au1LrF7xTUg865+ZVXonbNAe4xMyqx4z7ORpv\nIPT8qHa7m1m2c25BTDvHlt6ryNieo9k6/ETG+sTt5WrRosVWPy9YsCBOy/SVnZ2dkdsdLRP3wZQ3\nC/n7iJZlxviAF3wm0pyv7urOmVmLyLBdk5HHQxDtB0+m7od69ept9Tty0KBBIVazHZMcOue+cc61\ndc4d5pyr4X9tV5XBx58I8S9AftTiXLyBza2j2lUDLgemOec2+ounAiVA25i3bQd87ZyL3LPsU2BF\nQLv2eLf0mLHzWyKSPnp2WcNDz5cNPpHBzQOBwpE9OfOvWaHUJyISK7T+ZzO7zP/2JLzPzAvMbDmw\n3Dn3kZk9hvfZmY93qulI4G68ADMw8j7OudlmNg54ysyy8CY5vBFoiDf/T6TdcjN7AuhlZuvYMslh\nU6BFVLsSM+sDPGtmS4D38eY46gh0033MRLZYsriUj35sG9jj44DWvMqht+zDqbqiXUSSSMLhx8zO\nwgsTBwOx07E659y527nu8WyZRNEBz/rfTwfOwTv9dANwLVATr9flH8ADzrnvY96rI95szP3xxvV8\nCTRzzn0Z0+4evNmhewD1gW+B1s65KTEbM9TMSvEmRbwdWATcpNmdRbaY+3UpG26dxF4UBQafSVzA\nif33IwOHvolIkkso/Pj3tRqC1wPzHd5l6Fs12d4VO+fKPeXmnBsBjEjwvTbghZTbt9HO4fUaDSyv\nnd92GDAskfWLZJr8fJjTZyGjeS4w+OTzZz647nFaNlkc9HIRkVAl2vPTE3gVuMY5V1yJ9YhIkrsj\n523+xWCM4FNdG4Gvhwzk1vOyMm5ws4ikhkQHPDcARij4iGS2JYtK+BeD2YWyc/lEBjh3u2Qs2Y0S\nvpZCRKTKJfoJ9TmQeRMTiMhmH75XzEnXdos7c3Mp0PK08VxxkyYxFJHkluhprx7AGDP71jn3UWUW\nJCLJ6YRHh/AHfogbfPZmLJP6Bc0jKiKSXBINP7lALeADMyuk7H2vnHPukLIvE5F08GSfAt7mnTJd\nxZHg0/O6XCa1jr0IVEQkOSUafv7BlsvSRSSD9OhcyFc/XRG3x6fdWaPorOAjIikk0dtbdKzkOkQk\nCc3IuYav+F/g4Oa11KDrpePofKOCj4iklsy6w6CIJOyFJ9bxapzgswno1Wkc11yl4CMiqSfRSQ6v\n3lYb59wrO1+OiCSDFwcVMmpKq7iXs1/SZBy3KviISIpKtOfn5TjLo8cBKfyIpIGxw9YxMrcVu+EC\ng8+xh0xiUP8aIVUnIrLzEg0/hwYs2we4CLgK787oIpLiJk8q4YnX28YNPgM4iEEvKviISGpLdMDz\nTwGLfwK+MDMDbsMLQSKSomZ8XMqpz7xATQrLBJ8SoN35E7nutpohVSciUnEqYg76j4ELK+B9RCQk\nP84v4ZgHHucyJm71oRAZ3HwVryj4iEjaqIirvZoA6yrgfUQkBG+OKuSBV9qwT0CPz3p2oyPD+G+j\n/cMqT0SkwiV6tdd9AYuzgGPwen2eqciiRKRqfPheMQ+/ciV7sT5wEsNarGHosE+4vmEIxYmIVJJE\ne376BizbgDfuZwDwUEUVJCJVY/p0OP7RodSME3xKMDpd+wkNG4ZQnIhIJUp0wHNFjA0SkSTx1N1L\nmfB5e6pRduBf5MqufRjNW1dWfW0iIpUtbqgxswIzO9H//iUzC7rcXURS0ITP27Mr8YPPfgzjrbx6\nVV+YiEgVKK9HZ09gd//7jsB+lV6NiFS65x4rpBqUOdVVCszkZLp3nsL4vIZVX5iISBUp77TXT0AX\nM4sEoBPMLO589s65jyq0MhGpcCOHlzB0WofAMT4bqcbwdg/Q9grd8k9E0lt5n3IPA0OBDnifjc/F\naWf+89UqtjQRqUi9cibwKUPi3q/r4pNf484OCj4ikv7iftI5514ysynAEcAHQA/gm6oqTEQqzoN9\nS8oNPg0ZwMsDa4dTnIhIFSv3zzzn3C/AL2Y2EpjsnFtYNWWJSEUZNWQdk2a0DQw+K6nN9c1G8vLt\nul+XiGSORC9171TZhYhIxWuX8xWL6Rm3x+e2K8dy47U61SUimUXz94ikqccHFJUbfE6lKx0UfEQk\nAyn8iKShyROLeeXD9oHBZy01+OvpU3gor1VI1YmIhEt/9omkmcljVvPwy63ZjeAenx6txnBvV/3X\nF5HMpU9AkTRyZc53LOWmuKe6DuRxRnetGU5xIiJJQqe9RNLE8KElgcEHvPDTomkuo/OODaEyEZHk\novAjkgbeeqOEHm/cEzf4lAI9e8edoF1EJKPotJdIinvq9sW89WWncoPPPoxkQtWXJiKSlBR+RFLY\n7TcW8u/v4wefXM7m24fuZsJJ6uQVEYnQJ6JIiprxYREzvm8dN/isJ4vZ997JiQo+IiJb0aeiSAqa\n/e8SrhlwDXtQHBh8SoAbLh7L6Wepc1dEJJbCj0iKmTm9iM69zqcBywODzxLq0vaCSXToXiuM8kRE\nkp7+LBRJIW+OK+GeFztSi+BTXRO5iF+e7s71R+nvGhGRePQJKZJCsl+cQANWBgafYqrx+R03caSC\nj4hIudTzI5Iirm25ggUMi3s5e4uTXuOu8/RfWkRkW/QnokgKuDrnC34obBM3+AwA7nqodtUXJiKS\nghR+RJLc+JGFLOKuuPfrurb5GE7PywunOBGRFKTwI5LEJrxaxBOjW8UNPmc3HEH7nvXCKU5EJEVp\ngIBIkrox5x/M5eG4weewGmMZPmzfcIoTEUlhCj8iSejinALWBAQf8MLPwTzCK5MUfEREdoROe4kk\nmSf7FLCGK+IGn1n8mVfyTgyhMhGR9KDwI5JEpo1fw6T8+MHnWw7jja59q74wEZE0otNeIkliYM5I\n3mN0YPApBZ7nEtzTXblAkxiKiOwUfYqKJIHvvi4uN/i8SXPWP9JVszeLiFQAfZKKhOyjf5bw11u7\nxj3V9R0N+ebemzn+RP13FRGpCDrtJRKi9yas494hl1KT4OAzj2ye6zKYS87Sf1URkYqiT1SRkHz4\nfgm9h1wVN/gsYHc+Gvwslxyp/6YiIhVJ/egiIVi2DPZ+JJe9WB/3fl1vPzSBRgo+IiIVTuFHpIoN\nH1zIiW07cRvPxQ0+Fx0+lGNPygqhOhGR9Kc/K0Wq0KIfS/n729fTgKVxg89fuI4Hn8sOoToRkcyg\nnh+RKvRhl7lxg08J0OnyyTyY1zqEykREMod6fkSqyCU5y/iNW+P2+Fx19jhu6KJTXSIilU09PyJV\n4Lqc6aymbdzgsz9DuOGeuiFUJiKSeRR+RCrZlTnf8R0PlpnEMBJ8mp8xmXF5jcIpTkQkA4USfsys\ngZkNNrOZZva7mZWa2cEB7Wqb2YtmttzM1plZnpkdE9BudzN71MyWmFmh/75nBLQzM+tlZgvNbL2Z\nzTazVnFq7GJm35hZkZnNM7PrK2brJZPc03kZS7kpbvA5gme5+z6d6hIRqUph9fw0Av4GFAAf4f0u\nCPIOcB5wE9AK2A34wMwOiGn3EnAtcC9wIfALMM3Mjo1p9yBwHzAIaA58Cow3s+bRjcysC/A8MB5o\nBrwOPKcAJNuj501FzPypbdzgU5sxvJB3RDjFiYhksFAGPDvnpgP/B2Bm1+IFnK2YWUvgVOBs59xH\n/rJ8YCFwJ3CLv+w4oA3Q0Tn3ir/sI2AO8ABwib9sP6AnMNA596S/mulmdjjwMDDVb1cNLySNdM7d\nF9WuAdDfzF50zm2qwN0haejKnO+20eNzL2/l1QunOBGRDJfMY35aAEsiwQfAObcGyAVaRrW7GCjG\n652JtNsEvAY0M7Pd/MXN8XqOxsSsZzTwJzM7xP/5VGDfgHajgH2Av+zENkkGGDmsuNzgU59neSHv\nrHCKExGRpA4/RwNfByyfAxxsZjX8n48CFjrnigLaZeGdYou02+Cc+yGgnfnPR9ZLwLpj24mUcUeH\nZfR5+I9xg89ph4zhNZ3qEhEJVTLP81MX7xRXrAL/ax2g0G+3qpx2daO+rk6wHQHvGdtOZCszcnL4\nF8QNPn8+IpfHn60eSm0iIrJFMocfkZQx+MkS3qRs8AEv/NTnWV5T8BERSQrJHH5W4fXuxIrtmVkF\nlLlMPqpdQVS72gm2w1/3r+W0KyM3N3fz940bN6ZJkybxmqatOnXqkJ2dWfelmvt1Ke3e7RE3+HzH\nYXz2Q/OAV6a3TDwWgmg/eLQfPJm6H/Lz85k1a1bYZWyWzOFnDpATsPwoYJFzrjCq3SVmVj1m3M/R\neAOh50e1293Msp1zC2LaOWBuVDvzl0eHn8hYn7nE0aJFi61+XrBgQZyW6Ss7OzujtvvBzj/y/k9d\n4gaflezJmFufoGkG7ZOITDsW4tF+8Gg/eDJ1P9SrV2+r35GDBg0KsZrkHvD8NtAgerJCM6uFdxXY\npKh2uXgDm1tHtasGXA5Mc85t9BdPxbt3ZNuY9bQDvnbO/eT//CmwIqBde2AlMGMntknSyLx5lBt8\nCoHJw96g6QU1yr5YRERCE1rPj5ld5n97Et7vjgvMbDmw3L+8/W0gHxhtZnfiDVbu5b/m0cj7OOdm\nm9k44Ckzy8IbJH0j0BBv/p9Iu+Vm9gTQy8zWAV8AVwJN8QJVpF2JmfUBnjWzJcD7wLlAR6Cbc66k\nIveDpK7u3ZvSleDgUwr0aD2Rqxomc+eqiEhmCvOTeTxbZnZ2wLP+99OBc5xzzswuBB7zn6sOzASa\nOud+jnmvjsAAoD/euJ4vgWbOuS9j2t0DrAV6APWBb4HWzrkp0Y2cc0PNrBRvUsTbgUXATc65oTu1\nxZI2+uaMZWPAWdlI8Lmi6Ti6XlezyusSEZFtCy38OOe2ecrNObca6Ow/ymu3AS+k3L6Ndg4YdwK0\n1AAAHopJREFU6D+2te5hwLBttZPM0y7nKxbz0ubTXS7qaylQhxlM7F1Y3luIiEiIknnMj0jSaZMz\nj8X03Gqcj+GFnsXUp9MlE/nyh/rhFSgiItukAQkiCXqo5wp+oXvgJIYO6N1uFB06hFObiIgkTuFH\nJAGdL1rK/A3t487e3JRr6KvgIyKSEhR+RLahVc5qCogffA7kcUbnHRtOcSIist0UfkTK8fbLBRRw\nRdzg838MZmzekeEUJyIiO0QDnkXiePedUv4+Jn7waXbsWAUfEZEUpJ4fkQCv5vTjBT6JG3wa7T6K\nFx/fN5ziRERkpyj8iMR4e0JJYPABL/zUZTwT3gm6R66IiKQCnfYSifLPqcV0G3J13ODjgAl5Cj4i\nIqlMPT8ivsED1jDuw8vIIv79uhoygpervDIREalICj8iwC3XrGH2/y6L2+OzHni89yRebqo7tIuI\npDqFH8l4Ax8sZcb/ys7jA1t6fB67J5fTm1av+uJERKTCacyPZLRlS0roOv0ZalIYN/gcz/2cfraC\nj4hIulDPj2SsD98vocUjN3My38UNPrUZw1t59UKoTkREKovCj2Sk63M+5FsGBJ7qKgWK2JUne4/n\nraY1Q6hOREQqk057Scbp0Wpx3ODjgCXUZ0DPSTRR8BERSUsKP5JR8iYX89XaTuUPbr5hOOc0z6r6\n4kREpEoo/EjGWLKohI5PdS83+BxOH1pcpuAjIpLOFH4kI0yaBIuvzeNwFgQGn/nsx83XTWFY3plh\nlCciIlVIA54lIwx95hTWcl6ZtO9NYLgrQ258mUsv1X8HEZFMoJ4fSXszcnIoZG92w5W5Q/tGjJaN\nx3PhpTrVJSKSKfSnrqS1zlcVMh/KjPNxwFpqcHSdsbz8oG5ZISKSSdTzI2lr4tgi5ixvHTf4XN18\nPC+/ruAjIpJp1PMjaWlo/2WM/ahtYPApBe7rPJbuV+hUl4hIJlL4kbTT7eo1fP1L2eADXvgZALS4\nQj0+IiKZSqe9JK3cen0hX/1yWdzg8y3ZnJ6XF0JlIiKSLBR+JG2MH7KC/yxoya7En8Rwwr1PVn1h\nIiKSVBR+JC28/04Rz05oE/dGpaXA/gyhyVk63SUikuk05kdS3vvvFNHr6SviBp82jODMwQcy7sgQ\nihMRkaSjnh9JaYsWwcVP92UvCgNPdc3iJI6+7wCOVPARERGfwo+krGmTS1h67VQa83ncMT7v9+nH\n6WfoMBcRkS102ktS0pLFpZz31DWcwy9lEnwpXvj5A70Zeqbm8hERka3pT2JJOXnTSlnaaRLnBgQf\nByyhHrd0ncbQvKYhVCciIslOPT+Scr57bDXjeS7wVNdKatPzwmFc10q5XkREgin8SEoZmTOQN/gg\nsMenFLiq6VjuukWHtYiIxKffEpIy+t6+jg/5IO7g5uZcxd29dUiLiEj59JtCUsIzTxQz7cuyc/k4\nYBNw+WljuLtfvXCKExGRlKLwI0nvopw1rOZystgUeIf2vndO4aYcHcoiIpIYjQqVpNal9WrWcVnc\n4NOJszlbwUdERLaDfmtI0mqb8zU/c2vgqa71ZNHsuHH0faxmSNWJiEiqUviRpPTBlKK4waeYarQ9\nfyJ9b9MEhiIisv0UfiTp3JDzAfMYGBh8SoHavM47Cj4iIrKDNOZHkkqnnPxyg8/htcfzTl6tcIoT\nEZG0oJ4fSRpjB69gIX3KBB/wwk8DnmTM+NohVCYiIulE4UeSwi1XLWP28rZxg09rXmVM3n4hVCYi\nIulGp70kdPf0WFdu8BnCJVw8fJ8QKhMRkXSk8COhGja0lMnfdIgbfD4nmxX3deXAg3WoiohIxdBv\nFAnNjLxCHn+jPfuwJjD4rGN33r1nMKefocNUREQqjn6rSChmflJK17+35ACWBQafAvaiySGvc/rZ\nuqRdREQqlgY8Syj+2+8X9iH4VNfP1GPakJEMaqTDU0REKp56fqTKPdJ7DWPpWObgi8zlM7TncBoq\n+IiISCXRbxipUh0uXMFPxW3iBp8rzxrLDc2rh1CZiIhkCoUfqTKXN1/Dsk1t4s7ePAC44d59Q6lN\nREQyh057SZW4KmcuyzZdFhh8NgFNj5vI6Xl54RQnIiIZReFHKt177xSzhJvj9vhccMqb9H2sZjjF\niYhIxtFpL6lUbXO+5mdujRt8Dskay8gBulGpiIhUHYUfqTQtclaztpzgU6/am7w+WcFHRESqVlKf\n9jKzs8ysNOBRENOutpm9aGbLzWydmeWZ2TEB77e7mT1qZkvMrNDMZprZGQHtzMx6mdlCM1tvZrPN\nrFVlbmu6GT+2hFVcHjf4HMDTvD5VwUdERKpeUocfnwO6AU2iHn+NafMOcB5wE9AK2A34wMwOiGn3\nEnAtcC9wIfALMM3Mjo1p9yBwHzAIaA58Cow3s+YVtE1p7eVn1vH3ly4jCxc4iWF1fufVvKPCKE1E\nRCRlTnvNc859FvSEmbUETgXOds595C/LBxYCdwK3+MuOA9oAHZ1zr/jLPgLmAA8Al/jL9gN6AgOd\nc0/6q5luZocDDwNTK2UL08QfD1vHWi5lV4Jnb57CGfS+b2YIlYmIiHhSoecn9ndorBbAkkjwAXDO\nrQFygZZR7S4GioHXo9ptAl4DmpnZbv7i5ng9R2Ni1jMa+JOZHbIjG5EJHupfwjJOjRt8SoG5/e/m\njDInGkVERKpOKoQfgDFmVmJmK8xsjJkdFPXc0cDXAa+ZAxxsZjX8n48CFjrnigLaZQGNotptcM79\nENDO/OclxrTcYp75qDs1KYwbfNo3HcWJTXSjUhERCVeyn/b6DXgMmA6sAU4AegMzzewE59wKoC7e\nKa5YkUHRdYBCv92qctrVjfq6OoF24itYVkK3QRdyEME9Poupx03nDOPmXjUCXi0iIlK1kjr8OOdm\nA7OjFn1sZh8DnwHdgftDKUw2mzi+hLYv9A4MPpuAx7iF4vvP5+a/pEono4iIpLukDj9BnHP/MbPv\ngFP8Ravwendi1Y16PvL14HLaFUS1q51AuzJyc3M3f9+4cWOaNGkSr2la+G5eKU1fuIXGfBHY4zOB\nlhw86iaanJZZwadOnTpkZ2eHXUbotB882g8e7QdPpu6H/Px8Zs2aFXYZm6Vc+AkwB8gJWH4UsMg5\nVxjV7hIzqx4z7udovIHQ86Pa7W5m2c65BTHtHDA3XiEtWrTY6ucFCxbEaZn63nhxNY+Pa80exB/c\nPO++Gzit/o+k8W4IlJ2dndb/9onSfvBoP3i0HzyZuh/q1au31e/IQYMGhVhN6gx43szMTgL+AOT7\ni94GGkRPVmhmtfCuApsU9dJcvIHNraPaVQMuB6Y55zb6i6cCJUDbmFW3A752zv1UcVuTmr76ooSn\nxl1ZbvAZAJx2RjpkaxERSTdJ/dvJzEYBPwD/wRvwfCJwN/A/YLDf7G28IDTazO7EG6zcy3/u0ch7\nOedmm9k44Ckzy8IbJH0j0BBv/p9Iu+Vm9gTQy8zWAV8AVwJN8QJVRhs1Cv74yntksSkw+GzEuPHy\nCbTpohuViohIckrq8IN3CupK4GagBrAUeAPo65wrAHDOOTO7EO+qsGeB6sBMoKlz7ueY9+uI1ynR\nH29cz5dAM+fclzHt7gHWAj2A+sC3QGvn3JSK3sBUM/aVU1hHTtwen8H9vqLNaUtDqExERCQxSR1+\nnHMP482qvK12q4HO/qO8dhuA2/1Hee0cMNB/iK91zo8UklPmXOnmO7TzEB+3q5FxY3xERCS1pNyY\nHwnHQ3etZjldAm9UOo9GtP3bNEbmnRRSdSIiIolL6p4fSQ6P9ipg2hdXBAaf9WQxvveTdGmqHC0i\nIqlBv7GkXA/1KWTyv4ODTynQ76bxnN60ejjFiYiI7AD1/Ehcl+YsZRXt4waf/RjG+Et0ywoREUkt\n6vmRQC8+sabc4NPsxPGMz2sYSm0iIiI7Q+FHynj/nSJGTvlb3OBz4Unj6PVI0B1AREREkp9Oe8lW\nHs4ZwVRejd/j02QSvfrrVJeIiKQuhR/ZLHdiSbnBpw6jmKjgIyIiKU6nvQSAGR8Wc/1zPQKDz0aM\ndue/ycS8+iFVJyIiUnHU8yP8OK+IGwe0Yx9+C+zxuf/mt+l8kS5nFxGR9KCenwxXUAB/7N6PujHB\nB7zw05x7+KuCj4iIpBH1/GSwNjnz+IXuGGVT8Cbg7/TknAFnh1CZiIhI5VHPT4b68Uf4he7sQtmD\noBT4nsNYdctfOeWUqq9NRESkMin8ZKjbu2RjEHiq62fq0e/SQTS7UB2DIiKSfvTbLQMNe7qQFRwW\nGHxWUpvHrh9B579lhVGaiIhIpVP4yTCTJ5XwzDsdyvT6OOBbDmD0bUO5+HwFHxERSV8KPxnktmtW\n89n/riSLTRhe4Il8LQWmPDqCs4/XmVAREUlvCj8ZomXOCn6jzVaTGBpe6HHAvgznDQUfERHJAPpt\nlwEmjC4sE3zACz0F1KbtRZN4I+/gkKoTERGpWur5SXP/mFZCv5EdAoNPMdUY2G0sXVrqMBARkcyh\n33pp7I6ct/kXg+PeqPSUg17jCQUfERHJMPrNl6Y++6S43OCzN2OZ9FLtcIoTEREJkcb8pKF58+Dw\nfkMDg89KatOjwyQm5e0bUnUiIiLhUs9Pmpn9RSkz71rJWHIDe3yeuH0srZrpn11ERDKXen7SyPix\nJRx81/OMoDO74rZ6zgEn8wznKviIiEiG02/CNDFyRCktXh1Kc95jDwoBNsefTRiPcwv3T/xDeAWK\niIgkCYWfNDBvHqx5dS2nks8uuM2nu0qBEnZlAYdS1Ps8atYMs0oREZHkoPCT4kpKoFf3IyngoM0D\nnAupxu6UUkwW79KcuxpcxwtN9U8tIiICCj8prWXOClbSjtX+vboi9+mqwSa+5XDu4UE6TarLCzVC\nLlRERCSJKPykqMJCWEm7zTcpjYjcr6sXAzjhvr2poeAjIiKyFYWfFLRoEXS59i8UxwQf8Hp+NgFX\njatD3bohFCciIpLkdKl7Cupy7V+YTtMyy0vxgs8+jFLwERERiUM9Pynkn1OLOeHxF1hOK/ZiHRvZ\nlSxKAK/H51G60WhcCybWVaYVERGJR+EnRZSUwPGPv8jZTKcmv7MLjl0ooZgsStiFdzmf+sNbUFvB\nR0REpFwKPymizfm7sIKJ7MKWcT2GUUI1ZnMsC/pfx0kHK/iIiIhsi8JPCvjkE1jOuZsHNxveYK11\n1GAa5/FB5+u4rIn+KUVERBKhroIkt2YN9OvXdPM8PtE6MYzSMTdw2RUKPiIiIonSb80ktmB+KVO7\nruRzTgS8012RiQwdcPnY/dh33xALFBERSUHq+UlS778Pk7uuZQD304gFrGMPwLucvRTYj2EKPiIi\nIjtA4ScJDX22hL0eyeN++nEgiymlGo7dWEUdvuRPZNl6xuc1DLtMERGRlKTwk2TWrIHj3/qAc/mQ\n3djELmyiOoUYDodjPo14441Pwi5TREQkZSn8JJm/XXYaTZnOPqwEYCGHUsgerKAuH3AWI87pTK1a\nIRcpIiKSwhR+ksTMT0oZnPMbz9OduhSwB+upxRp2o5Qh3EgjvmeP3G707JUVdqkiIiIpTVd7JYHi\nYvi831pOZRYH8jMr2ZdSqgGwmAbcx71MnDid6tVDLlRERCQNKPyErLgYrrzyVDowjCL24Df2phZr\n+IlD+Ipj+ZTGTJmWzy7qoxMREakQCj8hKimBiy88nldpz5/5gmKyeJ3WZFHMSuryKY35eO+96K7g\nIyIiUmH0azUkxcXQseMpvMp1nM6nVMNRj+W0IJdXaM8NDKHl8L15/Q39E4mIiFQk9fyEoLQUbr75\nRH79tSaNmM9GvEHMa/Eu43qbi5k48UNq1gyzShERkfSk8BOCi5rVYDZNqM+vVKOEddRkI9XZjWLm\n04ixYxV8REREKovOqYRgNh05jIXUoIjqbKAm6yhgb2ZwKqObddZtK0RERCqRen5CUJ9fcX7udFRj\nE7vyZ75g4sQP6aEeHxERkUqlnp8QLGV/jFIAjFKWsr/G+IiIiFQRhZ8QnFXjMX7gUAqpzg8cylk1\nHlPwERERqSI67RWCsZNqsITnWBL5OdRqREREMot6fkRERCSjKPyIiIhIRlH4ERE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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc00b12ef28>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fc00b083c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(cc_data, 'credit card', 'loan_amnt', 'funded_amnt',\n", " [0.0, 35000.0, 0.0, 35000.0], 'loan amount', 'funded amount',\n", " 'standard')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "e3100bd5-6527-473e-9156-96a017e69fd6" }, "outputs": [ { "data": { "image/png": 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IMGxIKhiyeZt08sknnRwudjokRxzHFiKpI4Zq6ojgOLZQ1PxmSikVlGZUVEjU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\nRMXMzMzqlhMVMzMzq1v/B+qHCWpZKK0QAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbfeb82b550>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fc00aaa3f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(cc_data, 'credit card', 'home_ownership', 'funded_amnt',\n", " [-1, 6, 0.0, 35000.0], 'home ownership', 'funded amount',\n", " 'standard')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "8ba8d5d3-f566-446b-9c06-4c7db18f69ee" }, "source": [ "### Search string: \"medical\"" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "723b89c0-a0ff-4bf2-9a60-7001262ca572" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------------------------------------\n", "medical\n", "----------------------------------------\n", "Total number of samples: 7351\n", "% of all samples with target=0: 92.4092%\n", "% of all samples with target=1: 7.5908%\n", "\n" ] } ], "source": [ "medical_data = get_data('medical')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "60e2a8af-00b7-4802-9440-7cad0af70719" }, "outputs": [ { "data": { "image/png": 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PPRXttBPJVV9f32eVSVZWFkVFRdHjvXv3Mnfu3KiuJk2ahNlspqmpqV9eu93e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LnZ9mlnZ7xo/Hq9mRzhp+2T3PPX1lZf1FUh7OPtdPPpPH/ltgv5adZ13aSxMYXbbdLSksrT\n2yih5vxDPQpRKGrE0SqMjORHRJwqMy2mnNFomAUUaiwefXQvXm/5tOm7dgX5zndWcOmSQVtbkne+\nczBPY5H7HL6jI87SpTH6+6v3IxyGj33snROPIySf+9xh7r7bft7/zW92c+hQCz6fydat57j1Vrut\nS5fgU5/aTDTqIRBI8vWv78bvLz43hZqBj3+8l29/O9+/0Sjcd9/tZDI6um7y2c/2cOlScZTNUhqL\nhoYE/+N/rCaVMvB4Mnz72y/T0kJZjUUpuxsbi8+7o7HIFXUWaiy+/OW9FTUWlchkHN+2Tvi2r2aN\nxZIl8bLp4VUwrJlDRd5UKK4wTlTJwoiI5aj1B3GmhWj1Rt7Mpd5Jiduw+IOr/x7DHSbTGcRtdKNp\nWsn+pVLw/PMd9Pd7AcGqVWN88IN9uCdlDlgWDA25iUTceDwmDz54GG+JlfCsr/vdrHh9Jx2ps9wr\nIzzDB0DAG28EePXVDpYujfHRj/Zy8ODN/PKXAf76r/1kMnDyZICjR/0MD3sAwfCwh3AYli8vbiuR\ngJ/8ZDGRiBePJ8PVV0eLlu8NA7xei1hMx+OxOH48wOCg8wjAy+io3afe3kZM077D/9u/vYZo1IXf\nm+Se1LMs5yx9yS4yqQCggabxy9XvYqDNa/vkK26EgHXrwkQiLkxTMDrq4pvf7OaXv5y8uG/f3sue\nPUGefbaL4WE3LS0pfvKTDr773atwuUwyGbAsQSKhYxi24NI537nnotJ4sCx46aUgTz3VRSql09CQ\nJp2Gp57qyk4mV6+OFkWEvfvu/rww5ufPT+4KccqlUvDd7y5ncNBHKBTnbW8L43bX9t1Sk5Irg1qx\nUCjqpNZcIQtBdFYo/KwmWD2w4wTNPcfIuDwY6SSRNdezfseqkmV37FjLq6+2Y5q2jkLXTTZtGmLH\njsPZMp/+9M2cOtWErtsX3xUrxvja1/YX1eX4etWRn3L1+YOcGwngJcFuNvN9PghY3HjjGMmk4OJF\nF9GoPYGQEnQ9QzCY5vx5L/bT4XzBZ+E5/OpXr2F42JctJ4TFkiUJgsF01kc/+EEnly65c8pIli+P\nE43qNDZmME2BrtuPfiwLzp71EYvZAsx75VNs5lUS+PASZw8b+b3nWrJ9HBjwcfx4I5oGzc1pjhxp\nKrBbEgymsgLK9etHkVIwMOCjv99LLKYxPOxB0wRjY7bAVQiBlBKPJ83VV8fLnu9y42HXriB/9mc3\nYJqTglldN7n11iH6+710dCRYvDie9x3I/X7s3dvK2Jgrz4dOu6XGwEc+cqam79ZC+A7OJEq8qVDM\nMxaC6KxewapxPkzGZW9hzbg8GOfL/4ifP++biE9hv5ZS5/x5X16ZwUEfum7/X9ft16VwfN0SvZBt\nP4GP5ZzBvtBNij6jUQ9iolEhIJMxcnZwFAs+C8+hMylxykkpitKcO5OE3DKplMDlstOxS2mLMg3D\nFn8mk0bWpuWcJYEv24cu+vL6OD6uI6U2MTmZtHXS7nwBpXMOx8dtYezYmBshtIlVFg1ngiWELQSt\ndL7LjYfBQe+EyDRfdOq06Qh2y0X9LJcqvtwYqPW7tRC+g/MRNbFQKGaIhSA6q1ewmukMYqRtEaSR\nTpLpLB/GvLMzjhBWzi4Nk87OeF6ZUMjefgj23WooFKcUjq9HA0uy7XuJc4blOKsPTh8CgSTOSq2U\nYBiZiW2WkyLLXMFn4TkMBJJ55YSQRWnO/f50URm3W5JO2+nYhZDoukUmAw0NaTyeTNamMyzDSzzb\nhz668vrY0GAihIWuywnfFNqdL6B0zmFDgy3ibGpKIaU1MaGzJuyzfeHxZCqe73LjIRRKoOv5olNd\nt7JtOoLdclE/y6WKLzcGav1uLYTv4HxE37Fjx5W2Ydp46KGHdmzbtu1Km6FYoFgWvPJKkF/8Ikhf\nn5nNhFmOrq4YyaROJiO4+upxNm8OVyw/Uzh2v/ZaG8PD7qp253LjjSP093tJpTSuvTbK9u29FZ9R\nB9/RwtljOjJpEu9eztoHu9H00o3dcssge/e2E4m40HXJhg1D/PEfH87enQLceecF9u1rJ5nU6eoa\n55FH9mc1Hrn98vkyBINJwm1XERvKEPDEeGVsPc9wL0JI7r33LFLaffjjP36d/fvbiMUMmptT/N7v\nvUFTUwZNS+esiEj+9m9/ytq1xefwfe/r4yc/sZN+uVwmt98+QCqlEY/rLF6c4Pd//w3uvfcMP/zh\nUjIZDZ8vzdatfei6ZNGiJO94x0UWLUoQjxuk0xrr1o1wzz3nOHiwjXRa8KZ7BW4zjpsUR1jDv38i\nQEOjyI4nny9DMmmvWHi9Jh//+An27GlHSoGmmdx771nicRfBYJIPfegs9957jlRKx+/P0NycYt26\nUYQAl8tiyZIYIyMGUgoaGtI8/vjPGBmZPN/btvXy6quTY+d97zvHwEDxeOjqitHZOc7eve2Ypl3X\nH/7hUQIBu83ly8e56qpxpIT9++26NmwIc+hQK2fO+Fm1aozly8dJp4vH2R13XOB//+/FxOMG7e0J\nvvKVn7NiRW3frc7OGAcP2m0EAmm2bj2nNBY5PPHEE+zYseOh6a5XaSwUihpxntd2dLTQ3z86b57X\nztXnzFO1q9TxwKz1NVfzUE5HUKvtuUGzTp5sACRvf7un5DibzvNZra6ZbKtUoLDp1knM1bE/V1C7\nQhSKK8x8fV47V+0eHPTidkvOnPEzPm4QjbqKVPuVVP0XLng5cqSFaNRFIJCmtTWJrtt9lBL6+30c\nOtTCq68G2bgxnA3dXY3cNoNBe+k8HM7/fyiUYGDA0TwU6wiq7aYpPCe9vf4JvYXB8LAbkDz/vIdk\nUicSyfdL4bEDA9683CC17pDYtMkOV37iRIDxcZ2GBjPbFkyml9d1WLYsVrWtajswCu0+dcrPihWx\n7OuZ0EnM1bG/0FETC4WiRupNQjZXmEoytJkkFEpw4EAro6P2tslo1GD37mDeHaWTprtUXpPDh1s4\nf96LywXnz+scPtzC3Xf3Ew57GBjwceKEvXPi7NkGolGj5m29uW0eONACCFauHM/7fzjsyd5xNzRk\nGBszaG01s/510r17PJKLF708/nj+lt/CcwLQ329fBEdGXKRSGk1NGum0i9On/Xl+KTw2lXIzNOSp\nmvul0JdHjwY4fdrPwIAH09S5dMnEMPzZVOg9Pc3oOhPbgWHx4njFtiqdq1J2O3qNauNyKuN3ro79\nhY6aWCgUNeIkzTp2zKC1VVbNwDhXmKuZIDdtCvP0012MjRkEAmlWrIgV3VFWu+NsasqQyWh4vbY4\n0+nbuXM+LEtDSsmlSy58vkzVu1VnlWHv3nb8fpP160dJpZztk2T/LyUMDPjIZGxtwerVo3R0TAb+\n2rzZTlYXj+uMjWkYhsXZs/6ijKOWBXv32hfxxsYMup4gFtNpa0sxOurG7RYTeT7MPNsLz2d/v5ej\nR1uyqw7t7cnCruX50rH/zBkfQtjxJKS0cLkkjY2TbXk8kmXL7BUF04Q1ayIV27pwwcvu3e2Mjblp\nakrR2ppvR6HduYHCKo3LqYzfuTr2FzpqYqFQ1MiePUGkFFx/fYb+fsGePcF58bx2rmaC3LMniM9n\n0tSUwc4C6uc974nmlal0x9nVFSMc9mZ3MnR1xbJ9tVNwN06kNgevV6+6I8BZZbAsnfPn7chQzc0p\nnImF220CgrNn/VlNhZSCjo4EH/5wcRK3sTEDlwvicY3hYXfR3bymQSCQztNVrF49xsmTflwuC7/f\nSzJphwvPtb3wfD72WHd2tWNszEVHh7vQlDxfOpoQv99iZMSFpll4vQKfL4PHM9mW4/dc3Uiltl56\nKZTdGjo46OOll0L8+q9P+qXUOKxlXE5l/M7Vsb/QURMLhaJG1PPa6WVw0Et39zhnz9o6hUAgU3RH\nWemOc/v2Xh5/nDwdg0NbW4pVqy5x7pwdyOrqq2NV71Ynk4vZCSxiMZ0Pf7gfsHUVd91lT3pefLGD\njo4Ey5bZu2tKjYO1a0cZHfUQjbpYtChNc3Oq5Nhx3uvuHicctu/077rLjuR57NgKxsbG2bgxXNH2\ntrYUHR0JxscNWlsztLWlSpZz6rDDoyfo6opx9qyfwUEPPl+GUCjJpk35bRX6vVJbyaSO1ysxTTtl\nu53UTfFWRE0sFIoama8ai7mK48/ly2NZxX6h6LDSHWelUOWLFycYGopz1VXl6y5k6dIYFy/ak0ev\nN8P69cPcdltx25pm6w/sgFKlx8GSJQluuGE0bwdEKT2BsyqQSgluuSWc19ft2wM1RXh1+urUvXhx\n6XGZ68uenmY0DTo67BwdpXxc6r1KbXV2xhkddePxQDpNUUwSxVsHNbFQKGrEuWszzUba2qY3K+hb\nkZl8/n05dVdaAam37nr0BFPtf719nSnNwoMPHs4mFevsjPPgg4fLVaNY4KiJhUJRI84dX3d3gN7e\nhTupmEripnLHlnofbFHg7t1B4nGdnp4An/zk9GRejcXgq1+9Jpvx86abwjQ2VrYxk4GDB1sYHPQR\nDrvJZMgm2Motv2GDreHo65tMKhaJ5Is3c1cHSrWXyeRn9nzXu/r5y7+8HoAbbgjz5S+vxdZ2LOc7\n3/kpoVDt/t+1K5jdEltpO+gHP9hXdF4TCfj852/OJvx65JH9eUnfqq0gvfvd/VlBqqNBqneLr0oW\nNv9RAbIUijqpNQnZfGUmAhKVC2b1D/9wFeGwF00Dl8vkttsGLzsTay6/8RubGR72ZUNVt7XF+cd/\n3F3RxnIJzwrLnzvnIxz2Tuz6MGhsNPH7M2UDZJVq77nnOujpacblgvFxDbfbTmQmBCWSilk899xP\na/L5yZN+nC2xheeulvNaa9K3cnb85CcdRCIepISWlhTvec+FmsaOCmR1ZVABshQKxawwEwGJyr0f\ni7myIbylFEVJry73TtZJNOYk17ITh1WzsTjZlWXBq6/aKwANDRmWLYtx/ryPpiaLsTFtIqmYQWtr\numSirXLtnT/vw+WyPxdCEI/rOSs1xcnQKpFbf+722EJbBga8DAz4GB83aGjI0N6eLPJvrUnfytmR\nSunZ41Mpveaxo4TRCwu12KRQKPKYSuKmcseWej8USuD3pzFNJiYAsijplRN0aWzM3q7pBG+qhteb\nyUk0Zosxq9tYnOxq9+4g0ahBPK5z8aKH3t4GOjvjJJMCw7AmkoqVT7RVrr3Ozjhpe/MJUkp8PpNM\nhjJJxSqvKufW73abE9tii8/d8LB7Iu+LTn+/l+Fhd5F/fb50TUnfytnhdpuYpv1YqXCbbK19UMnC\n5j9qxUKhUOQxE+K+cu+bJjz7bBfxuM6NN44UCSYv9072Ix85zRNPrCCdNnC5MnzkI6er2vjII/uL\n9AU/+lEX3d0xzp6F8XGdQCDNpz99jG99qzursbjhhtEijUU1n2zYEM5qLFatsjUWr71mT5qiUcnZ\nswGcRyG/8iu1izGdLbHhcPG5m9wqqtPaatLWliry7/vff56dO0N5PqiVzZvzg35V2yZbzUeK+Yua\nWCgUijxmIiBRufdvvz3M7beXb+tyQzJ3diZ429tGSKV03G6Tzs7yAaYqUWpLrNtdfptrKUq153bD\njh2TuyYsC1wu+8I6Nmbg91+iudlDJJLk2msrp6qvtT/ltooW+rdWTUUpO267LVxyi24txypNxcJB\nTSwUCsW0US35Vr04YdRPnbLrc8KoV9NeSAnDwx7Gx100NKSpRaP+uc/dzMmTAYQQRKMuPvvZm/nI\nR84QjdpiCOcOvFQyL2crae6Ol3q0Ibl5Njo64gwNuYlEBMFggm3bLk/MWspOyF8VsCxK+vdy29u1\nK5i3YlHrrhDFwkJNLBSKOch0XqDrFUBWy+558aL9fL5weyVQNflWvba88kqQgwdbGR93EQ57eOWV\nILffHq6a8OrVV4OcPt1IOq3hcnl49dVgxZURgL6+BjKZyWiRp083cvRoM/G4QSRi8MYbgewOl+ef\nX5JdDenpsR9b5NoCVLSv8Py2tKQmhJU6sZhOImGwfLlJJmOHjq+0ClAua+kzz3QxOupm2bIY6bRg\n9+4gLS2pvGiZhf7dtSuIYVC3WNay4Bvf6Gbv3iDptEYgkK4r8dtUxqjanjr3UBMLhWIOUu0CXQ/V\nLsKVypfK7ulyyWyujKGheF59TlhssJ/ZF+7yqNeWZ59dysWLPnRdEosZPPvsUm6/PVxVe7FnT5BE\nQkcISCR09uypLvocH88PQW3vavBx8aIHw4A33giwe7d9Rz466sYw7LDfw8NuNmwYKbKlkn2F51cI\nCyk1PB5JX58Pl0uSSMClS2727q08sSiVtfTsWT/nz/swTY0jR5rx+Uw8HhPDkHnnrdC/3/nOCjZs\nGK75/OTa8MYbARIJnXRaQ9fB76+e+K1cH6q1W295xeyi5ngKxRyk2gW6HuoVQBZuX7S3ME7+f3zc\nwOORJbdXOqmwgWxq7KnYEo/raJpdXtMk8bhtS7VdBKYp0DSJEBJNs/NX1I8kEjEwDFtk2tw8eaEU\nE9UJAT6fWXLHSyX7Cs9vMqnT0WHvqvD5TAzDymunEoU+PXfOTyql4/FYCAHptE46bW9bLTxvhf51\nzq1TVz3bRZubM+i6nJjMibp2hUxljKrtqXMPNbFQKOYg1S7Q9VDvVr5y2xed/zc0lN9euX17L+vX\nDxMIJFm/frhol0e9ttx44ygul32hdblMbrxxFLB3EaxZE6GpKcWaNcXh1bu6xtE0ia7bF8yurvFq\nbppI8y2zfy0tSa67LoqmmQSDSUKhOKFQgo0bwzQ3J3G7MzQ3J9m6ta/Ilmr2FZ7fzs44ixfHWb06\nSnf3GC0tKbxei+bmJBs3Vr4TL/Tp0qUx3G6TxsYMHk8GrzdNS0sy22bueSv076pVY5e17dOeTMXp\n7r5EQ0OKzs44d93VX/PujqmMUbU9de6hIm8qFHUyG5E356vGYrpticXggQc2Mjzspa0twaOP7sXv\nr15XYWjqL35xP7/4ReV2h4fhvvtuJ5PRMQyT73xnJy0tpUORVxNvVvNH4fndtq2XffvsOtrbE7zx\nRoDR0UW0tl6selZUptoAABY6SURBVO5L1bVnz6SIcsOGMEKUPm+5ocU7O+N84QuHee21+rULUxVu\nKo3FlUFF3lQo3kJUytxZL/Vu5ZvJrX/VcmgUXhy++MW1jI560HXB6KiHL35xLQ89ZG/TrPSc3e2G\nj3zkTLbu114LcuxY5WfyX/nKWgxDoGmgaYKvfGUtd989mfvCmVgVsnv3ZN0XL9oah/b2VNk+WZat\nAWlvT7F6dTRbZvNmW3S5e3eQ119vwet14Xa3VhVUFgowHaFqLds+9+0LsnRpnO5uezVj377gZV2g\nNc3eiXP6dAPj4y5On25ASqoKZnOPnytjVDF11MRCoVBcEWoR4J040YRlaRM5PzROnGjKflbpOXth\n3dGoi0WLUiXLOhw50jKxK0RiWToHDrQhBNncF9GoK3vRLVf34KCPSMRg3bpo2T6V67fz/oEDrUQi\nbnw+gab5qgoqywlca6HQh3v3BgkE0pclipyKHYqFhVo8UigUV4RaBHgNDWY2BoWU9muHSs/ZC+t2\nypQq62AYZl4YcJDZ3BeGMZn7olLdkYhBc3OmYp+q5VPJZDQ0bVKAWk1QWU7gWguFPsztU72iyKnY\noVhYqImFQqGYMs4z9ief7GLXriCWVf2YWgR4H/tYLz5fCsuS+HwpPvaxycdDlQSSwWCCkyf9HD3a\nxMmTfjZsyCl7/QgfsJ6m68knCe7ahWPsHXcMANZErgyLdetGSua+KLR748Yw118f4eJFN0JAKmVP\ngsr1KRRKkEgIzpzx8/rrAYaG3FjWpD9CoTiZjMy2u3KlLaiUEk6e9NPb25jn40IB5rp1oyXPRalz\nVOjDDRvCnDzZwNGjAU6ebCj7+KcU5YS2tXA540cxd1GPQhQKxZS5nLgCteSH0HVYvfpSNhiVnnMT\nXP05u8j+CTFZNrhrF809PUiPB0/Yfi+8ZQu6bm8pdcSbV10VY/XqaNncF7l2795tP0IIBlP09jYQ\nDru55ZbSuTI2b7ajiTqrG5ZlB69yyo6OuohGXXg8blyuBO9+dz+GYQf9AkEwmKKnpznr409+shch\nyIo3r702WvFRS+H7uT58+eUguTtj6qHQjsIdQZVQcSkWFmpioVAopszlxBWoRYBnJ9Maz3tdC5WO\n8w4OIj32xUt6PHgHBwE4f95PU5NJKmXhdkvOn/fz27/dW1IEWWh3bv9XrhynqSlVtm+aBu3tKdat\ni2bfu3DByze+Ye/suHTJmLBdQ4gkQ0Nefu3X+ibyiLiBfB9rGqxeHc0KRgcGak9dXyigvXjRy8qV\nk1uba/U3TE1wrOJSLCzUoxCFQjFlZiquwOXWW+m4RCiESCYBEMkkiVAo+1k0qmOaGtFoffqAqcZh\nOHy4hQMH2ohGPbz5ZgOvvx4gkdCy6c0rtVGY+nx42F1z6vpaj51pVFyKhYVasVAoFFNmptJeX269\nlY4Lb94M2CsXiZUrs6/XrRtldNRNNOpm0aIU69bVrhGo187C8ufO+bJ37F6vRSql4fVa+HyJbG6P\ncm0U3u03NtpxKmpJXf/00101HTvTqLTpCws1sVAoasRZNt65swldD05fUB7LIrh7t32hC4XsC908\ni/bjPNZwfPT0010zHrioVBwMsN+7cMHLSy+FSCZ1OjvjbNgQxu2eNDa8ZUtRfW1tCS5c8BKLuRge\nNliyxBZJluuD035/v5fXX28BoKsrxr339gG2GLEw9oRzzMDAZLAqy7LFnv39bqTUSCQE6bTG4cMu\nvF7Jbbf1O2aXfLzS1pbgqae6iMUM/P4Mv/mbJ0qWczKZOhqITZvCBIMJDhxozWpY7rgjynPPdWQD\nZuX5rcZzcDlBsSqJRKc7Y65i5lGnR6GoEWfZuKPDoL9/Ujw3VYK7d5cUE85HpluEV6m+Up+BHWNi\n9+52Bgd9eL2S0VE3Dz+8lh07Dlds63vfW0406sayBJalsX9/G21t6bJ9cNo/cqSZ8+d9BAIm4bCd\nMG716soCyoEBXzaRWzoNliXweEyGh12k02CaGvG4IJl08eKLHbzzneV9+OKLHUQiLkAQiZQvXyqx\n3XXXRckVa37ve8sZHPThckFPT3W/TeV8l0t2V1jPdCbkU8wOamKhUNTITAnMyokJ5yPT7aNK9ZX7\nzOORjI250XU7eZjHA+fP+6q2dfGiD7dbkk7b0TdjMaNiH5z2o1EXLhekUhrNzXYSsPb2VEUBZW4i\nNxvB4sVJWltNfvnLBgxDYhgCr9fiwoXKtl+44KOhwcp7XYpSie3a21N5Ys1jx5pxuez/u1zV/TaV\n812Y7M7ewVNcz3Qm5FPMDvNrvVWhuILMlMCskphwvjHdPqpUX6nPnPeamlKYpr1dNZ2Gzs54DW3F\nMU0QQmJZZJOtleuD01YgkCadBrfbyiYBK2e3835uIrfJ5G5m9j3TBMOQNdne2RknbS+sVCxfKrFd\nsZ211VXog8J+1kK5ZHeF9UxnQj7F7KBWLBSKGnGe4ZtmI21txRkrL5dyYsL5yHSL8DZtsmM+nDo1\nqQuopa3m5iQ//GEn8biLUMhOrlWN//7f9/OpT20mGvXg96f51V89w9KlCSwLnnyyWDPitNfWlszT\nWGzf3otlkadVuP/+k3nHNDcnOXGik0OHmgmF4nz4w2cYGfHS0eEm0JjAfPIAi5P9jDYvov2dN5Rs\nH2ydwh139HP8eBPj4wZdX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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc00b07a748>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fc00b095128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(medical_data, 'medical', 'loan_amnt', 'int_rate',\n", " [1e2, 1e5, 5.0, 30.0], 'loan amount', 'interest rate',\n", " 'semilogx')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "0881c1d6-b874-4d1e-b286-703c461bde1e" }, "outputs": [ { "data": { "image/png": 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CrcSdd95JTk4OVqt1SHm2t7cTiUQoLi4eMJ7FYuGuu+7CaDTyuc99jszMTLZt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QF+ve8/cr/9Bo2k6KCPDnopESRCAQ0bQ4QAgSHC1fNXcM4rR3ifFyu92Da4rijvC9FAiggG7fSz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\nAvgo8AdACVgjaXY5g6QLSaMvzgCuJL0H/wQsqijnVuBDwC3A+cCXgA9n83n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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc00b00e6d8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fc00b12e358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(medical_data, 'medical', 'annual_inc', 'int_rate',\n", " [1e3, 1e7, 5.0, 30.0], 'annual income', 'interest rate',\n", " 'semilogx')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "980f6a8e-bb81-4cd6-848b-af025c730c53" }, "outputs": [ { "data": { "image/png": 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jlIo4fyDSX+IqKzG+7qvGbieusrLTtP6ZuUdmVrJ/1InszDiH/JG1fQ6qVPf8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GfX2RiQmdVEonFLL9DiYfn/cIfkDj4/MmeKPOlbIAOKK2YwwngBAdTZPkly69aF/z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\nf34Vr7xSSy5n4vO5XHedKj3u61O9Uaqrbaa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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc00afa9c88>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fbfeb94ceb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(medical_data, 'medical', 'annual_inc', 'loan_amnt',\n", " [1e3, 1e7, 0.0, 35000.0], 'annual income', 'loan amount',\n", " 'semilogx')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "2901b99c-d5e2-4bad-9db3-16f3fd30896b" }, "outputs": [ { "data": { "image/png": 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yderUeuuqNVDyIyLSHLZsgYQE7+eEBO91MysqKuKMM85okn2lpKQwYcIE4uLi\naN++PXfeeSdLly6ts/yCBQvo06cP1157LWbGSSedxMUXX1yr9ach8+bN44YbbuCkk04iJiaG3/3u\nd+Tk5NQauzNlyhQ6depEXFxco/ZZVFSEz+eje/fu9ZaLjY3lnnvuoV27dlx44YV06NCBdevWAXD2\n2WczcOBAAAYNGsQVV1zBkiVLarY1M6ZNm0ZCQgJxcXG8/PLLjB07luzsbKKjo78zhubxxx/ngQce\noHv37sTExDB16lRefvllfD4fr7zyCmPGjGHYsGHExMQwffr0OltjCgsL6zy37t27U1BQAHgtTYHP\n1YJftyVKfkREmkOvXlBa6v1cWuq9bsVKS0u56aabOOaYY+jcuTPnnHMOe/bsqfMDMzc3lxUrVpCS\nkkJKSgrJycnMmzePnTt3NvqYeXl59O7du+Z1+/btSU1NrdXy0aNHj0M6j+TkZKKiomp1n4WSmppK\nVNS3H5mJiYns378fgI8++ojvf//7pKen07lzZx5//PGaxCJUXHl5ebWuHktISCA1NbXmdW5uLhMm\nTKipqwEDBhATE8M333zznW0TExNrbdvYc9uxYwdpaWlA2+nKOhRKfkREmsPYsZCdDSkp3vPYseGO\n6Ig89NBDfPXVV3z88cfs2bOnptWnOvkJ/kDt2bMnw4cPp7CwkMLCQoqKiiguLubRRx9t9DEzMjLI\nzc2teX3gwAF2795dK7E41A/yhIQEsrOzeeWVVw5pu0BXXXUV48ePZ/v27ezZs4ebbrrpO0lgYFzd\nu3dn27ZtNa9LS0vZvXt3zetevXrx1ltv1aqrAwcO0L17d7p3787WrVtrypaUlNTaNlBiYiLZ2dkh\nW9defPHFmvFHkUjJj4hIc4iKgvHj4fbbveeopv/3W1VVRVlZGVVVVVRWVlJeXt4kVxdVVFRQXl5e\n86iqqmLfvn0kJCSQlJREYWEh9913X61tunbtWmvs0EUXXcT69et59tlnqays5ODBg3zyySc1Y34a\n48orr+Spp57i888/p7y8nClTpjB06NAjnoPnv/7rv5gzZw4PPfQQhYWFAKxatYorr7yyUdvv37+f\n5ORkYmJiWLlyJfPmzau1PjgRuvTSS5k/fz4rVqzg4MGD36m7m266iSlTptR05+3atatmDNGll17K\nggULWL58OQcPHmTq1Kn1dk89+OCDPP300/zlL39h//79FBUVcffdd7NixQruvffeOmNs65T8iIi0\nETNmzCDnA5D0AAAgAElEQVQxMZHf//73PPfccyQmJvLAAw/UrO/YsSPLli075P2OHj2axMREEhIS\nSExMZNq0afziF7+gpKSEtLQ0zjjjDH7wgx/U2uZnP/sZL730Eqmpqfz85z+nQ4cOvPvuu7zwwgtk\nZGSQkZHBb3/7WyoqKhodx4gRI5g+fToXX3wxmZmZbN68mRdeeKFm/eF232RnZ7N48WLee+89+vbt\nS1paGjfffDOjR4+uc5vAY82aNYt77rmHTp06MWPGDCZOnFhnWYABAwbw5z//mYkTJ5KRkUFSUhLp\n6ek145R+9rOfMW7cOM4//3w6derEGWecwcqVK2u2ffTRR7nyyivJyMggNTW13q6+YcOG8c477/DK\nK6/QvXt3+vTpw6pVq1i2bBl9+/atM8a23hVmkZbtHS1m5hYuXBjuMMIuKyurUVeKtGWqA09brIeR\nI0dG3DdkOfoOHDhA586d2bBhQ60xTW2BmRHqs9H/txS2DEstPyIiIs1swYIFlJaWcuDAAe644w5O\nPPHENpf4tGRKfkRERJrZa6+9RkZGBj169GDjxo21uu/k6IsOdwAiIiKR5oknnuCJJ54IdxgRSy0/\nIiIiElGU/IiIiEhEUfIjIiIiEUVjfkREGql79+5tfv4TkabU0D3TwkXJj4hII82dO/eQt2mL8x0d\nDtWDp6nrobIS5szJYvv2RDIzS5g0aRPR+mRvkKpIRESklYqOhhtvVFJ5qDTmR0RERCKKkh8RERGJ\nKEp+REREJKIo+REREZGIouRHREREIoqSHxEREYkoSn5EREQkoij5ERERkYii5EdEREQiipIfERER\niSi6vYWIiEiY+HyQk5NGfn486ellZGcXEKVmiaNOyY+IiEiY5OSksXZtJ+LiHAUFcQAMG1YQ5qja\nPuWXIiIiYZKfH09cnAMgLs6Rnx8f5ogiQ7MnP2Z2vpm9Z2Y7zKzMzLaa2d/N7HtB5Tqb2ZNmtsvM\n9pvZQjMbFGJ/cWb2BzPLM7MSM1tuZmeFKGdmdqeZbTazUjP7zMwuriPGH5nZF/74vjSzm5quBkRE\nRDzp6WWUlxsA5eVGenpZmCOKDOFo+UkBPgFuBUYCvwUGAjlm1jOg3ALgfH+5i4EY4H0zywja32zg\nBuBuYDSwA3jHzE4MKjcDmAo8AlwA5AAvmdkFgYXM7EfAY8BLwCjgRWCWEiAREWlq2dkFDBiwl44d\nKxgwYC/Z2eryag7NPubHOfcC8ELgMjP7GPgSuBT4k5mNA7KBc51zS/1lVgCbgV8DP/cvOwm4Epjk\nnJvrX7YUWAPcD4z3L+sC3AHMdM79yX/YJWZ2LPAg8La/XDu8JOlp59zUgHKZwHQze9I5V9XEVSIi\nIhEqKkpjfMKhpYz5KfQ/H/Q/jwXyqhMfAOdcMTAfGBew3VigAq91prpcFV5yNcrMYvyLL8BrOXou\n6LjPAieYWW//62wgLUS5Z4BU4MxDPjMREREAn4/Ed9+lxz/+QdqyZd6lXhIWYbvay8yigHbAMXit\nL3l82yI0AFgdYrM1wDVmluicK/GX2+ycC+4kXQPEAv2AL/zlyp1zG0OUM//6XLzuN0IcO7Dcksaf\npYiIRLrKSvjdrzrzzuqhxFBFMlEsP2E0aUDBsGHhDi8ihbPl5yOgHFgHDAJGOOeq2/5SgKIQ21S3\nECU3slxKwPOeRpYjxD6Dy4mIiDTKU09l8ebqYcRSRRQQg48z/u8N4vPzwx1axArnPD9XA0lAFvBL\nYJGZDXPObQljTEdk/vz5NT8PGTKEoUOHhjGa8EhOTiYrKyvcYYSV6sCjevCoHjyRWg8VFfD22z2J\npwLzLzMgGh+dTziB2AipkxUrVvDRRx+FO4waYUt+nHPr/D9+bGZvA1/jXfl1C17LS3KIzYJbZoqA\nXvWUKwwo17mR5fAf+5t6yoU0ZsyYWq83bdpUX/E2KSsrKyLPO5DqwKN68KgePJFaD/fdN4gDB4wy\nYknwJ0AOOEg7VmdlQYTUSXp6eq3PyEceeSSM0bSQAc/Oub3ABrwxOuCNsRkYougAYIt/vE91uT5m\nFjwr1EC8gdAbAsrFmVlwij0Q7324NqCchTj2AP/zWkRERBpQUeZj0U+3cmrO84ypep10tlBKLD6g\nlFim3zwP3ccifFpEzZtZV+B4vk1WXgcyAycrNLMkYAzwWsCm8/EGNl8WUK4dcDnwjnOu+uqxt4FK\n4KqgQ18NrHbO5fpf5wAFIcpdA+wGlh3O+YmISGRZ+qvtdNv8f6RSyBBWMCpqGemJ+/iPk/az7J03\nOPcSDSENp2bv9jKzV4FPgc+BYuA4vHl7KoCH/cVeB1YAz5rZr/EGK9/pX/eH6n055z4zs78D/21m\nsXjzAN2CdwXZlQHldpnZw8CdZrbff/wrgOF4CVV1uUozuwd41MzygEXACGAScJtzrrLJKkJERNqs\nhPx8DrZLIC7KR3l5HL3I5ZRTCnnyyRK2bQt3dBKOMT85eC0z/4nXarMVeB94sHqws3POmdlo4I/A\no0A8sBwY7pzbHrS/ScADwHS8cT2rgFHOuVVB5aYA+4DbgW54V5ld5px7K7CQc+5xM/PhTYr4S2AL\ncKtz7vEjP3UREYkEpenpdNr8DQfbJdAx5gAH+mRx332riY2NjAHOLV04Znj+AwGtN/WU2wPc6H/U\nV64cL0n5ZQPlHDDT/2jo2E8ATzRUTkREJJSz/5DJ0l95LUCl6f04+w+Z4Q5JAoTzUncREZE2KTY+\nivP+3BPo2WBZaX4tYsCziIiISHNRy4+IiMhh8PkgJyeN/Px40tPLyM4u0NXrrYSSHxERkcPw4Ydp\nzJ7dj5KSaBITK6mshHPO0R3aWwPlqCIiIofK5+Ob/1nDFflPMLJkAUW7Y3juuT7hjkoaSS0/IiIi\nh8Dng92z13B80TYO+NqTwXaIgiUHLgx3aNJIavkRERE5BDk5aZStK4L4OJyDUpdAL5dLv377wh2a\nNJKSHxERkUbw+WDZsjQWLerGhoN96NtjNx07VtIh5gDRfVO4667V4Q5RGkndXiIiIg2oqIA77hhM\nXl4iMTGO/ORxEAf9sjYTf1xPBk3OVHNCK6LkR0REpAEzZw4iN7cDZkZFBUACS793IbHn7SQ7u0CJ\nTyujX5eIiEgD8vISiIlxAERFwcGDxnnn7WTYMM3t0xrpVyYiItKAjIxS2rXzER3twzlHRkaJ1+Ij\nrZKSHxERkQZMmbKaAQP2kppaxuDBhTz00Kdq8WnFNOZHRESkAbGxcN99upqrrVDeKiIiIhFFLT8i\nIiLoRqWRRMmPiIgIXuKzdm0n4uIcBQVxAAwbpkHNbZFyWhERESA/P564OO9y9rg4R35+fJgjkqNF\nLT8iIhKxfD5Y/q8Uohb8m975K9ndPoMdp51J+cF29O1bFu7w5ChR8iMiIhFp2Yc+Pr1/H6N5nlQK\nWRd9HP06fkP7tVW4cf+heXzaMCU/IiISkT69fx/ZfEQPtpNEMRWVseyiD99rv4nkYb3DHZ4cRRrz\nIyIiEcfng15spYwE9tIJMDqxl5iqMioz08IdnhxlavkREZGI4vPB7NlZlFJMJnms5zhiqWA3Kewf\ndDx9J2WFO0Q5ypT8iIhIRMnJSWPduiTyu50DO6EXW5jLNbSfeAyTJhdqbp8IoORHREQiSn5+PJ06\nVVJREcfX6eeyJaqK8eO3aU6fCKLkR0RE2ryKCpg5cxB5eQnExVUxYMBeAPbujea444p1ZVeEUfIj\nIiJtms8Hd9wxmNzcDsTEOKKifAB8//v5uo1FhFLyIyIibVpOThp5eYmYGZWVRnQ0lJe3Y8KEbeEO\nTcJEua6IiLRp3hifg/i8Bh8OHjQyMkrDG5SElVp+RESkTaq+S/umTR3IzDyAc1BcHENGRglTpqwO\nd3gSRkp+RESkTaq+S3taWgXFxTEMGrSXoUMLNMZHlPyIiEjbFHiX9r59D9CxY4UuZxdAyY+IiLQV\nPh9pOTnE5+dTlp5OetoYCgriiItzlJeb7tIuNZT8iIhIq1dR5mPXzfOJL/icXR06k3XOLsYOAgaM\nIz8/nr59yzSXj9RQ8iMiIq2bz0t8svPeospi2FtUxqYl3ejZLZ9hE5TwyHcp+RERkVbL54Pds9fQ\ne+dqKomls9sDBpX74ylLHxzu8KSFUvIjIiKt1rJlaXT+6ACxlkaMKweDaFfBxrQTyczODnd40kIp\n+RERkVZr5co0+lUeQ1KnfNweKPfFsTVjEF0eG4OuZ5e6KPkREZFW7YOkC4mKgq7R26jM6MIZD/ZU\n4iP1UvIjIiKt1umnF1Bc3I3liaOIja3ivPN2QpQGOUv9lPyIiEirNWyYN1tzfn58zR3aRRqi5EdE\nRFqtqCg0a7McMiU/IiLS4lVWwpw5WWzfnkhmZgmTJm0iWp9gcpg0IkxERFo2n4/VMzYwaPGLDN6y\niFX/7sycOVnhjkpaMeXNIiLSoqXl5ND9610UW3sySncA8On288IclbRmavkREZEWLT4/n7jO7aiq\nMsotni6l28jMLAl3WNKKqeVHRERaHJ8PcnLSyM+P58zd/Tj9+BX8m26U76niwDF9mDRpU7hDlFZM\nyY+IiLQoJSVw/fXZ7N0bR0JCFTvPGwtxcMo5GyhLT6drdpb6LeSIKPkREZEWw+fzEp/CwgTA2L8/\nincX9aDbtRfSZcK2cIcnbYRyZxERaRF8Ppg9O4s9e+IB8y81SkvbkZ5eFs7QpI1R8iMiIi3CsmVp\nfPRRKuD8DwBHp07lmrlZmpSSHxERaRFWrkyjqiqKTp0OYuYDfKSklPLUUzm6T6k0KY35ERGRFqND\nh0rMIDbWR0ZGKQ8++JkSH2lySn5ERKRF8O7QHkP79lX+O7TvUOIjR4WSHxERaRF0h3ZpLkp+RESk\n2QVOYlid6OgO7dJclPyIiEizy8lJY+3aTsTFOQoK4gAlPtJ8mr031cwuNbN/mNkWMysxsy/NbKaZ\ndQgo09vMfCEeVWaWFLS/ODP7g5nl+fe33MzOCnFcM7M7zWyzmZWa2WdmdnEdMf7IzL4wszJ/fDc1\nfU2IiEQen8+7pH3Rom58800CzkFcnCM/Pz7coUkECUfLzx3ANuC3/ueTgWnAcOCMoLIPAPODlu0L\nej0buBD4JbAZuA14x8yGOuc+Dyg3A/hPYArwKXAF8JKZjXbOvV1dyMx+BDzmP/Z7wAhglpnhnHv8\ncE5YRESgoszHGzd/Q/uCVfSO6cEn3UcC0LVrKX37ahJDaT7hSH4ucs7tDni91MyKgDlmNtw590HA\nus3OuZV17cjMTgKuBCY55+b6ly0F1gD3A+P9y7rgJV0znXN/8m++xMyOBR4E3vaXa4eXJD3tnJsa\nUC4TmG5mTzrnqo7k5EVEIpHPB2/c/A3H5H1OeVQC6ZU7YAfkdj2HAQP2anCzNKtm7/YKSnyqfYw3\nl3nmIe5uLFABvBiw/yrgBWCUmcX4F18AxADPBW3/LHCCmfX2v84G0kKUewZIBc48xPhERCJeZSXc\nemsaMXkFlLgEfD4oI5GMg9s477ydNVd5iTSXlvJ2G443l/kXQct/Z2YHzWyPmb1mZoOC1g/Aax0K\nbi9dA8QC/QLKlTvnNoYoZ/71AAP9z6sbKCciIo3w7JxK/vfChdz47iTOcB8SR4k3zsdXyoG0dLX4\nSFiE/Wovf5fSNGChc+5T/+JyvHE37wK7gOOBu4BlZnaac269v1wKUBRit4UB66uf9zSyHCH2GVxO\nREQaUFYGxz73PiP4gDLiiacUgOV2FvkZxzL6sa5q8ZGwCGvyY2btgdfwuq4mVy93zu0EbgkouszM\n3sFrgbkLuK4542ys+fO/HZs9ZMgQhg4dGsZowiM5OZmsrKxwhxFWqgOP6sETyfVw8cVduYtZlOFd\nyVVGAgZ8MfIGHn00Mru6IvX9sGLFCj766KNwh1EjbMmPmcUDC4BjgLOdc3n1lXfObTOzfwGnBywu\nAnqFKF7dQlMYUK5zI8sBJAPf1FMupDFjxtR6vWnTpvqKt0lZWVkRed6BVAce1YMnkuth69burKc/\nPXnf3/JTxnqO5Re/WMnXX4c7uvCI1PdDenp6rc/IRx55JIzRhCn5MbNo4BVgMHCec27tYe5qDTDe\nzOKDxv0MxGtN2hBQLs7Mspxzm4LKOWBtQDnzLw9MfqrH+hxunCIiESFw5uaEhINM5V4A+rOe9RzL\nV1edy5CwD7iQSNfsb0EzM2Ae3iDn0c65jxu5XS+8q61eDVg8H2+80GV4V2RVX65+OfCOc+6gv9zb\nQCVwFTA9YPurgdXOuVz/6xygwF9ucUC5a4DdwLJGnaSISATy+eBvf8ti5co02rWDjIxSnIOHy+6l\nZ08fM2bkMERzGUoLEI78exZwKd58OqVmNiRg3Tbn3HYz+yPgA1bgdTUdjzcpYiUws7qwc+4zM/s7\n8N9mFos3yeEteF1pVwaU22VmDwN3mtl+vp3kcDgwJqBcpZndAzxqZnnAIrxJDicBtznnKpuwHkRE\n2gyfD2bPzmLRou4cPBhFXJyP8nLjxBP3cscdX/q7e8IdpYgnHMnPBXhdTXf5H4Gm4U1OuAa4GbgB\n6IDX6vIecL9z7qugbSbhzcY8HW9czypglHNuVVC5KXizQ98OdAPWAZc5594KLOSce9zMfHiTIv4S\n2ALcqtmdRURCq6iA//zPwWzc2BGfrx3t2vkwg3btInBEs7QKzZ78OOf6NKLMU8BTjdxfOV6S8ssG\nyjm8VqOZ9ZXzl30CeKIxxxcRiXS/e2AAgzYsZlTVNr6mF/N9Y6loZ3TvXs7pp2seH2l5NOxMREQO\nS2Wl19WVtjyHU/nIm7WZ7UQBH6eOZOLELbpTu7RIapMUEZHD8tRTWbz5ZgY92EoZiYB324rjEjZx\nww0bOeusyJzLR1o+vS1FROSQ+XywbFk6paUxbKNXzezNHdod4ISL2qnFR1o0dXuJiMghy8lJo6LC\nMHO8zlgAsqI302FId7remKWv1tKi6e0pIiKHLD8/nuOP30dS0kHaxTjejB1DzmlXMejufqivS1o6\ntfyIiEijvDh7P3c//xNSKaI/yZyZ/C4nngh790Zz3HHFTJ68SXmPtApKfkREpH6VlWTNmcOf/v4P\n4qnAEUUG3/CvovP5/cD3SU8vIztbg5ul9VDyIyIi9cqaM4eUzz4jjgoMAB+OKFIpYsKEbWGOTuTQ\nKU8XEZE6+XxQumoX+cVJHPR/X/YSIMduksMZmshhU/IjIiIhVd+va8XuQRzcV8mqdidSQTsqiSKP\nrsy48q/hDlHksKjbS0REQsrJSWPduiTWdb2bqryZ9I9bzwe9BhM98xKIjubycAcocpiU/IiISEj5\n+fF06lRJQUEcj/aYSlRUFePHbWNYtCYwlNZNyY+IiISUnl7Grl3ezM3Vl7NnZyvxkdZPyY+IiADe\njUrnzMli+/ZEMjNLuPbaTQB06VKuy9mlTVHyIyIigJf4fPZZCnFxjl274gG48cZNYY5KpOkphxcR\nEQC2b08kLs4BEBfn2L49McwRiRwdavkREYlggV1dBQWxVFUZCQmO8nIjM7Mk3OGJHBVKfkREIthT\nT2Xxr3+l45wBjqSkCpKSKsjMLGHSJHV5Sduk5EdEJEL5fLBsWTp798YSHQ0xMVWYGffeuzrcoYkc\nVRrzIyISgcpKfPz9ynwmbH+SEQcWUFHmKCuLIiGhKtyhiRx1avkREYlAb93yDccVfU4piWSSh5VD\nTqdRXHSRblQqbZ+SHxGRCFI9wLnbjv+j1CUAjnISOMZyiT/3G848U5MYStun5EdEJEJUVMCPf3w6\nu3Yl8ANfb7qxg3LiSbAStiQfx+TJmzSJoUQEJT8iIhGgssLHK9cUMKFwNlvpxRs2Ghz0jsolv/ux\nTJiVpsRHIoaSHxGRCLB65iaOL9rIAf8YHxy8GTuGCRO2ahZniTjK80VE2rDKSnjyySwKPj1AmcVj\nOMpIoCdb6NKlVHP5SERSy4+ISBtVUebjlesK6Lbn/+js8olylZRHJRLvStmafBz/8z8ridangEQg\nve1FRNognw/euPkb+hd+ThkJRFGFz6I5ENeZhMHtuWxKGtGx4Y5SJDyU/IiItEE5OWm0L1hFuSWA\nM0pJpMiS2Tl2HOdpjI9EOI35ERFpg/Lz49ndIYNEK8HMEU8p+zp31RgfEZT8iIi0SenpZRSfczpf\nJP8H+2M7sSXzRC55Ok1jfERQt5eISJvgq/Sxe84aorcXUJmZxpBrBwKQ3+0U2qd/j/OyCzSPj4if\nkh8RkTZg95w1JH72Jb64eGJ3FVAEDLtR2Y5IKEp+RERasU3rK9l16/tcyFvsJ4nt/U4hPjGe6O26\nR5dIXfS1QESkFdt16/uM4ANiqKIvG8nc8G+iysuozEwLd2giLZZafkREWiGfz7ucfQBfUUY8ZcQB\n0IFitp98PKmTBoY5QpGWSy0/IiKt0LJlabz7bnfW0594ygDjAB14iwvpcuMJREXr37tIXdTyIyLS\nyvgqfRQ/s4rhuxaxocNAbL/jWL5iPcfS5dFzwx2eSIvXqK8GZna2mXWoY10HMzu7acMSEZFQfD74\nfMYmeuetokPFHk6u+jcbU05m9gWPMGThSLL66zutSEMa2y76PjCgjnXH+deLiMhRVFAAo0YNJ3dZ\nGXvKO1BVBaW+RHq6rZx+uq7uEmmsxiY/Vs+6OKCqCWIREZF6XHXVOUAUW+hNPGVUVkXRIfoAHQYk\nMWyYkh+RxqqzfdTMjgGyAhadGqLrKwGYDGxp8shERKQWny8KMOYzFoBe5NLh5AxOvDtLszeLHIL6\nOoevA+4FnP/xZ2q3ADn/60rg1qMVoIhIJKushDlzsti+PZHqf8eOKF5nHODjnakfKPEROUT1JT9z\ngA/wEpzFeAnO2qAy5cB651zh0QhORCSi+XysnrGJQes/oWtCD3b0+QEbNycBRlSUj+eeW6LER+Qw\n1Jn8OOdygVwAMzsX+NQ5t6+5AhMRiWSVlbB6xiai/3c9cVEJnOg+hkT49MzzuPfe1eEOT6RVa9Q1\nkc65JUc7EBER8fgqfSz5+VYmfPU3Yt1B1nMsue5Yutg2MjNLwh2eSKvXqOTHzGKBO4ErgV7gn0f9\nW845p8klRESOUPU8PkM3vEWSby8d2U+8lRJfWcH6/hcyadKmcIco0uo1NmH5A96Yn7eAV/HG+oiI\nSBNavrSC46c/yeWsIJpKvuJYqogm2qpI7BHHyVOzdFMikSbQ2OTnUuBe59wDRzMYEZFI5fPB8dOf\nZBg5RFNJR/bxPb5gY9RxlHdOJvnqgRRpdLNIk2jsX1IHIOdoBiIiEql8Pvjb37LoxwYOEkspieyj\nIwaU9O5Jp1tPo+jM7HCHKdJmNDb5mQ/o/l0iIkfBhx+mMX9+JhvoRwwVAFQSzfsMp+qxH7P77LPQ\nNe0iTaex3V5/BuaamQ94E/jOvD7OOY3CExE5DM8914fy8mh+yDzm8UP6sYEN9OP1y37LpKjicIcn\n0uY0Nvmp7vK6D2/W51DaHXE0IiIRwueDf/0rjQULMtmypT3OGc5imeheJirKcemludxwg75TihwN\njU1+JuPNqy4iIkfI54PZs7N4//2ulJTEEBUFVVWOdu2gXTsfffrs44YbNqmnS+Qoaewkh3OOchwi\nIhHjww/TeOutDPbti8UMEhMPYhZFu3aOwYMLmTJltRIfkaNIExOKiDST/fvhhhuyKSyMr1nmnFFS\nEk23bqWcdVY+N96ori6Ro62xMzzPbqCIc87d0ATxiIi0SRUVcNllZ1NZGY13v2gAR1SUDzPHmWfm\na/ZmkWbS2IbV7wPnBj0uASYB4/2vG8XMLjWzf5jZFjMrMbMvzWymmXUIKtfZzJ40s11mtt/MFprZ\noBD7izOzP5hZnn9/y83srBDlzMzuNLPNZlZqZp+Z2cV1xPgjM/vCzMr88d3U2PMTEQnlgQcGBSU+\nnoSESoYMKeBHP9pEtNriRZpFo5If59wxzrk+QY9OwHBgJ14i1Fh3AJXAb4ELgFnAT4B3g8otAM7H\nu63GxUAM8L6ZZQSVmw3cANwNjAZ2AO+Y2YlB5WYAU4FH/MfNAV4yswsCC5nZj4DHgJeAUcCLwCwl\nQCJyuHw+WL26c9BSh5mPk08u4q67dJd2keZ0RN8znHNLzexPePMAndnIzS5yzu0OeL3UzIqAOWY2\n3Dn3gZmNA7KBc51zSwHMbAWwGfg18HP/spPwbrY6yTk3179sKbAGuB+vVQoz64KXdM10zv3Jf9wl\nZnYs8CDwtr9cO7wk6Wnn3NSAcpnAdDN70jlXdSh1JCKRzVfp4/P7NzB5/+ds4hjmMw6HYebj1Vc/\noEOHhvchIk2rKa4n2ASc0tjCQYlPtY/x2oIz/a/HAHnViY9/u2K8mabHBWw3FqjAa52pLlcFvACM\nMrMY/+IL8FqOngs67rPACWbW2/86G0gLUe4ZIJXGJ3giIvh88O5Pc0j4bB3d43ZzpuUwPuqfJCVV\ncNddq5X4iITJEbX8mFk03rifbUcYx3C8eYTW+l8PBEK1A68BrjGzROdcCTAA2OycKwtRLhboB3zh\nL1funNsYopz51+f6j0uIYweWW3JIZyYiEenykZvI5yZG4P1ze4BbSEzszIDojcRemMdZZxWEO0SR\niNXYq70Wh1gcC/THaxG5+XAD8HcpTQMWOuf+7V+cgtfFFaz6thrJQIm/XFE95VICnvc0shwh9hlc\nTkSkTj4f5HMTUXjfmhxwF7N43H6OndCTyZM1gaFIODW25SeK787wvA94FXjBOffB4RzczNoDr+F1\nXU0+nH20JPPnz6/5eciQIQwdOjSM0YRHcnIyWVlZ4Q4jrFQHnkitB1+lj3d/msMIvr2uy6ofwwYz\n/s/ZREVHXuYTqe+HYP+fvTuPb6pK/zj+edK9soPAWClYER1BnXFhGUVhFJdRxB0RGXFBR0Ud199P\nRlFRUX/qqCjuoqPihhuLA446ICiLCwMjuLBKlR3KVktI03t+f9y0hNpC0LZpku/79cqrzc1D7pNL\nenZCq10AACAASURBVPPknHPPSdXjMHPmTGbNmhXvNCrEOsNzj5resZll41/R1Q442jm3IurhDfit\nO5VVbpnZAOTvJK4oKq7ypRbVxRHZ9+qdxFWpd+/eO9xfsiT15uwoKChIydcdTcfAl4rHwfNgzu2L\nSP9iIQ7/G2N5y48D2l//G74v/D6eKcZNKr4fqpKqx6Fly5Y7fEaOGDEijtnUzIDn3RYZK/QWcChw\nknPu60oh89k+/ibagUBhZLxPedw+kUIqWkf81qRFUXFZZla53O7IjmONysf2VN73gZGflfMUEfF5\nHuuf+4oOsyaxV+mP/B+D8aDi1pKn1NUlUk/E/KdoZgeZ2ZuRSQfDkZ9vmNlBu7NDMzPgFfxBzn2c\nc59XETYOyIuerNDMGuFfBTY2Km48/tijs6Pi0oBzgPedc6WRzZPw5xbqX2k/5wPznHPLIvdnAOuq\niBsArAc+je1VikiqaTzlU3LfmsLe3jJ+z3/Yh9X8nRu4fM83+Pf7H/DGB6nX1SFSX8U64PkI/Kuc\ntuIXJquA1vjFyMlmdrRz7ssY9/k4cBb+fDpbzaxL1GM/OueWR/YxE3jZzG7CH6x8cyTm/vJg59wc\nM3sdeNjMMvEHSV+B35XWLypurZn9HbjZzIqB2cC5+AVY76i4sJndCow0sxXAh8Cx+Fe0DXbOhWN8\njSKSQkJBj833TqWT+54ScgkTIJ9C3sk8m2an9SQQmBPvFEUkSqwDnu/Bv/z7WOfclvKNZtYQv0C4\nB3825licSPnFD/4t2h3AMOecM7OTgQeAkUA2MB3oESmOog0E7gbuxB/XMxc4wTk3t1LcEPxB2lfj\nF27fAWc75yZGBznnnjIzD39SxBuAQuBK59xTMb4+EUkhmzfDS2du5jbWkUGYxmymmD2YT0eWH96d\nu27YTGFhvLMUkWixFj9dgQHRhQ+Ac26Lmd0H/CPWHTrn9okxbiNwSeS2s7ht+EXKDbuIc8DwyG1X\n+34GeCaWPEUkdXkenHdedwbxBN/wWw7mv2QSJkQGs5r25Lbb5pGeru4ukfom1uKn8mXuu/u4iEhS\n8TwYNaqAbdsyKKQteSwHjBasY2767zn9hZYa4CxST8Va/MwChpjZh5W6vfYA/gd/fI6ISEoIheD6\n6w9l6VJ/fYrxnArAcvL4kb0Z9E4jMrNV+YjUV7EWP0OAKcAyM5uAv3J6a+BPQC7+wGERkaS3ZkWY\npRdMZhiPs4AODOUOPDIYRx+yskp55ZVpZFaefENE6pVYJzn8zMy6AkOBE/An/SsCJgN3Oue+qr0U\nRUTqB8+DpRdM5limECSbNkwG4M6sYZx22o9atkIkQcS8sKlz7r/4l6iLiKQez2P9qPmcxEQyKCNI\nFkGy6cAC9tmnWIWPSAL5Vau6i4ikghUr4K0LttCNFeTRiH1ZDMBPNGAB+/Hgg7NV+IgkkJiLHzM7\nEX8m5Tb48+5Ec865Y2oyMRGR+sDz4IILejCYxwiSy2R6AtCAzXxED/b5R08yM+OcpIjsllhneL4J\nuBdYi79eVqg2kxIRqQ+8sMd/71rCYObRmlWkUUaQXKbTjbZ99+KIiw5Si49IAoq15Wcw8BT+Eg9l\ntZiPiEi9sf6F+TRfuJRimpJOGWHSWE9TCjmYwy9qGKeloUXk14r1T7cRMEaFj4ikglAIbrutE7Pf\n8VhR1Ig9ckNsJZdVtOYxBnPmPxqjJh+RxBXrX+/7+EtciIgkveHDOzF3blOWlrUjPbyNbdvSadVo\nE22PzOb996ew117xzlBEfo3d6fZ6x8wc8C9gQ+UA59ySmkxMRKSuBYP+zM0LFjQGjLGBUyHNaGvL\n8PZtR9db2qjBRyQJ7M7aXlvwV0+/q5qYtBrJSEQkDsIhj9f6FXF88YscQFvG05uwl8a7aX1o1XIr\nl/ZZRCB9XbzTFJEaEGvx8wLwB+Ah4Ft0tZeIJBHPgynXL6dT8dcEySWPFQCM41QyM8u46KJFdOum\nwkckWcRa/PQErnTOvVCLuYiI1LlwGO66qxOdF/6XIDkABMkhn2VkZZVx6qk/cswxKnxEkkmsvddr\ngdW1mYiISDy88EIBCxc25Me0fHLYCjiyKWG57c0pp/zIwIEaziiSbGJt+RkBXGFm7zvnvNpMSESk\nTngeTabNoN24uRy5rR3j7BTIhL3LCgnutw+XPtiI9EwVPiLJKNbipynQCfjazD7g51d7OefcbTWa\nmYhILfE8WD9qPosmbiA3GKarm4nDGJvWhyO6rmfo0Hm6qkskicVa/Pwt6vcOVTzuABU/IlLveR6M\nGlXA76d9RrikIYEAbHPZtGMZ2dll3HKLCh+RZBdT8eOc06lARBKe58EFJ+SyjH1Jw//Wdi/Xskd6\ngO+y9uKggzaSHvNyzyKSqFTUiEhKKG/xWcaRpOOf/ALA//IQc3MPZ9kh3RkyZF6csxSRuqDvOCKS\nEmbMaMF33zUiDbDINovc+o9pQSDwdfySE5E6FXPLj5ldamb/MbMSMyurfKvNJEVEfg3Pg5kzW7Bl\nSwZl+N1dRH6WoTVKRVJNTH/yZvZn4FHgcyAbeB54GdgMLAaG1VaCIiK/RigE11xzKFOntmT16mya\ns5Aw4AFhYGDPl+KcoYjUtVi7vf4K3APcCVwCPO6cm21mTYEpwPraSU9E5JcLh2HQoM6sWLEHZoaZ\no2HjNvTp9gPXXvstgQBcFO8kRaTOxdrYux8wFf/LkgdkAjjnNuAvdnpNrWQnIvIrPP98AStX5gKG\nc/6ttNTo2nWdurpEUlisf/5bgXTnnANWAQVRjxUDe9V0YiIiv1SoOESg70Pc/MZpvObOIY1SAJyD\nBg1KtUipSIqLtfj5iu2TG04DhphZNzM7Argdf6V3EZG4C4Wg9Oyn2L/oS5qwiSOZwSv0AxyZmWVc\neulCtfqIpLhYx/w8Dewb+f1W4EPgk8j9LcBpNZyXiMhu8zy47rpDeSW8lFKyACglkw4sYs89t9Kj\nx2q6d1erj0iqi3WG59ejfl9kZh2BbkAuMN05p7OJiMSVF/b4711LOHHhV5SQSyvWUEoWGYRYntOW\nK69cSLduGusjIr9wkkPn3E/4rT8iIvXC+hfm03zhUoLWhE85EoBcSvjeCsh+5VKObKDvaCLi03cg\nEUloXthj7TNf0WzCv2m6aQWZGWFCgT0Yw7kc13Qm//nb7WQ2yIx3miJSj2h5CxFJWJ4H/71rCXt8\ntYysUBZtvGV4Afgxuw1Frdpz1YAFHHmkWnxEZEcqfkQkIXke3H9PPjd8ei97s5z1NOV7a8MeGSE2\n7t+Jk4a3JpCuwkdEfk7Fj4gkpE8+aUGPKaNoyzJyCdKIzaS7Msa2vZzcPr9T4SMi1dKYHxFJSBMm\n7E0HFrCKvdhEI4Lk4DByzjlYkxiKyE6p5UdEEkY4DCNHFjBhQlsAFtCBNkxmPS3IJsgUjuGoo4vi\nnKWI1HfVFj9m5gEu1idyzqXVSEYiItUYNaq88PEbrYcyDBhKBxawgP3Iua9nXPMTkcSws5afYWwv\nfgx/8eMcYDywGmgNnIK/7tdztZijiAihEIwbtzfRvfUeGdzC3fTtu4yBA5eQrrZsEYlBtacK59zt\n5b+b2S3AMuAE51xJ1PY9gPeBcC3mKCIprqQEzjmnO9u2VT5lOcBxySVL4pGWiCSoWAc8XwbcH134\nQMVMzw8Af6npxEREAIqLoU+fHmzbloHfCF3OAR7PPTclPomJSMKKtfhpAVQ3RWom0Lxm0hER2S4c\n8hh99gYG8xinMhbDizziyMsrZuLEKeTnxzVFEUlAsfaQfwHcYWbTnXMryjeaWR5wO/B5LeQmIilu\n3vAlHBZeTJBc8vBPPePoQ1ZWKU8//ZnG+IjILxLrqeNq4N/AEjObiT/guRXQFSgBzqud9EQkFXke\nzJjRAr75Ev86CwiSQz7LAI833phGppbrEpFfKKZuL+fcf4D2wINAGXBQ5OcDwH7OuTm1lqGIpJRg\nEC66qDP33HMg8zYX0DC9GMORTQmr0vN4550p5ObGO0sRSWQxNxo759YDf6vFXEQkxYVDHq+dW8Tp\nP42ikHze5lRcGhzUbAnpv92Li4c0Jl0tPiLyK+1Wj7mZtcDv6moOjHfOFZlZNhByznk7/9ciItXz\nPJh87XI6/fQNQXIqxviMtdM46Or5/E6rs4tIDYmp+DEzA/4PuAr/6i4HHAEUAWOBT4A7aylHEUly\nV/YrYva6ARxLiDDG/3FDZIzPD+TmhrVWl4jUqFgvdb8ZGIw/63MXdpxsYzz+TM8iIrvN82D2ugHk\nECIAZOC4iQfIZis/0IbBg78joCWYRaQGxXpKuQQY5pwbDsyu9NgiYN8azUpEUoIX9vjvsEVkE6r4\nRmVAOo7P0zqztmtXjjlGrT4iUrNiHfOTB8ys5rEQsEfNpCMiqSIYhLf/vI79NywmTIAMPAy/Tz1I\nJulnHsqtF36tVh8RqXGxnlaWA52qeewQYGnNpCMiqeLGGw+l4YY1bCWX+7meUgJ4wFYyObTFSwwa\npIVKRaR2xHpqGQMMNbPZbG8BcmbWAbgeeLo2khOR5OSFwly08B7+wDQM+IBePMx1rNrnIE56ci9G\nqrVHRGpRrKeY24FvganAwsi2McBXkfv31nhmIpKUNm6E2Sd/RPeyjyklk+YU0ZUZzM7sTOPzO6mb\nS0RqXawzPG8FegADgenAh/jreV0K9HLOhWopPxFJIhs3wtln92BfFhMkBzBW8hu2Bhqw34378Yej\niuKdooikgN2Z4bkMeClyExHZLcGgX/hAgAV0oA2TCZJNNltp1m1fevTQVV0iUjfi0sBsZnlm9qiZ\nTTezn8zMM7P8SjFtI9sr38rMrFGl2Cwzu9/MVphZSeR5u1exXzOzm81sqZltNbM5ZnZGNTkOMrNv\nzCxoZt+a2WU1exREUsuNNx6Kf8oxhnInH9GTtTRnCscQHHJOvNMTkRRSbcuPmS3Fv+o0Js65gt3Y\nb3vgLOBL/HFEx+8k9m78iRSjbal0fxRwEnAD/pVng4H3zayrc+6/UXF3AdcBQ/DnKzoXGGNmJzvn\nJpUHmdkg4MnIvj8CjgUeNzOcc0/txusUSXlFRdC//9GEw2mUz4/qkc4tDAc8xoyZQkDrdYlIHdpZ\nt9fH7Fj8HAu0Aj4FVkd+PxJYhV8gxMw59zHwGwAzu5idFz9LnXOfVfegmR0C9AMGOudejGybCszH\nn5H6tMi2PfGvTBvunHuo/DWa2X74A7YnReLS8IukfzjnhkbF5QF3mtmzkS5AEdmFYBD69u1BeYuP\nf0op/+kXPk2axDFBEUlJ1XZ7OecGOucudM5dCMwAioF9nXN/dM71c879Eb8FpzjyeLycij/R4hvl\nGyLFyWvACWaWEdl8IpABjK70718GDjKztpH73YAWVcS9hL+g61E1mr1IkvI8uOyyzmwvfIj89Djg\ngI2MH6/CR0TiI9YxPzcCtznnfoze6Jz7AbgD+J+aTizKPWZWamYbzWysmVWebPFA/NahYKXt8/EX\nYW0fFbfNObe4ijiLPA7QMfJz3i7iRKQaoRCcefqeHL7iQwbzKKfyLoYHONLTy3j00dlkZ8c7SxFJ\nVbFe7bU3ULm4KLcNf/mLmrYNf9zNv4C1wAHA34BPzewI59yCSFwzYEMV/74o6vHynxtjjKOK56wc\nJyJVOK3XGtZzPrNxeMA9XE8eywEYx6mMHj01vgmKSMqLteXna+BGM9vhu5qZ5eC3Cn1d04k551Y5\n565wzr3rnPvUOfcccHTk4b/V9P5EpGas53wycASANOBmHiRIDvksY8iQr2imrw8iEmextvzcBLwH\nFJrZP9k+4PlPQGP8K61qnXPuRzP7BOgctXkDkF9FePkptigqrqoRBlXFATTFf53Vxf3M+PHbL0rr\n0qULXbt2rS40aTVt2pSCgt258C/5pOoxKCmBk0/eiyW4HUb4BIAcSkhr+1suvLARgUCjnTxL8knV\n90NlOg6+VD0OM2fOZNasWfFOo0JMxY9z7iMz+z1wC9Ad/0qtlfhdUnc5576tvRR3aT5wmpllVxr3\n0xF/IPSiqLgsMytwzi2pFOfY3npVPranIzsWP+Vjfapt5erdu/cO95csWVJNZPIqKChIydcdLRWP\nQSgEp5/enVAogzBGRqQAckAZUNThQI5/sBnff59axwVS8/1QFR0HX6oeh5YtW+7wGTlixIg4ZrMb\nkxw6575xzvV3zu3rnMuN/Dy/LgufyESIR7F9cVXw5wDKBM6OiksDzgHed86VRjZPAsJA/0pPez4w\nzzm3LHJ/BrCuirgBwHr8S/1FJCIchkGDOhMKZQBGc1ZSGhneXIrRjNH0GtmGzGwt2iUi9UPMy1vU\nNDM7M/Lr4fgtLX8ys7XAWufcVDN7APDwC50i/AHP/4tfwAwvfx7n3Bwzex142Mwy8Sc5vAJohz//\nT3ncWjP7O3CzmRWzfZLDHkDvqLiwmd0KjDSzFfjrmB2Lv67ZYOdcuIYPhUjC8jwYNqwTK1Y0oPxy\n9p9oSTZhMjM9hgyZx7tHatkKEalfYi5+zOwY/GIiH6h8kapzzh27m/sew/ZJFB0wMvL7x8Af8buf\n/gJcDDTAb3X5CBjmnFu441MxEH825jvxx/XMBU5wzs2tFDcEf3boq4HWwHfA2c65iZVezFNm5uFP\ningDUAhcqdmdRXb0ySct+OKLFmyfx6ec4/zzN9OtmwofEal/Yip+IutaPYHfArMA/zL0HUJ2d8fO\nuZ22gTvnngeej/G5tuEXKTfsIs7htxoN31lcJPYZ4JlY9i+SajwPpk1rwf/934GUlkb/KfszN591\nViE33wzffx+nBEVEdiLWlp/rgVeAi5xzoVrMR0TqualTPObevYV85nEiSxhPn6jhzY5u3dYxaNAS\nAoHUu6JFRBJDrMVPHvC8Ch8RmXv3FroxiyA55LECMMZxGuDYf/9NDB06j4DGNotIPRbrKepLQF/j\nRFJYOAxPP11APj8QJAcgMnlhIYGAR17eTzz88GzS43YZhYhIbGItfq4G/mpmR+8yUkSS0qhRBYwd\nuzeFtCWbrQBks5VC2tC161qefvozFT4ikhBiPVWNBxoBk82shJ+ve+Wcc21//s9EJBls3AhjxuQD\naYznVADyKaSQNqztcjDD7qi8DrCISP0Va/HzEdsvSxeRFBIOQ9++x1DeUOwIRMb4eBxwwCYeun12\nXPMTEdldsS5vMbCW8xCReuiZh4q5/5+Xs5kNrKcp+7GAEA0AR4cOm3joIY3xEZHEo2syRKRKoaDH\nY/+8kDxWkU2IvVjNQjoAjkCgjEceUeEjIokp1kkO/7yrGOfci78+HRGpD8JheOuCdRzH5sgMpg5/\n3a4NBAJlvP76xyp8RCRhxXr6eqGa7dHjgFT8iCSBcBiuueZQji96kRJyaUhxRQG0nqa8954KHxFJ\nbLF2e+1Txe1w4A5gIdClVrITkTr3wgsFLFrUmELa8jQXs4UGhAlQRBNu/NMTKnxEJOHFOuB5WRWb\nlwGzzcyA64DzajIxEal7ngdz5zbF86zikvYfaMdya8Ml4xoyKFvDBEUk8dXEd7hp+MWPiCQoz4OJ\nE1vw8MMHQUUnlzGOPgQCHl26rCMzW3P5iEhyqInipytQXAPPIyJx8uGH5YVPdMuOR2ZmGYcdVsQt\nt6jwEZHkEevVXkOr2JwJdAJOBh6ryaREpG79/e8d+fkQQGPIkK858sh18UhJRKTWxNryc3sV27bh\nj/u5G7inphISkbrheTB1SjN+GLmAy8tGUkhbxnMqjgD+hZwe3bqp8BGR5BPrgGeNchRJIqEQXHvt\noRywYArdmE2QHPJYAcA4+gAeo0dPIaC/fBFJQtWe2sysyMwOjfw+ysz2qbu0RKQ23X57JxYsaEI+\nPxAkB4AgOeSzjKysUsaMmULLlnFOUkSkluzse90eQFbk94HAnrWejYjUic8/3xMwCsknm60AZLMV\nL685EyZMo0mT+OYnIlKbdtbttQwYZGblBdDvzSy7umDn3NQazUxEalwwCDfeeGjF/fK5fPJZxsq0\nTgx8vHG8UhMRqTM7K37uBZ4CLsAf/fh4NXEWeTytZlMTkZo0c1qIDsOeZTSLWER7zuMVysiqGOMz\n/t0pZFf79UZEJHlUW/w450aZ2USgAzAZuBr4pq4SE5Ga1WHYsxzJDErJ5Ehm8Arn0ZcxBAIeo0d/\nrMJHRFLGTq/2cs6tBFaa2T+A95xzS+smLRGpKaEQDB/eiREsopRMAErJpD2LOOCATTz66Ow4Zygi\nUrdivdT9wtpORERq3rz/eky+fgu/5zVKyKUVaygliwxCLKI999+vwkdEUo/WZxZJUuEwTL5+C92Y\nRZAcPuVIAHIpYRHtWTD0Erqqq0tEUpCmMBNJQuGwP4lh9Dw+W2nA65zL1Uf+i2YfXEHX7plxzlJE\nJD5U/IgkGc+DO+7oxLffNqaQtjvM41NIG4YM0SKlIpLa1O0lkkTWrfF4tf8WDuc1WtKWCZwC+PP4\nFHIwPR9sSKYafEQkxan4EUkWnseS/h8ymC9ZRwv2ZjkA4zgNKGPixCmk6y9eRETFj0gyCAbhn5eu\n5nS+IIMy2vAjAD+yN+D4zW9KVPiIiETodCiS4EqKPV44cxP9vHfIYhsBHGEyaME6CmlD06ZbefLJ\nz+KdpohIvaHiRySBhUMe3571Ly73/kMWQTIJESKTUtL5jCOw3gfz2uAZBHRpg4hIBRU/IgnK8+Df\nf11Or7L/kEGYAB4hMllNK16mPwv3P4aHB89R4SMiUomKH5EE5IU95gxbwhEL3yeLYKSry2/xeZnz\nWd3lDzwwVIWPiEhVVPyIJJhwMEyw991czycAOGA6HSmlFZ9xBGu7dOWuuzSXj4hIdVT8iCSQcBi+\nHzCRS/kEi2xzwB+Yz5kM45MmvXhp6Kx4pigiUu+pUVwkgTz9dAEHbZxVUfgAWOT2xV7H8dLoWZrE\nUERkF1T8iCSIcBjefbctxTTARW13kdtzz32mwkdEJAYqfkQSQDAI/ft3wznjSS5jFgfhQcVtT57R\nJIYiIjHS6VKknvM8+MtfOlNUlAMYYzkDj3TyWcaKtDwGjWvMmEx9jxERiZWKH5F6LByGu4YdyGHL\nP+R0fqCQfMZzKuPoQ1ZWKa+9No10dXWJiOwWFT8i9dT3i8Ksvnwy93ELBnxAL/JYDjg+aXYCo0fP\nUFeXiMgvoFOnSD1UUuzR+PIn6c2nZBOklAzKSOMjjmX/rKUM+ocKHxGRX0oDBUTqmXAYXjl3I92Y\nRTbbSCNMDltpyzIaZ/zE7/sEyM6Od5YiIolLxY9IPRIMwnnndWPPbSsIkgORFdo9jJ/Yg9LD96P5\nhR3jnaaISEJTw7lIPfHOq0H6jbqLORRSTAO+oiOHMZsctrKS1jzb9gZOuH0frdclIvIrqfgRqQc8\nD/qNuotDmEcZaTRlEwCTOBFwFHU+nF63tVXhIyJSA1T8iMRZOAy33NKJtymkjDQAykinAcVclfk4\n//M/33D00evinKWISPJQ8SMSR+GQx+izVnD31ntpzCayCBEklzTKKCSfG2/8hqOOUuEjIlKTVPyI\nxIvn8dNf3+aOrRNpyBY20ZjmrMcoYy6duKxgFI/0WBHvLEVEko6KH5E4afzxDNzCBTTgJzIJ41HK\nCtowh4M5hzeYOHJKvFMUEUlKGj4pUsc8D956vQlFw6eyP9+RSRAoI4NSyjAWsB+vvjpFkxiKiNQS\nFT8ideyTT1qw97NjaEshEKCUTMpIYzWteJvTKLmpJy1axDtLEZHkpe+WInXIKy5hwPAzyWcZYTJY\nSju2kcUaWnCMfcybb0+jQYN4ZykiktzU8iNSR0pKoN3p15NfVojhkU2Qffie9TRnCsfw7jgVPiIi\ndUHFj0gdmDYN+vTpQQvW4AjgkUYZRjqlfEQPMu7uqfW6RETqiLq9RGpZYSEMG9YDCLCKVuzLUhwB\nHLAyK5/D3u2lwc0iInUoLi0/ZpZnZo+a2XQz+8nMPDPLryKuiZk9a2ZrzazYzD4ws05VxGWZ2f1m\ntsLMSiLP272KODOzm81sqZltNbM5ZnZGNTkOMrNvzCxoZt+a2WU18+ollQSDcPHFPfD/1Izf8R8W\nsw8lZLOMdix47SEVPiIidSxe3V7tgbOAImAq4KqJmwAcD1wJnAFkAJPNbK9KcaOAi4FbgJOBlcD7\nZnZwpbi7gKHACOBEYAYwxsxOjA4ys0HAk8AY4ATgDeBxFUCyO4JBOOus7pQXPgCl5NKRb2nOer4d\n+wSBBrlxzVFEJBXF5Tunc+5j4DcAZnYxfoGzAzPrA3QDejrnpka2zQSWAjcBf41sOwToBwx0zr0Y\n2TYVmA8MA06LbNsTuB4Y7px7KLKbj81sP+BeYFIkLg2/SPqHc25oVFwecKeZPeucK6vBwyFJaMki\nj/cu38IgnqCQtoznVBwB/DrfY+jQKeSq7hERiYv6POC5N7CivPABcM5tBsYDfaLiTgVC+K0z5XFl\nwGvACWaWEdl8In7L0ehK+3kZOMjM2kbudwNaVBH3EtAcOOpXvCZJAeEwvHf5Froxi+ZsoBsz6M04\nyguf556bQvefdcqKiEhdqc/FT0dgXhXb5wP5Zlb+vflAYKlzLlhFXCZ+F1t53Dbn3OIq4izyePl+\nqWLfleNEfia4OcQ3Ha/jUa7iFMZjlBEkh3yWkZVVyvjxU8j/2eg2ERGpS/V5qGUz/C6uyooiP5sC\nJZG4DTuJaxb1c2OMcVTxnJXjRHZwaa+PWcBdFd8ofmIlZ/IWE+hNIQfz5pvTdDm7iEg9UJ+LH5GE\nUl74WOT+HkAjNjGDrpz8RAMVPiIi9UR9Ln424LfuVFa5ZWYDUFVHQnlcUVRckxjjiOx79U7ifmb8\n+PEVv3fp0oWuXbtWF5q0mjZtSkFBQbzTqHOe5xc9Vmn753Sl5SU9OO74qhodk1uqvhcq03Hw6Tj4\nUvU4zJw5k1mzZsU7jQr1ufiZD/SqYvuBQKFzriQq7jQzy6407qcj/kDoRVFxWWZW4JxbUinOg3mX\nrwAAIABJREFUAV9HxVlke3TxUz7W52uq0bt37x3uL1mypJrI5FVQUJBSr7uoCPr2PRpIoxT/jWSU\nD22GMacPZ+CZs0mhQ1Ih1d4L1dFx8Ok4+FL1OLRs2XKHz8gRI0bEMZv6PeB5HJAXPVmhmTXCvwps\nbFTcePyBzWdHxaUB5wDvO+dKI5snAWGgf6X9nA/Mc84ti9yfAayrIm4AsB749Fe8Jkky5557NP53\niAA3cCMeVNxu4BouueJHTWIoIlLPxO20bGZnRn49HP/L8p/MbC2wNnJ5+zhgJvCymd2EP1j55si/\nub/8eZxzc8zsdeBhM8vEHyR9BdAOf/6f8ri1ZvZ34GYzKwZmA+cCPfALqvK4sJndCow0sxXAh8Cx\nwEBgsHMuXJPHQRKX54FzaZR3do3gHpbSjXyWUdKsJac/3yK+CYqISJXi+Z10DNtndnbAyMjvHwN/\ndM45MzsZeCDyWDYwHejhnFte6bkGAncDd+KP65kLnOCcm1spbgiwBbgaaA18B5ztnJsYHeSce8rM\nPPxJEW8ACoErnXNP/apXLEnjuZFBrnz3LpZRSCH59GQyYbIZRx8yMjwmvDqFQH1uVxURSWFxK36c\nc7v8aHDObQQuidx2FrcNv0i5YRdxDhgeue1q388Az+wqTlLPisIw9707iNasoYx0mrKRyfSkO9MB\nj5kzCymqdli8iIjEm76biuyGUHGIwy++mjxWkY5HOqWkU0Y+hRxwwEbee28KTaq6plBEROoNDcUU\niVFREdD3WdqxrOKy9jQcjjCF5PPII7PV1SUikgB0qhaJwcaN0LdvD9qziG1kESIDhz9YbRUtGXna\nLSp8REQShFp+RHYhFIKzz+4BBFhEe1pFpn/yML6nLV88N4KL8/WnJCKSKHTGFtmJcMjjrQHrGMxj\nFNKW/oxmNP1pzyIW0Z4G71zCXg30ZyQikkh01hapRjgMYy9ezX5FXxEkhzxWANCXNwGP11+fQmaD\n+OYoIiK7T6MURKrw9IggDU66hbtXDeY8XiFAmCA55LMM8BgzZgrNmu3yaUREpB5S8SNSSTjkcfv4\n6ziM2eQQpBWrGcg/yGYrheTrcnYRkQSn4kckSnlXVx4rMAzD4RGgAVuYQVc+adKLzMx4ZykiIr+G\nih+RiFAILrzgcH6/agplpBEgTPka7V/zWyZlnsxzz8+Kd5oiIvIrqfgRwV+k9NprD6Xzmn/TnCK+\n5rdsJZcyYCntOI63GTt2Gg00wFlEJOHpai9JeSUlMHBgNzZsyOF4fmAeBxEik8004UfyuC7nUd59\nezrp+msREUkKOp1LyvvLXzqzYUMOYBSSTx7L+Y4DyGYrM+jCK6+p8BERSSY6pUvKCofhwfvyuXLl\ncDqwgAV04DbuACCfZSy3TlzwRkNyc+OcqIiI1CgVP5KSPnovyMkP38m7zCGAYxH70oYfAbiFu/nD\nH9Zy663z1OIjIpKEdGqXlLN5o8eAh69jX5aQThkA7VnMIvajAwvo3Hktt902TwuViogkKZ3eJaWE\nQvDi2ZvJYwUB/AvZDUinjAYUs4D9uOMOFT4iIslMp3hJKcNuP5A/MZEwAcCjjAAOCJHO5xxGzn09\n1dUlIpLkdJqXlOB5MP2TZvT+fASd+YzVtCKdMrIIsY5mDOYRBr3XgoMz9X1ARCTZ6UwvKeHTT1uw\ndtTXdOZzNtIMSGMR+/MifyaPH+k3piXpKnxERFKCWn4k6fXr9S0ruSqyUAXMpBNraEUp6bzHn/jn\nxGnq6hIRSSH6qitJLRzyWMlVBKDi1pV5lJLGFxxG/1cbqvAREUkxKn4kaXkeTL5uecUVXbD96q45\nfziHwyYeR7MW+hMQEUk1+s4rSSkU9Hj34tX0WjMeh9/dVd7t5YBDbmuvy9lFRFKUTv+SdIqL4dlT\nN1Gw5ivCZPAF++JBxe03PKrCR0QkhanlR5JKOAx9+3bnUvcEQXJYwP4A/MDBvMz5/O6Wdrx6TFGc\nsxQRkXjS919JGp4Hd9zRiVAog0Laks1WHMYy2vIy5zOOUzmyuwofEZFUp5YfSQolJXBGn8N4hYtp\nzyIW055X6UseqyikDeM5heefn6LuLhERUfEjiS8chvPO/QNT+COd+JptZNGKNTigL2/SpcsaJg2b\nqsJHREQAdXtJgguHYfDgQzl26yTasxgwstlGOmW0ZxFNm27l9tu1UKmIiGynjwRJWJ4Hw4Z1YvHi\nxuTzA+toToAyHEYW21hEe158cYYmMRQRkR2o+JGE5Hlw9eB27DljOoN5jNasZAJ/opC9CZLJPA5k\n4nmXkJ0d70xFRKS+0XdiSTjBIFx0UWe6rf2QbswkSA7plBEmk6f4C8stj0snNKa/FioVEZEq6NNB\nEs511x3K2rUNyOcHguQAsJVcVtGaJ9Ku4Pw3m2qFdhERqZZafiShnNZrDetpQTqOMmAEV1JMc7LZ\nSiEHM2HCVI3xERGRndLXY0kYRUWwnvPJwBHAr9yvZiTracoMutB0gFZoFxGRXdNHhSSEjUUeo/tu\n4UzcDiu0pwGPMZiXXppC69ZxTFBERBKGih+p9zZv9Piu70cM5oufrdAexnjvvSlkZsY3RxERSRzq\n9pJ6LVji8c3ZH3EG79CaVXzGYRWrs5diNOdlFT4iIrJb1PIj9dair0N0umYYg5mHwyimIQCT+SOP\ncQX9X2/Mu83inKSIiCQcFT9SL4XD0OyaZ+nE16ThkU4pAFtowGcczoDXG9JEhY+IiPwC6vaSemfS\nhDBfnvQBx/ERGYQpI40wGRiOtzmd3445libN9NYVEZFfRi0/Uq98OjnERY/8lXx+II0wARyQSSnp\nzONADhl7LNm5KnxEROSXU/Ej9UawxOPY4cPYlyUYhofDw9hGBh9yLEWPXEJ7FT4iIvIrqfiReiFc\nEmLP027jUL6I6otNJ0Q6T3A5h008jmZ6t4qISA3Q12iJu41FHg373MEhbg4GUZMYllFIGz7701ma\nuVlERGqMih+JK8+DV87dVNHV5fDn8HFAIW249IhxXHnNxjhnKSIiyUTFj8SN58GzT+ZzmXuKHIIY\nZTgCeARYSWvuanw3Q+/6kYDepSIiUoPUmSBxM/CEQgrpRQC/pScIBMhgBb/h1rS7Oe/Flip8RESk\nxumjReKipAQKuZgA/hifAJANzOZQfpc7j/PHtdYl7SIiUiv06SJ1bmORx3N9Nu0wuJnI79PTjuL1\nMbO0XpeIiNQadXtJnVq3xmNxf3+FdthxhXYP+O2bx6nwERGRWqWWH6kzG9eFCfR/kosZRQcW8BkH\nVBQ9HpDPfWQ3UD0uIiK1S580Uic+nBDkikcG0Zo1gBEkC4B/R1Zov3hsY17MjW+OIiKSGlT8SO3z\nPP78yHXsxSrA7+bKZhtZBCtWaM9V4SMiInVE3V5SqyaOD1N0wpO0Z3HFAGcHgGMq3dl3tFZoFxGR\nuqWWH6k1S74Ocs0Iv6srDS+q8IFVtCTw6qU0a6HCR0RE6la9/uQxs2PMzKviVlQpromZPWtma82s\n2Mw+MLNOVTxflpndb2YrzKzEzKabWfcq4szMbjazpWa21czmmNkZtflak0045NH9muvIYxXpeIBf\n+JQRYAH78fg1z9CkhWpvERGpe/W6+IlwwGCga9TtuEoxE4DjgSuBM4AMYLKZ7VUpbhRwMXALcDKw\nEnjfzA6uFHcXMBQYAZwIzADGmNmJNfSaktq6NR5fnvwRBVFdXeWtPu/ShzFDX+O4U7Ljm6SIiKSs\nRPnq/a1z7rOqHjCzPkA3oKdzbmpk20xgKXAT8NfItkOAfsBA59yLkW1TgfnAMOC0yLY9geuB4c65\nhyK7+djM9gPuBSbVyitMEt8vKr+cfdoO3VwO2ExD/pz2POOO/DyOGYqISKpLhJYf28XjvYEV5YUP\ngHNuMzAe6BMVdyoQAt6IiisDXgNOMLOMyOYT8VuORlfaz8vAQWbW9pe8iFRQUgIzTniOI/gSA8Kk\n4zDCBCiiCYN4nDfe/lzrdYmISFwlysfQaDMLm9k6MxttZm2iHusIzKvi38wH8s2s/CLqA4Glzrlg\nFXGZQPuouG3OucVVxFnkcamkuBj69OlBBxZSTAOCZFNMI7bQgMn0ZBBPc8E7LXVJu4iIxF197/ba\nBDwAfAxsBn4P/A2Ybma/d86tA5rhd3FVVj4ouilQEonbsJO4ZlE/N8YQJxGhENxx+hJK6VUxvuc7\nmuDRiM/pTj9eYcxbn5DbIN6ZioiI1PPixzk3B5gTtWmamU0DPgOuAm6LS2JSoaTY46UzN/AFl+0w\nsLkDG7mXy3jnkMG8d+8npNfrd5qIiKSShPtIcs79x8wWAJ0jmzbgt+5U1izq8fKf+TuJK4qKaxJD\n3M+MHz++4vcuXbrQtWvX6kKTQjgMtx70Jed6r+2wQnt5AbT4wpt4bchGAoGC+CUZB02bNqWgILVe\nc1V0HHw6Dj4dB1+qHoeZM2cya9aseKdRIeGKnyrMB3pVsf1AoNA5VxIVd5qZZVca99MRfyD0oqi4\nLDMrcM4tqRTngK+rS6R379473F+yZEk1kYkvVByi+PSRvMC/SCe8w6h0F7mde+5svv8+PvnFU0FB\nQVL/38dKx8Gn4+DTcfCl6nFo2bLlDp+RI0aMiGM2iTPguYKZHQ7sD8yMbBoH5EVPVmhmjfCvAhsb\n9U/H4w9sPjsqLg04B3jfOVca2TwJCAP9K+36fGCec25Zzb2axOR5YP1HchqTyCJMGv6q7OVFjwcc\nwVW6qktEROqlet3yY2YvAYuB/+APeD4U+F/gB+DRSNg4/ELoZTO7CX+w8s2Rx+4vfy7n3Bwzex14\n2Mwy8QdJXwG0w5//pzxurZn9HbjZzIqB2cC5QA/8giqlhUMeH139A7eUfEx6ZMmKcj+RwwX8g4vG\nNub+XFU+IiJSP9Xr4ge/C+pc4BogF1gFvAnc7pwrAnDOOTM7Gf+qsJFANjAd6OGcW17p+QYCdwN3\n4o/rmQuc4JybWyluCLAFuBpoDXwHnO2cm1jTLzDRzBm2hK6LJ2F4OLaP7/GAuRzMsK8OY9Wq1GvS\nFRGRxFGvix/n3L34syrvKm4jcEnktrO4bcANkdvO4hwwPHKTiI/GFnPjrJtoxBbKMIKkk00YD1jE\nvjxyyi3cp3l8RESknqvXxY/UH+tWhfnfx/rTkBIMCESWKf2KTvyTkzh47HFcpq4uERFJAPq0kl0q\nLobFAyazR6TwgfLFSo3bGcqBbx1HtgofERFJEPrEkp3avNHj+dM3cRITKYu094A/xmcVLRn4TlMa\nNNLbSEREEoc+taRak94NcsDZg3mRC9iXpayjMaUEKAOKyeWJwU/QQEtWiIhIgtGYH6mSF/a4aOR1\ntGcxEMCLbJ9PRyZyEvu+1JNjW+vtIyIiiUctP/IzoRCMvWQ1eawAAgTwIm8Uxx3cRsd3etFChY+I\niCQofYLJDk7vtYr1DOCEyP1S0vBrZI/l7MUFYxqqq0tERBKaWn6kQjjkUcQA0vHfGAZkUMZWsljE\nvoy68u80aqK3jIiIJDa1/Ajgr9f10dU/cDw/X519SN4T9Hm2FSemq/AREZHEp08zIRiECy84nOMW\nv7rDdn8aQzj56d8QUOEjIiJJQp9owrXX/I4rVt1LR+azjiY7rM7enP+QmRnnBEVERGqQur1S2Mg7\ni3h56gC+JYQDNtAYCLCSVqxlT+7gVl5/b1280xQREalRavlJYS9PHUAOIQJAGtCcTTgCbKER/+Qk\nBoxpolYfERFJOip+UtTfh24km1DF4OZyHsa79OF344/TlV0iIpKU9OmWgjYXhXl7xrk7FD7lY3ym\n0p0D3upFZrbeGiIikpz0CZdiiovhm76TyaTsZ8XPA1xO9luXaqFSERFJavqUSyHr1mxfod2L2u7w\nFyo99P0zyG2kMfAiIpLcVPykiEmT4NX+W+jGLIppyGpaUoZf+IRIo1+3fxDQu0FERFKAvuangKIi\nePDBHgzmMYLkMJk/ArCaVkzkJH77ek+ua6a3goiIpAZ94iU5z4N+/Y4BAhTSljxWECSH6RzJDLpw\n4TuNtVCpiIikFBU/SeycXktYw2WE8Lu3WrEAgHyWUcjB9ButFdpFRCT1qPhJUsE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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbffce20128>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fc00b003ef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(medical_data, 'medical', 'loan_amnt', 'funded_amnt',\n", " [0.0, 35000.0, 0.0, 35000.0], 'loan amount', 'funded amount',\n", " 'standard')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "956aa427-cb84-43aa-9c6f-5c647f2b7451" }, "outputs": [ { "data": { "image/png": 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gwYMpKChgy5YtgPU05vr6+mbPhSouLmbQoGOjCV8TFeWIjIknUnPqQAKpydSc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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fc020c8bdd8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fc00ad6a0b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(medical_data, 'medical', 'home_ownership', 'funded_amnt',\n", " [-1, 6, 0.0, 35000.0], 'home ownership', 'funded amount',\n", " 'standard')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e3f77ce0-9aaf-4a60-8571-5c1be2cc3b20" }, "source": [ "### Search string: \"debt\"" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "a9daebdd-7cdd-4e45-8c07-14d551a5e529" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------------------------------------\n", "debt\n", "----------------------------------------\n", "Total number of samples: 440749\n", "% of all samples with target=0: 94.1545%\n", "% of all samples with target=1: 5.8455%\n", "\n" ] } ], "source": [ "debt_data = get_data('debt')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "6380a294-3f85-40a4-9892-e7e16ba6505f" }, "outputs": [ { "data": { "image/png": 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s+M53jO+BblHpAVVVCQQCqKpKJBIhGAwOyi6EUChEMBiMf1RVpaOjA4fDgcvl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\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4hBGr7yGs+9E0COt+YvUn1rLYurWEvj4PsZiLvj6P7QsyHLS3+3ESFPv6PEllvfVWctk7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\nhhm0gibteJDvcNuMDqFgZlr0M58oc6hhgv2cSw2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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc00ac6aa20>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fbfeb8763c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(debt_data, 'debt', 'loan_amnt', 'int_rate',\n", " [1e2, 1e5, 5.0, 30.0], 'loan amount', 'interest rate',\n", " 'semilogx')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "59eae0af-bd8b-4ec6-8582-c026f4e67859" }, "outputs": [ { "data": { "image/png": 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27VqGDBmC0+lk6dKlvSTp94+aClEojoOCAhfhMMdsY3HWWXVRG4v0dM8RNhb7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\nGLX0chtDNLGc80gIvOjYyPSxlO/wSfayA1m2kSThiq8JwuEsmibQNBuv1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2Z++tChSFFg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\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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbffce345c0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fc00b024080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(debt_data, 'debt', 'annual_inc', 'int_rate',\n", " [1e3, 1e7, 5.0, 30.0], 'annual income', 'interest rate',\n", " 'semilogx')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "9804c800-3e58-40cf-9e24-388398c440ab" }, "outputs": [ { "data": { "image/png": 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LuP3227FarQ3Kv/LKK9x///3nlW/bto3HH3+cHTt24PF48Hg83HbbbefJFRYW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\nykzLjgrqUjIsPOcCkyeQU6e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6FgmPxwJITkcmEVpWTJUMJdpRyUHZhyJTwcjIKEbXYds2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BOITOk3bpVJK8zq02NOVlNdVsHcIk5IiGkD+swk0uvzEYpHZZP6XelJfabPC68Mpc4bjS11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\nPCcbzeezEYFp0yJpZTgVa7VvXzlKQVFRnMFBH83NIQIBi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Gq5KoJqzoley5sLvZ43D3ot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\njUajueHRCo1Go9FoNJobnv8fJ8i3lxTUxeAAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbfeb901438>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fbfeb8a8630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(debt_data, 'debt', 'annual_inc', 'loan_amnt',\n", " [1e3, 1e7, 0.0, 35000.0], 'annual income', 'loan amount',\n", " 'semilogx')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "80af9c05-ee0f-466e-b7a6-cb40c1615405" }, "outputs": [ { "data": { "image/png": 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zdR7x8fGUKlUqR/dZMJUrV6ZUqT8/MmNjY9m9ezcAX375Jddccw1Vq1alUqVK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\ncYmUNOEmPzcDg51zTx3PYEREotX4Mft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GA28DvSqpRhEpcpM8j9ch9OrNu9FPwUdEsibT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\nyHtzW2nER0REip7Cj/DwrUsY+fbGwQf88POXas8xfITO6hIRkeKgCc8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"text/plain": [ "<matplotlib.figure.Figure at 0x7fc00afa9048>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fbffce3c278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(debt_data, 'debt', 'loan_amnt', 'funded_amnt',\n", " [0.0, 35000.0, 0.0, 35000.0], 'loan amount', 'funded amount',\n", " 'standard')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "_cell_guid": "4eb3a83e-47ee-49f5-8048-89bc5fee5e87" }, "outputs": [ { "data": { "image/png": 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zH4HAtNR09rscUcubzZXkU0A6JeRTwGyurH+nY9DN/J2u7CaBarqym5v5u9sh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\nIiUoQ1m53axm74prsLpNd0/eNErZzKwRTlQsF//NoTzKXJ5hGo8yl//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jpHEfvxvlfXyQ\nNL7lr0izfR4jta6cWXWOZyT9kjQFubzl5obsvLVac+qN/5vZmiyfJHXVTCYNpr2FdL1Ge72bSd1S\nxwG7Ag8DXyQlE/XEUn28Vr1zSQnmRdk5bgA+XjVQ2KzlKMKrbJuZtapsUbdVwIcj4sKi4zHLm2f9\nmJmZWdNyomJm1vrcNG4Tlrt+zMzMrGm5RcXMzMyalhMVMzMza1pOVMzMzKxpOVExMzOzpuVExczM\nzJrW/wdijjL9TW2kyAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbfeb83c7b8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7fc00ab08470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_two_fields(debt_data, 'debt', 'home_ownership', 'funded_amnt',\n", " [-1, 6, 0.0, 35000.0], 'home ownership', 'funded amount',\n", " 'standard')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "fbf1cb5b-a2fd-4ada-85f2-af6dadfa0e24" }, "source": [ "# Decision tree classifer for predicting the loan status\n", "A decision tree classifier (scikit-learn) is used to predict the **loan_status**. A binary classification system is used, in which the values for the **loan_status** field are classified as follows:\n", "\n", "* 0: \"Fully Paid\" or \"Current\"\n", "* 1: \"Late\" (for any time period) or \"Charged Off\"\n", "\n", "The loan status category (0 or 1) is hereafter referred to as the \"target\"." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "_cell_guid": "b405b49c-e982-4f38-80be-f2d2b54988a1" }, "outputs": [], "source": [ "def create_classifier(f, t, nt):\n", " \"\"\"Create classifier for predicting loan status. Print accuracy.\n", "\n", " Arguments:\n", " f (list of tuples) -- [(sample 1 features), (sample 2 features),...]\n", " t (list) -- [sample 1 target, sample 2 target,...]\n", " nt (int) -- number of samples to use in training set\n", " \"\"\"\n", " training_set_features = [] \n", " training_set_target = [] \n", " testing_set_features = []\n", " testing_set_target = []\n", " print(\"Number of training set samples:\\t{0}\".format(nt))\n", " print(\"Number of testing set samples:\\t{0}\".format(len(f)-nt))\n", " print(\"\")\n", " # Build training set\n", " for i in np.arange(0, nt, 1):\n", " training_set_features.append(f[i])\n", " training_set_target.append(t[i])\n", " # Build testing set\n", " for i in np.arange(nt, len(f), 1):\n", " testing_set_features.append(f[i])\n", " testing_set_target.append(t[i])\n", " clf = tree.DecisionTreeClassifier()\n", " clf = clf.fit(training_set_features, training_set_target)\n", " n = 0\n", " n_correct = 0\n", " n0 = 0\n", " n0_correct = 0\n", " n1 = 0\n", " n1_correct = 0\n", " # Compare predictions to testing data\n", " for i in range(len(testing_set_features)):\n", " t = testing_set_target[i]\n", " p = clf.predict(np.asarray(testing_set_features[i]).reshape(1, -1))\n", " # Category 0\n", " if t == 0:\n", " if t == p[0]:\n", " equal = \"yes\"\n", " n_correct += 1\n", " n0_correct += 1\n", " else:\n", " equal = \"no\"\n", " n += 1\n", " n0 += 1\n", " # Category 1\n", " elif t == 1:\n", " if t == p[0]:\n", " equal = \"yes\"\n", " n_correct += 1\n", " n1_correct += 1\n", " else:\n", " equal = \"no\"\n", " n += 1\n", " n1 += 1\n", " else:\n", " pass\n", " n_accuracy = 100.0 * n_correct / n\n", " n0_accuracy = 100.0 * n0_correct / n0\n", " n1_accuracy = 100.0 * n1_correct / n1\n", " print(\"Accuracy of predicting testing set target values:\")\n", " # Accuracy - manual calculation:\n", " print(\" All samples (method 1): {0:3.4f}%\".format(n_accuracy))\n", " # Accuracy - scikit-learn built-in method:\n", " print(\" All samples (method 2): {0:3.4f}%\".format(\n", " 100.0 * clf.score(testing_set_features, testing_set_target)))\n", " print(\" Samples with target=0: {0:3.4f}%\".format(n0_accuracy))\n", " print(\" Samples with target=1: {0:3.4f}%\\n\".format(n1_accuracy))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "8328d771-ae2a-4e35-8cb0-4f843eafbb18" }, "source": [ "### Search string: \"credit card\"" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "_cell_guid": "15300381-1253-4c4b-9891-4e2f7d968314" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of training set samples:\t2000\n", "Number of testing set samples:\t177301\n", "\n", "Accuracy of predicting testing set target values:\n", " All samples (method 1): 85.7305%\n", " All samples (method 2): 85.7305%\n", " Samples with target=0: 88.8355%\n", " Samples with target=1: 16.4277%\n", "\n" ] } ], "source": [ "create_classifier(cc_data[0], cc_data[1], 2000)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b788eb22-7d52-4faf-a009-d80ecea87d5d" }, "source": [ "### Search string: \"medical\"" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "_cell_guid": "dd80fbb1-e01f-4cc6-bfff-f5fc3aeadc84" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of training set samples:\t2000\n", "Number of testing set samples:\t5351\n", "\n", "Accuracy of predicting testing set target values:\n", " All samples (method 1): 84.1899%\n", " All samples (method 2): 84.1899%\n", " Samples with target=0: 88.2504%\n", " Samples with target=1: 16.7763%\n", "\n" ] } ], "source": [ "create_classifier(medical_data[0], medical_data[1], 2000)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1d33b7a2-ee1c-41c4-aa18-db5ba6293b0d" }, "source": [ "### Search string: \"debt\"" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "_cell_guid": "bec3df7f-88a3-4c94-8d30-de7881eda1c9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of training set samples:\t2000\n", "Number of testing set samples:\t438749\n", "\n", "Accuracy of predicting testing set target values:\n", " All samples (method 1): 83.2560%\n", " All samples (method 2): 83.2560%\n", " Samples with target=0: 87.2797%\n", " Samples with target=1: 18.1687%\n", "\n" ] } ], "source": [ "create_classifier(debt_data[0], debt_data[1], 2000)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b2bdd08e-676e-47d2-a1c1-ba7903e90dea" }, "source": [ "# Conclusions\n", "A decision tree classifier was used to predict the loan status category (0 or 1) for loan data associated with specific search strings. Loans with a **poor** loan status category (target=1) were predicted with an accuracy in the range of 16-18% for the three search strings investigated.\n", "\n", "The ability to accurately predict loans that are likely to end up with a **poor** outcome is valuable for lenders since this reduces the chance of funding a loan that results in a net financial loss.\n", "\n", "# Limitations\n", "\n", "* The **poor** loan data was plotted after the **good** loan data. Consequently, many of the **good** loan data points are hidden underneath the **bad** loan data points, resulting in an over representation of the **bad** data points in the plots.\n", "* The decision tree classifier was tested with only a single training set for each of the three search strings.\n", "* The date/time features of the data have not been taken into account.\n", "\n", "# Future work\n", "\n", "* Improve data visualization so that fewer **good** loan data points are hidden under the **bad** loan data points.\n", "* Test the decision tree classifier with multiple training sets for each of the three search strings.\n", "* Improve the prediction accuracy.\n", "* Consider the date/time features of the data.\n", "\n", "***Comments/critiques are welcomed, thanks!***" ] } ], "metadata": { "_change_revision": 247, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/320/320604.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "d08c1bbb-1d53-3940-1f5a-4b886026a525" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "7dd7b651-31d9-b888-99eb-e0749d930872" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "calculating weights for digit 0, samples 4132\n", "calculating weights for digit 1, samples 4684\n", "calculating weights for digit 2, samples 4177\n", "calculating weights for digit 3, samples 4351\n", "calculating weights for digit 4, samples 4072\n", "calculating weights for digit 5, samples 3795\n", "calculating weights for digit 6, samples 4137\n", "calculating weights for digit 7, samples 4401\n", "calculating weights for digit 8, samples 4063\n", "calculating weights for digit 9, samples 4188\n" ] } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "numClasses = 10\n", "numEig = 28*28\n", "picSize = 28*28\n", "#read input data\n", "trainData = pd.read_csv(\"../input/train.csv\")\n", "testData = pd.read_csv(\"../input/test.csv\")\n", "trainData.sort_values(by=['label'], inplace=True)\n", "trainY = trainData.iloc[:,0].values\n", "trainX = trainData.iloc[:,1:].values\n", "testX = testData.iloc[:,:].values\n", "#generate eigenface\n", "trainMean = np.mean(trainX, axis=0)\n", "trainX = trainX - trainMean\n", "cov = np.cov(trainX.T)\n", "w, v = np.linalg.eig(cov)\n", "ws = np.sort(w)\n", "ws = ws[::-1]\n", "for i in range(0,numEig):\n", " v[:,i] = v[:,np.where(w==ws[i])[0][0]]\n", "\n", "v = v[:,:numEig].real\n", "#free memory\n", "del trainData, testData, cov, w, ws\n", "\n", "\n", "#generate weights\n", "omega = np.zeros((numClasses, numEig ,picSize))\n", "for i in range(0,numClasses):\n", " trainDigit = trainX[np.where(trainY==i)]\n", " print(\"calculating weights for digit %d, samples %d\" % (i,len(trainDigit)))\n", " for k in range(0,len(trainDigit)):\n", " tmp = v.T * trainDigit[k]\n", " omega[i] += tmp\n", " omega[i] /= len(trainDigit)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "df985b69-d023-8dc2-4cce-8c34ce94a038" }, "outputs": [ { "data": { "image/png": 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0Wng6r9FooN1uz7XGB4OBGwLBhTFkzLSmfXk/ChdwStny/YDF8W6ymIWMmJw4\npfr6/f7Cb0XOTw5wl63Yx2C0j53t7W03KQqJLa8hoCp4AG7YVaPRUKvDm80mbm5u3Lrdbs+JMrc7\nDV8qjq4jtz9uoyR+vV4vMbLSiqhoxjY+zlzeDIQey/Qyj6Sek9BqyMadL72cJLhS2GQxJvdFgD+l\nLEWX25J2vry7TJ6PVkxI27WGhi/65uukPlxNfLnta89XzUaL7s7ODmq1Gl69eoV3330Xr1+/XogC\noyhaEFjqO5PRIV+63e5CKpAbrRbp0mPt4msRMvBWcEkoZQtYpqW1/hxZhawNp9AicFn1LFODhg5V\nwB8dHbkxtprdUZ8nRXRke9oiMyrS7kKRt0ReO15XwAVXpvL4WhZfyW4OsjtZ2aqdi2wMyPTgY4pS\nskSLdvnjUD9uKMrljaMoeju7GAC3DkW5clIcXxoZWLzLmq8hIBd5LF657OuW0NLJWnbHF5XHcbyw\nXgcbLboU6b58+RIf/vCH8Vmf9VkLfQXc2dEsP81m0/V/Up+tLI0nZycLO0LOT+s38LUSpfPjqRW+\nv/wjagUW0iB544B/nrZwo32Oju8uFItFJ7rvvvsu3nnnnYXCJGoAdbtdV4hHfZ98ikc5dEazO0KK\nY1K6mTsY+b7QtdZsTG7jAq51pUiSbI/v95xJI7xa1oFHkXw7XSNgfpYnKbq+8bgy0qVzo2NpRVNS\ncGXVPk36IrODd4l0pe9K04Bbt/BuvOjWajWcnJzgvffew0c+8pGF1lCr1XJjIK+vr/H+++874eUL\noIvmKuHH1ITReHxoAkKR7uHhIV6/fo333nsPwPz1pWFVdO/hDz74wAkvVcq3Wi3VkdCxZIONN8II\n3z6+TIzMcmhOlY7FxTbpN+HbtfM0/IQa1knDhHz9uXwsNfB2LmMuwGn6dGWmTp63HD3CF5/gUo2N\nFE3e/+yLdNMIb+h35t9HCq+vsbgsGy26dNF5C4sbUBRFrkN/b2/P3dhgMpm4yjq6RZrWWS8F/Lm3\nwo0p3O7I2cjMBhW8kd3RDQnIVskhhWb84elXHgWEIt2khiN3VPz7aN+RHyckojK6NqYk/W5AOsFN\nmnVKRrp8SBgXJzk6Qp6HTBVLm5bfZ2tra26uAxqrLseo0ygLOsdCobBQccw/R36mfE3+dvL3C/UV\nr7s/F3gGostL07e3t918oPRDS9El58cFt1QqLcypLKel80UExvNDa+zJ1Ja0r0qlMtcIJEclZwST\n/bkAFmyS8YttAAAgAElEQVQvjR1qdQb0eFlb9kUCJrDLo4mB3OYbupVmBipeRCVFl/ebaotMFWsN\nM7k/H/JIwxjlnbRImHmjQEt5EyGhlbboE9hQ1mfdwrvxoiudH43BpR+bZnDa3d1FuVxGr9fD1tbW\nnEOkeYTlXX9yuZwrRNikuV6N+6E19mS/0mg0mrOxwWAwN7kJbef2xicq4X2maQVXcyhaPxd/npQq\n9qXgTIDTkSZLEBLAtGLLBY1Hu+Px2M25LSee4GjRte8ceVEnny9AE11aqMCQF3jxvlv+e8n1sgIp\n7dUn6tanewfowvOUnTSsQqEwl+YbDodu2r69vT3n8GQ1qRRccyoGIRt7lIrj/Us8vUwV5blczs0g\nRrbHx+Tyu8DQcWhYDZBcnCdfA7DQGAgVT/lII87G/UiKdENTPvK+Uh7lknDyWcZCUSGPdGUBHy28\nqj2Xy81N1OObApUEmTcMeL8tkFyE6rPdpLRyGlFdtfhutOjy4Tbdbhftdnuhj4zfCYfGG+7s7Cz0\n6crqPzlHKFWkpkGrpEtajMeJr1+UhmnJGZVoofG13O6oUcgnOZE3r+B2R85zOByqxSIy6uTFMfTY\nN0We77uFfoekvlt5Hr7XjSm+CNcX6fqEVy7cZ5H98THX2mM67mg0cuNo5a38+KgQeh+PZimbqPXn\n8vQyT3trs1D5bCuUPk5aNNbldzdadKki+c2bNyiXy+o0ZcPhcG4cZKfTmZtTmJwgCTHdDUZOONHt\ndoOTFBCUWpT3ndQmppDbjadBv99Hs9nE6ekpSqXS3DhYWsbjsYtgac0ng9DsjtLQspvDN4ZaOhNp\nc3Lcpa9Yix8zTSScJL5GMvI39KWWNdGVw9O0ebRltMyFbnt7G8PhEMVi0TXK6Dxofz7XMi1SsPP5\n/MKUtDRJjIx0KQqn86S0txzimWZJeo+voUzITNGqxXfjRffq6gpv3rxBLpdDp9NZGFc4mUwWxkJS\nq50Wur3Uzs7OXNGBLHBJK7pyekX5+fJ+oRSRGE8Duo3j2dkZcrkcut3uwrSJABbG3/rsrlgszkWj\nsqBKppc5PLrU7kMbskF+VyB+fJ+o820mtqvBl1aWRU2asPq2cx9I2yaTiSuO0ibAkOllsg1eZaw1\nBnhfLi8apLu7UZRL2RztPKV4+n6PtAuvV5DRLq9RkH2/qxLfjRfd6+tr5PN5DAYDXF5ezqVY+DRo\nfEkyWOrP1YZuJBHH8dy8ufRYRs3dbtddaCvSelpQpJvL5TAYDNBsNhdSfGR3vFiEDzPidicdjhyu\nJu1OK2KSdidtjdsfNQr4TFeycIU7JvmZfFtSYZd2vr7v8Vy6WXy/6V0iXRnt8vfRuFdKMxPU1cH9\nmkw1azM++URXFlLRVKgU6cruEtlA1aJYHjjJx3J/nqam/x19d+DtGGVu7+sSXOCZiO5gMMD19TVK\npdLC7ccomuAXiU+6zfvb+Hi3QqFwp3OaTCZzd4jhsxDRQk6ZIlyas9Z4GpDoDodDtFotnJ+fL0xz\nR9eYL7yegJ5rfbmhFK8v0ozjWJ3xik9zSk5PFghykZV9xr4iKu080jqukAg/R+H1RbtpBFcTX+7v\nALhGFsHnM6AsChdSel3WKfBzkpEuX2RqmXxwaPFFrVpfdyi1TPsAb6NaLrjSxiy9vCSj0cjd4Jvu\neMIFl0RXGiSfnqzX66n3CZX3PuUGEFp4gRZvRW5vb88NW+p0Om4qSpqdSJso4a6FL8Z6oIYS3ZGJ\nbmLAG218yAZ3LHRzCm5/2pAPKWi+lj+3yzh+OzaY9xuT3VEfsbxtIE1Fycel+zIvoZSy2ebd8QmL\nLwIM9XX6+of5Z8kolro6qJCKdztw3yNFl4YMyQky+DZ+tyNNINP8Lr6F/15aKpk/9h1/HXa70aJL\nDpCqislI+P1itdSxNnepHOMmRVe2skItNt5fQoZJ/cV0jvIeuK1Wa67giya/5/0q5tgeFvr96Xrw\noWS8opkLrkzbcfvSFnKcwLzoyoajXKIocnZPRS/UkCyXyy5FSFX+XHwpGqbHZHe8KDHpN5GP+fn7\n3vNc08uEFu2GIj6fyGiNMHlsnm6lGaFIcHO53EIBnuzrj+N4wc9RIaC2cDun/fn5cXwjOZLswSfG\n/LeVj7Ngo0WXBImGBJHh5PP5OcGVhhsqUOCPtSiW903IMn2evqPPowhHCn2v15sT3JubGzQaDVxf\nX+P6+hoA7E4sjwjpDPiEKfSYbl6hVVdqQimLYrhzAt46C95Q9Nkc2R29j7+H7yej3U6n42yOhorQ\nf2mZyvo0DnLZ92wCaaI5WidFuyHB9WXiADjB5elVnnqlPlxZSwDMD//idkzvS7qxgc+X0rnxmgc5\nppxe9/1WcpvvNb49KfpdBRstuuQc+BRn/K49MhWXpkXJHZ9cKFKRA9Ll3KdyvbOz44oLaBkMBgt3\nOjo9PXW3vaKqU+7YrcL5YZDiwKvN6XGaqMUXmWj9UXQsAM65aXeTCd3abWdnZ65/jaY7lX2/p6en\n7s4vvLKZvp8v2tWqnh8ywniq+NKmIRsKpVvl7867ILgAk7+jPlx5UwFg/tpK0eVdddzfyRoFrSFJ\nkMCmzayEhDRJeLPiWYguJ/Qj+17TtmsXkgphqP+Cp1N4XwYJK7Xydnd3Ua/XcXh4iMPDQxwcHGA0\nGjmxJeEtFouIogj9ft85RuCt4NIfx1gv3B58YsPTc1EUzbXS5bhHLqZalXDo84G3s6pJm5P1C6VS\nCeVy2TnGnZ0dVKtV7O/vu2U0Gs2lktvttrO7wWDgujfI1qgARWt4+NYmtlOW+R1CgUCS4IZe186F\nC642eYp2xx4SXZnJ8c37zDMsmtDK/5hv8f1WSWK77O+/SjZadHnkSa0qfj9cXoyU5tZPGr4LydPZ\nVIRC00nSut1uo1Qqod1uu+eURt7a2nLnF0URSqWSc5C3t7cuYudRCZ8S0Fg9af+k3O7IwfCxtXSD\nDI5M1yX1XYWiFd5/vL297Qq5aFhQp9PB3t7egi3e3Nw4R0uZob29PVQqFezv76Pb7c7ZHYkyTzdq\n55tWcNN8z01imQaIT3S0u07xSU8oI9br9QBgQRi1iE8OofTd6ccn1LK7RIq+/F7a9+PfQd5gRt7b\nXJvQRUbl0scnLfK7rpKNFt3t7W03A0q5XMbu7u7cLEC3t7duOj5+oZaF/jx80gxgcTpAcsK3t7dz\nUcje3h6ur69RrVbdIqdGo4KXer3uJswoFAq4urpyY9FoViPjYSkUCi5dWy6XsbOzszA2W1aASqFN\nylrw1+T83zT96WAwmJsa8Pb2di4abjabaDQa7v9RqVTm7JLWZHdk14VCAdfX106gu93uwjn5zpWe\n+5yvEYZnS7jPkiJFYitv46fNNyAjQi2dmxRl8usZir7l9/Clj+Vd3ORjKcCa+Erhlf3Cvu8mf+9V\n8yxE9+DgAIeHh6hWq+6m9Pn8269ODktW5aWBHAiPMGS0IQtiZMELT/3Rul6v4+DgAPv7+zg4OHDO\nj2Yuon487vjkeDtj/WiRSqFQQKlUQr1ex/7+PsrlsusqoHG2URTNOQY5gXwaAZNZFXpMN0+QkYfs\nU6MGH5+qr1aruVQz2Vgau0sSUd95y+/63NLPab6vFoHxUQua6HLhJf/DRZeLrxRGX/QZinZ5V4qW\nCvd9J8qQSIHUbp+qRbpScDXhldFuKPqV34+f66rYaNEtFAool8s4PDzEq1evcHh4iPPzczexBR9z\nyB3XstAFkQYk++y4MfK0S6FQWBjPdnJygg996EPI5XKo1WrO+QFwg853dnac42s2m6pxG9lDkW69\nXseLFy9Qr9dRLBZdQ4+iXHIW/M8fwidk3IGQ3Wl9WTzqoQafnLTj+PgYw+EQhULB2V2pVHKCq9kd\nb+yldU6haPe5Ca8PrZtBRoe+ubS1oWk01aM2SQb3Tb6uDS26ldcq1Jfs+x5amlw2HnyLFF9NeOXs\nbb7oetXi6mPjRZcijuPjY5ycnDhhojsM0VhZXsjCo4Zlo967CPfW1tZCdR9NylGr1TAajZwjj6LI\njX8rFApot9u4urpyM2gZDw81oqrVKg4ODnB0dOSuzWQycd0D5DS485Lim8b+qLEnbU86Eq2/TQ5v\no8k5uN3t7u460aYZhEJ2lySa/PV19589JXy/mya2PMrltSN8iGO/35/rtx2Px96pbblIAumGMmkR\nshYF+74LFeFRA5QLZpLYSuEN3bDD10fti+J9fn9VNrrRoitbXPl8HuVy2aXf9vb23KQTfOn3+wsp\njHU7BxJs6jfzGRifhPyuxV/G+iG7I0Gjuw1RtEiTTNB8x71eb6H1PhgMVGeWlHpOEzGTw+ORMQA1\nwpCT30vHqhXIGPdDCrAUXB7VyoiVv4dHw5Rq5tFumiFp2mMtmuWzVhUKBYzHY7em86Yxvzzbx78P\nfSc+CRBf5Nzh/GYdWto5rQBrmaZ1+daNFl1gvqoun8+jVCo5waVKYDnzU6fTmSu2Wvd0i7wPmJB3\nG+INAd73LMfLGQ+P7EYg0c3n8y7tTFXE/JaS0rFQ6tmHL/XoOyctyuBjiAGoKT1NdJMiGuPukOBq\n0a2vH1dL43LBpS4DOcmPb/IMQBda/ljWDdD4byl2dFN6+h5S6Pk50pr+C/w/Im8QQ35RCm+ojzcp\n8qXff9ls0zJstOhK50eR7t7enjOAXq/nqjhpaTabuLm5cQZB5fbEOgSOD7mgFGQo0vU5QONxQE6J\nN/bI7uI4Rr/fn5v4hB7TzFF0U/BQGoyj7eMbosH7gumzaDsV//lE12dvyw4FSstz69/1Rbg8apUp\nZV/1MU/T8kpmbYazkOhKseWBjCwQpZvcSx9F50X2zT9TFoDJe5xrkS6/pzSJbppqZvpdtP+V/G+t\ny69uvOjK9LIcqD0cDnF5eYnLy0uUSiXXV8oFl5wfn/BglRdEduZHUTQX4dLCW4Oh4SbGw6JFunKa\nz9Fo5Kb1bDQa2NnZcYVWZHdpxEaLdklM+fnI9/iiZOn8qO4hKdKVQiF/D227T1Cfk8j64L+N1pdL\njTIZ4dK+tA+NEedFdJrohgRXW/gx6LhSbEOVwfz41NDjGRYuuDLalcIr08uysEwOr5KVzKEGraWX\nl0SOk+31enN9bJVKBXEcO2co5wTlxS88XSFb/TzN6yOU9uNREb87hzwX3u/b7/fR7XZd2nnZoU7G\n+iB7oevE7Y7G7wKYm45xd3d34VrLyCaUHtPSf5ozlch5xvlk9OSUeXELfSfKumh25xPZEM9RaH3f\n2Zdepv5PLry+CJennmXhFH8cshW5jQs0txkSdJ/YcqHV/KSWXfH145LP09LKmuDepZCKr9fBRosu\ntZioyjKKIhweHrqK5b29PVeRWa/XXRqQpmnkYxZJtHl/gtaikhGGjGJ5C5YMmM6Bj5d89eoV3nnn\nHRwfH+Pg4ADVahU3NzfO2G5ubnB1dYWbmxs3yUeS8BvZMBqNcHt7i0aj4eYs3t/fd3ZHFcC7u7sA\npuPJaYIKmqSiWq2iXq870dYyHjztK1PRHLI5uVAfM19evnyJly9f4ujoCPV6HZVKBa1Wy2VeWq0W\nGo1GKrtbdUboOeGLdHk/vCa4sh9Xi2xDk2P4FlnhrN3YJSRmdI58fgSCirx4DQv5WS2VLNPJ96la\npvPi63Wz0aJLk7dfX19ja2vLXQw+3zFNEEDR73A4RK1Wm5uT9vDwUC224kZAfwZgPt3Gy+L5kA5u\nxDSJR71ed8vLly/x+vVrHB8f4/DwELVaDf1+HwBcP/Tl5aWJ7iMjiiLXJ9VoNFz6jOyO5jsmuysW\ni6hUKhiPx6hWq6hUKqjVaq7Bp91ij2zON+kArbXGHnei29vbbkhdvV5HrVbDyckJTk5OcHR0hP39\nfVQqFXdv4F6vt3Rjj0ds2muGHym2WgRK+/GsiLy9oxyTG5oxyjdHs5xfgMSWCqd4XYB2rXnam54T\nvN+Zi65PeLVxuqHiKV8qOcuUMmfjRbfT6WBra3p3IZo9Z3d3F7VazRko9eGSwXU6HdTrdTQaDRwd\nHaHRaODq6grX19e4urpy/b5UTUeiqv0p+IUnByQNuVAooFKp4PDwECcnJ25M8cuXL3FycjIX6QJA\nv99Hs9nE1dUVms2mc34WVTwsdP0p0iUBJrsjweWOka87nQ5qtZqbyYoX99GSz+dxe3vrBFcbF84b\neTIFzRt71NDc39/HixcvcHx87NZcdFutFuI4dnZ3eXmJZrPpbnDPax34OZg93h+tn55fc3qN3/NW\nzjjlm30qjejSc1+l8nA4dDUySYILYG6EBm2jLgtNdLng+gqn0sxIpYmtLxpfNxstuoPBAO1224lv\nq9VyU90dHx/PRR/8biwUSd7c3Ljl7OwMZ2dnbgo8AHOFLzS+VmslxnHs+mP4PmTEFOnSzFnvvvuu\nc3x056Fqtepu60fpZYt0Hw9ccMjeaN1ut91kGd1u1zlJ6kqgpdfroV6vu6lKm80mLi4uUCqVsL29\n7YSW9+9TY0vaHKUgeYNPa+xRpHtycoJ33nnH2dzBwYFLL1OKPG2kyxufJrzLERIt+TvLlLI22x3f\ntkyfraxqljUnVIRaLBadwHG47+PRJE2Dyr8DF13qTpH9uFJwQ5EuF1xfpXKa331dbLToTiYTN7EF\nXYRGo4GLiwtUq1WUSiWMRiN3T1Fa6GKRYwTg0ifkrPb29pyQk2MdDAbus3nLjvfB0bg13idSrVbx\n+vVrvH792qX2KpUK8vk8RqMRWq0WxuMxLi4ucHl56aLum5sbN8kCFVMZqyEp9cmLQ+T7qJAqjmPX\n10+ZiUqlgr29PUwmE9d/S8VwsvsDeDuUjBpndFcqfpcfPnkLnRsvpqFz4MUv+XwelUoFL1++xKtX\nr1w3Bre7druNyWQyZ3O8P5fOe9UOy9LO8yQVa5Jf4uNeeeNKSyUD+u1J+ftkHy7PzFBDju9HE3XQ\n3dxI6OWsWHTOtJaiq0Wz2hjcpH7b0AiPkADz81sHGy26vCVIFXLNZhNnZ2fOsVxeXrrCFXKCsjVG\nBSeTycTN53x0dLRwxyISPt7KkoO+x+Pxwo2dKcrlEQa1JCmtCAC/+Zu/iTdv3uDs7AzX19dotVqu\nmi9pIgVjPci0Km3jk5ZEUYRWq4WLiwvkcjk3XIjuKEX2RxkUuo6UAQHg+n61iQIo2uR2p4kun2qU\n5lTmN9UguxuNpvdybjabiOMYb968wfvvv4/z83M0Gg202+2l7Y5HvSaq6dDSniHh5X2/WvGTloUD\nFq+HjJRJZLlQ8hoZKXpyLDEXXW7jtEjR9c2pLKPYNH212u/00H7yWYgupRgAuAkIqCV/fn7uCleq\n1SpqtZorcKG0c7FYdMVWdMcVPo6WUh/cSPhaii6/oT2lGHnVNBV08TFqnU4HH3zwAT7zmc/g9PTU\niS6fNOOhjcl4C3cKcRyj1Wo5wb29vcXl5aVqd7KrI4oiV93M7Y5sjsYpcpvTlslkotod2R6tKSXO\nl9PTU5ydneH8/BzX19cuq0O3xZR25+vfNbFNj/y9+G+spZmlgPrEV27Tji8rnOkORXyftIIrBV5O\nsEKi67uRgTanshbZauNtfb+r/L5Zs/Giy1PLk8kENzc3TnAvLi7cDbr39/ddAQtVctbrdeRyOden\nxluNlDrU5qrlBQGa6JKw0kITI/AB7HQz+0ajgfPzc5ydnbn08sXFhRNdWaVnPDwyKhmPx2i1WhiN\nRm4Im6xW5wVU9Xp9bhKXcrk810cmU27S5rRJ4OM4Xrh9JNkdH6vL+5PPz89xfn6Oq6srtzQajdR2\nZ/Z4f2S0ywUsaX9fYZScZ1lGiLL/lqeTec0AF1kt0yKHNlGELD+Pj/3mNp1mhikuuKEhQfK5/K2y\nZKNFF1hsFZLgkvHt7u661C6ld+n2ZjSWl/rYaBIDGmcpjW0wGCyUuctWGx8aQmlFPpMLHYv6aRuN\nBt68eYNPfepTLuVH69vb2wf6VQ0NcoZanxIJLrc7auzRcnx87LowKpXK3K33KAKmLA2PJjS700SX\n0tm0SLsbj6czYVF6+TOf+Qw+/elPzw2Vu7m5UW9ar6XYtd9G7mPR7yL0u/A1f40XZdI2WnNxkVGm\nNmQIwELEKCfQoFuh8uNRtbSMPGWky8+Rfx/+/whFujK97OvD9UW6WhbmoRuDGy+6EtkyoiEdrVZr\nbsYXvv3q6mouMqU0ILA4nZmczEA6v8lkgk6n4xwxjbeURQTn5+dzfWk0RIP68LShIsbDIfsrtT87\ndwxkX4VCwdkd7dPr9dBut9FoNOYmTaFZymRLXbM7OYSCnCnfP5fLJdpdo9FwNQskyLwKn/CJZ8jB\nmeAmIwWYttF6MpnMRY+0XUbFXOySIkEiVOksBZ2fL+9rJSGWEa9skIa6RTSRTYtstPj+n1ny7EQX\nmP/Bx+Mx+v0+Op0OgLdTkt3e3rohG7zIimYOogiYT7FGYs1bbHLsGBV19Xo9dDodN76NT37Q6XRw\nfX2Ni4sLl1K+ublxY9WsUvnxklTEAby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"text/plain": [ "<matplotlib.figure.Figure at 0x7ff2f14e1d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#generate weights\n", "orig = testX[np.random.randint(0, len(testX))]\n", "omega_m = v.T * (orig - trainMean)\n", "\n", "#find best match\n", "dist = np.zeros((numClasses))\n", "for i in range(0,numClasses):\n", " dist[i] = np.linalg.norm(omega[i] - omega_m)\n", "i = dist.argmin()\n", "\n", "#reconstruct\n", "recon = v.T * omega_m\n", "recon = np.sum(recon,axis=0) + trainMean\n", "match = v.T * omega[i]\n", "match = np.sum(match, axis=0) + trainMean\n", "\n", "#show result\n", "fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3),\n", " sharex=True, sharey=True)\n", "\n", "ax1.imshow(orig.reshape(28,28), cmap=plt.cm.gray)\n", "ax1.axis('off')\n", "ax1.set_title('testX', fontsize=10)\n", "\n", "ax2.imshow(recon.reshape(28,28), cmap=plt.cm.gray)\n", "ax2.axis('off')\n", "ax2.set_title('reconstruct', fontsize=10)\n", "\n", "ax3.imshow(match.reshape(28,28), cmap=plt.cm.gray)\n", "ax3.axis('off')\n", "ax3.set_title('match', fontsize=10)\n", "\n", "plt.show()\n", "plt.close()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "95ce924a-706c-da12-f485-06d3d1f60a45" }, "outputs": [], "source": [ "#make a submission\n", "out_file = open(\"submission_dr_eigenface.csv\", \"w\")\n", "out_file.write(\"ImageId,Label\\n\")\n", "\n", "for k in range(0,len(testX)):\n", " #generate weights\n", " omega_m = v.T * (testX[k] - trainMean)\n", " #find best match\n", " dist = np.zeros((numClasses))\n", " for i in range(0,numClasses):\n", " dist[i] = np.linalg.norm(omega[i] - omega_m)\n", " out_file.write(str(k+1) + \",\" + str(int(dist.argmin())) + \"\\n\") \n", " \n", "out_file.close()" ] } ], "metadata": { "_change_revision": 26, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/320/320748.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "ff5eebce-4926-b0fe-5415-af4d4271b31d" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "06278dc5-8601-4aad-8dd5-f4f6fc23cde9" }, "outputs": [], "source": [ "import numpy as np \n", "import pandas as pd\n", "\n", "data=pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "8657e6c7-99c6-415f-b683-0765084e3a49" }, "outputs": [ { "data": { "text/plain": [ "(array([ 1920., 1930., 1940., 1950., 1960., 1970., 1980., 1990.,\n", " 2000., 2010.]), <a list of 10 Text xticklabel objects>)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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bn3W15/R/Xi91/8xmKyLIzIF/g176jGxmfioiNgbeCRze2nwycBOwtM4ktuUy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\ngdcB5+DFXpIkSRqxKhd7HQS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"text/plain": [ "<matplotlib.figure.Figure at 0x7f867705c940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from dateutil.parser import parse\n", "#Showing how the massive crashes (fatalities>50) change whith year\n", "#getting just years\n", "years=[]\n", "for i in range(len(data)):\n", " years.append(parse(data.Date[i]).year)\n", " \n", "data.Fatalities=data.Fatalities.fillna(data.Fatalities.mean())\n", "temp=zip(years,data.Fatalities)\n", "temp=[(x,y) for (x,y) in temp if y>50]\n", "temp=pd.DataFrame(temp,columns=['massive_years','Fatalities'])\n", "counts=temp.massive_years.value_counts()\n", "plt.figure(figsize=(11, 7))\n", "plt.bar(counts.index,counts.values)\n", "plt.ylabel('Number of Massive Crashes(fatalities>50)',fontsize=15)\n", "plt.xlabel('Year',fontsize=15)\n", "plt.yticks(fontsize=15)\n", "plt.xticks(fontsize=15)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "b7c63cac-e64f-4041-b8e4-82ce4dd1ff57" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 174, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/320/320866.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "b79ea8b9-65cb-87c4-7dee-1aff704fdb40" }, "source": [ "##Kagglers teams and users graph\n", "\n", "The goal of this script is:\n", "\n", "* to show how different users form teams,\n", "* to find centers of community among Kagglers (it may help you if you want to compete as a team:)),\n", "* to see the largest teams in the history of Kaggle competitions\n", "* to look at overloaded graph :)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "c680d231-8d06-df36-28bc-d1f1d5d442a9" }, "outputs": [ { "data": { "text/html": "<script type='text/javascript'>if(!window.Plotly){define('plotly', function(require, exports, module) {/**\n* plotly.js v1.14.1\n* Copyright 2012-2016, Plotly, Inc.\n* All rights reserved.\n* Licensed under the MIT license\n*/\n!function(t){if(\"object\"==typeof exports&&\"undefined\"!=typeof module)module.exports=t();else if(\"function\"==typeof 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r=this.computedMatrix;if(e){for(var n=0;16>n;++n)e[n]=r[n];return e}return r},d.idle=function(t){this.center.idle(t),this.radius.idle(t),this.rotation.idle(t)},d.flush=function(t){this.center.flush(t),this.radius.flush(t),this.rotation.flush(t)},d.pan=function(t,e,r,i){e=e||0,r=r||0,i=i||0,this.recalcMatrix(t);var a=this.computedMatrix,o=a[1],s=a[5],l=a[9],c=n(o,s,l);o/=c,s/=c,l/=c;var u=a[0],f=a[4],h=a[8],d=u*o+f*s+h*l;u-=o*d,f-=s*d,h-=l*d;var p=n(u,f,h);u/=p,f/=p,h/=p;var g=a[2],v=a[6],m=a[10],y=g*o+v*s+m*l,b=g*u+v*f+m*h;g-=y*o+b*u,v-=y*s+b*f,m-=y*l+b*h;var x=n(g,v,m);g/=x,v/=x,m/=x;var _=u*e+o*r,w=f*e+s*r,k=h*e+l*r;this.center.move(t,_,w,k);var A=Math.exp(this.computedRadius[0]);A=Math.max(1e-4,A+i),this.radius.set(t,Math.log(A))},d.rotate=function(t,e,r,a){this.recalcMatrix(t),e=e||0,r=r||0;var o=this.computedMatrix,s=o[0],l=o[4],c=o[8],u=o[1],f=o[5],h=o[9],d=o[2],p=o[6],g=o[10],v=e*s+r*u,m=e*l+r*f,y=e*c+r*h,b=-(p*y-g*m),x=-(g*v-d*y),_=-(d*m-p*v),w=Math.sqrt(Math.max(0,1-Math.pow(b,2)-Math.pow(x,2)-Math.pow(_,2))),k=i(b,x,_,w);k>1e-6?(b/=k,x/=k,_/=k,w/=k):(b=x=_=0,w=1);var A=this.computedRotation,M=A[0],T=A[1],E=A[2],L=A[3],S=M*w+L*b+T*_-E*x,C=T*w+L*x+E*b-M*_,z=E*w+L*_+M*x-T*b,P=L*w-M*b-T*x-E*_;if(a){b=d,x=p,_=g;var R=Math.sin(a)/n(b,x,_);b*=R,x*=R,_*=R,w=Math.cos(e),S=S*w+P*b+C*_-z*x,C=C*w+P*x+z*b-S*_,z=z*w+P*_+S*x-C*b,P=P*w-S*b-C*x-z*_}var O=i(S,C,z,P);O>1e-6?(S/=O,C/=O,z/=O,P/=O):(S=C=z=0,P=1),this.rotation.set(t,S,C,z,P)},d.lookAt=function(t,e,r,n){this.recalcMatrix(t),r=r||this.computedCenter,e=e||this.computedEye,n=n||this.computedUp;var i=this.computedMatrix;c(i,e,r,n);var o=this.computedRotation;h(o,i[0],i[1],i[2],i[4],i[5],i[6],i[8],i[9],i[10]),a(o,o),this.rotation.set(t,o[0],o[1],o[2],o[3]);for(var s=0,l=0;3>l;++l)s+=Math.pow(r[l]-e[l],2);this.radius.set(t,.5*Math.log(Math.max(s,1e-6))),this.center.set(t,r[0],r[1],r[2])},d.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},d.setMatrix=function(t,e){var r=this.computedRotation;h(r,e[0],e[1],e[2],e[4],e[5],e[6],e[8],e[9],e[10]),a(r,r),this.rotation.set(t,r[0],r[1],r[2],r[3]);var n=this.computedMatrix;f(n,e);var i=n[15];if(Math.abs(i)>1e-6){var o=n[12]/i,s=n[13]/i,l=n[14]/i;this.recalcMatrix(t);var c=Math.exp(this.computedRadius[0]);this.center.set(t,o-n[2]*c,s-n[6]*c,l-n[10]*c),this.radius.idle(t)}else this.center.idle(t),this.radius.idle(t)},d.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},d.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-(1/0),e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},d.getDistanceLimits=function(t){var e=this.radius.bounds;\nreturn t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},d.toJSON=function(){return this.recalcMatrix(this.lastT()),{center:this.computedCenter.slice(),rotation:this.computedRotation.slice(),distance:Math.log(this.computedRadius[0]),zoomMin:this.radius.bounds[0][0],zoomMax:this.radius.bounds[1][0]}},d.fromJSON=function(t){var e=this.lastT(),r=t.center;r&&this.center.set(e,r[0],r[1],r[2]);var n=t.rotation;n&&this.rotation.set(e,n[0],n[1],n[2],n[3]);var i=t.distance;i&&i>0&&this.radius.set(e,Math.log(i)),this.setDistanceLimits(t.zoomMin,t.zoomMax)}},{\"./lib/quatFromFrame\":36,\"filtered-vector\":20,\"gl-mat4/fromQuat\":134,\"gl-mat4/invert\":137,\"gl-mat4/lookAt\":138}],38:[function(t,e,r){\"use strict\";function n(t,e,r){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2))}function i(t){return Math.min(1,Math.max(-1,t))}function a(t){var e=Math.abs(t[0]),r=Math.abs(t[1]),n=Math.abs(t[2]),i=[0,0,0];e>Math.max(r,n)?i[2]=1:r>Math.max(e,n)?i[0]=1:i[1]=1;for(var a=0,o=0,s=0;3>s;++s)a+=t[s]*t[s],o+=i[s]*t[s];for(var s=0;3>s;++s)i[s]-=o/a*t[s];return h(i,i),i}function o(t,e,r,n,i,a,o,s){this.center=l(r),this.up=l(n),this.right=l(i),this.radius=l([a]),this.angle=l([o,s]),this.angle.bounds=[[-(1/0),-Math.PI/2],[1/0,Math.PI/2]],this.setDistanceLimits(t,e),this.computedCenter=this.center.curve(0),this.computedUp=this.up.curve(0),this.computedRight=this.right.curve(0),this.computedRadius=this.radius.curve(0),this.computedAngle=this.angle.curve(0),this.computedToward=[0,0,0],this.computedEye=[0,0,0],this.computedMatrix=new Array(16);for(var c=0;16>c;++c)this.computedMatrix[c]=.5;this.recalcMatrix(0)}function s(t){t=t||{};var e=t.center||[0,0,0],r=t.up||[0,1,0],i=t.right||a(r),s=t.radius||1,l=t.theta||0,c=t.phi||0;if(e=[].slice.call(e,0,3),r=[].slice.call(r,0,3),h(r,r),i=[].slice.call(i,0,3),h(i,i),\"eye\"in t){var u=t.eye,p=[u[0]-e[0],u[1]-e[1],u[2]-e[2]];f(i,p,r),n(i[0],i[1],i[2])<1e-6?i=a(r):h(i,i),s=n(p[0],p[1],p[2]);var g=d(r,p)/s,v=d(i,p)/s;c=Math.acos(g),l=Math.acos(v)}return s=Math.log(s),new o(t.zoomMin,t.zoomMax,e,r,i,s,l,c)}e.exports=s;var l=t(\"filtered-vector\"),c=t(\"gl-mat4/invert\"),u=t(\"gl-mat4/rotate\"),f=t(\"gl-vec3/cross\"),h=t(\"gl-vec3/normalize\"),d=t(\"gl-vec3/dot\"),p=o.prototype;p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-(1/0),e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},p.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},p.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,i=0,a=0,o=0;3>o;++o)a+=e[o]*r[o],i+=e[o]*e[o];for(var s=Math.sqrt(i),l=0,o=0;3>o;++o)r[o]-=e[o]*a/i,l+=r[o]*r[o],e[o]/=s;for(var c=Math.sqrt(l),o=0;3>o;++o)r[o]/=c;var u=this.computedToward;f(u,e,r),h(u,u);for(var d=Math.exp(this.computedRadius[0]),p=this.computedAngle[0],g=this.computedAngle[1],v=Math.cos(p),m=Math.sin(p),y=Math.cos(g),b=Math.sin(g),x=this.computedCenter,_=v*y,w=m*y,k=b,A=-v*b,M=-m*b,T=y,E=this.computedEye,L=this.computedMatrix,o=0;3>o;++o){var S=_*r[o]+w*u[o]+k*e[o];L[4*o+1]=A*r[o]+M*u[o]+T*e[o],L[4*o+2]=S,L[4*o+3]=0}var C=L[1],z=L[5],P=L[9],R=L[2],O=L[6],I=L[10],N=z*I-P*O,j=P*R-C*I,F=C*O-z*R,D=n(N,j,F);N/=D,j/=D,F/=D,L[0]=N,L[4]=j,L[8]=F;for(var o=0;3>o;++o)E[o]=x[o]+L[2+4*o]*d;for(var o=0;3>o;++o){for(var l=0,B=0;3>B;++B)l+=L[o+4*B]*E[B];L[12+o]=-l}L[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;16>n;++n)e[n]=r[n];return e}return r};var g=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;g[0]=i[2],g[1]=i[6],g[2]=i[10];for(var a=this.computedUp,o=this.computedRight,s=this.computedToward,l=0;3>l;++l)i[4*l]=a[l],i[4*l+1]=o[l],i[4*l+2]=s[l];u(i,i,n,g);for(var l=0;3>l;++l)a[l]=i[4*l],o[l]=i[4*l+1];this.up.set(t,a[0],a[1],a[2]),this.right.set(t,o[0],o[1],o[2])}},p.pan=function(t,e,r,i){e=e||0,r=r||0,i=i||0,this.recalcMatrix(t);var a=this.computedMatrix,o=(Math.exp(this.computedRadius[0]),a[1]),s=a[5],l=a[9],c=n(o,s,l);o/=c,s/=c,l/=c;var u=a[0],f=a[4],h=a[8],d=u*o+f*s+h*l;u-=o*d,f-=s*d,h-=l*d;var p=n(u,f,h);u/=p,f/=p,h/=p;var g=u*e+o*r,v=f*e+s*r,m=h*e+l*r;this.center.move(t,g,v,m);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+i),this.radius.set(t,Math.log(y))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,a){var o=1;\"number\"==typeof r&&(o=0|r),(0>o||o>3)&&(o=1);var s=(o+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var l=e[o],u=e[o+4],f=e[o+8];if(a){var h=Math.abs(l),d=Math.abs(u),p=Math.abs(f),g=Math.max(h,d,p);h===g?(l=0>l?-1:1,u=f=0):p===g?(f=0>f?-1:1,l=u=0):(u=0>u?-1:1,l=f=0)}else{var v=n(l,u,f);l/=v,u/=v,f/=v}var m=e[s],y=e[s+4],b=e[s+8],x=m*l+y*u+b*f;m-=l*x,y-=u*x,b-=f*x;var _=n(m,y,b);m/=_,y/=_,b/=_;var w=u*b-f*y,k=f*m-l*b,A=l*y-u*m,M=n(w,k,A);w/=M,k/=M,A/=M,this.center.jump(t,H,G,Y),this.radius.idle(t),this.up.jump(t,l,u,f),this.right.jump(t,m,y,b);var T,E;if(2===o){var L=e[1],S=e[5],C=e[9],z=L*m+S*y+C*b,P=L*w+S*k+C*A;T=0>N?-Math.PI/2:Math.PI/2,E=Math.atan2(P,z)}else{var R=e[2],O=e[6],I=e[10],N=R*l+O*u+I*f,j=R*m+O*y+I*b,F=R*w+O*k+I*A;T=Math.asin(i(N)),E=Math.atan2(F,j)}this.angle.jump(t,E,T),this.recalcMatrix(t);var D=e[2],B=e[6],U=e[10],V=this.computedMatrix;c(V,e);var q=V[15],H=V[12]/q,G=V[13]/q,Y=V[14]/q,X=Math.exp(this.computedRadius[0]);this.center.jump(t,H-D*X,G-B*X,Y-U*X)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,a){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter,a=a||this.computedUp;var o=a[0],s=a[1],l=a[2],c=n(o,s,l);if(!(1e-6>c)){o/=c,s/=c,l/=c;var u=e[0]-r[0],f=e[1]-r[1],h=e[2]-r[2],d=n(u,f,h);if(!(1e-6>d)){u/=d,f/=d,h/=d;var p=this.computedRight,g=p[0],v=p[1],m=p[2],y=o*g+s*v+l*m;g-=y*o,v-=y*s,m-=y*l;var b=n(g,v,m);if(!(.01>b&&(g=s*h-l*f,v=l*u-o*h,m=o*f-s*u,b=n(g,v,m),1e-6>b))){g/=b,v/=b,m/=b,this.up.set(t,o,s,l),this.right.set(t,g,v,m),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(d));var x=s*m-l*v,_=l*g-o*m,w=o*v-s*g,k=n(x,_,w);x/=k,_/=k,w/=k;var A=o*u+s*f+l*h,M=g*u+v*f+m*h,T=x*u+_*f+w*h,E=Math.asin(i(A)),L=Math.atan2(T,M),S=this.angle._state,C=S[S.length-1],z=S[S.length-2];C%=2*Math.PI;var P=Math.abs(C+2*Math.PI-L),R=Math.abs(C-L),O=Math.abs(C-2*Math.PI-L);R>P&&(C+=2*Math.PI),R>O&&(C-=2*Math.PI),this.angle.jump(this.angle.lastT(),C,z),this.angle.set(t,L,E)}}}}},{\"filtered-vector\":20,\"gl-mat4/invert\":137,\"gl-mat4/rotate\":141,\"gl-vec3/cross\":23,\"gl-vec3/dot\":24,\"gl-vec3/normalize\":27}],39:[function(t,e,r){\"use strict\";function n(t,e){this._controllerNames=Object.keys(t),this._controllerList=this._controllerNames.map(function(e){return t[e]}),this._mode=e,this._active=t[e],this._active||(this._mode=\"turntable\",this._active=t.turntable),this.modes=this._controllerNames,this.computedMatrix=this._active.computedMatrix,this.computedEye=this._active.computedEye,this.computedUp=this._active.computedUp,this.computedCenter=this._active.computedCenter,this.computedRadius=this._active.computedRadius}function i(t){t=t||{};var e=t.eye||[0,0,1],r=t.center||[0,0,0],i=t.up||[0,1,0],l=t.distanceLimits||[0,1/0],c=t.mode||\"turntable\",u=a(),f=o(),h=s();return u.setDistanceLimits(l[0],l[1]),u.lookAt(0,e,r,i),f.setDistanceLimits(l[0],l[1]),f.lookAt(0,e,r,i),h.setDistanceLimits(l[0],l[1]),h.lookAt(0,e,r,i),new n({turntable:u,orbit:f,matrix:h},c)}e.exports=i;var a=t(\"turntable-camera-controller\"),o=t(\"orbit-camera-controller\"),s=t(\"matrix-camera-controller\"),l=n.prototype,c=[[\"flush\",1],[\"idle\",1],[\"lookAt\",4],[\"rotate\",4],[\"pan\",4],[\"translate\",4],[\"setMatrix\",2],[\"setDistanceLimits\",2],[\"setDistance\",2]];c.forEach(function(t){for(var e=t[0],r=[],n=0;n<t[1];++n)r.push(\"a\"+n);var i=\"var cc=this._controllerList;for(var i=0;i<cc.length;++i){cc[i].\"+t[0]+\"(\"+r.join()+\")}\";l[e]=Function.apply(null,r.concat(i))}),l.recalcMatrix=function(t){this._active.recalcMatrix(t)},l.getDistance=function(t){return this._active.getDistance(t)},l.getDistanceLimits=function(t){return this._active.getDistanceLimits(t)},l.lastT=function(){return this._active.lastT()},l.setMode=function(t){if(t!==this._mode){var e=this._controllerNames.indexOf(t);if(!(0>e)){var r=this._active,n=this._controllerList[e],i=Math.max(r.lastT(),n.lastT());r.recalcMatrix(i),n.setMatrix(i,r.computedMatrix),this._active=n,this._mode=t,this.computedMatrix=this._active.computedMatrix,this.computedEye=this._active.computedEye,this.computedUp=this._active.computedUp,this.computedCenter=this._active.computedCenter,this.computedRadius=this._active.computedRadius}}},l.getMode=function(){return this._mode}},{\"matrix-camera-controller\":28,\"orbit-camera-controller\":37,\"turntable-camera-controller\":38}],40:[function(t,e,r){function n(t,e){return a(i(t,e))}e.exports=n;var i=t(\"alpha-complex\"),a=t(\"simplicial-complex-boundary\")},{\"alpha-complex\":41,\"simplicial-complex-boundary\":44}],41:[function(t,e,r){\"use strict\";function n(t,e){return i(e).filter(function(r){for(var n=new Array(r.length),i=0;i<r.length;++i)n[i]=e[r[i]];return a(n)*t<1})}e.exports=n;var 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this.length>t.length?this.clone().ior(t):t.clone().ior(this)},i.prototype.iand=function(t){this.sign=this.sign&&t.sign;var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},i.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},i.prototype.ixor=function(t){this.sign=this.sign||t.sign;var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},i.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},i.prototype.setn=function(t,e){r(\"number\"==typeof t&&t>=0);for(var n=t/26|0,i=t%26;this.length<=n;)this.words[this.length++]=0;return e?this.words[n]=this.words[n]|1<<i:this.words[n]=this.words[n]&~(1<<i),this.strip()},i.prototype.iadd=function(t){if(this.sign&&!t.sign){this.sign=!1;var e=this.isub(t);return this.sign=!this.sign,\nthis._normSign()}if(!this.sign&&t.sign){t.sign=!1;var e=this.isub(t);return t.sign=!0,e._normSign()}var r,n;this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++){var e=r.words[a]+n.words[a]+i;this.words[a]=67108863&e,i=e>>>26}for(;0!==i&&a<r.length;a++){var e=r.words[a]+i;this.words[a]=67108863&e,i=e>>>26}if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},i.prototype.add=function(t){if(t.sign&&!this.sign){t.sign=!1;var e=this.sub(t);return t.sign=!0,e}if(!t.sign&&this.sign){this.sign=!1;var e=t.sub(this);return this.sign=!0,e}return this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},i.prototype.isub=function(t){if(t.sign){t.sign=!1;var e=this.iadd(t);return t.sign=!0,e._normSign()}if(this.sign)return this.sign=!1,this.iadd(t),this.sign=!0,this._normSign();var r=this.cmp(t);if(0===r)return this.sign=!1,this.length=1,this.words[0]=0,this;var n,i;r>0?(n=this,i=t):(n=t,i=this);for(var a=0,o=0;o<i.length;o++){var e=n.words[o]-i.words[o]+a;a=e>>26,this.words[o]=67108863&e}for(;0!==a&&o<n.length;o++){var e=n.words[o]+a;a=e>>26,this.words[o]=67108863&e}if(0===a&&o<n.length&&n!==this)for(;o<n.length;o++)this.words[o]=n.words[o];return this.length=Math.max(this.length,o),n!==this&&(this.sign=!0),this.strip()},i.prototype.sub=function(t){return this.clone().isub(t)},i.prototype._smallMulTo=function(t,e){e.sign=t.sign!==this.sign,e.length=this.length+t.length;for(var r=0,n=0;n<e.length-1;n++){for(var i=r>>>26,a=67108863&r,o=Math.min(n,t.length-1),s=Math.max(0,n-this.length+1);o>=s;s++){var l=n-s,c=0|this.words[l],u=0|t.words[s],f=c*u,h=67108863&f;i=i+(f/67108864|0)|0,h=h+a|0,a=67108863&h,i=i+(h>>>26)|0}e.words[n]=a,r=i}return 0!==r?e.words[n]=r:e.length--,e.strip()},i.prototype._bigMulTo=function(t,e){e.sign=t.sign!==this.sign,e.length=this.length+t.length;for(var r=0,n=0,i=0;i<e.length-1;i++){var a=n;n=0;for(var o=67108863&r,s=Math.min(i,t.length-1),l=Math.max(0,i-this.length+1);s>=l;l++){var c=i-l,u=0|this.words[c],f=0|t.words[l],h=u*f,d=67108863&h;a=a+(h/67108864|0)|0,d=d+o|0,o=67108863&d,a=a+(d>>>26)|0,n+=a>>>26,a&=67108863}e.words[i]=o,r=a,a=n}return 0!==r?e.words[i]=r:e.length--,e.strip()},i.prototype.mulTo=function(t,e){var r;return r=this.length+t.length<63?this._smallMulTo(t,e):this._bigMulTo(t,e)},i.prototype.mul=function(t){var e=new i(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},i.prototype.imul=function(t){if(0===this.cmpn(0)||0===t.cmpn(0))return this.words[0]=0,this.length=1,this;var e=this.length,r=t.length;this.sign=t.sign!==this.sign,this.length=this.length+t.length,this.words[this.length-1]=0;for(var n=this.length-2;n>=0;n--){for(var i=0,a=0,o=Math.min(n,r-1),s=Math.max(0,n-e+1);o>=s;s++){var l=n-s,c=this.words[l],u=t.words[s],f=c*u,h=67108863&f;i+=f/67108864|0,h+=a,a=67108863&h,i+=h>>>26}this.words[n]=a,this.words[n+1]+=i,i=0}for(var i=0,l=1;l<this.length;l++){var d=this.words[l]+i;this.words[l]=67108863&d,i=d>>>26}return this.strip()},i.prototype.imuln=function(t){r(\"number\"==typeof t);for(var e=0,n=0;n<this.length;n++){var i=this.words[n]*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[n]=67108863&a}return 0!==e&&(this.words[n]=e,this.length++),this},i.prototype.muln=function(t){return this.clone().imuln(t)},i.prototype.sqr=function(){return this.mul(this)},i.prototype.isqr=function(){return this.mul(this)},i.prototype.ishln=function(t){r(\"number\"==typeof t&&t>=0);var e=t%26,n=(t-e)/26,i=67108863>>>26-e<<26-e;if(0!==e){for(var a=0,o=0;o<this.length;o++){var s=this.words[o]&i,l=this.words[o]-s<<e;this.words[o]=l|a,a=s>>>26-e}a&&(this.words[o]=a,this.length++)}if(0!==n){for(var o=this.length-1;o>=0;o--)this.words[o+n]=this.words[o];for(var o=0;n>o;o++)this.words[o]=0;this.length+=n}return this.strip()},i.prototype.ishrn=function(t,e,n){r(\"number\"==typeof t&&t>=0);var i;i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=n;if(i-=o,i=Math.max(0,i),l){for(var c=0;o>c;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o){this.length-=o;for(var c=0;c<this.length;c++)this.words[c]=this.words[c+o]}else this.words[0]=0,this.length=1;for(var u=0,c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip(),this},i.prototype.shln=function(t){return this.clone().ishln(t)},i.prototype.shrn=function(t){return this.clone().ishrn(t)},i.prototype.testn=function(t){r(\"number\"==typeof t&&t>=0);var e=t%26,n=(t-e)/26,i=1<<e;if(this.length<=n)return!1;var a=this.words[n];return!!(a&i)},i.prototype.imaskn=function(t){r(\"number\"==typeof t&&t>=0);var e=t%26,n=(t-e)/26;if(r(!this.sign,\"imaskn works only with positive numbers\"),0!==e&&n++,this.length=Math.min(n,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},i.prototype.maskn=function(t){return this.clone().imaskn(t)},i.prototype.iaddn=function(t){return r(\"number\"==typeof t),0>t?this.isubn(-t):this.sign?1===this.length&&this.words[0]<t?(this.words[0]=t-this.words[0],this.sign=!1,this):(this.sign=!1,this.isubn(t),this.sign=!0,this):this._iaddn(t)},i.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},i.prototype.isubn=function(t){if(r(\"number\"==typeof t),0>t)return this.iaddn(-t);if(this.sign)return this.sign=!1,this.iaddn(t),this.sign=!0,this;this.words[0]-=t;for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},i.prototype.addn=function(t){return this.clone().iaddn(t)},i.prototype.subn=function(t){return this.clone().isubn(t)},i.prototype.iabs=function(){return this.sign=!1,this},i.prototype.abs=function(){return this.clone().iabs()},i.prototype._ishlnsubmul=function(t,e,n){var i,a=t.length+n;if(this.words.length<a){for(var o=new Array(a),i=0;i<this.length;i++)o[i]=this.words[i];this.words=o}else i=this.length;for(this.length=Math.max(this.length,a);i<this.length;i++)this.words[i]=0;for(var s=0,i=0;i<t.length;i++){var l=this.words[i+n]+s,c=t.words[i]*e;l-=67108863&c,s=(l>>26)-(c/67108864|0),this.words[i+n]=67108863&l}for(;i<this.length-n;i++){var l=this.words[i+n]+s;s=l>>26,this.words[i+n]=67108863&l}if(0===s)return this.strip();r(-1===s),s=0;for(var i=0;i<this.length;i++){var l=-this.words[i]+s;s=l>>26,this.words[i]=67108863&l}return this.sign=!0,this.strip()},i.prototype._wordDiv=function(t,e){var r=this.length-t.length,n=this.clone(),a=t,o=a.words[a.length-1],s=this._countBits(o);r=26-s,0!==r&&(a=a.shln(r),n.ishln(r),o=a.words[a.length-1]);var l,c=n.length-a.length;if(\"mod\"!==e){l=new i(null),l.length=c+1,l.words=new Array(l.length);for(var u=0;u<l.length;u++)l.words[u]=0}var f=n.clone()._ishlnsubmul(a,1,c);f.sign||(n=f,l&&(l.words[c]=1));for(var h=c-1;h>=0;h--){var d=67108864*n.words[a.length+h]+n.words[a.length+h-1];for(d=Math.min(d/o|0,67108863),n._ishlnsubmul(a,d,h);n.sign;)d--,n.sign=!1,n._ishlnsubmul(a,1,h),0!==n.cmpn(0)&&(n.sign=!n.sign);l&&(l.words[h]=d)}return l&&l.strip(),n.strip(),\"div\"!==e&&0!==r&&n.ishrn(r),{div:l?l:null,mod:n}},i.prototype.divmod=function(t,e){if(r(0!==t.cmpn(0)),this.sign&&!t.sign){var n,a,o=this.neg().divmod(t,e);return\"mod\"!==e&&(n=o.div.neg()),\"div\"!==e&&(a=0===o.mod.cmpn(0)?o.mod:t.sub(o.mod)),{div:n,mod:a}}if(!this.sign&&t.sign){var n,o=this.divmod(t.neg(),e);return\"mod\"!==e&&(n=o.div.neg()),{div:n,mod:o.mod}}return this.sign&&t.sign?this.neg().divmod(t.neg(),e):t.length>this.length||this.cmp(t)<0?{div:new i(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new i(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new i(this.modn(t.words[0]))}:this._wordDiv(t,e)},i.prototype.div=function(t){return this.divmod(t,\"div\").div},i.prototype.mod=function(t){return this.divmod(t,\"mod\").mod},i.prototype.divRound=function(t){var e=this.divmod(t);if(0===e.mod.cmpn(0))return e.div;var r=e.div.sign?e.mod.isub(t):e.mod,n=t.shrn(1),i=t.andln(1),a=r.cmp(n);return 0>a||1===i&&0===a?e.div:e.div.sign?e.div.isubn(1):e.div.iaddn(1)},i.prototype.modn=function(t){r(67108863>=t);for(var e=(1<<26)%t,n=0,i=this.length-1;i>=0;i--)n=(e*n+this.words[i])%t;return n},i.prototype.idivn=function(t){r(67108863>=t);for(var e=0,n=this.length-1;n>=0;n--){var i=this.words[n]+67108864*e;this.words[n]=i/t|0,e=i%t}return this.strip()},i.prototype.divn=function(t){return this.clone().idivn(t)},i.prototype.egcd=function(t){r(!t.sign),r(0!==t.cmpn(0));var e=this,n=t.clone();e=e.sign?e.mod(t):e.clone();for(var a=new i(1),o=new i(0),s=new i(0),l=new i(1),c=0;e.isEven()&&n.isEven();)e.ishrn(1),n.ishrn(1),++c;for(var u=n.clone(),f=e.clone();0!==e.cmpn(0);){for(;e.isEven();)e.ishrn(1),a.isEven()&&o.isEven()?(a.ishrn(1),o.ishrn(1)):(a.iadd(u).ishrn(1),o.isub(f).ishrn(1));for(;n.isEven();)n.ishrn(1),s.isEven()&&l.isEven()?(s.ishrn(1),l.ishrn(1)):(s.iadd(u).ishrn(1),l.isub(f).ishrn(1));e.cmp(n)>=0?(e.isub(n),a.isub(s),o.isub(l)):(n.isub(e),s.isub(a),l.isub(o))}return{a:s,b:l,gcd:n.ishln(c)}},i.prototype._invmp=function(t){r(!t.sign),r(0!==t.cmpn(0));var e=this,n=t.clone();e=e.sign?e.mod(t):e.clone();for(var a=new i(1),o=new i(0),s=n.clone();e.cmpn(1)>0&&n.cmpn(1)>0;){for(;e.isEven();)e.ishrn(1),a.isEven()?a.ishrn(1):a.iadd(s).ishrn(1);for(;n.isEven();)n.ishrn(1),o.isEven()?o.ishrn(1):o.iadd(s).ishrn(1);e.cmp(n)>=0?(e.isub(n),a.isub(o)):(n.isub(e),o.isub(a))}return 0===e.cmpn(1)?a:o},i.prototype.gcd=function(t){if(0===this.cmpn(0))return t.clone();if(0===t.cmpn(0))return this.clone();var e=this.clone(),r=t.clone();e.sign=!1,r.sign=!1;for(var n=0;e.isEven()&&r.isEven();n++)e.ishrn(1),r.ishrn(1);for(;;){for(;e.isEven();)e.ishrn(1);for(;r.isEven();)r.ishrn(1);var i=e.cmp(r);if(0>i){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.ishln(n)},i.prototype.invm=function(t){return this.egcd(t).a.mod(t)},i.prototype.isEven=function(){return 0===(1&this.words[0])},i.prototype.isOdd=function(){return 1===(1&this.words[0])},i.prototype.andln=function(t){return this.words[0]&t},i.prototype.bincn=function(t){r(\"number\"==typeof t);var e=t%26,n=(t-e)/26,i=1<<e;if(this.length<=n){for(var a=this.length;n+1>a;a++)this.words[a]=0;return this.words[n]|=i,this.length=n+1,this}for(var o=i,a=n;0!==o&&a<this.length;a++){var s=this.words[a];s+=o,o=s>>>26,s&=67108863,this.words[a]=s}return 0!==o&&(this.words[a]=o,this.length++),this},i.prototype.cmpn=function(t){var e=0>t;if(e&&(t=-t),this.sign&&!e)return-1;if(!this.sign&&e)return 1;t&=67108863,this.strip();var r;if(this.length>1)r=1;else{var n=this.words[0];r=n===t?0:t>n?-1:1}return this.sign&&(r=-r),r},i.prototype.cmp=function(t){if(this.sign&&!t.sign)return-1;if(!this.sign&&t.sign)return 1;var e=this.ucmp(t);return this.sign?-e:e},i.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=this.words[r],i=t.words[r];if(n!==i){i>n?e=-1:n>i&&(e=1);break}}return e},i.red=function(t){return new h(t)},i.prototype.toRed=function(t){return r(!this.red,\"Already a number in reduction context\"),r(!this.sign,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},i.prototype.fromRed=function(){return r(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},i.prototype._forceRed=function(t){return this.red=t,this},i.prototype.forceRed=function(t){return r(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},i.prototype.redAdd=function(t){return r(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},i.prototype.redIAdd=function(t){return r(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},i.prototype.redSub=function(t){return r(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},i.prototype.redISub=function(t){return r(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},i.prototype.redShl=function(t){return r(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},i.prototype.redMul=function(t){return r(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},i.prototype.redIMul=function(t){return r(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},i.prototype.redSqr=function(){return r(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},i.prototype.redISqr=function(){return r(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},i.prototype.redSqrt=function(){return r(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},i.prototype.redInvm=function(){return r(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},i.prototype.redNeg=function(){return r(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},i.prototype.redPow=function(t){return r(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var m={k256:null,p224:null,p192:null,p25519:null};s.prototype._tmp=function(){var t=new i(null);return t.words=new Array(Math.ceil(this.n/13)),t},s.prototype.ireduce=function(t){var e,r=t;do this.split(r,this.tmp),r=this.imulK(r),r=r.iadd(this.tmp),e=r.bitLength();while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):r.strip(),r},s.prototype.split=function(t,e){t.ishrn(this.n,0,e)},s.prototype.imulK=function(t){return t.imul(this.k)},n(l,s),l.prototype.split=function(t,e){for(var r=4194303,n=Math.min(t.length,9),i=0;n>i;i++)e.words[i]=t.words[i];if(e.length=n,t.length<=9)return t.words[0]=0,void(t.length=1);var a=t.words[9];e.words[e.length++]=a&r;for(var i=10;i<t.length;i++){var o=t.words[i];t.words[i-10]=(o&r)<<4|a>>>22,a=o}t.words[i-10]=a>>>22,t.length-=9},l.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e,r=0,n=0;n<t.length;n++){var i=t.words[n];e=64*i,r+=977*i,e+=r/67108864|0,r&=67108863,t.words[n]=r,r=e}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},n(c,s),n(u,s),n(f,s),f.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*t.words[r]+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},i._prime=function y(t){if(m[t])return m[t];var y;if(\"k256\"===t)y=new l;else if(\"p224\"===t)y=new c;else if(\"p192\"===t)y=new u;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);y=new f}return m[t]=y,y},h.prototype._verify1=function(t){r(!t.sign,\"red works only with positives\"),r(t.red,\"red works only with red numbers\")},h.prototype._verify2=function(t,e){r(!t.sign&&!e.sign,\"red works only with 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r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),a=r.isub(n).ishrn(this.shift),o=a;return a.cmp(this.m)>=0?o=a.isub(this.m):a.cmpn(0)<0&&(o=a.iadd(this.m)),o._forceRed(this)},d.prototype.invm=function(t){var e=this.imod(t._invmp(this.m).mul(this.r2));return e._forceRed(this)}}(\"undefined\"==typeof e||e,this)},{}],80:[function(t,e,r){\"use strict\";function n(t){return i(t[0])*i(t[1])}var i=t(\"./lib/bn-sign\");e.exports=n},{\"./lib/bn-sign\":71}],81:[function(t,e,r){\"use strict\";function n(t,e){return i(t[0].mul(e[1]).sub(t[1].mul(e[0])),t[1].mul(e[1]))}var i=t(\"./lib/rationalize\");e.exports=n},{\"./lib/rationalize\":76}],82:[function(t,e,r){\"use strict\";function n(t){var e=t[0],r=t[1];if(0===e.cmpn(0))return 0;var n=e.divmod(r),o=n.div,s=i(o),l=n.mod;if(0===l.cmpn(0))return s;if(s){var c=a(s)+4,u=i(l.shln(c).divRound(r));return 0>s&&(u=-u),s+u*Math.pow(2,-c)}var f=r.bitLength()-l.bitLength()+53,u=i(l.shln(f).divRound(r));return 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n(t,e,r){this.plot=t,this.shader=e,this.buffer=r,this.bounds=[1/0,1/0,-(1/0),-(1/0)],this.numPoints=0,this.color=[0,0,0,1]}function i(t,e){var r=a(t.gl,l.vertex,l.fragment),i=o(t.gl),s=new n(t,r,i);return s.update(e),t.addObject(s),s}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"typedarray-pool\"),l=t(\"./lib/shaders\");e.exports=i;var c=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]],u=n.prototype;u.draw=function(){var t=[1,0,0,0,1,0,0,0,1],e=[1,1];return function(){var 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d=n[u],p=e[c];if(Array.isArray(p[0])&&(p=e[u]),3===p.length&&(p=[p[0],p[1],p[2],1]),!isNaN(d[0][c])&&!isNaN(d[1][c])){if(d[0][c]<0){var g=f.slice();g[c]+=d[0][c],o.push(f[0],f[1],f[2],p[0],p[1],p[2],p[3],0,0,0,g[0],g[1],g[2],p[0],p[1],p[2],p[3],0,0,0),i(this.bounds,g),l+=2+a(o,g,p,c)}if(d[1][c]>0){var g=f.slice();g[c]+=d[1][c],o.push(f[0],f[1],f[2],p[0],p[1],p[2],p[3],0,0,0,g[0],g[1],g[2],p[0],p[1],p[2],p[3],0,0,0),i(this.bounds,g),l+=2+a(o,g,p,c)}}}this.lineCount[c]=l-this.lineOffset[c]}this.buffer.update(o)}},f.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{\"./shaders/index\":122,\"gl-buffer\":118,\"gl-vao\":226}],122:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n vec4 worldPosition = 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i(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function a(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);y=new Array(r+1);for(var n=0;r>=n;++n){for(var i=new Array(r),a=0;n>a;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(var a=n;r>a;++a)i[a]=t.NONE;y[n]=i}}function o(t){switch(t){case p:throw new Error(\"gl-fbo: Framebuffer unsupported\");case g:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case v:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case m:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function s(t,e,r,n,i,a){if(!n)return null;var o=d(t,e,r,i,n);return o.magFilter=t.NEAREST,o.minFilter=t.NEAREST,o.mipSamples=1,o.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,a,t.TEXTURE_2D,o.handle,0),o}function l(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function c(t){var e=n(t.gl),r=t.gl,a=t.handle=r.createFramebuffer(),c=t._shape[0],u=t._shape[1],f=t.color.length,h=t._ext,d=t._useStencil,p=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,a);for(var v=0;f>v;++v)t.color[v]=s(r,c,u,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===f?(t._color_rb=l(r,c,u,r.RGBA4,r.COLOR_ATTACHMENT0),h&&h.drawBuffersWEBGL(y[0])):f>1&&h.drawBuffersWEBGL(y[f]);var m=r.getExtension(\"WEBGL_depth_texture\");m?d?t.depth=s(r,c,u,m.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):p&&(t.depth=s(r,c,u,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):p&&d?t._depth_rb=l(r,c,u,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):p?t._depth_rb=l(r,c,u,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=l(r,c,u,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var b=r.checkFramebufferStatus(r.FRAMEBUFFER);if(b!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(var v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),i(r,e),o(b)}i(r,e)}function u(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var l=0;i>l;++l)this.color[l]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var u=this,f=[0|e,0|r];Object.defineProperties(f,{0:{get:function(){return u._shape[0]},set:function(t){return u.width=t}},1:{get:function(){return u._shape[1]},set:function(t){return u.height=t}}}),this._shapeVector=f,c(this)}function f(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var a=t.gl,s=a.getParameter(a.MAX_RENDERBUFFER_SIZE);if(0>e||e>s||0>r||r>s)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var l=n(a),c=0;c<t.color.length;++c)t.color[c].shape=t._shape;t._color_rb&&(a.bindRenderbuffer(a.RENDERBUFFER,t._color_rb),a.renderbufferStorage(a.RENDERBUFFER,a.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(a.bindRenderbuffer(a.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?a.renderbufferStorage(a.RENDERBUFFER,a.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?a.renderbufferStorage(a.RENDERBUFFER,a.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&a.renderbufferStorage(a.RENDERBUFFER,a.STENCIL_INDEX,t._shape[0],t._shape[1])),a.bindFramebuffer(a.FRAMEBUFFER,t.handle);var u=a.checkFramebufferStatus(a.FRAMEBUFFER);u!==a.FRAMEBUFFER_COMPLETE&&(t.dispose(),i(a,l),o(u)),i(a,l)}}function h(t,e,r,n){p||(p=t.FRAMEBUFFER_UNSUPPORTED,g=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,v=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,m=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var i=t.getExtension(\"WEBGL_draw_buffers\");if(!y&&i&&a(t,i),Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]),\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var o=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(0>e||e>o||0>r||r>o)throw new Error(\"gl-fbo: Parameters are too large for FBO\");n=n||{};var s=1;if(\"color\"in n){if(s=Math.max(0|n.color,0),0>s)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(s>1){if(!i)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(s>t.getParameter(i.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+s+\" draw buffers\")}}var l=t.UNSIGNED_BYTE,c=t.getExtension(\"OES_texture_float\");if(n.float&&s>0){if(!c)throw new Error(\"gl-fbo: Context does not support floating point textures\");l=t.FLOAT}else n.preferFloat&&s>0&&c&&(l=t.FLOAT);var f=!0;\"depth\"in n&&(f=!!n.depth);var h=!1;return\"stencil\"in n&&(h=!!n.stencil),new u(t,e,r,l,s,f,h,i)}var d=t(\"gl-texture2d\");e.exports=h;var p,g,v,m,y=null,b=u.prototype;Object.defineProperties(b,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return f(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return t=0|t,f(this,t,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t=0|t,f(this,this._shape[0],t),t},enumerable:!1}}),b.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},b.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":222}],124:[function(t,e,r){r.lineVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat inverse_1_0(float m) {\\n return 1.0 / m;\\n}\\n\\nmat2 inverse_1_0(mat2 m) {\\n return mat2(m[1][1],-m[0][1],\\n -m[1][0], m[0][0]) / (m[0][0]*m[1][1] - m[0][1]*m[1][0]);\\n}\\n\\nmat3 inverse_1_0(mat3 m) {\\n float a00 = m[0][0], a01 = m[0][1], a02 = m[0][2];\\n float a10 = m[1][0], a11 = m[1][1], a12 = m[1][2];\\n float a20 = m[2][0], a21 = m[2][1], a22 = m[2][2];\\n\\n float b01 = a22 * a11 - a12 * a21;\\n float b11 = -a22 * a10 + a12 * a20;\\n float b21 = a21 * a10 - a11 * a20;\\n\\n float det = a00 * b01 + a01 * b11 + a02 * b21;\\n\\n return mat3(b01, (-a22 * a01 + a02 * a21), (a12 * a01 - a02 * a11),\\n b11, (a22 * a00 - a02 * a20), (-a12 * a00 + a02 * a10),\\n b21, (-a21 * a00 + a01 * a20), (a11 * a00 - a01 * a10)) / det;\\n}\\n\\nmat4 inverse_1_0(mat4 m) {\\n float\\n a00 = m[0][0], a01 = m[0][1], a02 = m[0][2], a03 = m[0][3],\\n a10 = m[1][0], a11 = m[1][1], a12 = m[1][2], a13 = m[1][3],\\n a20 = m[2][0], a21 = m[2][1], a22 = m[2][2], a23 = m[2][3],\\n a30 = m[3][0], a31 = m[3][1], a32 = m[3][2], a33 = m[3][3],\\n\\n b00 = a00 * a11 - a01 * a10,\\n b01 = a00 * a12 - a02 * a10,\\n b02 = a00 * a13 - a03 * a10,\\n b03 = a01 * a12 - a02 * a11,\\n b04 = a01 * a13 - a03 * a11,\\n b05 = a02 * a13 - a03 * a12,\\n b06 = a20 * a31 - a21 * a30,\\n b07 = a20 * a32 - a22 * a30,\\n b08 = a20 * a33 - a23 * a30,\\n b09 = a21 * a32 - a22 * a31,\\n b10 = a21 * a33 - a23 * a31,\\n b11 = a22 * a33 - a23 * a32,\\n\\n det = b00 * b11 - b01 * b10 + b02 * b09 + b03 * b08 - b04 * b07 + b05 * b06;\\n\\n return mat4(\\n a11 * b11 - a12 * b10 + a13 * b09,\\n a02 * b10 - a01 * b11 - a03 * b09,\\n a31 * b05 - a32 * b04 + a33 * b03,\\n a22 * b04 - a21 * b05 - a23 * b03,\\n a12 * b08 - a10 * b11 - a13 * b07,\\n a00 * b11 - a02 * b08 + a03 * b07,\\n a32 * b02 - a30 * b05 - a33 * b01,\\n a20 * b05 - a22 * b02 + a23 * b01,\\n a10 * b10 - a11 * b08 + a13 * b06,\\n a01 * b08 - a00 * b10 - a03 * b06,\\n a30 * b04 - a31 * b02 + a33 * b00,\\n a21 * b02 - a20 * b04 - a23 * b00,\\n a11 * b07 - a10 * b09 - a12 * b06,\\n a00 * b09 - a01 * b07 + a02 * b06,\\n a31 * b01 - a30 * b03 - a32 * b00,\\n a20 * b03 - a21 * b01 + a22 * b00) / det;\\n}\\n\\n\\n\\nattribute vec2 a, d;\\n\\nuniform mat3 matrix;\\nuniform vec2 screenShape;\\nuniform float width;\\n\\nvarying vec2 direction;\\n\\nvoid main() {\\n vec2 dir = (matrix * vec3(d, 0)).xy;\\n vec3 base = matrix * vec3(a, 1);\\n vec2 n = 0.5 * width *\\n normalize(screenShape.yx * vec2(dir.y, -dir.x)) / screenShape.xy;\\n vec2 tangent = normalize(screenShape.xy * dir);\\n if(dir.x < 0.0 || (dir.x == 0.0 && dir.y < 0.0)) {\\n direction = -tangent;\\n } else {\\n direction = tangent;\\n }\\n gl_Position = vec4(base.xy/base.z + n, 0, 1);\\n}\\n\",r.lineFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nuniform vec2 screenShape;\\nuniform sampler2D dashPattern;\\nuniform float dashLength;\\n\\nvarying vec2 direction;\\n\\nvoid main() {\\n float t = fract(dot(direction, gl_FragCoord.xy) / dashLength);\\n vec4 pcolor = color * texture2D(dashPattern, vec2(t, 0.0)).r;\\n gl_FragColor = vec4(pcolor.rgb * pcolor.a, pcolor.a);\\n}\\n\",r.mitreVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 p;\\n\\nuniform mat3 matrix;\\nuniform vec2 screenShape;\\nuniform float radius;\\n\\nvoid main() {\\n vec3 pp = matrix * vec3(p, 1);\\n gl_Position = vec4(pp.xy, 0, pp.z);\\n gl_PointSize = radius;\\n}\\n\",r.mitreFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n if(length(gl_PointCoord.xy - 0.5) > 0.25) {\\n discard;\\n }\\n gl_FragColor = vec4(color.rgb, color.a);\\n}\\n\",r.pickVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 a, d;\\nattribute vec4 pick0, pick1;\\n\\nuniform mat3 matrix;\\nuniform vec2 screenShape;\\nuniform float width;\\n\\nvarying vec4 pickA, pickB;\\n\\nfloat inverse_1_0(float m) {\\n return 1.0 / m;\\n}\\n\\nmat2 inverse_1_0(mat2 m) {\\n return mat2(m[1][1],-m[0][1],\\n -m[1][0], m[0][0]) / (m[0][0]*m[1][1] - m[0][1]*m[1][0]);\\n}\\n\\nmat3 inverse_1_0(mat3 m) {\\n float a00 = m[0][0], a01 = m[0][1], a02 = m[0][2];\\n float a10 = m[1][0], a11 = m[1][1], a12 = m[1][2];\\n float a20 = m[2][0], a21 = m[2][1], a22 = m[2][2];\\n\\n float b01 = a22 * a11 - a12 * a21;\\n float b11 = -a22 * a10 + a12 * a20;\\n float b21 = a21 * a10 - a11 * a20;\\n\\n float det = a00 * b01 + a01 * b11 + a02 * b21;\\n\\n return mat3(b01, (-a22 * a01 + a02 * a21), (a12 * a01 - a02 * a11),\\n b11, (a22 * a00 - a02 * a20), (-a12 * a00 + a02 * a10),\\n b21, (-a21 * a00 + a01 * a20), (a11 * a00 - a01 * a10)) / det;\\n}\\n\\nmat4 inverse_1_0(mat4 m) {\\n float\\n a00 = m[0][0], a01 = m[0][1], a02 = m[0][2], a03 = m[0][3],\\n a10 = m[1][0], a11 = m[1][1], a12 = m[1][2], a13 = m[1][3],\\n a20 = m[2][0], a21 = m[2][1], a22 = m[2][2], a23 = m[2][3],\\n a30 = m[3][0], a31 = m[3][1], a32 = m[3][2], a33 = m[3][3],\\n\\n b00 = a00 * a11 - a01 * a10,\\n b01 = a00 * a12 - a02 * a10,\\n b02 = a00 * a13 - a03 * a10,\\n b03 = a01 * a12 - a02 * a11,\\n b04 = a01 * a13 - a03 * a11,\\n b05 = a02 * a13 - a03 * a12,\\n b06 = a20 * a31 - a21 * a30,\\n b07 = a20 * a32 - a22 * a30,\\n b08 = a20 * a33 - a23 * a30,\\n b09 = a21 * a32 - a22 * a31,\\n b10 = a21 * a33 - a23 * a31,\\n b11 = a22 * a33 - a23 * a32,\\n\\n det = b00 * b11 - b01 * b10 + b02 * b09 + b03 * b08 - b04 * b07 + b05 * b06;\\n\\n return mat4(\\n a11 * b11 - a12 * b10 + a13 * b09,\\n a02 * b10 - a01 * b11 - a03 * b09,\\n a31 * b05 - a32 * b04 + a33 * b03,\\n a22 * b04 - a21 * b05 - a23 * b03,\\n a12 * b08 - a10 * b11 - a13 * b07,\\n a00 * b11 - a02 * b08 + a03 * b07,\\n a32 * b02 - a30 * b05 - a33 * b01,\\n a20 * b05 - a22 * b02 + a23 * b01,\\n a10 * b10 - a11 * b08 + a13 * b06,\\n a01 * b08 - a00 * b10 - a03 * b06,\\n a30 * b04 - a31 * b02 + a33 * b00,\\n a21 * b02 - a20 * b04 - a23 * b00,\\n a11 * b07 - a10 * b09 - a12 * b06,\\n a00 * b09 - a01 * b07 + a02 * b06,\\n a31 * b01 - a30 * b03 - a32 * b00,\\n a20 * b03 - a21 * b01 + a22 * b00) / det;\\n}\\n\\n\\n\\nvoid main() {\\n vec3 base = matrix * vec3(a, 1);\\n vec2 n = width *\\n normalize(screenShape.yx * vec2(d.y, -d.x)) / screenShape.xy;\\n gl_Position = vec4(base.xy/base.z + n, 0, 1);\\n pickA = pick0;\\n pickB = pick1;\\n}\\n\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 pickOffset;\\n\\nvarying vec4 pickA, pickB;\\n\\nvoid main() {\\n vec4 fragId = vec4(pickA.xyz, 0.0);\\n if(pickB.w > pickA.w) {\\n fragId.xyz = pickB.xyz;\\n }\\n\\n fragId += pickOffset;\\n\\n fragId.y += floor(fragId.x / 256.0);\\n fragId.x -= floor(fragId.x / 256.0) * 256.0;\\n\\n fragId.z += floor(fragId.y / 256.0);\\n fragId.y -= floor(fragId.y / 256.0) * 256.0;\\n\\n fragId.w += floor(fragId.z / 256.0);\\n fragId.z -= floor(fragId.z / 256.0) * 256.0;\\n\\n gl_FragColor = fragId / 255.0;\\n}\\n\",r.fillVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 a, d;\\n\\nuniform mat3 matrix;\\nuniform vec2 projectAxis;\\nuniform float projectValue;\\nuniform float depth;\\n\\nvoid main() {\\n vec3 base = matrix * vec3(a, 1);\\n vec2 p = base.xy / base.z;\\n if(d.y < 0.0 || (d.y == 0.0 && d.x < 0.0)) {\\n if(dot(p, projectAxis) < projectValue) {\\n p = p * (1.0 - abs(projectAxis)) + projectAxis * projectValue;\\n }\\n }\\n gl_Position = vec4(p, depth, 1);\\n}\\n\",r.fillFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n gl_FragColor = vec4(color.rgb * color.a, color.a);\\n}\\n\"},{}],125:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s){this.plot=t,this.dashPattern=e,this.lineBuffer=r,this.pickBuffer=n,this.lineShader=i,this.mitreShader=a,this.fillShader=o,this.pickShader=s,this.usingDashes=!1,this.bounds=[1/0,1/0,-(1/0),-(1/0)],this.width=1,this.color=[0,0,1,1],this.fill=[!1,!1,!1,!1],this.fillColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.data=null,this.numPoints=0,this.vertCount=0,this.pickOffset=0,this.lodBuffer=[]}function i(t){return t.map(function(t){return t.slice()})}function a(t,e){var r=t.gl,i=s(r),a=s(r),c=l(r,[1,1]),u=o(r,f.lineVertex,f.lineFragment),h=o(r,f.mitreVertex,f.mitreFragment),d=o(r,f.fillVertex,f.fillFragment),p=o(r,f.pickVertex,f.pickFragment),g=new n(t,c,i,a,u,h,d,p);return t.addObject(g),g.update(e),g}e.exports=a;var o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"gl-texture2d\"),c=t(\"ndarray\"),u=t(\"typedarray-pool\"),f=t(\"./lib/shaders\"),h=n.prototype;h.draw=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0],r=[1,0],n=[-1,0],i=[0,1],a=[0,-1];return function(){var o=this.plot,s=this.color,l=this.width,c=(this.numPoints,this.bounds),u=this.vertCount;if(u){var f=o.gl,h=o.viewBox,d=o.dataBox,p=o.pixelRatio,g=c[2]-c[0],v=c[3]-c[1],m=d[2]-d[0],y=d[3]-d[1],b=h[2]-h[0],x=h[3]-h[1];t[0]=2*g/m,t[4]=2*v/y,t[6]=2*(c[0]-d[0])/m-1,t[7]=2*(c[1]-d[1])/y-1,e[0]=b,e[1]=x;var _=this.lineBuffer;_.bind();var w=this.fill;if(w[0]||w[1]||w[2]||w[3]){var k=this.fillShader;k.bind();var A=k.uniforms;A.matrix=t,A.depth=o.nextDepthValue();var M=k.attributes;M.a.pointer(f.FLOAT,!1,16,0),M.d.pointer(f.FLOAT,!1,16,8),f.depthMask(!0),f.enable(f.DEPTH_TEST);var T=this.fillColor;w[0]&&(A.color=T[0],A.projectAxis=n,A.projectValue=1,f.drawArrays(f.TRIANGLES,0,u)),w[1]&&(A.color=T[1],A.projectAxis=a,A.projectValue=1,f.drawArrays(f.TRIANGLES,0,u)),w[2]&&(A.color=T[2],A.projectAxis=r,A.projectValue=1,f.drawArrays(f.TRIANGLES,0,u)),w[3]&&(A.color=T[3],A.projectAxis=i,A.projectValue=1,f.drawArrays(f.TRIANGLES,0,u)),f.depthMask(!1),f.disable(f.DEPTH_TEST)}var E=this.lineShader;E.bind();var L=E.uniforms;L.matrix=t,L.color=s,L.width=l*p,L.screenShape=e,L.dashPattern=this.dashPattern.bind(),L.dashLength=this.dashLength*p;var S=E.attributes;if(S.a.pointer(f.FLOAT,!1,16,0),S.d.pointer(f.FLOAT,!1,16,8),f.drawArrays(f.TRIANGLES,0,u),l>2&&!this.usingDashes){var C=this.mitreShader;C.bind();var z=C.uniforms;z.matrix=t,z.color=s,z.screenShape=e,z.radius=l*p,C.attributes.p.pointer(f.FLOAT,!1,48,0),f.drawArrays(f.POINTS,0,u/3|0)}}}}(),h.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0],r=[0,0,0,0];return function(n){var i=this.plot,a=this.pickShader,o=this.lineBuffer,s=this.pickBuffer,l=this.width,c=this.numPoints,u=this.bounds,f=this.vertCount,h=i.gl,d=i.viewBox,p=i.dataBox,g=i.pickPixelRatio,v=u[2]-u[0],m=u[3]-u[1],y=p[2]-p[0],b=p[3]-p[1],x=d[2]-d[0],_=d[3]-d[1];if(this.pickOffset=n,!f)return n+c;t[0]=2*v/y,t[4]=2*m/b,t[6]=2*(u[0]-p[0])/y-1,t[7]=2*(u[1]-p[1])/b-1,e[0]=x,e[1]=_,r[0]=255&n,r[1]=n>>>8&255,r[2]=n>>>16&255,r[3]=n>>>24,a.bind();var w=a.uniforms;w.matrix=t,w.width=l*g,w.pickOffset=r,w.screenShape=e;var k=a.attributes;return o.bind(),k.a.pointer(h.FLOAT,!1,16,0),k.d.pointer(h.FLOAT,!1,16,8),s.bind(),k.pick0.pointer(h.UNSIGNED_BYTE,!1,8,0),k.pick1.pointer(h.UNSIGNED_BYTE,!1,8,4),h.drawArrays(h.TRIANGLES,0,f),n+c}}(),h.pick=function(t,e,r){var n=this.pickOffset,i=this.numPoints;if(n>r||r>=n+i)return null;var a=r-n,o=this.data;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}},h.update=function(t){t=t||{};var e=this.plot.gl;!!t.connectGaps;this.color=(t.color||[0,0,1,1]).slice(),this.width=+(t.width||1),this.fill=(t.fill||[!1,!1,!1,!1]).slice(),this.fillColor=i(t.fillColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);for(var r=t.dashes||[1],n=0,a=0;a<r.length;++a)n+=r[a];for(var o=u.mallocUint8(n),s=0,f=255,a=0;a<r.length;++a){for(var h=0;h<r[a];++h)o[s++]=f;f^=255}this.dashPattern.dispose(),this.usingDashes=r.length>1,this.dashPattern=l(e,c(o,[n,1,4],[1,0,0])),this.dashPattern.minFilter=e.NEAREST,this.dashPattern.magFilter=e.NEAREST,this.dashLength=n,u.free(o);var d=t.positions;this.data=d;var p=this.bounds;p[0]=p[1]=1/0,p[2]=p[3]=-(1/0);var g=this.numPoints=d.length>>>1;if(0!==g){for(var a=0;g>a;++a){var v=d[2*a],m=d[2*a+1];isNaN(v)||isNaN(m)||(p[0]=Math.min(p[0],v),p[1]=Math.min(p[1],m),p[2]=Math.max(p[2],v),p[3]=Math.max(p[3],m))}p[0]===p[2]&&(p[2]+=1),p[3]===p[1]&&(p[3]+=1);for(var y=u.mallocFloat32(24*(g-1)),b=u.mallocUint32(12*(g-1)),x=y.length,_=b.length,s=g,w=0;s>1;){var k=--s,v=d[2*s],m=d[2*s+1],A=k-1,M=d[2*A],T=d[2*A+1];if(!(isNaN(v)||isNaN(m)||isNaN(M)||isNaN(T))){w+=1,v=(v-p[0])/(p[2]-p[0]),m=(m-p[1])/(p[3]-p[1]),M=(M-p[0])/(p[2]-p[0]),T=(T-p[1])/(p[3]-p[1]);var E=M-v,L=T-m,S=k|1<<24,C=k-1,z=k,P=k-1|1<<24;y[--x]=-L,y[--x]=-E,y[--x]=m,y[--x]=v,b[--_]=S,b[--_]=C,y[--x]=L,y[--x]=E,y[--x]=T,y[--x]=M,b[--_]=z,b[--_]=P,y[--x]=-L,y[--x]=-E,y[--x]=T,y[--x]=M,b[--_]=z,b[--_]=P,y[--x]=L,y[--x]=E,y[--x]=T,y[--x]=M,b[--_]=z,b[--_]=P,y[--x]=-L,y[--x]=-E,y[--x]=m,y[--x]=v,b[--_]=S,b[--_]=C,y[--x]=L,y[--x]=E,y[--x]=m,y[--x]=v,b[--_]=S,b[--_]=C}}this.vertCount=6*w,this.lineBuffer.update(y.subarray(x)),this.pickBuffer.update(b.subarray(_)),u.free(y),u.free(b)}},h.dispose=function(){this.plot.removeObject(this),this.lineBuffer.dispose(),this.pickBuffer.dispose(),this.lineShader.dispose(),this.mitreShader.dispose(),this.fillShader.dispose(),this.pickShader.dispose(),this.dashPattern.dispose()}},{\"./lib/shaders\":124,\"gl-buffer\":118,\"gl-shader\":197,\"gl-texture2d\":222,ndarray:253,\"typedarray-pool\":278}],126:[function(t,e,r){var n=t(\"gl-shader\"),i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvoid main() {\\n vec4 projected = projection * view * model * vec4(position, 1.0);\\n vec4 tangentClip = projection * view * model * vec4(nextPosition - position, 0.0);\\n vec2 tangent = normalize(screenShape * tangentClip.xy);\\n vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(tangent.y, -tangent.x) / screenShape;\\n\\n gl_Position = vec4(projected.xy + projected.w * offset, projected.zw);\\n\\n worldPosition = position;\\n pixelArcLength = arcLength;\\n fragColor = color;\\n}\\n\",a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float dashScale;\\nuniform float opacity;\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\\n discard;\\n }\\n float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n if(dashWeight < 0.5) {\\n discard;\\n }\\n gl_FragColor = fragColor * opacity;\\n}\\n\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX 1.70141184e38\\n#define FLOAT_MIN 1.17549435e-38\\n\\nlowp vec4 encode_float_1_0(highp float v) {\\n highp float av = abs(v);\\n\\n //Handle special cases\\n if(av < FLOAT_MIN) {\\n return vec4(0.0, 0.0, 0.0, 0.0);\\n } else if(v > FLOAT_MAX) {\\n return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n } else if(v < -FLOAT_MAX) {\\n return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n }\\n\\n highp vec4 c = vec4(0,0,0,0);\\n\\n //Compute exponent and mantissa\\n highp float e = floor(log2(av));\\n highp float m = av * pow(2.0, -e) - 1.0;\\n \\n //Unpack mantissa\\n c[1] = floor(128.0 * m);\\n m -= c[1] / 128.0;\\n c[2] = floor(32768.0 * m);\\n m -= c[2] / 32768.0;\\n c[3] = floor(8388608.0 * m);\\n \\n //Unpack exponent\\n highp float ebias = e + 127.0;\\n c[0] = floor(ebias / 2.0);\\n ebias -= c[0] * 2.0;\\n c[1] += floor(ebias) * 128.0; \\n\\n //Unpack sign bit\\n c[0] += 128.0 * step(0.0, -v);\\n\\n //Scale back to range\\n return c / 255.0;\\n}\\n\\n\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if(any(lessThan(worldPosition, clipBounds[0])) || any(greaterThan(worldPosition, clipBounds[1]))) {\\n discard;\\n }\\n gl_FragColor = vec4(pickId/255.0, encode_float_1_0(pixelArcLength).xyz);\\n}\",s=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return n(t,i,a,null,s)},r.createPickShader=function(t){return n(t,i,o,null,s)}},{\"gl-shader\":197}],127:[function(t,e,r){\"use strict\";function n(t,e){for(var r=0,n=0;3>n;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function i(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;3>r;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function a(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function o(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.dirty=!0,this.pixelRatio=1}function s(t){var e=t.gl||t.scene&&t.scene.gl,r=g(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var n=v(e);n.attributes.position.location=0,n.attributes.nextPosition.location=1,n.attributes.arcLength.location=2,n.attributes.lineWidth.location=3,n.attributes.color.location=4;for(var i=l(e),a=c(e,[{buffer:i,size:3,offset:0,stride:48},{buffer:i,size:3,offset:12,stride:48},{buffer:i,size:1,offset:24,stride:48},{buffer:i,size:1,offset:28,stride:48},{buffer:i,size:4,offset:32,stride:48}]),s=d(new Array(1024),[256,1,4]),f=0;1024>f;++f)s.data[f]=255;var h=u(e,s);h.wrap=e.REPEAT;var p=new o(e,r,n,i,a,h);return p.update(t),p}e.exports=s;var l=t(\"gl-buffer\"),c=t(\"gl-vao\"),u=t(\"gl-texture2d\"),f=t(\"glsl-read-float\"),h=t(\"binary-search-bounds\"),d=t(\"ndarray\"),p=t(\"./lib/shaders\"),g=p.createShader,v=p.createPickShader,m=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],y=o.prototype;y.isTransparent=function(){return this.opacity<1},y.isOpaque=function(){return this.opacity>=1},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||m,view:t.view||m,projection:t.projection||m,clipBounds:i(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount)},y.drawPick=function(t){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||m,view:t.view||m,projection:t.projection||m,pickId:this.pickId,clipBounds:i(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount)},y.update=function(t){var e,r;this.dirty=!0;var i=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),\"opacity\"in t&&(this.opacity=+t.opacity);var a=t.position||t.positions;if(a){var o=t.color||t.colors||[0,0,0,1],s=t.lineWidth||1,l=[],c=[],u=[],f=0,p=0,g=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],v=!1;t:for(e=1;e<a.length;++e){var m=a[e-1],y=a[e];for(c.push(f),u.push(m.slice()),r=0;3>r;++r){if(isNaN(m[r])||isNaN(y[r])||!isFinite(m[r])||!isFinite(y[r])){if(!i&&l.length>0){for(var b=0;24>b;++b)l.push(l[l.length-12]);p+=2,v=!0}continue t}g[0][r]=Math.min(g[0][r],m[r],y[r]),g[1][r]=Math.max(g[1][r],m[r],y[r])}\nvar x,_;Array.isArray(o[0])?(x=o[e-1],_=o[e]):x=_=o,3===x.length&&(x=[x[0],x[1],x[2],1]),3===_.length&&(_=[_[0],_[1],_[2],1]);var w;w=Array.isArray(s)?s[e-1]:s;var k=f;if(f+=n(m,y),v){for(r=0;2>r;++r)l.push(m[0],m[1],m[2],y[0],y[1],y[2],k,w,x[0],x[1],x[2],x[3]);p+=2,v=!1}l.push(m[0],m[1],m[2],y[0],y[1],y[2],k,w,x[0],x[1],x[2],x[3],m[0],m[1],m[2],y[0],y[1],y[2],k,-w,x[0],x[1],x[2],x[3],y[0],y[1],y[2],m[0],m[1],m[2],f,-w,_[0],_[1],_[2],_[3],y[0],y[1],y[2],m[0],m[1],m[2],f,w,_[0],_[1],_[2],_[3]),p+=4}if(this.buffer.update(l),c.push(f),u.push(a[a.length-1].slice()),this.bounds=g,this.vertexCount=p,this.points=u,this.arcLength=c,\"dashes\"in t){var A=t.dashes,M=A.slice();for(M.unshift(0),e=1;e<M.length;++e)M[e]=M[e-1]+M[e];var T=d(new Array(1024),[256,1,4]);for(e=0;256>e;++e){for(r=0;4>r;++r)T.set(e,0,r,0);1&h.le(M,M[M.length-1]*e/255)?T.set(e,0,0,0):T.set(e,0,0,255)}this.texture.setPixels(T)}}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=f(t.value[0],t.value[1],t.value[2],0),r=h.le(this.arcLength,e);if(0>r)return null;if(r===this.arcLength.length-1)return new a(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],o=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),s=1-o,l=[0,0,0],c=0;3>c;++c)l[c]=s*n[c]+o*i[c];var u=Math.min(.5>o?r:r+1,this.points.length-1);return new a(e,l,u,this.points[u])}},{\"./lib/shaders\":126,\"binary-search-bounds\":128,\"gl-buffer\":118,\"gl-texture2d\":222,\"gl-vao\":226,\"glsl-read-float\":129,ndarray:253}],128:[function(t,e,r){arguments[4][21][0].apply(r,arguments)},{dup:21}],129:[function(t,e,r){function n(t,e,r,n){return i[0]=n,i[1]=r,i[2]=e,i[3]=t,a[0]}e.exports=n;var i=new Uint8Array(4),a=new Float32Array(i.buffer)},{}],130:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=u*o-s*c,h=-u*a+s*l,d=c*a-o*l,p=r*f+n*h+i*d;return p?(p=1/p,t[0]=f*p,t[1]=(-u*n+i*c)*p,t[2]=(s*n-i*o)*p,t[3]=h*p,t[4]=(u*r-i*l)*p,t[5]=(-s*r+i*a)*p,t[6]=d*p,t[7]=(-c*r+n*l)*p,t[8]=(o*r-n*a)*p,t):null}e.exports=n},{}],131:[function(t,e,r){function n(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}e.exports=n},{}],132:[function(t,e,r){function n(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],133:[function(t,e,r){function n(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],f=t[10],h=t[11],d=t[12],p=t[13],g=t[14],v=t[15],m=e*o-r*a,y=e*s-n*a,b=e*l-i*a,x=r*s-n*o,_=r*l-i*o,w=n*l-i*s,k=c*p-u*d,A=c*g-f*d,M=c*v-h*d,T=u*g-f*p,E=u*v-h*p,L=f*v-h*g;return m*L-y*E+b*T+x*M-_*A+w*k}e.exports=n},{}],134:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,f=n*s,h=i*o,d=i*s,p=i*l,g=a*o,v=a*s,m=a*l;return t[0]=1-f-p,t[1]=u+m,t[2]=h-v,t[3]=0,t[4]=u-m,t[5]=1-c-p,t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],135:[function(t,e,r){function n(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,f=n*l,h=n*c,d=i*l,p=i*c,g=a*c,v=o*s,m=o*l,y=o*c;return t[0]=1-(d+g),t[1]=f+y,t[2]=h-m,t[3]=0,t[4]=f-y,t[5]=1-(u+g),t[6]=p+v,t[7]=0,t[8]=h+m,t[9]=p-v,t[10]=1-(u+d),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}e.exports=n},{}],136:[function(t,e,r){function n(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}e.exports=n},{}],137:[function(t,e,r){function n(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],d=e[11],p=e[12],g=e[13],v=e[14],m=e[15],y=r*s-n*o,b=r*l-i*o,x=r*c-a*o,_=n*l-i*s,w=n*c-a*s,k=i*c-a*l,A=u*g-f*p,M=u*v-h*p,T=u*m-d*p,E=f*v-h*g,L=f*m-d*g,S=h*m-d*v,C=y*S-b*L+x*E+_*T-w*M+k*A;return C?(C=1/C,t[0]=(s*S-l*L+c*E)*C,t[1]=(i*L-n*S-a*E)*C,t[2]=(g*k-v*w+m*_)*C,t[3]=(h*w-f*k-d*_)*C,t[4]=(l*T-o*S-c*M)*C,t[5]=(r*S-i*T+a*M)*C,t[6]=(v*x-p*k-m*b)*C,t[7]=(u*k-h*x+d*b)*C,t[8]=(o*L-s*T+c*A)*C,t[9]=(n*T-r*L-a*A)*C,t[10]=(p*w-g*x+m*y)*C,t[11]=(f*x-u*w-d*y)*C,t[12]=(s*M-o*E-l*A)*C,t[13]=(r*E-n*M+i*A)*C,t[14]=(g*b-p*_-v*y)*C,t[15]=(u*_-f*b+h*y)*C,t):null}e.exports=n},{}],138:[function(t,e,r){function n(t,e,r,n){var a,o,s,l,c,u,f,h,d,p,g=e[0],v=e[1],m=e[2],y=n[0],b=n[1],x=n[2],_=r[0],w=r[1],k=r[2];return Math.abs(g-_)<1e-6&&Math.abs(v-w)<1e-6&&Math.abs(m-k)<1e-6?i(t):(f=g-_,h=v-w,d=m-k,p=1/Math.sqrt(f*f+h*h+d*d),f*=p,h*=p,d*=p,a=b*d-x*h,o=x*f-y*d,s=y*h-b*f,p=Math.sqrt(a*a+o*o+s*s),p?(p=1/p,a*=p,o*=p,s*=p):(a=0,o=0,s=0),l=h*s-d*o,c=d*a-f*s,u=f*o-h*a,p=Math.sqrt(l*l+c*c+u*u),p?(p=1/p,l*=p,c*=p,u*=p):(l=0,c=0,u=0),t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=d,t[11]=0,t[12]=-(a*g+o*v+s*m),t[13]=-(l*g+c*v+u*m),t[14]=-(f*g+h*v+d*m),t[15]=1,t)}var i=t(\"./identity\");e.exports=n},{\"./identity\":136}],139:[function(t,e,r){function n(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],d=e[10],p=e[11],g=e[12],v=e[13],m=e[14],y=e[15],b=r[0],x=r[1],_=r[2],w=r[3];return t[0]=b*n+x*s+_*f+w*g,t[1]=b*i+x*l+_*h+w*v,t[2]=b*a+x*c+_*d+w*m,t[3]=b*o+x*u+_*p+w*y,b=r[4],x=r[5],_=r[6],w=r[7],t[4]=b*n+x*s+_*f+w*g,t[5]=b*i+x*l+_*h+w*v,t[6]=b*a+x*c+_*d+w*m,t[7]=b*o+x*u+_*p+w*y,b=r[8],x=r[9],_=r[10],w=r[11],t[8]=b*n+x*s+_*f+w*g,t[9]=b*i+x*l+_*h+w*v,t[10]=b*a+x*c+_*d+w*m,t[11]=b*o+x*u+_*p+w*y,b=r[12],x=r[13],_=r[14],w=r[15],t[12]=b*n+x*s+_*f+w*g,t[13]=b*i+x*l+_*h+w*v,t[14]=b*a+x*c+_*d+w*m,t[15]=b*o+x*u+_*p+w*y,t}e.exports=n},{}],140:[function(t,e,r){function n(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}e.exports=n},{}],141:[function(t,e,r){function n(t,e,r,n){var i,a,o,s,l,c,u,f,h,d,p,g,v,m,y,b,x,_,w,k,A,M,T,E,L=n[0],S=n[1],C=n[2],z=Math.sqrt(L*L+S*S+C*C);return Math.abs(z)<1e-6?null:(z=1/z,L*=z,S*=z,C*=z,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],d=e[6],p=e[7],g=e[8],v=e[9],m=e[10],y=e[11],b=L*L*o+a,x=S*L*o+C*i,_=C*L*o-S*i,w=L*S*o-C*i,k=S*S*o+a,A=C*S*o+L*i,M=L*C*o+S*i,T=S*C*o-L*i,E=C*C*o+a,t[0]=s*b+f*x+g*_,t[1]=l*b+h*x+v*_,t[2]=c*b+d*x+m*_,t[3]=u*b+p*x+y*_,t[4]=s*w+f*k+g*A,t[5]=l*w+h*k+v*A,t[6]=c*w+d*k+m*A,t[7]=u*w+p*k+y*A,t[8]=s*M+f*T+g*E,t[9]=l*M+h*T+v*E,t[10]=c*M+d*T+m*E,t[11]=u*M+p*T+y*E,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t)}e.exports=n},{}],142:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];return e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}e.exports=n},{}],143:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];return e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}e.exports=n},{}],144:[function(t,e,r){function n(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];return e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]),t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}e.exports=n},{}],145:[function(t,e,r){function n(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}e.exports=n},{}],146:[function(t,e,r){function n(t,e,r){var n,i,a,o,s,l,c,u,f,h,d,p,g=r[0],v=r[1],m=r[2];return e===t?(t[12]=e[0]*g+e[4]*v+e[8]*m+e[12],t[13]=e[1]*g+e[5]*v+e[9]*m+e[13],t[14]=e[2]*g+e[6]*v+e[10]*m+e[14],t[15]=e[3]*g+e[7]*v+e[11]*m+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],d=e[10],p=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=d,t[11]=p,t[12]=n*g+s*v+f*m+e[12],t[13]=i*g+l*v+h*m+e[13],t[14]=a*g+c*v+d*m+e[14],t[15]=o*g+u*v+p*m+e[15]),t}e.exports=n},{}],147:[function(t,e,r){function n(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}e.exports=n},{}],148:[function(t,e,r){\"use strict\";function n(t,e){for(var r=[0,0,0,0],n=0;4>n;++n)for(var i=0;4>i;++i)r[i]+=t[4*n+i]*e[n];return r}function i(t,e,r,i,a){for(var o=n(i,n(r,n(e,[t[0],t[1],t[2],1]))),s=0;3>s;++s)o[s]/=o[3];return[.5*a[0]*(1+o[0]),.5*a[1]*(1-o[1])]}function a(t,e){if(2===t.length){for(var r=0,n=0,i=0;2>i;++i)r+=Math.pow(e[i]-t[0][i],2),n+=Math.pow(e[i]-t[1][i],2);return r=Math.sqrt(r),n=Math.sqrt(n),1e-6>r+n?[1,0]:[n/(r+n),r/(n+r)]}if(3===t.length){var a=[0,0];return c(t[0],t[1],t[2],e,a),l(t,a)}return[]}function o(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;3>o;++o)r[o]+=a*i[o];return r}function s(t,e,r,n,s,l){if(1===t.length)return[0,t[0].slice()];for(var c=new Array(t.length),u=0;u<t.length;++u)c[u]=i(t[u],r,n,s,l);for(var f=0,h=1/0,u=0;u<c.length;++u){for(var d=0,p=0;2>p;++p)d+=Math.pow(c[u][p]-e[p],2);h>d&&(h=d,f=u)}for(var g=a(c,e),v=0,u=0;3>u;++u){if(g[u]<-.001||g[u]>1.0001)return null;v+=g[u]}return Math.abs(v-1)>.001?null:[f,o(t,g),g]}var l=t(\"barycentric\"),c=t(\"polytope-closest-point/lib/closest_point_2d.js\");e.exports=s},{barycentric:151,\"polytope-closest-point/lib/closest_point_2d.js\":153}],149:[function(t,e,r){var n=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n , view\\n , projection;\\nuniform vec3 eyePosition\\n , lightPosition;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec4 m_position = model * vec4(position, 1.0);\\n vec4 t_position = view * m_position;\\n gl_Position = projection * t_position;\\n f_color = color;\\n f_normal = normal;\\n f_data = position;\\n f_eyeDirection = eyePosition - position;\\n f_lightDirection = lightPosition - position;\\n f_uv = uv;\\n}\",i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution_2_0(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\n\\n\\nfloat cookTorranceSpecular_1_1(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution_2_0(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular\\n , opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if(any(lessThan(f_data, clipBounds[0])) || \\n any(greaterThan(f_data, clipBounds[1]))) {\\n discard;\\n }\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n \\n if(!gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = cookTorranceSpecular_1_1(L, V, N, roughness, fresnel);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\",a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if(any(lessThan(f_data, clipBounds[0])) || \\n any(greaterThan(f_data, clipBounds[1]))) {\\n discard;\\n }\\n\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\",s=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) || \\n any(greaterThan(position, clipBounds[1]))) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n }\\n gl_PointSize = pointSize;\\n f_color = color;\\n f_uv = uv;\\n}\",l=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec2 pointR = gl_PointCoord.xy - vec2(0.5,0.5);\\n if(dot(pointR, pointR) > 0.25) {\\n discard;\\n }\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\",c=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_id = id;\\n f_position = position;\\n}\",u=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if(any(lessThan(f_position, clipBounds[0])) || \\n any(greaterThan(f_position, clipBounds[1]))) {\\n discard;\\n }\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\",f=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute float pointSize;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) || \\n any(greaterThan(position, clipBounds[1]))) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n gl_PointSize = pointSize;\\n }\\n f_id = id;\\n f_position = position;\\n}\",h=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n}\",d=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n gl_FragColor = vec4(contourColor,1);\\n}\\n\";r.meshShader={vertex:n,fragment:i,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:a,fragment:o,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:s,fragment:l,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:c,fragment:u,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:f,fragment:u,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:h,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{}],150:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s,l,c,u,f,h,d,p,g,v,m,y,b,x,_,w,k,A,M,T){this.gl=t,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=d,this.triangleCount=0,this.lineWidth=1,this.edgePositions=p,this.edgeColors=v,this.edgeUVs=m,this.edgeIds=g,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=b,this.pointColors=_,this.pointUVs=w,this.pointSizes=k,this.pointIds=x,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=T,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],this.clipBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this._model=I,this._view=I,this._projection=I,this._resolution=[1,1]}function i(t){for(var e=A({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;256>n;++n){for(var i=e[n],a=0;3>a;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return k(r,[256,256,4],[4,0,1])}function a(t,e,r){for(var n=new Array(e),i=0;e>i;++i)n[i]=0;for(var a=t.length,i=0;a>i;++i)for(var o=t[i],s=0;s<o.length;++s)n[o[s]]=r[i];return n}function o(t){for(var e=t.length,r=new Array(e),n=0;e>n;++n)r[n]=t[n][2];return r}function s(t){var e=v(t,S);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function l(t){var e=v(t,C);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function c(t){var e=v(t,z);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function u(t){var e=v(t,P);return e.attributes.position.location=0,e.attributes.id.location=1,e}function f(t){var e=v(t,R);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function h(t){var e=v(t,O);return e.attributes.position.location=0,e}function d(t){var e=t.gl,r=s(e),i=l(e),a=c(e),o=u(e),d=f(e),p=h(e),g=b(e,k(new Uint8Array([255,255,255,255]),[1,1,4]));g.generateMipmap(),g.minFilter=e.LINEAR_MIPMAP_LINEAR,g.magFilter=e.LINEAR;var v=m(e),x=m(e),_=m(e),w=m(e),A=m(e),M=y(e,[{buffer:v,type:e.FLOAT,size:3},{buffer:A,type:e.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:x,type:e.FLOAT,size:4},{buffer:_,type:e.FLOAT,size:2},{buffer:w,type:e.FLOAT,size:3}]),T=m(e),E=m(e),L=m(e),S=m(e),C=y(e,[{buffer:T,type:e.FLOAT,size:3},{buffer:S,type:e.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:E,type:e.FLOAT,size:4},{buffer:L,type:e.FLOAT,size:2}]),z=m(e),P=m(e),R=m(e),O=m(e),I=m(e),N=y(e,[{buffer:z,type:e.FLOAT,size:3},{buffer:I,type:e.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:P,type:e.FLOAT,size:4},{buffer:R,type:e.FLOAT,size:2},{buffer:O,type:e.FLOAT,size:1}]),j=m(e),F=y(e,[{buffer:j,type:e.FLOAT,size:3}]),D=new n(e,g,r,i,a,o,d,p,v,A,x,_,w,M,T,S,E,L,C,z,I,P,R,O,N,j,F);return D.update(t),D}var p=1e-6,g=1e-6,v=t(\"gl-shader\"),m=t(\"gl-buffer\"),y=t(\"gl-vao\"),b=t(\"gl-texture2d\"),x=t(\"normals\"),_=t(\"gl-mat4/multiply\"),w=t(\"gl-mat4/invert\"),k=t(\"ndarray\"),A=t(\"colormap\"),M=t(\"simplicial-complex-contour\"),T=t(\"typedarray-pool\"),E=t(\"./lib/shaders\"),L=t(\"./lib/closest-point\"),S=E.meshShader,C=E.wireShader,z=E.pointShader,P=E.pickShader,R=E.pointPickShader,O=E.contourShader,I=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],N=n.prototype;N.isOpaque=function(){return this.opacity>=1},N.isTransparent=function(){return this.opacity<1},N.pickSlots=1,N.setPickBase=function(t){this.pickId=t},N.highlight=function(t){if(!t||!this.contourEnable)return void(this.contourCount=0);for(var e=M(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=T.mallocFloat32(6*a),s=0,l=0;a>l;++l)for(var c=r[l],u=0;2>u;++u){var f=c[0];2===c.length&&(f=c[u]);for(var h=n[f][0],d=n[f][1],p=i[f],g=1-p,v=this.positions[h],m=this.positions[d],y=0;3>y;++y)o[s++]=p*v[y]+g*m[y]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),T.free(o)},N.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=b(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(i(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var s=[],l=[],c=[],u=[],f=[],h=[],d=[],v=[],m=[],y=[],_=[],w=[],k=[],A=[];this.cells=r,this.positions=n;var M=t.vertexNormals,T=t.cellNormals,E=void 0===t.vertexNormalsEpsilon?p:t.vertexNormalsEpsilon,L=void 0===t.faceNormalsEpsilon?g:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=x.faceNormals(r,n,L)),T||M||(M=x.vertexNormals(r,n,E));var S=t.vertexColors,C=t.cellColors,z=t.meshColor||[1,1,1,1],P=t.vertexUVs,R=t.vertexIntensity,O=t.cellUVs,I=t.cellIntensity,N=1/0,j=-(1/0);if(!P&&!O)if(R)for(var F=0;F<R.length;++F){var D=R[F];N=Math.min(N,D),j=Math.max(j,D)}else if(I)for(var F=0;F<I.length;++F){var D=I[F];N=Math.min(N,D),j=Math.max(j,D)}else for(var F=0;F<n.length;++F){var D=n[F][2];N=Math.min(N,D),j=Math.max(j,D)}R?this.intensity=R:I?this.intensity=a(r,n.length,I):this.intensity=o(n);var B=t.pointSizes,U=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]];for(var F=0;F<n.length;++F)for(var V=n[F],q=0;3>q;++q)!isNaN(V[q])&&isFinite(V[q])&&(this.bounds[0][q]=Math.min(this.bounds[0][q],V[q]),this.bounds[1][q]=Math.max(this.bounds[1][q],V[q]));var H=0,G=0,Y=0;t:for(var F=0;F<r.length;++F){var X=r[F];switch(X.length){case 1:for(var W=X[0],V=n[W],q=0;3>q;++q)if(isNaN(V[q])||!isFinite(V[q]))continue t;y.push(V[0],V[1],V[2]);var Z;Z=S?S[W]:C?C[F]:z,3===Z.length?_.push(Z[0],Z[1],Z[2],1):_.push(Z[0],Z[1],Z[2],Z[3]);var K;K=P?P[W]:R?[(R[W]-N)/(j-N),0]:O?O[F]:I?[(I[F]-N)/(j-N),0]:[(V[2]-N)/(j-N),0],w.push(K[0],K[1]),B?k.push(B[W]):k.push(U),A.push(F),Y+=1;break;case 2:for(var q=0;2>q;++q)for(var W=X[q],V=n[W],$=0;3>$;++$)if(isNaN(V[$])||!isFinite(V[$]))continue t;for(var q=0;2>q;++q){var W=X[q],V=n[W];h.push(V[0],V[1],V[2]);var Z;Z=S?S[W]:C?C[F]:z,3===Z.length?d.push(Z[0],Z[1],Z[2],1):d.push(Z[0],Z[1],Z[2],Z[3]);var K;K=P?P[W]:R?[(R[W]-N)/(j-N),0]:O?O[F]:I?[(I[F]-N)/(j-N),0]:[(V[2]-N)/(j-N),0],v.push(K[0],K[1]),m.push(F)}G+=1;break;case 3:for(var q=0;3>q;++q)for(var W=X[q],V=n[W],$=0;3>$;++$)if(isNaN(V[$])||!isFinite(V[$]))continue t;for(var q=0;3>q;++q){var W=X[q],V=n[W];s.push(V[0],V[1],V[2]);var Z;Z=S?S[W]:C?C[F]:z,3===Z.length?l.push(Z[0],Z[1],Z[2],1):l.push(Z[0],Z[1],Z[2],Z[3]);var K;K=P?P[W]:R?[(R[W]-N)/(j-N),0]:O?O[F]:I?[(I[F]-N)/(j-N),0]:[(V[2]-N)/(j-N),0],u.push(K[0],K[1]);var Q;Q=M?M[W]:T[F],c.push(Q[0],Q[1],Q[2]),f.push(F)}H+=1}}this.pointCount=Y,this.edgeCount=G,this.triangleCount=H,this.pointPositions.update(y),this.pointColors.update(_),this.pointUVs.update(w),this.pointSizes.update(k),this.pointIds.update(new Uint32Array(A)),this.edgePositions.update(h),this.edgeColors.update(d),this.edgeUVs.update(v),this.edgeIds.update(new Uint32Array(m)),this.trianglePositions.update(s),this.triangleColors.update(l),this.triangleUVs.update(u),this.triangleNormals.update(c),this.triangleIds.update(new Uint32Array(f))}},N.drawTransparent=N.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||I,n=t.view||I,i=t.projection||I,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;3>o;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,contourColor:this.contourColor,texture:0};this.texture.bind(0);var l=new Array(16);_(l,s.view,s.model),_(l,s.projection,l),w(l,l);for(var o=0;3>o;++o)s.eyePosition[o]=l[12+o]/l[15];for(var c=l[15],o=0;3>o;++o)c+=this.lightPosition[o]*l[4*o+3];for(var o=0;3>o;++o){for(var u=l[12+o],f=0;3>f;++f)u+=l[4*f+o]*this.lightPosition[f];s.lightPosition[o]=u/c}if(this.triangleCount>0){var h=this.triShader;h.bind(),h.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}if(this.edgeCount>0&&this.lineWidth>0){var h=this.lineShader;h.bind(),h.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()}if(this.pointCount>0){var h=this.pointShader;h.bind(),h.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()}if(this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0){var h=this.contourShader;h.bind(),h.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind()}},N.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||I,n=t.view||I,i=t.projection||I,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;3>o;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255},l=this.pickShader;if(l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0){var l=this.pointPickShader;l.bind(),l.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()}},N.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=L(i,[t.coord[0],this._resolution[1]-t.coord[1]],this._model,this._view,this._projection,this._resolution);if(!o)return null;for(var s=o[2],l=0,a=0;a<r.length;++a)l+=s[a]*this.intensity[r[a]];return{position:o[1],index:r[o[0]],cell:r,cellId:e,intensity:l,dataCoordinate:this.positions[r[o[0]]]}},N.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=d},{\"./lib/closest-point\":148,\"./lib/shaders\":149,colormap:100,\"gl-buffer\":118,\"gl-mat4/invert\":137,\"gl-mat4/multiply\":139,\"gl-shader\":197,\"gl-texture2d\":222,\"gl-vao\":226,ndarray:253,normals:152,\"simplicial-complex-contour\":154,\"typedarray-pool\":278}],151:[function(t,e,r){\"use 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u=l[c],f=u.x,h=u.text,d=u.font||\"sans-serif\",p=u.fontSize||12,g=s(d,h).data,v=1/(n[i+2]-n[i]),m=n[i],y=0;y<g.length;y+=2)e.push(g[y]*p,-g[y+1]*p,(f-m)*v);a.push(Math.floor(e.length/3)),o.push(f)}this.tickOffset[i]=a,this.tickX[i]=o}for(var i=0;2>i;++i){this.labelOffset[i]=Math.floor(e.length/3);for(var g=s(t.labelFont[i],t.labels[i]).data,p=t.labelSize[i],c=0;c<g.length;c+=2)e.push(g[c]*p,-g[c+1]*p,0);this.labelCount[i]=Math.floor(e.length/3)-this.labelOffset[i]}this.titleOffset=Math.floor(e.length/3);for(var g=s(t.titleFont,t.title).data,p=t.titleSize,c=0;c<g.length;c+=2)e.push(g[c]*p,-g[c+1]*p,0);this.titleCount=Math.floor(e.length/3)-this.titleOffset,this.vbo.update(e)},u.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":162,\"binary-search-bounds\":164,\"gl-buffer\":118,\"gl-shader\":197,\"text-cache\":273}],164:[function(t,e,r){arguments[4][62][0].apply(r,arguments)},{dup:62}],165:[function(t,e,r){\"use strict\";function n(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-(1/0),-(1/0)],this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}function 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t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT),t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,d=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var p=0;2>p;++p)if(f[p]&&n[p]<=0&&n[p+2]>=0){var g=e[p]-n[p]*(e[p+2]-e[p])/(n[p+2]-n[p]);0===p?o.drawLine(g,e[1],g,e[3],d[p],h[p]):o.drawLine(e[0],g,e[2],g,d[p],h[p])}}for(var p=0;p<l.length;++p)l[p].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var v=this.borderLineEnable,m=this.borderLineWidth,y=this.borderLineColor;v[1]&&o.drawLine(r[0],r[1]-.5*m[1]*i,r[0],r[3]+.5*m[3]*i,m[1],y[1]),v[0]&&o.drawLine(r[0]-.5*m[0]*i,r[1],r[2]+.5*m[2]*i,r[1],m[0],y[0]),v[3]&&o.drawLine(r[2],r[1]-.5*m[1]*i,r[2],r[3]+.5*m[3]*i,m[3],y[3]),v[2]&&o.drawLine(r[0]-.5*m[0]*i,r[3],r[2]+.5*m[2]*i,r[3],m[2],y[2]),s.bind();for(var p=0;2>p;++p)s.drawTicks(p);this.titleEnable&&s.drawTitle();for(var b=this.overlays,p=0;p<b.length;++p)b[p].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}}}(),h.drawPick=function(){return function(){var t=this.pickBuffer;this.gl;this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}}(),h.pick=function(){return function(t,e){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}}(),h.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},h.setDataBox=function(t){var e=this.dataBox,r=e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3];r&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},h.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},h.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,o=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/o,10,10/o]),this.borderColor=(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=i(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=i(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=i(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t&&!t.titleEnable),this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=i(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=i(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),\nthis.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=i(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var s=t.ticks||[[],[]],l=this._tickBounds;l[0]=l[1]=1/0,l[2]=l[3]=-(1/0);for(var c=0;2>c;++c){var 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y=[0,0,0],b=[0,0,0],x=[0,0,0];p.draw=function(t){t=t||m;for(var e=this.gl,r=t.model||d,i=t.view||d,a=t.projection||d,s=this.bounds,l=f(r,i,a,s),c=l.cubeEdges,u=l.axis,h=i[12],p=i[13],_=i[14],w=i[15],k=this.pixelRatio*(a[3]*h+a[7]*p+a[11]*_+a[15]*w)/e.drawingBufferHeight,A=0;3>A;++A)this.lastCubeProps.cubeEdges[A]=c[A],this.lastCubeProps.axis[A]=u[A];for(var M=g,A=0;3>A;++A)o(g[A],A,this.bounds,c,u);for(var e=this.gl,T=v,A=0;3>A;++A)this.backgroundEnable[A]?T[A]=u[A]:T[A]=0;this._background.draw(r,i,a,s,T,this.backgroundColor),this._lines.bind(r,i,a,this);for(var A=0;3>A;++A){var E=[0,0,0];u[A]>0?E[A]=s[1][A]:E[A]=s[0][A];for(var L=0;2>L;++L){var S=(A+1+L)%3,C=(A+1+(1^L))%3;this.gridEnable[S]&&this._lines.drawGrid(S,C,this.bounds,E,this.gridColor[S],this.gridWidth[S]*this.pixelRatio)}for(var L=0;2>L;++L){var S=(A+1+L)%3,C=(A+1+(1^L))%3;this.zeroEnable[C]&&s[0][C]<=0&&s[1][C]>=0&&this._lines.drawZero(S,C,this.bounds,E,this.zeroLineColor[C],this.zeroLineWidth[C]*this.pixelRatio)}}for(var 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o=t(\"extract-frustum-planes\"),s=t(\"split-polygon\"),l=t(\"./lib/cube.js\"),c=t(\"gl-mat4/multiply\"),u=t(\"gl-mat4/transpose\"),f=t(\"gl-vec4/transformMat4\"),h=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),d=new Float32Array(16),p=[0,0,0,1],g=[0,0,0,1],v=[new n(1/0,-(1/0),1/0),new n(1/0,-(1/0),1/0),new n(1/0,-(1/0),1/0)],m=[0,0,0]},{\"./lib/cube.js\":172,\"extract-frustum-planes\":177,\"gl-mat4/multiply\":139,\"gl-mat4/transpose\":147,\"gl-vec4/transformMat4\":227,\"split-polygon\":178}],181:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, color;\\nattribute float weight;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 coordinates[3];\\nuniform vec4 colors[3];\\nuniform vec2 screenShape;\\nuniform float lineWidth;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n vec3 vertexPosition = mix(coordinates[0],\\n mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\\n\\n vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\\n vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\\n vec2 delta = weight * clipOffset * screenShape;\\n vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\\n\\n gl_Position = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\\n fragColor = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\\n}\\n\",a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n gl_FragColor = fragColor;\\n}\";\ne.exports=function(t){return n(t,i,a,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec3\"},{name:\"weight\",type:\"float\"}])}},{\"gl-shader\":197}],182:[function(t,e,r){\"use strict\";function 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a=t(\"gl-buffer\"),o=t(\"gl-vao\"),s=t(\"./shaders/index\");e.exports=i;var l=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],c=n.prototype,u=[0,0,0],f=[0,0,0],h=[0,0];c.isTransparent=function(){return!1},c.drawTransparent=function(t){},c.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||l,o=t.view||l,s=t.projection||l;this.axes&&(i=this.axes.lastCubeProps.axis);for(var c=u,d=f,p=0;3>p;++p)i&&i[p]<0?(c[p]=this.bounds[0][p],d[p]=this.bounds[1][p]):(c[p]=this.bounds[1][p],d[p]=this.bounds[0][p]);h[0]=e.drawingBufferWidth,h[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=o,n.uniforms.projection=s,n.uniforms.coordinates=[this.position,c,d],n.uniforms.colors=this.colors,n.uniforms.screenShape=h;for(var p=0;3>p;++p)n.uniforms.lineWidth=this.lineWidth[p]*this.pixelRatio,this.enabled[p]&&(r.draw(e.TRIANGLES,6,6*p),this.drawSides[p]&&r.draw(e.TRIANGLES,12,18+12*p));r.unbind()},c.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},c.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":181,\"gl-buffer\":118,\"gl-vao\":226}],183:[function(t,e,r){\"use strict\";function n(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function i(t,e){var r=null;try{r=t.getContext(\"webgl\",e),r||(r=t.getContext(\"experimental-webgl\",e))}catch(n){return null}return r}function a(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(0>e){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){var r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function o(t){return\"boolean\"==typeof t?t:!0}function s(t){function e(){if(!_&&H.autoResize){var t=w.parentNode,e=1,r=1;t&&t!==document.body?(e=t.clientWidth,r=t.clientHeight):(e=window.innerWidth,r=window.innerHeight);var n=0|Math.ceil(e*H.pixelRatio),i=0|Math.ceil(r*H.pixelRatio);if(n!==w.width||i!==w.height){w.width=n,w.height=i;var a=w.style;a.position=a.position||\"absolute\",a.left=\"0px\",a.top=\"0px\",a.width=e+\"px\",a.height=r+\"px\",F=!0}}}function r(){for(var t=O.length,e=j.length,r=0;e>r;++r)N[r]=0;t:for(var r=0;t>r;++r){var n=O[r],i=n.pickSlots;if(i){for(var a=0;e>a;++a)if(N[a]+i<255){I[r]=a,n.setPickBase(N[a]+1),N[a]+=i;continue t}var o=h(A,q);I[r]=e,j.push(o),N.push(i),n.setPickBase(1),e+=1}else I[r]=-1}for(;e>0&&0===N[e-1];)N.pop(),j.pop().dispose()}function s(){return H.contextLost?!0:void(A.isContextLost()&&(H.contextLost=!0,H.mouseListener.enabled=!1,H.selection.object=null,H.oncontextloss&&H.oncontextloss()))}function y(){if(!s()){A.colorMask(!0,!0,!0,!0),A.depthMask(!0),A.disable(A.BLEND),A.enable(A.DEPTH_TEST);for(var t=O.length,e=j.length,r=0;e>r;++r){var n=j[r];n.shape=G,n.begin();for(var i=0;t>i;++i)if(I[i]===r){var a=O[i];a.drawPick&&(a.pixelRatio=1,a.drawPick(V))}n.end()}}}function b(){if(!s()){e();var t=H.camera.tick();V.view=H.camera.matrix,F=F||t,D=D||t,z.pixelRatio=H.pixelRatio,R.pixelRatio=H.pixelRatio;var r=O.length,n=W[0],i=W[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-(1/0);for(var o=0;r>o;++o){var l=O[o];l.pixelRatio=H.pixelRatio,l.axes=H.axes,F=F||!!l.dirty,D=D||!!l.dirty;var c=l.bounds;if(c)for(var f=c[0],h=c[1],d=0;3>d;++d)n[d]=Math.min(n[d],f[d]),i[d]=Math.max(i[d],h[d])}var g=H.bounds;if(H.autoBounds)for(var d=0;3>d;++d){if(i[d]<n[d])n[d]=-1,i[d]=1;else{n[d]===i[d]&&(n[d]-=1,i[d]+=1);var m=.05*(i[d]-n[d]);n[d]=n[d]-m,i[d]=i[d]+m}g[0][d]=n[d],g[1][d]=i[d]}for(var b=!1,d=0;3>d;++d)b=b||Z[0][d]!==g[0][d]||Z[1][d]!==g[1][d],Z[0][d]=g[0][d],Z[1][d]=g[1][d];if(b){for(var x=[0,0,0],o=0;3>o;++o)x[o]=a((g[1][o]-g[0][o])/10);z.autoTicks?z.update({bounds:g,tickSpacing:x}):z.update({bounds:g})}D=D||b,F=F||b;var _=A.drawingBufferWidth,w=A.drawingBufferHeight;q[0]=_,q[1]=w,G[0]=0|Math.max(_/H.pixelRatio,1),G[1]=0|Math.max(w/H.pixelRatio,1),v(B,H.fovy,_/w,H.zNear,H.zFar);for(var o=0;16>o;++o)U[o]=0;U[15]=1;for(var k=0,o=0;3>o;++o)k=Math.max(k,g[1][o]-g[0][o]);for(var o=0;3>o;++o)H.autoScale?U[5*o]=H.aspect[o]/(g[1][o]-g[0][o]):U[5*o]=1/k,H.autoCenter&&(U[12+o]=.5*-U[5*o]*(g[0][o]+g[1][o]));for(var o=0;r>o;++o){var l=O[o];l.axesBounds=g,H.clipToBounds&&(l.clipBounds=g)}if(T.object&&(H.snapToData?R.position=T.dataCoordinate:R.position=T.dataPosition,R.bounds=g),D&&(D=!1,y()),F){H.axesPixels=u(H.axes,V,_,w),H.onrender&&H.onrender(),A.bindFramebuffer(A.FRAMEBUFFER,null),A.viewport(0,0,_,w);var M=H.clearColor;A.clearColor(M[0],M[1],M[2],M[3]),A.clear(A.COLOR_BUFFER_BIT|A.DEPTH_BUFFER_BIT),A.depthMask(!0),A.colorMask(!0,!0,!0,!0),A.enable(A.DEPTH_TEST),A.depthFunc(A.LEQUAL),A.disable(A.BLEND),A.disable(A.CULL_FACE);var S=!1;z.enable&&(S=S||z.isTransparent(),z.draw(V)),R.axes=z,T.object&&R.draw(V),A.disable(A.CULL_FACE);for(var o=0;r>o;++o){var l=O[o];l.axes=z,l.pixelRatio=H.pixelRatio,l.isOpaque&&l.isOpaque()&&l.draw(V),l.isTransparent&&l.isTransparent()&&(S=!0)}if(S){E.shape=q,E.bind(),A.clear(A.DEPTH_BUFFER_BIT),A.colorMask(!1,!1,!1,!1),A.depthMask(!0),A.depthFunc(A.LESS),z.enable&&z.isTransparent()&&z.drawTransparent(V);for(var o=0;r>o;++o){var l=O[o];l.isOpaque&&l.isOpaque()&&l.draw(V)}A.enable(A.BLEND),A.blendEquation(A.FUNC_ADD),A.blendFunc(A.ONE,A.ONE_MINUS_SRC_ALPHA),A.colorMask(!0,!0,!0,!0),A.depthMask(!1),A.clearColor(0,0,0,0),A.clear(A.COLOR_BUFFER_BIT),z.isTransparent()&&z.drawTransparent(V);for(var o=0;r>o;++o){var l=O[o];l.isTransparent&&l.isTransparent()&&l.drawTransparent(V)}A.bindFramebuffer(A.FRAMEBUFFER,null),A.blendFunc(A.ONE,A.ONE_MINUS_SRC_ALPHA),A.disable(A.DEPTH_TEST),L.bind(),E.color[0].bind(0),L.uniforms.accumBuffer=0,p(A),A.disable(A.BLEND)}F=!1;for(var o=0;r>o;++o)O[o].dirty=!1}}}function x(){_||H.contextLost||(requestAnimationFrame(x),b())}t=t||{};var _=!1,w=(t.pixelRatio||parseFloat(window.devicePixelRatio),t.canvas);if(!w)if(w=document.createElement(\"canvas\"),t.container){var k=t.container;k.appendChild(w)}else document.body.appendChild(w);var A=t.gl;if(A||(A=i(w,t.glOptions||{premultipliedAlpha:!0,antialias:!0})),!A)throw new Error(\"webgl not supported\");var M=t.bounds||[[-10,-10,-10],[10,10,10]],T=new n,E=d(A,[A.drawingBufferWidth,A.drawingBufferHeight],{preferFloat:!0}),L=m(A),S=t.camera||{eye:[2,0,0],center:[0,0,0],up:[0,1,0],zoomMin:.1,zoomMax:100,mode:\"turntable\"},C=t.axes||{},z=c(A,C);z.enable=!C.disable;var P=t.spikes||{},R=f(A,P),O=[],I=[],N=[],j=[],F=!0,D=!0,B=new Array(16),U=new Array(16),V={view:null,projection:B,model:U},D=!0,q=[A.drawingBufferWidth,A.drawingBufferHeight],H={gl:A,contextLost:!1,pixelRatio:t.pixelRatio||parseFloat(window.devicePixelRatio),canvas:w,selection:T,camera:l(w,S),axes:z,axesPixels:null,spikes:R,bounds:M,objects:O,shape:q,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:o(t.autoResize),autoBounds:o(t.autoBounds),autoScale:!!t.autoScale,autoCenter:o(t.autoCenter),clipToBounds:o(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:V,oncontextloss:null,mouseListener:null},G=[A.drawingBufferWidth/H.pixelRatio|0,A.drawingBufferHeight/H.pixelRatio|0];H.autoResize&&e(),window.addEventListener(\"resize\",e),H.update=function(t){_||(t=t||{},F=!0,D=!0)},H.add=function(t){_||(t.axes=z,O.push(t),I.push(-1),F=!0,D=!0,r())},H.remove=function(t){if(!_){var e=O.indexOf(t);0>e||(O.splice(e,1),I.pop(),F=!0,D=!0,r())}},H.dispose=function(){if(!_&&(_=!0,window.removeEventListener(\"resize\",e),w.removeEventListener(\"webglcontextlost\",s),H.mouseListener.enabled=!1,!H.contextLost)){z.dispose(),R.dispose();for(var t=0;t<O.length;++t)O[t].dispose();E.dispose();for(var t=0;t<j.length;++t)j[t].dispose();L.dispose(),A=null,z=null,R=null,O=[]}};var Y=!1,X=0;H.mouseListener=g(w,function(t,e,r){if(!_){var n=j.length,i=O.length,a=T.object;T.distance=1/0,T.mouse[0]=e,T.mouse[1]=r,T.object=null,T.screen=null,T.dataCoordinate=T.dataPosition=null;var o=!1;if(t&&X)Y=!0;else{Y&&(D=!0),Y=!1;for(var s=0;n>s;++s){var l=j[s].query(e,G[1]-r-1,H.pickRadius);if(l){if(l.distance>T.distance)continue;for(var c=0;i>c;++c){var u=O[c];if(I[c]===s){var f=u.pick(l);f&&(T.buttons=t,T.screen=l.coord,T.distance=l.distance,T.object=u,T.index=f.distance,T.dataPosition=f.position,T.dataCoordinate=f.dataCoordinate,T.data=f,o=!0)}}}}}a&&a!==T.object&&(a.highlight&&a.highlight(null),F=!0),T.object&&(T.object.highlight&&T.object.highlight(T.data),F=!0),o=o||T.object!==a,o&&H.onselect&&H.onselect(T),1&t&&!(1&X)&&H.onclick&&H.onclick(T),X=t}}),w.addEventListener(\"webglcontextlost\",s);var W=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],Z=[W[0].slice(),W[1].slice()];return x(),H.redraw=function(){_||(F=!0,b())},H}e.exports=s;var l=t(\"3d-view-controls\"),c=t(\"gl-axes3d\"),u=t(\"gl-axes3d/properties\"),f=t(\"gl-spikes3d\"),h=t(\"gl-select-static\"),d=t(\"gl-fbo\"),p=t(\"a-big-triangle\"),g=t(\"mouse-change\"),v=t(\"gl-mat4/perspective\"),m=t(\"./lib/shader\")},{\"./lib/shader\":166,\"3d-view-controls\":167,\"a-big-triangle\":169,\"gl-axes3d\":170,\"gl-axes3d/properties\":180,\"gl-fbo\":123,\"gl-mat4/perspective\":140,\"gl-select-static\":196,\"gl-spikes3d\":182,\"mouse-change\":241}],184:[function(t,e,r){\"use strict\";e.exports={vertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec2 offset;\\nattribute vec4 color;\\n\\nuniform mat3 viewTransform;\\nuniform vec2 pixelScale;\\n\\nvarying vec4 fragColor;\\n\\nvec4 computePosition_1_0(vec2 position, vec2 offset, mat3 view, vec2 scale) {\\n vec3 xposition = view * vec3(position, 1.0);\\n return vec4(\\n xposition.xy + scale * offset * xposition.z,\\n 0,\\n xposition.z);\\n}\\n\\n\\n\\n\\nvoid main() {\\n fragColor = color;\\n\\n gl_Position = computePosition_1_0(\\n position,\\n offset,\\n viewTransform,\\n pixelScale);\\n}\\n\",fragment:\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\",pickVertex:\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec2 offset;\\nattribute vec4 id;\\n\\nuniform mat3 viewTransform;\\nuniform vec2 pixelScale;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragColor;\\n\\nvec4 computePosition_1_0(vec2 position, vec2 offset, mat3 view, vec2 scale) {\\n vec3 xposition = view * vec3(position, 1.0);\\n return vec4(\\n xposition.xy + scale * offset * xposition.z,\\n 0,\\n xposition.z);\\n}\\n\\n\\n\\n\\nvoid main() {\\n vec4 fragId = id + pickOffset;\\n\\n fragId.y += floor(fragId.x / 256.0);\\n fragId.x -= floor(fragId.x / 256.0) * 256.0;\\n\\n fragId.z += floor(fragId.y / 256.0);\\n fragId.y -= floor(fragId.y / 256.0) * 256.0;\\n\\n fragId.w += floor(fragId.z / 256.0);\\n fragId.z -= floor(fragId.z / 256.0) * 256.0;\\n\\n fragColor = fragId / 255.0;\\n\\n gl_Position = computePosition_1_0(\\n position,\\n offset,\\n viewTransform,\\n pixelScale);\\n}\\n\",pickFragment:\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n gl_FragColor = fragColor;\\n}\\n\"}},{}],185:[function(t,e,r){\"use strict\";function n(t){if(t in h)return h[t];var e=u(t,{polygons:!0,font:\"sans-serif\",textAlign:\"left\",textBaseline:\"alphabetic\"}),r=[],n=[];e.forEach(function(t){t.forEach(function(t){for(var e=0;e<t.length;++e){var i=t[(e+t.length-1)%t.length],a=t[e],o=t[(e+1)%t.length],s=t[(e+2)%t.length],l=a[0]-i[0],c=a[1]-i[1],u=Math.sqrt(l*l+c*c);l/=u,c/=u,r.push(i[0],i[1]+1.4),n.push(c,-l),r.push(i[0],i[1]+1.4),n.push(-c,l),r.push(a[0],a[1]+1.4),n.push(-c,l),r.push(a[0],a[1]+1.4),n.push(-c,l),r.push(i[0],i[1]+1.4),n.push(c,-l),r.push(a[0],a[1]+1.4),n.push(c,-l);var f=s[0]-o[0],h=s[1]-o[1],d=Math.sqrt(f*f+h*h);f/=d,h/=d,r.push(a[0],a[1]+1.4),n.push(c,-l),r.push(a[0],a[1]+1.4),n.push(-c,l),r.push(o[0],o[1]+1.4),n.push(-h,f),r.push(o[0],o[1]+1.4),n.push(-h,f),r.push(a[0],a[1]+1.4),n.push(h,-f),r.push(o[0],o[1]+1.4),n.push(h,-f)}})});for(var i=[1/0,1/0,-(1/0),-(1/0)],a=0;a<r.length;a+=2)for(var o=0;2>o;++o)i[o]=Math.min(i[o],r[a+o]),i[2+o]=Math.max(i[2+o],r[a+o]);return h[t]={coords:r,normals:n,bounds:i}}function i(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.offsetBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.bounds=[1/0,1/0,-(1/0),-(1/0)],this.numPoints=0,this.numVertices=0,this.pickOffset=0,this.points=null}function a(t,e){var r=t.gl,n=o(r,f.vertex,f.fragment),a=o(r,f.pickVertex,f.pickFragment),l=s(r),c=s(r),u=s(r),h=s(r),d=new i(t,n,a,l,c,u,h);return d.update(e),t.addObject(d),d}e.exports=a;var o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"text-cache\"),c=t(\"typedarray-pool\"),u=t(\"vectorize-text\"),f=t(\"./lib/shaders\"),h={},d=i.prototype;!function(){function t(){var t=this.plot,n=this.bounds,i=t.viewBox,a=t.dataBox,o=t.pixelRatio,s=n[2]-n[0],l=n[3]-n[1],c=a[2]-a[0],u=a[3]-a[1];e[0]=2*s/c,e[4]=2*l/u,e[6]=2*(n[0]-a[0])/c-1,e[7]=2*(n[1]-a[1])/u-1;var f=i[2]-i[0],h=i[3]-i[1];r[0]=2*o/f,r[1]=2*o/h}var e=[1,0,0,0,1,0,0,0,1],r=[1,1];d.draw=function(){var n=this.plot,i=this.shader,a=this.numVertices;if(a){var o=n.gl;t.call(this),i.bind(),i.uniforms.pixelScale=r,i.uniforms.viewTransform=e,this.positionBuffer.bind(),i.attributes.position.pointer(),this.offsetBuffer.bind(),i.attributes.offset.pointer(),this.colorBuffer.bind(),i.attributes.color.pointer(o.UNSIGNED_BYTE,!0),o.drawArrays(o.TRIANGLES,0,a)}};var n=[0,0,0,0];d.drawPick=function(i){var a=this.plot,o=this.pickShader,s=this.numVertices,l=a.gl;if(this.pickOffset=i,!s)return i;for(var c=0;4>c;++c)n[c]=i>>8*c&255;return t.call(this),o.bind(),o.uniforms.pixelScale=r,o.uniforms.viewTransform=e,o.uniforms.pickOffset=n,this.positionBuffer.bind(),o.attributes.position.pointer(),this.offsetBuffer.bind(),o.attributes.offset.pointer(),this.idBuffer.bind(),o.attributes.id.pointer(l.UNSIGNED_BYTE,!1),l.drawArrays(l.TRIANGLES,0,s),i+this.numPoints}}(),d.pick=function(t,e,r){var n=this.pickOffset,i=this.numPoints;if(n>r||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}},d.update=function(t){t=t||{};var e=t.positions||[],r=t.colors||[],i=t.glyphs||[],a=t.sizes||[],o=t.borderWidths||[],s=t.borderColors||[];this.points=e;for(var u=this.bounds=[1/0,1/0,-(1/0),-(1/0)],f=0,h=0;h<i.length;++h){f+=l(\"sans-serif\",i[h]).data.length+n(i[h]).coords.length>>1;for(var d=0;2>d;++d)u[d]=Math.min(u[d],e[2*h+d]),u[2+d]=Math.max(u[2+d],e[2*h+d])}u[0]===u[2]&&(u[2]+=1),u[3]===u[1]&&(u[3]+=1);for(var p=1/(u[2]-u[0]),g=1/(u[3]-u[1]),v=u[0],m=u[1],y=c.mallocFloat32(2*f),b=c.mallocFloat32(2*f),x=c.mallocUint8(4*f),_=c.mallocUint32(f),w=0,h=0;h<i.length;++h){for(var k=l(\"sans-serif\",i[h]),A=n(i[h]),M=p*(e[2*h]-v),T=g*(e[2*h+1]-m),E=a[h],L=255*r[4*h],S=255*r[4*h+1],C=255*r[4*h+2],z=255*r[4*h+3],P=.5*(A.bounds[0]+A.bounds[2]),R=.5*(A.bounds[1]+A.bounds[3]),d=0;d<k.data.length;d+=2)y[2*w]=M,y[2*w+1]=T,b[2*w]=-E*(k.data[d]-P),b[2*w+1]=-E*(k.data[d+1]-R),x[4*w]=L,x[4*w+1]=S,x[4*w+2]=C,x[4*w+3]=z,_[w]=h,w+=1;var O=o[h];L=255*s[4*h],S=255*s[4*h+1],C=255*s[4*h+2],z=255*s[4*h+3];for(var d=0;d<A.coords.length;d+=2)y[2*w]=M,y[2*w+1]=T,b[2*w]=-(E*(A.coords[d]-P)+O*A.normals[d]),b[2*w+1]=-(E*(A.coords[d+1]-R)+O*A.normals[d+1]),x[4*w]=L,x[4*w+1]=S,x[4*w+2]=C,x[4*w+3]=z,_[w]=h,w+=1}this.numPoints=i.length,this.numVertices=f,this.positionBuffer.update(y),this.offsetBuffer.update(b),this.colorBuffer.update(x),this.idBuffer.update(_),c.free(y),c.free(b),c.free(x),c.free(_)},d.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.offsetBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":184,\"gl-buffer\":118,\"gl-shader\":197,\"text-cache\":273,\"typedarray-pool\":278,\"vectorize-text\":280}],186:[function(t,e,r){r.pointVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute float weight;\\n\\nuniform mat3 matrix;\\nuniform float pointSize, useWeight;\\n\\nvarying float fragWeight;\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n gl_PointSize = pointSize;\\n fragWeight = mix(1.0, weight, useWeight);\\n}\\n\",r.pointFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\n\\nvarying float fragWeight;\\n\\nfloat smoothStep(float x, float y) {\\n return 1.0 / (1.0 + exp(50.0*(x - y)));\\n}\\n\\nvoid main() {\\n float radius = length(2.0*gl_PointCoord.xy-1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n vec4 baseColor = mix(borderColor, color, smoothStep(radius, centerFraction));\\n float alpha = 1.0 - pow(1.0 - baseColor.a, fragWeight);\\n gl_FragColor = vec4(baseColor.rgb * alpha, alpha);\\n}\\n\",r.pickVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n gl_PointSize = pointSize;\\n\\n vec4 id = pickId + pickOffset;\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) * 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n fragId = id;\\n}\\n\",r.pickFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n float radius = length(2.0*gl_PointCoord.xy-1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n gl_FragColor = fragId / 255.0;\\n}\\n\"},{}],187:[function(t,e,r){arguments[4][62][0].apply(r,arguments)},{dup:62}],188:[function(t,e,r){\"use strict\";function n(t,e,r,n,a){4*h>=a?i(0,a-1,t,e,r,n):f(0,a-1,t,e,r,n)}function i(t,e,r,n,i,a){for(var o=t+1;e>=o;++o){for(var s=r[o],l=n[2*o],c=n[2*o+1],u=i[o],f=a[o],h=o;h>t;){var d=r[h-1],p=n[2*(h-1)];if((d-s||l-p)>=0)break;r[h]=d,n[2*h]=p,n[2*h+1]=n[2*h-1],i[h]=i[h-1],a[h]=a[h-1],h-=1}r[h]=s,n[2*h]=l,n[2*h+1]=c,i[h]=u,a[h]=f}}function a(t,e,r,n,i,a){var o=r[t],s=n[2*t],l=n[2*t+1],c=i[t],u=a[t];r[t]=r[e],n[2*t]=n[2*e],n[2*t+1]=n[2*e+1],i[t]=i[e],a[t]=a[e],r[e]=o,n[2*e]=s,n[2*e+1]=l,i[e]=c,a[e]=u}function o(t,e,r,n,i,a){r[t]=r[e],n[2*t]=n[2*e],n[2*t+1]=n[2*e+1],i[t]=i[e],a[t]=a[e]}function s(t,e,r,n,i,a,o){var s=n[t],l=i[2*t],c=i[2*t+1],u=a[t],f=o[t];n[t]=n[e],i[2*t]=i[2*e],i[2*t+1]=i[2*e+1],a[t]=a[e],o[t]=o[e],n[e]=n[r],i[2*e]=i[2*r],i[2*e+1]=i[2*r+1],a[e]=a[r],o[e]=o[r],n[r]=s,i[2*r]=l,i[2*r+1]=c,a[r]=u,o[r]=f}function l(t,e,r,n,i,a,o,s,l,c,u){s[t]=s[e],l[2*t]=l[2*e],l[2*t+1]=l[2*e+1],c[t]=c[e],u[t]=u[e],s[e]=r,l[2*e]=n,l[2*e+1]=i,c[e]=a,u[e]=o}function c(t,e,r,n,i){return(r[t]-r[e]||n[2*e]-n[2*t]||i[t]-i[e])<0}function u(t,e,r,n,i,a,o,s){return(e-a[t]||o[2*t]-r||i-s[t])<0}function f(t,e,r,n,d,p){var g=(e-t+1)/6|0,v=t+g,m=e-g,y=t+e>>1,b=y-g,x=y+g,_=v,w=b,k=y,A=x,M=m,T=t+1,E=e-1,L=0;c(_,w,r,n,d,p)&&(L=_,_=w,w=L),c(A,M,r,n,d,p)&&(L=A,A=M,M=L),c(_,k,r,n,d,p)&&(L=_,_=k,k=L),c(w,k,r,n,d,p)&&(L=w,w=k,k=L),c(_,A,r,n,d,p)&&(L=_,_=A,A=L),c(k,A,r,n,d,p)&&(L=k,k=A,A=L),c(w,M,r,n,d,p)&&(L=w,w=M,M=L),c(w,k,r,n,d,p)&&(L=w,w=k,k=L),c(A,M,r,n,d,p)&&(L=A,A=M,M=L);var S=r[w],C=n[2*w],z=n[2*w+1],P=d[w],R=p[w],O=r[A],I=n[2*A],N=n[2*A+1],j=d[A],F=p[A],D=_,B=k,U=M,V=v,q=y,H=m,G=r[D],Y=r[B],X=r[U];r[V]=G,r[q]=Y,r[H]=X;for(var W=0;2>W;++W){var Z=n[2*D+W],K=n[2*B+W],$=n[2*U+W];n[2*V+W]=Z,n[2*q+W]=K,n[2*H+W]=$}var Q=d[D],J=d[B],tt=d[U];d[V]=Q,d[q]=J,d[H]=tt;var et=p[D],rt=p[B],nt=p[U];p[V]=et,p[q]=rt,p[H]=nt,o(b,t,r,n,d,p),o(x,e,r,n,d,p);for(var it=T;E>=it;++it)if(u(it,S,C,z,P,r,n,d))it!==T&&a(it,T,r,n,d,p),++T;else if(!u(it,O,I,N,j,r,n,d))for(;;){if(u(E,O,I,N,j,r,n,d)){u(E,S,C,z,P,r,n,d)?(s(it,T,E,r,n,d,p),++T,--E):(a(it,E,r,n,d,p),--E);break}if(--E<it)break}l(t,T-1,S,C,z,P,R,r,n,d,p),l(e,E+1,O,I,N,j,F,r,n,d,p),h>=T-2-t?i(t,T-2,r,n,d,p):f(t,T-2,r,n,d,p),h>=e-(E+2)?i(E+2,e,r,n,d,p):f(E+2,e,r,n,d,p),h>=E-T?i(T,E,r,n,d,p):f(T,E,r,n,d,p)}e.exports=n;var h=32},{}],189:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a,o,s){for(var l=r,c=r;n>c;++c){var u=t[2*c],f=t[2*c+1],h=e[c];u>=i&&o>=u&&f>=a&&s>=f&&(c===l?l+=1:(t[2*c]=t[2*l],t[2*c+1]=t[2*l+1],e[c]=e[l],t[2*l]=u,t[2*l+1]=f,e[l]=h,l+=1))}return l}function i(t,e,r){this.pixelSize=t,this.offset=e,this.count=r}function a(t,e,r,a){function l(i,a,o,s,c,u){var f=.5*o,h=s+1,d=c-s;r[_]=d,x[_++]=u;for(var p=0;2>p;++p)for(var g=0;2>g;++g){var v=i+p*f,m=a+g*f,y=n(t,e,h,c,v,m,v+f,m+f);if(y!==h){if(y-h>=Math.max(.9*d,32)){var b=c+s>>>1;l(v,m,f,h,b,u+1),h=b}l(v,m,f,h,y,u+1),h=y}}}var c=t.length>>>1;if(1>c)return[];for(var u=1/0,f=1/0,h=-(1/0),d=-(1/0),p=0;c>p;++p){var g=t[2*p],v=t[2*p+1];u=Math.min(u,g),h=Math.max(h,g),f=Math.min(f,v),d=Math.max(d,v),e[p]=p}u===h&&(h+=1+Math.abs(h)),f===d&&(d+=1+Math.abs(h));var m=1/(h-u),y=1/(d-f),b=Math.max(h-u,d-f);a=a||[0,0,0,0],a[0]=u,a[1]=f,a[2]=h,a[3]=d;var x=o.mallocInt32(c),_=0;l(u,f,b,0,c,0),s(x,t,e,r,c);for(var w=[],k=0,A=c,_=c-1;_>=0;--_){t[2*_]=(t[2*_]-u)*m,t[2*_+1]=(t[2*_+1]-f)*y;var M=x[_];M!==k&&(w.push(new i(b*Math.pow(.5,M),_+1,A-(_+1))),A=_+1,k=M)}return w.push(new i(b*Math.pow(.5,M+1),0,A)),o.free(x),w}var o=t(\"typedarray-pool\"),s=t(\"./lib/sort\");e.exports=a},{\"./lib/sort\":188,\"typedarray-pool\":278}],190:[function(t,e,r){\"use strict\";function n(t,e,r,n,i,a){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.weightBuffer=n,this.shader=i,this.pickShader=a,this.scales=[],this.size=12,this.borderSize=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.bounds=[1/0,1/0,-(1/0),-(1/0)],this.pickOffset=0,this.points=null,this.xCoords=null}function i(t,e){var r=t.gl,i=o(r),s=o(r),l=o(r),c=a(r,u.pointVertex,u.pointFragment),f=a(r,u.pickVertex,u.pickFragment),h=new n(t,i,s,l,c,f);return h.update(e),t.addObject(h),h}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"binary-search-bounds\"),l=t(\"snap-points-2d\"),c=t(\"typedarray-pool\"),u=t(\"./lib/shader\");e.exports=i;var f=n.prototype;f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.xCoords&&c.free(this.xCoords),this.plot.removeObject(this)},f.update=function(t){function e(e,r){return e in t?t[e]:r}t=t||{},this.size=e(\"size\",12),this.color=e(\"color\",[1,0,0,1]).slice(),this.borderSize=e(\"borderSize\",1),this.borderColor=e(\"borderColor\",[0,0,0,1]).slice(),this.xCoords&&c.free(this.xCoords);var r=t.positions,n=c.mallocFloat32(r.length),i=c.mallocInt32(r.length>>>1);n.set(r);var a=c.mallocFloat32(r.length);this.points=r,this.scales=l(n,i,a,this.bounds),this.offsetBuffer.update(n),this.pickBuffer.update(i),this.weightBuffer.update(a);for(var o=c.mallocFloat32(r.length>>>1),s=0,u=0;s<r.length;s+=2,++u)o[u]=n[s];c.free(i),c.free(n),c.free(a),this.xCoords=o,this.pointCount=r.length>>>1,this.pickOffset=0},f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.scales,o=this.offsetBuffer,l=this.pickBuffer,c=this.bounds,u=this.size,f=this.borderSize,h=n.gl,d=n.pickPixelRatio,p=n.viewBox,g=n.dataBox;if(0===this.pointCount)return r;var v=c[2]-c[0],m=c[3]-c[1],y=g[2]-g[0],b=g[3]-g[1],x=(p[2]-p[0])*d/n.pixelRatio,_=(p[3]-p[1])*d/n.pixelRatio,w=Math.min(y/x,b/_);t[0]=2*v/y,t[4]=2*m/b,t[6]=2*(c[0]-g[0])/y-1,t[7]=2*(c[1]-g[1])/b-1,this.pickOffset=r,e[0]=255&r,e[1]=r>>8&255,e[2]=r>>16&255,e[3]=r>>24&255,i.bind(),i.uniforms.matrix=t,i.uniforms.color=this.color,i.uniforms.borderColor=this.borderColor,i.uniforms.pointSize=d*(u+f),i.uniforms.pickOffset=e,0===this.borderSize?i.uniforms.centerFraction=2:i.uniforms.centerFraction=u/(u+f+1.25),o.bind(),i.attributes.position.pointer(),l.bind(),i.attributes.pickId.pointer(h.UNSIGNED_BYTE);for(var k=this.xCoords,A=(g[0]-c[0]-w*u*d)/v,M=(g[2]-c[0]+w*u*d)/v,T=a.length-1;T>=0;--T){var E=a[T];if(!(E.pixelSize<w&&T>1)){var L=E.offset,S=E.count+L,C=s.ge(k,A,L,S-1),z=s.lt(k,M,C,S-1)+1;z>C&&h.drawArrays(h.POINTS,C,z-C)}}return r+this.pointCount}}(),f.draw=function(){var t=[1,0,0,0,1,0,0,0,1];return function(){var e=this.plot,r=this.shader,n=this.scales,i=this.offsetBuffer,a=this.bounds,o=this.size,l=this.borderSize,c=e.gl,u=e.pixelRatio,f=e.viewBox,h=e.dataBox;if(0!==this.pointCount){var d=a[2]-a[0],p=a[3]-a[1],g=h[2]-h[0],v=h[3]-h[1],m=f[2]-f[0],y=f[3]-f[1],b=Math.min(g/m,v/y);t[0]=2*d/g,t[4]=2*p/v,t[6]=2*(a[0]-h[0])/g-1,t[7]=2*(a[1]-h[1])/v-1,r.bind(),r.uniforms.matrix=t,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointSize=u*(o+l),r.uniforms.useWeight=1,0===this.borderSize?r.uniforms.centerFraction=2:r.uniforms.centerFraction=o/(o+l+1.25),i.bind(),r.attributes.position.pointer(),this.weightBuffer.bind(),r.attributes.weight.pointer();for(var x=this.xCoords,_=(h[0]-a[0]-b*o*u)/d,w=(h[2]-a[0]+b*o*u)/d,k=!0,A=n.length-1;A>=0;--A){var M=n[A];if(!(M.pixelSize<b&&A>1)){var T=M.offset,E=M.count+T,L=s.ge(x,_,T,E-1),S=s.lt(x,w,L,E-1)+1;S>L&&c.drawArrays(c.POINTS,L,S-L),k&&(k=!1,r.uniforms.useWeight=0)}}}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(n>r||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":186,\"binary-search-bounds\":187,\"gl-buffer\":118,\"gl-shader\":197,\"snap-points-2d\":189,\"typedarray-pool\":278}],191:[function(t,e,r){\"use strict\";function n(t,e){var r=a[e];if(r||(r=a[e]={}),t in r)return r[t];for(var n=i(t,{textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),o=i(t,{triangles:!0,textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e}),s=[[1/0,1/0],[-(1/0),-(1/0)]],l=0;l<n.positions.length;++l)for(var c=n.positions[l],u=0;2>u;++u)s[0][u]=Math.min(s[0][u],c[u]),s[1][u]=Math.max(s[1][u],c[u]);return r[t]=[o,n,s]}var i=t(\"vectorize-text\");e.exports=n;var a={}},{\"vectorize-text\":280}],192:[function(t,e,r){function n(t,e){var r=i(t,e),n=r.attributes;return n.position.location=0,n.color.location=1,n.glyph.location=2,n.id.location=3,r}var i=t(\"gl-shader\"),a=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) || \\n any(greaterThan(position, clipBounds[1])) ) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = 1.0;\\n if(distance(highlightId, id) < 0.0001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1);\\n vec4 viewPosition = view * worldPosition;\\n viewPosition = viewPosition / viewPosition.w;\\n vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n \\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\",o=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) || any(greaterThan(position, clipBounds[1]))) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = pixelRatio;\\n if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n scale *= highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1.0);\\n vec4 viewPosition = view * worldPosition;\\n vec4 clipPosition = projection * viewPosition;\\n clipPosition /= clipPosition.w;\\n \\n gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\",s=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(position, clipBounds[0])) ||\\n any(greaterThan(position, clipBounds[1])) ) {\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float lscale = pixelRatio * scale;\\n if(distance(highlightId, id) < 0.0001) {\\n lscale *= highlightScale;\\n }\\n\\n vec4 clipCenter = projection * view * model * vec4(position, 1);\\n vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = dataPosition;\\n }\\n}\\n\",l=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(dataCoordinate, fragClipBounds[0])) ||\\n any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\\n discard;\\n } else {\\n gl_FragColor = interpColor * opacity;\\n }\\n}\\n\",c=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if(any(lessThan(dataCoordinate, fragClipBounds[0])) || \\n any(greaterThan(dataCoordinate, fragClipBounds[1])) ) {\\n discard;\\n } else {\\n gl_FragColor = vec4(pickGroup, pickId.bgr);\\n }\\n}\",u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},d={vertex:s,fragment:l,attributes:u},p={vertex:a,fragment:c,attributes:u},g={vertex:o,fragment:c,attributes:u},v={vertex:s,fragment:c,attributes:u};r.createPerspective=function(t){return n(t,f)},r.createOrtho=function(t){return n(t,h)},r.createProject=function(t){return n(t,d)},r.createPickPerspective=function(t){return n(t,p)},r.createPickOrtho=function(t){return n(t,g)},r.createPickProject=function(t){return n(t,v)}},{\"gl-shader\":197}],193:[function(t,e,r){\"use strict\";function n(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,\nt}function i(t,e,r,i){return n(i,i,r),n(i,i,e),n(i,i,t)}function a(t,e){this.index=t,this.dataCoordinate=this.position=e}function o(t,e,r,n,i,o,s,l,c,u,f,h){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=o,this.glyphBuffer=s,this.idBuffer=l,this.vao=c,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.pickId=0,this.pickPerspectiveShader=u,this.pickOrthoShader=f,this.pickProjectShader=h,this.points=[],this._selectResult=new a(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.dirty=!0}function s(t){return t[0]=t[1]=t[2]=0,t}function l(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function c(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function u(t){for(var e=S,r=0;2>r;++r)for(var n=0;3>n;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}function f(t,e,r,n,a){var o,f=e.axesProject,h=e.gl,d=t.uniforms,p=r.model||x,g=r.view||x,v=r.projection||x,y=e.axesBounds,b=u(e.clipBounds);o=e.axes?e.axes.lastCubeProps.axis:[1,1,1],w[0]=2/h.drawingBufferWidth,w[1]=2/h.drawingBufferHeight,t.bind(),d.view=g,d.projection=v,d.screenSize=w,d.highlightId=e.highlightId,d.highlightScale=e.highlightScale,d.clipBounds=b,d.pickGroup=e.pickId/255,d.pixelRatio=e.pixelRatio;for(var _=0;3>_;++_)if(f[_]&&e.projectOpacity[_]<1===n){d.scale=e.projectScale[_],d.opacity=e.projectOpacity[_];for(var S=E,C=0;16>C;++C)S[C]=0;for(var C=0;4>C;++C)S[5*C]=1;S[5*_]=0,o[_]<0?S[12+_]=y[0][_]:S[12+_]=y[1][_],m(S,p,S),d.model=S;var z=(_+1)%3,P=(_+2)%3,R=s(k),O=s(A);R[z]=1,O[P]=1;var I=i(v,g,p,l(M,R)),N=i(v,g,p,l(T,O));if(Math.abs(I[1])>Math.abs(N[1])){var j=I;I=N,N=j,j=R,R=O,O=j;var F=z;z=P,P=F}I[0]<0&&(R[z]=-1),N[1]>0&&(O[P]=-1);for(var D=0,B=0,C=0;4>C;++C)D+=Math.pow(p[4*z+C],2),B+=Math.pow(p[4*P+C],2);R[z]/=Math.sqrt(D),O[P]/=Math.sqrt(B),d.axes[0]=R,d.axes[1]=O,d.fragClipBounds[0]=c(L,b[0],_,-1e8),d.fragClipBounds[1]=c(L,b[1],_,1e8),e.vao.draw(h.TRIANGLES,e.vertexCount),e.lineWidth>0&&(h.lineWidth(e.lineWidth),e.vao.draw(h.LINES,e.lineVertexCount,e.vertexCount))}}function h(t,e,r,n,i,a){var o=r.gl;if(r.vao.bind(),i===r.opacity<1||a){t.bind();var s=t.uniforms;s.model=n.model||x,s.view=n.view||x,s.projection=n.projection||x,w[0]=2/o.drawingBufferWidth,w[1]=2/o.drawingBufferHeight,s.screenSize=w,s.highlightId=r.highlightId,s.highlightScale=r.highlightScale,s.fragClipBounds=P,s.clipBounds=r.axes.bounds,s.opacity=r.opacity,s.pickGroup=r.pickId/255,s.pixelRatio=r.pixelRatio,r.vao.draw(o.TRIANGLES,r.vertexCount),r.lineWidth>0&&(o.lineWidth(r.lineWidth),r.vao.draw(o.LINES,r.lineVertexCount,r.vertexCount))}f(e,r,n,i,a),r.vao.unbind()}function d(t){var e=t.gl,r=y.createPerspective(e),n=y.createOrtho(e),i=y.createProject(e),a=y.createPickPerspective(e),s=y.createPickOrtho(e),l=y.createPickProject(e),c=p(e),u=p(e),f=p(e),h=p(e),d=g(e,[{buffer:c,size:3,type:e.FLOAT},{buffer:u,size:4,type:e.FLOAT},{buffer:f,size:2,type:e.FLOAT},{buffer:h,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new o(e,r,n,i,c,u,f,h,d,a,s,l);return v.update(t),v}var p=t(\"gl-buffer\"),g=t(\"gl-vao\"),v=t(\"typedarray-pool\"),m=t(\"gl-mat4/multiply\"),y=t(\"./lib/shaders\"),b=t(\"./lib/glyphs\"),x=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];e.exports=d;var _=o.prototype;_.pickSlots=1,_.setPickBase=function(t){this.pickId=t},_.isTransparent=function(){if(this.opacity<1)return!0;for(var t=0;3>t;++t)if(this.axesProject[t]&&this.projectOpacity[t]<1)return!0;return!1},_.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;3>t;++t)if(this.axesProject[t]&&this.projectOpacity[t]>=1)return!0;return!1};var w=[0,0],k=[0,0,0],A=[0,0,0],M=[0,0,0,1],T=[0,0,0,1],E=x.slice(),L=[0,0,0],S=[[0,0,0],[0,0,0]],C=[-1e8,-1e8,-1e8],z=[1e8,1e8,1e8],P=[C,z];_.draw=function(t){var e=this.useOrtho?this.orthoShader:this.shader;h(e,this.projectShader,this,t,!1,!1)},_.drawTransparent=function(t){var e=this.useOrtho?this.orthoShader:this.shader;h(e,this.projectShader,this,t,!0,!1)},_.drawPick=function(t){var e=this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader;h(e,this.pickProjectShader,this,t,!1,!0)},_.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||0>e)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;3>i;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},_.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},_.update=function(t){if(t=t||{},\"perspective\"in t&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(\"projectOpacity\"in t)if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{var r=+t.projectOpacity;this.projectOpacity=[r,r,r]}\"opacity\"in t&&(this.opacity=t.opacity),this.dirty=!0;var n=t.position;if(n){var i=t.font||\"normal\",a=t.alignment||[0,0],o=[1/0,1/0,1/0],s=[-(1/0),-(1/0),-(1/0)],l=t.glyph,c=t.color,u=t.size,f=t.angle,h=t.lineColor,d=0,p=0,g=0,m=n.length;t:for(var y=0;m>y;++y){for(var x=n[y],_=0;3>_;++_)if(isNaN(x[_])||!isFinite(x[_]))continue t;var w;w=Array.isArray(l)?b(l[y],i):l?b(l,i):b(\"\\u25cf\",i);var k=w[0],A=w[1],M=w[2];p+=3*k.cells.length,g+=2*A.edges.length}var T=p+g,E=v.mallocFloat(3*T),L=v.mallocFloat(4*T),S=v.mallocFloat(2*T),C=v.mallocUint32(T),z=[0,a[1]],P=0,R=p,O=[0,0,0,1],I=[0,0,0,1],N=Array.isArray(c)&&Array.isArray(c[0]),j=Array.isArray(h)&&Array.isArray(h[0]);t:for(var y=0;m>y;++y){for(var x=n[y],_=0;3>_;++_){if(isNaN(x[_])||!isFinite(x[_])){d+=1;continue t}s[_]=Math.max(s[_],x[_]),o[_]=Math.min(o[_],x[_])}var w;w=Array.isArray(l)?b(l[y],i):l?b(l,i):b(\"\\u25cf\",i);var k=w[0],A=w[1],M=w[2];if(Array.isArray(c)){var F;if(F=N?c[y]:c,3===F.length){for(var _=0;3>_;++_)O[_]=F[_];O[3]=1}else if(4===F.length)for(var _=0;4>_;++_)O[_]=F[_]}else O[0]=O[1]=O[2]=0,O[3]=1;if(Array.isArray(h)){var F;if(F=j?h[y]:h,3===F.length){for(var _=0;3>_;++_)I[_]=F[_];I[_]=1}else if(4===F.length)for(var _=0;4>_;++_)I[_]=F[_]}else I[0]=I[1]=I[2]=0,I[3]=1;var D=.5;Array.isArray(u)?D=+u[y]:u?D=+u:this.useOrtho&&(D=12);var B=0;Array.isArray(f)?B=+f[y]:f&&(B=+f);for(var U=Math.cos(B),V=Math.sin(B),x=n[y],_=0;3>_;++_)s[_]=Math.max(s[_],x[_]),o[_]=Math.min(o[_],x[_]);a[0]<0?z[0]=a[0]*(1+M[1][0]):a[0]>0&&(z[0]=-a[0]*(1+M[0][0]));for(var q=k.cells,H=k.positions,_=0;_<q.length;++_)for(var G=q[_],Y=0;3>Y;++Y){for(var X=0;3>X;++X)E[3*P+X]=x[X];for(var X=0;4>X;++X)L[4*P+X]=O[X];C[P]=d;var W=H[G[Y]];S[2*P]=D*(U*W[0]-V*W[1]+z[0]),S[2*P+1]=D*(V*W[0]+U*W[1]+z[1]),P+=1}for(var q=A.edges,H=A.positions,_=0;_<q.length;++_)for(var G=q[_],Y=0;2>Y;++Y){for(var X=0;3>X;++X)E[3*R+X]=x[X];for(var X=0;4>X;++X)L[4*R+X]=I[X];C[R]=d;var W=H[G[Y]];S[2*R]=D*(U*W[0]-V*W[1]+z[0]),S[2*R+1]=D*(V*W[0]+U*W[1]+z[1]),R+=1}d+=1}this.vertexCount=p,this.lineVertexCount=g,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(S),this.idBuffer.update(new Uint32Array(C)),v.free(E),v.free(L),v.free(S),v.free(C),this.bounds=[o,s],this.points=n,this.pointCount=n.length}},_.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/glyphs\":191,\"./lib/shaders\":192,\"gl-buffer\":118,\"gl-mat4/multiply\":139,\"gl-vao\":226,\"typedarray-pool\":278}],194:[function(t,e,r){\"use strict\";r.boxVertex=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\",r.boxFragment=\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n gl_FragColor = color;\\n}\\n\"},{}],195:[function(t,e,r){\"use strict\";function n(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-(1/0),-(1/0)],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}function i(t,e){var r=t.gl,i=o(r,[0,0,0,1,1,0,1,1]),l=a(r,s.boxVertex,s.boxFragment),c=new n(t,i,l);return c.update(e),t.addOverlay(c),c}var a=t(\"gl-shader\"),o=t(\"gl-buffer\"),s=t(\"./lib/shaders\");e.exports=i;var l=n.prototype;l.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],d=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],p=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),d=Math.min(d,c[2]),p=Math.min(p,c[3]),!(f>d||h>p)){o.bind();var g=s[2]-s[0],v=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,g,h,i),o.drawBox(0,h,f,p,i),o.drawBox(0,p,g,v,i),o.drawBox(d,h,g,p,i)),this.innerFill&&o.drawBox(f,h,d,p,n),r>0){var m=r*u;o.drawBox(f-m,h-m,d+m,h+m,a),o.drawBox(f-m,p-m,d+m,p+m,a),o.drawBox(f-m,h-m,f+m,p+m,a),o.drawBox(d-m,h-m,d+m,p+m,a)}}}},l.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},l.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{\"./lib/shaders\":194,\"gl-buffer\":118,\"gl-shader\":197}],196:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function i(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}function a(t,e){var r=o(t,e),n=s.mallocUint8(e[0]*e[1]*4);return new i(t,r,n)}e.exports=a;var o=t(\"gl-fbo\"),s=t(\"typedarray-pool\"),l=t(\"ndarray\"),c=t(\"bit-twiddle\").nextPow2,u=t(\"cwise/lib/wrapper\")({args:[\"array\",{offset:[0,0,1],array:0},{offset:[0,0,2],array:0},{offset:[0,0,3],array:0},\"scalar\",\"scalar\",\"index\"],pre:{body:\"{this_closestD2=1e8,this_closestX=-1,this_closestY=-1}\",args:[],thisVars:[\"this_closestD2\",\"this_closestX\",\"this_closestY\"],localVars:[]},body:{body:\"{if(255>_inline_34_arg0_||255>_inline_34_arg1_||255>_inline_34_arg2_||255>_inline_34_arg3_){var 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e=n(t,i,s,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,o,a,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,o,s,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":197}],217:[function(t,e,r){arguments[4][21][0].apply(r,arguments)},{dup:21}],218:[function(t,e,r){\"use strict\";function n(t){if(t in l)return l[t];for(var e=[],r=0;t>r;++r)e.push(\"out\",r,\"s=0.5*(inp\",r,\"l-inp\",r,\"r);\");for(var n=[\"array\"],i=[\"junk\"],r=0;t>r;++r){n.push(\"array\"),i.push(\"out\"+r+\"s\");var a=o(t);a[r]=-1,n.push({array:0,offset:a.slice()}),a[r]=1,n.push({array:0,offset:a.slice()}),i.push(\"inp\"+r+\"l\",\"inp\"+r+\"r\")}return l[t]=s({args:n,pre:u,post:u,body:{body:e.join(\"\"),args:i.map(function(t){return{name:t,lvalue:0===t.indexOf(\"out\"),rvalue:0===t.indexOf(\"inp\"),count:\"junk\"!==t|0}}),thisVars:[],localVars:[]},funcName:\"fdTemplate\"+t})}function i(t){function e(e){for(var r=a-e.length,n=[],i=[],s=[],l=0;a>l;++l)e.indexOf(l+1)>=0?s.push(\"0\"):e.indexOf(-(l+1))>=0?s.push(\"s[\"+l+\"]-1\"):(s.push(\"-1\"),n.push(\"1\"),i.push(\"s[\"+l+\"]-2\"));var c=\".lo(\"+n.join()+\").hi(\"+i.join()+\")\";if(0===n.length&&(c=\"\"),r>0){o.push(\"if(1\");for(var l=0;a>l;++l)e.indexOf(l+1)>=0||e.indexOf(-(l+1))>=0||o.push(\"&&s[\",l,\"]>2\");o.push(\"){grad\",r,\"(src.pick(\",s.join(),\")\",c);for(var l=0;a>l;++l)e.indexOf(l+1)>=0||e.indexOf(-(l+1))>=0||o.push(\",dst.pick(\",s.join(),\",\",l,\")\",c);o.push(\");\")}for(var l=0;l<e.length;++l){var u=Math.abs(e[l])-1,f=\"dst.pick(\"+s.join()+\",\"+u+\")\"+c;switch(t[u]){case\"clamp\":var h=s.slice(),d=s.slice();e[l]<0?h[u]=\"s[\"+u+\"]-2\":d[u]=\"1\",0===r?o.push(\"if(s[\",u,\"]>1){dst.set(\",s.join(),\",\",u,\",0.5*(src.get(\",h.join(),\")-src.get(\",d.join(),\")))}else{dst.set(\",s.join(),\",\",u,\",0)};\"):o.push(\"if(s[\",u,\"]>1){diff(\",f,\",src.pick(\",h.join(),\")\",c,\",src.pick(\",d.join(),\")\",c,\");}else{zero(\",f,\");};\");break;case\"mirror\":0===r?o.push(\"dst.set(\",s.join(),\",\",u,\",0);\"):o.push(\"zero(\",f,\");\");break;case\"wrap\":var p=s.slice(),g=s.slice();e[l]<0?(p[u]=\"s[\"+u+\"]-2\",g[u]=\"0\"):(p[u]=\"s[\"+u+\"]-1\",g[u]=\"1\"),0===r?o.push(\"if(s[\",u,\"]>2){dst.set(\",s.join(),\",\",u,\",0.5*(src.get(\",p.join(),\")-src.get(\",g.join(),\")))}else{dst.set(\",s.join(),\",\",u,\",0)};\"):o.push(\"if(s[\",u,\"]>2){diff(\",f,\",src.pick(\",p.join(),\")\",c,\",src.pick(\",g.join(),\")\",c,\");}else{zero(\",f,\");};\");break;default:throw new Error(\"ndarray-gradient: Invalid boundary condition\")}}r>0&&o.push(\"};\")}var r=t.join(),i=c[r];if(i)return i;for(var a=t.length,o=[\"function gradient(dst,src){var s=src.shape.slice();\"],s=0;1<<a>s;++s){for(var u=[],d=0;a>d;++d)s&1<<d&&u.push(d+1);for(var p=0;p<1<<u.length;++p){for(var g=u.slice(),d=0;d<u.length;++d)p&1<<d&&(g[d]=-g[d]);e(g)}}o.push(\"return dst;};return gradient\");for(var v=[\"diff\",\"zero\"],m=[f,h],s=1;a>=s;++s)v.push(\"grad\"+s),m.push(n(s));v.push(o.join(\"\"));var y=Function.apply(void 0,v),i=y.apply(void 0,m);return l[r]=i,i}function a(t,e,r){if(Array.isArray(r)){if(r.length!==e.dimension)throw new Error(\"ndarray-gradient: invalid boundary conditions\")}else r=\"string\"==typeof r?o(e.dimension,r):o(e.dimension,\"clamp\");if(t.dimension!==e.dimension+1)throw new Error(\"ndarray-gradient: output dimension must be +1 input dimension\");if(t.shape[e.dimension]!==e.dimension)throw new Error(\"ndarray-gradient: output shape must match input shape\");for(var n=0;n<e.dimension;++n)if(t.shape[n]!==e.shape[n])throw new Error(\"ndarray-gradient: shape mismatch\");if(0===e.size)return t;if(e.dimension<=0)return t.set(0),t;var a=i(r);return a(t,e)}e.exports=a;var o=t(\"dup\"),s=t(\"cwise-compiler\"),l={},c={},u={body:\"\",args:[],thisVars:[],localVars:[]},f=s({args:[\"array\",\"array\",\"array\"],pre:u,post:u,body:{args:[{name:\"out\",lvalue:!0,rvalue:!1,count:1},{name:\"left\",lvalue:!1,rvalue:!0,count:1},{name:\"right\",lvalue:!1,rvalue:!0,count:1}],body:\"out=0.5*(left-right)\",thisVars:[],localVars:[]},funcName:\"cdiff\"}),h=s({args:[\"array\"],pre:u,post:u,body:{args:[{name:\"out\",lvalue:!0,rvalue:!1,count:1}],body:\"out=0\",thisVars:[],localVars:[]},funcName:\"zero\"})},{\"cwise-compiler\":109,dup:115}],219:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"./doConvert.js\");e.exports=function(t,e){for(var r=[],a=t,o=1;a instanceof Array;)r.push(a.length),o*=a.length,a=a[0];return 0===r.length?n():(e||(e=n(new Float64Array(o),r)),i(e,t),e)}},{\"./doConvert.js\":220,ndarray:253}],220:[function(t,e,r){e.exports=t(\"cwise-compiler\")({args:[\"array\",\"scalar\",\"index\"],pre:{body:\"{}\",args:[],thisVars:[],localVars:[]},body:{body:\"{\\nvar _inline_1_v=_inline_1_arg1_,_inline_1_i\\nfor(_inline_1_i=0;_inline_1_i<_inline_1_arg2_.length-1;++_inline_1_i) {\\n_inline_1_v=_inline_1_v[_inline_1_arg2_[_inline_1_i]]\\n}\\n_inline_1_arg0_=_inline_1_v[_inline_1_arg2_[_inline_1_arg2_.length-1]]\\n}\",args:[{name:\"_inline_1_arg0_\",lvalue:!0,rvalue:!1,count:1},{name:\"_inline_1_arg1_\",lvalue:!1,rvalue:!0,count:1},{name:\"_inline_1_arg2_\",lvalue:!1,rvalue:!0,count:4}],thisVars:[],localVars:[\"_inline_1_i\",\"_inline_1_v\"]},post:{body:\"{}\",args:[],thisVars:[],localVars:[]},funcName:\"convert\",blockSize:64})},{\"cwise-compiler\":109}],221:[function(t,e,r){\"use strict\";function n(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}function i(t){var e=x([y({colormap:t,nshades:N,format:\"rgba\"}).map(function(t){return[t[0],t[1],t[2],255*t[3]]})]);return b.divseq(e,255),e}function a(t,e,r,i,a,o,s,l,c,u,f,h,d,p){this.gl=t,this.shape=e,this.bounds=r,this.intensityBounds=[],this._shader=i,this._pickShader=a,this._coordinateBuffer=o,this._vao=s,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=f,this._contourVAO=h,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new n([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=p,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[_(m.mallocFloat(1024),[0,0]),_(m.mallocFloat(1024),[0,0]),_(m.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-(1/0),-(1/0),-(1/0)],[1/0,1/0,1/0]],this.snapToData=!1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.dirty=!0}function o(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||F,o=e.showSurface,s=e.showContour;for(r=0;3>r;++r)for(o=o||e.surfaceProject[r],n=0;3>n;++n)s=s||e.contourProject[r][n];for(r=0;3>r;++r){var l=D.projections[r];for(n=0;16>n;++n)l[n]=0;for(n=0;4>n;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],k(l,t.model,l);var c=D.clipBounds[r];for(i=0;2>i;++i)for(n=0;3>n;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return D.showSurface=o,D.showContour=s,D}function s(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=B;n.model=t.model||R,n.view=t.view||R,n.projection=t.projection||R,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.contourColor=this.contourColor[0],n.inverseModel=A(n.inverseModel,n.model);for(var i=0;2>i;++i)for(var a=n.clipBounds[i],s=0;3>s;++s)a[s]=Math.min(Math.max(this.clipBounds[i][s],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=V;var l=U;for(k(l,n.view,n.model),k(l,n.projection,l),A(l,l),i=0;3>i;++i)n.eyePosition[i]=l[12+i]/l[15];var c=l[15];for(i=0;3>i;++i)c+=this.lightPosition[i]*l[4*i+3];for(i=0;3>i;++i){var u=l[12+i];for(s=0;3>s;++s)u+=l[4*s+i]*this.lightPosition[s];n.lightPosition[i]=u/c}var f=o(n,this);if(f.showSurface&&e===this.opacity<1){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;3>i;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=f.projections[i],this._shader.uniforms.clipBounds=f.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(f.showContour&&!e){var h=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,h.bind(),h.uniforms=n;var d=this._contourVAO;for(d.bind(),i=0;3>i;++i)for(h.uniforms.permutation=I[i],r.lineWidth(this.contourWidth[i]),s=0;s<this.contourLevels[i].length;++s)this._contourCounts[i][s]&&(s===this.highlightLevel[i]?(h.uniforms.contourColor=this.highlightColor[i],h.uniforms.contourTint=this.highlightTint[i]):0!==s&&s-1!==this.highlightLevel[i]||(h.uniforms.contourColor=this.contourColor[i],h.uniforms.contourTint=this.contourTint[i]),h.uniforms.height=this.contourLevels[i][s],d.draw(r.LINES,this._contourCounts[i][s],this._contourOffsets[i][s]));for(i=0;3>i;++i)for(h.uniforms.model=f.projections[i],h.uniforms.clipBounds=f.clipBounds[i],s=0;3>s;++s)if(this.contourProject[i][s]){h.uniforms.permutation=I[s],r.lineWidth(this.contourWidth[s]);for(var p=0;p<this.contourLevels[s].length;++p)p===this.highlightLevel[s]?(h.uniforms.contourColor=this.highlightColor[s],h.uniforms.contourTint=this.highlightTint[s]):0!==p&&p-1!==this.highlightLevel[s]||(h.uniforms.contourColor=this.contourColor[s],h.uniforms.contourTint=this.contourTint[s]),h.uniforms.height=this.contourLevels[s][p],d.draw(r.LINES,this._contourCounts[s][p],this._contourOffsets[s][p])}for(d=this._dynamicVAO,d.bind(),i=0;3>i;++i)if(0!==this._dynamicCounts[i])for(h.uniforms.model=n.model,h.uniforms.clipBounds=n.clipBounds,h.uniforms.permutation=I[i],r.lineWidth(this.dynamicWidth[i]),h.uniforms.contourColor=this.dynamicColor[i],h.uniforms.contourTint=this.dynamicTint[i],h.uniforms.height=this.dynamicLevel[i],d.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),s=0;3>s;++s)this.contourProject[s][i]&&(h.uniforms.model=f.projections[s],h.uniforms.clipBounds=f.clipBounds[s],d.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));d.unbind()}}function l(t,e){var r=e.shape.slice(),n=t.shape.slice();b.assign(t.lo(1,1).hi(r[0],r[1]),e),b.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),b.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),b.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),b.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))}function c(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function u(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function f(t){if(Array.isArray(t)){if(Array.isArray(t))return[u(t[0]),u(t[1]),u(t[2])];var e=u(t);return[e.slice(),e.slice(),e.slice()]}}function h(t){var e=t.gl,r=L(e),n=C(e),i=S(e),o=z(e),s=p(e),l=g(e,[{buffer:s,size:4,stride:P,offset:0},{buffer:s,size:3,stride:P,offset:16},{buffer:s,size:3,stride:P,offset:28}]),c=p(e),u=g(e,[{buffer:c,size:4,stride:20,offset:0},{buffer:c,size:1,stride:20,offset:16}]),f=p(e),h=g(e,[{buffer:f,size:2,type:e.FLOAT}]),d=v(e,1,N,e.RGBA,e.UNSIGNED_BYTE);d.minFilter=e.LINEAR,d.magFilter=e.LINEAR;var m=new a(e,[0,0],[[0,0,0],[0,0,0]],r,n,s,l,d,i,o,c,u,f,h),y={levels:[[],[],[]]};for(var b in t)y[b]=t[b];return y.colormap=y.colormap||\"jet\",m.update(y),m}e.exports=h;var d=t(\"bit-twiddle\"),p=t(\"gl-buffer\"),g=t(\"gl-vao\"),v=t(\"gl-texture2d\"),m=t(\"typedarray-pool\"),y=t(\"colormap\"),b=t(\"ndarray-ops\"),x=t(\"ndarray-pack\"),_=t(\"ndarray\"),w=t(\"surface-nets\"),k=t(\"gl-mat4/multiply\"),A=t(\"gl-mat4/invert\"),M=t(\"binary-search-bounds\"),T=t(\"ndarray-gradient\"),E=t(\"./lib/shaders\"),L=E.createShader,S=E.createContourShader,C=E.createPickShader,z=E.createPickContourShader,P=40,R=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],O=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],I=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];!function(){for(var t=0;3>t;++t){var e=I[t],r=(t+1)%3,n=(t+2)%3;e[r+0]=1,e[n+3]=1,e[t+6]=1}}();var N=265,j=a.prototype;j.isTransparent=function(){return this.opacity<1},j.isOpaque=function(){if(this.opacity>=1)return!0;for(var t=0;3>t;++t)if(this._contourCounts[t].length>0||this._dynamicCounts[t]>0)return!0;return!1},j.pickSlots=1,j.setPickBase=function(t){this.pickId=t};var F=[0,0,0],D={showSurface:!1,showContour:!1,projections:[R.slice(),R.slice(),R.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]},B={model:R,view:R,projection:R,inverseModel:R.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1},U=R.slice(),V=[1,0,0,0,1,0,0,0,1];j.draw=function(t){return s.call(this,t,!1)},j.drawTransparent=function(t){return s.call(this,t,!0)};var q={model:R,view:R,projection:R,inverseModel:R,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};j.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=q;r.model=t.model||R,r.view=t.view||R,r.projection=t.projection||R,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.permutation=V;for(var n=0;2>n;++n)for(var i=r.clipBounds[n],a=0;3>a;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var s=o(r,this);if(s.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;3>n;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=s.projections[n],this._pickShader.uniforms.clipBounds=s.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(s.showContour){var l=this._contourPickShader;l.bind(),l.uniforms=r;var c=this._contourVAO;for(c.bind(),a=0;3>a;++a)for(e.lineWidth(this.contourWidth[a]),l.uniforms.permutation=I[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(l.uniforms.height=this.contourLevels[a][n],c.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;3>n;++n)for(l.uniforms.model=s.projections[n],l.uniforms.clipBounds=s.clipBounds[n],a=0;3>a;++a)if(this.contourProject[n][a]){l.uniforms.permutation=I[a],e.lineWidth(this.contourWidth[a]);for(var u=0;u<this.contourLevels[a].length;++u)this._contourCounts[a][u]&&(l.uniforms.height=this.contourLevels[a][u],c.draw(e.LINES,this._contourCounts[a][u],this._contourOffsets[a][u]))}c.unbind()}},j.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;2>u;++u)for(var f=u?a:1-a,h=0;2>h;++h)for(var d=h?l:1-l,p=i+u,g=s+h,v=f*d,m=0;3>m;++m)c[m]+=this._field[m].get(p,g)*v;for(var y=this._pickResult.level,b=0;3>b;++b)if(y[b]=M.le(this.contourLevels[b],c[b]),y[b]<0)this.contourLevels[b].length>0&&(y[b]=0);else if(y[b]<this.contourLevels[b].length-1){var x=this.contourLevels[b][y[b]],_=this.contourLevels[b][y[b]+1];Math.abs(x-c[b])>Math.abs(_-c[b])&&(y[b]+=1)}for(r.index[0]=.5>a?i:i+1,r.index[1]=.5>l?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],m=0;3>m;++m)r.dataCoordinate[m]=this._field[m].get(r.index[0],r.index[1]);return r},j.update=function(t){t=t||{},this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=c(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=c(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=c(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=f(t.contourColor)),\"contourProject\"in t&&(this.contourProject=c(t.contourProject,function(t){return c(t,Boolean)})),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=f(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=c(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=c(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds);var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var n=(e.shape[0]+2)*(e.shape[1]+2);n>this._field[2].data.length&&(m.freeFloat(this._field[2].data),this._field[2].data=m.mallocFloat(d.nextPow2(n))),this._field[2]=_(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),l(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;2>o;++o)this._field[2].size>this._field[o].data.length&&(m.freeFloat(this._field[o].data),this._field[o].data=m.mallocFloat(this._field[2].size)),this._field[o]=_(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var s=t.coords;if(!Array.isArray(s)||3!==s.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;2>o;++o){var u=s[o];for(y=0;2>y;++y)if(u.shape[y]!==a[y])throw new Error(\"gl-surface: coords have incorrect shape\");l(this._field[o],u)}}else if(t.ticks){var h=t.ticks;if(!Array.isArray(h)||2!==h.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;2>o;++o){var p=h[o];if((Array.isArray(p)||p.length)&&(p=_(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var g=_(p.data,a);g.stride[o]=p.stride[0],g.stride[1^o]=0,l(this._field[o],g)}}else{for(o=0;2>o;++o){var v=[0,0];v[o]=1,this._field[o]=_(this._field[o].data,[a[0]+2,a[1]+2],v,0)}this._field[0].set(0,0,0);for(var y=0;y<a[0];++y)this._field[0].set(y+1,0,y);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),y=0;y<a[1];++y)this._field[1].set(0,y+1,y);this._field[1].set(0,a[1]+1,a[1]-1)}var b=this._field,x=_(m.mallocFloat(3*b[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;3>o;++o)T(x.pick(o),b[o],\"mirror\");var k=_(m.mallocFloat(3*b[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(y=0;y<a[1]+2;++y){var A=x.get(0,o,y,0),M=x.get(0,o,y,1),E=x.get(1,o,y,0),L=x.get(1,o,y,1),S=x.get(2,o,y,0),C=x.get(2,o,y,1),z=E*C-L*S,P=S*M-C*A,R=A*L-M*E,I=Math.sqrt(z*z+P*P+R*R);1e-8>I?(I=Math.max(Math.abs(z),Math.abs(P),Math.abs(R)),1e-8>I?(R=1,P=z=0,I=1):I=1/I):I=1/Math.sqrt(I),k.set(o,y,0,z*I),k.set(o,y,1,P*I),k.set(o,y,2,R*I)}m.free(x.data);var N=[1/0,1/0,1/0],j=[-(1/0),-(1/0),-(1/0)],F=1/0,D=-(1/0),B=(a[0]-1)*(a[1]-1)*6,U=m.mallocFloat(d.nextPow2(10*B)),V=0,q=0;for(o=0;o<a[0]-1;++o)t:for(y=0;y<a[1]-1;++y){for(var H=0;2>H;++H)for(var G=0;2>G;++G)for(var Y=0;3>Y;++Y){var X=this._field[Y].get(1+o+H,1+y+G);if(isNaN(X)||!isFinite(X))continue t}for(Y=0;6>Y;++Y){var W=o+O[Y][0],Z=y+O[Y][1],K=this._field[0].get(W+1,Z+1),$=this._field[1].get(W+1,Z+1);X=this._field[2].get(W+1,Z+1);var Q=X;z=k.get(W+1,Z+1,0),P=k.get(W+1,Z+1,1),R=k.get(W+1,Z+1,2),t.intensity&&(Q=t.intensity.get(W,Z)),U[V++]=W,U[V++]=Z,U[V++]=K,U[V++]=$,U[V++]=X,U[V++]=0,U[V++]=Q,U[V++]=z,U[V++]=P,U[V++]=R,N[0]=Math.min(N[0],K),N[1]=Math.min(N[1],$),N[2]=Math.min(N[2],X),F=Math.min(F,Q),j[0]=Math.max(j[0],K),j[1]=Math.max(j[1],$),j[2]=Math.max(j[2],X),D=Math.max(D,Q),q+=1}}for(t.intensityBounds&&(F=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;V>o;o+=10)U[o]=(U[o]-F)/(D-F);this._vertexCount=q,this._coordinateBuffer.update(U.subarray(0,V)),m.freeFloat(U),m.free(k.data),this.bounds=[N,j],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===F&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[F,D]}if(\"levels\"in t){var J=t.levels;for(J=Array.isArray(J[0])?J.slice():[[],[],J],o=0;3>o;++o)J[o]=J[o].slice(),J.sort(function(t,e){return t-e});t:for(o=0;3>o;++o){if(J[o].length!==this.contourLevels[o].length){r=!0;break}for(y=0;y<J[o].length;++y)if(J[o][y]!==this.contourLevels[o][y]){r=!0;break t}}this.contourLevels=J}if(r){b=this._field,a=this.shape;for(var tt=[],et=0;3>et;++et){J=this.contourLevels[et];var rt=[],nt=[],it=[0,0,0];for(o=0;o<J.length;++o){var at=w(this._field[et],J[o]);rt.push(tt.length/5|0),q=0;t:for(y=0;y<at.cells.length;++y){var ot=at.cells[y];for(Y=0;2>Y;++Y){var st=at.positions[ot[Y]],lt=st[0],ct=0|Math.floor(lt),ut=lt-ct,ft=st[1],ht=0|Math.floor(ft),dt=ft-ht,pt=!1;\ne:for(var gt=0;3>gt;++gt){it[gt]=0;var vt=(et+gt+1)%3;for(H=0;2>H;++H){var mt=H?ut:1-ut;for(W=0|Math.min(Math.max(ct+H,0),a[0]),G=0;2>G;++G){var yt=G?dt:1-dt;if(Z=0|Math.min(Math.max(ht+G,0),a[1]),X=2>gt?this._field[vt].get(W,Z):(this.intensity.get(W,Z)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(X)||isNaN(X)){pt=!0;break e}var bt=mt*yt;it[gt]+=bt*X}}}if(pt){if(Y>0){for(var xt=0;5>xt;++xt)tt.pop();q-=1}continue t}tt.push(it[0],it[1],st[0],st[1],it[2]),q+=1}}nt.push(q)}this._contourOffsets[et]=rt,this._contourCounts[et]=nt}var _t=m.mallocFloat(tt.length);for(o=0;o<tt.length;++o)_t[o]=tt[o];this._contourBuffer.update(_t),m.freeFloat(_t)}t.colormap&&this._colorMap.setPixels(i(t.colormap))},j.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;3>t;++t)m.freeFloat(this._field[t].data)},j.highlight=function(t){if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(var e=0;3>e;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;var r;if(r=this.snapToData?t.dataCoordinate:t.position,this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=m.mallocFloat(12*i[0]*i[1]),o=0;3>o;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var s=(o+1)%3,l=(o+2)%3,c=this._field[o],u=this._field[s],f=this._field[l],h=(this.intensity,w(c,r[o])),d=h.cells,p=h.positions;for(this._dynamicOffsets[o]=n,e=0;e<d.length;++e)for(var g=d[e],v=0;2>v;++v){var y=p[g[v]],b=+y[0],x=0|b,_=0|Math.min(x+1,i[0]),k=b-x,A=1-k,M=+y[1],T=0|M,E=0|Math.min(T+1,i[1]),L=M-T,S=1-L,C=A*S,z=A*L,P=k*S,R=k*L,O=C*u.get(x,T)+z*u.get(x,E)+P*u.get(_,T)+R*u.get(_,E),I=C*f.get(x,T)+z*f.get(x,E)+P*f.get(_,T)+R*f.get(_,E);if(isNaN(O)||isNaN(I)){v&&(n-=1);break}a[2*n+0]=O,a[2*n+1]=I,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),m.freeFloat(a)}}},{\"./lib/shaders\":216,\"binary-search-bounds\":217,\"bit-twiddle\":50,colormap:100,\"gl-buffer\":118,\"gl-mat4/invert\":137,\"gl-mat4/multiply\":139,\"gl-texture2d\":222,\"gl-vao\":226,ndarray:253,\"ndarray-gradient\":218,\"ndarray-ops\":252,\"ndarray-pack\":219,\"surface-nets\":272,\"typedarray-pool\":278}],222:[function(t,e,r){\"use strict\";function n(t){v=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],m=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],y=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function i(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(0>e||e>i||0>r||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function a(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}function o(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function s(t,e,r,n,i,a,s,l){var c=l.dtype,u=l.shape.slice();if(u.length<2||u.length>3)throw new Error(\"gl-texture2d: Invalid ndarray, must be 2d or 3d\");var f=0,h=0,v=o(u,l.stride.slice());\"float32\"===c?f=t.FLOAT:\"float64\"===c?(f=t.FLOAT,v=!1,c=\"float32\"):\"uint8\"===c?f=t.UNSIGNED_BYTE:(f=t.UNSIGNED_BYTE,v=!1,c=\"uint8\");var m=1;if(2===u.length)h=t.LUMINANCE,u=[u[0],u[1],1],l=d(l.data,u,[l.stride[0],l.stride[1],1],l.offset);else{if(3!==u.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===u[2])h=t.ALPHA;else if(2===u[2])h=t.LUMINANCE_ALPHA;else if(3===u[2])h=t.RGB;else{if(4!==u[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");h=t.RGBA}m=u[2]}if(h!==t.LUMINANCE&&h!==t.ALPHA||i!==t.LUMINANCE&&i!==t.ALPHA||(h=i),h!==i)throw new Error(\"gl-texture2d: Incompatible texture format for setPixels\");var y=l.size,x=s.indexOf(n)<0;if(x&&s.push(n),f===a&&v)0===l.offset&&l.data.length===y?x?t.texImage2D(t.TEXTURE_2D,n,i,u[0],u[1],0,i,a,l.data):t.texSubImage2D(t.TEXTURE_2D,n,e,r,u[0],u[1],i,a,l.data):x?t.texImage2D(t.TEXTURE_2D,n,i,u[0],u[1],0,i,a,l.data.subarray(l.offset,l.offset+y)):t.texSubImage2D(t.TEXTURE_2D,n,e,r,u[0],u[1],i,a,l.data.subarray(l.offset,l.offset+y));else{var _;_=a===t.FLOAT?g.mallocFloat32(y):g.mallocUint8(y);var w=d(_,u,[u[2],u[2]*u[0],1]);f===t.FLOAT&&a===t.UNSIGNED_BYTE?b(w,l):p.assign(w,l),x?t.texImage2D(t.TEXTURE_2D,n,i,u[0],u[1],0,i,a,_.subarray(0,y)):t.texSubImage2D(t.TEXTURE_2D,n,e,r,u[0],u[1],i,a,_.subarray(0,y)),a===t.FLOAT?g.freeFloat32(_):g.freeUint8(_)}}function l(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function c(t,e,r,n,i){var o=t.getParameter(t.MAX_TEXTURE_SIZE);if(0>e||e>o||0>r||r>o)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var s=l(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new a(t,s,e,r,n,i)}function u(t,e,r,n){var i=l(t);return t.texImage2D(t.TEXTURE_2D,0,r,r,n,e),new a(t,i,0|e.width,0|e.height,r,n)}function f(t,e){var r=e.dtype,n=e.shape.slice(),i=t.getParameter(t.MAX_TEXTURE_SIZE);if(n[0]<0||n[0]>i||n[1]<0||n[1]>i)throw new Error(\"gl-texture2d: Invalid texture size\");var s=o(n,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,s=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,s=!1,r=\"uint8\");var u=0;if(2===n.length)u=t.LUMINANCE,n=[n[0],n[1],1],e=d(e.data,n,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==n.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===n[2])u=t.ALPHA;else if(2===n[2])u=t.LUMINANCE_ALPHA;else if(3===n[2])u=t.RGB;else{if(4!==n[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");u=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,s=!1);var f,h,v=e.size;if(s)f=0===e.offset&&e.data.length===v?e.data:e.data.subarray(e.offset,e.offset+v);else{var m=[n[2],n[2]*n[0],1];h=g.malloc(v,r);var y=d(h,n,m,0);\"float32\"!==r&&\"float64\"!==r||c!==t.UNSIGNED_BYTE?p.assign(y,e):b(y,e),f=h.subarray(0,v)}var x=l(t);return t.texImage2D(t.TEXTURE_2D,0,u,n[0],n[1],0,u,c,f),s||g.free(h),new a(t,x,n[0],n[1],u,c)}function 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n=t(\"./scene2d\"),i=t(\"../plots\"),a=t(\"../../constants/xmlns_namespaces\");r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex={x:/^x([2-9]|[1-9][0-9]+)?$/,y:/^y([2-9]|[1-9][0-9]+)?$/},r.attrRegex={x:/^xaxis([2-9]|[1-9][0-9]+)?$/,y:/^yaxis([2-9]|[1-9][0-9]+)?$/},r.attributes=t(\"../cartesian/attributes\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,a=i.getSubplotIds(e,\"gl2d\"),o=0;o<a.length;o++){var s=a[o],l=e._plots[s],c=i.getSubplotData(r,\"gl2d\",s),u=l._scene2d;void 0===u&&(u=new n({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),l._scene2d=u),u.plot(c,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var a=i.getSubplotIds(n,\"gl2d\"),o=0;o<a.length;o++){var s=a[o],l=n._plots[s];if(l._scene2d){var c=i.getSubplotData(t,\"gl2d\",s);0===c.length&&(l._scene2d.destroy(),delete n._plots[s])}}},r.toSVG=function(t){for(var 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i(t,e){for(var r=0;2>r;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1}var a,o,s=t(\"../../plots/cartesian/axes\"),l=t(\"../../plots/cartesian/graph_interact\"),c=t(\"gl-plot2d\"),u=t(\"gl-spikes2d\"),f=t(\"gl-select-box\"),h=t(\"./convert\"),d=t(\"./camera\"),p=t(\"../../lib/html2unicode\"),g=t(\"../../lib/show_no_webgl_msg\"),v=[\"xaxis\",\"yaxis\"];e.exports=n;var m=n.prototype;m.makeFramework=function(){if(this.staticPlot){if(!o){a=document.createElement(\"canvas\");try{o=a.getContext(\"webgl\",{preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0})}catch(t){throw new Error([\"Error creating static canvas/context for image server\"].join(\" \"))}}this.canvas=a,this.gl=o}else{var e,r=document.createElement(\"canvas\"),n={premultipliedAlpha:!0};try{e=r.getContext(\"webgl\",n)}catch(t){}if(!e)try{e=r.getContext(\"experimental-webgl\",n)}catch(t){}e||g(this),this.canvas=r,this.gl=e}var i=this.canvas,s=this.pixelRatio,l=this.fullLayout;i.width=0|Math.ceil(s*l.width),i.height=0|Math.ceil(s*l.height),i.style.width=\"100%\",i.style.height=\"100%\",i.style.position=\"absolute\",i.style.top=\"0px\",i.style.left=\"0px\",i.style[\"pointer-events\"]=\"none\";var c=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");c.style.position=\"absolute\",c.style.top=c.style.left=\"0px\",c.style.width=c.style.height=\"100%\",c.style[\"z-index\"]=20,c.style[\"pointer-events\"]=\"none\";var u=this.mouseContainer=document.createElement(\"div\");u.style.position=\"absolute\";var f=this.container;f.appendChild(i),f.appendChild(c),f.appendChild(u)},m.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(a),this.glplot.setDirty(),this.glplot.draw();var e=this.glplot.gl,r=e.drawingBufferWidth,n=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var i=new Uint8Array(r*n*4);e.readPixels(0,0,r,n,e.RGBA,e.UNSIGNED_BYTE,i);for(var o=0,s=n-1;s>o;++o,--s)for(var l=0;r>l;++l)for(var c=0;4>c;++c){var u=i[4*(r*o+l)+c];i[4*(r*o+l)+c]=i[4*(r*s+l)+c],i[4*(r*s+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=n;var h=f.getContext(\"2d\"),d=h.createImageData(r,n);d.data.set(i),h.putImageData(d,0,0);var p;switch(t){case\"jpeg\":p=f.toDataURL(\"image/jpeg\");break;case\"webp\":p=f.toDataURL(\"image/webp\");break;default:p=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(a),p},m.computeTickMarks=function(){this.xaxis._length=this.glplot.viewBox[2]-this.glplot.viewBox[0],this.yaxis._length=this.glplot.viewBox[3]-this.glplot.viewBox[1];for(var t=[s.calcTicks(this.xaxis),s.calcTicks(this.yaxis)],e=0;2>e;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=p(t[e][r].text+\"\").replace(/\\n/g,\" \");return t},m.updateAxes=function(t){var e=s.subplotMatch,r=\"xaxis\"+this.id.match(e)[1],n=\"yaxis\"+this.id.match(e)[2];this.xaxis=t[r],this.yaxis=t[n]},m.updateFx=function(t){var e=this.fullLayout;e.dragmode=t.dragmode,e.hovermode=t.hovermode};var y=function(t){var e=t.xaxis.range,r=t.yaxis.range;t.graphDiv.layout.xaxis.range=e.slice(0),t.graphDiv.layout.yaxis.range=r.slice(0);var n={lastInputTime:t.camera.lastInputTime};n[t.xaxis._name]=e.slice(),n[t.yaxis._name]=r.slice(),t.graphDiv.emit(\"plotly_relayout\",n)};m.cameraChanged=function(){var t=this.camera,e=this.xaxis.range,r=this.yaxis.range;this.glplot.setDataBox([e[0],r[0],e[1],r[1]]);var n=this.computeTickMarks(),a=this.glplotOptions.ticks;i(n,a)&&(this.glplotOptions.ticks=n,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),y(this))},m.destroy=function(){this.glplot.dispose(),this.staticPlot||this.container.removeChild(this.canvas),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.glplot=null,this.stopped=!0},m.plot=function(t,e,r){var n,i,a,o=this.glplot,l=this.pixelRatio;this.fullLayout=r,this.updateAxes(r);var c=r.width,u=r.height,f=0|Math.ceil(l*c),h=0|Math.ceil(l*u),d=this.canvas;for(d.width===f&&d.height===h||(d.width=f,d.height=h),n=0;n<t.length;++n){var p=t[n],g=e[n];a=this.traces[p.uid],a?a.update(p,g):a=p._module.plot(this,p,g),this.traces[p.uid]=a}var m=Object.keys(this.traces);t:for(n=0;n<m.length;++n){for(i=0;i<e.length;++i)if(e[i][0].trace.uid===m[n])continue t;a=this.traces[m[n]],a.dispose(),delete this.traces[m[n]]}var y=this.glplotOptions;y.merge(r),y.screenBox=[0,0,c,u];var b=r._size,x=this.xaxis.domain,_=this.yaxis.domain;y.viewBox=[b.l+x[0]*b.w,b.b+_[0]*b.h,c-b.r-(1-x[1])*b.w,u-b.t-(1-_[1])*b.h],this.mouseContainer.style.width=b.w*(x[1]-x[0])+\"px\",this.mouseContainer.style.height=b.h*(_[1]-_[0])+\"px\",this.mouseContainer.height=b.h*(_[1]-_[0]),this.mouseContainer.style.left=b.l+x[0]*b.w+\"px\",this.mouseContainer.style.top=b.t+(1-_[1])*b.h+\"px\";var w=this.bounds;for(w[0]=w[1]=1/0,w[2]=w[3]=-(1/0),m=Object.keys(this.traces),n=0;n<m.length;++n){a=this.traces[m[n]];for(var k=0;2>k;++k)w[k]=Math.min(w[k],a.bounds[k]),w[k+2]=Math.max(w[k+2],a.bounds[k+2])}var A;for(n=0;2>n;++n)w[n]>w[n+2]&&(w[n]=-1,w[n+2]=1),A=this[v[n]],A._length=y.viewBox[n+2]-y.viewBox[n],s.doAutoRange(A);y.ticks=this.computeTickMarks();var M=this.xaxis.range,T=this.yaxis.range;y.dataBox=[M[0],T[0],M[1],T[1]],y.merge(r),o.update(y),this.glplot.draw()},m.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=this.fullLayout;this.cameraChanged();var i=r.x*t.pixelRatio,a=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===n.dragmode)this.selectBox.enabled=!0,this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],t.setDirty();else{this.selectBox.enabled=!1;var o=n._size,s=this.xaxis.domain,c=this.yaxis.domain,u=t.pick(i/t.pixelRatio+o.l+s[0]*o.w,a/t.pixelRatio-(o.t+(1-c[1])*o.h));if(u&&n.hovermode){var f=u.object._trace.handlePick(u);if(f&&(!this.lastPickResult||this.lastPickResult.trace!==f.trace||this.lastPickResult.dataCoord[0]!==f.dataCoord[0]||this.lastPickResult.dataCoord[1]!==f.dataCoord[1])){var h=this.lastPickResult=f;this.spikes.update({center:u.dataCoord}),h.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(u.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(u.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio];var d=h.hoverinfo;if(\"all\"!==d){var p=d.split(\"+\");-1===p.indexOf(\"x\")&&(h.traceCoord[0]=void 0),-1===p.indexOf(\"y\")&&(h.traceCoord[1]=void 0),-1===p.indexOf(\"z\")&&(h.traceCoord[2]=void 0),-1===p.indexOf(\"text\")&&(h.textLabel=void 0),-1===p.indexOf(\"name\")&&(h.name=void 0)}l.loneHover({x:h.screenCoord[0],y:h.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",h.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",h.traceCoord[1]),zLabel:h.traceCoord[2],text:h.textLabel,name:h.name,color:h.color},{container:this.svgContainer}),this.lastPickResult={dataCoord:u.dataCoord}}}else!u&&this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,l.loneUnhover(this.svgContainer))}t.draw()}},m.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return s.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../lib/html2unicode\":381,\"../../lib/show_no_webgl_msg\":392,\"../../plots/cartesian/axes\":405,\"../../plots/cartesian/graph_interact\":412,\"./camera\":436,\"./convert\":437,\"gl-plot2d\":165,\"gl-select-box\":195,\"gl-spikes2d\":215}],440:[function(t,e,r){\"use strict\";function n(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]),\"zoomMin\"in e&&(r[0]=e.zoomMin),\"zoomMax\"in e&&(r[1]=e.zoomMax);var n=a({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),l=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],c=0,u=t.clientWidth,f=t.clientHeight,h={keyBindingMode:\"rotate\",view:n,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:n.modes,tick:function(){var e=i(),r=this.delay,a=e-2*r;n.idle(e-r),n.recalcMatrix(a),n.flush(e-(100+2*r));for(var o=!0,s=n.computedMatrix,h=0;16>h;++h)o=o&&l[h]===s[h],l[h]=s[h];var d=t.clientWidth===u&&t.clientHeight===f;return u=t.clientWidth,f=t.clientHeight,o?!d:(c=Math.exp(n.computedRadius[0]),!0)},lookAt:function(t,e,r){n.lookAt(n.lastT(),t,e,r)},rotate:function(t,e,r){n.rotate(n.lastT(),t,e,r)},pan:function(t,e,r){n.pan(n.lastT(),t,e,r)},translate:function(t,e,r){n.translate(n.lastT(),t,e,r)}};Object.defineProperties(h,{matrix:{get:function(){return n.computedMatrix},set:function(t){return n.setMatrix(n.lastT(),t),n.computedMatrix},enumerable:!0},mode:{get:function(){return n.getMode()},set:function(t){var e=n.computedUp.slice(),r=n.computedEye.slice(),a=n.computedCenter.slice();if(n.setMode(t),\"turntable\"===t){var o=i();n._active.lookAt(o,r,a,e),n._active.lookAt(o+500,r,a,[0,0,1]),n._active.flush(o)}return n.getMode()},enumerable:!0},center:{get:function(){return n.computedCenter},set:function(t){return n.lookAt(n.lastT(),null,t),n.computedCenter},enumerable:!0},eye:{get:function(){return n.computedEye},set:function(t){return n.lookAt(n.lastT(),t),n.computedEye},enumerable:!0},up:{get:function(){return n.computedUp},set:function(t){return n.lookAt(n.lastT(),null,null,t),n.computedUp},enumerable:!0},distance:{get:function(){return c},set:function(t){return n.setDistance(n.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return n.getDistanceLimits(r)},set:function(t){return n.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",function(t){return t.preventDefault(),!1});var d=0,p=0;return o(t,function(e,r,a,o){var s=\"rotate\"===h.keyBindingMode,l=\"pan\"===h.keyBindingMode,u=\"zoom\"===h.keyBindingMode,f=!!o.control,g=!!o.alt,v=!!o.shift,m=!!(1&e),y=!!(2&e),b=!!(4&e),x=1/t.clientHeight,_=x*(r-d),w=x*(a-p),k=h.flipX?1:-1,A=h.flipY?1:-1,M=i(),T=Math.PI*h.rotateSpeed;if((s&&m&&!f&&!g&&!v||m&&!f&&!g&&v)&&n.rotate(M,k*T*_,-A*T*w,0),(l&&m&&!f&&!g&&!v||y||m&&f&&!g&&!v)&&n.pan(M,-h.translateSpeed*_*c,h.translateSpeed*w*c,0),u&&m&&!f&&!g&&!v||b||m&&!f&&g&&!v){var E=-h.zoomSpeed*w/window.innerHeight*(M-n.lastT())*100;n.pan(M,0,0,c*(Math.exp(E)-1))}return d=r,p=a,!0}),s(t,function(t,e){var r=h.flipX?1:-1,a=h.flipY?1:-1,o=i();if(Math.abs(t)>Math.abs(e))n.rotate(o,0,0,-t*r*Math.PI*h.rotateSpeed/window.innerWidth);else{var s=-h.zoomSpeed*a*e/window.innerHeight*(o-n.lastT())/100;n.pan(o,0,0,c*(Math.exp(s)-1))}},!0),h}e.exports=n;var i=t(\"right-now\"),a=t(\"3d-view\"),o=t(\"mouse-change\"),s=t(\"mouse-wheel\")},{\"3d-view\":39,\"mouse-change\":241,\"mouse-wheel\":245,\"right-now\":255}],441:[function(t,e,r){\"use strict\";function n(t,e){for(var r=0;3>r;++r){var n=s[r];e[n]._gd=t}}var i=t(\"./scene\"),a=t(\"../plots\"),o=t(\"../../constants/xmlns_namespaces\"),s=[\"xaxis\",\"yaxis\",\"zaxis\"];r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=/^scene([2-9]|[1-9][0-9]+)?$/,r.attrRegex=/^scene([2-9]|[1-9][0-9]+)?$/,r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){var e=t._fullLayout,r=t._fullData,o=a.getSubplotIds(e,\"gl3d\");e._paperdiv.style({width:e.width+\"px\",height:e.height+\"px\"}),t._context.setBackground(t,e.paper_bgcolor);for(var s=0;s<o.length;s++){var l=o[s],c=a.getSubplotData(r,\"gl3d\",l),u=e[l],f=u._scene;void 0===f&&(n(t,u),f=new i({id:l,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),u._scene=f),f.plot(c,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=a.getSubplotIds(n,\"gl3d\"),o=0;o<i.length;o++){var s=i[o];!e[s]&&n[s]._scene&&n[s]._scene.destroy()}},r.toSVG=function(t){for(var e=t._fullLayout,r=a.getSubplotIds(e,\"gl3d\"),n=e._size,i=0;i<r.length;i++){var s=e[r[i]],l=s.domain,c=s._scene,u=c.toImage(\"png\"),f=e._glimages.append(\"svg:image\");f.attr({xmlns:o.svg,\"xlink:href\":u,x:n.l+n.w*l.x[0],y:n.t+n.h*(1-l.y[1]),width:n.w*(l.x[1]-l.x[0]),height:n.h*(l.y[1]-l.y[0]),preserveAspectRatio:\"none\"}),c.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.setConvert=t(\"./set_convert\")},{\"../../constants/xmlns_namespaces\":370,\"../plots\":454,\"./layout/attributes\":442,\"./layout/defaults\":446,\"./layout/layout_attributes\":447,\"./scene\":451,\"./set_convert\":452}],442:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\"}}},{}],443:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat;e.exports={showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,\ntitle:i.title,titlefont:i.titlefont,type:i.type,autorange:i.autorange,rangemode:i.rangemode,range:i.range,fixedrange:i.fixedrange,tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,tickformat:i.tickformat,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth}},{\"../../../components/color\":303,\"../../../lib/extend\":377,\"../../cartesian/layout_attributes\":414}],444:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"./axis_attributes\"),o=t(\"../../cartesian/axis_defaults\"),s=[\"xaxis\",\"yaxis\",\"zaxis\"],l=function(){},c=13600/187;e.exports=function(t,e,r){function u(t,e){return i.coerce(f,h,a,t,e)}for(var f,h,d=0;d<s.length;d++){var p=s[d];f=t[p]||{},h={_id:p[0]+r.scene,_name:p},e[p]=h=o(f,h,u,{font:r.font,letter:p[0],data:r.data,showGrid:!0,bgColor:r.bgColor}),u(\"gridcolor\",n(h.color,r.bgColor,c).toRgbString()),u(\"title\",p[0]),h.setScale=l,u(\"showspikes\")&&(u(\"spikesides\"),u(\"spikethickness\"),u(\"spikecolor\",h.color)),u(\"showaxeslabels\"),u(\"showbackground\")&&u(\"backgroundcolor\")}}},{\"../../../lib\":382,\"../../cartesian/axis_defaults\":406,\"./axis_attributes\":443,tinycolor2:274}],445:[function(t,e,r){\"use strict\";function n(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open 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a=t(\"arraytools\"),o=t(\"../../../lib/html2unicode\"),s=t(\"../../../lib/str2rgbarray\"),l=a.copy1D,c=[\"xaxis\",\"yaxis\",\"zaxis\"],u=n.prototype;u.merge=function(t){for(var e=this,r=0;3>r;++r){var n=t[c[r]];e.labels[r]=o(n.title),\"titlefont\"in n&&(n.titlefont.color&&(e.labelColor[r]=s(n.titlefont.color)),n.titlefont.family&&(e.labelFont[r]=n.titlefont.family),n.titlefont.size&&(e.labelSize[r]=n.titlefont.size)),\"showline\"in n&&(e.lineEnable[r]=n.showline),\"linecolor\"in n&&(e.lineColor[r]=s(n.linecolor)),\"linewidth\"in n&&(e.lineWidth[r]=n.linewidth),\"showgrid\"in n&&(e.gridEnable[r]=n.showgrid),\"gridcolor\"in n&&(e.gridColor[r]=s(n.gridcolor)),\"gridwidth\"in n&&(e.gridWidth[r]=n.gridwidth),\"log\"===n.type?e.zeroEnable[r]=!1:\"zeroline\"in n&&(e.zeroEnable[r]=n.zeroline),\"zerolinecolor\"in n&&(e.zeroLineColor[r]=s(n.zerolinecolor)),\"zerolinewidth\"in n&&(e.zeroLineWidth[r]=n.zerolinewidth),\"ticks\"in 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n&&n.showbackground!==!1?(e.backgroundEnable[r]=!0,e.backgroundColor[r]=s(n.backgroundcolor)):e.backgroundEnable[r]=!1}},e.exports=i},{\"../../../lib/html2unicode\":381,\"../../../lib/str2rgbarray\":394,arraytools:49}],446:[function(t,e,r){\"use strict\";function n(t,e,r,n){for(var a=r(\"bgcolor\"),l=i.combine(a,n.paper_bgcolor),c=Object.keys(o.camera),u=0;u<c.length;u++)r(\"camera.\"+c[u]+\".x\"),r(\"camera.\"+c[u]+\".y\"),r(\"camera.\"+c[u]+\".z\");var f=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),h=f?\"manual\":\"auto\",d=r(\"aspectmode\",h);f||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===d&&(e.aspectmode=\"auto\")),s(t,e,{font:n.font,scene:n.id,data:n.fullData,bgColor:l}),r(\"dragmode\",n.getDfltFromLayout(\"dragmode\")),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}var i=t(\"../../../components/color\"),a=t(\"../../subplot_defaults\"),o=t(\"./layout_attributes\"),s=t(\"./axis_defaults\");e.exports=function(t,e,r){function i(e){if(!s){var r=-1!==o[e].values.indexOf(t[e]);return r?t[e]:void 0}}var s=e._has(\"cartesian\")||e._has(\"geo\")||e._has(\"gl2d\")||e._has(\"pie\")||e._has(\"ternary\");a(t,e,r,{type:\"gl3d\",attributes:o,handleDefaults:n,font:e.font,fullData:r,getDfltFromLayout:i,paper_bgcolor:e.paper_bgcolor})}},{\"../../../components/color\":303,\"../../subplot_defaults\":460,\"./axis_defaults\":444,\"./layout_attributes\":447}],447:[function(t,e,r){\"use strict\";function n(t,e,r){return{x:{valType:\"number\",dflt:t},y:{valType:\"number\",dflt:e},z:{valType:\"number\",dflt:r}}}var i=t(\"./axis_attributes\"),a=t(\"../../../lib/extend\").extendFlat;e.exports={bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\"},camera:{up:a(n(0,0,1),{}),center:a(n(0,0,0),{}),eye:a(n(1.25,1.25,1.25),{})},domain:{x:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]},y:{valType:\"info_array\",items:[{valType:\"number\",min:0,max:1},{valType:\"number\",min:0,max:1}],dflt:[0,1]}},aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\"},aspectratio:{x:{valType:\"number\",min:0},y:{valType:\"number\",min:0},z:{valType:\"number\",min:0}},xaxis:i,yaxis:i,zaxis:i,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\"],dflt:\"turntable\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\"},_deprecated:{cameraposition:{valType:\"info_array\"}}}},{\"../../../lib/extend\":377,\"./axis_attributes\":443}],448:[function(t,e,r){\"use strict\";function n(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}function i(t){var e=new n;return e.merge(t),e}var a=t(\"../../../lib/str2rgbarray\"),o=[\"xaxis\",\"yaxis\",\"zaxis\"],s=n.prototype;s.merge=function(t){for(var e=0;3>e;++e){var r=t[o[e]];this.enabled[e]=r.showspikes,this.colors[e]=a(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness}},e.exports=i},{\"../../../lib/str2rgbarray\":394}],449:[function(t,e,r){\"use strict\";function n(t){for(var e=new Array(3),r=0;3>r;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}function i(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,i=t.fullSceneLayout,c=[[],[],[]],u=0;3>u;++u){var f=i[s[u]];if(f._length=(r[u].hi-r[u].lo)*r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(f._length)===1/0)c[u]=[];else{f.range[0]=r[u].lo/t.dataScale[u],f.range[1]=r[u].hi/t.dataScale[u],f._m=1/(t.dataScale[u]*r[u].pixelsPerDataUnit),f.range[0]===f.range[1]&&(f.range[0]-=1,f.range[1]+=1);var h=f.tickmode;if(\"auto\"===f.tickmode){f.tickmode=\"linear\";var d=f.nticks||a.Lib.constrain(f._length/40,4,9);a.Axes.autoTicks(f,Math.abs(f.range[1]-f.range[0])/d)}for(var p=a.Axes.calcTicks(f),g=0;g<p.length;++g)p[g].x=p[g].x*t.dataScale[u],p[g].text=o(p[g].text);c[u]=p,f.tickmode=h}}e.ticks=c;for(var u=0;3>u;++u){l[u]=.5*(t.glplot.bounds[0][u]+t.glplot.bounds[1][u]);for(var g=0;2>g;++g)e.bounds[g][u]=t.glplot.bounds[g][u]}t.contourLevels=n(c)}e.exports=i;var a=t(\"../../../plotly\"),o=t(\"../../../lib/html2unicode\"),s=[\"xaxis\",\"yaxis\",\"zaxis\"],l=[0,0,0]},{\"../../../lib/html2unicode\":381,\"../../../plotly\":402}],450:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;4>r;++r)for(n=0;4>n;++n)i[n]+=t[4*r+n]*e[r];return i}function i(t,e){var r=n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])));return r}e.exports=i},{}],451:[function(t,e,r){\"use strict\";function n(t){function e(e,r){if(\"string\"==typeof r)return r;var n=t.fullSceneLayout[e];return g.tickText(n,n.c2l(r),\"hover\").text}var r,n=t.svgContainer,i=t.container.getBoundingClientRect(),a=i.width,o=i.height;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+a+\" \"+o),n.setAttributeNS(null,\"width\",a),n.setAttributeNS(null,\"height\",o),A(t),t.glplot.axes.update(t.axesOptions);for(var s=Object.keys(t.traces),l=null,c=t.glplot.selection,u=0;u<s.length;++u)r=t.traces[s[u]],r.handlePick(c)&&(l=r),r.setContourLevels&&r.setContourLevels();var f;if(null!==l){var h=x(t.glplot.cameraParams,c.dataCoordinate);r=l.data;var d=r.hoverinfo,p=e(\"xaxis\",c.traceCoordinate[0]),m=e(\"yaxis\",c.traceCoordinate[1]),y=e(\"zaxis\",c.traceCoordinate[2]);if(\"all\"!==d){var b=d.split(\"+\");-1===b.indexOf(\"x\")&&(p=void 0),-1===b.indexOf(\"y\")&&(m=void 0),-1===b.indexOf(\"z\")&&(y=void 0),-1===b.indexOf(\"text\")&&(c.textLabel=void 0),-1===b.indexOf(\"name\")&&(l.name=void 0)}t.fullSceneLayout.hovermode&&v.loneHover({x:(.5+.5*h[0]/h[3])*a,y:(.5-.5*h[1]/h[3])*o,xLabel:p,yLabel:m,zLabel:y,text:c.textLabel,name:l.name,color:l.color},{container:n});var _={points:[{x:p,y:m,z:y,data:r._input,fullData:r,curveNumber:r.index,pointNumber:c.data.index}]};c.buttons&&c.distance<5?t.graphDiv.emit(\"plotly_click\",_):t.graphDiv.emit(\"plotly_hover\",_),f=_}else v.loneUnhover(n),t.graphDiv.emit(\"plotly_unhover\",f)}function i(t,e,r,i){var a={canvas:r,gl:i,container:t.container,axes:t.axesOptions,spikes:t.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1};if(t.staticMode){if(!f){u=document.createElement(\"canvas\");try{f=u.getContext(\"webgl\",{preserveDrawingBuffer:!0,premultipliedAlpha:!0})}catch(o){throw new Error(\"error creating static canvas/context for image server\")}}a.pixelRatio=t.pixelRatio,a.gl=f,a.canvas=u}try{t.glplot=h(a)}catch(o){y(t)}var s=function(t){var e={};e[t.id]=c(t.camera),t.saveCamera(t.graphDiv.layout),t.graphDiv.emit(\"plotly_relayout\",e)};if(t.glplot.canvas.addEventListener(\"mouseup\",s.bind(null,t)),t.glplot.canvas.addEventListener(\"wheel\",s.bind(null,t)),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",function(t){d.warn(\"Lost WebGL context.\"),t.preventDefault()}),!t.camera){var l=t.fullSceneLayout.camera;t.camera=b(t.container,{center:[l.center.x,l.center.y,l.center.z],eye:[l.eye.x,l.eye.y,l.eye.z],up:[l.up.x,l.up.y,l.up.z],zoomMin:.1,zoomMax:100,mode:\"orbit\"})}return t.glplot.mouseListener.enabled=!1,t.glplot.camera=t.camera,t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=n.bind(null,t),t.traces={},!0}function a(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var a=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\",r.appendChild(a),this.svgContainer=a,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=w(e[this.id]),this.spikeOptions=k(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],!i(this,e)}function o(t,e,r,n){for(var i,a=0;a<e.length;++a)if(Array.isArray(e[a]))for(var o=0;o<e[a].length;++o)i=t.d2l(e[a][o]),!isNaN(i)&&isFinite(i)&&(n[0][r]=Math.min(n[0][r],i),n[1][r]=Math.max(n[1][r],i));else i=t.d2l(e[a]),!isNaN(i)&&isFinite(i)&&(n[0][r]=Math.min(n[0][r],i),n[1][r]=Math.max(n[1][r],i))}function s(t,e,r){var n=t.fullSceneLayout;o(n.xaxis,e.x,0,r),o(n.yaxis,e.y,1,r),o(n.zaxis,e.z,2,r)}function l(t){return[[t.eye.x,t.eye.y,t.eye.z],[t.center.x,t.center.y,t.center.z],[t.up.x,t.up.y,t.up.z]]}function c(t){return{up:{x:t.up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]}}}var u,f,h=t(\"gl-plot3d\"),d=t(\"../../lib\"),p=t(\"../../plots/plots\"),g=t(\"../../plots/cartesian/axes\"),v=t(\"../../plots/cartesian/graph_interact\"),m=t(\"../../lib/str2rgbarray\"),y=t(\"../../lib/show_no_webgl_msg\"),b=t(\"./camera\"),x=t(\"./project\"),_=t(\"./set_convert\"),w=t(\"./layout/convert\"),k=t(\"./layout/spikes\"),A=t(\"./layout/tick_marks\"),M=a.prototype;M.recoverContext=function(){function t(){return r.isContextLost()?void requestAnimationFrame(t):i(e,e.fullLayout,n,r)?void e.plot.apply(e,e.plotArgs):void d.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")}var e=this,r=this.glplot.gl,n=this.glplot.canvas;this.glplot.dispose(),requestAnimationFrame(t)};var T=[\"xaxis\",\"yaxis\",\"zaxis\"];M.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,l,c,u=e[this.id],f=r[this.id];for(u.bgcolor?this.glplot.clearColor=m(u.bgcolor):this.glplot.clearColor=[0,0,0,0],this.glplot.snapToData=!0,this.fullSceneLayout=u,this.glplotLayout=u,this.axesOptions.merge(u),this.spikeOptions.merge(u),this.updateFx(u.dragmode,u.hovermode),this.glplot.update({}),a=0;3>a;++a)l=u[T[a]],_(l);t?Array.isArray(t)||(t=[t]):t=[];var h=[[1/0,1/0,1/0],[-(1/0),-(1/0),-(1/0)]];for(a=0;a<t.length;++a)n=t[a],n.visible===!0&&s(this,n,h);var d=[1,1,1];for(o=0;3>o;++o)h[0][o]>h[1][o]?d[o]=1:h[1][o]===h[0][o]?d[o]=1:d[o]=1/(h[1][o]-h[0][o]);for(this.dataScale=d,a=0;a<t.length;++a)if(n=t[a],n.visible===!0){if(i=this.traces[n.uid])i.update(n);else{var g=p.getModule(n.type);i=g.plot(this,n),this.traces[n.uid]=i}i.name=n.name}var v=Object.keys(this.traces);t:for(a=0;a<v.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===v[a]&&t[o].visible===!0)continue t;i=this.traces[v[a]],i.dispose(),delete this.traces[v[a]]}var y=[[0,0,0],[0,0,0]],b=[],x={};for(a=0;3>a;++a){if(l=u[T[a]],c=l.type,c in x?(x[c].acc*=d[a],x[c].count+=1):x[c]={acc:d[a],count:1},l.autorange){for(y[0][a]=1/0,y[1][a]=-(1/0),o=0;o<this.glplot.objects.length;++o){var w=this.glplot.objects[o].bounds;y[0][a]=Math.min(y[0][a],w[0][a]/d[a]),y[1][a]=Math.max(y[1][a],w[1][a]/d[a])}if(\"rangemode\"in l&&\"tozero\"===l.rangemode&&(y[0][a]=Math.min(y[0][a],0),y[1][a]=Math.max(y[1][a],0)),y[0][a]>y[1][a])y[0][a]=-1,y[1][a]=1;else{var k=y[1][a]-y[0][a];y[0][a]-=k/32,y[1][a]+=k/32}}else{var A=u[T[a]].range;y[0][a]=A[0],y[1][a]=A[1]}y[0][a]===y[1][a]&&(y[0][a]-=1,y[1][a]+=1),b[a]=y[1][a]-y[0][a],this.glplot.bounds[0][a]=y[0][a]*d[a],this.glplot.bounds[1][a]=y[1][a]*d[a]}var M=[1,1,1];for(a=0;3>a;++a){l=u[T[a]],c=l.type;var 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n=t(\"../../components/color\"),i=t(\"./subtypes\");e.exports=function(t,e){var r,a;if(\"lines\"===t.mode)return r=t.line.color,r&&n.opacity(r)?r:t.fillcolor;if(\"none\"===t.mode)return t.fill?t.fillcolor:\"\";var o=e.mcc||(t.marker||{}).color,s=e.mlcc||((t.marker||{}).line||{}).color;return a=o&&n.opacity(o)?o:s&&n.opacity(s)&&(e.mlw||((t.marker||{}).line||{}).width)?s:\"\",a?n.opacity(a)<.3?n.addOpacity(a,.3):a:(r=(t.line||{}).color,r&&n.opacity(r)&&i.hasLines(t)&&t.line.width?r:t.fillcolor)}},{\"../../components/color\":303,\"./subtypes\":575}],565:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/graph_interact\"),a=t(\"../../plots/cartesian/constants\"),o=t(\"../../components/errorbars\"),s=t(\"./get_trace_color\"),l=t(\"../../components/color\");e.exports=function(t,e,r,c){var u=t.cd,f=u[0].trace,h=t.xa,d=t.ya,p=h.c2p(e),g=d.c2p(r),v=[p,g];if(-1!==f.hoveron.indexOf(\"points\")){var m=function(t){var e=Math.max(3,t.mrc||0);return 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n={},i=t(\"./subtypes\");n.hasLines=i.hasLines,n.hasMarkers=i.hasMarkers,n.hasText=i.hasText,n.isBubble=i.isBubble,n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.cleanData=t(\"./clean_data\"),n.calc=t(\"./calc\"),n.arraysToCalcdata=t(\"./arrays_to_calcdata\"),n.plot=t(\"./plot\"),n.colorbar=t(\"./colorbar\"),n.style=t(\"./style\"),n.hoverPoints=t(\"./hover\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scatter\",n.basePlotModule=t(\"../../plots/cartesian\"),n.categories=[\"cartesian\",\"symbols\",\"markerColorscale\",\"errorBarsOK\",\"showLegend\"],n.meta={},e.exports=n},{\"../../plots/cartesian\":413,\"./arrays_to_calcdata\":555,\"./attributes\":556,\"./calc\":557,\"./clean_data\":558,\"./colorbar\":559,\"./defaults\":562,\"./hover\":565,\"./plot\":572,\"./select\":573,\"./style\":574,\"./subtypes\":575}],567:[function(t,e,r){\"use strict\";var 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h=i(l.position,l.delaunayColor,l.delaunayAxis);h.opacity=t.opacity,this.delaunayMesh?this.delaunayMesh.update(h):(h.gl=o,this.delaunayMesh=v(h),this.scene.glplot.add(this.delaunayMesh))}else this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose(),this.delaunayMesh=null)},M.dispose=function(){this.linePlot&&(this.scene.glplot.remove(this.linePlot),this.linePlot.dispose()),this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose()),this.errorBars&&(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose()),this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose()),this.delaunayMesh&&(this.scene.glplot.remove(this.delaunayMesh),this.delaunayMesh.dispose())},e.exports=h},{\"../../constants/gl3d_dashes\":368,\"../../constants/gl_markers\":369,\"../../lib\":382,\"../../lib/gl_format_color\":380,\"../../lib/str2rgbarray\":394,\"../scatter/make_bubble_size_func\":570,\"./calc_errors\":580,\"delaunay-triangulate\":114,\"gl-error3d\":121,\"gl-line3d\":127,\"gl-mesh3d\":150,\"gl-scatter3d\":193}],582:[function(t,e,r){\"use strict\";function n(t,e,r){var n=0,i=r(\"x\"),a=r(\"y\"),o=r(\"z\");return i&&a&&o&&(n=Math.min(i.length,a.length,o.length),n<i.length&&(e.x=i.slice(0,n)),n<a.length&&(e.y=a.slice(0,n)),n<o.length&&(e.z=o.slice(0,n))),n}var i=t(\"../../lib\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/text_defaults\"),c=t(\"../../components/errorbars/defaults\"),u=t(\"./attributes\");e.exports=function(t,e,r,f){function h(r,n){return i.coerce(t,e,u,r,n)}var d=n(t,e,h);if(!d)return void(e.visible=!1);h(\"text\"),h(\"mode\"),a.hasLines(e)&&(h(\"connectgaps\"),s(t,e,r,f,h)),a.hasMarkers(e)&&o(t,e,r,f,h),a.hasText(e)&&l(t,e,f,h);var p=(e.line||{}).color,g=(e.marker||{}).color;h(\"surfaceaxis\")>=0&&h(\"surfacecolor\",p||g);for(var v=[\"x\",\"y\",\"z\"],m=0;3>m;++m){var y=\"projection.\"+v[m];h(y+\".show\")&&(h(y+\".opacity\"),h(y+\".scale\"))}c(t,e,r,{axis:\"z\"}),c(t,e,r,{axis:\"y\",inherit:\"z\"}),c(t,e,r,{axis:\"x\",inherit:\"z\"})}},{\"../../components/errorbars/defaults\":331,\"../../lib\":382,\"../scatter/line_defaults\":567,\"../scatter/marker_defaults\":571,\"../scatter/subtypes\":575,\"../scatter/text_defaults\":576,\"./attributes\":578}],583:[function(t,e,r){\"use strict\";var n={};n.plot=t(\"./convert\"),n.attributes=t(\"./attributes\"),n.markerSymbols=t(\"../../constants/gl_markers\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.moduleType=\"trace\",n.name=\"scatter3d\",n.basePlotModule=t(\"../../plots/gl3d\"),n.categories=[\"gl3d\",\"symbols\",\"markerColorscale\",\"showLegend\"],n.meta={},e.exports=n},{\"../../constants/gl_markers\":369,\"../../plots/gl3d\":441,\"../scatter/colorbar\":559,\"./attributes\":578,\"./calc\":579,\"./convert\":581,\"./defaults\":582}],584:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../lib/extend\").extendFlat,s=n.marker,l=n.line,c=s.line;e.exports={lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country names\"],dflt:\"ISO-3\"},mode:o({},n.mode,{dflt:\"markers\"}),text:o({},n.text,{}),line:{color:l.color,width:l.width,dash:l.dash},marker:o({},{symbol:s.symbol,opacity:s.opacity,size:s.size,sizeref:s.sizeref,sizemin:s.sizemin,sizemode:s.sizemode,showscale:s.showscale,line:o({},{width:c.width},a(\"marker.line\"))},a(\"marker\")),textfont:n.textfont,textposition:n.textposition,hoverinfo:o({},i.hoverinfo,{flags:[\"lon\",\"lat\",\"location\",\"text\",\"name\"]}),_nestedModules:{\"marker.colorbar\":\"Colorbar\"}}},{\"../../components/colorscale/color_attributes\":311,\"../../lib/extend\":377,\"../../plots/attributes\":403,\"../scatter/attributes\":556}],585:[function(t,e,r){\"use strict\";var n=t(\"../scatter/colorscale_calc\");e.exports=function(t,e){var r=[{x:!1,y:!1,trace:e,t:{}}];return n(e),r}},{\"../scatter/colorscale_calc\":560}],586:[function(t,e,r){\"use strict\";function n(t,e,r){var n,i,a=0,o=r(\"locations\");return 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\").plot,n.moduleType=\"trace\",n.name=\"scattergeo\",n.basePlotModule=t(\"../../plots/geo\"),n.categories=[\"geo\",\"symbols\",\"markerColorscale\",\"showLegend\"],n.meta={},e.exports=n},{\"../../plots/geo\":426,\"../scatter/colorbar\":559,\"./attributes\":584,\"./calc\":585,\"./defaults\":586,\"./plot\":588}],588:[function(t,e,r){\"use strict\";function n(t,e,r){function n(t,n){h(t,e,n,r)}var i=t.marker;if(n(t.text,\"tx\"),n(t.textposition,\"tp\"),t.textfont&&(n(t.textfont.size,\"ts\"),n(t.textfont.color,\"tc\"),n(t.textfont.family,\"tf\")),i&&i.line){var a=i.line;n(i.opacity,\"mo\"),n(i.symbol,\"mx\"),n(i.color,\"mc\"),n(i.size,\"ms\"),n(a.color,\"mlc\"),n(a.width,\"mlw\")}}function i(t){for(var e=t.lon.length,r=new Array(e),n=0;e>n;n++)r[n]=[t.lon[n],t.lat[n]];return{type:\"LineString\",coordinates:r,trace:t}}function a(t,e){function r(e){var r=t.mockAxis;return c.tickText(r,r.c2l(e),\"hover\").text+\"\\xb0\"}var n=e.hoverinfo;if(\"none\"===n)return function(t){delete t.textLabel};var i=\"all\"===n?v.hoverinfo.flags:n.split(\"+\"),a=-1!==i.indexOf(\"location\")&&Array.isArray(e.locations),o=-1!==i.indexOf(\"lon\"),s=-1!==i.indexOf(\"lat\"),l=-1!==i.indexOf(\"text\");return function(t){var n=[];a?n.push(t.location):o&&s?n.push(\"(\"+r(t.lon)+\", \"+r(t.lat)+\")\"):o?n.push(\"lon: \"+r(t.lon)):s&&n.push(\"lat: \"+r(t.lat)),l&&n.push(t.tx||e.text),t.textLabel=n.join(\"<br>\")}}function o(t){var e=Array.isArray(t.locations);return function(r,n){return{points:[{data:t._input,fullData:t,curveNumber:t.index,pointNumber:n,lon:r.lon,lat:r.lat,location:e?r.location:null}]}}}var s=t(\"d3\"),l=t(\"../../plots/cartesian/graph_interact\"),c=t(\"../../plots/cartesian/axes\"),u=t(\"../../lib/topojson_utils\").getTopojsonFeatures,f=t(\"../../lib/geo_location_utils\").locationToFeature,h=t(\"../../lib/array_to_calc_item\"),d=t(\"../../components/color\"),p=t(\"../../components/drawing\"),g=t(\"../scatter/subtypes\"),v=t(\"./attributes\"),m=e.exports={};m.calcGeoJSON=function(t,e){var r,i,a,o,s=[],l=Array.isArray(t.locations);l?(o=t.locations,r=o.length,i=u(t,e),a=function(t,e){var r=f(t.locationmode,o[e],i);return void 0!==r?r.properties.ct:void 0}):(r=t.lon.length,a=function(t,e){return[t.lon[e],t.lat[e]]});for(var c=0;r>c;c++){var h=a(t,c);if(h){var d={lon:h[0],lat:h[1],location:l?t.locations[c]:null};n(t,d,c),s.push(d)}}return s.length>0&&(s[0].trace=t),s},m.plot=function(t,e){var r=t.framework.select(\".scattergeolayer\").selectAll(\"g.trace.scattergeo\").data(e,function(t){return t.uid});r.enter().append(\"g\").attr(\"class\",\"trace 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f=m.calcGeoJSON(e,t.topojson),h=a(t,e),d=o(e),p=e.hoverinfo,v=\"all\"===p||-1!==p.indexOf(\"name\"),y=null;c&&i.selectAll(\"path.point\").data(f).enter().append(\"path\").classed(\"point\",!0).on(\"mouseover\",r).on(\"click\",n).on(\"mouseout\",function(){l.loneUnhover(t.hoverContainer),t.graphDiv.emit(\"plotly_unhover\",y)}).on(\"mousedown\",function(){l.loneUnhover(t.hoverContainer)}).on(\"mouseup\",r),u&&i.selectAll(\"g\").data(f).enter().append(\"g\").append(\"text\")}}),m.style(t)},m.style=function(t){var e=t.framework.selectAll(\"g.trace.scattergeo\");e.style(\"opacity\",function(t){return t.opacity}),e.each(function(t){s.select(this).selectAll(\"path.point\").call(p.pointStyle,t),s.select(this).selectAll(\"text\").call(p.textPointStyle,t)}),e.selectAll(\"path.js-line\").style(\"fill\",\"none\").each(function(t){var e=t.trace,r=e.line||{};s.select(this).call(d.stroke,r.color).call(p.dashLine,r.dash||\"\",r.width||0)})}},{\"../../components/color\":303,\"../../components/drawing\":326,\"../../lib/array_to_calc_item\":373,\"../../lib/geo_location_utils\":379,\"../../lib/topojson_utils\":396,\"../../plots/cartesian/axes\":405,\"../../plots/cartesian/graph_interact\":412,\"../scatter/subtypes\":575,\"./attributes\":584,d3:113}],589:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../components/colorscale/color_attributes\"),a=t(\"../../constants/gl2d_dashes\"),o=t(\"../../constants/gl_markers\"),s=t(\"../../lib/extend\").extendFlat,l=t(\"../../lib/extend\").extendDeep,c=n.line,u=n.marker,f=u.line;e.exports={x:n.x,x0:n.x0,dx:n.dx,y:n.y,y0:n.y0,dy:n.dy,text:s({},n.text,{}),mode:{valType:\"flaglist\",flags:[\"lines\",\"markers\"],extras:[\"none\"]},line:{color:c.color,width:c.width,dash:{valType:\"enumerated\",values:Object.keys(a),dflt:\"solid\"}},marker:l({},i(\"marker\"),{symbol:{valType:\"enumerated\",values:Object.keys(o),dflt:\"circle\",arrayOk:!0},size:u.size,sizeref:u.sizeref,sizemin:u.sizemin,sizemode:u.sizemode,opacity:u.opacity,showscale:u.showscale,line:l({},i(\"marker.line\"),{width:f.width})}),connectgaps:n.connectgaps,fill:s({},n.fill,{values:[\"none\",\"tozeroy\",\"tozerox\"]}),fillcolor:n.fillcolor,_nestedModules:{error_x:\"ErrorBars\",error_y:\"ErrorBars\",\"marker.colorbar\":\"Colorbar\"}}},{\"../../components/colorscale/color_attributes\":311,\"../../constants/gl2d_dashes\":367,\"../../constants/gl_markers\":369,\"../../lib/extend\":377,\"../scatter/attributes\":556}],590:[function(t,e,r){\"use 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I,N=w(t),j=t.marker,F=j.opacity,D=t.opacity,B=s(j,F,D,x),U=C(j.symbol,x),V=L(j.line.width,x),q=s(j.line,F,D,x);for(O=i(N,j.size,x),u=0;T>u;++u)for(I=_[u],this.scatterOptions.sizes[u]=4*O[I],this.scatterOptions.glyphs[u]=U[I],this.scatterOptions.borderWidths[u]=.5*V[I],f=0;4>f;++f)this.scatterOptions.colors[4*u+f]=B[4*I+f],this.scatterOptions.borderColors[4*u+f]=q[4*I+f];this.fancyScatter.update(this.scatterOptions)}else this.scatterOptions.positions=new Float32Array(0),this.scatterOptions.glyphs=[],this.fancyScatter.update(this.scatterOptions);this.scatterOptions.positions=new Float32Array(0),this.scatterOptions.glyphs=[],this.scatter.update(this.scatterOptions),this.expandAxesFancy(o,l,O)},E.updateLines=function(t,e){var r;if(this.hasLines){var n=e;if(!t.connectgaps){var i=0,a=this.xData,s=this.yData;for(n=new Float32Array(2*a.length),r=0;r<a.length;++r)n[i++]=a[r],n[i++]=s[r]}this.lineOptions.positions=n;var 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r,n,i,a=e||10,o=0;2>o;o++)r=this.scene[T[o]],n=r._min,n||(n=[]),n.push({val:t[o],pad:a}),i=r._max,i||(i=[]),i.push({val:t[o+2],pad:a})},E.expandAxesFancy=function(t,e,r){var n=this.scene,i={padded:!0,ppad:r};m.expand(n.xaxis,t,i),m.expand(n.yaxis,e,i)},E.dispose=function(){this.line.dispose(),this.errorX.dispose(),this.errorY.dispose(),this.scatter.dispose(),this.fancyScatter.dispose()},e.exports=u},{\"../../components/errorbars\":332,\"../../constants/gl2d_dashes\":367,\"../../constants/gl_markers\":369,\"../../lib\":382,\"../../lib/gl_format_color\":380,\"../../lib/str2rgbarray\":394,\"../../plots/cartesian/axes\":405,\"../scatter/get_trace_color\":564,\"../scatter/make_bubble_size_func\":570,\"../scatter/subtypes\":575,\"fast-isnumeric\":117,\"gl-error2d\":119,\"gl-line2d\":125,\"gl-scatter2d\":190,\"gl-scatter2d-fancy\":185}],591:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/xy_defaults\"),s=t(\"../scatter/marker_defaults\"),l=t(\"../scatter/line_defaults\"),c=t(\"../scatter/fillcolor_defaults\"),u=t(\"../../components/errorbars/defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function d(r,i){return n.coerce(t,e,f,r,i)}var p=o(t,e,d);return p?(d(\"text\"),d(\"mode\",p<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(d(\"connectgaps\"),l(t,e,r,h,d)),a.hasMarkers(e)&&s(t,e,r,h,d),d(\"fill\"),\"none\"!==e.fill&&c(t,e,r,d),u(t,e,r,{axis:\"y\"}),void u(t,e,r,{axis:\"x\",inherit:\"y\"})):void(e.visible=!1)}},{\"../../components/errorbars/defaults\":331,\"../../lib\":382,\"../scatter/constants\":561,\"../scatter/fillcolor_defaults\":563,\"../scatter/line_defaults\":567,\"../scatter/marker_defaults\":571,\"../scatter/subtypes\":575,\"../scatter/xy_defaults\":577,\"./attributes\":589}],592:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"../scatter3d/calc\"),n.plot=t(\"./convert\"),n.moduleType=\"trace\",n.name=\"scattergl\",n.basePlotModule=t(\"../../plots/gl2d\"),n.categories=[\"gl2d\",\"symbols\",\"errorBarsOK\",\"markerColorscale\",\"showLegend\"],n.meta={},e.exports=n},{\"../../plots/gl2d\":438,\"../scatter/colorbar\":559,\"../scatter3d/calc\":579,\"./attributes\":589,\"./convert\":590,\"./defaults\":591}],593:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/colorscale/color_attributes\"),o=t(\"../../lib/extend\").extendFlat,s=n.marker,l=n.line,c=s.line;e.exports={a:{valType:\"data_array\"},b:{valType:\"data_array\"},c:{valType:\"data_array\"},sum:{valType:\"number\",dflt:0,min:0},mode:o({},n.mode,{dflt:\"markers\"}),text:o({},n.text,{}),line:{color:l.color,width:l.width,dash:l.dash,shape:o({},l.shape,{values:[\"linear\",\"spline\"]}),smoothing:l.smoothing},connectgaps:n.connectgaps,fill:o({},n.fill,{values:[\"none\",\"toself\",\"tonext\"]}),fillcolor:n.fillcolor,marker:o({},{symbol:s.symbol,opacity:s.opacity,maxdisplayed:s.maxdisplayed,size:s.size,sizeref:s.sizeref,sizemin:s.sizemin,sizemode:s.sizemode,showscale:s.showscale,line:o({},{width:c.width},a(\"marker\".line))},a(\"marker\")),textfont:n.textfont,textposition:n.textposition,hoverinfo:o({},i.hoverinfo,{flags:[\"a\",\"b\",\"c\",\"text\",\"name\"]}),hoveron:n.hoveron,_nestedModules:{\"marker.colorbar\":\"Colorbar\"}}},{\"../../components/colorscale/color_attributes\":311,\"../../lib/extend\":377,\"../../plots/attributes\":403,\"../scatter/attributes\":556}],594:[function(t,e,r){\"use 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E&&a.mergeArray(E,M,\"ms\"),M}},{\"../../lib\":382,\"../../plots/cartesian/axes\":405,\"../scatter/colorscale_calc\":560,\"../scatter/subtypes\":575,\"fast-isnumeric\":117}],595:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),c=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function d(r,i){return n.coerce(t,e,f,r,i)}var p,g=d(\"a\"),v=d(\"b\"),m=d(\"c\");if(g?(p=g.length,v?(p=Math.min(p,v.length),m&&(p=Math.min(p,m.length))):p=m?Math.min(p,m.length):0):v&&m&&(p=Math.min(v.length,m.length)),!p)return void(e.visible=!1);g&&p<g.length&&(e.a=g.slice(0,p)),v&&p<v.length&&(e.b=v.slice(0,p)),m&&p<m.length&&(e.c=m.slice(0,p)),d(\"sum\"),d(\"text\");var y=p<i.PTS_LINESONLY?\"lines+markers\":\"lines\";d(\"mode\",y),a.hasLines(e)&&(s(t,e,r,h,d),l(t,e,d),d(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,d),a.hasText(e)&&c(t,e,h,d);var b=[];(a.hasMarkers(e)||a.hasText(e))&&(d(\"marker.maxdisplayed\"),b.push(\"points\")),d(\"fill\"),\"none\"!==e.fill&&(u(t,e,r,d),a.hasLines(e)||l(t,e,d)),d(\"hoverinfo\",1===h._dataLength?\"a+b+c+text\":void 0),\"tonext\"!==e.fill&&\"toself\"!==e.fill||b.push(\"fills\"),d(\"hoveron\",b.join(\"+\")||\"points\")}},{\"../../lib\":382,\"../scatter/constants\":561,\"../scatter/fillcolor_defaults\":563,\"../scatter/line_defaults\":567,\"../scatter/line_shape_defaults\":569,\"../scatter/marker_defaults\":571,\"../scatter/subtypes\":575,\"../scatter/text_defaults\":576,\"./attributes\":593}],596:[function(t,e,r){\"use strict\";var n=t(\"../scatter/hover\"),i=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r,a){function o(t,e){m.push(t._hovertitle+\": \"+i.tickText(t,e,\"hover\").text)}var s=n(t,e,r,a);if(s&&s[0].index!==!1){var l=s[0];if(void 0===l.index){var c=1-l.y0/t.ya._length,u=t.xa._length,f=u*c/2,h=u-f;return l.x0=Math.max(Math.min(l.x0,h),f),l.x1=Math.max(Math.min(l.x1,h),f),s}var d=l.cd[l.index];l.a=d.a,l.b=d.b,l.c=d.c,l.xLabelVal=void 0,l.yLabelVal=void 0;var p=l.trace,g=p._ternary,v=p.hoverinfo.split(\"+\"),m=[];return-1!==v.indexOf(\"all\")&&(v=[\"a\",\"b\",\"c\"]),-1!==v.indexOf(\"a\")&&o(g.aaxis,d.a),-1!==v.indexOf(\"b\")&&o(g.baxis,d.b),-1!==v.indexOf(\"c\")&&o(g.caxis,d.c),l.extraText=m.join(\"<br>\"),s}}},{\"../../plots/cartesian/axes\":405,\"../scatter/hover\":565}],597:[function(t,e,r){\"use strict\";var n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"../scatter/colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./plot\"),n.style=t(\"./style\"),n.hoverPoints=t(\"./hover\"),n.selectPoints=t(\"./select\"),n.moduleType=\"trace\",n.name=\"scatterternary\",n.basePlotModule=t(\"../../plots/ternary\"),n.categories=[\"ternary\",\"symbols\",\"markerColorscale\",\"showLegend\"],n.meta={},e.exports=n},{\"../../plots/ternary\":461,\"../scatter/colorbar\":559,\"./attributes\":593,\"./calc\":594,\"./defaults\":595,\"./hover\":596,\"./plot\":598,\"./select\":599,\"./style\":600}],598:[function(t,e,r){\"use strict\";var n=t(\"../scatter/plot\");e.exports=function(t,e){var r=t.plotContainer;r.select(\".scatterlayer\").selectAll(\"*\").remove();for(var i={x:function(){return t.xaxis},y:function(){return t.yaxis},plot:r},a=new Array(e.length),o=t.graphDiv.calcdata,s=0;s<o.length;s++){var l=e.indexOf(o[s][0].trace);-1!==l&&(a[l]=o[s],e[l]._ternary=t)}n(t.graphDiv,i,a)}},{\"../scatter/plot\":572}],599:[function(t,e,r){\"use strict\";var n=t(\"../scatter/select\");e.exports=function(t,e){var r=n(t,e);if(r){var i,a,o,s=t.cd;for(o=0;o<r.length;o++)i=r[o],a=s[i.pointNumber],i.a=a.a,i.b=a.b,i.c=a.c,delete i.x,delete i.y;return r}}},{\"../scatter/select\":573}],600:[function(t,e,r){\"use strict\";var n=t(\"../scatter/style\");e.exports=function(t){for(var e=t._fullLayout._modules,r=0;r<e.length;r++)if(\"scatter\"===e[r].name)return;n(t)}},{\"../scatter/style\":574}],601:[function(t,e,r){\"use strict\";function n(t){return{valType:\"boolean\",dflt:!1}}function i(t){return{show:{valType:\"boolean\",dflt:!1},project:{x:n(\"x\"),y:n(\"y\"),z:n(\"z\")},color:{valType:\"color\",dflt:a.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:a.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var a=t(\"../../components/color\"),o=t(\"../../components/colorscale/attributes\"),s=t(\"../../lib/extend\").extendFlat;e.exports={z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"data_array\"},surfacecolor:{valType:\"data_array\"},cauto:o.zauto,cmin:o.zmin,cmax:o.zmax,colorscale:o.colorscale,autocolorscale:s({},o.autocolorscale,{dflt:!1}),reversescale:o.reversescale,showscale:o.showscale,contours:{x:i(\"x\"),y:i(\"y\"),z:i(\"z\")},hidesurface:{valType:\"boolean\",dflt:!1},lightposition:{x:{valType:\"number\",min:-1e5,max:1e5,dflt:10},y:{valType:\"number\",min:-1e5,max:1e5,dflt:1e4},z:{valType:\"number\",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:\"number\",min:0,max:1,dflt:.8},diffuse:{valType:\"number\",min:0,max:1,dflt:.8},specular:{valType:\"number\",min:0,max:2,dflt:.05},roughness:{valType:\"number\",min:0,max:1,dflt:.5},fresnel:{valType:\"number\",min:0,max:5,dflt:.2}},opacity:{valType:\"number\",min:0,max:1,dflt:1},_nestedModules:{colorbar:\"Colorbar\"},_deprecated:{zauto:s({},o.zauto,{}),zmin:s({},o.zmin,{}),zmax:s({},o.zmax,{})}}},{\"../../components/color\":303,\"../../components/colorscale/attributes\":309,\"../../lib/extend\":377}],602:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.surfacecolor?n(e,e.surfacecolor,\"\",\"c\"):n(e,e.z,\"\",\"c\")}},{\"../../components/colorscale/calc\":310}],603:[function(t,e,r){\"use strict\";var n=t(\"d3\"),i=t(\"fast-isnumeric\"),a=t(\"../../lib\"),o=t(\"../../plots/plots\"),s=t(\"../../components/colorscale/get_scale\"),l=t(\"../../components/colorbar/draw\");e.exports=function(t,e){var r=e[0].trace,c=\"cb\"+r.uid,u=s(r.colorscale),f=r.cmin,h=r.cmax,d=r.surfacecolor||r.z;if(i(f)||(f=a.aggNums(Math.min,null,d)),i(h)||(h=a.aggNums(Math.max,null,d)),t._fullLayout._infolayer.selectAll(\".\"+c).remove(),!r.showscale)return void o.autoMargin(t,c);var p=e[0].t.cb=l(t,c);p.fillcolor(n.scale.linear().domain(u.map(function(t){\nreturn f+t[0]*(h-f)})).range(u.map(function(t){return 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n={};n.attributes=t(\"./attributes\"),n.supplyDefaults=t(\"./defaults\"),n.colorbar=t(\"./colorbar\"),n.calc=t(\"./calc\"),n.plot=t(\"./convert\"),n.moduleType=\"trace\",n.name=\"surface\",n.basePlotModule=t(\"../../plots/gl3d\"),n.categories=[\"gl3d\",\"noOpacity\"],n.meta={},e.exports=n},{\"../../plots/gl3d\":441,\"./attributes\":601,\"./calc\":602,\"./colorbar\":603,\"./convert\":604,\"./defaults\":605}]},{},[12])(12)});});require(['plotly'], function(Plotly) {window.Plotly = Plotly;});}</script>", "text/plain": "<IPython.core.display.HTML object>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# -*- coding: utf-8 -*-\n", "import plotly\n", "plotly.offline.init_notebook_mode() # run at the start of every ipython noteboook\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import sqlite3\n", "import plotly.plotly as py\n", "from plotly.graph_objs import *\n", "import networkx as nx\n", "\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = [20,10]" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "25391e72-6dca-20f6-c24c-b55ca888e363" }, "outputs": [], "source": [ "conn = sqlite3.connect(r'../input/database.sqlite')\n", "teams = pd.read_sql_query(\"select * from Teams\", conn)\n", "users = pd.read_sql_query(\"select * from Users\", conn)\n", "teammembers = pd.read_sql_query(\"select * from TeamMemberships\",conn)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "5a52659f-8d96-7e24-f02e-e60ff59f39d5" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:10: SettingWithCopyWarning:\n\n\nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n\n/opt/conda/bin/ipython:11: SettingWithCopyWarning:\n\n\nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n\n" } ], "source": [ "teams_q = teammembers.groupby('TeamId').UserId.count()\n", "teams_q = teams_q[teams_q > 1].reset_index() ## I work with teams that have more than one participant \n", "teammembers_cut = teammembers.merge(teams_q,on='TeamId')\n", "users_q = teammembers_cut.groupby('UserId_x').TeamId.count().reset_index()\n", "teammembers_cut = teammembers_cut.merge(users_q,left_on='UserId_x', right_on='UserId_x')\n", "teammembers_cut = teammembers_cut.merge(teams, left_on='TeamId_x', right_on='Id')\n", "teammembers_cut = teammembers_cut.merge(users, left_on='UserId_x', right_on='Id')\n", "\n", "tm4graph = teammembers_cut[['TeamId_x','UserId_x']]\n", "tm4graph['TeamId_x'] = 'Team_' + tm4graph['TeamId_x'].astype('str')\n", "tm4graph['UserId_x'] = 'User_' + tm4graph['UserId_x'].astype('str')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "d7a7decc-e357-c855-bb11-a90a1622fd25" }, "outputs": [], "source": [ "## Implementation of force atlas to networkx from here https://github.com/tpoisot/nxfa2/blob/master/forceatlas.py\n", "\n", "from scipy.sparse import spdiags, coo_matrix\n", "import scipy as sp\n", "import numpy as np\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "## Now the layout function\n", "def forceatlas2_layout(G, iterations=10, linlog=False, pos=None, nohubs=False,\n", " kr=0.001, k=None, dim=2):\n", " \"\"\"\n", " Options values are\n", " g The graph to layout\n", " iterations Number of iterations to do\n", " linlog Whether to use linear or log repulsion\n", " random_init Start with a random position\n", " If false, start with FR\n", " avoidoverlap Whether to avoid overlap of points\n", " degreebased Degree based repulsion\n", " \"\"\"\n", " # We add attributes to store the current and previous convergence speed\n", " for n in G:\n", " G.node[n]['prevcs'] = 0\n", " G.node[n]['currcs'] = 0\n", " # To numpy matrix\n", " # This comes from the spares FR layout in nx\n", " A = nx.to_scipy_sparse_matrix(G, dtype='f')\n", " nnodes, _ = A.shape\n", "\n", " try:\n", " A = A.tolil()\n", " except Exception as e:\n", " A = (coo_matrix(A)).tolil()\n", " if pos is None:\n", " pos = np.asarray(np.random.random((nnodes, dim)), dtype=A.dtype)\n", " else:\n", " pos = pos.astype(A.dtype)\n", " if k is None:\n", " k = np.sqrt(1.0 / nnodes)\n", " # Iterations\n", " # the initial \"temperature\" is about .1 of domain area (=1x1)\n", " # this is the largest step allowed in the dynamics.\n", " t = 0.1\n", " # simple cooling scheme.\n", " # linearly step down by dt on each iteration so last iteration is size dt.\n", " dt = t / float(iterations + 1)\n", " displacement = np.zeros((dim, nnodes))\n", " for iteration in range(iterations):\n", " displacement *= 0\n", " # loop over rows\n", " for i in range(A.shape[0]):\n", " # difference between this row's node position and all others\n", " delta = (pos[i] - pos).T\n", " # distance between points\n", " distance = np.sqrt((delta ** 2).sum(axis=0))\n", " # enforce minimum distance of 0.01\n", " distance = np.where(distance < 0.01, 0.01, distance)\n", " # the adjacency matrix row\n", " Ai = np.asarray(A.getrowview(i).toarray())\n", " # displacement \"force\"\n", " Dist = k * k / distance ** 2\n", " if nohubs:\n", " Dist = Dist / float(Ai.sum(axis=1) + 1)\n", " if linlog:\n", " Dist = np.log(Dist + 1)\n", " displacement[:, i] += \\\n", " (delta * (Dist - Ai * distance / k)).sum(axis=1)\n", " # update positions\n", " length = np.sqrt((displacement ** 2).sum(axis=0))\n", " length = np.where(length < 0.01, 0.01, length)\n", " pos += (displacement * t / length).T\n", " # cool temperature\n", " t -= dt\n", " # Return the layout\n", " return dict(zip(G, pos))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "372cdeb4-c03a-82ca-b552-f2b2664938d0" }, "outputs": [], "source": [ "axis=dict(showline=False, # hide axis line, grid, ticklabels and title\n", " zeroline=False,\n", " showgrid=False,\n", " showticklabels=False,\n", " title='' \n", " )\n", "\n", "layout=Layout(title= \"Kaggle teams/users universe\", \n", " font= Font(size=12),\n", " showlegend=True,\n", " autosize=False,\n", " width=800,\n", " height=800,\n", " xaxis=XAxis(axis),\n", " yaxis=YAxis(axis), \n", " margin=Margin(\n", " l=40,\n", " r=40,\n", " b=85,\n", " t=100,\n", " ),\n", " hovermode='closest',\n", " annotations=Annotations([\n", " Annotation(\n", " showarrow=False, \n", " text='', \n", " xref='paper', \n", " yref='paper', \n", " x=0, \n", " y=-0.1, \n", " xanchor='left', \n", " yanchor='bottom', \n", " font=Font(\n", " size=14 \n", " ) \n", " )\n", " ]), \n", " )" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "1eb6bb20-7612-b220-edd8-eab2bc43fea3" }, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "", "KeyboardInterruptTraceback (most recent call last)", "<ipython-input-6-6d7e0aa528c7> in <module>()\n 2 G=nx.Graph()\n 3 G.add_edges_from(tm4graph.values[0:edges_to_use])\n----> 4 pos = forceatlas2_layout(G,iterations=300, nohubs=True) \n 5 N = G.number_of_nodes()\n 6 E = G.edges()\n", "<ipython-input-4-4902d6306182> in forceatlas2_layout(G, iterations, linlog, pos, nohubs, kr, k, dim)\n 53 for i in range(A.shape[0]):\n 54 # difference between this row's node position and all others\n---> 55 delta = (pos[i] - pos).T\n 56 # distance between points\n 57 distance = np.sqrt((delta ** 2).sum(axis=0))\n", "KeyboardInterrupt: " ] } ], "source": [ "edges_to_use = 22000\n", "G=nx.Graph()\n", "G.add_edges_from(tm4graph.values[0:edges_to_use])\n", "pos = forceatlas2_layout(G,iterations=300, nohubs=True) \n", "N = G.number_of_nodes()\n", "E = G.edges()\n", "labels = G.nodes()\n", "\n", "Xv_teams=[pos[k][0] for k in labels if \"Team\" in k]\n", "Yv_teams=[pos[k][1] for k in labels if \"Team\" in k]\n", "Xv_users=[pos[k][0] for k in labels if \"User\" in k]\n", "Yv_users=[pos[k][1] for k in labels if \"User\" in k]\n", "\n", "labels_team = [teammembers_cut.iloc[0:edges_to_use,:]\n", " .loc[teammembers_cut.TeamId_x==int(k.replace('Team_','')),'TeamName']\n", " .values[0] \n", " for k in labels if \"Team\" in k]\n", "labels_users = [teammembers_cut.iloc[0:edges_to_use,:]\n", " .loc[teammembers_cut.UserId_x==int(k.replace('User_','')),'DisplayName']\n", " .values[0] \n", " for k in labels if \"User\" in k]\n", "\n", "Xed=[]\n", "Yed=[]\n", "for edge in E:\n", " Xed+=[pos[edge[0]][0],pos[edge[1]][0], None]\n", " Yed+=[pos[edge[0]][1],pos[edge[1]][1], None] \n", " \n", "trace3=Scatter(x=Xed,\n", " y=Yed,\n", " mode='lines',\n", " line=Line(color='rgb(200,200,200)', width=2),\n", " name='Links',\n", " hoverinfo='none'\n", " )\n", "trace4=Scatter(x=Xv_teams,\n", " y=Yv_teams,\n", " mode='markers',\n", " name='Teams',\n", " marker=Marker(symbol='dot',\n", " size=[teammembers_cut.iloc[0:edges_to_use,:]\n", " .loc[teammembers_cut.TeamId_x==int(k.replace('Team_','')),'UserId_y']\n", " .values[0] \n", " for k in labels if \"Team\" in k], \n", " color='rgb(146,209,81)',\n", " line=Line(color='rgb(50,50,50)', width=0.5)\n", " ),\n", " text=map(lambda x: ['Team: '+u''.join(x[0]).encode('utf8').strip()\n", " + '<br>Users: '+str(','.join(x[1]).encode('utf8'))+'<br>'], \n", " zip(labels_team, [teammembers_cut.iloc[0:edges_to_use,:]\n", " .loc[teammembers_cut.TeamId_x==int(k.replace('Team_','')),'DisplayName'].values.tolist()\n", " for k in labels if \"Team\" in k])),\n", " hoverinfo='text'\n", " )\n", "trace5=Scatter(x=Xv_users,\n", " y=Yv_users,\n", " mode='markers',\n", " name='Users',\n", " marker=Marker(symbol='dot',\n", " size=[teammembers_cut.iloc[0:edges_to_use,:]\n", " .loc[teammembers_cut.UserId_x==int(k.replace('User_','')),'TeamId_y']\n", " .values[0]*0.5 \n", " for k in labels if \"User\" in k],\n", " color='#000000',\n", " line=Line(color='rgb(50,50,50)', width=0.5)\n", " ),\n", " text=map(lambda x: ['User: '+u''.join(x[0]).encode('utf8').strip() \n", " + '<br>Teams: '+str(','.join(x[1]).encode('utf8'))+'<br>'], \n", " zip(labels_users,[teammembers_cut.iloc[0:edges_to_use,:]\n", " .loc[teammembers_cut.UserId_x==int(k.replace('User_','')),'TeamName'].values.tolist()\n", " for k in labels if \"User\" in k])),\n", " hoverinfo='text'\n", " )\n", "\n", "data1=Data([trace3, trace4, trace5])\n", "fig1=Figure(data=data1, layout=layout)\n", "plotly.offline.iplot(fig1)" ] } ], "metadata": { "_change_revision": 56, "_is_fork": 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": 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0000/320/320908.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "0d435094-b5f8-6eab-a8ab-81e3dc6603e8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "genderclassmodel.csv\ngendermodel.csv\ngendermodel.py\nmyfirstforest.py\ntest.csv\ntrain.csv\n\n" } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "7fde0c9b-e34e-325d-9660-575190d523f6" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Survived</th>\n <th>Pclass</th>\n <th>Name</th>\n <th>Sex</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Ticket</th>\n <th>Fare</th>\n <th>Cabin</th>\n <th>Embarked</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>Braund, Mr. Owen Harris</td>\n <td>male</td>\n <td>22.0</td>\n <td>1</td>\n <td>0</td>\n <td>A/5 21171</td>\n <td>7.2500</td>\n <td>NaN</td>\n <td>S</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n <td>female</td>\n <td>38.0</td>\n <td>1</td>\n <td>0</td>\n <td>PC 17599</td>\n <td>71.2833</td>\n <td>C85</td>\n <td>C</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>1</td>\n <td>3</td>\n <td>Heikkinen, Miss. Laina</td>\n <td>female</td>\n <td>26.0</td>\n <td>0</td>\n <td>0</td>\n <td>STON/O2. 3101282</td>\n <td>7.9250</td>\n <td>NaN</td>\n <td>S</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " PassengerId Survived Pclass \\\n0 1 0 3 \n1 2 1 1 \n2 3 1 3 \n\n Name Sex Age SibSp \\\n0 Braund, Mr. Owen Harris male 22.0 1 \n1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n2 Heikkinen, Miss. Laina female 26.0 0 \n\n Parch Ticket Fare Cabin Embarked \n0 0 A/5 21171 7.2500 NaN S \n1 0 PC 17599 71.2833 C85 C \n2 0 STON/O2. 3101282 7.9250 NaN S " }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('../input/train.csv', header=0)\n", "df.head(3)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "ac6ce15d-5c22-edc5-e4a6-3e2d52a07173" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 12 columns):\nPassengerId 891 non-null int64\nSurvived 891 non-null int64\nPclass 891 non-null int64\nName 891 non-null object\nSex 891 non-null object\nAge 714 non-null float64\nSibSp 891 non-null int64\nParch 891 non-null int64\nTicket 891 non-null object\nFare 891 non-null float64\nCabin 204 non-null object\nEmbarked 889 non-null object\ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.6+ KB\n" } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "a02b5da9-8142-4183-c046-2b1102bb0178" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Sex</th>\n <th>Pclass</th>\n <th>Age</th>\n <th>Survived</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>33</th>\n <td>male</td>\n <td>2</td>\n <td>66.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>54</th>\n <td>male</td>\n <td>1</td>\n <td>65.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>96</th>\n <td>male</td>\n <td>1</td>\n <td>71.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>116</th>\n <td>male</td>\n <td>3</td>\n <td>70.5</td>\n <td>0</td>\n </tr>\n <tr>\n <th>170</th>\n <td>male</td>\n <td>1</td>\n <td>61.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>252</th>\n <td>male</td>\n <td>1</td>\n <td>62.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>275</th>\n <td>female</td>\n <td>1</td>\n <td>63.0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>280</th>\n <td>male</td>\n <td>3</td>\n <td>65.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>326</th>\n <td>male</td>\n <td>3</td>\n <td>61.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>438</th>\n <td>male</td>\n <td>1</td>\n <td>64.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>456</th>\n <td>male</td>\n <td>1</td>\n <td>65.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>483</th>\n <td>female</td>\n <td>3</td>\n <td>63.0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>493</th>\n <td>male</td>\n <td>1</td>\n <td>71.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>545</th>\n <td>male</td>\n <td>1</td>\n <td>64.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>555</th>\n <td>male</td>\n <td>1</td>\n <td>62.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>570</th>\n <td>male</td>\n <td>2</td>\n <td>62.0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>625</th>\n <td>male</td>\n <td>1</td>\n <td>61.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>630</th>\n <td>male</td>\n <td>1</td>\n <td>80.0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>672</th>\n <td>male</td>\n <td>2</td>\n <td>70.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>745</th>\n <td>male</td>\n <td>1</td>\n <td>70.0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>829</th>\n <td>female</td>\n <td>1</td>\n <td>62.0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>851</th>\n <td>male</td>\n <td>3</td>\n <td>74.0</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " Sex Pclass Age Survived\n33 male 2 66.0 0\n54 male 1 65.0 0\n96 male 1 71.0 0\n116 male 3 70.5 0\n170 male 1 61.0 0\n252 male 1 62.0 0\n275 female 1 63.0 1\n280 male 3 65.0 0\n326 male 3 61.0 0\n438 male 1 64.0 0\n456 male 1 65.0 0\n483 female 3 63.0 1\n493 male 1 71.0 0\n545 male 1 64.0 0\n555 male 1 62.0 0\n570 male 2 62.0 1\n625 male 1 61.0 0\n630 male 1 80.0 1\n672 male 2 70.0 0\n745 male 1 70.0 0\n829 female 1 62.0 1\n851 male 3 74.0 0" }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['Age']>60][['Sex', 'Pclass', 'Age', 'Survived']]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "37461b76-cd72-b73e-56a6-060d6f56df26" }, "outputs": [ { "data": { "image/png": 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B95Nd4es1o19CM/ouNYBm9NJFETP6Y4DnmdmxwHOBfcBVwLb689uANw15GSIiMoTWC727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\nde7+M5Z3hXe6+zP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"text/plain": "<matplotlib.figure.Figure at 0x7f858f91b6d8>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pylab as P\n", "df['Age'].hist()\n", "P.show()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "403d1842-4f21-13c9-19c6-3a1d6cad699a" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Survived</th>\n <th>Pclass</th>\n <th>Name</th>\n <th>Sex</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Ticket</th>\n <th>Fare</th>\n <th>Cabin</th>\n <th>Embarked</th>\n <th>Gender</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>Braund, Mr. Owen Harris</td>\n <td>male</td>\n <td>22.0</td>\n <td>1</td>\n <td>0</td>\n <td>A/5 21171</td>\n <td>7.2500</td>\n <td>NaN</td>\n <td>S</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n <td>female</td>\n <td>38.0</td>\n <td>1</td>\n <td>0</td>\n <td>PC 17599</td>\n <td>71.2833</td>\n <td>C85</td>\n <td>C</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>1</td>\n <td>3</td>\n <td>Heikkinen, Miss. Laina</td>\n <td>female</td>\n <td>26.0</td>\n <td>0</td>\n <td>0</td>\n <td>STON/O2. 3101282</td>\n <td>7.9250</td>\n <td>NaN</td>\n <td>S</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " PassengerId Survived Pclass \\\n0 1 0 3 \n1 2 1 1 \n2 3 1 3 \n\n Name Sex Age SibSp \\\n0 Braund, Mr. Owen Harris male 22.0 1 \n1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n2 Heikkinen, Miss. Laina female 26.0 0 \n\n Parch Ticket Fare Cabin Embarked Gender \n0 0 A/5 21171 7.2500 NaN S 1 \n1 0 PC 17599 71.2833 C85 C 0 \n2 0 STON/O2. 3101282 7.9250 NaN S 0 " }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Gender'] = df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n", "df.head(3)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "ea017a66-797d-329c-1175-4ec210f32427" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Survived</th>\n <th>Pclass</th>\n <th>Name</th>\n <th>Sex</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Ticket</th>\n <th>Fare</th>\n <th>Cabin</th>\n <th>Embarked</th>\n <th>Gender</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>Braund, Mr. Owen Harris</td>\n <td>male</td>\n <td>22.0</td>\n <td>1</td>\n <td>0</td>\n <td>A/5 21171</td>\n <td>7.2500</td>\n <td>NaN</td>\n <td>S</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n <td>female</td>\n <td>38.0</td>\n <td>1</td>\n <td>0</td>\n <td>PC 17599</td>\n <td>71.2833</td>\n <td>C85</td>\n <td>C</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>1</td>\n <td>3</td>\n <td>Heikkinen, Miss. Laina</td>\n <td>female</td>\n <td>26.0</td>\n <td>0</td>\n <td>0</td>\n <td>STON/O2. 3101282</td>\n <td>7.9250</td>\n <td>NaN</td>\n <td>S</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " PassengerId Survived Pclass \\\n0 1 0 3 \n1 2 1 1 \n2 3 1 3 \n\n Name Sex Age SibSp \\\n0 Braund, Mr. Owen Harris male 22.0 1 \n1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n2 Heikkinen, Miss. Laina female 26.0 0 \n\n Parch Ticket Fare Cabin Embarked Gender \n0 0 A/5 21171 7.2500 NaN S 1 \n1 0 PC 17599 71.2833 C85 C 0 \n2 0 STON/O2. 3101282 7.9250 NaN S 0 " }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(3)" ] } ], "metadata": { "_change_revision": 128, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/320/320942.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "a60e357e-1450-8a69-4431-d4ac2f51b812", "collapsed": true }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "3d20e958-0405-c4d2-b594-57a7d641d31c" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module has been deprecated in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n \"This module will be removed in 0.20.\", DeprecationWarning)\n" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import xgboost as xgb\n", "from scipy import sparse\n", "from sklearn.feature_extraction import FeatureHasher\n", "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n", "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, scale\n", "from sklearn.decomposition import TruncatedSVD, SparsePCA\n", "from sklearn.cross_validation import train_test_split, cross_val_score\n", "from sklearn.feature_selection import SelectPercentile, f_classif, chi2\n", "from sklearn.linear_model import LogisticRegression, SGDClassifier\n", "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n", "from sklearn.metrics import log_loss\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "e226c6e3-4017-eee8-778e-2062b0f13d05" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "# Read App Events\n" } ], "source": [ "##################\n", "# App Events\n", "##################\n", "print(\"# Read App Events\")\n", "app_ev = pd.read_csv(\"../input/app_events.csv\", dtype={'device_id': np.str})\n", "# remove duplicates(app_id)\n", "app_ev = app_ev.groupby(\"event_id\")[\"app_id\"].apply(\n", " lambda x: \" \".join(set(\"app_id:\" + str(s) for s in x)))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9f662b97-c722-85ba-f61c-785c885db26d" }, "source": [ "" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "91deb3f7-201a-a3c2-1fb2-33962f49a7e3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "# Read Events\n" }, { "name": "stdout", "output_type": "stream", "text": "# Read Events\n" }, { "ename": "NameError", "evalue": "name 'app_ev' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-4-33b6fb1ec59e> in <module>()\n 4 print(\"# Read Events\")\n 5 events = pd.read_csv(\"../input/events.csv\", dtype={'device_id': np.str})\n----> 6 events[\"app_id\"] = events[\"event_id\"].map(app_ev)\n 7 \n 8 events = events.dropna()\n", "NameError: name 'app_ev' is not defined" ] }, { "name": "stdout", "output_type": "stream", "text": "# Read Events\n" }, { "ename": "NameError", "evalue": "name 'app_ev' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-5-33b6fb1ec59e> in <module>()\n 4 print(\"# Read Events\")\n 5 events = pd.read_csv(\"../input/events.csv\", dtype={'device_id': np.str})\n----> 6 events[\"app_id\"] = events[\"event_id\"].map(app_ev)\n 7 \n 8 events = events.dropna()\n", "NameError: name 'app_ev' is not defined" ] } ], "source": [ "##################\n", "# Events\n", "##################\n", "print(\"# Read Events\")\n", "events = pd.read_csv(\"../input/events.csv\", dtype={'device_id': np.str})\n", "events[\"app_id\"] = events[\"event_id\"].map(app_ev)\n", "\n", "events = events.dropna()\n", "\n", "del app_ev\n", "\n", "events = events[[\"device_id\", \"app_id\"]]\n", "\n", "# remove duplicates(app_id)\n", "events = events.groupby(\"device_id\")[\"app_id\"].apply(\n", " lambda x: \" \".join(set(str(\" \".join(str(s) for s in x)).split(\" \"))))\n", "events = events.reset_index(name=\"app_id\")\n", "\n", "# expand to multiple rows\n", "events = pd.concat([pd.Series(row['device_id'], row['app_id'].split(' '))\n", " for _, row in events.iterrows()]).reset_index()\n", "events.columns = ['app_id', 'device_id']" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d609164d-eed4-66ac-acb5-701c7f5a635b" }, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "ef102a5f-75b0-14ff-79f0-9b4cbfffb424" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "c9c19dcd-c121-85b4-6b5b-490a78105f8c" }, "outputs": [], "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1e8b351f-db02-b67a-9cb2-1d4973bde186" }, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "7e3cbd54-b62b-4412-6a4d-beff4c2b9907" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "ab3d70d5-40b7-5ecf-e498-1aba00ceb2c6" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "b49c990b-b30a-2e24-fd4d-6b2c65bfe6be" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "8ed63ad5-f4ee-0138-a763-f5c80d92d18f" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "f03ac2da-2732-f4b8-46ec-5ca7199dfccc" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "23d97900-de22-9010-ea4d-8805936ac30e" }, "outputs": [], "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "85d6de4c-2f91-7cf4-53af-8833ae3fe5e6" }, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "16b86c6a-a539-bdce-1082-a0fc43152d47" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "9d2535e0-e1d9-0951-5fee-68d64163207f" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "73b8ee29-f65a-0491-0066-55946408c117" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "11a448d2-2087-670b-007d-ba1b46a79974" }, "outputs": [], "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "91b09a5c-2d06-a057-e01b-860629e24e64" }, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "a81e25f6-de66-9b0b-0b4e-3296c2c09b4d" }, "outputs": [], "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9f912d65-3b42-07a1-6c66-137606db9f17" }, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "a5a54a46-4323-40eb-e3e2-7669ff90532a" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "ee38bab9-1d80-2f86-dcf1-b0fbf6dee250", "collapsed": true }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "5674aef8-930b-d972-3fa8-62372478c50c" }, "outputs": [], "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1b7f1aa4-ce19-16ae-be40-7e6e18c6a013" }, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "89fdbb4d-acef-45ac-bc96-ba90f6b76260", "collapsed": true }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "a0a461b0-1f60-fdf6-ae55-525df33c7078" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "b34444a3-73d8-86e4-b8b7-ad6cfa4734dd" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "b78818a3-34a8-b3f8-58cc-05144fa4434a" }, "outputs": [], "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "ca180e69-6514-da42-0329-a94a8f5d3ca5" }, "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "00142261-d6f9-a026-4675-f98e4c35fe56" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "bb81818c-20a3-c7c3-dfd4-5ab8c4459469" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "0d3f2ebc-812b-eb79-1531-eb9191beec48" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "a486eff5-be37-c323-e37a-f73417abd94b" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "09d29732-71a6-40ac-7c67-ddc58bf95244" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "d2bae88c-0c8e-9efc-9b5b-56505d9d94a2" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "ff5dd07c-2bbb-bfe6-01e5-a30cb9b73f5f", "collapsed": true }, "outputs": [], "source": [ "" ] } ], "metadata": { "_change_revision": 37, "_is_fork": 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", 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0000/322/322308.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "45c72ca3-5ca3-b547-2a6c-7fc9f21dc434" }, "outputs": [], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "cfe7342b-326d-519d-223e-2e1c927ef86d" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2d893deba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "act_df = pd.read_csv('../input/act_train.csv',sep=',')\n", "\n", "sns.countplot(x='outcome',data=act_df)\n", "sns.plt.show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "ee68aaef-4d05-ca0a-6a21-afd6cf3b3299" }, "outputs": [ { "data": { "image/png": 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dAev/FclRDmZ2AuBtKVohs5vCdMoXzOwvZjY9tK0lOa3SPPFfJplZ8irwsLv/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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2dab4b9588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x='activity_category',data=act_df,hue='outcome')\n", "sns.plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "daeb7c8a-c478-a7b2-d90a-74b79ce1949a" }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "01482521-bd1a-adb7-b496-99181bb1d606", "collapsed": true }, "source": [] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "706d9a47-a9b3-d555-c1e6-08b06d74f490", "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "b60a3916-1fe7-dfb1-21d6-970b2b27a0f2" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 19, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/322/322326.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "3e63c197-0ba8-5dd4-17e2-2d9e871d1af7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "genderclassmodel.csv\ngendermodel.csv\ngendermodel.py\nmyfirstforest.py\ntest.csv\ntrain.csv\n\n" }, { "ename": "OSError", "evalue": "File b'titanic_train.csv' does not exist", "output_type": "error", "traceback": [ "", "OSErrorTraceback (most recent call last)", "<ipython-input-1-a15c4b7b29dd> in <module>()\n 14 # Any results you write to the current directory are saved as output.\n 15 \n---> 16 titanic = pd.read_csv(\"titanic_train.csv\")\n 17 print(titanic.describe())\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\n 560 skip_blank_lines=skip_blank_lines)\n 561 \n--> 562 return _read(filepath_or_buffer, kwds)\n 563 \n 564 parser_f.__name__ = name\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)\n 313 \n 314 # Create the parser.\n--> 315 parser = TextFileReader(filepath_or_buffer, **kwds)\n 316 \n 317 if (nrows is not None) and (chunksize is not None):\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)\n 643 self.options['has_index_names'] = kwds['has_index_names']\n 644 \n--> 645 self._make_engine(self.engine)\n 646 \n 647 def close(self):\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in _make_engine(self, engine)\n 797 def _make_engine(self, engine='c'):\n 798 if engine == 'c':\n--> 799 self._engine = CParserWrapper(self.f, **self.options)\n 800 else:\n 801 if engine == 'python':\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)\n 1211 kwds['allow_leading_cols'] = self.index_col is not False\n 1212 \n-> 1213 self._reader = _parser.TextReader(src, **kwds)\n 1214 \n 1215 # XXX\n", "pandas/parser.pyx in pandas.parser.TextReader.__cinit__ (pandas/parser.c:3427)()\n", "pandas/parser.pyx in pandas.parser.TextReader._setup_parser_source (pandas/parser.c:6861)()\n", "OSError: File b'titanic_train.csv' does not exist" ] }, { "name": "stdout", "output_type": "stream", "text": "genderclassmodel.csv\ngendermodel.csv\ngendermodel.py\nmyfirstforest.py\ntest.csv\ntrain.csv\n\n" }, { "name": "stdout", "output_type": "stream", "text": "genderclassmodel.csv\ngendermodel.csv\ngendermodel.py\nmyfirstforest.py\ntest.csv\ntrain.csv\n\n" }, { "name": "stdout", "output_type": "stream", "text": "genderclassmodel.csv\ngendermodel.csv\ngendermodel.py\nmyfirstforest.py\ntest.csv\ntrain.csv\n\n" }, { "ename": "OSError", "evalue": "File b'train.csv' does not exist", "output_type": "error", "traceback": [ "", "OSErrorTraceback (most recent call last)", "<ipython-input-4-7e4a3d2b6d5e> in <module>()\n 14 # Any results you write to the current directory are saved as output.\n 15 \n---> 16 titanic = pd.read_csv(\"train.csv\")\n 17 print(titanic.describe())\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\n 560 skip_blank_lines=skip_blank_lines)\n 561 \n--> 562 return _read(filepath_or_buffer, kwds)\n 563 \n 564 parser_f.__name__ = name\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)\n 313 \n 314 # Create the parser.\n--> 315 parser = TextFileReader(filepath_or_buffer, **kwds)\n 316 \n 317 if (nrows is not None) and (chunksize is not None):\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)\n 643 self.options['has_index_names'] = kwds['has_index_names']\n 644 \n--> 645 self._make_engine(self.engine)\n 646 \n 647 def close(self):\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in _make_engine(self, engine)\n 797 def _make_engine(self, engine='c'):\n 798 if engine == 'c':\n--> 799 self._engine = CParserWrapper(self.f, **self.options)\n 800 else:\n 801 if engine == 'python':\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)\n 1211 kwds['allow_leading_cols'] = self.index_col is not False\n 1212 \n-> 1213 self._reader = _parser.TextReader(src, **kwds)\n 1214 \n 1215 # XXX\n", "pandas/parser.pyx in pandas.parser.TextReader.__cinit__ (pandas/parser.c:3427)()\n", "pandas/parser.pyx in pandas.parser.TextReader._setup_parser_source (pandas/parser.c:6861)()\n", "OSError: File b'train.csv' does not exist" ] }, { "name": "stdout", "output_type": "stream", "text": "genderclassmodel.csv\ngendermodel.csv\ngendermodel.py\nmyfirstforest.py\ntest.csv\ntrain.csv\n\n" }, { "name": "stdout", "output_type": "stream", "text": " PassengerId Survived Pclass Age SibSp \\\ncount 891.000000 891.000000 891.000000 714.000000 891.000000 \nmean 446.000000 0.383838 2.308642 29.699118 0.523008 \nstd 257.353842 0.486592 0.836071 14.526497 1.102743 \nmin 1.000000 0.000000 1.000000 0.420000 0.000000 \n25% 223.500000 0.000000 2.000000 NaN 0.000000 \n50% 446.000000 0.000000 3.000000 NaN 0.000000 \n75% 668.500000 1.000000 3.000000 NaN 1.000000 \nmax 891.000000 1.000000 3.000000 80.000000 8.000000 \n\n Parch Fare \ncount 891.000000 891.000000 \nmean 0.381594 32.204208 \nstd 0.806057 49.693429 \nmin 0.000000 0.000000 \n25% 0.000000 7.910400 \n50% 0.000000 14.454200 \n75% 0.000000 31.000000 \nmax 6.000000 512.329200 \n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n RuntimeWarning)\n" } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output.\n", "\n", "# Looking at the data\n", "titanic = pd.read_csv(\"../input/train.csv\")\n", "print(titanic.describe())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "986fa832-49ac-74ab-825b-18592f2e9317" }, "outputs": [], "source": [ "# Missing data\n", "# Fill missing data with median\n", "titanic[\"Age\"] = titanic[\"Age\"].fillna(titanic[\"Age\"].median())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "6116ee47-4d1b-020d-81aa-df4889814a53" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": " PassengerId Survived Pclass Age SibSp \\\ncount 891.000000 891.000000 891.000000 891.000000 891.000000 \nmean 446.000000 0.383838 2.308642 29.361582 0.523008 \nstd 257.353842 0.486592 0.836071 13.019697 1.102743 \nmin 1.000000 0.000000 1.000000 0.420000 0.000000 \n25% 223.500000 0.000000 2.000000 22.000000 0.000000 \n50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n75% 668.500000 1.000000 3.000000 35.000000 1.000000 \nmax 891.000000 1.000000 3.000000 80.000000 8.000000 \n\n Parch Fare \ncount 891.000000 891.000000 \nmean 0.381594 32.204208 \nstd 0.806057 49.693429 \nmin 0.000000 0.000000 \n25% 0.000000 7.910400 \n50% 0.000000 14.454200 \n75% 0.000000 31.000000 \nmax 6.000000 512.329200 \n" } ], "source": [ "# Check that the Age column has been filled and now has 891 rows\n", "print(titanic.describe())" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "9297cdc8-14ee-8ad2-2656-37f514df2cdc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": " PassengerId Survived Pclass \\\n0 1 0 3 \n1 2 1 1 \n2 3 1 3 \n3 4 1 1 \n4 5 0 3 \n\n Name Sex Age SibSp \\\n0 Braund, Mr. Owen Harris male 22.0 1 \n1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n2 Heikkinen, Miss. Laina female 26.0 0 \n3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n4 Allen, Mr. William Henry male 35.0 0 \n\n Parch Ticket Fare Cabin Embarked \n0 0 A/5 21171 7.2500 NaN S \n1 0 PC 17599 71.2833 C85 C \n2 0 STON/O2. 3101282 7.9250 NaN S \n3 0 113803 53.1000 C123 S \n4 0 373450 8.0500 NaN S \n" } ], "source": [ "# Non-numeric columns\n", "# I want to see all the column names, numeric and non-numeric \n", "\n", "print(titanic.head())" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "2eba4f44-b4f1-a058-1179-0e8260f90c3b" }, "outputs": [ { "ename": "NameError", "evalue": "name 'count' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-9-f7cd7624f803> in <module>()\n 1 # I want to find how many values in the Cabin column are missing\n----> 2 print(count(titanic[\"Cabin\"]))\n", "NameError: name 'count' is not defined" ] }, { "name": "stdout", "output_type": "stream", "text": "204\n" }, { "name": "stdout", "output_type": "stream", "text": "204\n891\n" }, { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-12-4eb1ab1ea834>, line 3)", "output_type": "error", "traceback": [ " File \"<ipython-input-12-4eb1ab1ea834>\", line 3\n =\n ^\nSyntaxError: invalid syntax\n" ] }, { "name": "stdout", "output_type": "stream", "text": "204\n" } ], "source": [ "# I want to find how many values in the Cabin column are missing\n", "print(titanic[\"Cabin\"].count())\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "75229cb3-5d1b-778e-8a74-97e55ac77014" }, "outputs": [], "source": [ "# Converting the Sex column\n", "titanic.loc[titanic[\"Sex\"] == \"male\", \"Sex\"] = 0" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "271f8ae8-fd98-7294-d8f7-f70bc3c01a3c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "0 0\n1 female\n2 female\n3 female\n4 0\n5 0\n6 0\n7 0\n8 female\n9 female\n10 female\n11 female\n12 0\n13 0\n14 female\n15 female\n16 0\n17 0\n18 female\n19 female\n20 0\n21 0\n22 female\n23 0\n24 female\n25 female\n26 0\n27 0\n28 female\n29 0\n ... \n861 0\n862 female\n863 female\n864 0\n865 female\n866 female\n867 0\n868 0\n869 0\n870 0\n871 female\n872 0\n873 0\n874 female\n875 female\n876 0\n877 0\n878 0\n879 female\n880 female\n881 0\n882 female\n883 0\n884 0\n885 female\n886 0\n887 female\n888 female\n889 0\n890 0\nName: Sex, dtype: object\n" } ], "source": [ "# Checking if \"male\" has been replaced by 0\n", "print(titanic[\"Sex\"])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "1d5c2b6b-6a73-1d2f-cab6-706a95609595" }, "outputs": [], "source": [ "# Doing the same for female\n", "titanic.loc[titanic[\"Sex\"] == \"female\", \"Sex\"] = 1" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "5766a9b0-939b-ef95-ff70-f2a11bf79d14" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "0 0\n1 1\n2 1\n3 1\n4 0\n5 0\n6 0\n7 0\n8 1\n9 1\n10 1\n11 1\n12 0\n13 0\n14 1\n15 1\n16 0\n17 0\n18 1\n19 1\n20 0\n21 0\n22 1\n23 0\n24 1\n25 1\n26 0\n27 0\n28 1\n29 0\n ..\n861 0\n862 1\n863 1\n864 0\n865 1\n866 1\n867 0\n868 0\n869 0\n870 0\n871 1\n872 0\n873 0\n874 1\n875 1\n876 0\n877 0\n878 0\n879 1\n880 1\n881 0\n882 1\n883 0\n884 0\n885 1\n886 0\n887 1\n888 1\n889 0\n890 0\nName: Sex, dtype: object\n" } ], "source": [ "# Checking female has been replaced\n", "print(titanic[\"Sex\"])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "_cell_guid": "47b3c47f-faad-ed79-97b2-a12cbe0c96fd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "889\n" }, { "name": "stdout", "output_type": "stream", "text": "0 S\n1 C\n2 S\n3 S\n4 S\n5 Q\n6 S\n7 S\n8 S\n9 C\n10 S\n11 S\n12 S\n13 S\n14 S\n15 S\n16 Q\n17 S\n18 S\n19 C\n20 S\n21 S\n22 Q\n23 S\n24 S\n25 S\n26 C\n27 S\n28 Q\n29 S\n ..\n861 S\n862 S\n863 S\n864 S\n865 S\n866 C\n867 S\n868 S\n869 S\n870 S\n871 S\n872 S\n873 S\n874 C\n875 C\n876 S\n877 S\n878 S\n879 C\n880 S\n881 S\n882 S\n883 S\n884 S\n885 Q\n886 S\n887 S\n888 S\n889 C\n890 Q\nName: Embarked, dtype: object\n" }, { "name": "stdout", "output_type": "stream", "text": "889\n" }, { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-21-e05c642dc401>, line 4)", "output_type": "error", "traceback": [ " File \"<ipython-input-21-e05c642dc401>\", line 4\n print(titanic[])\n ^\nSyntaxError: invalid syntax\n" ] }, { "name": "stdout", "output_type": "stream", "text": "889\n" }, { "ename": "AttributeError", "evalue": "'Series' object has no attribute 'distinct'", "output_type": "error", "traceback": [ "", "AttributeErrorTraceback (most recent call last)", "<ipython-input-23-c2d2208c0218> in <module>()\n 2 # First how many of the values are \"Nan\"?\n 3 print(titanic[\"Embarked\"].count())\n----> 4 print(titanic[\"Embarked\"].distinct())\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py in __getattr__(self, name)\n 2670 if name in self._info_axis:\n 2671 return self[name]\n-> 2672 return object.__getattribute__(self, name)\n 2673 \n 2674 def __setattr__(self, name, value):\n", "AttributeError: 'Series' object has no attribute 'distinct'" ] }, { "name": "stdout", "output_type": "stream", "text": "889\n['S' 'C' 'Q' nan]\n" } ], "source": [ "# Converting the Embarked column \n", "# First how many of the values are \"Nan\"?\n", "print(titanic[\"Embarked\"].count())\n", "print(titanic[\"Embarked\"].unique())" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "_cell_guid": "4aacf763-6f40-ab6e-b6a9-1f4d1bbf54b1" }, "outputs": [ { "data": { "text/plain": "0 S\n1 C\n2 S\n3 S\n4 S\n5 Q\n6 S\n7 S\n8 S\n9 C\n10 S\n11 S\n12 S\n13 S\n14 S\n15 S\n16 Q\n17 S\n18 S\n19 C\n20 S\n21 S\n22 Q\n23 S\n24 S\n25 S\n26 C\n27 S\n28 Q\n29 S\n ..\n861 S\n862 S\n863 S\n864 S\n865 S\n866 C\n867 S\n868 S\n869 S\n870 S\n871 S\n872 S\n873 S\n874 C\n875 C\n876 S\n877 S\n878 S\n879 C\n880 S\n881 S\n882 S\n883 S\n884 S\n885 Q\n886 S\n887 S\n888 S\n889 C\n890 Q\nName: Embarked, dtype: object" }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 2 values are missing so will convert those to S\n", "# Converting S to 0, C to 1, and Q to 2\n", "# Need to save the results or else the filled values aren't saved\n", "\n", "titanic[\"Embarked\"] = titanic[\"Embarked\"].fillna(\"S\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "_cell_guid": "ef57ffb8-2d57-7fcf-ecac-94ba41d2dce1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "889\n" }, { "name": "stdout", "output_type": "stream", "text": "['S' 'C' 'Q' nan]\n" }, { "name": "stdout", "output_type": "stream", "text": "['S' 'C' 'Q']\n" } ], "source": [ "# Checking that there are no \"nan\" values any more\n", "print(titanic[\"Embarked\"].unique())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "4409f26a-d953-8d1d-e597-9c3548a6ae2f" }, "outputs": [], "source": [ "# Converting \n", "titanic.loc[titanic[\"Embarked\"] == 'S'] = 0\n", "titanic.loc[titanic[\"Embarked\"] == 'C'] = 1\n", "titanic.loc[titanic[\"Embarked\"] == 'Q'] = 2\n", "print(titanic[\"Embarked\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "6df5a6d7-0155-2ff3-f6b1-4e170d3c0ea8" }, "outputs": [], "source": [ "# Linear regression for survival predictions\n", "# import linear_model and cross_validation\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.cross_validation import KFold\n", "\n", "# columns to use in linear regression prediction\n", "predictors = [\"Pclass\", \"Sex\", \"Age\", \"Sibsp\", \"Embarked\", \"Fare\"]\n", "# First I will check how just Age can be used to predict survival\n", "\n" ] } ], "metadata": { "_change_revision": 924, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/322/322480.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "98f8f786-ed56-210b-12b9-06ab4ee41d94" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "12492e4d-5f66-b128-384b-aceb6dc55e81" }, "outputs": [], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "551a429c-8526-cbe7-2c4a-dbfa5ab1d34a" }, "outputs": [], "source": [ "train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])\n", "test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])\n", "ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])\n", "\n", "df_train = pd.merge(train, ppl, on='people_id')\n", "df_test = pd.merge(test, ppl, on='people_id')\n", "del train, test, ppl" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "7f9f089c-c992-e613-86a6-fa3065c0cf9f" }, "source": [] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "76de3985-4ec8-9a33-e29e-a3ad7f1eb5db" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start of date_x: 2022-07-17\n", " End of date_x: 2023-08-31\n", "Range of date_x: 410 days 00:00:00\n", "\n", "Start of date_y: 2020-05-18\n", " End of date_y: 2023-08-31\n", "Range of date_y: 1200 days 00:00:00\n", "\n" ] } ], "source": [ "for d in ['date_x', 'date_y']:\n", " print('Start of ' + d + ': ' + str(df_train[d].min().date()))\n", " print(' End of ' + d + ': ' + str(df_train[d].max().date()))\n", " print('Range of ' + d + ': ' + str(df_train[d].max() - df_train[d].min()) + '\\n')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b8534be0-0f9b-d713-467a-32895e6a3037" }, "source": [] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "2793bc37-e9a8-b2b2-0e7b-84eb5255c07d" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f386db0b7b8>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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IotDQgpgRdf75dgMbFG9FlCOGlQMAACC6+vpskFMouIlaRRRBFFAZgig0tCBa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\nHFF57z/kZMO0wvKy10s5+XsR7xkomlfz1LwqjqitLeDznwe+4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46/X98RRWXl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5JucxJo5r6ogilgFViCKHynIjHVF1C1Fnz4ppjeSgaR8UzSsGvcWJpSGIZiNlKmU7olzmlnJE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VFEiYO36wvqYTKYcLLvZPJrXcNd+NF7P8LxnuOy1xH/nbfMvwUvnXspbT3meR4vX3gZt86/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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f386da8a0b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "date_x = pd.DataFrame()\n", "date_x['Class probability'] = df_train.groupby('date_x')['outcome'].mean()\n", "date_x['Frequency'] = df_train.groupby('date_x')['outcome'].size()\n", "date_x.plot(secondary_y='Frequency', figsize=(20, 10))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "fef5b0e8-22ec-8baf-8b75-f58519e585a7" }, "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "000dd8bb-15ca-5d5f-eaca-260becae2e06" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f386e248cf8>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Vr3wF5eXlePzxxx2t/9BDD+HJJ5/Eiy++iObmZmzYsAFSyr5E3Ycccggef/xxbN++HTNm\nzMDMmTMBAJWVlbj55puxbt06LF26FLfeeiv++c9/pjzOpk2b+l5v3LgRu+66a9/75HC4XXfdFXV1\ndX3v6+rqUFpaipqaGtv9XXjhhdh7772xbt06NDc347rrrhuUdNyqLE4wC98rLy/HzJkz8eCDD2Lx\n4sV0QxFCiEvicQpRhJDCgY4oQrwTjVKIygTxOIUokoLq6mosWLAAc+fOxRNPPIGuri7EYjE888wz\nuPLKKwet397ejvLycowcORIdHR34xS9+0Se0RKNRLFmyBK2trSguLkZVVVVfzqOnn34a69atAwBU\nVVWhpKTEMhzwt7/9LZqbm7Fp0ybcfvvtOOOMM1Kue+aZZ+J3v/sdNmzYgPb2dlx11VU444wz+vYv\npcRvfvMbdHV14aOPPsKf/vSnvv21tbWhuroaFRUVWLNmDe655x5PZTGjpqYGjY2NAxKoA8CsWbNw\n//3348knn6QQRQghLqmvV40cClGEkEKAjihCvENHVPBISSGK2DBv3jzceuutuPbaazFu3DhMmjQJ\nd999t2kC83PPPReTJk1CbW0t9ttvPxxxxBEDPn/wwQcxZcoUjBgxAn/4wx+wZMkSACoE7hvf+Aaq\nqqpw5JFHYu7cuZYz582YMQOHHHIIDj74YJx88smYM2dOynXnzJmDWbNm4Wtf+xqmTp2KiooK3HHH\nHX2fCyFw9NFHY9q0afjmN7+JK664AsceeywA4Oabb8ZDDz2E6upq/PjHPx4kMgkhHJclVeLyPffc\nE2eeeSZ23313jBo1Clu2bAEAHHHEESgqKsLBBx88KNyQEEKIM9auBaZNoxBFCCkM6IgixDt0RAWP\nzvxTqEKUSA6zCvRgQkiz4wkhBoV7kdQUFRVh7dq12H333cMuSuAce+yxOPvssy2FtmR4PRFCSD/3\n3Qc8/TTw/PNAkvGUEELyjpUbVuK7j3wXjVc0hl0UQnKWq68GHn0U+PjjsEuSv0QiQHk58Le/Ad/5\njj/7TPSDXWbHzix0RJGs5Y033sA777yD73//+2EXhRBCcpa1a4G996YjihBSGPRKOqII8QodUcHT\nm6imCtURRSEqB0kV4pZPnHfeeTjuuONw++23o7KyMuziEEJIzrJ2LbDXXhSiCCGFQW+cOaII8Qpz\nRAVPoQtRJWEXgKRPb2/+P1zvv//+sItACCF5wbp1Sojq7VWJMQtgLIMQUsDQEUWId+iICp5CF6Lo\niCKEEELyFCn7k5WXlNAVRQjJf+iIIsQ72hHFtLvBQSGKEEIIIXnJ9u1AaSkwcqT6X6iNHUJI4UBH\nFCHeiUaVCNXTE3ZJ8hcKUYQQQgjJS9atU24oQAlRdEQRQvIdOqII8U4kov4zPC84KEQRQgghJC/R\nYXkAhShCSGGgRai4jIdcEkJyF91eoBAVHBSiCCGEEJKXUIgihBQaOiyP4XmEuIeOqOChEEVImpx1\n1llYunRpys+XLFmCb33rW472tWjRIhx11FGOjx2JRLD33nujsbHR8TYkM6xbByxfHnYpCCFG1q4F\npk5VrylEEUIKAe2IYngeIe6hIyp4KEQRW3bbbTdUVFSguroaVVVVqK6uxpYtW8IuVih88MEHeP/9\n93HKKaekXOess87Cs88+63ifwmIu8WOOOQYLFy7se19WVobzzz8fN9xwg+P9k8ywciXwwANhl4IQ\nYoQ5ogghhQYdUYR4RzuiurrCLUc+QyGK2CKEwNNPP43W1la0tbWhtbUVu+yyy6D1envz/4F37733\n4uyzz075eSbOwZlnnolFixYhyh5VVhGJFG5FSki2wtA8QkihQUcUId6JRgEh6IgKEgpRxBFSykHL\n6urqUFRUhIULF2Ly5Mk49thjAQCvvvoqjjzySIwcORIHHXQQVq5c2bfNhg0bMH36dAwfPhzHH388\nLrnkEsyaNQsAsHLlSkycOHHAMaZMmYIXX3yxrww33ngjpk2bhrFjx+KMM85Ac3PzgLI88MADmDx5\nMsaNG4frr7++bz/xeBzXX389pk2bhurqahx22GHYvHkzLr74YvzsZz8bcMwZM2bg9ttvNz0Pzzzz\nDI4++ui+94sWLcJXv/pVzJs3D2PGjMGCBQsGhdstX74ce+21F0aOHIm5c+di+vTpA1xOUkpcfvnl\nGDVqFKZOnYrnnnsOAPCrX/0KL7/8Mi6++GJUV1fj0ksvBQDU1tZi1KhRePXVV03LSMKBQhQh2UUk\nArS0AGPHqvcUogghhYB2QsXibJQQ4pZIBKiuphAVJBSiiGdeeuklrFmzBs899xzq6+tx0kkn4eqr\nr0ZTUxNuvvlmnHbaaX05jc466ywcdthh2LFjB371q19h0aJFA0LTrMLU7rjjDixduhQvv/wy6uvr\nMXLkSFx00UUD1vn3v/+NTz/9FM8//zx+/etf45NPPgEA3HLLLfjLX/6CZ599Fq2trVi4cCEqKiow\ne/ZsPPzww33bNzY24oUXXjB1PXV2dmL9+vXYc889Byx/7bXXMG3aNGzbtg1XXXXVgO+xY8cOnH76\n6bjpppvQ2NiIPffcE6tWrRq0vc77dPnll2POnDkAgGuvvRZHHXUU7rzzTrS2tuKOO+7o22avvfbC\ne++9l/JckcxDIYqQ7KK7GxgyRI1oAhSiCCGFQZ8jiqF5hLgmGgVGjKAQFSQUoogjTj31VIwaNQqj\nRo3Cd7/73b7lQggsWLAAQ4cORXl5ORYvXowTTzwRxx9/PADg2GOPxaGHHoply5Zh06ZNePPNN/Hr\nX/8apaWlOOqoo3DyySc7LsO9996L6667DuPHj0dpaSmuvvpqPProo4jH431lmT9/PsrKyvDFL34R\nBxxwQJ9Yc9999+G6667DtESMxv7774+RI0fisMMOw/Dhw/HCCy8AAB5++GFMnz4dY8aMGXT85uZm\nCCFQVVU1YHltbS0uuugiFBUVoby8fMBnzzzzDPbbbz/MmDEDRUVFuPTSS1FTUzNgnd122w1z5syB\nEAKzZ89GQ0MDtm3bZnkuqqqq+txgJDugEEVIdtHTAxirZApRhJBCQDuhGJpHiHsiEWD4cApRQVLo\nQlRJ2AVwiliQ2inkFHnN4PA6pzzxxBM45phjTD+bMGFC3+u6ujo88sgjePLJJ9UxpUQsFsPXv/71\nPhfT0KFD+9afPHkyPv/8c0dlqKurw3e+8x0UFRX17bu0tBRbt27tW8co8lRUVKC9vR0AsGnTJuy+\n++6m+z333HOxePFiHHvssVi8eDF+8pOfmK43YsQIAEBbWxtGjx7dtzw5nNBIfX39oM+N5wvAgHxb\n+ty0t7dj3LhxKffb1tbWVx6See6/H5g9u99pAagObqFWpIRkIxSiCCGFCJOVE+KdaBQYPZpCVJBQ\niMoRvIhIvhzfJEeUxhhON3HiRJx77rm49957B623ceNGNDU1oaurq09w2bhxY5+wVFlZiU7D3d7b\n24vt27f3vZ80aRIWLlyIr3zlK4P2XVdXZ1n+iRMnYt26ddhnn30GfXbOOedg//33x/vvv481a9bg\n1FNPNd1HRUUFpk6div/85z8DymAVTjh+/HgsXbp0wDKnwpvVvlevXj0otxXJDFIC558PnHYaYDTH\n0RFFSHZBIWogp58O/PGPaoSXEJK/MFk5Id6hIyp4Cl2IYmieR5IFqnPOOQdPPvkkli9fjng8ju7u\nbqxcuRL19fWYNGkSDj30UFxzzTWIRqP417/+1eecAoA99tgD3d3deOaZZxCLxXDttdcioufOBPDj\nH/8Yv/zlL7Fx40YAwPbt2weIPFZi2QUXXID//u//xtq1awEAH3zwAZqamgCo0LpDDz0Us2bNwmmn\nnTYovM7ICSecMCD5uh0nnngiPvzwQyxduhS9vb248847Bzi47KipqcFnn302YFl9fT2amprw5S9/\n2fF+iH90dwPxeP+0rhoKUYRkFz09QFlZ//uSksIWolauBBKPPUJIHkNHFCHeYY6o4Elk1ynY/hOF\nKAdYOX6SP5swYQKeeOIJXH/99Rg7diwmT56Mm2++uS+P00MPPYRXX30Vo0ePxm9+8xvMnj27b9vq\n6mrcfffdOP/88zFhwgRUVVUNCGO77LLLMGPGDBx33HEYPnw4jjjiCLz++uspy2J8P2/ePMycObNv\n2wsuuABdXV19n8+ePRsffvghzj33XMtz8cMf/hCLFy+2XMfI6NGj8de//hWXX345xowZgzVr1uDQ\nQw+1FLuM5b7sssvw17/+FaNHj+4LGXzooYcwe/ZslJaWOi4H8Y9EtCeFKEKyHDqiBtLT0z/6SAjJ\nX+iIIsQ7dEQFT6E7ooSVi8b3gwkhzY4nhLB08+QzCxYswLp16/DAAw+EWo6XX34Zs2bNwoYNG2zX\nPeecczBz5kyccsopaR9HSokJEyZgyZIlOProo9PePhKJ4MADD8RLL71kmlAdKOzrKROsXw/svrv6\nv9tu/csvvBB45x3g1VdDKxohxMCrrwKXXgro8YoZM4Af/ABIEX2d9wwZArz3HpA08SshJM+44eUb\n8MsXf4k1c9dgzzG84Qlxw7hxwI9/DDQ3A7//fdilyU9eegk4+mjVNlu40J99JvrB3pNrZ4CcyRFF\ngiMajeL222/HD3/4Q0frp+OIAoDly5fjS1/6EoYMGYLf/va3AOA6rK6srAwff/yxq22JP9ARRUhu\nQEdUP1LSEUVIoUBHFCHe0Y6o+vqwS5K/FLojiqF5Bc6aNWswcuRIbN26FZdddlkgx1i1ahWmTp2K\ncePG4emnn8YTTzxhGZpHshsKUYTkBpFI7ghRb7wR7P7196YQRUj+wxxRhHgnGmVoXtAUuhBFR1TI\nXHPNNaEef6+99kK7VhYC4pprrgn9exL/oBBFiHc2bQKuvRYwmWDVN3LFEdXcDBx7LNDaGtwxenrU\nf9ZRhOQ/dEQR4p1IhMnKg6bQhSg6ogghaUEhihDvfPwx8MorwR4jV4SoSGRwfeI33d3qPx1RhOQ/\ndEQR4g0pVZuejqhgoRBFCMkZHn4YePLJcMughSjtMNBEo4VbkRKSLlu2BC+K5IoQFY0GL0Tp+opC\nFCH5Dx1RhHgjGgVKSoDKSgpRQUIhygFCiG8JIdYIIf4jhPi5yefVQoilQoh3hRAfCCHO872khBC8\n+aaa9SlMOjrUfzqiCHFPQ0Pw90uuCFGxmBp9DVIkYmgeIYUDHVGEeCMaVW2GoUMpRAVJb68S/DLV\nNhFCFAkh3hZCLE28HymEWC6E+EQI8ZwQYrhh3V8IIT4VQqwWQhxnWH6wEOL9hC50m2F5mRDi4cQ2\nq4QQk+zKYytECSGKANwJ4HgA+wI4UwixV9JqcwF8JKU8EMAxAG4RQjD/FCE+E4mEP6LP0DxCvNPQ\nQEeURpcpyLLREUVI4UBHFCHeiESAsjKgooJCVJD09qp2Wgb7T5cBME4/fyWA56WUewJ4EcAvAEAI\nsQ+AmQD2BvBtAHcLIURim3sAnC+l3APAHkKI4xPLzwewU0r5BQC3Afgfu8I4EYsOB/CplLIuUbCH\nAcwAsMawjgRQlXhdBaBRSun4lE6ePBn9340Qb0yePDnsIgRGNog9FKII8Q4dUf3o8xCJAEOGBHMM\nClGEFA50RBHiDe2IohAVLJkUooQQEwCcAOA6APMSi2cAODrxehGAFVDi1CkAHk7oORuEEJ8COFwI\nUQegSkqp5zp+AMCpAJ5L7EvPTvYolJHJEidCVC2ATYb3n0OJU0buBLBUCFEPYBiA7zvYbx8bNmxI\nZ3VCCpZoNPyOFIUoQrxDR1Q/mXREsY4iJP+JxdWNTkcUIe6gIyozZNgR9TsAlwMYblhWI6XcCgBS\nyi1CiHGJ5bUAVhnW25xYFoPSgjSfJ5brbTYl9tUrhGgWQoySUu5MVSC/kpUfD+AdKeWuAA4CcJcQ\nYphP+yaEJMim0LzkZOUUoghxDh1R/RgdUUFBRxQhhUNfaB4dUYS4go6ozJApIUoIcSKArVLKdwFY\nhaFJPw9rt4ITR9RmAMZkUxMSy4z8AMANACClXCeEWA9gLwBvJu9s/vz5fa+nT5+O6dOnOygC8UJT\nE/DXvwI/+lHYJSFeyQaxp71djZLQEUWIO6RUQtTQocEex0yI6u4O9phuYI4oYmTRIuALXwCOOCLs\nkpBcpS9PReafAAAgAElEQVQ0j44oQlyhHVFDhqjnZzwOFPllXyF9+CFErVixAitWrLBb7UgApwgh\nTgAwFECVEOJBAFuEEDVSyq1CiF0AbEusvxnARMP2Wv9Jtdy4Tb0QohhAtZUbCnAmRL0BYJoQYjKA\nBgBnADgzaZ06AN8A8G8hRA2APQB8ZrYzoxBFMsOaNcBdd1GIygeyJTRv1ChzISrsshGSC7S3qxHG\nsrJgj9PTA1RV9b/PdkcUQ/MIADzzjLpHKEQRt9ARRYg3tCOqqEiJUV1dQGVl2KXKP/wQopKNPQsW\nLBi0jpTylwB+CQBCiKMB/FRKOUsI8T8AzgNwE4DZAJ5IbLIUwENCiN9BhdxNA/C6lFIKIVqEEIdD\naUTnArjDsM1sAK8BOB0q+bkltkJUIsbvYgDLoUL57pNSrhZC/Fh9LP8A4FoA9wsh3k9sdoWdAkYy\nR09Pdo6Ck/TJBtdRKiEqGg2/bITkAg0NwLhxwdvdcyU0T5eJoXkEAJqb2WYh3uCseYR4IxrtHyzT\n4XkUovxHC1EhPvNuBPCIEGIOlLFoJgBIKT8WQjwCNcNeFMBFUkodtjcXwP0AhgBYJqV8NrH8PgAP\nJhKbN0KZlyxx4ohC4gB7Ji271/C6ASpPFMlCKETlD9ngOrJyRMViKuyIk2ASkpqGBmDCBGD16mCP\nk2tCFEPzCKDSCSTnICQkHThrHiHeiERUmwFgnqgg0UKUzr+bCaSUKwGsTLzeCRXVZrbeDUikXkpa\n/haA/U2W9yAhZDmF0Z4FAIWo/CEbXEft7cDo0ebJygEVR04ISU1DAzBxIpOVaxiaR4w0N1OIIt6g\nI4oQb5g5ooj/ZHjWvKyDQlQBQCEqf8gGR1RHR2pHFFC4lSkhTtGOqKDv5UgkN4QohuYRI3REEa/Q\nEUWIN5IdUV1d4ZYnX6EQRfKeSMS7ENXcDGxOniuRZJxsyRE1cqS5EFVaGn75CMl2GhqA2lrlHpR+\nTpSbBB1R/VCIyg2kpCOKeKdX9qK0qJSOKEJcQkdUZqAQRfKenh4lEngJmXrwQeCGQVGiJNNk66x5\n8biqRIcMKdzKlBCnbNkCjB8PFBcHez/nihCVSUcU66fsprNTXQ8UoogXeuO9KCsuoyOKEJdY5Yha\ntkxFRxDvxOMUokieoxt0Xhp27e3Z2YEpNMJ2RMViqgzDhw/sNOqREzqiCLGnoaFfiAryfskVIYqO\nKKJpalL/KUQRL/TKhBBFRxQhrrByRP30p8AHH4RTrnxDO6KysW2WCShEFQC6QeclPK+zkwJDNhB2\njqiODmDYMFVpGjsKkYh6YJWU8DohxA4tRJWU0BEFcNY80k9zs/pPIYp4gY4oQrxh5YhqaGBb3y8Y\nmkfyHgpR+UPYjqj2diVElZUNdERRiCLEOXREDYSheURDRxTxAzqiCPFGKkdUVxfQ0sJnqV/09qrz\nXKjns+CEqH/8o3/ErVDQjXsKUblP2DmiUglR+oFFISp45s1TjQAntLXRAZJt9PSo32X0aDqiNJkK\nzRPC+/nOxvOXT9ARRfyAjihCvJHKEdXQoP6zre8PdEQVGNddB/z732GXIrP45YhiAzx8stkRVVpK\nISoTPPoosGmTs3Vnz1ZJJUn2sGULMG4cUFRER5QmU46oigpvQtT27cAXv+hfmchgmpqAMWMoRBFv\nxOIxOqJcEuRMriR3SOWI0kJUNrYlchEKUQVGJNJv/S4U/BCiOjoK9ybJJsLOEcXQvPCJRp3PVrJu\nHWc2yTb0jHkAHVGaTDmiKiq81U+trcDOnf6ViQymuRnYZRcKUcQbvbIX5SXldES54EtfAurqwi4F\nCRs6ojKDUYgqRBG4IIWoQgvNY46o/CEazQ5HFJOVh0ckon4HJ2zcmJ3CQyGj80MBwd8vuSJEZSpZ\nuVdHVNiO1EKgqUkJUV7aK4T0hebREZU227ZRcCf2jig+C/2ht1e1zYqKgHg87NJkHgpRBQCFqPxA\nyvAdUXrWPDqiwsOpI6qtTdV1ufB7NDUVzgPYKERlIjRPNySB8ISoeNy63tLnIOjQvMpKb/VnTw9z\nrgVNczNQU0NHFPFGX7LyPHBE/exnwNatmTteTw/vP0JHVKbo7VVtwULtPxWkEFVooXlMVp4f9PYq\nMSrs0LzKSgpRYeJUiNJ5pLLRAZPMrFnA88+HXYrMkOyIymRoXlj35z33AAsWpP48k44oL9+fjqjg\n0Y4odoSJF/LJEfXUU87zQvpBdzcdiYSOqExBIarAoCPKHRSiwkd30rIhNI9CVHg4Dc3buFH9z4Xf\no7MzsyO+YdLQoDraQOEkK9+82XoAKJM5ohial93QEUX8IJ8cUV1dwbpFk+nu5v1HrB1Ro0blxiBn\nLkAhqsAoREdUT4+6wClE5Ta6IRK2I4pCVHjE4+rPiSNKC1G50FiIxYDGxrBLkRky5YiScuCIJhCe\nENXcbN2RikZV2XIhNM9N/SYl8MEH7o9bSNARRfwgnxxRXV2Zq7elZGieH2zdmvth3Mb2w9ChA4Wo\nSZPY1vcLClEFRqE6ooYPpxCV6+hOWjY4opisPBx0Y5RCVO5inDUvSEeUHs0sMjzls1mIqqgI3hFV\nWek9NE+HSKdDXR1wzDHuj1tIcNY8YmTrVuAb30h/u3xzRGWq3o5GVf3G0DxvzJoFrFwZdim8kcoR\ntWULMHGif22XNWuAGTP82VcuQiGqwKAQ5Y7Oztzo0OYz+vxnoyNKj5wUakWaKfQ5dxKat2kTMG5c\nbvwesVjhzNKTKUdUclgekL1CVCymGrpBO6K8huZpcSTdxPpdXUpobWtzf+xCoamJoXmkn6Ym4PXX\n098u3xxRmQrN0/cd7z9vNDXlvphnliMqFlPfbfx4/9qWDQ39A6eFCIWoAiPfQ/POPhtYv37gMq9C\nlJR0RGUD2eSIMgvNKy0t3Io0U6TriJo6NTcE5EJxREmppsauqVHvg3RE5ZIQlUlHlNccUUD6v5ne\nrq7O/bFTceONwIYN/u83LOiIIkZiMSXgdnWlt12+OKKiUVVnZare1v0E3n/eaGvL/bawmSNq61Zg\nzBjVtvDr+2XS8ZeNUIgqMPLdEfXeeyoxrJFIxJsQpR+EhXiDZBPMEUX0w9ppsvLdd8+N36NQHFGR\nCCBE/yhjITmirI6rHVG5MGsekP4+dKfObyGqvV3NRvj22/7uNyxiMdXZGT1anet0QyBJ/qHvtW3b\n0tuuN96LsqLcd0TpdnumHVG57uYJm/b23BdXzBxR2tFdUuLf9yt0owOFqAKit1dZ6tva0rfW5wo9\nPYMfIF4dUTouuBBvkGxCPxSy1RFFISp49Dm3c0TF48DnnwNTpuRGY6hQHFFdXSrpp6ZQHFFNTfaO\nqKFDcyc0z60jym/n0rJl6rmebic9W2lpAaqr1X0RdPJ6khu4FqLyxBGlnWB0ROUW+eqI0rP++tnW\npyOKQlTBEI2qhnllJdDaGnZpgqGnZ7CFmUJUfhCJeO9IeYXJysPFaWjetm3AiBFAVVVuPOALxRHV\n2anuYU0hOaKc5IjK19A8XVf6LUQ99hgwYYIKl8gHmpqAkSPV6+RnDClM9L2W7jWeLzmidHuejqjc\nQUrVVs71trCVI6q01L/vV+g5iClEFRC6szxiRP6G56VyRFVXu3+wdHQEO3JPnBGJKNdAmL9DR4cS\novRotQ6doBCVGZwmK9+4Uc1qkiu/R6E4opKFqEw7ooqL1f9Mitnd3erPSY6oTDiivJxv3UlL9/wF\n4Yjq7ASefRaYMyd/HFHNzap9Bqhrl51h4sURVV5Sjlg8Bx6AFtARlXt0dipXeq6LK6kcUTo0j0KU\nP1CIKiAKRYhK5YhKN9mjprNTbV+IN0g2ocNXwnZEVVaqStPYiaYQlRmcOqI2bgQmTQrPAZMuvb3K\nyp4LZfVCcmheph1RQOaviZYW9b/QHVHjxvmbI+q554BDDwX23Td/hCijI2rIEHaGiQ+OKIbmpUU+\nzZq3YwewfXvmj6sHCnO9LWx0RJWVqWfnpk3+C1FdXbl/rrwQj1OIKhh0Z3nkyPydOc/MEeU1WXln\np3JUFeINkk1kgyNKh+YBA/NEUYjKDLph4FSIypXfQ5cx38PzwnZEAYOFqGg02JyJetAnzFnzdH7I\n8vLwZs3bc8/BjqjHH1eCkhseewz43vfUDIz5EpqX7IjKh84w8YZbR1QsHkN5cTlD86DqvIsvdrau\n7ifkgxvxjjuAW2/N/HHb2tT/XGh7WWF0RAmhntHr1gWTrDzfByGtMDqiCvE8FKQQla+OKCmDyxFV\nXV2YN0g2kU05ogAKUWEQiSgh3S40b9Om3HJExWKqXi40ISobHFGXXgo8+mgwZQDUs9YuEXnQoXn6\nXHitn9wmK+/pUbmc2toGisiLFgEPPeSuHE8/DXznO8pplY+OKApRBPDgiGKy8j66u4G77nL2rMmn\n0LympnBEei1E5ULbywqjIwoYKET5mSOKycrpiCoY8t0RFYspMSqIWfPoiAofHZrn9XdYubL/QZkO\nOgFjZaV6b+wo6AdWoVakmSIaVfWXE0fUxIn+NhaCJBZTHep8zxNlFpoXpCPK2IjUJAtRjY3BDsw0\nN6vfNszQPC1EFReH54gaMgSYPHlgeN5bbwFvvJF+Of7xD2D//dXsRfnkiGpqoiMqm7jrLuCTT8It\nQyymroW0c0TlSbJy3W73ItLr+spuAAvIr9C81tZwRPp8dEQB6hn9+efMEeU3vb1AUVHh9p8KUojK\nV0eUfnAYHVHaJeUlWTmFqOxAd2a8Oih+/nNg1ar0t+vu7hebgMGOqNLSwq1IM0U0quqvdELz/HrA\n9/QAN97oz76S0UJUoTmi3AgjUqrGoB1OHVFBj0Y2NdkLUUGH5vklRLlNVq5Fwd126xeitm1THaWN\nG9Ofxfell4Djj1evR4xQHcxMzaoVJM3NdERlE48/DixbFm4ZYjFg1109OKJyXIjywxGlt3UyAJlP\noXmtrcwR5QUzR5SUagDEbyEq18+VF+iIKiAKRYgyPkCiUXWBV1ZSiMp1dGie199hxw53DXxjWB7A\n0LwwiET6w2StznO6ycqjUWDmTOCWW1Kv09Bg/bkXYjHl7Mh3R5RZaF6698s77wCnnWa/nlMhKujR\nSO2IsjqGdkRle2ieF0dUeblyROk8UW+9BRxyCHDAAcDbb6e3v/b2fudQUREwdmw4HS6/oSMqu+jp\nUfVNmGghyrUjKk9C8/xwRDkRonp6VDsvH+69lpZwHVG57vIxc0SNGuXPs9RIV5cSuMJMOxImFKIK\nCD0qma+heWaOKN0AHjLEmxDFWfPCx69Z8xob3V0LFKLCJxpVDYPKytSuqO5u1fmvqXH2e8RiwDnn\nAC+8YB2G0dUVXOO0UBxRyaF5bpKVd3Y6mwFV1/3JZNoR5SQ0L2hHVHd3uKF5WgjbbbeBQtShh6q/\nN99Mb3/J11G+hOfREZVd9PSkL5L6TSymHBg7d6Z379IR1U+6jigvqTyyCYbmecPMETV+vHpt5raP\nRoHXX0//OJ2d/dsXIhSiCohCdETpBrBXIWrYMHWzSOm9nNnOJZeo0IcwuPzy1I1vY2ie298hFlPX\nPoWo3EQ3DCorU+d7+PxzoLZWOSXsHFHxODBnjhLm77jD2lXR1RWcY6WQHVHpCiORiLMGW7aE5qWT\nIypoR5RfoXluHFE6NE8LUW++qRxRhx2Wfp6oZCEqXxKW0xGVXfT0AGvWOBO+gyIWU+2eESOcPx+k\nlIjLOEqLS/PGEeWljk7XETV8eH7ce62t6plrl8rAb/JFiDJzRGkhyiz/6HvvAT/6UfrHoRCl2ia5\nktPVbyhE5RFmjii/hKjKymBneMomPv54YELZTHLnnakbW1qIEsL9dOvaceKHEGXsKFCIygy6YTBs\nWOrGlQ7LA+wfbP/4hxrxfvxxldzcSojq7FS/t99idDyu/grBEWWWI8qNqJFrQlRNTXbkiAorNE8f\n35isXIfm+eGIyhchio6o7EJHEXzwQXhliMXUfTtunHPXX1zGISBQUlSSF46oYcO8ifRuHFH5cO+1\ntqp6JNNhy+3t6tme68KKnSMq+TkYibi7Tv0QW3MZOqIKiHyfNS9IR1RFhb+Jj7OZtjZ3s8p5pbtb\n/aWqyHVYlhdBUItcbq6Fjg57R5SbjjVxjpPQPKMQZXfPbtkCHHywur/tOrO6seD376sfwqNH578j\nymzWvEJwRI0dm1+z5nlJVr5hg+pQt7cDu+8O7Lmn6iilc+3na2heU1NuCVHr1/eP5ucjPT3KsRdm\nnigtRNXUOBdbe2UviouKUSyK88IRpfNCusWNIyofQvNaWlQdm2mRvq1N1WO53hY2c0Ttsot6bSaa\nRKPurlNdh2br+dq8Odj9U4gqIArVEVVW5k2I6ujod0QVwk3S1uZsmlu/aWlR/1N12PwQe7wIUe3t\n6jrQMDQv8+gRqmHDUl+jDQ0D7dNWDYOdO1XyScA+4bGuV/zuHOqOxqhRdEQ5wWljL5uSlY8ebZ2M\nVDui8jk0r7xcNeKbm4F//Uu5oYRQIbQHH6wcUk7JZ0eUk9C81lbgsccyVy4zWlqAI48EHnkk3HIE\nSU8P8OUvZ4cQlY4jqjfei2JRjOKi4rxwRFVXZy5Zeb44ovSAzW67hSNEjRiR+23hZEdUVRUwYYJ6\nnUqIyjdH1KZNwH77BXsMClF5wIcfOlvP6IjKZyHKKDL4laxcO6IK4SZpbw/HEaWvSascUVrscduZ\n2rHD+hhWWOWI0g+sQrlGwkKPUNklK9edVLvfwyhEjRxpPQ28H7P3mKE7GoXgiCrEHFHa5WKsL5Ip\nhNC8sjIlOk2aBPztb0qI0qSbJ8pMiMp1R5SUg3NEpWqzvPIKcP31mSubGb/4haqv8rnO6ukBvvKV\ncBOW9/YWtiOqu9u7I6oQQ/Pa2tR5C0Okb29X7apsFFbSIdkR9etfA+efr16nSlZu953N8s11dqpn\nYzaer9deCz5HHoWoPODoo1WIiR1GR1S+huaVlgYbmlcIN0lYjigtRFk5okpLw3VEWYXm6bDBQrhG\nwsJJaJ6u5wB7R1RjoxKAANUQGD26X6xMJihHlO5oFIIjyo9Z8/wUoqTMTGjeiBHWQlQuJSsfOtS9\nIwpQeaKeekrlhtKkmyfKLDQv1x1RnZ3q2tTnycoRVVcXbqfllVdUXr1LLsl/Ierww4GPPkrvmr/0\nUuDdd/0pAx1RmXVE5UtoXmtrvxCV6RxR+RKal+yIGjOmPyrCLP+oE0fUsccONo90dnoXW4Pi9deD\n/x0pROUBPT3OhCXdQausdJ9ULZvp6VENfrNk5brScNMILyQhSsrgckQ9/zwwa1bqz+0cUUbXkZcc\nUUVFTFaeqzgJzTMKEOk4ogDr0UM6oryTbY6oaFQlig9biIpGlbCSiRxRXh1RFRXuc0QBKlSktdV/\nR1Q2ClFXXgksXuxsXWN+KEANnqV6Fm7cGF6nJRJRM0P97nfA1Kn5XWf19Kh6ubZWzZ7nlHffVbO3\n+gFzRClhiI6o9GhpCc8RlS9CVLIjyojbHFFNTYOFQX2NZ+P5eu214GeMpxCVB0SjzoWo8nKVlyEf\n80SZjWToBrgQ1g07KwpJiOrqUh2zIBxR69ZZN1qdOKL8yBE1fnwwjigKUcHjJDQvHUdUshBllScq\n6BxRlZWqrLk+EmtFts2aF3RuBikHClGpjpOpZOVeZ37VQpQXR9Ruu6lOypQp/Z9PmaJ+i4YGZ/vL\nldC8t992LmAY80MB2euIWrwY2HVXYOZMJdLkq4tTC9RlZcBBB6WXJ8pPlyUdUZlPVl5dnfvP4dZW\n1R8KU4jKRoePU3p7VR1QXGz+uVlbPxazH6js7h54Hcbj6pobNixz5+s//wHmzh28fPXqgVpCLNYf\nlhxkv4ZCVB4QizkTlYwdtHwVoswcUfo7uw3P050nu6ng8wFdQQYhRG3fbl1JO8kR5XXWvB071Ogm\nhajw+f73nYUUG3Eamqc7vXb3rDE0D7ButOmZTYJwRBUXK7E8nzt2gL+z5tmN0DkRovRvGlQDsLtb\nOTCHDHGWIyoXQvPcCFHG5/DUqcoBJUT/50KkF55nJkRt3x7sqK0b1q8H6uudrZvsiLITosJ6znz2\nGXDUUf31Vb46ovQzXQiVTD8dIcpq9t90ceOIisVjKCkqyStHVCaTlY8YkfuOKB2aZzcJSxC0t+e+\nI0qL0MbnlBG3jqhkIaq7u799kCkh6vPPgVdfHbz8mmuA227rf796tRp0GDKEQlSQ5LwQFY+rPwpR\n1o4owLsQVQg3ia4ggwjN27bNuqK1c0TpB4NXRxSFqOxgxQrVUUsHp6F5up4zSyhpJJscUUD+54ny\nyxEF2Asq2eCIMrpc7HJE6dxLQYgpfofmeXFEnXaaebiaFyGqvFyVK5tyX/b2KsHIqRCVjiMqzNC8\nHTtUrhQgv+srY/1x0EHpJSzv6vJfiErLEaVD8+iIAqC2FSI9R1RPT/YJ2+lgzBHF0Lz00YOeqUiV\nrLy317pt0tMz8Do0Gh0yVadHo+bt57Y2NQuqvu5ff13lyLNrR3uFQlSOoy+OdHJEAaqSyKZGmx+Y\nOaKMDWA/hKhctpo6IZUjqq0N+M53vO3bqxDlx6x5hSpEbdoE3HJL2KXoR88QlSoxeCqyITQvqBxR\nQH47DAD/ckQB9nWxUZA0ko1CVNCzbvoVmtfTo+49N44oo0tx7NjB6zjNE9Xbq85XssiYbQnL6+tV\nOZ2GGzp1RMViwObN4QpR2kWaz/VVshD17rvOhYmgQvO2bXNWhr7QvDxxRPmRrHz4cOeOKN3ez+U8\nutmQIyqX+0vGdqQZqZKVG/+b0d09sH/V2akGVTIpRMVi5kJUe7sKJdfJ1LUQFXQ0EIWoHEf/aHRE\n0RHlB21tqpOQ/MBuaACee87bvp0KUVaheV4dUX6G5vmVrFzK4Bs8N98MPPRQsMdIB91QT1eISjc0\nz+r3iEbVPqqr+5eFmawcKMzQvHTvFyeNPSD7HFGlpdaOqJIS63W84Fdonttk5XaNekA5ot54w76j\nrUMZkkMmsi1h+fr1wLRpzh1RW7ao76BJJUTV16vPwurkNTYWniNq7Fj12ulvGYQjqrJS3b9OxJR8\nckTp5OFeHVGjRjl3ROmZtnM5PE/niNKDa5lyd0kZTmje++/7m9fLiSMqXSFKysGOqK6uzKd+SeWI\nam8Hvv515YoCBjqiKEQFhyMhSgjxLSHEGiHEf4QQP0+xznQhxDtCiA+FEP+02t8nn6g/P/DiiMpn\nIUpXuhSi0qOtTSXzTq6kWlq8j/Jt22afI8rONeA1R1RjIzBhgrvroKMjGEfUZZcBP/1p+uVxSksL\nsHBhdo1O6foq3dwF6YbmWY0yaQdCkeEpkA2hefnqMADMQ/OCdESZCVFGZ2vQQlRTk70jSn//4uLg\n8kSEHZqX6rcwUlurfpuNG63XSxYzNdmWsHz9euXyamlxVmfU1alE7ppUQlRdncqzlQ2hedodl+uJ\nnc1IvmbHj3cudHZ3+++IApy7/uiIGkgspp71Th1RQ4ZYh8b6jV8zLBrRoXn6u7S2+n8MM7q7Vbsr\n6LxCyVx4IfDb3/q3P7vBEyshymrAKR4fHJo3dGhmI260IypZnGxvB+bMUUJUR4dKan7ggfkTmieE\nKBdCvJbQaj4QQlyTWD5SCLFcCPGJEOI5IcRwwza/EEJ8KoRYLYQ4zrD8YCHE+wld6DbD8jIhxMOJ\nbVYJISbZlctWiBJCFAG4E8DxAPYFcKYQYq+kdYYDuAvASVLK/QCcbrXPBx4A/vd/7Y7sDH1xuHFE\n5WNonrY46orAr2TllZWFIUS1t6vkdMkP7JYW9d9L7ig7R1RLi+pMBOWIisfVNb/rru4aGDt3Dszh\nYexYug2teeEF4Pe/D1Z8+OMfgT32cNeQ6+oCXnrJ/zLp+iqo0DwnycqTw/IAOqKCxiw0z22OKLuG\nkfE6MJKcrFyIcEPzotH+3z9oR5QfoXluc0TZOaJ0wnK78LxUQlS2heatX68Eo5oaZ5MybNgATJ7c\n/95KiJo2LTuEqHxOWJ4sRI0d6+z6kjIYRxSgriUnYms+OaL8yhHlxhGVKYH16KPVrNJ+okPzgMy6\nRdvagKqqzKcy6ehQzn+/6qIgHFG6Ps+GHFHx+ODru70dOOYYVc777wf23VfdC0G7tfTshEFfM1LK\nHgDHSCkPAnAggG8LIQ4HcCWA56WUewJ4EcAvAEAIsQ+AmQD2BvBtAHcL0efFvgfA+VLKPQDsIYQ4\nPrH8fAA7pZRfAHAbgP+xK5cTR9ThAD6VUtZJKaMAHgYwI2mdswA8JqXcnPiylt2rSEQ1OvxAXxzp\nOqLyNTRPP0CM7gUvjiidjyKb8//4SVsbsMsuqnKMx/uXayHK7ahKb69qwNqF5o0bZ58jym14SUuL\nqvCrqtw1MOrr1aioJtkRpd1aTq+R1lbg/PPVX1Ajb7EYcMcdwM9/7q5hvGoV8F//5X+5dH3lJTTP\na7JyMyHKzhFVUkJHlBeSRQQvycr9Cs3zOtpuhRMhKhbrb/AG1Rj1OzQvCEcUoBxEdgnLrRxR2SZE\nTZmiBj6c5Imqq3MmRG3cGJ4jSsrBM43ma3he8jXr9Poyhuv7gfH54LQM+eiI8nK9Z7sjqqsr/baQ\nHTo0DwhHiMr0LOMdHWog46ab/NmfE0eUWbJyva0Zut+RKjQvk0IUMLgN3d6ufruZM4EFC1RYHpA/\njigAkFIm5kpGOYASABJK01mUWL4IwKmJ16cAeFhKGZNSbgDwKYDDhRC7AKiSUuphswcM2xj39SiA\nY+3K5ESIqgWwyfD+88QyI3sAGCWE+KcQ4g0hxCyrHfb0+CdEuXVE5WtoXnm5aqTqG95rsnKtVguR\n20LUH/7g7Lu3tamH15Ah/VObA/3XilshqrHRPheSFqJSPfy9hubp3BZuroNYTG1fU9O/zGto3k9/\nCnzzm8CMGcE1eP7+d2DiROCII9w9SJqa+kVIP2luViFxboQoHZrnNVl5cocKsG6wdXZ6n0bajEJJ\nVgOFa1wAACAASURBVN7bq87dkCH9y4JOVu5EiPKaf8QKp44oLURlc2heb68anHATcuHEEQV4c0Rl\nY2jelClq8MIut5CUzoWoujq1X8CbqOiGtjZVLuN9la91llshSrct/AzNKy5WrwvZEeU1NK+yUtVf\ndvvp7u6/xjPliIrF/I9Q0aF5gPW1+9ln/h63rU21zzLdX+rsBK6/HrjvPjWZg1fsHFFukpWbCVHG\nZOWZOl/6OEYhKh7vj/6ZOVMNyH7pS+qzTOSIKirKzDUjhCgSQrwDYAuAfyTEpBop5VYAkFJuAaCz\nNSbrP5sTy2qhtCCNURfq20ZK2QugWQiRNOQ9EL+SlZcAOBjKuvUtAP8thJiWauUghCg3jqh8DM3z\n2xFlDCXJtMLvJ9dco+J97dCjGVVVAyspr46obdvsO3zNzaqh5cQR5eZ30MKDm+tg2za1rRYMAHVd\nuRWiPvkEeOopNZOdcT9+87vfKUeTVe4tK3buDEawbmoCJk1KP0dUuqF5Vr+HmSNqxAh1z5t1ALu6\n1Od+i4a9vQMdUfnoLgD6BQRjkulscUQFKUTpmdBSiUxGITKbQ/N0HVdamv4+0nFEvfXWQDduMrkU\nmqcdUXZCVFOTuheMod+pOsIbNyrBKpMj6BpjWJ4mX+us5Fk3x41z9rzyO4S7kB1ROszRj2TlpaWq\nXWvniurpUW3ETCYrj0aDFaJSOb03blRhgX6iXTWZDs3r7AS+8AUVYXDttd73F0SOKLPQPO2IyuT5\nMnNEaUGsqAg45BA1eP3Vr6rP8mnWPCllPBGaNwHK3bQvlCtqwGo+HlLYrVBitwKUAmZMNjUhsczI\n5wB2SCm7AXQLIV4CcACAtck7mz9/Pl57TTVQn3pqOk46abqDIqRGP6TCnjVvwwYV43ysrQktOMwc\nUT09qlIEvAtRueyIam931oDRs+YNG9Yfpgf4I0RNmGA9mmfniDKKPW46U3raaTfXQXJYHqDK0tPT\n7/RKJzTvs8+AAw5QDYWgLODbtwOrVwOnnqp+NzcNY+2IknLwLFVeaG5WjYb169PbTjconSYrLy5W\nndp4fGBScsBciBJCdbT07IpGdIOYjih3mAkIXhxRdveZEyGqs1Pdg6lETa80N6t8PkD2OKK8ClFu\n83o5cUSNHavaJmvXqrx2ZuSCI6qnp/+Z50SISs4PBVg7ooxClNFhGDRmQlS+1llmjqhPP7Xfzu8J\nEJJzRK1ebb9NGI6o9euBSy8FnnzSv31Go+qZXFHh3RFlFKKSndBGjI6oTAlRQTiinOSI2rnTf9dX\nWKF52s3z85+r+vGWWwbmo0yXIHJEpXJE6XD3TCYrBwa2oY2zggsB/Pvf/Z/lQmjeihUrsGLFCsfr\nSylbhRAroMxDW4UQNVLKrYmwO323bAYw0bCZ1n9SLTduUy+EKAZQLaW0HKpx4oh6A8A0IcRkIUQZ\ngDMALE1a5wkAXxVCFAshKgB8CYDp42L+/PnYZ5/5AOZj4sTpDg5vTTSqGgZuZs3zs+J76ingzjv9\n258bgnZEZVrh94t4XHW0nApRw4YF44iqrbVO4heLqU6I3ax5XhxRY8a4s1w3NKgOhRHdsdS20nQq\n0vr6/v1pQctv3nkHOOggb7Nx7dypvo++n/yiqUkJUW6SlZeV2TuidD1nFVJrJkQBqRttQTmiknNE\n5aO7ABicqBwoDEeUkxxRmXJEeQnN87IPp44oQIXnWeWJyoUcURs3qvq9pMRZjqjksDzA3JGhQ/gm\nTQrHEWUWzlxIQpST6ytIR5TTcx2GI2rdOtUP8CMsSqPvda/Xup4QIh1HVCZD8+wcUe3t6U8U5CRH\nVEuL/3VIGKF5sZj6KytT98i4cc4miLAiKEdUZWXq0LwwHVFGISqZTITmFRd7Ey+nT5+O+fPn9/2Z\nIYQYo2fEE0IMBfBNKK1mKYDzEqvNhtJ0kFh+RmImvCkApgF4PRG+1yKEODyRvPzcpG1mJ16fDpX8\n3BJbISoR43cxgOUAPoJKXLVaCPFjIcSPEuusAfAcgPcBvArgD1LKj1PtMxJRHaR03QBmRKPqxmtt\ntbaz6+MG5Yiqr08/zMZvUjmivMyalw+OqK4u1YB1KkRVVfU7ojQtLer7exWiUlXQLS3qmrQKU7Ny\nRM2YYS+WeAnNS+WIikQG3lduhKigRt7eeUdNvWosa7roxpHfeaKam9VU5e3t6T18jcnKnYTmAakf\n8GadKiC1jZ2OKG+YCVFuHFF2o46abBCimprSyxGVbmM0FgPuvdd+Pd258iM0z81zMDnMyYrDDrPO\nE5ULoXk6LA9wliPKTIgyey7s3KnOY3U1Q/OCxu2secb8pH5gfD5UVg7M3ZmKMBxR+rn17LP+7bO7\nW93rbtsvmmRHVCqkHDignS2heXfcofIfpYOTHFEtLf73aYyz5mWqv6TD27Rrf5ddvAtRThxRbpKV\njx2bOll5ps5XukJUHoXmjQfwTyHEuwBeA/CclHIZgJsAfFMI8QlUcvEbASCh4zwC4GMAywBcJKXU\nYXtzAdwH4D9QE9rpmu8+AGOEEJ8C+AnUjHyWOMoRJaV8Vkq5p5TyC1JKXcB7pZR/MKxzs5RyXynl\nF6WUv7faX0+PGtHyI09ULKYqzGThwIwgk5VnkxBldERZJSvv7rZvlOeDEKWvi3SEKDNH1IQJ7gUJ\nO0eUdg5YiTJWOaJefNHe4edFiLJyRAUlRDU1qYaRW959VzmijOVKd3+6g+F3nqimJtWBSXeWOJ2s\nXAtRZt8nudOb6iGaTY4onYxWd+q8/O7ZipmAkA2OqLCTlRs7muk6F7duBebNs1/Pj9A8Y56pdH4z\nKfvvWye4dUQNH67q9Uy5GKwwClFOQvOcClHaDQVkjxCVr+K5V0dUEKF5Q4c6cyeH4YhqbFT9imXL\n/NunX44ofQ7thCjtnCoqylxonk4dYNV+3bkzfaeZkxxRQTiidI4oJ7+ZlKquv+QS1YZ3S/Iglx9C\nlJ0jyk2y8p6ewUKUdkRlMuLGLjQvmVwIzXOClPIDKeXBUsoDE1rNdYnlO6WU30joPMdJKZsN29wg\npZwmpdxbSrncsPwtKeX+CV3oMsPyHinlzMTyLydm27PEr2TlaRGJqPwHfghRWrV1knzc2EEbPtx7\nR9dIfb3/04+mi1GIMjqiUglRP/kJ8OCD1vvMByFKVzZeHVETJ3p3RKUSQ3SHzWrky2rWvGjUfqRQ\nN6J1st10OmRG4Uij3VtBCVEnnQS8957zMiZjdEQJ4a4x54cj6oUXgL/8ZeAyncRZ52NyijEXV2mp\neacz2RGV6iGaSoiyckRZhY66JbmjUVSUOv9VLuOXIypXQ/NShd0lO6LSub46OtR5tTuHyUKUm+e+\n2xx9+p5NztGWikMOUXVXqno0lRClc8k4cYwk88gj/oq/6QpRTnNEGQWrsELzCtURlQ3JyocOzV5H\n1I4dwPe+p573fn33ri7Vbi8uVvenWxHdaWhed3d/zjU3A5Zu0PWc1T3U3p6esBKJqP3qejLToXlO\nHFGffqrap6efDrz2GvDyy+6P2dHhvxDlJUeUlSNq1ChVt+hr2eiIKvTQvFztY3slFCGqpwfYc0//\nhKiSEmcOJ2OHubxcXfh+JWmtr+/PJxMWxtA8JzmiNm+2H2UoZCHKWEk1N6uRWC9CVE1N6nPo1REV\njdpfy9oRJUT6tmurZOXG0X4/haiWFveCREeHylOy114Dy5tuA1GLNV6EqOefHzxK2tSk6qyxY9MT\nooyNg1TheWaOqFSheek4ojo7lYAfZI4oAPjGN4D//V9/j5EN+Jkjyq7RFo+nduGYJSsP0xGln+F6\nnXTKoq9/J3lPystV3VdUZB/Gb4bb0Dynico1I0aounHNGvPPUwlRgLtceJ2dwPe/76/r0yhEjR6t\nfh+reqOuToUqGzF7LugZ84DwHFGFkiMqeUCjslKJIXbtDL8dUcZZVSsqnDmiYvEYSopKMu6I2ntv\nNcj+yiv+7NN4r3uZyMFpaJ6xv5ApR5STmc/b2tKbiEG7oXSompUQ5UXgM8NpjqhnngG++EWVW+z0\n09PPgWXEzBFll5fPDjsXr9n30++tkpUPGaLqEt2212XPZH1u5ojSv5sZQYbmSan+iopyt4/tlZwX\nonQF68QRldwgHDPGvwZEfb3at9X+Hnss2JCTdB1RO3bYd4L1TAxA7t4k7e3qGnEiRGlbbfID2w9H\n1LhxqUf8nTiiUo3Ia2uz3UihMSdQuqNddqF5Whxxeo1s3tw/K1uqvFha5HLD++8D++wzcETHrSNq\nyhRvQtTmzYMbUfr3HjMmvZBeY+Mg1cx5yfWcVbJypzmi9HmrrAzWEQWo2V5uusl7Qyrb8HPWvMpK\n62tZXwNmMz1myhElpfPQPLeOKH39OxWiAPfheW6TlaeTqFxjlSfKSohyk+z9s8/Ufz9diEYhqqjI\nvmOU66F5heCIEsJZeF6QOaLSDs3LcI6oMWOAb3/bv/A8473u5Xp344jKlBCl61K7ZOXpOHyMYXlA\n/z2aPACh23V+9muMs+ZZ/V7r16uZo4uKnCWRtyKo0DwrR5Qe0DGeUyfJyocMGfh9w0pWXlY22BGl\nZ5hPJsjQPD3Zk9XEQvlOaKF5fjqitBCVjiMKSB2Cki5dXepm2n331PuTUqneQU6vrG/yZEeUMVm5\n8SG+Y4e9EGes4DKZTG7ZMv9cEe3tarTVyblP5YjyU4gyq9CMQlSqh3+qWfOM7gYrjI3odIUoP5OV\nx2KqLDU16n2qBk93t/vK/913+8PyksubDjt3qg6VF7fA5s2DGwXaEeU2NA8wd0SZ5aNJdc1ZheYl\ndzZ0gziIxmmyEPWFLwAXXABcaUhx2NSk3BC5jJ+OKDshyio5trEeD1KI6uxUZdDlCCJZebqOKGDw\nOW9tBd56y/5YmXJEAdZ5ovx2RK1bp/576QglYxSiAOvwvPZ29Z3Gjh24PFsdUWaheanaUTt2AI8+\nGny5gsBMQHUiRHV1qTZUUDmi0grNy6AjSrvlTjghGCHKS8JyN46oTIXmOXVEbd/uvN5taRkoRJWW\nqvfJgrEWovysR7SgYfec+Owz1WcE/BGitGEA8C80z+q5ZSac2OWI6u5W15cx9UkYycpjMVVvZ0No\nng7LC/o42UxojqiaGtVpsnMx2WEMzXPjiLISop56CvjnP+3L0NCgOunjxqXuVOqZ2/yYKTAVQTmi\nwgjNe/994De/8ed47e2qwk83WbmuKKX0T4hK1VEwhual64jS5ygoR1QsprbVwpEmlRBl5zbYtk2V\nwxiOYyZueHFEvfNOf6Ly5PI6pbdXdXQnTfLmiKqvH9woMDqivITmJbsYtFBldMKkSirZ0TGwsaYx\nywWiG8ReZ+8xI1mIAoCrrlIhjStXArfeCkydCsyd6+9xM40e+TMSlCPKyoWTqWTlRjcUkLru85Ks\n3I0QlXzOV64Efv5z+2O5TVaeK44ov4So9nb1t8su/cushCjtckp27+lnodFFHrYjKlWOqMZGc7f7\nm2+qGb9yES9ClNnsqpEIsGRJ+uUw1g9OQ/PCckSNHq1E5K1b/Rk48csR5TRZeViOqKFD7YUoKZ0b\nB1pb1TVoxOzaDUKIcpojyijW56IjChjctoxG1W+ZriOqoiKzycqjUaUZZMOseRSiQnRElZcrl4pX\nV5QxNM9vR9QzzwC//rV9GXS+GythSzeY/XCBpcIsR1SqWfOiUXW+7DrBxiR4ma4o6uv9mQq3vb1f\n+LTKb6A/HzZsoCOqu7vflu5GiOrqUr+N1ZTTThxRqXJE6f3ZfTe3QtS2baqxnSwWuE1Wnpz43KzD\nAfjviEq3IdfcrBozI0Z4D83bsaO/89vbq66t6mp3OaKMoXnJv3lyXg/A/L7VjiyzBMpm9WImHVGA\naqjcdBNwzDFqNpnbbkt/1pxsQ4/8GXHT8PBbiArKEdXUNFiI8jtZua6j7eplq9C8SMTZ/e0lWXm6\njqiDDgI+/ND8XGS7I2r9etW2MwpL48enDs0zS1QOqLqpuHjg9wk7WblZjqghQ1LnG+3s9F+4zxRm\ndYiZWzaZ7m5VpyR/77o64Kc/Tb8crkLzQnBEaZGyuBg4/njVf/BKd7c/jihdxzoRoow5ojLliBo1\nSv2uqe7n9nZVHqfiSnJoHmAtRPkdmjdsmHX9JKX/jqggkpXbPbfMHFFW6Ru00JkNoXkjRmTHrHkU\nokJ0RPklROkK1o0jyk6I6ugAVqzob6ilQnesrfanL/hMCFFOHFHaSp6tjqhIRI0W/PGP3velRyjs\nRvM6OvpzgBgrypYWJUhUV7sTorZvV8fWM7dZ5YiyckSlmjXPSWheR4f6XrpRk04jw2zGPKBfNPMq\nRJl1OAD3jqhYDPjoI5UIMrm86TTkdu5U9YoTkTsVbW2qPCNH9t9r2jZeVJR+jii70DyzTq/ZaE6q\nsDzA/D7RIkqmHFEAcPbZwOrVypn6zW/mvhCVKjQvXUeUbuzZ5YhyIkR1dqrGV3KuBz/QQq7GKkeU\n/v3DCM3TgzJ2uA3Nc+OIqqxULsAPPhj8WRCOqLIybx2hjg7g5puBe+5RM/Eaw/IAe0dUcqJyjfE5\nFYmoNp525mYylAMYPJhjJFXC8o6O/BKinMycl0rcdir4JmMWmmeXazUMR5RRpPza14BVq7zvM9OO\nKO1YAdKf0MYtWvAYPjx1PdzWpkQbL0KUWf8sLEfUjh393xnwX4iqqVGuPC/PdCeOKDMhqqLCPjTP\n+H3DmDVPt8kZmpcdhOaIKivzT4gqKXHviLISYtrblQ38/vut9+tEiNIN5iBD8/RNPnSovRC1Y4cq\nc7YKUdEocNZZwEsveU9arCsYOyFKP0CAgY4oLRJVV7trSOmwPMCZI8qs4ao7q8XF7nJEJTeg03FE\nmSUqB9zniDITtpKdNvG42q+bB9Mnn6hE6MmJB9MVUZqalFgzfLh7R5ROym4coWpuVg9BwFtonlmy\ncrPcQGajOVZC1PDh6towXh9BOqKMsyIZEULlEgTU/dPUlHkHhJ+kSlbuxhFl1dgD0nNEBTUaaRzN\nB5w5osIIzYtGnd3fbpOVu3FEASrExyw8LwhH1H77eUtW/sQTqp30/vvApk3q2W3ETogyc0QBA+sb\n7bDTLs5MO6La2tRz0+y+GjXKPGF5vjmivITm9fRYu15SYRSiSkrMB62SMTqi4jIOGeQsQVDfVX9v\nQJ0nPxLYh5GsPNOz5unf18pM0N6uckc6zbGrB4+NmInFYeWIWr++3w0F+C9E6TxMXlLfOHVEGc+d\nnSPKLll5JvuX6TiiGJoXLDnviNKheUE5oi66CFi0yHrUOh0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tlhJ8uGauByFGlPb5HT5M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\nB8QBUTnNyn3PhRfHuoN8aoqSq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yZyeURJYQJwrmtefHE/I4rbwWVdALilee3xJaA7+oyo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\nXEc4Lq8hTHkCQNJEYDkhY+/4LGi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"text/plain": [ "<matplotlib.figure.Figure at 0x7f386e72d588>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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XA9hu2HZndFk5gB2G5Tuiy3ptI6XsAtAghBhqVyCG5hHf6egA9u3zuxSEEEII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\nHVRKHVNKBSulKurK+yilNuquUbuzv8o5Qzrt1X3mjj+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"text/plain": [ "<matplotlib.figure.Figure at 0x7f386e72d7b8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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+FJ9uEqZ9DtQDUeBOa+1/G2PqrLWDfPPUWmsHG2P+ALxhrZ0bn/7fwLNADXCd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PyZPKlMcj8+P9/4DmVShWwILl5MuVe9xz5gTn9WR0bIT80m91NBKvWcfcz6W28FvvAF9/nzfdyy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\nI1/WS1LJKana/e3o8Hdkgk/jccDrcmlSUo3OjWb+rzeTajI6ienYNM5Mn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t2PLxob5Tfr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POgOdGJ4dVvUqywWnAzSX6umTT6Mz0JmRl3bVd2EqSe06cna/ttp8sqAQpMTBRGQCiVQC7XXt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\n+oaQAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f386e72d5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "date_y = pd.DataFrame()\n", "date_y['Class probability'] = df_train.groupby('date_y')['outcome'].mean()\n", "date_y['Frequency'] = df_train.groupby('date_y')['outcome'].size()\n", "# We need to split it into multiple graphs since the time-scale is too long to show well on one graph\n", "i = int(len(date_y) / 3)\n", "date_y[:i].plot(secondary_y='Frequency', figsize=(20, 5), title='date_y Year 1')\n", "date_y[i:2*i].plot(secondary_y='Frequency', figsize=(20, 5), title='date_y Year 2')\n", "date_y[2*i:].plot(secondary_y='Frequency', figsize=(20, 5), title='date_y Year 3')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "134daec9-4e41-7710-236a-5d217452fc09" }, "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "019d1d6c-c44c-3d51-1d81-c255f088cf23" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f386e90beb8>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GxEWD7H97fnuE7LO9jqGXy432/A72+DNk1ZB/IjvG68kqTGHgZYWfo+ZzmFJaAhxNdpXL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zpf7L2cHBY7mbyBroU5EbeBDpKri73HxYIjq1z7+t5eUGPGo4XJTBKP1TjIsgouUpniXcKR+RAO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ua9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\nk0J5/cp/ssj1Q95atZz0aP8CWVGSsmfqo85KDrLw0EHrFZXKjra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WAeVBWAWgHiqrskdYBQAA0AJUVgHlQ1gFoJ7QQ7lcTkO7zBBWAQAA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rk6JxCxqmbcgV3KhR8DXCmu4MBUy1afYAnkMu6nlG1WNqXl6gAwnYnWWVVSSjg6c9S3nfd8Pd93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\n+AvAQqMiajEAgzGtDxA5/2kAOYy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eeVSQ5QUAAAAAQCUEsgBUlS0tLO6RFcdSOi25b96+ycgCAAAAANSC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Jnd179hhDUZ06sHnEQObunsvA+QNZcWjFP1ZqGzFnIfkiG5PLP/leoXzBUuw5u9ujNKwDZw9T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\nJSPIVENWamKCLlEA8aoaBxQlKeUFjNfhD8w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BH4H3gGlJrqmqecBjwA7gYuAvYDkwg+Yu\n+FVJlk1wrqOBjcCMJJv31WeSJEnqM4MsSZKkIdDevbUgyaKua5EkSRpW9siSJEnqWFU9CJwDnNd1\nLZIkScPMIEuSJGk/qao7gEuA0DSOD/BckiWdFiZJktQTPlooSZIkSZKkXnDVQkmSJEmSJPWCQZYk\nSZIkSZJ6wSBLkiRJkiRJvWCQJUmSJEmSpF4wyJIkSZIkSVIv/A3msoTh3n3ByAAAAABJRU5ErkJg\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f386f010550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "date_x_freq = pd.DataFrame()\n", "date_x_freq['Training set'] = df_train.groupby('date_x')['activity_id'].count()\n", "date_x_freq['Testing set'] = df_test.groupby('date_x')['activity_id'].count()\n", "date_x_freq.plot(secondary_y='Testing set', figsize=(20, 8), \n", " title='Comparison of date_x distribution between training/testing set')\n", "date_y_freq = pd.DataFrame()\n", "date_y_freq['Training set'] = df_train.groupby('date_y')['activity_id'].count()\n", "date_y_freq['Testing set'] = df_test.groupby('date_y')['activity_id'].count()\n", "date_y_freq[:i].plot(secondary_y='Testing set', figsize=(20, 8), \n", " title='Comparison of date_y distribution between training/testing set (first year)')\n", "date_y_freq[2*i:].plot(secondary_y='Testing set', figsize=(20, 8), \n", " title='Comparison of date_y distribution between training/testing set (last year)')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "a6b70be3-608b-1ce2-3811-5851dfd361ea" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correlation of date_x distribution in training/testing sets: 0.853430807691\n", "Correlation of date_y distribution in training/testing sets: 0.709589035055\n" ] } ], "source": [ "print('Correlation of date_x distribution in training/testing sets: ' + str(np.corrcoef(date_x_freq.T)[0,1]))\n", "print('Correlation of date_y distribution in training/testing sets: ' + str(np.corrcoef(date_y_freq.fillna(0).T)[0,1]))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "fef8f45c-4a7e-c9af-21fc-e69fcb71206d" }, "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "f62bad74-4050-a587-baef-8966980187fc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "date_y correlation in year 1: 0.237056344324\n", "date_y correlation in year 2: 0.682344221229\n", "date_y correlation in year 3: 0.807207224857\n" ] } ], "source": [ "print('date_y correlation in year 1: ' + str(np.corrcoef(date_y_freq[:i].fillna(0).T)[0,1]))\n", "print('date_y correlation in year 2: ' + str(np.corrcoef(date_y_freq[i:2*i].fillna(0).T)[0,1]))\n", "print('date_y correlation in year 3: ' + str(np.corrcoef(date_y_freq[2*i:].fillna(0).T)[0,1]))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "0eca140b-3abf-e3ed-1f92-5fc8c29d60d3" }, "source": [] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "eea41b73-fe97-6076-6c1a-069a9fe6cd81" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "date_x_prob AUC: 0.626182\n", "date_y_prob AUC: 0.720296\n", "date_x_count AUC: 0.465697\n", "date_y_count AUC: 0.475916\n" ] } ], "source": [ "from sklearn.metrics import roc_auc_score\n", "features = pd.DataFrame()\n", "features['date_x_prob'] = df_train.groupby('date_x')['outcome'].transform('mean')\n", "features['date_y_prob'] = df_train.groupby('date_y')['outcome'].transform('mean')\n", "features['date_x_count'] = df_train.groupby('date_x')['outcome'].transform('count')\n", "features['date_y_count'] = df_train.groupby('date_y')['outcome'].transform('count')\n", "_=[print(f.ljust(12) + ' AUC: ' + str(round(roc_auc_score(df_train['outcome'], features[f]), 6))) for f in features.columns]" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "234385e5-a9fa-0917-4d0c-8f67072cf62d" }, "source": [] } ], "metadata": { "_change_revision": 98, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/322/322536.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "365f75f0-91e0-f08d-ebd9-d82a8f615151" }, "source": [ "### Kaggle Predicting Red Hat Business Value" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "75364ee1-3685-dbfe-605f-e8d517435585" }, "source": [ "As this my first kernel, I would very much appreciate some feedback. While i'm confident that most of my process, particularly the initial clearning and transformation from cateogrical to numeric are correct, i'm more unsure about the steps I took in re: to train, test, split, etc. \n", "\n", "Thank you to everyone who contributes with tips and kernels of their own!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "8247b89f-1e06-b57b-4900-849e429abd3d" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module has been deprecated in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "# importing libraries\n", "\n", "import numpy as np\n", "import pandas as pd\n", "from IPython.display import display, HTML\n", "\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.ensemble import RandomForestClassifier" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "7d4c624e-c081-b36c-333e-7c95102fb96a" }, "outputs": [], "source": [ "# reading in data\n", "\n", "people = pd.read_csv('../input/people.csv')\n", "activity_train = pd.read_csv('../input/act_train.csv')\n", "activity_test = pd.read_csv('../input/act_test.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "c31f5f1b-44d7-d4ac-0b85-8cd775ecc026" }, "outputs": [], "source": [ "# merging the dataframes into train, test\n", "\n", "df = activity_train.merge(people, how='left', on='people_id' )\n", "df_test = activity_test.merge(people, how='left', on='people_id' )" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "19648fdb-3613-10bd-11a0-d5133b640d92" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2197291, 55)\n", "(498687, 54)\n" ] } ], "source": [ "# the shape of the dataframes\n", "\n", "print (df.shape)\n", "print (df_test.shape)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "1820388b-32de-699e-61c9-3a43d6cc7774" }, "outputs": [], "source": [ "# filling NaN values first\n", "\n", "df = df.fillna('0', axis=0)\n", "df_test = df_test.fillna('0', axis=0)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "47929a4c-d8c6-a526-be24-9247afa27bc1" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>people_id</th>\n", " <th>activity_id</th>\n", " <th>date_x</th>\n", " <th>activity_category</th>\n", " <th>char_1_x</th>\n", " <th>char_2_x</th>\n", " <th>char_3_x</th>\n", " <th>char_4_x</th>\n", " <th>char_5_x</th>\n", " <th>char_6_x</th>\n", " <th>...</th>\n", " <th>char_29</th>\n", " <th>char_30</th>\n", " <th>char_31</th>\n", " <th>char_32</th>\n", " <th>char_33</th>\n", " <th>char_34</th>\n", " <th>char_35</th>\n", " <th>char_36</th>\n", " <th>char_37</th>\n", " <th>char_38</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>ppl_100</td>\n", " <td>act2_1734928</td>\n", " <td>2023-08-26</td>\n", " <td>type 4</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ppl_100</td>\n", " <td>act2_2434093</td>\n", " <td>2022-09-27</td>\n", " <td>type 2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>ppl_100</td>\n", " <td>act2_3404049</td>\n", " <td>2022-09-27</td>\n", " <td>type 2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>ppl_100</td>\n", " <td>act2_3651215</td>\n", " <td>2023-08-04</td>\n", " <td>type 2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>ppl_100</td>\n", " <td>act2_4109017</td>\n", " <td>2023-08-26</td>\n", " <td>type 2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 55 columns</p>\n", "</div>" ], "text/plain": [ " people_id activity_id date_x activity_category char_1_x char_2_x \\\n", "0 ppl_100 act2_1734928 2023-08-26 type 4 0 0 \n", "1 ppl_100 act2_2434093 2022-09-27 type 2 0 0 \n", "2 ppl_100 act2_3404049 2022-09-27 type 2 0 0 \n", "3 ppl_100 act2_3651215 2023-08-04 type 2 0 0 \n", "4 ppl_100 act2_4109017 2023-08-26 type 2 0 0 \n", "\n", " char_3_x char_4_x char_5_x char_6_x ... char_29 char_30 char_31 char_32 \\\n", "0 0 0 0 0 ... False True True False \n", "1 0 0 0 0 ... False True True False \n", "2 0 0 0 0 ... False True True False \n", "3 0 0 0 0 ... False True True False \n", "4 0 0 0 0 ... False True True False \n", "\n", " char_33 char_34 char_35 char_36 char_37 char_38 \n", "0 False True True True False 36 \n", "1 False True True True False 36 \n", "2 False True True True False 36 \n", "3 False True True True False 36 \n", "4 False True True True False 36 \n", "\n", "[5 rows x 55 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# taking a look at the first few rows\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "0763c2b3-f72d-53db-ec5d-3d6894c276f5" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>people_id</th>\n", " <th>activity_id</th>\n", " <th>date_x</th>\n", " <th>activity_category</th>\n", " <th>char_1_x</th>\n", " <th>char_2_x</th>\n", " <th>char_3_x</th>\n", " <th>char_4_x</th>\n", " <th>char_5_x</th>\n", " <th>char_6_x</th>\n", " <th>...</th>\n", " <th>char_29</th>\n", " <th>char_30</th>\n", " <th>char_31</th>\n", " <th>char_32</th>\n", " <th>char_33</th>\n", " <th>char_34</th>\n", " <th>char_35</th>\n", " <th>char_36</th>\n", " <th>char_37</th>\n", " <th>char_38</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>ppl_100004</td>\n", " <td>act1_249281</td>\n", " <td>2022-07-20</td>\n", " <td>type 1</td>\n", " <td>type 5</td>\n", " <td>type 10</td>\n", " <td>type 5</td>\n", " <td>type 1</td>\n", " <td>type 6</td>\n", " <td>type 1</td>\n", " <td>...</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>76</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ppl_100004</td>\n", " <td>act2_230855</td>\n", " <td>2022-07-20</td>\n", " <td>type 5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>76</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>ppl_10001</td>\n", " <td>act1_240724</td>\n", " <td>2022-10-14</td>\n", " <td>type 1</td>\n", " <td>type 12</td>\n", " <td>type 1</td>\n", " <td>type 5</td>\n", " <td>type 4</td>\n", " <td>type 6</td>\n", " <td>type 1</td>\n", " <td>...</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>90</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>ppl_10001</td>\n", " <td>act1_83552</td>\n", " <td>2022-11-27</td>\n", " <td>type 1</td>\n", " <td>type 20</td>\n", " <td>type 10</td>\n", " <td>type 5</td>\n", " <td>type 4</td>\n", " <td>type 6</td>\n", " <td>type 1</td>\n", " <td>...</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>90</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>ppl_10001</td>\n", " <td>act2_1043301</td>\n", " <td>2022-10-15</td>\n", " <td>type 5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>90</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 54 columns</p>\n", "</div>" ], "text/plain": [ " people_id activity_id date_x activity_category char_1_x char_2_x \\\n", "0 ppl_100004 act1_249281 2022-07-20 type 1 type 5 type 10 \n", "1 ppl_100004 act2_230855 2022-07-20 type 5 0 0 \n", "2 ppl_10001 act1_240724 2022-10-14 type 1 type 12 type 1 \n", "3 ppl_10001 act1_83552 2022-11-27 type 1 type 20 type 10 \n", "4 ppl_10001 act2_1043301 2022-10-15 type 5 0 0 \n", "\n", " char_3_x char_4_x char_5_x char_6_x ... char_29 char_30 char_31 char_32 \\\n", "0 type 5 type 1 type 6 type 1 ... True True True True \n", "1 0 0 0 0 ... True True True True \n", "2 type 5 type 4 type 6 type 1 ... False True True True \n", "3 type 5 type 4 type 6 type 1 ... False True True True \n", "4 0 0 0 0 ... False True True True \n", "\n", " char_33 char_34 char_35 char_36 char_37 char_38 \n", "0 True True True True True 76 \n", "1 True True True True True 76 \n", "2 True True True True True 90 \n", "3 True True True True True 90 \n", "4 True True True True True 90 \n", "\n", "[5 rows x 54 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_test.head()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "414e25ae-54d6-cd11-caa1-61381ca5664b" }, "source": [ "### preprocessing" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "1666455d-a2b0-6ba1-acca-08e0e890bc77" }, "outputs": [], "source": [ "# a multi-column LabelEncoder()\n", "\n", "# this solution for applying LabelEncoder() across multiple columns was suggested in the following thread\n", "# http://stackoverflow.com/questions/24458645/label-encoding-across-multiple-columns-in-scikit-learn\n", "\n", "# I like this solution but is it the most efficient? Would another method be more practical, particularly if \n", "# applied to different type of model \n", "\n", "class MultiColumnLabelEncoder:\n", " def __init__(self,columns = None):\n", " self.columns = columns \n", "\n", " def fit(self,X,y=None):\n", " return self\n", "\n", " def transform(self,X):\n", " output = X.copy()\n", " if self.columns is not None:\n", " for col in self.columns:\n", " output[col] = LabelEncoder().fit_transform(output[col])\n", " else:\n", " for colname,col in output.iteritems():\n", " output[colname] = LabelEncoder().fit_transform(col)\n", " return output\n", "\n", " def fit_transform(self,X,y=None):\n", " return self.fit(X,y).transform(X)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "079a6421-c271-ab52-0704-e2737390961a" }, "outputs": [], "source": [ "# defining a processor \n", "\n", "def processor(data):\n", " data = MultiColumnLabelEncoder(columns = ['people_id','activity_id', 'activity_category', 'date_x', 'char_1_x', 'char_2_x',\n", " 'char_3_x', 'char_4_x', 'char_5_x', 'char_6_x', 'char_7_x', 'char_8_x', 'char_9_x',\n", " 'char_10_x', 'char_1_y', 'group_1', 'char_2_y', 'date_y', 'char_3_y', 'char_4_y',\n", " 'char_5_y', 'char_6_y', 'char_7_y', 'char_8_y', 'char_9_y']).fit_transform(df)\n", " \n", " bool_map = {True:1, False:0}\n", "\n", " data = data.applymap(lambda x: bool_map.get(x,x))\n", " \n", " return data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "ecf4e844-05ec-8807-0a00-1f2777487d91" }, "outputs": [], "source": [ "# applying processor to training data\n", "\n", "df_encoded = processor(df)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "218ce148-8b8e-a67e-e196-6e067ba90bb5" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>people_id</th>\n", " <th>activity_id</th>\n", " <th>date_x</th>\n", " <th>activity_category</th>\n", " <th>char_1_x</th>\n", " <th>char_2_x</th>\n", " <th>char_3_x</th>\n", " <th>char_4_x</th>\n", " <th>char_5_x</th>\n", " <th>char_6_x</th>\n", " <th>...</th>\n", " <th>char_29</th>\n", " <th>char_30</th>\n", " <th>char_31</th>\n", " <th>char_32</th>\n", " <th>char_33</th>\n", " <th>char_34</th>\n", " <th>char_35</th>\n", " <th>char_36</th>\n", " <th>char_37</th>\n", " <th>char_38</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>503691</td>\n", " <td>405</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>832759</td>\n", " <td>72</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>1289703</td>\n", " <td>72</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>1406406</td>\n", " <td>383</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>1623050</td>\n", " <td>405</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>36</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 55 columns</p>\n", "</div>" ], "text/plain": [ " people_id activity_id date_x activity_category char_1_x char_2_x \\\n", "0 0 503691 405 3 0 0 \n", "1 0 832759 72 1 0 0 \n", "2 0 1289703 72 1 0 0 \n", "3 0 1406406 383 1 0 0 \n", "4 0 1623050 405 1 0 0 \n", "\n", " char_3_x char_4_x char_5_x char_6_x ... char_29 char_30 char_31 \\\n", "0 0 0 0 0 ... 0 1 1 \n", "1 0 0 0 0 ... 0 1 1 \n", "2 0 0 0 0 ... 0 1 1 \n", "3 0 0 0 0 ... 0 1 1 \n", "4 0 0 0 0 ... 0 1 1 \n", "\n", " char_32 char_33 char_34 char_35 char_36 char_37 char_38 \n", "0 0 0 1 1 1 0 36 \n", "1 0 0 1 1 1 0 36 \n", "2 0 0 1 1 1 0 36 \n", "3 0 0 1 1 1 0 36 \n", "4 0 0 1 1 1 0 36 \n", "\n", "[5 rows x 55 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_encoded.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "49025faf-bbdb-f416-8f79-f8d1a8c746fc" }, "outputs": [ { "data": { "text/plain": [ "people_id int64\n", "activity_id int64\n", "date_x int64\n", "activity_category int64\n", "char_1_x int64\n", "char_2_x int64\n", "char_3_x int64\n", "char_4_x int64\n", "char_5_x int64\n", "char_6_x int64\n", "char_7_x int64\n", "char_8_x int64\n", "char_9_x int64\n", "char_10_x int64\n", "outcome int64\n", "char_1_y int64\n", "group_1 int64\n", "char_2_y int64\n", "date_y int64\n", "char_3_y int64\n", "char_4_y int64\n", "char_5_y int64\n", "char_6_y int64\n", "char_7_y int64\n", "char_8_y int64\n", "char_9_y int64\n", "char_10_y int64\n", "char_11 int64\n", "char_12 int64\n", "char_13 int64\n", "char_14 int64\n", "char_15 int64\n", "char_16 int64\n", "char_17 int64\n", "char_18 int64\n", "char_19 int64\n", "char_20 int64\n", "char_21 int64\n", "char_22 int64\n", "char_23 int64\n", "char_24 int64\n", "char_25 int64\n", "char_26 int64\n", "char_27 int64\n", "char_28 int64\n", "char_29 int64\n", "char_30 int64\n", "char_31 int64\n", "char_32 int64\n", "char_33 int64\n", "char_34 int64\n", "char_35 int64\n", "char_36 int64\n", "char_37 int64\n", "char_38 int64\n", "dtype: object" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_encoded.dtypes" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "b7bb9b2b-fb5f-5663-6c8e-a507081f954d" }, "outputs": [], "source": [ "# applying processor to test data\n", "\n", "df_test_encoded = processor(df_test)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "8099ffd3-67ff-6a2a-7225-0a422af39d8d" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>people_id</th>\n", " <th>activity_id</th>\n", " <th>date_x</th>\n", " <th>activity_category</th>\n", " <th>char_1_x</th>\n", " <th>char_2_x</th>\n", " <th>char_3_x</th>\n", " <th>char_4_x</th>\n", " <th>char_5_x</th>\n", " <th>char_6_x</th>\n", " <th>...</th>\n", " <th>char_29</th>\n", " <th>char_30</th>\n", " <th>char_31</th>\n", " <th>char_32</th>\n", " <th>char_33</th>\n", " <th>char_34</th>\n", " <th>char_35</th>\n", " <th>char_36</th>\n", " <th>char_37</th>\n", " <th>char_38</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>503691</td>\n", " <td>405</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>832759</td>\n", " <td>72</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>1289703</td>\n", " <td>72</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>1406406</td>\n", " <td>383</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>1623050</td>\n", " <td>405</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>36</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 55 columns</p>\n", "</div>" ], "text/plain": [ " people_id activity_id date_x activity_category char_1_x char_2_x \\\n", "0 0 503691 405 3 0 0 \n", "1 0 832759 72 1 0 0 \n", "2 0 1289703 72 1 0 0 \n", "3 0 1406406 383 1 0 0 \n", "4 0 1623050 405 1 0 0 \n", "\n", " char_3_x char_4_x char_5_x char_6_x ... char_29 char_30 char_31 \\\n", "0 0 0 0 0 ... 0 1 1 \n", "1 0 0 0 0 ... 0 1 1 \n", "2 0 0 0 0 ... 0 1 1 \n", "3 0 0 0 0 ... 0 1 1 \n", "4 0 0 0 0 ... 0 1 1 \n", "\n", " char_32 char_33 char_34 char_35 char_36 char_37 char_38 \n", "0 0 0 1 1 1 0 36 \n", "1 0 0 1 1 1 0 36 \n", "2 0 0 1 1 1 0 36 \n", "3 0 0 1 1 1 0 36 \n", "4 0 0 1 1 1 0 36 \n", "\n", "[5 rows x 55 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_test_encoded.head()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "cd84c14e-f2e1-f240-46a1-1d7724f86735" }, "outputs": [ { "data": { "text/plain": [ "people_id int64\n", "activity_id int64\n", "date_x int64\n", "activity_category int64\n", "char_1_x int64\n", "char_2_x int64\n", "char_3_x int64\n", "char_4_x int64\n", "char_5_x int64\n", "char_6_x int64\n", "char_7_x int64\n", "char_8_x int64\n", "char_9_x int64\n", "char_10_x int64\n", "outcome int64\n", "char_1_y int64\n", "group_1 int64\n", "char_2_y int64\n", "date_y int64\n", "char_3_y int64\n", "char_4_y int64\n", "char_5_y int64\n", "char_6_y int64\n", "char_7_y int64\n", "char_8_y int64\n", "char_9_y int64\n", "char_10_y int64\n", "char_11 int64\n", "char_12 int64\n", "char_13 int64\n", "char_14 int64\n", "char_15 int64\n", "char_16 int64\n", "char_17 int64\n", "char_18 int64\n", "char_19 int64\n", "char_20 int64\n", "char_21 int64\n", "char_22 int64\n", "char_23 int64\n", "char_24 int64\n", "char_25 int64\n", "char_26 int64\n", "char_27 int64\n", "char_28 int64\n", "char_29 int64\n", "char_30 int64\n", "char_31 int64\n", "char_32 int64\n", "char_33 int64\n", "char_34 int64\n", "char_35 int64\n", "char_36 int64\n", "char_37 int64\n", "char_38 int64\n", "dtype: object" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_test_encoded.dtypes" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b0f47b63-aa60-dc45-855b-8b31326b63cf" }, "source": [ "## modeling\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "dc4fefff-58cb-dc71-1298-6382144eb80f" }, "outputs": [], "source": [ "# defining X and y (features and target label)\n", "\n", "X = df_encoded\n", "y = X.pop('outcome')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "3af920d4-6b60-356c-c391-b77fdd1cb1dc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2197291, 54)\n", "(2197291,)\n" ] } ], "source": [ "# shape of X and y\n", "\n", "print (X.shape)\n", "print (y.shape)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "982b0669-74f9-20b2-7a34-57b42260a42c" }, "outputs": [], "source": [ "'''\n", "\n", "train, test, split the data. hold out 25% for test\n", "\n", "generally if not provided a test set, this would be the way to move forward.\n", "yet I feel something is off in my process and would love feedback!\n", "\n", "'''\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=7)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "3995c761-6775-9222-4177-03e1d803ce17" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "model score 0.992874865971\n" ] } ], "source": [ "# random forest classifier\n", "\n", "model = RandomForestClassifier(77, n_jobs=-1, random_state=7)\n", "model.fit(X_train, y_train)\n", "print (\"model score \", model.score(X_test, y_test))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "_cell_guid": "82ff7025-2a1f-bbac-0c69-ac3f9565ca85" }, "outputs": [ { "data": { "text/plain": [ "array([0, 0, 0, ..., 0, 0, 0])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# predicting test data\n", "\n", "pred = model.predict(X_test)\n", "pred" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d17fb93e-819d-b699-6542-372b013ce5ac" }, "source": [ "### Final thoughts\n", "\n", "While the X_test a split of our traning data, as achieved through train, test, split, it is not the same as the actual test set provided in the data by kaggle. For submission, this would be an issue. For me, this competition is unique in although fairly straightforward, I haven't had to label encode categoricals on a provided test dataset. Generally these are untouched until model prediction time.\n", "\n", "Hopefully some other less experienced users such as myself can make some use of my inital sets in this process." ] } ], "metadata": { "_change_revision": 220, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/322/322554.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "3e63c197-0ba8-5dd4-17e2-2d9e871d1af7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "genderclassmodel.csv\ngendermodel.csv\ngendermodel.py\nmyfirstforest.py\ntest.csv\ntrain.csv\n\n PassengerId Survived Pclass Age SibSp \\\ncount 891.000000 891.000000 891.000000 714.000000 891.000000 \nmean 446.000000 0.383838 2.308642 29.699118 0.523008 \nstd 257.353842 0.486592 0.836071 14.526497 1.102743 \nmin 1.000000 0.000000 1.000000 0.420000 0.000000 \n25% 223.500000 0.000000 2.000000 NaN 0.000000 \n50% 446.000000 0.000000 3.000000 NaN 0.000000 \n75% 668.500000 1.000000 3.000000 NaN 1.000000 \nmax 891.000000 1.000000 3.000000 80.000000 8.000000 \n\n Parch Fare \ncount 891.000000 891.000000 \nmean 0.381594 32.204208 \nstd 0.806057 49.693429 \nmin 0.000000 0.000000 \n25% 0.000000 7.910400 \n50% 0.000000 14.454200 \n75% 0.000000 31.000000 \nmax 6.000000 512.329200 \n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n RuntimeWarning)\n" } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output.\n", "\n", "# Looking at the data\n", "titanic = pd.read_csv(\"../input/train.csv\")\n", "print(titanic.describe())" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "986fa832-49ac-74ab-825b-18592f2e9317" }, "outputs": [], "source": [ "# Missing data\n", "# Fill missing data with median\n", "titanic[\"Age\"] = titanic[\"Age\"].fillna(titanic[\"Age\"].median())" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "6116ee47-4d1b-020d-81aa-df4889814a53" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": " PassengerId Survived Pclass Age SibSp \\\ncount 891.000000 891.000000 891.000000 891.000000 891.000000 \nmean 446.000000 0.383838 2.308642 29.361582 0.523008 \nstd 257.353842 0.486592 0.836071 13.019697 1.102743 \nmin 1.000000 0.000000 1.000000 0.420000 0.000000 \n25% 223.500000 0.000000 2.000000 22.000000 0.000000 \n50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n75% 668.500000 1.000000 3.000000 35.000000 1.000000 \nmax 891.000000 1.000000 3.000000 80.000000 8.000000 \n\n Parch Fare \ncount 891.000000 891.000000 \nmean 0.381594 32.204208 \nstd 0.806057 49.693429 \nmin 0.000000 0.000000 \n25% 0.000000 7.910400 \n50% 0.000000 14.454200 \n75% 0.000000 31.000000 \nmax 6.000000 512.329200 \n" } ], "source": [ "# Check that the Age column has been filled and now has 891 rows\n", "print(titanic.describe())" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "9297cdc8-14ee-8ad2-2656-37f514df2cdc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": " PassengerId Survived Pclass \\\n0 1 0 3 \n1 2 1 1 \n2 3 1 3 \n3 4 1 1 \n4 5 0 3 \n\n Name Sex Age SibSp \\\n0 Braund, Mr. Owen Harris male 22.0 1 \n1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n2 Heikkinen, Miss. Laina female 26.0 0 \n3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n4 Allen, Mr. William Henry male 35.0 0 \n\n Parch Ticket Fare Cabin Embarked \n0 0 A/5 21171 7.2500 NaN S \n1 0 PC 17599 71.2833 C85 C \n2 0 STON/O2. 3101282 7.9250 NaN S \n3 0 113803 53.1000 C123 S \n4 0 373450 8.0500 NaN S \n" } ], "source": [ "# Non-numeric columns\n", "# I want to see all the column names, numeric and non-numeric \n", "\n", "print(titanic.head())" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "2eba4f44-b4f1-a058-1179-0e8260f90c3b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "204\n" } ], "source": [ "# I want to find how many values in the Cabin column are missing\n", "print(titanic[\"Cabin\"].count())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "75229cb3-5d1b-778e-8a74-97e55ac77014" }, "outputs": [], "source": [ "# Converting the Sex column\n", "titanic.loc[titanic[\"Sex\"] == \"male\", \"Sex\"] = 0" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "271f8ae8-fd98-7294-d8f7-f70bc3c01a3c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "0 0\n1 female\n2 female\n3 female\n4 0\n5 0\n6 0\n7 0\n8 female\n9 female\n10 female\n11 female\n12 0\n13 0\n14 female\n15 female\n16 0\n17 0\n18 female\n19 female\n20 0\n21 0\n22 female\n23 0\n24 female\n25 female\n26 0\n27 0\n28 female\n29 0\n ... \n861 0\n862 female\n863 female\n864 0\n865 female\n866 female\n867 0\n868 0\n869 0\n870 0\n871 female\n872 0\n873 0\n874 female\n875 female\n876 0\n877 0\n878 0\n879 female\n880 female\n881 0\n882 female\n883 0\n884 0\n885 female\n886 0\n887 female\n888 female\n889 0\n890 0\nName: Sex, dtype: object\n" } ], "source": [ "# Checking if \"male\" has been replaced by 0\n", "print(titanic[\"Sex\"])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "1d5c2b6b-6a73-1d2f-cab6-706a95609595" }, "outputs": [], "source": [ "# Doing the same for female\n", "titanic.loc[titanic[\"Sex\"] == \"female\", \"Sex\"] = 1" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "5766a9b0-939b-ef95-ff70-f2a11bf79d14" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "0 0\n1 1\n2 1\n3 1\n4 0\n5 0\n6 0\n7 0\n8 1\n9 1\n10 1\n11 1\n12 0\n13 0\n14 1\n15 1\n16 0\n17 0\n18 1\n19 1\n20 0\n21 0\n22 1\n23 0\n24 1\n25 1\n26 0\n27 0\n28 1\n29 0\n ..\n861 0\n862 1\n863 1\n864 0\n865 1\n866 1\n867 0\n868 0\n869 0\n870 0\n871 1\n872 0\n873 0\n874 1\n875 1\n876 0\n877 0\n878 0\n879 1\n880 1\n881 0\n882 1\n883 0\n884 0\n885 1\n886 0\n887 1\n888 1\n889 0\n890 0\nName: Sex, dtype: object\n" } ], "source": [ "# Checking female has been replaced\n", "print(titanic[\"Sex\"])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "47b3c47f-faad-ed79-97b2-a12cbe0c96fd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "889\n['S' 'C' 'Q' nan]\n" } ], "source": [ "# Converting the Embarked column \n", "# First how many of the values are \"Nan\"?\n", "print(titanic[\"Embarked\"].count())\n", "print(titanic[\"Embarked\"].unique())" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "4aacf763-6f40-ab6e-b6a9-1f4d1bbf54b1" }, "outputs": [], "source": [ "# 2 values are missing so will convert those to S\n", "# Converting S to 0, C to 1, and Q to 2\n", "# Need to save the results or else the filled values aren't saved\n", "\n", "titanic[\"Embarked\"] = titanic[\"Embarked\"].fillna(\"S\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "ef57ffb8-2d57-7fcf-ecac-94ba41d2dce1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "['S' 'C' 'Q']\n" } ], "source": [ "# Checking that there are no \"nan\" values any more\n", "print(titanic[\"Embarked\"].unique())" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "4409f26a-d953-8d1d-e597-9c3548a6ae2f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "0 0\n1 1\n2 0\n3 0\n4 0\n5 2\n6 0\n7 0\n8 0\n9 1\n10 0\n11 0\n12 0\n13 0\n14 0\n15 0\n16 2\n17 0\n18 0\n19 1\n20 0\n21 0\n22 2\n23 0\n24 0\n25 0\n26 1\n27 0\n28 2\n29 0\n ..\n861 0\n862 0\n863 0\n864 0\n865 0\n866 1\n867 0\n868 0\n869 0\n870 0\n871 0\n872 0\n873 0\n874 1\n875 1\n876 0\n877 0\n878 0\n879 1\n880 0\n881 0\n882 0\n883 0\n884 0\n885 2\n886 0\n887 0\n888 0\n889 1\n890 2\nName: Embarked, dtype: object\n" } ], "source": [ "# Converting \n", "titanic.loc[titanic[\"Embarked\"] == 'S'] = 0\n", "titanic.loc[titanic[\"Embarked\"] == 'C'] = 1\n", "titanic.loc[titanic[\"Embarked\"] == 'Q'] = 2\n", "print(titanic[\"Embarked\"])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "6df5a6d7-0155-2ff3-f6b1-4e170d3c0ea8" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module has been deprecated in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n \"This module will be removed in 0.20.\", DeprecationWarning)\n" } ], "source": [ "# Linear regression for survival predictions\n", "# import linear_model and cross_validation\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.cross_validation import KFold\n", "\n", "# columns to use in linear regression prediction\n", "predictors = [\"Pclass\", \"Sex\", \"Age\", \"SibSp\", \"Embarked\", \"Fare\"]\n", "# First I will check how just Age can be used to predict survival\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "_cell_guid": "46688dd5-6cb8-5915-6cec-662016019138" }, "outputs": [ { "ename": "NameError", "evalue": "name 'LogisticRegression' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-16-fc9dd678737e> in <module>()\n 3 \n 4 # Initialize the algorithm\n----> 5 alg = LogisticRegression(random_state=1)\n 6 \n 7 # Compute accuracy for all the cross validation folds\n", "NameError: name 'LogisticRegression' is not defined" ] }, { "ename": "ImportError", "evalue": "No module named 'sklearn.logistic_model'", "output_type": "error", "traceback": [ "", "ImportErrorTraceback (most recent call last)", "<ipython-input-17-95ab52867df5> in <module>()\n 1 # Logistic regression\n 2 from sklearn import cross_validation\n----> 3 from sklearn.logistic_model import LogisticRegression\n 4 \n 5 # Initialize the algorithm\n", "ImportError: No module named 'sklearn.logistic_model'" ] }, { "ename": "NameError", "evalue": "name 'pandas' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-18-cdb3c1aebd2f> in <module>()\n 15 # Now I have to repeat the same procedure on the test set\n 16 \n---> 17 titanic_test = pandas.read_csv(\"../input/test.csv\")\n 18 titanic_test[\"Age\"] = titanic_test[\"Age\"].fillna(titanic[\"Age\"].median())\n 19 titanic_test[\"Fare\"] = titanic_test[\"Fare\"].fillna(titanic_test[\"Fare\"].median())\n", "NameError: name 'pandas' is not defined" ] }, { "ename": "NameError", "evalue": "name 'pandas' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-19-886e9f04e9ed> in <module>()\n 36 \n 37 # Submit file\n---> 38 submission = pandas.DataFrame({\n 39 \"PassengerId\":titanic[\"PassengerId\"], \n 40 \"Survived\": predictions\n", "NameError: name 'pandas' is not defined" ] }, { "ename": "ValueError", "evalue": "array length 418 does not match index length 891", "output_type": "error", "traceback": [ "", "ValueErrorTraceback (most recent call last)", "<ipython-input-20-dc997a080293> in <module>()\n 38 submission = pd.DataFrame({\n 39 \"PassengerId\":titanic[\"PassengerId\"], \n---> 40 \"Survived\": predictions\n 41 })\n 42 \n", "/opt/conda/lib/python3.5/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)\n 222 dtype=dtype, copy=copy)\n 223 elif isinstance(data, dict):\n--> 224 mgr = self._init_dict(data, index, columns, dtype=dtype)\n 225 elif isinstance(data, ma.MaskedArray):\n 226 import numpy.ma.mrecords as mrecords\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/frame.py in _init_dict(self, data, index, columns, dtype)\n 358 arrays = [data[k] for k in keys]\n 359 \n--> 360 return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)\n 361 \n 362 def _init_ndarray(self, values, index, columns, dtype=None, copy=False):\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/frame.py in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)\n 5229 # figure out the index, if necessary\n 5230 if index is None:\n-> 5231 index = extract_index(arrays)\n 5232 else:\n 5233 index = _ensure_index(index)\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/frame.py in extract_index(data)\n 5287 msg = ('array length %d does not match index length %d' %\n 5288 (lengths[0], len(index)))\n-> 5289 raise ValueError(msg)\n 5290 else:\n 5291 index = _default_index(lengths[0])\n", "ValueError: array length 418 does not match index length 891" ] }, { "name": "stdout", "output_type": "stream", "text": "418\n" }, { "ename": "ValueError", "evalue": "array length 418 does not match index length 891", "output_type": "error", "traceback": [ "", "ValueErrorTraceback (most recent call last)", "<ipython-input-21-9247945db3ef> in <module>()\n 39 submission = pd.DataFrame({\n 40 \"PassengerId\":titanic[\"PassengerId\"], \n---> 41 \"Survived\": predictions\n 42 })\n 43 \n", "/opt/conda/lib/python3.5/site-packages/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)\n 222 dtype=dtype, copy=copy)\n 223 elif isinstance(data, dict):\n--> 224 mgr = self._init_dict(data, index, columns, dtype=dtype)\n 225 elif isinstance(data, ma.MaskedArray):\n 226 import numpy.ma.mrecords as mrecords\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/frame.py in _init_dict(self, data, index, columns, dtype)\n 358 arrays = [data[k] for k in keys]\n 359 \n--> 360 return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)\n 361 \n 362 def _init_ndarray(self, values, index, columns, dtype=None, copy=False):\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/frame.py in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)\n 5229 # figure out the index, if necessary\n 5230 if index is None:\n-> 5231 index = extract_index(arrays)\n 5232 else:\n 5233 index = _ensure_index(index)\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/frame.py in extract_index(data)\n 5287 msg = ('array length %d does not match index length %d' %\n 5288 (lengths[0], len(index)))\n-> 5289 raise ValueError(msg)\n 5290 else:\n 5291 index = _default_index(lengths[0])\n", "ValueError: array length 418 does not match index length 891" ] }, { "name": "stdout", "output_type": "stream", "text": "418\n" } ], "source": [ "# Logistic regression\n", "from sklearn import cross_validation\n", "from sklearn.linear_model import LogisticRegression\n", "\n", "# Initialize the algorithm\n", "alg = LogisticRegression(random_state=1)\n", "\n", "# Compute accuracy for all the cross validation folds\n", "accuracy = cross_validation.cross_val_score(alg, titanic[predictors], \n", " titanic[\"Survived\"], cv=3)\n", "\n", "# Take the mean of the folds\n", "mean = np.mean(accuracy)\n", "\n", "# Now I have to repeat the same procedure on the test set\n", "\n", "titanic_test = pd.read_csv(\"../input/test.csv\")\n", "titanic_test[\"Age\"] = titanic_test[\"Age\"].fillna(titanic[\"Age\"].median())\n", "titanic_test[\"Fare\"] = titanic_test[\"Fare\"].fillna(titanic_test[\"Fare\"].median())\n", "titanic_test.loc[titanic_test[\"Sex\"] == \"male\", \"Sex\"] = 0\n", "titanic_test.loc[titanic_test[\"Sex\"] == \"female\", \"Sex\"] = 1\n", "titanic_test[\"Embarked\"] = titanic_test[\"Embarked\"].fillna(\"S\")\n", "\n", "titanic_test.loc[titanic_test[\"Embarked\"] == \"S\", \"Embarked\"] = 0\n", "titanic_test.loc[titanic_test[\"Embarked\"] == \"C\", \"Embarked\"] = 1\n", "titanic_test.loc[titanic_test[\"Embarked\"] == \"Q\", \"Embarked\"] = 2\n", "\n", "# Initialize your model\n", "alg = LogisticRegression(random_state = 1)\n", "\n", "# Train algorithm on the training data\n", "alg.fit(titanic[predictors], titanic[\"Survived\"])\n", "\n", "# Predictions\n", "predictions = alg.predict(titanic_test[predictors])\n", "print (len(predictions))\n", "\n", "# Submit file\n", "#submission = pd.DataFrame({\n", "# \"PassengerId\":titanic[\"PassengerId\"], \n", "# \"Survived\": predictions\n", "#})\n", "\n", "# Generate a csv\n", "#submission.to_csv(\"kaggle.csv\", index=False)\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "e235abdf-47d4-2cdc-0281-a5f7f9813af2" }, "outputs": [], "source": "" }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "414cfda5-cc09-c570-fa50-dd67f65e7f12" }, "outputs": [], "source": "" } ], "metadata": { "_change_revision": 177, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/322/322568.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "dde07dc9-6285-691f-427b-c71f08b8bc46" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "database.sqlite\n", "otp.csv\n", "trainView.csv\n", "\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import datetime\n", "\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# You may want to define dtypes and parse_dates for timeStamps\n", "#dateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')\n", "def dateparse(x): \n", " try:\n", " print (\"Inside DateParse\")\n", " return pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')\n", " except TypeError as err:\n", " print('My exception occurred, value:', err.value)\n", " return None\n", "\n", "d=pd.read_csv(\"../input/trainView.csv\",\n", " header=0,names=['train_id','status','next_station','service','dest','lon','lat','source',\n", " 'track_change','track','date','timeStamp0','timeStamp1'],\n", " dtype={'train_id':str,'status':str,'next_station':str,'service':str,'dest':str,\n", " 'lon':str,'lat':str,'source':str,'track_change':str,'track':str,'date':str,\n", " 'timeStamp0':datetime.datetime,'timeStamp1':datetime.datetime})" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "7772f8af-8c48-afe7-bd75-85ca7d34143e" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>train_id</th>\n", " <th>status</th>\n", " <th>next_station</th>\n", " <th>service</th>\n", " <th>dest</th>\n", " <th>lon</th>\n", " <th>lat</th>\n", " <th>source</th>\n", " <th>track_change</th>\n", " <th>track</th>\n", " <th>date</th>\n", " <th>timeStamp0</th>\n", " <th>timeStamp1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>9216</th>\n", " <td>4</td>\n", " <td>Suburban Station</td>\n", " <td>LOCAL</td>\n", " <td>Temple U</td>\n", " <td>-75.18373</td>\n", " <td>39.95694</td>\n", " <td>Marcus Hook</td>\n", " <td>NaN</td>\n", " <td>5</td>\n", " <td>2016-03-23</td>\n", " <td>2016-03-23 00:00:01</td>\n", " <td>2016-03-23 03:21:01</td>\n", " <td>NaT</td>\n", " </tr>\n", " <tr>\n", " <th>276</th>\n", " <td>1</td>\n", " <td>Manayunk</td>\n", " <td>LOCAL</td>\n", " <td>Norristown</td>\n", " <td>-75.21183</td>\n", " <td>40.01689</td>\n", " <td>Wilmington</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2016-03-23</td>\n", " <td>2016-03-23 00:00:01</td>\n", " <td>2016-03-23 00:00:45</td>\n", " <td>NaT</td>\n", " </tr>\n", " <tr>\n", " <th>777</th>\n", " <td>0</td>\n", " <td>30th Street Station</td>\n", " <td>LOCAL</td>\n", " <td>Trenton</td>\n", " <td>-75.17201</td>\n", " <td>39.95482</td>\n", " <td>Chestnut H East</td>\n", " <td>NaN</td>\n", " <td>4A</td>\n", " <td>2016-03-23</td>\n", " <td>2016-03-23 00:00:01</td>\n", " <td>2016-03-23 00:00:01</td>\n", " <td>NaT</td>\n", " </tr>\n", " <tr>\n", " <th>778</th>\n", " <td>2</td>\n", " <td>Germantown</td>\n", " <td>LOCAL</td>\n", " <td>Chestnut H East</td>\n", " <td>-75.16215</td>\n", " <td>40.03613</td>\n", " <td>Trenton</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2016-03-23</td>\n", " <td>2016-03-23 00:00:01</td>\n", " <td>2016-03-23 00:00:01</td>\n", " <td>NaT</td>\n", " </tr>\n", " <tr>\n", " <th>474</th>\n", " <td>1</td>\n", " <td>Melrose Park</td>\n", " <td>LOCAL</td>\n", " <td>Glenside</td>\n", " <td>-75.13471</td>\n", " <td>40.04293</td>\n", " <td>Airport</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2016-03-23</td>\n", " <td>2016-03-23 00:00:01</td>\n", " <td>2016-03-23 00:00:01</td>\n", " <td>NaT</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " train_id status next_station service dest \\\n", "9216 4 Suburban Station LOCAL Temple U -75.18373 \n", "276 1 Manayunk LOCAL Norristown -75.21183 \n", "777 0 30th Street Station LOCAL Trenton -75.17201 \n", "778 2 Germantown LOCAL Chestnut H East -75.16215 \n", "474 1 Melrose Park LOCAL Glenside -75.13471 \n", "\n", " lon lat source track_change track \\\n", "9216 39.95694 Marcus Hook NaN 5 2016-03-23 \n", "276 40.01689 Wilmington NaN NaN 2016-03-23 \n", "777 39.95482 Chestnut H East NaN 4A 2016-03-23 \n", "778 40.03613 Trenton NaN NaN 2016-03-23 \n", "474 40.04293 Airport NaN NaN 2016-03-23 \n", "\n", " date timeStamp0 timeStamp1 \n", "9216 2016-03-23 00:00:01 2016-03-23 03:21:01 NaT \n", "276 2016-03-23 00:00:01 2016-03-23 00:00:45 NaT \n", "777 2016-03-23 00:00:01 2016-03-23 00:00:01 NaT \n", "778 2016-03-23 00:00:01 2016-03-23 00:00:01 NaT \n", "474 2016-03-23 00:00:01 2016-03-23 00:00:01 NaT " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.head()\n", "d['timeStamp0'] = pd.to_datetime(d['timeStamp0'], format='%Y-%m-%d %H:%M:%S')\n", "d['timeStamp1'] = pd.to_datetime(d['timeStamp1'], format='%Y-%m-%d %H:%M:%S', errors='coerce')\n", "\n", "d.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "587ab6ed-eb54-626d-288b-7276267d6eec" }, "outputs": [ { "data": { "text/plain": [ "\"\\ndef getDeltaTime(x):\\n r=(x[1] - x[0]).total_seconds() \\n return r\\n\\n# It might make sense to add delta_s to the next version\\nd['delta_s']=d[['timeStamp0','timeStamp1']].apply(getDeltaTime, axis=1)\\n\\n\"" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", "def getDeltaTime(x):\n", " r=(x[1] - x[0]).total_seconds() \n", " return r\n", "\n", "# It might make sense to add delta_s to the next version\n", "d['delta_s']=d[['timeStamp0','timeStamp1']].apply(getDeltaTime, axis=1)\n", "\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "0a1bc78f-84df-b417-ea23-c50a70a1c995" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 512, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/322/322662.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "bb2e9698-34c6-c194-04a1-f7e79de57cbc" }, "source": [ "Update 2016-08-06: Note that I am just getting started here. Please come back for more." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "09409333-52c4-b978-488a-7e6ce1cf0345" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cdc_zika.csv\n", "\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "bf68da13-af53-7717-ff5a-e5871fce9008" }, "source": [ "### Loading and cleaning data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "5433f629-ab89-c569-d7a3-20c9187f64d6" }, "outputs": [], "source": [ "import os" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "65aa8418-1a8f-1320-f692-cd16d1e58f6e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Removed 7 out of 107619 rows with missing report_date.\n" ] } ], "source": [ "# load data into pandas DataFrame with low_memory=False to suppress warning\n", "zika_df = pd.read_csv(os.path.join('..', 'input', 'cdc_zika.csv'),\n", " low_memory=False)\n", "\n", "keep_rows = pd.notnull(zika_df['report_date'])\n", "zika_df = zika_df[keep_rows]\n", "print('Removed {:d} out of {:d} rows with missing '\n", " 'report_date.'.format(len(keep_rows) - sum(keep_rows), len(keep_rows)))\n", "\n", "# clean report_date as some dates are delimited by underscores and some by hyphens,\n", "# then convert to DatetimeIndex and set as index\n", "zika_df.index = pd.to_datetime([d.replace('_', '-') for d in zika_df['report_date']],\n", " format='%Y-%m-%d')\n", "zika_df.sort_index(inplace=True)\n", "zika_df.index.rename('report_date', inplace=True)\n", "zika_df.drop('report_date', axis=1, inplace=True)" ] } ], "metadata": { "_change_revision": 1329, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/322/322963.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "bdb15564-ec82-6812-d6dd-e8a5dd639220" }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import sklearn.linear_model as sk\n", "\n", "full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv',low_memory=False)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "de864226-f82e-0406-bb04-282b789874bd" }, "outputs": [], "source": [ "## Pull out pass plays and sacks\n", "Pass_Plays = full_data_set.loc[full_data_set.PlayType=='Pass']\n", "Sack_Plays = full_data_set.loc[full_data_set.PlayType=='Sack']\n", "## Form a single set\n", "P_S_data = pd.concat([Pass_Plays,Sack_Plays])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "caf14aae-9bf3-b528-d365-bbe834819221" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Drive</th>\n", " <th>qtr</th>\n", " <th>down</th>\n", " <th>TimeUnder</th>\n", " <th>TimeSecs</th>\n", " <th>PlayTimeDiff</th>\n", " <th>yrdline100</th>\n", " <th>ydstogo</th>\n", " <th>ScoreDiff</th>\n", " <th>PosTeamScore</th>\n", " <th>DefTeamScore</th>\n", " <th>Sack</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " <td>15.0</td>\n", " <td>3561.0</td>\n", " <td>39.0</td>\n", " <td>62.0</td>\n", " <td>10</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " <td>14.0</td>\n", " <td>3506.0</td>\n", " <td>38.0</td>\n", " <td>49.0</td>\n", " <td>10</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3.0</td>\n", " <td>11.0</td>\n", " <td>3328.0</td>\n", " <td>25.0</td>\n", " <td>36.0</td>\n", " <td>22</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " <td>10.0</td>\n", " <td>3280.0</td>\n", " <td>4.0</td>\n", " <td>66.0</td>\n", " <td>10</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " <td>10.0</td>\n", " <td>3254.0</td>\n", " <td>26.0</td>\n", " <td>68.0</td>\n", " <td>10</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Drive qtr down TimeUnder TimeSecs PlayTimeDiff yrdline100 ydstogo \\\n", "2 1 1 1.0 15.0 3561.0 39.0 62.0 10 \n", "4 1 1 1.0 14.0 3506.0 38.0 49.0 10 \n", "9 1 1 3.0 11.0 3328.0 25.0 36.0 22 \n", "11 2 1 1.0 10.0 3280.0 4.0 66.0 10 \n", "12 2 1 1.0 10.0 3254.0 26.0 68.0 10 \n", "\n", " ScoreDiff PosTeamScore DefTeamScore Sack \n", "2 0.0 0.0 0.0 0 \n", "4 0.0 0.0 0.0 0 \n", "9 0.0 0.0 0.0 0 \n", "11 0.0 0.0 0.0 0 \n", "12 0.0 0.0 0.0 0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# https://github.com/maksimhorowitz/nflscrapR/blob/master/R/PlayByPlayBoxScore.R\n", "# description of columns which allows us to create\n", "good_columns = ['Drive','qtr','down','TimeUnder','TimeSecs','PlayTimeDiff','yrdline100','ydstogo']\n", "good_columns += ['ScoreDiff','PosTeamScore','DefTeamScore']\n", "good_columns += ['Sack'] #this is our result field\n", "uncleaned_data = P_S_data[good_columns]\n", "uncleaned_data.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "e7492979-43cb-31ed-f69a-d286c4c0b220" }, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 4, 5])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "uncleaned_data.qtr.unique() #checking what OT is assigned as" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "2760129c-dad4-912e-7cb9-fbdadb721997" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:3: 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", " app.launch_new_instance()\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Drive</th>\n", " <th>down</th>\n", " <th>TimeUnder</th>\n", " <th>TimeSecs</th>\n", " <th>PlayTimeDiff</th>\n", " <th>yrdline100</th>\n", " <th>ydstogo</th>\n", " <th>ScoreDiff</th>\n", " <th>PosTeamScore</th>\n", " <th>DefTeamScore</th>\n", " <th>Sack</th>\n", " <th>qt1</th>\n", " <th>qt2</th>\n", " <th>qt3</th>\n", " <th>qt4</th>\n", " <th>qt5</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>1.0</td>\n", " <td>15.0</td>\n", " <td>3561.0</td>\n", " <td>39.0</td>\n", " <td>62.0</td>\n", " <td>10</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>1.0</td>\n", " <td>14.0</td>\n", " <td>3506.0</td>\n", " <td>38.0</td>\n", " <td>49.0</td>\n", " <td>10</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1</td>\n", " <td>3.0</td>\n", " <td>11.0</td>\n", " <td>3328.0</td>\n", " <td>25.0</td>\n", " <td>36.0</td>\n", " <td>22</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>2</td>\n", " <td>1.0</td>\n", " <td>10.0</td>\n", " <td>3280.0</td>\n", " <td>4.0</td>\n", " <td>66.0</td>\n", " <td>10</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>2</td>\n", " <td>1.0</td>\n", " <td>10.0</td>\n", " <td>3254.0</td>\n", " <td>26.0</td>\n", " <td>68.0</td>\n", " <td>10</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Drive down TimeUnder TimeSecs PlayTimeDiff yrdline100 ydstogo \\\n", "2 1 1.0 15.0 3561.0 39.0 62.0 10 \n", "4 1 1.0 14.0 3506.0 38.0 49.0 10 \n", "9 1 3.0 11.0 3328.0 25.0 36.0 22 \n", "11 2 1.0 10.0 3280.0 4.0 66.0 10 \n", "12 2 1.0 10.0 3254.0 26.0 68.0 10 \n", "\n", " ScoreDiff PosTeamScore DefTeamScore Sack qt1 qt2 qt3 qt4 qt5 \n", "2 0.0 0.0 0.0 0 1 0 0 0 0 \n", "4 0.0 0.0 0.0 0 1 0 0 0 0 \n", "9 0.0 0.0 0.0 0 1 0 0 0 0 \n", "11 0.0 0.0 0.0 0 1 0 0 0 0 \n", "12 0.0 0.0 0.0 0 1 0 0 0 0 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## Split quarter into a set of five binary variables\n", "def quarter_binary(df,name,number):\n", " df[name] = np.where(df['qtr']==number,1,0)\n", " return df\n", "\n", "for x in [['qt1',1],['qt2',2],['qt3',3],['qt4',4],['qt5',5]]:\n", " uncleaned_data = quarter_binary(uncleaned_data,x[0],x[1])\n", "\n", "del uncleaned_data['qtr']\n", "uncleaned_data.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "620ba7aa-1e55-7329-8de1-36652d2e128f" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 236, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/322/322985.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "55ac0791-fcdf-4171-99bd-178699041fd6" }, "outputs": [], "source": [ "import numpy as np\n", "import cv2\n", "import os\n", "import glob\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "ecff4fc1-9a3e-4979-9c73-924da0385cbe" }, "outputs": [], "source": [ "# Load images into dictionary where the keys\n", "# represent patients (first label on training images)\n", "\n", "def load_cv2_images(folder):\n", " imgs, masks, img_ids = {}, {}, {}\n", " for i in range(47):\n", " imgs[i+1] = []\n", " masks[i+1] = []\n", " img_ids[i+1] = []\n", " \n", " paths = glob.glob(folder+'*.tif')\n", " paths = [p for p in paths if 'mask' not in p]\n", " \n", " for p in paths:\n", " # Read in greyscale image and append to path\n", " index = int(p.split('/')[3].split('_')[0])\n", " try:\n", " imgs[index].append(cv2.imread(p, 0))\n", " masks[index].append(cv2.imread(p[:-4]+'_mask.tif', 0))\n", " img_ids[index].append(p.split('/')[3])\n", " except:\n", " pass\n", " \n", " for i in range(47):\n", " imgs[i+1] = np.array(imgs[i+1])\n", " masks[i+1] = np.array(masks[i+1])\n", " \n", " return imgs, masks, img_ids\n", " \n", "imgs, masks, img_ids = load_cv2_images('../input/train/')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "70bd3c12-06b1-463f-8114-cc1ecd47281f" }, "outputs": [ { "data": { "text/plain": [ "dict_keys([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "imgs.keys()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "3212266d-8881-408e-ae3f-05ed22e3a9ad" }, "outputs": [ { "data": { "text/plain": [ "((120, 420, 580), (120, 420, 580))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "imgs[1].shape, masks[1].shape" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "fc47dacb-be68-4665-ab6d-8971537ad329" }, "outputs": [ { "data": { "text/plain": [ "865" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The function below will find any\n", "# image similar to the input image\n", "\n", "def find_pairs(compare_img, compare_mask, compare_id,\n", " imgs, masks, img_ids,\n", " compare_index, matches):\n", "\n", " threshold = 23000000\n", " for i, (img, mask, img_id) in enumerate(zip(imgs, masks, img_ids)):\n", " if np.abs(compare_img - img).sum() < threshold \\\n", " and i != compare_index \\\n", " and (compare_mask.sum() == 0) != (mask.sum() == 0):\n", " matches.append((compare_img, compare_mask, compare_id, img, mask, img_id))\n", "\n", " return matches\n", "\n", "matches = []\n", "for j in range(47):\n", " for i, (img, mask, img_id) in enumerate(zip(imgs[j+1], masks[j+1], img_ids[j+1])):\n", " matches = find_pairs(img, mask, img_id,\n", " imgs[j+1], masks[j+1], img_ids[j+1],\n", " i, matches)\n", "len(matches)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "9fa0bb7b-2537-4c47-b3fe-de630029237e" }, "outputs": [ { "ename": "NameError", "evalue": "name 'plt' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mNameError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-6-9f5594caa960>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[0munique\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\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---> 10\u001b[1;33m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 11\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[0mi1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined" ] } ], "source": [ "# Print the matches, avoiding duplicates\n", "\n", "repeats, unique = [], []\n", "for i, m in enumerate(matches):\n", "\n", " # Using pixel sums as an ID for the picture\n", " if m[0].sum() not in repeats\\\n", " or m[3].sum() not in repeats:\n", " \n", " unique.append(m[0].sum())\n", " fig, ax = plt.subplots(2, 2)\n", " if m[1].sum() == 0:\n", " i1, i2 = 1, 0\n", " else:\n", " i1, i2 = 0, 1\n", " \n", " ax[i1][0].imshow(m[0], cmap='hot')\n", " ax[i1][0].set_title(m[2])\n", " ax[i1][1].imshow(m[1], cmap='hot')\n", " ax[i1][1].set_title(m[2][:-4]+'_mask.tif')\n", " \n", " ax[i2][0].imshow(m[3], cmap='hot')\n", " ax[i2][0].set_title(m[5])\n", " ax[i2][1].imshow(m[4], cmap='hot')\n", " ax[i2][1].set_title(m[5][:-4]+'_mask.tif')\n", " \n", " fig.subplots_adjust(hspace=0.4)\n", " plt.show()\n", " \n", " repeats.append(m[0].sum())\n", " repeats.append(m[3].sum())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "ca0087aa-d546-4040-867e-5654742deaf1" }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Number of cases found\n", "len(unique)" ] } ], "metadata": { "_change_revision": 46, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/323/323056.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "f6291e87-316b-7197-394a-8158afa6da18" }, "source": [ "This is an attempt to find out what factors are involved in determining the likelyhood of a QB's getting sacked. It's very much a work in progress!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "bdb15564-ec82-6812-d6dd-e8a5dd639220" }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import sklearn.linear_model as sk\n", "from sklearn import preprocessing\n", "\n", "full_data_set = pd.read_csv('../input/nflplaybyplay2015.csv',low_memory=False)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "de864226-f82e-0406-bb04-282b789874bd" }, "outputs": [], "source": [ "## Pull out pass plays and sacks\n", "Pass_Plays = full_data_set.loc[full_data_set.PlayType=='Pass']\n", "Sack_Plays = full_data_set.loc[full_data_set.PlayType=='Sack']\n", "## Form a single set\n", "P_S_data = pd.concat([Pass_Plays,Sack_Plays])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "caf14aae-9bf3-b528-d365-bbe834819221" }, "outputs": [], "source": [ "# https://github.com/maksimhorowitz/nflscrapR/blob/master/R/PlayByPlayBoxScore.R\n", "# description of columns which allows us to create\n", "good_columns = ['Drive','qtr','down','TimeUnder','TimeSecs','PlayTimeDiff','yrdline100','ydstogo']\n", "good_columns += ['ScoreDiff','PosTeamScore','DefTeamScore']\n", "good_columns += ['Sack'] #this is our result field\n", "uncleaned_data = P_S_data[good_columns]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "af8ed74c-4217-e477-c63d-e687c22d3553" }, "outputs": [], "source": [ "#uncleaned_data.loc[uncleaned_data.down.isnull()==True].head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "e7492979-43cb-31ed-f69a-d286c4c0b220" }, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 4, 5])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "uncleaned_data.qtr.unique() #checking what OT is assigned as" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "2760129c-dad4-912e-7cb9-fbdadb721997" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:3: 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", " app.launch_new_instance()\n" ] } ], "source": [ "## Split quarter into a set of five binary variables\n", "def quarter_binary(df,name,number):\n", " df[name] = np.where(df['qtr']==number,1,0)\n", " return df\n", "\n", "for x in [['qt1',1],['qt2',2],['qt3',3],['qt4',4],['qt5',5]]:\n", " uncleaned_data = quarter_binary(uncleaned_data,x[0],x[1])\n", "\n", "del uncleaned_data['qtr']\n", "#uncleaned_data.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "8b7ed31d-c7e1-d2a3-719d-64961b5032ad" }, "outputs": [], "source": [ "## We have some null values in the down columns which I can't explain, drop them and any other \n", "## nulls\n", "\n", "cleaned_data = uncleaned_data.dropna()\n", "explanatory_variables = cleaned_data.columns" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "620ba7aa-1e55-7329-8de1-36652d2e128f" }, "outputs": [], "source": [ "def pandas_to_numpy(df):\n", " y = df['Sack'].values\n", " del df['Sack']\n", " X = df.values\n", " X = preprocessing.scale(X) # 0 mean and 1 std norming\n", " return X,y" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "047a94a7-9394-20af-9daf-029509f280e0" }, "outputs": [], "source": [ "X_all, y_all = pandas_to_numpy(cleaned_data)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "06301217-8a1b-dafa-fa63-6bca5ed08860" }, "outputs": [], "source": [ "logreg = sk.LogisticRegressionCV()\n", "logreg.fit(X_all,y_all)\n", "coef_array = np.abs(logreg.coef_) #careful now with signs when interpreting results" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "be6c5162-64c2-9dac-e360-006c22965d7e" }, "outputs": [], "source": [ "x = np.arange(1,coef_array.shape[1]+1,1)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "c25138cf-b052-6596-1d21-797273da5868" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 0x7f94b0752390>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f94b0752470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x,coef_array,marker='x',color='r')\n", "plt.axhline(0, color='b')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "c83df925-2e32-9b32-6935-cd440b9331f7" }, "outputs": [ { "data": { "text/plain": [ "'down'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "explanatory_variables[1] #note 0 vs 1 based indexing ~ this is the largest value" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "ec41b8c7-2a8b-e8ae-8cc3-0faeaf7f532f" }, "outputs": [], "source": [ "## Next phase is to add a boring constant variable to point out how often a pass play...\n", "## ...will not end in a sack" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "3e07b88a-4e24-8f52-eaa5-8178476c2823" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 133, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/323/323155.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "1688f230-a798-e5be-0eff-e6448b7cbbb1" }, "source": "" }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "90a2ed25-74e3-8d02-d0f4-e5b2cac1f99d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "consumer_complaints.csv\ndatabase.sqlite\n\n" }, { "ename": "NameError", "evalue": "name 'sqlite3' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-1-25e0d1b1f997> in <module>()\n 12 print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n 13 \n---> 14 con = sqlite3.connect(\"../input/database.sqlite\")\n 15 cur = con.cursor()\n 16 sqlString = \"\"\" \n", "NameError: name 'sqlite3' is not defined" ] }, { "name": "stdout", "output_type": "stream", "text": "consumer_complaints.csv\ndatabase.sqlite\n\n" }, { "name": "stdout", "output_type": "stream", "text": "consumer_complaints.csv\ndatabase.sqlite\n\n" }, { "name": "stdout", "output_type": "stream", "text": "consumer_complaints.csv\ndatabase.sqlite\n\n" }, { "name": "stdout", "output_type": "stream", "text": "(1290253, 'Mortgage', 'I have an open and current mortgage with Chase Bank # XXXX. Chase is reporting the loan payments to XXXX but XXXX is surpressing the information and reporting the loan as Discharged in BK. This mortgage was reaffirmed in a Chapter XXXX BK discharged dated XXXX/XXXX/2013. Chase keeps referring to BK Law for Chapter XXXX and we keep providing documentation for Chapter XXXX, and the account should be open and current with all the payments \\n', 'JPMorgan Chase & Co.')\n" } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import sqlite3\n", "import nltk\n", "import numpy as np\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "import scipy\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "con = sqlite3.connect(\"../input/database.sqlite\")\n", "cur = con.cursor()\n", "sqlString = \"\"\" \n", " SELECT complaint_id, product, consumer_complaint_narrative, company\n", " FROM consumer_complaints\n", " WHERE product = \"Mortgage\" AND \n", " consumer_complaint_narrative != \"\"\n", " \"\"\"\n", "cur.execute(sqlString)\n", "complaints = cur.fetchall()\n", "con.close()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "4f84ce6a-f4f7-10cc-b863-1d9474fa4b64" }, "outputs": [], "source": "" }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "535a500e-a23a-5aac-539d-0fd25fb4b321" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "(1292603, 'Mortgage', 'I was offered a trial modification by my servicer - Carrington. I accepted the offer. The first payment was due in the month of XXXX. As a member of the XXXX XXXX XXXX XXXX, I received 30 day XXXX in the month of XXXX. None-the-less I called Carrington to make the XXXX trial modification payment over the phone. Carrington refused to take the payment over the phone but instead demanded that I send via XXXX XXXX. Because of my service I explained to Carrington that I would have limited access to XXXX XXXX and I faxed my XXXX. Carrington stood firm in their unreasonableness. I was able to get to a XXXX XXXX office on XXXX XXXX, but Carrington refused to accept the payment because they claimed it was late. My Authorized XXXX Party representative called Carrington on XXXX separate occasions - XXXX XXXX, XXXX XXXX, and XXXX XXXX, 2015. The purpose of each call was to determine whether or not there was a scheduled foreclosure sale date. Each time the Authorized XXXX Party Representative was told there was not a foreclosure sale date. Carrington completed a foreclosure sale on XXXX XXXX, 2015. At the time of the foreclosure sale I remained out-of-town due to the XXXX mentioned above. \\n', 'Carrington Mortgage Holdings, LLC.')\n" } ], "source": [ "complaint_list=[]\n", "for i in range(len(complaints)):\n", " complaint_list.append(complaints[i][2])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "2b7de80e-4977-c39d-046c-48b084712667" }, "outputs": [], "source": "" }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "aa9d7182-88b9-abac-51d9-6728efa7dc92" }, "outputs": [], "source": [ "stopwords = nltk.corpus.stopwords.words('english')\n", "stopwords.extend(['wells', 'bank', 'america', 'x','xx','xxx','xxxx','xxxxx',\n", " 'mortgage', 'x/xx/xxxx', 'fargo', 'mortgage', '00'])\n", "vectorizer = CountVectorizer(stop_words=stopwords)\n", "dtm = vectorizer.fit_transform(complaint_list)" ] } ], "metadata": { "_change_revision": 180, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/323/323429.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "98f8f786-ed56-210b-12b9-06ab4ee41d94" }, "source": [ "## Exploration of the date features ##" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "12492e4d-5f66-b128-384b-aceb6dc55e81" }, "outputs": [], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "551a429c-8526-cbe7-2c4a-dbfa5ab1d34a" }, "outputs": [], "source": [ "train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])\n", "test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])\n", "ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])\n", "\n", "df_train = pd.merge(train, ppl, on='people_id')\n", "df_test = pd.merge(test, ppl, on='people_id')\n", "del train, test, ppl" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "7f9f089c-c992-e613-86a6-fa3065c0cf9f" }, "source": [ "First just so we know what we're dealing with, let's take a look at the range of the two date variables" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "76de3985-4ec8-9a33-e29e-a3ad7f1eb5db" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "Start of date_x: 2022-07-17\n End of date_x: 2023-08-31\nRange of date_x: 410 days 00:00:00\n\nStart of date_y: 2020-05-18\n End of date_y: 2023-08-31\nRange of date_y: 1200 days 00:00:00\n\n" } ], "source": [ "for d in ['date_x', 'date_y']:\n", " print('Start of ' + d + ': ' + str(df_train[d].min().date()))\n", " print(' End of ' + d + ': ' + str(df_train[d].max().date()))\n", " print('Range of ' + d + ': ' + str(df_train[d].max() - df_train[d].min()) + '\\n')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b8534be0-0f9b-d713-467a-32895e6a3037" }, "source": [ "So we can see that all the dates are a few years in the future, all the way until 2023! Although we now though that [this is because the data was anonymised](https://www.kaggle.com/c/predicting-red-hat-business-value/forums/t/22642/data-question/130058#post130058), so we can essentially treat these as if they were the last few years instead.\n", "\n", "We can also see that date_x is on the order of 1 year, while date_y is 3 times longer, even though they both end on the same day (the date before they stopped collecting the dataset perhaps?)\n", "\n", "----\n", "\n", "We'll go on more into looking at how the two features relate to each other later, but first let's look at the structure of the features separately.\n", "\n", "### Feature structure ###\n", "\n", "Here I'm grouping the activities by date, and then for each date working out the number of activities that happened on that day as well as the probability of class 1 on that day." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "2793bc37-e9a8-b2b2-0e7b-84eb5255c07d" }, "outputs": [ { "data": { "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f0baef39588>" }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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IotDQgpgRdf75dgMbFG9FlCOGlQMAACC6+vpskFMouIlaRRRBFFAZgig0tCBa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\nHFF57z/kZMO0wvKy10s5+XsR7xkomlfz1LwqjqitLeDznwe+4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46/X98RRWXl\n7cSkI2pra1K0SmXlBGEHcrHddEcUOaKIIuQpKy86Nc82IYo6otpNXMiQ79WyD/SWgdXV6rE8uZ14\nNM90RxStXWfJ83I8CkD9FV8x/pwWzvmnGGPPYIxtcs7Px/9eFaIWDXmjRNE8e2lrNK+JsvI0R1Te\nqXlJ/VcymgcgsSPKdVxwRHTSbhkmO6K2tkScQm6XysoJonlMR/OqOKJIiCLykqesvKgjyrZoHnVE\ntZv4Q+BFj+WZYGMDuPba6tsx4YhyXXJEFSXPLcLnAVzHGLuaMdYF8CYAt6lfwBi7Vvn4hQC6OhFq\n0VEdUSRE2UkY6aN5ruMijFJmbhpkYwM4rxwdSYXe8oTF0ExZuc4RpZ2al+GI0rm9ggDojB1RnqOP\n5pEjqp3I8bVRVJ8jisrKCaI5TJeV6zqiso5vzyMhiiiGaUeUjdE8ckS1m7gwv+hF5SZ4wxuA3/md\n6tvp96dF6LIdUbqEh4SEqFkyX2LOecgYeyeAj0IIV/+Vc34XY+zt4q/5bwP4PsbYDwEYAdgH8MY6\nd9pWul1xATtzBrj88qb3htBhQzSv3xe57/198XGusnLYVVYej+b54fQXO06eaJ43/n59NM9loqyc\nTtrtgrGJkGnCESWfdFFZOUHYgeqIarIjiqJ5RBHyCFGL1hFFZeXtIn4+JEdUNp3OxDlfhdXVWUeU\n6WgeCVGz5FpCcM4/AuD62Of+i/LxbwD4DbO71j48D/jGN4QIRTdJdpIUzWNg4ODgnIPVPAuasUk8\nb2WFw498baF30x1RqdE8ZWqe53jY86dXbq47juYluL2CAOgg2xHFweF2OACaz90mpBBV1RG1vT1Z\nYMQ7ouhiThDNYNIRVbUjyjYhgLCX9XXgiSeyHVFnz2ZvazQS/0nHri3oHFFUVt4e4mkEckTNDyor\nbwaqPzNItwt8/esUy7OZpGgeY+xQjJoHUojyIx8dp6MVv5ruiPJ94dySxB1R8nXMjOYliGwdZ1xW\nnjA1T/5OOp35/tuJ6phwRMknUXKhrzqiKJpHEM0hz+8mBGHqiCLmhUlHlHRD1fzcsjCqI8r3xbpN\njrcn7EcXzSNH1HzQlZWTEFU/JEQZxPOEmnr11U3vCZGEGimL00RheZJjCFA6otj8O6IcZ1I4LlEd\nUVlT8+T3JkXzREfUOJrn6svKARHPc0mIah2mHFHAtBBFjiiCaJ66O6LyHN/UEUUURZaVp3W/FBWi\nbEN1RMlYnm1iGZGMLppH57j5EHdEle2IIiGqGCREGUSeLMgRZS8hD7XRPKAZISpJqAGadUQBszcI\nall51tQ8+cQ8bWqeWlaeVO7HmAPXm0+JPGEOUx1RAJWVE4RtmJyaVzWaRzdpRF4GA+DCBeH0Trou\n5S0r39qyb2IeMO2IoqLy9kFl5c2hi+aZ7IjiXKyJSYiahoQog8g3LAlR9qJ2G8VpSojSRdeAWFk5\nn29ZOTB7gyCjeZzzmal5fjRbVp7liPLG0TzP1UfzAMCBS0JUCzHpiJIdUVRWThB2oDqimiwrjyK6\nSSPyMxiIdZfnJbuEFtERRbSHeEcUlZXPDxPRPPmQRkcYinsjcihOQ0KUQeSCiKJ59qJ2G8UhR9Q0\n8QuijOZFPAIDg8PE6cNzvfSOKDepI0p8PqmsHAAc5sJ1KZrXNqR7qcrTnzRHFEXzCKI5THdExaN5\neYUoYOLSJYgs1teFgJR2c5nXEWWrEBV3RFFRebsgR1RzeN7kugaYj+ZRLE8PCVEGIUeU/dgWzUuK\nrgET9bzJaF7cEdXrzfZspXVEpU3N82Q0z+0kPkFw0Gw0L4xCfPrhTzf289uKdC/J93AZdB1RVFZO\nEM1juiOqjCNKrrfoJo3Ii3REpV078jqitrbsFKJURxRF89qHriOKHFHzgTFx/Mt4numychKi9JAQ\nZZBuV9x0XXFF03tCJGFjNC9JiJJuEob5l5UDs44oGc2Li3lZU/OSOqKkU6qbFs1ruKz8q09+FW/5\n4Fsa+/ltRe2IqqusnIQogmgGk0JUlY4ogIQoIj95HVFnzybHayRPPUUdUYR5yBHVLGpP1HBotiOK\nhCg9JEQZZDAArrmG1GuboWheftTIRBRNivvyOqKCkMOPfG0HVt6OKAYHbqc5R9Sev4dhOGzs57cV\ntSOqrCNKnkdlR1S8rJwu6ATRDCbLyqt0RAF0k0bkZzAQN5lpQtQNNwBXXQW84x2iXDgJ26N5nFNH\nVBuJPwAmIWq+qELUN78p7umLQEJUcUiIMshVVwFf/GLTe0GkYUs07+hR8bQqqUMJiJWVo9mychnL\nY2xWiPIcb6as3HWBkIuvY5pmvjCcCFGpjii4cOYkRP2vr/4v3PnYnVOf2/P3MApHc/n5i0QdjijX\nFYtrmeEnRxRBNIPpsnJdR1TWdqkjiiiKfKiR9t7yPOC224B/+AfgZ34mWYyyNZrnOJNjihxR7YOi\nec2iRnO/9jXgWc8q9v0kRBWHhCjD0EnfbmyJ5skcf1KHEmCXI0oWlQOzr2FSNC/gyW4v4YgaR/M6\nXnJH1ByjeR+670P47COfnfocCVHlMOmIkkIUMHFFUVk5QTSH6bJy6ogi5kGnI+oFskTO9XXg9tuB\nT34S+M//Wf81tkbzgMn6ksrK2wdF85pFOqJ2dkREt2jns+uSEFUUEqKIpSItmuc6LsJoPu4buVCw\nOZqnc0QBs69hUjQvxEgbywPEv63bGUfzOp3EEzfgzM0Rte/vY9efHpdDQlQ5TDiiXFf8J59iy+3K\nEnRyRBFEM1BHFNFWBoN8145jx4C3vx340pf0f29rNA+YxPPIEVUPdd4nxKN55IiaL/2+cETdfTdw\n/fXFr2+dTnK/HDn59ZAQRSwVtkTzighRjDVTVq46omRROZCvI6qQIyormufO59++5+9hz9+b+dww\noI6oophwRAFiQZ3kiKILOkE0A3VEEW1lfT3/jf1gIJwROmyN5gHTjigSosxycXQR1/7f19a2fXJE\nNYucmve1rwHPfnbx76doXnFIiCKWCtuieaMw3TV02BGV1ppZE+qT6qloHs+O5glHVHrsUP5dLyWa\nN8+y8j1/D7sjvSOqide/zcgnslUcUXI7qhClClx0QSeIZqi7IypP5I+EKKIMeR1R8muThCibo3ny\n+ktl5ebZ8/fw8PbDta0JSYhqFhnNIyFqfpAQRSwVtkzNa0s0T3VETUXzFFeZ53rwQ11Zefq/Tf5d\nt5MyNY/Pr6x8P9BH8zg4Qt7c5L42YsoR5XmzQtRoRI4ogmgSGxxR0tVCZeVEEUwKUW1wRFFHlFlG\n4QgRj7Af7NeyfSorbxZZVl6mqBwgIaoMJEQRS4Vt0Tw/srusXO2IKhLNO+yISpgIGAQ4/Luum9wR\n5WB+ZeW6aJ4UpiieVwwTHVEA8OpXAydPTv5MZeUE0Tzy2mDiOKSOKGKerK9XF6I4tzuaRx1R9SEf\nul4cXaxl+/GOKHJEzRfpiLrrLnJEzQsSooilwsZonq1ClHpBLDM1L+R+auywNy4r73nJjijAAXPm\nGM3TOKIAUGF5QUw5on7/96cX0lRWThDNIwtZTTmi4tE8EqKIuijqiLqo0Rv29sS1yFY3HnVE1Ycf\niZPVzjDBKlcRckQ1S78PnD8PPPoocG2JKjASoopDQhSxVNgYzUtyDR2WlaO5svIkR1SuqXk8SBT9\ngkCUlAOzHVF/+IfARz4y3g46cDqJKpVR9v19bVk5QEJUUUw5ouJQWTlBNI8cUU1l5UTbKOKIWl/X\nO6JsjuUB5Iiqk7odUdQR1Syrq2JS5rXXlhMAZWxdBwlRekiIIpYKm6J5BwdjR5ST7YjiaKasXOeI\n0kXz5FMiiesCEYJUkS3JEfU3fwN84hPiY4fPT4hKKisHSIgqiilHlG670hFFF3SCaAbVEVVVEC4b\nzZM3CXSTRhTBREeU7UKUfNBJZeXmkWvdOqN5cUcUnePmR78PfOEL5fqhgGxHFD1AnYWEKGKpsC2a\n54d2d0TpysrjU/M8x9NH8+BnOqI6TmemrHxvD3j88fF20AXc+YhAuo4o+edhSB1RRajLESW3S44o\ngmgO6Ygy0REVX7hH40tdloAtj39b41GEnRQRotbWRF9MFFt+2dwPBQjhgsrK60E6onZG9UXz4h1R\nFM2bH/0+cM895fqhgHQhirpN9ZAQRSwVtkXzhikdUVEkFuNNdkQlRvOc7GhehCC1I8rzGL76E19F\nz3OnrKyqEMXgAa6v3YZJgiiAH/nUEWUIGaEz7YiisnJzvOuOd+FbO99qejeIFiIX23V0ROXdpvwa\ncgsQRSgSzXMcEdWJ90Q99RSwsWF+30zR64l99DwSMUxTtyOKonnNsroqhhHUIUSRk18PCVHEUmFL\nNK/TARgDDvzsjigbHFFZ0Tx9WXl6R5TrAtefuH7mxK0KUQ73wOYgRO37YhQvdUSZQUbo6nBEUVm5\nGd5/1/vx9fNfb3o3iBYiezBMdkTxcfo877HNmPjZdJNGFGEwKCbO6OJ5tkfzul3g7FmK5dXBoSOK\nysoXkn5f/J+EqPlBQhSxVMRFFJV5Cz69HrA/zJ6ax1gzZeVJjqg8U/McB4hYcjRPvdmQMQ/J/j7w\nxBPj7fD5OKL2/D0wsMSOqGFA0bwi1NURRWXl5jgIDmYcgASRB9URVfU4dJzx9WJ8iSuyWPc8EqKI\nYhSJ5smvjwtRtkfzej0SoupiHh1R8WgenePmx+qquB6dPl3u++V6Nx7nBUiISoKEKGKpCKPQimge\nIISdAz9fRxTndpWVx6fmyadEEtcFIp5cVq5Gq3SOqCeeECdyNi9HVLCPzf6m1hG15q2RI6ogdXZE\nUVm5GQ6CgxnhlSDyYNIRBUxfA4pss9OhmzSiGM98JvDc5+b/+iRHlM3RPHJE1ce8p+ZRWfl86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5JucxJo5r6ogilgFViCKHynIjHVF1C1Fnz4ppjeSgaR8UzSsGvcWJpSGIZiNlKmU7olzmlnJE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VFEiYO36wvqYTKYcLLvZPJrXcNd+NF7P8LxnuOy1xH/nbfMvwUvnXspbT3meR4vX3gZt86/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\n", "text/plain": "<matplotlib.figure.Figure at 0x7f0bbead5e48>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "date_x = pd.DataFrame()\n", "date_x['Class probability'] = df_train.groupby('date_x')['outcome'].mean()\n", "date_x['Frequency'] = df_train.groupby('date_x')['outcome'].size()\n", "date_x.plot(secondary_y='Frequency', figsize=(20, 10))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "fef5b0e8-22ec-8baf-8b75-f58519e585a7" }, "source": [ "This plot shows some very interesting findings. There appears to be a very apparent weekly pattern, where on weekends there are much less events, as well as the probability of a event being a '1' class being much lower. \n", "\n", "We can see that during the week the classes are pretty balanced at ~0.5 while on weekends they drop to 0.4-0.3 (this could be very useful information). \n", "\n", "We can also see some very big peaks in number of activities around the September-October time frame, which we will look into later in the EDA. But first, let's do the same with the other date feature, date_y!" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "000dd8bb-15ca-5d5f-eaca-260becae2e06" }, "outputs": [ { "data": { "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f0bafbb35c0>" }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Vr3wF5eXlePzxxx2t/9BDD+HJJ5/Eiy++iObmZmzYsAFSyr5E3Ycccggef/xxbN++HTNm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\nnSPKLll5JvuX6TiiGJoXLDnviNKheUE5oi66CFi0yHrUOh0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tlhJ8uGauByFGlPb5HT5M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\nB8QBUTnNyn3PhRfHuoN8aoqSq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yZyeURJYQJwrmtefHE/I4rbwWVdALilee3xJaA7+oyo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\nXEc4Lq8hTHkCQNJEYDkhY+/4LGi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"text/plain": "<matplotlib.figure.Figure at 0x7f0baef390b8>" }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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XA9hu2HZndFk5gB2G5Tuiy3ptI6XsAtAghBhqVyCG5hHf6egA9u3zuxSEEEII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\nQohsIYEoIYQQQgghhBBCCJEt/g9WYUfxgqumQgAAAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f0baef39198>" }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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+FJ9uEqZ9DtQDUeBOa+1/G2PqrLWDfPPUWmsHG2P+ALxhrZ0bn/7fwLNADXCd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PyZPKlMcj8+P9/4DmVShWwILl5MuVe9xz5gTn9WR0bIT80m91NBKvWcfcz6W28FvvAF9/nzfdyy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\nI1/WS1LJKana/e3o8Hdkgk/jccDrcmlSUo3OjWb+rzeTajI6ienYNM5Mn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t2PLxob5Tfr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POgOdGJ4dVvUqywWnAzSX6umTT6Mz0JmRl3bVd2EqSe06cna/ttp8sqAQpMTBRGQCiVQC7XXt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\nVfvx5cCbqEDV+1VbSk+iBvrPBXax/bH27JXtz1HB15fZPuEvvjy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"text/plain": "<matplotlib.figure.Figure at 0x7f0baf0a7518>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "date_y = pd.DataFrame()\n", "date_y['Class probability'] = df_train.groupby('date_y')['outcome'].mean()\n", "date_y['Frequency'] = df_train.groupby('date_y')['outcome'].size()\n", "# We need to split it into multiple graphs since the time-scale is too long to show well on one graph\n", "i = int(len(date_y) / 3)\n", "date_y[:i].plot(secondary_y='Frequency', figsize=(20, 5), title='date_y Year 1')\n", "date_y[i:2*i].plot(secondary_y='Frequency', figsize=(20, 5), title='date_y Year 2')\n", "date_y[2*i:].plot(secondary_y='Frequency', figsize=(20, 5), title='date_y Year 3')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "134daec9-4e41-7710-236a-5d217452fc09" }, "source": [ "There also appears to be a weekly structure to the date_y variable, although it isn't as cleanly visible. However, the class probabilities appear to swing much lower (reaching 0.2 on a weekly basis)\n", "\n", "We have to take these class probabilities with a grain of salt however, since we are hitting very low numbers of samples in each day with the date_y (in the hundreds).\n", "\n", "----\n", "\n", "### Test set ###\n", "\n", "However, all of this information is useless if the same pattern doesn't emerge in the test set - let's find out if this is the case!\n", "\n", "Since we don't know the true class values, we can't check if the same class probability appears in the test set, however we can check that the distribution of samples is the same." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "019d1d6c-c44c-3d51-1d81-c255f088cf23" }, "outputs": [ { "data": { "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f0bb0391198>" }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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veAWXXXYZ7e3tPP300yxevJiXv/zlvOY1r+k7l37VXf0fH3/88bzqVa8CsuVz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zpf7L2cHBY7mbyBroU5EbeBDpKri73HxYIjq1z7+t5eUGPGo4XJTBKP1TjIsgouUpniXcKR+RAO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ua9\nkd3V1RyebxVx6+bNm3/zvs//eZ93/+O79VQOQ9evWBU0G/TTYH8iq6KJyzYLkcPJWS+itkm905B8\nXm01MGrVE1kBh5vqPiexz11bQ1edu5XwHQa70UG5OZ7cVDsl3BZ1iiy3Sb6Tz2Yuj1GcbG4HgwJC\ne3TfeDaHTVCv3HRaTPT3lKZXW00ME6jh4GbZQneKl1ZHSVeXOh678vl9KjpLqrkh+UwURf7fl/6C\nk4OPqi4r3IVLnONKcnQw1dVUCLqUEUYWrY1K884wA7vtzl9PGEWXbJl7qZNX1GRiF3NBHy3N/qfz\n58tf40P3vk3RvcKmBa6k9yeykuUsPvPhMbUgCOhaYS4dQDTAsPSopUvy2OlpHgm9hb9+fvLyQlGE\nP/szePw9+X29q74e/zpLniXZnKHeqUNHnsjyeqHXG5UWuk1uML0+iqwr6VVi9oMPMfx+ENpe8g35\ncfEf/vET/E+//Qu3FHMp8TwfeHCkgPrQ4kf4i8ufUfQ8zz4Lfdd1lr0HK7K8NgetfXxAb8d272Xe\nvHQwkWUZhLme2N8n64WVTTRtN3ORww9E5uagldm/tLAmZpnyqFRkaQ2yXmzn1m/wuaufY6O0Mfbd\nP/0TPJn8e156p5/iT5v5yNOzfOzXf+/An/OXfymiWfgy71l656HPNDUFg3J4Xz/PXL1Ar9/nfU8c\nvsdF3G5aHEyQnEuco3ptnMg6fVKLfudtfOHGFw79OXtRqbcRDRWWovs/n1MbZi0rv+6kynlcxvFY\n4KVf+e88++v/263yx3cvvJuOdY3nV68f+DyldoGgQ97vd94bY7OsjMhq9Ct4bcoP7jxmL9n6/mv+\nU+vPIW49wmOPanAa3OTr/0pk3VFkG1lou3jo7OF+Fh/8ILQKwX1lkluFDHatssVn0XGalxMHE1m/\n89wf8aDuhxWpZiyDECv76BJXkmlM/f2TwMemH0Hj3ObR0OPotXqmPdLyhttRG2R56JQyws6lC+7b\nDUupgm0vTCagZ6LRGZU5ZEs1tAr9eObcs2xVNxT/vBe2LzBrkVcEWfQWTgdO81DkEVJteUVWtVOF\ngZb5KXXqFIveDLqWJEDKFjqIxiKLYeXv7MR0kI4+I+mO0myCxqbeHwsYdgLs6+n0RkTWQBzQELIc\nm1J3guS12+gK+5cWNnUJ6p06S56DT95ux15F1hdufIF3L7xb1f9/NeE0jnctbHd7iPoasyF1ifNe\nGLBSqNXpaSsEneoTJIfJBvoG5YqUKRVFaOo3OTN/OJEVCgE1qdnyLsrNOmbt5IosAINgJX8zcs+3\n00x7DieyDNjIV+8+s/dkoYrQcdwqO59zzyA4N9nZGUb2uWIbtG08VmWk5Kw3TKq+f9K2ls5iQfn8\nXo74aev3Ly1s6ROcnFJGCJvNApp6hEtb0iC91m5g0qonUyJuN/X+fkTWOm5BeRdXp1G+k1WtV8Kr\nksjyWeWbHewUcliFyYisQADEmo/czaBxp5jHoVd/r9u9JGutFnrN5B3zHEKYa4nReOtpavgcyoms\nNx1boqiVBunpeppKs84PvV95s5fbETDMsVYcEVk9bZWgW9kcsuhtlFt3Zq3IVSqYNa8vkWXWOEnJ\n+E8obTKxi4Wwj55ePj7s9rvUtVt89B2HHwwDzLsXiFf2J7IyjQxBu7J1zNyXjlE5xIs7iPUAJ5YM\n/ODb3szF6tMTdQJMJuHDH4Zf+NPP8fQjUf7gz8cPAWqdGhcyF5jvf4BUVV6RNWjtT2SBtLRwYHx9\nFFmb1VVOhA5WzPj9INbliX2AQm+TzOxv8cd/lWE9UaFnTvLuB0Zx3a/+uw+S78f54ksvH/o8F1bz\nCJrBvl3cd+GzO2lxeGlhupamO+jw+H1TB17n0kZYy+5PZH31whUc7cP9sWBIZJW3pvYtLWxpsszK\nSWMOgFEnr8h6/lIGcaDhD85JDcXbbfjpf19DfP9P8z8+9j+o/EKZn7vnv/IPiYMJxT/5xwu4rXZm\nXbOHPlM0Cu18mMQ+iqx/fmkFfW2RqSkFTWR8btqagwmS5zbP0V5/iOXbOM7paTCufBeffuqvDv05\ne3ExnkLbDB7YHdNnjLBZkB8X2bqyjul6rZ6P3fMxWsf+lLr82T4AtX6ByD4+qiejMbIdZaWFLVG5\nchkgYDu4K+nfvfQcgc6j2GzgUmjKf7fhDU1krWYSUIkwdfAaBgzlvlSDxPPyRFayksVtUJbMPzB9\nmrXqwUTWK42/54ce+i5F93NoQmxk5YmseC6D/QCfAaPOyBPzb+bf3D9M+GMBL22t/KAVRZGuQd5v\nSw4+S4CUTKILsJbOYOqrIz8EAeiZKdZGQXq2UkOnkMg6EZ4l291Q/POul1/hvvDBpW33Ts9TEuSJ\nrEQlhVgLqTbrNWgNoOmRyY4kMy+vpNC1g+hUmK/EpozQtktOyhoNEKyTKbI0GmBgoNocbZiFZgFt\nz05s6mCz/dvhdVjpa/cvLbxWf5ZHpx5VXf5oMw0VWYK2xxdXvsgHlj+g6v+/mnCZnZTb0uRhPVVA\naLsw6CdfOo1aK+VGnb6uSsijXpGlETQIPQvJvPT9l0oiA8cmp6KHE1l+P/QrQRKV8fldadUw646m\nyDJprJRu7uqVQZr5wOFElhE7+erdp8hKFipoeqPAwWqwoh1YuLA2THDWUwU0HY/isb4YClPs7p+0\nbReyOLTKlTzHpn0MDAXZrma1Goi2BEsqvPmMnSgX49Igvd5pYppAkRX1umkK8rL2V7bWiVqUlTwC\nuC1OWQPgxqCM36GutDDokG92kKqMlxMohcUCQstLPJe7ea8cbqN6RZbLZkbUjg57aq0mRs1kiiwY\ndr/dyI3GW19bx+dUPr/fcmqBnnWDWn1PeVH6Kr3UiVsdnSbBjH2ereqQyOr2u4iaNkG3sjFm09up\ntu9Q18J6BYv29SWyrFoXWRnD/bpYIOxSrsiaD7sRjWVa7XE571YhC00v83PKYpST4QXSnf2JrEI7\ny7RHWfBkJ8xa5mAi65tX4xibMYxG+Njb76drifPMi8pNynfx0Y9CZClD510/xbztNL/69382ds2z\nW89y0n0///jnMVYSMkRXu8GgbZE1GPd6h7HW7nces4ee/vVRZOUGqzy4cDiR1at6yTfk32VNzBDQ\nLfLxz/0af/3kBezNU+h1ozHi8+h4k/6n+Phff+rQ51krXydiXD50rww4HXSFwxVZL+ycR0zex4kT\nB9/PZzq4wcpLmzcIGQ5Wie3C7QZNPcpGYZzIGgyga8gyF1JPZHX640RWspqGCx/j09/8Y8mB9ic/\nCbztF3nP8cd55/w7MelMfM+D76Jkekly3V6srcG2/st88OThZYUAVivommHW91EsfemFVcJGZca6\nsYCbvn5/gqTda3Mlf4kHp85IjN5hOJd+62e+g69vf2ks5j0IV7YSmLoHxz0Re5hkTf73K7byBBUe\nEnzfvR9Dc+qzJPcZYv1Bn5ZYZsonf+B2dn6GikaZIqtNhYCKA/CQ00vlgK6k34g/x4PhYeWYz+qm\nvI8VxN2MNzSRdXkriakXVlTPLwhgIchKUp6YydQz+C3KNt23nz5NTrs/kdXpd+jocnzgzYebLgN4\nDKF9F9ntUhqX/uAk8DPf+Rl+7IEfA2A+5KNnkD9xq3XqIArMhJUFi2F7kFxD/qR/K5/BrlHJ8ACa\ngYlyfRSk5ys1DCgjsu6fnaWqVe6RleECjy8fTGSdjszTd64h0wyLG8kUumZoqGRSAUEQ0AxMXLw2\nIuwubCSwDNTVYPh8IFbDbBZHY6PRANGcm0iRBaBthNksjk4g0rU01INEVVbw+Z02+tq6bPOEWg0u\nlJ7lsSl1ZYUAVtNQkXWx/A1izhhTDgUs9WsEr9VFpXs7kZVH15nM6H0XZq2VXKMAAw1el8rBdRPa\nvoNUUUr6XF4roUGDy3x4Uq/VgrEb4obMelhr17Hqj6bIMumGnRn7gz4tIc9i5PDxatbenWbvyUIF\nfV9KONr7Mc7Hh4HHZq6gylfpVCxEjf1LC5PlHG6j8vnttOuhY2crOx6AxHfaYKzgtyofs3aiXE9K\ng/RGt4FFp57ImvG76WrlA6O1wjpL/lnF9/LaHNR748lNixJBhzpFVtjpoS7TqStTky8nUAqz6GM9\nNdx/s/Ucfqv6e3nsQ0Xv7tpa7zQxTqCG20XYFma7PNpDBro6AZfy+W0zmdF1Ajx1YRRoP331Kub6\n8YnLCgHumZ4l140zEAdD9XPHgdOpjAy2m2zUu3dmrSg1Ru3OXy84DC5yNRm/Rk2eKa9yIkuv0yJ0\nnKwmxufkla00+nZwLJHcDw/MzVPRbOybOFf7GWIKlSleQ0QSl8jhhdUNPJpZAPRaHdOaR/jU335j\n7LrDVFpr6yLrp36S77/v+/n0R36NZOgP+eY3pf/pyfiT2HJPQMPPTmk8Bs5X6mgHVtl35fUOywp3\n8xKXyUVPW6JSPaA1+WuAXg+aplXecupgIstkAm3Hw/Y+nc1augyffPcnKc79Pr/+t19i3jJurP5b\nP/yjXBp8lrXkwcTiTvMGc47DVfpBl4Oe9nAi6yuXz2NvnMF8yPIYsUdI1fcfX1vlBGGr8iB42hll\nsyjT3bcMgjVDxKGyWkVnoDMYLy3MNjLM9d9Lo+jgaxtfA2B9HX7tz16kPPMZfv3dv37r2rPHfdB0\nc35Tnlx+4QWw3vNl3rVweFnhLtyGEKv7EMzn1lY4eYjabxcRvxlRFGl25X3wLmQu4Bos8ugD8jHG\nD3yXh0j/MT72i19U9uDA9VQCGwdbucx4wuQ78r9fuZsn7FS2f5/0n6RnX7+l0r8d2UYW/cBN0K+X\n/f7REzN0zZv0eodLTLuaCiG38v0o6vFSG+xPZG21L/G+s/cC4LftbwVxN+MNTWTdSCaxC8o9h1y6\nABtZeWKm0MoSUiiDfuu9S/Rs6zRa8l0+VjM7UAsRCio72QpYQ6Tr8olNqprGdwjBFrQFMemGpQdR\ntxfMeRqN8Qmxls4iNP2HLvq7iLoCFLvyxN9OOYtTN4EqqG+mtEeRVajVMArKTocfXJyja93Y19Ng\nL1rtPi3bFd579uDSh3nPHIJnja2t8e9uJFOY++qM3ndhEYOcuzr6m15LJHBr1UX9Gg0YO2Eub0mJ\nrL5pstJCAH09xlp+lJAkq2n65aDqhMTjNIAojNX1i+KwtPClrHp/LLhJZOnaPJn6PB9c/qDq//9q\nwm93Ue9Jk4eNbA5j/2gG9BadlUwjCR276o5mu9D17aSL0kDvpfVNLL3D1Vi7sGuCrGXG151ap4bN\neDRFlkU37FqYa+TQdl1MR+Q38L0wa+0UG3cfkZUpVzDcZvbs08e4lh7Oo61cHpOoPJk8Pe+lr6nT\n6o13NgXI1NUrLvUdP1e2xve2y5tJDJ3QoR2K9sKri7KelyGyDOrJlKmAlQE92bKJXGebY6Fpxffy\nORw0BuMnDh1NiZBbnSIr6vXQklGKFZr5Q8teDoJN42MzNwwaS+08Yaf6e+16Se52sWq0m5h0kxNZ\n0+7wcL0BBgMR9HUCLnWkpHuwzDNXR+WFz69dJWY93Bj5IJxctKLrO0jVUsOS0ZZD8XroNNmod++M\nxKXUquAwvL5ElsvootAYJ7I62gKzAXX7kb7jYy01ntjcSKQxD5THO8fmLQht975eQU0hy5JCKwW/\neX9lxC6uJOJEraPD4feceDP/cP0rEuKqUgH/fecoFuUTQlGEpP8zbDdu8Etv+yXeNv84dm+dX/r9\nFyTXfT3+dVa/+jghh59MbVyRla/W0Yvye+QukbULnUaHTrSRl+m4+lri6moDzAUW/IcTNBa8bOXH\n10NRhJ4hw9uOPcD7pj9KevH/4uGZcT+q+5cDzHU/yM/8wR8e+HNygxWWfIereCJeJ319ed8u87t4\nPn6eecvB/lgAMU+YfPuATsGNJNMu5bnkYjBKqjFu9p7NDRDNBdV7iFFnpDsY3yNL3TTf8Y4gmld+\nhN/82h8hivAj/2EF4/d+P598z69K4gSNBhyNs3zxdNzfcAAAIABJREFU5Zdkf8bVG22K9qd5+9zh\nzb52EbSE2S7J56arhRXedFyZIsvpFKDlJluTJ0nOJc5hyD405o+1F//pQ9/Ft+p/xWc/q+hH8s83\nnmbJfv+B1ywEwpQH8uOiPsgzpaATNQytavSilatb8l6lqVoKQyssa8YO4HPYEPoWXlnd3+t0Fz1t\nhbBH+X404/Ps64vY7XfpaEq889EhxxB0HW7KfzfiDU1krecSePXKM3CfKch2SZ6YqfQzTLmVKYws\nRgPaVoCXVuU38JfXtzC2phWfbEWdIfLtfQz1mhnCNuVG3Ca9EaFvIp4e3zRXEln0HeUJ0lwgSE2U\nJ/4ytQxeBUaet0Mjmqg0RglcsVHDpFF2OhxxBBGMVa6tH1CIfBNffWUVXTt4qJl2xB5BNBa5sdEY\n+24tk8KumYzIChsWeXlz1NlyI58gaFF/fG0Vw9xIjsZGozGULk9SWghgaMYkbatXUmn0nSD7NMXc\nFzYb0LVS60iTiU4H0HY4n3mZh6MPq34+201F1ld2Ps8Hj72+RFbI6aIpShPn7UIey4QeOruw6q2k\nG0mEjh394fyOLAw4yFZuU2TtbOIWlBNZHkOQzcL4etjo1bEbj6bIshmsVFt10vU0Ql0ZUWrT2yg3\n7z4iK1cdN3uOWGNsFIdEVrJUUDUmYjEBKtOsFzdkvy+0soQc6gJh0yDAWmo8CLqeTGBVqQQNWqJs\n35actnpNLAb1iiy/X0BouSm2xoOjirDF6WnlRFbQ6aDN+L7W1ZaJeNQpsmIBLx3deHBX6uQJ2Caf\n3w69l+3CUJFV6eWY8qgnsrRaoGeiVB8e+DS6LUzayT2yFgJhije73xZrTegZMRqUl7gDTJmWOb81\n6tR5NXeVe0JHI7IWFkBbnWO9uE6mVEXo2hU3OHCabTT7d4bIqrYrOM2vL5Hlto6XuYO6JhO7MIle\nNtLjqv21TAqHRnmsGYvBIL/Ajfy4AkQURTqmHe6dU6ZyiTrC5FoHE1nrpQ0WfbO3/v3x9/xbqnN/\nyt8/OYqP/suffIX8Rx7i6+fly3SKReCR3+S/v/+/YdKZ0AgafuKRH+bL+T8kczPUbfVanNt5Ec32\nm3jXYwHy7fEYuFCrY9jnANbrZayBhRm3bJfU1xJPX1zH0plFqzl8Ujl0XhLF8fUwV+iCsULU4+FT\nH/1PaLQDPvyYPHH0Sx/4Gf6p+P/QaMp3YAeoCtssKzi88LmMIAqSxlByuFp6mfsjhxNZS6EIFXF/\nRVaxl2TOr3yfXJ5x0Rv0xjrpbaSKaHt29Fp1gZ1ZL09k1cQMS5EAP/rw9/GPG5/nJ//gt3lq+TE+\n8Y6f4Afv+8Gx62OG+3lm7UXZn/HN7W8RMizjNssbjsthyhkmLePnmUhAy7rCW04pI7I0GtB23Gxm\n5EmS53e+RfHyuNH7Xnz0zIcRF/6RX/nkeK52O3o9kZebn+Pn3/edB153PBqmqRn//URRpCXkmQ0q\njwUcTHMtJaOIAJLVJEI9hO+AcMDcmeHF1YN9soYl+B2CHuUHW3MhL11dXpYU3imnoOEnNjNcI6IK\nTPnvRryhiaydSpKgVTmLHrLLmxsD1Mgwo8Kgz9yZ4fy6/KC7vL2FbaA8QJ/1hCn15ImsUi/NlFtd\nRzld18u6zInbejqLWVT+Oy6EAjS18u8r18wQtKonsrSimXJjpMgqNWqKjaUFQcDUifGt6xuHXvvV\nS6/g7R1cVghDvyF7f5bz8fGSxa1SCo9hMiJrwbPI9dyIyEpUkkw51RNZLn1IUqPeaEBHl51YNWBq\nz7BZGQV5K8k0dkHd+IJh/Txt27BcdQ+qVTDPvcSSZwmbQT0ZYjMbwZ6k1q1wNnxW9f9/NRFyO2kL\n0uQhWcpjm9BDZxc2g418O4W2N3lyZMJOtiJN6ldzcYJm5URWwBIiJeOR1ezXcJiOpsiyGazUOnVS\n1TS9cpCwgmXabrRT7dx9Zu+FWnXM7HnOM0OyPlz/05UCdp3yZNJmA13xJM/cuCT7faWXY8qtjqi2\nCX42suNE1nougUulEnTGFSXTkAb9zX4D2wRElssFYlP+JLZj3Ob+ReWlwyG3g46Mb8rAUGLar47I\nmg146BvGE8tqX3k5gRy8Zh+Z6nDvbZBjRqVaZhdC30yxOtwnm93msIHIhDgxHaZ+s/ttulhH6Kmf\n28u+JVaKI0VWsnuVNx8/GpG1uAjd7BzrpXVSxQq6vvL10GW10RrcmbWi1htvd36n4bO6qHakhyrF\nahOEAT6nunlpFXxs5seJrK1iGo9RebxjsYChtsBL8XEiK57LQs/MbERZDDDrjVDsHVxamG5vcM/M\nSJG16F3gUdMP8fEv/p/AMMn73fjPIrQ8PH1Nfm1NpQDnFif9J2999lOP/iDC6b/gN35rON+e33ke\nR+ckP/BRO3NBP9W+TGffegOjRv69ezzjRJZF8JBv3NkE8YX1VfxaZaVfLqOHdGV8Pby6lUXb8aIR\nNMRcM7z4k9/ivafeJHuP73vrw5gFD7/22a/Kft/pQNeUYDl0OLlptwNtp2xzj120ei3y4ipPnDi5\n7zW7ODEdpqndnyitCQmWlQQpNzE/J2DpjRu+r6WzGPsTdBTXG+iK46WFTW2apUiQ//XHfLD6Hv7o\n4u/yu2/6Cj//bf+LrM/YmeBZLhf2UWSVX+Re7wFMkQzmA/Kld5cugca7ypJXGZEFoO+72czt0/gl\nfg5D9kEOOtfyWXw8GH6Iq70vHlo+/HufP49WK/Dhxw6xlpkN0zUlx0ieercOopapoPJ912eYZi23\nD5FVS9Iv76/IArCJYdYy8vn2LqqdKrQduN3KvYejQRP09WOiA4BLm0l0zcgt+5xpv5uOdvzA5G7H\nG5rIyqiUg065AxRkTlcA2tosiyHlxIxbiHElIU9krWS38OqVE1kLoRB1jTyRVRMzxHzqCCNj30c8\nOx6obOZz2DXKF9ljU356hqwsk1vuZQk71S/YOtFMZQ+RVWnVsOiUEx4e5nhla+PQ617YusCc9XAi\nC8Cvn+dKetzwPVVNEbRMRmTdO7XIdmNEZGXbCeb9ysfqLgKmMDtlKZHV0k5eWhgwxFgvjoiseD6N\nx6ieyNLrQehZKdzmYFqrgXZ2Mn8sAIfFCJo+753/oKpyqNcCUa+TrkYqb89U87gMRyOy7CYrlUEK\n3WDCukLArHGM+UltVzeJOZX58gFEnUGyrfF1p9Wv47IcTZHluFn6s5ZJo2sFFZUzO0x2ap27T5FV\nqFew6KSJ7IlwjPxgOI+ytTxOgzpVREh7iqeuXpb9ri6q73rk1PvZLo7vbdulJD6TOiJr3h+l0JcG\n6O1+A5tJPZmi0YCuO34Sm8zXELVtFiPK31vYO+6b0mwNwFAh6FJHNITdTjBUJQbmAA0xR9Qz+fwO\n2LzkGsO9t63JsxCa7MBB6JtveUkO1XCTE1n3zIXpGIZ7SK5cR9NXP7fPxpZJdodEVr1Tp6XN8I4H\nZid+JrjZ5bEwx6XEGulyBf1A+d/QY7XTFu/MWtHoVfBaX18iK+h0UR/c5teYLqBpe251U1UKh85L\nsjR+0Jmspgha1MUCPs0Cr2yNE1nnbsQxNGKK/Gth2ACjLqOM2IuqJs4jx2cln/329/wfXBH/lhd2\nXuF//9tPMShN86D+B3g5IU9kbSU6DAwlAnsOYaed0zwy/RC/+61P87O/8VV+74Xfp3bxcb7v+2Ap\n6qMp5Md8wMqNOiaNPCH8xBPwiU9IP7Np3RSad1aRdSW1SsyhjMjyWb3kGuNjYiWZwdgbvav7Qvcd\nGJfN6R/i/M412e8yGdC6dphyHL4fabUgdBwki/t3LrycvYy+ssiZew7vWn96NkjPkKM3kLeDaRuS\nnJpRHp/PzQ0N328vq90qZLGq6Di8C4vBSFeUKrJ6gx59XYnlKS+xGLyt9Md8Z/pFfvSD++c1bz12\nloT4omzOlhTP8+js4eq1vViODP08b7/f1bUqfV2FsE35OzOJbnby40RWo9tgtXSDR+buPXS9+N77\nvovu8l+Tk7eBvoXf+ern+Dbvdx7aVGAqYIOBluRtNh35Rh5Ny6uq0VfENs12VZ7ISlSTtPMHE1ku\nfZDN/P7eqcCwPLntwKLi7MLrBbEhP78vbyWw9Ed/w8NM+e9WvKGJrGIvwbwKOehcIEilL8949owZ\nlqPKR23ANMNqXl6+HC9tEbYoJ7KOT4Xo6OUHcEubZjGsLriw4LtV3rAXiVIWl0H5IjsdNkHXIuvN\nUB1kmPGqV2TpMVFrjUoLq62aKuVOyDTLjczGodfdqFzgTPheRfecsc2zXhonsnKtFBHnZETWQwsL\n1Awrt/y8yoMEJ6bUK7IijjDphpTIagqTlxaeCMfYro7GbaKcJmhT/3cE0PRsZErSZKJWg0FkMn8s\nALtlGJR8YOn1LSsE8Hn0aAYmyUlGvpnHYz6a2bvDbKWpTaEXJyeyLDo7hYZ08812N1kOKldkzXiD\nlGQ88NpiDZflaIosh9lKs1dnNZXGplDx5zTZ75iBsxqUmhVseunf6r5YjLp2OI8KzYKiNs17cdJ3\nivP7JFstbZaFiLox5jX7ZdvEp+oJVcEmwPFIlLogDdA7gyZ2k3pFFoBBdI8Z0b9wYxt9c0pVEh7x\n2hnoKpKgOpGrQc+CXqtT9Uw67dD0eiMtfa62Nk/MPzmRFXb5KHaGCVNfX2ZpSnkZx15oB+ZbpYWt\n/tGIrMWIB1HXoFBpDrsE99XP7cdPLVHVD0sLX4hfh+IiS4vqyhNvhyBAyDjPxZ11cpUqRpSvhz67\njY5wZxRZTZXtzl8LBF1OmuJtfo3pPPqeOgIdwGPyka6Nx4e5Zpopl7p4J2pZ4Noe5fkuLmxuYheV\n70XHo2Ha+gMUM/UBPcsWjx6X3vPMcRdzm/+Zf/v//SSfOv8r/HDov3FP8BSrVfm19epOElM/OEbG\nfPyJn8X97t/m91f+C1/7ep/54o9z7BhMRwxoejaKt7WkLzfr+3b29fvhve+VfmbXeyjd4W5g8eoq\nJxSacQfsXootmSqOTAYryuPDKfuMxLpiLzIZEO0Jog5l5abanoNUYX9F1rnt83S3z3Ds2OH3Cvh0\n0PQSz40f9jTabUR9leMx5ev+3Bz0CtExRdZOKYNDO4Eiy2CgfxuRlanloOkhHBqus3/+GTN/8kcH\nlyx+271hej0N2xWpf1ezCU3HeZ44ro7Imp0yoelbx0jY81ureDXzqrqSmwU3qdL4HLiWu4ajv8Cj\nDx5OSL5r/p0IM09z/fr+11QqcGnwN/zcIWWFMNyDdK0wF9ala0++mWfQ8B5IPN2OWfc0mZY8kRXP\np9A1w7JdTnfhN4fYqRxMZCXyFbQ9h+IDAhh1U94pyBDVGaliP+K3IArynqZ3M97QRFZdSHIsojxI\nX44EaWjGE7dirQ4MiPqVB3gzjhm2q/ILdrq5TcytQpEVCCFaU7LtebuGDMen1BENdq2PVGV80GZq\nWXxm5TPTYABNM8D1xPg7a2kyzKmhq29CL5iptkaKrFpXHZE165pls7Jx6HUZ4QKPH1OmyFr0zZNs\njhNZpV6KmHcyIut4YBGtf5WVFej3oa1PcM+seiIr5g1R6Eg9smri5Iqs+xemKfUTt06mMo00Uy71\niiwAQzvCRl5aDlCtQst/REXW9sO8c0G5IeVrBZcLhLaLcnt0Klhs5whM0IlsL5xmK6KmoypxG7uH\nyUG2LCV9KmxyekZ58rAYClFjfG53qOM5aMdV8nwWK81+nXg+jdugbHy5rTYa/buPyCq3K9iN0kT2\n/vkYPVucdnvoq+SzqksoH1s8xUZjPNlq99oMtE0WIupK5UK2APmGjClxJ0HMrW7dOR2L0DakJCqE\njtjAYZ6MyLIIbnYK0gD2Qlxd+T1AwGMEUUuzOzoI2c6V0HbVvatd6Lse4hlpgN7T55kLTT6/Z7w+\nqr08+XoRWk6CgcnIHkNrmpX8cE9q91tYjZN7ZGk0ArpWiPNrKQrVOjrUE1ln52cZWBMkMm2+cv4q\n7t5xxX5WB2HWOcdqfp1ctYJJo5ws8jlt9O4QkaW23flrgajXRUe4za8xX8A4UD9WfVYfeZnT+VIv\nRcynLhZY8i6wXRtXZF3PxPHplKuDT8R8DPQVWl35BOrZi0l0XY/sPPj5x3+C7XwRXvp3/McfOcZj\nC6fIiPJE1momgVMYJ1K+ffnbWf/5G2z84tcJPP1n/M8fXQYgEgFN00/2trW12qpj0SufR06Dm0rn\nziqycoNVHpibV3RtxOWh0h1/vs1CBqdW+ZhY8O+fzG8mm4i6uuJDH33fSVqunfhNfP7CV/A134Tx\ncP4DQRjGqxfi4+WrlzaTaBohjAbl6fDsLDRSUbbLUiIrXVUnFtiFxWCkh7S08PpOBm0rcKvsy+Xi\nUC/b+XkBEmd5dkNaXnh9pQf+y5wJK8uJdhGNDjud396I4UZ2g4hF2djahV3nIVOR6axcjkNx7kB/\nrF3Mu+fBUOPc1f1L8H7nL9bQuVK8/7SyHMTSD3N1R/r7pat5xJoXt4pzqOXgNCVRfuyvZ5MErQfn\nkmF7iGzj4NJCtSX4uzD0vKynx9f8zUISv3nEoVitQ1P+RPGNpcp6wxJZA3FAW5/mVEw50bA45WQg\ntMdagF7fyaJpBVSdDi8GYmQ68kRWobfFUlC594fL5AJ9k/iO9LnK9SZoO0wH1A1cl8FLujp+4pZv\nZQnYVHbT6AW5vjN+itExDE0I1UIvSBVZ9W4Nh0l50nw8NEu6O+5ntRe1dp22YYd3PXB4m1+Ae6fm\nKTBOZNWFFItB9eWAMFxw+/Z1Ll/tk04DjgSzXvVE1kIgTEUcLbLVZpuu2BiOmQlw+oQBfddPojrc\n0IudNLP+yYgsUzvGWkGqSlzPbyPqmix6lNfO74XZpMH0mW/iVKOdfY3gdoPYdFJqjU7Cq708oSN4\n6AC4rMMA2KyZnMiK+hzs5EfPNRhA27TJAwsqiKyol66mRLfflXzeFWp47EdTZLmtVtpinUQlTUBh\nuYrHZqc1uPuIrFqngvM2Istv9SLo2lzbqFLrFwg51I2Jd589RkW7Mvbus/UcNHwEAurKhaIuP8XO\n+DpdERMsBNStO7EpI7Qdkm5dXRo4lLa7vQ02rZtUWRoYXU9t49Ep3yPhZhDfdpIuj07pk4Uy+r66\njoW7MAw8bOVGyVu710bUdIiFJp+XswEvTSHHWiqPpu2buJlDePAwX195Hhiq4SYp69wLcz/MpXiS\nQrWOYQIiy6DTY2zN8PXza69Kx8JdnIzMkWiuD8t3VRBZfoeNnvbOEFldoUJQRbvz1wIzfhe927xL\nEsUCFtQrskIOL6X2eHxYF9IshdUd3J2OLpDtr46VHm0U40zZlBNZdpsGoR5kJS2vSPjmtQ3sA/n7\nffS79bQ+9SyP1n+Z+Xl4530naViv0B8Mxq6NF3bwGvZfD4NBOHcOfvzHh/8Oh6FXDox1Lqy166o8\nA90mD7XenUsOGw1oW1Z59JgyRdaU10tdlCk3rWTwmJTH+SciMxQH8nnR9UQSSz+sWMVjwEGmLK/I\n6g16fG3n7zlrU67ctwzCXNuW8XvaTGLsqovzLRaw9KNcS0mVT7lmFt8Eh8wWo5E+UhL3eiKNsafS\nH1kHvu5ZvnxJavj+1OVrWHpTqn1rp6agXw6RrErf21Z1g3n3rKp7OY1ucvXxObBe3KCyOauIyBIE\ngSntAzyz/sK+1/zek5/jLYEPKWpyAODQRFhJSwnOjXQeQ9+ruGEbwOnpaRo6eSJru3y4DdK0O0ix\ne7AiK10a76CtBGa8t7op70WymiC6p9RXEA425b9b8YYlsnKNHLQdzE4roONvIhIREBoBMnVpwL+S\nzGLsqVt8Tk/NUEG+tLCm3eLUlPLTZkEQMHRCXNuRsrFXtzJomgG0WnVJjcfkI98cD1RK3SxRl7rf\n0yoGxgzoWt02orbJfER9AmHUmKm3R4Rds1fDaVa+uJ6ZnaMibBx4zVcvX0JXOo7Po6zc5MGFeZrG\nNYmBYH/Qp6PLsRydTPlk0Vuw4OPc9W1urLfAUMVrUU+AnJgO09KNNpFSO49d51Ml6d2LY8eAUox4\naTh266RZjkxGZNn7MdYKG5LPXs4+j6fxyMTPp9PBygqqpLOvFWw2GDRd5OqjBKIu5om4j0ZkuW8S\nWbf7LqnBmdgCidaoi1gi1QVrRhVpMRXRoml7x06be5oaPsfRFFkem5WOWCfbSBN2KBtfPrudNnef\n2XutVx0zex42nhh2mamLBcJudQnlA/daoBrlYlJalhPPZRGaPqwquYYZn5/qYFyR1dAlOBZVR2TZ\nbKCpRbmWHJ02d4UmLutk5LLD4CZTlZ72rxe2CFvVKbJgWG6yk9tDZBVLGMXJSH0LUrn9diEPTe/w\nVHJCLEZ9dLR5VhI5jL3JS5AfjjzMC6mbRJbYxH5EIsuhCXMjlaRUr2NksrntFZZ5buU61wpXuTfy\n6hBZZ+anqYopCs0CVr1yAjHotjPQ3Zm1oqerEHJPTm6+Goh6XQwMJXp7LH5SlTw2FU0mbt3L7aPa\nH09qOvoUx6fVxQKn5rwMBuJY6VGyGWfeo5zIgqFi5sqWfHnhhc04QeOs7HdeL/zIx1z83L8fJq6z\nYSealocXVsfj80QtQch6cGmbIIziD5sNtG0/61lpzlDvNrAZlS/SHrOb+uDOKbKu3eiDc5NF75yi\n62eDHtqa8efL1jOqrCfuX5imoZdP5tdyOzg0yvciE25SMgfyAM9sPoO5PccTZ1QIBjQRVrPjiqwb\nySR21B9Yh61TrGWliqxSJ0vIrv6A32I00L9NkbWRyWAT1N9ryX6WcztSIuu5jfOENerKCgF8PhiU\nI6znpb9nprPBycisqnu5TfLdi8/HN7B2Zw/s6LcXpz0PcqlwTva7fB42TH/Dz77z8LLCXXiNYTYL\n0nVnM5fHKqjbv49HphnYtmQrq7LNJIuhg8fYfCBEVTyYyMpWKpgE9XmDTbNPV9J2klmv9LkMfTdb\n+5jy3614wxJZK+kkQj087G6hED4fiLUgW0UpMbORyWAR1S0Y98/P0DJujp1ENbtNetoKp+fU3c8y\nCHEjKR3EN5LqGXkAv9Une+JWG2SZ8akjZhy6AFt56SYez2URGv6JAn6D1kSjM1JkNQc13FblgfXD\ny7N0LBv09+/wy1cvXcDXVy6hPRmeQ3Stk82O/pb5Zh6h7SIanvBIHYiYFnl5c4WLGynMvfBE5uUL\nU3ZEUbzV5rfcy+LST0auwbC2v5ubYSU3HLttXYaTscmIrGn77Jgi62ruOiHt4V1kDkJUmYXCaw6N\nBvQ9F4k9yqeWkCemdMfdB17HMAC26iZPjt528jQ1yyV2xY0vruygb4fQaZR7BYVCIFbHO7kOtHV8\njqMpsrwOK12hTqGTJuZVNr78Tjtd4e5TZDX7Fby28eDBSYyLW3HaQp6oR11CaTCAvXWKf3pRavi+\nmsypPlSBmx1mNVIiSxShZ0pwOqZeCWruRrm0NQpe+0JjYiLLY/YMS+32IFnfJuZWp8gC0PUdJPKj\ncpNMpYxJmEyRZdN6SO3p1LWWyqPreI9Eoi9EnQx0NVbTKcxMTni//8xDbA++hSiKrwqR5TOFieeT\nlBo1jPuYVB+GmHWZi4nrpHpXePOJV4fIOr6kR98Ks9G4iM2gPEAPuq2grzEYHNK+6lXAQFch6nt9\nFVluixNMJYrF0e+brRVwTtB4ZMbnpYE0Pmx3uwz0FY7PqLvf3JyAtrrAalFaXlgYxDkRUUdkWQZh\nriflOxfeyG4w69r/fp/+NHxwjzjH3jrFVy+OlxfmWjtMu9Sth1b8rKWka2ujW8dhVj6PfDYPDfHO\nJYfPXdrG2PdhVtjtNBayMxDadPpSMqXYyRBxKs9lzsxHGViSNJrjAfpWKYHPoDy4C3Qf5JX8N2W/\n+5srf0v9xQ/x3d+t+Hb4zZExwgKGnX09+glsPzzjZu+VfpbIBE2wrEYjfUGqyIrn07j06mPzh6bu\nZ6UuLS28lD/PMad6IksQwN5b4Pyehg7tNjQMG9w7M6vqXj6bm7KMT9y19AYxp/J7vWnuAbb78oqs\nFy4XEYPnee/yOxTfL2wNk7hNcZYo5nHo1K2FU44ooi3J9o507IuiSGWQ4sT0wWrX5UiIpvbg0sJc\nrYxFq34vcuq9sl1Jy4MES2Hp2Dchb8p/N+MNS2Rd3kxi7oVVBZ0aDRi6gTHl02Yhg12jjng6PutC\nHGjI1aRy783SNlSiTEXVvVqHJsRGTkpkrU1AsAGEHD4qvXH2taXJMhdUt8h6jUESFen7upHIoOtM\nZhBu0pqpd0aKrLZYU+XHM+3xg77Bjfj+Ce+L28o7FgLYjXZ0AxsvrYzef7KaQqyGCE7G8QCw5F1k\nJb/K1Z0EDs1kJYrhsIBYWOBy9ioA1X4Wt3FyIkWvBxcxXl6PU26XEXtGFmYmS5KOh2Mk6lIia7Ww\nwpJPmZz9jQCj6JLUi3d1eWaDR1NkeW+W7dmNkxNZ94ZPgv8K128MyydeicexDZSXFcLwtJl6iLWs\ndN0Z6GoE3EdTZPmdNnpCnZqYZjGkbBIFnHZ62ruQyBIreGzjf6uAIcZKLk5HV2A2oH5MxMyn+MaK\nNNlaT2exTND1aCnqp6uXHjgksk3QN4mqVIsBOIQI1/cosvqaBm7bZESWz+oeMzrOd7dZVlF+vwuj\n6CBdGimycrUSVs1kiiyH3kO2Ngru4pkcxv7R5rbHrYGWm4vJ6zh0k6/T7/m2EP2mneu5FXq0cFgm\n98gCiNiG3W8rrf1Nqg/DieASq+WrNEw3eN9DChyWFWBhYdi5cKt7fqx89yCYjFroGynWmodffAS0\n2n3QNwi4jrYeHhUGrQFB1A/n9E0UmgXcJvVzez7ko62VElk3EhmEph+LWV3cGotBL73ASkFKZDX0\ncc7MqSOynNow6zl5RdZOPa5KARLVn+Jb8XEiq9RPsOBXd1Lm0geI56REVrNfx6mCyPLb3bKKp9cK\n59ZW8WmVx2GBgIDQ8owp6yr9DDMe5bG+2WD/Pz3lAAAgAElEQVRA2/bx0sr43zFZ2yFkVU4YxXic\nC5Unxz4XRZG/PP93zHc/xLwKm6awLUyqPk6U7pSTBMzq4/Nj4SjZjpTIqpNhxjsBkWUyMriNyEpW\nM/jM6vOsbzs5R7NflVQebXXO8+CUeiILIKhb4kp6pP7f2gK9f4MF76yq+wTsbqoy5bWblQ2WA8rv\n9f4zD1K1n0OmcpjnblzH2VvGqFNeqTXtDpNrjXtkuYzqYgGjzoi+5+FiXBpPV9oVxIGWY3MH7yEn\nZoL0jClkGk7eQqFWwTpBJYfH7CVbG+cEmrokp2ekc9KicY1ZQdzteMMSWdeTSeyoZ9FtQpDVlDTg\nT1WyuFUa9On1oKvP8OKaNJG/uLWFrjGtqN38XngNIXbK0gmwmU+rMlrcRdTjpS6OK7K6hqzqUrmg\nbbwUcy2dxTSYTBVk0pklZr1danjsyoNEQRAwt2d57tr+PlkrlYvcFz6t6rls3Xle3hjdcyWVQtsM\nKTKS3A/3TS+QaK2wmkngN6kfqzBUbpgyb+YfLj0NQF3M4lVh2C+HqC3GlUSceC6NUA/inTB3u38u\nRmGwIfks2V7hzMxk/lh3I+ydY1xIDwNiURQZGAssRNQnD3vhdw4DYMcRiCy70Y5p4OfJC8Mxey21\niU+njsgSBLAMgqwkR0R1r98HXQuf82gKEL/TSl9Xpa3NsqywWUXIY6N/FxJZHSoEHOPBw4wjxmY5\nzsCYZy6kfkzcFz7Fpaw02doqZCciQBbDPkRTgVZ7FN1diifRt5R7kuyF1xBlozAK0gfaBm77ZGMi\nYHdT6UgDo6pmi3ti6ksLTYLUNyVfL2HTTUZkuU1eien1Vj5/JBUV3OyC1PFxJXtNdSC8Fz4fmAsP\n8/kXn6dHE6flaPNxxhMm20pSadax6iYjZR6aX2ZT/yW07YCqxjgHIRqFfn6OEhs4TeoCdKFrI118\nbcsLh10xrWjVmKW8RtD1XGxlRwenpU4e/wSNRxajPvqGvCRhurSZwtBVH2va7aCvLXJhe0RkVdtV\n+kKLexfUrWM+Y4Sd8jgBIopQGGzwwKJyYuyY5xRX8+NEVl27w7GIuljMZ/GzU5LGwK1+XZWPZ8jh\noaO5c8nh5dQqMbtyIsvvB7HhIVeXJrsNIc18UGV1SXeal9bGfbLynQQzLuUk4ttOnabUyZKqSfOi\nK7krlOsdfvQDKjvweSPk2uPjK9NMEnWqJ7LumQvREHMSn8u2Vr1YAMBqMjAQpGq4bCNN2K5+Tp46\nJaDLneW57edufVYynuetJyYjsmK2JdbLIyJrYwMGjg1mXbOq7hNyuWkMxudArrfBmVnlc/t0dBb0\nLVmy9ML2KkGDOhP6+UCYcl96r1x9srXVNpjm8ra0tDZZS6JthJk7pMp3yusCXYtMcf/DmWEHbfVE\nls/qoXBbV9Juv0tfX+D0nHS82nRuMtV/JbLuCDZyCTwG9YuPWx8knpcqjDL1DD6Leubb2ovxSly6\nYF/c2sLWVx+gB62hsQU7UU7jMap/rhmvj5ZGSmQ1O21EbUu1r1XEGaRwm4nwZj6DTUVL3r0w6Uw0\ne6OJ2tXU8Kv04wlojvHMtav7fp/lMm89eUrVPf26eS4lRobvN5IpLIPJOhbu4r6ZRXqOFa4lpIZ6\nanHc8ha+cPEpABrC5B0Ld7EUiLFRinMpnsbQDU5cSnP2mJ8urVtljwAV3QpvOv4vh8gK9h7mldzQ\nqyZZLEPPjMtuONI9A+5hEugyH61cJaI9xbOrFwFYL24SsaojsgAc2iDr2dF6mK82oGvBoD/a1uBx\nGgARoWsjFlXGBgfcZtD0xgzQX290NRUCrvG/1YJvhu3WNRD6E5ViPn7iJImeNNlKVXK4jernt1Gv\nR+jaubE1CkCu7iQw9yZbd0LWUdmEKIqI2iaeCYmskNtNfU8AK4rQMW1zdlG9IsusdZCrjYisUrOM\nwzhZaaHX4qHUHikQkuU8Nu3RiCwA48DLTusaPsvRSpAXTQ/zpSvP09c0cVqPRmQthkKU+klqHXXd\n1vbiidNLiI4tPP1Xp6wQhip5jzBMPNwWdcS+tm8jU3r1iax0LU2xORyvO/kK2u7rW1a4C8PAJWnw\nUe0WCNrVE+hBhxvMRQrFEem9kkxjFSeLd/zaBS7sjLz+LifiCJUZPB51gUXIKq+YSaVg4Fzj/lll\nfk8AZ2dOstORrq3dLnRNCU5MqVNkhe3jZu8dsYHbpnwehZxuuvo7p8jaKK9yMqycyDKbQWh5hz6B\ne9DRZxQfRO3CpZnhSmLcJ6s82GHOr3w/etc7NWh33sxT8ackn3/24t/Ru/ghvud71I2vxVCYijg+\nvgrdBLM+9bnk4rwOXcd3K28TRegZsyyE1e/fdrORgUaqyCp2MkTd6vOsxUXon/shfuMbvwnAdjFN\nX2jx2En1eSnAo8tLJNo3btnoXF4rIWgGuE0qWvoBUY+bFlKCpNQq0Rf73LesfB0TBAFn/QH+4fx4\neeFqcY1Zp7qKkGORMHWNlMgqtvMEVTbwAfBop1nJSsd+opKkVwoTO4SrE4Rhd+FLG/uXF5bbFdUH\nPjBs8FHuSuf2WiYNjQBej9QU32mQN+W/m/GGJbJ2KkmCVvWLj98SIFGWDpRCK0vIrn7x8WhnuJqU\nElkr6W08OvULRsQZIt+WElmZRoaAVT0jPxv00dVLB+1qMofQ9GE0qlv8Y94Alb70fe2UMjj1kxFZ\nFr2Z1h4iq6+p4XeqI7KOu+/jxcR52e8KjSIdarz9QXVJ0pR1nrXiiMhaz6ZwaI5GZC15FtEHV1jP\nJZjzTU5k/ZuH38wr5acRRZGWJkvQdjQi675YjEw7zo1EGpswee3k8rKAUBqSYgCleoO+McdjJ9Un\nqHcrpjQPca16jv6gz2oyj7Z9NA8dAJdDBz0jLpWJ2+045j7NpcyQyEo2NlWb6wJ4jSG2iqN1J1uq\nI/SOrrZwOAToWhFrQcIKl2mHQ4COjWr77jJ872mrhD3jwcOpqRh5/Uto2h5VHW938e2PHqdpXKXd\nHRF3mXqWgHWy+W3o+iUdZlczCVXmunsx44qSbQ2JrE6/AwMdDpty/7W9mPJKA9h4sgbaNjM+9Um4\nVeegsIfIKndKE3dwDdg9khL8dDWPU390Issm+KgYrhFyHI3Ieiw2JNEHmiYu29GIrBNTYRqaJLVO\nTZVJ9V4cC09Bz8Ss7dUjsgCmbUOCQs6H7iDo+nay5Vd/rfiRP/+PfPzv/m9g8nbnrwVMgpNkUdp4\nJOxSP4d0Gh2aroO1xOhe69nUROp/gAXrGV7JjZLKl9Y2MXdiqvfJKVeYvIxi5oULdbDvsORV1oUa\n4K0nT1I2XGUgjsi6bBYExw7TTnVr4pTHT6EtPcztUMejgsiKejz09XcmORRFyInXeXRJ+fsCMA48\nbKT3dHFtiwzMGZbC6mL9kHmatdw4kdXQJjgeUU4injoFwubjfOGitLzwM9/6O07pPkRIZXh+YipC\nUzs+vmokWQ6p3yfn5oByjPXSUBVfrQ3AnGPao37dt5qMiLcRWdVBmjmf+jmp18NS63tZyW3wzOYz\nfOnieUylM6pzv1088bCHQVd/qzLnwtYGHs2saqX3jN9NRyudA/FSHE1lluVllXmp/kGejY8bviea\nq5wMqSOyTsfCdIzScVHtTdbUKWSZZrMkHfsrqRTaRhingvM2Uz/I9cT+hu+1TmWiA/CI20vttgYf\nr6wnMHbG7Zk8Zvetg5w3Ct6wRFamkVRt2ggQdgTHSuXKvQxTEzDfIfPMmNl1vLRFyKw+kZ/1hSj3\npQO40E6rMlrcxULYy8CYlxjRX9/Jou+qX2DnQwEagvR9pWsZPBOoBgDMehPt3qi0sK+rEVTpx/PY\n3H1sNOWJrK9euoS+dBK/X+VpjXeeneaIyNoupfAaj0ZkLXgW6NhWEW0J1XL2vfj+D03TrVu4nLlG\nW5cl5DgakfXoiRmq2jjr2cnMJHfh9YKmGuPC5nAOPHlhDX19buhf8i8EixEvFjHItfw14pk8hiN0\nItuFzQZ0rHhlfJfU4MHYKTZbw1PnQm+T42H1iqygNUiqOiKqM6Ua2v7R/WDsdqBjRWgEFTfk0OlA\n6NjJlO+u8sKBvkLEOx48nJ2PMXDE0fcmIz8ifjO65hRfOT9SMxRa2YkJELPoZ3WPKXG8kMA7gWoZ\nYDEYpdgfEln1TgN6ZtXl8ruY8bslZTUvrmxjaE1NVPLoMDgpNUdEVrVbGhphT4Cw00u9P0rc8vU8\nXvPR57dD70M0logesbvpdzz0ABnhFQa6Gi7b0TyyTs+G6RqT1Lt17BMSWRpBg7W9yJmpV5fIWvIP\niSyfXV2Arhdt5Kqv/lrxtbWn+MLLw7KcSdudvxawal2ky6NGBy2hwNSEvgCGnpfV5Ei1v1NO4zNN\nFu/cGzhDpr1xK/m5tB3HhfpDlXlfhLI4TjR85dIFPIMTqhqZnDnhgIaX1fzILmJ1u4qgEXGo8GID\nmAv4qfRv6+wrqGuIEna7wVw4sEnRq4WdHRB9l3h0Xp29hlXwspkbJbsbiRoCWuwmdevFjHOG7ar0\ngL/fh655h+MqOuhqNPBo+HG+sjoispLVJOu1y/zEe59Q9UwA98wF6Rmy9AfSP0JLn+TkjPp9cnoa\neltneX5r2CFwPVlC6FlV+TPtwmY2IGqkpYVNTYalyGSCgcce0XO2+Ql++alf5pkb5/EPJisrBHjg\nARjklriSHZYX3shuELbMqr7PTMBN3yAlSK5nN+jnZplVebvT3ge4UhpXZJWEVR6YV0dkzUdcoOlQ\nqDZufVYX88z4Jmik4Zwm1ZASWVe2k7h0ytZWmxBiNb0/kVXvVfBY1e9H0z4vTUFKZF3dSWIdjM9H\nr9VNufOvRNYdQbGXYN6vfvGZ8QQpdqQKozpZYj71C8asO0aiLl2wk/UtZlzqFVmLwRB14TaTuH6G\nabd6osFlN0DPRHqPl8h6OotZVE+AHIsG6eil7yvXyKpqybsXVqOZ1mCoyOoP+qDp4Hepy5Ded/Ze\nisbzsqZ4X7t0GT/qygoBTkXmKQxGRFaqliJgPRqR5TA6MGlsCJEXj0RkzcyAvfgWPvPkU3QNk3VF\n2YuzpxwMukauFS8RMB/BzR5wC7O8sLYBwDevr+Ie/MspKwR4xztAl36Yb25/k61cHtMRPXQAtFoQ\nekcnst5+z2lKxosMBlDTbXLvrHoiK+oKkm/vLS2soxscXZGl14PQtWIZqBtfmr6NVOHuIbLanQHo\nawRkVKP3zEZgoMU4mNwzzTc4xZfPj0pgKr0cU+7J5rddE2AzP0q4krWEKnPdvTg9NUtdt0m1XaVU\nb0LXgnZCfnou5KFnKNwqGb24uYVdnKzMwWlyUG6N9rV6r4zPNpkiK+rx0BRGRFaxncc3gS/G7fCY\nhveY8R+NFHvzwzbE4jyiron7iIqsqCsA5gL5WhmneXKi+j+//8f5uQ8r7wilBPfNDIksv1PdemgQ\nbBSqr64ia7O0TZMCKe05eoMe2fJk7c5fC9h0LnLVkYqqqysw459s7TGLPuLZEZGVqqUITeDHAzA/\nq8PfeYRvbH0DgJV8nKBJPZG1FA7T0I6Xfr2wfZ4Fq7pE3GYDQ+kUT14dra2Xt3YwdSOqCfSlaICm\nICWy+po6XqdyjyyH0Q7aNqXKa182//LFJqJjkyWPOkWWQ+clWRqth9d2MugnaOq0FJwm25Ym89ms\nCPYEM251+9FH3nQ/qfb6LRP6X/znX0F45Qf4no+oJ4sCPh00vcT3dGFv9zoMDEVOzarfc3U6cDcf\n5MnVbwGwmsqi7062dzssRtCOFFmiKNI1ZDg2PVme9YlPwDOf+iFeTr7CPyQ+w4JtciLL5QJre4mn\nLg6JrM3KBvPuWdX3ifjNwEDikfzi2gb2wSw6lWLvtyw8yA5SRVanA23rKo8uqyOytFoBbSvEhfUR\nid7W5JmboKnTgn+afE869lczSQIWZVyFWyetkLgdjUEFn039wd1c0EtHe1uVViaBWz/+XAG7m5qM\nKf/djDcskVUTkhyLqCey5gIBqgMpMdPSZlgIqV+AloMz5LpSRVa+t8ViQH2QfmwqRNsgHcCTGC3C\n0HBW2/ZJTtw281nsGvW/4+KUE1HTprGn02CpmyHsmJDIMpjo9IcLWbVdh64Vu11dYPHg4hwYy1xa\nG/cceHH7EovOk6qf64H5eeqGEZGVb6eYch6NyAKYMi8i+i8xpVLOfjseCb2FL1x8mr4xS8R1NCLL\n5wNtNcb12reIOI9GZEUsQ+N4gFd2Voia/2URWW99KxQvPswz8edJlPL/f3v3HSZVdT5w/PvOzM72\n3vtSFqR3RGmKHbsSYy+JxljSLNFojDGaWKKJ+RljEpOgiV1j1MSKBRE7XUA6LLC99zaz5/fHnWUX\n2AXmzlZ8P8/Dw+69dw9nL2fuPfe97zmHCEfgD7oAIdvPYXhCTkBlTMs5AhO7mS3bW/GE7WTqcP8D\nWUMSU6j2dFx3ymvrcLX1zApdjrZwohz+tS+XN5LiqoETyCosrwdPKK4uojhulwtnQzphYj+QNTx6\nDF/u6HjYqqeU7CR7AZBYdyIFnSYlLmsusH3dyc2MJqRwHi+se4GK2gYcXnsrFgJkpYbhKBvD619Z\nD7qbincTH2Rv+HFsWBQ1LR1ZKY2miqQoe4GsrMS4vTp31a1lpNiYF2NfiRHW/9/Q5MACWZGREF07\nHdocREcEBVSWy+HC1RpPhdlGdJj9QPUtx/yAUUkjAqrLviaPSIGmKFJj/ft/DJYIKup7NpD1wqcf\nE1pyDI7aTN5asZayuhpCHQMjkBUbEkNxtRXIMsbgddtbZAIgwpnA7vKOtl/RXExmrL3+zogR4Myf\nxdKd1oI0u2vyyIr2P5A1MiMRj6tyvzkSN9euYkq6/w/iiey9KuyW4gKi8G9+LIBR2dZUHZ2HKXqd\n9STFHPrnSERwtMSQX3HwB0RjYNEiDrh62YEsWb+BmLbhBDn9u2bEhMRRXNPRJrYWlRDa5n8/f0xm\nJtWyz5Qru6twEESE27++xQnHBeEomMHSvI/ZWLaRp1Y/yxnRvyDGxiVfBIKaU1mb1xEs3bCrGGlM\nIjzM3luasbHTWFZgBVR2lJQSanMRrIhQN7haaGuz/tOrGmvA6yY7zd4LjNxcWHB2MKMqbybfu4aJ\nKfYDWQBDonL5bLMVyCpt3eHXCqLt3G5BmmPZVdrxGViXv4PUUP/LOnp0Fh6vh4Lajv/LjVsbkdAK\nsmP9/4yHtqaxfrdVlqfNg9dZzxA/55MGGJ2eSa1j70DW7qpCMmMOLVaRFJZCQU33c2Q1mRoSu1h4\n6GCGpMbQ5qrD0+bZs21XZSHJofv3D61J+av22z6QDcpAljGGFncRY7NtLJmakUyjs6OhGGPwBJeS\n6+dqfgDjMrOpdex9wa517GJ0uv+BrBFpyZiwIpqaOu5eza4SRqTZCzS4PQnklXTclAqqSokO8v93\njIgQpCGJHWUdD0i1bSVkxdsLZEWEhNJirKBYSVUd0hqBvwsCOcRBVOM43li+Zr9922rXMTXL/4ys\nKbnpeKVpz0NltbeInITAA1njMqy3A6kR9ob4tLto9mw2NHwE4WUkR/bA8BeTTUXQarJtjMHvbFhC\nNnm+ObK2Vm4hN+HwCmRFRMCYmOks3vwFRTVlRLl6JpCVue5hspMCKyssKIzQlgwWvv0lIg4SIv2/\n8Q5PSaZeOq6HlXX1uOmZQJarLYK4YP/al9tEUtbDWRaBKKyowenpvuMQ7skmMoA2MSVrNJurOx62\nmp2l5NqYLBas1bWKO01KXOUttD03X04OsPK7PPb536msa8DRZj8jyOWC0a5TeXTR6wDsqNhFari9\nQFZceBT1no6MrGapJvlQJp/owpDkeLzujpch9cbevBj7SvUFw3LTAy/riKjpvmGdAU7MB4S1peKJ\n2kJMAIGs3pCbK/DIZjL9nDMt1BlJdUNg14r2h9B2/13zEWMiZpPtnMGzH31GRb295c57w9D0aDbv\ntoK4dc2NYITUBHsB5uigeAprOl501niLGZJory8waxYUfzmTD3dYgaySljxyE/0PZKWlOpH6JIrr\n936QK3Ws5rgxE/0ub2jkGL4q6ri27qjIJy7I/+thVnoQNEdS0WA9gLd6W0G8xEf7lxXkaklgS1H3\n2Rbtnnq2hROvWsKqVfYiWct3rSUnzL9hhQAJYfGUN3RcD/PKSogQ//v5U4dn0Ry8z/Cq/AJCWv0/\n98OGQUjxHF5dtYQb3rgVlv6U+35hv/8b7k3j690dwY91OwsJbrHfNz9l2hGUNudT3VTN7opSwsXe\nvdvldIDXRUOzFcTdmF+MozGZoADeX9xxB6x4/CpiC87j6OH+PxN1NjFzBBtKN9PSAvVBOxiflWOr\nHGdrLHklHYGs7RV5DI3zv6zhw4W2gil8vqvj+v3Jhm2EtmTjdPgflIyUVLYUWRlZFY0VSFMMyUn+\nh0cmDMmkJWSfbMTGIoYmHVobS41MprSx+2tEi9SQ3MXCQweTmOCAppi9Pt9F9QWkdxFgS4+PpVk0\nI6vXlTdUYFpDGZLpf+d6VFYC3qCqPZHJ6sY6aHOSkex/h2DC0FRag8po9lgpoXUtdXhpZlS2jWU7\ng8OQtmC25lsdFU+bhzZ3JSMy7XWGQ9ri2VXe0VEprislIdT/i6wIBLUksXFXRyCr0VFKTqK9C3Z4\nSAitxsrIKq6sw+Gx99CcFTyBj7fuP09Wmaxn3jj/L9qhIU7GlP+c7zx/IwD1UsRwf2eT7MK49OG4\nnW7iQu1nbQBccPwReJw1ELM14FULAVJCssHpYXhKYIGsMenZFDVbgayi5i1MzDq8AlkAZx05kbz6\nrymszyc2pGcCWW+9BUf0wFQzyY4xvPb1G4Q0+5+NBZCbEU+rs3rPW/DK+jqCHT3zoBtkwkmJ8K99\nBUsk5TUDJyPrYJM9xzmyiQqy/9leMHMyxe5PKK/00Gba8LorGJFhr6OeGpVEeVNHIKveYX9uvpAQ\nuHDaKWws2c7yghU42+xnZAFcMmM+n5Ragayiht0MibM3tDAxKooGb0cgq9VR5XcmT7vMpGhMUB3N\nrVZfoEnszYuxr4z4BDBCVpJ/qzp1Ze7w6eAJ8ftlT1dinKkQWejXamt9ISsLXM1JhzyXXrswV4TV\nf7Npd81upj0+jS/zv9yzbU3VUk4aNYvZQ2bwyc7PbC933htGZsdQXl9BZSVsLypHmuNst4u4kARK\n6zpedDY4ihiRbq+/ExkJk5JmsLJwJc2eZqrJY2yG/4GsqCigLpVtpR2BhuKSNjzxX3HMqPF+lzcp\ndSJbGjrm0cmvySc5zP9sjbAwcDQmsqXQurY2tDZAa7i1OIkfUswU3li1/wTV7epa6rjzrd9x+cqh\nyGUnsPCtVX7XFWBz9VrGp/ofyEqKjKOyqaNN5FeWEOu2MbQwLQnjrqaovGMUx5bifCKN/+deBI5K\nm8MLm55k6daVXJz7Q4b5N3JsL9GuVLaVdAwh21RYSISxH8iad4wLd/lElhcup7CmlGhXAH1zbzA1\nDdaz5MbdJQR77CULtMvIgO9eGkblX59n1Aj/h2J2NndcLoXNm9m1C1yJOxgWn2OrnOC2WHaVdQRJ\nCht3MCbd/2tFaChE1kxj0def79m2Km8r8Q57jSPenUpeudUuCqvLMQ3xtrL+clNSMaFllFd1zHdW\n5SnkiIxDu7ZmxadQ1dp9IMvjqCEl1v/7UUiItSrpztKOz3d5SwFDu3jR2dWk/APdoAxkbSosxFGf\nak2a7Ke0VCc0xFNUY92UNuWX4GhMsjX/R2aGE2rTyKvcDcCu6l1IbQYZGfbenrqbU9iw22rEO8vK\noSmW6Eh7K0WFOxLIr+wIZJU3lZJsY2VGgNC2JLYWdwSyWoJKGO7nSibtokJC8WDd4Eqr7Q9jGp8y\nnvXle2dkldRU4nHUcexke2/77zj5WrZWbOeVDa/gkXqGpQX+IDI8bjhpkf7Py7CvoCAhzTMLXC3E\nhwX+sDXEt8LdqKzAAlnTRuRQIzsAqHFtYeaoAHoZA9SpJ4XirBzFptZ3SQwLPBsOrBVvAl39EGBE\nzFg2tr1BNPYCWRlpThxN8XsWwKhuqCfE0TMZWQk1xzEmbqpfPxPsiKC8buAEskqqawlq677jMCl2\nLmPjJ9suf9bIUcQ7hnLzwpcora2ElgiSE+29hk2PTaSqtdN12l3AmCz7Q5q/d6UL51eX8c8Nf8Rl\nAgtkXX/ONBqllC827aDCs5sRKfau0YlRUTTTEcjyBFWRkWAvIyvI5cBRl8Hzn1qZJC2ucrITA7+2\nDk1OwNEcS5DT3r27s7NmTCDyjf8EXA5AYoj1wJYQ1TOf757icsH770OSn12KcFcE1c32rxXvbv0A\np3HzyOePAVDZWEW1cwuXHD+Zi+YcxU5jBbL8nRy8t0xJn0jYEUv5+GPYUVJBUKv9tpqZEM/Oso7+\nYWtwMaMz7fcFTpkXSVTrSD7d/SnNrlImDPX/uiMCwa2pbNjdEWh4b8U23N444sL874vNGTmeOlNC\nfo21aEVpk/2h1sHeRDb4XuZWN9ZDa5jfi19MSzmapTs/7nb/hf++kD+//QHfCXuVc1Jv4LUtL9iq\na7FZx8xc/1/mpsXG77WKa1FdMQmh/vfznQ4HQU0ZLN+0e8+2vIoCW9lwAAuOmk69pwbvO7/hrjsC\nW/QiMTiNnZUdgdLtpQXEuOzfIydNgta8aSzZssy3CJb94JN4g6n1BbK2FRcTbgLrmwPceitMnQpD\nhwZWzvwjc2kM3cKGDYa2qB3kxOTYKieEWAo6Da+tlh1My7VX1jDXbD7K+2jP9xtKtpIRZu8XzYjM\nYVPl14D1kiDIE2/rJUGQ04WrKZlVWzvaWIOzkInDDi1YOjQphVq6H1rodXW98NChcHvi2V7c8fmu\nNYVdvujMSortsxVWe8qgC2RtqdjC+l2FttJUweo0OZuT+HqX1Vi2FJbithn5Dg6GoPpsVudZwws3\nl+zCVGdicyEZwkwKmwutQNaG3cUEtRq4Q4AAACAASURBVCTZftiNdiVQ1Cl1vLqljHSbcytFOpLZ\nUWqdr/qWeowxZKXYe6sbGRqKBysjq6ymjiBjr1N9zMgJFHj3zsh6e+V6QmpHExZm76SdfYYb1/sP\n8r3XrsHRmExKSuCRhqlpUzl+yPEBlwMwJ3s2juY4v1bv6c7odCvwMW5IYDfLo8am4nFVUlBegzes\ngCNH+v92ZaCbNAkkfzqlrlUk98AcOj1pSuYYvEkrSXLbC2QlJkJbTce4/OqmOkKdPZOxMarip0xN\nn+TXz4Q5I6lsGDiBrNLqGoLpvuPw8s+v5IkbLwjo3/jBlBt5budDbMwvxdmUaHtS9ZzEROqN9YKm\nurEO42hlWLq9IA9YqxWlFl/B19XLCSKwycbDQh0M8ZzCg6++QZ1zF+Oz7WVkpcRG0SJWIMsYMO5q\nMhPtZWQBXBT7R655+3IqGitoC6pmSGrgLy+OnZzNjKxpAZcDMHWKg3/cObtHykqLsjrT8ZEDKyML\nYPZs/wP7EcER1Ld0n5G1o2oHcxbOobS+tMv9z33+Pt7Ft/HiVy9T2VjJv7/4FHfpNHKHupk3bjRE\nFLC5ZIet5c57w3FDjqM1ahP/+2gnu8sqAlpkYtaEFPLr8igpgaraZkxQXUBt//jjwbt9Fs+ufQ6p\nS2VItr0+SqRJY0txx0Pg4g2rScHe/D5HTnPC9nm8veU9ACq9+QxN9D8rCCBCkthWbLWjkqp6xBPu\nd3s9e+rR7PB80uW+htYGFm3+gKSP/8Wjt0/hJyecx86oF6ir8294YUUFtMauZe4o/zOyshLiqW/r\nGHpU1lhCSqTNKUS8mazJ65h2paC2gKRQe+f+5ONDMI+t5Lq555Ma2AwdpEWmUljXESjdXV1IUqj9\nQl0uOCJyKu+uX0Z5U2lAoyWkzU19k5XJs7OihJigwDKywJoP98svrazCQKTERuFqC+efb36Nw9FG\nbIi9a0W4I5aiaitIUt1UjZcWpoyy16eemnw0m2pX0OSxnid31m4lN9Hei/Tr5y1gnXmJxtYmdpSU\nE9Jmv58f5slk7S5reGFjaxNtznom5B5aeSPSkmlydZ2R5W3zYlwNpCbYu3+HmHjyOmVkNQUVdDk9\nU3pCxF4LDwwGgy6QdfeH97CpsIBIsX/xCfUmsynferuyvaSEMBur+bWLbMti7U7rgr1u127CPRm2\ng09RjhTyyq1GvLWwhBCv/SBDbHACZQ2do6+lZMbbnEQ4OImCaut8FdaUQn0icXH2fsmosBA8YmVk\nldfV2Z6P59Rp42gIX79nWAjAkq/XkeKwPxbc7YbLjz6NiIYxtNWkkBz4CxFGxI/g8TMeD7wg4JpT\n5pEWYe8BcF/TR2RDaxjxkYG9nU+It7Ia/rboQ9xNmQQHMqh/gHI4YELCkQCkxgysQNaxY6wOa0aU\nvUBWUBAEtSSzqcAKZNU21RMW1DMZG3ffDaee6t/PhLsiqWocOIGs8roaQhyBrS55MLctOB2Ps5rf\nv/kfgr3270WTRiTR6N7JovdaWb+zEEdDGm63/WC8CFx//kiCCmcRJAH2hIEFE05lUd7rtIbsZtJw\nexlZqXFReBxWIKuipgkwRIXZf0v/15tOw7X1dE7664XQHEVifOAvCdKj0vj4mrcCLgesB6UFC3qk\nKHLifRlZ0QMvkGVHVHAEdQcIZC1c8g5LN61l7mNn7XnYaWeM4ZOC9zkp49uweT5/W/Yk/1m2lNzg\nWdZiOQ4nqWYa5TGLiLWx3HlvCHIGcWzq2byZ9yL5leWEYj+Qdc64UyH3TZ56sYb1O0twNiXhDGD8\n6rRp0LhxFs9/9SKO2mxsTltHTFAqeRUdgYZVhasZGW0vkJWeDunNx/PMZ4sAqJcCRqTYewEeHZRI\nXqnVBy6rbsDp9f8zdM6ssTQH57OtsHy/fYt3LMZRMok/PRSD2w1HD51IiNvB395Y3kVJ3Vv2VQ0S\nXsrQ2CF+1y87KY5mR6fFLzwlZMTaC6bEu7LYUNgxV1BJYz7pUfbOfXo63PWDUdx6S+CPq9nxaZQ3\ndxq6Wl+4J8Bv17xRU1lbsYzq1lJSogIJZAVT12gFEPKriokPCTyQ1ZPiJZfXNywi1pFje4RJgiub\nr0pXArChKA+qcsjOtlfW8XMiCK0dwxf5XwBQ6t3KhEx7gaz5R2cTUjWZB/77H3aVBbaoU7RksqnI\navvrd1pznR3qMOQx2Sl4Q4r2TPrfWUVdHbREEBFu73MQLnEUVFqB6qaWVtqCKxk7ZP825nIJ0mz/\n5WB/GHSBrJfXvs7nhUuId9u/+EQ6ktlWYj247a4oJcph/4IRH5TFxmJrjqBNRbuIddoPNMS7U9hd\nZQWy8sqKbU202C4hPJ6Kpo6MrCZHKUOT7V1kk0KTKaptz2ArwdVsbygmQHR4KG2ORowxVNbVESz2\nHppT4yNwNabx3qpNe7atKljHiDj/Vyzs7IrLhepn/kTIV9cRElgWc4+bPXwSm3/6WY+UdfKUUVw5\n4ZoeKSvCk8N/Vr9HjDn85sdqd/rk6QBk2k237CWzR4+ANqetyXXbhZtkthZZn++6ljrCg3rmQXfK\nFIj188VdpDuS2qaBM9l7RX0NYc7efZB1OR2cGv8TXit7gDDsD12dkjWKSVkjOeOFU/hg3TpCWgJb\nKRXgootAPv8RMa2BT+h241knUhX9IbiaSY229xCekRiNN8gKZO0qrcLRGh3QsO2QEPjLtx5g9fZ8\nHE3xfi8DPpgM96UzJB4mgazokEgaPN1fK/67Zgkjd93HjjWZTL3ncjzejlXntpRvo76pld/dNpJp\nci2/XfwYy0qXcFxuR/bbjIwZEJVPfMTACGQBfH/2eeyOfoG80goinfbvRUnhSUyNO44/L32Or3cV\nEewJ7K2dywWzs2dS3VJBuMf+vSgpJJWCmo5A1vbG1UzPtr/i2nlTTuDToncxxtAcnM/YbHtZQYlh\nieRXWxlZZdX1uIz/n6HQYBcxDdP51+L9+3DPfPEmzu2nMHOm9b2IMCPy2/xrhX/DCz9Yt55Y7yhb\nE14PT4unNah8z3yZtW0l5NhcACA1PJMdlR2BrEpvATlx9s49wC9+4X9foivDk1OpbutoX+WtBWTH\nBxbIOmduLnXeciocX5MRaz+Q5TDB1DVZgazShhK/5xftbUNjcqlPXkSajVUG2/3shO+ztO4JimvK\n+XzjDsJacmw/S86fD40b5/DWhiUYA/XurcwYaS+QJQKnpn6Hvy//B4XV5UQFBXBtDc5kR4XV9ldt\nKSTEc+hzDybFRIBxkF+2/30tv6wGR2uU/USZoHiKqq1A9bq8YhxNCYSGdH3yXZ4e+LD1oUEXyDKf\n/ogltQtJDrPfSY91J7Gz3HpwK6gpIcZt/+KTHp7Nx+WvcMrTp/DczgfJdtkfUpAcnkJRnRXI2lVV\nTEyQ/QtZUkQC1S2d5kAIsrcyI7RPImy9jdpaVEKw136ALTkqllZ3KY5fOXlw+4UBBesSvRNYtKZj\nnqwd9euZnhPg6hwTISN0BJnllwVUTm8JcfVMdC0yOJLHv/Vgj5SVEJTN+qb3yAg9fANZl84fCY2x\nDOuJNL0eFBIUTETTKKaP8P8NbLsYVwo7yqzrTn1rPZHB/TeHTmRwBLUtAycjq6qxhog+WLXs95dd\nRpvXSZTT/r0oyBnEZz98jdyoCfx85QVEEuA4DCAmBs4fv4DJVfcEXFZSVAzJbVMJbs6wHXxKioqC\n4Gq+f41hU141ztbA3xx++5xQJm19jpgdVwRc1kDWPowgJmxgzZFlV2J0BDXN1V3uM8awvuFDrj/t\nGNb/5gl2Ve9i9s/u37P/iQ8/IKL0WEaPFv7vxplUlropCf6Ui4+ZseeYc6dbXydGDpxA1skjj8WZ\nsJ2lW5cT5Q5sAZlbT7ySbTF/54t1xUQS+MI2px+ThrNmKPFO+4GstMhMtjWuos1YQcdK92pOHG8/\nkHXluUNprg/l8+1rMeFFjLA5Ni0lKolS34qwFbX1uLEXDB4bdTSLNuw/vPDNLW8yP/eUvebluXrW\neazxvoAxhz68cPnOtWSF2usDZ6aGIgUzeHbNSwA0OUvItTkX7pC4TArqO4YW1kk+uTaz4XrSqIw0\nGl0dGVm1ppDc5MDqdeR0B6ZgCrURq8hKCCSQ1TG0sKKlmPSYgZWRNTk7F3IWM8TGKoPtFpyYSVLZ\nuVzzxP+xJi+PxCD7ZUVEwJT4Oby66kNKy7y0Re5kYpb9fvAvzz+L3d6VrK9cTlwAizrlRA1jVcUn\nGAPrdxUS7fTvmhPUnMy6vP2HFxZW1OA8wMJDBxMbEk+Jb4GPtXmFBB9geqagNs3I6lXnZf8QmmLJ\n7GLZyEOVHJ5MoW9OmJL6UhLD7F8wpsQdS1LjHK6afBU/Dd7B9OgzbZeVHpNCaesOnv/qRZZUPUVy\niP3srrSYBGrbrBtvc4sXE1zF8DR7H87MuGSqPMW0eltZXfgV4di/WI8bHsefs2r5udfDlSW1/Hr2\nH2yXNTxyAl/u6pgnq9K1jhMmBBbIEoHLL6dHhhV+U2RFZ9MSs5YRCYdvICszw8mt4RuZPsr+W8Xe\nsu0X73PBrBkHP7Ab6UFjWVz8Mi3eFhpa64gM7r+MjejQSOpb+zeQ1dICNb75xKsba4l09/6DbE56\nGOPqbiQ9eFRA5TgdTj658yESPvsLI7zn9kjdbrsNLuuhuP654+aTGmFvWCFYgduxyaN5NmEkFz3+\nS9xt9ucAaycCC+8fw/UTbgu4rIFsaGIqghDqCmy+s4HikuOmUxr+IYXFrfvt21SSR4unlQtOzCUn\nI4SPfvwMnzsfZNFnVjbGf1a9z6yMeQBMnCiMbbwWV9kkpozt+Kwff4R1TR2RPXACWS6Hi7HOc8iL\neprY4MCyg0894kRCEgp4YfmigF6atjvhBPBuOIXsUP/nZ2o3I+EETGsw9y29jx1FVXiDy5k52v4C\nMrm5EF12Ar/577M4WqMJCbK3eltmXCIVzdbL3Iq6etw2h1qfNGom62r2nvB9c/lmapsa+N4Zewfs\nFsweh2kN4dVlX7Kv4rpivG3e/bZvrlrH+BR75z88HKa33sitrz1EW5vBE1zCiAx7z0YjU7Iob+3I\nyGoKKmB0Rv/3ncbmJONxl+05d02uQkZnBvbCJzgY0sRa1GaIzVEvAE4TTEOzlZFV4y0hO2FgPYTM\nHJUL7nrGpOUEVM69p93Ca4V/YnXxGjKjAptT94rjZ7Gx/jM+Xr+doNYEwtz2722jR4SQVHwBq1tf\nIjHC/rX1D9+5nHI2ceotz7Gl2P852EK8KWwu2D+QVVRVc8CFhw4mISyeikYrkLWxoOCALzpDjWZk\n9aoH7o4i4rX/cUzmSbbLGBoznE/aHibqngS+bPsz6ZH2L7CTsnMpe+phnv75OTy/MJ70AK7Vo9Iy\nyYt4gfMf+jM1b/+YS4+4znZZE1LGUx28jp3VO9mcX440x+AOspfDOTQphdLQpcTeH8vbBc8yrNn+\nA5LDAVdfDXf/ysFf/xTKt861mVcKTMsaz5Y6K5C1s7QSr7OOo8faf0hq9/3vw2OPBVzMN8aIJOtm\nNDHr8A1kAdx7R+KAG24KkBieGNDwqqMjLiG4JZWb3rmJRm890aH9l7ERGxZJg7f/AlnGwFE/+DOp\n117CspWt1LbUEBXSNw+yb93+M56+7qaAy4mIgPd+dyn3XnxeD9QKRo6EU07pkaK45+yreP7K+wIq\nY821K3n/mmc5/6RhnDakZ37H0aPhrrt6pKgBKy0yjcsmXhbwCroDxdj0IcSRy6+fW7Tfvic+WEJs\nzZw9c3mOz87m+LjvcNk/7qKtzbCp9X2uPnHenuOfu/lq7h/35l7DNhLDExmdOJqRNh/me8t5Y84D\ndz0J4YFlZDkdTs7KuYKqoY+TGBZ4RlZuLmR+9UeOSTjfdhlpKUGM+fo5HvniEX797iOE148NaO4u\ngHlDjuftwn8FNNR6SFIS1Z5SjIHqhgaCHfZe9lwy70iqwpbR0NQRfH3my7dwbDuZOXP2/lw6ncKo\ntvN49MPn99pe2VjF8N+N5YiHJ/HO1nf22ldk1jIz134g8YVfn0pxZR1/evMDCKkkI87eA/34nEzq\nnFZGVqvHS1toCWOyA29jgUpJCoKmWP739Ts0tTbjDS5jbIALHgEcmW6NxMlNCyCQhZt6XyCrUeyv\nDN9bRiXlAjBpSE5A5Vx2+nBiyk9kmecJRiYHVtb5Z8Ziyofyz+UvEu0NfMX0S8d9FxxeUqLtB7LS\nk0J5/cp/ssj1Q95atZz0aP8CWVGSsmfqo85KDrLw0EHrFZXKjra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WAeVBWAWgHiqrskdYBQAA0AJUVgHlQ1gFoJ7QQ7lcTkO7zBBWAQAA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rk6JxCxqmbcgV3KhR8DXCmu4MBUy1afYAnkMu6nlG1WNqXl6gAwnYnWWVVSSjg6c9S3nfd8Pd93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\n+AvAQqMiajEAgzGtDxA5/2kAOYy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"text/plain": "<matplotlib.figure.Figure at 0x7f0bb03526d8>" }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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eeVSQ5QUAAAAAQCUEsgBUlS0tLO6RFcdSOi25b96+ycgCAAAAANSC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Jnd179hhDUZ06sHnEQObunsvA+QNZcWjFP1ZqGzFnIfkiG5PLP/leoXzBUuw5u9ujNKwDZw9T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\ngg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f0bb03525f8>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "date_x_freq = pd.DataFrame()\n", "date_x_freq['Training set'] = df_train.groupby('date_x')['activity_id'].count()\n", "date_x_freq['Testing set'] = df_test.groupby('date_x')['activity_id'].count()\n", "date_x_freq.plot(secondary_y='Testing set', figsize=(20, 8), \n", " title='Comparison of date_x distribution between training/testing set')\n", "date_y_freq = pd.DataFrame()\n", "date_y_freq['Training set'] = df_train.groupby('date_y')['activity_id'].count()\n", "date_y_freq['Testing set'] = df_test.groupby('date_y')['activity_id'].count()\n", "date_y_freq[:i].plot(secondary_y='Testing set', figsize=(20, 8), \n", " title='Comparison of date_y distribution between training/testing set (first year)')\n", "date_y_freq[2*i:].plot(secondary_y='Testing set', figsize=(20, 8), \n", " title='Comparison of date_y distribution between training/testing set (last year)')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "a6b70be3-608b-1ce2-3811-5851dfd361ea" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "Correlation of date_x distribution in training/testing sets: 0.853430807691\nCorrelation of date_y distribution in training/testing sets: 0.709589035055\n" } ], "source": [ "print('Correlation of date_x distribution in training/testing sets: ' + str(np.corrcoef(date_x_freq.T)[0,1]))\n", "print('Correlation of date_y distribution in training/testing sets: ' + str(np.corrcoef(date_y_freq.fillna(0).T)[0,1]))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "fef8f45c-4a7e-c9af-21fc-e69fcb71206d" }, "source": [ "This gives us some interesting results. For date_x, we observe in the graph (and also in the high correlation value) that the training and testing sets have a very similar structure - this provides strong evidence that the training and testing sets are split based on people, and not based on time or some other unknown factor. Once again, we also observe the peaks (outliers?) in the September/October region.\n", "\n", "However, the date_y is less clear cut. There is a low correlation between the two sets, although there is definitely some relationship that we can see visually. There appears to be very many spikes in the test set in the first year (what could this mean?) That being said, in the last year of date_y the relationship between the two sets is much more apparent. Let's try looking at the correlations over the years." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "f62bad74-4050-a587-baef-8966980187fc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "date_y correlation in year 1: 0.237056344324\ndate_y correlation in year 2: 0.682344221229\ndate_y correlation in year 3: 0.807207224857\n" } ], "source": [ "print('date_y correlation in year 1: ' + str(np.corrcoef(date_y_freq[:i].fillna(0).T)[0,1]))\n", "print('date_y correlation in year 2: ' + str(np.corrcoef(date_y_freq[i:2*i].fillna(0).T)[0,1]))\n", "print('date_y correlation in year 3: ' + str(np.corrcoef(date_y_freq[2*i:].fillna(0).T)[0,1]))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "0eca140b-3abf-e3ed-1f92-5fc8c29d60d3" }, "source": [ "Wow, that is definitely a huge improvement over time! Something about the structure of the first year of date_y doesn't match up, so we should keep that in mind (If anyone has any theories I would love to hear them).\n", "\n", "----\n", "### Probability features ###\n", "\n", "To wrap up the first part of this EDA, I'm going to try turning the date class probabilities into features that we could use in our model, and then we can take a look at the AUCs that they give." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "eea41b73-fe97-6076-6c1a-069a9fe6cd81" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "date_x_prob AUC: 0.626182\n" }, { "name": "stdout", "output_type": "stream", "text": "date_y_prob AUC: 0.720296\n" }, { "name": "stdout", "output_type": "stream", "text": "date_x_count AUC: 0.465697\n" }, { "name": "stdout", "output_type": "stream", "text": "date_y_count AUC: 0.475916\n" } ], "source": [ "from sklearn.metrics import roc_auc_score\n", "features = pd.DataFrame()\n", "features['date_x_prob'] = df_train.groupby('date_x')['outcome'].transform('mean')\n", "features['date_y_prob'] = df_train.groupby('date_y')['outcome'].transform('mean')\n", "features['date_x_count'] = df_train.groupby('date_x')['outcome'].transform('count')\n", "features['date_y_count'] = df_train.groupby('date_y')['outcome'].transform('count')\n", "_=[print(f.ljust(12) + ' AUC: ' + str(round(roc_auc_score(df_train['outcome'], features[f]), 6))) for f in features.columns]" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "234385e5-a9fa-0917-4d0c-8f67072cf62d" }, "source": [ "It looks like the date probability features have very high predictive power! I think we might be onto something here.\n", "\n", "Anyway, that's all I've got for now. I'll be back with more graphs and text soon, in the meantime if anyone has any theories or questions feel free to ask/discuss in the comments.\n", "\n", "**Make sure to upvote if this was useful (and motivate me to make more!)**" ] } ], "metadata": { "_change_revision": 69, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/323/323526.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "61eb3899-723c-e7b8-3be5-ca7449798435" }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "# system import\n", "import gc\n", "import psutil\n", "\n", "# data import\n", "import numpy as np\n", "import pandas as pd\n", "\n", "# visualization import\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# machine learning\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "aca3ced8-7ac8-9919-3bc6-4c582579760e" }, "outputs": [], "source": [ "# loading act_train.csv, act_test.csv\n", "act_train = pd.read_csv(\"../input/act_train.csv\")\n", "act_test = pd.read_csv(\"../input/act_test.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "922059de-aa6c-06e3-a120-fe897757d931" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/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", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", "/opt/conda/lib/python3.5/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", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:19: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:20: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" ] } ], "source": [ "# rows where char_10 are not null\n", "act_train_char_10 = act_train[act_train[\"char_10\"].notnull().values]\n", "act_test_char_10 = act_test[act_test[\"char_10\"].notnull().values]\n", "\n", "drop_list = [\"char_1\", \"char_2\", \"char_3\", \"char_4\", \"char_5\", \"char_6\", \"char_7\",\n", " \"char_8\", \"char_9\"]\n", "\n", "# drop char_X rows from dataframe\n", "act_train_char_10.drop(drop_list, axis = 1, inplace = True)\n", "act_test_char_10.drop(drop_list, axis = 1, inplace = True)\n", "\n", "# rows where char_10 are null\n", "act_train_char_X = act_train[act_train[\"char_10\"].isnull().values]\n", "act_test_char_X = act_test[act_test[\"char_10\"].isnull().values]\n", "\n", "drop_list = [\"char_10\"]\n", "\n", "# drop char_10 rows from dataframe\n", "act_train_char_X.drop(drop_list, axis = 1, inplace = True)\n", "act_test_char_X.drop(drop_list, axis = 1, inplace = True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "ca0599f4-f82d-143f-f04f-07917c1c0373" }, "outputs": [], "source": [ "# loading people.csv\n", "people = pd.read_csv(\"../input/people.csv\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "48828100-fa82-b9f3-2129-78e381fde096" }, "outputs": [], "source": [ "# for char_10 != null\n", "# left join activity dataframe with people dataframe on people_id column\n", "act_train_char_10 = pd.merge(act_train_char_10, people, on = \"people_id\", how = \"left\")\n", "act_test_char_10 = pd.merge(act_test_char_10, people, on = \"people_id\", how = \"left\")\n", "\n", "\n", "# for char_10 == null\n", "# left join activity dataframe with people dataframe on people_id column\n", "act_train_char_X = pd.merge(act_train_char_X, people, on = \"people_id\", how = \"left\")\n", "act_test_char_X = pd.merge(act_test_char_X, people, on = \"people_id\", how = \"left\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "d23e779e-1464-cb98-82a5-7ba22e5d8d26" }, "outputs": [], "source": [ "# for char_10 != null\n", "# drop both date columns\n", "act_train_char_10.drop([\"date_x\", \"date_y\"], axis = 1, inplace = True)\n", "act_test_char_10.drop([\"date_x\", \"date_y\"], axis = 1, inplace = True)\n", "\n", "rename_dict = {\"char_10_x\":\"char_10_act\", \"char_10_y\":\"char_10_peo\"}\n", "\n", "# renaming columns according to rename_dict\n", "act_train_char_10.rename(columns = rename_dict, inplace = True)\n", "act_test_char_10.rename(columns = rename_dict, inplace = True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "b29bb27d-c4eb-4459-e055-3bbcf6e959a1" }, "outputs": [], "source": [ "# for char_10 == null\n", "# drop both date columns\n", "act_train_char_X.drop([\"date_x\", \"date_y\"], axis = 1, inplace = True)\n", "act_test_char_X.drop([\"date_x\", \"date_y\"], axis = 1, inplace = True)\n", "\n", "rename_dict = {\"char_1_x\":\"char_1_act\", \"char_2_x\":\"char_2_act\", \"char_3_x\":\"char_3_act\",\n", " \"char_4_x\":\"char_4_act\", \"char_5_x\":\"char_5_act\", \"char_6_x\":\"char_6_act\",\n", " \"char_7_x\":\"char_7_act\", \"char_8_x\":\"char_8_act\", \"char_9_x\":\"char_9_act\", \n", " \"char_1_y\":\"char_1_peo\", \"char_2_y\":\"char_2_peo\", \"char_3_y\":\"char_3_peo\",\n", " \"char_4_y\":\"char_4_peo\", \"char_5_y\":\"char_5_peo\", \"char_6_y\":\"char_6_peo\",\n", " \"char_7_y\":\"char_7_peo\", \"char_8_y\":\"char_8_peo\", \"char_9_y\":\"char_9_peo\"}\n", "\n", "# renaming columns according to rename_dict\n", "act_train_char_X.rename(columns = rename_dict, inplace = True)\n", "act_test_char_X.rename(columns = rename_dict, inplace = True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "e67730d5-c685-2074-faf7-b96cbf5ded95" }, "outputs": [], "source": [ "# convert True = 1 and False = 0\n", "\n", "act_train_char_10 = act_train_char_10.astype(str)\n", "act_test_char_10 = act_test_char_10.astype(str)\n", "act_train_char_X = act_train_char_X.astype(str)\n", "act_test_char_X = act_test_char_X.astype(str)\n", "\n", "act_train_char_10 = act_train_char_10.replace([\"True\", \"False\"], [1, 0])\n", "act_test_char_10 = act_test_char_10.replace([\"True\", \"False\"], [1, 0])\n", "\n", "act_train_char_X = act_train_char_X.replace([\"True\", \"False\"], [1, 0])\n", "act_test_char_X = act_test_char_X.replace([\"True\", \"False\"], [1, 0])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "c80d1b21-1e31-566b-001e-68764b97104c" }, "outputs": [], "source": [ "# convert string value to numerical value\n", "\n", "col_replace = [\"activity_category\", \"char_10_act\", \"group_1\", \"char_1\", \"char_2\", \"char_3\", \"char_4\", \"char_5\", \"char_6\",\n", " \"char_7\", \"char_8\", \"char_9\"]\n", "\n", "for col in col_replace:\n", " act_train_char_10[col] = [s.split(\" \")[1] for s in act_train_char_10[col].values]\n", " act_test_char_10[col] = [s.split(\" \")[1] for s in act_test_char_10[col].values]\n", " \n", "col_replace.append(\"char_38\")\n", "\n", "act_train_char_10[col_replace] = act_train_char_10[col_replace].astype(int)\n", "act_test_char_10[col_replace] = act_test_char_10[col_replace].astype(int)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "8f330a71-8601-a133-cb18-f1cb7ae2ebdc" }, "outputs": [], "source": [ "# convert string value to numerical value\n", "\n", "col_replace = [\"activity_category\", \"group_1\", \"char_1_act\", \"char_2_act\", \"char_3_act\", \"char_4_act\",\n", " \"char_5_act\", \"char_6_act\", \"char_7_act\", \"char_8_act\", \"char_9_act\", \"char_1_peo\",\n", " \"char_2_peo\", \"char_3_peo\", \"char_4_peo\", \"char_5_peo\", \"char_6_peo\", \"char_7_peo\",\n", " \"char_8_peo\", \"char_9_peo\"]\n", "\n", "for col in col_replace:\n", " act_train_char_X[col] = [s.split(\" \")[1] for s in act_train_char_X[col].values]\n", " act_test_char_X[col] = [s.split(\" \")[1] for s in act_test_char_X[col].values]\n", "\n", "col_replace.append(\"char_38\")\n", "\n", "act_train_char_X[col_replace] = act_train_char_X[col_replace].astype(int)\n", "act_test_char_X[col_replace] = act_test_char_X[col_replace].astype(int)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "8a7b4508-4889-4ed5-0a22-5bc16e937764" }, "outputs": [], "source": [ "# features\n", "\n", "features_char_10 = list(act_train_char_10.columns.values)\n", "features_char_X = list(act_train_char_X.columns.values)\n", "\n", "features_char_10.remove(\"people_id\")\n", "features_char_10.remove(\"activity_id\")\n", "features_char_10.remove(\"outcome\")\n", "\n", "features_char_X.remove(\"people_id\")\n", "features_char_X.remove(\"activity_id\")\n", "features_char_X.remove(\"outcome\")\n", "\n", "# labels\n", "\n", "label = [\"outcome\"]\n", "\n", "# validation set size\n", "\n", "VALIDATION_SIZE = 10000" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "96677942-1883-2900-a499-c3a6b560aef0" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/utils/validation.py:525: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Accuracy_Char_10 = 0.790820917908\n", "Accuracy_Char_X = 0.840715928407\n" ] } ], "source": [ "# Logistic Regression\n", "\n", "log_reg_char_10 = LogisticRegression()\n", "log_reg_char_10.fit(act_train_char_10[features_char_10].ix[VALIDATION_SIZE:],\n", " act_train_char_10[label].ix[VALIDATION_SIZE:])\n", "\n", "print(\"Accuracy_Char_10 = \" + str(log_reg_char_10.score(act_train_char_10[features_char_10].ix[:VALIDATION_SIZE],\n", " act_train_char_10[label].ix[:VALIDATION_SIZE])))\n", "\n", "log_reg_char_X = LogisticRegression()\n", "log_reg_char_X.fit(act_train_char_X[features_char_X].ix[VALIDATION_SIZE:],\n", " act_train_char_X[label].ix[VALIDATION_SIZE:])\n", "\n", "print(\"Accuracy_Char_X = \" + str(log_reg_char_X.score(act_train_char_X[features_char_X].ix[:VALIDATION_SIZE],\n", " act_train_char_X[label].ix[:VALIDATION_SIZE])))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "8164aa3e-39c6-06e6-2e38-d8c28f376920" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy_Char_10 = 0.872812718728\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/utils/validation.py:525: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Accuracy_Char_X = 0.840715928407\n" ] } ], "source": [ "# Decision Tree Classifier\n", "\n", "dtc_char_10 = DecisionTreeClassifier()\n", "dtc_char_10.fit(act_train_char_10[features_char_10].ix[VALIDATION_SIZE:],\n", " act_train_char_10[label].ix[VALIDATION_SIZE:])\n", "\n", "print(\"Accuracy_Char_10 = \" + str(dtc_char_10.score(act_train_char_10[features_char_10].ix[:VALIDATION_SIZE],\n", " act_train_char_10[label].ix[:VALIDATION_SIZE])))\n", "\n", "dct_char_X = LogisticRegression()\n", "dct_char_X.fit(act_train_char_X[features_char_X].ix[VALIDATION_SIZE:],\n", " act_train_char_X[label].ix[VALIDATION_SIZE:])\n", "\n", "print(\"Accuracy_Char_X = \" + str(dct_char_X.score(act_train_char_X[features_char_X].ix[:VALIDATION_SIZE],\n", " act_train_char_X[label].ix[:VALIDATION_SIZE])))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "b88840f5-74dc-1057-b7b9-f3461328271a" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Accuracy_Char_10 = 0.832216778322\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/utils/validation.py:525: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Accuracy_Char_X = 0.840715928407\n" ] } ], "source": [ "# Random Forest Classifier\n", "\n", "rfc_char_10 = RandomForestClassifier()\n", "rfc_char_10.fit(act_train_char_10[features_char_10].ix[VALIDATION_SIZE:],\n", " act_train_char_10[label].ix[VALIDATION_SIZE:])\n", "\n", "print(\"Accuracy_Char_10 = \" + str(rfc_char_10.score(act_train_char_10[features_char_10].ix[:VALIDATION_SIZE],\n", " act_train_char_10[label].ix[:VALIDATION_SIZE])))\n", "\n", "rfc_char_X = LogisticRegression()\n", "rfc_char_X.fit(act_train_char_X[features_char_X].ix[VALIDATION_SIZE:],\n", " act_train_char_X[label].ix[VALIDATION_SIZE:])\n", "\n", "print(\"Accuracy_Char_X = \" + str(rfc_char_X.score(act_train_char_X[features_char_X].ix[:VALIDATION_SIZE],\n", " act_train_char_X[label].ix[:VALIDATION_SIZE])))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "631000fa-632f-059c-6023-8c9e1c8a9d52" }, "outputs": [], "source": [ "# generate submission file\n", "\n", "(act_char_10, prediction_char_10) = (list(act_test_char_10[\"activity_id\"].values), rfc_char_10.predict(act_test_char_10[features_char_10]))\n", "(act_char_X, prediction_char_X) = (list(act_test_char_X[\"activity_id\"].values), rfc_char_X.predict(act_test_char_X[features_char_X]))\n", "\n", "activity_id = act_char_10 + act_char_X\n", "activity_id = list(map(str, activity_id))\n", "outcome = list(prediction_char_10) + list(prediction_char_X)\n", "outcome = list(map(int, outcome))\n", "\n", "submission = pd.DataFrame({\n", " \"activity_id\" : activity_id,\n", " \"outcome\" : outcome\n", " })\n", "\n", "np.savetxt('kaggle.csv', submission, delimiter=',', header = 'activity_id,outcome', comments = '', fmt='%s,%d')" ] } ], "metadata": { "_change_revision": 163, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/323/323646.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "8dead67d-cf27-b39b-df9d-803de90089ea" }, "source": [ "In this notebook I try to look at duplicate rows characteristics and on variables distributions - with regards to the outcome and between train and test and possible deduplication " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "4ccf6d9b-067d-e5f7-224a-cac067983ae8" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "baseDir = \"../input/\"\n", "people = pd.read_csv('{0}people.csv'.format(baseDir)).drop_duplicates()\n", "act_train = pd.read_csv('{0}act_train.csv'.format(baseDir)).drop_duplicates()\n", "act_test = pd.read_csv('{0}act_test.csv'.format(baseDir)).drop_duplicates()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "5b16e709-44e9-c04c-de6a-eda5a1cd688b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(189118, 41)\n", "(2197291, 15)\n", "(498687, 14)\n" ] } ], "source": [ "print(people.shape)\n", "print(act_train.shape)\n", "print(act_test.shape)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "8c059617-f666-d681-6f32-ff3ea65d7c99" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "23291 duplicate people rows\n", "0 duplicate people ids\n", "983413 duplicate train rows\n", "0 duplicate train rows with different outcome\n", "0 duplicate train activity id\n", "197519 duplicate test rows\n", "0 duplicate test activity id\n" ] } ], "source": [ "print(\"{0} duplicate people rows\".format(people.drop('people_id',axis=1).duplicated().sum()))\n", "print(\"{0} duplicate people ids\".format(people['people_id'].duplicated().sum()))\n", "print(\"{0} duplicate train rows\".format(act_train.drop('activity_id',axis=1).duplicated().sum()))\n", "print(\"{0} duplicate train rows with different outcome\".format(\n", " act_train.drop(['activity_id'],axis=1).drop_duplicates().drop('outcome',axis=1).duplicated().sum()))\n", "print(\"{0} duplicate train activity id\".format(act_train['activity_id'].duplicated().sum()))\n", "print(\"{0} duplicate test rows\".format(act_test.drop('activity_id',axis=1).duplicated().sum()))\n", "print(\"{0} duplicate test activity id\".format(act_test['activity_id'].duplicated().sum()))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "771fe72c-85f3-645f-8fb5-cb5859518114" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 rows duplicated between train and test\n", "[] columns diff between train and test\n" ] } ], "source": [ "unqTrain = act_train.drop(['activity_id','outcome'],axis=1).drop_duplicates()\n", "unqTest = act_test.drop(['activity_id'],axis=1).drop_duplicates()\n", "total = pd.concat([unqTrain,unqTest],axis=0)\n", "print(\"{0} rows duplicated between train and test\".format(len(total) - len(total.drop_duplicates())))\n", "print(\"{0} columns diff between train and test\".format([c for c in act_train.columns if c not in act_test.columns and c!='outcome']))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e11bad3a-e59a-ff64-40c1-96ee38832943" }, "source": [ "since column names are anonimized - let's give some explanatory names" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "4fac1b41-e85c-dc6f-9a29-e6e4c437279a" }, "outputs": [], "source": [ "def addPrefix(df,suffix, exclude):\n", " for c in df.columns:\n", " if c not in exclude:\n", " df.rename(columns={c:suffix+c},inplace=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "b531bc67-de24-fb7b-6c72-a95fd9af6437" }, "outputs": [], "source": [ "addPrefix(people,'ppl_',['people_id'])\n", "addPrefix(act_train,'act_',['people_id','activity_id','activity_category','outcome'])\n", "addPrefix(act_test,'act_',['people_id','activity_id','activity_category'])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "871744cc-181f-2c85-c20f-5f9eb6468fa1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2197291, 55)\n", "(498687, 54)\n", "Index(['people_id', 'activity_id', 'act_date', 'activity_category',\n", " 'act_char_1', 'act_char_2', 'act_char_3', 'act_char_4', 'act_char_5',\n", " 'act_char_6', 'act_char_7', 'act_char_8', 'act_char_9', 'act_char_10',\n", " 'outcome', 'ppl_char_1', 'ppl_group_1', 'ppl_char_2', 'ppl_date',\n", " 'ppl_char_3', 'ppl_char_4', 'ppl_char_5', 'ppl_char_6', 'ppl_char_7',\n", " 'ppl_char_8', 'ppl_char_9', 'ppl_char_10', 'ppl_char_11', 'ppl_char_12',\n", " 'ppl_char_13', 'ppl_char_14', 'ppl_char_15', 'ppl_char_16',\n", " 'ppl_char_17', 'ppl_char_18', 'ppl_char_19', 'ppl_char_20',\n", " 'ppl_char_21', 'ppl_char_22', 'ppl_char_23', 'ppl_char_24',\n", " 'ppl_char_25', 'ppl_char_26', 'ppl_char_27', 'ppl_char_28',\n", " 'ppl_char_29', 'ppl_char_30', 'ppl_char_31', 'ppl_char_32',\n", " 'ppl_char_33', 'ppl_char_34', 'ppl_char_35', 'ppl_char_36',\n", " 'ppl_char_37', 'ppl_char_38'],\n", " dtype='object')\n" ] } ], "source": [ "train = pd.merge(act_train,people, on='people_id', how='left')\n", "print(train.shape)\n", "test = pd.merge(act_test,people, on='people_id', how='left')\n", "print(test.shape)\n", "print(train.columns)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "83831c23-cdcd-d63d-2419-170e9edb53f8" }, "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "81a26288-2310-2844-7f60-be54b228fcfc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1172993, 55)\n", "(295128, 54)\n" ] } ], "source": [ "trainUnique = train[~train.drop(['people_id','activity_id'],axis=1).duplicated()]\n", "print(trainUnique.shape)\n", "testUnique = test[~test.drop(['people_id','activity_id'],axis=1).duplicated()]\n", "print(testUnique.shape)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "f4345eb2-e819-1001-f81c-13f20d6d1b59" }, "outputs": [], "source": [ "nonCategoricalColumns = ['people_id','activity_id','outcome','ppl_char_38','ppl_date','act_date']\n", "valCounts = {}\n", "def calcCountSuffix(df,exclude):\n", " for c in df.columns:\n", " if c not in exclude:\n", " cnt = len(df[c].value_counts())\n", " valCounts[c] = cnt\n", "calcCountSuffix(trainUnique,nonCategoricalColumns)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "1bea29cd-2bee-7ebd-610c-3d77aaacf9aa" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/pandas/core/frame.py:2754: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " **kwargs)\n" ] } ], "source": [ "def addCountSuffix(df,exclude):\n", " for c in df.columns:\n", " if c not in exclude:\n", " cnt = valCounts[c]\n", " df.rename(columns={c:c+\"_cnt_\"+str(cnt)},inplace=True)\n", "addCountSuffix(train,nonCategoricalColumns)\n", "addCountSuffix(test,nonCategoricalColumns)\n", "addCountSuffix(trainUnique,nonCategoricalColumns)\n", "addCountSuffix(testUnique,nonCategoricalColumns)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "bacf7b7b-9ff8-882d-e0b7-9a4e059ceb06" }, "outputs": [], "source": [ "def getColumnsBySuffix(df,minValue,maxValue,exclude):\n", " return [c for c in df.columns if c not in exclude if int(c.split(\"_\")[-1])>=minValue and int(c.split(\"_\")[-1])<=maxValue]\n", "def drawViolin(df, minCnt,maxCnt,indexFrom, indexTo, size=3.5):\n", " g = sns.PairGrid(df,\n", " x_vars=getColumnsBySuffix(train,minCnt,maxCnt,nonCategoricalColumns)[indexFrom:indexTo],\n", " y_vars=[\"outcome\"],\n", " aspect=.75, size=size)\n", " g.map(sns.violinplot, palette=\"pastel\");" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "c512cbc4-30f6-d3d0-7446-9165794a0b1c" }, "outputs": [], "source": [ "sam10k = trainUnique.sample(10000)\n", "sam100k = trainUnique.sample(100000)\n", "sam500k = trainUnique.sample(500000)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e6331bf1-ae49-0d08-d7ba-38afe4ac54aa" }, "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "fbb46d81-a602-6c0d-889d-489a12e1740f" }, "outputs": [ { "data": { "image/png": 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wAmCmxXRbWwtnz55geNiMWh9HxpKdRKXkB/w8sixj6anDUHMaj9NGcnIq27bt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\nhgYWLlxCVtaaGd+pv0+lUjFnTiIajT/lZaUYikYv5D1Cva0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VlQBER0ejUln1q0IIIYQQ\nQgghhBACKwdhbt++zc9+9rOxpUhGo5E//OEPpKSk2Do+IYQQQgghhBBCiBnBqkGYX//61/zmN78h\nMzMTgJycHH71q1+xb98+mwYnhBBCCCGEEEIIMVM4WdOov79/bAAGIDMzk/7+fpsFJYQQQgghhBBC\nCDHTWDUI4+bmxuXLl8d+vnLlCm5ubjYLSgghhBBCCCGEEGKmsWo50t///d/zt3/7tzg7OwMwPDzM\n73//e5sGJoQQQgghhBBCCDGTWDUIk5aWxqlTpx7aHUmtVts0MCGEEEIIIYQQQoiZxKrlSJcuXWJg\nYID4+Hji4+Pp7+8nJyfH1rEJIYQQQgghhBBCzBhWDcL89re/xdPTc+xnT09Pfvvb39osKCGEEEII\nIYQQQoiZxqpBGLPZjEKh+PaXnJwYGRmxWVBCCCGEEEIIIYQQM41VgzAeHh7k5eWN/ZyXl4e7u/uk\nBJCdnc2GDRtYv349f/zjH8dtl5+fT0pKCqdOnZqU1xVCCCGEEEIIIYSwJ6sK8/7iF7/gv/7X/0pc\nXBwAZWVl/O///b8n/OImk4lf/epXvP/++wQGBrJjxw7Wrl1LbGzsI+3+1//6X6xYsWLCrymEEEII\nIYQQQggxFawahElPT+fEiRPcunULgPnz5+Pj4zPhF8/PzycyMpLQ0FAANm7cyJkzZx4ZhPnggw9Y\nv349t2/fnvBrCiGEEEIIIYQQQkwFq5Yj/frXv8bHx4fnnnuO5557Dh8fH379619P+MX1ej3BwcFj\nP+t0Opqbmx9p89VXX/H6669P+PWEEEIIIYQQQgghpopVgzDXrl175LGrV69OejCW/OY3v+EXv/jF\n2M9ms9kuryuEEEIIIYQQQggxmR67HOmLL77giy++oL6+nr/9278de7ynpwdXV9cJv7hOp6OhoWHs\nZ71eT2Bg4ENt7ty5w9/93d9hNpsxGAxkZ2ejUqlYu3btY/+2n587KpVywjFOBqVy2OLjGo0HGo2X\nnaNxfPJ+WSY5Pb3Je2aZ5PX0Je+XZZLT05u8Z5ZJXk9f8n5ZJjk9vU339+yxgzDR0dGsWrWK27dv\ns2rVqrHHPT09yczMnPCLp6amUlNTQ319PVqtlhMnTvC73/3uoTZnzpwZ+///9t/+G6tXr37iAAyA\nwdA34fgmS2dnr8XH29t7GRlR2zkaxzcd3i+t1v5fbsnp6W06vGeS147/GTmS6fB+SU47/mfkaKbD\neyZ57fifkSOZDu+X5LTjf0aOZjq8Z4/L68cOwiQmJpKYmMiaNWvw9fWd9MCUSiXvvvsub7/9Nmaz\nmR07dhAbG8u+fftQKBS89tprk/6aQgghhBBCCCGEEFPBqt2RfvnLX6JQKB55/F//9V8nHEBWVhZZ\nWVkPPbZz506Lbf/lX/5lwq8nhBBCCCGEEEIIMRWsGoRZvXr12P8PDg5y8uTJR7aRFkIIIYQQQggh\nhBDjs2oQ5nvf+95DP2/bto133nnHJgEJIYQQQgghhBBCzERWbVH9XQqFAr1eP9mxCCGEEEIIIYQQ\nQsxYVs2E+dnPfjZWE8ZsNlNcXMyyZctsGpgQQgghhBBCCCHETGJ1TRiFQkFvby9eXl788Ic/JC0t\nzdaxCSGEEEIIIYQQQswYVg3CLFy4kJ///OcUFhYCkJKSwv/8n/+T8PBwmwYnhBBCCCGEEEIIMVNY\nVRPmH/7hH3j11VfJz88nPz+fV155hV/+8pe2jk0IIYQQQgghhBBixrBqEKa9vZ0dO3agUChQKBRs\n376d9vZ2W8cmhBBCCCGEEEIIMWNYNQjj5ORERUXF2M+VlZUolUqbBSWEEEIIIYQQQggx01hVE+bv\n/u7v2L17N0lJSQAUFRXx29/+1qaBCSGEEEIIIYQQQswkVg3CZGVlceLECfLy8gCYN28eGo3GpoEJ\nIYQQQgghhBBCzCRWDcIAaDQaVq9ebctYhBBCCCGEEEIIIWYsq2rC2FJ2djYbNmxg/fr1/PGPf3zk\n+WPHjrFlyxa2bNnCrl27KC4unoIohRBCCCGEEEIIISbG6pkwtmAymfjVr37F+++/T2BgIDt27GDt\n2rXExsaOtQkPD+ejjz7Cy8uL7Oxs3n33XQ4cODCFUQshhBBCCCGEEEI8vSmdCZOfn09kZCShoaGo\n1Wo2btzImTNnHmozf/58vLy8xv5fr9dPRagTMjDQP9UhCCGEEEKIJzCZTFMdghBCiBluSgdh9Ho9\nwcHBYz/rdDqam5vHbX/w4EGysrLsEdqkqq+vneoQhBBTrLe3Z6pDEGJSmc1mi4+PjIzYORIhJo9e\n3zTVIQghBDD+eVZM/wHzKa8JY63c3FwOHTrEz3/+86kO5alVVlZYfHxgYMDOkQhhW8PDw1MdgsOq\nra2e6hDEE0z3E7q9tbe3WXy8oaHOzpGIp9Xd3TXVITisiorSqQ5BPMHgoOX+s1ywipmmsbFhqkNw\nWI2N9VMdwoRMaU0YnU5HQ8O3yaXX6wkMDHykXVFREb/85S/5z//8T3x8fKz6235+7qhUykmL9VkN\nDAxQU1Np8bmmpmrmzp1j54gc39CQ5c6ht7cLWq2XnaNxHI6S0wBms+UldnV15SQlxdg5mumhurrc\n4uMajQcajeS1Iygpybf4uJeX86w+9ozn7FnLF6uVlSWsXr3CztE4DkfKabB8sVpScof09BQ7x+L4\nhoaGKCu3vAGEydSPVhtu54gchyPl9aVLX1t8vL29kTlzIuwcjeNTKi3fIHNxYVaf2xwppxWKQYuP\nFxTcIitrqZ2jmR5qaiz3q93dldMir6d0ECY1NZWamhrq6+vRarWcOHGC3/3udw+1aWho4Gc/+xm/\n/e1viYiw/sBqMPRNdrjP5Nat6xiNRovP3bhxg7S0DBQKhZ2jcmxnzpyz+Pj585fIylpr52gsm4ov\nt6PkNMDXX2dbfPzcuWwSEtJwc3O3c0SOzWBop7ra8kyYysp6RkbUdo7Istmc18PDw3z9teWO/dmz\n51m7dr2dI3JsAwMD5OXlWXyuqKiIsrJafHx87RzVo2ZzTgOcO3fR4uPXr18nNXUhWq3OzhE5ths3\nrjI4YPli6Pz5i/j5BVt8zt5mc163t7eSk5Nj8bmTJ08RFhaHs7OznaNybJWVNRYfv3LlOq6uU3+c\nhtmd0wCXLl22+HhFRQU5OdeJi4u3c0SOra+vj7sFBRafy829zsqVjjEI87i8ntLlSEqlknfffZe3\n336bTZs2sXHjRmJjY9m3bx/79+8H4N/+7d/o7Ozkf/yP/8HWrVvZsWPHVIb8VMxmM9evX4FxBlla\nW1ulXsx31NZWc+fOLYvPXb2aS0vL+DWDhH3U1dVy48YVi88NDQ3y+eefyZTg77hyxXKHESAv74Yd\nIxGWmM1mvvrqC7q7uy0+f+PGFerq5Fj9oOvXLz92+eHly5Yv/oX91NfXcfXq+Meeo0c/ZWjI8oDD\nbDQ0NETu5QvgZLnPVlJSRHt7q52jEg8aHBzk8OGD4y4d7enplj6IBbdv37T8+J28cW8UC/vR6xvJ\nyblg+UmFE198cZTOzg77BuXgrl69hGmc+nN5+denRXmEKa8Jk5WVxcmTJzl16hQ//vGPAdi5cyev\nvfYaAP/8z//M5cuXOXz4MEeOHOGTTz6ZynCfyv0Ttlfw+KOX437pZqG2tlaOfHZw3OdNJhOHDu2j\np8fyhZKwvdbWZo4cGX+LeDf/cCoqyjhz5kvpBN1jMLRz584t1F6W78wVFxdgMLTbOSrxoJyc89y5\nk4ezl3bcNocP75dB4Ht6erq5ejUHpavlybTO3i7k59+ktbXFzpGJ+1pamjl8eP+4x2HfyPm0t7dx\n6ND+adFZtYcrVy7R29ODJtXy7CCz2czZs1/JuW2KDA0NcujQPtrbW/GJmGexjatfCKWlRXz11Rfy\nOd3T2tpCcXGhxef6enu5deuanSMSD2pra+XTT/dhMlkeUNAmZTEw0M/Bgx/J9c89HR0Grt+4isrd\n8izy/r4+rl3LtXNUT2/KB2FmKpPJxMWL50ChQBOzyGIbN00oVVXl1NVZniY4m+j1jew/8AED/f3o\nlkdabBOQHkJXVyf79v1VLlqnQGNjPfv3f0h/fx+Byasttgmetx4Xn0Bu3rzGqVOfz/pdUkY77acw\nmUxoF4Y9to10GO3PbDaTnf01Fy+eQ+3uQ2j6SxbbBaasZmCgnwMHPqShYXoXgpsMX399kuHhYQIW\nhFh8XpsRhtls5vTpzyWvp0B9fS37939Af38f2uRVFtsExGfiFZJAbW01n3zyEX19jjMtfyq0tOi5\nfPkiak9nNKlBFtu4BXtRUVFKYeEdO0cnenp6OHDgI+rqavAOSUSbsNxiu5D5L+LiE8itW9f5/PPP\nZv0A4/1ZnuNxUjtx8eI5KdQ9RRob69m3/wN6e3sISFxpsY1v+Fz84zMxGNrZu/cvs3423mhOf8mI\n0Yh2keV+tdJVRU7uBYe/VpRBGBu5ffsWbW0t+Eak4ezhZ7GN/5zRQktnz56e1R3V4uIC9u79C709\nPQQ/F41vfIDFdpr5QWgXh2EwtPPRR+9RXW254LGYXGazmTt38ti79y/09fcRPP9FfMItF3RUql2J\nWr4bVx8d+fk3OHjwo1m9NXNR0V3Ky0vxCPPBM9ryumu3IE/Ky0spKrpr5+hmt4GBfo4cOcDlyxdx\n9vAjauUbqNwsr931CUsheP6L9PX3sW/fX7h9+9asPWYXFxdSXFyIe7AXPgmWj9We4b54x2qoq6vh\n5k25y2ovZrOZW7eus2/fX+kf6Cc4/SV8w+dabKtwUhKWsQ3v0CTq6mr54IP/pKlpdu7CMTw8zLFj\nhzGZTISsjUWptlyoM3h5JE4qJ05/9YUsDbCjuroa/vrX/6CxsR6fiFTCMrahcLJ8+aJ0diNq5R7c\n/EIoKLjN3r3v09FhsHPEjuPmzWvU1lbjEW55UxPt4jCGhoY4efL4rD2nTQWz2Ux+/k0+3vsX+np7\nCJq3Hr9Iy7O7AHQpawhIWE5Hh4EPPniP0tIiO0brWPLyblBZWYZnhA9esZavrwOXRjBiNHLixBGH\n3vVSBmFsYGCgn/Pnz+KkVBM4zl0oADffYLzDkmlqauDOHcsFDmeywcEBvvjiKEePfsqIeYSITYkE\npFu+swqgQEHQ8khC18YyMDjAgQMfcvbs6Vl/p8OW+vp6OXr0E7744ihmhYqIzJ1oYhY+9ndUrh5E\nPfeDsbus77337xQXWy6eNZN1dXVy+qsvcFI5EbouFgWW6wwEPdCx7+rqtHOUs1NlZRnv/fn/UVZW\ngoc2iujVb487WH6fJmYhkct2YnZS8eWXxzhy5OCsG2Ds7u7i1Knjozn9fNy4OQ0QsiYWpauKb859\nRWurLOOyte7uLj79dB+nT3+OQuVC5PLX0UQveOzvON0biNEmPUdXVycffvgeFy58M6tmMN6fKdDW\n1oL/vGC8ozXjtlV7uRC8OoahwUGOHv1UamnY2PDwMGfPnmbfvr/S29eLLnUdoQu3jDsAc5/K2Y2o\nrD34Rs5Dr2/i/ff/yM2b12bdIENTUwNnvzmNyk2NbpnljU18EgLwjPSlsrJc6njZSU9PN4cP7+fk\nyeMonNRELt+Ff+zix/6OQqFAl7Ka0EUvM2wc4ciRg3z++WcMDFjeqXSm0usb+frsKZQuKsKenzNu\nH8Q72g+fhAAaG+vJzra84YIjkEEYG8jO/np0GnDSStTj3Fm9L2juOpyUas6dO0N//+yYDmw2m7l7\nN5///M9/486dPNwCPYh7fR4+cf5W/b4mNYiYV1Nx9nPj2rVc/vznf6esrMTGUc8uJpOJvLwbvPfe\nv1NSUoS7fzixa97BKyjWqt9XqpwJX7KDoLQXGBwa5ujRTzl8eP+suSM1MjLCsWOHGBwYICgrGhdf\nt3HbOnu7EvxcNIMDAxw/fmhWXQDZW1dXJ0ePfsonn+ylr6+XwORVRC5/HZXz+J/Pgzx1scSu+RHu\nARGUlRXzpz/9X27cuOrQd1omy8jICEePfsrAwABBWVG4ah6/A5raw5nQdXGMGI189tknDA0N2SnS\n2WVkZISrV3N4771/p7KyDI/AaGLW/BDPwGirfl+hUBCYtJLIFa+jcvMiJ+c877//RyorLW/9OdPc\nuHFltB+i8yRoZdQT2/slB+KbFEhTUwOnTp2YdRf29mA2mykrK+b99/8f167lonb3JWrlHgLmLLV6\nN1EnpZrQhZsJXbQFE0589dUX7N37F/T6RhtH7xi6u7s4dHg/ppERwjbMQe1uuSadAgXh6+NRezpz\n/vxZysosb88uJm5kZIRr13L503v/d3SGtDZq9Fits65fDeAbkUrM6rdx9Q3m7t18/vSn/ztrZub2\n9fVy5MhBRoxGwtbPQe3l8tj2oWticfFz4+rVHIddQqr8x3/8x3+c6iBsoa9vajp8dXW1fPXVF7h4\nBRCyaAsKhRMjwwO0lz+6m4x/XAbOHj4onJR0NJTQ19fHnDkJUxC1/dTWVnP8+GFu3LjKiHmEwCXh\nhL4wB7XHtyeIkUEjbbcePVEGpIeMFYJUe7qgSQnEbDLRUd1KYeEdGhrqCAjQ4ulp+23JPDwe/+W3\nBXvldHV1JceOHSI//wYmFASmrCEk/UVULt9edD0up5XOrsBo595dE4p3aBIDXc3o6yrIyxutWK7T\nBaNSWS7qORN8/fUpSkuL8IkPIGh5JAqF4rF57RHuw6Chn5aqJgYGBoiJiZuCqGduXg8ODpCbe4Hj\nxw/T0qLHzS+E8MxX8QlLfqhTb01eK51d8Y1IQ+niTre+ioryYkpLi/D29sbPT2P1RcJ0c/bsKUpK\nrM9ppasKV407I4NG2iubMRjaSUhIsvv7M1Nz2mw2U1JSxGefHRxdyqh0JijtBYLSXkB1L1fBupwG\ncPbwwzdyPiPDgxiaKigouI1e30hAgBYPD0+b/3umQllZCV9+eQyVu5qY7XNRuY0WeXxcXqtc1XhF\n+dFT3UFDZS1OTk6Eh1uuY2dLMzWvm5ub+Pzzz8jNvcjA4CD+c5YSnrEdF8+HZyhZm9euPjp8IlIZ\n6uuktaGCvLwbdHd3odMF4+Ji//fQHgYGBjh48CM6OgwErYzCLynwsTmt9nLBPdSbzqIWSkuKiYyM\nxsvL2+5xz9ScNpvNlJYW89lnByksvAtOaoLSnido3vqHbgBZm9MqVw/8IuehUKrp1FdQVlpEeXkp\nvr5++Po+fkbvdGU0Gvnkk720trYQuDQc/3t1ux6b1x7OeET40FHYQllJMRERUXh7W16WZ0uPy+uZ\nexU0BYxGIydPHgcgZMFGnJwsryv+Lv+4JXTW3uXOnTySkuYSFRVjyzCnRGNjPRcunKOqavTumnec\nP8FZUTh7uz7hN8fnpFYSvDIav2QdjecqqKoa/S8+Polly7LQagMnK/xZobGxnvPnz47V2vEJn4tu\n7tonzuZ6Ehcvf6JW7qGrroCmO19x+fJFbt26zpIly0hPX4yzs+U7NNNVfv5Nbt68iou/O6Hr4qy6\n6FQoFISui2OgrY+bN68SGKgjLS3dDtHObENDQ9y8eZXLly8xODiAytWT0Hlr8IlIndBggEKhwD92\nMT6hyegLztJadYtDh/YTHBzKihWriIyMnlGDMXfu5HHjxv2cjn2qf1vwiij6m3spKSnkypVLLFli\nuaCmsI7ZbKaysozz57+hublptPh/7GK0SVlWz+gaj1LtQkj6i2iiF9CYf4ry8lLKy0tJSkph2bLn\n0Gism606HTQ2NnDs2CEUSgWRW5KeeFf1QU4qJyK3JFG+L58LF77B29uHlJQ0G0Y787W3t3Lhwrmx\npcueuliCUp/Hxdty3amnoXbzImLpDnqaK2nKP8Xt27coKLhDevoilixZhru7x4Rfw1EMDw9z+PA+\nWlr0aNKCxi2e/l3uOi/CX4yn+ngRn376Mbt2/YCAAOlDT4TZbKaqqpzz578ZnYF1b6MWbVLWQzc1\nn4XCSYk2YTm+4ano736NvvYOBw58SHh4JCtWrCYsLHyS/hVTz2QyceLEYRoa6vBJCCBwifX/NleN\nOxEbE6g6Usihw/vZ/fqbaDQTP6ZMFhmEmUSXL1+kvb0VTcwi3P2tTxKFkxMhCzZS8c17nDp1grfe\n+glqteVtt6YTs9lMbW01ubkXxi7sPcJ9CFoeiXvQ5M1WcfV3J3rbXHpqOmi6WE1JSSElJYXExSWQ\nmbmCoCDrTkKzVV1dLTk558cGyDwCY9ClrMbNL3jSXkOhUOATnoJXcDztFVdpLb5EdvbXXL2ay6JF\nS0hPXzwj7krV1lZz+vTnKF1VRG5OROls3UAsgNJZSeTmRMr35XP69Of4+Wmm5A7rTDAw0M+NG1e5\nfv0KAwP9KNWuBKaswT92EU6qyRv0U7l6ELpgE/5xS2guOEdjQxEHD35EcHAomZkriImZM+0HYxoa\n6jh56gRKl/s5/XTdBoXSiYiXEijfm0d29tcEBGiJjY23UbQz1/0lGjk5F8aWVPiEpaBNysLFa3IH\nR1x9dUStfIMefTnNd7+hsPAuRUUFJCYms2TJiml/g8NgaOfTT/diHBkmclPiM/VH1B7ORG1NpuLA\nbb788hju7h5ER1u/rECMamtrJTf3AoWFdzCbzbj6BqNLWfVUSzSs5RkYTeyaH9FRk09LYTbXruWS\nl3ed9PRFLFqUiYfH9B6MMRqNHD68n7q6WnziAwhZFfNU5x/vWH9C18VRf7qM/Qc+ZNfOH8yogVd7\nMZvNlJeXkJNzYazYuXdYMoFJWbh4Te4AgNrdm7DFW+/1Qb6htracvXvfJyIiiszMlYSHR07rPojZ\nbObMmZOUlBThEeY9WgfmKf89XpF+hK6Lpf50GQcPfszu3W/ZZcWENWQQZpK0tbWSe/kiKjcvAlMs\nb9/7OG5+wfjHLaGtNJecnGyystbaIEr7uD/17sqVSzQ2jm7n6hHuQ2BGOJ7jVGifDJ4RvsSG+9Bd\naaD5Si1lZcWUlY1OrczIWDbj7k5PhNlspqamipyc89TWVgPgHhBJYNJKPLRRNntdJ5WagPhl+EUt\noK3sMm3lVzh//ixXruSwcGEGCxYsxs1tYncIpkpHh4Ejnx3EjJnIjYmPrQMzHhdfNyI2JlJ1+C5H\nPjvInjfembHTS22hp6eb69cvc+vWdYaGhlA6u6FNyhqdzqt+9ll3T+LqrSVi6Q76DY20FF2gsbGY\nQ4f2o9XqWLJkGQkJyTg9oZikI+rp6ebIkYOYTCNEvZTyTDkNoxeskZuTKD9wm+PHD7NnzzsOdTfK\nkY2MjFBUdJcrVy7R2toCgHdoEtrElbj62G4wRKFQ4BUUh6culu6GIlqKLlBYeJfCwrvExcWzZMkK\nQkJCbfb6ttLX18snn3xMf38fIWti8I599otMV393IrckUnmogM8+O8iuXW+i01ne3lo8rKVFT27u\nBYqKRme+uHgHEpiUhVdIgk37aQonJ/yi5uMTPhdD1U1aiy9x5UoON25cZd68hWRkZDrMBdrTGK3Z\n9QnV1ZV4xfgRvn4OCqenfx81KTpMwyYav6lg/4EP2LXzB9IHsZLJZKK4uIDLly/S0jJajN4rJJHA\npJW4+uhs+tpufsFELt9FX1stzYXZ1NRUUlNTRUhIGEuXLp+2N4Rycy9w69Y1XAPcidyUhJPq2fpR\nmhQdxp4h9Dk1fPLJ6EwvFxfb9QmtJYMwk+B+dX3TyAihaetRqp/tjn5gUhZd9YVcvZpLSso8/P2n\nVyfVaDRSUHCbK1dzMLS3AeAVoyFwcRjuwfY5qSkUCrxjNHhF+9Fb20nz1Tqqqyuprq5gs3qKAAAg\nAElEQVQkMDCIJUuWER+fNC0viCaDpRF6j8BotIkr8QiwXD3fFpTOrgQmP4f/nCW0l1+jrewyly5l\nc/VqDvPnL2LRoqV4ek6fOgRDQ0McOrSfgf5+QtfFTmiw0TPch5DVMdSfKefw4f3s3v32jFuyNdkM\nhnauXMnhzt08TCMjqFw80M1diV/0gmc+Hj8LN79gIjJfYaCzmZbiC7TUF3L8+GHOnz9LRsYy5s6d\nN21qIY2MjPDZZ5/Q29tD0MoovCItb7FuLTedJ2HPx1H7ZQmHDx9gz54fSl4/xvDwMLdv3+Lq1ZzR\nXdMUCnwiUtHGL5+UJRrWUigUeIcm4RWSSE9TGS3FFygrK6GsrITw8EiWLl0xbW5wGI1GDh0aLRCv\nzQjDP23isz09Qn0I3zCHmhPFfPrpXvbseWdK6mlMF3p9I5cunR8rAOvqG4Q2cSVewfF2zSEnpQr/\n2MX4RaXTUXWL1pJL9wbwr5Gams6SJcumpH7EszCZTBw/fpjy8lI8I3yJeCkRhfLZ+7gB84MxG0do\nulDN/v0f8Prrb0pOP4bRaOTu3XwuX744unW9QoFP+FwCEpbj6q21ayzu/uFErdhNX3s9rcUXaWgo\n4dCh/QQEBLJ06fJpdUPozp08Llz4BrWXC1Fbk8dqgj4rbUYYw71DtOQ3ceTIQXbseB2l0vrZ6rYw\nPXqDDq6srJiamio8dbF4hTx7YV0nlTNBqc9Te/kTzp49zY4duyYxStsZGhoiL+86V6/l0tvTg8JJ\ngV9KIAELQ5+4g4atKBQKPCN88YzwpU/fTeu1eprLmjh27BA+Pr7T7oJookZnJxWRk3Oe5mY9AF4h\nCWjjl+OmmbrlWkq1K9rEFWjiMjBU3qStNJerV0fvSs2fv4CMjGUOf1fKbDZz8uQx2tpa0MwLQjN3\n4ndCNalB9Lf20prXxMmTx9i0adu0uMixt9bWZnJzL1JUdBez2Yyzhx/+8Zn4RqThpJy677arTyDh\nGdsY6mmntTSXjuo8Tp/+nEuXslm8eCnz5i10+AGICxfOjq7Bjg+wuq7Ak/gmaunTd9N2s5HTpz/n\npZdelrz+jvvn0ytXc+nr7UHhpEITswj/OUtx9pjYQNhEKBQKvILn4BkUR19rNS3Fl6itraC2tnpa\nLL8zm818+eUxGhvr8U3UosucvJsOPnMCCMoapCm7ikOH9vP662/OiCXlk2l08CV7bCdLN79QtIkr\n8Ayyrm6arTgpVWhiF+EbnU5ndT4tJRe5desa+fk3SEtLZ+nSFQ49AGE2mzl9+nNKSgrxCPUmcnPi\nM88WeJB2URgmo4nm3FoOHvyIXbt+MG1nKdvK6ED5TS5fvkRPTzcKJyV+0QsIiM/E2WNqZw+5a0KJ\nyHyVgc5mWksu0lpXwPHjh7l48RxLl64gOTnVoQdj6upqOHnyOEoXFVHfS0btOfGbaQqFgpBVMRh7\nhqipqOL06c9Zv37TlB5/ZscVqA2ZTCays8+CQkFQ2vMT/jC9QhLw0EZRWVlGbW21Q9eEuF/08srV\nHAb6+3FSKwlYEELAgpBJ+cJMFnedFxEbExns6Kf1ej2GghZOn/6cnJzzLF26gtTU+TN2MGa0MFgF\n2dlnxgZffMJSCEhcYfcR+sdRqpwJmLMETcxCOqrzaC2+yPXrV7h16zrp6YtZunS5w3YAbt++RVFR\nAe7BXoRkWbctrDVCsqIZaOmlqKiAyMgYKdT7gPb2Ni5ePDe6KwyjU9m1CcvxDktCoXCcjoWzp4aQ\n9JfQJmXRVnYZQ8V1vvnmKy5fvsTSpcuZP3+RQx57amuruXIlB2df16cuxPskQSui6GvopqDgNnFx\n8SQkJE/a357OjEYjeXk3yMk5T39/H04qZwLil+Efl4HK1XFmBSoUCjy0UXhoo0aX3xVfoLFhdPmd\nThdMVtYah9xc4Nat6xQW3sHt/7N339FR3Xf+/59TVUZdo94LQgWJjugYTLFNEaK54rpxyjebzW6y\nPmeLs443Z3ePk83uOSkb+5fETtxwx2B6MWAQIEAUoQLqvY1GGo3qtPv7YyyMqRKaJvF5nONzjDQz\n96PRS3fufd/PfX8i/UfcMH00tNOjGersp72klcOH97Fq1RqHvv541d3dxdGjh7h6tQwA39BYwjIW\nownzrNlTcrmC4KTpBCVMxdBwmY7yr7hw4RyXii8wfdos5s9fhLf32BpfO8OpU8e5dOk83uEaEtZl\nIFc57sp+eG4c1kH76jPbt3/Ili1b3T5zwBPYbDaKiy9w4sRR+vp6kStUhE6aS2hq7pgXsnA078Bw\nYmfnE575ALorBXTXX2TPnh2cPPkVixYtZfLkTI/6OwT78uqff/4RNkkiac1kh17Ml8llxD2cRtVH\nxRQXXyAyMppp02Y67PVHy/OO/saZiopy9HodQQnTHNJwSSaTEZ61lJojb3Ly5HGPLMLYbDYuXTrP\n8eNHGBjoR+GlJHxuHNpp0WOeLuZMXkE+xDyYSvjceHRFTegvtnLw4B5Onz7BkiUPkp6e5XE7o7HQ\n6do5dGgf9fW1gH21o7D0RQ5v4uhIcoWSkOSZBCVOw1B/iY6yrzh79hSXis8zb+4iZs3K9ajqvdHY\nw+HD+1B4KYl7ePKYpgDfSKaQE/dQGpXvXuTw4X0kJaV49BU5VxgY6OfYsS8pLj7/dRPHSMIzFuMX\n6blX4AFU3n5ETnkQbdp89JWFdFYW8uWXBzhz5hSLFy8jM3NsqzU5ksViYe/enSCDuFVpo27Eezfy\nr3Nd8c4FDhzYQ0JCkkee3LiKfanpMo4cOUhPjwG5Um2fHZgyZ8wraDibT3AU8XO/vv2u/Cvamsr4\n6KN3SUhIYtmyVWi1nlHo1+t1fPnlfpQ+KhJWT3bITIEbyWQyopemMNDex6VL50lKSiEtLcPh2xkv\nTCYTBQXHOFdUiM1qxSc4mvDMJWjCR9cs1tVkcjlBCTkExk2hu6GYjrJjnDt3mpKSSyxYsIRp02Z6\nzDFIVdXVb27XyMtE4eXYfbVMJiNqSRLmPhONFQ0cPryPFSseceg2xpuqqgq+/HI/XV165Ap7j8PQ\nSbkovTy7qbNaE0z0jNVo0xeiu1pAd+15du78lMLCApYtW0VsrOvaEdyJJEns2bOD/v5+opYk4Rfn\n+NmfcpWChDXpVL5nP7aOi4snNNQ9n1VuP2M+duwY//Ef/4EkSWzcuJEXX3zxpsf84he/4NixY/j4\n+PBf//VfZGR4zgdbUZF9TXdt2jyHvaZvSAy+2gTq6qrR6To85kAG7Ms67t+/i/b2VuRqxbgovtxI\npVETtSiJsJkxdJxtovNiK1988RkXLpxl5crVbvtjdBSr1cqpU8c5deo4NpsNv4gUIrKW4R3k3MZg\njiSXKwhOnE5gXDb66nPorhzn6NGDlJdfZtWqtR7T/PDIkQOYzWZiVqSiDnD87C91gDeRixJpOljJ\nkSMHWLt2o8O3MR5IkkRJySW+/PIAg4MDqP1Dich8AP/odI8+oL+RUu1DeOYSQlJmo6s4ib7yDLt3\nf87lyxdZufIRgoPdXyC9ePEc3d1dhE6LclovL69gH8LnxtF2oo7TpwtYsmT8NqIfC6Oxh4MH91BZ\neRWZXEFI6hzCJi/w+AP6G3kHhhOXu5GBrhbaSr6krq6av/zlDebOXUhu7gK3zvaSJIn9+3djtVqJ\nf3DSqJaiHi25Uk78w2lUvHuBg4f2kpiYjFrtObOCXaWxsYHdu7djMHSj8gkgOvtBAmI874r7ncjk\ncoITphIYm4W+6gwd5V9x6NBerlwp4eGH89zerLavr489e3YiU8hJWJuOSuOc21tlMhlxKydR1TXA\nhQvnSE6eRErKJKdsy5P19/dx+PA+yspKQCYjOGkmYekLPW7my92ofQOJnvYw2q9XdGxrLOH99//C\n9OmzWLx4mdv3V5cvX6Surga/xGBCpzluhdYbqQO8iVmeSv0X5ezd+wVPPPGsW/ZPbi3n2mw2/v3f\n/50//elPfPHFF+zatYuqqqpvPebo0aPU19ezf/9+Xn31Vf7t3/7NTaO9WU+PgcbGBjRhCQ6fXRCS\nNAPg2nR7d5MkiaKiM7z33pu0t7cSlBFO2jMziJgbP64KMNdT+qqJWpxE2tPTCUgOobGxgbff/hNl\nZZfdPbR7Njg4wIcfvkNBwTHkXhri520hYcHj46oAcz25Qol2Ui6pK75PUHwObW2tvPPunz3id9TZ\nqaO8vBSfcA3Bmc5bpSQ4KxyfcA3l5aV0duqcth1PZbVa2bt3J3v27MBkthAx5UFSH3yRgJiMcXVQ\nfz2lly+RUx4kdcX38ItMpb6+lr/89Y9UV1e4dVw2m43ThQX2AntunFO3pZ0ehcpPTVHRGUymIadu\nyxM1NTXw1ltvUFl5FV9tAinLv0tUzspxV4C5nk9wFIkLnyBu7hbkXhoKCo7x4YdvMzDQ77Yx1dfX\n0tBQh39SMIGpzi9yeoX4EjYrhr7eXi5cKHL69jxNUdEZtm37CwZDN9q0+aSu/D6BseN3lrFcoUSb\nNo9JK/8fAdHpNDY28NZf3qCursat4xqeiR4xPx6fcOferihXKYh7KA2ZQsbBg3uwWq1O3Z6naW9v\n462//H+UlZXgExxDyoMvEj394XFXgLme2i+E2Dn5JC15Fi9/LefPn+Xtt/9kbyzsJhaLheMnjiBT\nyIl90LG3Qd9KYGooASkhNDc3XutV5WpuLcJcunSJhIQEYmJiUKlUrF69mkOHDn3rMYcOHWL9+vUA\nTJ06FaPRiE7nGSciNTX2gpF/tONn5vhFTUImV1BdXenw174XJ04c4dChvci9FCRtyCJu1SSnVd5d\nTR3oTcK6DOIfmYxVZu8yf/78WXcPa9SGhgZ57723aGysJyA6ndTl38U/Ks3dw3IIpZcvMbPWET//\nMZAp+eKLz7h06bxbxzS8/bDZsc5dUlMmI2x2LGDvP3M/sdlsfPLJ+1y+fBHvoChSVnwXbdo8ZPKJ\ncV+6WhNE/LxHiZmVh8Vq49NPP3BrgbG2tpq+3l6C0sNQ+ji3sahcqSA4OxKLxcyVK2VO3Zanqaur\n4YMP3mZwaJDIqQ+RuOgpvPxC3D0shwmITiN1+fcIiM2kqamRd999022FmKKiMwBOLypeTzs9BrlK\nQVFRIZIkuWy77lZUVMihQ3tRqH1JXPw0EVOWIVdMjAbFSm8NsbkbiZm5FovFyiefbHNbIcZo7OHy\n5Quog33QTnfNwgreWg0h2ZH09BgoLS12yTY9QWtrM++//xf6eo2EZy0l6YFnPKqf4lj5hsaSvOxv\nCE3NRa/v5N1336SrS++WsVRWXqHXaCQkJ9KpMxavFzHf3vLDXed8bi3CtLW1ERX1zXSjiIgI2tvb\nv/WY9vZ2IiMjv/WYtrY2l43xTlpbWwB7iB1NoVTjHRhBR0eb26vOw0saqwO9SX1yKn7xrlmhQaVS\nodVqXbbKQGCaltTHclD6qjh8eB/NzU0u2a6jHD16iM5OHcFJM4nN3YhC5e30bbr6d+QfmUrSkmdQ\nqH04dGgv3d1dLtnurVRXVyJXyfFPcv7Jk39SCHKlnKoq986UcLUzZ07ap6ZGpJC0+GnUvhNv3yOT\nyQiKzyZx0VZkChX79u9y29Woa/2jJrnmtqjh7Qxv935gMg2xe/fn2CSJ+HmPEpoyyyWzBFy9r1ao\nvIidnW9f+a5Lz5dfHnDJdq9ntVqpq6vGK9gH30jXXbVWeCvxTw7GaOxBp2u/+xMmgO7uLr788gBK\nbz8SFz+NRuuaHhMu31cnTCVu7mZsX6+2ZbFYnL7dG1VWXsVms6GdFoVM7roZRsMFn+EmyxOd1Wpl\n9+4dmExDxM5eT9jkBS5p/O/qfbVcoSQyZwUR2cvp6+tl//5dbikeV1SUAxCS5byZ5TfyDvXFJ9KP\n+voaBgcHXLbdYZ7RXWqcMhp7APu0LmdQ+wVjs9no7+9zyuuP1PCU2vg16S5b9UilUpGXl8dLL71E\nXl6ey3ZGXiG+xK6c9HXz4fEzldhkGuLixSLUfqFETl3psoN6d/yOvAPDicxegcVi4cKFcy7Z5o1s\nNhtdXZ14azVOafJ4I7lSjneYhu5uPTabzenb8xRnz55GofYhZlYecqVr8uWuXPuGxBCRvRyzyeS2\nGU/DJ4zOnt4+zCvYB7lSTkfH/XGiClBeXkpvr5HQSfPwj0x1yTbdlWmZTEbklOV4B0ZQUnKJvr5e\nl2x3mMHQhdlsxjfa9bcNaKLtTdR1ug6Xb9sdiooKsdlsRGYvd1nzf3fl2j8ylZDkWfT0GNxSkGhu\nbgTALy7QpdtVB3qjDvS+tv2Jrqamks7ODoISpxEYN8Ul23RXpgFCU3Pxi0ihvr6WtrZWl213mE7X\ngVytwCvUtQ3pfaMCkCQJvd71M4Dc2swjIiKC5ubma/9ua2sjPPzbFbDw8HBaW78JQ2trKxERd+9v\nERzsi1Lp7Cnr9hkqd5tuKVPc+m2+3deHyRX22338/FSEhbnv3kN9lw6FlxJvrWv+MGRKOYGBgcyZ\nMweAOXPmcOTIEWQuONkF0MTYD54MBr1b3/cb3SnTHR2DAPiERCN3wa0aMoXy1r+ju2TaUXy19qnl\nZvOAW35Hg4ODSJI0qtUIbpffkeZa4aXEZrMRGOiFt7fzZzm5yu1ybTab6e/vw1cb77JVYtyda43W\nPjXWZOp3S65lMvvVL7l6ZPuQsWZaJpMhVyuQJKtH7WvH6k77apvN3v/GVTMF3J1pmVyOT0gsg4Y2\nVCqbS3/PAwP2mZKjXTVmrLm+fptKpTRhsn2nXEuSfUaIT6hrbvtyd659Q2PprDyNQuHaTAPIZPYL\nMYpR3DLqiEwPb3Oo1z2fT85wp0xXVdkzrQm9T/bVMhm+oXH0tlWhVLr+M9lsNqHwVo7qIrIjcq30\nsb+/Xl64/Gd2axEmOzub+vp6mpqaCAsLY9euXfz617/+1mMefPBB3n33XR555BEuXLhAQEAAWu3d\nl4Lu6nL+/cdyuf3ts1mG7njrh8rbD7VfCKbeb6psar9QVN53vtpoNdsP1vr6rHR0GB0w4nsTGRFN\nd3kJvfXd+Cc4vyO8SqOm3zZIYWEhc+bMobCwkAHboMt60Biu2nsOhYdH3/Z9d8cH0J0ybTLJUau9\n6GurxmYxIVc6971SefvRb5K+9TvqN0HEXTLtKD3NVwDw8wtyy9+GJEnI5XIsA+YRP0elUaMO9sbU\nNXjta17BPiPOtWXAjFwup6fHhNE48u2OhiflWpIkgoND6NY3Ye43oPJ1/lU/t+e6qRQAf/8Qt+R6\n+DPNOmBG6Xv3XI4105JNwjpoQRWodtrP60mZBvDx+brI31SGX0SK08fi7kzbrBZ6WytQKpVYrSqX\n5nro637P5j7TqJ431lwDmPvt27TZlE75mT0t115fF8qNzVcITZ3j9LG4O9fDxyByubfL99UKhX1G\nutk4NOLeXY7I9PA2fX1874tMq1T2Juk9zeUExmc7fYa5uzMtSdJ1ufZxea7Vam+MXUYkmzTi2+wc\nkWtLn/142mSSuTzXbr0dSaFQ8PLLL/P888+zZs0aVq9eTUpKCtu2beODDz4AYMmSJcTGxrJixQp+\n9rOfedTqSIGB9v4EQ8a7NwqOy92E2s8+RVPtF0pc7t2Xmh0y6lAqVfj6unZq1o1mzJiDXC6nYdcV\nBtpcM5045pFU9hzey2uvvcaew3uJfsQ107Z767tpOlyNSqVi6tQZLtmmI6jVambMmIVlqI/6kx9i\nszrnJP16UbM3snv/EV577TV27z9C1OwNTt8mgLGlgvaSw6jUaqZNm+mSbd5IJpMRHh7JYEcf1qGR\n3xOesDodr2AfwP5BEb968oieZzVZGOzoIzw8ctyuMjFaMpmMOXPmI9ms1J14H8uga/Y97sq1obGE\n9rJjeHv7kJ09zSXbvJFWa5+J2t868vf6XjMNMNjRh2STCA2dOI0O72bSpHR7cbH2Ap2VhS7Zprsy\nbbNaaDj1MeaBHqZOnYmPj49LtjssICAQLy9v+pt6Rt3jYCy5BuhrNAAQHj4+VyYcrRkzclGrvWgv\n+ZK+jlqXbNNdue6sOouh4TLh4ZGkprp+8YPo6BgAjLWj64k31kwPtPdi6TMRFRUzqueNV3FxCcTE\nxGJsuUpH2VGX9ElxV6YlSaK56AsGu1uYPDmTkBDX3FJ4vaioaGwWG/2toyuEjDXXvQ3dKJVKtFrX\nH4fIpAnaut0VFbzS0mJ27dpOeNYywibPH9FzrObBETVMtQz1c2X3/xAbE8fjjz8z1qGO2fDPKlPI\niVyYQOi0KJecDFqHLKOeSnwvJKuN9sIG2gsbkcvkbNz4OImJybd9vDsq9nfLtNVqZceOj6msvIp3\nUBSxs/Pw8r/7rLGxGmmmx0qSbOiunqSj7CgKuZxNm54gLi7B6du9nRMnjlJQcIyYB1MIyY68+xOu\nM9pcd15qpflwFfPnL2bBgiWjHeqIeVquJUniyJGDnD17CqWPPzEz1+EXnuSScbkq1zarhfbSo3RW\nnESt9mLLlqeIinLNihc3am62r2QTOFlL/MOjO5C5l311y7EadEXN5OVtJi0tfVTPHSlPyzSAXq9j\n27a36evrJThpBpHZy50+exFcl2mAIWMnTWc/Z6CrmcTEFPLzt6BUun7y9e7dn1NSconE9Zn4J45+\nJu+95NpsHOLKW+fQhoTx7LPfHfU2R8ITc11ZeYXPd3yChIyoqQ8RlDDVNceJrtpX26x0lB5Fd7UA\nX18Njz661S0nbkNDQ/zf//0vkhrSnpmBYoS3jw671+Pq+j1XMFzRsWHDY6SkTBr18+/GEzPd02Ng\n2wdvY+juIjBuClFTH0Khdn7WXLmvNg8YaS7aRW9bJRERUWzZ8iTe3q4tmIN9sYtPPnmfoIww4laN\nvrh5L7nuazRQ/fFlUlMnk5+/ZdTbHIk75VrxyiuvvOKUrbpZf//opp/eCz8/f86cOYXVNEBI0shm\nTchHeG9fd/0lelsrmDFjNjExrlta8XbCwiKIjIymtqaKrsoOeuu78Q71dfoyYq5oemqs66Z+VzmG\nik4CAgLJz3+U+PjEOz5Ho3FNg+Lr3S3TcrmcSZPS6e010lxfSVfdBWRyBd5BUcjkznsfR5rpsRjs\nbqOx8FO66y6i8dWQn7/FrQUYgJCQUIqKChns7CckO3JUqxSMJtc2i43GvVeRLBKrV69HrXZe9jwt\n1zKZjMTEZORyOXU1FXTXX8IyYMQnOMbpJ62uyHVvew0Npz/G2HKFwKBgNm541G0FGLB/ppWXl2Bo\n1hOUHobCe+TvwWj31ZZBM437KvDx8mHFikeQO2kf5WmZBvDx8SUlJY2Ghlr0zVX0NJai9gt1+jLV\nrsi0zWqhs/I0jWc+xdxvIDMzmzVr8t1SgAH7bJiLF4sY6hogeErEqIsC93IM0vpVLQNtvSxevIyI\niNEV6EfKE3MdEqIlKjKayopyuhvLGOxuxTc0HoXKyceJLsj1QFcLDSc/sN+aEhjEo49uJTTU+Re5\nbkWpVGI2m6mvqUGy2kbdJuBeMt3b0E3rV3VERESyZMmDTimueWKmvby8SUtLp6Ghns7magwNxag0\nwaj9Qp1aYHRFpiVJwlB/iYZTHzHU005CQhIbNjzmlgIMQFBQMFeulNHVoCMgJWTUt8qNNteSJNGw\n7ypmo4mHHlpHQEDAqJ4/UnfKtSjCjIFKpaK1tZmOlnr8IpJR+TjmFyhJEs3nv8Bm6uehh9bi5eX6\nHdOthISEkpWVg8HQTWtdM10lbQzpB/AK9R3xfamepL/NSOPBKtpP1WPpN5OdPY38/C0jmobniR8W\nMFyImYxWG05dbRWG5qv0NJag8vFH7a8dd7eymAeMtBYfoOX8bswDPaSlpbNp0+MecfuCWu3FwMAA\njbV1yJVyNDHO6VnScaaRnspOpk+fTXp6plO2McwTcy2TyYiLSyA5OZWmpgb0LdV01RSBJOEdFOmS\nRtSONmhop6noCzpKj2Ad6mfq1BmsX7/l2i2u7iKTyfD29ubq1XLMfSaC0px3ktF6vI7+ph4WLFji\n1IKqJ2Ya7IWY7OxpWK1WGmorMDRcpr+zEe+AcJQu6gHgSJIk0dNUSsOpj+lpKsPby4tHHslj3rxF\nTiuwjYSfnz96vY7WuiYUPkqnL1Xd12ig+UgNISGhrFy52mmfuZ6a6+DgEDIzp9DR0UZHUzX6mnNI\nNis+wdHIxuG+2jxgpPXiPlou7sUy1EdOznTy87fg5+fexrTR0bGUl5egr+nAJ8Lv2u0YzmDuM1H3\nWSmSxcb69Y+65WTVWUaSaS8vL6ZMmYpcLqe+thJDQwl9ujq8A8JQ+YzPBsW97TU0nv6YrpoiFDJY\nunQlDz74kEtXY7qRTCYjKCiI0tJiBtp7Cc4Md+oS7PpLreiL25g0KZ05c+Y5bTuiCONEvr4aSkou\nYR3qJzAuyyGv2dtaib6ykPT0TLKzpzvkNR1FrVaTnp5FfHwiHR3tdNa3o7/YylBnP14hPiNq5uhu\n/S1Gmg5V0Xq8DlP3IHFxCaxfv5lp02aO+Gqdp35YDNNqw8jJmY7VaqG5oQZDYynGlgqUXzeJ9vRi\njHmwl/bSIzSd3cFgVzOhoVpWr17P3LkL3fohcaOYmFiKL1/EUNuJf1Kww5tHD7T30rivAl9fDevX\nb3b61WRPzrWfnz85OTPw9dXQ0tyAoaWCrprzSJIN78AIl1w5GqtBQxstF/bSemkfpl49sbHx5OVt\nZurUGSgUnnGCotWGU1tbja6uDZ8wDV4hju9JNrwPDg4O4eGH1zn1JN2TMy2Xy0lMTGbSpHS6u7vo\naLYXGAcN7Xj5hY6LYoy9+FJG45ntdFWfA6uJGTPmkJe3ichI983qul5sbDzFxRcw1OjxTx79FdaR\nsgyYqd1eis1kJT//MQIDnddM3JNz7eXlTVZWDgEBgTQ3N2JoqaS79uuZuYHh46IYYxnqp738GM1n\nP2egu4WwsHDWrMln5sxcFB7wWaNQKIiNjePy5Yv0VHXinxyC0tfxx0Y2i5W6z9mGgqAAACAASURB\nVMsY0g+wZMlyp14I8uRMy+Vy4uISmDw5k54eA+1NNXTVnmewq8W+0Mo4KMZIkkS/ro6mczvQlR/H\nMtRHZmY269dvITEx2SPOC4KDQzAYummuacBqtt3TLaQjMdDeS/3uq3h7ebFx42Num2EuijBjFBgY\nRF1dDbrmGvzCk8a8gock2Wgo/BTrUB9r1mxAo9E4aKSOFRgYRE7OdMLDI+nq6qSzvgP9pVYGWntR\n+alR+Xt5xB/0MEmSMNZ20XSwkraT9Zi6B4mNjeehh9ayYMGSUV/V8OQPi2EqlYqkpFTS07MYGOin\ntamGnsYSjK0VKNU+HjkzxtxvoL30GE3nPmegswF/Pz8eeGA5q1atcUujsLtRKpWEacMpLSmmt8FA\ncEa4w26hsw5ZqPmsBOuAhby8zdeapjqTp+daLpcTFRVDTs4MlEolrS0N9LRU0lVThM1qxisgHLnS\nc4p0w/o7G2m5sIfW4gMMGXVERESxatVqFi1ahr+/Zx28yWQyoqNjKS4+T09tl/22JLXjTjqsQxZq\nPy3BOmRl/frNBAc79xYcT880gEbjR1ZWDtHRsXR16elsqbEXY7pbUWkCHTbL1pEkmxVDw2Wazmyn\nq+YcNlM/mZnZrFu3iczMKR5VLFer1YSGaikrvUxvfTdB6WHIVY4tBEg2ifovyhns6GP+/MVkZeU4\n9PVv5Om5lslkREREMm3qTORyOS1N9fS0XKW77pJHF2MsQ310lB+n6cxn9Ovq0Gg0LF26khUrHnH6\nvmq0/Pz8CQgI5Gp5GcZqPQGTtA7toSjZJBp2XaG3wUBGxhQeeGC5U48ZPT3TAL6+vmRkTCEuLoHu\n7i50LfZizIC+GbUm0CWrOI6WJEn0tlXTXLSTjvLjmPsNJCWlsnbtBmbMmI2Xl2t6z4xUQkIyFRVX\n0Ne0oQ7wxifMsefB5j4TNZ+WYB20sH79ZiIiohz6+je6U65FY14HaGpq4L333sI7KIrkpc8hk937\nSZi++hwtF/aQlZXDI4/kOXCUziNJEjU1lZw6dYKmpgYAfCL9CZsVQ0CKe2ddSFYb3Vd16M41Maiz\nL0WXlJRCbu6CMU2B98QGYnej07Vz8uRXlJfbl8FV+4cSlraAwLgpTu0ZMxJDvXp0VwowNFxCstnw\n9w9g7tyFZGdP85gZAndy9OhBCgtP4p8UTMK6jDFnXpIk6naUYazpYs6ceSxZstxBI72z8ZbroaFB\nzp0r5Ny5QgYHB5ArVAQlTkc7KdftB0P2A58qdFcL6NfVAxAVFcP8+YtJSkrxuALojYqKznDo0F58\no/1J2jgFuWLs+whJsp+o9lTpmTt3IYsWLXXASO9svGVakiRqa6spKDhGc3MjAJqwBLRpC9CEJ7k9\nNzarme7ai+gqTmLuNyCTycjMzGbevIUEB3teofx6x49/ycmTx/GNCSApP8thBXNJkmj+shr9pVaS\nkyexYcOjTv89jbdc9/f3c+bMSc6fP4PZbEbppSF00lyCk2eicEFT6rsxDxjprDhlv33KakGj8WPu\n3AXXCv6e7PTpAo4dO4Q6yJvkzdkOmekl2ez9MgxXdMTHJ7Jp0xNOPxYbb5mWJIn6+loKCo7S2Gg/\n9/HVxhM2eQGacPfPLJEkGz3NV9CVn2DQ0ApAcvIk5s9f5PErXOn1Ot55901MpiES8zPxi3PMrdo2\ns5Xqjy8z0NbLokVLmTt3oUNe907ulGtRhHGQXbu2U1paTNTUhwhJmXVPr2EZ7KPywP8hl9n4mxf+\nH35+nj8V+UZNTQ0UFhZQWXkVsC8Xpp0VQ3B6GDIHHMSPlM1sRX+5DV1RM2bjEDKZjPT0LObMmUd4\n+Ngb5Y23D4vr6fU6Tp8uoLS0GJvNhso3CO3k+QTF57j8lo7Bng50V05gaCwBSSI4OITc3AVkZmaP\ni+LLMJvNxscfv0ddXQ1hs2KIXJg4ptdrPV5Lx9kmEhKS2LTpCZf1VBivuTaZTBQXn6ew8CS9vUZk\nMjmBcVPQTp7vkhXCridJNnqaytFdOcGgoQ2ApKRUcnPnExsb7/YDs5GSJImdOz/lypVSQqZGErM0\nZcyv2V7YQFtBPXFxCWzZ8pRLcj1eMy1JEg0NdZw+fYLa2moAfIKj0U5egH9UmstzZDUP0VVzjs6K\n01iG+lAolOTkTGP27Hlu72U0UvZMf8KVK2UEpmmJe9gx72PH2UZaj9cRFhbBE08849Sp7cPGa677\n+/s4d+4054rOYDaZUKh9CE3NJSRlttMb+N6Kud+A7upJumrPI9ms+Pn5k5u7gOzsaR41m+tujh07\nzOnTJ/AK9SV545Qx3ZokSRJNh6routxGdHQsmzc/iVrt/ELZeM00QGNjPadOnaCmphIA76AowiYv\nwD96ssv31cOzFDuuFGDq7QQgPT2TOXMWOK1RuDPU19fy0UfvglJG8ubsMc+Ikaw2aneW01vbxZQp\nU3noobUu+d2IIowL9Pb28uc//x9mq0Tqiu/e0/ThxsLPMDSWsGzZKmbOnOOEUbpOZ2cHhYUnvznR\n9/cibHYMwZkRTl3xyGqyoi9uRXeuCUu/GaVSSU7OdGbNmuvQA8Xx/GExzGDo5syZk1y6dB6r1YrK\nJwBt+kKCEqY6vdnpUI+O9vJj9DTaZ+WEhYUzd+5C0tIy3NrEcSwGBgZ4550/0d3dReyqSQRn3Nvt\nQ11l7TTuqyA4OISnnnrepZ3qx3uurVYrZWWXOX36BHq9/eAjMDaLsPRFeAU4txgjSTZ6GktpL/8K\nk7ETmUzG5MkZ4+7A53omk4l33/0zOl0HMctTCJly7z9HT7Weuh1l+PsHsHXr37jsVtvxnmmA1tYW\nTp8+ztWr5QB4BYQTnrEI/+h0px9EWs1D6KvO0FlxCqt5EJVazYzps5g5MxeNZvxdKDKbzXz00Ts0\nNTWinRFN1OKxLXnfVd5O494K/Pz9eerJ5/H3d82tY+M914ODA9dmMQ4N2ZfjDZ00l5DUOS6ZGWMe\nMNJRfpzuuvNINhuBgUHk5i5gypSp4+oC0DBJkjh8eD9FRYV4azUkb5oyqtXtrn+dliM1dF5sISIi\nki1btuLt7ZrbVcZ7pgHa2lo4ffoEV66UAeAVEEZYxmICXLCvlmxWuuuL6Sj/CnO/AblcTlZWDnPm\nzPfI2/lHoqzsMl988RlKjZqULdmoA+8ti5Ik0bi/ku6ydpKSUsjPf9Rlf+eiCOMiFy8WsX//Lvyj\nJxM/d/OonmtsraS+YBuRkdE8+eRz4/ZE9EZGYw9nzpzkwsUirBYLKn8vIubFE5Qe5tCu1zarDX1x\nKx2nG7EMmFGr1cyYMYeZM3Px9XV8Y8mJ8GExrLfXyJkzpzh//ixWqwWVbyDhmQ/Yb1Ny8IeGqd9A\ne+kRDA2XQZKIiIhk/vzFpKS4/squM+j1Ot5+58+YLSaSNk5BEz26A/K+5h5qPrmMSqlm61PPExLi\n2lkcEyXXkiRRUVHOyZNf0d5un40SGDeF8MwHUGsce9VekiSMzVdoK/3yWvFlypSp5OYu8LgeAvei\nu7uLt9/+I0OmIZI2jT7TAIP6fqq2XUJuk/HEE886/R7s602UTIP94sapUycoK7uMJEl4B0YQnrUU\nvwjH395ms1rQV51Bd7UAq2kALy9vZs+ey/Tps112UuYsAwMDvPfem+j1nUQtTkI7494aCPfWd1O7\nvRSVSs0Tjz9LWJjz+3YNmyi5Hhoa4vz5M5w5c4rBwQEUXr6ETV5IcPJMp1wMspgG0F05gb7qLJLN\nQlBQMPPmLSIjY8q4LL5cT5IkDhzYzcWLRfhG+ZOYn4VCPbqfqfVEHR1nGtFqw3nssa34+Dj++Pl2\nJkqmATo7dZw+fYLS0mIkScIrMJyIzKX4RaY6fF8tSZJ95kvZMUx9XSgUCnJyZjBnzjwCAjyvR81o\nnT17mi+/3I9XsA/JW7LvaTXe4VxHRkbz6KNbXTKza5gowriIJEls2/ZXGhvricvdREBM+oieZ7WY\nqDr4OtZBI1u3/g3h4RFOHqnr9fb2Xrsf2Gq14q31JWpJ0pjv85MkiZ4qPa1f1WIy2K/SzZ41l5kz\n5zh1BsFE+rAY1ttr5PTpAi5cPIfNal9SMjJnBb6hcWN+bavFhO5KAZ0Vp5BsFsLCwlmw4AFSUydG\n8eV6NTVVfPLJ+yh8VaQ+noPKb2RTrM29Q1S+dwnrgJlNm54gMTHZySO92UTLtSRJVFZepaDgKO3t\nbcjkCkJSZhOWvhCFauwnkgP6ZlqLD9Df2XCt+DJ37kKCgpzT0d9d6upq+Oijd1H4qEh9YuSZBnsj\n3sr3L2LqHmTNmnwyMqY4caQ3m2iZBtDrOzl58qtrxRhNeBKR2cvxDhz7sYMkSfQ0ltJWchhzv+Fa\n8WXGjDl4ebn+dhFnMRi6effdN+nr6yV+TTqBqaO7UjzY2U/VB5fACls2P+nUZdZvZaLlemhoiLNn\nT3Hm7CnMJhNqTTCROSvwi5zkkGMEyWZDX3OOjrJjWE0D+PsHMH/+4mtLD08UkiSxa9d2ysou45/4\ndY+6EV7w1J1vpuVoDcHBITz22DMub4kw0TIN0NXVSUHBdfvqsEQispfjE+SY2bF9ujpaLx1ksLsF\nuULB1Jzp5OYucNmMPFc5cuQgZ86cxDc6gKQNo+vnpS9upelQFUFBwTz55HP4+rp2wZs75VqsjuRA\nMpmMmJhYLl46T19HPcGJ00fUY6P98mF626rIzZ1PZma2C0bqemq1mqSkFLKychgaGqS5tpHusg5M\n3YNoov3vaaUCU88gjfsq6ChsRDLbmD59NuvzNpGSMgmlk1dIGQ9d3EdLrfYiOTmVrMxs+vp6aW2s\nprvuIpbBXny18ffcL6a3rYr6gm30tlag0WhY/uBDrFjxCKGhnrc6kyMEB4egUqmprqigv9VIcEb4\nXX9OySZR+3kZQ/p+HnhgBVlZ7tkPTLRcy2QyQkO1TJ06g+DgEFpamuhuqaK7vhi1Jvie+8VYzUO0\nXT5E8/ldmAd6SE1NY/36LeTkTHfp7WOuEhQUjFp9XaZHOJNRkiQa9lylv8XI7NnzmD17rgtG+20T\nLdMAPj6+pKWlM2lSOgZDN+1N1XTVnsdmMeEbGnfPq86YevU0Fn5KZ8UpsFmYNWsueXmbSEpK8fjm\npKPl7e1NfHwiJSXFGCp1+CcGj7ihqWXATM3Hl7H0m3nkkTxSU9OcPNqbTbRcK5VK4uMTycmZgdVq\npamhGkNDCQNdLWi0cWMqmg90NVNXsA1D/SVUCgULFy5l9er1REXFTLhjEJlMRkpKGi0tzbTXNmPp\nNxOQfPcZmT1VnTTur8RXo+Hxx54hIMD1J/ETLdPwzb46LS2Dnp7r9tXmoTHtqy1D/fYVFy8dwDLY\nS0ZGFvnrt5CZmT2hiuXDEhKS0Ot1tNQ0Yuk14Z88skVfehsM1O++io+PD4899rRbZgaJJapdyMfH\nFySJupqr9iZfEXduZjhoaKepaCdBQcGsWbNh3E+HvBsvL28mTZpMSkoabW0tdNa10V3WgbdWg1fQ\nyD9ku6902E9aO/uJi0tg48bHycrKQaVyzRSzifhhMczb25vJkzNJTEymubkJfXMV3Q3F+ARHox7F\nijM2q5mWC3tpLT6AZDGRmzufdes2TcgDnxtFR8fQ2dlBa20Tkk3CL/7OM77aTtZjKO9g8uQMli5d\n4bb3Z6LmWiaTERYWwbRps5DL5TTUV2NouMyQUYdfePKoCoz9nQ3UffUOfe01BIeEkrduE3PnLnTK\nbY+eJCoqhs5OnT3TEnfNNEDnxVY6zzcTF5fAI4/kuSXXEzXTABqNhszMbKKjY2lqaqCruRJDYwk+\nITGj6ksnSRL66nM0nPoIU5+elJRJbNzwGOnpWU6/oOFOfn7+aLVhlJVexljbTVDG3ZeulmwSdTvL\nri1F7a7+fRM11yqViuTkVNLSMtDrdXQ0V9NVexGll2bUswckm5X2kiM0Fe3EOtRHTs501q/fQlJS\n8oSa/XIjuVzOpEmTqa6upLOmDZVGjU/E7We1DOn7qf28DIVMwZYtW9Fqw1w42m9M1EwD+Pp+s69u\nbm6076sbLuMTHD3qlRyNrZXUn3iPfn0j4eGRrF+/mZkzc8f9baJ3MlxcrKmpQlfbhtJXhW/knWdO\nmY1D1H5aAlaJjRsfd+lt0NcTRRgXi4qKoby8hK6WKgJjMlF63frgXJIkGgs/w9zfzerV+W7b8bmD\nn58/2dnT8PLyoraqmq6ydpDJ0MQE3PFAXbJJtBytofV4HUq5kpUrV7Ns2UqXTy+byB8WwwICAsnJ\nmY5MJqO+tpLu+kvIlWp8Qu5eRDH1dVN3/D162yoJC4u4ViSb6EXGYTKZjMTEZMrLS9HXtOOfEIzK\n/9aZ6W8x0niggsDAIDZufNytJz0TPddyuZz4+EQmp2XQ1taKrqmKnuZyNGGJKL3uvA+RJAl9VSFN\nZ7Zjswwxb95C1qzZMCH6voyEPdMplJeXoK9uxy82EHXA7Q/6Bjv7qd91BR9vb7ZseQovL/ccIE70\nTIN99t3wDIKG2koM9ZeQq7zxDbn7MqRWi4mmczvorDiJj7c3Dz+8loULl+LjM/FmdN1KaKh9Nlxd\nVTVDXQMEpt15hmb76Qa6S9tJTU1jxYpHRMHcSXx9NWRl5eDvH0B9XTXdjWWY+3vwi0hGNoICinmw\nl/qTH2JouExgYNDXJ6pzXNoLwp0UCgWJicmUll6iu7qTgNTQW66YZLPaqN1eitk4xMMPryMpaeyr\n4N2riZ5pGN5XT8dms9FYV0V33SVkIzyuliQb7SVf0nJhDzJsLF68jIcfXjch+r6MhFwuJzk5lZIS\ne6b9k0NuO3txuFg+pB9g2bJVpKdnuni03/DIIozBYOAHP/gBv//97zly5AjLli27aQpVa2srP/jB\nD/jjH//Itm3bsFgsTJ06dUSv784ijFwuJzAwkPKyEkz9BoLibn0PfG9rBbqrBSQnT2LBgsUuHqX7\n2W/fiiM5OZXa2mr0Ve2YegYxG4fobzXe8j/d2Sa6yzrQasN49NGnSExMFldXnWj4pDUuLoHqmiq6\nG8uxWc34hMQgSTYkm/Wm/0w9OmqPv4upT//1lafNbpna6m5KpZLw8EguX75If6uRkCkRN93CYb8N\nqRRrv5n8/C0ub8R7o/sl176+vmRl5WC1WqivqaCnoQSvwAiQbFhN/bf8T3f1JB1lx/DVaNiQ/yjZ\n2dMn9NXUW1EqlURGxlBcfIG+5p6vM33ze2A/ACrHbBxizZoNREXdW+NTR7hfMj180hUTE0d1deW1\nfbUmLPG2z7GaB6k/8T59bdXExMSyZctTxMTETfiZijeKi0ugsbGejtoWVH63nzXQ32KkcX8F/v4B\nbN78hFuXML4fci2TyYiIiCIjI4vGxno6myrp19USEJNxx9mLQ716ao/+haGedtLSMti8+Qm3f7a6\ng7e3D8HBIZSXlTDQ1ktwVsRNf9vtpxswXNWRnT2NefMWuWmkdvdDpuGbfXVsbLx9X91UjtU8eMcG\n6zablaYzn9NVW0RgUDCbNz/J5MkZ992+2svLi9DQMMpKi+lvMRKSFX7LW6M7L7SgL24jNXWyW2eX\nw51z7bbGvL/85S8JCgriO9/5Dm+88QY9PT389Kc//dZjOjo60Ol0ZGRk0NfXx4YNG/j9739PSsrd\nK7XuaMx7veub9CY98Cy+IbE3fb/68B8Z6mnn2We/e1/NgrmV3l4jH330Hjpd+10fGxsbR37+Y26d\nejcRG4jdjdHYw4cfvnNt6d+7eeCBFW7pAeFp9u37gkuXzhP1QBLaad8+GdVdaKHlSDU5OdNZtWqN\nm0b4jfsx15cvX2Tv3p2M5KMwJCSUzZufvG+uPN3O4cP7OXfuNBHz4gnPvblxt/5yG00HK0lPz2Tt\n2o1uGOE37sdM9/QY+PCjd+ka4b46MzObhx5ae9/MVLwVo7GHP//5/7BiZdIzM266wipZbVS+f5FB\nXT+PPfa0yxvx3uh+y7XZbGbv3h2Ul5ei9gu5Yz+vAX0TlqE+Fi58gLlzF953J6o3+uKLzygru0zM\ngymEZH9zS5fJMMjVv55H46PhhRe+j1rt3l4i91umwX7u8+GH79LZ2YFcoYLbZFWSbEhWCzExcWzY\n4N7zH0+wd+9OiosvELkokbCZ357xaTYOcfWv51ErVDz//A/QaFx7p8SN7pRrt3VaO3ToEO+88w4A\n+fn5bN269aYiTFhYGGFh9uKERqMhJSWF9vb2ERVh3E0mk7Fw4QNs2/ZXOsqPkzD/sW9939hSwaCh\njYyMKfd9AQbstyc9+eRz1NfXYrNZb/s4e/O2pAnXJHA88PcP4LHHnuGrrw7T19d328fJZJCRMcXl\nq6B4qkWLllJWXkJHYSMhWRHXeg7YzFY6ChtQq9UsWrTUzaO8f02ZMhV//wCuXCkDbl+IGV4lxtW3\nPnqiBQuWUFZWTMfZJoKnRHzrhNVmttJWUIdSqWLp0pVuHOX9KyAgkMcfe4YjRw7Q23vnk4y4uATm\nzVt035+o+vsHsHjxgxw8uIe2k/XELk/91vf1l9sY1PWTnT3N7QWY+5FKpWL16nzUam8uXSrC1Ku/\n7WPlcjkPPriKGTPc06/H0zzwwHIqK6/QdqoBTVzgtb/11oI6JKuNBx5Y7vYCzP3Kz8+fxx9/mgMH\ndtPV1XXHx0ZERPLggw+5dQaep1iyZDkVFeV0nG4gOCscpfc370nbqXpsZitLlj3s9gLM3bjtTFav\n16PV2ivZYWFh6PW336ECNDY2Ul5eTk5OjiuG5xBxcQlfN2GqZMjYiZf/N0sgdlaeBiA3d4G7hudx\n1Gq1W1YZEEZOo9Hw0ENr3T2MccXXV8PMGXM4deo4XaXthE61NwfrKm3H0m9m7twF4sTezRISkkhI\nSHL3MMYNLy8v5s9fwsGDe9AVNRO1KPHa9/TFrVj6zcybtxA/P9df2RTsNBoNq1evd/cwxpWpU2dQ\nVHQGfUkbYTNj8Aq298WxWWy0n25ApVKJgrkbyeVyVq1azdKlK5Ak2x0epxAnqtfx8/Nn2rRZnDlz\nkqtvFX3re6GhWtLTs9w0MgHsC7qsW7fJ3cMYV3x8fJg7dyFHjhxEV9RM+Gz73SbmXhNdpR2EhmqZ\nMmVk7UvcyalFmOeeew6dTnfT13/84x/f9LU7XYXp6+vjRz/6Ef/8z//s8VWtG82cOcfeCbv2PJHZ\nywEYMnbSr6sjPj6JsLBwN49QEARnmz59NoWFBbQcq6W9sBEA66AFuVzO9Oniap0w/mRnT+Pkya/Q\nF7cSnhuHXCUHCXTnW1CpVMycmevuIQrCqMjlchYsWMzOnZ/SdKgS32h7LzOTYRBLv5k5c+ah0dx+\nlRnBNe6X5rqONHfuAiwWMybTNz1QZDIZU6fOvO9nwQnj09SpMzl56jgdhY10fH1cPWzOnPnjomef\nU4swb7755m2/Fxoaik6nQ6vV0tHRQUjIrVeYsFgs/OhHPyIvL4/ly5ePeNvBwb4ole6/vzk4eBYH\nD+7FUF9MxJRlyGRyuusvAbBw4Ty33AMpjE+ekmlh9MLC/Fm2bBnnz5//5oveMH36dJKS3LNsnqcQ\nuR6/5s2by8GDByn9/alvfT03N5f4+Ag3jcr9RKbHr5CQ2ZwoOIK+UU9fY8+1ryuVSpYvX0pQ0P17\nzCZyPZ758/jjW9w9CI8jMj2+bdywgaKib8/uCgwMZNGiueOibYVbG/MGBgby4osv3rYxL8BLL71E\ncHAw//RP/zSq13d3s6XrDTfmDIjJQK70ore1EqyD/PCHPxVTJsep+7GBmDDxiVwLo9Hf38e+fV8w\nNDR07WtKpYrlyx8iKCjYjSP7hsi0MFr9/X03NaD38/P3mEyDyLUw8YhMCxPRnXLttiJMd3c3P/7x\nj2lpaSEmJob//d//JSAggPb2dl5++WVef/11zp07x1NPPUVaWhoymQyZTMbf//3fs3jx3Zdz9qQ/\nrIaGOrZt++u3vpaZmS3u1x7HxIeFMBGJXAsTjci0MBGJXAsTjci0MBF5ZBHG2TztD6u3txez+Zt7\nMQMDg8bF/WrCrYkPC2EiErkWJhqRaWEiErkWJhqRaWEi8sglqu83fn6imZsgCIIgCIIgCIIg3M/E\nVAxBEARBEARBEARBEAQXEEUYQRAEQRAEQRAEQRAEFxBFGEEQBEEQBEEQBEEQBBcQRRhBEARBEARB\nEARBEAQXEEUYQRAEQRAEQRAEQRAEFxBFGEEQBEEQBEEQBEEQBBcQRRhBEARBEARBEARBEAQXEEUY\nQRAEQRAEQRAEQRAEFxBFGEEQBEEQBEEQBEEQBBcQRRhBEARBEARBEARBEAQXEEUYQRAEQRAEQRAE\nQRAEF3BbEcZgMPD888+zatUqXnjhBYxG420fa7PZyM/P53vf+54LRygIgiAIgiAIgiAIguA4bivC\nvPHGG8ybN499+/aRm5vL66+/ftvH/vWvfyUlJcWFoxMEQRAEQRAEQRAEQXAstxVhDh06RH5+PgD5\n+fkcPHjwlo9rbW3l6NGjbN682ZXDEwRBEARBEARBEARBcCi3FWH0ej1arRaAsLAw9Hr9LR/3H//x\nH7z00kvIZDJXDk8QBEEQBEEQBEEQBMGhlM588eeeew6dTnfT13/84x/f9LVbFVmOHDmCVqslIyOD\n06dPO2WMgiAIgiAIgiAIgiAIruDUIsybb7552++Fhoai0+nQarV0dHQQEhJy02OKioo4fPgwR48e\nZWhoiL6+Pl566SVee+21u247LMx/TGMXBE8jMi1MRCLXwkQjMi1MRCLXwkQjMi24k0ySJMkdG/7l\nL39JYGAgL774Im+88QY9PT389Kc/ve3jCwsL+fOf/8wf/vAHF45SEARBEARBEARBEATBMdzWE+Y7\n3/kOBQUFrFq1ilOnTvHiiy8C0N7ezne/+113DUsQBEEQBEEQBEEQBMEp3DYTRhAEQRAEQRAEQRAE\n4X7itpkwgiAIgiAIgiAIgiAI9xNRhBEEQRAEQRAEQRAEQXABUYQRBEEQuL/3tAAADytJREFUBEEQ\nBEEQBEFwAacuUT0RGY1Gdu7cyRNPPOHU7bz77rv85S9/oaGhgZMnTxIUFOTU7TmLq96vn/70p1y+\nfBmVSkVOTg6vvvoqCoXCqducqDIyMkhPT0eSJGQyGb/73e+Ijo6+5WObmpr43ve+x86dO108Ss/S\n3d3Ns88+i0wmo6OjA7lcTkhICDKZjI8++gilUuxq3U3kevRErj2byPToiUx7PpHr0RO59mwi06N3\nP2R6/P8ELmYwGHj//fedXlSYOXMmS5cuZevWrU7djrO56v1at24dv/rVrwD4yU9+wkcffcRjjz3m\n1G1OVD4+Pnz22WfuHsa4EhQUxPbt2wH47W9/i0aj4bnnnrvpccMfwILriVyPnsi1ZxOZHj2Rac8n\ncj16IteeTWR69O6HTIsizCj9+te/pr6+nvz8fObPn49Op2PFihUsX74csM/IeOSRRzAYDBw4cACj\n0Uh7eztr167lhz/8IQA7duzg7bffxmKxkJOTwyuvvHJTgNLT0wF7uMYzV71fixcvvvb/2dnZtLa2\nuu6HnGBulbmmpiZeeuklBgYGAPjZz37GtGnTvvWYyspK/umf/gmLxYLNZuM3v/kN8fHxI/r9TVT1\n9fV8//vfJyMjg/Lyct544w3y8vI4c+YMALt376agoIBf/OIXdHZ28sorr9DS0oJcLudf//VfycnJ\ncfNPMHGIXDuOyLVnEJl2HJFpzyFy7Tgi155BZNpxJlKmRRFmlH7yk59QUVFxraJ55swZ3nrrLZYv\nX05vby8XLlzgtdde4/PPP6e4uJhdu3bh5eXFpk2bWLp0Kd7e3uzevZtt27ahUCj4+c9/zo4dO8jL\ny3PzT+Ycrn6/LBYLO3bs4F/+5V9c+WNOKENDQ+Tn5yNJEnFxcfzmN79Bq9Xy5ptvolarqaur4x/+\n4R/45JNPvvW8bdu28cwzz7BmzZprHxhVVVX3Vd5vpaamhl/+8pdkZmZitVpv+qAc/vcvfvELvvOd\n75CTkyOmozqByLVjiVy7n8i0Y4lMewaRa8cSuXY/kWnHmiiZFkWYMZo9ezavvvoqXV1d7Nu3j5Ur\nVyKX2/sdL1iwgICAAABWrlzJuXPnUCgUlJSUsGnTJiRJYmhoiNDQUHf+CC7l7Pfr5z//ObNnz2bm\nzJku+XkmIm9v75umTZrNZl599VXKyspQKBTU1dXd9Lxp06bxhz/8gZaWFlauXElCQgKnTp2itLT0\nvs07QFxcHJmZmXd9XEFBAbW1tdeumBiNRkwmE2q12tlDvC+IXDuWyLX7iUw7lsi0ZxC5diyRa/cT\nmXasiZJpUYRxgLy8PD7//HN2797Nf/7nf177+vWVuevvWduwYQN///d/P6LXnojTy5z1fv32t7+l\nq6uLf//3f3f8oO9zb731Flqtlp07d2K1Wpk6depNj1mzZg1Tp07lyJEjvPjii7z66qtIkkR+fv6I\n8z4R+fr6Xvt/uVyOzWa79u+hoaFvPfbjjz8WDaVdSOT63olceyaR6XsnMu25RK7vnci1ZxKZvncT\nJdNiiepR0mg09PX1fetr+fn5/PWvf0Umk5GSknLt6ydOnKCnp4fBwUEOHjzIjBkzmDt3Lnv37kWv\n1wP2xrXNzc233Z4kSeO6L4yr3q+PPvqI48eP8+tf/9q5P9B94FZ5MxqNhIeHA7B9+3asVutNj2lo\naCAuLo6tW7eybNkyrly5wrx580aV94no+vdTJpMRGBhIfX09NpuNAwcOXPve/Pnzefvtt6/9u7y8\n3KXjnOhErh1L5Nr9RKYdS2TaM4hcO5bItfuJTDvWRMm0mAkzSkFBQcyYMYO1a9eyePFi/vEf/5HQ\n0FCSk5NZsWLFtx6bk5PDD3/4Q9ra2sjLyyMrKwuAH//4xzz//PPYbDZUKhX/9m//dtNSZW+//TZ/\n/OMf6ezsJC8vjyVLlozLGR6uer9eeeUVYmJi2LJlCzKZjBUrVvCDH/zAZT/nRHKr2VdPPPEEf/u3\nf8v27dtZtGgRPj4+Nz1mz5497NixA6VSSVhYGN///vcJCAgY0e9vIrvx/fzJT37C888/j1arJSsr\nC5PJBMDLL7/MK6+8wqefforNZiM3N5eXX37ZHUOekESuHUvk2v1Eph1LZNoziFw7lsi1+4lMO9ZE\nybRMGs/TLDzEwMAAeXl5fPrpp/j5+QHw2WefUVJSwr/+67+6eXSeR7xfgiAIgiAIgiAIwv1I3I40\nRidPnmT16tVs3br1WkFBuD3xfgmCIAiCIAiCIAj3KzETRhAEQRAEQRAEQRAEwQXETBhBEARBEARB\nEARBEAQXEEUYQRAEQRAEQRAEQRAEFxBFGEEQBEEQBEEQBEEQBBcQRRhBEARBEARBEARBEAQXEEUY\nQRAEQRAEQRAEQRAEFxBFGA+Qnp7OwMDAqJ9XWFjIxo0bnTAiu7a2Np5++mlmzZrFpk2bnLadzz77\njLq6ujs+RpIkfvSjH/Hwww+zfv16XnjhB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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4978289e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "drawViolin(sam10k,2,2,0,6)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "b2af99bc-c4a7-8cf4-7471-2f9465a49ee0" }, "outputs": [ { "data": { "image/png": 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Mp2MIbUoeuasPRnxW9FXGHRZM977GM9xPRkYWhw4dm1ZHarYkSeLBgx+4cuV8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TAAAg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\n+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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4978289518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "drawViolin(sam10k,2,2,6,11)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "9203bef2-2ff3-44f7-d379-82d27cf3ea3b" }, "outputs": [ { "data": { "image/png": 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V95/yONz0XG7FUm9GrdGwc8c+CgvnTeh7Z/MD4uHDe1yqOY/P6/Vvr162YdI7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\n7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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971d40048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "drawViolin(sam10k,2,2,11,16)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "cf4229c8-72b4-dfe5-137a-29e1277287d3" }, "outputs": [ { "data": { "image/png": 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wWR9yuZxNmzazYsXk1zrhkM6QZ88ec/nyOfySRFpFDUnFa8O+uHudo/Q8PoHd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\n3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ZG3ba86XUR1WilJSQuYN2+hDUr4nd7ybLMBMIcPH+add94BIDk5mR07dtxWIQ0ICCAg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\nLFq0qOvnhIQE3nrrr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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971e46da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "drawViolin(sam10k,2,2,16,21)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "cda2d979-888f-76c4-5065-40f8119617b8" }, "outputs": [ { "data": { "image/png": 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nCQaDJM/LIHvt3Jjue54sn9OL6UIrw62DKJVKNm3aTnX12x2lNhPj+WdHx8nk\npJatIL1idczq5ueCAR/WZ9cYaL6JFAyyYMF7rF+/KWqzpX6/n3PnTlJX9wilNo7cTSUYiqIzUxxO\nI+1DdJ9rxj/qY968+WzZsnPWtzde5vV6OXHiK1pbm1Eb0ilY/iGqKM34+91Ouu58xWi/kaysHA4c\n+Dispy5MxMBAP6dOHXsx+J2xoiCUVG4GDH4/J0kS9qZ++q4Z8Y14SE5JZeuWXWFpb8S8Q/q73/2O\nS5cukZqayvHjx1/7mv/23/4bV65cQavV8j//5/+ksvLNKZ2n8oXq6Gjj22+P4PV6MORUkL1oO0p1\n5A7yngxnfyc9d4/hG7WRnZ3Lvn2H0Osj+yUbGLDy5z//kdFRJ2mLc8haNWdGfZFe5RvxYDzxDJfZ\nQVFRKXv2HHyrh8RMfEBIksT335/i4cN7KNU6cpfsJT6zKEylm1q5Blt/wPzke0Biy5adzJ+/MOL3\nDQaDnD9/hocP76JQK8ndVDKtl3+9DSko0X+vh74bnchlMjZu3EZNzXtv/L2ZGM8Q2v985OgXeD0e\nUoqWkLlgE/Iw7xWdqIDPg+n+dwz31KOPT+DQR5+SmhqeZErj8fv9nDr1Lc+e1aPUqcjbXELCnMge\n4xJpzxs+pvOtBLwBFi1awgcfbHljtseZFs/Dw3aOHv2C/n5r6Oi49/dEfKn5RLntFrrvfoPHbiE1\nNY0DB34V8f3/fr+fb789QltbM9oMPQU7K1EZZt7g93MvtzfmzClm376PZm1742V+v5+vvvozRmM7\n+oy55C87GPGtQa8KBgOY7p/A3vmE9PQMPv74r9FooruPvrGxnpMnv8Xv98/owe/ngr4AfdeNDDzs\nRSaTUVu7kcWLl71x0m68eFb8/ve//32YyzkhiYmJHDx4kHPnzvHJJ5/84ueXL1/m2rVr/OUvf2He\nvHn81//6X/nwww/feN3RUe+kyvP06WOOHz9KICiRs2hHaF+dcnolqgBQ6RJJKqzB5xqmv6eVpqYG\niovLIvYlczgcfPb5/2F01El2bRGZy/JjPls8VQq1kqTKDFxmB+YOE0NDg5SVVb7x36XXR/+hONl4\nhlDD7tSpYzx58hBNYiZz1v412qTpcd6cTCZDl5KLPmMuw6ZGmhufotfHk5WVE7F7SpLEuXMnefTo\nPpo0HUUHq6flvo2Jkslk6HMN6HMNjLQP0tz4jIQEA5mZ2eP+3kyLZ4Du7i4OH/4Mv99P7pJdocRF\nbzhEPRo8ENshAAAgAElEQVTkCiWG3ArkShVD3c949qye4uKysJ5L+KpAIMDx40dpanqGLieBuQeq\n0WbMjKOVxiOTydCk6TGUpuLsttPdZsTpdFBcXDpuHT2T4rm/38IXX/wrdruN5KLF5C87SJx2+tVF\nSo2epMIagj43g6YWGp7VM3dOccQGwSVJ4rvvvqGlpZH4giTm7q+KyfFx4fS8veG2OLF0mOjvt1JR\n8eZMxjMpnl/n1Kljoc8xq5TCFR9FLCnXeGQyOQnZ5fg9owz0tGAydVNVtSBqbdg7d25w9ux3oJCR\nv62MzOUFyOOm/1GO45Ep5CTMSSY+P5GRdhttzc04nU6KikomXT/H/Am+ZMkSDIaxK+Dz58+zd+9e\nAGpqahgZGaG/vz8iZenp6eb06ePIFCrmrPk1yXPCc47UcwGfO2zXAlDEqcldsof0ijXY7Ta++eYw\nPp8vrPeA0MPh7NkTjDqdZK0uJG3h+A3ccAl4/BG/h1wpp3BXJbqcBBob66mvfxLxe0bbgwd3efr0\nMdrkHOas+auwNXjCGc+61DzmrP1rlGo958+fxmTqCdu1X3X//g88fvwATbqeog/nRy0RVzTiGSA+\nP5G5B6tRaJScOXOCnp6uqNw3WlyuUY4fP4o/4Cdv2UGSChaE5brhimeZTEZa2QqyF23H5Rrl2LGj\nEamXn7t69SItLU3o8xOZu7+aOH10BlCjFc/qJC1FH85Hk67n8eMH3L17Oyr3jTS3281XX/8Fp9NB\n1oJN5CzcNuVzzF8VzjparlCSvXArWQs2M+p08PXXX+J2u8J2/ZfV1z+hsbEeXY6Bwt0VyJWRb7xH\nq71RsLMCfZ6BlpZGnjx5GPF7xlJTUwP19U/QJueQv2z/lLOdTyWeZTIZ2Qu3kpBTTnd354uEjpHW\n2FjP5cvniUtQU3xoPomlaVG5b7TqZ31uIiW/WoAmXc+jR/e4c+fmpK8V8w7pm1gsFrKyfprNyczM\nxGw2h/0+kiRx8uQ3BCWJvGUHwprkxW230Hz2f/Ps+P9H89n/jdtuCdu1ZTIZGfPWkTz3PaxWM7dv\nXwvbtZ/r6+ultbUZfV4iaYtzw379V7n7nbT+6yMsh1tp/ddHuPudEb2fXCknf0sZcqWca9cuMZu2\nVXu9Hq5cOY9CpSN/+UEUqql3viIVzxpDOrnv732R/CASXC4X169fQqFWMmfvvKgkxoh2PANoUnUU\n7qwA4MKFs7Mqpm/duo7DMUJG5ToMORPPKvyqSMVzytz3SJ67mP5+C48e3QvLNV81NDTIvXu3USVq\nKNxViVwZ+Ud6LOJZoVYyd988FBolN25cZnR0NOL3jLTr1y9jtw2RVraS1JJlYb12JNscqSVLSStf\nhd1u49q1S2G77nOSJHH16kXkcQryt5RGvDMai/ZG3pYy5CoF165dnFV186tu3LiKTC4nd8nuKe2H\nDlc8y2QychZtR6HScuvWdQKBwKTL9Da8Xi+nz5xAHqdgzp7KqBzlEov6OS5BzZy984iLV3H16gVs\ntqFJXWfad0ijxWTqxmYbIjG/mviM8CZW6bp9BK8jlC7Z6xik6/bRsF4fIGvBJuRKNXV1j8NewTU3\nNwCQtig7Kkscek62sP2Drfzd3/0d2z/YiulkS8TvqUrUYChJZXjYjsXSF/H7RUtzcyM+n4+U4iVh\nmxmNZDzHZ8wlPqOI3t6eSVdq4zEa2/B4PKQuyo7aTFIs4hlAn5dIwpxk+vpMDA/bo3LPSJMkiYZn\nT1GotKSWrQjLNSMZzxnz1oJMRkPD07Bd82VNTc8IBoNkLMtHoYrOErBYxbNSpyLtvVy8Xi/t7dG5\nZ6QEg0Hq65+gVOvJmLcu7NePdJsjo3IdSk089fV1YW/Um819jIwMYyhJicrqlZi0NxLUJJam4nQ6\n6e2N3GqgWLLZhrBazegzilEnTG1WMJzxrFTrScyvxu120d3dOaVyvUlLSyNej4e097Kjdq5orOrn\nOL2KzJWFSJL04hz3iZr26fcyMjLo6/upg9DX10dm5ps3/Ccn61BOYGStpye0Zl6bHN69az6348UX\n6TmvYwCf20GcJnz7fOSKONSGdBxDPaSlxb8x8cNESFJo6l+dHPlN4D6nF51cw9KlSwFYunQply5d\nwuf0RrwDofrx36dWy2KSSGA8E43n52Sy0GenSQzPntFoxLMmKQuHpQ2VSgr751BXF1piFq2HQyzj\nGUCTpmOkYwjwTKuYnmw8S5LEqNOBNiU3LAmMIh3PSrWeOK0Bl8sZkfff5wvNFGrSopN0L+bxnB76\nd/r9rhkdz263G7fbRXxm8ZSXMb4qGnW0TC5Hk5iFw9xCYqIarTZ8bYOBgVAHbba3N9QpoVhWKoPT\nKpZh8vXzy9xuG8CUz8+NRDw/z/CrUkU2aVQw6AFAmxmdzzfW9bM2K/R5+HyTq5+nRYd0vBm9DRs2\n8Nlnn7F9+3YePnyIwWAgLe3Noy1DQxNb0iNJoQ/LNdgDxe9P6HfHvW7g9eu4x/r7yQr43HhH+omP\nT2BgILxT9DJZKEw8Q64XlWikSP4gdrudO3fusHTpUu7cuYPdbifVH4zofQE8g6GY8XikcbPMxeLh\nMdF4fk6SQp+d294XluWN0Yhnl60XAI8n/Onn4+JCHVG31RmVrLqxjGcAlzVUF8jl2jHfy5kUzwA6\nnR73yADBgG/Kx2JEOp59bgd+9whaQ1ZEzvZTq0MNAJfVGZVERrGOZ/eP8axS6Wd0PEuShEqlxm03\nhyWOf3btKNTRwYAft91MXJyKkREfDkf4ru3zhQbTPUOR2Z/6sunQ3vD55LOmvfEyj+fH/x0ZmNJ1\nIhHPnpFQHhqfTxaRevk5mSzUrxjtG4nKcUWxrp9He0PvZVzc5NobMe+Q/va3v+X27dvYbDZqa2v5\nzW9+g8/nQyaTcejQIdatW8fly5fZtGkTWq2W//E//kdEypGbm0d6eibWrjqSCmvCvmw3kiRJwvzk\nPAGfm4XLw7OM7WXl5VXcvn2D/vsmEopSIr5s1+fz8e2333Lp0iXsdntEE4I857W7GW4ZJDk5hYyM\n6ZGBNhxKSytQq88w2PoDSYU1qHSJsS7SuBzmNpyWdvLyCkhODn8FXlg4F5VKxcADE8nVmVE5QzcW\n8QwwYhzCYbSRlZUT1fMwI23+/Bpu3bpOf9MtMirXxLo447LWX0YKBiN2lFFJSRmXL3+P5VYXicWp\nUTlzNFbx7Bvx0H/PhFIZx5w5xVG5Z6TIZDIWLnyPO3duYmm4Qlb1hlgXaUKsDVfwu0d4//0VYW8P\nZGZmkZycgq1pgMzl7ogv241Je2PEg62xH4MhkezsyGWUj6XExCQyMrKwWtpwD1vRGCJ//NXb8Lsd\nDHc9RavVkZdXENF7lZZWoNN/z8B9E0llaVFZmRWz+tnhwXKzE4VCMennXcz3kP7DP/wD165do66u\njkuXLnHgwAE+/vhjDh069OI1f//3f8+5c+c4duwYVVVVESmHTCZj48atyBUKum7+BWd/ZNeWh4sk\nSVieXmSo4wFpaeksXhze5AgQekAUF5fi7BnGcqc77Nd/HZ/PR39/f1S+TEF/kM6TjUiBICtWrJnx\nx9m8TKVSsWbNegJeF103D+P3Rn7UebLcdgvdd75CrlBQW7sxIvfQaDSsX7+ZgDeA8VgDfleUKuso\nxjOAy+Kg+3QzcrmczZu3R+We0bJ48XLi4xOwNlxmuKch1sUZ00DrDy/q5aqq8GQCflVycgorVqwJ\nnXH43TMC3sgm6Xgu2vHsH/XRcayBgMfP+vWbwrpENFaWLVtFYmISA003sTbemBHJbSRJor/pJv1N\nN0hMTGLZslVhv4dMJmP58tVIgSCd3z0jGIXZnai3N757huQPtTfCub1qulm1ah2SFKTn7jGC/vAd\nJTNZkhSk5/53BHxuVq6M/HuvUqnY8MEWgv4g7V89jUqCIYh+/exzeGg/+hSfw8vKlWsxGCY38TF7\nvwmTkJdXwO5d+5GCfozX/sRA691p/ZAI+Nx03TpCf9MNkpKS+fDDT4mLi8wZT9u27SYhwYDlZifW\nu9HplEZDwBug45t6XGYHVVULItZwjKWFCxdTU/MebnsfHZf/Be+oLdZF+gWntYP2K/9CwOdmy+Yd\nZGdHLpvz/PkLQ++H1Unb4To8tunbSZ8MR5eN9qNP8bt8bNq0/Y3nkM40Op2OffsOoVTG0XXnKwbb\n78e6SD8jSRKWhiv0PTqDVhsqa6TqZYAVK9ZQUlKOs8tO+1d1+ByeiN0rFjxDLtoOP8FtdbJgwSJq\nat6LdZHCQqPR8tFHv0avj8fy9AKme8cJBqLTgJyMYMCP6f4JzHXn0enj+fDDTyM2MPD8WeyyOOn4\npp6ANzpHWETa84FQV5+DysrqiK2cmC5KSsqYP38hblsvXbePEgzzVrWJkCQJ0/2TOPqaKSiYw6JF\n4duaN56Kiio2bdqOf9RH658fM1QfvozX08GIcYiWzx7hGXLx/vsrpjRIJTqkrygtreDDDz9Fo9bQ\n9+g0XTf/gs8VuTXmk+Uwt9F6/h8Z6W0kP7+QTz75G+LjI7fXQKvV8dFHnxKfkEDfNSOmS20EA9FZ\nlx4pXrubtiNPcHbbKS0tZ/PmHbEuUkTIZDI2bdrOkiXL8Yz003bhD4z0NsW6WEDoIWFtvE7Htc8g\n4GPHjr1UV9dE9J4/ez8GR2n5/BG2Z9aI3jMapKCE+aaR9qNPkXxBtm/fw4IFi2JdrIjIysrm0KG/\nQqvR0vvgJKb7302LxnzA66Lr9hGsDVcwGBL55JP/i6Sk5IjeUy6Xs2fPwVADvs9B858eMtw2+OZf\nnOYkSWKo3kLL56HGzpIly9m8ecesWsGSlJTMr3/9b8jMzMbW+Zi2C39gdHD6ZV11DZlou/gHbMZH\nZGZm81e//jcR2VLxnEwmY/PmHaGBlm47bYfr8NrDe457tHmH3bQfrcPRaaO4uJQtW3bOqlgey6ZN\n2ykqKsVhbsV4/QsC3uh/jsGAn+4fvsZmfEhmZhZ7934U1fd+4cLF7N59EKVcSffZZozfPZvxA4cB\nj5+ei610fF1P0Btkw4YtrFu3YUrvq+L3v//978NXxOljdHTyywOSkpKprKzGbO7DampjyPgQhUqL\nJilrwm92wOdmsPXOL/4+tWTppM6E9Huc9D46g/nJ90h+LytWrGbr1t2o1ZHfC6fV6igrraC9vY3B\ndguOTjvx+Ylh3bcU8PgZeNj7i79PW5QT1vsMtw7Q8U0DvmEP8+cvZPv2vSgUb5dVTq+P/Hv9qqnE\nM4Qe8HPnFqPXx9Pe1oStsw6/x4kurXBC2UrDGc/eURvdt49i63iIPj6B/fsOUVIy9cRLb+P5+5Gc\nnEJ7awtDTVbcVif6XAMK1cyLZ5fZQcexBuxNAxgMiRw48AnFxaVv9bszMZ4BEhIMlJZW0NXVyYCp\nhZHeJrQpeRPKvBjOeHZaOzDe+DOuwR7y8ws5ePATEhOTJnSNyZLJZJSUlKPT6elobWXomQWvzY0+\nJwF5XPiyuEYrnr12N91nmum/10OcMo5t23a/9X7FmRbParWGqqoFeDxuuo0t2IyPCPq9aFPzJpVJ\nOpwxHfR7MddfwnT/BAGPk4ULl7Br1350ushndZbL5ZSXV+J0Oulp78RWb0GVrEUTxsSK0W5veO1u\nqqtr2LFjL0rl211/psXzq+RyOWVlFQwO9tPX1cpIbyP6jCKU6rf7HKcazz7XCF03v8RhbiU3N48D\nBz5Bo4n8cUKvSktLp6Kiir4+E/1GM4N1ZuQKOZoMPTJ5eDrH0YhnSZKwPbNiPNaAs3uYlJRUDhz4\nmNLSiinXz6JDOga1Wk1V1QL0+ng6jW3Ye57hMLeiScwkTvv2M5HhejhIUpCh9vt03TqMa7CH9PQM\nDhz4FfPmzY/qSI9Go6G6uobhYTu9Hd0MPbWg1CpDX6owlCPSX6iA24/pQit9143ICY3Crlq1bkJl\nn8kPiKysHIqLy+juNjJoasXeVYfakIFK/3azOOGIZ0mSfozlI3gdAxQXl3Lw4K9IS4t+0oP09EzK\nyyuxWPqwvvSQ0IbpIRGNeO673kHP9y34nT6qqhawb9+hCc1ezOR41mq1VFfX4Ha76TG2YOt4iISE\nLjUPmezNC4DCEc9Bv5e+J9/T+/A0kj+0h2br1l1RGSR8mUwmIzs7h5KSMvr6eunvMDNUZ0GuUqDN\niJ8R9XPQH6T/Xg9dJxvxDLooKJjDgQO/Ij+/8K2vMRPjWS6XU1RUSn5+Id1dRoZMLdg7HxOnS0SV\nkDahzy5cdfSIqZHOHxvyBkMie/Z8yHvvvf/WA7fhEBpoKcNgSAwNHD4faMkzIJ/isSQQnfq553wr\n5lfaGxPZuzgT4/lVoU5pJT6fj66OZmydj1HHp6I2vPnEjKnEs7O/E+P1z/GM9FNRUcXevR9FvV5+\nmUajZf78hSQkGOjqNGJr7cfe1E+cXoU6RTvlOjrS8ezottP13TMGH/chk+SsXrWOHTv2TmjPqOiQ\nTpJMJiMrK4fq6hqcTge9Xa0MdTzANzqMLiUXufLN5/qE4+EwOtBF180j2IwPUSrlrFu7gS1bdsYs\nc6ZCoaC0tILExCSMHe3Ymq04TcPoshNQaqe2VypSXyhJkhhuHQyN6vQMk5mZxYEDn1BUVDLha830\nB4ReH8/8+YuQpCBdxlZsnY/xuYbRpxYgV4z/Hk81nj2OQbpuH2Go7R5xcUo2b97B2rUbUKkif0bW\nWLRaHdXVNcTHJ9Bl7MDW1o+9ZQBVkgZ10tT2SEUsnoMSQ3VmjCee4eyyk5SUzO7dB3n//eVvPfL+\n3EyPZ7lcTnFxKTk5eXR2tmMzNTPc24Q2OeeNg4dTjWeHpR3j9T/jtLS9GCmO9iDhq0Lf74XodHo6\njR3YW0PxrE7WTjljaaTr587jz7A3D6BRa9i8eQe1tRNPYDST4zkxMYkFC95DJoOuznbsXU8ZHexG\nm5wTtRklz0g/3T98Q3/TDQj6WLZsFbt2HSAlJfLHZI0lMzOL0tKK0OxShxlbvYW4BPWUG/GRjueO\nbxsYNT1vb/yKoqKSCZd3Jsfzy56vSkpJSaW1pQlbVx0Bnxt9+pxxBw8nE8+SJDHQfJOeu98iBXzU\n1m6itnZjVAdTxiKTycjMzGb+/EUEAn5Mxm5sTf2MGIdQJU6tjo5UPLusTnrOtWC+0Ynf6aOyspq9\nez+kpKRswomhRId0ilQqNWVlleTnF2I29zLY28ZQxwPkShXapOxxK5ipPBz8bgemh6foe3wWv8fx\n4+zHR8yZUxTzzGwymYyMjCyq5s1ncHAQi9HEYJ0ZAG1WwqRnlyLxhfIOe+g+24z1dhcEYOXKtWzb\ntmfSe25nwwNCLpdTWFhEUVEJvb09DJpCHVOVPgV1wtgNj8nGsxQMMtB8i+7bX+FzDlFSUvZi5mM6\n7KMJDT5lM3/+QrxeDz0dXdieWXH1OdCk61HqJjfQEol4HjHa6PzuWWh1gkzB6tW1bN++d9INxtkQ\nzxDKODt//kLcbhemzlaGjA+RAn50qfnIxqgvJxvPAZ+Hvsdn6Xt8FsnvYenSFezcuT9qS3TfJDRb\nmkt19UI8Hjemji5sDVZcZgeaDP2kBw4jEc+j5hG6TzXRf68HyRfkvfeWsmfPh+Tk5E6qbpjp8axQ\nKCgomEtF+TxstkEsPW0Mtd8n8JbLeCcb00G/F0v9ZXrufovXOcScOUXs33+IioqqadGQ1+n0zJ+/\nkLi4ODrbO7A1WRk1O9BnJ0w69iLT3ggtN7fe6YZgKNPstm273+n2xsvS0zMoKSmjs7ODwZ4WnJZ2\n9BlFKOJeH5sTjWe/10X3na8YaruHXh/P/v0fU1lZPS3aGS+Li4ujqKiEiooqHI4RzJ0mbA0WRk0j\naFJ0xMVPfJA+3PHssbkwXWyj92IbXps7lPh19wEWL1466WXPokMaJomJSdTUvIdOp6Ony4i9p5GR\n3iY0SVnEaV8/WznZ0Z3Btnt03TqMe8hEZmYWe/YcZPHipahUsVtu8DpqtZrKyirS0tLp6jJia+tn\nuHkAdcrkRnrC+YUKBl5a/jUwGtrXdeBjysoqp9Shn00PiPj4BObPX4RCoaDTGFrC6x0ZQJc+57WH\ntU8mnj3D/XTe/BJb52N0Wi3btu1m1apa1Oro7+N4k7g4FcXFZZSUlDM4OIC1s4/BJ2b8o150mRPf\njxfOeHYPjtJ9phnLrU78oz6qq2vYt+8jiopKRDz/SKlUUlJSRm5u/ouljyO9jWPuLZ1MPDutHRiv\nf47T2kFaWjr7939MdXXNtGi0v0qlUlFSUk5xcRmDg/30d5oZfDw94tk77Ak1di614xvxUFJSxt69\nHzJv3vwJz/K/bLbEs1aro7KymszMLHp6urD1/riMV5+MOmHspY6TielhUxOdN/6Mw9yCwWBg27bd\nrF69Hp0u8ucmToRMJiMvr4CKiioGBvqxGnsZfGIGGWgzJz4QHu72hvWl5eah9savKCurEPXzK/R6\n/U9bv7pasXc+QZOYhSr+l1uHJhLPLlsfxqt/wj1korBwLh9++OuYbAWaCK1WR0VFFUVFpdjttlCb\no86MZ2A0NBg+gcHDcMWzz+ml71oHPedacPePkpmZ9WOdUDvllZmiQxpGL488j4466e1qCyUh8HnQ\npRYge2X0cqIPB89IP503D2PruI8qLo716zexefOOaTPy/joymYy0tHRqFizC6/XR09HJUIMFz5AL\nXY4BhWoCSXPC9IVy9gxj/LYee1M/GrWWTZu2sX795rA8YGfbA0Iul5OfX0hZaQVmcy/9P+4t1ST+\ncm/pROI5NLByl67bR/G57FRWVnPgwMdkZeVMu9HKV8XHx1NVtYDMzCzMfb0MGa0MPekDuQxtZvxb\nN3zCEc9+t4++q0a6z7XgtbnIyytg794PWbhwSVgGqGZbPEMoMd2CBaHZwR5jCzbjQ2RyJdqUvJ/F\n3kTiORgMYHl6CdOD714klNuxYx+JiZM7cy2a4uMTqK6u+Vk8Dz7pQyYjtL80ivEc8Aaw3O6i61QT\nbquTjIwsdu7cx/Llq9Fqp56wZjbFs0wmIyUljZqal5fx1uEZtqJPK3jttqGJxLTf7aTn/gms9Zd+\ntjw3PT1zWtfRWq2WefPmk5ycQnd3508D4akTGwgPV3vD0W3HeKwBe1M/Wo2WTZu2s379prAkf5pN\n8fyy51u/9Pp42tqasHU+Ca06TMmdVB1t76qj89ZfCHhdrFixhi1bdsZ0v+hEJSQkUFW1gNzcfAYG\n+hnotDD4pA/fBAYPpxrPAW8A651uuk42Mto7QnJSCps2beODD7aQnJwSljphvHgOXxqxd4xer2f7\n9j3Mn7+Q06ePM9Bym5G+ZvKW7keblDXh60mSxFDbPfqenEMKBigvn8eGDVvQ698+Y2SsqdUaNm7c\nSnV1DWfPfoe5sRdHh42s1YUkV0fnARdw++m92s7Q09BZTzU177FmzQez4iD1SEtLS+eTT/5v7ty5\nwfXrlzFe+5z0ijWkV655qwQxLwv4PPTcO8aIqRGNRsuWLfsoK6uIUMkj43nm0rlzS3j48C7Xb1yh\n72oHQ3VmsmuLSCiM7CCRFJQYemqm77qRgNtPUlIytbUbKSkpn9aNxelCpVKzadN2SkrKOHnyGOa6\n8zj7O8l7f8+Yy8PG4nON0HX7KK7BbhKTktm1c19Ez8qNhJfj+dGje1y/fpm+a0YG68xkr5uLYW7k\njvGA0DPO3thP79UO/E4v8fEJrFmznqqqBSKe3yAuLo7Vq9dTWTmf06ePY+ppwGk1krtkNwlZE8+D\nADDS14rp3rf4PaPk5OSxdetOUlOn92zSy2QyGfPmzae4uJRr1y7x4MFd2o8+Jakyney1c6ecz+Jt\n+N0++q50vDhbcuHCJaxZsz4mWVxnIplMxsKFi0lPz+Tbbw9jfvI9XucQ2TVb3rrNIUkS/U03sDy9\nSJxKxc7d0cvWHwlz5hRRWDiXpqZnXLlynsFHfdgarGQsyydtYTYyRfi360mShK3BSt+1DvyjPnR6\nPas/qI36yh8xQzpFoSQEi/D5fC9StitUOrTJOcDbje4EfB66f/iagZbbaNQaduzYx8qVa2Oa6GUq\nQstAQ0k1uowd2Fr6cfbY0eUYUGrGf0hMZYTH3hJKrT5qGiY9PYN9+z5i4cIlYT+UfraOWMJPS6KK\nikowGtsZ7GnCNWQiIbsMuUL5VvHsdQzScfWPuAa6yc8v5NChX8+4xvvL5HI5OTl5LPhxBYDJ2IWt\nwYJnYBRd7vgrACYbzy6rE+PxBgafmFHKlKxZU8uOHXtJS8sIe+N9NsczhPaWVlcvCGVS7mll2PSM\n+MxilCrtW8Wza6iXjqt/xDvST2VlFQcOfBzxs0UjSS6Xk52dS03NewQCfno6OkP7pS0O9DkGFOqx\n43Ky8ewZHKXzu0b6H5iQBWH58tXs2rWf7OzJ7RMdz2yOZ50ulIBNo9HS0dGMrfMJwYDvx8Qwoffx\nTTEtSUEsTy/S+/AUEGTduo1s3rxjRg1+v0ypVFJUVEJRUQlmcx8Dz5MeGd6c9GhK7Y3mfozfNjBq\nGiEjI5N9+w5RU/PelJabv85sjufnDAYDFRVVGI3tDPS04Bnpx5BTjkwmHzee5XFqzE++p7/xGgkJ\nBj7++K/IyyuIatkj4fmqw4ULF6PT6TD1dIcy8rYMoE7RjbkKYDLx7LI66fzuGQMPe5FL8h/r5gPk\n5uZFJFeNWLIbYQqFgrlzi8nJyaW9rYWh7gYCXhfxmUUEfZ5xHw6+0WGM1z9ntN9Ifn4hH330KTk5\nM7fx/txPS5trsNkGMXeYGKozo1ArQ0sex3hITOYLFXD76T7XjOVmJ7IgrFmznm3bdkdsmfO78ICI\nj09g3rwFWK1mLN2tOCztGLLLkaTguPHsGurFeO0zfK5hlixZzo4de6flXtHJiIuLo7i4lOLiUqxW\nC/1GcyixkF6FJk332pieaDwHA0HMtzrpOduMz+GlsrKK/fs/Zu7cqe0THc+7EM9xcSoqK6sJBPx0\ntpubrLcAACAASURBVDcz3F1PfEYRMoVy3HgO7Rf9goDX9SJTY7gbnLGiVMYxd24JpaUVP+7H62Pw\nqRmFSjFmHT3ReJaCEtYfuuk61YzX7qakpIz9+0P7+CM18j7b41kmk5GTk0dxUemPiWGacdvNxGeX\nIpcrxm3Ay+Ryuu98jc34kOTkFD48+AllZZWzYob6+UC4Wq3G2NGBrdGKe3CU+ILEMY+ImXR742wz\nlltdyIIy1qz5gG3bdk/o6IuJmO3x/FwoH0k1PT3dWHta8TgGMeSUj9uG7m+6wUDzLVJT0/j447+e\n0HFnM8HzwcMF8xeFki22d2JrsOAf9aHPTUT+ymzpROJZCgSx3Omi+0wzvmEPZWUV7N//MaWl5RGd\nFRUd0ihJTk6hvHweHR3tDPY04x21oUsrYLD1h1+8NrVkKQG/h44r/4rXMcDChYvZtWv/rFvqoVKp\nqaioIjU1DWNHG0MtoZH4+MKk166Jn+gDwtkzTPvXTxk1jZCdncuHH35CaenUkgi8ybvygIiLi3uR\nAc7UGcqGF59ZwlD7/V+8NrVkKT7XMMarfyLgdbFx4zZWrFgzKxo6r3re8NFqdXT+eOyRZ3CU+IIk\n5MrJPyA8Qy46vqlnuGmAhHgDu3fvZ9myVRFPZPauxLNMJmPOnCK0Wh2tzQ0M9zxDnzkXe+fjX7w2\ntWQpHscAnde/ACnArl37WbBg0ayMZ71eT1XVAgyGxFA8t/Qzanbw/7N338FRnXmi97+ns0Ird7dy\nBKGAiCaDyAYbDLbBBhzHeOyduVs18+5bM1u1W9eeueOZ2S1vXVft3vvubk2yZ7AN2CTbyBgM2Igo\nwAgEQjnHVs6p1X3eP2RkY1rQQuog6fn8JZ1zWs9Pp399+nnOeYI+2v+ea/Ro8nmgvY+KT/Noy2vE\n29ubxx/fyvLlq9DpnDt8Yqrks6+vLykpszCb62ioLqGnqRL/yBRsVovdCnxg3Dyqrxymu6GU6OhY\nnn32+Qn9pN8eSZKIiIgiKSkVs7mepvJ62vMb0Rl87T5VGn19o52yw7fpqeskPDzy2/qGc4dQTJV8\nhqGn3TNmpFBdXUlTTQmyzYpXYJjdfFZqvGgqOEdgUDA7dryEXv9wsxhPBHduhsfHT6emppqW8gba\nC5vwifBD7fNdT0pH83mgvY/yI7dpL2jC10fPE09sY8mSFS55eODRDdLMzEz+7u/+jj179tDb28v8\n+fPv2t/V1cXPfvYz/vjHP/Lhhx+i0+lITk5+4N911wdKp9ORkjKTysoKmmqKsQ700d/RcM9xATGz\nqLqwH0tPG8uWrWTlyrVuX8rFWYa6HxhJTf32iVt5He2FTXhH6FH73p2cjn6gZFmmObuOqi8KkQes\nw1Oru2JWwKn0BSFJEgkJiXR3d1NTUURvm5nBvs57jvOPSqXy4kdYB3p47LEtzJ49zw3Rus6dHgDJ\nyTOpr6+jsbyetoImfCIf7guivaiJ8iN5WDr7mTlzNtu27SQkxOiS/2Uq5TNAWFgEPj6+FBfdpqex\nDNvgvbH4RSRRdfEjZOsATz75DImJD/7OmciG1sYLJTV1Fk1NDTSU1w01JMP0aPTf5Yej+dxR1kLZ\noVwG2vtISkph+/bnMJnCXPK/TKV8VqlUJCWl0tLSTH1VCb2tdfiaEmgpvfcmeH9HAz1NlSQmJvHk\nk89O2CFBjvDy8iI1dRZKpZKKklJa8xpRaBR4h+rvnjBnNPWN63VUf1GETdQ3nGposqMZFBXl01JT\niFYfTFd98T3H9TRV4uXlza5dL+PnN7aZXycKvX7oZrjNZqWytJzW242o9Vq8DEN56Eg+d1a0Un74\nNgPtfaSkpPH00zsxGFxT1wAPbpDabDZee+013n33XV577TV+97vfsXDhQoKCvnvs/u6776LX6/mP\n//gPNm7cyE9/+lNeeeWVBzbe3PmBUqlUJCYmU1ZWTEtdmd1j+jub6Gs3s3DhUlasWD0p77z/kEaj\nJSUlDYVCQXlxCW15jWiDvNAFfzcTnSMfKNlqo+ZkCY1Xa/D29mH79l0unRhjqn1B3FnQurGxgYba\nEfK5o5GBzibS09cwb94CF0foPjqdjtTUWQBUlJTSlteILtgbbdBQTj8on2VZpvFyNbWnS1EpVWza\n9CRLlqxAqXRdt9Cpls8AoaHhQ1/q5fdWdAD62s1YuttYt+6x4fd3KrhzjdZoNJQVl9CW3zDqCk/j\ntRpqThSjkJRseHQTy5evHvdx/Pcz1fJZoVAwbdoMzOZ6GqpLkGUrfW33vkeWnnbi4qaxdet2j1yi\naLxJkkRUVAwxMXGUlBbRWtzIQEc/fnGBw7NKO1TfsMnUnC6h8Uo13l7ebNu2i5kzZ4v6hhOpVGpi\nYuLIycmmu7kS2Tpo97inn97hshtdnkKhUBAbG09oaDilJYW0FjYgKRR4h+ux9Vvvm8+t+Q1UZhSi\nkCUefXQTK1asdvkQlPvls1sfyeXk5BATE0NERARqtZpNmzZx6tSpu46RJInu7m4Auru7CQgImBBj\neHQ6HVu3PoN6hLuQPU2VxMbGk56+xsWRuZckSSxZsoJt23ahUiipzCig+ca9H6CR2AatVBzNp/V2\nAyZTGC+//NqkGMTu6RQKBY89tmXEbjG9LTUkJExn4cKlLo7M/RQKBcuXr2Lr1mdQoKTiaAFtBY0P\nfJ0sy9Sfq8B8sRI/P3+ee+4VkpJSXRCxALBs2aoRKzN9bfXMmJHMnDnz7e6fzCRJYsGCJWzf/hwa\nlYbqE0UOXaNlWcZ8sZL6zHJ8fH15btfLpKXNmRI3W91NqVSyadOTBAQE2h1SAUNPVzZvfmpKNEa/\nLyIiipdfeo3Q0HDa8hqoOJqPbdDm0GttgzYqM/JpvWXGZArlpZdeIyoqxskRCwDBwQaWLVuFdaDP\n7v7k5JnExMS5OCrPkZAwnRdeeBW93g/zhQoaLlff9/jWvAaqvyhCo9Hw7LMvMmvWXBdF6ji3NkjN\nZjNhYd9VCEwmEw0Nd3dvff755ykuLmb58uVs3bqVf/7nf3Z1mA8tMDCIRSNU0FUqNRs3PjFlv6zj\n46exa9fLQzOIfVVKyy3zA18jW21UZhTQWdZKbGw8O3e+hK/v5B034Gl0Oh1Ll660u0+pVLF+/eNT\nNp8BEhOT2LHjBTRqNVVfFNJR2nLf4xsuVdH0TQ1BQcE8//wrGI0mF0UqwJ0bCavt7lOpVKxZs2FK\n53NsbDzPPfej4Wt0a+79r9GNV2toyKrC3z+AF57fTWhouIsiFWDo+rxx4xMj7l+5ct2km6PCUb6+\nenbseJHY2Hg6y1qp+rwA2Sbf9zWyTabqeCEdJS1ER8d+O05xanQN9RTz5y8csY73yCOLXByN5wkK\nCua5536Ev38ADRcraS9utntcd10nNV8Wo9Vq2bXzJSIjo1wcqWM8ftDiuXPnSElJ4dy5cxw5coTf\n/OY3w09MJ4KRnnjMmjV3yl/cTKYwdux4CZ2XFzUni+koG7kCLzPUbeZOY/Spp3ZM6jEwnmqkO5Kp\nqWlTPp+Bbye6eAGVUkXVsUL6W3vsHtdR2jJced+xQ9xYcZfg4GC721NT08R7AoSEGNmx4yW0Oh01\np0roMXfZPa6zohXz+Qr0er9vx3Q5Z8ZR4f7udFG1Jzw80sXReBaNRsNTT+0gOjqWjtIWar8uve/x\ndZlldBQ1ExkZzdNP70SrdX3X2alOpVKRljbH7j4vL2+726caPz9/tm/fhVanw3yhwu4xdV+XIiHx\n1FM7MBpDXRyh49za99VkMlFbWzv8u9lsxmi8e3DtoUOHeP311wGIjo4mMjKS0tJS0tLS7vu3AwO9\nUY0w1bcrKZUWu9uXL1+MwSAqPAaDntd+/GP+67/+i+ovioh5Isnuce0FTbTmNhAREcGPf7x7yjVG\nPT2fly0T+XyHwZCEQrGLPXv2UHvG/pjb+gsVaLVafvzjVzGZpt6TUU/P5yVLFol8/pbBoOfll17i\nj3/6I3Uj5fO5CtRqNa++upvw8Kn3ZNRT8hlg2bIlVFTc+z4FBfkQFCRy+sc/3s1//td/UZ9ThzbA\n/ozPnWWtNF+vw2g08tprr+Ll5dyZoT2NJ+XzwoXzuHjx7D3bRT5/x2DQ88z27bz//vt291v7Bnns\nsceYN2+miyMbHbc2SNPS0qisrKSmpgaDwUBGRgbvvPPOXceEh4dz8eJF5s+fT1NTE+Xl5URFPfhx\nc+sITyZcrb3d/tPcvj6ZxsZ7ZyudinS6ANate4wvvviMunP27/A0ZFWh0+nYtOlp2tv7gX7XBvk9\n7qioeno+W60qkc/fExoay6xZ88jJsT+eS7bYWL3xURQKb7efN5HP97LZ1G5/XzyJv7+JRQuXcunS\nebv7bQNWVq9dj1qtd/t5m8r5DODjY38txpaWbqxW100u5ck2b3qKv/7tT5gvVdrdX39h6AbL5s3b\n6OoapKvLfTk91fO5t9f+eF+Rz3cLC4sjJiZuhJtRwaSkzHP7tRnun89u7bKrVCp544032L17N5s3\nb2bTpk0kJCSwb98+9u/fD8BPf/pTsrOzeeKJJ3jllVf45S9/SUBAgDvDFpxg5szZJCRMp7fe/gdG\ntsqsWbMBf3/x3gsTw6pV69CM0M3LZApj5szZLo5IEB7ekiXpI3ZjDgkxMHfuIy6OSLBnKo97dlRg\nYDDpK9ZgG7Da3W8bsLJ8+WqCg0NcHJkgPLwFC5bY3f7II4snxLKSbp+uNj09nfT09Lu27dy5c/hn\no9HIn//8Z1eHJbiYJEmsWbOB0tJiZPneyQaMxlBSUu7fTVsQPIlWq2XO7Plcvnzhnn0LFiwRFUdh\nQlGpVMybt5DMzFP37HvkkUUin4UJZc6c+XzzzWXa2u6du8LPz39KLV8mTA5BQfbnRJgosxF7fpNZ\nmDICAgJJTLQ/hnT+/IWiwiNMOElJKXa3h4VNvXF2wsSXkDDd7vaoqFjXBiIIY6RQKJg71/6yTnPn\nLpgQT5QEwRETpe4sPnGCR0lJsb8IvVhrVJiIdDr7k2FMlC8IQfg+tdr+mC2Rz8JEFBc3ze72+Hj7\n2wVBcB7RIBU8SkiIwe52UeERBEEQBGG8qFT2R62NdONFEATnEQ1SwaOIhqcgCIIgCIIgTB2iQSoI\ngiAIgiAIgiC4hWiQCoIgCIIgCIIgCG7hUIO0ubmZX/ziFzz//PMA5Ofns3fvXqcGJgiCIAiCIAiC\nIExuDjVI/+f//J/Mnz+fjo4OAOLj4/nwww+dGpggCIIgCIIgCIIwuTnUIDWbzezatQulUgmARqMR\nazQJgiAIgiAIgiAIY+JQq/KHU2N3dHQgy7JTAhIEQRAEQRAEQRCmBvuLMP3A+vXrefPNN+nu7ubQ\noUN8+OGHbNu2zdmxCYIgCIIgCIIgCJOYQw3S1157jU8//ZSOjg7OnDnDiy++yNatW50dmyAIgiAI\ngiAIgjCJOdQgBdiyZQtbtmwZ9wAyMzP5/e9/jyzLbNu2jddff/2eY7KysviXf/kXBgcHCQwMZM+e\nPeMehyAIgiAIgiAIguBaDjVIm5ubef/996msrGRwcHB4+7//+7+PqXCbzcZbb73Fe++9h9FoZPv2\n7axdu5aEhIThYzo7O/nNb37DX/7yF0wmEy0tLWMqUxAEQRAEQRAEQfAMDjVI/8f/+B+kpKSwZMmS\n4Zl2x0NOTg4xMTFEREQAsGnTJk6dOnVXg/Szzz7j0UcfxWQyARAUFDRu5QuCIAiCIAiCIAju41CD\ntLe3l1/96lfjXrjZbCYsLGz4d5PJxM2bN+86pry8nMHBQV588UV6enp48cUXefLJJ8c9FkEQBEEQ\nBEEQBMG1HGqQzp49m4KCAmbMmOHseO5htVq5ffs2f/3rX+np6WHnzp3MnTuXmJgYl8ciCIIgCIIg\nCIIgjB+HGqQ7d+7khRdeIDQ0FK1WO7z9wIEDYyrcZDJRW1s7/LvZbMZoNN5zTGBgIFqtFq1WyyOP\nPEJ+fv4DG6SBgd6oVOPXvfhhKZUWu9uDgnwICtK7OBrPJ86XfSKfJyZxvuwT+TwxifNln6fkM4j3\naDTEubJP5PPENNHPlUMN0l/+8pf85Cc/ISUlZVzHkKalpVFZWUlNTQ0Gg4GMjAzeeeedu45Zu3Yt\nv/3tb7FarQwMDJCTk8Mrr7zywL/d2tozbnGORXt7t93tLS3dWK1qF0fj+SbC+TIYXP/BFvk8MU2E\n8yXy+V6e9P54kolwvqZyPsPEeI88xUQ4VyKfPf898hQT4VzdL58dapBqtVpeffXVcQvoDqVSyRtv\nvMHu3buRZZnt27eTkJDAvn37kCSJHTt2kJCQwPLly9myZQsKhYJnn32WadOmjXssgiAIgiAIgiAI\ngms51CBdsWIFmZmZpKenj3sA6enp9/zdnTt33vX7q6++6pQGsSAIgiAIgiAIguA+DjVIP/roI/7w\nhz/g4+ODRqNBlmUkSeLixYvOjk8QBEEQBEEQBEGYpBxqkB48eNDZcQiCIAiCIAiCIAhTjEMN0oiI\nCAYHBykrKwMgLi4OlcqhlwqCIAiCIAiCIAiCXQ61Km/evMnPfvaz4e66g4OD/J//839ITU11dnyC\nIAiCIAiCIAjCJOVQg/R3v/sdv//971myZAkAFy9e5K233mLfvn1ODU4QBEEQBEEQBEGYvBSOHNTb\n2zvcGAVYsmQJvb29TgtKEARBEARBEARBmPwcapB6eXmRlZU1/Pvly5fx8vJyWlCCIAiCIAiCIAjC\n5OdQl91//ud/5uc//zkajQYAi8XCf/zHfzg1MEEQBEEQBEEQBGFyc6hBOmvWLE6cOHHXLLtqtdqp\ngQmCIAiCIAiCIAiTm0Nddi9cuEBfXx+JiYkkJibS29vLxYsXnR2bIAiCIAiCIAiCMIk51CB9++23\n8fX1Hf7d19eXt99+22lBCYIgCIIgCIIgCJOfQw1SWZaRJOm7FykUWK1WpwUlCIIgCIIgCIIgTH4O\nNUh9fHy4cePG8O83btzA29t7XALIzMxk48aNbNiwgT/84Q8jHpeTk0NqaionTpwYl3IFQRAEQRAE\nQRAE93JoUqNf/vKX/P3f/z3Tpk0DoLi4mP/7f//vmAu32Wy89dZbvPfeexiNRrZv387atWtJSEi4\n57j//b//N8uXLx9zmYIgCIIgCIIgCIJncKhBOnfuXDIyMrh+/ToAc+bMwd/ff8yF5+TkEBMTQ0RE\nBACbNm3i1KlT9zRI9+zZw4YNG7h58+aYyxQEQRAEQRAEQRA8g0Nddn/3u9/h7+/PypUrWblyJf7+\n/vzud78bc+Fms5mwsLDh300mEw0NDfccc/LkSZ577rkxlycIgiAIgiAIgiB4DocapFevXr1n25Ur\nV8Y9GHt+//vf88tf/nL4d1mWXVKuIAiCIAiCIAiC4Fz37bJ77Ngxjh07Rk1NDT//+c+Ht3d1daHT\n6cZcuMlkora2dvh3s9mM0Wi865hbt27xD//wD8iyTGtrK5mZmahUKtauXXvfvx0Y6I1KpRxzjGOl\nVFrsbg8K8iEoSO/iaDyfOF/2iXyemMT5sk/k88Qkzpd9npLPIN6j0RDnyj6RzxPTRD9X922QxsXF\nsWrVKm7evMmqVauGt/v6+rJkyZIxF56WlkZlZSU1NTUYDAYyMjJ455137jrm1KlTwz//0z/9E6tX\nr35gYxSgtbVnzPGNh/b2brvbW1q6sVrVLo7G802E82UwuP6DLfJ5YpoI50vk87086f3xJBPhfE3l\nfIaJ8R55iolwrkQ+e/575Ckmwrm6Xz7ft0GalJREUlISa9asISAgYNwDUyqVvPHGG+zevRtZltm+\nfTsJCQns27cPSZLYsWPHuJcpCIIgCIIgCIIgeAaHZtl98803kSTpnu3//u//PuYA0tPTSU9Pv2vb\nzp077R77L//yL2MuTxAEQRAEQRAEQfAMDjVIV69ePfxzf38/x48fv2dpFkEQBEEQBEEQBEEYDYca\npE899dRdvz/99NO8+uqrTglIEARBEARBEARBmBocWvblhyRJwmw2j3csgiAIgiAIgiAIwhTi0BPS\nn/3sZ8NjSGVZpqCggKVLlzo1MEEQBEEQBEEQBGFyc3gMqSRJdHd3o9fr+fGPf8ysWbOcHZsgCIIg\nCIIgCIIwiTnUIJ0/fz6/+MUvyMvLAyA1NZV/+7d/IyoqyqnBCYIgCIIgCIIgCJOXQ2NIf/WrX/Hs\ns8+Sk5NDTk4OzzzzDG+++aazYxMEQRAEQRAEQRAmMYcapC0tLWzfvh1JkpAkiW3bttHS0uLs2ARB\nEARBEARBEIRJzKEGqUKhoLS0dPj3srIylEql04ISBEEQBEEQBEEQJj+HxpD+wz/8A88//zzJyckA\n5Ofn8/bbbzs1MEEQBEEQBEEQBGFyc6hBmp6eTkZGBjdu3ABg9uzZBAUFOTUwQRAEQRAEQRAEYXJz\nqEEKEBQUxOrVq50ZiyAIgiAIgiAIgjCFODSG1JkyMzPZuHEjGzZs4A9/+MM9+z/77DO2bNnCli1b\n2LVrFwUFBW6IUhAEQRAEQRAEQRhvDj8hdQabzcZbb73Fe++9h9FoZPv27axdu5aEhIThY6Kiovjg\ngw/Q6/VkZmbyxhtv8NFHH7kxakEQBEEQBEEQBGE8uPUJaU5ODjExMURERKBWq9m0aROnTp2665g5\nc+ag1+uHfzabze4IVRAEQRAEQRAEQRhnbm2Qms1mwsLChn83mUw0NDSMePzHH39Menq6K0IT3KS9\nvc3dIQiCIAiCMMlZrVa72wcHB10ciTCSkd4jYfJx+xhSR126dIlDhw7xi1/8wt2hjIuBgX53h+CR\nSkuL7G4XDVXP1tTU6O4QPFJPT7fd7bIsuzgSYTQaG0e+MTqVibz1fA0NoheZo6qqKuxur6wsc3Ek\nwkgqKsR7MVW4dQypyWSitrZ2+Hez2YzRaLznuPz8fN58803+9Kc/4e/v79DfDgz0RqVSjlusD0uS\n7Dc8KyqKSEmZ5uJoPJvNZqOoKN/uvvLyQubPT3NxRJ7DU/IZ+uxuzcu7weLF81wci+fLzq60u72v\nrx2DIdrF0XgOT8lnhWLA7vb8/ByWLJnv4mg8X01Nid3tstyHwaB3cTSew1PyGeDo0Zt2twcF+RAU\nNHXfI3uKi/NG2J7Po4+ucXE0nsNT8tlms3Hz5jW7+/R6zZS+5tijVFrsbvf3102Ic+XWBmlaWhqV\nlZXU1NRgMBjIyMjgnXfeueuY2tpafvazn/H2228THe14Ba61tWe8w30oV65k291+/vx5kpPn4uPj\n4+KIPFdhYR5tbfafhGZnZ7No0Uq0Wq2Lo7qXOz7YnpLPZ86cs7s9Ly+Pa9duERUV4+KIPJfNZiMr\n67LdfRcuXCI0NNa1AY1gKufztWvX7W7Py8sjOzuXyMipe9PAnqysK3a3X7p0hYCAUBdHY99Uzuea\nmiry8uw3snJzi0hKSnFxRJ6rocFMaWmp3X3l5eXculWIyRRmd78rTeV8zs6+OuK8MV9+eZoNGza7\nOCLPVlpabnd7dvYt5s59xLXBjOB++ezWLrtKpZI33niD3bt3s3nzZjZt2kRCQgL79u1j//79APzn\nf/4n7e3t/K//9b948skn2b59uztDHpW2tlbOnz9jd9/AwACff35EdIH6ls1m48KFzBH3DwwM8M03\nWS6MSPih6upKrly5NOL+z44epru7y4UReba8vFt0dnbY3VdSUkRzs+jm7E5tba2cPfvViPuPHj1M\nb69nVMw8QVtbKyUl9odU5OXlinPlZn19vRw9enjE/adPH6e3t9eFEXm28+e/vu/+s2fvv19wrvr6\nWr766gSSSmN3f05ONsXFYhnI78vJsX+D9ebN7AnR1nD7GNL09HSOHz/OiRMneP311wHYuXMnO3bs\nAOC3v/0tWVlZHD58mCNHjnDgwAF3huuwrq4uDhz4EIvFfpcw75BoystL+fLLYxMiUZzt5s3rNDY2\n4Dct2O5+pU7F5csXRqzgC87V0FDP4cMfjZirIYlL6O7q5MCBvaLSw9ANlLNnv7rvFfbrr0+Kz76b\ndHV1cuDgXgYG7F+fgxIW0dnZwcGD++jvF+P9Ac6d+3rEfYODFi5dOu+6YIS7DAwMcOjQPjo62gmK\nX2D3mO7uLg4d2ifmrwDKy0spLi5EZ/K1u98rTE9ZWfGIc1oIztXU1MjBg/uwWq2EzXrU/kEKJUeP\nHh5xHPBU09zcOOKQN7O5nvJy+8MtPInbG6STUVtbK/v376G1tYXAOPvj6kJnPYrO38SNG99w4kTG\nlJ5JrLu7izOZp1ColYTMD7d7TMj8CCwWC6dOfeHi6ITq6kr273+fvr5ejKn2x9UExM4jMHYuDQ31\n7N+/h46OdhdH6VnOnz9DZ2cHQTPtd2P0CtNTWlpMYaH97nWC87S2tgxdn1uaCYiba/eYoIRH8I+a\nSV1dDR9//MGUf/JfXV1JXt4ttMFedverfDVcu3ZZPPV3g56ebg4c+JCammr8IlMImma/QaoPm05t\nbTUff/zhiJOtTQUDAwMcP34UJAnToii7x5gWRSIpJE6c+FzckHKx2toa9u37Gz093YTN3oiPIdbu\ncWGzNjBotXLgwIcUFxe6NkgPI8syp04dv+8xp06f8PjZo0WDdJxVVJSxZ8+faWlpInj6YoKnL7F7\nnFKtI2b5c+j8TeTkZPPxxx/Q1TX1Kj2yLHPiRAb9fX2YlsWg9rbfPcM/MRifCD+KigrIzc1xcZRT\nkyzLZGdfZf/+PfT19xE+bzP+kfbHIEmSRNjcxwmMm09jo5m//e1PVFaWuzZgD1FZWc7Vq5fQ+OsI\nnm2/QRq6NBqFSsGJExniqb8LlZeX8v77f6GlpZngxCWETF9q9zhJkoh4ZAv+0WnU1dWwZ8+fqa+v\ntXvsZGexWDh27FMAjIvtj6k1LYrEZrNx7Nhn2Gw2V4Y3pZnNdezZ82dqaqrwi0wh8pGtSJL9ap1p\n5nr8o1Kpra3+Np/rXBytZzh9+jgdHe0Y5oejC/a2e4w20BvDggg6Ozs4efKYiyOcmmRZ5vr17qDq\neAAAIABJREFUb9i79z16+3oJm/s4QQkjj3v0NcUTtegZrDIcPryf8+fPTNlrT05ONhUVZfhE+Nnd\nH5BsoLWlecQhhJ5CNEjHicVi4fTp43z00fv09/cTNvdxQtPWIUnSiK9RaX2IW/ky+vAkqqoqePfd\n/6ag4LYLo3a/7OyrFBcX4hPlP2LlHUBCImL9NBRqJV+ePEZra4sLo5x6hror7uXkyWNIKh2xy58n\nMHbOfV8jSRJhczYSOnsjfX197N+/h9Onj2Ox2J/5bTLq6urks6OHQJKIeiwRxQgzFWr8dISmx9LX\n18dnnx2c0j0kXOFO74qPP/6A/oF+wudtInTm2vtenyVJQcT8LRhTVtHZ2cEHH7zL+fNnptx7dfr0\ncdraWgmZF4630X4XR9/oQPxnhFBXV3PfuQCE8WGz2bh48Szvv/+XocZVcjqRC55CUow8M6qkUBDx\nyJMYU1bS0dHOBx/8hQsXMqdUJf727ZvcvHkdncFnxJsrdxgWRuFl8uX27ZvcunXDRRFOTV1dnRw+\nvJ8vv/wcSaUlZukugkboXfh9+rDpxKW/jNrbnwsXMtm796+0tDS5IGLP0djYwOnTx1FqVZiW2p9U\nMmR+BBp/HZcvX6CszHO77ooG6RjJskxRUQHvvvvffPPNZbT6YOJW/sihDxOAQqUhatE2QmdvoN9i\n4dNPD3L48P4p0eCqra3hq69OoNSpiNow/b6VQwBtgBfha+KxDAzwyScfT6mGjqtYrVauXLnEX/7y\n35SVleBjjCdhzasjdpv5IUmSCE54hNiVL6HxDeabby7z7rv/TWFh/qQfL2mxWDhy5GN6ursJWxGD\nd+j9Z0cMSgvFPzGEmppqTp78YtKfH3eQZZni4qHr87VrV9DqQ4hL/xGBsfa76v6QJEkYkpYTs/w5\nlFofLlzIZM+eP02ZcUu3bt0gJycbncFnxMrOHRGrE1D7abl48SylpcUuinDqqaqq4G9/+xPnzn2N\nQutDzLJdGJPTH/j9CXfyeQUxy55DofXh/Pkz/PWvf5wSvVnM5nqOHz+KQqMk+vEZKFT3r/4qlAqi\nH5+BUqvixImMKdtDwplsNhvXrl3hL3/5b0pKivAxxBC/5lV8TfEO/w2vwDDiV7+KX0QKtbXVvPfe\nHzl//syUqB/29PRw+PB+BgcHiVg/DbWv/R6GSvVQzktKic+OHqKlpdnFkTpG+etf//rX7g7CGXp6\n7E9WMZ7M5jqOHfuUS5fOMTAwQPD0RUQufBqNT8DwMVZLHy0l9y79EDxtIUqNDhj6kvAOisAvIpm+\njgbM1aXcuHGNgYEBQkPDUKnUTv9fXK2rq2voafJAP7FbkvEyDN15t/YP0nz93q5EIXPDUepUeBl8\nGOy20Fxmpr29lenTkxz6Ih5PPj6uX3rG2fk8VHEv5MgnH5OfdwsUasJmbyB01vrhPAXH8hlA7eVH\nYOwcZJuNtrpS8vNzqa6uxGAw4uvr+ethjZYsy2RkHKa8vJSAZAOhy2ORJOm++azSqdHHBtJZ3kpN\nWSVqtZqICPtjmpxpMuYzDC3rcPf1eTGRC58a9fUZQOMTSEDsHAYHemipLeHWrRu0tDRhNIbi5WV/\nXOVEV1tbzZFPDqDQKIl7OhW1t+a++az21eAdrqctr5Hi4kISp8/Ay8t+l0hnmqz53NrazJdfHuPM\nmZP09HQTEDOb6MXPoPO/e+12R3Ja4xtIYMwcBvt7aKkrITc3h6amRoxGk1veM2cbqm/soa+vl+hN\nSfiED3VtfFB9Q6lToTP40JrXQElpEUlJqS5fem4y5rMsy5SXl/LJJx+Tm5uDLCkJnfUoobM3oNLc\nfT11JJ8VKjX+kclo/Qx0N1ZQUVZEbm4OPj6+hIQYXF5HdAWLxcKhQ/tobGzAsDCSkNlh981nXbA3\nal8NrYUNlJeXkJSUilptvwHrTPfLZ7euQzpRNTc3cf78meHutT7GOMJmbUDrFzKmv6vVBxO74kU6\nam5jvnmSy5cvcP3GNyxauJR58xag0bh/Dc7xMDg4yCeffExXVyehy2PwjQ548Iu+J2xlHH1N3eTl\n5WIwmFi0aJmTIp38ZFmmoqKMs2e/GroDLEkExT+CITkdlXZsFROFUk1o2loCY2dTn/MllZUl/O1v\nfyIxMYlly1YREmIYp//CvWRZ5vTpExQU5OET4UfE2mkOfwEq1EpityZTsi+HM2dO4eurJyUlzckR\nT26trS2cP3+GvLxbwPhdn5VqHRHzNhMYO5f6G8fJz79NYWE+s2bNZcmSFZPqRkt7exuHD3+EzWYl\n9vEUtAGONbq9TXoi1iVQfbyIgwf38fzzu/H2nnwNHFfq6Gjn4sWz3Lx5HVmW8QoMJ3T2BryDIsb0\nd5UaHRHzNxMUN4+6nOMUFuZRVJTPzJmzWbo0HT8//3H6D9zLYrFw+PB+Ojs7MC2Nxi8+aFSv18cG\nEroilvqz5Rw+vJ+dO19Go3F9RX6yqK2tJjPz9HAvk4CYOZhSV6HS2R8OMBr+Ecn4GuNpLDhHS3EW\nR48e5vLli6xYsZq4uIRJ0zC12WwcPXqImpoq/BNDMC1xbL3swFQT/W19NF6p5uDBfezY8YJHtStE\ng3QUGhvNXLp0jvz8oYaoLiAMU+pqfIxx45bokiThH5mKPiyRlpKrNBVe4OzZr7hy5RLz5y9k3ryF\n6HS6B/8hD3VnEqPa2moCkgyEzB/9l6pCpSB6cxIle2+QmXma4GAD06YlOiHayWvoDmUJFy6cpba2\nGgC/iGSMySvHXHH/Ia0+hJhlu+hqKKUh92sKC/MpLMwnKSmFxYtXYDAYH/xHPFhW1nmuXbuMNtib\nmCeSH9gV7IfUvlpin0yh9KObHDv2KV5e3sTFJTgp2smrpaWZS5fOcfv2TWRZRudvwpi6Gl/T+FZE\nvIMiiFv1Ch3Vt2m4/TXXr3/DzZvXmT17HgsXLkWvtz+xxETR19fHwYP7hma5XBWPPiZwVK8PTDbS\n39JL45Vqjhz5iGeffQGVSlQ1RqutrZWsrPPcunUDm82GRh+MKWUV+vDx7RXkFRRO3Mof0VlbQMPt\nr7l58zq5uTmkps5i0aJlBAaOrgHnSWRZ5vPPj1BfX0tAsgHDgsiH+jsh88Lpb+3FfKuejIzDbN36\nDAqFGPE2GnfGl9/pzu9rSsCYuhqvgJHnDnkYSrWW0JlrCYqdS0PeGRqqcjl4cC/h4ZEsXZpObGz8\nhG6YyrLMsWOfDs+9Evnog4e7fZ9paTSD3QPU367l0KH9bNu2C7XaM3phim8JB9TW1pCVdW54ammd\nfyiGpOXow2c4LbEVSjUhiUsIjJtHc/FlWkouc/78Ga5cucTcuY8wf/4ifHx8nFK2M129mkVubg5e\nJl8i1j18RVHtoyFmSzKlH93k6NFDvPDCq5PmiZsz3emae+nSueExMfrQ6RiS0/EKDHNq2b7GeHwM\ncXTWFdGYl0l+/m3y828zffoMFi9eTmio/SV/PFlOTjZnz36FWq8l7skUlLqHu6TqQnyI2ZJM2eHb\nHDnyMTt2vEh4+NiegEwVTU0NXLp0nvz8XGRZRutnwJC0Ar+IZKddnyVJwj8qFb+IJForbtBUcJ5r\n165w/cY1ZqXNYeHCpfj7j67nhyewWq188snHNDc3EjwnjJA5D3dNMC2NZqC9j5rCKo4d+5TNm5+a\n0JVAV2pqaiQr6zx5ebeQZRmNbxCGGcvwj0pDclIjSJIk/CKS0Icn0l55i8aCc9y8eZ1bt26QnDyT\nRYuWEhIy8W4cnj37FYWF+fhE+hGxzvGeKz8kSRIRq+MZaO+juLiQzMzTrFq1bpyjnZxqaqq5eDFz\neDId75BojCmr8Alx7Knew9L4BhG54ClCEpfScPsMtbWFHDjw4bcN0xXExk68J6ayLHPy5Bfcvn0T\nr1D9Q90AlySJiHXTsA5YqSqu4NNPD/Dkk8+iVI48IZqriAbpCGRZprKynEuXzg0P+PcKisAwYzm+\noQ9/YRstpVqLMXkFwdMW0lr2Dc1FWWRlnefq1SxmzRqq+EyUrjUVFWWcOXMSlY+GmCeSRpyB1FFe\nRl8i1k+j6lghhw/v56WXfoxWO3GfHjuTzWYjPz+XrKzzNDUNrRXoF55ESNLycb9DeT+SJOEXnog+\nbDpd9cU05p+jqKiAoqICYmPjWbx4OVFR9588xVOUlhZx4kQGSp2K2KdSUOvH1vXFJ9KfqMcSqczI\n59ChvTz//O4J/XTC2czmOi5ePDe8GLjWz4gheQV+4/wE6X4khZKguHkExsymrfImjQXnuH79G3Jy\nsklJSWPRomUEBQW7JJaxGqrsHKOyshx9fBBh6XEP/bckSSLy0elYuvrJz88lKCiYZctWjmO0k099\nfS0XL56juLgAYOjGyoxl+EWmjLiUy3iTJAUBMbPwj55JR3UejQVDPQ5u377JtGmJLF68grCwiXHj\nMDc3h6ys82gCdERvTkKhHNs5lJQKYjYlUbI/hytXLhIcHEJa2v1nnp/KqqsruXDhLBUVpcC3DdHk\ndIcnSBwvOn8T0Uuepbe1jsb8s982TPcSGhrO0qUriI8f3RNGd8rMPM3161fRhfgM3QDXPFwdWlIM\nrQJQ8WkepaXFZGQcZvPmp93+1F80SH9AlmVKS4u5ePEsdXU1wNAYJMOMZXiHxLgtcZVqLSGJSwlK\nWEBb+Q2aii6SnX2VGzeuTYiuNV1dnRw9eggkiN40A7Xv+PRbD5hhoK+xm8arNXzxxWds2bJ9wlxc\nXMFms5GXd4uLF88OzdwsSfhHpxGSuBSdn/ueKEuShD5sOr6h0+huLKep4Bzl5aWUl5cSGRnNsmUr\niY6OdVt8D9LQUM+nnx4EhUTs1hR0QeMzTs5/WjDhqxOoPV3CwYN7eeGF3eh0k3PSnIdlNtdx4ULm\ncI8Vr8BwQmYsRx/mvoqFpFASGDuHgOhZtFfn0lQw1NUyNzeH5OSZLFmynKCg8e0KP96ys68Oz6gb\n/VgikmJs51KhUhCzOZmSfTe4cCGTkBADM2bYX8d4KquuruLSpbPDT5C8AiMImbEUfVii+/JZUgz1\nAIhMobOuiKbC8xQXF1JcXEhsbAJLliwnMtK5T7jGwmyu5/iJDBQaJbFbklHpxqdLolKnImZrMiV7\nc/jyy88xGIwTsmePM9XW1nDu3NfDDVEfQyyGpBX4GNx7o9krMGyoYdpWT1P+Oepr8zl0aD+hoeEs\nX77K47vyXrlyicuXL6AJ9CLu6YfvjXWHQqkgZnMSZUduU1CQh5fXcdat2+jWcyAapN9TUVFGZubp\n77oyhs3AkLQMr0DPueAolGqCEh4hMG4u7VW3aCy4MNy1Ji1tDkuXpnvcGKahPu+f0dPTQ9jKuOEZ\n7saLaWkM3XWdFBbmD5+HqW5oOaJ8MjNP09ragiQpCIydS8iMpWh8RjcmzJkkScLXGIevMY6e5ioa\n889RXV3C/v17iIqKYdWqdR73hd/b28Phwx9hsViI3pyEd9j4TmYTPCsUS0cfjVdr+OyzQ2zbtsvt\ndy49QWtrM2fOnB5+IuoVFIkxeQU+Rs+pSEgKBQHRafhHzaSjNp/GvLPcvn2TvLxbpKbOYvnyVR53\nfYah8V1ffXUClZea2C3JKNTj031L5a0mZmsKJXtvcOyLzzAYTBPmibGzmc11ZGZ+RXn5na6MMRiS\nluNjiPWcfP5ej5aepgoa889SXl5CeXkJMTHxrFy5BpPJuUM9RstisQyt7Tw4SMyWZLTjdLPwDm2A\nF1EbEyn/5DaffXaIl19+XUxyxNAY/jNnTg7fKPQxxmFMTsc72PUzx9+PV0AoUYu309feQGP+Wepr\n8jhw4EMiIqJYvfpRj+wBUFSUz9dff4naR0PcUymovMcn3xRqJbFPJFN64CbXr18lICCQBQsWj8vf\nfhiiQQo0Nzdy6tRxKirKgKHJXQxJK+6ZTt2TSAolATGz8Y9Oo6Mmn8a8THJyssnNzWH+/IUsWZLu\nMRfJ3NwcystL8I0OIPghxyTdj6SQiNqQSNH72Zz+6gRxcQmTasbL0aqvr+PUqS+ora3+riGatAyN\nt2ePafMOjiJm2S56WmpozMukqqqEPXv+THJyKqtWrfeI9/TOpFwdHe0YF0XhP805lWvT0hj6mnoo\nLy/l6tUsFi5c4pRyJoL+/j7OnRuaPMhms+EVGIExZeW4TiY33iRJwj8iGb/wpKHJYvLOcOvWDfLz\nc1mwYAmLFy/3mIl+LBYLGRlHsNlsxDyWPOau5z+kC/YmYt00qr4o5NixT9m16+UpfYOlq6uTr7/+\nkry8XODbJ0jJ6U4fUzcWkiThY4jFxxBLT3MVDXmZVFSU8re/lZKUlMrq1Z5xfYahcaOtrS0Ezw0f\n9Yy6jtLHBRIyP4Kmb2rIzDzFunWPOaWciWBgoJ/z5zO5du0yNpsN7+CooTGibn4i+iA6fyNRi7bR\n21ZP4+0z1NQU8f77fyY1dRYrV67Fx2fss/6Oh+bmJjIyjgz1OHkyBY3f+A5LU+pUxD6ZQvHeHM6c\nOYnRaCIm5uGHa4yF278RMzMz+f3vf48sy2zbto3XX3/9nmN++9vfkpmZiZeXF//6r/9KcnLyuJQ9\nODhIVtZ5Ll06h81mw8cYjyl1tdMndxlPkqTAPzIFv4ikoTFMt89w+fJF8vNv8+ijjxMXN82t8Vks\nFs6e/QpJqRjTJEYPovHTEroshtqvSrl48Szr1z/ulHI82eDgIBcuDL3/siyjD0/ClLoarX5iPZHw\nDoogZtkuuhvLqb95kry8XEpLS1iz5lFSU2e5tRFSWJhHYWE+3hF+GBc5786vpBgag1f0fjZnz33F\n9OkzPLpLvrOUlhZz/PhRuro60fgEYpq5ZtxnGXWm708W01Zxg4bbmVy8eJaCgtts3LiFiIiHm/Vz\nPGVlnf+2Ah826iW4HBWQZKCjpJnaompycrKZM2e+U8rxZLIsc+PGNc5knmKgv39olv6Zq/E1xrs7\ntFHxDo4idvnzdDWUYb51mvz8XEpLi1m5ci2zZ89z62ezubmJa9cuownQEbrMuQ0i05JoOktbuH79\nG2bPnj/hZ4t/GNXVVXz++RHa29tQe/sTkbZuQl2fYeiJafTSHUP1jZwvyc3NobS0mI0bNzNt2gy3\nxmaz2fj880+wWCxEPZaIl8E5E5mqfbXEbE6i5NuZ/l955ScuX28XwK23KW02G2+99RZ//vOfOXr0\nKBkZGZSUlNx1zJkzZ6isrOTEiRP85je/4Ve/+tW4lN3T08NHH73PhQuZKLU+RC1+htjlz02oxuj3\nSZKCwJjZTFv/U0ISl9LZ2cmBA3u5cCETWZbdFldubg5dXZ2EzA0b9zs7PxQ004QmQEdOTjZdXV1O\nLcvT9PR0s3fvX8nKuoDKy4+Y5c8TvXj7hGuMfp+PIZb41a8SNucxLFYbx459yrFjn2K1Wt0Sj81m\nG7q5opCIXDdtzGPsHkTlrSZsZRw2q5Vz5752almeRpZlLlzI5ODBvXR3d2NITidh/U+cOnOuM93p\nqTDt0Z8SlLCAlpZm9u59jxs3rrk1rp6eHq5ezULlrSZ0qXMr8GGr4lGoFFy4mMng4KBTy/I0g4OD\nHD16mC+//JxBK4TNeYz41bsnXGP0+3yNccSv3k3Y3McZlOHLLz/n6NFDbn1vL106hyzLhC6PHfXs\no6OlUCkITY9FlmUuXTrr1LI80fXr37Bv319pb28jJHEp09b/dMJen+Hb+saaVwmdtZ6+/n4OH/6I\nzMzTbq0/37p1g/r6WvxnhBAww7lzfniH6TEujKCzs4NLl845tayRuLVBmpOTQ0xMDBEREajVajZt\n2sSpU6fuOubUqVM8+eSTAMyePZvOzk6amprGVG5vby8ffPAXamqq8ItIIWHdT/ALd++dkPGiUKkx\nzVxD/OpXUXv7c/78GU6d+sJt8dy8eR0knNJV94ckpYLgOeHfTuRz0+nleYqenm4+/PC9oQtXdBoJ\n6/4OX6N7ulyMN0mSCIqfT8K6v8MrMJzc3Bw+/fSAW74kyspKaG1tITDFiDbQNRMN+SeGoAvxoaDg\n9pS6yXLmzCnOnz+D2tuf+NWvYkxOR6Fw/7T0Y6VUaQibvYHYFS+iUOs4cSKDq1ez3BZPXt4tLJYB\nQuZHjNu40ZGofTQEzQqlu6uLkpJCp5blSaxWKwcOfEh+fi7ewZFMW/8TguLnT9iK+/dJkkRQ3Dym\nrfsJ3sFR5Off5uOPP3DLTcO+vj4KCm6jCdThl+Ca3iT62EC0wd4UFubT09PjkjI9wfXr3/Dll5+j\n1HgTm/4SpplrUCjd3uFyzCRJQfC0RcSvfhWNbxBZWec5e/a0W2IZutFxDkmpIGxFrEvKNCyIROWt\nJjv7Kv39fS4p8/vc2iA1m82EhX3XUDGZTDQ0NNx1TENDA6GhoXcdYzabx1TuqVNf0NbWSvC0RUQu\nfAql2vmPptVqNSEhIS5bgFYXYCJ+1W60/kays6+6pQLQ399PfX0tPuF+4zar7oP4Tx96InhnqZ6p\n4MyZU0Nd7qYtImL+FpQq548ddnU+a7z9iVnxAj6GWIqLC4dudLhYYWEeAIGpJpeVKUkSganGb9eP\nLXBZue5UXV3JlSsX0eiDiVv5I3QBzj/frs5nH0MMcSt/hErny5nMU7S0jO0m68O6k1MBSa6ZcTsg\neahbY1HR1MhlgCtXLlJVVYE+LJGY5S+g9nLNWEtX5rTaS0/M8ufRh82gurqSy5cvOL3MH6qursRq\ntRKQaHDdsk+SRMCMEGw2G9XVFS4p0906Ozs4ffo4Sq330Heyi8Y+uzKfdf5GYle8+G2j9AJ1dbVO\nL/OHGhrqaW9vw396sMvqzwqVkqBZoVgsA5SXl7qkzLvKd3mJbtbd3U1e3i10AaGYZq51yYVLrVaz\ndetW/vEf/5GtW7e6rNKj0vkQueApALfchW9uHlrvUmd03eBwtY8Gta+Ghsax3bSYKHp6url16wZa\nP+Okz2elSkPEI1tQKNVuqfA0NTUiKSS8TK6d7ODOrNR3Pk+T3ZUrlwCImPeESyrv7spnrT6Y0FmP\nYrNauXbtqkvK/KHm5kbUflrUPq6ZAE8X4o2kUtDc7J4GuDtcvZqFUuNNxPwnXPYUyR05rVCqiJj/\nBEqtt1vqG43ffud7hbr2+uxlGrpGNTRMjTpHdvYVrFYrptQ1Lls6zh35rPbSEz53aC6Sq1cvOr28\nH7rTCPaN8ndpub5RQ/MI1NbWuLRccPOkRiaTidra7+48mM1mjMa7B4YbjUbq6+uHf6+vr8dkevAd\n88BAb1Sqe7sg9fW1AeAdFInkgpn+JKUKf39/Fi5cCMDChQv5+uuvkVz0xaTVh6DUeNHV1YHB4NpZ\n8Nrbhy4ao1m8Vxph3MdI2+1RaJTY+q0u/3+daaR8rq/vBoYmApoK+az28kPtE0BPj+vzeXBwAKVO\nNaqxo+ORz9+tNzZ5cnqkfAYYGOgFScIrKMLpcbg7n+8siTA42OeW93ZgYABVoOON0bHmsyRJKLUq\nBgcHJk0uw8j5bLFY6O3twTskBqXGNd383ZnTSo0OnZ+R7sZy/P21Lp3pX60eui4rtY7/n+NyfdYq\nh8ufLDl9v+uzLA+NEfYOds2EbO7MZ6+gof/RarW4/L290+ZWejne+B6XfPYaOq8Khc3l/7NbG6Rp\naWlUVlZSU1ODwWAgIyODd955565j1q5dywcffMDjjz/O9evX8fPzIyTkwQuMt7ba788vy1pUKhWd\n9UWYrOucfsdSrfOlZ0Dm8uXLLFy4kMuXL9MzACada+7i9TRVYh3oJSgomsbGTpeUeUdfnw2AwV6L\nw69R+2jQBOoYaP2u/7o20MvhO/iyLDPYY8FLp3fa/+uOL52R8tliUaBSqek0F2OzDk76fO7vaKK/\noxGTKczl+azR6LC2NGMbtDk8YcZY8xnA0j0AgCSpnfI/e1I+A+j1ASBX0llXhF94olPjcHc+d9YO\ndV318fFzeT4DaLU6+nv6HT5+zNdnqw1rnwWNXjslrs+yLBMQEEh7SzWW3g7UXs5fg9adOW3p7aSn\nuRp//wDa2vqQJMdza6xstqEG1GCP6+ob3y9PlpVT4vqs0Qyt69pZV4hW/+C6+Fi5M58764aGunl5\n+br8+izLQ3U5S5frrs8Als6h+oZSqXF5Pru1y65SqeSNN95g9+7dbN68mU2bNpGQkMC+ffvYv38/\nACtXriQyMpL169fz5ptvjnmWXS8vL2bNmoulp52qrAPYbM4ffB+2YBufn/iat99+m89PfE3Ygqed\nXiYMVd6rLx8EYMEC169jGBQUgiRJ9NaPLqljNiUNTxqjDfQiepPjE05ZOvqx9g0SEjI1pmDX6byY\nO/cRBns7qco6iM3q/BkO3ZXPlt4OKi99BMDixctdUub3hYVFINtkemo7RvW6seQzQHdVOwDh4c5/\nYugJFixYgiRJ1F77jL4253eDc1c+dzWUUX/zJCqVmnnzFrqkzB8KDQ3H0jXAQLvjE1iMJZ97zF3I\nVpnQUM9bfN4ZJEli4cKlyDYrFef3Mdjvmolv3JHTg/09VFzYi2wbZOHCpS6ftOnOsiuuvj731A3V\nb6ZKnWPu3AVotToa8jLpMrtmnKE78rmvzUzd9S9QKBQsXLjU6eX90J1rZGd566heN9Z87ixv+bZ8\n19c3JNmdcxo70f1a9oODgxw+vJ/y8lK8AsOJeGSrS5bHsFr6UKqdu/TJHe1Vt6i9fgybpZ/16x93\n27pvH3/8AeXlpSS+PG/UM5Na+wdH1f0GoOFyFeYLlWzYsJlZs+aO6rWOcscdy/vls8Vi4ciRjygv\nL8U7OJKIR7ai8Ql0ekyuzOfupgpqrnyCpbeDRYuWsWLFapdXeKqqKti372/4JQQR88To10J+mHyW\nrTYK3ruG3Gvlpz/9f9Dpxr/bn6flMwzNzv3FF5+hVOsIm/s4/pEpTo/JVfksyzKtpd9Qf+skEjJP\nPfks8fHuWS/6znk2LIgc9bqND5PPVceLaMtrYNu2XU77nz0tn2VZ5tSpL8jOvoraO4DQAKf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0G0q6urJTX1FJcuXcBqteLhF0Lo5Afs0pW3bVTGDKqunsJsaMbDw5NZsxKYMWO2XebWHExXrmRz\n7Ngh3LzdiUqOQ+/f9wO3xvxaCo7moHfXs2XLk4SEjLBDSzvnLHk2Gg18800G586d4fbtFnR6L4In\nLiAwcpbNz8hbzEaqr52m+noqVouJYcP8mDt3IVOmxNu9i7W9WSwWDh7cS0FBHkHTRjH6gb5dBVIU\nhaJPr1F/rYpJk2JZtSpxUPdZzpJnk8lEVtZ5UlNP09zchNZNT1D0XIKj56K18Ylwq9lI1fVUqq+f\nwWo24u3jS8Kc+cTHz3DIEyt3OnHic86dO4NPmD8RiZP7dbyhWKzkf5xDU2EdM2cmsHTpcju0tHPO\nkufm5ibS0v5RmOp9AgiNfQC/MZPt8vvQVH6T8uwvaK0r6yhE585dOCQGRxyo06e/4tSpk3gEehO5\naQpuXr3/jlrNVvKPXKa5uJ4pU+Ltfg/0d0lBamOtra1kZKSSnp6K0WhA7xPAiKkP2uRA3mo2Unn1\nNNU3zqJYzAQHh7BgwWKnKETbKYrCsWMHuXo1h8CpIxizrG9TtJhbTeTubesKtmZNEjExsXZqaeec\nZQfRrq6uljNnviY7OwtFUfAJiWDk1IfwHD7wokhRFBpLr1N+6XOMTTW46/XMmpnArFlzHfKMe1fO\nnz9HSspf0Qd4EbV5ap92ELcrmrj50SU0Vti8eavd5v/qirPl2Wg0kJGRRtq5MxgNtv19VhSF+sIs\nyrO/xNzahLePL/PmLiQubrrDF6J3Mhha+eCDPVRXVzFmWRSBU0f2+rnlZwupOFvEmDHhbN5s/3ug\nv8vZ8mwymbhw4TypqadoaWnGzXMYoZMXM/y+eBvm+QTm1ka8vX1ISJhPfPxMhy9E2ymKwscfH+D6\n9SsExIYy5sHxff7cSr7IpSarjKioaNav3zyog944W56bmho5c+bvZGWdx2q14h0Uxsi4FXgF2Oai\ngqGphvKsv9FY1jZmxqRJsSxY8IDDXhHtjKIofPnl38jISMV79DDGJU3p1byjd54sjI6eyNq1Gwd9\nAKfu8iyj7PaDm5sbY8dGEBc3HbPZ/O30GtnfTg4c3u9uYk3luRSc3kdT2XV8fHxYtnQFDz30CMHB\noU5TjMI/Jgq/efMG1XnluPv2fqoBRVEo/L+r3C5vYu7cBcycmWDn1t5rqI9611eenl5ER09kwoRJ\n1NfXUVFyk9r8b9rOlAeF97tbjel2AyXpR6m88hVWUyvTp89i/fpNREVNcKqDd2gb2dFsNlGYm8ft\n8iaGTwzuVVdHU7ORvIOXsLSaO0asHGzOlmedzo3w8PuIj5uBxWLmVtGdv8/9n7zd0FhN0dmPqMk9\nhxYrCQkLWLtmA2FhY4fUqIy24Obmxrhx47mcc5G6G5X4jh3eq65hDTdruJWSi5+fP8nJW/HwGPyT\nTs6XZx2jR4cxbdpMtFott4rzqC+5QnNlPt5B4f3uxmtorKYo9QA1uefQKFbmzp3P2rUbCA+PcMjb\ngbqi0WiIippAXt4NqvLK0Xm59Wnk3eqsUirOFhESEsqGDY8N+r7L2fKs13sQFRXN5MlTaWpqoKy4\n7XjDYryNT/DYfh9vKFYLlVdOUXLuMIbGKsLCxrJ+/WZmzJiNl5dj98L6Lo1GQ0REJLW11ZTmFWOs\nb8VvfM9THFWcLaQmq4wxY8JJTExW5Xsu077Yibu7O5GR45k4cTKVlRVU3Wr7Yul9AvD0C+n1dqwW\nM6UX/kpZ1t9QzEYSEuazdu1GRo8e41SF6J10Oh0REZFkX86iLrcKv6hA3Lx77oZUea6Y2ovlRERE\nsWLFalU+H2fbQbTz9vZh8uSpjB4dRsmtYmpv3aCh+DLegWG4e/XtLG1d0SUKT+3D0FBBePh9JCU9\n+u0AXI4zwEtf3XffOCoryynLL8FitDAsovuuQW3dwC5jqL7N/fcv/XbY/MHnrHl2d3dn3Li23+eq\nqsq2ydsLMvv8+6woCjU3Myg6ewBTSx3R0RPZsGEL0dExTnXg/l2enl6MHDGKy9kXacyvZfjkULRu\nXb9fY0Mr+Yey0Wp0bN60VbUrEs6aZ51Ox9ixEUyZEk99fT3l3x7Ia9088AoY3et9YVue0yk6+xGm\n5jrGj5/Ixo3teXauE4XtdDod48aNJyfnEnW5lfiE+6PvxQmWlrJGiv7vKl6eXiQnP6HKgE7OmmdP\nTy9iYmIJCxtLaWkJNbduUF+cjVfAKNy9+zZysaGhioLT+2gozsbHx5eVD69h8eIHh/RI0AOl0WiI\njIzumOJI5+GG96iu32/DzRpufXETf//hJCdvU20ebClI7czb25spU+Lx9R1GQX4udUXZWIy38Q2N\n7HEnYWypo+Dv/0tT2fWOM3CxsXFOfaDTztPTi6CgEHIuX6K5uJ6A2BHdXlVqKW2k6HjbfHabNz+u\n2uiVzrqDaBcQENhx9b+o4AZ1hVno9N54BYzu8blWq4XSC8epyP4SNzcdDy57mGXLVjjMyIwD0b6D\nuHHjKtV5FXgG++AZ2PXVi/IzhdRfrWLixMksXbpctZNPzp5nb29vYmPj8PHxpTD/JnVFl7CYWvEN\nGdfjZ241m7h1/hjV107j5enJI4+sZeHCB1S58qeG9vut8nNvYmowdDldl6IoFB67gqGuleUPPUJU\nVPRgNvMuzp5nDw8PYmJiCQkJpaAgj7qSKxgaqxk2MqrHq0tWs4mSjI+pvn4WL08vHnlkHQsWLHaq\n2ye64uHhyciRo8jOzqKpsI6AyaHddnO0GMzkH2qbnzExMXlQx6m4k7PnefjwAOLipmO1WiguyKWu\nIAud3gvPgFG92ic2lFyh8Mw+TC1t90Qmffv/ylkv5txJq9USGTmey5cvUnezkmGRgZ1O12VqNpJ/\nOBstWjZvflzV+2iHZEFaX1/Ps88+y5tvvsmJEydYunTpPRV7WVkZzz77LL///e/Zt28fZrOZ+Pje\nDR0/mF8o+Mfk7dHRMRQWFlBTch1DQyWew0diNd7G0sl/hsZqCk7vxdhUQ1zcdNat2zSk5160h8DA\nIFpaWijJKwQFfMd2PrKo1dx2Ncly20RiYjLBwb2/wmFrzr6DgPYzylGMHh3Gzdzr1BXnoFjM+IRE\ndPlDbzWbKDr7EQ3F2QQHh/Jo8lYiIqJcYsfQTqfTERZ2HxcvXaCpoJaA2NBOp85oKW2k+LPr+PsP\nZ8OGLap2YXaFPLf9Po8mOnoiBQV51JTcwNBQybDRE7uc8shiMlB4pu0WilGjxpCcvI3Ro+0/j+ZQ\nM2ZMOAUFeVTll+EV4oNHJydZai6WU5NVxvjxE1m8eJmq33lXyDNAUFAIkyZNobT0FlUlubRUFeAT\nEoFiNWM1G+/5z2xsoejsAZrKcxk9Oozk5K2MHh3mUr/P7SOX59/IxdRsxH9811N+3Poyl+biBubN\nW0hcnDq9V8A18qzVaomIiCQsbCy5udepLc7Bajbi08NFneobadw6fwydVsuqVeuZN2+R090O1BO9\n3oPg4GAuX75ES1kjgVNG3POZFX92ndaKZpYuWc748RNVammb7vKs2qBGr732GsOHD+eZZ57h7bff\npqGhgd27d9+1TmVlJVVVVUyaNInm5maSkpJ48803iYqK6nH79rwpuydGo4GDB/dRXFzYq/UXL36Q\nOXPm2blVQ5fRaORPf3qL+oZ6ordO63TC34q0IspPFzJ9+iwefHClCq38B2cbZKAn9fV17P/oA+pq\na9Dpvbo8gLdazFjNBiIjx7N27UanGRSjP86dO8OJE58TMDmUsOV3Xy1SrArXP8jEUN3Co48+QXj4\nfSq1so2r5dloNHDo0IcUFRW0ZbmLAx5FUUCxMnHiJFatSnSJXitdqaqq5M9/fhudjzsTnpxx15Ul\nS6uZq3sy0Clann76WdW7yblani0WC59++jE5Odm9Wj8mZjKPPLLeZfNstVr54IM9lJXdYlxSbKcn\nwZuK6sk7eInQ0JFs3bpd1c/K1fLc0FDPgQP/S3V1FV6BYV3OKa1YzTRX5OHj48umTY8N6sj0Q9En\nn3xMdnbWPYPQNRXWkXcomzFjwtmy5UnVT0B1l2fVTiWkpKTw/vvvA5CYmMi2bdvuKUhDQkIICWm7\nEubj40NUVBQVFRW9KkjVpNd7sHHjY6SmnqKpqfsvdts9TpMGqWVDk16vZ+nSFRw+/CGlX+UxLvHu\nUXNNzUYqz5Xg7e3NokVLVWql6/L3H86WR5/k+PFj1NXVdrvu2LFTWLp0hcse7LSbOTOB7OyLVF4u\nJ2jaqLsG7arNLsdQ3cLUqdNUL0ZdkV7vwYYNW/jii+NUVlZ0u254+FgWLVrqdIMW9VVwcAgzZswh\nPf0sNZfKCJ72j+77lRklWAxmFjr5PVtDlU6n45FH1hMSMoLy8rJu1w0NHcGcOfNdOs9arZbly1fx\n3nu/59aXN4neNv2uW4UUq0LpyZsALF++yuX3ZYOtbUC0Jzh0aB9lZcXdrus/PIBNG7cQEND1lW5X\ncf/9S7l6NYeK1KK2e0m/LTzLTrddGBvMuUb7S7WCtKamhuDgtvtRQkJCqKmp6Xb94uJirly5Qlxc\n3GA0b8Dc3d1ZuPABtZvhMKKiohk7NoLCgnyabzXgM9qvY1lleglWk4UFDzyg2o3Yrs7X15cNG7ao\n3QyHodVqWbx4GQcO/C8VqUWEPdR2lVRRFCrSinFzc5PfBxW5u7uzYsVqtZvhUBIS5pOZmUFVxi38\nxrUNWKRYrFRfKMXbx4fp02ep3ELXpdVqSUhYoHYzHMaIESOZOnUaWVnfUHelkoDJoR3L6q9V0VrV\nQmxsHKNG9TxugrA9Hx8ftm7djsnUfddhNzd3lz65cidf32HEx88gIyOV6+9n3rVs/PgJjBihzj3Q\nfWHXgvSpp56iqqrqnsd37tx5z2PdVe7Nzc08//zz/Pu//7tLDI7iijQaDfPn3982YlhGSUdBajGY\nqb1Yhu+wYUydOk3lVgrRexERkYwYMYry3FIu56betWz69FlyNUk4FG9vH6ZMiSMzM4OrezLuWjZj\nzmyX7qIvHM+8eYu4dOkClenF+E8IAjSAQmV6ccfxiFCPRqNBr5cLEH0xf/4itFotJpOp4zGdTsuM\nGXNUbFXv2bUg3bNnT5fLgoKCqKqqIjg4mMrKSgIDOx8i3mw28/zzz7Nu3ToefPDBXr92QIA3bt0M\nUS+GnuDgWL76ajS3cm+R83Ya0NZ9xmq2smjhQkaO7HzAI1cgeXZMGzcmcfLkSe68Vd/d3Z1Vqx7G\nz891C1LJs2NavXolHh5umM3mjsc8PDx46KElTjfXX19Inh1PSMgw4uLiyMzMJPv1s3cti4uLIzp6\nrEotU5/k2VENIzw8Ue1G9Juqgxr5+/uzY8eOLgc1AnjxxRcJCAjg3/7t3/q0fTVvyhb9d/PmdU6c\n+Byr1drxmLe3D4mJm/Hy6t8E4LbmaoMMCOcmeRbORPIsequmppqTJz/HbLZ0PObmpuP++5cSFKTe\nSP53kjwLZ9JdnlUrSOvq6ti5cyelpaWMGTOG3/zmN/j5+VFRUcFLL73EW2+9RUZGBlu3bmXChAlo\nNBo0Gg27du3i/vt77kohXyhhL7KDEM5E8iycieRZOBPJs3AmQ7IgtTf5Qgl7kR2EcCaSZ+FMJM/C\nmUiehTPpLs8yPJUQQgghhBBCCFVIQSqEEEIIIYQQQhVSkAohhBBCCCGEUIUUpEIIIYQQQgghVCEF\nqRBCCCGEEEIIVUhBKoQQQgghhBBCFVKQCiGEEEIIIYRQhRSkQgghhBBCCCFUIQWpEEIIIYQQQghV\nSEEqhBBCCCGEEEIVUpAKIYQQQgghhFCFFKRCCCGEEEIIIVShWkFaX1/P9u3bWbFiBU8//TSNjY1d\nrmu1WklMTOT73//+ILZQCCGEEEIIIYQ9qVaQvv3228ybN4/jx4+TkJDAW2+91eW67777LlFRUYPY\nOiGEEEIIIYQQ9qZaQZqSkkJiYiIAiYmJfP75552uV1ZWxsmTJ9m0adNgNk8IIYQQQgghhJ2pVpDW\n1NQQHBwMQEhICDU1NZ2u9/Of/5wXX3wRjUYzmM0TQgghhBBCCGFnbvbc+FNPPcEKiagAAA1LSURB\nVEVVVdU9j+/cufOexzorOE+cOEFwcDCTJk0iNTXVLm0UQgghhBBCCKEOuxake/bs6XJZUFAQVVVV\nBAcHU1lZSWBg4D3rnD9/ni+++IKTJ09iMBhobm7mxRdf5Fe/+lWPrx0SMmxAbRdiKJE8C2cieRbO\nRPIsnInkWahBoyiKosYLv/baa/j7+7Njxw7efvttGhoa2L17d5frp6Wl8cc//pHf/e53g9hKIYQQ\nQgghhBD2oto9pM888wynT59mxYoVnD17lh07dgBQUVHBP/3TP6nVLCGEEEIIIYQQg0S1K6RCCCGE\nEEIIIVybaldIhRBCCCGEEEK4NilIhRBCCCGEEEKoQgpSIYQQQgghhBCqsOu0L85u0qRJxMTEoCgK\nGo2GN954g9GjR3e6bklJCd///vc5duzYILdy6Kirq+N73/seGo2GyspKtFotgYGBaDQaPvroI9zc\nJI5qkjz3jeR5aJM8951keuiSPPed5Hlok0z3jbPn2bFbrzIvLy8OHz6sdjMcxvDhwzly5AgAr7/+\nOj4+Pjz11FP3rNf+4yQGl+S5byTPQ5vkue8k00OX5LnvJM9Dm2S6b5w9z9JldwA6G6C4pKSExx9/\nnKSkJJKSksjMzLxnnRs3brBp0yYSExNZt24dhYWFABw9erTj8Z/85Cedbt8ZFRYWsmrVKnbv3s3q\n1aspLS1l9uzZHcs/+eQT/uM//gOA6upqfvCDH7Bx40Y2b95MVlaWWs12OpJn25A8Dw2SZ9uRTKtP\n8mw7kuehQTJtG86SZ7lCOgAGg4HExEQURSE8PJzf/va3BAcHs2fPHvR6PQUFBbzwwgscPHjwruft\n27ePJ598ktWrV2M2m7FareTm5vLJJ5+wb98+dDodP/3pTzl69Cjr1q1T6d0Nrry8PF577TUmT56M\nxWK55+xO+9+vvvoqzzzzDHFxcdKFw8Ykz7YjeVaf5Nm2JNPqkjzbluRZfZJp23GGPEtBOgCenp73\ndDcwmUy88sor5OTkoNPpKCgouOd506ZN43e/+x2lpaUsX76c++67j7Nnz3L58mU2btyIoigYDAaC\ngoIG662oLjw8nMmTJ/e43unTp8nPz+8489XY2IjRaESv19u7iU5P8mw7kmf1SZ5tSzKtLsmzbUme\n1SeZth1nyLMUpDb2pz/9ieDgYI4dO4bFYiE+Pv6edVavXk18fDwnTpxgx44dvPLKKyiKQmJiIrt2\n7VKh1erz9vbu+LdWq8VqtXb8bTAY7lr3wIED6HS6QWubK5M894/keWiSPPefZHrokTz3n+R5aJJM\n948z5FnuIR2AzvqnNzY2EhoaCsCRI0ewWCz3rFNUVER4eDjbtm1j6dKlXL16lXnz5vHXv/6Vmpoa\nAOrr67l165Z938AQcudnqdFo8Pf3p7CwEKvVyt/+9reOZfPnz+e9997r+PvKlSuD2k5nJnm2Hcmz\n+iTPtiWZVpfk2bYkz+qTTNuOM+RZrpAOQGejWD322GP84Ac/4MiRIyxatAgvL6971vn00085evQo\nbm5uhISE8M///M/4+fmxc+dOtm/fjtVqxd3dnZ/85CddDoHtbL77Wf7oRz9i+/btBAcHExsbi9Fo\nBOCll17i5Zdf5tChQ1itVhISEnjppZfUaLLTkTzbjuRZfZJn25JMq0vybFuSZ/VJpm3HGfKsUVxl\nGCohhBBCCCGEEEOKdNkVQgghhBBCCKEKKUiFEEIIIYQQQqhCClIhhBBCCCGEEKqQglQIIYQQQggh\nhCqkIBVCCCGEEEIIoQopSIUQQgghhBBCqEIKUiGEEEIIIYQQqpCCVAUxMTHcvn27z89LS0tjw4YN\ndmhRmytXrpCUlERiYiJr1qzhxz/+MSaTqWP5/v37Wb58OcuXL+fVV1+1SxsOHz5MQUFBt+soisLz\nzz/PypUrWb9+PU8//TRFRUV2aY/omSPmuby8nCeeeIJZs2axceNGu7VB8ux4HDHPKSkpJCUlsWbN\nGtasWcOePXvs0gbJs+NxxDz3dCxiK5Jnx+OIeW5nNBpZtWqV3Y45HD3PUpCqQKPRqPJcaAtjVyIj\nI9m/fz+HDx/m2LFj1NXV8eGHHwJQVFTEG2+8wf79+/nss8/Iy8vj448/HlBbOnPo0CHy8/N7XC8x\nMZFPP/2UI0eOsHTpUl566SWbt0X0jiPm2cfHhx/+8If813/914BevyeSZ8fjiHkOCQnhrbfe4tix\nY+zdu5e9e/eSkZExoLZ0RvLseBwxz90tsyXJs+NxxDy3++///m+mT58+oDZ0x9Hz7KZ2AxxZTEwM\nzz33HCkpKRgMBnbt2sXy5ct7XNZdqNu99dZb/OUvf0Gr1eLt7c3evXsBMJvN/PjHPyYzMxOtVsuv\nf/1rIiMjqaqq4oUXXqC5uRmj0cjixYvZvXs3AK+//jrXr1+nqamJ0tJSPvzwQ4YNG3bPa+r1+o5/\nG41GWltbO77An332GQ899BDDhw8HYPPmzRw+fJh169Z1+R5yc3P5+c9/TmVlJQDbt29n/fr1bNu2\njalTp5KZmUllZSUrV67khRde4NChQ1y6dIlXX32V3/zmN7z44ovMmzfvnu1qNBqWLFnS8fe0adN4\n9913e/xMRfdcKc++vr7MnDmTtLS0Xn8+kmfH4kp5jouL61jm6+tLZGQkt27dYubMmV2+B8mzY3Gl\nPHe3rCuSZ8fiSnkGSE9Pp6CggKeeeoorV670+B5cMs+K6LeJEycqb775pqIoinLz5k1lzpw5SnV1\nda+WtbS0dLndQ4cOKcnJyR3r1NXVKYqiKKmpqUpsbKySk5OjKIqi/M///I+ye/duRVEUxWAwdKxv\nMpmUJ554Qvn6668VRVGU3/72t8qSJUs6ttOd8vJyZd26dcqMGTOUXbt2KSaTSVEURfnZz36m/OEP\nf+hY78KFC8ratWu73I7ZbFaWL1+uHD9+vOOx9tffunWrsmvXLkVRFKWxsVFJSEhQCgoKOpadOHGi\nx3be6V//9V+VX/7yl316jriXK+W5XWpqqrJhw4YetyN5djyumGdFUZQbN24o8+bNUyoqKrrcjuTZ\n8bhannuT9XaSZ8fjSnluaWlRkpKSlIqKil4dc7hqnqXL7gC19wUfN24csbGxXLhwoVfLunPixAm2\nbNmCl5cXAP7+/h3Lxo0bR0xMDADx8fEdfb8tFgv/+Z//ybp160hKSuLGjRvk5OR0PO/++++/aztd\nCQ0N5ciRI5w6dQqTycRnn33WqzZ/V15eHlarteOs1nffx8MPPwy0nc2PioqisLCwX6/zzjvvkJeX\nx86dO/v1fHE3yXPnJM+OydXyXFFRwXPPPcfLL79MSEhIl9uRPDsmV8pzX367Jc+OyVXy/Ktf/YrH\nH3+ckJCQXl3hddU8S0E6QN2FqzfB6ysPD4+Of+t0OsxmMwB79uyhsbGRAwcOcPToUZYtW4bBYOhY\n19vbu0+v4+npycqVKzl27BgAo0aNoqSkpGN5aWkpo0aNssn70Gq1WCyWPm/jvffe45NPPuGdd965\na3ui/1wlz7YmeR6aXCnP1dXVbN++nR07dtx1IDPQ9yF5HjpcKc+9WdZbkuehyVXynJGRwRtvvMGy\nZcv40Y9+xNWrV7u93a0v78OZ8iwF6QAdOnQIgPz8fHJycpg2bVqvlnVnyZIl7N27l+bmZgDq6up6\nfE5jYyMhISG4u7tTX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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971ea32e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "drawViolin(sam10k,2,2,21,26)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "6a30c5d2-6f4f-bd5d-a51c-c7e13d881385" }, "outputs": [ { "data": { "image/png": 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vz7VrhsrRUqvVbN++Bz+NBt21GurPl2OzOv9yjLPZzFZqT5bQ+k0DQUHBbN36\nskc8oNRREhKSWL58JebeTqryPsRk1Ls7JADMvV1Uff0Rpu425s1bRFraiycaHQ2ZTMaGDVsIDAyi\n5WYdjZcrkSZA3kqSRMutOurPl+Pj48umTdtRKkfXNyIKlW+RyWSsW5fLwoVLMRn1VFx6H33VXY/5\nkraa+6m/c5z6O8dRq1Ts3LnvhZOWjZZSqWTbtt2EhobTdr+RmpMlWE3OnzPCWfrae6j4vJBeXTcZ\nGVlkZ69yd0hOM3/+YubNW0R/VxsVl/8v/V1tbomjp62WyssfYuk3snr1S0ydOt3p+/T3D+D7r7xG\neHgk+gc6yvcXOP32S2fqaeyi9JP7GErbiI2NY9++vx520qzxauHCpSxevByTsYPKS/+X7uZKt8bT\n015HxaX36e9sYc6c+eTkrHHq/jQaLXv3/hUhIaG03Wuk4osHmLr6nbpPZ7L2Wag+XkzT1Wr8NBr2\n7v3BmCYmdfvMtM4y2pkSZTIZCQlJBAeHUFlRRkddEX0dTfiGxqNQjWzGR6u5j/bymwPeD0ldgELt\nPaJtdTdXUnNtPz2t1YSHR7Br1ytERTn3eq5arSY9PYOGhlpaqpowlLXjF+PvsNHh1n4LbfcG3u4X\nOjvaoXdudBS3UH2sGIvRxLx5i1i79nt2PetnPM7wCU9yODExGUmyUV3xGEPtA7wCwu2ewNAReauv\nukfdzUNIVjPr1uUye/a8Ef0OY+Hl5c306TPo6THSUFmL/oEOySbhG6VBNorHz3+XK/LWarKgu1pN\n/fkyrL0W5s5dSG7uVry8XpyT4zFvZTIZ8fGJ+Pr6UVFeQkdNITKZHN8Q+y/fOSJvJUmivfwW9bcO\nI1lMZGevYtky51zy+S612ovp07Po6NDTVFVPx6NmFD4qvMP8HLZ/V+SuoayNqqNF9DV3Ex+fyK6d\nrxAYGPTCnxsub0WhMoSwsHCmT8+kubmJloYK9FV3USi98A6KcukHx9LfQ+O9U+gKzyFZ+lm4cCkb\nN27Dz881DwZUKlVMnz4Di8VCbXkl+oc6kIFvlP+YPzzO/tBYeszUnSml+WYdSoWS3NytzJ9v/0PO\nxmOD/9SThj+JgIBAysuK6agpBMmGb+iLr3+PJW9tVjMN907RUpSHl9qLrVtfdst4CoVCQWrqVCIj\no6mpqaKjshVDcQvqAG/Ugd5jyl1n5q0kSRhKWqk+Vkx3TQcBAYFs2/Yys2bNtau4hvGdt1FR0SQk\nJFFZWY5BXowOAAAgAElEQVS+/jHGlir8whNRqF7cXo61vTX3dVN38xDtFbfx8fZh69aXycyc6dIH\nAiqVStLS0tFotFRXVdJR2oKxvhOfSI1D7mRzZu6auvqpO1tG841asMHSpStYty7XruIaRKEyal5e\n3mRkZKHRaKmpqcRQX0K3rgKfoGi7nqQ8lg+OJEkYagqouf45ve11hIdHsn37HjIysuxusBxFLpeT\nmJhMVFQ01dVV6Cta6KpsxydCM6beFWd9aP7S2BfRq+smOjqWXbv2ERc3sonAxnOD/1R4eCTJyal/\nbvhLMbbWoAlPHrZ3cLR529/ZSvXVTzHqygkPj2T37u8TFeX4uyRGIjg4hJkz52C1WqmvrqWjuIWe\nxk58wvxQjvIZQM7K257GLmpPltB2rxGZDRYvXs7GjdsIChrZozzGe976+weQmfnnnoW6Cjqq7qH0\n1uAdEDFs0TCW9tZQX0TNtf30dzaTmJjCrl2vEB7u3OdSDUUmkxEZGUVGRhZ6fTu66gb0hTqsJgu+\nkVrkytG3/87IXZvFRsvtempPltDX2kNMTCw7d+wlLW1kdzCJQmUMniZNZkYW3d2dNNVV/PlWSxO+\nIXHI5EMPaht1g//nGRXby2+ikMHy5av43vc2uf1W2qCgEGbMmEV3dxeNVXW0P2jGZrbgF+2PbBQP\nLHTGh8Zk6KP21GNa79QjQ86K7FWsW5eLr+/Ie6DGe4P/lEajJTMzC72+DV1dOYbaQrwDwod8ovJo\n8rajppDa6wew9HUxa9Y8Nm/eMapj7gwKhZKkpBTS0qbR0dFBc3Uj7YVNmLtN+ERoUKhHNjDV0Xlr\n6uyj/kI5jZcrMXebSEubxtatL5OWlj6qQbMTIW9VKhVTp04nICCQqqoyDHVF9HU24xeWhFw5eM/C\naPLWau6j4e4JWh5dQiGDlSvXsXr1ert7AZzJy8uLadMyiIiIpKGhHn1lC/pHzSi9laO+HOTI3JUk\nia6KdqqPF9NZ1oa3lw9r136PVavWO7y9dfvtyeOFRqNl06YdZGbO4uzZk7SV3qCzvpjoOblowh0z\nK5pks9Faep2Wojwkm5WUlCmsXv0SAQGBDtm+I3h7+5Cbu5WMjCzOnDlB650GDI/biF6ZjH+y4x7i\nOFI2q43WOw203KzFZrGRkJDE2rUbRnw2OlF5e/uwZcsu7t69xcWLZ6m++ilh05YTlp49pq5tm9VC\n4/2v6Ki6h0qtZmPuDpcMmh2N0NBwdu3aR2VlGRcvnqXtgQ5DSQuh82IJmxPt9MdFfJe130LLrTpa\n7zYiWW1ERESxcuXaEff8TVQymYzMzJnExSVw8uRR6upKKG+vJ2buZjQRyWPefk9bLXW3jmDuMRAZ\nGU1u7haCg188magryWQyUlOnkpCQzK1b18nPv0rd2TLaCpqIWpGEX7R7Tl772npovFxBd40BuVzO\n3LkLWLJkBd7eIxt7aS/RozJCQUHBZGU96Uquqymno6YAq6kXv9CEAb0rI6nw+7vaqLl+AENNAb4+\nvnzve5tZtiwHb2/PnMslMDCIrKw5SJJEXVU1HcUt9LUY8Yv2t/vx5I6q7o31nVQffYThcSs+3j6s\nW5fLihVr8PEZ290RE+HM9NtkMhlRUTEkJ6dSVVWBvv4xvfpGtJGpyBV/Od725q25p5Oaa5/S3VT6\n5FLPy98nNjbeafE7SlBQMDNnzkWj0dJYX0dHZSsdxS0o/VR4hfi+eAzPGPNWsknoHzZT82Ux3dUd\naDQa1qz5HmvWOOakZKLlrbf3k0vwKpWa6spSOmoKQJIGjLeyN28lSaKt9AZ1t48gWfpZvHg5GzZs\nwc/Pcyf5VCgUxMUlkJGRRU+PkcaqOvQPmzEZ+vCN0trdKzjW3LX2WWi6UkXd2TJMhj4SE1PYtu1l\nMjKyRn3r8VOiR8XBVCoVOTlrmDZtOidOHKG9/BbG5kriFu3ESzvyiryjppDGuyexWc2kp2eyevVL\n+Ph4ZoHybSqViuzsVUyfnsnZsyepK6+lu9ZAxJIEQmZGOn0QmrX/yYemvfDJ9NYzZ84lO3ulxxZ3\nniIyMpof/ODHfPnlIaqqyqjM+5CEJXtQ+do/u3GfQUf11f1Y+rrIyMhi7doNqFTOm7be0eRyObNm\nzSU9PZMbN65w+04+tace017QRPTqFLyDnXMLcK+um/oL5fTqulEqVSxduoL58xePq2PnDnK5nIUL\nlxAfn8ixYwdpKf6aXn0DsQu2j+huTJvVTP3t43TWP8LPT8OmTdvHVQ+Wv38AGzduY9aseVy48BW6\noiY6y9uJWBxPyMwoZHLntLmSJNFR3ELT11VYeswEBgaxatU6kpOnuGSwsehRGQONRsuMGbPo7++n\nrrqMjpoCvLShz4qVF1X4ks1G0/3TND+8iFKlJHfDFpYsyR53jZavrx+ZmTPRav2pra6io7yV7joD\nfjH+KL2H/l3GUt13VeqpOvIIY10noaFhbN26m9mz56Ic4vr1aEy0M9NvU6lUpKdnPsndqlIMdUVo\nI6eg9PJ9Yd72tNVS9fXHWE09rFixhhUrVo/bCciUSiWJicmkT8ugs9PwbOCizWp7cmfbIA3/aPLW\nZrbS+HUV9efLsHSbSE/PZPv23aSmpjn82E3kvNVqn4y3amnR0VxXTldTKdqoqShUXi/MW0t/D9VX\nPsHYXEFMTBy7d786prk93MnfP4AZM2aj0Wipq62ho7yVzsp2fCO1w97gMJrcNRn6qDlRQts3Dcgl\nOUuXriA3dyuhoWEOLVLEYFonUigUJCenEhISSkV5CfraByi9/PAJih72gyNTKKi7eQhDbSGhoWHs\nfvnVcVXZf5dMJiMiIorMzJl0dHSgq6pH/0CH0keFd/jgA79G2+A3XKx48swTi8SSJdnk5m51yjie\nidzgw5O/WXJyKiqVmsryYjobStBGpSGTyYbM2/7uNqqvfgI2C7m5W5k1a65Lb990Fh8fH9LTM4mI\niKSurhZ9ZQtdFe34Rg2cN2ikeWts6KTq8CO6qzsICgpm8+YdLFiw2GkDNid63iqVKqZNy6Cvr4+6\n6lK6Gh+jjZ4KMGTeSjYrVVc+ps/QREZGFps373TaeApXeXKjRzSZmbO+dTlIhwRPbnAYY5srSRLt\nhU3UfFmCSd9LcvIUduzYS2pqmlPuPBWFiguEhoaTmJhM6eNi9HVFKL38UGuCB/3gBCfPpeHOcbqb\nyp5MiLPr+2i1nvN8i7FQq9VMnTqdkJDQJ+MgSlvoa+tBGx844La6kTb4fa1GKg8/pLu6g7CwCHbu\n3Me0aRlOu117ojf4T8XExOHl5UVF2Z8fZhiVhr7ymwHr+cdMo/b650iWfjZt2j6hnjfzVHBwKFlZ\nc+jv76Ouogb9Qx0KbyU+EZpnDb+9eftkCvF66k4/xtpnYcGCxWzevJPg4BCn/g6TIW9lMhlJSSnP\nJjU06irQRE4ZNG+DkuZQd/MQfYYmZs+ex7p1ueO2B3AwarWaKVOmERUVQ3VNJR0VLRjrDGgSAlGo\nn29H7c1da5+F2tOltN5pQK1Ss379RrKzVzn1srpHFyp5eXn8x//4H/noo4/o7e1l7ty5zy3v7u7m\n9ddf59///d/55JNP8Pb2Jj39xc9ccEeDr9FoSU2dSnHxQ/R1RXhpw+huKh2wnrmvm+6mUpKSUtm+\nfTdqtWNmevUUMpmM0NBw0tMz0eka/zyrbRua+MDnJi0aSaFieNxK9dEiLEYzc+cuZNOm7U6/XXsy\nNPhPRUfHYjabqa0qxdTVjrmnY8A6Pe31WHoNrF27gczMmW6I0jUUCgUpKVOIjo6hsqIMfWkL5q5+\nNIlByOQyu/L26fN52gua0Gi17Nixl6ys2S6ZA2my5O3TSQ37+/uprSqlv6sVc49hwHqWHgPG1moy\nMrJYty53QvQADiYoKJgZM2bT0dH+ZGbbohZ8o7WotX/JB3tyt6+9h8qDD+hp7CI2Np7du18lNtb5\nD0ocLm/d+qwfm83GO++8w3vvvceXX37JiRMnKC8vf26djz/+mClTpnD06FE+/PBD/uf//J9YLJ77\nzJng4BB27dqHUqlE9+DsoOt0NRQTGRnN5s07xjxS2pP5+wewe/erLFiwBFNHH+X7C+iuHfgFOBxJ\nkmi+WUvNyRIUMgVbtuxi1ap1E/q4uUt29iri4xMxtlYNutzU1Upm5kxmzZo76PKJJikplR/84MdE\nRkajf9RM9bEibGbrC3/O2meh8tBDOsvbiYtL4Aev/nhc3A01XuXkrCEpKYWe1ppBl3c1lRIdHcv6\n9RsnbJHylLe3N5s372TVqvVP8vCLhxhK7X+4rrHeQMVnhZgMfSxcuJTdu191+/xd4OZCpaCggISE\nBGJiYlCpVOTm5nL+/Pnn1pHJZBiNTx4qZjQaCQwM9PgvqYiIKFavfgmbZfCzDJVKzaZN2ydcT8pg\n5HI5K1asJjd3K1gkqo4U0VnRbtfPSpJE05VqdNdq0Gr9eeWV10hLm+bkiCcvuVzOSy9tGvLz5evr\nx6pV610clXv5+wewZ88PSE5Opbu6g6pjRdhsQz/Z1mqyUnn4IT2NXaSnZ7Jr1ysue9zFZPU0b4dq\nTxUKJRs2bJlQl3uGI5PJmDt3ATt27EEpV1BzssSuYsVYb6Dq8CMks43vfW8z2dmrXD4L+lDcGoVO\npyMqKurZ64iICJqbm59b55VXXqGsrIxly5axZcsW3nzzTVeHOSozZswiMjJq0GXz5i2y6yFNE8n0\n6TPYvn0PCpmcmhMldNcN7KL9ruabdbTeqScoOIR9+/6asLDxOUJ/PAkICCQzc9agy+bNW+QRM3a6\nmkqlYuvWl0lNTcNYa6DpSvWg60lI1J4soVfXTWbmTHJzt06aL0d302i0zJ69YNBlWVmzJuXEj0lJ\nqbz88quoVGpqTz0etje7X99D1dEisMGWLbs87tKuZ5RLw7hy5QrTp0/nypUrHDlyhF/+8pfPelg8\nmUwmY/78JYMumz4908XReIakpBS2bduNTIKaY0WYOvuGXFdf3Ezz9ZonZ7S7X8Xf3/45PoSxmTFj\n8EJlypSpLo7EcygUCjZu3E5UVAxd5YP3CLYX6uiq0pOYmDyhx0J4qqHa1ays2S6OxHNER8ewY/se\nZMioPfkYc/fgvfz158uxmazk5m4lNTXNxVG+mFuvoURERNDQ0PDstU6nIzz8+bPmQ4cO8ZOf/ASA\n+Ph4YmNjqaioYMaMGcNuOyjIF6XSvWczcnkqx48PfD8szJ/g4Ilxl89IhYXNRCYzc+DAAeovVgy6\njsnQS8P5Cry8vPjJT348ICcmMk/IW4XCPOj7kzlvn/rrv/4Bv/3tbzGZBjb4rXfq0Wg1/OAH30ej\n8dxZTp3Bk/M2KipkUudtWFgm/f1bOHz4ME3XqgZdx9xlIicnh+XLF7k2ODu5tVCZMWMGNTU11NfX\nExYWxokTJ/jd73733DrR0dFcv36duXPn0traSlVVFXFxcS/ctl7f46yw7WYwDB5De7sRq3V8Term\nSAkJU5kxYxaFhfcGXd54pQab2cq6TVuQyXxoaelycYRPhIW5vnHzjLwdvMdysuftE0rmzVvEtWt5\nAxdJsDJnHb29Er297slZEHn7XSJvISUlg8TEAqqqygddHhQUzJw5S9zW1sLweevWSz8KhYK33nqL\n1157jY0bN5Kbm0tKSgr79+/ns88+A+Bv/uZvuHv3Lps2beKHP/whb7zxBoGBnvOQPmF0Vq5ch/cQ\njwnoa+4mNXXqhJynQxj/0tMHv8QQGhrqsQ9kFCY3mUzGmjVDD4RfunSFR4+ncvvtM9nZ2WRnZz/3\n3p49e579Pzw8nPfee8/VYQlO5uXlxYL5i8nLuzDo8pycNS6OSBDsM1SDnpExS4xLETxWUFAIKSlT\nKC8fOLdXVFSMGyKyn8cPphUmrtTUwQdnJidPmZSj9IXxLTk5xd0hCMKwhuql9vQCWxQqgtsMNV9H\nerq45COMPyrVxJ8XSRjfPL3nZCiiUBE8znj9MAmCIHgyT5nAbaTGZ9TChDZeP0yCIAiC44lvBEEQ\nBEEQPJYoVARBEARB8Fh2FSptbW384he/4JVXXgGguLiYTz/91KmBCYIgCIIg2FWo/P3f/z1z586l\ns7MTgOTkZD755BOnBiYIgiAIgmBXoaLT6di7d++ziY7UarUY8CgIgiAIgtPZVW18d76Lzs5OJEly\nSkCCIAiCIAhP2TWF/tq1a3n77bcxGo0cOnSITz75hB07djg7NkEQBEEQJjm7CpUf//jHHDt2jM7O\nTi5fvsyrr77Kli1bnB2bIAiCIAiTnN0PJdy8eTObN292eAB5eXn8+te/RpIkduzYwU9+8pMB6+Tn\n5/Ob3/wGi8VCUFAQH330kcPjEARBEATB89hVqLS1tfGnP/2JmpoaLBbLs/f/+Z//eUw7t9lsvPPO\nO3zwwQeEh4ezc+dOVq9eTUrKXx7u1dXVxS9/+Uvef/99IiIiaG9vH9M+BUEQBEEYP+wqVP7zf/7P\nTJ8+ncWLFw/5iPPRKCgoICEhgZiYJ892yc3N5fz5888VKsePH2fdunVEREQAEBwsnqorCIIgCJOF\nXYVKb28v//AP/+Dwnet0OqKiop69joiIoLCw8Ll1qqqqsFgsvPrqq/T09PDqq6+ydetWh8ciCIIg\nCILnsatQmTlzJiUlJUydOtXZ8QxgtVp59OgRH374IT09PezZs4fZs2eTkJDg8lgEQRAEQXAtuwqV\nPXv28P3vf5/IyEi8vLyevX/w4MEx7TwiIoKGhoZnr3U6HeHh4QPWCQoKwsvLCy8vL+bNm0dxcfEL\nC5WgIF+USsddphoNhcI86PvBwX4EB2tdHI3nEcdnIJG3nk8cn4FE3o4P4/UY2VWovPHGG/yn//Sf\nmD59ukPHqMyYMYOamhrq6+sJCwvjxIkT/O53v3tundWrV/NP//RPWK1WTCYTBQUF/PCHP3zhtvX6\nHofFOVoGg3HQ99vbjVitKhdH43k8/fiEhbn+gyvy1vN5+vERefs8T/m7eAJPPkbD5a1dhYqXlxc/\n+tGPHBbQUwqFgrfeeovXXnsNSZLYuXMnKSkp7N+/H5lMxu7du0lJSWHZsmVs3rwZuVzOyy+/TGpq\nqsNjEQRBEATB89hVqCxfvpy8vDyys7MdHkB2dvaA7e7Zs+e51z/60Y+cUigJgiAIguDZ7CpUDhw4\nwO9//3v8/PxQq9VIkoRMJuP69evOjk8QBEEQhEnMrkLliy++cHYcgiAIgiAIA9hVqMTExGCxWKis\nrAQgKSlpwBOVBUEQBEEQHM2uaqOwsJDXX3/92WUfi8XCv/zLv5CRkeHs+ARBEARBmMTsKlR+9atf\n8etf/5rFixcDcP36dd555x3279/v1OAEQRAEQZjc5Pas1Nvb+6xIAVi8eDG9vb1OC0oQBEEQBAHs\nLFR8fHzIz89/9vrmzZv4+Pg4LShBEARBEASw89LPm2++yd/+7d+iVqsBMJvN/O///b+dGpggCIIg\nCIJdhUpWVhZnzpx57q4flUpMSSwIgiAIgnPZdenn2rVr9PX1kZaWRlpaGr29vWKyN0EQBEEQnM6u\nQuXdd99Fo9E8e63RaHj33XedFpQgCIIgCALYWag8nTL/2Q/J5VitVqcFJQiCIAiCAHYWKn5+fty/\nf//Z6/v37+Pr6+uQAPLy8njppZdYv349v//974dcr6CggIyMDM6cOeOQ/QqCIAiC4PnsGkz7xhtv\n8F/+y38hNTUVgLKyMv71X/91zDu32Wy88847fPDBB4SHh7Nz505Wr15NSkrKgPV++9vfsmzZsjHv\nUxAEQRCE8cOuQmX27NmcOHGCe/fuATBr1iwCAgLGvPOCggISEhKIiYkBIDc3l/Pnzw8oVD766CPW\nr19PYWHhmPcpCIIgCML4Ydeln1/96lcEBASwYsUKVqxYQUBAAL/61a/GvHOdTkdUVNSz1xERETQ3\nNw9Y59y5c+zbt2/M+xMEQRAEYXyxq1C5ffv2gPdu3brl8GAG8+tf/5o33njj2WtJklyyX0EQBEEQ\n3G/YSz+nTp3i1KlT1NfX87d/+7fP3u/u7sbb23vMO4+IiKChoeHZa51OR3h4+HPrPHjwgJ/97GdI\nkoRerycvLw+lUsnq1auH3XZQkC9KpWLMMY6FQmEe9P3gYD+Cg7UujsbziOMzkMhbzyeOz0Aib8eH\n8XqMhi1UkpKSyMnJobCwkJycnGfvazSa5x5SOFozZsygpqaG+vp6wsLCOHHiBL/73e+eW+f8+fPP\n/v93f/d3rFy58oVFCoBe3zPm+MbKYDAO+n57uxGrVczs6+nHJyzM9R9ckbeez9OPj8jb53nK38UT\nePIxGi5vhy1Upk2bxrRp01i1ahWBgYEOD0yhUPDWW2/x2muvIUkSO3fuJCUlhf379yOTydi9e7fD\n9ykIgiAIwvhh110/b7/99nMTvj31z//8z2MOIDs7m+zs7Ofe27Nnz6Dr/uY3vxnz/gRBEARBGD/s\nKlRWrlz57P/9/f2cPn16wC3EgiAIgiAIjmZXobJt27bnXm/fvp0f/ehHTglIEARBEAThKbtuT/4u\nmUyGTqdzdCyCIAiCIAjPsatH5fXXX382RkWSJEpKSliyZIlTAxMEQRAEQbB7jIpMJsNoNKLVavkP\n/+E/kJWV5ezYBEEQBEGY5OwqVObOncsvfvELioqKAMjIyOB//a//RVxcnFODEwRBEARhcrNrjMo/\n/MM/8PLLL1NQUEBBQQG7du3i7bffdnZsgiAIgiBMcnYVKu3t7ezcuROZTIZMJmPHjh20t7c7OzZB\nEARBECY5uwoVuVxORUXFs9eVlZUoFO59roMgCIIgCBOfXWNUfvazn/HKK6+Qnp4OQHFxMe+++65T\nAxMEQRAEQbCrUMnOzubEiRPcv38fgJkzZxIcHOzUwARBEARBEOwqVACCg4Ofm0pfEARBEATB2UY1\nM60j5eXl8dJLL7F+/Xp+//vfD1h+/PhxNm/ezObNm9m7dy8lJSVuiFIQBEEQBHewu0fFGWw2G++8\n8w4ffPAB4eHh7Ny5k9WrVz/3wMO4uDg+/vhjtFoteXl5vPXWWxw4cMCNUQuCIAiC4Cpu7VEpKCgg\nISGBmJgYVCoVubm5nD9//rl1Zs2ahVarffZ/8YyhiU+SJHeHIAiCIHgItxYqOp2OqKioZ68jIiJo\nbm4ecv3PP/+c7OxsV4QmuJHBoHd3CIIgCBPOeD0JdPsYFXvduHGDQ4cO8Ytf/MLdoQhOVlVV8eKV\nBKcZr42Zu/X0GN0dgiAMq729zd0hjIpbx6hERETQ0NDw7LVOpyM8PHzAesXFxbz99tv84Q9/ICAg\nwK5tBwX5olS6d1I6na560PcDArwJC9O6OBrPYzQO/qGpqChlx46tz57YPZl4Qt5WVDwa9H2zuZuw\nMPF8L4XCPOj7LS31ZGVNc3E0nsET8nao9sRm6xV5+2fXr9cO+n5wsB/BwZ77neTWQmXGjBnU1NRQ\nX19PWFgYJ06c4He/+91z6zQ0NPD666/z7rvvEh8fb/e29foeR4c7IpIkcebMuUGXXbmSz7JlK1wc\nkee5di1/0PdbW1u5e/chcXEJLo7oee4oJt2dtxaLhTNnzg667NSp04SExEzKAvLbOjq6B33/7t37\nZGUtcHE0A03GvAU4c+b8EO+fIzAwctLnrSRJz+ZC+y6drgOrVeXiiJ43XN669dKPQqHgrbfe4rXX\nXmPjxo3k5uaSkpLC/v37+eyzzwD4t3/7NwwGA//9v/93tm7dys6dO90Zst3u3LlJU1PDoMtu3rxG\nW1uriyPyLP39fTx6VDjk8vz8ay6MRnjq/Pmv6OrqHHRZU1MD+flXXRyR56mtrRr0/ebmJhoa6l0b\njABASUkRZWWDT11RU1NFYeE9F0fkecrKSujq6hp0WXHx4L2onkImTdAL0i0tg/9BXKGiopTDhw8g\nU6qxmvoGXScwMIh9+/4aPz+Ni6PzDFeuXOL69a8HXeYToaFX183evX9FbKz9vWiO5o4zU3fm7c2b\n17h8+TxqbSimroGFtNLLD6uph9zcraSnZ7ohQveTJIkPPvj/aG1tGXR5YmIKu3btc3FUz5tsedvY\n2MBnn32ExWpDslkGLJcr1cgkKzt37iM+PtH1AXoASZL46KM/oNM1Dbrc19ePn/zkp6hU7utV8dge\nlYmovLyUo0cPIiEnctaGQdcJSppDR4eeAwc+HvLsdSLr6NBz89Z1FN6DX3kMnRcDwPnzp7HZbK4M\nbVKSJIkbN65w+fJ5VD5aomcPnrdRs3ORKdScOHGEhw8LXBylZ3j4sGDIIsU3WktVVTnl5aUujmry\namio5+DBTzBbzERmrRl0nahZL2GT4NChz6iurnRxhJ6hqOgBOl0T2uSgQZf39Bi5ffuGi6OynyhU\nHESSJO7cucnhw59hlSBu0U58g6IHXTdkymKCU+bT2trMn/70Pk1NjS6O1n0kSeLs2VNYLRbCFsQO\nuo5vuIbA9DCam5u4c+emiyOcXMxmM6dOHePrry+i9NGSsPxVVD7+g67rHRBOwrK9yBRqTp48Sl7e\nhUlVSBqN3Vy8dBaZcvBmM3xBLDK5jLNnT9Lf3+/i6Caf4uKH7N//R/r6+4iZsxFNROqg6/mGxBM7\nfxsWq5WDBz+hoODupLqzrbe3l4sXzyBXygmbEzPoOgpvJddvXEGvb3dxdPYRhYoD9PT0cOTIAS5c\nOI1C7Uvi8lfRRg7+oQGQyWREZq0jInM13d1dfPzx++TnX5sUH56CgrtUVZWjSQjEP2XoB1tGLU9C\n6aPi6ysXJ/14HmfR6Rr56KM/8PBhAT5B0STnvIaXZviHjfoGx5Kc80PUfkHk519l//4P6eiY+PPe\nSJLEyZNH6evtJXTu4CcgXkG+hM2Poaurk7NnT0yKz7M7mEwmTp/+kuPHDyHJ5MQv3k1gwsxhf8Y/\nZhoJy/YhU3px+vSXnDhxmP7+wS/LTySSJHHu3Cl6enoIXxiHSus16HrhC+KwWix89dUxjzz5EIXK\nGNWHrMwAACAASURBVEiSxIMH93n//f9DWdlj/MISSV71I3yDB69av00mkxGatpiEpXuRq33JyzvP\nJ598QHPz4NcQJ4K2tlYuXDiDwktJzJpUZAw9Cl/pqyJ6dQpWi4Xjxw9hsQy89iyMjtls5uuvL/Kn\nP71PW1srwSnzScx+FZWPfWMbvPxDSV75Gv4x6dTX1/HBB7/n9u18j2zgHOXatTyqqirQJgYRNH3g\nFApPhS+IwydSS1HRQ+7du+PCCCeHyspyPvjw9xQU3MU7IIKknB8Oe1L4bX6hCSSvfA2f4BiKih7y\n/vv/L6WlxRO6oHz4sIDi4of4RmkJnTv095I2JQj/1BDq6mq5ceOKCyO0jyhURqmpqYH9+//IqVPH\n6DeZiZixhoRlrwzZbT4UTUQKKat/jH9MOg0Ndfzxj3/g/PnT9PS4/3Y/RzKZTBw9ehCLxUzMmhTU\nQ1T23xaQGkJQZgQtLTouXDjjgignNkmSePy4iPff/z/cuHEFhZcfCUv3EjVzPXLFyAbRKdQ+xC7Y\nTsy8zdiQc/HiGT788N+pqalyTvBu9PhxEdeu5aHSehG7fsqwBbZMISd+w1SUPiouXDg9IY+HOxgM\nHRw79gUHD36CwdBByJRFJOX8EG//sBFtR+0XRFL2XxGWno2xx8iRI59z+PBn43YitOE0N+s4e/Yk\nCrWCuJfSkMmHyVtkxKxJQaX14urVyx436aZb51EZj/T6dq5cuURx8UMAtFFpRM5ch9o3cNTbVHr5\nEbdwB926chrvfcU339zkwYP7LFy4hDlzFqBWqx0Vvls8GZdykra2FkJmRhEwJdTun41ekURvUxf3\n798hJiaWjIwsJ0Y6cdXX13Lp0jkaGuqQyeSEpi0hbNoy5MrR55ZMJiMwPgtNRAq6Bxdprb7HZ599\nRErKFLKzVxEaOnTPw3jR0FDPlyeOIFcpSNicjtJHhclkHfZn1P5exOdOpfLQQw4fOcAr+/56QhwL\nd+jr6+XGjat8881NrFYrPkExRM3+Hj6BkaPepkwuJzw9m4DY6TTeO0V5eSmVleX/f3v3HV1VmS/+\n/31aeu89hFBSIKEmEDpSRMAQQLHAOOKoU7561ety3Tv3OjrqUue67vxmrXGcUcdhRlRQERhABtBA\nQk0ChAQCCSW9956TU/fvj0guSHpySpLntZZrmZx99vPs8Dn7fPZTiY2dxfz5i3F0dBzBK7AMtVrN\n/v1fodfrCV0fgY2rXb/vUdqpCFk7lYKvr3Dw4Dds2/Yz3Nx6HnxrbiJRGaDm5ibS0k5z5UoWkiRh\n5+aP77TlOPmEjVgZTr7hhK94lsbCTGrzTnHq1AkuXswgPj6B2NjZFp06NhyXLl3g2rUr2Ps54bdo\nwqDeK1cpCFkbwa1d2Rw79i3e3j74+Az9JjXeVFdXceZMSvdMFOeAqfhGL8fW2XPEylDaOhI4ex3u\nYTOpzkkmP/8mBQW3iIqaTkLCYqu52Q1WQ0M9e/fuwmDQE/pgJPbeA/8CcwxyJXDlJMqO3mTPnl08\n/viTODsPrrV1PNNqtVy8mM7582loNJ2oHFzwi1qGa/C0EVu4zdbZi9CFW2mtuE51TjKXLl0gJyeb\n2bPjmTt3PnZ2/X+5WyOj0cjBg9/Q3NyET3wQLuED/6w7+DkTsGwi5d/ns2/flzz++JPY2PTf+m1q\nIlHpR1eCcoacnCyMRiM2Tp74RC7GJSjKJCsdyhVKPCfF4RYaQ92NNBryMzhx4jsyMs4RF5dAbOys\nUZWwVFSUceLEMZT2KkLWRiDvZcZEX2zd7QlePZnig3n885972LbtZ6P2JmIudXW1nD2byvXruQA4\neAbjO205Dp6mW0rcwSOQCYu20VZ1i+qrJ7h69TK5uTlMmzaD+fMX4uIysO0vrEFbWxt79nyBWq0m\n8L5wXML6HmTcE/dIH3StWqrPFrNnzxc8+uhPRdz2Q6fTcenSBTIyzqJWd6Cwscd32n14hM9Frhj5\nryuZTIZLYATO/pNpKLxE3fXTpKWd5tKlC8ydO49Zs+KwtbX8F/VgnDjxHcXFhThP9MBn3uDXofKY\n5oe6tp267Cq+/fafbNjwkMVX9RWJSi+amhpJSzvN1auXf0hQPPCOWIRrcDQymemH9ihUdvhGL8Vz\nUhz1N9NoKLjAiRPHSE8/w9y585k5c47VJyxqtZoDB77BaDQSuiZyQONSeuMS7on3nEBqL5Rz5MhB\nEhM3W/zDY40aGuo4e/YkubldXZP27gH4RC3B0WeiWf5eMpkMZ//JOPlNoqX8GjXXTnL5ciY5OVnE\nxMxi3rwFVt+yoNVq2bt3V9cT6bxgPKYPvQXPe24g+nYtddmV7N//FZs3P4ZSKW67P6bT6cjOziQ9\n/QwdHe3IlbZ4Ry7Gc1I8CpXpEwWZXIFn+BzcQ2NpKLhA3Y2znD6dwoUL6d0Jy2jogs/JySYzMwNb\nTweC75885M98wOIwNPVqbt26zrlzp0hIWDzCNR0c8Yn5kebmJs6dO/V/CYqzZ1eCEhRllgTlx5S2\nDvhOW47n5HnU30qnIf88qanfc/58VwvLjBnW2SXUtdfRIVpbW/CZF4xTyNDH8NzmmxBKR2UrN2/m\nkZ2dyYwZs0egpmNDS0szZ8+eJCcnu6tr0tUPn6glOPlNskhCJ5PJcA2KxiUgkubSHGryTpKVdYEr\nV7KYNWsu8fEJ2Ns7mL1e/TEajRw6tJfq6ircp/niEz+8FiiZTIb/kjB07VpKbxXz3XeHuf/+9SLJ\n/oHBYODKlSzOnTtFW1srcqUNXlMX4jk5HqWNvdnrI1eq8JoyH/ewWTTkn6f+ZhqnTp3gwoV05s9f\nSGzsbKtNNGtqqjh27FsUtkpC10egsBl6PWUKOSFrp3Lri2zOnEnF3z+QsLDwEazt4FjnX9wC1OoO\nzp07xaVLF7oTFJ+IxbgERVokQfkxpa0DvtHL8PohYam/lUFKynecv5DGooVLiY6OQS63fD1vy8u7\nyo0beTgGuuATNzLdDTK5jKD7p3DrsyxSUr4jLCwcV9fhJ0CjmVarJS3tFBcupGMwGLB19sInainO\nAVOt4stQJpfjFhqDa3A0TcWXqc07yfnz58jOzmT+/EXMnh2HQmHZXXfvdOZMKvn5N3EKcSVw2ci0\nQsnkMoLvn0zB1xpycrLx9vZlzpz4Eajt6CVJEvn5N0hJ+Z7GxgZkCiWeU+bjNXk+SlvLJ7AKlS3e\nEQvxCJ9D/c106m+lc/z4Mc6fT2PJkvuIiIi2is/XbTqdjgMH92IwGAhdG4mt2/CTPKW9itB1EeR/\neZnDh/fz058+a7EtX8Z9omI0Grl06Tynz6Si1WhQObrhH7nEbF08g6Wwsccnaikek+Kpv3GO+vwM\njhw5yIUL6axc+QBBQZbfzlyj6SQ5+ShypZygVZP7nBY3WDbOtvgvCaPs2E2Sk4+wceMjI3bu0SYv\n7yrHT3xHe1srKnsX/KKW4Boy3SrjViZX4B42E9eQ6TQUXKTu+mlSU7/n8pVLrFyxhtDQkRuUPlRF\nRQWkpZ3umrXzQAQyxcj9HeVKBaHrI7j1RTapqd8TFBSMn1/PC8eNdY2NDXz33eGu5exlMtwnzsZ7\n6sIBr+NjTgqVHT5RS7pWEr9xlob88xw6tI9Ll86zcuVavL2tYzbXmTOpNDbU4znDH5eJgx9P1Rt7\nXyd8F4ZSdbKI48ePsn79phE792BY/I528uRJ7r//flavXs1HH33U4zFvvfUWq1atIjExkdzc3BEr\nu76+jl27/sHx48cwGGX4xaxk0oqf42alN/s7KW3s8Z22nMkrf4lbaCx1dTXs2vV3kpOPotVqLVq3\n8+fTUKs78I4LGtC0uMFyi/TGMdCF/PyblJWVjvj5rZ1G08mhQ/s4eHAvHR1qvCMWMWnlL3ALjbX6\nuJUrlHhNjmfSql/iMXEOjY0NfPXVZxw/fsyii/rpdDqOHj3U1frxwNRe96EaDpWTLUGrp2A0Gjly\n5NCYXiCvJ5IkkZV1kb///SOKiwtx8plI+H3PEDBjjVUmKXdS2jrgN30Fk1Y8i3PAVMrLy/h051/J\nyDhn8X/HhoZ6LlxIw8bFFr8FoSN+fq8ZAdj7OZOXd81i6wJZ9K5mNBp58803+eSTTzh06BDffvst\n+fn5dx2TmppKSUkJx44d44033uC1114bkbILC/PZufMTKirKcAmKYtKqX+A5Kd4kI8tNSeXgQuDs\n9YQteQJbZ08yMzPYvfsftLW1WaQ+er2ezEvnUdqr8JppmidGmUyG7w8fyPPnz5mkDGvV0tLMp5/+\nldzcHOw9Apm04hl8opYgV1rfOKW+KG3s8Z9xPxOXbsfGyZOLF9PZvftTiy1rnpmZQUtLM54z/XHw\nM92XpnOoG+5RPtTWVpOTk22ycqyN0Wjk6NFDfPfdYSSZgqC5GwhZ8OigF2yzNBsnD0LmPUTI/IeR\nK+1ITf2egz90uVhKWtppJEnCb9EE5KqR70aVyWUELO1q8TxzJnXEzz8QFk1ULl++TGhoKIGBgahU\nKtauXUtycvJdxyQnJ7NhwwYAYmNjaW1tpa5ueHu/FBTcZO/e3egNBoLmJhEctxGl7ehe5MfBM5iJ\ny5/GLXQG1dVVfPHFDtRq869um59/A01nJ25RPib50NzmGOCCnZcjBQU3LXKdltDe3sbuL3fS1NTY\ntTLn4iew6WdvHmtn7+5P+PKncA2OprKynK+++hydTmfWOhiNRjIvnUeuUozYeKq++CaEIJPLuHgx\nY0wv336bJEn8618HuHIlCzs3f8JXPDOi66FYgrP/FMJXPIODVwg3buSyf//XFvm31Gq15F2/ho27\nHS6TRm5tpB9z8HPGMdiVsrISi+ztZdFEpbq6Gn9//+6ffX19qampueuYmpoa/Pz87jqmurp6yGV2\ndnZy5MghJGSELnwc1+DoIZ9roFQqFV5eXiafnSNXKAmYtRavKQk0NzdZZNn50tISoGv5e1NzmeSB\n0WikoqLM5GVZg9OnU2huasRr6gJ8p92HzMSDp80Wt0obAuck4ho8jaqqCi5eTDdpeT9WXV1JW2sr\nrlM8UdiavkVV5WSLc5g7dXU1NDc3mbw8SysouNm14KN7ABMWbR30NiODZa64Vdo6EprwKI7eE7qv\n0dyKiwsx6PW4TvYyeeLnNrWr9ev24pHmZN0d2iZw40Yu7e1teE1JwNFr8IvhDJZKpSIxMZFXXnmF\nxMREk394ZDIZPlFLsXP15dq1K6jVapOW92P19bUA2A1iFc+hur1SaF1drcnLsrTOzk6uXMnqmo0W\ntcTkNyXzx60c/xlrUNjYc+GCeROVqqpKABwDzbcgnUNA15d1dXWl2cq0lAsXMgAImL3e5GuimDtu\n5UoVAbPXIZMrzB630LVuEmDS7srb7H2d7irTnCw6IMPX15eKiorun6urq/HxuXsUtY+PD1VV/7ej\ncFVVFb6+vv2e293dAaXy3q4Hvb7ri9vBM2io1R4wmUKJq6srcXFxAMTFxZGSkoLMxONgZHI59u4B\ndDZXo1Tq8fY250A1IzK5bEAr0Mp6Oaa33/+Y/Id1AlQqmZmv0XR6i9uGBh2SJGHvHmDyAbOWiluF\nyhZbZ086GsrN+u9pa9v19xzoANrhxu2dZdnZKcZE7PYWtwAaTQdypS22zgPf42soLBW3Ng5uKO2c\n6OzsMPu/5e0lXRS2A+tmH07s3m5tlMmM5r9Os5b2I9OnT6ekpITy8nK8vb359ttv+f3vf3/XMffd\ndx+ff/45DzzwAFlZWbi4uODl1X/ANzb2PG7B0bFr3Y2Wyhs4+Zp2ARuVnRMdWomMjAzi4uLIyMig\nQwu+dqadi2406GmrzkelUmE02lBb22rS8u6kUKiQjBIGjb7fZnSVow027nZoG/9vAKWtuz0qx4Gt\nAKlXd41lMBoVJrlGS3yB9Ba3Wq2ESqWivaYQo0E36N2OB8NScatTt6JurMLVxdWsMavXd7VO6doG\nNltuuHELoG/vKkurZcSv1ZriFsDFxY3q6mo66ktN2optqbjtbK5B19GMb1CIWeMWwGDoil29emAz\n5oYTuwb17bFj5r/fWjRRUSgUvPrqq2zfvh1Jkti8eTPh4eHs3r0bmUzGli1bWLJkCampqaxcuRJ7\ne3veeeedYZU5adJUXFxcaSy4iL2bP+4TZozQ1fTMf+4mDh/bS0pKCh1a8J+70aTlSUYjZRn70Klb\nmD3b/PtUeHp6U1iYj7q6bUCr0YaujaDk2+toGtXYutsTsnbqgMtSV7f9UKZpn9SsgY2NDTNnziUj\n4yzlFw8SNGeDSceomDtujXotZRl7kYx64uMXmLSsH/P375qd1l7ahGfMwJbLH07cArSVNN9V9lg2\nZ858bt68Tvn5/UxY8gQ2DqbrYjN33Oo72ylN3wNAXNx8k5bVE1/frjGe7eUtAx4XONTYba9oAbDI\n+j8yaYwOO+8r4+tac+RTOjvVeE1J6Orzl5t2dUyDrhOFyrQbkuk6Wii7sJ+OuhJCQiawadOjZl/u\nuaiogK+//hyP6X4E3jfwFquBtMDcSZIkbvw9E2O7gf/3//7dJH3Rlngy7StutVot33zzBWVlpTj7\nTyFg9nqTLzNujrjVdjRTlrEPdUMZU6dGsW5dkllXWZYkiR07PqShsY6p22ejchp4cj/YuAXorO/g\n5s5LBAWF8OijTwy2uv2ytrgFSE8/w8mTx1HZuxAUl2TSzTHBPHGrbqqiLGMv2rYG4uISWLLkPpOW\n1xOdTsef//z/YVAYmfrUHOSDWKRwMLErSRK3vshGU9fBs88+b5L9uvqK23E3mBbAy8uHhx/eiqur\nG3U3zlKY+g/UjaYd1GbKD40kSTQWZ5N//GM66kqYPHkqSUkPW2RPiuDgUJydXWjKrenumhmIwd7s\nW/Ib0DZ3EhERZZV7HZmCjY0NmzY9SkjIBForb1CQ/BFtNQUmLdPUcdtcmkNB8seoG8qIjJzG2rUb\nzL4VhEwmY86ceCSjRNXp4kG9d7BxK0kSlScLAZgzZ96g3juaxccvYPHi5eg7Wyk8+Sk111IxGky3\nwJ9J49ZooO7GOQpTdnQnKYsXLzdZeX1RqVRMnz4TfYeOxpzBzYYdTOy2FjXSWdvO5MlTLbKpqOL1\n119/3eylmkFHR9/9zU5OTkybNoOWlmYqS/NpLLqEXt2KvXsAcqX175J5W0dDGWXp39BYcBGFDJYv\nX83SpSsstnGWXC5HLpdTkH8LSW/EOcx9xMuQDEZKDl/HoNazdm0SDg6mmWHk6Gj+7d37i1uFQklU\n1HQUCgXFhTdpKrmCpqUWe49Akz9BjqTO5hrKMvZRfysdhRxWrnyAhQuXWmy/Km9vXwoKblJfXIO9\ntyO2HqbZb6bxWg31mRWEhoaxaNEyk8zessa4BQgKCiE4OJSiogKaKm7SUnYVG0d3bJw8Rs2aKm01\nhZSe+4rmsqvY29mTmLiJmTPnWLT+3t6+ZF/OpK28Cfco3xFfv8qoN1B86DpGjYH16zfh6Gj+++24\nTVQAlEolU6ZEEhgYTHV1JQ2VBTQWXMSo12Ln5mfSAYvD1dlUTUXmt1TnHEff2UpERBRJSVuYMGFk\nNlIbDh8fP/LyrtJQXIvzBPdBNaUPRO3Fcppv1BEbO5vp0003xshab/gymYzg4FDCwydTW1tDXUUB\njQWZGPQa7Nz8rTpudR0tVF35jsqsw+g6mggPn2wVcSuTyQgICOZKTjYtRQ24TvYa8WX0O+vaKTmU\nh0plw+ZNj2JnZ5puO2uNWwBXVzdiYmag1xsoL8mnuTSH9roSbJ09Tb6+ynCom6qouHiQ2txUDNoO\nYmNnsWHDQ/j4DGxMkynZ2NigUCgpvHULbXMnrpM9R/SzVHmqiLbCRmbNimPatNgRO++PiUSlH25u\n7sTEzMLR0YmqqnKaq/JpLMhEMuiwdfW1qht/Z3M1ldlHqbp8FG1bA4GBwaxbl8TcufOxtbWOJ2q5\nXI6Xlw9Xcy7TUd6Ce7TPiG3wpq5tp+zIDRwcHNmQuNmk3T7WfMMHcHJyZvr0Gbi6ulFZWUZzZT6N\nhZkgSdi5+lrVdhB6TTs1uScpv/BPOhsr8PDwYs2aB1mwYAn29qYdZzNQjo6OODo6cfN6Hm2lzbhF\neA9omv1A6Du0FOy5iqFTz7p1SQQGmm6MhrXHrVKpJCwsnClTImhpaaGmvICmoiw6m6qwcfZEZeJZ\nOoOhaamjMvsIVdlH0bY3EhwcSmLiQ8TGzrKqLmd//wBKS4upLapC6aAasXVVWvLrqUwtwt3Dk8QH\nN5t0p/O+4nZcDqbti06nIyvrAunpZ1GrO5ArbfAIn4vnpHiLbj+ubqqiNu8UrRXXga7R3osWLWXC\nhHCLt6D05vjxY1y8mI57tA9BKycP+3wGrYH8XdloGtVs2vQIEycO/5x9scZBib3R6/VcunSBtLTT\ndHaqUdjY4zVlPh4T51i0K1Ov6aD+ZhoN+ecxGnQ4O7uwcOFSoqKmW6ybpz/JyUfIzDyPU4groYlR\ngxqg2BOjzkDBnhzU1W0sWLCEhITFI1TTno2muAUoKyvh5Mlkysu7Vph2DojAJ3Ixdq6W25lY09ZA\nbe5JmktzAPD19WPRouUWb/nrS9c+YB/TqekkbPM0HAOG10KlaVSTvysbmVHG448/afLWo77iViQq\nvdBqtWRnXyQj4xwdHe1dCcvEOXhOnmfWhEXdVEVt7klaK28A4O8fSELCYsLCrDdBuU2v1/PFFzuo\nrq4iaNVk3KOGfuORJInSIzdovl7HnDnzWLZs5QjWtGej7YYPoNFoyMzM4Pz5NDSaTpS2jnhNTcA9\nbLZZW1gMuk7qb6ZTfysdo16Lo5Mz8+IXEBMz02LjpwbKaDSyf/9X5OffxC3Cm6DVk4f8WZOMEsUH\nc2ktbCQ6OoY1ax40+ed2NMatJEkUFRVw5kwqlZXlALgEReETudjkC8XdSdveSG3uKZpKr4Ak4e3t\ny4IFS5g0aYrV32+ha0n9r7/+HIW9kkmPxqJyHlrrmkGjJ3/3ZTSNah54IJHo6JgRrum9RKIyDDqd\njuzsi6RnnKOjvQ250gbPyfPwnBRv0uWgNa111FxLpaU8F4CAgCASEhZbdUbfk6amRv7x6cfo9DrC\nt0zH3mdozbp1WRVUphQSEBDEI4/8xKRNkLeNxhv+bZ2dnVy4kMaFi+notFqU9s74RC7GLSTWpOuv\nGA16GvLPU3f9DAZdJw4OjsTHL7C6pvL+aLVavvrqMyory/GaHYj/ogmDPockSZR/f4vGqzVMmDCR\njRsfEXHbD0mSKCy8xenTKVRXV4FMhltIDD6RS1A5mG4Mi76zndq8UzQWZiJJRjw9vVmwYAlTpkSM\nqvstwIUL6Zw4cQx7XycmPjQNeS8rBvfmzuR69ux4li9fZaKa3k0kKiOgK2HJJC3tNGp1BwobB7wj\nF+MRNmtEb/z6zjZqrqXSWJwFkoSfXwALFy4ddQnKnfLzb7B375fYuNox6dHYQQ9SbK9ooXBPDvZ2\n9vzkJ0+bbXrcaL7h36ZWd5CRcZbMzPPo9Xpsnb3wnb4CZ79JI1pO11TjK9RcTUGnbsHW1o74+ARm\nzpyLjc3omUV3p46ODr74YgeNjQ0ELJuIZ6x//2+6Q3VaCTVppfj6+vPIIz8x299hLMStJEncunWd\nU6dSqK+vRSZX4jkpDq+IhShGsCvTaNBRdyON+pvnMOq1uLm5s3DhUqZOjbLarsn+SJLEkSMHycnJ\nHlKLYNWZYmrPlxEaGsbmzY+Z7e8gEpURpNVquXgxnYyMc2i1GmxdvPGPXY2j94RhnVcyGqjPP09t\n7imMeg2enl4sWrSMSZOmjtoE5U6nT5/g3LnTOE90J3R95ICvSd+h49bnWeg7dDz88FZCQiaYtqJ3\nGAs3/NtaW1s4cyaVnJxsJEnC2X8KfjErsXEc/vTxzqZqKrP/RUd9GQqFgtmz44iPX2CyWS3m1NTU\nyOef/40OdQcTEqNwnjCwv1dTXi2lR27g4uLK1q3bcXQ03wDRsRS3RqORq1cvc/p0Cm1trSjtnfGb\nvgKXwKhh3xdbK29SmX0UXUcTDg6OJCQsJiZmpllavUxNr9eze/enVFaW478kDK+ZA1tNtvlWPSWH\n8nBzc2fbtqfM+hnuK27FrJ9BUigUBAeHMn16LBqNhoqSfJpKLqNXt+LgFTqkcQCdzTWUnPuS5pLL\n2KhULF26gvvvX4+Xl/eYSFIAgoJCqagoo6aoErmtAkf//ltFJEmi5Ns8OmvbWbx4uVn6Se9k7bMn\nBsPW1pZJk6YyeXIEdXW1XVOaCy8hV9r8sNHh4OPMaDRQcy2V8ov/RNfRwpQpEWzc+AhTp0ahVI6e\nbp6+2NnZExwcytWrl2nJr8dlkidK+76vTV3TRvGBPGxUKh555Ce4uo78WkJ9GUtxK5PJ8PX1Y8aM\n2chkMspLCmguu0ZnczWO3qFDGiiu16qpuHiImmsnkPQa5syZR2LiQwQFhYzaVpQfk8vlhIWFc+1a\nDk0FtTiFuvW7TIS2uZPi/ddQyJQ8/PDjuLr2vwXKSBLTk03AxsaGSZOmEB4+mYqKchoq8mkuu4qD\nZwgq+4E90UiSREPBBcrS96BXtxIdHcOmTY8QEjJhzCQot8lkMkJDJ3Lt2hWaCutwnujR70ZY9VmV\nNGRXMWFCOCtXPmD2v8lYuuHf5ujoyLRpsbi7e1BSUkhT+fWuzeJ8Jw6qSV3TWk/JmV20lOfi4uLK\n+vWbmDdvIXZ21jFFfiQ5O7vg7OzCjet5tFe04B7lg0zecywaNHoK917FoNaRmPiQSach92Ysxq1C\noSAkZAIREdHU1tZQW15AU/Fl7Fx9sHHyGPB52utKKD71OeqGMvz9A9m8+VGio2OsfoD3UNja2uLj\n48vVq5dpL23GY5pvr8tESEaJ4gO5aJs7Wb16HWFhpt2wtyciUTEhJydnYmJmIklGSgpv0lRyGZWD\nG3auvn2+z2g0UJl5iLobZ7G3s+fBBzcSH79gVA04HCwbGxs8Pb3IvZZDR2UrHtG93/A1TWpKgsG9\n0AAAEplJREFUDl3Hzs6Ohx563OybK8LYvOFDV9Lo7e1LdHQMjY31VJcV0FJ2FQev0AEl2W3V+RSf\n+QJdRzPTp88gKWkLXl7eJq+3Jfn4+NHa2kJFYSmSQcI5tOenzYoT+bSXNv8wPmeOmWvZZazGLYC9\nvT3R0THY2dlR9MPKzHKlDfYegf0+yDQWXqLs/F4kvZaFC5eyZs2DZu2SswQ3N3f0eh0lBUUYdYZe\nuy7rMitovFrD1KlRLFy41CIPylaZqDQ3N/PLX/6SDz74gJSUFJYvX37Pl1FVVRW//OUv+etf/8ru\n3bvR6/XExg5sZTxzfXCgq5ktNDQMf/8AbuXfoKn0KgqVHQobewxa9b3/aTqoyDxES3kufn4BbNmy\nFX//QLPV15I8PDxpaWmmoqgUua2yx7n+kiRR+q/raJs6WbPmQQICgixQ07F9w4euxDEiIhqlUklh\n/nWaS3Nw8Arpc3fb5rJrlKbvQYbE/fevJyFh8Zh8Gu1JSEgYeXlXaSyqw2Wi+z0tgm2lTVSmFuHj\n48u6dRst1o0w1uO2axXhICaETqQg/yaNZXmAhINXaK9fsHU306nKPoKdrR0bN25h2rTYMddq3Zug\noBCu38ilobAGlx5asnVtGkq+vY6drR2bNj1mscHvVrng23vvvYebmxtPP/00H330ES0tLbz88st3\nHVNbW0tdXR2RkZG0t7ezceNGPvjgA8LD+2+WMtXgrv7LrebLLz9Dre7o99jQ0DCSkraM6VaUnqjV\nHXzyyQdoDFqm/nQ2Soe7r7+loIHiA7mEhYWzadOjFruhjKVBif25cSOXgwf3gkyBba+tgRLqxgpU\nKhWbNz1GUJD5uzUsrbAwnz17vsAx2JWwjdHdsSlJErc+z6azrp1t257Cz29ggxdNYTzFbUtLM7u/\n3ElzUyMymRx6uVdIRgOOTs48smUbHh6eZq6l5d3e1d4x2JWJm6bd9VrZsZs0Xqth9ep1xMTMtFAN\n+45biz0KJScn89lnnwGQlJTEtm3b7klUvL298fbualJ2dHQkPDycmpqaASUqluLt7ctjjz3RPR20\nN87OLsTHLxg3T6N3srd3ICFhCcnJR6g5X0rAkondr0mSRNWZYmQyGcuWrRw3Tz2WNmVKJA8+uImj\nR79F01TR63HOTs6sX7+JwEDLtHJZWlhYOGFh4RQW5tNe1oxTcFcXUMv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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971ad6978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "drawViolin(sam10k,2,2,26,31)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "44ce33e6-5158-b19c-4a45-7c9ffe3caae8" }, "outputs": [ { "data": { "image/png": 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8nU4MtQ7g4sVz2L598YLuYhBFER999EeEwiE8X1kOszo1E3jrLGbU51rRNDK0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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971b18630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "drawViolin(sam100k,3,6,0,6,8.0)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "_cell_guid": "b6fe66c3-1a68-386d-5178-bcaf395a5af1" }, "outputs": [ { "data": { "image/png": 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g25MLv79ftUK3IzIrBM2I5+5ESibdxYvnYHLko2j9c8mfezi9yYaSDd+HVm/C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V/kZMFks0PCkEY/bDLhXHsnBHwBNm9mf2ExkZiYjDVrNuDu3Zu4dOdzbFnzQ8iks50damU03tv8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\nqLKyMqXrutJ1Xa1bt04dPnzYOPbRRx9FPXfAn3/+q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"text/plain": [ "<matplotlib.figure.Figure at 0x7f498497d8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "drawViolin(sam100k,6,7,0,5,5.0)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "_cell_guid": "82357cb5-532e-4907-3bd4-305b18dfe0ee" }, "outputs": [ { "data": { "image/png": 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DkSkiIrp7MJgi6rGNjTU4NBfcTmONIEmSEPQPHorRnUKhgK2tTQwOempFEAYHjQpsYiFi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\nHSHrrVq1iiVLltDQ0MCOHTtYt24d169ff+jzEQIg4v89ASEeR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"text/plain": [ "<matplotlib.figure.Figure at 0x7f497274cac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "drawViolin(sam100k,8,8,0,5,8.0)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "_cell_guid": "6f4590d8-74b7-edfb-68ab-fef54f57fecc" }, "outputs": [ { "data": { "image/png": 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gHGoMDww8VWESU6M8U7woQY6kAs8j3thYBS/yh1ohHMUISV7+/t4eZJ5HfERz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\nA/sCh0dF2ZClAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4971e554e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "drawViolin(sam100k,9,9,0,5,8.0)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1cd2040d-954b-4d03-9c2a-44abd1159c0a" }, "source": [] }, { "cell_type": "code", "execution_count": 23, "metadata": { "_cell_guid": "5207d6ef-c6f0-9124-996a-b5e77be77cd5" }, "outputs": [ { "data": { "text/plain": [ "['act_char_1_cnt_51',\n", " 'act_char_2_cnt_32',\n", " 'act_char_3_cnt_11',\n", " 'act_char_8_cnt_18',\n", " 'act_char_9_cnt_19',\n", " 'act_char_10_cnt_6515',\n", " 'ppl_group_1_cnt_29899',\n", " 'ppl_char_3_cnt_43',\n", " 'ppl_char_4_cnt_25',\n", " 'ppl_char_7_cnt_25']" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "getColumnsBySuffix(trainUnique,10,10000000,exclude=nonCategoricalColumns)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "39180b6b-5ab9-dcac-e724-76865597939c" }, "source": [] }, { "cell_type": "code", "execution_count": 24, "metadata": { "_cell_guid": "81be37ee-eee3-55d6-aec7-65e9f5ac8d2e" }, "outputs": [], "source": [ "def createDataForDistributionsPlot():\n", " train['set'] = 'train'\n", " trainUnique['set'] = 'trainUnique'\n", " test['set'] = 'test'\n", " return pd.concat([train,trainUnique,test],axis=0)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "_cell_guid": "1d16a02f-f818-0406-fb6d-74c7e209d733" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:3: 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", " app.launch_new_instance()\n" ] } ], "source": [ "trainAndTest = createDataForDistributionsPlot()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "_cell_guid": "6117192c-e8a0-9bdc-0164-1a193f730a34" }, "outputs": [], "source": [ "def drawDistributions(column):\n", " gb = trainAndTest.groupby([c,'set'],as_index=False).count()[[c,'set','activity_id']]\n", " gb['c_freq'] = gb['activity_id'] / np.where(gb['set'] == 'train',len(train),np.where(gb['set'] == 'trainUnique',len(trainUnique),len(test)))\n", " sns.barplot(x=c, y='c_freq', hue='set', hue_order=['train','trainUnique','test'], data=gb) " ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "_cell_guid": "742f7695-4dd2-4e95-43da-4aa262157950" }, "outputs": [ { "data": { "image/png": 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OYTi3yyWH46tAx/Cb8+m1BgplDgB+VlfzjZ7/8M+K2N050FH8wvnPct2m8+O1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\n3s7KytIjjzyiJUuW6IEHHjj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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971d49f60>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971e2e7b8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4983cace10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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VFdKu2wN8KTIyRBZLqK9juI3PHzoKX3z2PF7mCQkJstlsKi0tlcViUUFBgRYs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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f497271f588>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f49c195e6a0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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IiDAFB/t2DtyqqnCf9gcEUmRk+BnnPgbQuhge5omJiXI4HCotLVVUVJQKCgo0Z86cRm1S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\nYQ4AgMkR5gAAmJx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"text/plain": [ "<matplotlib.figure.Figure at 0x7f49c195e6a0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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m55cwLy4u1tChQ5WWlqYFCxY027527VqNGDFCI0aM0JgxY7R9+3Z/lAUAQLsQ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\nftK7d29t2bJF55xzTpu2AUB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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971d49278>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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R5gAAmBxhDgCAyRHmAACYHGEOAIDJEeYAAJgcYQ4AgMkR5gAAmBxhDgCAyRHm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\nuc3fGThdMZ85ECB9+/bV5s2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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4984ab43c8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f497271f588>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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R5gAAmJxfwry0tFQjRoxQRkaGlixZ0mL7unXrNGbMGI0ZM0bjx4/X9u3b/VEW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\nfmKxWE56Xby1bQDQFs7MAZM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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971cef908>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4972728b70>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f49849af208>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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AAEwuIGFeWlqqESNGKCMjQwsXLjxu+9q1azV69GiNHj1a48aN09atWwNRFgAA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\nTGLs2LFyu90t1vXv318PP/y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"text/plain": [ "<matplotlib.figure.Figure at 0x7f498492f320>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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OMAcAwOIIcwAALC4oYV5SUqKhQ4cqIyNDCxcubLF93bp1GjFihEaMGKExY8Zo\n27ZtwSgLAIAOIdzsD/B6vSooKNCSJUsUHx8vl8ul9PR0JScn+9r07t1by5cvV5cuXVRSUqL8/Hy9\n+OKLZpcGAECHYPqZeWlpqZKSkpSYmKiIiAhlZmaquLi4WZtLLrlEXbp08f3t8XjMLgsAgA7D9DD3\neDxKSEjwLTudTlVWVp6w/apVq5SWlmZ2WQAAdBimX2Zvj/fff19r1qzRH//4x1CXAgCAZZge5k6n\nUxUVFb5lj8ej+Pj4Fu22bt2qGTNm6A9/+IO6devWZr+xsdEKD7cHtNaampiA9geEUlxcjByOLqEu\nw28cf+goQnHsmR7mqampcrvdKi8vl8PhUFFRkebNm9esTUVFhe6++2499thjOvfcc/3qt6bmYMBr\nra6uC3ifQKhUV9epqqo21GX4jeMPHYWZx96JfiSYHuZ2u135+fnKy8uTYRhyuVxKTk5WYWGhbDab\ncnJy9PTTT+vAgQOaOXOmDMNQeHi4Vq9ebXZpAAB0CEG5Z56WltZiUFtubq7v79mzZ2v27NnBKAUA\ngA6HN8ABAGBxhDkAABZHmAMAYHGEOQAAFkeYAwBgcYQ5AAAWR5gDAGBxhDkAABZHmAMAYHGEOQAA\nFkeYAwBgcYQ5AAAWR5gDAGBxhDkAABZHmAMAYHGEOQAAFkeYAwBgcYQ5AAAWR5gDAGBxhDkAABZH\nmAMAYHGEOQAAFkeYAwBgcYQ5AAAWR5gDAGBxhDkAABZHmAMAYHGEOQAAFkeYAwBgcYQ5AAAWF5Qw\nLykp0dChQ5WRkaGFCxe22L5z507l5uYqNTVVixcvDkZJAAB0GOFmf4DX61VBQYGWLFmi+Ph4uVwu\npaenKzk52deme/fueuihh7RhwwazywEAoMMx/cy8tLRUSUlJSkxMVEREhDIzM1VcXNysTVxcnH7w\ngx8oPNz03xYAAHQ4poe5x+NRQkKCb9npdKqystLsjwUA4IzBADgAACzO9OvaTqdTFRUVvmWPx6P4\n+PhT7jc2Nlrh4fZT7udYNTUxAe0PCKW4uBg5HF1CXYbfOP7QUYTi2DM9zFNTU+V2u1VeXi6Hw6Gi\noiLNmzfvhO0Nw/Cr35qag4Eq0ae6ui7gfQKhUl1dp6qq2lCX4TeOP3QUZh57J/qRYHqY2+125efn\nKy8vT4ZhyOVyKTk5WYWFhbLZbMrJydHevXs1evRo1dfXKywsTEuXLlVRUZE6d+5sdnkAAFheUIaP\np6WlKS2QmfDgAAAKmUlEQVQtrdm63Nxc3989e/bUxo0bg1EKAAAdDgPgAACwOMIcAACLI8wBALA4\nwhwAAIsjzAEAsDjCHAAAiyPMAQCwOMIcAACLI8wBALA4whwAAIsjzAEAsDjCHAAAiyPMAQCwOMIc\nAACLI8wBALA4whwAAIsjzAEAsDjCHAAAiyPMAQCwOMIcAACLI8wBALA4whwAAIsjzAEAsDjCHAAA\niyPMAQCwOMIcAACLI8wBALA4whwAAIsjzAEAsLighHlJSYmGDh2qjIwMLVy48LhtZs+erSFDhigr\nK0tbtmwJRlkAAHQIpoe51+tVQUGBFi1apPXr16uoqEg7duxo1mbjxo1yu9168803NWvWLD388MNm\nlwUAQIdhepiXlpYqKSlJiYmJioiIUGZmpoqLi5u1KS4u1siRIyVJ/fv3V21trfbu3Wt2aQAAdAim\nh7nH41FCQoJv2el0qrKyslmbyspK9erVq1kbj8djdmkAAHQI4aEu4HRTf6Aq1CUEzaHaakXs/SbU\nZQRFfU2d9tSdOeM999TVKTXURZyEM+X4O5OOPenMOv5CdeyZHuZOp1MVFRW+ZY/Ho/j4+GZt4uPj\ntWfPHt/ynj175HQ6W+3X4egS2EIlORw/1NurfhjwfgG0jeMPOHmm/1RKTU2V2+1WeXm5GhoaVFRU\npPT09GZt0tPTtXbtWknS5s2b1bVrV/Xs2dPs0gAA6BBMPzO32+3Kz89XXl6eDMOQy+VScnKyCgsL\nZbPZlJOTo6uvvlobN27U9ddfr06dOmnu3LlmlwUAQIdhMwzDCHURAADg5J0ZIxIAAOjACHMAACyO\nMAcAwOJ4zhwBcdFFF6lv374yDEM2m02///3vdfbZZx+3bXl5uSZPnqx169YFuUqgY9q/f79uu+02\n2Ww2VVVVKSwsTHFxcbLZbFq1apXCw/m/+o6Of8MIiE6dOumll14KdRnAGal79+6+x3ufeuopde7c\nWbfffnuLdv/+sY2Oh8vsCIjjPRRRXl6ucePGadSoURo1apQ2b97cos327dt14403Kjs7W1lZWXK7\n3ZKkV155xbf+4YcfPm7/AFrndruVmZmpadOmadiwYdq9e7cuu+wy3/ZXX31VDz30kCRp3759mjJl\nilwul2666SaVlpaGqmycBM7MERBHjhxRdna2DMNQ7969NX/+fPXs2VOLFy9WZGSkdu3apfvuu09/\n+tOfmu1XWFioW2+9VcOGDVNjY6O8Xq927NihV199VYWFhbLb7Zo5c6ZeeeUVZWVlhejbAdb15Zdf\n6re//a369eunpqamFmfm/16ePXu2Jk6cqIsvvphbYRZEmCMgzjrrrBaX2Y8ePapZs2Zpy5Ytstvt\n2rVrV4v9LrnkEi1YsEC7d+/WkCFDlJSUpPfff1//+Mc/5HK5ZBiGjhw5oh49egTrqwAdSu/evdWv\nX7822/31r39VWVmZ7ypYbW2tGhoaFBkZaXaJCADCHKZZsmSJevbsqXXr1qmpqUn9+/dv0WbYsGHq\n37+/3nnnHU2aNEmzZs2SYRjKzs7W1KlTQ1A10LFER0f7/g4LC5PX6/UtHzlypFnb1atXy263B602\nBA73zBEQx7unXVtb65tUZ+3atWpqamrR5quvvlLv3r01fvx4XXvttdq2bZuuuOIKvf7666qurpYk\nHThwoNlkPQD8d+yxabPZ1K1bN7ndbnm9Xr311lu+bYMHD9ayZct8y1u3bg1qnTg1nJkjII43Qnbs\n2LGaMmWK1q5dq6uuukqdOnVq0ea1117TK6+8ovDwcDkcDt15553q2rWr7r33XuXl5cnr9SoiIkIP\nP/zwCR91A3Bi/3ls3n///crLy1PPnj2VkpKihoYGSVJ+fr4eeeQRrVmzRl6vV4MGDVJ+fn4oSsZJ\n4N3sAABYHJfZAQCwOMIcAACLI8wBALA4whwAAIsjzAEAsDjCHAAAiyPMAQCwOMIcOM317dtXhw4d\navd+H374oUaPHm1CRd8qLi7WqFGjNHz4cA0fPlyLFy/2a1sgvfTSS8d95/+xDMPQ3XffrRtuuEEj\nR47UhAkT9NVXX5lSDxAqvAEOOM2dyvzTpzp3dWvzXzscDj377LNyOByqq6vTqFGjdPHFF2vAgAGt\nbgukNWvWKC4uTklJSa22y87O1jXXXCNJWr58ufLz87VkyZKA1gKEEmEOBEnfvn111113qbi4WEeO\nHNHUqVM1ZMiQNrf585LGZ599VuvXr1dYWJiio6O1YsUKSVJjY6NmzJihzZs3KywsTPPmzdP555+v\nvXv36r777lN9fb0aGhp09dVXa9q0aZKkp556Sl988YXq6uq0e/durVy5Ul26dGnxmRdffLHv75iY\nGJ1//vmqqKjQgAEDWt12Ijt27NCcOXNUVVUlScrLy9PIkSM1fvx4paamavPmzaqqqtINN9yg++67\nT2vWrNFnn32m2bNn64knntAvf/lLXXHFFS36tdlsviCXvp2pb+nSpW3+MwUsxQAQFBdeeKHx9NNP\nG4ZhGDt37jQuv/xyY9++fX5tO3jw4An7XbNmjZGTk+Nrs3//fsMwDOODDz4wUlJSjC1bthiGYRjP\nPPOMMW3aNMMwDOPIkSO+9kePHjVuueUW49133zUMwzDmz59vXHPNNb5+/LF9+3bjiiuuMCorK9u1\n7d8aGxuNIUOGGG+88YZv3b8//+abbzamTp1qGIZh1NbWGoMGDTJ27drl2/bOO+/4XadhGMaDDz5o\nPProo+3aBzjdcc8cCCKXyyVJ6tOnj1JSUvTpp5/6ta0177zzjsaMGeObyKZbt26+bX369FHfvn0l\nSf379/fdK25qatJvfvMbZWVladSoUdq+fbu2bNni2y8tLa1ZP62prKzUXXfdpUceeUQOh8Pvbcf6\n8ssv5fV6fVcj/vN7DB06VNK3Z/nJyclyu91+1fafnnvuOX355Ze69957T2p/4HTFZXYgiIxWLpm3\ntu1kRUVF+f622+1qbGyUJC1evFi1tbVavXq1IiIiNGPGjGZzWx87B3Zr9u3bp7y8PE2aNKlZELe1\n7VS+R1hY2HGn023LsmXL9Oqrr2rp0qXN+gM6As7MgSBas2aNJKmsrExbtmzRJZdc4te21lxzzTVa\nsWKF6uvrJUn79+9vc5/a2lo5HA5FRETI4/GouLi4vV9FNTU1ysvL080336xRo0b5ve14+vTpI7vd\nrjfeeMO3zp/vERMTo9ra2jbbFRYW6sUXX9Tzzz9/3Pv/gNVxZg4EUWNjo7Kzs3X48GEVFBQoNja2\nzW1tjUgfOXKkKisrlZOTo/DwcHXu3FnLly9vdZ/x48frnnvu0fDhw9WrV6/jDhxry3PPPaddu3Zp\n5cqVKiwslM1m0y233KLs7OxWtx2P3W7X008/rVmzZumpp56S3W5XXl6eRowY0eL7H7uck5OjRx99\nVIsWLTrhALj6+nrNnDlTiYmJysvLk2EYioqK0sqVK9v9nYHTFfOZA0HSt29fbd68WWeddVa7tgFA\nW7jMDgSJzWY74X3x1rYBQFs4MwcsYvTo0fJ6vc3W9e/fX4888ohlPnfVqlVavny571K58a+X0syd\nO9c36v5kmdk3cLojzAEAsDg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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971e74c50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f49848fa7f0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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ffqm9e/eqT58+x2wXG9tdkZH2oNZaVxcT1P6AcIqLi5HD0TPcZQAIAdPDPDk5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\n0sX/2W63q6mpSZL01FNPqb6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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971e2e7b8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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p+wMCKS4uUjZbVKBjeI33H7qKQLz3fF7mqampcjqdqqyslM1mU0lJiebMmdNq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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4971e1e9b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4984a3ca90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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ERDB+uBDmAE4Zvfo41DsuIdhlmK5pf52i+x9WnwGxwS4lIJr2fq0dSx/R/ujo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\nzAfHuvLYY49pz549evLJJ1V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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4983cace10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4984ad7860>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f49849eeac8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f49849eeac8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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XFRVp6NChSktL0/z585ttX7NmjUaMGKERI0ZozJgx2rFjhz/KAgAgKIQa/QEu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\nOZ9qGwC0hDNzwCRGjx4tl8v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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4984a25048>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4984a02470>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f499a6ec0f0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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w8PA2+42ICJa/v7VDa62uDunQ/gBfiowMkc0W6usy3Mbxh87CF8eex8M8ISFB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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4984a02470>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4984948898>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971da90f0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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LSko0ZswYpaamatmyZa22b9y4URMmTNCECRM0adIk7dy5MxhlAQDQKYSb/QFe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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4971da90f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "for c in getColumnsBySuffix(trainAndTest,2,2,exclude=nonCategoricalColumns + ['set']):\n", " drawDistributions(c)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "_cell_guid": "04749d12-aaff-fd99-9216-d0f4505cf44e" }, "outputs": [ { "data": { "image/png": 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WQTAYkJ39BzIyfoVer7d1OERE1ACrtyzIZDIsXboUsbGxEAQB0dHRCAoKws6dOyGRSDB1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\neeyxxzAYDJSUlIjjNDU1ER4eTlVVlYgaOlMobW1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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971afd8d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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DIaED+e67I/j++++QnDwTgiCguroGly7pMHTob5CW9ireeCMNI0c+gNDQ3/y3QkDjW0YT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\nPPDAA3j//feRn5+PQ4cOWf0+3N3dIZP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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4984adfb70>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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ilVemQqUK5UWARERErdGjx52Iy17YZvuL+s8+xZg7N8tuOyVlnN32bbdFYODAh2+YN3r0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\n+uqrQr9+/Zya56qMs2fPFvb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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971cef908>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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qq69WeXl5MGsBAAAB5vce/c9+9jONHz9eYWFhioyMlGEYslgsWr9+fTDrAwAA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\nbY8nWGN/7rnnVFdXp6KiosA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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4972728b70>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f49849b27f0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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rAFwQpW0iIqL2aHMNQHZ2NiorK2/6+qFDh1BcXOzRThEREZG42rwDMHLkSDz9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\nqDvgboBEREQSxHhLREQkQfwIgEginnv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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971adaef0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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3330LAwc+gkmT/tgGFbYdfgyQiIg6lV8vBzxo0GAUF+/EpUuXAACVlRZUV1ejsrISt912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\nXd3kPH9/f8e1/cbcddddKCkpAXC1vyN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"text/plain": [ "<matplotlib.figure.Figure at 0x7f498498a240>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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VwsfHx7js5eWF7OxskzbZ2dnw9vY2aaPVai09NCIiIlnhhXxEREQyYWvpAl5e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\nVWnRogVu3rxpHLeHh4dJ/xq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"text/plain": [ "<matplotlib.figure.Figure at 0x7f492d385ba8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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3QyKRYPTo0ejXrx8SExPh7++v/tghAJw7dw6HDh1Chw4dEBwcDIlEgpkzZ8LX1xeTJk3C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\noqIi9bF6+FgcPXoU5eXl2L1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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971ae3780>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971adaef0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4984a256d8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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iAgMBERERAVDYugNERNQ2iUYjtNpLACD9n1ouBgIiIrKKkqKrWHP8FyhTVMi+mInZUNm6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\ney+tra2cO3euR+yu7Thu3DiuXbumTaLrGvvGdty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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4984b26128>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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FABERkQSxACAiIpIgFgBEREQSxAKAiIhIgizaOwEiImqaoNEgK+uqOF3/byJjsQAgIjJz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\nwsICTk5OsLW1RVBQEAoLC3H06FHU1dX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"text/plain": [ "<matplotlib.figure.Figure at 0x7f49c195e6a0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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v1gIAADzM7bvur7/++tM+AgcAAFovt4/or732Wt1zzz3y8/NTYGCgjDGy2Wza\nsGGDN+sDAABnwe2gnz59uubMmaM+ffq4PVEOAABoWW4HfXh4uEaMGOHNWgAAgIe5fWg+dOhQLVu2\nTAcPHtTRo0dd/wMAAK2X20f0c+fOlSTNnDlTNpvNdY1+27ZtXisOAACcHbeDfvv27d6sAwAAeAF3\n1QEAYGEEPQAAFkbQAwBgYQQ9AAAW1qRlagH4Vl1dnQoLC5rc74ILerH4FABJBD3gE80N7KKiXdox\n7wXFhIS43WdfZaWSZz2huLiLmvx5AKyHoAd8oLCwQFkrHldI17Am9Sv5ulgTQ8IUGxbupcoAWB1B\nD/hISNcwhcd0blKfyrLDksNLBQFoF7gZDwAACyPoAQCwMIIeAAALI+gBALAwbsYDmqg5j8oVFe3y\nUjUA0DCCHmiiwsICTXvqNQWHR7rdp3TPDp07yItFAcAZEPRAMwSHRyosopvb7SsPlUr61nsFAcAZ\ncI0eAAALI+gBALAwgh4AAAsj6AEAsDCCHgAACyPoAQCwMJ8EfX5+vkaMGKHhw4dr3rx5p7xfUFCg\n8ePHKyEhQa+88kqT+gIAgDPzetA7nU5lZ2frpZde0po1a5Sbm6udO3fWa9OpUyc9+uij+vnPf97k\nvgAA4My8HvSbN29Wjx49FBsbq4CAAKWkpCgvL69em4iICF122WXy9/dvcl8AAHBmXg/6kpISdev2\n3Qxi0dHR2r9/v9f7AgAAbsYDAMDSvD7XfXR0tIqLi13bJSUlioqK8lrfzp2D5O9vb16xrVh5eUhL\nl9CoiIgQRUaGtnQZXmeV34VVxgF4khX/Lrwe9AkJCSoqKtLevXsVGRmp3Nxc5eTknLG9MabZfSWp\nvLzKY7W3Jg5HZUuX0CiHo1KlpRUtXYbXWeV3YZVxAJ7UVv8uGgp+rwe93W5XVlaWMjMzZYxRenq6\n4uLitHz5ctlsNmVkZKisrExpaWk6cuSI/Pz8tHjxYuXm5io4OPi0fQEAgHt8skxtYmKiEhMT6702\nfvx4189du3bVunXr3O4LAADcw814AABYGEEPAICFEfQAAFgYQQ8AgIUR9AAAWBhBDwCAhRH0AABY\nGEEPAICFEfQAAFgYQQ8AgIUR9AAAWBhBDwCAhRH0AABYGEEPAICF+WSZWgCAZ9XV1amwsKDJ/S64\noJfsdrsXKkJrRdADQBtUWFigrBWPK6RrmNt9KssOK/um6YqLu8iLlaG1IegBoI0K6Rqm8JjObrc3\nTqeKinZAUXyMAAATEElEQVQ167M4E9B2EfRo9ThFCXjGEUeldsx7QYdCQprUb19lpZJnPcGZgDaK\noEerxylKwHNiQkIUGxbe0mXAhwh6tAlNPUUJADiBx+sAALAwgh4AAAsj6AEAsDCu0QNAC2vOkyXN\nfUwO7Q9BD0tq7vPCPJKHllBYWKBpT72m4PBIt/uU7tmhcwd5sShYBkEPS2rO88I8K4yWFBweqbCI\nbm63rzxUKulb7xUEyyDoYVk8LwwABD18jGuRAOBbBD18imuRaA2YVhntCUEPn+NaJFoa0yqjPSHo\nAbRLTKuM9oIJcwAAsDCCHgAACyPoAQCwMK7RA2izmnv3fHMe2WS2RbRVPgn6/Px8zZ49W8YYpaWl\nadKkSae0mTVrlvLz89WxY0fNmTNHl156qSRpyJAhCgkJkZ+fn/z9/bVy5UpflAygDWjO45pS8x7Z\nZLZFtFVeD3qn06ns7GwtXLhQUVFRSk9PV1JSkuLi4lxt1q1bp6KiIr333nv64osvNGPGDL3++uuS\nJJvNpiVLlig8nBnOAJyqqY9rSs1/ZJPZFtEWef0a/ebNm9WjRw/FxsYqICBAKSkpysvLq9cmLy9P\nqampkqS+ffuqoqJCZWVlkiRjjJxOp7fLBADAkrwe9CUlJerW7btv29HR0dq/f3+9Nvv371dMTEy9\nNiUlJZJOHNFnZmYqLS3NdZQPAADc0+pvxlu2bJmioqLkcDg0ceJE9erVS/3792/psgAAaBO8HvTR\n0dEqLi52bZeUlCgqKqpem6ioKO3bt8+1vW/fPkVHR7vek6SIiAglJydry5YtDQZ9585B8ve33h2u\n5eXu3wDUUiIiQhQZGdpgm9Y+DiuMQWo/47DCGCTrjMMKrPi78HrQJyQkqKioSHv37lVkZKRyc3OV\nk5NTr01SUpKWLl2qkSNHatOmTQoLC1PXrl119OhROZ1OBQcHq6qqSuvXr9fkyZMb/Lzy8ipvDqfF\nOByVLV1CoxyOSpWWVjTapjWzwhik9jMOK4zhZJvWzp1xWEFb/V00FPxeD3q73a6srCxlZmbKGKP0\n9HTFxcVp+fLlstlsysjI0KBBg7Ru3TolJye7Hq+TpLKyMk2ePFk2m011dXUaNWqUBg4c6O2SAQCw\nDJ9co09MTFRiYmK918aPH19ve/r06af0O//887V69Wqv1gYAaFtYZrhpWv3NeAAA62pOaBcV7dKO\neS8ohsmL3ELQAwBaTGFhgbJWPK6QrmFu9yn5ulgTQ8KYvMhNBD0AwCOae3Qe0jVM4TGd3e5TWXZY\ncjS1uvaLoAcAeERz1h5ozroDaBqCHgDgMU1de6C56w7AfaxHDwCAhRH0AABYGEEPAICFEfQAAFgY\nQQ8AgIUR9AAAWBiP150BcykDAKyAoD+D5kzLWFl2WNk3TW+XcykDAFongr4BTZ2W0TidKira1eTP\n4SwAAMBbCHoPOuKo1I55L+gQKyoBAFoJgt7DYkJCWFEJANBqcNc9AAAWZvkj+ubePd+ca+0AALQ2\nlg/65iybKLF0IgDAGiwf9FLTl02UWDoRAGANXKMHAMDCCHoAACyMoAcAwMIIegAALIygBwDAwgh6\nAAAsjKAHAMDCCHoAACyMoAcAwMIIegAALIygBwDAwgh6AAAsjKAHAMDCCHoAACyMoAcAwMJ8EvT5\n+fkaMWKEhg8frnnz5p22zaxZszRs2DCNGTNG27Zta1JfAABwel4PeqfTqezsbL300ktas2aNcnNz\ntXPnznpt1q1bp6KiIr333nt6/PHH9dhjj7ndFwAAnJnXg37z5s3q0aOHYmNjFRAQoJSUFOXl5dVr\nk5eXp9TUVElS3759VVFRobKyMrf6AgCAM/N60JeUlKhbt26u7ejoaO3fv79em/379ysmJsa1HRMT\no5KSErf6AgCAM/Nv6QJOxxjj0f0dOVTa5D5HKxwKKDvctM8pr9S+yqZ9d9pXWakEd/ffxHH4agyS\nNcbR2sYgWWMc3v43xd+3+/j7dnP/Fvn7PsnrQR8dHa3i4mLXdklJiaKiouq1iYqK0r59+1zb+/bt\nU3R0tGpqahrt+0ORkaE/2L5SH6648myG0CowjtbDCmOQrDEOK4xBYhytiRXG8ENeP3WfkJCgoqIi\n7d27V9XV1crNzVVSUlK9NklJSXrrrbckSZs2bVJYWJi6du3qVl8AAHBmXj+it9vtysrKUmZmpowx\nSk9PV1xcnJYvXy6bzaaMjAwNGjRI69atU3Jysjp27Kg5c+Y02BcAALjHZjx9QRwAALQazIwHAICF\nEfQAAFgYQQ8AgIW1y6CvqKjQq6++6vXPWbp0qYYNG6bevXvr4MGDHt+/r8YxZcoUjRgxQqNGjdJv\nf/tb1dXVeXT/vhrHb3/7W40ZM0ZjxozR/fffr6NHj3ps374aw0mzZs3SFVdc4fH9+moc06ZNU1JS\nklJTUzV27Fht377dY/v25e/i6aef1vDhw5WSkqK//OUvHt23r8YxYcIEjR07Vqmpqbr++us1efJk\nj+7fV+PYsGGDxo0bp9TUVE2YMEG7d+/22L59PYZRo0Zp2rRpcjqdntmxaYd2795tbrzxRq9/zrZt\n28zevXvNkCFDTHl5ucf376txrFu3zvXzQw89ZJYtW+bR/ftqHJWVla6f58yZY+bNm+exfftqDMYY\ns2XLFvPwww+bK664wuP79tU4pk6dat577z2v7NtXY3jjjTfMI4884to+cOCAR/fvy39TJ917773m\nrbfe8ug+fTWOYcOGmYKCAmOMMUuXLjVTp0712L59MQan02kGDRpkdu3aZYwx5o9//KNZsWKFR/bd\nKmfG87acnBwVFRVp7NixGjBggMrKypScnKyhQ4dKOnEEO3LkSB06dEjvv/++KioqtH//fo0aNcr1\nbfevf/2rlixZotraWv3oRz/SjBkzZLPZ6n1OfHy8JM/P9OfrcSQmJrp+TkhIqDe5UVsaR3BwsKQT\nv49jx46d8n5bGIPT6dSTTz6pnJwcrV271mP1+3ocJ8fiDb4aw7Jly5STk+PajoiIaJPjOKmyslIf\nf/yx6/HmtjYOPz8/VVRUuMbS2ORqrW0M5eXlCgwMVPfu3SVJ1157rebNm6f09PSzH4BHvi60MXv2\n7Kn37ezTTz8199xzjzHGmIqKCpOUlGTq6urMqlWrzMCBA82hQ4fMsWPHzI033mi+/PJL880335i7\n7rrL1NbWGmOMmTFjRoPfggcPHuyVI3pfj6OmpsaMHTvWbNy4sc2OY+rUqWbAgAHmZz/7mTl27Fib\nG8OiRYvMokWLjDHGXH755R6r39fjmDp1qhk2bJgZPXq0mTNnjqmurm5zY7j66qvNCy+8YMaNG2fu\nvPNOU1hY6LEx+HIcJ7355pvmvvvu8+gYfDmOzz77zFx99dVm0KBBJiUlpd4ZvLYyhsGDB5svv/zS\nGGPMrFmzzKhRozxSf7s8ov+hq666So8//rjKy8v17rvvatiwYfLzO3H7wnXXXaewsDBJ0rBhw/TP\nf/5TdrtdW7duVXp6uowxOn78uLp06dKSQ5Dk/XHMnDlTV111lfr169dmxzFnzhwZY5Sdna3c3FyN\nGzeuzYxh//79eueddzx+Ldj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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971e1e9b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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FHYuv1wEAupyAgABVV5+drfSaa65Vbu5fdOrUKUlSaWmJysvLVVpaqh49emjcuAm65Zaf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\nuL9oN/L0ez9x4kQ9//zznTL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"text/plain": [ "<matplotlib.figure.Figure at 0x7f49848f1c18>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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0mgxo6NCh/NInIiLqBjQuALKysrBhwwZIpVLI5XLV8r179+olMSIiItIfjQuA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\nEREZIRYARERERogFABERkRF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np6erfv2OHz8eaWlpmDt3LiQSCXJzc3Hy5Em19ndjVlVV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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4971adc9b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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88UbdfffdCgoKUrt27WQYhmw2m3bs2GFmfQAAoBl8DvrZs2dr4cKF6tevHxPl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\n+Gt8H3/8sXHdddcZw4YNM5K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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4971ec6a90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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eHpK7ExERUQ+QnNT33nsvUlNTYbFY8PLLL+Pee++FwWCQszYiIiLqJsl33Scl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\nW4vo6GhMmjQJwMUj2Lvuugt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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4984b26128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "for c in getColumnsBySuffix(trainAndTest,3,52,exclude=nonCategoricalColumns + ['set']):\n", " drawDistributions(c)\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "a313a111-cb52-85f2-f3cd-ad3be5fa98b4" }, "source": [] } ], "metadata": { "_change_revision": 269, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/323/323931.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "87df8b7f-d735-b37a-a191-42b5167d5f2d" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "b73e986c-8a08-6aa0-c079-6d75e66e0ff0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2016-FCC-New-Coders-Survey-Data.csv\n", "\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "c2cfdc76-ef7b-d7aa-71f1-20cc6d8af5ca" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (21,57) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] } ], "source": [ "data=pd.read_csv(\"../input/2016-FCC-New-Coders-Survey-Data.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "57253875-2049-2269-83fb-fb2f89a1c02a" }, "outputs": [ { "data": { "text/plain": [ "array(['Age', 'AttendedBootcamp', 'BootcampFinish', 'BootcampFullJobAfter',\n", " 'BootcampLoanYesNo', 'BootcampMonthsAgo', 'BootcampName',\n", " 'BootcampPostSalary', 'BootcampRecommend', 'ChildrenNumber',\n", " 'CityPopulation', 'CodeEventBootcamp', 'CodeEventCoffee',\n", " 'CodeEventConferences', 'CodeEventDjangoGirls', 'CodeEventGameJam',\n", " 'CodeEventGirlDev', 'CodeEventHackathons', 'CodeEventMeetup',\n", " 'CodeEventNodeSchool', 'CodeEventNone', 'CodeEventOther',\n", " 'CodeEventRailsBridge', 'CodeEventRailsGirls',\n", " 'CodeEventStartUpWknd', 'CodeEventWomenCode', 'CodeEventWorkshop',\n", " 'CommuteTime', 'CountryCitizen', 'CountryLive', 'EmploymentField',\n", " 'EmploymentFieldOther', 'EmploymentStatus', 'EmploymentStatusOther',\n", " 'ExpectedEarning', 'FinanciallySupporting', 'Gender', 'HasChildren',\n", " 'HasDebt', 'HasFinancialDependents', 'HasHighSpdInternet',\n", " 'HasHomeMortgage', 'HasServedInMilitary', 'HasStudentDebt',\n", " 'HomeMortgageOwe', 'HoursLearning', 'ID.x', 'ID.y', 'Income',\n", " 'IsEthnicMinority', 'IsReceiveDiabilitiesBenefits', 'IsSoftwareDev',\n", " 'IsUnderEmployed', 'JobApplyWhen', 'JobPref', 'JobRelocateYesNo',\n", " 'JobRoleInterest', 'JobRoleInterestOther', 'JobWherePref',\n", " 'LanguageAtHome', 'MaritalStatus', 'MoneyForLearning',\n", " 'MonthsProgramming', 'NetworkID', 'Part1EndTime', 'Part1StartTime',\n", " 'Part2EndTime', 'Part2StartTime', 'PodcastChangeLog',\n", " 'PodcastCodeNewbie', 'PodcastCodingBlocks', 'PodcastDeveloperTea',\n", " 'PodcastDotNetRocks', 'PodcastHanselminutes', 'PodcastJSJabber',\n", " 'PodcastJsAir', 'PodcastNone', 'PodcastOther',\n", " 'PodcastProgrammingThrowDown', 'PodcastRubyRogues',\n", " 'PodcastSEDaily', 'PodcastShopTalk', 'PodcastTalkPython',\n", " 'PodcastWebAhead', 'ResourceBlogs', 'ResourceBooks',\n", " 'ResourceCodeWars', 'ResourceCodecademy', 'ResourceCoursera',\n", " 'ResourceDevTips', 'ResourceEdX', 'ResourceEggHead', 'ResourceFCC',\n", " 'ResourceGoogle', 'ResourceHackerRank', 'ResourceKhanAcademy',\n", " 'ResourceLynda', 'ResourceMDN', 'ResourceOdinProj', 'ResourceOther',\n", " 'ResourcePluralSight', 'ResourceReddit', 'ResourceSkillCrush',\n", " 'ResourceSoloLearn', 'ResourceStackOverflow', 'ResourceTreehouse',\n", " 'ResourceUdacity', 'ResourceUdemy', 'ResourceW3Schools',\n", " 'ResourceYouTube', 'SchoolDegree', 'SchoolMajor', 'StudentDebtOwe'], dtype=object)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns.values" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "008f0205-79e8-637a-a0c2-421119a04890" }, "source": [ "Above is a list of features for every individual coder. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "8d84daea-a26f-74f8-ab26-ca50aa94c230" }, "outputs": [], "source": [ "# Looking out for missing values and removing unwanted features" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "4df09065-f80f-016c-51b2-7f2334f2fe3f" }, "outputs": [], "source": [ "def feature_summary(data):\n", " features=pd.DataFrame()\n", " features_names=[]\n", " features_counts=[]\n", " features_missing=[]\n", " names=data.columns.values\n", " for i in names:\n", " features_names.append(i)\n", " features_counts.append(data[i].value_counts().count())\n", " features_missing.append(data[data[i].isnull()].shape[0])\n", " features['name']=features_names\n", " features['value counts']=features_counts\n", " features['missing']=features_missing\n", " return (features)\n", " " ] } ], "metadata": { "_change_revision": 9, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/324/324023.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "765cc74f-e8e2-b2ee-7640-e49e2ed67dea" }, "source": [ "xgboost rmse issue" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "e3409e42-93e1-d814-a20b-149e60bb1e7c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cliente_tabla.csv\n", "producto_tabla.csv\n", "sample_submission.csv\n", "test.csv\n", "town_state.csv\n", "train.csv\n", "\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "bdd0aa7c-eef1-8eed-38bc-0c6029529d9e" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module has been deprecated in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import numpy as np\n", "import xgboost as xgb\n", "from sklearn import datasets\n", "from sklearn.cross_validation import train_test_split" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "8a81ed3a-1cc1-953b-1214-b07862aebf92" }, "outputs": [], "source": [ "# Load data\n", "boston = datasets.load_boston()\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " boston.data, boston.target, test_size=0.33, random_state=42)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "6094984b-9048-d136-8949-f1734b094fd6" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0]\ttrain-rmse:24.1233\teval-rmse:22.6468\n", "[1]\ttrain-rmse:23.8956\teval-rmse:22.4312\n", "[2]\ttrain-rmse:23.6701\teval-rmse:22.2184\n", "[3]\ttrain-rmse:23.4469\teval-rmse:22.0075\n", "[4]\ttrain-rmse:23.226\teval-rmse:21.7982\n", "[5]\ttrain-rmse:23.0071\teval-rmse:21.5908\n", "[6]\ttrain-rmse:22.7905\teval-rmse:21.3865\n", "[7]\ttrain-rmse:22.5759\teval-rmse:21.1839\n", "[8]\ttrain-rmse:22.3636\teval-rmse:20.9824\n", "[9]\ttrain-rmse:22.1532\teval-rmse:20.7832\n", "[10]\ttrain-rmse:21.945\teval-rmse:20.5865\n", "[11]\ttrain-rmse:21.7388\teval-rmse:20.3916\n", "[12]\ttrain-rmse:21.5346\teval-rmse:20.2013\n", "[13]\ttrain-rmse:21.3324\teval-rmse:20.0107\n", "[14]\ttrain-rmse:21.1322\teval-rmse:19.8242\n", "[15]\ttrain-rmse:20.934\teval-rmse:19.64\n", "[16]\ttrain-rmse:20.7377\teval-rmse:19.4573\n", "[17]\ttrain-rmse:20.5434\teval-rmse:19.2776\n", "[18]\ttrain-rmse:20.351\teval-rmse:19.0957\n", "[19]\ttrain-rmse:20.1603\teval-rmse:18.9182\n", "[20]\ttrain-rmse:19.9717\teval-rmse:18.742\n", "[21]\ttrain-rmse:19.7848\teval-rmse:18.5678\n", "[22]\ttrain-rmse:19.5997\teval-rmse:18.3959\n", "[23]\ttrain-rmse:19.4165\teval-rmse:18.2241\n", "[24]\ttrain-rmse:19.2351\teval-rmse:18.0557\n", "[25]\ttrain-rmse:19.0555\teval-rmse:17.8891\n", "[26]\ttrain-rmse:18.8776\teval-rmse:17.724\n", "[27]\ttrain-rmse:18.7016\teval-rmse:17.5612\n", "[28]\ttrain-rmse:18.5272\teval-rmse:17.3982\n", "[29]\ttrain-rmse:18.3539\teval-rmse:17.241\n", "[30]\ttrain-rmse:18.1827\teval-rmse:17.0813\n", "[31]\ttrain-rmse:18.0129\teval-rmse:16.928\n", "[32]\ttrain-rmse:17.8442\teval-rmse:16.7744\n", "[33]\ttrain-rmse:17.6779\teval-rmse:16.6193\n", "[34]\ttrain-rmse:17.5125\teval-rmse:16.4687\n", "[35]\ttrain-rmse:17.3493\teval-rmse:16.3154\n", "[36]\ttrain-rmse:17.188\teval-rmse:16.1658\n", "[37]\ttrain-rmse:17.0275\teval-rmse:16.0194\n", "[38]\ttrain-rmse:16.8685\teval-rmse:15.8737\n", "[39]\ttrain-rmse:16.712\teval-rmse:15.7287\n", "[40]\ttrain-rmse:16.5561\teval-rmse:15.5859\n", "[41]\ttrain-rmse:16.4027\teval-rmse:15.444\n", "[42]\ttrain-rmse:16.2499\teval-rmse:15.3041\n", "[43]\ttrain-rmse:16.0986\teval-rmse:15.1666\n", "[44]\ttrain-rmse:15.9487\teval-rmse:15.0297\n", "[45]\ttrain-rmse:15.8004\teval-rmse:14.894\n", "[46]\ttrain-rmse:15.6543\teval-rmse:14.7571\n", "[47]\ttrain-rmse:15.5089\teval-rmse:14.6241\n", "[48]\ttrain-rmse:15.3649\teval-rmse:14.4927\n", "[49]\ttrain-rmse:15.2223\teval-rmse:14.3624\n", "[50]\ttrain-rmse:15.0819\teval-rmse:14.2317\n", "[51]\ttrain-rmse:14.9423\teval-rmse:14.1041\n", "[52]\ttrain-rmse:14.8038\teval-rmse:13.9777\n", "[53]\ttrain-rmse:14.6674\teval-rmse:13.8565\n", "[54]\ttrain-rmse:14.5323\teval-rmse:13.7364\n", "[55]\ttrain-rmse:14.3985\teval-rmse:13.6175\n", "[56]\ttrain-rmse:14.2661\teval-rmse:13.4998\n", "[57]\ttrain-rmse:14.135\teval-rmse:13.3835\n", "[58]\ttrain-rmse:14.0047\teval-rmse:13.2659\n", "[59]\ttrain-rmse:13.8761\teval-rmse:13.152\n", "[60]\ttrain-rmse:13.7489\teval-rmse:13.0398\n", "[61]\ttrain-rmse:13.6229\teval-rmse:12.9283\n", "[62]\ttrain-rmse:13.4982\teval-rmse:12.8181\n", "[63]\ttrain-rmse:13.3746\teval-rmse:12.7114\n", "[64]\ttrain-rmse:13.2523\teval-rmse:12.6034\n", "[65]\ttrain-rmse:13.1313\teval-rmse:12.4965\n", "[66]\ttrain-rmse:13.0108\teval-rmse:12.3871\n", "[67]\ttrain-rmse:12.892\teval-rmse:12.2841\n", "[68]\ttrain-rmse:12.7745\teval-rmse:12.1804\n", "[69]\ttrain-rmse:12.658\teval-rmse:12.0779\n", "[70]\ttrain-rmse:12.542\teval-rmse:11.9726\n", "[71]\ttrain-rmse:12.428\teval-rmse:11.8699\n", "[72]\ttrain-rmse:12.315\teval-rmse:11.7722\n", "[73]\ttrain-rmse:12.2032\teval-rmse:11.6718\n", "[74]\ttrain-rmse:12.0917\teval-rmse:11.5709\n", "[75]\ttrain-rmse:11.982\teval-rmse:11.4749\n", "[76]\ttrain-rmse:11.8735\teval-rmse:11.3771\n", "[77]\ttrain-rmse:11.7653\teval-rmse:11.2785\n", "[78]\ttrain-rmse:11.6587\teval-rmse:11.1878\n", "[79]\ttrain-rmse:11.5534\teval-rmse:11.0929\n", "[80]\ttrain-rmse:11.449\teval-rmse:11.0016\n", "[81]\ttrain-rmse:11.3449\teval-rmse:10.9095\n", "[82]\ttrain-rmse:11.2425\teval-rmse:10.8222\n", "[83]\ttrain-rmse:11.1411\teval-rmse:10.7337\n", "[84]\ttrain-rmse:11.0407\teval-rmse:10.6453\n", "[85]\ttrain-rmse:10.9407\teval-rmse:10.555\n", "[86]\ttrain-rmse:10.8424\teval-rmse:10.4693\n", "[87]\ttrain-rmse:10.745\teval-rmse:10.3844\n", "[88]\ttrain-rmse:10.648\teval-rmse:10.297\n", "[89]\ttrain-rmse:10.5526\teval-rmse:10.2163\n", "[90]\ttrain-rmse:10.4581\teval-rmse:10.1341\n", "[91]\ttrain-rmse:10.3644\teval-rmse:10.0519\n", "[92]\ttrain-rmse:10.2712\teval-rmse:9.96851\n", "[93]\ttrain-rmse:10.1794\teval-rmse:9.88945\n", "[94]\ttrain-rmse:10.0885\teval-rmse:9.81143\n", "[95]\ttrain-rmse:9.99845\teval-rmse:9.73554\n", "[96]\ttrain-rmse:9.90877\teval-rmse:9.6553\n", "[97]\ttrain-rmse:9.82047\teval-rmse:9.58031\n", "[98]\ttrain-rmse:9.73294\teval-rmse:9.50605\n", "[99]\ttrain-rmse:9.64628\teval-rmse:9.43131\n" ] } ], "source": [ "dtrain = xgb.DMatrix(X_train, y_train)\n", "dvalid = xgb.DMatrix(X_test, y_test)\n", "watchlist = [(dtrain, \"train\"), (dvalid, \"eval\")]\n", "params = {\"objective\": \"reg:linear\",\n", " \"eval_metric\": \"rmse\",\n", " \"eta\": 0.01,\n", " \"max_depth\": 6,\n", " \"silent\": 1,\n", " \"nthread\": 1}\n", "num_boost_round = 100\n", "gbm = xgb.train(params, dtrain, num_boost_round,\n", " evals=watchlist, verbose_eval=True)\n", "y_pred = gbm.predict(dvalid)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "31836e54-ae85-a7ca-f0a1-5e0ef07a4335" }, "outputs": [ { "data": { "text/plain": [ "9.4313072345019364" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sqrt(np.mean((y_pred - y_test) ** 2))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "7ceabe1d-f1e3-e392-766b-3225e5226378" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 391, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/324/324025.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "41e89d5a-ffcf-c13d-30c1-af45941768f4" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (21,57) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] } ], "source": [ "# Importing libraries and the dataset\n", "\n", "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import colorsys\n", "plt.style.use('seaborn-talk')\n", "\n", "df = pd.read_csv(\"../input/2016-FCC-New-Coders-Survey-Data.csv\", sep=',')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b84b205a-0879-5216-022a-fde54ddbd61a" }, "source": [ "**Distribution of Age**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "abf02a40-a827-f72f-b07f-08d7e382fbe3" }, "outputs": [ { "data": { "image/png": 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mZqguSkuStLUNzFertdZBaKm5Oe9dND09zejoDKOjnH3s3/9o3TuH9+HL8z2U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\nSZK0yfXNhC8iXhsRn4/4/9u7/1iv6jqO488X15t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"text/plain": [ "<matplotlib.figure.Figure at 0x7f35b44aa278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.Age.hist(bins=100)\n", "plt.xlabel(\"Age\")\n", "plt.title(\"Distribution of Age\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1acc25f2-ac47-aff2-234b-f9f0d5fbedbc" }, "source": [ "Most learners are in the 20-30 year gap, being 25 the most frequent age for new coders." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e2216228-96b6-9ce6-0866-f0b085b5c24b" }, "source": [ "**Distribution of Gender**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "4b4fc1d4-25fd-7c37-cd76-87fdb3e435ef" }, "outputs": [ { "data": { "image/png": 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74wUVFZ+3GY2890xrvZfe+nYe6KOovCQ3FvQ6HeZNKZtcFHBvqMzzbfikvu12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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f35985bbeb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = df.Gender.value_counts().index\n", "N = len(df.EmploymentField.value_counts().index)\n", "HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]\n", "RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))\n", "patches, texts = plt.pie(df.Gender.value_counts(), colors=RGB_tuples, startangle=90)\n", "plt.axes().set_aspect('equal', 'datalim')\n", "plt.legend(patches, labels, bbox_to_anchor=(1.05,1))\n", "plt.title(\"Gender\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "a7e81a6d-ee72-aa65-11f3-eddc667aef28" }, "source": [ "New coders are mostly men, with a very high proportion with respect to women" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1894a1e3-a9e9-91d5-557e-647031b13b90" }, "source": [ "**Distribution of Job role interest**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "673b84bf-10ea-12c0-ec91-4f02a63a6090" }, "outputs": [ { "data": { "image/png": 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kp6fTsWPHv91nQkICRqNRLxM5O6vdokULFi5cyP79+zl9+jT9+vXjjjvu4ODBg1X2dfjw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VAAAg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\nXftdNXYDjn+HssUYo+4Bj+NYr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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3585f64748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "N = len(df.JobRoleInterest.value_counts().index)\n", "HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]\n", "RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))\n", "labels = df.JobRoleInterest.value_counts().index\n", "colors = ['OliveDrab', 'Orange', 'OrangeRed', 'DarkCyan', 'Salmon', 'Sienna', 'Maroon', 'LightSlateGrey', 'DimGray']\n", "patches, texts = plt.pie(df.JobRoleInterest.value_counts(), colors=RGB_tuples, startangle=90)\n", "plt.axes().set_aspect('equal', 'datalim')\n", "plt.legend(patches, labels, bbox_to_anchor=(1.25, 1))\n", "plt.title(\"Job Role Interest\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "465fa8ef-3c6c-18d1-6d11-541f54da5828" }, "source": [ "The interest of new coders seems to lie in Web Development (both front and back-end), followed by Data Science." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "92557a98-50d0-c0ae-5ae6-1d474afb8ecd" }, "source": [ "**Distribution of Employment field**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "f8d63a06-f28a-0932-d30c-84f5b28256b4" }, "outputs": [ { "data": { "image/png": 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ZQmpqqnX7vffeS5s2bbCzs2Ps2LEkJSWxcOFCnJycCA4OJjY2lp9++gmAL774AoCnn34a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\nxdBoNPjyyy8xZ86cdp9PRkYGVq9ejZkzZ0KtVuPpp5/GvHnzOpTvpZdeQ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3585d0f8d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "N = len(df.EmploymentField.value_counts().index)\n", "HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]\n", "RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))\n", "labels = df.EmploymentField.value_counts().index\n", "patches, texts = plt.pie(df.EmploymentField.value_counts(), colors=RGB_tuples, startangle=90)\n", "plt.axes().set_aspect('equal', 'datalim')\n", "plt.legend(patches, labels, bbox_to_anchor=(1.3, 1))\n", "plt.title(\"Employment Field\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9b505aeb-0ea5-7cce-dd40-ed942691c272" }, "source": [ "New coders mostly belong to the Software Development professional field. This makes sense, since it is a field of constant change and developers need to update and broaden their knowledge and improve their skills in order to be aligned with the rapid changes that take place in the market." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "2fe9e3d0-056e-a367-e37d-e8a289691943" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 69, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/324/324276.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "41e89d5a-ffcf-c13d-30c1-af45941768f4" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (21,57) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] } ], "source": [ "# Importing libraries and the dataset\n", "\n", "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import colorsys\n", "plt.style.use('seaborn-talk')\n", "\n", "df = pd.read_csv(\"../input/2016-FCC-New-Coders-Survey-Data.csv\", sep=',')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b84b205a-0879-5216-022a-fde54ddbd61a" }, "source": [ "**Distribution of Age**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "abf02a40-a827-f72f-b07f-08d7e382fbe3" }, "outputs": [ { "data": { "image/png": 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mZqguSkuStLUNzFertdZBaKm5Oe9dND09zejoDKOjnH3s3/9o3TuH9+HL8z2U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\nSZK0yfXNhC8iXhsRn4/4/9u7/1iv6jqO488X15t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"text/plain": [ "<matplotlib.figure.Figure at 0x7f207c6b04e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.Age.hist(bins=100)\n", "plt.xlabel(\"Age\")\n", "plt.title(\"Distribution of Age\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1acc25f2-ac47-aff2-234b-f9f0d5fbedbc" }, "source": [ "Most learners are in the 20-30 year gap, being 25 the most frequent age for new coders." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e2216228-96b6-9ce6-0866-f0b085b5c24b" }, "source": [ "**Distribution of Gender**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "4b4fc1d4-25fd-7c37-cd76-87fdb3e435ef" }, "outputs": [ { "data": { "image/png": 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74wUVFZ+3GY2890xrvZfe+nYe6KOovCQ3FvQ6HeZNKZtcFHBvqMzzbfikvu12\nLoA7tnwBG3LynVrHSGtSSqiqCr0+NTYp4XUboiHkO53zZ+Xn/27xhAmnsqAlDQEA8pDLnaoq+e8z\nhirzfIGL5tTePLU052WHxVSsdR4af2vXrkVtbS1cLhdKS0tx11139S+Js2PHjv4Nxk866SSsXbsW\nugG3hCiKggceeAATJkyAz+fD3LlzsXXr1v7HV65ciRUrVuDaa6+F1+tFUVERHnts8Gow69evR2Vl\nJTweD1asWIFIJDLo8X379uELX/gC8vLyUFBQgOuuuw6hUKj/cZ1Oh7Vr12LWrFlwOByDjp/sWNKI\nDlHm9X7p1OLip+eWlNToUnAbkXR36LiZqrCkjTWH1aS7cE7N/M/XFm8qznIv1joPja+ioiL87ne/\nQ0dHB1588UWsX78eTzzxBBRFweLFi3HSSSehqakJv/zlL/H4448P2n7p3nvvxcaNG/Hyyy+jpaUF\nV111FRYuXIj29vb+5/z85z/HkiVL0NbWhrVr1+L666/Hvn37AACvvvoqrr/+ejz22GNobW3FggUL\n8Nxzz/V/bDQaxfz58zF58mTs2bMH7733Hg4cOIAbb7xx0Oewfv16/OxnP0MoFMJJJ500xl+x0cOS\nRtRLCKGr8vvXLSgv/+ak7Ow8rfPQYH3l7LAJh6rkeWwc6ITAKbVFVadOLH68PNd7q9Z5aPxcdNFF\nKC7uGUSdNm0arrjiCvzhD3/A3/72N+zZswff/OY3YTKZUFpaiptvvnnQx65btw4PPfQQSkpKIITA\nypUrkZeXh1//+tf9z5k/fz7OP//8/mN5PB689dZbAICnnnoKX/jCFzB//nzodDpcccUVOPnkk/s/\nduPGjQCA++67DyaTCW63G6tXr8aPf/zjQeeK2267DaWlpRBCpNSkCd6TRgRACOGYmJX17LmVlQud\nA5ezp+TRe8KVh9yDpqiS57FxNKEgkOO2WVZXF/grd3zaej3vU0t/zzzzDB555BHs3LkTiUQC8Xgc\nc+bMwYEDB5CdnQ2z+bPFpEtKSvr/3tzcjFAohMWLF/ePrkkpkUgksH///v7n5eUN/p3Ybrejs7MT\nALB//37MmjVr0ONlZWX9f9+9ezf27NkDn8/X/z4pJfR6Perr6/tfe2CuVMKTG2U8v81WOSs//8dn\nV1ScbEyRm0kzkei7J02i/1pKPKHAqOOaKOMt1+uwLzm55trfbN1RJIS4TEoZOvZHUSrav38/rrji\nCrzwwgtLqeDjAAAgAElEQVQ499xzodfrcdttt2Hr1q0oKChAY2MjotFof1Hbs2dP/8cGAgE4HA5s\n2rQJdXV1x3X8goIC7N69e9D7du/ejaqqKgA95WvChAnHXL4kVU8TqZmaaJQUud3nzszP//V5VVUs\naKliwDWMSDQBm9HI85gGHFaT7qLP1Zw/szL/D267hRMK0lQoFIKUEoFAAHq9Hn/729/w1FNPAQDm\nzJmD4uJi3HnnnYhGo9i1axe++93vDvr4G2+8Ebfeeis+/vjj/td7+eWXUV9fP6zjX3HFFXj++eex\nefNmKIqCp59+Gq+//nr/44sWLUIsFsODDz7YP1ngwIEDeOGFF0bj09ccR9IoY5V7vTd/vqjo9tqs\nrByts9Cx9Y2gSfWzkbRIJAGH2WTSLlVmM+r1OG9m1cn2fxp/k+NxXNkQDL2pdaZU1tocTrpj1NTU\nYPXq1bjgggsQj8cxb948LFu2DG+99Rb0ej02btyIVatWISsrCxUVFVixYgW++tWv9n/86tWrsXbt\nWixZsgQHDhyA3W7HnDlzsG7duiMec+DEg9NOOw3r1q3D1VdfjdbWVlxwwQW47LLL+h+3Wq145ZVX\ncMcdd6CmpgahUAj5+fn4l3/5F1x44YWHvV6qEdz1gzKNEEJU+nzfObOs7Oo8p9OudR46uuejO5um\nnFOete2lD5sXXTAh8IcXP2y4cP6EHADYeyAYDx9IGGsKA1rHzHh/33Fg1zu7Gr68r7n9V1pnSXZC\niBlbt27dOnBB2XTZceAHP/gBHnnkEXzwwQdj8vrpZtu2bairq6uTUm4b6nGOpFFG6S1o3z+vqupK\nn9XKEZiU0v8LZf+vxaGueMxtNafOVK00dnJVQZnDYnqsNMdz3+6G4ONa50k1RqMxJXcB+POf/4y8\nvDyUl5fjnXfewUMPPYQVK1ZoHSttsKRRxhBC6Kp8vh+eX139RW6QnoI+u9zZfw9aVzimlLjc2mWi\nQSYWZeXZzMb/qsjz5X9ysHW11nlo7O3btw/Lli1DS0sLsrKycOmll+KOO+7QOlbaYEmjjCCE0Ff7\n/U8uqq7+FxeX2EhJ/cNn8rPFa7u746o1wNNYMinN9ngtRsN/VuT5DJ8cbL1H6zw0ti677LJB94jR\n6OKsKEp7QgjDBL//mQsmTLiMBS2l9YykDehriiqVVJ1an85yvQ7bWdPKb6rI892ndRaiVMazG6U1\nIYSxJhD42ZKamkscJhO/31OYlLJnotOAHQZUZfA+npQ88n1Ox5lTy75Skeu7W+ssRKmKP7QobQkh\nzBOzsn65ZMKEC7lJeloQqiqh1+s+G0ljSUtqBX6XY97U0v8sz/XernUWolTEkkZpSQhhm5SV9dKS\nCRPOt6bQPm00tN6lgkQirsKg/2zRI1VVuYZQkisKuF3zppTdyf0+iUaOJY3SjhDCPiU7e+OSmpqz\nzQbeVJ4OpAT0QohEQoXJqO8/bykKO1oqKM5yu0+fXHp3ea73Jq2zEKUS/gSjtCKEMEzKyvrZBRMm\nzOc2T+lDKiqEXkBJqDCbDP3/sAN3H6DkVprt8cpJJfeU5Xjjuxra/p/WeZJNuixmS6OLJY3ShhBC\nVPv9/3t+dfVCFrT0oqoSep0O8bgCk+Gzf1ypSF4NSCFlOV6fqsrVvUXtMa3zJJPt27fj+vuXwxWw\njelxOprD+N7dT6fkwrmZiCWN0kaF1/vQuZWVX+AkgfQjVQmdTiCRUGEx6/uHAFSVJS3VVOT5/BK4\nvzTHo+xuCP5Q6zzJxBWwwZfv1DrGYebNm4cFCxbgrrvu0jpKxmFJo7RQ7vXeclZ5+XVebvWUlqQq\nYTAIXSKuJCwmowEAVCkhJc9hqagyzxdIKOoDRQF3A/f6TH3xeJyXT8cIfwullFfi8fzLKUVFdxS4\nXA6ts9DYUFUJnV6HWFRJmHrXI47FFFgMeo6apqiawkD2pJKsRwIuW43WWejIbrjhBrz66qv4+te/\nDpfLhdraWqxcuRLLly/HypUr4ff7cdNNN6G7uxtLly5FXl4e3G43Zs6ciU2bNvW/zoYNG1BVVYV1\n69ahqKgIfr8f//Zv/9Y3cxuxWAzXXnstcnJy4PF4MGHCBPz85z/X6tNOGixplNLynM7TZuTl/XeV\n35+ldRYaOz3rowldLKYkLOaewbNINAGbib++p7I51YWVlXm+p4QQLq2z0NDWrVuHuXPn4p577kFH\nRwfef/99AMDzzz+P888/H83NzfjWt74FVVWxdOlSfPLJJ2htbcXll1+OpUuXoqWlpf+19uzZg8bG\nRuzcuRN///vf8bOf/QzPPvssgJ4St3XrVnz44YcIBoN45ZVXMGnSJE0+52TCkkYpK2CzTZiUlfXY\n9NzcYq2z0NiSPRMHRDyuSmtfSYsk4LSYeXk7hQkhsGB6xcxJxVnPCSE42yeFnHrqqbjkkksghIDF\nYoHdbseyZctgs9mg1+tx6623wmQy4Y033uj/GJvNhjVr1sBoNKKiogJnnnkm3nzzTQCAyWRCKBTC\nP//5TyiKgoKCAtTUcJCVJY1SkkmvD1T5/c+cWlw8QessNPZk70haPJqQfduvdoVjEZeVe7GmOoNe\nh/Pqqs6uyvf9j9ZZaPhKS0sHvR2JRHD99dejoqICHo8HXq8XwWAQTU1N/c/Jzs6G+GwtatjtdnR2\ndgIAli9fjmuuuQY333wz/H4/LrnkEnzyySfj8rkkM5Y0SjlCCOvErKxfLCgvP2ng//CUvlRVwmDQ\n6eIxRfZd7gyFY3GX3axxMhoNdotJd9a08i+W53q/onUWOpxOd3hVOPR93/rWt/Daa69h8+bNCAaD\naGtrg8fj6b/n7Fj0ej1uu+02vPHGG9i7dy+sViuuvvrqUcmfyljSKKUIIURNILDh/OrqufohThyU\nnmTPxAGdKqH2/XDoCsdVt40lLV3keBy2ORMKbysKuBdpnYUGy83Nxccff3zU53R2dsJsNsPr9SIa\njWLNmjUIBoPDPsbmzZuxbds2JBIJmM1m2O126LneJaevU2op83juOqu8fImJ//NmFFWRMBh0eql+\n9mt5PK4oJm77lVYmFASyW0PdjwRcto+bO8IfaJ1nvHU0h5PyGDfffDNWrlwJn8+HgoICzJo167Dn\n3HLLLdi2bRvy8/Ph9Xpx0003oaysbNjHaGhowPXXX499+/bBZDLh5JNPxmOPcb1jMdyhSCKtFbhc\nC+YWFz9Vm5WVo3UWGj/PR3c25U30+ywdUX1zQ1fzObPKAgCw+bVdzfPKe/5O6UNKid9u+3jr6x8d\nOFNK2a51nrEghJixdevWrQNX/ee2UJlp27ZtqKurq5NSbhvqcf4aSinBZjTmzC4sfIQFLTOpilT1\nep1eDvitMqGoipaZaGz0zvisC0VizwkhzpdSZsS/s9Fo5FZNdBje1ENJTwhhqPL7nz6tpISL5mQo\nVZWq3qiDKiEHvE/LSDSGDHodzq2rOrsi1/uw1lmItMSSRkmvwut96OyKivk6zuTMWKqiSoNe9OwF\n1fe+BO/VSGcOi0nMmVC4PM/nPEvrLERaYUmjpFbocl1wSlHRCofJxO/VDKYqUtUZdFDVAe9TJVt7\nmqvK9wcqcr0PCSGSb9dxonHAH3yUtEx6fVa13/9ghc/n0zoLaUtVVFWv10HKz65xSpa0jHDG5NLp\nNYWBH2idg0gLLGmUlIQQugmBwJOnFhdP1DoLaU9VpDQYdMCAkTSFJS0jGA16nDap5ILiLDdXNqWM\nw9mdlJQqvN6vLSgvP4sL1hIAqIoKg0GHQXehqTx/ZYp8n9NeW5R1u9Nq/l1nd3S/1nlGQ99G5ZTZ\njvV9wJMcJR2/zTZ1Xmnp1W6Lhd+fBKBnJE1v0EHKntEzRVEhJDfkziRzqgurDrZ2/lAIca6UUj32\nRyS17cuXL6/TOgQljSMukMcfgpRUhBC6k3JzvzM5Oztf6yyUPD4bSesZSotEE7CZDLzcmUF0OoEz\np5XPD0Vi9wBYrXWeEyGljAMYcvFSooF4LYmSSpnHc/sZpaVzuXE6DdR/T5qEAHpKmt1sMmmdi8aX\nx24xzKjIuzbbYz98XyKiNMSSRknDYTIVT8zKupaXOelQqiqh0wvI3pIW7o4rTouZu6tnoCklOfml\n2Z61Qgj++1PaY0mjpCCEEOVe76N1+fmlWmehJCSl1Ol0QO89aeHueNRl48/oTHXm1PI51fn+72id\ng2issaRRUih2u6+dW1JyJncVoKHI3u2gpOw5Z4W6YgmPnSUtU1lMBkwvz7044LKdpHUWorHEkkaa\nE0L4qv3+m7PtdovWWShZ9c4Y+GwkTTosvCUtk9UWBrKLAu7/EryBldIYSxppbmJW1qNzCgsnaJ2D\nkpdUASklBIQOABRFJnRcQy+jCSHw+YlFpxUFXFzkltIWz3KkqUKX6+LPFRaeZ+APXDoqKRMJFQaD\nrm+dtFRfJ4tGQZbLbi7P8d4ghLBpnYVoLPAnI2lGCGEv83rvKXK7uXkyHZWUkIm4CqOh55SlKim/\nmCmNklNqi6ZW5vm+qXUOorHAkkaaqfL5vjm3uHi61jkoBUhASagwGfR9lzvlsT6EMoPZaMCUkuyL\n3XZLtdZZiEYbSxppwmo05k0IBC4wG7gkGg2D7LncaTH1fMOoKjsafWZqaU5BSZb7W1rnIBptLGmk\niTKP579Pys0t1joHpQYJIBFXYDTqekqaIjmjj/oJIfC5mqJ5hX7XpVpnIRpNLGk07nxW66TJ2dln\n6zlZgIZPxuOqaukdelUVyW8eGiTf57SXZHtuE0JwbRZKGzzR0bgrdLkemJiVla11DkohUiIeVxIW\ns6H3TZY0OtypE4tnVuR6U3rzdaKBeKKjcZXrcMybkZd3OtefpJGQEjIeUxJmkwFSSkiVJY0OZzMb\nUVuYdbnVZMzXOgvRaOCJjsaNEEIUuFx3l3m9bq2zUIqRQCymqGazHvGECpPewHMXDemkityS0hzP\n17XOQTQaeKKjcZPvdF42u6DgFK1zUGqKRxVptRjQHYnDauS0YBqaXqdDZZ5vgcmgz9I6C9GJYkmj\ncSGE0Be5XF/OcTisWmehFCQlYjFFWkwGRKIJOK0m7q5ORzS9LLeoPNe7RuscRCeKJY3GRbHbff3n\niopmaZ2DUpSEVBKqajIZEA7HYy6rmSNpdEQGvQ7lud5zhBA+rbMQnQiWNBpzQghzqcez0mOx6LXO\nQqlJQkLKnq2gusKxuNtq0ToSJbkZ5XllVfm+r2mdg+hEsKTRmCtxu788u6BgmtY5KHVJCUjZsxVU\nVziuuO0saXR0RoMeZTnehUIIl9ZZiI4XSxqNqd4ZnRfbTVxfko6fAETfpuqRaEKxmHi1k46triKv\nqjLPd4/WOYiOF0sajak8h+OS6bm5M7TOQalNSkj0btepqD1ljehYzEYDSrLdi4QQdq2zEB0PljQa\nU/lO51XZdjuH0ejESAm19560vhE1ouGYVVlQU57rvUvrHETHgyWNxozPaj2pJhCYo3UOSguir5qx\npNFIWEwGlGS5lwgheCMjpRyWNBozBS7XbZU+n0frHJT6eicOAABURZUax6EUM6uqYFJZjuc2rXMQ\njRRLGo0JIURWmcczl3t00mgQ+Gx2p6Kwo9HI2MxGFPpdFwkhuAwQpRSWNBoTVT7f3VNzcgq1zkHp\no/9ypyrZ/GnEppXlTs71OC7WOgfRSLCk0agTQliK3O6zjHr+0kqjRQr0Xu6UquQ3Fo1YwGUz5vkc\nl2udg2gkWNJo1BW73TfMyMubpHUOSiMSUCUEAEiV5y06PkUB92whREDrHETDxZMdjSohhChwOi9y\ncPFaGkVSQkCVUFUJSJ636PhMLsnOr8j13qx1DqLh4smORlXAZjt3ak4OF6+lUSYBQBeJJmA1Gnje\nouNiMuiR63XMF5zRRCmCJzsaVXkOx/I8p9OsdQ5KN0JIFYhEE7CbOUxLx29iUda0gMs2T+scRMPB\nkkajRghhy3M6Z2mdg9KRBKTUdUfiqsNi4i8BdNzyfU5rntdxjdY5iIaDJY1GTYHTedXUnJxKrXNQ\nGpIQEhDhcDzmtpp5qYqOmxAChX7XHCGEU+ssRMfCkkajJs/pPI8TBmgsCEBASl1XOB5327m7D52Y\nqWU5ZSXZ7v/QOgfRsbCk0agw6vV5xW43JwzQmFBVKXRC6LrCMdVp5dVOOjFWkxF5XudCrXMQHQtL\nGo2KIpfrhtpAIEfrHJSepCp1RoMOiYSqGPQ8bdGJq87313kdVv5iSUmNZzsaFTkOxxzuMEBjRVWl\nzmTUC0VVFa2zUHooy/E487yOf9c6B9HRsKTRCTMbDGUlbvc0rXNQepKyv6TplUTfDp5EJ0YIgSy3\nfarWOYiOhiWNTliRy/UfVX6/T+sclJ5kTy8TZqPBoKpS6ziURoqz3DVWk7FG6xxER8KSRicsx+GY\nbdDxW4nGhqpKASmF2aQ3qIpkS6NRU5rtcRX4nVdqnYPoSPiTlU6I1WisLvV4eMmAxoxUIaQEzGaD\nTlEl10ijUWPQ6xBw2Th5gJIWSxqdkDyH41/LvV631jkofUkphVQlzCY9VEVlSaNRleNxTBRCeLTO\nQTQUljQ6IQGbbQovddJYUlUpVFUKi9kAqYJTiGlUTSj0FxT4ncu1zkE0FP50peMmhDB6LBbedEtj\nSvbckyZ7SprkOYtGld1sQrbbPlfrHERD4QmPjpvXYplX6fOVaZ2D0lvfSJpBr4NBx2FbGn0+p3WK\nEMKgdQ6iQ/GER8ct225fkm2388RGY0pKCakCEIDNaOTlThp11fn+Kq/DMl/rHESHYkmj4+a32WqF\n4H3cNLakKgUgZSymwGE2mbTOQ+kn22035HocS7XOQXQoljQ6LkIIT8Bmm6B1Dkp/UkoBCNkdiced\nNjNLGo06IQQCLhuXEqKkw5JGxyXP4Vha5fPla52D0p+qSqHTQXZ1xeMuq1nrOJSmirLctWajoVLr\nHEQDsaTRccmy209zmvkDk8aeVAEhIUPhWMJtt2gdh9JUabbHned1XKp1DqKBWNLouHgsllqtM1Bm\nkKoqhE4nu6MJ1WbiPBUaGyaDHl6ndZLWOYgGYkmjETPodKUFTme11jkoM6iqhBBQlYSqcAUOGksu\nq7lC6wxEA/GMRyNW6HJ9sYxbQdE4kaoUOgGpKFLVOgulN7/TWiqE8Gmdg6gPSxqNmMdimWDSc7kq\nGh89I2lCqipLGo2t0mxPTo7Hfo7WOYj6sKTRiDnN5mKtM1Dm6B1Jg6qoUusslN7cdgt8Diu3iKKk\nwbtwaUSEEIYF5eWFWuegzCFVCaETqqpIrpxMo64jHMXuxrZQUzDcHYkm1HhEmaV1JqI+LGk0Ikad\nrrrA5eL6aDRuVFUKIQQUhQNpdGIi8QT2N7dH9jV1hCORRDwWU/ROvckyKZDtmJqT6wCAV/fsaRVC\nCCklv+FIcyxpNCI5Dsc5uQ6HVesclDmklNDpAKi8PYOGT1FV1LeFErsagqGu7lgsGlVgUHXmCV6/\n63R/ieVIM4VzHY5CAKUAdo1nXqKhsKTRiLjN5lqLgd82NH6kCiEgpKpKljQakpQSrZ3dcldDsLMt\n1B2NRhOqEpemEofbMSuQ7zF7h3/OKnC5nPlO57kAHh27xETDw5+2NCJOs7lI6wyUWXpnd0JKnq+o\nR1ckht2Nwa6G1lAkEk0kYjHFEDDbrZOzsl2e3BPblcJmNMJjsUwZpahEJ4QnPRo2IYSYX1bGkkbj\nSkopIKXeYtBzJC0DxRIKPm3pjO1tau8KR+KxaDShswqjZaIv4JyYnWUXYvTnkzhNptJRf1Gi48CS\nRiORn2WzFWgdgjKLlBKKIvVWi5GL86U5VZVobO9SdjW0hTrDsWg0moBIwFzh9jlO8Rd5DeO044Td\nZOJ5jpICSxoNW67DMb/A5fJonYMyjASUhGpwWcxmraPQ6JFSoiMcxa6GYKilI9zdHUmoiZhqKrA7\n7dMCOW6b26RZNrvRGBBC2KWUXZqFIAJLGo2Ay2ye6TRpd+KkzBVPqDqX1cyRtBTWHYtjX1N794GW\nznAkkkhEowm922ixTg5kO6b3Ln+RLLLsdr9OiDIA/9Q6C2U2ljQaNrfZXDQW938QHYNQFAmXnQNp\nqSKhqDjY1hnf3RAMhbvj8WgsIYyqzlTjy3Kf7i+xHmn5i2ThtVhM2Xb7TLCkkcZY0mjYLAaDX+sM\nlJlURYXbxpKWjFQp0dIRlrsagp3tXZFoJJKQMgFjqdPjnO0v8Jp8qfdjxmEywWowTNQ6B1Hq/d9D\nmjHq9T6tM1DmEUJAQsLE9fmSQmd3FLsbgl2Nwa7uSDShxGOqIcdit08KZLlcJ7j8RbIQQsBhMmVp\nnYOIZz0aFiGE+byqKk4aIE3opFC0zpCJovEE9jd3RPY3d4TDkXg8FlP0dmG0TApkO6bk5Ni1zjeW\nbEZjQOsMRCxpNFz5PquVJY3GnRCAALiP4hhTVBUNwVBid0Mw1Bnu2UZJpwpztdvnPNVXbNEn+X1k\no81iMLCkkeZY0mhY3GZzpcdiSaoZWJQ5dDrBkjaKpJQIdkWwq6Gts7WjOxKJKqoSV01Fdpd9Rla+\nx+LhjwaTXp/FjdZJa/w/kYbFaTZP5fIbpBW9TqhaZ0hl4WgcexqD4YOtoe5INJ6IRVWDz2S1TsnK\nds7IzXdqnS8Z+axWH4BcAAe1zkKZiyWNhsVqMBSYeeM2aUQndBzNGKZ4QsGnrZ2xPY2fbaNkhsEy\n0Z/lPCNQYkv25S+Shd9m8zpNphqwpJGG+FOXhsVqNHq1zkCZiyNpQ1OlRFN7l7q7IdjZ0RWNRqIJ\nICFN5S6f43P+Qq9Rz/V/j5fHYoHbYpkFYLPWWShzsaTRsFgMBpY00sx47dmY7DrCUexuaAs1tYe7\nI9GEGo+pxjyrwz4pK9vtcPF2hNFk0uthMxq5hydpiiWNhsWs17OkkSYkAH0GXqOLxBPY19Qe2d/c\nEY5EEvFYTNE79WbLpECWY2qSbaOUrkx6vVXrDJTZWNJoWIx6PZffIE0IKYVeiLQuaQmlZ/mLXQ3B\nUFd3LBaNKDBInXmC1+863V9iycCOmhQMOh1LGmmKJY2OSQhhOreykiNppBmH2ZQ2m8ZKKdHa2S13\nNQQ720Ld0WgkIdWENBY73I5ZgXyP2cvTcrIw6HTpsYUCpSyeDWg4PA6Tib9RkiYkBOzm1F3/pSsS\nw+7GYFd9aygSjSYSsZhqyDLbrJOysl2eNNlGKV1xJI20xpJGw2E3Gwz8aULakBJOizkldlePJRQc\naOmI7W1sD3VHE/FoJKGz6oyWSf4s56Sc7LTeRikd6YXgeY80xZJGw+Ew6/U8WZFmXLbk62iqKtHY\nHlJ2NQRDnV2xaDSagE4Vpgq3z/l5X7GPM1JTn54jaaQxljQ6JqvB4DHp9fxeIU1ICeG2a1vSpJRo\nD0exq76ts6UjHIlGFTURV00FNqd9WiDHbXOn7NVYOgq9ECxppCn+4KVjsptMARMXxSSNSCnhtIxv\nCeqOxbGvqb37QEtnOBJJJGIxRe82mK1TsnKcJ+XmcRulDKHnxAHSGEsaHZNJr/ezpJFWBCDHcgmK\nhKLiYFtnfHdDMNTVHY/Foglhgt5S4w24TveXWLn8RebSCWHlJuukJZY0OiYBOHl/DWllNH8+qlKi\npSMsdzW0dbZ3RSORSAIyAVOZ0+OY7S/wmnw8JdJnzHq9GYAVQFjrLJSZeEaiY9IJYdGzpJFWTqCj\ndXZHsbsh2NUY7OqORBNKPKYacyx266RAlsuVa3GNXkhKRzaj0QzABZY00ghLGg2HRS/SZi1RSjFC\nDq+mReMJ7G/uiOxv7giHI/F4LKboHcJomRjIdkzJyeHyFzRiFoPBip6SVq91FspMLGl0TDoh9IIl\njTQy1NVORe3ZRml3fTDU2R2LRaMK9KowV3v8zlN9xRz5pVEhe8ZxVa1zUOZiSaNj0gnBWQOkGQGJ\ntlA3djW0dbZ2dEciUUVV46qp0O62z8jK91i4jRKNkYSqJgDEtc5BmYtnNzomnRD8PiHNZNscse3v\nNsanZGU7Z+Tmc/kLGjdKT0mLaZ2DMhd/+BJRUvvXqdN5PxlpIqGqCljSSEO8cYOOKa6qMS4TRESZ\nprek8XInaYYljY5JlbIjofLeWSLKLIqUKjiSRhpiSaNjUqUMxlnSiCjDcCSNtMaSRscUV5TWmKJo\nHYOIaFypUipSSp78SDMsaXRM3YlEM0saEWUaFjTSGksaHVMoFmuLKwqvdxJRRpFAQusMlNlY0mg4\nuqKKEtE6BBHReJJSsqSRpljSaDi6oolEVOsQRETjSQK83EmaYkmj4QhFWNKIKMMoqtqtdQbKbCxp\nNBxdUUXhNHQiyigJVe3UOgNlNpY0OiYppaKoKhd0JKKMElMUljTSFEsaDYsiJYf9iSijxBSlQ+sM\nlNlY0mhY4orSqnUGIqLxFOVIGmmMJY2GJaooLVpnICIaL1JKRBIJjqSRpljSaFi643GWNCLKGOF4\nHNFEYrfWOSizsaTRsHTF4y1SSq1jEBGNi45oVG3t7n5H6xyU2VjSaFi64/Ed4ThX4SCizNDa3R2M\nKsoerXNQZmNJo2Fp6e5+KxiJcPVtIsoI7dFoEECj1jkos7Gk0bDEFGVXS3c370sjoowQU5Q2KaWq\ndQ7KbCxpNFxtHT2/WRIRpb24orRpnYGIJY2GRUopY1yGg4gyRDgeb9I6AxFLGg0bSxoRZQJVSnRE\nowe0zkHEkkbDxrXSiCgTBCMRtEejf9U6BxFLGg1bVzzezLXSiCjdHezsbGkOh/+sdQ4iljQatvZI\n5K/BSETrGEREY6qlu7teStmgdQ4iljQatqZweMu+jg7eTEtEaa07Ht+vdQYigCWNRkBK2dLa3c2T\nFxGltc5YjOc5SgosaTQiXbHYbq0zEBGNlbiioCMa3aV1DiKAJY1GKBiJ7OXkASJKV03hcKKpq2uT\n1jmIAJY0GqFgJPIqJw8QUbo60NFR351IvKN1DiKAJY1GqHfyADcdJqK0FIrFPpVSdmudgwhgSaMR\n4qPZc74AABScSURBVOQBIkpn4XicOw1Q0mBJoxHrisX2aJ2BiGi0qVKiLRLZrXUOoj4saTRinDxA\nROmoIRSKN3Z1vah1DqI+LGk0Ym2cPEBEaWhnW9uujmiU20FR0mBJoxFrDoe37G1v5+QBIkor7dHo\nh1LKhNY5iPqwpNGISSlbmsLhj7XOQUQ0WhRVRXM4/L7WOYgGYkmj49LW3c2TGRGljU87OyP1odBP\ntc5BNBBLGh2XpnD4923d3Zw9QERpYVcwuDMcj/9D6xxEA7Gk0XFp7Or61cetrXu1zkFENBraI5EP\npZSq1jmIBmJJo+Mipexq7e7+SOscREQnKq4oaOnu3q51DqJDsaTRcWsOh99VuV4aEaW4ve3toYOd\nnc9qnYPoUCxpdNzqQ6Gf7O/o4B53RJTS9nV0fBJVlA+0zkF0KJY0Om6dsdibO9vaeMmTiFJaR8/6\naLwsQEmHJY2Om5RStvI+DiJKYV2xGBq7urjLACUlljQ6IY1dXS+3RyL8DZSIUtK7TU2793d0rNc6\nB9FQWNLohNSHQj//oLl5j9Y5iIiOR30o9JaUMqR1DqKhsKTRCZFShpvC4X9qnYOIaKQ6olFZHwr9\nn9Y5iI6EJY1O2MHOzt92RqNaxyAiGpF3Gxs/+bSz8ymtcxAdCUsanbADnZ3rtzc2csN1IkopDV1d\nb0kpuYwQJS2WNDphUsrug52db2idg4houIKRiFofCr2kdQ6io2FJo1FxMBR6uj4Uimmdg4hoOP7Z\n2LijPhT6qdY5iI6GJY1GRXM4/Nv3mpo4gYCIUkJjV9c2KSVvpqWkxpJGo0JKqTaEQn/jXp5ElOxa\nwuFEfSj0C61zEB0LSxqNmn0dHd/7uLW1XescRERH825T04eNXV0vaJ2D6FhY0mjUdMVi7+9sa/uH\n1jmIiI5ESomGUGiblDKhdRaiY2FJo1FVHwptiSZ47iOi5LS3vT30aWfn97XOQTQcLGk0qnYHg+v+\n2di4X+scRERDea+p6c3W7u6/aJ2DaDhY0mhUSSlb93d0bNU6BxHRoUKxGD7t7HxR6xxEw8WSRqPu\nYCj0dGNXF6e2E1FS+cfBg+/v6+jgpU5KGSxp/7+9Ow+OszzsOP6+0lpaSbvaXVmybNnyIYPdmKsc\nYcZ42pKGSdL0j5RAgZJJO8kkJKSlZcDhyExLSdrhaKbAtIGEQobSZhJIMKXlaDCnjTG+8Ckfklar\n1d7n++77vntor6d/4DaFgQHb0j7v7n4/Mx7/+/vH8lfv7vs8mHdxy3rm3ViMp2kAbKMuhBI2jLeE\nECXZW4BPikjDvBNCiIhhPGXMzdVlbwEARVGU4+l0ejaXu0/2DuBUEGlYECHDeGRvNHpY9g4AUBRF\nmchkdhQqFb/sHcCpINKwIIQQlbBhvMBxHABkCxtGIWIYP5K9AzhVRBoWzLSm3bsvFpuSvQNAezuU\nSOxJFQqvyN4BnCoiDQtGCGEGdf21Wp2vpgGQI1cq1SKG8XMhuFgYzYdIw4IK5nLfP5xMxmTvANCe\n9sVihyKm+bjsHcDpINKwoIqVSsSfzW7jl1gAjZYvl8VsLreFezrRrIg0LLiwYdw7lc3qsncAaC/v\nhMP7Z3T9ftk7gNNFpGHBZYvFA8fS6R2ydwBoH7lSqRbM5Z4UQpRlbwFOF5GGhoia5kNBXTdl7wDQ\nHnaGw3tmc7l/lr0DOBNEGhoiZppb90ajr/PdNAALLV0olEO53KNCiJrsLcCZINLQMCHDuGM8lUrI\n3gGgte0Kh9+JmOYTsncAZ4pIQ8NoxeKxI8nki1XOTQOwQGKmWQwbxkOci4ZWQKShoY6n09/dG41y\nfx6ABbEnGt0RNc0tsncA84FIQ0MJITKTmcwvi5WK7CkAWsyMrpthw7hH9g5gvhBpaDi/pt29Mxw+\nKHsHgNYhhFD2x2LbEpb1muwtwHwh0tBwQohSQNMe04pFHqcBmBeT2Ww2bBh3y94BzCciDVKEDOPh\nt0OhXbJ3AGh+lVpNeTcWeyFdKOyRvQWYT0QapBBC1MOGcV/YMPKytwBobjvD4aPH0+mbZO8A5huR\nBmmipvn87kjkDd6UB3C6kvl8aTKTeVAIkZO9BZhvRBqkms3lNh9MJMKydwBoPnUhlLdmZ18PGcZj\nsrcAC4FIg1RasXj8UCLxpFUu8zgNwCk5EI+Hgrp+EwfXolURaZBuWtPuemNmZqfsHQCah1UuiyPJ\n5JN6qcTh2GhZRBqkE0JUZ3O5206k02nZWwA0h23B4M5pTftb2TuAhUSkwRYSlrVjbzT6q7lqVfYU\nADY3lc1mgrr+PSEEPzDQ0og02MZkNnvLm8HgPtk7ANhXpVZT9kQiz8Ut603ZW4CFRqTBNoQQxWlN\nu2Myk8nI3gLAnnaEQkdOZDI3y94BNAKRBluJmeYruyORp7iAHcAHhXI5Y1rT7hVCmLK3AI1ApMF2\nJrPZW16fmdktewcA+yhWKsr22dmng7r+M9lbgEYh0mA7Qoi5oK5vPp5OJ2VvASCfEEJ5ZXr67YlM\n5s9lbwEaiUiDLcUta/ueSOSJXKnE21tAm9sXi80EdP0bQoiy7C1AIxFpsC2/pt35st//crVelz0F\ngCRxyyocTiTuzRQKx2RvARqNSINtCSHq46nUV14PBA7K3gKg8eaqVeX1QGDLjK7/RPYWQAYiDbYm\nhNCnstnvHE4korK3AGisVwOB3ScymRtk7wBkIdJge3HLentfLPbDhGUVZW8B0BgH4/FQQNNuFELw\n7x5ti0hDUwho2gOvBgLPlLg2Cmh5qXy+tD8efyCZz78rewsgE5GGpjGRyXxzq9+/UwghewqABVKp\n1ZTXAoHnZ3T9QdlbANmINDQNIUQpoOt/tisS8cveAmD+CSGUl/3+PcfS6a8LfhsDiDQ0l0yhMDme\nTP71jK7rsrcAmF9vh0ITE5nMn3DtE/AeIg1NZzaX+/n2YPAJY26uJnsLgPkxnkzGxlOpv9BLJZ6U\nAycRaWhKfk279YWJif/gRQKg+YVyOWNPNPqDiGFslb0FsBMiDU1JCFE/kclc//zExNZKjQdqQLPS\nS6XKGzMzjwU07RHZWwC7IdLQtIQQ5SPJ5FUvTU29U+c7xkDTKVQqyouTk1v8mrZZ9hbAjog0NDUh\nhHkslfqjrX7/QV4GA5pHpVZTXpiYeHkik/kqb3ICH45IQ9MrVCqJY+n0tW/Nzh6XvQXAx6sLobw0\nNfXOeCp1lRCiInsPYFdEGlqCViyeGE+lvrEvGp2RvQXARxNCKK8FAoePplJfFkJYsvcAdkakoWXE\nTHPH/nj85mOpVFz2FgAfbkcoNHEinf5KsVKJyd4C2J1D9gBgPoVyuedWe71LnA7HfWt8Pp/sPQB+\nY3sweOJQInF9Mp8/LHsL0AyINLScGV3/lzGfb4nT4bhzmdvdJ3sP0O6EEMr22dnjhxKJ61L5/EHZ\ne4BmofJSDVrVWQMD3//MmjU3r+jvd8veArQrIYSyLRg8diSZvJYnaMCpIdLQ0sZ8vtt+d9WqO/jo\nE2g8IYTyZjB47HAi8cfpQmFc9h6g2fBxJ1ratKbdv8bnM6v1+t1nL148JHsP0C6EEMrrMzPjR5LJ\nazKFwlHZe4BmRKSh5QU07ZFVXm+hUq/fs2FoaJnsPUCrOxloRw4lEldrxeIJ2XuAZkWkoS0Edf1f\nV3o8ZqVWe/CCpUtHZe8BWtX/noM2nkpdpRWLk7L3AM2MSEPbmM3ltizv77cq9fqPLxkZWSN7D9Bq\nhBDKq4HA4cOJxJV6qeSXvQdodrw4gLazzO3edP7w8E8vGx1dJ3sL0CoqtZrya79/10Qmc22uVArK\n3gO0AiINbWnY5frtDUNDP/u9Vas2qKoqew7Q1Kxyuf7i5ORLR1Op67jqCZg/RBra1kBPz7r1g4O/\nuGJs7EJHBzekAacjYVmFrdPT/z6VzX5HCFGTvQdoJUQa2pqqqr5zlyx56otnn31F76JFPFIDTsFE\nJpPaGQr947Sm3St7C9CKiDS0PVVVHesWL370irGx65b09fXI3gM0gz2RyPTBROK2UC73jOwtQKsi\n0oCTxny+OzaOjt6yjkNvgY9Uq9eV1wKBgxOZzNeS+fx+2XuAVkakAf/PqMdz5Yahofs2rlhxNi8U\nAO9XqlaVlyYn3zyaSl1drtXSsvcArY5IAz7A63SuPWtg4N8+t3btxm4HRwkCiqIoWrFY+e+pqWdP\nZDJ/KoSYk70HaAdEGvAhVFV1rl+8+KdXjI1dOdTX55S9B5BpPJmM7YvFfjytaT8Q/KcBNAyRBnwE\nVVXVNV7v7ZcuX37zp4aGhmXvARqtXKsprwUCewOa9pdxy9opew/Qbog04GOMuN2/v8bn+4fLV6++\nqKuzU/YcoCGippnfFgw+ezyd/pYQoiB7D9COiDTgE1BVtW/94sU/2rRy5ZUrPZ5+2XuAhSKEUHZH\nIv7xVOqeoK4/LnsP0M6INOAUjHo815w9MHDXppUrN3BLAVqNVS7Xt/r926Y17evG3FxA9h6g3RFp\nwClSVXVgw9DQY5evXv0FDr9Fq5jKZtM7Q6En/Zp2G9c7AfZApAGnaZXXe+OGoaHNly5fPtbBmWpo\nUtV6XdkWDB6eyma/GzGMX8veA+A3iDTgDLi6ukbHfL7HPjs29hmv07lI9h7gVMzmcsbOUOjFY+n0\njUIIXfYeAO9HpAFn6ORRHd+7YOnSb18wPLyCmwpgd6VqVdkWDO6d0fW/ixjGc7L3APhwRBowTwZ6\nejaMejw/vGx09PKlLhffVYPtCCGUo6lUfH88/vRUNnu7EKIkexOAj0akAfNsRX//9au93ls2rVx5\nce8iPgGFPWSLxfL2YHDHbC53a7pQ4GJ0oAkQacACUFW1a63Pd9f6wcHrL162bHUnx3VAknKtpuwM\nhY76Ne3h2VzuYa51ApoHkQYsoJ5Fi5aN+XwPXDIy8vkxn88rew/ax8mPNhMHE4nnJjKZzUIIU/Ym\nAKeGSAMaYKnLdfmK/v67f2fVqo28BYqFFjaM/J5IZEfYMG5PFwoHZO8BcHqINKBBVFXtWOnx3LTW\n57th4+joBu4BxXyLmWZxbzT6dsQ0H4yZ5vOy9wA4M0Qa0GCqqrrXLV7892u83i9ePDKylljDmUpY\n1tyeaPSdsGH8U9yytvC9M6A1EGmAJKqqes4aGPib1V7vH14yMrLe6XDInoQmky4UKrvC4V1hw3g4\nZlm/IM6A1kKkAZKpquoa8/nuXOXxfOnTy5efw7Ed+DjZYrGyKxzeGzaMn0RM80niDGhNRBpgE6qq\nOld7vZtXejxXf3pk5Hx3dzdXF+B99FKptisc3hcyjMfChvG4EKIuexOAhUOkATajqmrXSo/nr0b7\n+6+5dPnyCz1OJ19aa2NCCCWg6+bxdHpP1DR/FTaMR4UQNdm7ACw8Ig2wKVVVHaP9/d9a3t9//fnD\nwxeNuN1O2ZvQOHPVqnIwkQgGdX1H1DQfyhaLu2VvAtBYRBpgc6qqqkO9vVcsc7u/ucrj2Xje8PAK\n3ghtXal8vnIgHj8Qs6yXpzXth0IIXfYmAHIQaUATUVV1cMznu3WZy/XZ84eHLxh2ubpkb8KZq9Xr\nyrF0OjWtaTsjhvFkIp9/lu+bASDSgCakqmrHUG/vHyxzu7+63O2+9Lzh4TW8Fdp89FKpfiiROB4x\njG0hw7g/Xy4HZG8CYB9EGtDkVFV1r/J4vr3U5fr8mM930VkDAz4udLevbLFYP5pKTaYLhQNxy3o+\nblm/FELMyd4FwH6INKCFuLq61i1zu28Y6u29cKnLde76wcElHJIrX6ZQqB1NpSZShcKBhGX9ZyKf\n3yKEKMveBcDeiDSgRamqumSVx/O1ob6+ywZ7e8/9rcHBNV6nk7PXGiRdKFSPplKTqXx+fyKffzaZ\nzz8nhKjI3gWgeRBpQBtQVbVnuK/vy8Mu1xcGenouOHtgYN2I292tqjTbfKnV60rYMIozuu5PFwoH\nE/n8M8l8/r+EEFXZ2wA0JyINaDOqqnZ4nc5NS12u6wd7e89b3NMzttrrXeZ1OhWi7ZOrC6EkLKsy\nrWkBvVSayBSL43HLerpQqRzgzUwA84FIA9qcqqqeod7eKwZ6ei739fSMubq61q7o71+53O3uWcR5\nbP+nWq8rEcMozuZyQatc9meLxclkPv9cbm5uBx9jAlgIRBqA91FVtaO7s/OcpS7Xl3w9PZ/q7+4e\n8zmdq9f4fEs93d1t8bStXKsp6UKhGjXNhDk3FzPL5elssXg8blnPlqrVQzwpA9AIRBqAj6Wq6sBQ\nb+/nBnp6Nrm7u5c6HY7h7s7OJb6ensElfX0DPqdTbcanbuVaTUnl89WYZSWscjlWrFRiVrkcNebm\nptOFwpvFavWIECIveyeA9kSkATgt6nuP1IbcXV3neJ3Oje7u7hV9ixYtcTocS7odjmGf0zm4pK9v\nwNXVpTgdjoY/gavV60qxWlWsclkx5+ZMY27OLFarVqVWM0rVatQsl2PmezH2xskYKzR0IAB8DCIN\nwLw7GXDD/d3d57i7utYv6uwc6e7sdDsdjr5FnZ3uRR0dLkdHR29nR4ezQ1V7OlTV2amqTqfD0e3o\n6OisC1GvCyE++LdQFKEoSv3kx411RVFEXYhKtV43K/W6Ua7VjHKtZpaqVaNUrerlWm1GL5UmStVq\nTFGUJE/FADQTIg2ALaiq2qEoiktRFIfyXoDVPvhH8AMLQBsh0gAAAGyIC/4AAABsiEgDAACwISIN\nAADAhog0AAAAGyLSAAAAbIhIAwAAsCEiDQAAwIaINAAAABsi0gAAAGyISAMAALAhIg0AAMCGiDQA\nAAAbItIAAABsiEgDAACwISINAADAhog0AAAAGyLSAAAAbIhIAwAAsCEiDQAAwIaINAAAABsi0gAA\nAGyISAMAALAhIg0AAMCGiDQAAAAbItIAAABsiEgDAACwISINAADAhog0AAAAGyLSAAAAbIhIAwAA\nsCEiDQAAwIaINAAAABsi0gAAAGyISAMAALAhIg0AAMCGiDQAAAAbItIAAABsiEgDAACwISINAADA\nhog0AAAAGyLSAAAAbIhIAwAAsCEiDQAAwIaINAAAABsi0gAAAGzofwAPC8l7rmz+vAAAAABJRU5E\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2066546d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = df.Gender.value_counts().index\n", "N = len(df.EmploymentField.value_counts().index)\n", "HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]\n", "RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))\n", "patches, texts = plt.pie(df.Gender.value_counts(), colors=RGB_tuples, startangle=90)\n", "plt.axes().set_aspect('equal', 'datalim')\n", "plt.legend(patches, labels, bbox_to_anchor=(1.05,1))\n", "plt.title(\"Gender\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "a7e81a6d-ee72-aa65-11f3-eddc667aef28" }, "source": [ "New coders are mostly men, with a very high proportion with respect to women" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1894a1e3-a9e9-91d5-557e-647031b13b90" }, "source": [ "**Distribution of Job role interest**" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "673b84bf-10ea-12c0-ec91-4f02a63a6090" }, "outputs": [ { "data": { "image/png": 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kp6fTsWPHv91nQkICRqNRLxM5O6vdokULFi5cyP79+zl9+jT9+vXjjjvu4ODBg1X2dfjw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VAAAg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\nXftdNXYDjn+HssUYo+4Bj+NYr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"text/plain": [ "<matplotlib.figure.Figure at 0x7f20665462b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "N = len(df.JobRoleInterest.value_counts().index)\n", "HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]\n", "RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))\n", "labels = df.JobRoleInterest.value_counts().index\n", "colors = ['OliveDrab', 'Orange', 'OrangeRed', 'DarkCyan', 'Salmon', 'Sienna', 'Maroon', 'LightSlateGrey', 'DimGray']\n", "patches, texts = plt.pie(df.JobRoleInterest.value_counts(), colors=RGB_tuples, startangle=90)\n", "plt.axes().set_aspect('equal', 'datalim')\n", "plt.legend(patches, labels, bbox_to_anchor=(1.25, 1))\n", "plt.title(\"Job Role Interest\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "465fa8ef-3c6c-18d1-6d11-541f54da5828" }, "source": [ "The interest of new coders seems to lie in Web Development (both front and back-end), followed by Data Science." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "92557a98-50d0-c0ae-5ae6-1d474afb8ecd" }, "source": [ "**Distribution of Employment field**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "f8d63a06-f28a-0932-d30c-84f5b28256b4" }, "outputs": [ { "data": { "image/png": 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ZQmpqqnX7vffeS5s2bbCzs2Ps2LEkJSWxcOFCnJycCA4OJjY2lp9++gmAL774AoCnn34a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"text/plain": [ "<matplotlib.figure.Figure at 0x7f20662e6860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "N = len(df.EmploymentField.value_counts().index)\n", "HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]\n", "RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))\n", "labels = df.EmploymentField.value_counts().index\n", "patches, texts = plt.pie(df.EmploymentField.value_counts(), colors=RGB_tuples, startangle=90)\n", "plt.axes().set_aspect('equal', 'datalim')\n", "plt.legend(patches, labels, bbox_to_anchor=(1.3, 1))\n", "plt.title(\"Employment Field\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9b505aeb-0ea5-7cce-dd40-ed942691c272" }, "source": [ "New coders mostly belong to the Software Development professional field. This makes sense, since it is a field of constant change and developers need to update and broaden their knowledge and improve their skills in order to be aligned with the rapid changes that take place in the market." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2fe9e3d0-056e-a367-e37d-e8a289691943" }, "source": [ "**Job preference per age**" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "348e5681-40f5-4a14-08af-14bb3b7a5adb" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f206a27af60>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Prl27Vmjbnp6efPPNN6Snp/Pll18SGRnJypUrzcqsWbOGjRs3kpqaSp06dZg4caLp2OzZ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\nkQAAAAymRAIAADCYEgkAAMBg/xtli1zUjr5uygAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f206627eeb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_ageranges = df.copy()\n", "bins=[0, 20, 30, 40, 50, 60, 100]\n", "df_ageranges['AgeRanges'] = pd.cut(df_ageranges['Age'], bins, labels=[\"< 20\", \"20-30\", \"30-40\", \"40-50\", \"50-60\", \"< 60\"]) \n", "df2 = pd.crosstab(df_ageranges.AgeRanges,df_ageranges.JobPref).apply(lambda r: r/r.sum(), axis=1)\n", "N = len(df_ageranges.AgeRanges.value_counts().index)\n", "HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]\n", "RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))\n", "ax1 = df2.plot(kind=\"bar\", stacked=True, color= RGB_tuples, title=\"Job preference per Age\")\n", "lines, labels = ax1.get_legend_handles_labels()\n", "ax1.legend(lines,labels, bbox_to_anchor=(1.51, 1))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c29b6390-911f-802f-7cee-9bde1d136c08" }, "source": [ "The interest to become a freelance worker increases with age, being the main preference for people older than 60.\n", "\n", "People younger than 30 would like to work for a startup or start their own business, while this preference decreases significatively with age.\n", "\n", "Working for a medium-sized company is the job preference for people between their 20s and their 50s." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b3e9e272-e1f2-3781-ecfe-8619f15c9dda" }, "source": [ "**Employment field and Under-employed**" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "18f20abe-e4e8-8c2c-ae22-225170a038b5" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f20662f1cf8>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SUbrwpCRJ0ibDD0CSes7GCJHDrLt8+basfTLySd1226Pnbu7r66Ovr2+t22+44QbOP/XV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\nuUT6IGAPuv7OziCVPr7e+3cGwZsjl57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9Ailfs2hci+0HsvHVrsDDJE+BL9ekAioIgqDP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rA5fuFQj7N9f3upIuqRNN32Gs3xDNurS3oe8IDttSrHizGiWQEyKNunt5ml10maCaxm2ymsExHR\nW7Insn/dDnR+IN/Lgk2cRfaaRH+5VdLbbf+i69xbgBtrBYqxJwPFEbkG+K2kPwIrSDplsAelNUpE\nRPsyiOxfL+s63qhaiojecQhwgaRzgOUlfZPSz+/tdWPFWCPpdcCewHq23yFpK2DFwfr19rn3Uvbr\nT2w+HlcxS0REdMly1oiIhqTNgX+m3FiZAnzb9h/qpoqxRNL7gROBM4G9bK8i6VXAsbbfUDVcD5P0\nM9u71c4RERFFBpEBQNN/69XAfNXvbB9ZJ1FExNgj6Q+UwePvugo4LQfcl723iyZpHWB94B7b02rn\niYjoVxlEBpK+RFnKdwPwZNcl2965SqiIiDGoM3BsjjsFnJYBHrG9euV4PUvSqpTZ279rThn4JWVA\nPqNasIiIPrVM7QDREz4GvN721rZ36nrLADIiYsm6U9J2A85tB/ypRphR5Ljm/UspeyM3owwkj62W\nKCKij6WwTkCpxHpd7RAREX3gCOB8SccD4yR9GvhX4J/qxup5uwKb2X6s+fj2pl3KrRUzRUT0rcxE\nBsCpwEdrh4iIGOtsnwe8H9iGUrxpZ+Ajti+sGqz3ddpOdZvbnI+IiJZlT2SfknQx834gLwPsQFlO\n9UD342zv2nK0iJ4haQKl4Mmc2lki+pmkHwCrUGZtJwMbAscAj9ves16yiIj+lOWs/euKAR9fXiVF\nRG+bDNwh6RDb59YOE9HHPgWcBfyZeTdALwL2qZYoIqKPZSYyImIIkt4ATAB2tv3humkiQtJ6zGvx\n8cCiHh8REUtHBpGBpI8D19i+vuvcq4FX2z6pXrKIiIiIiOg1GUQGkqYAf9vda0vSGsD1tjesFiyi\nRZI2AR61/YikFYHPAnOAo23/pW66iIiIiN6R6qwBsOogzZpnAGvUCBNRydnAus3xkcC7gN2Z158u\n4q8m6URJu0tauXaWiIiIxZVBZABMkbTtgHPbAFNrhImoZCLwh+b4H4DdgDc37yOWFAFHA9MlXS3p\nCElvkDSudrCIiIjhSnXWADgR+LGkI4A7gE2AQ4CjqqaKaJeAZSW9BHjK9mQASeOrpooxxfYnACSt\nD+wCvAn4KeXncb7XIiJiVMggMrB9iqRlgf0pvbcmA1+z/Z2auSJadg3wLcqS1l8CSNqQsrQ7YomR\n9ALg5cAWwJbAM5R2FREREaNCCuv0uWbwuBVwg+3ZtfNE1CJpA8peyNnAZ2xPl/SPlKJTB9dNF2OF\npMsoA8drgEuASbZvqBoqIiJihDKI7HOSBDwBrOR8M0Qfk7RhZwnrgPMb2J5SIVKMQZKmUuoR/Aq4\nmDKIfKRuqoiIiJFJYZ0+1wwcbwU2qJ0lorKbhjj/v62miDHN9gRgZ+D3wB7AbZKul5Q96BERMWpk\nJjKQdACwN6Vi4BRgbuea7atq5Ypok6THbY8fcG4c8KDttLuJJUrS8sAOlArA+wDjbS9bN1VERMTw\nZBAZSJo7xCXnl5oY6yRdDBh4A3DZgMsTgHts79JyrBijJH2OUpF1O2A6MKl5u8T2AzWzRUREDFeq\nswa2s6w5+tkVzfsdgSu7zs8FHgR+3HqiGMu2Bc4H9rf9x9phIiIiFkdmIiOi7zVVig+htLZ5pnae\niIiIiF6WQWR0KrTuA7wRWIvSdB0A2zvXyhXRllQpjjZJeill+fTA/28Pr5UpIiJiJLKMMQC+CnwF\nuAd4LXA9sDmQ3mXRF1KlONoi6X3AjcBHgS8A72je71AzV0RExEhkJjKQNBnY3faNkh61vZqk1wIH\n2X5X5XgRrUiV4miDpFuAw22f0/X/7d7AS20fVDtfRETEcGQQGUiaZXvl5vgRYB3bczq/4FSOF9GK\nVCmONkiaBaxi212DyOdRqgCvVztfRETEcKQ6awDcJ2mC7anAXcBbm8FkCoxE30iV4mjJTGCV5v00\nSZtRWn2sWDVVRETECOSXpgD4DrBVc/wN4DxKq4MTqiWKiBibLgH+vjk+p/n4WuDCaokiIiJGKMtZ\nYwGSXkypUnlb7SwRbUmV4mhb8z33fmA8cLrtpytHioiIGJbMRMYCbN+bAWT0oVQpjla5+KHtkzKA\njIiI0SQzkRERpEpxLD2SDhnO42wfubSzRERELAkZREZEkCrFsfRIunQYD3OWTUdExGiR6qwREUWq\nFMdSYXun2hkiIiKWpAwiIyKKTpXiqcyrUizgsJqhIiIiInpNlrNGRAwiVYojIiIiBpdBZERERERE\nRAxbWnxERERERETEsGUQGREREREREcOWQWREREREREQMWwaREdHzJN0t6aDaOWqT9BVJN43wcy6X\ndMIiHnOxpFP+unQRERHRLzKIjIhhkfQ9SXMlzWned96m1s422kjaQ9LcQc4P9hrPkfR3zUOOAnZs\nN21ERETE/NInMiJG4tfA+yj9EzvmVMoymgkYqjT2YK/xowC2nwKeWrrRIiIiIhYuM5ERMRKzbT9s\n+6Gut+nw3JLTwySdJGmmpAclfULScpJOkDRD0n2SPtH9hM1s2ycl/ZekJyXdM/AxA0l6oaQfN1/n\nCUkXSvqb5tpKkmZJ2mPA52zefK2XdX3dj0s6p3mOyZLeLWllSWc2z3GXpHcNeJ51m+uPNF//15L+\ntuv6XpKelvQ6Sf/b/JmukfTK5vqOwFldGeZIOm0Rr/EzzeO/IunmAXn2knRz8zVvl3SopCFvEEpa\nc8Br/amFvdYRERERA2UQGRFL0gHArcCrgOOAbwI/Be4GXtOcO0HS5gM+7zDgIuAVwDHA8ZLespCv\n8zNgArALsB1l1u6/JS1n+wngbGCfAZ+zD3C17T90nfs8cD6wZfOcZzSfezHwyubaDyStASBpBeDS\n5nPfCLwauBaYJGntrucdB3wF+OfmtZjZPC/AVcDHmuN1gPWAf1nIn3Wg52YwJe0L/DtwOLAZ8HFg\nT8rrOZQzgJcCuwJvBd4EbD2Crx8RERF9LoPIiBiJXSQ9PuDt9K7rk2yfYPsuyuBmJvCs7W/YvtP2\n0cB0ygCs2/m2T7L9Z9vHAecCBw4WQNKbKAO899m+zvZNlOWfawAfaB52MrCTpA2bzxkHfBD47oCn\nO9P2D5u8hwIrAHfYPr0590XgBZSBKs3XWR74kO0bm7yfA+4HPtQdE/ik7att/wn4MrCppPWbWcXH\nALpmHB9fyGt83WCvQ+OLwGdt/9j2ZNuXAF8A9h/itdsMeDOwj+0rbd/SvC75WRARERHDlj2RETES\nVwIfYf79ek90Hd/YObBtSQ8BA6uJPkSZget29SBfZ6hqrJsB05pBXudrPSrpFmDz5uPfS/pf4KOU\ngdbfUwZ//znguW7qeo6ZkmYDN3ede0LSU115twLWB2ZJ3S8BywMv6fr42QEznvdTXrN1gHuG+HN1\nDHyNZw/2IEnrAi8CviPp212XlgGWl7RGZ6lxl80oe1iv6fozzpT0x0VkioiIiHhOBpERMRJP2b57\nIdefGfCxhzjXxszXycBhkg6jDCbPbgrTdBtp3mUog8x3M/9AGmBW1/HAYkOdJajD+XMv6jXu6DzX\nfsD/DHJ9xjCeIyIiImLEsoQpInrBawd8vD0w1OzYrcA6kjbunJC0OvBy4Jaux50NjKfsS3wjCy5l\nXRzXAxOBmbbvGvD2yAieZ3aTe+BAdNhs3w88AGwySJa7bA9W/fVWyv/7z73eklalzFBGREREDEtm\nIiNiJJaTNHApKran/ZXPu7uk3wGXAG+jLD/dfbAH2p7ULFU9S9L+lAHZ14CHmVe8BttPSjqbUqjn\nZtvX/5UZAX4I/Cvwc0mHAncCL6TsM7zY9pXDfJ7OTOPukq4Anrb95GLk+SLwTUmPAxc057YAXmX7\n4IEPtn2bpIuAkyXtR5k9/Spp0xIREREjkJnIiBiJnSj7+zpvDwD3S1qWwfseDvfclyiVQm8EPgsc\naPvChXzObsAUSkXXy4G5wFtsD9w/+F1gOeCUvyLbc+ds/wXYgTLjeQZwG2Xgugnl9ViY7ue5ATgW\n+A7wIKWK7YjZPo1SjfUfKbOkVwGfBiYP9nUbewK3U167XwGXUSrMRkRERAyLBl/xFBHRDklzgffa\nPmcpPPdulJ6ML7Q9a1GPj4iIiIhFy3LWiBhzJD0fWJfStuP0DCAjIiIilpwsZ42I2pbGcoiDKEs2\nn6L0TYyIiIiIJSTLWSMiIiIiImLYMhMZERERERERw5ZBZERERERERAxbBpERERERERExbBlERkRE\nRERExLBlEBkRERERERHD9v8YMT8759mHSAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f20662575f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df4 = pd.crosstab(df_ageranges.EmploymentField,df_ageranges.IsUnderEmployed).apply(lambda r: r/r.sum(), axis=1)\n", "df4 = df4.sort_values(by=1.0)\n", "N = len(df_ageranges.EmploymentField.value_counts().index)\n", "HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]\n", "RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))\n", "ax1 = df4.plot(kind=\"bar\", stacked=True, color= RGB_tuples, title=\"Under-employed per Employment Field\")\n", "lines, labels = ax1.get_legend_handles_labels()\n", "ax1.legend(lines,[\"No\", \"Yes\"], bbox_to_anchor=(1.51, 1))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "821dfd32-055c-fef1-32e0-946376a7169b" }, "source": [ "The field where people feel less under-employed is software development, followed by software development and IT. On the contrary, food and beverage is the field where employees feel most under-employed." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "f8288d72-552c-6b02-4441-90f55a824e16" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 67, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/324/324293.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 50, "metadata": { "_cell_guid": "d2d2f1ea-c6ab-3549-6c91-92cd60f4a5a1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "genderclassmodel.csv\ngendermodel.csv\ngendermodel.py\nmyfirstforest.py\ntest.csv\ntrain.csv\n\n" } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "_cell_guid": "66032765-5eed-a226-a1d7-0599aca59680" }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline\n", "\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC, LinearSVC\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.naive_bayes import GaussianNB" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "_cell_guid": "faafa552-cd96-654c-daa2-603de1a28377" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Survived</th>\n <th>Pclass</th>\n <th>Name</th>\n <th>Sex</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Ticket</th>\n <th>Fare</th>\n <th>Cabin</th>\n <th>Embarked</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>0</td>\n <td>3</td>\n <td>Braund, Mr. Owen Harris</td>\n <td>male</td>\n <td>22.0</td>\n <td>1</td>\n <td>0</td>\n <td>A/5 21171</td>\n <td>7.2500</td>\n <td>NaN</td>\n <td>S</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>1</td>\n <td>1</td>\n <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n <td>female</td>\n <td>38.0</td>\n <td>1</td>\n <td>0</td>\n <td>PC 17599</td>\n <td>71.2833</td>\n <td>C85</td>\n <td>C</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>1</td>\n <td>3</td>\n <td>Heikkinen, Miss. Laina</td>\n <td>female</td>\n <td>26.0</td>\n <td>0</td>\n <td>0</td>\n <td>STON/O2. 3101282</td>\n <td>7.9250</td>\n <td>NaN</td>\n <td>S</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>1</td>\n <td>1</td>\n <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n <td>female</td>\n <td>35.0</td>\n <td>1</td>\n <td>0</td>\n <td>113803</td>\n <td>53.1000</td>\n <td>C123</td>\n <td>S</td>\n </tr>\n <tr>\n <th>4</th>\n <td>5</td>\n <td>0</td>\n <td>3</td>\n <td>Allen, Mr. William Henry</td>\n <td>male</td>\n <td>35.0</td>\n <td>0</td>\n <td>0</td>\n <td>373450</td>\n <td>8.0500</td>\n <td>NaN</td>\n <td>S</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " PassengerId Survived Pclass \\\n0 1 0 3 \n1 2 1 1 \n2 3 1 3 \n3 4 1 1 \n4 5 0 3 \n\n Name Sex Age SibSp \\\n0 Braund, Mr. Owen Harris male 22.0 1 \n1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n2 Heikkinen, Miss. Laina female 26.0 0 \n3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n4 Allen, Mr. William Henry male 35.0 0 \n\n Parch Ticket Fare Cabin Embarked \n0 0 A/5 21171 7.2500 NaN S \n1 0 PC 17599 71.2833 C85 C \n2 0 STON/O2. 3101282 7.9250 NaN S \n3 0 113803 53.1000 C123 S \n4 0 373450 8.0500 NaN S " }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Read in data sets\n", "train_df = pd.read_csv(\"../input/train.csv\", dtype={\"Age\": np.float64}, )\n", "test_df = pd.read_csv(\"../input/test.csv\", dtype={\"Age\": np.float64}, )\n", "\n", "train_df.head()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "_cell_guid": "357ad263-8026-8871-b478-159f43b48cf4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 12 columns):\nPassengerId 891 non-null int64\nSurvived 891 non-null int64\nPclass 891 non-null int64\nName 891 non-null object\nSex 891 non-null object\nAge 714 non-null float64\nSibSp 891 non-null int64\nParch 891 non-null int64\nTicket 891 non-null object\nFare 891 non-null float64\nCabin 204 non-null object\nEmbarked 889 non-null object\ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.6+ KB\n" }, { "name": "stdout", "output_type": "stream", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 12 columns):\nPassengerId 891 non-null int64\nSurvived 891 non-null int64\nPclass 891 non-null int64\nName 891 non-null object\nSex 891 non-null object\nAge 714 non-null float64\nSibSp 891 non-null int64\nParch 891 non-null int64\nTicket 891 non-null object\nFare 891 non-null float64\nCabin 204 non-null object\nEmbarked 889 non-null object\ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.6+ KB\n" }, { "name": "stdout", "output_type": "stream", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 12 columns):\nPassengerId 891 non-null int64\nSurvived 891 non-null int64\nPclass 891 non-null int64\nName 891 non-null object\nSex 891 non-null object\nAge 714 non-null float64\nSibSp 891 non-null int64\nParch 891 non-null int64\nTicket 891 non-null object\nFare 891 non-null float64\nCabin 204 non-null object\nEmbarked 889 non-null object\ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.6+ KB\n" } ], "source": [ "# Training stats\n", "\"\"\"\n", "Takeaways\n", "Age is null for some rows\n", "Cabin is almost always null\n", "Embarked is null for only 2 rows\n", "\"\"\"\n", "train_df.info()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "_cell_guid": "0271cf35-6ea4-af50-3f54-c95b053c63f7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 418 entries, 0 to 417\nData columns (total 11 columns):\nPassengerId 418 non-null int64\nPclass 418 non-null int64\nName 418 non-null object\nSex 418 non-null object\nAge 332 non-null float64\nSibSp 418 non-null int64\nParch 418 non-null int64\nTicket 418 non-null object\nFare 417 non-null float64\nCabin 91 non-null object\nEmbarked 418 non-null object\ndtypes: float64(2), int64(4), object(5)\nmemory usage: 36.0+ KB\n" }, { "name": "stdout", "output_type": "stream", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 418 entries, 0 to 417\nData columns (total 11 columns):\nPassengerId 418 non-null int64\nPclass 418 non-null int64\nName 418 non-null object\nSex 418 non-null object\nAge 332 non-null float64\nSibSp 418 non-null int64\nParch 418 non-null int64\nTicket 418 non-null object\nFare 417 non-null float64\nCabin 91 non-null object\nEmbarked 418 non-null object\ndtypes: float64(2), int64(4), object(5)\nmemory usage: 36.0+ KB\n" } ], "source": [ "# Test data stats\n", "\"\"\"\n", "Takeaways\n", "Fare is null for one row\n", "Cabin null for most\n", "Age null for some\n", "\"\"\"\n", "test_df.info()" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "_cell_guid": "7ff01fa5-5329-993a-4908-80b01a5ea2c9" }, "outputs": [ { "ename": "NameError", "evalue": "name 'train_na' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-6-e8aef0fca7d3> in <module>()\n 3 train_df[\"Embarked\"] = train_df[\"Embarked\"].fillna(\"S\")\n 4 \n----> 5 test_df[\"Age\"] = test_df[\"Age\"].fillna(train_na[\"Age\"].median)\n 6 test_df[\"Fare\"] = test_df[\"Fare\"].fillna(0)\n", "NameError: name 'train_na' is not defined" ] } ], "source": [ "# Fill in missing data\n", "median_age = train_df[\"Age\"].median()\n", "train_df[\"Age\"] = train_df[\"Age\"].fillna(median_age)\n", "train_df[\"Embarked\"] = train_df[\"Embarked\"].fillna(\"S\")\n", "\n", "test_df[\"Age\"] = test_df[\"Age\"].fillna(median_age)\n", "test_df[\"Fare\"] = test_df[\"Fare\"].fillna(0)\n", "\n", "test_df[\"Cabin\"] = test_df[\"Cabin\"].fillna(\"Missing\")\n", "\n", "train_df[\"Cabin\"] = train_df[\"Cabin\"].fillna(\"Missing\")" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "_cell_guid": "b031363d-4dc3-ad65-2593-a6d2afe2146b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 12 columns):\nPassengerId 891 non-null int64\nSurvived 891 non-null int64\nPclass 891 non-null int64\nName 891 non-null object\nSex 891 non-null object\nAge 891 non-null float64\nSibSp 891 non-null int64\nParch 891 non-null int64\nTicket 891 non-null object\nFare 891 non-null float64\nCabin 204 non-null object\nEmbarked 891 non-null object\ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.6+ KB\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 418 entries, 0 to 417\nData columns (total 11 columns):\nPassengerId 418 non-null int64\nPclass 418 non-null int64\nName 418 non-null object\nSex 418 non-null object\nAge 418 non-null object\nSibSp 418 non-null int64\nParch 418 non-null int64\nTicket 418 non-null object\nFare 418 non-null float64\nCabin 91 non-null object\nEmbarked 418 non-null object\ndtypes: float64(1), int64(4), object(6)\nmemory usage: 36.0+ KB\n" }, { "name": "stdout", "output_type": "stream", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 12 columns):\nPassengerId 891 non-null int64\nSurvived 891 non-null int64\nPclass 891 non-null int64\nName 891 non-null object\nSex 891 non-null object\nAge 891 non-null float64\nSibSp 891 non-null int64\nParch 891 non-null int64\nTicket 891 non-null object\nFare 891 non-null float64\nCabin 204 non-null object\nEmbarked 891 non-null object\ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.6+ KB\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 418 entries, 0 to 417\nData columns (total 11 columns):\nPassengerId 418 non-null int64\nPclass 418 non-null int64\nName 418 non-null object\nSex 418 non-null object\nAge 418 non-null object\nSibSp 418 non-null int64\nParch 418 non-null int64\nTicket 418 non-null object\nFare 418 non-null float64\nCabin 91 non-null object\nEmbarked 418 non-null object\ndtypes: float64(1), int64(4), object(6)\nmemory usage: 36.0+ KB\n" }, { "name": "stdout", "output_type": "stream", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 12 columns):\nPassengerId 891 non-null int64\nSurvived 891 non-null int64\nPclass 891 non-null int64\nName 891 non-null object\nSex 891 non-null object\nAge 891 non-null float64\nSibSp 891 non-null int64\nParch 891 non-null int64\nTicket 891 non-null object\nFare 891 non-null float64\nCabin 204 non-null object\nEmbarked 891 non-null object\ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.6+ KB\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 418 entries, 0 to 417\nData columns (total 11 columns):\nPassengerId 418 non-null int64\nPclass 418 non-null int64\nName 418 non-null object\nSex 418 non-null object\nAge 418 non-null float64\nSibSp 418 non-null int64\nParch 418 non-null int64\nTicket 418 non-null object\nFare 418 non-null float64\nCabin 91 non-null object\nEmbarked 418 non-null object\ndtypes: float64(2), int64(4), object(5)\nmemory usage: 36.0+ KB\n" } ], "source": [ "# Confirm\n", "train_df.info()\n", "test_df.info()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "_cell_guid": "65216ad7-48ad-97a1-e376-e2060fafcf52" }, "outputs": [ { "data": { "text/plain": "<seaborn.axisgrid.FacetGrid at 0x7f22dfe06cf8>" }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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f5083i8gsnKJkScBLqvpv571+C/B/gCqgHPgrVV0bee0gkFf9mqpO9Dper63e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\nQmCC\n", "text/plain": "<matplotlib.figure.Figure at 0x7f22dfe06dd8>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the data to explore it further\n", "sns.factorplot('Embarked', 'Survived', data=train_df, size=4)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "_cell_guid": "14f02301-7f28-7fd6-a2f5-408e7e52b76c" }, "outputs": [ { "data": { "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f22dfe11a90>" }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f22dfdae358>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x=\"Embarked\", data=train_df)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "_cell_guid": "d61e8b2c-2482-a5bd-6970-6e6bb168230b" }, "outputs": [ { "data": { "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f22e0004eb8>" }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ADAIqgMfd/a/BW0uB6WZ2QFBHHzPb+eD7lQRHI2Y2mPRYhUhkdMQgvV0hMNfM\nPg/8lfSX+LXuvsrM/gVYYWabgP9q0a+1aYl/CawDFmV84be2biVwPfCNnQ3u/lDwpf+cmTWT/qXt\nbuB14AZgkZlVkR50fq5jP6pIdjTttoiIhOhUkoiIhCgYREQkRMEgIiIhCgYREQlRMIiISIiCQURE\nQhQMIiISomAQEZGQ/w/iuNdG8524HAAAAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f22dfd5f438>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x=\"Survived\", hue=\"Embarked\", data=train_df, order=[0,1])" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "_cell_guid": "751735e8-7024-b41d-c742-6fcda93660f9" }, "outputs": [ { "data": { "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f22dfcf85f8>" }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LzH6gMyJWAvOAe4ezf0nS81fv7ar3A8/5q/353K4aEXOBQ4H/APbIzDXFNroiYvdisdnA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\nklTy/wGYF9haR33RkAAAAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f22dfddd6d8>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x=\"Survived\", hue=\"Pclass\", data=train_df, order=[0,1])" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "_cell_guid": "91e652ec-a129-2426-fb1a-51c1bc46d42d" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Survived</th>\n <th>Pclass</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Fare</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>891.000000</td>\n <td>891.000000</td>\n <td>891.000000</td>\n <td>891.000000</td>\n <td>891.000000</td>\n <td>891.000000</td>\n <td>891.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>446.000000</td>\n <td>0.383838</td>\n <td>2.308642</td>\n <td>29.361582</td>\n <td>0.523008</td>\n <td>0.381594</td>\n <td>32.204208</td>\n </tr>\n <tr>\n <th>std</th>\n <td>257.353842</td>\n <td>0.486592</td>\n <td>0.836071</td>\n <td>13.019697</td>\n <td>1.102743</td>\n <td>0.806057</td>\n <td>49.693429</td>\n </tr>\n <tr>\n <th>min</th>\n <td>1.000000</td>\n <td>0.000000</td>\n <td>1.000000</td>\n <td>0.420000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>223.500000</td>\n <td>0.000000</td>\n <td>2.000000</td>\n <td>22.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>7.910400</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>446.000000</td>\n <td>0.000000</td>\n <td>3.000000</td>\n <td>28.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>14.454200</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>668.500000</td>\n <td>1.000000</td>\n <td>3.000000</td>\n <td>35.000000</td>\n <td>1.000000</td>\n <td>0.000000</td>\n <td>31.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>891.000000</td>\n <td>1.000000</td>\n <td>3.000000</td>\n <td>80.000000</td>\n <td>8.000000</td>\n <td>6.000000</td>\n <td>512.329200</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " PassengerId Survived Pclass Age SibSp \\\ncount 891.000000 891.000000 891.000000 891.000000 891.000000 \nmean 446.000000 0.383838 2.308642 29.361582 0.523008 \nstd 257.353842 0.486592 0.836071 13.019697 1.102743 \nmin 1.000000 0.000000 1.000000 0.420000 0.000000 \n25% 223.500000 0.000000 2.000000 22.000000 0.000000 \n50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n75% 668.500000 1.000000 3.000000 35.000000 1.000000 \nmax 891.000000 1.000000 3.000000 80.000000 8.000000 \n\n Parch Fare \ncount 891.000000 891.000000 \nmean 0.381594 32.204208 \nstd 0.806057 49.693429 \nmin 0.000000 0.000000 \n25% 0.000000 7.910400 \n50% 0.000000 14.454200 \n75% 0.000000 31.000000 \nmax 6.000000 512.329200 " }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df.describe()" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "_cell_guid": "d7b417d8-319b-68de-dc97-0feaa8d8a1ac" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Pclass</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Fare</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>418.000000</td>\n <td>418.000000</td>\n <td>418.000000</td>\n <td>418.000000</td>\n <td>418.000000</td>\n <td>418.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>1100.500000</td>\n <td>2.265550</td>\n <td>29.805024</td>\n <td>0.447368</td>\n <td>0.392344</td>\n <td>35.541956</td>\n </tr>\n <tr>\n <th>std</th>\n <td>120.810458</td>\n <td>0.841838</td>\n <td>12.667969</td>\n <td>0.896760</td>\n <td>0.981429</td>\n <td>55.867684</td>\n </tr>\n <tr>\n <th>min</th>\n <td>892.000000</td>\n <td>1.000000</td>\n <td>0.170000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>996.250000</td>\n <td>1.000000</td>\n <td>23.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>7.895800</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>1100.500000</td>\n <td>3.000000</td>\n <td>28.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>14.454200</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>1204.750000</td>\n <td>3.000000</td>\n <td>35.750000</td>\n <td>1.000000</td>\n <td>0.000000</td>\n <td>31.471875</td>\n </tr>\n <tr>\n <th>max</th>\n <td>1309.000000</td>\n <td>3.000000</td>\n <td>76.000000</td>\n <td>8.000000</td>\n <td>9.000000</td>\n <td>512.329200</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " PassengerId Pclass Age SibSp Parch Fare\ncount 418.000000 418.000000 418.000000 418.000000 418.000000 418.000000\nmean 1100.500000 2.265550 29.805024 0.447368 0.392344 35.541956\nstd 120.810458 0.841838 12.667969 0.896760 0.981429 55.867684\nmin 892.000000 1.000000 0.170000 0.000000 0.000000 0.000000\n25% 996.250000 1.000000 23.000000 0.000000 0.000000 7.895800\n50% 1100.500000 3.000000 28.000000 0.000000 0.000000 14.454200\n75% 1204.750000 3.000000 35.750000 1.000000 0.000000 31.471875\nmax 1309.000000 3.000000 76.000000 8.000000 9.000000 512.329200" }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df.describe()" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "_cell_guid": "d20ee4c5-9dff-4d2c-37e8-85b02298bd56" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:1: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n #!/opt/conda/bin/python\n/opt/conda/bin/ipython:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n if __name__ == '__main__':\n/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n/opt/conda/bin/ipython:5: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n \n/opt/conda/bin/ipython:6: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n sys.exit(IPython.start_ipython())\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:8: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:9: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:12: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:13: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:1: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n #!/opt/conda/bin/python\n/opt/conda/bin/ipython:2: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n if __name__ == '__main__':\n/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:5: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n \n/opt/conda/bin/ipython:6: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n sys.exit(IPython.start_ipython())\n/opt/conda/bin/ipython:8: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:9: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:12: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:13: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" } ], "source": [ "test_df[\"Sex\"][test_df[\"Sex\"] == \"male\"] = 0\n", "test_df[\"Sex\"][test_df[\"Sex\"] == \"female\"] = 1\n", "\n", "test_df[\"Embarked\"][test_df[\"Embarked\"] == \"S\"] = 0\n", "test_df[\"Embarked\"][test_df[\"Embarked\"] == \"C\"] = 1\n", "test_df[\"Embarked\"][test_df[\"Embarked\"] == \"Q\"] = 2\n", "\n", "train_df[\"Sex\"][train_df[\"Sex\"] == \"male\"] = 0\n", "train_df[\"Sex\"][train_df[\"Sex\"] == \"female\"] = 1\n", "\n", "train_df[\"Embarked\"][train_df[\"Embarked\"] == \"S\"] = 0\n", "train_df[\"Embarked\"][train_df[\"Embarked\"] == \"C\"] = 1\n", "train_df[\"Embarked\"][train_df[\"Embarked\"] == \"Q\"] = 2" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "_cell_guid": "567cbcad-6781-ca7b-a822-0fa24bb7ef3e" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:3: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import sys\n/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n" }, { "ename": "TypeError", "evalue": "unorderable types: method() < int()", "output_type": "error", "traceback": [ "", "TypeErrorTraceback (most recent call last)", "<ipython-input-35-2810d5381f3c> in <module>()\n 5 \n 6 test_df[\"Child\"] = float(0)\n----> 7 test_df[\"Child\"][test_df[\"Age\"] < 18] = 1\n 8 test_df[\"Child\"][test_df[\"Age\"] >= 18] = 0\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/ops.py in wrapper(self, other, axis)\n 761 other = np.asarray(other)\n 762 \n--> 763 res = na_op(values, other)\n 764 if isscalar(res):\n 765 raise TypeError('Could not compare %s type with Series' %\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/ops.py in na_op(x, y)\n 681 result = lib.vec_compare(x, y, op)\n 682 else:\n--> 683 result = lib.scalar_compare(x, y, op)\n 684 else:\n 685 \n", "pandas/lib.pyx in pandas.lib.scalar_compare (pandas/lib.c:14261)()\n", "TypeError: unorderable types: method() < int()" ] }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:3: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import sys\n/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n/opt/conda/bin/ipython:7: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:8: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" } ], "source": [ "# Feature engineering\n", "train_df[\"Child\"] = float(0)\n", "train_df[\"Child\"][train_df[\"Age\"] < 18] = 1\n", "train_df[\"Child\"][train_df[\"Age\"] >= 18] = 0\n", "\n", "test_df[\"Child\"] = float(0)\n", "test_df[\"Child\"][test_df[\"Age\"] < 18] = 1\n", "test_df[\"Child\"][test_df[\"Age\"] >= 18] = 0" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "_cell_guid": "b9f55406-0d57-b96a-0231-2482364f9190" }, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-36-252ab0d09770>, line 1)", "output_type": "error", "traceback": [ " File \"<ipython-input-36-252ab0d09770>\", line 1\n letters =\n ^\nSyntaxError: invalid syntax\n" ] }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:3: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import sys\n/opt/conda/bin/ipython:6: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n sys.exit(IPython.start_ipython())\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:3: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import sys\n" }, { "ename": "TypeError", "evalue": "Can't convert 'Series' object to str implicitly", "output_type": "error", "traceback": [ "", "TypeErrorTraceback (most recent call last)", "<ipython-input-38-54053f369327> in <module>()\n 2 train_df[\"CabinStartsWith\"] = \"\"\n 3 train_df[\"CabinStartsWith\"][train_df[\"Cabin\"] != \"\"] = train_df[\"Cabin\"][0]\n----> 4 train_df[\"CabinStartsWith\"][train_df[\"Cabin\"] != \"\"] = letters.find(train_df[\"CabinStartsWith\"])\n 5 \n 6 test_df[\"CabinStartsWith\"] = \"\"\n", "TypeError: Can't convert 'Series' object to str implicitly" ] }, { "ename": "KeyError", "evalue": "'CabinStartsWithLetter'", "output_type": "error", "traceback": [ "", "KeyErrorTraceback (most recent call last)", "/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py in get_loc(self, key, method, tolerance)\n 1944 try:\n-> 1945 return self._engine.get_loc(key)\n 1946 except KeyError:\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4154)()\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4018)()\n", "pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12368)()\n", "pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12322)()\n", "KeyError: 'CabinStartsWithLetter'", "\nDuring handling of the above exception, another exception occurred:\n", "KeyErrorTraceback (most recent call last)", "<ipython-input-39-a273424dde22> in <module>()\n 1 letters = \"ABCDEFGHIJKLM\"\n 2 train_df[\"CabinStartsWith\"] = \"\"\n----> 3 train_df[\"CabinStartsWithLetter\"][train_df[\"Cabin\"] != \"\"] = train_df[\"Cabin\"][0]\n 4 train_df[\"CabinStartsWith\"][train_df[\"Cabin\"] != \"\"] = letters.find(train_df[\"CabinStartsWith\"])\n 5 \n", "/opt/conda/lib/python3.5/site-packages/pandas/core/frame.py in __getitem__(self, key)\n 1995 return self._getitem_multilevel(key)\n 1996 else:\n-> 1997 return self._getitem_column(key)\n 1998 \n 1999 def _getitem_column(self, key):\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/frame.py in _getitem_column(self, key)\n 2002 # get column\n 2003 if self.columns.is_unique:\n-> 2004 return self._get_item_cache(key)\n 2005 \n 2006 # duplicate columns & possible reduce dimensionality\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py in _get_item_cache(self, item)\n 1348 res = cache.get(item)\n 1349 if res is None:\n-> 1350 values = self._data.get(item)\n 1351 res = self._box_item_values(item, values)\n 1352 cache[item] = res\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/internals.py in get(self, item, fastpath)\n 3288 \n 3289 if not isnull(item):\n-> 3290 loc = self.items.get_loc(item)\n 3291 else:\n 3292 indexer = np.arange(len(self.items))[isnull(self.items)]\n", "/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py in get_loc(self, key, method, tolerance)\n 1945 return self._engine.get_loc(key)\n 1946 except KeyError:\n-> 1947 return self._engine.get_loc(self._maybe_cast_indexer(key))\n 1948 \n 1949 indexer = self.get_indexer([key], method=method, tolerance=tolerance)\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4154)()\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4018)()\n", "pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12368)()\n", "pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12322)()\n", "KeyError: 'CabinStartsWithLetter'" ] }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n" }, { "ename": "TypeError", "evalue": "Can't convert 'Series' object to str implicitly", "output_type": "error", "traceback": [ "", "TypeErrorTraceback (most recent call last)", "<ipython-input-40-f4c6b956d72a> in <module>()\n 3 train_df[\"CabinStartsWithLetter\"] = \"\"\n 4 train_df[\"CabinStartsWithLetter\"][train_df[\"Cabin\"] != \"\"] = train_df[\"Cabin\"][0]\n----> 5 train_df[\"CabinStartsWith\"][train_df[\"Cabin\"] != \"\"] = letters.find(train_df[\"CabinStartsWith\"])\n 6 \n 7 test_df[\"CabinStartsWith\"] = -1\n", "TypeError: Can't convert 'Series' object to str implicitly" ] }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n/opt/conda/bin/ipython:5: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n \n/opt/conda/bin/ipython:6: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n sys.exit(IPython.start_ipython())\n/opt/conda/bin/ipython:7: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:8: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:9: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:10: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:15: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "ename": "NameError", "evalue": "name 'tedt_df' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-41-aac2d1a07f58> in <module>()\n 14 test_df[\"CabinStartsWithLetter\"] = \"\"\n 15 test_df[\"CabinStartsWithLetter\"][test_df[\"Cabin\"] != \"\"] = test_df[\"Cabin\"][0]\n---> 16 test_df[\"CabinStartsWith\"][tedt_df[\"CabinStartsWithLetter\"] == \"A\"] = 0\n 17 test_df[\"CabinStartsWith\"][tedt_df[\"CabinStartsWithLetter\"] == \"B\"] = 0\n 18 test_df[\"CabinStartsWith\"][tedt_df[\"CabinStartsWithLetter\"] == \"C\"] = 0\n", "NameError: name 'tedt_df' is not defined" ] }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n/opt/conda/bin/ipython:5: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n \n/opt/conda/bin/ipython:6: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n sys.exit(IPython.start_ipython())\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:7: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:8: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:9: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:10: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:15: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:16: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:17: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:18: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:19: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:20: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:21: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:22: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n" }, { "ename": "KeyError", "evalue": "'cannot use a single bool to index into setitem'", "output_type": "error", "traceback": [ "", "KeyErrorTraceback (most recent call last)", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in _set_with_engine(self, key, value)\n 759 try:\n--> 760 self.index._engine.set_value(values, key, value)\n 761 return\n", "pandas/index.pyx in pandas.index.IndexEngine.set_value (pandas/index.c:3600)()\n", "pandas/index.pyx in pandas.index.IndexEngine.set_value (pandas/index.c:3426)()\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3977)()\n", "pandas/index.pyx in pandas.index.Int64Engine._check_type (pandas/index.c:7634)()\n", "KeyError: True", "\nDuring handling of the above exception, another exception occurred:\n", "KeyErrorTraceback (most recent call last)", "/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py in get_loc(self, key, method, tolerance)\n 1944 try:\n-> 1945 return self._engine.get_loc(key)\n 1946 except KeyError:\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4154)()\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3977)()\n", "pandas/index.pyx in pandas.index.Int64Engine._check_type (pandas/index.c:7634)()\n", "KeyError: True", "\nDuring handling of the above exception, another exception occurred:\n", "KeyErrorTraceback (most recent call last)", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in setitem(key, value)\n 700 try:\n--> 701 self._set_with_engine(key, value)\n 702 return\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in _set_with_engine(self, key, value)\n 762 except KeyError:\n--> 763 values[self.index.get_loc(key)] = value\n 764 return\n", "/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py in get_loc(self, key, method, tolerance)\n 1946 except KeyError:\n-> 1947 return self._engine.get_loc(self._maybe_cast_indexer(key))\n 1948 \n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4154)()\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3977)()\n", "pandas/index.pyx in pandas.index.Int64Engine._check_type (pandas/index.c:7634)()\n", "KeyError: True", "\nDuring handling of the above exception, another exception occurred:\n", "KeyErrorTraceback (most recent call last)", "<ipython-input-47-41b651994233> in <module>()\n 2 train_df[\"CabinStartsWith\"] = -1\n 3 train_df[\"CabinStartsWithLetter\"] = \"\"\n----> 4 train_df[\"CabinStartsWithLetter\"][train_df[\"Cabin\"] is not None] = train_df[\"Cabin\"][0]\n 5 train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"A\"] = 0\n 6 train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"B\"] = 1\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in __setitem__(self, key, value)\n 751 # do the setitem\n 752 cacher_needs_updating = self._check_is_chained_assignment_possible()\n--> 753 setitem(key, value)\n 754 if cacher_needs_updating:\n 755 self._maybe_update_cacher()\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in setitem(key, value)\n 727 pass\n 728 \n--> 729 self.loc[key] = value\n 730 return\n 731 \n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in __setitem__(self, key, value)\n 130 key = com._apply_if_callable(key, self.obj)\n 131 indexer = self._get_setitem_indexer(key)\n--> 132 self._setitem_with_indexer(indexer, value)\n 133 \n 134 def _has_valid_type(self, k, axis):\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)\n 312 else:\n 313 \n--> 314 indexer, missing = convert_missing_indexer(indexer)\n 315 \n 316 if missing:\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in convert_missing_indexer(indexer)\n 1822 \n 1823 if isinstance(indexer, bool):\n-> 1824 raise KeyError(\"cannot use a single bool to index into setitem\")\n 1825 return indexer, True\n 1826 \n", "KeyError: 'cannot use a single bool to index into setitem'" ] }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/lib/python3.5/site-packages/pandas/core/ops.py:716: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n result = getattr(x, name)(y)\n" }, { "ename": "TypeError", "evalue": "invalid type comparison", "output_type": "error", "traceback": [ "", "TypeErrorTraceback (most recent call last)", "<ipython-input-48-31689b95ceb8> in <module>()\n 3 train_df[\"CabinStartsWithLetter\"] = \"\"\n 4 train_df[\"CabinStartsWithLetter\"] = train_df[\"Cabin\"][0]\n----> 5 train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"A\"] = 0\n 6 train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"B\"] = 1\n 7 train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"C\"] = 2\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/ops.py in wrapper(self, other, axis)\n 761 other = np.asarray(other)\n 762 \n--> 763 res = na_op(values, other)\n 764 if isscalar(res):\n 765 raise TypeError('Could not compare %s type with Series' %\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/ops.py in na_op(x, y)\n 716 result = getattr(x, name)(y)\n 717 if result is NotImplemented:\n--> 718 raise TypeError(\"invalid type comparison\")\n 719 except AttributeError:\n 720 result = op(x, y)\n", "TypeError: invalid type comparison" ] }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n" }, { "ename": "KeyError", "evalue": "'cannot use a single bool to index into setitem'", "output_type": "error", "traceback": [ "", "KeyErrorTraceback (most recent call last)", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in _set_with_engine(self, key, value)\n 759 try:\n--> 760 self.index._engine.set_value(values, key, value)\n 761 return\n", "pandas/index.pyx in pandas.index.IndexEngine.set_value (pandas/index.c:3600)()\n", "pandas/index.pyx in pandas.index.IndexEngine.set_value (pandas/index.c:3426)()\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3977)()\n", "pandas/index.pyx in pandas.index.Int64Engine._check_type (pandas/index.c:7634)()\n", "KeyError: True", "\nDuring handling of the above exception, another exception occurred:\n", "KeyErrorTraceback (most recent call last)", "/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py in get_loc(self, key, method, tolerance)\n 1944 try:\n-> 1945 return self._engine.get_loc(key)\n 1946 except KeyError:\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4154)()\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3977)()\n", "pandas/index.pyx in pandas.index.Int64Engine._check_type (pandas/index.c:7634)()\n", "KeyError: True", "\nDuring handling of the above exception, another exception occurred:\n", "KeyErrorTraceback (most recent call last)", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in setitem(key, value)\n 700 try:\n--> 701 self._set_with_engine(key, value)\n 702 return\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in _set_with_engine(self, key, value)\n 762 except KeyError:\n--> 763 values[self.index.get_loc(key)] = value\n 764 return\n", "/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py in get_loc(self, key, method, tolerance)\n 1946 except KeyError:\n-> 1947 return self._engine.get_loc(self._maybe_cast_indexer(key))\n 1948 \n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4154)()\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3977)()\n", "pandas/index.pyx in pandas.index.Int64Engine._check_type (pandas/index.c:7634)()\n", "KeyError: True", "\nDuring handling of the above exception, another exception occurred:\n", "KeyErrorTraceback (most recent call last)", "<ipython-input-49-0284f3f5d59e> in <module>()\n 2 train_df[\"CabinStartsWith\"] = -1\n 3 train_df[\"CabinStartsWithLetter\"] = \"\"\n----> 4 train_df[\"CabinStartsWithLetter\"][len(train_df[\"Cabin\"]) > 1] = train_df[\"Cabin\"][0]\n 5 train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"A\"] = 0\n 6 train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"B\"] = 1\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in __setitem__(self, key, value)\n 751 # do the setitem\n 752 cacher_needs_updating = self._check_is_chained_assignment_possible()\n--> 753 setitem(key, value)\n 754 if cacher_needs_updating:\n 755 self._maybe_update_cacher()\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in setitem(key, value)\n 727 pass\n 728 \n--> 729 self.loc[key] = value\n 730 return\n 731 \n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in __setitem__(self, key, value)\n 130 key = com._apply_if_callable(key, self.obj)\n 131 indexer = self._get_setitem_indexer(key)\n--> 132 self._setitem_with_indexer(indexer, value)\n 133 \n 134 def _has_valid_type(self, k, axis):\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)\n 312 else:\n 313 \n--> 314 indexer, missing = convert_missing_indexer(indexer)\n 315 \n 316 if missing:\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in convert_missing_indexer(indexer)\n 1822 \n 1823 if isinstance(indexer, bool):\n-> 1824 raise KeyError(\"cannot use a single bool to index into setitem\")\n 1825 return indexer, True\n 1826 \n", "KeyError: 'cannot use a single bool to index into setitem'" ] }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n" }, { "ename": "KeyError", "evalue": "'cannot use a single bool to index into setitem'", "output_type": "error", "traceback": [ "", "KeyErrorTraceback (most recent call last)", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in _set_with_engine(self, key, value)\n 759 try:\n--> 760 self.index._engine.set_value(values, key, value)\n 761 return\n", "pandas/index.pyx in pandas.index.IndexEngine.set_value (pandas/index.c:3600)()\n", "pandas/index.pyx in pandas.index.IndexEngine.set_value (pandas/index.c:3426)()\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3977)()\n", "pandas/index.pyx in pandas.index.Int64Engine._check_type (pandas/index.c:7634)()\n", "KeyError: True", "\nDuring handling of the above exception, another exception occurred:\n", "KeyErrorTraceback (most recent call last)", "/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py in get_loc(self, key, method, tolerance)\n 1944 try:\n-> 1945 return self._engine.get_loc(key)\n 1946 except KeyError:\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4154)()\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3977)()\n", "pandas/index.pyx in pandas.index.Int64Engine._check_type (pandas/index.c:7634)()\n", "KeyError: True", "\nDuring handling of the above exception, another exception occurred:\n", "KeyErrorTraceback (most recent call last)", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in setitem(key, value)\n 700 try:\n--> 701 self._set_with_engine(key, value)\n 702 return\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in _set_with_engine(self, key, value)\n 762 except KeyError:\n--> 763 values[self.index.get_loc(key)] = value\n 764 return\n", "/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py in get_loc(self, key, method, tolerance)\n 1946 except KeyError:\n-> 1947 return self._engine.get_loc(self._maybe_cast_indexer(key))\n 1948 \n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4154)()\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3977)()\n", "pandas/index.pyx in pandas.index.Int64Engine._check_type (pandas/index.c:7634)()\n", "KeyError: True", "\nDuring handling of the above exception, another exception occurred:\n", "KeyErrorTraceback (most recent call last)", "<ipython-input-65-0284f3f5d59e> in <module>()\n 2 train_df[\"CabinStartsWith\"] = -1\n 3 train_df[\"CabinStartsWithLetter\"] = \"\"\n----> 4 train_df[\"CabinStartsWithLetter\"][len(train_df[\"Cabin\"]) > 1] = train_df[\"Cabin\"][0]\n 5 train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"A\"] = 0\n 6 train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"B\"] = 1\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in __setitem__(self, key, value)\n 751 # do the setitem\n 752 cacher_needs_updating = self._check_is_chained_assignment_possible()\n--> 753 setitem(key, value)\n 754 if cacher_needs_updating:\n 755 self._maybe_update_cacher()\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in setitem(key, value)\n 727 pass\n 728 \n--> 729 self.loc[key] = value\n 730 return\n 731 \n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in __setitem__(self, key, value)\n 130 key = com._apply_if_callable(key, self.obj)\n 131 indexer = self._get_setitem_indexer(key)\n--> 132 self._setitem_with_indexer(indexer, value)\n 133 \n 134 def _has_valid_type(self, k, axis):\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)\n 312 else:\n 313 \n--> 314 indexer, missing = convert_missing_indexer(indexer)\n 315 \n 316 if missing:\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in convert_missing_indexer(indexer)\n 1822 \n 1823 if isinstance(indexer, bool):\n-> 1824 raise KeyError(\"cannot use a single bool to index into setitem\")\n 1825 return indexer, True\n 1826 \n", "KeyError: 'cannot use a single bool to index into setitem'" ] }, { "ename": "KeyError", "evalue": "'cannot use a single bool to index into setitem'", "output_type": "error", "traceback": [ "", "KeyErrorTraceback (most recent call last)", "<ipython-input-66-7fcb7f061578> in <module>()\n 2 train_df[\"CabinStartsWith\"] = -1\n 3 train_df[\"CabinStartsWithLetter\"] = \"\"\n----> 4 train_df[\"CabinStartsWithLetter\"].loc[len(train_df[\"Cabin\"]) > 1] = train_df[\"Cabin\"][0]\n 5 train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"A\"] = 0\n 6 train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"B\"] = 1\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in __setitem__(self, key, value)\n 130 key = com._apply_if_callable(key, self.obj)\n 131 indexer = self._get_setitem_indexer(key)\n--> 132 self._setitem_with_indexer(indexer, value)\n 133 \n 134 def _has_valid_type(self, k, axis):\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)\n 312 else:\n 313 \n--> 314 indexer, missing = convert_missing_indexer(indexer)\n 315 \n 316 if missing:\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in convert_missing_indexer(indexer)\n 1822 \n 1823 if isinstance(indexer, bool):\n-> 1824 raise KeyError(\"cannot use a single bool to index into setitem\")\n 1825 return indexer, True\n 1826 \n", "KeyError: 'cannot use a single bool to index into setitem'" ] }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n" }, { "ename": "KeyError", "evalue": "'cannot use a single bool to index into setitem'", "output_type": "error", "traceback": [ "", "KeyErrorTraceback (most recent call last)", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in _set_with_engine(self, key, value)\n 759 try:\n--> 760 self.index._engine.set_value(values, key, value)\n 761 return\n", "pandas/index.pyx in pandas.index.IndexEngine.set_value (pandas/index.c:3600)()\n", "pandas/index.pyx in pandas.index.IndexEngine.set_value (pandas/index.c:3426)()\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3977)()\n", "pandas/index.pyx in pandas.index.Int64Engine._check_type (pandas/index.c:7634)()\n", "KeyError: True", "\nDuring handling of the above exception, another exception occurred:\n", "KeyErrorTraceback (most recent call last)", "/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py in get_loc(self, key, method, tolerance)\n 1944 try:\n-> 1945 return self._engine.get_loc(key)\n 1946 except KeyError:\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4154)()\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3977)()\n", "pandas/index.pyx in pandas.index.Int64Engine._check_type (pandas/index.c:7634)()\n", "KeyError: True", "\nDuring handling of the above exception, another exception occurred:\n", "KeyErrorTraceback (most recent call last)", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in setitem(key, value)\n 700 try:\n--> 701 self._set_with_engine(key, value)\n 702 return\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in _set_with_engine(self, key, value)\n 762 except KeyError:\n--> 763 values[self.index.get_loc(key)] = value\n 764 return\n", "/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py in get_loc(self, key, method, tolerance)\n 1946 except KeyError:\n-> 1947 return self._engine.get_loc(self._maybe_cast_indexer(key))\n 1948 \n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4154)()\n", "pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3977)()\n", "pandas/index.pyx in pandas.index.Int64Engine._check_type (pandas/index.c:7634)()\n", "KeyError: True", "\nDuring handling of the above exception, another exception occurred:\n", "KeyErrorTraceback (most recent call last)", "<ipython-input-67-0284f3f5d59e> in <module>()\n 2 train_df[\"CabinStartsWith\"] = -1\n 3 train_df[\"CabinStartsWithLetter\"] = \"\"\n----> 4 train_df[\"CabinStartsWithLetter\"][len(train_df[\"Cabin\"]) > 1] = train_df[\"Cabin\"][0]\n 5 train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"A\"] = 0\n 6 train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"B\"] = 1\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in __setitem__(self, key, value)\n 751 # do the setitem\n 752 cacher_needs_updating = self._check_is_chained_assignment_possible()\n--> 753 setitem(key, value)\n 754 if cacher_needs_updating:\n 755 self._maybe_update_cacher()\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in setitem(key, value)\n 727 pass\n 728 \n--> 729 self.loc[key] = value\n 730 return\n 731 \n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in __setitem__(self, key, value)\n 130 key = com._apply_if_callable(key, self.obj)\n 131 indexer = self._get_setitem_indexer(key)\n--> 132 self._setitem_with_indexer(indexer, value)\n 133 \n 134 def _has_valid_type(self, k, axis):\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in _setitem_with_indexer(self, indexer, value)\n 312 else:\n 313 \n--> 314 indexer, missing = convert_missing_indexer(indexer)\n 315 \n 316 if missing:\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py in convert_missing_indexer(indexer)\n 1822 \n 1823 if isinstance(indexer, bool):\n-> 1824 raise KeyError(\"cannot use a single bool to index into setitem\")\n 1825 return indexer, True\n 1826 \n", "KeyError: 'cannot use a single bool to index into setitem'" ] }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n/opt/conda/bin/ipython:5: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n \n/opt/conda/bin/ipython:6: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n sys.exit(IPython.start_ipython())\n/opt/conda/bin/ipython:7: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:8: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:9: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:10: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:15: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:16: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:17: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:18: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:19: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:20: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:21: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:22: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n/opt/conda/bin/ipython:5: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n \n/opt/conda/bin/ipython:6: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n sys.exit(IPython.start_ipython())\n/opt/conda/bin/ipython:7: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:8: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:9: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:10: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:15: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:16: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:17: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:18: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:19: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:20: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:21: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:22: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n/opt/conda/bin/ipython:5: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n \n/opt/conda/bin/ipython:6: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n sys.exit(IPython.start_ipython())\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:7: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:8: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:9: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:10: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:15: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:16: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:17: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:18: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:19: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:20: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:21: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:22: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n/opt/conda/bin/ipython:5: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n \n/opt/conda/bin/ipython:6: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n sys.exit(IPython.start_ipython())\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:7: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:8: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:9: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:10: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:15: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:16: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:17: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:18: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:19: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:20: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:21: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:22: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n/opt/conda/bin/ipython:5: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n \n/opt/conda/bin/ipython:6: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n sys.exit(IPython.start_ipython())\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:7: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:8: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:9: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:10: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:15: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:16: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:17: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:18: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:19: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:20: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:21: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:22: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n/opt/conda/bin/ipython:5: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n \n/opt/conda/bin/ipython:6: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n sys.exit(IPython.start_ipython())\n/opt/conda/bin/ipython:7: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:8: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:9: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:10: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:15: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:16: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:17: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:18: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:19: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:20: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:21: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:22: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:4: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n import IPython\n/opt/conda/bin/ipython:5: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n \n/opt/conda/bin/ipython:6: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n sys.exit(IPython.start_ipython())\n/opt/conda/bin/ipython:7: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:8: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:9: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:10: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:11: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:15: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:16: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:17: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:18: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:19: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/bin/ipython:20: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:21: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n/opt/conda/bin/ipython:22: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" } ], "source": [ "letters = \"ABCDEFGHIJKLM\"\n", "train_df[\"CabinStartsWith\"] = float(-1)\n", "train_df[\"CabinStartsWithLetter\"] = \"\"\n", "train_df[\"CabinStartsWithLetter\"][train_df[\"Cabin\"] != \"Missing\"] = train_df[\"Cabin\"].str[0]\n", "train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"A\"] = 0\n", "train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"B\"] = 1\n", "train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"C\"] = 2\n", "train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"D\"] = 3\n", "train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"E\"] = 4\n", "train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"F\"] = 5\n", "train_df[\"CabinStartsWith\"][train_df[\"CabinStartsWithLetter\"] == \"G\"] = 6\n", "\n", "test_df[\"CabinStartsWith\"] = -1\n", "test_df[\"CabinStartsWithLetter\"] = \"\"\n", "test_df[\"CabinStartsWithLetter\"][test_df[\"Cabin\"] != \"Missing\"] = test_df[\"Cabin\"].str[0]\n", "test_df[\"CabinStartsWith\"][test_df[\"CabinStartsWithLetter\"] == \"A\"] = 0\n", "test_df[\"CabinStartsWith\"][test_df[\"CabinStartsWithLetter\"] == \"B\"] = 1\n", "test_df[\"CabinStartsWith\"][test_df[\"CabinStartsWithLetter\"] == \"C\"] = 2\n", "test_df[\"CabinStartsWith\"][test_df[\"CabinStartsWithLetter\"] == \"D\"] = 3\n", "test_df[\"CabinStartsWith\"][test_df[\"CabinStartsWithLetter\"] == \"E\"] = 4\n", "test_df[\"CabinStartsWith\"][test_df[\"CabinStartsWithLetter\"] == \"F\"] = 5\n", "test_df[\"CabinStartsWith\"][test_df[\"CabinStartsWithLetter\"] == \"G\"] = 6" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "_cell_guid": "5f3a2ec7-ff68-9a9e-80ca-e5123e06bdf5" }, "outputs": [ { "data": { "text/plain": "0 -1.0\n1 2.0\n2 -1.0\n3 2.0\n4 -1.0\n5 -1.0\n6 4.0\n7 -1.0\n8 -1.0\n9 -1.0\n10 6.0\n11 2.0\n12 -1.0\n13 -1.0\n14 -1.0\n15 -1.0\n16 -1.0\n17 -1.0\n18 -1.0\n19 -1.0\n20 -1.0\n21 3.0\n22 -1.0\n23 0.0\n24 -1.0\n25 -1.0\n26 -1.0\n27 2.0\n28 -1.0\n29 -1.0\n ... \n861 -1.0\n862 3.0\n863 -1.0\n864 -1.0\n865 -1.0\n866 -1.0\n867 0.0\n868 -1.0\n869 -1.0\n870 -1.0\n871 3.0\n872 1.0\n873 -1.0\n874 -1.0\n875 -1.0\n876 -1.0\n877 -1.0\n878 -1.0\n879 2.0\n880 -1.0\n881 -1.0\n882 -1.0\n883 -1.0\n884 -1.0\n885 -1.0\n886 -1.0\n887 1.0\n888 -1.0\n889 2.0\n890 -1.0\nName: CabinStartsWith, dtype: float64" }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df[\"CabinStartsWith\"]" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "_cell_guid": "4c3ef55f-db29-53b7-0118-326e7a425035" }, "outputs": [ { "ename": "TypeError", "evalue": "cannot convert the series to <class 'int'>", "output_type": "error", "traceback": [ "", "TypeErrorTraceback (most recent call last)", "<ipython-input-43-b5a5fcc86ff6> in <module>()\n----> 1 train_df[\"Family\"] = int(train_df[\"Parch\"]) + int(train_df[\"SibSp\"])\n 2 test_df[\"Family\"] = int(test_df[\"Parch\"]) + int(test_df[\"SibSp\"])\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in wrapper(self)\n 76 return converter(self.iloc[0])\n 77 raise TypeError(\"cannot convert the series to \"\n---> 78 \"{0}\".format(str(converter)))\n 79 \n 80 return wrapper\n", "TypeError: cannot convert the series to <class 'int'>" ] }, { "name": "stderr", "output_type": "stream", "text": "/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py:132: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n self._setitem_with_indexer(indexer, value)\n" } ], "source": [ "train_df[\"Family\"] = train_df[\"Parch\"] + train_df[\"SibSp\"]\n", "train_df['Family'].loc[train_df['Family'] > 0] = 1\n", "train_df['Family'].loc[train_df['Family'] == 0] = 0\n", "\n", "test_df[\"Family\"] = test_df[\"Parch\"] + test_df[\"SibSp\"]\n", "test_df['Family'].loc[test_df['Family'] > 0] = 1\n", "test_df['Family'].loc[test_df['Family'] == 0] = 0" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "_cell_guid": "30bc3a24-ae6a-8caf-c54e-5e9eed3310dc" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PassengerId</th>\n <th>Survived</th>\n <th>Pclass</th>\n <th>Age</th>\n <th>SibSp</th>\n <th>Parch</th>\n <th>Fare</th>\n <th>Child</th>\n <th>CabinStartsWith</th>\n <th>Family</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>891.000000</td>\n <td>891.000000</td>\n <td>891.000000</td>\n <td>891.000000</td>\n <td>891.000000</td>\n <td>891.000000</td>\n <td>891.000000</td>\n <td>891.000000</td>\n <td>891.0</td>\n <td>891.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>446.000000</td>\n <td>0.383838</td>\n <td>2.308642</td>\n <td>29.361582</td>\n <td>0.523008</td>\n <td>0.381594</td>\n <td>32.204208</td>\n <td>0.126824</td>\n <td>-1.0</td>\n <td>0.904602</td>\n </tr>\n <tr>\n <th>std</th>\n <td>257.353842</td>\n <td>0.486592</td>\n <td>0.836071</td>\n <td>13.019697</td>\n <td>1.102743</td>\n <td>0.806057</td>\n <td>49.693429</td>\n <td>0.332962</td>\n <td>0.0</td>\n <td>1.613459</td>\n </tr>\n <tr>\n <th>min</th>\n <td>1.000000</td>\n <td>0.000000</td>\n <td>1.000000</td>\n <td>0.420000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>-1.0</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>223.500000</td>\n <td>0.000000</td>\n <td>2.000000</td>\n <td>22.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>7.910400</td>\n <td>0.000000</td>\n <td>-1.0</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>446.000000</td>\n <td>0.000000</td>\n <td>3.000000</td>\n <td>28.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>14.454200</td>\n <td>0.000000</td>\n <td>-1.0</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>668.500000</td>\n <td>1.000000</td>\n <td>3.000000</td>\n <td>35.000000</td>\n <td>1.000000</td>\n <td>0.000000</td>\n <td>31.000000</td>\n <td>0.000000</td>\n <td>-1.0</td>\n <td>1.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>891.000000</td>\n <td>1.000000</td>\n <td>3.000000</td>\n <td>80.000000</td>\n <td>8.000000</td>\n <td>6.000000</td>\n <td>512.329200</td>\n <td>1.000000</td>\n <td>-1.0</td>\n <td>10.000000</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " PassengerId Survived Pclass Age SibSp \\\ncount 891.000000 891.000000 891.000000 891.000000 891.000000 \nmean 446.000000 0.383838 2.308642 29.361582 0.523008 \nstd 257.353842 0.486592 0.836071 13.019697 1.102743 \nmin 1.000000 0.000000 1.000000 0.420000 0.000000 \n25% 223.500000 0.000000 2.000000 22.000000 0.000000 \n50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n75% 668.500000 1.000000 3.000000 35.000000 1.000000 \nmax 891.000000 1.000000 3.000000 80.000000 8.000000 \n\n Parch Fare Child CabinStartsWith Family \ncount 891.000000 891.000000 891.000000 891.0 891.000000 \nmean 0.381594 32.204208 0.126824 -1.0 0.904602 \nstd 0.806057 49.693429 0.332962 0.0 1.613459 \nmin 0.000000 0.000000 0.000000 -1.0 0.000000 \n25% 0.000000 7.910400 0.000000 -1.0 0.000000 \n50% 0.000000 14.454200 0.000000 -1.0 0.000000 \n75% 0.000000 31.000000 0.000000 -1.0 1.000000 \nmax 6.000000 512.329200 1.000000 -1.0 10.000000 " }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 16 columns):\nPassengerId 891 non-null int64\nSurvived 891 non-null int64\nPclass 891 non-null int64\nName 891 non-null object\nSex 891 non-null object\nAge 891 non-null float64\nSibSp 891 non-null int64\nParch 891 non-null int64\nTicket 891 non-null object\nFare 891 non-null float64\nCabin 204 non-null object\nEmbarked 891 non-null object\nChild 891 non-null float64\nCabinStartsWith 891 non-null int64\nCabinStartsWithLetter 0 non-null object\nFamily 891 non-null int64\ndtypes: float64(3), int64(7), object(6)\nmemory usage: 111.5+ KB\n" }, { "name": "stdout", "output_type": "stream", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 15 columns):\nPassengerId 891 non-null int64\nSurvived 891 non-null int64\nPclass 891 non-null int64\nName 891 non-null object\nSex 891 non-null object\nAge 891 non-null float64\nSibSp 891 non-null int64\nParch 891 non-null int64\nTicket 891 non-null object\nFare 891 non-null float64\nCabin 891 non-null object\nEmbarked 891 non-null int64\nChild 891 non-null float64\nCabinStartsWith 891 non-null float64\nCabinStartsWithLetter 891 non-null object\ndtypes: float64(4), int64(6), object(5)\nmemory usage: 104.5+ KB\n" }, { "name": "stdout", "output_type": "stream", "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 891 entries, 0 to 890\nData columns (total 16 columns):\nPassengerId 891 non-null int64\nSurvived 891 non-null int64\nPclass 891 non-null int64\nName 891 non-null object\nSex 891 non-null object\nAge 891 non-null float64\nSibSp 891 non-null int64\nParch 891 non-null int64\nTicket 891 non-null object\nFare 891 non-null float64\nCabin 891 non-null object\nEmbarked 891 non-null int64\nChild 891 non-null float64\nCabinStartsWith 891 non-null float64\nCabinStartsWithLetter 891 non-null object\nFamily 891 non-null int64\ndtypes: float64(4), int64(7), object(5)\nmemory usage: 111.5+ KB\n" } ], "source": [ "train_df.info()" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "_cell_guid": "8f826d94-ea3b-8950-be53-6a6fa9bcef5d" }, "outputs": [ { "data": { "text/plain": "0 1\n1 1\n2 0\n3 1\n4 0\n5 0\n6 0\n7 1\n8 1\n9 1\n10 1\n11 0\n12 0\n13 1\n14 0\n15 0\n16 1\n17 0\n18 1\n19 0\n20 0\n21 0\n22 0\n23 0\n24 1\n25 1\n26 0\n27 1\n28 0\n29 0\n ..\n861 1\n862 0\n863 1\n864 0\n865 0\n866 1\n867 0\n868 0\n869 1\n870 0\n871 1\n872 0\n873 0\n874 1\n875 0\n876 0\n877 0\n878 0\n879 1\n880 1\n881 0\n882 0\n883 0\n884 0\n885 1\n886 0\n887 0\n888 1\n889 0\n890 0\nName: Family, dtype: int64" }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_df[\"Family\"]" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "_cell_guid": "16342daa-8aee-69a9-fd38-4417fe2c87bf" }, "outputs": [], "source": [ "X_train = train_df[[\"Pclass\", \"Sex\", \"Age\", \"Parch\", \"Fare\", \"Embarked\", \"Child\", \"Family\", \"CabinStartsWith\"]].values\n", "Y_train = train_df[[\"Survived\"]].values\n", "\n", "X_test = test_df[[\"Pclass\", \"Sex\", \"Age\", \"Parch\", \"Fare\", \"Embarked\", \"Child\", \"Family\", \"CabinStartsWith\"]].values" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "_cell_guid": "a9d2235c-5b82-0e14-ba61-07bb5f67f1a7" }, "outputs": [ { "data": { "text/plain": "0.98316498316498313" }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Random Forests\n", "random_forest = RandomForestClassifier(n_estimators=100)\n", "random_forest.fit(X_train, Y_train)\n", "Y_pred = random_forest.predict(X_test)\n", "random_forest.score(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "_cell_guid": "8e046656-6706-3444-c5ba-87a8c25ade99" }, "outputs": [ { "data": { "text/plain": "0.84175084175084181" }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn = KNeighborsClassifier(n_neighbors = 3)\n", "knn.fit(X_train, Y_train)\n", "Y_pred_knn = knn.predict(X_test)\n", "knn.score(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "_cell_guid": "9dd82a08-00f9-16e7-fdea-ff4d3d4c73cb" }, "outputs": [ { "data": { "text/plain": "0.76430976430976427" }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Gaussian Naive Bayes\n", "gaussian = GaussianNB()\n", "gaussian.fit(X_train, Y_train)\n", "Y_pred_nb = gaussian.predict(X_test)\n", "gaussian.score(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "_cell_guid": "28b25af5-6b93-d692-bbf8-e3c280c7bac5" }, "outputs": [ { "data": { "text/plain": "0.88327721661054992" }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Support Vector Machines\n", "svc = SVC()\n", "svc.fit(X_train, Y_train)\n", "Y_pred_svm = svc.predict(X_test)\n", "svc.score(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "_cell_guid": "29a21b69-7cd1-4e36-5bfc-8d06fe41c0be" }, "outputs": [ { "data": { "text/plain": "0.81369248035914699" }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Logistic Regression\n", "logreg = LogisticRegression()\n", "logreg.fit(X_train, Y_train)\n", "Y_pred_log = logreg.predict(X_test)\n", "logreg.score(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "_cell_guid": "c16913bb-f88c-e9e6-30c3-00222ec40993" }, "outputs": [ { "data": { "text/plain": "0.70258136924803594" }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Linear SVC\n", "linreg = LinearSVC()\n", "linreg.fit(X_train, Y_train)\n", "Y_pred_lin = linreg.predict(X_test)\n", "linreg.score(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "_cell_guid": "d827fcc5-5ef3-ec95-37cc-55c7b9daabc3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": " Survived\n892 0\n893 0\n894 0\n895 1\n896 1\n897 0\n898 0\n899 0\n900 1\n901 0\n902 0\n903 0\n904 1\n905 0\n906 1\n907 1\n908 0\n909 1\n910 0\n911 0\n912 0\n913 1\n914 1\n915 0\n916 1\n917 0\n918 1\n919 1\n920 1\n921 0\n... ...\n1280 0\n1281 0\n1282 0\n1283 1\n1284 0\n1285 0\n1286 0\n1287 1\n1288 0\n1289 1\n1290 0\n1291 0\n1292 1\n1293 0\n1294 1\n1295 0\n1296 0\n1297 1\n1298 0\n1299 0\n1300 1\n1301 1\n1302 1\n1303 1\n1304 0\n1305 0\n1306 1\n1307 0\n1308 0\n1309 1\n\n[418 rows x 1 columns]\n(418, 1)\n" } ], "source": [ "pid = np.array(test_df[\"PassengerId\"]).astype(int)\n", "my_solution = pd.DataFrame(Y_pred, pid, columns = [\"Survived\"])\n", "print(my_solution)\n", "\n", "# Check that your data frame has 418 entries\n", "print(my_solution.shape)\n", "\n", "# Write your solution to a csv file with the name my_solution.csv\n", "my_solution.to_csv(\"titanic_solution_rf.csv\", index_label = [\"PassengerId\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "ca01e213-2048-5525-d66e-39e204c4973e" }, "outputs": [], "source": "" } ], "metadata": { "_change_revision": 1622, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/324/324878.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "554c0be6-85d0-78b5-de27-126222b9c30c" }, "source": [ "## EA player stats per league and game frequencies" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "f0a08ebf-f53f-38ff-799d-188b0e48350d" }, "source": [ "We'll look at the distribution of average player ratings per team for each league. The averages are determined using the Player_Stats table in combination with the Match table. We also plot the frequency of games for each league." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "6f97dfb4-7dcb-4288-7227-5f8441563272" }, "outputs": [], "source": [ "import sqlite3\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns\n", "import datetime as dt" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "80a9d86f-739d-0217-915b-809e09c6022f" }, "source": [ "\n", "### Dataframe manipulations (SQLite and Pandas)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "8c149a15-d9dc-ec27-20fd-df4b01a7a714" }, "outputs": [], "source": [ "conn = sqlite3.connect('../input/database.sqlite')\n", "c = conn.cursor()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "95e0478b-f464-91ad-aca4-d9f40eea8de7" }, "source": [ "**League ID dictionary**\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "85b6603c-6480-2663-38c1-5d30b0087b0b" }, "outputs": [ { "data": { "text/plain": [ "{1: 'Belgium Jupiler League',\n", " 1729: 'England Premier League',\n", " 4735: 'France Ligue 1',\n", " 7775: 'Germany 1. Bundesliga',\n", " 10223: 'Italy Serie A',\n", " 13240: 'Netherlands Eredivisie',\n", " 15688: 'Poland Ekstraklasa',\n", " 17608: 'Portugal Liga ZON Sagres',\n", " 19660: 'Scotland Premier League',\n", " 21484: 'Spain LIGA BBVA',\n", " 24524: 'Switzerland Super League'}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ids = [i[0] for i in c.execute('SELECT id FROM League').fetchall()]\n", "names = [i[0] for i in c.execute('SELECT name FROM League').fetchall()]\n", "id_league = {i: n for i, n in zip(ids, names)}\n", "id_league" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "67d2188d-8f26-9880-2151-bce249f46ca2" }, "outputs": [], "source": [ "# Country ID\n", "# Country id\n", "ids = [i[0] for i in c.execute('SELECT id FROM Country').fetchall()]\n", "names = [i[0] for i in c.execute('SELECT name FROM Country').fetchall()]\n", "id_country = {i: n for i, n in zip(ids, names)};" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "10b1f61c-304d-a1ac-80a9-0b3428d17a10" }, "source": [ "**What EA Sports FIFA stats do we have?**" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "dc0b0a09-0189-ea27-db3f-d158c517d23f" }, "outputs": [ { "data": { "text/plain": [ "[(0, 'id', 'INTEGER', 0, None, 1),\n", " (1, 'player_fifa_api_id', 'INTEGER', 0, None, 0),\n", " (2, 'player_api_id', 'INTEGER', 0, None, 0),\n", " (3, 'date_stat', 'TEXT', 0, None, 0),\n", " (4, 'overall_rating', 'INTEGER', 0, None, 0),\n", " (5, 'potential', 'INTEGER', 0, None, 0),\n", " (6, 'preferred_foot', 'TEXT', 0, None, 0),\n", " (7, 'attacking_work_rate', 'TEXT', 0, None, 0),\n", " (8, 'defensive_work_rate', 'TEXT', 0, None, 0),\n", " (9, 'crossing', 'INTEGER', 0, None, 0),\n", " (10, 'finishing', 'INTEGER', 0, None, 0),\n", " (11, 'heading_accuracy', 'INTEGER', 0, None, 0),\n", " (12, 'short_passing', 'INTEGER', 0, None, 0),\n", " (13, 'volleys', 'INTEGER', 0, None, 0),\n", " (14, 'dribbling', 'INTEGER', 0, None, 0),\n", " (15, 'curve', 'INTEGER', 0, None, 0),\n", " (16, 'free_kick_accuracy', 'INTEGER', 0, None, 0),\n", " (17, 'long_passing', 'INTEGER', 0, None, 0),\n", " (18, 'ball_control', 'INTEGER', 0, None, 0),\n", " (19, 'acceleration', 'INTEGER', 0, None, 0),\n", " (20, 'sprint_speed', 'INTEGER', 0, None, 0),\n", " (21, 'agility', 'INTEGER', 0, None, 0),\n", " (22, 'reactions', 'INTEGER', 0, None, 0),\n", " (23, 'balance', 'INTEGER', 0, None, 0),\n", " (24, 'shot_power', 'INTEGER', 0, None, 0),\n", " (25, 'jumping', 'INTEGER', 0, None, 0),\n", " (26, 'stamina', 'INTEGER', 0, None, 0),\n", " (27, 'strength', 'INTEGER', 0, None, 0),\n", " (28, 'long_shots', 'INTEGER', 0, None, 0),\n", " (29, 'aggression', 'INTEGER', 0, None, 0),\n", " (30, 'interceptions', 'INTEGER', 0, None, 0),\n", " (31, 'positioning', 'INTEGER', 0, None, 0),\n", " (32, 'vision', 'INTEGER', 0, None, 0),\n", " (33, 'penalties', 'INTEGER', 0, None, 0),\n", " (34, 'marking', 'INTEGER', 0, None, 0),\n", " (35, 'standing_tackle', 'INTEGER', 0, None, 0),\n", " (36, 'sliding_tackle', 'INTEGER', 0, None, 0),\n", " (37, 'gk_diving', 'INTEGER', 0, None, 0),\n", " (38, 'gk_handling', 'INTEGER', 0, None, 0),\n", " (39, 'gk_kicking', 'INTEGER', 0, None, 0),\n", " (40, 'gk_positioning', 'INTEGER', 0, None, 0),\n", " (41, 'gk_reflexes', 'INTEGER', 0, None, 0)]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c.execute('PRAGMA TABLE_INFO(Player_Stats)').fetchall()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "a4e92175-839e-d91d-4b88-72ba47ae0db8" }, "source": [ "**Getting player stats for each game in the database**\n" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1b578824-235d-2169-a8b6-9974f28adf33" }, "source": [ "For each game we'll determine the mean overall_rating statistic for __4 player groups__: \n", "\n", " - F=forward (striker), M=midfield, D=defense, G=goalie.\n", "\n", "Doing this task requires us to iterate over the Match table and get the stats for each player on the home and away teams using the Player_Stats table. Multiple rows of statistics exist for each player and we'll select the one whose datestamp most closely aligns with the game date.\n", "\n", "The player position is determined using the player's 'Y' coordinate from the Match table. These coordinates are integers ranging from 1 to 11 (0 and None are assumed to be unknown). Based on the distribution below we'll define positions as follows:\n", "\n", " - Y=1 -> G\n", " - Y=3 -> D \n", " - Y=5-7 -> M \n", " - Y=8-11 -> F \n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "90bd73a1-f412-a07c-d837-54e8436b2a8d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Player Y value: # of instances in database (home players)\n" ] }, { "data": { "text/plain": [ "Counter({0: 22,\n", " 1: 24115,\n", " None: 20130,\n", " 3: 95293,\n", " 5: 3102,\n", " 6: 15641,\n", " 7: 58108,\n", " 8: 23067,\n", " 9: 2782,\n", " 10: 31864,\n", " 11: 11271})" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols = \", \".join([\"home_player_Y\"+str(i) for i in range(1,12)])\n", "c.execute('SELECT {0:s} FROM Match'.\\\n", " format(cols))\n", "Y_array = c.fetchall()\n", "\n", "Y = np.array([a for row in Y_array for a in row]) # flatten\n", "from collections import Counter\n", "print('Player Y value: # of instances in database (home players)')\n", "Counter(Y)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "dd053529-13d1-0c83-313a-4ac626e4947a" }, "source": [ "__Warning__: _very ugly function below to pool EA player stats for each game into a list. You may want to_ __skip down to the next section where we start visualizing the data.__" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "7159f436-708b-59da-9afc-0a4ff3ed64df" }, "outputs": [], "source": [ "EA_stats = {'player': ', '.join(['overall_rating']), #'attacking_work_rate', 'defensive_work_rate',\n", "# 'crossing', 'finishing', 'heading_accuracy', 'short_passing',\n", "# 'volleys', 'dribbling', 'curve', 'free_kick_accuracy',\n", "# 'long_passing', 'ball_control', 'acceleration', 'sprint_speed',\n", "# 'agility', 'reactions', 'balance', 'shot_power', 'jumping',\n", "# 'stamina', 'strength', 'long_shots', 'interceptions',\n", "# 'positioning', 'vision', 'penalties', 'marking',\n", "# 'standing_tackle', 'sliding_tackle']),\n", " 'goalie': ', '.join(['gk_diving', 'gk_handling', 'gk_kicking',\n", " 'gk_positioning', 'gk_reflexes'])}\n", "\n", "\n", "def getTeamScores(match_id, team, EA_stats,\n", " printout=False,\n", " group='forward_mid_defense_goalie'):\n", " ''' Return the cumulative average team scores for \n", " a given EA Sports FIFA statistic. If particular EA\n", " stats are not in the database that stat is taken as\n", " the overall player rating. If any positional stat is\n", " unavailable (i.e. no goalie information) that stat is\n", " taken as the average of the others for that team.\n", " team : str\n", " 'home' or 'away'\n", " EA_stat : dict\n", " Names of statistics to cumulate for goalie and players.\n", " e.g. {'player': 'overall_rating, heading_accuracy',\n", " 'goalie': 'gk_diving, gk_handling'}\n", " printout : boolean\n", " Option to print out debug information,\n", " defaults to False.\n", " group : str\n", " How to group scores:\n", " 'forward_mid_defense_goalie': output 4 values\n", " 'all': output 1 value (currently not implemented)\n", " '''\n", " \n", " if team == 'home':\n", " player_cols = ', '.join(['home_player_'+str(i) for i in range(1,12)])\n", " player_Y_cols = np.array(['home_player_Y'+str(i) for i in range(1,12)])\n", " elif team == 'away':\n", " player_cols = ', '.join(['away_player_'+str(i) for i in range(1,12)])\n", " player_Y_cols = np.array(['away_player_Y'+str(i) for i in range(1,12)])\n", " \n", " # Get the player ids from the Match table\n", " c.execute('SELECT {0:s} FROM Match WHERE id={1:d}'.\\\n", " format(player_cols, match_id))\n", " player_api_id = np.array(c.fetchall()[0])\n", " \n", " # Return dictionary of NaN if all items in the list are null\n", " # WARNING: I've hard-coded this dictionary\n", " if False not in [p==0 or p==None for p in player_api_id]:\n", "# raise LookupError('No player data found for Match table row_id={}'.\\\n", "# format(match_id))\n", " return {'F': np.array([np.nan]), 'M': np.array([np.nan]),\n", " 'D': np.array([np.nan]), 'G': np.array([np.nan])}\n", " \n", " # Remove any empty player entries (if player_api_id == None or nan)\n", " empty_mask = player_api_id != np.array(None)\n", " player_api_id = player_api_id[empty_mask]\n", " player_Y_cols = ', '.join(player_Y_cols[empty_mask])\n", " \n", " # Get the player positions from the Match table\n", " # We only care about the Y position to designate\n", " # forwards, midfielders, defense, and goalie\n", " \n", " c.execute('SELECT {0:s} FROM Match WHERE id={1:d}'.\\\n", " format(player_Y_cols, match_id))\n", " player_Y = c.fetchall()[0]\n", " \n", " def givePosition(Y):\n", " ''' Input the Y position of the player (as opposed\n", " to the lateral X position) and return the categorical\n", " position. '''\n", " if Y == 1:\n", " return 'G'\n", " elif Y == 3:\n", " return 'D'\n", " elif Y == 5 or Y == 6 or Y == 7:\n", " return 'M'\n", " elif Y == 8 or Y == 9 or Y == 10 or Y == 11:\n", " return 'F'\n", " else:\n", "# sys.exit('Unknown value for Y: {}'.\\\n", "# format(Y))\n", " return 'NaN'\n", "\n", " player_pos = np.array([givePosition(Y) for Y in player_Y])\n", " \n", " # Get the match date\n", " \n", " def toDatetime(datetime):\n", " ''' Convert string date to datetime object. '''\n", " return dt.datetime.strptime(datetime, '%Y-%m-%d %H:%M:%S')\n", "\n", " c.execute('SELECT date FROM Match WHERE id={}'.\\\n", " format(match_id))\n", " match_date = toDatetime(c.fetchall()[0][0])\n", " \n", " # Lookup the EA Sports stats for each player\n", " # The stats are time dependent so we have to\n", " # find the ones closest to the match date\n", " \n", " def getBestDate(player_id, match_date):\n", " ''' Find most suitable player stats to use based\n", " on date of match and return the corresponding row\n", " id from the Player_Stats table. ''' \n", " c.execute('SELECT id FROM Player_Stats WHERE player_api_id={}'.\\\n", " format(player_id))\n", " ids = np.array([i[0] for i in c.fetchall()])\n", " c.execute('SELECT date_stat FROM Player_Stats WHERE player_api_id={}'.\\\n", " format(player_id))\n", " dates = [toDatetime(d[0]) for d in c.fetchall()]\n", " dates_delta = np.array([abs(d-match_date) for d in dates])\n", " return ids[dates_delta==dates_delta.min()][0]\n", " \n", " def fill_empty_stats(stats, stat_names):\n", " ''' Input the incomplete EA player stats and corresponing\n", " names, return the filled in stats list. Filling with\n", " overall_rating or averaging otherwise (i.e. for goalies\n", " where there is no overall_rating stat). '''\n", " if not np.sum([s==0 or s==None for s in stats]):\n", " return stats\n", " stats_dict = {sn: s for sn, s in zip(stat_names, stats)}\n", " try:\n", " fill = stats_dict['overall_rating']\n", " except:\n", " # Either a goalie or player with no overall rating\n", " # Filling with average of other stats\n", " fill = np.mean([s for s in stats if s!=0 and s!=None])\n", " filled_stats = []\n", " for s in stats:\n", " if s==None or s==0:\n", " filled_stats.append(fill)\n", " else:\n", " filled_stats.append(s)\n", " return filled_stats\n", " \n", " positions = ('G', 'D', 'M', 'F')\n", " average_stats = {}\n", " for position in positions:\n", " if printout: print(position)\n", " if position == 'G':\n", " stats = EA_stats['goalie']\n", " else:\n", " stats = EA_stats['player']\n", " position_ids = player_api_id[player_pos==position]\n", " average_stats[position] = np.zeros(len(stats.split(',')))\n", " for player_id in position_ids:\n", " if printout: print(player_id)\n", " best_date_id = getBestDate(player_id, match_date)\n", " c.execute('SELECT {0:s} FROM Player_Stats WHERE id={1:d}'.\\\n", " format(stats, best_date_id))\n", " query = np.array(c.fetchall()[0])\n", " query = fill_empty_stats(query, stats.split(', '))\n", " if printout: print(query)\n", " if sum([q==None or q==0 for q in query]):\n", " raise LookupError('Found null EA stats entry at stat_id={}'.\\\n", " format(best_date_id))\n", "# sys.exit('Found null EA stats entry at stat_id={}'.\\\n", "# format(best_date_id))\n", " average_stats[position] += query\n", " if printout: print('')\n", " average_stats[position] /= len(position_ids) # take average\n", " \n", " # Take average of goalie stats\n", " try:\n", " average_stats['G'] = np.array([average_stats['G'].mean()])\n", " except:\n", " # Missing info: (average_stats['G']) = 0\n", " pass\n", " \n", " # Insert missing stats\n", " insert_value = np.mean([v[0] for v in average_stats.values() if not np.isnan(v)])\n", " for k, v in average_stats.items():\n", " if np.isnan(v[0]):\n", " average_stats[k] = np.array([insert_value])\n", " \n", "# # Return a dictionary of numeric results as strings for storing in SQL table\n", "# return {key: ' '.join([str(v) for v in value]) for key, value in average_stats.items()}\n", "# ''' THE LINE ABOVE NEEDS A FIX - UNABLE TO ADD STRINGS LIKE THIS TO SQL TABLE ''' \n", " return average_stats" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "4d9a3132-918a-9838-51ce-0e3af2de1768" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "G\n", "67949\n", "[71 61 59 61 72]\n", "\n", "D\n", "38906\n", "[64]\n", "\n", "39878\n", "[68]\n", "\n", "39977\n", "[65]\n", "\n", "36849\n", "[64]\n", "\n", "M\n", "37128\n", "[68]\n", "\n", "26224\n", "[66]\n", "\n", "38353\n", "[64]\n", "\n", "21753\n", "[68]\n", "\n", "F\n", "17276\n", "[69]\n", "\n", "38947\n", "[66]\n", "\n" ] }, { "data": { "text/plain": [ "{'D': array([ 65.25]),\n", " 'F': array([ 67.5]),\n", " 'G': array([ 64.8]),\n", " 'M': array([ 66.5])}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Test of the function above\n", "avg = getTeamScores(999, 'home', EA_stats, printout=True)\n", "avg" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "b69c7aba-2db3-b026-d93e-a58bca11de6d" }, "outputs": [ { "data": { "text/plain": [ "{'D': array([ nan]),\n", " 'F': array([ nan]),\n", " 'G': array([ nan]),\n", " 'M': array([ nan])}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Null test of the function above\n", "avg = getTeamScores(5, 'home', EA_stats, printout=True)\n", "avg" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c41ddf9b-034a-9fff-2c1b-25515cf36c64" }, "source": [ "Iterate through table rows and store results in lists.\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "24557671-4c83-bac8-a1f4-c0bb6c0f8c94" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n", " warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n" ] } ], "source": [ "# Get row ids for our Match table\n", "all_ids = c.execute('SELECT id FROM Match').fetchall()\n", "all_ids = [i[0] for i in sorted(all_ids)]\n", "\n", "hF, hM, hD, hG = [], [], [], []\n", "aF, aM, aD, aG = [], [], [], []\n", "for i in all_ids:\n", " h_stats = getTeamScores(i, 'home', EA_stats, printout=False)\n", " hF.append(h_stats['F'][0])\n", " hM.append(h_stats['M'][0])\n", " hD.append(h_stats['D'][0])\n", " hG.append(h_stats['G'][0])\n", " a_stats = getTeamScores(i, 'away', EA_stats, printout=False)\n", " aF.append(a_stats['F'][0])\n", " aM.append(a_stats['M'][0])\n", " aD.append(a_stats['D'][0])\n", " aG.append(a_stats['G'][0])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d571385e-60d1-faf7-cba6-c1e2bf653822" }, "source": [ "Load results into a Pandas dataframe along with desired columns from Match" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "e544399b-0810-7d15-42e0-47547ffac584" }, "outputs": [], "source": [ "df = pd.read_sql(sql='SELECT {} FROM Match'.\\\n", " format('id, country_id, league_id, season, stage, '+\\\n", " 'date, home_team_api_id, away_team_api_id, '+\\\n", " 'home_team_goal, away_team_goal'),\n", " con=conn)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "138d3fb3-ce6e-fe33-7354-457431df5562" }, "outputs": [], "source": [ "features = ['home_F_stats', 'home_M_stats', 'home_D_stats', 'home_G_stats',\n", " 'away_F_stats', 'away_M_stats', 'away_D_stats', 'away_G_stats']\n", "\n", "data = [hF, hM, hD, hG, aF, aM, aD, aG]\n", "\n", "for f, d in zip(features, data):\n", " df[f] = d" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "e26c9e59-1b94-4018-3f96-12ca99962b94" }, "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>country_id</th>\n", " <th>league_id</th>\n", " <th>season</th>\n", " <th>stage</th>\n", " <th>date</th>\n", " <th>home_team_api_id</th>\n", " <th>away_team_api_id</th>\n", " <th>home_team_goal</th>\n", " <th>away_team_goal</th>\n", " <th>home_F_stats</th>\n", " <th>home_M_stats</th>\n", " <th>home_D_stats</th>\n", " <th>home_G_stats</th>\n", " <th>away_F_stats</th>\n", " <th>away_M_stats</th>\n", " <th>away_D_stats</th>\n", " <th>away_G_stats</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2008/2009</td>\n", " <td>1</td>\n", " <td>2008-08-17 00:00:00</td>\n", " <td>9987</td>\n", " <td>9993</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2008/2009</td>\n", " <td>1</td>\n", " <td>2008-08-16 00:00:00</td>\n", " <td>10000</td>\n", " <td>9994</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2008/2009</td>\n", " <td>1</td>\n", " <td>2008-08-16 00:00:00</td>\n", " <td>9984</td>\n", " <td>8635</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2008/2009</td>\n", " <td>1</td>\n", " <td>2008-08-17 00:00:00</td>\n", " <td>9991</td>\n", " <td>9998</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>2008/2009</td>\n", " <td>1</td>\n", " <td>2008-08-16 00:00:00</td>\n", " <td>7947</td>\n", " <td>9985</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id country_id league_id season stage date \\\n", "0 1 1 1 2008/2009 1 2008-08-17 00:00:00 \n", "1 2 1 1 2008/2009 1 2008-08-16 00:00:00 \n", "2 3 1 1 2008/2009 1 2008-08-16 00:00:00 \n", "3 4 1 1 2008/2009 1 2008-08-17 00:00:00 \n", "4 5 1 1 2008/2009 1 2008-08-16 00:00:00 \n", "\n", " home_team_api_id away_team_api_id home_team_goal away_team_goal \\\n", "0 9987 9993 1 1 \n", "1 10000 9994 0 0 \n", "2 9984 8635 0 3 \n", "3 9991 9998 5 0 \n", "4 7947 9985 1 3 \n", "\n", " home_F_stats home_M_stats home_D_stats home_G_stats away_F_stats \\\n", "0 NaN NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN NaN \n", "\n", " away_M_stats away_D_stats away_G_stats \n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "6a8daae2-01f7-0cff-8dd3-29a88f4de9ec" }, "source": [ "Let's do some dataframe manipulations:\n", "\n", " - getting rid of the NaN rows\n", " - converting to datetimes\n", " - add league and country names\n", " - calculate averages of EA stats" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "56e2b34d-4f90-3fea-d4bf-4791bb2fb2c6" }, "outputs": [], "source": [ "# Dropping NaNs\n", "df = df.dropna()\n", "\n", "# Adding a game state column:\n", "# a list of the form [H, D, A]\n", "#\n", "# state = [1, 0, 0], result = 1 => Home team win\n", "# state = [0, 1, 0], result = 2 => Draw\n", "# state = [0, 0, 1], result = 3 => Away team win\n", "H = lambda x: x[0] > x[1]\n", "D = lambda x: x[0] == x[1]\n", "A = lambda x: x[0] < x[1]\n", "state, result = [], []\n", "for goals in df[['home_team_goal', 'away_team_goal']].values:\n", " r = np.array([H(goals), D(goals), A(goals)])\n", " state.append(r)\n", " if (r == [1, 0, 0]).sum() == 3:\n", " result.append(1)\n", " elif (r == [0, 1, 0]).sum() == 3:\n", " result.append(2)\n", " elif (r == [0, 0, 1]).sum() == 3:\n", " result.append(3)\n", "df['game_state'] = state\n", "df['game_result'] = result\n", "\n", "# Convert to datetimes\n", "df['date'] = pd.to_datetime(df['date'])\n", "\n", "# Map leagues names using dictionaries from earlier\n", "df['country'] = df['country_id'].map(id_country)\n", "df['league'] = df['league_id'].map(id_league)\n", "\n", "# Average stats for teams (for each game)\n", "f = lambda x: np.mean(x)\n", "df['home_mean_stats'] = list(map(f, df[['home_F_stats', 'home_M_stats',\n", " 'home_D_stats', 'home_G_stats']].values))\n", "df['away_mean_stats'] = list(map(f, df[['away_F_stats', 'away_M_stats',\n", " 'away_D_stats', 'away_G_stats']].values));" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "91bb1ed1-8cb0-538d-e826-7135c5a1e603" }, "outputs": [ { "data": { "text/plain": [ "id int64\n", "country_id int64\n", "league_id int64\n", "season object\n", "stage int64\n", "date datetime64[ns]\n", "home_team_api_id int64\n", "away_team_api_id int64\n", "home_team_goal int64\n", "away_team_goal int64\n", "home_F_stats float64\n", "home_M_stats float64\n", "home_D_stats float64\n", "home_G_stats float64\n", "away_F_stats float64\n", "away_M_stats float64\n", "away_D_stats float64\n", "away_G_stats float64\n", "game_state object\n", "game_result int64\n", "country object\n", "league object\n", "home_mean_stats float64\n", "away_mean_stats float64\n", "dtype: object" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Here is what we have ...\n", "df.dtypes" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "02715f6d-c681-6c70-599e-8ad3d0cde524" }, "source": [ "\n", "### Visualizing EA Sports FIFA stats" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b2de5a65-5ac6-3686-b5e3-f53094076d8f" }, "source": [ "If you've read how we got the stats for our dataframe df, then you know we averaged player stats for both teams in each game and binned the results for forwards **F**, midfielders **M**, defence **D**, and goalies **G**. We also have columns in `df` named `home_mean_stats` and `away_mean_stats` for the average of these 4 quantities.\n" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "21c026ae-b823-284a-8d1f-4b77814c5691" }, "source": [ "\n", "Looking for correlations among our positional EA stats for each team. Using `seaborn`'s `pairplot` function we can easily do this, plus generate histograms on the diagonal (where scatter plots would be pointless)." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "d07adae3-a0ea-c6ad-0b95-931b42b1424f" }, "outputs": [ { "data": { "image/png": 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yFxzry8nJUVufqm1uZWXFPVYVmAEgl6NcIBDIjSS/c+cO97i6ulplIAiA3N0C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ftdx6oOF4uJnDbyH/CJXJ9J6p5Tkiv86JVlplQmLzMfzdLpl4gkclitZlpcNtySnVxR1hVQjGyn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\ntfuD5ApFWTFicSlDXSWBIY7oKUfxj7wxikGv5a2REAcGOyuBbYn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7YR9MxdpEuVRifHys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\nZHtVN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KNCmE/7mYnZQU8IlaV6xixRjhs5vm7VcwATzLSo9JYKGEscMz+RisJspnJk3wUv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Wjl+jKb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\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eMXduevmG7CE1IT0VSSHLyubtiZ13Ua7IJDIFQk0O/v2ZFSmUR3f0KV0e1Y48tYX5wO5+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7//7kP1dbKJ2gzpbB69JPLt3X2EohnN1IpeEpFEeRrQZJBochjp8Fjo63TisBhYCKfqDLhff3f2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1lycIjaoAXQNjaL8ygo23SDPb1cqJfR1SwTi3N4RioxoHPu0U32fQKmHUF6BvyAePPwy5LDlB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hlTW4WiArmsQuAZLCxnsG96sGEvzhVkbB4No6SomFvOoCfowlIsjyMn9HHevdN60YWx0JiP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2ljayd/r8Mh68pw0LK2ThI5uT0d5Evg8Lx4IzMUbh5/6trbBwLH7x5hTSkownH+wHVNSM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KrvBDjx/pRqzFnoBz8oeWVlP1g8Vl+vj+///LTyeF+vj2d/+dHGB+XnaTQZ6PBKkPlmUzoO7ev1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Xq/lSUyk3NdFLtVZnaUOmP+olbhJLaGo+8/RVpVpjYizKB9dVwi1fqnDgtj7SuRJ2wcovzy+w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\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hWXgrqULVsXe1+MC6nFWjXXZGcUnRrOPaP9mLAzv78NH5ZfR1hrCSLGKgM4gzl+PW5q9U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\nTGJsuAtADVcWc6jValgXciBXrOJPIu1N3512r2kQBQ7H319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WW7RMPItKtYZTZ6MYqQUo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1yItNEC2kVXYTnn9jYsXwtx6oBwD8y3PCTbE4hkhN1ZWaN3+6/I8DIyG8Pp4z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\n/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kh/uMkgaVD9HbKYzLP7nrU889qlOlLI3tHeugZdi89OW5ODr2xqx4TptpNs+bxjpf3Z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FpZjGY4tGstd9/WjBEBQTB8It/tJ4Eb8XtuWtuI89EBZpdSBBvsrAk6WFxM0NPiIp+T1DzB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RKGfOXmNqfMAsqtw3GiXkk/nJKx9YZId+8qurPHp4G6ePjVBQK2b8c7OOvMbEc6FkMFJf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\nzLubK1l9ZBuokzXWrwMDYS87xsKUKjUKpeZJukjQobPsIh0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WdZ6ffX2mbl3W3388XYAo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PBh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\nf4+OqZhBwbsgAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7face69758d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.pairplot(data=df[features])\n", "plt.suptitle('EA Sports FIFA positional game ratings correlations', fontsize=30, y=1.02);\n", "plt.show();" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "4297ea18-1c66-a9a5-3f1b-5fbb29a199d2" }, "source": [ "\n", "The figure above can be understood in terms of 4, 4x4 quadrants.\n", "\n", "The top left quadrant shows how home scores are largely correlated with eachother, the same is true for away teams as seen in the bottom right quadrant.\n", "\n", "The other two quadrants are also redundant, looking at these we see a nicely distributed dataset. Looking at the histrograms we see that our features are normally distributed meaning we have a good amount of data from which to draw accurate conclusions." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "128995d2-32d0-4c71-45bc-2ef0ba81d2a0" }, "source": [ "Now let's plot the **game frequency** for all leagues. With our data we can look back to the 2008/2009 season." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "39d81f03-30af-3f0e-c80c-ee8c8aa4e1e8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bin_width = 28.5 days\n" ] }, { "data": { "image/png": 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4xkzkIKameeSsZ0RkDgszkYOYmuaRs55JA6eJJSljYSZyIHvdy5Xsi9PEkpSx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\n5tbSAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7face3b9a828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.date.hist(bins=100)\n", "plt.title('Frequency of games in all countries')\n", "plot_width = (df.date.max() - df.date.min()).days\n", "bin_width = plot_width/100\n", "print('bin_width = {0:.1f} days'.format(bin_width))\n", "plt.show();" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2405d77a-bb9b-4884-b852-a2a02d6eee11" }, "source": [ "Let's split the leagues up and visualize how our data is divided. We'll plot the **frequency of games for each league**." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "af759ec8-71d9-c3d3-2ba5-b138d5bd857f" }, "outputs": [ { "data": { "image/png": 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OHYqkpCRkZWVh+/bt8PHxwd9//y17v6Sw5XfW3LF86KGHTG7mqpLKVMb//PNP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\nfPfdd3C5XHj22Wdv+9Cn2+2G1+tV/b7x8bI3bNiA+++/3/8zP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"text/plain": [ "<matplotlib.figure.Figure at 0x7face084c748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.FacetGrid(df, col='league', col_wrap=4)\n", "g.map(plt.hist, 'date', bins=100)\n", "for ax in g.axes.flat: \n", " plt.setp(ax.get_xticklabels(), rotation=45)\n", "plt.suptitle('Game frequency by league', fontsize=20, y=1.04);" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2f12dd8e-754e-1cfb-39af-6464742d0fd6" }, "source": [ "Exactly how does the amount of data from each league compare?" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "b47cd40b-d43b-8c46-656e-f88c74423853" }, "outputs": [ { "data": { "image/png": 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vvPEGdu3aha5du2Lx4sUICgrSL+vg4IDNmzdj//796Nu3LwIDA/Hbb7/dcYZ/TpszZw68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\nFhYKIYR48eKFCAsLE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"text/plain": [ "<matplotlib.figure.Figure at 0x7face2cc1ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "league = np.unique(df.league.values)\n", "N_entries = np.array([len(df[(df.league == L)]) for L in league])\n", "N_entries = N_entries/N_entries.sum()\n", "ax = sns.barplot(league, N_entries)\n", "ax.set_ylabel('Percentage of data - total')\n", "plt.setp(ax.get_xticklabels(), rotation=45, ha='right')\n", "plt.title('Amount of game data from each league')\n", "plt.show();" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "_cell_guid": "bdd8f10a-938a-b46a-5e67-2083a282d036" }, "outputs": [ { "data": { "image/png": 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KBQUFOH78OA4ePIgePXoAeHBS1N69e3H//n1cv34dv/76q97rqFOnDpKSkh57CaqDgwPc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\nERERkT/E/wGfomU/78p8twAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7face6a6edd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "league = np.unique(df.league.values)\n", "N_entries = np.array([len(df[(df.league == L)&(df.season == '2015/2016')]) for L in league])\n", "N_entries = N_entries/N_entries.sum()\n", "ax = sns.barplot(league, N_entries)\n", "ax.set_ylabel('Percentage of data - 2015/2016')\n", "plt.setp(ax.get_xticklabels(), rotation=45, ha='right')\n", "plt.title('Amount of 2015/2016 game data from each league')\n", "plt.show();" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "665c265c-0f42-7609-9258-aaffaea5e082" }, "source": [ "As promised, we'll compare the mean EA rank for players on teams in the different leagues. Using data from every match in the dataframe, we'll look at a **violin plot of mean player stat distributions**.\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "_cell_guid": "e03d8dcc-de0c-16f5-251d-12474ad73cab" }, "outputs": [ { "data": { "image/png": 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TyR2Q9pG7Re0j5dR+52JZKaU4fvwoO3ZsY/PmjRw8uB/wNVcbHBVHenxPkqO7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\nc88dcv3R0VHe/va309vbi6ZpOJ1OvvzlL7N+/fqjcjxnn302O3fuPOz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Xt3wHLcRBSKAk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\nErZt23bEz1tbW8v11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qkoxsn/Z0RLpdCaGklVVqBV7kfr7ADS87nmzaO0dBGLFi1h+vQZ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\nFK3Taa+uWp8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"text/plain": [ "<matplotlib.figure.Figure at 0x7face2c999b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12, 10))\n", "sns.violinplot(x='home_mean_stats', y='league', data=df)\n", "plt.title('Average EA Sports FIFA player ratings per team since 2009', y=1.02);" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d473e02a-ac93-f6c7-3308-95b07d02f725" }, "source": [ "Another **violin plot, this time using data from only this season**.\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "_cell_guid": "6faf457c-f00e-daa1-8e8b-d07a4bbba89e" }, "outputs": [ { "data": { "image/png": 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UaWkn2bZtCz//vJmSkmIAuvgHMbzjTfQMbtWsg6Xfstps7MrOYFN6Mofyc7D9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2bxyiuv4O/vz7/+9S/y8/P54Q9/2OezBbjwwgt5+OGHe5Om9957j+eeew6Xy9W77CuvvMK1117L\nsmXLehOnY7dxos9y2rRpg21qIcQYpqoqnZ2dtLW10NjYSEODjbq6Wmprq6moKMdmqz+6sKKANRQl\nPhIlKQZCzaPWG6A1taF+9AW0dgx5W0ajEavVis1m6/MF17AKDkR3fh6KJeikV1WMPihp8ZAW7/ki\nsaUdra4JmtrQWtvpaO+io64GjhTe+C6jD/gZISwYTH6eYZOBAZ4eo6BACApAOc0V+xS9zvOsW4QF\nJqR5jqu5Ha22AWobaatt5KuvdvLVVzt71wkLCyc+PoGoqGgiI6OxWiMICwsnJCQEkylQeqaEOEsN\n6uo0Y8YMysrK+MEPfgDA22+/TUREBHV1dTzwwAM89thjIxqkyWRixYoVvPDCC73JA8D27dspKSnp\n7QXq6uo6YeJx/vnnA5Camkpjo6frf+vWrWzbto0VK1b0Ji1lZWVER0cTExPTb8IE8Pzzz/PRRx8B\nUFtbS1lZGbm5uRiNRubOnQtATk4O27dvB+Crr77iqaeeAuCSSy7h8ccfB2DChAnce++9OJ1Ozj//\nfLKysvrd3+OPP97v8LwLLrig9+9bt27l448/5tlnnwU8kzdWV1ezc+dOrrnmGgAyMzPJzMzsXefI\n55adnU1TUxM2m43GxkaCg4OJjIykquroGPBJkybxzDPPUFtby6JFi0hMTOwTy4k+S0mahDh71dfX\nsWbN38jPPzCk7WgauN0uBizmqtd5hmjp9Z6/d3Wj5Zeh5ZcNad9D1mn3HMAQGY1Gbr31VpYsWcLG\njRt5+umnRyZxau1AXfexp0dnJBh9wGiA734kyuH/qBp0dXvaz9YCHL/ocFNSYtDlTRjcsooCliBP\nUjkuBfBMCkx9E1pDC1pDC43NbTR+/VW/6+v0enQ6Hd9Nm+Li4rn++ptITEweyqEIIUbRoJKmnTt3\n8uqrr/b+e/78+fzoRz/i1Vdf5cILLxyx4I51zTXXsGLFij49JZqm8dprrw2q+o3RaOyz3hE33ngj\nl19+eZ9lq6qq8Pfve0M58s3Rjh07+Pzzz3n99dcxGo1cffXV9PT0APSZ00Kv1/f21CiK0u83T9Om\nTePFF19k8+bN/PrXv+bHP/4xl1xyyXHLnegXiYCAvuPHn3zySZKSkvpd9vtccMEFvP/++zQ0NPTb\npsuWLWPixIls3ryZn/3sZzz44IPk5eX1Waa/z1IIcfb69NNNfPvt3pHZuKJ4kiTD4SRJp8Bxv4qO\nMk0bloQJwGq1smTJEgCWLFnCa6+91ueLq2F1JO4R6xFRzrimGgol0B8CY1FSYj334047Wm0jlNei\nVdZBj7N3WdXt7nfoYWlpCdu2fSZJkxBebFBJU3NzMz09Pfj6emZLdzgctLa2oihKn56fkXAkYQgO\nDmbp0qW8+eabXHrppQDMmjWLF154geuvvx6AgwcPkpWVhclkoqPjxEMljmzzvPPO489//jPLli0j\nICCAurq6EyZgR9Zpb28nKCgIo9FIcXExe/bsOW6Z75oyZQr//Oc/ufjii3nnnXd6X6+uriYqKorL\nLrsMh8PB/v37TyppOtZ5553HP/7xD/7f//t/ABw4cIDs7GzOOeccNmzYQF5eHgUFBeTn5/e7/tKl\nS7nvvvtoaWnhxRdfPO79iooK4uPjufrqq6muriY/P5+8vLzv/SwtFsv3xi6E8E7Ll/+QoKAQmpub\negvmfLdwzrH/VpSjv6u73So9Pd3Y7Xa6ujrp7Oygra2N1tYWzxdRnu6no88JGQ0QHooSaUGJDofI\nsDNiriX3ax8Oy9A8m83Gxo0be3uabDbbMER3AsGB6C9fNKyb1DQNHE6w90CPA1xucKuHk1/FU9Xu\nyPA8X6PXDGHTOu1Q14Rma/L0jDW2eo7zO3x9fQkJsRAcHIzZbMZkCsTfPwC9XgcoBAQEMG/ewtN/\nAEKIYTOopGnp0qVcccUVLF26FKD3wt7Z2UlsbOyIBnjshfUnP/kJa9eu7X3t3nvv5cEHH+Tiiy9G\nVVWmTZvGAw88wPz587n99tv5+OOPewtB9LfNWbNmUVJSwhVXXAF4hgE+9thj6HTHV1w6ss7s2bN5\n5ZVXuOiii0hOTmbSpEn9xnqse+65h7vuuov//d//ZeHCoxfNHTt28Oyzz2IwGDCZTDzyyCP9rn/k\nmSZN07BYLDz33HPHLXPLLbfw0EMPsXz5cgBiY2N55plnuPLKK1m1ahUXXXQRqampjB8/vt9409LS\n6OzsJCoqivDw8OO2/9577/HOO+9gMBiwWq3cfPPNg/osJWkS4uzl5+fHkiXDO9pA0zS6urpobGyg\nrq6WmpoqKirKKSsrpaa6Gq3ahrY739MDFR2OkhSDkhiN4u87rHEMlu78vGF5psnhcPD000/z2muv\nnZZnmoZC0zRo6/Q891PfjNbUCi0d/SYT/VIUCPDzPNdkNkFgAASZUIJMEBwIAX6jklT1HldNA9Q2\noNU0QkfXMWErREVFEx+fSFxcPNHRMURFRRMebpVnmYQYAxRtMN0YwMcff8yOHTsAOPfcc/tUgRPi\nVMnM2t5LZkb3bt7Yfh0d7RQVFbBv3zd8881eqqoqPG8oCsRFoKTFoyTFjEoP1NlaPe8ITdM8PS4l\nVZ4Keu2dx2xWT2RkFBERkYT0Uz3P5XLR02Onq6uLjo52WltbaW5uoqm5qf8qegYDBJt6K+h5qugd\nrqbnM3yFIjS3Ck2tnmIWdY2eIXdd3b3vB5hMZKRnkZGRSVpaBklJKccN3T8V3njuCQ9pO+9mtZqH\ntP6gkyYhRoJcfLyX3Dy829nQfnV1tXz55U62b9/KoUMlnhd9jSjp8SjZyZ5ftMWQaB12tILDBTcO\n97r4+fszYXwu48ZNIC0tg7i4+D7P9A6WqrppaWmhvr6O+vq6wxPaHv6pq8HZX29bgJ+nV8ps8pQQ\nD/RHCfAHf9+jk9sa9J5nqjTNM0zwyLDBzm60tg5o6UBrboOmNjhmSo/g4BAyM7PJyhpHVlY2sbHx\n/Y48Gaqz4dwbq6TtvNtpSZra29v5+9//zoEDB3qLHgC88MILQ9q5EHLx8V5y8/BuZ1v7VVdXsWXL\nJ3z22Wba2lo9L8Za0eWkQkKUDJ06CZ5epUbUb4vhUA1oGkZfX/LOncH06TMZN27CKSVJJ0NVVZqa\nGqmpqaa6uora2mpqaqqpq6ulsbFhUM/6DsRg8CEhIYGkpBTS0z3zMEVEnJ7/T862c28skbbzbkNN\nmgZ11bvnnntITU3l0KFD/PznP+fNN98kJydnSDsWQgghhktMTCw/+tF/snLlj/jyy518+OF75Ocf\nQK2yeXomxqWgZCaiGL+/2upYpakalFWj7i2E+mYAEhKSWLToAvLyZg7L0LTB0ul0hIdbCQ+3MmHC\nxD7vOZ1OGhsbsNnqPcP8mpro6Gijo6Od7u7u3ufBFEWH0WgkICCAwEAzYWHhhIeHEx0di9UaMeKJ\nnxDi7DKoK0ZZWRlPPvkkmzZtYtmyZSxevLh37h8hhBDiTGEwGMjLm0Fe3gzKy8v44IN3+fe/P8P5\n+TdoXx7wDN0bl4ISevKTu56tNLcbragCbU9hb0GLKVOmsXTpcjIzs8+4XjofHx+ioqKJiooe7VCE\nEGPIoJKmI3Mc+fj40NLSQnBwME1NTSMamBBCCDEUCQmJ/PSnN3PFFVfxySeb2LRpI037S9H2l0JU\nGEpWEkpyDMoY7XHQHE60g4fQvi2GTjt6vZ5Zc+Zz4YUXExsbN9rhCSHEGWVQd4qkpCRaWlpYvnw5\nV1xxBWazWYbnCSGE8ApmcxAXX7yCiy66mK++2sWmTRvZt+8btNpGtG17UJJjUVLjICYcZQQe/D/T\naG2daPtL0A4eAqcLo68vC5cu44ILlmGxhI12eEIIcUY66ep5u3btor29ndmzZ8t4YDFk8kCl95IH\nYr3bWG+/uroatmzZzNatn9LU1Oh50c+IEheJkhgNMVYUP+PoBjmMNFWF8lrUg4egog6A4JAQFi+6\nkAULzicwUCoNni5j/dzzZtJ23u20lRwvLS2luLiY888/n46ODlwuFyEhIUPauRBy8fFecvPwbtJ+\nHqqqUlBwkB07trNj5xe0tjQffTM8BCU6HCUqDCIspzzH0WjpnVupuBKttMpTdhtITU1n0aKlnHvu\ndHx8pDDG6SbnnveStvNupyVpWrduHX/7299wOp1s2rSJkpISHnzwQf7v//5vSDsXQi4+3ktuHt5N\n2u94mqZx6FAJe/bsZt++bygszMd97OSrgQEoEaGeZMp6+M8zrBqf1uOAahtaRZ1nEtrDiZIpMJBZ\nM2czZ84CEhOTRjfIMU7OPe8lbefdTkvJ8RdeeIE333yTq666CoCUlBQaGhqGtGMhhBDiTKIoCsnJ\nqSQnp/KDH6ykp6eHkpIi8vMPUlxcQFFRAR0lVVBSRe+3jUGmowmUNRTCgk9bIqVpGnTaob4Zra4R\nrbYRGls9k7rieZZr8pyZ5OXNOC1zKwkhxNlsUFdQHx8fTCZTn9f0ev2IBCSEEEKcCXx9fcnOziE7\n21P4SNM0GhpslJQUU1p65KcEe3ElFFceTaRCAlHCDydQYcFgCUbx9x1SLJpbhbYOtKY2aGpFa2yF\nhpbeniTwTNiampHF+PG5jB+fS0pKKjqd3KuFEGI4DCppCgkJobS0tHeuhrfffpuoqKgRDUwIIYQ4\nkyiKgtUagdUaQV7eDMCTSNXX11JaWkJJSTGHDpVQeqiE7qIKKKo4mkj5GSE4ECXIBIEB4O/nKTzh\nYwD94Yp9bhVcbs8QO3sPdNrROuzQ1gEdXaD2HU0fFhZOcs5EUlMzSEtLJyUlrXeKECGEEMNrUM80\nlZaW8stf/pKSkhIsFgt+fn4888wzJCQknI4YxVlMxgZ7Lxnb7d2k/UaOqqrU19dx6FAJFRVllJeX\nU11dic1Wz0kWrAUg0GwmKjKa2Ng4YmPjmDAhm6AgK0FBwSMQvRhpcu55L2k773baque53W4OHTqE\npmkkJyfL8DwxLOTi473k5uHdpP1OP6fTSVNTI42NDbS1tdLR0UF3dzculwtNU/Hx8cFo9MVkCiQo\nKIjQUAthYWH4+wf02Y60nXeT9vNe0nbebUQLQdjt9j7/jomJAcDhcADg7+8/pJ0LIYQQY4WPjw+R\nkVFERsrwdiGE8DYDJk2TJ09GUZQ+wwmO/FtRFA4cODDiAQohhBBCCCHEaBowaTp48ODpikMIIYQQ\nQgghzki60Q5ACCGEEEIIIc5kkjQJIYQQQgghxAAkaRJCCCGEEEKIAUjSJIQQQgghhBADkKRJCCGE\nEEIIIQYgSZMQQgghhBBCDECSJiGEEEIIIYQYgCRNQgghhBBCCDEASZqEEEIIIYQQYgCSNAkhhBBC\nCCHEACRpEkIIIYQQQogBSNIkhBBCCCGEEAMwjHYAQgghxhaXy0lNTTV1dbU0NzfT2dmB0+kENHx8\njAQEmAgKCiI01EJYWDgWiwWdTj/aYQshhBjDJGkSQggxotxuNwUFB9m7dzf79++jrPwQbpdr0Osb\nDAYiIiKJioohKiqayMgoIiIiCQsLIzTUgp+f/whGL4QQQkjSJIQQYoSUlBTz2Wef8PkX/6ajvd3z\nok6HYglDFxaOEhwCpkAUX18wHO5JcrnB4UCzd6F1dkJHO+72NqobbFRXV/W7Hx+jkQD/AHx9fTEY\nPLc1VVVxOp04HA4cDgdOlxPV7T4cgg6jry9BZk9vVnR0DImJyaSnZxAfn4hOJyPXhRBC9CVJkxBC\niGHjcDj4/PNtfPDhe5QdKgVA8fdHlzUOXWISSlQMio/PSW9X0zTotqO1tqK1t6G1tUFnB1pXF65u\nO60OB3R0gKp6VlAU0OtBr0cJDPT8qdMBCpqq0uNyUt/RTn19Hfn5B3r3Yw4KZvKkKUyfPoucnPEy\nLFAIIQQgSZMQQohh0N7exkcfbeTDD9+nvb0NFAUlMRl9ZjZKXPzhhOXUKYoC/gEo/gEQFT1MUYPm\ncqG1taI12NBqqmmvrGDLlk/YsuUTQi0W5s1dyPz55xMaahm2fQohhPA+kjQJIZKvWMEAACAASURB\nVIQ4ZXV1tbz33j/Z8tknOB0OFF9fdLmT0Y8b7+nhOcMpBgOKJQwsYZCRhaZpaHW1qEUFNBcXsn79\n67z99jry8mZwwQXLSElJHe2QhRBCjAJJmoQQQpy0oqIC3nvvn+zc+TmapqEEmtFPOQddZvYpDb87\nUyiKghIVjS4qGi1vJmpxIeq+vWzfvpXt27eSkZHFBRcsY8qUaej1MnRPCCHGCkmahBBCDIrT6WTn\nzs/54IP3KC4uBEAJC0efOwldcuqQh+CdaRQfH/RZ49BlZqNVVeL+dg8FBQcpKDhIWFg48+adz5w5\n87BYwkY7VCGEECNMkiYhhBADqqmp4tNPPc/5tLe3AaDEJ6KfMBElOsbzvNFZTFEUlLh4dHHxaM3N\nuPd/Q2NhAW+++Qrr1r1KdnYO5547gylTpsmzT0IIcZZSNE3TRjsIMXbZbO2jHYI4RVarWdrPi31f\n+7W0NLNz5+ds2/bZ0V4lX1+UjCz02TkoQcGnK9QzkuZwoJYUoRbmo9XV9r4eGxtPdvY40tIySU5O\nISoqatgr8Mm5592k/byXtJ13s1rNQ1pfepqEEEIAnqIOu3d/ya5dX1BQcNBT5ltRUGLj0WVkoktM\nRjHIbQNAMRrRZ41DnzUOrb0dtawUtaKcqroaqqoq+OijjYBnDqmY6FhiYmKJjo7p82M0+o7yUQgh\nhBgsufsJIcQY1d3dTX7+Ab75Zg979+6mpqa69z0lMgp9ciq6lFSUANMoRnnmU8xm9ONz0Y/PRXO7\n0RptaPV1aI0NuJqaKKssp6ystO86ikJERBQJCQkkJaWSkpJKamoa/v4Bo3QUQgghBiJJkxBCjBEO\nh4Pi4kIOHNhHYeEBDhw4gNvt9rxpMKAkJKJLSEKXkIQSIL+8nwpFr0eJiIKIqN7XNE2Djna01ha0\nlha0lma0lmbqmpuoq6th584vPOsqComJSWRljSMnJ5esrGz8/PxH61CEEEIcY0wlTdnZ2WRlZeFy\nuUhNTeWRRx7B1/fEwyMmT57M7t27h7zfqqoqbrrpJjZs2HDc6xdeeCEpKSmekr2KwnXXXccll1xy\nUvtevXo1N95440nHtWDBAtatW0dISEif14fruIUQo8vh6KG4uIgDB/Zz8OA+iooKcDqdve8r4VZ0\nMXHoYuNQoqJRpIT2iFAUBcxBKOYgiEvofV3TNOjsRGuoR62vR6ur4VB5OYcOlfL++/9Cr9eTkZFF\nbu5kJk6cRFxcwllfdEMIIc5UYypp8vf3Z/369QDcddddvPzyy1x33XUnXP503JwSEhJ6YzrVfT/z\nzDMnTJqOJGP9OdnXhRBntvb2NgoLCygsPEh+/kFKSoqO9iQBiiUMXXQMuphYlKgYlAG+NBIjT1EU\nCAxECQxEl5QCgOZyodXXolZVolZVcuDAPg4c2Merr75IaKiFCRMmMnNmHnFxaQQHj+1iHEIIcTqN\nqaTpWNOmTaOgoACANWvWsG7dOgBWrlzJtdde22fZrq4ubrnlFtra2nC5XPz85z9n4cKFVFVVccMN\nNzB16lR2795NZGQkf/3rXzEajXz77bfce++9KIrCzJkzTznOpqYmbrnlFm6++WbGjRvHnXfeSWdn\nJy6XiwceeIDNmzfT09PDihUrSEtL44477uD6669n4sSJ7N+/n7/97W+sXr2ab7/9lp6eHpYsWcJt\nt90GHP6WE89zDf/1X//F4sWLueyyy3pfP9Fx2+127rjjDurq6nC73dxyyy0sXbqUp59+ms2bN9Pd\n3c3kyZN58MEHT/m4hRADa29vo6LC86xMaWkJJSVF1B1TxQ1FQQkLRxcVjS46BiUyGsXPb/QCBrSu\nLnC7RjWGAekNoz4sUTEYUGLi0MXEwTmg2btQKyvQKitorqxgyxZP6XeAuLh4MjOzSUvLICUlbUQq\n9QkhhPAYU0nTkWTA5XKxZcsW5syZw759+1i/fj1vvPEGbrebyy+/nLy8PLKysnrX8/X15emnn8Zk\nMtHc3MwVV1zBwoULASgvL+eJJ57gd7/7HXfccQcbN25k+fLl3HPPPdx///1MnTqVRx999IQxlZeX\ns2LFit4eofvuu4+pU6cC0NjYyM0338ydd97JjBkzWLNmDbNnz+bGG29E0zTsdjtTp07lpZde6u2t\nqqqqory8nEcffZTc3FwAfvGLXxAUFISqqlx77bUsXryYjIwMFEWhs7OTO++8kxUrVnDxxRcDR3ua\nTnTcn332GZGRkaxevRqAjo4OAK6++mpuvfVWAH71q1+xefNm5s2bNyxtJ8RYoaoqPT09dHZ20NHR\nQVtbC83NzTQ2NtLQUE99fR01NdW0tbX2XdHoixIbjxIRiS4qGsUagWI0js5BfIfa1Ihr00Zobf3+\nhQdgNBqxWq3YbDYcDscwRfcdwcEYFi5Bd4ZMWKv4B6BPz4T0TDRNQ2uwoVVVolZXUllbQ2VlBZs2\nfQB4rtkxMXHExMQSERFFeHg4FksYwcHBmM1BmEyB+Pj4jPIRCSGEdxpTSdORHhnw9DStXLmStWvX\nsmjRot5nmxYtWsSuXbvIysrqTbI0TeOPf/wjO3fuRKfTUV9fT2NjIwCxsbFkZmYCkJOTQ1VVFe3t\n7XR0dPQmP5dccgmfffZZvzGdaHie0+nkuuuu4/7772fatGkATJgwgXvvvRen08n555/fJ7E7VkxM\nTG/CBPCvf/2L119/HZfLRUNDA0VFRWRkZKBpGrfeeis//elPWbZs2XHbOdFxZ2Rk8Mgjj/D4448z\nd+7c3vi2b9/Os88+i91up62tjfT0dEmahDgBh8PBI4/8joKCgye3oqKA2YwSn4hisaCEhaNVVqBW\nV3oKDbS2oBbmj0zQp6qzA4Y4JaDRaOTWW29lyZIlbNy4kaeffnpkEqfWVlzr3wDTmVkxUJecgiFv\nJvpJUw5X6ms4XKnPhqOhgdKyQ5SWFg9qWzfccAtz5swf4YiFEOLsoBvtAE4nPz8/1q9fz/r167n3\n3nsxfM98I0d6XDZs2EBzczNvvfUWb731FhaLhZ6eHsBzIz9Cr9fjcnmGngx1zmCDwcD48eP7JFvT\npk3jxRdfJDIykl//+te8/fbb/e7L3/9otaXKykrWrFnDCy+8wDvvvMPcuXP7/KIxZcqUEyZ0Jzru\npKQk1q9fT0ZGBn/605/4y1/+gsPh4MEHH+TJJ59kw4YNXHbZZb2fkRDieA5HDyUlRSe9nhIWji4y\n2jPs7vAPvr7AGfosoqYNOWECsFqtLFmyBIAlS5ZgtVqHvM0T0tRhiXmkKXo9SrjVU8QjMtrzZ0TE\noNevqqoYweiEEOLsMqZ6mvpLZKZNm8aqVav42c9+htvt5qOPPuKxxx7rs3x7ezsWiwWdTsfnn39O\ndXX1cds5ltlsJigoiK+++oopU6YcVzVvsB5++GFuv/12/v73v3PDDTdQXV1NVFQUl112GQ6Hg/37\n93PJJZdgNBpxu93o+6l81dHRQUBAACaTiYaGBrZs2UJeXl7v+7fffjtPPfUUv/3tb7n//vsHPO6a\nmhoA6uvrCQkJYfny5ZjNZt544w16enpQFIXQ0FA6OzvZuHFj7y84QojjBQaaWb36eez2LpxOJ06n\ng54eBz093djtdjo7O+noaKOtrZXm5maamhqx2eppaGxAbbD13Zh/AIrVis4aiRIZiWKNRDmDhmE5\nXl875KF5Nput97qyceNGbDbb9690qoJDMF525chtfwg01Y1aW4NWXYVaW41WXweuo8+JKYpCWLiV\nqMgowsLCsVgsBAeHYjabCQgw4e8fgJ+fH76+voSHj2DiKYQQZ5kxlTT1VxVu3LhxrFixgpUrVwJw\n+eWX9w57O7L88uXLufnmm7n44osZP348qamp37uvhx9+mHvuuQedTsesWbNOuFxFRUWfZ5ouvfRS\n/vM//xNFUVAUhccff5xbbrmFwMBA/P39efbZZzEYDJhMJh555JHemJcvX05OTg533HFHn+1nZWWR\nnZ3N0qVLiY6O7h0yeOzx3XfffaxatYo//OEP3HXXXSc87pQUT3WngoICHn30UXQ6HT4+Pvz2t7/F\nbDazcuVKLrroIqxWKxMmTPjez0iIsc5oNPbprR4Ml8uFzVZPVVVFbyGIktISmsvLcJeXeRY6XARC\niYpGFxXj6YEYxSIQhoVLcG36AFpbTnkbDoeDp59+mtdee22En2kKwbBw8chs+xRp7e2oleWoleVo\n1VVwTNn42Nh40tMzSE5OJSkphdjYuAGn0hBCCHFqFG2o48iEGAKbrX20QxCnyGo1S/udQVpamikq\nKjhccjz/+HLjoRaU6BhPJb3oGJRRmDRVqucNjuZ0otXWoFZVoFWUox2TbEZGRnHOOdNISckiK2sc\nZrN5FCMVp0Kund5L2s67Wa1Du15K0iRGlVx8vJfcPM5sRya2zc8/wIED+ygsKsB5TO+MYglDiYk9\nPLFtzBk1nG+s0bq70errUOtq0Wqr0Wz1oKoAGI2+jBs3ntzcSeTmTiIyMkrOPS8n7ee9pO28myRN\nwqvJxcd7yc3Du7hcToqLi3onSy0szMd5ZJiXTocSGYUuPgFdQhIEh8gk1yNAczo9FQ5bmtFaWtCa\nG9EaG6Hj6HmkKApJSSnk5Ixn/PiJZGRkHVcmXM497ybt572k7bybJE3Cq8nFx3vJzcO7BQf78u9/\n72Lfvm/45ps9HDpU0vueEhyMkpSCLjnV82yUJFAnRVPdaE1NnjmVmpvQmpuhtRmts/O4ZYOCgklK\nSiElJZX09AzS0zPx9x94iKCce95N2s97Sdt5N0mahFeTi4/3kpuHd/tu+7W2trB379d89dUu9u79\nGofDM2WAEhyCLi0DXXoGSqA8P9MfTdM88yVVlKNWVaLZ6uCY58kALJYwoqNjDv/EEhMTS3x8AsHB\nISe9Pzn3vJu0n/eStvNuQ02axlT1PCGEEP0LDg5h9ux5zJ49j56eHvbu/ZrPP9/G7t27cH65A/eX\nO1Di4tFn53gm1tWNqWn++qU1N+MuykcrLkI7PMROURTi4xJIS0snOTmVhIREYmLi+syfJ4QQwvtI\n0iSEEKIPX19fzjknj3POyaOrq5MdOz7n008/pqioAFdlBUqgGd24HHSZ41DGWHlrTXWjlpag7v8W\nra4WAD8/fybPOI8pU6aRk5MrFe2EEOIsJEmTEEKIEwoIMDFv3kLmzVtIeXkZmzZtZOvWLTh2fI77\nqy/RZWahH5+LYg4a7VBHlNbdjXpwH+r+fWhdnmeTJkyYyNy5C5g8eSpG49hKHoUQYqyRZ5rEqJKx\nwd5LxnZ7t6G0X2dnB5s3b2LjB+/S3NQEioIuJRXdhEnowq3DHOno0pqbcO/7BrWoAFwu/Pz8mDt3\nAYsWXUBkZPSoxCTnnneT9vNe0nbeTZ5pEkIIcVqZTIFcdNElLFlyEV988W/++a+3qSwuQi0uQomJ\nRT9hIkpcgtdW3dOcTtSyUtT8A2g11QCEh1tZvPhC5s1b8L3V7YQQQpx9JGkSQghxSgwGA7NmzWHm\nzNl8880e3n33Hfbt+wZXdRVKUDC67Bx06Zkofn6jHeqANE2DtjbU2mrUynK0inJwuQAYN248ixZd\nwOTJ09Dr9aMcqRBCiNEiSZMQQoghURSF3NxJ5OZOoqyslA8+eI9//3srri/+jXvn5yjxCeiSU9HF\nJw65cISmqtDZgdbWhtbZAV1daN12cDjA5UJT3Ydj0oFe7/kxGECnB70OUEBVweUEux2tswOtpQUO\nl1gHiIyMZvr0mcyePXfUhuAJIYQ4s8gzTWJUydhg7yVju73bSLdfe3s7W7d+ytatmykvL/O8qCgo\n4VaUyCiUMKtnEl1TIPj5wZES5m43OBxodjt0daC1t3sSpLYWtNZWaG/zJD3DRKfXExkRSWJiMmlp\nGeTmTiQqKuaMHloo5553k/bzXtJ23k2eaRJCCHHGMZvNLF26jKVLl1FZWcGuXTvYu3c3xcWFqLb6\nU9pmgMlEdHIqUVFRWK0RhIVZCQkJJSgoGJMpAKPRF4PBc1tzu924XC6cTgcOhwOn04nb7UbTNPR6\nPUajL0FBQQQFBcuwOyGEEN9LkiYhhBAjKi4unri4eH7wg0vp7rZTVnaI8vIy6utraW5upqOjHZfL\nhaZpGI1GAgJMmM1BWCxhhIWFExkZSWRktMx/JIQQYtRI0iSEEOK08fPzJzMzm8zM7NEORQghhBg0\n3WgHIIQQQgghhBBnMkmahBBCCCGEEGIAkjQJIYQQQgghxAAkaRJCCCGEEEKIAUjSJIQQQgghhBAD\nkKRJCCGEEEIIIQYgSZMQQgghhBBCDECSJiGEEEIIIYQYgCRNQgghhBBCCDEASZqEEEIIIYQQYgCS\nNAkhhBBCCCHEAAyjHYAQQgghTszh6KG1tZX29jb0egOqGoHdruHv749OJ999CiHE6SBJkxBCCHGG\n6O62U1hYwMGD+zl4cD/l5WV0d9v7XdbHaCQ+LoHExCQSE5PJzs4hOjoGRVFOc9RCCHH2k6RJCCGE\nGEWqqnLgwD62bPmEnTs/x+l0et5QFHQhEejD4lH8A1H8AkF1ozm6wdmNu72ZkkOllJQU9W7Lao0g\nN3cyU6ZMJScnF71eP0pHJYQQZxdJmoQQQohR4HD08Mknm9i48V1stjoAdEHhGLPHo49ORR+VhGL0\nH3AbmtuF2lKP21aBuzKfhqoCNm3ayKZNGzGbg5g+fSYzZ84mNTVdeqCEEGIIFE3TtNEOQoxdNlv7\naIcgTpHVapb282LSfqOnp6eHjz/+gH/+823a2lpRDD7okyfik3ku+qjkISU3murGXXcIV8keXCV7\n0Lo7AYiJiWX+/EWcd94cAgPNw3Uo4hTIuee9pO28m9U6tGufJE1iVMnFx3vJzcO7Sfudfqrq5tNP\nP+GNN17xJEs+vvjkzMJnwlx0fqZh35+munFXFuAs/BLXoW9AdWMw+JCXN53zz19KWlr6sO9TfD85\n97yXtJ13G2rSJMPzhBBCiBG2d+/XvPzyC1RWVqAYjBgnLcQ4YQ7KCCRLRyg6PYaEbAwJ2aj2DlyF\nu3Ae/IJt2z5j27bPSE5OZdGiC8jLm4nRaByxOIQQ4mwgPU1iVMk3Nt5LvnHzbtJ+p0dNTRUvvfQ8\ne/bsBhQMGdPwnXYBOlPwqMSjaRru6kKc+7bhKt8PmoY5KJiFCxaxcOFiQkJCRyWusUTOPe8lbefd\nZHie8Gpy8fFecvPwbtJ+I6uzs5O33nqdDz54H1V1o49OxXfGxejDYkc7tF5qexOO/f/GdfALNIcd\nvV7P9OkzWbLkIpKTU0c7vLOWnHveS9rOu0nSJLyaXHy8l9w8vJu038hwuVx88smHrFv3Oh0d7ejM\nFozTL8aQmHPGVq/TnD04C7/EuW8raks9AOnpmSxZchHTpp0rZcuHmZx73kvazrvJM01CCCHEKNM0\njS+/3Mkrr7xIXV0Nio8vxnOWYhw/B8XgM9rhDUjx8cU4biY+2TNwVxXg+PYzCgsPUliYT6gljPMX\nLmbevIUEBY3OkEIhhDgTSE+TGFXyjY33km/cvJu03/DQNI09e3azfv1rlJQUg6LDJ3s6ximL0Pl7\nb2lvtaUex76tuAq/RHP2YDD4MH36TBYsWERaWsYZ22vmDeTc817Sdt5NhucJryYXH+8lNw/vJu03\nNKqq8vXXX/H2229SUlIEgCE511PkISRilKMbPpqjG2fBTpz7tqG2NQCQkJDI/PmLmDHjPEymkav+\nd7aSc897Sdt5N0mahFeTi4/3kpuHd5P2OzVdXZ1s2bKZDz98j/r6OsCTLBmnLEJviR7l6EaOpqm4\nq4txHtiOq+xbUFV8fHyYNu1cZs+eT07OeHQ6efZpMOTc817Sdt5NnmkSQgghRpCqutm/fx/btm1h\n584v6OnpBr0PPpnn4jN+9lmdLB2hKDoMsekYYtNRu9pwFuzCVbCT7du3sX37NoKCgjn33OlMnz6L\n9PRMdDrdaId8UlTVTV1dHVVVFVRXV9HU1EhzczMtLU10dXXhcrlQVRVVVTGZTAQGmgkMDMRiCScm\nJpbo6BhiY+OkZLsQZzHpaRKjSr6x8V7yjZt3k/YbmMPRw8GD+/n666/YsfMLWluaAVACQ/HJno5P\n1nR0IzgxrTfQNA21vsyTQB36Bq27E4Dg4BAmTpzMpElTyMmZQEDAmfU5qaqb6upqSkqKDv8UU1FR\njsvlPH5hvQHF6A86Peh0gAIOO1qPHTj+16fQUAupqemkpqaTkZFJSkoqhu8UApFzz3tJ23k3rxie\nl52dTVZWFi6Xi9TUVB555BF8fX0Hvf7q1au58cYbhz2uqqoqbrrpJjZs2DCo1//85z9zzjnnMGPG\njCHt96WXXuL111/vfZDW5XJRWFjIu+++S0pKCrt27eKRRx6ho6MDRVG47rrruPzyywF48sknefbZ\nZ/n444+xWCwATJ48md27dx+3nzfeeIPnn38eRVHQNI0777yTBQsWDCn24SYXH+8lNw/vJu3Xl8Ph\noLS0mIKCg+TnH2D/gX04HQ4AFF9/DMkTMaRPRR+ZiKJ4Vy/K6aCpbtzVxbhKvsZVth+tuwMAnU5P\nSkoKGRnZZGWNIy0tA7P59BXI0DSNxsYGDh0qoaSkmOLiQkpKiunuth9dSKdHZ4lCFxqFPjQKXWgk\nSmAouoBg8PXvt+iFpqpoDjtaezNqSx1qSz1qcy3u+nI0+9HzysdoJD0tg8zMcWRnjyM1NY3Y2HA5\n97yUXDe9m1cMz/P392f9+vUA3HXXXbz88stcd911g1pXVVWeeeaZEUmaTtbtt98+LNu56qqruOqq\nq3r//cQTTzBu3DhSUlKw2Wzcfffd/PWvfyUrK4uWlhZ+8pOfEBkZydy5c1EUBYvFwpo1a/jlL38J\n0O8Fva6ujtWrV/PWW29hMpmw2+00NTUNOXa32y1zdgghvJamabS0NFNbW0NFRTllZaVUVJQd7mlw\n9S6nC4nEJysLQ3w2+qhklNP4vI7a1QZu1/cveDroDegCgr53MUWnxxCXgSEuA01TURsqcZUfwFVx\nkKLiYoqKCnn33XcACAsLJzExiYSERKKiYoiMjCIiIhKzOeiUq/K53W5aWpqx2eqprq6iqqqCqqpK\nysoO0dHR95dcXUgEhoQc9NYE9NZ4dJZoFP3J/Tqk6HQofibwM6G3xvW+rmkaWkcz7vpy3LWluGtL\n2L//W/bv/5b168FgMJCZmUlychqpqRmkpaXLkD4hvMRpf6Zp2rRpFBQUALBmzRrWrVsHwMqVK7n2\n2mupqqri+uuvZ+LEiezfv5/x48fT09PDihUrSEtL44477ujTC/Tcc8/R1dXFbbfdxt69e7nvvvvQ\n6/XMmDGDzz77jA0bNlBVVcWvfvUr7HbPN0u/+c1vmDRp0knHvmrVKubPn8/ixYv59NNP+Z//+R8C\nAgKYPHkylZWVPPPMM+zdu5eHH34Yh8OBr68vv//970lKSjrhNnfu3Mn777/fm1SuXbuWH/7wh2Rl\nZQEQEhLC3XffzVNPPcXcuXMB+OEPf8j69eu54YYbCArq/2bW2NhIYGAg/v7+gCdxjY31zET/+uuv\n8+qrr+JyuUhISOCxxx7D19eXiooK7rrrLux2OwsWLOD5559n9+7d7Nixgz/96U8EBQVRWlrK+++/\nzzvvvMM//vEPXC4Xubm5PPDAA2iaxr333su3336LoihceumlXHvttSf9OQshxKlyOHpoaWmhpaWZ\nlpZmmpqaaGiw0dhow2azUVtb43km6Vh6A7rQKHwik9BHJaOPTEJnOv1zErmbarB/9AJaq+2k1jMa\njVitVmw2G47DvWPDSQm24n/+NYN+dktRdIcTkgR8py5Bc/bgri/zJBG2Cpoaq2n8ahdffbWrz3o+\nRiPBQcEEHf7x8/PD19cXo9EXnU7X+0yR2+3Cbu+iq6uLzs4OWlpaaG5uor+BM4rZgiE5F114HPrw\nOPTWeBRf/2H5XPo/dgXFbEFntuCT6vk9Q+vuwlVbgru2BHdNCfv272ffvn2961gsYSQkJBIfn0h8\nfALR0TFYrZFSmVCIM8xpSZqOXMhcLhdbtmxhzpw57Nu3j/Xr1/PGG2/gdru5/PLLycvLw2w2U15e\nzqOPPkpubi4AGzdu7E0qqqqqTrife++9l4ceeojc3Fwef/zx3tfDwsJYs2YNRqORsrIyfvGLX/Dm\nm2+e8vE4HA7uv/9+1q5dS0xMTG+PD0Bqaipr165Fp9Oxfft2/vjHP/LnP/+53+20tbWxatUq/vCH\nPxAQEABAUVERK1as6LPchAkTKCoq6v23yWTi0ksv5fnnn+f/s3fn8VXVd+L/X3fNXXKT3Jt9JTsh\nbIEQdhUQREE7dca2M3bqOp2Zb21d5mf1i19n2krRdlq1tjK22o62TltKnUJFUFwQEJV9D2HJvpB9\nz83d7/n9ccOVCIQtJBx8Px+P+4jce+45n5O355y8z+dz3p/vfOc7Z71QFBQU4HA4uPHGG5k5cyY3\n3XQT8+fPB+Cmm27iK1/5CgA/+9nPeOONN/j617/OihUruPvuu1myZAmrVq0adMfvyJEjrF+/npSU\nFCoqKtiwYQOrVq1Cp9Pxgx/8gDfffJPc3Fyam5vDCW1fX9+l/HrFNc7tdqMoQWDgrqwCoBAMKoN+\nhj4792vwspz1ODidRqMh9L+0Bq1WA2gG3jv/a/B3zt67e7rT23K1zmfjdGpxufqHZV0XMso7FKNg\n6DmYoBLqjQj/ERwgGAzg9wfw+334/X78fj9er/e0lwe3243H48blcg380eykv7+fvr4+ent76O3t\nPTMh+jytDvQG0OhApwv1IGl1KG4n/ppS/DWlQ3//ClKc3TBwbFwoo9HIAw88wOLFi9m4cSMrV64c\n9sRJ6W6l/y/PoxmuRFKrC60rGEAJBiEYgGAQnz9AW3sbbW0XlzSGDkot6LShuGq14biiKARa6wi0\n1nHqiSV99iRMM24bnn25kOaZLBgyJ2DInACEyrgHWutCvVEtNXS21tGxI4D13AAAIABJREFUfy/7\n9+8d9D2rNZK4uHhiYmKIjg69rNZIrFYrFosFk8mM0WjEaDQSGWkjISFxxPZJiC+iEUmaTvUUQain\n6Y477uAPf/gDixYtCj/btGjRInbv3s38+fNJSUkJJ0wXqre3F6fTGf7erbfeyubNm4FQsvbUU09R\nVlaGTqejpqbmsvansrKS9PR0UlJSAFi6dCmrV68Ot+Pxxx8PbyMQCJxzPd///vf58pe/fEm9Xt/4\nxjf48pe/zH333XfWz7VaLb/5zW84dOgQn376KT/60Y8oLS3l29/+NseOHeOFF16gp6cHl8vF3Llz\nAdi3bx//9V//BYR+f//5n/8ZXt+kSZPC+7t9+3aOHDnCHXfcgaIoeDweYmNjmT9/PvX19fzwhz/k\nhhtuCK9XiFPef38jv/3tr0e7GeIapTFZ0ViiUbyuz/6Q1mpDzyBptaF/X6UUJXjRCRNAfHw8ixcv\nBmDx4sWsXr16yJuLl2wg4R22mwADSY7mXKMeB26McK6EPHT3Arg6b0oMRWM0hSsRnhJ0Owl2NIZe\nPe0Eu5pxnizHWdPHhf7J8sgjjzN16rQr1GohxIgkTSaTKdxTdCFODSk75fS7mHq9nmDwswuLx+M5\n7/pee+014uLiWLduHYFAgMmTJ19wWy7WCy+8wMyZM3nxxRdpaGjgrrvuOutya9as4eTJk/z0pz8d\n9H5OTg6HDx8eVLDh0KFD5OXlDVrOZrNx66238vvf/37Ii9jEiROZOHEis2fP5oknnuDb3/42y5Yt\n46WXXiI/P581a9awc+dOYOg74qfHRFEUbr/9dh555JEzlvvrX//Ktm3b+NOf/sTbb7/N008/fc51\nii+e9PQMdDrdkDcThLhUp6q3aYwmNGYbGksUGms02kg7GpsdbaQDbXQcGmsMmquwJHbf6h9f9NC8\n1tZWNm7cGO5pam29yF6aC6SNjsf61cevyLohVEhCcfWhuHpDL3c/is8Dfi+K3xdKKDVa0GhCvUhG\ncyjOEWY0Zhtaa8w5izZczRS/j2BnI4H2RoKdTaGkqbst1Ot4lup85xIREUF8fPyVa6gQYmSH551u\n2rRpLFu2jH/+538mEAjw/vvv85Of/OSs3zcajfj9fvR6PbGxsXR0dNDd3Y3ZbGbz5s1cd9112Gw2\nrFYrBw8eZNKkSWzYsCH8/d7eXpKTQ2Ox165de9l/sGVlZVFfX8/JkydJSUk5Y1uJiaEu8lPPa31e\nXV0dzz//fHgY3+m+/vWv87WvfY2bbrqJgoICOjs7efbZZ/nOd75zxnruuece7rjjjrPuT0tLC21t\nbRQWFgJQVlYW7inq7+8nLi4On8/HunXrwu0tKirinXfeYcmSJaxfv/6c+z9r1iy+9a1vcffdd+Nw\nOOju7sbpdGI2mzEYDCxatIjMzEwee+yxoX6N4gto7NhxvPbaqtFuhkA9VaCCwQBerxePx4vH48bt\nduN2u+jvHzw8r68vNDyvp6d74HmmLvoaz5FA6PRoo2LR2pPQOlLQxSajjUu7oIIHV5J54V243/8d\nwYtInLxeLytXrmT16tVX7JkmbXQ8poVnvwF4sT5LEk4SbG8k2NNGsKcdpa8Dghff03Y6jd6IJjIG\nbUwC2phEtPZEtI5ktDEJI1rIYyhBZ3fo2abmGgItNQTbT4aGJ57G4YglPq2AxMQkYmNDw/NiYmKI\niooZeFbZgsViwWAwnGMrQogrYUSSprPd+SksLOT222/njjvuAOCrX/0qBQUFZx1W8NWvfpUvfelL\njB8/np/85Cd861vf4o477iApKYns7OzwcitWrAgXgigpKQmXNb3zzjv5zne+w9q1a7nuuuvO6Mk6\nm6qqKubNmxcejrBs2bLwZxEREXzve9/j/vvvx2KxMHHixPA+/tM//ROPP/44L730Urhww+e98sor\neDyecCJ0ahtPPvkkxcXF/Od//idPPvkkTmforuk999xz1nXZ7XYWLVrE7373uzM+8/v9/PjHP6a1\ntZWIiAgcDgff//73AXjooYf4yle+QmxsLJMmTQpvZ9myZXz3u9/lV7/6FXPnzj1nWdicnBwefvhh\n7rvvPoIDs8J/73vfw2g08sQTTxAMBtFoNIOe9RJCiEuh1eowmcyYTGbg4p6p8ft9dHR00D7wnExr\nawtNTY00NTXS2HgSd2czVB4IL6+xOdAlZqJLzESflo82Km6Y92ZoOkcy1q8+fknV87oAw8BreBt1\nYdXzziXY3xtKEpqqCDRWEuxsPGPIXVRUNAnZucTGxhEdHU10dAyRkTbMZnO4EIROpws/A+f3f74Q\nRCcdHe20t7fR0tKCq7oFOPzZBvSGUHIcl4YuIQNdQgaaqLgR6ZUK9nYQaKzA3xgqBKH0tIc/0+p0\nZGdmkpWVy5gxmaSnjyEtLW3g/3UhxNXmmprctr+/P1xQ4eWXX6atrY0nnnjiim/rBz/4AZmZmaqv\nFOd2uzGZTABs2LCB9evXs3Llyiu6TTXc6RZnp5aeCnF2X/T4nZq/p7a2mtraGioryzl+/BhO52cF\nbLTR8ejSC9BnFKJLzrkqh/RdbZRgkGBrbbjceLD9sxuher2BrKxsMjOzyMjIJCMjk+TklAu6kXnB\n21cUuru7aGio5+TJeqqrq6iurqS+oZ7gaaMyNBEWtPHp6AZe2ri00HDOy0iklICfYMdJAs21BFpr\nQ0lSX1f4c7PZwtixBRQXTyElJYvMzEyMxgufs1KMvi/6eVPtVDG57UjZsGEDL7/8MoFAgNTUVJ55\n5hns9isz/8Frr73G2rVr8fl8FBYW8sMf/vCiJuy9Gu3evZvly5ejKArR0dE8/fTTpKenX9FtyslH\nveTioW4SvzMFg0EaG09y7FgZBw/u4/Dhg+HnZjWWKPS5UzDkFqOLTRnlll5dFCVIoKkKf+UB/FWH\nwpO76vR6CsaOo7BwIgUF48jKyhm1IWVer5fa2moqKk5QUXGC8opyWluaBy2jMZpDQ/rCk9uGnonT\nRJjDVflAE5rU1u0ETz/B3o7QxLbdrQS7WgYNtYu02SgYW0hBwTjGji0kIyMDrVYnx56KSezUTZIm\noWpy8lEvuXiom8Tv/Hw+H0ePHmHnzk/ZsePTcIl2bUIGxvHXoc+aeNGTol5LAl0t+I/vwndiD0p/\nDwCRkTamTZtOUVEx48dPuKqHmvX29lJVVUFlZTk1NdU0NNTR3Nw0qNjUhYqIMJGamkpWVi65uXnk\n5OSSmJh8xnPLIMeemkns1E2SJqFqcvJRL7l4qJvE7+J4vV4OHNjH1q0fcuDA3tCzqJYoDOPnYCyc\ng8ZoGu0mjgjF58FXsR/fsZ0EW0K1sM1mC9Onz2TGjNkUFk5Ap7s6ii5cCp/PR1NTI52d7XR2dtLZ\n2UF/fz/BYIBAIICiKFgsViIjbURGRuJwxJKSkord7rjgoX1y7KmXxE7dJGkSqiYnH/WSi4e6Sfwu\nXXNzI++9t5EtWzfhdrnQRFgwTLwB4/hrN3kKdDbhK9uO/8RuFK8bjUbDhAmTuP76+UydWoLRaBzt\nJqqGHHvqJbFTN0mahKrJyUe95OKhbhK/y+dy9fPuu2+z4e119DudaCIsGKcsxFA4+5oYtqcEg/hr\nj+Ar3UbgZDkA0TF25s+7kXnzbiQ2dmSrC14r5NhTL4mduknSJFRNTj7qJRcPdZP4DZ9TydP69W/i\ncvWjtcVinL4EfdYk1U22CqB4+vEd24nvyMcEezsBGDduPIsW3cyUKdPQ69WfEI4mOfbUS2KnbpI0\nCVWTk496ycVD3SR+w6+3t5e//vV/ef/9dwgEAmgTMzHN+ht08Ve2CulwCXa14C3dhv/4bhS/F6Mx\ngrlzr2fhwptJT88Y7eZdM+TYUy+JnbpJ0iRUTU4+6iUXD3WT+F05zc2NrFr1e3bv3gGAPn8aESVL\nLmuS2CtFUYIE6o/jPfwRgfpjAMTGxrFo0c3Mm3cjVmvkKLfw2iPHnnpJ7NTtcpMm6WMXQgghhlFi\nYjIPPfQoZWWlvP76q9Qd302g6hCGyfMxTrwBjX505io6neJ14zuxJzQEr6sFgPz8AhYvXkJx8XRV\nV8ATQogrQXqaxKiSOzbqJXfc1E3iNzKCwQCbN2/ijTdW0dvbg8YaTUTJEvS5U9BozpzD50oLdLXg\nO/IJ/uO7UHwedDodM2fOYfHiJWRl5Yx4e76I5NhTL4mdusnwPKFqcvJRL7l4qJvEb2T19ztZt24t\n77yzHr/fh9aRgnHqQvSZE6548qT4ffirD+Er206gqRIAu93BggU3MX/+jURHx1zR7YvB5NhTL4md\nuknSJFRNTj7qJRcPdZP4jY62tlbeeOOPfPLJNhRFQWtPwjhlIfqsiWi0wzckTlGCBJqq8ZfvxV91\nAMXjAmD8+IksWLCIqVNLpAreKJFjT70kduomSZNQNTn5qJdcPNRN4je6GhsbePPNNXz88VYURUFj\njkSfV4whfzo6e+IlrVMJ+Ak0VeKvO4q/6iBKXxcA0dExXHfdPObNu5HExKTh3A1xCeTYUy+JnbpJ\n0iRUTU4+6iUXD3WT+F0dmpsbee+9d9i2bStOZx8A2pgEdEnZ6JJz0CVkoLFEnVE8QlGCKM5ugu0n\nCbQ3EGitI3iyAsXvBcBkMlNSMoPZs6+jsHA82mHsxRKXR4499ZLYqZskTULV5OSjXnLxUDeJ39XF\n5/Oxd+9utm3bTFnZETwe96DPNYYIMEdCIAA+N4rXAwy+fCclJTN58hQmT57K2LHjMBqNI7gH4kLJ\nsadeEjt1k5LjQgghhMoZDAZmzJjFjBmzCAQC1NRUUVZ2hLq6arq7u+nu7qa3twe90UBUogODIYLo\n6BjGjMkkIyOTjIwxOByxo70bQghxzZKkSQghhLiK6HQ6srNzyc7OPevncrdbCCFG3shPEiGEEEII\nIYQQKiJJkxBCCCGEEEIMQZImIYQQQgghhBiCJE1CCCGEEEIIMQRJmoQQQgghhBBiCJI0CSGEEEII\nIcQQJGkSQgghhBBCiCFI0iSEEEIIIYQQQ5DJbYUQQlwRwWCAhoZ6GhtP0traSl9fLx6PGwCDwYjV\nasVudxAXF09qahpRUdGj3GIhhBDi7CRpEkIIMWxaWprZu3c3+/fvobz8OB6P54K/Gx0dQ05OLrm5\n+YwbN57MzGz0erlMCSGEGH1yNRJCCHFZvF4v27d/zNatH3LsWFn4/ZiYVDLG5GJ3pGGzxWMyR6HX\nR6BBgz/gxePuo7+/k56eZro6G+hor2Hv3t3s3bsbAJPJzPjxE5g8eSpTphQTE2MfrV0UQgjxBSdJ\nkxBCiEvS3+/k3Xff4b333qanpxuA5JTxZOfMJD2jCIvl4pMcp7OD5qbjNDWW0dBwiD17drFnzy4A\ncnLyKCmZSUnJDBISEod1X4QQQoihaBRFUUa7EeKLq7W1d7SbIC5RfLxN4qdilxM/t9vNxo3r2bBh\nHf39ToxGC2MLFlBQeCM2W/ywtrOnu4m62v3U1uylqekoihIEQgnUrFlzmDlzDtHRMcO6zaudHHvq\nJvFTL4mdusXH2y7r+5I0iVElJx/1kouHul1K/ILBANu2beXPf/4jXV2dREREMnHSUsYVLsRgNF+h\nln7G7e6lpno3VZU7aDxZiqIoaLVaJk0q4vrr5zNlSjF6veGKt2O0ybGnbhI/9ZLYqdvlJk0yPE8I\nIcR5lZcf57e//Q3V1ZXo9UYmT/kbJk5aitFoGbE2mEw2xhbMZ2zBfFz93VRWfkr5iY/Zv38v+/fv\nxWaL4rrrbmD+/EUkJSWPWLuEEEJc+6SnSYwquWOjXnLHTd0uNH69vT386U+/Z8uWTQBk58ymZPrX\nsEbGXukmXrDOjjqOH9tCefk2PO4+AMaPn8jChYuZMmUaOp1ulFs4vOTYUzeJn3pJ7NRNepqEEEIM\nu2AwyEcfbWbVqv+hr68XuyOdWbPvJim5YLSbdga7I50Zs/6RadO/RnXVLo6VbaK09BClpYeIjY3j\nxhtvYt68G7HZoka7qUIIIVRKeprEqJI7Nuold9zUbaj41dXV8tprr3D8+FH0+gimFv8dhRMWo9Wq\np8ems6OOsiPvU35iG36/B73ewOzZc1m06BYyM7NGu3mXRY49dZP4qZfETt2kEIRQNTn5qJdcPNTt\nbPFzu12sWfMG77yznmAwwJjMEmbM+kcir6KheBfL6+3nxPGtlJW+R09PMwB5eWNZtOhmSkpmqLJw\nhBx76ibxUy+JnbrJ8DwhhBCXRVEUdu78lD/84Xd0dLQTaYtn5qy7yBgzZbSbdtmMRgvjJ9xM4fib\nqK8/RFnpu5w4cYATJ44RFRXNDTcsYN68G2XeJyGEEEOSniYxquSOjXrJHTd1OxW/6uoqfv/71zh6\n9AharZ6Jk5YyecqX0OsjRruJV0xPdxNHyzZx4vhWPJ7PCkfccMONFBeXYDQaR7mFQ5NjT90kfuol\nsVM3GZ4nVE1OPuolFw91Cwb7eeWV/+bjj7eiKArp6UXMmPWPREUnjXbTRozf76W6aifHyjbR3Hwc\nAIvFysyZs5kz53pyc/PRarWj3MozybGnbhI/9ZLYqZskTULV5OSjXnLxUKeOjnbeemstH374Pn6/\nH7sjnekz7iQ1beJoN21UdXed5PixrVSUf0x/fycAcXHxzJw5h5kzZ5ORkYlGoxnlVobIsaduEj/1\nktipmyRNQtXk5KNecvFQl/r6OjZuXM+2bVvw+/3YbPFMKf47snNmX5W9KaMlGAzSeLKUivKPqane\njc/nBiAxMZmSkhmUlMwkKyt7VBMoOfbUTeKnXhI7dZOkSaianHzUSy4eVz+fz8fevbv48MP3KS09\nBEBUVCKTir5Ebt4ctFqpBTQUv99Lfd1+Kiu2U193AL/fA4DDEUtxcQlFRcWMGzceg2FkK/DJsadu\nEj/1ktipm+qSppdeeon169ej1WrR6XT84Ac/YNKkSRe1jqNHj9Lc3MwNN9wAwJo1azh8+DD//u//\nftnte/HFF7Fardx7771nvL969WpiY2Px+/088sgjLFiw4LK393k///nPKSkpYdasWZf0/eH8XYwE\nOfmol1w8rk4+n4+jR4+wY8cn7Nq1g/5+JwBJyQWMn3Az6RlTpWfpEvj9HurrDlJTvYe62r14vf0A\nREREMG7ceCZOLGL8+AmkpKRd8V6oSzn2gsEAfr+fYDBI6LKvQavVotfr0enUM//WtUDOneolsVM3\nVZUc379/P1u2bGHt2rXo9Xq6urrw+XwXvZ6ysjIOHz4cTpqAERkqce+993LvvfdSUVHB17/+dbZv\n3z7o80AgcNkXnwcffPCilj/bNq+WcfdCiCsvEAhQX1/L0aNlHDlymNLSQ3g8oSFlFoudCRNvIL9g\nHjExKSPSnv7+LgKBiz+vDzedzoDFEjNs69PrI8jMKiEzq4Rg0E9z03Fqa/dRX7uf/fv3sn//XgCi\noqLJzx9Lbm4+mZnZZGZmYbVGDls7ALxeLy0tzXR1ddLV1Ul3dxddXV309HTT29tDb28vTqcTl6sf\nt9uNx+MmEAicc31arZaIiAgiIkxYLBYsFiuRkTZsNhs2WxTR0TFER8dgt9ux2+3ExDgwm83Duk9C\nCHG1G9GkqbW1Fbvdjl4f2mxMzGcXtIMHD/L000/jcrmIiIjgtddeQ6/X873vfY/Dhw9jMBj4v//3\n/zJlyhR+/vOf4/F42Lt3L//8z/88aBsffvghL730En6/n5iYGH7605/icDh48cUXOXnyJHV1dTQ1\nNXHXXXfxjW98Awj1fq1du5a4uDiSkpKYMGHCkPuRk5ODXq+no6ODn/zkJxiNRsrKyiguLubBBx9k\n+fLllJeX4/f7+fa3v82CBQtYs2YN77//Pi6Xi5qaGu677z58Ph9//etfiYiI4OWXXyYqKoply5Yx\nf/58brrpJkpLS/nRj35Ef38/drudH/3oR8TFxfGNb3yDcePGsXfvXm699Vbuueee8/7uP/74Y37x\ni1/g9XrJyMjgmWeewWw2s3LlSjZv3ozb7WbKlCk89dRT4Xg8+eST6HQ6Zs2axUcffcS6devO6Mn6\n13/9V+6//35KSkrOuQ0hxOXzej10dHTQ0tJEU1MTDQ111NXVUlNThdfrDS9ni0ogN28eGZnFJCWN\nRaMZmV6lzo46Pnj/BXq6my76u0ajkfj4eFpbWwfty+WKik7ixoUPYXekD9s6AbRaPckphSSnFDJj\n5tfp623jZMNhGhuP0NhYxu7dO9m9e2d4+ZgYO8nJKcTFxRMbG0tUVDSRkTZMJjNGozHc8xcIBPD5\nfLjdblyufvr6+ujr66Wnp5vu7i66u7vo7Oykr+98d7o1RERYMBgsRJgcREaa0OkMaHU6tBodDNxY\nCwYDBIN+AgEffp8Hn99NR0cPjY2NKEpwyC2YTGbsdgcOhwO73UFMjP20VwxRUVHYbFFYLFbp2RRC\nXBNGNGmaM2cOK1eu5Oabb2bWrFksWbKEkpISfD4f//Zv/8YLL7zA+PHjcTqdRERE8Lvf/Q6tVsu6\ndeuorKzk/vvvZ+PGjTz44IOUlpby5JNPAqEhaadMmzaN1atXA/DnP/+ZV155hccffxyAqqoqXn/9\ndXp7e7n55pu58847KSsr4+2332bdunV4vV7+9m//9rxJ04EDB9BqtTgcDgCam5vD23z++eeZNWsW\nTz/9NL29vdxxxx3Mnj0bgPLyctauXYvL5eKmm27iscceY82aNTzzzDOsXbuWu+66K7wNv9/P8uXL\neemll7Db7WzYsIHnnnuOp59+Ovz5G2+8cUG/987OTl566SVee+01TCYTr7zyCv/93//NAw88wDe+\n8Q0eeOABAB577DE2b97MvHnz+H//7/+xYsUKJk2axLPPPjtofWfryRpqG0KIM7lc/bz77tu0tbWe\n8VkgEKC/34nT6Rz4g7kbp7PvjOU0Gi0x9jQy47NISMwnOaWQsiPvUV25k5rq3SOxG2FOZyeKcu7e\njHMxGo088MADLF68mI0bN7Jy5cphS5x6uptY+5cnsVrtw7K+c8nMns70GXeSXzAPgL6+dlqaT9DR\nXkN7ew3dXY2UlR0BLm80vMFoxmqxk5ySjsXiwGKJwWKNwWyOwWyJxmyOxmSyERFhvaxkWVEUfD4X\nbncvblcPLlc3rv5u+vs76e/vxOnspL+/i46ODhobG4Zcl1arxWq1YrFEYrVaMJstmM1mTCZz+Abq\nhUhMTGbhwpswmeRGnBBidIxo0mSxWFizZg27d+9m+/btPPLIIzz66KMUFhaSkJDA+PHjAbBarQDs\n2bMn3BuUnZ1Namoq1dXVQ26jsbGRhx9+mJaWFvx+P2lpaeHP5s2bh16vx263ExcXR1tbG3v27GHR\nokUYjUaMRuOQzym9+uqrvPnmm1itVn72s5+F37/55pvD/71t2zY2bdrEb37zGyD0fMHJkycBmDFj\nBmazGbPZTFRUFPPmzQMgPz+f48ePD9pWVVUVJ06c4L777kNRFILBIAkJCeHPlyxZMuTv4XQHDhyg\nvLycf/iHf0BRFPx+P0VFRQB8+umn/OY3v8HlctHT00NeXh7FxcU4nc7ws2a33normzdvvuRtCCHO\n9OGHH/DGG6su+nsx9jTS0iYxJnMasXGZ6PWjPxGrogQvKWECiI+PZ/HixQAsXryY1atX09Aw9B/i\nF9e2AIoSHLEeN4DIyFgiI2PJzpkZfs/v9+Lsa6O1tZKurga6u5pwubrxePoIBv2ggN4QgdFgxmyJ\nwWp1YItKIDo6maioRMyWmBGLtUajwWi0YDRaiIpKHHJZv99Lv7ODjo462tuqaW+vprOjDqezAwhV\nI+zt7aW3d3ieA7n11r8ZlvUIIcTFGvHSSRqNhpKSEkpKSsjPz2ft2rUUFhZyIfUoLmSZ5cuXc//9\n9zNv3jx27tzJiy++GP7s9FnetVrtkGO8z+bUM02fZ7FYBv37F7/4BZmZmYPeO3DgwBmzzJ/699na\noigKeXl5rFp19j+qLmbYm6IozJkz54weI6/Xy1NPPcVf/vIXEhMTefHFF/F4PEOuS6fTEQx+Nmzj\n1PLn2oYQ4uxmzpzNiRNHKS8/EX7v8+e4YDBIf79z0Pmhq7Oers56Dh/aQKQtnri4TOLic0hMzCcu\nPpvpM+5k+ow7R2w/Tnlj9aOXNDSvtbWVjRs3hnuaWlvP7Hm7HNHRyfzdV38yrOs8n2AwQGdnPe1t\n1XQM9DR1dzfidHacd9jb2Wi1OszmaMyWGCwWe6iHyWLHYrUPvBeD2RyFyRSFVjt8RR0URcHj6cPl\n6sHV3zXQ0zTw0xl6Ofs7cPV3EQwOfT3VaDSYzRYMBgMajWbg96A5YxlFUc74qdVqyM8fx9y51w/b\nvgkhxMUa0aSpqqoKrVbLmDFjgFBBh9TUVLKysmhra+Pw4cNMmDABp9OJyWRi2rRprFu3jhkzZlBV\nVUVjYyNZWVlUV1fT13fmUBUAp9MZ7pE5fdjeuZSUlLBs2TL+5V/+Ba/Xy4cffsjf//3fX/I+zp07\nl9dffz38zE9ZWRnjxo276PVkZWXR2dnJ/v37KSoqwu/3U11dTW5u7nm/+/k/vCZPnszy5cupra0l\nIyMDl8tFc3MzsbGxaDQa7HY7Tqcz/IeLzWbDarVy8OBBJk2axIYNG8LrSk1N5Y9//COKotDU1MTB\ngweH3Mbnk0chRIjDEctDD333vMsFg0GcTiednR20t7fR0tJMc/OpZ5pqqK7aRXXVLiBUrCApuYD0\njClkjJmK1eq40rsRduPCh9j0/s/p7m68qO95vV5WrlzJ6tWrh/2ZpujoZBYsvLjiOpciEPDT2lJO\n48kjNDUepbW1Ilye/JSYGDu5uXk4HLFER596psmEwWBEp9OFRxT4fN5BzzSFeml6QsUeOutoa60c\noiUaIiKsRJgiMRqtRBgtGIwm9PoIdDojOp0ejUaLRqMd6B0MEgj4CQZ9+Hwe/H4PXm8/Xo8Tj8c5\n0At27mRIp9MRHR1DclJ2+Hmm6OhTzzRFDzzXZCMy0obZbJFnm4Qky7FWAAAgAElEQVQQqjaiSVN/\nfz/Lly+nr68PnU7HmDFjeOqppzAYDDz//PMsX74ct9uN2Wzm1Vdf5c477+R73/set912GwaDgR//\n+McYDAZmzJjByy+/zO23335GIYgHHniABx98kOjoaGbOnHneYR6FhYXccsst3HbbbcTFxTFx4sTL\n2sdvfetbrFixgttuuw1FUUhLS+OXv/zlGcudr8KdwWDghRde4Ic//CG9vb0Eg0HuuusucnNzz/vd\nNWvW8MEHH4Tv1P3pT3/imWee4d/+7d/wer1oNBoefvhhMjMzueOOO1i6dCnx8fGD9n3FihXhQhAl\nJSXYbKEyjcXFxaSmprJ06VJycnLCQyodDsc5tyGEuHRarXagipmNjIwxgz5TFIX29jYqKk5w9OgR\nyspKqa87QH3dAT79+LckJY0lJ28O2dkzMRiv7LMgdkc6f/fVn1yz1fM+z9nXTl3dfurrDtB48kh4\nElyA1NR08vLyycrKISMjk9TUtGEpiqMoCk5nHxqNl8rKejo7O06roNc9qHpeV2cbfr//oreh1Wox\nmy1ER9uIjEwkKiqa6OhooqNjiImxY7c7BqroOYiKipZESAjxhSGT24qz6u/vDw87fPnll2lra+OJ\nJ54Y9u3IfAfqJfNVXJ1aW1vYv38PO3Z8yvHjR1EUBb0+guzc2Ywfvxi7I+38KxFnUBSFrs56aqp3\nU1Ozh/a26vBnSUnJTJxYRGHhBAoKxhEZeXlzgZzPhRx7oWIOoV4rt9uNz+fD7/cN9GiFhrxpNBr0\nej16vQGTyRQuOy7TVlxZcu5UL4mduqlucluhDhs2bODll18mEAiQmprKM888g90+/BWo5OSjXnLx\nuPp1dLSzdetmtmz5IFylLzVtEkVTvkxiUv4ot+7qpygKnR11VFXuoKpqR/iZLZ1OR2HhBIqKiikq\nmkpCwtDFEoabHHvqJvFTL4mduknSJFRNTj7qJRcP9QgGA+zbt5e3317HsWNlACSnFFJc8lUSEs7/\nnOQXTW9PCxUVn1JZ/gldXaEh3kajkcmTp1BSMpPJk6dgsVhHrX1y7KmbxE+9JHbqJkmTUDU5+aiX\nXDzU6fjxo6xd+waHDh0AYEzmNKZN/3uio5NGuWWjy+d1UVW5gxMnPqK56RgAer2BoqKpzJw5m8mT\np2IymUa5lSFy7KmbxE+9JHbqJkmTUDU5+aiXXDzUrampml/96hXKy4+j1eoYN/4mpky9HaPRcv4v\nXyMURaG1tYJjZR9SVbk9XPFu3LjxzJ17A9OmTR/VHqVzkWNP3SR+6iWxUzdJmoSqyclHveTioW7x\n8TZaWnrYtWs7q1b9D62tLZjN0Uyb/vfk5s29posB+HxuKso/4eiR9+noqAUgLi6e66+fz3XXzSMu\nLn6UWzg0OfbUTeKnXhI7dbvcpGnEJ7cVQghxddBoNEyfPouiomLefnsdb775Fz7a8itOHNvCrDn3\nXHOV9rq7myg78h4njm/F53Wh1WqZNm0GCxYsYvz4iVI+WwghxDlJT5MYVXLHRr3kjpu6nS1+bW2t\n/M//vMqePbvQanWMn3gLU6bejl4fMUqtvHyKEqS+/hBlpe9SXxd6jismxs78+QuZP38hdvvITQA8\nXOTYUzeJn3pJ7NRNepqEEEIMi7i4eB5++DH27dvDb3/3Gw4deIuqyh3MnHUXGWOmjHbzLorH46T8\n+EeUHXmPnp5mAPLyxrJo0S2UlExHrzeMcguFEEKoiSRNQgghBpkypZhx48bz17++wYYNb/H+u8+S\nMaaYGbP+EZvt6n7ep621iqNHN1FZ/gl+vwe93sD1189n4cLFZGXljHbzhBBCqJQkTUIIIc5gMpn4\n2tf+kTlzbuC1117h2LE9NNQfZNLk25g4+Vb0euNoNzHM43FSVbGdY8c2095WBYR6zW68cTE33DAf\nmy1qlFsohBBC7eSZJjGqZGywesnYbnW7mPgpisInn3zEH//4Ot3dXVitDopLvkpO7mw0mtEpnhAM\nBjjZcJjyE9uoqd5NIOBDo9EwZUox8+cvYtKkyWi1ulFp25Umx566SfzUS2KnbvJMkxBCiCtKo9Ew\nZ871TJ06jTffXMM776xn6+ZfcujgeqZM/VvGZE4bkRLlwWCQlubjVFXuoKpqB25XDwBJSclcf/18\n5s69QZWFHYQQQlz9pKdJjCq5Y6NecsdN3S4nfm1trfzv//6Jjz/eiqIoxMSkMmHiLWTnzh72YXt+\nv5fGk0eordlDbc1eXK5uAGy2KKZPn8XcudeTk5N3Tc8r9Xly7KmbxE+9JHbqJpPbClWTk496ycVD\n3YYjfo2NDfz1r3/h008/JhgMYDRayM6ZRXbOLBIS8y5peFwwGKSzs47Gk0c42XCYpsYy/H4vAJE2\nG9OKpzN9+iwKCyeg012bw+/OR449dZP4qZfETt0kaRKqJicf9ZKLh7oNZ/w6Otp577132LZtC11d\nnQBERFhJTCogISEXuyONSFsCZnNUeM6nQMCHx92Ls7+T3p4WujobaG+vpq21Ep/PHV53amo6kydP\nYerUaeTl5V+zzyldDDn21E3ip14SO3WTpEmompx81EsuHup2JeIXCAQoLT3E3r272LdvDx0d7Re9\njuTkVPLy8hk3bjyFhRNwOGKHtY3XAjn21E3ip14SO3WTQhBCCCGuCjqdjkmTipg0qYh77vkmHR3t\nVFScoLGxkdbWZvr6+vB4Qr1IBoMRi8WCw+EgLi6B1NR00tLSsFiso7wXQgghxJkkaRJCCHFFOByx\n0lMkhBDimjA6E2wIIYQQQgghhEpI0iSEEEIIIYQQQ5CkSQghhBBCCCGGIEmTEEIIIYQQQgxBkiYh\nhBBCCCGEGIIkTUIIIYQQQggxBEmahBBCCCGEEGIIkjQJIYQQQgghxBAkaRJCCCGEEEKIIUjSJIQQ\nQgghhBBDkKRJCCGEEEIIIYagH+0GCCGEEOKLxefzcfJkA01NjbS1tdDe3obT6aS/34nf70dRFHQ6\nHWazGbPZQkxMDHZ7LAkJiSQnp2C3O9Bq5b6vEGLkSNIkhBBCiCuqra2VsrJSTpw4Rnn5CU421BMI\nBi55fSaTmYyMMYwZk0VOTh65uXkkJCSi0WiGsdVCCPEZSZqEEEIIMay8Xg9HjpSyf/9eDh8+QHNz\nU/gzg85IenQWyVFpJNlScZjjsFtisRoiMRss6LV60GgIBgN4/G76/f30uLvocnXQ6mympa+Rpt56\nTpw4xvHjR3nvvbcBiI6KJn9sAWPHjmPs2EIyMjLQanWj9SsQQlxjJGkSQgghxGXr7e1l377d7Nmz\ni8OHD+D1egGI0JsYn1hEfvx4sh15pESlo9NewJ8fWj1GfQQ2okmMTD7jY6/fQ0NPHTWdFdR0VlDZ\ncZxdu3awa9cOACxmC/ljx1FQUMi4cYWMGZOFTidJlBDi0kjSJIQQQohL0t3dze7dO9i1aztlZaUE\ng0EAEiNTGJ9RxPjEyWTacy8sSbpIRn0EWY5cshy5ACiKQkd/GxUdx6hoP0ZF21H279/D/v17ADBF\nmMjNyyc/v4D8/AJycnIxmczD3i4hxLVJkiYhhBBCXLDe3l52797Bjh2fcOTIYRRFASAjJpvJydOY\nmFxMQmTSiLdLo9EQa40n1hrP9PS5AHS5OkIJVPsxytuPcvjwQQ4fPhhePj19DLm5eeTk5JGdnUtK\nSooM6RNCnJUkTUIIIYQYktvtZt++3XzyyTYOHdwfLuIwxp5DUcp0ipJLsFtiR7mVZ4oxOyhOm0Vx\n2iwA+jy9VHeeoLLjBNUd5dTVV1FbW82mTe8Bod6oMZlZZGfnkJmZTVZWNomJyVKpTwghSZMQQggh\nzhQMBikrK2Xbti3s3rUDt8cNQGpUBsVpsyhKmY7DEjfKrbw4kRE2JiRNZULSVAACQT8ne+qo7qyg\ntquKuq5Kjh87yrFjZeHvnEqkMjOzyMzMJjMzW3qkhPgCkqRJCCGEEGGtrS1s3foh27Ztoa2tFQCH\nJY7r8hZRnDabJFvKKLdw+Oi0etJjskiPyQq/5/G7aeiupbarivruauq7qzl+fHAiZTRGkJmZSVZW\nDllZOWRn50iPlBDXOEmahBBCiC84n8/H3r272Lz5A0pLD6EoCkZdBNPTr2N6+lyyY/PRar4YCUGE\n3kR2bD7Zsfnh905V6qvvrqKuq5q6rmrKT5zg+PFj4WUsZgtZ2Tnk5OSSnZ1HTk4uMTH20dgFIcQV\nIEmTEEII8QVVV1fLli2b+PjjrfT19QKQ7chnRsZ1FKVMJ0JvGuUWXh0+X6kPTiVStQPD+qqo6ayk\ntPQQpaWHwsvExsaRnZ07kEjlkpmZDdhGYQ+EEJdLkiYhhBDiC6S3t5cdOz7ho48+pLKyAoBIo40F\nOUuYkXEdidfQ8LsrKZRI5ZHlyAu/1+9zUtdZRU1XBTWdldR0VrBr13Z27doOhCr2paWlkZ6eSWZm\nFhkZmWRkZGKzSSIlxNVOkqazeOmll1i/fj1arRadTscPfvADJk2adNHr2bRpExUVFXzzm9+84O9M\nmTKFffv2DXrvxRdfxGq1cu+99wLw6quvsnr1agwGA1qtllmzZvHoo4+GJ+0rKyvj9ttv59e//jVz\n584963YWLFhAZGQkWq2WYDDIQw89xI033gjAuHHjKCgoIBgMotPp+I//+A+KiopYuHAhv/71r8nM\nzAyv5+mnnyYhIYF/+qd/AmDFihVs3LiRrVu3XvA+CyGEuLLcbhf79u1l+/aPOXBgL4FAAI1GQ2HC\nZGZkXM/4pCL0V2AupS8ai8HK2IQJjE2YAAzMHeVqo7azktquqtBzUo3V1NXV8cknH4W/FxNjJy0t\nndTUNJKTU0lKSiYxMQmHwyEFJ4S4SsgZ8nP279/Pli1bWLt2LXq9nq6uLnw+3yWta8GCBSxYsOCi\nvqPRaIb8/I9//COffPIJf/7zn4mMjMTv9/Pqq6/idruxWq0ArF+/nmnTprF+/fpzJk0ajYbXX3+d\n6OhoqqqquP/++8NJk9lsZs2aNQBs27aNZ599ltdff52lS5eyfv16HnjgASB0Mdi4cSOrVq0K//uD\nDz4gJSWFnTt3Mn369IvadyGEEMOntbWFQ4cOsG/fHkpLD4avZcm2NErS51CcNptoU8wot/LaptFo\niLXEE2uJZ0rqDACCSpA2ZwsN3TU09NTS0F1LU2/DoDmkTtHpdMTGxhEbG4fDEYvdbicmxk5UVDQ2\nWxRWqxWrNRKTyUREhAmj0XjevyOCwQBerw+fz4fP58Xn8w76t9/vx+/3EwgECAaD4Xm4Qu3RotXq\nMBgM6PV6jEYjEREmIiIiMJvNmEwmSfLENUuSps9pbW3Fbrej14d+NTExn11QFixYwC233MLWrVsx\nm808++yzpKen8+GHH/LSSy/h9/uJiYnhpz/9KQ6HgzVr1nD48GH+/d//nWXLlmG1Wjl8+DDt7e18\n97vf5aabbrro9v3qV7/iD3/4A5GRkQDo9fozerLeeecdXn31Ve688068Xi9Go/GM9SiKEp65vbe3\nl+jo6EGfnXL6Z0uXLuWRRx4JJ027du0iNTWV5ORkAHbs2EFeXh633HILb731liRNQggxQrxeLydP\nNlBZWU55+XGOHS2jpbU5/HmyLY2JWcVMTZ1Bki111NrZ4+7CF7y0G5GjyaA1EDVMCaZWoyUhMomE\nyKRwIgXg9rlo6Wukqe8kbc5m2pwttDlb6Oxup6yl9ILXr9Pp0Ov1aLVaNBotDFzvA8FAOBG6kkwm\nMxaLBavVisViJTIykshIGzabbeBnFJGRNqKiorDZooiKisZkMp032RNitEnS9Dlz5sxh5cqV3Hzz\nzcyaNYslS5ZQUlIS/jw6Opp169axdu1aVqxYwS9/+UumTZvG6tWrAfjzn//MK6+8wuOPPw4M7jlq\na2tj1apVVFRU8H/+z/+56KSpr68Pl8tFSsq5x5vv3buX9PR00tPTmTFjBlu2bGHRokVnXfbuu+9G\nURTq6+v52c9+Fn7f4/Fw++2343a7aWtr47e//S0A+fn5aLVajh07xtixY1m/fj1Lly4Nf2/9+vXc\neuutzJ8/n+eff55AIBAeMiiEEOLiKYqC1+vF5eqnt7eXvr5eSkvd1NaepL29jebmZpqbG2lqahx0\nw8tssDAhaSr5cYUUJk4mzpowinsBjT31/PeuX9DqbBqW9RmNRuLj42ltbcXr9Q7LOs8n3prEfSXf\nITkq7Yqs32Qwk2HPJsOefcZnvoCXHnc33e5Oej099Hl76PP00u9z4vI58fg9ePxufEEv/qCfQNAf\nujmqBNFotGg1GrQaHXqtHp1Wh15rQK/VY9AZQy+tAYPOgF5rQKfVo9fq0Wq0aDUDideAoBJKvvxB\nH4GgH2/Aizfgwev34Pa7cPvduH399Lv6aettw+WrvaB9NxgMAwlU1KCfNlt0ONmKjIzEao0MJ2QR\nESYp8S5GlCRNn2OxWFizZg27d+9m+/btPPLIIzz66KN8+ctfBmDJkiUA3HrrrTzzzDMANDY28vDD\nD9PS0oLf7yct7ewn1IULFwKQk5NDe3v7JbXv9Ivitm3b+OlPf0pPTw/PPfccRUVFrF+/PtzGJUuW\nsHbt2nMmTaeG59XV1XH33Xezfv36cPf6qeF5+/fv57HHHuOtt94CQr1NGzZsIDc3lw8++ICHHnoI\nCJWr3bJlC8uWLcNisTBp0iS2bdvGDTfccEn7KYQQatLQUMcvfvE8DQ11I75tDRp0Wh06rT78B69O\nq6OhOzT068OKd0a8TZ/X7e4kqASGZV1Go5EHHniAxYsXs3HjRlauXDkiiVOrs4mfbPkPok1SRrwo\npYS/Gf/3510uqARx+fpxevsGXr30eXtxekI/T/13r7eHPlcvDd0N+AJVw9pWk8nMgw/+f0ycOHlY\n1yu+eCRpOguNRkNJSQklJSXk5+ezdu3acNJ0es/RqTscy5cv5/7772fevHns3LmTF1988azrPX2Y\n3OnJz4UK3WWx0tDQQGpqKnPnzmXu3Ln867/+Kz6fj2AwyMaNG9m0aRO//OUvURSFrq4u+vv7sVgs\nZ6zvVBvS09OJi4ujvLyciRMnDlqmqKiIzs5OOjo6cDgcLF26lPvuu49p06YxduxYHA4HEErgent7\nue2221AUBY/Hg8lkkqRJCPGFsGvXjhFPmHQaHQadcSBZ0qHT6ICrb4hTUAkOW8IEEB8fz+LFiwFY\nvHgxq1evpqGhYdjWP5SgEkAZ6L0R56fVaLEaI7EaI8/6uT/op8/TE0qgPD30eXrocLXT3HuSxt4G\nWvoa8V/mcE6328W2bVskaRKXTZKmz6mqqkKr1TJmzBggVIkuNfWz8d8bNmzgm9/8JuvXr6eoqAgA\np9NJQkJo6MOpHprzOVfSdL5k6pvf/Cbf//73ee6557DZbOEEBeDTTz+loKCAX//61+Hlly1bxrvv\nvhtO+s6mvb09nIh9vg0VFRUEg0Hs9tCdtfT0dOx2O88++yx33313eLm33nqLFStWhHu5XC4XN954\nIx6Ph4iIiCH3SQgh1O5LX7qdrKwcnM4+FAU0GsI/gTPeO/VTo9GgKAqBQACfz4ff78fn8+LxePF6\nPbjdLlwuF/39Tvr6+ujt7aWnpwuXy0VACRDwu8Jt0Gn1pESlk2kPzSeUFzsOmyn6HC0eWSs+eHzY\nhua1traycePGcE9Ta2vrsKz3QiREJvPEgh+N2PYuRCDoDw3PC7jxBrwETh+eh4IGBoba6QZ6Ik8N\nzwsNyTPojJc1cbGiKHgDXjx+Fx6/G5evH5ffhcvnxOl10u/roz/c09QX7l1y+vpw+frPu36NRoPV\nGhqaFxkZGp5nsVgwmy2YzWYiIiIGimBEYDQaMRgMA8dV6Pt6vZ7Jk4suef+EOEWSps/p7+9n+fLl\n9PX1odPpGDNmDE899VT4856eHr70pS8RERHBc889B8ADDzzAgw8+SHR0NDNnzrygO17neuDR4/Ew\nb948FEVBo9Fwzz33DPr8zjvvxOVy8ZWvfIWIiAgsFgvFxcWMGzeOFStWnDEUb9GiRaxateqMpEmj\n0XDXXXeh1WoJBAI8+uij4V4jr9fL7bffHk6efvzjHw9q79KlS3nuuefC23K73Wzbtm3Q78lsNjNt\n2jQ2bdrELbfcct7fhxBCqJlWq2Py5Ckjsq34eBu1tS10dISeaWpqaqShoY66uhpqa2uo66rio6r3\ngFABiMLEyUxImsoYe/Zl/XF8Oe4r+Q6v7n6Rlr7Gy16X1+tl5cqVrF69ekSfaUqITObead8ekW0B\nuP0uOpytdLja6XS10+3upNt15jNN3oDnsrelOy2R0g96pkl32vVfIagoBIJ+/AMvX8CLL+BF4cJH\nz+i0OiJtNuJi48KFIE79PPOZpiisVotU5BNXBY1yKePEvqAWLFjAX/7yl0EV9cTlaW3tHe0miEsU\nH2+T+KmYxE+9hoqd1+ulpqaKY8fKKC09xLFjZeFS49GmGCanTGdq6kzGxGSPSrUyqZ43mKIodLs7\naextoKmnnua+Rlr6GmlxNtHn6Tnn90wRJiJtoeIIoR4XCyZTqOS4Xq8PV88LDdccqJ4XCBII+Ad6\nM32nlRr3Dvz7s5Ljp4b8BwOBQemQTqtFq9ViGNjOmSXHzVgs1tOq50UOVM8LvWy2KMxmi2or5cl5\nU93i4y9vEmnpaboIaj3IhRBCfDEYjUby8saSlzeWW2/9Mh6Ph9LSQ+zZs5M9e3aytfJdtla+S7w1\nkZL0OZSkz8Vujh2x9l2JxEMtTs3PVNdVRX13dahQR08tTm/foOU0Gg3x8QlkJWaRkJBIXFw8sbHx\nOBwO7HYH0dExMuxdiFEgPU1iVMkdG/WSO27qJvFTr0uNnd/v4/Dhg3zyyUfs3r0Tn8+HBg1j4ycw\nc8wNTEiagl4r91KHS4+7i5rOSmq6KqjtrKSuu/qMZ3gSEhLJyMgkPT2D1NR00tLSSEhIwmAwjFKr\nxVDkvKlu0tMkhBBCiPPS6w0UFRVTVFSMy9XPjh2fsmXLJo6WH+Jo6yEijTZK0ucwI+MGkmznng9Q\nnMnjd1PfXR1KkgYSpS5Xx6BlEhOTKcqeysSJhcTHpzJmTCZm85mVbYUQVyfpaRKjSu7YqJfccVM3\niZ96DXfsGhrq2LJlE9s+2kJvX2i9mfZcZmRcx5SUGZgM5mHb1rXAG/BysruWuu5q6rqqqO2qornv\n5KDKs1FR0eTk5JKdnTfwMwerNVR2W4499ZLYqdvl9jRJ0iRGlZx81EsuHuom8VOvKxU7n8/Hvn27\n2bz5Aw4fPoiiKBh0RiYlFVOSPof8+PGjVn1vtHj8Hk721FLfXU1dVzX13TU09TYQVILhZSIiIsjM\nzCY7O5ecnDyys3OIi4s/53PQcuypl8RO3WR4nhBCCCEum8FgYPr0WUyfPou2tlY+/ngrW7d+yJ6G\nT9nT8ClRphimpMygOG0W6dGZ11xxJK/fQ0NPLbVdVeFiDc19jYN6kIzGCHJy8waSpByysrJJTk6R\nkthCfAFIT5MYVXLHRr3kjpu6SfzUayRjpygKJ04c5+OPt7Bjxyc4nU4A4qyJTE2dQVHKdJJtaapL\noIJKkNa+Jqo7y6nuDBVqaOytH9SDZIowMSYzi8zMbDIzs4YtQZJjT70kduomw/OEqsnJR73k4qFu\nEj/1Gq3Y+f0+Dh48wKefbmPv3t14vaFJVRMikylKmc7k5GmkRKVflQmUP+CjtquKyo7jVHWcoKrj\nBP0+Z/hzg8FwWu9RLllZ2SQlJQ/MdTS85NhTL4mduknSJFRNTj7qJRcPdZP4qdfVEDu3283+/XvY\nufNT9u/fG55AN86awMSkqUxMKibTkTtqz0B5/R6qOyuoaD9KefsxajsrBk2qmxCfSF5+Prm5Y8nN\nzSMtLQO9fmSeWLga4icujcRO3SRpEqomJx/1kouHukn81Otqi53b7eLAgX3s2rWd/fv34vGEeqCs\nxkgK4icyLnES+XGFV3Ri236fk+qOcio7jlPRfozarkoCwQAQmiw2PX0MBQXjyM8fx9ixBcTE2K9Y\nW87naoufuHASO3WTQhBCCCGEGDUmk5kZM2YzY8ZsvF4vR44cZs+enRw4sC9cRAIgyZZKliOPLHse\n6TGZJEQmobuEyXRdvn4ae+pp6KmhrquGms4KWvoaUQjdA9ZoNGRmZlNQMI6CgvGMHVsQLvcthBCX\nSpImIYQQQgwLo9FIUdFUioqmoigKtbU1HD58gNLSQxw7dpSmmgY+rdkMgE6rI96aiMMcT4zZgdUY\nicVgRafVo9FAIBjE43fj8vXT7emk+/9v787joyzvvY9/ZskyWzKTTBJCZInscAh4IA9SrQtS4IBU\nEOmjbfEcbevRF9ZqOepB61NBkFaOKygFqq31uFUEPAgcFSiIyKqyKIKIQJAlJpB19uV+/pgwipAB\nFQgj3/frNS9muee6rzs/7pn8cl337wrUUOWrpCFUd9Q+s7Oy6dqtO507d6VLl2507NhJi8aKyCmn\npElEREROOZPJRLt27WnXrj3Dhl1FNBpl7949fPrpJ1RU7KGiYg8HD+zn4Bf7T7o9r7eA0uLetG5d\nQrt2pbRrV0pJSYlKfovIaaekSURERE47q9VKaWkHSks7HPW8z+fj8OFqfD4fPp+PWCyKYRiYzRZs\nNht2ux2320NurhuLRcmRiLQMJU0iIiLSYhwOBw6Ho6W7ISKSUsvUAhUREREREUkTSppERERERERS\nUNIkIiIiIiKSgpImERERERGRFJQ0iYiIiIiIpKCkSUREREREJAUlTSIiIiIiIikoaRIREREREUlB\nSZOIiIiIiEgKSppERERERERSUNIkIiIiIiKSgpImERERERGRFJQ0iYiIiIiIpKCkSUREREREJAUl\nTSIiIiIiIikoaRIREREREUlBSZOIiIiIiEgKSppERERERERSsLZ0B0REJP3F4zFCoTDRaIRYLJZ8\n3mKxYrVaycjIwGrVV46IiKQnfYOJiMgJNTTUU1GxhwMH9roopEwAACAASURBVFNZeYCqqi+oqamh\ntrYGv89HMBQ8YRsWs4WsrCyybTays7Ox2ezY7UduDux2B06nE4fDmfzX5XLhcuXgdDqxWjPOwJGK\niIgcS0mTiIgcxTAM9u6t4OOPP2THju3s2PEJhw8fOmY7q9lCbqaToiwPNkcWWZZMMsxWzCYzJsAA\n4kacSDxKJB4lHIsQioUJBsI0NNbwRfQg0XjsmHabY7c7yMnJIScnl9xcN263m9xcNx5PHm63G7c7\nD4/Hg9PpwmQynbofiIiInPOUNImICIFAgC1bNvLBB++xefNG6uvrkq/lZDroVdCJtq5iSpyFFDny\nKbC5cWbYv3NyEo5F8EeD+CNB/JEAjZEAvkgAXyRIY8RHYyRAfdhHY9hPQ9hH/eF6Kg8exMBotk2r\n1Yrb7cHjyWu6eXC7E7fcXHfTLRen04XFYvlO/RcRkXODkiYRkXNUIODnvffWs27darZs2UQ0GgUg\nN8vJD1r3okf++XTytKPQ5jltIzeZlgwyLRm4s1wn/Z64Eac+7KM+5KM21HDUrSbYQE2ontqGBnYe\n2kHciDfbjslkwulw4srJwel0Nd0S0wIdDgcOhxO73Z58bLc7kvd1fZaIyLlFn/oiIueQcDjMpk0f\n8P77a1i7Zi2RaASA85xF9CnqxgWFXWmfU3xWT28zm8y4s1y4s1y0pVWz28WNOPUhHzWheupCjcnE\nqj7soy7UmBi5CvtoqK7hwP79KUevvi4724bT+eU1Vzk5uU1TB91NUwU95OUlRrqys22n4rBFRKQF\nKWkSEfmei8fjbN/+Me++u5J1a1fjD/gBaO0o4MLSnvyfon+i2Olt4V6eemaTGXe2C3f2iUex4kYc\nfySILxqkMezHHwnga5o26IsE8EeDTdMGA8lphI2NfvbV1BKORVK2bbfZ8eTlk5/vxev1kp9fgNfr\nxestpKCggNxcN2azVgARETmbKWkSEfkeMgyDiordvPvuO6xZsypZyMGTncNlpRfRv7gXbVxFZ/WI\n0plkNplxZtpxZtopsud9o/eGouHE6FW4kfpQI3WhRmpCTdMEgw0cDtZx+Isq9u3be9z3Z1gzyPd6\nKSgoxOstSCZTRx7n5OQqqRIRaWFKmkREvicMw2DPnt2sX7+GtWtXU1l5AACbNYtLSv6ZH7Quo0te\ne8wm/QJ+KmVZMymwZlJg96TcLhANUh2o41CglupALYeCtVQ13a8+XMPBgweO+z6r1Up+vjd5a9Om\nNdnZrmShC7fbg8vlUmIlInIandakacaMGSxcuBCz2YzFYmHChAmUlZWd8H1PPPEE5eXl9O/fn2ef\nfZZrr72WrKys79yf6dOn43A4uOGGG75zW+PHj+fyyy9n0KBBRz2/adMmJk+eTDgcJhKJ8C//8i/c\neuut33l/J2vevHl8+OGH3HfffWdsnyLSckKhENu2bW26Tmk9hw5VA4kCC+VFPbiwdU/KvJ3ItGiN\no5Zms2bTxpVNG1fRcV8PRkNUB+qoDtRQHailKlDDoUAd1cFaDtXUsrXyYLNtW8yWxDVVubnk5OTi\ndDpxOl3JYhY2mx2bzUZWVjZZWVlkZWWRkZFBRkYmVqsVi8XSdEvct1otmM0WJWIiIk1OW9K0ceNG\nVqxYwfz587FardTW1hKJpJ73fcRtt92WvP/ss89y1VVXfeek6asr1J9Od999N0888QSdO3fGMAw+\n++yz07q/WCx2TMlcTbcR+f4KBAJ89tmnbN++jW3bPuLTHZ8kiznYrdn0Ly7jn4u60svbmSxrZgv3\nVr6JbGsW57kKOc9VeNzXw7EIh4P11ATrOBSspyZYf1SRi7pQI5Wf72dPbPcp65PJZMJqsWKxWrFa\nrWRYrWRkZpKRkZlYqDg7+6iFihNVBr8skOFyuZqKZOSSman/jyKSvk5b0lRVVYXH40mWZXW73QBs\n2bKFWbNmMW3aNJYsWcK4ceN47733iMfjDB06lCVLliRHcSorK/niiy+4/vrr8Xg8/Ou//itPPPEE\nJpOJQCBANBplyZIlfPjhh/zxj3/E7/fj8Xj4wx/+gNfrZcyYMXTr1o3333+fK6+88qj+vfLKK7z8\n8stEo1Hatm3L1KlTycrKYvz48TgcDj788EMOHTrEnXfemRxNmjhxIqtXr6a4uLjZcrM1NTV4vYkL\nqk0mEx06dACOHeUaPnw4M2fOxDAMfvnLX9KjRw+2bt1Kp06deOihh8jKyuKjjz7iD3/4wwmP69/+\n7d9OGI9Vq1Yxbdo0wuEwbdu2ZcqUKdhsNp588kmWL19OMBjkggsuYOLEiQBs3ryZ3/3ud1gsFvr3\n78/KlStZsGDBMSNZN998M7/4xS8oLy9vdh8i8s3FYjGqq6s4cGAf+/Z9TkXFHvbs2c3+/Z9jGIkq\nbyZMtHEV0dPbkZ7eTnTytMVqbpl1h2pDDURi0RbZ99kgw2L9RmXTv41MSwatHPm0cuSn3C4UC+OL\nBGgMB5JFLALRIIFomFAs1LTIcIRIPEo0HiMSjxKLx4kZMWJGnFi86V8jlnw+Go8RNWJEwzHCAT++\neD3hpjZOls1mS66TdaTC4FcfH7nvcuVo/SwROeuctqTpoosu4sknn2TIkCH079+foUOHUl5eTvfu\n3dm2bRsA7733Hp07d2bLli1Eo1F69ep1VBtjxozhL3/5C8899xy5ubkADBgwAIDbb7+dfv36EY1G\nmTRpEjNmzMDj8bBo0SIeeeQRHnzwQQCi0Shz5swBEonLEYMGDWL06NEAPPbYY8yZM4ef/exnAFRX\nV/PSSy+xc+dObrnlFgYNGsSbb77Jnj17WLx4MV988QXDhg3jmmuuOea4r7/+eoYMGUK/fv24+OKL\nGTly5An/urZr1y6mTJlC7969ueeee3jhhRcYM2YMDzzwwEkd14nU1NQwY8YM/vrXv5Kdnc3s2bN5\n5plnGDt2LGPGjGHs2LEA3HXXXSxfvpzLLruMe++9l8mTJ1NWVsbDDz98VHvHG8lKtQ+Rc1k8Hica\njRAOhwmHw4RCQYLBIIFAAL/fh8/no6Ghnvr6emprD1NTU0N1dRWHDx8iHj96jaFsSyad3W3p6G5D\nR3cbOnva4cy0t9CRJXzeUMm0D17ioP/QGdtnZmYmBQUFVFVVEQ6Hz9h+T6SVPZ9fX3At5zUz/e5M\nybJkkmXJJC8797TvKxaPEYyFCURDyUqDvqbqgg1hH40Rf7LEe324kdqTWJzYZDLhcDjJaWb9LLvd\ngc1mw2azk52d3TTlMJPMzMSUw8zMTKzWjOS0Q82+EJFT4bQlTXa7nXnz5rFhwwbWrFnDHXfcwX/8\nx38wYsQI2rZty86dO9myZQs33HAD69evJxaL0bdv3+O2deSvqkfMnj0bm83Gddddx44dO9ixYwc3\n3ngjhmEQj8cpLPxyasPQoUOP2+b27dt5/PHHqa+vJxAIcPHFFydfGzhwIAAdOnTg0KHELwIbNmxg\n2LBhABQWFnLhhRcet92xY8fy4x//mFWrVvH666+zcOFC/va3v6X8WbVu3ZrevXsD8OMf/5j//u//\n5uKLL/5Wx3U8mzZt4tNPP+W6667DMAyi0Whyf6tXr+bpp58mEAhQX19Pp06d6NOnDz6fL3n92ZVX\nXsny5cu/9T5EzkWLFi3gxRdTn/vHY8JEbpaT83NKKLTnUezwUuzw0sbVikK754RFHF7a9gbrD370\nbbv9jdWE6omlWED2VMvMzGTs2LEMHjyYN954gyeffPKsSZwO+g/x/96dgScrp6W7cta6uHVvRnce\nmEykEmtnJRKqulBjsgJhQ9j/rdbPSuWBB/5I+/bnn5K2ROTcc1oLQZhMJsrLyykvL6dz587Mnz+f\nESNG0KdPH1auXElGRgb9+/fnP//zP4nH49x1110nbPPdd9/lzTff5PnnnwcSCVWnTp146aWXjrt9\nc9PDxo8fz4wZM+jcuTPz5s1j3bp1yde+OjL09YTtZLRp04Zrr72W0aNH079/f+rq6rBYLEf91TgU\nCjX7fpPJ9K2P63gMw+Ciiy46ZsQoHA4zceJE5s6dS1FREdOnT0/ZL6DZ42huHyLnqlAo+K3eZzGb\nsVmzsFuzsVmzsFmzsVuzsVuzzrqqd3EjfkYTJoCCggIGDx4MwODBg/n73//Ovn37zmgfUokZceKG\ngVmjG82ymC3YrdmEYxHC8cQUv0g8mpzuF4l9OW0wYokSjJ2apDgaPXenj4rId3fakqZdu3ZhNptp\n164dAB9//DElJSUA9O3bl7vvvpuRI0fi8Xiora3l0KFDdOrU6Zh2nE4njY2NuN1u9u3bx8SJE3nm\nmWeSiU1paSk1NTVs3LiR3r17E41G2b17Nx07dkzZP7/fj9frJRKJsGDBAoqKjj+d4kjSVF5ezssv\nv8yIESOorq5m7dq1DB8+/JjtV6xYwaWXXgrA7t27sVgSFY1KSkpYsWIFAB999BGff/558j379+9n\n06ZN9OrVi9dff50+ffp86+P6ap+P6NWrFw888AAVFRW0bduWQCBAZWUl+fn5mEwmPB4PPp+PN954\ng8GDB+NyuXA4HGzevJmysjIWLVqUbKukpIQXX3wRwzA4ePAgmzdvTrmP9u3bn7C/It9HI0eOZujQ\nHxONRohEIk0VNcOEQiGCwSB+v59AwE9jYyONjUem59VQU3OY6upqDlTvOKZNd5aL9jmt6eg+j47u\nNnRwtzmmKt61XQdzbdfBZ+owufvtx8/o1LyqqqrkZ9Ubb7xBVVXVGdv3ySh2ePnDD2878YbfI3Ej\nTiAaIhANfWV6np+GcGJqXsNXpue9/8XH/GPv+hMmQhaLBZcrhwJvq+QUvSNT8xyOxPS87OzE7Ug1\nwERFwMymqoCJ6XlHpuhZrVbMLXS9n4h8P5y2pMnv9/PAAw/Q2NiIxWKhXbt2ySIDvXr14tChQ5SX\nlwPQpUuX5DS4r/vJT37CL3/5S4qKiigvL6euro6xY8diGAZFRUXMnDmTxx9/nEmTJtHQ0EA8Huf6\n66+nY8eOKecx33bbbYwePZr8/HzKysrw+XzH3e5IGz/60Y9Ys2YNw4YNo3Xr1lxwwQXH3f61115L\nFkCwWCw8/PDDmEwmBg8ezGuvvcbw4cMpKyujtLQ0+Z7S0lKef/55xo8fT8eOHbnuuuvIyMj4VscF\nibLjS5cuxTAMTCYTL7/8MlOmTOG3v/0t4XAYk8nE7bffTvv27bnmmmsYNmwYBQUF9OzZM9nG5MmT\nk4UgysvLcbkSFzj36dOHkpIShg0bRocOHejRowcAeXl5ze5D5Fx15Be5byMQ8FNZeZADB/Y3FYLY\nzZ49u9lYtZ2NVdsByDBb6ZB7Hv/k7UhZQSfauIrO+GjUry+4lukbX+aAr/qM7C8cDvPkk0/y97//\n/ay7pqnY4eXW3v+3pbuBYRgEY6FkIYhANIg/GiIYDRGMhRMjPLEvR3ii8UShhy8LQcSbRhFjTUUg\nEsUhIvEoUSNGJBYlHE+0caS9k2Eymchx5VBU0PorBSA85Obm4nZ7yMnJxe12k5OTi93u0LVIInJW\nMRnfZv6ZnDL79u3j5ptvZsGCBS3dlaP4/X7s9sQF5rNmzaK6upp77rnnlO+nqqrhlLcpZ0ZBgUvx\nawG1tTV8+uknfPLJNj7++CP27NmdHF12Z7n458Ku9CnqRte80jNaSU/V805/9TzDMGgI+5rKjdcl\nyo+H6qkNNVLXVHI8MbLjJ2ac+mU2LGYL1gwrmRmZZGRmJoswHCk5brPZcDgcOByJkuNOZ05i7aic\nHFyuxL8a7dFnZzpT7NJbQcF3+4w+rdc0Sfpavnw5s2bNIhaLUVJSwpQpU1q6SyICuN0e+vbtR9++\n/QBoaGjgww83s2nT+2zc+D7L9q5n2d71ODJs9CnqxoXFPemWV3raR6BOd8JwLogbcWpDDYmFbf2J\nBW6rA7WJxW0DdRwO1qUs8Z2VlUVurpv2rlZNSUtzi9sm1lnKbFpv6avT2I6+WbFYzMkFb7XQrYic\nyzTSJC1Kf7FJX/qL29knFovxySfbWL9+DevXr6W2tgZIJDT/p9U/8YPWvWifU6xpTy0kFo9RG2qk\nOlBDdbCO6kANhwKJf6sCicSouREilysHr7eA/Px8SkqKsdlc5OXl4/Hk4fEk1jvKzta6eOlAn53p\nS7FLb991pElJk7QoffikL315nN3i8Rjbt29j9ep3WLduDT5fI5C47qZ/6zL6F5dRaM9r4V5+P8SN\nOP5I8GtltBO3mmA9h4P1HA7WURNqIN5MtcHcnFy8BYUUFBTi9XopKCjC6/Xi9RaSn+896to4nXvp\nTfFLX4pdelPSJGlNHz7pS18e6SMajbB580ZWrVrJBx9sIBJJXLjfIfc8Liwuo7xVdzzZWlsoFo/h\niwbxhQP4In580SD+SBB/9MtFW5P3wwEaIn4aI34aw/6UpdfNZjNutwev10tenhev10t+fkFTglSA\n11vwjQqG6NxLb4pf+lLs0puSJklr+vBJX/rySE9+v48NG9axYcNqNm7cmKiyiYmO7jb0LepG78Ku\ntHLkt3Q3T5lQLExtsIGaUEOyWEJiAVUf9eGmRVSbEh9/9OTX1jKZTNjtdpxOV1ORg1xycnKaqr+5\n8XjycLs95OXl4Xa7T2kBBJ176U3xS1+KXXpT0iRpTR8+6UtfHumtoMDFjh0VrFu3hnXrVvPJJ9uS\nVfha2fPp4e1A9/zz6expR06mo4V7e6xwLJJIgEIN1IYaqQk1UBuqb0qQ6pseN+CPpE6EjqwHdKRw\ngtPpxOFwNhVQcDRVg/tyfSC73dFUYMHeYpXgdO6lN8UvfSl26U1Jk6Q1ffikL315pLevx6+2toaN\nG9/ngw/eY+tHWwiGvkw2iuz5tM9pTbucVpQ4Cymy5+O1u8kwf/cCrIZhEIlH8UeC+KKBLxdHDScW\nSD2ySGpiodRGGsI+6sM+AtFQynYdDicejyc54uN2e5IFE3JzE2sB5ebmYrPZ064whs699Kb4pS/F\nLr0paZK0pg+f9KUvj/SWKn7RaJSdO3fw8ccfsWPHdj7d8Qn+gP+Y7VyZdnIzXTgzbNgyssmyZGA1\nW7GYzJgwYWA0LZYaSy6GGoqFCUbDBGNhAtEggWiIaPzk1hQym824nC5yct1NU+ESSVAiMXI3VZJL\n3M/M/HaLCqcDnXvpTfFLX4pdetM6TSIickpZrVa6dOlGly7dAIjH41RXV1FRsZv9+/dTWXmAqqov\nqK2t4XBtLZ83Vp502yaTKbkgqsvtodBmx263f2UqnBOHw9E0Vc6VnDbncuXgcDi0VpCIiLQIJU0i\nIpKS2WymsLCIwsKi474ej8fw+/2Ew2Gi0Six2JcLsFosiYVTv1xMNUOJj4iIpB0lTSIi8p2YzRac\nzu827UFERORspj/3iYiIiIiIpKCkSUREREREJAUlTSIiIiIiIikoaRIREREREUlBSZOIiIiIiEgK\nSppERERERERSUNIkIiIiIiKSgpImERERERGRFJQ0iYiIiIiIpKCkSUREREREJAWTYRhGS3dCRERE\nRETkbKWRJhERERERkRSUNImIiIiIiKSgpElERERERCQFJU0iIiIiIiIpKGkSERERERFJQUmTiIiI\niIhICtaW7oCcW+LxOKNGjaKoqIg//elP1NXVcccdd7Bv3z7OO+88HnvsMVwuV0t3U75mwIABOJ1O\nzGYzVquVOXPmKHZppKGhgXvvvZcdO3ZgNpt58MEHad++veKXBnbt2sUdd9yByWTCMAz27t3Lb37z\nG6666irFLw389a9/Zc6cOZhMJjp37syUKVMIBAKKXZp49tlnmTNnDgCjR4/m+uuv13ffWeyee+5h\n+fLl5Ofns2DBAoCU8Zo5cyavvvoqFouFe++9l4svvjhl+xppkjPqb3/7Gx06dEg+njVrFv379+eN\nN96gX79+zJw5swV7J80xmUw899xzzJ8/P/kFotilj8mTJ3PppZeyePFiXnvtNc4//3zFL02UlpYy\nf/585s2bx9y5c7HZbPzoRz9S/NJAZWUlzz33HHPnzmXBggXEYjEWLlyo2KWJHTt2MGfOHF599VXm\nz5/P8uXLqaioUPzOYldffTVPP/30Uc81F69PP/2UxYsXs2jRImbPns2ECRM40dK1SprkjDl48CAr\nVqxg9OjRyeeWLl3KyJEjARg5ciRLlixpqe5JCoZhEI/Hj3pOsUsPjY2NbNiwgVGjRgFgtVpxuVyK\nXxp69913adu2LcXFxYpfmojH4wQCAaLRKMFgkKKiIsUuTezcuZNevXqRmZmJxWKhb9++vPnmmyxb\ntkzxO0v17duXnJyco55r7nxbtmwZQ4cOxWq1ct5559GuXTs2b96csn0lTXLGPPjgg9x1112YTKbk\nc4cOHcLr9QJQUFDA4cOHW6p7koLJZOLGG29k1KhRvPLKK4Bily4+//xzPB4P48ePZ+TIkdx3330E\nAgHFLw0tWrSIK6+8EtD5lw6Kioq44YYbuOyyy7jkkktwuVz84Ac/UOzSRKdOndiwYQN1dXUEAgHe\nfvttDh48qPilmcOHDx83XpWVlRQXFye3KyoqorKyMmVbSprkjFi+fDler5du3bqlHP78akIlZ48X\nX3yRefPmMXv2bJ5//nk2bNhwTKwUu7NTNBpl69at/PSnP2XevHnYbDZmzZql+KWZSCTCsmXLGDJk\nCHBsvBS/s099fT1Lly7lH//4BytXriQQCPA///M/il2a6NChA7/61a+44YYbuOmmm+jWrRtm87G/\nNit+6eW7xEtJk5wR77//PsuWLeOKK65g3LhxrF27ljvvvBOv10t1dTUAVVVV5OXltXBP5XgKCwsB\nyMvLY+DAgWzevJn8/HzFLg20atWKVq1a0bNnTwAGDRrE1q1bFb808/bbb9OjR49knBS/s9+7775L\nmzZtcLvdWCwWBg4cyAcffKDYpZFRo0Yxd+5cnnvuOXJycigtLVX80kxz8SoqKuLAgQPJ7Q4ePEhR\nUVHKtpQ0yRnx29/+luXLl7N06VIeeeQR+vXrx9SpU7n88suZO3cuAPPmzeOKK65o4Z7K1wUCAXw+\nHwB+v5933nmHzp07M2DAAMUuDXi9XoqLi9m1axcAa9asoWPHjopfmlm4cGFyah6g+KWB1q1bs2nT\nJkKhEIZh6NxLQ0emcu3fv5+33nqL4cOHK35nua/PZmouXgMGDGDRokWEw2H27t1LRUUFZWVlKds2\nGScqFSFyiq1bt45nnnmGP/3pT9TW1nL77bdz4MABSkpKeOyxx465iE9a1t69e7n11lsxmUzEYjGG\nDx/OTTfdpNilkW3btnHvvfcSjUZp06YNU6ZMIRaLKX5pIhAIcPnll7NkyRKcTieAzr80MX36dBYu\nXIjVaqV79+5MmjQJn8+n2KWJn/3sZ9TV1WG1Whk/fjz9+vXTuXcWOzKTqba2Fq/Xy69//WsGDhzI\nb37zm+PGa+bMmcyZMwer1XpSJceVNImIiIiIiKSg6XkiIiIiIiIpKGkSERERERFJQUmTiIiIiIhI\nCkqaREREREREUlDSJCIiIiIikoKSJhERERERkRSUNImIiIiIiKSgpElERM4ZXbt2JRAItHQ30tK6\ndetYtWrVSW27bds2Fi9efJp7JCJy5ihpEhGRc4bJZGrpLqStdevW8c4775zUtlu3blXSJCLfKybD\nMIyW7oSIiMiZ0LVrV+644w7eeust6urquPPOOxk0aBAAb7/9No8++ijxeJy8vDwmTpxImzZtWLdu\nHZMnT6asrIyNGzeSkZHBQw89xPTp09mxYwfFxcVMnz6d7OxsIpEIjz76KBs2bCAcDtOlSxfuv/9+\nbDbbcfuzb98+Ro0axU9+8hNWrlxJKBRi6tSpvPTSS2zatAmbzcZTTz1Ffn4+ALNnz+att94iGo1S\nVFTEpEmTyM/PZ/Xq1Tz++OOEw2Gi0Sg333wzQ4cOBWDMmDH07NmTjRs3UlVVxZAhQxg3blyzP6Nd\nu3Yxfvx4gsEgsViMq6++mosuuogbb7wRwzAoLCxk6NCh3Hjjjdx0003U1dURCoXo2bMnEydOpLGx\nkZEjR+Lz+SgpKaFv376MGzeOu+++m507d2K1WiktLeXRRx89xdEVETmNDBERkXNEly5djOeff94w\nDMN47733jB/+8IeGYRhGdXW1ceGFFxo7d+40DMMwXnnlFWP06NGGYRjG2rVrjR49ehjbtm0zDMMw\nJkyYYFx66aVGZWWlYRiG8atf/cp45ZVXDMMwjKeeesqYMWNGcn9Tp041HnnkkWb78/nnnxtdunQx\nVqxYYRiGYfz5z382+vbtm9zX/fffbzz22GOGYRjGa6+9Ztx3333J977wwgvGuHHjDMMwjPr6eiMe\njyeP5ZJLLjHq6+sNwzCMn//858Ydd9xhGIZhNDQ0GP369TP27NnTbJ8mTZpkzJw5M/n4SDvTpk0z\n/vjHPx61bW1tbfL+XXfdZbz00kuGYRjG3Llzjdtuuy352ltvvWX84he/OKZNEZF0YW3ppE1ERORM\nOjIC07t3b6qqqgiHw2zevJlu3bpx/vnnAzBq1CgmTJiA3+8HoLS0lC5dugDQvXt39u/fT2FhIQA9\nevSgoqICgGXLluHz+fjf//1fACKRCF27dk3ZH4fDwSWXXJJsu1WrVsl99ejRg9WrVyfb/uijjxgx\nYgQAsViMnJwcAA4dOsT48ePZs2cPFouF+vp6du3aRVlZGQBDhgwBwOl00qFDByoqKmjbtu1x+1Ne\nXs5//dd/EQgE6NevHxdeeOFxt4vH4/z5z39m5cqVxGIxGhoamh1R69KlC5999hkPPPAA5eXlXHbZ\nZSl/JiIiZxslTSIics4wmUxkZWUBYDYnLuuNxWIAGF+brf7V65+OvAfAYrEc8zgUCiXb+P3vf0+/\nfv1Ouk+ZmZkp245Go8m2b7nlFq6++upj2rj//vu54oormD59OgCDBw9O9unr/TebzcljPp5BgwZx\nwQUXsGrVKmbPns2rr77K1KlTj9luwYIFfPDBB7z4hg/phwAAAihJREFU4ovYbDZmzpzJ7t27j9tm\nmzZteP3111m9ejUrVqzg0UcfZcGCBUcdu4jI2UyFIERE5Jzx9cToyONevXqxfft2du3aBcDcuXPp\n3r07drv9G7U/YMAA/vKXvyQTFp/Px86dO79Rn1K1/cILL1BfXw9AOBxm27ZtADQ0NFBSUgLAqlWr\nkiNf30ZFRQVer5cRI0YwduxYtmzZAiRGqRobG5PbNTQ04PF4sNlsNDQ08Prrrydf+/q2lZWVmM1m\nrrjiCsaPH09NTQ11dXXfuo8iImeaRppEROSc8fXqeUce5+Xl8dBDDzFu3DhisRh5eXnHHV05kZtu\nuolp06ZxzTXXYDKZMJvN3HrrrXTo0OGk+9Scq666itraWn7+859jMpmIx+P89Kc/pWvXrowbN44J\nEyYwbdo0evbsedSUwOaOuTmLFy9mwYIFZGRkYDKZ+N3vfgfAwIEDufXWWxk5ciRDhw7luuuuY+nS\npQwdOpT8/Hz69u1LMBgEoH///jz99NOMGDGC8vJyfvjDH/Lwww8DiWl9//7v/05BQcFJHbeIyNlA\n1fNERERERERS0PQ8ERERERGRFDQ9T0RE5DT7/e9/z6ZNm5JT4wzDwGq1MmfOnBbr0y233MLBgweT\njw3DoHXr1jz11FMt1icRkbOVpueJiIiIiIikoOl5IiIiIiIiKShpEhERERERSUFJk4iIiIiISApK\nmkRERERERFJQ0iQiIiIiIpLC/wez5qFjJfFbQAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7face3c383c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12, 10))\n", "sns.violinplot(x='home_mean_stats', y='league', data=df[(df.season=='2015/2016')])\n", "plt.title('Average EA Sports FIFA player ratings per team in 2015/2016', y=1.02);" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "_cell_guid": "4fbf4c87-3b93-e1de-8a29-8dddbb9e514f" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 135, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/324/324947.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "e0df1d03-6b2e-377f-dea3-d645bc740264" }, "source": [ "assignment 3" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "a56bc4da-5f7e-9c06-b306-d248d4d535e9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NationalNames.csv\n", "NationalReadMe.pdf\n", "StateNames.csv\n", "StateReadMe.pdf\n", "database.sqlite\n", "hashes.txt\n", "\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "9331f55b-1ba0-b368-2934-730d392351f0" }, "outputs": [], "source": [ "%matplotlib inline\n", "import warnings\n", "warnings.filterwarnings(\"ignore\", message=\"axes.color_cycle is deprecated\")\n", "import numpy as np\n", "import pandas as pd\n", "import scipy as sp\n", "import seaborn as sns\n", "import sqlite3" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "468558fe-2535-16e7-60e0-ae7eeeaebf9c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[('NationalNames',), ('StateNames',)]\n" ] } ], "source": [ "con = sqlite3.connect('../input/database.sqlite')\n", "cursor = con.cursor()\n", "cursor.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n", "print(cursor.fetchall())" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "0a804770-1e86-36c0-2341-36e244f7ce6c" }, "outputs": [], "source": [ "# helper method to load the data\n", "def load(what='NationalNames'):\n", " assert what in ('NationalNames', 'StateNames')\n", " cols = ['Name', 'Year', 'Gender', 'Count']\n", " if what == 'StateNames':\n", " cols.append('State')\n", " df = pd.read_sql_query(\"SELECT {} from {}\".format(','.join(cols), what),\n", " con)\n", " return df\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "f9301940-2a79-bd77-76d9-965a3b2da746" }, "outputs": [], "source": [ "df = load(what='NationalNames')\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "04f756b3-4870-1ac1-cef9-842637909804" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fe7c39968d0>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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DAE6nk8zMTN/jHA4HTqcTp9NJRkbGZdcBampqfD8LCwsjPj6epiYNYY4mna7egN/97WJj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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fe7c3932f98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.query('Name==\"Alice\"')[['Year', 'Count']].groupby('Year').sum().plot()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "8c1133ff-d781-7140-8a28-e61c13134913" }, "outputs": [], "source": [ "\n", "df2 = load(what='StateNames')\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "ac1a9fe1-4157-fde8-c78a-c9478e5e7432" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fe75ed91240>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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2Ke98dYYPtpyjtt7EhMF+FJXV8O/NCaRkleHjbt9kuZLohRCiEzKZzKzenEhe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\nXP5RLh+pRMr8+GLmxxfT5enhQPsRer02Ckx5FJuLLppXQafQclvWeta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HMxKq1DLMiTr6\n+7zsfa8GgMI5Y9/aXJ26DLPayHtNH1LeW0l5byWFxnzuzNlAim70qVwnClfAzTPlLxCMBPnK7IdJ\njUkaNqwmGAzTWNuLPlZFwqfS18YnxRCfrKe5zsaAw0uMYehLQnXL4LY9gPnsC5Jj7x4iHjex967D\nH2xEkKoIejvpbdqCZcZ9CMKnd1KCOLuP4O4rJeQfrAshSJToLUuuCpG/UkgkcszpN6GNm0VT+RYM\neivdNX8kEE4mJn4eltTZSM5mBwuHPPjdrbh6T+IbqMUXMPDxgZkEAnL6bMkY41xAHQPWfcQmrZna\nG4sSZQqJCv048AQ9/PHUn2kcaGGOZRZfKbofwgJH9jUglUlYsnqwSppUJmHNLXm89copfKd0LFpe\nxPsfVaDRKdjwhdkTakP/LEqVnCWrZzBnURrOfh8xBhVK1eAEaet28dYrpy5L7AtN+RQY86ix1/Ne\n04ec6aumuaSVnyz8Pib15ZVtHQvhSJhnT2+mz2dnQ9Z65liKRjy3uc5GMBBm9oKUIcI6e34Kuzqq\nqCjpYNmN2UOurWqxn3fEsyToiAQD2D94H4lKhZApATfEZ99Pf+cefAO12NveJy51MLWtt3/w/6GA\nHUGQoTEUoombhTomZ1rEx09H1PpE8pZ8k7KDu5BFThMb046/r53G7vcJRpJQygeQCv3nz7c7DBw9\nUYgp3siaz+Wx9/0aDhyawU1r3fRb96LQpqCOmX4+JVGiXAmis8wYCIaD7O84wo6m3TiDLhYlzOfB\ngk1IJVKOH2rC7Qwwf3n6eU95gJSMOPKKEqip6OL9rRXIZBJuvWfWsDHvk4FKLUelvtBGaYrX8fkv\nzblssRcEgXxjDvnGHPa2HeRvNW/wP+Uv8MMF35nwusvBcJA9bQfY03YAAL1Ch16hIxgOUutooNhc\nxK2Z6y7aRk3FYGbB3KKh2/PZM+M5sLueylOdLFqZOcScUtXsIF4QkEoEDCYNA/v2EO7vx3Dbenzu\nBhSaZJTaVMxZm+iqeR5X73EkUhVBvw2voxIQ0McvJTZxzaht+Nc7UqmUeatuJuBfS2tdHQM9JehU\nzWiUrQRDUvocBuyOWPrsMfQPxLFw1eCLrUQisH5jIa89d5zDR/NYvvgUtqZtJBV8G6l8vLnxo0S5\neokK/SgIR8Icth5ne+OHOPz9KKUKbs+6hVsyb0QiSHAN+Cg53IJaK2fekqFhQcvWZtNcb8PvC7Hu\njgLik6begW2I2ItQOHf8HsqrU5bR5uzgYOdRXq7acj6c8HKJiBGOWUt4u2EHdr8DtUyFRqbG6u6m\n1dkOQLI2kYcK77toKJ3PG6S1oQ9zvA6jeehkL5VJKJyTxMlDLdRVdjOz+BMTRG2bgx6Hl3QkGC06\nJALYP3gPQS5HNtcMjnp05sFYeYlUiSX7S3TV/JmBrv0AKLSpGNNuQ6G+umsITBUKpYzsopnATAL+\nIN3t7UiCGnTqCEpjGHMoTEaOmTjTJ3Z8fayKGzfM5P2tp2lszSErrZqB7gPEpYy+dn2UKNcKUaG/\nBKIo8p+lz1DnaEQukbEufTU3p9+ITjEoFh53gHdeLSMUjLDippxht+M1WgUb75+L1xMkNfPKbWtf\nigvE/v0aRBGK5o1P7AVB4N78O+lwWznWdZKMmFRuTFs57rEFIyHKek7zQfMe2lwdyCQy1qffwM0Z\nN6CRaxBFEX84gCvoIk5pQDqC89s56qu6iUREcoriRzynaF4yJYdbKD/RTv7sRARBoKS2h6ffrECD\ngABYEnV462oJ9vSgX7Ycr/sMgkSJxvCJyUCmiMGS/SUc7TvRGArRmuZFbfAThEIpJ3VG5qjOzcoz\nU7wwldMnIqQmteDqOU5M/DKk8ulV0SxKlMkmKvSXoNvbS52jkayYDP5u9gMXeJd73AHeerkUu83D\nnEWpFBSP7IhmiteNeGwqMcXr+Pz9g2K/b0cNoigya37KuNqSS2Q8OvtB/vnY79ha9w6J2ngy9GmE\nxTARMUIoEsYX9uEN+fCGvHhDgxX1DMpYDMpYdHItXZ4eDnQc4aj1JK6gGwGBxYnzuT3rlgts/4Ig\noJIpL8g4eDFqK7oByC0YWeh1MSoyc8001vTS1TFAZZeTzTtrkMsk3LEwlfrj7ZgTdAwc2gGAakkW\nzuAhdOZFSD6TFlihTiA+54GxPsIoE8zSG2dgbe+nsjqV4qJa+q37MabdOtXDuqoQxTAeRxVuWykS\nqRK1YSbqmNyoCeoqIir0l6DGXg/A4sT5FxX5ZWuzr9pVm8miY+OX5vLWK6V8/EEtiDBrwfjE3qCM\n5e9mPcjvSp7mD6V/GtO1MkFKSAwDoJNrWZe2mhXJi0nQjizOo8HZ76OzrZ/kdMMlfSNmzU+hsaaX\n7e9Vs7/XhV4j5web5tBWPmjfN5lUuI4fQxZnJKjqgiDnt+2jTD+kUgkr1+ey7S/95Oe2g+0kMQnL\nkSmmNhz0aiAcdOOyncTVe5xw8JMoFo/jDAhSVPosNIYiNHGF0z5XwfVOVOgvQe1Zoc+L+8QT2zXg\n451Xy64JkT+H0aLl8/efFfudtXR3DqBQyUAEUQRdjJLiRalDEvUMR44hi6/NeoBDHceQCBIkggSp\nIEEiSM/a2FWo5WpUUiXekA+7/2xJXZ8DnVzL0qSFFFuKkE+QR3rtmXNOeJd+YfBIISyT4O11k6VR\n8HdfmkuSRUfprjoEAZSddQx4vejXrcDnbECpTUOhvrwXkSiTS0JyDKmZJiqrU5k7u4YB68cY02+f\n6mERCvTTb/0Yb68SmW4+ctX0qQzp6j1JX9t7IIYRJAp0lsXozQsRxTBeRxUeRxW+gTp8A3U42j9A\na5qH3rwQmfLaLMJ0tRMV+osgiiI1jnpiFHpMChP1Vd1Ul3fR0mBDFLlmRP4cRrOWjffP5a2XT1F9\numvI8b4eN2tvnzmq+51rmcVcy/SoGV57phuJVCA73zLiOe29brbsqae0rhcjkI0EsyfE9s2l5BbF\n09vlIs6sxXNk0MFOWqADD+jMC6/QXUS5HOYvy+CtV/ooyO8AWykxCSuQKafGX0YUIzh7jtDfuQcx\nEsRtAziEOnYmMQkrUGrHt5s2UWNztO/E2XMEiVRNbNIatMY5F2zTK9QJxCatIejvw20rwWUrwdl9\nEGf3QVT6bOQqMxKZBqlMi0SuRaFOjO6gTDFRob8IVk83zoCLua4VvPiHQwT8IWCwoEnRvGQK5iRd\nMyJ/jjiTlvu/sRhHn5fBWxu8v307aqip6EKrV7L0hhlTOsax0NUxQF+Pm6xc8/k8Ap/l3UNNbN3X\ngChCbmosm27IwaKVU1lmpbrcSsXJDgCMcUrcR0+jTM/AH2hAIlWjMRRcwbuJMl6S0w0kphg4U5nK\nvDlV9Fv3YcrYeMXH4Xe309f6LkGvFYlUTVz6rRgMsbTV7cLbX4W3vwqFJhmFNhWFOgGFOgG5Kv6K\n5FuIhH30Nm7B56xHrrJgmfHFi74MyZVGDMnriE1cg9tegav3GD5nPT5n/ZBzpQoDKl0mSl0G6pgZ\nUYfIK0xU6C9Cjb0eSUhG6EwsStVg8Zi8WQmYLNPTsW6ikCtkQ9Lj3vqFWWz7Swklh1vQxSjH7bB3\nJenuHGD7a2UAFMwd3lGyb8DHGx83EqtV8NAtM5mTYzr/8rZ0zQwWr8qkpaGP5vo+0vzNhMJhtCvn\n4QmVDaZWjSa8uWpYsCKDd151UFDQAX1lxCQsR64aeZdnonHZSuhreQcQ0RrnYEhZj1SmIc6iJyjJ\nxO9qYqDrAD5nAwFPxycXChI0hiJi4pdMSpEeURQJeNqwtbxNyNeLKiYHc+bdSKSjy/UhSGToTHPQ\nmeYQCgwQCbkIB92EQx7CQScBTxt+VwvuvlLcfaWAgNowE715IUpd5jW3WJqORGepi1Bjr0fjGrQ5\nFc1PPp/x7npErVFw+33FbH3xJB9/UItWpyAr78pNkmOlvdnOe1tOEwqGWXNrHhnZw9s/PzjWSjgi\ncueqGczNHSzvK0YiiMEgEqUSiURCZo6ZzBwzzf/7FUISCfJcE/SCUpdxJW8pymWSmhmHJTGG0xWp\nLJx3hp6Gv5GQ+8gVSaLj7DmOvW07Eqkac9YXUOmzLjguCAIqfRYqfRaRcICgr5ugt4uAtwufswmP\nvRyPvRylLh29ZenZ7X1h8J8gIEaChENuIkEX4aALERFtXNGIYn2u5oLbXoHHcYZwwAHwqZTMF/ri\nhP9fe3ceH1V9L/7/dc7sWzKTZDLZE0gCQSDsLmwKKtQFF1Crt+62trZqN71t9d7ee9tf/T5ue6ve\ne+1tsbW3V6u1rYpWRaXgigugCIEASdiyZ7JMJrNv55zfHwNRJAECIUDyeT4eebTOnDnzmY/jvM/5\nLO93SsXfG8HuMA06MgbpraUYD88Tkn4/L7HQfsK+GqL+nUT9O9GbcrDnzMLmmozOMLpvoE4lEegH\noWoqDf49ZMfSwT2vUMwxZTgtXHrtVF56Zgt//9tOll4lU1px+iwgOmjXtnZe+Uv6Tv7iKydTXjXw\nBUkomuSdLW24HCbOLjLR9/56IrXbieyoRQmHyLxgETnLr0VnsRBvayO+fx+2qdUkU+mtemYR6M8o\nkiQxa24pr78QxBeoIitjF517nsFTefMJbxXT1BTBrk2oSgRbVvUhIwWBzo/wt65B1tvIrbjxqImT\nZJ0Rk60Ik60ofW5NIxbcQ7BzA7HgHuKhpmNqU1/bm2TkLcCRM7t/5ElV4oR9Wwl2f0wq1g2AJBux\nuqZiy6rGkpFedByLJqnf7qWrI0hPZ4jengiqqqHXy1RO9jB1VuGQtgxLkoTRmofRmofDfQ6JSAvB\nro+J+Hfgb30Df+sbmOyl6fTQzkki6A8zSdM07VQ3YjgNVMzkeLSG2nlo4yNU77sQtcszznHZAAAg\nAElEQVTEbd+ed1gq2RPldjuGrb0jqXFPD6+/sB1V0Zgys4DzFpUPWIHvZFAUddCV/5qmse2TVj5Y\ntxvdgVTDRWVZg57rpfX7eGn9Pr5p3EXGjo39j+ucTmSDgWRXF3pXFrk33kxsz258q1/Bc+fXCVo+\nBDQKp3xvTA07nqnf18/TNI2//P5jertDXLncTzK0DZO9lNzyrxz3NEwsuB9f86uk4j39j5lsJdhz\nZpFK+Olrfwud3k5u5c0YzDmHvX4o/ZqIdhLu+RQlGQI0tAPbYiRZj05vQ2ewI+vtKMk+At4P0dQ4\nOmMmGZ75JKNewr4aNDUBkg5r5kSsrimYM8r7t8fFY0m2bmqhZlMLyUR6q6veIJPttuPKttLa5CfY\nFwMgvyiTaWcXUVaZc9z/HSjJMJHe7UT8O4iHmw88KqWDvussrJmTDhtxUVNRVDWBzuA4bOQhlQgQ\nDzUSj7Ris1pIKFb0Jhd6oxO90TVoVcnRwO0efN2DuKMfRH3vHtAAvwlnlmXYg/yZrLQ8mxU3z2Lt\nyzvYvrmN1iY/Fy2bRI7n5C2wUVIqn3zQyKcbmigodnL2wnF4Cj4bIgz4o7z9Wh2tjX4sVgOXXDP1\nkOe/KJZIsfbjZiqVbjJ2bMTg8eA8fxHWyVMxFhSgpVL4Vr+Cb/UrtP33o0h6PbLZjHnSOPp2/x2r\nc/KYCvKjxcG7+r+/tIP33y9i4fwEsWAd3fufJ2fctYcFjiNRU1F629YS7vkUAHvOHEz2EsI9m4kF\n9xEPp++8dYaMdJA3DX7ReayMllyMRUuP6Vh7zmwCHesJdm+it/nV/rbYPfOwZ888JIBGwgl2bmlj\ny8YWEvEUFquBOfPLKK3IJsNpQZbT33VV1Wja28P2T1pp3tfbn59i/sUVx7V2SWew4cg9B0fuOaQS\nASL+nUT8tcRD+4mH9tPb/Bomexk6vZVUopdU3IeqpC80kOQDAdyJrLMQj7T2T0EAhL7wXrLOgi17\n+oFtgKdPhtKRIO7oB7Gy5v+oa2qkcvtCJk7NY/FlVcNy3s870++QUkmFj97ey7ZPWpFliQVLKk8o\nX/5g2pv9vP16Pf6eCAajrv9Oo6wym7MXjMPbFuCDN/eQTCiUVmSz/CszicWTRzznmo1NPLuugW/7\n12Lpaaf4gX/GMv7wqnXx1la8//d7Ynv3kLFgIfbLpuNrfgVX0aU43GNra92Z/n09SNM03n6tjl01\nHZRVOJk5bRvx0D5kvQ1ZNiLJeiRJD7IunfhYkjm4+0RTE6hK/MBfFDQFgzmXrJLL+4faAZJxH6Hu\nzSRjXWQVXXLE/eWf79dkUiHQG6Xvc3/RSIKS8dlUnpV7XBUvUwk/YV8NBrMbS+ZEQCIaSdLZFqCl\nsZfWRj++rjCQLmU9/ZwSpswsxGA88t1vb3eYD9/aS+OeHiQpnWxqzoKyI87hH3ub+9JBv7eWRCRd\n0wJJh97oQm9yIctGUgk/qYQfNZVuu6wzY7KXYLKVYrIX43Ra6Pa2HbhA6CUaaEBNRdKfM6MCq/Ms\nlGSQZKyLZKybVNyH3piJ0ZqP0ZKP0ZqPwZp/xiQDOtIdvQj0A1A1lX9879/I7irB2VDO+V+acFIC\n2Gj54Wza28O6V3YRiyS59Nqpgy58G6pQIMbmD5uo/TS9AnnqrELOXjiObm+IDe/upaMl0H+s0aRj\n/kWVTJjiITc344j9mlJUfvCbDynr2MnS9vU4zjmX/K99Y9DjNVUlsmsnlvHl+DpWE+ndTn7VXRgs\np+9ixJNhtHxfIT0FtPqv22jZ30v17FwmlNeSjHSgaSk0NXXgfxVAPeR1kqRH0pmQdSZknRmrcxKO\n3HORpGMbEtY0jUgoQbAvRqAvRrAvRiKWwtseINAbJRxKDPpavUGm8iwPZ03Px53nOGxEKRFP0drY\nS9NeH10dIWSdhE4no9PLyLJELJokHIwTCSVQ1c9+9nV6mfyiTErGZzFpWv6QLyYa9/Tw/trd9PVG\nMVsMzJxbwuQZBej1wzNMnkoGQVMHHKoHUJUEaiqCzph5SJ988fuqqSki/h0EuzZ9dvFwgCTp0Ztc\npBJ+NPWzmwRJNmB2jMeSOQFLRmX/2gFViaMkQ+kLB0lCknTp74asP9COYx8ZGi4i0A9RU7CFf9/0\nX0zvWESqycJ1t88+KbnqR9MPZ2d7gBef3oJOJ3H1TTMHrBB3UCQUp72lD29bEL1eJsNpJsNpIcNp\nJhJOsL+hh8bdPXR3pgffXDlWLrhk4iELIjVNo2mvj0/eb8RiM7BgyQTsjvSCqiP1q6om2b31CYLB\nIM7uXiRfnJwl12LLn3zUvb2aptFW+yiaplA45ftjbuh+NH1fAeKxFKv+uJne7gjzLqygek7RgMel\nfyLTP5PH+wOuKCrbPm7hkw8aScSVAY9xZJrJdKX/O8h0Wcl0Wch0WdAbZBpqvezY2k4oEAfSQd9q\nM2Kzm7DajUTDCTpaA/0BXNZJoHFIQJdlCas9/Rqbw4gr20ZRmQtPQQY6/YkFJkVRqdmU/nzJhILN\nYWL2vFImTs07pmyaJ8ORvq+JSDvxUBN6kwuD2d0fnDVNJRXvIRFpJxFpJxrYQyre3f86nTETNRU5\n5GLgi3RGJxm5c7FnTx/R7bci0A/R2qZ3WLX7VWbVXY4albj9O/P756iG02j74WzY4WXt33aS4TSz\n4pZZh6xr6GjtY+eWdtpb+ujrjR71XLJOorDEybgJOVRV5w/px+JI/Rro/gR/86uoKsifO6WsM5NX\n9Y309qBBJOM+2nc8hsU5Cfe4a4+5PaPFaPu+QroOwgtPbiYSTjB7Ximewkyy3TasduOwXci1Nft5\n9416ersjmMx6CktdZDjNODLTf2Xjskmp6lGDrapqtOz3UbetA78vSiSUIBL+bAQgN99B8fgsSsZn\nkZufgSxLaJqGklJRFBWjSX/SL05j0SSfftTE9k9aSaVUMpxmZpxbwoTJnhFbsHvQcH1fk7EeooF6\non31pOI+5AOLHnV6O7LeCmhomgKqgqrEiPbVoWkpZL2djNxzsefMPqzo1ckgAv0Q/Xrr79np3cOk\nzRdTVOZi2fXThqFlhxuNP5wb3t3L5g+aKCx1ctl11XhbA3z8/n5aG9OLZIwmHXlFmeQXZZJXmImm\naQT8MQL+KAF/DL1BprQ8m6Iy13HNR8Lg/appGk3b/gc12UP86VYMViv53/k68Vgjwa4NWDKrcI+/\nbtDzphOevIyr6BIc7jnH1bYz2Wj8vgJ0dQR56Zkt/Ws/AExmPbkFGUyqzqOsMueoF5rN+3xseGcf\nAX+0/04802Uh4I9RX5tOJz1pWj7nXjD+sIW9J9KvqqoSCSfR6+XTasFwOBRn8wdN7NjShqpqmC0G\nJs8oYPLMAmz2kal6d6q+r0oyRLDzI4LdH6OpCWS9DVfhknSCrZN4oSVW3Q+Boirs9u8jL1EMcMSV\n28Lhzl4wjt6uCPsaunlm5Yb+ocaiMhczzyuhoMR52Je9cIS2o8dD+5CUHvz7NWyBODnLb8SaU4VF\nm0gi2kG0bxcR/85B09rGgo2ASJQz2rjzHNxw59l0tPTR0xXGd+Cvea+P5r0+rDYjVdPymFSdjyPT\nfMj3t6czxIdv7aF5Xy8AmS4L3d4Qne2fBZicXDsLllaelFwcsiz3T1mdTmx2EwuWVDLjvBJqN7dS\n+2lbetfMR01MnJrH2QvHYbWd/LvcU0FnsOMsvIgMzzyCXRsJeN+np3EVYd9WXMWXDsvui6ESgf4L\nmkOtxJQ4eckS4oCnUAT6oZAkiQuXVbHqqU/p6QpTUp7FrLmlp0XCoUBneq+8fXMbhuISMubOA9Jt\nziq+nPZdv8HX/Bpm+zhk/aEZxTRNIx7aj6yzjGjaVGFk2OwmyqtyKf/c5hpfd5gdW9qo2+Zl8wdN\nbP6gCb1BTs+L24zoDXJ/gC8qc3HuBeNx5zlQVY1QIEZfbxQlpVJSnoUsn5p56lPN7jBxzvnjmTm3\nlPrtXmo2NbNzazt7dnVxzvnjOGt6wUmZFj0dyPqDRYGq8bW8Riywm/advyYzbyEZueeN6Py9CPRf\n0NC7FwBjwEEchTwR6IfMYNRz1Y0ziIQTOLOsp7o5ACSCHUT76tE6YwSDVqb8091In/vxNZizycxb\nSF/7W/jb1h5WxlRJ+FGSASyZx1a9TzjzZeXYmH9RJeecP549OzvZV99N6MCq9fbevvQxbhvnLRpP\n8bis/u+FLEsHFpdaTmXzTysGg+5AIbA8tm9uY9N7+3hvTQM7t7azcOmEUT1yqje5cI+/gah/J76W\n1+lrf4uwbxtZxZcclgr5pLVhRN7lDFLvTyfKiXZrOLOtw7IndCwymvTHPcc+3FJ9ftrXrkQaL9Gx\nA2I3fBNj7uE15DM8c4n01hLq2ZzOGOYo638uFkoP24u0t2OPwaCjqjqfqurPCiOpqkosmk4sIy78\njp0sy1TPLqKiys2Hb+2lvtbLC09uZvxEN2cvKMN1hN06ZzJJkrC6zsKcMR5/21uEujfRufsprK6p\nuAovPukpf8fmeNIgFFVhj38f+VoxqaRK3ii+yhwrEu1tNP37T6FQIxWFJxLnUz2lZMBjJUlHVsky\nQMLX/AqJqLf/ufiBQG/6XPAXxi5ZTm9vE0H++FjtJi5cNokr/2E6ufkO9tZ18ecnNrHulZ0E/Eff\nlXOmknVmsoovIW/iVzFaC4j0bqNt56/wt79NKhE4+gmO0+lxy3WaaA61ElcSFCbHEUPMz5/pYk2N\ntD78HzBOQjLJvNdQQmFeFq4jLF4y2Qpx5J5DsPMjOnatxGDxYHNVEwvuOzA/f/hIgCAIx6egxMny\nm2eyf3cPG9/dR/12L7t3dDJ5RgGz5pVisY7OBXtGawGeCbcT6v4Ef/tbBDreJdDxHpbMCdhzZmN2\njB/Wi0gR6D/n4Py8LewiRlIE+jNYtKGB1v96GDUWwzrvLBQSbGrO5+JzDi8q8kXOgosx2YrT5TT7\nGvBH/w6AJXOiuIMThGEmSRLjKnMoq8hm985ONr67j22ftFK3vYOZ55UydVbhiO/BHwmSJONwz8GW\nNY1I73aC3R8T7asj2leHrLdhtpdiOvBnMLtP6LdHBPrPqffvASDRI2M06Y6Y3U04ffm3bKXlkV+g\npVJk3bmMiG47reFSIkkD0yuOHuglScLqnITVOQklFSHSW0s0sBuH+5wRaL0gjE2SJFF5lofxE93U\nftrGx+v389Hbe9m+uZWCYifRSIJoOEk0kk7hezDhUIbTjD3DjF4vI+vS6X51OhmL+cwYDZB1Ruw5\nM7FlzyARaSPU/QnRwG4i/h1E/DsOHGPBYPFgtHgwWDwYzNkoyQiphI9UvJdUwo/b/fVB30ME+gMO\nzs/nGfII9sYpHuc67e/eVCVOtK/uwL/43NO+vSMh+PEmOn63EgD3XV8hKH2EJBl5vdaDy2GixDO0\nRS86vRWHe86YTJAjCKeCTpdesDdxiofNHzZR83FLf9Ihg1GHxWpAL0l0dx6ar+CL1rxUS0VVLtVz\ninDnnbzKmsNFkiRMtkJMtkI0TSMV9xEPNRILNZIIt/RX9DseItAfcHB+vkSpJAJ4ToN930cS7avH\n1/wqSjL9RdebstP1m51njcmgr6VSdL/wHL1rXkc2m/HcdQd96juQUonZL6Ol18eiGcdfN1sQhJFl\nMhs4b1E5M84tSZfOtRkxfG4IX9M0wsE4gb4Y4WAcJaWiqhqqohGPp9izs5P6Wi/1tV7yCjOYODWP\nHI+drBxb/1SAoqh0dQRpb+6jsz2II9OEpyATT2HGERMRqapKsC+dKyEUiBMKxAkH44RDcSxWIzke\nO+48B9m5dkzmoYdZSZIwmLMxmLOx58xMv6eSIBnzkoh6ScV86Ax29CYXelMWeuORy+6KQH9AfW96\n2N7am02EBCXjRz570bFQUhF6W94g0rsNkJG77EhmSGm9BDreI9DxHrLeni61aM3DaClAZ3SgpqKo\nqShKMkwq0Is+7oSgSirQhxIMgqKAJB2oximhqSpaIoGWTKAlk8h2OzlXLkdnP7nbQI5Hyu+nfeX/\nEG2ox+DJo+qH32FfywuoiQiuoktZvc0G+Jh2DMP2giCcXswWw4DpfSVJwp6RHrYfyNJlk9m8qYlt\nH7fQtNdHR2vgwOsgM8uKxWKgyxsklVS/8MoWAGwOExlOc38FQJ1ORkmp9PVGCPhjhxQM+qKDIxAA\nrmwrJeXZlFVkk1eUcdzJk2SdEZOtGJOteMivFYH+gIbevaBBqFXFYjOQm3/8Qz2qksDf/iZmxzis\nmROP4fgYfR3rCXV/jMGcgz1nFlbn5P5CCOlhnG4i/jqCXR+hpiIYDG6iL+4mtX93+iR6CbnMiq7C\nDgUQSzUQCzQc+X19UZRdIZTdIUgeveRBpLaWwnu/gzEv/6jHDvh+iQSS4fj2HWupFIGPPqTv3beR\nDAYMWdnos7PR2e34Vr+CEghgnzWb3FtupaP7ZVKxbhzuc3C4Z7Nl90eYDDomlQ5eE1wQhNFFkiVK\nDhT58fsitDX56ekM0dMZpqcrhL8nQpbbRn5xJgXFTnLzHYQCcbxtATpa+/C2BWhv7jvsvCazHnee\ng8ysdD0DR4YZe4YJm8OEzW4iHIrT7Q3R1RGk2xuio7WPrRub2bqxGZNZT8n4LMqrcikZn3XCVQOP\nlQj0HJif79tHoVJGLJKiqjrvuId4VSVB155niIebCHV/gqfy5kGvwDRVIdi1ib6Od1BTEWS9lUSk\nHV/Ty/S2rMGWNRVJNhDtqyMV9wHpuskOx7n4Vq5G6fWTs/waLJUTiLc0p//qm4j9vRGMGnKOCTnf\nimTVowbjEFOQJBOmshI0TwoKQC6wYFich17nRJbMyJIFWbaABCpRVC2CooRRI3FizzfQ9ND/R8E3\n78ZaNXA++IE/p0r383+hd80b6Ox2TKXjMJeVYi4dh2ViFTrr4NnzDgZ436t/I9nVlS45p6ocstNW\np8P95RvIWLQQX9PLRPsaMGdU4iy8mA5fBK8vwswJbgzDVB9bEIQzizPLekiWTk3TSKXUQ6YCADKc\nFgpKnIccpyrpYxVFRZaloxYPOvheFZPSW3FTSYXWJj+Nu3to3NNDw45OGnZ0YjTpGDfBTcUkN5ku\n64FRAwm9XkZv0IntdcOtKZien88LlREHyiqyj+s8qhI/EOSbMdlLiYea6Nr7V/ImfvWw8qexUCO1\ndauJR7qQZCOZ+Ytx5J6DmooQ6vmUcM+nhLo/BkCSDVgyq7BkTkQfz6D1l/+J0ufHfd31uJZ8CQBL\n5YT+cyvRKNG6XUR21BLZUYuaiGOfNgvHotlYKicg6dJf7lTcT9i3lbC/llS8F7SB62TLehuaMYb5\n+vHE/tJIyyP/gefGm7FUTiDZ3UWyq5tkTzemoiIcZ597SGpZNZmg44nfEvp4E/qsLJBlIttriGyv\nSX82gwH7jFlkzJuPddJZSLKMGo8T3d1AtG4XwU0bSHZ1Ien1ZCxehGX+BEyZBUhRE0qvj6SvB3NJ\nGWRJdNT9FiXhx+4ch7NkBSDx3NvpKZmZE8SwvSAIaZIkHRbkBztOp5dO6M5bb9BRWp5NaXk2mqbR\n1RFk984udu/spG5bB3XbOg57jSxLWO1GrDYjNnt6tCAzy3LgIsKCzWEi0BulyxuiuyNIT1eY2++Z\nP/jnEGVqYU3jW7y05zXmNFxOPKBx+7fnYTAO7RpIVeJ07nmaRLgFq3My2WVXE+zaiL91TTo5QuWt\nSLI+XZbV+z597W8BYM+ZiUWqIrh+A8GNH6FGImiqCpqKXGxBMhjRJTMwZLsx5LgJbvwIpa8P95dv\nwHXx0iF/1sFomoamxFFSoQML/CT0xkx0BgeSrCfUvRlf8ytImEisakVpGziLkzG/gOyrrsY+czZq\nOEzrY/9JbHcDlgkTKfjWvehsNpRgkFhTI9HdDQQ3biDpTX/R9S4XelcWscb96TUDkA7wC84n46K5\n+H1/JxnrBMBgzsWWPR2bayph31b8bW8CKhmeBVRMvYzunghvb2nlydfrmFjs5P4bZoza4hkjZbSW\nqT3VRL+eHKd7v2qaRkdLH/saeojHkigptX/kIB5NEQnFCYcTqMqxhegf/3LZoM+JQA/8assTNLQ3\nMnHrYorHZ3H5ddVHPP7g1gc1FUZJRVGVCKHuzSQirVhdU8guvQpJktE0DV/TS4R9NdiyqnEWLqGn\n8UVigd3oDA6ypWl0vbqBaH0dADqHA4PbDZKcviuWJJRwmGR3F1o83v/+7uu/guuii4f8OU9UqPsT\nfM2vIssW5BorsmLGkOPGkJOD3ukisOFDAu+vB1XFVFKKGo+T9HbgOPscPLd9Fdlw+JCXpmnE9u4h\n8P56gps2oMbjmEvLsEyswjJhIpbKCUQj9fS2rEZTk9iypqOpCSJ9u0D7bBGNrLeTU3Y1Zsc43G4H\nW3d28JM/bMKgl/m3288ma5AFO8KxO91/OM9Uol9PjtHQr5qmEY+l+lf4+30R+nxRgoEYGU4Lbo+d\nnDwHObk2CgoHX3l/1NvWBx54gLfffpvs7GxefvllAPr6+vjud79La2srRUVFPProozgc6cVrK1eu\n5Pnnn0en0/Hggw8yf356OKG2tpYf/vCHJBIJFi5cyIMPPghAIpHgBz/4AbW1tbhcLh555BEKCgoA\nWLVqFb/5zW8AuOuuu7jqqqtOoMsGdnB+viBSCRx92D6VDNK99y8kIq2HPff5IA+flT9NxroJ+2qI\n9NWhKXHMjnKUD8M0vfNk+nWTziLz/EXYp89A0h/+r0TTNJRQkGRXF7LJhKmw6EQ/9nGx58xCQ6O3\neTVMl8kqu+yQwi/WqklkLb2Unr+tIrhxAwCuL11KzvJrDhnO/zxJkrCUV2ApryD3H25EUxRkU3pb\ni5qK0tu6hrBvK5JsIqfsGqyuswBQkmHCvduI9G5Hb3TiKroEnSGd4CiZUlj5t1oSKZWvXn6WCPKC\nIJyRJEnq33VwIrkAjhroly9fzk033cQ//uM/9j/2+OOPc9555/G1r32Nxx9/nJUrV3Lfffexe/du\nXnvtNVavXk1HRwe33XYba9asQZIk/vVf/5Wf/exnVFdX87WvfY333nuPBQsW8Nxzz5GZmcmaNWtY\nvXo1v/jFL3jkkUfo6+vjV7/6FatWrULTNJYvX86FF17Yf0ExXA7Oz7sC+aSA0vLBA3083EL33r+g\npEKYMyowWjzIOiuy3oremIHJXtof5A+SZD05466jo+53qKkwmfmLUeuT+N/5P2zl43HfdifGvLwj\ntlGSJPSODPSOU5+S15EzGzSN3pbX6Nz9JGbHeJwFizFa0xdnxrw88u+8i6zLlpHy+7FNnnLM55b0\netBJRPvq+y+M0BSM1gJyylagN312xaoz2MjIPZeM3HMPO8//vbqT5s4QC6cVMLtK5KYXBGFsO2qg\nnz17Nq2th969rlu3jj/+8Y8AXH311dx0003cd999vPnmm1x66aXo9XqKioooLS2lpqaGgoICwuEw\n1dXpIfGrrrqKtWvXsmDBAtatW8e9994LwNKlS/npT38KwPr165k3b15/YJ83bx7vvfcel1566fB9\nemB79w5kRYfSZSQ714Yjc+C7v1DPp/iaV4Om4ixcgsN9zjGvitQbM8ir+lp6DrwjSPMzP0O22aj6\n4f0EpTOvZrXDPQejtYC+9jeJBffSUbcXi3MSzvxFGMzpRW+mwqJBRx7SUx89xMMtKMkgqhJDVeKo\nSpR4qAk1FQbAYHZjy5qGw302knxsayZq9vTw0rt7yM+2csOFlcPzgQVBEM5gx7Xq3ufzkZOT/kF3\nu934fOmtX16vl+nTp/cf5/F48Hq96HQ68j5313rwcYDOzs7+53Q6HQ6HA7/fj9frJT8/f8DXDJfG\nQDN/b3qHnHARmgqlgwzb+1vXEuj8AFlnJrtsBZaM8iG/l97gQIlLNP/6/6EpCgVf+wbm3FyCZ+gc\nkslWSG7FTcSC+/C3vUnUv5Oofxf2nFlk5p3fP4x+UCreS6SvjnioiXi4CTUVGfC8ss6C3X02tqxq\njJb8IW0xaewI8puXtqPXydy5bDImo9hOJwiCMCzb64Zzv99IrQ2MpqI8sf1pVE2lWp1DBxHKBsic\nlkoECHR+gN7owl3xFQym48uYp6kq7b/9DameHrKvvBrblKkn+hFOC2bHODwTbifaV4e/bS2h7o8J\n+7aRmTcfS2YV0b46Iv4dJCJt/a/RGTKwuqZgshWjN7mQdebP/vTWw6Y/jkWHL8LDf9lCPKHwg5vn\nUHoG5LYWBEEYCccV6LOzs+nu7iYnJ4euri6ystLBz+Px0N7e3n9cR0cHHo/nsMe9Xi8ejweA3Nzc\n/uMURSEUCuF0OvF4PGzYsOGQc5177uHzsV/kclnRHyUxiqZpPPLBs/TEfFw96Uu0PZ/C5jAxeWoB\n0he2YHU2bQMgf/xCcotKj/r+A1Hicfb/4UkitdtxzZ7FxFv/oX9xmts9SgJS7hyKy2fQ3fwRbXv/\njr9tHf62dennJJmM7Am4PNNwZFdgNA9vwaCeviiP/nUrwUiSb10zjXnTCobt3MKhRs339TQj+vXk\nEP2adkyB/ot32YsXL+aFF17gzjvvZNWqVVx44YX9j993333ceuuteL1empqaqK6uRpIkHA4HNTU1\nTJ06lRdffJGbbrqp/zWrVq1i2rRpvP766/3BfP78+TzyyCMEg0FUVeWDDz7gvvvuO2pbe3sHHhL+\nvPdaP+Sjls2UZ5ZxljKDhtBWqqrz6O4JHXZsV+sWABRd2ZC3amiaRnjrFrqefYZkdxcGj4esm26n\nuyc9Bz0atn98kWSdRn7VBALe90nGu7FkTMDirEKnt6IBgRAQOryfj1comuTfn95MZ2+UqxeOZ9aB\n6ZfR1q+ng9H4fT0diH49OcZavx7pouaogf773/8+GzZswO/3c8EFF3DPPfdw55138u1vf5vnn3+e\nwsJCHn30UQAqKiq45JJLuOyyy9Dr9fzLv/xL/53bj3/8Y370ox8Rj8dZuHAhC45zr4AAABcqSURB\nVBcuBODaa6/l/vvvZ8mSJTidTh5++GEAMjMz+eY3v8mKFSuQJIm7776bjIwTX3XeHGzjuYaXsRms\n3Db5H9j2TjoBy0Db6tRUlFiwEaO1AL1xaNXsEl4vXc8+TXhbDeh0uJZeQvayK5DNZ97iu6GS9Rac\nhRedlHNrmkZnb5TdrX3saQuwfW8P3X0xLppdxOXnHd+IiyAIwmg25hLm/OLjx9gfaOKu6tvIiRWw\n6o+fkpll4brbZx825B/21dDT+CKZ+YvIzFtwTO+vJhL4XnuV3tdeRUulsE46C/cNN2IqOHw4eaxd\ncZ6ohhY/v3mplt7gZ8mDDHqZhdUF3HBxJfKBi0rRryeH6NeTQ/TryTHW+vWE7uhHk0AiyP5AExNd\nFVQ5J/DX36dzyS+6tGrAef2IfxcAVmfVMZ0/vK2GzmeeItnVhd7lwn3dDdhnzxE10IfB9r09PPbC\nNhRV4+xJuZQXZlJRmElxrh29bmQqQAmCIJyJxlSgr/OlS7pOyprAx+834vdFmTqrkPyiw4flVTVJ\nLLAbvSkbvenIBVHiLc30/O1FQps/AVnGteRLZF9x5ZgYph8JH+/qZOXfapFlibuXTxV15QVBEIZg\nTAX6Xb50ffZ8pZj1HzXhyDRzzvnjBjw2FtiLpqWwZk4c8I5cUxRCWz7Fv+7v/bnqzRWVeG68GVPR\nwGVphaF7b2sbf3h9FyaDjm9fU83EksHzOQuCIAiHGzOBXtM0dvU2YNfZqX3Lh6bBBZdMHLRKXbQv\nPWxv+cKwfbKnh8CH79P37tukDiQKsk6ajPPCi7BVTxs0p7swdG9ubuGPa+qxmfV878vTGZd/6lMA\nC4IgnGnGTKD3Rrrwx/uY1jcfX1eYSdPyKSob+O5Q01SiffXoDA6M1kLURILQp58QWL+eyK4doGlI\nJhOZFyzGufhCTAWFI/xpRr/1Ne38cU09GTYj918/nUK3/VQ3SRAE4Yw0ZgL9Ll8DaBLsy8BiNXDe\nosHT2MZDjahKFLtrNloySdNP/oVERzrhj7miksx587HPPhudRczBnwwbd3r539d2YjPruU8EeUEQ\nhBMydgJ9bz3WkBMlAeMmuzGZB//o/avtM6vwv7mWREc79tlzyLlqxVErzQknZsvubn778g5MBh3f\n+/J0ikSQFwRBOCFjItArqkJD717yQ5MAKC0fPF+9pmlE++qQdWb0uhx8q19Ftlrx3HQrOptt0Ned\nKoFwAoNexmI69n+VKUVFVTU0DdQDaRTMRt2Aiw4TSYXN9V14e6OcO9mDx2UdtrZ/nqZpfNrQzW9e\nqkUnS3zn2mliTl4QBGEYjIlAvy/QREyJk9GXC3qZwtLBV27HQ/tQkgGsrmp6X38DNRIm55rrTrsg\nn1JU/rSugbc2p0sIW0w6shxmXBkmMq1GrGYDNosem9mAoqh0+CJ0+CK0+yL0hRKHnS8rw8SkEheT\nylxMKs2iLxznvZp2NtR6icRTAPxt/T5mTXTzpXNKGV8wPEE4EE7w/vZ23t3ajtcXQa+TuGfFNCYU\nO4fl/IIgCGPdmAj0db4GDHEzSkBPyXgnBsPARW9UNZmuOQ9YzBNpW/swelcWzsUnJ53r8fIFYvz6\nxe3saQuQl2XF7bTgC8bwBWK0docHfZ0EZGeaqSpxotfL/ZnkVFVjf0eQ97d38P72jkNek2k3ctnM\nUvKyrKz9pIWP67r4uK6LCcVOZk5wU1k0tKQ18aRCY0eQvW0BGlr81OzpQVE19DqZcyd7uHh2sbiT\nFwRBGEZjItDv6m3A4U9XyystH7jmPECg/R1ScR8O9zmE1nyElkySfeVVyEbjSDX1qHbu9/Gbv9US\njCQ5d7KHW5ZWHVJ3PRpPEYwmicSShKMpwrEksiSRl2Ul12XBONhFjqbR0hliZ2Mvuxp7MRh0zJuS\nx5TxWegObBmcOyWPXY29vLahie37fNQ3+wEwGmTG52dwVlkWMya4Kci2HjIN4O2NsGGHl0/ru2nu\nDPVPFwAUum0snFbAeZPzsFsMJ6PLBEEQxrRRH+ijqSj7A81MCM8FoGSQ+flEpI1A54fojE4sUhVd\n7z2LMb+AjPPmjWRzB6VpGm9sbOavb+9GliS+cvEEFs8sPGxe3WLSH5ivH9qOAFmSKPE4KPE4WHp2\nyYDHSJLEpLIsJpVl0e2PUt/iZ3dLHw2tfexq8rOryc8L7+7Fk2VlZmUODquRjTu97O9I55vW6yTG\nFTgYn59JeWEG4/MzyM40ixTBgiAIJ9GoD/T1vXvRUmDodeDMsZLhPDwAappCT9PLgEZ2yeX4nvob\naBo5y1cg6Y5c234kJFMKf3itjg9rO3DajXzzqqlUDJC2dyTlOC3kOC3MnZIPpMvFbtvTw+aGLrbt\n7eG1DU1A+gJi6vhszjkrlxmV7iEtGhQEQRBO3Kj/1d3la8AWzEZTpEGH7QPeD0hGvdiyZ6A0RQht\n/gRzeQW26TNHuLWH84fiPPbCNva2BRhfkMHdy6fitJtOdbMOY7cYOG9KHudNySORVNixv5dQNMm0\nimwc1tNn6kMQBGGsGfWBvq63AWdfeu/7QIE+Geuir+NddHo7dtMMmn/5/5D0enJvuPGUDikrqkp9\nk5/fvrIDfyjBeZPzuPWSiRgGqLJ3ujEadEyvFIVnBEEQTgejOtA3BVrwhruY0jcDo0lPXtGhq7lV\nJUH3vudBU3DmL6HjV4+jhsN4brkNc1nZiLd3b1uAnY0+6prTc9+xhIIkwXWLKlh6drGYyxYEQRCG\nbNQG+pSa4qmdf8EUdUBMT8kkF/LnCs5omoav+VWSsU7sObMJvPg+8eYmMhdeQOaC80e0re09YZ5d\nt5tte3v6H8vLsjKh2Ml5kz2iYpsgCIJw3EZtoH99/5u0hTuYpZ5PnMOH7UPdHxPp3YbRWgh1OoIf\nfYh5fDnuG74yYm0MRZP8aW0Db25uQVE1JpW6WDSjkMpiJ5k2Ma8tCIIgnLhRGeibg6280fgmLpOT\nzNY8OglSPP6zbXXxUDO9LW8g663Y1Om0//V/0DkyyL/rbmTDyOzl3r6vh9+9spNAOIHbaebLiyuZ\nUZkjhucFQRCEYTXqAv3BIXtVU7k8axlb1vfgKczAcmDlt5IM0b3/OUDDYZpLx3/+FoD8u76FwTUy\nQ+T1zX4ee34bANdcUM7Fs4sx6EUde0EQBGH4jbpA/0bjW7SG2pnDPLa92gvAtDnF/c/3NL2Mkgxi\nt86i67E/oiXi5H/9m1gnTByR9jV5g/znczUoqsaDt51Nmfv0yqEvCIIgjC6j7jby9f3rKOyZSHRj\nJkjwpRVTKK9yA5CK9xILNGAweOj9zeuo0Sh5t38Vx+w5I9I2ry/Cw3/eQiye4o7LJzHnLFHyVhAE\nQTi5Rt0dfX7zJFxtpVisBi69diq5nyuQEvJtASD6zh6UYJDcm2496SlukymVWCKFP5Tgv56rIRBJ\ncuOSCZwrgrwgCIIwAkZdoHe1leLMsnDZddWHpLvVNI1wz1a0pEZqWxfuL9+A8/wLhv39NU1j065O\nXnxvH13+KIqqHfL81QvGsXhm0bC/ryAIgiAMZNQF+vMWlVNVnYf5C5XQ4sF0nXmlPkjG2fNwXbx0\n2N97b1uAZ9c1sLu1D71OoizPgdmow2TUYzbqqCjK5PxpBcP+voIgCIIwmFEX6KefUzzg46GeTwFQ\n6iNk33vlsL1fSlHZ3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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe75edc39e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tmp = df2.groupby(['Year', 'State']).agg({'Count': 'sum'}).reset_index()\n", "largest_states = (tmp.groupby('State')\n", " .agg({'Count': 'sum'})\n", " .sort_values('Count', ascending=False)\n", " .index[:5].tolist())\n", "tmp.pivot(index='Year', columns='State', values='Count')[largest_states].plot()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "b761de3a-e066-6744-d828-8e0cdd3a1a5e" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 183, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/324/324967.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "9005e0d5-64e9-ecd3-8c34-e8fefc1a2358" }, "source": [ "" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "a9e3c535-024a-a340-281d-0e0613228b5d" }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from math import *\n", "\n", "import nltk\n", "from nltk.tokenize import word_tokenize\n", "from nltk.probability import FreqDist\n", "from nltk.corpus import brown\n", "from nltk.corpus import stopwords\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "12ac681d-b21e-df17-54ff-909b951804e1" }, "outputs": [], "source": [ "#This needs to be changed to whatever your directory i\n", "masterDF = pd.read_csv(\"../input/emails.csv\")" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "bb54349e-223f-255c-4b61-7a498ecbd767" }, "source": [ "" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "1e9bda6d-8c5d-2a36-7bfe-fd5d85073644" }, "outputs": [ { "ename": "NameError", "evalue": "name 'masterDF' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-1-aaf68b1697bb> in <module>()\n----> 1 messageList = masterDF['message'].tolist()\n 2 #print messageList[11]\n 3 \n 4 bodyList = []\n 5 \n", "NameError: name 'masterDF' is not defined" ] } ], "source": [ "messageList = masterDF['message'].tolist()\n", "#print messageList[11]\n", "\n", "bodyList = []\n", "\n", "#Janky first attempt at a split!\n", "for message in messageList:\n", " #Split at the filename\n", " firstSplit = message.split(\"X-FileName: \", 1)[1]\n", " #Get everything after the file extension\n", " secondSplit = firstSplit.split(\".\")\n", " #Some error checking if the file type isn't included\n", " if len(secondSplit) > 1:\n", " secondSplit = secondSplit[1]\n", " body = ''.join(secondSplit)[4:]\n", " bodyList.append(body)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "0e648a72-b304-c3bb-f62e-3e85dac8be94" }, "outputs": [], "source": [ "#Join all of this text together\n", "textBlob = ''.join(bodyList)\n", "\n", "textTokenized = word_tokenize(textBlob)\n", "textFreqDist = FreqDist(textTokenized)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "041917ec-7075-4c01-f88e-7385ec849506" }, "outputs": [], "source": [ "#Get a frequency distribution from Brown\n", "brownFreqDist = FreqDist(i.lower() for i in brown.words())" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9a8fb120-9005-93c0-0870-1071683a308f" }, "source": [ "" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "7706a203-3539-ec33-45bf-1b5111aab10d" }, "outputs": [], "source": [ "uniquenessList = []\n", "\n", "#Compare the occurance of a word to its occurance in the Brown Dataset\n", "for word in textFreqDist:\n", " brownOccurances = brownFreqDist[word]\n", " textOccurances = textFreqDist[word]\n", " if brownOccurances > 5 and textOccurances > 5 and word.isalpha():\n", " uniquenessList.append((word, log10(float(textOccurances) / float(brownOccurances))))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "6fc85aa3-e33e-3115-ee57-b1bfea1282ec" }, "outputs": [], "source": [ "uniquenessList.sort(key=lambda tup: -tup[1])\n", "\n", "for i in range(10):\n", " print(\"(%s, %f)\" % (uniquenessList[i][0], uniquenessList[i][1]))" ] } ], "metadata": { "_change_revision": 8, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/325/325017.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "c0c53fd1-f980-acd9-e5c7-2a7e256caf40" }, "source": [ "In this notebook I would like to look at all of the Bears vs Packers games, and try to create a model that can predict whether or not the next play will be a run or a pass based on Down, Quarter, Yards to Go, and Score Difference, and Position on the football field.. I am only examining downs 1 through 3 because 4th down is a special circumstance." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "64951167-a1ba-66a7-5a78-8980c9cd3f9e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nflplaybyplay2015.csv\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module has been deprecated in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] }, { "data": { "text/plain": [ "Index(['Unnamed: 0', 'Date', 'GameID', 'Drive', 'qtr', 'down', 'time',\n", " 'TimeUnder', 'TimeSecs', 'PlayTimeDiff', 'SideofField', 'yrdln',\n", " 'yrdline100', 'ydstogo', 'ydsnet', 'GoalToGo', 'FirstDown', 'posteam',\n", " 'DefensiveTeam', 'desc', 'PlayAttempted', 'Yards.Gained', 'sp',\n", " 'Touchdown', 'ExPointResult', 'TwoPointConv', 'DefTwoPoint', 'Safety',\n", " 'PlayType', 'Passer', 'PassAttempt', 'PassOutcome', 'PassLength',\n", " 'PassLocation', 'InterceptionThrown', 'Interceptor', 'Rusher',\n", " 'RushAttempt', 'RunLocation', 'RunGap', 'Receiver', 'Reception',\n", " 'ReturnResult', 'Returner', 'Tackler1', 'Tackler2', 'FieldGoalResult',\n", " 'FieldGoalDistance', 'Fumble', 'RecFumbTeam', 'RecFumbPlayer', 'Sack',\n", " 'Challenge.Replay', 'ChalReplayResult', 'Accepted.Penalty',\n", " 'PenalizedTeam', 'PenaltyType', 'PenalizedPlayer', 'Penalty.Yards',\n", " 'PosTeamScore', 'DefTeamScore', 'ScoreDiff', 'AbsScoreDiff', 'Season'],\n", " dtype='object')" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n", "\n", "\n", "df = pd.read_csv('../input/nflplaybyplay2015.csv',low_memory=False)\n", "df.columns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "7ba4506d-568b-729f-19bb-caf92ae62799" }, "outputs": [], "source": [ "df = df[df['posteam'] == 'CHI'] # Bears on offense\n", "df = df[df['DefensiveTeam'] == 'GB'] # Packers on defense\n", "\n", "used_downs = [1,2,3] # Downs that are being used in predictions\n", "df = df[df['down'].isin(used_downs)]\n", "\n", "valid_plays = ['Pass', 'Run', 'Sack']\n", "df = df[df['PlayType'].isin(valid_plays)]\n", "\n", "pass_plays = ['Pass', 'Sack']\n", "df['is_pass'] = df['PlayType'].isin(pass_plays).astype('int')\n", "\n", "df = df[['down','yrdline100','ScoreDiff','ydstogo','TimeSecs','is_pass']]\n", "\n", "X, test = train_test_split(df, test_size = 0.2)\n", "\n", "y = X.pop('is_pass')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "d851a7ac-bfdd-1a0d-8fb3-c0e5b2f2e7b6" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "4edfcb98-f8f5-809d-d223-f9cb680cc64d" }, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=1000, n_jobs=1,\n", " oob_score=False, random_state=None, verbose=0,\n", " warm_start=False)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf = RandomForestClassifier(n_estimators=1000)\n", "rf.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "f0a47d72-9f0c-8843-dd88-4f51ee19a1d9" }, "outputs": [], "source": [ "test_y = test.pop('is_pass')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "828e77ba-14fb-5cd5-57e5-895b8f62b9bb" }, "outputs": [ { "data": { "text/plain": [ "0.61538461538461542" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf.score(test,test_y)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "362511bb-bb6f-7ad2-2fd1-2e1152721167" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 100, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/325/325098.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "c0c53fd1-f980-acd9-e5c7-2a7e256caf40" }, "source": [ "In this notebook I would like to to create a model that can predict whether or not the next play will be a run or a pass based on Down, Quarter, Yards to Go, and Score Difference, and Position on the football field.. I am only examining downs 1 through 3 because 4th down is a special circumstance." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "64951167-a1ba-66a7-5a78-8980c9cd3f9e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nflplaybyplay2015.csv\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module has been deprecated in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] }, { "data": { "text/plain": [ "Index(['Unnamed: 0', 'Date', 'GameID', 'Drive', 'qtr', 'down', 'time',\n", " 'TimeUnder', 'TimeSecs', 'PlayTimeDiff', 'SideofField', 'yrdln',\n", " 'yrdline100', 'ydstogo', 'ydsnet', 'GoalToGo', 'FirstDown', 'posteam',\n", " 'DefensiveTeam', 'desc', 'PlayAttempted', 'Yards.Gained', 'sp',\n", " 'Touchdown', 'ExPointResult', 'TwoPointConv', 'DefTwoPoint', 'Safety',\n", " 'PlayType', 'Passer', 'PassAttempt', 'PassOutcome', 'PassLength',\n", " 'PassLocation', 'InterceptionThrown', 'Interceptor', 'Rusher',\n", " 'RushAttempt', 'RunLocation', 'RunGap', 'Receiver', 'Reception',\n", " 'ReturnResult', 'Returner', 'Tackler1', 'Tackler2', 'FieldGoalResult',\n", " 'FieldGoalDistance', 'Fumble', 'RecFumbTeam', 'RecFumbPlayer', 'Sack',\n", " 'Challenge.Replay', 'ChalReplayResult', 'Accepted.Penalty',\n", " 'PenalizedTeam', 'PenaltyType', 'PenalizedPlayer', 'Penalty.Yards',\n", " 'PosTeamScore', 'DefTeamScore', 'ScoreDiff', 'AbsScoreDiff', 'Season'],\n", " dtype='object')" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n", "from sklearn.svm import SVC\n", "\n", "\n", "df = pd.read_csv('../input/nflplaybyplay2015.csv',low_memory=False)\n", "df.columns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "7ba4506d-568b-729f-19bb-caf92ae62799" }, "outputs": [], "source": [ "'''\n", "Boiler-Plate/Feature-Engineering to get frame into a testable format\n", "'''\n", "\n", "# Only use downs 1-3 since 4th is too unpredictable\n", "used_downs = [1,2,3] # Downs that are being used in predictions\n", "df = df[df['down'].isin(used_downs)]\n", "\n", "# Don't include kicks, kneels, spikes, etc.\n", "valid_plays = ['Pass', 'Run', 'Sack']\n", "df = df[df['PlayType'].isin(valid_plays)]\n", "\n", "# create a column that has 1 for pass/sack, 0 for run\n", "pass_plays = ['Pass', 'Sack']\n", "df['is_pass'] = df['PlayType'].isin(pass_plays).astype('int')\n", "\n", "# select your features and classifier from full data frame\n", "df = df[['down','yrdline100','ScoreDiff','ydstogo','TimeSecs','is_pass']]\n", "\n", "# train/test split on data\n", "X, test = train_test_split(df, test_size = 0.2)\n", "\n", "# pop the classifier off the sets.\n", "y = X.pop('is_pass')\n", "test_y = test.pop('is_pass')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "4edfcb98-f8f5-809d-d223-f9cb680cc64d" }, "outputs": [ { "data": { "text/plain": [ "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n", " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf = RandomForestClassifier(n_estimators=10)\n", "gb = GradientBoostingClassifier(n_estimators=10)\n", "sv = SVC()\n", "rf.fit(X, y)\n", "gb.fit(X, y)\n", "sv.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "828e77ba-14fb-5cd5-57e5-895b8f62b9bb" }, "outputs": [ { "data": { "text/plain": [ "0.65147494927423133" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Score of Random Forest Classifier\n", "rf.score(test,test_y)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "362511bb-bb6f-7ad2-2fd1-2e1152721167" }, "outputs": [ { "data": { "text/plain": [ "0.64226627126580305" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Score of Gradient Boosted Classifier\n", "gb.score(test,test_y)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "ae9a863b-7161-594e-cf8b-817a1f795bf0" }, "outputs": [ { "data": { "text/plain": [ "0.59513032620571249" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Score of Support Vector Classifier\n", "sv.score(test,test_y)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "b51c49ed-37aa-0ff7-5a45-e8e648edea80" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 578, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/325/325101.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "c0274348-ecaf-4bc2-4b88-6b2364a3c674" }, "source": [ "# Simple SGD classifier" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "71e793e4-b469-5f2b-a144-b64abcd62790" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "test.csv\n", "train.csv\n", "\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "43665611-932b-93d1-f4c9-4138998bb3f6" }, "outputs": [], "source": [ "#taken from\n", "#Digit Recognizer in Python using Convolutional Neural Nets\n", "#https://www.kaggle.com/kobakhit/digit-recognizer/digit-recognizer-in-python-using-cnn/comments\n", "\n", "dataset = pd.read_csv(\"../input/train.csv\")\n", "target = dataset[[0]].values.ravel()\n", "train = dataset.iloc[:,1:].values\n", "test = pd.read_csv(\"../input/test.csv\").values" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "b7ea8b1c-d95b-7b62-7241-99bf58d11079" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', n_iter=100, n_jobs=6,\n", " penalty='l1', power_t=0.5, random_state=None, shuffle=True,\n", " verbose=0, warm_start=False)\n" ] } ], "source": [ "from sklearn import linear_model, svm, metrics\n", "classifier = linear_model.SGDClassifier(n_iter=100, n_jobs=6, penalty=\"l1\")\n", "print(classifier)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "33609e3f-de9b-362c-e43e-44b761ca61df" }, "outputs": [ { "data": { "text/plain": [ "SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', n_iter=100, n_jobs=6,\n", " penalty='l1', power_t=0.5, random_state=None, shuffle=True,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifier.fit(train, target)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "c3edd4c7-dae8-d9be-0a8f-d2a761f1cf50" }, "outputs": [], "source": [ "pred = classifier.predict(test)\n", "np.savetxt('submission_of_mine.csv', np.c_[range(1,len(test)+1),pred], delimiter=',', header = 'ImageId,Label', comments = '', fmt='%d')" ] } ], "metadata": { "_change_revision": 91, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/325/325103.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "8d2c4e55-13cc-d7a1-4033-e094bdeb6ac7" }, "source": [ "Before starting the classification model, Let's try to analyse data. My interest is to find the group, cahr_10 and outcome distribution. I am using seaborn count plot to show the outcome distribution for each fields. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "45c72ca3-5ca3-b547-2a6c-7fc9f21dc434" }, "outputs": [], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "cfe7342b-326d-519d-223e-2e1c927ef86d" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb6b333bbe0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "act_df = pd.read_csv('../input/act_train.csv',sep=',')\n", "\n", "sns.countplot(x='outcome',data=act_df)\n", "sns.plt.show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "ee68aaef-4d05-ca0a-6a21-afd6cf3b3299" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb6cd80d5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x='activity_category',data=act_df,hue='outcome')\n", "sns.plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "daeb7c8a-c478-a7b2-d90a-74b79ce1949a" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb6b206bdd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "fig.set_size_inches(30, 20)\n", "h = sns.countplot(x='char_1',data=act_df,hue='outcome',ax=ax)\n", "h.set_xticklabels(h.get_xticklabels(),rotation=50)\n", "sns.plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "29c13703-082f-209d-8a78-d94f05b8dce5" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb6b21236a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "fig.set_size_inches(30, 20)\n", "people_df = pd.read_csv('../input/people.csv',sep=',')\n", "group_based_ppl_count = people_df.groupby(['group_1']).count().sort_values(by='people_id',ascending=[0])\n", "group_based_ppl_count = group_based_ppl_count.reset_index()\n", "group_based_ppl_count = group_based_ppl_count.ix[:20,]\n", "g = sns.barplot(x='group_1',y='people_id',data=group_based_ppl_count,ax=ax)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "01482521-bd1a-adb7-b496-99181bb1d606" }, "source": [ "To get the outcome distribution of top 20 groups. I have created a joint data by merging people and activity. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "706d9a47-a9b3-d555-c1e6-08b06d74f490" }, "outputs": [ { "data": { "image/png": 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Xm/tod1/c3TunTH+UMQ3I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ki4SPkoKm4ApkUxTigi9sFeSr1iJjypHdztC5d7cBY4fFeV01Q2WUcTNLRTNsy/xl4HPUH1w6On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\nxscnAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb6982ae940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#merging people and activity \n", "\n", "people_df = pd.read_csv(\"../input/people.csv\",sep=',',parse_dates=['date'])\n", "activity_df = pd.read_csv(\"../input/act_train.csv\",sep=',',parse_dates=['date'])\n", "\n", "def sanitizepeople():\n", " sn_fileds= [\"char_1\",\"group_1\",\"char_2\",\"date\",\"char_3\",\"char_4\",\"char_5\",\"char_6\",\"char_7\",\"char_8\",\"char_9\"]\n", " for filed in sn_fileds:\n", "\n", " if \"group\" in filed:\n", " people_df[filed] = people_df[filed].str.lstrip('group ').astype(np.float)\n", " elif \"char_\" in filed:\n", " people_df[filed] = people_df[filed].fillna(\"-999\")\n", " people_df[filed] = people_df[filed].str.lstrip('type ').astype(np.float)\n", " else:\n", " people_df['year'] = people_df[filed].dt.year\n", " people_df['month'] = people_df[filed].dt.month\n", " people_df['day'] = people_df[filed].dt.day\n", "\n", " people_df1 = people_df.drop(['date'],axis=1)\n", "\n", " return people_df1\n", "\n", "def sanitizeactivity():\n", " sn_fileds= [\"date\",\"activity_category\",\"char_1\",\"char_2\",\"char_3\",\"char_4\",\"char_5\",\"char_6\",\"char_7\",\"char_8\",\"char_9\",\"char_10\"]\n", " for filed in sn_fileds:\n", "\n", " if \"char_\" in filed or \"activity\" in filed:\n", " activity_df[filed] = activity_df[filed].fillna(\"-999\")\n", " activity_df[filed] = activity_df[filed].str.lstrip('type ').astype(np.float)\n", " else:\n", " activity_df['year'] = activity_df[filed].dt.year\n", " activity_df['month'] = activity_df[filed].dt.month\n", " activity_df['day'] = activity_df[filed].dt.day\n", "\n", " activity_df1 = activity_df.drop(['date'],axis=1)\n", "\n", " return activity_df1\n", "\n", "\n", "people_nrm_df = sanitizepeople()\n", "activity_nrm_df = sanitizeactivity()\n", "j_df = pd.merge(people_nrm_df,activity_nrm_df,how='left',on='people_id',left_index='True')\n", "\n", "\n", "fig, ax = plt.subplots()\n", "fig.set_size_inches(30, 20)\n", "\n", "j_top20grp_grpby = j_df.groupby(['group_1']).sum().sort_values(by='outcome',ascending=[0])\n", "j_top20grp_grpby = j_top20grp_grpby.reset_index()\n", "\n", "top20group = j_top20grp_grpby['group_1'].astype(np.int).tolist()\n", "top20group = top20group[:50]\n", "\n", "j_top20grp_df = j_df.loc[j_df['group_1'].isin(top20group)]\n", "\n", "j_top20grp_df = j_top20grp_df[['group_1','outcome']]\n", "\n", "h = sns.countplot(x='group_1',data=j_top20grp_df,hue='outcome',ax = ax)\n", "h.set_xticklabels(h.get_xticklabels(),rotation=50)\n", "sns.plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "b60a3916-1fe7-dfb1-21d6-970b2b27a0f2" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 207, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/325/325211.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "c0c53fd1-f980-acd9-e5c7-2a7e256caf40" }, "source": [ "In this notebook I would like to to create a model that can predict whether or not the next play will be a run or a pass based on Down, Quarter, Yards to Go, and Score Difference, and Position on the football field.. I am only examining downs 1 through 3 because 4th down is a special circumstance." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "64951167-a1ba-66a7-5a78-8980c9cd3f9e" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", " DeprecationWarning)\n" ] }, { "data": { "text/plain": [ "Index(['Unnamed: 0', 'Date', 'GameID', 'Drive', 'qtr', 'down', 'time',\n", " 'TimeUnder', 'TimeSecs', 'PlayTimeDiff', 'SideofField', 'yrdln',\n", " 'yrdline100', 'ydstogo', 'ydsnet', 'GoalToGo', 'FirstDown', 'posteam',\n", " 'DefensiveTeam', 'desc', 'PlayAttempted', 'Yards.Gained', 'sp',\n", " 'Touchdown', 'ExPointResult', 'TwoPointConv', 'DefTwoPoint', 'Safety',\n", " 'PlayType', 'Passer', 'PassAttempt', 'PassOutcome', 'PassLength',\n", " 'PassLocation', 'InterceptionThrown', 'Interceptor', 'Rusher',\n", " 'RushAttempt', 'RunLocation', 'RunGap', 'Receiver', 'Reception',\n", " 'ReturnResult', 'Returner', 'Tackler1', 'Tackler2', 'FieldGoalResult',\n", " 'FieldGoalDistance', 'Fumble', 'RecFumbTeam', 'RecFumbPlayer', 'Sack',\n", " 'Challenge.Replay', 'ChalReplayResult', 'Accepted.Penalty',\n", " 'PenalizedTeam', 'PenaltyType', 'PenalizedPlayer', 'Penalty.Yards',\n", " 'PosTeamScore', 'DefTeamScore', 'ScoreDiff', 'AbsScoreDiff', 'Season'],\n", " dtype='object')" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline\n", "\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn import grid_search\n", "from sklearn.preprocessing import LabelEncoder\n", "\n", "df = pd.read_csv('../input/nflplaybyplay2015.csv',low_memory=False)\n", "df.columns" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "7ba4506d-568b-729f-19bb-caf92ae62799" }, "outputs": [], "source": [ "\"\"\"\n", "Boiler-Plate/Feature-Engineering to get frame into a testable format\n", "\"\"\"\n", "\n", "# Only use downs 1-3 since 4th is too unpredictable\n", "used_downs = [1,2,3] # Downs that are being used in predictions\n", "df = df[df['down'].isin(used_downs)]\n", "\n", "# Don't include kicks, kneels, spikes, etc.\n", "valid_plays = ['Pass', 'Run', 'Sack']\n", "df = df[df['PlayType'].isin(valid_plays)]\n", "\n", "# create a column that has 1 for pass/sack, 0 for run\n", "pass_plays = ['Pass', 'Sack']\n", "df['is_pass'] = df['PlayType'].isin(pass_plays).astype('int')\n", "\n", "# select your features and classifier from full data frame\n", "df = df[['down','yrdline100','ScoreDiff', 'PosTeamScore', 'DefTeamScore',\n", " 'ydstogo','TimeSecs','ydsnet','is_pass','Drive']]\n", "\n", "# train/test split on data\n", "X, test = train_test_split(df, test_size = 0.2)\n", "\n", "# pop the classifier off the sets.\n", "y = X.pop('is_pass')\n", "test_y = test.pop('is_pass')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "4edfcb98-f8f5-809d-d223-f9cb680cc64d" }, "outputs": [], "source": [ "parameters = {\n", " # 'n_estimators':[1, 5, 10, 30, 50, 100, 200],\n", " # 'min_samples_leaf':[10, 12, 14, 16, 18, 20],\n", " # 'max_features':[.2,.5,.8, 1.0]\n", " }\n", "clf = RandomForestClassifier(n_jobs = -1, oob_score=True,\n", " n_estimators=100, min_samples_leaf=12, max_features=.8\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "828e77ba-14fb-5cd5-57e5-895b8f62b9bb" }, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features=0.8, max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=12,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=100, n_jobs=-1, oob_score=True, random_state=None,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# clf = grid_search.GridSearchCV(rf, parameters)\n", "clf.fit(X,y)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "362511bb-bb6f-7ad2-2fd1-2e1152721167" }, "outputs": [ { "data": { "text/plain": [ "0.71484314031528018" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.score(test, test_y)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "b51c49ed-37aa-0ff7-5a45-e8e648edea80" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 41, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/325/325602.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "1fab96bf-9f9d-33b0-db72-c35e15a74ca6" }, "outputs": [], "source": [ "import pandas as pd\n", "import csv as csv\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from time import time\n", "\n", "train = pd.read_csv(\"../input/train.csv\", dtype={\"Age\": np.float64}, )\n", "test = pd.read_csv(\"../input/test.csv\", dtype={\"Age\": np.float64}, )\n", "\n", "data = np.array(train)\n", "test_data = np.array(test)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "94fa40b0-4ee3-5b3c-739e-4b0d7d82752a" }, "outputs": [], "source": [ "# The size() function counts how many elements are in\n", "# in the array and sum() (as you would expects) sums up\n", "# the elements in the array.\n", "\n", "number_passengers = np.size(data[0::,1].astype(np.float))\n", "number_survived = np.sum(data[0::,1].astype(np.float))\n", "proportion_survivors = number_survived / number_passengers" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "0f34dbb1-e027-0669-04b5-4078fd040de2" }, "outputs": [], "source": [ "women_only_stats = data[0::,4] == \"female\" # This finds where all \n", " # the elements in the gender\n", " # column that equals “female”\n", "men_only_stats = data[0::,4] != \"female\" # This finds where all the \n", " # elements do not equal \n", " # female (i.e. male)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "a6e93374-e5e0-ad8b-d603-30f454f6d80d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Proportion of women who survived is 0.742038216561\n", "Proportion of men who survived is 0.188908145581\n" ] } ], "source": [ "# Using the index from above we select the females and males separately\n", "women_onboard = data[women_only_stats,1].astype(np.float) \n", "men_onboard = data[men_only_stats,1].astype(np.float)\n", "\n", "# Then we finds the proportions of them that survived\n", "proportion_women_survived = np.sum(women_onboard) / np.size(women_onboard) \n", "proportion_men_survived = np.sum(men_onboard) / np.size(men_onboard) \n", "\n", "# and then print it out\n", "print('Proportion of women who survived is %s' % proportion_women_survived)\n", "print('Proportion of men who survived is %s' % proportion_men_survived)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "667a0134-6781-df90-e8cf-4996519f9cb0" }, "outputs": [], "source": [ "prediction = test_data[0::,3] == \"female\"\n", "for row in prediction:\n", " if row == \"True\":\n", " row = '1'\n", " else:\n", " row = '0'\n", "\n", "submission = pd.DataFrame({\"PassengerId\": test_data[0::, 0], \"Survived\": prediction})\n", "\n", "submission.to_csv(\"FirstAttempt.csv\", index=False)" ] } ], "metadata": { "_change_revision": 483, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/325/325654.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "72c13538-c15d-2fed-8dda-6ba7f8af6987" }, "source": [ "The word is climate change is one of the biggest existential threat that humanity is facing. Hoping to throw some exploratory light on the matter with the given data. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "f2757c04-d8ea-9788-36ef-9a6d461ad5b5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GlobalLandTemperaturesByCity.csv\n", "GlobalLandTemperaturesByCountry.csv\n", "GlobalLandTemperaturesByMajorCity.csv\n", "GlobalLandTemperaturesByState.csv\n", "GlobalTemperatures.csv\n", "\n" ] } ], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "6740af67-88aa-c968-f343-370629690e49" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "DatetimeIndex: 3192 entries, 1750-01-01 to 2015-12-01\n", "Data columns (total 8 columns):\n", "LandAverageTemperature 3180 non-null float64\n", "LandAverageTemperatureUncertainty 3180 non-null float64\n", "LandMaxTemperature 1992 non-null float64\n", "LandMaxTemperatureUncertainty 1992 non-null float64\n", "LandMinTemperature 1992 non-null float64\n", "LandMinTemperatureUncertainty 1992 non-null float64\n", "LandAndOceanAverageTemperature 1992 non-null float64\n", "LandAndOceanAverageTemperatureUncertainty 1992 non-null float64\n", "dtypes: float64(8)\n", "memory usage: 224.4 KB\n", "None\n" ] } ], "source": [ "global_temperatures = pd.read_csv(\"../input/GlobalTemperatures.csv\", infer_datetime_format=True, index_col='dt', parse_dates=['dt'])\n", "print (global_temperatures.info())" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "a856e045-f297-0e7b-c394-5e54e90573e9" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f94f137ed68>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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F3gEHD9IvmJOSAfi9w8QEqZiSFz9uUTRAi8D0tNulRhxJjE9pLAtuCr2v5YYj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\nOFShl17lhpPNIKRyGBdV2CLUcsMpMAlR6M+fp3cfHU3TJh+EiDydDq8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"text/plain": [ "<matplotlib.figure.Figure at 0x7f94f137e588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "global_temperatures[global_temperatures.index.year > 2000]['LandAverageTemperature'].plot(figsize=(13,7))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c7816922-5738-98f6-59a1-34e35c94a540" }, "source": [ "The oscillation basically depicts the seasonal variance in average temperature - To gain a better insight let's try grouping the average temperature by year and plotting the average temperature change over years." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "ff9ba7f4-8125-15b0-912e-3d73794d86c1" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f94f0e4da20>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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vmffdcYccn63d17ZuFbdfzyx7wQXA+PHy/2wL/717ZYBk3QZrLHnmTFP433WX\nDNZ0+sJO//4Sl9L5fg2Ff+bJeeFvz8b7dfyrq6Mx2ZPfqI9TD39AhH+yA1K+cuCAHESKisQpysRU\n51FCH5wp5kgyysslQ8t9hQAigN99179Zcscd4oDffnvw422QGVp/+UuJ0UycGP+Yk/B/+20R1Fam\nT3eefdaJujrRC4MHyxWIYcOS64bq6vhC1y5d5KrD0qXmfdu2ifB3Qk/gle6GDW7FvfrKeVWVeZ9V\nc82YIS0933wTuO8+4K9/dX+NkhL5zCj8s09eCH9r1Mev4z9tWjTiPm5RH7cfPB3/ePbvl7wgIJeK\nC63At6YG6NuXjj9Jzr59dPyJycaNYpjt3On9Oe3tklt/4gm52jp1qv9zqWHIc/wKf6XiY64aJ+Gv\nC3utTJ/u3fHXbr925b3k/N3O0aefDixZYt7eulUKeZ0oLpZ/Xn6nzz+f2PR8/XXg4MH4+7WjP2BA\nvOOfTPhPny7f33XXSUvVESPcX1/rGwr/7JPzwt8e9fHr+J9+ejTiPvaoT1GRFC27ufdOPfwBCn89\nxfeoUYWX86+pkZMexRxJRnm5xAlaW3Nz3hMSLps2iai1z8SaiLfekuPNxRdL3v5Tn/KXmQckStK1\nq1yhDQu78G9qEoFv7+Bz2mkiMr3oBS38NakKf6vjr6M+bniJ+1RWAldeGVtoa8UwgOuvBx54IP6x\n5mb5Dnr0iC/u1efQQ4fM+6zCv6REinm//31g7tzE26ifY8/4jxghgwfDkIHJkSNi3JH0kfPC3x71\n8ev4z5oVHeFvdfyBxHEfOv7x6KgPIMK/EB3/44+n40+SU14uv5E+fThQJOL4n3mmP+H/l7+I06sZ\nOtTZUU7Exx8Dc+b4e04y7MJ/0SJxpu0tjktK4rvSuOEk/JNFfbSLbmfMGDH0dDTKi/DXBcBu6PkN\n1q93fnzNGnHtn3/eeTu1lujbVzSHLuYtKxMd4ub4A3L14tZbE28f4O74f+EL8n0tWCBtXadNM6+s\nkPSQF8I/aFef1lYZfUcl6mN1/IHCFP733SeXjoNgj/oUkuOv57MYNYpCjiSmqUmOKwMHUvgTaQ29\nZQtw+eXehX99PfDqq7E926Mm/HVM9oUXZDIsJ2bM8Bb32bFDWnlqhg9P7vjX1jqfo5US3bFsmXz2\n27e7R30Ab47/889LxyM34f/SS9K+dPfu+POiVUsoZfbbB2TZadMSC/8+fbwJdTfhX1IC/OtfEhX6\nj/9gzCevmBiEAAAgAElEQVQT5LzwD9rVRw8OJk+WkXtDQ3q2zyt0/IUXXpBezUEo5KhPY6NkQQcN\nouNPErNvH3DccXKypvAne/aIeTZrlnfh//TTkpm3NpiIivDv31+KaGtrxbl++mn3SaW8FviGGfUB\nzJx/RYUYfn37uq8nmfCvrwc++AD4v//XXfi//DJwxRXApZfGu/727bQW+JaVyWfkFvXxQ48eco6y\nC39ADLs335Rz+PTp/tdN/JHzwt+pq48X4d/WJhn6oiIpSlqzJn3b6IWwhH9xce4Kf8MQ9yVo9OrA\ngcIt7tX7Q9++FHIkMfv2mRlaCn+yaZO4xRMnehf+jz0WG/MB/Av/o0clYz9zpvfneEW7/u+8I4Ld\n3l1H49XxT4fwX7o0ecwHSC78X3tN6hfOOMO5xuLAAXmds88W8Z9M+OsC385OOYeedlpix98PJSXx\nGX/N6NESObPvVyR88kL427v6eIn6aOEPyOQa2Y77OEV9evWSwhsn8tHxLy8X8b9nj8QR/FLIjr/e\nH/r0oeNPElNebgp/DhTJxo0i/IcNEzFuFZkvvCAFuFbKykRg2uMzTsK/o8NdIK9ZI6LXnr0PAy38\n//EPmeTLjdNOk3N/Z2f8Y62twD//KW1A29tlnRp7xt/p+YmE/6xZ0gZz8+bkwj/Z7L3PPy+Cfvx4\n+W3bzcJXXpE5Grp3By68UF7Xuj434X/okBwfRo2KF/5OtQteuPLK2MiUnb59RcOR9JLzwt+pq48f\nxx+QH3+2C3yDOP5uffxzVfivWCEHxMmTxQnyi1X4l5bKAapQ5jSg40+8YhX+dPzJpk1yzFUq1vU3\nDOnW8s47sctv3QqcfLKca6w4Cf+333afGCwdMR/N6NGync8/LxN+uTFwoDjQ27bFP3bttdKt6Otf\nl3VZ36/V8e/oAE45RQS1lUTCf/Bg+ffyy6k5/kePSj7+sstEz0yYIN+nlZdfBj7zGfl/r14y4Zk1\nTusk/OvqZIA3apRsZxhRHwD405+cdQvJLDkv/J26+nh1/PUPWY/6s0Vbm4j1Xr1i7y+0jP/KlXLp\nNchAzDBioz5duhTWJF50/IlXyssl4w9wfyGm4w/ECv9t2yTiYnV7ARGBTjltJ+G/Z4+s337VAEi/\n8H/wQRmg6H3dDbd+/mvWSPvLq6+On4124EC5Gt/SAjzzjGTrd+0yH29vl6vWibL7p58OvPFG4sJe\n/Vpuwv/ddyWqrM97U6fG5vyPHJHB18UXm/ddcQXw3HPmbTfH3yr8w4r6kGiQF8Lf3tXHr+M/blzs\njzbT6JiPvTI+kfDPxz7+K1bIQTiI8D98WMR+nz7mfYUU96mtld8BJ/AiyaDjH02amuQ85LdANhUM\nw3T8gVjh/+qrcky1ur2AiEAn4d+nj0RerDFNbby8/nr88ukW/tu2uRf1WpkxI77A1zBksGKN91hR\nSq4uV1QAP/uZaIgDB8zHtTjukkBhzZ4tJmUqjv8zz4iQ19iF/3vvyX3WXP2ll8pgQMeIKfwLj5wX\n/kEn8LIK/549sxsJcYr5AO7CX7dudMrZ5bLw9+L4L1wo/aZ/+9vYA621h7+mkAp8rY4/hRxJBIV/\ntGhvl6uTQ4aIEE6USQ+byko5Z2hhOHGi2df+tdeAiy6KF/6HDjkXaColrr91+fJy4KyzJI5i5cAB\nEZDJ3O6gjB4tovtzn0u+rFNnn0OH5PxrNZLsjBghVxUA4Etfij0fJYr5aE4/XT6zRJl3wF3419aK\n8P/iF8377ML/ySeBz3429nmDBsl59q23nLdVd/XRwr93b9EceqBA4Z/75LTw7+wUd9M+461fx7+4\nOLrC36m49/BhaY1lz1gCuSv8KyrkOxk1Svr4btxoTiJi5a9/BaZMkUHCxIlyG4jt4a8pJMffmvGn\n408Swa4+0WL/fvnb3CzGRrJuMcnYtcvsYZ8Mq9sPAJMmiePf3Ax8+KEMQuxRHzfHH4iP+5SXA1/7\nmtQJWI/nS5aI453IEU+FU04BHn1UticZU6bET+K1Z4+7268ZMQK4+27gRz+SgZv+HgFvwn/GDOAX\nv4iPEdlxE/5//rO499ZZj63Cv7ZWirO//OX451rjPskcf6XMnH9rq3MsmeQWOS38Gxriq8CDOP5F\nRbJzZ2vqeqeOPoD8uJwc/0QHle7dc7OgVbv9urf48cfHFyl1dkpR0g9/KO3knnsO+PnP5SRnLezV\nFKrwp5AjbrS2yr6iBRH3l+xjHYjZHfMgfOITIqy9YM33A+LA79wpPdVnzhQ32qvjDzgL/5kzpZ2m\ndZvSGfMBxBi79lpvy44YIeab9XfgVfiPGQNcdZWYTn4d/+Ji4Ac/SL59TsK/rQ24917g5ptj7x89\nWkR7fT3w+OOS7Xf6ri6/XCb1am9PXtwLmHGf+npx+zmzbm6T08LfHvMBgjn+gPwIjxwJd/u84jfq\nk+ig0q2bvDevjk9UWLFChL/GKe6zYoV83+PHy+158+Tve+85R32mTvXWozlXaG2VfcWJfCzubWwE\n7ror21uRX1RUyO9EmyV0/LOPNXo1YIAYQU5XO73Q3Cyi1etxz+749+wp+8d994loHDLEWfj7cfxH\njpTIkI77tLVJjCidwt8PXbrIAGf7dvM+L8L/qquAP/xBfktOwj9oy0s7AwdKTZ/1nP7cc8AJJ8Se\nMwF5L3oG3wceAG680Xmdo0eLufbBB4kd/+OPl/uGDBHhz5hPfpCS8FdK3aKUWq+UWquUekIp1d32\n+FylVJ1SauWxf3ektrmxOO2EQRx/QByCbDnlfoW/W2EvID/8rl2DnziyxcqVsTP2OQl/a1syQFyH\nm26Sg69T1GfOHGDdumBzAkSRZ56Ry+ZO5KPjv3u31HKQ8LCKTKBwhf8f/iBRlihgdfy7dJEMtj1e\n4xUtXr0Kf7vjD0iE8vXXpU+/vZUj4D3q09goZsWAAcCnPmUK/+9/X+IpF1zgbRszwfjx/oX/3Lky\nezEggyVr1Ke2Nrnj75WePWW/sMZ+f/tb4Hvfc15+6lQR/a2tso1uXHmltDt1Ev5VVTKQGTFC7tP7\nAYV/fhBY+CulRgD4PwCmG4YxDUBXAE5lSe8bhjH92L+fBn09J5wmkvDq+Le2xjv+2RL+blEfN+Ff\nUWH+IJ2IWs6/sjJ5ka3d8Z8+PV74v/SSZBqtXHutdChYsSLe8e/ZUyZn++ij4NseJerrZbZHJ/SJ\npnt3iURF6fsPSlOTvK9cu3oVZQ4ejM0959MVIq+8+CLwne9I15ooYG2vCvifAdfK1q1ybvDSFe3l\nl8UYOeWU2PsnTpSByJQpIvKam2OPJ16jPnpAo5TMKrt9O/DTn0pR6d/+Fq2JmiZMiO3l70X4W9GO\nvz5WeYn6+MEa91m6VM6pl1/uvOzUqRKFveGGxJGcK64AnnpK9JI1s9+/v+xHgwebGklHfSj884NU\noz5FAHorpboC6AXAqSwpbWkwp6hPUMc/m8Lfb3Gv3bWzEzXhf++9csB348ABeZ8nnGDeZ59Rsbxc\nDsZnnhn73L59gWuuETfJLvwBcTzeey/ltxAJmpvlc3ASBfpEo1Ri1/+RR5xnmYwi2jF0m72a+Ke2\nNnYCnUJz/LdtkwmZbrpJsuxRwOr4A6kL/6uukghPonPAffcB3/iGDH7s7v3s2VLUq1T8FYjOTrni\n7EX4Wwc03btLNPPuu2Xg5XS+yyZBHH8rvXqJnmhokNthC3/r7L1PPQVcd537wGnqVPm8v/KVxOuc\nOlV0jz5vaAYMkNizzvcDZtTHSXOR3COw8DcMowLAbwDsBbAPQJ1hGG85LHqGUmq1UuoVpdRJDo8H\nxmn0WQgZ/1wT/uXl4iy5sWaNOPPWg8+gQfKZfPCB3H7lFcmcdu0a//ybbpK/9qgPkH/CH4hvPQfE\nZkoTubjf/KacuHMBHdGqrc3uduQT9uxxIQn/piZpbbhggZgFURH+YTv+p54qRacbNzov8z//I/8W\nLxaRb+eLXwR+9SvztjXnX18v7R2dOsrZt91+nvrxjyVClK4WnqmQquMPxOb80+n4v/UWcOGF7sue\nfTbwz3+6D840Sonrb99OrUWswp9Rn/zCQUZ5QynVH8DlAEYDqAfwtFLqGsMwnrQstgLA8YZhNCul\nLgbwPADX6Srmz5//v/+fN28e5unqTRecoj5BJvACspvxb2hwPsi4dfUpL5fMpBvFxdES/vv2ifDv\n7HRu37Zjh/PJ4O67xb26+WY5STm1JQOkOO2Xv4zPqgJyhWDlSvkce/ZM7X1km+Zm2WeXL4+difHI\nEdmfe/eW226O/9Gjsl+4TQoXNfR7qK1NPNAl3rELknyqCUnGo49KEec3vykCLUrCP0zH/+tfN2ej\nPfXU2Mfb26WF5LvvJu8fr7FO4JQo5mPfdvv7stZwRQ2r49/QIMdJ65UxL+ic/4knpk/4Hzwo7VpP\nP9192Z493WNAdq65Jr6Go6hIxL9d+DPqE20WLlyIhQsXelo2sPAHcAGAnYZh1ACAUupZAGcC+F/h\nbxhGo+X/ryml/qiUGqifY8cq/L3g1tUnn6I+TiLN2mbLiSg6/k1N4qKMGRP/+K5dwNix8fd/7nPi\nSF1/vZyo/vY399dwa4vWp49kVZcsMbsA5SotLdIaz+7463y/vmLi5vjry9C5Ep3Rjr/brJXEP7W1\nZqcOoLAc/5deElGslLizTU3yO+nbN3vbZBhijITp+J94okQlV66Mbwbw+utyDLZ28kmG1fFPVNgL\nxGf8p0zxt/3Z4rjjRNQ2Nppuv9+WlXbHP6yuPoAp/N95R65iW7VLKsyYIfUAdgYMcI76UPhHF7tZ\nvmDBAtdlU8n47wUwRynVQymlAJwPIKbzulKq1PL/0wEoN9HvlXfeMQto3Lr6eHX8rZcrsx318VPc\nm2tRn337pMPO2rXOj+/c6TwgAOTg8/rrctk66AFn7lzg/feDPTdKNDcD55wTL/y9urha+Oei40/C\nwb6vFIrwP3xYYoM6IqGUHHN27crudlVXy5Vda3FlUOFfXS3nviFDnJsjAMBf/uJ+5dQNq/BP5vjr\nZTs7k5+nokSXLmI+7dgRLOYDxAr/MLv6AKbwf/PNzHRD6t+fjn8+k0rGfymApwGsArDm2N0PKKVu\nVErdcOz25461+1wF4LcA/j3ROpMJ9tWrpX2WntjJLeqTa45/Q4N3x7+lRU5iiQ6+URL+WmyefXZi\n4e/k+GuUSi0Xmi85/+ZmcdAaG2OFgf0kk8zxzxXhz4x/+NTWxmf8C6Grz1tviflgNVjGjs1+3Mfu\n9gPBhf+2beL2KyURnzVrYs+FNTViovx7wrNwPNaWnol6+ANy7unTR87NuST8ATPnH1T4W1t6piPq\nU10twv+TnwxvvW588Yux9R/M+OcXKXX1MQxjgWEYkw3DmGYYxlcMw2gzDON+wzAeOPb4HwzDmGoY\nxmmGYZxpGEbC+QST9Vt/6CERw6+/LrfDnMArqn387bEMfaJINNV52ML/738PPi+A3t5TTgku/FPl\n7LOlBVpUBkNBaW6WHP/06bGuv5Pjnw/CXzvRjPqEh31f6dlTfhfZmrU8U7z0UuwcIEA0hL+TOA4q\n/HXMB5DzYmlpbMHqP/4htWF+Iyg65gEkj/oA5vbbi5ajjs7579kT22HOK9aWnvYBdqoMHChx1c5O\nabeabm69Ndbx112Famoo/POBSM3cm0j4HzkiGe+f/tQU/mFO4JXtjL9T1MepuLe8PHG+Hwhf+N9w\nA7BoUbDnauE/bZpzZx/dpz3Mg6SdkhJxc5YtS99rZIKWFtknZsyQAl9NvkZ9mppEZOS74//KK/EC\n1DCCib/9+2MnErJj31eUksFkvkxy50Rnp3zGURT+9laeQDjCHzALfDVBYj6Av6gPINtfVibnZ+uc\nEVFn/PjUHH8t/A8flgF1WDl8QH6zixaJ2++39iAMunWT88ru3RT++UDOCP/nnpMD2de+JlnNlpbU\nJvCKUjtPP1GfsrLkl0/DFP6trXIg04Mtv2jXZ+JEOaDar2Botz/dB7Ozz47OTJ1BaW6WfcJe4OvU\nojEfinsbG2WQm+/Cf/58Ob5ZWbYs8aybbnzve8A997g/7uRE5ntnn6VLRbza64iiIPydXPGwhL8u\n8AXkeLFnT+I2kG7Yhb8Xx3/1aplILNGV6agxYYLp+AcV/vv3hx/zAWR9hpGZmI8bgwfL74XCP/eJ\n1M8ykSB58EHpyNC/v7jHixaFO4FXtqI+huFv5l4vuckwhb/u+a6nW/eLdvy7dRPxb+8tne6Yj2bW\nrNx3/JubTcc/WdQnXxz/fBf+LS0ikjZsiL1/zRoRIX4iODU1MoCor3d+vKND9gu7yZDvBb4vvxw/\n4zcQHeFvP5737i3nBb9XYZwc/zfflEz/hRcCd97pPA9KMqztPL1GfVauzK18P5C64z9smDj+Ycd8\nAPP4fv754a7XD4MHi26i8M99IiX83Q50O3dKPlz3pr3oInGgU5nAq7U1edRnzRo5MFuprHQ+iQSl\nsVEGHU4H5FSEf1iDmKoqEexlZUCF07zMSbBeyp42LT7nv2uXe0efMJk1KzYek4to4T92bGyBr1On\nlnzJ+I8ald8Z/5Ur5bdvF/7r18txrKzM+7qefFLWpb9nO3V1YjDYXdh8F/5O+X5Acty7d2d3Jmun\n4l6lRDzb+6snorNTRKu1CcKsWXIOOeMMeZ/21p5eCRL1yUXhP3KkiPbaWmD4cP/P11Gf6urwHf+x\nY4Hvf995kspMoQd8FP65T04I/4cfBr70JRHngAj/l14S8a4nLdKEmfH/7W+BP/859r4PPpC8aFiZ\nWLeYD+Bc3JsNx7+0VFqIvfGG/+dbT2xOwj9Tjv+JJ8p70c5VLqInIVNK3LyPPpL77V198sXxL4So\nz8cfyyR1GzeabYoBEf7dukl7Qa88/DDwla+4C3+3CEI+d/apqhIHd86c+Md69ZLPI4ihERZux3O/\ncZ99++Q8Yp2TYNAg2b++973U5ioYPFiOnZ2d3h3/rVtzq7AXMFt6Jmue4UavXvKb3bUrfOHfrx/w\nm9+Eu06/DB4s+sraepbkJjkh/Jcvj822zZwpB6L+/eOz4V26yAHKehJ1wkvGv6VF8qH2bQHcp0P3\ni1tHH0BE3pEjse8l2eRdQLjCv6pKfvD6KotfoiL8u3SJL4rNNbTjDwDf/rb827bNn+Pfu3fuCP+m\nJtOFy1c+/lhmYe7bN9bdX78eOO8878J/9Wo5Jl5xhbvwd4sg5LPjv3WrXLEsKnJ+3G/c5/e/D7dx\ngpPjD3gT/kuWyJWM8vL4mE+YdOsm+0htrfeMP5B7jj8gV0yCxHw0w4ZJu/GwhX8UGDzYWXOR3CMn\nhH9HR6xILyqSgYDTJSelvM3e6yXjr4W/VXgvWyYO+Pr1idfvFbeOPoCIVfuAJBuO/6BBIvzffNPb\n1RQr1uI1Lfytn2emoj5A7uf8rcL/qquAn/xEMp9bt3p3/EtLc6+4N5+jPh9/LG70lClm3OfQIfn9\nnnuud+H/yCPi9g8YEMzxz1fhb4+/2PEj/BsbxT3fvTuUTcPhw/I9Ow3GvAr/TZvECHvwwfQJf0DE\nflmZnDf79Em8bC4L//HjUxP+paX5K/yHDGHMJ1/IGeFvd2wuush9J/SS8/cS9WlpkZOlPvl2dkpR\n5Re/GJ7wTxT1AWJz/keOeGuRFrbjP2iQHMSHDYufNTYRbW3y+elcYmmpDGYqK+V2Rwewd29qB1o/\nzJyZP8IfAK6/HrjtNhEiXh3/0tLcc/zr6pJfwctFysvlNz12bKzw37ABmDoVGDfOm/A/elTy/V/5\nipgIfoV/Pnf1CVP4r1ol5wDd8CBVtNvv5KAOGZJc+O/eDdx4I/DUU9LsYsqUcLbLiSFDRNAOGZLc\n8c1l4f+FL8jvKCha+KezPXW20I4/yX1yVvh//vPA3Xc7L9+1azDh7xT1GTrUjPts3y47/ic+EV+M\nF5REUR8gVvhXVHhrkRa246+LufzGfSor5UCovzulRHy//77cLi+Xz7dHj3C2NRna8c9FEdnWJttt\n7w39ne/IYMwal0rk+A8bljvCv7FRfm89e+ZnBn3JEnH7lQJOOsk8pqxfLyLOq/B/7z2Js4wZk1j4\nF2LUJ0zhr2OCYV2Bcurhr/Hi+O/eLd/5OefI+/z2t8PZLie08E9W2AvktvCfMSNYG13NsGFSU5KP\njv9xx2W3uJiER6SEv1sEob09Xvj37i0HPCe8Rn26dzdvuzn+c+fKCRoQ0ThrlrhxmYj6ALEFvl7y\n/YC8l7Adf8CM+3jFKb963XVyWRrIbMwHkCsL7e2yXbmGnrzLienTY124fHD8dTvD3r1FrOZj3EfH\nfIBYx3/9etPx37kz+UB1+XJzPUEcfwp/b+tatkzOGWE5/olim16Fv55hNuwJo+wMHix1bcny/YD8\nXufMERFcaGhhnI/C/4ILgL//PdtbQcIgUsLfj+OfiCCOv1vGf+5c0/Ffvlwc6+OPF8EeRtGhn6iP\nl3w/kD7H/6ST/HUZcRL+V14pOf8dOzJX2KtRKnfbetpjPolIlvHPBeHf2irfV/fuchLNxwJfu/Df\ntEmiJFr4Dxggx7Jknaj0cQmQ40Vbm/PvPyqO//79/o4jQTEMEf7jx7sv49fxP+ec8KM+TvgV/unG\nGvVJRpcu0nEsnQORqJLPwr9Ll+T1HSQ3yFvh77e4183xP/tsEaqtrabj36VLrEOXCsmiPr16ZV/4\na8df95b22vfaaVbK4mLT9c+08Adyt8DXr/BP5PjnQnFvU5N5khkwIP+Ef1ubZMZnzZLb/fuLW793\nrxn1AbzFfVaskIgCIIOlfv2cv/+otPN84AHgrrvS/zoHDsjxJlHeetgwOQYna89cVyfRxbPOCu/q\nUyqOf329HOP1sTndDBkigygvUZ9CJp+FP8kfckb4+5lxMGhxr1PGf8gQEaerVknLvOnT5bEpU8KJ\n+ySL+gwfbl5xyIbw1+089Xp1WzcvuDlaX/+6dCHZsoXC3yu6h78X9GDRPkDLJce/sdGcoyMfhf+a\nNWYmXzNlikTpevQwndVkwv/QITmGjBtn3ldS4hz3iYrjv2OHiPKwaW2NPe4ni/kAYuJMmpS8acGK\nFcBpp4kgDzPqE9Tx37NH3P5MtVbUs7Z6cfwLGR1vovAnUSZnhH+6HX+3qE/PnsDs2cCjj8pBWle1\nT50ajuOfLOrzs59J28a9e71n/NPl+APm7IRecCtemzRJihFfeCGzGX9AIhHLl+dega8fx79LF1nW\n+ntqb5eB7eDBuSH8rY7/wIH5l/H/4AOZUdXKlCnAP/4hxxZNMuG/YoWYEdaCf7ecf1Qy/tu3p0f4\n33CDHCs1XoQ/IJ1cHn888TLLlsmxY+DA8IT/6tXunXj0bLluV1czGfPR22P9S5zRjn8+dvUh+UNe\nCv8w23n27AmcfrqcGHSOFgivwDeZ4z9lCnDLLcA3viHCP5OOf3u7RACsLbz8Cn83R+uGG+Q7yLTj\nP2yYfO/ZnK0zCH6EPxAf32hokAhQrkzgle+O/xtvxE5KCEgNzbvv+hf+Ouaj8Sv83WpCWlvTM9P1\njh2S8w+T6mopPHzlFfM+r8L/S18Cnn46cQRO11EMGhTOIHTPHjnfuG2fvrpaV+f8eLaEP6M+iRk2\nTH6znN2WRJm8FP5eHP/W1sTCv7NThGlxsTj+TU1mHheQk/O6dak7x8ky/gDwgx/ICXjlyvQK/87O\n2OfV1IjosrqJYQn/q64Crr02O50f+vd373wSVfwKf7uYa2gQQWgtFo8y+ZzxP3pU+q6ff37s/VOm\nyG/Qr/C3GhKAu/D3G/V54gkRxWHS2CjbceBAuFfd/vIXaRywfbs45YB34T9ihBRZP/ec+zLLl8vx\nf9CgcBz/RYukUDhRVCdR3GfXLjr+UaRnT9kHObstiTKREv5ujkumHH9rxv/IEYn/KCUn5J49Y0+w\nw4bJiStZ54VkJIv6ALKdjzwi7Si99NENKvz/8Y/YXtDWfL/Gq/A3jMTCv0cP4LHHsnOAzMUJi/xk\n/AFnx7+kJLY9bJSxOv5Rifq8/75k81Plgw/E3be77yedJH/9CP/ly1N3/N2E/8cfS3cWr8X8Xtix\nQ8R4t27hDb4NQwqGb7oJmDcPeOstud+r8Adk0qZHH3V+7NAhcd7HjQsv6qOFfyISCf9MO/76PEDH\nn5DcJ1LCP0oZf6vQ6tpV8ui69R5gDghSjfski/popk0Tl8fL59C9e3xsyQvV1bF1C/Z8P+Bd+NfU\nyGcaxUueudi3nI5/drcHkAkDn3469fW8/jpw4YXx9/fvL5G+k0827xsxQkSn07Hx4EH5Xq2FvYCz\n8NffudPg0a2rz7Jlst9t3Jj4/fhhxw7ZXj9XDpPx3ntyXDzrLPlc33hDBgPbt3sX/pdfLldUy8ri\nH9Mxny5dwnf8ExEl4d+7t+wnnMCJkNwnL4V/GBl/u8P6yU/GdxYKo8DXS9RH49UdD+r4t7TEuovW\nybs0Tifsa64BXn019r5Ebn+2yXT7wjAII+OfS8I/ihn/ZcukE0uqvPGGTIbnxAMPmO8bELE5Zoxz\nr3md77cfF5yEv5vbDzgPhI8cATZvlvjMRx8lfj9+2L5d+uoPGxZezv/++4Ebb5TP4aKL5POtqJD3\n5cVUAcSk+PznnYt8dWEvIAPqo0e9GStuV0qqquT4eMopiZ+v2yc7kWnhr5SYXHT8Ccl9ckL4O83c\nm4ggE3jZoz4tLXIySMTkyTKpSVA++khOuNbi2TBIRfjr9oBA7ORdmqFD44X/2rVSlGhlzRozuhA1\ncjXqE4bjb50XIspEravPvn0iJlOd9fnAARFts2d7f45b3MepsBdwF/5unUachP/q1dJ56xOfCFf4\nh+34HzoEvPaa1AsBsu7iYuD55727/Zovf9lZ+C9ZYtZ3KeVtf9y6VVox61nfrSxeLB2dkp3TRoxw\nvgJRVyfnr0z18NeMHp3Z1yOEpIecEP5BHP9UJ/Dykqnu2zd4XvqFF4DLLgOeespfdtsLqQh/wBQZ\nXoki7jwAACAASURBVKI+hiFC5uOPY5f78EO59B5FcjXqk2rGv18/2ecNQ/b/KJNOx3/FCuAzn/H3\nnGXLpJVuqo7/m2+KmPYzL0kQ4a8H75raWnfHv1cvOfZZj5l6ssIzzoi28L/vPmkUoAc1Sknc549/\n9C/8Tz9dBhLWjl+dnXIsO/ts875kcZ+2NimKPussmazQfo7wEvMBZOC1ZUv8/Xv2yFUgFpASQoIQ\nKeHf3Ozc6SFIxt+L49+9u3k7UcbfjaAC+/HHgW99S+Ixl1zi//nJSFX4b98uf70U9+oT4KpVsWLy\nww/j+5RHBbeZbaOE/XcQVsYfyI24Tzoz/h98ALz8sr+WrsuWSewlVeH/+uvuMR83Tj7ZnMRPYxhm\n9tyO36iPUjLIshovWvhPnSpXOcK64qKjPmEI/+Zm4N57gVtvjb3/wgulLsGv8O/SRQT5+++b923Y\nIJ1srNn2ZC09f/ITOW4+84x8P7ffHvu4V+E/aZLErexkOuZDCMkvIiX8u3aNnz0XCDZzbyYc/27d\ngjmnDz4IPPRQbHvQMElF+JeUJHf8Dx40henu3eJMjR4t7U0BER07dgCnnhr4LaSVqDv+hiGF49Zc\nd1gZfyA3hL/V8S8pkfeS7DetaW5OPLBbvVo+yxdf9L49y5YBF1wgDnDQbjSdneL4OxX2JuKKK4B/\n/SvWxV+yRMwKp3kwnIS/WytPjf03sWyZOOBdu4p4tQ88gtDaClRWAscfH07G/5FHgDPPlMillfPO\nExHvV/gDwLnnSrGwZvHiWLcfiO/s89JLwNy5wB13SI3Ggw8CDz8sA6p77wWefRZ4+21ZtrFRBiWn\nn558WyZMkGOA3cSi8CeEpEKkhH/v3s7RmXQ5/sky/uly/A8cSO+Bu7g4uPCfOjWx49+zp7xvLUL0\nSWj2bDPPunSpzCZqvaISJaIu/FeulNoRa57cbzvPXHD8lywB/vAH58esjn9Rkbwfe3zFjXvuAa6/\n3v3x1aul9WOivu1WtLs+a5bMoxE05791qww4/M5WPWiQ9Py3dhS6/36ZBM8p7uHX8QdiB4oNDZIt\n17PKhhX32b1bPr9u3VJ3/NvbgV//GvjhD+Mf698f+OpXna+GJGPu3FjHf9GieOFvj/q8/76Zf//n\nP2VAoucnGTBATJ6rrpIZgu++WwyRZPVjgPxOhw+Xbm5WKPwJIakQOeHvlPPPRDvPrl3FkdPP8yr8\ngzj++/end+KqVBz/k082hb+T4w/EnrT1SWjOHDPn/9FH0Y35ANGP+jz7rPy1xlv8Ov79+8fGEazC\nPyoFvs8/L9EXJ6yOP+Av7rNrl4h6J0e5tVXiE7feKvup28yoVnbsEGE8bJh0qgoa99m4MbZVpx+u\nvdYsPK2rk/f35S87LxvE8R85UuJ5gNQOnHKKeZV1zpxwhL+O+QDBhP899wB//rPsu089JTUXbseZ\nBx/0P8AC5H3v22d201m8OD6WY4/67NoFXHop8NOfyhwCn/pU7PIXXij70LnnSiHyFVd43x6nuA+F\nPyEkFfJS+Adp56lUbM7fa9THr8BuaZF/YXfysZKK8J861Yz6ODn+gLPwnz3bFP4ffiiX4KNK1B3/\nZ5+V+FQqwn/MmFin0O74eylK/9e/vL9eEJYtc3fxrY4/IG61V+FfViZO6SOPxD+2ebO4s0OHymRP\nr7ySfH1Ll5qxvJEjUxP+9liKVy65RNop7t4N/PWvIi6HDnVeNojj/6tfiXteURH7fgER/kuXpj6R\nly7sBfwL/6NHgR//WAY8J5wA3Habs9ufKno+gEWLgL175XX1YEVjj/rs3OkcubIyaJDUdX30UXxN\nQiKchH+mZ+0lhOQXeSn8gzj+QGzcJ11RnwMH5KSXzo4MqQj/CRPkpNbc7M/xnzJFREN1tQwAouz4\nR1n4b9ok2/bJT6Ym/MePN6/cAP6jPuXlwMUXO9fchEFnpwh/N8fdyfH3WmBaVgb8v/8n7rBdrK5e\nbdaeXHmlt7iPLnQFUov6bNoUvMVtcbH0mf/rXyVHfsMN7suWlPhr5wlId6BvflP64VvfLyDFrUOG\npD6Rl13479/v3MzBiYUL5Rjz6quSwb/11vQ0RgDEmX//fTPfbz9WW6M+hiHvK5nwDwodf0JI2OSl\n8Pfi+Le2Ogt/P45/kKjPgQPpjfkAqQn/3r3lpLJ9u4gyJ7HgJPx1EeBf/iInRjc3MgpEOerz7LPA\nZz8b73D7zfiPGiXfkRbufoX/Bx/I36DtapOxbZv81rw6/n6iPmVl8hmWlEj0wopV+H/mM1Jsm+yz\n0IWuQOqOfypzW1x7reTaW1qkJagbblGfRI4/IMWpe/dKq2F744Ewcv7WqE/v3nL89Voo/dJLZgvW\nSZOAm29On3miC3yd8v1ArPDX+2SiQVUq2IV/ZaU5lwAhhAQhL4V/UMc/E1GfdOf7gdSEf8+ecnJe\nvlwEhFM3JS38dQ9/Xdg2e7bkcKMc8wGi7fhbhb/V4fbr+HftKt+L7gzkV/gvWiR/3ebWSJWlSyU7\n7Sb87Y6/16jP4cOy7w8cKO71/ffHPr5mjSn8Bw+WIvQ333RfX3u7DBZ0v3yvGf+NG2NrDDo6pLh3\n0qTkz3Vjzhxx3t2KejW9e8v3az0GJov6AHLcePRRqUOwx1tOP10GQKlgdfwB73Efw5AOTJddltrr\ne2XGDBmkvPaas/C3/jZ1zCddg5BJk+RKkb4y8tprUjPAHv6EkKBESvj36hUvNDo75SDn50AXJOMP\nZMbx378/tid0Ouje3duU8naswv/jj91nhtQn7Opq+cy0oJwzRyaXiXLMB4iu8N+1S9zqs8+Od7j9\nCn8gNu7jt7h38WL5zaVL+Ov2mA0NztlxJ8ffS9SnrEyudigFXHMN8M47Zr9+w4h1/AHg8sulp78b\nGzeKy19SIre9Rn1+8YvYjkV79shAw/qe/KKUXMG4+ebky9mvaiUr7tWcdpp0lepiOzPMmpWa8O/o\nEJPAGokZNsyb8F+7Vo7VQesj/NK9u5gYVVXyedixOv67dqUv5gPIQA+QbQGkJuXTn07f6xFC8p9I\nCX+ndp7t7f7cfiBzGf+oOv4DBsjn6Fe06fc8bpy0WnQq7AVM4W/Pms6eLX+j7vhHNerz/PMiRIuK\nwhH+EyY4C/9kxb319eLOTpmSXsf/jDPkPTkNwoJ29dHCH5D3e911IsIBcep1K0nNRRcBb7zhnjXf\nssVsawl4j/rU18f2g0+lsNfK6NHe2uTa4z5eHP9EnHKKfBZBu0Ht2yevb92Hdc4/Gdrtz6TLfe65\nYmQ4XfG0Cn8vhb2poJQZ92ltlfkALr44fa9HCMl/Iif87ULDb8wHSO74G4bzpGBWx//IkfQV96Zb\n+BcVidu7dau/51kd//Xrkzv+duE/fLhMWDN1atAtzwxRdfw3bDCz5Hah6zfjD5iOf2dnrIOeLOrz\n4Yfi8JaUpCfj39oqk71Nny7drZziPnbH/7jjYic0c8Mq/AHgP/8TeOIJea7d7QdEVHV0SM2BE/v3\ny36tGTxYBHWyoueGBhk86885lcLeIFiFf0eH/F9ftQhCjx4ycFm9OtjzN26UTlVWvEZ9rPn+TPHN\nbwL/8z/Oj+moj2GkX/gDpvBftEj+r68CEEJIEPJS+CebwKutTZaxO0h+M/5Boz7pFv6A+3TvibAK\n/85Od8d/6FBn4Q/IxEh+v69ME1Xhb41jhBn10e65/l6SCX/dzcSt5iZV1q0TsdSnj4hRe2eftjb5\nV1xs3nfBBeJ2Jhto24X/0KESjbnjDmfhr5Rkpt3mE9BduDRdugAjRiSP+9TXyzFGT2oXluPvFavw\nLy+X9+Bn9nMnUon7rF0rVw2suAn/piZzHoaKCtmH7b30083Qoe5zLvTsKb+lpiYR/kHmC/CDPpa/\n/DJjPoSQ1MkJ4e/3hFVUlDjq09bmfLncnvFPNrti0KhPujP+QGrCf/Ro+QyTOf652k9aC8ogdRDp\nxCr87cWsqQh/a8wHSC78Fy0SoZUu4W9tF1lSEu/4a7ffOjAvLZV9evHixOu2C38AuOUWaQf5t7/F\nC39AhP8bbzivz2mg7iXnX18v69Vxn2w6/tu3S+wrVWbNkohWENauBaZNi73PLeN/yy0yUJs8WeKD\nF10UH8vMNjruk+6MP2Aey5nvJ4SEQU4I/3Q4/k4nkkz08Y+q46+vXHTrJv9Gj3Z3/HUEY9263BT+\nQDRdf2uvdV3MqmNpra2xDrgXRo8Wx7SqKlb4JyruPXpUijvnzJHl0hH1WbrUjDS5CX9rvl/z6U8n\nLsQFnIV/nz7S13/TJmfhf8EF0rfd6bfsNFD30tmnoUFy6e+9J9/hpk3Zc/y3bYvv0hOEVBz/devi\nhb9Txv/11+XfRx8BBw/KvAU//3mw10wngwbJ9pWVmV3N0sWkSbJ/NjY6FxsTQogfUhL+SqlblFLr\nlVJrlVJPKKXifHSl1D1KqW1KqdVKKYfTrkmYGf9kjr+b8PfbztNP1McwMpPxB/wLf/v7HT/e3fFX\nSk7aK1ZQ+IeJ1fHv0UP24+Zms97Eb3Fjt27iTq9dG+/4uwn6FSski923b2Yc//7946M+jY3O3W8u\nvTSY8AeAr38dWLDA2fkeNEje84cfxj/m9Hv1UuBbXy8TTC1dKq5wz56Z7b1uF/5hOP4nnSRXOtwm\nXXOjtVXqjexXPOxRn/p64BvfAB58ULa/Wzdg7txoHmMGDpTWsKWl/gfkfhkzRo4Bl1zCNp6EkNQJ\nLPyVUiMA/B8A0w3DmAagK4CrbctcDGCcYRgTANwI4E+J1unUzjOTjn+QjL8fx18LzVRa+nll4kQ5\n4SfrbqSxv98f/lAusbtRWiriMd1uV7qIYmcfe8tFnfMPEvPRjB8vYt5r1Mc6aVE6hL/ORev8tB/H\n/9RT5TG3onXDcBf+3boBP/6x+7HELe4TJOpz9Khsy9Ch8jt87LHMuv1AeqI+XbuK47xihb/nbdki\n4t1+PLUL/+9/X8TtJz+Z8qamnUGDZK6TdOf7Adl3TzyRMR9CSDikGvUpAtBbKdUVQC8AFbbHLwfw\nGAAYhrEEQIlSyjXh7tTOM6jwD+r4+23n2d7ufdr5TMV8ABlcDBokM3F6wf5+zzsvsagvLRXXyyoo\nc4moOf7t7bLvWz/PMIT/hAkS3fEq/D/6CDjrLPm/00A8VcrKRDjrGhsn4e/m+CuVOO5TWyu//SD7\npJPwNwyJc9hnoU7m+NfXy/tSShzrP/85s/l+ID1RHyBY3Mcp3w/ETgT42msy58KvfhXOdqabQYPk\nc0h3vl/z0kvS6pcQQlIlsPA3DKMCwG8A7AWwD0CdYRhv2RY7DkCZ5fa+Y/c5kql2nq2t4UR9lBKh\n4TXuk0nhD4jL6DXu47ddZGlpNC/BeyVqwr+uTsSideIkXeCbquO/apV34X/woGTYAeeBeKrYfwNO\n7TzdHH9A4j6vvOL8WFkZcPzxwbZrzhwRyIcOmffV1srnbi/yT5bx18IfEOFfUZE9x7+jQ6JGURT+\nvXvL8bOsTGYjfughuRKXCwwcaHanygRjx8ZPqkYIIUEI3OBNKdUf4uiPBlAP4Gml1DWGYTwZdJ1P\nPTUfGzYA8+cD8+bNw7x580Jx/A8flpP69OlyO6yoD2DGfbxMqpOpfL9G5/y9TPhSaMI/alEfp5lV\ndYFvnz7+e/hrxo8XIe1V+Fv7vffubc56GxZ24V9SIm1hrbg5/gBw/vnAl74EVFbK76myEvjUp2QQ\n7hbz8UL37lJwvHy5+Xtx68CVLOpj7aJ0zjmybdlw/OvrZYAyaFDwgaOdWbMkBuiHtWuBb3/b+bHS\nUuDaa6UQ+rzzUt++TDFokJxHMiX8CSEkEQsXLsTChQs9LZtKZ+cLAOw0DKMGAJRSzwI4E4BV+O8D\nYD0Vjzx2nyM33TQf69eL8NcEmbnX7vgvXAj8/vfmpfywinsBfwW+mWrlqZk0SQrQvODXVT7jjOAi\nKwpEzfF3E/61tdJdKRXHH4jv6uPm5NfXm8umI+PvJPz9OP69ewPz5smg88QTZX3PPisCOxXhD8iM\n1bt2mbfdBurDhsmVkfZ251bDVsd/4EDgttuAGTOCb1cQSkpkABJmzAeQz6ix0d/VSzfHH5DjYVmZ\n+1WcqKIbH1D4E0KigDbLNQsWLHBdNpWLh3sBzFFK9VBKKQDnA9hkW+ZFANcBgFJqDiQO5DpXY3Fx\nfLFsGI7/0aOxQiesjD/gr8A301EfP519/Dr+l1wil+dzlVwS/qlEfU44QSICfhx/vWw62nlWViYX\n/okcfwB45hnZrnXrRFQ/+qjcn6rwHzs2dnZgt99rt24yGLO3otRYB08AcNddqc2aGwQd9QmrsFej\nlMzMvXGjt+WrquT7dItgXXONtOzMRMODMNEdmjJR3EsIIWGSSsZ/KYCnAawCoH3lB5RSNyqlbji2\nzKsAdimltgO4H4DLBV/BKZsfRsa/tdW78A8S9Ylqxj+dwj/XyZWojxb+Qb+b4mIRXV6Ef2eniDSd\ns06X4z98uHnbqZ1nIscfkN+uPiZ86Uvi+Dc1hS/87bP2Wpk4UZxsJ6xxqWyhhX9YrTytJDquGIZ0\nT/rXv+S27t/v1obyppuAM88Md/sywaBB8jvK5BVcQggJg5TKhQzDWGAYxmTDMKYZhvEVwzDaDMO4\n3zCMByzLfMcwjPGGYZxiGMbKROtz6saTScc/SMbfz+y9iYREOhg+XN5HTU3yZQtN+OeS49/SklpG\ne8KEWCHqJvwbG+V19O8tW1GfxsbEwt/K8OHSheiZZ6SDVSYcf0BaTr75pvNjdsc/G1iFf5hRH0CE\n/5Ytzo8tWCDfxXXXyTKJYj65zNixwBVXsK8+IST3iFSfAKf++x0dzjnaRKTq+FtnsU1GlKM+SiU+\nSVuh8M8uTsJ/4EAZtKUS9QGAe++N7QHuJvytMR8gPe08vWb8/UQ/vvpVifuE5fjr9ryJanIuvFBm\nmHUiSo5/2FEfwN3xv+8+4IkngHffBX72MxHGixfnp/AfPhx4MnAbC0IIyR6REv5hRX3sAwgnx9+p\nC4/O+PsRwX6LezMp/AE5SW+yV144UGjCv2/f6Av/MDL+gBTBWoW0W3bfLlgz1c7TaeZer44/IC0+\n160Tx3/kyODb1r+/HDuqq+V2oi5c06dL68+ysvjHrMW92aJfP9l3du2SgtwwmTgxXvi/9x7w05/K\nYGjoUJkpee5c4Omn81P4E0JIrhIp4R9W1KeoKHY9fh1/PyLYq+NvGJmP+gDee/kXmvDv0yd3Mv5h\nfzdujr89ohJ21Ke9Xa5gDBli3tenjwy2rQN1v45/cTFw9dXyeaX6OVnjPokG6l26ABdc4DzbbxSi\nPn36yOcYZitPzQknSFcj6zH1hReA73wntsvN734H3H67zLhMCCEkGkRO+KfL8W9pkeJFIHnGv6Ul\nftIeN7wW97pNBpRunNw5JwpN+BeS429HC3/7jNP2qE/Ywv/QIRGi1t+zUvJd6FlmAf+OPyAdpj7x\nidS30Sr8kw3UL7rIOe4ThahPUZGI/7BjPnrd48cDW7ea9y1ZIpOgWSkulshPIR1XCCEk6kRK+IcZ\n9bE7/oDpciZr53nkiL+ojxfHPxsxH0BO/Nu3J1+u0IR/LmT80yX8u3YVx9o+YLVHVMLO+Lv9Buxx\nH7+OPwCcfDLwz3+mtn2AKfw7O2WgMnSo+7IXXgi8/Xb8VcooOP6AbEPYhb0aq6HQ2irzhcycmZ7X\nIoQQEh6REv5hRn3sjj9gXprORtQnW8JfT0pk/1ztFKLwz5WoT9jCH5Dv2p7fd3L8m5vjrwwExd7D\nX2Mv8A3i+IeFFv7V1bJdiQr8R4yQf8uXx94fBccfkO8yHY4/ENs0YO1a6Wev28ASQgiJLpET/nbH\nP8jMvW6OvxY6ra3Joz5+hL+XqE+2hH+vXhKvKC+Pvf+OO2LFVqEJ/1yI+hQXy/518GD4302vXvE5\nf7vwLyqS19eT2qWKvYe/xi78gzj+YaGFv9ff60UXxef8o1DcC6Rf+GvHf8kSYPbs9LwOIYSQcImU\n8E/XBF7pdPy9Rn327XMWPZlgwgTp561pawN++cvYnuWFJvxzIeoDyH379qXH8bcLfyfBGmbcx01M\n24X/4cO5I/yd2npGJerz3/8NnH9+etZtjfo45fsJIYREk0gJ/3RN4GV3/JNl/NMR9VmzRnLI2WD8\n+Fjhv22bfAbWXHUhCv+oRH06OkRcO4nFTAp/u+MPhNvS00vGv7VVlkulLWcqjBolkaSyMm8duM48\nE1ixIjYOFZWoz4UXpm8AMnGiFPd2dgIff0zHnxBCcoXICf9cdPy9RH1WrgRmzPC2zrCxF/iuWyd/\nC134NzaGl1/3wne/C+zeHX9/XZ0ItC4Ov8YBA4CKiuwK/0w6/jt2iPh2mmcjE3TrBhx3HLB0qTfH\nv08fiQjq2bE7O2W/yve8e79+MmBbu1a+15NOyvYWEUII8UKkhH+XLiLEdNtNINjMvUEd/6AZ/2SO\nf1OTCL5snRztUZ/16+VvIQv/rl3Dza97YeHC2BaImpoa55gPILP3trWF/904FfdGIeqzZYvkx7PJ\n2LHARx95n3Nj5EizhqaxUT4zv2ZFLjJpEvDYY2JoFML7JYSQfCBSwh+Id/0z7fgHifokc/zXrAGm\nTEncISSdjB8f7/gfd5zkyjWFJvyBzMd9WlpMZ9iKW74fMO8P2/H3UtwLZMbxt0Z9Nm+WGEk2GTNG\nfiNei/FHjTKFf1Ty/Zlg4kTgiSeY7yeEkFwiksLf6taHMYFXa6u4itkq7l25Epg+3dv60oG9pef6\n9cA55xS24w9kvrNPc3PsYEuTDeGfiYz/7bcDv/+9edur459t4T92rFx59Cr8rY5/VDr6ZIJJk6Tj\nFPP9hBCSO0RO+Nvd+qCOv3XwcPSouIrauWxrc84Qp6uPf7aFf69ewODBUrDY1CSZ8ZkzKfwz3dkn\nFcc/HVEfL119UnH8338fePpp+X9Tk/zunNxwq/DfvDkaUR8gWNQnKoW9mUB/TxT+hBCSO0RO+IcR\n9XFy/AcMSE/G30txb7aFP2DGfTZuFEd1yBAK/0xHfXLR8Q+a8e/okIjbihXy/nQPf6Xily0pkX3R\nMKLj+APBHf9Cifqccgowd2722hQTQgjxj8+y2fQTRtTHyfH3Ivy7dZMBQ1OTd7cvmeN/5IgUdGar\nladGF/gWFwNTp8bmqoHCFP6ZjPp0dsp+6NfxHzhQ/mZi5l4n0Ro06rNtGzB0KDB5MvDmm1JT4iak\n+/eX166qkttDhvh/vTAZO1aOBYMHe1u+UKM+paVSsE4IISR3iJzjH0bUJ6jjr5QI+fr68Ip7168X\n0d2jh/ftTwda+K9bJ4MQCv/gUZ/2dm9zN1jR7npUoj724t6ODrltnzgraNRn1SrgtNOAT38aeOWV\nxBNi6aiPLux1uiqQSQYNkt+t1+OOPepTKI4/IYSQ3CNywt8u2tvbU5/AS2f8tfBvbXXvsFNcLII4\nrOLeKMR8ADPqs369KfzZ1SdY1Od3vwN+9CN/z9EiO0jUp7jYucd/KtijPnq2XPvrBI36aOF/ySXA\na69JXUki4V9XF42Yj+bEE70vO3Kk1M8YRmE5/oQQQnKPSAr/MKI+9naeXhx/QJz52lrvDn2yqE9U\nhL/V8bdHfTo65DMpLs7uNmaaoFGfqipg8WJ/z9H7XhDHP+yYDxAv/N2y6UEdf73fn3CCRHdefjl5\n1CcKhb1B0JOvNTQUVnEvIYSQ3CNywj+sqI99Ai+r459I+Adx/BNFfaIi/MeNE8f/yBFxKK3C/8gR\nGehkO2KRaYJGfRobxdH2E/fRV1TcHH+d5bdz/PHAt77lfxuTYRf+boI1SMbfMEzHH5C4z5tvugv/\nHj2kBmLt2ug4/n7RcZ9CKu4lhBCSe0RO+KdrAi+vjr9f4e/k+JeVAXv3Snxiwwbg1FP9bX866NlT\nhNfUqSLw+/YV4dfeXpgxHyB41KepSb7zNWu8P6e5WQpc/Tr+PXsCd97pfxuTYS/udcum2x3/Q4eA\n6urE6y4rk9+X7vby6U8n7ouvlAw6li3LD+FPx58QQkhUiaTwt0d9uvrsPZSK49+jh3/hb3f8Z82S\n2SwHD5ascO/e/rY/XUyYYHYX0mKrvr5whX/QqE9jo3x2S5d6f05Li+wP7e1yhcVKIuGfLrxGfewZ\n/1/9CvjtbxOv2+r2A8CZZ8rnlag9Zv/+MggbN87b9kcNLfxZ3EsIISTKRFL455Lj71Tc29Agefoj\nR4Dly/1tezo5/XTgjDPM27rAt1CFfypRn7lz/Qv/Xr1kP7THfbIh/O1dfbxGfcrKgH37Eq/bHm/r\n1k2iPjNmuD+npAQYMyZ360zo+BNCCMkFIif805nxt87cm0j4d3YGj/oYRmxm3u+2p5O77gKuu868\nrXP+hSz8g0Z9zjsPWLLE+3Oam0VsDxwYK/w7OmR9mXaJnTL+XqI+FRVAZWXiddsdf0CugiX6LZSU\n5GZhr8bq+FP4E0IIiSqRE/5hd/XRf/v18+74A8GLe1tb5T1ESfC7UejCP5Woz+zZIvSscyEkQn/G\nAwfG5vzr6syuMJkkaNRn375gwj8ZJSW5m+8HWNxLCCEkN4ik8A9zAq/WVnHle/WKFf7duzs/V7fx\nDOr455KILnThHzTq09QkQnX6dClI9UJzs3zG9qhPNmI+gHNxr1vURwt/wxDhX1Hhvt6qKrmKMnas\nv+2ZMEGiaLkKoz6EEEJyAZ9ls+knjKhPUZF51eDoUXHxrVnl/9/e3cfYVdd5HP98O9NaOtAHlraU\nFqRFEFqzQnkWkFFAU4i4uihrNlExEYIoZrNxxQ3GYvaPdWMMmmCErI8bH1aNiomsCsqErDxTqtAi\nTxYXChYqtEhbKDP97h+/c5zTO/f5/u6d35nzfiWT3jn3zJnT6ZnTz/3e7/memBX/Mgf/RYtC8J83\nrzz7HFMvPf4HHhiq/nffLZ13XuuvyXv83fev+E9X8F+yJNxNN/fii6HHvlbx92bHjvB788ILrvex\n7gAAHK9JREFUjX+H7rsvTLHqdDTs5z7X2fqpyYP/nj1U/AEA6Uqy4l9s9en2zr2tKv79avUpU/Cv\n+sW9Bx0UAm+n8uB/yintX+BbrPinEPxXrpSefXbyhU87rT5bt0qHHx5uyLVtW/3t3nqr9OY392ef\nU7ZoUSgyTExU83cJAFAOSQb/flT889YGdyr+uaq3+ixY0Hnwdw9BeGQkVPzvuissayWv+Nde3Pun\nP4Xq+6ANDYVRs7//ffi8nVafp5+WDjsszOdv1Od/883tvQMy05iFF0Xz51fvRngAgPJILvjHmupT\nW/EfHg4fe/eGj2Zz/OfMaf9iy9o5/i+/XJ4QTfAPle5O7N0bgt2cOSHoSWHEZSuNKv5bttRvsRmE\nNWvCDeak5hX/PXsm+/uXLw/Bv16f//bt4e7Qp53W3/1O1YoV9PcDANKWXPCPMdWnuI284i9Ntvu0\nqvh3EoJr5/iXKURXPfjnx0XtDbWaydt8pPAC4I1vlB54oPXXNar4P/FEGsG/0TjPoaHwc9qzZ//g\nX6/i/6tfSWed1fh3a6Yj+AMAUpdk8I95A6+84i/1J/jXa/XJJwOlrurBX+q86p+3+eQOPbRxv3tR\ns4r/kUe2//1jWr1a2rw5PG42fz7v889bfQ47rH7wv+WWarb55Fas4MJeAEDakgv+9Vp9hjucPdRL\nxX/u3M6Ce5kv7s1HS5Zpn2PrtM+/WPGXpKVL2wv+jSr+qbf6SJN9/s0q/u7V7e/PUfEHAKQuuXGe\nsW7gNTERwkhtxX/Xrv5X/MsSoosV/+mYLJOC+fM7q/jXC/6d9PgXb+D16qshQOfXCgzaqlXhRctL\nLzVu9ZEmR3pu3Rqq/WZTe/wfeyz8fY47rv/7narzzgvvAAEAkKquK/5mdoyZ3W9mG7I/d5rZlTXr\nnG1mO7J1NpjZ1a22G6PVxyxcnLtvX/97/Gsv7i1r8C/LPsfWa6tPpxX/4g28nnwyVM+nqyd+aCjc\nLfeBB8LvSfHvVVRs9WlU8b/5Zuncc6s90eZ1r5Pe9a7p3gsAABrruuLv7o9IOkGSzGyWpKck/bjO\nqre5+4XtbjfGVJ/idvrd4197cS9TfcolRqvPs8+2/rr8Z5wHf/fpbfPJrV4t3Xln8zGUIyPhxdH2\n7eHv6z41+N9yi/Tud/d/fwEAQPdi9fifK+lxd6/X9NBRDTBGq09xO93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"text/plain": [ "<matplotlib.figure.Figure at 0x7f94f137ecc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "global_temperatures.groupby(global_temperatures.index.year)['LandAverageTemperature'].mean().plot(figsize=(13,7))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d2f3ad7d-935d-2f27-0fc8-33f196c684a2" }, "source": [ "That seems about correct. I'm guessing the instruments we had in the early years had huge uncertainty, which is why we see the data in the initial years with large variation. \n", "Anyways bottom line is the average temperature has gradually increased over the year as seen in the plot.\n", "\n", "Note: pandas rolling mean function with window = 12 will not provide the analysis we are looking for." ] } ], "metadata": { "_change_revision": 820, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/325/325674.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "f948729e-5bf7-496c-82b7-5b4e4e0be315" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "import os\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.cross_validation import KFold\n", "from sklearn.metrics import log_loss" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "55de2a4f-715c-48f8-be5d-deeb6ee92d20" }, "source": [ "# Phone brand and device model based benchmarks\n", "\n", "The task in this competition is to predict a user's age and gender group using information about their mobile phone model and usage patterns.\n", "\n", "Since many of the devices have no events data it's interesting to know how much information we can extract from phone brand and device model only. This notebook contains some benchmark approaches of inferring age and gender from data contained in `phone_brand_device_model.csv`.\n", "\n", "CV scores of different approaches obtained by 10-fold cross-validation are as follows:\n", "\n", "* 2.485 - 1/n_classes benchmark\n", "* 2.428 - class probabilities benchmark\n", "* 2.420 - predicting gender from phone brand\n", "* 2.417 - predicting gender from device model\n", "* 2.402 - predict gender-age group from phone brand\n", "* 2.395 - predict gender-age group from device model\n", "* 2.391 - mean of last two\n", "\n", "Some notes from the exploratory analysis:\n", "\n", "* Some device models could belong to different brands. So the correct way to label encode device model is to concatenate with brand first.\n", "* `phone_brand_device_model.csv` contains double entries for 529 device_ids. Most of these duplicate rows are identical and can be safely dropped, but six devices actually have different information in their rows. Of these 1 belongs to the train set and 5 to the test set.\n", "\n", "## Demographic data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "d152ad32-d74b-430a-b1f1-2512da20c1f3" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>device_id</th>\n", " <th>gender</th>\n", " <th>age</th>\n", " <th>group</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-8076087639492063270</td>\n", " <td>M</td>\n", " <td>35</td>\n", " <td>M32-38</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-2897161552818060146</td>\n", " <td>M</td>\n", " <td>35</td>\n", " <td>M32-38</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-8260683887967679142</td>\n", " <td>M</td>\n", " <td>35</td>\n", " <td>M32-38</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " device_id gender age group\n", "0 -8076087639492063270 M 35 M32-38\n", "1 -2897161552818060146 M 35 M32-38\n", "2 -8260683887967679142 M 35 M32-38" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gatrain = pd.read_csv('../input/gender_age_train.csv')\n", "gatest = pd.read_csv('../input/gender_age_test.csv')\n", "gatrain.head(3)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "f291bb29-8a4f-4616-9fe8-4258f019f752" }, "outputs": [], "source": [ "letarget = LabelEncoder().fit(gatrain.group.values)\n", "y = letarget.transform(gatrain.group.values)\n", "n_classes = len(letarget.classes_)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "093854be-39f1-436d-b58e-ab15e0dada48" }, "source": [ "## Phone brand and model data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "dfbf25d0-6136-4680-9c85-881a58f0e824" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>device_id</th>\n", " <th>phone_brand</th>\n", " <th>device_model</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-8890648629457979026</td>\n", " <td>小米</td>\n", " <td>红米</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1277779817574759137</td>\n", " <td>小米</td>\n", " <td>MI 2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>5137427614288105724</td>\n", " <td>三星</td>\n", " <td>Galaxy S4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " device_id phone_brand device_model\n", "0 -8890648629457979026 小米 红米\n", "1 1277779817574759137 小米 MI 2\n", "2 5137427614288105724 三星 Galaxy S4" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phone = pd.read_csv('../input/phone_brand_device_model.csv',encoding='utf-8')\n", "phone.head(3)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c26a9891-24da-49a9-aaea-a0eb6fa122f8" }, "source": [ "### Duplicate devide_ids" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "a0c38a59-ac12-461a-ad23-af017db7f192" }, "outputs": [], "source": [ "phone = phone.drop_duplicates('device_id', keep='first')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "081f957a-3946-416a-8a3c-3e9298e28f8e" }, "source": [ "### Any models that can belong to different brands?" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "f432cc4d-f797-4fe4-a179-b201392c898b" }, "source": [ "Some device models can belong to more than one brand. So the correct way to label-encode device models is probably to concatenate with brand first." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "b57ec496-6f73-4a01-b0f1-3febd0b68487" }, "outputs": [], "source": [ "lebrand = LabelEncoder().fit(phone.phone_brand)\n", "phone['brand'] = lebrand.transform(phone.phone_brand)\n", "m = phone.phone_brand.str.cat(phone.device_model)\n", "lemodel = LabelEncoder().fit(m)\n", "phone['model'] = lemodel.transform(m)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b210418e-6771-4a28-93db-8f70f3a24af4" }, "source": [ "### Gender ratios by phone brand" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "80daeeab-43eb-4397-81ea-7a216702e4a5" }, "outputs": [], "source": [ "train = gatrain.merge(phone[['device_id','brand','model']], how='left',on='device_id')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "2de703fa-36b5-449a-844b-8f1fa5b32a9b" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>device_id</th>\n", " <th>gender</th>\n", " <th>age</th>\n", " <th>group</th>\n", " <th>brand</th>\n", " <th>model</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-8076087639492063270</td>\n", " <td>M</td>\n", " <td>35</td>\n", " <td>M32-38</td>\n", " <td>51</td>\n", " <td>843</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-2897161552818060146</td>\n", " <td>M</td>\n", " <td>35</td>\n", " <td>M32-38</td>\n", " <td>51</td>\n", " <td>843</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-8260683887967679142</td>\n", " <td>M</td>\n", " <td>35</td>\n", " <td>M32-38</td>\n", " <td>51</td>\n", " <td>843</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " device_id gender age group brand model\n", "0 -8076087639492063270 M 35 M32-38 51 843\n", "1 -2897161552818060146 M 35 M32-38 51 843\n", "2 -8260683887967679142 M 35 M32-38 51 843" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ptrain = gatrain.merge(phone[['device_id','brand','model']], how='left',on='device_id')\n", "ptrain.head(3)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "67b42d32-c197-46e4-9540-2713b9c82603" }, "source": [ "### Benchmark: predict gender-age group from phone brand" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "3a5fd260-f866-4385-9ecc-011c7387372e" }, "outputs": [], "source": [ "class GenderAgeGroupProb(object):\n", " def __init__(self, prior_weight=10.):\n", " self.prior_weight = prior_weight\n", " \n", " def fit(self, df, by):\n", " self.by = by\n", " #self.label = 'pF_' + by\n", " self.prior = df['group'].value_counts().sort_index()/df.shape[0]\n", " # fit gender probs by grouping column\n", " c = df.groupby([by, 'group']).size().unstack().fillna(0)\n", " total = c.sum(axis=1)\n", " self.prob = (c.add(self.prior_weight*self.prior)).div(c.sum(axis=1)+self.prior_weight, axis=0)\n", " return self\n", " \n", " def predict_proba(self, df):\n", " pred = df[[self.by]].merge(self.prob, how='left', \n", " left_on=self.by, right_index=True).fillna(self.prior)[self.prob.columns]\n", " pred.loc[pred.iloc[:,0].isnull(),:] = self.prior\n", " return pred.values\n", " \n", "def score(ptrain, by, prior_weight=10.):\n", " kf = KFold(ptrain.shape[0], n_folds=10, shuffle=True, random_state=0)\n", " pred = np.zeros((ptrain.shape[0],n_classes))\n", " for itrain, itest in kf:\n", " train = ptrain.iloc[itrain,:]\n", " test = ptrain.iloc[itest,:]\n", " ytrain, ytest = y[itrain], y[itest]\n", " clf = GenderAgeGroupProb(prior_weight=prior_weight).fit(train,by)\n", " pred[itest,:] = clf.predict_proba(test)\n", " return log_loss(y, pred)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "faa51ded-e522-4223-9e08-37a91c0a382d" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f1f468fa518>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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H4pMmTcKKFStw8eJFGI1GrF271ua2dO/eHefPn292ztBTTz1ldoVE/X/1r1kyduxY7Nix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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f1f492a1da0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weights = [0.5, 1., 5., 10., 20.,40.,100.]\n", "res = [score(ptrain,'brand',prior_weight=w) for w in weights]\n", "plt.plot(weights, res)\n", "plt.title('Best score {:.5f} at prior_weight = {}'.format(np.min(res),weights[np.argmin(res)]))\n", "plt.xlabel('prior_weight')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "08a816e6-9364-4281-bfcd-308ec4a622a6" }, "source": [ "### Benchmark: predict gender-age group from device model" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "98279f0f-c400-4a28-aa66-248eec6e19f7" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f1f468837f0>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZszF9+nQsXLhQPWrQ+rzz7T2gnj17Ii0tDRs2bMDo0aMRFRWFjz76SGuAFBcX46uvvsLp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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f1f81aefeb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "weights = [0.5, 1., 5., 10., 20.,40.,100.]\n", "res = [score(ptrain,'model',prior_weight=w) for w in weights]\n", "plt.plot(weights, res)\n", "plt.title('Best score {:.5f} at prior_weight = {}'.format(np.min(res),weights[np.argmin(res)]))\n", "plt.xlabel('prior_weight')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "0a650f8e-6285-44ae-b4f3-cd72aaea5532" }, "source": [ "What if we combine predictions from phone brand and device model?" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "1bc60841-9ddb-4563-91c8-8077d65519ca" }, "outputs": [ { "data": { "text/plain": [ "2.3909032414810976" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kf = KFold(ptrain.shape[0], n_folds=10, shuffle=True, random_state=0)\n", "predb = np.zeros((ptrain.shape[0],n_classes))\n", "predm = np.zeros((ptrain.shape[0],n_classes))\n", "for itrain, itest in kf:\n", " train = ptrain.iloc[itrain,:]\n", " test = ptrain.iloc[itest,:]\n", " ytrain, ytest = y[itrain], y[itest]\n", " clf = GenderAgeGroupProb(prior_weight=40.).fit(train,'brand')\n", " predb[itest,:] = clf.predict_proba(test)\n", " clf = GenderAgeGroupProb(prior_weight=40.).fit(train,'model')\n", " predm[itest,:] = clf.predict_proba(test)\n", "log_loss(y, 0.5*(predb+predm))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "3b11dd56-4473-4b9a-99a4-7d79268ca0d6" }, "source": [ "Down to 2.391.\n", "\n", "## Make a submission" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "6039cc6d-65a7-4d34-a489-4839415af277" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>device_id</th>\n", " <th>brand</th>\n", " <th>model</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1002079943728939269</td>\n", " <td>51</td>\n", " <td>857</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-1547860181818787117</td>\n", " <td>51</td>\n", " <td>860</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>7374582448058474277</td>\n", " <td>31</td>\n", " <td>717</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " device_id brand model\n", "0 1002079943728939269 51 857\n", "1 -1547860181818787117 51 860\n", "2 7374582448058474277 31 717" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ptest = gatest.merge(phone[['device_id','brand','model']], how='left',on='device_id')\n", "ptest.head(3)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "5b6941b8-7ce2-48c4-ad81-e8e646e91759" }, "outputs": [], "source": [ "clf = GenderAgeGroupProb(prior_weight=40.).fit(ptrain,'brand')\n", "predb = clf.predict_proba(ptest)\n", "clf = GenderAgeGroupProb(prior_weight=40.).fit(ptrain,'model')\n", "predm = clf.predict_proba(ptest)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "6d16ec6a-fadb-4a0a-b50d-ee36d4530396" }, "outputs": [], "source": [ "pd.DataFrame(0.5*(predb+predm), \n", " index = ptest.device_id, \n", " columns=letarget.classes_).to_csv('pbm_subm.csv', index=True)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "f7c914e7-a972-5628-bbfc-874d2c90443c" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 210, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/325/325705.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "932ebb10-ffd5-4181-52ae-7ed367a85a6e" }, "source": [ "This notebook shows which user traits make each demographic group more or less likely. It uses the linear model built in the [parent script](https://www.kaggle.com/dvasyukova/talkingdata-mobile-user-demographics/a-linear-model-on-apps-and-labels). What can the coefficients of logistic regression tell us about demographic groups?" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "10e55365-83e8-f7b6-a26e-aaa376cad62a" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "%matplotlib inline\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import os\n", "from sklearn.preprocessing import LabelEncoder\n", "from scipy.sparse import csr_matrix, hstack\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.cross_validation import StratifiedKFold\n", "from sklearn.metrics import log_loss" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "29ab3f35-c097-465e-e2a5-4efe1aa836aa" }, "source": [ "## Load data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "e76e779c-b6b2-d800-1150-b920c610e9ff" }, "outputs": [], "source": [ "datadir = '../input'\n", "gatrain = pd.read_csv(os.path.join(datadir,'gender_age_train.csv'),\n", " index_col='device_id')\n", "gatest = pd.read_csv(os.path.join(datadir,'gender_age_test.csv'),\n", " index_col = 'device_id')\n", "phone = pd.read_csv(os.path.join(datadir,'phone_brand_device_model.csv'))\n", "# Get rid of duplicate device ids in phone\n", "phone = phone.drop_duplicates('device_id',keep='first').set_index('device_id')\n", "events = pd.read_csv(os.path.join(datadir,'events.csv'),\n", " parse_dates=['timestamp'], index_col='event_id')\n", "appevents = pd.read_csv(os.path.join(datadir,'app_events.csv'), \n", " usecols=['event_id','app_id','is_active'],\n", " dtype={'is_active':bool})\n", "applabels = pd.read_csv(os.path.join(datadir,'app_labels.csv'))\n", "labelcats = pd.read_csv(os.path.join(datadir,'label_categories.csv'),\n", " index_col='label_id',squeeze=True)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "bff3d889-5bc9-5182-7e56-bbd9858216ba" }, "source": [ "## Feature Engineering" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "f81678cf-e762-19b5-0a67-cf6430689886" }, "outputs": [], "source": [ "gatrain['trainrow'] = np.arange(gatrain.shape[0])\n", "gatest['testrow'] = np.arange(gatest.shape[0])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "1e93897e-518c-8ea3-4e55-b204f0d08985" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Brand features: train shape (74645, 131), test shape (112071, 131)\n" ] } ], "source": [ "brandencoder = LabelEncoder().fit(phone.phone_brand)\n", "phone['brand'] = brandencoder.transform(phone['phone_brand'])\n", "gatrain['brand'] = phone['brand']\n", "gatest['brand'] = phone['brand']\n", "Xtr_brand = csr_matrix((np.ones(gatrain.shape[0]), \n", " (gatrain.trainrow, gatrain.brand)))\n", "Xte_brand = csr_matrix((np.ones(gatest.shape[0]), \n", " (gatest.testrow, gatest.brand)))\n", "print('Brand features: train shape {}, test shape {}'.format(Xtr_brand.shape, Xte_brand.shape))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "850876c4-f13a-7854-f875-9f68617a6333" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model features: train shape (74645, 1667), test shape (112071, 1667)\n" ] } ], "source": [ "m = phone.phone_brand.str.cat(phone.device_model)\n", "modelencoder = LabelEncoder().fit(m)\n", "phone['model'] = modelencoder.transform(m)\n", "gatrain['model'] = phone['model']\n", "gatest['model'] = phone['model']\n", "Xtr_model = csr_matrix((np.ones(gatrain.shape[0]), \n", " (gatrain.trainrow, gatrain.model)))\n", "Xte_model = csr_matrix((np.ones(gatest.shape[0]), \n", " (gatest.testrow, gatest.model)))\n", "print('Model features: train shape {}, test shape {}'.format(Xtr_model.shape, Xte_model.shape))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "d4e074f1-a1cd-c69e-f2e1-d2ea6aae8a27" }, "outputs": [], "source": [ "appencoder = LabelEncoder().fit(appevents.app_id)\n", "appevents['app'] = appencoder.transform(appevents.app_id)\n", "napps = len(appencoder.classes_)\n", "deviceapps = (appevents.merge(events[['device_id']], how='left',left_on='event_id',right_index=True)\n", " .groupby(['device_id','app'])['app'].agg(['size'])\n", " .merge(gatrain[['trainrow']], how='left', left_index=True, right_index=True)\n", " .merge(gatest[['testrow']], how='left', left_index=True, right_index=True)\n", " .reset_index())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "74a7f87d-6eb8-7553-addb-d32b72382506" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Apps data: train shape (74645, 19237), test shape (112071, 19237)\n" ] } ], "source": [ "d = deviceapps.dropna(subset=['trainrow'])\n", "Xtr_app = csr_matrix((np.ones(d.shape[0]), (d.trainrow, d.app)), \n", " shape=(gatrain.shape[0],napps))\n", "d = deviceapps.dropna(subset=['testrow'])\n", "Xte_app = csr_matrix((np.ones(d.shape[0]), (d.testrow, d.app)), \n", " shape=(gatest.shape[0],napps))\n", "print('Apps data: train shape {}, test shape {}'.format(Xtr_app.shape, Xte_app.shape))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "b9bd3b7c-9146-ae81-ef83-e92e5bcd6868" }, "outputs": [], "source": [ "applabels = applabels.loc[applabels.app_id.isin(appevents.app_id.unique())]\n", "applabels['app'] = appencoder.transform(applabels.app_id)\n", "labelencoder = LabelEncoder().fit(applabels.label_id)\n", "applabels['label'] = labelencoder.transform(applabels.label_id)\n", "nlabels = len(labelencoder.classes_)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "a2f082a8-8e42-a914-aa0d-f1b53ca17788" }, "outputs": [], "source": [ "devicelabels = (deviceapps[['device_id','app']]\n", " .merge(applabels[['app','label']])\n", " .groupby(['device_id','label'])['app'].agg(['size'])\n", " .merge(gatrain[['trainrow']], how='left', left_index=True, right_index=True)\n", " .merge(gatest[['testrow']], how='left', left_index=True, right_index=True)\n", " .reset_index())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "8c4897b6-877a-2dc4-a546-a61dda27eb65" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Labels data: train shape (74645, 492), test shape (112071, 492)\n" ] } ], "source": [ "d = devicelabels.dropna(subset=['trainrow'])\n", "Xtr_label = csr_matrix((np.ones(d.shape[0]), (d.trainrow, d.label)), \n", " shape=(gatrain.shape[0],nlabels))\n", "d = devicelabels.dropna(subset=['testrow'])\n", "Xte_label = csr_matrix((np.ones(d.shape[0]), (d.testrow, d.label)), \n", " shape=(gatest.shape[0],nlabels))\n", "print('Labels data: train shape {}, test shape {}'.format(Xtr_label.shape, Xte_label.shape))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "31c19090-6cab-8935-c0dd-5dc7af47d5c9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All features: train shape (74645, 21527), test shape (112071, 21527)\n" ] } ], "source": [ "Xtrain = hstack((Xtr_brand, Xtr_model, Xtr_app, Xtr_label), format='csr')\n", "Xtest = hstack((Xte_brand, Xte_model, Xte_app, Xte_label), format='csr')\n", "print('All features: train shape {}, test shape {}'.format(Xtrain.shape, Xtest.shape))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "bc5e2845-0597-4bab-18c2-75deace40f40" }, "source": [ "## Build model" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "100a586a-d05b-5c3c-fb80-3cd20f07505e" }, "outputs": [], "source": [ "targetencoder = LabelEncoder().fit(gatrain.group)\n", "y = targetencoder.transform(gatrain.group)\n", "nclasses = len(targetencoder.classes_)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "b9a67b06-83ec-cbce-d810-1a4ce78f3a9d" }, "outputs": [ { "data": { "text/plain": [ "LogisticRegression(C=0.02, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='multinomial',\n", " n_jobs=1, penalty='l2', random_state=None, solver='lbfgs',\n", " tol=0.0001, verbose=0, warm_start=False)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf = LogisticRegression(C=0.02, multi_class='multinomial',solver='lbfgs')\n", "clf.fit(Xtrain, y)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "880e5f6e-0191-9833-1d27-b5bc79739e31" }, "source": [ "## Look at coefficients\n", "\n", "Logistic regression has a matrix of coefficients of shape (n_classes, n_features). Since all our features are on the same scale we can directly compare coefficients values. Positive coefficients mean that this feature makes this class more likely, negative - less likely." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "45cb26b8-fc84-ddcc-f58f-4eec40059a3d" }, "outputs": [ { "data": { "text/plain": [ "(12, 21527)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coef = clf.coef_\n", "coef.shape" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "f61f34a4-7169-acac-fbfd-a4945f608c92" }, "outputs": [], "source": [ "# convert feature index to feature name\n", "def map_feature(n):\n", " m = n+0\n", " if m < len(brandencoder.classes_):\n", " return 'brand {}'.format(brandencoder.classes_[m])\n", " m -= len(brandencoder.classes_)\n", " if m < len(modelencoder.classes_):\n", " return 'model {}'.format(modelencoder.classes_[m])\n", " m -= len(modelencoder.classes_)\n", " if m < len(appencoder.classes_):\n", " return 'app {}'.format(appencoder.classes_[m])\n", " m -= len(appencoder.classes_)\n", " return 'label {}'.format(labelcats.loc[labelencoder.classes_[m]])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "32b0f2b2-ea17-70f7-f644-4c2fe2dd3456" }, "source": [ "Here is a plot of 10 largest positive (red) and negative (blue) coefficients for each gender-age group." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "a6f5372f-3100-f3fd-22e0-1cc734ea03c1" }, "outputs": [ { "data": { "image/png": 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Xd8PX9xibN++iefNWWKdclStfvjxhYWH8+68fKpWKAwe8efLkEQkJyR3M/PkL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yKYQQQgghhBDC6CT5FEIIIYQQQghhdJJ8CiGEEEIIIYQwOkk+hRBCCCGEEEIYnSSfQggh\nhBBCCCGMTpJPIYQQQgghhBBGJ8mnEEIIIYQQQgijk+RTCCGEEEIIIYTRSfIphBBCCCGEEMLoJPkU\nQgghhBBCCGF0knwKIYQQQgghhDA6ST6Fom7dunh4eFCuXDnKli1LuXLlOH36NP369aNq1apUrlyZ\nnj17EhQUpGyTkpLC9OnTqVmzJpUrV2by5MmkpaW9cB979+6lQYMGlC9fnurVqzNq1CgeP36s1DVm\nzBjq1q1L+fLladmyJb/++qvR+y2E+LhkFusiIiJo3749lStXpmLFirRr146zZ88q27wsdr1Kly5d\nKFasGOnp6YDEOiHE+5NZ/IuKilLKd+3aRbFixdi5c6ey7G3inxDPk+RTGFi1ahVnz57l3LlznD17\nFnNzc7y8vNi/fz9//PEHpUqVol+/fsr6q1ev5urVq+zdu5d9+/Zx5coVVqxY8cL6y5Urx/fff8+Z\nM2c4ePAgqampLFy4EIC0tDRy586tlA8aNIjBgwcTFhZm9H4LIT4uz8c6BwcHpk2bxvHjxzl16hQ9\ne/akb9++SsL4stj1Mnv27CEtLQ2VSqUsk1gnhHifno9/Tk5OAMTHx7Nq1SoKFy5ssP6bxL+lS5ey\ndOlSo/dBZF2SfAoDOp3O4H3p0qVp3bo1OXLkwMTEhK5duxIUFMTDhw8BOHz4MJ06dcLGxgZ7e3s6\nd+7M//73vxfW7+LiQs6cOQFIT0/HxMSEu3fvAmBlZcXXX39N7ty5AfD09MTV1ZUrV64Yo6tCiI/Y\n87HO3NycAgUKoFar0el0qNVq4uPjiYuLA14eu14kISGBZcuWMWLECIPlEuuEEO/T8/HviXnz5vHl\nl19iZ2dnsPyfxD8hXsT0fTdAZC2nTp3CyckJW1vbTMvT09OJiIggISGB7NmzZ7rOmTNn6NOnDwkJ\nCVhZWbF8+fJM14uOjubOnTsUKlTonbVfCCFeplmzZty+fZu0tDTatGmDg4ODUva6seuJ+fPn06FD\nB2XQ9iIS64QQ79vFixe5cuUKkyZNYu/evRnK3zT+CfEiknwKA/3798fUVP+1qFSpksHUiYiICCZP\nnsyoUaOUZTVr1uTbb7+lUqVKpKWlsXnzZgA0Gs0Lk8/y5ctz+vRp7t+/j6+vr3L0/1larRYfHx9a\ntmyJu7v7u+yiEEK8MNbt3r2blJQUDh48SEpKisE2rxO7nrh06RLnzp1j3LhxL51OK7FOCPFvez7+\nLV68mEmTJjFhwoQXbvMm8U+Il5HkUxhYvnw5VapUybA8JiaGHj160KlTJxo1aqQs79u3LwkJCbRo\n0QILCwvatGnDtWvXcHR0ZM+ePYwfPx6VSkWFChVYvXq1QZ3Ozs7UrFmToUOH8sMPPyjLdTodPj4+\nmJubM27cOON1Vgjx0XpRrAP9FNxGjRrRqFEjPv30U4oWLWpQ/nzsej7WrVq1ismTJzNmzBhUKtUL\np7hJrBNCvA/Px7/vvvuOYsWKUbp06Vdum9nY7auvvuLMmTOoVCo0Gg0qlYpvv/0W0F8vunLlSuN0\nRGRJknwKA5kNkuLj4+nRowf16tWjd+/eBmUWFhaMHTuWsWPHArB9+3ZKlCgBQNOmTWnatOlL95ea\nmkpISIjBstGjRxMbG8vq1asxMTF5m+4IIUSmXpQQPkur1RISEpIh+QTD2PV8rHv06BFXrlxh8ODB\ngP4GQzqdjlq1arFo0SLKly8PSKwTQrwfz8e/P//8k9OnT3P06FEA4uLiuH79OtevX1fGd896fuz2\nbHL5ZBbJ119/bYymiw+AJJ/ipRISEujevTvly5dnyJAhGcojIyNRqVQ4Oztz/vx5VqxYwYwZM15Y\n3549e6hQoQK5c+cmNDSURYsWUbVqVaV8/PjxBAUFsWHDBszNzY3SJyGEeN6FCxfQarWULl2a9PR0\nNm3axIMHD/Dw8ABeHbueZWNjw7Fjx5T3YWFhtGnThh9//BF7e3tAYp0Q4r9j1qxZJCcnK+/79+9P\ngwYN+Pzzz4E3i39CvIokn0Lx7KMAnjh48CBXrlzhr7/+Uu5iq1Kp2Lt3Ly4uLoSEhDBiBCdLuQAA\nIABJREFUxAhiY2NxcXHBx8fnpQEpMDCQuXPnEh8fj62tLbVr12bo0KGAfoDm6+uLhYUF1apVU/Y1\nefJkmjRpYoQeCyE+RpnFupSUFKZOncq9e/cwNTWlSJEirF69WnkEwctiV2aevcnQk2loOXPmRK1W\nS6wTQrw3mcW/7NmzG9ynw9zc3GDZm8Y/IV5GpXuduUdCCCGEEEIIIcRbkOd8CiGEEEIIIYQwOkk+\nhRBCCCGEEEIYnSSfQgghhBBCCCGMTpJPIYQQQgghhBBG907vdqvVphEbm/guqzQqe/ts0l4jykrt\nzUpthazXXicnm/fdhHdKYp1xSXuNKyu1Nyu1FT68WAdZK95lte+LtNe4slJ7s1Jb4e1i3Ts982lq\nmrUeki3tNa6s1N6s1FbIeu390GS1z1/aa1zSXuPJSm39UGWl/4Os1FaQ9hpbVmpvVmrr25Jpt0II\nIYQQQgghjO6dTrsV4gmdTodGo0Gj0bzvprwWjcbsvbXVwsIi04c+CyH++7JarIP3G+/elDHaKjFX\niDcnse6fk5hjSJLP/7jp0yfh7JyLnj2/euW6bdo0Y+TIcZQvX/FfaNnLJScnk3LiHKaPU9h38k/8\n/zzOkoFD3nezXszOGtO4x//6blNStSSXq4ClpeW/vm8hxNt7NtZlGe8p3v0j77itEnPfrzcZp9Ss\nWZFt234kTx7XN97P22z7tqZMGYeraz66devFuXNnWLBgNt9+uz3TdUNCQmjWrDkHDhw1apv8/Hax\nf38AS5as+sd1GDPWnQ+8xaIffNkwYkym5eEPoukxZyZ7Z859s4pfEj9Co6PoNH0yh+cvedPmZurM\nzRvM2b6FbeMmGSyXmJORJJ8fgcjICCZNGmtw1EWn0+Ho6MTkyTMYNWoY8fHxBmUqlYqpU2dhb++A\nVqvlu+828PPP+4iKisLGxoaCBQvxxRftqVixygv3a25mRpq5CjNTM0zUaizNLYzaz2f5//kHUzdv\n5OsWrelYz1tZ3mzMCCZ17UnZwkUM1re0sMDSXKu8P3vrBhM3rmP3tNn/aP+Xg26z2u8nrofcwUSt\nplzhogz9vB05bW0zrKvNZHshsrrg4CCmTp1AaOg9VCoVRYsWY9Cg4bi5uQMwfPhALlw4r8Sl1NQU\n8uVzY9OmrQb1nDt3hoEDv6JLlx7KQbjjx3/ju+82cvv2X1hYWFCtWk0GDBhCtmzZAFi2bBHHjh0l\nNvYBTk7OdOrUlQYNGgNw4cJ5hg8fqOxXfzQ/ialTZ1O7dh0AVq9eTkCAH0lJSRQpUpQhQ0bg7l7A\noF0hIXfp0qU9tWp5Mq9jO9LMVQRFhDN50zpCo6NApaJY3vwMadMOd5fc+j5qtczfsZWjF86Tlp5O\n6QIF+aZ9Jxxt7QzqPnvrBv0XzaNbg8b0btJcWR6X8Ij5O7bxx5VLqNVqqhUvxcSuPZS6Z23dzOHz\nZ7GyMKdjPW/a1/0sw//L3hN/MOW7jYzu8CVNq9UA4Oczp1jrv5vohw+xMDOjaomSDG3THuu/B0t1\nh34NT34/dDqSU1NpXasOQ9u0e2WsG7JsERf+uqVsn6rVkj+XC5tHTyAyNob2U8Yb1J2UksLAVm2U\ntj8fm98FiblZw9ucKXrRtp07f0FkZCQAyckaTExMMTExQaVS0blzNzp37vqP95mZsmXLGySerVo1\nZvz4KZQpUw6AvHnzGj3xfOJdnHl7Mq5716oUL0mV4iWV98+P1dxz5+HQP0gSXxY/LMzMUcE7G5ua\nm5qhVqkyrU9ijiFJPj8CyckaypWrkOHs6bhxIwEwNTVj2bI1BmXLly8iOVl/dGvMGB8ePHjA+PFT\nKFRIHwjOnj3N8eO/vzT5fN9yZLNm88/7aVXTEyuLNwsuOt3bBepHiYm0qFGLKp+WwMTEhLnbv2fK\n5o0s7D/oH9cpRFbi5KQ/uPXJJ3nQ6XT873/bmTBhtJJczp272GD9AQP6UKFCJYNlWq2WxYvnUaJE\nKYPljx8/pmvXnnh4lCU1NZWJE0ezfPlihg/XxzQrKyvmzFlI3rz5uHr1MsOGDcTVNR8lS5bCw6MM\nP//8q1LXuXNnGDlyKFWqVAXgl19+JiDAjxUr1pErlwurVy9nypTxrF+/2aANCxbMpnjxEgbLnG3t\nmNajD584OqHT6dhx9BDj1q9m8+gJAGw7fJArwUFsGTsRa0srZmz5lnm+W5nRq+/TPqelsXDndkq6\nGSa7ACNXr6CEmzu7p87Gwtyc22GhStka/92ERkexe+osoh7G0X/RPArk/oTKnz5t46PERDbtD6Cw\nq+HZII8CBVkxxAcHmxxoUpKZseU7VvvtYsjn7QA4NH+psm5ScjKNRw/Hq1wFpc6XxboFz8W8fgvn\nUrHYpwDksncwqDvsQTRtJo6lbtnyGfouPj46ne6db/vdd77K6wED+tCgQWMaN272wnrS0tIwMfl4\nbgTzPqSlp2Oi/u/fgubfaufH8J2T5PMdaNOmGS1btmH//r2EhYVSr159evfux7RpE7l48QIlSpRk\nypRZZM+eHYDffjvKqlXLiI6OpnDhIgwbNpL8+d0AuHnzOjNnTiU0NIQqVaoBhgnQ778fY+3aFYSH\nh+PuXoDhw0dRsGCht2p/ZkH6yaJTp05w5swptm3bhaOjo1JeqVIVKlV6mnhu3ryRPXt2ERsbS65c\nuejatSct8uXOdH8Ldm7jyPlzJCQlkc/ZmUGt21KmUGEAhi5fjJtLbga2agPA2PWrsbKw4Jt2HWk0\najgrB/tQ4JM8AMQ+ekTL8SP5acosbP/+bJ/l5uJCjmzWbPnlAD0aNc1QnqrVsnTXTg6dPYPaRE0d\nj3J83bI12rQ0hi5fjDZNqxzx3zF+Kg45cvDdz/vY/fsxEjRJVCj6Kd+064TN32dbnlW1REmD95/X\nrku/hW84XUQII3j+b7VXr37UquUJQECAH7t3/0iRIkXZv38vjo5ODBkyQpkiN2BAH0qWLM3p0ye5\nezeYcuUqMnr0BGxsMt5y3do6O9bW+r/LtLQ0VCo1YWH3Mm1TeHgYFy+eZ8wYw+lK27ZtplKlqsTG\nxhgsr/fMbAYLCwuaNm3J+vWrlWXdu/dWXhcvXhIPjzJcuXKRkiUNk9gnffb09MLCQn+WLyIijNKl\nPXD5+2xl/foN8fXdYrDNwYP7sbGxwc2tAHfuBD/ts5UV1lZW+j6np6NWqQmNinrazwcPqPxpCeyy\n6z+veuUqsugHX4O6t/xygMqfliD20SOD5SeuXeV+XCwrWvooB8YKu+Z92o8Txxn/ZXelDS2q18T/\nzz8Mks/lP/1A2zpeHLl4zqBuZ3sH5XV6ug4TtZp7UfczfFYAh86dwSG7DR5//+68SawLexDNhb9u\nMf7LbpmW7/3zD8oWKkyuZ9ojPlzXrl1h0aJ5BAcHYWlpSe3adRgwYCimpk+HpseP/4av71YSExNp\n1KgJ/fo9PZjh5/cT27ZtJiYmhk8/LYGPz+g3fvzD8+MfP79d7Nu3l0KFinDgQABt2rTjs88aMHv2\nNAIDb6FWq6lcuSrDhn1DtmzWAFy/fo1Zs6YQFhZK1ao1SEtLU+o7ffoks2ZNZceO3UycOIbo6CiG\nDx+IWm1Cz559aNq0IfXr1+fYsVMAREXdZ86cGVy+fBFbW1s6deqqJMdr1qwgNPQeJiZqjh37ldy5\nczN27CQKFy4KwKZN6/D3301cXBwuLi707t2fGjVqvfIzSE7WMHPmVE6ePE5aWjr58uVn7txF5Mhh\nS0JCAosXz+PEieOo1WpaVa1CN++n46kfjx1l2+GDRD2MI7dDTiZ17Yl77k+oMfArfpw8AxeHnABM\n3LiOvM7O9GjUlFPXrzF9yyZaVK+F75FDVCtRkvoVKjN9yyZ+nDyT8RvWEP0wjiHLF6FWq+nduDk1\nSpWmzaSxHF+qj/MJSYks3OnLn9cuY6I2oUmVavT6e5bI3fuRzPj+W26FhmBuZkbFop8yqWvPzP//\ngZ9+P8Za/92ggo5e3rSrWw+AVXt2ERJ1H7VKxe+XLzHsi3b8n72zDogqa+PwM0NKN9jdHVhri4rY\n69q96q4bxtqtqIuugGLHmmtjK62Crrp2t5iEdNcgA/P9MXhhJFX0k937/KNzz7nnvvfemR8n3vc9\nNo2sWXP0EH63byCVSOnYqAm/9O6Lei6DxX2+p3C7dJHlP/6CIcq+/5YtmwgNDaFy5SpMmTKTSpUq\nA8oV8ZEjR3D48BECAwPx9b2Y7/vMyMhgzZqV+Ph4oKenT//+g3BxcRK+R9nfm5qaGt269WT06B8L\n/C58Kb7+qYZiwt9/+7Fq1Qb27TvChQt/M3XqRMaNG4+7+2kyMjI4dGg/AAEBr7G3n8ukSdNwcztF\n8+YtmTHjN+RyOXK5nNmzp9G1a3c8PHxp396Gc+d8hWs8fPiQZcsWM2PGXDw9fenV61tmzpyMXP75\nFvRv3LhGrVp1VAaeuVGmTFk2bNiKj885Ro36gaVLFxMVF5dr3VrlK7J79gJOObrQ2boZc7ZuIi3z\nHuYMHYnX1cvcePoEr6uXefz6FVP6DURdTZ3OjZvide2K0I7P9atYV6+Z68ATQIKEH3r05oDfaRKS\nc+6dtN3LnYevXrF79gKOL13Kg9cv2e7pjramFit/mYCZoRG+K9bi67wGU0NDXM+e4fzdO2ycPB03\nB0f0S+jgeGBPoZ7jLf+nVCpZqlB1RUQ+J+//Vhcvnkd0dJRQ/vDhfcqUKYe7+xlGjfqBOXOmkZBt\nIOTt7cGcOQs5ccIbNTUpLi75u6bb2rbHxqYVq1c7M3z497nW8fJyp379hlhZWQnHQkND8PA4yahR\nYwu8p9u3b+Zwi31HaqqMR48eUrFi5RxlMpmMs2d9scs2OdWxYxeCg4MJDAxALpfj6XkycyJQSVJS\nIlu3bmL8+Ml5rq50mjqRdr/9wspD+xlpaycc79myFXeePyMyLhbZ21S8rl2mZbZV3ZCoKNwv/8No\nu+4oUG37wasXlLOwxH7nNrpM/43vlztwy/8poFx9jIyPo0q2+LYqZcryIuRNtvNf8iTwNd+2bper\nzXeeP8Nm6gQ6Tp3A2ds3Gdg+p8suKAe5XZu1yLUM8tc6zyuXaFClqtAhfR+vq5fplu1Zi/y7kUrV\nmDBhMp6evmzcuJ0bN65z9OghlTrnz59j27Y9bNu2m/Pnz+Hmdjzz+Fl2796Jg4MTbm6nqF+/Afb2\ns4vErnv37lCxYiXc3U8zZMgIFAoFI0eO4eRJH3bvPkhIyBt27NgCQFpaGrNnT6V79954ePjSunVb\nzp8/+16LysmihQt/x8zMHGfnNfj4nKN//8HK0mxeVvPnz6J06TKcOOGNvb0DGzas5s6drMmiCxfO\nYWvbHW/vszRr1oKVKx2FsnLlyrNp03Z8fM4xfPj32NvPJSYmpsD7dXc/SWpqKseOeeHp6cuUKTPR\nzHQdXbx4HiVKlODgwRNs2LCVC3fv4nbpIgA+16+w09uDxaPG4uu8hmVjf8ZAVzfbHedNeEwssrdv\nOb7kD6YNGJJ5jvKsRaPGYmZohMsvE/F1XiMMBrO3uXDnNrS1NDliv5QdM+byz4N7gl2bThzlmzr1\nOO20mnNr1/Jdm/b52nLnuT+H7R1Y8fNEtnu5C7oK8PedW9haN+eM82o6NrJmq8dJngQGsGeOPX/N\nmsfdF8/4y8czR5ub3Y5z6vpVNv42HVMDAx4/foSjowOzZs3H09OXbt16MmvWFJX+u4eHB87Oa/Hy\n8gPyf59Hjx7k5s1r/PXXAbZs2cW5c34q36Ps723r1t1cunQRd/cTBbyVL4c4+Cwi+vbtj5GREWZm\nZtSv34BatepQpUpVNDQ0aNOmHU+fPgHA1/cULVu2onFja9TU1Bg0aBhv377l/v27PHhwj/T0dPr1\nG4iamhrt2nWkZs1awjVcXV3p3bsvNWrUQiKRYGvbDQ0NDR48uFfk9/PuOxwXF4tJto5CfHw8trbt\nsbVtR4cO3wjH27XrKNTr0MGG0qXLcO/ly1zb7mLdDH0dHaRSKYM6dCJNnsbrsFAATA0MmD5wCIv+\n2saqw64sGDFa8J/v2qwFPtkGn15XL9G1ad6dIICqpctgXaMWu0555SjzuXaF0XbdMdTTw1hfnzF2\nPfC8ejnPto5e+JtxPXtjZmiEupo6o+2643vrBhkZGfna4B8cxDYvN8Z/+12+9UREvgTv/1bLlCnL\nw4cPhHITE1NBgzp27ETZsuW5dOmCUN6lix0VKlRES0ubMWN+ws/vTL7ucV5efnh7n+W336ZRJdPD\n4X28vT1UBoAAq1Y5MXbsTwUmabh27TLe3h6Mzea6mh1Hx6VUq1ZdxVPjHWfPnsHIyIj69RsKx8zM\nzKhbtz6DB/fFxqYVZ8/6Mn78ZKF8y5ZN9OjRBzMz8zxtOuW0itNOq5nSfxBVS2etTpY1t8DS2Jge\nc6ZjM3Uir8NC+b5rd6F85aH9/Ni9d64xQ+ExMVx9/JAm1WvgscyZQR07MX3TOuKSkkhJlSEB9DJX\nXQF0tbVJzszymJGRgdOBPUwdMDhPm+tXrsJpp9Wc/H05Q2y6YGmSc/UxJCqKW8/8sWuW+wCxIK3z\nvHqZ7s2/ybXs9rOnRCcm0L5hozxtFPl3Ub16DWrVqoNEIsHKyoqePftw+/YNlTpDh45AT08PCwtL\n+vcfzOnT3gAcP36EYcNGUq5ceaRSKUOHjsTf/ykhISGfbJeVVUl69foWiUSCpqYmZcuWo1GjJqip\nqWFkZET//oO4desmAHfvKuPW+/btn6mZnYWVyLzISy/fvAnm8eOHjBv3K+rq6lSrVgM7ux54eXkI\ndRo0aIS1dTMkEgldunTj2TN/oax9exuMM70GbGy6ULJkSR4/fljg/aqrqxMXF0tg4GshPl9bW5vI\nyAiuX7/K+PGT0dTUxMjImMGdOnHqxlUATvxzkWGdu1KtbDkAylpYYGFkrLzHgq6ppsZoux6oq6mh\nqaGRx3PK/dzw2BiuPX7EpL790dTQwFhfn/7tbQS71NTUCImOJCI2Fk11depWyjnx+A4JMMauB5oa\nGlQtXQa7Zi3wuX5VKK9XuSotMz1mtDQ08L5+lbHdemKoq4uRnj6ju/bA8+oloX6GQsHKQ/u5/cyf\ndROnCp5xHh4n6NOnH9Wr10AikQh/77K/nxEjRmBmZoampiaQ//v08ztD//6DMTU1Q19fnyFDRgjt\nRESEq7w3Y2Nj+vUbKPx2vgZEt9siIvsATUtLC5Nsf7i1tLRISVGuvEVGRmJpmeWOKpFIMDe3ICIi\nHKlUmqNDk73umzdvuHr1KocOKYPXFQoF6elyIiMj+FwYGBgSFBSY7bMBXl5+BAcHMWjQt8JxT083\nXF33CsIvk6UQ+57b2Dv2nPbm5KWLwspocqqMuKREobxV3Xo4u+6jnKWlimjUrlARbS0tbvo/wdTA\nkODICFrXq1/gPfzQvRejHR2E2bN3RMTFYZntvVmZmBIZF5tnO6HRUczYvB6pkKhEKaDRCfE5Eoa8\nIzA8nMnrVzGl3yDqVfo092gRkaIgt99qXLbv/fsaZGVVUkVjLCwsVcrS0tKIjY3F2Ng4z2tqaWnT\nq1dfune3Yc+ewxgZZf1e7ty5TXR0NO3adRSO+fr6kpycTPv2Nrk1J3D//j3s7eexZMkfuWa1XLdu\nFa9evWT16o25nu/l5S4kInrHtm2befz4AUePemJiYoK3twfjx49j925XAgJecf36FbZv35tre9nR\n1tSkT6u22M6YzIH5izDS02f5gT28lcs55eiCtqYmf/l4MWmdC1unzeb8vTsky2R0aJR7vKOWpgYl\nTc3o3kI5eOvU2JodXu7cffGMBpWrogCSZCmCS29iSgo6mQP3Q3/7UaVMWWqVr1ig3WaGRjSrWZt5\n2zazc+Y8lTLPq5eoX7kKJU1zrlwWpHW3n/kTnRCf5+DS48pl2jdo9EUT04n8fwkMDGDNmpU8efKQ\n1NRU0tPTqV69pkodc/PsemNFZGQkAKGhoaxa5czatS5AVqLEsLAwSpfO3RuqsGTXOIDo6ChcXJy4\nd+8OKSnJpKdnCHoXFRWJhYWFSv13LvsfSlRUJIaGRmhly1FhaVmSly8vCp+z9zW1tbWRyVKEzx4e\nJ3F13UdYWKiQSC0unz7NO+zsehAVFcn8+bNITk6mSxc7xo79idDQENLS0ujRozMACkUGkvR0Spoq\nPeHCY6IpY573JFx+mBjo5+qqWhjCoqNJk6dhN3NKpl2gQEGpTLsm9h3AppPHGLV8CaaGhgxo1zHP\nCTMAi2x/u6xMTLn+5JHw2fK9v2uRcbEqE3NWJiZExGY94/ikRE7+c4FlP/yskmskLCwMP7/THDiw\nN9NmZf89Ilt4Q3bPH8j/fUZGRqh8T7P/Pyws9L33pgAUlPyKvO/EwecXxszMjJcvn6scCw8Pw9xc\nKV4R78XZhIWFUiYzrsfKyorhw79n2LDc42WKknczTk2aWHPkiCuRkRF5zvSHhobi6OjA6tUbqVOn\nHgAjRgzKdXbv9rOn7D7tzfqJU6mY+UPoPG2iSt0Nx49SoWRJQiIjOXX9Kp2yJSGxa9YCz6uXMTUw\noH3DxmioF/wVLm9pRbv6jdjh5aHilmBuaEhoVJSQiTI0OkoYREpycRqxNDZh7tCR+c6iZSckKooJ\na1cw2q4HXaybFeocEZHPSW6/1VGjBqv8/t6fzAoLC6V167bC5/DwsGzthaChoaEymMyL9PR0ZDIZ\nERHhKvW9vNxp27a9ygrn5cuXefLkEb16KWM7ExMTUVNT5/nzZyxdqownfPr0MbNnT2XOnAU0ykx+\nk52tWzdx9eol1q79U8iCm53w8DBu3brB9PdS+z975k/Hjp2FUIOuXbsLcWl37twiNDSUvn27AwqS\nk1PIyEhn0LMnbJuWc4uA9IwMZG/fEhEbi5GePs+CgxjXsw96JZT29G/XgS3uJ4hLSuLGk8c8DnxN\nt1lTlfeckoKampTnb4L444dfqFKqDBfv3VVp/52e6evoYGZgiH9QkJDM51lwkOD+euPpY2498+ef\n+0ovmfjkJB69fMXToECm9B+Uw255ejpvMjv52fG6epkRXexyHC+M1nlevUS7+g1zHVympqXhe+s6\ny3/8JddzRf6dODkto3r16ixatBRtbW1cXfephBqB8nf6LkN2aGio8Lu0sLBkxIjv6dTJVqW+ubk+\nERG5T3wXlveTDW7YsAZNTU127XJFT0+Ps2fPsG6dMmGaqakZ4eE5+22VCtlPyI6ZmTlxcbGkpsqE\nGPSwsFDMzCwKOBOCg4Nwdl7G6tWbqJ0Zhz18+IBCJW1SV1dn1KixjBo1ltDQECZP/pXy5SvQqFET\ntLVL4OmpfCcymQzzQH9SkpWuohbGJgRFRNBMdb4ANakUTXV1ZG+ztmSJSoijbLZBem59rOzkl/DR\n0tgYbS0tfBxX5VpuamDA7CHDAXgeFsgoBwcaVqme66QZQFhMDKUz+7dhMdEqiwnv22luZERodBTl\nMgd7odHRmGf7e2akp8+coSOYu20zjj/+Sp3McBALCwuaNRvL4MHDC3XPb94E5/s+TU3NVMYLYZne\ng8prWaq8t68R0e32C9OhQyf++eciN29eRy6Xs3fvLjQ1NalTpx516tRDXV2dQ4f2I5fLOXfOl0eP\nstzh+vfvz7Fjh3n48D4AKSkpXLp0gZSUlLwu98lYWzenYcMmzJo1hYcP7wuxqffvZ3WCZLIUJBIJ\nhoZGZGRk4O5+glevXuTaXnJqKupqahjo6pEml7PV4yRJslSh/Jb/UzyuXGLh8NHMHTYK54P7VFYj\nba2bce7OLbyvXSnQ5TY739t1x+3yRRJSsmI/OzVpynYvd2ITE4iOj2ebpxtdM13zTAwMiEtKJCnb\ns+3Tqi0bThwlNDM+LiYhgb/v3s71euGxMYxf7Uy/th3o/U3BAf8iIl+C3H6rL16oTobFxEQLGuTr\ne5qAgFc0z+Yq6e3twevXr5DJZGzduon27Tvm2lG4du0K/v5PyMjIICkpkbVrV2JgYCh0JEG5b5yf\n36kcLreTJk1i374j7Nixjx079tGqVRt69OjN7MyssS9ePGPq1IlMmjSNFi1a5bi2cmsob1xc1uea\nDAmUg966detTKjOB2Ttq1qyFn98ZYmKiUSgUeHm5k56eTpkyZejV61tcXY+xY8deduzYR+/efWnW\nrCUbpyhn4K8+fsjTwADlPaeksOqwKwa6OlTInOCqWa4CnlcukZSSgjxdzqFzfpgZGmGoq8uPPXrj\numAJu2bPZ9fs+bSuV59eLVszd6hysrFtg4bEpyTjeeUSGRkZ+N68QURsrLDK2LVZC7Z7uZOQnMzL\n0BCOXzxPt8z3Nn/Y9+yft0hou06lSoy268G4nn2U7/TaFcIykzqFREWxye2YMIh9x90XyljV9zPR\nFkbrUtPSOHPzurBq+z5nb9/EQEeXRgW4K4r8u0hOTkJHRxdtbW1ev37FsWOHctTZu/cvEhISCAsL\n5dCh/djYKFdzevfuy65d23n5UtnXSExMxM/v9GezU1u7BDo6OoSFhbJvX1bm63r1GqBQKDhy5CDp\n6emcOXOKp08f59mWqakpb7JlqYYsN9ySJUtRo0ZNNm1aR1paGv7+T/DwOImtbc4Jn/fPlclkSKVS\njIyMSE9P58SJoyrJ0PLj5s3rvHjxHIVCQYkSJVBXV0cqVcPCwpIGDRqxZs1KkpOTUCgUBIaHc/uZ\nMiayV8tW7D7txdPAAEDp/RAeq4xJrFqmLN7XrpCRkcHF+3e5+/xZoWwRnpOBgXLLquz3mvmvhbEJ\nDatUY9VhV5JkMhQKBUERWXaduXldWI3U09FBIpGiJs19MKsAtnm6kZqWxrPgIDyu/EOnJnnvQdup\ncVO2ebgRl5hITEIC273cc/RFG1erwYLh3zNj83oeB7wGlKvLR44cFNxmk5OTuXjxPKmpslyvk5KS\nku/77NDBBlfXfURGRhIfH6/yncztvQUHB6nEDv+/EVc+i4T3v9R5z9iUK1ee+fMY9fgSAAAgAElE\nQVQXsWLFciIjI6hatRp//LFSyO72+++O/PHHYv78cwPNm39D27YdhHPr1KnDjBlzWblyOUFBQWhp\naVGvXgMaNHjXGSi6vZey9yUdHBzZtWs7ixbNJyoqAn19AypXrsKKzBT5FSpUZODAofz44yikUim2\ntt2EVZX3aV6zNs1r1qa//Vx0tLQY2MFGcGtIkslYtGsbUwcMxtTQEFNDQ3q2bM2SXTtw+XUSoBSd\n6mXLERwRIWTILQylTM3o2rQ5R89n7ac1yrYbyakyhjrYI5VK6dCgMSMzXfDKW1rRqUlTvl0wG4Ui\ng31zFzGgvdItcOJaFyLj4jDW18emcRPa1GuQ43on/7nAm6hItnicZIvHyXd7t+DrXDSbGYuIfAy5\n/Vbrvff9rVWrDkFBgXTvboOJiSlLlizHwMBAKO/SxY4lSxYQGPiahg0bM23arFyvlZiYgIuLIxER\nEWhpaVGzZm2cnVejkS2+5/z5s+jrG9DwvQGNjo6OEOsCSrfdEiVKCAPJ/fv3EBcXy7Jli1m6dBEA\nJUuWFPbT27x5PRoamgwY0Edwx3t/Dz8fH89cZ6GHDBlBbGwMI0cOJjVVRunSZXFwWC5k7s3uElei\nRAk0NTUx1NMjJVlOYnIKzq77iIiLRUtDg1rlK+LyyyTBQ2P8t/1YcXAf39nPIT09nUolS/PHDz8r\n29LSUnHT0tLQoISWlhAzZKCji+OPv7J8/24cXfdSwdIKx3G/YJiZ4GNst54s37+b3vNmoq2pyfDO\ntjTLzBmgW6IEumTFg2qqq6OrrS3s4/ky9A3rjh0mMSUZfR1dWtauy0+9+qg8F88rl2jXoFGObasK\no3V/37mFvo5OnoPLgpIYifybyOpc/PrrJJYv/529e3dRrVp1OnbszM2b17NqSiS0bt2W0aOHkpyc\nhJ1dD7p1U2Y0bdOmHTJZCgsXziYsLBRdXT2srZvRv38f4dwCLSnkdmqjR//IkiULsbVtT5kyZenU\nyZYjRw4CoKGhgYODI8uWLWHTprW0bNmaNvkkuBk6dBSrVjmxZs1Kvv/+B7p376Jih739UhwdHejZ\nswuGhkaMG/erSkx6XvdQuXIV+vYdwJgxw1FXV6dr1245tqjKi8jICBwdHYiKiqJEiRLY2HShUyel\n18n8+YvZsGE1Q4f2Jzk5ibKmpgyz6QooJ/ATUpKZu20zUfHxlDRVZru1MDLmt+8GsmTXdlzP+tK+\nQSNa59JPyo/hnbuy8tABVh9xZbRdT76pU1elh2s/cjTrjh1m0OL5pKSmUsrMjBGdlYP0B69e4nLo\nAEmyFCyMjZk2YLBKRm+V54cy3r3vgllIkDCyS7d8J8FG2/VgzdGDDP59IVKJBJvG1gzvbJujXvNa\ndZg5eBhTN67hjx9+pmaXbkyZMhMnp2UEBwehra1NvXoNaJLHQLeg99m793cEBQUxYsQA9PT06du3\nP/fu3RHKVd9bMqVKlS7yPWw/BYniUzZSyoVPdXf4khSFe8aX5GPtDQh4hbe3Z46EHHPnzmDJkj+E\nf7Ozbt0q+vYdkMMHvbC8755RlPy+ewfmRsYqm69/KsbGusTEJBVZe4VF9jYVed0GBSZVeZ/i+N39\nt1Hcnn9h7PX0dMPN7XiOfX/fMX78j3TpYkf3Ivzt5UZx+n5/Tq37XPy/9O5jKGpbP1ZzC8u/Ueug\n+OhdcdIOKF72ilr3cRRWcz7lu3Dx4nnWrFnB/v1HP+r8j+FTtE5c+fyP4OPjqTIrolAohO0TXrx4\nxoQJ41TK3rwJpm/fAV/czoJ4ExXJuTu32Dlz/v/bFBEREREREREREZEvikwm486dWzRp0pSoqEh2\n7Pgz3xX3rw1x8PkfoFy5Chw8mPf+Pnv3Hv6C1nw8m92Os9/vNCO72OUZOC4iIvL5KKybmoiIiIiI\niMjnQaFQsHnzeubNm0mJEtq0bNmmUHtify2Ig0+Rz8bbtDSVbGefyvDOtoJvvextagG1PwxZqnqR\nt1kY3qbJxaxfIl8NXbt2p2u2fSffJ68tS/7rFLXWfW7+X3r3MRS1raLmioh8PKLWfTifQ3NKlCjB\n1q27irjVL0eRx3yKiIByViY1tXh0bv7faGlpiStKIiLFFFHrih+i5oqIfDii1n08ouaoIiYcEu39\nbBQne4uTrVA87f23Udyev2jv50O09/NRnGyFf6fWQfHRu+L4fRHt/XwUJ3uLk60gJhwS+QpRKBTI\nZDJkstz3MPrakMk0vqit4iyYiMi/g+KmdfDl9e5TKEpbRd0VEfl4RK37cETNyR1x8CnyWUhNTeXt\nlVuoJxWT2AAjXdRjv0w67rdpclIbNflsqf5FRES+HMVO6+CL6t0nU0S2irorIvJpiFr3YYiakzfi\n4LMQ9OvXk5kz59G4ce6bwWandWtr9u8/SunSZT74Op9ybnZCQ0Po168n585dQSr9cqkVfHy88PJy\nZ8UK5ebimhoapGsWzYzP4b/PstXjJLK0txxbvAwDHd1Paq/P/JnMGTKCJtVrAqCtpYW2ZuH2rvrZ\nxYmuTZvTo2WrHGVhMdEMWrKAM06r853tKj67ZIn8WxB1rHC8r2OFoSi17lO5/cyfpXv/4sD8xbmW\nL961nfIlLRluY1dk19zifoKgiAgWjhxdZG2+40O0uSBE3RUR+TS+Jq0rDEWpHx+DqDm5Iw4+i5hP\nWV4vyqX5z73Mn1vHsHNnWzpnZqMtSuTp6aw+cpBt02dTuVTpIm+/KLE0NsHXufCdVhGRrxFRx4pe\nxz4F98v/cOKf82yaPKPAug2qVM1z4Pk5ET3LRP6rvHr1kiVLFhAcHIREIqF69RpMnDiVChUqCnWe\nPHnMmjUrePLkMTo6JRg2bBTffTcQAH//p7i4OPL8uT86Orr07NmHkSPHAHDz5nVWrXIiLCwMdXU1\n6tdvyG+/TcfMzBwAX9/THDy4F3//p9SqVSfPjOSenm44ONgzY8ZcunfvBYCT01K8vT0FnZXL09DQ\n0MDb+xyg1Edn52Xcv38PTU1NWrVqy8Keykkrebqc+du38CjgFaHR0ayfOJWGVasJ19vifoId3h5o\namiAQgESCbtnL6CUqZlykn7x/CzRUChIefuWCd/2Y1CHTtz0f8Kvq5zR1tISzp3WfzBdm7UAICI2\nFscDe7j93J8SmlqM7GJHn9ZthWufv3eHjSeOEhIdRY1y5Zg2YCgVrUoCkCaXs+7YYc7cvE6qPI3O\njZvyW7+BqL03wRkQHsYwB3s6NGzMghHKSbX7L1+w2e04jwNfoyaV0qhqdSZ/NxBTQ0MA9vue5uA5\nX2ITE9HR1qJ9g0aMqV1PaDO/9/xfQxx8FjGfkr+pOCUeVigUSCSSL2JzdHwcafI0KmSKx4fyzlYR\nEZHCIerY14VCoUCCqGEiIl8j5ubmLFq0lFKlSqNQKDh8+AALFsxm5859AMTFxTJ16gQmTpxCu3Yd\nSUtLIyIiTDjf3n4u7dp1YN26PwkODuLnn8dQtWp1vvmmNRUrVsbJaTXm5hbI5XI2b16Pk9NSli1b\nAYChoSH9+w/m9etX3Lx5PVf7EhIS2L17B5UqVVY5PnXqLKZOnSV8dnCwV/EycXZehrGxCSdP+pCQ\nEM+ECT9xQE+b3i3bAVC/clUGdrBhzpZNuV63U2NrYeCWHUtjE3xXrBU+v4mKpN/CuXRo2DjrmRoZ\nc3zJH7m2u3DnFqqVKceysT/xPOQNv6xyoryVFY2qVicgPIyFO7bi8stEaleoyOGLfkzbuBbX+YuR\nSqXs9PbgSWAA++bZk56ewZSNa9ju6caYbj1VruHsupda5SuqHEtITqZ3qzY0r1kbNTU1nA7sYfHu\nHbj8MhGANvXqY9e8BQY6uiQkJzNj8zqOHj3EkCHDgfzf838NcfD5gTx69IBVq5x59eol2tratG3b\nnvHjJ6OunvUoL126gKvrPpKTk7Gz687PP08UytzcjrN//26io6OpWbM206bNxsrKqsDrJiUl4uKy\nDD+/s6ipqdG1a3fGjBmHRCIhIyOD9etX4+Xlhq6uHgMGDFE59313u23bNhMcHMi8ecrZ8Tt3brNx\n42pevnyJrq4uY8aMo2vX7ly6dIE//9xAcHAQenr6dOvWk++//wGAX39V/mtr2x6JRMLKlesICHjF\nyZPHWL9+CwC3/f35Y89eAsPDKWthyW/fDaBupvj97OJEgypVuf7kMc/eBFGvYmXsR43FUFfVnTYg\nPIwRS5V2dpo2kVrlK7J2wmTuvniGy6EDebZdr3Jlbj59ytOgAPbMWUjpzFnC7Dx8/Qpn131EJcTT\nydqaSX0GoKGuTkJyMgt3buXhq5ekKzKoW7EyMwYNxcLIWDg3KCKc75c78DoslCbVqzN36Cj0dXQI\niYri2wWzuLh6I1KplKSUFFyOuHLpwT2kEindmrcU9ioNDg5i6dJFPHv2FHV1DRo3tsbe3qHA74KI\nyKfy/9SxNWtWcunSxXx1zMDAgO++G6Ry7v9Lx+7du8Pq1c4EBgZStmw5Jk6cQp06ytns8eN/pHbt\nuty/cpGngYF56tg7jl38m92nvElITqZ+5SpMHzgEM0OjHLoBWe79dStVxvHAHuTpGXSY/Cvqamr4\nOK7in/v3WHP0IGGxMehpl2BgBxsGd+zMTf8nLNyxlRO/LwfgSWAADnt2EhQRQYvadXIMYS/cu8Nm\nt+OEREVRsWQppg8cQpU8XKZfvAnG5bArjwNfo6GmzoD2HRneuWuOenO2bOT282e8TUujSpkyTB8w\nhIolSwHkaXdcYiKLdm3n7vNnSKQSKpcszYbfpuX9ZRIRKYDdu3dw8uQxYmJisLS0ZOzYn2nTph2g\nXAk8ceIo1apVx9vbAzMzc377bbqgL+PH/0idOvW4fv0qAQGvaNTImtmzF6CvnzPDp66uHrq6egCk\np6cjkUh58yZIKN+/fw/NmrXAxqYLAOrq6pQrV0EoDwsLoVMnZb+gdOky1KvXgJcvn/PNN60xNs7q\nd2RkZCCVSgkOzmr7nb1ubsfyfA6bNq2lX7+BnDlzKs86KSkpnD3ri6PjKuFYSEgIffsOQF1dHWNj\nE6ytm/H8zRvlPWT+/uHTPVQ8Lv9DwypVsTQ2KbBuSmoqN/2f4jB6HFKplKqly9ChQWPcLl2kUdXq\nXH30kAZVqgj9wbE9erD28BFuPXtK42o1uHj/LsM6dUWvhA4A/dt1YP2xIyqDz1PXr6Kvo0tFq5IE\nRYQLx1vUrqNiy3dtO/Czi5PwuVS2fmZGRgYSiUTle5Dfe/6vIe61/IFIpWpMmDAZT09fNm7czo0b\n1zl69JBKnfPnz7Ft2x62bdvN+fPncHM7nnn8LLt378TBwQk3t1PUr98Ae/vZhbrukiUL0dDQwNX1\nONu27eHatSucPKkUmxMnjnD58kV27NjHli27OHv2TCFaVIpFaGgI06ZN5LvvBuHufprt2/dStWp1\nAEqU0GHu3EV4e5/D0dGF48cPc+GC0h1j3bo/AfDxOYePzzlqZ/4o34lQQkI841etYkB7G7yXr2RQ\nBxumbFhNfHJW4LfP9avMHz4Kr2UreCuXs/e0dw4ry1lYsneuPQBnnFazdsJk4pOTmLphTb5te129\nwuwhw/F1XoOViWmuT8D72hVWj5/M4YUOvHzzhu1e7gBkKBT0aPENx5f8wfHFf6CtqYmz616Vcz2v\nXmbe8FG4L3VCKpHi7LrvvSerZNGubWioqXPYfil/zZrH1ccPcb/8DwB//rmBZs1a4OV1lqNHPfju\nuwH5vTARkSLj/6lj6uoF69jhw4e/Ch2Lj49n+vTf6NdvMB4eZxgwYDDTpk0iPj5esMDP7zRLxozJ\nV8cArj95xMYTR1k6ZhzuSx2xNDZh3rY/37uTnFSwKsn0gUOpW7ESvivW4pPZOXTYu5NZmRq3d+5C\nmlSrkdXWOze6dDkzNq/HrllLfBxd6NCwMX63bwr1ngQG8PuencwaPBwfRxf6tGrDtE1rkafnjFRK\nlsmYsHYlLWvXxd3BiUMLf6dJ9Ro56gG0qF2Xw/a/4/mHM9XLlmPBji1CWV527z3jg6WxCd7LV+K5\nbAXjevbJ44mIiBSOMmXKsmHDVnx8zjFq1A8sXjyP6Ogoofzhw/uUKVMOd/czjBr1A3PmTCMhIWur\nC29vD+bMWciJE96oqUlxcVme7/VsbdtjY9OK1audGT78e5Xr6Osb8NNP39OjR2dmzpxMWFioUN6v\n3yA8Pd2Qy+UEBLziwYN7WFs3F8rDwkKFtg8c2MOQISMK/QwePrzPkyeP6N37u3zrnT17BmNjY+rX\nbyAc699/EGfO+JCaKiMiIpyrVy/Tqm7dQl/7wr27dJn+G0N+X8iR82fzrOd19TLdmrdUORaTEE+3\nWVPpu2A2LocPIHur3F9U6QUCCrK8VRQoeP4mONe2MxQKyK88Q0F4bAxJmRlxk1JS+NP9BBO/7V+g\nR8wt/6dUypxUe4fP9St0nDIB25mTefHmDd269RLKCnrP/yXEwecHUr16DWrVqoNEIsHKyoqePftw\n+/YNlTpDh45AT08PCwtL+vcfzOnMzsjx40cYNmwk5cqVRyqVMnToSPz9n6qIUG7ExERz5co/zJ49\nGy0tLYyMjARRAPDzO0O/foMwMzNHX1+fYcNGFvp+Tp3yxtq6KR07dkJNTQ0DAwOqVKkKQIMGjQQ3\njUqVqtCxY2du3bqpcn5eP84rVy5R3tKSLtbNkEqldGrSlPKWJblw745Qp3vzlpQxt0BTQ4OOjZrw\nNCgwX1vfXevi/XuUtci/7W7NW1LBqiRSqTSHL/87+rXrgLmREfo6Oozr3Ruf61cBMNTVpV2DRmhq\naFBCS4sRnbty65m/yrldmzanolVJtDU1+aFHb87cup7jWUTFx3PpwX0m9e2PloYGRnr6DGxvg+8t\n5fdFXV2d0NAQIiLC0dDQoG7d+vnev4hIUfH/1LEJEyYXqGMGBgZfhY5dunSBsmXL0bmzLVKpFBub\nLpQvX4GLF/8W6nTpYkdZi4J1zPv6VXq0aEXVMmVRV1Pn517fcu/lc0KzdYY/BHU1dV6EvCFJJkOv\nhA7VypbLUefeixekZ6QzoH1H1KRSOjRsTM3yFYTy4xfP822rttQsXwGJRELXZi3QVNfg/ssXOdq6\neP8upgaGDOxgg4a6OiW0tHK4pb2je4tv0NbUQl1NndFde+AfHCR07vKyW11Njaj4WN5ERaImlVK/\ncpWPei4iIu9o164jJpmTzx062FCmTFkePnwglJuYmNKv30DU1NTo2LETZcuW59KlC0J5ly52VKhQ\nES0tbcaM+Qk/vzP5Dki8vPzw9j7Lb79NE/QHIDw8DC8vdyZNms6RI+5YWZVi4cI5QnnLlq04e/YM\nHTt+w9Ch/enevRfVs03sWFpa4eXlh7v7GcaO/YmyZcsX6v4zMjJYsWI5kwsRK+7l5YGtbTeVY/Xr\nN+TFi+d07tyWvn27U716Ddo1bFioa9s0tmb/vEV4/bGCmYOGsc3TjVM3ruWod/vZU6ITE2jfsJFw\nrIJVSf6aNR/3pU6snTCFJwEBrDp8EAAdbW3qVarCNk933qal8TjgNX63byJ7q8zAa12jJrf8n3LL\n/ynydDkbjx1Dnp4ulDevVYcDZ08Tm5hAVFwcB8/5Agjlm92P0+ub1pgbGeV7f/7BQWzzcmP8t6qD\n+s5NmnHGeTUHFyyhZ8tWGGdbzS3oPf+XEN1uP5DAwADWrFnJkycPSU1NJT09neqZGVPfYW5uKfzf\nysqKyMhIAEJDQ1m1ypm1a12ArHijiIgILC3zdlkLDQ1BLpfTqlUrMjIUmeKnEM6JjIzAwiLrmpaW\nhY+NDA8PyzMr5cOH99m4cS0vXjxHLk8jLS2N9u1tCtVuVFQkJU1VVxytTEyIiI0VPpsaGAr/19bU\nJCU1tVBtR8bF5ljNfL9ty2yuKnmR3Y22tJkZkXHK82Vv3+Jy6ACXHz0gMSUZhQJSUmUqsaMW2QSl\npIkJ8vR0YhMTVdoPi4lCnp5O99lTAWXcvAKFcN1ffpnI5s0bGDt2BAYGBgwYMIRu78UdiIh8Dv6f\nOtarl61w3teuY5GREVi9F2tuaWlFZGSE8Dl75yI/HYuMjaVGtk5jCS0tDHX1iIiNxcww/45Obiwb\n+xPbPN1Yf+wwVUqX5edefahTUTWmKzI+DnNDVS0smU07Q6Oj8LxySeiAKRTKBG8RcXE5rhcWE0OZ\nXMIX3icjI4MNJ47id+sGsUmJSDKjVeMSE9DV1s7T7qGdbJUrDmtdkAA9v2mdq0uviEhh8fR0w9V1\nLyEhIQDIZCnExWX1E8ze+z5bWZVU+W1n1yMrq5KkpaURGxur4gr7Plpa2vTq1Zfu3W3Ys+cwRkZG\naGlp06ZNO2Gg8f33Y+nWzYbk5CTi4jKYMmU8U6bMxMamC9HRUcyZMx0TE5Mcq5X6+vrY2nZj5MjB\nHDvmWWAW8CNHXKlSpSo1a9bOt15oaCi3b99g5sy5wjGFQsGUKePp1asvmzZtJzk5mSVLFrDS1ZVx\n3b/Ntz1AJUdH3UqV6d+uI363btDpvWzrHlcu075BI7Q1tYRjJvoGmOgbAFDS1JRfevdl2sa1zBg0\nFAD7UWNYvn8PvebNoLSZOV2bNudFiNIduLylFfOGf4+T616i4uPo1bo1FaxKCv2ukbbdSExJYdjS\nRWipa9Dzm9b4BwViamDA08AArj1+xF+z5ud7b4Hh4Uxev4op/QZRr1Luk2RlzC0ob2XFqlVOLFvm\nTHx8fKHf838BcfD5gTg5LaN69eosWrQUbW1tXF33cS7zD/c7wsPDhCxnoaGhmJmZAUohGzHie8Hn\nu7BYWFiiqanJlStXiIxMzFFuampGeHhW8HpYWIhKuba2tsomu9ndTiwsLHn06AG5YW8/l+++G8iK\nFWtRV1dn9Wpn4oROSf4+/qamZlyKUp3RD42JzuEz/zEoY6RUVy7eb7swyTnCY2KE/wdHRgodwL1n\nfAiMCGP79DkY6+vjHxTIiGWLVQaf4THRwrkh0dFoqKlhpKdHaHTWcUsjEzQ1NPBe7qISEyF7m4oc\nZad1xgzl7Ofdu7eZNOkXGjRo9MlbVIiIFMT/U8c8PM7kGiP0NeqYmZk5Z8++/1xCaf6ei1hhMDMy\nUlnlTElNJS4pEQtjY7Q0NADlxJdO5p5wUfFZA8DcQqpqlCvP8h9/IT0jg4NnfZmzdXOOBB1mBoZE\nxMWoHAuNjqZKWWXWcEtjY0ba2jGiS8HbrlgaG3PqxtUC63lfu8KFe3dYO3EKViamJKYk02naJMFJ\nLi+7S2hpMeHbfkz4th8vQ97w8yonaleoiE2zxvleT0QkN0JDQ3F0dGD16o1CjPaoUYNVVi6zDzRB\n6d7aOlvW1Ox6FBoagoaGBkYFrIiBMu5TJlO6qhoZGVG5cpUcmvfuc2BgIGpq6nTOnGgxMzOnY8fO\nXLp0MddBiVwuJzY2hqSkpFzjT7Nz48Z17ty5JazmxsfH4+//lGfPnjJpUlY8tY+PB3Xr1qdkNhfS\n+Pg4wsPD6Nu3H+rq6hgYGNClix27/1xXqMHn++SW2C01LQ3fW9dZ/uMvBZ6fke1cS2MTnH8aL3ye\nv/1PFS+M9g0a0b6BciVVXUvCQV9famV6fGhpaDCl/yCm9FfmFDh24W+ql1NOCt569pTQ6Ch6z5sh\nLDqkZyh4GRrCjhnKgXlIVBQT1q5gtF0Pulg3y9dmeXo6IZmD4jdvgj/oPf/bEd1uP5Dk5CR0dHTR\n1tbm9etXHDt2KEedvXv/IiEhgbCwUA4d2o+NTWcAevfuy65d23mZ6dKUmJiIn9/pAq9pamqGtXVz\nHBwcSE5OQqFQEBwcxO3M2J0OHWw4dGg/ERHhxMfHs3v3XyrnV61anTNnfJDL5Tx+/FAllqpzZ1tu\n3LiKn99p0tPTiY+Pw9//KaAMQNfX10ddXZ2HD+9z6lRWLJOxsRESiUQl8D07TZu2ICAsjFPXr5Ke\nkcGpG9d4HRpCq490Lc0uWS1r1yUo4tPbPvS3H+GxMcQlJbHp+HFhRi45VYaWhia62trEJSWxxeNE\njnO9rl3hVWgIsrepbHE7ToeGTYQ/Ju9sNTU0pFnNWrgcPkCSTLlyGhwZwZ3nzwBlrFhEZjC7np4+\nUqnki+5nKPLf5f+pY6tXryhQx+Li4r4KHWvR4huCggI5fdqb9PR0zpzx4dWrV3zzTZsC7/d9Ojdu\nitvli/gHB/E2LY0NJ45Sp0IlLI1NMNLTx9zIGK9rl8nIyODkPxcIztYxNtE3IDw2RojFlKfL8b52\nhaSUFNSkUnS0tZBKc45Q61aqhJpUDdezZ5Cnp+N3+yYPX78Uynt904aj58/x4JXyWEpqKv/cv5fr\n6u03deoRHR/PAb8zpMnlJMtkwnnZSU5NRUNDA30dXVJSU1l//KgwxM/P7ov37wrJPXS0tVGXqolZ\nykU+GpksBYlEgqGhERkZGbi7n+DFi+cqdWJiojl0aD9yuRxf39MEBLyiefNvhHJvbw9ev36FTCZj\n69ZNtG/fMdfv5LVrV/D3f0JGRgZJSYmsXbsSAwNDYfKuW7ee/P33WZ4980cul7Njxxbq1WuAjo4u\nFSpUQKFQcPq0NwqFgqioSHx9T1GlinLbknPn/AgIeI1CoSAmJoY1a1ZSrVoNYeCZkZHB27dvkcvl\nKv8HmDt3IXv2HGTHjn3s2LGPGjVq8v33Y/nhh59V7Pfycs/hdWVoaETJkqU4duww6enpJCQkcOqU\nJ9XKlhXqpMnlpKalAfBWLudt5v8B/r57m4TkZAAevHqJq98Z2tRXddk9e/smBjq6NMqMz3/HjadP\nhIm6sJho1h8/QptssaivQkNIlsmQp8vxvHqZa48fMbhDJ6H8ccBrMjIyiElIYN6WLbSp15BymR42\nEbGxgpfb/ZfP2e7lzg+ZcZm9W7XlkL0Df82az67Z8+nTqi2t6tRl1a+/ARAeG8P41c70a9uB3rn8\nDTjxz3liMmOGX4a8Ye+ZUzRq1ASAcuXK5fue/2uIK5+FIktsfv11EsuX/+qGRagAACAASURBVM7e\nvbuoVq06HTt2VklvLZFIaN26LaNHDyU5OQk7ux5CwHGbNu2QyVJYuHA2YWGh6OrqYW3dTHABy+8P\n7bx59mzfvpGhQ/uTnJxMqVKlhaDzHj36EBgYyMiRg9DV1WPQoGHcupVl05gx41i4cA52dh1p0KAR\nnTp1JT5zVt3S0gpHx1WsXevCsmWL0dPTZ+zYn6hatRqTJ09n7VoXVq5cToMGjejYsZMQjK+lpc3w\n4d/z00+jSU9Px9l5tYq9BgYGrJk4kaW797B8/x7KmJvj/NMEDHR0M+/1Y9+AMibT6afxrDi4/6Pb\nliChc5OmTFyzkqj4OGysrRmZGe8wsL0N87dvwXbGb5gbGTOoYyfO382KJ5VIwLZpcxb9tZ2A8FAa\nVa3O9EHDcrV1/vDRrDt2mEGL55OSmkopMzMGZr7vR48esnr1CpKSkjAxMWHSpKkqM48iIkXL16Fj\n69evKVDHDAwM6N9/yFegY4YsX74SFxcnnJyWUaZMWRwdXTAwMCjwXt/HukZNfuzem1mb15OQkkLd\nSpVZnJl1F2DW4GEs37+HjSeO0qNFKxV3ribVa1CxZCnsZk1FTSLlpIMjnlcv43xwHxkZGZSzsGLR\nyLE5rqmups6ysT/hsPcvNp08TsvadYQVAVCuQs4aMhxn170ERYSjpaFJvcpVVPbre4eOtjarx/+G\n88H9bPU4gaaGBgPa21A7216GAHbNWnDl0QN6zJmGoa4uP3TvzbHMBE9AnnYHhofj5LqXuMRE9HV0\n6dumXY5OqYhIYalQoSIDBw7lxx9HIZVKsbXtRr16DVTq1KpVh6CgQLp3t8HExJQlS5YLv21Qxnwu\nWbKAwMDXNGzYmGnTZr1/GQASExNwcXEkIiICLS0tatasjbPzajQyPRoaNWrCDz/8zLRpE0lNTaVe\nvfosWLAEAD09PX7/fTkbNqzGyWkZWlpatGrVRkhYFBkZztq1LsTGxqCjo0PDho35/fesxEfe3h44\nONgLWmRj0wpb227Mnr0gMwtvlp0aGpro6Oiio5N18P79e0RERNCuXccc9/X7746sWuXErl07UFNT\no0GDRkzt11co779oLmGZ3l6/rVOGYRxZtBQrE1NO37jG77t3kpYux8LImBFd7OjaVDW5jueVS8Le\nndl5GhTAwp1bSExOEXJw/Nijt1B+5dEDdnh5kJr2lmplyuHy6yQM9fSE8pWH9uMfHISGmjp2LZrz\nY7es5GXBkeHY/7WN2ASl18mvvftiXUMZcqKloSF4oYAyNEJTQ0PIXn7ynwu8iYpki8dJtnicFPYg\nfbe3+93nz9l44hiyt6kY6enTtn5DhmdqvI6Obr7v+b+GRFHEG5xFRCQUXOkrwdxcX7T3MyGTyTAP\n9CclOWfWxK8RY2NdYmKSCq5YBMjepiKv2wDtTPe6j6E4fRdAae+/jeL2/EV7Pw/FTevgy+rdp1JU\nthaF7haGf6PWQfHRu8Jqh6enG25ux4WM1+8zfvyPdOliR/fuvXItLypErfu8/D+17kM1pzh9F+DT\ntE708RMRERERERERERERERH57IhutyKfjbdpaUL66q8dWaq6sI/U5+Ztmlyc9RER+RdRnLQOvqze\nfSpFZauouyIfghhvnDui1hUeUXPyRhx8inwWtLS00GzWjLji4kJgro/8C9kqRfl8REREij/FTuvg\ni+rdJ1NEtoq6K5Kdrl2707Vr9zzLV6/e+AWtKR6IWvdhiJqTN+LgU+SzIJFI0NbWRls7reDKXwHF\nyVYREZGvh+KmdVC89K442Soi8m9G1DqRokJcERYRERERERERERERERH57IgrnyKfBYVCgUwmU9kU\n/mtGJtMoUlu1tLTEmBERkf8AxU3roOj17kMQtVFEpHgiap0SUcM+nWI5+OzXryczZ86jcWPrAuu2\nbm3N/v1HKV26zAdf51POLS58rnTiqampvL1yC/UkZWB6aHQ0g5Ys4IzTKqTSwi+4e129jPvlS6yZ\n8Fuu5TM2r6djwyZ0tm5aYN1J61bRuUlT7HLZVwojXdRjiyYd99s0OamNmhQqvbanpxsnTx5j/fot\nH3WtqVMnYGPTBdvMPUpFRESy+JC/FR/L+1qXnYGLFzB94OCvb7/KItS7D+FDtFFE5L+Mg4M9FhaW\njBkzrsC6X0LnIH+ty42C+mRfhAK0bs9pb0Kiopg6YHChmhM1rGgoloPPD+FTZifyO/fXX3/gzp1b\n7Nixj8qVszYDnzVrKhcunGPNmk00yLaZd178mwe4mhoapGsqn6GWhiYSQFtT64MGnxrqGqhJpWhr\n5h60verX3wpdVyqRoqGmnmu5tpYW2ppFt3fVh7RU2O/otm2bCQ4OYt68RcIxJ6fVH2iZiIhIUZNd\n67IjkYCmukYOzQmLiWb+9j+RkHWOAgVmhkb8PvpHpm9aR3xSkkqZBAkOY8dhom/wyfYWtd59CMVn\nh0ARkeJNWFgo9vZzVfoYCoUCMzNzFi1ayqxZU4iPj1cpk0gkLFnyB8bGJsjlcnbt2s6pU15ERESg\np6dHjZJWfNemI81q1irw+gX1yYqa9IwMOk2dwNqJU6hVviIAPteuMX3DBrZOmyUc87p6me1e7hyY\nv5jRdj0/+Dqihn06//rBp0Kh+CznSiQSypUrj5eXO7/8MhGA+Pg4Hj68h7GxSaGv8SmD4/T0dNTU\n1D76fBERERERJV9ST2Vv39K4Wg1+eM/jZM4WZYZNDTU1Nk6erlK25ugh3qaJiTNEREQKR2qqjEaN\nmuRYPZ03byYA6uoarFv3p0rZ+vWrSE1VrmzOmTONqKgo5s9fTJUq1UhNlfHC5yTnbt4p1ODzS6Mm\nlVK3UmVu+fsLA83rT55QwdJK5did5/40qlrt/2nqf55iP/h89OgBq1Y58+rVS7S1tWnbtj3jx09G\nXT3r1i5duoCr6z6Sk5Oxs+vOzz9PFMrc3I6zf/9uoqOjqVmzNtOmzcbKyqpQ1+7UyZYTJ47y888T\nkEgknDrlTZs2Hbh48e9C2ffrrz+gUCgYOXIQEomUmTPn0aGDDRcvnmfLlg2EhIRQsWIlpk6dJayu\n9uvXk969++Lj40VgYACnTv3NwIF9+Pbb/nh5uRMWFkqzZi2YO9ceDQ0NEhISWLx4Pg8f3icjI4M6\ndeoxbdoszM0tPvnZtm5tzZQpM9m/fw9xcbF06tSFyZNnAJCRkYHz/v2cuHgRPe0SDOzYKd9rhcdE\ns+LQAe4880eBgk6NmzKl/yBAOeu/5shBTl66gL6OLlP7D6ZF7ToA/OziRNemzenRslWONq88esiK\ng/uIjo+nS9NmKMh7MmHt4cM8ePEKqVTKPw/uUc7CkjlDR1I1c0X6VWgIy/fvwT8oEAtjY8b17EPr\nuvUBWLxrO5rqGgRHRnD/1QuqlSnLNPullC9fgdDQEPr168m5c1eEFd/8XJ1XrXLm3DlfkpISKVu2\nPOPHT6Z+/QZcuXKJXbu2A/D332epUKE8f/65S6UthULBzp1bcXM7ztu3b2nWrAWTJk1FV1dPsGP2\n7AVs2bKR1NRU+vcfxPDh3+f7XkREijsPHz5g5UpHoqOjaN26LVOnzkJDQ4Nbt26wePF8+vbtj6vr\nXqytmzNx4tR89XL8+B+pX78hN25c4/nzZ9SpU4+ZM+dhnnktzyuX2OR2HNnbVAa2t/lom3NVqk+Y\nSBURESl6+vXrSZ8+/fD29uDNm2BsbDrzww8/8/vvC7l79w61a9dh8eI/0NPTA+DChXNs2rSOyMhI\nqlatxpQpMzE3rwvA06ePWbZsCcHBgTRv3hJQXZjIr1/4seS2wPLu0LVrV7hx4xr79x/DzMwMALlc\nnZZ16tCwUg2h/l8+npy4eJ7oxASsjE34sUdv2tZvmOv1Vh7az9nbt0hMSaGchQUT+w6gQZWqAExe\nv5oKViWZ8G0/AOZu20wJLS1mDByC3aypbJw0jUqlSgMQk5BAn/kzOb74Dwwzn+076leuyu1nTxli\n0xmAG48fM6yzLb63bgrHbj/zZ2RmqNIW9xMERUSwcORoQqKi+HbBLOYNG8nmk8dJTUtjQPuOQl2R\noqPYZ7uVStWYMGEynp6+bNy4nRs3rnP06CGVOufPn2Pbtj1s27ab8+fP4eZ2HIDTp0+ze/dOHByc\ncHM7Rf36DbC3n13oa5uZmVOhQiWuXr0MgJeXe47Yu/zsW7t2MwA7d+7Hx+ccHTrYZArQYmbMmIun\npy+9en3LzJmTkcuzFvrPnPHB2Xk1Xl5+wky9n99pVq5cy8GDJ3j+3B8Pj5MAKBQZdOvWkyNH3Dl8\n2A1tbW1WrFheZM/20qULbNu2ix079uLre1p4Fu7uJ7hw7x67Zi1g+4y5+N26ked1MjIymLJhDaVM\nzTi25A9O/u5IpyZZsQsPXr2kglVJvJe7MMSmMw57dhZoe2xiArO2bOCnnn3wWr6SMmYW3H3+PN9z\nzt+7g02jJpxyXEWnxk2ZsWkd6RkZyNPTmbpxLc1r1cbzjxVM7jeQBTu2EBAeJpzrc/0qo+164LN8\nJZVLlWbp0izX2A9Z3a5ZszY7d+7H09OPTp26MH/+DNLS0mjWrAXDho2iQ4dOnDr1N8eOHctxrrv7\nCby8PFi7djOursdJTk7K8a7v3bvD/v1HcXFZz44dWwgIeFVo20REiiOnT3vh4rKOAweOERDwmp07\ntwplUVGRJCYmcviwO9OnzymUXp4+7c3cufa4uZ0iLe0tBw/uA+BlyBscD+zBfuQY3BwciUtKIiI2\n9oveq4iIyJfl77/9WLVqA/v2HeHChb+ZOnUi48aNx939NBkZGRw6tB+AgIDX2NvPZdKkabi5nfof\ne2cdFmXWxuF7YJgZ6Qa7C7tj9VNpwVpdu2N1d+3udl0DuwsDRUVsDARzLSzsVbGDDukamO+PwYER\nUFBcl933vi4umPfU874z8+PEc55D48ZNmThxNHK5HLlczpQp42ndug3Hj5+hVStbzp8/o2ojL/3C\ngubmzetYWVVXDTxzo4SZORvGTuLMklUMdGrLrG1biMjiypsVq9Jl2TllJj6Ll2PfoBFTt2wgNeMe\npvbqx8lrV7n55DEnr13l0auXjO3cDbGmGPt6DTl53U9Vz6kb12hQuWq2gSdAnQqVuPtc2dd7HxdL\nYkoKNnUb8PDlC9W1lyHB1MkY9IJye0RW7j57xr5Zv7NyxBhcT3jxKiT48w9MIF8U+sFn5cpVsLKq\njkgkwtLSknbtfuT2bfWBTq9efdHV1cXc3IIuXXrg6+sNwN69e+ndux+lSpVGQ0ODXr36ERDwhJB8\nfNAcHZ05ccKL169fEh8fR7WMFbn82Jd19unIkUN06NCJKlWsEIlEODo6o6WlxYMH91R5Onfujqmp\nGRKJJMu1bhgbm6Cnp0fTps0JCHgCgL6+AS1atEIikVCkSBF69+7HnTv+ebq3vNjeu3d/tLV1sLCw\npG7d+gQEPAaUgtzTzg4zQ0P0tLXpY++UazsPX70gPCaaYR06IdXSQksspma5zBm9oiYmtG3aDJFI\nhFOjpoTHRBMZm7O4feDKg/uUL1qclrXroqmhQTdrW0z0P71XqkrJ0qr8PWzsSJXLuf/iOfdfPCcp\nJZk+9q0Ra2pSr1IVmlWvic+Na6qyP1SvQa3yFRBrihnk1JaHDx8QFhb6yfZywt7eET09PTQ0NOja\ntScpKam8fv0qT2V9fLzp1q0HlpZFkclkDBkyjNOnT5Geng4oB8EDBgxBS0uLChUqUr58RQICAvJt\no4BAYaJTp66Ympqhp6dHnz4DVPoPoKmpycCBQxCLxUgkkjzppZNTW4oXL4FEIsHa2o5nz5TfobO3\nb9GsRi2VDgxp216IiCgg8C+nU6cuGBoaYmpqSq1atbGyqk6FChXR0tLif/9ryZMnyj7RmTM+NG3a\njHr1GqCpqUn37r1JSUnB39+fBw/ukZaWRufO3dDU1KRlSxuqZnFrzUu/sKD4IFnR0e8xNjZRXY+J\niaFDB0ea/fYbLUb9prpuXaeeqm9lU7c+Jc3NefjqRY51OzRohJ62NhoaGnS3tiNVnqoa2Jno6zOh\nW0/m7HBlxX4PZvYdqNor2rpRE05lGXyevHaF1g1zCBwJVCtTlqSUFJ6+e8udZ0+pV7kyUi0tipua\nqa4VMzHFPJftcSJgkHNbtMRiKhYvQYXiJQl4+yZvD08gzxR6t9s3b16zatUyHj9+SHJyMmlpaVSu\nXFUtj5mZhepvS0tLwsPDAQgMDGTFiiWsXr0cyNxsHRYWhoVF3lxvW7RoyerVyzAwMMDBIfsAKy/2\nZSUkJAhv72N4eu5V2ZSWJic8PCzL/WR3mc0qEjKZjIgI5T0mJyexYsUSrl27SlxcLAqFgsTERNW9\nfoq82J51f6tMJiMxMRFQrihYGmemFTXOfR9sSFQURY1Ncg1EZKJnkNlGxoA7MTkZ9HK3PSz6PeZG\nRmrXLD6zFzdrfpFIhJmhIeHR71EoFJgbqpe1NDZRW9XIWraIVIq+vh7h4WH52v8L4O7uxvHjR1Sf\n0cTEBKKj87Z6EhERhoVF0UwbLYuSlpZGZGSk6pqx8cfvV0K+7BMQKGxk1UtLy6JqWmpoaKS2RSMv\nevmx1n7QvLD36pojk0gx0NH5ZvclICDw/cmqB1KpVO1/rFQqVf2PDQ8PV/v/LBKJMDMzJyQkhLi4\nZExNzchK1rx56RcWNPr6BrzNMujS19fn0KGTJN28RLtJk1TXj/tdZs8ZX4IiIgBITEkmOi4uxzp3\n+Xpz9MolIqKjAUhITiI6PjNvsxo1WeKxm1IWFtQoV151vVqZssikUm4FPMZE34B34WE0r1krxzYk\nWlpYlSmDf8AT3kWEUb+yMtJ4zXLlVddqZ1n1zImsQd1kEomyvylQoBT6waeLywIqV67MnDl/IJPJ\n8PDYreauABAaGkKZMsqNxsHBwSo3AktLS3r27IedneMXty+VymjcuCmHDu3Hw+PwF9mXFXNzC/r0\nGUDv3v1zzZOf2fTdu3fy9u0bNm3agZGREQEBTxg4sFeeBp/5tT0rxsYmBGcZ9ARl+ftjLIyMCI6M\nJD09PV+RcD+FqYEBF+6qtxkSlbsNAKFRUaq/FQoFoe+jMDUwRKFQZCsbHBlB6SwTFFnLJiQnExMT\ni5mZOWKxFgBJSUloa2sDEBkZkWP7d+74s3u3GytXrqds2XIAtG5trVoZ/9z7ZWJiRkhIUKaNwUGI\nxWKMjY0JzeIiLCDwXyLrZz84OChbJy8rX6OXpgYGau5ZSSnJRMf//ceZCAgI/PMwNTXlxQv1rT+h\noSFYWFgglSZk85QKCQmmRImSQN76hQXFB0e8+vUbcOCAB+HhYblqZnBkBAvc3VgzcpxqsNjnjzk5\n7iW9/fQJO329WTtyHGWLFgPAfvxItbzrDh+kTNGiBIWH43PjGnb1G6rSnBo14cS1q5jo69OqTj20\nxLkPX2pn7PsMioygT2sHAGpVqIj3tasERoTT6X+t8vdQBAqcQu92m5AQj7a2DjKZjFevXnLokGe2\nPO7uO4iNjSUkJBhPzz3YZmw67t69O25uW3nx4jkAcXFxnD3rm28bhgwZyurVG3NcLf2cfcbGJgQG\nvlO9btv2Rw4d2s/Dh/cBSExM5MqVi6rZ9fySkJCAVCpFR0eHmJhoXF035qPs559tbrRoYY27ry+h\n76OISYjHzedkrnmtSpfF1MCANYcPkJSSTEpqKnefP81zWznxQ7WavAgK4vwdf9LS09l71vezrrqP\n3rxS5d99xgeJWIvqZctRvWxZikgkuPmcRJ6Wxs0nj7l0/56aMF5+cI+7z5+SKpfjetwLK6tqmJqa\nZbjjmHHq1HHS09Px8jrMu3dvc2w/ISEBsViMgYEBqampbN26iYSEzM6rkZExwcFBuUZhtrOzZ+9e\nd4KCAklISGDjxrXY2NirBvRfE/lZQKCwcuDAPsLCQomJicbNbSs2Nva55v0avbSuU4+L9+9y9/lT\n5GlyNnodEb5zAgICAFhb23H58iVu3bqBXC7H3d0NiURCnTp1qF69JmKxGE/PPcjlcs6fP8Nffz1Q\nlS3ofmFeaNCgMXXq1Gfy5LE8fHgfuVxOWpqcO08z+2aJKSmIRBoY6Ooq+zdXLvEsMDDH+hKSkxFr\naqKvo0uqXM6W40eJT8pcUfQPeMJxvyvM6jOQab37s2TfbsKzeH05NmjE+Tv+eF/3y9Xl9gO1K1Ti\nZsBjQqKiKF9cGaSoVrkK3Ap4QsC7t59c+RQU+++hkK58Zs5ADxs2ikWLfsfd3Y1KlSpjY2PPrVs3\nMnOKRDRv3oKBA3uRkBCPk1NbnJ2VUUZtbW0JCYlk1qwphIQEo6OjS4MGjWiVEaXwUzPdWdNMTEwx\nMcl5U/bn7BswYDDz5s0kJSWFCROm0KqVLRMnTmPZskW8ffsWqVRKzZq1qV27XrZ7z+l5fEyXLj2Y\nPXsqzs62mJmZ0a1bL7VovJ8+y/TzzzY3nJzaEvnoHr3nz0G3SBF62Nhz68mjHPNqaGjg8sswluzb\nTftpk9AQibBv0FBt32dud5ubCQa6uswfNIQlHruZ57YNx4aNc63vA/+rUQvfm9eZvcOVkmbmLBz8\nG5oaGoAGi38dzqI9O9nufRxzQyNm9h1AKfNMd277+o3YfOwo9188o2KJkkyes0CVNnHiNFxcFrBh\nw1ratGlPjRo5u4s0atSEhg0b0717R4oU0aZLlx6Ym2dOaFhb23Lq1AmcnGwoVaokGzZsV3sPnJ3b\nEx4eztChP6uCFI0aNT7Ls1J/WMJ+NIF/PyLs7BwYPXoYERHhNG/e4pMRnr9GL8sWLcb4Lj2Y4bqJ\npNQUulvbYW5olGv+/N+K8H0VEPhn8fF3MvfvaKlSpZkxYw5Lly4iPDyMihUrsXDhMsRiMWKxmN9/\nX8zChXPZtGkdjRv/QIsW1qqyVapU/YJ+4RfeUZaq5s9fjJvbVubMmUFERBi6unpUKWqpOl+9rGVR\netjYMcjlDzRFGrRu1IRauUTgbVy1Go2rVqPL7GloS6V0s7bFImObQnxSEnPcXBnXtQcmBgaYGBjQ\nrmlz5rltY/mwUQCYGxlTuWQp3oV93m22ZrnyxCcl0TRLDBYDXV0MdXXREosp8YnTHrK9o4LsfhNE\nigKemg0Liy3I6r4pZmZ6gr3fiKSkJMzeBJCYUDiO49115iRP37xjZt+B+S47120rFkbGqjP7klKS\nkdeojUwmK2gzVRSmzwIo7f23Udiev2Dvt+FLtO5VSDAnr11lSNsOatenbF7P/EG/qH5nZdWBfXRu\naY1llj1mX4qRkQ5RUX+/S/CXaGNh+izAv1ProPDoXWH8vHwre1+/fom39wl+/vlXtevTpk1k3ryF\nqt9ZWbNmBZ06dc3xyMHv3a/7fec2zAyNsp2P/CkKWuu+Zf+uMH52v5RCuvIpICAgICBQeDl5/ara\n9gKFAmIz3OyfBb5j6AoXtbTA8DA6t7TOVo+AgIBAbpw6dYJ79+6oXisUCmJjlQOc58+fMmLEL2pp\ngYHv6NSp699u5+cIjAjn/B1/tk+a8b1NESgAhMGngMBXIipAlxcBAYF/P6UtLDmYxTX/Y/bOmPs3\nWiMgIPBvpFSpMuzbdyTXdHf3/X+jNV/ORq/D7DnrSz8HJ4qafL3nh8D3Rxh8CnwzUlJTSUpJ+d5m\n5IlBbdoQ9T6epJT8h9Qe17U7gKpsSqq88EfyEhAQyDOFSesAkpLFX6R1X4ugjQIChZvvoXV97B3p\nY688lSK/ulXQWidoWMEgDD4FvglSqRRJo0ZEFxb/dTM95AVkqwbK+xcQEPj3U+i0DgpU7/KDoI0C\nAoUXQesEDSsohMGnwDdBJBIhk8mQyVK/tyl5ojDZKiAg8M+hsGkdCHonICCQfwStEygohMGnwDdB\noVCQlJREUlLS9zYlTyQlaeXbVqlUKhxXIiDwH6ewaR18md4VFIJuCggUTgStE/SroBAGnwLfhOTk\nZFL8/BHHF5J9UIY6iN/nPRx3Sqqc5Lr1v+lxKgICAv98Cp3WQb71rqAQdFNAoPDyX9c6Qb8KDmHf\n7N9I587tuHnzep7yNm/egHfv3n5RO19TtiCRaGkhk0i/y8/DVy/pMnt63stIM/+Oio2j1ZjhSMS5\n2y/REuZtBP59FDaNCg4OonnzBqSnp391Xfnh1KmTjBkzXPW6ILXu2NUrdJwxBafJ40iRy7+6vu7z\nZnL/xfNc9e5TP2PWrsLnxvUc06Lj43GaPA6pliTPtgi6KSDwbciPdn8NuWldjjrzT/jJo9YJ+vX3\nIjzJfyhfs6z/qbLDhg3m4cMHiMViJBIJtWrVZuzYSRgXwMHlfyc/zpjE1J59qV+5aq55vuoZfnFJ\nAYH/Bt9Ko75nXTkRHBxE587tOH/eDw0N5Xytvb0j9hnRFwsSeVoaKw/sw3XCFMoXK17g9RckFkbG\nnFmy6nubISAg8A8mJCqSGVs3qR1Jp0CBqYEhvw8cwoQNa4iJj1dLEyFi/s+/YKyn/z1MFvgbEAaf\n/1AUCsU3KSsSiRg7diLOzu2IjY1l2rQJrFy5lFmzfs+WNz09XdXZEhAQEMjKt9KofxoKhQKRSPS3\n2BwZE02qPJUylkW/qPwHWwUEBARyIy0tDU1Nzb+lraSUFOpVqsLgNu3Vrk/dvB4ALU1N1o+ZoJa2\n6qAnKalCkKB/M8Lg8zvx118PWLFiCS9fvkAmk9GiRSuGDx+DWJz5lly5chEPj90kJCTg5NSGmTOn\nqdK8vA6zZ89OIiMjqVq1GuPHT8HS0jJPbX/oROnp6dGihTWHDysPGp4/fzYSiZSQkCBu3/ZnwYIl\n1KxZmw0b1nD2rC9yeSrNm7dixIgxSCQSAHbt2o6Hx240NDQYOHAwCxf+zp49BzExMWX6li1INLQI\nigzH/2kA5YoWY06/QRQzNQNgmecezt32Jy4xkVLm5ozs1JXaFSoCsPnYEV4EByHV0uLcHX+KGpsw\nvXd/qpQqzeztWwiJjGTc+tXKdlu3oaetQ473ud37OLvP+KAtlTGk19GligAAIABJREFUbQccGjQC\n4PL9e6z3OsS7sDD0ihShs3UrelpnrmQogCNXLrLl2FEAulnb0dPWnoiYGDrNnMy+mfMokpH38eNH\njB07nMOHT/5tgi4g8K35Eo367beRqrQv1aj4+DiWL1/A2bPn0NTUpHXrNgwa9AsikYj09HTWrl3J\nyZNe6Ojo0rVrT7WynTu3Y9Kk6dSr1wAAV9eNvHv3hunT5wJw585t1q9fyYsXL9DR0WHQoF9o3boN\nV65cZNOmdbx79xZdXT2cndsxYMBgQOktAuDo2AqRSMSyZWt4/folR48eYu3azQDcDghg4S533oSG\nUtLcgtE/daVGufIA/LbchdoVKnLj8SOeBr6lZtnyzO7/MwY6Omq2vw4Noe8fSjvtxo/EqnRZVo8Y\nw93nT1nuuTfXumuWL8+tJ0948vY1u6bOoniGvmbl4auXLPHYTURsDC1q1mb+r0MAiE1IYNb2LTx8\n+YI0RTo1ypZnYvdemBsaqcq+DQtlwKL5vAoJpn7lykzr1R89bW2CIiLoOHMyl1auR0NDg/jERJYf\n8ODKg3toiDRwbtyUwW3aIxKJeBsWyu+7tvPkzRu0ZFLq1WvI7NnzP/tZEBAQyDsPHz5g2bLFREZG\n0Lx5C8aNm4yWlhb+/jeZO3cGnTp1wcPDnQYNGjNy5Djmzp3Bw4f3SU9Pp3r1mowfPxkzM3MAhg8f\nQq1adbh58zrPnj2levWaTJo0nQ/qcsLvChu8DpOUkky3VrZfbHOOU3qFaHJS4MsQlrW+ExoamowY\nMYYTJ86wfv1Wbt68wcGDnmp5/vzzPK6uu3B13cmff57H09Mz4/o5du7czvz5Lnh5+VCrVm1mz56S\nbxvev3/P+fNnqFSpiuqar683ffsOwsfnAjVq1GLdupW8e/eG7dv3sGfPIcLDQ9m6dRMAV69exsNj\nNytXrmfPnoP4+9/MNuvue+s6Pzu3x3fxCoqbmrH+6CFVmlXpsuycMhOfxcuxb9CIqVs2kCqXq9Iv\n3ruLff2GnHZZSbPqNXHxcAdgZt+BWBgbs+TX4ZxZsirHgSdAREwMMfHxeM1fzPTe/Vng7sbr0BAA\nikilzOozgNNLVrLktxHsOX2aC3dvq5W/9eQxnrPns3zYKHb6nOTG478w0denXqXKnL19S5Xv1Knj\n2No6CANPgX8VX6JRXl6HM65/uUbNmzcLLS0tPDwO4+q6i+vX/TiaoRtHjhzg6tVLbNu2m82b3Th3\n7nQealRqUnBwEOPHj+Snn7pz7JgvW7e6U7FiZQCKFNFm2rQ5eHufZ/Hi5Rw+vJ+LF88DsGaNUu9O\nnTrPqVPnqVaturLWDK2LjY1h+IoVdG1li/eiZXS3tmXsupXEJGS6kp26cY0ZffpzcsFSUuRy3H29\ns1lZytwC92mzATjtspLVI8YQkxDPuHWrPln3yWt+TOnZhzNLVmGZy/YJ7+t+rBw+hv2z5vMqJIR1\nBw8CkK5Q0LbJDxyet5DDcxcik0hYkqGzHzhx7SrT+/Tn2B8uaIg0WOKx+6Mnq2SOmytammL2z/6D\nHZOnc+3RQw5f/hOAjV6HaVy1Gsf+WMzu3Qf56aeun3rDBAQEvgBf35MsX76GvXsP8fr1K7Zv36JK\ni4gIJy4ujv37jzFhwlQUinScndtx4MAx9u/3QiaTsXTpoo/q82batNl4efmQmprCvn3K7/6LoEAW\n793F7H6D8Jq/mOj4eMLev/9b71WgcCMMPr8TlStXwcqqOiKRCEtLS9q1+5Hbt2+q5enVqy+6urqY\nm1vQpUsPjh07BsDhwwfo3bsfpUqVRkNDg169+hEQ8ISQkOA8tb18+WJat7ZmwICemJqaMmzYKFVa\n8+YtqF69BgASiYSjRw8xfPgYdHV1KVKkCL169cPX9xQAZ8/64uTUltKlyyCVShkwYEi2tlrUqkOV\nDDsdGjTiyds3qjSHBo3Q09ZGQ0OD7tZ2pMpTeZXlHmqVr0DjjGfk2KgJTz8KUPI5NziRCAa37YBY\nU0ydipVoWr0Gp2/dAKBOxUqUy9hTVb5YcZyaNME/4Ila+UHO7ZBqaVG+WHGcmzTl1A3lZv7WDZtw\n6sY1QOma7OvrjaOj0ydtERAobHyJRvlmDKq+VKOioiLx87vMlClTkEqlGBoa0qVLd06f/qA5p+nc\nuTumpmbo6enRu3e/PN+Pj483DRo0xMbGDk1NTfT19amQ4WlRu3ZdymWsJpYrVwEbG3v8/W+plc9N\nb/z8rlDawgKHBo3Q0NDArn5DSlsU5eK9O6o8bRo3pYSZORItLWzq1lfTwZz40Nal+/coaf7pup0b\nN6WMZVE0NDTQzGWbROeW1pgZGqKnrU0/RyeOXb4MgIGODi1r10WipUURqZS+9q3xfxqgVrZ1w8aU\ntSyKTCJhcNsOnPa/ke1ZRMTEcOXBfUZ16oJUSwtDXT26tbLFN0MzxZqaBEdGEPb+PVpaWtSoUeuT\n9y8gIJB/OnXqqtLGPn0GqPQYQFNTk4EDh6jifejrG9CiRSskEglFihShd+9+3Lnjr1afk1Nbihcv\ngUQiwdrajmfPlNpw9vYtmtWoRa3yFRBrihnStr3g7i+QLwS32+/EmzevWbVqGY8fPyQ5OZm0tDQq\nfxQ8x8zMQvW3paUloaGhAAQHB7NixRJWr14OZO7zCQsLw8Li825to0aNp81H/vcfMDfPbDMqKoqk\npCQGDuytuqZQpKs6HuHh4VStaqVW9uNOiYm+gepvmURCYnKy6vUuX2+OXrlERHQ0AAnJSUTHx2Up\nm7nZXKYlISU1NV/7UPW0tZFqaaleWxqbEB6tnJ27/+I5644c4HlgIKlpcuRpaVjXqafKKwI11zNL\nYxOeBwYC8L+atVm0ZychIcEEBQWiq6tHlSqZz0FA4N/Al2hUeHg48OUaFRwchFwup1mzZqSnKzL0\nRKEqEx4epqZRFhZ53xsZGhpC8eIlckx7+PA+69ev5vnzZ8jlqaSmptIqj65kERHhFDVRX3G0NDZW\nWwn4lA5+ivDo99lWMz+u28LI6ONi2ciqZUWNTQjNKJ+UksJyz71c/esBcYkJKBSQmJyktnfU3Mg4\nS1lj5GlpvI+LU6s/JCoCeVoabaaMA5RecwoUWGSUHfbjT2w4eohfly9Gd7sr3bv3wtm5XZ6egYCA\nQN744DILYGlZlPDwMNVrQ0MjtS0TyclJrFixhGvXrhIXF4tCoSAxMVHtu581EKVMJiMxMRGAsPfv\nMc+iOzKJNNs2AgGBTyEMPr8TLi4LqFy5MnPm/IFMJsPDYzfnz59RyxMaGkKZMmUBZWfO3FwpLObm\nFvTtOwA7u4KPtph19srQ0BCZTIabmwempqbZ8pqYmKgGxAAhIcF5nv26/fQJO329WTtyHGWLFgPA\nfvzIPAf1EOUhHm1sQgJJKSnIMvanhkRFqiJIztq2mc4tbVgxbBRiTTHrvA4QGhGlVj40KpJSGZ3e\nkMhITA0MAWWo8Za16+Ljc5LAwHc4OAirngL/Pr5Eoz7oxJdqlLm5BRKJBD8/P8LD47Klm5iYEprh\nOg8QEhKkli6TydQOFI+MjFCr+6+/HuTY7uzZ0/jpp24sXboasVjMypVLiM6YFPtc7GsTE1OuRESo\nXQuOiqRJhnvu12BqYEhQhPoK7Md150ULQ6MytS0oMgJzQ6WWuZ8+xZuwELZOmIqRnh4Bb9/Qd8Fc\ntQ5oaFRklrKRaGlqYqirS3Bk5nULQ2MkWlp4L1qe4/8AYz19JvfoQ1JKMrdFGkyYMJratevmOhkg\nICCQf7JqY3BwEKY57P/+wO7dO3n79g2bNu3AyMiIgIAnDBzYK09By0wNDNS81JJSkomO//vPDRYo\nvAhut9+JhIR4tLV1kMlkvHr1kkOHPLPlcXffQWxsLCEhwXh67sHJSTnI6dChE25uW3nx4jkAcXFx\nnD3rW+A2ikQi2rbtwMqVS4jK6LyEhYVy7dpVAKyt7Th+/CivXr0kKSlJbX/B50hITkasqYm+ji6p\ncjlbjh8lPunTqwFZh6Um+ga8y1hlyTW/AjYdO4I8Tc7tp0+4fP8uNnXrq9rX19ZGrCnmwcsXeF26\nlK0t1xPHSEpJ4XngO7yuXsIuI4gJgH39hpw6dYJLly4Ig0+BfyVfolG2tvbAl2uUiYkpDRo0Zv78\n+SQkxKNQKHj37i23M/ZYW1vb4um5h7CwUGJiYti5c4da+YoVK3P69CnkcjmPHj1U2xNqb+/IzZvX\nOHvWl7S0NGJiognIcLVPTExET08PsVjMw4f38fHJdFczMjJEJBLlei5pw4ZNeB0Sgs+Na6Slp+Nz\n8zqvgoNo9oWupVl1rmm1GrwN+/q6PS+cJfR9FNHx8Wz3Po5TkyaA0ttEqiVBRyYjOj6ezcePZCt7\n8rofL4ODSEpJZrPXYazr1Fd1Tj/YamJgQKOqVizfv5f4JOXK6bvwMNVWhjO3bhL6Xvk/RFdXFw0N\nkRBJXUCggDlwYF+GNkbj5rYVGxv7XPMmJCQglUrR0dEhJiYaV9eNeW7Huk49Lt6/y93nT5Gnydno\ndaRQRTAX+P4IK59/K5mzScOGjWLRot9xd3ejUqXK2NjYcytjPyIoB37Nm7dg4MBeJCTE4+TUlp9+\n+onw8Dj+97+WJCUlMmvWFEJCgtHR0aVBg0YqN7FPzVrl1y//119H4Oq6kSFD+hETE42pqTk//tiJ\nhg0b07hxU376qSsjRgxBQ0OTvn0H4u19XBUJ91M0rlqNxlWr0WX2NLSlUrpZ237WfSyr5b3tHVnq\nsZs1hzzp5+hMjxxE1tTAAH1tbdpMGY9MImVi996UynDZG9+tJyv2e+DisZs6FSvh1KQJ4VHRqrIi\nlPtCO8+aigIFvWwdaVAl0+WwetlyiEQiKlWqkidXZwGBwsHXaZSzs9Kd/2s0avr02Wzdup5evbqQ\nkJBAsWLF6dmzLwBt2/7Imzdv6NevOzo6unTv3ht//0ybBg36hVmzpuLkZEPt2nWxs2tNTIzye21h\nYcnixStYvXo5CxbMRVdXj59//pWKFSsxZswEVq9ezrJli6hduy42NnbExsYCIJXK6NNnAL/+OpC0\ntDSWLFmpZq++vj6rRo7kj527WLRnFyXMzFjy6wj0tXUy7vVL3wHlnkyXX4ezdN+eL65bhAj7+g0Z\nuWoZETHR/K9mHX7t0IGE+FS6tbJlxtbNOE4cjZmhEd1t7PjzbuZ+UpEIHBs2Zs6OrbwODaZuxcpM\n6N47S92ZzOgzkDWH9tN97gwSk5MpZmpK74yV74evX7Bs/x7iE5MwMjVl1KhxFM3weBEQECgIRNjZ\nOTB69DAiIsJp3rwFffoMyDV3ly49mD17Ks7OtpiZmdGtWy8uXbqQWdsnxKVs0WKM79KDGa6bSEpN\nobu1nZpr/9ffirB/9N+OSFHA0xVhYbEFWd03xcxMT7C3AHn16iV9+nTl7NkrpKSkYPYmgMQE+ecL\n/gMwMtIhKirvbiNJKcmM3rEVBwenXPfPfkv+6Z+FjzEz0/veJhQ4he35C/Z+G5KSkgqV1kH+9a6g\nSEpJRl6jNjKZLM9lCtNnAf6dWgeFR+8K4+elsNj7JVr3KiSYk9euMqRtB7XrUzavZ/6gX1S/s7Lq\nwD46t7TONXp3fihIrfsS/coPhemzAF+ndcLKp8BXceHCOZo0+YHExETWrVtJs2b/+0+4Uz16/Yqn\nT5+waNHy722KgICAgICAgMA/kpPXr3L3+VPVa4UCYjOOi3oW+I6hK1zU0gLDw+jc0vpvt1Pg70MY\nfAp8FYcPH2D+/FloampSp049xoyZ+L1N+ubM2eHKhbu3GTpiLEWKFPne5ggICAgICAgI/OMobWHJ\nwTkLck3fO2Pu32iNwD8FYfAp8FV8vP8pKympqSSlpPyN1nw5ScliklLydvzBhG49GdWpKxoZwYsE\nBAT+2xQmrYP86V1BkpIqF6IcCggUYv7LWifoV8EhDD4FvglSqRRJo0ZEFxb/dTM95PmwVQPlPQoI\nCPy3KXRaB/nWu4JC0E0BgcLLf13rBP0qOITBp8A3QSQSIZPJkMlSv7cpeaIw2SogIPDPobBpHQh6\nJyAgkH8ErRMoKITBp8A3QaFQkJSUpHbg+z+ZpCStHG2VSqX5Pp5GQEDgv0Nh0zrIXe++JYKWCggU\nbv6rWidoV8EjDD4FvgnJycmk+Pkjji8kewMMdRC/Vw/HnZIqJ7lu/W8WVltAQKDwU+i0DnLUu2+J\noKUCAoWf/6LWCdr1bRAGnwLfDImWFmmSwjFbJJNKkUmyn11VeE7uExAQ+F4UJq2D3PXuWyJoqYBA\n3ujcuR2TJk2nXr0Gn83bvHkD9uw5SPHiJfLdzpeU/Vqt+3HGJKb27Ev9ylXZ7n2cwIhwJvfo88X1\nBUVE0HHmZC6tXJ/jMX8FoXWCdhU8wuDzH8CbN6/p27c7rVrZMH36HNX15OQkVq1azrlzvsjlaVhZ\nVWXp0rUAjBs3gjt3bqtcAVJTUyhVqgzbt+8mKiqKFStcuH37FklJSZQrV55hw0ZhZVVdVff27Vs4\ncuQg8fFxNG78AxMmTEVbWxuA8PAwlixZwJ07t5HJZPTpM4AOHTqpyt68eZ01a1bw7t0bDA2N6Nmz\nL+3a/ahKDwx8x9KlC7l3+xYSsRZtmvzA0A6dSJXLWbR3F9cf/UVsQgLFzcz4te2PNKmmtOuDiBSR\nSpWHPYlE9LZzpL+jMwC7fL057neFoMgIjHT16Ni8BT1tHVTt3n3+lOWeHrwMCaK4iSnjuvakVvkK\n2Z73PLdtHPO7jOes3yluagbA5PXrOXr5MlpisartY/MXq8o0b94AmUx5rIpIJMLGxp6JE6cCcOKE\nF56ee3n79jU6OrrY2jrwyy/DVEI4d+50bty4RnJyMsbGJvTo0Zs2bTIPXD592oetWzcSFhaKubkF\ngwf/RvPmLQHw8HDH03Mv0dHv0dbWwdrajqFDR+bhUyUg8M/C19ebbds2ExISjImJKVOmzKRmzdo8\neHCfzZvX8fjxI9WRTXPmzASUM81xcXGsWOHC1auXEYlEdOjQiQEDBqvq/emntkRFRaKpqfx3Vr16\nTZYuXaVK/5TWrV27El9fb+Lj49DXN6Bdu4707t0PUOry2rUruHfvLgpFOlWqVGPkyLGUKlUagOfP\nn7F69XIeP/6L2JhoLq/eqHa/L4ODcNnrzqM3rzDS02NYh59oUauOKv36o79w8XAnNCqKamXKMq13\nP9Wh6puPHWGb93EkWloqPdo5ZSbFTEwBGLrCheeBgaSmySlmYsog53b8r2ZtVd1bTx7j8MULxCUl\n0rRaDSZ17412lpn7y/fusWDnLl6HhKCvo8PIjl2wrltPzf7jfpeZ67aNKT360LZpM+U9B75j5YF9\nPHrzipj4+Gz3DOBz4xpbTngREhmJiYEB03v3p3LJkmp5tm7dhKvrRpYvX6vqYLu7u3HypBfBwcEY\nGhrSocNP9OjRO/sHSUBAQMXXuIN+quywYYO5c8efbdt2Uz5LP2rUqlWc8/dn7chx1KlY6YvbBujr\n4PRV5T9QeKb9BD4gDD7/ASxbtggrq2rZri9c+Dvp6em4u+9HT0+f8PC3qjQXF/UjToYPH0L9+g0B\nSExMwMpK2VEyNDTi6NFDTJgwCk9PL2QyGSdOeOHjc5ING7aiq6vH7NlTWbZsEVOnzgJgzpzpVKxY\nmd9/X8zz588YMeIXSpcuQ5069ZDL5UydOp6hQ0fRtm0HHj16yPDhv1CtWg3Kl6+AXC5n9OihtG/f\nkZUD+pGcmMbr0BAA0tLTsDQyZsOYCVgYGXPp/l2mum7AfeosVadLBJx2WZmrKM7sM4AKxUvwNiyU\nEauXY2FkjG29BsQkxDN+/Rom9ehNy1p18L7ux/j1qzgw5w90i2iryt959pR3EWE5ilVvO0cGt2mv\nep01PLdIJGL79t0UK1Y8W7nk5GRGjhyLlVV13r9/z8SJo9m9242ePfsC0KtXfyZMmIZUKuX161cM\nHz6YSpWqUKlSFcLDw5g3bwYLFy6jYcPGXLlykenTJ+Hp6YWhoSHNmrXA0bEN+vr6xMbGMm3aBDw9\n9zB06JAcn4+AwD+R69evsmHDGubM+YOqVasRHh6uSouNjaF9+440bNgETU1Nli5dyOTJk/njj2UA\nrFy5hOTkZPbv9yIyMoKRI3+laNFitG7dBlB+NxcvXkHdHI4++pzWtWnTnn79BqGtrU14eDijR/9G\n6dJl+N//WhIXF0uzZi2YMmUW2trabN26icmTx7JrlycAYrEYGxs72rZtz8wZk9XaTUtPZ8KGNXT6\nX0tWjRjDrYDHjFu3mh2TZ1DS3JzouDgmb17H1F79aFa9JuuPHmKa60Y2j8usx65eA2b2HZjj8xz9\nUzfKWFoi1hTz4OULhq9ayr6Zv2Oir8+xq5fxvu7HpnGT0dMuwoytm3HxcGdGnwEAvAgKZNyaNczo\nM4AGlasSl5RIXEKCWv2xCQls9z5BuaLF1K6LNTWxrdeATi1aMXHDmmx2+f31kLVHDvD7wCFYlS5L\nePT7bHnevXvLuXOnMc2Y+MvK9OlzKF++Im/fvmHMmGFYWFjSrVvHHJ+BgICAch/mtygrEokoVao0\nJ08eU014x8TEcO/ZM4x09b64TQEBQDiyZufObXTt2gF7+xb07t2FCxfOqdJOnPDi118HsmzZIhwd\nW9KrV2du3ryuSh8+fAgbNqzh55/74uDQgsmTxxEbm7+Qzr6+3ujp6WVzr3j9+iWXL//JhAlT0dc3\nQCQSYWVllWMdQUGB3L17GwcH5QphsWLF6dKlB0ZGxohEItq1+5HU1FRev34JwKVLf+Lk1A5TUzNk\nMhk9e/bl9GkfkpOTSUxMxN//Jn369EdDQ4MKFSrSsqU1x44dAZQdxYSEBOztWwNQpYoVZcqU4eXL\n5wAcP34UMzNzOnbsglRLCy2xmPIZAzaZRMpAp7ZYGBkD8EP1mhQzMeXR61eqe1EA6bkIYk9bByqV\nLIWGhgalLCz5X83a3H3+DIB7z59hoq9Pq9p1EYlEODZsjKGuHudu+6vKp6Wns2TfbsZ16UF+5Vqh\nUOQq1B06dKJmzdqIxWJMTU2xt3fk3r07qvSyZctlCc+tAES8e6ecSAgNDUFPT5+GDRsD0KRJM2Sy\nIqr0YsWKo6+vD0B6ehoikYi3b9/k03oBge+rda6uG+nXbxBVqyon2UxNTTE1Va7iNW7clJYtbdDW\n1kYqldKpUxf8/TO/t5cv/0mPHn2QSCRYWhalTZv2Kj36QG7fzZy07swZpdYBlCpVWrUKqlCko6Gh\nofp+Va1aDWfndujp6aGpqUmXLj14/foVMTExqrLOzu0oXbpstnZfBQcRHhNN11a2iEQi6lWqQs3y\n5Tl57QoAZ+/colzR4rSqXRctsZifndsS8PYtr0OC8/Q8KxQvgVgzc+44LS2d0KhI5T3fv0vbJj9g\nZmiITCKlt50jp2/dIDlVGfFx68ljdLO1pVHVamhoaKCvrUOxjwaCaw8foGsrGwx0dNWul7KwpE2T\nHyhrWTRHuzYfP8LA1m2xyngmpgaGmBoYquVZunQRv/46ArFYfe67R4/eVKxYWanvpUrTrFkLNR0V\nEPgv8tdfD/jllwE4OraiQ4fWLFu2CLlc3RH0ypWLdOnSnjZt7Fi7doVampfXYXr16oyTkw1jx44g\nODhvGgNgZ+fI6dOnVPp69qwPNvXqKT3EMlAoFOw4dYKfZk7BceJoprluJDbLZNYJvyt0mD4Jx4mj\n2XbymFr9m48dYda2LarXt58G8POSBdiNG0mHaRM57ncZgMv379FnwVxsxo6gw7SJbP5I/wUKH//5\nwWeJEiVZt24Lp06dp3//wcydO53IyAhV+sOH9ylRohTHjp2mf//BTJ06Xq3T5e19nKlTZ3HkiDea\nmhosX74oz23Hx8exZcsGhg8fk63z9PDhAywsirJly3ratLGlb9/unDp1Ksd6Tp48Rq1adbC0tMwx\nPSDgMXK5nBIlSuaYnp6ejlyeytu3b1AoFIhEIrKao1AoXcwAjIyMsbV14NixI6Snp3P//l1CQkKo\nleFO9uDBPSwsLJkyZRwthw9n6AoXngW+y7HdiJgY3oSGqM2ui4Afp0+i/bSJzHPbRnRcXI5lAW4/\nC6BcsWK5pitQqLW9+7QPdStWUg2GP2b/hXM4TBhN/4XzOHv7Vrb0YcMG0769I9OmTSA4OCh3u277\nU7ZsebVrS5YsxNa2GT17dsbU1IwmTZRubFWqWFG6dBkuXfqT9PR0Llw4h0QioUKFTDcXH5+TODi0\noE0bO549e0r79p0QEMgv30vr0tPTefToL6KiIunW7Uc6dnRm2bJFpORyUPnt27eoWLHiR1czBSk9\nPV2lRx+YM2cabdvaM2bMcJ4+DfikLampqWoTODt3bsPO7n907OhMUlIS9vaOudplYmKqmgzKLwoF\nPAsKBJSrjxVLZO6zkkmklDAz43lGOsDFe3dxmDCanr/P4sCf57LVN3bdKlqM+o1BLn9Qt2IlqpYu\nk2O76QoFqXI5bzI8UB68fIFCoaDn77NoO2U8s7dvISYhMyDHg5cvePzmFR0zXP/zSnp6Oo9evyIy\nNoafZk2l/bSJuHi4k5KaeczBmTO+SCQSGjdu+tn67t71p2zZcvmyQUDg34aGhiYjRozhxIkzrF+/\nlZs3b3DwoKdanj//PI+r6y5cXXfy55/n8fI6nHH9HDt3bmf+fBe8vHyoVas2s2dPyXPbpqZmlClT\njmvXrgLKvkibpk3V+qse507z5907rB8zAa/5i9Eros3ivbsApc4t3ruL2f0G4TV/MdHx8YS9V/eG\n+ODkFhQRwZh1K+na0oaTi5axY8oMKpYoBUARqZRZfQZweslKlvw2goMXL3Dh7u38PUiBfxT/+cFn\ny5Y2GGe4fFpb21KiREkePnygSjc2NqFz525oampiY2NHyZKluXLloirdwcGJMmXKIpXKGDToV86e\nPZ1nN4jNmzfQtu2PObofhYWF8vz5U/T09Dl06CSjR49n4sTI68FGAAAgAElEQVSJqtXLrHh7H8fJ\nqW2ObcTHxzFv3kwGDBiMtrYOAI0bN8HL6xDBwUHExcXh7r4DgKSkJLS1talRoxbbtm0mJSWFx48f\ncf78GZKTM0NV29jYs23bZlq1asKwYYMZPPhX1T2EhYVy5owPHTt2wXf5cppWq8GEDWuQp6Wp2SVP\nS2PW9s04N/6BUhbKQbOhri6uE6ZyaO4Ctk2cRkJyEjO3bc7xvjZ5HQaFgjYZnZjqZcsTHhON783r\nyNPSOHb1Mu/CwkjK6OCGREVy+PIFBju3z7G+Po6O7Js1jxMLlvBzm/bMddvKg4zVXIDVqzexb98R\n3N09MTExZcKEUaSnp2erx8vrMI8f/0X37r3Uro8dOxEfnz9Zu3YzLVq0QktLCwANDQ0cHJyYNWsq\nrVo1Ye7c6YwfPwWpNHN/lp2dI97e59mz5yAdOnTC2Ng4x3sQEPgU30vrIiMjkcvlnD9/hnXrtrBt\nmztPnjxm+/Yt2fI+fRrAtm1bmDBhgupao0ZN2LlzOwkJCbx9+4bjx4+qhc6fOXMe+/YdxdPzKHXq\n1GPs2GHExysnrT6ldR/o1asfPj4XcHXdhYODEzofrfaB0kNh2bJFDB8+5rP3C8oVQmNdPXb5eiNP\nS8Pvrwf4P32i0qOE5GR0M/aQf0BHVoSEDJ21rdeAPdPncHLhUiZ1743rCS98sqxEAyz5dThnlq5m\n2W8jaFQ1c9tGY6tqHLl8kaCICOISE9jpc1J5zxlth76P4sjFiywc/Bv7Zs0jKSWFJR67AeUA0mXv\nLsZ17ZGn+8xKZGwM8rQ0zt2+xcaxE9kxeQZP3rxha8ZqR0JCAhs3rmXUqHGfrWvLlg0oFAqcndvl\n2w4BgX8TlStXwcqqOiKRCEtLS9q1+5Hbt2+q5enVqy+6urqYm1vQpUsPfH29ATh8+AC9e/ejVKnS\naGho0KtXPwICnhCSRw8LAEdHZ06c8OL165fEx8dTs7z6xPrBixf4pV0HTA0MEWuKGejUhjP+N0lP\nT+fs7Vs0q1GLWuUrINYUM6Rt+1y3VPncvEbDKlbY1muAZoZHRsWMQEh1KlaiXMaiQflixbGr1wD/\ngCd5vgeBfx7/+cHniRNe9O/fA0fHVjg6tuLFi+dEZ9mn8vHA0NKyKOHhYarX5uYWammpqam8f599\nn8u4cSOws/sf9vYt8PE5SUDAE27c8KNLl+452iWVStHS0qJv34GIxWJq165Lo0aNVDNQH7hz5zaR\nkZG0bGmTrY7k5GQmThxD9eo1VfsPAZyd22Nr68Dw4UPo06crdes2yLgXcwBmzJhLYOA7OnVqw9Kl\nC3FwcMLMTJn26tVLZs6czPTpczh/3g83Nw927tzBlSuXVHbXrFmb+vUbItbUpKetA9HxcbzMslKo\nUCiYtX0LErGYsVnuv4hUSpUMkTTS02Nslx74PXpIYnLm3kuAfefOcPK6H0t/G6FyPTPQ0WHR4N9w\nP30K58nj8PvrAQ2rWGFuZATAcs+9DGjdVi3oRlaqlimDvrYOGhoaNK1WA4f6jbhwN9Plq1YtpVut\njo4uI0eOIygoiJcvX6jVceHCOTZtWsuSJavQ1zfI1oZIJKJGjVqEhoZw6JBy5vL6dT/WrVvJmjUb\nOX/ej1WrNrBgwdwcV2+KFy9BmTJlcXH5I8d7EBD4FN9L6z64nP/0UzeMjIzR1zegW7eeKs34wNu3\nbxg/fiSjRo2nbt26quujRk1AIpHQvfuPTJkyDjs7R5VWgTLAkEQiQSqV0rt3P3R19bhzRzkr/jmt\ny0rFipWQSCRs3rxe7XpUVBRjxgynY8cu2NjY5fJ01RFrarJwyFAu3r9Lmynj2H3GF9u69TE3VOqR\ntlRK/Ednz8UnJaKdMelUxrIoJgbK7RY1ypWnS0sbzvrfzNaOpoYGja2qc/WvB1zMcFFt26QZdvUa\n8NuKxfT8fTb1K1dR3nOGFkq1tOjUsiUlzMyRSaT0dXDiysP7AHheOEuFEiVVbrP5QaolAaBzSxuM\n9fQx0NGhu40dlx/cA2DHji04OjphYZGzh84H9u/fi7f3cRYvXpnNNVdA4L/GmzevmTBhNO3bO+Do\n2JJNm9YSHR2tlsfMLKs2W6r21AcHB7NixRJat7amdWtrnJxsEIlEhIWFkVdatGjJrVs32L/fA9ss\nAR4/EBwZwcSNa7EfPxL78SPpPncmYk1NImNjCHv/XqU7oPTwMNDRybGdkKhIVQDIj3nw8gVDV7jQ\neuIYbMcpVz6j43P3ihP45/OfVvbAwEAWL57PypXrqV69JgD9+/dQm83P2vkCCAkJpnnzFqrXoRmu\nTADBwUFoaWlhaKi+xwWyBwjy8NhNcHAwnTq1ARQkJCSSnp7Gy5cv2LLFjfLllW5nH9xgIefIZCdP\nHqNFi1bZziBKTU1l8uRxWFhYMn68upuFSCRiwIDBqoiR165dxdTUTDXAtLCwZNGiZar8s2dPU+3V\nevHiGaVKlaFBg0YAlCxZiqZNf8DP7zJNmvxA+fIVuXfvbjY7s/L7zu28j4tj2W8j0MwhNLaarajv\nAT16+SI7fb1ZP3pCtr1EtStUwnWCMgJtWno6nWZMpoetPQA3Hj/i7vNnrM7irjLI5Q/G/NQNu4xA\nTR8/o9xWdTKvZ6ZfvXqZxYvns3jxis+6iqWlpan2dD59GkDt2nWpVEnZQaxSxQorq+rcuOFHhQof\nux6CXC4nMBc3ZgGB3PieWgeotCUTdS0LDg5i9Oih9O//cza3Vz09PWbMmKt6vWHDGpUe5UTW7+7n\ntO5j0tLS1L5fsbGxjB07jObNW6ii4OaV8sWKs27UeNXrn5cswDnDU6Ns0WIcv3pFlZaYnMzbsLBs\nAX5yuqcc7U5P413G+ycSiRjk3I5BGauGfn89wMzQSDXwrVAs92MVbj55hP/TAC7fVw4YYxLiCXj7\nhidv36hNFOaEnra2qo2c8Pe/RUREOAcP7gPg/fv3zJgxiZ49+9Ij46gFL6/D7Nq1g7VrN6v2BAsI\n/JdxcVlA5cqVmTPnD2QyGR4euzl//oxantDQEMqUUU4YBQcHq7475uYW9O07ADu7nLcS5AWpVEbj\nxk05dGg/bm4ekByjlm5hZMy0Xv2oUa58trKmBga8yrLKmpSSTHR8zmduWhgZ8+CjCf0PzNy6ic4t\nbVgxbBRiTTHLPfcKg89Czn965TMxMRGRSISBgSHp6ekcO3Yk216iqKhIPD33IJfLOXPGl9evX9K4\n8Q+qdG/v47x69ZKkpCS2bNlAq1Y2eQp93b59Rzw8DrFtmzvbtu2mQ4dONG3aXHVEQK1adTA3t8TN\nbStpaWncvXuba9eu0bBhE1UdycnJnD3rk83lVhmRdgIymUwV1TErMTExqsHPixfPWb16GQMG/KxK\nf/XqJQkJCcjlcry9j3P9uh/duvUEoGLFyrx794Zbt24AysiFly9fVA2U7O1b8/DhPfwz3C52n/HB\nUFePMhkBKhbuduNVSDAuvwxV27QOytmt1yHBKBQKouPiWOa5h7qVqqCTMbA+ee0q648eYuXw0RQ1\nMcl2X0/evEaelkZ8YiIrD3hgYWxMwyrKIE37Zs3DbcoM3KbMYMfkGYDSbe3D0Qfe166RmJyMQqHA\n768HeF/3o1lGJ/3Fi+cEBDwhPT2dhIQEVq1ahrm5uSrQyM2b15k7dzrz5i2iSpWqajZFRUVx+vQp\nEhMTSU9Px8/vCr6+p6hfXzl4r1rVirt37xCQ4ULy5Mkj7t71p0IFZQhzL69DREVFqezYuXObqqyA\nQF75nloH4OzcDk/PvURFRRETE4OHhzs//NAcULrqjxz5K506dVE7sukD7969JSYmmvT0dK5cucTR\no4fo128QoBwg37t3B7lcTkpKCu7uO4iOjqZGjVrAp7VOoVBw+PAB1b7Whw/vc+DAPlXU8ISEeMaM\nGUrNmrUZMmRojveVkpJCamoKCiAlNZXULIFAnr57S0pqKkkpyezy9SYyJka1TaBlrTq8CArk3O1b\npKSmsvn4USqVKKnagnDh7m1V0I4HL1/gcfY0/8vQqlchwVx5cJ/k1FTkaWmcuHaVO08DqJOhGTEJ\n8aqB6IugQFYe2MdApzYqu9o0+YED588TGB5GUkoybj4nVVo3o/cA9kyfo9LKKqVKM9CpLb9keV8+\n3GdO99ymcVP2nTtDVGwsMQnx7D3jS7OM98LFZQVubnvZtm0327btzti+MJWOHbsAcOrUCTZtWsvy\n5WuwzCWgkYDAf42EhHi0tXWQyWS8evVS5TWVFXf3HcTGxhISEoyn5x5sMybdO3TohJvbVl68UG4h\niouL4+xZ33zbMGTIUFav3qjm/fKBH5u1YN2RgwRnxA+Iio1V7ce0rlOPi/fvcvf5U+RpcjZ6Hcl1\nEs2hQSNuPH7EmVs3SUtPJzpeOfEFym0K+traquje3jf81Mp+ebxfge/Ff3rls3z58nTr1oshQ5SR\nXR0dnamZ5aw0ACur6rx9+4Y2bWwxNjZh3rxFagEnHBycmDdvJm/evKJOnXqMHz/542ZyRCqVZomA\nCkWKFEEikWCQsZonFotZsGAJCxbMZefO7VhaWrJo0SLVGXOg3Eyup6dPnTrq57Pdv3+Xq1cvIZVK\ncXBoCShnw11cVlCzZm2io5XHgYSFhWJoaETnzt3Vzp3087vCjh2uJCcnU6lSZZYuXaWyq3jxEkya\nNJ3lyxcTEhKMjo4uDg5OqvKlSpVm+vS5LF++mDmREVQqWYrFvwxDrKlJcGQEhy79iUSshdOksWQY\nxqTuvbCv34jA8DDWHTnI+7hYdGRFaFClKnP6D1LZtdHrMDEJ8fRf9Lvq7DvHBo2ZkDEw3unrzeUH\n9xAhorFVNRYO/k1V1vCj0OAiwEBHV3mOHrDj5Ekev3qNAgXFTEyZ0rMPNctXQI6yU+7i8gdhYWEU\nKVKE6tVrsmjRcjQ1NQHlOYLx8fGMHz9StVJdq1ZtFi9egUgk4uBBT1xcFqBQpGNhUZSRI8fSNOPc\nvNq169K//89Mnz6RqKhIDA2N6Nt3oKoDfPfuHTZuXEdiYiKGhkZYW9syaNAvefqMCQh84HtqHUDf\nvgN5//493bt3RCqVYmNjR5+Moz+8vA4TFBSIq+smXF03oVAo0NAQ4e19HoDHjx+xcuUS4uPjKFmy\nFDNnzqN0RnCdhIQEXFwWEBj4DqlUQoUKlViyZKXK7s9p3YUL59i4cQ2pqXJMTU3p3LkbnTopB0Pn\nz5/l8eNHvHz5kmPHjgJKHd250wNzcwuCg4Po3LkdIpEIEdBi9FCKGptwYI7SLf7Etascvfwn8vR0\napevyMrho1XbBAx19fg/e2cdEFXWxuFnYGAIaVBUVOxusQMpEXN1zbVzXWtV7MQObF1da3XtLtJ2\n1+61VsVARVo6ZmBgvj8GroyU++0a6H3+0bnn3Hvec+/ld0+85z0LBv/Ikr27mLVtM1VtSzMnywDg\nqZvXmbdjG6lpSgqbmtG3lRutMyJiq1QqNvkcI3BLKNpaEmysijB34FAqlFAH54hJSGD8+jWER0dj\nalSI7i2daN+4mXDtto2aEJscz8AlC0ACjapUY2yX7gAY6utjyLu1qLpSKYZ6esIAYOZezBLIsc79\nW7clJjGBrh7TkOnq4FTHjn6t3EhLT8PIyFjDQ0dbW0qhQkbCsY0b1xMXF8egQX0FHXVxac2iRfM+\n+D0TEfk6eDeoN2LEzyxePI9du7ZToUJFHB1dhMF/UGtSs2YtGDiwF0lJibi5taNNRmyL5s3tkcuT\nmTVritBes7NrQMuWTsK5uVqQJc3CwhILC0thrXzWtG4t1Uu+Rq9ZQWRsLGZGRjjVrUfzGrUoXbQY\n47v2ZMaWjchTU+jh4Jyrd0QRM3OW/TSKVYf2MW/nNoz09RnariPlbUrg3q0nqw7tx3PfbmqXr4BT\nHTsSkt9F1BX3+Sx4SFT/ZpOgHIiI+GdbjXxOrKyM8rTX19cLL6+jrF27Mcf0kSOHZnS8cg5i81+T\nn71fEnK5HKvXASQnKfPP/AVgZmZIdLSmO4g8RYGyeq1sLs1fAgXpXQC1vV8bBe3+i1r3cShoWgc5\n693H5N9oaUF6F+Dr1DooOHpXEN+XgmLvt6h1n7IdWJDeBfh3WvdNu92KiIiIiIiIiIiIiIiIfBq+\nabfbf8uHrnf6VlGvd8p5L78vDblCijxFM6puSqpSHJ0REUHUuvwoSFoHOevdx0TUUhGRr4NvTetE\n7fo4iG63or0fBZVKhbGxboGxN7d7K5PJvsiGd0F6F+DrdEUraPdftPfjUNC0Dj7P/f1/tbQgvQvw\ndWodFBy9K4jvS0Gx91vVuk/VDixI7wL8O60TZz5FPgoSiQQ9PT309FI/tykfREGyVURE5MuhoGkd\niHonIiLyzxG1TuS/QpxNFhEREREREREREREREfnoiDOfIh8FlUqFXC4XQnN/6cjlOtls/VJdbkVE\nRL4cCprWQc569zERtVREpODzNWudqFGfFrHz+ZXTpUt7Jk2aTt26dp+0XIVCQcrV2/SaNJkJ3XtS\np3zFf3T+zlP+hLx9i3u3nh/JwvcwNUQa8y4cd0qqEkWdel/kNisiIiJfDplaJ00sOEE43te7j4mo\npSIimsyf70HhwkU+aM/uz9WGy4msWud37QreVy6zetSYz21W3nyA1oka9ekRO58iHw1dHR0kEtCV\n6qCnK/tH5w50a/+RrMoZPZkMPV3NvasKzk5WIiJfHoGBL5g7dyZv3gQhkUioWLESo0e7Y2tbWsjz\n+PEjVq9exuPHjzAw0Kd37/58/313AAICnrBixRKePQvAwMCQ9u2/o1+/QQDcunWDlSs9CQsLQyrV\npmbN2owZMwFLSysAzpw5xf79uwgIeEKVKtVYtWp9jjb6+noxf74HEydOE/Yw9fRcgL+/rzAKrlSm\noqOjg7//eQBCQ0NYunQh9+/fQ1dXl6ZNWzCrvRt6ujKUaUpm/LaJv18FEhoVxS+j3aldvoJQ3ibv\nY2z190FXRwdUKpBI2DFlJsUsLAmLjqLHnBmQOfquUpGcksKoTl3o4eDMrYDHjFi5FD2ZTDh3fNee\ntG7QCICImBiW7N3JnWcB6OvK6NfKje+atRDK/vPeX6w/dpiQqLdUKlmS8d16Udq6KACpSiVrjxzk\n9K0bKJSpuNStz5gu3dHW0lyZ8yo8jN7zPXCoXZeZfQcCcP/FczZ4HeXR65doa2lRp3xFxn7fHQsT\nEwD2nz/LoeWLiYuLxcDAEAcHZ4YPH41WxrXzes4iIiI5ExYWiofHNI3ZOpVKhaWlFbNnL2Dy5HHE\nxcVppEkkEubOXYSZmTlKpZLt23/j5Ek/IiIiMDIyomzZcnTt2gM7u4a5lquro0OargQdqQ7aWlr/\nuG33b/C+com5O7YyomNnfnBqJRxvP3UCHv0GaWhtJlnbdrcCHjNr62aOzVucLd+HtPcePLjPpk3r\nePz4Edra2tSuXZfRo8dhYWH5f9fpW0XsfIoIpKWloa2t/bnNEBER+QqwslI3gooVK45KpeLgwb3M\nnDmFbdt2AxAbG4O7+yhGjx6Hvb0jqampRESECed7eEzD3t6BtWs38uZNED/9NIjy5SvSpEkzSpcu\ni6fnKqysCqNUKtmw4Rc8PRewcOEyAExMTOjatScvXwZy69aNHO2Lj49nx46tlClTVuO4u/tk3N0n\nC7/nz/cQOkoAS5cuxMzMnOPHTxAfH8eoUcPYW0iPjo3tAahZtjzdHZyYuunXHMt1rmsndNyyUsTM\nnDPL1gi/g99G0mXWNBxq1313T03NODp3UY7XnbVtExVsSrJw8DCehQQzfKUnpaytqVO+Iq/Cw5i1\ndTMrho+mqm1pDl48y/j1a9g3Yw5aWlps8/fh8etX7J7uQVpaOuPWr+Y3Xy8GtdEcBFy6bxdVSpXW\nOBaflETHps1pWLkq2traeO7dyZwdW1kxfDQATatVx6nvQKysChMfH8+0aRM4cGAPXbuqvVryes4i\nIiI5o1DIqVOnXrbZ0+nTJwEgleqwdu1GjbRfflmJQqH20Jg6dTxv375lxow5lCun7rTdunWDy5cv\n5tn5/NwYGxiy46Q/nZrZoy/7Zx1f9Zjd/+9aGx8fR4cOnahfvxHa2tosW7aI+fNns3Tpqv/7mt8q\nYsChb4CHDx/Qq1dX3NwcWbBgNqmp6shft2/fpFOnNuzcuY0OHVqxYMFs4uPjmTBhDG3bOuPm5siE\nCWOIiAgXrjVy5FA2bVrPsGEDcXFpwdixI4mLixXS/fy8+f77dnTu3JZNXl652vQg8AVtJruTdaef\nc3du0Xu+B6CeIfDYthmAMWtXcuCPsxrn957vwfm/bgNw9/lTBiyeh7P7aAYsns+958/+5R0TEfl6\n2bFjK926dcTFpQW9e3fljz/OCWm+vl4MGzaQ5csX4+pqT69eXbh587qQPnLkUH79dS2DB/elVasW\nTJ7sTnx8zqHhDQ0LUaxYcUA9sCWRaBEcHCSk79mzkwYNGuHk1AqpVIq+vj4lS9oK6WFhITg7uwJQ\nvLgNNWrU4sUL9d+2mZkZVlaFAUhPT0dLS4s3b95du25dO1q2dMLSMvcR6V9/XUOXLt0xNjbJNU9y\ncjLnzp2hdet2wrGQkBAcHJyRSqWYmZljZ9eAZ8HBAEi1pXRr6UiNMuX+9fohnyuXqF2uPEXMzPPN\nm6xQcCvgCf1auaGlpUX54jY41KqL1+WLAFz7+yG1ypWjepmyaGlpMbhdOyJiYrj99AkAF+/fpUsL\nBwrpG2BSqBBd7R2EczM5eeMaRgaG1KtYSeN4o6rVcKhdFwM9PWQ6OnzfwkFDg4taWGJkZAxAenoa\nEomEoKDXQnpez1lE5HPRpUt7du3aTt++PXB2bs6iRXOJjo7C3X0ULi4tGDNmOAkJCUL+CxfO07t3\nV1q3dmDUqB95+TJQSHvy5BEDBvSiVasWzJw5GYVCc9/Jixf/pH//nri6tmTYsIE8e/b0X9uf0y6K\nmYeuX7/KzZvXWbhwGZUqVUEqlSKVSqlfvyGjRo0T8r//rbh48Y9cy1t+YA8dpk3Ecdwo+i+ay52n\nAULa2F9WserQfuH3tC0bmLdzG8o0JS4TfuZ58BshLTo+Hvsxw4nNcm+zYmttTbXSZdh1+kSO6alK\nJcsP7KHdlPG0mzqe+du3o0xTIk9RMPaXVUTGxuAwdgQO40byNjYWlUrFzlMn6NOnG23bOjFz5uRc\nv2kNGzbG3t4RAwMDZDIZnTt35f79v3K9JyK5I3Y+vwFOnfJjxYq17N17hFevXrIto1MH8PZtJAkJ\nCRw86M2ECVNRqdJp06Y9hw55c/CgF3p6eixbtvi96/kzbZoHXl4nSU1NYffuHQC8ePGcpUsXMWPG\nHPbuPUJMQgIRMTE52lTVtjT6Mhk3Hj8Sjp28cY1WOYy4udSrz4nr14TfL0KCCYuOokm1GsQlJeK+\nbjXdWjrhv3g5PRycGLduFXFJn2Y9k4hIQcPGpgTr1m3mxInz9O8/hDlzphMV9VZIf/jwPjY2JfH2\nPk3//kOYOnW8xsfY39+HqVNnceyYP9raWqxYkd2FKSuuri1xcmrKqlVL6dNngEY5RkbGDBs2gHbt\nXJg0aSxhYaFCepcuPfD19UKpVPLqVSAPHtzTGJEPCwsVrr13705++KHvB9+Dhw/v8/jx33Ts+H2e\n+c6dO42ZmRk1a9YSjnXt2oPTp0+gUMiJiAjn2rUrNK1e/YPLvnDvLq0mjOGHebM49Oe5XPP5XbtC\nm4aNNY5Fx8fRZrI7nWdOYcXBvcLm6SqVCgmg4l2DU4WKZ1kadVlJV6kgr/R0FeEx0SRmBOpITE5m\no/cxRnfqmmOjNiu3A55QpmgxjWNnzpykVasWtG3rzLNnT+nQobOQlt9zFhH5XPzxx1lWrlzH7t2H\nuHDhD9zdR/PjjyPx9j5Feno6Bw7sAeDVq5d4eEzj55/H4+V1koYNGzNx4hiUSiVKpZIpU8bTunVb\nfHzO0LKlE+fPnxHKePjwIQsXzmHixGn4+p6hQ4dOTJo0FqXy4y38uXnzOlWqVMtzcA6yfysWLJjD\n29jYHPNWKVWaHVNmcnLJClzsGjB186+kZtRhaq9++F27ws0nj/G7doVHLwMZ16U7Um0pLnXr43f9\nqnCdEzeuYVexMiaFCuVYjgQJQ9p1ZO/ZU8QnJWVL/83Pm4eBgeyYMpMdk2dy99kzfvP1Rk9XxvLh\no7A0MeXMsjWcWboaCxMT9p07zaUH91i+fC1HjvhhZGTM0qULP+g+3rlzi9Kly+afUSQbYufzG6Bz\n525YWlphZGREnz4DOHXKX0jT1tZm4MChSKVSdHV1MTY2oUWLlujq6qKvr0/v3v34K2OGMRM3t3YU\nL26Drq4uDg7OBAQ8BuD8+TM0adKMGjVqIZVKGf7dd3mO/jvXtePEDbXoJMrlXHpwH5d62RfVt6hZ\nm6dvXhMWHQWA/42rtKhVB6m2Nhfv36NE4SK0smuAlpYWzvXqU6pIUS7cE0ejRERywt7eEXNzCwAc\nHJywsSnBw4cPhHRzcwu6dOmOtrY2jo7OlChRisuXLwjprVq5YWtbGplMj0GDhnH27Ok8OyR+fmfx\n9z/HmDHjKVeuvHA8PDwMPz9vfv55AocOeWNtXYxZs6YK6Y0bN+XcudM4OjahV6+utG3bgYpZZt2K\nFLHGz+8s3t6nGTx4GCVKlPqg+qenp7Ns2WLGjp2Yb14/Px9cXdtoHKtZszbPnz/DxaUFnTu3pWLF\nStjXrv1BZTvVtWPP9Nn4LVrGpB692eLrxcksM8uZ3Hn6hKiEeFrWriMcs7Uuyu+TZ+C9wJM1o8bx\n+NUrVh5UzyYY6OlRo0w5tvh6k5KayqNXLzl75xbyFLWLnV2lytwOeMLtgCco05SsP3IEZVqakN6w\nSjX2njtFTEI8b2Nj2Z/ROM5M3+B9lA5NmmFlappn/QLeBLHFz4uRnTQ79Q4Ozvj7n2fPnsN07NgZ\nc/N3s7n5PWcRkc9F585dMTU1xdLSkpo1a1GlSjXKlcTUThMAACAASURBVCuPjo4OzZvb8+SJuu1z\n5sxJGjduSt26dmhra9OjR29SUlK4f/8uDx7cIy0tTdBUe3tHKleuIpSxb98+OnbsTKVKVZBIJLi6\ntkFHR4cHD+795/XJbI7FxsYI3wCAuLg4XF1b4upqj4NDE+H4+9+K4sVtuPfiRY7XbmXXACMDA7S0\ntOjh4EyqMpWXGYOJFsbGTOj+A7N/38LKg/uY2XegsFa0dYNGnMjS+fS7dpnW9RvlWY/yxW2wq1SF\n7Sf9sqWduH6VgW5tMSlUCJNChRjRqRO+167keq3DF/5gkFs7LCwskUql9Os3mHPnTpOenp6nDU+f\nBrB162aGZywvEPlniJ3Pb4BM9zQAa+uiREZGCL9NTc2QSt8t/VUo5CxePI/vv2+Hq6s9I0YMISEh\nXqNxmVW09PT0SE5OBiAyMoLChYsIafoyGSaGhrna5WLXgPN/3UaZpuTcnVtUKlmKwjm4mBno6dGo\nanVO3lA30k7euI5rxsh4ZGwM1lnsAbA2N891xlVE5FvH19dLcPFydW3JixfPiY199/eSGbQnk/c1\nI+vfuLV1UVJTU4nJ5+9NJtOjQ4fOzJ07U8grk+nRvLk9FStWQkdHhwEDBnP//l2SkhKJjY1l3LiR\nDBgwhLNnL3PokDdXr17myJED2a5tZGSEq2sbJk8el2+DAeDQoX2UK1eeypWr5pkvNDSUO3duanQ+\nVSoV48aNxN7ekdOnL+LldYr4+HiW79uXb7mg7kBamJggkUioXqYsXe0dOXv7ZrZ8Plev0LJWHY1g\nHuZGxthmBAgqamHB8I6dOXfnlpDu0X8QbyIj6DB9Ip77dtG6fkMKm5kBUKqINdP7DMBz3y7aThlP\nbGIittZFKWyqTu/n2oYKNiXpvWA2Q5ctokXN2ki1tbEwNubJ61dcf/Q33Vo65Vm31+HhjP1lJeO6\n9KBGmXI55ile3AZb29J4ei4A1I3eD33OIiKfmqxtHZlMpjFoIpPJSE5Wz7xFRkZSpEhRIU0ikWBl\nVZiIiHAiIyOyaWrWvMHBwezZs4PWrR1o3doBV9eWwnkfC2NjE96+jczy2xg/v7Ns3rwDpTJVOP7+\nt+LlyxfE5OKSuvOUP93nzMDZfTTO7qNJlMuJTXznOtu0eg3S09MpWaQI1bOss69qWxo9mYxbAY95\nGRbKm8gImtWomW8dhrTtwKE/zxEVH6dxPCI2liJZnlsxS0siY3P/PoVGvWXalo18911rWrd2oFev\nLkilUqKionI9JyjoNePHj+bnn8dTvXr+topkRww49A0QHv4uiEdoaEg2IczK7t07CAp6zcaNv2Nm\nZkZAwBMGDuwlRErLCwsLS411DskKBbGJubu/lrYuirW5BZfu3+fkjWu41Kufa16XevXZ7HOcWuXK\nkaJMpW4F9dYtliamhLy9pZE3NDqKRlWr5WmriMi3SGhoKEuWzGfVqvVUq1YDgP79e2oMLr3f6AkL\nC6VZlqip7+uJjo4OpvnMiIF63adcrnZVNTU1pWzZ7OsiM3+/fv0abW0pLi6tAXWH2NHRhcuXL+bo\nKqtUKomJiSYxMREjI6M87bh58wZ//XVbmM2Ni4sjIOAJT58+4eefxwv5TpzwoXr1mhTN4kIaFxdL\neHgYnTurGyjGxsa0auXGjo1r+bFtp3zvwftIJJJss8aK1FTO3L7B4qHD8z0/Pcu5RczMWTpspPB7\nxm8bNYIDtaxVh5a11DOpUpmE/WfOUKWULQAyHR3Gde3BuK49ADhy4Q8qllTPJN9++oTQqLd0nD4R\nlQqSFXLS0lW8CA1h68RpAIS8fcuoNcsY6NaOVnYN8rRZqVQSnOHuGxz85h89ZxGRLxFLS8ts65TD\nw8OEgf+scTNArak2NiUAsLa2pk+fAfTu3f+j25kpF/Xq2XHo0L4cO8aZ5PSt6Nu3R45eLneePmHH\nKX9+Ge1O6Qy9dBk/WiPvuqOHsS1alJDISE7euIZzlvaeW4NG+F67goWxMS1r10VHmn/XpFQRa+xr\n1mGrn4/Gd8TKxITQt2+FSN7BkZFYmqi/TxKyt2GLmJkzoXtPKrbr9EFbrYSGhjBmzHD69x+Mi4tr\nvvlFckac+fwGOHRoPxER4cTFxbJ9+284OrrkmjcpKQmZTIahoSFxcbFs2bLhg8uxt3fk0qUL3Lv3\nF0qlkl8OH853fZBLvQbsPXeKO88CcKhTL9d8jatWJzTqLRu8juFUx07jeFBEGCdvXCMtPZ2TN6/z\nMjSEJtXE0SgRkfeRy5ORSCSYmJiSnp6Ot/cxnr8XoCs6OooDB/agVCo5c+YUr14F0rDhO1csf38f\nXr4MRC6Xs3nzr7Rs6ZjjwNT161cJCHhMeno6iYkJrFmzHGNjE2GrlTZt2vPHH+d4+jQApVLJ1q2b\nqFGjFgYGhtja2qJSqTh1yh+VSsXbt5GcOXNSiMp4/vxZXr16iUqlIjo6mtWrl1OhQiWh45menk5K\nSgpKpVLj/wDTps1i5879bN26m61bd1OpUmUGDBjMkCE/adjv5+dNm/eivZqYmFK0aDGOHDlIWloa\n8fHxnDzpS4USJYQ8qUolioygbilKJSmp72YS/rh7R1in9CDwBfvOnqZ5TU2X3XN3bmFsYJhtb+Sb\nTx4TmrE2Nyw6il+OHqJ5lrWogaEhJMnlKNOU+F67wvVHf9PTwVlIf/TqJenp6UTHxzN90yaa16hN\nySLWgHqblszZgfsvnvGbnzdD2qi3nunYtAUHPObz++QZbJ8yg++atqBpteqsHKHe3y88JpqRq5bS\npYUDHZs0z/YeeF+5RExMNKCOC7Bjx1bq1VN3UEuWLJnncxYRKQg4ODhz6dJFbt26gVKpZNeu7ejq\n6lKtWg2qVauBVCoVNPX8+TP8/fe7ZQ5du3blyJGDPHx4H1AHObt8+YLgUfYxsLNrSO3a9Zg8eRwP\nH94X1qbev39XyJPTtyIw8HmO10tSKJBqa2NsWIhUpZLNPsdJlL8LqnQ74Ak+Vy8zq89ApvXuz9L9\nuzVmI10zvOD8r1/N1+U2KwPc2uJ15SLxye/WfjrXq89vft7EJMQTkxDPL4cP07q+2lPO3NiY2MQE\nErPc2++atmCj93Eh3kB0dDQXLpzPsbyIiHBGjx5G585dad/+uw+2UyQ74sznV48EZ+dWjBkzgrdv\nI2nWrIVG0I/36dq1Jx4eU2nTxgkrKyu6d++lEeEsr9nP0qXLMHbsBGbNmopcLqevk6Pg1pUbzvXs\nWHfsEI2qVs/TRVdHKqVFrTp4X77ITx3ezTCYGBriOWwky/bvYfGendhYWbF02Kg8ryUi8q1ia1ua\n7t17MXRof7S0tHB1bUONGrU08lSpUo2goNe0beuEubkFc+cuxtjYWEhv1cqNuXNn8vr1S2rXrsv4\n8ZPfLwaAhIR4VqxYQkREBDKZjMqVq7J06Sp0dHQAqFOnHkOG/MT48aNRKBTUqFGTmTPnAlCoUCHm\nzVvMunWr8PRciEwmo2nT5oJ2RUaGs2bNCmJiojEwMKB27brMy7J3m7+/D/Pnewh65eTUFFfXNkyZ\nMhNDw0JklQcdHV0MDAwxMHh38P79e0RERGBv75itXvPmLWHlSk+2b9+KtrY2tWrVwb3LuwA6XWdP\nIyzDZWvM2hUAHJq9AGtzC07dvM68HdtITVNS2NSMvq3chIZRJr5XLwt7d2blSdArZm3bREJSMiaG\nhtjXqsPQdh2F9Kt/P2Crnw+K1BQq2JRkxYifNYJ2LD+wh4A3QehoS3Fr1JChbd41nt5EhuPx+xZi\n4hMobGbGiI6dsatUGVDPisoynhmol1Po6ugIGnv80gWC30ayyec4m3yOC3uQnlm6Wn0vXzxn0+C+\nyOVyTE3NcHBwEraHMDAwzPM5i4h8Pt5v6+Te9ilZshQzZsxm2bLFREZGUL58BRYtWi4saZo3bwmL\nFs1h48Z1NGzYhBYtHIRzq1WrxsSJ01i+fDFBQUHIZDJq1KhFrVqZWyz9u6jZGjXIcqn585ewfftv\nzJ49g7dvIzAyMqZs2XIsy9juKadvReYM6Ps0rFyVhpWr0tVjGgYyGd0dnCiS4fKfKJcze/sW3Lv1\nxMLEBAsTE9o3bsbc7VtZMeJnAAqbmVOxREneRERQK0tcgPwoZmFJ6/oNOfznu85if9c2JCnk9Jrv\ngQQJbo0b0S9j6USpItY416tPp5lTUKnS2T1tNt1aOpKapmTSpLFERb3FzMwcBwdnmjZtka08L6+j\nhIQEs2XLRrZs2Sh4BJ44kXNnVSR3JKr8pqb+IREROfuDf4lYWRmJ9n4k5HI5Vq8DSE76eBHb/kvM\nzAyJjn7nIixPUaCsXuuD3DA+BwXpXQC1vV8bBe3+f4i9vr5eeHkdzbY/XCYjRw6lVSs32rbt8F+b\nqEFBer8LmtZBdr37mPxbLS1I7wJ8nVoHBUfvCuL78v/Y++pVIP7+vgwePEzj+LRpE5k7d5Hwb1bW\nrl1J587dsLa2/r9s/ZhaN2/HVqxMzRjyH39bPkTrvpT2XkF8d/9fxJlPERERERERERERkQLEiRO+\n3MsS2V+lUgnbYj1//pRRo37USAsOfkPnzt0+uZ35Efw2kvN/3WbbpBmf2xSRT4TY+RQRERER+SDy\nCzomIiIiIvLxKVnSlv37j+WavmvXwU9ozf/PBq+j7Dl7in6t3ChqYZH/CSJfBWLnU+SjkZKaKuwT\n96UjV0iFDdsBUlKVYjQukW+O1q3b0rp121zTV61a/wmtKTgUJK2D7Hr3MRG1VETk6+G/1ro+Lq70\nyYga+zE06UO0TtSoT89/vuZTRATULh4Kxadp3HwsZDKZONMjIiKSJ1+D1n1sRC0VESn4fM1aJ2rU\np0UMOCTa+9EoSPYWJFuhYNr7tVHQ7r9o78dDtPfjUZBsha9T66Dg6F1BfF9Eez8eBcnegmQriAGH\nRL5AVCoVcrkcuVz+uU35IORyHQ1bxVEwERGRD6GgaR1k17v/GlE/RUS+Pr5WrRP16tMjdj5FPgoK\nhYKUq7eRJhaQdVCmhkhj1OG4U1KVKOrU++xht0VERL58CpzWgYbe/deI+iki8nXyNWqdqFefB7Hz\nWUDo0qU9CxbMp1y5avnmbdbMjj17DlO8uM0/LuffnPs+ujo6pOkWjNEkPZkMPd13e1cVnB37RERE\nPjcFSesgu97914j6KSLydfI1ap2oV58esfP5FfJv3Ae+NdeDkLdv6TRzMg+3b//cpoiIfFUEBr5g\n7tyZvHkThEQioWLFSowe7Y6tbWkA9u3bxYEDe4mNjcHAwBAHB2eGDx+NlpY67uD337cjOjoKbW31\nZ6patRosW7YagMuXL7B9+1aeP3+GTCajceNmjBw5BgMDAwB69+5KWFiYYItCIadRoyYsXLiM2NgY\nJk0ax6tXgaSlpVO6dGl++mk01avXFPLv3buTXbt+R6FQYG/viLv7ZKRSzc/l69ev6Nu3B82b27P0\nh+7Z6r/Z5zibfI6zeuQY6lWsDMCeM6fYf/4MMQkJGOjJcKpjx8jvvkdLS4uw6Ch6zJkBmRqsUpGc\nksKoTl3o4eAMgP/1q6w7dpi4xATsKlVhWq9+GGXUefWh/fxx7w5RcfFYmZrS16U1rRs0Euz5895f\nrD92mJCot1QqWZLx3XpR2rqokL7++GG8r1xCrkihQokSuHftSemixQC1Ti7Zu5P7L56hq6ODfa06\njP2+O1paWrwIDWH2ts28iYwAiYQKxUswbOI0KlasBMCuXdvx8/MiNDQUU1NTOnb8np49ewMQHR3N\nypWe3LlzC7lcTpkyZRkx4meqVMl/kFVE5EshNDSELl3ao69vgEqlQiKR8MMPfejbd6BGPqVSSd++\n3UlOTubQIW/h+KhRP/L8+TOUylSKFi3GwIFDadq0hZB+4oQfGzasJTY2Fju7BkyePAMjo3fr7a5f\nv8q6dat5/folRkbGjBw5hpYtnQBIT09n06b1+PgcJykpCRubEqxevR5Dw0J4ei7A399XaPcplano\n6Ojg739ew+6ctC6z7aQvk4FKBRIJvZ1d6e/aRrPOaUp+mOeBPCWFo3MXAeSrdW9jY1m4ezuPXr0k\nMi6Ww7MXYG3+bhuWVKWSRbt3cPbOLfRluvzg1ErQSIAbj/9m9eEDBEVEYGFiTE9HFzo2aZ7tuY1Y\nuZSbAY+z1TdrnVu2dGT69NnC85s1ayqPH/9NaGgIq1f/Sq1adYRz8vumqfPsZv/+PcTERFGkSFEW\nLlyKjU2JbOV/7Yidz6+QfxND6lsLfqxChQT4tmotIvLxsbKyYvbsBRQrVhyVSsXBg3uZOXMK27bt\nBqBp0xa4urbF2NiY+Ph4pk2bwIEDe+jatSegHghbsmQlderUy3btxMRE+vUbRM2atUlNTWXWrCn8\n8ssq3N0nAbB9+z6N/F26dMAho3Gir2/A5MnTsbEpiZaWFn/+eY6JE8fi5XUSLS0trl69zK5dv7Nq\n1a9YWFgyefI4Nm/+laFDh2tcc/nyxVSpUjXHur+JjODM7ZtYmphoHG9eoyZuDRthbGBIfFISkzeu\nY9+5M3R3cKKImTlnlq0R8ga/jaTLrGk41K4LwPPgNyzas4PlP42mYokSzN/5O4v37GDOgCHqeslk\nLB02ipKFi/Ag8AVj1q6gROHCVCtdllfhYczaupkVw0dT1bY0By+eZfz6NeybMQctLS1O3byO95VL\nbBg7CWtzc9YfO8ysbZvZNmk6AEv27sTMyAifhUuJS0pi5KplHPzjHF3sHbAyMWHewKEUs7RCpVKx\n+/QJ5s2bye+/7xXqMn36bMqWLU9Q0GvGjh1BkSLWODo6k5ycRJUqVRk9ehympmYcP36ECRN+5sAB\nL9ENTqRAIZFI8Pc/l+cA/s6d2zAzMyc5+Y3G8dGj3SlVyhapVMrDh/f5+efh7NlzCHNzC54/f4an\n5wI8PVdSoUIlFi2ai6fnAjw85gPw4sVzZs+ezvTps6lXrz4JCQkkJLwLWrNp03oePLjPhg1bKVy4\nCC9ePEdXVwaAu/tk3N0nC3nnz/fQ6ChlkpvWSYDTnqvyrPP2k/6YGxsTHBkpHMtP6yRaEhpVrUbf\nVm4MWbow2zU3eh/jTWQEx+YuIiI2huErl1KmaDEaVK6KMi2NSRvXMfK7LnRo0oyg6DD6zJlDNdsy\nlMvi0ed//Spp6enkZnluda5ZszbduvVk+vRJ2dLy+6YdP34EH5/jLF26kpIlbQkOfoORkXGu9+5r\nRux8FkD+/vsBK1cuJTDwBXp6erRo0ZKRI8dqjMxfvnyBfft2k5SUhJtbW376abSQ5uV1lD17dhAV\nFUXlylUZP34K1tbW+ZYbEhLMvHmzCAh4TJUq1ShRoiSJiQlMnz4HgOnTJ3H37m0UihTKlCnLrK7f\nU8ysCABztv+Gnq4uwZGR/PUsgPI2JZg/aBjbT/jic/US5sYmzOk/mPIZI0CRsTEs3bebO08DMNDT\no1tLR7raO+ZsV8YI3MQevdjsfRyA7g7O/ODkAqg71NtP+nHs4p8kyJOpV7EyE7v3wsjAgJ+WLwGg\n3qBBAKwaMYZyxYv/o+chIlKQ2LFjK8ePHyE6OpoiRYowePBPNG9uD4CvrxfHjh2mQoWK+Pv7YGlp\nxZgxE6hb1w6AkSOHUq1aDW7cuMarV4HUqWPHlCkzNUbhMzE0LIShYSEA0tLSkEi0CA4OEtKLFXv3\nd5aenoZEIiEo6LXGNXIbDHNyaiX8XyaT0a7dd2zZsiHHvLdv3yQuLoYWLVoCoKurS8mStsL1JRIt\nEhLiiYuLw9TUFD8/b9q06UCpUuo8/fsPxsNjqkbn89Qpf4yMjLC1LcPLl4HZylyydxcjOn7P4r07\nNI4Xs7TKUud0JFoSgiLCc7Tb58olapcrTxEzcwD8b1yjWfWa1CxbDoCh7TrSfc4MkhUK9GUyBrVp\nL5xb1bY0NcuW596L51QrXZZrfz+kVrlyVC9TFoDB7dqx5uAhbj99Qt0KlQiJekvNsuWFTd5d6zdk\nz9nTwvVC3kbSpYUDUm0p5kbGNKxSjechwQAU0jegkL569jUto07BwcHCuZmznAAlS5aiadMW3Lv3\nF46OzhQrVlxomAG0b/8da9eu4NWrQCpUqJTjfRER+VA+ldaBWkvS09PR1tbOMT04+A0nT/ozcuQY\nFi2aq5FWNuNvOpO0NCXh4WGYm1tw8qQfTZs2p0aNWgAMGvQjvXp1ITk5GX19fX7/fQsdO3amfv2G\nABgbG2NsrO7QxMfHs3//HrZt203hwuq2WOnSZXK0Lzk5mXPnzrBkyUqN43lpnQpIV6nQzqXzGRwZ\nwYnrVxnduSsLduXuXfa+1pkbGdOpmT1p6ek5Tgz4Xr3MjD4DMNTXx1Bfn45NmuF95RINKlclLimR\nJLkc14z7Ub1MGWyti/IiNETofCYmJ7PF14sZfQYw2HNBtutnrXPWb5JUKqVLF/XMb06d9Ly+aSqV\nit9+28i0aR7C9ydr/m8NcV/VAoiWljajRo3F1/cM69f/xs2bNzh8+IBGnj//PM+WLTvZsmUHf/55\nHi+voxnHz7Fjxzbmz/fEy+skNWvWwsNjygeV6+ExjSpVquHtfZr+/Qfj7+8DWcaNGjVqwt69R/Hy\nOkn58hWZvEGzMXjm1k2Gtf8O/8Ur0NGWMthzAZVKlsJ/8Qpa1qrDioPqkXKVSoX7+jVUKFESrwWe\nrB41ln1nT3P174d52nfryWMOeMxnxYif2XHSjxuP/wZg37nT/Hn3L9aPnYDX/CUY6RuwZO9OANaN\nmaA+d/NmzixdTbVchFlE5GvBxqYE69Zt5sSJ8/TvP4Q5c6YTFfVWSH/48D42NiUz/s6HMHXqeOLj\n342k+/v7MHXqLI4d80dbW4sVKxbnWZ6ra0ucnJqyatVS+vQZoJF28qQfrVq1oG1bZ549e0qHDp01\n0mfPnka7di6MHTuSp08Dci3jzp1buTaq/Py8adHCAZlMcyatb98eODg0ZsoUd9q164ipqSmgnkko\nV66CkK9cufJER0cTFxcHQGJiAps3/8rIkWNz7ByfvnUDmY6URlVzdh09ceMqjuNG4TppLE/fBNGx\naXZ3MAC/a1do07Cx8PtFSDDli79zzypuaYWOVMqr8LBs58pTUvj7VSBliubcuElXqQAVz4LVMzDO\nde14ExHOq/AwlGlKvK5c0rC/m4MTJ29eQ56SQnhMNFce3s9WP2f30diPGc7qwwfp2bNPjuUC3L17\nO9dnFRDwGKVS+U26oYn893xKrZNIJHTp0p5Ondowf74HsbExGukrVnjy44/D0dXVzfH8CRPG4ODQ\nhKFD+1O7dl0qVaoCQGDgc8qVKy/kK17cBh0dXV6/fgnAgwf3UKlU9O3bnY4dWzNnzgyhDs+fP0Uq\nlXL27Ck6dGhFz56dOXRof47lnzt3GjMzM2rWrCUcy0/rJMB30yfRYdpE5m7fSmxCgkb60v17GNah\nE7o6OrneN8iudXkRn5REZFysxixmOZsSwmCYuZExzvXqc/zyBdLT07n95AmhUVHCoB3AumOH6dTM\nHvMcZh3zq3N+5PZNCw8PIyIinGfPntKpUxu6du3A5s2//uPrfy2Inc8CSMWKlahSpRoSiQRra2va\nt/+OO3duauTp1asvhQoVonDhInTt2pNTp/wBOHr0EL1796NkyVJoaWnRq1c/AgKeEBYWmmeZYWGh\nPHr0kIEDhyKVSqlRoxZN32s0ubm1Q09PD6lUSu/e/Xjy+jWJWUJct6hZmwolSqIjldKiZm1kOjq4\n1m+IRCLBqa4dARkjRA8CXxCTkEB/1zZoa2lRzMKS9k2acermtTxtHNSmPTIdHcoWK06bRo05ceM6\nAIcv/MGP7TtiaWKKVFvKQLe2nLl9k/T0dFQZ42rfmruxyLeLvb0j5hnrZxwcnLCxKcHDhw+EdHNz\nC7p06Y62tjaOjs6UKFGKy5cvCOmtWrlha1samUyPQYOGcfbs6Tz/fvz8zuLvf44xY8ZrNKIAnJ1d\n8fc/z549h+nYsTPm5uZC2syZc9m//zgHDhyndu26jBs3gsTEhPcvz/XrV/D392Hw4GHZ0hQKOefO\nnaZNllnBTLZt282JE38wc+ZcjfWeyclJFCpUSPhtYGCISqUiKSkJgE2bfqVdu++wzDKLmUmSXM76\n44cZ26VHrvfDpV4DTi9dxf6Zc+nUtAXmxtkbQHeePiEqIZ6Wtd+tJ0pSKCikr6+Rz1BPj6QcthFY\nvGcHFWxK0qCyugFrV6kytwOecDvgCco0JeuPHEGZloY8RR210tLEhBply9Ft9nTsx4zg3J1bjO7U\nVbherbLleR4SjOO4UXScNpHKJW1pXqOWRpknPVdyynMVozt1yTaTk8nmzb+iUqlyfB6JiQnMnTuT\nAQOGYGBgmNvtExH5YD6V1pmYmLJx4+8cOHCczZt3kJSUhIfHdCH9/PmzqFTpGus432fx4uWcPPkH\nnp6rhFlMgKSkZMGDJBNDQ0NBjyIiwvH392X+fE/27DmMQiEXOsnh4WEkJMQTFPSaAwe8mDNnEVu2\nbODGjextKT8/H1zfW6+Zl9aZFirElglTOTJnIVsnTiNJIWfm1k1C+rk7t1CpVNl04n1y0rq8SFbI\nkYCGFr6vg8517dji40Wz0T/Re84cfmzfkcKmZgD8/TKQuy+e0dXeIcfr51XnDyG3b1pEhofL9etX\n2bFjH6tWrefUKX+8vI78X+UUdES32wLI69evWL16OY8fP0ShUJCWlkbFjIAWmVhZFRH+b21tTWSG\nv31oaCgrVy5lzZoVAMLi+IiICIoUyd31NjIyEmNjE2QymXCscGFrwjNG3dPT0/n117WcO3daGPGT\nALEJ8RhmrN3J2siS6epo/tbRIUmhANSL0SNiYnAZPzrDRvVIfa2MhqvD2BHCQvU90zyEsjLFBcDa\n3ILnGa5foVFvmbjhF7QyzlGpQKqtTVR8HJJcPf5FRL5OfH292LdvFyEhIQDI5ckao/Tvf3StrYsS\nGRkh/M5038pMS01NJSYmBjMzM3JDJtOjQ4fOfU0EWQAAIABJREFUtG3rxM6dB4VZxkyKF7fB1rY0\nnp4LmDdP7QpfrVoNIb137374+Xnx1193aNy4qXD8/v17eHhMZ+7cRTlG6D537gzGxqbUrFk7R7t0\ndHRwdHShV68ulC9fkbJly6Gvb6DRyU1MTEAikWBgYEBAwGNu3LjKb7/tyvF6m3yO07p+I8F9LC9s\nrApjW7QYi/fsZOF7HWefq1doWasOerrv9NZAJiNRnqyRLzE5GYP31kauPrSfFyHBrB3tLhwrVcSa\n6X0G4LlvF2/jYunQrBm21kUFzdzkc5yHLwM5Pm8x5kbG+F67wvCVnuyePhtdqZSf166kU7MWbHKf\nRJJCwdztW1lz5AAjOn6vUbaeri7tGzelw6xp1KxZW+M5Hzy4F39/H375ZXO24E0KhYKJE8dSrVoN\nfvihb773TkTkQ/hUWqevry8E2DIzM2Ps2Al06OBKc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/ODUyvhePupE/DoN4ja+WwRc/v2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ZAtDW1qFQIUNxI2SRL8q/qXUKhYING1YTFvYCLS1typSxwsPDS/A68PE5Snh4OJs3b2Dz5g3C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jXA0NCggJxq/V+0aB6jR4/TqEN2djbXrl2hd28vtLS0KFeuPE5OLQgO3g+oHcSsrCxcXFoD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\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Giup0OvLnd2by5OmMGTOMhw8fGpUpFAqmTp2Jo6PTm6iyeA0k+XyDTpz4i+XLFzN27CTU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SqNqV+/A716TQcgJCSIVauc6dJlLJUrN8xRt6/vZq5f92XEiLXo6+cMhgTKkTBLy6oUKlRU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\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XLgRTq1aegymVSsnMzHz3OkspHuCPPyKEeIvbt2+ye/cOISV1LhERx9HV1adRoyZKxy9f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Rk/8A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\nKw9oHynJnt2R/fv3kJKSQnR0NEePHpYanaVLl8HOzoHNm9eTkpLCvXt3uH3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mptjZ2ePu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fo6WlxcKFy1i4cC47dmzHysqKGTPmUK2aDQCdO3clISGefv16IpFAhw5d6NixCwDXr1/j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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb15a819438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "N = 10 # Number of positive and negative traits to plot\n", "fig, ax = plt.subplots(4,3, figsize=(16,32), sharex=True)\n", "ax = ax.flatten()\n", "for i in range(12):\n", " ax[i].set_title(targetencoder.classes_[i])\n", " ind = np.argsort(coef[i,:])\n", " for n in range(N):\n", " ax[i].barh([-(N-n)-1], [coef[i,ind[n]]], color='b',alpha=0.2)\n", " ax[i].text(0, -(N-n)-0.6, map_feature(ind[n]),\n", " horizontalalignment='center',\n", " verticalalignment = 'center')\n", " ax[i].barh([N-n], [coef[i,ind[-n-1]]], color='r',alpha=0.2)\n", " ax[i].text(0, N-n+0.4, map_feature(ind[-n-1]),\n", " horizontalalignment='center',\n", " verticalalignment = 'center')\n", " ax[i].set_ylim(-N-1,N+1)\n", " ax[i].set_yticklabels([''])" ] } ], "metadata": { "_change_revision": 200, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/325/325724.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "72c13538-c15d-2fed-8dda-6ba7f8af6987" }, "source": [ "The word is climate change is one of the biggest existential threat that humanity is facing. Hoping to throw some exploratory light on the matter with the given data. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "f2757c04-d8ea-9788-36ef-9a6d461ad5b5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GlobalLandTemperaturesByCity.csv\n", "GlobalLandTemperaturesByCountry.csv\n", "GlobalLandTemperaturesByMajorCity.csv\n", "GlobalLandTemperaturesByState.csv\n", "GlobalTemperatures.csv\n", "\n" ] } ], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "from matplotlib import pyplot as plt\n", "import seaborn as sbn\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "6740af67-88aa-c968-f343-370629690e49" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "DatetimeIndex: 3192 entries, 1750-01-01 to 2015-12-01\n", "Data columns (total 8 columns):\n", "LandAverageTemperature 3180 non-null float64\n", "LandAverageTemperatureUncertainty 3180 non-null float64\n", "LandMaxTemperature 1992 non-null float64\n", "LandMaxTemperatureUncertainty 1992 non-null float64\n", "LandMinTemperature 1992 non-null float64\n", "LandMinTemperatureUncertainty 1992 non-null float64\n", "LandAndOceanAverageTemperature 1992 non-null float64\n", "LandAndOceanAverageTemperatureUncertainty 1992 non-null float64\n", "dtypes: float64(8)\n", "memory usage: 224.4 KB\n", "None\n" ] } ], "source": [ "global_temperatures = pd.read_csv(\"../input/GlobalTemperatures.csv\", infer_datetime_format=True, index_col='dt', parse_dates=['dt'])\n", "print (global_temperatures.info())" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "a856e045-f297-0e7b-c394-5e54e90573e9" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f59fee03390>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TDy9c6WFwf3h6cA/0w+/+9v4COSpnGYrXXt7BwLV6UZz94NDHpa0BTNN44t/GA6cXc3l2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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5a18c73400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "global_temperatures[global_temperatures.index.year > 2000]['LandAverageTemperature'].plot(figsize=(13,7))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c7816922-5738-98f6-59a1-34e35c94a540" }, "source": [ "The oscillation basically depicts the seasonal variance in average temperature - To gain a better insight let's try grouping the average temperature by year and plotting the average temperature change over years." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "ff9ba7f4-8125-15b0-912e-3d73794d86c1" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f59fb599668>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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QrQQsx3IAgOkWIsDntuMn3nYYuyYaI0R7DYmAIUNRFD0dSJKpEkAQBEEQBGGGKFV0C3Wq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pF1uphMqVW0JbsmaorGd7oB4KBUa7ovANxiw/G6SMSKhCXEI3JXhUPQmLTMwQAoIpQgCIIILIHd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\niJbThRo5XkipuzrQh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"text/plain": [ "<matplotlib.figure.Figure at 0x7f59fe5ec780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "global_temperatures.groupby(global_temperatures.index.year)['LandAverageTemperature'].mean().plot(figsize=(13,7))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d2f3ad7d-935d-2f27-0fc8-33f196c684a2" }, "source": [ "That seems about correct. I'm guessing the instruments we had in the early years had huge uncertainty, which is why we see the data in the initial years with large variation - as seen in below plot. \n", "Anyways bottom line is the average temperature has gradually increased over the year as seen in the plot.\n", "\n", "Note: pandas rolling mean function with window = 12 will not provide the analysis we are looking for." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "6955b9d8-3055-61c4-0d9f-8e993e6e0748" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f59fbbf9630>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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f9NttmrW+oh+PhnRsKqXXZ1ZbFuR6FX1vvKb3uFqtK1+qKh51BiGlYmHZtjS/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\nK/TyrUf28Y2H95KX7aGzz0c0ZrOqMpctq4rZahZTlJexkKc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"text/plain": [ "<matplotlib.figure.Figure at 0x7f59fbc078d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "global_temperatures.groupby(global_temperatures.index.year)['LandAverageTemperatureUncertainty'].mean().plot(figsize=(13,7))" ] } ], "metadata": { "_change_revision": 115, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/326/326100.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "55342cb6-ea89-07c1-d3ae-53f7976f4dec" }, "source": [ "**Titanic survival prediction in Python with XGBoost tutorial**\n", "==========================" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b83785e3-b726-8f98-56a3-6869c2ae9b23" }, "source": [ "This notebook runs through most of the basic components of a ML script on the Titanic dataset, using...\n", "\n", "- Python\n", "- Pandas\n", "- Sci-kit learn\n", "- XGBoost\n", "\n", "\n", "The goal is to use a simple and easy to understand implementation of:\n", "\n", "- feature engineering\n", "- feature selection using Greedy Search (RFECV)\n", "- hyperparameter tuning using Grid Search\n", "- XGBoost classifier\n", "\n", "\n", "What this script doesn't do:\n", "\n", "- aim for a high score on the leaderboard. On this small dataset with the answers publicly available, the public leaderboard ranking doesn't mean much anyway.\n", "- we are not guarding against overfitting." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "9a860601-6eb3-c841-29f0-8abb20b95ee9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", " DeprecationWarning)\n" ] } ], "source": [ "from IPython.display import display\n", "\n", "import re\n", "import pandas as pd\n", "import numpy as np\n", "import xgboost as xgb\n", "\n", "from sklearn import preprocessing\n", "from sklearn import cross_validation\n", "from sklearn.model_selection import KFold\n", "from sklearn.feature_selection import RFECV\n", "from sklearn.grid_search import GridSearchCV" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "72bfc886-9112-ed09-3193-74af2c0cb3a1" }, "source": [ "Functions to generate new features\n", "-------------------" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "5fb97dc3-09cb-dfe5-a059-ba42a2078006" }, "outputs": [], "source": [ "def extract_maritial(name):\n", " \"\"\" extract the person's title, and bin it to Mr. Miss. and Mrs.\n", " assuming a Miss, Lady or Countess has more change to survive than a regular married woman.\"\"\"\n", " \n", " re_maritial = r' ([A-Za-z]+\\.) ' # use regular expressions to extract the persons title\n", " found = re.findall(re_maritial, name)[0]\n", " replace = [['Dr.','Sir.'],\n", " ['Rev.','Sir.'],\n", " ['Major.','Officer.'],\n", " ['Mlle.','Miss.'],\n", " ['Col.','Officer.'],\n", " ['Master.','Sir.'],\n", " ['Jonkheer.','Sir.'],\n", " ['Sir.','Sir.'],\n", " ['Don.','Sir.'],\n", " ['Countess.','High.'],\n", " ['Capt.','Officer.'],\n", " ['Ms.','High.'],\n", " ['Mme.','High.'],\n", " ['Dona.','High.'],\n", " ['Lady.','High.']]\n", " \n", " for i in range(0,len(replace)):\n", " if found == replace[i][0]:\n", " found = replace[i][1]\n", " break\n", " return found\n", "\n", "\n", "def father(sex, age, parch):\n", " if sex == 'male' and age > 16 and parch > 0:\n", " return 1\n", " else:\n", " return 0\n", " \n", " \n", "def mother(sex, age, parch):\n", " if sex == 'female' and age > 16 and parch > 0:\n", " return 1\n", " else:\n", " return 0\n", " \n", " \n", "def parent(sex, age, parch):\n", " if mother(sex, age, parch) == 1 or father(sex, age, parch) == 1:\n", " return 1\n", " else:\n", " return 0\n", " \n", " \n", "def extract_cabin_nr(cabin):\n", " \"\"\" Extracts the cabin number. If there no number found, return NaN \"\"\"\n", " if not pd.isnull(cabin):\n", " cabin = cabin.split(' ')[-1] # if several cabins on ticket, take last one\n", " re_numb = r'[A-Z]([0-9]+)'\n", " try:\n", " number = int(re.findall(re_numb, cabin)[0])\n", " return number\n", " except:\n", " return np.nan\n", " else:\n", " return np.nan\n", " \n", " \n", "def extract_cabin_letter(cabin):\n", " \"\"\" Extracts the cabin letter. If there no letter found, return NaN \"\"\"\n", " if not pd.isnull(cabin):\n", " cabin = cabin.split(' ')[-1] # if several cabins on ticket, take last one\n", " re_char = r'([A-Z])[0-9]+'\n", " try:\n", " character = re.findall(re_char, cabin)[0]\n", " return character\n", " except:\n", " return np.nan\n", " else:\n", " return np.nan\n", " \n", " \n", "def expand_sex(sex, age):\n", " \"\"\" this expands male/female with kid. Cause below 14 years old, male or female is irrelevant\"\"\"\n", " if age < 14:\n", " return 'kid'\n", " else:\n", " return sex" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "8ba4e062-45c8-ee49-bb3e-57492b953a02" }, "source": [ "Function to add the new features to our data set\n", "-------------------------" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "2e69cc2c-58fa-f4a6-ee3c-19a4d0c7106f" }, "outputs": [], "source": [ "def feat_eng(data):\n", " # create feature 'Title', which extracts the persons title from their name.\n", " data['Title'] = list(map(extract_maritial, data['Name']))\n", "\n", " # Extract features from cabins\n", " data['Cabin_char'] = list(map(extract_cabin_letter, data['Cabin']))\n", " data['Cabin_nr'] = list(map(extract_cabin_nr, data['Cabin']))\n", " data['Cabin_nr_odd'] = data.Cabin_nr.apply(lambda x: np.nan if x == np.nan else x%2)\n", " \n", " # Family features\n", " data['Father'] = list(map(father, data.Sex, data.Age, data.Parch))\n", " data['Mother'] = list(map(mother, data.Sex, data.Age, data.Parch))\n", " data['Parent'] = list(map(parent, data.Sex, data.Age, data.Parch))\n", " data['has_parents_or_kids'] = data.Parch.apply(lambda x: 1 if x > 0 else 0)\n", " data['FamilySize'] = data.SibSp + data.Parch\n", " \n", " # Extend the male/female feature with kid. Cause for kids gender doesn't matter.\n", " data['Sex'] = list(map(expand_sex, data['Sex'], data['Age']))\n", " \n", " # Create bins for Fare and Age\n", " data['FareBin'] = pd.cut(data.Fare, bins=(-1000,0,8.67,16.11,32,350,1000))\n", " data['AgeBin'] = pd.cut(data.Age, bins=(0,15,25,60,90))\n", "\n", " data.head(8)\n", " return data" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "075440c3-28cd-ece5-2b49-0d28bcd0ba7b" }, "source": [ "Function to handle missing data\n", "---------------------" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "5f8cbd5a-3d6c-ad2a-0354-e4326a02ccfb" }, "outputs": [], "source": [ "def missing(data):\n", " # If Age is null, we impute it with the median Age for their title.\n", " data.loc[(data.Age.isnull()) & (data.Title == 'Sir.'), 'Age'] = data.loc[data.Title == 'Sir.', 'Age'].median() \n", " data.loc[(data.Age.isnull()) & (data.Title == 'Officer.'), 'Age'] = data.loc[data.Title == 'Officer.', 'Age'].median()\n", " data.loc[(data.Age.isnull()) & (data.Title == 'Miss.'), 'Age'] = data.loc[data.Title == 'Miss.', 'Age'].median()\n", " data.loc[(data.Age.isnull()) & (data.Title == 'High.'), 'Age'] = data.loc[data.Title == 'High.', 'Age'].median()\n", " data.loc[(data.Age.isnull()) & (data.Title == 'Mrs.'), 'Age'] = data.loc[data.Title == 'Mrs.', 'Age'].median()\n", " data.loc[(data.Age.isnull()) & (data.Title == 'Mr.'), 'Age'] = data.loc[data.Title == 'Mr.', 'Age'].median()\n", "\n", " # There is one row without a Fare...\n", " median_fare = data['Fare'].median()\n", " data['Fare'].fillna(value=median_fare, inplace=True)\n", "\n", " # ... and 2 rows without Embarked.\n", " mode_embarked = data['Embarked'].mode()[0]\n", " data['Embarked'].fillna(value=mode_embarked, inplace=True)\n", "\n", " # deal with the NaN's in some of our newly created columns\n", " data['Cabin_char'].fillna(value=-9999, inplace=True)\n", " data['Cabin_nr'].fillna(value=-9999, inplace=True)\n", " data['Cabin_nr_odd'].fillna(value=-9999, inplace=True)\n", "\n", " # after our feature engineering, we don't need some of the original features anymore\n", " data = data.drop(['Name','Cabin','Fare','Age','Ticket'], 1)\n", "\n", " data.head(8)\n", " return data" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "8f9b0941-69bd-2789-80c9-3802ebd09dc8" }, "source": [ "MAIN SCRIPT STARTS HERE\n", "=====================\n", "Preparing the training set\n", "----------------------------" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "eaee3795-3265-3e77-171e-0f3bd02f0b8b" }, "outputs": [ { "data": { "text/plain": [ "'Unaltered training set:'" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. 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Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "5 Moran, Mr. James male NaN 0 \n", "6 McCarthy, Mr. Timothy J male 54.0 0 \n", "7 Palsson, Master. Gosta Leonard kid 2.0 3 \n", "\n", " Parch Ticket Fare ... Cabin_char Cabin_nr \\\n", "0 0 A/5 21171 7.2500 ... NaN NaN \n", "1 0 PC 17599 71.2833 ... C 85.0 \n", "2 0 STON/O2. 3101282 7.9250 ... NaN NaN \n", "3 0 113803 53.1000 ... C 123.0 \n", "4 0 373450 8.0500 ... NaN NaN \n", "5 0 330877 8.4583 ... NaN NaN \n", "6 0 17463 51.8625 ... E 46.0 \n", "7 1 349909 21.0750 ... NaN NaN \n", "\n", " Cabin_nr_odd Father Mother Parent has_parents_or_kids FamilySize \\\n", "0 NaN 0 0 0 0 1 \n", "1 1.0 0 0 0 0 1 \n", "2 NaN 0 0 0 0 0 \n", "3 1.0 0 0 0 0 1 \n", "4 NaN 0 0 0 0 0 \n", "5 NaN 0 0 0 0 0 \n", "6 0.0 0 0 0 0 0 \n", "7 NaN 0 0 0 1 4 \n", "\n", " FareBin AgeBin \n", "0 (0, 8.67] (15, 25] \n", "1 (32, 350] (25, 60] \n", "2 (0, 8.67] (25, 60] \n", "3 (32, 350] (25, 60] \n", "4 (0, 8.67] (25, 60] \n", "5 (0, 8.67] NaN \n", "6 (32, 350] (25, 60] \n", "7 (16.11, 32] (0, 15] \n", "\n", "[8 rows x 23 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "'After handling missing values:'" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Sex</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Embarked</th>\n", " <th>Title</th>\n", " <th>Cabin_char</th>\n", " <th>Cabin_nr</th>\n", " <th>Cabin_nr_odd</th>\n", " <th>Father</th>\n", " <th>Mother</th>\n", " <th>Parent</th>\n", " <th>has_parents_or_kids</th>\n", " <th>FamilySize</th>\n", " <th>FareBin</th>\n", " <th>AgeBin</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>male</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>S</td>\n", " <td>Mr.</td>\n", " <td>-9999</td>\n", " <td>-9999.0</td>\n", " <td>-9999.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>(0, 8.67]</td>\n", " <td>(15, 25]</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>female</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>C</td>\n", " <td>Mrs.</td>\n", " <td>C</td>\n", " <td>85.0</td>\n", " <td>1.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " 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<td>S</td>\n", " <td>Mr.</td>\n", " <td>-9999</td>\n", " <td>-9999.0</td>\n", " <td>-9999.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>(0, 8.67]</td>\n", " <td>(25, 60]</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>male</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>Q</td>\n", " <td>Mr.</td>\n", " <td>-9999</td>\n", " <td>-9999.0</td>\n", " <td>-9999.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>(0, 8.67]</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>7</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>male</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>S</td>\n", " <td>Mr.</td>\n", " <td>E</td>\n", " <td>46.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>(32, 350]</td>\n", " <td>(25, 60]</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>8</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>kid</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>S</td>\n", " <td>Sir.</td>\n", " <td>-9999</td>\n", " <td>-9999.0</td>\n", " <td>-9999.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>(16.11, 32]</td>\n", " <td>(0, 15]</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass Sex SibSp Parch Embarked Title \\\n", "0 1 0 3 male 1 0 S Mr. \n", "1 2 1 1 female 1 0 C Mrs. \n", "2 3 1 3 female 0 0 S Miss. \n", "3 4 1 1 female 1 0 S Mrs. \n", "4 5 0 3 male 0 0 S Mr. \n", "5 6 0 3 male 0 0 Q Mr. \n", "6 7 0 1 male 0 0 S Mr. \n", "7 8 0 3 kid 3 1 S Sir. \n", "\n", " Cabin_char Cabin_nr Cabin_nr_odd Father Mother Parent \\\n", "0 -9999 -9999.0 -9999.0 0 0 0 \n", "1 C 85.0 1.0 0 0 0 \n", "2 -9999 -9999.0 -9999.0 0 0 0 \n", "3 C 123.0 1.0 0 0 0 \n", "4 -9999 -9999.0 -9999.0 0 0 0 \n", "5 -9999 -9999.0 -9999.0 0 0 0 \n", "6 E 46.0 0.0 0 0 0 \n", "7 -9999 -9999.0 -9999.0 0 0 0 \n", "\n", " has_parents_or_kids FamilySize FareBin AgeBin \n", "0 0 1 (0, 8.67] (15, 25] \n", "1 0 1 (32, 350] (25, 60] \n", "2 0 0 (0, 8.67] (25, 60] \n", "3 0 1 (32, 350] (25, 60] \n", "4 0 0 (0, 8.67] (25, 60] \n", "5 0 0 (0, 8.67] NaN \n", "6 0 0 (32, 350] (25, 60] \n", "7 1 4 (16.11, 32] (0, 15] " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "'After handling categorical values:'" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Cabin_nr</th>\n", " <th>Cabin_nr_odd</th>\n", " <th>Father</th>\n", " <th>Mother</th>\n", " <th>Parent</th>\n", " <th>...</th>\n", " <th>Cabin_char_F</th>\n", " <th>Cabin_char_G</th>\n", " <th>FareBin_(0, 8.67]</th>\n", " <th>FareBin_(8.67, 16.11]</th>\n", " <th>FareBin_(16.11, 32]</th>\n", " <th>FareBin_(32, 350]</th>\n", " <th>FareBin_(350, 1000]</th>\n", " <th>AgeBin_(15, 25]</th>\n", " <th>AgeBin_(25, 60]</th>\n", " <th>AgeBin_(60, 90]</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>-9999.0</td>\n", " <td>-9999.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>85.0</td>\n", " <td>1.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " 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<td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>46.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>8</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>-9999.0</td>\n", " <td>-9999.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>8 rows × 36 columns</p>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass SibSp Parch Cabin_nr Cabin_nr_odd \\\n", "0 1 0 3 1 0 -9999.0 -9999.0 \n", "1 2 1 1 1 0 85.0 1.0 \n", "2 3 1 3 0 0 -9999.0 -9999.0 \n", "3 4 1 1 1 0 123.0 1.0 \n", "4 5 0 3 0 0 -9999.0 -9999.0 \n", "5 6 0 3 0 0 -9999.0 -9999.0 \n", "6 7 0 1 0 0 46.0 0.0 \n", "7 8 0 3 3 1 -9999.0 -9999.0 \n", "\n", " Father Mother Parent ... Cabin_char_F Cabin_char_G \\\n", "0 0 0 0 ... 0.0 0.0 \n", "1 0 0 0 ... 0.0 0.0 \n", "2 0 0 0 ... 0.0 0.0 \n", "3 0 0 0 ... 0.0 0.0 \n", "4 0 0 0 ... 0.0 0.0 \n", "5 0 0 0 ... 0.0 0.0 \n", "6 0 0 0 ... 0.0 0.0 \n", "7 0 0 0 ... 0.0 0.0 \n", "\n", " FareBin_(0, 8.67] FareBin_(8.67, 16.11] FareBin_(16.11, 32] \\\n", "0 1.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 1.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 1.0 0.0 0.0 \n", "5 1.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 1.0 \n", "\n", " FareBin_(32, 350] FareBin_(350, 1000] AgeBin_(15, 25] AgeBin_(25, 60] \\\n", "0 0.0 0.0 1.0 0.0 \n", "1 1.0 0.0 0.0 1.0 \n", "2 0.0 0.0 0.0 1.0 \n", "3 1.0 0.0 0.0 1.0 \n", "4 0.0 0.0 0.0 1.0 \n", "5 0.0 0.0 0.0 0.0 \n", "6 1.0 0.0 0.0 1.0 \n", "7 0.0 0.0 0.0 0.0 \n", "\n", " AgeBin_(60, 90] \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 \n", "5 0.0 \n", "6 0.0 \n", "7 0.0 \n", "\n", "[8 rows x 36 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# read the training set\n", "train = pd.read_csv('../input/train.csv')\n", "display(\"Unaltered training set:\")\n", "display(train.head(8))\n", "\n", "# feature engineering\n", "train = feat_eng(train)\n", "display(\"After feature engineering:\")\n", "display(train.head(8))\n", "\n", "# treat missing values\n", "train = missing(train)\n", "display(\"After handling missing values:\")\n", "display(train.head(8))\n", "\n", "# convert categorical values to numerical\n", "train = pd.get_dummies(train, drop_first=True)\n", "display(\"After handling categorical values:\")\n", "display(train.head(8))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "59d421f3-4353-a005-90d2-c7b774bc16b3" }, "source": [ "Training our first XGBoost model\n", "------------------" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "da67dd5a-af28-d135-c10a-02b4b07d74d1" }, "outputs": [], "source": [ "X = np.array(train.drop(['Survived','PassengerId'], 1))\n", "training_features = np.array(train.drop(['Survived','PassengerId'], 1).columns)\n", "#X = preprocessing.scale(X) --- not needed for XGboost?\n", "y = np.array(train['Survived'])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "d706bb96-632e-112f-24cb-1c0c3071a9a5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.73333333 0.71111111 0.82222222 0.73333333 0.82222222 0.71111111\n", " 0.77777778 0.8 0.77777778 0.86666667 0.86666667 0.84090909\n", " 0.90909091 0.84090909 0.77272727 0.81818182 0.88636364 0.88636364\n", " 0.84090909 0.75 ]\n", "Accuracy: 0.808 stdev: 0.06\n" ] } ], "source": [ "clf = xgb.XGBClassifier()\n", "cv = cross_validation.KFold(len(X), n_folds=20, shuffle=True, random_state=1)\n", "scores = cross_validation.cross_val_score(clf, X, y, cv=cv, n_jobs=1, scoring='accuracy')\n", "clf.fit(X,y)\n", "print(scores)\n", "print('Accuracy: %.3f stdev: %.2f' % (np.mean(np.abs(scores)), np.std(scores)))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "65092356-5b57-21b5-644f-4ca555bac71a" }, "source": [ "Feature selection with Greedy Search (RFECV)\n", "---------------------" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "bfa09d29-b0cc-d535-87a0-c3c118a061d2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "features used during training: \n", "['Pclass' 'SibSp' 'Parch' 'Cabin_nr' 'Cabin_nr_odd' 'Father' 'Mother'\n", " 'Parent' 'has_parents_or_kids' 'FamilySize' 'Sex_kid' 'Sex_male'\n", " 'Embarked_Q' 'Embarked_S' 'Title_Miss.' 'Title_Mr.' 'Title_Mrs.'\n", " 'Title_Officer.' 'Title_Sir.' 'Cabin_char_A' 'Cabin_char_B' 'Cabin_char_C'\n", " 'Cabin_char_D' 'Cabin_char_E' 'Cabin_char_F' 'Cabin_char_G'\n", " 'FareBin_(0, 8.67]' 'FareBin_(8.67, 16.11]' 'FareBin_(16.11, 32]'\n", " 'FareBin_(32, 350]' 'FareBin_(350, 1000]' 'AgeBin_(15, 25]'\n", " 'AgeBin_(25, 60]' 'AgeBin_(60, 90]']\n", "\n", "features proposed by RFECV: \n", "['Pclass' 'Cabin_nr' 'FamilySize' 'Sex_male' 'FareBin_(32, 350]']\n" ] } ], "source": [ "featselect = RFECV(estimator=clf, cv=cv, scoring='accuracy')\n", "featselect.fit(X,y)\n", "\n", "print(\"features used during training: \")\n", "print(training_features)\n", "print(\"\")\n", "print(\"features proposed by RFECV: \"),\n", "print(training_features[featselect.support_])\n", "\n", "# Note that for our feature Sex, which consists of male/female/kid, the classifier only needs to\n", "# know if a person is male or not. The classifier expects women and children to have equal\n", "# chance of survival. Which makes sense when we think about \"Women and children first!\"." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "71863b79-d69a-9e4b-b550-05bacaaf9595" }, "source": [ "Training our XGBoost model again after feature selection\n", "-------------" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "6773f2b3-643c-b3d8-a05c-b2406fbae92c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.8 0.75555556 0.86666667 0.77777778 0.84444444 0.71111111\n", " 0.8 0.82222222 0.77777778 0.91111111 0.86666667 0.86363636\n", " 0.90909091 0.90909091 0.77272727 0.84090909 0.88636364 0.86363636\n", " 0.88636364 0.75 ]\n", "Accuracy: 0.831 stdev: 0.06\n" ] }, { "data": { "text/plain": [ "XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,\n", " gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,\n", " min_child_weight=1, missing=None, n_estimators=100, nthread=-1,\n", " objective='binary:logistic', reg_alpha=0, reg_lambda=1,\n", " scale_pos_weight=1, seed=0, silent=True, subsample=1)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "selection = np.append(training_features[featselect.support_], ['Survived','PassengerId'])\n", "train2 = train[selection]\n", "\n", "X = np.array(train2.drop(['Survived','PassengerId'], 1))\n", "training_features = np.array(train2.drop(['Survived','PassengerId'], 1).columns)\n", "#X = preprocessing.scale(X) --- not needed for XGboost?\n", "y = np.array(train2['Survived'])\n", "\n", "clf = xgb.XGBClassifier()\n", "cv = cross_validation.KFold(len(X), n_folds=20, shuffle=True, random_state=1)\n", "scores = cross_validation.cross_val_score(clf, X, y, cv=cv, n_jobs=1, scoring='accuracy')\n", "print(scores)\n", "print('Accuracy: %.3f stdev: %.2f' % (np.mean(np.abs(scores)), np.std(scores)))\n", "clf.fit(X,y)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "01339d0d-ce1d-c75c-f437-00c41245e6a2" }, "source": [ "Hyper parameter tuning using Grid Search\n", "---------------------------" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "ae5a5ff1-65cc-b518-fa86-0e091b0da66c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'reg_lambda': 1, 'learning_rate': 0.004}\n", "0.8372615039281706\n" ] } ], "source": [ "# just as an example, tuning 2 parameters.\n", "# first try a wide range, e.g. [0, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0]\n", "# and then narrow it down.\n", "grid = {'learning_rate':[0, 0.001, 0.002, 0.004, 0.006, 0.008, 0.010], \n", " 'reg_lambda':[0, 0.01, 0.10, 0.50, 1]}\n", "\n", "search = GridSearchCV(estimator=clf, param_grid=grid, scoring='accuracy', n_jobs=1, refit=True, cv=cv)\n", "search.fit(X,y)\n", "\n", "print(search.best_params_)\n", "print(search.best_score_)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "4639a593-e9bd-4e2a-e786-8a05f21d587e" }, "source": [ "Making predictions\n", "-------------------" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "4034f690-0b24-fc3a-b0c0-fdfc1ea65a62" }, "outputs": [], "source": [ "# read test set\n", "test = pd.read_csv('../input/test.csv')\n", "\n", "# pull the test set through our feature engineering and missing values functions\n", "test = feat_eng(test)\n", "test = missing(test)\n", "\n", "# deal with categorical values\n", "test = pd.get_dummies(test, drop_first=True)\n", "\n", "# remove features deemed unworthy by our feature selection (RFECV)\n", "test2 = test[training_features]\n", "# the above line removes several features incl. PassengerId.\n", "# So we prefer to keep our 'test' variable as it is, cause a few lines below\n", "# we will need the passengerid feature.\n", "\n", "X = np.array(test2)\n", "#X = preprocessing.scale(X)\n", "y_predict = clf.predict(X)\n", "dfresult = pd.DataFrame(y_predict, test.PassengerId)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "5ceead6d-613f-44d7-f329-2278018b7091" }, "source": [ "Write submission to disk\n", "-----------------" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "2640e025-452c-5cbf-45fd-f3aedf7dff9e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "done.\n" ] } ], "source": [ "dfresult.columns = ['Survived']\n", "dfresult.to_csv('predictions.csv')\n", "print(\"done.\")" ] } ], "metadata": { "_change_revision": 420, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/326/326282.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 22, "metadata": { "_cell_guid": "ca4b2839-5fab-bb76-8fc0-e85624e5128e" }, "outputs": [ { "ename": "AttributeError", "evalue": "module 'tqdm' has no attribute 'pandas'", "output_type": "error", "traceback": [ "", "AttributeErrorTraceback (most recent call last)", "<ipython-input-1-b94dba671e65> in <module>()\n 4 # For displaying progress.\n 5 import tqdm\n----> 6 tqdm.pandas()\n 7 \n 8 # For plotting data.\n", "AttributeError: module 'tqdm' has no attribute 'pandas'" ] } ], "source": [ "# For handling data.\n", "import pandas\n", "\n", "# For plotting data.\n", "%matplotlib inline\n", "import seaborn\n", "import matplotlib.pyplot as plot\n", "seaborn.set(style = \"darkgrid\", palette = \"husl\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "_cell_guid": "c0f74294-15a1-23cc-6a3a-562d4283964e" }, "outputs": [ { "ename": "SyntaxError", "evalue": "unexpected EOF while parsing (<ipython-input-2-7aad1c60d986>, line 3)", "output_type": "error", "traceback": [ " File \"<ipython-input-2-7aad1c60d986>\", line 3\n data.columns = [\"char\", \"level\", \"race\", \"charclass\", \"zone\", \"guild\", \"timestamp\"\n ^\nSyntaxError: unexpected EOF while parsing\n" ] }, { "ename": "OSError", "evalue": "File b'input/wowah_data.csv' does not exist", "output_type": "error", "traceback": [ "", "OSErrorTraceback (most recent call last)", "<ipython-input-12-3e387492ca22> in <module>()\n 1 # Load data.\n----> 2 data = pandas.read_csv(\"input/wowah_data.csv\")\n 3 data.columns = [\"char\", \"level\", \"race\", \"charclass\", \"zone\", \"guild\", \"timestamp\"]\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\n 560 skip_blank_lines=skip_blank_lines)\n 561 \n--> 562 return _read(filepath_or_buffer, kwds)\n 563 \n 564 parser_f.__name__ = name\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)\n 313 \n 314 # Create the parser.\n--> 315 parser = TextFileReader(filepath_or_buffer, **kwds)\n 316 \n 317 if (nrows is not None) and (chunksize is not None):\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)\n 643 self.options['has_index_names'] = kwds['has_index_names']\n 644 \n--> 645 self._make_engine(self.engine)\n 646 \n 647 def close(self):\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in _make_engine(self, engine)\n 797 def _make_engine(self, engine='c'):\n 798 if engine == 'c':\n--> 799 self._engine = CParserWrapper(self.f, **self.options)\n 800 else:\n 801 if engine == 'python':\n", "/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)\n 1211 kwds['allow_leading_cols'] = self.index_col is not False\n 1212 \n-> 1213 self._reader = _parser.TextReader(src, **kwds)\n 1214 \n 1215 # XXX\n", "pandas/parser.pyx in pandas.parser.TextReader.__cinit__ (pandas/parser.c:3427)()\n", "pandas/parser.pyx in pandas.parser.TextReader._setup_parser_source (pandas/parser.c:6861)()\n", "OSError: File b'input/wowah_data.csv' does not exist" ] } ], "source": [ "# Load data.\n", "data = pandas.read_csv(\"../input/wowah_data.csv\")\n", "data.columns = [\"char\", \"level\", \"race\", \"charclass\", \"zone\", \"guild\", \"timestamp\"]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "_cell_guid": "ad2b20e7-7436-4a94-ab70-019a9bff266d" }, "outputs": [ { "ename": "NameError", "evalue": "name 'data' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-3-2a92ad032d66> in <module>()\n 1 # Parse timestamps.\n----> 2 data[\"time\"] = data[\"timestamp\"].progress_apply(pandas.to_datetime)\n", "NameError: name 'data' is not defined" ] }, { "ename": "NameError", "evalue": "name 'data' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-13-ab97f0e6e4fb> in <module>()\n 1 # Parse timestamps.\n----> 2 data[\"time\"] = data[\"timestamp\"].apply(pandas.to_datetime)\n", "NameError: name 'data' is not defined" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "", "ValueErrorTraceback (most recent call last)", "pandas/tslib.pyx in pandas.tslib.array_to_datetime (pandas/tslib.c:39919)()\n", "pandas/src/datetime.pxd in datetime._string_to_dts (pandas/tslib.c:85647)()\n", "ValueError: Unable to parse b'03/23/08 17:31:59'", "\nDuring handling of the above exception, another exception occurred:\n", "KeyboardInterruptTraceback (most recent call last)", "<ipython-input-24-ab97f0e6e4fb> in <module>()\n 1 # Parse timestamps.\n----> 2 data[\"time\"] = data[\"timestamp\"].apply(pandas.to_datetime)\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in apply(self, func, convert_dtype, args, **kwds)\n 2218 else:\n 2219 values = self.asobject\n-> 2220 mapped = lib.map_infer(values, f, convert=convert_dtype)\n 2221 \n 2222 if len(mapped) and isinstance(mapped[0], Series):\n", "pandas/src/inference.pyx in pandas.lib.map_infer (pandas/lib.c:62658)()\n", "/opt/conda/lib/python3.5/site-packages/pandas/util/decorators.py in wrapper(*args, **kwargs)\n 89 else:\n 90 kwargs[new_arg_name] = new_arg_value\n---> 91 return func(*args, **kwargs)\n 92 return wrapper\n 93 return _deprecate_kwarg\n", "/opt/conda/lib/python3.5/site-packages/pandas/tseries/tools.py in to_datetime(arg, errors, dayfirst, yearfirst, utc, box, format, exact, coerce, unit, infer_datetime_format)\n 289 yearfirst=yearfirst,\n 290 utc=utc, box=box, format=format, exact=exact,\n--> 291 unit=unit, infer_datetime_format=infer_datetime_format)\n 292 \n 293 \n", "/opt/conda/lib/python3.5/site-packages/pandas/tseries/tools.py in _to_datetime(arg, errors, dayfirst, yearfirst, utc, box, format, exact, unit, freq, infer_datetime_format)\n 427 return _convert_listlike(arg, box, format)\n 428 \n--> 429 return _convert_listlike(np.array([arg]), box, format)[0]\n 430 \n 431 # mappings for assembling units\n", "/opt/conda/lib/python3.5/site-packages/pandas/tseries/tools.py in _convert_listlike(arg, box, format, name)\n 396 yearfirst=yearfirst,\n 397 freq=freq,\n--> 398 require_iso8601=require_iso8601\n 399 )\n 400 \n", "pandas/tslib.pyx in pandas.tslib.array_to_datetime (pandas/tslib.c:41972)()\n", "pandas/tslib.pyx in pandas.tslib.array_to_datetime (pandas/tslib.c:40293)()\n", "pandas/tslib.pyx in pandas.tslib.parse_datetime_string (pandas/tslib.c:31806)()\n", "/opt/conda/lib/python3.5/site-packages/dateutil/parser.py in parse(timestr, parserinfo, **kwargs)\n 1162 return parser(parserinfo).parse(timestr, **kwargs)\n 1163 else:\n-> 1164 return DEFAULTPARSER.parse(timestr, **kwargs)\n 1165 \n 1166 \n", "/opt/conda/lib/python3.5/site-packages/dateutil/parser.py in parse(self, timestr, default, ignoretz, tzinfos, **kwargs)\n 550 effective_dt = default\n 551 \n--> 552 res, skipped_tokens = self._parse(timestr, **kwargs)\n 553 \n 554 if res is None:\n", "/opt/conda/lib/python3.5/site-packages/dateutil/parser.py in _parse(self, timestr, dayfirst, yearfirst, fuzzy, fuzzy_with_tokens)\n 1039 \n 1040 # Process year/month/day\n-> 1041 year, month, day = ymd.resolve_ymd(mstridx, yearfirst, dayfirst)\n 1042 if year is not None:\n 1043 res.year = year\n", "/opt/conda/lib/python3.5/site-packages/dateutil/parser.py in resolve_ymd(self, mstridx, yearfirst, dayfirst)\n 462 else:\n 463 if self[0] > 31 or \\\n--> 464 self.find_probable_year_index(_timelex.split(self.tzstr)) == 0 or \\\n 465 (yearfirst and self[1] <= 12 and self[2] <= 31):\n 466 # 99-01-01\n", "/opt/conda/lib/python3.5/site-packages/dateutil/parser.py in split(cls, s)\n 186 @classmethod\n 187 def split(cls, s):\n--> 188 return list(cls(s))\n 189 \n 190 @classmethod\n", "/opt/conda/lib/python3.5/site-packages/dateutil/parser.py in __next__(self)\n 176 def __next__(self):\n 177 token = self.get_token()\n--> 178 if token is None:\n 179 raise StopIteration\n 180 \n", "KeyboardInterrupt: " ] } ], "source": [ "# Parse timestamps.\n", "data[\"time\"] = data[\"timestamp\"].apply(pandas.to_datetime)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "_cell_guid": "74dcfeca-2005-bb6e-4a30-ab2d5cc01807" }, "outputs": [ { "ename": "NameError", "evalue": "name 'data' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-4-d4c579181fff> in <module>()\n 1 # Create set of characters that reached level 80 and record the last timestamp at level 70 as well as the first timestamp at level 80.\n----> 2 last70 = data[data[\"level\"] == 70].groupby(\"char\", as_index=False).last()\n 3 ding80 = data[data[\"level\"] == 80].groupby(\"char\", as_index=False).first()\n 4 ding80.columns = [\"char\", \"level\", \"race\", \"charclass\", \"zone\", \"guild\", \"timestamp\", \"ding80time\"]\n 5 last70.columns = [\"char\", \"level\", \"race\", \"charclass\", \"zone\", \"guild\", \"timestamp\", \"last70time\"]\n", "NameError: name 'data' is not defined" ] }, { "ename": "NameError", "evalue": "name 'data' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-14-d4c579181fff> in <module>()\n 1 # Create set of characters that reached level 80 and record the last timestamp at level 70 as well as the first timestamp at level 80.\n----> 2 last70 = data[data[\"level\"] == 70].groupby(\"char\", as_index=False).last()\n 3 ding80 = data[data[\"level\"] == 80].groupby(\"char\", as_index=False).first()\n 4 ding80.columns = [\"char\", \"level\", \"race\", \"charclass\", \"zone\", \"guild\", \"timestamp\", \"ding80time\"]\n 5 last70.columns = [\"char\", \"level\", \"race\", \"charclass\", \"zone\", \"guild\", \"timestamp\", \"last70time\"]\n", "NameError: name 'data' is not defined" ] }, { "ename": "ValueError", "evalue": "Length mismatch: Expected axis has 7 elements, new values have 8 elements", "output_type": "error", "traceback": [ "", "ValueErrorTraceback (most recent call last)", "<ipython-input-25-d4c579181fff> in <module>()\n 2 last70 = data[data[\"level\"] == 70].groupby(\"char\", as_index=False).last()\n 3 ding80 = data[data[\"level\"] == 80].groupby(\"char\", as_index=False).first()\n----> 4 ding80.columns = [\"char\", \"level\", \"race\", \"charclass\", \"zone\", \"guild\", \"timestamp\", \"ding80time\"]\n 5 last70.columns = [\"char\", \"level\", \"race\", \"charclass\", \"zone\", \"guild\", \"timestamp\", \"last70time\"]\n 6 characters = pandas.merge(ding80[[\"char\", \"race\", \"charclass\", \"guild\", \"ding80time\"]], last70[[\"char\", \"last70time\"]], on=\"char\")\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py in __setattr__(self, name, value)\n 2683 try:\n 2684 object.__getattribute__(self, name)\n-> 2685 return object.__setattr__(self, name, value)\n 2686 except AttributeError:\n 2687 pass\n", "pandas/src/properties.pyx in pandas.lib.AxisProperty.__set__ (pandas/lib.c:44748)()\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py in _set_axis(self, axis, labels)\n 426 \n 427 def _set_axis(self, axis, labels):\n--> 428 self._data.set_axis(axis, labels)\n 429 self._clear_item_cache()\n 430 \n", "/opt/conda/lib/python3.5/site-packages/pandas/core/internals.py in set_axis(self, axis, new_labels)\n 2633 raise ValueError('Length mismatch: Expected axis has %d elements, '\n 2634 'new values have %d elements' %\n-> 2635 (old_len, new_len))\n 2636 \n 2637 self.axes[axis] = new_labels\n", "ValueError: Length mismatch: Expected axis has 7 elements, new values have 8 elements" ] } ], "source": [ "# Create set of characters that reached level 80 and record the last timestamp at level 70 as well as the first timestamp at level 80.\n", "last70 = data[data[\"level\"] == 70].groupby(\"char\", as_index=False).last()\n", "ding80 = data[data[\"level\"] == 80].groupby(\"char\", as_index=False).first()\n", "ding80.columns = [\"char\", \"level\", \"race\", \"charclass\", \"zone\", \"guild\", \"timestamp\", \"ding80time\"]\n", "last70.columns = [\"char\", \"level\", \"race\", \"charclass\", \"zone\", \"guild\", \"timestamp\", \"last70time\"]\n", "characters = pandas.merge(ding80[[\"char\", \"race\", \"charclass\", \"guild\", \"ding80time\"]], last70[[\"char\", \"last70time\"]], on=\"char\")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "_cell_guid": "e4c589fc-a0b7-bc96-b214-ded0273e3d21" }, "outputs": [ { "ename": "NameError", "evalue": "name 'characters' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-5-e55171b93dc3> in <module>()\n 1 # Create leveling time column.\n----> 2 characters[\"leveling_time\"] = characters[\"ding80time\"] - characters[\"last70time\"]\n", "NameError: name 'characters' is not defined" ] }, { "ename": "NameError", "evalue": "name 'characters' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-15-e55171b93dc3> in <module>()\n 1 # Create leveling time column.\n----> 2 characters[\"leveling_time\"] = characters[\"ding80time\"] - characters[\"last70time\"]\n", "NameError: name 'characters' is not defined" ] }, { "ename": "NameError", "evalue": "name 'characters' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-26-e55171b93dc3> in <module>()\n 1 # Create leveling time column.\n----> 2 characters[\"leveling_time\"] = characters[\"ding80time\"] - characters[\"last70time\"]\n", "NameError: name 'characters' is not defined" ] } ], "source": [ "# Create leveling time column.\n", "characters[\"leveling_time\"] = characters[\"ding80time\"] - characters[\"last70time\"]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "_cell_guid": "7be38ed3-bfdc-1fe1-0699-0feee558d6f1" }, "outputs": [ { "ename": "NameError", "evalue": "name 'characters' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-6-f0c5419ad322> in <module>()\n 1 # Remove high outliers in leveling time.\n----> 2 mean_leveling_time = characters[\"leveling_time\"].mean()\n 3 std_leveling_time = characters[\"leveling_time\"].std()\n 4 characters_no_slowpokes = characters[characters[\"leveling_time\"] - mean_leveling_time <= 3 * std_leveling_time]\n", "NameError: name 'characters' is not defined" ] }, { "ename": "NameError", "evalue": "name 'characters' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-16-f0c5419ad322> in <module>()\n 1 # Remove high outliers in leveling time.\n----> 2 mean_leveling_time = characters[\"leveling_time\"].mean()\n 3 std_leveling_time = characters[\"leveling_time\"].std()\n 4 characters_no_slowpokes = characters[characters[\"leveling_time\"] - mean_leveling_time <= 3 * std_leveling_time]\n", "NameError: name 'characters' is not defined" ] }, { "ename": "NameError", "evalue": "name 'characters' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-27-f0c5419ad322> in <module>()\n 1 # Remove high outliers in leveling time.\n----> 2 mean_leveling_time = characters[\"leveling_time\"].mean()\n 3 std_leveling_time = characters[\"leveling_time\"].std()\n 4 characters_no_slowpokes = characters[characters[\"leveling_time\"] - mean_leveling_time <= 3 * std_leveling_time]\n", "NameError: name 'characters' is not defined" ] } ], "source": [ "# Remove high outliers in leveling time.\n", "mean_leveling_time = characters[\"leveling_time\"].mean()\n", "std_leveling_time = characters[\"leveling_time\"].std()\n", "characters_no_slowpokes = characters[characters[\"leveling_time\"] - mean_leveling_time <= 3 * std_leveling_time]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "_cell_guid": "3756f309-42b2-8fd9-3eb6-a93350010a84" }, "outputs": [ { "ename": "NameError", "evalue": "name 'characters' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-7-8081878c41fc> in <module>()\n 1 # Who was the top 10 fastest to hit 80?\n----> 2 characters[characters[\"leveling_time\"].isin(characters[\"leveling_time\"].nsmallest(10))].sort_values(\"leveling_time\")\n", "NameError: name 'characters' is not defined" ] }, { "ename": "NameError", "evalue": "name 'characters' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-17-8081878c41fc> in <module>()\n 1 # Who was the top 10 fastest to hit 80?\n----> 2 characters[characters[\"leveling_time\"].isin(characters[\"leveling_time\"].nsmallest(10))].sort_values(\"leveling_time\")\n", "NameError: name 'characters' is not defined" ] }, { "ename": "NameError", "evalue": "name 'characters' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-28-8081878c41fc> in <module>()\n 1 # Who was the top 10 fastest to hit 80?\n----> 2 characters[characters[\"leveling_time\"].isin(characters[\"leveling_time\"].nsmallest(10))].sort_values(\"leveling_time\")\n", "NameError: name 'characters' is not defined" ] } ], "source": [ "# Who was the top 10 fastest to hit 80?\n", "characters[characters[\"leveling_time\"].isin(characters[\"leveling_time\"].nsmallest(10))].sort_values(\"leveling_time\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "_cell_guid": "c11d1412-4901-6a8c-1dfe-9fcdb3c3b5bd" }, "outputs": [ { "ename": "NameError", "evalue": "name 'seaborn' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-8-c21bd2f90a13> in <module>()\n 1 # Plot leveling time versus class.\n----> 2 seaborn.boxplot(x=\"charclass\", y=\"leveling_time\", data=characters_no_slowpokes)\n", "NameError: name 'seaborn' is not defined" ] }, { "ename": "NameError", "evalue": "name 'characters_no_slowpokes' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-18-c21bd2f90a13> in <module>()\n 1 # Plot leveling time versus class.\n----> 2 seaborn.boxplot(x=\"charclass\", y=\"leveling_time\", data=characters_no_slowpokes)\n", "NameError: name 'characters_no_slowpokes' is not defined" ] }, { "ename": "NameError", "evalue": "name 'characters_no_slowpokes' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-29-c21bd2f90a13> in <module>()\n 1 # Plot leveling time versus class.\n----> 2 seaborn.boxplot(x=\"charclass\", y=\"leveling_time\", data=characters_no_slowpokes)\n", "NameError: name 'characters_no_slowpokes' is not defined" ] } ], "source": [ "# Plot leveling time versus class.\n", "seaborn.boxplot(x=\"charclass\", y=\"leveling_time\", data=characters_no_slowpokes)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "_cell_guid": "7c9feb04-008c-cc87-14ca-2cc3b3dd165a" }, "outputs": [ { "ename": "NameError", "evalue": "name 'plot' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-9-8e4ccc22d07b> in <module>()\n 1 # Plot leveling time versus guild.\n----> 2 plot.figure(figsize=(45,10))\n 3 seaborn.boxplot(x=\"guild\", y=\"leveling_time\", data=characters_no_slowpokes)\n", "NameError: name 'plot' is not defined" ] }, { "ename": "NameError", "evalue": "name 'characters_no_slowpokes' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-19-8e4ccc22d07b> in <module>()\n 1 # Plot leveling time versus guild.\n 2 plot.figure(figsize=(45,10))\n----> 3 seaborn.boxplot(x=\"guild\", y=\"leveling_time\", data=characters_no_slowpokes)\n", "NameError: name 'characters_no_slowpokes' is not defined" ] }, { "data": { "text/plain": "<matplotlib.figure.Figure at 0x7f716a08ebe0>" }, "metadata": {}, "output_type": "display_data" }, { "ename": "NameError", "evalue": "name 'characters_no_slowpokes' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-30-8e4ccc22d07b> in <module>()\n 1 # Plot leveling time versus guild.\n 2 plot.figure(figsize=(45,10))\n----> 3 seaborn.boxplot(x=\"guild\", y=\"leveling_time\", data=characters_no_slowpokes)\n", "NameError: name 'characters_no_slowpokes' is not defined" ] }, { "data": { "text/plain": "<matplotlib.figure.Figure at 0x7f716a08eb38>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot leveling time versus guild.\n", "plot.figure(figsize=(45,10))\n", "seaborn.boxplot(x=\"guild\", y=\"leveling_time\", data=characters_no_slowpokes)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "_cell_guid": "ef0573b5-2f77-b350-4f92-52ff6508dc0a" }, "outputs": [ { "ename": "NameError", "evalue": "name 'seaborn' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-10-b7264fa64ed1> in <module>()\n 1 # Plot leveling time versus race.\n----> 2 seaborn.boxplot(x=\"race\", y=\"leveling_time\", data=characters_no_slowpokes)\n", "NameError: name 'seaborn' is not defined" ] }, { "ename": "NameError", "evalue": "name 'characters_no_slowpokes' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-20-b7264fa64ed1> in <module>()\n 1 # Plot leveling time versus race.\n----> 2 seaborn.boxplot(x=\"race\", y=\"leveling_time\", data=characters_no_slowpokes)\n", "NameError: name 'characters_no_slowpokes' is not defined" ] }, { "ename": "NameError", "evalue": "name 'characters_no_slowpokes' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-31-b7264fa64ed1> in <module>()\n 1 # Plot leveling time versus race.\n----> 2 seaborn.boxplot(x=\"race\", y=\"leveling_time\", data=characters_no_slowpokes)\n", "NameError: name 'characters_no_slowpokes' is not defined" ] } ], "source": [ "# Plot leveling time versus race.\n", "seaborn.boxplot(x=\"race\", y=\"leveling_time\", data=characters_no_slowpokes)" ] } ], "metadata": { "_change_revision": 189, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/326/326306.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "bf2a343c-70e5-b78c-46ee-ae59d3ab3e72" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "95100524-f70f-5b23-fa68-2585aac217fc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3-Airplane_Crashes_Since_1908.txt\n", "plots.png\n", "plots_2.png\n", "\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "3244b714-7738-066c-3df0-11a378daebc5" }, "outputs": [], "source": [ "crashes=pd.read_csv(\"../input/3-Airplane_Crashes_Since_1908.txt\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "eab31b05-112c-e642-5db2-cd1e56d8945f" }, "outputs": [ { "data": { "text/plain": [ "Date object\n", "Time object\n", "Location object\n", "Operator object\n", "Flight # object\n", "Route object\n", "Type object\n", "Registration object\n", "cn/In object\n", "Aboard float64\n", "Fatalities float64\n", "Ground float64\n", "Summary object\n", "dtype: object" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "crashes.dtypes" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "f8c3916d-47ab-4a50-d65a-d7413fd18617" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Date</th>\n", " <th>Time</th>\n", " <th>Location</th>\n", " <th>Operator</th>\n", " <th>Flight #</th>\n", " <th>Route</th>\n", " <th>Type</th>\n", " <th>Registration</th>\n", " <th>cn/In</th>\n", " <th>Aboard</th>\n", " <th>Fatalities</th>\n", " <th>Ground</th>\n", " <th>Summary</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>09/17/1908</td>\n", " <td>17:18</td>\n", " <td>Fort Myer, Virginia</td>\n", " <td>Military - U.S. Army</td>\n", " <td>NaN</td>\n", " <td>Demonstration</td>\n", " <td>Wright Flyer III</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>During a demonstration flight, a U.S. Army fly...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>07/12/1912</td>\n", " <td>06:30</td>\n", " <td>AtlantiCity, New Jersey</td>\n", " <td>Military - U.S. Navy</td>\n", " <td>NaN</td>\n", " <td>Test flight</td>\n", " <td>Dirigible</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>0.0</td>\n", " <td>First U.S. dirigible Akron exploded just offsh...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>08/06/1913</td>\n", " <td>NaN</td>\n", " <td>Victoria, British Columbia, Canada</td>\n", " <td>Private</td>\n", " <td>-</td>\n", " <td>NaN</td>\n", " <td>Curtiss seaplane</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>The first fatal airplane accident in Canada oc...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>09/09/1913</td>\n", " <td>18:30</td>\n", " <td>Over the North Sea</td>\n", " <td>Military - German Navy</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Zeppelin L-1 (airship)</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>20.0</td>\n", " <td>14.0</td>\n", " <td>0.0</td>\n", " <td>The airship flew into a thunderstorm and encou...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>10/17/1913</td>\n", " <td>10:30</td>\n", " <td>Near Johannisthal, Germany</td>\n", " <td>Military - German Navy</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Zeppelin L-2 (airship)</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>30.0</td>\n", " <td>30.0</td>\n", " <td>0.0</td>\n", " <td>Hydrogen gas which was being vented was sucked...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Date Time Location \\\n", "0 09/17/1908 17:18 Fort Myer, Virginia \n", "1 07/12/1912 06:30 AtlantiCity, New Jersey \n", "2 08/06/1913 NaN Victoria, British Columbia, Canada \n", "3 09/09/1913 18:30 Over the North Sea \n", "4 10/17/1913 10:30 Near Johannisthal, Germany \n", "\n", " Operator Flight # Route Type \\\n", "0 Military - U.S. Army NaN Demonstration Wright Flyer III \n", "1 Military - U.S. Navy NaN Test flight Dirigible \n", "2 Private - NaN Curtiss seaplane \n", "3 Military - German Navy NaN NaN Zeppelin L-1 (airship) \n", "4 Military - German Navy NaN NaN Zeppelin L-2 (airship) \n", "\n", " Registration cn/In Aboard Fatalities Ground \\\n", "0 NaN 1 2.0 1.0 0.0 \n", "1 NaN NaN 5.0 5.0 0.0 \n", "2 NaN NaN 1.0 1.0 0.0 \n", "3 NaN NaN 20.0 14.0 0.0 \n", "4 NaN NaN 30.0 30.0 0.0 \n", "\n", " Summary \n", "0 During a demonstration flight, a U.S. Army fly... \n", "1 First U.S. dirigible Akron exploded just offsh... \n", "2 The first fatal airplane accident in Canada oc... \n", "3 The airship flew into a thunderstorm and encou... \n", "4 Hydrogen gas which was being vented was sucked... " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "crashes.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "64901f0e-7cf8-336f-b3b2-05a3f2fc0637" }, "outputs": [ { "data": { "text/plain": [ "['07', '12', '1912']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "crashes[\"Date\"][1].split(\"/\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "5ff3873a-2579-08c2-1040-ac84a1c3d425" }, "outputs": [], "source": [ "def parse_year(string):\n", " return int(string.split(\"/\")[2])\n", "crashes[\"Year\"]=crashes[\"Date\"].apply(lambda x: parse_year(x))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "d066203e-a60f-e35d-e65e-5b3785200347" }, "outputs": [], "source": [ "def parse_month(string):\n", " return int(string.split(\"/\")[0])\n", "crashes[\"Month\"]=crashes[\"Date\"].apply(lambda x: parse_month(x))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "a21c506c-cb04-fefd-09b0-7b1f4f38de70" }, "outputs": [ { "data": { "text/plain": [ "{nan,\n", " 'ERA Helicopter',\n", " 'Southwest Airlift',\n", " 'Ejercito del Aire',\n", " 'Empresa Aviacion del Pacifico',\n", " 'Military - Kenya Air Force',\n", " 'North Cay Airways',\n", " 'Air taxi - Cuiaba Air Taxi',\n", " 'Portsmouth Aviation - Air Taxi',\n", " 'Lineas Aéreas Costarricenses LACSA',\n", " 'Slick Airways - Cargo',\n", " 'Military - Union of Burma Air Force',\n", " 'TACA',\n", " 'Varney Airlines',\n", " 'Plane Sailing',\n", " 'Bazair',\n", " 'Executive Funds',\n", " 'Air Taxi - Haines Airways Inc.',\n", " 'Savanair',\n", " 'Air Taxi - Papillion Helicopters Ltd.',\n", " 'ArctiCircle Air Service',\n", " 'Shanxi Airlines',\n", " 'Air Taxi - Bohemia Inc.',\n", " 'Braathens',\n", " 'AVISPA',\n", " 'Aero Sudpacifico',\n", " 'Jack N. Boswick - Air Taxi',\n", " 'Khors Aircompany',\n", " 'President Airlines',\n", " 'Cathay PacifiAirways',\n", " 'Arall',\n", " 'Aerevias Centrales Air Service',\n", " 'Trans World Airlines / Eastern Air Lines',\n", " 'Air Taxi - Alpine Aviation',\n", " 'Trillium Air',\n", " \"Chalk's International Airlines\",\n", " 'DHL',\n", " 'Superior Aviation',\n", " 'Tyee Airlines - Air Taxi',\n", " 'ANT Air Taxi',\n", " 'Union des Transportes Aeriens',\n", " 'PacifiCoast Airlines',\n", " 'Latecoere Airlines',\n", " 'Military - Fuerza Aérea de Chile',\n", " 'Canair Cargo',\n", " 'Air Taxi - Sun Western Flyers Inc.',\n", " 'Tulakes Aviaiton - Air Taxi',\n", " 'North East Bolivian Airways',\n", " 'RRC Air Service',\n", " 'Wien Consolidated Airlines',\n", " 'Military - Russian Navy',\n", " 'Military - Nigerian Air Force',\n", " 'North Central Airlines / Air Wisconsin',\n", " 'Streamline',\n", " 'Volare Aviation Enterprise',\n", " 'Francisco Cruz',\n", " 'OlympiAirways',\n", " 'Skyline Sweden',\n", " 'Air Tahiti',\n", " 'Resort Airlines',\n", " 'LACSA',\n", " 'Pathet Lao Airlines',\n", " 'Independent Air Travel',\n", " 'Eastern Provincial Airways',\n", " 'TACV-Cabo Verde Airlines',\n", " 'South PacifiIsland Airways',\n", " 'Sky Cabs',\n", " 'International Jet Charter',\n", " 'Air Taxi - MajestiAlliance',\n", " 'Kodiak Airways',\n", " 'New England Airways',\n", " 'El Magal Aviation',\n", " 'Wenela Air Services',\n", " 'Temsco Helicopter - Air Taxi',\n", " 'Norte Jet Táxi Aéreo',\n", " 'Jay Hawk Air - Air Taxi',\n", " 'Military - Força Aérea Brasileira',\n", " 'Aero Asahi',\n", " 'Trans Mediterranean Airways',\n", " 'Munz Northern Airlines',\n", " 'Líneas Aéreas Suramericanas',\n", " 'Iran Air',\n", " 'United Airways',\n", " 'Knight Air',\n", " 'Aeromexico / Private',\n", " 'Winship Air Service - Air Taxi',\n", " 'Baron Aviation',\n", " 'Propair',\n", " 'Military - United States Navy',\n", " 'Aerovias del Valle',\n", " 'Sempati Air',\n", " 'Antillian Airlines',\n", " 'Chicago Helicopter Airways',\n", " 'SUDENE',\n", " 'Malaysia Airlines',\n", " 'TACSA',\n", " 'Libya Arab Airlines',\n", " 'GLM Aviation',\n", " 'Si Fly - U.N. charter',\n", " 'Inex Adria Aviopromet (Yugoslavia)',\n", " 'Broward Aviation Services - Air Taxi',\n", " 'Evolga',\n", " 'Madrid Taxi Aéreo - Charter',\n", " 'Starair',\n", " 'Ecuato Guineana de Aviacion',\n", " 'Minerva Airlines',\n", " 'Business Jet Services',\n", " 'ArctiPacific',\n", " 'Aeroservicios Empresariales de Durango',\n", " 'Paraense Transportes Aéreos',\n", " 'Skyline Aviation - Air Taxi',\n", " 'TanaMana Aviation',\n", " 'Manchurian Air Lines',\n", " 'Inter City Flug',\n", " 'Yug Avia',\n", " 'C and C Aviaiton - Air Taxi',\n", " 'Paradise Airlines',\n", " 'Mamer - Shreck - Air Taxi',\n", " 'Aramar',\n", " 'East Anglian Flying Services',\n", " 'Air Taxi - Richmor Aviation',\n", " 'Hoseba',\n", " 'Vieques Air Link',\n", " 'Continental Air Transport',\n", " 'Bulair TABSO',\n", " 'Aviation Development Corporation',\n", " 'Transportno Aviatsionno Bulgaro-Soviet Obshchestvo',\n", " 'Kam Air',\n", " 'Malert Airlines',\n", " 'Servicios Aéreos Especiales',\n", " 'Ross Aviation - Air Taxi',\n", " 'Corporacion Boliviano de Fomento',\n", " 'Yemen Airlines',\n", " 'ComvaAviation',\n", " 'Empresa Nacional de Transp. Trabalho Aereo',\n", " 'Sankuru Air Service',\n", " 'TANS Peru',\n", " 'TropiAir - Air Taxi',\n", " 'Cubana de Aviacion',\n", " 'British Airways Helicopters',\n", " 'Air Tahoma',\n", " 'Pan ArctiOil',\n", " 'Military - Royal British Navy',\n", " 'AREA',\n", " 'American Air Export & Import Co.',\n", " 'Nordeste Linhas Aéreas',\n", " 'Military - U.S. Air Force / Military - U.S. Army',\n", " 'Carib Air Transport',\n", " 'Aerocosta Colombia',\n", " 'Interior Airways - Air Taxi',\n", " 'Military - US Army',\n", " 'Military - El Salvador Air Force',\n", " 'Panorama Air Tour',\n", " 'Del Rio Flying Service - Air Taxi',\n", " 'Ceskoslovenske Aerolinie',\n", " \"Gor'ky Eskadril'ya\",\n", " 'Edinburgh Air Charter',\n", " 'Alitalia',\n", " 'Transocean Air Lines',\n", " 'Air Guadeloupe',\n", " 'Middle East Airlines',\n", " 'Boeing Air Transport',\n", " 'Ukvozduchput',\n", " 'TARS',\n", " 'Aer Turas',\n", " 'Transoriente Colombia',\n", " 'Military - Sri Lanka Air Force',\n", " 'Air taxi - Air Safaris Inc.',\n", " 'Douglas Aircraft',\n", " 'Spartan Air Services',\n", " 'Rotor Airs In- Air Taxi',\n", " 'Air-Lift Commuter',\n", " 'Air Taxi - Peninsula Airways',\n", " 'Swedish Air Force',\n", " 'Hamilton Aviation',\n", " 'Military - Angolan Armed Forces',\n", " 'Cruzeiro do Sul',\n", " 'Arrow Airlines',\n", " 'Aeronautical Services - Air Taxi',\n", " 'Military - Soviet Navy',\n", " 'Air Taxi - Talon Air Services Inc.',\n", " 'Alva Aircraft Service - Taxi',\n", " 'Daimler Airways / Grands Express Aeriens',\n", " 'Willow Air Service - Air Taxi',\n", " 'Military - Fuerza Aérea Argentina',\n", " 'Chicago Southern Airlines',\n", " 'Uzbekistan Airways',\n", " 'Private Wings',\n", " 'Overseas National Airways',\n", " 'Skyways',\n", " 'Commercial Air Taxi',\n", " 'United Nations Mission',\n", " 'CORAL Colombia',\n", " 'Transamazonica',\n", " 'North Western Air Transport',\n", " 'Southern Express',\n", " 'Aero Tropical',\n", " 'Servicio Aéreo Panini',\n", " 'Tiramavia',\n", " 'Air France',\n", " 'Aquilla Airways',\n", " 'Rhoadgs Aviation - Air Taxi',\n", " 'Northwest Orient Airlines',\n", " 'Air Gaspé',\n", " 'Airfast Indonesia',\n", " 'Helicol Colombia',\n", " 'Aerolineas El Salvador',\n", " 'Military - Peruvian Army Aviation',\n", " 'Itek Air',\n", " 'Europe Aero Service EAS',\n", " 'Inter Island Air - Air Taxi',\n", " 'Djibouti Airlines',\n", " 'Mohawk Airlines',\n", " 'Phoenix Airlines',\n", " 'Syktyvkar Avia',\n", " 'Valley Air Service',\n", " 'Palair Macedonian',\n", " 'Air Karibu',\n", " 'Tura Air Enterprise',\n", " 'Military - Algerian Air Force',\n", " 'Military - Government of Equatorial Guinea',\n", " 'Bali International Air Service',\n", " 'Galesburg Aviation - Air Taxi',\n", " 'Transniugini Airways',\n", " 'Sasco Air Lines',\n", " 'Zen Nippon',\n", " 'Aero Ejecutivos',\n", " 'Panair do Brasil',\n", " 'Gander Aviation',\n", " 'SceniAir Lines',\n", " 'College of the Ozarks',\n", " 'Aero Cozumel',\n", " 'Lineas Aereas del Caribe',\n", " 'US Air Express/Air Midwest',\n", " 'Inex Adria Aviopromet / British Airways',\n", " 'LATI',\n", " 'US Airways Express',\n", " 'Imperial Airways',\n", " 'Hlavka Aviation - Air Taxi',\n", " 'Military - Taliban Militia',\n", " 'Saha Airline Services',\n", " 'TAME',\n", " 'All Nippon Airways',\n", " 'Air Taxi - Eidoarie Inc.',\n", " 'Trans Air Services',\n", " 'Mililtary - Soviet Air Force',\n", " 'ArctiWings & Rotors',\n", " 'Ministerstvo Obshchestvo Mashinostroyeniya',\n", " 'Military - Kenyan Air Force',\n", " 'Petroleum Helicopter',\n", " 'Air Turks & Caicos',\n", " 'PacifiAir',\n", " 'New York Airways',\n", " 'National Parks Airways',\n", " 'Walker - Watts Aviation - Air Taxi',\n", " 'Lionair',\n", " 'Wien Air Alaska',\n", " 'Scandinavian Airlines (SAS)',\n", " 'Transportes Aéreos Salvador',\n", " 'Corporate Airlines (American Connection)',\n", " 'Air St. Martin',\n", " 'American Airways',\n", " 'Military - Luftwaffe',\n", " 'PacifiSouthwest Airlines',\n", " 'Air Mauritanie',\n", " 'Festus Flying Service - Air Taxi',\n", " 'Lancashire Aircraft Corporation (Skyways)',\n", " 'Rutaca',\n", " 'North Continental Airlines (Robin Airlines)',\n", " 'Fragtflug',\n", " 'Avanti Aviation -Air Taxi',\n", " 'Japan Air Lines',\n", " 'CAAK',\n", " 'Mustique Airways',\n", " 'Military - Chilian Air Force',\n", " 'Lineas Aéreas Costarricenses',\n", " 'Corp. Aeronautica de Transportes',\n", " 'Taxair',\n", " 'Military - Republiof China Air Force',\n", " 'Societa Aerea Mediterranea',\n", " 'Transportes Aereos Don Carlos - Charter',\n", " 'Tracep',\n", " 'Red Aircraft Service - Air Taxi',\n", " 'Air Taxi - Petroleum Helicopters Inc.',\n", " 'Evergreen International Airlines',\n", " 'Central Airlines',\n", " 'Military - Salvadoran Air Force',\n", " 'Air Taxi - Richards Aviation Inc.',\n", " 'General Airways',\n", " 'Calair',\n", " 'Sky Executive Air Services',\n", " 'Cie Air Transport',\n", " 'NewCal Aviation',\n", " 'Aeroespresso',\n", " 'Lake Central Airlines',\n", " 'Caribbean International Airways',\n", " 'Fuerza Aérea Argentina',\n", " 'Maritime Central Airways',\n", " 'California Air Charter',\n", " 'Air Memphis',\n", " 'G & W Aviation',\n", " 'Interflug',\n", " 'Trans Isle Air',\n", " 'Northern Air Cargo',\n", " 'Lineas Aereas de los Libertadores',\n", " 'Siamese Airways',\n", " 'Iberia Airlines',\n", " 'Invicta International Airlines (UK)',\n", " 'Skystream Airlines',\n", " 'Imperial Comm - Air Taxi',\n", " 'Lineas Aéreas la Urraca',\n", " 'Miltiary - U.S. Airforce',\n", " 'Seaview Aviation',\n", " 'Pan International',\n", " 'Regionnair',\n", " 'Boeing KC-135E',\n", " 'West Australian Airways',\n", " 'Aeroflot / Military - Russian Air Force',\n", " 'Military - Venezuelan Navy',\n", " 'Kinair Cargo',\n", " 'Provincial Air Services',\n", " 'TPI International Airways',\n", " 'Pan American Grace Airlines',\n", " 'Pan American World Airways / KLM',\n", " 'Airwave Transport',\n", " 'Military - Servicio Aereo Nacional',\n", " 'Military - U.S. Army',\n", " 'Care Flight International',\n", " 'Hallo Bay Air - Air Taxi',\n", " 'Sierra PacifiAirlines',\n", " 'Lineas Areas Unidas',\n", " 'British European Airways / Military - Soviet Air Force',\n", " 'Lina Congo',\n", " 'American International Airways',\n", " 'Onzeair',\n", " 'Ortner Air Service - Taxi',\n", " 'Lineas Areas del Centro',\n", " 'Apache Airlines - Air Taxi',\n", " 'Military - U.S. Navy / NASA',\n", " 'Transportes Aereos Orientales',\n", " 'Vladivostokavia',\n", " 'MALERT',\n", " 'Air Charter',\n", " 'Air Malawi',\n", " 'Pan African Air Charter',\n", " 'Kenya Airways',\n", " 'Military - Sudan Air Force',\n", " 'Astrd Wing Aviaiton - Air Taxi',\n", " 'Trans-Luxury Airlines',\n", " 'Galaxy Airlines',\n", " 'Texas International Airlines',\n", " 'Aviaimpex (Macedonia)',\n", " 'Henry Webber Air - Air Taxi',\n", " 'Globe Air',\n", " 'STAAP',\n", " 'Independent Air Inc.',\n", " 'ATESA',\n", " 'Military - Royal Canadian Air Force',\n", " 'Islands of the Bahamas Inc',\n", " 'Kamchatavia',\n", " 'Pinnacle Airlines/Northwest Airlink',\n", " 'Aérotaxi Cachanilla',\n", " 'CC Air',\n", " 'Santa Barbara Airlines',\n", " 'Filipinas Orient Airways',\n", " 'American Eagle',\n", " 'VOTEC / VOTEC',\n", " 'Aerovanguardia',\n", " 'Rutas Internacionales Peruanes',\n", " 'Uralex',\n", " 'Union Aéromaritime de Transport',\n", " 'Priorty Air Charter',\n", " 'Sudan Airways',\n", " 'New Zealand National Airways',\n", " 'JAT Yugoslav Airlines',\n", " 'Military - Argentine Navy',\n", " 'Turkish Airlines (THY)',\n", " 'Safe Air Complany',\n", " 'Fly 540',\n", " 'Southwest Airways',\n", " 'MerriAviation - Air Taxi',\n", " 'Malu Aviation',\n", " 'Aero Clube de Volta Redonda',\n", " 'Ala Littoria',\n", " 'Tomahawk Airways',\n", " 'Military - Russian Army',\n", " 'Transportes Aereos Peruanas',\n", " 'Vnukovo Airlines',\n", " 'Skyline Transportation Company',\n", " 'USAir / Skywest Airlilnes',\n", " 'Airlines of Australia',\n", " 'China Airlines (Taiwan)',\n", " 'Volga-Avia Express',\n", " 'Derby Aviation',\n", " 'Indian Airlines/Alliance Airlines',\n", " 'Loma Linda University - Air Taxi',\n", " 'Sociedad Aeronautica Medellin',\n", " 'Airco Charters - Air Taxi',\n", " 'STASA',\n", " 'Military - U.S. Air Force / Military - U.S. Air Force',\n", " \"Harrington's Inc\",\n", " 'Sosoliso Airlines',\n", " 'Petroleum Helicopters Inc',\n", " 'PacifiAirways',\n", " 'Corporate Air',\n", " 'Military - Centrafricain Airlines',\n", " 'Hankins Airways - Air Taxi',\n", " 'Aeroextra',\n", " 'ClassiWings',\n", " 'Private - Daewoo Shipbuilding',\n", " 'Village Airways',\n", " 'BOAC',\n", " 'Flying Tiger Line',\n", " 'Faucett',\n", " 'Staer Air',\n", " 'Luthi Aviation - Air Taxi',\n", " 'Aerovias Cuba International',\n", " 'Stikine Air Service - Air Taxi',\n", " 'Aerovias de Guatemala SA',\n", " 'Air Ohio - Air Taxi',\n", " 'State Airlines',\n", " 'Union Flights',\n", " 'Somali Airlines',\n", " 'T.A. Intercontinentaux',\n", " 'Transport Aerien Intercontinentaux (France)',\n", " 'Rambar Aviation - Air Taxi',\n", " 'Presidental Airways Inc. - Air Taxi',\n", " 'Eagle Air',\n", " 'Aerolineas La Paz',\n", " 'Theif River Aviation - Air Taxi',\n", " 'Kodiak Western Alaska Airlines',\n", " 'Military - Uruguayan Air Force',\n", " 'Copterline',\n", " 'Tadair',\n", " 'Fiji Air Services',\n", " 'Rico Taxi Aéreo',\n", " 'Bar Harbor Airlines',\n", " 'Associated Aviators',\n", " 'NLM (Nederlandse Luchtvaart Maatschappij)',\n", " 'Royal American - Air Taxi',\n", " 'Merpati Nasantara Airlines',\n", " 'Fine Air',\n", " 'Planemasters',\n", " 'Gulf Aviation (Kalinga Airways)',\n", " 'Krasnoyarskie Avialinii',\n", " 'King Air Charter',\n", " 'Jordan International Airlines',\n", " 'Handley Page Transport',\n", " 'Green River Aviation - Air Taxi',\n", " 'Far Eastern Air Transport',\n", " 'SAVIARE S.A. - Air Taxi',\n", " 'Air Caraibes',\n", " 'Viaçao Cometa',\n", " 'Sibir (S7)',\n", " 'Metro Air Systems',\n", " 'Venezuelian Government',\n", " 'Transports Aériens de la Guinee-Bissau',\n", " 'Scottish Airlines',\n", " 'Rapid Air Transport',\n", " 'CAAC Air TraffiManagement Bureau',\n", " 'Avio Linee Italiane',\n", " 'TAA',\n", " 'Air Moorea',\n", " 'Military - U. S. Navy',\n", " 'Linair Express',\n", " 'Fuerza Aérea Venezolana',\n", " 'Chartair - Air Taxi',\n", " 'Cabo Verde Airlines',\n", " 'Air Angles Inc.',\n", " 'Trans-Canada Air Lines',\n", " 'Hesler Noble - Air Taxi',\n", " 'Commuter Airline - Air Taxi',\n", " 'Coastal Cargo',\n", " 'Hornbill Airways',\n", " 'Khalatyrka',\n", " 'Moyer Aviation - Air Taxi',\n", " 'Aerolineas Condor',\n", " 'Private KNBC Los Angeles',\n", " 'Air America',\n", " 'Tartarstan Airlines',\n", " 'Transasia Airways',\n", " 'McKinley Air Service - Air Taxi',\n", " 'Mango Airlines',\n", " 'Military - Malaysian Air Force',\n", " 'United Airways of New Zealand',\n", " 'Wyman Pilot Service - Air Taxi',\n", " 'China General Aviation Corporation',\n", " 'Shanair Inc. - Air Taxi',\n", " 'Reed Aviation - Air Taxi',\n", " 'Ababeel Aviaition',\n", " 'Lehigh Acres Development Inc.',\n", " 'Trans Service Airlift',\n", " 'Star Aviation',\n", " 'Military - Indian Air Force',\n", " 'China Southern Airlines',\n", " 'Quebecair',\n", " 'North Cay Air - Air Taxi',\n", " 'Eurasia',\n", " 'North Sea Aerial and General Transport',\n", " 'PacifiOverseas Airways',\n", " 'Village Aviation - Air Taxi',\n", " 'Paradise Air',\n", " 'East Coast Jets',\n", " 'Tamair',\n", " 'Nationair (chartered by Nigeria Airways)',\n", " 'CATA Linea Aerea',\n", " 'AtlantiSoutheast Airlines',\n", " 'RepubliAirlines',\n", " 'Malev Hungarian Airlines',\n", " 'PacifiAlaska Air Freight',\n", " 'IMSS',\n", " 'Aero National - Air Taxi',\n", " 'Tajikistan Airlines',\n", " 'Air Taxi - Monument Valley Air Service',\n", " 'Aerolineas Centrales de Colombia',\n", " 'Zakavia',\n", " 'Wiggins Airways',\n", " 'Luxair',\n", " 'Air Taxi -Wilderness Aviation Inc.',\n", " 'Lineas Aéreas Cave',\n", " 'Trans International Airlines',\n", " 'FedEx',\n", " 'Trans National Airlines',\n", " 'Central Air Services',\n", " 'Azerbaijan Airlines',\n", " 'Japan Aviation Corporation',\n", " 'Military - Guatemalan Air Force',\n", " 'Astro Air Taxi',\n", " 'National Airlines',\n", " 'Ambler Air Service',\n", " 'Hughes Airwest / Military - US Marine Air Corps',\n", " 'Magistralnye Avialinii',\n", " 'Air Methods / ClassiHelicopters',\n", " 'Permaviatrans',\n", " 'Aerovias Brasil',\n", " 'Terry Air Inc.',\n", " 'Rwanda Government',\n", " 'Military - Zambia Air Force',\n", " 'American Export Airlines',\n", " 'Wein Alaska Airlines Inc.',\n", " 'Dodita Air Cargo',\n", " 'Jack Harter Helicopters',\n", " 'Air Taxi - Ozark Skyways Inc.',\n", " 'Aloha Island Air',\n", " 'Air Orient',\n", " 'Aitalia',\n", " 'Metro Aviation',\n", " 'BKS Air Transport',\n", " 'Air Taxi - Air Vegas Inc.',\n", " 'Soundsair',\n", " 'Military - Dostum-Galboddin Militia',\n", " 'Military - U.S. Navy',\n", " 'Duncan Aircraft Sales',\n", " 'AVENSA',\n", " 'Military - Royal HelleniAir Force',\n", " 'Albancion Circulo',\n", " 'Aerotuy airline',\n", " 'Deutche Lufthansa',\n", " 'Compagnie Sila',\n", " 'Krasnoyarskavia',\n", " 'YPF',\n", " 'Vincent Aviation',\n", " 'Shelter Cove Sea Park',\n", " 'Alaska Helicopter - Air Taxi',\n", " 'GP Express Airlines',\n", " 'Air Ferry',\n", " 'Air Haiti International',\n", " 'Livingston Helicopter - Air Taxi',\n", " 'Centurian Air Cargo',\n", " 'British International Helicopters',\n", " 'All Nippon Airways / Japanese Air Force',\n", " 'American Airlines / Private',\n", " 'Flugfelag Austurlands',\n", " 'Sun West Airline - Air Taxi',\n", " 'Midwest Express',\n", " 'Mexican Government',\n", " 'Inex Adria Aviopromet',\n", " 'Universal Jet Aviation',\n", " 'Orion - Air Taxi',\n", " 'Military - Unified Yemen Air Force',\n", " 'Tampa Air Center - Air Taxi',\n", " 'Superior Airways',\n", " 'Britannia Airways',\n", " 'Pioneer Airlines',\n", " 'Bangladesh Biman',\n", " 'Riddle Airlines',\n", " 'Eastern Air Lines',\n", " 'Lineas Aéreas Sud Americana',\n", " 'Northern Thunderbird Air',\n", " 'Flight Safety Australia',\n", " 'Military - Israel Air Force /Military - Israel Air Force',\n", " 'Karibu Airways',\n", " 'Catalina Airlines',\n", " 'Military - Belgian Air Force',\n", " 'Airwork',\n", " 'Servicios Aereos Nacionales',\n", " 'Winchester Air',\n", " 'Far East Aviation',\n", " 'Urcupina',\n", " 'LADE',\n", " 'Lineas Areas Venezolanas',\n", " 'Military - Macedonian Air Force',\n", " 'United Air Lines / Trans World Airlines',\n", " 'USAir',\n", " 'Military - U.S. Air Force / Military - U.S. Navy',\n", " 'Greenbriar Airways',\n", " 'Aviogenex (Yugoslavia)',\n", " 'Leeward Islands Air Transport',\n", " 'California Air Freight',\n", " 'Frigorifico Cooperativo Los Andes',\n", " 'Nahanni Air Services',\n", " 'Blue Water Aviation Services',\n", " 'Salmon Air - Air Taxi',\n", " 'Ceskoslovenské Aerolinie',\n", " 'Kish Airlines',\n", " 'Compania Dominicana de Aviacion',\n", " 'STAP',\n", " 'Union of Burma Airways',\n", " 'Rousseau Aviaiton',\n", " 'Nordair',\n", " 'Bankair',\n", " 'Military - U.S. Marine Corps',\n", " 'Nuna Air',\n", " 'Air Chaparral - Air Taxi',\n", " 'Ozark Air Lines',\n", " 'Aer Lingus',\n", " 'Military - Portuguese Air Force',\n", " 'Tar Heel Aviation',\n", " 'Military - French Navy',\n", " 'Aerotaxi Manaus',\n", " 'Air Taxi - Island Air Service',\n", " 'Catalina - Vega - Air Taxi',\n", " 'Downeast Airlines',\n", " 'Wayumi Air Taxi',\n", " 'West Aviation',\n", " 'Devlet Hava Yollari',\n", " 'Crossair',\n", " 'Beatty Flying Service - Taxi',\n", " 'Darbhanga Aviation',\n", " 'Air Taxi - Bigfoot Air of Alaska, LLC',\n", " 'Reeve Aleutian Airlines',\n", " 'Myanmar Airways',\n", " 'Pulkovo Airlines',\n", " 'Air Taxi',\n", " 'Kalinga Airlines',\n", " 'Volga Air',\n", " 'Fuerza Area Angolaise',\n", " 'Loide Aereo Nacional',\n", " 'Societe Indochinoise de Raviteillement',\n", " 'Bay Land Aviation',\n", " 'Skywest Airlines / Private.',\n", " 'Ghana Airways',\n", " 'Trans World Airlines / Castleton Inc.',\n", " 'Military - Slovak Air Force',\n", " 'Aero France',\n", " 'Heli-USA Airways',\n", " 'Purdue Airlines Inc.',\n", " 'Military - Georgian Air Force',\n", " 'China Eastern Airlines',\n", " 'Airline Transport',\n", " 'LOT Polish Airlines',\n", " 'Cheremshanka Airlines',\n", " 'Korean Air',\n", " 'Australian Aerial Services',\n", " 'Fun Air',\n", " 'Hewa Bora Airways',\n", " 'Middle States Airlines',\n", " 'Executive Airlines',\n", " 'Transbrasil',\n", " 'Lebanese International Airways',\n", " 'Southern Cross Airways',\n", " 'Kivu Air Services',\n", " \"Military - Armée de l'Air Malgache\",\n", " 'New England Aviaiton',\n", " 'Ford Air Freight Inc.',\n", " 'Avesca Colombia',\n", " 'Military - U.S. Army / Military - U.S. Army',\n", " 'Air Georgian',\n", " 'Guinea Airways Limited',\n", " 'Scanex Air',\n", " 'Aeronorte',\n", " 'Jetcraft',\n", " \"Amee de l'Air\",\n", " 'Wright Air Service - Air Taxi',\n", " 'Ace Flying Service',\n", " 'Frontier Flying Service',\n", " 'Glow Air/Air Castle - Charter',\n", " 'Skyways of London',\n", " 'Summit Air Charters',\n", " 'Nile Delta Air Services',\n", " 'Azov Avia Airlines',\n", " 'Taxi Aéreo Cesar Aguiar',\n", " 'Iran Air Tours / Military - Iranian Air Force',\n", " 'Air Littoral',\n", " 'Asa Pesada',\n", " 'West Caribbean Airways',\n", " 'Vnokovo Airlines',\n", " 'Military - U.S. Air Force / U.S. Air Force',\n", " 'Borneo Airways',\n", " 'Naysa Aerotaxis',\n", " 'Survair',\n", " 'Taos Airways - Air Taxi',\n", " 'Utility Helocopter - Air Taxi',\n", " 'Servicios Aeronauticos Sucre (SASCA)',\n", " 'Orient Airways',\n", " 'Lineas Aéreas Trans. Brasileira',\n", " 'Bristol Aeroplane Co.',\n", " 'Scibe Airlift Cargo Zaire',\n", " 'Agco Corp',\n", " 'Sundance Helicopters',\n", " 'Channel Flying Service - Air Taxi',\n", " 'Sabang Merauke Raya Air Charter',\n", " 'Military - U.S. Navy / Military - U.S. Navy',\n", " 'Linea Expresa Bolivar',\n", " 'Amazonese Importacao e Exportacao',\n", " 'Air US / Private',\n", " 'Expresso Aéreo',\n", " 'MarkAir Commuter',\n", " 'General Aviation Inc.',\n", " 'Islands Nationair',\n", " 'Flugefelag',\n", " 'Turkish Airlines',\n", " 'Military - Iranian Air Force',\n", " 'L.J. Simmons - Air Taxi',\n", " 'Air Central',\n", " 'Great Western and Southern Air Lines',\n", " 'SL Aviation Services',\n", " 'PenAir',\n", " 'Romanian Banat Air',\n", " 'Skyline Airways',\n", " 'Avioriprese Jet Executive',\n", " 'Air Albatross',\n", " 'Fairways',\n", " 'TAUSA',\n", " 'Africa One Congo',\n", " 'China Flying Dragon Aviation (Feilong Airlines)',\n", " 'North PacifiAirlines',\n", " 'Laoag International Airlines',\n", " 'Euro Asia Aviation',\n", " 'Bakhtar Afghan Airlines',\n", " 'Military - Spanish Air Force',\n", " 'Civil Air Transport',\n", " 'MAP - Ministerstvo Aviatsionnoi Promyshlennosti',\n", " 'Military - Colombian government',\n", " 'Franco-Roumaine',\n", " 'Conquest Airways - Air Taxi',\n", " 'Corporate Mobility Inc. - Private',\n", " 'Cameroon Airlines',\n", " 'Loftleidir IcelandiAirlines',\n", " 'Embry Riddle Company',\n", " 'Lineas Aereas Suramericanas',\n", " 'Air 70',\n", " 'Compania de Aviacion Faucett SA (Peru)',\n", " 'Dutch Continental Airways',\n", " 'Grand Canyon Airlines',\n", " 'Naturelink',\n", " 'Transporturile Aeriene Romano-Sovietice',\n", " 'Trans Canada Air Lines',\n", " 'TAC Colombia',\n", " 'Aktiebolaget Aerotransport',\n", " 'Trans Luxury Airlines',\n", " 'Transpolar',\n", " 'Top Air - Air Taxi',\n", " 'Blue Bird Air Service',\n", " 'American Virginia',\n", " 'Trans Continental and Western Air',\n", " 'Zantop Airways',\n", " 'REAL / Military - U.S. Navy',\n", " 'Air Inter',\n", " 'Military - Deutsche Luftwaffe',\n", " \"Military - People's Liberation Army\",\n", " 'Orcon Inc. - Air Taxi',\n", " 'Air Luxor',\n", " 'Air Senegal / Gambia Airways',\n", " 'Aeroflot / Aeroflot',\n", " 'Aeroflite Services',\n", " 'Military - Fuerza Del Peru',\n", " 'Qantas',\n", " 'Mid PacifiAir',\n", " 'Transaviaexport Airlines',\n", " 'Cascade Airways',\n", " 'Avia Air Aruba',\n", " 'Military - Royal Belgian Air Force',\n", " 'Aerovias Rojas',\n", " 'Sinquanon Management - Air Taxi',\n", " 'SELVA Colombia',\n", " 'Air Taxi - Air Grand Canyon Inc.',\n", " 'Regional Compagnie Aerienne Europeenne',\n", " 'Las Vegas Airlines',\n", " 'Rijnmond Air Services',\n", " 'MaRoberston-Miller Airlines',\n", " 'Military - Israel Air Force',\n", " 'Military - U.S. Army Air Corps',\n", " 'TAME Ecuador',\n", " 'Viking Air Transport',\n", " 'Austria-Flugdienst',\n", " 'Dirgantara Air Services',\n", " 'General Air',\n", " 'Air Logistics',\n", " 'Warsaw Aviation - Taxi',\n", " 'Alaska Travel',\n", " 'Atran',\n", " 'Military - Royal Jordanian Air Force',\n", " 'Mid-AtlantiFreight',\n", " 'Trans Caribbean Airways',\n", " 'Fuerza Aérea Panamena',\n", " 'Vehu Akat',\n", " 'Aéreo Ruta Maya',\n", " 'Antilles Air',\n", " 'Vostok Aviakompania',\n", " 'Evergreen Alaska Helicopters, Inc.',\n", " 'Aerolift Philippines',\n", " 'Fujita Koku Kabushki Kai',\n", " 'Air Taxi - Martin Aviation LP',\n", " 'Century PacifiLines',\n", " 'Fuerza Aérea Nicaragua',\n", " 'Merpati Nusantara Airlines',\n", " 'MAP',\n", " 'Air Taxi - Tyee Airlines Inc.',\n", " 'Aero PaLease - Air Taxi',\n", " 'Blue Hawaiian Helicopters',\n", " 'Transportes Aereos Nacionales',\n", " 'Military - Egyptian Air Force',\n", " 'Military - Angolan Air Force',\n", " 'Hang Khong (Vietnam)',\n", " 'DNL',\n", " 'Casino Air - Air Taxi',\n", " 'BHS - Brazilian Helicopter Service Air Taxi',\n", " 'Layman Weikle - Taxi',\n", " 'Syndicato Condor',\n", " 'DTA Angola Airlines',\n", " 'Procurator Generale de la Republica',\n", " 'Linea Aérea Mexicana de Carga',\n", " 'Military - U.S. Air Force / Military U.S. Air Force',\n", " 'Military - Royal Lesotho Defence Force',\n", " 'Wien Alaska Airlines',\n", " 'Lockheed AC-130H Hercules',\n", " 'Alia Royal Jordanian Airlines',\n", " 'East African Airways',\n", " 'Inter-Island',\n", " 'Iran National Airlines',\n", " 'Aerovias Condor',\n", " 'Private - Purdue Reasearch Foundation',\n", " 'Air Taxi - Minuteman Aviation Inc.',\n", " 'Aeroservice S de RL',\n", " 'Societe Alpes Provence',\n", " 'VASP',\n", " 'Perris Valley Aviation',\n", " 'United Air Lines / Military - U.S. Air Force',\n", " 'Military - Peruvian Air Force',\n", " 'Price Aircraft Company',\n", " 'Spencer Airways / Ceskoslovenske Aerolinie',\n", " 'Military - Colombian Air Force',\n", " 'Condor Flugdienst',\n", " 'Air Guinee',\n", " 'Baikal Air',\n", " 'Trans World Airlines',\n", " 'Alas del Sur',\n", " 'Private Charter',\n", " 'Fairflight Ltd.',\n", " 'Military - U.S. Army Air Forces / Military - U.S. Army Air Forces',\n", " 'Airborne Express',\n", " 'Compagnie Air Transport',\n", " 'Military - Greek Army',\n", " 'Military - HelleniAir Force',\n", " 'ELK Aviation Co. (leased from Eminex)',\n", " 'Eitos',\n", " 'Eagle Cap Leasing',\n", " 'Burma Airways',\n", " 'Trafik-Turist-Transportflyg',\n", " 'Helicopteros Nacionales de Colombia',\n", " 'Austral Lineas Aeras (Argentina)',\n", " 'AECA Cargo',\n", " 'Wrangell Air Service',\n", " 'Air Taxi - Islip Airco Inc.',\n", " 'North Canada Air',\n", " 'American Airlines / Military - USAF',\n", " 'Syrian Airways',\n", " 'Military - Royal Lao Air Force',\n", " 'Island Air',\n", " 'Eurojet Italila',\n", " 'Air Ocean',\n", " 'Chester Airport',\n", " 'Linea Aérea Nacional',\n", " 'Military - Japan Air Self Defense Force',\n", " 'Perimeter Airlines',\n", " 'Coval Air',\n", " 'Emery Worldwide',\n", " 'Air Taxi - Northern Arizona Aircraft Inc.',\n", " 'Ukraine Aviation Transport Company',\n", " 'Eagle Commuter',\n", " 'Hudson Air Service - Air Taxi',\n", " 'Private - Omniflight Helicopters',\n", " 'Pulkovo Aviation Enterprise',\n", " 'Military U.S. Navy / Military - U.S. Navy',\n", " 'Lineas Aéreas Mineras',\n", " 'Conesul Taxi Aéreo',\n", " 'Aero Eslava',\n", " 'Ladeco',\n", " 'Air Outre-Mer',\n", " 'Air taxi',\n", " 'Military - Federal Nigerian Air Force',\n", " 'Heliandes',\n", " 'Air Martinique',\n", " 'Paramount Airlilnes',\n", " 'Military - Brazilian Air Force',\n", " 'Air Rhodesia',\n", " 'Ryan Blake Air Charter',\n", " 'Dan-Air Services',\n", " 'Avirex',\n", " 'Airbus Industrie',\n", " 'Executive Jet Sales Inc.',\n", " 'Frigorifico Maniqui',\n", " 'Dwyer Flying Service - Private Charter',\n", " 'Sunflower Airlines',\n", " 'Aerolatino (Aerocaribe)',\n", " 'City-Jet',\n", " 'Air Rouergue',\n", " 'Guernsey Airways',\n", " 'Halvorson - Air Taxi',\n", " 'Piedmont Airlines',\n", " 'Southeast Skyways - Air Taxi',\n", " 'Air Taxi - Las Vegas Flyers Inc.',\n", " 'Aero Air',\n", " 'TAM Paraguay',\n", " 'Lion Air',\n", " 'Paukn Air',\n", " 'Líder Táxi Aéreo',\n", " 'Guardia Nacional de Venezuela',\n", " 'Channel Air Lift',\n", " 'Military - Royal Thai Air Force',\n", " 'Provincetown - Boston Airlines',\n", " 'Yacimientos Petroliferos Fiscales',\n", " 'Security Air',\n", " 'Air West Airlines Ltd.',\n", " 'Trans Canada Air Lines / RCAF',\n", " 'Military - U.S. Army / Military U.S. Army',\n", " 'Mt. McKinley Airfreight',\n", " 'Continental Express',\n", " 'Blackhawk Int. Airways - Private charter',\n", " 'Sabah Air',\n", " 'Private - Facilities Management Co.',\n", " 'Bahri Aviation',\n", " 'Galaxy Kavatsi Airlines',\n", " 'Military - Russian Air Force',\n", " 'BAFIN',\n", " 'Guyana Airways',\n", " 'Lignes Aeriennes Latecoere',\n", " 'Lloyd Aero Boliviano',\n", " 'Red Bank Air Taxi',\n", " 'Sarit Airlines',\n", " 'Jamair',\n", " 'Aeropeca',\n", " 'Oefag Flugdienst',\n", " 'Oeste Linhas Aereas',\n", " 'Stavropol Airlines',\n", " 'Alpena Flying Service - Air Taxi',\n", " 'China Northern Airlines',\n", " 'Paraense Transportes Aereos',\n", " 'XL Airways leased from Air New Zealand',\n", " 'Dan Air Services',\n", " 'Bharat Airways',\n", " 'Uzu Air',\n", " 'Havasu City Air - Air Taxi',\n", " 'TABA',\n", " 'Air Taxi - Island Airlines',\n", " 'Air Taxi - Ohana Helicopter Tours',\n", " 'Aviation Charter - Air Taxi',\n", " 'Air India',\n", " 'Proteus Air / Private',\n", " 'NY, Phil., Washington AW',\n", " 'ERA Aviation - Air Taxi',\n", " 'TACA International Airlines',\n", " 'PacifiWestern Airlines',\n", " 'SADELCA',\n", " 'Ozark Air Lines / Private',\n", " 'Hyannis Air Service - Air Taxi',\n", " 'Air São Tomé',\n", " 'Ocean Airlines',\n", " 'Military - Royal Saudi Air Force',\n", " 'Aerolineas La Gaviola',\n", " 'Necon Air',\n", " 'Aero Pantanal - Air Taxi',\n", " 'Enimex',\n", " 'Spanish Air Force',\n", " 'Soldotna Air Services',\n", " 'Saint Lawrence Airways',\n", " 'Air Taxi - TAJ FBO Co.',\n", " 'Aeroleasing - Air Taxi',\n", " 'EDELCA',\n", " 'American Airlines',\n", " 'Colonial Air Transport',\n", " 'Don Zimmerman - Air Taxi',\n", " 'Trigana Air Service',\n", " 'Sowind Air',\n", " 'Aerosweet Airlines (LVOV)',\n", " 'Travel Air Flug',\n", " 'Air Canada',\n", " 'Ameriflight',\n", " 'New York Helicopter',\n", " ...}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set(crashes[\"Operator\"].tolist())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "92e44166-1011-95f3-9a13-c4dde88dc43d" }, "outputs": [], "source": [ "import re\n", "def military_private(string):\n", " string=str(string)\n", " if re.search(\"[Mm]ilitary\",string)!=None:\n", " return \"military\"\n", " if re.search(\"[Pp]rivate\",string)!=None:\n", " return \"private\"\n", " return \"airline\"\n", "\n", "crashes[\"category\"]=crashes[\"Operator\"].apply(lambda x: military_private(x))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "5ed95afb-8c51-7103-2a6c-8502d245798a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Aboard Fatalities Ground Year Month\n", "count 5246.000000 5256.000000 5246.000000 5268.000000 5268.000000\n", "mean 27.554518 20.068303 1.608845 1971.300304 6.643888\n", "std 43.076711 33.199952 53.987827 22.387541 3.546162\n", "min 0.000000 0.000000 0.000000 1908.000000 1.000000\n", "25% NaN NaN NaN 1954.000000 3.000000\n", "50% NaN NaN NaN 1973.000000 7.000000\n", "75% NaN NaN NaN 1990.000000 10.000000\n", "max 644.000000 583.000000 2750.000000 2009.000000 12.000000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n", " RuntimeWarning)\n" ] } ], "source": [ "print(crashes.describe())" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "8fb752c5-bc7e-b8a2-0700-4f8a3cbac957" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 328, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/326/326551.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "ee64eb9c-96df-4a1c-ce4f-336579929471" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "\n", "train_types = {'Agencia_ID':np.uint16, 'Ruta_SAK':np.uint16, 'Cliente_ID':np.uint32, \n", " 'Producto_ID':np.uint16, 'Demanda_uni_equil':np.uint32}\n", "\n", "test_types = {'Agencia_ID':np.uint16, 'Ruta_SAK':np.uint16, 'Cliente_ID':np.uint32, \n", " 'Producto_ID':np.uint16, 'id':np.uint32}\n", "\n", "df_train = pd.read_csv('../input/train.csv', usecols=train_types.keys(), dtype=train_types)\n", "df_test = pd.read_csv('../input/test.csv',usecols=test_types.keys(), dtype=test_types)\n", "df_client = pd.read_csv('../input/cliente_tabla.csv')\n", "df_product = pd.read_csv('../input/producto_tabla.csv')\n", "df_town = pd.read_csv('../input/town_state.csv')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "ee39a4bc-feee-7ab0-65ae-4a838d1a102b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "Train Data\n Agencia_ID Ruta_SAK Cliente_ID Producto_ID Demanda_uni_equil\n0 1110 3301 15766 1212 3 \n\nTest Data\n id Agencia_ID Ruta_SAK Cliente_ID Producto_ID\n0 0 4037 2209 4639078 35305 \n\nClient Data\n Agencia_ID Town State\n0 1110 2008 AG. LAGO FILT M\u00c9XICO, D.F. \n\n" }, { "ename": "NameError", "evalue": "name 'df_product' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-2-6bac2bb50ed0> in <module>()\n 2 print('Test Data\\n', df_test.head(1), '\\n')\n 3 print('Client Data\\n', df_client.head(1), '\\n')\n----> 4 print('Product Data\\n', df_product.head(1), '\\n')\n 5 print('Town Data\\n', df_town.head(1), '\\n')\n", "NameError: name 'df_product' is not defined" ] }, { "name": "stdout", "output_type": "stream", "text": "Train Data\n Agencia_ID Ruta_SAK Cliente_ID Producto_ID Demanda_uni_equil\n0 1110 3301 15766 1212 3 \n\nTest Data\n id Agencia_ID Ruta_SAK Cliente_ID Producto_ID\n0 0 4037 2209 4639078 35305 \n\nClient Data\n Cliente_ID NombreCliente\n0 0 SIN NOMBRE \n\nProduct Data\n Producto_ID NombreProducto\n0 0 NO IDENTIFICADO 0 \n\nTown Data\n Agencia_ID Town State\n0 1110 2008 AG. LAGO FILT M\u00c9XICO, D.F. \n\n" } ], "source": [ "print('Train Data\\n', df_train.head(1), '\\n')\n", "print('Test Data\\n', df_test.head(1), '\\n')\n", "print('Client Data\\n', df_client.head(1), '\\n')\n", "print('Product Data\\n', df_product.head(1), '\\n')\n", "print('Town Data\\n', df_town.head(1), '\\n')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "f372bc8a-f9a7-4774-03d7-46a92cefbebf" }, "outputs": [ { "data": { "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b696ff048>" }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LF+uee+6JapEAACSKZLsJwWBQW7Zs0a5du5Sdna3i4mLl5+crJycnNMfn82nz5s169tln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\nYII=\n", "text/plain": "<matplotlib.figure.Figure at 0x7f9b6b46c0b8>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(np.log1p(df_train['Demanda_uni_equil']), kde=False)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "2b8b24b0-7ccb-c044-0d88-7f3771129dfd" }, "outputs": [], "source": [ "agencies_subset = np.zeros(len(df_train))\n", "for i in range(4):\n", " this_agency = df_train['Agencia_ID'].unique()[i]\n", " agencies_subset += df_train['Agencia_ID'] == this_agency" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "6c1c9121-8ac4-7e7f-d6e4-276358621577" }, "outputs": [ { "data": { "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f9b696bd160>" }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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UkJCgrq4uSVIwGFRycrJ9rMfjUTAYVDAYVFJS0lXjktTR0WFvi46OVkxMjLq7uycnIQAA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\npUILOwAAAABJRU5ErkJggg==\n", "text/plain": "<matplotlib.figure.Figure at 0x7f9b6708cfd0>" }, "metadata": {}, "output_type": "display_data" }, { "ename": "TypeError", "evalue": "'Series' objects are mutable, thus they cannot be hashed", "output_type": "error", "traceback": [ "", "TypeErrorTraceback (most recent call last)", "<ipython-input-10-eaaf0e2e1566> in <module>()\n----> 1 agencies_subset = df_train['Agencia_ID'] in df_train['Agencia_ID'][0:4]\n 2 sns.distplot(df_train.loc[agencies_subset, 'Demanda_uni_equil'], kde=False)\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py in __contains__(self, key)\n 844 def __contains__(self, key):\n 845 \"\"\"True if the key is in the info axis\"\"\"\n--> 846 return key in self._info_axis\n 847 \n 848 @property\n", "/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py in __contains__(self, key)\n 1232 \n 1233 def __contains__(self, key):\n-> 1234 hash(key)\n 1235 # work around some kind of odd cython bug\n 1236 try:\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py in __hash__(self)\n 804 def __hash__(self):\n 805 raise TypeError('{0!r} objects are mutable, thus they cannot be'\n--> 806 ' hashed'.format(self.__class__.__name__))\n 807 \n 808 def __iter__(self):\n", "TypeError: 'Series' objects are mutable, thus they cannot be hashed" ] }, { "ename": "TypeError", "evalue": "'Series' objects are mutable, thus they cannot be hashed", "output_type": "error", "traceback": [ "", "TypeErrorTraceback (most recent call last)", "<ipython-input-11-40adf9a52f02> in <module>()\n----> 1 agencies_subset = df_train['Agencia_ID'] in df_train['Agencia_ID'][0:4]\n 2 print(agencies_subset)\n 3 sns.distplot(df_train.loc[agencies_subset, 'Demanda_uni_equil'], kde=False)\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py in __contains__(self, key)\n 844 def __contains__(self, key):\n 845 \"\"\"True if the key is in the info axis\"\"\"\n--> 846 return key in self._info_axis\n 847 \n 848 @property\n", "/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py in __contains__(self, key)\n 1232 \n 1233 def __contains__(self, key):\n-> 1234 hash(key)\n 1235 # work around some kind of odd cython bug\n 1236 try:\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py in __hash__(self)\n 804 def __hash__(self):\n 805 raise TypeError('{0!r} objects are mutable, thus they cannot be'\n--> 806 ' hashed'.format(self.__class__.__name__))\n 807 \n 808 def __iter__(self):\n", "TypeError: 'Series' objects are mutable, thus they cannot be hashed" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "", "KeyboardInterruptTraceback (most recent call last)", "<ipython-input-12-97b0a29e164b> in <module>()\n----> 1 agencies_subset = [i in df_train['Agencia_ID'][0:4] for i in df_train['Agencia_ID'].values]\n 2 print(agencies_subset)\n 3 sns.distplot(df_train.loc[agencies_subset, 'Demanda_uni_equil'], kde=False)\n", "<ipython-input-12-97b0a29e164b> in <listcomp>(.0)\n----> 1 agencies_subset = [i in df_train['Agencia_ID'][0:4] for i in df_train['Agencia_ID'].values]\n 2 print(agencies_subset)\n 3 sns.distplot(df_train.loc[agencies_subset, 'Demanda_uni_equil'], kde=False)\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in __getitem__(self, key)\n 622 key = check_bool_indexer(self.index, key)\n 623 \n--> 624 return self._get_with(key)\n 625 \n 626 def _get_with(self, key):\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in _get_with(self, key)\n 628 if isinstance(key, slice):\n 629 indexer = self.index._convert_slice_indexer(key, kind='getitem')\n--> 630 return self._get_values(indexer)\n 631 elif isinstance(key, ABCDataFrame):\n 632 raise TypeError('Indexing a Series with DataFrame is not '\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in _get_values(self, indexer)\n 690 try:\n 691 return self._constructor(self._data.get_slice(indexer),\n--> 692 fastpath=True).__finalize__(self)\n 693 except Exception:\n 694 return self._values[indexer]\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py in __finalize__(self, other, method, **kwargs)\n 2653 if isinstance(other, NDFrame):\n 2654 for name in self._metadata:\n-> 2655 object.__setattr__(self, name, getattr(other, name, None))\n 2656 return self\n 2657 \n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in name(self, value)\n 308 if value is not None and not com.is_hashable(value):\n 309 raise TypeError('Series.name must be a hashable type')\n--> 310 object.__setattr__(self, '_name', value)\n 311 \n 312 # ndarray compatibility\n", "KeyboardInterrupt: " ] } ], "source": [ "print(agencies_subset)\n", "#sns.distplot(df_train.loc[agencies_subset, 'Demanda_uni_equil'], kde=False)" ] } ], "metadata": { "_change_revision": 190, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/326/326660.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "f013415a-da8e-39eb-c86e-26e3babd9298" }, "source": "" }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "bef7ed5c-054e-11cf-d37e-8a8e4cfda1e0" }, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-1-cb7b10af47d9>, line 8)", "output_type": "error", "traceback": [ " File \"<ipython-input-1-cb7b10af47d9>\", line 8\n from sklearn.linear_model import\n ^\nSyntaxError: invalid syntax\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from collections import Counter\n", "import string\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.linear_model import LinearRegression\n", "\n", "\n", "pd.options.mode.chained_assignment = None\n", "\n", "\n", "def get_title(name):\n", " name = name.split(',')[1]\n", " name = name.split('.')[0]\n", " return name.strip()\n", "\n", "\n", "def get_title_grouped(name):\n", " title = get_title(name)\n", " if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:\n", " title = 'Rare'\n", " elif title in ['Ms', 'Mlle']:\n", " title = 'Miss'\n", " elif title == 'Mme':\n", " title = 'Mrs'\n", " return title\n", "\n", "\n", "def get_deck(cabin):\n", " if isinstance(cabin, str):\n", " if cabin[0] == 'T':\n", " return np.nan\n", " return cabin[0]\n", " return cabin\n", "\n", "\n", "train = pd.read_csv('../input/train.csv')\n", "test = pd.read_csv('../input/test.csv')\n", "full = pd.concat([train, test])\n", "\n", "# feature engineering described in previous notebooks\n", "full['Embarked'].fillna('C', inplace=True)\n", "full['Fare'].fillna(8.05, inplace=True)\n", "full['Title'] = full['Name'].apply(get_title_grouped)\n", "full['Deck'] = full['Cabin'].apply(get_deck)\n", "full['Family size'] = full['Parch'] + full['SibSp']" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "4fe2b59e-9470-ebbc-4145-5fa5ef64ecc1" }, "source": "" }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "17ce955e-f6af-73c0-e90c-7a687506b65b", "collapsed": true }, "outputs": [ { "ename": "NameError", "evalue": "name 'full' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-2-e928c0a8d14c> in <module>()\n----> 1 ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]\n 2 plt.hist(ticket_nums, 50)\n 3 plt.xlabel('Ticket number')\n 4 plt.ylabel('Count')\n 5 plt.show()\n", "NameError: name 'full' is not defined" ] }, { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7fcdd5a58550>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]\n", "plt.hist(ticket_nums, 50)\n", "plt.xlabel('Ticket number')\n", "plt.ylabel('Count')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1e636a3d-744e-fc22-cf81-dd4862257bf9" }, "source": "" }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "2308e6cb-90b4-80d7-6b65-25d72479372a", "collapsed": true }, "outputs": [ { "ename": "NameError", "evalue": "name 'ticket_nums' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-3-0ebec8f00a93> in <module>()\n----> 1 ticket_nums = [num for num in ticket_nums if num < 2000000]\n 2 plt.hist(ticket_nums, 50)\n 3 plt.xlabel('Ticket number')\n 4 plt.ylabel('Count')\n 5 plt.show()\n", "NameError: name 'ticket_nums' is not defined" ] }, { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7fcdd5a58780>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ticket_nums = [num for num in ticket_nums if num < 2000000]\n", "plt.hist(ticket_nums, 50)\n", "plt.xlabel('Ticket number')\n", "plt.ylabel('Count')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "6dd1ff9d-c27f-ea85-524b-ee7109c1b255" }, "source": "" }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "a10cb9be-68fe-6c9a-ccf4-47011cdcf282", "collapsed": true }, "outputs": [ { "ename": "NameError", "evalue": "name 'full' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-4-c505df4732ea> in <module>()\n 6 return int(ticket_num)\n 7 \n----> 8 full['Ticket number'] = full['Ticket'].apply(get_ticket_num)\n 9 full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)\n 10 \n", "NameError: name 'full' is not defined" ] } ], "source": [ "def get_ticket_num(ticket):\n", " ticket_num = ticket.split()\n", " ticket_num = ''.join(char for char in ticket_num[-1].strip() if char not in string.punctuation)\n", " if not ticket_num.isdigit():\n", " return np.nan\n", " return int(ticket_num)\n", "\n", "full['Ticket number'] = full['Ticket'].apply(get_ticket_num)\n", "full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)\n", "\n", "full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "3670eadc-b9be-4360-a7f0-d2c109b01e2a" }, "source": "" }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "6d92c58f-907a-0395-bcfd-1bde43236cfd" }, "outputs": [ { "ename": "NameError", "evalue": "name 'LabelEncoder' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-5-6b71f2996209> in <module>()\n 2 to_encode = ['Embarked', 'Sex', 'Title']\n 3 for col in to_encode:\n----> 4 encoders[col] = LabelEncoder()\n 5 encoders[col].fit(full[col])\n 6 full[col] = full[col].apply(encoders[col].transform)\n", "NameError: name 'LabelEncoder' is not defined" ] }, { "ename": "ValueError", "evalue": "bad input shape ()", "output_type": "error", "traceback": [ "", "ValueErrorTraceback (most recent call last)", "<ipython-input-17-6b71f2996209> in <module>()\n 4 encoders[col] = LabelEncoder()\n 5 encoders[col].fit(full[col])\n----> 6 full[col] = full[col].apply(encoders[col].transform)\n 7 \n 8 age_train = full[full['Age'].notnull()]\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in apply(self, func, convert_dtype, args, **kwds)\n 2218 else:\n 2219 values = self.asobject\n-> 2220 mapped = lib.map_infer(values, f, convert=convert_dtype)\n 2221 \n 2222 if len(mapped) and isinstance(mapped[0], Series):\n", "pandas/src/inference.pyx in pandas.lib.map_infer (pandas/lib.c:62668)()\n", "/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/label.py in transform(self, y)\n 141 \"\"\"\n 142 check_is_fitted(self, 'classes_')\n--> 143 y = column_or_1d(y, warn=True)\n 144 \n 145 classes = np.unique(y)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/utils/validation.py in column_or_1d(y, warn)\n 559 return np.ravel(y)\n 560 \n--> 561 raise ValueError(\"bad input shape {0}\".format(shape))\n 562 \n 563 \n", "ValueError: bad input shape ()" ] } ], "source": [ "encoders = {}\n", "to_encode = ['Embarked', 'Sex', 'Title']\n", "for col in to_encode:\n", " encoders[col] = LabelEncoder()\n", " encoders[col].fit(full[col])\n", " full[col] = full[col].apply(encoders[col].transform)\n", "\n", "age_train = full[full['Age'].notnull()]\n", "age_predict = full[~full['Age'].notnull()]\n", "lr = LinearRegression()\n", "lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])\n", "predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))\n", "age_predict['Age'] = [max(0., age) for age in predicted_ages]\n", "\n", "full = pd.concat([age_train, age_predict]).sort_values('PassengerId')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d0eb017d-7cd3-f8d6-843a-a6173e174e5b" }, "source": "" }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "27a1262b-1d16-7281-3492-f5ab7c2bbfc1" }, "outputs": [ { "ename": "NameError", "evalue": "name 'age_train' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-6-5179a79709e2> in <module>()\n----> 1 ages = age_train.Age\n 2 ages.plot.kde(label='Original')\n 3 ages = full.Age\n 4 ages.plot.kde(label='With predicted missing values')\n 5 plt.xlabel('Age')\n", "NameError: name 'age_train' is not defined" ] }, { "ename": "NameError", "evalue": "name 'age_train' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-18-5179a79709e2> in <module>()\n----> 1 ages = age_train.Age\n 2 ages.plot.kde(label='Original')\n 3 ages = full.Age\n 4 ages.plot.kde(label='With predicted missing values')\n 5 plt.xlabel('Age')\n", "NameError: name 'age_train' is not defined" ] } ], "source": [ "ages = age_train.Age\n", "ages.plot.kde(label='Original')\n", "ages = full.Age\n", "ages.plot.kde(label='With predicted missing values')\n", "plt.xlabel('Age')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "f90216cf-bf81-37ed-6cfa-51652e09ea09" }, "source": "" }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "e64668ae-2e49-a1a1-8e05-b2274d203310" }, "outputs": [ { "data": { "text/plain": "Counter({nan: 1015,\n 'A': 22,\n 'D': 46,\n 'E': 41,\n 'G': 5,\n 'C': 94,\n 'B': 65,\n 'F': 21})" }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Counter(full['Deck'].values)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "5df2b07f-373f-7e02-dc2a-438b357ac61b" }, "source": "" }, { "cell_type": "code", "execution_count": 20, "metadata": { "_cell_guid": "2dfeec3a-2664-3d89-2564-d35d0898133c" }, "outputs": [ { "ename": "NameError", "evalue": "name 'full' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-8-b3b67487236f> in <module>()\n----> 1 full_with_deck = full[full['Deck'].notnull()]\n 2 full_without_deck = full[~full['Deck'].notnull()]\n 3 \n 4 full_with_deck_means, full_without_deck_means = [], []\n 5 for col in full_with_deck:\n", "NameError: name 'full' is not defined" ] }, { "ename": "TypeError", "evalue": "unsupported operand type(s) for /: 'str' and 'int'", "output_type": "error", "traceback": [ "", "TypeErrorTraceback (most recent call last)", "<ipython-input-20-b3b67487236f> in <module>()\n 5 for col in full_with_deck:\n 6 if col not in ['Deck', 'PassengerId']:\n----> 7 sum_means = np.nanmean(full_with_deck[col].values) + np.nanmean(full_without_deck[col].values)\n 8 full_with_deck_means.append(np.nanmean(full_with_deck[col].values)/sum_means)\n 9 full_without_deck_means.append(np.nanmean(full_without_deck[col].values)/sum_means)\n", "/opt/conda/lib/python3.5/site-packages/numpy/lib/nanfunctions.py in nanmean(a, axis, dtype, out, keepdims)\n 655 arr, mask = _replace_nan(a, 0)\n 656 if mask is None:\n--> 657 return np.mean(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims)\n 658 \n 659 if dtype is not None:\n", "/opt/conda/lib/python3.5/site-packages/numpy/core/fromnumeric.py in mean(a, axis, dtype, out, keepdims)\n 2883 \n 2884 return _methods._mean(a, axis=axis, dtype=dtype,\n-> 2885 out=out, keepdims=keepdims)\n 2886 \n 2887 \n", "/opt/conda/lib/python3.5/site-packages/numpy/core/_methods.py in _mean(a, axis, dtype, out, keepdims)\n 70 ret = ret.dtype.type(ret / rcount)\n 71 else:\n---> 72 ret = ret / rcount\n 73 \n 74 return ret\n", "TypeError: unsupported operand type(s) for /: 'str' and 'int'" ] } ], "source": [ "full_with_deck = full[full['Deck'].notnull()]\n", "full_without_deck = full[~full['Deck'].notnull()]\n", "\n", "full_with_deck_means, full_without_deck_means = [], []\n", "for col in full_with_deck:\n", " if col not in ['Deck', 'PassengerId']:\n", " sum_means = np.nanmean(full_with_deck[col].values) + np.nanmean(full_without_deck[col].values)\n", " full_with_deck_means.append(np.nanmean(full_with_deck[col].values)/sum_means)\n", " full_without_deck_means.append(np.nanmean(full_without_deck[col].values)/sum_means)\n", "\n", "bar_width = 0.35\n", "opacity = 0.4\n", "x_index = np.arange(len(full_with_deck_means))\n", "\n", "plt.bar(x_index, full_with_deck_means, bar_width, alpha=opacity, color='b', label='With deck value')\n", "plt.bar(x_index + bar_width, full_without_deck_means, bar_width, alpha=opacity, color='r', label='Missing deck value')\n", "plt.legend()\n", "plt.ylabel('Ratio of means')\n", "plt.xticks(x_index + bar_width, [col for col in full_with_deck if col not in ['PassengerId', 'Deck']])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "cc66c461-6313-2a65-603b-6133d4877c1b" }, "source": "" }, { "cell_type": "code", "execution_count": 21, "metadata": { "_cell_guid": "ce541531-bdd7-2a35-1ddf-1440fb7b0908" }, "outputs": [ { "ename": "NameError", "evalue": "name 'full' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-9-5bc0f148642a> in <module>()\n----> 1 full['Deck'].fillna('N', inplace=True)\n 2 \n 3 encoders['Deck'] = LabelEncoder()\n 4 encoders['Deck'].fit(full['Deck'])\n 5 full['Deck'] = full['Deck'].apply(encoders['Deck'].transform)\n", "NameError: name 'full' is not defined" ] }, { "ename": "ValueError", "evalue": "bad input shape ()", "output_type": "error", "traceback": [ "", "ValueErrorTraceback (most recent call last)", "<ipython-input-21-5bc0f148642a> in <module>()\n 3 encoders['Deck'] = LabelEncoder()\n 4 encoders['Deck'].fit(full['Deck'])\n----> 5 full['Deck'] = full['Deck'].apply(encoders['Deck'].transform)\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/series.py in apply(self, func, convert_dtype, args, **kwds)\n 2218 else:\n 2219 values = self.asobject\n-> 2220 mapped = lib.map_infer(values, f, convert=convert_dtype)\n 2221 \n 2222 if len(mapped) and isinstance(mapped[0], Series):\n", "pandas/src/inference.pyx in pandas.lib.map_infer (pandas/lib.c:62668)()\n", "/opt/conda/lib/python3.5/site-packages/sklearn/preprocessing/label.py in transform(self, y)\n 141 \"\"\"\n 142 check_is_fitted(self, 'classes_')\n--> 143 y = column_or_1d(y, warn=True)\n 144 \n 145 classes = np.unique(y)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/utils/validation.py in column_or_1d(y, warn)\n 559 return np.ravel(y)\n 560 \n--> 561 raise ValueError(\"bad input shape {0}\".format(shape))\n 562 \n 563 \n", "ValueError: bad input shape ()" ] } ], "source": [ "full['Deck'].fillna('N', inplace=True)\n", "\n", "encoders['Deck'] = LabelEncoder()\n", "encoders['Deck'].fit(full['Deck'])\n", "full['Deck'] = full['Deck'].apply(encoders['Deck'].transform)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "066c7934-0fda-d7e6-22c6-508b78d472d7" }, "source": "" }, { "cell_type": "code", "execution_count": 22, "metadata": { "_cell_guid": "66e9339a-ca57-41c0-5c62-affaaf23e2d8" }, "outputs": [ { "ename": "NameError", "evalue": "name 'full' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-10-102550b3a8d3> in <module>()\n----> 1 train = full[full.PassengerId < 892]\n 2 test = full[full.PassengerId >= 892]\n 3 \n 4 rf = RandomForestClassifier(n_estimators=100, oob_score=True)\n 5 rf.fit(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])\n", "NameError: name 'full' is not defined" ] }, { "ename": "ValueError", "evalue": "could not convert string to float: 'N'", "output_type": "error", "traceback": [ "", "ValueErrorTraceback (most recent call last)", "<ipython-input-22-102550b3a8d3> in <module>()\n 3 \n 4 rf = RandomForestClassifier(n_estimators=100, oob_score=True)\n----> 5 rf.fit(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])\n 6 \n 7 rf.score(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])\n", "/opt/conda/lib/python3.5/site-packages/sklearn/ensemble/forest.py in fit(self, X, y, sample_weight)\n 239 \"\"\"\n 240 # Validate or convert input data\n--> 241 X = check_array(X, accept_sparse=\"csc\", dtype=DTYPE)\n 242 y = check_array(y, accept_sparse='csc', ensure_2d=False, dtype=None)\n 243 if issparse(X):\n", "/opt/conda/lib/python3.5/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\n 379 force_all_finite)\n 380 else:\n--> 381 array = np.array(array, dtype=dtype, order=order, copy=copy)\n 382 \n 383 if ensure_2d:\n", "ValueError: could not convert string to float: 'N'" ] } ], "source": [ "train = full[full.PassengerId < 892]\n", "test = full[full.PassengerId >= 892]\n", "\n", "rf = RandomForestClassifier(n_estimators=100, oob_score=True)\n", "rf.fit(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])\n", "\n", "rf.score(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b87d63bb-c976-b7b0-7e1b-bc75930c2b5a" }, "source": "" }, { "cell_type": "code", "execution_count": 23, "metadata": { "_cell_guid": "e2c21fe8-1edb-6e53-8f05-cc1904c2c2d0" }, "outputs": [ { "ename": "NameError", "evalue": "name 'rf' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-11-7654d4fee680> in <module>()\n----> 1 rf.oob_score_\n", "NameError: name 'rf' is not defined" ] }, { "ename": "AttributeError", "evalue": "'RandomForestClassifier' object has no attribute 'oob_score_'", "output_type": "error", "traceback": [ "", "AttributeErrorTraceback (most recent call last)", "<ipython-input-23-7654d4fee680> in <module>()\n----> 1 rf.oob_score_\n", "AttributeError: 'RandomForestClassifier' object has no attribute 'oob_score_'" ] } ], "source": [ "rf.oob_score_" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "8075840e-e0e6-7f6f-35e6-1afa1befd87f" }, "source": "" }, { "cell_type": "code", "execution_count": 24, "metadata": { "_cell_guid": "743444b7-55af-bdae-3251-5b4c82ed29be" }, "outputs": [ { "ename": "NameError", "evalue": "name 'train' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-12-7fa2b5fd761a> in <module>()\n----> 1 features = list(zip(train.drop(['Survived', 'PassengerId'], axis=1).columns.values, rf.feature_importances_))\n 2 features.sort(key=lambda f: f[1])\n 3 names = [f[0] for f in features]\n 4 lengths = [f[1] for f in features]\n 5 \n", "NameError: name 'train' is not defined" ] }, { "ename": "NotFittedError", "evalue": "Estimator not fitted, call `fit` before `feature_importances_`.", "output_type": "error", "traceback": [ "", "NotFittedErrorTraceback (most recent call last)", "<ipython-input-24-7fa2b5fd761a> in <module>()\n----> 1 features = list(zip(train.drop(['Survived', 'PassengerId'], axis=1).columns.values, rf.feature_importances_))\n 2 features.sort(key=lambda f: f[1])\n 3 names = [f[0] for f in features]\n 4 lengths = [f[1] for f in features]\n 5 \n", "/opt/conda/lib/python3.5/site-packages/sklearn/ensemble/forest.py in feature_importances_(self)\n 359 \"\"\"\n 360 if self.estimators_ is None or len(self.estimators_) == 0:\n--> 361 raise NotFittedError(\"Estimator not fitted, \"\n 362 \"call `fit` before `feature_importances_`.\")\n 363 \n", "NotFittedError: Estimator not fitted, call `fit` before `feature_importances_`." ] } ], "source": [ "features = list(zip(train.drop(['Survived', 'PassengerId'], axis=1).columns.values, rf.feature_importances_))\n", "features.sort(key=lambda f: f[1])\n", "names = [f[0] for f in features]\n", "lengths = [f[1] for f in features]\n", "\n", "pos = np.arange(len(features)) + .5\n", "plt.barh(pos, lengths, align='center', color='r', alpha=opacity)\n", "plt.yticks(pos, names)\n", "plt.xlabel('Gini importance')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "847ab81f-ab55-7f36-25f1-cc1d9a33747d" }, "source": "" } ], "metadata": { "_change_revision": 199, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/326/326868.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "f013415a-da8e-39eb-c86e-26e3babd9298" }, "source": [ "This isn't much. Just a slight extension of the contributions I've seen so far to this competition.\n", "\n", "This is my first Kaggle notebook so I'm sure it's riddled with mistakes.\n", "\n", "First up, let's read in the train and test data and combine into one full dataframe to work on. We then apply the feature engineering described in the excellent notebook by [Megan Risdal](https://www.kaggle.com/mrisdal/titanic/exploring-survival-on-the-titanic/notebook)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "bef7ed5c-054e-11cf-d37e-8a8e4cfda1e0" }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from collections import Counter\n", "import string\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.linear_model import LinearRegression\n", "\n", "\n", "pd.options.mode.chained_assignment = None\n", "\n", "\n", "def get_title(name):\n", " name = name.split(',')[1]\n", " name = name.split('.')[0]\n", " return name.strip()\n", "\n", "\n", "def get_title_grouped(name):\n", " title = get_title(name)\n", " if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:\n", " title = 'Rare'\n", " elif title in ['Ms', 'Mlle']:\n", " title = 'Miss'\n", " elif title == 'Mme':\n", " title = 'Mrs'\n", " return title\n", "\n", "\n", "def get_deck(cabin):\n", " if isinstance(cabin, str):\n", " if cabin[0] == 'T':\n", " return np.nan\n", " return cabin[0]\n", " return cabin\n", "\n", "\n", "train = pd.read_csv('../input/train.csv')\n", "test = pd.read_csv('../input/test.csv')\n", "full = pd.concat([train, test])\n", "\n", "# feature engineering described in previous notebooks\n", "full['Embarked'].fillna('C', inplace=True)\n", "full['Fare'].fillna(8.05, inplace=True)\n", "full['Title'] = full['Name'].apply(get_title_grouped)\n", "full['Deck'] = full['Cabin'].apply(get_deck)\n", "full['Family size'] = full['Parch'] + full['SibSp']" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "4fe2b59e-9470-ebbc-4145-5fa5ef64ecc1" }, "source": [ "Now let's consider the 'Ticket' column. This consists of either a number or a number with some kind of prefix, e.g. `STON/O2. 3101282` (there are 4 that are just 'LINE', disregard for now). The prefix seems to consist of some kind of abstract reference to the embarkation point. \n", "\n", "Maybe there is more to it, who knows, but I have played around with it and not found much use for it. So let's throw it away and consider the just the number. As always, let's start with a chart or two to see what we're dealing with." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "17ce955e-f6af-73c0-e90c-7a687506b65b" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f19e0f0f630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]\n", "plt.hist(ticket_nums, 50)\n", "plt.xlabel('Ticket number')\n", "plt.ylabel('Count')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1e636a3d-744e-fc22-cf81-dd4862257bf9" }, "source": [ "From the histogram, we can see almost all of the ticket numbers are grouped somewhere under 500k, except for a few just above 3 million. Let's zoom in on the ones under 500k." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "2308e6cb-90b4-80d7-6b65-25d72479372a" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f19e0f0f940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ticket_nums = [num for num in ticket_nums if num < 2000000]\n", "plt.hist(ticket_nums, 50)\n", "plt.xlabel('Ticket number')\n", "plt.ylabel('Count')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "6dd1ff9d-c27f-ea85-524b-ee7109c1b255" }, "source": [ "Just from eyeballing the graph, it looks like we have 4 clusters of ticket numbers (and also the one at 3 million): at 50k, 120k, 200-250k and 300-400k. \n", "\n", "It wouldn't be unreasonable to think that perhaps this ticket number could correspond to whereabouts on the ship the holder was staying, which could be fairly important. Furthermore, the fact that the numbers come in clusters seems to me to make it more likely that the number has some meaning by it, and may be a useful tool in our predictions.\n", "\n", "So let's add the ticket number to our dataframe (we will set the 4 that are 'LINE' to be the median ticket number).\n", "\n", "We also drop the columns that we have engineered into more instructive features." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "a10cb9be-68fe-6c9a-ccf4-47011cdcf282" }, "outputs": [], "source": [ "def get_ticket_num(ticket):\n", " ticket_num = ticket.split()\n", " ticket_num = ''.join(char for char in ticket_num[-1].strip() if char not in string.punctuation)\n", " if not ticket_num.isdigit():\n", " return np.nan\n", " return int(ticket_num)\n", "\n", "full['Ticket number'] = full['Ticket'].apply(get_ticket_num)\n", "full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)\n", "\n", "full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "3670eadc-b9be-4360-a7f0-d2c109b01e2a" }, "source": [ "Great! Now our data contains what we hope are the most useful columns that we could extract from the original data.\n", "\n", "As considered in previous notebooks, we now have to fill in the considerable number of missing values in the `Age` and `Deck` columns (the missing values in the `Deck` column stems from the missing values in the original `Cabin` column).\n", "\n", "Let's consider age first.\n", "\n", "We know that this is a continuous variable, so what are our methods for filling it in? We could use some kind of imputer, but I'm going to do it a little differently, and treat it as a machine learning problem. So we are trying to predict the missing age values, using the rows containing age values as the training set.\n", "\n", "Ultimately this prediction of the age values isn't the be all and end all, so let's keep it simple and use logistic regression.\n", "\n", "Before we apply our logistic regression, we need to change the categorical data to numerical. We therefore encode our categorical columns with the `LabelEncoder` module from `sklearn`.\n", "\n", "Note that given `Deck` has many missing values, let's not use that in the prediction of `Age`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "6d92c58f-907a-0395-bcfd-1bde43236cfd" }, "outputs": [], "source": [ "encoders = {}\n", "to_encode = ['Embarked', 'Sex', 'Title']\n", "for col in to_encode:\n", " encoders[col] = LabelEncoder()\n", " encoders[col].fit(full[col])\n", " full[col] = encoders[col].transform(full[col])\n", "\n", "age_train = full[full['Age'].notnull()]\n", "age_predict = full[~full['Age'].notnull()]\n", "lr = LinearRegression()\n", "lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])\n", "predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))\n", "age_predict['Age'] = [max(0., age) for age in predicted_ages]\n", "\n", "full = pd.concat([age_train, age_predict]).sort_values('PassengerId')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d0eb017d-7cd3-f8d6-843a-a6173e174e5b" }, "source": [ "So we have filled in the missing age values. As a sanity check, let's chart the densities of the ages before and after our prediction." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "27a1262b-1d16-7281-3492-f5ab7c2bbfc1" }, "outputs": [ { "data": { "image/png": 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2SyzGZMhTYlHV2ap6J9AU1xIqs0VkvojcLSLFfBmgMVn5dV0ixY/VoXhx5xd/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\nG8FzvzyX2WopXtzVJfbYY3D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"text/plain": [ "<matplotlib.figure.Figure at 0x7f19c0b7ac50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ages = age_train.Age\n", "ages.plot.kde(label='Original')\n", "ages = full.Age\n", "ages.plot.kde(label='With predicted missing values')\n", "plt.xlabel('Age')\n", "plt.legend(prop={'size': 9})\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "f90216cf-bf81-37ed-6cfa-51652e09ea09" }, "source": [ "Largely the same, so let's move on. Next up, the `Deck` column.\n", "\n", "This is trickier as there are many more people missing deck values than with them:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "e64668ae-2e49-a1a1-8e05-b2274d203310" }, "outputs": [ { "data": { "text/plain": [ "Counter({nan: 1015,\n", " 'E': 41,\n", " 'F': 21,\n", " 'A': 22,\n", " 'C': 94,\n", " 'D': 46,\n", " 'G': 5,\n", " 'B': 65})" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Counter(full['Deck'].values)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "5df2b07f-373f-7e02-dc2a-438b357ac61b" }, "source": [ "Let's plot out the average values of the other (continuous) columns for those *with* decks and those *without* decks." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "2dfeec3a-2664-3d89-2564-d35d0898133c" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f19c316ceb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "full_with_deck = full[full['Deck'].notnull()]\n", "full_without_deck = full[~full['Deck'].notnull()]\n", "\n", "full_with_deck_means, full_without_deck_means = [], []\n", "for col in full_with_deck:\n", " if col not in ['Deck', 'PassengerId']:\n", " sum_means = np.nanmean(full_with_deck[col].values) + np.nanmean(full_without_deck[col].values)\n", " full_with_deck_means.append(np.nanmean(full_with_deck[col].values)/sum_means)\n", " full_without_deck_means.append(np.nanmean(full_without_deck[col].values)/sum_means)\n", "\n", "bar_width = 0.35\n", "opacity = 0.4\n", "x_index = np.arange(len(full_with_deck_means))\n", "\n", "plt.bar(x_index, full_with_deck_means, bar_width, alpha=opacity, color='b', label='With deck value')\n", "plt.bar(x_index + bar_width, full_without_deck_means, bar_width, alpha=opacity, color='r', label='Missing deck value')\n", "plt.legend(loc='upper center', prop={'size': 9})\n", "plt.ylabel('Ratio of means')\n", "plt.xticks(x_index + bar_width, [col for col in full_with_deck if col not in ['PassengerId', 'Deck']])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "cc66c461-6313-2a65-603b-6133d4877c1b" }, "source": [ "There are some large differences! In particular, those with missing deck values are on average younger, in a worse class, paid a lot less for their fare, have a very different ticket number and were much less likely to survive.\n", "\n", "So for whatever reason, the fact that a passenger is missing a deck value actually seems to be quite important in our analysis. As such, we don't actually want to fill it in, so let's classify those without a deck as being on a deck of their own.\n", "\n", "We also encode the `Deck` column. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "ce541531-bdd7-2a35-1ddf-1440fb7b0908" }, "outputs": [], "source": [ "full['Deck'].fillna('N', inplace=True)\n", "\n", "encoders['Deck'] = LabelEncoder()\n", "encoders['Deck'].fit(full['Deck'])\n", "full['Deck'] = encoders['Deck'].transform(full['Deck'])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "066c7934-0fda-d7e6-22c6-508b78d472d7" }, "source": [ "Now to make our survival predictions.\n", "\n", "As everyone else has noted, a random forest is almost certainly the best tool for the job here, so let's implement." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "66e9339a-ca57-41c0-5c62-affaaf23e2d8" }, "outputs": [ { "data": { "text/plain": [ "0.99887766554433222" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train = full[full.PassengerId < 892]\n", "test = full[full.PassengerId >= 892]\n", "\n", "rf = RandomForestClassifier(n_estimators=100, oob_score=True)\n", "rf.fit(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])\n", "\n", "rf.score(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b87d63bb-c976-b7b0-7e1b-bc75930c2b5a" }, "source": [ "So we fit the training data well. To see how well we fit the test data we can use the OOB error." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "e2c21fe8-1edb-6e53-8f05-cc1904c2c2d0" }, "outputs": [ { "data": { "text/plain": [ "0.83052749719416386" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf.oob_score_" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "8075840e-e0e6-7f6f-35e6-1afa1befd87f" }, "source": [ "\n", "Not bad.\n", "\n", "Of course, we need to really plot out the error against the number of trees to see when we have enough, but I'll leave that to you.\n", "\n", "Finally, let's see how important our features were to the model." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "743444b7-55af-bdae-3251-5b4c82ed29be" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f19bc9612b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "features = list(zip(train.drop(['Survived', 'PassengerId'], axis=1).columns.values, rf.feature_importances_))\n", "features.sort(key=lambda f: f[1])\n", "names = [f[0] for f in features]\n", "lengths = [f[1] for f in features]\n", "\n", "pos = np.arange(len(features)) + .5\n", "plt.barh(pos, lengths, align='center', color='r', alpha=opacity)\n", "plt.yticks(pos, names)\n", "plt.xlabel('Gini importance')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "847ab81f-ab55-7f36-25f1-cc1d9a33747d" }, "source": [ "Ticket number turned out to be useful, but deck not so much. Interestingly, the title feature was less important for me as for some of the other users. Perhaps it has a correlation to one of the variables I created?" ] } ], "metadata": { "_change_revision": 359, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/326/326886.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "f38b2717-da79-6cf8-50c3-97fa1fc8a620" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "ca4274b6-5d50-e76c-8bc3-357cf34fd001" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "genderclassmodel.csv\n", "gendermodel.csv\n", "gendermodel.py\n", "myfirstforest.py\n", "test.csv\n", "train.csv\n", "\n" ] } ], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "31607f9d-9039-94b6-c3ba-d3eb1bbc1ed5" }, "source": [ "**load data**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "0112e4a8-7709-1b4c-b4e7-b61a5446f33d" }, "outputs": [ { "ename": "OSError", "evalue": "File b'../input/test.csva' does not exist", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mOSError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-2-6868b14189b5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mdf_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../input/train.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'../input/test.csva'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[0;32m 560\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[0;32m 561\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 562\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 317\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 643\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 644\u001b[0m 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798\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 799\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 800\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 801\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 1211\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'allow_leading_cols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1212\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1213\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1215\u001b[0m \u001b[1;31m# XXX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas/parser.c:3427)\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader._setup_parser_source (pandas/parser.c:6861)\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mOSError\u001b[0m: File b'../input/test.csva' does not exist" ] } ], "source": [ "df_train = pd.read_csv('../input/train.csv')\n", "df_test = pd.read_csv('../input/test.csva')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "762ababc-c72e-58ea-378a-5ac8b5056b64" }, "source": [ "**data analysis**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "e427a7cc-8a1b-ebbc-0f26-d83209327cb6" }, "outputs": [], "source": [ "# survive vs gender\n" ] } ], "metadata": { "_change_revision": 161, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/327/327044.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "58ee480a-9c0b-3ee4-4bd6-c5c069a35814" }, "source": [ "## Picking the correct indicators to explore\n", "- Need to explorer only a particular area of interest ...\n", "- The database is so rich with so many indicators that it is desirable to have a better way of picking required indicators.\n", "- So I have created a new Indicator list using which specific topics like for eg: Health, Food, Energy etc. can be searched for. Then within each topic required indicators can be more easily picked." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "0498e283-9946-c8f3-efe2-2f77afc30166" }, "outputs": [], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import re" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "f86098eb-0a7b-9690-b8a0-11498d9fe5df" }, "outputs": [], "source": [ "# read in file as data frame\n", "df = pd.read_csv('../input/Indicators.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "a4eb7085-08bd-fa86-8479-2d30b1d64204" }, "outputs": [], "source": [ "# Create list of unique indicators, indicator codes\n", "Indicator_array = df[['IndicatorName','IndicatorCode']].drop_duplicates().values" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "6f0793b7-f7bf-805c-5fba-7d54d439c1a4" }, "source": [ "### Change IndicatorNames\n", "- Create a new list of IndicatorName and IndicatorCode such that special characters like \"(\", \")\", \",\" are replaced just by spaces\n", "- This new list (modified_indicators) can be used to search for specific topics as done below" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "8fb3417e-4b31-d82f-55d3-fc08ac960317" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1344, 2)\n" ] } ], "source": [ "modified_indicators = []\n", "unique_indicator_codes = []\n", "for ele in Indicator_array:\n", " indicator = ele[0]\n", " indicator_code = ele[1].strip()\n", " if indicator_code not in unique_indicator_codes:\n", " # delete , ( ) from the IndicatorNames\n", " new_indicator = re.sub('[,()]',\"\",indicator).lower()\n", " # replace - with \"to\" and make all words into lower case\n", " new_indicator = re.sub('-',\" to \",new_indicator).lower()\n", " modified_indicators.append([new_indicator,indicator_code])\n", " unique_indicator_codes.append(indicator_code)\n", "\n", "Indicators = pd.DataFrame(modified_indicators,columns=['IndicatorName','IndicatorCode'])\n", "Indicators = Indicators.drop_duplicates()\n", "print(Indicators.shape)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "51a764e5-74a6-2161-ea74-23462647f94b" }, "source": [ "### Define Key word dictionary on specific topics\n", "eg: Topic \"Health\" contains all indicators which have the words desease, hospital, mortality, doctor etc.\n", "\n", "- Feel free to change it to your requirements" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "45ede2de-9c03-4e12-307a-5111256163a7" }, "outputs": [], "source": [ "\n", "key_word_dict = {}\n", "key_word_dict['Demography'] = ['population','birth','death','fertility','mortality','expectancy']\n", "key_word_dict['Food'] = ['food','grain','nutrition','calories']\n", "key_word_dict['Trade'] = ['trade','import','export','good','shipping','shipment']\n", "key_word_dict['Health'] = ['health','desease','hospital','mortality','doctor']\n", "key_word_dict['Economy'] = ['income','gdp','gni','deficit','budget','market','stock','bond','infrastructure']\n", "key_word_dict['Energy'] = ['fuel','energy','power','emission','electric','electricity']\n", "key_word_dict['Education'] = ['education','literacy']\n", "key_word_dict['Employment'] =['employed','employment','umemployed','unemployment']\n", "key_word_dict['Rural'] = ['rural','village']\n", "key_word_dict['Urban'] = ['urban','city']" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "6ca5ab89-0bec-e172-041e-6ffdd0ee7841" }, "source": [ "### Pick required fields\n", "- Now within specific topics we cah chose what ever indicators we are interested in\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "53436362-bdaa-5849-632d-6407d3621f82" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['hospital beds per 1000 people' 'SH.MED.BEDS.ZS']\n", "['mortality rate adult female per 1000 female adults' 'SP.DYN.AMRT.FE']\n", "['mortality rate adult male per 1000 male adults' 'SP.DYN.AMRT.MA']\n", "['mortality rate infant per 1000 live births' 'SP.DYN.IMRT.IN']\n", "['mortality rate under to 5 per 1000' 'SH.DYN.MORT']\n", "['maternal mortality ratio national estimate per 100000 live births'\n", " 'SH.STA.MMRT.NE']\n", "['births attended by skilled health staff % of total' 'SH.STA.BRTC.ZS']\n", "['maternal mortality ratio modeled estimate per 100000 live births'\n", " 'SH.STA.MMRT']\n", "['mortality rate infant female per 1000 live births' 'SP.DYN.IMRT.FE.IN']\n", "['mortality rate infant male per 1000 live births' 'SP.DYN.IMRT.MA.IN']\n", "['mortality rate neonatal per 1000 live births' 'SH.DYN.NMRT']\n", "['mortality rate under to 5 female per 1000 live births' 'SH.DYN.MORT.FE']\n", "['mortality rate under to 5 male per 1000 live births' 'SH.DYN.MORT.MA']\n", "['community health workers per 1000 people' 'SH.MED.CMHW.P3']\n", "['ari treatment % of children under 5 taken to a health provider'\n", " 'SH.STA.ARIC.ZS']\n", "['external resources for health % of total expenditure on health'\n", " 'SH.XPD.EXTR.ZS']\n", "['health expenditure per capita current us$' 'SH.XPD.PCAP']\n", "['health expenditure per capita ppp constant 2011 international $'\n", " 'SH.XPD.PCAP.PP.KD']\n", "['health expenditure private % of gdp' 'SH.XPD.PRIV.ZS']\n", "['health expenditure public % of gdp' 'SH.XPD.PUBL.ZS']\n", "['health expenditure public % of total health expenditure' 'SH.XPD.PUBL']\n", "['health expenditure total % of gdp' 'SH.XPD.TOTL.ZS']\n", "[ 'out to of to pocket health expenditure % of private expenditure on health'\n", " 'SH.XPD.OOPC.ZS']\n", "['out to of to pocket health expenditure % of total expenditure on health'\n", " 'SH.XPD.OOPC.TO.ZS']\n", "['health expenditure public % of government expenditure'\n", " 'SH.XPD.PUBL.GX.ZS']\n" ] } ], "source": [ "feature = 'Health'\n", "for indicator_ele in Indicators.values:\n", " for ele in key_word_dict[feature]:\n", " word_list = indicator_ele[0].split()\n", " if ele in word_list or ele+'s' in word_list:\n", " print(indicator_ele)\n", " break" ] } ], "metadata": { "_change_revision": 8, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/327/327075.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "0176c69b-cefe-4078-b4fc-e30d1245e2cf" }, "source": [ "In this notebook, analysis have been done based on the airplane crashes database. We are going to find out:\n", "\n", " - Finding the operators and types with highest crashes \n", " - Fatalities based on the decades\n", " - Finding the operators with high percentage of survivors\n", " - Comparing the number of aboard, fatalities and survivers \n", " - Locations with high number of crashes" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "9093959d-62b8-fd8a-da34-f87bca856226" }, "outputs": [], "source": [ "import pandas as ps\n", "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "import matplotlib\n", "from matplotlib import cm\n", "import pylab\n", "import string\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "026744de-e309-612e-b620-1177cbbdb322" }, "source": [ "Let's read the data and explore a bit:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "6943fe6e-cd6b-b9a8-24ce-2062fdbf691d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Date Time Location \\\n", "0 09/17/1908 17:18 Fort Myer, Virginia \n", "1 07/12/1912 06:30 AtlantiCity, New Jersey \n", "2 08/06/1913 NaN Victoria, British Columbia, Canada \n", "3 09/09/1913 18:30 Over the North Sea \n", "4 10/17/1913 10:30 Near Johannisthal, Germany \n", "\n", " Operator Flight # Route Type \\\n", "0 Military - U.S. Army NaN Demonstration Wright Flyer III \n", "1 Military - U.S. Navy NaN Test flight Dirigible \n", "2 Private - NaN Curtiss seaplane \n", "3 Military - German Navy NaN NaN Zeppelin L-1 (airship) \n", "4 Military - German Navy NaN NaN Zeppelin L-2 (airship) \n", "\n", " Registration cn/In Aboard Fatalities Ground \\\n", "0 NaN 1 2.0 1.0 0.0 \n", "1 NaN NaN 5.0 5.0 0.0 \n", "2 NaN NaN 1.0 1.0 0.0 \n", "3 NaN NaN 20.0 14.0 0.0 \n", "4 NaN NaN 30.0 30.0 0.0 \n", "\n", " Summary \n", "0 During a demonstration flight, a U.S. Army fly... \n", "1 First U.S. dirigible Akron exploded just offsh... \n", "2 The first fatal airplane accident in Canada oc... \n", "3 The airship flew into a thunderstorm and encou... \n", "4 Hydrogen gas which was being vented was sucked... \n" ] } ], "source": [ "fileR = ps.read_csv(\"../input/3-Airplane_Crashes_Since_1908.txt\", sep=\",\")\n", "print(fileR.head())" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e0617b81-f6c1-3c72-a127-5996880bef68" }, "source": [ "Finding the operators and types with highest crashes\n", "--------------------------------------------------" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "cdf9e691-05e2-4615-f8e7-870ef562f792" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f750de714a8>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TW6bMzMysT5tuuikLLbQQ559//lzb//73v3PxxRezzTbbAPDEE0/Mvi4iePLJJ1lhhRVY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\nDD5Nim60a7/MJKVXziy9hpGApDHA9iXqmFTTrDVJW9j+o6Q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3kO4/LaHOK4E9qyyVCGcq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\nBUEQBEEQDIP/D0XrUg6N8XClAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f750dd5d198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "matplotlib.rcParams['figure.figsize'] = (10,5)\n", "ops = fileR[\"Operator\"].value_counts()[:20]\n", "ops.plot(kind=\"bar\",legend=\"Operator\",color =\"g\",fontsize=10, title=\"Operators with Highest Crashes\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "ecd2986e-4f62-88d9-9fd2-50a8ecdc510d" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f750dd5d1d0>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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HR/RYRQzfoCRpFeCFiHhS0jLAxcBXgW2BxyPia5I+C6wYEYflAeinA1uSuvcuBTaIjkCS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\nJJ0CfIWB7ge2T+ogVhVZmYqIiFjLSfoP4HOUxYS7Z87bbn1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"text/plain": [ "<matplotlib.figure.Figure at 0x7f750de71518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "types = fileR[\"Type\"].value_counts()[:20]\n", "types.plot(kind=\"bar\",legend=\"Types\",color =\"g\", fontsize=10,title=\"Types with Highest Crashes\")" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "cd6b8199-86ff-5b31-32e5-aa9da3be8506" }, "source": [ "Fatalities based on the decades\n", "------------------------------------------------------------\n", "\n", "Let's explore a bit more on the years of crashes." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "99b87600-7bdc-49c4-6300-ef8fb23f2b87" }, "outputs": [], "source": [ "fileR['Date'] = ps.to_datetime(fileR['Date'])\n", "fileR['year'] = fileR['Date'].dt.year\n", "fileR['month'] = fileR['Date'].dt.month\n", "fileR['day'] = fileR['Date'].dt.day\n", "sub_years = [1900,1910,1920,1930,1940,1950,1960,1970,1980,1990,2000,2010]\n", "years_legend = list(string.ascii_letters[:len(sub_years)])\n", "fileR[\"year_group\"] = \"\"\n", "for i in range(0,(len(sub_years)-1)):\n", " fileR.loc[(sub_years[i+1]>fileR[\"year\"]) & (fileR[\"year\"] >= sub_years[i]) , [\"year_group\"]] = years_legend[i]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "323fc5dd-ab1f-4a72-cce4-5bc549c9d33f" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f75212b00b8>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ShzYYtpLMS/JHSa5Pcn/75x8lmddXN5LkliT3Jbk4yf597Vsn+ViS1UnuSXJ+kj2m+oQk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\nLZN8KMk5SS4FzknyuCSfSfL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"text/plain": [ "<matplotlib.figure.Figure at 0x7f750e16f860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "matplotlib.rcParams['figure.figsize'] = (10,5)\n", "fileR[[\"Fatalities\",\"year_group\"]].groupby(\"year_group\").count().plot(kind=\"bar\",fontsize=14,legend=True,color =\"g\", title=\"Fatalities based on decades\"\n", " )" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "ae2291ff-3d9b-f269-3928-0ea7ea0edcab" }, "source": [ "Let's create a better visualisation chart by using pie chart and find out the distribution of the fatalities:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "1378ceaf-c0d7-20e3-2d2c-db9224c4bad3" }, "outputs": [ { "data": { "image/png": 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h2KkjIpwx9Sxv58TOY1wu9/3H8ViG0SqZgMfokJJzMuXAzn1/19rgOSNuDl8G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\nA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"text/plain": [ "<matplotlib.figure.Figure at 0x7f750e1d7588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = [\"1900-1910\",\"1910-1920\",\"1920-1930\",\"1930-1940\",\"1940-1950\",\"1950-1960\",\"1960-1970\",\"1970-1980\",\"1980-1990\",\"1990-2000\",\"2000-2010\"]\n", "sizes = fileR[[\"Fatalities\",\"year_group\"]].groupby(\"year_group\").sum()\n", "explode = (0, 0, 0, 0, 0, 0, 0, 0.1, 0.1, 0,0) \n", "colors = cm.Set1(np.arange(20)/30.)\n", "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n", " autopct='%1.1f%%', shadow=True, startangle=45)\n", "plt.axis('equal')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "896271ad-5ee4-0d8d-c0b9-818e99ec0d93" }, "source": [ "It is interesting to see that in the interval of 1980-1990 compare to 1990-2000, there were less crashes but more fatalities." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "547176c8-41b2-84cc-929d-2943a6820ff8" }, "source": [ "Finding the operators with high percentage of survivors\n", "--------------------------------------------------------" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "06bf7c9e-1d30-55e1-1644-103921361b95" }, "source": [ "In this step, let's find out the percentages of the survivors:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "45c54c0f-eb5c-b805-d5f8-c594052ebf78" }, "outputs": [], "source": [ "subfile2 = (fileR[[\"Aboard\",\"Fatalities\",\"year\",\"Operator\",\"Type\"]].groupby(\"Operator\").sum())\n", "subfile2[\"survived\"] = subfile2[\"Aboard\"]- subfile2[\"Fatalities\"]\n", "subfile2[\"percentageSurvived\"] = subfile2[\"survived\"]/subfile2[\"Aboard\"]\n", "subfile3= subfile2[subfile2[\"year\"]>max(fileR[\"year\"])] # Exclude the records with one observation" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "02d789af-cd09-08e5-efdf-9d5ac07822fb" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Aboard</th>\n", " <th>Fatalities</th>\n", " <th>year</th>\n", " <th>survived</th>\n", " <th>percentageSurvived</th>\n", " </tr>\n", " <tr>\n", " <th>Operator</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>Southwest Airlines</th>\n", " <td>245.0</td>\n", " <td>0.0</td>\n", " <td>4005</td>\n", " <td>245.0</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>British Airways</th>\n", " <td>510.0</td>\n", " <td>13.0</td>\n", " <td>15668</td>\n", " <td>497.0</td>\n", " <td>0.974510</td>\n", " </tr>\n", " <tr>\n", " <th>Cubana de Aviacon</th>\n", " <td>351.0</td>\n", " <td>17.0</td>\n", " <td>3979</td>\n", " <td>334.0</td>\n", " <td>0.951567</td>\n", " </tr>\n", " <tr>\n", " <th>Karibu Airways</th>\n", " <td>31.0</td>\n", " <td>2.0</td>\n", " <td>4014</td>\n", " <td>29.0</td>\n", " <td>0.935484</td>\n", " </tr>\n", " <tr>\n", " <th>Continental Airlines</th>\n", " <td>666.0</td>\n", " <td>83.0</td>\n", " <td>15780</td>\n", " <td>583.0</td>\n", " <td>0.875375</td>\n", " </tr>\n", " <tr>\n", " <th>General Airways</th>\n", " <td>29.0</td>\n", " <td>4.0</td>\n", " <td>3897</td>\n", " <td>25.0</td>\n", " <td>0.862069</td>\n", " </tr>\n", " <tr>\n", " <th>Airwork</th>\n", " <td>59.0</td>\n", " <td>9.0</td>\n", " <td>3957</td>\n", " <td>50.0</td>\n", " <td>0.847458</td>\n", " </tr>\n", " <tr>\n", " <th>Trans World Airlines / Private</th>\n", " <td>168.0</td>\n", " <td>28.0</td>\n", " <td>3961</td>\n", " <td>140.0</td>\n", " <td>0.833333</td>\n", " </tr>\n", " <tr>\n", " <th>Skyways of London</th>\n", " <td>63.0</td>\n", " <td>11.0</td>\n", " <td>3897</td>\n", " <td>52.0</td>\n", " <td>0.825397</td>\n", " </tr>\n", " <tr>\n", " <th>A B Aerotransport</th>\n", " <td>17.0</td>\n", " <td>3.0</td>\n", " <td>3868</td>\n", " <td>14.0</td>\n", " <td>0.823529</td>\n", " </tr>\n", " <tr>\n", " <th>Skypower Express Airways</th>\n", " <td>32.0</td>\n", " <td>6.0</td>\n", " <td>3997</td>\n", " <td>26.0</td>\n", " <td>0.812500</td>\n", " </tr>\n", " <tr>\n", " <th>Union Aeromaritime de Transport</th>\n", " <td>85.0</td>\n", " <td>17.0</td>\n", " <td>5869</td>\n", " <td>68.0</td>\n", " <td>0.800000</td>\n", " </tr>\n", " <tr>\n", " <th>Saha Airline Services</th>\n", " <td>208.0</td>\n", " <td>42.0</td>\n", " <td>3997</td>\n", " <td>166.0</td>\n", " <td>0.798077</td>\n", " </tr>\n", " <tr>\n", " <th>China Eastern Airlines</th>\n", " <td>437.0</td>\n", " <td>91.0</td>\n", " <td>7979</td>\n", " <td>346.0</td>\n", " <td>0.791762</td>\n", " </tr>\n", " <tr>\n", " <th>Servivensa</th>\n", " <td>36.0</td>\n", " <td>8.0</td>\n", " <td>3992</td>\n", " <td>28.0</td>\n", " <td>0.777778</td>\n", " </tr>\n", " <tr>\n", " <th>Loide Aéreo Nacional</th>\n", " <td>80.0</td>\n", " <td>18.0</td>\n", " <td>5861</td>\n", " <td>62.0</td>\n", " <td>0.775000</td>\n", " </tr>\n", " <tr>\n", " <th>Lloyd Aéreo Boliviano</th>\n", " <td>35.0</td>\n", " <td>8.0</td>\n", " <td>5906</td>\n", " <td>27.0</td>\n", " <td>0.771429</td>\n", " </tr>\n", " <tr>\n", " <th>Flota Aérea Mercante Argentina</th>\n", " <td>51.0</td>\n", " <td>12.0</td>\n", " <td>5843</td>\n", " <td>39.0</td>\n", " <td>0.764706</td>\n", " </tr>\n", " <tr>\n", " <th>SATCO</th>\n", " <td>29.0</td>\n", " <td>7.0</td>\n", " <td>3936</td>\n", " <td>22.0</td>\n", " <td>0.758621</td>\n", " </tr>\n", " <tr>\n", " <th>Central Air Transport</th>\n", " <td>89.0</td>\n", " <td>22.0</td>\n", " <td>3894</td>\n", " <td>67.0</td>\n", " <td>0.752809</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Aboard Fatalities year survived \\\n", "Operator \n", "Southwest Airlines 245.0 0.0 4005 245.0 \n", "British Airways 510.0 13.0 15668 497.0 \n", "Cubana de Aviacon 351.0 17.0 3979 334.0 \n", "Karibu Airways 31.0 2.0 4014 29.0 \n", "Continental Airlines 666.0 83.0 15780 583.0 \n", "General Airways 29.0 4.0 3897 25.0 \n", "Airwork 59.0 9.0 3957 50.0 \n", "Trans World Airlines / Private 168.0 28.0 3961 140.0 \n", "Skyways of London 63.0 11.0 3897 52.0 \n", "A B Aerotransport 17.0 3.0 3868 14.0 \n", "Skypower Express Airways 32.0 6.0 3997 26.0 \n", "Union Aeromaritime de Transport 85.0 17.0 5869 68.0 \n", "Saha Airline Services 208.0 42.0 3997 166.0 \n", "China Eastern Airlines 437.0 91.0 7979 346.0 \n", "Servivensa 36.0 8.0 3992 28.0 \n", "Loide Aéreo Nacional 80.0 18.0 5861 62.0 \n", "Lloyd Aéreo Boliviano 35.0 8.0 5906 27.0 \n", "Flota Aérea Mercante Argentina 51.0 12.0 5843 39.0 \n", "SATCO 29.0 7.0 3936 22.0 \n", "Central Air Transport 89.0 22.0 3894 67.0 \n", "\n", " percentageSurvived \n", "Operator \n", "Southwest Airlines 1.000000 \n", "British Airways 0.974510 \n", "Cubana de Aviacon 0.951567 \n", "Karibu Airways 0.935484 \n", "Continental Airlines 0.875375 \n", "General Airways 0.862069 \n", "Airwork 0.847458 \n", "Trans World Airlines / Private 0.833333 \n", "Skyways of London 0.825397 \n", "A B Aerotransport 0.823529 \n", "Skypower Express Airways 0.812500 \n", "Union Aeromaritime de Transport 0.800000 \n", "Saha Airline Services 0.798077 \n", "China Eastern Airlines 0.791762 \n", "Servivensa 0.777778 \n", "Loide Aéreo Nacional 0.775000 \n", "Lloyd Aéreo Boliviano 0.771429 \n", "Flota Aérea Mercante Argentina 0.764706 \n", "SATCO 0.758621 \n", "Central Air Transport 0.752809 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "highSurvive = subfile3.sort_values(by=\"percentageSurvived\", ascending=False)[:20]# sorting the values \n", "highSurvive" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "52e57842-6ac3-7b1c-63bd-f08eba369323" }, "source": [ "Let's plot it:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "ba2b7f3e-e993-36ba-30f6-ad8b1cea875c" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f750dfdbf60>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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EY7rMzCY2t1iZWV9uITMzmztusTKzvgbdQjZt+WkZcy4svdzSzLph1jzFMzOrbdSJlaRd\ngU8AywB/Az4aEWcNc/8tgb2ANYEHgLOB3SPin2PaYzObb81LIuduTjObSEbVFShpG+Ag4AvAWsA5\nwK8lLd/n/isBPwXOLPd/JbAQ8Msx77GZmZnZBDXaMVa7AUdGxJER8Y+I+DBwM7BLn/uvTbaGfTYi\nro6IS4EvA6tKWnLMe21mNkYeP2ZmbRixK1DSgmSitH/XTacC6/d52AXAQ8D7JB0BLALsAJwfEbfP\n896amVXiGZZm1obRtFgtBUwFuj9NZgM9f7pFxHXAFsA+5Piq/wFrAK+d5z01MzMzm+BamRUoaWng\nCOD7wHHAomSSdSKwSa/HzJgx47Hr06dPZ/r06W3smpnZuJiXGY/gWY9mE8HMmTOZOXPmqO47msTq\nVuARYOmu7UsD/c72DwB3R8SnOxskvQu4XtL6EXFO9wOaiZWZ2fxmMpSuACdyZr10N/jsvffefe87\nYmIVEQ9JugjYHDipcdPmZAtULwuTyVjTo+VfFyU1M2uZEzmz8THarsADgR9IuoCsR7ULWc/qUABJ\n+wLrRsRm5f6/BD4qaU+yK3Ax4EvAdcBF9XbfzMwmAk8GMEujSqwi4oRSJmEPMqG6DNg6Im4od5kG\nrNy4/xmStgU+CewO3AucB2wVEfdV3H8zMzOzCWPUg9cj4lBKC1WP23bsse0E4IR53zUzMzOzycXj\nnczMbNJxgVebqLwIs5mZTToenG8TlRMrMzOzETiRs9FyYmVmZjbBeJbl5OUxVmZmZk9wHrNWj1us\nzMzMnuDcQlaPW6zMzMxsoObnFjK3WJmZmdlATYbJAPM6EcCJlZmZmc3X5iWRm9ckzl2BZmZmZpU4\nsTIzMzOrxImVmZmZWSVOrMzMzMwqcWJlZmZmVokTKzMzM7NKnFiZmZmZVeLEyszMzKwSJ1ZmZmZm\nlTixMjMzM6vEiZWZmZlZJU6szMzMzCpxYmVmZmZWiRMrMzMzs0qcWJmZmZlV4sTKzMzMrBInVmZm\nZmaVOLEyMzMzq8SJlZmZmVklTqzMzMzMKnFiZWZmZlaJEyszMzOzSpxYmZmZmVXixMrMzMysEidW\nZmZmZpU4sTIzMzOrxImVmZmZWSVOrMzMzMwqcWJlZmZmVokTKzMzM7NKnFiZmZmZVeLEyszMzKwS\nJ1ZmZmZmlTixMjMzM6vEiZWZmZlZJU6szMzMzCoZdWIlaVdJV0u6T9KFkjYcxWM+KukKSfdLulHS\nl8a2u2ZmZmYT1wKjuZOkbYCDgJ2Bs4EPAL+W9PyIuKHPYw4EXgV8ArgMWBxYpsZOm5mZmU1Eo0qs\ngN2AIyPiyPL3hyVtBewC7NF9Z0nPAz4IrBkRVzZuumQsO2tmZmY2kY3YFShpQWBt4LSum04F1u/z\nsNcBVwGvknSVpGskHSXpGWPaWzMzM7MJbDRjrJYCpgKzu7bPBqb1ecwqwErANsC7gXcCqwGnzNNe\nmpmZmU0Co+0KnFtTgCcB74yIqwAkvQv4h6R1I+KC7gfMmDHjsevTp09n+vTpLe2amZmZ2ejNnDmT\nmTNnjuq+o0msbgUeAZbu2r40MKvPY24GHu4kVQAR8U9JjwArAMMmVmZmZmYTRXeDz9577933viN2\nBUbEQ8BFwOZdN21OzhDs5WxgAUkrdzZIWpXsUrx2pJhmZmZmk9Fo61gdCOwg6b2SVpP0dbJ0wqEA\nkvaV9LvG/X8H/Bk4UtJakl4MHAGcGxEXVtx/MzMzswljVGOsIuIESUuSpRWWIetSbd2oYTUNWLlx\n/5D0GuAbwJnAfeQswo9X3HczMzOzCWXUg9cj4lBKC1WP23bssW02OSvQzMzM7AnBawWamZmZVeLE\nyszMzKwSJ1ZmZmZmlTixMjMzM6vEiZWZmZlZJU6szMzMzCpxYmVmZmZWiRMrMzMzs0qcWJmZmZlV\n4sTKzMzMrBInVmZmZmaVOLEyMzMzq8SJlZmZmVklTqzMzMzMKnFiZWZmZlaJEyszMzOzSpxYmZmZ\nmVXixMrMzMysEidWZmZmZpU4sTIzMzOrxImVmZmZWSVOrMzMzMwqcWJlZmZmVokTKzMzM7NKnFiZ\nmZmZVeLEyszMzKwSJ1ZmZmZmlTixMjMzM6vEiZWZmZlZJU6szMzMzCpxYmVmZmZWiRMrMzMzs0qc\nWJmZmZlV4sTKzMzMrBInVmZmZmaVOLEyMzMzq8SJlZmZmVklTqzMzMzMKnFiZWZmZlaJEyszMzOz\nSpxYmZmZmVXixMrMzMysEidWZmZmZpU4sTIzMzOrxImVmZmZWSWjTqwk7Srpakn3SbpQ0oajfNxz\nJN0l6c55300zMzOziW9UiZWkbYCDgC8AawHnAL+WtPwIj1sQOA6YObbdNDMzM5v4RttitRtwZEQc\nGRH/iIgPAzcDu4zwuK8AlwA/HsM+mpmZmU0KIyZWpdVpbeC0rptOBdYf5nGvBl4FfGgsO2hmZmY2\nWYymxWopYCowu2v7bGBarwdIWhY4DNguIu4d0x6amZmZTRILtPS8RwMHR8SF5W+N9IAZM2Y8dn36\n9OlMnz69lR0zMzMzmxszZ85k5syZo7rvaBKrW4FHgKW7ti8NzOrzmE2AV0iaUf4WMEXSg8CuEXF4\n9wOaiZWZmZnZRNHd4LP33nv3ve+IiVVEPCTpImBz4KTGTZsDJ/Z52Jpdf78B+CywLnDTSDHNzMzM\nJqPRdgUeCPxA0gXA2eRswGWAQwEk7QusGxGbAUTE5c0HS1oXeDQirqi142ZmZmYTzagSq4g4QdKS\nwB5kQnUZsHVE3FDuMg1YuZ1dNDMzM5scRj14PSIOpbRQ9bhtxxEe+33g+3O3a2ZmZmaTi9cKNDMz\nM6vEiZWZmZlZJU6szMzMzCpxYmVmZmZWiRMrMzMzs0qcWJmZmZlV4sTKzMzMrBInVmZmZmaVOLEy\nMzMzq8SJlZmZmVklTqzMzMzMKnFiZWZmZlaJEyszMzOzSpxYmZmZmVXixMrMzMysEidWZmZmZpU4\nsTIzMzOrxImVmZmZWSVOrMzMzMwqcWJlZmZmVokTKzMzM7NKnFiZmZmZVeLEyszMzKwSJ1ZmZmZm\nlTixMjMzM6vEiZWZmZlZJU6szMzMzCpxYmVmZmZWiRMrMzMzs0qcWJmZmZlV4sTKzMzMrBInVmZm\nZmaVOLEyMzMzq8SJlZmZmVklTqzMzMzMKnFiZWZmZlaJEyszMzOzSpxYmZmZmVXixMrMzMysEidW\nZmZmZpU4sTIzMzOrxImVmZmZWSVOrMzMzMwqcWJlZmZmVsmoEytJu0q6WtJ9ki6UtOEw991Y0k8l\n3STpHkmXSNqxzi6bmZmZTUyjSqwkbQMcBHwBWAs4B/i1pOX7PGR94FLgzcAawCHAYZLePuY9NjMz\nM5ugFhjl/XYDjoyII8vfH5a0FbALsEf3nSNi365Nh0rahEy0jp/XnTUzMzObyEZssZK0ILA2cFrX\nTaeSLVOjtRjw37m4v5mZmdmkMpoWq6WAqcDsru2zgVeOJoik1wCbMneJmJmZmdmk0vqsQEkbAD8E\nPhQRF7Udz8zMzGy8jKbF6lbgEWDpru1LA7OGe2CZOfhL4HMRcdhw950xY8Zj16dPn8706dNHsWtm\nZmZm7Zo5cyYzZ84c1X1HTKwi4iFJFwGbAyc1btocOLHf4yRtBPwC2DMivjlSnGZiZWZmZjZRdDf4\n7L333n3vO9pZgQcCP5B0AXA2ORtwGeBQAEn7AutGxGbl7+lkUvVt4HhJndauRyLi1rk4FjMzM7NJ\nY1SJVUScIGlJsrTCMsBlwNYRcUO5yzRg5cZDtgeeAnyiXDquBVYZ606bmZmZTUSjbbEiIg6ltFD1\nuG3HHn+70rqZmZk9oXitQDMzM7NKnFiZmZmZVeLEyszMzKwSJ1ZmZmZmlTixMjMzM6vEiZWZmZlZ\nJU6szMzMzCpxYmVmZmZWiRMrMzMzs0qcWJmZmZlV4sTKzMzMrBInVmZmZmaVOLEyMzMzq8SJlZmZ\nmVklTqzMzMzMKnFiZWZmZlaJEyszMzOzSpxYmZmZmVXixMrMzMysEidWZmZmZpU4sTIzMzOrxImV\nmZmZWSVOrMzMzMwqcWJlZmZmVokTKzMzM7NKnFiZmZmZVeLEyszMzKwSJ1ZmZmZmlTixMjMzM6vE\niZWZmZlZJU6szMzMzCpxYmVmZmZWiRMrMzMzs0qcWJmZmZlV4sTKzMzMrBInVmZmZmaVOLEyMzMz\nq8SJlZmZmVklTqzMzMzMKnFiZWZmZlaJEyszMzOzSpxYmZmZmVXixMrMzMysEidWZmZmZpU4sTIz\nMzOrZNSJlaRdJV0t6T5JF0racIT7rylppqR7JV0vac+x766ZmZnZxDWqxErSNsBBwBeAtYBzgF9L\nWr7P/RcFTgNuBtYGPgLsLmm3Gjv9mGuqPpvjOZ7jOd7gYzme4znefBVvtC1WuwFHRsSREfGPiPgw\nmTTt0uf+7wSeAmwfEVdExE+A/YCPjXmPm/5d9dkcz/Ecz/EGH8vxHM/x5qt4IyZWkhYkW51O67rp\nVGD9Pg97GfDHiHiwse23wLKSVpyXHTUzMzOb6EbTYrUUMBWY3bV9NjCtz2Om9bm/hnmMmZmZ2aSm\niBj+DtIywI3ARhFxVmP7nsC2EfH8Ho/5LXB9RLyvse1ZwLXAyyPiT133H34nzMzMzCaQiFCv7QuM\n4rG3Ao8AS3dtXxqY1ecxs/rcP3o9pt/OmZmZmU0mI3YFRsRDwEXA5l03bQ6c3edh5wKvkPSkxrYt\ngJsi4tp52VEzMzOziW60swIPBHaQ9F5Jq0n6OrAMcCiApH0l/a5x/2OBe4GjJK0h6U3Ap4CvVtx3\nMzMzswllNF2BRMQJkpYE9iATqsuArSPihnKXacDKjfvfKWlz4NvABcB/gf0j4qCaO29mZmY2kYw4\neN3MzMzMRsdrBc6FUtNrviTpKZI2G2SdMUnPlrTQ/BqvEXe+fd+0RdIKkh43qUVphfHYp1okPXNe\nbqsYf74/1+dXkp4qaZHx3g8b3qRpsZK0MXB/p1SDpB2A9wF/Az4eEXdXjvdh4MaIOKn8fQSwPXAV\n8LqI+EfNeCXGS4FXAs+kK+kt1e5rxjoKOD8iDi6TDC4C1gAeBN4YEb+uHO9LwD8i4vvlC/NU8ljv\nALbqLsExCeN9PiIetx5m+b/9cUS8rma88tzPA3YCVgV2jojZkl4NXBcRf20h3jb0f39WPT5JjwDL\nRMR/urY/HfhPREytGOttwP8i4tTy9/8B7yc/W3aIiJtrxSrPP7BjK897FPPxuT4IZZzwqJSVRmrH\n/wA5Tnm5sukGYL+IOLh2rBLvucBbgBWA5iQ0IuI9LcVcojz//9p4/hLjXmDFiLila/uSwA0RsXCV\nQBExKS7AX4DXl+vPAx4ADgYuBQ5pId6/yNpdABsBdwFvA34E/KKFeJ8AHgWuBGYCZzQup7cQ72bg\nJeX6W8ii/s8EPg38qYV41wIvK9dfBdwCrAd8HThjPoh3E/Dhrm0LAr8ALm4h3ibAfcCvy7mwStn+\nSeCkFuLtDzxEfkkeBXyveWkh3qPAM3psXxG4p3Ksy4EtyvWXAPeX/8fTgWNbOrZn9ti+Qu1jK887\nX5/rJc6TgL3L5+f9ZImgxy6VXrPRXMYcq0fsz5LfP3uRCeorgRnAncCnW4j36vJ/eC6ZfJ9Nlkn6\nL3BK5ViLAl8rz995vWaTE+YWbeHY+p17y5INN1XijGrw+gTxbKDzK/zNwGkRsWtp5TmJ/usWzqvl\nGFqa8bXAiZGD+P8K/LFyLMiFqj8cEd9q4bl7eRrQ+cW8Ffll/B9Jx5OTFGpbmvyVBflhe0JEnC/p\nduDC+SDe1sAZkm6LiB+WloGTgeWBTVuI9yXgMxFxkKS7GttPJ99Ltb0beEdE/LiF536MpG+UqwHs\nW35hdkwlv6Avrhx2RaDTAv1G4KcR8RVJp5JLcVUh6SvlagD/1+PYXsbQZ1xN8/u5DvB5YBtgX/KL\nendgJeDtwONakudWRIznsJmdgfdHxHGNbb+X9E/yc+DLlePtA+wdEfuWz5Z3kT8cjyaTrSokLU4m\nbasAx5M/cCBbU3cGNpO0YUTcWSHWruVqkBUOmj1cU4GNyaS8ismUWD1K/gdAZuwnl+uzgKe3EO9O\n8lfd9WSJXJgmAAAgAElEQVTNrv3L9oeANsYKLAb8qoXn7WcWsKakm4Etya4PgKeSx1jbbeQX2A1k\nTbNPl+0LkEsdTep4EXGJpNcDv5B0P7AjmZxvGhG31Y4HvADYtsf2W2nnfJhC/YSmlxeUfwU8n/zF\n3PEg8GfggMox7yd/OUN+thxZrt/R2F7DK8q/IpOo5nn2INlKXvtLEub/cx2yN2HniPiNpAOAn0XE\nVZKuID+/v9NS3EF4Jjm7vtv5PL4Qdw3PI3tmIN8fC0fE/ZL2AX5JtibV8DnyO/25MVRhAHisO/5U\nMvH/VIVYneRawMfJfKLjQbIVd1cqmUyJ1QXAnpJOIz+gOh8OK5FN3bWdCnxX0p/J1rLOOIQ1GGrJ\nquk48tdkK33mPRxJnjw3kc2vvy/bXwr8vYV4JwHHSroSWJKhloC1yC+UyR6PiPijpG3JpP9vZFJ1\nexuxgP+RZU6634trkUtQ1XYY8E6yC6I1EbFJGZdzAvCeiLhrpMdU8Efgq5LOAtYhu8sAnkv+sKoi\nIl4OIOlEBndsMP+f65AJRqfF425giXL9N8B+tYNJWoBsPe01BukHlcNdSf6I2qdr+7YMtbTWdBdD\njQc3k99/l5H5wtMqxnkT2UtzQ/cNEXG9pE8CB1EhsYqIZQAknUuWimptHBdMrsTqo2Th0dcDX4yI\nq8r2t1KxebLhA8AXyRPnLY0vyJeQSVBt1wN7S9qAHDc2xy/JiKj1K6HzfPtI+ht5fCdGRKdl4GFa\n+CACPkaOvVgB+GRE3FO2LwMcMhnjSTqlz023AveQBXKB+oO7yS/KL0t6C9m83Zn8cADww8qxIL+o\nti316Xq9P2tOrpgCvIEcU3L5CPet4YPke+ItZKvHTWX71lTsCoTHvpDfSI4Huqzmc/fzBDjXAa4j\nx8lcRyZvW5KD9F9OjkWsRtJqwM/J2o0ik9UFyHPiAaB2YjUDOEHSRgytdrIB2X311sqxAP4EbEie\ne78kf3S8iHzf1vyuXR64ZJjb/8LQYP0xK7Ozly2XVhOrSTMrsJ8yhfeRyKV3Ji1Jw7WCRUSsUjne\nM6JrZsT8RNICEfFwyzG+N9r7RsSOlWM/maEfGlPI5uwFgZ+Qi6NXPR8knTHMzRERVceRSfoX+YNm\nEN2PAyXpauANEXHpeO/L/ELSvsDdEfHF8mPjOLIrcjmyOHW1sWSSfkN+Mb+X7GZdC1icTBo/FxGn\n1YrViLk2sBvZPQ5wBfDViPhLC7FWAZ4aEZdKWphcMWUDsuXsYxFxXaU4s4HXRESvbs7OD8VTIqJa\nd6ekG4HNIuKKWs/ZM85kS6wkrUNOL/9FRNxTano8UPtLVNLFwOHAMW03G44HSQ8CpwBHAL+JAbwR\nJL0A+H/k6/eeiLhZ0huAa2t/QEi6Bfg+cETrJ5E0BViNLHNQtezHKGI/H1ibTK7+HBEDaQVpm6Tt\ngXcA74yIW1uO9VPyXP9VRDw60v0rxNsJeB15bHe0Ha/E3JpshV8F2LJ0tbwPuCYifj/8o+cp3sDO\n9T7xXwasD1wZEb+o/Ny3ARtHxGWS7gDWi4h/KEsCfTMiXlgz3vxK0nHAkyOiZykLSSeT3+1vrxjz\nc2RL4/sj4pFaz9tt0nQFSloa+BnZrx3Ac4CryYF091N/JtQvySnX+3c+eNv4AOpF0lPJVoB7Rrzz\nvHs1OcD6JOC2UuvmqEYXa1WStiATuV+Ts+SeUm5aFdiB7Pqp6bPk8X1M0p/IL84ftZT4BDmwe3Xa\nG0MyhzL+4JslabyisX0hctzCV/o+eGxxFyLHXARwVUTc30YcsvzIysCNkm4gu1YfU/nL6x6ya/WO\nch58LyL+WfH5u72fTMRvLi3V3ce2Xs1gkrYj13U9nByc3ylYO5X8jKv6uTYO5/rjRMR5wHktPb3I\ntXAhS0ksR451uoE8N9oJKi1L7xpyf24x5hI94tUaN7oPcH4Z93QAQ+P9VicHmK9BjgOs6UVkN/EW\nki7l8efe22oEmTQtVpKOBRYhT8zrgBdFxNWSNiO/YJ4/3OPnMabIAeU7kr8wbybr9hxVqzm0K95A\ni8CVmEsA25HH+GLgTLIV66SaX5olufl+ZJHCuxh6/dYGfh4Ry9aK1RX3+cB7yIHXTwVOJFuxzh72\ngXMf56/kr6A2xvv1ijfoIpMLklO7P0gO1hU5nuSbwB4tdD3uNdztEbF35XiLMXQerAOcRSYiJ0ZE\n7TE6+w53e0R8pnK8S4B9I+L4rnPvRcCpNbtaSryBn+saYJFXSX8AvhYRJ5fvpaeT58ZOwAtrt1hJ\nejFwDJmMd8+qjBbO9RXJRHw6cw7MV+14pWXxe+RMxE4yIrLb8T0RcU6tWCXesOOjI+IdVQJF5QJc\nbV3IomFrlut3MVQQcWVaKKrXI/6SwGfI1rGHyUGtW1V8/oEWgeuzDx8ox/cocDs59fuplZ77HmCl\nPq9ftcJsw8SfCnyIoeKBfydrpUyp9PxbkwNL16L8YGn5ePoV0NwIuLWFeAeSPyy2J1seOq0PNwMH\ntH28g7yQv5S/Rg56/h85Vf/5471fYzieTrXp7nNvVeC+FuIN/FxngEVeyRaPN5Xrq5Atxo+StcKm\nt3BsF5CzG9cnZ8Gv2Ly0EO90sqzJtmRytXHz0kI8kT9mti2XdQbxGdrmZdJ0BZLNyQ/22P4M8iRq\nTcmq30MWoLuJzLCXAX4s6fCI+GiFMIMuAgeApGXIL8sdyFkax5MtVsuSieQ6wGYVQt1OtsT9u2v7\nSxgqJlidslDnm8jXb1OyJaJzfHuSHxw1+vBPIKcoXwQ8LOmB5o0RsViFGJ2xY1Eul0tqNjlPJQfR\njnpQ/VzYlvwF2ay1dlXZn8PJrrvqJG1Kdg0E8LeImNlGnEa8ZckJAa8hf0CdBDwLuFTSZyKiWg0t\nSesz57G11XV1E1k64tqu7RuRS3TVNh7n+kCKvAJExG8b168Gnq9cEuW/UTKFylYHXhwR1QpYjmA9\nsnJ+6+M1y/l2S0RcSKN4rKQFywSrm/o/ekxxlyMnAgTw94ioWqJmMiVWfyC//D9b/g5JU8muszYG\nXz6TrDa9I/nL7hRyltJpjfscDZxGloIYq4EWgVOuffUesoDfZcA3gB9GYzCtpAuoV+fmWHK82tvI\nN/MCZbDnAbSQCEh6CXl87yCnQf8A+GDzw0nSL6hXCfqDlZ5nJJ8jf+EdDHyFbNHseBD4d0QMN4Nv\nXi1O7y/hqxiqGVRN+eA7mRyY3/lwXVbSheT6dtU+cEs35+vJ98vm5DTvrwDHRRmTJ+l15HtozIlV\nGS/6Y3KmVad47NMl/RF4a3R171ZwGPCNMlgd4FmSXkEe44zKsWDA53oxqCKvPUV79eogq/FPo2Jl\n8BFcAzy57SDl/TGDHILSbSpwmqT9omJdsDJ++RDyh2KnW/XR0qW7S9Qa1zzeTWZz0Vy4OjlQ8DTy\nC+Qn5C+UWcCqLcR7kGxe/jiwVJ/7LEalta/I2kD/12P7XsAlLRzfHWQ/+trD3OcpwF6V4i1I1ld6\nhGw2f7hcPxqY2sLxPUIOnn0zsECf+yxCC+vcDeJCdkcsOMB45wHf7rH9EODcFuKdRNbMWbmxbRXg\nHHJR65qxbiUTnG+SY2R63WcJcgZdjXgnkD+iVmtse37ZdnxLr98XyS7Bzpp29wGfbynWQM/1EvOn\nZMvUnuWze9myfUtyQeiasRYif9CfSk5aubR5aeHYNi3n32bkj+wlm5eW4p0KPLuN16oR5/fAjsPc\n/u5a36+N5/wu+WNwy/L5vwg5jvpfwHdqxZk0g9cBJE0j1wR8bHo5+WFfvfK6pFdERBtrAvaL9yby\nA3cmPYrARcRPK8dbOCLuHfmedUlalfyFMgX4S7Q0+0rSihHR3fXRKmVtqe1odO+QrR4PDPvAscdd\ngsdXf67a6qEsTvgrsqp7p8vqZWSX6tYRcVbleHeS41X+3LV9HeD3EbF4xVjvIgeptzqkoBHvDrKW\nzgVd29cjB5NXbwEsz78w+d6cAlweLZcGGdS5XmItTyb5KwBfj4gjy/aDyHGU1QrYSjqS7G48kWxN\nneNLNOpPrGiWAGnGqj6YvMS7i2yxmkpOUJmjlFHUG9ZwM7Bh9JmJXt4/Z0fEtBrxynPeSvY8zeza\nvgm5puUzqsSZTInV/E4DLALXFXcaj/9irj7rcX4maXVygOliDC2k+wKyZXCrqFxLqzRpf5UcH/bU\n7ttrf9iWmMuSExxWK5uuAA6OFsZBlMRq4+73fjlHTq+ZWA1a+eLaMCIu6dr+YuDMWl9cjeedRrba\ndq/HtjzwUETMrhlvfqdcTPptEfG7AcXbeLjbI+LMyvG2HyHe9yvFuZfsMen52VhmdF8UEQvXiDdc\nTElrAOdHxCJV4kymxKr84lqL3rU8ftJCvB3JMTq91oOqWgl90JQri3+DXLz0Sd23t/TFvA05/qHX\n61d1yZcyaH0Phl6/BZu3t/Ar7zSyq+VdUVZjL1P4jyGL4G1ZOd7B5ODjPcgxLTuTg6x3IZcRaWPZ\npYFRFgd8BvCOiLi+bFuB7GK6JfoUFRxDvE3of67Xrip/Cjnu5+2dpKYkP8cCd0XE6yvH+x3ZcnpE\n1/b3AttExBY145XnHti53iN2m7WXUNZVe2VEtLFO3xOGpMuBL0XEMX1ufzfwmahYSkm5gsQsYPso\nSzuVnoajgKVrneuTZvC6sl7VcWTNkG5BNlvWjLc7OSvuO+QX2MFk8beNqDCAtUe8t5JVZk/p2v56\ncizNjyuHPIAslvYGcrzae8iZPB8hx5VVJWl/cpD/GfRoPm/B58lZnPuSU+d3J6cqv52hlc5r2gBY\nt5NUAUTEnZL2oJ1Cha8hk7gzlTWtzo2IoyVdT45NGHNiVbr/RiUi/jDWeF0+TE4YuVrSY4PXydbA\nOrVmCkk7kOMNTyZnif6MnEW3MpkY1/ZhsgDxdZL+XbatBPyTnCxT2zrArj22/xHYv3awcTjXR6y9\nRN3vh6+QhYd3jgG2TJQW416J/5jPPUlLdpLPMsOxr4pJ6snAFyWd1t1qWn5ofJ7659/HyJ6FGyR1\nWsNfTI4FrPbjd9K0WCkXEb0A+GwbXQ894l1ZYv1Ycxa52xNYISJ2qhzvb8BHupuXS0J5UESsWTne\nDWRrwB9Lt8tLIuJfkt5BTqvfvHK82cAHWkgQ+8W7hpzl8Zvy+q0VEVdJ2oX8tfmWyvFuB14bXYVH\nJW0I/Cwiev0gGEu8u4HVI+K68lq+KSLOl7QScFlEPK57cB5iPEp+KXVmzzQL+DX/bquFU+SA3ce6\nHtvofpF0GXmOHd51rn+LXH/u0y3EnEKuftDsVm1lSZ3yXlk/utYmlPRCMiGv0v3ReN6Bnusl5unk\nBIMD6D3uqVp3maSfA68gu/kv5/ELktdufV+WbM3ciKHzseq5p0bB4cZ5/7i7UXFMl6RFyVnvy5HL\nj3VmoD+f/HF4I/DS5o/VinF3YM5z7/sRcVetGJOmxYr8Rfe6QSRVxfLkiw45g6Yz7uG4sr1qYkXO\neOo1uPNf5bbalmCors0dZEvgv8iZWIe3EG8KOYNmUJYmP/QA7maoJMBvgP1aiPdz4LvKdeA6LVQv\nJ1s8T+n7qHl3DVm75zpyduxbyPflq6m3cntzIOdLyS+tLzK0wv3LyfInn6wUbw6lNeC0cmnTKkAn\nYXuAoTFr3yInk1RPrEoC9fNyadufyC7iXbq2f4DeJV7GatDnOgyw9hI5i/TkAcTpOIicVbk6+Xpt\nRX6+7UOOya1hU7L+WOd66y0uEXGXspbbfsC7GPqOvZOsp/jp2klVJy45A7g1kymxOpsse9/KWnY9\nzAKWIr+4riW/RC5maJ202v5L7yJ+zyWrF9d2FfmFch2Zsb9d0vlkMc02arIcRi4rM6OF5+7lOrLr\n6DoyYdySLN75cjJRru0j5K+uP5IfgpBfMKdQp85Zt6PJLp4/kh9Mp0j6IDmbZ/caASKiU2MJSZ8n\nW1SbSc7Vkv5Ddo38skbMJuWivR8jv1Ag36cHRkTtL7XbGKp1dCOwJjl1/ukMrXNXlaSt6H1sv24h\n3B7A6aWF6vSybVOyC6RG8d9ugz7XYUC1lwAioo3u2uFsDLw6Iv6uLAh8S0ScrSxC/Hkq/PBotuh1\nz5hri6RlS0PJ+yXtTCaLAma10XLbiLsG+XndPPcOioi/1YoxmRKrQ4EDSrPoX3l882vthShPJ9cH\n/DNZqftryoJmLyHLItT2sxLjTVGKWEp6HrmUSNVSC8VRwAvJX+RfBn5BFrmcQv0FrSFbjLaVtDn5\npdX9+lWbDl2cTA6ePQ/4OnBcaU1ajhbGlUTE/4DXS3oOc3ZdtbIoczQWWY6IUyWtSf5q/2d0TeOv\nZHV6V82+kaHjrUbSx8kVB35Avlchk+JjJe0ZFSugk8npFuTnyglkMc3NyfdP9dYySR8iZ3T+iKFz\n++XATyXtHhHfqBkvIs6T9HIy4e4M+v8LsGv3zMRKBn2uQ35m7Stp17bOuW6SVmGotMoVkVXY2/AU\nspUM8kfvM8lioZeTn+FVaXDrkF4vaZmI+E9JpKqXTepWvsOPJVvdO+sQvgy4WNJ2EVHlu30yjbEa\nLoOt1u/biDeFrH/ycPl7G3KA8pVkIbHai84uRha0fClDb7BlyO6drdpoEu2KvwLZAvLPiPjrSPef\nh+cfrhp41J551SP+SymvX0T8ooXn7/z6GghJq0VErar4o4l3Idnyt2OURYklPYWspP3siFincryb\nyYK53+3avhOwT0QsUzHWksBCEXFTOe93Z+hc/0JJmquRdCM5G+rbXdt3BT4XLS1IPijjca5rQLWX\nSqzFyB/bbyYHPUO2tJwEvLfmWJ0S73zyXPiNpJ+SQxv2INc+fX1EPKdyvEeBaT0Sq2WBqyKiSitu\nvzhtknQ18IOImNG1fS9ypmCVYTeTKbFacbjbY8DFINtSfuWtVf78C1kMsfqLJGmBTtJoY1c+JP5F\ntgDOBGa2mWiVeH8muwSP755V00K8dclWzQXJVgjIOl2PkN0UVVvJyhfli7tbHyQ9myw22foyJW0p\ng8nX6nNsF1eaeDAes7zGjQZUe6nE+h65IPL7GWr12IDsVTk7It5bK1aJtx05M/wo5VJdvyG7qR8g\nk4ETK8X5WLm6P7A3mcB1TCUH7D8rInotQTMv8cYjsbqXXF2h+9x7DrnCSZWaWZMmsRo05eKdM8vl\n/PktCSkf7ucwnx6jcvHqMxhAklPirUpO9Z5OjolYnqFE64yoXFeqjJfZjiwfsQzZdX00cHK0VFFf\n0iIlZnM2zbFRa32tOWMdTS5M/OWu7Z8iPxi3qxjrMPK9cuYgWh0lHQdcGBFf7dr+cWC9iNimQoyB\nz/J6opB0G/CG6FqZQ1me5OSoPAO4R/yFyXPwuoi4daT7z8XzXlOurkh2+z/SuPlBclHt/4uIP1WK\n9yhZDmfYz4+I+FKNeCXmb8gfokd1bd8B2C4qzYaf0ImVcpmXn0fEQ+V6X1G5QKikL5BfkOuSYwTO\npeUkRNLTgK3pXatkn8qxNiOPbzo9jjEizun74NHHOAV4Z2Q9p2FnxkX9KcrvI49vY3Jc1VUMqDWp\nxF+NnC33TnJ9tNa+vJSVmbcjuyaeBPw0It7VVrxBKE3zu5Ez2jqzEF9WLgfS+DUdEQeOMdYx5FT2\ngbxPJH2W7G48kzmXB9qYnAjw2ELoEXHwPMbYmGw9eVjSdIaZcBMVShGM57ne2IelydllqwJ7RsSt\nkjYAboqIa4Z/9FzFuRdYJyIu79q+JvCnqFy+YtBKV+6bIuK/LcfptPI/MszdIiJWH+b2uY35fnLA\n/3HMee69Hfg/GuNII+JX8xxngidWjzUVDnqMVWMfnkI2+04vl5cC99fssy9xXkbOrHqAnOZ+I9kS\n8QDw74ioPkixEbtzjNtRMREoTeYfjpxWO+yq9tHiTJtGa9Lm5BpfUyOi6sSNMjZnHWCTEmsDcrbZ\nTPILulpXxDD78BJy7McL2zgflEugbETvatpjSm56xBrtF2FUGxeRXXGdHxsbUVodI+J5NZ6/EWe0\ng3Rjsoy3Gu9zXbnU0e/J2YFrkAtcXy1pBvDciNi2YqzTyJIA7+q0DpfW3B8Ai9Vo9ZD0DbLq+D3l\nel/RzmSA1o1TV+BoZxuOKaeY0LMCI2JKr+sDthhZduGZ5HTQh8lp+7XtTy7X8RHypN2UbCI9jvyy\nrE7SMxlKBDYll0Q5m0wGxqz5Adpm4tRPSXbWZej4NiCLB85sIdz/gPvJcUjHATsPYtyfpOXISuTb\nkTOEziNnd9aOsx1wJPn+v4U5W0CCbEWqJiJWrvl8o3Q1OXalc64vQ4/lnsaq5sD70ZB0MdlNfFxb\nLbXjfa6TNda+HhF7lfF5Hb+lfjX73crz3iipOd7wXupV734BQ8twvWCY+7XSMqLBLEk0Hq06rZRP\n6TahW6zGk3Ittulkf/OfyGb7mcB5EfFAC/HuIJdEuVLS/4CXR8QVZdDwsS3M/LicoWObSemWaOPY\nxoOkXzFnq9GZZMtRK8mOpLPIFquryPE6Z5R4tw37wHmP914ymdqIbE7/IXBMzS6PrnhXkeUB9oyI\n4ZruW1NalG6IiPsrP+8nyXN9Q3Jae+dcb+390mMflifr97QxxOBLZPK9PHlsRwMnRcTdwz5wElGu\nHrFWaaVqVs9fCfh7RCxUOd7CPH684Q+jzJidzDTCkkS1EufxaLEalAmdWI00rqqphTFWj5K/zL9F\nlkG4KFr8z5J0C7BBSaz+QRZj/I1yhe8La/fbK9eUW5ys4dNJBP5c8xhHGmvR1MIYqwfIsSonMpTk\nVBvo2Sdmd7fx2mTSc0ZEVK0NJmkWmegcE+3UreqOdzfZxdhWrZ7ueF8C/hER35cksp7UpuRrunVE\nVFt/sXGuHwAcFRG31HruPvFmkF2Mx5S/f0mOrbydLK1yYUtxNwS2Bd4KLExWfT86IsZc3HU8z/US\nfzbwqoi4qCux2go4LCJWqB1zUJSFcn/RRtLdJ95AliRSFh3eN4aZbCPp2VGxLpmk/wfMjoiflr+/\nDbyPXE7nzbViTfTEaiD9oX1id8/yWhQ4i6Ev6aoFSSX9lqyv8UNJ3yG/lL9Jjnl6akS8vGa8ErPX\nMf6RTAS+VuH5hx1r0VS7+6BHkrMOJckhj6+1JSnKINpNyeVl3kYLg9clTR1ky5GkE8gZT1VnNw4T\n71pgm8jilq8iq9q/mtLlGRGbVIz1Sh7/PpnJ0EzBqq2OyoWX3xER50rakixY+EayVek5EdFGNfRm\n/AXIZVE+T6XxeON5rpf4hwHTyKTxVrJbPMjCy6dHxJiWfhnniVT3kqtFnEgmwmeP8JCxxruF7DEZ\nSKHVHvEXIpfo2gnYsOZnp3IN4PdHxMwyseG35ALlbyDrVr6hSpyJnFhNJG3P8pK0DrBoRJwh6Rnk\nQMhOkcIdo4WinY3YU8mq3TvR3vEtDDwwjt1Iq5JF9do6vreRX8ybkMsQzQL+wFCX0j8qxFid7NZ4\ntFzvK7pmLFWIvROwJ/m+7LXyQe0vk/vJwqM3KBdDVkR8oHQHXhgRS4zwFPMatzmRYzvyw3bB4R81\n1zHuJxOo6yV9HXhyROysrKVzfkQ8rWa8rtjPIluttiMHeZ8VERtXjjHwc11ZtPNXZEK1CHn+LU2O\nGX1VjLEkyHhOpFIuGvwW8nXbhFym61iytbp6kWBJXwQeiq4imm0rk2/eS/7AeIBcPePEiBiu4Ozc\nxrgPeF7k4vX7ka/p9splbmZGxDNGeIpRmdCD1zskLQgcA3w2IgayVmCfWV4LkQPXZ9aO12z+L10R\nW9eO0SRpPYYSgQ3IqsV/JpfamFk51lSyC+dFDC2M3KoyMH86Q6/fc4HZZHXkmS2EPIgcv3IQlRKp\nHi4jf5X/p1x/3Er3jb9rzwr8Tvn3sz1uayPebQzV09mCoYWQFyCPsaquiRzN5HjMpQh6uJ0c73Q9\nOdh5z7J9CvX/HztlXN5KJlMbkIt2H0OOCbqucqyBn+sAkStTbChpU3LZsSnk0IbfDf/IUT//uE2k\niqzk/j3ge8rq528nk6zPSPpzRKxbOeTAliSStDj5vnwf8ByyhXFRsqWqjffPXeQElevIWeIHle33\nU3Fg+6RIrErz6xbAZwYY9n8MJRszyRfgrLH+8plAzgIuZCgZaO3YIuKR0rVTfYbVMGaRSwP9gXaT\nnU7XyhfJ+lE3thGjeD45FqhzfWAG/WVCJsDHlqb7Jckme8hVCap2UUi6gqHE+0zga7T4fiHXBzym\nxH0mWUkbMhlp44fjLPJ98yPgoxHxlxZiAON2riNprYi4OCJOZ2ih6flO5LJL3wKuBT5HJpG1rQ5c\nXK53rwNacwzu0WQX3J/I9Vx/HFleYswFcofxO+BQ5RJdq5GtnJDHXG2iyqTpCpR0BLnQZc3FV4eL\n92ryw7W1REo5VXfjiPivpL8yfBG/anWsSiKwJVnMrtUB3Y2Y25NNvO8cwCDyKWTicW0MaOaTpHuA\n1WMwJRYWJFs5vhsR17cdbzyU9+hHyGK5R3WSAUm7AXdFxOEVY/0/2k2kuuM9iSwQugJwREScX7bv\nDtwdEYdUjDUF+ADw/Wh5vdFGzIGd642Yj5ItZEeTLXG9FgyvFeuLZNXz73Rt3xlYLiL27P3IMcfd\nhKFCwAA/IbsDq3WVDZKkh4H9gAOiUYxU0kPk5IPqLVbK5Z32I8+9b0fEKWV71e7PyZRYdSoxn0m2\ntMyR8ETFAoWlOft+WnpxG3H2AvaPiHvLTKHhEqu9K8e+nyyi9++azztMvL8CK5O1WW7g8a9fzcRR\nZB/96oMagCnp9+SJWnWs0TDx7gbWHNTrV2K+GvgU+esuyC+y/WIMFYrHW0lSrwdeGRF/G+/9qW2c\nzoWBneuNmM8lk453AKuQLfJHk60gdwz32HmIdR1ZmfzCru3rlnjDrms7D/H2J7v/Oq2bxwCnRMul\ncSQtRVaxv7iNWGUSwHvJ7vdTyfGbvyDrgbX63du2SdEVWOwA/JccnNh9YlYtUDio5uxmsjTogYLA\nJfOdlpUAACAASURBVMCzyfWfBqHVqbtNERHKkhXPoHK30TC+CxwgaQVyHF73l0nVWaRklemNGNDr\np1wi6GCyXlanivwrgJMl7RIRR7YQc2Gy66+7SGFEpVmdZZjBQwy4WGFptVqT3gUYqyWq43QuDOxc\n74iIK4G9gL0kvZRMsr4IfFPSLyPirRXDPZMcA9jtNnLAfG3rA18CfhQDWDC7DJY/kmwZC3Ls09WS\nDiVrrc2oEaf8CP2JsobbjmS5k8PJ8+HFkq6Illp+SstVr3OvTjIXEb70uADbk78OlhpQvIOAtQd4\nfFuTydUbyIrrSzYv4/3/X+n4zia/mDWAeI8Oc3mkhXg7kcX7vkwOTH5V89JCvH8CH+yx/UPAlS3E\n24wcF9T6/yc52/doYIG23ycl3sbltRvUe2Wg58JEuZDLj/2lhffLlcD2PbbvQNYnG/djH+PxHdx4\nv9wNrFK2vwa4pOXYmwHHk+UlZgGHVH7+NTvvia5L1XNv0nQFDtqgm7MlnUMuBtmcsfPvmjG64jWn\nDD9uVllM8hXvlUUCFyJ/kTxMdoc8Juqv9Ths839UHns1DlO+HwDWiK7upFL+4G8R8eTK8f4GXEDO\nBG57weyfk8nOfeRsy+5zvXbx2r+SJSs+R+/K1lW7XQZ9LownSSszVCrj2eTklWMiYtR1tkYR4+Nk\n6ZZPMTRQ/pXAvmTX+FcqxBjPulk3AG+MiAs0Z7HVTrfgojXj9Yi/ALmc1BuB90bEiyo+93lkV+MX\n6H3uVRlnOaG7AjW+C1EOtDk7ItaXtApDNWb2kXQumWT9KOqvNF6twGI/ymUmVolcZf4uhh9DVvvD\nvfp6ecOpnTiNwkDWvGroTE/u7k7agoqzaRpWAl7XdlJV3ErOQhyUVcgvroFUsWcA58I4n+tI+gD5\nuflSMjk+klwKrPos3Yj4ahl/9A2Ghos8SK5VOOakqvgxQ6VVhvsuaqPUydPo3dW5KNm6U4WyMO/T\nI+KExrZPAzPI3OR35A+eml4AvKRWAtXPhG6xknQG+QH0v3K9r6hYiXkiKMXStiUHLT49Igb9RTpm\nZXbQ8RHxQLneV0R8f7jbJ6Jx/lW5PFkO5OoYwElcZs59kxxfdU7ZvAHwLuBDEXFY5XinAgfFJB4Y\n34+k08mWjd+OeOdJYrzP9TKg/Diydaq1YspdMRchJ3JAzlifL9ZelDSTLB1zUEmSXxgR10g6BFgx\nIl5VKc5pwK+jTDxT1lY8DziCXHtxd/L13L1GvBLjArLkSLvV6ydyYvVEVgZgvhPYBlg4Ip5a4Tlf\nQjblPlqu9xX1B1u3TtKSUQZ3lsGJfUWFQaAah2rMJaE6maH6NVeTa1xdWuP5R4j9RuDjDNXQuoKc\n1fqzFmK9iWyuP5Deld4n3fuzQ9LryIHV+9H72NqYZr40mQSvSi6kfatySY+boqWFuwdJkgbxA+OJ\nQNL6ZN2448nvoMPJKv3rARvVOveU652+OiIuKn/vTy6ls2H5+63AFyLieTXilefcmPxc+Qy9z72+\n6xbOVZzJ/l4sfervjYjPVXiu8W7O7kwZ3pasOn0G2RX4k6hQT6tHItCp3N1tIGOsygy695BL9ox5\nirKkR4Bluo7vcXdjwGPIJG0eEadVeq5jye6OL5BjZXYDHo4W1pIc5f48GVi1djLQdqKqcaohV2J3\nH1sndivvTUlrk7NIryG/IFcrY2ZmAM+NiG1rxuuzD1XP9fKc4/ZDUVlT6h1kPaQ5Zo9H/H/2zjta\nkqrq4r9NkCggiAQlCggMOYogOYMoOX9klSBBcs4gMEpGMiICkjOSs0iULDlHCZJz2N8f5/a8np5+\nYebdqn71qL1Wr+lX3VOnOlWde84+e3vJDPvvsQLeEi+7xIuk2YGdCM/a0Qih7MNyVgPVZO2U/r4T\nuNr2QenvaYFHcxQVmmI2fnttf++5fnsDmmPVHdKo8uqEBkbDO6nfiRUx4fRhul8qR0ehBDs3oXh7\nAnCu7Tcyh5mOLuXu6TLvu09Q6Ab9irAwWIqYsPlLpt0vSdiFQAkcsp4g6YfECPEmBF8o18VyMUJ4\n8eYU55/EKPTYtj/LFGNkMDNx0s2dqBb9/byILhJ32fIAparmE2PsR9veNy0YG7iW+H4WgoJ/6xB6\nhg0e0n30sFAk4/dT0sbAiUTleHHChmUm4jv7t0xh+vqdLIJjRUqgemzpZsDrRAX15bRAm5sueycI\nTldu/axCreIaqFTFKmXRDaPgCYkS5TDl4ipDofz6N9uPD4BjmaF1+ivDPmclTrAbENNXPwR+Yfsf\nOeP08Vi+6/Dfyr3f0YFfEq+z4bN1HmEkmqXdkqpyP2xOuhWq70NcolhoU+w5CU+2Sk+RDmakSvxc\nqUrVPOU1LWHqPXbmeKX81tMk7ku2XeZUrqRHCf7fqS3v53GEcv5uvexiQCNVF9vBwGcOL9sccU4g\n/Hh3A1Yhvi9T2v4iPb4+sK3tBXPEKxMDvmKlECtbj/ihzkysNNcmPH6OLoKP0MOxZC9nN2B7z25i\nZmt19gRJYxMO6lsAi5BpFSRps7TPWen67G4lViKlTtJJWiQdy+pAzvLyT4jv5/8Ro/rnEInVhgV9\nP1snc76m/Uq90khj1wvQvt3y14Jjj0N8VzdvcD4y7380Qieo3Ws7v+1/GnV8Skx6tWJmotqTBWX/\n1luSpVdsZ5tY6wXTExNrEK+tcS45jvCVrXRiRYgOd1txSYn6GcAutr/qR5x9CFueGwi9rI0aSVXC\npkAWCkUrEge33W8vT5HGA0CQrKcb8AFRst6AIHE3tn9J2DQUHX9MQoDxWkID5j/A/gXH/A7Rv7+B\nuGg+X2CseYDjiRba60QbcomM+/8KOBAYu2V7WZ/fD4jpkicIm6KricQ41/5vJxwBTiU4O4W+PkLI\n7i3igti4fUNIBgzbVvT72nQ8c1KMqOXMhCjpV+k38EV6nZ8DHxT4euYHTiJM2N8lPPZyx5gBeCy9\nnmaBwq+ALwqIdzJwOTFF+iHRspqWEAg+MmOcjv3W02/iWGDBIuOkWC8Ds6f7DwHrpfsLA+8XFHMO\nwvLlPkLf7UzC0qqIWGsRydXuBL1iyXT/eUIEdbv0fme5DhLdp9HbbJ8Y+E7m1zYZcS1vFQj9Oud5\nbMBXrIgT6YTpNjYh7lU4OtG66qbVuYcztzolTUiQ5Dcn7AouI/rZizh/heUEYEtgeYWb+bnOVEru\nDpJE9NK3SP/eR1zMFnSaQMmIhYjE9GSX4zW3ZQkxhqE3UjCQbWKnBUcR1kBzEQrMcxG/iT+Th085\nDJK+R0zMbUZ8T8YGfkMkVV/29H9HEUcTQsCLEPzQ+YBJiNe8SwHxdiIWFG8B4xI+epMR6to538vS\nf+tN2JPobNwp6Tm6RJaLsPG5ndBvewQ4HzhG0jIEjyx7hSVNkV6c4jauP4sAD0hazfYVmUP+Fvi9\nhyfF36SwRtrO9mKS3gT2J2yE+gV34+XoYux7jiI0AOclfgerEDy9vYHfZ4tSRMabOcNsVIyuIRKc\ny9PfX1BMRWAzQkujUe5cgpiKKKoC8V3iJH4vsZr8C9FGKireWSnODQQ5cby0vbBVJcNX4D4DriJW\nt3MWEOtAYkX5NPGjb9gxFPV+zk0kVu8SVgk7ED/UUipyRd/oqqqUbdnzDmlFDrwP/CTdXwx4OFOM\npYiR8k+ICdxNgAmK/uyI6uIcbV7bksSUW1FxlySSrF2ApQuKUdpvvZv4UxGK6A+l7+ZdwNaZY0xM\ncIFI14Zd03VpKDBRAa/pYdpUh4ADKMBihrjOzthm+0zAJ+n+tI37VboRXZkF0v0PiKlYiATrn9ni\ndPqFjuSbMk26WL6QfjQXACsBY2aMUWo5m5Jbnen1HQx8r4zX1yb+dIRUwMsEH+lCYO0CPr/RW7YX\nfbEcm6h63JxifU20IL9XVMwybuk31+utgLj/oyspfgZYMt3/ca4TevquHA5MXfJ35V1gunT/WWDx\n3K9tINyK/q33If48FOAV2IH38TNghjbbZyTI5LnjPUHo07VuP4IYdoBomb/S6fdmFF7bB8C06f6L\nwMLp/nQ5f3vDOTsPdNh+0fb+xJuwAkGwu4guCYEcaJSzb5e0raRJM+67HVpbnUVjLaKt8oqkSySt\nmsaiS4Ht5x1E/GnSsYxOcAdyYRfCY+oVSUdKmjvjvruF7c9sn+VwAJiFOAntALwhqfTJx1xIv7le\nbwWEfpTgbwHcA+yaxP32Z0RbnVHF1cBWwB8lrZKmOsvAY4S1BkSleqckCLwnIfiaBZLmTHpLzdvW\nl/ScpDclnZikawpBCb/1tpC0iEIl/DoiWc0lgdApvEm0rloxL/DfAuLtCPxO0mOS/pJujxISRI12\n2fxEG7RqeIpISCEqgZsn8dwtiGpWHnQ6g8yQgU4K7JB5n2W2rkptdTbF/RHRV36WaLt8TfCu1IHP\n8AcF7HMhgqP2AXEh+4qMpPw+HkNDfuGyst/Tqt+A5YDV0v3pCZX3b4iLzOIZ40xOTHE9RSzQjicq\nVrMU+NpWBtZK92dMv8FviCrdMhnjXEGorDf+njWdV64leF4fNj9e0uea/bee9jsEOIQgWH+Rztnr\nAuNkjPFhOp/0dMtOXk/n6feIxHuJdNursa2g93Oq9H5enG6H0FLZreKNIN9vnu4vkH5zX6fr/Hq5\n4lRKx6oTaMgdEHykiQny4AW2zysg1jTEB78JMQp6EcG5us7FkGgbcZcmiOy/JDgfl9gulSRdFBR+\nXusSn+GChJjlBbYP6+iB1egRksYFPnfTCH0akX7XBZ20JC1OfE9Wp8v89gLbd2eOM5rtb1q2TUlM\nc/ZnfL01zqtEcnp3+vsAwth6rvT3ZsSidLZcMTuFpKh9D3A24VmYnTSvnj0QhxAVnTFtZ+0ApGGc\n7YlK0pRp82tEVfyYnL+H1L04GDje5RvLl440yDWEmLzPVrGqE6s+IunOrEAkICvaHqvAWCKmTjYj\nSHWf2Z6oqHhNcb9HaDFtanvO3p5fNUgaQnx+69merNPHMyqQdAiR+N7b6WMpCqkl9xlRIS5Np64p\n/oQE53FTQlgzp2r3GMRrm8v2o7n2202sVsuQW4Dbbe+d/v4x8IALsOcqE+k93ZKYQny75NhTEJzO\njYiuw24ucDo46TriAgSOm2J8RAyOvFBUjE4gJY3PACsUfV6pFMeqk7D9je2rbK9KlEmLjGXb19pe\nK8Xav8h4TXHftX30YEyqAGw/ZnsHog1aVUwBXC3p1cSRWb4ojpykRdNFq1SkKtWLtIj3lRj/fdvH\n256XkELIue+vCImFMs69bxEyMY1kdV6gufr2HaIFWWmk9/QIMor+9gZJE6RFztNExWMp278oMqmC\nYQnV55KKfK3XEhOkgwqp6zMGPYif5kKdWI0CbGdTK+5DrLdsH1lWvG8DimyrFg3bmxAaROsSk1bH\nAW9LOl/Seqnakgs3E+1vEuF5koz77g0HAn+Q9P0SY44A2w8UsNtDgYMzf1btcAuwr6TpiTYSxGfa\nwKzEhPVgwEOEBlmhkDSmpO2JIYPVgY1tL2T7toLirSnpUEm/TH8fSHC93pd0fUG/yRuBQyQdJWlD\nSas13wqIVyb+TAyLFDqoUplWoKT/A86z/XnL9u8A67hgi4saNQYqJM1G8ON+SSg03wFcSvCDRnlq\nSNLbwEq2704clsmK4K50E/sRYvp3TOAVIokcBttzlHEcRUDSvYSy/OgE2br1tS2QKc60xADO9ARB\nd1vbf256/FLgWds7tt1BhSBpBeAPhBzP/Yz4nvZbbFLhXXcgITC5P3CKC7TRkbQjkYQ/RHxfTiZE\nUI8kqi7bAlfm5sOm33p3cM7WeNmQdAExGPM+MRXY+j1ZK0ucCiVWXwNTtFaLUsb+ZpU/7MGKdHHs\n0xesyhfKgYREgm4kWTf3h6Qv6SSCO/I6MUzxCiP6FAJge/pRjdNN7P3o4bvjkF2pJCQd2tPjtnfP\nGGsMolX1lu3XWh6bk9AieidXvLTfiQkC9FKEpdRwnZEiOF0tyUDz90ZkSgZSjE8JY/WPunue7W37\nGyvFexI4xPaZkhYGbiOmSS9Kj68AnOjMvrWDGZLO7elx2+tmiVOhxKrtijnpFN1oe+LOHFmN7iCp\nz3YHVb5QDlakIYoVCUmAPxFKz21Js7b/WOKhVRKSFgXuzDn5NxAh6RLCkeBkYnptuIuM7TMLiLlY\nT4/bvjVDjFvofaFo21n4SW2GDz4nFPufTH//kJhmK4WL2Jiwtn1qGfFyQtLUwMtFTRSPEG+gJ1ZN\nVY8hhL9W80lpdEJ87upcJbxvC9KqslvkKJ3XGDyQdAbRSipsGqkl3nPA/K3VFEkTAf/OXSFriTEO\nYaj7dM6R8+6q7oMNkj4g9LiyylR825CKCZM3vi+SPiQmZZ9Lf08GvFZ0t0bSQsSE+tpEzlDakEAu\nlP3bq4IJ84Xp39kI0bfmEuwXBPnyotxBO1TO3grYmuCWzGb7OUm7Ac/Zzq1y+zY9r74q11pNJ56+\nth4rPWJeNhJpHkljEyRhE/yczwoKOS3tv4NjkXmqU9JfgHtsn5A4m/cQC7kvJK3qfMbryrSfgY43\n6aFVVhQUJva/IdTWN7X9uqRfAS8WNIRQBuaQ1FjkChiSFhcAhQ12JIrNRoQ8zU+Ia+9mwJVFxSwY\npf72Bnxi1WgRSXqBEH77vOf/kQ2n0UM5OzfSpMkuwGEECbOBVwnhudyJ1RItf49JvN4tyet4PwyS\nNiGm2aamZZQ+UwVimwz7qNEGiatzKPEef4c4UX0u6VhC/TnLpGXL1NFKkt5v+nt0YqHzfI5YTVgO\nOCbdX4UwRp+c0LHajxAFzoWB3SLIgz2BAyRtZLuUBEvSsoRrxT8IqYBx0kM/JkSXf1XGcRSAaxk+\nKbis5fGs3ydJyxHJ1MqENMefiEm63TqhKVdVDPhWYANKnn0NjlVanawNPGa7R0LaKMYrtZwt6Qlg\nR9tXNZd8FaKWt9kuZdRd0uqE5P8Kmfe7M7A7cBLhoXcCUflYFBhq+6Cc8WrkhaQ/EUnxbsTUIcDP\niWTrbNs7ZYrTICGbEVeZXxIV6h1tZ1s5Jy7LDLZfkXQqYUuyY5qqe8T2dzPF+YYgPn/a0/Nsb5oj\nXqeQ6BvTEonwi8TnNgxFDKpIuhs4M1Udm8+f8wJX2J6yl10MOCicOHpFrnZ1Kl58BpxF/KZfSNu/\npENivbmQfntD6aWSavuAHPEGfMWqCecTH/jpSdvmNqKS9DtJUxZAni27nD0NYTzbii/pWn2VgQeJ\nZCc3tgB+bftCSdsAx6UT397Ea68xEpD0M8Ly5f70d8O25zFiddnjxXsUsB7RXrm6aduzkt4iPBmz\nJFa2RwOQ9DzBsSpDSfsNYDZJrxPVq1+n7ePTkhRkwCSE8XqpSHycDYkKzt62306TZq/Zzl0BvLD3\np2THbISpdiv+R9Jiqxpy8vv6iMmJitiDwMslxy4Dv2B4jnYrTAzo9BtVSqzmAO5K99cAnrE9fxJO\nOwLInViVXc5+DpiHWOE1Y0WglJWCQs13e4r5Uf2I4K5ArNgbHKdz0/YtcgZLXJk96Wo9DqdOPgjk\nOY4FDgLulzQD4Sl5NmGFNDr526ITEmbBrXgWyG63ZHu63PvsAacTlaTXCDmJG9P2BYEnMsfaoGzy\neqra3Ei0UIcQ58u3gWWAmYikOVesMYB7gbtzyzj0gv8RKvMvtGyfh5AJqdE7piZ4VUOJAsbfgb8x\neNrXi5X126uS8vo4dFWQlib66RCmulksZiQ9IulhSQ8TF+VlgTclPd7Y3vR4bgwFjksidAIWSnIF\nBxMnwqyQ9KGkD5puHxKiaRsBO+eOR1QFGmTLF4GF0v0GETo3Gv5dfyRsO3YGjgfeAbYqIF7ZmJEQ\nDoRYaNyYWkibERpWufEQIUjYiu2IFW52SFpJ0m2S3pb0lqRbJa2YO04q/29K8CkXsf1FeugrgvOY\nLVTGfY0MhgJH256b4atl1xLTj9mQpCQuJnhqZeIc4AhJPyLe5zGSBMNQILt4tKSxJG0qaaikIyRt\nLKkw/9gyYPtN20fYnoU4p0xAKPWPAfwm0VKqilJ/e1WqWD0NrCbpIiLhaSQbkwHvZYrRiRI2ALbP\nSKu9Q4Bxibbna8SI+3kFhGytaHxDeIvdbfvdAuLdRBCD/00MBhwpaS1iRZmbmA+wFvBb29dIGgpc\nZvtZSY8TK/WTCohZNhoLoyWJqR2I1XkR00K7EB6FS9NVOf4pMCVhTp4VkjYneHhnAw3do58Dl0ja\n0vbpOeM1RBdbtuXWW+rUVOC8RMLditeJ82duNOxlXihg391hL6Jq+yLxPv+H+H2cTSxOs0HSrARJ\nfkLgkbR5C2B/ScvbfjxnvE7A9u3A7ZK2BdYnFh6/k/RkSryqhlJ/e1Uir69GtI3GIFbny6btewIL\n286+ku0UEodstMGkdyNpNOI1fZX+XptYLT8FnJRrqqwp3ifAzLZfStyZlW3fL2k64KGqyy1Iuplo\nw11LXDyG2H5aIUL5V9vTFhBzSkIOZOa06XHgBLcoemeK9TRRZTmuZfvvgN/ZniljrLWA92xfl/7e\nh+BZPUZ4wb2eKc5GlDvZ3Ij7X2DF9P1vJnYvD5xse+rM8Qq3l+kh9o+J6ebRgP/Ybsdb7W+M64FP\ngA1tf5C2TUC0zcayvVzumAMBkuYANrO9XaePZWSRuj9H2P6klHhVSaxgGAFzSuLC+E3atiAxxZOV\nC1H2FGInoNAkWo8wY4VY5Z1bAPG5W+VbSQKmsv1S5nhPEBfFuyTdDvzD9iGS1gOOtF3ESr00SJqL\nqPRNRbyePdL2o4EfOJM1Q6egUJkeYvuZlu0zEL/BbG0XSf8Btrd9naR5gDuBfYDlgTdsZ+MgdQKS\nTiaIyWsS3Ko5iNbIZcBNtnfIHK9we5mmWEsBkzTr/EnanUjqxiC8Etexnaur0Vi0zW/7sZbtswN3\n2R4vV6yW/ZelIVejn6hSKxCHoex/JU0m6S3b3xQoh1D4FGKafOqroGVuL7Z5CLG3cegqZ28KHCxp\nJdv/zhmPIM5OQUxbNmPi9FhuMvklhObRXcDRwLmStiAIrtk5a2XD9oME8bgVe5N/kq0TeIlo2T7T\nsn1ZRhzw6C+mIVwdAFYFLrV9uKTriIpg1bETMTH3FkEzuINoAf6TYjTrWjXyisRuNOmMSVqAaP2d\nRlRUdyb4sjl5o5/RfmBjwvRYVqgkDbka+VCZxErSmMQPZksiGZgJeE7SYYSy7gmZQ5Yxhdjc5hgf\n+D0xIfevtG0hYIFMsVpxMnGC3cT2xzDMC+r09Nh8meOJ9knk+BRwMnKTkW2SeHiZ1HrMqYHUaUia\njRihvz6VuT+hG6PkimEocGxTBQni89sQ+F3mWJ/RRbZeivgNQAxzlE3Czo7UrlpE0pIEp3E0whbo\nhoLi9duXbyQwO5FcNbAm4ce4BUD63R9E3sTqCuCUtFBrXCMWInibl3f7v0YdhxPTzb9lRA250cgk\ndVIjHyrTCpR0ELA68SM6B5g98QRWB3a1vUDmeM0cnQuJ9uOBkqYiLs5ZtaUUthpP2T6kZfvuREtk\ng8zxPgXmbRV9S5Mf9+V6fZIaitZbA2cQF/4GRicSxy9sZ5tOSkn434A9bLeTCKg8UhX1IuIEa8Ks\n9TmFwOWHuds7nYCkVYEdgQZZ9nGCJ9GqPt3fOJcSi7U7iIrftLZfU6hQH2P7JznjfRugkuxlNKJR\n8Z2Ed+xB6e9pgUed0d9OYSlzJqGL1FjEjEYkVRvbfr+7/zuK8d5gRA05JK0EnGp7ipzxBjMkjQt8\n1qASFYXKVKyIjH1T27e29PAfpX1LpL8oYwqxGasRq8lWXEAolufGEwRfrVUjawqCUJ4Ls6d/RVwg\nv2h67AtiSnBoxnjY/lJhcVHE+zZQcCRBCp6S+K42cD5wVEeOKBNS62NZwnHgkhJCbkPYdqxBTJI2\nyPgrUGArUNJ8ROJxpe2PU8X488aAR+ZYC9K972k7GY3+xCrTXub1tN+Xk9zB3ERy3MB3ySzImvha\nv5Q0I02DHK18wIwoVUNusELS6EQVek4K1oasUmI1Je25FWNQzOvYn5hC/CMxhdjgci0HFGHo+TGw\nOCNyShZn+CrPKENhLN3AXsAxkg5g+PH5vRi+tN4v2F4ixT4D2K4xRVMCLiaS1axJ2wDCMsCytt8I\n/v8wPE0I/RUKSeMQrbmnnVkh2vZXki4mLlqFi0zafoWoPrRu376IeGkI5zKiWmtCk+w5wpftM0Ib\nLGe8nYh20jOM6HtalIbc791lL9PALUQFMif+ARyuMKtfhTiP3t70+ByMeE7NgjSF+wHwVsEVkIaG\n3NYt24vUkFshxZseWM72y0kC5XnbN/b8vwcmbH8t6UVafGqLQJUSq8cIq5UXWravRYz0ZoXti9Mk\n25R0CTFCTJmMoHmTAUcCx6dVbHOisxFhBJsDbzPilM45TdsaV+jLyEwmt71Jzv31AS8Be0n6OXAf\nI458/6nk48mN8WifcE/C8FXBLEit6nvSxfI7BBdwCPCFpFVt5zQqhpK1kNLE1cpE9eMk2++l0f13\nC5AHOBL4L/FZNU/DXkAo6ufGdoQe3nG9PjMPyrSX2YdYRN1ACEhv5C6BV4iBnOtzBuwA37dsDbn1\ngRMJq6ql6HKtGD0dSyUTq4QDgT9I2sAF2mVVKbHaH/hb4jiNDqwpaWZCLmClIgI2phBbthUyhZim\nkF4gToJrpc2PEyeKXAKaZU7rDId04dqO7tsRuY1ZNwbeJVasrfs2UR2oMu4ANqAr6XaSrtiJqAzk\nxnJAgy+3CtFimZy4cO1H02RWJuwH/DHpzxSqhZQkHG4gBikmIhKc94gL50TA5rliJSwFLGX73ZZq\n47MUU22cgPaJTlEozV4mXRwXlTQh8JHt1sGNNcnv+bovUeHcgFiYNnAPsCshbJsNtm+TNBPDa8hd\nQEEackTytIXtv6cqVQN3kclLr4PYCZgOeFXSK4x4XslyHapMYmX7CoWQ3x6ESvi+BD/nF7mmjmBB\ndwAAIABJREFUWxLRevfEdzimp+fm5iWkfZ5PMSrkjf2XOa3TihOIUfYLiCmvQqcmXK7XXCewC3Cr\nwgfuO8SE0BCCI/ezAuJ9jy6pjOWBi2y/qfAT27OAeA0l+Ytpo4VE3orqUcB1RCLVzJ+8nBi4yI1x\naF9VnJQCJmQJSsPyZL7g94CGvcxajGgvU8T7SXeE8QKqjVA+35eUQBXxO2uHGemaTG/GR3R5vFYV\npbirVCaxArB9LcXqysxOV9lz9p6eWEWk0fUHbX+T7neLAnSsfgWsWdSId09InJaieRClwvYjCiXk\n3xEX6h8Qv42jGxNSmfEGMJtCxX45QpkcospThI5OmdXVnwE/TRyM5u0vEe2W3LiNqKjukf52Itbu\nSjFtlpcJu5WFgYdp+bwKaIu3s5dp0A6y2st0CGXzfZunLKcn1M8LmbJMeI1IEFtf46K0J9FXBrb3\nLyNOZRIrSXsQhpD3FjE1A11E69b7gwj3Ee2bN9N9095DKXdFAIIPVMQFvy06wIMoHYl0vWtJ4U4H\nziNOul/TlQAsSEyYZkUHqqtjttk2NTFFlBuNauP8wFjEgMwQYvorqylywuZEteFnjFjNzN4WT4KV\n60vamy7drAdsP93z/6wMSuX7tkxZLkWxU5YQOobHNLUBp0pc1cPJx/cd1KhMYkWQ9PYBvpT0L4JH\ncgtBqC1iPHkfYKhbvIXSNNTOtqvYa56OUF9u3C8ThwO/l/RblyOeVioPohNIJPLZaM9Zy8qpsX2A\npMeIZOOCJoLwV8BhOWM1UJYWEtEG/D1dRsVWeL/tT1dLMhts/ye9ti0JKYCxiRb58c7kS9gSr9Tf\netO58zli2rGxvcrnzmaUzfctc8qywfedkCD9j00UND4nPtPjc8crGmlyc3rbb6f3r9vrjzN5yFZG\nIBSGG/FejJAhmI84sd/pzMaXkr4GpnCLEbKkSYA3ndHvqmw0VXOOzz0q30PMKwgxy/eJ1kBrO2KV\nzPGepYsH0Ww8+xPgbtuV1n9JnJVziQpkK5z7+ylpUiffzDLQskpfEZglfX47Aj+3nW2VrjCXvjn9\nOT0hpzIDMbiyaJmvezBgMJ87G0jisXsA85KU7IEDnIy8M8f6mBCJfqHlXDYdoZ81du6YKe64hI9s\nw9A69xBAKVCT+Xm63y1sn5kjZpUqVjjMgW+Q9AhxcV6JKL/+vIBw3VmwzE1MvVQWSUBzK8qt2rxN\n+PeVhdJ5ECXjOGLFuhcjahMVgVclXU54sF1TQtWxtFW6Q2V9LoKU3GhdnQyc7UyG5JIWHYnjuS1D\nvE4O4gzac2eTeO3dthcrKWxpU5YAkk4nNAc/JCgjje3jAcfa3jR3zCLRnCzlSpx6Q2UuMGnCZHGC\n1Do1cDdwKyGUeFf3/3Ok4zRKhSY4Oc0niNGJ0uiJueK1xP4e0fKcmhYRswLK59cSqsin9/bEHOiA\njlWpPIgOYHpg1dRuKQMrAZsQGm7vJF2rv7g4y6BStJA0vP3R6RT3e7iF4TmNrdpxreeZ/qKvgzjZ\nEuROnjvLgksWr00oe8pyI0Ik+sOW7eMA/0dIrAwaJL3KTQnf3Gly7LMyiRXwd4IfNJRoYWVRI2+D\nbYiT3enEeGszefUL4AXb7UZR+wVJPyX4HJ8TY9evEqPznxPJQe7E6kbgkDRZ1k4n6OLM8YBSbTxK\n1z0rGXcT72NRNhrDwfb1wPUKn7T1iSRrD0m3ElWsi2znlAooZZXu8uyPJm26vyBxHjuY4Q3X9yCI\n7f1GhwZxOnLu7ABKFa+lpClLhTOH0u17kprPyaMT583/tvu/VUNaUP2KGOxYirBx+0u2ALYrcUtv\nwFnECPR7hMP4jsSJVgXEWwwYs8TXdzshwChipTA94Ut4E7B+AfG+6eH2dQHxJiMqi98QU2XTp+0n\nERIBRbynyxFVzY+IqcQ7CBuY0r63BX5fVgEeIcj5cxJciGG3ko5ha0J36RsiEfoDMH6mfR9G6J39\nCPiAmOpcDHge2Cfz6zgN2KnEz+5+YJk225chpuc6/v3q5+tbDBij08dR4OtbgUiufgVMRVRQh90K\njDs94We5FmE8nXv/jXNzd7evgD07/f738zXOSkzBvkkkql8BK+SOUynyegPJamJx4kS0KqG4O0mG\n/U7sJCin4X31RoAzC89Jeh+Y3/ZTkt4DFrL9eBrJPsf2jDnjlQ1J5xA2LBsTyXGDgLk00befpZPH\nVzW0CBPC8K0luyCCsKQpiFbBxkTScyGRmExJVH3etr10hjhjEivIdYjX9A1dq/SNPaLCdn9i7Qvs\nQCThhdsfSfoUmMf24y3bZwXutz1O+/85UjF65FU1w/lNmGclFmdPpr+XIb4zjwGH5/zsOoGW394I\n4rU5f3vpd/AyodT/WK79dhNrMeI13ASszvB8uC+IadwilN4Lh6TNgC2IxOoi4K/E7/1z4lqU1ZS5\nSq1AJI0GzE8kVUsSE4Iiyng58JakxjRLq6/esMOgGJ2nZiXm/wLTEJY2H1GMSGHZKNvGYxhS+6pV\njqDSJFqg1ERU0moED2FZQmH6GILc/X7Tc+4lk6aVy9VC2phy7Y8eA/aVtIkTOT5NPO+THsuBvgoc\nF7GyPp1Qs38yteIvIzhmWxPK3UW3XYtGaRqHjlb1lxQ/nIKTdlyaNnzZg0hQmeiMHAos7ibKQsu1\nKBsqk1hJ+gchbjcOUUq/hTjh3WH74x7+68hgSbqy9LIFQv9NJI1PEa/tIIVi+AaEWnJ2lEyWL9XG\nQ9I0BFF2cYZ/bUUlxqWiUQ0oEWcQ8g4L2e6O/P86mZW1PaIW0nSE8vReGWOUrem2JXAlMWnZ+G3P\nTrRbsvD/3FmB45mJ8xlE6+pu2ytKWoL4HlU6sXL54rXHArunRLwQcexm2H5R0rhpUradRl4h/NuC\ncQLxu1te0lnAuS5QRqUyrUBJhxIJR85EqrtYYxCWHZeWVfpMpO7v2r5Z0qREqXJhItHaxPYjmeP1\nSJZ3ZlNkSVcCD9veI00PzUG0BM8n2gZr9biDkY93E2GgO5Q2cgQdODn2G5JWBK5Pq9gVe3quMwuE\nShrXxQ2M9Bb7O0RrYjNiwfNSUcmQpPEBXLBmTxraWJ8uU93HiZZ/1nNbma2kppgfArM7dJeuBG61\nfUSavnoyR6uz01B54rUNDcDFgE+JanFrqzq3BuDSxCKqHb2mMJpB0Wg5jyxCDHAtB8xr+6GssaqS\nWJWNJMo2q0sS0Cwbkm4nhBC3I8jBcxI/2HOB02yfnTnerERP+0HiJHElTTYezjy2L+kjwv/t0Zz7\n7SQSt2Nyh/lxT2X6Qk9+kiZnxArnSwXEmZ3gRWxAfE9OJb6b9xQQa3tCff2HadNrREX8KFf8JCnp\nZWC53DySHuL9i/BDvJJQtV/A4W25EHC+7anKOI5cSN/DRxvfA5UoXpvi9Sip4MxSNgqHhXsJCZJK\ncqp6Q6PyTXD/JiY+ywtsn5dl/1U6Z0hakODqtCtP5iZg3kjIOlSx7NkrOkGWTxfkLRlerbgQG48k\nIrtxD22rykHSWLY/b9zv6bmN52WMPSHBq1qLlqQqxcuSyEn6LiGJsTlRzbkIOJvQtMpOMk0xDycq\n1EcwvPzBTsAptrNIIHQKknYhWo2ltJIUYqiXEsnwmU6CkqnrMJPt1Ys+hpxI79+CwLq2v5B0N/G6\nGuK1jUGceYErbFeaE5uKCnPkXuwORCTe9grE+WZF2z2eV/uKKnGsdiL85p5hxNZOEdnhKcDQVL5u\np/P077b/axSRphAPpvvEMYuHURNKJ8vbfoPw8CsD2wGHStrKdilaT0WjOVnKnTj1AUOJquavgIsJ\nIvsPifc5pxL6q0RyczRwcaP9WBTJNGFzYHPbFzZtu0nSkwTptdKJFeFMsRjB6Sq8lWT7tkRnmMD2\nu00PnUTInlQNfySkRG4gRIdLEa9tRYkagP8EfkIMFg1qJIL+VcBVkn6Qa7+VSayIE/i2to8rKV7D\nuLfdRFAR5OfTCMuHkynHoqQTZPlxgbIIkZcBYxGTSZ8TeiXN8XInqoWjN15VM3JzrIhV3bq2b1d4\nwd1v+zxJrxNckwt7/u99xudEpWNCQqm7rAtxu+/8w7R8TyuKt4nKX2lIkgrvtmx7ocxjyIX0WnaW\n1EhACxWvlTRGc7KUzsuXAQsQ14UZiYGOPxGDP9v1N2YLTiSKClMSWnmtvq5ZiwoDBW7xtuwPqpRY\nTUD7VUJRKHtSaClCNPDukuLtCXw33d+LIMsfSyRa2S0LeiNEkj9R3Sbz/gYCruzj84p4Pyeiy3vx\nfeJzfIaoLp2aMc6URFVsM+Lkfj0hDFzkQuOvhBRA6wVqyxS70sjNwekL0gTgurSfOF6y7OPJAduX\np7tFW8zsKOkZ241k+EiiqzAJMfDTwAXEOTs3Goukk9s8VvmJ6jJQpcTqXGB5SjIO7gBp/U2iDVcK\nbN/XdP8toiIBgKTvFxDyaKLkWgoh0iWZbZaMTk5TPUsoP79EtIzXkXQPsBoZjXWTftUFwAVJMmNj\ngvs0BrC/wqPwuvS8XBgLWE/ScnT5ji5IJHlnN4tt5uRyltjaKRWSNiaqHpcQcieXEcr50xG+jFVH\n0RYzVwIXS5rc9vGUrwFYdlFh0GFAk9cl/b7pz3GA7Ykpk4cZsTyZWx25mSj8Q4LcOi5wue3bc8ZK\nMdYmiMEbFTnqLekA2/v08PgkwE2258wct3RCZCqhb0hcvPa2/bakhYHXbD9f1nHkRhqhPw3YzyWZ\nMEvagZDFOEbSksTJf0yiVbZdkS16xdVkWaKKtQrwme2JMu7/5j4+1TmqLe1aO4n8fBLx2vrd2kn6\nWIuli/Ej9FDxK0Ba5VFimvLUFnL3cYRLxm4543UKkqanIPHaNCxymu01JH0AzJcGjZrfzwWAfziD\n60iNvBjoiVVfL362PX2mmD8hyLkzEwnc+sD1RCvSRHK1hu1Lc8RrivsIMC1RZn2RERPHLCe/lODs\n2u5CmAj0N0U4z50jXtO+ryNOtqW0c9OEzo2Et9wQYOZ0MtqPmExar4zjKAppqnOuTiWIaahjPuBp\nZ9ZY6yXupMAGto8sK2ZuqAR7J4VNzxG2P0n3u4Xt/fsbryX2J4RUzQuS3gaWtP2wwgT9FtuT54w3\nUKACxGvTfkvVAEwxxyAS/3at3L/mjjfYMKATq05AIcY2DtHXXoeYqLmR0NOB6GnPa/unmeOWcvKT\ntDLRQ9/E9rlN2yciXud3CNn/d3LEa9r/asBBBOGycEJkqkLcZnvfllXeQsDfbU+TM17ZkHQm8G/b\nR5cUbzhC7WCCpGm7I1ZL+pntOzPH+y/R2nm05bs5HaGXNF7OeGVDoZu1okO76iHgMNvnpGrx1bYn\n7PAhZoNKEK9V+RqAMwNXEC1BEY4AYxDn7M+rNvjTW8W2GbkKGJXhWEnaBxjqFvVnhcfWzs5nwfJT\ngkT+oKTbCKLuCWksE0nH0sXDyIbcq8Ye4lwpaQvgdEn/s31tKjtfTySUi+VOqhLKJkTOS5zsWvE6\nMFnmWJ3AE4Tf3EK0lwPJzUV8T9KdxATpLcA9gyjRekjS1raH8X+Svs1+wK4EBysnSrV3aod03lyb\nkJlYJPPubydat48QVZVjFEbMSxHnmcpD7cVr93AB4rW2/5PibUlMzY5N8BAL0QAkfB7vJya430j/\nTgj8meCXVQ25Jpb7jMpUrNKId8MguXn7JMCbzidQOEzdOv09bEWZ/p6M4OhUejJCoTR9ELAmcQGZ\nkKhUvVFQvB4rRLmHBVJVYEXb97dUBZYHTrZdqPFz0UgyB93BzixSmNpUixFk5PmJ1eu/SIlW7qpO\nmZD0G0Kr6HLgt0SCczbwI0Jk9obM8Upv7TTFnp/Q7VqbWNBcbnujzDEmBsa2/VpKUHemy57rINvv\n5YxXFtQB8do+HNMMzqzTJ+kdYoH9aKIcLGD7yTT5eGxuTt5gRGUqVjDMPLcVc5NxKimhNU7h2Wcq\nKe9J14jymMMdQOZEzvZRKSm9kpguWayopCrFK3vK8jKiorNm4xAkTQscRsmaPkXA9hQlx7uBEEhs\nVDt+RvAP9yOqjZVdaNg+SdKtxEXyUUJa4noiMc99boEQHL01JTljEUndsNZO7mAKs/UNiQruDETF\n4zeEenjO6UoAmt+zVOk/LHeMDqET4rUjQNLYhLn1FoTnXe7fnujSj3uL0Ox6ktDomiFzrEGJAZ9Y\npRWd0+05Sc1JzujESeLEzGH/phCVJO3/lETIhPxtgQYOJFaRhxL8rp0JMvs6wN65gki6vGXTl0S7\n86TmE4QzqDEnXtUVDtPg1Xp6rvMLhO5ErCTfIgYO7iBagP+kmuXsjkOhTLwEUbVaEpiKeD9v6dxR\nZcPrhODjbMSU1zUFJVWltXYkLUVcfFcB7iZaPBcB7wB35k6qJP2aSNYa09RDCNPlr9Lf4xGDM91O\nJQ9wdEq8FgBJ8xDJ8brpWC4hFja58SjhsvAccA+wa+oYbUFo11Uakjahe421PENwA70VKGkjIoM+\nnZBbeL/p4S+AF2z/q93/HcV4fRJ4c37jy+eBLW1fk5LJuWw/K2lLgui6RqY4pb0+DQDTYIU0QGMk\n+t+52zplQuFpt79D8+jwnp7rzP52kv5D2B7dTSRStwJ3uQBrnVS9nY74bX8uaS5gByJBvtT5DcIX\nJfSVXieqcAsRQyo3ExykIjiHhUPSV8SwyHFuMsmW9CUFtK5a6RoKmYC5BguNQiFz0hCvXYyoap5F\nVDrnKqIVmPiv6xPtxxmJSvzaFNh6VOi5jWf7YoWkxFWExc3bwFq2bykibhmQtDOwO2GvtAOhizkD\nYVU01PZBWeIM9MSqgdTfzb7KGihIFbGZbb+U+DMrJ37QdMBDVZvEqJEfkv4FrGD7vXS/O9j2zzLH\nfplYqd9OJBw3E4lq1hOIwmj9auB7hNr0OgT36VXgG2AW4Le2T8kY8wvCh3S/purKdMRFc/rcfLW0\n/+8Q1bF29k5ZJElSdXpJ4B/Ea7nK9tcFJlbfCn4qDOOMbgxsQlQ+LiJEQ7OJ10o6i0jk7iY+vwvT\noqqQz6+XY5kYeDf3771sSHqKGDK4sIV7uzcwte0tetlF3+JU5X1KH2y3KKpsXxYkPUEQZe+SdDsh\n/HaIpPWAI21XepItVQXubJ0kkzQ6MTJ8W6Y44wG/sP339PcJRNm+ga+B7W1/3O7/1+gekn5MtAEX\nJ1bs3yUlWs6kKyXpJmIS6WDg/wh7pVNs75Ee34vQkZsrR7y0z0Xbff8S8XqPXKvYpv0uQ1wo25m+\nZq3eSpqcSAA2JZLV8wmx4zlsP54rTor1rUmsGpCKE69NFcfDiErKu03bC02s0ndmDNuvtGz/EfCl\n7f8WEbcMtBQw3gSWdSgAzEBMOmcx0a6SwejbBF+mu1vVcQkxjgxBjtw/tQf/Ql4vtk7hZto7v0+U\nHsuFTQgF+wY2JFpYk6bbcoQv3KCEpClT8pEdtp+1fRpxoV6b+M4uT3ik5cLcwIG2HwP2Jb4z5zU9\n/ndCST8bukvqbX+TO6lKOJ4YGpmOaG+O03QbN2cg22/Y/oPtmYgJ4AkIXuU/JA1NFcIaowgHrnVM\nck4F5JTNWYuQOnhF0iWSVk3tyKLxN+I82YrlqL535htAw7LtRaLtD9EOzFZlqlLFarGWTWMSJ+Et\ngb1sn1P+URUHST8lJq+est1X890Bi7SanczhS9i8fSbgvlytTkl3AIc02iltVs3rEhWrQXNBSVW/\nVYhV8/KE/EhuuYUFiErVEsTk2ljAv+mSW7g2U5zSqh4KXa4Vncb/JR1KqJX/L/39faLdmVWao/U1\nlY3E29mAqGLNlauClD67zejiwZ5FDJE0KhwTEdXHQVOxKgOpUrQJsaCZKN3+DziniNacpPcIiYWn\nWrbPRPAqs1R1OgFJpwKv2N5P0m+JQbG7CB7u+d+6VmB3kLQ6QTBdodcn1ygdTVOIKxHj+s1k59EJ\nnsnjtpfPFO8NYEEneYfEDVqk6e8ZgfsHA2ctneg2J06ykwJ/Bc4Abi+A+/QFIRp4S7rdUUQ7NRGg\nJ28k4IkAPaeTdU/mxKo1iWtHtn7ddtbKvqRzCb5Txw2JJc1t+4FM++ppQKWBrK3ObxsUenKbA78k\nEthLbG+ZOcZHwM9sP9yyfQ7gX66wM0Bq74/WxKVcmy6NtZNy8eMGvNxCH/AgweivPBTjtNsDs6ZN\njxP8qqx2LyWjMVEl4F3g06bHviBkELIRkQmC9TiNP2xP1fL4GLRohFUJCg2ptYiT67wEsXsLwt/y\niAIJrd8riZcmQuOpwcUbl2hbNZTKizxntRMkKmLl+VvgbIWf5aOMaO9UmhdbrqQq7atK1JJKwklP\nTqFN1uAg5sbdRCeoNWHbGri3gHilILVRDyZa8S8C2D6P4akGWVDpxErS+EQi8nKnj6W/kLQ+UXG4\niZiKgrDXuUfSxgNhdTsqcJJtkPQCQcIs+uL8MjA7YfnSDnNS7e/La4Q5+JnE5Oj7QBkihWenMvrV\nTvZOBaGVo1J5Mdc2WI7gU65IaCE1J28mzgM1anSLRGY/Ot1yY0/gplShuiltW5Kg3ixdQLxS4NBT\n3IqQWCgUlUms1CUUOmwTsZr9mND5qDoOBva2fUjzRkm7E9YzlUysGnBJXoiE5sp+kq6wPZzvWpoY\n3Dc9p8pw060sfEys7N6X9BfgDNtP5w5S4vcE2r+HZbynQ4HjCHmHejq1xoBCmkz/KeEQ0BB2fgDY\nyvZDnTuyLLiWSBJPLzJIZThWCqHQZnxDTAPe3TyKWlVI+pjgkjzTsn0Gwlcs67RQ2UhyGQcTK/V2\n2j25yOs/IE4CXxEXrwYBc2ZgmxR3brd4TlYFCjuLNQmS8PxEkngWUdkpRKSwKfYExCJmE2A+oo17\nKnCB7U97+r8DEYkTdD1dvL8VCOHTZpeFpXNzghKXa27bz+bcb43ykCofWxOTnbM5tJB2A56zfX5n\nj27UIWkMQo7jUtuvdfp4ciN9bvsQ08XtzOuzOIBUJrEa7FAYs15q+9SW7ZsDq1ednC/pEqKUfDLR\nzhrui2f7zIyxpiFsjpalizdj4Dpi1fV8rlidRCLibwpsBEwOnEOQ128uuF2Hwq5kc4Iv9DlRzToq\ntzZSkVDnXBbOIKarTsq53xrlQGFgvwuhMfUHYEhKrDYEtrBdac5vWuTP6vL9XQtHLwMW2QYrKpVY\nSRqLWDHPSlwoHwPOdQG2GmVAw/vnTUH4Pl1EjH9CcKxWI1oGhfeFi0RapS9j++4SY05Ml2noM664\niGx3SHILvyCSrBUIheR24pO54k1Jl+r05ITo5BTAMsDutnPqWg06KFSetyPaEg8zInn9TwXEXILu\n/dGWzB1vMEMh5ryj7as0vHr3EOA225N0+BD7BUk3Er6Vuf1bvzWoTGIlaVbgGkLg7pG0eXZi5HT5\nKq2UG+jjeDIMghFlSc8Av3QIP9YoCCnp2cj2oZn3OyYx4r0pkUA9QExznmv7o/ScVYC/OpPy9GBF\nEv7tDnYmI9imeBsTFdxLgFUJv7mZiDbW32xvkzPeYIekTwn17hdbEquZgAdz0DYk9Vk7zU0+kDkg\naR3gEOAY2rfLKjulLun/gPNaizEKi6l1ck3kVimxup7gPmxo+4O0bQKC1D2W7XZKsTUGCJJeyFrE\nRf+jTh9PjZGDpLeJtuo5hMjjw22eMxHwgO3pCj6WpV1hM+2yIelRok17aksicBzwke3dCoi5Cd1X\nyLImjmVD0mOEKPUlLe/n9sAGtufLEOMb+jhIUQAHsJR2WSegFqPwpu2TEMLKWV5bZaYCCRGv+RtJ\nFYDtDyTtSVfrrMbAxV7AtMCbkl5kxPbHHJ04qBp9xg4ESf2z7p7gUDAvJKmS9EOi9bgpYVFU2ZN7\nBzA9Ic4LwYcbP90/jhB7zZpYSdoZ2B04idAYPIFoyS9KXvujTmEocJykcYnFxkKJX7UL+XSl5m+6\nPxNhEn4i0DBfXwj4DbBrpnjNKHRh1GGI9gnr1HQ5BvQbVUqsPiOk/FsxYXqs0pD0+54eL4J3UTIu\n7PQB1OgX3u0uqZK0R6tMSA4k7tgviQnIZQk+0onABbljFQ1JxxD8s4/T/W5he9vM4d8hDLMBXiXc\nDh4GJqFJTDcjtgB+bftCSdsAx6WKzt5EUlxp2D4jTc8dQkj+nEUM5GybBCdzxLi/cV/Sn4AdbDef\nQ2+S9CTB1Ts3R8ym2IORtP4IXfIqzQLEEIu0aejSj+x/vAq1As8ksvgt6KpQLUSsiu7JPblTNtrw\nLsYkCMGfEiXKSpfPa1Qbkt4nfPX+2bJ9T4LIm80/TNJP6LLq+ZhoP+5KtFyyykk0qzEXeUGRdDOw\nqu330v1uYXuJzLHPIWyc/pg+rx2AKwjpk3tsr5E53icEB+klSW8Cy9p+MEnH3JPzu9JpKPwkRytS\nviVxuuZ0e+++LJyuNjFXIOQkpgeWs/1ymlB/3vaNueMVDUn7prv7An8EmukoXwAvABfZ/oIMqFLF\najtCbfp24Ou0bTTC0mP7Th1ULrTjpSi8ys4gr+VLRyFpSZqmOm3f0tkjqtFHbA1cLmmJBr9K0l7A\n7wkl8SyQdDtRUbkIWMv2rWl7ES2P0tSYm5Ol3IlTH7ANMHa6fyih8bYwMc15UAHx3gC+D7xEWIcs\nRFiPzUC5oraFw/bbJYR5AdiKEa9zW5GsWXIiuYCcSGjULUWXBdjoRLuzcomVk/BwcgA5rydKQw5U\npmLVQFr1zJL+fLxVUHOwQdLchOv2jJ0+lv4gcWQuIfztGsJzUwL3ESv5QSdGlxuSdunrc20fXkD8\n7Qg+ziLAekRStaztbP5hqUR/PHBy8wSppC8poGKV9n0RYYpcqBpzU7x9CHunT1q2jwPsbPuAMo6j\nKCisj16xvZ+k3wJHEl2GeYhz2RYdPcBRQOoo9JVMnnuqc3ni3PkiXd2aBQnO6mq2/5E53kPAobb/\n3kLOnxO4zvZkOeN1CmnYplWoOoskT+USqwZSj3vswT5hpjBqvdmZlMk7hXTxmhJYz0nCMILgAAAg\nAElEQVSgU9L0xFTna7nbEYMRklotZCYHxgMaq+bvE5Ozr9meqaBjOJCoXplIqu7v5b+M7P7nJtqA\n6xEr9b8SHJKXKS6xKkWNuSleKZNJbeJOSXvXg6zj85JGI9pjX6W/1yYqZE8BJ9n+sqf/PxAhacem\nP8cnFhX3MDyZfAHgj0UkxpKmIkyRZ06bHgdOtJ3d9zS1cmdpIyfxY+BR20Xw8kqBusSjF2f4aVXx\nbRIIlbQUMImbbAIU1gH7Ea3MGwj9ifc6c4R5oOHFQiE+6CmIi9hztlcq/6jyQSEQunjrSVzSfMCN\ntifszJFVE6lc/1tgk0bVNlVzTwNOtX1WhhjdDVRsT7TkhyVVuYcr1GXdsylRIRuNqJad6swWVmWP\nl6d4k9l+q2X70oQu2KSZ481NLGBmhmFOBA1Ueny+E1B4ZT7VOrCh8HUdYnuDzPHGaCSpZUChObil\n7etbEqtNCD7lbGUdS25IuokYghtKeweQW7PEqUBidT3wj8aJW9ICRDn0NCJr35kQudu5c0fZf7Q5\nuZvwQryJ+DK/Xv5R5UNKrBaz/UDL9nmBm+rEauQg6VlgjTbv5zwECbPfI9NtBiq6g4scrkgJY4PM\nPgnxfamcxZO6jOTHIyqLzSff0Qke1Im2t84c915iMvAA2l9MiuDpjAvMRfsKWaUVvdO5bJ5WGkr6\nnv47d3dB0lsEv/g0lyCEnSgHmxC/uWuAlYm241DCBeT4oo+hKEj6CPip7UeLjFMF8vrsDK+zsiZw\nZ6NPL+llgoBZ6cTK9mi9P6vSuBE4VtK6jfK1Ql34KCpIhhwAmIL2v98xgCwciBzJWQ6kC9huaaJt\nZfJpBZWNbYiK0enAngyvm/MF8ILtf7X7j/3ErITp81O9PjMDGpU3Igluham+BtnHRCupld+7OF0G\n3jmxB5Ho/F7S3QSp/LyiaDC2D5c0IWFQPjZwM6F/NrTKSVXC84S5eqGoQsXqM2DGpovxncDVtg9K\nf09L9H3H73YnNTqOxBG4nJj4aiavPwKsYvuVTh1bFaEw7Z4S2NT2g2nbXMRJ9w3bKxccf9BwHCWJ\n4K9sTYgjzpZaH7sRbfjze9zByMdbjFgclsI1knQXsIvt20qK9xhwL7DHYBxKSRWdA4mJ7WZf142I\nis5hBcWdhVhUbEDwvC4gqlj/7PE/jnq8cYmkfDTgP4Pkt74kUajZqsjBtyokVs8TPJJbFCbM7wEr\nN7Q0JM0O3OKKGl8mvZA/A3O4SVU+PTYh8BCwve1LO3F8OZEuYEvTRMB0bU0ySpA0OaHvtDixmoQg\nY95KDAi8kSnOoOc4KqxIdgEOA/5A8GSeU6hpb2F70QwxJm5MHCnMwbtFjsmklhhzEWKWexELmVbX\ng6zm5JI+Js5nz+bc70CCpLUICaBhE+rA0bmT8G5ij05ILRxBSCE8TVT+T7bdV//ZbyVSO34somr6\nOSE9Mgy52rhVSKxOAOYjssxViGx9Sichr0Ti3db2gp07ylGHpKuIClzbEqukLQnz4uXLPbIaVYCk\nORg+UX2kp+ePwv4HPcdR0hMEj/GqFrLuEOC2HIu25klAde8Dl20yqU2MBmm9dVsR5PzrCG/CbErW\nNUBhFLwaUbVaEriD+B1OCWwL3G57nX7sv0dHgAac3xmgNEjaqKfHbZ+ZI04VOFb7ABcTK+OPCBPf\nZnXUTYlecFUxBzG62x1uIvgYlcS3qSLXCTjEOkcwRM6IbwPHcRqgHZn1S/JZviwJNCpDZQiEli1C\n2owTgaFJ3qFdhSyrvMNgRxpI2ZQwtf6SkCDZppkzl6gB9/Uz1DaEqOtzjDg92sDArsT0glyJU28Y\n8ImVQ9l20XQR/sj21y1PWZPh5emrhkmBHse9aU8CrQq2AY5oTaoAbL8v6TBCNqBOrHqBpD6Lftru\ns5hoL5gIaNZbWpjhPbXuBX6YKVan8BwhXtk6HbcikEU3y10K8mMAQ4BLi+Qf5RobH0U0PO1ObvNY\nJcnraRJwettvN013tkXuqUDiN3Yd8Gvgsm6kF14gdNj6gwuIrtDzRCXsQhesUN4JKBxNNgR+DOyd\nPtOFCf2/vk5C94gBn1g1YLut83RufkAH8ApRtWoVf2xgDsI4taoY1BW5kvHzPj4v56rydeIE9HLi\nOM4N7N30+Hfp4nhVFUOB4xJZV8BCiV+1C5knEG1/JekI4Kqc++0LUgVpaoYXRqQAUvuAmCbNjN8B\nH6b725Qce/reJDFsf0xMDo4ybK+duHkbADsRU9x/Jwjy/a2GDQgkeZ8bieRxCMFTextYBpiJECbu\nf5yBzrEa7JB0NPGhzmv705bHxiXKu9fb3q4Tx9dfpKnO2W23TRwVRqIPucJqvoMZg53j2ICkLQhy\n91Rp02vAvrZPKyDWjYTpcyl6TimhOgdYlEi6RVPynZtjVaMYaHif1f/Y7tHMO0O8+YiFxTqE88FC\nbrFhqhoUBui32d63hU+5EPB329PkiFOZitUgxsHAGsBTko4DnkjbZ6FL9+aQbv5vFTDYK3KlQ9KY\nhIbOCi7A4qUFg53jCIDtU4BTJH2fsGN5s7f/0w+cQnCQpqa9hU5uDtJRhHH9rERbaXlC6+wAYIcc\nARTOEVc4TK1bXSSGQ9UFQhtQSYbyCp/VS4l29TCpGklF+6w+QZhnL0hUcwZDAj4vsFmb7a+TSf8P\n6orVgIDCv+jPwHIMP71zLbB1rr5vJzDYK3KdgqRXgaVdghJziteW45haBx+1JFuVgqT1CD/OUtwN\n2rgsNKOIKb3/AivZvi9xheaz/ZSklQiOyU8zxPgGmLxp6rE7ZH99ZUMlG8qrZJ/VpLO2GTGB+CCh\n11WYIGmZSL+FFW3f31KxWp6Qq5g6S5w6sRo4kPQ9YAYiuXramT3ROgFJPwAeIAj63VXk5rH9384c\nYTUhaS+Cy/LrNgMdNUYCkl4iCPjPArc0bkVVAtJCqlv0xqcZhXgfEFO5L0h6AdjA9h2SpiMqLePm\njDfY0YFEpxSfVYWzwcaE+OhfgTNsP9Hjf6oYJJ1MmNevSXCr5iCKGJcRVll5Krh1YlWjaAzmilyn\nIOkC4v18n5BbaG0nrdWJ46oqFD5viwOLpVsj0brZ9m86eGj9hqR7gH1sXyPpUqKluydByP6l7Rk7\neoAVQ1mJTku8xVywz2qqNL4E/IMWiYxmVFzHagJiqnkOwrPzDaIF+E+ikvVxD/+973HqxKpGWRiM\nFblOQdK5PT1ue92yjmUwIalaLwBsQRD1Ry+idZVkFxag/ZTeXzPHWh8Y0/ZfkibSNYSEy+cEZ+6C\nnPFSzB8RZPl2Jsx/yh2vTJSV6DTt9xJClqfVZ/Vs4C3bPXLaRiLOLfQ+UWzbS+aI10kkftw8xHfz\n387sAFInVjVq1PhWI6nJL06Iai5MtAhupaslmLs1NzNwBdHKFUEsH4OoEnxegA5Sa/xxCbX+l5JO\nYO79r08YTX8FvMXwF2vbnj53zDJRVqLTFK/2Wa0Y6sSqRo0KIxFpZyEuXk/YricsRxKpBfIWoWd1\nnu2XCo53DeF5uhnRipgLmJBol+9lu9ApSxVsoC3pWeA8ghg/6Ph/vSQ6v2wkW5lj1j6r/UDZDiB1\nYlWjRgUhaXziRLEeXby1bwi9oi1zcQW+DZB0EMGrmp+QsbiZrmrVOwXEe4doJT0q6X1gAdtPpmms\nY23PkSlORwy0JX1EXMCey7nfgYQ60akWVLInb51Y1ahRQUg6hfCf24owY4VQZj+OINBWmnDdCUga\nB/gZXST2BYAnbc+ZOc7/CMmD5yQ9Q0x23iTpx8Ajuab01CEDbUnnA5fY7pEHONiQhnSOyDE4Iqkn\nt4rhUHXOWhlQeJoubfvJbh7/CXHe/FGOeLVAaI0a1cSqwBotooTXJAXx84E6sRp5TAB8nyBcT06Q\nyr9fQJxHgTkJj8J7gF0lfU0Q5p/JGKc0A+0WUdDrgcMkDaG9CfOgEAhtg4mA1TPt63d9fJ6BOrHq\nHaV68taJVY0a1cS4QDvtrzfTYzX6CEl/JipUPyHe01uBPxKtwLYr3H7iYGLUG8JG5yqi/fg2kFMm\no0wD7QvbbNujzbZKmjCXDduD0W+xkyjVAaROrGrUqCbuBvaRtJG7fPvGIi7Ud3f0yKqHiYCjKS6R\nGg62r226/xwwS1Kwf9d5uRmlGWjbHq33Z9XIiZytxx5iNCq3w1D0cEdBuAo4UNLVbu8AcgAZjdFr\njlWNGhWEpLkJPSIRyvYQF85vgOVsP9SpY6sxMKBviYH2QIGkOQlNpFIqckXFS1NyxxDV0++0Pl5F\nS6KyHUDqilWNGhWE7QeSWvjGdE0mXQGcafvDjh1YRSFpDmAnukx1/0NUAx4tINZYxNDBErQX0Fwg\nU6hSDbTLHmkvG5Iu7+UpheqPlYihBAfwV8T3Z1OiZbwdsGMHj2uU4fCw/Bnx/TyE9g4g2WzV6opV\njRoVgqTRB6M2UCchaRXiAnI7XROWi6TbaravyBzvr8DKhD/Zf2lRu7a9e+Z4pRholz3SXjYkndGX\n59nepOhjgUIrVq8Q4qe3J5X5eWw/I2ldYFPby+SMVzbKcACpE6saNSqEpBF0O3ATQXi+PzMv51sH\nSQ8T8gD7tmw/gEgEcsstvJf2e2vO/XYaZY+0f9tRYGL1ETCr7ZfSZ7qG7bslTUuYdo/X4w5q1K3A\nGjUqht0InaVdgMOA9yXdRiRaN9l+pIPHVlXMBJzVZvtZxPucG28SE4CDDaWOtA92dLD1+CwwPWHI\n/DiwTjLyXg34X0ExBxXqSY4aNSoE28fZXsP2pARZfX/igrUP8KCkNyWd19GDrB7eBOZts31e2kta\n9Bd7AIeklsRgQmOkvTtkHWn/FuCdXm7PA1kNuxP+Qtfn+AdCE+8L4AhiMVejF9StwBo1BgEkTQJs\nD2wLjF/FyZ1OQdLeBCn3CODOtHlhgsx+hO2DM8ebALiIqDy+wYgCmpU0KZZ0NLAMMG83I+33Adfb\n3q4Tx1dj1JAMpucj+Eh1RbwPqBOrGjUqiHRxXpSYLFsCGEIoet9G6DFd1sHDqxSS79v2RHI1Zdr8\nGpFoHZObwybpUsKX8Bzak9f/mDNeWSh7pL1GjYGKOrGqUaNCkHQ4UemYg0ikbiUMg2/Pbab7bYSk\n7wIUKVkh6WNgSduDTsg1iVb+GViO9iPtz3fq2Gr0HZK2ArYGpgNmS76WuwHPNZt612iPmrxeo0a1\nsBNBKt0BuMz2ax0+nkGDZII8S7r/n6SKXgReIpPi+UCD7ReBFcsYaa9RDCRtT9dwzB+aHnqVqDzW\niVUvqCtWNWpUCJKWoqv9Nx/wIlGxuoVoAdaJ1kgi8dNOI9TJG1NtAq4kdHveyRxveeD3wFa2c5ou\n16jRb0h6AtjR9lWSPgTmTBWrIcBttuvJzl5QJ1Y1alQUksYDfk60BhcH5gFeAG62/ZuOHVjFIOkS\nYEZi+qnRnluQaGk9Y3u1zPE+BMYizIg/B75qftz2YFHwrlFBSPoUmNn2iy2J1UzAg7Zrk/deUCdW\nNWpUHJJGI8jQvyR4EfVU4EhA0ifAUrb/1bJ9IeCG3IKIkjbq6XHbZ+aMV6PGyEDSY8Beti9pSay2\nBzawPV+HD3HAo+ZY1ahRMaQptnnoagkuAoxPTJhdSSiy1+g73gI+brP9E0IvKCvqxKnGAMdQ4Lgk\nkSFgIUkbEryrTTt6ZBVBXbGqUaNCkHQFkUhNQFz0byHZ23RnJVKjZ0jaDFgf2ND2q2nbD4Ezgb/b\nPrWAmGOlmA3T58eAc20PSlJ7jWpB0hbAXsBUadNrwL62T+vcUVUHdWJVo0aFIOlioiJ1s+1HO308\ngwGSHgGmBcamSxn8h8BnhLr1MNjuSVm8r/FmBa4hkuOG4OLswPvA8rYf72+MGjVGBYlWMDPwou2P\nJX0fGM32mx0+tEqhTqxq1KjxrYakfXt/VsD2/hniXU+0GTe0/UHaNgHwN2As28v1N0aNGqOCRDP4\nnDBhridWRxF1YlWjRo0aJSKR5ee3/VjL9tmBu3KT5WvUGBmkCu6vW4c5avQdtQlzjRo1vtWQdKmk\nlVMbpAx8BkzUZvuE6bEaNTqJXYChkuZKFawaI4k6sapRo8a3HR8D5wGvSDpE0owFx7sCOEXSwpJG\nT7dFgJOAywuOXaNGbzgfWAC4H/hM0gfNtw4fWyVQtwJr1KjxrUfiOK0PbEIo2t8BnApcYPvTzLEm\nIiYOfwF8nTaPRiRVG9t+P2e8GjVGBrXOWv9RJ1Y1alQQyYuNhgebpJ8AawKP2b6kk8dWdSTrjs2B\n3xJE3vOAo3JP60mageRNCDxek4Vr1BgcqFuBNWpUExcCawBImhi4E9gYOEvSth08rkpD0pSEgv3K\nhNXMRYSWz8OSdsqw/zElvSFpiO1nbF+RbnVSVWPAQNJkknaS9OckuUBqXU/X6WOrAurEqkaNamIu\nIpkCWJ0wY54R2AjYqlMHVUWkZGcNSVcT7+OvgMOBKWxvZntF4j3eq7+xbH8JfEmIgtao0XFImqrl\n73mBJ4nW+GaE3hrAMsDB5R5dNVEnVjVqVBPjAB+m+8sAlzn6+vcAU3fsqKqJ1wni+LPAvLYXsH2K\n7Y+annMb8G6meMcCu0uqLcVqDARsLOmopr+HAkfbnptohTdwLbBwqUdWUdQcqxo1KoikNXMC0ap6\nCljB9r/SavMq25N39AArhOSDdoHtUqQOki3RYsCnwKO0+BTaXqWM46hRA0DS+MDpwDe210mTf3Ml\n4+VmE+ZpgSdsj93Bw60E6opVjRrVxEHAMYSH1wNNYn7LAA927KiqiXe7S6ok7VFAvLeJhPhq4CXC\n87H5VqNGabD9ke21gH+mTZ8C32vz1JmB2tqmD6grVjVqVBSSpiY87e61/VXatgjwXu0j2HdIeh9Y\n0fY/W7bvCexoe+LOHFmNGv/f3r3H2lWXaRz/PqJSqFDwAmWIl9ZbkbHQAo0gYOAQiWJUlMIAHZnp\nyAw4GRASKoMo/xiiyEV0xqIiKjZeQEAFxBSmSEZbBSlQES2KSooKWpRaW26Wxz9+6+DusedCz9p7\n7XXO80lOuvfal/Wettnr3b/L+/aepE8D0ym7jNcCsylrAr8BLLN9WoPhtUISq4iY1CQtAC4GDrG9\nqjp2NnA6cLjt27p03n2BlwPXVQ1vpwKPDybJEU2oarp9i5JQTQUeBHaljGi92faGEV4eJLGKaC1J\nbwMGgF0YMq1fDe3HGEk6FTgTOBA4jpJUvbEbSZWkXSnf/udRRgJeWa1h+RTwmO1T6z5nxDMl6VBg\nLuWzZaXtmxoOqTWyKyWihSSdS+nptZyyzmrTyK+Ikdi+uKrXcxsl2TnM9u1dOt1FwEPACyhrrAZd\nSdkxGNE428uAZYP3qxHVY21f2lxU7ZARq4gWkvRbyvqfLzUdSxtJOn2Yh94L/D+lTxoAti+s+dwP\nAQO27x6y62oGcLftqXWeL2I8JO1PqWd1DCVneF7DIfW9jFhFtNNzgR80HUSL/dcwxzcBB1Q/UEav\nak2sKDXIntjC8RcBPSn5EDESSS+gFBt+N/BqypqrfwOuazKutsiIVUQLSTqPsvvv3KZjiWdG0nXA\nKttnVSNWsylTglcAm7I+Lpoi6XBKMvUWyhe3JcBiyqjqPU3G1iYZsYpop2cDZ0gaAFZR2qQ8zfai\nRqKaAKqK6FOGVF6v0yLgFkn7AdsCFwB7AtNIZetoiKRfUUZMvwicYftX1fHFDYbVSikQGtFO+wM/\nBbYHXgcc1PFzYINxtYakAUlHDzl2JvBn4BFJ35a0U93nrb75v5ay8WApMIWycH2O7fvqPl/EGE0H\n7qIUGF7TcCytlqnAiJiUJN0I3DC4OF3SPOD7wGeBnwBnAEtsn9HDmF5h++e9Ol/EIEm7UNZVLQSe\nD3yFMhX4PUqLm0wFjlFGrCJisnotcEvH/fnActsnVsnWKUDX+/ZJmiJpgaRbgNXdPl/Eltj+ne2P\n2t4DOArYEbiZsuzgPyTt2WiALZIRq4iWknQA8E/ASyi7BJ9m+82NBNUikh6jFOdcU91fDnzL9oeq\n+y+jlD/oyvZySXMpO62OBR4HrqE0g765G+eLeKYk7QAcTxnF2hdYXSVeMYKMWEW0kKTjgO8ALwbe\nRNm+/zJKmYBfNxZYu/yW0lIGSdsCc4AVHY/vQEl4aiNpmqT3SFpJGS2bVp1nwPZ7klRFP7G93vYl\ntucBe1PWBMYoklhFtNN/A6faPpKSVJ1O2Vl2BaW3V4zuBuC8qnXHR4ANlOKgg2YDta13kvRF4AHg\nHZTehNNtL6jr/SO6yfaqtFsamyRWEe00k5IYQEmsprrM619EqUMTo/sgZXv5TZSpjhNtdxbuXAjc\nWOP5jgU+Dsy3/YU0s42YmFLHKqKd/kiZQoIy9fca4EeUqaW0RBkD22uBgyVNA/5se2i/xfmU0gt1\nOZqypuoBSUuBy0kl64gJJyNWEe30XeDQ6vZVwMVVIb8v0dE4NUZne90Wkips/2HICNZ4z3O17SMo\nLUJWAudTpm2fBcyRpLrOFRHNya7AiBaqas5sZ/t+SdsAZ1Oqdt8LnGP74UYDjDGRdBhl6vZtwDrg\nGtsnNxtVRIxHEquIlqlarryLUhogC9UnAEk7U/5NF9req+l4YnKrPmPmseVSLpc3ElSLJLGKaCFJ\nG4E9bN/fdCwRMXFImgVcC8wABGyirMd+Enjc9o4NhtcKWWMV0U63AhnZiIi6fQy4nbIRZiOwB6U4\n6J3AOxuMqzWyKzCinf4HuEDSP1A+BDfbup++XhGxlfYD3mB7g6SngGfbXilpEfAJSn23GEESq4h2\nuqL685PVn4Nz+qpub9PziCJiIhBlpArg98DulB6WDwCvaCqoNkliFdFO6dcVEd1wN2WZwS8oSw7e\nJ2kTcCI1diKYyLJ4PaJFJF1GaWWzvulYYnyqadxdGLLW1fbKZiKKAEmHUzo5XC1pJnA9pfbaWuCY\n9LMcXRKriBapvjnuZvt3TccSW0fSHGAJMIsy7dLJtjONG31F0vOBPzoJw5hkV2BEu6Q6d/t9GlgD\nHETp+Tij42dmg3FFIOkySTt0HrP9B2D7asQ8RpERq4gWqXbp7Gr7903HEltH0gZgju17m44lYqjh\nRsUlvRB40HbWZo8if0ER7fPgaG3lMp3U134ETKe0H4roC9V0n6qfnSX9pePhbYAjgIeaiK1tklhF\ntM+/A480HURstbOA8ySdTUmynux8sJp2iei1tZRSLQa2VAfPwDk9jailMhUY0SLVVOD0LF5vr+rf\ncFDnB7DI4vVoiKQ3UP4PLqNUWO9M8J8A7rf9myZia5uMWEW0S74Jtd8hTQcQMZTtWwAkzQDW2H5q\nlJfEMDJiFdEiGbGKiG6TtCvwn8BrKF/mfgwstp01VmOQxCoiosdy4Yp+Jen1wLcpC9VXVIf3pxSz\nPdz2iuFeG0USq4iIHsqFK/qZpBWUTRUnDU4HSnoWcAnwj7YPaDK+NkhiFRHRQ7lwRT+T9Ciwt+3V\nQ47PAu6wvV0zkbVHKq9HRPTW3sAFnYuDq9sXAnMaiyqiWEfpAjDUDFLmZUySWEVE9FYuXNHPvgJ8\nVtLxkmZUPwuAS4EvNxxbK6TcQkREbw1euBYBy6tjrwc+Qi5c0bxFlHpWl/G3HOFJYDFwZlNBtUnW\nWEVE9JCk5wIfBU7i7y9c77P9RFOxRQyStD3w8urufbY3NhlPmySxiohoQC5cERNTEquIiIhJTNI3\nx/pc22/tZiwTQdZYRUR0WXXhWmD7T6NdxHLhigY83HQAE0kSq4iI7nuYv/V5zEUs+ortf206hokk\nU4ERERGTnKTZwN1pvjx+qWMVERERdwAvHLwj6XpJuzUYT2tlKjAioockTQFOBQYo/QE3+4Jre3YT\nccWkpyH3DwbSvmYrJLGKiOitTwJHAldSCoRmPUbEBJLEKiKit94OzLd9U9OBRHQwf5/kJ+nfCkms\nIiJ6ayOwpukgIoYQsETS49X9KcBnJG1WuDblQEaXXYERET0k6RRgT+Ak5wM4+oSkz43leSnNMLok\nVhERPSTpWuAgYB1wD6VP4NMyIhDRbpkKjIjorbXANU0HERHdkRGriIiIiJqkQGhERERETTIVGBHR\nA5LWs+Xt6+uA1cB5tpf2NqqIqFumAiMiekDSCcM8tBOwD3AMcJTta3sXVUTULYlVREQfkHQacLTt\n/ZuOJSK2XtZYRUT0h+uBWU0HERHjk8QqIqI/TAEeazqIiBifJFYREf3h3cCdTQcREeOTXYERET0g\n6ePDPDQNmAvMBA7uXUQR0Q1ZvB4R0QOSbh7moT9Ryi0stv3LHoYUEV2QxCoiIiKiJlljFREREVGT\nJFYRERERNUliFREREVGTJFYRERERNUliFRF9Q9Lukr4u6V5JP5N0kaSuloWRdIKk6d08R0RMHkms\nIqKfXA1cbftVwKuAHYBzx/umkkb6rPsXYPca3y8iJrF8OEREX5B0KPCo7csBXGrBnAYslHRyNZJ1\ns6TVkj7Y8brjJf1A0kpJiyWpOr5e0vmS7gBeJ+kDkm6VtErSJdVz3gnsCyypXr+tpIHq9l2SLpX0\nnOq5v5T0YUk/BI7q6V9ORLRGEquI6Bd7Ard3HrC9Hrif0iViP+BIYC9gvqS5kmYBxwAH2J4LPAUc\nX718KrDC9hzby4FP2J5nezawvaQjbF8F/BA4rno9wOeA+bb3Ap4DnNwR0lrb+9q+ov5fPyImgiRW\nEdHvBBi40fYjth8DrgIOBAaAfYDbqpGpQ4EZ1es2UaYWBw1I+r6kVcAhlESu8xwArwZ+Yfu+6v4X\n2LzNzFfr+7UiYiJKr8CI6Bf3MGSKTdKOwEuAvwx57mCyBfB52+/fwvs9Wk0nImlb4H+BubZ/I+kc\nYMowcWiY4wAbRv4VImKyy4hVRPQF2/8HbCdpAYCkbYDzKVNzjwKHSdpJ0nbA24HvAcuAoyS9qHrN\nzpJeXL1lZ4I0hZKIPSzpeWyewK0HdqxurwZeKmlmdf+fge/U+otGxISWxCoi+idBjt4AAACaSURB\nVMmRwNGS7gV+CmwEzqoeu5UytXcncKXtlbZ/ApwNLJV0F7AU2K16/tONUG2vAz4D/Bi4oXqvQZ8H\nLpG0srq/EPha9X6bgE8Nfb+IiOGkCXNE9D1JJwD72D6l6VgiIkaSEauIiIiImmTEKiIiIqImGbGK\niIiIqEkSq4iIiIiaJLGKiIiIqEkSq4iIiIiaJLGKiIiIqMlfAc7aXfZKXwPEAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f750e11ecf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "highSurvive[\"percentageSurvived\"].plot(kind='bar', color=\"g\",fontsize=14, title=\"Operators with high percentage of survivers\")" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "605bf395-66f1-9502-9334-5c9bcf28548e" }, "source": [ "Comparing the number of aboard, fatalities and survivers\n", "----------------------------------------------------------" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "2825a36b-c108-ca49-c4f4-ca4278d0b407" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f750b5a9cc0>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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rIxOMsLb7/CoqICfHPHbSlQnm74MMmRVEmAmCIAwBno5Zv4VZWx1pY9OYmj41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\nIqbEKSbooHdVlUGxAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f750e063eb8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "subfile = (fileR[[\"Aboard\",\"Fatalities\",\"year\"]].groupby(\"year\").sum())\n", "subfile[\"survived\"] = subfile[\"Aboard\"]- subfile[\"Fatalities\"]\n", "pylab.plot(subfile[\"Aboard\"],label=\"Aboard\")\n", "pylab.plot(subfile[\"Fatalities\"],label=\"Fatalities\")\n", "pylab.plot(subfile[\"survived\"],label=\"Survived\")\n", "pylab.legend(loc='upper left')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "243e2e85-6d21-554c-03ab-58c499aa65c4" }, "source": [ "Locations with high number of crashes\n", "-------------------------------------------" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "916ff1e6-2d2a-2b0a-7493-b90069c4928e" }, "outputs": [], "source": [ "test = fileR[\"Location\"]\n", "loc = test.str.split(\",\")\n", "countries=[]\n", "for i in range(0,len(loc)):\n", " if(not(type(loc.iloc[i])==float)):\n", " dim = len(loc[i])\n", " countries.append(loc[i][dim-1])\n", " else:\n", " countries.append(\"\")\n", "fileR[\"countries\"] = countries" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b8663bc5-2470-7b79-0ac8-b00a0fec7e8b" }, "source": [ "\n", "Let's make a plot from less safe counteries:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "9ec3e143-e3d9-f81e-9ee5-4bddd10877d0" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f750b4632e8>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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MzMzMhpX+JGOnA3cAN+XHbUAA9b2LZ+V1AKOBVyPimW7KtAFzGsSbXShjZn3k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\n65LeI2spzZgPlDZ44B00nmz1KVLzZdl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k6UMRsXDQc4ceY4JXxu4D5kY2En04WVhvr4ho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\nZmYNcjBmZmZm1qD/B17J72AqUOhRAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f750e0ae240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "countrySub = fileR.groupby(\"countries\").sum()\n", "dangerousCountries = countrySub.sort_values(\"Fatalities\",ascending=False)\n", "dangerousCountries[\"Fatalities\"][:20].plot(kind = \"bar\", color = \"g\", fontsize=14, title=\"Highest fatalities based on the location\")" ] } ], "metadata": { "_change_revision": 85, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/327/327240.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "e181e05b-9494-7a1f-b7c3-74fa6f371217" }, "source": [ "##A test run with the Titanic dataset##" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "254377e0-9bf7-a324-fbc5-bc2fdd32de30" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "genderclassmodel.csv\n", "gendermodel.csv\n", "gendermodel.py\n", "myfirstforest.py\n", "test.csv\n", "train.csv\n", "\n" ] } ], "source": [ "# imports\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "# type shift-enter to see input files" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "21893d8d-4a46-0e11-0def-ec50a707e380" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of records in titanic_df= 891\n", "Number of records in test_df = 418\n", "['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']\n" ] } ], "source": [ "# get titanic & test csv files as a DataFrame\n", "titanic_df = pd.read_csv(\"../input/train.csv\", dtype={\"Age\": np.float64}, )\n", "test_df = pd.read_csv(\"../input/test.csv\", dtype={\"Age\": np.float64}, )\n", "\n", "# print basic info\n", "print(\"Number of records in titanic_df= {}\".format(len(titanic_df)))\n", "print(\"Number of records in test_df = {}\".format(len(test_df)))\n", "print(list(titanic_df.columns.values))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1985fa0b-6312-13af-2107-5402775a90e1" }, "source": [ "**Look more closely at Age and Sex...**\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "991344f6-0ede-f79b-d525-3c3b3b2d622a" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>Sex</th>\n", " <th>female</th>\n", " <th>male</th>\n", " </tr>\n", " <tr>\n", " <th>Survived</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>81</td>\n", " <td>468</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>233</td>\n", " <td>109</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Sex female male\n", "Survived \n", "0 81 468\n", "1 233 109" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Cross tabulate the sex of passengers that survived \n", "pd.crosstab(index=titanic_df['Survived'], columns = titanic_df['Sex'])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "824c833e-96be-c4f6-bdf0-d9567d624373" }, "outputs": [ { "data": { "text/plain": [ "Survived\n", "0 30.626179\n", "1 28.343690\n", "Name: Age, dtype: float64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Identify the mean age of those that survived and those that didn't\n", "titanic_df['Age'].groupby(titanic_df['Survived']).mean()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "1b0d5039-f920-b5b5-a7b0-c2527f6bce26" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "87cfe718-a5fd-1e40-8558-1a70dd94f7dc" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 23, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/327/327301.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "c054b4e9-e402-d2bc-aff7-c8c9b097efa8" }, "source": [ "Instead of predicting per-pixel mask you can predict only 6 numbers mu_x, mu_y, sigma_x, sigma_y, sigma_xy, scale that will define a 2d gaussian:\n", "![](https://wikimedia.org/api/rest_v1/media/math/render/svg/c6fc534bfde62d6d2b3b743b0c3fa2fb7fc3174a)\n", "![](https://www.mathworks.com/matlabcentral/mlc-downloads/downloads/submissions/37087/versions/6/screenshot.jpg)\n", "\n", "Thus, instead of autoencoder-like networks you can use simple CNNs. \n", "\n", "I've got ~0.67 on LB using this Gaussian2D layer. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "4e7d4837-543f-d656-a237-8532c8e7dda0" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n" ] } ], "source": [ "import math\n", "import numpy as np\n", "\n", "from keras.layers import Input\n", "from keras import backend as K\n", "from keras.engine.topology import Layer\n", "\n", "from skimage.util.montage import montage2d" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "dbe4d549-aac5-7f61-d27d-524dbdaa9f77" }, "outputs": [], "source": [ "def nbimage( data, vmin = None, vmax = None, vsym = False, saveas = None ):\n", " '''\n", " Display raw data as a notebook inline image.\n", "\n", " Parameters:\n", " data: array-like object, two or three dimensions. If three dimensional,\n", " first or last dimension must have length 3 or 4 and will be\n", " interpreted as color (RGB or RGBA).\n", " vmin, vmax, vsym: refer to rerange()\n", " saveas: Save image file to disk (optional). Proper file name extension\n", " will be appended to the pathname given. [ None ]\n", " '''\n", " from IPython.display import display, Image\n", " from PIL.Image import fromarray\n", " from io import BytesIO\n", " data = rerange( data, vmin, vmax, vsym )\n", " data = data.squeeze()\n", " # try to be smart\n", " if data.ndim == 3 and 3 <= data.shape[ 0 ] <= 4:\n", " data = data.transpose( ( 1, 2, 0 ) )\n", " s = BytesIO()\n", " fromarray( data ).save( s, 'png' )\n", " if saveas is not None:\n", " open( saveas + '.png', 'wb' ).write( s )\n", " display( Image( s.getvalue() ) )\n", " \n", "def rerange( data, vmin = None, vmax = None, vsym = False ):\n", " '''\n", " Rescale values of data array to fit the range 0 ... 255 and convert to uint8.\n", "\n", " Parameters:\n", " data: array-like object. if data.dtype == uint8, no scaling will occur.\n", " vmin: original array value that will map to 0 in the output. [ data.min() ]\n", " vmax: original array value that will map to 255 in the output. [ data.max() ]\n", " vsym: ensure that 0 will map to gray (if True, may override either vmin or vmax\n", " to accommodate all values.) [ False ]\n", " '''\n", " from numpy import asarray, uint8, clip\n", " data = asarray( data )\n", " if data.dtype != uint8:\n", " if vmin is None:\n", " vmin = data.min()\n", " if vmax is None:\n", " vmax = data.max()\n", " if vsym:\n", " vmax = max( abs( vmin ), abs( vmax ) )\n", " vmin = -vmax\n", " data = ( data - vmin ) * ( 256 / ( vmax - vmin ) )\n", " data = clip( data, 0, 255 ).astype( uint8 )\n", " return data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "b9f244f7-cfab-9dd7-3ad8-9bd6404c869e" }, "outputs": [], "source": [ "class Gaussian2D(Layer):\n", " def __init__(self, output_shape, **kwargs):\n", " self.output_shape_ = output_shape\n", " self.height = output_shape[2]\n", " self.width = output_shape[3]\n", " self.grid = np.dstack(np.mgrid[-1:1:(2. / self.height), -1:1:(2. / self.width)])[None, ...]\n", " super(Gaussian2D, self).__init__(**kwargs)\n", "\n", " def call(self, inputs, mask=None):\n", " mu, sigma, corr, scale = inputs\n", " mu = K.tanh(mu) * 0.95\n", " sigma = K.exp(sigma) + 0.00001\n", " corr = K.tanh(corr[:, 0]) * 0.95\n", " scale = K.exp(scale[:, 0])\n", "\n", " mu0 = K.permute_dimensions(mu[:, 0], (0, 'x', 'x', 'x'))\n", " mu1 = K.permute_dimensions(mu[:, 1], (0, 'x', 'x', 'x'))\n", " sigma0 = K.permute_dimensions(sigma[:, 0], (0, 'x', 'x', 'x'))\n", " sigma1 = K.permute_dimensions(sigma[:, 1], (0, 'x', 'x', 'x'))\n", " grid0 = self.grid[..., 0]\n", " grid1 = self.grid[..., 1]\n", " corr = K.permute_dimensions(corr, (0, 'x', 'x', 'x'))\n", " scale = K.permute_dimensions(scale, (0, 'x', 'x', 'x'))\n", " \n", " return K.tanh(scale / (2. * math.pi * sigma0 * sigma1 * K.sqrt(1. - corr * corr)) *\n", " K.exp(-(1. / (2. * (1. - corr * corr)) *\n", " ((grid0 - mu0) * (grid0 - mu0) / (sigma0 * sigma0) +\n", " (grid1 - mu1) * (grid1 - mu1) / (sigma1 * sigma1) -\n", " 2. * corr * (grid0 - mu0) * (grid1 - mu1) / sigma0 / sigma1))))\n", "\n", " def get_output_shape_for(self, input_shape):\n", " return self.output_shape_" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "a130d1fe-49b6-845c-2f36-1ac5d61d24f9" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mu_input = Input((2,))\n", "sigma_input = Input((2,))\n", "corr_input = Input((1,))\n", "scale_input = Input((1,))\n", "g = Gaussian2D(output_shape=(None, 1, 100, 100))([mu_input, sigma_input, corr_input, scale_input])\n", "\n", "n = 3*3\n", "mu = np.random.normal(size=(n,2))/3\n", "sigma = np.random.uniform(-3, -2, size=(n,2))\n", "corr = np.random.normal(size=(n,1))/5\n", "scale = np.random.normal(size=(n,1))\n", "\n", "gaussians = g.eval({mu_input: mu.astype('float32'), \n", " sigma_input: sigma.astype('float32'), \n", " corr_input: corr.astype('float32'), \n", " scale_input: scale.astype('float32')})\n", "\n", "nbimage(montage2d(gaussians.squeeze().clip(0, 1)))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "fdda3142-d6f3-fb71-62bc-e2e3cc334e81" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 37, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/327/327528.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "1ccb5f16-828d-8ca9-f735-9d7400ab6189" }, "source": "" }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "1d86997b-98f3-ed63-cd1e-acdde6908ed2" }, "outputs": [], "source": [ "import json\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import os\n", "import pandas as pd\n", "import tensorflow as tf\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "b4163fdd-1c33-a3a0-c99b-ca78e781f908" }, "outputs": [ { "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: '../SETTINGS.json'", "output_type": "error", "traceback": [ "", "FileNotFoundErrorTraceback (most recent call last)", "<ipython-input-3-a494250ec2a1> in <module>()\n 1 project_dir = os.path.join(os.path.dirname('__file__'), os.pardir)\n----> 2 settings = json.loads(open(os.path.join(project_dir, 'SETTINGS.json')).read())\n 3 train_path = os.path.join(project_dir, settings['TRAIN_DATA_PATH'])\n 4 test_path = os.path.join(project_dir, settings['TEST_DATA_PATH'])\n 5 \n", "FileNotFoundError: [Errno 2] No such file or directory: '../SETTINGS.json'" ] }, { "name": "stdout", "output_type": "stream", "text": "(42000, 785)\n(28000, 784)\n" } ], "source": [ "train_data = pd.read_csv('../input/train.csv')\n", "test_data = pd.read_csv('../input/test.csv')\n", "\n", "print(train_data.shape)\n", "print(test_data.shape)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1cf336f7-0828-54c9-a713-c2cd519db1af", "collapsed": true }, "source": "" }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "c365def8-fa67-3553-c04b-a5977677d90d" }, "outputs": [ { "ename": "NameError", "evalue": "name 'train_data' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-4-0fe542aac03a> in <module>()\n 2 for row in range(10):\n 3 for column in range(10):\n----> 4 entry = train_data[train_data['label']==column].iloc[row].drop('label').as_matrix()\n 5 axarr[row, column].imshow(entry.reshape([28, 28]))\n 6 axarr[row, column].get_xaxis().set_visible(False)\n", "NameError: name 'train_data' is not defined" ] }, { "data": { "image/png": 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B+mK2zx8BnmzSZwBmcp8OvIi5EVwXZKyIPamtW7fqFVdcoZWVlVpbW6vJycn60EMPNepT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\n3eZLqrYBmAss8vJZDsthOZxzHI6UxXK4owCOEXp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"text/plain": "<matplotlib.figure.Figure at 0x7fd1580a7ef0>" }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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rSRikyLj+0BCxhxhUjFVpYfE3fXamop/ZAb7xIE51HXOLmgHBomoBQOJCj+Xe\nVHNR5oGWLeV1pFAOZ2qsB1oFkFcA4+YsoHDcGf7aevWNI6xoM/oWzR8cp0pBKKXGSgRPHgtzhX86\nq+mQSJVk8RcAyZFQb4gSA5SHkeZoSZ9Bm4HXF4VhIyvyYmPIE/JrkDjQYHAEt8+aw/M4MGlErzoX\nGdhiE1FjU1gJWBg60PTuOoRxpAASrs9wI6lPJ/468nbghLGJHet3/sCghqAUNR4QZ0hm1OwtrB8s\nA55gPkeH9qOTiKQ2Sshozy0u/ylDAmx95im4FpksCwzf3tbQEM6UDuHelPrMXaDB+0VvhENZVR8l\n9wJa6ybC3s2i2QUwaanKtL5nqR0Wj37mHuA+rUde4XG4LkU3soUNFvwIvdpcwvKRlNvRE9krNQXK\nP+xRZmcqLWjLnGZ3CbgYzd1k6Bf0JV1nd2Z378HYPI/Ga/JqIm7GoyJNJxdwb2nH8vX1EWMCpm2t\nMCxLkigRa1naGziamvH93SlEA/ot67F26HLOt3wCyM8JgwQDinPT0bCGcMuWHLlc0d3J2myR+JN6\n5zIzWulh1zyTyNXmpEeHE3Uxl2bjofYV+LKbFzHLhXzyKWbpBSeUHJSg8O2lSNaTa1JlTaA80X2P\nQAE1vM0fkf3oxE6eAM+X3sXDJZZkjChFhs2TKDJSQL865NyqhAX0gEGAMz86NYUFqSDyamBJ0QA6\nx05h8c1fxC27hAYjYsLoNv827VU3A/cx8LRm9u0Z/J3Tm5zih6KqkePM108nYtsGJhy041OHoMo8\nAZVckEWrNsdIrlUbgDRpOoXLfmOgWw+aeUwk9GE4hdJ0Cs2McGtZkzaBPqQlizMBo1xYgmqJTLRK\nNm9y4Nh00m7logLk+J7iWctrNNGPx6y6ChrWphSraoCxKa2WGhFe1b7iBaiqofeFBJN78cQeuM/5\nKfEkF9xCIzUPfX11Jl31xfx2JIHL0xEmBv5NMimNeHNJiwYeIdXwmtMMjeGmKPU4h5jdyeQ1fzPq\n6iPyq5tTCJRKMzCUPsYuKgoJYGwN2ub92Hv+CxHUSFgumUEqphSamSKdvgn3eptBJnoVOdA15Im/\nBbd62FOqLEw478cws0qjQb9zfOH/G4mqibg2jGel6SaMupkw7W5fSkuEPkffwdicoyFziU1LovuB\nHyH307uQ5RszXejP9i8W8lWTk4wL38epFzkKtK2U0J5dl+2Zo1E/kMDMMDPEnHQKzGqFaeEdkEBg\nZkuErow4c3w9AAAgAElEQVT6Jt6bv6JG9XsMMr7Mybp1CX3cE/W42wRUb0WEquOrysvrhgj0VFgU\nwRD3DcwpsMW9kx8LopdQ9VwY1XKLyUaHyPxMXlc7FBs7xvj5IjW0YmvGfEC8p9KXZDc/w64WS7Do\nl8kitcncXAnXhm0j9dQjNM2T2fNXO0D4ZVEAB5dpAl7yztqqJOC0KHbfpX/baJ6NUGWt7XRKimNF\nta2ZmsdXrRPwvKCOd9+rTMo6zFepMxi7xRG4IaoW0GJw4yTuud5Bq58zzy8mQd6nh36XcwJKRvaJ\nSLZTi+3Mf705BhgHyFIBNcQc+wEIv5bLIr6Wv4kVt8t2YJcZYMrKp+3gFshXN7pCdC5vjp0KRwl8\nv42lqMGf45FfqW8OzlVmEb1QLBqEYnbEnEU7TABxL1w5z4m6AlFXoN3Lc3Z9PKAF962QV9v7v4QG\nOUfjGZ57mftaJ0W3nm2izaY/bIi72IlBg37BYLMj8mtG/N9Bz0WGoUUATsAXWdsg+fPG8j9jNv8d\nD17pkaOVWyn1v8F/RcdrXMe/nNwRPxLsff5734/YGBqn4v3jXtycRleK/cTH6nz7eDjfAiwOpTJ/\nE32pAaU3kon60Rkmfv6kqKI6qQIF/4fITFdn9Oqh8Kjyg1wqm1ji2SzrDhMrpj1FdVKFDoWO/zEd\nFa1FoaNsWhTVSRUoUKCgAvhvZAdRoECBgv/PUThTBQoUKKgAFKWeFToUOv7HdFS0FoWOsmlRlHou\nNwodb6PQ8TZl1OHkQckIN1ZNheEzwGylH/Bmkb3/SmljhY73+bCWz+rm29lns2bdaVKXe3N1yCI6\nWlpAbavPafKzGN1LioO+2JVRKxejTpY0Mg6nV/MYMDb+9w9UNqrGdFWKppeL0FFH6rgb59LHJ5d9\nrbaz1Pcyrvr6uBL96mXUy+zfm6lwtHGepMRWgy85swzCen6L/a/vOtL/m1jWN2KE+y2ckeKKlPo1\no1E3/P9nbfBnrDOVMczzBraWCXScP4aJjQPpEutJFaM5/I4plZHLc9nk40xwbkjEfPGi9DXrD6bW\nDnnC5dbxgTQdvx/11dYssp3JHTcdBA2h9ByDj1oHQi8/pHaxGtjBkeTKu5mVhd4NpfR+9juWPxhz\npINwJVSsHdMZ7nyI516PiQIIeEYvzr91TEuLQFozH3gsmI53GffzQ0yWH8OhUTzfRQYjPRMI+f/H\nHamdC+t/9iOv5z2M0x9Tu5cN/aKfc8ZelzVhQ3mSKlaRP7BwNGez8zbMHZ7TeJ435bnJfaIzVcH5\ncFfqfLeFmanjiUiTcLenBU3QpKTTGS6F9uJ52Ke1/Mn8NoZf23hT7FNAeWpdfyqjvtFlTshcnvzt\nzU03+TYNdZBZQ868eMbG9KKLGpya3ZwJC7sgRDKLZx1d8O3/XB6MF1RMA36iAeA1W75/S98RAGx3\nUQVjTZJz9Cn+jNjj8jJmTBYBSf24f+TMq21KBeak6BoxrMNRQLgM93W062FwfiXaJhB46jv+oi1V\nSGPSyg1Y/hFLRqkWzzzsYLN4jtR0UG10l+3DXiOFjdFjkT4/LopdgxoyrnVZRFw0nJo6gr0d3k6R\nOFpahFK7/USYd+PQ06aUZIkXkm3Z2IlVDYaR1TEdvV52TMkPI+N8GtX2z+LxBm3i8swB4atC6hpr\no9vGlZMX2uG/D24CE9f+yaY5TsjyynbtfpIzrTlYh8WTXTnlMYCIHTpADquXu4NkFPdPWeGb9z3P\nX5bVFQvdIqgC2tfEyMMno5PZIbIa5iP1aMuhw18CYFRcgEWNq1AqwaCTDkc2byfmlD0YGEF6xSdD\n9jg7BuuWN3AaXEqrm1cJaNScHgGn8Vkm36+87CcAvACrZkasuj+EiMgP1LEXgC7bShm8eS0h9m+W\n3a7DgoIvGV73JNwXtlTI4K3buO8Gz8+vYqtLNhAEwDW6yQ9QNoZJ1ogW5lrHg8VnplIr9SEbO2zm\n8Nk0hM/iBejp0d49nKN7oF0P0O7wE17vHvPiJ3LMOIwulqRTRXhdAOjwrdNiQuIdyTSsj+8ZV0j8\njZYTa1I84AbhX62k8JQYWcYas9xlNakX5+L/Rk5326tSrG1q8CS8bOVkPsGZavL97RPciIZN26dD\nwRt3VyULDqRDrnINENuZAlqGoBF3n1dnh2BI+HJuR6Dji/dvpBoMdQCq4tP9OtmAo3YGpAuT7fam\nrzI3ceVIIEBjkMBiWeNX+5/OXITfCvm/o80sSX+mJ4iO92jmwdixbhx29+Rm0OtEK6Els9g8rhsP\nQoTutmjTiJukAHNzp/HBCYOSZIgUbygq6U5LdijnUxTclcNuwlYZeJNqNdKpk38ZdQ1wbQVuR1bQ\n8VxdmjfvThsPuHh4Ok3vhnDb/S9yBjVEdtYAnogkbkoL+HE6Wm3asDraFMhlUpIEy6rDOfNoC6dt\nn4sgwoafS3bySPkvAFq5aODkpcFmz3RqBjzGMKcFT8qYzarcztTIpYiHznk4hg4ApTNv7Rs0ag9a\nvqBaml/eZj8TdeYErhbZ5se7y5bjlMm6ewmntrDTzARhuylv6JDJ/+g5+zkN9v7yypGWjm5A1O0a\nJNwQfllxFWMZKzK8UGkDV6tbwmX5jcbC3YpM9ceY1nIhO9MJ417hJB8RqpxvLQ675dFJF+h+jO6D\nYtA750iXBdupmxIJQEBqK4Kih7D/iC7IhJ0Mc2ppyWOdfCwclFnWYwgQKai9d5EARq4GWO2dCLl5\nnG0ewlkW4H0cUIWeI2rjonJZVE0ArDuDjhI8fGaG5cgqfBUWSJumB1nVaS5XGouTeW7EN9d5pHyI\nTG0Hkme0YX34DJrGnaQLE5AmQuOQy9xwLduwYbmdqXlCPK63b+LV+giw6a19P7mM4lQbqBJ1lOho\nMSstamMfE0WhCqAuotkPYsKX59ejGxnL7EZBSP+6iLhZ1CVkRddAEvO6e7T9Zg/ipeLEZxjWbUZR\n2GIWTAhGOv468olIVSaqHOavYvim90lKfCJp2FWdfke6CqQij+7acDELgqwXce1uOOoZuUR/I68Y\nLyeAVjUC6LyiE8O/b4NwtTI0GJ11iD/zoFHVUmb7+DL04n74IQByRSgvHSnBL6YjRppFxMZ9+BzI\nfVqN0qwiTGrUITtLlw9XqRKGagENqet+ieqaf6NeR4MliTO5dsEIioW60b5N0xPBxAFqhe7s8W6A\nq/Q27UfPJB1w1IILIzsCV8rUVrmvsKJnPWiTnUt64Ll39qiiGlbCJbtWxBiJ1J18wah2MnKOniQ8\n3xH/9D6i2n4LvaqcGbIO08f3+fX7tUhvnkDschQAcXk5xDnUe/XeU7qYH6feQcVYX1jDtkb8fr0r\npRkyxs1wI6fOROIbeBPXby62j49jWgN8NtsRufFL+nmNE1BIJHN+WkuxDgRGS8nK06FGc0eUT33F\n3K/jmPv1M6LmTyAvGq7PjgN1S+GkVDXkYmF1+SRhoIz01pdYt8CCX6YsoVGNeEBHONtAcZ6E+DRD\n7sZV++gxTTtdQVYKc8JGUpIiliOVYGFczGalbygijllVHzPj0hquRamL5kgBUnvIx4erd/enye8m\n7NjSmxyp/Ds4PmQ4gTllv+GV35miTU5deD87eX0S90H0E1vS0g0/8EnhKLG9SZEaVNeLpWn1q//+\nAYGwH6DH30fTyaxhSUKouOUg3iTCXwm/iL4EWn2B07TqACSv9adPsq+gdnvUDudCNTVS2rclSNeD\nocmz6ZG9jWGqs3gcA8FdtzFPfwG/D1FC6IG566NKOGu/gHOm2/jDfjSdr37PrK72sMsHdvny8JoK\nhTaGGNtm4tBBwKVJasCLlT1GTlb8yQ+UznIieWkYy2x9oUcL4WyXkbqP5deye6u/RLKoima39mys\n+gcFVzLJUf8CqgOxN0Wy/5oNN7/FYyZk+6fSpV9f/P3k22XAPf2m8Kjsww3ldqYSQDnhAw2tqcL2\nFNC5qIosWtyQf7UnyWjpg9mOupz4xUZU268wms/87cPQzkxm0YNLhJ8Sf53tu1yIcaHfmpEsWlCC\nxyBwJIKgmX6C2TNdrIfmwc54n+6Ib7wL/omqBD+KZ8KUE3RYCL5b4l9cMHmCaXhJso4updtcOJUQ\nT/BxVSiIemv/paR2qOTaoKebgYW5cBUAdE3TMW0YQxFw7JveHCOXX042pcYgkJ6DEUG7QLsyggde\nMHcu4Vvlta9PeomR3libqb6ZrDjXksD8/lyenkmpZjardgBNW4lg/20SAm/g9sMCdq4O48fQ5zwK\nkk9WLlgAN1aZlKutcnu9XGUNUvLhze6Jnr0TCzfNpmETCBtQB0rEKaT3ihdzMLGNnwOV8ESoZcXN\nZRYU6oNRQw9gN6/WlZrY03HwcwY3yaD99VqCynDvY8A83yC6bSt+sUUCSJB5L2Js5jYA/H42Axdh\nVjv4umkwobEzb40/tnTBtG8oM3/6QRCbH8PePILpvqPo9409SD4QyHBLSmtZPBJpBkp+wqWhlCFB\nggRVoP6ZcKiiSchtR4KCeqMC5DfJQ7m6yEXk3uDaI/kkw83zu0SxZ/Z1e5pFneDo8nmsj3q9jFH+\nC1RStJMMHnz3O6luFxi/YAFW6lDV/BoQWq5myu1MjY3Py2ObNn79Yktjhi4Loq1NIAsyg5DuLf74\nhwXGprIizwY3IMQrgQfdWjK1uBvoGEM7Ty5f8mW/3Qp63t1PT6X95PxavjtduTA2xuZ5CBLPU1wJ\nbPPOThmztJcJZ/sf8PPeSJCOBjouD0S1+yTShj0n2tDm+tds672bNsGWQG/5TvUxDA5WItwki2wg\nTcCCE7nhGtQukI/LaVy6wYV+W3AOrk7D7vIJwuICFWTFlVFXV86JX+R/nx0szuz5rvvDCXxYg/PT\nXrgeJWXumjgzrBtwp3zOq2LR4OdOu7hwCiK/70mqV0C5Wyi3M71vqMFv1fsRt7oqvfcYEeAwAIeh\nm1jTeCfS8BNUSgE3ZShRgv7+lyvFvpZJChpVocnVQO5NGk/EvIlsvGDBo7bx6MU8pVlTfQbUPcv1\n9QKO5yYnE303BwmwpNpWTI2LMCUe05a1eTZrEXd+i8G4Gvh5HAWpePWYGk0J48mTuhy+1EM0mwAF\nWen8PnwozerVpfBEAB3cRnOMhlzCm4MF1XF0W0D0nWwMDTSxFPAuXJqbwpR7o+iywJToVBUO+xXS\n3O1bLqwL4xmm1LaUUfpI7KWEcqa1vIiqUim27UxQyxRnWMrVIoYUvaryN1p6NNHRZqLDOqw9MyDn\nA+OHImFgYk7whVhUNOHGnhZ8SoXjct+SC8ILCArvRE+P9qy+OwVtj3x87+7gzu/iz1q/JDDSEdPU\nvyvNfu7Sh4Rc/IH2bb4naAwkWlUleNtAVCdo80C9gECfqoC/4DoC9buTm1GEx1r/11Eugb74BoJV\nLXhk7Q6B4pdb7tg5g7VHov/9wApFRv5yKS5fLaSPxi8kTnJAVvMyTg/CqZHwjPCWHQDYNrYN99J1\ngdR/bu5zCAnENeS9uCM5fiBGuOR7KCkRklCbNqWXkKo3J6NAEyFCnt9lQpV1tAzYTdOJ9whMbsjX\n53yICG6D7GjllL1+Sdtqj7GJjyBJw5poExlE/ftn3uUT+je53CEXjkPr4zNebIspfzMVSPgjC5LQ\nrUQFiWxoAxtelhKOAcZJ5ANBD9QAkZ48YmLk+XZcPBkT542L6g1MT3Uh2KERo3r2huPHgXBxtLyB\ncUwOlZH4BoBfb+CPPayUcZnmQHP59n0vDyhCUEf6H0VZtYRWAyNgEYS1qwunhHekAL/8lkXz4w60\naeVLBn/jlqZOtyqDgHui2P8YMbZmZN4ppU8TWPGJz2WfOVj0X0mPlc5WhkFv4Waqy8a70UiVhPQG\nfnTHj+5Q9+XGE1TG7+V62/PfDxKN/8r5+t9i+pml3NCeTmqOCOkrM9OY2MqMl7lF51YBeCa83X9E\nhrPOQaQ4s7rDbPj7zie1oij1rEDB/0FKCmG7nxvgyP6/ISOv8tZFVz4S/Pa+WO+79NMcKShKPSt0\nKHT8z+moaC0KHWXToij1rECBAgUVgKI6qQIFChRUAApnqkCBAgUVgKLUs0KHQsf/mI6K1qLQUTYt\nilLP5Uah420UOt7m33WYjfkCT7/GzN8CyuM/dvx/pbSxQsf7CFDq+RUqWrh0LGaPzwG2td9B7Qmm\nFdLsJ6FqRV9ugN1/u0pnhWNgirtdDn19Sjmh680PePOn52Z6rVNldI0boCre99GUaMDoo/t72Yaj\n9EVN0fT8V3AwTcK9mzaefo2xqgU9t4+ubEmVhpJpAzr2S2JUHyndq0tR0mxY2ZI+mwpxpg132jH1\n8Q7ivO5RcPEpQ8+uZ+BuMQphvY/WTBs6cAzlr2tXiv3KoTY/+e6hd8ouvL0W4/w1DJsITgXJjF46\nlzbpx5hl8BgwFl6KoTHNbIvoEPTxomwN0wJR+c5NeC1v0RHl34aCnXDlpf8ZGb0mRtHl3AyMTeHR\nkNkcj3etJC2ViJouc4YnEjhzCT3D99Em4jhdM4+zLOBCpcrS6G3LZofzzLT3pUf3T8t693nOVEUL\nO5v29BvWm/THCbS2ceZM4G4SS7XpNv4iKiZOn9V8udHUY1HARpp71qVkdogoJttft+TukI0kGHrj\n5xNMgybdwPTjT2WC0KstmcOuUnsW1PN4htmPC+Sv3T8yz+s8MiVoWrqU+prmgktxMXhK1xr7wPbD\nWf2Nv7Am10jgjP+AkpY6NU0NqS91ZoU0guvfD2TLV7V42nwg8b7exPt6075LbawlNTGvYYB2Ix1Q\nEq5ChPOSHqjPOUVRIVwoasuyhaoQL0KqSo2qDN6iw4Ma3qzg9WsR3oypk0AjuzzULVT/vZ0KQYc2\nWxyx372Vy5NvoBuXQbS6JZla2kzL/hF5Ju1KwNiMrtXvkiELwyVaxphWZ0C1/L3rz3KmLbV1WJHY\nB31dKGw1mKYaCzk5tAokw12HHAx7Jn1O8+XGxDGbjFrxNK1+BbGSRyxcMZpze9PYlwqZXidZlNiE\nbx2DaD9D3BNDSRO2bmkKx9/Mpp/MzaWPuL5nDfdSIDnPQnAdmQ69eHYRzg348P4Otx/QKzqC4jWP\nBFRhjpGjGzPM1zDIpSedO/2GyuZYcmQQ6Q+7PeWvjqeHMFVpOBNtdzLE7RJmw4QZehjcOJSFp+VP\noaWzm3HY5CNJT4RAo4gGl86TaKhF6LYpmK6tS1dbaLFJh65PfVhReyXTxzxGRVvo6hiajGmYzNhz\nfdEfLCFo/SwC6kzkZ7fx5IxxQWkSVF3eAjTEcuwvqcK2LjdoGhzIic4TGacxmWebq+C980m5W/oM\nZzqP7it+JzI3mab2tky51AQixEvt9iHMbjzFeWcQeYfEq0HV++liGhxpwbR7pUxbXkrJlS54/bWX\n0bY/UXED3v9CIPRTA7cZH5jsdGpEt27TUEacyPTcBio0nASc//C58Gy6ITanoPS4cD2H4JMLmSrt\niMWNGPJKptMo5RSuhU+JyFtPuy/Ok186nToPOlGtBuSVgOTcHWr4Hmd+zSWC6MkIikZ6GVzc4eTe\nehAuYrKZ9HRmXFhFC+l37B+rx9dT+9Lg0XzaT5jGpalDsPgTqizciVrDQcLq0NAjJ+oxT37Nx9Qd\nDk1WY8cVQyJ985GVSFgbDlNnteFE7DFhdbzL4hYk7/Ml8Os2XNkoITk9D8lIJeYF/MDWnfFQpexV\nED4hNl8C1nW42UuF6+NA7YdeuH3fAJAnl61q/hztohxAQgniJr01md2crPxrsHaVaDaTQ4pp26sD\nL2f4+sxqQu1+o5i1bgyHfSzp7TUdSHvjE1ZQ0d9Lii9Nmx2i8bOX+VItgGpU6ZdLc4sLRNwBy9ow\n3Gk9y673hDihqmIassd2IONGr+BjpUnc8/+mVfhfwEWBNMDObmANPOnjjreyFnAdCq/jqwkQwMJf\ntAAP0F8IRm/0IOYHARX7QGBmnYOzVRIEwul4F6Qxwg+1vEfSQV7fSl/fUjcstCXjyBaceo1n6PA9\n+FzRRrgCkBKUZaAKXB4nY7Hvde4lNEB5UTiEZiApAi0nPRYcGAGIValDk3n7ZlN1VDWOj3UDbgPg\n9dcc8mzXYPn7TzQwVOJ22seLEb5J+Z1pNWPaWt3mzAaoquXAhEWteLNL3Vn3T2zVHmHzzIxV0eJ2\n83ffnMb8mLZ8SmLXisOEJyfhklYnHL1+xyf4LPlvnMDNJ/zOAw0lBl2qWKvR10KJvqbDsGBof2Ad\nN1dqoRqahcXBSEqA2AdQq/hXdndLYvh294o1/hK7rkRPn/QPB8j46uxB9vxth/yyEpajLt3A/2Ml\nnFMg44zgGnouSkEyXF5jKn1sM5j+zgGeL7Jq+Qpb7PBj7NroyRrG08J7Kj7qAVBwXhhDBbm4eGeh\n873cW6h6nsGVM1QBUo7LD3mg3J8bU0TM7WqlT6hRdX6rvwH47fX2v0K5f743fBVPhkbZe7nldqZf\nap9nZPxhcgyc6WOwEp68mWxYRpNWT4g/WcyUKycobXGovM1/OtWNUY1MRZIuVP3zf0ajkT51amWz\nwXENhmcfc+oaFAJZbicA0GykzZMYQ/anFEGfRkCYIDp2TfRm7XWoWxsStO0wWNeZ2FbGWD5KRH1y\nAKlXr9GjnyVHD1pXuO29+4eT4ApfzDlKdPJG6hjdIXzZA9BKJ9Pemm9+ziPQCWQUIaQzNagJBlEw\nb/1sHuBJSTVjYg2sIL8IolMQMz+i5I2BtEJlqFcjCUmjZvTxHyrf6Cvv0RibQlydliy/0E40bW9i\nt7I+DL4unAFZJpO/b4JKx218pz0T2SMpJvfTSS1MohT5eGPXkh2cx49s00RiE3QR+neqrxPKvhb7\n0fn2dVl01FUxsnfla6Uu+OsO4UmWJWXNxVs+ZzqmJd+e9ybTvT59Li2EqLeztrfdqobZkmBygNIS\ncSNVLXyc8ZtSlbtN68hTd4pIm8GJ1NG+Q8uf/EmurcuS0Fl0YDmlwO94A6lUdSvkfJ4N+Sm5L5Lu\nnxJEy1jTBUQ2MsO6ViBHi1uTNiUTeSmXegRViaAkOZZqscnIO8IVS1WSiAM0ll7lG1xRAzoDMi1N\nYt1a077FaW4BfbkL6wzYuL0vhH16yrOPcb92S7IcbMk9uZzp+FJg5khgzZboaebxqKUrl/eWAuLU\nZr84SYXvakHMQ6jj9wv9auUS7r/lveOSE6CWTiAgrjNdOXESXIB2+y8APwpur/jsT6zAnqrD2lIr\nR4vej4dS0wVqacLZyzCCMdToXZ2xv04gJU3YKraq94vR6l0AvFzGaYWndyAd/9iLigvUqBIO52zL\n3F7ZPZ6KC8G3vucB4P/UCqLeHltqs02NEePmUvgM2gbXJq+VuGUqjkj7EZdnxbVUceuQq7gYYaeV\nhq2vP4zQY86ZuYQt7oMS8pHRIGoQhAEnfE3IjxB++MH3CFy8Gc/uA7VIO/yMlzWxlL/uyd9WhsQ5\nVePGVw0Esd1550W+2w0NbGF2K/juW+hgC0rJeVj7nuZOpvw7seAU/fcdoUdzYbKr+59xZNNtGzSu\njCELKLwVThN/Xxz37eGLvZO4UfK+MxOKcA0XDuuPAUASnkL42TxGHzTl6B/X8Wa+/OX5FJA7XLH5\ntuE2ikCse8srkn6O49pjI5SBk92/xiXoGhZX+1JNE6K2PuXhiZnCi9CBV0Gm9V0IbreFJlt2cVYy\nisSg8pedLrMzNR35lEMlmWTUrI9f0uDXH7dwZE7HC0xaPYc4QGlSTfr17Q0ElVvMp6OOJF2Z2lZx\ncO2GiHZBnQJUXzisEyre2A2ozvqpbuS427HLZyfwRFQ971OdMbUK8L9ah+pPb5PxdCTSnwSKPtri\nh9LwFXRKu4hSwGmUNu7GOe48063/H3vnHRXV9bXhZ+gg0pEmKBaKFWn2Fo29txR7QzRqNJYoxt5j\nL4mAvZcoWLCXKGCjiKIIFlQQpA299++PiTUmAcO9+Ms3z1qsBXNn7n4Z7mzOPWef/Qaxb7NsflJ5\n0yB+tAyiddgpTm4V0Coj9hUTWtowq08Ql4cvQlvdnqwBfdC3hBLNJ+DcQ7jY7/Iqjuik9zvYb52W\nQ43Vi7HnDtsXHmPt0eqUGqjzYslQcTS9RtGCMA8JJjqgmJT6z8+vaBrK3pcq4bmUlpxncIsGrFm6\nCZMW4NUCaoxrImj4UB0rBjOBkrCF+JuMJiDakIVDt+N5o5DWBX6QoUS5xpvlFdA0UxG0OzAp8BbD\nnvyGzwpDjMLu8fgVDNsHzWbM4mVcPGKYc71Bpy4+h/QxOmAC7cQLC5CbX4XCDJnb4uikfcR5h5No\na8xJx8E8Gif86Hz0wmJc1bfjNPNdexAtWN6NnTVcqT06geiUCEqaw0HNPRy5k46wnl25kPL7Oz8+\ngyhd6q0KoAgoUn4Fz8Uqf8mA46e4CFykJwYY49I+keQdN0DEHc+h6TWQugSwO9oZ/3MgicqgcdRp\nGgPRf1TPJR4dym+u4m7DbjlWyu+rS3HsCsq/H/7nF1Q095/yVW844OfFrX0GNP16LqE/JHBioBtO\nLCPdSkfQ8IVR2fjsN8bJeyxZhVWIfGVK0VLZZyNbuQqFYVWQze2XbR2mzMk0fp8ezhI1rmaHsIJq\n4ASXAT1ekausQaNJxpgO2YS4I1IZvTufxyrlCYMf7AOOihq75EECgb2tsazThKgHdygxMOTwzGkE\nDc0SR0BRJlrecZj16UUpoEAptzo7ctFtGpJCeF7NnPq9TRihOY0Hp4UslP8bFJUpjFdGEQnTw9cB\nPuJr0DHAuoEmegtuoNsYSBfRTTc9nVeep/nS9hC9z8Xhen87Uh19rtq3JCK8PtenpMAAsQ0HFdF0\nT0FCMZ66E0hRVERmLigmL6k17glrblvh93Uc8dMnENi/ETrtQwkA0gqE3khQSkZUMcG8Lld7e00Y\ntsnCP1sLUKXCkyk54eyr68LIJ5MIA5oDR92H8GpZFonJOkw74kBlJFKAguvm3B0+hdKJlbO/N2Rp\nHrrV0YgAACAASURBVCHdFjKyyjbuqzcUL5ECt280IibPhB+OO1AM5Lh3YwHjYDkoLIzBt1YbIjbm\n8HrutDLQr52KVd3ndPi9FLaLH3+QexotNGNQH7KdXOD6Qnfoc1N8IeHhnOgCJ+j/zoOPxdcBKCmp\n077zaTgNto9fQUblNCcq6ulN7oJBKC04zP7VwOpQigGdJbbgXSmS3qBKCeWpKCjXbb7XEyler+2M\nAVzf2VOTXUlWvpQyI/IMX7eYADyrJA3AmWB2Iuwcz8d4cDmaDox9+4ArvC3MNoa4yqy5lZH8xIAn\nkdacKnAERfFGpd3ci9g/6md+VynkMTB5Coxr4s6OPmIVhX++DMqoirKGzNP4ef+qcEvEabl3KCnO\nZubcZuC9j8ANyvhehYaDYIL7DIgRsbTyAyQSaNExlj2Xyj4I+bQdUJ8VEr5Q7UKlJtJK53P7m3xA\naRy/FZvym7gb4jjjqoSuqxvM6A6Gxcyakw2Ffv/8wv8HFO5Jpvq3Wjzu68KOgYZU7kaXNOi7BKfX\n26+PAIhbDfQhW1vb43+ifG0r5VbPcv77rBK58Ph/gKPjlXjYcBgPj9cG4itbzmeHy4qe5X6N3OpZ\nrkOu4z+mo6K1yHWUTYvc6lmOHDlyKgC5O6kcOXLkVAByd1K5DrmO/5iOitYi11E2LXJ30nIj1/E+\nch3v8xc6HOaxOlgRCZAcMJplztUF1vE3WsqFXMef+biWf7Ga34hqnRWZ1NYDNQ0Jnh5teRJeOeUV\nNk2rsKHeZq627cLyEWXvjP1fwqBKNg6aERglwcMS3rQ1e42dAxxP7Iv0pZBbSf+eqmMs6XDpKEUv\nwIfBgFBNqj8vlFoaEdhbkZAQULDow7QB3wDyEi0ABZPGfBm3gySgpLMD6sbZGCUnceZqawqyXla2\nvHLxyck04PQSToxJRuN8NKrAKLdCOowJY5n5DI5PVoJ48Yr4LeNLKL4TzfGTPajoTun/jA7QC1BD\nr28iueFVyI24BsSIqiJH25Svez9laNRxEktl+zbSTasS5WpCQakq1frcp312JIM7H4TzIm8tMTHA\nzSUcVZ/faGH0kNu/uuLTzZb/D8lUwUaHH5tGcH4mRLXuy+7kfvBQnkgBDKbW5MTBJRTXvINuFJyJ\nDEYpsRCNnGwGFtxmsLEHxIu9q7EmytNbsnB1WzLX92P5qkYQ+6pMryx3MlWvUptfunpzpXsIr3fO\n5gJKyy7iB7RmKkv7Qr9LvjzKvMFf2VdUJBlKOqgWQnjyC8FjvYt+7zZ4fDULpeFTeVoIRd6yN7Tm\nsbp84zKUwmTxdpXkvEpn5JbGjOTd9nrKGFwowbhHN+a/cqTYOgZ0xCvwVzRQwTy7JeeGtcN7lwE1\n9BRRsa/F6cs9QeIrZo/mj4gzoqpVJspqBVTLSEI9Jw9pjgYvK7jReyO1Ihz3byRBAzIUS8l5WAl9\n9j6CRNkIO5O7pOmNYecrZzhowjH9CfQt2c0X/b3gufC7j+qqJRCQeIcYixFY5RXSeN41+qtcpZ/t\nOVrNmsrP8xYxc4xArhAfYKBuwJhdITT5aiFxayGniTbFS07yYNAv9PdexqO4Av7pgi13Mh2y6CVF\nnt4UAPWQNZjL+SPM6w0u3t7wnc1UJivNhwfCjxTrlIQi1QbF0d0oXrtX8HivMTxxn59PjABGAFBi\nI+F76QqixyXQzPAVfsll846peKpCF2cWOKzD6GYChlvmkdHamsMOX8J6L3EkNOzOPI0Z6ESuwlup\nBydrD2NDq2UE/v6EPZl92NHWheVXDcTRUssCtwY+WCRHyy5UCQQG2aHaMIYqOlkMjAnD0SCNqdW/\nZsOyCoyr2JqvzDbw7C48Wu+O5xRlhO3YVUYM6+FudYHqCtux0vBk8LcbIBi6BkUS9Xs0WAHPhZdx\nc7MZv34FMx+bEjDJjLx7BmTfPkBcZ3VyUxU497QHojRa/a4Ha8PHEjfyLnlqFtyo2gOpk4TqKqX4\nbv+VHfvcaTnQlX9qeFLOZFqTRY9Hsv8RGAHuv3vRYcASVJPvgHs7vm9+lX2N4RWgWxTM2JqebH3g\n8Km/YplRNYgiMx8UexZQvFbwcG+IIPXDB7AdV0iiRwaF0hhk75LY1MFlfzZOM9qQfA7slECruAb1\nFfeCnxg7gdTo5S5hgKsz+s1gnkUgwUtvo+VelSvj76K92p66O08SmNib5U3mQYjw/WddjLypFeJD\n/DtTcMNtg2nVFBp3vM2q1tUgY2fFBz5ZG5Xup2jkrsM018+kH4COBsM73CTp0HasOkL9zDwKZi4H\noGCsDgMSvgMFkfbEZz7FuUUOiw5q8FPwTxRjAXMnMHKxOvUijZlSuwMgsJb5Pbix0InzgMtyMJt/\nA5K2gidAa8b5XqZlmznAP3dcK0cyVWH9wdMc+Ea2EzzMYy5n2t9jnlscD5fBlb11UXD9jm5jnuGw\naxWZT6W0aCZlK5YI/W8upEEjLAoDUVXKp0DQSH9P3XaaPDuvgpKDLmr2tWCr2AqUuV8yj+sKz0kA\nvpyoxXmNidweFw81n8ALJaBIuPAKNvzY8AFNty7lyKjBJBboErzvDFDC5Bs+lJbCyYye8GArTiwE\nHgmn5R18X3SnRWYoSvoZnNb/Gb+n9hAeBdNCgTN/PKuipz90ufqsG6EGElZFTnrviKZBEV1HaWFQ\nR2b53GnZKVIHm7D62lge+isDCRWs5R1VBSaM9f+R0Ib96aX2JQWXlgMS6NUITf9Yfqo2EDHbWEqO\n3MXaBgZvN6fBtoM0/mU6Z/v3YXLjugidSLX1GjJz4Q+cA6qMcKD5z25QsBVQYWxtA75O6ciqM1OA\nvDKdr+xF+82syFz1BIDrsyaxcZysIfJwr+/w/PU4dl4gOdeZM7vUObDLk1wgZ98toG25fsFPITC/\nKbFFZnicmPTPTxaQxvklJCdGUS9eg+veIt3CvkcR3zh1w2vEfpIDJ6Hdo5S5jZdx0O4wNyQraLjT\nTrDI+n31GGq9H4dXS/HLHMlvd5tzZZ8+UALGBry4r48hcPb06w73gbz13hGQb8ayXm8+8QXRaKjn\noa/rCyWngFBBw9r3TeLp7hTiTG0IirV8e2BuX/a6H+DLg3Op5bKLWi67sExIxtnrAUNeLsItQOB/\nMB26crq7KxPutyTT5/VoWZdLiWt5Ka0DKeIuThbevMg1ozG0meRKM+lVlttdZL1fG5KzNP75xf+S\n6h7qlOKHiwssueJKdGoooMxkt3s4pI7DNxW+XLEeKFuFUJmT6dCkXRg/v0eRmgaxHpZAMgCPIgq5\nNSGE6UamlHZZA0XJRA65R5dADTKBk0G9y/9bfiJ1455ROR2UNLF37smgmz+iWgTxS+pTmGuAGHbG\n71PKg2ADLux6zDonPWy6/IBk8Hy0Jk5nWvIUxrgOYs8Bb1BRr9Cote1r4aP+Pd3ynhC1tjPrHjei\n+M5bm+/6phF0tz9Jz6aqECRiY2iLrjw8WJ07L0to0sYc+/maWHwtQYyNf3qSZHKycsl9qkHKBdk/\nVu2Ojdi692ueDXjOyyIj2n9Tlxnbo7HTkjLFYi+qUcksGbtBUF2ppzaz3MOId+3Zp/14FsfVIUQl\nBUGJmK34FLC0z6ah1TUS75Ry+1knrl65Doki2T2PLcS2pg4ZLirUyX9GE+04ujjWoMWRI8SlgEEV\nA9b+5AsElel0Zb7NfxQJjYE4Y1tuG9WG1L+f69p6fTp2LOKxYyjQt6xhPo0kCeo5Eq6H6IKBJUjF\nbMeniN1gbVYcdeQuYK0GJ/bVYFLdO9Tv+gj0ZLW3S1J+JGb56xHKbRH1ybjZzhD7R7YozS9CrWMN\n8s5UXFmSV9AizipA1R/rM2P3eMD/veP5LwrIzMjkxuNOIm1gNgISIDqEFZLuPNGuh9ttA/Qii3C1\nvAhjpsE2YUdgWknpqOa8e3vYkoXXFpNTGEE6puyovpjFB3NwillCX9VI6hdf5ingt7IpdBFU2juY\nsfm7bdwNbsC3CSMRdAroT0iwH1jCaL2j5D/W5ospCuy4XR1UlKBAnMm6cKUwNqjPZs0vP7LQeDmk\nQ0SQJ+F/HL/m8i2R68r+WS1/nWkGUPTPo7+zVXpizyKm9oHpx8sdpXzkS1AohkdxaqCnW1ab6wrB\n3CGX4RprCP1joe9pBtS/vJViIPvu2+dNZBBKSIBSqgxzYPwe8TQCDDHeReOYe4TesSPvScXeQtkp\nDuai9xVeDPVicZYX3X4Ek+Xa7H85jBmDNpCjHESi/1YU0K/QuH9FoMtinF4Gwtmt7Cl1hJcAOShb\nAbWzsHRX5Pk2YTWk2BqSmV0FW8N7fH0qlUM9h5AX3AmARVYv0BodT7GXBT+bQwHwNBoub/6ZaV3E\nc2kAE8b+FsLGTg5s22VKWe05KgIDzSyOpKwmZrcD7TRc8Th7hbjuuqCkiFgLHyXSAnylBThHpNKj\n+DzLW88i+MULAOadAsWeXwEXyny+MifTEhsDiqXq2JYEM7/HNhbGjoaL4fypjtTMHJuMSLa5O/Gw\nlRaKx68AAhuo+d6jk0UCx9SNQVfYUB+SLVXimWE9Og97xNY9o96UhxXztlQMZP/zlYDaFLLqTh94\nz7pCKJRQamnGiIf+OOQEEK/Sk6Nz+sDSkAqNUloqoWPf3jBlIVvy57FtuzVWyb4ocobrmZ48ephP\nPOAw+CBtjDyxVPXF196a58eFGQm5jtrA6YOtWW72PWmkU604CYUTFth0DaG2XzbP+wifNK6escPa\nrApdk5bjZDocO4a/mfRZ9VgFXGE9YGwCiR0dSFGz4Mwafd71IRISRfsmeLOR3i5rOecqRcxEir0F\nC0ZvZWbyNLw0NIEY1Hc8wyDBVFQfzteUlq7nlKU5gyxUAbD7yhTFnmspTyKFciTTh9IGOPa+yp3t\nUej/fo6JyrF8tTmN7KYKTHmxkYhjjTj3QztSlivx5EwUETkS7rduAf5iOFEWcWFzbywH7WLklBuM\nn7YHZ9fdkO7/zy/9l6REKbJzfxOu6xpwB/syvKIEHgi7+AGAXV8OzxhH0oJMNPMekp3Yl8VF/Shc\nKlTReBas92U8XUDDgBohsgVKnaR0Dk1x58F6UJiqxWIvF56oQv+GdvQ6Lsx8+p3hUp4stWX48kXE\noI5maTZ91NJwt3JlwT4XeCHCNRkThUe+KbeUd9L959tozXR/77Dzl3Bl6QjcB3cmI/QJCfeKEbMG\n9fqORez0sOf8RAfgvGhxQZHJY27Q8XkoP+7uBhTj0iuYhEm3sbHWgvyWImp5i8MXL3gakoaBBVxS\nGgNvbvbLTpmTaY40iw4XT3LBYyBhS5Op/jSYgImyY6PpCkDYITAwlaCqbs1at2U8HCoFYsst6lNY\n3asTG40vYz1zBTeRQE2dd+fYBaSUrKhi7kSZUikdy5U1qFY3B52HJWTZ2SNxM2ff3T7ELltI2g/K\nmBkZstLvALccFfiUC6T8FEFOPFHBagBEoYa2GaR4NKKx8x9z5wv7waznQMWOkF9T+iiBKT92xWb7\n95hWfUmQlzMzlibCMD9EKQJ/TZKUe0i5N9MIlGKZc3suAEsdFsDFu3AxGLG31CopljBkURwPXSKR\nNq5PaZGYiRRMamZhd9OH8d/vIXv9C5o0rkJx1WpIGtZgR7vO8LByjB9tgvTQvp9AnWGtmbSnNp9i\ng1S+OdNoLzrNnkHrBonkRyuj0zqLDoP9yYtS5szy9kgopZ7ZM45LW5Ay9F65xfw7QpjbcBmrHNdh\n0/0VfCfC6K/S6YHLyM2Yp4cxuiCIi6HQaC6EtG/EnibTMeMGZ3Prk+IoTj3nx7Hl5TEIHun49qFZ\nIuzCipQS0U76R6r6DBpmFHmy1MH8jx8qwaL1D0w22LE4YDEjRvhweUKg6PHV83Ixi0+g29dn6GqR\nTJP6J7hd2oNfnCbzfL0IpXJ/gep3Yai5wowx02HPp20kKf8CVEoMfr4ABeAn4YLf672zsrnTW4Gm\nyDaZik/6xVBc+OIPW/bPYNuewJS8dIJb4HQ2kLlPH4BlB3h0CR69AFIBW2T9oyoPxUv2XOkId1Ka\nItsbJ6fyaMC5if357qs1lZJIAV5qVOfU+RU0SArCftkJJiy/TJBGAKLUHP8N95LtWWy7nQltPn1H\nXgUY6n1uzpifmx7hUDB/3Z/xj9rN569HPJ/Pe1DccT+zmQ+P5Ym0UpFosU7Ni+sjjPF/IrJN7DsU\nPsthswJAfdnXBt9K0/IuT8/ns4MuYN8a7nxaVy+5O6kcOf8fKM1kXZ4ZGjcGknJPpKL4/yHSfdOZ\nS1P+TZ9ZuTupXIdcx39MR0Vrkesomxa5O6kcOXLkVAByd1I5cuTIqQDkyVSOHDlyKgC51bNch1zH\nf0xHRWuR6yibFrnVc7mR63gfuY73KZ+OUUomePZ3Renwh6/7XKyN5Tr+TIVbPb9GjQnb0zAdvRJb\nz+qsueDCjfOqkCm+7XP12j3wUHWie58SWFYRb/7/Jg1apaLi/wx7U/AvdiYioRK6R3wm9BnTgNmJ\nA0lsKOsVkLg0CSmyrQylQI5xDXbVWEnMbTG22r5PvzGptMnbxOFi4Ruof35IAD1MR0vo9uw86MON\nguY8PF0MxZVwvVoYUL19KU13XyC2lRW3VNrClfIV8P+rOdNGfVXZZnOQJnNWogQ8cYlhaPhGaqxv\nDMoC9zB9jc/wN99WXZZMxENgoDihPz9UMArozNrcvfTBh2aZPrT6PgGoLq6Moa0JHObJ7IVSqPa+\n44Bx36qstTnNbhN/ULQRXMren4Zy5SSELU0idGkSCciSaCmyTl6G8VHUeyp+3eVwt0d0DPEgeV8K\n8+4uFj1+ZWN+oT3+Whs4t3ILId/PJ2TVLKZ19abdxUrwTdMZy9GxG5gUuoGR+DD45W7Wv1gMvdpT\nnhT5iclUCZMmJVidzCA5IpRUHT3u/PQ9p4N2oy5VZ5pLF3w7fYO+Wo1PO3156LH7zbebdSdiZWUO\nTcTdbVPd3JQvgswJPHSMds0LQKS+nR+i7NSWSc4teBKciokhZJhVoZ7bL7xyGouyeUPRdMwx3sz5\nPXHoxV7EvPo7jW60arPLZibqmUGcUjMER+GcCBQ1JKxYd4HNNfNQBAw1IKFGAxIwJgFjrPvZ8MXZ\nmjy2bcyTNomC6XgPY1vQNMCgVTWcwl+QGJzPy7G9iC0NECV8VQtb7vmtJ99zCYWPljDooTn1L3+B\n+eUvsPulJ6AquAYFFRi8PYet3/5Eq7oraWQwkOA+PgRbXsT/Ym0u/TJecA3v05LYb6sj/TmNbXE7\n6cF8gof0RVUziBHGa9Cwr1LmM33abb6uPd85uVHz4T20f6zCyoiR+C+1hCXPkDaeyDaDZdy7GE67\nTk855iOWdYc19bY9pbetBzwW18nObYInB65/zcb1LdjWbA/bdXqx/Gw1UTUAqKrF4bdlAXoT8tGs\nFkqstQn1nBI4tPcUI/vdxPOlAWU1B/s3/F7Shm4cJ9CvBq+SdHjdt+G7/UkE9Syh6am6jP96ADx/\nIpgGM9s8VB9KyQXMdOChVj82Wg6CKNnt/Dov4HW/lXCROszHhwP2zErahYr/LToogNu1tuQ9Fv5v\nAtB07hOOf1nMkyE/oOHzEJUwb6wjtbBpXcCob29Sh+kI3ddUp0kzTB5foIt0Akjvv3fsnGVHTo7K\nAEkPOH8ZMnP/4iwViSp3f4VTffvwJDwM4mHXUgdMc0zRbhcKCWVf8/qEkWlV3Pt5oOR5j7x8+OHV\nVfy9NKFU5vkTfi+Hln3DyCiAHzstA4S/lQOoH1iVPUegSzcRPYb+YMI8d/y/n8be5ybU8bmNzp0A\naFCW3qYVS5bfA86PL+VgqQpbwxw542XG6r32TJkBCV5JQFVRdAzX2QdAXngexdLXiUqDWd2XYgJM\n7HkAsu8jZGIvRpF8lCkB9DTgbGpTuCr+vOiHBG4bj+SRN5lA60PF+D3OQLTW8kBenhr7tn+B50lH\n9kU64oUVvn52tOp1ETF6Vpam1uVyYT3+1HpwyU94GC8mvf5Zbug0xUJfrNv9K3Q9BOPubWOCTzbg\nQBv32tTRWERxaDVyEso+Mi13Mq3XogWa10+iBhyduownFz6yl3WtBFXAdzJw8qvyhvgk1t+aSomB\nBmfWdRUl3nsU+vBmhS9zK2m3ezCi2g7xdQCgCzo2gDIGXSwY0/IVdwvA4M2Si8CoavLyoczN0dz6\nbUy1Vs3YrZBC9BAH0hv6YKDcEHcXoRpVQ2ywOrov9FEAQl/Bumo/4jiiCl+2iqD6GD1QsxAs9l/R\nfKAGlxZlo6IHCQv6w6BFosaPTTRHb4gBkPLe41VVFclqryOKhtSYkxgGZDD7cCFd6xbQLbYJCwc8\nY8hPTYl/acDENtdosW0F0X/Yh4jBvIz+FJiAeZ3p3GzVi2mbOhEFSNZnQFHcP77+NeW+zXeec4GY\n7kDAYC4MUIJXH/lvlvuAFrtViXukClM8yxvik9A9kk2iRh3CDAwR0ne8LFzoaoV9THDlBN84lPXb\n2hO+vwctpqymMD+c4utw1ukrCBSmGfO72Go4U+fqSvIBT6cf4JGsr+wPX68Cf5CcimJU3k6Ui6vQ\nUiWciitX+ZBClt0bxo3jfuzvAwGRMCByJuo1lGmRG0StXhosCW3Lkwix5rdL+anmL4T9lk6csS07\nTvQGIkWKLSN8XQpbzLpD6fv/xNq6xXN74X5xROSkcyFUm741ntEs6RCJet8w1rsnUIt9GwGuiKPj\nHZa42NHEwpHkzu1wJ5VZA5bAlBtEUb41n7KPTLU0aPpbdZp3X0DH+grMGv4zJdEf96uxNlbi9+H5\nqNZQgmdlz+yfioFdDSIfa1OiU4h6DXFXZjVUVGiiU4RtkxRe30bX+FWFYt3K2Vy2psVklPPCsey9\njJdPwmnsJMFpSAqvAoMQ2mDH2rANEzfOJCZOiiqgpmyO+aUOdDpuRsdtsumXqsVS9K1TKG2kwrzn\nUwXVE5UYg1mf+fiO/hWdho2o0lCVEmkh6sGRxB65z4iIzdQZ00ZQDTLUsO/Si5hV0ZQALuZPubFs\nHE3G9hQh9jtI03l2T5MPe9yO0tsDJH30JYKQkce353yY3s2Hy6GNmL0oFCMnfSqvdWQRIdF5RJ8/\nR9T529zLqPtJUsr+iW9tSwu3LaQCuw2HQfhfjzj31xtSfiX/gn7WJygwiMC5sISUc+LWqNlqvuBO\n86X4793NZPxY860Pw25M41nn5sh6NtZCdgNgCAi/KDXNsTl+NqM4Y7ee+/OnkKKkAMqpgscF2N94\nCKlDZda4McCsXc6M69iG+ZNdCL0LmsCl9nO5aDeERTHz8D5tLIoun+0JjI1045pdfy40nU2rLapI\nkH1eJl5wQb2VnrACajRlRYgj6cgmPY4EFnKiay47UhyhlnhVFh+nHuFPlSnSELEbZxdn8jMkPDnw\nmEPN8zns3RCvOXvoPyIfNMRasP5rmuy+T2kpxJSzpLBcRVSFEtB2UCGu7t99COzhpQnGRuD169hy\niflUQg83IzEMrEZmQLK4ZVHBKg54aoxlZb2pdN4Si9qBYLoeiOa7rbNZzCBOVBlOyay5xNpPJPiL\n7yhZt5CSQ0JuKEji0EljfINSObzQghd+Pbi/qw706CFgTBmOvoEY3RnMbF2YbQejonP46WUO330R\ngKkEuhRX5+wpBc7tMQJpKFDxGzsO+N7H3ucjv2tOOEf31uXsFRVaj7+HArKLP95QBVVbYRfm3L+Z\nwf0EmVttLeCX7Idkjm3K+WOwZcxyQWP/M5YEKelSrCLenZRSNxWQlgC5UFLKs6DHtOwzhJ71j+MS\nVRktlk0/+ugd12blOku538H04AIsbsQAlh+cyQJU7FnX8ggBiXd4WezA1jxxnAZbDQqjBNjUzEWU\neO8RH8e4qC7obvud2OASftm4HdWwdQxUu8pcrSB6KwahkByEWcsgHIKCUJgfhIJLUMXrsLQHicEH\nD6YzLmkYwXe/YU7yYgRfzS/Yylj7Oiimzkfx7nwwXwnmK2lR9QTxpdDbV+hKi1I0Ht5iQA8n3NYF\nYWrz/u+r2KIRXzSDlR7jKUF2s2tSBGmBwi3MaThU5U6yCSVATncHjs/wxHn8fmy8btPIFA4/dBIs\ndpnopM91v55k54hT6QFQNPkKR0cMpeHod9NPKiO2zKbgixhGLCiAKsLXvAJotrLg55J7gMwAEgUN\ncnINcOkH5TU7LPu/gVDoUQfuPoY6CfsI+DmEWe2nvDlca/lV1E48RDU0GKtfdBl7fy1suFUuMZ9G\nKX0v70N7FcwYpQ8kixDzA4JCmR3U/o8f/vCeyvv9beWP4GWv1Qn8ZhFLDzfieOQHq9SFoXxvtZq2\nN2chu2DEdX+cuOc5TSbuwXYSPBog9PUgwevVGOqzCK055xiqEUaDQFNKkFDrfCyHPHSx5DlF49Io\nBYz22LF6/HTIFq7eNb9QjfQc2Up58cUonKNmUvQgjepqMLVqAMH7TgsWuywY7yulgVkoXoViWiyX\nMmv+Ilob/cJ7labPItllUB8/LXfOFc4iXgTn1u9ijrHMazfwCwA2kgyMku/Qe0EAeJXvb1P2ZBoV\nTJe0nXT8LYSeg0dwZWYYnXh7G19VARQaq6Bi2YwRy74lJkKkVbkeC4j0WYR2ezuYUQmJ9LMgGe0e\n9yl53gUrrTQeJ1jAqwwwMkbNJIfGD+7/8ykEQNm0IfbfHadEAXaeHAfJwi9G7rlWj339BpJ/6jck\n0hfEOL1AEUgEzHlBFU14bNUEvQY1mbzMiWwBEylAcWgSsT1t6GYHWRFSitAkpU1T3PSnEuxduYkU\nYIDiUVQlWYhSNvcOCqFr2LbIg06PLnI/4ioUZskOSONZNnMo2oOtiN8tbDJV1dGluLQKA7oeYpt5\nNaxTqtF5uw96t9UIblR+Y73yTVCke3F3fCPMJs3Hdo1s3k8T2DPJHfNXoKh/lSOezRB1dKj2SG5k\nRS6bf/mB4eeXkj5An6shrTlrMpSZs6+RcTGahROnYdlrN5wUPpm9i1GPVzwKyqJuFKAaC4iwSVHY\nmAAAIABJREFU4HQtnCFV+jNHSw/bYg+i02Rpwrk2/DhoA9VPPuJY7S7gFYzQu31e479UE6vRPUiI\nKqW4sRnnQjqCb2Xab79lzR03VtNU9Lhp6Rr0WzOR793WUHQ1i8i2SpyI7ctjr3rM6jKE+em1Efod\nsjF/hFPDk6gP/43kGmMYWuCNyoMSpuxxA8r/WSl3HpJKQ9m9BmDe2wc3xSHbXWyNzGJYRI4e4lvm\ngeM/P/W/jEeEEWn9+2Hl6YUVx3HgOC28m+Dc7xaLPbYAzypNW1RNc3ztW8BjcZIX2eEszTbmvWs0\nEliewm0MIUzsGuBUdmx3kH27HyBM5Ph/hR4HvpSASmXELuFmtCI3Xa2hUU803c1JzzKiaA/sKv6W\nq9uFr8G9l9qY5EnDSGi+lYlHHzHw6ENSlvwCZH3S+f7FoO7zsRP+vLRUDvnBaewJbgi8U2qzH9i/\nrLIkkR2Rxz1pHRyKNHi01Yg388miIb8u/p5SqiiAynfAukqUEXqKLB1QfL2BYwOIMrcfE8eE5qbA\nfBgA8Cv/5pqR3yHLEYxU32zO0YxzgPiJVM4/k87S0lEkHyz7/nM5f43c6lmuQ67jP6ajorXIdZRN\ni9zqWY4cOXIqALk7qRw5cuRUAPJkKkeOHDkVgNzqWa5DruM/pqOitch1lE2L3Oq53Mh1vI9cx/u8\n1RHY2JOUrRp0dv6ULmqfi7WxXMefEczq+fOi8UxTvv15HOvcjxHvGipsMBUbVAwUqdPtESrk8+iJ\nLbm+UihN+efXCoipbTU6pF0mLK7gzTyOkY0mvtJaZEqFbXGmY5BPI4MnlEQU4I/41i1v0aBKK1O6\n3MzFZIsfQ24eJv5WAQUOOqxS/ZmoR1ok+gtrY3LxHnz5m/iW5/97mKA2qirdMs8S552GRR9N4nSM\n8N1mB7yobHFlpmKS6Rd94Yo3bBzLdP0VNJpyjNxfGzJuwkhIEr5Zwbt0q+1DKSB9ZQwIm0z3TNtK\n8OYU9EOSUKSYxGRDurbI4dfJEznp0hTSzwsa/6PMGcvvse2p1foZZxvZsJWxVC3NZNrIRfSxOk2m\n9Lqg4c0M2+GatApHhWKsSj6WTGsBrYHdHzlWcajbGtLb0Z9R/h5kPHVixM1TaCRfJm2fDvmn61H6\nUgr+gkr4LNFRasf6at/y+5Ex7G7lCJR/D3pFoqI8nIM9uuB/V5V6+U/IKMlDJ1yZTDVNOlXL5qdE\nK+DjTegrBOPWBCwbys5RsnL97je0mP98GUGDy587/tUCVI0aFqzY95TTd+w4tvUsnpOrYzB4M6+S\n4sgZeIE5LqdAU5wGwABKOoZoLtNGEShadEHwePPqu/KtTSItPFXp0hqGzk7C0Cob22FruVTagl5B\nuiDRElzHeyw9h/WuuSiP/oleTgM42y+PiJZdUU/QRi1Q4FGSsgE13cKIlhaz+t76jz5lVO+zPLGu\nBUbCtkvUU61P22MH6OgRS7+fuxERcYM7Seo8I5/Y7hdJchXewuVzpGqQIa9exdFy4hKOj5tGdaXu\nmNALG5wAdVG1mJo34sWeWkTfiuFbxzS2DDqI+pWBZClWoTQ0lXCTLDDWFCy+ho42Ow1+wW9UHOa1\n1WmrYsytMUWsG9OfJhYNAI1yne/TRqbKjXB3XAZKieQMeckz4LF7XW4z6s1TdGtG0X/pcn4y0WRJ\nlsknhSkvtXWfYmJ5HruqwAPh470YEkZTxoLD+48baGvQoMsvNHOcQtiwfUTuyRBezBtevvmuqkUN\nug+5zYGXjrQN3sOT3EdQKGDoxv0YMNQMna9gVktr4M8t93Q0NUnWBTKFvWPptekosQ/bwDhxPMj+\nkhwFZG2hxXWA+Ct2pY8gATjn0IvHh20YvWEe9b8/R14RDOMCIOydy2uqaBaywmY6ryYr4d3+J37Y\nJqG05C4T6MBMF1tqP11EQ/1CeCzc+2ZWx4BSqTcdZkPPkJm8TDTFpGEST9uHs/L+QDopXYNnZe9+\nV/6RqaIJ0z0fknYzmDS/lxQAzbY5sKlkEAHEvfmK0y9Eb6wqVeLEu5ibHDYi7koSZ+99L1rMjyFN\nV+Do4UZIgMhA60pQoAia44juO4o6NfJR2PsLfg8jIVvYkan9lpskAAWHgYyPNYuoxpDibdToBrzy\nFVSLo1owhVS+BQbPlPn7EZ8eUwJzCVh9GgSZYzYAuv/xfXPS2gahBezZbsetNDUWfudM0rKGf2z2\nFa/jiXpJAeYZz/GMWoHvkXxKS143/1VjcI1FDOoBs65MhFypYBr6/epPzguwCynm5bk4yAwm7nA0\nfo2cueML82cuLdf5ypdMFWxw/SYdu0lz0RpjjnnHVmg16IPTtJUQ8v7cS3VzI9K25lNtjGG5Qvwb\nep9YS/eOMEexvmgxP0aT3rqMsvGhcUMEne75M4rQ1J7Zo+I4Y1+T8Zd+YN0+O2TdPIXnzlBdrNvA\nc+DwnX3Ah3u+HbhwSBQpKFCMhFJQsadvn3SObdhB4KaFBG5aiNNyY1qZFlHe27hP4VGYGqD7F0et\nWdfBH+ftHkRuukfXCUKMCqWArG+q8egCPlxycxhViNHG+4waqQSCN717BwVAE4yuvZMsbSy57TaJ\nwBPW1FG/AlwVVML33Txp2Qc499ZyW6+7Dd/sW4ESsHC3gMl03p7z1AueR+5kS9aETOP70KlMT+wP\n6X7vP7FZX+ZG/oyiW3VWhlwtl6BPZn5fopYm4dljFBArTsw/YcTAPQq0D1mDSgTMTdwMiX7//LIK\nQUIHt1wmxi7CKj0et/hpHHpiSbafiC0RI3w5nLGDujUgt+cJZncN492E6hogG42uazlecCktpgXT\n8eA1JhYv4qtL67mm2x2XZsdY2GwT82aPZ0ZDD2xsy+fxU15s9qhzXbEKfPHxAUXg7uW8MM5kUcIk\nmrkUMsD6jKB6rJrJUmnxVO0/HtHES3sXiTHQr/ACIJ5/WlqpLifzvqH7iFU4jJHNPS0dvI/rK3PY\nO2gUyQdEWB2c+eED3TlWuggjv09blCtzMm3ULw/1JafJRZMd4SOIDE5FmhhKTuL7Htz6ao3w6/Y1\n1yPT8NSYxuPgI58krFzoGXPh7ED0a0nIPVUZZUlqqDXR5qemB+k2cz61VaKYXmsHwQlJkCeO9bSG\ngya1kh7gVj0Yt0v9KH6ejK3tY5T1xW1WeSg+m99rDyY+FpzCd9P7pBo2RiaYNmnG/AkzaWwIl3o2\nF1zHUNetPPNPQ6txKtd/nM7GUfUJcQrFx0lKz2qxvAxSZ8ycVWCgL5iGfsOOMjw7mIlP/fjwVr/G\nZmsuDb+PurMJEd6atDc4gd02YSf6fW864qBgibtkDAAmQ83YvSmJvB9suX9GXL/6ouwC7jU14wnF\n2G1LJuq3pZj8GorboRCuTdeBQuHnmCeab+TaCXh+YzFNqtXDf2lP7pwJR1ECRYC5ZSzlaclX5mSq\n7PWA0ogkqlfT46b3X926GzNz4EHuzI/AuokhEfvESSSWBk/wj0wmO8eSK8ntRIn5Bi1tBnco4Fsn\nfxqG+VE4qiGXrAYRli7q/T0FUfn4Pu/F4BHLaL8ygHNuhzjZbDeLal4CQ+GdSd8QL+VeTE2ub5nD\n4xfQvNdkfjJxY5LTDHYHQWFTBUqqC7/I8WxsFtf13dnlOJVNczWh6J3byURPbrbrSfUffJg9Tshb\n2xMcrTYLxxoeOG8xg2pve83+ELSRIuDnwwvQcHNidOJmUjoIPCW2w5euJVM5u9YJiZINM9WPolRc\nhCRAwppRF/jr6QhhuLLWgK9XgjUe3BpYhKfNWvK+OY5YI2TfcWYYGrXCq0UJK4y/4t7PCTT6ygK9\nsa1RBvoFHwB0yny+MidTBWQ5+uY1Ff48FwbQg5PFv1G69ygDm0DPOwGkRojTkcqwrxVVk1PwOzmO\nqLvidvqvM7U1bS/Pp66nF5pqsL3nJrzPjIPkDybOa86BtsIVsRdJC3h0KYbfXfM54KqH2cIptA5y\np13rdLylzphvsREs9of4PVbi1IRqxJcsIce+BjF3U5B4ykZdO7VGcVfFTnAN0uwYdibE8cpTysf8\njfZf6UJWhj0lG26BkXB/l/2qKqTb1Kbn+KH8bj+A7amP2Z7wGKvIW0iAhEc1WLGsA12upPDlq7OC\n6XiDXg7dXPxYVPQN2Z5elJRCtv9DpKsDAOH/Lm/RYE2HeDaOgFb1IFoCxd1eQZF4VQ/SjFAGx89k\n+uJi4u8O4E7qRr48PBM/h7oAVGv7GGP7slswlbk0qkjDlmqFt9E2CmOa3UV8z7TBtvFTFE1TiXlY\nh+8s+/BQMZbaNW1o9nQd5ApuySnDYj5zLytQ0BlmdHYAxJqjlCE9m0FGQxtK7kcQLIVezdvRC1kh\njCLQuB6gCvccjhBNTfLrqlKwW4sDQpYoASAhPvgRzR/MwXP/WeYu2YKbYSekSYIHllGawnoFCTTb\ngZ1hFgsyexORB5397uH9srooEgxbqdDbxJBtv31kDj01mAaacaRYF9DS3pfrW4WpZ4wLlvB90lw8\nnTaxPrwO2IG2VjoNCzWws87B26wZqztNJXqWBnBKEA3vkVKE5i0VjLR7kpF+ijxAoWVD4u82hewX\nwsdHmdbNUtEdWIrt6a08Nx/Hoj4OtHviyqB73gTSSQQN7xJM6dxghs+tz2vvum3jJrCSHWiRjhr5\nlLX+tszJNKTQkGO75jN08CQMY/bRn30YxCugkFNK4vNScp6DeUALpnWcS3SGeB47ky6uItQarvTb\nA6niJlKAtJAANulNp0iz8UeOlmKcdACUskkIMSQNHYpQIq8wVwAldYGPOG3mh+JyzJOzygpYKFgj\nxVSA2H9FKdzyJW1mG6LWggkwN+dHQBy31ClmV5ntcJFtcRfB//0FDee+iiQ8ysMs3IyQKDvg6cdP\nUhFEh+IS3Ql0ZPOzqnkqmBRnYsE5eqgsh1lXhYv9Jwo5EqqFN63x5BSxwK8vZxBTkIusDkNYzDRb\nMFN9AE8XZ3DEZSi7fm/CgiP7USgqETx2eVACJLJ6kDJT9tX8whzODJYyeuAj1L7thwHGEFeNkkdG\nWI1z5vu+wQx2/pLojFsIWxn+FuXGnTCx/pGaBhrEnjUTJeafKMwjKuElsVk+H/k6TXCSNsFxpsQE\nK5MVnE1ecDpQUOEyhjzLR797I0Dt/QNmVbHXvUOAbUue6VpUeNyyULv2K3KLZMk0OiVKtLi369iy\n5KApV6O/xOPobUzt2mPa3YmhQYpsCJzH0+JiLnq0IUcqYCJ9Qz6kvYK0V+QnvCBzjgFnpdBl8O8i\nxP4zpT0mkQp0fmxHTPQLKBRnnvLu5S9Qu5fCzvNXuHbRDEfdONTaZ6DdWJUZxfP++QSikEs1XVD3\nfEbDlLJbgZd7B1TObweYwgfGbR4AJ8t7qn+NcrMCSu5BfON6vLjnC0IM+P5HODdDh3GqwVhUC+T4\nomZcuN0Ny6ZP+PLpeX6NmU7bhF9JixDRgvsdmtQNBGRjv75DEvDeK862xZNLzYh0ceWeXzLWD6LY\nMvgbVDMLSA+qytXBfbiYYMOVoaqiaPmQXz3a045UijdFAzVEj79p1kSSfaBZwm1APNPFCAVrkvUf\nUa/fTr7ofhHL+BiSkupwrOlO8PjzjrnKIYDBO2B1X9g62BuTpY3K9Kr/6a5ROR5X+Yl5cLmylVQ+\n0mOhLNPqy9BUC5Z1cqXt5cUY7IWCrSNRWL0Hmddx5XB+1FDmshFtYMJeM0Cs8rUcwjwhDB1YoAO8\n+6GoZOfSiGiu4lhJJdF1adlqId5/dXh1f5h+TJDIrXuuZs7NAJwsF2MfCbsiV7BZox4gtv3235EB\nzWVrHj/0WQ5L8/7xFfA/nkxlyO1835Dhw15F2Pu6b2MRMBIqM5EC3H/hw6A3vSQrsz2h/FqR8QS7\nMTdxi/oO2v25IZB6RKhwN3nxQSy1VADmywZBGrl8XolURssTq6nOAS5NtaKs3ef+A8lUjhw55aVk\n2zmW0BMI+tOx3G1lnyf8r3JzXCbQE/zL3opPbvUs1yHX8R/TUdFa5DrKpkVu9SxHjhw5FYDcnVSO\nHDlyKgC5O6lch1zHf0xHRWuR6yibFrk7abn5Kx3zYV4w3DNl55xveel8DSUkuLUMgOs+IuooL3Id\n7yOejjnF+dRTXEEcMJ0AXvcdrRgd5dPy18h1/BlB3EkV+WarKs2n/0hzF9jcZiu7eyZTGdXzQWM9\nuXYpg99rz8Tnkohb03Q1aD2pgA0hCjy6ADYZcM4ZVAElStmstYBZNdqRFfWxzvMVhTb6lto4KntT\ntU1VctDA/3IbMtJ0IDsdCiq3NOqjKGmAki7kCVxoqWbPIKutqKqAqi6QB+hBxHVD/KWVsyOsdZ9i\nrDVf0UZxB7cGNOOSQx+YXQnmi58VasyZGctkyRbWp7Ri48WOZL8oqmxR5eJfJlMT6sxcjZmmLR7X\nrAm9rIpYW0n/hAIUPc8mo4UxYja5xVQHk4dBnD8lezNfIatmlMgkkRd4Cd0cC7KoJpyGvt1wudoX\nU4Vw1ENUKECZemm3KMpVo2mtPGbv2cALZ/H7FvwdxiW6DJp5nY2LagoaJ1BnEQEJwUiUQCUVKAKJ\nFOxdHfBfUgnJdEMPert/gV54Jj9MDCDx93MknSySCft/iwM3Zk7FICqSvnqX+F5xcWUL+iT+1QKU\nTb90TOpl0r/rIbZJJ+K/ZwQoCG8F8VcUAjnC9fr9OLEF1GmjzEubJqhZNaHtTnMk1lUpQrYDv0Ca\nT8CJLcJq8M6nW60SJCkQ6TGV2lWNUDcqwcEomIKYMCY5d8R9QjAqja2E1VFmNBlzIoCvbh8WOI4q\nD+OD0S1VJ29zfwyPNOC51yhGu8UyYYn4PmHmE2tybMsgTKsWcMr5e8I2nyYprBgKRB6AKFalSfVE\n5p18hb/SQlYO2U2tXzpiUcsJ6/pimj9q4GTyBWdXrEP1eSlWhb5k1a7Kw8ryHrQ0oKFhEWbTexEa\ntI5wi4UsOJpU5pf/q5FpU/UEnumMgG1eAGx6MZkBw5I4uksdELcLTHQrU/CIEzUmAGlSlk02hP4L\nZR28XMPpMv4l9oYX0fV/RClwfQMwuhVsF8qKwYu+mktw1knkjKMpyyXVqdpPm2+khykqbMRkveVc\n+NWHXms6cHSaQBI+4OstqSjOSWJ/ii3vO3Mq0dxNmzZTtnG2+yQQ8u62lTNJ/hCr3xmPsS3IkTb4\n44DYjqUqfOP+ilrbrlDYr5jfLrniHWCGyAZhoNaIVfob8bIYgFvOFnR3KLBv81AaZIazfnprInLB\n+BtNhhXsgyfCWmFLNJWwb1rA9VodaBazmzvHUsAhl5/W9+GMZl3yk8XMqC1pPeceDZ/dptfZfVSN\nXsoYF3c6t97L93N/ZUEZ51r/xci0M2tPr+LM6bdNkGcNWIPZ/hRQMPr0034iFqUi3tp/jGPBcDUY\n8nM4t16fcP8qKCPb3xvpA7StJ2h46bUHnElNBO5CqZTMY5F4XuuJs3Iy56LBEHgwzUBQDe/iNWAh\n6gNyUbf/oJF4lcaMWu2Bv04NDuYKe5utNbSQPKAwXJkcqSKgCw0nCxrzo3zdCUfXX2hr4MeqI254\nB+ggeiJFkY0bNpIVe41hNyfR5pwqrY5fxH18LSbO6Eav3Diu9Hcn5mAWLguE9aIC0FjjwneX56KS\nl8OdzY+hRMqUwlASY1OoNUWTokyR6t9Ne1Bs3Ymvl85gbut9dLHPouWRnQTciWNtFVdMfyq7o+8n\nJ9MG/mpsa1uPDOt3PqB5C2mxJBkDc3G73QNi2X2XmSojWlOIbCasGOgj/cu2EhWPYR2aDc5hvNpL\ndNI9UQIO999IhK5YXaPUGX/Rk7xcNYqK3r/5mdRzL4WqwRSXtCBya9kaSHwqi1bMAaAOv+FXfwoB\nc6cQ11AfqgjXWf9D1Ex1mPd0OUbAnqxFBMd8+OFUBvQEVqGIes8GhLkXo6AmIXpRB7RNZgG+f5gQ\nSIAbNNC7RFs78Jzb/e9PVwH073uYa1+Nhb1/OIA2asKwVrNRqzqUea6DBI8PgIo2czWXcT4Gbo5Z\ni8mSfXDzIK06hNGyuylbq65Ee55XmU/3ycn0dGlP8NUnJvLdOVIJFw43QV+lyaee9j/DvhpDKUF2\nrZYCjZeXzzb2k6k+lgMKK+l3fxcN85YSmyZ7uFueBzZq4rRi6OwmpcPIH2mYkUrh43cS5v6xmN+4\nSM4v7Vka4ozQiy4B3zijCBTO/pavuwWiWEeVCwcgYOBiQJzJ9fEbw3F+cY4lPwSwz1/lfYPFPmNZ\nvSOUwH4beb+jVcUyZVkwI+7NwyzEH6czNfHc7Pj+E+o24pLlJhruOsqyjXvgWQHl8T76FEqA+mZh\nb35ek7CUm+e/ZlxuW0RbQFatQmS2AV3qQbRbDbzXr+es6Rp6Pd9F79PjUFjzAMPnZXeF+LRkqtyT\nsNaxKHUEit6fPFd8cZMqGuLdTgKgokFeqjLGWtDES/hu4WVBXzkJFWST0opAZtnnsf8VQR6OBCop\nE/NQjab7jGnX3ZhYjSZwOgzPVSuEDa5qQP/2MDx8HYmGYJQlpYlqNF84pOEdfJLrg6tTt3kp2xYO\nBNKE1QIcWGaER9BlZiyvS+wqHxzmr+VSh+Fc2B3ECHcRuv1rODJ+tScJM2oQsfY0spJBfbAxxviH\nZhw/Xp2sTbe5dvYFM05dEkSCVmNVSu4kYBn/CMn6HnT7YiTJie82EFdicaYfD19c5cqAGZweFA+t\n60ENYfu8hj/UQ2HvTTbfDGbtgEAUzSP5znkwxYUvBY37HpmvsGyXxNwiS7r0G8+VmcVESyIoeiZF\nXwECv/yRh7plv04+bahiBHdi4EkHW/jtg2Ov64LEpKEtl/wMUDHN5YFVyf+1d95RUV1fG36G3oug\nIEpRUEFFpFnB3hIr2COxi2iKxvIzYkFULLFrggrW2JIoWGKJPdYo1YKAiiKIFCkC0tt8f4zGmgSQ\nueOXzLMWC7iXuftdM8yec8/ZZ7+QJHD899Dj5Oo3RqZKs5vBUunHdeo9Edo4QlYM6z1e3FIaOnC5\nrD8Gn0vviVFyscQ95w6ehcs5qzOCTLMcLJz0uTRjM7kzy/jREXSB1XHLiX2Q8Y/XqykeOV187Zcs\nSjrXBTGYkAxStnDxtv6NQwXG/Hxz4J/H6no70vbuDrrp7GUPm7BfG4nGwB3EfdsUqPnnRSmtnHZu\npaS5ujJ/oyvw5jSD1/cJ2Hy3haBFi9i3yQRSEyUOOFImqtdd8G5LSduj1NaFgMHfwBbhm0P7/dQX\n03GdMSpPIyx7BkFFtciMA8OArqzd3gAyK7+oXfP3fflm0nDl+Hu0IU0FRKr1uK7XCqE8hv4KR7tk\nRpifJuqSZL50jD6Y3N4BCGQyeO2t/pAZBVwF+ivwPqPOGkBMiP5IrlyGZSHbOdMqA8iDq+C93Jvk\nwIUYTIAuXcvh7EJpCKgkZfysPRBblqHmpYHE2lh68/vtI4I5oVaP8ERnIAJw5Ijd14SvDad0hCcO\neFHeEfovhcmztZBGMs1KLWPYgpdzoG8mUisfI1yn+REw9mtOrVaGrMQaj/9XFBcpE6w1koFc5WkO\nXN3yPsdjAShP5HEAPHZpw+8p3bn0AFpuqkXf8e2BqlUHVXvOVAykYPzmQT0H7ObGkptzr7qXrRaf\n6+7ATu0mDik3GBexQ8rRzGCgAwx0wEYvAWfCcSYcJyJo9LkGDHSg2aAWPPtFsrSgBIj2A0cFSqRv\noeJSmx82S0qBDvsOl1qcsAQHVrQL4kyrR8Cr3V4mAzqy939w0cdLxon0JZLbprAW+mCk9g9/+2Gc\n7jSApkY3mDz3V1ovqMfM4TsJGxpOTgGUuAfg0AQyhw2h7Sp/4JEUlbx7u6ggaor798d56mFKVoYi\nZL22a1GAhXSl1rbY7DyA8Xh9dO2V+dV/PSJH4auAXuLRIYio0kgszPWZv2cM1bm9rt7INC2acZ1A\nZ80pTtHzxUErQo0WErPLkKRnwn3CARDdCuKDeWajx4PmFhAlxVgT7Jh+dSJlgGFRDBoUUMGLVfvb\nt8gqNaR+9F1SSiTJVASM2icb2xBRE33mfHKf4onn0XHTZdXP1kin/leE562+vN2RXN2tPp+zGGdD\nS5attwWeSiF2FTGQ1C+218nkSHTly16qw9qEjtxx3sjzGVuwZAsgGRuO7AhLR3iz1zud8N+bQ4bw\nz8sfnfz4o1ERG24O4sH1tzba3DgNJc+lGr97YD7DXc7wldJVPNKnkDE5FMVFjSgLT5Nq3PdTH4+Y\njUQnVhCwcxWRox5TnfdJ9ZJp6QNM9DL5tngCtraliAtFzCwORqSWzcjY5VAs7NbFpNYicuNB7VkJ\nuvF5SHOltn+gJ3WIoBzJB3jpi+/lgOKNBOqQQCGvPtd0untw6kDOX1ytBtDVwMoijUclFhioZaBM\nKfqRmeSscOdInd6EjionzqEpqTY9uH9QHSHrGwtvG2Bb24iO99cClS8xqRGUtPlu1u94NDzKhNSN\nJC59TKmZGl/5LUMMRPTXh8tSfF0A4mPoqBbBELepqB68R6P2sN56D9O3FsCFMCRztgLbuIgUGD/r\nHr8tv01i9P94kKbOO0PRPOmXNl4KtWOMqgj9gEcUbtLD8gSIk2WzpbZ2RB9iHCbgtLsJ0zyq755b\n/TnTQ7upN76INc+WYPuJBmt+cWXUqamA8HvAUx+Ana0ZT0W1OajijjQ9ZW7SgnTq/rm49HIasuLF\nz6IXP7/cm6/4XAlypLfg0lo/g2mO+zia14UmetFoUkDDyLvEzwwg3UKTGy6e7GxqT96SBwheKB73\nEI/4IQieSAGUISdYxLYYaM8kxuhCQTM98h9kE7ZpCdu+MwKkfweVEXMY/5jOQGdJLfSVi//0EKmi\nKdKlwfe36GSrTPsCZ0iTzfpC6Rf72eq4jB8+/YxzaXCt3UTKN8bKQEkLgq/251dbVwZ8N5XK+j29\njw9YgMriqy1OgBP8aWQo3Crt68T8UUBPRr/4TbrmXI8w51FVrHmlvEBp1yCaknMptHgEoK+kAAAe\nRElEQVS0B5Akcvvpxtxb4U53hdnwaAtcllXXqOdQflM2oQuf4xfTDeX0H5mmtB73WT4EhD/nyvFt\n7O6ajBCJ9GNkbEomjYxu0z66BMoEqn1+D8VFIk5eKeAk8yUHrooQolzubTw2neWKVwgN+/cj6/CH\nlVV+4Gr+x+T2+DFpEY6A804E8GYR9vRVwCrgxTzdf5csSmuvZTmw/OX+6q7Vv437/48zneoPYobZ\nVkiUXSJ9E9m+b10XXyN/vDte50bwIaNSkLuTypHzHyKGKeVjSMqRpd32x8WSNFdSIy3g4YclUpC7\nk8p1yHX863TUtBa5jsppkbuTypEjR04NIHcnlSNHjpwaQJ5M5ciRI6cGkFs9y3XIdfzLdNS0FrmO\nymmRWz1XGbmON/l/pMOqLjPOpDOnyXxarD3A40lRvLttUMo61niyxb8e1636EHjC8W/+8GOxNpbr\neJf3a/lX3ea7h2qz2cMfanURNrChGe5aJgR4bMAXX84tWsPU7lHUGl/5xrIfjgLYWTIUbRZ6HGAZ\nvmzS9mXFZw+ABgLq+HhpKiqm7aSVhCiLmbd/KpaO9QSNr22tQ9Dx9tR5uT3uP4xIzZxOA5/hRDhO\nhCMS2DNOGtTYS6o8wxGdawPQuTaAPWvPEeoZwPA9aoBrTYX4R0RA7u50MBUuJqjg3eACnfMmQt1W\nbPW9SrClJ59eC2Jy5GpwcRFEhY6NEqPanaYHMxhW+yHHJ+xj/9r1GO/dzcAQ4e23JxDA6pYBWBsL\n1BW7EmzW8CertQVzSrbhkPOYHb1WAcI1Mu+tH4VCTDLfdgrlYNZoweJ+dDTowEWrlfSJ2cVAjjKQ\noyhojZW1KgBsDNMJ/aF6hosfXrRf15DBWnoM+aEfd1dKDuVqQERdGBM4m9juYUSe/uAolUKTPLrZ\nAjelu6X0TURs7DGEZ15LYdwO4CTfY85z02nYhK/mOx1//ocrIN2mGrnlWph1U2dczBNYcwqIpX5E\nKU8mNGWYUzBBtJJq/JcoaYgx7GmHycEUzGupQ+prJ0Vi5m0M4dApD24H3xdEz+tEFd0nqK4fYSUJ\nfNtxM/1XTwEtd8gTxq30uX4DmvkpEd3lqCDxXkdRRRHjEa1Y8eVCGm2O51xgCrXMoegR1N/ozLCt\ncygOk64jKYCSswU/OG4gZts90GqKpq454pwbaFqlkXtD6uH/EnXNFhxdNgetH3M4+kX1XI4/bGRq\naUZ32xQGPvwcZX043GATh1nIFa2vedLVhYx2EG7r9M/XqRHa4bFsBylDawNC/rMW88wvFMYF8Kor\ndgZBFqb06wJxdqVomglgW3svi0UD68LvAbzsjVkn15BmT2JYnfSt9OO/oLZGDp4xnvT5DNYmDiU2\ntfarky+WANyDPQTT8zqRLecy6NpBoDcXTiiwtelmKP7wnS+VQ8yQfnsI15Ge19PfYVw/h+UM59iF\nVnx13IfD9f0JtezNKQ1/cs7cZdnuwwKosGCD3UnMth3ikqk/MwzmMdNwLrbdYd717QLE/yv64Ge4\nFvG9WGa3GYZZK2BBnypf5YOS6fiiUoacXUKeBRxv5kZofAqhlLP7qT7HTtugqwq+qz8kQhVY35xb\nQY9YkLpIoIB/T96lbKybg57NM9QMC//5AVLg6SJXDH4T84eZMOZxAKHj1yGKzcS5UTmX4t5tCCPy\n+k0wLW/zS5wecUqalI9rTWmMHTfCH0KpsFYZq5guaLyXpFMHtV6u7JnWhmtJKVx9nEbAWSd+LUjj\naZvmfKa/S/oiTJ3I3PITGd83ZteDDLhfRHFCNH5l85mStVn68f+CPeWLedhTmUFXl5BzIY/ajYCh\nf7c4+H6qmUzNGL/ehHZPvNBVhvjaMwg4/eaiT9a4kdw+BzqpX1QvRBXZYzYEQ0WwDhDQkOtv0YU0\nyD9dn8ybOgLH1mSAax0OLnBmUcc5vGvUJT0CX3r2+b7bVd9+fAUioDxstGB6XqeefT4Wprms2SrG\n5pO7oKrxzw+qQdr8cpfs0bqCxnxJycMCBo0ZDcS856wwzUaafXKT5hZQ4XmPyKHeRHb+nPX+x+ms\nuZeYbtB8sBLUd2B1ywdEjvClaycdNGopSlWTy2d63FM8TLaSLdnhxR90rWokUxHe407gtGwiT4FA\nlx/xu27Om+33LNn2cCwtFWF6pzUfJLCyNIl6iJIyqGrKqOXbW2h7W7HtKBz3cINyYVuLudXSYETG\ndApt6xH6u4qgsf9kwoR3DvUOELr5rxrQgrlHnuBTPwD3yE0UrL9DOWD7+BLoCJjY7DxJ0tdBpfyc\ncDHfpuD97w01ilmsMFfq4e8EZLBAaTpztoViNBiM2sGgR9dwvBvHsTswNGYzX+YuROX2bn7bA8vv\nz0ZHv5MUFRlicSGMCcdg948v7qLigWq+RFVOpvYNrWj16AAZyaDX04PTZ3NA/Hoi1SB0x1xCtt1n\n6ZDNECtMq68cLW0KrRUp6Ctdi9rKocTihOVQ34H4RSUIYqoDGJkW0DPMgqniL0mIKUKECFPV56Bk\n/M8PriFyVAxQ1wSfwPqcDvyJueGJGLu3ZdCcJBQVJMPWzIza/3CVD0WPNnYWBJ04zEYG0tRrL90K\noVu5iOLTY1ACKgR20TVYokyIgRnZ6oqgooz+8LakmPiysnYSKMnO+6ilTx/qLQhjp+sgAaI940ac\nFkljj2IyyAcTPx9Mlizgk/SrDMgxRzEqiXq54eg2V2fDjFDcWvxOoUiKHkSWIG4OJ406YpwXi3Gn\ntuzqFkh06j8/9H1ULZmKXOn+MIjHZ6HZJmP2xGq8lUjBVacuSTMLaGJhxr3oWtVTVQ2y9XVRLyhH\nI+PDhuo1gZm6Ci1PPWV80wsI6f00eu5dvu86hqRZTbD70oCCA0ks1liJm0okDUZI1zzuJbtVxpAx\n6RMALk+4i6LjdiYG96KZ31YAzKzg/KK60hWh3h+fB2Mw3HKNE2038bPpULqLPOl4YxzzXMdJN/Zf\nsOOToZSgQgWKmBg5svjSVHYmw9OKAlAWvnTtJdfLWmNgB0oJO2WkQAx58eycXu9PW/SgmzYkrzzK\n4xMnyYmToifUgwwSzynjP3U4rdfUol7nRywJm03TtkA1KpCqUBplRqj1V5yMiaHdMHD08uTNj3Zr\neNyf0Ume9F+VgUJuEpwSzpHT/HESR7QtOVGvB5ApWNz3UdapMz79xDBppaBxl09syXJawsvF+6Y+\nbPbywuXrAHoeH4wX7ZGmtTFARp4mK1e2ApxfHaxrQp+64ThGBHIwzoHYuGykOixUVSVTEQ7ensGR\neymAmfRiVZK2/EEEzQD4OWg8Gp2ecBp4vlgRJsmwv2gUrDeaTpaSASCDhVJFRT63v4v6lqvYIBl6\niBo6Qpoe5Et/euxSqTlcTiH8siqQAi0yJHtcvq76jqtKj0z12iiyNxfUNcD7uS+SN4MOtLej//h8\n1g7252gzDZ5vyEIn/5qgiRQ1R+4sqktJsSp5OVrCxQWUDVVpOs4cp3H1aNu7Hu5qZZxr0IpGW2Th\nrSN68yt6IRMDJkpO2T8HhBy1v9KhXpqMQUGYZOThYorU76+zb1NcawBjbFcy3iQPdRdjXjhy4eJ+\nHYDW9inwVLgk5hy4D9dfIvi0x3MutHrC8QJQHN+VjTvdBNPwLsaQroD9kavwXDYVJxbj6tPZaCWO\n7pCzaACKyoBWBijK5g6zIAos21XvjrrSI9NP1U/QSDmOolL49so65qqpkl+uxrnH+tQ3esZWz/Uo\nRa4i/WgIeXlnqiWmurTtd4mMqESK7umQ/ShX0NhNtJKZ/2AZLs653L8Ht4rg8BYY6HGcwPBrwC9I\njKBlQH1DOjYMpjQKdgzqAeek7Mb5F+gVZGCeITFLE3s0h8vSjvgHsw26s0UpmY7PV+HcojHbVi7k\nulsmjZOvIwZWaU9A2n5hrxM/8Q5nh83G9aelFIsgb44bi070gnCh6lzfQ5sm7AsToaKL4CapL3Fb\nl0iyegEbB15l9qUhJJeC5a0EQDb1uNlL3fgmx4zqvGcrPTINMXWiyNaEglK4kp3FhaIUbpbGo6Ke\nhr5iY272ekp43DESc9OhQtg5oD9y25NbbIa9zkNGdVUXNLZqejHug3IxPb2BQFtvWho34bvToTzu\n0oqwZZ2gfVvBtJg2tqNpSBvcF+fS3LAtvyzdzJjI5XTzacq1ySaC6XibcRcKEGUUIrY35riXME45\nT689pd/NJdisrM+jX4oZOGkK0Qbf0uXMj9jrOpC6V9iyKCjD7yd9bNuZ4x98nhmLe5MTniywhjcZ\nZHeYzIoygmf3l5kGTfJRAlKGKKBzLwljSzg0Rzbz2gA3dT6l4ED1FrEr/Z8dd1KNy86debKyJapq\nJSAGhSXPeNJMg/v79ZCp2+NvjzH2NiFj9wPuKgg7+kozrsNPl5vj7nidCZkJuOFHZsejTKAN0AYh\nra8bfxHDquA5RGwB/YI1KG4yZefQLxnt68D76wuFpSJ8jLANPmIv4TRxMno2qlg6Z/FreiY6UY85\npjURnghvSQ75LLndmlvn0wBZ10MrItqhA2Vi4oOMkdQEyQ6voW2I7F6Ph7p2xPnJaJhs58rFtYaI\n66YBVZ8urPwwIS2D4KPmcPT1BQwVCC5DVhbPr8hg0hIHwAEShfWGT3qghMcDdwD20xCQjQ85wNkp\nJbR8aZ0LL3zaRcBDWUkCwCt4B4GAn+9i4N1ifulSTHZMMeExSoCR5OuJkL0b3iT4tg0fwwebsmop\nPWftI2chPFWwQlbJdKWeL6cmr6B4dWu6D/kBDhxFVtNiHbckcnK0OVfqdAAiqvz4f5E7qSwtYz8m\nm+mPSYsEr6UO3Grh8d5dUXJkhBjqmYgRNxwLV6/ITEZR8XI6+PuAP8CvMtMB4LJ7F8SbwZ3qDQ7/\nRclUzsfKUfrCLdksfsl5P6WlCkzaOIiEJAvebO3138VvXYcXP1VvylJu9SzXIdfxL9NR01rkOiqn\nRW71LEeOHDk1wH/cPEGOHDlyagZ5MpUjR46cGkBu9SzXIdfxL9NR01rkOiqnRW71XGXkOt5EruNN\nKqnDoS9LI5yYPTcUFr/PZudjsTaW63gXKVg9W6tr0FEhnNaGUbQYLwaaAnofcskPRBkX63Rcxgu7\nP//jQw16NGNE2wz81l/jO3xZMeoymioOshaGdUdDfMy302OJkDbYf4ceIOwWZENdJfZoT8HODIiU\nYou5/0eYjdXlzPxt5O30w0VZljmk+lQ/mR4byYZaP+AmPsrggkMMiwxkKku45LURq24da1Bi5TGx\nyWe46zl+Xv2jwJEtgJGg6wnangLHfpsJ/LT1GD7ZSxgU8wNZT63YHXKENufP4rpd9vWExk6FaCcl\n0qy7DBwRGhiieXowBqGfMu94EqH+AXzNBn783F9AEW4c6bEOixtXmNc3BI5VfafNvw9HBt3YxMSS\nNbjaXsSiSxjU0RZWQqdmLDS/xDcEcPLLAH4MOQP6VbNpr1YyNZ1qR4qXJbeeJFIuBnFBOcrhKZhy\nk5BN9/jfmS40+aILEtsI4TBMhcY/x1JvpxDb9USAIe2a6XDqm/WkKloSoG7OdrEpd/134DC8Hc3t\nnwIC2oZ80pdj1CdzwmW0nyeS13oIq064crvVXXTsYceGiUDVjcKqi66NKii/PpMkZlSdHQztqsAa\nZ6EdSrXo3kuPdT2b4+3ZCY05+9mx3ACFRgoYnhamPFDZUJELs3pz7W4Z8/ruIvyHY4AAzrXvoIWO\nvRq69mrQwBhQBm3h3Bjepu7wPGYdz+TBstuUp4kE8qV4iQgDB2u+b7eAWmoXsLJP548rkNfqChta\nzgcq70FV9R1Qyrp4Bu9i52NQBqy8tXlsZkMqxhiLUymeFEIW4B0znlkuU0m9XFTlENXF85tYMp5Y\nwlfSt401clXFOeE+iwsOMfOSNw2UfqXARB3l1DKiVucR0Hgoic4FBBVPZU+0ISBAz4CnuSzqsgmD\ndMhtksalA02AGGZtTCPGB7aO8YZrQu0L787q5j7MShxBRmmC5FCrPtT+YSELv5sPp04IpAOgFt59\nonDcOJO7eiMIcVYkDy2u3ndFUzObYxdzgQKpq+g6wJB7O5+j9mVjzq0TMIl2d2CT8kJJr4YKKCkx\n4ZFzIWLElMY3pFl6LCk6TTARRYIIclpq811cGzKfCGABpKRAp/RzeJTsgHZZzPLtRZRNJzj5XPqx\nAcztGOu0isJLBcR+4kZ+gYhommJ70xgH8TXQawjZ9yt1qSon0xYt8rBxOM7tQNBW86T/EnsgBUQi\nRIjZsTGH+El3ESfep2/DEwQKNhKyxO2iL/sGt0WIN0ZaeDN8TPZTqlCb02F1AafXeiOIWfV4KRUd\nnCH6O24Tyi3et8hQw4Rf4E/j4tsAMTC9D/0nOXMVCPZ3R6j9z8blxqQrnkXVZTlcliTT8qBWbDeF\n37/oJ5gOAIWRjcm8uY10a4hvosWhgJejsFgKgHQhROydj/tnivSZDHXn/g+4I0RUatkpMl/Pl/z9\nkn/OUkCZcOoFSMbEipylANDNu0QuknGY0gVQwRpJYxgpo6BAchMTLpn1YHrb9Qx/koPCNTvpx33B\nSO+T6E68jLa+I1svNfvz+C1RKprDFIjzG4lV+3mVulaVb/OzenxCQmAx6oCifTiSfb0iEINYLOLI\nF1Oow4ssLWAV64CluQRFwvn4TsIELAjH+cEKnO8NQpJFVXl1SyCC4qMonPYhxGMQmzo4g521MLpe\nw9DJljX7pnEVyBj1NUliIbolqeMyxJL5ila00AGjEIkful3Heiw1BRVPfRTNhZzTVmd+sj9Nom6S\nEQsDL24m9CtfmipYAcL1NJ1ks44Ka3BRO84biVTREETN/vJxH4rqzSeU7o9AUVsRfUdrahk7oO3l\nQHzAZxQP6o62gYPka9joPx+jo96QCmWB/NtEFQxweYaiwlFatNpP+5krELJZj+3DaADEDZSRJCwN\nzFxqMV7FhAFztnMmvF+lr1X1kWnnMFgqMb76uY87/FHyxvmcaYXkrwRhneJrMXL5asIsLbmZaYxg\nbcPFF9kWep7T9GDaLxt5rqZGZj9tHuRY8e23iyBsL0/iVYlJBOZ1AM9YQWRZzO6C58EpKCT/gurz\nGNymgPXBOpAn/WbENkvbMH39QGKBi23ciFX0hnQVxunOIRc4HdqLhymWSF4jTcASkFa3eQOOhGzl\nzqAYmpXDr0emMSZiODMWONPDbRdJMV3IFeAlmfG/y/RxO8v1YneSAxJA2xUm2RBq4kRFmCaiMi1a\n/bQauFDjsTNqNWbdgFBEe/LISThNboYybHp5thEACkOaMPbuZiyAQRrg6zOYtNnqCNEKT4VSRmbu\nwlXvCuHbI9A65AHkST3uS1amz+ArLmKUH0XooscQokRUjBIVZQ94pOnG1AB3IK5S16pyMt16axJ7\ngArgvpYVEP3G+VLTCsqAR5r1uVK7LVDy7kVqGMO+2ly7q0JhngFJgcJ4MOgYltAjQImSDmE0LUwm\nPsyIsi9S+GNRERqm+YSUNaHOZdh2fCC1lgD7pS4JgIquvny/1BdVZXhs2RylFAP4KYucgp4gxakG\nTcPGHNywlCfDfYlGMkbXO3UQbw6ixKuJF7fa+xjrfx1r34dQDPYLkkjtKI1kKsK0hRmZrW5SpmfI\n9gNe/NRPEThKUeFUGqmvpY6RN7mcl0Ls11BRpu7WYqIyIXyZNWbfi/l5QTeuz4Ifa9tjWJqBZmIc\nU3ftZ+3ndWo8fGlWLknbXrzuxcrvnFduqc+EqBAaRV+gwSgtumT9TvKsXxGqp2i5dT1W3HbAvO45\n7j6cyekuwiVSgLRtsUSErcSgzU7OLFWipXoRRpp3eaoDk/tPhg2V99ipcjL9qdFAxAQhBr6JWM90\nur121pIhBf5kA6WpRkSLbRHCZ8c95SzWz2NZM/kozAuVejyA3kZxzPY+iI/mZg4WloBTKpIlOSUM\nzWpzSzUOv2lL0QgJIgn4VmkBh1y0ib0s3ZIPr9w+PBnggMnpG+yy9aJP/EFIC4QuwDnpxTV2T+bi\niueoA806gPmLO9cKMVzZBM+BT1qCghaw8SF3bbTYnDKRuns2kVqFFdN/pI4r5OVDQQKfuv6B6BaU\nfd2BnyboA5L64zlhq1jDWsrTpD8sNTMpQMM8g27D1dh2piebzAeS6m9MqFt3tjwbjKduMm2DvXCa\ndYi1CFtWp9CrAVPNT+G0fy+WtWBK7lKSfxVgbv8FJoO1cRaHYbH1HJGDP2XzHQ2ENX0EyCfYCYLV\n54GWGo2a1mN6ahvEgaOgZ0iVrlTlZLrq2iy+IQglQPOsCaAPPAPzPpSfdWatlWRUEvPrQGglTEfz\nG2FNJD3uI+0BYZLpvTsFVIRqcND59d6HpUApGVeK6T8khWvnoNUsFywKEojYEMjQxuCSm0a+jvTq\nGgNCHV88BfZw8HFVKjs+iAcBoBLahTknNsH8g3DxxR2LgScHqE8c4FhUDsHrgKIXXzU9NybmO18f\nXCddobSREx3C57GZZFi4n1d3K5aUf6bIQqDIxwl8pbsQpvMoHf1HsVjmw5ywjniH7uEP57sQ4QQ9\ne+N1WZEr2fDp4VPQ8YBUtbyBsiJdaj/EePNe8qeb0mW7B7kHBXTM0NeguUYSJ7/dwBTTrUxeI+MN\nJYV3oBB+39WP1Jng2NMJeFqlS1R5iUj3yH3qW0tuF/Kb36G+pRo6NrasKJ/GWqsXf9OzNb+0kr1X\nuTTRG9qQQi8Fps+5z8jGSZKDVo0ZYPOEoDmryYyM49nmvnRZ8Q0NN4xluv0R4kzM2f9pDa+QmtSD\nTg6A6TunDE3ysXaOxbYRkCzt5sx5zHc2g/lLkNhyiEBUm+GTlhAHaMx2gdiFQA6S0Yd0FhmUMsq4\nYAC3Sm4woddRxqt70m98BP3GP8L9szj2eMxl4WMw7u1A+Xbpl2cVD/HkOfBdGBTqQ71+0cwPCOdC\nr89YdlKRUH0tHq5xp0DARKrSxpB+S9XpsmshhWNtWZswmtwsAeuhAfu+nVhQdA1xSz86DwJFgT70\n/5YGnhzrBb/Yj6WqiRSqMTJ9kPyUKP1+KBKExtlo1u/YjO71IsLWx1ABPHFyY+Pt7kDlarNqAjHC\nu8acP9uSlM2/c0A8Ff276VjvETPe4DAXEnsz09+d7KwIsiaKeLm4khB5gxl6QzDUza5RHaO/jEQv\nMIQRPQrwUxjAod9evSncViRiMuESxpGNwLJqtyw1QVMLDRou3oGyjQa/PnUSIKIIv3Xd0Cz8nEaz\ntZmwbBTZpZAfKdkuqlBawZO4YiYEQbvhnjwtkX5R1OPzsSQZDkA94xBJz8AFP+pOgehFvbm30IEL\nw5rxeE6U1HW8QpmvNeKwnLmOhrOaMu738SRdz0ayCiIcQcsGs/7L1pJfLJAM62Sxf+E1Jq1bi8Ik\nyIquXm18lZNp0bNsCimnmSpkFUP8iEgqkDwXRg5NmdX4CwgT1vlRBIyzgbnBws33VGQk4j7QHThG\nAsABmIEJcOMvHlFOQrYGCdk1WY4jpnWtKEoSwrkYDwpm7pjt68v+J/0x2/aEmDFKZC4yx85yPpVd\nkaxJWn0Vjvq0ApQVHDm9uw1CGMllZiiRSTqJM7I4yw5Y151LTdqikVjCrRaNmbh8A/8beAxIkboW\ngKL0OEpUc8iiLqrhA9i6ezLP1pTDjIMv/iIGweZiAIvfhtB5VmNM7QxosXYZFMvGXFCfbDAWY2vV\nm32rtiGWtXeZqiNabgtItrHh5/pdIbTq1iXV8oBaqd2Xyz8kcf2zMECy7KIxwowvNAMg4HR1LvlB\nmNbKoqwAoCEgWy9yYRGxfs9Ehs2vTZdNwRgm+tFpuB/xDWBlK2+yn+YQEVgfWSRSgOhzLRjlCH5m\n7eCO0I6c5UA8TAnAlTGvHQ8WWAcsLe4AdHixk1fAedE3UMKj9X30946nrBymtx4EN2Tn0jpucSC9\nVNaybE4HGlzZSVn5fRB4I+nruJrcxOBZIp3LtJh/sHrVQNUz1EtMxGWEH9P3hdFy9BxyksbhOaQf\nnBc+kQIENe/C18kXQasp5FW+lOHfQMyl+/hcssWH5q8OxgPxkt4BZAi3nfdtQo6m0Jn5EP7xOab+\n99Cm5LoyVqEXOd92Fae3yrazWrB/NMF0x5PuwD2ZagEoiq9NGRkozFKG2dUrz6q+O6n4KquGA/hA\nbaiOz3SNcTGcjvQFAmSnQeZ8rAnrY9X130JRuww1kwKsOndkSpAhL8vEZMvH87+RblWAqoEDzrOn\nUd31HrnVsxw5/wEqSkq5P6krY7Y/hvSHspbz0fEoLoeZcX35kIVzudWzXIdcx79MR01rkeuonBa5\n1bMcOXLk1AByd1I5cuTIqQHkyVSOHDlyagB5MpUjR46cGkCeTOXIkSOnBpAnUzly5MipAf4PjpuH\nv/uZ1BsAAAAASUVORK5CYII=\n", "text/plain": "<matplotlib.figure.Figure at 0x7fd1246dae80>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, axarr = plt.subplots(10, 10)\n", "for row in range(10):\n", " for column in range(10):\n", " entry = train_data[train_data['label']==column].iloc[row].drop('label').as_matrix()\n", " axarr[row, column].imshow(entry.reshape([28, 28]))\n", " axarr[row, column].get_xaxis().set_visible(False)\n", " axarr[row, column].get_yaxis().set_visible(False)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d4beb0f4-8cbc-4521-6c98-a06e22c607b6" }, "source": "" }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "fecfcb0a-5f87-7f6e-f25f-7bc79d9c94fd" }, "outputs": [], "source": [ "# input tensor\n", "x = tf.placeholder(tf.float32, [None, 784])\n", "\n", "# weights (w) and biases (s) \n", "W = tf.Variable(tf.zeros([784, 10]))\n", "b = tf.Variable(tf.zeros([10]))\n", "\n", "# model output (y)\n", "y = tf.nn.softmax(tf.matmul(x, W) + b)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "f9b48945-8f81-e009-5fef-c99812f78968", "collapsed": true }, "source": "" }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "5b1c6695-454d-6356-0ede-7a1a4ccf0154" }, "outputs": [], "source": [ "# target\n", "y_ = tf.placeholder(tf.float32, [None, 10])\n", "# cross entropy\n", "cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2371934e-6a5b-3051-3b83-a04412e82dd1" }, "source": "" }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "606c2198-ac2e-0b55-46ac-d2ee4759cf5d" }, "outputs": [], "source": [ "train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n", "correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d1331a13-604e-29d4-047d-48167aea01b2" }, "source": "" }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "9478e396-2d85-a2bb-00eb-364e01c7941f" }, "outputs": [ { "ename": "NameError", "evalue": "name 'train_data' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-8-bfc9b79a18e1> in <module>()\n 1 train_val_ratio = 0.7\n----> 2 train_data_size = len(train_data)\n 3 train_set = train_data[:int(train_data_size*train_val_ratio)]\n 4 val_set = train_data[int(train_data_size*train_val_ratio)+1:]\n 5 \n", "NameError: name 'train_data' is not defined" ] } ], "source": [ "train_val_ratio = 0.7\n", "train_data_size = len(train_data)\n", "train_set = train_data[:int(train_data_size*train_val_ratio)]\n", "val_set = train_data[int(train_data_size*train_val_ratio)+1:]\n", "\n", "init = tf.initialize_all_variables()\n", "saver = tf.train.Saver()\n", "sess = tf.Session()\n", "sess.run(init)\n", "\n", "train_eval_list = []\n", "val_eval_list = []\n", "for i in range(100):\n", " batch = train_set.sample(frac=0.1)\n", " batch_xs = batch.drop('label', axis=1).as_matrix()/255.0\n", " batch_ys = pd.get_dummies(batch['label']).as_matrix()\n", " val_xs = val_set.drop('label', axis=1).as_matrix()/255.0\n", " val_ys = pd.get_dummies(val_set['label']).as_matrix()\n", "\n", " sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n", " \n", " train_eval = sess.run(accuracy, feed_dict={x: batch_xs, y_: batch_ys})\n", " val_eval = sess.run(accuracy, feed_dict={x: val_xs, y_: val_ys})\n", " \n", " train_eval_list.append(train_eval)\n", " val_eval_list.append(val_eval)\n", "# " ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d04c8e9b-8499-7797-c65a-4d0e96d5c1f3" }, "source": "" }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "5f42f37e-c699-37d7-d6bf-72bbdcbe3fde" }, "outputs": [ { "data": { "text/plain": "<matplotlib.legend.Legend at 0x7fd11f59a5f8>" }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7fd11f752cc0>" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(train_eval_list, label='train set')\n", "plt.plot(val_eval_list, label='validation set')\n", "ax.set_xlabel('Epoch')\n", "ax.set_ylabel('Accuracy')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "64f8acda-5698-076f-19b2-c6b6c25ed5b9" }, "outputs": [ { "ename": "NameError", "evalue": "name 'settings' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-10-b5ac488208c1> in <module>()\n----> 1 model_path = os.path.join(project_dir, settings['MODEL_PATH'], \"logistic_regression.ckpt\")\n 2 saver.save(sess, model_path)\n 3 sess.close()\n", "NameError: name 'settings' is not defined" ] } ], "source": [ "saver.save(sess, \"logistic_regression.ckpt\")\n", "sess.close()" ] } ], "metadata": { "_change_revision": 143, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/327/327693.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "8cdfb2fa-ee26-29b8-9294-faa28b049bc7" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "8ee5c2c6-4d21-dff7-f039-0423e50c169b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all_star.csv\n", "appearances.csv\n", "batting.csv\n", "batting_postseason.csv\n", "college.csv\n", "database.sqlite\n", "fielding.csv\n", "fielding_outfield.csv\n", "fielding_postseason.csv\n", "hall_of_fame.csv\n", "hashes.txt\n", "home_game.csv\n", "manager.csv\n", "manager_award.csv\n", "manager_award_vote.csv\n", "manager_half.csv\n", "park.csv\n", "pitching.csv\n", "pitching_postseason.csv\n", "player.csv\n", "player_award.csv\n", "player_award_vote.csv\n", "player_college.csv\n", "postseason.csv\n", "readme.txt\n", "salary.csv\n", "team.csv\n", "team_franchise.csv\n", "team_half.csv\n", "\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "03f10960-1e4e-6765-486c-153d13749e0d" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " 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<tr>\n", " <th>44125</th>\n", " <td>woodal02</td>\n", " <td>2015</td>\n", " <td>2</td>\n", " <td>LAN</td>\n", " <td>NL</td>\n", " <td>5</td>\n", " <td>6</td>\n", " <td>12</td>\n", " <td>12</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>1</td>\n", " <td>292.0</td>\n", " <td>0.0</td>\n", " <td>36</td>\n", " <td>4.0</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>44126</th>\n", " <td>woodtr01</td>\n", " <td>2015</td>\n", " <td>1</td>\n", " <td>CHN</td>\n", " <td>NL</td>\n", " <td>5</td>\n", " <td>4</td>\n", " <td>54</td>\n", " <td>9</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>0</td>\n", " <td>419.0</td>\n", " <td>12.0</td>\n", " <td>48</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>44127</th>\n", " <td>wootero01</td>\n", " <td>2015</td>\n", " <td>1</td>\n", " <td>MIL</td>\n", " <td>NL</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>0</td>\n", " <td>30.0</td>\n", " <td>0.0</td>\n", " <td>8</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>44128</th>\n", " <td>worleva01</td>\n", " <td>2015</td>\n", " <td>1</td>\n", " <td>PIT</td>\n", " <td>NL</td>\n", " <td>4</td>\n", " <td>6</td>\n", " <td>23</td>\n", " <td>8</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " <td>2.0</td>\n", " <td>0</td>\n", " <td>310.0</td>\n", " <td>6.0</td>\n", " <td>36</td>\n", " <td>3.0</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>44129</th>\n", " <td>wrighmi01</td>\n", " <td>2015</td>\n", " <td>1</td>\n", " <td>BAL</td>\n", " <td>AL</td>\n", " <td>3</td>\n", " <td>5</td>\n", " <td>12</td>\n", " <td>9</td>\n", " <td>0</td>\n", " <td>...</td>\n", " 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" <td>0.0</td>\n", " <td>1.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>44132</th>\n", " <td>wrighwe01</td>\n", " <td>2015</td>\n", " <td>2</td>\n", " <td>LAA</td>\n", " <td>AL</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>9</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>24.0</td>\n", " <td>2.0</td>\n", " <td>3</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>44133</th>\n", " <td>yateski01</td>\n", " <td>2015</td>\n", " <td>1</td>\n", " <td>TBA</td>\n", " <td>AL</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>20</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>0</td>\n", " <td>92.0</td>\n", " <td>10.0</td>\n", " <td>18</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>44134</th>\n", " <td>youngch03</td>\n", " <td>2015</td>\n", " <td>1</td>\n", " <td>KCA</td>\n", " <td>AL</td>\n", " <td>11</td>\n", " <td>6</td>\n", " <td>34</td>\n", " <td>18</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>5.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>500.0</td>\n", " <td>3.0</td>\n", " <td>44</td>\n", " <td>4.0</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>44135</th>\n", " <td>zieglbr01</td>\n", " <td>2015</td>\n", " <td>1</td>\n", " <td>ARI</td>\n", " <td>NL</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>66</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>0</td>\n", " <td>263.0</td>\n", " <td>46.0</td>\n", " <td>17</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>44136</th>\n", " <td>zimmejo02</td>\n", " <td>2015</td>\n", " <td>1</td>\n", " <td>WAS</td>\n", " <td>NL</td>\n", " <td>13</td>\n", " <td>10</td>\n", " <td>33</td>\n", " <td>33</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>2.0</td>\n", " <td>8.0</td>\n", " <td>1</td>\n", " <td>831.0</td>\n", " <td>0.0</td>\n", " <td>89</td>\n", " <td>8.0</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>44137</th>\n", " <td>zitoba01</td>\n", " <td>2015</td>\n", " <td>1</td>\n", " <td>OAK</td>\n", " <td>AL</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0</td>\n", " <td>37.0</td>\n", " <td>1.0</td>\n", " <td>8</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>44138</th>\n", " <td>zychto01</td>\n", " <td>2015</td>\n", " <td>1</td>\n", " <td>SEA</td>\n", " <td>AL</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>13</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>0</td>\n", " <td>76.0</td>\n", " <td>4.0</td>\n", " <td>6</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>44139 rows × 30 columns</p>\n", "</div>" ], "text/plain": [ " player_id year stint team_id league_id w l g gs cg ... \\\n", "0 bechtge01 1871 1 PH1 NaN 1 2 3 3 2 ... \n", "1 brainas01 1871 1 WS3 NaN 12 15 30 30 30 ... \n", "2 fergubo01 1871 1 NY2 NaN 0 0 1 0 0 ... \n", "3 fishech01 1871 1 RC1 NaN 4 16 24 24 22 ... \n", "4 fleetfr01 1871 1 NY2 NaN 0 1 1 1 1 ... \n", "5 flowedi01 1871 1 TRO NaN 0 0 1 0 0 ... \n", "6 mackde01 1871 1 RC1 NaN 0 1 3 1 1 ... \n", "7 mathebo01 1871 1 FW1 NaN 6 11 19 19 19 ... \n", "8 mcbridi01 1871 1 PH1 NaN 18 5 25 25 25 ... \n", "9 mcmuljo01 1871 1 TRO NaN 12 15 29 29 28 ... \n", "10 meyerle01 1871 1 PH1 NaN 0 0 1 0 0 ... \n", "11 paborch01 1871 1 CL1 NaN 0 2 7 1 1 ... \n", "12 pinkhed01 1871 1 CH1 NaN 1 0 3 0 0 ... \n", "13 prattal01 1871 1 CL1 NaN 10 17 28 28 22 ... \n", "14 spaldal01 1871 1 BS1 NaN 19 10 31 31 22 ... \n", "15 stearbi01 1871 1 WS3 NaN 2 0 2 2 2 ... \n", "16 woltery01 1871 1 NY2 NaN 16 16 32 32 31 ... \n", "17 wrighha01 1871 1 BS1 NaN 1 0 9 0 0 ... \n", "18 zettlge01 1871 1 CH1 NaN 18 9 28 28 25 ... \n", "19 bentlcy01 1872 1 MID NaN 2 15 18 17 16 ... \n", "20 brainas01 1872 1 WS3 NaN 2 7 9 9 9 ... \n", "21 brainas01 1872 2 MID NaN 0 2 2 2 1 ... \n", "22 brittji01 1872 1 BR2 NaN 9 28 37 37 37 ... \n", "23 buttefr01 1872 1 MID NaN 3 2 6 5 5 ... \n", "24 cummica01 1872 1 NY2 NaN 33 20 55 55 53 ... \n", "25 fishech01 1872 1 BL1 NaN 10 1 19 11 9 ... \n", "26 malonma01 1872 1 BR1 NaN 0 3 3 3 3 ... \n", "27 martiph01 1872 2 BR1 NaN 2 7 10 9 9 ... \n", "28 martiph01 1872 1 TRO NaN 1 2 8 3 0 ... \n", "29 mathebo01 1872 1 BL1 NaN 25 18 49 47 39 ... \n", "... ... ... ... ... ... .. .. .. .. .. ... \n", "44109 westma03 2015 1 LAN NL 0 0 2 0 0 ... \n", "44110 whitlch01 2015 1 NYA AL 1 2 4 4 0 ... \n", "44111 wielajo01 2015 1 LAN NL 0 1 2 2 0 ... \n", "44112 wilheto01 2015 1 SEA AL 2 2 53 0 0 ... \n", "44113 wilkad01 2015 1 LAA AL 0 0 1 0 0 ... \n", "44114 willije02 2015 1 PHI NL 4 12 33 21 0 ... \n", "44115 wilsoal01 2015 1 DET AL 3 3 59 1 0 ... \n", "44116 wilsocj01 2015 1 LAA AL 8 8 21 21 0 ... \n", "44117 wilsojo03 2015 1 DET AL 0 0 1 0 0 ... \n", "44118 wilsoju10 2015 1 NYA AL 5 0 74 0 0 ... \n", "44119 wilsoty01 2015 1 BAL AL 2 2 9 5 0 ... \n", "44120 winklda01 2015 1 ATL NL 0 0 2 0 0 ... \n", "44121 wislema01 2015 1 ATL NL 8 8 20 19 0 ... \n", "44122 wojcias01 2015 1 HOU AL 0 1 5 3 0 ... \n", "44123 wolfra02 2015 1 DET AL 0 5 8 7 0 ... \n", "44124 woodal02 2015 1 ATL NL 7 6 20 20 0 ... \n", "44125 woodal02 2015 2 LAN NL 5 6 12 12 0 ... \n", "44126 woodtr01 2015 1 CHN NL 5 4 54 9 0 ... \n", "44127 wootero01 2015 1 MIL NL 0 0 4 0 0 ... \n", "44128 worleva01 2015 1 PIT NL 4 6 23 8 0 ... \n", "44129 wrighmi01 2015 1 BAL AL 3 5 12 9 0 ... \n", "44130 wrighst01 2015 1 BOS AL 5 4 16 9 0 ... \n", "44131 wrighwe01 2015 1 BAL AL 0 0 2 0 0 ... \n", "44132 wrighwe01 2015 2 LAA AL 0 0 9 0 0 ... \n", "44133 yateski01 2015 1 TBA AL 1 0 20 0 0 ... \n", "44134 youngch03 2015 1 KCA AL 11 6 34 18 0 ... \n", "44135 zieglbr01 2015 1 ARI NL 0 3 66 0 0 ... \n", "44136 zimmejo02 2015 1 WAS NL 13 10 33 33 0 ... \n", "44137 zitoba01 2015 1 OAK AL 0 0 3 2 0 ... \n", "44138 zychto01 2015 1 SEA AL 0 0 13 1 0 ... \n", "\n", " ibb wp hbp bk bfp gf r sh sf g_idp \n", "0 NaN NaN NaN 0 NaN NaN 42 NaN NaN NaN \n", "1 NaN NaN NaN 0 NaN NaN 292 NaN NaN NaN \n", "2 NaN NaN NaN 0 NaN NaN 9 NaN NaN NaN \n", "3 NaN NaN NaN 0 NaN NaN 257 NaN NaN NaN \n", "4 NaN NaN NaN 0 NaN NaN 21 NaN NaN NaN \n", "5 NaN NaN NaN 0 NaN NaN 0 NaN NaN NaN \n", "6 NaN NaN NaN 0 NaN NaN 30 NaN NaN NaN \n", "7 NaN NaN NaN 2 NaN NaN 243 NaN NaN NaN \n", "8 NaN NaN NaN 0 NaN NaN 223 NaN NaN NaN \n", "9 NaN NaN NaN 0 NaN NaN 362 NaN NaN NaN \n", "10 NaN NaN NaN 0 NaN NaN 1 NaN NaN NaN \n", "11 NaN NaN NaN 0 NaN NaN 53 NaN NaN NaN \n", "12 NaN NaN NaN 0 NaN NaN 8 NaN NaN NaN \n", "13 NaN NaN NaN 0 NaN NaN 288 NaN NaN NaN \n", "14 NaN NaN NaN 0 NaN NaN 272 NaN NaN NaN \n", "15 NaN NaN NaN 0 NaN NaN 11 NaN NaN NaN \n", "16 NaN NaN NaN 0 NaN NaN 283 NaN NaN NaN \n", "17 NaN NaN NaN 0 NaN NaN 31 NaN NaN NaN \n", "18 NaN NaN NaN 0 NaN NaN 233 NaN NaN NaN \n", "19 NaN NaN NaN 0 NaN NaN 252 NaN NaN NaN \n", "20 NaN NaN NaN 0 NaN NaN 140 NaN NaN NaN \n", "21 NaN NaN NaN 0 NaN NaN 17 NaN NaN NaN \n", "22 NaN NaN NaN 1 NaN NaN 473 NaN NaN NaN \n", "23 NaN NaN NaN 0 NaN NaN 78 NaN NaN NaN \n", "24 NaN NaN NaN 0 NaN NaN 347 NaN NaN NaN \n", "25 NaN NaN NaN 0 NaN NaN 78 NaN NaN NaN \n", "26 NaN NaN NaN 0 NaN NaN 86 NaN NaN NaN \n", "27 NaN NaN NaN 0 NaN NaN 106 NaN NaN NaN \n", "28 NaN NaN NaN 0 NaN NaN 59 NaN NaN NaN \n", "29 NaN NaN NaN 0 NaN NaN 356 NaN NaN NaN \n", "... ... ... ... .. ... ... ... ... ... ... \n", "44109 0.0 0.0 0.0 0 11.0 2.0 0 0.0 0.0 NaN \n", "44110 0.0 2.0 2.0 0 84.0 0.0 9 0.0 0.0 NaN \n", "44111 1.0 0.0 0.0 0 40.0 0.0 8 0.0 1.0 NaN \n", "44112 3.0 2.0 2.0 0 267.0 20.0 24 4.0 3.0 NaN \n", "44113 0.0 0.0 1.0 0 10.0 1.0 1 0.0 0.0 NaN \n", "44114 3.0 4.0 5.0 0 553.0 3.0 83 2.0 4.0 NaN \n", "44115 1.0 2.0 2.0 0 273.0 16.0 19 2.0 2.0 NaN \n", "44116 2.0 11.0 10.0 1 553.0 0.0 59 2.0 4.0 NaN \n", "44117 0.0 0.0 0.0 0 4.0 1.0 1 0.0 0.0 NaN \n", "44118 0.0 4.0 2.0 0 244.0 3.0 21 2.0 0.0 NaN \n", "44119 1.0 0.0 1.0 0 149.0 2.0 14 0.0 2.0 NaN \n", "44120 0.0 0.0 0.0 0 8.0 0.0 2 0.0 0.0 NaN \n", "44121 4.0 2.0 4.0 3 478.0 0.0 59 4.0 5.0 NaN \n", "44122 0.0 1.0 0.0 0 79.0 2.0 13 0.0 2.0 NaN \n", "44123 1.0 3.0 1.0 0 161.0 0.0 28 0.0 1.0 NaN \n", "44124 2.0 5.0 2.0 0 509.0 0.0 50 11.0 1.0 NaN \n", "44125 2.0 1.0 2.0 1 292.0 0.0 36 4.0 2.0 NaN \n", "44126 5.0 2.0 1.0 0 419.0 12.0 48 1.0 2.0 NaN \n", "44127 0.0 1.0 1.0 0 30.0 0.0 8 1.0 0.0 NaN \n", "44128 3.0 3.0 2.0 0 310.0 6.0 36 3.0 2.0 NaN \n", "44129 3.0 2.0 5.0 0 204.0 0.0 30 0.0 2.0 NaN \n", "44130 0.0 2.0 1.0 0 310.0 3.0 38 1.0 1.0 NaN \n", "44131 0.0 0.0 0.0 0 7.0 2.0 1 0.0 1.0 NaN \n", "44132 1.0 0.0 0.0 0 24.0 2.0 3 0.0 1.0 NaN \n", "44133 0.0 0.0 1.0 0 92.0 10.0 18 0.0 0.0 NaN \n", "44134 0.0 5.0 0.0 0 500.0 3.0 44 4.0 2.0 NaN \n", "44135 3.0 2.0 1.0 0 263.0 46.0 17 1.0 0.0 NaN \n", "44136 3.0 2.0 8.0 1 831.0 0.0 89 8.0 2.0 NaN \n", "44137 0.0 0.0 0.0 0 37.0 1.0 8 0.0 0.0 NaN \n", "44138 0.0 1.0 2.0 0 76.0 4.0 6 0.0 0.0 NaN \n", "\n", "[44139 rows x 30 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"../input/pitching.csv\")\n", "df" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "4dcc8e92-5855-1433-5665-5b0eaba63683" }, "outputs": [], "source": [ "df_sg = df[(df.gs == df.g)]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "c713dae8-049b-1adf-9de0-ed736a545b23" }, "outputs": [], "source": [ "Y = df_sg.w/(df_sg.gs)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "d15e33ed-5848-cd95-d060-8ada2448bd0c" }, "outputs": [], "source": [ "Y_class = np.floor(Y)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "d9ae7ece-f783-036e-37fa-886a37825ff4" }, "outputs": [ { "ename": "NameError", "evalue": "name 'X' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mNameError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-6-253bcac7dd80>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'X' is not defined" ] } ], "source": [ "X" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "b55f945f-9c1a-b736-73be-b41a2cb335f3" }, "outputs": [], "source": [ "from sklearn import svm" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "1ffe7409-9aec-9e18-e066-fc4da514aa54" }, "outputs": [ { "ename": "NameError", "evalue": "name 'X' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mNameError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-8-24005fe30990>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mclf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msvm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSVC\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[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'X' is not defined" ] } ], "source": [ "clf = svm.SVC()\n", "clf.fit(X, Y_class) " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "08449f87-1099-f5f2-1ca3-a3da3a921a3b" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "55ba3027-17d8-3403-1e40-8381cedf2c48" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 244, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/327/327702.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "ba21bab2-0a78-65d7-2963-d786b37bcdfe" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "96562a7e-7a64-5748-db55-8951498a3d74" }, "outputs": [], "source": [ "import pandas as pd\n", "import csv as csv\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# machine learning\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.neighbors import KNeighborsClassifier" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "38ec5f31-9191-1a29-9d87-5c5b3af3e747" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 12 columns):\n", "PassengerId 891 non-null int64\n", "Survived 891 non-null int64\n", "Pclass 891 non-null int64\n", "Name 891 non-null object\n", "Sex 891 non-null object\n", "Age 714 non-null float64\n", "SibSp 891 non-null int64\n", "Parch 891 non-null int64\n", "Ticket 891 non-null object\n", "Fare 891 non-null float64\n", "Cabin 204 non-null object\n", "Embarked 889 non-null object\n", "dtypes: float64(2), int64(5), object(5)\n", "memory usage: 83.6+ KB\n", "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 418 entries, 0 to 417\n", "Data columns (total 11 columns):\n", "PassengerId 418 non-null int64\n", "Pclass 418 non-null int64\n", "Name 418 non-null object\n", "Sex 418 non-null object\n", "Age 332 non-null float64\n", "SibSp 418 non-null int64\n", "Parch 418 non-null int64\n", "Ticket 418 non-null object\n", "Fare 417 non-null float64\n", "Cabin 91 non-null object\n", "Embarked 418 non-null object\n", "dtypes: float64(2), int64(4), object(5)\n", "memory usage: 36.0+ KB\n" ] } ], "source": [ "if __name__ == \"__main__\":\n", " \n", " # get titanic & test csv files as a DataFrame\n", " train_df = pd.read_csv(\"../input/train.csv\")\n", " test_df = pd.read_csv(\"../input/test.csv\")\n", " \n", " train_df.head()\n", " train_df.info()\n", " test_df.info()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "b4048631-33c0-61c5-784b-3888d26a74e0" }, "outputs": [], "source": [ " #drop un-insightful columns\n", " train_df = train_df.drop(['PassengerId','Name','Ticket','Cabin'], axis=1)\n", " test_df = test_df.drop(['Name','Ticket','Cabin'], axis=1)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "d713ff92-6f9d-9ebf-27c7-5ecf2e35efa2" }, "outputs": [], "source": [ " #feature by feature analysis\n", " #Gender: replace male/female with integers\n", " mapping = {'male':1,'female':0}\n", " train_df['Gender'] = train_df['Sex'].map(mapping).astype(int)\n", " test_df['Gender'] = test_df['Sex'].map(mapping).astype(int)\n", " train_df = train_df.drop(['Sex'], axis=1) \n", " test_df = test_df.drop(['Sex'], axis=1)\n", " \n", " #Embarked: fill na and replace C/Q/S with integers\n", " mapping = {'C':0,'Q':1,'S':2}\n", " train_df['Embarked'].value_counts()\n", " train_df['Embarked'] = train_df['Embarked'].fillna(\"S\")\n", " test_df['Embarked'] = test_df['Embarked'].fillna(\"S\")\n", "\n", " train_df['Embark'] = train_df['Embarked'].map(mapping).astype(int)\n", " test_df['Embark'] = test_df['Embarked'].map(mapping).astype(int)\n", " train_df = train_df.drop(['Embarked'], axis=1) \n", " test_df = test_df.drop(['Embarked'], axis=1) \n", " \n", " #Age: fill na with medium age\n", " median_age = train_df['Age'].dropna().median()\n", " train_df['Age'] = train_df['Age'].fillna(median_age)\n", " test_df['Age'] = test_df['Age'].fillna(median_age)\n", " \n", " #Fare: fill na with medium fare\n", " median_fare = train_df['Fare'].dropna().median()\n", " test_df['Fare'] = test_df['Fare'].fillna(median_fare)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "4d1e0743-2c32-a150-d07b-a2b5244549b3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "random forest classifier score: %f 0.979797979798\n", "logistic regression score: %f 0.803591470258\n", "SVM score: %f 0.897867564534\n", "knn score: %f 0.809203142536\n" ] } ], "source": [ " #machine learning\n", " train_data = train_df.values\n", " test_data = test_df.values\n", " \n", " X_train = train_data[:,1:]\n", " y_train = train_data[:,0]\n", " \n", " X_test = test_data[:,1:]\n", " idx = test_data[:,0]\n", " \n", " #random forest classifier\n", " rfc = RandomForestClassifier(n_estimators=100) \n", " rfc.fit(X_train, y_train)\n", " score_rfc = rfc.score(X_train, y_train)\n", " out_rfc = rfc.predict(X_test)\n", " print (\"random forest classifier score: %f\", score_rfc)\n", " \n", " #logistic regression\n", " logreg = LogisticRegression()\n", " logreg.fit(X_train, y_train)\n", " score_logreg = logreg.score(X_train, y_train)\n", " out_logreg = logreg.predict(X_test)\n", " print (\"logistic regression score: %f\", score_logreg)\n", " \n", " #SVM\n", " svc = SVC()\n", " svc.fit(X_train, y_train)\n", " score_svc = svc.score(X_train, y_train)\n", " out_svc = svc.predict(X_test) \n", " print (\"SVM score: %f\", score_svc)\n", " \n", " #knn classifier\n", " knn = KNeighborsClassifier(n_neighbors=5)\n", " knn.fit(X_train, y_train)\n", " score_knn = knn.score(X_train, y_train)\n", " out_knn = knn.predict(X_test)\n", " print (\"knn score: %f\", score_knn)\n", " \n", " \n", " #write out predictions \n", " #predictions_file = open(\"titanic_pred.csv\", \"wb\")\n", " #open_file_object = csv.writer(predictions_file)\n", " #open_file_object.writerow([\"PassengerId\",\"Survived\"])\n", " #open_file_object.writerows(zip(idx, out_rfc))\n", " #predictions_file.close() \n", " " ] } ], "metadata": { "_change_revision": 98, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/327/327813.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "1fab96bf-9f9d-33b0-db72-c35e15a74ca6" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import random\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics\n", "import sklearn.ensemble as ske\n", "import tensorflow as tf\n", "from tensorflow.contrib import skflow" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "94fa40b0-4ee3-5b3c-739e-4b0d7d82752a" }, "outputs": [], "source": [ "titanic_df = pd.read_csv(\"../input/train.csv\", dtype={\"Age\": np.float64}, )" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "0f34dbb1-e027-0669-04b5-4078fd040de2" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "2f3a7bb2-6c96-f69d-f97e-cae568194b51" }, "outputs": [ { "data": { "text/plain": [ "0.3838383838383838" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_df['Survived'].mean()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "27068383-665e-6df2-68a3-89c367aeb661" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " </tr>\n", " <tr>\n", " <th>Pclass</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>1</th>\n", " <td>461.597222</td>\n", " <td>0.629630</td>\n", " <td>38.233441</td>\n", " <td>0.416667</td>\n", " <td>0.356481</td>\n", " <td>84.154687</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>445.956522</td>\n", " <td>0.472826</td>\n", " <td>29.877630</td>\n", " <td>0.402174</td>\n", " <td>0.380435</td>\n", " <td>20.662183</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>439.154786</td>\n", " <td>0.242363</td>\n", " <td>25.140620</td>\n", " <td>0.615071</td>\n", " <td>0.393075</td>\n", " <td>13.675550</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Age SibSp Parch Fare\n", "Pclass \n", "1 461.597222 0.629630 38.233441 0.416667 0.356481 84.154687\n", "2 445.956522 0.472826 29.877630 0.402174 0.380435 20.662183\n", "3 439.154786 0.242363 25.140620 0.615071 0.393075 13.675550" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_df.groupby('Pclass').mean()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "0d961ec7-12d0-9a42-9170-c5e777185a6a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pclass Sex \n", "1 female 0.968085\n", " male 0.368852\n", "2 female 0.921053\n", " male 0.157407\n", "3 female 0.500000\n", " male 0.135447\n", "Name: Survived, dtype: float64\n" ] } ], "source": [ "class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()\n", "print(class_sex_grouping['Survived'])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "20b23df4-c25a-d6a7-a969-57e99e913f66" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fcfb43669e8>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fcfb42fb2b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "class_sex_grouping['Survived'].plot.bar()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "c438c83b-464e-9934-ebba-8677446d8ee0" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fcfb4051438>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1JD89mVADGE+ukeRNwDcGzLWYOfvTQkYwZ98Gz9nalvjPAf+Z8e6U/dcv38h4d8qvV9Xv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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fcfb4366e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "group_by_age = pd.cut(titanic_df['Age'], np.arange(0, 90, 10))\n", "age_grouping = titanic_df.groupby(group_by_age).mean()\n", "age_grouping['Survived'].plot.bar()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "2ae759f6-9453-143a-c49d-ad7ec3b9ca3e" }, "outputs": [ { "data": { "text/plain": [ "PassengerId 891\n", "Survived 891\n", "Pclass 891\n", "Name 891\n", "Sex 891\n", "Age 714\n", "SibSp 891\n", "Parch 891\n", "Ticket 891\n", "Fare 891\n", "Cabin 204\n", "Embarked 889\n", "dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_df.count()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "2823623c-a76d-11b1-1f6c-8e7a49eb8d54" }, "outputs": [], "source": [ "titanic_df = titanic_df.drop(['Cabin'], axis = 1)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "dcfabd25-4a70-4077-2617-9ae21a2a7f3b" }, "outputs": [], "source": [ "titanic_df = titanic_df.dropna()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "4c658342-67af-24a2-8405-89cc1ed6e123" }, "outputs": [ { "data": { "text/plain": [ "PassengerId 712\n", "Survived 712\n", "Pclass 712\n", "Name 712\n", "Sex 712\n", "Age 712\n", "SibSp 712\n", "Parch 712\n", "Ticket 712\n", "Fare 712\n", "Embarked 712\n", "dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_df.count()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "823aa7c4-714a-09df-b65b-18e10e46c109" }, "outputs": [], "source": [ "def preprocess_titanic_df(df) :\n", " processed_df = df.copy()\n", " le = preprocessing.LabelEncoder()\n", " processed_df_sex = le.fit_transform(processed_df.Sex)\n", " processed_df_embark = le.fit_transform(processed_df.Embarked)\n", " processed_df = processed_df.drop(['Name', 'Ticket'], axis = 1)\n", " return processed_df" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "5ae788d1-8c25-d977-c8b4-4ea5c8f90ad0" }, "outputs": [], "source": [ "processed_df = preprocess_titanic_df(titanic_df)" ] } ], "metadata": { "_change_revision": 564, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/327/327848.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "cff138ea-73cd-e5d5-b094-4fd7b1317d5d" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "4f8196a4-b433-68cf-4b92-c3eb5bbdf813" }, "outputs": [], "source": [ "import json\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import os\n", "import pandas as pd\n", "import tensorflow as tf\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "ececde47-de1f-c8d9-5443-ef8d5fd723ac" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(42000, 785)\n", "(28000, 784)\n" ] } ], "source": [ "train_data = pd.read_csv('../input/train.csv')\n", "test_data = pd.read_csv('../input/test.csv')\n", "\n", "print(train_data.shape)\n", "print(test_data.shape)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "61d44394-a7e7-34a8-cf43-c5b1d0fca334", "collapsed": true }, "source": [] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "715c02cc-917b-c165-da46-305f6bc62c74" }, "outputs": [ { "data": { "image/png": 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rSRikyLj+0BCxhxhUjFVpYfE3fXamop/ZAb7xIE51HXOLmgHBomoBQOJCj+Xe\nVHNR5oGWLeV1pFAOZ2qsB1oFkFcA4+YsoHDcGf7aevWNI6xoM/oWzR8cp0pBKKXGSgRPHgtzhX86\nq+mQSJVk8RcAyZFQb4gSA5SHkeZoSZ9Bm4HXF4VhIyvyYmPIE/JrkDjQYHAEt8+aw/M4MGlErzoX\nGdhiE1FjU1gJWBg60PTuOoRxpAASrs9wI6lPJ/468nbghLGJHet3/sCghqAUNR4QZ0hm1OwtrB8s\nA55gPkeH9qOTiKQ2Sshozy0u/ylDAmx95im4FpksCwzf3tbQEM6UDuHelPrMXaDB+0VvhENZVR8l\n9wJa6ybC3s2i2QUwaanKtL5nqR0Wj37mHuA+rUde4XG4LkU3soUNFvwIvdpcwvKRlNvRE9krNQXK\nP+xRZmcqLWjLnGZ3CbgYzd1k6Bf0JV1nd2Z378HYPI/Ga/JqIm7GoyJNJxdwb2nH8vX1EWMCpm2t\nMCxLkigRa1naGziamvH93SlEA/ot67F26HLOt3wCyM8JgwQDinPT0bCGcMuWHLlc0d3J2myR+JN6\n5zIzWulh1zyTyNXmpEeHE3Uxl2bjofYV+LKbFzHLhXzyKWbpBSeUHJSg8O2lSNaTa1JlTaA80X2P\nQAE1vM0fkf3oxE6eAM+X3sXDJZZkjChFhs2TKDJSQL865NyqhAX0gEGAMz86NYUFqSDyamBJ0QA6\nx05h8c1fxC27hAYjYsLoNv827VU3A/cx8LRm9u0Z/J3Tm5zih6KqkePM108nYtsGJhy041OHoMo8\nAZVckEWrNsdIrlUbgDRpOoXLfmOgWw+aeUwk9GE4hdJ0Cs2McGtZkzaBPqQlizMBo1xYgmqJTLRK\nNm9y4Nh00m7logLk+J7iWctrNNGPx6y6ChrWphSraoCxKa2WGhFe1b7iBaiqofeFBJN78cQeuM/5\nKfEkF9xCIzUPfX11Jl31xfx2JIHL0xEmBv5NMimNeHNJiwYeIdXwmtMMjeGmKPU4h5jdyeQ1fzPq\n6iPyq5tTCJRKMzCUPsYuKgoJYGwN2ub92Hv+CxHUSFgumUEqphSamSKdvgn3eptBJnoVOdA15Im/\nBbd62FOqLEw478cws0qjQb9zfOH/G4mqibg2jGel6SaMupkw7W5fSkuEPkffwdicoyFziU1LovuB\nHyH307uQ5RszXejP9i8W8lWTk4wL38epFzkKtK2U0J5dl+2Zo1E/kMDMMDPEnHQKzGqFaeEdkEBg\nZkuErow4c3w9AAAgAElEQVT6Jt6bv6JG9XsMMr7Mybp1CX3cE/W42wRUb0WEquOrysvrhgj0VFgU\nwRD3DcwpsMW9kx8LopdQ9VwY1XKLyUaHyPxMXlc7FBs7xvj5IjW0YmvGfEC8p9KXZDc/w64WS7Do\nl8kitcncXAnXhm0j9dQjNM2T2fNXO0D4ZVEAB5dpAl7yztqqJOC0KHbfpX/baJ6NUGWt7XRKimNF\nta2ZmsdXrRPwvKCOd9+rTMo6zFepMxi7xRG4IaoW0GJw4yTuud5Bq58zzy8mQd6nh36XcwJKRvaJ\nSLZTi+3Mf705BhgHyFIBNcQc+wEIv5bLIr6Wv4kVt8t2YJcZYMrKp+3gFshXN7pCdC5vjp0KRwl8\nv42lqMGf45FfqW8OzlVmEb1QLBqEYnbEnEU7TABxL1w5z4m6AlFXoN3Lc3Z9PKAF962QV9v7v4QG\nOUfjGZ57mftaJ0W3nm2izaY/bIi72IlBg37BYLMj8mtG/N9Bz0WGoUUATsAXWdsg+fPG8j9jNv8d\nD17pkaOVWyn1v8F/RcdrXMe/nNwRPxLsff5734/YGBqn4v3jXtycRleK/cTH6nz7eDjfAiwOpTJ/\nE32pAaU3kon60Rkmfv6kqKI6qQIF/4fITFdn9Oqh8Kjyg1wqm1ji2SzrDhMrpj1FdVKFDoWO/zEd\nFa1FoaNsWhTVSRUoUKCgAvhvZAdRoECBgv/PUThTBQoUKKgAFKWeFToUOv7HdFS0FoWOsmlRlHou\nNwodb6PQ8TZl1OHkQckIN1ZNheEzwGylH/Bmkb3/SmljhY73+bCWz+rm29lns2bdaVKXe3N1yCI6\nWlpAbavPafKzGN1LioO+2JVRKxejTpY0Mg6nV/MYMDb+9w9UNqrGdFWKppeL0FFH6rgb59LHJ5d9\nrbaz1Pcyrvr6uBL96mXUy+zfm6lwtHGepMRWgy85swzCen6L/a/vOtL/m1jWN2KE+y2ckeKKlPo1\no1E3/P9nbfBnrDOVMczzBraWCXScP4aJjQPpEutJFaM5/I4plZHLc9nk40xwbkjEfPGi9DXrD6bW\nDnnC5dbxgTQdvx/11dYssp3JHTcdBA2h9ByDj1oHQi8/pHaxGtjBkeTKu5mVhd4NpfR+9juWPxhz\npINwJVSsHdMZ7nyI516PiQIIeEYvzr91TEuLQFozH3gsmI53GffzQ0yWH8OhUTzfRQYjPRMI+f/H\nHamdC+t/9iOv5z2M0x9Tu5cN/aKfc8ZelzVhQ3mSKlaRP7BwNGez8zbMHZ7TeJ435bnJfaIzVcH5\ncFfqfLeFmanjiUiTcLenBU3QpKTTGS6F9uJ52Ke1/Mn8NoZf23hT7FNAeWpdfyqjvtFlTshcnvzt\nzU03+TYNdZBZQ868eMbG9KKLGpya3ZwJC7sgRDKLZx1d8O3/XB6MF1RMA36iAeA1W75/S98RAGx3\nUQVjTZJz9Cn+jNjj8jJmTBYBSf24f+TMq21KBeak6BoxrMNRQLgM93W062FwfiXaJhB46jv+oi1V\nSGPSyg1Y/hFLRqkWzzzsYLN4jtR0UG10l+3DXiOFjdFjkT4/LopdgxoyrnVZRFw0nJo6gr0d3k6R\nOFpahFK7/USYd+PQ06aUZIkXkm3Z2IlVDYaR1TEdvV52TMkPI+N8GtX2z+LxBm3i8swB4atC6hpr\no9vGlZMX2uG/D24CE9f+yaY5TsjyynbtfpIzrTlYh8WTXTnlMYCIHTpADquXu4NkFPdPWeGb9z3P\nX5bVFQvdIqgC2tfEyMMno5PZIbIa5iP1aMuhw18CYFRcgEWNq1AqwaCTDkc2byfmlD0YGEF6xSdD\n9jg7BuuWN3AaXEqrm1cJaNScHgGn8Vkm36+87CcAvACrZkasuj+EiMgP1LEXgC7bShm8eS0h9m+W\n3a7DgoIvGV73JNwXtlTI4K3buO8Gz8+vYqtLNhAEwDW6yQ9QNoZJ1ogW5lrHg8VnplIr9SEbO2zm\n8Nk0hM/iBejp0d49nKN7oF0P0O7wE17vHvPiJ3LMOIwulqRTRXhdAOjwrdNiQuIdyTSsj+8ZV0j8\njZYTa1I84AbhX62k8JQYWcYas9xlNakX5+L/Rk5326tSrG1q8CS8bOVkPsGZavL97RPciIZN26dD\nwRt3VyULDqRDrnINENuZAlqGoBF3n1dnh2BI+HJuR6Dji/dvpBoMdQCq4tP9OtmAo3YGpAuT7fam\nrzI3ceVIIEBjkMBiWeNX+5/OXITfCvm/o80sSX+mJ4iO92jmwdixbhx29+Rm0OtEK6Els9g8rhsP\nQoTutmjTiJukAHNzp/HBCYOSZIgUbygq6U5LdijnUxTclcNuwlYZeJNqNdKpk38ZdQ1wbQVuR1bQ\n8VxdmjfvThsPuHh4Ok3vhnDb/S9yBjVEdtYAnogkbkoL+HE6Wm3asDraFMhlUpIEy6rDOfNoC6dt\nn4sgwoafS3bySPkvAFq5aODkpcFmz3RqBjzGMKcFT8qYzarcztTIpYiHznk4hg4ApTNv7Rs0ag9a\nvqBaml/eZj8TdeYErhbZ5se7y5bjlMm6ewmntrDTzARhuylv6JDJ/+g5+zkN9v7yypGWjm5A1O0a\nJNwQfllxFWMZKzK8UGkDV6tbwmX5jcbC3YpM9ceY1nIhO9MJ417hJB8RqpxvLQ675dFJF+h+jO6D\nYtA750iXBdupmxIJQEBqK4Kih7D/iC7IhJ0Mc2ppyWOdfCwclFnWYwgQKai9d5EARq4GWO2dCLl5\nnG0ewlkW4H0cUIWeI2rjonJZVE0ArDuDjhI8fGaG5cgqfBUWSJumB1nVaS5XGouTeW7EN9d5pHyI\nTG0Hkme0YX34DJrGnaQLE5AmQuOQy9xwLduwYbmdqXlCPK63b+LV+giw6a19P7mM4lQbqBJ1lOho\nMSstamMfE0WhCqAuotkPYsKX59ejGxnL7EZBSP+6iLhZ1CVkRddAEvO6e7T9Zg/ipeLEZxjWbUZR\n2GIWTAhGOv468olIVSaqHOavYvim90lKfCJp2FWdfke6CqQij+7acDELgqwXce1uOOoZuUR/I68Y\nLyeAVjUC6LyiE8O/b4NwtTI0GJ11iD/zoFHVUmb7+DL04n74IQByRSgvHSnBL6YjRppFxMZ9+BzI\nfVqN0qwiTGrUITtLlw9XqRKGagENqet+ieqaf6NeR4MliTO5dsEIioW60b5N0xPBxAFqhe7s8W6A\nq/Q27UfPJB1w1IILIzsCV8rUVrmvsKJnPWiTnUt64Ll39qiiGlbCJbtWxBiJ1J18wah2MnKOniQ8\n3xH/9D6i2n4LvaqcGbIO08f3+fX7tUhvnkDschQAcXk5xDnUe/XeU7qYH6feQcVYX1jDtkb8fr0r\npRkyxs1wI6fOROIbeBPXby62j49jWgN8NtsRufFL+nmNE1BIJHN+WkuxDgRGS8nK06FGc0eUT33F\n3K/jmPv1M6LmTyAvGq7PjgN1S+GkVDXkYmF1+SRhoIz01pdYt8CCX6YsoVGNeEBHONtAcZ6E+DRD\n7sZV++gxTTtdQVYKc8JGUpIiliOVYGFczGalbygijllVHzPj0hquRamL5kgBUnvIx4erd/enye8m\n7NjSmxyp/Ds4PmQ4gTllv+GV35miTU5deD87eX0S90H0E1vS0g0/8EnhKLG9SZEaVNeLpWn1q//+\nAYGwH6DH30fTyaxhSUKouOUg3iTCXwm/iL4EWn2B07TqACSv9adPsq+gdnvUDudCNTVS2rclSNeD\nocmz6ZG9jWGqs3gcA8FdtzFPfwG/D1FC6IG566NKOGu/gHOm2/jDfjSdr37PrK72sMsHdvny8JoK\nhTaGGNtm4tBBwKVJasCLlT1GTlb8yQ+UznIieWkYy2x9oUcL4WyXkbqP5deye6u/RLKoima39mys\n+gcFVzLJUf8CqgOxN0Wy/5oNN7/FYyZk+6fSpV9f/P3k22XAPf2m8Kjsww3ldqYSQDnhAw2tqcL2\nFNC5qIosWtyQf7UnyWjpg9mOupz4xUZU268wms/87cPQzkxm0YNLhJ8Sf53tu1yIcaHfmpEsWlCC\nxyBwJIKgmX6C2TNdrIfmwc54n+6Ib7wL/omqBD+KZ8KUE3RYCL5b4l9cMHmCaXhJso4updtcOJUQ\nT/BxVSiIemv/paR2qOTaoKebgYW5cBUAdE3TMW0YQxFw7JveHCOXX042pcYgkJ6DEUG7QLsyggde\nMHcu4Vvlta9PeomR3libqb6ZrDjXksD8/lyenkmpZjardgBNW4lg/20SAm/g9sMCdq4O48fQ5zwK\nkk9WLlgAN1aZlKutcnu9XGUNUvLhze6Jnr0TCzfNpmETCBtQB0rEKaT3ihdzMLGNnwOV8ESoZcXN\nZRYU6oNRQw9gN6/WlZrY03HwcwY3yaD99VqCynDvY8A83yC6bSt+sUUCSJB5L2Js5jYA/H42Axdh\nVjv4umkwobEzb40/tnTBtG8oM3/6QRCbH8PePILpvqPo9409SD4QyHBLSmtZPBJpBkp+wqWhlCFB\nggRVoP6ZcKiiSchtR4KCeqMC5DfJQ7m6yEXk3uDaI/kkw83zu0SxZ/Z1e5pFneDo8nmsj3q9jFH+\nC1RStJMMHnz3O6luFxi/YAFW6lDV/BoQWq5myu1MjY3Py2ObNn79Yktjhi4Loq1NIAsyg5DuLf74\nhwXGprIizwY3IMQrgQfdWjK1uBvoGEM7Ty5f8mW/3Qp63t1PT6X95PxavjtduTA2xuZ5CBLPU1wJ\nbPPOThmztJcJZ/sf8PPeSJCOBjouD0S1+yTShj0n2tDm+tds672bNsGWQG/5TvUxDA5WItwki2wg\nTcCCE7nhGtQukI/LaVy6wYV+W3AOrk7D7vIJwuICFWTFlVFXV86JX+R/nx0szuz5rvvDCXxYg/PT\nXrgeJWXumjgzrBtwp3zOq2LR4OdOu7hwCiK/70mqV0C5Wyi3M71vqMFv1fsRt7oqvfcYEeAwAIeh\nm1jTeCfS8BNUSgE3ZShRgv7+lyvFvpZJChpVocnVQO5NGk/EvIlsvGDBo7bx6MU8pVlTfQbUPcv1\n9QKO5yYnE303BwmwpNpWTI2LMCUe05a1eTZrEXd+i8G4Gvh5HAWpePWYGk0J48mTuhy+1EM0mwAF\nWen8PnwozerVpfBEAB3cRnOMhlzCm4MF1XF0W0D0nWwMDTSxFPAuXJqbwpR7o+iywJToVBUO+xXS\n3O1bLqwL4xmm1LaUUfpI7KWEcqa1vIiqUim27UxQyxRnWMrVIoYUvaryN1p6NNHRZqLDOqw9MyDn\nA+OHImFgYk7whVhUNOHGnhZ8SoXjct+SC8ILCArvRE+P9qy+OwVtj3x87+7gzu/iz1q/JDDSEdPU\nvyvNfu7Sh4Rc/IH2bb4naAwkWlUleNtAVCdo80C9gECfqoC/4DoC9buTm1GEx1r/11Eugb74BoJV\nLXhk7Q6B4pdb7tg5g7VHov/9wApFRv5yKS5fLaSPxi8kTnJAVvMyTg/CqZHwjPCWHQDYNrYN99J1\ngdR/bu5zCAnENeS9uCM5fiBGuOR7KCkRklCbNqWXkKo3J6NAEyFCnt9lQpV1tAzYTdOJ9whMbsjX\n53yICG6D7GjllL1+Sdtqj7GJjyBJw5poExlE/ftn3uUT+je53CEXjkPr4zNebIspfzMVSPgjC5LQ\nrUQFiWxoAxtelhKOAcZJ5ANBD9QAkZ48YmLk+XZcPBkT542L6g1MT3Uh2KERo3r2huPHgXBxtLyB\ncUwOlZH4BoBfb+CPPayUcZnmQHP59n0vDyhCUEf6H0VZtYRWAyNgEYS1qwunhHekAL/8lkXz4w60\naeVLBn/jlqZOtyqDgHui2P8YMbZmZN4ppU8TWPGJz2WfOVj0X0mPlc5WhkFv4Waqy8a70UiVhPQG\nfnTHj+5Q9+XGE1TG7+V62/PfDxKN/8r5+t9i+pml3NCeTmqOCOkrM9OY2MqMl7lF51YBeCa83X9E\nhrPOQaQ4s7rDbPj7zie1oij1rEDB/0FKCmG7nxvgyP6/ISOv8tZFVz4S/Pa+WO+79NMcKShKPSt0\nKHT8z+moaC0KHWXToij1rECBAgUVgKI6qQIFChRUAApnqkCBAgUVgKLUs0KHQsf/mI6K1qLQUTYt\nilLP5Uah420UOt7m33WYjfkCT7/GzN8CyuM/dvx/pbSxQsf7CFDq+RUqWrh0LGaPzwG2td9B7Qmm\nFdLsJ6FqRV9ugN1/u0pnhWNgirtdDn19Sjmh680PePOn52Z6rVNldI0boCre99GUaMDoo/t72Yaj\n9EVN0fT8V3AwTcK9mzaefo2xqgU9t4+ubEmVhpJpAzr2S2JUHyndq0tR0mxY2ZI+mwpxpg132jH1\n8Q7ivO5RcPEpQ8+uZ+BuMQphvY/WTBs6cAzlr2tXiv3KoTY/+e6hd8ouvL0W4/w1DJsITgXJjF46\nlzbpx5hl8BgwFl6KoTHNbIvoEPTxomwN0wJR+c5NeC1v0RHl34aCnXDlpf8ZGb0mRtHl3AyMTeHR\nkNkcj3etJC2ViJouc4YnEjhzCT3D99Em4jhdM4+zLOBCpcrS6G3LZofzzLT3pUf3T8t693nOVEUL\nO5v29BvWm/THCbS2ceZM4G4SS7XpNv4iKiZOn9V8udHUY1HARpp71qVkdogoJttft+TukI0kGHrj\n5xNMgybdwPTjT2WC0KstmcOuUnsW1PN4htmPC+Sv3T8yz+s8MiVoWrqU+prmgktxMXhK1xr7wPbD\nWf2Nv7Am10jgjP+AkpY6NU0NqS91ZoU0guvfD2TLV7V42nwg8b7exPt6075LbawlNTGvYYB2Ix1Q\nEq5ChPOSHqjPOUVRIVwoasuyhaoQL0KqSo2qDN6iw4Ma3qzg9WsR3oypk0AjuzzULVT/vZ0KQYc2\nWxyx372Vy5NvoBuXQbS6JZla2kzL/hF5Ju1KwNiMrtXvkiELwyVaxphWZ0C1/L3rz3KmLbV1WJHY\nB31dKGw1mKYaCzk5tAokw12HHAx7Jn1O8+XGxDGbjFrxNK1+BbGSRyxcMZpze9PYlwqZXidZlNiE\nbx2DaD9D3BNDSRO2bmkKx9/Mpp/MzaWPuL5nDfdSIDnPQnAdmQ69eHYRzg348P4Otx/QKzqC4jWP\nBFRhjpGjGzPM1zDIpSedO/2GyuZYcmQQ6Q+7PeWvjqeHMFVpOBNtdzLE7RJmw4QZehjcOJSFp+VP\noaWzm3HY5CNJT4RAo4gGl86TaKhF6LYpmK6tS1dbaLFJh65PfVhReyXTxzxGRVvo6hiajGmYzNhz\nfdEfLCFo/SwC6kzkZ7fx5IxxQWkSVF3eAjTEcuwvqcK2LjdoGhzIic4TGacxmWebq+C980m5W/oM\nZzqP7it+JzI3mab2tky51AQixEvt9iHMbjzFeWcQeYfEq0HV++liGhxpwbR7pUxbXkrJlS54/bWX\n0bY/UXED3v9CIPRTA7cZH5jsdGpEt27TUEacyPTcBio0nASc//C58Gy6ITanoPS4cD2H4JMLmSrt\niMWNGPJKptMo5RSuhU+JyFtPuy/Ok186nToPOlGtBuSVgOTcHWr4Hmd+zSWC6MkIikZ6GVzc4eTe\nehAuYrKZ9HRmXFhFC+l37B+rx9dT+9Lg0XzaT5jGpalDsPgTqizciVrDQcLq0NAjJ+oxT37Nx9Qd\nDk1WY8cVQyJ985GVSFgbDlNnteFE7DFhdbzL4hYk7/Ml8Os2XNkoITk9D8lIJeYF/MDWnfFQpexV\nED4hNl8C1nW42UuF6+NA7YdeuH3fAJAnl61q/hztohxAQgniJr01md2crPxrsHaVaDaTQ4pp26sD\nL2f4+sxqQu1+o5i1bgyHfSzp7TUdSHvjE1ZQ0d9Lii9Nmx2i8bOX+VItgGpU6ZdLc4sLRNwBy9ow\n3Gk9y673hDihqmIassd2IONGr+BjpUnc8/+mVfhfwEWBNMDObmANPOnjjreyFnAdCq/jqwkQwMJf\ntAAP0F8IRm/0IOYHARX7QGBmnYOzVRIEwul4F6Qxwg+1vEfSQV7fSl/fUjcstCXjyBaceo1n6PA9\n+FzRRrgCkBKUZaAKXB4nY7Hvde4lNEB5UTiEZiApAi0nPRYcGAGIValDk3n7ZlN1VDWOj3UDbgPg\n9dcc8mzXYPn7TzQwVOJ22seLEb5J+Z1pNWPaWt3mzAaoquXAhEWteLNL3Vn3T2zVHmHzzIxV0eJ2\n83ffnMb8mLZ8SmLXisOEJyfhklYnHL1+xyf4LPlvnMDNJ/zOAw0lBl2qWKvR10KJvqbDsGBof2Ad\nN1dqoRqahcXBSEqA2AdQq/hXdndLYvh294o1/hK7rkRPn/QPB8j46uxB9vxth/yyEpajLt3A/2Ml\nnFMg44zgGnouSkEyXF5jKn1sM5j+zgGeL7Jq+Qpb7PBj7NroyRrG08J7Kj7qAVBwXhhDBbm4eGeh\n873cW6h6nsGVM1QBUo7LD3mg3J8bU0TM7WqlT6hRdX6rvwH47fX2v0K5f743fBVPhkbZe7nldqZf\nap9nZPxhcgyc6WOwEp68mWxYRpNWT4g/WcyUKycobXGovM1/OtWNUY1MRZIuVP3zf0ajkT51amWz\nwXENhmcfc+oaFAJZbicA0GykzZMYQ/anFEGfRkCYIDp2TfRm7XWoWxsStO0wWNeZ2FbGWD5KRH1y\nAKlXr9GjnyVHD1pXuO29+4eT4ApfzDlKdPJG6hjdIXzZA9BKJ9Pemm9+ziPQCWQUIaQzNagJBlEw\nb/1sHuBJSTVjYg2sIL8IolMQMz+i5I2BtEJlqFcjCUmjZvTxHyrf6Cvv0RibQlydliy/0E40bW9i\nt7I+DL4unAFZJpO/b4JKx218pz0T2SMpJvfTSS1MohT5eGPXkh2cx49s00RiE3QR+neqrxPKvhb7\n0fn2dVl01FUxsnfla6Uu+OsO4UmWJWXNxVs+ZzqmJd+e9ybTvT59Li2EqLeztrfdqobZkmBygNIS\ncSNVLXyc8ZtSlbtN68hTd4pIm8GJ1NG+Q8uf/EmurcuS0Fl0YDmlwO94A6lUdSvkfJ4N+Sm5L5Lu\nnxJEy1jTBUQ2MsO6ViBHi1uTNiUTeSmXegRViaAkOZZqscnIO8IVS1WSiAM0ll7lG1xRAzoDMi1N\nYt1a077FaW4BfbkL6wzYuL0vhH16yrOPcb92S7IcbMk9uZzp+FJg5khgzZboaebxqKUrl/eWAuLU\nZr84SYXvakHMQ6jj9wv9auUS7r/lveOSE6CWTiAgrjNdOXESXIB2+y8APwpur/jsT6zAnqrD2lIr\nR4vej4dS0wVqacLZyzCCMdToXZ2xv04gJU3YKraq94vR6l0AvFzGaYWndyAd/9iLigvUqBIO52zL\n3F7ZPZ6KC8G3vucB4P/UCqLeHltqs02NEePmUvgM2gbXJq+VuGUqjkj7EZdnxbVUceuQq7gYYaeV\nhq2vP4zQY86ZuYQt7oMS8pHRIGoQhAEnfE3IjxB++MH3CFy8Gc/uA7VIO/yMlzWxlL/uyd9WhsQ5\nVePGVw0Esd1550W+2w0NbGF2K/juW+hgC0rJeVj7nuZOpvw7seAU/fcdoUdzYbKr+59xZNNtGzSu\njCELKLwVThN/Xxz37eGLvZO4UfK+MxOKcA0XDuuPAUASnkL42TxGHzTl6B/X8Wa+/OX5FJA7XLH5\ntuE2ikCse8srkn6O49pjI5SBk92/xiXoGhZX+1JNE6K2PuXhiZnCi9CBV0Gm9V0IbreFJlt2cVYy\nisSg8pedLrMzNR35lEMlmWTUrI9f0uDXH7dwZE7HC0xaPYc4QGlSTfr17Q0ElVvMp6OOJF2Z2lZx\ncO2GiHZBnQJUXzisEyre2A2ozvqpbuS427HLZyfwRFQ971OdMbUK8L9ah+pPb5PxdCTSnwSKPtri\nh9LwFXRKu4hSwGmUNu7GOe48063/H3vnHRXV9bXhZ+gg0pEmKBaKFWn2Fo29txR7QzRqNJYoxt5j\nL4mAvZcoWLCXKGCjiKIIFlQQpA299++PiTUmAcO9+Ms3z1qsBXNn7n4Z7mzOPWef/Qaxb7NsflJ5\n0yB+tAyiddgpTm4V0Coj9hUTWtowq08Ql4cvQlvdnqwBfdC3hBLNJ+DcQ7jY7/Iqjuik9zvYb52W\nQ43Vi7HnDtsXHmPt0eqUGqjzYslQcTS9RtGCMA8JJjqgmJT6z8+vaBrK3pcq4bmUlpxncIsGrFm6\nCZMW4NUCaoxrImj4UB0rBjOBkrCF+JuMJiDakIVDt+N5o5DWBX6QoUS5xpvlFdA0UxG0OzAp8BbD\nnvyGzwpDjMLu8fgVDNsHzWbM4mVcPGKYc71Bpy4+h/QxOmAC7cQLC5CbX4XCDJnb4uikfcR5h5No\na8xJx8E8Gif86Hz0wmJc1bfjNPNdexAtWN6NnTVcqT06geiUCEqaw0HNPRy5k46wnl25kPL7Oz8+\ngyhd6q0KoAgoUn4Fz8Uqf8mA46e4CFykJwYY49I+keQdN0DEHc+h6TWQugSwO9oZ/3MgicqgcdRp\nGgPRf1TPJR4dym+u4m7DbjlWyu+rS3HsCsq/H/7nF1Q095/yVW844OfFrX0GNP16LqE/JHBioBtO\nLCPdSkfQ8IVR2fjsN8bJeyxZhVWIfGVK0VLZZyNbuQqFYVWQze2XbR2mzMk0fp8ezhI1rmaHsIJq\n4ASXAT1ekausQaNJxpgO2YS4I1IZvTufxyrlCYMf7AOOihq75EECgb2tsazThKgHdygxMOTwzGkE\nDc0SR0BRJlrecZj16UUpoEAptzo7ctFtGpJCeF7NnPq9TRihOY0Hp4UslP8bFJUpjFdGEQnTw9cB\nPuJr0DHAuoEmegtuoNsYSBfRTTc9nVeep/nS9hC9z8Xhen87Uh19rtq3JCK8PtenpMAAsQ0HFdF0\nT0FCMZ66E0hRVERmLigmL6k17glrblvh93Uc8dMnENi/ETrtQwkA0gqE3khQSkZUMcG8Lld7e00Y\ntsnCP1sLUKXCkyk54eyr68LIJ5MIA5oDR92H8GpZFonJOkw74kBlJFKAguvm3B0+hdKJlbO/N2Rp\nHrrV0YgAACAASURBVCHdFjKyyjbuqzcUL5ECt280IibPhB+OO1AM5Lh3YwHjYDkoLIzBt1YbIjbm\n8HrutDLQr52KVd3ndPi9FLaLH3+QexotNGNQH7KdXOD6Qnfoc1N8IeHhnOgCJ+j/zoOPxdcBKCmp\n077zaTgNto9fQUblNCcq6ulN7oJBKC04zP7VwOpQigGdJbbgXSmS3qBKCeWpKCjXbb7XEyler+2M\nAVzf2VOTXUlWvpQyI/IMX7eYADyrJA3AmWB2Iuwcz8d4cDmaDox9+4ArvC3MNoa4yqy5lZH8xIAn\nkdacKnAERfFGpd3ci9g/6md+VynkMTB5Coxr4s6OPmIVhX++DMqoirKGzNP4ef+qcEvEabl3KCnO\nZubcZuC9j8ANyvhehYaDYIL7DIgRsbTyAyQSaNExlj2Xyj4I+bQdUJ8VEr5Q7UKlJtJK53P7m3xA\naRy/FZvym7gb4jjjqoSuqxvM6A6Gxcyakw2Ffv/8wv8HFO5Jpvq3Wjzu68KOgYZU7kaXNOi7BKfX\n26+PAIhbDfQhW1vb43+ifG0r5VbPcv77rBK58Ph/gKPjlXjYcBgPj9cG4itbzmeHy4qe5X6N3OpZ\nrkOu4z+mo6K1yHWUTYvc6lmOHDlyKgC5O6kcOXLkVAByd1K5DrmO/5iOitYi11E2LXJ30nIj1/E+\nch3v8xc6HOaxOlgRCZAcMJplztUF1vE3WsqFXMef+biWf7Ga34hqnRWZ1NYDNQ0Jnh5teRJeOeUV\nNk2rsKHeZq627cLyEWXvjP1fwqBKNg6aERglwcMS3rQ1e42dAxxP7Iv0pZBbSf+eqmMs6XDpKEUv\nwIfBgFBNqj8vlFoaEdhbkZAQULDow7QB3wDyEi0ABZPGfBm3gySgpLMD6sbZGCUnceZqawqyXla2\nvHLxyck04PQSToxJRuN8NKrAKLdCOowJY5n5DI5PVoJ48Yr4LeNLKL4TzfGTPajoTun/jA7QC1BD\nr28iueFVyI24BsSIqiJH25Svez9laNRxEktl+zbSTasS5WpCQakq1frcp312JIM7H4TzIm8tMTHA\nzSUcVZ/faGH0kNu/uuLTzZb/D8lUwUaHH5tGcH4mRLXuy+7kfvBQnkgBDKbW5MTBJRTXvINuFJyJ\nDEYpsRCNnGwGFtxmsLEHxIu9q7EmytNbsnB1WzLX92P5qkYQ+6pMryx3MlWvUptfunpzpXsIr3fO\n5gJKyy7iB7RmKkv7Qr9LvjzKvMFf2VdUJBlKOqgWQnjyC8FjvYt+7zZ4fDULpeFTeVoIRd6yN7Tm\nsbp84zKUwmTxdpXkvEpn5JbGjOTd9nrKGFwowbhHN+a/cqTYOgZ0xCvwVzRQwTy7JeeGtcN7lwE1\n9BRRsa/F6cs9QeIrZo/mj4gzoqpVJspqBVTLSEI9Jw9pjgYvK7jReyO1Ihz3byRBAzIUS8l5WAl9\n9j6CRNkIO5O7pOmNYecrZzhowjH9CfQt2c0X/b3gufC7j+qqJRCQeIcYixFY5RXSeN41+qtcpZ/t\nOVrNmsrP8xYxc4xArhAfYKBuwJhdITT5aiFxayGniTbFS07yYNAv9PdexqO4Av7pgi13Mh2y6CVF\nnt4UAPWQNZjL+SPM6w0u3t7wnc1UJivNhwfCjxTrlIQi1QbF0d0oXrtX8HivMTxxn59PjABGAFBi\nI+F76QqixyXQzPAVfsll846peKpCF2cWOKzD6GYChlvmkdHamsMOX8J6L3EkNOzOPI0Z6ESuwlup\nBydrD2NDq2UE/v6EPZl92NHWheVXDcTRUssCtwY+WCRHyy5UCQQG2aHaMIYqOlkMjAnD0SCNqdW/\nZsOyCoyr2JqvzDbw7C48Wu+O5xRlhO3YVUYM6+FudYHqCtux0vBk8LcbIBi6BkUS9Xs0WAHPhZdx\nc7MZv34FMx+bEjDJjLx7BmTfPkBcZ3VyUxU497QHojRa/a4Ha8PHEjfyLnlqFtyo2gOpk4TqKqX4\nbv+VHfvcaTnQlX9qeFLOZFqTRY9Hsv8RGAHuv3vRYcASVJPvgHs7vm9+lX2N4RWgWxTM2JqebH3g\n8Km/YplRNYgiMx8UexZQvFbwcG+IIPXDB7AdV0iiRwaF0hhk75LY1MFlfzZOM9qQfA7slECruAb1\nFfeCnxg7gdTo5S5hgKsz+s1gnkUgwUtvo+VelSvj76K92p66O08SmNib5U3mQYjw/WddjLypFeJD\n/DtTcMNtg2nVFBp3vM2q1tUgY2fFBz5ZG5Xup2jkrsM018+kH4COBsM73CTp0HasOkL9zDwKZi4H\noGCsDgMSvgMFkfbEZz7FuUUOiw5q8FPwTxRjAXMnMHKxOvUijZlSuwMgsJb5Pbix0InzgMtyMJt/\nA5K2gidAa8b5XqZlmznAP3dcK0cyVWH9wdMc+Ea2EzzMYy5n2t9jnlscD5fBlb11UXD9jm5jnuGw\naxWZT6W0aCZlK5YI/W8upEEjLAoDUVXKp0DQSH9P3XaaPDuvgpKDLmr2tWCr2AqUuV8yj+sKz0kA\nvpyoxXmNidweFw81n8ALJaBIuPAKNvzY8AFNty7lyKjBJBboErzvDFDC5Bs+lJbCyYye8GArTiwE\nHgmn5R18X3SnRWYoSvoZnNb/Gb+n9hAeBdNCgTN/PKuipz90ufqsG6EGElZFTnrviKZBEV1HaWFQ\nR2b53GnZKVIHm7D62lge+isDCRWs5R1VBSaM9f+R0Ib96aX2JQWXlgMS6NUITf9Yfqo2EDHbWEqO\n3MXaBgZvN6fBtoM0/mU6Z/v3YXLjugidSLX1GjJz4Q+cA6qMcKD5z25QsBVQYWxtA75O6ciqM1OA\nvDKdr+xF+82syFz1BIDrsyaxcZysIfJwr+/w/PU4dl4gOdeZM7vUObDLk1wgZ98toG25fsFPITC/\nKbFFZnicmPTPTxaQxvklJCdGUS9eg+veIt3CvkcR3zh1w2vEfpIDJ6Hdo5S5jZdx0O4wNyQraLjT\nTrDI+n31GGq9H4dXS/HLHMlvd5tzZZ8+UALGBry4r48hcPb06w73gbz13hGQb8ayXm8+8QXRaKjn\noa/rCyWngFBBw9r3TeLp7hTiTG0IirV8e2BuX/a6H+DLg3Op5bKLWi67sExIxtnrAUNeLsItQOB/\nMB26crq7KxPutyTT5/VoWZdLiWt5Ka0DKeIuThbevMg1ozG0meRKM+lVlttdZL1fG5KzNP75xf+S\n6h7qlOKHiwssueJKdGoooMxkt3s4pI7DNxW+XLEeKFuFUJmT6dCkXRg/v0eRmgaxHpZAMgCPIgq5\nNSGE6UamlHZZA0XJRA65R5dADTKBk0G9y/9bfiJ1455ROR2UNLF37smgmz+iWgTxS+pTmGuAGHbG\n71PKg2ADLux6zDonPWy6/IBk8Hy0Jk5nWvIUxrgOYs8Bb1BRr9Cote1r4aP+Pd3ynhC1tjPrHjei\n+M5bm+/6phF0tz9Jz6aqECRiY2iLrjw8WJ07L0to0sYc+/maWHwtQYyNf3qSZHKycsl9qkHKBdk/\nVu2Ojdi692ueDXjOyyIj2n9Tlxnbo7HTkjLFYi+qUcksGbtBUF2ppzaz3MOId+3Zp/14FsfVIUQl\nBUGJmK34FLC0z6ah1TUS75Ry+1knrl65Doki2T2PLcS2pg4ZLirUyX9GE+04ujjWoMWRI8SlgEEV\nA9b+5AsElel0Zb7NfxQJjYE4Y1tuG9WG1L+f69p6fTp2LOKxYyjQt6xhPo0kCeo5Eq6H6IKBJUjF\nbMeniN1gbVYcdeQuYK0GJ/bVYFLdO9Tv+gj0ZLW3S1J+JGb56xHKbRH1ybjZzhD7R7YozS9CrWMN\n8s5UXFmSV9AizipA1R/rM2P3eMD/veP5LwrIzMjkxuNOIm1gNgISIDqEFZLuPNGuh9ttA/Qii3C1\nvAhjpsE2YUdgWknpqOa8e3vYkoXXFpNTGEE6puyovpjFB3NwillCX9VI6hdf5ingt7IpdBFU2juY\nsfm7bdwNbsC3CSMRdAroT0iwH1jCaL2j5D/W5ospCuy4XR1UlKBAnMm6cKUwNqjPZs0vP7LQeDmk\nQ0SQJ+F/HL/m8i2R68r+WS1/nWkGUPTPo7+zVXpizyKm9oHpx8sdpXzkS1AohkdxaqCnW1ab6wrB\n3CGX4RprCP1joe9pBtS/vJViIPvu2+dNZBBKSIBSqgxzYPwe8TQCDDHeReOYe4TesSPvScXeQtkp\nDuai9xVeDPVicZYX3X4Ek+Xa7H85jBmDNpCjHESi/1YU0K/QuH9FoMtinF4Gwtmt7Cl1hJcAOShb\nAbWzsHRX5Pk2YTWk2BqSmV0FW8N7fH0qlUM9h5AX3AmARVYv0BodT7GXBT+bQwHwNBoub/6ZaV3E\nc2kAE8b+FsLGTg5s22VKWe05KgIDzSyOpKwmZrcD7TRc8Th7hbjuuqCkiFgLHyXSAnylBThHpNKj\n+DzLW88i+MULAOadAsWeXwEXyny+MifTEhsDiqXq2JYEM7/HNhbGjoaL4fypjtTMHJuMSLa5O/Gw\nlRaKx68AAhuo+d6jk0UCx9SNQVfYUB+SLVXimWE9Og97xNY9o96UhxXztlQMZP/zlYDaFLLqTh94\nz7pCKJRQamnGiIf+OOQEEK/Sk6Nz+sDSkAqNUloqoWPf3jBlIVvy57FtuzVWyb4ocobrmZ48ephP\nPOAw+CBtjDyxVPXF196a58eFGQm5jtrA6YOtWW72PWmkU604CYUTFth0DaG2XzbP+wifNK6escPa\nrApdk5bjZDocO4a/mfRZ9VgFXGE9YGwCiR0dSFGz4Mwafd71IRISRfsmeLOR3i5rOecqRcxEir0F\nC0ZvZWbyNLw0NIEY1Hc8wyDBVFQfzteUlq7nlKU5gyxUAbD7yhTFnmspTyKFciTTh9IGOPa+yp3t\nUej/fo6JyrF8tTmN7KYKTHmxkYhjjTj3QztSlivx5EwUETkS7rduAf5iOFEWcWFzbywH7WLklBuM\nn7YHZ9fdkO7/zy/9l6REKbJzfxOu6xpwB/syvKIEHgi7+AGAXV8OzxhH0oJMNPMekp3Yl8VF/Shc\nKlTReBas92U8XUDDgBohsgVKnaR0Dk1x58F6UJiqxWIvF56oQv+GdvQ6Lsx8+p3hUp4stWX48kXE\noI5maTZ91NJwt3JlwT4XeCHCNRkThUe+KbeUd9L959tozXR/77Dzl3Bl6QjcB3cmI/QJCfeKEbMG\n9fqORez0sOf8RAfgvGhxQZHJY27Q8XkoP+7uBhTj0iuYhEm3sbHWgvyWImp5i8MXL3gakoaBBVxS\nGgNvbvbLTpmTaY40iw4XT3LBYyBhS5Op/jSYgImyY6PpCkDYITAwlaCqbs1at2U8HCoFYsst6lNY\n3asTG40vYz1zBTeRQE2dd+fYBaSUrKhi7kSZUikdy5U1qFY3B52HJWTZ2SNxM2ff3T7ELltI2g/K\nmBkZstLvALccFfiUC6T8FEFOPFHBagBEoYa2GaR4NKKx8x9z5wv7waznQMWOkF9T+iiBKT92xWb7\n95hWfUmQlzMzlibCMD9EKQJ/TZKUe0i5N9MIlGKZc3suAEsdFsDFu3AxGLG31CopljBkURwPXSKR\nNq5PaZGYiRRMamZhd9OH8d/vIXv9C5o0rkJx1WpIGtZgR7vO8LByjB9tgvTQvp9AnWGtmbSnNp9i\ng1S+OdNoLzrNnkHrBonkRyuj0zqLDoP9yYtS5szy9kgopZ7ZM45LW5Ay9F65xfw7QpjbcBmrHNdh\n0/0VfCfC6K/S6YHLyM2Yp4cxuiCIi6HQaC6EtG/EnibTMeMGZ3Prk+IoTj3nx7Hl5TEIHun49qFZ\nIuzCipQS0U76R6r6DBpmFHmy1MH8jx8qwaL1D0w22LE4YDEjRvhweUKg6PHV83Ixi0+g29dn6GqR\nTJP6J7hd2oNfnCbzfL0IpXJ/gep3Yai5wowx02HPp20kKf8CVEoMfr4ABeAn4YLf672zsrnTW4Gm\nyDaZik/6xVBc+OIPW/bPYNuewJS8dIJb4HQ2kLlPH4BlB3h0CR69AFIBW2T9oyoPxUv2XOkId1Ka\nItsbJ6fyaMC5if357qs1lZJIAV5qVOfU+RU0SArCftkJJiy/TJBGAKLUHP8N95LtWWy7nQltPn1H\nXgUY6n1uzpifmx7hUDB/3Z/xj9rN569HPJ/Pe1DccT+zmQ+P5Ym0UpFosU7Ni+sjjPF/IrJN7DsU\nPsthswJAfdnXBt9K0/IuT8/ns4MuYN8a7nxaVy+5O6kcOf8fKM1kXZ4ZGjcGknJPpKL4/yHSfdOZ\nS1P+TZ9ZuTupXIdcx39MR0Vrkesomxa5O6kcOXLkVAByd1I5cuTIqQDkyVSOHDlyKgC51bNch1zH\nf0xHRWuR6yibFrnVc7mR63gfuY73KZ+OUUomePZ3Renwh6/7XKyN5Tr+TIVbPb9GjQnb0zAdvRJb\nz+qsueDCjfOqkCm+7XP12j3wUHWie58SWFYRb/7/Jg1apaLi/wx7U/AvdiYioRK6R3wm9BnTgNmJ\nA0lsKOsVkLg0CSmyrQylQI5xDXbVWEnMbTG22r5PvzGptMnbxOFi4Ruof35IAD1MR0vo9uw86MON\nguY8PF0MxZVwvVoYUL19KU13XyC2lRW3VNrClfIV8P+rOdNGfVXZZnOQJnNWogQ8cYlhaPhGaqxv\nDMoC9zB9jc/wN99WXZZMxENgoDihPz9UMArozNrcvfTBh2aZPrT6PgGoLq6Moa0JHObJ7IVSqPa+\n44Bx36qstTnNbhN/ULQRXMren4Zy5SSELU0idGkSCciSaCmyTl6G8VHUeyp+3eVwt0d0DPEgeV8K\n8+4uFj1+ZWN+oT3+Whs4t3ILId/PJ2TVLKZ19abdxUrwTdMZy9GxG5gUuoGR+DD45W7Wv1gMvdpT\nnhT5iclUCZMmJVidzCA5IpRUHT3u/PQ9p4N2oy5VZ5pLF3w7fYO+Wo1PO3156LH7zbebdSdiZWUO\nTcTdbVPd3JQvgswJPHSMds0LQKS+nR+i7NSWSc4teBKciokhZJhVoZ7bL7xyGouyeUPRdMwx3sz5\nPXHoxV7EvPo7jW60arPLZibqmUGcUjMER+GcCBQ1JKxYd4HNNfNQBAw1IKFGAxIwJgFjrPvZ8MXZ\nmjy2bcyTNomC6XgPY1vQNMCgVTWcwl+QGJzPy7G9iC0NECV8VQtb7vmtJ99zCYWPljDooTn1L3+B\n+eUvsPulJ6AquAYFFRi8PYet3/5Eq7oraWQwkOA+PgRbXsT/Ym0u/TJecA3v05LYb6sj/TmNbXE7\n6cF8gof0RVUziBHGa9Cwr1LmM33abb6uPd85uVHz4T20f6zCyoiR+C+1hCXPkDaeyDaDZdy7GE67\nTk855iOWdYc19bY9pbetBzwW18nObYInB65/zcb1LdjWbA/bdXqx/Gw1UTUAqKrF4bdlAXoT8tGs\nFkqstQn1nBI4tPcUI/vdxPOlAWU1B/s3/F7Shm4cJ9CvBq+SdHjdt+G7/UkE9Syh6am6jP96ADx/\nIpgGM9s8VB9KyQXMdOChVj82Wg6CKNnt/Dov4HW/lXCROszHhwP2zErahYr/LToogNu1tuQ9Fv5v\nAtB07hOOf1nMkyE/oOHzEJUwb6wjtbBpXcCob29Sh+kI3ddUp0kzTB5foIt0Akjvv3fsnGVHTo7K\nAEkPOH8ZMnP/4iwViSp3f4VTffvwJDwM4mHXUgdMc0zRbhcKCWVf8/qEkWlV3Pt5oOR5j7x8+OHV\nVfy9NKFU5vkTfi+Hln3DyCiAHzstA4S/lQOoH1iVPUegSzcRPYb+YMI8d/y/n8be5ybU8bmNzp0A\naFCW3qYVS5bfA86PL+VgqQpbwxw542XG6r32TJkBCV5JQFVRdAzX2QdAXngexdLXiUqDWd2XYgJM\n7HkAsu8jZGIvRpF8lCkB9DTgbGpTuCr+vOiHBG4bj+SRN5lA60PF+D3OQLTW8kBenhr7tn+B50lH\n9kU64oUVvn52tOp1ETF6Vpam1uVyYT3+1HpwyU94GC8mvf5Zbug0xUJfrNv9K3Q9BOPubWOCTzbg\nQBv32tTRWERxaDVyEso+Mi13Mq3XogWa10+iBhyduownFz6yl3WtBFXAdzJw8qvyhvgk1t+aSomB\nBmfWdRUl3nsU+vBmhS9zK2m3ezCi2g7xdQCgCzo2gDIGXSwY0/IVdwvA4M2Si8CoavLyoczN0dz6\nbUy1Vs3YrZBC9BAH0hv6YKDcEHcXoRpVQ2ywOrov9FEAQl/Bumo/4jiiCl+2iqD6GD1QsxAs9l/R\nfKAGlxZlo6IHCQv6w6BFosaPTTRHb4gBkPLe41VVFclqryOKhtSYkxgGZDD7cCFd6xbQLbYJCwc8\nY8hPTYl/acDENtdosW0F0X/Yh4jBvIz+FJiAeZ3p3GzVi2mbOhEFSNZnQFHcP77+NeW+zXeec4GY\n7kDAYC4MUIJXH/lvlvuAFrtViXukClM8yxvik9A9kk2iRh3CDAwR0ne8LFzoaoV9THDlBN84lPXb\n2hO+vwctpqymMD+c4utw1ukrCBSmGfO72Go4U+fqSvIBT6cf4JGsr+wPX68Cf5CcimJU3k6Ui6vQ\nUiWciitX+ZBClt0bxo3jfuzvAwGRMCByJuo1lGmRG0StXhosCW3Lkwix5rdL+anmL4T9lk6csS07\nTvQGIkWKLSN8XQpbzLpD6fv/xNq6xXN74X5xROSkcyFUm741ntEs6RCJet8w1rsnUIt9GwGuiKPj\nHZa42NHEwpHkzu1wJ5VZA5bAlBtEUb41n7KPTLU0aPpbdZp3X0DH+grMGv4zJdEf96uxNlbi9+H5\nqNZQgmdlz+yfioFdDSIfa1OiU4h6DXFXZjVUVGiiU4RtkxRe30bX+FWFYt3K2Vy2psVklPPCsey9\njJdPwmnsJMFpSAqvAoMQ2mDH2rANEzfOJCZOiiqgpmyO+aUOdDpuRsdtsumXqsVS9K1TKG2kwrzn\nUwXVE5UYg1mf+fiO/hWdho2o0lCVEmkh6sGRxB65z4iIzdQZ00ZQDTLUsO/Si5hV0ZQALuZPubFs\nHE3G9hQh9jtI03l2T5MPe9yO0tsDJH30JYKQkce353yY3s2Hy6GNmL0oFCMnfSqvdWQRIdF5RJ8/\nR9T529zLqPtJUsr+iW9tSwu3LaQCuw2HQfhfjzj31xtSfiX/gn7WJygwiMC5sISUc+LWqNlqvuBO\n86X4793NZPxY860Pw25M41nn5sh6NtZCdgNgCAi/KDXNsTl+NqM4Y7ee+/OnkKKkAMqpgscF2N94\nCKlDZda4McCsXc6M69iG+ZNdCL0LmsCl9nO5aDeERTHz8D5tLIoun+0JjI1045pdfy40nU2rLapI\nkH1eJl5wQb2VnrACajRlRYgj6cgmPY4EFnKiay47UhyhlnhVFh+nHuFPlSnSELEbZxdn8jMkPDnw\nmEPN8zns3RCvOXvoPyIfNMRasP5rmuy+T2kpxJSzpLBcRVSFEtB2UCGu7t99COzhpQnGRuD169hy\niflUQg83IzEMrEZmQLK4ZVHBKg54aoxlZb2pdN4Si9qBYLoeiOa7rbNZzCBOVBlOyay5xNpPJPiL\n7yhZt5CSQ0JuKEji0EljfINSObzQghd+Pbi/qw706CFgTBmOvoEY3RnMbF2YbQejonP46WUO330R\ngKkEuhRX5+wpBc7tMQJpKFDxGzsO+N7H3ucjv2tOOEf31uXsFRVaj7+HArKLP95QBVVbYRfm3L+Z\nwf0EmVttLeCX7Idkjm3K+WOwZcxyQWP/M5YEKelSrCLenZRSNxWQlgC5UFLKs6DHtOwzhJ71j+MS\nVRktlk0/+ugd12blOku538H04AIsbsQAlh+cyQJU7FnX8ggBiXd4WezA1jxxnAZbDQqjBNjUzEWU\neO8RH8e4qC7obvud2OASftm4HdWwdQxUu8pcrSB6KwahkByEWcsgHIKCUJgfhIJLUMXrsLQHicEH\nD6YzLmkYwXe/YU7yYgRfzS/Yylj7Oiimzkfx7nwwXwnmK2lR9QTxpdDbV+hKi1I0Ht5iQA8n3NYF\nYWrz/u+r2KIRXzSDlR7jKUF2s2tSBGmBwi3MaThU5U6yCSVATncHjs/wxHn8fmy8btPIFA4/dBIs\ndpnopM91v55k54hT6QFQNPkKR0cMpeHod9NPKiO2zKbgixhGLCiAKsLXvAJotrLg55J7gMwAEgUN\ncnINcOkH5TU7LPu/gVDoUQfuPoY6CfsI+DmEWe2nvDlca/lV1E48RDU0GKtfdBl7fy1suFUuMZ9G\nKX0v70N7FcwYpQ8kixDzA4JCmR3U/o8f/vCeyvv9beWP4GWv1Qn8ZhFLDzfieOQHq9SFoXxvtZq2\nN2chu2DEdX+cuOc5TSbuwXYSPBog9PUgwevVGOqzCK055xiqEUaDQFNKkFDrfCyHPHSx5DlF49Io\nBYz22LF6/HTIFq7eNb9QjfQc2Up58cUonKNmUvQgjepqMLVqAMH7TgsWuywY7yulgVkoXoViWiyX\nMmv+Ilob/cJ7labPItllUB8/LXfOFc4iXgTn1u9ijrHMazfwCwA2kgyMku/Qe0EAeJXvb1P2ZBoV\nTJe0nXT8LYSeg0dwZWYYnXh7G19VARQaq6Bi2YwRy74lJkKkVbkeC4j0WYR2ezuYUQmJ9LMgGe0e\n9yl53gUrrTQeJ1jAqwwwMkbNJIfGD+7/8ykEQNm0IfbfHadEAXaeHAfJwi9G7rlWj339BpJ/6jck\n0hfEOL1AEUgEzHlBFU14bNUEvQY1mbzMiWwBEylAcWgSsT1t6GYHWRFSitAkpU1T3PSnEuxduYkU\nYIDiUVQlWYhSNvcOCqFr2LbIg06PLnI/4ioUZskOSONZNnMo2oOtiN8tbDJV1dGluLQKA7oeYpt5\nNaxTqtF5uw96t9UIblR+Y73yTVCke3F3fCPMJs3Hdo1s3k8T2DPJHfNXoKh/lSOezRB1dKj2SG5k\nRS6bf/mB4eeXkj5An6shrTlrMpSZs6+RcTGahROnYdlrN5wUPpm9i1GPVzwKyqJuFKAaC4iwSVHY\nmAAAIABJREFU4HQtnCFV+jNHSw/bYg+i02Rpwrk2/DhoA9VPPuJY7S7gFYzQu31e479UE6vRPUiI\nKqW4sRnnQjqCb2Xab79lzR03VtNU9Lhp6Rr0WzOR793WUHQ1i8i2SpyI7ctjr3rM6jKE+em1Efod\nsjF/hFPDk6gP/43kGmMYWuCNyoMSpuxxA8r/WSl3HpJKQ9m9BmDe2wc3xSHbXWyNzGJYRI4e4lvm\ngeM/P/W/jEeEEWn9+2Hl6YUVx3HgOC28m+Dc7xaLPbYAzypNW1RNc3ztW8BjcZIX2eEszTbmvWs0\nEliewm0MIUzsGuBUdmx3kH27HyBM5Ph/hR4HvpSASmXELuFmtCI3Xa2hUU803c1JzzKiaA/sKv6W\nq9uFr8G9l9qY5EnDSGi+lYlHHzHw6ENSlvwCZH3S+f7FoO7zsRP+vLRUDvnBaewJbgi8U2qzH9i/\nrLIkkR2Rxz1pHRyKNHi01Yg388miIb8u/p5SqiiAynfAukqUEXqKLB1QfL2BYwOIMrcfE8eE5qbA\nfBgA8Cv/5pqR3yHLEYxU32zO0YxzgPiJVM4/k87S0lEkHyz7/nM5f43c6lmuQ67jP6ajorXIdZRN\ni9zqWY4cOXIqALk7qRw5cuRUAPJkKkeOHDkVgNzqWa5DruM/pqOitch1lE2L3Oq53Mh1vI9cx/u8\n1RHY2JOUrRp0dv6ULmqfi7WxXMefEczq+fOi8UxTvv15HOvcjxHvGipsMBUbVAwUqdPtESrk8+iJ\nLbm+UihN+efXCoipbTU6pF0mLK7gzTyOkY0mvtJaZEqFbXGmY5BPI4MnlEQU4I/41i1v0aBKK1O6\n3MzFZIsfQ24eJv5WAQUOOqxS/ZmoR1ok+gtrY3LxHnz5m/iW5/97mKA2qirdMs8S552GRR9N4nSM\n8N1mB7yobHFlpmKS6Rd94Yo3bBzLdP0VNJpyjNxfGzJuwkhIEr5Zwbt0q+1DKSB9ZQwIm0z3TNtK\n8OYU9EOSUKSYxGRDurbI4dfJEznp0hTSzwsa/6PMGcvvse2p1foZZxvZsJWxVC3NZNrIRfSxOk2m\n9Lqg4c0M2+GatApHhWKsSj6WTGsBrYHdHzlWcajbGtLb0Z9R/h5kPHVixM1TaCRfJm2fDvmn61H6\nUgr+gkr4LNFRasf6at/y+5Ex7G7lCJR/D3pFoqI8nIM9uuB/V5V6+U/IKMlDJ1yZTDVNOlXL5qdE\nK+DjTegrBOPWBCwbys5RsnL97je0mP98GUGDy587/tUCVI0aFqzY95TTd+w4tvUsnpOrYzB4M6+S\n4sgZeIE5LqdAU5wGwABKOoZoLtNGEShadEHwePPqu/KtTSItPFXp0hqGzk7C0Cob22FruVTagl5B\nuiDRElzHeyw9h/WuuSiP/oleTgM42y+PiJZdUU/QRi1Q4FGSsgE13cKIlhaz+t76jz5lVO+zPLGu\nBUbCtkvUU61P22MH6OgRS7+fuxERcYM7Seo8I5/Y7hdJchXewuVzpGqQIa9exdFy4hKOj5tGdaXu\nmNALG5wAdVG1mJo34sWeWkTfiuFbxzS2DDqI+pWBZClWoTQ0lXCTLDDWFCy+ho42Ow1+wW9UHOa1\n1WmrYsytMUWsG9OfJhYNAI1yne/TRqbKjXB3XAZKieQMeckz4LF7XW4z6s1TdGtG0X/pcn4y0WRJ\nlsknhSkvtXWfYmJ5HruqwAPh470YEkZTxoLD+48baGvQoMsvNHOcQtiwfUTuyRBezBtevvmuqkUN\nug+5zYGXjrQN3sOT3EdQKGDoxv0YMNQMna9gVktr4M8t93Q0NUnWBTKFvWPptekosQ/bwDhxPMj+\nkhwFZG2hxXWA+Ct2pY8gATjn0IvHh20YvWEe9b8/R14RDOMCIOydy2uqaBaywmY6ryYr4d3+J37Y\nJqG05C4T6MBMF1tqP11EQ/1CeCzc+2ZWx4BSqTcdZkPPkJm8TDTFpGEST9uHs/L+QDopXYNnZe9+\nV/6RqaIJ0z0fknYzmDS/lxQAzbY5sKlkEAHEvfmK0y9Eb6wqVeLEu5ibHDYi7koSZ+99L1rMjyFN\nV+Do4UZIgMhA60pQoAia44juO4o6NfJR2PsLfg8jIVvYkan9lpskAAWHgYyPNYuoxpDibdToBrzy\nFVSLo1owhVS+BQbPlPn7EZ8eUwJzCVh9GgSZYzYAuv/xfXPS2gahBezZbsetNDUWfudM0rKGf2z2\nFa/jiXpJAeYZz/GMWoHvkXxKS143/1VjcI1FDOoBs65MhFypYBr6/epPzguwCynm5bk4yAwm7nA0\nfo2cueML82cuLdf5ypdMFWxw/SYdu0lz0RpjjnnHVmg16IPTtJUQ8v7cS3VzI9K25lNtjGG5Qvwb\nep9YS/eOMEexvmgxP0aT3rqMsvGhcUMEne75M4rQ1J7Zo+I4Y1+T8Zd+YN0+O2TdPIXnzlBdrNvA\nc+DwnX3Ah3u+HbhwSBQpKFCMhFJQsadvn3SObdhB4KaFBG5aiNNyY1qZFlHe27hP4VGYGqD7F0et\nWdfBH+ftHkRuukfXCUKMCqWArG+q8egCPlxycxhViNHG+4waqQSCN717BwVAE4yuvZMsbSy57TaJ\nwBPW1FG/AlwVVML33Txp2Qc499ZyW6+7Dd/sW4ESsHC3gMl03p7z1AueR+5kS9aETOP70KlMT+wP\n6X7vP7FZX+ZG/oyiW3VWhlwtl6BPZn5fopYm4dljFBArTsw/YcTAPQq0D1mDSgTMTdwMiX7//LIK\nQUIHt1wmxi7CKj0et/hpHHpiSbafiC0RI3w5nLGDujUgt+cJZncN492E6hogG42uazlecCktpgXT\n8eA1JhYv4qtL67mm2x2XZsdY2GwT82aPZ0ZDD2xsy+fxU15s9qhzXbEKfPHxAUXg7uW8MM5kUcIk\nmrkUMsD6jKB6rJrJUmnxVO0/HtHES3sXiTHQr/ACIJ5/WlqpLifzvqH7iFU4jJHNPS0dvI/rK3PY\nO2gUyQdEWB2c+eED3TlWuggjv09blCtzMm3ULw/1JafJRZMd4SOIDE5FmhhKTuL7Htz6ao3w6/Y1\n1yPT8NSYxuPgI58krFzoGXPh7ED0a0nIPVUZZUlqqDXR5qemB+k2cz61VaKYXmsHwQlJkCeO9bSG\ngya1kh7gVj0Yt0v9KH6ejK3tY5T1xW1WeSg+m99rDyY+FpzCd9P7pBo2RiaYNmnG/AkzaWwIl3o2\nF1zHUNetPPNPQ6txKtd/nM7GUfUJcQrFx0lKz2qxvAxSZ8ycVWCgL5iGfsOOMjw7mIlP/fjwVr/G\nZmsuDb+PurMJEd6atDc4gd02YSf6fW864qBgibtkDAAmQ83YvSmJvB9suX9GXL/6ouwC7jU14wnF\n2G1LJuq3pZj8GorboRCuTdeBQuHnmCeab+TaCXh+YzFNqtXDf2lP7pwJR1ECRYC5ZSzlaclX5mSq\n7PWA0ogkqlfT46b3X926GzNz4EHuzI/AuokhEfvESSSWBk/wj0wmO8eSK8ntRIn5Bi1tBnco4Fsn\nfxqG+VE4qiGXrAYRli7q/T0FUfn4Pu/F4BHLaL8ygHNuhzjZbDeLal4CQ+GdSd8QL+VeTE2ub5nD\n4xfQvNdkfjJxY5LTDHYHQWFTBUqqC7/I8WxsFtf13dnlOJVNczWh6J3byURPbrbrSfUffJg9Tshb\n2xMcrTYLxxoeOG8xg2pve83+ELSRIuDnwwvQcHNidOJmUjoIPCW2w5euJVM5u9YJiZINM9WPolRc\nhCRAwppRF/jr6QhhuLLWgK9XgjUe3BpYhKfNWvK+OY5YI2TfcWYYGrXCq0UJK4y/4t7PCTT6ygK9\nsa1RBvoFHwB0yny+MidTBWQ5+uY1Ff48FwbQg5PFv1G69ygDm0DPOwGkRojTkcqwrxVVk1PwOzmO\nqLvidvqvM7U1bS/Pp66nF5pqsL3nJrzPjIPkDybOa86BtsIVsRdJC3h0KYbfXfM54KqH2cIptA5y\np13rdLylzphvsREs9of4PVbi1IRqxJcsIce+BjF3U5B4ykZdO7VGcVfFTnAN0uwYdibE8cpTysf8\njfZf6UJWhj0lG26BkXB/l/2qKqTb1Kbn+KH8bj+A7amP2Z7wGKvIW0iAhEc1WLGsA12upPDlq7OC\n6XiDXg7dXPxYVPQN2Z5elJRCtv9DpKsDAOH/Lm/RYE2HeDaOgFb1IFoCxd1eQZF4VQ/SjFAGx89k\n+uJi4u8O4E7qRr48PBM/h7oAVGv7GGP7slswlbk0qkjDlmqFt9E2CmOa3UV8z7TBtvFTFE1TiXlY\nh+8s+/BQMZbaNW1o9nQd5ApuySnDYj5zLytQ0BlmdHYAxJqjlCE9m0FGQxtK7kcQLIVezdvRC1kh\njCLQuB6gCvccjhBNTfLrqlKwW4sDQpYoASAhPvgRzR/MwXP/WeYu2YKbYSekSYIHllGawnoFCTTb\ngZ1hFgsyexORB5397uH9srooEgxbqdDbxJBtv31kDj01mAaacaRYF9DS3pfrW4WpZ4wLlvB90lw8\nnTaxPrwO2IG2VjoNCzWws87B26wZqztNJXqWBnBKEA3vkVKE5i0VjLR7kpF+ijxAoWVD4u82hewX\nwsdHmdbNUtEdWIrt6a08Nx/Hoj4OtHviyqB73gTSSQQN7xJM6dxghs+tz2vvum3jJrCSHWiRjhr5\nlLX+tszJNKTQkGO75jN08CQMY/bRn30YxCugkFNK4vNScp6DeUALpnWcS3SGeB47ky6uItQarvTb\nA6niJlKAtJAANulNp0iz8UeOlmKcdACUskkIMSQNHYpQIq8wVwAldYGPOG3mh+JyzJOzygpYKFgj\nxVSA2H9FKdzyJW1mG6LWggkwN+dHQBy31ClmV5ntcJFtcRfB//0FDee+iiQ8ysMs3IyQKDvg6cdP\nUhFEh+IS3Ql0ZPOzqnkqmBRnYsE5eqgsh1lXhYv9Jwo5EqqFN63x5BSxwK8vZxBTkIusDkNYzDRb\nMFN9AE8XZ3DEZSi7fm/CgiP7USgqETx2eVACJLJ6kDJT9tX8whzODJYyeuAj1L7thwHGEFeNkkdG\nWI1z5vu+wQx2/pLojFsIWxn+FuXGnTCx/pGaBhrEnjUTJeafKMwjKuElsVk+H/k6TXCSNsFxpsQE\nK5MVnE1ecDpQUOEyhjzLR797I0Dt/QNmVbHXvUOAbUue6VpUeNyyULv2K3KLZMk0OiVKtLi369iy\n5KApV6O/xOPobUzt2mPa3YmhQYpsCJzH0+JiLnq0IUcqYCJ9Qz6kvYK0V+QnvCBzjgFnpdBl8O8i\nxP4zpT0mkQp0fmxHTPQLKBRnnvLu5S9Qu5fCzvNXuHbRDEfdONTaZ6DdWJUZxfP++QSikEs1XVD3\nfEbDlLJbgZd7B1TObweYwgfGbR4AJ8t7qn+NcrMCSu5BfON6vLjnC0IM+P5HODdDh3GqwVhUC+T4\nomZcuN0Ny6ZP+PLpeX6NmU7bhF9JixDRgvsdmtQNBGRjv75DEvDeK862xZNLzYh0ceWeXzLWD6LY\nMvgbVDMLSA+qytXBfbiYYMOVoaqiaPmQXz3a045UijdFAzVEj79p1kSSfaBZwm1APNPFCAVrkvUf\nUa/fTr7ofhHL+BiSkupwrOlO8PjzjrnKIYDBO2B1X9g62BuTpY3K9Kr/6a5ROR5X+Yl5cLmylVQ+\n0mOhLNPqy9BUC5Z1cqXt5cUY7IWCrSNRWL0Hmddx5XB+1FDmshFtYMJeM0Cs8rUcwjwhDB1YoAO8\n+6GoZOfSiGiu4lhJJdF1adlqId5/dXh1f5h+TJDIrXuuZs7NAJwsF2MfCbsiV7BZox4gtv3235EB\nzWVrHj/0WQ5L8/7xFfA/nkxlyO1835Dhw15F2Pu6b2MRMBIqM5EC3H/hw6A3vSQrsz2h/FqR8QS7\nMTdxi/oO2v25IZB6RKhwN3nxQSy1VADmywZBGrl8XolURssTq6nOAS5NtaKs3ef+A8lUjhw55aVk\n2zmW0BMI+tOx3G1lnyf8r3JzXCbQE/zL3opPbvUs1yHX8R/TUdFa5DrKpkVu9SxHjhw5FYDcnVSO\nHDlyKgC5O6lch1zHf0xHRWuR6yibFrk7abn5Kx3zYV4w3DNl55xveel8DSUkuLUMgOs+IuooL3Id\n7yOejjnF+dRTXEEcMJ0AXvcdrRgd5dPy18h1/BlB3EkV+WarKs2n/0hzF9jcZiu7eyZTGdXzQWM9\nuXYpg99rz8Tnkohb03Q1aD2pgA0hCjy6ADYZcM4ZVAElStmstYBZNdqRFfWxzvMVhTb6lto4KntT\ntU1VctDA/3IbMtJ0IDsdCiq3NOqjKGmAki7kCVxoqWbPIKutqKqAqi6QB+hBxHVD/KWVsyOsdZ9i\nrDVf0UZxB7cGNOOSQx+YXQnmi58VasyZGctkyRbWp7Ri48WOZL8oqmxR5eJfJlMT6sxcjZmmLR7X\nrAm9rIpYW0n/hAIUPc8mo4UxYja5xVQHk4dBnD8lezNfIatmlMgkkRd4Cd0cC7KoJpyGvt1wudoX\nU4Vw1ENUKECZemm3KMpVo2mtPGbv2cALZ/H7FvwdxiW6DJp5nY2LagoaJ1BnEQEJwUiUQCUVKAKJ\nFOxdHfBfUgnJdEMPert/gV54Jj9MDCDx93MknSySCft/iwM3Zk7FICqSvnqX+F5xcWUL+iT+1QKU\nTb90TOpl0r/rIbZJJ+K/ZwQoCG8F8VcUAjnC9fr9OLEF1GmjzEubJqhZNaHtTnMk1lUpQrYDv0Ca\nT8CJLcJq8M6nW60SJCkQ6TGV2lWNUDcqwcEomIKYMCY5d8R9QjAqja2E1VFmNBlzIoCvbh8WOI4q\nD+OD0S1VJ29zfwyPNOC51yhGu8UyYYn4PmHmE2tybMsgTKsWcMr5e8I2nyYprBgKRB6AKFalSfVE\n5p18hb/SQlYO2U2tXzpiUcsJ6/pimj9q4GTyBWdXrEP1eSlWhb5k1a7Kw8ryHrQ0oKFhEWbTexEa\ntI5wi4UsOJpU5pf/q5FpU/UEnumMgG1eAGx6MZkBw5I4uksdELcLTHQrU/CIEzUmAGlSlk02hP4L\nZR28XMPpMv4l9oYX0fV/RClwfQMwuhVsF8qKwYu+mktw1knkjKMpyyXVqdpPm2+khykqbMRkveVc\n+NWHXms6cHSaQBI+4OstqSjOSWJ/ii3vO3Mq0dxNmzZTtnG2+yQQ8u62lTNJ/hCr3xmPsS3IkTb4\n44DYjqUqfOP+ilrbrlDYr5jfLrniHWCGyAZhoNaIVfob8bIYgFvOFnR3KLBv81AaZIazfnprInLB\n+BtNhhXsgyfCWmFLNJWwb1rA9VodaBazmzvHUsAhl5/W9+GMZl3yk8XMqC1pPeceDZ/dptfZfVSN\nXsoYF3c6t97L93N/ZUEZ51r/xci0M2tPr+LM6bdNkGcNWIPZ/hRQMPr0034iFqUi3tp/jGPBcDUY\n8nM4t16fcP8qKCPb3xvpA7StJ2h46bUHnElNBO5CqZTMY5F4XuuJs3Iy56LBEHgwzUBQDe/iNWAh\n6gNyUbf/oJF4lcaMWu2Bv04NDuYKe5utNbSQPKAwXJkcqSKgCw0nCxrzo3zdCUfXX2hr4MeqI254\nB+ggeiJFkY0bNpIVe41hNyfR5pwqrY5fxH18LSbO6Eav3Diu9Hcn5mAWLguE9aIC0FjjwneX56KS\nl8OdzY+hRMqUwlASY1OoNUWTokyR6t9Ne1Bs3Ymvl85gbut9dLHPouWRnQTciWNtFVdMfyq7o+8n\nJ9MG/mpsa1uPDOt3PqB5C2mxJBkDc3G73QNi2X2XmSojWlOIbCasGOgj/cu2EhWPYR2aDc5hvNpL\ndNI9UQIO999IhK5YXaPUGX/Rk7xcNYqK3r/5mdRzL4WqwRSXtCBya9kaSHwqi1bMAaAOv+FXfwoB\nc6cQ11AfqgjXWf9D1Ex1mPd0OUbAnqxFBMd8+OFUBvQEVqGIes8GhLkXo6AmIXpRB7RNZgG+f5gQ\nSIAbNNC7RFs78Jzb/e9PVwH073uYa1+Nhb1/OIA2asKwVrNRqzqUea6DBI8PgIo2czWXcT4Gbo5Z\ni8mSfXDzIK06hNGyuylbq65Ee55XmU/3ycn0dGlP8NUnJvLdOVIJFw43QV+lyaee9j/DvhpDKUF2\nrZYCjZeXzzb2k6k+lgMKK+l3fxcN85YSmyZ7uFueBzZq4rRi6OwmpcPIH2mYkUrh43cS5v6xmN+4\nSM4v7Vka4ozQiy4B3zijCBTO/pavuwWiWEeVCwcgYOBiQJzJ9fEbw3F+cY4lPwSwz1/lfYPFPmNZ\nvSOUwH4beb+jVcUyZVkwI+7NwyzEH6czNfHc7Pj+E+o24pLlJhruOsqyjXvgWQHl8T76FEqA+mZh\nb35ek7CUm+e/ZlxuW0RbQFatQmS2AV3qQbRbDbzXr+es6Rp6Pd9F79PjUFjzAMPnZXeF+LRkqtyT\nsNaxKHUEit6fPFd8cZMqGuLdTgKgokFeqjLGWtDES/hu4WVBXzkJFWST0opAZtnnsf8VQR6OBCop\nE/NQjab7jGnX3ZhYjSZwOgzPVSuEDa5qQP/2MDx8HYmGYJQlpYlqNF84pOEdfJLrg6tTt3kp2xYO\nBNKE1QIcWGaER9BlZiyvS+wqHxzmr+VSh+Fc2B3ECHcRuv1rODJ+tScJM2oQsfY0spJBfbAxxviH\nZhw/Xp2sTbe5dvYFM05dEkSCVmNVSu4kYBn/CMn6HnT7YiTJie82EFdicaYfD19c5cqAGZweFA+t\n60ENYfu8hj/UQ2HvTTbfDGbtgEAUzSP5znkwxYUvBY37HpmvsGyXxNwiS7r0G8+VmcVESyIoeiZF\nXwECv/yRh7plv04+bahiBHdi4EkHW/jtg2Ov64LEpKEtl/wMUDHN5YFVyf+1d95RUV1fG36G3oug\nIEpRUEFFpFnB3hIr2COxi2iKxvIzYkFULLFrggrW2JIoWGKJPdYo1YKAiiKIFCkC0tt8f4zGmgSQ\nueOXzLMWC7iXuftdM8yec8/ZZ7+QJHD899Dj5Oo3RqZKs5vBUunHdeo9Edo4QlYM6z1e3FIaOnC5\nrD8Gn0vviVFyscQ95w6ehcs5qzOCTLMcLJz0uTRjM7kzy/jREXSB1XHLiX2Q8Y/XqykeOV187Zcs\nSjrXBTGYkAxStnDxtv6NQwXG/Hxz4J/H6no70vbuDrrp7GUPm7BfG4nGwB3EfdsUqPnnRSmtnHZu\npaS5ujJ/oyvw5jSD1/cJ2Hy3haBFi9i3yQRSEyUOOFImqtdd8G5LSduj1NaFgMHfwBbhm0P7/dQX\n03GdMSpPIyx7BkFFtciMA8OArqzd3gAyK7+oXfP3fflm0nDl+Hu0IU0FRKr1uK7XCqE8hv4KR7tk\nRpifJuqSZL50jD6Y3N4BCGQyeO2t/pAZBVwF+ivwPqPOGkBMiP5IrlyGZSHbOdMqA8iDq+C93Jvk\nwIUYTIAuXcvh7EJpCKgkZfysPRBblqHmpYHE2lh68/vtI4I5oVaP8ERnIAJw5Ijd14SvDad0hCcO\neFHeEfovhcmztZBGMs1KLWPYgpdzoG8mUisfI1yn+REw9mtOrVaGrMQaj/9XFBcpE6w1koFc5WkO\nXN3yPsdjAShP5HEAPHZpw+8p3bn0AFpuqkXf8e2BqlUHVXvOVAykYPzmQT0H7ObGkptzr7qXrRaf\n6+7ATu0mDik3GBexQ8rRzGCgAwx0wEYvAWfCcSYcJyJo9LkGDHSg2aAWPPtFsrSgBIj2A0cFSqRv\noeJSmx82S0qBDvsOl1qcsAQHVrQL4kyrR8Cr3V4mAzqy939w0cdLxon0JZLbprAW+mCk9g9/+2Gc\n7jSApkY3mDz3V1ovqMfM4TsJGxpOTgGUuAfg0AQyhw2h7Sp/4JEUlbx7u6ggaor798d56mFKVoYi\nZL22a1GAhXSl1rbY7DyA8Xh9dO2V+dV/PSJH4auAXuLRIYio0kgszPWZv2cM1bm9rt7INC2acZ1A\nZ80pTtHzxUErQo0WErPLkKRnwn3CARDdCuKDeWajx4PmFhAlxVgT7Jh+dSJlgGFRDBoUUMGLVfvb\nt8gqNaR+9F1SSiTJVASM2icb2xBRE33mfHKf4onn0XHTZdXP1kin/leE562+vN2RXN2tPp+zGGdD\nS5attwWeSiF2FTGQ1C+218nkSHTly16qw9qEjtxx3sjzGVuwZAsgGRuO7AhLR3iz1zud8N+bQ4bw\nz8sfnfz4o1ERG24O4sH1tzba3DgNJc+lGr97YD7DXc7wldJVPNKnkDE5FMVFjSgLT5Nq3PdTH4+Y\njUQnVhCwcxWRox5TnfdJ9ZJp6QNM9DL5tngCtraliAtFzCwORqSWzcjY5VAs7NbFpNYicuNB7VkJ\nuvF5SHOltn+gJ3WIoBzJB3jpi+/lgOKNBOqQQCGvPtd0untw6kDOX1ytBtDVwMoijUclFhioZaBM\nKfqRmeSscOdInd6EjionzqEpqTY9uH9QHSHrGwtvG2Bb24iO99cClS8xqRGUtPlu1u94NDzKhNSN\nJC59TKmZGl/5LUMMRPTXh8tSfF0A4mPoqBbBELepqB68R6P2sN56D9O3FsCFMCRztgLbuIgUGD/r\nHr8tv01i9P94kKbOO0PRPOmXNl4KtWOMqgj9gEcUbtLD8gSIk2WzpbZ2RB9iHCbgtLsJ0zyq755b\n/TnTQ7upN76INc+WYPuJBmt+cWXUqamA8HvAUx+Ana0ZT0W1OajijjQ9ZW7SgnTq/rm49HIasuLF\nz6IXP7/cm6/4XAlypLfg0lo/g2mO+zia14UmetFoUkDDyLvEzwwg3UKTGy6e7GxqT96SBwheKB73\nEI/4IQieSAGUISdYxLYYaM8kxuhCQTM98h9kE7ZpCdu+MwKkfweVEXMY/5jOQGdJLfSVi//0EKmi\nKdKlwfe36GSrTPsCZ0iTzfpC6Rf72eq4jB8+/YxzaXCt3UTKN8bKQEkLgq/251dbVwZ8N5XK+j29\njw9YgMriqy1OgBP8aWQo3Crt68T8UUBPRr/4TbrmXI8w51FVrHmlvEBp1yCaknMptHgEoK+kAAAe\nRElEQVS0B5Akcvvpxtxb4U53hdnwaAtcllXXqOdQflM2oQuf4xfTDeX0H5mmtB73WT4EhD/nyvFt\n7O6ajBCJ9GNkbEomjYxu0z66BMoEqn1+D8VFIk5eKeAk8yUHrooQolzubTw2neWKVwgN+/cj6/CH\nlVV+4Gr+x+T2+DFpEY6A804E8GYR9vRVwCrgxTzdf5csSmuvZTmw/OX+6q7Vv437/48zneoPYobZ\nVkiUXSJ9E9m+b10XXyN/vDte50bwIaNSkLuTypHzHyKGKeVjSMqRpd32x8WSNFdSIy3g4YclUpC7\nk8p1yHX863TUtBa5jsppkbuTypEjR04NIHcnlSNHjpwaQJ5M5ciRI6cGkFs9y3XIdfzLdNS0FrmO\nymmRWz1XGbmON/l/pMOqLjPOpDOnyXxarD3A40lRvLttUMo61niyxb8e1636EHjC8W/+8GOxNpbr\neJf3a/lX3ea7h2qz2cMfanURNrChGe5aJgR4bMAXX84tWsPU7lHUGl/5xrIfjgLYWTIUbRZ6HGAZ\nvmzS9mXFZw+ABgLq+HhpKiqm7aSVhCiLmbd/KpaO9QSNr22tQ9Dx9tR5uT3uP4xIzZxOA5/hRDhO\nhCMS2DNOGtTYS6o8wxGdawPQuTaAPWvPEeoZwPA9aoBrTYX4R0RA7u50MBUuJqjg3eACnfMmQt1W\nbPW9SrClJ59eC2Jy5GpwcRFEhY6NEqPanaYHMxhW+yHHJ+xj/9r1GO/dzcAQ4e23JxDA6pYBWBsL\n1BW7EmzW8CertQVzSrbhkPOYHb1WAcI1Mu+tH4VCTDLfdgrlYNZoweJ+dDTowEWrlfSJ2cVAjjKQ\noyhojZW1KgBsDNMJ/aF6hosfXrRf15DBWnoM+aEfd1dKDuVqQERdGBM4m9juYUSe/uAolUKTPLrZ\nAjelu6X0TURs7DGEZ15LYdwO4CTfY85z02nYhK/mOx1//ocrIN2mGrnlWph1U2dczBNYcwqIpX5E\nKU8mNGWYUzBBtJJq/JcoaYgx7GmHycEUzGupQ+prJ0Vi5m0M4dApD24H3xdEz+tEFd0nqK4fYSUJ\nfNtxM/1XTwEtd8gTxq30uX4DmvkpEd3lqCDxXkdRRRHjEa1Y8eVCGm2O51xgCrXMoegR1N/ozLCt\ncygOk64jKYCSswU/OG4gZts90GqKpq454pwbaFqlkXtD6uH/EnXNFhxdNgetH3M4+kX1XI4/bGRq\naUZ32xQGPvwcZX043GATh1nIFa2vedLVhYx2EG7r9M/XqRHa4bFsBylDawNC/rMW88wvFMYF8Kor\ndgZBFqb06wJxdqVomglgW3svi0UD68LvAbzsjVkn15BmT2JYnfSt9OO/oLZGDp4xnvT5DNYmDiU2\ntfarky+WANyDPQTT8zqRLecy6NpBoDcXTiiwtelmKP7wnS+VQ8yQfnsI15Ge19PfYVw/h+UM59iF\nVnx13IfD9f0JtezNKQ1/cs7cZdnuwwKosGCD3UnMth3ikqk/MwzmMdNwLrbdYd717QLE/yv64Ge4\nFvG9WGa3GYZZK2BBnypf5YOS6fiiUoacXUKeBRxv5kZofAqhlLP7qT7HTtugqwq+qz8kQhVY35xb\nQY9YkLpIoIB/T96lbKybg57NM9QMC//5AVLg6SJXDH4T84eZMOZxAKHj1yGKzcS5UTmX4t5tCCPy\n+k0wLW/zS5wecUqalI9rTWmMHTfCH0KpsFYZq5guaLyXpFMHtV6u7JnWhmtJKVx9nEbAWSd+LUjj\naZvmfKa/S/oiTJ3I3PITGd83ZteDDLhfRHFCNH5l85mStVn68f+CPeWLedhTmUFXl5BzIY/ajYCh\nf7c4+H6qmUzNGL/ehHZPvNBVhvjaMwg4/eaiT9a4kdw+BzqpX1QvRBXZYzYEQ0WwDhDQkOtv0YU0\nyD9dn8ybOgLH1mSAax0OLnBmUcc5vGvUJT0CX3r2+b7bVd9+fAUioDxstGB6XqeefT4Wprms2SrG\n5pO7oKrxzw+qQdr8cpfs0bqCxnxJycMCBo0ZDcS856wwzUaafXKT5hZQ4XmPyKHeRHb+nPX+x+ms\nuZeYbtB8sBLUd2B1ywdEjvClaycdNGopSlWTy2d63FM8TLaSLdnhxR90rWokUxHe407gtGwiT4FA\nlx/xu27Om+33LNn2cCwtFWF6pzUfJLCyNIl6iJIyqGrKqOXbW2h7W7HtKBz3cINyYVuLudXSYETG\ndApt6xH6u4qgsf9kwoR3DvUOELr5rxrQgrlHnuBTPwD3yE0UrL9DOWD7+BLoCJjY7DxJ0tdBpfyc\ncDHfpuD97w01ilmsMFfq4e8EZLBAaTpztoViNBiM2sGgR9dwvBvHsTswNGYzX+YuROX2bn7bA8vv\nz0ZHv5MUFRlicSGMCcdg948v7qLigWq+RFVOpvYNrWj16AAZyaDX04PTZ3NA/Hoi1SB0x1xCtt1n\n6ZDNECtMq68cLW0KrRUp6Ctdi9rKocTihOVQ34H4RSUIYqoDGJkW0DPMgqniL0mIKUKECFPV56Bk\n/M8PriFyVAxQ1wSfwPqcDvyJueGJGLu3ZdCcJBQVJMPWzIza/3CVD0WPNnYWBJ04zEYG0tRrL90K\noVu5iOLTY1ACKgR20TVYokyIgRnZ6oqgooz+8LakmPiysnYSKMnO+6ilTx/qLQhjp+sgAaI940ac\nFkljj2IyyAcTPx9Mlizgk/SrDMgxRzEqiXq54eg2V2fDjFDcWvxOoUiKHkSWIG4OJ406YpwXi3Gn\ntuzqFkh06j8/9H1ULZmKXOn+MIjHZ6HZJmP2xGq8lUjBVacuSTMLaGJhxr3oWtVTVQ2y9XVRLyhH\nI+PDhuo1gZm6Ci1PPWV80wsI6f00eu5dvu86hqRZTbD70oCCA0ks1liJm0okDUZI1zzuJbtVxpAx\n6RMALk+4i6LjdiYG96KZ31YAzKzg/KK60hWh3h+fB2Mw3HKNE2038bPpULqLPOl4YxzzXMdJN/Zf\nsOOToZSgQgWKmBg5svjSVHYmw9OKAlAWvnTtJdfLWmNgB0oJO2WkQAx58eycXu9PW/SgmzYkrzzK\n4xMnyYmToifUgwwSzynjP3U4rdfUol7nRywJm03TtkA1KpCqUBplRqj1V5yMiaHdMHD08uTNj3Zr\neNyf0Ume9F+VgUJuEpwSzpHT/HESR7QtOVGvB5ApWNz3UdapMz79xDBppaBxl09syXJawsvF+6Y+\nbPbywuXrAHoeH4wX7ZGmtTFARp4mK1e2ApxfHaxrQp+64ThGBHIwzoHYuGykOixUVSVTEQ7ensGR\neymAmfRiVZK2/EEEzQD4OWg8Gp2ecBp4vlgRJsmwv2gUrDeaTpaSASCDhVJFRT63v4v6lqvYIBl6\niBo6Qpoe5Et/euxSqTlcTiH8siqQAi0yJHtcvq76jqtKj0z12iiyNxfUNcD7uS+SN4MOtLej//h8\n1g7252gzDZ5vyEIn/5qgiRQ1R+4sqktJsSp5OVrCxQWUDVVpOs4cp3H1aNu7Hu5qZZxr0IpGW2Th\nrSN68yt6IRMDJkpO2T8HhBy1v9KhXpqMQUGYZOThYorU76+zb1NcawBjbFcy3iQPdRdjXjhy4eJ+\nHYDW9inwVLgk5hy4D9dfIvi0x3MutHrC8QJQHN+VjTvdBNPwLsaQroD9kavwXDYVJxbj6tPZaCWO\n7pCzaACKyoBWBijK5g6zIAos21XvjrrSI9NP1U/QSDmOolL49so65qqpkl+uxrnH+tQ3esZWz/Uo\nRa4i/WgIeXlnqiWmurTtd4mMqESK7umQ/ShX0NhNtJKZ/2AZLs653L8Ht4rg8BYY6HGcwPBrwC9I\njKBlQH1DOjYMpjQKdgzqAeek7Mb5F+gVZGCeITFLE3s0h8vSjvgHsw26s0UpmY7PV+HcojHbVi7k\nulsmjZOvIwZWaU9A2n5hrxM/8Q5nh83G9aelFIsgb44bi070gnCh6lzfQ5sm7AsToaKL4CapL3Fb\nl0iyegEbB15l9qUhJJeC5a0EQDb1uNlL3fgmx4zqvGcrPTINMXWiyNaEglK4kp3FhaIUbpbGo6Ke\nhr5iY272ekp43DESc9OhQtg5oD9y25NbbIa9zkNGdVUXNLZqejHug3IxPb2BQFtvWho34bvToTzu\n0oqwZZ2gfVvBtJg2tqNpSBvcF+fS3LAtvyzdzJjI5XTzacq1ySaC6XibcRcKEGUUIrY35riXME45\nT689pd/NJdisrM+jX4oZOGkK0Qbf0uXMj9jrOpC6V9iyKCjD7yd9bNuZ4x98nhmLe5MTniywhjcZ\nZHeYzIoygmf3l5kGTfJRAlKGKKBzLwljSzg0Rzbz2gA3dT6l4ED1FrEr/Z8dd1KNy86debKyJapq\nJSAGhSXPeNJMg/v79ZCp2+NvjzH2NiFj9wPuKgg7+kozrsNPl5vj7nidCZkJuOFHZsejTKAN0AYh\nra8bfxHDquA5RGwB/YI1KG4yZefQLxnt68D76wuFpSJ8jLANPmIv4TRxMno2qlg6Z/FreiY6UY85\npjURnghvSQ75LLndmlvn0wBZ10MrItqhA2Vi4oOMkdQEyQ6voW2I7F6Ph7p2xPnJaJhs58rFtYaI\n66YBVZ8urPwwIS2D4KPmcPT1BQwVCC5DVhbPr8hg0hIHwAEShfWGT3qghMcDdwD20xCQjQ85wNkp\nJbR8aZ0LL3zaRcBDWUkCwCt4B4GAn+9i4N1ifulSTHZMMeExSoCR5OuJkL0b3iT4tg0fwwebsmop\nPWftI2chPFWwQlbJdKWeL6cmr6B4dWu6D/kBDhxFVtNiHbckcnK0OVfqdAAiqvz4f5E7qSwtYz8m\nm+mPSYsEr6UO3Grh8d5dUXJkhBjqmYgRNxwLV6/ITEZR8XI6+PuAP8CvMtMB4LJ7F8SbwZ3qDQ7/\nRclUzsfKUfrCLdksfsl5P6WlCkzaOIiEJAvebO3138VvXYcXP1VvylJu9SzXIdfxL9NR01rkOiqn\nRW71LEeOHDk1wH/cPEGOHDlyagZ5MpUjR46cGkBu9SzXIdfxL9NR01rkOiqnRW71XGXkOt5EruNN\nKqnDoS9LI5yYPTcUFr/PZudjsTaW63gXKVg9W6tr0FEhnNaGUbQYLwaaAnofcskPRBkX63Rcxgu7\nP//jQw16NGNE2wz81l/jO3xZMeoymioOshaGdUdDfMy302OJkDbYf4ceIOwWZENdJfZoT8HODIiU\nYou5/0eYjdXlzPxt5O30w0VZljmk+lQ/mR4byYZaP+AmPsrggkMMiwxkKku45LURq24da1Bi5TGx\nyWe46zl+Xv2jwJEtgJGg6wnangLHfpsJ/LT1GD7ZSxgU8wNZT63YHXKENufP4rpd9vWExk6FaCcl\n0qy7DBwRGhiieXowBqGfMu94EqH+AXzNBn783F9AEW4c6bEOixtXmNc3BI5VfafNvw9HBt3YxMSS\nNbjaXsSiSxjU0RZWQqdmLDS/xDcEcPLLAH4MOQP6VbNpr1YyNZ1qR4qXJbeeJFIuBnFBOcrhKZhy\nk5BN9/jfmS40+aILEtsI4TBMhcY/x1JvpxDb9USAIe2a6XDqm/WkKloSoG7OdrEpd/134DC8Hc3t\nnwIC2oZ80pdj1CdzwmW0nyeS13oIq064crvVXXTsYceGiUDVjcKqi66NKii/PpMkZlSdHQztqsAa\nZ6EdSrXo3kuPdT2b4+3ZCY05+9mx3ACFRgoYnhamPFDZUJELs3pz7W4Z8/ruIvyHY4AAzrXvoIWO\nvRq69mrQwBhQBm3h3Bjepu7wPGYdz+TBstuUp4kE8qV4iQgDB2u+b7eAWmoXsLJP548rkNfqChta\nzgcq70FV9R1Qyrp4Bu9i52NQBqy8tXlsZkMqxhiLUymeFEIW4B0znlkuU0m9XFTlENXF85tYMp5Y\nwlfSt401clXFOeE+iwsOMfOSNw2UfqXARB3l1DKiVucR0Hgoic4FBBVPZU+0ISBAz4CnuSzqsgmD\ndMhtksalA02AGGZtTCPGB7aO8YZrQu0L787q5j7MShxBRmmC5FCrPtT+YSELv5sPp04IpAOgFt59\nonDcOJO7eiMIcVYkDy2u3ndFUzObYxdzgQKpq+g6wJB7O5+j9mVjzq0TMIl2d2CT8kJJr4YKKCkx\n4ZFzIWLElMY3pFl6LCk6TTARRYIIclpq811cGzKfCGABpKRAp/RzeJTsgHZZzPLtRZRNJzj5XPqx\nAcztGOu0isJLBcR+4kZ+gYhommJ70xgH8TXQawjZ9yt1qSon0xYt8rBxOM7tQNBW86T/EnsgBUQi\nRIjZsTGH+El3ESfep2/DEwQKNhKyxO2iL/sGt0WIN0ZaeDN8TPZTqlCb02F1AafXeiOIWfV4KRUd\nnCH6O24Tyi3et8hQw4Rf4E/j4tsAMTC9D/0nOXMVCPZ3R6j9z8blxqQrnkXVZTlcliTT8qBWbDeF\n37/oJ5gOAIWRjcm8uY10a4hvosWhgJejsFgKgHQhROydj/tnivSZDHXn/g+4I0RUatkpMl/Pl/z9\nkn/OUkCZcOoFSMbEipylANDNu0QuknGY0gVQwRpJYxgpo6BAchMTLpn1YHrb9Qx/koPCNTvpx33B\nSO+T6E68jLa+I1svNfvz+C1RKprDFIjzG4lV+3mVulaVb/OzenxCQmAx6oCifTiSfb0iEINYLOLI\nF1Oow4ssLWAV64CluQRFwvn4TsIELAjH+cEKnO8NQpJFVXl1SyCC4qMonPYhxGMQmzo4g521MLpe\nw9DJljX7pnEVyBj1NUliIbolqeMyxJL5ila00AGjEIkful3Heiw1BRVPfRTNhZzTVmd+sj9Nom6S\nEQsDL24m9CtfmipYAcL1NJ1ks44Ka3BRO84biVTREETN/vJxH4rqzSeU7o9AUVsRfUdrahk7oO3l\nQHzAZxQP6o62gYPka9joPx+jo96QCmWB/NtEFQxweYaiwlFatNpP+5krELJZj+3DaADEDZSRJCwN\nzFxqMV7FhAFztnMmvF+lr1X1kWnnMFgqMb76uY87/FHyxvmcaYXkrwRhneJrMXL5asIsLbmZaYxg\nbcPFF9kWep7T9GDaLxt5rqZGZj9tHuRY8e23iyBsL0/iVYlJBOZ1AM9YQWRZzO6C58EpKCT/gurz\nGNymgPXBOpAn/WbENkvbMH39QGKBi23ciFX0hnQVxunOIRc4HdqLhymWSF4jTcASkFa3eQOOhGzl\nzqAYmpXDr0emMSZiODMWONPDbRdJMV3IFeAlmfG/y/RxO8v1YneSAxJA2xUm2RBq4kRFmCaiMi1a\n/bQauFDjsTNqNWbdgFBEe/LISThNboYybHp5thEACkOaMPbuZiyAQRrg6zOYtNnqCNEKT4VSRmbu\nwlXvCuHbI9A65AHkST3uS1amz+ArLmKUH0XooscQokRUjBIVZQ94pOnG1AB3IK5S16pyMt16axJ7\ngArgvpYVEP3G+VLTCsqAR5r1uVK7LVDy7kVqGMO+2ly7q0JhngFJgcJ4MOgYltAjQImSDmE0LUwm\nPsyIsi9S+GNRERqm+YSUNaHOZdh2fCC1lgD7pS4JgIquvny/1BdVZXhs2RylFAP4KYucgp4gxakG\nTcPGHNywlCfDfYlGMkbXO3UQbw6ixKuJF7fa+xjrfx1r34dQDPYLkkjtKI1kKsK0hRmZrW5SpmfI\n9gNe/NRPEThKUeFUGqmvpY6RN7mcl0Ls11BRpu7WYqIyIXyZNWbfi/l5QTeuz4Ifa9tjWJqBZmIc\nU3ftZ+3ndWo8fGlWLknbXrzuxcrvnFduqc+EqBAaRV+gwSgtumT9TvKsXxGqp2i5dT1W3HbAvO45\n7j6cyekuwiVSgLRtsUSErcSgzU7OLFWipXoRRpp3eaoDk/tPhg2V99ipcjL9qdFAxAQhBr6JWM90\nur121pIhBf5kA6WpRkSLbRHCZ8c95SzWz2NZM/kozAuVejyA3kZxzPY+iI/mZg4WloBTKpIlOSUM\nzWpzSzUOv2lL0QgJIgn4VmkBh1y0ib0s3ZIPr9w+PBnggMnpG+yy9aJP/EFIC4QuwDnpxTV2T+bi\niueoA806gPmLO9cKMVzZBM+BT1qCghaw8SF3bbTYnDKRuns2kVqFFdN/pI4r5OVDQQKfuv6B6BaU\nfd2BnyboA5L64zlhq1jDWsrTpD8sNTMpQMM8g27D1dh2piebzAeS6m9MqFt3tjwbjKduMm2DvXCa\ndYi1CFtWp9CrAVPNT+G0fy+WtWBK7lKSfxVgbv8FJoO1cRaHYbH1HJGDP2XzHQ2ENX0EyCfYCYLV\n54GWGo2a1mN6ahvEgaOgZ0iVrlTlZLrq2iy+IQglQPOsCaAPPAPzPpSfdWatlWRUEvPrQGglTEfz\nG2FNJD3uI+0BYZLpvTsFVIRqcND59d6HpUApGVeK6T8khWvnoNUsFywKEojYEMjQxuCSm0a+jvTq\nGgNCHV88BfZw8HFVKjs+iAcBoBLahTknNsH8g3DxxR2LgScHqE8c4FhUDsHrgKIXXzU9NybmO18f\nXCddobSREx3C57GZZFi4n1d3K5aUf6bIQqDIxwl8pbsQpvMoHf1HsVjmw5ywjniH7uEP57sQ4QQ9\ne+N1WZEr2fDp4VPQ8YBUtbyBsiJdaj/EePNe8qeb0mW7B7kHBXTM0NeguUYSJ7/dwBTTrUxeI+MN\nJYV3oBB+39WP1Jng2NMJeFqlS1R5iUj3yH3qW0tuF/Kb36G+pRo6NrasKJ/GWqsXf9OzNb+0kr1X\nuTTRG9qQQi8Fps+5z8jGSZKDVo0ZYPOEoDmryYyM49nmvnRZ8Q0NN4xluv0R4kzM2f9pDa+QmtSD\nTg6A6TunDE3ysXaOxbYRkCzt5sx5zHc2g/lLkNhyiEBUm+GTlhAHaMx2gdiFQA6S0Yd0FhmUMsq4\nYAC3Sm4woddRxqt70m98BP3GP8L9szj2eMxl4WMw7u1A+Xbpl2cVD/HkOfBdGBTqQ71+0cwPCOdC\nr89YdlKRUH0tHq5xp0DARKrSxpB+S9XpsmshhWNtWZswmtwsAeuhAfu+nVhQdA1xSz86DwJFgT70\n/5YGnhzrBb/Yj6WqiRSqMTJ9kPyUKP1+KBKExtlo1u/YjO71IsLWx1ABPHFyY+Pt7kDlarNqAjHC\nu8acP9uSlM2/c0A8Ff276VjvETPe4DAXEnsz09+d7KwIsiaKeLm4khB5gxl6QzDUza5RHaO/jEQv\nMIQRPQrwUxjAod9evSncViRiMuESxpGNwLJqtyw1QVMLDRou3oGyjQa/PnUSIKIIv3Xd0Cz8nEaz\ntZmwbBTZpZAfKdkuqlBawZO4YiYEQbvhnjwtkX5R1OPzsSQZDkA94xBJz8AFP+pOgehFvbm30IEL\nw5rxeE6U1HW8QpmvNeKwnLmOhrOaMu738SRdz0ayCiIcQcsGs/7L1pJfLJAM62Sxf+E1Jq1bi8Ik\nyIquXm18lZNp0bNsCimnmSpkFUP8iEgqkDwXRg5NmdX4CwgT1vlRBIyzgbnBws33VGQk4j7QHThG\nAsABmIEJcOMvHlFOQrYGCdk1WY4jpnWtKEoSwrkYDwpm7pjt68v+J/0x2/aEmDFKZC4yx85yPpVd\nkaxJWn0Vjvq0ApQVHDm9uw1CGMllZiiRSTqJM7I4yw5Y151LTdqikVjCrRaNmbh8A/8beAxIkboW\ngKL0OEpUc8iiLqrhA9i6ezLP1pTDjIMv/iIGweZiAIvfhtB5VmNM7QxosXYZFMvGXFCfbDAWY2vV\nm32rtiGWtXeZqiNabgtItrHh5/pdIbTq1iXV8oBaqd2Xyz8kcf2zMECy7KIxwowvNAMg4HR1LvlB\nmNbKoqwAo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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe55825ef28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, axarr = plt.subplots(10, 10)\n", "for row in range(10):\n", " for column in range(10):\n", " entry = train_data[train_data['label']==column].iloc[row].drop('label').as_matrix()\n", " axarr[row, column].imshow(entry.reshape([28, 28]))\n", " axarr[row, column].get_xaxis().set_visible(False)\n", " axarr[row, column].get_yaxis().set_visible(False)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "652255c7-749b-ae95-e959-a38da0326896" }, "source": [] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "713c8059-97e4-3502-1e23-24f8cc7e4fa6" }, "outputs": [], "source": [ "# input tensor\n", "x = tf.placeholder(tf.float32, [None, 784])\n", "\n", "# weights (w) and biases (s) \n", "W = tf.Variable(tf.zeros([784, 10]))\n", "b = tf.Variable(tf.zeros([10]))\n", "\n", "# model output (y)\n", "y = tf.nn.softmax(tf.matmul(x, W) + b)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "524a701d-6c13-5ec0-7146-8a98817cc25c", "collapsed": true }, "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "a72c174e-d5ed-6696-05c7-a0e2a3a77dd6" }, "outputs": [], "source": [ "# target\n", "y_ = tf.placeholder(tf.float32, [None, 10])\n", "# cross entropy\n", "cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "cbafb893-3ea4-1c80-9b9a-67bc2c599591" }, "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "b544a343-db20-e087-adde-f8993f49f594", "collapsed": true }, "outputs": [], "source": [ "train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n", "correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n", "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "4a1ee75d-d9d9-d759-2f64-188959a7493c" }, "source": [ "In order to train teh model we first need to initialize all variables. Second we will iterate over many epochs and evaluate the model using a train and test set. Finally, to keep this trained model for further use, we can just save the session." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "6ec9b6ad-ff9c-e262-4281-016f2efa831c" }, "outputs": [], "source": [ "train_val_ratio = 0.7\n", "train_data_size = len(train_data)\n", "train_set = train_data[:int(train_data_size*train_val_ratio)]\n", "val_set = train_data[int(train_data_size*train_val_ratio)+1:]\n", "\n", "init = tf.initialize_all_variables()\n", "saver = tf.train.Saver()\n", "sess = tf.Session()\n", "sess.run(init)\n", "\n", "train_eval_list = []\n", "val_eval_list = []\n", "for i in range(1000):\n", " batch = train_set.sample(frac=0.1)\n", " batch_xs = batch.drop('label', axis=1).as_matrix()/255.0\n", " batch_ys = pd.get_dummies(batch['label']).as_matrix()\n", " val_xs = val_set.drop('label', axis=1).as_matrix()/255.0\n", " val_ys = pd.get_dummies(val_set['label']).as_matrix()\n", "\n", " sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})\n", " \n", " train_eval = sess.run(accuracy, feed_dict={x: batch_xs, y_: batch_ys})\n", " val_eval = sess.run(accuracy, feed_dict={x: val_xs, y_: val_ys})\n", " \n", " train_eval_list.append(train_eval)\n", " val_eval_list.append(val_eval)\n", "\n", "saver.save(sess, \"logistic_regression.ckpt\")\n", "sess.close()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "735184ef-844e-2f74-df48-b9a7c7c2022e" }, "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "2b828202-8f64-c49c-12b4-7ef7aeaac4f2" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7fe523569898>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JqKjiQc7bdjBjBpx+ups+4YTi1UtVxeTJ7mVGxpjQqAo94B8FnhWRX4DFwEKg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\nVwpVcQFkZmbSsGFDIiMjmT9/Pm+99Va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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe5237a1278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(train_eval_list, label='Train set')\n", "plt.plot(val_eval_list, label='Validation set')\n", "plt.xlabel('Epoch')\n", "plt.ylabel('Accuracy')\n", "plt.legend(loc=4)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c1b4b8ca-7e9f-c0f8-a463-019d0d598071" }, "source": [] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "8bf89a0b-7a61-8321-f33a-5eabefc1a98c" }, "outputs": [], "source": [ "saver = tf.train.Saver()\n", "with tf.Session() as sess:\n", " saver.restore(sess, \"logistic_regression.ckpt\")\n", " predict = sess.run(y, feed_dict={x: test_data.as_matrix() / 255.0})\n", " pred = [[i + 1, np.argmax(one_hot_list)] for i, one_hot_list in enumerate(predict)]\n", " submission = pd.DataFrame(pred, columns=['ImageId', 'Label'])\n", " submission.to_csv('submission_logreg.csv', index=False)" ] } ], "metadata": { "_change_revision": 430, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/327/327861.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "bde37af6-94c1-e0fc-4c8a-69f683a8d4c9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd \n", "import matplotlib.pyplot as plt\n", "from patsy import dmatrices\n", "import seaborn as sns\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.cross_validation import train_test_split, cross_val_score\n", "from sklearn import metrics\n", "\n", "%matplotlib inline\n", "\n", "df = pd.read_csv('../input/train.csv')\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "cbf1cbf6-3298-06bd-5c84-0426a98d2677" }, "outputs": [], "source": [ "from sklearn.pipeline import make_pipeline, make_union\n", "from sklearn.preprocessing import Imputer, StandardScaler\n", "from sklearn.base import BaseEstimator, TransformerMixin\n", "\n", "class ColumnSelector(BaseEstimator, TransformerMixin):\n", " def __init__(self, columns):\n", " self.columns = columns\n", " \n", " def transform(self, X, *_):\n", " if isinstance(X, pd.DataFrame):\n", " return pd.DataFrame(X[self.columns])\n", " else:\n", " raise TypeError(\"This transformer only works with Pandas Dataframes\")\n", " \n", " def fit(self, X, *_):\n", " return self\n", " \n", "cs = ColumnSelector('Age')\n", "\n", "cs.transform(df).head()\n", "\n", "age_pipe = make_pipeline(ColumnSelector('Age'),\n", " Imputer(),\n", " StandardScaler())\n", "\n", "df.Embarked = df.Embarked.fillna('S')\n", "\n", "class GetDummiesTransformer(BaseEstimator, TransformerMixin):\n", " def __init__(self, columns):\n", " self.columns = columns\n", " \n", " def transform(self, X, *_):\n", " if isinstance(X, pd.DataFrame):\n", " return pd.get_dummies(X[self.columns], columns = self.columns)\n", " else:\n", " raise TypeError(\"This transformer only works with Pandas Dataframes\")\n", " \n", " def fit(self, X, *_):\n", " return self\n", " \n", "one_hot_pipe = GetDummiesTransformer(['Pclass', 'Embarked'])\n", "\n", "class TrueFalseTransformer(BaseEstimator, TransformerMixin):\n", " def __init__(self, flag):\n", " self.flag = flag\n", " \n", " def transform(self, X, *_):\n", " return X == self.flag\n", "\n", " def fit(self, X, *_):\n", " return self\n", "\n", "gender_pipe = make_pipeline(ColumnSelector('Sex'),\n", " TrueFalseTransformer('male'))\n", "\n", "fare_pipe = make_pipeline(ColumnSelector('Fare'),\n", " StandardScaler())\n", "\n", "union = make_union(age_pipe,\n", " one_hot_pipe,\n", " gender_pipe,\n", " fare_pipe)\n", "\n", "X = df[[u'Pclass', u'Sex', u'Age', u'SibSp', u'Parch', u'Fare', u'Embarked']]\n", "\n", "X_1 = union.fit_transform(X)\n", "\n", "new_cols = ['scaled_age', 'Pclass_1', 'Pclass_2', 'Pclass_3',\n", " 'Embarked_C', 'Embarked_Q', 'Embarked_S',\n", " 'male', 'scaled_fare']\n", "\n", "Xt = pd.DataFrame(X_1, columns=new_cols)\n", "Xt = pd.concat([Xt, X[[u'SibSp', u'Parch']]], axis = 1)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "b96a9a1e-9267-ce35-d5b5-d38e40a2d5aa" }, "outputs": [], "source": [ "X = Xt\n", "y = df[u'Survived']\n", "\n", "from sklearn.feature_selection import RFECV\n", "from sklearn.svm import SVR\n", "\n", "estimator = SVR(kernel=\"linear\")\n", "selector = RFECV(estimator, step=1, cv=3)\n", "\n", "rfecv_columns = selector.fit_transform(X,y)\n", "\n", "rfecv_columns = Xt.columns[selector.support_]\n", "X = X[rfecv_columns]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "e61b0e4e-8e10-4b95-3d41-ee1240b97d0c" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", " DeprecationWarning)\n" ] } ], "source": [ "from sklearn.cross_validation import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y)\n", "\n", "from sklearn.grid_search import GridSearchCV\n", "\n", "logreg_parameters = {\n", " 'penalty':['l1','l2'],\n", " 'C':np.logspace(-5,1,50),\n", " 'solver':['liblinear']\n", "}\n", "\n", "lr = LogisticRegression(solver='liblinear')\n", "mdl = lr.fit(X_train,y_train)\n", "\n", "gs = GridSearchCV(lr, logreg_parameters, cv=5)\n", "\n", "gs.fit(X_train,y_train)\n", "\n", "predictions = gs.predict(X)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "967b65ce-8d65-d822-3c15-b1de2fc23e3a" }, "outputs": [], "source": [ "submission = pd.DataFrame({\n", " \"PassengerId\": df[\"PassengerId\"],\n", " \"Survived\": predictions\n", " })\n", "\n", "submission.to_csv('titanic.csv', index=False)" ] } ], "metadata": { "_change_revision": 131, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/327/327932.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "1fab96bf-9f9d-33b0-db72-c35e15a74ca6" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import random\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics\n", "import sklearn.ensemble as ske\n", "import tensorflow as tf\n", "from tensorflow.contrib import skflow" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "94fa40b0-4ee3-5b3c-739e-4b0d7d82752a" }, "outputs": [], "source": [ "titanic_df = pd.read_csv(\"../input/train.csv\", dtype={\"Age\": np.float64}, )\n", "test_df = pd.read_csv(\"../input/test.csv\", dtype={\"Age\": np.float64}, )" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "0f34dbb1-e027-0669-04b5-4078fd040de2" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "20b3bc68-1f67-bfd7-f6bf-152563c47cc1" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>892</td>\n", " <td>3</td>\n", " <td>Kelly, Mr. James</td>\n", " <td>male</td>\n", " <td>34.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>330911</td>\n", " <td>7.8292</td>\n", " <td>NaN</td>\n", " <td>Q</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>893</td>\n", " <td>3</td>\n", " <td>Wilkes, Mrs. James (Ellen Needs)</td>\n", " <td>female</td>\n", " <td>47.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>363272</td>\n", " <td>7.0000</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>894</td>\n", " <td>2</td>\n", " <td>Myles, Mr. Thomas Francis</td>\n", " <td>male</td>\n", " <td>62.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>240276</td>\n", " <td>9.6875</td>\n", " <td>NaN</td>\n", " <td>Q</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>895</td>\n", " <td>3</td>\n", " <td>Wirz, Mr. Albert</td>\n", " <td>male</td>\n", " <td>27.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>315154</td>\n", " <td>8.6625</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>896</td>\n", " <td>3</td>\n", " <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n", " <td>female</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3101298</td>\n", " <td>12.2875</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Pclass Name Sex \\\n", "0 892 3 Kelly, Mr. James male \n", "1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n", "2 894 2 Myles, Mr. Thomas Francis male \n", "3 895 3 Wirz, Mr. Albert male \n", "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n", "\n", " Age SibSp Parch Ticket Fare Cabin Embarked \n", "0 34.5 0 0 330911 7.8292 NaN Q \n", "1 47.0 1 0 363272 7.0000 NaN S \n", "2 62.0 0 0 240276 9.6875 NaN Q \n", "3 27.0 0 0 315154 8.6625 NaN S \n", "4 22.0 1 1 3101298 12.2875 NaN S " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "2f3a7bb2-6c96-f69d-f97e-cae568194b51" }, "outputs": [ { "data": { "text/plain": [ "0.3838383838383838" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_df['Survived'].mean()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "27068383-665e-6df2-68a3-89c367aeb661" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " </tr>\n", " <tr>\n", " <th>Pclass</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>1</th>\n", " <td>461.597222</td>\n", " <td>0.629630</td>\n", " <td>38.233441</td>\n", " <td>0.416667</td>\n", " <td>0.356481</td>\n", " <td>84.154687</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>445.956522</td>\n", " <td>0.472826</td>\n", " <td>29.877630</td>\n", " <td>0.402174</td>\n", " <td>0.380435</td>\n", " <td>20.662183</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>439.154786</td>\n", " <td>0.242363</td>\n", " <td>25.140620</td>\n", " <td>0.615071</td>\n", " <td>0.393075</td>\n", " <td>13.675550</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Age SibSp Parch Fare\n", "Pclass \n", "1 461.597222 0.629630 38.233441 0.416667 0.356481 84.154687\n", "2 445.956522 0.472826 29.877630 0.402174 0.380435 20.662183\n", "3 439.154786 0.242363 25.140620 0.615071 0.393075 13.675550" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_df.groupby('Pclass').mean()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "0d961ec7-12d0-9a42-9170-c5e777185a6a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pclass Sex \n", "1 female 0.968085\n", " male 0.368852\n", "2 female 0.921053\n", " male 0.157407\n", "3 female 0.500000\n", " male 0.135447\n", "Name: Survived, dtype: float64\n" ] } ], "source": [ "class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()\n", "print(class_sex_grouping['Survived'])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "20b23df4-c25a-d6a7-a969-57e99e913f66" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f12dc111860>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f12dc0f9cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "class_sex_grouping['Survived'].plot.bar()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "c438c83b-464e-9934-ebba-8677446d8ee0" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f12dc110fd0>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1JD89mVADGE+ukeRNwDcGzLWYOfvTQkYwZ98Gz9nalvjPAf+Z8e6U/dcv38h4d8qvV9Xv\nDBTtIElOAi4GzuLxD/IZwC3Au6rqK8MkO9iMnAGezvznBHgm8GfMSc6G38sw/rc5N+8lNP9/aFXf\nz6ZKHA7sOnkNTzywOU8/nQ9I8j0AVfV3Q2dZjjn700JGMGffhsrZXIm3Isk/BZ5dVV9etPwHquqL\nA8V6giQbAKrqq0meDbwSuLuq7hw22fKSvKeqfm3oHEtJ8n3Ay4C7qmrn0Hn2S7IR+FpVPZIkwNuA\n04C7gCur6h+GzLdfktcy3jj7v0NnWUmSVwF7q+ruJGcCZwA7qurGVXn9tVLiSf5XVb106BwASd4I\nXAJ8DTgWeFtVbZ8899dVddqQ+fabXE74XYx/BbyY8X/oO4BXAP+1qq4aLt3jkly6eBHwFh6f1/Xf\nrHqoRZJcV1XnTu5vYfz5j4AzgffM0a6+O4BNVfVwkouBk4HrGO8OoKrePmS+/ZJ8h/FlrT8JfJhx\noT86bKonSnIJ43mI1wM3AWczzvxq4Paq+g9HPENLJb7MtVMCvL+qnr2aeZaS5PPAOVX1QJJNjMvm\nV6vqY0lur6qXDRwRGP/gA14OfBewG3jeZIv8mYxnavrBQQNOJLkX+HPGx0L2H8B+H/ArAFX1wYGi\nHTD9uSb5NPAzVfWVJM8C/rSqTh024ViSu6rqlMn9zwGnV9Vjk8dfmKOctzP+wfIGxhPRvAT4GPDh\nqvrzIbNNS3In42zfxWRu4ckPyGMZl/hLjnSG1r7scy3wIWafofLUVc6ynGOq6gGAqrotyY8CH0/y\nHGZnH8q+qnoYeDjJl6vqqwBV9Y3M11R6pwC/zvhLXr9SVfcnuWgeynvK9Pv1lP0HtKrqwSSPDZRp\nlnuTnFVVfwb8DfAcYPf+/blzpCbHua4Erpzs9nsj8N4kJ1bVc4aNd0BVVU19xvv/HTzGKp3911qJ\nfxF4X1XdsfiJJD82QJ6l/H2Sk/fvD59skS8w/rX1xYMmO1glOXbyrbJ/sX9hkqcyR6efVtXfA/8u\nyQ8BH0pyI3OUb+LUJP+H8W8K/yjJP5t87k8Bjhk427R3AFcn2QZ8E/j85DfHZwC/PGSwRQ46ZXiy\ngXEpcGnGU0XOixuT/AXjjcj/AXwkya2Md6d8ajUCtLY75ZXA7qr62xnP/XBV/dUAsZ4gyanAt6vq\nS4uWH8v4YjgfGibZwSYHue5ffDAryQnAi6rqfw6TbGmTg3G/BJxRVT87dJ6VJHkG4/fyM0NnmZbk\nRcDzGW/I3Qds379bZR4kWaiq0dA5ukhyBuMt8luTnMz4K/h/C/zBarynTZW4JOlg8/YrqSTpEFji\nktQwS1ySGrYmSjzJliQvHzrHSpJ8MMlvJzni544eDnP2p4WMYM6+rWbONXFgM8l7gJcC66vqnKHz\nLCXJ6Ywv2LWpqi4cOs9SzNmfFjKCOfu2mjnXRIlL0tGqtS/7OD1bj8zZnxYygjn7Ng85m9oSj9Oz\n9cqc/WkhI5izb/OQs7USd3q2HpmzPy1kBHP2bR5ytnZ2itOz9cuc/WkhI5izb4PnbG1L3OnZetRw\nzrmb+myJ93KuppCDNt5LWDKn08jNytBSicOBXSdOz9Yzc/anhYxgzr4NlbOpEk+SWiFwlzFDSvLj\nVfUnQ+fYL04jd8RkzqeQA5xG7jBlDqaRa22f+C1J3jn5gA9I8pQkZyX5IOOjwvNsLqY8A/ZPI7cT\n+GiSOydfUNjvd4ZJ9UQZTyP3GeDWJL8IfJzx9c//MMkvDBpuIsmli26/CfzS/sdD59svyXVT97cw\n3j3xL4EbkrxtqFwzfILH++m9jD/vzwKnA1cMFWqGa4E9SX43yU8mWfVrx7d2nvhm4O3AhydbEA8x\nnhZpHeP95JdU1e0D5gMgyQ1LPQXM0wwqvwb8UD0+jdzvJvnVqvoY83Wg+ALGk2nMnEaO+fjB+FM8\ncQq584DPDZZotukJFS4EzpqeRo75+eG9bjLrFMCP8fg0cr+X5AsD5lpsJ49PI/fvgQ8kWdVp5Joq\n8ap6BLgcuDzjCRaeBXxnnr7oM/FK4GeBby1aHsaTqs4Lp5HrTwtTyIHTyPVt8GnkmirxaZNvQj0w\ndI4l3Ao8POsn8eRc93nhNHI9aWQKOXAaub4NPo1cUwc21a+Mp5F7uKruWbTcaeQOw+RAXDNTyIHT\nyD1ZmYNp5CzxI6CVs2jM2Z8WMnbNYM7u5iHnPP66txa0chaNOfvTQkYwZ98Gz+mW+BEw2Vf7duBn\ngP1n0TyV8T7Hm4HL5+QsGnP2ZImM02dODZ4R2ngvwZyHlMESP7Lm/CyaA8zZnxYygjn7NlROS1yS\nGuY+cUlqmCUuSQ2zxCWpYZa4jhpJzk3yWJK5mBVG6oMlrqPJecBfAFuHDiL1xRLXUSHJ04AzGc9M\nvnWyLEkuT3JXkpuS3JjkdZPnTksySrI9ySeTHD9gfGlJlriOFluAP66qLwEPJnkZ8DpgY1WdArwV\nOAMgyXrgN4HXV9XpwAeA9wwTW1pes1cxlA7RVuCSyf1rgTcz/vf/+wBVtTfJLZPnXwC8BPiTycWs\n1gH3r25cqRtLXGtexpNHnAW8JOPrjx/D+LraH1vqjwB3VNWZqxRRetLcnaKjwU8DV1fV91XV91fV\nc4GvMJ6d/PWTfePHAwuT8XcDz07yIzDevZLklCGCSyuxxHU0eBNP3Or+KHA842tU3wlczXgqtW9O\nJp94A3DxZCKC25nsL5fmjddO0VEtydOq6ttJjmM8Ee+ZVfW1oXNJXblPXEe7j09mtTkW+C8WuFrj\nlrgkNcx94pLUMEtckhpmiUtSwyxxSWqYJS5JDbPEJalh/x+Uy878E9WmGQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f12dc0a60f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "group_by_age = pd.cut(titanic_df['Age'], np.arange(0, 90, 10))\n", "age_grouping = titanic_df.groupby(group_by_age).mean()\n", "age_grouping['Survived'].plot.bar()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "2ae759f6-9453-143a-c49d-ad7ec3b9ca3e" }, "outputs": [ { "data": { "text/plain": [ "PassengerId 891\n", "Survived 891\n", "Pclass 891\n", "Name 891\n", "Sex 891\n", "Age 714\n", "SibSp 891\n", "Parch 891\n", "Ticket 891\n", "Fare 891\n", "Cabin 204\n", "Embarked 889\n", "dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_df.count()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "882ef892-eafd-7af1-c57a-80dd54ff9414" }, "outputs": [ { "data": { "text/plain": [ "PassengerId 418\n", "Pclass 418\n", "Name 418\n", "Sex 418\n", "Age 332\n", "SibSp 418\n", "Parch 418\n", "Ticket 418\n", "Fare 417\n", "Cabin 91\n", "Embarked 418\n", "dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df.count()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "2823623c-a76d-11b1-1f6c-8e7a49eb8d54" }, "outputs": [], "source": [ "titanic_df = titanic_df.drop(['Cabin'], axis = 1)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "6e0f2996-a48f-ae65-1ccc-ee47de8aa84b" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "dcfabd25-4a70-4077-2617-9ae21a2a7f3b" }, "outputs": [], "source": [ "titanic_df = titanic_df.dropna()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "fdf65fb5-9112-bc07-d7b3-10c9e6026eaa" }, "outputs": [], "source": [ "test_df = test_df.dropna()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "4c658342-67af-24a2-8405-89cc1ed6e123" }, "outputs": [ { "data": { "text/plain": [ "PassengerId 712\n", "Survived 712\n", "Pclass 712\n", "Name 712\n", "Sex 712\n", "Age 712\n", "SibSp 712\n", "Parch 712\n", "Ticket 712\n", "Fare 712\n", "Embarked 712\n", "dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_df.count()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "f969483c-b6c3-d903-09a8-d7eeb0643cef" }, "outputs": [ { "data": { "text/plain": [ "PassengerId 87\n", "Pclass 87\n", "Name 87\n", "Sex 87\n", "Age 87\n", "SibSp 87\n", "Parch 87\n", "Ticket 87\n", "Fare 87\n", "Cabin 87\n", "Embarked 87\n", "dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df.count()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "823aa7c4-714a-09df-b65b-18e10e46c109" }, "outputs": [], "source": [ "def preprocess_titanic_df(df) :\n", " processed_df = df.copy()\n", " le = preprocessing.LabelEncoder()\n", " processed_df.Sex = le.fit_transform(processed_df.Sex)\n", " processed_df.Embarked = le.fit_transform(processed_df.Embarked)\n", " processed_df = processed_df.drop(['Name', 'Ticket'], axis = 1)\n", " return processed_df" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "5ae788d1-8c25-d977-c8b4-4ea5c8f90ad0" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", 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<td>884</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>28.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>10.5000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>884</th>\n", " <td>885</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>25.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.0500</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>885</th>\n", " <td>886</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>39.0</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>29.1250</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>886</th>\n", " <td>887</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>27.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>13.0000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>887</th>\n", " <td>888</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>19.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30.0000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>889</th>\n", " <td>890</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30.0000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>890</th>\n", " <td>891</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>32.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.7500</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>712 rows × 9 columns</p>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass Sex Age SibSp Parch Fare \\\n", "0 1 0 3 1 22.0 1 0 7.2500 \n", "1 2 1 1 0 38.0 1 0 71.2833 \n", "2 3 1 3 0 26.0 0 0 7.9250 \n", "3 4 1 1 0 35.0 1 0 53.1000 \n", "4 5 0 3 1 35.0 0 0 8.0500 \n", "6 7 0 1 1 54.0 0 0 51.8625 \n", "7 8 0 3 1 2.0 3 1 21.0750 \n", "8 9 1 3 0 27.0 0 2 11.1333 \n", "9 10 1 2 0 14.0 1 0 30.0708 \n", "10 11 1 3 0 4.0 1 1 16.7000 \n", "11 12 1 1 0 58.0 0 0 26.5500 \n", "12 13 0 3 1 20.0 0 0 8.0500 \n", "13 14 0 3 1 39.0 1 5 31.2750 \n", "14 15 0 3 0 14.0 0 0 7.8542 \n", "15 16 1 2 0 55.0 0 0 16.0000 \n", "16 17 0 3 1 2.0 4 1 29.1250 \n", "18 19 0 3 0 31.0 1 0 18.0000 \n", "20 21 0 2 1 35.0 0 0 26.0000 \n", "21 22 1 2 1 34.0 0 0 13.0000 \n", "22 23 1 3 0 15.0 0 0 8.0292 \n", "23 24 1 1 1 28.0 0 0 35.5000 \n", "24 25 0 3 0 8.0 3 1 21.0750 \n", "25 26 1 3 0 38.0 1 5 31.3875 \n", "27 28 0 1 1 19.0 3 2 263.0000 \n", "30 31 0 1 1 40.0 0 0 27.7208 \n", "33 34 0 2 1 66.0 0 0 10.5000 \n", "34 35 0 1 1 28.0 1 0 82.1708 \n", "35 36 0 1 1 42.0 1 0 52.0000 \n", "37 38 0 3 1 21.0 0 0 8.0500 \n", "38 39 0 3 0 18.0 2 0 18.0000 \n", ".. ... ... ... ... ... ... ... ... \n", "856 857 1 1 0 45.0 1 1 164.8667 \n", "857 858 1 1 1 51.0 0 0 26.5500 \n", "858 859 1 3 0 24.0 0 3 19.2583 \n", "860 861 0 3 1 41.0 2 0 14.1083 \n", "861 862 0 2 1 21.0 1 0 11.5000 \n", "862 863 1 1 0 48.0 0 0 25.9292 \n", "864 865 0 2 1 24.0 0 0 13.0000 \n", "865 866 1 2 0 42.0 0 0 13.0000 \n", "866 867 1 2 0 27.0 1 0 13.8583 \n", "867 868 0 1 1 31.0 0 0 50.4958 \n", "869 870 1 3 1 4.0 1 1 11.1333 \n", "870 871 0 3 1 26.0 0 0 7.8958 \n", "871 872 1 1 0 47.0 1 1 52.5542 \n", "872 873 0 1 1 33.0 0 0 5.0000 \n", "873 874 0 3 1 47.0 0 0 9.0000 \n", "874 875 1 2 0 28.0 1 0 24.0000 \n", "875 876 1 3 0 15.0 0 0 7.2250 \n", "876 877 0 3 1 20.0 0 0 9.8458 \n", "877 878 0 3 1 19.0 0 0 7.8958 \n", "879 880 1 1 0 56.0 0 1 83.1583 \n", "880 881 1 2 0 25.0 0 1 26.0000 \n", "881 882 0 3 1 33.0 0 0 7.8958 \n", "882 883 0 3 0 22.0 0 0 10.5167 \n", "883 884 0 2 1 28.0 0 0 10.5000 \n", "884 885 0 3 1 25.0 0 0 7.0500 \n", "885 886 0 3 0 39.0 0 5 29.1250 \n", "886 887 0 2 1 27.0 0 0 13.0000 \n", "887 888 1 1 0 19.0 0 0 30.0000 \n", "889 890 1 1 1 26.0 0 0 30.0000 \n", "890 891 0 3 1 32.0 0 0 7.7500 \n", "\n", " Embarked \n", "0 2 \n", "1 0 \n", "2 2 \n", "3 2 \n", "4 2 \n", "6 2 \n", "7 2 \n", "8 2 \n", "9 0 \n", "10 2 \n", "11 2 \n", "12 2 \n", "13 2 \n", "14 2 \n", "15 2 \n", "16 1 \n", "18 2 \n", "20 2 \n", "21 2 \n", "22 1 \n", "23 2 \n", "24 2 \n", "25 2 \n", "27 2 \n", "30 0 \n", "33 2 \n", "34 0 \n", "35 2 \n", "37 2 \n", "38 2 \n", ".. ... \n", "856 2 \n", "857 2 \n", "858 0 \n", "860 2 \n", "861 2 \n", "862 2 \n", "864 2 \n", "865 2 \n", "866 0 \n", "867 2 \n", "869 2 \n", "870 2 \n", "871 2 \n", "872 2 \n", "873 2 \n", "874 0 \n", "875 0 \n", "876 2 \n", "877 2 \n", "879 0 \n", "880 2 \n", "881 2 \n", "882 2 \n", "883 2 \n", "884 2 \n", "885 1 \n", "886 2 \n", "887 2 \n", "889 0 \n", "890 1 \n", "\n", "[712 rows x 9 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "processed_df = preprocess_titanic_df(titanic_df)\n", "processed_df.count()\n", "processed_df" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "88ff81db-32ed-c705-6d7b-dc8134dc7e79" }, "outputs": [ { "ename": "NameError", "evalue": "name 'processed_test' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mNameError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-19-482495fdd1bc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mprocessed_test_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpreprocess_titanic_df\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mprocessed_test_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mprocessed_test\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'processed_test' is not defined" ] } ], "source": [ "processed_test_df = preprocess_titanic_df(test_df)\n", "processed_test_df.count()\n", "processed_test" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "_cell_guid": "9c4809cf-95d6-4fe2-f240-f3c4a0e38811" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 3. 1. ..., 0. 7.25 2. ]\n", " [ 2. 1. 0. ..., 0. 71.2833 0. ]\n", " [ 3. 3. 0. ..., 0. 7.925 2. ]\n", " ..., \n", " [ 888. 1. 0. ..., 0. 30. 2. ]\n", " [ 890. 1. 1. ..., 0. 30. 0. ]\n", " [ 891. 3. 1. ..., 0. 7.75 1. ]]\n" ] } ], "source": [ "X = processed_df.drop(['Survived'], axis = 1).values\n", "Y = processed_df['Survived'].values\n", "print(X)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "_cell_guid": "1a2593b7-4958-393e-dce6-fb3279d4f9b8" }, "outputs": [], "source": [ "#x_train, x_test, y_train, y_test = cross_validation.train_test_split(X, Y, test_size=0.2)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "_cell_guid": "09c22e25-4bdf-fb6e-ef6c-48de0a7f9318" }, "outputs": [], "source": [ "clf_dt = tree.DecisionTreeClassifier(max_depth=10)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "_cell_guid": "db85787e-3711-6842-8bc0-58d0ca9ea4d3" }, "outputs": [ { "ename": "NameError", "evalue": "name 'x_test' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mNameError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-23-a767ec9d551e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mclf_dt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mclf_dt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'x_test' is not defined" ] } ], "source": [ "clf_dt.fit(X, Y)\n", "clf_dt.score(x_test, y_test)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "_cell_guid": "4062ede9-588e-7887-6f0b-83487385f897" }, "outputs": [], "source": [ "shuffle_validator = cross_validation.ShuffleSplit(len(X), n_iter=20, test_size=0.2, random_state=0)\n", "def test_classifier(clf) :\n", " scores = cross_validation.cross_val_score(clf, X, Y, cv=shuffle_validator)\n", " print(\"Accuracy: %0.4f (+/- %0.2f)\" % (scores.mean(), scores.std()))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "_cell_guid": "11a59980-552a-c4be-b3b8-e7f122f5ddc4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.7664 (+/- 0.03)\n" ] } ], "source": [ "test_classifier(clf_dt)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "_cell_guid": "6ee4e41f-119d-0eb9-69f6-efa4fb03ebca" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.8175 (+/- 0.03)\n" ] } ], "source": [ "clf_rf = ske.RandomForestClassifier(n_estimators=50)\n", "test_classifier(clf_rf)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "_cell_guid": "13d4e15e-75d3-1e5d-2fd6-5a77ba2d19b8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.8224 (+/- 0.03)\n" ] } ], "source": [ "clf_gb = ske.GradientBoostingClassifier(n_estimators=50)\n", "test_classifier(clf_gb)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "_cell_guid": "726946b5-5322-1048-db9e-c191c5a78e59" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.8192 (+/- 0.03)\n" ] } ], "source": [ "eclf = ske.VotingClassifier([('dt', clf_dt), ('rf', clf_rf), ('gb', clf_gb)])\n", "test_classifier(eclf)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "_cell_guid": "993a00ea-772c-c385-0c71-6d5fde6c80a7" }, "outputs": [], "source": [ "def custom_model(X, Y) :\n", " layers = skflow.ops.dnn(X, [20, 40, 20], tf.tanh)\n", " return skflow.models.logistic_regression(layers, Y)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "_cell_guid": "aa825497-d404-f267-ef7e-b7bae9a01432" }, "outputs": [ { "ename": "NameError", "evalue": "name 'x_train' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mNameError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-30-7c40d5b61b59>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mtf_clf_c\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mskflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTensorFlowEstimator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_fn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcustom_model\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_classes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m256\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.05\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtf_clf_c\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maccuracy_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtf_clf_c\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'x_train' is not defined" ] } ], "source": [ "tf_clf_c = skflow.TensorFlowEstimator(model_fn=custom_model, n_classes=2, batch_size=256, steps=1000, learning_rate=0.05)\n", "tf_clf_c.fit(x_train, y_train)\n", "metrics.accuracy_score(y_test, tf_clf_c.predict(x_test))" ] } ], "metadata": { "_change_revision": 624, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/327/327983.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "190cf3dc-feac-e980-35c9-8880435d1682" }, "source": [ "A simple exploration of the exact same file appearing under different names, in several cases even under two different patients." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "64937856-9bc3-a2af-7033-089c42660341" }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import os\n", "import hashlib\n", "\n", "# first we collect some basic information about the images\n", "records = []\n", "for name in os.listdir('../input/train/'):\n", " if 'mask' in name or not name.endswith('.tif'):\n", " continue\n", "\n", " patient_id, image_id = name.strip('.tif').split('_')\n", " with open('../input/train/' + name, 'rb') as fd:\n", " md5sum = hashlib.md5(fd.read()).hexdigest()\n", "\n", " records.append({\n", " 'filename': name,\n", " 'patient_id': patient_id,\n", " 'image_id': image_id,\n", " 'md5sum': md5sum,\n", " })\n", "\n", "df = pd.DataFrame.from_records(records)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "39deecbc-4a4a-403e-0af9-db1a00828b87" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "39\n" ] } ], "source": [ "# select the files that occur more than once\n", "counts = df.groupby('md5sum')['filename'].count()\n", "duplicates = counts[counts > 1]\n", "print(len(duplicates))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "db746bf7-abeb-6382-6301-59b5386c698c" }, "outputs": [ { "data": { "text/plain": [ "md5sum\n", "701faf62cb080158b31709c27e277a50 3\n", "c3f97cc6652b35f0b7abd9ee11702c69 4\n", "e9e74d865e68d76695012d14b1173b0d 3\n", "eefefa05d6665e30edb28bf47dd38c57 3\n", "fa592220c7cfea00705e96e9c983661d 3\n", "Name: filename, dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# some files occur more than twice\n", "duplicates[duplicates > 2]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "1257e534-c81b-16d2-c62d-56421a0209f5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " filename image_id md5sum patient_id\n", "1721 18_95.tif 95 0d7179bc19f09daf991e04c3cd4dcc84 18\n", "5376 17_94.tif 94 0d7179bc19f09daf991e04c3cd4dcc84 17\n", "------\n", " filename image_id md5sum patient_id\n", "490 18_44.tif 44 20865b51bad8f1f413ed89511b5ed618 18\n", "1903 17_7.tif 7 20865b51bad8f1f413ed89511b5ed618 17\n", "------\n", " filename image_id md5sum patient_id\n", "1125 17_35.tif 35 35590717275315dc0028cbc577d72b8d 17\n", "5317 18_54.tif 54 35590717275315dc0028cbc577d72b8d 18\n", "------\n", " filename image_id md5sum patient_id\n", "851 17_83.tif 83 5590fe1b2667e17f2cdc1fde4f48392d 17\n", "973 18_102.tif 102 5590fe1b2667e17f2cdc1fde4f48392d 18\n", "------\n", " filename image_id md5sum patient_id\n", "1560 17_57.tif 57 c0cc4596003b7cf3b16eb59bcf785bf8 17\n", "3598 18_50.tif 50 c0cc4596003b7cf3b16eb59bcf785bf8 18\n", "------\n", " filename image_id md5sum patient_id\n", "1282 18_75.tif 75 f81117c5e9c7bbb4fe8281d23e576ca5 18\n", "4622 17_117.tif 117 f81117c5e9c7bbb4fe8281d23e576ca5 17\n", "------\n" ] } ], "source": [ "# there also appears to be some strange mixup between patients 17 & 18\n", "for md5sum in duplicates.index:\n", " subset = df[df['md5sum'] == md5sum]\n", " if len(subset['patient_id'].value_counts()) > 1:\n", " print(subset)\n", " print('------')" ] } ], "metadata": { "_change_revision": 129, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/328/328019.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "b858c8f2-97a2-8cbd-0d86-382f99c2b6e1" }, "outputs": [], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "5b5a96ee-a843-870e-251e-ed09a80e0eab" }, "outputs": [], "source": [ "people = pd.read_csv('../input/people.csv',\n", " dtype={'people_id': np.str,\n", " 'activity_id': np.str,\n", " 'char_38': np.int32},\n", " parse_dates=['date'])\n", "act_train = pd.read_csv('../input/act_train.csv',\n", " dtype={'people_id': np.str,\n", " 'activity_id': np.str,\n", " 'otcome': np.int8},\n", " parse_dates=['date'])\n", "act_test = pd.read_csv('../input/act_test.csv',\n", " dtype={'people_id': np.str,\n", " 'activity_id': np.str,\n", " 'otcome': np.int8},\n", " parse_dates=['date'])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1d54ae6e-5a34-47c7-def1-512b8e93f032" }, "source": [ "## Checking to see how dates of the train and testing set are distributed. Are we predicting future values or a random sample " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "3910f043-fbc1-94fe-ce5e-8d58e36d8eeb" }, "outputs": [ { "data": { "image/png": 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Byy10wEJEUCHIbOaAuRVidTpgTuiAFQ6Dg/k/1L27G9i1a7KPgpDcQwcst1CA\nhYhCHgVZksSrzdQBowDLHYODsXOfrzzyCHDrrZN9FITkHgqw3EIBFiKCLMRqO1JBV8J3S8LPlgAz\nDhhDkLnBCLCgcgazQVeXuGCEhA2GIHMLBViIyOZckEEXYk0nBFlWRgesUBgclGtlYGCyj8Sb7m4K\nMBJO6IDlFgqwEGE7SfmaA2ZcOpOEr3W8cHSjtDR9AcUcsMlhcFB+53MeGAUYCSNjY/JDByx3UICF\niGw6YNkqxGr+Vsr7PRMNQSpFByzXFIoAYydEwoZpQ+mA5Q4KsBDhNwl/0ybgqqu8t+NWBywIATY2\nJo5XUVGiAEvGRAXYjBkxB2x0NDgRSbwpBAHW00MHjIQPCrDcQwEWIvwm4Tc3A3v2eG8nW5XwjUOn\nVCwJP9UISGDiAmzOnJgDVlREFywXFIIAMw5YPg8UICRoTBtK9zd3UICFCL8hyKGh5B2kWw5YEJ2V\n7XbZDliyBHwgUYD9/vfAW28lf09/PzB7NtDZKX9Pm0YBlguMAMvnUhTd3XI957NIJCRo6IDlHgqw\nEOE3BDk0lLyDzFYOmH18Jgl/Ig7YN78JPPdc8vcYB6y1Faiqks/DUhTZp1AcMPs3IWHAPFhTgOUO\nCrAQ4QxBes0FmcoBy1YOmH18E80Ba2oS9ytVI2IcsNZWcb/KyuiA5YJCEWBFRQzFkHAxMgLMnMnr\nPpdQgIUIOwSZbC7IdB2woHLAgghBPv64/PYjwObMAY4eFQesvJwCLBcUigCbP58OGAkXIyPyMAqw\nLcwVFGAhIp0Q5OCgt0OWrTpg9vGlk4RvF2J9/HHgzDNTP8XZDpgJQbLRyT6Dg3Ku812ALVxIAUbC\nxciIPMxWV9MFyxUUYCHC7yhIkwvlVa08W5XwvUKQfhwwI57WrgX+5m/Sd8CYA5YbBgeBWbPyV4CN\njMi1VF/PToiEC1uAMQ8sN1CAhYh0RkEC3p1kNnPAMknC11rm8Vu2zH8O2OgoHbBcYgRYvo6C7OkB\namqA2lo6YCRcGAFWU0MBlisowEJEOiFIwLuTdMsB0zpzF8wZghweTi8J3zhodXX+QpBz5sjrycgB\nu/hiKXgbNvLdAevuFvFFAUbChkktYQgyd1CAhYh0RkEC3p2kMwdMqWAS8e3jM3ld6SThDw3FGpBk\nT3BmMuhZs+TvyQhBHjoEtLTkbn/5QqEIsJoadkIkXJjIBkOQuYMCLCTY0/wAyavHp+uAAfGJ8BPF\ndrvM9tLSHTAjAAAgAElEQVQJQQ4NiZOVqgEZHJT1amrk78kIQfb05G8YLpvkuwDr6aEDRgqDxkbg\nG98Ibnt2CJIPH7mBAiwk2NP8ADGB4xY2TDcHzGwvUwFjhyDN9tJ1wIywSibA+vtFdFVVyd/TpuU+\nBNnbG14BNnNm/gowOwSZy07o+uuBHTtytz9S+OzZAzz8cHDbYxJ+7qEACwl2Aj4gQswePWgzUQcs\nUwHjDEEOD6fngA0PxxywZJ2nEWCVlfL3ZDhgYRZg+eyAdXeLgK+p8XbAmpqAG24Ibp9dXcA99wAH\nDgS3TTL1SVWvMV2YhJ97KMBCgu0uGbxcn2QOmNaxG9UmiBwq5yhIv0n4xs3zG4I0AkypmBOWyxyw\noSE5Xgqw/MNPEv6rrwKPPBLcPp96Su7PMF4PZOJkS4AxCT93UICFBNtdMniJjqEhuRHdbm4jiIoc\nV042QpDpJOEPD8cE2LRpkmTvNSjACDBA1s21A2bEYb6KkGyS72Uo/CThb9sm9eOCqH0HAI89JvdT\nvp4Tkp9kU4DRAcsNFGAhwRmCBJI7YF55Om75X8m2lQ6ZhiDNKMiiIgkvejVOtgAzDliy4+/qAn7x\ni4l9JjdMxx7GDncqOGDbtqWeL9UvY2Mye8O7352/54TkJ4ODFGCFDgVYSHALQSZzwGbOdL+53fK/\nzLaCEGBuSfjpjoIEktvo6TpgL74IfPvb6X8eL0zjRgGWf/gRYNu3y++jRzPf38GD8tBxyinhvB7I\nxBkakh+vckLpYgQYi1LnDgqwkOAWgkzmgM2Y4d5JOmuAGYIOQZqwolu+mRM3AZYskdTNAUuWA7Z9\nO9DRkf7n8YIOWP4LMK8Q5NiYXA/LlgFtbZnvr6cHmD5drsEwXg9k4qSaMi5dTFubrEQRCRYKsJDg\nFoLMRwfMGYLMxAHzI8CMA5YsBGkEWFA5P2HNARsdld+1tfn72VM5YI2N8r/ly4NxwPr6Ytdgvp4T\nkp8MDsrvoIS77YBRgOUGCrCQkO4oSC8HzCsHLOhRkBOphG/KUAD+Q5Cf+ATwtrclF5Dbt8txBNVB\nhjUEOTgIVFTId2QmWs83OjrEkTIDOZzhnW3bgBNOkHlEgxBgvb2yr2nTwnc9kMxIVS4oXWwHjCHI\n3JDCWyBTBbdk9lQOWFdX4v+y6YDt2wfMnx+/vYkk4QP+HbDrr4/tz0tgbd8uoqGzU7abKSbsFLYO\n1wgwpURw9PXJvJ35REsLUF8vAznM/WGuFSAmwIqKgnPAqqsZgiTpk00BRgcsN9ABCwkDA7HCo4Yg\nc8CCGAW5di3wnvfI60zqgAH+c8Dsbbgdf1ubbHf58uDywHp7gblzw9fhGgEG5K/j09wMzJsnrysq\nYmEew86dwKpVwTlgdggyH88HyV+yJcAYgswdFGAhwU2A5VMOWCQCrFsXE2DpTkVk1wED/DtgBjcB\neeGFwIMPAscfL4I0KAHW0yMuS9g6XKcAy7ecp5ERcTlnz5a/y8sT7489e0SMZ0OAeZ2PoJKsydQi\nWzlgDEHmjpQCTCm1RinVopTaaC2boZR6Uim1Qyn1hFKqzvrfrUqpXUqpbUqpS6zlZyilNiqldiql\n7rSWlymlHoi+52Wl1OIgPyAR7M7PMNEcsGwIsA0bxHmwQ5ATnYwb8J8D5nX8WgPPPQf80z8FL8B6\ne0WA5ZsAyTZOAZZvtYZaW0VYmYEguRRgyRzBxYuBrVsz3xeZWqSaszdd6IDlHj8O2C8AXOpYdguA\np7XWqwCsBXArACilTgRwFYATAFwG4G6lzPTP+AmA67XWKwGsVEqZbV4PoF1rvQLAnQC+m8HnIR4E\n6YB5JeFnIsCeeSbmftnb8+OAFRdLeYCBgcxCkPa56O2VZe96F3DaacE7YGEPQU6f7p5jOJk0N4sw\nNjgF2Ogo0NAALF2auxDkwIDs51vfynxfZGrBJPzCJ6UA01q/AMDZ9VwB4J7o63sAXBl9/QEAD2it\nR7XW+wHsAnC2UmoegBqt9evR9X5lvcfe1m8BXDiBz0FSYAuw4cgweod7A68DlskoyJdeErFjb89v\nEr6ZWLyvL7gQZEeHiNDHHwduuknOR2dn+p/LDeOAhV2ABVlbLQjs/C9Argk7B6yhQb638nKpZZYL\nAWZGZT7xBLBrV+b7I1OHwUFp+5iEX7hMNAdsrta6BQC01s0A5kaXLwDQYK3XFF22AECjtbwxuizu\nPVrrCIBOpdTMCR4X8cDu/H7x5i9wy9O3pHTAchmC3LYNOPnk2N/pJOGb9fv64kdBZhKCbG8X0VVS\nIj9BhyDD7oAFKWiDoqUlXoBVVMTfHyb8CIgDFkQhVjsE6Xa/dXTIMX3wg/IwQIhhaCjY0dQMQeae\noJLwAypRCQBQqVch6WI7YId6DqF7qNs1x2VsTBLia2vTS8LPZBTk8DBw4ECscwPSC0ECsk5vb3Ah\nSOOAGZiEnzmF6IA5BdiyZfLaOGCZFuf144DNmCG5kUEIPjJ1MJEKhiALl4nWAWtRStVrrVui4cUj\n0eVNABZZ6y2MLvNabr/nkFKqGECt1rrda8e33Xbb+OvVq1dj9erVE/wI4cIWYK39rRgYHcB0F9fK\nJLJ7PZFnIwds925JNDbiyWzPJOH7qb3lFGDphiCdx286PkOQgqG3V7atlLegnYo4HbB8FGBLl8b+\ndgqwvXtjDwmmoGxPjzysTBS/AmzWLHGJCTFkS4DRAcuMdevWYd26db7W9SvAFOKdqUcAfArAdwB8\nEsDD1vL7lFL/FxJaPA7Aa1prrZTqUkqdDeB1ANcCuMt6zycBvArgw5Ckfk9sATaVOHhQbqaamuxs\n3+78WvtbMTg66OqADQ5Kx2I6BK1FKBiyMRfk9u1S3NJte35DkGVlmQkwp4PX3p7ogAUVMuvpiS++\nGUYBNn26OEr5REsL8M53xv52c8A+/OHY3yYRPxcCLKik/8ni+98HPv/5xOnQyMQZHKQDlo84jaHb\nb7/dc10/ZSh+DeAlyMjFg0qpTwP4NoCLlVI7IEnz3wYArfVWAA8C2ArgMQA3aD1u0t8IYA2AnQB2\naa3/FF2+BsBspdQuAP8EGWEZOr7wBeB3v8ve9uMcsL5WDIwMuIom44CVlMiN6CxEmY0csO3bpdSD\nTTqTcZv1e3piAqyuztthmYgDFnQOWE1N+IpvTqYDduBA6pGEzhCksxCrHYIEghFFvb0xMT4wkBjS\ntAVYkCFIrWVQQTKCqj82NgZ86UvyUEOCI9lo9YlgHq6ZhJ87UnoLWuuPefzrIo/17wBwh8vy9QBO\ncVk+BCldEWp2785uwUVbgB3tP4rpFdNdHTC7lpapTWSXr0gmwFLVdRoZkcbYDjUCElqxS1CY7aWb\nhG9KRwCS5N7a6r6unxwwk4RvCDoHLIzTz0xmEv4f/wjcdx/wla94r5MqB+zQIWDBgtjfQQgw44CZ\nqY8GB+Pvt87OWAgySAfsjTeAq6/2HlnZ1gYcdxxw5Ii/B6BkmFSGzk5gzpzMtkViMARZ+LASfh6g\nteSXON2mIHGGIAdGkztggHseWCaTcf/wh8C//3vicq8QpN/JuIHEMhRz50rn4UY6ZSgMdMAyZzKT\n8F9+2ft6MKSqAzY4GH/dBCnAAPfrIVshyIYGaXO87tl160QwHTiQ+b7MaOR8y/krdJiEX/hQgOUB\n7e1Ad3dmdbRSYRywMT2Gtv42zxwwW4DV1SW6FJmEIJubE8MQWosAW7UqfrkdgkzHAbNzwCIR94EE\nkxmCHBuT/ScrPTBVmcwQ5EsviasTiXgf28BA/HfuvD+GhuJnkwhagLldD3YSfhCjLg2HDsm1uHu3\n+//XRjNx9+7NfF8UYNlhcDD4ECQdsNxCAZYHmEYuFwKsY6ADER1JmgNmBJZbmMgZIjH4KUPR2ZkY\nZu3vl07R7vgACckUFcn+JiLAlPIOQ04kBFlZKceZqUvZ3y+deHExHbBchSBbWuT7nD7dOw/JDLqw\nB5zYOWBax98bQGoB9sor8tCRDL8OWFWV3A9BXS+HDsnv7dvd/792LXDmmcEMlOjult8UYMFCByye\nAwekXl4hQQGWB+RCgJnO72j/UUwrnYaB0YGUDpibS+E2pyTgzwHr6kpsLIwb5EZZmXRQE6kDBriH\nIUdH5cfp4rk5YHYIUqlg8pZM0jUQbgGWSwfs5ZdldGN9vXcYsrNTBJqNfX8Y8VVktZipBNg//ZN7\nyN3GrwDzs790OHRIXLUdOxL/19Qk5+lv/zZYByzfCu8WOslmLJkIhV4Jf+tW4PXXU6+XT1CA5QF7\n92Y+lU8qjAPW2t+KRXWLxkOQyXLA3PJ0MhFgbg7YwECiG2Vvs7/fvwNmSmgY3ASY2Z9ylPt1K0Ph\ndOWCEA1dXbFSI2EWYFVVsQnUs80rrwDnnCMJ4F4DM7q6JORuYwswt+t+1izvkYn9/cDGjcD993u7\npmb+UnP951qArV7t7oA99xzw7ncDK1YE44AxBJkdslmGYmQkuHB3rjh4MOa2FgoUYHnA3r3AypXZ\nTcIfF2B9rVhct3g8BJkPDphbSNNsc2DAnwNmHC3b2XITYG7hR/O+ZA4YkLy2mF+OHImNtKuqCm8O\nWFCOoh8aGqR8RDIB5scBc173yQTRa6/JJO5nnAH84Q/u6wwMyDaNq5YsBwxILvjS5dAhGXns5oBt\n3QqceqqcszDkgLW0pA4VTwaNjcD69d7/z0YZitJSuR6LiyVSUEg0NIgAGxub7CPxDwVYHrB3r4wC\nzFUIcmHNQgxHhlFWppM6YF45YF4CLNXxe+WAJXPA+vr8O2BAagcsmQAzxx+JSKfhdESqqzMXTPZI\nO1PmIyw4r51MHcXOTuDTn069nsnvSlaaxMsBMw9FTncVSC7AXngBOP984Nprgd/8xn0dO/wIeDtg\nRhgG7YBdcIEIMKfTsXOnPBAuXy4OWKZOSHe3dOj5GoL88Y+BH/1oso8ikf/4D/lxIxIRoeE1ZdxE\nsEe4F2IifkODXKuF9FBLAZYH5EKA2SHIudPmoqy4DCgdzKkD1tnp7oB5CbDS0vRCkECiAGtp8bc/\nOwTZ1SViy1m122vU4sgI8LGPAW++mfo47VpTUzEEuWkTsGaN+/+c106mifg7dgC//KX3pOuGtjYR\nYHPmpJcDZk/G7Xbd+xFgS5cmXoOGvr74abac18PQkFxbRqQFJcCGhkQUrVol943z+HbtEgE2fbr8\nP9N99vQACxfmrwPW3Z2fnfbjj3sflz1lXNAOGFCYeWAHD8rvrq7JPY50oACbZEZH5Wl05cocCbC+\nVsyumo3K0kqo0sGUDphfAeZnFGRXV/oOWH+//yR8cxyGdByw4uLY3Ixu4UdAGju3EOSePdJYXnKJ\nhJ6SMdUF2NNPA//1X+7/m6gD9oc/uIcVGhvl96ZN8uTrFXpob5fwXTZywNrbE/fb2yuJ/+eeK9v0\n6hB6e+MdMGdnaoqwmnzFoIqxHj4s12BRkYQZ9+2L/U9rccBWrJC/jQuWCT09wKJF+SvAenuzWwR7\nIhw8CGzZ4i3AnFPGJWNwEHjxRQktJ8MpwAptJGRDg/QZhZQHRgE2yRw6JB1DdXVuQpCt/a2YM20O\nKkoqoIsHkjpgQSbhDw7KTzoOWLpJ+MDEBRgQmw7GLQEf8A5Bbt8OnHce8P73ixhIxlQXYHv2SAfu\nxkQcsOZmGY3nVq/KTKXz1lvA3XcD//AP7tvwE4JMlQPmdt2Xlopocgqsz39ehsPPmSPb9BJgbiFI\n+/py1qILajqiQ4eAY46R1/PniyAzHD4sx2TE6PLl3tXy/dLTAyxe7M/tHBnxFvDZoqcn/+7Dxx+X\nc5bMAauoiJ+z14tPfUqux1tSTPJnC7BCC0GOjckD2apVdMBIGjQ0yNOhnW+SDYwDdrT/qDhgJZXQ\nxQOB5oAlE2BdXfIkPxEHLB0BZifhu5UdSLY/40AY58Ht/14C7Pjj/VV3b2mZ2kn4e/aIS+N2Hibi\ngD3+uPz2EmALF8pow4ceEsfASSQiT8TTp2fmgNn3hY0zLPjss8Azz8Ryd9yKGRtS5YC5CbAgHLBk\nAszkfxlOPhnYvDmz/XV3A0uW+HPA1q8Hbrwxs/35oa9P3FogPx2wJ54Q0eQlDM31WFycOv9240bg\n619PPRdnITtgra0yunzePDpgJA1sAZaLEGT7QDtmVs5EZWkldHHucsC6uqQD9OOAHeqRKpGlpXI8\nuQhBArEO0K7VZeMVgtyxQwSYn1F9tgM2FZPw9+yRhtDNNXFzwFJ1yo8+KqE3tzBYYyPwvvdJeOX5\n591FWmenJCoXF08sB8xOwne77p2iaPt24OKLY6VGqqtjxYadpCvAggpBJhNgJv/LcOqpqV3d7m7g\nq1/1/r9xwPwIsJdecnfKg+bBB4EvfjF2fPl2H27fLqVTUoUggeROutZSoPT009MTYIXmgJl+NFnI\nPx+hAJtkvATYtm2pY/bpYDqQ7qFu1JXXoaKkAmM+HLB0BFgyAdnZKY290y53E0Qn3X0S2gfax92s\niYYgTedo5+j4EWDOjtHgxwFzCrBdu4CPfjT2tz0KcqqFICMRyV15z3v8CbBZs7wdKUAE/TPPAJ/5\njLcDdvnlIhDOO086GKeTYcKPQPA5YECiAHNeX0VFIsbcnsqd15lTkHd3i3g01NamHnDgh/37Y5OK\nuzlgJv8LAE45RRyUZGzaBPyf/+MdBjNJ+F1dqUsEvPii/E4lFjLlj3+MddT55oBpLdf28cenDkEC\nyduRo0dlvaVL03fACkmAHTwo/WhtLR0wkgbmwnEKsJ/+VKp3//nPme9D65gD1jPcg5ryGlSWVGKs\nKLkDFmQOWFeXdFZFRfE3trMQa2Qsgs7BTrT1t40LsHTqgNnrlpVJ52c3PJkIMLccMDOXpZcAa2iQ\nMgSm8zlyZOoKsIYGETmnnOKeB+a8dk45RfK3vHjpJcnpOPdcdwHW2CjbWLxY8u+WLEmsW2US8AG5\n/jo63EXARHLAzDbtvCy368vrqdxNgNkCK5VAmwhai6t40UXyt1OA7dgRL8CWLJEOLVnnvXev3Mde\n4ranR0RwVVVyAam1fOczZmRXgA0PA08+GftO8s0BM+kaxxyTehQkIO2Sl+jYv1/ElzmnbiL5jjuk\nDEchhyAbGqQdyNQBO/dc99p42YICLA1uuin4L8c4YPaQd0AahA98ALjqqswt1ZERET4lJeKA1ZbX\noqKkApGi5A5YVVXi/IcTHQVpOjin6HAWYu0Zlha6Y7AjbQesvDyxwr3TxfMrwNzWcQtBHjki4a3Z\ns90dw4EBOYfr1kkDWF2dOnRgRvflG7feKs6sF3v2SNL2ypX+HLC3v11yfrxckTfeAN7xDuC44xJD\nkJGIuInHHAPcfjtw9dWynlOomRIUgFxHtbXuiexB5YCZBx0br8EGToE1c2a88PBTJyxdNm+W+/Tt\nb5e/bQE2OioO1DnnxNYvKhKRmywMaUTv/v3u/+/ulgehZDl/7e3y3Zn9BVVw1o3nnpNrtKtLBEm+\nOWCmT/By3IH46zHZ4Iz9+0VEV1RIG+m2vS1bxOWcjBDkhg3BzN/Y2Ciubl1dZg7Ynj0xFzYXUICl\nwWOPAX/5S7Db9ErC7+8H3vte4LLLgLvuymwfpuL2mB5D/0g/qsuqUVlaiYhK7oC5VSufqAPW2Sk3\nR2VlfGPnFETdQ3L3dAx0jDcGfgWYc35HQBp++6k76BCkcb8A947WdJhPPRWfgG/259xed7eUBghi\nCpig+fGPJeTnVTV8zx459pUr/Tlgs2ZJ5+H1ULNxo3TGxx4rHYmdR9XcLO8vK5NRXvPmuZdMsEOQ\ngEww/dhjifvycsD85IDZzk86Dpgz19DZkaYaJTkRfvtb4EMfij2o2ALs9dclVGjywwypBNi+fbI9\nLwHW0xMTYF45kpdeKrMGnHturLxHtnjqKXFMKyrkO+jtzS8HzO4TzNy1TuwcsGSDM4wDBiQKfENj\no+SJTYYD9uSTEg7OdF9Hj4r7Xls7ccNCa3lASFVKKEgowHyitUxSG8TUHDbGOnWGIE3j+7WviQDL\nRNUPDorw6R3uRVVpFYpUkThgKnkZCiDxqTWTEKSXA+YmwDoHO9MKQRoHzEmQAswtBLlrVyxk4yXA\nTj5ZRlzZCfiAe0jptdekIfSqnj5Z9PZKR/DXf+1dnds4YCtWyHlxhjvcrp2zzvJ+qNm0STr/ykpp\nXE3ZCSDWSdl4CTATggSAr3xFJsh2dmpuDliqQqxAYmJ/OgKsoSGWiwUkJtmnKtSaLk1NwL33xjsO\nc+eK6BsdBf70J3ngc3LqqcnzwPbuFfGUSoAlG3Rx+DDw//4f8K1vZV+AHT0qQtOMUM1XAaaUd9jZ\nzgFLNjjjwAF/Asz0a6b4dK4csBdekM+SLBXBD21tIkQzccAGBuQz53JCbwown3R0SCNsFy3MlMFB\nuVjmzk0UYKYhX7FCOvCXX574fkxYxIQfAaCypBIjSAxBmnCBISgBZhwwU2vL4Oywugalp5poCNKJ\nM3HZjwDr7/d2wJwhyMOHY46BVwjynHNk+e9+l+iAORvXl18WUfLgg+7HOFG0TgxTDA3JdDT/8z+p\n33/4sLgl7363t2O1d6+IoJkz5Xqwc4sAbwHm1uCNjkq48+ST5W9neNFNgLmFKu0QJCDHP29evMAd\nHZXvwTny1U8OWH19fCX5dATYvn3i7hmcTobzQaCyMnXNJy/27RP377rrJLfUUFIiHfiRI1Ly473v\nTXzv8uXJ2729e+U6chNgWsc7YG4CzFybF18s7d3MmdkNQZrvqK5ORoQC+RmCBLxdT2cIcqIOmDEW\n9u+Pf9DNRRL+2JiE+973PuCVVzLb1tGjch1n4oCZUcfbtmW3JJQNBZjF8897W+RNTfI7SAessVE6\n76IibwEG+BsKngwvATaqJQRpN+jOTsHp6nh1RKZkhFfnYBww04kYkoUg880BcwtB2mFFLwds2jTg\nnnuAn/88loBv78/m5ZeBf/kX6RjcEs8nyltvSSdp87//twjKv//71OUzTOmClSu9BZh97Zx8cnxd\nLuM4OcW0lwDbvVsEnxFFTnersVFcDJvlyxPPmTMEqRTw8Y9LnSWDGW1Y5GgNU03GDSTWmvMSYG7n\n13mvGefH3EPO67C0VByKiYRrtmyRvK+vfjUxT3L+fJlGa+dOCQE6qavzTp4fHJQQ7PnnuwuwwcFY\nrSqv5Pr+fjn35rx5OTVBYXI86+qkXQ9yPsUgsAWYVx5YOjlgyQRYe7u0yWbaKUMuQpA7dsh38Ld/\nm7kAC8IB6+yUtnzVKslNywVTXoB973v+nqb6+uQJ7Nhj4xtnQ1OTPGEHJcAOHJARjuZGcybh241v\nqhyMVBjR1DPUg5oysbcqSiowNDaQMOu9s1Own1pHR+Wpxc2RKi5GwrZs7CR8XzlgATlgzhIA2RBg\n9qjGkRF3If3e94rD8LGPJe7PMDYmDdH550tOzLp1ST9yWnR0xIcFm5tFED7+OHDllZKou3ixt4A2\nDpi5B9zqWh04INsBgJNOii/g6SXcTzrJXdBt3CgPHoY5c+I7j/Z2aXBtliyRUcX2Z3CGIAFxgtav\nj/1t3FknqSbjBhLnG/VKwnc+lY+NybGa8wWISKmoiF2vbtfhRMOQvb3xzrbN/PkSlr3qKvc8ymRD\n+w8ckOtm+XJ3AWbcL0A+q9s6xr0wZDsEaR6Kpk8XIT97toiNVCUycoUfAWbfT14OmNaxJHzAXYCZ\nB5mlSxNHkGfqgF1xhZSR8cLMl/rOd3oLsJER4NvfTr2voByw6dOBs8/OXRhySguwsTHgX/9VRr2k\nYv164LTTxBX4058S/9/UJBdKS0vqJ4OhIaldlKwRueYa4DvfAd71LvnbhPBMI2ALhUwFmKsDVlqJ\nwdHBuKd8Mw2PnYRrCzDjAjifoA3JRkLaSfipHDAFNaEk/FzkgDlDkHZdLzNowW4A7DIbF14ojo+9\nP7tx3blTztG8ebKdIGo+Gfr6pNE2CfStrdLwzp4N/PCHUnOuq8vbCTMO2LRpIobMxLf29nt7RZAA\niQ6YlwAzT6xO4WfyvwzV1fHnw61YbkWFOCm2AHaGIM2x7dkTuw6NO+vETw6YccDM8fsNQR4+HHsg\nsbE706AFmFtxYUAE2MsvA5/9rPv/kwmwvXtl4IURV87v0RZgXqNjjx6NF9PZDkG6OWAVFfkThjSl\niYDkOWCpQpBtbdIumocLLwG2YIF8f7b4ztQB01r63R//2HudF1+U+n2rVsmxun2GnTtl9HWykGAk\nIvfXjBmZO2AzZgAnnug9nZqTO+/0l8LhRcEKsF27JKcmGQcOSAOwYYNcEMlGN7z6qgx5P+EE96H2\nTU1ykS5cKNtNxq23AmvWAGvXuv8/EpFj+stfRCAC0nnbeVR2Q37SSTLazstdSoWbAKsoqcDAyEDc\nPvfvl6dZk4gJxAswr07IkCwPzE7Ctxs6Zx2w7qFuLKhdgM6h9JLwy8qCHQXpVYYiWQgSSEw0dpbZ\ncB4zEHvSfOmlWAgo2RD0iWC2ZRxcu8hnUZE0wvPmeY9wNA4Y4D7K0XQaJozn1wErLY2NRrPZulW2\nYXB+j16CwikWnCFIQDqu44+PJZYnc8BSCbCKCvkxwtWvAHM6zYZZs2LiwzlZN5A9AXbWWeIMupFK\ngB17rHw/VVWJtcCcAsytYzPhI0O2Q5B2Dlhjo5wX5+jsiTCR3Dy3bTQ2ph+CdBMvdgI+kNwBW7Ik\nWAfs0CHp0/78Z+82xThgRUVyDM6cUSDmjifrc81sF6bMzEQdMBOlWbAglnKUioceSq1DklGwAuzR\nR4F//ufkF/3GjXIhbdgg4uuCC7zXf+UVcbiOP17EjpOmJvlili1LHobcuBH47/8Gvvxlb+dtxw7p\n7JIVfrSffqurpZGcaE7QeAhyuCcuB8zpgO3bJ5/Pxs4vmIgA01qGGm/bJu6IHwdsSd2SuBywfHHA\nUoUggcQ8MKfA9NonIC6EqcEUtAAzAsdNgBnmz/duLO3pa9w6Ujv8CIh42ro1dr8lu3a8BIp9LdbU\nxNF+Or4AACAASURBVIu0nh7/AswZggTiw5BeDpi5nrVOfvx2HlimAiyVAzbRYqzJBNhHPyouqBdm\n9K8zRKc18Otfy8AGwD3EaF9nXqNjnSHIXAgwM+F4U1NMPGaaB3bhhZmXKWptlWMz15BXEr6fMhR2\n/hfgfl6bmtxDkJkm4W/eLNMfffCDkv/qpLlZjuWEE+RvL+fKCDCvEbZAvIPqtZ3mZqkrmAyThJ9M\ngD3wQKzt01r6++efT77dZBSsANu5U566k13wGzdKMdMNGySvq78/Pl/DxjhgS5fKOs6b0U2AvfZa\n4gX9yiuSv3Plld5fzBtvuD9t2mLI2ZCfcsrEJ8W1HTCTA1ZZWomB0XgHzDzN2tg3t1ciMgCMjo0m\nCLAdO6Ti9j/+o1T2P+EE9xww2yHqHurG4rrFgeaAOQWYlyNlRI/XKEhz7KYjGhiQc2K7J85aR8n2\nZ7aZCwFmtmVGs7mVXZg3z/0pFEjtgLk9bVdXx0pHJBMwboMX9u2L356fECSQ+ATc1uY+sfoZZ8Qa\nZC8HrKgoForxKsQKxOeBuQlur8/nJcDMA49XCHIi14WXYAVELL/jHd7vLSpyHwH8xz9KZ3fVVfL3\nnDmJoUPbAaurk+04rzFnCNJ2AbOBHYIMygFrbBS3J9PRy1u3SgjMkMwBS5UD5keAeTlgmYYgN2+W\nUP+FF7ontL/4orj9xjH3cq62b5fvJtko3La2mICvqpJz4xSPP/0p8KUvJT9mPw7YvfdKuRRAHkqL\ni+V9ZjRtuhSsANuxQ2zzZPbfxo2SCNjRIa5Uaam7e9XYKF/asmVyQo87LrGDcRNg//iPiQn7mzbJ\nhXfGGeJYueXUvPGG/N+JEWCRiFz8zmlbghBgzhCkHwfM3Nxenejg6CCO+cExKC0ficu/+epXJaFx\n8+ZY7aF0HLDSUrlBnaPT3PArwNycH0MqB6yoKP74jftl58S5hSBTOWB9fdL4HDgQSzzPhgM2Z05y\nByxZCNLpgDkT5+1kX4MdhkzHAevslAbU7pTdQpBuSeW2AzY2Jq/d3K0zz4wXYG7rALFE/FQOmBFg\nboI73RBkrnPA/GCf1127JER2zTXAv/1bLGXBrc5XY2N8iN5NvHuFIIMI6bmRDQfsySclevLww8nX\na2kB/vAH7/87B5/4CUHW1cn36xQdznsyWQ7YCSdI+2DINAS5ebP0WXPnxo8SNpjwo8ErzL1jB7B6\ntX8HTCn3OVPXrpXE+mQDLYwDNn++HLPbQKPeXqnpCEhff9ppksf2wguJ665bl1rUF6wAM8l5990n\nU5G4VQ7fuBF429vkJO3dK0Uk3QSYcb9MR+oWhjQC7NRTxXUbGhJl77xozIVXVibiw21aAy8BVlEh\nDb3ptO2OfcmS+EKU6WCPgowLQUYG41wrt07B7hC8OqGtrVvR2t+Kkmnd49uKRGQEzE03xT9ZpRwF\nOdyNJdOXjDtgftwvQDoXrw7Zvhnd3I6m7ia81PBSSgEGxDeIdgK+YaIhyFdfFVFgzlU2HLBTTpl4\nCNJ2wJYvT7yPnCFIs555ck1HgJnr0L7+/eaA2SGI3l45v3ZOo2HRopgT09GRmCdmMIn46QiwbIYg\n80GAmZGPGzbIA67BrdL966/Hpj0C3AWY0wGrrJTvPltJ8XYO2OCgnBdnu5QuTzwBfPGL8p098wzw\nn//pvt7jj8uDu5e4fOutRAGWKgm/qMhdXKXjgK1YES8igghBnnxycgF23nmxv90EmNYiwC69NLkA\nsx0wsy37fuvrkz63ttY9vchgHsRKS+VcuUXLensl8tXdHRso9Fd/5R7t+uQnU4+mLEgB1tsrF9IH\nPgD83d9J6OZHP4pfp79fBMuqVSLC3vUueSJ3E2om/8vgFGBDQ/LlzJ0rF81f/iJfwvBw/BetdcwB\nA4BLLhHnzWZsTGruJHPA3Bpx56S5bnip+7gQZHmsDIXTAUsVgvQair+xRbKZi6s7xwWYmdbEdNoG\nPw7Y4rrF6B7qRknpWNIE/O6hbqz4oZSh/+u/Bn7yk8R1nGUo3EJvv9/2e3znxe/4FmAmFOPM/wLS\nD0GaBvall+Ln4MuGADv11Jhw6ury74D19cm1blwit1CTmwCz10snBLl/f+J1OJEcMLfv2mBPTuyW\nqG8w90ey47c7GT8CbGxMRoiuWpW4LTv8lm8CzHwGUzza+R25FVr9y1/8CTBnnp5bGLK9XZymTBgZ\nkfNvjw6sqUlsl9IhEhFX5NJLpU9673uBm292X3fzZnmYTzb9li3AvELOZno5g1sY0k8SfmtrbOSy\nUsCbh990TSfxy/e/L+2YCaU6Z4oA5H7avDn+unDL3WptlWM666zkIUjn9eO83158UfLR3vUuedD1\nwjhggHcYsrdX+rRnn41NlXb22fFlbcy2Dh5MPZK9IAXYrl0SJiwuBr7+dSnn8PDD8U8Vu3dLOK20\nVKo/f+Ur3gn0r76aKMDskZAtLXKRFhVJQ3T88THBZ180RjEby/1zn5MnI7uExJEj4o65JQabxt6t\n4U0mwHbvFgFSUiI32apV8fPdjQuw4cQyFM5RkKkEmFsntKlFPmBRZdf4tp58UgSoE/tJ04RabVHX\nPdSNGRUzUF1WjbHS7qQO2L6Ofdjdvhv9I/0oKXEPI9nOydCQNL6VlTIv5lN7ngIA7O3Yi8M9h8cb\nu2RhQ3s6IucISCBRTPgJQfb3iyi3S1RkIwS5apU0amYGBj85YN/7noSPjzkm5kiZwpy2Re8mwOx5\nEs2109DVgKt+c1Xcel4OmE06OWB+BJg9ObEfAZYsB8w4YKZOnvOhwVmIdeNG2Z+zkCwQu9+0ds9F\nzAcHzE28A+7X/s6dEoEw+HHAAHex8Mwz8sCdSWjSnFOlYteGccAmKsC2bZP2fOFCSbtYvz6xHqBh\nyxa5l0wYyyYSEeFiHuAB73bAOY2VU4CZGmDJBJjWieH3a35/DZ7d/+yEHbCHHpLjv+ACuUZmz5Z9\n2m3F5s3Sf9sPpm45YGaeXTMXrBfOELZTvK9dC7znPRLlevVVcSB/+9vE7djnIpkAu+IK4Be/EBPm\n1FNFZDofPMwI61QjMgtSgO3cKTey4dRTYxevwf5STj9dRuq4zRU3MiL2pN35rVgRP+LQ+QW/+93y\nBZ5ySvwJNu6X6ahqa4FbbhGRaOjsTN3Yp+uAfelL8rTR3y/i9LLL4pW+STq1Q5AVJRUYGB2IE31D\nQ4kJy9XVco4GBrwF2MYjG1GkiqAqO1MKMPtJ04Tn7FCTyVObUTEDI8UdSQXY/s79AIDWvlbPdWwB\nZjpkpYBdbbtw+a8vx0hkBHs79+JwrwiwtjYRyG5hK8BfCNK+Gf2GIFta4t3CbDhgtbUSejtwwH8I\n0nQmF18cW1ZcLO81ne3wsDxY2B0CIA2TU7xvad2Ch7Y/hJFIrHV3dtzOBHwgvRwwc08my+0CYh2S\n/eTrJJ0cMLfrGZDvcng41qE9/bQMTnHDdKSDg7HK9zb5IMDcxDuQ6IC99ZbkFtnC1RTLtXG2r4C7\nADtwQN6byUT1dokZpwM20RDkvn2x+WCPOUb6I69pl7ZsAW64wV2A7d4tD0H2de3VDphpvwxOAdbR\nIYaBff1XVckDgt3+lpTEl+852n8UW1q3JE3Cb24G7r8/cXl3t3znd90lVQoA2X5dXfx3uWGD9Mk2\nbiHIHTvkobG+Xv7ndd07Q5B2uwOIC3vOOeJU3XOPTMb+uc8lmjF+HbB/+Af5TLNnS1TNbZCNmdsy\nVU2yghRg5osxKCXW7yOPxJaZqrY2bg7Y5s3SKNgNilHtBucT8rvfLRfyxRfHCzCT/2XzwQ/Gx4FT\nJfx6CTATznGrBbZnjyTEVlTIhbhqVXz82uS4xI2CLIl3wIyb4+w8lIqNzErmgJ0+73SoipgDtnGj\nXPBObAfM7XMaATa9YjqGiztcQ5AvN7yMgZGBmADrjxdg33nhO/jzvj8DcBdgALCzbSdGx0axu303\n9nbsRXNvMyoqx8aHgXsRdAjSuG5OFyAbDpgpZ9Lc7D8E2dEhUyP99Kfxy+0OsrVVrjunWHZzTxu6\nGjAyNoJd7bGKnH4cMDsEqbV7jSzAvwNmfwbn/f3m4Tfx07/IB/abA3bkiPd3bQr0mvOVTICZp3fn\nRNyGfBFgXg6YLTpefz3+wRYQgeIcMeY3BHnwoAjSZNXVAXEmvMSU7SqadjhTB8zpNAHxAkxr4G/+\nRh6g29qA66+XBG1nW+4MPwLuOWBjY4kDppwCzO2YlJLzbx7knX3RmB5D+0A7thzZEpeEH4nEH8Nt\nt0lU6Utfincjn31WXCbnPeDMA9uwQdKCbNwE2NatIuCLiiTn0MsFc7adznPR1ibHcPrp8vPHP8qx\n33JL/HZSOWCm3TnuOHHAXnwxNo2TmwBLVj/PUJACzOmAARJ3twufuj3VHnOMNIL2xfTKK4lDsJ2V\nmJ3b+qu/Emv0nHPiT/COHbLcxjmtRrKnbZOE7xaCNJPmOgsdap3oGDgnCDZPCM5K+HYOWHNzYjjN\n/gxeAuxI3xEMRYZw0tyTgIouDA25l2cw2A2dmzs07oBVzsBIcaerA/aZ//kMntjzhKsD1jnYiW8+\n9008vvtxAMkFGCCOzL6OfSgtKsVQcRs6OpILsFQhSOd8d14hyNeaXsPQ6ND4+Th6NH4UUjYcsGnT\nYteGm4sxe7acI/vJt73d/Xq1BZjzCdTeXoIA65aRJJuPxIb0+skBKy+XDsu4sV4DNIIQYL/Z+hv8\n6LUfje/XTw6YKV3j5XYuWiQCYmhIGu7Vq93XM+csWS26yRZgXiFI58PH+vXxeT5ALFxjOnet3QWY\nlwP2vve5u0eGb31L2vNf/jK27OmnJdkciP+OgnLA3EYA2wKsu1tCc1//ugiKefOk/3IKSbf+w60d\nOHxYjtn+Pt0EmPOYAOkDjbBw3h9dg12I6Mi4A2a+o//+b+AjH5HXjY1SauOtt+Qc227mU0/FO+UG\npwB7881EAeY2UGXLllgx5uOOk/e54Wx/7NQHINaGVVRIru3y5XL/OfPKUjlgg4Pu7U5lpYhUu1r/\nW2/JKM8pKcAaG0UR2yxaFC86zLQCNsXFclHaJ/711xOdmro6uVHNBejshKZPl7j/zJnxF42zEwXk\nZo9EYjf3RB0wwD0M2daGhPyn+vp4J8NMx2IXYq0oqcDg6CDq6uTCcwunGeywiLMT2tSyCafMPQXT\ny6dDl0sIsrVVzoPblEV2Q+d0DMb0GHqHe1FdVo0ZFTMwVJTogGmtsbdjLzY0b8D+rv2YVjoNR/pi\nd/d/vflfqCuvw5ZWmQenqkoExehoogCbWTkTz+5/FuUl5Vg2Yxm6xw5D69QOWLIQpPPJ3SsEee0f\nrsXafWsxbZocV19ffGOYDQFWXR0vwJydaFFRYtKsV35UJgJsQc2C8bxBIL7xdctdAeRaMmI6mZiw\nk3knKsBebHgRW1u3omuwK60csGQCzITe3nhDOl+vhzDjNnd3e9eim8h1kakAs8+rlwPmDLs1NCQK\n6eJiucZMW93XJ8uc581NgB08CHz601Jvy23AUUMD8IMfAHffHT/46aabYsn7dgjSfIZMHTBnsjsQ\nfy7M51izJpbfde21wK9+Ff+etjb3/sP5fTvDj0D8aHUgfj5JG9uBdPZFbQNtmF4xHZuPbEZpqR5/\nEDt8WHKZu7okyf666+QaPvHE+IjSs89KrpWTuXNjgmhsLFadwMbLATMC7KabpNqBW1g0lQPmZnrM\nmRMv0iIRuUfMNeEmwLzuIaXi53sdHZVjP++8KSrATAdv4+ekA3Kj2NMaNDa6N/b2DeQ1TN2ZOOi2\nnlKyzN5WsnyTZALMLUnazS2YNy9ejJpq4PYoyMoSKcS6eLE0bMkcsGQCbGPLRpxafyqmV0yHLusa\nF2BmZI0Tu6Fzfs7e4V5UlVahuKgYMypmYBCJOWCHew9jcHQQb7W8hf2d+3HmMWfGhSDvfv1ufPfi\n72LLERFgSsUSuO0GZ2f7Trx/5fvx6K5HsWzGMsyvmY/OiJzcTEKQzuvQLSw1EhnBno492NW+C1VV\ncv5nzYoXrNkIQTodMLdO1BmGzFSAtbbGV5I/2HUQlx13GTa3xhwwO0m9q0uEoJtwsgWY18TS6Tpg\nR4/GP6wNR4ax/tB6nDbvNLzW9JqvHLCaGmnAjx71FmCLF0u7s3NnrPq3G2Vlclx79ngLsMl2wLxy\nwJwhSDdnC4h/kPS6drxCkOeeK23L+vXSkV92Wez/R46I0L3uOnEgDh2S9nHbtlhna4cgS0rkdRAO\nmJsAM9d0e7ukpixeHBMUV18toTA7r9HtXnNrB/bskfDj4OggXjwodY7sAr6AnHu3NtgWFs7742j/\nUayYuQJlxWXoLz40bkC0tYkZsWaNiMYvfEGWH3tsvACzc+FsbAds7145N87P6RRgnZ3yY4yWSy8V\nF8yZCjE8LH24PX+x3QaPjsp37mzrnO20aQ9Nzcl0BBgQL8D27ZN29JhjpmgSfmtrYtKmeQIwMWkv\noWNPG2K25RRzQHwH47Ut59BZP+EaPw6YV/jBzQFzy5cxnaw5F8YBcyvEumSJdAxu4TRDUgfsiDhg\ndRV1iJR1jidku51TINEBc5agMMc3u2o2+vTRBAG2t2Mv5lTNwYbmDTjQeQBnHXPWeAiyZ6gHh3oO\n4aMnfxRH+o6gd1iUkrm5nQ7Ylcdfif2d+0WAVc9H27A/AZYsBGl3HJGINFxO52Rf5z6Mjo1iV1tM\ngDmvZ2fV/UxxhiC9wki2yI9EpINw62z9CDBTg8tMBG5ywC5fcXlCCNI0VG6i1mAmQ0/WENoNuVeF\ne8OsWdJ5VlXFwgobmjdg+czluGTZJXi58WVfOWBKSSezf793vp9xwHbudO+khiOxR/tFiyQcFZQA\nM+GRZLmIqZhICNItuR6Id2G82gqnA9bbK/fD7Nkiuh5/XEJhr7wSW8cIvvJyyQl+8EFZT6lYZ+ts\nc+rqgskBSxaC7OiQz3jPPbFQ3uzZkkv80EOx97jdR24CzDhgzx94Htc/cv349mxB4TayFEjhgPW3\nYXbVbJw892QcwZZxt6m9XaJEX/6y5BqbwUJ2TnVXl/Q3bveb7aq75UkDiX3ptm2x/C/DjTfGj+4H\nZLq/k06K73ftc2HmiXQW866uFnFm+iJnzrhb/bJUAsxc+83Ncp6nZA7Y2JicLOeFWl4eLTYafaJw\nS8IHEvOjjhxxf1KwGwAvF8AZt/ZyypxiLpMQpDNJ2m3EWHW1NDrGqWlvB2qnj2AkMoLKEmmFzVRE\nRoAF4YCpii60t0/cAbMFWH11PbrHmhNCkHva9+CiZRehta8Vw5FhnDD7BBzplzulqacJC2sXorio\nGKtmr8K2VqklYpwTI8B6h3vRMdCBi5ZdBAWFY6cfi/nV89HSexiVle4dn46qWZMD1t8vT1/Ojmja\ntNiN7TUqbvvR7agoqcDO9p2oqpLz72wsi4rkXAdVjNI0HslywID4p1rT0bqNCPUjwID4a6e8XKOx\nuxHvOfY9aOxuRP+IXAj2fZTsOjTfY7JpdWxX2o8Dtnu3I/x48EWct+g8vHPhO/FK4ysJOWBP7nkS\nH/ntR3D7utvjtlVfL/dishDkgQMyStkpwH639Xeo/349nj8g1RwXLpQh+EEJMHOf+ZlRwgs/Sfim\nEx0b887tAuIfJL0Et1OAHTwobohSwOWXS0f80EPS6Zk2374Ob7xR8sF+/nMZ8GAcMDsECYioWLZs\n4g5Yb6+cX2d75wxBzpwpgstOnXn72+PrgXk5YM7v2zhgjd2N2Ne5D5GxiG8BtmBBTIC5OWCzqmbh\npDkn4YjeEueAffrTIqTs6XyWLYul85iCrm5pJ7aYaWpyD406o0l2/pdh6dLEEbSPPiolmGzsc+Fl\nnpjBZV7rzZghx2OXz/ArwIypMyUFWEeHNMRuo+Ps5Du3HDAg/mLQOjMHzHmCvdZzOmCphrynkwPm\nVVHbrk3U2wsUVUr+l4reITVlNRgYGcD8RcPjAizdHLDIWATbjm7DyXNPRl15HYqndeLAAW9RC/h3\nwOqn1aNrtMXVAVsxcwVOrT8VS6cvxZxpc8YdsMbuRiysleJKJ845cTwPzCnAdrXtwnEzj0N1WTWW\nTl86HoJs7m1GVVXiub/3rXtx/i9k3gxTfsFtGiIgftSo16i4HUd34MJjL0zqgAHBhiGNAzZ3rjTA\ng4PuHbxdp8nL0QUmJsDGKtpQUVKBuoo6HD/7+PECvnbjlcwB85MD5nRqUpWh2LMnvtN7/uDzOHfR\nueMCrKxcY2BAxHZRySj+/tG/x7kLz8Vdr92Fg12x3iCVADMhSKcA29O+B//r0f+FsxecjdeaXgMg\nHZQtwNYfWj/+ADARAZZp+BHwF4IsLpb9mJzGkhL369+PAHOGIE31fUCSm7dulf+vXBmbIcS+Ds8+\nW/KVNm0S18YtBAlIbpGZ/HoiDpg5Lmc74CbAnDjNAL8hSOOANXY3YjgyjIbuhrQcMK8QZNtAG2ZX\nzsbKWSvROrY7ToCtWCHn2RaQtgPmlXMGxPe59pRmNs6+dMuW+DkxgdhAFnvk5R//ODEBBsTngTkf\n/EpKpL2xRaE9r6kTuw0z/Z/XxOA2BSfAvAQT4O/E2xd9V5cICrfQgh8HzLxvcFCEjqm1lGxbyUKQ\nJtzhFYL0ygFzOmBA7HMax617uHNc3ABAcVEx5tfMR+mMJhw8KNtNNgrSTYDtbt+N+mn1qCmvwfSK\n6UBF17gA8/qO7KRSNwFWVy4twrzqeehTLQnVwvd27sWyGctwWv1pIsCq5ozngNkC7KQ5J2FrqxSG\ncwqwnW07sXKWDKP94AkfxNkLzsb86vk43HsY06bJuR/TYzjjZ2fg47//OP75yX/G1tataOhqwKJF\n0tgkC9mazsMrAX9H2w5cuvxSNPU0obxq2PN8BSXATMHbykq5LnbvlnPi9rS6alXsqTxZgdJ0BJgp\n/tpf2oBFddJKn7/o/HHHx68DZocgvRrCdJPwbQesd7gXa/etxWXHXYb5NfOhlMKMBa3Ys0dysx7a\n8XscU3MMbn7nzfjQCR/Crzf9enxb9fWxcKYbxgHbvTtegL3Z/CbOX3w+rjrxKmxokVmLFy6MhSDb\nB9px1n+ehR++9kMA+SPAvOZTNWFILwEATNwBM2G+8nKZ5Pn97493RZwhz2uvlQ5/1ar46Z3cvqOJ\nOmBuCfiAPwHmzLd0u4+cSfhayzVkBBgg7bBTgLml6QCpQ5CzqmahvroevfrIeAjSpLA4nXDbLTcO\nmBu2ALOnNLOZNi3WjwKJk5IDcs2VlsbO665dci84E/rt3NNk04zZho2bMHQ+BKQa/BMKByzZje1H\ngNkXQzKnxo8DBsQ6D5Nv4mbz+9nWkb4jKCofSHsUpJcDZm5uc1Pv69iHpdOXxq2zqHYR2kYbUF0t\nTxzphiA3HdmEU+ulcE1dRR1GSzqxf3/yEKSdq9fREd9B7uvYhwW1Us2zvroeXZFm/PrX8e/f074H\ny2Ysw6XHXYr/3955h0dVbW38t9NIIYV0SEIoaRAgAaT3rnTFhkpRVESaXtvl0ysWFEQUBUFQVBBB\nEFCQ3ouU0EJCDTVACCmEkB5S9/fHnpnMZGYCKlfQe97nyZMz5+zZc846u7x7rbXX6lynM74uvhY1\nYJE+kRxLVzvtjAmYhwecyjhFuJdidh/3/Jho/2j8q/sbgrG6uChfoJyiHCJ9Iln80GIeCHmAjec3\nUqeOmmhvR2No7T2evn6aRr6NCHILItfuguE7lXGnCJh+0hFC3XNVxKSyBuzPEjB9UMSbNyHfNokg\nN0XAOtXpxM5LOwE1qN28qfzl7rQGTP+cV3Ov8supX8yeIS+v4hlXn15Nu9rt8HJWDxPkFoR3/SQO\nH1btfnrMdF5rq2wwQ6KGsPDoQoNmSu8DZo2A+fio9uDoaDrpHU8/TmPfxkT7RxOfqqI3BgVhCIey\n5/IeGvk24v1d7xOfGn9PEDBrPmBQ4YhfuU3oN8WAKQn4vSZIPaZPh/ffx7CJCCy3Q29v04nWUnYB\n+OMaMGsL4MoEzJoyQE/A9K41lctVHgNOnlTv0t8fruRewb+6P2evn8XNTfUhffT9W2nApLRsgvR2\n9sbXxZfcsnSTKACW+re/f8WC6M9qwPRJtPVt7MIFy76Sxu/72DFlxq28kHRyUkQtL+/WGjA9X7h6\n1ZwY/h4CZkkDZim6f2X87QjYndSA3Yoo3EoDBhWNpqoXfSsNmJSS/j/253S1H34XASsvt74C0z+n\n/t6NtT56BLkHkZSdRHCw+k39QCilJLOwYvSzRsCOph2lsa/yqPRw9KCIqjVguy7tYnfqBhwdVcOu\nrNI+knqEZv7N1P27+JGWb54N9cINpQEbGDGQV9u+io+LjyEMhTEB6xDcgb1Je8kvzjfTgB1PP05j\nP1NP0JquNQ3piFxcYOO5jfQJ7cOEDhPoUb8Hver3MiFgVREFfcetygQZ7h1OmFcYN2zOGmRcGXeS\ngOknHb3DsbUJtHZt1S8KCqpePf4RE2SuzWUDAesY3JHdl3dTVl5mMvjeDgGrygdMv+GhqKjifecW\n5dJ7UW+Grxpu4uyufzb9/yUnlvBY5GOG60HuQbjUTCI2Fqo5lhObEkvP+iq9Q7ugdhSUFBCfpkiT\nn5/SAlhzdBdCybbypKLfxBLpG8nZzLMUlRYZJjIXF2USfbjhw7zW9jVmHph5xwhYSm4KH+z6wOp3\npJSGWHlQ8X6krNoUY0kDllecR5M5TTifqULY364G7Pr1CnOTsQkS1Jjn53drAgamE621sfWP5oK0\nFm/rdjVg+rkoN1fdV2XXmurVlfZaPw5s2KB2BAoByTnJdK7TmXOZ5xCiYtyR0voGiOrVlTZXv8uw\nchgKLycvfF18yS4z1YBZkqsQavGfmPjnNWBg2sasETrj952cbP039ePO7ZogLRHDyvFAfw8B+8dq\nwG6HgFnKcaVHZQ2YtbqMhX87GrDb9Zex5IS/78o+9ifvJ8/2UpUmyMBAtWLSr0xSUtTvWyqrBbRJ\nwgAAIABJREFUJ2D6znM28yyhnqajf6BrIFdyrhAcrBqLfmBadGwRYTPDSM5RzgL16yu1d26uKQGL\nS42r0IBVcye3JIucHDVYWiK2cw/PZeHRhQZbfuVV7ZHUIzSt2RQAL2cvcopyTCbMvOI8souyqela\n0YNdHVwpKS+hsKTQhIB5OnnSOrA1686uM0zcei3l8fTjNPI1SrgGBhOkk7PExQU2XdhkmGwBetbv\nydYLW/H2LSU3V63QKk8cGQUZlJaXGrR8lkyQNwpvcLP0JjWr1yTUM5Rr5WqS+28SsMoDh5+fdQJm\na6ve99mzd84EqSdg2bLCBOnr4kst11oGAqNX4d+uCVL/PGXlZWbl9AOf/n2/ue1NmtdsTgPvBuy4\nuMPkGUD125yiHLYnbmdA+ADD9dputZHul0lLA3uPdNyqueFkrxiWEIJBDQaxMkFtY/PzUwsi4/e9\nN2kv49aP4+M9HwNqojYjYGnHaOTbCEc7R+rVqMepjFOGSUVPwDrU7sDjjR5nZcJK7B2Lf1ebsLZr\n9NN9n/LW9rfYcsE8qmm5LOfVTa8S/kU4R1KOmMg0P78ij6Yl6ImHsQP+kZQjlMtytl9U2Sluh4A5\nOam2qCdFFy6YRn7X43YImLF2yJoJ0jhDx++BtQWwcUgOawsZ493q1u7dzk7FDtMHId24UQUdB7Xg\n7BzcmXM3VN48PQHLzlbPY5xiyBh6R/yqNGDZpemGoMdlZdY1u3o/sKo0YPr5LiPDugYMKtrYtWsV\nC8XKqEzAjNOfXbhxgfHrxwMV405V87KxwiYl5c+ZICs74fv6VsSgrAp/SwJ2KxNkYWHFLrLK0K+G\nystvzwRZlW8XmGrArE1Wxto0S07402Om09S/KXk2V6p0wndwUIOXPo5ZZX8SY+hNkMYasFAv08JB\n7kEk5SgNmPEgOPvgbKL8o3jql6coKy/Dw0N1tH37KmRaVFrEzks76VynM6BMkNlF2QTVlpw6ZS5X\nKSU7L+7kzPUz1K6tOqwxASstL+V4+nGi/KIAsBE2+DhXaLcSMhLo8F0H+of3x0ZUNFshhMEP7ErO\nFYOGBeCRho+w/NRyatRQ7zw7G6pVL+RS9iUzbaBrNVccbB2wd8vAzjmPQ1cP0alOJ8P1mq41qeVa\ni6Np8QQHq1ybxkRh2Yll1Pu8HvPj5ps44Vd+j2vPrqVd7XYIIQj1CiWt5K/VgIF611X5RunNkH/E\nCX9f0j7e2/meoZwxAbtefpG6HhX28k7BnQwpo/ShKG7XBOnqqjSwgdMDDb4w5VLF7NCr/vUTzMbz\nGxnXahwPRjxoIEz6Z9D/33lxJy0DWuLuWCGYIPcg0guTqFMHbGskUdvdNPrzgPABJgQMwMlJciL9\nBC+ufZFHlz1KNdtqzD40G1ATtXEGj4KSApJykgxtMdo/mrjUOEPic3vnAo6mHaVVYCtqu9cm3Duc\nw1lbbltTU1CgJqgrV0wnjpyiHL6N+5aPe3zM65tfN8hNj0m7JrH3yl7e6fQO7+x8x0T2+m391mBs\ngtS36diUWGo41mBb4jaDrK5dUxN7Ve/buI1VTr2jh/GEbG3XpfGON9UnpcF0rMef0YD9UR8wR0f1\nuzduWC5zJOUIuy/vpmVLFTA8P1+NwV27QmFJIbnFubQObM3Z6xVjSEaGufkxNiWW7t9356mfnwIq\nzJCVw7RcL1Q+YJ5OnhSU5VBUWmIwP1ryFwU1/xw9WrUGTAjlzxUXp/qksdLjWv41gy+lfi6tvDA3\nRlUE7IsDXzDr4Czyi/P/sAZs2Yll5BapbbWVs9j8Xg2YXrNfFf52BMxStHk9bkfoDg5qMLlVuAR9\n56/Ktwt+nwasvNx81XHy2kl2XNzBWx3fIockwyrtSPEyvj3yrVldISEVicLPnVOfLcGSBszMBOlW\nQcD0ZCI+NZ7L2ZdZ/+R68orzWH1mNaB2Hl26VEHAdlzcQaRPJD4u6mU42Dpgb2NPUN0CysvN31Fi\nViL5JfmczjhNYJA0I2AJGQkEugUaAsWC8gNLy1M6+ql7ptI7pDdLBi0xe1a9H5ixBgxgYMRANpzb\nQMOoQg4fVrK/TgIhniE42JovD0M9QynzOEuqwx6a1WxGdQfT3hbtH83RtKPUqaMSvOonjj2X9zB+\nw3iGRg1l16VdJiZIB+ebJlqXb458w4imIwy/l1JkTsCKSpUjx50kYLerAQPltKwnYNYWFfoJpqzM\nNMH8hK0T+GjPRwZioh/kbt6EjJKLJn6IQ6KGMD1mOjlFOXh4qN8z3txwo/AG/7f1/+jwXQeOpR0z\nM0F+d+Q7HO0cGb5yOD0X9uTRZY8CGLI75OdDLlfJKMigsV9jBkYMZGXCSgPhcHJSJktPT9iauJVu\ndbuZPKO+f0RGgvBIMiH3AG2D2nI19yqJNxIN48j5aj/TZUEXnOycODbqGB/1+IjMwkwyCjJ47z0V\nHkGPk9dOEuYVhr2tUidF+UVxJOUIDg66eIUO+2ni1wRne8XiH4t8jHWXltw2UTh6VE1oO3ZUvP/z\nmecZs24MPer14JU2r2AjbFhzZo3hO3Gpccw8MJPljyznjfZvcPjqYbYnbsfOTskqJcW87aTmpfLy\nhpeRUpqYIPVk6HDKYV5s8SLbErchpcTeXsk8La2CgOUU5XAk5QhfHPiCF9e+iJTSYIUoKFB1WjJd\n3Y4GDCraYUEBrC56lVbzWhGXGme4bk0DduNG1bH4qjJBGgditdaP9GZIs3RYJ5bR/rv2TNo1iRYt\nFAHbsgWaNVPyv5p7lQDXAEI8Q8xCURgTsLLyMgb9NIge9Xrw6+lfuVl6k8BAtQCuvEtYrwGzETa4\n23tRQIbBAd8annhCBWitSgMGioBt26bmW+O5dOqeqQxfOZyruVcNc+nly9brskbACksK+T7+e2q7\n1+bg1YMGv7+quEBlJ3xvv2KGrhzKl4e+BP68Bgz+gQTsdkyQVQkdKsjJrUyQ+jQl+rr+veXfJilU\noIKA3Y6/TF5ehYOgHhO2TuDf7f5NA+8GZMkrBh+wmNyfeGHNC8w9NNdktRYSorbOQ9UErFYt1VCv\nXwf3GqVcyrpEvRqmS0i9D1jr1hXJgeccmsNzzZ7DwdaBV9u8yvSY6YBKqwAVBGzNmTX0C+tnUp+H\nowd+wdkWY2nturSLXvV7Uc2uGjWCUrlwQclfr/Y9knKEpv5NTb7jX92f1Dzlpbrj4g6eaPyEIYyG\nMXxcfDiXeY684jy8nSuYjLezNxHeETjVO0RMjOogySXm5kc9wrzCKK9xlut2Rw2+aMZo7NuY4+nH\nqVNHTe5+fkpzN3rdaKb3ms6LLV5kT9IeExNkrvdWui7oyi+nfuF85nlOpJ+gf3h/AEK9QkkqMCVg\nsSmxeE715K1tb+HkUnLHTJCVNWD6gUHftvKL81lxcoWSQ5gKg1C5TR9IPsAHuz7gYPJB7O0xhNCo\nXl2ZSmKuxHAp+xLrn1zPqLWjyLqZRe3aiszdvAmpRaYbQdoGteX+kPt5a9tbREeroJr6CblclvPE\nz09wMesiA8MH0vfHvpQ6pRg0YI4uxSw6toh1T6zDRtjQOrA1e5P2ciTlCG5uanCuXh12J+2kY3BH\nbIQN4d7hNPRpSKt5rYhNiTVkqfD0hC0XttCtnikBq+1em6ScJBo2BOlmTsBsbWzpF9aPVadXGch4\nqs1BxrUaxye9PqGGUw1shA3Nazbn8NXD+PtbdsDXo0udLmy6oHLmBAVBmoilRa2KbNbd63XncFrM\nbROw2Fj1f9s2cK5exqRdk2j9TWt8XXz57P7PEEIwpuUYQ+LxS1mXeHTZo0zrMY0AtwAc7Rz5dsC3\nPLr8UTaf30ydOqrOytrTD3Z9wGf7P2Nr4laLGrDDKYcZ1GAQzvbOhp3JkZFqEZOToya69t+2Z8gv\nQ9ifvJ+VCStJyEgwaCD0JMfSIjgwUL3rsrLbM4Xn58PV8qNE+0fTY2EPEjISANU/jKPSgzIftWyJ\n2UYgPQoL1dhvyWTu6qqul5TcmoAZb5YCFax41NpRLBm0hLjUOAMBmztXxeMCZX4McAvAxcEFLycv\nErMSLRKwNWfW4Ofixxvt36CJXxN2XtxJo0aKnOuVAbMOzGLuoblqF6STugmvan4U2qRVKVNQTvD6\n569MNoznrchIRSCNSXT2zWy+jfuW3qG9mX1wtkEDZuwbnFmYybCVwwyuBtYI2E8nfqJlQEseavAQ\ne5P23rYGLCNDWbmuXYOr8giuDq58vv9zikqL/hABq9wO/5EEzNtbTRh6p0499EKvKtYWVETDr6wB\nyyzM5KvDX3E07aiBNOknofOZ5/l478e8vuV1k7p+jxO+sV/a1gtbGbVmFPGp8YxuOZpAt0BulCVx\ns0hSUADJRQksfHAhMw/MpP137Q1mltvVgDVtqmzzCQlAjYv4V/fH0c7UJhvopsw3rVqpRLG5Rbks\nObGEZ5s9C8CghoNIvJFIbEos7VUYLBwdVcdafWY1/cJNCZi7ozs+QVkmeSD1Ed83nt9Ip+BOhHuF\nI7xPExOj3oM+zldsSizNapqSHr0jflJ2ErnFuTT0qbQvWYdBDQbx1va3CHALMCNoLWq14GLxIZyd\nVdu4kH/MZNIzRqhnKK0eOIttTcskrbFfY46lHzOYHPz9YUHcAjydPHk08lEivCPIupmFcLtqWLnf\nrH6aTnU6MeLXETT/qrmB3IKa4DOL0sGuEB8fNSA9uuxRpnafyuYLm7nsOf+OacCExyWGrxyOlBJ/\nf3Bzl3we8zm+03xp8XULouZEMXjFYM5eP0vbtipRcEZGxcRxKesS9/9wP4lZiTy+4nGKSouoUUP5\niukHm4/2fMSrbV6lY3BHetXvxecxn9Osmaon4Xw+hWW5+Fc3na2mdJ/CN0e+oXO3YpYtqwgLM23v\nNPKL81kwcAGvtH2FPqF92F/6lcGn6RzrCfcOp4FPAzYN2cR7Xd7jjXZv8O7Od3FzUyYrd3dF3DsH\ndzb83qYhmxjdYjR9F/flcvZlPD1BuqSSnJtM85rNTe4tyD2Iy9mXiYyE8uoV/mvGeKzRYyw6tsiw\nTf865prm+2rdx6Grh0zOSSn55sg3Jlq35rWak3Uzi3OZ5+jQAbKqVewyBgjxDCEp5zKlFBn8QC1h\n82Y1vsXGqqTDqamQ5raOH4//yJGRR5jWc5rhPTwW+RgHkg8wP24+7b9rz+gWoxkWPcxQV8/6PZnX\nbx5vbnuTpk1Vvj/jSeVS1iUWH1/MR90/YuqeqXh5qd/Wk4D84nwSbyQS6RtJr/q9WHpCJWls1UoF\n0fT2hovZF0jPT+foqKMsfHAhA8IHsObMGsO4ac3/Cyo0mElJysfL2oRnrAFLLznHG+3eYEq3KfRZ\n3IcO33VgY8Zc0tJMNc5ff63q3b3bcp36jQGWiKE+P2BWVtUETL8T0rjMa5tfU200rC8l5SV4BKaS\nmqoIqyEptpG2v1f9Xqw+vdoiAft8/+eMazUOgD6hfVhzZg3Nmqm2kZUFLq4lfPDbB7y+5XWq2VWj\nmp3axeLp6EuhTbrVHZDGGDNGLRiMh97S8lKi5kTR5ps27Lq0i4YNVfoo/YJbSskHv33A/SH3M7XH\nVL46/BXOboUGE6ReAzb30Fy+j//e4D+oJ2BSmhKwH479wDNNn6FNYBsDAUtPvz0NWHq6esb9V/fw\ncMOHaeLXxNCnKxMwe+cCE5P93qS9JGQkmLxrd/cKJcs/joDpTZAvb3yZ5l8151jaMfKK80xUsHpH\n98zCTB5d9ii7L+/mzPUzvLj2RUrLS/H1rdCA6QnYrku7CP8inMXHFjPi1xF41Cg30YDNOTSHMS3G\nkJCRwK5LuwDVyNzcpYkJMik7iRGrRtBqXis+2v0R+cX5hhepv6/41HgGrxhMfc/6rH9yPY52jrhW\nc8VOOJBXeoO8/DKSC87RN6wv8S/E06NeDwYsGUB+cb7BIR6qJmAODioz/Zo1cNPJfFIAZbrLKcrh\nZqlK477o2CK61OliCAVhZ2PHC/e9wLzYedSurRr/Tdt0Hlj0AMEewUT6mIYq9nD0wMM/G19fle/w\nvZ3vEfBpAD0W9mBv0l561u9JuFc4Ra5nOHSoYpVzo/AGi4+rUA/G8HPxIzUvlZ2XdtIpuJNF7RfA\niKYjcLJzMjE/6nFfrfs4ePUgrVurwfrUdesasFCvUK4UnrVaprGvImD6sB9+fjDn8Bxea/saQghs\nhA3tgtqRJPYY/E3yHc8wqMEgzow9w+kxp5nUdZKhPjsbO2q71cE1+BxXCs/Q9tu2DAgfwOiWoxnc\naDBZTnF3jIDlef3GgvgFbLmwhbFjoeewWD6N+ZQtQ7YwqcskvuzzJSOajuCXhF8ID1dtedeuiknh\nvZ3vMeq+UczrP4+GPg354sAXeHmp1CJeXnDq2in2Je3j6aZqif5Wx7eYeWAmuSVZ9O8P18suUtM5\n2Owdejt7E+oZinvEEU6dUqS2oKSAj/d+zDf9vzGY5zrX6UxyaZxBA5ZQsokHIx40qev55s+zNXEr\nbbrcYO5cHQG7tMPgpwjKt3B49HBebv0yT/38FB9+CFk1ttG5TmdsbUwDHQW4BpCWl0bX7qUENLxs\npgED6Fa3Gym5KZzKOKFSrpSZ7zZuXrM5h1JMCdgPR3+goKSAoVFDTe6tb2hf1pxZwyefQKo8arJY\ncLB1oI5HHRwDzpqYyzZuNE0s/MIL8PHHapJ94QV17objEfqH9TfrI072TgyPHs7b29/mq75fMb71\neLNn7F6vOyeunaBJ02J27FCTSmxKLF8f/pouC7rwcuuXean1S5y8dhKfyOP89luFFuBwymEifSNx\nsHXg9XavM+vgLFJyU2jVClavVn1o47mN9Kzf0+Db2TesL2vPrjWMmxcuWA61o0dEhNLyeXpa91XS\nzw15hUVklqQQ7BHMiGYj+KDrB3Sp04VVZ3+mQQPVnkGRuUmT4PPPTdMdGePSJXPzY2FJIfPj5jNy\n9Uhq1FAmWykrdscWlhQaMnSAuQly/5X97E3ayxvt30AIQbR/NMeuxdG8ucpxqa/nSs4VAl3Vu3wk\n8hGWnVxmeEa9kiLmSgxnM8/ycMOHTeQaHS2Ji1Pjws7UNdT3rM9zzZ4zaL8AvJ18KbJLv6UJEmDw\nYFi1yvTcgrgF1HCqwdAmQ3ll0ys0bKhMuTVrKvL10E8PsePiDqZ0m0KYVxghniHk1dhHdnaFBqy4\nrJiZB2bydPTT/HD0B0B9PyNDyUzvY5VRkMGB5AP0Du1Nm6A27Luyj+hoyf79usDtHiUWN+voNWB6\nB/w9SSoLxvhW45l9cLZFDdjcjCd4cOmDFJYUEpsSy4AlA+g0vxPnivaSlWVqoTuYfJDq7lWslPgb\nErCrVyHdJo5Vp1cxpfsU2n7bFu+p3jSd25SLpTEGAubimUOn+cqJ+qGlD9F1QVc2nNvAvNh5Bg2Y\n3gS5KmEVD//0MEsfXsq2YcpPYfPVZeTmqhfk7nWT+fHzGdtqLFO6TeHZX58lISOBhrMactllucEE\n6eiRTe/FvfF08uSDrh8QmxpLxKwIdlxdzc2b6vfcPYt5fMXjTO81nVfbvkoDn4rMvD4OQWTLJHJs\nLuLl6IuLgwu2NrZM7DSRSJ9I3tz2pkEDZhyQ72ruVRbGL2TuobmczzxvSPHSr59So+fYnzHbAQlq\nwK/lWsugXZtzaA6j7htlUuaRho/wS8IvlMtyNm+GTfnT8Kvux5YhW8wmU/dq7tRtkMWTT0p6/tCT\nmCsx7H56Nxdfusilly5R37M+YV5hZNmepqSkgoC9s+MdHop4yCw0hH91f9Ly0thxcQedgjthDbY2\ntnzV7yueaPSE2bUWtVpw6Ooh2rQBN/dyDiQfMNN06BHqGUpCRgKnrp2yqG0LdAvkZulNPGplUK0a\nXCiIIy0vzWS3ZPva7TlTuNsQiDXH4TThXuF4O3vjV93PTGYRPmHMWHSWp1c9zTPRz/BJr08AFcfs\nht3JO2aCzK+uUkZN+m0S3t5wIncXfUL7EOUfRa+QXvSo34OHGjzEilPKDDlokDLJ/JD8HyK+iODX\nM7/yWjsVA2tKtylM3TuVhx8p5+OP1UQ7de9UxrQcY/BXCvEMoXdob+bFzmPgQMDjIkGudSzeX5vA\nNsRl7KNdOzUhLzq6iDaBbUw2jUT5RXHxZrzBB+zCzUO0DGhpUo+TvRMtA1oS2m0P166Bk0+Kwf+r\nMl5u8zLxafG07Z7B7qtbzPy/AOxt7ZWPY/UUbGpY1oDZ2tgypMkQFsQvwNevnLTic4R4mq6KKmvA\nfrv0G69seoXZvWebkb6+YX1ZfWY1peWlJGQkEOlrushp4NMA+5qnDGZIKWHYMOjSRU1K+jAwCxYo\n7Xe/fmqRmWEbb6JNM8bkbpM5O/YsD4Q+YPG6i4ML9WrUwz30OCkpUOC7g54Le7L5wma+6f8Nb3V8\nCwdbBwaED+Ci3QYyM1UybEf3HEavG83T0YqU161Rl2ein2Hijom0alURS2/j+Y3cH3K/4fe61O1C\nbEosLl43yMyscMDfdH4Tbb9pS36xaafo0gWWL7euqdlzeQ/OPte4dg0y5UX8nIKws1Gq98cbPc7Y\nlmOJuRJD46gy4tWmXPbtU+PT008rLW9l8yRUOODnFOXw4toXaTq3KV5Tvfjx+I8sP7Uc55qXOX9e\nLWb03f7/tv4fTec2ZcO5DUCFBkxPWN/f9T4T2k8w9KNoPxUfbt48eOutit8+ln7MsCmkW91unL5+\nGuGRZNCAeXlJXt/8Ou92ftegcW/k2wghBJeK4vDxUWbSeUe+4vlmz/N2p7eZ+cBMQ/0+zr4U2+sI\nmJc026hhDFvbis1gy08uZ8gvQ3hr+1tM7T6VZ5s9S+KNRMpdL+PqqojOurPruJh1kT3P7DH0qTaB\nbSj2i+HQoQrf4Plx84n0jeTDbh+y6vQqCkoKsLOD5s1Vnk+99uuXU7/Qq34vnO2dqeVaC7dqbvg3\nPsWhQ2pzwMuH7sfxA0c6fteR9WfXM279OH468ROenmreTkqCmrWkSkNWux096vUgoyCDdNvDZk74\n5woPUlRaROD0QDrN78TcvnP5ss+X/Hvvc2RlVSh1DiQfoO23bcmrudaq3OBvSMBSU2HWybd5u+Pb\nvHDfC5wafYqcCTm80uYVxu8YYvA/yPZdS5BbEEsfXsqihxYxvdd0fn7sZybumIirTxZpaYqtrk6f\nydj1Y1nzxBq61u2KjbBhcrfJfLh7Eu7uavWV77uNCO8IQjxDeKzRY/QP70/jLxvjZO/EBdv15OSo\nFcyamxNoE9iGqT2m0r1ed5Y+vJSFDy5k6MoheHgVc+EClAfuxtXBlSebPGn2bP4ugcScusKVmwmE\neUYYzgshmNxtMt/Hf493QA4XLyrW7ugIzq7FdFnQhZWnV7I7aTcd53fEY4oHn+77lN69Vcc/WbiN\nNkFtLMoz0C2QpOwkTqSfILMw08wPJtQrFB9nH/Yl7SMsDLZc2MTI5iMNmgljeDh64OCWSa8nEzif\neZ61T6w123kZ7h1OSvFpQ1ykvUl7WXJiCe93fd+sPr/qfhxOOcyaM+rdVIXWga0Zed9Is/MR3hGk\n5KXQuEUW1eudoIZTDYOGrzJCvUI5mnYUv+p+JpsB9BBC0Mi3ESWexxg+HL498g3PNH3GZBLtXKcz\n+69tqXAetjHXiJj8pmcox7P3ciL9BGNajjGcj/SNJEOc+NMErLxcrXSzHY/ybud3ScpOYv+V/ey6\nvIuOwR1NyupjCiVlJzFokDq38eqPfNjtQ2JGxKhsB7p783D0oPPjcdjZgYtvOisTVjK6xWiT+oZH\nD2fxscV06wZOtS5St4ZlNYZ+1dqzJ/j5S2YcmMH4VqaamBDPELJL0zl7OYfDccUkFhwn2j/arK6O\ntTtyMO03Ro+G0sAK/6/KsLOxo33t9mxP3G7RAV+PIDdlhkzKNvcB02NY9DB+OPoD7XpfwsPR02zz\nRr0a9SgpK+HktZPEpsQy6KdBLHpoEa0CW5nV1b1ed2JTYtmeuJ0AtwCzuhp4N8AxMMHgB6NPw9O3\nL4wcCdu3q1yJTZoo0uLsrMx9qfIoUf5RFu/f3tbeYHqyhvtq3UeBx0EAbnhsY2Tzkfz0yE90qdvF\nUKZL3S7suLSdHj0UCZhxdhztgtqZLOomdJjA8pPLKXdJJihIOT5vv7idHvV6GMo42zvTqU4n0tzW\nmZggP933KdcLr/Pc6ucoLS+lpKyEKzlX6NJFmV29vGD92fU8tPQhFh1dBKiJsNv33TjpPIdr1yDH\n9hzBrvVNns3HxQc/Fz/8Gp00ELBt29RuQwcHFWn94EFzmeh902YfnM2l7EvM7TuXzDcy2fjURrrU\n6YJdvd/Ytw88fAqInhPNm1vfZPHxxax4dAVDfxnKjP0z8PUrN5ggc1wOE5caZ9Aig25nbFocISEV\n/kcHkg+wNXErTzV5yvD+BoQP4KRcwfXrSvaprhu4XnjdRMMqhGBwo8EsOraIZs3A1Tub3Zd3M6jh\nINyquZm4lPi6VBCwi+4LCP8inIPJB9l9eTdPrHgC76nehM0MM+zABNhwbgNj1o2hfVB7Zj4wk1aB\nrbC3tad/eH9WJvxCw4aKgE3ZM4U32r1hMoe0CWrDTe99bNqkFg42NS7z5rY3mdZDmcs7BXfipQ0v\nUVZeRvfuaoEREKC0aUtPLOWRho8Y6uod0pvNl1fRujVczykg/vp+0l9NZ0iTIby6+VWc7Jx4Y8sb\nfLp/KoGBsHAhuASex9bGlmD3YGxtbHm22bOsT//aRAOWeTODovJ81j25jqMvHOX8uPM81OAhBoQP\nIDX/KsX26SQnQw3ffAavGEyLWi3I87CiPtXhniFgQoj7hRAJQogzQog3rJVzqnGD35J2GBpWoFsg\nDrYODI0aSkFpAdWDz7B0KaRU30D/8P4IIehRvwePRD6iHC/r9eCc6zcsWAAZN0qYGTeJTUM2maym\nu9XrRkZBBp6hZ5g0CW74rKVvaF/D9Y+6f8TKx1ayZNASzpRs5XqmJPOG5EjeGv7V5l9J33zGAAAe\n90lEQVQmWo7OdToT7h2OY/hv7N8POb4b6B3a2+KzNQkOYtKMJF6fepomtSJMrgW4BdCtXjdWnF2I\nl5fyxQgJUQ6U9WrUY8WjK1j44EKS/5XM4ecPM23vNFw9inh/Sj6xN7bTJ7SPxd9sHdiaX0//amjE\nliarhxo8xM+nfiYtL41L2ZfMNA96tA1qy9qza1l9ZjV9w/paNBmGe4VzKuMkfv4Sr8BMBq8YzLx+\n80yc5/Xwc/FjT9Ie3ur4lpkm4HZha2NLU/+myJqHGPG+qT9QZXg4euDt7G3VRAnKDHkh7xjTZxay\n+Phiw+pej/tq3UdWcSb51c5x5mIuN8UNi5oTPUK9QpkXO4/u9bqbTII1q9ekjGIyCq9Z/e6tkJqq\n21q9DDJsj9KsZjNGNh/JnMNz2H15Nx1qdzApb29rzyMNH2HWwVk0aQLj30mkoCyXgREDqe9pOmnd\nX/9+dl7ZyJw54NbqZx4IeYAaTqbOFp2CO5Gal0pibgIjXkmkYc06Fu+zTWAb9iXtY/RoeOK1/RSX\nFZsRblsbWxr5RTJ7xVFa9ztBHfe6ZuQEVADe3y7/xoQJEHF/1e+7a52uzDsyj9LyUiK8IyyWqe1e\nm1MZp0jPT6eWq+UARhHeEUrD2fN7Gviaa5qFEIxrNY4Pf/uQCVsn8F6X9+hRv4eFmpS26anGTzF+\nw3iLGqsI7wg8Qk+xebP6vH8/Bh/OffuUo3bXrjBhgsqBCDBtRh65JFe5ELgVWtRqwcmsQ9StC6mO\nu+gQ3MGsTOc6ndl9eTfde5aCbRGbk1byXpf3TMYBTydPhjQZwoz9M2jVCgprbTTZTa3HoAaDSLBd\nzpkzatORne95YlNiiRkRQ0ZBBk2+bELErAgaf9mY+o0zsLcH28DDPL3qabrW7co7O9+h9bzW9Fnc\nh2ebPcs5uZHERMhzOEc9D3O/jbZBbSn130ucbmOknoABtG6tZAvw7bdK+wJK2xgQXMSM/TOY3G0y\nLQNaGvxsOwZ3xK/lLmbOBJt6O5BIDqcc5rNen9EnrA97R+xlQfwC9jLNYILcU/wl41qNM/HV1Ycm\n0SOvOI+Ra0YytftUk/72SMNHiMldZlAsHC6bz/hW4w2aPj2ebPwkPx7/keimZdiEbKNtUFuDts0Y\nvi6+FDukce0anLZdQevA1jyw6AHGrR+nsja8EM+IpiN4bvVzlMty0vLSGPrLUFY8uoKR9400mD31\n73LFqRW8/jp4NN5Hck6yyXVQY8DhtBj69pPk5cGk2NG83Pplw6Lh+we/5/yN8zy/+nm6dVPm9VoB\nkje2vEFafhp9wirmt4cbPsyKUyu4/34QQTE09m1MDacaPNf8OU68eIKPenzEzuE7mbJ7CnO/LeDX\nXyHbbw296vcytNWno59m3aWlZNyoCOSV6RBPuEcTbIQNAW4B+Loo/yVbG1vaBbXDucEujh+H/KBV\nhHmF8U7nd7ju/DcgYEIIG+ALoBcQCQwWQlgcEf+z6Fe61u1qpqEQQtA3tC9PvbeGdu3LuVJtI73q\n9zL7/gv3vUC83dd8/73kuXemEuYdZjb42ggbBoQPYNjkVWRlSa66rDV5wbY2tvQJ60OEdwQ29sVs\nOZTImYwz2NhgSHFjjN4hvQnsso59++B6jQ0m6nZjBLoFki2TuC4SLE4IY1qMYcaBGTRoVMKIEVCv\nUQYf7v6QT3t+alKusZ+KrL30xFIa9NtIq8BWZpOjHuNajWNB/AIWHl3Io5GPWizzSMNHWHRsEQuP\nLqRznc5mnVqPull1WXd2HT8c/YG+YX0tlgn3DkcIgVeTA+yxm0TvkN5mzvx6dAjuwPZh2000Q38E\nbQLbsObMamIzTf2BLCHUM5RGPtYJWK/6vfjmyDf8dOInWtRqQbCHqROI3o+n66jVLNt2Fh/bEIuk\n1vj3souyzeS1c+dOAqtFklJ60uw7589XpBzR/7eECROgY0dISLpGmSgkyC2Ip5s+zdLjS3F1cLWo\nCfxPx//wdezXXM6+RMO+m+ler7vF++8VojID9OkDiS5LTSLI62FrY8vjjR5n8bHFpBReNEuFpUeI\nZwiFpYXkcIVNaQsYFjXMInmP8osiVcbz+L8O0SroPot1tQ5srYK72hUSm7nDEMttx44dZmW71O3C\npvOb6Fa3W5X+hW9seQNfF1+LWl89Hm74MDMOzLBKcsa0HMP6c+s5l3mOZ5o+Y7UegLGtxnIq45TF\nzSINvBtQ6nGKjRvV5wMH1E49Z2flDB0To0xy3brBq6+X8HnM56SKWBr4NLDab28H4qLgUMohmjQr\nIlUcom1QW7My3s7e1PGog2/0YTya7iDSN9IwSRnj5TYvM+/IPJ54NoO0oLk81+w5szIDwgdwrmwr\nqZm5nDgB669/wbCoYdRwqsHGpzYy44EZzB8wnycbP8nkve/Svj1k1FrM882fZ0zLMRx+/jCTu01m\n5/CdfNzjY66UxpOUnk0m5wj1skzAkm33cOyYMjfGxVXs/O7RQ4VaWLFC9ak331R5JuPi4Ey1RTT2\na2xGljsGdyRR7iIqCgoC1vFEoyfY8NQGBjcezI4dOwjxDGFOnzlsuTGXmP3lxMYXsz/nFwY3GmxS\nT4R3BIUlhSw/uZzMwkweWPQAzWs2N2i/9OhWrxtJhQnk2iSxekMB8fkbzHwkQZmwfV18cQjfRlnd\njfSs19OsDECIvx/VfdP5/sdCzhbv5PP7Pyfj9QxiR8byervXCXAL4NW2r3Kz9CYvbXiJ8RvGMzx6\nOO1qtzOrq3u97py+fpoGHU7x85WZjGs1zqwt6nfd9n7yPDUbn+ZgygFeafOK4bpbNTdWD17Nrsu7\nSPNcibMzZAf9yPJ1y9kxbIcJiewQ3IHL2Zdp3DER54Y7LY75td1r0zqwNde8f2bFCrjm8zODGgwy\nuZ8I7wiKau40jLE5TvE09rGsRe4U3InA9juYNg2ueizj8cjHaRnQknTbwxbL63FPEDCgJXBWSnlJ\nSlkCLAEGWCq4K3OJibrRGH3C+nCydA1PvRKPt6u7RZNHu6B22NrYYF9/NzsO/8DwqOEW63ow4kE2\nJK7iYr5u27SPuQZGCEH3kK60enw76a6b6BTYw+JA3iesDze817J5/xWKHZNNtpYbI8gtiMs5lzmV\nccoiAesY3JFg92B6vvkFly+Da/+JDG402MSPTI9X27zKv7f8mym7pzAwfKDF3wNF+gZEDKBcllvV\nbEX6RjK6xWhe2/ya1Q4LcHjfYQZEDOD8jfNWTYY2woaRzUdS85HJ7Mqez5sd37Ran4Otwy0J0+3g\n1bavsuzkMjac22ASXNUSetbvWeVv9g/vj3s1d8ZtGGdx4gDoF96P0nqrmbH4NC3qVa11CPMKQyDM\ntKI7duygjnMk6fIE8fHKlFhYqMxMzZqpWF1t2ypH88rb5PPzYepU2LQJ5s2DGUuOEVWzCUIIfF18\n6Rfez6IGA9TAM6bFGJ5e9TTLTy43MQ0Zo1NwJw6nHCY2JZa41Dh6hZgvdgCebfYscw7NYfvF7VZN\nkEII+oX1418b/8VPJ38ym1z0iPKLIj4tnkNXD1ntQ872zjT2bcx7O98jPT/dMDFaImBRflHUcKxh\n1fwIimgObjTYjGhXxqAGg8gszLRKwNyqufFpz0+Z3Xu2xRh0xgjzCmNY1DC61Olidi3CO4LUkjMc\nji0nO1sRMJvQjey/sp9RoyTPjSxl8smnmbF/Bq9seoUJWyfwzKpnDAGO/yiSjyVz5voZ7hv2E2Ge\n4bhVs7y9q0udLhzIXMej/1lF/7D+FsvU8ajDs02fZVryABLy9/FYI3PyXsOpBu2D2zF+1lqWbjvF\nstM/8HKblwHduFuvOx2COzCx00SWnFhC034xJLkt5fFGjwNK3l3qdqGhT0Oc7J1oV7str8/ZSnCz\nczQOMCdgHWp3YFfyZtx8s3j5ZWjcMpMxm5+h3bftSPWfz4svwsMPw6xZajdfvXpQZlPAwqR3+E/H\n/5jV19i3Mal5qbz9UTr5tdab+Nfp2+J9te7D3cWZ9xb8Rq2Om2jg1dBMW25va8/Pj/3MqLWjCP8i\nnNYBrfmq31dm84yDrQMDIvozZtYKvvttA60C7zPTKurxetvXWZD+ErZhG6z225puvtRtlM5rX+wg\nyi8KTydzT3xbG1tWPb6K64XXOZB8gLc7vW2xrmp21RjXchyvbHqF9efWMzx6uMVybQLbUOT3G33e\nURuCKpvFne2d+W7Ad4zf+CKtul/liNPHtC5tbcjdqoedjR0DIway+cZsmvTfYXXMfzr6ab6L+45W\nXdNIzD9G93rdTa73D++PQ+NVBjNkgVs80TWtELA6nbCtv5PLabkkV9vGgIgBeDh64GljJaKsHlLK\nu/4HDAK+Mvr8FDDDQjnpOMlRZt/MlpaQV5QnXT90lU2+bCLHrhtrsYyUUs4+MFvavmsrq/esbrWu\notIi6THFQ4bOCJXj1o2zWtc3sd/I6Nn3ScdR7eWCwz9aLFNWXib9p/nLqC+j5ODlg63WFZMUI6u9\nX026fOAi0/LSLJY5nXFaen3kJf9vy/9Jn6k+MiM/w2p9uy7ukoOWDpIpuSlWy0gp5dWcq3LbhW1V\nlikrL5P/2fafKuuaOHGijE+Nl+/ueLfKutLz0qX9e/by6ZVPV1nuTmLdmXWy7Tdt70hdh5IPyUaz\nG8mi0iKL1/OL86XnR54y6sso+X9b/u+W9R24csDs3MSJE+UL86dL+oySLj7pctjwcvlA73LZ64kE\nOXvPfDnll1/lh4t2yhkb1sha0cdk9H0FcuJEKWNjpazbKF32evy8/HrbJjn0l6Gy96LecszaMYa6\n0/LSZHJOstX7KSotkhO2TJDV3q8mr2RfsVrutU2vSdcPXeWwX4ZV+XwJ1xLkgB8HWO1rUkpZWFIo\ne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"text/plain": [ "<matplotlib.figure.Figure at 0x7f7f074ceb00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "act_train['date'].groupby(act_train.date.dt.date).count().plot(figsize=(10,5), label='Train')\n", "act_test['date'].groupby(act_test.date.dt.date).count().plot(figsize=(10,5), label='Test')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b5a442db-34c8-7062-e477-d3e90f629319" }, "source": [ "This clearly shows that we are looking at a random distribution to test, rather than a time later in the future. Now check the distribution of the good and bad events." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "6e15a5d8-7073-f56f-f79c-7ddd9f50b04b" }, "outputs": [ { "data": { "image/png": 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373wfiYiIyIJiZjjnsnb1ZlIBOwa418xCeC3LrzvnHjazrWZ2FpAGWoAPAzjndpjZg8AO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"text/plain": [ "<matplotlib.figure.Figure at 0x7f7f47837898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "goods=act_train[act_train['outcome']==1]\n", "bads=act_train[act_train['outcome']==0]\n", "\n", "goods['date'].groupby(goods.date.dt.date).count().plot(figsize=(10,5),label='Good')\n", "bads['date'].groupby(bads.date.dt.date).count().plot(figsize=(10,5),c='r',label='Bad')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2fbd6b81-c6ae-a0e2-a24a-f71b72b3ea49" }, "source": [ "Most of the \"bad\" events are in the peak around Oct 2022! Finally, look to see if any people are better or worse bets on the return." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "cb0eeaf1-0a43-c75b-7a11-443be4295a7c" }, "outputs": [], "source": [ "positive_counts=pd.DataFrame({'positive_counts' : act_train[act_train['outcome']==1].groupby('people_id',as_index=True).size()}).reset_index()\n", "negative_counts=pd.DataFrame({'negative_counts' : act_train[act_train['outcome']==0].groupby('people_id',as_index=True).size()}).reset_index()\n", "hstry=positive_counts.merge(negative_counts, on='people_id',how='left')\n", "hstry['positive_counts']=hstry['positive_counts'].fillna('0').astype(np.int64)\n", "hstry['negative_counts']=hstry['negative_counts'].fillna('0').astype(np.int64)\n", "hstry['diff']=hstry['positive_counts']-hstry['negative_counts']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "2c83eb58-9da9-3959-70e2-4e520fb3a9c1" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>people_id</th>\n", " <th>positive_counts</th>\n", " <th>negative_counts</th>\n", " <th>diff</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>62042</th>\n", " <td>ppl_64887</td>\n", " <td>7051</td>\n", " <td>1</td>\n", " <td>7050</td>\n", " </tr>\n", " <tr>\n", " <th>68274</th>\n", " <td>ppl_97427</td>\n", " <td>1487</td>\n", " <td>0</td>\n", " <td>1487</td>\n", " </tr>\n", " <tr>\n", " <th>61144</th>\n", " <td>ppl_60206</td>\n", " <td>736</td>\n", " <td>0</td>\n", " <td>736</td>\n", " </tr>\n", " <tr>\n", " <th>67693</th>\n", " <td>ppl_9442</td>\n", " <td>730</td>\n", " <td>0</td>\n", " <td>730</td>\n", " </tr>\n", " <tr>\n", " <th>49900</th>\n", " <td>ppl_359707</td>\n", " <td>556</td>\n", " <td>0</td>\n", " <td>556</td>\n", " </tr>\n", " <tr>\n", " <th>51171</th>\n", " <td>ppl_366313</td>\n", " <td>555</td>\n", " <td>0</td>\n", " <td>555</td>\n", " </tr>\n", " <tr>\n", " <th>530</th>\n", " <td>ppl_102941</td>\n", " <td>524</td>\n", " <td>0</td>\n", " <td>524</td>\n", " </tr>\n", " <tr>\n", " <th>48447</th>\n", " <td>ppl_352378</td>\n", " <td>501</td>\n", " <td>0</td>\n", " <td>501</td>\n", " </tr>\n", " <tr>\n", " <th>23505</th>\n", " <td>ppl_22266</td>\n", " <td>452</td>\n", " <td>0</td>\n", " <td>452</td>\n", " </tr>\n", " <tr>\n", " <th>44764</th>\n", " <td>ppl_333492</td>\n", " <td>438</td>\n", " <td>104</td>\n", " <td>334</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " people_id positive_counts negative_counts diff\n", "62042 ppl_64887 7051 1 7050\n", "68274 ppl_97427 1487 0 1487\n", "61144 ppl_60206 736 0 736\n", "67693 ppl_9442 730 0 730\n", "49900 ppl_359707 556 0 556\n", "51171 ppl_366313 555 0 555\n", "530 ppl_102941 524 0 524\n", "48447 ppl_352378 501 0 501\n", "23505 ppl_22266 452 0 452\n", "44764 ppl_333492 438 104 334" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hstry.sort_values(by='positive_counts',ascending=False).head(10)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "815f0b00-9ee8-4bce-2216-5051848726c1" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>people_id</th>\n", " <th>positive_counts</th>\n", " <th>negative_counts</th>\n", " <th>diff</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>51952</th>\n", " <td>ppl_370270</td>\n", " <td>12</td>\n", " <td>53656</td>\n", " <td>-53644</td>\n", " </tr>\n", " <tr>\n", " <th>32015</th>\n", " <td>ppl_267205</td>\n", " <td>18</td>\n", " <td>566</td>\n", " <td>-548</td>\n", " </tr>\n", " <tr>\n", " <th>64005</th>\n", " <td>ppl_7497</td>\n", " <td>5</td>\n", " <td>516</td>\n", " <td>-511</td>\n", " </tr>\n", " <tr>\n", " <th>27872</th>\n", " <td>ppl_245327</td>\n", " <td>2</td>\n", " <td>458</td>\n", " <td>-456</td>\n", " </tr>\n", " <tr>\n", " <th>2211</th>\n", " <td>ppl_111738</td>\n", " <td>16</td>\n", " <td>427</td>\n", " <td>-411</td>\n", " </tr>\n", " <tr>\n", " <th>49621</th>\n", " <td>ppl_358257</td>\n", " <td>90</td>\n", " <td>333</td>\n", " <td>-243</td>\n", " </tr>\n", " <tr>\n", " <th>11548</th>\n", " <td>ppl_16074</td>\n", " <td>53</td>\n", " <td>300</td>\n", " <td>-247</td>\n", " </tr>\n", " <tr>\n", " <th>21781</th>\n", " <td>ppl_213733</td>\n", " <td>7</td>\n", " <td>287</td>\n", " <td>-280</td>\n", " </tr>\n", " <tr>\n", " <th>61862</th>\n", " <td>ppl_63924</td>\n", " <td>19</td>\n", " <td>270</td>\n", " <td>-251</td>\n", " </tr>\n", " <tr>\n", " <th>66943</th>\n", " <td>ppl_90586</td>\n", " <td>7</td>\n", " <td>240</td>\n", " <td>-233</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " people_id positive_counts negative_counts diff\n", "51952 ppl_370270 12 53656 -53644\n", "32015 ppl_267205 18 566 -548\n", "64005 ppl_7497 5 516 -511\n", "27872 ppl_245327 2 458 -456\n", "2211 ppl_111738 16 427 -411\n", "49621 ppl_358257 90 333 -243\n", "11548 ppl_16074 53 300 -247\n", "21781 ppl_213733 7 287 -280\n", "61862 ppl_63924 19 270 -251\n", "66943 ppl_90586 7 240 -233" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hstry.sort_values(by='negative_counts',ascending=False).head(10)" ] } ], "metadata": { "_change_revision": 261, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/328/328101.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "f948729e-5bf7-496c-82b7-5b4e4e0be315" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "import os\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.cross_validation import KFold\n", "from sklearn.metrics import log_loss" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "55de2a4f-715c-48f8-be5d-deeb6ee92d20" }, "source": [ "# Phone brand and device model based prediction\n", "\n", "## Demographic data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "d152ad32-d74b-430a-b1f1-2512da20c1f3" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>device_id</th>\n", " <th>gender</th>\n", " <th>age</th>\n", " <th>group</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-8076087639492063270</td>\n", " <td>M</td>\n", " <td>35</td>\n", " <td>M32-38</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-2897161552818060146</td>\n", " <td>M</td>\n", " <td>35</td>\n", " <td>M32-38</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>-8260683887967679142</td>\n", " <td>M</td>\n", " <td>35</td>\n", " <td>M32-38</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " device_id gender age group\n", "0 -8076087639492063270 M 35 M32-38\n", "1 -2897161552818060146 M 35 M32-38\n", "2 -8260683887967679142 M 35 M32-38" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gatrain = pd.read_csv('../input/gender_age_train.csv')\n", "gatest = pd.read_csv('../input/gender_age_test.csv')\n", "gatrain.head(3)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "f291bb29-8a4f-4616-9fe8-4258f019f752" }, "outputs": [], "source": [ "letarget = LabelEncoder().fit(gatrain.group.values)\n", "y = letarget.transform(gatrain.group.values)\n", "n_classes = len(letarget.classes_)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "093854be-39f1-436d-b58e-ab15e0dada48" }, "source": [ "## Phone brand and model data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "dfbf25d0-6136-4680-9c85-881a58f0e824" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>device_id</th>\n", " <th>phone_brand</th>\n", " <th>device_model</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>-8890648629457979026</td>\n", " <td>小米</td>\n", " <td>红米</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1277779817574759137</td>\n", " <td>小米</td>\n", " <td>MI 2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>5137427614288105724</td>\n", " <td>三星</td>\n", " <td>Galaxy S4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " device_id phone_brand device_model\n", "0 -8890648629457979026 小米 红米\n", "1 1277779817574759137 小米 MI 2\n", "2 5137427614288105724 三星 Galaxy S4" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "phone = pd.read_csv('../input/phone_brand_device_model.csv',encoding='utf-8')\n", "phone.head(3)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c26a9891-24da-49a9-aaea-a0eb6fa122f8" }, "source": [ "### Duplicate devide_ids" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "a0c38a59-ac12-461a-ad23-af017db7f192" }, "outputs": [], "source": [ "phone = phone.drop_duplicates('device_id', keep='first')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "081f957a-3946-416a-8a3c-3e9298e28f8e" }, "source": [ "### Label encode the brand and model" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "f432cc4d-f797-4fe4-a179-b201392c898b" }, "source": [ "Some device models can belong to more than one brand. So the correct way to label-encode device models is probably to concatenate with brand first." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "b57ec496-6f73-4a01-b0f1-3febd0b68487" }, "outputs": [], "source": [ "lebrand = LabelEncoder().fit(phone.phone_brand)\n", "phone['brand'] = lebrand.transform(phone.phone_brand)\n", "m = phone.phone_brand.str.cat(phone.device_model)\n", "lemodel = LabelEncoder().fit(m)\n", "phone['model'] = lemodel.transform(m)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b210418e-6771-4a28-93db-8f70f3a24af4" }, "source": [ "### Combine gender-age and brand-model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "80daeeab-43eb-4397-81ea-7a216702e4a5" }, "outputs": [], "source": [ "train = gatrain.merge(phone[['device_id','brand','model']], how='left',on='device_id')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "67b42d32-c197-46e4-9540-2713b9c82603" }, "source": [ "### Benchmark: predict gender-age group from N groupings " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "3a5fd260-f866-4385-9ecc-011c7387372e" }, "outputs": [], "source": [ "class GenderAgeGroupProb(object):\n", " def __init__(self):\n", " pass\n", " \n", " def fit(self, df, by, n_smoothing, weights):\n", " self.by = by\n", " self.n_smoothing = n_smoothing\n", " self.weights = np.divide(weights,sum(weights))\n", " self.classes_ = sorted(df['group'].unique())\n", " self.n_classes_ = len(self.classes_)\n", " \n", " self.group_freq = df['group'].value_counts().sort_index()/df.shape[0]\n", " \n", " self.prob_by = []\n", " for i,b in enumerate(self.by):\n", " c = df.groupby([b,'group']).size().unstack().fillna(0)\n", " total = c.sum(axis=1)\n", " prob = (c.add(self.n_smoothing[i]*self.group_freq)).div(total+self.n_smoothing[i], axis=0)\n", " self.prob_by.append(prob)\n", " return self\n", " \n", " def predict_proba(self, df):\n", " pred = pd.DataFrame(np.zeros((len(df.index),self.n_classes_)),columns=self.classes_,index=df.index)\n", " pred_by = []\n", " for i,b in enumerate(self.by):\n", " pred_by.append(df[[b]].merge(self.prob_by[i], how='left',\n", " left_on=b, right_index=True).fillna(self.group_freq)[self.classes_])\n", " pred = pred.radd(pred_by[i].values*self.weights[i])\n", " \n", " pred.loc[pred.iloc[:,0].isnull(),:] = self.group_freq\n", " return pred[self.classes_].values\n", " \n", "def score(ptrain, by, n_smoothing, weights=[0.5,0.5]):\n", " kf = KFold(ptrain.shape[0], n_folds=10, shuffle=True, random_state=0)\n", " pred = np.zeros((ptrain.shape[0],n_classes))\n", " for itrain, itest in kf:\n", " train = ptrain.iloc[itrain,:]\n", " test = ptrain.iloc[itest,:]\n", " ytrain, ytest = y[itrain], y[itest]\n", " clf = GenderAgeGroupProb().fit(train,by,n_smoothing,weights)\n", " pred[itest,:] = clf.predict_proba(test)\n", " return log_loss(y, pred)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "20036d7f-75d6-0b8d-10d5-16558a735041" }, "source": [ "### Optimize the choice of smoothing and weighting " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "2182c254-113c-6230-20dd-9a57cd226b51" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f3c14902828>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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sjkdERGTlpkHPojlERESAuwa9gkFPREQEuGvQ26rjceqeiIg8m1sGPW9VS0RE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\nYQx6IiIiCfsfAfBxrJgjrDMAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f3c148eeb38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "n_smoothing = [1,5,10,15,20,50,100]\n", "res = [score(train,['brand','model'],[s,s],[.5,.5]) for s in n_smoothing]\n", "plt.plot(n_smoothing, res)\n", "plt.title('Best score {:.5f} at n_smoothing = {}'.format(np.min(res),n_smoothing[np.argmin(res)]))\n", "plt.xlabel('n_smoothing')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "faa51ded-e522-4223-9e08-37a91c0a382d" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f3c14886e48>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FarWaQ4cOcfjw4UrrBFCr1QwfPpyPP/6Y4uJiEhISqvxcur+/P1u3bsXV1RUzMzN69erF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\nuavFmPq0H57s1Z4L5EhUgxD4T1IW9qQ0LmDz0pT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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3c18348080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "brand_weight = [0,0.2,0.4,0.6,0.8,1.0]\n", "res = [score(train,['brand','model'],[15,15],[b,1-b]) for b in brand_weight]\n", "plt.plot(brand_weight, res)\n", "plt.title('Best score {:.5f} at brand_weight = {}'.format(np.min(res),brand_weight[np.argmin(res)]))\n", "plt.xlabel('brand_weight')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "3b11dd56-4473-4b9a-99a4-7d79268ca0d6" }, "source": [ "## Make a submission" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "6039cc6d-65a7-4d34-a489-4839415af277" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>device_id</th>\n", " <th>brand</th>\n", " <th>model</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1002079943728939269</td>\n", " <td>51</td>\n", " <td>857</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>-1547860181818787117</td>\n", " <td>51</td>\n", " <td>860</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>7374582448058474277</td>\n", " <td>31</td>\n", " <td>717</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " device_id brand model\n", "0 1002079943728939269 51 857\n", "1 -1547860181818787117 51 860\n", "2 7374582448058474277 31 717" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test = gatest.merge(phone[['device_id','brand','model']], how='left',on='device_id')\n", "test.head(3)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "5b6941b8-7ce2-48c4-ad81-e8e646e91759" }, "outputs": [], "source": [ "clf = GenderAgeGroupProb().fit(train,['brand','model'],[15,15],[0.4,0.6])\n", "pred = clf.predict_proba(test)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "6d16ec6a-fadb-4a0a-b50d-ee36d4530396" }, "outputs": [], "source": [ "pd.DataFrame(pred, \n", " index = test.device_id, \n", " columns=clf.classes_).to_csv('pbm_subm.csv', index=True)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "f7c914e7-a972-5628-bbfc-874d2c90443c" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 1415, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/328/328147.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "aba53a01-cc53-a04f-5cfc-5ca644de275b" }, "source": [ "Using Trueskill to compute the 2016 kitefoil rankings" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "5f9e29dd-c831-3ff9-0d70-0110ab0ce459" }, "outputs": [], "source": [ "\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import trueskill as ts" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "d10c8f81-b7c4-5df5-a78a-f8e35453229a" }, "outputs": [], "source": [ "dfResults = pd.read_csv('../input/201608-SanFracisco-HydrofoilProTour.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "b92f7a60-28c5-c550-18d9-b153fa29c020" }, "outputs": [], "source": [ "def cleanResults(numRaces,dfResults):\n", " for raceCol in range(1,numRaces+1):\n", " dfResults['R'+str(raceCol)] = dfResults['R'+str(raceCol)].str.replace('\\(|\\)|DNF-|RET-|SCP-|RDG-|RCT-|DNS-[0-9]*|DNC-[0-9]*|OCS-[0-9]*','')\n", " dfResults['R'+str(raceCol)] = pd.to_numeric(dfResults['R'+str(raceCol)])\n", " return dfResults" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "e7b1546f-4bff-c8c5-699f-740b6d4aa3b7" }, "outputs": [], "source": [ "def doRating(numRaces,dfResults):\n", " for raceCol in range(1,numRaces+1):\n", " dfResults['Rating'] = ts.rate(list(zip(dfResults['Rating'].T.values.tolist())), ranks=dfResults['R' +raceCol].T.values.tolist())" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "2dbb7f1b-3bd0-f7a3-2424-d90e3b2f51ef" }, "outputs": [ { "ename": "KeyError", "evalue": "'Rating'", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mKeyError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 1944\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1945\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1946\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4154)\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4018)\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12368)\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12322)\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'Rating'", "\nDuring handling of the above exception, another exception occurred:\n", "\u001b[1;31mKeyError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-5-56bd10c12c24>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdfResults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdoRating\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdfResults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-4-537d21e80952>\u001b[0m in \u001b[0;36mdoRating\u001b[1;34m(numRaces, dfResults)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdoRating\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumRaces\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdfResults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mraceCol\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mnumRaces\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdfResults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Rating'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdfResults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Rating'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mranks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdfResults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'R'\u001b[0m \u001b[1;33m+\u001b[0m\u001b[0mraceCol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\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[0m", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1995\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1996\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1997\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1998\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1999\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 2002\u001b[0m \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2003\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2004\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2005\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2006\u001b[0m \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m 1348\u001b[0m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1349\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1350\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1351\u001b[0m \u001b[0mres\u001b[0m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3291\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3292\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 1945\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1946\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1947\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\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 1948\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1949\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4154)\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4018)\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12368)\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12322)\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'Rating'" ] } ], "source": [ "dfResults = doRating(16,dfResults)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "eb06e2c7-0371-9418-f718-0d7ccfb2ebc4" }, "outputs": [], "source": [ "dfResults['Rating'] = ts.Rating()\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "a20ce728-648e-168f-cc2a-721a9294118b" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "be0160b1-3843-145a-89a2-7264b0bf6ed6" }, "outputs": [ { "data": { "text/plain": [ "0 trueskill.Rating(mu=25.000, sigma=8.333)\n", "1 trueskill.Rating(mu=25.000, sigma=8.333)\n", "2 trueskill.Rating(mu=25.000, sigma=8.333)\n", "3 trueskill.Rating(mu=25.000, sigma=8.333)\n", "4 trueskill.Rating(mu=25.000, sigma=8.333)\n", "5 trueskill.Rating(mu=25.000, sigma=8.333)\n", "6 trueskill.Rating(mu=25.000, sigma=8.333)\n", "7 trueskill.Rating(mu=25.000, sigma=8.333)\n", "8 trueskill.Rating(mu=25.000, sigma=8.333)\n", "9 trueskill.Rating(mu=25.000, sigma=8.333)\n", "10 trueskill.Rating(mu=25.000, sigma=8.333)\n", "11 trueskill.Rating(mu=25.000, sigma=8.333)\n", "12 trueskill.Rating(mu=25.000, sigma=8.333)\n", "13 trueskill.Rating(mu=25.000, sigma=8.333)\n", "14 trueskill.Rating(mu=25.000, sigma=8.333)\n", "15 trueskill.Rating(mu=25.000, sigma=8.333)\n", "16 trueskill.Rating(mu=25.000, sigma=8.333)\n", "17 trueskill.Rating(mu=25.000, sigma=8.333)\n", "18 trueskill.Rating(mu=25.000, sigma=8.333)\n", "19 trueskill.Rating(mu=25.000, sigma=8.333)\n", "20 trueskill.Rating(mu=25.000, sigma=8.333)\n", "21 trueskill.Rating(mu=25.000, sigma=8.333)\n", "22 trueskill.Rating(mu=25.000, sigma=8.333)\n", "23 trueskill.Rating(mu=25.000, sigma=8.333)\n", "24 trueskill.Rating(mu=25.000, sigma=8.333)\n", "25 trueskill.Rating(mu=25.000, sigma=8.333)\n", "26 trueskill.Rating(mu=25.000, sigma=8.333)\n", "27 trueskill.Rating(mu=25.000, sigma=8.333)\n", "28 trueskill.Rating(mu=25.000, sigma=8.333)\n", "29 trueskill.Rating(mu=25.000, sigma=8.333)\n", "30 trueskill.Rating(mu=25.000, sigma=8.333)\n", "31 trueskill.Rating(mu=25.000, sigma=8.333)\n", "32 trueskill.Rating(mu=25.000, sigma=8.333)\n", "33 trueskill.Rating(mu=25.000, sigma=8.333)\n", "34 trueskill.Rating(mu=25.000, sigma=8.333)\n", "35 trueskill.Rating(mu=25.000, sigma=8.333)\n", "36 trueskill.Rating(mu=25.000, sigma=8.333)\n", "37 trueskill.Rating(mu=25.000, sigma=8.333)\n", "38 trueskill.Rating(mu=25.000, sigma=8.333)\n", "39 trueskill.Rating(mu=25.000, sigma=8.333)\n", "40 trueskill.Rating(mu=25.000, sigma=8.333)\n", "41 trueskill.Rating(mu=25.000, sigma=8.333)\n", "42 trueskill.Rating(mu=25.000, sigma=8.333)\n", "43 trueskill.Rating(mu=25.000, sigma=8.333)\n", "44 trueskill.Rating(mu=25.000, sigma=8.333)\n", "Name: Rating, dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfResults['Rating']" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "91c76e6f-7e84-9f74-2608-2a1c65589e9d" }, "outputs": [ { "ename": "TypeError", "evalue": "Rating cannot be a rating group", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mTypeError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-8-f3935eaf5e79>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRating\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRating\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mranks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/trueskill/__init__.py\u001b[0m in \u001b[0;36mrate\u001b[1;34m(rating_groups, ranks, weights, min_delta)\u001b[0m\n\u001b[0;32m 698\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 699\u001b[0m \"\"\"\n\u001b[1;32m--> 700\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mglobal_env\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrating_groups\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mranks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmin_delta\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 701\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 702\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/trueskill/__init__.py\u001b[0m in \u001b[0;36mrate\u001b[1;34m(self, rating_groups, ranks, weights, min_delta)\u001b[0m\n\u001b[0;32m 475\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 476\u001b[0m \"\"\"\n\u001b[1;32m--> 477\u001b[1;33m \u001b[0mrating_groups\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeys\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalidate_rating_groups\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrating_groups\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 478\u001b[0m \u001b[0mweights\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalidate_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrating_groups\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 479\u001b[0m \u001b[0mgroup_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrating_groups\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/trueskill/__init__.py\u001b[0m in \u001b[0;36mvalidate_rating_groups\u001b[1;34m(self, rating_groups)\u001b[0m\n\u001b[0;32m 272\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'All groups should be same type'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 273\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mgroup_types\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mRating\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 274\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Rating cannot be a rating group'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 275\u001b[0m \u001b[1;31m# normalize rating_groups\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 276\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrating_groups\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\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: Rating cannot be a rating group" ] } ], "source": [ "ts.rate([ts.Rating(),ts.Rating()],ranks=[1,0])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "fe61caff-9a99-9e00-630d-491bcbbcb8ae" }, "outputs": [], "source": [ "r1,r2 = ts.rate([(ts.Rating(),), (ts.Rating(),)], ranks=[0, 1])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "0d2a13d1-2c3a-f3b1-08ab-4a58ae8e0c3c" }, "outputs": [ { "data": { "text/plain": [ "(trueskill.Rating(mu=29.396, sigma=7.171),)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r1" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "0a83d522-9b8f-da0e-ea11-7f785ed9145b" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 842, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/328/328194.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "1fab96bf-9f9d-33b0-db72-c35e15a74ca6" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import random\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics\n", "import sklearn.ensemble as ske\n", "import tensorflow as tf\n", "from tensorflow.contrib import skflow" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "94fa40b0-4ee3-5b3c-739e-4b0d7d82752a" }, "outputs": [], "source": [ "titanic_df = pd.read_csv(\"../input/train.csv\", dtype={\"Age\": np.float64}, )\n", "test_df = pd.read_csv(\"../input/test.csv\", dtype={\"Age\": np.float64}, )" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "0f34dbb1-e027-0669-04b5-4078fd040de2" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Sex Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. 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<td>0</td>\n", " <td>315154</td>\n", " <td>8.6625</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>896</td>\n", " <td>3</td>\n", " <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n", " <td>female</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>3101298</td>\n", " <td>12.2875</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Pclass Name Sex \\\n", "0 892 3 Kelly, Mr. James male \n", "1 893 3 Wilkes, Mrs. James (Ellen Needs) female \n", "2 894 2 Myles, Mr. Thomas Francis male \n", "3 895 3 Wirz, Mr. Albert male \n", "4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female \n", "\n", " Age SibSp Parch Ticket Fare Cabin Embarked \n", "0 34.5 0 0 330911 7.8292 NaN Q \n", "1 47.0 1 0 363272 7.0000 NaN S \n", "2 62.0 0 0 240276 9.6875 NaN Q \n", "3 27.0 0 0 315154 8.6625 NaN S \n", "4 22.0 1 1 3101298 12.2875 NaN S " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "2f3a7bb2-6c96-f69d-f97e-cae568194b51" }, "outputs": [ { "data": { "text/plain": [ "0.3838383838383838" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_df['Survived'].mean()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "27068383-665e-6df2-68a3-89c367aeb661" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " </tr>\n", " <tr>\n", " <th>Pclass</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>1</th>\n", " <td>461.597222</td>\n", " <td>0.629630</td>\n", " <td>38.233441</td>\n", " <td>0.416667</td>\n", " <td>0.356481</td>\n", " <td>84.154687</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>445.956522</td>\n", " <td>0.472826</td>\n", " <td>29.877630</td>\n", " <td>0.402174</td>\n", " <td>0.380435</td>\n", " <td>20.662183</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>439.154786</td>\n", " <td>0.242363</td>\n", " <td>25.140620</td>\n", " <td>0.615071</td>\n", " <td>0.393075</td>\n", " <td>13.675550</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Age SibSp Parch Fare\n", "Pclass \n", "1 461.597222 0.629630 38.233441 0.416667 0.356481 84.154687\n", "2 445.956522 0.472826 29.877630 0.402174 0.380435 20.662183\n", "3 439.154786 0.242363 25.140620 0.615071 0.393075 13.675550" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_df.groupby('Pclass').mean()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "0d961ec7-12d0-9a42-9170-c5e777185a6a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pclass Sex \n", "1 female 0.968085\n", " male 0.368852\n", "2 female 0.921053\n", " male 0.157407\n", "3 female 0.500000\n", " male 0.135447\n", "Name: Survived, dtype: float64\n" ] } ], "source": [ "class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()\n", "print(class_sex_grouping['Survived'])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "20b23df4-c25a-d6a7-a969-57e99e913f66" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f6208077128>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f6208062588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "class_sex_grouping['Survived'].plot.bar()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "c438c83b-464e-9934-ebba-8677446d8ee0" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f61e815ebe0>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1JD89mVADGE+ukeRNwDcGzLWYOfvTQkYwZ98Gz9nalvjPAf+Z8e6U/dcv38h4d8qvV9Xv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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f61e80f8198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "group_by_age = pd.cut(titanic_df['Age'], np.arange(0, 90, 10))\n", "age_grouping = titanic_df.groupby(group_by_age).mean()\n", "age_grouping['Survived'].plot.bar()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "2ae759f6-9453-143a-c49d-ad7ec3b9ca3e" }, "outputs": [ { "data": { "text/plain": [ "PassengerId 891\n", "Survived 891\n", "Pclass 891\n", "Name 891\n", "Sex 891\n", "Age 714\n", "SibSp 891\n", "Parch 891\n", "Ticket 891\n", "Fare 891\n", "Cabin 204\n", "Embarked 889\n", "dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_df.count()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "882ef892-eafd-7af1-c57a-80dd54ff9414" }, "outputs": [ { "data": { "text/plain": [ "PassengerId 418\n", "Pclass 418\n", "Name 418\n", "Sex 418\n", "Age 332\n", "SibSp 418\n", "Parch 418\n", "Ticket 418\n", "Fare 417\n", "Cabin 91\n", "Embarked 418\n", "dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df.count()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "2823623c-a76d-11b1-1f6c-8e7a49eb8d54" }, "outputs": [], "source": [ "titanic_df = titanic_df.drop(['Cabin'], axis = 1)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "6e0f2996-a48f-ae65-1ccc-ee47de8aa84b" }, "outputs": [], "source": [ "test_df = test_df.drop(['Cabin'], axis=1)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "dcfabd25-4a70-4077-2617-9ae21a2a7f3b" }, "outputs": [], "source": [ "titanic_df = titanic_df.dropna()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "fdf65fb5-9112-bc07-d7b3-10c9e6026eaa" }, "outputs": [], "source": [ "test_df = test_df.dropna()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "4c658342-67af-24a2-8405-89cc1ed6e123" }, "outputs": [ { "data": { "text/plain": [ "PassengerId 712\n", "Survived 712\n", "Pclass 712\n", "Name 712\n", "Sex 712\n", "Age 712\n", "SibSp 712\n", "Parch 712\n", "Ticket 712\n", "Fare 712\n", "Embarked 712\n", "dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic_df.count()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "f969483c-b6c3-d903-09a8-d7eeb0643cef" }, "outputs": [ { "data": { "text/plain": [ "PassengerId 331\n", "Pclass 331\n", "Name 331\n", "Sex 331\n", "Age 331\n", "SibSp 331\n", "Parch 331\n", "Ticket 331\n", "Fare 331\n", "Embarked 331\n", "dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_df.count()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "823aa7c4-714a-09df-b65b-18e10e46c109" }, "outputs": [], "source": [ "def preprocess_titanic_df(df) :\n", " processed_df = df.copy()\n", " le = preprocessing.LabelEncoder()\n", " processed_df.Sex = le.fit_transform(processed_df.Sex)\n", " processed_df.Embarked = le.fit_transform(processed_df.Embarked)\n", " processed_df = processed_df.drop(['Name', 'Ticket'], axis = 1)\n", " return processed_df" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "5ae788d1-8c25-d977-c8b4-4ea5c8f90ad0" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " 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<td>0</td>\n", " <td>30.0000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>889</th>\n", " <td>890</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>30.0000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>890</th>\n", " <td>891</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>32.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.7500</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>712 rows × 9 columns</p>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass Sex Age SibSp Parch Fare \\\n", "0 1 0 3 1 22.0 1 0 7.2500 \n", "1 2 1 1 0 38.0 1 0 71.2833 \n", "2 3 1 3 0 26.0 0 0 7.9250 \n", "3 4 1 1 0 35.0 1 0 53.1000 \n", "4 5 0 3 1 35.0 0 0 8.0500 \n", "6 7 0 1 1 54.0 0 0 51.8625 \n", "7 8 0 3 1 2.0 3 1 21.0750 \n", "8 9 1 3 0 27.0 0 2 11.1333 \n", "9 10 1 2 0 14.0 1 0 30.0708 \n", "10 11 1 3 0 4.0 1 1 16.7000 \n", "11 12 1 1 0 58.0 0 0 26.5500 \n", "12 13 0 3 1 20.0 0 0 8.0500 \n", "13 14 0 3 1 39.0 1 5 31.2750 \n", "14 15 0 3 0 14.0 0 0 7.8542 \n", "15 16 1 2 0 55.0 0 0 16.0000 \n", "16 17 0 3 1 2.0 4 1 29.1250 \n", "18 19 0 3 0 31.0 1 0 18.0000 \n", "20 21 0 2 1 35.0 0 0 26.0000 \n", "21 22 1 2 1 34.0 0 0 13.0000 \n", "22 23 1 3 0 15.0 0 0 8.0292 \n", "23 24 1 1 1 28.0 0 0 35.5000 \n", "24 25 0 3 0 8.0 3 1 21.0750 \n", "25 26 1 3 0 38.0 1 5 31.3875 \n", "27 28 0 1 1 19.0 3 2 263.0000 \n", "30 31 0 1 1 40.0 0 0 27.7208 \n", "33 34 0 2 1 66.0 0 0 10.5000 \n", "34 35 0 1 1 28.0 1 0 82.1708 \n", "35 36 0 1 1 42.0 1 0 52.0000 \n", "37 38 0 3 1 21.0 0 0 8.0500 \n", "38 39 0 3 0 18.0 2 0 18.0000 \n", ".. ... ... ... ... ... ... ... ... \n", "856 857 1 1 0 45.0 1 1 164.8667 \n", "857 858 1 1 1 51.0 0 0 26.5500 \n", "858 859 1 3 0 24.0 0 3 19.2583 \n", "860 861 0 3 1 41.0 2 0 14.1083 \n", "861 862 0 2 1 21.0 1 0 11.5000 \n", "862 863 1 1 0 48.0 0 0 25.9292 \n", "864 865 0 2 1 24.0 0 0 13.0000 \n", "865 866 1 2 0 42.0 0 0 13.0000 \n", "866 867 1 2 0 27.0 1 0 13.8583 \n", "867 868 0 1 1 31.0 0 0 50.4958 \n", "869 870 1 3 1 4.0 1 1 11.1333 \n", "870 871 0 3 1 26.0 0 0 7.8958 \n", "871 872 1 1 0 47.0 1 1 52.5542 \n", "872 873 0 1 1 33.0 0 0 5.0000 \n", "873 874 0 3 1 47.0 0 0 9.0000 \n", "874 875 1 2 0 28.0 1 0 24.0000 \n", "875 876 1 3 0 15.0 0 0 7.2250 \n", "876 877 0 3 1 20.0 0 0 9.8458 \n", "877 878 0 3 1 19.0 0 0 7.8958 \n", "879 880 1 1 0 56.0 0 1 83.1583 \n", "880 881 1 2 0 25.0 0 1 26.0000 \n", "881 882 0 3 1 33.0 0 0 7.8958 \n", "882 883 0 3 0 22.0 0 0 10.5167 \n", "883 884 0 2 1 28.0 0 0 10.5000 \n", "884 885 0 3 1 25.0 0 0 7.0500 \n", "885 886 0 3 0 39.0 0 5 29.1250 \n", "886 887 0 2 1 27.0 0 0 13.0000 \n", "887 888 1 1 0 19.0 0 0 30.0000 \n", "889 890 1 1 1 26.0 0 0 30.0000 \n", "890 891 0 3 1 32.0 0 0 7.7500 \n", "\n", " Embarked \n", "0 2 \n", "1 0 \n", "2 2 \n", "3 2 \n", "4 2 \n", "6 2 \n", "7 2 \n", "8 2 \n", "9 0 \n", "10 2 \n", "11 2 \n", "12 2 \n", "13 2 \n", "14 2 \n", "15 2 \n", "16 1 \n", "18 2 \n", "20 2 \n", "21 2 \n", "22 1 \n", "23 2 \n", "24 2 \n", "25 2 \n", "27 2 \n", "30 0 \n", "33 2 \n", "34 0 \n", "35 2 \n", "37 2 \n", "38 2 \n", ".. ... \n", "856 2 \n", "857 2 \n", "858 0 \n", "860 2 \n", "861 2 \n", "862 2 \n", "864 2 \n", "865 2 \n", "866 0 \n", "867 2 \n", "869 2 \n", "870 2 \n", "871 2 \n", "872 2 \n", "873 2 \n", "874 0 \n", "875 0 \n", "876 2 \n", "877 2 \n", "879 0 \n", "880 2 \n", "881 2 \n", "882 2 \n", "883 2 \n", "884 2 \n", "885 1 \n", "886 2 \n", "887 2 \n", "889 0 \n", "890 1 \n", "\n", "[712 rows x 9 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "processed_df = preprocess_titanic_df(titanic_df)\n", "processed_df.count()\n", "processed_df" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "_cell_guid": "88ff81db-32ed-c705-6d7b-dc8134dc7e79" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", 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<td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>381</th>\n", " <td>1273</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.8792</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>383</th>\n", " <td>1275</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>19.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>16.1000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>385</th>\n", " <td>1277</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>24.0</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>65.0000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>386</th>\n", " <td>1278</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>24.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.7750</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>387</th>\n", " <td>1279</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>57.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>13.0000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>388</th>\n", " <td>1280</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>21.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.7500</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>389</th>\n", " <td>1281</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>6.0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>21.0750</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>390</th>\n", " <td>1282</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>23.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>93.5000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>391</th>\n", " <td>1283</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>51.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>39.4000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>392</th>\n", " <td>1284</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>13.0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>20.2500</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>393</th>\n", " <td>1285</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>47.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>10.5000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>394</th>\n", " <td>1286</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>29.0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>22.0250</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>395</th>\n", " <td>1287</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>18.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>60.0000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>396</th>\n", " <td>1288</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>24.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.2500</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>397</th>\n", " <td>1289</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>48.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>79.2000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>398</th>\n", " <td>1290</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>22.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.7750</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>399</th>\n", " <td>1291</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>31.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.7333</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>400</th>\n", " <td>1292</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>30.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>164.8667</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>401</th>\n", " <td>1293</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>21.0000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>402</th>\n", " <td>1294</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>22.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>59.4000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>403</th>\n", " <td>1295</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>17.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>47.1000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>404</th>\n", " <td>1296</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>43.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>27.7208</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>405</th>\n", " <td>1297</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>20.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>13.8625</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>406</th>\n", " <td>1298</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>23.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>10.5000</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>407</th>\n", " <td>1299</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>50.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>211.5000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>409</th>\n", " <td>1301</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>3.0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>13.7750</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>411</th>\n", " <td>1303</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>37.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>90.0000</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>412</th>\n", " <td>1304</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>28.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.7750</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>414</th>\n", " <td>1306</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>39.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>108.9000</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>415</th>\n", " <td>1307</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>38.5</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.2500</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>331 rows × 8 columns</p>\n", "</div>" ], "text/plain": [ " PassengerId Pclass Sex Age SibSp Parch Fare Embarked\n", "0 892 3 1 34.5 0 0 7.8292 1\n", "1 893 3 0 47.0 1 0 7.0000 2\n", "2 894 2 1 62.0 0 0 9.6875 1\n", "3 895 3 1 27.0 0 0 8.6625 2\n", "4 896 3 0 22.0 1 1 12.2875 2\n", "5 897 3 1 14.0 0 0 9.2250 2\n", "6 898 3 0 30.0 0 0 7.6292 1\n", "7 899 2 1 26.0 1 1 29.0000 2\n", "8 900 3 0 18.0 0 0 7.2292 0\n", "9 901 3 1 21.0 2 0 24.1500 2\n", "11 903 1 1 46.0 0 0 26.0000 2\n", "12 904 1 0 23.0 1 0 82.2667 2\n", "13 905 2 1 63.0 1 0 26.0000 2\n", "14 906 1 0 47.0 1 0 61.1750 2\n", "15 907 2 0 24.0 1 0 27.7208 0\n", "16 908 2 1 35.0 0 0 12.3500 1\n", "17 909 3 1 21.0 0 0 7.2250 0\n", "18 910 3 0 27.0 1 0 7.9250 2\n", "19 911 3 0 45.0 0 0 7.2250 0\n", "20 912 1 1 55.0 1 0 59.4000 0\n", "21 913 3 1 9.0 0 1 3.1708 2\n", "23 915 1 1 21.0 0 1 61.3792 0\n", "24 916 1 0 48.0 1 3 262.3750 0\n", "25 917 3 1 50.0 1 0 14.5000 2\n", "26 918 1 0 22.0 0 1 61.9792 0\n", "27 919 3 1 22.5 0 0 7.2250 0\n", "28 920 1 1 41.0 0 0 30.5000 2\n", "30 922 2 1 50.0 1 0 26.0000 2\n", "31 923 2 1 24.0 2 0 31.5000 2\n", "32 924 3 0 33.0 1 2 20.5750 2\n", ".. ... ... ... ... ... ... ... ...\n", "381 1273 3 1 26.0 0 0 7.8792 1\n", "383 1275 3 0 19.0 1 0 16.1000 2\n", "385 1277 2 0 24.0 1 2 65.0000 2\n", "386 1278 3 1 24.0 0 0 7.7750 2\n", "387 1279 2 1 57.0 0 0 13.0000 2\n", "388 1280 3 1 21.0 0 0 7.7500 1\n", "389 1281 3 1 6.0 3 1 21.0750 2\n", "390 1282 1 1 23.0 0 0 93.5000 2\n", "391 1283 1 0 51.0 0 1 39.4000 2\n", "392 1284 3 1 13.0 0 2 20.2500 2\n", "393 1285 2 1 47.0 0 0 10.5000 2\n", "394 1286 3 1 29.0 3 1 22.0250 2\n", "395 1287 1 0 18.0 1 0 60.0000 2\n", "396 1288 3 1 24.0 0 0 7.2500 1\n", "397 1289 1 0 48.0 1 1 79.2000 0\n", "398 1290 3 1 22.0 0 0 7.7750 2\n", "399 1291 3 1 31.0 0 0 7.7333 1\n", "400 1292 1 0 30.0 0 0 164.8667 2\n", "401 1293 2 1 38.0 1 0 21.0000 2\n", "402 1294 1 0 22.0 0 1 59.4000 0\n", "403 1295 1 1 17.0 0 0 47.1000 2\n", "404 1296 1 1 43.0 1 0 27.7208 0\n", "405 1297 2 1 20.0 0 0 13.8625 0\n", "406 1298 2 1 23.0 1 0 10.5000 2\n", "407 1299 1 1 50.0 1 1 211.5000 0\n", "409 1301 3 0 3.0 1 1 13.7750 2\n", "411 1303 1 0 37.0 1 0 90.0000 1\n", "412 1304 3 0 28.0 0 0 7.7750 2\n", "414 1306 1 0 39.0 0 0 108.9000 0\n", "415 1307 3 1 38.5 0 0 7.2500 2\n", "\n", "[331 rows x 8 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "processed_test_df = preprocess_titanic_df(test_df)\n", "processed_test_df.count()\n", "processed_test_df" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "_cell_guid": "9c4809cf-95d6-4fe2-f240-f3c4a0e38811" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1. 3. 1. ..., 0. 7.25 2. ]\n", " [ 2. 1. 0. ..., 0. 71.2833 0. ]\n", " [ 3. 3. 0. ..., 0. 7.925 2. ]\n", " ..., \n", " [ 888. 1. 0. ..., 0. 30. 2. ]\n", " [ 890. 1. 1. ..., 0. 30. 0. ]\n", " [ 891. 3. 1. ..., 0. 7.75 1. ]]\n" ] } ], "source": [ "X = processed_df.drop(['Survived'], axis = 1).values\n", "Y = processed_df['Survived'].values\n", "print(X)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "_cell_guid": "e55440fd-a8d3-3e95-4797-f34dcabca083" }, "outputs": [], "source": [ "X_test = processed_test_df.values" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "_cell_guid": "1a2593b7-4958-393e-dce6-fb3279d4f9b8" }, "outputs": [], "source": [ "#x_train, x_test, y_train, y_test = cross_validation.train_test_split(X, Y, test_size=0.2)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "_cell_guid": "09c22e25-4bdf-fb6e-ef6c-48de0a7f9318" }, "outputs": [], "source": [ "clf_dt = tree.DecisionTreeClassifier(max_depth=10)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "_cell_guid": "db85787e-3711-6842-8bc0-58d0ca9ea4d3" }, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_dt.fit(X, Y)\n", "Y_test = clf_dt.predict(X_test)\n", "clf_dt.score(X_test, Y_test)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "_cell_guid": "b6a2cc69-969b-a15f-0fbd-2b9d11703454" }, "outputs": [], "source": [ "submission = pd.DataFrame({'PassengerId': processed_test_df['PassengerId'], 'Survived': Y_test})\n", "submission.to_csv('clf_titanic.csv', index=False)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "_cell_guid": "6ee4e41f-119d-0eb9-69f6-efa4fb03ebca" }, "outputs": [], "source": [ "#clf_rf = ske.RandomForestClassifier(n_estimators=50)\n", "#test_classifier(clf_rf)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "_cell_guid": "13d4e15e-75d3-1e5d-2fd6-5a77ba2d19b8" }, "outputs": [], "source": [ "#clf_gb = ske.GradientBoostingClassifier(n_estimators=50)\n", "#test_classifier(clf_gb)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "_cell_guid": "726946b5-5322-1048-db9e-c191c5a78e59" }, "outputs": [], "source": [ "#eclf = ske.VotingClassifier([('dt', clf_dt), ('rf', clf_rf), ('gb', clf_gb)])\n", "#test_classifier(eclf)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "_cell_guid": "993a00ea-772c-c385-0c71-6d5fde6c80a7" }, "outputs": [], "source": [ "#def custom_model(X, Y) :\n", "# layers = skflow.ops.dnn(X, [20, 40, 20], tf.tanh)\n", "# return skflow.models.logistic_regression(layers, Y)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "_cell_guid": "aa825497-d404-f267-ef7e-b7bae9a01432" }, "outputs": [], "source": [ "#tf_clf_c = skflow.TensorFlowEstimator(model_fn=custom_model, n_classes=2, batch_size=256, steps=1000, learning_rate=0.05)\n", "#tf_clf_c.fit(x_train, y_train)\n", "#metrics.accuracy_score(y_test, tf_clf_c.predict(x_test))" ] } ], "metadata": { "_change_revision": 178, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/328/328596.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "9c957606-deb8-73f7-0e61-e674e741fc7f" }, "outputs": [ { "ename": "SyntaxError", "evalue": "unexpected EOF while parsing (<ipython-input-1-238182fc6d0b>, line 19)", "output_type": "error", "traceback": [ " File \"<ipython-input-1-238182fc6d0b>\", line 19\n print(\"Most ambiguous by year: %s\" % str(yearly_ambiguity.idxmax().apply(lambda x: x[1]))\n ^\nSyntaxError: unexpected EOF while parsing\n" ] }, { "name": "stdout", "output_type": "stream", "text": "Average ambiguitiy: 0.008810887620090621\nAverage by year: Year\n1880 0.000000\n1881 0.000000\n1882 0.000000\n1883 0.000000\n1884 0.000000\n1885 0.000000\n1886 0.000000\n1887 0.000000\n1888 0.000000\n1889 0.000000\n1890 0.000000\n1891 0.000000\n1892 0.000000\n1893 0.000000\n1894 0.000000\n1895 0.000000\n1896 0.000000\n1897 0.000000\n1898 0.000000\n1899 0.000000\n1900 0.007359\n1901 0.000000\n1902 0.009130\n1903 0.009445\n1904 0.009325\n1905 0.008639\n1906 0.008505\n1907 0.007974\n1908 0.007407\n1909 0.007428\n ... \n1985 0.009672\n1986 0.011035\n1987 0.011095\n1988 0.010574\n1989 0.010609\n1990 0.009952\n1991 0.011768\n1992 0.009721\n1993 0.009236\n1994 0.006967\n1995 0.007648\n1996 0.008622\n1997 0.010846\n1998 0.013672\n1999 0.013627\n2000 0.013938\n2001 0.014045\n2002 0.013733\n2003 0.011929\n2004 0.012542\n2005 0.011526\n2006 0.011641\n2007 0.009798\n2008 0.009233\n2009 0.007822\n2010 0.009019\n2011 0.008613\n2012 0.007363\n2013 0.007222\n2014 0.008436\nName: Count, dtype: float64\nMost ambiguous by year: Year\n1880 Albert\n1881 Albert\n1882 Albert\n1883 Albert\n1884 Albert\n1885 Albert\n1886 Albert\n1887 Albert\n1888 Ada\n1889 Ada\n1890 Ada\n1891 Agnes\n1892 Ada\n1893 Ada\n1894 Ada\n1895 Ada\n1896 Ada\n1897 Agnes\n1898 Ada\n1899 Agnes\n1900 Willie\n1901 Agnes\n1902 Willie\n1903 Willie\n1904 Willie\n1905 Willie\n1906 Willie\n1907 Willie\n1908 Willie\n1909 Willie\n ... \n1985 Jessie\n1986 Jaime\n1987 Jessie\n1988 Jessie\n1989 Jessie\n1990 Taylor\n1991 Jessie\n1992 Jessie\n1993 Jessie\n1994 Casey\n1995 Casey\n1996 Casey\n1997 Casey\n1998 Avery\n1999 Peyton\n2000 Peyton\n2001 Peyton\n2002 Riley\n2003 Peyton\n2004 Peyton\n2005 Peyton\n2006 Peyton\n2007 Peyton\n2008 Dakota\n2009 Dakota\n2010 Dakota\n2011 Dakota\n2012 Charlie\n2013 Charlie\n2014 Charlie\nName: Count, dtype: object\n" } ], "source": [ "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "names_data = pd.read_csv(\"../input/NationalNames.csv\")\n", "\n", "frequent_names = names_data[names_data['Count'] > 1000]\n", "indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']\n", "\n", "# Number between 0 and 1 representing ambiguity, from certain to totally ambiguous\n", "# Assumes only two options\n", "def ambiguity_measure(grouped_frame):\n", " return (2 * (1 - (grouped_frame.max() / grouped_frame.sum())))\n", "\n", "ambiguity_data = ambiguity_measure(indexed_names.groupby(level=['Year', 'Name']))\n", "yearly_ambiguity = ambiguity_data.groupby(level='Year')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "eca3b285-43ad-fe80-3bce-b487eb476ab2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "Average ambiguitiy: 0.008810887620090621\n\n\nAverage by year: Year\n1880 0.000000\n1881 0.000000\n1882 0.000000\n1883 0.000000\n1884 0.000000\n1885 0.000000\n1886 0.000000\n1887 0.000000\n1888 0.000000\n1889 0.000000\n1890 0.000000\n1891 0.000000\n1892 0.000000\n1893 0.000000\n1894 0.000000\n1895 0.000000\n1896 0.000000\n1897 0.000000\n1898 0.000000\n1899 0.000000\n1900 0.007359\n1901 0.000000\n1902 0.009130\n1903 0.009445\n1904 0.009325\n1905 0.008639\n1906 0.008505\n1907 0.007974\n1908 0.007407\n1909 0.007428\n ... \n1985 0.009672\n1986 0.011035\n1987 0.011095\n1988 0.010574\n1989 0.010609\n1990 0.009952\n1991 0.011768\n1992 0.009721\n1993 0.009236\n1994 0.006967\n1995 0.007648\n1996 0.008622\n1997 0.010846\n1998 0.013672\n1999 0.013627\n2000 0.013938\n2001 0.014045\n2002 0.013733\n2003 0.011929\n2004 0.012542\n2005 0.011526\n2006 0.011641\n2007 0.009798\n2008 0.009233\n2009 0.007822\n2010 0.009019\n2011 0.008613\n2012 0.007363\n2013 0.007222\n2014 0.008436\nName: Count, dtype: float64\n\n\nMost ambiguous by year: Year\n1880 Albert\n1881 Albert\n1882 Albert\n1883 Albert\n1884 Albert\n1885 Albert\n1886 Albert\n1887 Albert\n1888 Ada\n1889 Ada\n1890 Ada\n1891 Agnes\n1892 Ada\n1893 Ada\n1894 Ada\n1895 Ada\n1896 Ada\n1897 Agnes\n1898 Ada\n1899 Agnes\n1900 Willie\n1901 Agnes\n1902 Willie\n1903 Willie\n1904 Willie\n1905 Willie\n1906 Willie\n1907 Willie\n1908 Willie\n1909 Willie\n ... \n1985 Jessie\n1986 Jaime\n1987 Jessie\n1988 Jessie\n1989 Jessie\n1990 Taylor\n1991 Jessie\n1992 Jessie\n1993 Jessie\n1994 Casey\n1995 Casey\n1996 Casey\n1997 Casey\n1998 Avery\n1999 Peyton\n2000 Peyton\n2001 Peyton\n2002 Riley\n2003 Peyton\n2004 Peyton\n2005 Peyton\n2006 Peyton\n2007 Peyton\n2008 Dakota\n2009 Dakota\n2010 Dakota\n2011 Dakota\n2012 Charlie\n2013 Charlie\n2014 Charlie\nName: Count, dtype: object\n" }, { "name": "stdout", "output_type": "stream", "text": "Average ambiguity: 0.008810887620090621\n\nAverage by year: Year\n1880 0.000000\n1881 0.000000\n1882 0.000000\n1883 0.000000\n1884 0.000000\n1885 0.000000\n1886 0.000000\n1887 0.000000\n1888 0.000000\n1889 0.000000\n1890 0.000000\n1891 0.000000\n1892 0.000000\n1893 0.000000\n1894 0.000000\n1895 0.000000\n1896 0.000000\n1897 0.000000\n1898 0.000000\n1899 0.000000\n1900 0.007359\n1901 0.000000\n1902 0.009130\n1903 0.009445\n1904 0.009325\n1905 0.008639\n1906 0.008505\n1907 0.007974\n1908 0.007407\n1909 0.007428\n ... \n1985 0.009672\n1986 0.011035\n1987 0.011095\n1988 0.010574\n1989 0.010609\n1990 0.009952\n1991 0.011768\n1992 0.009721\n1993 0.009236\n1994 0.006967\n1995 0.007648\n1996 0.008622\n1997 0.010846\n1998 0.013672\n1999 0.013627\n2000 0.013938\n2001 0.014045\n2002 0.013733\n2003 0.011929\n2004 0.012542\n2005 0.011526\n2006 0.011641\n2007 0.009798\n2008 0.009233\n2009 0.007822\n2010 0.009019\n2011 0.008613\n2012 0.007363\n2013 0.007222\n2014 0.008436\nName: Count, dtype: float64\n\nMost ambiguous by year: Year\n1880 Albert\n1881 Albert\n1882 Albert\n1883 Albert\n1884 Albert\n1885 Albert\n1886 Albert\n1887 Albert\n1888 Ada\n1889 Ada\n1890 Ada\n1891 Agnes\n1892 Ada\n1893 Ada\n1894 Ada\n1895 Ada\n1896 Ada\n1897 Agnes\n1898 Ada\n1899 Agnes\n1900 Willie\n1901 Agnes\n1902 Willie\n1903 Willie\n1904 Willie\n1905 Willie\n1906 Willie\n1907 Willie\n1908 Willie\n1909 Willie\n ... \n1985 Jessie\n1986 Jaime\n1987 Jessie\n1988 Jessie\n1989 Jessie\n1990 Taylor\n1991 Jessie\n1992 Jessie\n1993 Jessie\n1994 Casey\n1995 Casey\n1996 Casey\n1997 Casey\n1998 Avery\n1999 Peyton\n2000 Peyton\n2001 Peyton\n2002 Riley\n2003 Peyton\n2004 Peyton\n2005 Peyton\n2006 Peyton\n2007 Peyton\n2008 Dakota\n2009 Dakota\n2010 Dakota\n2011 Dakota\n2012 Charlie\n2013 Charlie\n2014 Charlie\nName: Count, dtype: object\n" }, { "name": "stdout", "output_type": "stream", "text": "Average ambiguity: 0.008810887620090621\n\nAverage by year: Year\n1880 0.000000\n1881 0.000000\n1882 0.000000\n1883 0.000000\n1884 0.000000\n1885 0.000000\n1886 0.000000\n1887 0.000000\n1888 0.000000\n1889 0.000000\n1890 0.000000\n1891 0.000000\n1892 0.000000\n1893 0.000000\n1894 0.000000\n1895 0.000000\n1896 0.000000\n1897 0.000000\n1898 0.000000\n1899 0.000000\n1900 0.007359\n1901 0.000000\n1902 0.009130\n1903 0.009445\n1904 0.009325\n1905 0.008639\n1906 0.008505\n1907 0.007974\n1908 0.007407\n1909 0.007428\n ... \n1985 0.009672\n1986 0.011035\n1987 0.011095\n1988 0.010574\n1989 0.010609\n1990 0.009952\n1991 0.011768\n1992 0.009721\n1993 0.009236\n1994 0.006967\n1995 0.007648\n1996 0.008622\n1997 0.010846\n1998 0.013672\n1999 0.013627\n2000 0.013938\n2001 0.014045\n2002 0.013733\n2003 0.011929\n2004 0.012542\n2005 0.011526\n2006 0.011641\n2007 0.009798\n2008 0.009233\n2009 0.007822\n2010 0.009019\n2011 0.008613\n2012 0.007363\n2013 0.007222\n2014 0.008436\nName: Count, dtype: float64\n\n" }, { "ename": "TypeError", "evalue": "unorderable types: SeriesGroupBy() > int()", "output_type": "error", "traceback": [ "", "TypeErrorTraceback (most recent call last)", "<ipython-input-5-4fed1f870662> in <module>()\n 2 print(\"Average ambiguity: %s\\n\" % str(ambiguity_data.mean()))\n 3 print(\"Average by year: %s\\n\" % str(yearly_ambiguity.mean()))\n----> 4 most_ambiguous_excluding_invalid = yearly_ambiguity[yearly_ambiguity > 0].idxmax().apply(lambda x: x[1])\n 5 print(\"Most ambiguous by year: %s\" % str(yearly_ambiguity.idxmax().apply(lambda x: x[1])))\n", "TypeError: unorderable types: SeriesGroupBy() > int()" ] }, { "name": "stdout", "output_type": "stream", "text": "Average ambiguity: 0.008810887620090621\n\nAverage by year: Year\n1880 0.000000\n1881 0.000000\n1882 0.000000\n1883 0.000000\n1884 0.000000\n1885 0.000000\n1886 0.000000\n1887 0.000000\n1888 0.000000\n1889 0.000000\n1890 0.000000\n1891 0.000000\n1892 0.000000\n1893 0.000000\n1894 0.000000\n1895 0.000000\n1896 0.000000\n1897 0.000000\n1898 0.000000\n1899 0.000000\n1900 0.007359\n1901 0.000000\n1902 0.009130\n1903 0.009445\n1904 0.009325\n1905 0.008639\n1906 0.008505\n1907 0.007974\n1908 0.007407\n1909 0.007428\n ... \n1985 0.009672\n1986 0.011035\n1987 0.011095\n1988 0.010574\n1989 0.010609\n1990 0.009952\n1991 0.011768\n1992 0.009721\n1993 0.009236\n1994 0.006967\n1995 0.007648\n1996 0.008622\n1997 0.010846\n1998 0.013672\n1999 0.013627\n2000 0.013938\n2001 0.014045\n2002 0.013733\n2003 0.011929\n2004 0.012542\n2005 0.011526\n2006 0.011641\n2007 0.009798\n2008 0.009233\n2009 0.007822\n2010 0.009019\n2011 0.008613\n2012 0.007363\n2013 0.007222\n2014 0.008436\nName: Count, dtype: float64\n\n" }, { "ename": "AttributeError", "evalue": "'Series' object has no attribute 'columns'", "output_type": "error", "traceback": [ "", "AttributeErrorTraceback (most recent call last)", "<ipython-input-6-31408769e0bf> in <module>()\n 2 print(\"Average ambiguity: %s\\n\" % str(ambiguity_data.mean()))\n 3 print(\"Average by year: %s\\n\" % str(yearly_ambiguity.mean()))\n----> 4 most_ambiguous_excluding_invalid = yearly_ambiguity[yearly_ambiguity.max() > 0].idxmax().apply(lambda x: x[1])\n 5 print(\"Most ambiguous by year: %s\" % str(most_ambiguous_excluding_invalid))\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/base.py in __getitem__(self, key)\n 328 if isinstance(key, (list, tuple, gt.ABCSeries, gt.ABCIndex,\n 329 np.ndarray)):\n--> 330 if len(self.obj.columns.intersection(key)) != len(key):\n 331 bad_keys = list(set(key).difference(self.obj.columns))\n 332 raise KeyError(\"Columns not found: %s\"\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py in __getattr__(self, name)\n 2666 if (name in self._internal_names_set or name in self._metadata or\n 2667 name in self._accessors):\n-> 2668 return object.__getattribute__(self, name)\n 2669 else:\n 2670 if name in self._info_axis:\n", "AttributeError: 'Series' object has no attribute 'columns'" ] }, { "name": "stdout", "output_type": "stream", "text": "Average ambiguity: 0.008810887620090621\n\nAverage by year: Year\n1880 0.000000\n1881 0.000000\n1882 0.000000\n1883 0.000000\n1884 0.000000\n1885 0.000000\n1886 0.000000\n1887 0.000000\n1888 0.000000\n1889 0.000000\n1890 0.000000\n1891 0.000000\n1892 0.000000\n1893 0.000000\n1894 0.000000\n1895 0.000000\n1896 0.000000\n1897 0.000000\n1898 0.000000\n1899 0.000000\n1900 0.007359\n1901 0.000000\n1902 0.009130\n1903 0.009445\n1904 0.009325\n1905 0.008639\n1906 0.008505\n1907 0.007974\n1908 0.007407\n1909 0.007428\n ... \n1985 0.009672\n1986 0.011035\n1987 0.011095\n1988 0.010574\n1989 0.010609\n1990 0.009952\n1991 0.011768\n1992 0.009721\n1993 0.009236\n1994 0.006967\n1995 0.007648\n1996 0.008622\n1997 0.010846\n1998 0.013672\n1999 0.013627\n2000 0.013938\n2001 0.014045\n2002 0.013733\n2003 0.011929\n2004 0.012542\n2005 0.011526\n2006 0.011641\n2007 0.009798\n2008 0.009233\n2009 0.007822\n2010 0.009019\n2011 0.008613\n2012 0.007363\n2013 0.007222\n2014 0.008436\nName: Count, dtype: float64\n\n" }, { "ename": "AttributeError", "evalue": "'tuple' object has no attribute 'apply'", "output_type": "error", "traceback": [ "", "AttributeErrorTraceback (most recent call last)", "<ipython-input-7-14442829ddbc> in <module>()\n 2 print(\"Average ambiguity: %s\\n\" % str(ambiguity_data.mean()))\n 3 print(\"Average by year: %s\\n\" % str(yearly_ambiguity.mean()))\n----> 4 most_ambiguous_excluding_invalid = yearly_ambiguity.filter(lambda x: x.max() > 0).idxmax().apply(lambda x: x[1])\n 5 print(\"Most ambiguous by year: %s\" % str(most_ambiguous_excluding_invalid))\n", "AttributeError: 'tuple' object has no attribute 'apply'" ] }, { "name": "stdout", "output_type": "stream", "text": "Average ambiguity: 0.008810887620090621\n\nAverage by year: Year\n1880 0.000000\n1881 0.000000\n1882 0.000000\n1883 0.000000\n1884 0.000000\n1885 0.000000\n1886 0.000000\n1887 0.000000\n1888 0.000000\n1889 0.000000\n1890 0.000000\n1891 0.000000\n1892 0.000000\n1893 0.000000\n1894 0.000000\n1895 0.000000\n1896 0.000000\n1897 0.000000\n1898 0.000000\n1899 0.000000\n1900 0.007359\n1901 0.000000\n1902 0.009130\n1903 0.009445\n1904 0.009325\n1905 0.008639\n1906 0.008505\n1907 0.007974\n1908 0.007407\n1909 0.007428\n ... \n1985 0.009672\n1986 0.011035\n1987 0.011095\n1988 0.010574\n1989 0.010609\n1990 0.009952\n1991 0.011768\n1992 0.009721\n1993 0.009236\n1994 0.006967\n1995 0.007648\n1996 0.008622\n1997 0.010846\n1998 0.013672\n1999 0.013627\n2000 0.013938\n2001 0.014045\n2002 0.013733\n2003 0.011929\n2004 0.012542\n2005 0.011526\n2006 0.011641\n2007 0.009798\n2008 0.009233\n2009 0.007822\n2010 0.009019\n2011 0.008613\n2012 0.007363\n2013 0.007222\n2014 0.008436\nName: Count, dtype: float64\n\n" }, { "ename": "AttributeError", "evalue": "Cannot access callable attribute 'where' of 'SeriesGroupBy' objects, try using the 'apply' method", "output_type": "error", "traceback": [ "", "AttributeErrorTraceback (most recent call last)", "<ipython-input-8-9c0be04ae6eb> in <module>()\n 2 print(\"Average ambiguity: %s\\n\" % str(ambiguity_data.mean()))\n 3 print(\"Average by year: %s\\n\" % str(yearly_ambiguity.mean()))\n----> 4 most_ambiguous_excluding_invalid = yearly_ambiguity.where(lambda x: x.max() > 0).idxmax().apply(lambda x: x[1])\n 5 print(\"Most ambiguous by year: %s\" % str(most_ambiguous_excluding_invalid))\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/groupby.py in __getattr__(self, attr)\n 493 return self[attr]\n 494 if hasattr(self.obj, attr):\n--> 495 return self._make_wrapper(attr)\n 496 \n 497 raise AttributeError(\"%r object has no attribute %r\" %\n", "/opt/conda/lib/python3.5/site-packages/pandas/core/groupby.py in _make_wrapper(self, name)\n 507 \"using the 'apply' method\".format(kind, name,\n 508 type(self).__name__))\n--> 509 raise AttributeError(msg)\n 510 \n 511 # need to setup the selection\n", "AttributeError: Cannot access callable attribute 'where' of 'SeriesGroupBy' objects, try using the 'apply' method" ] }, { "name": "stdout", "output_type": "stream", "text": "Average ambiguity: 0.008810887620090621\n\nAverage by year: Year\n1880 0.000000\n1881 0.000000\n1882 0.000000\n1883 0.000000\n1884 0.000000\n1885 0.000000\n1886 0.000000\n1887 0.000000\n1888 0.000000\n1889 0.000000\n1890 0.000000\n1891 0.000000\n1892 0.000000\n1893 0.000000\n1894 0.000000\n1895 0.000000\n1896 0.000000\n1897 0.000000\n1898 0.000000\n1899 0.000000\n1900 0.007359\n1901 0.000000\n1902 0.009130\n1903 0.009445\n1904 0.009325\n1905 0.008639\n1906 0.008505\n1907 0.007974\n1908 0.007407\n1909 0.007428\n ... \n1985 0.009672\n1986 0.011035\n1987 0.011095\n1988 0.010574\n1989 0.010609\n1990 0.009952\n1991 0.011768\n1992 0.009721\n1993 0.009236\n1994 0.006967\n1995 0.007648\n1996 0.008622\n1997 0.010846\n1998 0.013672\n1999 0.013627\n2000 0.013938\n2001 0.014045\n2002 0.013733\n2003 0.011929\n2004 0.012542\n2005 0.011526\n2006 0.011641\n2007 0.009798\n2008 0.009233\n2009 0.007822\n2010 0.009019\n2011 0.008613\n2012 0.007363\n2013 0.007222\n2014 0.008436\nName: Count, dtype: float64\n\nMost ambiguous by year: Year\n1880 Albert\n1881 Albert\n1882 Albert\n1883 Albert\n1884 Albert\n1885 Albert\n1886 Albert\n1887 Albert\n1888 Ada\n1889 Ada\n1890 Ada\n1891 Agnes\n1892 Ada\n1893 Ada\n1894 Ada\n1895 Ada\n1896 Ada\n1897 Agnes\n1898 Ada\n1899 Agnes\n1900 Willie\n1901 Agnes\n1902 Willie\n1903 Willie\n1904 Willie\n1905 Willie\n1906 Willie\n1907 Willie\n1908 Willie\n1909 Willie\n ... \n1985 Jessie\n1986 Jaime\n1987 Jessie\n1988 Jessie\n1989 Jessie\n1990 Taylor\n1991 Jessie\n1992 Jessie\n1993 Jessie\n1994 Casey\n1995 Casey\n1996 Casey\n1997 Casey\n1998 Avery\n1999 Peyton\n2000 Peyton\n2001 Peyton\n2002 Riley\n2003 Peyton\n2004 Peyton\n2005 Peyton\n2006 Peyton\n2007 Peyton\n2008 Dakota\n2009 Dakota\n2010 Dakota\n2011 Dakota\n2012 Charlie\n2013 Charlie\n2014 Charlie\nName: Count, dtype: object\n" } ], "source": [ "# Some basic metrics:\n", "print(\"Average ambiguity: %s\\n\" % str(ambiguity_data.mean()))\n", "print(\"Average by year: %s\\n\" % str(yearly_ambiguity.mean()))\n", "print(\"Most ambiguous by year: %s\" % str(yearly_ambiguity.idxmax().apply(lambda x: x[1])))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "53dec8f7-19ed-02f4-1df1-63ea0391c292" }, "outputs": [], "source": [ "# Graph ambiguity by name by year\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "ae59c4f2-447d-3d11-0386-e8bda8bc08a2" }, "outputs": [], "source": [ "# Change in ambiguousness over time\n", "# = Expected ambiguity of a name, by year\n", "# = For each year, sum(ambiguity * probability of name)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "d318172a-ce4e-e43a-4400-1a88b4c30186" }, "outputs": [], "source": [ "# Change in ambiguousness of names by various factors\n", "# Length of name\n", "# Vowels used\n", "# Initial character\n", "# Final character\n", "\n", "# Group by Year, Name.\n", "# " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "28026bf1-a81d-f9d1-cccd-05f32aeb9a89" }, "outputs": [], "source": "" } ], "metadata": { "_change_revision": 124, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/328/328714.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "e17cddda-2d7a-77e5-f3c7-b82b9617c421" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "ff48ef76-5fba-5040-5eeb-82af196e3fdb" }, "outputs": [], "source": [ "import math\n", "import pandas as pd\n", "\n", "names_data = pd.read_csv(\"../input/NationalNames.csv\")\n", "\n", "frequent_names = names_data[names_data['Count'] > 500]\n", "indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']\n", "\n", "# Number between 0 and 1 representing ambiguity, from certain to totally ambiguous\n", "# Assumes only two options\n", "def ambiguity_measure(grouped_frame):\n", " return (2 * (1 - (grouped_frame.max() / grouped_frame.sum())))\n", "\n", "ambiguity_data = ambiguity_measure(indexed_names.groupby(level=['Year', 'Name']))\n", "names_vs_years = ambiguity_data.unstack(level='Year')\n", "yearly_ambiguity = ambiguity_data.groupby(level='Year')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "ff48ef76-5fba-5040-5eeb-82af196e3fdb" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "2e28a540-968e-bda7-9407-d54866157f1e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average ambiguity: 0.011221908363761928\n", "Average by year: Year\n", "1880 0.000000\n", "1881 0.000000\n", "1882 0.000000\n", "1883 0.000000\n", "1884 0.000000\n", "1885 0.000000\n", "1886 0.000000\n", "1887 0.000000\n", "1888 0.006651\n", "1889 0.000000\n", "1890 0.006813\n", "1891 0.006912\n", "1892 0.005877\n", "1893 0.006228\n", "1894 0.005976\n", "1895 0.005563\n", "1896 0.005677\n", "1897 0.005810\n", "1898 0.005742\n", "1899 0.006079\n", "1900 0.004432\n", "1901 0.005741\n", "1902 0.005079\n", "1903 0.005201\n", "1904 0.005123\n", "1905 0.004900\n", "1906 0.004797\n", "1907 0.004388\n", "1908 0.004152\n", "1909 0.004139\n", " ... \n", "1985 0.009487\n", "1986 0.010049\n", "1987 0.009725\n", "1988 0.009655\n", "1989 0.011535\n", "1990 0.012105\n", "1991 0.012075\n", "1992 0.011078\n", "1993 0.015313\n", "1994 0.015313\n", "1995 0.016112\n", "1996 0.015297\n", "1997 0.017641\n", "1998 0.015811\n", "1999 0.014688\n", "2000 0.013975\n", "2001 0.014216\n", "2002 0.016378\n", "2003 0.015023\n", "2004 0.014072\n", "2005 0.012709\n", "2006 0.013264\n", "2007 0.015036\n", "2008 0.013794\n", "2009 0.012647\n", "2010 0.011683\n", "2011 0.011498\n", "2012 0.011514\n", "2013 0.012382\n", "2014 0.012909\n", "Name: Count, dtype: float64\n", "Most ambiguous by year: Year\n", "1880 Ada\n", "1881 Ada\n", "1882 Ada\n", "1883 Ada\n", "1884 Ada\n", "1885 Ada\n", "1886 Ada\n", "1887 Ada\n", "1888 Willie\n", "1889 Ada\n", "1890 Willie\n", "1891 Willie\n", "1892 Willie\n", "1893 Willie\n", "1894 Willie\n", "1895 Willie\n", "1896 Willie\n", "1897 Willie\n", "1898 Willie\n", "1899 Willie\n", "1900 Willie\n", "1901 Willie\n", "1902 Willie\n", "1903 Willie\n", "1904 Willie\n", "1905 Willie\n", "1906 Willie\n", "1907 Willie\n", "1908 Willie\n", "1909 Willie\n", " ... \n", "1985 Jessie\n", "1986 Kendall\n", "1987 Jessie\n", "1988 Jessie\n", "1989 Jessie\n", "1990 Infant\n", "1991 Jessie\n", "1992 Jessie\n", "1993 Harley\n", "1994 Skylar\n", "1995 Casey\n", "1996 Casey\n", "1997 Ashton\n", "1998 Avery\n", "1999 Peyton\n", "2000 Peyton\n", "2001 Jessie\n", "2002 Riley\n", "2003 Justice\n", "2004 Peyton\n", "2005 Amari\n", "2006 Jessie\n", "2007 Jaylin\n", "2008 Jaylin\n", "2009 Dakota\n", "2010 Dakota\n", "2011 Dakota\n", "2012 Dakota\n", "2013 Charlie\n", "2014 Charlie\n", "Name: Count, dtype: object\n" ] } ], "source": [ "print(\"Average ambiguity: %s\" % str(ambiguity_data.mean()))\n", "print(\"Average by year: %s\" % str(yearly_ambiguity.mean()))\n", "print(\"Most ambiguous by year: %s\" % str(yearly_ambiguity.idxmax().apply(lambda x: x[1])))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "5af95505-65c9-e40e-648f-3a112b4b586e" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f474c634cf8>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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xKn8k4x6LNB7E3bW3+n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C5k1DMac3/NFK2tUFUlHjPefVe0eEXjnHPEdS1L/GTxfxt79og60XPOSXzPYhF1LP/xH4Im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\nLPKhBkLJSoKEjB8Qg9fWVvFwIvHjmeol4CtB14ZuLTHC9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BCIRPCkED+ldjs6Qo0Doxvu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Va7PbTaPdjnMUg50BHCYTlRYLe6NkGKNi9YqqdJckydDulcrqlWm4s3vAmPhJpvhRAgpa\nQEOWZcpKygh6hr/Tgvhpw2arx7pXoXdc8qF8euHO4/D5DhCMOv8HAmLd8KldL+BZUxXQUfwAVFit\nfLm8nCfiOtN/smcPb1VVIZlMFPT2AlG20TTg7/CT71lAf/97+P1xgXBJrF56H6me3ctshry8Olqj\n1ErljnL27tnLuHGlnHfeZ1i4cCGOIgez5s5CbVa58htX8tnPfpZTT53HhAkSJZ45hpXuAFXOKjrc\ngvjZvBkOYaOktT9jq1c87HY7v0rRiJwjfnLIIYfRgdkMs2fDd78L99yTXZBHPKxWuOYaeOCBkb9W\nDlnD5zvA7t23MHnyIuQ0B5H5+Z+hoeE2Nm/+GsFg4gzZaCLdi2VVFVSXqwSe+S2E6qhHBIsFjj8e\n3nkn/XWCwayCnbu6/kFv7xvMmrUGt3sjXV1/z3BnDx/xI9q8kqt9AgHB4R5hHAGUFJYSC6Z8E759\nQ3GKH3vEc69r98rW6pXJHdhhqHN/4IEHuO666zAZDKwmTZrEBx98gKZpzJkzh92hhhSb2cZxdcfx\nXuvaiOInumo3GcyyGZ/qY/fu3bz55ps8/vjj/OAHP+Dcc7/KXXdpXHbZtxLWSVD89PdDYSGfufRS\ndrS1xZBS8bDZwFZeQHBADJjMpWbUHpUFC9Jz+GZM/GgaJosJi5R+xo9rrYv8I/OR5OHfzUgVP2Mu\nG0PT3U1Zr68cSk38OKY4cG9yC5tXivfIyOqVChZZjhA/zy6fyDe/KSbcs0Jrq2HeWjLiR9E0nFGK\nn+iMH7VfxbPdQ8FsHTtSlN1L9+fr88HrrwtVaIpzQVhdlwxKUKFcKafbN7KMn2W9vQn5PqCv+AGY\n5nTyjcpKWnw+vKNo9Tocwc5hxAc8C+JnhFavrVsj4c5gXOk+2hk/nkELZEn8yMhIkkRZaVkMmdnT\nA3l5e7Hb67G/56FrQvIfXUqrlywjy1as1ip8vmE1cfw1UjfgWVXBQPEDcF1dHY8fOBD5fd7V0sJf\nu7q4MxzIH6p0j9hG04C/3Y+9ooSyss/T2fmnxIONqzcPn6r1PlK9SnezGez2WOKnzFFG2942GhvL\nY8KdN3Ru4OT6k5Gj1H6TJ4PcPTU18ePqQFFEuVlJvoZ/5cCIiZ90kCN+csghh9HDiScKP02mbUXJ\ncOWVYuYtSnaZw8cHTdPYseM71NRcSX5+ZnKNmpqrsNsb2bXrxsO0dwKZiDOualhMV36jkPuPBjLI\n+VGUHgLrPhQhGElm4OPh93eyffvlTJ78HFZrJRMnPs6OHd/VDzdMgsNB/GhagEOH/kJFRXLiZ8cO\nQbwVFma/rYjdKyrjp8yeF0X8zBk94ucTrHPfu3cv//rXv7jkkkuSLud0Onnuuee4/PLLOemkk9i8\nWdxozm+cz/KOfeTlpRfuvG/fPs4991xefflVLvv2ZcyfP597772XdevWUV1dzUUXXcSDD1Zz0UVn\nJayra/UqLMR23nlM1TTWpgg/r2x0IrsGQdOwlFpQehQ+97nUxI+qqvz73//mlFNOSb7g8AqC+LGb\nsEjmtBU/g2sGY/J9YOTEj7nIjH2cfvtTOkhH8ZN/ZD6utS60t5Ln+0BilXu6CM/SB6pq+P2OOdnb\nvMAw4wdCOT/L9HN+oq1emqbFZPz0/6efwmMKka06Q5044ifh43z7bZg2TZy0UpwLwnlayaAEFAq0\nAlRNZUjRDxZOBZ+mMdZup0bHQh+f8RONWxsa2Ov1sq1cSZv4CSrJB+GjFezsO+iLUfwATHY4eOrA\nAd7u7cUfDKIoXdm3egFUVICmUe31DhM/E/MY2q5P/JAF8WPU6uUaNIHdQK2WxOqlBJTIebu0pBTN\npxEIERq9vWC378S3ogzzRi/rv5BcuZyO1QtIsHvFf+11K90VFSkJU3FEfj7THA7+3NnJw62tLDp4\nkH8deSRlEyaIDYRarTNS/IRUYlVVF9DZGWf3yiDcGfSbvQTxMzZB8dOxr4PGxsph4sfvZtWBVZzS\nEHsNmjIFXAfq2Na9jUAwloQKI5zxs2uXyI5vbNDof3/kip90kCN+csghh9HDD38o6k+z06euzwAA\nIABJREFUDK3URWmpsI099tjovWYOgLhwvboteWPgoUN/wePZTn39jzN+fUmSaG5eRHf33zl06K/Z\n7mZKZHKxPKfraV4uGwW1Txjz5qWV8zM4uJYVK45gbdt5KF84Oe2X1zSNbdsuo7r6IoqLTwSgtPQ0\niopOYO/e2zLa1cNB/PT1vYPNVovDMSHpciMJdg7DMdmBZ7tQ/GiaRqe7k3K7I3IjJhQ/cZXun8JW\nr0ceeYRLLrmEgjRm1CVJ4tprr+Wuu+5i/vz5rF69mhPrjmJ1bxCLpTQl8bNy5UqOO+44Zs+ezYLP\nLuDhRx+OEE9PPvkkN954I+eeey6TJ49BVRNtKjHET3hAZLdDYSGz6utZGSq0MMLY8VaCsgl8Pswl\nQvEzbx6sWBHJfNbFihUrqK+vp7KyMuV7BAxn/NhMmOUMFD9xjV4giJ+RWL1GirQyfiqsmPJNeN/Z\nCnEZlQmv161gLsvO6qVqGm/tn0SN1M7UqRm/xDCSWL2S5fyomoY9pC5WNQ3ZMWz10rV5hZGK+Hnl\nFWHzglFT/FhkC6WW0qwDnpVgkDN0bF6Q2OoVjRKLhW9WVrLoiyqkociSLKkVP6MR7Az6ip9bxo3j\nM/n53LJ7NxXvvce1g19hUY+THR5PysYiXUgSTJ5M1cGDtIeIn4JZBQysSFTipMr4KTabCWoaA3G/\nf0Or16AZzWqgVkuh+DFJ4sOyWqxgJdKo1dOjku/sY+8Nnag/rWYohWs0leLHnCbxM3HiRPbu3RtL\negdSXye/N3YsN+zaxX3797PkqKMEcWmxgNMJGzcCUerBNOBv92OtslJS8lmGhnYzNLQr6cEms3qF\nFT/RmzaZwGarY//+YfVTWV4ZfW19NDZWxbR6LW9dzimNscTP5MmwZ6eNckc5Lf0tusdQnV9Nh7uD\nzZsFLznhCBNDu4bw96o54ieHHHL4FMFmE3kCo43rroMnn4QktoEcMseLm1/k/L+cj1fVb7gIBv3s\n3Pk9mpufRpazy6SwWEqYOvVPbN9+pW5d6GggbeLn4EFq97zLffvPI5v7R13MmCEGLZ2dhov09LzB\n+vWfZcKEByjaCOs+vwxF6U3r5Q8efBqfr5WGhliSZ8KE+2hv/y0uV/LK7GgcDuInHZsXjCzfJwzH\nZAeeHT4wm+nz9mE327GbbZEbMadzKn7/QRQlamD1Kcv4GRgY4Le//S3f/e53M1rvwgsv5PHHH+dz\nn/sc/Vt20OUXGUhKUDEkfl5++WXOOOMMHnvsMX70ox9RXlIeY2mKRrjSPR6aqhGQQ8RPyOYVxuwF\nC1iRQg3X2Ahei2j2MpeaUXoV8vOFazjZqmm3eYURUvyYbWbR6pWJ1StO8WM2m0ek+Bkp0qlzByio\nV3BVzBVTysleryc7q5csScjAb5fUsJDkBF9KJCF+wNjuFa6jtssyvmAwxurV9+8+ik4yqBhLRvwE\ng7HET5LEVU3T0lb8WEwh4seTOfEzoKoEgC/q2LzA2OoVxiVjxuC1wF9N+iRENFJZvdRgkPUuF0eN\ncpV7GDU2G7c3NvLhzJnsPvZYTuNfbPCambd2LeM//JCrtm9neVyteEo0N1O1e3dE8VN4bCEDywcS\niKQAJCV+JEnSDXg2In5cAyY0m/61XlXVpK1eJlkQP2bZjGSX6A3l4fT0qJRsOgbZLmP6cklKwiRV\nnXuYMMzLa2RoKJb4id49m83G2LFj2bUrimhRVd1Wr2icUVrK2eXlvHXkkdTbo5SOFRUQso5lo/iR\nZQuVlefFhjxnSPzU1AjSp6Mj9iWs1sSMH3eHm8bGMQSDCpqm4fK78Kk+ppTHqscnTxbxYVMrjO1e\n4YyfLVvEkKlpvETB0QV41w7kiJ8ccsghByZNgrlz4bmPpyXq/xf8bevfUIMq7+zVz6g5dOglHI5m\nioqOG9F2CguPob7+x2zadN5hyftJm/h59lnkr3wZj5zP7tHioMxmYXE0yPk5ePAZtmxZyLRpf6PS\n+XnG39lLcc0C1q37bEryx+PZyZ49P2LKlN8jy7E3x1ZrFY2Nd7B9+xVoWnre+NEmfoJBha6uv1JR\ncV7KZUeL+Bna6QezmU53J5XOSiTJHCF+JMlEQcFsBgY+Gl7JIH/nv7XOfdGiRZx++umMGzcu43W/\n9KUv8bvf/Y4Lv/kjxh8qZemepbqKH03TeOCBB/jOd77D4sWLIwHSZtmMEt+KFoLZXKL7fdUCUYqf\nkM0rjFkXXcSKzk5obzfc58ZG8Mii2ctSakHtEZ9VqpyfjPJ9YNjqZTNhxowa0JfgRyPgCeDd48U5\n1Rnz+EitXiNFOoofgHzzXlzVJ6RcTu1WsZRmN9qwSBKL3zHzde0PoNNqlBZUVYy8aozr0IvnFdP3\ndiJpEVa62GQZX8jqFRwKorpU3BvdFB5r4C2tqYlsN+Hnu2IFFBfDxFB7ZZKOZb+/HVm2Y7GUJD/E\noCqIH1Pmih9N07g4ZLE5oVhfwZRM8QPQlJfH1/8AP1UP0Jviuytb5KThzls9HmptNgqz6Z2OQ3y4\nczxKzTInB1/jN1OOoPW443hl+nRqrFbO22ycn6KLyZOp3rqVjtCx2+psSGYJ797YSa9Uih/QD3g2\navUaGJAJ2owVP0mtXpJ4ziyb0fK0CPHT2wP2v5zIhAcnpKWUSWX1MlL86H3tE+xeaVwnZUni183N\nTIqfFK6tFeUYZJjx0zH8namsvICOjueHCTwD4kfT9HdTkhJzfsxmsFgSM36UQwpNTWPQNAVfwIcs\nyZzSeEqkkS6MSZOEtX1yyTRj4idk9dq8WexbUxMUzi1EyRE/OeSQQw4h/OAHIuQ5zYtDDsnh9rt5\ne8/bXDvnWhbvXKy7TFvbw9TWZqY8MEJt7bXY7fXs3PmDUXm9aKRF/GgaPPMM0uWXceKJoVr30YKO\n3UvTNPbsuZWWlp8xY8Y7FBefAMuWIc2azfjJj1BcfDLr1p2Goui3vASDKlu2fJP6+p/gdOp7KMaM\nuRzQOHhwUVq7OdrET1/fUuz28eTlNaRcdtQUP7sUMJnYP7CfKmdVDPEDOgHPnyKrl6qqPPTQQ3zv\ne6mb84ywYMECHn30C2x/pp/f/PE3CcSPqqpcc801PPPMM7z//vvMmjUr8pxRqxeAyVRAIJA4o62p\nGmo08VM0rLCYNnMm+yWJgSSEfUMDDGr5w4qfHiV0HCJbVw9DQ0N89NFHnHTSScneiliEiB+L3YJZ\nsqRF/Lg3unE0OxIyYj5Jq5cW0FD7VMwlqb+b+Z3v49LGp1wuW6sXAAGZk+ZDWa0d4tp70kZ7u1Al\nJTmJF88rpu+dxJwfJRROawspfsIZPwPvD1AwowBTnoG3KTziW78+MaIr3OYV2YjxBSYdtQ9EWb3M\npXR5MmuBvL+1lb0+H9YkxE5Y+WSEErOZ5u2wwFzEtTt3Jt1eKsXPaNm8QN/qFQ1F6cFsLkaSTEiS\nxBH5+fy4vp5+VaUnE/K1uZmCjRtRNQ13IIAkSUL180Gs3Ssd4kcv58dI8TM4IIOtn6DO5Ewyq5ca\niFX8YGeY+Om2UHFcO4XHFIpmvRT3xCnDndO0ekFiwLOkBlIqfgwxfnzknJGu4kcLaIKoDikeCwvn\noGkBBgdXiQUyzPiBxJwfsxnM5ljix+v2gg+KK5xomorb70aWZOY1zEt4PacTKiuhSj3GkPgJ17lv\n3iz48sZGKJpbRGB9f9q3HdkiR/zkkEMOnw6ceCIUFMBrr33Se/J/Aq/vfJ05dXO44IgLdImfgYEV\n+HwHKS//4qhsT5IkJk9+hp6e1+ns/POovGYYaRE/774rWuKOOUaX+Dl06GX27PkJHs+OzHfglFNi\niJ9gUGHbtkvo6fknRx+9XFT9btwIt94KX/gCkiQxfvwvKf5/7J13YBv1wf4/dxq2JXnP2HGc5cRJ\nyN5pGSE0EKBQWjqAsgq0rEJYhV8ZZW8CpC+lLaNAgVJGaZhv2QmBkL3IcOIMZ3jE25YtS7d+f3wl\nW1snO6y+fv5JrBu6k053933uGVlz/cqfSPJn3767sVozKCm5PObbSpLMqFF/Yc+eG/H5YlvNAjBU\nA9l2+C77Zm1ezc3CBTR0aP/eL3VIKkqrxvpsK+cvOZ9zJ577X0X8vP7665SWljJjxox+reeIIzws\nevJSPlr8EV3dXT3ET0dHB6eccgpVVVUsX76csrKykOXiEz8uNK0j4nWR8WNEtXpZrVYmjhrF2mee\nibmtw4ZBi+K3emVZUVtVDN1gwgTo6CBUmbdkCTz4IJ999hkTJkwgI4mkcENR0AwDi92C1WS4s3t9\npM0rsF/flOJHbVWxZFiQrQl+x243rn0f4j6QOES6r61eAFq3xC/ONISCxt/QkzQS2LxA/PYt6Ra6\ntobavQMZJSmS1EP86B49vs0rAP+j/ghRYLDNC+KeC8zk+4Df6mW1kS1nJ2X1WtraygP79vHcmDFx\nFT3xwp1BXH/TdIkL7fl80d7O6w0NsedNQPysPUzBzhDd6hWMaFXusiRxhNPJ5mQUZqNHI1VWhjR7\nfR3ET1sbWBztUc+r8axeit6r+JElGdKgqbmJlspu0KD0ZkEe2vw5W/GQqM7d5s/ISksbHmLHj3bY\nR1P8xAt3jouxY8XNAeYzfnwNPqw51p7znyRJoSHPsiweDgeRYfEUPxCd+LFYiqmvr+8h+KuqqrDl\n2fBoCoah4Pa50XQtItg5gDFjwNpyRFyr16HOQ1TuMDh0yK/4mZ2BsaUdm/Vw5RBExwDxM4ABDOC7\nAUmCq6+Ghx76prfkvwKvb3+d0ypOY1LRJNq97exq3hUy/eDBP1JScjmS1Nd+3khYrZmMG/cyO3de\n1jeCJQZMjdGffBIuvBAkKSrxU1f3NO3tq1m//kjWrZtDTc1fUZTEeQiAkLLU1UFtLarazubNJ6Eo\njUya9Al2TypcdZUIWP3Vr2DhQgA/+fOAn/wJVf60t6/k4MHHqKj4G5IU/zLtck2gqOhcdu1KrKQ6\nnIofXffR2LiE/PyfJpx30yZR4y73845DkiXU4m4uHgcPH/8wF029KArxM5P29pW99revI+PnMNW5\nL1q0iKuvvrrf6/F4dnDmCeeR+etMPN0eXvj7Cxw4cIDvf//7lJaW8tZbb5GZGTkojkf8WK3pMYkf\n1SIGIOFWL4Dp8+axproa9uyJWBbEeL9VdeFr6kC2ylicFrQODVkOs3tVV8MFF8CiRXz4wQfJ2bwA\n1evFIklIdsm01atjfUdEsDN8s1YvX4PPlM2LZctInTEEtV1DaYq/rX0lfnbtAk2ROHqeqHTvs+LH\nBPED0XN+lGCrV1DGT9vSttjBzgFMnAibNoVaYSorxWg9SAkX72TR1VUpiP0EUHSR8ZMtZ5u2etV4\nvZyxdSvPjRnDILs9LrGTyOoFkKJLtOgqz1RUcOnOnTT4CZBwSDYprtXrcCl+DM1AaVCwF8YjfqI3\nek1wOtkUL/09HCNGwL59h434OZAM8ZPmjnpejWf1UjUVq6V3mpQm0dTcxMbr95Hu8OAcOggwp5Qx\na/Wy2wehaW1omiBXTSl+NK3vxM/kyT32ULOKn0CwczAKC8+ivv4f6LoqxgkWS0ileyLix38a6IHV\nCrpuIy8vjzq/TXnnzp04i5x0+LowDIXtjeIzGJUbnfStqIDuuqFsa9wWNZA8zZaGTbaTXdhGS4vg\nze35doxMO8W+2ISmYRg8t/E5bv3kVp7Z8Awf7/mYPS17UDTz16MB4scEhg4disPhICMjg+LiYs4/\n/3y6BkJmBzCArx8//SlUVcG6dd/0lnyn4dN8vLPzHU4dfSqSJHHCyBNCVD8+Xz1NTW8yaFD8Oum+\nID19CkOH3saWLT9F08zV2nZ374tLwsQdxBsG3HmnSIo9+2wAjjgCGhpCo0fc7o2MGvUYs2fvZ8iQ\n39Pc/B5ffFHG1q1n0NT0vxhGnIGixQJHHYX309dZv/5I0tJGMm7cv7C8+C9/t6cbtmyBSy4R8/oR\nIH+ys+f1kD+a1sm2bWdTXv4YKSklpj6fsrI/0Nq6jJaWj+LOdziJn+bm93A6x5KamnjAdjhsXgBP\nrXuKlc61PLhqKqePPR0ggvix2wuw2XLp6qoUL/RD8dPQ1saHH36YeMMOQ537ihUrOHToEKecElmZ\nngwMQ8Pj2Y3DUc78OfOxpFi47977mDRpEmeffTZ//vOfYz5l7rPVKxDuHGb1Apg2Ywari4rgpZdi\nrBfUtHQa94p1ByrdIYj40TTx2/3d7yA9nQ/ffLNPxI9NlpFtMhbJimKC+IkW7AzfLPFjNt+H3buR\nxlTgmiRq3eNBbe5bnfsLL4DDLoFFF8RPXxU/+/dDaWnC2WIRP1Y/8dMdpPjpWNdBxpwEijD/o/6Q\ngfGSJXDKKaEsdZyThVmrV0/Gj2TO6qXoOj/bsoVLi4uZn5MTosyIOn8CxQ+AXZU4qCt8LzOTswsL\nuWTHjqiD0niKH90w2OB2M/kwKH6URgVrljWuClUofqIQPy5XcoqflBQoLaVIUXqIn/Sp6XR+2YnW\nHUQSGAZGIuInSrhzrDr31lawpHXGJH7ihTsHW3QtDgu1G2o5+EUXGfkNpKYKtaYZpYxZq5ckyaSk\nDKG7e6+YFuWwHz16NNu3b+85biRVQ7bGr5OPiSlTxLm9q8t0xk+0TCiHYxSpqaW0tvrvf6zWpIif\nsWNhxw4I8KAWi1hm8OBeu9fOnTvJLsmmw+dB1xWW71tORkpGRL5PABUVUF3lIN2ezv72/VHnSZcL\nKRzWTFlZ7+lGq8hgSEdk2xyIeIazXz+bh1Y8hKZrfLTnI25deivHPHsMrntcDHl4CEf+7UjOfv3s\n6DvqxwDxYwKSJPH222/T3t7Ohg0bWL9+Pffcc883vVkDGMD/PdhscP31YhDgD4UbQPL4ZO8njMod\nRUmGIBYWjFwQQvzU1PyV/PyfYbNFr47tL4qLL8HhqKCqamHc+drb17Bly89ZubKcqqrYWUMxiR+P\nB846C954A1auBH8jisUissKXLw8s34yqtpCaOgxZtpGXdzJHHPEqs2bt9tem38KKFUPYvv18tm07\nhy+/PJ1Nmxawfv3RrF07nVWrxvHFpctZlb2QwsIzKe/6NfLRx8Kjj8Lrr8MTT4gGiyiQJInhw+8n\nO/s4Nm6cx44dl5CRMZuCgtNNfZYAVquL8vI/smPHJXHDsw8n8dPQ8E/y8xPbvKD/xI9hGNz80c3c\ns/wefjB5MoNbhvZMCyd+IKD68du9otz1+h/qBnNwkVAUlm/YwMKF8Y/RWO+RLBYtWsTChQuxxN2o\nxOjursZuL8BicTBv2Dw0Q2PZJ8t45ZVXuPbaa2PeqALYLLYExE9sxU80qxfA9OnTWePxwD/+EfN9\n5QwXTXvFuq051p6A5x/8QPC12l33is/42mtpPe44tlVVMXt2coHziteLVZaRbH7Fj66HdviG75dm\n0Lm5E9fE6FavbyrjxzTx4z8pJiJ+dJ+O7tGxZCR/3L38MqSn+Z/Uf8VWL4CsoyNzfgID11R/uLMk\nS2AF51gnVlcCxd7YsVBZiQ2ll/gJz/eBBIqfJK1ekjnFz3W7dpFts/F7vx0zuH0pGtQExBCATYOD\nmrg+3D50KNu6ungpShtlPOJnR1cX+TYb2YchgTZRsDOAzxdp9YI+KH5AVLq3t/dUulscFhwVDtzr\ne9ejGgaoap/CnWMpfqwxiB81zvsEW70ALKkWqt+oxnVhKa6MIOLHhFLGbKsXhOb8RCN+cnJySEtL\no6amBnQdSdeR+hpKk5MjFDpbt2I1YVmD2MdMYeEve9u9wnY4QPzEOmTT0oQNPSBkCiweTvwUlBbQ\n7u3EMFS+OPAFeY7YbYkVFWJ98Zq9UpQiHJmdDBsWtH/lmZRGaazb0bSDWU/NwipbWXHBCu449g6e\nO+05lp63lOqF1bj/n5tl5y/jzrl3ctyw42JuFwwQP6YRuNAUFBRw/PHHs2HDBgB8Ph/XXnstZWVl\nDBo0iEsvvRSvnwleunQppaWlLFq0iMLCQkpKSngmyOseb9kBDGAAMXD55fCb34iR+8qV3/TWfCfx\n+jZh8wrgB8N/wKfVn+JRPOi6j5qax+Nmy/QXkiQxevRfaW39mPr6F0OmGYZOU9M7bNgwly1bfkxG\nxkxmzqyiqekdururo64v6n15TQ0cfbT4/9KlMGhQyORgu5fbvRGnc0KErcpmy6Wk5DKmTl3FxInv\nk5Exi+zs4ygo+AUlJb9l6NDbKC//E+PGvczE/L8z65pihjxUgzR/Pvzyl+L4nDnT1OcxfPh9ZGfP\np61tOeXlixMuE468vFNwOMawb999Mec5XMSPpnXT1PQW+fnmyKmNG8XD9b7Ap/k499/n8v7u91lx\nwQpKh2fQ5S3smR6d+JlFe7v/3BBFjRMgfeI+IFdVFF1ny5YttCWqDu4n8bNnzx4+/vhjzj///D6v\nI4Curl4FwtyhczEwGDJ4CHPnRs8iCEaijB9VTUD8RLF6jRo1ika3m8bGRqF6i/a+2S5aD7j9/+8N\neM7Ph1MHrUR7ZLFodJRlPiksZHZqKikpKQn3Jxiqz9dD/FgMC4okhTwVDkdXZRf2QXasGZGDmm9c\n8WOiyj2Y+OlYH/m99czWrGDNtsYlBKNhyxYxqHWlymLA9jVYvaLl/ARCjQNWLwDJIpEx20T+k8MB\nZWXYdleKU0RdnehhDv+txKhz13WV7u69pKbGD9A2DAPN0ATxY2QnVPy8VF/Pm01NPFdRgez/XhQj\nvpXLjOLHosE+TZAeqRYLz1ZUsLCqipqwcUc8q9fXGewMwuoVTfEz3uXiy85OdJMV4ICodG9o6Gn2\ngki7l2ZG8ePP+AkmIOMSP47YVq+4ip8gq1dFcwXtajv6FBcuVwN2exFgrg3LYhHER7TZwrOhgivd\nYwndeuxemoZukbH1VfEjSeI3uHataauXUq9EJX7y839OY+MSYVOLQfzE46eCc34Ci5eWloYQP4OH\nDqbd14lhKKyrW8cg16CY6zND/BgdhRiywvDhva91j8xgUEuo4uf1ba/z/ae/z6VTL+fWSX9jxTIH\n4bckNouNoVlDOXro0Zw76dzYO8oA8ZM0Dhw4wLvvvku5v+bx+uuvp6qqik2bNlFVVcXBgwe5/fbb\ne+avq6ujo6ODmpoannzySS677LKem8hEyw5gAAOIgSuugL/8BU4+GV599Zvemu8UdENnSeUSThvj\nJ37a2sju1DjKOZbP1y2hcdszZHQPx9VVCIcOCa3yVwCrNYNx416hqupKOju3o+teamv/xurV49m9\n+/cMGnQhM2fuorT0alJTSxk06Ffs3/9w1HVFED9r1wrC5dRThR8hLS1imXDix+WKL0lxOsdSXPwb\niorOoaDgdHJzTyQ7+xgyMqbjdI4jbdIJ2Fp1oTLaulWQk0moN4Tt6z5mzKjEak0QShoD5eWLOXBg\nMW73xqjTDxfx09z8Li7XZFJSihLOq6piLDV+fPLv09rdygnPn4Db5+ajcz8i35mPY7BOV3dBzzyS\nZItB/MRW/JgKA/cTP4ZhsDIRwdzPOvfFixdzwQUX4DoM9gmPp1eBUJYpngrvaNphalmrbI2ZFRDT\n6qUI4scSw+olyzJTp05l7Zw5MVU/qXnpdNT1Wr3UFv/31dHBovqzeOWYP/UQAx/W1THP5xP130kg\nhPjBghrQ88eAe4Ob9MnRB7jfOPGTjOJncnzFj9rUN5vXK68I57VV9g/Y+mP1Mkn8QKTdq6fO3R/u\nDIAhbDymMGEC8pebxMB4yZtwwgmiBCCAOPLA7u49pKQUY7HED9BWdAWLZEG2yWSRFTfceUtnJ7+t\nquK1ceNCVDWJiJ9E4c4AFhX2ab0kz7SMDC4uLubXlZUhJEYsxc+/Ghq4dtcuTvUrZ/sLb603oeIn\nltUr02olz2Zjt8ecXRwQle4HDvRYvSCS+FENAyOB4ifDasUqSbQEnT9i1bm3tYHN0ZU08aPpGjaL\nmKZ1aszbNg/fGB8NjS1kZvp6HlJZTRImsZ5NhB9XiRQ/EBTwrKoYFkuIJS1pZGfDl1+aD3eOofhJ\nSSkiPX0yLS0fROysooifcbzbseCcn4BTLFzxM2zkMNq8nXT6xBg+3xldxQ1QWCg2YYhtCtsatkWd\nx9NYiKdLClH8dBc6Mbo1Pv1fhb89ozH7zI/45Rl2Mp/eyzXH/Ibvf1/inHPgb3+LvS+J8BWXhh0+\nSJ980u91GMcc0+dlf+SXfrrdbubNm8ett94KwBNPPMHmzZt7ghJvuOEGzjrrLO666y4A7HY7N998\nM7Iss2DBAlwuF5WVlcyYMSPhsgMYwADi4Ic/hPfeE378XbtE/kOSTy3/L2LlgZVkp2WLULq33xZ3\n7w4HL6sedON8UmSVPIsD5CPEAp2d4nP+3vcO+7a4XBMZNuxuNm8+CV3vxukcx8iRj5CdfVzEE+jB\ngxeyevV4hg69GZst9MZTUYJuUF55BS69FP78Z/jJT2K+97Rpwtfd3g5u9wYyM/u5f7IsAmj7eQzK\nct8l9KmpQygvf5QNG+ZRWHgmZWU39wRjGoYBGv1+3KOqbezdexulpeYqx3fsEGPCZDmNfW37OPGF\nE5k3bB6Ljl/UU23rGKTh6c7B0AwkixRV8eNyTcLj2YmqusUNfJR614TqdD/xAyJ/Z/78+bHn7Yfi\np62tjWeffZZNwemS/UCw4kczNGRJ5sM9HzImf0zCZfsS7qwpOoYsiEva22HIkIh5pk2bxmq3m+Of\nfx6uvDLC9ugsdFGzpdfqFVD8cMUV+OYcw6Lqn3CWf94PP/mE5+bMEe2OSSikVJ8PmywjWYXixyvL\n8YmfGI1e8M1bvRINlsWMgvhxjnXSvbsbzaNFrTbvS5W7YQib19NPwwdSkNWrr4ofkxk/IIifpjea\nKLmsRChpEKRjQPGj+3QMxcAx3mHuvSdMQNq8CZvtTJTX3yLl/DNDpwcuLlHO68nm+0hWiSw9iyZv\ndOKnXVX58Zdf8uCIEUwKU9UksnKZCXeWVINDhB63N5aVMXPdOv5WV8ev/KrYcOIgctLVAAAgAElE\nQVTnkM/H5Tt3stHt5pVx4/helGD4vsCc4qeB9PTpUaeNd7nY1NnJSIfJ73r0aApXruTDMOJnz01B\nFeYmiB/oDXjO8RM30RQ/hiGemxU5PVEJ9XhWr+CMn/0P7qdlUAsdcgeNjW1kZ/dKd8y0ekHvJcoe\n9nGHK8VSU4f1PDRJqPhRVQyrBVs/7lkoKoKqqqTCndOnRyd1c3NPpbFxCXlh12OvV/x84/08JkyA\nP/5R/D/Y6rVmzRpaW1vp7u5m6OCh1DfuoNXWREVuBU67M+b6JEmoflJbJ7G1/YmI6YYBbQeKSO9I\nCVH8eLwSl+rTGXKZRkPBUlzFNTxy1Y+YNsFBebm4j3ryychykmTwnSF++kPaHA4sWbKEuXPnsmzZ\nMs466ywaGxvxer10dXUxderUnvl0/xPCAHJzc5GDTtYOhwO3201DQ0PCZQcwgAEkwOTJ8MUXQvmz\ncyc8/riJR/lfMQ4cEBafDRtg3jxhN/oWEVKBNi98PtE29dprsGABO+o2cN1bp3D7OJmZM6sg8BTn\nT3+CBx74SogfgEGDLgQgPX066emTYs6XklJCXt5pHDz4GEOH3hIyTVURFZi33S5GI++9J46NOEhJ\ngalT4fPPIS9vIyUll/V/Z74F33Nh4VlkZ8+nuvp2Vq0aQ2np1QwevBDJSEWySklbOoKhaZ1s2nQS\nWVlHUVh4jqll+prvc+NHN3LK6FO4e97dIa9bbCo2exfde7tJG5EWlfiRZTsu10Q6OtaQ3U/ip6Cg\ngM8//zz+vP0gfp588kkWLFjAYJOKh0TweHaSm3si4B9wyjY+2vMRl89IbN3sS527quroAT4hSsYP\niJyfF198Ec44Q0i/Fi0S/5ck2tvb+cKzl1f3ruEPR/ybWybfwszmmYJZWL6cvFXr2TVMiA9VtYba\n2lomX3edIK2TIH4Unw+rRaguZEM2pfgZfFX07+SbVvw4j4g94OidUYG0NGS7jGO0g84vO8mYHvnd\nKM3JN3pt2SKeB8ycCba1fqtJcTHU1ooRTTLnGE0TFqviYlOzZx2dxa5rdmEYRo/KRQoQP4ZBx5oO\npBQpbmBwCCZMgD//GZvNQPlsFSkv/z10+uGqcpdtyDaZbC261cswDM7fvp25WVmcWxSppDwcVi9U\nA68U6vexyzLPVVRw7MaNzMvOpiw1FckmoStiPPLPQ4dYWFXFuUVFPFtRQVo/M8iC4av1kTo0gVoq\nRqsX9Ob8/DhGfl4ERo+mcOvWEMVP2sg0NLeGt8ZLSnGKeeLHH/A8wf9Ew263o+s6Pp8Pu59d8XiE\nysRm1yPOq5rfZhor000zNKyyle4D3RxYfIDlpy0nZXUKjY2d5OT0LmNWKRNLlBpOKAYrfmIpYysq\nKnjzzTdBVdEtcv8UP2VlImcrmXDnGC1weXmnsm/f3Rg2G1LQud3nS9woGs3qFVD87Ny5k5EjR5Ln\nyKPS20GHrYWh2TNw2uKfhysqQDk0gq0dWzEMI+Teq6YG7EohLXXpIYqfqipw2lXmD70J501O/nD0\nH3oeegUwaVIvSdUXDFi9TCJAyBx11FGce+65XHvtteTl5eFwONiyZQvNzc00NzfT2tqaOA8A+rXs\nAAYwgCCUlAj6u74eFiz4yqxJUdHZCcuWwf33C3VJSYloKnjmGXHV/M1vhLTkxRf73fpzOGAYRi/x\n86c/wfDh4jMDJhZO5MjsRlKzf44cfCE/7zz47DNBrH0FkCSJ4uKL4pI+AZSWXsfBg//TUzcKgKYx\nuGUzUx74Bbz7riDdEpA+ARx5JHz2mY+uru04nUf0dRe+dbDb8ykv/yNTpqzA7V7PqlWjqd3/XL9s\nXrru5csvT8PhKGfkyEdME0h9JX62HNrCj8f8OHKCpuFwNtNVKY6BaMQPBNm9+kP8aBpHHnkkK1eu\nRI93U9rHOndVVVm8eDFXXWVOPWUGwSoEVVdJsabwyd5P0PTELVaJW72iKH5UAyPwWUaxeoFf8bN6\nNdxzD+rrr/PFTTdxe0UF358xg5KSEl7a+QW5vhSGDx/Ozs6dKNXNIsvtxRexZbuYOxfefx/eeust\njj32WCwnnQQffNBbw2ICqqKEZvzEUfwYhiGq3GMofr5R4qchOasXEDfguS9Wr5dfFkJRSQpSHKSl\ngdMJjYkbq0JQXy9CXsOlCDEQnPMTTIYEWr1al7ZizbCiexIPIgFxctq4UQQ8z/heJHGZoMrdjOIn\nUOUu2STStXS6lK4IBciSxkZ2ejw86o+RiFxH4nDnRMSPoRh0WyJJgvEuF9cMHsyvtm9HN4QduE5S\nOO3LL7mzupo3xo/nvhEjDivpA+bCnWNZvUA0e21KptkrP5/C1lbqg4KZJUkSdq+Vwu7l03XQtJAH\n9tEQHvAsSRJOpzPE7tXWBllZ0c+r8WxeIIgfu9XOnhv3UHxxMV2DumhtbaW52UtOTm++mVmljFmr\nl5mMn3CrV8CS1ieMGgWHDiVX5x7jmElLG4bdPoi2Ci1kZ80QP6Wl0N0tHjCEt3rt3LmT8vJych25\nNHe34/a1MTh9MC57fBlzRQUc3J2OTbZR564LmbZtGwzJKaS9PjdE8bN5iw8jdR8ndpzI7XNvjyB9\nQLTS7tghlEx9wQDx0wcsXLiQ999/n82bN3PRRRexcOFCGhoaADh48CDvvfdewnVIktTnZQcwgAGE\nweUSbRzjxonQ5z17Ei/TH+zfD9OnQ0GBsJgdPAinn95LQL35Jtx7rzi733or/PWvMGIEPPggEals\nXyO2NGzBp/mYYi+Du++Ghx7qmaYoDczM0VjRmh26kMMhCKyHo+frfJ1wOivISJ1K3ftXw403CkVV\ndja3bPoJSn6xqACK8rQ0Fo48Eiort5GaOgyLJTIH6LsOh6OcceNeYezYV6g7+By63EVT07tJK0t1\nXWXr1jOwWDIYNeqJiBDseOhLsLNu6FQ2VTI6d3TkRFXFkd5C1/a+Ez/JZPwUFxeTn5/P1q2RAY26\nrrNw4ULcut4nYve1115j6NChTJs2Lello0HTuvF6a0lNHQr0Kn6KXEVsqNuQcPm+1Lmrqo5h8Q8c\nooQ7AwwbNgyPx8OPf/xj8k88kV87nbTn5HBLZSWH7ruPD/9wEz9hGBMmTKPBewj1jY+FGnG6sHgc\nfzy8+67K/fffz29/+1tx3q2oSErvHpzxIxsyahzix3vAi2SVSBkUPUD6m7Z69Yn4WR+d+FGaFKw5\n5p/YB2xeP/uZ+DskY6Qvdq8DB0zbvAII5PwEq1xS/Vav1qWt2HJs5omfIUPA7camdKEcf3Lk9ARV\n7g5HlHNUGBRNwSpbBfGuQk5aTkizl2EY3F5dze1Dh5ISY4RqJuMn3vSA1bdbjn7uv7a0lE5N408H\nD/LvIV2cOOYA410u1k6bxowov+nDATNWr1itXiAUP0lVuksSRfn5IYofCM358WmaaKpKQKIFAp6D\nEW73amsTPHi0tsR4Ni8Q5267bKfhtQZKrynF7rLT1tpGU5NOfn6v0sSsUiYW8RNOGFqtOYCBorTE\nPPRLS0tpbW2lo6Wl/4qfCROgra3fGT8B5OWdStPU7qhWr3iQJLEpmzf3flYlJSXU1tZSWVlJeXk5\neY48Nh/ahk2WSLGmmFL8xAp43roVynJLMCSVrKze19dvdWMf3IV9mx1dif69pqbCyJExuxISYoD4\nMYHwE0BeXh7nnHMOd9xxB/fddx8jR45k1qxZZGVlMX/+fHbsiB2kGLyue++9N6llBzCAAcSBxSLq\nsy+9FI46Kvknj8ng7rsFwdTcLKxmjz4qrAvDh/dcYfbvfwhFaxVZRJ98Imq9160T81x9tciD+Zrx\n+rbX+dHoHyHdfrt4ZDtuXM+0mpq/oqcdyVu7lkUuePnlIpi1KXEN7VeCtWvhzDNh+HCGXPop+7uf\nRbfJcO21sGcPP5+0g30LHxZXxCQwezZ0d28kLS2x2ui7jMzMWYwf+7/INjtVVQvZuPE4OjrWmVrW\nMHQqKy9A07oYO/aFUDWYCWzalLziZ3/bfrJTs0lPieLlV1UcGa2miR+jL4off6WvomnYbDZmz57N\nihUrImbbtGkTjz76KP+zeXPSih/DMFi0aBFXX311UsvFQ3f3LlJTh/Z8R4ouBpzHDjuWj/Z8lHB5\nm2xDjfJZQuxWL00xElq9JEkiLy+Pffv2sXXrVjZt3syDK1Yw/9NPSXv6aaT776M05RBWazH121ej\nKmmCUPfj+OPhzTdfYPDgwRwTsP2fdBK89VbCfQpAVRRsVqspxY97gxvXpNhPc79pq1fSxE+cgGel\nKTmr1+bN4sm4n5MLHbD1JeB5/37Twc4BZM0VxE8w2ZEiSXh9Gu2ft2MrtKF5EivcgJ4Rn1XxoMw7\nIXL64ahy14XVS7KJ7JzctNyQgOe3m5rQDINT8mLXQysJMn4C7WaxYKgGWCDWUWuVZZ4dM4brd+/m\n6WEdPPNlIXcMGxaTiDoc8NXGH8QbhhGz1QugPC2Ng14v7iTOvenDhqHqOp1BjX7BxI+iqkgmCIhk\niJ++Kn5yvDlYM63YcmzYHXY63Z20tMjk5/eqKs0qZWJZvcKVZJIk+e1eu2NeJ2VZZvTo0eyqrBSt\nXv3J+Jk0CbxebIqScD90r47WqWHNjn3xzss7lcbJXRhBO2tG8QOC+Nm4MTgPyU5OTg6bN28Wip+0\nXNp9XWTYnXT6OuNm/ACMGROb+Nm2DUrSi5FzescBug51+x2MHZdD6rBU3Btjh/JPmiTSJPqCAeLH\nBHbv3s2xxx4b8tpjjz3GK6+8gt1u56677mLXrl20trayZcsWLr9ceOmPPvpo9u3bF3NdKSkpMZcd\nwAAG0Edcfjn8/Ofw61+LAdzhxv798M9/wk03iaCYKDAMjT17bqGtbXnvi1OnCsvX+vWCpJoyRRBG\nXyNe3/46Z9mnChLnttt6Xtd1hZqax5k06jY+3Sdq3UNQVASnnSYylL4J3HWXGBy88w6ZK9pJKZtO\nwyVjhU0tN9ecgiMKMjJg6tQNtLT0wYv0XYMGFlsK06d/SX7+T9m8+SS2bv0lHs/emIsYhsHOnVfQ\n3b2bI474F7KcXIV2Y6NwQ5aVJbep2xu3U5FXEX2iquLIbEtI/KSklGKxOHHLO5MnfvwtPoqqYrPZ\nmDNnTtScnw8++IB58+axaN062qNU+cbDihUraG5u5uSTo6gM+ojwgWggIHT+iPm8uePNhMubafUK\nV4tpqg7W+IqfmpoaDhw4wKxZsxg0KKgCd8IEQZzPmsVUz3IGv/8f6vZtRxk5OaSCpbRUxeO5k7PO\n+kPvsiefLHJ+TEIJKH6sErIuo0pSbOJnfexGL/gOEj8TXXRu7sTQIq+HyRI/AbVPYKwYMvDsC/GT\nRKNXAFlHZ9G6tBWfpvWQHSmyjLzZQ8qQFKyZSVi9ACZMwGaXUPOiqEVjVLmrqhtVbSIlJbFaKTjc\nWVd0ch25PYqfgNrn5rKynur2aOi34kcxkGxyXHXIaIeD9dOmsWR9MWPb+ljRnQR8db6YqjoATXMj\nSdaYalyrLDPG4WBLV1fU6dEgjR5NYXd3aLPXjAw61nagqzpeVUU2oaAJhDsHI7zZq7U1NvGj+q8t\nsaAZGoVdhTjGiOBqm9WGM91Jc7ONgoJegtAiSeiQsNY+ntUrnDAM2L3iXSfHjBnD7h07Dk/GD+DY\nty+hcslXL/J9JDn2ce5yTUG3G3T5eqMJfD5zBasTJsDq1b2tXiDsXgHFT64jF80Apy2FTqUzoeJn\n2DBxOizPGB9V8ZOTkoeWsbPnmvrGst1g6+SIIaVkzM6g/fP2aKsFBoifAQxgAAMIxV13we7d/es8\njIX77oMLL4xopglGV9cOdL2Lzs7NkROHDBFhyevXwx13xMzOMQyDnU2HL1dnb+te9rfvZ9qif8IN\nN0DQ08XGxn/hcIxiUM73mFw0mU/2fhK5gquvhv/5H/G49+uEroscpSuvFNpZWWbIkOvZv//+ngum\nqcyWGBg7dgNbtvx3K36gt8pdlm2UlFzMjBk7SEsbydq1U9m16zoUpSVimT17bqK9fQXjx7+FxWKy\nOSUIAZtXsnnS2xu3MyYvRguVquLI7khI/EiSRFHROdSpbydP/PhnCDyVjaX4+fDDD7nkkks4YcQI\nHlkWRSkXB4sWLWLhwoUxwz37gvCWoQDxc8LIE6hsqkx4Poln9ZJlK7JsQ9dDf/+akjjj5/e//z0L\nFixg165dUd7UChddREPWKEpqaqnLdaF2hn4mL774Ivn5xTQ2HtP74qRJglU0qZJWFaUn3FkypLhW\nr3iNXmKTvxmrl+7T0T061kwTJ7sg4seaacVWYMNTFVl9nUzGT7jNC8Jahfpq9UqS+EktTcWSIXJ+\ngjN+Ur/wkHV0FnKanBzx84tfYMtOj+7WDKmM7IXHs5O0tJGmbK+BcGfJJmEoBnmOvJ6A5/80N9Op\naQkDihMpehKFOwfO/4lUFaMcDuxWGUP5aotmtE4NQzGwZMQ+/4l8n9gqKPDn/CRDuo8eTVFrawjx\nY820klqWSufmTryahmxG8eMPdw5GNMVPvIyfeFYvzdAo6CrAOUaQC1bZSkZWBm1tTgoKeo8VSZKw\nmmj2ihvuHHbcBAKe410nKyoq2LNzZ/8VP2lpYLXi2LQp4bFpJhNKkiRyN2fS6PmgdzmTip8TThDi\n8l/9qjc+bvDgwVRXV1NeXo7D5sCVkkma1S6InwSKH5tNkD/p7ilsbYxU/MjYseTuo80r4h/+9K9N\n5A/ykGKXyZyTSdvnsWMhYhE/hmFQ+3Rt3O0aIH4GMIAB/PchJQVeeAGuv17E5B8uHDwoVDvXXht3\nto6OtUhSCm53FOIngCFDRE7NxRdHVSZta9zGUc8cFT+PpbHRtKrp39v/zfVdU5ArK4UqKggHDiym\npOQKABaMXMC7Ve9GruCII8TV5oUXTL3fYcO2beLuqaSk56WcnAXoukJLy/uAycyWKDAMg9zcjXz0\n0f8d4icAqzWdYcNuZfr0L1HVdlatGsX+/Q+haWJgv2/ffTQ2vs6ECf/Bau1bfW9fg50TKX7sLh96\nt47SpPiJn+jqi8LCc6j3votOaKZDwuMljPg54ogjqKmpoSnI6ujz+fjss8+YO3cutxxzDIu/+IKW\nlkjyLBp2797N0qVLOe+880zNbxaxFD92i51fjv8lT69/Ou7y8YgfiN7spQUyfgxDjHTCaqhXrVrF\ne++9x913382qVavwRkukdLlIsSj8e/4Saj3tKC2936eqqtx5551cdtkfeO+9oAGKJMGJJ5pW/QSI\nH8kmFD9KPMXPt9TqFVDnmApWDzvIYwU8K83m69w3bhQfWVAZrcj4CTyp76vVK8mMHxA5P51L23rs\nTymyjOsLD1lHZWFJsyRH/Bx5JLYsZ2ziJ8rJQpCsifN9ICjc2Rpq9TIMg9tMqH3AnOInLvGjGMg2\nCZ9hJMx4C69z/yoQGMTHO5YVpSFmo1cA453O5AKeKyooPHSIuhg5Pz5NM2X1GuxX/AR/lofb6pXv\nzu9R/FhlK+kZabjdueTmhpIfZuxeccOdw1gRs8TP3qoqdIvUP8UPQHo6qV9+aYr4sRUmvtHL25ZN\no+/j3uVMEj+lpcKaPn68eIaxaBHk5BSgqir5fmL2k/OWY5EMYfVKoPgB8axSbxgVovhpbBTb1NAA\nWUUt1Lvr0XSN5Z/C2BEZ2GyQMSex4mfjRvFcNABvrZfNP9zMwT/GPwcPED8DGMAA/jsxbhzcfDP8\n8peHr1HrvvtEhXBBQdzZ3O515OefFl3xE4zf/hZaWuD55yMmdavd1LnrONB+IPqyW7bA0KFi/zyR\nT3LD8caWf/HrFyqF2ijIotbRsRav9wC5uT8E4MTyE6MTPyAIr0WLvhoLXSwsXQpHHx3ykiTJDBny\nO/btuw/oO/Hj9R7AZrPxwQeFaCYjIb6rCCd+AkhJGcTo0X9h0qRltLYuY9WqCnbsuJyamr8wceL7\n2O3xn7jGQ1+CnUGQnvGIH8lmxVHhoKuyK6biB4Rk3WWroHFse/gqklL8WCwWZsyYwRdffNEzyxdf\nfMGoUaPIyclhZGEhPxo1ioeCwtLjYfHixVx44YU4nSZquZOAGIz2tgIFiB+AC6ZcwDMbn4lL7CQm\nfiKbvYTiRxJKQIsl5Nyi6zpXXHEFd999N6NHj+aYY47hhhtuiFxxejppmpva2hy8Pi8dTb3v8dJL\nL1FYWMgVV8xlzRoIebh/8smmc35UVe3J+JH12OHOSouC0qSQNjJ22Ps3RvyYtXlBVOKnY31kRpPS\npGDLMbfOcJsXhA06i4uTJ3727u0z8dP1aXuv1UuHjNXdZB6diZwmm8/48SOWIiLWycJsvg/0hjvL\nNjlE8fNhSwstisJPE9xPgEmrV5zRra7oIt8K0L4txE+CYOd4+T4BBCrdTWPECApraqgPu2fqIX5U\n1dTA2GmxkCbLNAYdNMkQP4msXjo6OR05IcSP02WlszOb7LD+DTMBz8lYvcwQP2PGjKF61y40We5f\nqxdAfj72HTsSqpbMKH4Asqqz8OjVeL1C+aIo5qxeIGIib7pJlAy+8w688cYtOJ1zewjKzNRcdF0x\npfgBQfzU7c1C1VUaOhvYv18kPIwdK059BaVu6tx1vLfrfdS9sxk9LAOrFdJGpqF7dLoPRFfY5+aK\nY2vPHvEAs/6letZMWkP61HQmfxH/pmuA+BnAAAbw34vLLxdqkbvu6v+6amoEQXPddQln7ehYS0HB\nWXg8u9D1OJ2LVqto/Lruuojg5EDWxqqDqyKXa2+HH/9YtISBqKc6EIMgAho6G5jw1mpcg4bAj34U\nMu3AgT9SUnJZTyDshMIJdCld0W0h8+aJbf7f/429T4cby5aJsO4wFBScgcezk/b2NX0mftzujWRk\nTKKgAL788jBs67cYsYifAJzOMYwfv4QxY55DURqYOPEDUlJKYs5vBn0JdobEih+sfuJne3ziB6Ao\n/afUzQmVTCdL/AAROT8ffvgh8+bNE39Yrdw8axaPP/54T0tnLLS2tvLcc899JXl+sRQ/IAImh2UN\n452d78Rc3hzxEzrI0lUDrES1eb344otomsY555wDwBNPPMFrr73GO++EbYPLhd3bwd69EsXFxTRo\nDWgeDU3TuOOOO/jDH/6AyyUxYwZ8/HHQcvPmwapV4r0TQAlS/EiaFFPx497gxjnBGTdH4puyepmu\ncodI4idGwLNZq1c0mxdECXdOxuqlquLE24eTRNbRWXg/7SCw5dk7Vby5FlKKUpK3ehGH+Imr+DFH\n/ATa9YLDnRu7GrmtupqbysqwmFBwJQx3Nmn1MtOeFLCkfZXw1noTDuLjNXoFMMHlYnNnp/mmSrud\nIl2nvr4+5OUe4kfXTVm9AIanprIriEByOp0hxE+8jJ94Vi/dEMdudnt2CPHjcDiQZS2iw8KM4icZ\nq5eZjJ/y8nLqDx5Ekw+D4mfwYKx79x4WqxeALNnJkWbS1PSGWM5kxk8AVqtQ0rz/Pkya9B6trc9y\nySXiGa0k2TAMxbTiRwQ8Sz0Bzxs2wGuvidd374aSUh/1nfU89t7bpFlcuFziu5IkSah+VsRX/axZ\nprD151upvr2a8W+PZ9htw2jpiH9/PkD8DGAAA/jvhSyLnJ8//xmiZHQkhfvvh3PPTVgXbhg6bvcG\nMjNnk5Y2jK6uyvjrnTYNfvGLkBYbEPJwgNU1q8PfAM47D449VtjEnn9e3I3PnAlRAmgB3l3zErd+\nbCA/8mjI41qvt4ampiUMGnRBz2uSJHHCiBOiq34kCa65JqQG/iuFYQjFTxTiR5ZtDB58Nfv3398P\n4mcDLtdEjjwyqWbo7yQSET8BZGUdxbhx/yQtbXi/3k9RoLJSOASTQYunhU6lk+L04ugzJEn85Gec\nRPvIbrzemvBVxIZ/Bp/Ph90ubjTDc34++OADjjvuOPGHzUaZw8HPf/5z7r///rj79+STT3LiiSdS\nUtI/Ui0citKKpnVit/d+bsHED8CFUy7kqfVPxVxHtNrhYFgs6RHNXpqqgyxFNHq53W5uuOEGFi9e\njOwfsObk5PD8889zwQUXUFsblEPgdCJ3d7F3t05JSQktrhbUFpWXXnqJ/Pz8HoLt+OPhjTeC3tzl\ngu99D957L+5nA36rl1WoLiRdihnu3La8jYwZ8eurv6uKH/f60HBuwzBEnbsJq9f69eLfyZNDXw/J\n+EnW6rVli1D79KEuPLU0FSnDQuke8Xf2Ki+Hponfal+IH6s1WcVPpXnFT7DVSzHIdeSyta2Wep+P\nX5hQ+4h16Ich3FkyRRJ8exQ/DQkVPwV2O3ZJ4mA0C2kMFKalUdfcHPKac6wTX62P7g7FNPFT4XCw\nPShYOlzx09UFDoc/NF8PPbjiWb1UXSW7MxvJkLAXis/IIlmw212kpkaSt2bIvJiKnyjHVWrqULze\nahRFj3mdTElJoaSwEK+m9Z/4GT4cubbWXLizCeIHq5U8Yw6NjUuA5BQ//sXRNHG7W1KylEGDxPVn\n3Dh45RUHhqEmpfjZvh3G5gnix+cTzylGjxbPaocNldnTsocPl/qYe7Q1JEs+c05mXLvXaEcX715R\nR2pZKlPXTSVjmjiPxrK+BzBA/AxgAAP478agQaKN6pe/hI5Iqbsp1NXBc89FkDPR4PFUYbPlYLPl\n4nSOT2z3AhHy/N57guTwQ9EULJIlUvHzwAPi5vqRR8TfkiS2669/FWqepyNzPJwPPELT3Fkhd+2G\nobN9+7mUlFyJzZYbMn9cu9cvfiFyd/paKZAMqqrEVXDo0KiTBw26kNbWj8nJqeoH8TNpgPj5CrB9\nu4ixciSZCV3ZVElFXkXs7IckiR+LPYO8lSnU1/faKZPN+AGYNWsWq1evRlVVOjo62LhxI9/73vfE\n/P676htvvJGnnnqKurq6qKtVFIXFixdz1VVXxf0M+gKPZycOR3nI5xZO/Pxs3M9YVr2M2o7o4Y/R\nBijBiJbxoytBip+gAfy9997LMcccw+zZs0PmP+qoo/j1r3/NOeecgx640drdVp4AACAASURBVLdY\nkFJTcVk85OWV0OJowdvo7VH7BPbpvPPg3/8Oy8M/6SRTOT+qqgrFj1VC1mQUohM/TW83kXtSbpQ1\n9OI7Qfz4fCEHeUpJCoZm4KvrzTbR3Jqw/6QmHhVFs3mBP+MnMOjMzxcyh7D8lJhYvVo8+OgjLN93\nMXqdsHSlr+ymdrrY36QzfhAfVVQRV5SThWEYfnWdyYyfoHBnXdHJc+SxuvkgN5aVYTVZl56I2Emo\n+FEMZJv87SF+ElS5gzmrF/gDnpPI+SnMyqI+bH7JIpE+PR3vAQWLSeJnjNMZl/hRFGEZimX1iqX4\n0XSNoQ1D6cju6Dn3WWUrVquDlJTI/bSa+E5jET/RsqEsFicWSwbd3e1xr5PDy8rwqmr/wp0Bxo5F\nam5OTPyYVPxgsZCjTaWtbTmq2pG04ifwk9R1aGpqwuut4/HH4fXX4fLL06ivL8Dtc5tS/IweLR6A\nVeQK4kdRhF05P18kRpRk5/HJ3k8Y1PQz5h2TEnK6yZidETXgWWlV2HbuNnI/2U/t+CJGPDAi5Byu\nx7mGwwDxM4ABDOD/Ak47DebOFc1QfcEDD8DZZwsSKQE6Otbhck0BwOkcHz/gOYD0dPjjH+E3vwH/\nkytFV5hQOIG1tWt7pL989BE8/DC8+mpklfxJJwlb1L33wsKFPVd597ZNHPPxHvIX/SVk9n377kfX\nvZSV3RSxOccNP47l+5bTpUSpSbXbRTbRokWJ96u/CKh9YtzQWq0uiosv5qSTHuxTq1dn58YQ4ufr\njC76uvF1Ez/9CXaO2egFPaRM2sg0PFWehMQPVitFH9mpq/ub+Ra4KMRPdnY2paWlbNq0iWXLljFj\nxgwcAVbLf1ddUlLCOeecwz333BN1ta+99hrDhw9nanA67mFCNOtJOPHjsrs4fczpPLvx2ajrSGT1\nslqjWL00Q9S5B1m99uzZw+OPP869994bdT0333wzHo+HBwNWVQCXi7GlHTgcJTTZmnjvtffIycnp\nVVUhbpSvvTaMfz/pJBHGkGDQoPq/S8kiYUgGahTix3fIR9f2LjKPjB9mbrPZvhmrVz8UP5IkkT45\nPcTupTb3z+YFYfkiFgsUFkJt/FaZHqxZA9Onm5s3Gr7nonytjmEYOFZ6ODBF7Mthz/gJG/0qSgOS\nZIl4YBILii4yfgKEygHNjtvbzJkm1T5wGMKdA1YvE3kwAYLqq4R5xU/ijLlkc34KCwupj/JlZ8zO\noPugYnpgHE3xE1znHvgJJhvurOoqZY1ldOT0kuxW2YpVdmCzRRI/Iaq7GIh1fMc6rtLShuPxNMW9\nTg4vLaVbUfuv+CkvB8MgvS12ixUkQfxYrVjVFDIy5tDc/B8UxVy4c9gqUFWoqamhra0NXdeZORNm\nzjTYseMIOn2duOyxCwACyMgQaRMFmmj2UhShBEtNheHDodBVyNratSh7ZnLkkaGn7fRp6XR+2Rlx\nLqtaWIWhGfzkgxFs2Rd5DA0ofgYwgAEMAIRC5tNPhcE2GdTXC7uYCbUPiGDn9HQxsDOt+AGh1qmo\nEAHSgE/zMSh9EHmOPCobK0X7yVlnCWtXrDDMigpYuVLILRYsgOZmWq78NW+eOIKMYb1PJ9vavuDA\ngYcZM+aFnmyfYGSmZjJl0JTote4gCKo334ybK3RYsGxZRLBzOEpKfsvs2S8D0VUWsaCqHXi9NaSl\nlTNsmOCWdu/ux7Z+y/FNED99CnZuiBPsDD2kTOrwVLr3dIORmPjJ/BIMQ6WjY1XwKhK+R/jN+Zw5\nc1ixYgUffPBBb74PhNxV33DDDfz973/nQNhvwzAMHnroIa6++uo4b9x3RAubDSd+QIQ8P7X+qaiZ\nGH0Jd9ZVQ7R6BVm9rrvuOq666ioGx6jptlqtvPDCCzz00EOsXu23sqanM2mkm7a2Ehpp5NWnXw1R\n+wRw5ZXi2OrJ+hk+XCRdrlkTc7sBlKAn7IbFQJMiJR7N7zaTfVw2sj3+rbHVf2x83VAaFWz5fSN+\noNfu1TNLk4I1J/Ggbe1a8XuJRuRGKEiSsXutXt0v4kef42T4Oo2urV0YDpmmInGsHPaMn7CTRTLB\nzuDP+LH01rk/39xNltEVN7MnYjMSZPyYtnqZyfj5Glu94sFMqxfA+CQVP0VDhlAf5QKQMSsDX51m\nWvFT4XCwLYHix2YDmxxpoVUUBYtFY+PG+RHrVXWVsoYyOvN698kqW7FIqVitUaxehzncGUTAs8fT\nHPc6ObS0lG6f2v9w55ISJJuN4bW1cYPHfXW+HutbXPi9Wnl5p9LY+O/E1/sosFhAUQx27dpFZmZm\nT3bflCkSO3ZMNG31AnFbLjeNYVvDNtrbxffgdouqd0VTaGtKoaPZyfjxoadti8OCc5yTjjWh19zO\nTZ0MvmIwI8dZcbtFO1gwDCO+4nKA+OknLrnkEu7yB8cuXbqU0j60EwxgAAP4GuByCdLk0kuTyyF4\n8EE488yQOvF46OhY26P4cbmSIH5AqH4WL4YdO3rk4dOLp7N27+fw058KJU/wgDMasrOF9WHiRJgw\ngdT1m1GvvKJnsqK0sm3bmYwe/VdSU2OfrxaMXMC7O2PYvbKz4ZxzxPZ+lYiR7xMMu72ApUvPoLl5\ncVKr7uzchNM5Dlm2IkkiH3vZsv5s7LcbXzfx0+dg56Y4wc4gzPdWK1aXFUuGBa3enpD4kVSNoqLz\nqK39G2CC+PHffYUTP7Nnz+bzzz8PDXb2v0fgrrqoqIgLL7yw574ggM8++4zW1lZOPvnkOG/cd8RS\n/ITflM8smYndYmdZdeTBHo/4WbNmDVu2dKKqoZkDmqILxc+hQ5CXxyeffMKaNWu45ppr4m5vWVkZ\njz32GGeccQbt7e3gcvGbs9wsW1bC3o5q8ux5zJ8fOShKTRXc+NVX09vEZ6LdK2D1AsACumGJGAk1\nvdVE7smJVRzfCatXNOInLOA5UA+fCLFsXiDyRULUBsXF5gKeu7uFZXjSpMTzxoBaYsObLnHw8YPo\nc5x4/YPfrzrc2eOpNB3sDL1WL9km0+DxsV9LQfG1JrV9iRQ/iq4nVvx8mzJ+DqfVK1nFz6hR1Dkc\nERLfjJkZKA06FpO7PjItjerubnz+4y6c+PH54lu9ZFmnpeV9urqqQqfpKqUNpXjyeoOjrbIFjDQk\nKTKuoD917rHa4ESzV2vc62RZcTEe72FQ/JSUgKYxqqYmJoFlGEZSih9Ulby8U2hufgdVVZOyegVW\nUVfXiMViobS0lP379wMwdarEjh1TTIc7gwhybqjOx+1zs3OPh5QUqK4WzyxWHlyJo24+c+ZIfrIp\n9HQTHvBsGAZdO7pwjHYgSeL0GZ66MGD1OgwYOnQoDoeDjIwMiouLOf/88+nys7yPP/44N954Y8+8\nMXMJBjCAAXzzmDkTLrtMhEWY6e8+dAieegqiVRBHgWEYfsWPIH5SU4ehKM0oismbvNJS0SV58cUo\nmg+bxcaMkhkMufURcUMdpjqK2WRhscCDD6I8cB+/Ok3i5Ek/7Zl/x45fk5NzInl5p8bdlBPLT+Sd\nqndiv8fCheKziZeb1NkpyLaFC+HJJ2Hduh4rW0JUV4t5RyW+wX711YU0Nj6FHketEA63W9i8Ajjh\nBCFi+m9F4Mb/60J/rF5mFD8AaSPSUKoTEz+oKoWF59DQ8Aqa5ulTxg8Ixc/y5cvZt28f04KzScLu\nqn/3u9/x8ssvs3fv3p7XFi1axFVXXdUTdHy4YVbxI0kSF06OHvIcj/i56667OO+8f/ODH9zLww8/\n3PMEVBCKwN69aGVlXHnllTzwwAOkpcWuQw/g9NNP59hjj+Wyyy4Dl4vyog5mzhzEnua9nDrv1Jj3\nU6efLnj8ZwOONRM5P8H1yULxE/qd6T6d5vebyV3wX0z8RFH8JCJ+4tm8IEq+iFnFz8aNIgDDxHES\nC6phUD3NSu0TtRBE/PQ148dsuHMy+T4QGu68ta2T3484grbuNjTdvB3NTLhzPOJHV/Qeq5cvgTok\nUDv/VcJX5yNlUEr8eUy0egGMcTjY1d3d8/0nQnpBAZos0xnW7GXPt6M5IV0zN6BPkWVKg5q9wlu9\nElu9xLWgoeHVkGmqrlLaWIqnsJf4cVl0dCMXXW+J2A4zKq5ox7dhGDEJxdTUYXi98YmfIcXFeLoV\nrFI/iZ+8PNA0Zm3bFnM/NLf4rVhcJhgc//U4JaWEtLQROBybk1b8WK2wY8duysvLGTx4cI+Cd/Jk\n2LlzCrJkvsa+ogIqKyXG5I9hS5Ubm02oy4cO1fnPrv/g2y1sXhB52s6ckxmS8+M96MWabsWaKXYo\nGvEzYPU6DJAkibfffpv29nY2bNjA+vXrY3r4BzCAAXzL8fvfizPrhAnwyivxsyEWLRJhxjEsC+Ho\n7t6DxeLCbhfefUmScTrH0dmZRFf4b38L7e0M/vdH2C12Tvq8kbK1VcJuFnaBXrNmMlVVV6Fp3VFX\n9cG0HJpnTaTIJZrIamufpKurkhEjHow6fzDGF4zHq3rZ2Ryl1h2ETnXu3MgwaU2DDz8UDWiDB8OL\nL4pspOXLBeGWnS2unhdcAI89JtrWosm0E+T7BGAYUF1dTkrKEFpbP0m4XwEEGr0COOUUsdlJPDj8\nTuHrVPzU14unnSZ/Nj3waT6qW6sZmTMy9kyq2pPUmDYiDWWvLT7x409tTU0tJT19mjnpdwziZ/To\n0TQ1NTFz5szQYM4w4icvL49LLrmEO+64A4Bdu3bx6aefcu6558bd/77CMAy/4qc8dDeiED8AZ088\nmzcq36C1O5SQjmZJCEBRFB555BfccsuxbNiwgfLycn7yk5/w+e6lGLIGe/fy5J49ZGVlcfrpp5ve\n9kceeYS1a9fy9/Z2tt/pZVrhCtqNDopdI2IuI0ni1HzTTX7eec4c2LMnrtIk2OqFBYwwxU/b8jYc\nox2mrATfWJ17ssSPPXRf0kal4a3xonaIbTdT5b56tVBZjR8ffXqfrV79DHYGoYI5ON2G4TOwfj8d\nr387ZEfyGT8xW72iKn7MV7mDUPxYZStViocOr8qvikvITM2kpTtyEB9zHQmIHbNWL/u3wOplaIY4\nlgviH3tmWr0AUi0WhqemhuTtxIMkyxR1dlK/Y0fENL0QcrzxM76CMSYo5yeW1St2nbuMxeKioeGV\nkGm+Nh8ZXRmoub3LZNu8qFoRqhrm66Hvih8dkAA5asbPMLq72+NeJ9PT0tBlic5m8za7qJBlyMvj\n2HXrYu5HoNHLlLgiaGfz8n5EdvYXfVL87NwZSfwMGwYej4s0X5npdfU0e+WPZW+1htUqLleNKasp\nzSjFs2sq3/++2O9wZ2nG7AzaP2/veQDrqfSQNrqXLB8gfr5CBD70goICjj/+eDb4P+nzzz+fW265\nJeoytbW1nH766RQUFDBixAj+6LdF1NfX43Q6aWnpPemvW7eOgoICNDMqhAEMYAB9h9Uqng4/9JCo\naJ88WVTFhF9wGhvhiSdMq30gNNg5gKRyfkAMav/yFyY//BITNzcw6t6/8uOfGXidqSGzeTy78Pnq\n8HoPsnbtNNzujRGrenPHm/xo9I8A6Ozcyp49v2fs2JewWFIj5g2HJEmcMPKE2HYvENXujzwiLrJb\ntojPqqwMrrtOXJG2bRPBq9dfD888I/w/TU3wl7+IbIeNGwXRVVwcVtWDqXwfEG8ty1BQ8HMaGv6Z\ncP4AwhU/OTlCEPZunN39LuPrJH4+/RSmTEnI2UVgV/MuyrLKsFviDL6DWJvUEan49iXI+LFYBBlp\nGBQVnUdd3d/6TPzIstwT8hyCKI9Tr7nmGpYsWUJVVRWPPvooF110EU6nuSfJycLnq0OWU7HZckJe\nDww4w5HnyGP+iPn8Y/M/Ql63ylYULfpNo8/nIyXFxbRp2Tz77LNUV1dzwgkn8Py6v/DY46fx/5Yt\n4w9LlvDoo48mpXx2OBz84x//4OrKSjauPsir/3kSD12s+ih+TsH06cL1et99iM9//nxxrokBVVWx\nBr5LK2iEEj9mbV7wDSp+Gvqn+JGtMs4jnLg3isGp0py4yj2ezQui5IuYtXr1M98H/MTPTDuZR2Zi\nH57ab6tXVC4vpuInCeJHF1avl5sbqbClkSLL5Kbl0tTVZH4d/W31Cg53NkP8fIWKH6VRwZplRbbF\nHoLqug9d78JqzTK1zgkuV3J2L02jbs+eiNe1fMjpNk/8VPSR+BH2I4mMjDl4vQfweHoDBj2VHmpy\na7Dae4+7TEs3PrUIrzcyy7CvGT/xVGTC6hWf+EFVkdJsNO5rjPvepjBsGENralCbm6NONp3vAyE7\nm5t7Kjk5q7FakzuerVbYtaua8vJySktLe4gfSYJh5Zuw1ZsnrYMr3Q/V2bBYhOJnWccznDnqNxgN\nFZSPF6qe8Cz51NJU5BQZzy6h/uqqFDavAKJbvQYyfg4rDhw4wLvvvkt5eXnc+QzD4Ic//CGTJ0+m\ntraWDz/8kEcffZT333+fwsJC5s6dy8svv9wz//PPP88ZZ5yBJVlacgADGEDykCTh7Vm1Cu68E267\nTTx9fPvtXgJo0SLhKRgyxPRq3e61PcHOASSd8wMwdSpVx0/n2tveR/rj/6CMGcWm+k0hs7S0fEBO\nznzGjv0nQ4Zcz8aNx7Fv3wMYhiCPDcPg3ap3WVC+AE3zsHXrzxk+/F6czjiNSWGIW+sOMGuWuNEf\nPVoMvHRdMCfr1sFVV0FRUeQyaWkwYwZcfLGooF+zRiiBXnopdD4T+T7Qe6EsKPgZDQ2vJ7zoAei6\nSmfnlzidoenDp5+efPb3dwVfF/FjGKIE7+KLk192W2OCYGeIsHr59lriEz+S1EP+5OX9iI6ONej6\nflPET1dXPa2tvdkxhmHQ0dHRW0MeQJS76uzsbK688kquueYann/+eS6//PL4+9UPxFIgxFL8AFww\n+QKeXP9kyGvxrF6KopCW1hvunJmZyUUXXcSjJ/ydM365GF9rK5dfcAGT+pDZMnHiRG4cP5VLWq4i\nRU0hy5XJ9q31CfmDe+6Bxx+HfftImPOjhil+dMMSQtb1hfiJaYP9CmAYRr+tXuC3e/lzfhJZvRLZ\nvCBKo5BZxU9/G70QKhdfgYXJyyaTKstfjdUr7HM0DI3u7t2kpcVRJYZvp67SrsMOxcNgWQxecx25\nNHmSJH4ShTvHmZ5Unbvtq1X8mAt2bsRqzTVNIk9wOpOrdM/Npf7VV2Fz6L2ZmmuQ05Uc8bMtiPgJ\nbvWKl/ETUPzIchp5eaeF2L28lV4O5B0IUZVmWD14vPl4PJG/LWsfW73ikYUpKaX4fN1YLHHECKr6\n/9l78/go6sP//zmz95H7JoRwE0BOORSrcqioiAfWW2hRqyK2ttbW1qtWq9aqn/5qrfbwaj0r34pI\nFQQ8EBQEBYJyhiuQECB3Nslmd2d3fn+8s/c1mwTQPvJ6PHy07M7Mzm5mZ9/v1/t1IFkN1FZGq5BS\nRkkJ+/v2RRcnaNFd7cZUnNgaGEDI77HNNhKPx4TRmLgxLBI6XZD4CVX8AJQM/BpqtDdzFhcLJXmJ\ncTSt9XZUFRwOlY9r36DUcRXmkh00K8J2GOu2nT5FqH4gmvgZMUKoh0LFbh0d/yOKn0+kT7r9X3dw\n6aWXkp6eTr9+/SgoKODBBx9MuP2GDRuoq6vj3nvvRafT0b9/f2666Sbe7JzczJs3j1deeQUAn8/H\nG2+8wdy5c7t1jr3oRS9ShCTB7NmituTee4Vi5fTTRV363/4Gv/51SofrEcVPJ9bdfCH/+PVMuPpq\nJhVPYuPhjWHPNzSsJCvrHCRJorBwLuPHb6S+filbtsygo+MgFQ0VeLweRuaNZO/en2O1jqSw8IaU\nzuGcgefw2aHPOOxIMAN7/nnxWR08KBRU8TwBiXDllWKG4cfhw9DQACNHJt3V/0NpNvfDah1GY+Oq\npPs4nRUYjUXo9Wlhj196KSxfLnJH/9dwooifjz4Szd5z5qS+7866nZTlpEr8SImJHwgMBHU6C3l5\nV2Iy/Stpxk/jkFaam7dSX/9aIDtq37596HQ6dkdaBOIkZ95xxx2sXbuWiy66iD59+iQ+x26gvb0i\npgIhEfFzzsBzqG2rZcuR4HJhMuLHaIyuc1cVlbzCgdyrpHPR1bHr27XgB/3O40z9FBZYF1Bc0Ies\nnMNE5GNHoW9fuP32TlHm+eeLqq84X17F68XQaX2S9BJqiOKnfXc73jYv9rHJ63lBKL8kSYomAI8j\nfO0+kETTiybEI35CAp6VegVDdvwvwhdfiCylRLfhqIyfPn2SEz8OBxw4AKeckni7JAgNNDbJMh3H\nI9w5Ygm+o6MSgyEfnc4aY+M45+n1UOX2ck5+NpJXfFa51lzq2rUrJRKpM3yqio/EE7pAq5eWOvfj\nbPVy1bh6rNHLj5QVP8XFHP3Rj2DmTKFM9r9uug+7yxKwQyZDdxQ/BoOMLBvIy/t+NPGTVxWmNk2X\n23E6c3C7j0bZTLtq9UpkD5RlA5AJJMin7CR+KrdX8swzz7Bp06au3xP79uVoXh6GQF1jOFzVLozF\nGhU/uuC9XZIkDh8+DaMxhUIXxOe1b9/BAPHjD3cG6Dvoa7yHtY9zJUmofpp3jcUneXC7IaOwiZmD\nz2PLhnRyh+/gSKtQcsW6bWdMyQgEPEcSP0ajWHf9JiRN4rPP/keIn6nq1G7/1x0sWbKElpYWVq9e\nzc6dO6mrS3zDPnjwINXV1WRnZ5OdnU1WVhaPPfYYx44dA+CSSy5hx44dVFZWsmLFCjIzM8PDInvR\ni16cOMiymKmWlwulyv33CzKif3/NhxBqgGjFj802itbWr1NeHW43y2z/ngiQnNhnIhuqN4S8lpem\npo/Iyjon8JjF0p+xYz8mJ+cCvvpqAl/s/B0zB51HXd1iGhqWM2zY31IOn083pfOrM37FqOdG8eAn\nD+JwxQhyHj4czjmHlE3UoTj9dGhsDA7APv1U1GxpCMIN/aHMz7+aY8feTLwD0TYvP/LzhXR2xYqU\nzl4zVDXaUXiicKKIn0cfFRPxrmQYJw12hmjiZ7924gegqGg+NtvLCaXfTcpXbL9qF0ZjGVZrES0t\nnwHw4Ycfcu6557J582bc7hBlWZxZY3p6OosWLYpq+OppdEXxo5N13DDuBl7YFAx5TkT8CKtXdJ27\nqqhIPg+PWh9mzvdlTZn5sdDWWsDjo+dySsMpFOUVobPU8OabQhKfCL/8pbhdrN+TK4iE1atjbufx\neoNWLwP4fMHJQf179eTM0q4ugBNv90pJ7QOJFT+bg4qfeFavI0eEmiqRzQtiBMsWFwviPtGNbtMm\nkbGXkH1NjtCJa6jiR7aknvGjtc49VZsXCKuXwwelNnPAQtWTVi9/sHOi6zdg9dKS8WM4vlYv9xE3\nxqKeafTyY1Sqih+jkaOjRwuv6LnnBqzmHny0Gxw4NiYorQiBn/hRVTUh8RNpoRV17hKSZCQzcyod\nHQdwOg+I53Z7qM6pDlP82OU2HK0Z2Gxumpois9mS/01jWr2SqMQkKRtVTXCNKgqKDOuXr2fNmjVc\nd9115Ofnc8UVV/DXv/6ViooK7ePe0lKcFgumTz6J+bSryoWpb+qKH4CamkkYDIcS7BDrECqVldUx\nFT+FA7fRcWhESscrK4NtG/LAdgy3W6XDvp0fjv0ha9dC6aiDHG1LrPjxBzy372wPy/iBcLtXTQ1s\n2fI/QvycbPgv3jPPPJMf/OAHSatKS0pKGDhwIA0NDTQ0NNDY2EhzczNLO2tjTCYTV155Ja+88gqv\nvvpqr9qnF734NkCW4aqrYPt2ETycAlyuQ8iyAZOpKOxxozEPWTbjclXF2TM2PF5PIOskUvHjcGzC\naCzCZApXEUiSjn797mb06A+wtC/myrzt7N69gBEj3kCv1y5fDsW9Z93LVzd/xd7GvQx9ZijPbXwu\nbg5IlyHLwme1qDPkUGO+D4T/UOblfZ/6+qVxw679EMHOsS0pl19+fOxeqiq4xZde6vlja3r9zoG/\n19tpjTkOWL8e9u6Fa6/t2v6pEj+GfAOqG3wtSSo7QgaCaWmT8Pn0FBV9FnPTpqa1bONBRiwehapa\nyMk5i7q6dwFYtWoV559/PkOGDGHz5s0xjx+J6dOnR2cC9TCczr1YLNFhyImIH4D5Y+fz+jev4/SI\n/IBkih+TKR1FCZ8Q+RQVPC5ebZ+DqgrFXFfQ1phNWm491jIr+fZ8ah1H+PGPhQs3EWw2eOQRwder\nF8Zv9wrN+IlU/KRi8/LjpBA/eT1A/Iyy076jHZ/HF7B6tbYKvuyJJ+CKK0RM24gRotTyxhsTv0yU\n2iA9XTBFLS3xd+qBYGcIn7iaZDkY7nwc69xTDXYG8Vve7FXpm2YJKGlSVfwkUmckC3aGzlavb0md\nuxarl9ZGLz9KTCacXi+17uRWb4B8g0FsO3euuMnMmAH796OoKq3mFlrWJ7h+Q5BtMGCRZWrc7i6E\nO0tIkgFZ1pObe2lA9eOt8EZZvWxyG62ONDIz1bB8WNCm+EnV6iWQA8S/Rr9cv566yibMVjP//ve/\n2bFjB+Xl5cyePZt169Yxbdo0SktLmT9/Pv9JNqgqLcXqcqGvqRHtEBFwVbtSs3qFrEDU1Q3FYGim\no6NS2/4AeDEaLWRmZlJcXEx1dXWAB0gv3IenJYcmjWW9IIifzZtlLBnteDwq7vSdTC05j40boWxc\nA0db4xM/9rF2nHuduI668Bz1YO4fntE5bhz4hyO//S2MHdub8dPj+OlPf8qqVavYunVr3G0mTZpE\nWloaf/jDH+jo6MDr9bJt2za+/PLLwDZz587l5ZdfZunSpb3ETy968W2CJKUsWYhl8/LDZjslZbuX\nu7POHeCU/FM40HQgoLhpbFxJVta5cfc1WIZz6ybomz2B/v0fJD19ckqvHYn+mf155bJXeP/a91m8\nczGnPHcKb+94u2czLkLtXhrzfSD8h9JkKsJuH0dDQ+KE5shGr1DMmSOiQjSOHzXjb38TdfGRQXwn\nCn7i5/XXRTPFz37W2YjUg3j0UaHA6MpCvqqqKRM/kiRhGiDjPZSEIXQA8wAAIABJREFU1Ayp7JEk\niaam+QwaFM3ANTevY9u2OQx3/YKswwV4PB7y8qZRV7cEr9fLRx99xIwZMzj99NP5/PPPw49/Elqe\n/HC7D2MyFUc9noz4Kc0sZUKfCSzeuRgAgy5xq5fFkhFt9fKqNDbBkKx6HnggZb48gNZjadjTjmEb\nZSNfn8/R1qPceaeIDNu2LfG+c+eKP+9y/UXxiZ8QxY+klwKtXkqzgmOjg6wZWSmd74lu9uopxY/O\npsNcaqZlazs1uzxc9gMDBQXC1VxdDZddJtoN6+vFZ5+Ms4zK+IHkAc89EOwM4RXmpm5m/MRt9YoI\nd25v39UlxU+zF/pZIxQ/PZTxk3wC33n//7YQPzVaFD/aGr38kCSJ0XY7X2tU/eQZDNT6/+A33ihk\nqtOno7S302bRTvxAUPVjs9loa2sLjIsSZfyIzDGp01JFwO7lc/vwVfk4mnk0zOplkx20tVjJzpZi\nEz9dCHdO2gSnZqKq0cSPqqo89thjvPX66+SMKcDj9tDSSfQWFxczb948/vnPf3Lo0CFWrlzJhAkT\nWLBgAdu3b49/gqWlFB89SusZZwjLbgRcVSkSPyFv1uPRIctFgQUcLfD5XJSWisUUq9WK3W4POH08\nEqT325/SWK6sTCyKZWZ68fkkJo3Mp3yzniFDoLQgM6HiRzbIpI1Po+6dOswDzcj68PuAX/Gze7dY\ntJw4sVfx021Eyidzc3OZN28eDz/8cFxppSzL/Pe//2XLli0MGDCA/Px8fvSjHwW+HABTpkxBlmXG\njx9/3FcEe9GLXhxfxAp29qMrAc/+JhAQE7IxBWP4quYrwB/sHJ/4WXtwLUNzRzGy7BmKixek9LqJ\nMK5oHCvmruDPF/yZh1Y/xBkvnsHag2t75uCnnQbNzaISqqoKxsQmZiIRWbqSn38Vx44lbvdqa4tt\n9QLhVBg2LObYo8uoqBDV0488kty6crzgJ37eeguefBKamoRLb9GinrGfbd0q8lpvSC1GKoDDjsNY\nDVayLEkm4BF/cEH8JNknYiBYVzeXvn3fxusNThJaWjbwzTeXUFb2L7Ldo0Cvx+12k5ExCp+vgy++\neIecnBxKSkqYMmUK69ati3v8Ew2X6zBGY1HU44pPQS8lVkPdOO5GXtgs7F56WY/Hl6jVKz3K6oWi\nUtNoYv6UXVx9tZjT792b2vmrXpW2I2Zsxmrso+1ku7I55jpGmk3ll7+EOMWpAcgy/PGP8JNny1Dj\nyNkUny8848cngaLQsLKBjDMy0NlSs6p+V61eIFaQd77XiuRQ+MNzBhob4fPPRUHjtdfC4MHaG/n0\nsSadyQKeeyDYGcLtTyZJOj6tXjEVP8NSOna9uwOjzoDdog8SP9aetXolU/yonpBWr2QZPwYJn+f4\n5VdpDXdOhfiBzoBnjTk/eUYjx0K/v7fdBnfcgWfLFtqM9bSsb9G8sOUnfnQ6HUajEafT2fkexKVj\nkKMJdWH1AkkS11Zm5jSczj00fbMPqVjCK3sDih9FcSCpXtodZnJzdV1S/KTa6gUgSRn4fOHqG7fb\nzQ033MCiRYu455e/RMow0HdAX3bt2hVjf4lhw4axcOFCpk+fzsaNG6O2CaBfP/rU1NB81lkiKDAC\nruquW728XpDlftTVLdG2P6AoHZSUDAj8O9Tu1eGTyR14gFDRbzKUlYmi4NxsPSBx8eTRrFkjEg0K\n7AVhGT+xiifSp6TT9GFTWL6PH2PGiIzye+4RRbsmUy/x023s27eP6dOnhz32l7/8hUWLFvHiiy/y\n0EMPAXD22WdzMGTQUVhYyOuvv05NTQ319fV8/vnnUccpKSnpVfv0ohf/A0is+BE5P6nA4/UEFD8Q\nzPnxettxODaSkRFfEbN8z3LOH3x+Sq+XCs4bdB6bbtnEbRNv47q3r2POv+ewtyHF2V4kZFn4DJ55\nBs44I0nfdhCR85vc3MtpaFgWNqkPhct1BJ/PhcnUN+4xL79c5Hv3BBQF5s0TsVGzZp1E4sej4vFJ\nrF4tyJmXXoI33hDS4AsuSH2yHonHHhMqIrM5+baxoEntAzGIHz2+qtSIn46OIhobp3DgwGt4vV4c\njq/4+uvZDBv2Ijk554fVuRuNRnJzL+a9917knHNEplaU4ieuT+T4Q1VV3O4j8YmfBIofgEuGXcLW\no1vZ27A3qdXLbM6MIn7cHSr1LjNXzGrHYhEFfc89l9p7cO5xYsxU0bsbsY22kVGXQb2uHqVZYeFC\nYSFMNGcAMYAeM8GAS8mOyWSGZvxIBkm0eilKl2xecOKJH3etu0eJn6NrHVhUhVOn6jFqzEyNhZiT\nzkTET329mAENS408iQVPHMVPj2b8RCl+Us/4OepqJ8doCWvLyrXmUufsmXDn0JDreAi0esky7m+D\n4kdDuHMqVi/oDHhORfETKev96U/x9umDd89mJL1Kx35tLQ+RzV5+u5c2q5f4HGTZQG7upRzd8CkM\nBlQCxE9HRyWNzj7ojV5yctKiiJ+utnopSZRiqpoeRvzU19dz7rnn0tTUxJo1a8i021FkKBlYwo6Q\ngOxYmDhxYmLiJzsb2eejeezYqFU31afiPuzG2Cf1Onfw57MX43BswONpTLBjEG63k759+wf+HRrw\n7PRC4eBKNm3SdjogCm49HsjPSAdgysjSIPFjKwgofiLr3P3ImJKBY7MjJvGTkSH+W7MGfvIT8MVZ\nvPGjl/g5idi4cSObN2/mqquuOtmn0ote9KIbiBfs7EdXmr1CFT8QzPlpavoUu31cVCNVKD7Y+wEz\nB81M6fVShSzJXD/6enbdvouJfSYy+fnJ3L3yblpc2iXSUbjiCli1SrPNC6LnN0ZjLunpp1NfH7va\n2a/2SRSEOWcOLFnSMyKOxx8XOSQ//rHICj9w4OQEPKuKysFqiWnTxCABxKBj82aYPh0mT4aHHgKX\nK/VjV1QIe0hXKtz90Ez8eL1hEzHzQAO+Q9mJ94kxENy8+Vy+972fcM01sykvv4ChQ/9Gbu5FwQ06\niR+DwUBOzsV88snnzJgxA4BBgwbhdruDTR8nUfGjKA3odFZ0Okv0cxqIH5PexPWjruelLS9pqHPP\nirJ6Nder5OkbSRsuiNQFC+Dll8PrZZOhtbwV2yCgtRX7aDv2Sjt1Uh2eBg8WiyBN77sv+XEe/UkH\nX/Aq7UejJ2xKBPGDKqO6FRrebyB7VpLrJwa+1VYvVU1M/Iyzo3zjwGvRI+m6F/geM1g2kdXryy9h\n/HjtVurDh4UCNAZClS7+jB9VFQQHkJJqRUvGj9frxO0+itlcqvm4AMdcTnKMZiR9UElzPMKdE8Fv\n9TJ+G6xex9wYC3q21QtSU/zkh1q9QqAUF6PPySG9bSMtK7U1QUU2e/kr3ZO1eul0BKxeIOxeTeV7\nUQepSD4pQPy4XJXUO0ow2zvIysqioaEh7FjdCndOSPykoaqCkNi9ezennXYakydP5j//+Q82mw0U\nBY+k0n9wf3bu3Jnw9ZMSP5LE0aIi3GazUH6HiCg8tR70GXp0Zo2qzBiKH4PBQGbm2TQ2amvucLvb\nKS4Ofs/DFD9eieKhVSkRP7t3g8kEVkX8ThYVwWefiTFYob0wYcYPQPrp6bgqXZiHRK+sqaqwFV58\nMVitoKq9GT/fSvzwhz/kvPPO409/+pP4AvWiF734zsLtrgG8cVUkNttInM7dSZn4UISGOwNMLBaK\nn8bGVQnzfapaqqhx1DChz4lpCTTrzfz6zF/z9YKvqW2vpeyZMl7Y9AJeXxfqfSZPFsEzKVhfY/1Q\nJmr3itfoFYoBA8QprFmj+TRiYvNm+NOfhLpGlkU1clqaaMw50VAVlT37Ja64Ivxxg0Hk8nz1lSjb\nGTVKcG+p4PHHYeFC8d66iq4qfswDjajVSVaGIwaC+/ev55lnfs/cuXq+/noly5adQ17epVGv4Sd+\nrNYpbNnSxJQpon5akqRw1c9JJH7i2bxAED+hqsF4uHH8jby05SW8Pm+SVi8bIOHzCXZQVaGlQaXE\ndyDQgDhwoPgav5m8XC+A1q2t2IfpweHAWGAk35hPna8OpVGcyw03wJ49cQu7ArB+XY+KgQ2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n0qSr2CIScF4idBR/uxNa0o/aw0ftjIxlEb+SzvM9ZY1/DVxK/Y9aNdVP+lmubPmql5sYam1U1x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\nL2GwM/SrEr5/apCn2ueJmdoiG2XOMlrkFvRePU79EkK41j3C6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DnTer0oarzx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caqPtXpqGJpoookJe3vSUdq+5c+eyZ88eOmwaucIINAdwu6VE4gdi7V42ip+AHogQ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GnTppGnf4Ze6g5TMYiizGefxT/v9vb1sa6ri0tLSlizxiZPnCOc1D5Zy5j/HEPOlJy0drNI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y9ZVmgLzVU+oRrVDeRrJ6+XO2ImH4cCirlamojX1txiO52t3qlUjGT1D7WHFxMWc5zuIv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\nv69kr5ywejU1qOimwaabNuFXVSpu6EHl5EpiZgyX4so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8ZIeEiEiAjBAGV199\nJTNnLssf1tsaGIad8RP/m+axekVbrKsAyBZWjj+XWqZy+BuHs/ux3ex5ek/en1RVFZdh2BMd7XAs\nlVTlDm0ifib0mEDMjPHRdrvaeNy4caU1ewFLKioYm8NmlKb4yTFLDy12r+dqi6qnUqFWqhgBI2eg\ncUVFBYcddhgff/xx3s+L8pF8ru7it/7F3Dzp5sTxmpkJAyRuMG43aIbM9PUX8Yl4IOs7U6MNR42y\nI2fefhuec/alaWVTyefg0eXlbDCiRIOC730PHnwQZsyAQJNg+acKn/+2B71+cwTfbujHwzt3MejT\nT9kSiRQNd3apNvGTuZvzKX6kYAw9Q7mtKPahHM/jE6ZAchwExY/kSRI/zc00ivLivOkxx8DmzbB9\nO34/HHkkfPpByt+zoQFybKdpBuntr+HdS9/lqF5Hceu8W23ip7XhzoBhNaP+9FY46ij0t9/H4RBE\nImBZZ1JX9zpz5sCpp0L//hXEYk2AbfXyOhxYspJ2Td29N0ZMVpAfeRh++1ubzXr00aw2xLjVK7Ih\ngmzIKJ0lNmywJ3CaTZsQSs340SyL22+Hww6DgQMh7BbEgtEE8WOYEp1O75RoxSqu+Cm2QBK5iB9F\nSVa6B1cEcXZz8nn952mKH78/O+PHEILY7hhb79iKpVstxI8DtyzjcXh46PSH+PPJf+aucXfxf2f9\nHwKBY4eLMpw8e/azvCJfxC/Mozmh/wncv3MPl3ftyn+efx4RDKIA+xc1svrC1Xw+9nNWTF/Bl/d+\nmXaNCpkmDkPBJfnxOX3sDe7FNCEieVOIn/RnDi8KkTzET7mrPCvc2W0GiSits3rZZJNINIaXig7i\n5xDA66+/TteuXRkyZAjnnHPO1706HehAaSgW7BxHIatXNErEsjoUP9+kcOcDzPhxeErP+OlQ/LQd\nrSF+IkUG28UyfkRMoHiUkjJ+CLaB+JEkok5KavWCXFYvHTR3WquX0DUMozGh+GlP4kd2yggcBYkf\ngO7dLwcEixYNQ1WrGDnyjaw691R8FYofXddxmGZ+q5cBHl8vdH0/ZXJzFvGjKDlyfgD69IH16+GI\nI3DdcT0jN55P9+bncQadWFaMSZP+h5oag5/97MaWsMwDQC7FT7FwZ0XkJH4AXN1djJo9ik03biIw\nL5BzGUVRKBMC8VVWuUObiB9ZkpnafyqLdy/GsAyGDh3K7t27aWzMzpzKxJJIhLG12aqdYhk/AGVj\ny5AcEk2fthwfpgmvv263NO3fn/P3HFUOjICRVeceR7Gcn6h/BB/VfsrnnTV+0PusxOs5rV4tUh6P\nB2KmxHFbTmavtJS1+9emLZba6AVQUwMvvwxPLPCzfoOGIkq7vh1VXs4GLcInHwm+8x047zw4/3wY\nPFxw6okyRxwBZV6Jt27rzLITxuJcWMMfN+8oavWShcCSpawShXyKHzkURXdmDx5Tc36EWZh0bguE\n0NElV4J8FY1NNFr+1Izp3HA44Mwz7Z2Obff6dH7K3zMQgBzbaZohu5lQkrlp0k28tu41NCuGsw1W\nL9NsRL3sOnjvPfSXX8O5dBFKcwOyfAa1ta/x+edwwgngcFQwbFgzkuTE7ZbwOhyQofjZXashxzOZ\nLroIFiyAv/wFLr00Tf4SD3euf7se/2A/ldUmGzaAKsu4ZZmwZaW1er02R/DKKxBvMA+5INocxuPx\nJESQfW/rx86/7SSyOVKc19F1pAMgflKtXo3zG/Ed52PpnqWM7z4+sUxOxY9l0fxZM1t/u5WVZ60k\nHAwjtbR6xXHWsLNYIC/gJfUlCHfBGy5DhDT23LkHvdECw2BbNMoLtbXcNnQoJ514Eit+9xzOCCy9\nbDX+I/x8a8u3GP/ZeGqfrWXdFeuwWmyeQdNE1RUMA3wOH/tC+9A0iKle3EIkFD9WShaeR6iEy125\nFT85rF5uI0Sk1Yof+80O4ucbiDPOOINgMMhzzz33da9KBzpQOkoJdobiVi/T/P9e8aOZWkmKn6+b\n8Ggfxc+BhTs7c0+idCADpRI/MSGICVFwFr6Y1YsoqB61JMWP1Wy2PtxZUYg6abPVy1b8eNIUP5YW\nQlG8yLLa7sSPrfhRixI/kiQzdOg/6dPnJoYN+1dLMLWMJOXeP6Uofkq1meSDrus4C1m9TIHqUOjU\n6XRG+YOlK34AqqvhhhsIPvMpXw74FWXGGo64MIL8ve+jfLKUP/5hABs3rmDChAkF25qKIjPjp4Rw\nZxSbh8gH32E+Rjw3gtXnrya0JltyKEkSnVQVUXTUWhoOptUL4OzhZ+OQHXz45YeoqsqoUaNK2udL\nNm1iXDQKO3emvZ6V8ZPjeiJJEjUX1rDvqR3w2GMwbJitBLvzTluS0Lu3PaC/9VaYNQu2bEGtVNAD\nel6Vy9SpU5kzZ05OpZhmWZjOanqX9+YXewbiWbU+8V6hcGePB6KGjNtwMDL2Ax789MG0xXLNefXq\nBf96UmKuVEXTmtII6s5OJ8q6KhrqLf7wh5TVEAKvS8LlssUtH30EW7aA+lpPntm3l4hpFlT8WLqF\nUKS0ATTkV/zYxE/2c1Ia8VPC/aS1sCwNg6TiJ7a/maBcXpqo5Nxz4d//BuycpaWfmMiewoofywol\nWr26lXVjYo+JNIeX4rCUVlm9LEvDsnQ7XHfUKLQ7/4jDo/LIojHUrI8RDtdyyilb8QVcpQ0AACAA\nSURBVPlse1PPnk0oihNVlfA6nVlWr337dOTU4fiwYbaMzOGAiRPh/feBFsVP2KL+7XrKhpVRXmWw\nYYP9kXJVpckwEhk/ixfJLFkueP99m5i0hCDsgUjItnrJsh3p5Oztpv8d/VlxxgoMXRS+lJhmyYof\nlyQRy2n1aiF+FjSy58g99KnoQ4U76e3LUvy0qAeNgEHN+TU4uzlZc/0aFNORzIZqgV6vo1bZ6+eo\nVJF9BlVDq9j5yF4C7+7nL5u3c3mnrkT/XssxHx7DvFnPIqkSh38+jt4/6Y1aruLu7WbMgjHo+3WW\nn7wcvV4n2KL4MQzwu/zURerQNNAyiJ9Uq5dbKETLC1i9MhQ/LiNIRG6t4sfeVre7w+rVgQ504KtA\ne1m9DiLx801Q/JiWiUCgSMqhT/y0Q8aP0yNKsmrlC3d2OjusXqVAGMWl+WWKQtg0ceZ4SEtFMauX\nFJNQvYWJHyEEIcPECpooZa23ekUOwOolSRronjTFjxVrQlXtWWGtna1eCcVPgYyfOHy+4fTqdT2S\nJCWq3PMhno2TD+2W8WOa6a1eZnI7JAMUVaK6+nTGVWp2a0oK8ip+UiB0CFWPZ63jFhY/1wVr1HC4\n/HJOuL6OJ07szs033cTJJ5/MHXfcYQcmtxJC1zGwVThAQcVPYvCgCIRZ+HypmlLFwHsHsvzU5US2\nRrLer1aUdlP8bA5sLqj42bLlNsLhdW0mfqb0n4Ju6cxaMwsoLeC5vr6e+vp6Bk6ebKsSUlAs4weA\nUIie2ov0/vsJiH+/BP/8px3Q8sEHtkrjgw/gu9+1w3kffxyOOQb1uLEY2wN5VS7HHnsslZWVXHTR\nRVn5mU2GQbmiMO9787i282lp2SlmyMxb524TPxKyaTEidA0zV86kIdqQWCwazd22dcopMLxfhDfe\n8VDEPQvAtm3Q9HYN/UZH075Ptyx8TinteaC6Ggb63YzSq9gWjRZU/AhdIOUgfvIpftRwBC3HJN7B\nJn6E0GzFT8s5GNnbRNRZInE6bRqsXAm7djFoEFS6LZq0pOLnM1WlNuN4iCt+4rjo8IsINi9sdbiz\nrRStSORc6ZITx5Hj+X3Phxh+64WYn3Xn22e8AthkhywHAReBgESZy5Wd8bM/hixl/L7Xa58fd9wB\nV14Jp5yCun0lZsikcX4j5UPLKa8wk8SPotBkmkSNKPfd42b9GokrrhbU1LSssxDoHoiGbKuXvW72\nId/j2h5UHleJ1mShSAVsmIaBdAAZP7bVqwkhBA3zG1jbey1H9joybZl8GT96QMdR42Do40NxH+Gm\n03KBf1v6xIoRMFAqVUBCcSsgNAZcMYBeP+uHFdZZ8uoufL/YTeOHjVzy4iWEuxoYko6WsevVMpWR\nL4/EP8HPF9/6gqZmHYcpYxhQ6a4kEA2gaaCrXtymSSQSaQl3Tq6PRyhE8hE/ORQ/Lj1EWGqd4sf+\nPQlfJoFdBB3ETwc60IG2oTWKn0JWr/8SxU8uoiKOQvk8umUHO0stFqpDmvhpB8WP4wDDnTsUP6VB\n6KUpfoKmiUeWCwY8F1P8yFG5KPETsyzKonYlsKy24tGjRfETPkDiR9LdaYofM9aMqtqzwgdD8SNK\nUPxkwrZ55b+mWpp18DN+ioU7mwJFlSgrn8wIP5AR5FxQ8dMCS7dAscNKrWoP+o++B2vXEv3DTXj+\n8SqX/PWvrPz731m4cCFHHXUUq1evbtU2mLEYMiDHSZ0C4c7xQaekgFWE+AHodmk3ev+sN0uPX0pk\nUzr5U6UoWIUkbyWiMdqIYRl08nTKu0x9/RxCoZVtJn66+roS1aPMWjsLIURJxM/SpUsZPXo08uTJ\nMH9+2nsFW70CAVvV078/zg2fsWnwn2i46Vk49tjkMrIMAwbAOefA734Hb7wBO3eiLnwHo1ng+/jj\nnBXmDoeDOXPmoGlaQj0fR6NpUqGq9PD3QB4zFpYuTa5vZv03pCt+NAnZsnBq3Tlt8Gk8/sXjicUy\nrV6pOPYUgR6Uuet3ha9VpmlzXKNP0Yg60wmKuOInEkm/B1ZXw+S6nmxrOb7zQRgCVLlkxY8aimA4\ns4+h1EF4KfeT1sKydHTJnSBfY/uaiDlLJE5dLtvjNcsmLicebrJjf1Lxs8HpZGvGhKNpBlFSclS+\nM+w7RKNrQNfbRPzEET8F33GezuaXlqC+LTM8chts2oSiVBCLBZEkD7t3g8/pyrJ61QU01EziJ47z\nzoM1a2D6dLzXn0Pfdb+mamAjLr+LsgqD9S0itnJVpV6LYgmL559TueQCCbcveQzqQqB7JCLhSOJ5\nO35ZlCSJQX8ZhCFg++1b8u4HyTCQSrzW5At3Ns1GIusjyG6ZJZElHNHjiLRlMhU/cduoETBQq1Qk\nSaL64moivdz0O2cHTYuS9xojYCBXOACB4nVgmTE76LrKTaep5TDZz7h7BnPYC4dRNamK02fMwGxu\nzjnpJSkSA+8dSO+f96Z2VTNKg33OVruraYw12sSPw4vbslIyfpL3GKepEC7PnUuQVPwkFVZOPUhI\nap3iRwgNm/hpHZXTQfx0oAMdaBvaS/FjGP9fK37i+T7x72hrJfxXgfbI+HF6im+HEPkVP253h+Kn\nFJRq9QqaZs6HtFS4FBeaqWGJ3MtImoTT6yxI/ARNk84xpXU2L7AVP4pC2CEOyOolGe4Mq1cwofhp\nd+LHIWGJ4hk/mShG/Ajt4Ct+9FgMZ9wHQC7iB1RVJiYU1gVVAoF30j5fkuJHE8iqjOyWkWUnQsRA\nkvCf9VOW/M2Bfu0Man7yE970+fjZt7/N5MmTue+++0oOfjZiMdL2QinhziolqTQAel3Xi76/7MvS\n45cSXpe8mFXKMlZbLowZqIvU0cXXJas5KxW6HkDXA20mfhRZocxZZttCdi9m/PjxRQOelyxZwtix\nY+G447KInzTFTzzcORyGX/wCBg2yw3jnz4cXX6TsiuPYO3Nvaes5oh9CdjH8h9fjywytaYHb7ebF\nF1+kV69eTJs2jbq6OgAaDYOK+Hk9enSW4idfnbvbDVFdQjYEhgHXH3k9d394N9fOvpa3N75NUziW\n99FH8gumd27moQdg7tz823X//fbxdv55Mo3R9GNTFwKfSyYSSVcWVVVB5z0VKMAXmaxOCoRuqz1L\nV/yEMXIcQ5mKH1p56S4GITR0koofra4ZzdMKxdw558BLLwEwaqjFll32CopAgL0+H4GM669phpDl\n5PlZ4a7A5RlJfWhrq6xeptmURfw4nfZj7uZwN364ax6hQTGMKUegLlyKpgWRZTemCfW1blCziZ94\nfXhOOJ1w3XXE5q4gbPRg+LoZTPrfl+hqBdm82T6O/IrCw0+HwXAz932JyrJ01Z1uWRgtxE+m4gdA\ndshYqkzD7P3s/tfu3OthmAek+LEnBRppWNBAxbEVLNq1KC3YGfJn/BgBA0eV/duaqRHs6aLxvu6s\nmL6Cutn2+a4HdKiwz3fFpWKZUbt1smVDm5wW3Xsm//5nXHwxZmMjdalMUwZ6XNEDMciFuTbGntf2\n09nbmeZYc4L48RhGTquXy1KIliv2OCljP7hVNyBhmlLCOefUchM/hRQ/lqUjRAfx04EOdOCrQnuF\nO+v6f4Xip83ET0uj14F8x1eF+Ax5MNh2xY+rBMWPptmtIpnPoobRofgpFaUSP82miUdRCip+JEnC\nqTjRzGy7DIAck1HcDuKlRrkQsiyqY3LrqtwhQfwEHQei+NGRdVe61UsPJhU/hpHMg2kH2FYvtSSr\nVyosK4YkFVH8OA6u4keLRHAoyUFIZquXZILikAnrYZY1edm///W0z5ek+NGSih9JcmFZ9v1Blp10\n6nIme6cBa9ciTZjABX/+MxvPOIN5L7/M5MmT2bRpU9FtMKLR9AyUEsKdJQUso3RFQ4+retDvjn4s\nnbKU0Gp7dFwly5jtoPgJRAJUubMH6KkwjACG0XbiB6DaW83UAVOZtWYWI0aMYNu2bWmKmUwkiJ8x\nY2D79rRA5tRQclWSqNqxAyZNsv1MX3wBTzxhZ5cANefXsH/WfqxY8fNZkiTUKif7R03iygeyG7bi\nUFWVxx57jMmTJ3PssceyY8cOGlusXgCMGGGTTy2TUKlWLyEElmWlKX5iOsiWha7DxJ4T+fj7H9Ov\nsh93zr+T097vyuYJ5/HUsqeoC9elrYdu6vQc4+f3x+zmkktgbw5+a+lSuPdeePJJ6F/uwdStNFuS\nLgQ+d7bip6oKGgIS3ZxOZuUJw4bktT9zgiSf4scVCWM4s68bqcSPpVlISntbvXR0nAny1ahvwvS0\nIiPr5JPtY6u2lv7dTfY2yOzdC1p9PfVlZdRnXH8zrV4ArrKjqA9uK6j4icViWYofRUkSP3FlRiQC\nH34IU6aVU9FlCvUv3ox679/Q62sBF4MGwaKFNhGhpwQBBxo1HErx4bjcpYIt5ncJ/XsRqmlxz/X/\n5jb1LnYv3sXeTTL/+ShMuddF9+7ZOVuGEBheiWgkafVyZMxN6LrE6FcOY/PPN9P4UY6g9xx17mbE\nZPOvN7PirBUIK/l7hVq9Guc3oh6jsjmwmcO7Hp62TK5Wr1TFD2DbzmUV6ZQKDn/9cNZdsY5dj+3C\nCBjgt5eR3Q5M0w66jhM/9YZBVcoET68ePVAcDl5/662C+z3iFlQOLWfPzP04FjkIaSE0DQynF3cK\n8ZMa7uwyFYTXspnbHIR1hdoZRRHx+RUcWogQrbV66YCM19u687KD+OlABzrQNrRHuHMsdlCJn0NZ\n8WMJC0vY1ZSpip9Dmvhph4wfVwmKn3zKp7jiJxzOajrtQAbaU/ED+XN+hBComopQnZSVJYQiWQiZ\nJlVRuU2KH6+qEnKKVrR6Jf32QsQzflxpih+hhdKJn/YOd6b1Vi8hDgHFTzSati8yFT9yi9UrYkRY\nE6qkvn4OQiT/LopSngjwzAehCSRZalH8uLCs5KC3S5dz2LfvJfuB+Ze/hNWrqXC5eH3jRm6vrOTs\n6dMxiuxXPTMDJUXxc8899yRCjNPCnR0gROseibtf1p0BfxjAsmnLCC4PUiFJmO2g+KmP1FPtqc77\nvhBWC/FTX3x0UADVnmqO6nUUL699GYfDwciRI1mWoorJRIL4UVU4+mh7lNsCy0pOsnRbsIA/XnKJ\n7WV69lno2zfte9y93fgO81H/dn1J66lWqay69uccOXcuFGjwkiSJP/zhD1x22WUcc8wxrFm/Pqn4\ncbls5dGqVYBN/Oxq3sXtt99O//79+dGPfpSoc/d4IBqTkAwrwd0O6TSEn0/6OR9e/iGPjd5At6bT\nmbV2FgP+MoDJ/5rMnz7+ExE9gm7pVI6pZNgXX3LZZYKLL04PDY9E4OKL4U9/gn79QHHKVKPycVOK\nZUUIfC6JWCyb+AkEoFJV2RSJsCaP9FXoAtlZmuJHCHBFgxg5rn9pip+Wc7Y9YVnpGT9mQzOmrxWK\nH4/HDlZ65RUkzaLnQIU334RYfT0Bvz9L8ZMa7hyHwzeGSLSJsJT/oaRUq1c0CvPm2TXunTqdSZ1/\nBeo/XyAWaUKynAwdIlj6uRcUlbAe4YFPHuD3H9xHVNNxFFL8xH+30QAD/CcPYvGvLuO3vz+Z8Z5V\n+I4dy6ILv8Wyz6fz7LMRuOkmJsycSd+PPoKtW8E00YXA9ErEorHE87aaMjdhWS3KocN8DPu/Yaw6\ndxXRbenP7ZJpIqeQzPVv1/PZyM+IbIyg7dXY/VhSKZTf6tVEw/wGNo3YxOiuo3Eq6deuuOIn/nwX\nVw/qgWRws2ZqCMkOdy4/spwx88fw5e+/JLQqhFRmr5/sdmAaYTuDroX4CRgGVSnr75ZllIoKXnrj\njYL7PWRZeFwO+t0/CN86H5FYBE0D05VK/KRPNjlNBctl5n1wL3dWozqSD7GOWJBm0Vqrl634ae3w\nqYP46UAHOtA2tIPVy4pE0EwzMQPR3jiUFT/ff+37XPDvC9AMLXHzO+SJn3bI+HGXUOeeL+tI1+3n\nd1nOOYHfgRS0hvgplvED+XN+YmYMj+nBkJWijV6VUaltVi9VpVm12qT4seNqNKQM4sfSQkmr18FQ\n/Ii2Zvzkv159JRk/sRjOQsSPAWqL4idCBU5nV5qaFiWXV4tbvSzNAokE8SNE8riqqjqJYHApmtYi\nlaipgYcfRpo3jylC8Oa2bbx35ZUFfVlGLJZO/LQofj7++GNuu+02rr/+eoQQaeHOkioVDXfOhW6X\ndGPQ/w5i2UnL6GNVoue7gLcCgWiAKk9+xY+tqBIHZPUCqHJX0a2sG82xZtbuX8u4cePy2r3C4TBb\ntmxhxIgR9gsZdi8hNGTJCQ88wJirr+aeu++GG27IywTXXFhD7czsWvhcUCtVYqaXf9x+O1x+OTQV\nPr5uuukmbr31Vn515pkY69Yl3xgzhtjnn/PCCy9w3drrOG7Gcezbt4+//e1vPPfcc0RDoUS4cyQm\nIZkip2jPZXRhYPP3ePn8l9nz0z3cPOlm3tr4FlfPvhrd0vH18CF7ZX46vRnTtIvL4vjlL+Gww+CS\nS+x/S6pEJQoLG5NkqW5ZlLklNC038WMB53TpwoMZzWpxWLqFnMPqlUvxE41ChRVCV7Ovy3HiJ15r\n3d6wrV7OBPEjGpugvJWteC3tXmbYZOBhCm+8AUZ9PQ05FT/BLOLHUlz0dAxgbePavD+RbfXKTfxE\nIrab8IQToFOn6dTVvYncdxiaxwWWk7rVb1J5yWPwyfnUR+pZtW8VDy56EE/vzdnhzjnQuKARJEC2\nr8s7u3p44axnmdh7Lz9970Nm3fMrXh/vh06d6Ll8OVP++ldbdTd2LHo0iumVicRyW73ihV2SBJ1O\n7UTvn/dmxZkrMIIpak/DRHI4iO2OseqCVay/Zj2DHxrMYc8fxtB/DGXLb7ag1doPZvmIn9h2Eyts\nsVxenmXziq9T3DYHuRU/ceInftx4B3sZt3AcPa7sgdzJ/jtJLgdGiuLHNE2aDYPKlHubW5aR/X4a\nwuGCjYZB08RpKeBVGX3FaDSh0bQ1iuHy4ta0nFYvh65iuQsQP2o1qtpC/AiBGgsRFK1T/FiWhhAy\nbnfrZkE7iJ8i8Pv9lJeXU15ejqIoeL3exGszZ878ulevAx34+tAOVq9oKITL4SiYZXAgOFQVP+9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7xYu4pZVbKKg3oflDPiIbnSXREE+4GPAJLbPj/bxI+cVfETidgiJ1ORccTDyGWZekVJafQC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0IUgWc36awdarM35UQeEKrWXGj5mV3de5V0w2hRComMDJtSZTSqofQ/pgS4Ro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QOXrL6E7Su2szK3MrS8dkJL8OLNL+YrL/0K+9+/n2+84huoQuXtP347l3zrT5h48kvcsvERDhcP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\njo50AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f474c634550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Graph of ambiguity of names over the years\n", "potentially_ambiguous_names = names_vs_years[(names_vs_years > 0).any(axis=1)]\n", "potentially_ambiguous_names.transpose().plot(figsize=(20, 10))" ] } ], "metadata": { "_change_revision": 82, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/328/328803.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "28299261-b289-3397-3746-a5a061baa365" }, "outputs": [], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "18bcaf30-5a52-2411-5442-c0cda003e044" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "ed2dc800-c433-0b21-e014-e51fd12b8545" }, "outputs": [], "source": [ "train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])\n", "test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])\n", "ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])\n", "\n", "df_train = pd.merge(train, ppl, on='people_id')\n", "df_test = pd.merge(test, ppl, on='people_id')\n", "del train, test, ppl" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "d062084d-5635-47c8-9dff-d5c4ff716e36" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start of date_x: 2022-07-17\n", " End of date_x: 2023-08-31\n", "Range of date_x: 410 days 00:00:00\n", "\n", "Start of date_y: 2020-05-18\n", " End of date_y: 2023-08-31\n", "Range of date_y: 1200 days 00:00:00\n", "\n" ] } ], "source": [ "for d in ['date_x', 'date_y']:\n", " print('Start of ' + d + ': ' + str(df_train[d].min().date()))\n", " print(' End of ' + d + ': ' + str(df_train[d].max().date()))\n", " print('Range of ' + d + ': ' + str(df_train[d].max() - df_train[d].min()) + '\\n')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "31ce7dbd-b9a5-2488-90d1-027fa49c1336" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 26, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/328/328841.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "aba53a01-cc53-a04f-5cfc-5ca644de275b" }, "source": [ "Using Trueskill to compute the 2016 kitefoil rankings" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "5f9e29dd-c831-3ff9-0d70-0110ab0ce459" }, "outputs": [], "source": [ "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import numpy as np\n", "import trueskill as ts" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "b92f7a60-28c5-c550-18d9-b153fa29c020" }, "outputs": [], "source": [ "def cleanResults(numRaces,dfResults):\n", " for raceCol in range(1,numRaces+1):\n", " dfResults['R'+str(raceCol)] = dfResults['R'+str(raceCol)].str.replace('\\(|\\)|DNF-|RET-|SCP-|RDG-|RCT-|DNS-[0-9]*|DNC-[0-9]*|OCS-[0-9]*','')\n", " dfResults['R'+str(raceCol)] = pd.to_numeric(dfResults['R'+str(raceCol)])\n", " return dfResults" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "e7b1546f-4bff-c8c5-699f-740b6d4aa3b7" }, "outputs": [], "source": [ "def doRating(numRaces,dfResults,dfRatings):\n", " for raceCol in range(1,numRaces+1):\n", " competed = dfRatings['Name'].isin(dfResults['Name'][dfResults['R' +str(raceCol)].notnull()])\n", " rating_group = list(zip(dfRatings['Rating'][competed].T.values.tolist()))\n", " dfRatings['Rating'][competed] = ts.rate(rating_group, ranks=dfResults['R' +str(raceCol)][competed].T.values.tolist())\n", " return pd.DataFrame(dfRatings)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "2dbb7f1b-3bd0-f7a3-2424-d90e3b2f51ef" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "07ea7b0f-96ad-d20c-3349-446934e396c4" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:13: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", "/opt/conda/lib/python3.5/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", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", "/opt/conda/lib/python3.5/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", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" ] } ], "source": [ "dfResults = pd.read_csv('../input/201608-SanFracisco-HydrofoilProTour.csv')\n", "dfResults = cleanResults(16,dfResults)\n", "dfRatings = pd.DataFrame()\n", "dfRatings['Name'] = dfResults['Name']\n", "dfRatings['Rating'] = pd.Series(np.repeat(ts.Rating(),len(dfRatings))).T.values.tolist()\n", "dfRatings = doRating(16,dfResults,dfRatings)\n", "\n", "dfRatings['mu'] = pd.Series(np.repeat(25.0,len(dfRatings)))\n", "dfRatings['sigma'] = pd.Series(np.repeat(8.333,len(dfRatings)))\n", "dfRatings['mu_minus_3sigma'] = pd.Series(np.repeat(0.0,len(dfRatings)))\n", "\n", "for i in range(0,len(dfRatings['Rating'])):\n", " dfRatings['mu'][i] = float(dfRatings['Rating'][i].mu) \n", " dfRatings['sigma'][i] = float(dfRatings['Rating'][i].sigma) \n", " dfRatings['mu_minus_3sigma'][i] = float(dfRatings['mu'][i] - 3 * dfRatings['sigma'][i]) " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "b5de9ec0-2853-88f2-5eed-577385c529d4" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n", " from ipykernel import kernelapp as app\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Name</th>\n", " <th>Rating</th>\n", " <th>mu</th>\n", " <th>sigma</th>\n", " <th>mu_minus_3sigma</th>\n", " </tr>\n", " <tr>\n", " <th>mu_minus_3sigma</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>1.0</th>\n", " <td>Johnny Heineken</td>\n", " <td>trueskill.Rating(mu=52.522, sigma=1.287)</td>\n", " <td>52.521645</td>\n", " <td>1.286807</td>\n", " <td>48.661225</td>\n", " </tr>\n", " <tr>\n", " <th>2.0</th>\n", " <td>Nico Parlier</td>\n", " <td>trueskill.Rating(mu=51.777, sigma=1.319)</td>\n", " <td>51.776708</td>\n", " <td>1.319282</td>\n", " <td>47.818862</td>\n", " </tr>\n", " <tr>\n", " <th>3.0</th>\n", " <td>Joey Pasquali</td>\n", " <td>trueskill.Rating(mu=46.623, sigma=1.223)</td>\n", " <td>46.623167</td>\n", " <td>1.223101</td>\n", " <td>42.953863</td>\n", " </tr>\n", " <tr>\n", " <th>4.0</th>\n", " <td>Nico Landauer</td>\n", " <td>trueskill.Rating(mu=43.082, sigma=1.175)</td>\n", " <td>43.081558</td>\n", " <td>1.175167</td>\n", " <td>39.556056</td>\n", " </tr>\n", " <tr>\n", " <th>5.0</th>\n", " <td>Adam Withington</td>\n", " <td>trueskill.Rating(mu=41.922, sigma=1.116)</td>\n", " <td>41.921617</td>\n", " <td>1.115800</td>\n", " <td>38.574217</td>\n", " </tr>\n", " <tr>\n", " <th>6.0</th>\n", " <td>Rikki Leccese</td>\n", " <td>trueskill.Rating(mu=43.811, sigma=1.972)</td>\n", " <td>43.811068</td>\n", " <td>1.971721</td>\n", " <td>37.895905</td>\n", " </tr>\n", " <tr>\n", " <th>7.0</th>\n", " <td>Stefaans Viljoen</td>\n", " <td>trueskill.Rating(mu=40.649, sigma=1.124)</td>\n", " <td>40.648765</td>\n", " <td>1.124414</td>\n", " <td>37.275523</td>\n", " </tr>\n", " <tr>\n", " <th>8.0</th>\n", " <td>Zack Marks</td>\n", " <td>trueskill.Rating(mu=40.037, sigma=1.106)</td>\n", " <td>40.036788</td>\n", " <td>1.106085</td>\n", " <td>36.718534</td>\n", " </tr>\n", " <tr>\n", " <th>9.0</th>\n", " <td>Toni Vodisek</td>\n", " <td>trueskill.Rating(mu=38.223, sigma=1.107)</td>\n", " <td>38.222988</td>\n", " <td>1.106964</td>\n", " <td>34.902098</td>\n", " </tr>\n", " <tr>\n", " <th>10.0</th>\n", " <td>Riley Gibbs</td>\n", " <td>trueskill.Rating(mu=36.185, sigma=1.100)</td>\n", " <td>36.185377</td>\n", " <td>1.099742</td>\n", " <td>32.886152</td>\n", " </tr>\n", " <tr>\n", " <th>11.0</th>\n", " <td>Seth Besse</td>\n", " <td>trueskill.Rating(mu=34.051, sigma=1.088)</td>\n", " <td>34.051387</td>\n", " <td>1.087891</td>\n", " <td>30.787715</td>\n", " </tr>\n", " <tr>\n", " <th>12.0</th>\n", " <td>Jacob Olivier</td>\n", " <td>trueskill.Rating(mu=32.130, sigma=1.088)</td>\n", " <td>32.129574</td>\n", " <td>1.088124</td>\n", " <td>28.865202</td>\n", " </tr>\n", " <tr>\n", " <th>13.0</th>\n", " <td>Xantos Villegas</td>\n", " <td>trueskill.Rating(mu=31.719, sigma=1.090)</td>\n", " <td>31.719069</td>\n", " <td>1.089903</td>\n", " <td>28.449361</td>\n", " </tr>\n", " <tr>\n", " <th>14.0</th>\n", " <td>Jon Modica</td>\n", " <td>trueskill.Rating(mu=30.266, sigma=1.086)</td>\n", " <td>30.266462</td>\n", " <td>1.086260</td>\n", " <td>27.007681</td>\n", " </tr>\n", " <tr>\n", " <th>15.0</th>\n", " <td>Andy Hansen</td>\n", " <td>trueskill.Rating(mu=29.342, sigma=1.083)</td>\n", " <td>29.342191</td>\n", " <td>1.083259</td>\n", " <td>26.092415</td>\n", " </tr>\n", " <tr>\n", " <th>16.0</th>\n", " <td>Kieran Le Borgne</td>\n", " <td>trueskill.Rating(mu=28.056, sigma=1.081)</td>\n", " <td>28.056360</td>\n", " <td>1.080702</td>\n", " <td>24.814255</td>\n", " </tr>\n", " <tr>\n", " <th>17.0</th>\n", " <td>Kai Calder</td>\n", " <td>trueskill.Rating(mu=27.907, sigma=1.082)</td>\n", " <td>27.907039</td>\n", " <td>1.082138</td>\n", " <td>24.660625</td>\n", " </tr>\n", " <tr>\n", " <th>18.0</th>\n", " <td>Ty Reed</td>\n", " <td>trueskill.Rating(mu=26.973, sigma=1.086)</td>\n", " <td>26.972698</td>\n", " <td>1.085640</td>\n", " <td>23.715779</td>\n", " </tr>\n", " <tr>\n", " <th>19.0</th>\n", " <td>Daniela Moroz</td>\n", " <td>trueskill.Rating(mu=26.955, sigma=1.082)</td>\n", " <td>26.955385</td>\n", " <td>1.081861</td>\n", " <td>23.709800</td>\n", " </tr>\n", " <tr>\n", " <th>20.0</th>\n", " <td>marvin baumeisterschoenian</td>\n", " <td>trueskill.Rating(mu=25.744, sigma=1.086)</td>\n", " <td>25.744486</td>\n", " <td>1.086269</td>\n", " <td>22.485679</td>\n", " </tr>\n", " <tr>\n", " <th>21.0</th>\n", " <td>Benjamin Petit</td>\n", " <td>trueskill.Rating(mu=25.501, sigma=1.080)</td>\n", " <td>25.500960</td>\n", " <td>1.080345</td>\n", " <td>22.259925</td>\n", " </tr>\n", " <tr>\n", " <th>22.0</th>\n", " <td>Alex Caizergues</td>\n", " <td>trueskill.Rating(mu=25.099, sigma=1.082)</td>\n", " <td>25.099062</td>\n", " <td>1.081762</td>\n", " <td>21.853777</td>\n", " </tr>\n", " <tr>\n", " <th>23.0</th>\n", " <td>Jordan Girdis</td>\n", " <td>trueskill.Rating(mu=24.720, sigma=1.080)</td>\n", " <td>24.719646</td>\n", " <td>1.079695</td>\n", " <td>21.480562</td>\n", " </tr>\n", " <tr>\n", " <th>24.0</th>\n", " <td>Peter Martel</td>\n", " <td>trueskill.Rating(mu=24.417, sigma=1.080)</td>\n", " <td>24.416681</td>\n", " <td>1.079866</td>\n", " <td>21.177083</td>\n", " </tr>\n", " <tr>\n", " <th>25.0</th>\n", " <td>Sam Bullock</td>\n", " <td>trueskill.Rating(mu=24.397, sigma=1.082)</td>\n", " <td>24.396634</td>\n", " <td>1.081877</td>\n", " <td>21.151002</td>\n", " </tr>\n", " <tr>\n", " <th>26.0</th>\n", " <td>chip wasson</td>\n", " <td>trueskill.Rating(mu=23.842, sigma=1.113)</td>\n", " <td>23.841944</td>\n", " <td>1.112608</td>\n", " <td>20.504120</td>\n", " </tr>\n", " <tr>\n", " <th>27.0</th>\n", " <td>William Morris</td>\n", " <td>trueskill.Rating(mu=20.765, sigma=1.080)</td>\n", " <td>20.765052</td>\n", " <td>1.079822</td>\n", " <td>17.525585</td>\n", " </tr>\n", " <tr>\n", " <th>28.0</th>\n", " <td>Mani Bisschops</td>\n", " <td>trueskill.Rating(mu=20.516, sigma=1.149)</td>\n", " <td>20.515838</td>\n", " <td>1.148639</td>\n", " <td>17.069922</td>\n", " </tr>\n", " <tr>\n", " <th>29.0</th>\n", " <td>will james</td>\n", " <td>trueskill.Rating(mu=19.701, sigma=1.083)</td>\n", " <td>19.701357</td>\n", " <td>1.083264</td>\n", " <td>16.451566</td>\n", " </tr>\n", " <tr>\n", " <th>30.0</th>\n", " <td>Amil Kabil</td>\n", " <td>trueskill.Rating(mu=18.372, sigma=1.080)</td>\n", " <td>18.371518</td>\n", " <td>1.080173</td>\n", " <td>15.130999</td>\n", " </tr>\n", " <tr>\n", " <th>31.0</th>\n", " <td>ariel poler</td>\n", " <td>trueskill.Rating(mu=16.708, sigma=1.114)</td>\n", " <td>16.708027</td>\n", " <td>1.114141</td>\n", " <td>13.365603</td>\n", " </tr>\n", " <tr>\n", " <th>32.0</th>\n", " <td>Sonny Swords</td>\n", " <td>trueskill.Rating(mu=16.321, sigma=1.081)</td>\n", " <td>16.320934</td>\n", " <td>1.081230</td>\n", " <td>13.077243</td>\n", " </tr>\n", " <tr>\n", " <th>33.0</th>\n", " <td>Michael Gilbreath</td>\n", " <td>trueskill.Rating(mu=14.809, sigma=1.114)</td>\n", " <td>14.808720</td>\n", " <td>1.113812</td>\n", " <td>11.467284</td>\n", " </tr>\n", " <tr>\n", " <th>34.0</th>\n", " <td>Will Cyr</td>\n", " <td>trueskill.Rating(mu=13.530, sigma=1.152)</td>\n", " <td>13.530063</td>\n", " <td>1.151890</td>\n", " <td>10.074394</td>\n", " </tr>\n", " <tr>\n", " <th>35.0</th>\n", " <td>Felix Louis N'jai</td>\n", " <td>trueskill.Rating(mu=13.204, sigma=1.087)</td>\n", " <td>13.203974</td>\n", " <td>1.086699</td>\n", " <td>9.943877</td>\n", " </tr>\n", " <tr>\n", " <th>36.0</th>\n", " <td>AnthonyGoldbloom</td>\n", " <td>trueskill.Rating(mu=12.707, sigma=1.083)</td>\n", " <td>12.706935</td>\n", " <td>1.083414</td>\n", " <td>9.456692</td>\n", " </tr>\n", " <tr>\n", " <th>37.0</th>\n", " <td>FraserNovakowski</td>\n", " <td>trueskill.Rating(mu=11.841, sigma=1.085)</td>\n", " <td>11.840566</td>\n", " <td>1.085455</td>\n", " <td>8.584202</td>\n", " </tr>\n", " <tr>\n", " <th>38.0</th>\n", " <td>Ben Turner</td>\n", " <td>trueskill.Rating(mu=11.588, sigma=1.086)</td>\n", " <td>11.587932</td>\n", " <td>1.086155</td>\n", " <td>8.329468</td>\n", " </tr>\n", " <tr>\n", " <th>39.0</th>\n", " <td>Craig Rawson</td>\n", " <td>trueskill.Rating(mu=10.125, sigma=1.160)</td>\n", " <td>10.124529</td>\n", " <td>1.159851</td>\n", " <td>6.644975</td>\n", " </tr>\n", " <tr>\n", " <th>40.0</th>\n", " <td>Loic Le Meur</td>\n", " <td>trueskill.Rating(mu=9.753, sigma=1.123)</td>\n", " <td>9.753415</td>\n", " <td>1.123391</td>\n", " <td>6.383243</td>\n", " </tr>\n", " <tr>\n", " <th>41.0</th>\n", " <td>Chris Brent</td>\n", " <td>trueskill.Rating(mu=9.526, sigma=1.090)</td>\n", " <td>9.526397</td>\n", " <td>1.089961</td>\n", " <td>6.256513</td>\n", " </tr>\n", " <tr>\n", " <th>42.0</th>\n", " <td>Kevin Growney</td>\n", " <td>trueskill.Rating(mu=8.484, sigma=1.133)</td>\n", " <td>8.483726</td>\n", " <td>1.132593</td>\n", " <td>5.085948</td>\n", " </tr>\n", " <tr>\n", " <th>43.0</th>\n", " <td>John Gomes</td>\n", " <td>trueskill.Rating(mu=7.670, sigma=1.311)</td>\n", " <td>7.669955</td>\n", " <td>1.311220</td>\n", " <td>3.736295</td>\n", " </tr>\n", " <tr>\n", " <th>44.0</th>\n", " <td>steve bodner</td>\n", " <td>trueskill.Rating(mu=7.569, sigma=1.322)</td>\n", " <td>7.569298</td>\n", " <td>1.322356</td>\n", " <td>3.602231</td>\n", " </tr>\n", " <tr>\n", " <th>45.0</th>\n", " <td>Peter Grendler</td>\n", " <td>trueskill.Rating(mu=6.377, sigma=1.330)</td>\n", " <td>6.377368</td>\n", " <td>1.329545</td>\n", " <td>2.388733</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name \\\n", "mu_minus_3sigma \n", "1.0 Johnny Heineken \n", "2.0 Nico Parlier \n", "3.0 Joey Pasquali \n", "4.0 Nico Landauer \n", "5.0 Adam Withington \n", "6.0 Rikki Leccese \n", "7.0 Stefaans Viljoen \n", "8.0 Zack Marks \n", "9.0 Toni Vodisek \n", "10.0 Riley Gibbs \n", "11.0 Seth Besse \n", "12.0 Jacob Olivier \n", "13.0 Xantos Villegas \n", "14.0 Jon Modica \n", "15.0 Andy Hansen \n", "16.0 Kieran Le Borgne \n", "17.0 Kai Calder \n", "18.0 Ty Reed \n", "19.0 Daniela Moroz \n", "20.0 marvin baumeisterschoenian \n", "21.0 Benjamin Petit \n", "22.0 Alex Caizergues \n", "23.0 Jordan Girdis \n", "24.0 Peter Martel \n", "25.0 Sam Bullock \n", "26.0 chip wasson \n", "27.0 William Morris \n", "28.0 Mani Bisschops \n", "29.0 will james \n", "30.0 Amil Kabil \n", "31.0 ariel poler \n", "32.0 Sonny Swords \n", "33.0 Michael Gilbreath \n", "34.0 Will Cyr \n", "35.0 Felix Louis N'jai \n", "36.0 AnthonyGoldbloom \n", "37.0 FraserNovakowski \n", "38.0 Ben Turner \n", "39.0 Craig Rawson \n", "40.0 Loic Le Meur \n", "41.0 Chris Brent \n", "42.0 Kevin Growney \n", "43.0 John Gomes \n", "44.0 steve bodner \n", "45.0 Peter Grendler \n", "\n", " Rating mu \\\n", "mu_minus_3sigma \n", "1.0 trueskill.Rating(mu=52.522, sigma=1.287) 52.521645 \n", "2.0 trueskill.Rating(mu=51.777, sigma=1.319) 51.776708 \n", "3.0 trueskill.Rating(mu=46.623, sigma=1.223) 46.623167 \n", "4.0 trueskill.Rating(mu=43.082, sigma=1.175) 43.081558 \n", "5.0 trueskill.Rating(mu=41.922, sigma=1.116) 41.921617 \n", "6.0 trueskill.Rating(mu=43.811, sigma=1.972) 43.811068 \n", "7.0 trueskill.Rating(mu=40.649, sigma=1.124) 40.648765 \n", "8.0 trueskill.Rating(mu=40.037, sigma=1.106) 40.036788 \n", "9.0 trueskill.Rating(mu=38.223, sigma=1.107) 38.222988 \n", "10.0 trueskill.Rating(mu=36.185, sigma=1.100) 36.185377 \n", "11.0 trueskill.Rating(mu=34.051, sigma=1.088) 34.051387 \n", "12.0 trueskill.Rating(mu=32.130, sigma=1.088) 32.129574 \n", "13.0 trueskill.Rating(mu=31.719, sigma=1.090) 31.719069 \n", "14.0 trueskill.Rating(mu=30.266, sigma=1.086) 30.266462 \n", "15.0 trueskill.Rating(mu=29.342, sigma=1.083) 29.342191 \n", "16.0 trueskill.Rating(mu=28.056, sigma=1.081) 28.056360 \n", "17.0 trueskill.Rating(mu=27.907, sigma=1.082) 27.907039 \n", "18.0 trueskill.Rating(mu=26.973, sigma=1.086) 26.972698 \n", "19.0 trueskill.Rating(mu=26.955, sigma=1.082) 26.955385 \n", "20.0 trueskill.Rating(mu=25.744, sigma=1.086) 25.744486 \n", "21.0 trueskill.Rating(mu=25.501, sigma=1.080) 25.500960 \n", "22.0 trueskill.Rating(mu=25.099, sigma=1.082) 25.099062 \n", "23.0 trueskill.Rating(mu=24.720, sigma=1.080) 24.719646 \n", "24.0 trueskill.Rating(mu=24.417, sigma=1.080) 24.416681 \n", "25.0 trueskill.Rating(mu=24.397, sigma=1.082) 24.396634 \n", "26.0 trueskill.Rating(mu=23.842, sigma=1.113) 23.841944 \n", "27.0 trueskill.Rating(mu=20.765, sigma=1.080) 20.765052 \n", "28.0 trueskill.Rating(mu=20.516, sigma=1.149) 20.515838 \n", "29.0 trueskill.Rating(mu=19.701, sigma=1.083) 19.701357 \n", "30.0 trueskill.Rating(mu=18.372, sigma=1.080) 18.371518 \n", "31.0 trueskill.Rating(mu=16.708, sigma=1.114) 16.708027 \n", "32.0 trueskill.Rating(mu=16.321, sigma=1.081) 16.320934 \n", "33.0 trueskill.Rating(mu=14.809, sigma=1.114) 14.808720 \n", "34.0 trueskill.Rating(mu=13.530, sigma=1.152) 13.530063 \n", "35.0 trueskill.Rating(mu=13.204, sigma=1.087) 13.203974 \n", "36.0 trueskill.Rating(mu=12.707, sigma=1.083) 12.706935 \n", "37.0 trueskill.Rating(mu=11.841, sigma=1.085) 11.840566 \n", "38.0 trueskill.Rating(mu=11.588, sigma=1.086) 11.587932 \n", "39.0 trueskill.Rating(mu=10.125, sigma=1.160) 10.124529 \n", "40.0 trueskill.Rating(mu=9.753, sigma=1.123) 9.753415 \n", "41.0 trueskill.Rating(mu=9.526, sigma=1.090) 9.526397 \n", "42.0 trueskill.Rating(mu=8.484, sigma=1.133) 8.483726 \n", "43.0 trueskill.Rating(mu=7.670, sigma=1.311) 7.669955 \n", "44.0 trueskill.Rating(mu=7.569, sigma=1.322) 7.569298 \n", "45.0 trueskill.Rating(mu=6.377, sigma=1.330) 6.377368 \n", "\n", " sigma mu_minus_3sigma \n", "mu_minus_3sigma \n", "1.0 1.286807 48.661225 \n", "2.0 1.319282 47.818862 \n", "3.0 1.223101 42.953863 \n", "4.0 1.175167 39.556056 \n", "5.0 1.115800 38.574217 \n", "6.0 1.971721 37.895905 \n", "7.0 1.124414 37.275523 \n", "8.0 1.106085 36.718534 \n", "9.0 1.106964 34.902098 \n", "10.0 1.099742 32.886152 \n", "11.0 1.087891 30.787715 \n", "12.0 1.088124 28.865202 \n", "13.0 1.089903 28.449361 \n", "14.0 1.086260 27.007681 \n", "15.0 1.083259 26.092415 \n", "16.0 1.080702 24.814255 \n", "17.0 1.082138 24.660625 \n", "18.0 1.085640 23.715779 \n", "19.0 1.081861 23.709800 \n", "20.0 1.086269 22.485679 \n", "21.0 1.080345 22.259925 \n", "22.0 1.081762 21.853777 \n", "23.0 1.079695 21.480562 \n", "24.0 1.079866 21.177083 \n", "25.0 1.081877 21.151002 \n", "26.0 1.112608 20.504120 \n", "27.0 1.079822 17.525585 \n", "28.0 1.148639 17.069922 \n", "29.0 1.083264 16.451566 \n", "30.0 1.080173 15.130999 \n", "31.0 1.114141 13.365603 \n", "32.0 1.081230 13.077243 \n", "33.0 1.113812 11.467284 \n", "34.0 1.151890 10.074394 \n", "35.0 1.086699 9.943877 \n", "36.0 1.083414 9.456692 \n", "37.0 1.085455 8.584202 \n", "38.0 1.086155 8.329468 \n", "39.0 1.159851 6.644975 \n", "40.0 1.123391 6.383243 \n", "41.0 1.089961 6.256513 \n", "42.0 1.132593 5.085948 \n", "43.0 1.311220 3.736295 \n", "44.0 1.322356 3.602231 \n", "45.0 1.329545 2.388733 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfRatings.index = dfRatings['mu_minus_3sigma'].rank(ascending=False)\n", "dfRatings.sort('mu_minus_3sigma',ascending=False)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "f575622e-8713-7607-1460-ed40ae6597aa" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 43, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/328/328872.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "e17cddda-2d7a-77e5-f3c7-b82b9617c421" }, "source": [ "# Gender Ambiguity in American Names\n", "\n", "Some names are very gendered, while others are unisex. How we think of these names changes over time though, along with how open parents are to giving their children gender-neutral names in the first place. We're going to dig into the numbers here and see if anything interesting comes up.\n", "\n", "I'm defining 'gender ambiguity' here as in the first script below: a number between 0 and 1 that describes how hard it is to guess a person's gender from the name alone. 0 means everybody with that name has the same gender, 1 means exactly 50% of people with that name have each gender. It's just double the probability you'd guess gender wrong from the name alone (which of course only goes up to 0.5).\n", "\n", "Note that the dataset is very definitive and binary about genders, so I'm following those simplifying assumptions here (sorry).\n", "\n", "**Also, I'm not super familiar with Pandas, so I'd love any suggestions or pointers to improve this!**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "ff48ef76-5fba-5040-5eeb-82af196e3fdb" }, "outputs": [], "source": [ "import math\n", "import pandas as pd\n", "\n", "names_data = pd.read_csv(\"../input/NationalNames.csv\")\n", "\n", "frequent_names = names_data[names_data['Count'] > 10]\n", "indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']\n", "\n", "# Number between 0 and 1 representing ambiguity, from certain to totally ambiguous\n", "# 0 = all the same gender, 1 = exactly 50% of each gender. Assumes only two options.\n", "def ambiguity_measure(grouped_frame):\n", " return (2 * (1 - (grouped_frame.max() / grouped_frame.sum())))\n", "\n", "# Various useful formattings of gender ambiguity data:\n", "ambiguity_data = ambiguity_measure(indexed_names.groupby(level=['Year', 'Name'])).rename(\"Ambiguity\")\n", "yearly_ambiguity = ambiguity_data.groupby(level='Year')\n", "\n", "ambiguity_with_counts = ambiguity_data.to_frame().join(indexed_names.groupby(level=['Year', 'Name']).sum())\n", "data_vs_years = ambiguity_with_counts.unstack(level='Year')\n", "data_vs_years[\"Total\"] = data_vs_years['Count'].sum(axis=1)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "4ba37e6b-a083-68c2-b772-a25374c9e8d0" }, "source": [ "# The most gender-ambiguous name in America, by year of birth." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "2e28a540-968e-bda7-9407-d54866157f1e" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Ambiguity</th>\n", " </tr>\n", " <tr>\n", " <th>Year</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1880</th>\n", " <td>Fay</td>\n", " </tr>\n", " <tr>\n", " <th>1881</th>\n", " <td>Dell</td>\n", " </tr>\n", " <tr>\n", " <th>1882</th>\n", " <td>Jimmie</td>\n", " </tr>\n", " <tr>\n", " <th>1883</th>\n", " <td>Marion</td>\n", " </tr>\n", " <tr>\n", " <th>1884</th>\n", " <td>Marion</td>\n", " </tr>\n", " <tr>\n", " <th>1885</th>\n", " <td>Tracy</td>\n", " </tr>\n", " <tr>\n", " <th>1886</th>\n", " <td>Jimmie</td>\n", " </tr>\n", " <tr>\n", " <th>1887</th>\n", " <td>Jimmie</td>\n", " </tr>\n", " <tr>\n", " <th>1888</th>\n", " <td>Gene</td>\n", " </tr>\n", " <tr>\n", " <th>1889</th>\n", " <td>Odie</td>\n", " </tr>\n", " <tr>\n", " <th>1890</th>\n", " <td>Theo</td>\n", " </tr>\n", " <tr>\n", " <th>1891</th>\n", " <td>Arlie</td>\n", " </tr>\n", " <tr>\n", " <th>1892</th>\n", " <td>Arlie</td>\n", " </tr>\n", " <tr>\n", " <th>1893</th>\n", " <td>Tommie</td>\n", " </tr>\n", " <tr>\n", " <th>1894</th>\n", " <td>Tommie</td>\n", " </tr>\n", " <tr>\n", " <th>1895</th>\n", " <td>Tommie</td>\n", " </tr>\n", " <tr>\n", " <th>1896</th>\n", " <td>Jodie</td>\n", " </tr>\n", " <tr>\n", " <th>1897</th>\n", " <td>Sammie</td>\n", " </tr>\n", " <tr>\n", " <th>1898</th>\n", " <td>Augustine</td>\n", " </tr>\n", " <tr>\n", " <th>1899</th>\n", " <td>Dana</td>\n", " </tr>\n", " <tr>\n", " <th>1900</th>\n", " <td>Earlie</td>\n", " </tr>\n", " <tr>\n", " <th>1901</th>\n", " <td>Sammie</td>\n", " </tr>\n", " <tr>\n", " <th>1902</th>\n", " <td>Beverly</td>\n", " </tr>\n", " <tr>\n", " <th>1903</th>\n", " <td>Ivory</td>\n", " </tr>\n", " <tr>\n", " <th>1904</th>\n", " <td>Alva</td>\n", " </tr>\n", " <tr>\n", " <th>1905</th>\n", " <td>Beverly</td>\n", " </tr>\n", " <tr>\n", " <th>1906</th>\n", " <td>Oral</td>\n", " </tr>\n", " <tr>\n", " <th>1907</th>\n", " <td>Ivory</td>\n", " </tr>\n", " <tr>\n", " <th>1908</th>\n", " <td>Augustine</td>\n", " </tr>\n", " <tr>\n", " <th>1909</th>\n", " <td>Alva</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>1985</th>\n", " <td>Farris</td>\n", " </tr>\n", " <tr>\n", " <th>1986</th>\n", " <td>Hoa</td>\n", " </tr>\n", " <tr>\n", " <th>1987</th>\n", " <td>Amando</td>\n", " </tr>\n", " <tr>\n", " <th>1988</th>\n", " <td>Brittain</td>\n", " </tr>\n", " <tr>\n", " <th>1989</th>\n", " <td>Alpha</td>\n", " </tr>\n", " <tr>\n", " <th>1990</th>\n", " <td>Adrean</td>\n", " </tr>\n", " <tr>\n", " <th>1991</th>\n", " <td>Chesley</td>\n", " </tr>\n", " <tr>\n", " <th>1992</th>\n", " <td>Ashby</td>\n", " </tr>\n", " <tr>\n", " <th>1993</th>\n", " <td>Bao</td>\n", " </tr>\n", " <tr>\n", " <th>1994</th>\n", " <td>Adison</td>\n", " </tr>\n", " <tr>\n", " <th>1995</th>\n", " <td>Ashby</td>\n", " </tr>\n", " <tr>\n", " <th>1996</th>\n", " <td>Aquarius</td>\n", " </tr>\n", " <tr>\n", " <th>1997</th>\n", " <td>Baley</td>\n", " </tr>\n", " <tr>\n", " <th>1998</th>\n", " <td>Adi</td>\n", " </tr>\n", " <tr>\n", " <th>1999</th>\n", " <td>Amory</td>\n", " </tr>\n", " <tr>\n", " <th>2000</th>\n", " <td>Alexiz</td>\n", " </tr>\n", " <tr>\n", " <th>2001</th>\n", " <td>Arlen</td>\n", " </tr>\n", " <tr>\n", " <th>2002</th>\n", " <td>Andree</td>\n", " </tr>\n", " <tr>\n", " <th>2003</th>\n", " <td>Aarya</td>\n", " </tr>\n", " <tr>\n", " <th>2004</th>\n", " <td>Carlin</td>\n", " </tr>\n", " <tr>\n", " <th>2005</th>\n", " <td>Anmol</td>\n", " </tr>\n", " <tr>\n", " <th>2006</th>\n", " <td>Abrar</td>\n", " </tr>\n", " <tr>\n", " <th>2007</th>\n", " <td>Arlen</td>\n", " </tr>\n", " <tr>\n", " <th>2008</th>\n", " <td>Adama</td>\n", " </tr>\n", " <tr>\n", " <th>2009</th>\n", " <td>Adi</td>\n", " </tr>\n", " <tr>\n", " <th>2010</th>\n", " <td>Atley</td>\n", " </tr>\n", " <tr>\n", " <th>2011</th>\n", " <td>Amory</td>\n", " </tr>\n", " <tr>\n", " <th>2012</th>\n", " <td>Devlyn</td>\n", " </tr>\n", " <tr>\n", " <th>2013</th>\n", " <td>Aalijah</td>\n", " </tr>\n", " <tr>\n", " <th>2014</th>\n", " <td>Adama</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>135 rows × 1 columns</p>\n", "</div>" ], "text/plain": [ " Ambiguity\n", "Year \n", "1880 Fay\n", "1881 Dell\n", "1882 Jimmie\n", "1883 Marion\n", "1884 Marion\n", "1885 Tracy\n", "1886 Jimmie\n", "1887 Jimmie\n", "1888 Gene\n", "1889 Odie\n", "1890 Theo\n", "1891 Arlie\n", "1892 Arlie\n", "1893 Tommie\n", "1894 Tommie\n", "1895 Tommie\n", "1896 Jodie\n", "1897 Sammie\n", "1898 Augustine\n", "1899 Dana\n", "1900 Earlie\n", "1901 Sammie\n", "1902 Beverly\n", "1903 Ivory\n", "1904 Alva\n", "1905 Beverly\n", "1906 Oral\n", "1907 Ivory\n", "1908 Augustine\n", "1909 Alva\n", "... ...\n", "1985 Farris\n", "1986 Hoa\n", "1987 Amando\n", "1988 Brittain\n", "1989 Alpha\n", "1990 Adrean\n", "1991 Chesley\n", "1992 Ashby\n", "1993 Bao\n", "1994 Adison\n", "1995 Ashby\n", "1996 Aquarius\n", "1997 Baley\n", "1998 Adi\n", "1999 Amory\n", "2000 Alexiz\n", "2001 Arlen\n", "2002 Andree\n", "2003 Aarya\n", "2004 Carlin\n", "2005 Anmol\n", "2006 Abrar\n", "2007 Arlen\n", "2008 Adama\n", "2009 Adi\n", "2010 Atley\n", "2011 Amory\n", "2012 Devlyn\n", "2013 Aalijah\n", "2014 Adama\n", "\n", "[135 rows x 1 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yearly_ambiguity.idxmax().apply(lambda x: x[1]).to_frame() " ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c69b8d62-a191-713e-b19c-184d7da2cfb9" }, "source": [ "# How has the ambiguity of names changed over the years?\n", "\n", "Filtered for an interesting selection here, as it's hard to look at all the names together. so we're only looking at names which at some point where reasonably ambiguous (more than a 5% chance you'd guess the gender wrong based on the name), and then the overall most popular 7 names from within those.\n", "\n", "I've love to include more names, but it's hard to match the colours with more lines sadly. If anybody wants to send me (here or on twitter: @pimterry) a good way to add labels to lines, or a different way to portray this, that would be great!\n", "\n", "Interesting points though: 'Willie' has been around for a long time, was once a very unisex name, and became steadily more male for 100 years (but has now basically disappeared). 'Ashley'5 appeared suddenly as a unisex option in 60s, and then settled towards being predominantly female over 25 years or so.\n", "\n", "General pattern in a lot of these is names starting off less gender specific, and becoming more gendered over time. 'Ryan' is the one obvious counter-example in the names shown here: it appeared in the 70s, all male, and is slowly but steadily moving in the other direction." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "5af95505-65c9-e40e-648f-3a112b4b586e" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fd82eef89b0>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PFaBE2xhIdmhvh+ef9/9vQF2dKlAiLuGZAFVVVcXGjRsVoEQVKMkO774LkybB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\nW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/Q4TjqQjBK6kOrvVxkDVbmPxmKx9MFm2+PrslC3JehUbLbEBmfg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\nT1stwb26IKx6srdryZeT0XpA3luBvKsc9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0ks770cWZLrzMADpV1QVPdAuhZaaFkjiSFA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\nVFRUVFRUGomqQKmoqKioqKioNJL/BxqpU91MYPZmAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd84065aa58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ambiguous_names = data_vs_years[(data_vs_years['Ambiguity'] > 0.1).any(axis=1)]\n", "popular_ambiguous_names = ambiguous_names.sort_values(by='Total', ascending=False).head(7).drop(\"Total\", axis=1)\n", "popular_ambiguous_names['Ambiguity'].transpose().plot(figsize=(10, 10))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c80424ba-843e-7a56-e977-b8e38cab22df" }, "source": [ "# How gender-ambiguous are the name's used each year?\n", "\n", "This is the simplistic measure: what's the average gender ambiguity, just of the names used each year?\n", "\n", "Note that this doesn't weight by how much those names are used - that's the next chart. This is essentially a measure of the ambiguity of the names *available* and in common-ish use (i.e. at least 10 births in a year)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "29f3db6e-3db9-b1d3-3035-1b130ca3c928" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fd7faae6be0>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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yL5HHznVlO88VmDJF+n//L/EGqqlk2l5ctqz4oWvAgOTtxdjQxTA9yk2moQvZ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\nvYDvVNW7Rvn94w9QkiRpBqmqUSef5g+x7Qpg3zYY/Qg4GTh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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd7fab16cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Gender ambiguity by name (not *person*! See the next chart)\n", "yearly_ambiguity.mean().transpose().plot(figsize=(10, 10))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d719b724-0563-e38a-e8ff-4de81a058fe0" }, "source": [ "# How gender-ambiguous is the average person's name, by birth year?\n", "\n", "The names available have become more ambiguous over time (with a dip in the 70s). Are we picking more gender ambiguous names though, given those options?\n", "\n", "Spoilers: yes! Not only are the total set of names in use fitting less neatly into one gender or another, people are picking more gender-ambiguous names overall. There's approximately a 2.5% chance you'll guess the gender of somebody in the USA born in 2014 wrong, almost double the odds a century ago. Slow changes, but what looks like a clear steady shift in the numbers." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "eda35afc-34cf-7554-2172-4afa0a7de28e" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fd7f9262080>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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uKacWLUo/JNa3n6tQ165w9tmOdqmyTJwIl18Ot9xSunBy1FGpLyxrTi3WNkOX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\ndSXqCI50SevH0KXcmDIF9t8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l+2zIzCNa8xEkdRpDl6SOk5mbImIh\nsDEzfx8RxwNHAveVF9TdmeK6ewCnRMRHKP4e7gccRHENPoCFFXddUgczdEnqVFvKG0AA38nMubUb\nRMSbKS52fGRmboyI71EEsl4vVNJTSV3BsxcldYNbgA9GxBiAiNgrIg4AXgv8Fng+IvYDZrSwj5I6\nnCNdkjpeZj4cEV8GbomInYDfAX+fmcsjogfoAZ4Alta+rAVdldTBPHtRkiSpAh5elCRJqoChS5Ik\nqQKGLkmSpAoYuiRJkipg6JIkSaqAoUuSJKkChi5JkqQKGLokSZIq8P+mD7j4KeNmIgAAAABJRU5E\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd7fab15080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# = SUM(probability of name in given year * ambiguity of name)\n", "total_people_per_year = ambiguity_with_counts['Count'].groupby(level='Year').sum()\n", "ambiguity_by_year = ambiguity_with_counts.unstack('Name')\n", "ambiguity_by_year[\"total_people\"] = total_people_per_year\n", "weighted_ambiguity = ambiguity_by_year.apply(lambda x : x['Ambiguity'] * (x['Count']/x['total_people'][0]), axis=1)\n", "weighted_ambiguity.sum(axis=1).plot(figsize=(10, 10))" ] } ], "metadata": { "_change_revision": 113, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/329/329077.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "88169e18-abbb-d747-6517-dc29e4669152" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sample_submission.csv\n", "test_sm\n", "train_sm\n", "\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "29b43f92-b534-d607-e812-7bcc08db9ad8" }, "outputs": [], "source": [ "import cv2\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "02a3e052-ae2b-4f0e-3ce3-9dc718a0dbba" }, "outputs": [], "source": [ "img = cv2.imread('../input/train_sm/set107_1.jpeg')\n", "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "f92c0cef-4b67-eccb-c28c-bfcf4837af7b" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f3e6249b710>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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yKN0W+xhNZyFDtRICCktHBHg4lsJoEMLhxK397Dk8g0xijNRIXaSY1Sz6TVJe\nju6UpNoAT4OVFVi2YtWqVSyWJZaosrS0zEfe/yG+eOft+L7i6msu54obr2VpvI5IfIK4gvGKSNm2\nHiVJQlfJplk3hInk8zdfzcfOuJDcUBqVpFg10EOl2eQDH3sH11x4K10Dec4+5538w1+fzTd+eA3v\nePOl1Os1rrv1Yj70zxfw9R9/hve++ROkU0Xu+/FlXHPpHfitGq0kQKmYKNLYlouT0oSBxHItdm1f\nZM1giqVKhZ6eLi684EO8752Xc+rpQ3zisk9w2zXf4d3nvYp3vurDfP77V3LnDV8l8JdYrhu0MXRl\nNdXIxZISJUFGTRQeibA5aWsH23dMkGC3U3yidlqqNYWUIE6c9kAzkrTn4zcdjCU4cdtaHn96Aik1\nabtJLS5gWwFukCH2EjavH2J4eJg4jnE9SSAy5B1NoFvoxKW706a8IDHCJ+NZ1BPozTvMlhMcS5AW\nmiSJWb9hPaOTU1iBYc0Gj4mpAMcJkZaFKzyyeU1nx1oSEZHPxoyP1gh1RK0R4GWyKF+RKIVwLaLg\nD531KPaxRI6+/k4m59pRdNoRRKH5L+O5t7eX2dlZbEcSxyECB8e1USoCYx+dA0K2hc9oi97+PHMz\nTUCBSJDkSGXAb7YAQaGYo14LMCbBsgVGuRRLNsv1dtTpWhGhEjiOiw4NUgryBYdaNcJx2x5w16To\n7S+wON9Aeu0g6Rm/9DPjtphtBzpZV5Mu5nFdl5ybYqZWIY4iWq2Iei2gWCwSxzHEhhO2rKFjMMfo\n7mnGZis4LnR2p6hXE4LkmaDMcMyWEof311E2BH6TUqmEEIIwDAnDkEw2j05CEqW58bOfelYC+2fr\nInjo0YdYXlpkdLzKLXecw3e/+QAnPf80nnjiICpOOPEFL2TvYyPUGj67902zZpVh7dpV9A9088jv\nh5maCdkwVOSJ7cNMji+zfmMvw4enyaQzOFaOOIkAgYpt4qSFSgSZVDdx0u5EJklyVKyVakcBgwNr\naDT89gCTkiQJMSjCKEAgyGY9/FgjhYslbaSQoGICX2FJSalUIAgNJklwHQtLSqQ0LFd9jNZ0dvfS\nbCYgNX4cIYyLFBbLTR8lJFZesFwNCMKEpUqdpdoiXsqlXA757cMP4UcxQRLx2PZHKS+UqdUqHHvC\nWirVkI60TaI0EkPGDTGk6Cg5rFrVyfZf7qDQB7UkYqDbIWq1aMWLPLVjB8vlGa644nyuuPwKxsam\nyWVzuI4FkiTzAAAgAElEQVTixhsv5/xzLuHGW8/j2itvZVV/imtvuZJvfe37zM5P0gp8YqXw3LYl\nxhiDQGA0jBya4ye/uJ1qzWfPE1Pcft+n+Myn/p1Wa4koFNzxhbt474ffyA3X3k4UGP7y5NP59cO/\nohZEIDSlUoFmdRnhZDFak2iFjSZWkPE8LM+wUPZxcejqTOFYFkkU4FiSrrzB1y4KgyAG46IQuHbC\nzEwdozUnnbiVmRkfYWKylkMgbVK2Yr5cxgjYsH411UoT2ygc20UFFl2dGRZm69hOQk93J7VagGMl\nBC1wpE1XMU3Vj0BIavUmSmkSqag02v7e3u4upisxYaIJYsHUXINyeZmJmZi6r7Esh0ZkI6IYYeUJ\nWjHr1vbTbCRgwLUTjGXj6TphVKZgFM8/doh6WGVoqJdSMU8mDVFUoVap0d2dRhqB0BaWpVBJiMSw\nZnA9leUyWilSmXYd1BhNox4BmoHBXmpVH0OCbTlEUdyOLv0AYzSu66KUxrYFlUodaWwGV5Vo1CIs\nYYiDAEtYQEwcJQgkaInARtiaZjNBGU0q5dJsBu2SWRS069ipFMvlOhqBsR3mFxosl33my2X8hqGn\nt5vFhQqFQqEtzkpx7NYu9u2dZGq8ipUJsRxBrC1agUYZ8KSN6yZ4doqpmRpGGIzWZDIZms0mWre/\nk23bPPq7A1hIurotfvvb3z8rF8GfbQS7Z/gQb3nDhdx9522cf/4lTC/McfzmzWw6XrDzoUle8tIX\n0WguMzK+xE1fuoCLzrqCmg5pJg3q5Qy33fZxzjn7Wq7+zCUcHJ3ivPd/hG/+7Ftc8bGryOfzPLRz\nD5s2dENYQGRDxnaNc+qpL8LKVsiX0ti2TbXi88T2wzzvtF72PDZJ/8Aaegc0tpVmYW4eL9VNbGK0\nksxO1+nrSSGFR2dnJ0u1ZZQKsJ22WBudQgjQVkB3Tw+V+TqIBMexj3rynomy+vr6mJtdQsg/rHDK\n5lL4Dd02y1up9g+AVGDaZfRsNkujWcMYhRQuBnU0/TNGk82laDbb/r1novdUKoXfjBBSkclkCAP1\nR9G9YvWaASYnZujMGHxfElsS7c+TSfdgW6CdEMuy6OnMYYsOXv03r+bee/+dqh9S6HBotSJqDYei\nB9KT+M0GmDRKKdLOAIvLE5z1sXcwOjLDzkee5pxz38eFn7yOaz97EWHD57OfvZ6PX/p+Lv7I5/B6\nBUsVH5kobNsm70TUE6/d1TqCMYZYGEqeYKkpEKq9kszIkFYIxWIRx5+jJtoT0NEx69b1kSJiZr5M\nxsmwuDRJojrp6smwemA9O/fuwmgb1/Xwk5BUyoLAQxtNT7/L9HwDrds/Ho4rcGSIClMYG5y0TbMV\n4yQBlpUn0YKNm9a0PbsyabscANeTRGH7Pm87aS1PPzGLtEOksFGJIJWN8RsGIWz6B3LMTjcw+EiR\nBgx9Ay5z0xFSOhhRxajMkSgzCzJk9eBaJicnAXOk4ajpH/SYnFSkZUIun1Cv5sh3Cxq1BK0Va3py\nLFZ9HC8hm8qRzefJiJClWpkkhjhyqfkxqayiXlF4XoauriILC0sEJkJgcHUB22mLcGL5rFnXy+RY\nnThpYdtgjKa3t5f55RrGaDzPI2mFOLaN1opERXR3d1Jerv+XpmWuYOM3NNpEbUFPBJatEcJCJIZs\nR45qtUrGyRGGCaWegGrNBqWxLbsdUTse0laccvIQjz8ySWi3F6e4rksct0tKzzRLW60WhUKBgcFe\nZmfKLMwv8qXbPvP/7xLBJRddy9h0jdtuu4w3/dPZfOXL1/HRcy5mbHyE73/3bh57ZBfXfuounHxI\nbzpHkrUJWyF7H59nYG2aaqtKs6nQJk1/qcQ9d9/Av33yCnaPHWbbtm1cefXZvPxlZ/Les9/EL376\nSwpeio5Smrm5KTK5IkopBgfWctYH38aHz7qSD537Dr52+/dIZypUKz5SOjy+bx9/cdKLmZg8zBvf\n9Hp+8R//iZuuIoRDM0gYOdQkiits23YsI8PzHH9yF489Mo42cNqpW9n19GHWD/XhHBHhZzrdxhiM\nttqNhuXldlnCMgiTprunyPzcclsIiZHCRUrZXs4qNB0dhfaANAnGKAQOtm2RqHYHNJPJ4Ps+lmW1\nBV1JSp15KpUKGPtIM0FjWZJExwhjkcmlaNQUUsbtCCB26Op0Wag0AMhnBX69PY6krZGRhbBChLBR\nwmZ9f4mxyTmMsVmzvsjESJ1c3iMI63jSxmgHJ+viOh6ZvKKyFJOyXZaXl1iVkswnPk2dIuta+IEG\nIUmZBoHI/fGYQSlFhKbkCRYb4Mp2imlkRBgLcrkcgznD8FJ7QYOHooXmtJOP4/HHR0FFCKuOoYfO\n7jTlchUrkawdSjMxVqeYtRHGoRm3MKJJMT9A2rLp7s2itaHZrDM+WSdXtIiMob7kk0mlCaIWBo9Y\nh0jhopTimK1DHDo4dmQlm0Al7e8ghYvtagZXdzI6vICwAlTsIaXAS4MlMvh+SKLqCFIgDJj2ktTj\nTxji6SdHEEK3V5eJHMKKMLqtAxuGBhkZnkIphetkiExMWhh8BJCQsiyiUOE4NlES4QmXvlV5xuaW\n2yb/pIkiR67g0KjF2EbT01tkYb5O34DL/HSMsGJ6B3IszSmEiI7+/mVdSKc66Oi0yWTyVCrLtFot\nZmcWcbPtxTZaOSgJiVKo2EbpGGgvYS2VStRqNYSQeGlF4AvyhXQ7ylQSKV28dMgxG4fYtXcEKSWF\nTIpKpYwgzfjUPFs2raW3t5OZmRkyGa8dcDhplIqxLQvbtturAh2HVquFZVlHFzqEYUizEWFZ7ZLM\nbV+49lkJ7J+tTeuXD+4m6xV559vOoRXWwAqYmV3ih9/8d7QJ+PrXvscnLjyTY7ccAwUblYQgQz59\nw7/ynve+my1b11EoZNiyeYDXve5VeIUAnRVceMmHec0ZL+aNZ5zND3/8We655W4Wxia49qYLWKpU\nELag0ajx+CPjLM7O8ZsHf0HGk1x/9Z246RqNRoCWDg8/upcXnfwSNm3ZwuS4z398+9d4OYcgMbRC\nm4N7WgwNreHfrrmYA7tm+NI3bub3vx7lvPPeyTvf+hZ+86vHue7685mZaOFaXQyPVegqWaS8BAuL\nJ586wG9+ehDPabJmVR8DvSWq8xGPPTTGqjUW4+NTSOFRyIFOypRKNkKFlBfmEVbIhg1D7QaIkKAM\nUtrkizZ+QxxtGrStRBG1Wg0pJT29JYAj4muQQqJNhN8IkFZIvpAmDhXogMWFKraGrnwRv6axpCST\nToOyMI7CTmVQ2AihGJ6eQzgptKUZm1hCpjTSs0iMSz2GBhWqjRbz5QXGJsrUWxFztWVCaZhJLBqJ\njdEJXV29SAuEVBSKLhwZ7Gk7C8ZBWwKMQxAeadQ4FjGaWLX9smEYkkr/wSpka0M+0ezYcZCEgNNO\n2YYyRYxpMT+/SKwVuJrDI0skRiAdh2m/RbWhCHSRycUK840Gjz45xo7dk8xXAqqJolGPqC01CGKX\njccOEimHOA7JpzyipEnBC5gcGUaohG3HraO/O8WJW0o8b+sAAwNZSl7A8tgMxTxkUlmE1FjEqDCm\n1SzT35PDsz2MAAcbiwzaWOzeNYkQNidt24q0HIwMCcMWGAfLhkMHx1E6IV/wUDrEIiTBYGtDd0eW\nJG43tlpBA2EkxjbtNfzaYmjtAJo8QmjqlRAB2GmPxbJPbDQT01USAUESMzHuE8Qxhc5uImURxFBP\nLKbLDUZmKzz61GFGxnwWKk1CkScME1pNQSt26eruIgoM2ZRAaIUj0uS9FrXaPJ4Tsn5dnq6ODOvX\ndZBL58hnPY7fUuKlL1qDHQsOHDjEqsEcWgtqfoB00yiZsH4wRSaVYd3qEuvW9+D7CQmSOApwHQfH\nadfln3keRqnUngvl5TqVcoNmIySV8rCddqbybPmzFdjv/uAixoZ3MzJxCH9e8Za3nEetUuOv//5d\nvO5vL+LRJ0d58MEf0VlsUMxCRzbD7ieq3H3XF7j9S7ey49FFjlnXxfLcEsOHh5nYM0oum2Wwv8jM\ncMTfnfEKrrnmVjYcW+D7P7qTi869iWIhjSXTJMrj7i9dwfWfvYCffX8Hn/3s+Rx/fB9Kx8RHvD+v\nePGLuOKKD/DU9sf5yc8+gxJlXM8+Yj/RpBzDTTdczuXn3sTb3/0WzvqXjyNNBhNmGFq3jhNOOJ3b\nv/B1luZmmJlZ4Bv3XY+KM9QbNuVywite8lJ++uBtCNPB8NgcIwd9brvzYu6861J++9MFPnn1hRze\nP4rtFEl0hsjXCMvCcrLsfmKY7b8dxjZpSh0uKcdm5nCFhx+eY+3qNDNjkvnZBs1GxM6d02RdFyLN\n9FQDy/HJ5TN4tgIdIUwGJTU7njzM/GIZ29h40mt3WGPBYmUR2zHERhMmAmTbXhdFIdIIPCeLbaXQ\npl3zlsLBGNO20SQJnZ2dRC0HRILRkiQ2lEqlI+vZbaRnYdsOjuMyNbsIAnK5HAFZUO1SR6haSFsf\nqZXro2MoCAKSJEGrdlZg2zYdHR1tZwHgSZemMBiRYJsqDz2+E2VCBtcXkCKFZUKSRCOFR0feY6Ec\nYClDqbOIiQSoiFakSByL7qLDYjXEUhphp1AmhbBa7NgxDkawedtqKqHCEmkaiaGpLWwLdh1YZHwC\nWqaL7XsmmZxaZLHpshwJjCWoVY80hDodoijNwJo+ZuaX0UiOPSYPKYtMyqcjJ+jr9njepgL1+gzH\nb8py6ra1dOegO92AJKCYl3QWU7TqDVzLkE8V0HGI9AIWFmNi4bK2fwiHDFp7RKqKkTHCgsOjsyQS\nLFdjpCLRMX5Sw8SCTUOrUMJDkZBOZ9sZlfFZmC8jLc2atYNHvMA2QattZetZ1UMQuSASbK8TbbkI\nGTIxuow2ht5V67AcG2NpIooImcFyM4xOlJmeDVmcrrNqULB+KMP+g/P8/qE9rFrTT6QEc9MRlrTQ\nSkJsk0un8KxO1m/Ksf2JfYxOLOFHTaIoIpPJHC0DxHGM53kopViYXyYMEoKwge0K3JSNIy3Sdsgp\nJ2181jr2ZyuwH/vAzSwtLbF61Rp++NCdjI1N8M//8g88/5TjmZsf4577LufwwVl27J7nwFiNn/96\nF1/+/tUEooDxStz3vatpxVn+5b3/zH8++ijvOPNcutet5zNXf40vfekebvvcN3nyqSd5cscE1177\nOZ56eiePPTSOm24yP7fMT376TS755M3U6svEcYMf/+gRpmeWCMKA9evXc92NF3DJhTcg7YBLLrqW\nTCZNebkMQLHDpbdrA2e++wIKRZs3vOl0urrz/Pqhu+np6eYLn7+FO279OH7N57LLLuXiS8/mkouu\nahu4DTz/Bc8nm7O56KLLCcMQ27ZRicX8tGD7o7vJlrJ8/oIvctyx/cwtLAGCrp4CKnIJWhb/dMZb\nue3OC8jns8zUqiwFIW844018/dvXsevxOl/91mWcedZ7Oby/wY9/+i2W/YRXv/bNxJHN/qebbFh7\nEn7doqOUQVgRu3ZOc/PN1zKya44164s4qRael2dkeI7lpYDlxYB9e2bwg0WKXkwYBti2JJeziFUd\nI9pdXa0Mcdy2Szm2g+d5VCoVbLv9/AgpLVKpNEtLS0dtdHEcI4Q4au627Xb6Jqw/Mq0bTRS30/52\nqSA5Wk+zbfvIw2ba/5/JZP5gehcJOdsw2JnBElkGelL05CRzh2dwRY3BwQ0YbTBGU600UUqRzWRo\n1JsYbcik00RxTDaRzNUN2mj6B7P4ftRehGBJBIJEBxzaPwvA1uM2oHW7tqd0SGJqSG+eAwcOIoBj\ntm4hjhOM1tQqIKVAKU15yWC5MeWy3/aYSsnh/TXCOGZo42YqoWFmqcnThyY4PNngwFiFx3dPUQ4c\n3EI3LeMSBRK/ESCEoKPk0fTLeClwtSFj1UirWZYqI+T+L+reM86yolzfvqpW2nl3ztM9GWaYASTK\nICKgKIp/8RwzBoI5oKBwEERykJwEPWDCAJhBUUwoSg7DMDl193SYzr27d957rVVV74fV03i++X7T\n/W36N7One/eqqqfu576vJ6M4Yn2GQ1cv4XVr+uhuStDVmKQ15WJKVRpj0NeeJqbiGAoM75siTkBf\nZ0fk3RWCRCK+yA6Ym8stuHUyKBVpzSOjEXWstbWVSiUKLiilsO2oZzAwOIBWSbINHmGoEGhqVUXc\nwIkbltHc6rDllVlefXmC1tZWpEgyODAFyAXqml4IFSjm82MoMc/endMoZVBKkU5nSKfTi6S3RCJB\nEATMz88v9CoEQRASi8VIWj69bWmSdplYrEp3d8u/vI/9226wZ539Hk5680ms6OvlnPd/nl8/+hPa\nGrp46q9P0dEd5+JzroVYBLoYn8rhxlJ85dOXsW/rNO2NDvfeeg+7dw3x0+//lr6uXlKZLja9/AIv\nbHyVzr40Tz77A2Zz8LM/PMDjj2/k42d/lN/+7h7+8ZcRbvzOJTz94jCNPc2cfvp/8YH3XMAjj32f\nsdEcu3YMMzJU4LxPfwXbK2HsAM+qk22KsXJpHyuWNZNyJOuP9piaHqGtM8bXLrqc1hZBfm6cSy6+\nhqXLWymWyrR3pnnjyWu469a7CSohtbqhPZvlv08/hQsv/HxEhlICSwQcsqoH4jmefuoF7rjnq8Qy\nkkItRADSDhgdzxFaFsmYpFQYZHTvGJVKGdfEGdgxzM8e+yG/+8nzvOktR/HTHzzJ1V/7Bhdd+DFu\nufZ6BnfO8p1vfY8vfvls+las5r1nnoqXUMznNeWawiiXH//oYdYd2cPQyBz5omHr5n5+/fu76Gtr\nYXholt/8+lacIEmhUsdxEySloVKpElQDHBFSys/jWZo9/bNsfXUfzfEEDQ0eHd0JVi5JIkRAIV/j\n6Zf7ycRTpOKCSnGGMBBs2TLJ9HyOIKhTr9UJAxlJBQsWHmmFhGHUEHTwcEz2/6ZtRADGQeDS2NiI\npcHSoKSiXk3RtaQTLRL49YBa4ICVJmYFTE7tIx3TtDd5CEvRlDZ4hCzpbuDYo7pY1tXG0s4MjW2G\nziaLpB0yOzFDU4PNiuVNuLbA9SLrkjAhMUuwt38Cg+Sg5Suok0GoWORkEAY34THQH8VZvZjAGI2w\nS3huHK0smpqylEshUlqUKzMoIXGATdu2YWvBYetXYqw0qDo6jCMQxDyPsakirpBYMRtjxdAWzM4p\nAu3Q2NJFueZQVzF86VGuuFTrDpt3jLNtV5H5qmJyPmA2XyOXrxJaSfq6uxidnMeyFFJEDeFkAman\nR0nYgt6OLE2pDM0NcVKpBC0tzbS1JCjMTdPcaCFNlcZMnHisxszkCLYd0rOkFduykdLgWBZGxEil\nNKWcjyU82lobOPqoFcQbbJ55ZojRsSLxpCQWjzMxM4u2FMKNDlzHSiCFRVJW6Gj3iMlGApmiWK9i\nx7woCl2rEQRRYtP3fXKzBcKAKCptXJJ2wOrOJN2NSVTokMkmOPzYw7DdJfzlic3/8j72b7vBbnzh\nBVavbiU3PU53Z4bmJpu//P5RTj3hJB793YOsPaI7CuQoKOaGCApFmpsauPhrZ3LHHTcyO13mib/+\nhDdsWM/IvmEe++M3CYoh53zqDL557y288fgzsFzB5z76Zf7rjLdRyM1x9devZ826lVxz3u3MDA3x\n6nN7eNd73sAxb3gz533+Ypb0rOSgVUdw+dVfwJea2XyAUpLG1qUUCgH79g+yc9cIE7MhD/3sKa6+\n7SISsVayTY0UCpoPve8yfvSLu3jp5VHeetqHeebZQb74xavZtHEvu7ZNks1WMdLw6GOP8OB3HqK9\np5FkJsvIrhLnfPId3H/j7dSr0/S2tNPYZbCEAV3FxoFQ0JRwuOO2q7jwf77Cvfd/HyM1Dg5fOv+9\nfOd/72RsYjdOVXPMUSvINLRw6ulvYGIsz733XcU5n3g/13z5Dm669TzuuPpWAm0wxmfPtlH+/Kc7\nCQqSWCKO63ps39bPwauXc9Z7bmTjpiE+//mPcdMN97NseSexZBOgMU4MYXm4sRS+cqkFLs89M8Dq\npb089PNvYzsWMzNlJoarDA7PMjRWYPXKw/jNL79NsVimUC0TkmLLlmm+95MbCOcb2LpzkoHdeRA+\nrgPhghygTYBlRZWLbSkCEzXwwtAQEwYjI+ZsEARUFtCSymgc4RA4czz/8hBCaKomRjEMCURI2biE\nYRbcNFNzigCXqkgyH2gmp+Z5dtMwO4ammc1XmJp0mJwsESqNIU2pWmPvwAwYh662XpJeivbWLJYJ\ngAqZWJ389DDLuzyOXtfJ4YcfzuvXLKEp6ZG0FUt6UuigTjYuSMWSCFHCcWrMTExhC1jet4RUrJla\nVWGCAE+msYRm8+ZthDqgq60TbQShNhjpo4RN2pMUKkVqlQqpeIxaEJBIxRkfm8GyLNYdugppous9\nVp7QOGhTY9/eEkpH4RwhJEqHbB2YxFgxUk0dBBh8QorVGIUalEPD8MQ8E7MFHC/BzFSN/r1DDA7O\nky9IYsk01arN3HwRRALbTbGkN8XE/nFcoVnalyURt0nbZRLSo7crzglv7CCdlAwOjBIGilhMI22b\nQlXhL7AjQIJxsXCw2Muy7hhFJdg7XEI5EilttDYUi5E+H49HBK75whx+YFC2hSdCsska7e0CV8aZ\nLjqsPmwlRx93OFNTFf7xXD/zBeuAcedfev3bugjOPfcTqFDS1ZFgatxHSZ/WtiyOlUIGIYFlRfaJ\nrEdutkCxWCGVSRB3G8jlJkimMnzmU5/h5ltu4WMfO4sbr7+H1x3dx9D2MY46dhUf+tTn+Mj7zmb5\nyk7uuutW+vft5cbr7ue2m77OhRddS1wJbrjlCr5w3sXc9L/XcOP132Lzpn5+8cidnPfpC7FdF18F\nWHZ05Qh8Q1dPM9OTRQJf0te5goGBflINJbxkllJBk043EJca15Xs2rqfm++7hDNOu4jbv3cpv/3V\nnwjK+/CrHgZNLayAThLzMnzgI2fw8PcfplrLIVyPFT1J+veXsURAa0uKqcki9Sr075jm6KPXs23n\nHnK1Iqe/9Sg2vjjIyae8nn379jM4MMuDD1/N+8/4NNrNkJubIZtM8Ogj3+Eznz6f675xKeVClft/\n/BBTU9Ps2TlGZ0cfbzv1OAaH9zA2MYkQkm1bBvn949/igs9exdLeg/nK18/kc5/5Kp7nIRybeDzq\n7ioV2b+q1SoCm662Li74ytlcc/kdTOZzOLZDX08b+/ePs3njKLd/6yp+9MAvyGYUQxMzTE0WmZ0K\n2HDCkex8aSsrVq7iyhu+zDfvuRN0jeH9eYTRSEsThi4hddJugmrZpy400rLwVJmqFUf5IZ7nccza\nFM9siUIpbW5APkgQKk0yJSlXQhSGJV1NTOyvYntVAt9glEMi6VKsRekiV4K/IPXGrSRhGNK7tJWh\nfRNoSlgigZIgtEIKg1YOvX3t9I+OIbQhZmrURQpHmMWD4KAV7ewaniJmQz2MeAUHr+plcM8QQlQj\nQDRxLLeMXwfXdljZ18L+6QpNzU04jiTmlcnl5sgXXDKJOOl0ioH+UVJpm1w5xDYO6axDsVBDa4V0\nYiwE3JB2QOhbLF/Rw0B/dH1Xuo4lUpH5fzqSvxqb0szlipETYcHS1Nvby8TEBHW/SoR7cEDoCOau\noKMry+R4Hi9mEQQ+GIfmliyzs7M4joMxEbg75mh8ZbBsm7asy/7JIdLJdkIl8LwYtZpPXWp04C7E\ncSMZwLINKrTx1TjLelYwOV3F90MQPrZtL9gkLRAax7EWAxfGGKiFxBJVmhuamSxFSbWYbfP2045k\nZGSEnbtmMSZy+cQcRVdPK0ODY9x8++3/2Tatcz/5Ofy6QFoKKaOIXbFQBUCZAo6dQghBpVLBcxfA\nLAt2JcuyoodjAchRrQS0pl0q1RqOHaMh24a0fZSsI0lz8LI2XnplF0op3nvm+3jit3/hyxd9jqu+\nei1lS/Cxsz7A97/7EDfdehXXXn0ztXo5EsWNRoiIpC5NHS0iY3ZlLuSe71/JlZfdjWvXKVarvPjc\nXp599mGe+vs/uOuqn3Lvj6/n+q/dwofPO4en/vo0c4VxJqajGKNt22glee7F3Xzls5/m+Zf+Rkdn\nFNv1teEA8N/z4tTqBYK6zc5t4/z8d/fywbd/kcOPWMVXLj+P977zE/zksXu57pLbeOup76J7RZqL\nPn49P3zsSi75/O0cu+Ew3n7au3j4J99mfHwcLSTxhE2xVCKXDzj5xDfw0A//hLQUIHnbOw/jd3/Y\njtaGE45bzkuvDHDxFz7DhhMP54sXfJF4PM62neNU0By+agmZVJr5YhljDIcc1Memp/bwpncezVPP\nbkFKkFa0MDCGW2+6gbJf4PKvX0+oqujQYdfOYX7z2K187P030d3TyHW3fIkrL7mJIj5rV7axc880\n0ryWUjLGYCmXuvAJlMFxIwiMFK8BW9b2SnaMRnrtqmbJYC4i1+swgeNIbLeKX42hjSGRUuSLEbfU\nMiEBms7OTsb3z+JIheMp6lVQuhY1xSy5GEfVWmMEaAGexQIHAVauWsbuncMgAuo6XHBBuFTDSLuU\n6rWfx7IsECGNjY3Mz0WWOMtWhMHCAyCiuG97Z7SB2UikpfCVIJXQ1KuSUCsScZtaFdo6UkxPzYER\nHLyyg737JvEcE4GwLUk8bdHT1ku1WkVbmuHhYbQOmcuVaGxrw/d9PEsQ+FAP8pgFm5wxBseJ0dTU\nzMT4OAZDPG4R+DaOCAkCQYhC6TqO40QHcClan719PQwPDyGliKpxx+f444/j6ee2YLRk1UGdDPSP\nYkyc4J/gPtF/HG2aybjCswzVCtQAFUabZ6VSiby1YbjQzAIhJMbUgTzrVq9jYGAfqEaU9AlVidPf\ndTzD/eOM5yqUSiVEGLCst5uxsTEKdRF9Bp73nx+VPevcL2BMFSniGKLOtDFigT+gMRRxHCeKwxkb\nISUCQxiqhVMNgiAyTWMEcUdS0xaOC5VygOvYtLXGmZksUa8rZExiWZLO1mbGxgpIS2GhcJwk2UaP\nXEZmeqMAACAASURBVG6auNNKoAvU6hVcx6KtqRnfUiSTMaT28HWFcrFET2cHH33/J7n22mvwMYTa\n55ZbvoEKfC69+CZuv/UaPve+T5Ja3gJ2M/n5URR1kDF6OjsjapOwqBcVd//gVi78/Pl4sTi1eo2O\nrm4mJsYAgTEhGI/+XSOcc9YH+dVDf0UFc7S2Jinj8M53vYsnf/8sG/du5ooLr+Yvz/2B7qYmxkYG\neeXFYU5+1wYu/erZXHrFdRRLRfxagMZCG8H2V/bz+mOXc+11X+Pplzdz2PqDeP87LuAvT/4vpVLI\n2Wedxy3fvIZLv3AVx514EFt27GF+rkxjcxvT42UOXe+SK/hIrQhVlXiig4SleeLpQVraMrzppNXU\nixXq1Qpt7S1s3z3Ljr0jFPM+J7xpJc/8vZ/eJcs5+2Pv5vnn/8rBa/t47um91IMy1bDOymXN9A/N\nIzERhjDQOJYNIdQWQCFSSoLAR4goay+EoDU+x0w9C8ARy9LsHSrjeC7FvKarCwplgwrq2LahVqsS\nTyWwHUlb5ypsVSaXL2IBgVAM78vR3tbIfHkG2yRwXY9SNQQdsnrVSnbu7UcJg6012lhI20IYg1IO\n3d2t7J8cwxCliWqBpr2zmZnJaYyCZDJOpVJGSB1Fc50YB69dyc7tA1EhoQMk8WhCQFjGsVKsXNnN\n4J4ZAlEGS2KMS8qFUj3EGIfmjEWuVAIRYJk4GkFXVzMT4zmMVjh2iXoQI5lI4pcVYWhIZouU5m0y\nSQs/UGDHOejgLnZuHyEVN4SBJgwVyZRNve6TSidYvmw5E/vz2G6N6TmNkHXC0KFQypPOJCmXqxgl\nEEKiUTg6w9JVLi2tzbz0zF4sy2Hpqk727tmD0I0Ejo/AxkYTUYtFdHMRZWKJkKDSRCACVCgQBLzp\nTafw+ON/IJVM4sVc/Loh8Ks0pCyO33Aof/vbCwT1RnTMRwUhRpQ5/e3Hsn3rboaHDMLUeMubN/D0\n00+jZYZCpboACI8A30EQcOdt/1pU9t92g7340uuZmBhBSnuhzI86s1FENaSlpYWZmZnIikEkbjc0\npijkKwt6kTjwXgtXyBApXLTxkVLied4iqyCbzS52iV0vev8DfAKtJA1NceZzVYQ0KBVt6LazMJ5E\nKxqb48zNlkknotMyCGNksl4EkiEgnWmmXC7T3phhaibCsTVmPeZKAUb7JFM22WwKzxFIOxF11q04\n9VqU+56dnacxk0QbTSmoR5n6TJZSMSQIA+676xbOP/MrxHpSvPOd7+SB7/2A7hW9HLJmPU888Q+0\ncrjwfz7NHdd/m1vvvpDv3P8rjj5uJYevP4ovfeViAqLqr6ejk9m5KuPj01x59cX85IHvUa/EMJbD\n7Ow8a1Yv4/jjDuLJJ//GX3+/jURTgo5egQoclBNj00vDHLRiOffc8zWuu/Zm8uUS0gQIYQi0zfbN\n+/jAe97Gez7wX3zz3juYnvGxKIBKUgw1AwP7+f4Dd/Ohd5/Pw7+8nQ9/8HOc/JZO5nKSSiVg185x\nhKM4an0zrptmriyxpMCx69SMh6mH2A7UlASj0Oq1Rpe0FEopUmaCitWF1pp3bFjNc6/uZa4SYfe6\nGjOM5nI4whCGPo6doCkbY3bGB1thaYUvEyTcGlXfxWDYcPRynnphJw2uoa40ylLERY1q6ODicsia\nVnbsnSGTtvHcBMlEE4l0wGSughUqtDZMjs/hxSV+HYQVJ50KmZlU2I4G1yVYCKEYLVFK093TxuRE\nLgITOQIdutheldCPxMH2jmamFsIBQRBE8JrWBNNTcxjq2HacEJD4CO0ipcOq1b3s2T24mCYUQtDd\n08LEeA6tDQ1NCQpzAcgATLTmsg1J8vPRDcWyFa6VpF7Lo2wJOk5Lk0MuVwEhECbEwqGnt5nh0Vls\nEVKtVnndkQchZIX+XTmUhqXLOxganMJXEq2jzfSfMYcmVCTTISknwWwdjLKQRCyDMAyJJ5zFiLvv\n+6hqnQ3Hr2frjh0E9QQIibAkQd3HEhXeePLr2LGjn7Bsc9Cq1fz95c1IFgDdVmQVtCxrMXxwYE/5\n32/d/J+9wR53wrs57bQjeerJXVRLmkPW2pT82ELXWGOJBBBZOxxXsGHDBnbt2cx8roJaIO5gLFIZ\nj2K+jhDgxRzqtagLiwjBWItywgH6VqlYQ5sozYGx0cZflB0sGXnkpKUXr/GOKxZjfAe0RynlIjTD\nduDA7L14wlnArBlsK4bSAZ7nUK8pDApjohis7fr4NQvLSDq7s4zsn6M1Y5Gf96kJQzYlqZZAOgGO\n8aLst67ipdtxHENTcwOBb3A8w+6dQ3T3dLN/fJIz3vk23vqOk7j2kju5+KrzuOvW+9k9OIJwBY4X\noJWFrttce9lFkKxx5UW30rOil2V9fWzb3M+XL/scAzt28eCPfsrFl32RC846H7urGYSDKyWrV76O\nj3zkdK664eboYRSapsZmZmZmCUOHN5/8Bk4//UQu/eo11AMfrBApYtR9n5c37yNtp+hb1kg+Z9PY\nlOR1R/Uysq+fQFu88MwAK5a18uGPv4+HH3yIZMLC1zbSRHxY3/ejq6orKNY0RhG5BwCMg+1Eh4io\nTkCiE601H37HsTz82IsAJFKSYh4MNZIxm7KSOArqgU8sFqOru4WR8ZkFp4JEKwsjKvh1jfBc1q9Y\nwvbB0cWBjloJjCgjhEWgHY5Yt4RXtwyjLE0KSd0Isp5m3rfB+MRicer1Gq0ZwWwpqu5WL02yY2Ce\n5qRFXfuoCjQ3xXC9Fhoa0qTSLkPT88RCwd7BXaRSkf2r5nvE4g5CVijXBHEaKIQazDxr1q5i+7Z9\nSAmWJ/F9hWVAU0VYSRobG5mbm8OzNH59AXaUTSwWLgee5wM6prReWzfSAq1DjHaJJTRzuTLxWGJx\nXR/wIjtScdKJ6xkcmGZgaByLOl2dnYxM5xdSWXJxpA+AARwpaGq0qFY0fhAQaCea4bVgC7NsQywW\no17TuNInFo/jyIB81WAQYOwFuHaA1CFvOHEt218ZoKdnKf17B6louXi4RGyFYJFJeyDdGKAXgVH3\n3X3jf/YGOzw5x7tPO4fTzziG88+7kNtvupahiUhodz1N4MsFVJvDyOg+urq6kJaNJWFubo7mpmZs\nR9LYHCMej/Gn32/FsgLWrFvKi88M07usiaXLmpnN5fDcFFpFhCKjFyZpqjBKB5kAKSEeS1Cu1LGE\nRTobp1AoIoWDZZvIE5lMLKIR4/EY1cpC9NUyhIEhmUxSKM4hxGsaq5CGKApo09CYJZebQQoXYdUx\n2sKzFeWCg5u0sF2bek0gZR0fhTAJWlIec6U5tNG4RlDHRusKjp2MKu/G1xaGCWrYTpy+pW3sGhjH\nNiHprGFuStPRpijmLUQK0AGd6T7a2lxyEzF0skBhRqHlPP9z2aVc9dWbCJnkENXMXHsju2bHSBAn\n6SX52Dnv5p47f00goio9FnPx/QAQHNzby2e/+EkuvvRS/JpEG0NrZyPjY1HgYGxQ8aNfXcr7z7iE\nXz92B/fe/RMq9TFmJyuU63m6Wo/ik5d8lO/dfA8j+0dxbIUWHv884F1rjeVAJRCE9RDXE7S3tTI8\nPIltGxDQ4FaZq8cQQvBfJ6/lkT9vixa+Iwl9SUdXE+MjUyg7RTpRoF5zgGixB0BzcxP5mTmMsfFi\nmsA3ZOOKmbqDRLPuoDXs2BHp1NKuY1Qa4frETIJCdZ6u1ixjBQvPVEhKQ1EL+nq7GNo3iyUFyQZF\ncS6if0nSSK/KuhU9vDowhKilsb1xgjBLzBX4gUFrWLkkzq6xkHhQxYQRB2HlwVl27Rgn5ThIxzBX\ntcl6Bep1sOwKa9aspVILSWZcqoUSWieYmZumUq3QkM2SLwmUingBSvkYLVm2bBmDQ3sRxsWyI6eG\n61nUalWkcGlqbmBmZgq0hxE15mZ9+pZ2LJr4pV3j+A0beGXj36n5WYQVsHKlxd7d1kLRIVDh/92z\nbNuiuzlFoT5Krpim7ivARCBrY/C8WDQPjwgEtG51Gy9vn8F1HAQaoyP7XhhAc4PgmKOW079jlHSm\nlV17R9FuFiPn8avRQX0gaICxFp6p16h71cBHG43jOP/5G+yHP/hZlvSs4Igj1/HHP/+CMJRgWyxf\nvpx9+/YB0YI6eHUb6w/fwPfu/yXzc6MctPIgtm59lXWHrKOGBmMY7J/k+De+kf/3gdP57FkX8vCv\nr+Pyi+5m6dIs46N5GhuTzM3PE6qQvr5uxibm8X2fPXvmsK0QrWqoIM3Jb+4lkYgRApWyz5N/2cma\nVW10drcyOTVBY7aVSqWCtiTVStSkMKJEKpaIqmoM2cZGcrlcVAkLF4i6tbZtY0mPVDpGoVD4P59H\nMpmkXIpYAq4nqVWj1m8EzbDp6elhYnwGg0JK0EpGs4tUgCU9EolovEZERooAMPFEpF8HviGZcqlV\nIzniwKutpYmxianoDypAijiuJ8FEwwibWzNMzUbfZ09nEzOTNdJpQb44h7Fd4qaMFU/S2JTBdW0s\n3UHSrjKeK1MozVOpFrFtD89xaG1txRIdPPPsXzj8iIN4z/veyR233YclPYwd4jhxivkatmsWxkSD\noI5wkthCkm3wmJ/zoxuAI/DrUA1CHBeOPKiJ57dNYemF4Yl2gbrJIoTglGN6eP6Fcdrak4xMFsiK\nOZRI4TgeHd1J/JohnopRrfiUgzqxuGFifIa420IqlWRqahpjXIwGpXyktCiGGsu2OWrdaja9shMh\nJUqXqBqX7tY08wvYypbWJmamo5BFqEJCHJa1NzAyMU2oasS8BHWtkZQQOoWUNmtWL2HrniGEhIQ0\nVOqGhpRFqWxQGJYu72JkaApDgBAQ+g493R6jYyWUUsTsGnWZwdVVjLQBl56OFkbGCmjjY8s6ihir\nutoYGBtB6YBsg0u+KFjZkWZwch6hDS2tjRSLRWxH49gOBkFzNolle9FEYb/O6MgY1ZpFZT6gozvJ\niW88mBde2EppLk2iycdYcarVGioEd4Fdq0KxKO05os6aQ1YzMDBAsaYI6hZhUI9+jlgM2wFPuCjl\nc/Rxh/DKqy8RVDMEJsC23EW2hwl8Djt8JYl4kZRJ8OLmPQRWA5VSHeG+Nire87zFChaipmS1WsWx\n4xFEaGFQ5oHwyl13XPufvcFeeMElXH71pYwNjnDbbXdSE4JKbYpMqp0wNOC6xHSFEzes5/G/9ZPP\n5zGBxYfecyg1EefpZ7eBsfD9gI2v9vPHP/+cr375SibGZ0km4zQ1Rlnk6ErgopVAmTKCyC+3ffMU\nzc0tXHTpJ/j6td/i/vuu4IbLb8J2NWFYZ8crRW785gU8+MCvsGPzVEsufe1NjO+foW4JBraPcMKp\n76CS7ydQkWxgqQDbdkmmPJ59eZzlSxvINLhUSzWE9Ni8dYi+njZa2zJ4XoypqaloNIYLtlRkMlny\npRpGVLBlGh2CoQ5EenBre5bZ6SLaluhQ4y5o0VorLBvS6XQ00VRaCx1VEV13FySOZcuXsG/fvqgS\nlB62I2lvb2V0ZAJpQXNzI7Mz+UVgeRBGmpQULsYYEonEwsKDWCxGrRpiCBavVdL2CWtxQhWiqeDa\naZZ3NTE6PEuZCh0dXczOzuJZDnUdJY5icYkOo6oimUyCsakHBYKaT2BspBG0tCWYnoykF2QdvxpH\nSYXtRNdYFUp8VSFlbCxXU1aRrrfhiAb6d08xU3BBe0AOTTyy8IjIGXDkuhW8vK0ftFi0/DiySj2M\n4sKHre3khR376G1qYnpmDE81gDWOslMQ1Dj+uKOZmi3RmHIJhY0fCCZndjMyUGRJXzejo8PEk60I\n7WPJFPlqgaOPWc3GF0axHAucAoEfx3Iiwhna4dBDVvLy9r3IoIznRWSwhCep+QugHidE6xhuUGI+\nqGLHErzh8OU8v2mIMADb9cG4OJahpm0sbdHcETA7aVA6IBaLkoPC8gnqFpYd3cJs20abEEtGuf2O\nzhbG9k9jRDW6zZkE2QbI5SNpJizN8ua3Hs3zz79KEHrELIGQAWXlLc4+k9Zrm5qQNjGjOeGN6/nL\n0xtRQeT+8H0/4sW6Ho7nYkQJv1SirbWFWkVSU2aREIdxiDkSaQccedQq6oUiIyNzlOpRewwrIpd1\n9TQyPjYHvGY3O6D1HphWXCgUIilPqYWDsIbneXiex43XX/GfDXvZ8uI+yrNzfOoLN1GVgmQymlGk\nlbfY7MqkM3hegou/dg7X33Q+gmhWz3PPbl7UTVtbWzls5Qq+dv5VDA8P8+DPv0G2IY7S0XW+paUF\nrdXiyem6Lpbl8I2bL+Wnv7qRiy64hXot5PJLv0lDUxSzLOVdfvyLbzK8e4xkWhDUolNuojRLkKiw\nY/sQxx21gTCITNyhL6hVFNNlzXS1yO+f2MlPf3kHJ5/0ViyyzFQ1z7w4yLfuu5HpMYvmliYmxqeR\nuomDVx/KxIhFNu5RLeSw/TwrOhtJyciDu+3VGbq6OlG6xnNPb6exOU5vSwbXRA/D9PQ0tvNa/j+f\nz7NtyzBhGDI0NLRI+h8dHWVoaGjR2nZgKsTk5OTi13K53OJobN+PtOnIxxlN+TzAzwSoVCoL+pWz\n+PAHdQdph3R2NxJzI5jG3rE8ddfHth1yuRyWZdHY2IgkgSSBX4v01YaGBorFIoVCgUqlghCvjdea\nnioh8BYrDPVPdp5/rkgAVPhalT4xMcv0vEPdhjWrmlAysv65MYM0GZRSvLytn4qJaE61Wm0h4hq9\np2VZbN1VAWPo7OygSoKKXaYUNlEMk/hOnL89u48te6YYmhnjuU3DbNu6l/xMApFspa2rCymX4vsB\nVlxQqhdYt2wJWzaN4cUVh61O4/hx2jIhLYmQQ5a3s25VBunPsnZZO+tW9GB7VeKpgFo1wHYDmlri\ni/PAYglNLBan0Vg8t3UQIQRrDs2ATkefxcLnlMpqZiecaKNOaYJalM/3/agh3NfXF41uEQLLMov/\nbmJiAsdxaGtrwxIpbEcxn6vS4NV5y5t6iMdTvPD0GK0tDdE8LeVRVNFv7cDcrjAMF3XPDYevoYbh\nD09uI6gnqPuVyEMtBOlMCscLOem4ZXiBIRFvZjxfJ1djES/o+z6aCie8vpUVPdEMufG5MpUgKi4O\n2DillFEhsKCtLgLGFyBISinq9ajJFQTBIgjmwB5xAPv5r7z+bSvYidkR/t8p53Pfwzdx59X3oZyQ\n0057O3/40y+I2RJtSxpcwwlveB1rDllPIpnEcz3+/MdHefrFvfQsaUdaOooWihiP/fY5lJ8i02Ko\nlCusWbuM+bkK2UaXmIyxZPlSduzYgZQWXkLyrje/hQd/9nd0NsP1l5/PdVddhlKaer3K6lWHMLyt\nQKxxjmoYcSRTqdTi+JnhnfP85NGb+Z8L7sT2ymhlsXR5B/sGxglDjee5fOJjn+Y7992JcolO4aqh\nsy1DoTQVefeMzb7d+4l7Tdx465e54947oi6xqSBtl1pFMjE6zS8evYmLvvQt3vGh/yadTPDje28m\ntNJopVh7yOsJtYLaBPtG+wmosW1jlXPOeQ+///NPGRup0tCQphbYgOaM0w+lbqrMzubQyvDcM7tY\nc8gSdu3OEfo2a9c2s3nLflzPZeXqNoYGp1l9cA8uBmX0YvfW6EhTS2fiFPKVxeGBSqlFkPk/oxWl\nJdHaX/QvSksvVsSVsh+h9xY6ug2NaUqlIjqoEwoXaaJu8QH8ouWE1CsxAoLFuKwQAj8ISFshhTDA\ndaKBhp3eBKOFDMm0hV8PsEyM5cu72DMwiu1EGv8BjKBSmkBIjl67kle370GrAMt1QdlIuw4IQqVZ\ns6KPnfvGkAIsrdBYrFrezcDAJKEJaGiMU8gHSCuIuuQ6TndnAyNjkziOQ1iror0UKRmgMJSV5Oh1\nq3hl807QiljMUDMezQlJseyDFrR1ZNg/XqO702VyvIxl2axa0cyOwTEaYy6FWhHbsvBsiKdaSKUd\nVvd2MzJexNcl9u7ZR1tbOxiXqclJ4vEEQgrqdUOtWiMWi+M4ipbWVgaHJ7Gd6HAJA3C86PpuC4vO\nTouDD1nFM09sx1chBx2yhJ17RtFKo0JrcbTNAbRkEARkUy6HH7GS557ai08d24pmkQVoEq6H0Qbp\nGTqafQrzCUrVOsZAqVhdGDppo2o1+nqXkcjU0UHA2EiV5pYYuUqweDAkPZdypYrteQRhLZoCoUMU\n7gK7QC0exge+T4jktgOe+gPvFYbhv+yD/betYC/4wl0cvHY5119yNxs37uT0006ho81h5dIlKCFB\nuVSMQ7lY4tWXXuGRn/+Mxx79KXEnxDY2O7fm2LVrlL39eX79s2d58qmfcMkVX2QqV+R7D95FpRjn\nAx/8MHufn0O6Rfbs3Y0Xk2x+dRfbn5/hxw/+lu1797B/Yozv/+/19HVnWLYkzd7+Is888xyNbTP4\n5MkkbfYP5MnPBWglSKcaeODhG/nUR79GIl6JYMCWxWD/frQ2LO1s5+ILPslA/6v4Uka/wHKJ737n\nUj78sffR0OiigjgyNPzu8R/yo4ev4mc/+xUqBIOPMhYiFBRzFb52xRd44s8v8MrL22hNdjE9tIPA\nshDS59VXBnjxuef55NnvYGZ+GmVcdrxa5H1nHsuZHzmF2QmHa274Mt/+zg0cvGIVv/ndrfTvGWFo\ncJr5WcnchM3Nt9/LfN7hff/9Tv70h/sZ2FvjRw98m65kM7s2zZGNt2PLKp0dWWJxh0TKsKyjkaVL\n2lixvAFPQmtbjK2vjrLxpS1s3TnA5m3jJFwLE+qFdFDkuBDYxB0LhxgYSTKZjnRhV2HbEnQKaWny\ncz5aGywrvhi4WLOiDQUoCXHXJ5BV1h/ShWcZHKH4wHtPxxUWnr2ApRMBQobkfGiOlfAsTTabZs3y\nBJbls3ZFE8cevhpdr7CkPYYWPo0pl0wwz46dg0hT4cij+pCBwJUapIMQHnFHsXtwDKEFq1cvQ+GC\npekfHUQLQUPGYq5cQ1mKmCtQIol264xNTWJZNmvX9yFdD1WtUTNVAiWI2XU2bd0BxqF3eRehjuEg\n8VGEysF2YGiyhDZFJmfK+MrCTTrsGswhjEfX8oMwNFBXFlUVZ2SqzK6d+3jsia1s3TZJueSjyTI7\nl2N8do6ytmlpTVKqapKOwdFgbEO9Os7+6RE8O4Jku65LT0cjB3X08MYjl/ChD57A3IzgH09uYelB\nSRzPZvuOCVRgLU7QFVJhCFAmoLMxxltPWUZg4Jln9xAKBUZiDLiuR9x2Cfzo2m58ycREnHzRp1wK\nqVctHC+BKZR507HL6Gy1WLk0TW60TM/KlcxXFXm/yqqeVuKOhV8pUa8HeK5DvRRgtI1fi/pYB/oS\nB244wKJNTakoHAGvBVl831+svv+V17/tBjs7M8nd3/o6sYTic+edzamnHc13v/tdRkZGFn9YYwxP\nvjBA4MXYsrvG85tyPPH3MUrKoXdZEqOiqunscz/Ixmf38507b+ZTZ36UKy+4nfHxcR7/xSNcce9X\nqFdtIGTzxgnWHnwkP3n8DrbvG+czn/8ga3syDOzPsW0gxxN/3s3937uRc889l47ONsJaE7OTkvt/\ncAO52TyD/RMUC5pLvno1c7kyr76QR7oFXn1lN9nGBKMj0+zamqe1pZsffvfPYOVJZ1Kc9ObjiTkN\nPPrgj5ib9ZF2CTdWZ/eufs7+8P+wb98+lFL09vYSjcOW3P/968kNjvK73z7N6zccymxhFy9tfGnx\nwbjhpsu58+6vc8MN36CQz2OM4dyPf5jPfvazfOm8S/ngR07jhise5n1nfIXVa9q46brvRKdzYLNn\n1yh9q9bT3grDg0VOPOFUXnppE5lslrtu+SHrT3xdlL668xKkbmZirECtAlIk2L1/muF90+zeOcJo\nrsQfH9/MUUcfyYWXfZULPnEu99915ULyJ6oStm4eYNPLu0lg6F3ajOXUGdg7ycvPj9DZ2cbOrdPs\nH50i07RAXDJlXC8EBEZHVUalEl3TjTE0NTWDgO7ubiBaGJs3b14kcv0z97OtrZ3mnrXka3HKRc2+\n/WW2D8yzc3iaF7buIXCSOHYGdIxSuYxMNVCVIdl0hm2bh7DcGkcdu5Z44JD1ijRkm2lPJ1m91CZt\nhRy5ppe1y9NkpE1Hc51KNU9PY4KDelupV+Jk7DCqVFWIsDRbNkbEraOOXY0O45E2rjyESRJLhoyN\nzmKMIZm2CWpJQh3NijLG0N3dsyiLVSrVRZlnx44dGGPo6OiIRsIb8NxIInDj84yNRLJHqRSglMC2\nLUaHo+ZlqqMTk3ZwXQcR66axYy3JZJKx0TnKRc0Zp59M3/plvLh1gt8+8hRNrS7CxNi0s0hZsajV\nw/+Valb3tDFRqPLEU2OL0BWIdHuly3T1NOF53mKFeuBqbowhmUizZJlNZ4PixFPWYlkxTnnzBhJJ\nh0987q3s2bOXvuVNVEqRn71cLhOLxRaoa5FsksoIMg2Set3geZFf/QBo+8DmCizO9jsQWjnw/Pz/\nufX/226wbV0Jzj77HNrb4Kyz38IlF15CJpOJCOmBRGqDpTySCQtb2Lzp5HWEJqCsDbVqmX2jM4RY\nLGlv52PnvgPpltHG5aPnno4jkjz++/s55g3H8NzfnkAJgdEZPvz+U7njns9w8Wcu54ZvfJ3vf+tR\ntKrhCgcZGtatW0NLspG//eV3vLJlAKQi9G2uvOxO3v/e06hUbBobs0xPG1537Hoe+ev1bHslzy9+\ncxPDAxVuu+9uvnz5Fzj17R/nih+cT226iZb2PnpWHMF1117PZDlPPJllz+4cmzbOcsst93Dmp89i\n05YBtmwa4um/b6M9m+KY43p46IePUy0VaWqSnPr2o7jjG7cwOTFLa1uWejHg0PXNXPm1C8jlphHS\n0JxOctbZp1GYn6WnrY8z3vlmliy1+OKXz+S8L5xFUCuhLIE0VT718Y9w4fkf4IYrv8m1V36atWsb\n+fH3Huehh6/npLeu59EH/8Yjj9/CNVfejqaAsWxsR1Cr+sRcj65lzbjxBlwMnZ1tXHbF57npc9EV\nSwAAIABJREFUyntZ0nUYTRmPztY2KkGV4f0FsoksZ7z7dLRlMTg8x+R0ja6WXv70x5v48x+2c/21\nF3PGO86gMdkaNWiAoCJQGOTCQhieKC8cPJqDVnViG80vf/0kNWVBWOO5F7ciLJ9Ql1GhvUjTKs8X\n6R/ej1I1OrtbqSqDpQNC3xBWLfB9BqdzhASsOPhgSqFBGCgrjSYNlsdzL2yjYPtYbpbpXJnpuSpj\nEyEbt+1nYnaWrXvLFOoKSYYgaCCXKzC8bxojFStXZckXHRrTNgTzuHaZmJxl9969NKdrnH7SOpav\n6uKYI9pY29tJb1eKpgZJaT5HNluhpzOLkJKY8ZkaG0Nqi+7OFixjYwKFUJp63aJUzrN/dBJlarz+\n6DXUw2izqgcOSgY0NWdIpRuwhSTh2ijpIS3N1PgsFi7peAKtBHP7R/CDMpde9gGWL8vwwx/+llI+\nT7ZBM1sM2T9RQwlIOTFEuNB0EuGCQ6FGU4OF8QW7hqap+iHlWgXbii2C0SuVCtooRoenF7VPrTUG\nMKLKSSe9jg/99zH0pJdx/OuPYMWyHrIpi6AaUJibZdMLO1jSkGDlymWk0yn6R8cWXAIGJcDgIbAo\nzAeUioqYl8BQI5NuXWQTuK672PR1XTeKDS9sutVqlVBLFM6/vI/92w49fNtpp9Pbt4z9E7P8/o9P\nkGlspVAqE08msXWZGgJTm8USARP7p2lqyNLenKYxroklYmijaW9QlPJ13vymU7jz5ltJJF1++oOf\ns6TPZt0Rh/LDB35Ifr6AxKMhG2PDCcexd9sIGze/wkXnf4pnnv47IQqtoSEh6W5eBgRs3bEFy7PR\nQYzv3n8NzS02N1z3A/7wj2/zowd+xdtOPYkdG1/htz9/jqtuupB7b3uAwV0zdGR6+cXPH6Cclzz1\n+6eYzk3T1riKM898I7965Df4oWT3nn6WLV/Obd+8gntu/yV2UdG7bAlHH3EM37j5Szz22N8Ym8wz\nPj1KVcDUlGLb9n3cct9tPPXIS7y8qZ8vff1CvnnrAySSHlrazM3X2fjKAMNbhtmy6e9M1+Z4+YWX\nOHhtC+VShVeeewGjfGzXp6eti/f89+k89NAvefHFbZz7yf/Hj7/9Y1IJl67eFq666jbi8TgdXU28\n+MIWkimJUg6JZIxSuYJtO0zlSgTGp7vNJTcS8vRftnLFNV/k4HUZvn7ZTUwWouvr+HCBb//sZh77\n1e/wTR2lauRHNA/8/Gp+9+t/UKwpPnTmSTz880eZz5cRUpFMNWCQaFNbHEdtYxNIBQZWLG9j175x\nYm4aywqRXgzb9pCOQ0Ma5isSeyH5Y0uLEilcv8J8zSfQAb3Le5nLl9EC7ESAX4/Gfs3O5zDCZkV7\nmtm5aJKqNhqIkYwHFEsKTIylKzqZns1jWZJ8uYAtHDKNSWZyESO3jsTXBssKGZ+oIS3BioOXMD0l\nwFIYmUWFAXUFe/bNkZutIFzJnsEZaqpAuSTRJo6QDvm8j+0kkG6IkElaE5pCuYYXD1m6tJ1abZ6G\nhKKrq5Ul3a00JBT5fJn2jMCzBY6jkbJGvRpAqGhqzlLNW0h8LByUFKRtTaWu0ZUan/vCOxgbK/LE\nb7agwkmIx9mzZ4JKVeDFvMgaFkYb6tz8HJl4hqRtSCV8Kn6cahVCHTWQPEcQc20MClvY2DKquF0n\nCiYIy8LCwnXg2Ncdji0rvLpxL22tLl48JAg0fqDRSBzPjgYlJuIcs2EVT/5phMCroKqS1gYHf2FD\ntC0Ly/bpbFuCBCqlAo7lolUVaUV/58BmKoRY3OTj8fji110pePr5XYwNb/vPHnr4yc98abEU/+dk\nR1NTE7OzcxggbirUZBywQEQjh9uaEuyfLeLiENZrGFsQd6OFWAs0zQ1p5nN1mpuTTE1FHfJE0iOo\nBLiuy7LlXUzOFEgmkygV8P9R995RllVl3v9n75NvqqpbuXPuppsmSBAESUYcxXnVMc1PUcHEmFBB\nohEdEURQ0BExoA5iziAYAAWEBpqmc66cw83hpL3fP86tgnmXv/Vz/Watd41nrVpV69xzT917zj7P\nfvbzfEO9XMS2LMpVwY03XM6NN9zB0Pgoti3p8Dr5xL9/jA9d8ikuv+Z9fOSSj7N+/Vq2HLOCl73+\nVZimwdTAKDd++VZsI8+BAwdZvqKT/r61jAyPcNyJx/Pq153Hr37wfZ4ZKmDpgFy2i1e/+gK+9/Xv\n8OVv/DsXv/Ya7LzASVc5+6xzeeihR1BGvCgGEoYBft0jLx0uvORNbH9mjAcf/C1dbRaRVCgpOLzP\n55vfu5Kbrv8aURgRobAMH1O2EcQVlHBRcUzKBl8Z1Gt1Dh+d5yd3f5lrr7iJ1RsyzE7XqNfKPLn9\nCIZMXAqEjHj+mctZv6yLei3Ctm3qjTqVRoMnHxvCb5qc++J+DJHlrRe/kTtv/yWzc8P4wmRqap6w\n6dLfn2Xtkg4OTkwQBibXf+oSnEyWO//jl7z5va/myzfcyVxxOlkeZhwa9aQup2gitNNqmhSpBRkM\nA848bRn3P7QHQ2cRMsFCqtjAsCQ92YCpSgqrVVc7edNS2ttdBqdLCDQjwxOEYUR3Twee5zIyNE8q\no6lUqkhh4XopSqFJXK9zymnr2Ll9HG34SG2hYxc7VSVoJmNNCx+hXbRWCeSLFKtWreTI8CCgsaUk\njlrsMmEjhWT9xmUcOTyEIVNEar7FiNI060mmnkqlaTYbCGGiabbstNuYmaoCCmKfQKbJGCE1nWSp\n61b3cHhgGgSYOiI2TFb3dTA6UUDqGNtMXAZ6ezuYL1cgkvT255idrmMQYMd13vbuf+H+P24naBo0\nVYWJmSICbxHFsYAkWdDnNUxNGDXJuikakSYImgidZKMLjaIwaiyqcMWRWOzkL/ykZczSJd20tZmE\nGEzN1ElZJZb0r8G2bYKgvgibk4bGshKWpGk4PPjwXk457wU8+JvH6WwLCc0EFWFLF6UaSGkSxyG2\nnSBUOvIdzJeqi+dbKBcsfJ+F7whArPjr9kGefOTH/9g42HdfcukihGhRWQgWtQja29sTo76/sUkp\n6evrY2JiYvEiLXQKBRag6ci3Otgt+l8QBC3IRoRtZcm2WZRLCRzDtm0a9ZCMFRFoiSFN+pe0MzVe\nIZVyqVQqrQepgW3lWda9nEzWStR4XInfCAiVYmxkHNdNcfwJW9j71Hau+OTVfPIzVyFJobWks6uH\nSy//AJ9432dI5Q3Of8mL+fkv/szNt15LEIfcdtO3ODh+BBmD0gGRNjFlils+/3GqQZnf/Wo7r7vw\nBVx12c0EqkAQSvY8McBNX7ucb3ziDtJruijrAEck13PhodDKRFHHkDaR1uzdPYlNljPP3ESsCpTr\nTaJYs/PJQU7YsoVP3vgRLvvQLdz0tY/w2Ss/RSgUjkzRkfeYmC5QmPN505v+ldPP2MytN9/E/FwF\npEF7NsfU3DxjE5P8y1vfwaP3/pm27CzVJoxPVzFFOyduXENPf4VqvY1URlOr1Bmfn8W0Apr1CBWm\nSWdNSpV4Ef4VhSUikcY1Tc58wfHc/9BOBArT0skY0haWFCzpajIwa2BIgRuB1PC8kzfw8FP7MRE4\npqYRQsoJE5NHy+KFL9jAo9tGWNpnMjbRaFGiK4Q6g4dPpqMdZWv6cxki5RD6DTwvzeChQbSTaByE\nkUmkKqjQRSlYsWIlw8PDmEYKpSNibeFaASo2iTBZujzD+Ghit7KgtZHJpigWC5iGQ1t7mmKxuGiS\nGAQB69av4uiR4WS8OpI1a9Zw8ODBRbp3X18fU5Nz2I6BEBD4ijWr+jgwOI6IEvSF0C62GRA2Uxhu\nhQvfdAF3/exhCOusWtXDkUOzKFMvmmMuaHkACAOkANMsIminUUuINf8nvhRYDKYJE0wmhBKdaBu6\nFvQtybFsWTeVQpEjgxW0SKyIlvZlMQ2T3t5eDDMmbiaqaM1mE9Ox2f34Uxx74rGsWb+Ku366DdMx\nKZcKrOxLMVuMyLSlECaopqBarxFriS0FlmVRr9cJdfKs/5+fNQpZjBPVRp2nnxrkycd+/o8dYN96\n8XuR2sBxTALfwLBAygSOo31BqC1ME7p6XKYmG0jDQMYVtPQAkVhex5J8vpO5+cnnFMrjRLJMGSBC\n0ukMjUbSQFkI3lIaJPTIBH+7YCGhVIyQErROlIBiQU9vnunpaQzDxDQswjAmVs1WIIcVK1dy5OhB\nHMsmijRxBMcev4a9e45gS4vICBGxQT7vUSwEGIR093YzOj5D1gTLzNDd1UZTNYlUjGlLVKgpFIoY\ntiaNx9Ufv4yPX3MDx524nMqsze6BfcSxQgjJbV++gZ/dcid7ZqeYrM2gBTi2TRA0yOayVMsRxCGr\nVq1kdHKMoFFn945x7v3zN/nAe68jnRaEUUgQCrrtHOMzsxTGbM5+1QZcJ8vg6DaCyCUMG5hGmqav\n6F+2Ctu2sOtzjJVKCd1QVCDOEceaPUdmWJ1bw2te28ns3DxPb59mZDTmZS8+g+L8PiZqlaSb7EZE\nvkfUgPa8SbkegqwiybBgDQIkMCCtEMC6Nb3sPDiK0GZL6jLJYE0pWN3RZLiUIo4DLCIMMU9Fd+Jq\nm45Oh4m5Bq5WaDNAR50Iq4COLTTQtyzDyHCIo5oklQmTvmWCuSloxnDimqXsOjyOZUfEkUGgNGtX\negwNBUirjqg3UXaarauWcmC4BFaVrRv62HOoQtqoYngWKoZcxiSXX4IRVsGCgcEJBDaV8jzZbIaa\nEuC7OClBM45plsBJRTQCgTIttixbyqHhCaSqIkgRCU1I0IIlaZQwkIZENCsIaaOlTXdvO1OT06go\nTXuuzlve+gbuuvNuHFuxet1ynt4zgmXk0MQtKqtoJSMaIk0u14FplvEDg2YQ0fCT5pvnuq3lNkRx\noojlOM7ixGgYBhqJrRK87ZLuNBu2LGXXrkMUGzbV8iyGIcnne2jUIgwzpjg/wvr1G+np6UTGBuNH\nBjn75c9jdGyY4mxAvrML14Hf/P4ZTjjteTz4p91sXCuYLSgClcD6TDvJUg1pE8cRmXSa3u5ORsan\n/kuTawETuzDJSSmYLxXYvXOKp5/8yT92gD3jrDei4xqVkuLk03tZt2wpk8UGruviWZJ8b57f/vIx\nmnXJySd2kM/nGRiZIJ3pY/vTOzh263pGhifJZttIZxI66ejoKJaZeKa7rkWt1iSbzWLZimw2S6lU\nAlhcKiwAkQFyuRzlcnkRvQCJsLAmXHy9WkkCtdIBlpkIRURRhJAxXV1dzM+VUdSJQxvDkGxcv4S9\nhycxtEKLOobIQtw6h7DYsGklB/YPI3RAU4FpWdhRTGRbCBK6ZBg45HIWpXoZHQn6+jLMzdTRxAih\nsGOLwDaI/AZOOpVcP89DSIXv+wlIu1nD930wUhiiTlBPQyjoWd7GbLFlG+5HvPYF53PfY4/xzmvf\nTmFyil/+/D6CIKAe+BgmScfb0Jxz5qmce8653HjzVyiXEsKBZVvEccDI0Cy3f/dLXPahL3LBOUvo\nXpLlp3c/zg23fQ4VhNz29e8xPjVFHFoYVkDga/L5mFoxRUiYAMXNJqjU4n0Iw3Dx7/7eDIOTZUzp\nIGQCYtfKxBBw+qY2njpcIwgauIZGhCYVQ9OZDamULEJTsHX9UvbuHwahMAwTrSCTSVOp+sRGxAnr\nV7Nr3wCGKRFAHCvyKUmhYaF1E8uBOLRphCVSRooYOPWENWx7ZgRBhNRNQsvC0qCVSdOEU4/ZxI69\nB1GxIu24NKKIjBFTDzRmaKFFgcjMkDZj6rHGkS6r1vRy5OAA+XaH2ZkySmoMXcJILaOvM0OmI4vn\n5AmbBSZmEuUtWwqm55LMN9+ZpVSKUFLhKotjNq9j6wkr+PKXf0JXdzubN66m0GgwMz2H1vJZDDPW\n4orS9QxyGZifq1GLBZGvWdAvWyB8KKVw3Rb21HCJomgR4QCQNhVaFjnhhBM4PDACRppqWdFoVrCE\nzboNXWitqZQicm0eu7bvpLe3H8cLOOMFx1Icm2SyMMeatasZHSpimhaF6TrHn/E8vvfDXxPHLt0Z\nh44OSSmQNJoJBtayLKLw2fHTqBaxvWyyv7WyW8jSn2tnPzYxTf/SPm770mf+sXGwgV9l86ZT+dMj\nPyIIPUZnqotQm7HJIr///U5Of+EL+fZ/3kK13GBgokwYu0RBlW9/4zaaZcWd37qdtcvXsmvHKA/8\nYSdawxNP7eXI4DjCiBkbrjFwZJr+3jTSiMi1uRzaN08YxKTTKY4cHsIxTKRSFAoF0CbDQ9OLD+3C\n9VWxpFqto3SDjs50C9heJ4qbyUB0XWZnZ0EoXCeNZYPtmBw8MoolYMXKJS1rZUWEBtNAyJgjh0Yx\npKCzu4uU5WCT+CqZOiJlGzQCjXRCQpKBL0zB9FyVWCpSuSyRtqkbMUpHSMsj8BXlUp2J8RkmxwsU\n5uoU5xs0AxstsmhlEKscWGDkBNPzM4k/lCkxPJvfP/0HArvKj751N3+472G8rEeus40tG9fjCI9s\nOiKsFJg4PMXnPnktlUIRz7JY2teBqQVaS5avWsWPf/AIy/ssHnxsgL/86RDnv+J8DGHziauv58jw\nEEGgaM+7iXGdoSgWLUJC8vk8yJis9SwQXEpJyjHBSTLzWnmGqLW880Ri/QGAKcl2tCcaukCsFEtW\npjmuL42hNN0dFp2iwb5D+zGtJhs3rUis4Ilo+kUEPg4Ru/eOopUinXJbTTbNTGjiy4iN61cQByZK\nxfRkMgRCQOzzxDMDoENWrOxHSw8zNlo6xRbtlsnTO/YgY9iyeQlhGGCoqGVBbrJkfRux5SLQSC+D\nFDYNHTI4OEIsHVavPgbldiPsDDK1hiAUTBaa7D8wzdM796O0SaGkKFYbFCoNMNvo7uikUpIYCt70\nunM49/xT2fXkfn758/tYsaobcDk8MsXMdJEwVItZnCEdLOmQTylOOKYXGTUZG2tQqsTEjRiBJlCa\nofFRQtRikErchBOpUNuRSBlhx4KsW+YFL9xIZ34pQwNl/IaJa0l01KCtLQdI9u0d5OCBYSYmp9mz\nb5SXvOLF7NxzGM8wsQ3Frn1zPP7wGE89so3puVmCOCb2BM/sfBxCyZlnnkQ1Dujq7cWvRQiSmnPi\nepzU4j3Po3/ZKpYt7ycKxSKL0fd9giAhuiCSZGl4YI7XvPz0vzuO/Y/NYF/7qkuR7gwd7b1oGkjT\nW+Qt+77PzienuPuH3+Kaj3+QXLaDUMHevYe55B2X8S9vPo3Xv/Y9dLT1cPMtV3P9jd/gus9fxLln\nvZvVa9byH3d8gvNffDFnn7MVz4Ph4QkUFs9sH+IX93+ZC197KYapeP/l7+L399xHvVFDYbLvqSHe\n8u638cRjD3H08BSlwgybjl3LyNGQ407OM3C4RKke8pLzliEFDA9OMT7boFEJOP55S0h73RTm55FG\nBBr8QKMIsC2XIDCQVogkRsUmtm0SRQrPaSMIa/hhk1x7jqDhg0g0PpUSOI5sqfabxMpHa9GShbOJ\n4zDJnOfnCWOBNAzQLXhTCy9rIInjVlZhSJq+j2VbGEqidGuJDSgd092WYr7UbN2kBlq36lWAQLNi\n1TKGh2ew3Qi/AULHdPammZttYkiJaSiiUOCYjZatimLDqiXUyg6GmcCZirUqpogpF+t09WSZnq2Q\nch38ZhMtXYIwpK9DMlN8Tm6gYgKtcA2LlBcyUdaYWNiGwg9jNAaOK3nJ6Sv548ODRFFMNqVoNi3a\n2iXT8yG2aSBVjYg0q/pTDI4UEULR3elSmodUzifGIIoUS3tTzDWz2FTZvLafmalp6rWYiZkCfT09\nVPwC5YogbWep+XNonUWFdaTtECnJqjV9HBkqYqs6aBtfa1JOlcjPEhGS72ynMF9NrFBUsrRuy0gq\nVY0flcg4Ho3Aw7IC0A4RinWr8hw9Ooc0osXA77g2fhNMKyKKNApwDQupAt79b2/goQcf59DeArEq\nku80KTUdBocn6O/vREeJm65hmGBJ4qrGSSlOPmkTDz22kyDQ2JaVaNUKgSENwkgAitnpCZYvW46x\nIAwuJIZpYotEOc4wZzj9jDPZ9tg+hHSp+o1WnTzG82IaDYVhu5imhWu6rFrTxVPbRgh1A8eWHH/C\nenpyKdraLXY9s4O9uyY450WbSOcyPP7YGMuW9dLe3smBgVFm5hLhppQjifwyvk4vZqlLenKMT8wn\nXl6OQEU+hrSIAd+PEUJjmhLLshJt3YbCS/dz2QdeSmf/+n/sEsHDf/krP7r7XpycpjBfWuxUJpTI\niDu+fitve8uH2XDsUgYHJlBK8flPfZpMHq752G18/sbLKc9W+OmPf0tTl7GNFKPjJcq1OaZnZvjC\n9ddz8w230dNvMDA8ReArdu7czYaVWykWi3zzOzfzs1/cRalWY3R0lGpdkE7led5xq/npz+6hLdvH\nN779ec5/6Zu5/093cuG/Xs4b3vAvHHfmMXzt+s/RUIJmXXLBBf9MV3s3N3zhBl7z2lexY/uf6OhM\n1J+UMnl6+zCr1nQzOjwHocVZL93KU0/upb0jw8T4FKFv8vwzVvPoX49gWSZdHR6F+QabtvTiaE1s\nJjqXbdl+ao2ZRVaKisGyBb4fghCUqg1s216cnRfuu27JvwFJvS4M6ezsRETP8u2TzrAgDH1MwyWd\nTlEozi6+nmS+ScNjQRGsUkkcJ6K4AcrDS5lUK8kxmaxLrRxiGj4RAkmKnr4ckxPJOU2RNCOXrehi\nYGga29BJI8N10Ji4okA9zC3SYwH81hJYqypNUhjCRrUylMDXpD2T889bz69/txchDFyrSruXYbwU\nY0jJCccfw1PP7MW1bJqNgNi2yKcUpaqN0lU2b+jj0KHk80ktiGxNzoyp101s2YGSo0SqA09WaGgB\n2Jx56ib+8sRBbFkjbWqqoYmrfQyZwnA0645dR7HQoMszma0lTqcqKlGYr5DPdzI7OwNYmIak5mts\nO2bj2mPZefAwkaphCg+pEtJGGCX2ND19WaYmShimbunSSlau7mVoYJo2V/Dmt7+Se37zKHMTBbAC\nHM+i1pAEQYhluhiGsagzsdAI7Wgz2LBxDduf3EslkGgSLYKuri4mJycXtSr8ZoQ0Yjoy7UlDWQgs\nW4AGR4R092ZYt24NTz2xFy0dvHTiLhK07uH8XJWOdB/tnQZjkzN4Xmv1EfkgTBQWUdzk1NO28uCv\nH+KVrz6Rrq4u6vUqjUbAxEiTfG8PI7MTzM3UkYZGK5k0m7XN6uVweCgmnWnpVURFOvJdxJHNfLlM\nxrVJpVLMlSr4zUSg3bQSq3fLsigX53jJ6Wfz8te+GMfJ/GOXCL777Z8S6xKGlHR159m7b46D+ybZ\ns2uYPXsKXPzu97H/wBR//P02TBvWL19Kd0+WW7/0Q7JtDhNHj/DZT3wBQzR4zzsv5uD+g2ze0sW6\n1auIGg2W9qcp1QsMDCeF7cP7D/PYX39Nb1eO977/X+lbmmZwYJDx8XGUUsyMVbjlxquYGp9nYnya\nb991FR9836c46fgtvO/ia5ibKHH6KcfxmY9cTaAEMvIYPVJm19P7efSvD9DZmWfn9seJMZieDakE\nkj37S/z8vm9wZN8Y61au4ye/v5X7fvsUH/7I+9l68vPBcPjpvbey6+lZHnnoB7zvnW9jZqrJV+64\nHks5IELCMOTwwUm0cCAUrFrRRSrtkMk5SbZopzGlTdwMOXpoGsdO4Zk2KdugOF0mn+8gl8slg1lb\neG4Ow0i0MRdYLMkSKUDFCZWxUptDCptVK9cRR61MWDpIYeO4BpVyo3WsJg6TLnelnGS+7e3t1Ko+\nWoYomegLaBEyOTmJNBSZrEukDWIZMTyWeJR5mXak5bWCqVr0Y1tMDoQC9RxUhE4ESbQy0SphAkVI\nmhWfXC5HbEhMSzFXDoklEIVse/JJwGH9uiUYpsBWAZVqhBDQZrscPDKBNASOFxBbAktr/NhCGyar\n1mRp0o4WEVbOBTwwBA8/thuhNf1LN1EKsoTKIDI95lSa2Zpk27YhDh6aZqbe5ODhKfYcOMT4dIVi\nlGV53xIacQq0m6xGDJdlve3sObgDRJOzTz4RTyryaUnaCujplGxZnccxHVas7OP0k1eTyaSxlWR2\n7CgXXfgiOlJw13d/S7VSw3Y1hulRqSWiL56XBNdGo4EGpKMxCDn9tI0EvuaRR/dR90HpZ5E9lfki\nnmmTtl0806a7s5N0OnGm9TwPwxTkc2lOOTbL80/bimV3seOZIfxI4MeaqZkmcSTo6cjQjBNgf6SK\nNJolVi7robc7j9I+sTQ47fkriVXC7Nqz6ygXvPlV/PnhSSbnQx544CDP7JlDOyn2HR1lerKS9BZC\nnYw1ZRCKmIOHZjGNOo16SBxDJDwsy6UwX0YpRSOMmS2WoVXeeLbpnahrFeeLfP8n9/H5z97+d8ex\n/7EBtqOjn3qjTmG+zu9++wifvf7dXPPxyzBlmv/84a3s31XlTw9/l/Nf9lpW9FgcHBvjkks+ygMP\n/oWZ6Sa3f+NufF2jUJjlyo9dipdpsvuJHbR3xJxz3lb+1wXvYs+uo2gRcGD/IC8861Qc2yXXbjEy\nupNPXPtx6o0aWoUcPnCU226/itmZOYQMed0rXsG7X/9pZgeOcNMtH8WyBfc/cCdf++rtrNu4DIQg\nCMtcefX7ufxjb+PIMwe5845PsnJVF6ZpIYRk6PA0//Tyk3nnGz6Ga+W55atXc927vsor//lMzjj9\nZH73owcIm4r3v/uTvOC0U7n+0z+jUiliik6+861vksoaxFaWOHLp613Gp657L6bhMXh0imo5xLbS\ni+wmpRQb1q/DdRzqlSqdXTnKhSq9fb1IFJ5j4fsBU1NTjI4OLWatCzXv1atX09vbRd+SPLt2HiXl\ndrB37wEeeeQxvJTNzGwJQwJEKNUakJZFHIfMz5dayveCgaHhxUZiysu04DqgFUhpkMtlKBbnEVpj\nWk5iF2RJ6vU6pmmQSiUgdM/zEo661kgjCRBCghRJuWPhc0spEKKlvC8kvt+k6SfuFUJE5rR8AAAg\nAElEQVRrBB6elGTaNI6Rp80O2L3vKEopsrksAoESdRAahEtbu0VQ60BoWLWmlzh0kIbPwcMDAPQv\nyVAuOIBCB1WU9LBsg/0HD6C0Yutx6wmbGQQhlqMTPy5bcWDfNKDI5zvAypGVPo/tOgyA1W4wWwvQ\nKuboyDxN1Y5pGvx123Ya2Njt7RQbWSbmQ/YcmWBgfI6xoVke2TaNbES8870vxXHy/OedD2Hne4lp\nUKtqSj40ggRpYhgmjUaTRrOJbZqYMmb98l6aQZOHHzrAfCNalOyDBLrU3d29eB8cJ4WXSrDUxx97\nPKaUZFM2LztvEyvWtDMyCI8+sp/RsTGiOKa7p5POzs6E2x9KZqarRL5sUVOzBL6k2QyYmS5hGlkE\nBk9vHyKKEoJHve5TrhRBJE7Bq1b3I6XB6NjYIvMqsWj3Fxttng5Y3tPN+lVdhHEV01Lk83mEMDBM\ngSGfTUZdL/meQrYw+GgKs3M878QtvOjlfezc/cDfHcf+x5YILrvs41QbNVRsY1kx73nn1Vz41ov5\n2BUf4vav38hZ576UwYEpVq9McXT0MDr2ePrpfWxcsZH/+P4NfPWmmzgwNokVa2xDEeCggyqG5RFH\nihNOeCEXXvwy3vL6T/Pdu67ngfsf5Zmdf2FiehpTOSxf0c7h4SkmJ2K+cOOn+eC/fYA1q9OksynS\nTopcezfVYoMdTw8xWZ7mhM0b8awa5VgzOVakOK9445suYM+OXfR0e3zow+/ghhvvZLYygkDwtn99\nO887aQsfuOQKztl6LLtnJgiqIbbjUa7M8cKzz+XVrzmXd110JbffcRNP73icW77wK75257VcddU1\nhAEYhmTPM9Pc/8B3+PD7L8e0zMQuSSSg/yhkkZFy7JZ+Tn/BKfT3bOSvf76PJx/Zztve926eevJP\nNKoRv7z/UapVD1OFbNjYC4a9eD80IWEQc2j/HD+/99tc8q6r+dLNH2f7E/v43HVf4pqbruS3d96F\n4ynqgSKbSpb0pbLFgX0jOE4KMx0T+pqOlMWJJy6l0Whi2ykMEwpzdby0Sb0Rt/QE8kxOTSGloBkm\n+qOjw2V6+zrxMpIV3e2MzZZbTbCAODKJVIglLbQqU4vd/1IiENi4psVJx2V49OkiSij6nQrlRopV\nq3vZPzjFmuUOUxNVan5Mb4/H7GwTaUmOXbOMwlwVO+PhuG2oWDJbmqPZrFOv1zBlE4FHGEZ4roOK\nIzasO4Ydew9hSTBFRBA5IGO0kCgVs3r1KoaGRlCmoM0wqQYhuXZJqSCQMjH4XJAEXMCLrlq9jIGj\niQ6HMAKE9khnDUJfEkcRff0eY2O1xEJ8eYYL/vml3POrxxkfm+KYLSsZGNpLtd6OZbAIkQIWg5Ht\nmMRBg/7+PFNT8/jKJIgiPM+jUqmQtt1FOchAJ+UDWxi05xyq1XlMp42RkRFOPvkklixpR0ifvc8M\ntxyh0ygVM1usAOCZJrOl0qJGgIVNe95garZGOp3GcRyIAurNBrGWi9dj5bJuDh6eSuzhZYhr2osl\nw4hkwvVbE6iUEtsw0HEV04qp+xmIDbSu4sdJacu2bUwk+U6XcqlJoJLJ2rQjKqXw2QnFlITFKi86\nexOWbZBOe/zTP1/0j10imJuZQ/kxacfi+s9fzZduvJ6v3/EFjjmui/6+FXz0Y+/gAx9+DRMTM6At\nNAHLepfz+tedz0c+fClDk5PYCNLZFLE0MWSM6XpoJNu3H6RaHuO+Xz1EPpfi3y7+GI8+dh9j4+Mo\nNDVV4PDwLErHzM+U+cbXb2Xz1jUow6Luw8R8mQNHjzBZLDA0Mc4vfvU10q6kFvgESjAxGfHtu7/E\nwOFtmF6ZiZkp3vjGD3LSqediKEF/Txs/+tGPueaqK+jpdaiWpnhqxxjbdw6SzYHjNBgdG+b8899J\nM4RIVbj2mtupRUVOO+El7N87imWZWAIe/NN3uOenv6WztwPdmoUTUkVS11qoyU6MF/j1D+7j0N6H\nMSjx4lc+j4mRJ8lkMmRch2W9y3nx2VvYuLKbYze2QzDH1NBBwqiEVgYDR6e45/d3cOk7r0U0gcjg\nN7/+Deeddxbfv/WbeDmPRgBKhdQ1VAObvfvned0bX8Svf/st/LLJjV+6mpNP3czY3DTFmo20JJOz\nPgEGSIN6CH2deWZmprGVx+oVeRzHw7IcrrjqA4T1kHzOINeZbel6ClS8IL4hieOQZtNfBIlnzRAt\nkr8DpSlWay2yCdhtaTQxBwcmkRoi3UU9sDHNNMWSiZAeEo8dh4sMFSIOjVTYfXiAvQNHmC2WqTYj\nlHQIaMPHRlkpmrFk/Yql7D2wHy+l2bJ5BdIWdOQi8jnoSEFfVmCbiq0bejjv1E3093axtEdSmC7T\n261Zs6oHQ4fYhibjJQHaIGRoeAAdabZuXouUNpap8euKeqjoyKWYHCywaW0Xl77/dcxMVfjO137C\nyuXtOJ5m176D+KGHJF6sjSodoFRMW3uOnKNp8wKmxucZnChTairqLThTrVbDMaCvr29R6MQWBo5M\ngk+9Iogjm9Ur+nn5y08nCJvMTdV44tFDRBE4jkOtVqVRKyRQMdtG6Zh1K1cjVSLwgvCp1yIsy6VW\n9ZmZLjBfayJlYlQaxwqtYWxkhCiKCcIqWln4cUy12aTuJ2QD3/eR0kCqGFPWMPFRyqVUMghDRajr\nhGiW9HSxYlk/qVSKWGgK5YAwjBZXRfWqIp1xEgJOLJGRIt/bzf7DBQ4cHOHRvx78u+PYfyuDFUIM\nAiUSVH6otT5VCNEB/BBYCQwCr9dal1rHXwm8A4iAD2qt7/9/Oa++4cbrmJ2dJfJzGHadUqlAYzZN\nNueBV+O6T17LVR+9mUDW0GZMb28fy3NtPPrIEEZXGdNwMAyHKPIxpEF3Tzcz0zMoJfnQB65g3QaL\nyz7wNb78tcu5/CNfoLs3xcDAAKHWLadOi4N7i9zzxy9y/ksu4syzNzM7U29Zg0i0luzfM0Y259KW\ny7J0mUOjYvLQ4zu5794fcsVVN7J5nce+wWlm5gZw5TF881vXcu1ln8GwIrSSdPbmGRmb5Kmdg/z5\n3v9kfKLGHd+7mcJcMmi2rN/ERRe/kYv/n0v5/o9v5xUvextGOmTDimNxc5PYwkVGDk9s2wVOjnPP\nWsuSvl4GpxKxjEY9XMT09nVnmRg4imO3ccGrT6LWbPLQHw+S68yQc1Mcf8aJSKGZGjlEX38Hltag\nNPc/tIu5muAl576SlBnz458+wo1feQ/XffbLPP3kEL+85z/48PtvQIkCpmnieibVZkSlFLEkn2Pb\nY6NUm1M88Oefc9FF13Layf0cGRpDGgEijhGml5A5ylViASkCfOW2bJglEpPhwVlMI8fVV7+Fu+76\nHlu2HMuO3XsW3RqUSixTMq5NYW4cI9ONjgWerFKNXYQ2EMKgPTXFXCWPYUmO39DGwNF5GlGSIUVR\nCMrBcOZQURsIhYokqkXdNA3r2YxYPqf+y3OUorRqOc96WDpCC02EYM2qpQwdqSDMKq6pqCmTtpSk\nUq0h8cg5IcVyTFu7w/xcDc/t4JitHezdPUomk6LRqGHGFna2SRyniBsR3UsEVV/SlurgpWedQiWs\n8uAf9lGvV1i1YSlPbB/Csg0wJObiZ9VIKQiCkGzOJesF1EpNis00YRxRqVfJZZLGUlJ7T97lGIA2\nKBSK5PP51nfXSGlwzMYlrF7by8RYjbm5CgcODeDZiSqX47hYtiSOYnryvQxPj2MYJoaO8VJJbbzW\nhKiZaAa3dbbh+wa+71Op18k4BkGgiXVix+MgiKwKtbKFEAaWLVpuu4mJaSqVoitt01QBpYKmFlXJ\nOCk68+2ten6i5ZpvcylXymiZoAOCIKC70yGMPXzfp6uriyCepVyMUVEiCRmGIZaXwiDGMA1u+Nyn\n/69ksAo4R2t9otb61Na+K4A/aK03An8CrgQQQmwGXg8cA5wPfFUsoI3/xrZ3/wTTszGzpRkavkEQ\npbCyAuE1qdcEn7v+KzRlESE0loyYGZ/mmUP76F9nkTI78BxFLitZtXoJy5a2Q1yjO5+jI5fiN7/+\nPp+55iay2QpxANVqiYGBYZSCbC6FihOfrEs/+lK+8vnfsnZVH9NTJaRMZN9UnECj1q/t4xc/+w59\nPTnKVU1AyDFrjyWXs3DNgIHhWWxMapMp7v7P67jyw1ejTU0z1GDBfGEO2/RoM9O8+fXXcePNn6Ve\n8TFkzPjANC9/0cs4emCQmi+49PKPUQmmWLl8DV/66kV05pdQ9zWztRov+qdX8LNf3shcJWDnoSOU\ny+UkYKAXKcLSkGzcvJTjTuqm6VeIdYUzzlnJkmVZupZ7/ODOu7jphu+xY/tBDEMQxAolIJP12Lh+\nCb+77yd8/0c/YnzyAL+790/s2T7C805cyXe+8T1iSgiRGMQ1m03CIKZeC3jPe1/Pxs3LWLN2GdKI\nyOfrHBocSOzRbYtYSGIVUK4U8KOQnnyOSJpoQyO0wDSSJd/PfnUb3/vhtTz8578QRjHF6lEAImIc\nK1wcioahscwMjmsn9GeZRrQMCzUBjVryQAkp2bhqDfVIIzFQkcbSBquXtKHCHJKYJUu6iESENBSW\nLTCcGESKMLQWVwULy/eFevXmYzYSSgutA6SpUbHARDEwNIYyi/T2dNAMwNaaajlAxClyOZtiXeCr\nJvUgxk6lMFMBe/dPooRJV18fzcAhtGyqTZdqQ2G4GcZGbU48fiu5tOZbd/+RX/xyG1YupNqw2LN/\nBM81MCWYWpGgRHTLo8rn3HM2o6IGI2MRpaZNoBoYlqajPYMOI3QYISJjUSs1xmJytkEs3URaUMRs\nPWENr3rlaWhsHvj1NiqHh3ly2xOMHZnGjzXPP2UDYaA55fTno4RHLajguBaGCZaXJgoFpuHhGCZu\nJ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X1MTEw0pSWxHMa2bXzfbwGjXacZU2xAGEg6O7taIBhFkc0Z8cErfh+1lnZRiHjpIqVE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\nIJEi3tyvXZNldNIlCGPtb9JS0RUXV7FIG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YJgdodDJqeimVHKOQvdFUgXhKri\n+R7DQxkiZhLfk0xOhkYVc2fPZmxsAtcB2ylSLHhEo0Vmz20N954WpTmdqtkYCmJ6hGTUoFrxsd0K\n6v8Dqey/rZLrM5/7WpiFpfi0tLSQyYQfjqKEnFVNp465JpKR8BoijHrmEFAjxAsMBTzfCfmZ06e/\ndFFrBHEhRD2p1fWqoeN5UxPZbLaubFEV8z2hh41NKTKTWVRVx5clCKJE4+FEXFVq+V1KCqH6BEFY\n/BfM6WLP7kE0w8HzHQzRSENaY2JqCkVVUA0F1/XRNJVULEqp6NHQZKIqURoa4ji2zehoDkUJcD0P\ny7fCQ6ZYJJFIkJvIcPMtVzE4MMr99/2OHZunUI0yMzqT+FIDJeAb/3kKUjd54DePcOMtl/DD718J\n8fAZyI7mueu+G/jtA39koHuYS684g/POWsX1t/yQSrnK7b98kOHx/tBsQwHPDfPGHLeKIkJlnJSS\niakKs5ub+cE5Z/O1L5/PPX+4mWceW8Mjf3qOFYcfwGuvb2bJotlcd9sl3Hz1bewZG8K3TbLDeVY/\nfDWnnPQ9Hn7iZi76zpUsOaCd0fGQgG9oNrqZwK8ZbhuqpBroCN8hYkpKVQ0pwu98XovOlBO61Zu6\noFgKqUZhPloof44nTFQZMKMjxdhojmIVbN9DQD1vbN8oFoM4ZiTAkQG2FbBjWx9SUQmCmkIoBpWS\nj6bpFAqFej7WB4+YwQffPwddC/HGsLsLcdCxkQLPvroHKfYGLE6LD6af4+kh2/Tat7jWQw4VBYWQ\nDZHU4P1HzAVR4vHHR9l/xWyG+4s4nhJGDzlRvn3KLLZvGmJLf4mjD5nHH17bjeqZ73mf6fdGVdBE\n2OR8+Lg5aMU8lip59q9bsIjV9mQY+S6p0NLSQjZr4QWhOVM0GmV4eBxDhXkLOhBeQGtKJVPxkb5A\n0yESiZCKKpSdEKKJmxEU1cZ1ZEhHI0AXgmQqysRYiUCVeL6gLW6SK1VxpBd+t9gIjPBgVBSi0QTt\nXTrZSRvfB001cNwKhUKJaCSFooQMmHlzWhgZK9VEFi6LFrQRjUbJFkoM9fYzd+5cPN+iWHDIZLJc\nc8P1/2e7ad3768d4+vE1zGhTEMSIJnQi8TSO47D2lU34VZXl729m+5YRKmWTQ49sxSorSOmh6xpS\nqgQE7NzVx7z5czCliyeNmo+kVotnDnOK8oXM3k5AMYnohBJSxSbdOIPcVP49stRoLNxAqmIST2qU\nix6KGtSLdDqdJp/PA9QJ3/vids3NzUxOTqKq6l47RhGgabXDQ1NrktXwqjZ9oIRYYXigZLNZggCa\nmtIUCnkaG5soT5Ux9QiBdKhUq+iGgWkauK5LU3MMgUbgmkQTOsNDEzS3xCmW8vh+qNhJRhL8/IrL\nufqKW0mlk3Tv2UpDqouTPn04jz/6D6bsLFJAR0cHIyNDuK6Paap4rkpHVyOjw1OhwsdXuOHGy7nh\n2vv4+MknsN/75vOlz53PA49ezje/vpKmpia+8/0vcs/t9+GrPp7vs33LKE8+cydnfOvnlHMWHZ1N\npBp1CtXwaplMJikULUw9IJB7ccJpWWw6ZZAruvgyFBjMa07SN15i7vwZFMcHqfgJFCVUtkUUjcAL\n+MAR83jhzU14bgKhOPVOMFDD70kX4EpqajCB50oc26G7dxjbkjWuawQtqlMqldACMzRfCSIk4wFf\n/dIHmT9Lw6vFUYfc3r1SVBXJ5t1TvLahr5Yq8N/X/4qatu/r09QuRVFQ1DCCOqoGHLyohV27epi0\nG4inkuF7yr0/q2NywMwyX/zCEcxc0MBXz12D75rv6Zr3XZ4MIZKPHzcPHYv8WIUnX9qJrxr1cMvp\ngj+jvYXJTAbLDrPCUqkUpZKFJx0OXjwX23HwpeQDhyzmnY3doNUcuzSBEBqBL3Gki66qtLZGyU6G\nqbJSBjQ1GPiuRqHikEhEQyk3FbJT4T5TtbD7VkWcciU06ZFIOjqaMI00u3btIh5P0dCkUyzYKEKj\npaUFxy0zOjpGItZGS1sEXShE44KBoQKGoTFz5kz6+nrwvPBz+NGPfvR/tlR2+aH709IcoerqWL4k\nly/TOzDKjt09fP4Lp3DPb66kkElw1fU3sPruq2gxE8ggBKZjcYOKXWHXzgIKMayCxIj7ICwUodO9\no0q6IUlP9wTvvDlAR2srC+c3o+Ozc/MYm9dPEI1G2bZ5iNde2kIhU2T7zlEULYzRqFarCEVFVXTK\nRQ9ViaHrKkgTXxbI5wsgApqaG2p4nYUnJY6USDwy2TGE4jOjfUatuHqYEZ2qIwm0CqWqg+0HxGJJ\n/BDKQqAj0BCKT7FQQdcixGNJyiUHVYmC1PGFghX4VL0AxTRRTQ3LlfiooMYYnSgxmp2kt38Y27co\nlKuUK1Ase7i+xmTBZeVVqxicHGZ7zy7aZs0mV5niiSfWUnZzCFUloEi1WiKZTNDZ1Uy6IUVLW4xC\nYYpESkVVbJIJeGbNHykURvnHy3/nx+dfyqGHtbNx/ShL9+vkW6d9jDde3Aiqh187eM4/9ytcf9kD\nZEcH+d2jP8OICEqlCr4SHmzlchnUcMgIEKCyeGELji/xBQRugOKCjsCrBOSrJfZbOpfh4RFcy0ER\nFsI3iMgKx5zQiVAkz7+xCykjCCVkJghFQTXDYjo8PIzQNKKqjlMMmMxU2LB1iC27xnFsn2Rap7Ut\nQeALSrkK1XxAueTRFHe55McncuCiNja8u43Hn9jEWFYhkx3FdcOiqGpa+FkqGlLRUVBRhYL0g/9t\nQZ1e/5x0O90l+65XL3CqdJjMDJErpYglE4hAIuTeoqwoCg4Wb45EuHH1OwS+z2GdMXyxF1s0TfM9\n7zuNCasRDc1IMDY6hIOKJ308GSAVgV/jk+eyedpaG4gaERRFx/cFiUQE37LxRNihR0yVnpEJCHwU\nRaKqIBWVpuYoqXScjs5WkB6FQh4VD4ew4SiWA1wJngiwnTLRmE5jY5yOzhZaWtM0NSdoTidIJoss\nWNTJrNmdpNMJHLtI1coSi8VQNZ9qxUXXDRw7YHBghLHRDIuWdFC1SqiKSbIpTsWCpfvNwqq67Onu\nx/N8TF3Btf7vv6P3fG7/8r/8/3nddedDxOIxQMHzXDzfR+LTt8fm0x/5EOec9SN2bh8g059h7dPP\nMpixAAVDayCfcxHEsawiH/7oB0imDLIZH98z2bKpl8fWrGKgN88jjz7A42tuZXR0jB09OcpegltX\n3cTja1bzxqt7eP7vf+aWW65hoDfHNddeSakgKZd81r/Tw67t/QSBQTQao6+/nx1bCjhenv7dglgs\nSmNTlLHxPkzToHtHHikkbSkTo7YhhBAMDQ7VzDAilMplpISxYQ9fOnTvGiYzmQUEyWSy7h0Qdhii\nlpfk1rXrU1NTNcK2Uc/JcpzpGHGPkZHRul5eEl7xS+UyEpi/YEHtNUEmm0UoGoGEwaFhXN9D1RR8\nGYR2db5JPueiKILx0SLZyTKT42WqZSgXfTQRI+8o7BiYoODZ7Ojvw9ECBkanePThu8nnp3jsT8+Q\nnRpEVTTmzmhkQVcr27dvRxg9rPjA/kQTafRICUUrkjKoDy3SpgRZux4DvueG1CwBvhcQj8VZvN9s\n0o0x9nSPs21rL66toSoqhl/l4PelqPqSNX8ZohC49aJRrdq1YZpH1bIIgoBYLIZlO+wZmmB7z0iI\nswZV4gmN1rYWqmVJIe9QKBTxXYgmKvzg+x+ltaWJPzz8MplqhYIryVR9nnt5HZu2TjGdJWbbNm7N\nOrNULof69yCoZ4ZND4mm1VP7/oH/XmSllGj75I8de/wSli1bCkKpwwzqPuYtEPoj68Jm2JF8+8yX\nOPP7R9MQUBcz2Ptg3dNLCMGra3vxfJ+5c+bWBQz70semvT8ctzag9HxyufAWkogncD2vZiAkGJ/I\nMndhE5Zlh42LZVGplCkUigwPZgl8A8dSSTcYaEq6JggKAElz3KibFuWyodx3amoq5Kt6UZoausjn\nbCYmJonFYkTN0GgoHo8Tj8eoVCzaO2O0tCWIxhWCQDI6XCESMejp6WPblgGMiIf0QyZBJpMhm6kQ\nBFVkUP6X69i/bYH1qhZTBQdf8Um3xFCEiePZ3Lv6MoYGB5nd1cQF551JMqWze7gXofgEgY9bo9IM\n9g7zj1ce5fW17xKLq0QiJvlclZdefITHHn0MxVN58rE/0NfdA4ASSLq39fLb397GK8++wsqrfsSm\nt7fz44sv4/OfO5UH7ruXnl05+nqHueDH53PQfxzJSScfw87N/Tz68J088NA1tKeX8OjTV2OVKpRz\nJVwZY+OGAZ59ejWabRJJRrFsj1hcx3ErKBrENJtSOYehR9i6eZCpXJWJ8SlOP/0b7N4yRmOjT6E0\nQTStsmXrOLu6x5ksOGzdmcHyq2ze1svWHX14wM7uQfKVIkEg6eyYReCHhtFB4OP4CmrEDN24FKWe\nb4Xw6O8bRqDT3tFSt7mzLBsZhBBFNl/GR5BuiKMqEYTikZ+qIqWkqampvrGi0ShoGpqmMtAXKoqi\nkQSKMDAjOp5iEihQ8hyGJ8dwZYAtFPYMjzA0Ns74lM9ILscFF63EcgVCixFJpNBQUKSF61h1kYEI\nXHb3TKELSeALFAV8WQ5dt6TLRT/9AUIECBGgmEkCI85b6yYRWhxDCbmrnueh6h6z587Atm10PYKu\nxXD8gEzWZevWUSbGqwS+JBITtDamsEoFRkcmqdhQslw0s8R/fvE42pqb+OMTa3FlBKkaeK6OVQ3w\nXIEMdHqGHfpHSngyQNFUPFdgVQN27Rh574PvB3UoSUiF5liCuW0dLJk5l/3mzSOmqSgyQJHvLbxS\nSlQE+AGaG5ofRRvTtDYn6kUT4SOU0BpT1UDFQEdFE0kGxyxO+fxcPE8lqH3G+w6NFQmaUJi/oIOY\nNOgfzoT4tLtXhCAFhMwqhXzOQqjV2nwD8vkis2fPDA8GXaVQsXECAV6JuTOaUJQwOluLppnZ3oqq\nSRQDFCNCruDi21WCgNptpoKqKmQm86ErHhqd7SZxU8Uul1CjHtlCiZgpMBQV6XpEzBQqKhMjY5RL\nLrFoivx4MWQeuWHHbkuf1ka9JnH2mcoGjAxPUSqViEaj2HaFatn/n2H2IrQ8Ta06szvjNCcbcCxB\n/84KN6+6g4cf/RWGEuNTn1nOpRdeRyaTJZet0L1rFIFAV1Xmdc5EYDOZLVAqWbhOwD13X4f0K2xa\nt4U5cxs49MAlrHn6LwRCQXoazz3/Gy7/+fk889fnOeLIOZgRi872Tr59xklUSyUefPAann/hN1z7\n01u54eoL+fuTb3P8h49lcCjLOV/9GblKH688tY05c2cTSIX8pMXJn/oI11x9PWbMpr9vIpRkGglU\nEUfBADUOQYT1bw2ydMkCHn1kNZlhj5deeIXjP74/42NVXFfh5b/u5Kk1v6K9q4nR3QP89pEbUbwm\nAtcncBX6ukeYO3Mu2zYMsm1HN88+/jrt7TqzZqVYtqyLnu4Mm9eNgbDZs3uCwcERWprbyGeKREyX\nwMszNjaBT4kZbTOJxjSGhsYolV1c6TM8Okk2X0AEPiphJ6aokmw2h0QB1aNs5Wt2hmGctpSSUimU\nkkYiEVwvwPVCM+zpTTk5mQvFCbqOpoUOTJVKBUWBSNSgWCzjOBbJZBpPiYYYXSBxHI9AhldSTSgk\nG5PYrmDzxj1s2tzL6tWrUU0DzTTIez75SoCjlgl8gW3bYRGTkmpVZWS0BIqC4wq2bt3Dzq2jVMth\ncW5sipJujFAsVpjI5LEdBR2FGelhvn3qMcztmMNLf38z5OYGYWGc7uymMeIgCAgEvLF+GDtQ8ZwQ\nJ87kszh+2KU6AXiORyXvEanCkUsX8cAvz+OXN5zJjVd9m5WXncpPL/gyZ3zlRJY0JVg6Zz6KkKho\nBCIUyQSEA914qhHPFVSqZWzbQdEFqBKkggxAoNQw4BCfvPXGFfzkltd4/Z0iv7v1WNoDH1Wk8PBR\n2GtVKKWkf88A3QO7GJkq1iGJfd24FEVBCoHlOMRjDZimgiQcNn5gxTyEs7fTDoKAwaGAiC4pFquU\ny2XGBgpYXg5d1TD0SFiQFYjFiiSTMeLxOKlUikrZIqoaSCUUdwyPWqgo+J5ev82pKkQjcVxH4vgB\n5XKVBQvbiURV0g1xLFujrSWFCGrULj/KVF5SKpUR0mR4MMvQ6EQtBUUjGo2Tr4Q36X91/dsW2LKv\nMjkxxeDQFLt3j7Bp006eePp2hBrF82yq0uaTnz6D2399M/29kwwPD3LXPbfhOXlMw8VWsiw/4Ius\nOOZwcpMOQwMFvvrFs1l5+XVsfifDlb+4iLvvWM3mrRuYPaOJ9pTO6mtX87lPnc3gzglSZpJf3nw/\nM2bE+dLnv0VjY5y33lzHd077CXfceylbt29CMyu0JQWVzAhf/MZx3HHr1ax9ZQ29gz1Ynk8i0sB3\nvnsys2bNw7YdNFXD82yy2SwApqlTsT38wKejfSbLD1vIj867mldef5BFi2chhImqG/hSZ86shVyz\n8kG61w1z1g/P5O4bV7Nl43YOfN/BPL3mHpxynO99/8uc+uVTOHTZoTy45lqGB3L0DpZ59fVd6FqU\nXz98BeveKPHHx3+FrsV44411HHjYERhaFEOPommCreunmDt3Hp7VwPLDl7NjVy8bN+xkcsSjq7md\nxYsX0NSUprV1Bls3jeB6Fq0p0F2ftG6i6yaON4X0Iwz0T9SvsuVyGUVREWpIQldVNdy4KHUIRFXV\ncFChqigqWFYFpMKM9pYwqQEFxyuxYFEXxx1/WC02XKIIgW7G2dI7yEQxRkLVyOcVKtUK5WqFeDyG\nazlIF1w/NATxPA+pCoSqYjsuG3b2s2nrLhxXopuCxuYYiWScUqlMNpsDKUjpMRbNSXDMcXOIJZfw\nzF/XYzlhcRMiNLfhfzEznmYiuL7P3/+2rW70PtA/DoQSTC3w+P7pJ3Ludz/GV75+GMccs4iq7xOg\n4PoBnhBE4xGOXLGcn950Dmd84wQWpqN0NasYfpg66/gBpqGiaBHGRifRNJUgEPhVC1OE0JIQKtNe\nGX7g0hot8NDvNyGDZnYMT3DqBX9mxsxGHrz14zSKIp5q1b9DXddZuDSBhkn/SDHM86oV2X2X53sE\nUlIuF9ENFWT4eY8MTJJKKvtYRqrkKz6ZqREWzWmtJYcUyGQckgkFqxJGH6FoNCTmUK3aIe3NtonH\noszsaEARIYtBKDqd7c3MaE/WzIgklYqLqvmomkupXMW2fWLxKL4HI8NjFCslZOBi1nwsdM3Cw2LO\nvCYamqI0NkdRdQ/T1LGqNoqiQi0K6l9d/7YFVvgeqVQDAp3h4XFW/3IlD//6UeIxG6GmcZWAqdEC\nd9x5FzoRVl2/ijtu/wWxVJJ8SSIwSba0k82N0ts7TNfMGdzz8O28s3GIe/50MWeffSlZV2BGGvD1\nCBNWlR1Do9z/wA1cfMUF/PwXV6JGAzLFQSamMrz75m5e/OsL7NzTzaUX38zLz73IgrkdfPX0r3HV\nykeIR9N85zsX8ur6fuJRE1MVXH/j9zAUnWw2G4be4WIaUQJfwVAEdrmAVFWi8SZO/tQRvPXqRqqV\ncbJjRbq39TDQP4IiDOa0dXH/by5ldLCX2fNmcOJJh1LMWhx++HwOPugIPnHit3no0evY+OYIO7Zu\nYLIwym2/uBMjGkMJfIZ2+7Q2G3zlc5dgCJ0rL7uenu4xlh9yNN/48sk0tjXjE6FUkCyYPYsde7p5\nZ8MWRgZK/ODM79DVNJs/r7mNoaFBdu3czZ7hLG+/u5sb77wKRUao+BqW1Ik2pfDtMhVL592NG7F9\nA8cyGOyrUsi77No5wLp3d5FOpxnqd4jHWmlrTiCCKmZEo793imw2Gzpd1boiw1SYyGXwpUEqbpMw\nGhgb7qZcsuuWepom2NM9BLbgo8fvzyfnJZnZEK9P/7PZAi1tSZpbk8QNM8RCJViOZOO2PrbuHsb3\nBLFYLJSyKhrHrjiC0lQRxxLE41GOef88li1LoAUxdu0qYrkOnvRDYYChg6qAGiYs+FLBC0Lakud5\n9QIbBAHFqo1fM6JW9Dh2YKMHKl/77GFsfultvGKOo44+loOWH0Kp4GM5AetfX0e5ZFOxnTAhQJok\nZzTzixsuRi05LD6wEylAlQGzOlrw7AIHHXQwpqjiBFVmtGqc9+1Won5IdZRSglQxzDi33vw1Xnt3\nBFfPgQDhp9k8UuKb5zzEJ487gHuvOIUUAqEYRIVElTqeV8WyqeP/0zS2aQe06YDHQj6UB4cx65DP\n2Sw/fCmajKDJ0Ahd4DGUi5LJDZLLFjFiCXyhMiMdxYiatRtBlVJQDtV1Qehx60iV0UyJuB4GgEZU\nhamyz9RUrm6AoyiSxqY40WiSaExHNxXyUxYNMZdkYxgFNTpRwlNqCQ5Sw7EDSrky+akituUhZJTA\nUmlr1UJjINUCNfYv17F/2wJrGBFKpRKBD2d+73QOPXwxm7a8iW15+L7H6ECV51/5FX3bdvLFL5zG\n3AWtTI5XcZyagXTG5aknr2eiJ8tHTlzOLbddxhc+dyonf/I/ufmaR2ib0V6blAaMj4/jOirr3t3G\n6tseYcUxSymVbWxM+nryLNvvEP74/B28/EYvh39oGRdd+X3W7XqbLd3DrDjhq9z19IVkizoDgwV+\ntvIicFxM0+DqX9zD9TesZnCoB1ULeN/CmcQ1SUTYJFOgm5Jtm3rZsn6QvoEdHHpYFy2tJqtuvxzV\nmCSViBIxBf392/ndQ4+SbCjzyY/P4amH1/Dmpp3k83neWfc3/uOwOeimz2tv/Y3DjzyEDx2/gqa2\nFJ4XoKkqTz59Bx/7+IcIKPLM365nfKRCV1czl195Gpf//Ar2dPcCkpH+Kr986Hyo+Jxw9DLKxQlW\n3/oQv37wSu656kHiyRggGB4c44hD3889dzyKaapYloOqKuTzeRzFZOe2XvZfspxfP3g1bS3zeeSx\ny1ACneWHHs3jT63ipRe2svreM5kYH2MyM0Q+5/PW67tZeeWFKCQxjUZc30MSOhxpQmPhoibyBY9i\n0UXQyMYN69/TSbS0NHH/7StZ1N7MW/EIXrSh7ingewG5rM2e7mFGpiwq0mTjzgH27B7D931isQid\nnc3YdoVyqUq5XGLt2hf5/Bc+yve+dQLHHZ1mdLjKSEaQ94ohx3kfN6l/VlxN46ICAVKEOKUbFljX\n0bCqoSKrId2OZytcdOFH6JzZyddO+xpRc2/i68TYJHoyyoFHHlbH/VwnHF6aRgyUgGvu+ClqT4ZD\n9puPomgsPbAF13V5/eUtrFixjPaGFspemUcetjjn3GXECECESsGuqOTs7/2eki6JKNP8V4kqFBzp\nc/ffezj7oidYdfWn+MZnOmlqMXCmikTiMQJ/L192+rq/r3R3usjalo3rVPF9n1fe6SYqi0SiYQev\nKAqRWBSJQaNu4Ls21WoFx7HYuSOH9H0iRgRdTVApCObMb8BxXWLxVOgNoNoIzUbVHVRVJ5CQSqVD\nrnoQhieGEd1FJsfLqGpowm7GEmQnBIVCEdt2iMcTtDfG0AyPppYYsUgHvm/XYCoTPaIhFIGm6uha\nkkr5fwBEkEoK0g0xonGF9W+8zt+fexVdi6CoAamIzv13XMO1V97PgiUzOPXLK2iKp0gkQms2obi0\ntcZQMTjsqGV87KSDuPzHN5OKJfjUZw6iUBhheLIXRVFJNTSy/OUAACAASURBVIQPbsKAf7z8CBdc\n/HUuu/BKihWbwPE5/NADuee+H3PpOVdx+c/PIC6aee7JZ9GNJkqFCgctns3gliJ33X4vFddl9/aN\nxJvasVyBj8NIZgwviGA5Gjv7xpmqBgSmwVRRYeP2HD+79GzWrLmFsdEyW7cPE/gqDakOEM3YThXH\nzRNJxBkZ7sN1Xf728iC33fkEn/vsx2lsbKRrZoCGx08vuZbsSAV7aoo/PPR3Xl3bS2tDFE11uOrq\nn/DaS3/hkIMX87entzM8MsShhyzh/LOuJKLFiCo6Bj6r7/wRl533e2KpOOdecAbxRBNHr+hEUQyq\neo5C1cb2JfNmdXDBD7+AVx3Fkxa+J0mmolQrDr5doaWhi0MPm82pn7iEH13weZ557G0SeiP5Qj9X\n/+whUukUPz7/13R1NOJ4cTzh8sHjlvPCmmeJ6zrF4hSKq5GI+ixe0IVQVHbvGsOXJkJ3+Mx/fpRY\nRKWpIVkPB+zZNcjqO1az5oW1eFUVX1bqXZWiGLhB6J61c1cfO7d2I12BGYG5s2bi2zaToxl8X6Fc\nrqLaFb755RNoT07Q3iwZ21PBlx4y8NBVE8006syGf177TvpdzycIwHUDVDWkQEnNZ+euQXRdZ+fO\nQY74jzmsfWEji/dbhEhLDjruaKqDg2h2lWoxj1uuhkwOVUWI0BBa1gzFA6EQOD4rb7+QwmiRzsY4\numPTlmzlgP3mgl1gbLAXU2/FTej86i6fr362jXQwiqJKrvj5h5kS7ehShDEthMGLKD4oEZKKSUVI\nzrv8z6x5JsvS/VTmzJvH21t6qLjl+vBLqVHApOejBYSDuiDk5OYmSixbNg8IMea3Ng3xgfcvrXNn\nq46N0Mv0jtskTCs0a1JM1LhCU5NCKglN6QTpZISxkRyeXcJUNSzLQlWTeDKMVSpbDlOFMlNTRRqS\nBiIA2wuolCVtMxpIphWa2xoQusbIRJl0GkxTo6mpkVymiq9WqJSCMLZnYoRUQsezQn/iXGGKQkmi\nKkWSyQhNTel/uY792xbY8QmLYqGCXRX0T47z1qZNuBIUw6DouFy18jrKpSGq1TLnnX8hl/7sQmKG\nSoPm0dKQprUjyXXXXIFTKfDic2/T3p7gK1//FDdcdRu2yNKQjNDeFqGcs1CCgGhUYtsVfnjOhYyV\npkDCrLYUMxvms+n1LZQreY770KFMjgwzOZnBCyTLli7gtl9dQnfvBjTdZc2Td/LOay8zOjpKIB1a\n2xqxqt7e072mWnGdACeQJLUU9913Pzs2dqNrHqpm4kiXvsFxAuHQMbODIEjjS4+RSYtAibJhQy/L\nD1nK5z57LIFvMTauMJGpsHtHlhvuOp8nnn+Dj332RG69/3KMSAIvMKk6PoFqY/sVrrtpFTfcfAlj\nowU6Z8XQVJ94XBL4DnfccQfvvvMOzz+/lltuvZu+nj1MDpf4yue+wrvv9NPYYDDWX2DenDauueY6\nYqaKKgU7to4yMjSFEJKuztksnNXMupd389wLt1CpDPP3v7zI9y76Tz73mU/Qs2sHD/3+KmJKePX3\nBJSzLj+9+HQOPOwwqtJGEzZts3XKlQi790xgBQEVz8fULGa0mjy/5iXyRYVIzU8UwJEeQTSJrymo\nUmBbAZoaDaf4UuJ5EsOIINBoaY0zd147lbLP8NAYju3jBRqBleGnP/g0l13wSRqSEI8mkNKhYBug\nKii6hmaGeN2+xfWfJdrTf5ciJOgD9egehEd2wsb1bPKFPB8/cRF5N8a2LbsYHbawXJv+3p2sffYp\nZnUmiegKgVtlanyA3W++hlP6v5h77yhJ6nL///Wp2Ll7ctjZvLCRsKxIEBHDVa4BA15z4OoVERNJ\ngqhIUEAUFhEEIygSVMCLJEXJcdlll81hNkyOPZ0rV32+f9TMLPi9gfM73/M71jl7tqe2u2u6t+qp\n5/N+3qGOEvmoMiKwXSLp4YceJx53GO25FIlsEl9GmAWD0kSVNxzVilt3cYg44k1z+N2jVd5y7GLO\n/NcuvnT+35DUXvN7z34W6RNGHmZCI1IMVq/O4lYMDuw9gG3F4YhxxxqCgKwGpx6/jG98fg2fP+UE\n1MjHm5av+66KpoaEeDzx4gFct4GUOlIqqBHIUKMWRrR3N6OhkFANbD9kqmQzNlpmZKLI8PgESJMj\nVi4gYCbeJaRRi2gpZEilDcyEiqbqqKpJGBIr8RTBZKlBrRKyZ1cftaqNY3u0tbcipcC24+7atuq0\ndzSTL6RjRkqgYJgKju2RzeTQVJ321nlEYUh/f//rrmP/tAV2ZnI5E4s9MDAQG0/ncqiqQpTU8HSB\npyuQMpm06tSikGRzN0UrpFSyGSlOMVK2mZyc4MD+AR5/7HlaWwuotGLoGUolC1VJ0NbWigyTXHT+\nZRjJBpqrkNLK+L5D1X2Fl195kpWrVnHjTddiphukMzqGFjK4ZwvS1dn+4k7y2W7e++6PEUb+dHpm\nwPDwMCDp7OxACIiicNY42QxMfv/HH/K9qy7k3r8+iKfIWV4rxMvEvr4+ADo6OmIesJQcd+zxrL3h\nm6xd+5N4wOBr7NjexzkXfJxzvnoNtmXz2AOPcuM1NzM5UUXKKM4go5MdB4ZR0y3cct3NSGqMjTSo\nhT6pljnY0uSFl0d500mHc/8ja3n6sX3cfMclLFh2OO/76Ee44tpv89RjW7n0lkvYs/UAqXQC35OM\njU5x0y1XsmdHlW2vFPnrg1uRBIyOTHHJxddy929eYuBAlQ1PP8Evb/wTq9+wgHQyhUjFYYgpqfCD\nK75MeVLw3N/+Sk+HRcLMMzEcY6u+7yMbFqtXNjGns5WSI9gzMBmbmoyMxJ2MprFo0cKDicHT8mYh\n4u9xfGwcM7TwyuMsXNxJtRLQ1zcxO/X2PJfvnvduLrvo/bSk4+FLtVrFMAxGR6rIKEEURkRhTEt6\ndVbVTJc8U0D/K94qwKsVk4ZpUK/VePdbTqQ91UTRCvnzI1uZ1x1hlYZZt24duVyOB//4GGNj+9AN\njz3rX2LevCWznV+tVsN1XZ5/ejdR5HHSx1czPlpHV/IxS0ImWDBvKemMzjFHFaiPNdixrY+cafLC\n5lFyuSwyOnh9/WM3PgOvFAoFjjkqFsQ0Gg1aWlqw7RmMXHLk0vl8eE0Xf/j5hXzy9BN58aVe5i4z\neeDur/Hnn5yLpmqUy2WWLV0KMv4edu0Y4C1vW4qqmLP/B5qmMTzsY5o+ljtFOp2mudBB99w0mqZh\nmiZB4NO/v4FQ4yy5GXvIXFMcV1+pVHA9D033yeY1LMueHZwuX7mAto4MmVzsxDY5WZw1Uo+iCKuS\nZ2pqinK5HFPB6h4dczTMZIiRCMnmtdlhXqFQeN117J/WrnDNcScQ+gFhFCCJEOh4fpwdpCom2WyC\nRt1CBhGqqoDUkJHE9QNUIens6qBWt9A0SaiAFDpeaFO1G6hqENvMBQqqoWK7NpECyWyakAQSgWFm\nqDmSuhUwMl6hVC+RTuSp10J8N0DKAKlneXHdOiarRfSEw+o1h2BbkuamFKFUEEKB0MHxXFRV0NIa\nc0YLhQK6JnE9hz/d9yD1So18Nku+OYPnxgU6vh7FNFWphiYgm01x+RVfZceO7QwPlWi4HprwyWVy\nnPbZj/LUE4/S0tWErkqiqAGqhpnQKBYnkL5PecThoUeu5MH7X0AxBV4QH6tWcUBGvOOk49nXu4nN\n6yeYtyTPwraj+ePv7+OCi07nZzfdgq4189ITz9PcXmB8agrL9yhOKWx9eQtvfefhlKckt/zqfG5Y\nex+/u+97bNy+m1f29HLxDefwwF1PccPPL2Fkb417/nQPjuMThjqmafLU8+vZtGk9JadOuS7RDZMg\ntICI+T2t1GsBqWyCXfvG8bwYv9RVhXRaxbIcIkUgxbSDU+Azd16KcjmkNFZE1yxURaWnJ8emXUWq\nFZcwcjEMSIZ1LvjGybznxMU0ajV0PUHAwfypWLiRYeOeSYgkAgWkQEwnrc7yT1UTIVSkDF/lsj89\nqQ9D1GnJsySGChbPayKbSfK+D7yVdc+u55G/b6Crp5v9+4d59s77mH/IQl5a9zKHruhh60sbWPfI\nI7znE6fi6waRlKQSSXzPQ9N15s5ri+lXbsTDf3mOZl0l15bAD2yGhoYYnqzwytZh+ken0FQdI5ch\nl8vyzIa9qOl8fH29aqo/4wWbSqUoFArs3r0bGfh0z2khiUqghWzcNkng1TnpqKWcf97HeONbjyJS\nDFRS5Js09CBHNt+KnksyX7PYsm2Yi7/9rzz28HrcMOYPm0ZEGCjUnQBEPAR0PcGqZU30D7loukLD\ncYgaEkVaCD0BEfiENDcn8T1JJmvQ3KpjV8F2XUCCohB6CoZhUqt7yEjguj4yrJLQc7iWR6VmoQiD\nXDYkCHWy2SxmykBEDQJXYiSShJGK5yv4foChJ6mUa1hOSE9XjpJl8eTjT7wuu8J/2gKrigKLl8xl\n89ZxDDOLqdhIEV9c42NTRKGJpgnMdArbiSkwuqZNFyYFq27j+5I5c+ZRq1VBBKgiBSIkmUziuRGI\ngETCmH4czXaQuaYcDdtBKJJIqkQohKFPw/IRqkI6mySMABFRr7soqoaiKdQbIUJVMRImnhtO0zr0\nOE8+kDi2h+s5FAp5JibL7DvQh+U4oMScRasRABFdXd3Ylk/KVHFtC1U3MJKxf8L6J1/hla3b8TyH\nVDIkmcljpjVefP4FEJK2lia653dTtxqkTA2rZmNqBjKyEZ7C2K5hSDeo2x6RjEgbBgoauqpx7tmf\n4+1vOZndO/Zw5rmf5dZf3Imp26xZvYZ77vozN9x8Bc8/8zyRbCDRsBoBt/zsKkw1wW9/+SA/+OG5\nXHX5zznjrE8xMW5z7+8eoDmTZ8+mbQwOjPDUYxv56GffwhNPv4gfQaE5pGd+Gw3bpWY5uERk1BSl\ncj+dc5qYKjao1h0OOaKbHdtLuH5c1DKZDAIL1y+jihyRjB2ygsAjlCp9O8aY16ryr+9czmS5zM59\nNSZdm7olMYVOR6bOWV97F0etmo8MIsxEzDjQNZNqxUJGCpqeQKg6lit5Ze9+kGpMpEdO6+5juhMo\n0zQtERs+v8pPdUafrykx0V8oYAiVE09YgPASLF05j8ce34Kvp0BR2LLuAEvXHMKHP/NOnnnoaTw1\nIG1kOPbEN1CrhKRacxzYfoCdr7zM3MXzMYwEstHAkXV+c9djbN07SlL6dC3KMznmMDw4xY4Rl4YV\nO2NNTpTY17ufbL6NbGsLybRJOp0g8COSSZNCUwZNm2FlHJiN8SmXPfr6h+jOhkx5AVMDdY49egkf\n+uA7aOtqQ6gmAkEgQ3Zt7KO5qYNcRwqBzrxli3jve49l3/Zhjj3hCP76zH58v86Cud0cedQSBg6M\nEMoYQhMqTE3ZNKcUCm2tuG58XliNkN7dg2QKaax6DV1LUi3HhblW9Uml03Q3p6haRtxQRTp1Z5yO\n5uz0gCpkbNwimVQRCIxkkogGCS2D7Qh8P6JaLRGEJpopSJgpLKtOIpGiKa+gKFmymTyKKmnUGniW\nzVPPPPO6Cuw/rV3hr399G2eeeTp/uvsXfPrjZ7Ps+EMYGB5HCAWFNNu37ieigRQZjj1hMZvWT5JK\nSt52/GLq9TqOGmAXq+zv204h18HObaPMXSiw6o1Z96yu7g5GRkaQkUokw1kC9IxzV1tbG6Wp2L18\npjOxbRtEjKs2NTVRKTeIZISUEWK6+6lUKiA1WlpaKBaLBx27pEQIycDAAAKdnjlzGBjom6XxGHqc\n+zM0NISMVLLJPK4jCEOf0E/E8le1gRrFN4J8roliaRKhKGSTMXWkWCwyUYp5tk1NrXjuJPl8nuKU\nTcv8PMPVYWzPIEGD+fPnM16ewjASmIbBz2/+HVbDp9YY45E/3E/oT3Hm1/6Na6+9lmWHH4KIbJLJ\nBJYbBxm+aU0PdqmPRx99lFM+dDx33XUfS5fPozw0zMsvr+ejH/8AH//MiZz7tR9zx+/XEoQ+F5x3\nJSEhqhoiGgH7ekeJQiN2qnerdLf7lBvNDPd7LD5kLr4XsXHdcHyDnObQNhoN1EgllerGC91psnsM\ny3QWUpy0ejGjUyovbJji+Q0jhJGC1x+weHET//GRN6KEGiLwSRQKs4UwCAICGedEzVCqhKqwfdsB\nhEwB/v+15H81nAOgaa/V+8fG3eG0oc/BS833fZqb26iUfCbHLTzPI9dkoOoBZcfg579+icOPW8mz\nz22l9dh2HnlsMye/80h0fSUtLS3MPWwRke3jCY+f/vJ+GpGJHXk0NTURBBb7944xWg7wRRrLKqNN\ne8XmCgaJtM/A8A5eftmNzznDIJU2CQMJCHTdIJEwX+NFIJHoVshTL5VZs2YFv/rV27j3dztYuKQt\nXmLLeNglFJ/DjzwERQ2JIgVFU4iiAFVX6WzpItMsMQKLSDHZ1zsyyxmeNdmJIiLbZMWSArv2lzBM\nDdu26WjNoundJJNJjHQSIQSrjiowPhzQaDRwHJtiQ0WKOpYlaW1XsOp5FNKUinHWWTKRIJ1OEIUB\nXiWIQzZtl5a2DJVKlZSaQsoIRbOQMjsreNHUEMetoapqbJ0aJFh4SMfrrmP/tBjs/n0vceiiJZz9\ntStpnxMyPjGOqips29pLz7xm/v7U7UR+locevpnnnt7FJz75AX516w1YEsarEbWii6ZqEJk8/+w2\nbr3jcjw7wSFLj2HTi8NkMx5jYxMoisohizuIpINuwM5tk7R1JBkfrfP00720tifY1zuJ6/roWh1T\ng1wqgSJDxsbGkdKmtSVLFBq4gcbgyBS+Bw1HMDwxRhAENDVnieT0kENqsXGLEjI0OEIUKrS3daMq\nSVzXx/ckvhefbKVaBV+GNDU14boOQeDT3dWG40ukYjBVrYGIcema5YFqIhVjNjZnaHScUEjc0Ac1\nhRAOlowQRgTJAnuHi9QtjUgRjBcb9A710zc+QNkO2bW/j3KjxPU33I6rRAyNDXP9j2+h5pYJUejp\namHP/iI/++UfMZMBw6P7iKgQ2SUi0WD+woWUqnu59OIr6WiXEIb86JqbibBYsqiFlKlQDZIIoaLr\nIUetXoCMDLYNJtE1hVVHzGdf7ygHBqYwkibHr1gwi3cChIoglUkgRMTQgUHyGYWUInAdSbFeYGJ4\niiWLCyxqTdITuqxRNc78zDtY9/xGdvUeYGCwTKPRoFwuU6vVqJQsEBqhkAg97lallOzdP4ISHiyu\nsbeDPIjBui4qgtYOh6SZwHHsWWgAmBVVzJL1RYiphSSTKsr0yiUMoV4OWHzYclwEQ2OT/PzebQxb\nOocf91b2Dls89LdNFPdvJVUw2bxhG7f//kXW3vwXXCNPABAKoijAtgK27Cixf6DO3sFRgkbcUMwk\np8owST7XQkdnE83NzbH5iWKQSMSBgULEN4DAtlg0p5XIKhF5FsVIoyIj+neu49m/7WLxqrkoWgpV\ni2EFqQiIEuQ68ySSB8MeVSFQpGD+0jYO9A5z3lfeixfZDIyUkJ7LUWtWvCYJWCZcdu6ZJHQnCUOP\nbDaL5wrmL2hF+h6eF2LbHkP9VUZHJ4giQa3m0FAC5rXE1LzQ11FVhXrDxjRBUQNUDYrFCkiFWr3M\n5LiLH8UudjKKk30bVhUhs4iwgVBCMtkEyUye9ladyLWQkYpiCqYqzj+Wq/92+6ctsA/ft4Wf3HwJ\n11//dRJKnlCqiEDw8EO/4V3/chzvf9dptDYbfOA9X+Ktx7+TtkIXl156PhMTFRQ1ont+J4qqMDw0\nxQXnncnZZ1zO+NAE+zfv54sXfJBqXRD4EX5g0ds3gO0n2PzCAHf+54850GvxwQ+9j9/cfQ1PPLSf\nn/3+GsaGqjQV2gmkSs3yCKRAETq6ojJSrOFLKA6PcOSi1Vh2jR27Rwhsi66eDIV0kvndrezeXsKp\nORRyEIQuuYyJ9F36B/eg6hp7908RBB4yiuWgoQyICGPyfRDjQX37i7P4YBhGhJHH1KSDGwXkC4WY\nviM9/MCetfKrVmN3L11LgtRi4ncY49bJlE6l5EzzMpsxjFhJVapaqGaCbFMBTY0VWtWGj6oYmEaK\nial6LKRUdHwpaDgB5ZrNQGmK5zZsZeeBvezqHcSRBgPjZS686GomJ8doak7SP1jD9XUUJUXoFomq\n4/z9qR04viCpuyxfuYiNrxzAlzGO6bsOz23vnzXJAUhogkqliKaDllRob8nS3tnKkoULaDarHL2q\nlU2PP8u/JhK8c/ECxkPJ6OZeomGLJr2JvfuGiHybVDJH0syRyWReg58qioITCTSzQCgANHxfUq87\nTE1NMTm8j9acwac/fwKf+OxbGdwLGg5dc+Ohiuu6s0V2htcKoCsuwteQQDKvcuyJRxMEIaqhU23U\nMQyDzdt24UWC0Smf8y64ib7RCo+8MMR/XPBrzvzGdfz5/pcZHBkBTTBenMQNfBzfo+pYpNrT1G1z\nesgnSGVaZy0NIxGrsYqTdRp1G91gNlF1JlV1qjzBYUesoKezmW1bd3LEUfGNL1TAkyHfv/Ecik6e\nxcuWIUIgNJA1G0MIhCpBxtHgiuMS2UWwbMIwIELgWQFr1iwgkknqAQwXQ8LII20kkF4AUkdEGrZU\necdJR+LaksAHxdBpWD6ZlE9be4bmlhSVssXKZQvRp7tzVQj2DhcxRAKr4cdR5paDxMM0MtiWjxuo\ntDSbLF/Ugx9aSCnJJpPUK+OUSiWSiSyeG5HJJRAyRa0SMDFmUak0cNy461UUwcTY68/k+qctsGt/\nehb1ms9pp38f34jNnDu7mkmnsvzuN/fx0Y+eyllnn8nxJ6zmu1d9kocffhgZpomiuKiMDJVxLIP3\nnvJO3vfB48jlU2iqyde/+3lGdwzOXqhhGBL4BmNjYxx99NFcdu41jI3UWbXiDfzokmtR0iqXfOEy\n5i3oZKpYQ1EUOrsLKGSng+HiSebWzXtJpFs4+4rPMtInuP33P0QIg8H+ErsGh7jvwW3cdd9VGHqe\nttZ5ECWolGsIIdGUDFs3H+C9p5zE5LjFvr1jvLx+FxnFJYVNIpHAMBX272nQNSfF/v37yeVy5PIp\nBvuqmMmQndsPsHHjKwAxZYrErAet7/sM9k/NYswzYYAzQY8QszYmJydnXzNTHKrVKp7nkUzG2WOv\ndk2aWUYZhkFTU1Oc8KnHgytt2rloxocgkQrRDYPyVOxipasqH3zvcuqhii1ix6eWtiRdc/Js3bz3\nNefCDOd05nczDIPW9gyZTJJsNsv8+fMZ6huku6DR0+GQEgJN03jDmqUsePNSphSf3//9JzR3t3DS\n246hMTVCYniM0ZEK5VI1hn2mj/PqOBYhxGyh9DyPWi2mNL317cfSNu8QLGmw8aVB1j23kyWHdlAL\nFCZGTCzLfk0U9MzwCKC1tRUpoVIuY2gtrDxsLpVyeVbT//LLL5PKKLS2Z9DNkEKzSWd37INcKBQI\ngoBkWxOZ9hby+Txz587F9+PvtDjh8vVzTkOo8eeZGbTNwCAzn0tRFE54y2oCX5v1TqhWKyxfuZD2\nVIbhPXsZKlkxNCLThLIyPcxT+cPaB1m+cj6GGSCFRRDV8PwKsjFB6JdBaZBMJkAJEMk0YUonYyRe\n47R1ximHI6Vkz549DA0N0d3dTSKRmP0OpJQ89tRuTDVerXieR73m0tHRzfBghZGhCqZpUq2WZ3Hv\ner1OOp1m4eIOMjkFMxmiahLfjyGadDpNc3OWob4G9XqdbDZLvV5nsH+KQ5d3ks6kSSRi74OBA3XM\nZEBbRyzDlUGWxYd0UCqVYrhh2qHs9Wz/tAX2X044mw++9wt86lMfJghsdveO8+yTezn9c99gaDhg\n45btXHPtT3jh2V189zvXsGfPPlzXpVEL2dc7Siqh0d5s8ql/ex+6nqZvX5VPnvZ21n7/Kvb1DSGj\nJIsXtpE1m1CViCMWvYErr/0KjgMdze0ceUQPff2T/P2vN1GyquiGRiR0PM9jcqKGUC1y+RRCzyCC\nkC989FR+eNUFfOYDX+Oe+6/lF1fdycK53Rh6As9O0pVr5rvn30Kk1tm5Zz9CwPwl8zBSOaoNn0Sy\niVM+9E5q9RLLli7j0Sduo9A9F19tYrJcYssrLid/6AQ2vjLB1TfcyLoXtrPhpZ1c+f21bN4wzoMP\n/IYTj/0X+nc1+OXPbyCp5uhozSKUgN7eAyQyOfA1OtuyGHpIS1uS0SGX5qZ22lp1UkmHlpYWtr0y\niWVX2ds3xMYNexGKRBdQq5RQVGjOZQk9F6SOH9gIJUQoYezXCrS0tMx2a7Zto+KyZGE3TugTSIGW\nMDFUH5Qx7nlgA7qWwxcmq1bMI/IciiMVQM4qpWYKlaZpJJNJUmkNTfcJfImZTFErlkhJm7ecdByF\nlgx+wyMQMbNk2Yql1LMKvUWL+++7l1qthOcF5OyQNatWMTVgYaYT6EmTUEgQKorQYxmpniSKJFYo\nCT1JpVKhu7ubbDbL5k17UCS4lh2brDsuV559OJ7tIRSBqhpIqSKlimmmZzH2MAxZuKQdw9RJpdNo\nmkJHRxfNqTzFYoVyuY5j+zgWjA6X0TBJmWmIVDQEhdYcS5YehuuF1Oo2fYMjDA6O4rpxEQmqFW68\n4XbCWeNDZl3OFEVBhDEXG6ny/LNbqdtFGnaNY45ZgKFKqhM1apFP/+QYdsOlLEMGhyp0d3ehRqCE\nkldGyrS3t6CqOm61jqEIMq0dkO/CSLZjmG1I00RNtSDUFIqWxlZUVNUkwGW8b4z3nXoSKgGOL5mq\nRehmQDIZi4TUaRpaIJIceVgntVo9DiTNpnF8lXQuNl2JIokbRgRK3J036gFWI2SqOESl5GHVJZqZ\nRoQKSdOlpZAgoQrMTAIznWbhvPnk83mMtMbEZIiuK7PnsGIqzO1sZ6rmEkURk84EI8PjNGdTqFqE\nqr3+kIL/tcAKIX4phBgTQmx+1b4mIcRfhRC7hBB/EULkX/VvFwkh9gghdggh3vmq/UcJITYLIXYL\nIdb+b8d96ynzSedbSadNtm8e5YiVK/jrM79i175BOWAkdwAAIABJREFU/vDgLXiew6c/+0H+9PDV\nPPHkbu564Ec4DZNipcg3L/ouquoxVfc4+5tX89Y3fQChhLzw3JNoCQNhSFR1iP6RftSUy5FLF7D0\n0Ax/uO0RBoan+NTn3szH/u3LHH/c8ezbOkG5BA17At9NMDExhVOz0YRBtWITRgHdXa20dCS59LvX\ncfMvvstTj25hsj7AWHEKKQRysI97/3w9qlZFxl5UBKFF38ABnNBl5ECDH1//Lb711atZtugI+vpe\n5tsXXs3EWJUoCqgMJfjPx6/myb88xdRImZ9e9WMSSpo3vfE4OlpKfOjUU/jOeTcQeILDj1hJIZvE\ntmpMVRyqnopwCqxasZiE6jI6VWZ83OWhR3bw49vOYmy8TCQ0yhXBM0/u4Ff3fAM/ypJNZjnhuOPQ\nZYJIUdH0BIbqUmlYREBnRwpDT5PNJWlqztLRlSdXMPCDBop0SCcEETZaIkvv/mGcQKVel7R2JKnW\nFSq1Jup2ROTZLFvayo7d+5isRZRthQB1trNG+GgEpAyFRNKkd73PgW2TlCsN9u/cxZmf/zhNLUkm\nJ4sogDQ1mjrb0UVilifbPreV++98lsC2UVTIrprP+OgUTcUq61/sjzXmepJICMLpKyKKIrzAiA2k\nSyXm9LRSqVRe45QFsGNXPyNjNRLK+Kxa69UWgq7rxqGdAeiqQIYCx67g+zZBtUwymeUDpxxDStEx\nkgmWrTx8Fq7I5psptHfR1t1DOp1GkCcSBkLoBEGMlcaxSeBVLRa1pxkaOUi7igdQsTnKDEd4hj9q\nlX06ct2k1SRBmANNp294NCbY62kAWrJpeg8MYSQamJqE0GH/YBVdkSihjp7tQug5pEiB1FGECVKP\nDeCnJ1hx5yoQ7sFom4GXNvGW5Z3ISGHzln7GxydpakmRTGmEYZz24IY2W7aP0rDKhIFCaarG0OA4\n7TmdQFoIoVKrSDLpDJH0aG5J0t6RxZcp2jI6tusSRZBKx8NmgcmWLQMUK1Umxov0D/QSBBG+p+Da\ngtbm2DAnnU7T0pphcmoC4dRjDi4mgUxSqcXx9YH/v1Wvg9vr6WB/DbzrH/ZdCPxNSrkUeAy4CEAI\nsQL4CLAc+FfgJnEwnvKnwOellIcChwoh/vE9X7O9smGUW2//Affe8xBLl/fgejXe867P8qUvn8Yn\n3//vbN/eSyHfzrcu+ClzW7Pccs1tjI6OEapZBkb3EYU6URiybt06OrpWsPbmS5g3dwmmacYRx0EG\n30lSLcHLO/axbnMfN/7qHu6+Zy13//Z5zjr7P/j3f/8Ep33xQn5x7xUkjS6MhM83zruI9o5mfN9C\n4rN/7yhPPbaDlzfsoL9viK+ecTV3//537Ns9SToLBUOw5PBl6KpGNq+jGQ0U0owMVfAcle1b93PY\nkfOYt6CNw49cyg0//Tbvf9+HCKYtAVVVZf6qJchAwfci7n3gOorjNd7zvrdxwcWf58rLfs3pX343\nnzvj3Qz0D3LZVZ/jikuvxUjEzkPFPSXuf+xmdq/vndWHR0GSn15zPtdffAfNSYfJcQvXUZk7t5tv\nnvETJvZOcOyqo7n00jNJJuNYjlD6RH52ehmnMjgwiePYFAoFxkdrDA+WKE5YTI5bOI5DV1cXRCa1\namxXqLkuXV0KffvKOI6DEvoYQYOVhy1m986xWW37TGGq1WpoWswayGQyZLNZ9m4a4O4HzuLUj7yJ\nj57yLs74xAe49a57MbQ8w4MlVJFF0zQs22JqagopJel0mqPeuIiBSOH+F8aoJvUY0mhuoqczj2fb\n04Yx2qxl4swp+/TTT1OpVGhtbcW2vNdEWcNBc+p48BVfdTM80pmlrqZpBEGAaZpYtsWjT25hvBK/\nbtu2bWiaxqmnnvyagtxoNJg3b940S8XBsQWFQoF8PjcL38wM/GLbxZAdO/Zw7mWnYave/xXFHfN5\nY1zY8zwajQZGIqC1pxU1l+KF5zYReGL288x0vDNZYOWJ9MEhX+ixY8cODMOYTdn9x+2/Ei4899xz\nB/0JMjrnnv8pVKHjuxqaptHZ2RnTJ2cUb0DZEXQ2ZWhqSVJoTsbuW1MeUShn02Nt20bTdALfZGSo\nwvhofVo1GeL5dSYn6hzYN07/4C4KzTqFppghkcvl8DwPy7IwEwaTY7EIyLZtylMepcmQto7k7Moj\nmUzS3VMgl4/dul7v9r8WWCnlM0DpH3a/H7ht+vFtwAemH58C3CWlDKSUB4A9wBuFEJ1AVkr50vTz\nfvOq1/yX29Frerjq8h/R3ZHnN7dej2+FWLUp/v0zp9A9p42///Vujlh1CE65yH3338zg3kGOP+YI\n/nbvz9i+cQPVho9uSDqau7nxtsu47oc/Yef+fUgPlh+6iFBogIIUdcIANm7YyxXfu5wrv38txckq\njz/2Iq1tJv/yjmO5+sIfsXH9frZvGeT441fgRxpSGNRrLseuOYY///nXPP74Fr725c/zuW98hAMD\n49xx/1qsok7ddhhzGpx71rmMFqewXMn6jVuIRJrePVN0dSzipRe3cPLbP8STL63noou+yejoGIcu\nnkdre47+fQ7SKfKFT3yVd578ZtY/uxUvkHzpjPeTNA0y2SSKK/nW2T/lpp9eyO7N47iBRKixMXN3\nZwJch55FBWpRQOiqLJhb4MijDscP6oRKkhCPtlSGW35+AU0tOY5/yxrOOOcjXPiN78xSvuZ0zyUQ\nAYgAL7DRzJgJsX/fAEJAKhVLFxNGSKY5yfZdffEFaAia0y5ScekbqhMAQkuwoifkDasXsnvrPkI1\nIprOgvF9H1WEHH3EQnra0hjCYPvLU9QqVW797cVce82dbN06yO13PsSi1cup1QIGxsvIKGBwYhRV\nS6J6ComMTjKZoFYNWHPMGjTp03ADKnYdW1XYWexjYmqKHgGVSjw0jPwAERz0Kh0eqNHemsXQfHz/\n4LJwhnI3U4AUYaLpCgEhQjKLE8+orhKJBL7v4toCTU5SHBzBCxo0nAbV8Qn8QHLl5Z8lsgxsy4+7\n2HwzMlIxjQT1Wp3mrjlE08f1fX9WPRZGEcP9w5x55hv5xY0Px+mur5LuBr7Esixs28au1dEUla6u\nLkLVYMvW7TRqdVSNWY/TfCFDGHmk0ia6oWCYKsOTo8yfl2ZOZxPpQpq773iCQCWGU/6LTSEgUONO\nVJFQLpaQegISOr4KwkjS17ubQzuTIG2eemYbg/v2ktQVEkYWZPy+Khq5fIJGuUF5wqJmeVT9kM6C\nQIgIzfABSUsuRUtLjmQqNpAfrVZRVWLKoeUh9ASHH7GcVLLA2EidiSmXqaKFIkJ0XSAJ8aTHwvkt\ntBZyJJIabuQxWbJpas7S1NREvV6nVBF4QQ3Lsv+n0vUP38X/t61dSjkGIKUcBdqn988BBl71vKHp\nfXOAwVftH5ze999uB/oP4Dl9RKrDD675MW9YM5c3v+0ELjjnQnIZn59cfzXXXfN98nmd8z5/AfkW\nwdwFBf7j9K9Sb5RQI51li5azeEEbZ376HIhcdKHhBSV27RhGKBoL5nZiKjkUTXDIwk6OPa5AeXyE\nOx+5im9/5wx+eOXPOOfizxKS44unf5i/P3or5553cQx2i4isbEFJWdx+952cc+G/ke9q48rzb+J9\n7z+Z7194PT0LEwRCR1UibEBRYPu2IRYuOJLbf7sWNdK59toLueTbX6ervYfLvvUFAiSjkyNs39XL\nMw/v5pf3fItGyeO6m7/LAw9u5Iorfs03r/kKp3/5cq78wU8ZrZT45Gcu5qrbz+OrZ3yHb5z/I557\nejtBvYqRUMh1N3PT2htp6dCYO7+Z/gNT9O2tMDU2Qu+BKTzFJmfaeH6dJx9/ge4Wg7Mu+hhOxSYU\naXRDsGv7ODt37UZKlVyuCRnmsSw7Tu8MVTShUitP0NoqCFFwGgqhnyL0A45941IGigblhoEMIPJs\nlh7aRiPRzIubxqgLkEGIYQgMVVIruvS0t2JXQiYmVCYHJ3joLz+gJZ3l5l/cw1StRs1zUJNpEukE\nmpIl8iMCBJlsnjBUEGmDRDZPJDSUQCGtC9KqTxgJHv7bLlRFYembjmb+Gw5lVUuazS/uRdOVg7aC\nMiSI6lSqNY4+aiXlqpzt6GY6uRlGw4wkFxGgKonZjmeGqiWEmFYf6uhphZ6WJhb25GmMTKER0Ltz\nHUhBx7zFXPXNj9CSSlKzQwxTkEinUE2dVC4T6+Vtm1qthm3HVDBhRkyOFTn91JX07rDwpINGDBF4\nvo/tOJjTnXEmk6G9ux3Lc5gsxQPPmRWCrsc3I0Q0Ky2dGWLqug6qiRVmqFcbJJMmY47FnT9+AIw6\nmpIHIiLpACGKGlCdGEJEHoGMB2i7d++eHVCqaixV1cIEF11yKr4wcQIfLZHmqDWHk0zGycwAQg0Z\nmQwpZG3CKETiU6/ZVF2TIEghI4FluVTqAQf69uJ7AjcIsb0MC+e2kUgKsvkE6YzGvoFxwjCmrJlm\nHInU09GBwMSq+/iuoP9AEcuyY7/ZdApBklwuN7sCMU0NQ1XQlOB1F8r/V0Ou/+fRtOvWr+O59Vt5\n5JEHePa5p9m8ez9jE+MUClk8kWTf6DChqWIrAV3LkliRxu5dvSSTceaVmarRu3c3qYzK6qO7EIrE\nMEMWLVrCvPnNzO/IY9lTFJoMDlsyj6t/dDY/u+k/yWQ0smGac846l2OPX8WPr/gdPe0qb37Las76\nyjex7BgIN8OIxcta+dZ5X2NkzyQbN+zh9t/eTq6Q4ytf/xihrDLYHyfLtre3EUYRUajSpLdzxIoc\n73nn6dxz3/X8/dFnuePO3/LL336Hu+68e/pEkogwzds/cAI3XnE35XKJH15yC8cfPYdCc4L77riH\nhOLR39+PBA5ZupCbLr6NVcccRuuiLL+450o6e1bg2GBZNmOT42zrneJvD+3ih2u/xbmXfolPfe5S\nLrnuAtyiRsPN46gR6ze8RP+Ux9Xf/gm3/upWpGJTrXhcf8PlbNm0h+1b9/PCM3vomZdi5WGL6ewu\nkM3rqKrHIYcupDQhAYUoNHnbyXMJgwqP/nVrLGEVEk0NWb3mUPr7B9nXa2OY0G66qKYZk9k1jbkL\n0iTTgo37dnDqu4/gIx99F9+57BZqkcvo2MRssB5IhBqQzWusXNLCkvlNNKwG9XoDRVVxvQDb9TB0\nk00bX+Hbl32GMLJxHGW28AVJjV3bhuh2oDTdqc90nvWaxdLlC5mYGMdIJGb18IZhzDIDZuCEMApj\nf4xQvib2WtO0WdxR0xQUkWfN0cupTnpEeQ/fdZFRxMvPPIasWjTN7eArX3w3raFDueJRrVVnu89G\nozGb0aVpGkEUYQ+Pcc03P0xzVwvDQ2Ui4mwvz/PoLrQQVBsERjwoHBwcxHEdzOkY7XQ6nprPwCKa\nppFIxOKCV4slEokEQijs2rmPJUubWLCog1xa44V9m/jBhXfh+ZNoahpdyxJFApBUHYk71mDLC+t5\n5ZVXCMMIy7Jew2ioCB/qNi1+CRmZrFu3hc2bt+C6DolEzIAJ/DiGPJ1IoakGrutiJgxcN6ApM4mU\nEQsXLsBMJMjlDfywhm6YeEGJ0aEqmqoS+BrFSYvRkRqFQgu+71Ov1xECKpUy6axCKqPQ0ZVH1SRz\n5hbiEMkwxHN9xsbGcByHvft6+ctfHuePf3iYx5549HXXsdcV2y2EmA/8WUp5+PTPO4CTpJRj08v/\nx6WUy4UQFwJSSnn19PMeAS4B+maeM73/Y8BbpJRf+m+OJy/85mWMjRVR1GnTDKmhqNEsxqVpGq7r\nooQxOVxKSb6QpVYrI0QsbUWaCMUjDGJMbMXyDrbvGAMiECGamsRMSnxPoIexBLNUbdDZ1sTExCSR\nAioeqkih6SqhCGk0LFKmSj7TxIqVKxg4MBRPuHMmDcchk8mwYf0WgjBW1lj2FKGfQFU1WjraWbl0\nCX95+AXGx6b49R2X862Lf8hV3/sENTfkV7/4G6OjsYpswfwFfOWs07j5B7/gnPO/zHVrf8t/3vsk\njzx1M+d+9bsIJcAJApJqE5d//xxGJ4f5j89eBrrK6lXtRFIhJKK9M8/EWA3fd+nuXMZpp/073/ve\npZRKVVqaOzn66B527I4ZGLGhRoiiefieIPAFlZJFe3Mb11x7IRs39LFidRvf/c41hCGokUq22WFy\nzEE14lhs16vzoZNP5Pd/egpPqLO4pS4UVh3ew/atg1iej6rGZHREHLcs0Ni1sUhLB7z/g6vZuGGI\n1Svb2bCpSKi4Ma6mCupFl0xWZX53hkMPXcL27TvI5ZPkCialUQsjo8fmOrZNaMdTYNd2OOLEI7jw\nO79FqilacyannHwYiqLSMmox0TvBlkTAG09YiUAlCF2qlQYbtw6wY/cwgWEyM/16Nb1vBmdVNJ07\nvlfgg18vomkBQeiTSqXwvQDHtWdpa1EU8bH3L6Ejm2XP3j7mzZ9DJpMmDCV5M00+28UhR61GapL+\nLXu5+obf0vBMNF3DQcEQGoq0sK0xfnDZGfzsuut4+/vex2N/f56+iRr1esCcrm56N+9BSZqIbBLP\nCXCDg+GYM3/PUNJmPkcUxeGbmoiHUop68AZh2za2E3LU8iYmJ4ZIpLrJaQZqRvDht70RXU9jJARu\nw6V/3wBN7Wk2vzLI8Se/ESnjfDchYwaOlPGNUdM0EqEg29zOp8+5BdM0ecdJK1lyyHxeeO4VqpY3\nS4kyVJ+0bmGHKXQtRRiGLFnURn/f+OzNQdM8PNfB8w10PfZSmNeTp1QOcVyHSqWCmcgQigaRq03X\nEUkmm2RicpSWpm5s2yGb1wh8Qb1mk0qlcL0auXSCTKqdhl+iWLFoSWuce/63X1ds9+uVyorpPzPb\n/cBpwNXAZ4H/fNX+3wkhriOGAJYA66SUUghREUK8EXgJ+Azw4//pgBOTE0giCoUmqlWbwHMQmESR\nTyaToVJtgBREogHSIJJgWQ18P2TRonn09/dPn9xxcTUTKjt3DiERdHZ2UZwsEYYeriuRkU4kBHa9\nhlQUpiwLB4GUAQomgR+SUHQsx469KlN5Bqcq9P/9GRQtNnWRaCgiwtB0EskUtiOwHA9FLYCwSZgK\nY+OjTIwP096TJZUvsPbqX2GaEZdecQ/Ct2nqamfhgg5kJNFDn9pQmSNWHM4jjzzHgf1buPIHZ/D1\nL17AkuVzGRoYJ6lGdKY1+vp2ce/tj3LHH6/lS2deiFB8NNJoQlAtWuhCJ9eU5duXnc7NN97N1FiV\n2+64mssv/yFbtm8ljAT5QoFGozZ94mlEqk6j7HDbrWspTozz5S9cQWt3RKP+DsKGSqQ5LD2sk62b\nBlDNPCEqIpiktWBwx33PIDUNGUgCqZBWXY5Ys4JNG/YBTHdNKooiEUIHqdC7dYxHH7uJtdfdyNYd\nkwRaxIatJdZt2cIxa1YAoPo1Pvj+w3AcB8eG8fES6VQez2sQBSnaO9qYLJaZGquQzQhUQyeh64hQ\nMryrj+9dfBoXfe8eKnWFSFFAFdgpsO0iWdGB78Z5S6oGu3YfQNcTCF1HQSeI/NmCM0Mhi/OiQqSM\n+cEyctEigzkLOxgdnkDVFOa0N1Mt+7O+BDt3jdMXlWgUp3D9JJ3dguaMRtWbQFU9Nq2rk1ZSLF5+\nGDf/7NsIEV+iw/t2MDSwn107dnL0m0+jd99+FvfM4YGHNpBvL7A0KvDy/k0U3RAKWTzHI2w4ICEM\n45VFGEIQhIThDNc5moYwLNLpNI7jYWixwkwXAhGK6WZGICOP3v113v7mZYwNVtGVIRSjm77hfpJJ\nk45584mSkqZFzWRMkzO/+Sk2rNtOJCWaZuD7B7Ht0I8wTRU/CmlpyZDSQupByLYdB6jWphDTznKN\nRjVezkcKq45cwLYtNUJNohkqtu3QWtCpWBJFDUmm8jQ1FyhO+LFxesNmZDSiVKmTMHMoikkUOCxa\n1M7wSHXahMek0bDp6pxLtdIAFEpTFq0tSTwj7pgdR9LT2cT4aJm6Y5NIJ5kqvn6hwf/awQoh7gBO\nAlqAMeKO9E/AH4C5xN3pR6SU5ennXwR8HvCBr0sp/zq9fw1wK5AAHpJSfv1/OKY867wLef7pXSxY\n1EG2ENCcbaVUq7Nn5zgLFnahRzZz585l/8g4mqZMLylAN2IbNaRGZ1c74+OjhIHAD+uoijGLp8hI\npa29icnJyWmT3/iCSSaT2HasgsrlU9Sq9iz+9moHeykl+Xw+VklxcLAwMzVWFIWmpqbY10BMU2ek\nhqpBGAYITFqb0oxVSiihSiRtBAlUGb+XLyPm93QyNlwhiCyC6XvhnI42isUiruvSPaeV8WKdjB5R\n812SqkE2reBGTC8DUzBtTlIpO6w8dAWbNm3GCSIOO3wpG195DlVvjg1vPI8wjOjoaGd8fBzNh49+\n6iMsXbaI73/nZr53zflENPjx2t+yd982srkUjptF0XyCsMGqec1s3zNOWepEXjS9zHI5pL2Zpu5W\nNu04QBgoqOrMlF2CiD1bk8kkP7rkq3zyQ9fgOiGOMcAhRy5k25a9zOmeTyYVks1mWX14ASENipMW\nnm/x4gsbOPOrn2Byosrf/vZ3jly5miiKaG9vx3Es0kLGk2kZokqf1Sccy+lf+QmhGtHdlOY97zoS\nBcG8iYCXn3iJ3p523nTiEjTN5O67H6a1ax7bDxQJPB1Nl7OJuzPcXCEEhmJyzMK9fOPsN+FYHXz6\n60+hJLLY0p9dDuu6PovLtrZnUNwqXe1N1EoBiaREESq5ZBIzGZHLaaxYeiiKotDT1sHQ2Ciu72Gk\nE6x/agNvOnEOknZuu/FulOYmLvjEv/HgrXfxQsNgJAypBA4ZqccWfJaFaZo0HB/LspgzZ86sRd/M\nzWLm9zpYB2J2Qt60CNWm+JxXQyplC13X6W5VMYQgYabJpZO84fCFvPnEN7Brfz+pVArP83jyoRf5\nl/e8CSfwiaZvEEEQEDje7ONEKo6aadHTRJk0X/zGrzBNnZNOXM6hhx7C40++TK1WJpfP4AUReVUF\npY7QmuMbXBSxemUrW3qLKNMhjumMQrVWJAqSpFIpmpqzWHaFSsmb9l1IkEjGXsGqGlMBVVUjkYqQ\nYQJErH5LJ0MqVRXP8+JmQKp4YYlyzY09DDyXH/3oR6+rg31dEMH/39v/4e49wywp6/zvz135xM5h\nuifnGYIMMCRFRV0X1xwwrAkDrsgKLoiKKAsiqCuIgCigIIIsiIiCikiGITuBiT2hp3s6TOfuk0/l\nuv8v6pwzM+4+1+Ob/3XxPPWmZ85MV9W5q+pXv/ANQgh53Inv5uGHb+eL51zK+td1MHBwFonBO9/5\nL1z9ndt50ykZKpGOCixftoCB/nE27xhGFSne9s/refqJLQRRmVNOW8vWbZP4QQ5V6hRyPiefPo+d\nW0dZsHAeVhLGRirM62kHBKqqEUQFElaWqhdgqiaWBXY1tqpBhDUXVBXfjz3am5qaKBQKR/SuZE0c\nRBEG6XSacrncgO1EUYRQQgJfoigqXV1dTE/H+qTd3d1MTk4evha0tbUxPT0NxNnf37uW1ntGpqXi\nOgAh6XQax3EQSkgqlaJSdhFKzepCavR0NTMxnsdMKzUXV4WU7lPxNTRdp7etiVxepbMpw0x+ks+c\nfQ4P/P5+DBMGB8fwFZVQRFhE9PSk2NFfrrkHKBCZpLMqy3s7mC1PMDnuESAavPx4jeIAi9QxDZOj\nV3XT3tSGpqkMj40zNLyfwf4c6eYsZiZFdWaMj37ozdi+z9jBaR768zMMlzR0T6NF0fj8eW+gp7uF\n8YP5WJijWsVQNNo7UzQ1p7ErQbwOlYAb7niaVAY+edbJ6BhY40WmNu/EWbSclrXtmEbAhg3b8KXF\n4HgRT0kgPA+VgDe95Xgef3wjmALTEdz741MZ7J/kytsHmJ8sc+Wlp7Jlf4XrfzbMrHBQAh3HcRqB\nra3FQlLFKflkknGbxLIsUlYMYUokEhgYGMkKp550AlIKgqhE6Gv0Dxxg44t9vPX09SzVNRKRxmC5\nwObRMV7YPVOTyBQNqFO5XCYSGp2dnQ1Ew+EQrvrgqY6GCMOQkBBFguH7nPHWpTy14QBCi4ONbduk\nkyrve8fJjI0WqBZLvPFtx9KR1tizfYy5Up7Xn3Iiy49ZwYHBIVw7IlIOmT7WWWNKFB/bNE0U6bHq\nmOWc9clrcaXOyqVtLF7SxvhUmdxcJcb/KnHC8o7TVrNt7wiWZVEuuYQyICxUUDNJgiB+tlJpk3LJ\naWTLHVmFuaqPIJbulDJiXmuagqsS+kEDOubZTuyuUIfsGRLHFqgqFGyHtUuaGRgqNNo9l/3nN/+h\nAPuaZXI1Z1r41Ee/Tdc8k4mJHK40+dsL+/nN3Y/y7e98grKv4SoJHKmzY2AEX02hiSQPPXwLf37o\neZ54+lf86pe/5PlndvPAfT+g1VrMzMwMjz9zD06xnT/84bcM7MuzZ2eBa6/7L1RVsG3LCC8+O4Br\ne2zbOEISWL5II6VJmluyDB+oItDJZrJs39ZPJqPiuRXmZmawLIvRkYm47CVARAJNFVRtl0q5giIE\nrc2th02hlVpgjBifnAAhSCYthodHjqCGQlALvtDcko1LTcLa8EGQyWSOwF0qCmi6Git6AZlME8Vi\nmTA8RPHTNI3946M4QiJIIEgQBhrlQCcMdFwb9o5MM1OZI0yGzFUEP775eoqVErsGZvG0AFVR6GlJ\nookC2/ucxkMKEQkTli3rZvfgHg4c9HEViWnG+FNdVYAYllXP+m27yuDAFK9s38PL2/YwODBGImXx\nk1u+zhlvW0YYhhiGVsN82ixZ1kugdtKm6Vx7+fu55Mr3cOsNz2MaacqlCtVqmWw2SyRDbMfDdWKD\nRN/3Of70JSB9PC/gqSdfRYkiou4sK485Cm9ghL5dewn8gETCwrZtgsAn9OISX1UzPP1YP1KFFkfl\nv69fxDXXPMlFdwxT9RPstps46yt7+evvdnPbtau4+UsnkFJVTEsHRUFT6y/H+GVjmGbDldVxPBzX\no1K1mbbnmMgpPPX4RsIg4PGH9/DU41vwvRLFU2MgAAAgAElEQVSndbVybNHk2NNPYbdeJW8G9I3H\ngyFVA9eNz7Va9XndsWvo6elpZNz12UW9xVFXsKq/9Nvb21FSsKo7wXtOmODpF0YJDZMwlIcYYSLF\n5PQMc7OzhIHEc8oMDxY48ZS1vP3Mkwix2b1zANc+5NFVP0YYho0g31CYAw5uHuKyr32UIAoYHp7E\ncVROOH4d2XSK0PfQVBWk5OWN2ynmq8xMl7CrEt+LOHpdZ5ydWhLdjKiUZ8lm07X1UCgVTTo7Y9Wv\nMFCQkYEXOIjIQsoQx3GpVquEstTA4eq6gVBd5s1rr1UgEaPDFexq5QjL+X9ke80G2D89chPprEoi\nkcKWKpoMufd3N3Hq69exe8cOpJJE9SMSpoYWqmx9ZQ+9PRk+8r4v8tEPncX4wYNcfOHXeOsZJ/PV\n8y5nbmovHzrr0zz5xHPgFPnWxddw+puPQVOS5GYm2b51kDefcQpPvXgH7ZlVPPP8L6naNn378kzk\nKjzz5Fbu+u3VBFWF6fECN//yv8jPOOi6Cmi88mI/v7j9JsJQpbO9CakHaJbFxz7+WXZvHyTbXKRS\nniTbHDE6XGDkQJ6Wlnb27R2ms7WVjjaBU56hpzPFskUdiMglkzWRxEG4b+cQ01M5Aj+Gk/iB3SgD\noyiiq7uNMBCoqoKiSEwzHmjkc0UUoZJImthVH0UYhJGLHllYqorj2ISBoKOzFUXGnmeGqZAwUpiq\nxsjwNM3ZCMOEuZKNoUeIUGfV6lYOjBUYL2UIhdMIlm0pnaOPsdjRN4AXJDE1BVMxSKqSNau6IARF\nlQjlUFaDgJJv49o2RBFCV7ErgpeeeI5lC+ZjaoIT1q2KBzJhgsJ0me998wN8/KNruez7D/KNb/+B\nolbhP6+6m8H9k6haPEk3EjpC0SmX4iGZaZrs2TrIBee9F8eWzM4peNLHU0JGFJuepMXo3/YzPZ1j\nXlcrMoS2bBJFxEM5N6jiU+LoTJk7bpjH+Rdv55W5dtKKiZFQUCMV1QzZnEtx9iX7+eXvNnPHdQu4\n4uwl3HPNSjqUESQBUsY6DdOzZSp2nNX5Ebh+hBdICDVkGDJblTz74kZ623VWhD7/HC3h/ed+jL2L\nBH96/ulYIjCRwa46SEXgOC5+UGLtih4uufBdjE0VGkHtcFPC+rr/PSEgl8vhOy7Z4CAjORc/kkg/\nFmqpB9h8scSuvilOOWktqqrR0drGiqMXk6/azBVcQkVHqAGRdCmXyw1IWRAEccUm9EaWGAQBARGO\n6XHcqsXoisT1FHbtHqRv+04W9HYRBS6+7RIFESXH5I0nL0NGEIgqmmKwY28BVVXxAo2KLfH8DFZC\nUCyU8EKBrbuEbhXL0hsQLWims0tHKBJJiG7oBG78Arcsi2rVoZRXmJ6ZiluKIoGP5Lh1C7BM6xDL\n8B/YXrMB9vMfP4+JySLjY7MIdCYOVmnvTLJ/x0b6940gFIWmpiyVqo3nwqc/8g6u+O6lzO+ax1cu\nfB9+1WNh7xK+cem/8W8XnEvP0qVccMFHuOfOP3LDTy/n7e86hg2PbeWhP93IbbfeycLeTnZu3cK/\nvPFzfOhTb+O2Gx8glUwQKhq5guSYtcu47KIb2Ll7GFVYdKTbWbZ8AULVGD04yXnnfoK/vfgihhow\nNVvFD1VeeG6AX99+P8vXLiA3l8YNDJ59tI91x67iw598P08/uYdfP3QnM5NzFEsOkZJGOmUGD0zj\n+IJXntnHzk1D9O8b4YafXMv0hEffziEMtRMhJLu2jzEzVqKtFVKmzqqlHaw/djGtbU1k0ibdLQa6\nIhGRT+AWSWseq5d0okSSpG6iRqALhUQK8vmYS9La2lbDgko0RWF+TxPFSoDnGfF02StDMMPLm8bx\nQhDIWjakIvFYvnYFf9tcQAZx786SCl3NFpqlsnfPCAsXd8Q+TEHcrlixsAUpVVwnwgmgWPWIVIEX\nemzeO8F9D7yCgklXdwuqqbBx+2627R7lSxf9jJ/etY1i5CNFBLLK5Zd9isULuxFeUy2IKBQKFRTD\naGRSqquwfHkrqnQpeQ7PvtgHYYDa3YxqBixKGejSopCboWNeB6YWgeZjCEGLmOb681fwne++mc99\n5QAzRie6CBtBTFFq1t1hiBP6bJ8y+NevDPL0U0/w0h9f4ftXvB7fgzAQeDLuzVqWRbHqUCpVG1J8\nebuMXSozeXACb+s4bSM+H/z8+9nVHfDEcy8hPA+haESGyuRETNF1HAfPKTO/rcrszDTPb30VR1bo\n7e06pEVwWHvg8L7r4XrFWpjgiitPYvP4anxfEkUKUVSzIY8EiUSC2bLP7sExVq/qxXegmLcJPAEY\nSGEQRCpeKBqBqI4Nrh/fPczOXNM0PEVl26tbuPALbycEZudcNB2WLOuMrdkNDUVXcQnYsmU3yYSF\nomh40sOPBEctbcFQfdqaLWyvxPR0AaEoaPgIPyRfimhrbyUMoFr1mJzJU66WkR4gBbqmoyVUCEpg\n6LS2Jenu7iaZ0jAUFYhQFNjXP4fnB8joHw+br9kAO7jP5oE/XU8+V2L3zklWrTiOay+/HlOWkFLy\n6sZhdm2fojPZwrqTerCrLpd+/SpGhysoqs45n76CSy//PAjJf3z5SlYsX8ZnPnMed95xNb+49l6e\n+eOLbHj5Tj78/i8j1CqtTYtZvmIpf3nsTtavX8fExEEAElIlLTSuv+kSpFLkS1/+BDf//HK+/71r\nGRoaQoYJvvD58/nQR87gscceO1SqV+HlZ++jq0dtlGCBp3Pd9Vfx9Us/x+0//g2PbriRH1x0DZlm\ng8CLLYVLgU5VVOnbOczF3z2fe/54L10di7njFw8wOzfBypUrufO+S+jb6vDUczfzyY+fw9yMw57h\nCXYPzvHiqweYnixRqQrGcyGhr4A0UWQrRTdgx95BgiA2s5MiQNU9XNeNOfNUqFTi8nrVwk4iz+fA\nwHRtWBOxam03VZkkX002WESxnzx0ZUyOXbGQrVv2oNZpp6rG6mPasG2bcjFEhoLx8XGamppqQz8Y\nGM7hCb/xwHmeF7dUlFh8Q1XV2jTXpVoJ2D8csXt0lJY2k1AIrIrPlZes512nnc7prz+FVWsWIkW1\nUQqnUkmCIGzQWYPAZ6J/lH/79MdwHIfxkdpxwwBlQZZV6SQTO0ZYdUwvS3vaSEQKy7p6ueXKDF/5\nbAcHBst85vwDzJoJ1Mj6H1lgvc9e5/1rmsbLB49mqprk1p/vAeKAE4VxBlksFhu013K5TLlcITec\nJz+cp5uQee88mT+XpvmPqx9C6AFhFOHVHBSUWpLhuS7FYhGJzYfOejetWg/f/KBKSzDH9Mx0Q0Wt\nrqhVP+d62Q40IFsLLYMLL91KNfIbhIN6cK7jf+0KDB2YQtMDHnvs0UMQsMPmOYcPzwzDaAz86pkz\n0FgjRVFIJpO85bTVIOP1mZmqMjI8QXt7G57nNTQXDtpJjj6qCynjIC2l5MDABFFgMTlewrIsPNui\nqVlDYAKxIlqhNE62Wael1SSVUZiZ9DDM2I0il8sR+DrNzc1EUcTstE2hUKitTdTAH+u6xsJFnaQy\n/z8IsD1L5/Hv53yLwozD8iXt/Oi6c+np6WJWpBgaKXDaaW/ggYduwA59tm0dY+/EBIWKy68euoK3\nvf6z3PfQrZz1/gt479u+xIMP38zgvkk+9rFPsHHTKF7kUJFlvviFywnDiJ1bcvQsMNi6eYhbbryN\n715wI8+8uIvexR2sXNHOmqNa+P3vHmdo7wE++ZF3csXlNzB2MCY9WIbK+959HNViRLHq4IUangtC\nVbnxJ3egShoc8OOPX8qvb3uQyy+5hq9980u8/OQ2fIoMjUzihR7NLRkidHAUBAZDO4Y591+/ys9/\nfjlTc7uRQZp3vucMLj73J5z7bx/mwM6QkZEX8SIdZEQUeMjQZ9HiHgBURYIukKokEB5+KIE0iUyW\nku3ihZBp7iAMBIKYF14pSdo7Muw8MIQrJVIVRH6JyC/z0gt7KJRL+LX+qa4ZHH90O8uXdpFsSbN7\n3xRCtzAMaEtJWtI+fX2TuAEIJSKSIa4TUijO0pRNIv2AUPiYHNmviyIV14n/r+/7RH4JJQrREhpS\n2mgyQWfHcm787pmc+U/H8ob17+aUN63hmcefZOumEVYf00I2m0TTQFWp2cnEWVQQOvh2xAnrO1C8\nEJsyO/ePYyga2rxekr09uBPTPPt0P6msh5ZM8u4zj0JTO1mxtI3fPDUECYeEooFwGucMoIlDOgSW\nZeGFkqobEOHxm5dVXh7Ra/9fEgQCX6q4ocD3dFwnRrc4uTwdArrXrWBQKjzz5DawkriWx6vbR9GS\nFno6gZqI3W2rjkFYE11HNPPEfVt421EzfO3bs1xz+Wo6vHwjEP29PkAd4aCqKsgIKSTX/WAVu6da\n0CO1kdnGwdxA0yw00yCT0RiZqlDJzfKZT34SxwtwQx+hiEO6t7qOah5SQhNCNGYAh0Pd7EpAGASE\npsbevgE+84HTiYTCgdFJCsUyQegio5iOLKVG4FXZvXeEpqTSCNxVYbFiSTvZthZUCZ5foFAoEoQ2\ngZR4Ucj0TIDvBoyP5ZkruJTdCqmE2aA167qgUA1J6jrtHU3kcnNUy5JMaxZd19A0FaRaI6T840Yw\nr9kA++tfX8WJx63j3e95C7fc9kMu+NK57BwcREj4yPv/law2yw++dTVu5CEjg82bdnDRRRdz1Xd/\nRGsSSlOzCC/Bp8/5V26+4VZGhka54+a7WbAwzbh9gEjT2L13kDXrerjnz99n08t7ePy525jMFbjw\nO5/k3t9ex9D+GXbtm2PvgQI/uv42Lr/+Gs75/CU0J5s498vng5AErsOXz7uSD7z/fC658NsoVDj7\nUx+kKdPFE48/xsZNQ3T3NjE55vPw77ezcmULW3eN8fjDD/GnP/2FRFZj0eIeFKEzNzmL9EOWLlzA\nqSeewF8feQbVLCGEoKkly/z2NHfddjep1jLvOWs9D/z5F7y8vR9UA8+VuK7HkiVL8AOXjs5MIzuR\nEpav7I4Ft4VPpewShSqR9Bg7OAUiYOmyhaQshda2JH07h5GBhh+FNCcERQ/ydkgglUZmCNDRJKlW\nNKamphk+OEOogOaHrFsxj7IXULUTKEpNC1UEKGqEldAIfBDoJFKHtFeBhkg0gC9VgpqYckerjpG2\nONg/xpuPX8YFn38TupfidUedxpvOOIXdffsZ3NmPHnmcdNoyTL2ZkcEq4+Pjte+vYnvghi4iYSCt\nkImDY7z9jBMp2j6bX53CV11sRVAm4KhMB4GMsbpHH93OU3/ZylT1RL581QGKUeaIwBPvP87cvDBo\nZK8AyWQyzhKR+KFH5Psg4wc1CAIc10VWXezZPIXpOYbHp8BIMuLBxIFRVCuDldRR1AgpQoanoOpV\nG24KWiLB2OwMHW2CUtkm29NC3kryri++kykrwZe/V+bKby6iW+SJdHlEtnr4i0FKSRQqzMPgpps2\nIjWjEYz/HmUkhA6Ghiqhu2cxxblhnn9m05FauqEgqtlm15lvmmo2EC9CCLwoaBCEokCFSMcwmnn7\nPx/HWe84Gcd2UVWdjs5WWrMp/Gp8bwh0JvKC5Qtb0dGwVJOKAwdnpinn8oRoqKaFFySwEj5ShAgl\nIpPNINQAJfIaw+CDU3kiGRAGGo4d4lQErW0GuVyeVCpLFAly+WlMMyYKJZIqmpoilf7fNRj+t+01\nG2A3PPcsYWTzhjevoZir4gUWEN8c69/QxHlfPxe9WW181tTUxPpTF+FN5bj+l1dzxaWX8dZ3ncZZ\nH3sLxx9/Anfe/WPuvvcGfvqzn1At67iuSzKZ4sDAFBd/9sfc9etr+f0fnmJP314SRob8XKx1qmhV\npifLPPnYw9z3iwfZsXUHz23YzAnrl4DUqYiQzXv7aGlWWb66nT1bI+6563Euu+I8Tn/Dm7jz7pt4\n/OFd3Parq7n1/sv47UNb+PHt32f3SI6XXtlNf1+VyfEK5byGY0c0tUYcHJ1j2ap2jl93PIpM8fa3\nfoyNL47Ts0hSLobMDZbZ9NR2RgemUTQt5rqrKpqus2vXLkaH8owO5xoPiWlq7OubazwE9X5bfe00\nTaNUKuH5NoVCKeafi4A1i5sZnSkT+jrIOGOql4yKojBvXifDQ2M41ZixlUwmSaYV+veMEQWJIx5g\nVVXJZrMNwL3necgwvlGFIhr7rv870PjzwoULYoWu3jZOev1C1h61iq4mQf+mnTz7x0eYnhtCt2z2\n7NnPSy9s5a9/eRY3GmTJkiWNYFQvc6MoQql6TB8Y5X0fPrnhszY+WkEjomSBmo1omirjexFtbe2s\nPqaZX97xe7q62lFU0Vi3epletzypr2vdh6teEruu2wg+QRBQLpepVCrIigNlpwHVMk2TOb+KRLDu\nxOXYto3n+Y0S1XVL5GdiyFTCSjI9VWV4104m94/Q3NxMwk1QkBof/8KGBgTrc1dX+MbX19ITDh8R\nWBvKarVrFCkB11zdyfOvptEP+7x+XeossPq1MQyDcrlMUVH48HvOaLQh6mtS3+rHq3//+jnU/yyl\nbAxqq4HHcN82qjNTnHb6Ubzy8iYqlQpNLSZC8xrn4noeE0NFWtoMuualaetIUSkq9C7MHoGOcCo6\ngRdbyORmbXLTEZkmswEXc12X7nlZkmlJtlkl3SSYGKs2rqGmaQiZJJVK4bouuVmPmZk5HOcft4x5\nzbrKppLtlMt5+rb10bdzO24YohslspkM06NlNr78LJ4aoRGgCJu3vvVE7rz1XlzfobRpO05a5ZLL\nv8DUZIFf3HIb73j3G7ns2z/AccsoSsj8eQtY0DmP5mQL11z/DdJNOj/70W18778uJNOU5ZZbbyFX\nrBKEAR//xCf4492PMDUzyEc/+V6u/N6FXHT+tyg5DhoK/3rWJ9jw0ga2vjjAXfddyomnHMWnz/4G\nl/7nV7n9F/cAKsuXr+Qb//F9WpqbSCdsXnxqM5rSzjXXXcLAnjGOWbeImdkRqi74ATz++CYU1eSC\nr32MlnQnN916Hj+98S+c+61zeMObT2agfxtDk7MYmkoUeiQtA1WNM0xEGDuYGhaKIvD9ABSfjuZm\nXMcGVdLTnUJGHprq09mZYno0BE1FCZJYqRK2Y3NwIiKQkphaLFEUgZQxxOyo1YvZ1T+A42nI0GPl\nsg40pcDUjEOkqmgahGFcDje3JvB9j2rZp7Ojm2q1WnML8FCViMBzEUrMg69je6H2MIqQYqHCqsVd\nBEFIW7aLrZt38IFPvolCLs+ru2d4/qVd5O1pzPQ8hidnGZ8M+cB738zM5Ay56SoRIU1NKTTVwLM9\nkAFCUXAqZUoFh/FZl0KxxJo187GaMphRSMaJ2FNW6J3fQjKZJps1mRmbpbklS8LScBxBFEmElEQ1\nacl6wKi7SFSrZaKaLXbcq5bguyhVB8uTGJkUoSrwhUJkWASBQFM1FEsy3D+MovoEnoHrRAgBQlMJ\nPIej1iwkjCIOjkzwL+8+jb8+Mcm8RWl8qaFGEKkSIVTqVuJ/fH6M75y7lr5X+8mHaeCQswFAGLkk\nfehu89nQZyNrxJjD2wr17xBrLwhSpmDlyk7mtXeD4bPh0c30LumAmrW5lOD7IbpmIKVo7C+KIjRd\nNIJ1FMRtCsdxEAmD6oECZ7zrFK6/eQNe6LN6+VJSaYtKxaNcseMWjAAvEFi6y2QuJPB9pBqRtUKK\nhRBNFRiqglRd2tMGiWwTfuDiCx9LhSjU0DUdNwyxKzblkkvZDiiWYq3jbBNUyyFhEAdZz86RSWsk\nkhlAUiravPDCP+Yq+5oNsOtOPBkpoOr72H6AFNDc0k0uX2JkfIxi1adS8rASTZQrPqMHp2ltacYT\nkoKlU6xUeWnDRna8up1yGPLUs8+jiCCe4kYVpC1pbrFIp1V27tjByNAI5VKV973/TK774XWMHBik\nqdWgKd1J384duHKWrGXQ297JTT//BWEUoik6GSPD+z94KsuXzCOd1Xjm8R389eFH+dQXPsLqNUu4\n+5b/5qY7LuPuu3/Hvr5p7rr/Km679UGk4vPIwzdz0XmXceMt32T7q9sJkRQcFy/0mNfcweXfu4iz\nP/E1NL2Eqbbx0GMvseHpDdiFcSan50g2GyzobaGnp4UTjl3B+hNPpbWliam5MWzbJ5HQEKixiLEK\nrheBohJ6Dp6rEMoyoUwyNxsiVAWkz6kn9LJnX4GqpxDJGt9eUdHDInpSQwnhqKMW0L9nAl9KwlAj\nlZJUKiFVW9QCfEAY+lgadHXPY3amgKYZBJ4gkdSoVFxQbDKZZhzbR9dNoIzEPCIzBIgigdQVJkaK\nrF7WjG4k6Jw3j317BgiF4Lh1S/jjo7uZ9TT6B2eYKbhU8nm6OjKgaEQiJGGlY9UoI0JTNNRAYFdc\niqNF/uVDp/PIn17FVSMWLugkkwCjNYuze5KZ2Qo9a9tRRBLdUGhvzZI2JaEtaG52md+dIKWZVB2H\nhKGh6hGppMrMVB7X95BCxDbfUYi0HWShBGFEZ1cXM4FHyXOJVBWlRr6INWlVFKHhS4XXn76aoYG4\n8kDEgcmrBqw5qptnn96CSgsPPraJjl4FxzZBHKroDg+QijR4fKPD1z7bzujuCtOhILQjFNVDSgVb\ny3PNv/Xwg5/nCGrncriYTf3v9WonDBwioXD0yjSVvMKCJQnaEr3kqwUMRUPR9IZyVt3Opj7AjK9p\ngBKB73qxHgWxLYz0AzKLWgh8j5c3bKQgBXOzOZqyBtVqgdCTGFYiZm0qIabi4Tk6qqEQeIJCKSKZ\nUvCcgDgv0OlepDMz7hNEPkgDTdioqosnE0gZolkGS+enCWwD17URUuLZGkHgIpQIGamEErp62pkY\nz4NUCMOIF17c8P/tAHvC+lMaDeh6mVGpOWTWJcQURcF1XaSUtLS0UMwXQI3Vg3RVw0PiEKAKjWQq\nge866JqBbio4IqLgVMhVSkzm8lT8KlP5Io888gQFxyfSDKQvan5NktbWJkYnJ5nMzcbAZ0KspAKR\nz+aNOxgbLZDLT2IYknTWZH5HD/fffS9oFfRIYdfWTbz3Y2/Cm7XxqlOkPZtHHt3Axz9zJr0Lu/jN\n3X9lrjiFFWmkdIO1a9bxyB/+wIc/+Ba++o0LeOrJx5iYzbG4q4VU2sT3fd72xqPIGCYGkrasiuJO\nsXZpJ8P9fbSk09ihIIpAUWJRZhnFD56pqyxZ3sH0hEckTUAjFOOEjs++4TKFwEblUMmLoqAKBTUU\nrFizkD19Y0gJSc0jY5pEikSgHipBZUwnVQXkCiWkhPa2NqrVOptMRSgB1YoDxMfQzZAwMhttgsNL\nPUSEjsLIRJ50SiGfz9cA4Rqe51IuzzI2lscLQqplOHn9Ano6WxvnPzU1Q1tbC1ZCgwgC26FcLuNX\nPVasW8Zfn3wK20tRmC6yZmUHrohIVCIyqGyfKdLZnUFVFSzDjL2dugy6m5tozSTp6lBYvbiLZfOz\njB8cYWjUA6VKFKlI10e6sSaA5gS0zusg71UpVMsIRUU3a26qNTtyTdNQtUPrPnpgBt/30DS9pgan\noYSSY45dSFtbC+3zijz7zDjtnSlcT0MK5Yjsv77Ve+aPbpSc80Gd/NggZ3/5Qp59ehMIQaLayVkf\nnMe9T1fRlKjR0qkH6COy15pFOsDJ6xaiaxrNze0kmiQ7/jZEx7xWJKJBD/77Xq7neaiaAjXvvPq+\ntVqrywDIV1i/ahVPbD2AkB6rVy5k/vz5FAtVbNertSsCkmoCYdiEkQoIhCI4dV0rXqSQSKo0NTUz\nNWmjmQWCMI4jWpQmmVIoOyCICDyN4uwMni8QqqjtGxYu1fFdC9MKyaSbcdwSnmcThnF82fDc0/9Q\ngH3N9mB9L07lfT+gWo5QVb0BFrft2FTu8F5JuVyGmoqO4zi4YQACXNvB86tMTo7jhgHzFnbHU20J\nRBIZRgSew+RYDhmGNLVmCTwPISWhEoEOwlCYmC6SyjZjpZKEaAjdwg0E1UhQ9DxK7hxzVZs5u8qB\n8Vn+8OdHmMhNkbclf3zkCUJV54WnX+b+hx5k7+AwJd1Emh6/vvtBrrnqRqTMk0km6ZnfBCJicPRv\nGIkqkxMlbvnJz7Fdj7e/6XWsPXoptifx8ZGeQzIZoelx1vvtqx7kgouvY8Xq5aBEhCFEUrKwow0h\nk4gIUom4hNu7bxo0FUGZ007qJHDSlAMDN/Qx0BoPhqIoKFFIssli2aolDO4fR4iIlat7aWnJUggD\nNNVq9DoRAUsW9BK5AY4vELWsStMFMnRImRph6KEqFqARqQKpKbhehoQRoshqQ/uh/lAGLjhCxQ0V\nNm/bj1QNSqVSQ0PiHW8/la//+7s49YRlrF3TTlsbJDJJxidcJqZn6dt5AKFIigWfkBirmi8Vmc7P\nsGfTfq757tcwpU+u7BCqWTRhIlZ34+Sm0SYKBLKWTSuApmAaKRKpDKlME5lsF1ZGw8oKJmdCMMAp\nhuA4eCWbyPVZvmw5TkpnulJBS6QwUhk00zjE2FMO61vKQ0pXrq1y9LFL8QMXTU2giPj7Bo5Lwkjy\n21+9TCoLhVxIIKMjyAP16X2dmgygCpfv/7bKmW9cyRN3XUkkQRcqbzm+n8+d/yoaTuOa13/WYWeH\n6y+oqkkQQHkuJAw9dvftY/nK1QgzdkuWBPjB/96nTCQSiPBQSyUIAlw3JiUoioKvgZpJsuKUHvSo\niBPCK1v6yc1Ooqqy1lqIXyA5x6e7LYEmDayEiqKEPL9xmmpFkptzGJ2YpFh0WLVwGQldxdIEvnDI\nFUNazQrdnSkyWZNAT9PeY2BZBomEiW5E5Od8fN+lUo7I5QoUcjHlXNMgttP9x7bXbIDdszPHzld3\nMjFTZnCkwvREha4OA0OBjtYkTWmTyKvS3mJhqBEqAfhQzQco0qO1tZXQlegygakkSBoZAA4OHUCT\nGk3JLDLSEUJF15IIoaCoEbm5EoiA5pY0UajgBy6qWuMruwHFQjzF1TUTz41ABGi6wA3ivlapVELi\no1oCLwLTUklmWgikilQVQlQ0M4WZsaMzmV4AACAASURBVKh6KnoyRTmKCDQNX0qm5lzQddJNbTik\n2DfWz67BfoaGhti/fz+DwweReBx//Drcqo0WeiRJIcMihqXiC5PmTAZTzSJEhBJ5jM1MoykVlq/I\nEDoGodAoViElJAsXtPHohgHKbixOIoVPIkrT2qyiEWEqJosWpulo7qR/7zjtaXjdql4G9o8yW/CR\nNddVVQpSVpwBDB+cwQ3jLKi1tRVFUSgUCjS3tVP1QtwooOr6tUzIJAhUWjIJwkBgGil0pV5KRkf0\nNJ3QJwhVtm4dRGohY1PjVBxJxQnjl065hCUterqOYXbS5fj1K3j1lRKXXHE+L/y1H1NoRIoOGYsV\nK1eyeOlCJC6d3U3oikvV8fnDQ0+jIQlMgZpN0eq4/PWxrVBjqwkhKE6XEDJEyBA0l0ceH+SBvwxT\nDgK0ooMeSExP0Lugh0Q6Rf/Iwfge00yE0JFSJQwPlfFCCEJiW22AwJfISEFqEXv2HMB1PNwowg/j\nNgEoeF6JUEmwevFCFDWBxEJKHwiAAEWJCEP3iKGhEDpqpHPdHxz+6c3zOH15hO2pfOGLJ+M0m41B\n1uFbPZOtV5H1oKvrOi9t3U0iKWtaF1Xe/d63MjI0A5FL+P8QYD3Piw0m4QicbT2AuyWf6UKFmdEy\nN1zxWXzfZGbKBiIWL+0lkYidM+IbRDI+a2Cak+i6iaYZNGUTLF8ScvLrVpFJSyxLsnPfOFHVx7Bi\nlqOuq6TaFjKTiwecYRjhuT5WQoCsafz6BouWtNPR0UoU+SiKJAgiiBJ4tvq/frf/bXvNBthTX7+K\nvzx6D1N7K1z17S9hWFDIB5SLATPTFWanbVQljSIyOFWFwNNx3BKK5jA+WuFvLx4gDG2SmZAgcBgZ\nPcienTN0ti+hZ0GabLNGe2eCvh2jKIoAEbJ9yySGEd9o+XwegMG9JVzXaUCI6jdbvbdUb1fUbarr\nN599mNdTpVKJ4UYdHQ3WTzyJjCfvxWKx8aDVbaJnZ2djVaOmpkPlY+2noiiEQcDftk7w3MvjbOvf\nT//uWb773U9z1ZWfYUvfCFgtDYpiGAY0NTWza6BEWXGIpMuxvT6FisPW3dMNa21N05CRSaq5Qm7W\nIQo1Vh/VRbEQMDo6xrzeJqbKAWN5+4i+miTGPbqu2/i8vhaKopDJZHBrYPh6plLfWkxIKz6lUimm\nPHoepmk2/r2egdUn80VfYboSEngGpZJLqVSiWCziuj4nnLSceb0JduzYz76D0zz39C6++4Mv0tly\nFN/76dcJNLN2/JjbbrU3oxk6mzZt4qKvfxqEpFI0qEQadqhgHr2IDB5nnnQCyBhuVKlUeH7TDkZm\nHB5/biv33r+VZCJkUWUcfapItdbGKkuf6UqRqggb3lW+7+M4TsOAsF5G1+1f6mSLenmvKApu1WDN\nUYuoVCpo6iEHAl3X0Q0jvo8iSWt65ohstX6/1u+XekZbv49+cJ/F0StNzlq/n8+fswXF1RvXq95S\nOLyPW4d41TdR88NSlFgs6NlnNzBv0WImJmKjycPRAvXvDodYY/XAnUgkGmLmdUSGaZrkqiWWLO8i\n5U0Cgl3bp6hWY0Uvzzu0r3K5TE/3UkqlEo7j4LsaA3sqDA0PUhnLowgDgcbrTugh8GPSQzqdxnds\nmoyw1g5RIMyQyWQaIjRRaFDI+czNzTXwtlEoSDVF6Nb/fcuY/+vbrt3P8c2v/JD/vv9a7rr9Tjra\ndaoO+IREiiBAEqouU3PT+LhkWlPYHti+i2H2ctdvrsFKtZArOrhhRLEguP9PPyYIPIYPTLN/cIbH\n/rKZ2++6ih3b+9m7ZYrb77uKSnEW264SBD5jo2Vuu+N6BvdP0b97hIGBKYQiyc8KtmzaQ1uLhpUQ\n9M5vZ0FvBqF4JFMqWzeN4fmSMFLYsXUUzawwOTnB3j0jaGqAroUgFRRVULXLmIpBcyZL4Ee1jEMg\nIxVNMykUp5FKAsXQcPwaU0i12N03RFVGiEQCJ0wxkrd54KGNPPxYH50Ll6MlJR0tzTRlsqgKTM4U\nEYogrSq8459OYetQQCmI9UHrjB4pJYqImCsLDD3B0cd1s2/PKOVSFdMSWAkFVehUy06jtGtubmF+\ndwtO4BEJC9eJH8REIoGpqhhG0Cjn/SBEqgoikKgSMkmVou0SKBqh0HFDQaQYOLZPJuWhcQguFBvZ\nqXiuj0CwY98kqVSCiYkJDuwfYnxkmsnhAseu6sQwApARRb/KA39+gt/e9xv6927n2ce2gfAIHIXQ\nUmKLaF0lrej0NOkoQUDRs/n9n15AJSLRmWHFW05BHBhm27YRFF1ycHyK0YMBf3lqC2tWrGG5anOC\nqrDAyEA6hZpN4RkqaiYJkURHIcSj6hQwNJ8z//kk3nHmyTTrER0tOdavy7Kgp7dRLnthcMSQLxAB\nI2OzZFMKoV9myaKWBiSqtbODSA0xVJtbrjkGONR7VVUTVTUbn8WZp9/IQi1TsHX7EB89+23c9JO3\nkFAPtRbqvxMPtpQjAvXhcCspDRRF47ln/0Y23U3o5vja1/+DnVsPks/nG0H1cMhaPYA7jnMEu6sO\nNXScmLwxPTbN7k3DXPW984migPFcFS+UnHryWtJW7GBbJ2rs6ptBDzxQVEpOkWIgODAwiONOxlWj\nAtv6pgBZY/RFlMo2PfObMM24r267Nr5dpSmjYSUVUul4QJcwY5PPRCJJGEZU8nk0eSgB+H/bXrMB\n9paf3U5LtcyyRe0EEdjliFDa9M5vJwx9oqhGwYs0FNUjN1cm09rM+IDCDTedx72/+i1OtYIidPp2\nDjGvU+Xsj1xMJEKkYhCKBKZo5Rvn30Ja7+bDH/sAP7z0TuYvWoaqWDhVjZNOOIpbbvg1mpCsWn40\n99xzNTs3HuR97zqZR//6S6bzHsMHZhk+MMf+wVnycyHbN+f4w4M/wi9qrFt7Aj++8Vq8SgtvOeNM\nIt+iubmJIIynnK7rISU4QVwyZ7JNjAxIdu+YolAssG3LEIlEku2bBnnhmdHGVLee7chIYzpXJl/x\ncPwkFSdgfGqOZ1/Ywr5dO7A0i7lSBSdQkWES0x9jNjfHb/7wIr6MRVYUNdFQB4qiiKQJlhGxZk0H\n/X3TzF+Uoa0jRRSq5HNx2ee6DooMWLi4BVWTjI7NNDL7MPJQwiSZVAwrmpmNLXaklCi6jhaAZvi0\ntqUoVQ9lWVHkxgLPkUJHZzOeo5NKm2jCbdwT9Yc8CAJ8qbOtbwrF6GCmrDI6WeaTZ6/nlOMXEVWM\nRnY4MzPDrgP7uPuuZ7nse18iookwCrDMJjwvJHAlJddnarLAv3/pTILIoFRWKdp5Ik1lLLSpjI+w\n55V+HvzjTrbvnOBTH30jyysuPfkRVrZnsbIm1VVLcE0DdBXNMpBSIQJc3yOVaiKTaUMKk/0D/YyO\njqC1tjI9k2XrtgrDoxMEAWiaiZQKjh/7WaVSKVRVUCn7LFu+kGrRYvmK3lqmGhE4FSzForVzMffd\nOogVBgSeH+tCHAZ3kzIgijxUoZAOOjC1kJ98dQHSWMzVP55ibLTKHT/pRvoJFCWqldGx+lkYHhpE\n1dsD9SoskQxRFYPT33w8jlPkycdeIVJd9uzp4/nnh8jlcrieHYuYq+ohyUIvQjcTREJteIPFxzrk\nFNvc2U6hXGLd6l4MPSAMJf17JhgYGCCV0RtVYRiGVALJccfMRwY1BpnQaO1dRqJtDZqAwPUoFkNO\nPXEFixfPx7IMVFVl5GCerKpgl10CXzI2a5NOWszmHfL5PEII5nX3gnBj0X0riaFlUI3iPxzHXrMB\n9ne//hXpYzq45vIraO8BKRwG9+V48vFXSSXaGT4wy9BgPGUt5S2kjFBsn29d8QmqxQSVSqXGv454\n09HH85vf3cmCxW1MTk8RBCF7X93Bnx69HqGUyeXH+OinTidSJhgZGSGSIcPDI1z0tbNZubaZ0DP4\nwFln8K2L7uG+B37G4oXLue3m+wiCugxbfOM4VclXLv4ol17yQ6YmCzzz9HMcvaqdnduHePaZl7nq\n6i8yMVEmDC0cx2V8bIIolARBSKGQ59kn+/jv+6/iggvPQfrNPPjwzTz31BCnnvY67vv9j5E1EWc4\nEpR/OCsniiKw81hCZd/BMSAu1dYc20rJbsMWBp7nNuwp1Ggs1iGoZzaWwepVK9m3e4I1a+czNuJg\nGHH55ntxIBZC0NHRztRkgcDTiKRolJDNzU3MX2jFWYkfEPiHDAAlPp1dGaLAYG620jg3TVURiqCl\n1USKKjPTsdW3XXXo7u46wueqoQjlB/h+wMBg7LGpmyp33b2R3zywCU+PS7h6lgQKnu8hELzy8ObY\nznw2F0vaqSqRjDG5bz71RHAL2I7Lts2jKIDekiGyVD583EpW92Q40RQM79nEys40+dBAX9mLXLqE\nTbsGUA/LAIMgBGKnCK8mUahpOi1N80in2rnqvBNjKBEiftEpSs1tIGq0bOrC71EkmZstIpQITVcb\n0/ze3h6iSDJ6cIw/v+RzwdkLkIetVf2+kLJmtaQoGFbAPTeexA9+0c/uGZOD5Sprj9K55cqdfP/i\nrtrvhY3ftyzrf1Bs6y2K9vZYZ8KyErieQyKRZLB/P+d88ROc+objKZXzCBHvr56Z1oO0rO2/Dv+q\n31dWzTPM832m7QIbN7zIt758FuXAZmJ8kiiKWLp0CYZhNGjJQRTw3MYhupJmzJBzHHw/ZM3RC9FU\ng2QigURlcN8Yubk5gqCmfSGhsyONpgdoeoimm5RKJRZ2ZWvawRVmZnI1NlpEsVhCCo9FC1b/w3Hs\nNRtgdw4eYO/QOCURMpOX9A+V+e3vfsXPfnYdL72wkz//+VbmJnKMD0/wpXPPxfcDzITJb+/9Ldf+\n8DJGRvtpz1icfPQKepZnueHqm1m2qIl0U8TUZJE1x65F0xLMzMxx3++v40ufu5Jte4cxExED+2d5\n9E+/4ic/uI6BnXO88Q3H8dNrbmfNsRYP3Hc/v777Tnbt2YGGTkdnC5GMvegX9XZx5tveQEdzGz/8\n/kXcfvd1fPGc7/DHP1/Pycev56qrrkeKECljiFl3d3dt8h7h2Sbzl3Rzy4/v59qrb+Cb3ziXH33v\nZhZ1rebcf/8Y3/rq5VSCENeJA2md3XN4YNXVkJPWr0RPJsh7SQjBD4uoUY5Nmweo+h5KcCRV0vYT\nsSeWEAgCFs2fx4H+vYjIZu+eQaQM0NU0XuAR+BUSugGRzcTsHL4n8IMqUkboQiFpmByztpeD41VK\n1YAoPPTwKKqCGkVMzpQan1mWBTK2vF65cD4trRae6xMGEk1NoCoWszMF5nUl0cX/1OAMUPClihcp\n+MLk4FTIaC7A9ePhRTabbayTLeDmG3/Ht79zMaGQKIZKJA7x4lEVXtmylQu/fBYgGZ6UuG6RUIWu\n5Ysx/IA1IRyzYiXLViyg843zSa3qRsu2IHQP24uvg2+H2CWX5uZmUqkMXu1zRRWEoWTfnlH6dh6g\nqzXuD0ahcijoNGipGuWyg2bG/lJhGLF33xinn34MB4djI8TQC2lvNohUQTrh4Rn/h7v3jJLsKu+9\nf3ufXLlzT0/3RM2MEgKBkASWCCKYZEAEAyYbHME25pIcsTEYbF+DAZtoeE24BAMGW3ghAyYrIIGy\nRqOJnXOornTSPnu/H05VdY+56y69X+7C71lrVq/uqVpVJ+xnP+EfLK541Dg+JYTIECLrZqOaxNIY\n6VOyl/jH917MS3/7NpaaPtISeHaFd73zPlaiAl/67G386StHEWYYUAiRzy92kxJ6G1yapjz64Rfj\ndrNB21X84Nb7+OEPTvPj799KVD9OpRjsCvKm3//tu0G4LlLm/endsopRFOXGpZZHnLpcefkRhhyf\nSEkW5zdpNLYJnB6EsnfJAo49bH8+w8CmFaYszU1TLEpsR7Nvsor2KkwM18h0TFBwwUgWNuugLTLl\n4FuSzWZKq9kmTXVOlW3ETA5V8YsWpVJAUK7SSf5/oAcrjI9jWSyvxRiTETYd3vqmP+Hr//otnvKk\nx/I3f/Yh9h/ex+TeC3j0leOkiU0rDWkrCI2NES7rrZg7TpzlntNzNMQGD5xusL4keMc7/pQ//MM3\n88TH/Sp/8e4/4LUv/5/MrZ3lve/9AItzHf7yb/6Qxz/h1/jmf5zhznuPs7DUYKvT4Bv/+iAHpw5w\n8mSDcnWIw0fKJGGbyfFRLt4/xjOffDU61kStiCPHDvCJj3wG163zuX/6JsY0EJbDjr1ZDmHyPB+V\n5q4I/88n3kmzucQ/fvLdHD42TNhp8OkvvIV3/NFHmdg3RNoRZDJmeLSWu9RqTaoh0QrHVigFd90x\nR5pUsN2YwSEHndhsRSWSGIwWYOR5C8a2itiWjedKHnHJQYR00MIiFi7FSj4oazdbSAMjw4PEaQdB\nAaNhbW0Dz7dxpE25YuhkETMLdSwbwk6SGyhKm8xESJnlpWu241w6NDBItQKOlXJueoHF+W0Efp+N\nhsgoVwpsrHU4dMEEJgvB0HdG2K30FIYhabYjgQc7cKMsy9k+G406YjDlO/92kk7YJo4VlmV3PatA\nKMNjHjFFFrWJopQ77tvIkSFDPoNH97DhZjTHCqSWz0DFYDl5BpwqiGOBSjWtsMnBCw70MaA9Bfwe\ntjRVijhJMCIlyVKSbGex/lc6qUo6XDgZYFkeUnhsri2wtZ1gO4JKqYZEoZKYwUqFTuLyrr/8LoXW\nLAkOwuRQREFGAYdr96/ykicd4IVvOk4i3e4HQhiF3LVa4K/efQkPrE7y9W/N8NrnbGA5ot+HtW1J\nmibdxMAgLQvb2FhWXkW4rosULs96ysOplBd4wi8+nAuPPQrXrpElKdKA7zlsbGycz9LTmp4nQJqm\n1Ou5G0UYhriuS7EQ0Iza3P6Dm/iVl1yNRnBmepFOK2Tv1Ej/+kopiaIOt95+PyXXQlqgjWJ728Fx\nCrQahuWVbWbnVthqLZEph6gDWhviUDMxllGp+VSrVYIgoLmdi3CXywG2LThzboWkk/uEbW02mZle\neshx7Oc2wGYoXNtBui5hK+J7N36G6dMLOKLAW//olbz011/Ec697Op/89Lv5/d98O2ubc1jY6Exj\nSYkRGmlJtAFpZ5w7t4RtGxYX6vzHN25hZmaBOG3x9X//Dko2kckAlx0d4szJTT7y3o/yxOsezste\nez2f/8pf8cCJ+7jiMb/AZ254D3/zvv/FF2/8AGubLeamV+i0UjbWG5xdWON/ffk/+NBHP0Vbhfzp\nH/wFIgrpKIvFlds4e/Y+XF+RqgaWk2I5KUMjBfYdGGV9NWR7o83dt9/GsWOT3H3nHdz4lW+wsbbA\nn7397cTxBj+66TiFICEQFmXb5oKJA/kQIYmoFSQ6FaSxJE5THNFgcnyU6fkGYZYvDmkZUtUGkexi\nSWlcz2LQszk2NcLczAKnTs8ADq7jYPcgOlmH/VNjLC1skihQJkMgqJQHiOOQw4cn2LNnGCyfer2O\nUglSWijdYnS8jOcUczV5A2CoVCrkAk4hzUTTUTbCskhThTE9Naac5rm6Usdom9npVSb3jiPE+QG2\nd/SgTrvtdFqtVv93KSRRpvjY+7/EW97yckzmIrCwLAdjukB4qVlaOMcfXP8kYstw9kyOJHGKAUsF\nSe2ig1jlAuXiKMo/gOwO7Y3MSLKMRqNFtVqhvr2105PuZmY9CFSqFVqC7Tr0OgS9Url3T3p96RFP\n88dvPMBIxcJxJffcfIIXPv8XCNs2tpBUiwFGp1g2BF7GiaURPvB3T8TKTwhLSgrC5dN/d5T5RYeP\n3ZggafW1BRCCRCmME/PhD99DTZ7iruVBZh6IeM5jdtS0QOO6Fo5jo3WGFjG2vUXguxQLhbw/Kxxm\nT08jGSJJfVrhOt//7m0YnctSaqX6KJocGtUlk5D31kulEiMjIyilqNVq3QzXYXB0iOJgjac9/goC\nKYiVRaeVYIzue+JJKRFAO4ZLLhnG8xwsS+B4kiRtMDhUwC2AkSmrS4LBIZ9SOQAk7cygshrr6+us\nrq6ytbWF6wpsN2c7WpaFkB4HD5UYGhqiWAyQ/x+i5s9tgN27Z4A4bmOLhJGhMn4JLrloL697/bNZ\nmtngs5/8NFdcexkmzTCBy/6pKYwR2JYPxuHio8eghzPMFJ4M0JnFDf/+j7zwxdfwnj9/P7fd9iXm\nzizR2E74+n98hOf90u9z0y3/xNxai9tvuY8LHzbAqZOLVMpDnDt1glc9+3f5rd97Ne/5o79namKI\nROV6k5mJSI1A24rjp4+TkNKQDe6bmcWzYbNpk9kFstTBd6ugPRqbknYr5JYfnuBzn34ff/uht/HP\nn/sKt951N9+9+cecnn0Au1RhO8pYXm3z6t98NYEtEFnKmdlzRKZOwY542MVjNFqaSGkyqSg6bRrb\nG5w8u4lKIFN01ZsSDu2pYuP32wqu61ItOQxPFDk3t8J2O58yV6vV7l0QDAwVCaMtlpa2wd6BiQEc\n2FdjZLDIqbML1FsSO5Nk6Y7Vc6VcY2WpjlIZWoNlZzkDzLMYHx3BDlykslGh7g/agiDg8JEJ0gSM\nthkeHgA0tlVgdWWLQwdGEUph0gRP2jiczxTS0iXOBIocmSDdwo7Kk9YsrrUYuWyC239wio3GJu0o\nQgtYXd3AZJJ6aHHB0y/CbEdsN+t850f3IbVEezbS9lBakBKRxBqT5hCmWNlEKmXP3kFc18OYHWgZ\n0A+ufek+DJ0sL293M9Z2t3uyLGMj8fjQB+4lbW0i8Ljiscco2obZsxt0VJ1iscgjLz2IAcoONLIG\nzdYWjjBok+HFi3zsPRfzG2+8mbOJh5EGwY7qVR++hcct02U++IlfxyLhR7NVRCPjMQe30QKEztdR\nz3bbtou86sW/CNrBkkH+05Y4lSKpUdxz9wmCYpWnP+saHMdDpTumormrgHdeldFDRfTO3/Ny5a2e\niE6WSc7e+wCPvHAMo+HO44tEUUS1JAhsNxdwtyVRlnL/fQv9vm4cx2xv+FhOSquREnUU7cxA0qIV\n5RKig0WfRMGRvTXK5QKeZxNrh2owAlknd+LFZmauzsbGBq1WSPbQeQY/vwF2ZakNwmFicoQsDXjj\n778JnJg3v/kdfPzjH2djq86HPvwx3vSGd+J6Br8gGBq1OLTf48hBjyiK2Dta5YLJYQQu1WrOpy8I\nyd/+2Uf48D/+AR/94L9x4FCF73/3k7z2VW/kdW94Jm/9H3/NwfEqz336NTz80sfwta/9M+N7quyd\nHOaXX/R8nvKUq9nYmmdtfRGtNeN7BkCX8ofHijCqgM4yjM4nrwcPHjxv4fRA1Z/+p7eBSonDhLvu\nqWPqEVYhAGPwfMm5xZyi++CJ0wSFAQ4cKuUaqTaUAo/NjVUwDnefqlOSDrYQPOspVzK/nqCtYRrx\nTq8T8kW+uKZp6hBJznevlgL2DAQ8OLtGO8sxkqI/dND4BUOrkaJVrevWS/88RkdHWF/NEDKf5DYa\njVzQOktxXE1QzLPfng6rAVynwN79AdLSbKxFzM2sdQkcKa7r4BdAyJDp6RmkBCnpqvGDMJpyuZxb\nwjiKC47uJdNdMLvhPFom0A9a+XDF7Pp7xhf/8YtccNAmiZO+klepVOq/P2x1eN51l5KmKYsLTZQO\nc6ZaptA6v7e9axqFbe699zhSaPbsGUbrHfxnb3jY60H2JukAzTpgFDrbwcnu7nNalkU71dy2YPj0\nJ56J46SMTxb59hdu5LHXPhopHYwwDA37qBiGh0sI3+XTHz3NeDbPhJ3xTx99Jq95w32otNi/Brur\nl92qZRmGX/3tb4EQ6Ezzb/dKxiqGw04Lze73CpxQU7RNP2hKKVFphiUtarUBarUKgV/E9RN+8L2f\n4PpxN5iL/J/IRdqlkH0UQe/oBVcpc8F1gMQW2E6B173xBehMEacJOnPZd2CMStXFoMm/mSBNKoxX\nbaq+ZGRkhFLVsLqySZbm5JU0TViY79BpbZEkCdv1iLXVBkhBp9PBtnM8cBzHtNt1XM8wPA6lYoWh\noQGkpGsT9dCOn1tX2de85nWEcdYX6T18YJIz0/N56WAZkmynR5QmEBQhDEO8LuvKGEMYt3CcEga4\n5IL93HdymkDaJGmKsR1GR2tsbGzgOUWK5SJr6+vUAp9Op8XeiQNE8SauW+XoReMsL7Rot5sIaZie\nn8dxbDApBpdCoUi55DI3u4zneYzvHWN5fiN/IC1FrPIHyfc9TNeW25IZWWzzhjf+Onsnh/ngBz7G\nzPQSlmUj7ZREWQQulIIp3vqHv81fvus9OflBKK656ijfv/kcGQppFSmIDs1khXYymGszaHleCS2l\nwJCRqS7UycQcPVRhfatDfdtFd107944Nsb6+ztRwibn1NtLKEPgIseNp73iaOBRI6SCFxchYkeWl\nDaSlSWObg/tHmF+s5xuO3BF6dizDxL4hFuY6+IFkeyvCdg0my72ZxkaKbG1uY9s2cZZDlJIk6Tuu\nttttGu24n51ICVEccezYYc6cniMzhjjd4c7vXrSusPrvM8YQ2Bbvetfv8Vuv/DOe+PRjjI+NEcdt\nir6PZQl822FoapLffv0HOXRkPxcfrrJnT6Gvb2rSPJjHcUx9dZEvf/M+DhyaYHaxSWp8bDvHc+7O\nTncTMZRKeeur9vDBT00TaRsst/9ddwdAAKUVDxtrkSQdrr32WkjgqsdcwjvfcyMvetklNLcVW5st\nHjy3xHZTohsxb31thZNnzvGJH01hk5Jh+oPM3YSBftuiS3o4T75Qa6IUfv/ZMd/6Xptz4RBpmm+E\nz3z8QfZOjRDYxfPcEnqbh7Q0szMrPONZ19FqGE6dOoHn2bsgXvln+77f/T3Z5Ta8c812426NMVQc\nj7e/77OsdwQF3+UpT7iMZrPOxpbqw/cs2+HAMCxvC3wvJwjMzS5xweFxGlGOqLAMTE0NkGQucSfO\nN2GTEAQWnbZBCEnBdagNuMwtblEqlbBtm4IHjXaC1vCuv/xzzH9nV1npOJSDAEdIMG3OzEyDUOyd\nHCPJzpc/2z+csG9imIdddIypkg2ozQAAIABJREFUAwdQ3R3XdSoYJCpLuO/0WQyC2vgY2rHJTMbi\n8jpKS9pxyOr6Ogaoje8hFjYn56aZW2syvbTGv934I+49dZqTswsoK0ALjzgzqEySKAdswfJyHeEV\nGayWWJlbYHjIw7Y6GJPhiW0GApuam/KICyc5uLfK/PQaMyfnGRgd4f1/8QHWVlapFaAymJAlFrZI\n6LRjrn3Cw/n4hz7C0tICStcxSch/3nSSVCdYRnPVIwbY6LRodAaJo9wvSNgugdNAiE4/MAohwLXw\npcNVj7qIhZWERsPFdQSBI/AsQylwOHpgCKUsBA6W5fZ7iCrrMDJYQnUyhG+zsb1NlqWkqgPASMkF\nK2Zmfvk8Bo+NYKBcQTouOsshYu1WC2k7mEwh7YShMcF2KwbbIe0OscaGa5DFjI0ELK5s0Il3BGCk\nlDjCouB7nD0zjxfAJZccwsZgmRRpzjeli7KUFE1iMlI0zaTD177ybQ4cHqLdSYi65XonzsiEodFp\n49mGqaESs4sL/OiOaUq14DymFEkHkg5bGysExs6FnS0Xp0va6OE6e4Gil2HnG4/FuZlFSpYG4f1M\nP7nXSpBugu/4rDRH+Is/eQqe3KZUKXDTt27iyKMrNFsSP3DxC4IkChmpCn7hSYc5G9p84TujaC/r\nix/1guDue7PbS6xXbfTaBq7rUgpsPvbNAs+6bpA3/FJGisu+UosHfnoflhT9jayXcduuR6FqE9iC\n9e2INMzYqJ9jYW6t/1k5a8vGtkV/aLa7/5xlOR44zlLiLCUTBstzyIRBSMlfv/t3wATEyrAVwuWX\nX0y5Uuhfc6MzlrddpoY8CgUPYVlMHZrE8R1s4yCURZQaZhaarK8ustVsEKYJaWIYGfKxtEAITSvq\nUG8qxoaGGBwcJIoiolRS6lqEP+Q49pBf+X/5mNgz0b/pmB3c5/T0dP9B6e3GjeYwp6c3OH36NLOz\nswBMTU0Bpl+aCXKA/urqKgAHDhw4f8GQ0wvPnTsHwKFDh/rDFMdx+2DrmZkZtNZceOHFqCTPPLbq\ndUJyQfD6Vp1I+jiVUVqZS5YJ2tqhqSzqqeDOB1Y5fnqbSBT40Jffzjvf9h6WGps0ooxypcLmioc2\nGscu4DgB//rVf+fMmTOMTQToeJyQoE95LFcd/vP7c9STvN/Uo1FeeKiIkw4gKHbPP8/mfJNw0QWC\n4w+cII09hJWDtQuFAo7jsLW1xYPn2qyFDYQQfWFh27YZGx9ma7NFEASEYcTKygqXPfwy2u02F196\nkKgjkaJ0XltCCMHwSCn3/QoFG+vb/UzScSzGJ2o5jnlzR5y7B2jvYUCXl5d/5j5lWYbtKjA5Djhs\nG04cn6ZcM335u15p3s8Eu62KXJ1LcddP5njbH7+OEw+c6dN0w7DTv44z5+Z5658+jzRNSVJFp6V+\nJiP1fZ+BvePsnSpjsgI6LeYthG6m1sOQ9uBJO+dhOHe2zuDgQJ9F1zvvXlYphODoYJuv/9Pz+fQ/\nHMPzbcb3jGJZFrXqKL/2vOv4xg23UCgUqFYqXHvFpajEZvrsMp/7lwRVdCkped516AXQ/6o30EM5\n9I7e63uEls/+yxajhwYI4ga/86oX8p73vzO3UGGHkpvLQQp+ettZvvW9ReaXWoRrCdVKhauvPdS/\nt7u9unotHKCviqdUblO/ezPtBeYkTam6GkdvkWUZd/70BM3tXGWvt0G5rkvY6TA46vZbDKrbsx+v\nCWw3oVJz8H2HoeEdy3shBDNn2iAUhUIhp9OmKUOjkpWVFaSUrK2tYdkJQeH/GLrOO35uWwSffv+7\nuefBaTJZQiUexolRWTO/kLZDrVTpm5SliWB7e5s43SJRLp1OiG+5VKpFBgZqzM5O50FG5xNjy7JQ\nOsZow9TUFPMry6AUKtVI1+9K6gHGZnBwkI2Njf7iEEKQqjzjEV0c7MbGBhibyalx5ufnAVDdJOqi\ni49w6uS5fJJs5w/VnbfP8bhrLmNufo4Ljkyyud0hTWMKnkOqcwO47e1tXNdlvOrSSkPWN3MTujiC\n4ZFVNteqdDJDmiiktYOl7B3/tef36IftQyUpZxY3McoApssMg7HhGmurmzhFSMIAYSWkSQZZSqHg\no7VCZQakS22gTH2zwwf+4qW84wOfo726SIs9CHoGdhaWsLCckFpxiI1Gm3K5TLMRIW2FJT0sOowM\nT7G6vkyUxqA9LNsghcvoeJlyUKTZ2GJ+s4VUWZ+37jgOeydcpk9HKJHi+RYq1biu20VKaNI0h2kF\nfpHVzUZfREQp1S+DhcrfM+ALCnbExJH9DNfKdDqdPvRHK83lV13KG3/vb2hnAVNjNZ5//aNz3Ql2\nFn+zsc5P7p5jaOoo3/vRPf3gCpyXlQK7SmjJZNAijlbZVGOorITvtnn4SMab//gXEY7iRz84y7/+\n2zTn6hqjC/hSUAsMz77+UpxE0Flf54FtxSMvOUxQsOlsbZOmLe66XxFZIQ+eW6HgD5HoCCGcfq+z\nZ8DZuyb5hrjD1uoFerUL6ikyi0Ic8/KXHkP6Fc6cnubMyQ2e+bzHYVDEoc2pkzMsz7cYHQt4wS9f\nR7HkcNuP7+RxT34SX/38DZQrxRw50r2P7XYeWH3f2XEP6NKFt7e3ufXHp7ns8v0cODBFu5XLWpYC\nl6prI6ser3zjZyjahqdceyHHLt7PT++YZ2NjA9d1iTJBYFLKBUPm1FhbW2N8fIiKZ9FKbZK0k1OA\n05Aki9HK62+G+w8Oc/rULJbl5tm5lRJ2ei0UietajIyM8KY3v+EhtQh+bgPsy1/5OpRSDPo2F146\nRCAcjp9cYS3MH1xP5t87UimOHZyH7TQmBwHumSiyvBBjVMIFR/dx5twihyeGmJk7g3RLaNpIilgy\n49D+vbSbmtqgw1Yzz46j5iapLmG7GUEhYGVpm9qAT2M76ksk+kG+gI0WpCJ/SF2hSbp4U8uyEIlk\n/6EhTp9bx1UOA5VxXvLq6/nIR/8nGA+EwnFdOpFC6BxGlWIx4krqsUbRbeBHLX75Bdfx6S/dSqfT\nAZHhF10810Ul+ZS312d0XRdP2mRpnaHhKrVajbPntrrMpRjITeiGhisIAUsLdWwH0sQgbYXMYOrA\nCLNzm4AFIgZjc2DfKPMzW5SqinpjBxxv2z5KpYDBshUqdRkaqlCvt6nWfLY2OpQKLogWloBGw5AJ\ncL0cG1urWCRxxsQ+h9OnO7tUnAxC2fiFjEKpyNp6SBhu43keI6M1tjYiPNtCWAZlIpLIAQyI3AE1\nCIIcrmV2lKJ6WWUgDY+5uMrcZsyFRw/3M6osi/EDj4nRUYo1n7e87UsUqzav+JXHUQCUlWEjcwfc\nTsoDDy5yz+klOumOUn/vc3o/lVIYKcBSJFtNDg0VefWLR3jEI/bynz+YYXNri5tv1SxHKbEu9Ev3\n3Rln0bJ4zcsuwqQO7dVtnvicX+Atb/skr/iVx2KER1Rvsr6+zn3TLaQvqG8I1jshUuysC6139GZ3\nVxvddbcLmgVaJ2RSMSw0z37GFQwMDGAsgesVuXjfAX7ywD3cffdZ1rcUXpbw1j/6VSwbbrv1TsIw\n7NoYSYTxaXeaSLcbvFOD6bkFJAm2nevH9lhYruvmNju2QCmHb9xwG65jeN7zrsSYAkcuGeZlv/pB\nIsvlCVcd4YpH7Of2u8+SpT5ZlvZ1Mp766BFufrDB6nKDqf1DGKNxowR7YLivKTw2NMDaSptSxekL\nONm2wJJFoihiaLhIoSSYn93EdX2Sdkil5vDn7/rLhxRgH7o94v/loz9JTOCuu9vEdorJLGoy4rqn\nXsXXv3EbnuflU0e9WwE/fxgtKVlZboHU2LbDyVMnEbKALvhk1hTShNiWJEtdMq04fq6OVi6TvmB1\noZ6XK1aGrUMIQ1aWWljSplYbYHNjASEUfpB/7vDwMBub8xQyw/DQMAurdQpWRqFUyMuN4QJZ4nN0\nn0+5MsLKcp2vfe6TZGmMwWJkdIDllRVst4Bt5Tv6tY+6lJt+fA+xk+Jqj0E3ZrMT8tFP3oRw0nzH\ntQwVy+Pw4Sp3PbAG5FP3NE1J05TBEUHB3kOhJJib3uovINuxcX2D7ws210NsN0EbQ5Lkm1W5WqS9\nvcHc3DJa21iWTaVaZWuzSdQRFCsZnU6I1m6/hNOZxrIkE5MDLC2u7BpiNKjX63iBzeCww+qSzba2\nEW4bV/goleBYgpGxIrPTG6wt+ef1LYUQCCskits0222EzPGIYRhSLBbZXA/BWidsD+AEATkDSZB1\ny/gwDNFaExRK55WSWmtUZnPP2TqjRUOj0WBwcLDLi6/gB/n3GB8bxXYUSaL51jfu5rnPvDRvYbRD\nPM8jy3LhGykkWu/0WXezs9I0RSLwFBwstfnbD78A4bRpb8e8+HXHkf46RgWkjo10PESyY3Xdw8T2\nWmWO42BwkLbNuR8e55LLLsiDsQ3lchnP8xge19x08yn2DkiGhkc5NbfeX1O7xbR7wbRHue45zPaG\nXikWv3H9o/EdKJZzkRkjBTd+/Q6iq0K+/6Npjh0o8KY/fAWdrS3uuf1eUp07OARB7jwQxTGPfNQU\nd/zkwa4Ic77hCFuclxCFYY7UKBaLO4mJnTIwVOUVv3YlQhY5c2aV7911nL2fN/z9e9/Ia970QWzb\nZnV1lcsuu4xbb76XarXM9vY2vu/zw3s2GRyqEFVAq9y65sixce45s9JPQsKwzeBwQKPRwnVzjd59\n+6ZYWlpkcCig2WyidF71xnGM6/g0mw9di+DnNsDGSiO0IrElIlPoVINxWMHii1+9BQcXKRNGBzRT\nRw7x45tO4nsVlOOhDVx64QHuv3+ajA5KJwjKaNPm1IkZbNviwMEDnJmeRzqCLNEYo7Aszfx0GyEz\nhoaH2NxooKXK7YqNxrUN02dXwHY4cGSKB0/MIBKF7UCU+FhGM78c4hYqTOwdYubsCvVOB71SR5gi\n0o4x83lpVHAjbKeGUhHra008u8LU+DCzs9MIu84Pb78PIwXluMrVV07x9W8fJ/UCcEL8wENrhaUt\nUrXNT+5tIbqTcq01JS/g4itGOPPACtXhEnOzq2Qyw+o+0Af3j3Lm3AoFPyCKmog0RBDgWAnlUoWw\n3SZKbFzPxnUtlDIEQRFZU3Qaq6w0YgLfx2hJpsGQUS5atFotFubWGRissrHWod3ZwpOCQtFDS4fV\njQYKiSvytkaaKIq+RblUIVaCMNFk5O2XXgZ3wcG9nDi5iBAehghLKjyvRKvdoNVugjCkWQksSaYT\nVBbjuA6lQplOpxtQjYM0HVxhkeisv9GkJqUVSY4eGmd1o0GxWOz2mEM8X5IJi9NnZnjHn7yKP3jH\np1jcapDaFtkumvLAkIcgF6Zpp3GfSaZsjU4tKskmT33CEK981WUszqZ8/CO38Mu/80O0Elx9cJ0R\nz2E+CnJxFey8VeK6dDqd82BUWmuQAUZVyWgTDBaZXVnnd3/jl3nHOz/FNdccIOpunrYjedSVU2xv\nJczMznPJviId7bK4HBLHHaRJMV3IFV33Aa0VKukQSJ/A0zzjqYcYLA7iFzRYNpHY5tvfeZDZ2ZAL\nD7g86poLePJzrmZlcYMf3PifSOmSmQzpCESyS5DIGB48Ps38/AwDw6PUalWwNfWtZp4cSUGaZiRJ\niu25RFGM73tstSNu/I+fEncC/vTtT+Pzn72Je84sI7RgHXhqfYWigK16Eyng3vunKRaGieJ8ThDH\nMQaH+vo64wNlNhvb7J2YZHp5CyuLiJTEcWziKGNk1Ge73UFhENCf81iWndO5lcUF45LlThutMxyn\n9pDj2M9ti+DFr/yt8wQveofWeaDtvu68/yuIGE81eOWrn86J02vcctOdPP7ax/HDe0/SbG5SsAdQ\n0sK2LFxLE6aKx1/5CG79yXGMAWF1yKwAK9xGu1WMUhy+YJJzZ88ipUCYAkpYZPEmlldGYuHoFokY\nwHcSwjQve1TcxnKqIEMuPnqM4w+eBRmDdhGpx54ph8XlFiZNKBR9mirDV5qLLt3DvfetE+oOnUbM\now+XuefkGmuhYHCoihCCcuDg2LmGrLZ2+SVlMTpVDAw6hHFu433w8ASzM3O5iIbtoKwGwhQpljzq\nGwov0MRRhic9VBYzPuKytNl1YJUpAi/PZMImV11zMTf96BS2nWc4QggMLsIKmRqdYHWz3l9Qw8PD\nrK6uMlCuYqyYKIlIY4sgKJJmDRIVEBDjFyV+qcrK0jaFQkC7lXTZPSnlYkrYASN9klh3hyigooSJ\nvYPUmwkIRaedARlp2n2Nl1IqDNNobPezsjjS2F3b6jgT55XGwsqoOGCaMVdcdTEDwzVyTVCHgpcP\n5A4eOshv/4+/J5OSsbExnvuUPXQaOlfntzNuvnWGOI5ZWr2TJL6IslzmfX/1KwTVLb70z3fyrW+v\nshAN4Pg9R1+N1imj5YhnPuJCPv6jGRzsbtaakqY7WWYP/iSlZrIoefGvPLbvbLA2u8D+yUlOnEkY\nn4pIsx3qaG9qn+M5W3SaNutbW7iFIlJazM9uU281iZKYkhtw7MI9XHpsb5fWbIF08YWLdJr8yw33\n0Vi3ue7qA1z15Es4dPgwM9PTLM6voYzGYM5zndWJ6mfFPRbbxIEBPvXx73LlYw9Tq+VebL0Mut1u\n96FQgQXr7ZCvfO1WTi9sEkUxHvCYX7iI+x9Y7DuW2EnKG//4+Xz7xrtoba1TrOaOxY2WYGAwH1Al\nse47Jj/28r08OBeBzLhoX5FTMxGNdoOC62FZEmFHBO4Q7XY7F4qv+szNrFMbqfT791ubLYzJK5N/\n+PBf/ffuwb7gpb/2M3/vl3fZ+crrfX6zlEihAIk2DhiNtMDWGa4tIQspBy5CQLEgGRkfYd9ImeHJ\nSXyvTLO1TnOzTbzV4vTiBu14jWYrV2N68uOu5rs3ncB1bDI7Q2ceMtsCUSBx4PD+UU6d3qDZbFL0\nXZAFpB2ThhHCDjhwaJyZc8t4NmRKkAk4cniUBx9YJlMRRTdipeOSpYqHTVgstxM2mj6hvY0VlfGD\nvHyrFFyk8Oh0OsRa4Xk5zAcVsX/fEBtrTZqdmMNHx5k9t4bWXfWroQKb221U4jEy7rG+EmG7KTqz\nGBxQbKwarCD//NytKEbiMzU1ycriLELaxGnubtrLLn2dUapY1FsGLXNb7lar1f9ZCiT1ZtdTSkCW\nGYzokCG46OBezp6uMzJZZnF+E9/3iMI8UxsZD2httVCJjekOv9I0ZWS0ho3E8w3Tc6tkOsZ1ykiZ\nl+GDQyU6YZskykHtxuS03M2NJp6fL4xWlPdmewsf4VCUiuufcQn337fA5P49+L5PmsbUygFSShrN\nBkMje/nzv/0ChUKBl/zSxQidl+7Czrj9pzmzyCzfxdveeT1//d4fc2ZFkBmPjopxpI3QFpnVa2EZ\npDQ4ssgn3vcIXvJ7d2GbqIuuECh1PoQqH7AqXvvCJ1ArWyBzVbXORh022zz99c/nvW//Ag+/cpIw\nDCmVSoRhmG8AQtButZEM0NyYpzTg4/keaZrlLrVZhiMk0lJYVg1kimXbaGPz9RvuYGMt46UvfAxH\nL6gycvgQqwsLLC1sdddcRqoVVhdz3FuTJs3F0XtDPWMMKqvw1a/cwNOffSVDQ4NgrL59ed5qyajV\naqRa8bkv38K9J85hdWGPSaYoF0qkUau/iduRz5OvGyMMI1I8Ep1vROubKaNjA/kcI8lbP57nUSm4\njA1IwshDaEPLJKQZpGGOV947OcHy0kq/ZVIqxOjMo9lJ+pvH+J7B3IG21eb9H/jb/949WImFZVwy\nK9cD7cFc8j6SQlndAYtxdk1tFTrrlT5dTJ+QaEsSI0i1T6vdZVS14NTqGjezhpTT2Dqf8koLMtvF\nkhZQIU5aOK7DV75zPzJVOFaGsFIcO6Xo+2S6TaAlM2fuY9gPODhcolIaxC4aisURbLtEq77F3NwM\nFRkRbjbQukhGzL0/3eDoiM9Gs06sCvzGS67i29+5m7MbHp20gcoMgRzCKoClBRN7DYtzTYSt0EZT\nLXq02yHlQZ9aqczachs/sDi8d5Jzp5eQUuDZOVpgdb1NbaBEfTNGJRaZrXEzxYXHjnDizANgu6Sx\nAjQg8IOAqYlRwnYTZSxEVyPAGIPrlNi7VzK/2GSz7WFkB3Q+rEvTDM/bJOmUaXSaSGkDhlSl+FaR\nTIcEtsPa+iYpijgEyAd00okYGiixutzAcXwykWIhcFyBAYrFIjMzM/2N1rZ8IEMbzYGD+5iZnutO\nwntwHYXr5RnN1lbeg/as3FSln8WalNBIvvmDn7A8t0ZQC5jwxgCJwuAiufTSI/iBi+606EjBd354\nL7/4+EfmzyUeQ8NVlpdDihdewq+++Q6QHkbmz6Xn5T1FYwH9IVu+YCMR4QF21ukz6HI34OC8taCU\nwjGCStHq3wNjDE6lSKPRYuX4CoP7J/ugfylzJEpf0hKDdJqUR8v4bgnbdpCWOm8IZwBhg9I2X/ri\nj1Gx5pWvegoDNZva8ADra1ss//RujLYxVi+5kVi6i0oQTnfTEmTCoOWO3YxKU/aOZDz9WZdSKxf7\nUDhEliuemSYkFl/46g9phLnl99S+vX3UR9xO0WmM0AahfR77qD288AXX8Fd/dgMHLlSkWYaRBVSq\nWJqbw7I1Y2NjOO5OL18JODHbZP8eQaFqUbUnWF5boVCt4jgOnVYTus9FsejTbNpM7B2kE61jWQKt\nU9IwIU3SnxkO/p+On9sA2wkTtN7x9elNVC3L4uqHH2V6dY16vU6mRB+KM1jNhwHb9RTT3XWUUrjC\nQmlF1u2/QVdgw2ik8EBnxMIg7FwkmUyRpAkuHo7lQwYJXRNFk2FSAWnGZqT7U1mosKA1UsYoNfcz\nEJ38+wdg51mFQxVPWpzuNFDZXgoy4h8+eyuVSgU7AJlJ/CCfVAttg5TMzhpct0DZz8ukNAqZnKiw\nvZWynmSQtRgcmmRmZhUpc2xirAxapt1epA2iRWt7jYqEVKUcf/AMRhcw5DAemUm8QsLw2BinTq9Q\nKOULiAwsBIWCT7nqsLCY6+3aloXnV0k7CQXXoXxgiLCTkGQqv7ZdFphvWQyN+qxvtQkjgWOKZNkm\na2vrSCkZH3VZXLLYXOtWKkYgLYXWNrajaWzn+EiMjbQ0RgfYQiFEjO2VWVxYRQoPaSeYzMWyNZaT\noHSD7e0MgYvRhlItYbuxI7vXO1rtMk972jinz8SMDiqCwCOOEmRBcObcAtVKiTf97vP5m/d/nSVb\nkxLhOkV0prnnp3fj1aq0Oy44HkLIXGylmxT0WiqwIxqutSbJYj7/yXsZ1m3WRKkvPN3pdPqc/bgd\ndq8PSCsDY2EJnzRTWK7NSqPJN756M895/TP46qe+w/iEz+bmJsVisc/5rw2O9UVvep/vCYOxDZm2\nCVWC59p85rO3kLUUf/TGlxF7qwxW97CyssKpB6d3sLL2Do4VdvQWVJa3dzJl+p+jVK5dEBQKzK/U\nOXLkCJ/6xA08+RmPpdOxCUo27bjDjd9eoNVqkWSmjyOWUtJRBqFC0khxZHIflz98hBe84jl885Of\np7WyTZyd5tzSIPv3jIHWTK+ltFOPtZWY4SGBZedecMPDwxhjGBwuc3p2C8cVVEoh8doyweheOmGE\n5ytK1ZSttVzG0PUMSbbO6GDAdlsT+EXiuIEjHeL0f+839r87fm4DbM9/qufR1JsoG2O46c4HkN2v\nnqStvBENrCyFKN1EEJB1oSmWZRGnbYRlE3ctf3uAfFI4dmmNTksyv7SjgNR7jciS3FFUnI9SOC9I\ndwP5borhbsuP3XjU3WwZgyTKDI+7wuWOezfpiCK+7/cplj1F/v8qSNwTUslFr3NudJq2gZCpPZPM\nTq+TZCnVajV32jUWnufSUap/XlP7BpiZa6KEjdS5+piQ+QLyiwlxaNNsNfvwIsjhVBYKpUOWFzuI\nHjvIZFSrA4TWBplpsbliIa0MrXOIDuQZm+/A8mIDv1gizJr9oCPthLGxMbY28qw1TdP8mmYa27U4\ndHQfc7MLXSWnXgYokF7K8GCB9RXRpaB2yRS+T6uRUqq4dFo+7Wa310ceVFt1B6XPz94AsgxKYoR9\no9u0Wi1c1+5f7yiK2FpucOW1l2NbLlnq8MC9mzzy0QWksHnZC57ITT89TSdzCNqSKNqh9Pbu1W6I\nWO85sXH43vE2z3zKMJ/5To5GsCyrjyDYTWF96QuejMxUX8NWWprFxS3uPdlkpLqIXH0C2/UOeyY9\nSqVS3z3AcRyiXT5nURTRaDTACgg8g05L3PDvtxC2Et71jtfTaU5T3muzdcqwtX6mD2nrywvuetZ7\n5yOEwLbsPjmhpzLWI1H0MLgbqwmXXZ5rc3z/Jz9laz2hGSlsmbdsjMz7sT2yiIojJoeKXP/SJxJv\nLPKE6yaYOX4nR68+xtT4RTz3+mfQUB4zp84hhWZ+bg6lJVEY0mg0GBgs9z2/eglaEORaGO1OigxK\njJQF0g4JsxIGl30HAlqtiEqtyPJCk317bWITUa36rC83uOTSQ9x//P6HHMd+bnuw17/gN9AmI1Fp\nn+rXezijKKJQKJyH5euXI10MXKFQ6P8dcr3JHrVvd4rf+1sYhhQKhf7DYIxBJobMzjO9NE2RmcR1\nUixbo1SRlC5wXEoOTu1h/5RDEifcfs9Gt9QoYtmKrFnP5dRMoQuKT7FMisgTNTK9I/ZhjOHCY0eZ\nO3ucVpL3qUqlEpALYQidMTleQ6mMlbU2RV/mQilln431Fn5gdw3yAoTQJHHGgUNjLMyvUww0mRK5\npbnJcYdCZn2A+dj4GGGnzfZWQqHo0WqFOC5YRrN/apJT52YwxkXaMTqzKRVcVCrYd9BnZraZey2F\nOUQnyzKyOMYJJNoYlMr1UkdHR3KVeC/DaB+draCTIVKi3JpaSpRuUgkq+IHFViPqGt0leVaUghcI\nbMtle3u7208FaaUIHMaGa3huiThtsrae9zVVlwqbb0ojCKlYmNskk7tUpXTCYKnC5GjAyPgA1WoZ\n33dZXV3Ftm2qpTIjQyXB0tn2AAAgAElEQVROzDT556/+gMByePmLrkLrtL8x3/qTk2TS5+TcGlI6\ndDpxV2/2fPhWb5Ol+/x8+UNP4EW/c0t/oNVLKJRSZDLm8qkxfvGplyCw0Fm+eWdGc9/xE1TL49x+\n690859GXM/GYo9x+2z3s3zeAyez+kK9nTihdG5yM//z2zZycjSAtsqegefPbfo2BYZf1tXWWF7fQ\nGlxPYrpDVJ2onaRC7rQodq+hHklg9/ll8Y4Xl9aaOFEcODzK373vy2wmfr6RdO+BUgqyHNlBJ2Kk\nWOGlr3gi5arDP3/2u7z4+ZeDXeDOWxe45rop1lY6FAqTfPsnt7Jwbo2RsQLP/aWXc6Je50Pv/CBX\nX3Mk17Xo6j/0dC1s26ax3WFra4s9e/bgO7nIy3Yrx9/6gQ8G7HxJ4nkeKlTMzp1hz9Q+pM6hZP/t\ntQhe+pxH8vLnXsnznnQ5h0YyxgshqBaWCQk8TeBJSoGNbzv4tg8KdCZyiqmn+mrxRkuSLEbpFMto\nbGwsY2EZC9IMS4OOElxfkqgOmowkzoVRQpOgsjyrKBQK+GUf4xTIRBkc0Rf/sG2bheU1bvrJIjff\nsdTPkNvtNs2Goqlc2uRQnF7zP8NCJYos9kjaKXEcE4YhcRxz/IETNOOcD54DrwWlIJ9qP/axe9jc\n7tBsx3hegB/A4OBQV/zaodPOM6FeNlRwA+obW4yOV7C9gHbHReChYxdbFyn6JQZrJVwL1lfraL3L\nNddIKkUBtsPpc4sg8s0pTRWu5TAyXGR8vEajYee9cJVbn2dJjGtp9h/eg1KSKNS5WpXIAeSulXFg\n3zBREqJ1FS26dE2pSHXMoX2ThElIJ8qB8ZblobN8IURxTNjJmFtc7Q7PBFJYCGFRrQUoMs5Oz7PV\nCMl0hCHBMhbVYgkLWF3ZYG52kaDg9rMaAC1stqOUdlxnq9kijFM6YZvh4WGGh4fAMiyv1rn6ikmi\nuqShEu568Cw4qj8JP3pwAjtRHJoo02qHCPt80Z1eJrWTUdlI6SCsECtuYVs2vm+TZQlap0jhMCIV\n1z35MJmRxEqBZbj7+P1oETF9ap6iFfGB970FMW5x8PA4d94+TzPM22f1ep2t7TV0akBrHMfn/h+v\nc+L0BheNeHzqI7/JBz76FvYdmqBcLlEul/nWt+5GW2GuA9AdtAkhUEbnvdVdFRxIwrZECI2KE4zK\nyBKFNAIV5TAt1U7Zbm8TxpKv3XgfN3zjLBRza5o4jslSRaxTVJih04QBmfH61z2NK66ZRJiIuXPL\n3D89y4nTaxTKPs9/9ZP5/MceoDzsUB4qcs0jL2bubBtjQ7WcMeCVec1vvQ6hU+J2JxeT9zxq5Qom\nywlESRrh+wVsB9pRSqro43bbrQ7NZpOtzSZbW01WltfZam5QHRzh7JlZjCXZbDQfchz7uW0RDA3W\niKKI2oBPEl3CkWPDODqHQXWSBJWarjp8B0u6YAyhSkC7wGFyPeG8RPMLQ7mtr0opFmrMzc0yOzvD\naju3Aa7VaiSdrt8UFpG9owLV+7d7l+6VW7szEstxMFoj2IHIQG44p1Sug6rSnUwqTlKMbePHdV70\ntF/gi9+/DVfmw43d/duJiQlcTyBU3u88cd9GXwavWIJqrcrM9CzSE7SaLWw7x+7pLJd7K/oOg0M1\n5uYbGBKELKNUjOuFeWtFpIQtH8cqEUchtu0jrXwTGBjMpQKV6uRuDEYhLYtKaYBS4KGUxdpKE+Gl\n7N6rC0FAo7nJ9LQ+j5Fk2V0UQaaYPdtEdMtLSzhoo7CSFpN79lDfCjFY+IFPoxHSVC3CMGR4pEqz\nEZIpjeO6tDsdXMfG9QT79o8yc26DQtHHsnMpwl57Y3S4zNrqdve6SizLYXJyLyfPzva/szYQkzC7\nKnlYLeCuu05yxeWXIHzRLcklRkK71eFhj6xx14lF7r97jQv2j1Mp5ILRSf0ch44WOLfU5tiBQaYX\nO3240n/VhI3juC9T+I2vPYjO8h5pzwLeslx8k3D9s64hiSXGxMzNLnDhhZew9P9S997Rkl31ne9n\nn1y56tbNnZOkVkYSkpCQEAgQWSIa2+AEGGdsw4zH4Rnwsz14xuAx4zjGD9sPMzYGkwwSYBEEKLVa\nrdDdkrrV3Tenqlu5Tp249/yxq6qleQ4av7feYs5aWrq3bnXFc377t7+/bzizzExhlv/8ex/kkQe+\nyV/+5Z+we+chPDfD9S/cx8bKgHJR53p5GRM/UXzjnpNsrvfIuwaf/9Qfc+Tb3+Lur3+NmckD2LbN\nzt1VBoOQbN7WW3wlxq9Xc3Cf4bn7DIjgO9++jxe/9EpQYmjRyVjoEscxkVR88SuP0Us94ihk85HH\n2Tk/palWQwgw6+bI53x+4ie+n2Pf/QaNhQ7TpSrNTkipYvPRj/ws//59f8Phyw+wdmaBX/sv7+Zr\nd93Jk8e/zitvvwJprhMEU3z2M1/ilW9+Fd16m4yy8M3zr/PM00+z/8ABMDVMkCY6bhxlkSpFPFRB\nAv+PDLhUhigkF150gCiKyPwvmBF8z0IE9339M6SJwPVM4khi2goz1uB6ZJxPuUyShCiK8H2fjFsk\nk7V0jpwQQ3s151mQAtIhiiLSVA4HCn0GA5/BIBwagsQkicR1XVqtFgVPqzm6/QGFYoVBGFGpVKhU\nJ7BchyQ2WVpapNFps7y0RKfbJU7AsfOkQ+J3qmxMy8QQ9hC3tbQblmViCgOEqbmRKoZhYY9iHRQ3\nOzuLjHxK5RytRqgHRk5ILpejXK6y3WrQ6cTAqKBrhUoSp0yUXTq+Hj70e4He7sQg6eOZDjt2TbO4\nuopl5KhWq2xtNhFmiGkalCo29c1QezdYAilNPE9gY3Dg0A6OP7FEqZSn04pxM5IktlH0KBbLdNr+\nGGPVlKeY2anKOGHAD1MsW2gPUdMmVX3ynotKoRW6FDMmvW6I4zjjgU29XieTyZDNZjUchKbjVaqa\npmaZNpsbDY0jDzH5JFbMVk022z5xOIRgpC7IcRxjGgZxYiDTlHIlpdkzEEKxswgX7d2FXSgOn09r\n0IXUbIdDhw/wk+/7Y1zX5Q2veh7zVQ3hJHKAJCLnzfPAw4/RT7M8dWpZv9+hGsu29XkbDt3BkiRh\nZ3bA7mrIAysFlFQYosR0uc2N111ApVLmxImTHLroUu698yFecsslvOjVL+P448fx+wEqbTG3Y4KN\nDYmjBFMHqnz4t/6eV7z6MAKDT3zmO0S+R8np8+d/8RHCuM3y8jLN5jbT01PUNgeYpuC665/P5tYa\nW/UWf/+p7/DSV1wxlo6OqEuWZUHybCy50+mMv+MRBmuKBLwin/rcg3R9fR5EUTSG6QzDwHMzGIaJ\nGa3xS7/649z7nUcJN+vM79tJlMZIFNOTk8RRwPyuIr/1O3dxy00HuPVFLyCRPv2uROJTKBTwcg6/\n/Et/zVXP38nOcpUf+aEb+OUPfZaerxkZtm0zMTExxNYdgiSlsd3Fss978aaJeFZjdD5xISZNLJIk\nxrTS8WP9zm+//39vmtZgECCwMAydryMiMMWwm4wjHFdTqQwD3IIG9lGGvq8QdNo9Jis6ekK5ijRO\nKORyKCXJuDZhmtDttiiUcmSyNlLFw85PkMoIECg1A6YJKBzDIQWUgjRRJKnEdjXfslLdhUrm4cbD\nAFixQRTrYtKPAhzLptFoaoGEaZDJZvAHPo5lafNmq4DtuETDrmZ5eZmHTqwSJQYbm+vMlIr0usP4\naqFwrBwzswUWzm4SJBFJIrCs80ObXNahkC8jCWi0JMIQQwOTFFPYlCol+m2flZUOk9Wd1Os1/EEH\nZMTkZJHNRpeNtR6mobfgQgpMobBMQeCnNFtdBDYDP9KBjX6EndGE835/cF7iKgxmpqdZXjlLNpth\na7OB41kYhsIQw46OgF075llb62ObFjLuMcAc5teP8E1FtTqJYaUgoDyRw7IM6httctk8mxsNcrnz\nzmdSwsx0kdp6neV6goE+bzzPZBDEGKYFscH8rikWFxsoJei0HZQRgxI0Bg7LjRZ78kWUADnEWC3T\n0nlUtmJ+ssBmo8c3vnmat7z+MjzDJpYCVxSI/AY3XH0Jj58+R+V5+zjx9AaNRhcx9C9wbBvHtUjS\nBAzJ615/GdH2aR5bETgZxfOvz5O3dlGrN9hswMq5LjvKDd73az/MU6fOcf+9Dw0HhAbCnWSzbmNY\nCXEUsW/3IS6+dC+f+fyj5DO72FWe4gO/926EG3HsoYeoN5u4rovtOWw3+himVmDdd98D5Asurmex\nd+8kwSDBdoamNgoMBCqVoBTCSEkloMzxYDZNE6SlwMzw5W88SqsHvTCAIf0xTRTdQR9LGshY8qJX\n7OXqa67kW/cc4b7vPEEmY5NOVfFTRbE4QavVodXpYwpJt5PwOx96B7/zm58lHHyXW156Mfm8S4rH\nIw+fYnG5RZJ2MAyLtVbI+3//KyiVkkYxdqGMEIpOp4Xj6DRnZVqUKhnifoA9tJU0DQPDtPH9AUoK\nTFMACtN0yFSytFotstkShmFoX+bneHzPdrBf+ruP47ouhUKBdqeGmSjy+fzYDAIY41uJpa31XNcd\ntvAZ4hBII3KeS2Qo+p0uE4XS0K5OYGScsYVdEAR4nofruuOBgJRy3AkppTuX0UQ3iQSGCcKQ4y3d\nqKiYpomVqDHjIbUNpK+39MJQ4FggBHZqkAiFsExkqrAshyhM6Pf7JEnCZq3Ok48cZf/hF3JmdY3N\n2oAoiqhUHUw3z3a9zoV7d3Dm3DpSnWc+GIZBueDQbAYIyyBNDEAhVUzWhVy2TK3ZIJfLk6YpMzMz\nLC0t42YUk6VJGtubdBIDW5kgEpQUZF3B/Pw8YdRnda3P9GyRZqM3psHtmCtR2w5JU6kXxCGUE4YR\nFxyssniuRTZn0euBsBOiMMGyBXE8IO+W8P0Bg0gPG+M41kXIccbu9oOwxY03vpDlU6cI+nXe9vbX\n8/efuYc9lxyiXm+ytro1NguRDJiq7qTTaNLp68dRRAgcimV7+DkZ1Lf6GLZPnGSR/5NS0LIMirbg\nisv3kct7eJ6HM/Qt1Qq2mN37dvPz/+5juE6Bt7z+AlQ3IFcpjV2Z4jjGMGH3zgu578j9+AOoNQcs\nL28RRRFhKsZsgUlnleuuvopBGNMOYsLYZma+wOMPnqGaUbzqTS9hYalBPp/FMoeJwErTCi3TPQ9B\nxHqgU5rM88mP38VPv+ctKCU59ehZtLhBECjdhZmuPcwk0z2WMOD5V19Ku90G4L//9be48ppd42HV\niAkjpUSYQzhNaUw9iiJag5i7v32KINJpGCMcP5bpcOCpSKKQQ7uLfN/33UosU55+qsbJJze45OJp\nLTQJ+iQYFIslVBxjGQNKxRKOqQfG21uSu75+L29928t5/NEnOH2mhmNnSJMWl1x6IbVaDSkl/UDP\nICYrBeysZPFch1Ipr1N0XQcVWjxw9FvMZKtUDuwFzsMCUkqSGJJUU7FkFGJ5mfFMA3R3+4cf/c//\n33SwQog/B14DbCqlLh/e9n7gXcDW8G6/opS6a/i3XwZ+DD0ifY9S6qvD268C/gLwgC8rpX7+X3re\nx06cJpfLUdtqMDNbYWayQq3dJ5fL0d7cfpZaZYS1iUSQKXjUt7QzfiaTod7sUK7kEaZFJwwoOHrF\nlf0YaeuiVCpM0ul0CHwfESVkczYZx4EkwTRsgiDEUgqV6NgJaSjdLcdyvHWyLAtXGTq+hHRMt3KE\ngxqaaAgh6Ha7WmcuLbxCnqPHjnDzLddjGGCkKfmii5Q2tj2HER7i7KnTtOKYZtPnsiv206v1kEaK\nqQSnziyzY+cM6+s1DFMbmvh9nyi1hllK6TBUL8UxJbGy2GzUcOzzRUAmLaarOTY3t1jqber3IiW2\nY6MSheOBZxksrDTYMT8NokO7pbFF15LsnNvBem2DNLEBgzSNdJbWXAmQBLFFKky6gwGpdLGUwjUc\nSmWP+rZJb5AShPJZi2Y25+H7XWwX5qZmkJHHRC7mmtuvwpYCIxlw5eUHaIQm3W53uJ3TSReG4dFq\nrBOnFoaZYpgplm0ikpSdczOcOLmB7QAiQSYukxM5tms+oYzGC5SUMJCKVqOBYVZ0NpR13nFfxib5\nbIaiiFjvbPF3n+rxulceIAckUmCZJranF+O1rRXmZyY0Hu6G3HrTCzVEMAjo1hs0G02KpX3MTJXZ\nc9Eh/uQTXybY7DKTU7z5DS+i1QlYWurgJylhs0++YJKxXSzbwrDMIcxiDultNomUzE3N8v7feg9H\n7r+ffi/BzQzTCpQi8TUU5pkCWwh8vzdk0fg8eP8xSpUs8zt24LpZ4rCPaRqa/x0PxTymAUPWix91\nOb3U5OGHFujFz0iKfYbJzcCPsRPJlZfP8JqXPI+zp9cJ+z3u+eZjXHjTNfiPrnDf0afY2mxy3TWT\nbLdcms1zJAx44yuvo1T28P0IZSr2XZDD+07Ao8fPcG6hCUaAaWd5y5tfRLcZsbzeIudGWIlueA7P\nKb5+7xZ7L52jUpnEkjZ3fuGrzM3N8LPv/hFarUVOnE5RYkCYPMMjVyRjW0dlawOYfD6v49KHDdlz\nPZ4LRPBx4L8Cf/U/3f4RpdRHnnmDEOIw8BbgMLAT+EchxCGl2+Q/Bt6hlDoihPiyEOI2pdRX/rkn\nFUIQhTHJ8IttNAYoNcBxXKanZykWCywsnCOfL9Drd8hms7Q6Pu1uRBDEVCYMgiDQ2F22QGO7iedl\nOFtbIl/IY5rg5rNDud4A17bJ5bNYEqLY1N6gpgcqQgjG4LYSECMplor6hEoVqZWipMTxdKZWPp8n\nkZIkTbBdl8XFBXK5HLZpEaUJtuuQy5bwwx4XXbyXjY0N8rk8tuNgmxaO56BSn7kD+2kN1lk5tcWe\nvRn6gwbbvgC/N1TPeKyvbeG4tpaGSjXWn+vBjMJ2FNXqBBsr28hEW8fpLs0mTSMsy2FzvYmSDpjD\nYuw6yEQOObYx1YkpWlsDFCnCMJBpgpeFrFNgfX0DLN3RJ3GCaRlUcgYuMWeWW6TSxxQ5HNfAEIps\nxgU1oNPWHVU6TJ+wbQfDtIijHu9455v5g//0MX7s+2+l3VpibuoiUA5RGlDb3sZzXbxcxJmTCwRh\nCEjm56ZZX6mh0Oo+YUA26xFFEblsnl63wfpaZ7jLMMYcU3/Q1I5rhvus8y9ODDLZIr1en2zOJRwm\n7eoOVnHkgYf55V9/Bz/1i39InQ7SyWsqUC4PqPFuJk1TLNshl4NyuUzY0sO9xACRdSlYVY3zy5iH\nv3Uv10zkqFx3kI2exVKtheHYRCpGphLHNgGNH3umSZqkCGEMGw1vLMR58oklpEwQwiVNQ4RwsCwT\n3x+MRTlRpGPD/b6vO3RHzwdmZmYoFYu87Udexl9+7PNcesUUaTJURMmUNJWkScyjj53g+KkYX4VI\nmeg5ybCJiJOYNJGQJrz25c/j5usPcvLYOXYdOsjp5RqnFjs8udTlsD9gY3ONnr/K4cOXUa7McOr0\nGp1uSKbo8p3vPMKNN1yGY2dYXVlk794ZXnHrYb5+zypvfcOLMG1JMFDEcUC56uI4CWFkkc3q99gc\npPzMe2/j937/2wThCi+47jA/897b+Ownz7F4rs7MTIknj30ZY24/e6aLxJHGkU3DwPFsTNMmCkLi\nNKHT6RKGAfl8nomJiedQNvXxrxZYpdR3hBB7/qka+E/cdjvwN0qpBFgQQpwGrhVCLAIFpdSR4f3+\nCrgD+GcL7De/9TiJHxGJgNmlEocPX0K31eHUmU2E0UfF2jUplSGJ7GAYAtISrqW3itlyFdvyMC3B\n9FxMrdGg0+6TcQ06QZ/JyRJxT1CrNZGmYnZuikqlwsrKMpY5QahiTJUCDhvrG7iuR3bIeRRCsrry\nNKVSiUKhgN8LEXFKO6cLfRoJetFgONCRpKag5fdwpMAt5igUi/gDHzeXwc1lhhiWZOXMMvPVKSoT\nFYQpyXklLrrIxPVgadPn1Mo2KtXcRoSJRGKrDGkSYTgmrufS7/eH7vCSbNYlGCjWVhsgwDDSoUXb\nAGFEmJZFrdElQWK6Q1Nm2yCOA3bOTbC+3iCRJolwMIRPrbaOKeDw7jlOLW/QHAwwzQxpEiBlhDBg\nz64plhe2wU6xTBfHVShpY6gQ0wpJApNI6S7IdiJMQxeGQjFLxs1ROxdg+Avc+LxZfvdDX+RDv3kb\na/WeLmypRbY6yQP3n2Ru56VIuUkua2OZedbX1kiFluWmMoUUZDxgeqZKsejRbqUMYp9RgTIMg0ql\nTG2rTTYX02wn4wIxmpo/cvJpOrU2N7/4cqanp7CHTBLDMSiZFeZnJ3ANRasf8aV/OMJbX/8CfL9L\nLp8fG50YhoESAseyIE0ws57miaJnCl4+j2GayCQhOzPLYDCg2Y6oZC18Iej2QoRhMFHSkE4caYOa\nRqPB5OQkWAZxomg0mlQqlXEyQNDvalWgZdHpaA1/sVhESi3UMBOBEopcLkc2645hsLW1Na2KnJpi\nfaPGnoMleq0ulekivbbg0SdPsHA2xFeCJB7OBTAwTXF+sKkEr3nldVx88QQ7Jueo1+q0A5+FU6v0\nApMzCwsMeiEnjq/xnp+/g09/8qtcfeVOvvalp7EyBsVySpgGLG0LLu35uK4kXy5x9NgqmUyJN7zt\nOjxSBoHAcgRhaBCFERcf2MHxU+tjAdDTK5Izf/B5im6GiUqBpaebTE3keMGLdoHSM4SP//F/5Ud/\n8f2stgS7Durw0kzGxvQ8Tj+1QhjHVGcnOLvY4IJ9u0nT/hhGeS7H/5sh188IId4OPAS8VynVBnYA\n9z3jPqvD2xJg5Rm3rwxv/2ePPfunUKbFoyceJZursLZ0hssvv5AwiqiWdrC9qTPM17d90tjSna7d\nI0kU/b5PmqziBwZRZHLXl08yOVUBEZEveJTLZTa2A4I05sILL+Sbdx/hxNN9XNdhqprFsUMWFhfY\nt2MP9cYKpmnQ6wZ4jskFF8zrbioW9DohSgqWl5aZqU6yXm8wMzNDv+8jVcTevXvpdjcIg5Rer8eu\n3XP0tnqsLtUpFItEaYNKtQJxSqvZozzh4XgGwpRkbR2dXSgUmJ6t4H/tOCfiJq6N3m4OfTNjI9GT\ncBVjGE3ARKmUXC5HEAxIkvMBcgD5ok2+aNNp6a1iLu+OC4shY2Ymi2xsbLK20aNQrNLr9RBYKJXi\nmCmD1OTJ1XWSVAwpXAOU0h2ol1WYdolYNWl0OkglMBMwjIj9u3Zy6twiiZWQSu2fOlGZIUp6LJ9L\nOLjT4oYXzOG+fC9GlPKq193EnXd/jL/4xJ287JUvYWujy5GHzyC8PKlMWdg6wkzeQ0rYbPd1sujI\nXkAMyNpZ8rkSa80WgzhLmmpMUBgWu6aKrLUG1LZaxAjypLgYxOhYa2A4XLX4oR96Meee3iKKErKO\nS5oKbRwkJSePn+bDv/Vu3vsbn6QdGBjZDI417FrN8/Hm50n4Wn8vDSARCNvU3NJnKLZGWV5JEunI\nE8NBqpQkMUAo8gUPx8gwUZ1gEIVY6HlBGsU0W3UKhQJpInAyOYTl0KlvYjo2szvmCcI+IjZoNpvM\nzs4ShiGFQkFjwlGA7YLfaJPfdxDLMPjtD3+A73/bT3Lx4YOsP3aWxpZBIlKiNBkOKM+nNEhMZJDy\nrh96FQd3FVhbWcGzXI4eO0alNEesfM6sb5LNOLzrR1/K57/8CI89cprXlq4mRnDi6Eku3W1x4PAV\nvOQVl/PAQ0v80V/epamWkaTdC/AKHlIFPHj/FjfdsHM8/9CfncXEhIVn1YiGMIUg4dob93PyKUlp\n1qKYmcTLWCwvDLjiwh38p9/9LS694jLe9/Nv4WvfPkIcaD5sN/BZW1pls9ul0/A5u9gA4OHek1x7\n9QEqlcpzLpL/1gL7R8BvKKWUEOI3gQ8D7/w3PtY/eTz1+EOUpirEjQYDTzC9b47W2iZxHFM/9wRR\n30cBr73yeo4+fB+GbXByZYPq/BQTGZdeN8KbnGBychftoMVEtUQU+9S3N5icmWZ1ZZ2puRkmp4rc\n8caX8fjJ07TbbU4+cQpkluXlFR5+ZI0g6Gt8zxWUchUeeOQMIHAsk9nZMnv27kBJg+7GCrtnZ6hv\nr6OkjWkpVlZWOHz4MI8cO45t22SzWR47tUAUxezOONTW62xstClUJ+i2O5TjmCdqfer1Gq+//YVj\nBUyu4HDNZTuoBz1OLzbJZrNj5dmIu6dSjzAMkFIzL/p9nyQ2sJ1hAKTjoBRkMh6ry02EOJ80OioE\nu/ZMsr7aJglt3LxJPp/XEMqgxux8HpOE7vJQ027r2BnTNKlUHaIoptMEy9hGSolr2yQoKhWLRmOb\nM+t1lOkg0BHZA18PGLstePs7Xsx3v3IvYaiLvhlL0g14y9uvhzTh0597glzeod6PuPaSfTz+2ONc\nfrDE8XN9hDFUNknNWgDJ1NQk7VqfYrHAZq9PFEWALnjFkkun3WHgxxg4pCT0Wi5utksU5EhJh/hb\nSqpSvnDnCSYmTaQlyHuupsANY2MAZg7OkMnoXciXvvBt3viGW7Rc1LXHg8dRIRjxR/XPCsPS23J7\nyJF9pstXmmojlO1mj3xBc5cnhrHRaRgzCAIMy9S0KNPUwXzJgHa7Tak4gWnqv2WzWXLFwngY1Wt1\nKJVKYx5qr9cbXnG6g/U7gpPHz3H5dYdxXZ96rc+3e0+AbYGIsRIXKYZYdCrHg6/3/fjrKZVTHn/w\nJMGOK7ntNa/l1//D72OWBI3aWQ5dXCAelNlqHGdxUfK6O17EgQO7+PiffoXvu/V5rJx5jM2uw62v\nugLLyHHD1Xv427+fHBpda+657/s4js037r6Hw4deS7FQepYnQpqm7N47ycKSpvapOOXIyQEH5lI2\nVlLONE5Rmh7wxLeO8WN3/jkvfPQ19Ac1vvT5Ixj5DLZtsba2RopHpx2xsdYGGWOZHr1unV57nbC3\n+Cwe8L92/JsKrFKq9oxf/wz44vDnVWDXM/62c3jbP3f7P3vMzF1ENm2R3b2DzOQsy21J1FhgoisJ\n05B+amln8eQehFW5zTEAACAASURBVDKQVo6Lr7qOja0lhG0xv2uWra0ezc4mbtaku72E9EOqnkW0\ntUFca7O42eLJ+x7BEQ7XvOQannzoNEoGPO95+5nIwNbaOsl0nmazxQ03XssjR4+ye7ZCsVBFKcgX\nXLY2utqYYyJPau+mPGvSlz38rW2Wz23xje/8PQN/QDab4667H6JQKNPpdjn9xAyWpZjfMUuYtqnV\nNti0HTJ5lwsP7mV7u8X0dJVcLkcqQ+yM4KbLdhN0O2x0jfFFPRI9YIBKDGxLYJpDzqWjL4BMJkOp\nYNDsRLRbAzzPGVOgwMAxJUmsOLu4hW1lmJiZ0k7uaUQma+Naku31lH7YwnHc4UVl4RiSnTurnF1s\nsWfvPJ3mFoOwhWGY+H6Pg3t3U9vYIpY5HKkZbzJVFIoeMg2IkwGpjPjqnfeiAhPbNjAMk3sfPM21\nL9jL2cUsa2tP4w86zO84hDTyrK88TbngcnatqzPWCDWKKx0cO0BhIiNFohzafoQIHYI4pZiz8TIu\n280BMjURQ5mqZzhIJPv37efEE+skgCICAamAAQkqtmhvbZPMTGCaCqUMTFcvbKcfP8Ovv+8H+OBv\n/xWbLU3fMwyDbqtNqVweQw6GYYyLs2EYkGFo+BKBbaDClCiKiKJoLL2tN9uUykUKWZtiPjuWdG9t\nbzMxWSVNIE3Atl36nS62bZP3CpgIsBSmZWJZOUJfF95sIU9KlqXVBnt2VkmShG63i+UoCoU85xZ6\n3H/kNPPlErmqx2PH/5Gf/clb+bsvPUitoZDSJoqj8YLhSfiVX3oru6YLPPLtoxQz89x2x8sYtGJa\n9TVsK+KWWw/y3W89wfaa5NXv2snXv7bK4lLAVu0JqlNgGVlWmj2+cnSdVsfh3bGg21tnzpnj7a+4\nmq2oRxTpBIEnnzhLt9bn7T9wO//Xxz7HD/zA64bBmdpD17Icstk8cbypv1tb8KKr9jAxKTDUOlJt\n8N6f+UH+m6VQvo9tZzl2YpP8dJGtzQaRBEwbU0h6/SblTEBlcprFs12KhRxzM1dyySU7MQ2Th48+\n8Jxq5XMtsIJnYK5CiFml1Mbw1zcAx4c/fwH4ayHE76EhgIPAg8NOty2EuBY4AvwQ8NF/6Qmvuf5i\njjxwgun5aRQJ5QmbTjuPWbY488jjeDmbPZdcxlqvz6EL9jM7O0e5YHB15iqtlArDMdWn0w5ZXVvm\n6qsv5+gjp7XaxNrGcUx27Jjn1LkznFlYZXp+p5781rcwHIvynF4TLjx4kEatxu5d+7AdkzjxyXoZ\nChmb7W6biXIFf6vO8a27qVar1Nod8oUMO3dP43omuZxLEASIxGbPoRkeOnKcSsUmny8CCceOPcie\n3Yc5eeIUhck+u+evYhAkOJkcXT/AtWykmaM0lef2V93C337hm2wP4mcpvgBSTNIE8pYFIsAQNoVi\nRq/mykKmDVAGricwTKk9G3JlGvUYqRSm4SJTg1w+g9/pkAza9IKUIFDEke68BA6GYeJaKbGCWruJ\nTHW3ZBgGRgrlokDKHE89vYxlZjEtSNOIrJNHmm2KjmIrUlrKqlLCwOLgBdN8/mtnxq5P3e/U8H2f\n4kSF7KBCEITMzk2hJKwsbYMYmcoYOIZBptJn0DeIE4+JqTz1xgZpkME2JPM7Jlleq5OK8/4VhmEg\nlUTKkDiNeerpBMsSkCjUMwzdjSSiG4Lod6g1+szMWThKoORQYptEmMYWVlaglMGd3zjCHbddR6Pf\nwbKEFrgY1rOMc0bGP6PXEQYJSRjoQahtk2hPN8qJJAkUMuPT6Qiq1TL1WpOpYplmvYFTKGKaJo1G\ng2hw3iTIdGxUolMX0lTQ8VMiZdPb7mE7JqZr0g8ibMfEzthkbZN+v89TZ7ZoRbB95gwPHI0wxRRJ\nI8fKQpfUHcaRJxaeIfmFd72OG2+8moePPUwY5Hj5m+4gDSPuvfse6s02O3fs5JKLd9LdgLib8sKX\nHsJshLTX+jiTRVQ/YHWlyWu+73V84qMfprMd8e53vob8ZJGpXbPUVrd53R3X8iefvJs4UpiGwfTs\nNO/5iVv4jV/4v8lVJQ/dcw8ve9NNSOmNvUhMJamUbJBZeoFP0w+YkNpQu9svsj3IEJkFfvHX/ory\n7ARWpzc8d20MzvvYbm00iQKXJOrwEz/5Wvbt28f6+jrHHn5sTNd6LsdzoWl9ErgFqAohloD3Ay8W\nQlyJNg9dAN4NoJQ6KYT4FHASiIGfUueJtj/Ns2lad/1Lz5vxyuzdu5dLLttDmsK5s2scPLQL3/e5\n6OAcE7PT9Ps6sVRL1wSOocYc1hHHEKA6laNY1jK3A/NTQ6vCFNczKJZdnnfVJZhWShLZ1LdrxFGC\n7/tYtqJSqTA1NcV2o0xts0+j0SSXmSKIYza6ERMTejuWn5skiQV13yfwTVzHZn21jSLG91NKpRL5\njE6hnZubwbZNHn3kSdLEYMfuMnEcsnffFDNTc+SMhMcffwIlEg4dOkQ2A+6Qjua6Oa69YoZvfned\nyLXGA5vh549haPZEsZSj0/aRg4gwDMllNaYbRRGuYQ4nxw6Nuj9WGenUWr1FnpubIwxbJEmo3asM\nLQE2TJ/5nTPU1xpI5VEuVmlvb9FoNJDSYGauyMqi3na6rovAAiE1y0H0kLFNfVOT1S3TQkl9vzhO\nxoohpRT1ep00TXVOViYmmxMsLWxpyEPoGOwRXWZ6rsLS4oCpqQqNdkgYhriuSxC2OHBwNwsLy4w8\nWG3bJokFpmFywe4sZsbj1FMNDFOwY1eeM6cajGJfRlvBgT9g7w6TtbU1pmeLCEO/Ttu2kULQC1Pe\n/YZb+MNPfZ1eq0iSaH7xiDsphTFegEb80Ewm8yxnLXvIUNAqvmQs4V5aXKM6VcX1HPr9nl7Alzdw\nMt7wc4upVCr0za4OYez3EZZJmmrcV2Bjew6dTkcHQPbbVCqV4Wtz9fY3SfjSlx9n0w+I4pCpUolP\nf36V519bxLE9Dl+yj5PHz5J1PX7512/n5htu5tzRJ2k2trnxppsQpsnRb9zDpz77ZS654iqePHGO\n29/4ctzcDdiey0Rpjj0HKzx03yNklfaAEErwK7/yK2wsrhK97kW87LbryVTypKmg1+uhui3+y599\nhsz+WZIkIV/MMu0ZfORDn+GTn/01fvqH/09ue+XNfPGzD/Hq195MkqTDAVWG/fv3c/L4AkopHj+1\nzNfuWic/UUC6Ee/9D39GQUXInElRlsE8n502ug4KhQK3v/6VXHnVPgyRpdPR5/PU1NSYb/1cj+9Z\nocF/+6OPkHE8Mhl7mE9k4Dgu2WyWKPLHdoajzHkYyducodwtIQwT4jghTgY4tkOj0SaXc4dFOcNg\noA0tBoNgTGr3fX/8mErpKav2NPDZqjfZbjSxHZswTul0+0xPz+itXqid6tNUkoZ9LDNHGAYcODTH\nIOhRLpdpNbsEvsH09AyN1jKFYoZiKUdtY5GzZxucemyVt77lWtbWN8hX59k5N8P+/QcoetroJJ/P\nE4Q94kTHqXz809+gE5njC3ZUEIQQCCTT1YqOlhnieyPpcC7vjm3k0uR8wmixlIUkJopCWl0ND8TR\nMIVUDijkMyg1IOxXyU7EdJoJM3NFNta3yXk2U1OTLC0uo0x7rDMXmKSqzwX7D9IbdKnVBuRyNn5f\nYlgDHMejVCqxtVVHpuf9HfL5Av1+j327Z1heXifjZeiH2sks8CEREa6SeJksQZIgU5OpqQnq9Sb5\ngoVjGHTqNWKnhCnOR2lbNhSzuuPZbveYmZlkc6ODTkVISWQXjNJ5pzYRIxA8//A0pqFTaicmqriu\n5sxatoYADhw4wM/94h+SuiaXHChzwzUXjXdSprKI4phCqfgsZ7iRQGDkODXaeo8udNOCVrNLoVDA\nUOm4qCsRITDw+xFSaGOhMAxptZ7h+O9mh+9ZoWxJHNrEcYRIE6pTWUxT0W71+eY9ZxlIgVSSJE21\naYtS9Ps9rr9+Hye+u8WOvR4//o6XctU1N9Npb0MEaRSRJD65YpX7v3svzWbI6uppshNT3HLj1Vxw\n4TWYeZOw0ybyB/zDP3yGs0+vcscP/gBf+tp3sV2LSWeGt7zjDr71j0eYrfQpljMkA4Ot9S222i3i\nKCBQFu12TD6fJ+dIvv25Y3z0kx/ip1/+C+x7+S5yuSKXXL6DJPLGzYY0LI4dPUGYSgzT4soL99Dq\np6xsLfPkibPYQrFnzx6azSbT83MYhkGtph3Q0jRFmD16vsPr3/hy/H7KmTNn2NrcxJAm/VaDfMHk\nP/7u7/3vLZXNZLNIqfC8PK6bZTDoAQopo7FaZnSMkkM9zyNJguHfxDj3vZCv0O/3mZqaHtJRJEkS\nDC+AiEKhwNbWFt1ul3KpipnTLlmRPxjjYY7jUq1WsR0X09Lb5TjSF0qj2SRTcagWy3S7XYrlKpmM\n7hAd4RETkaYp62sNXM9EmLqIb230WVpokiSSyy85zPve/Qa++cAxGm3F+tYKZhgRdX0uvPIgedcd\nihccMp6FIODqA1W+fqJG1jHo9ft4nndeN24Kms0mlQmdspkkepXO5/O0Wi1t3WZ6pEmIMPRgZXJy\nUjsGKYFpeEShzoIiNimUs/T6CQoTy45BOliWQRhGlMo5grDP6sY2pqsNa7RyLaVSyCGlw8LyBsWS\ntoOMgwjPzpIr5Kg3AsKgwSjaRjMaDBxPks1OE8qAVHl0Bj0s00MGJp4Ts2/nLGeWNvFDbWoSqRil\nJJYy2b93kuMntzGzFcxh8XKHab1hv0czdsjmFYawCcMERYAig+NKytkZthpNUHoAiLJRwNETm1x1\ncZH6JpTLEs3WUChpYirFoNvnjS+/jk/d/Rhnlvu88AUmUV9bLNqWRyafexYeO1L6jYaUI1e20d+E\nEFi2oFAo6OKrEpCSoBtgurb+7NMY2zLHfhyxkjgZjygKCFPtcfvgA8e45vlXkiQBaQxeVrK+3uXB\nRzYJ44BU6Ym7HwZ4uCQDiRAKy5DE9ZBffc9rmN5VYnNzC8OIOfPoSWobA7yswcOPH+eGm2/hc3fe\nRacr+OD/8bOUJir8we/+IR/43StIGj6nn3iUO//mPm5+7WVcccWlEGzjCo+BH9GkjYXEsTwWN9ep\nHTtHkmoYY6ZSIUhtmrWAfD6H5URY2SK33f5Cfu6n/iP//iPv4q/+4G85+IJpOpsxxSkPpTT7A1vx\n/Osv4oEHT2OaJg8dP83qyha33f4C/K06Z85ts9loYUkNbY0cwlzXotPx2d4o8+KXXMC3736Adr9H\nOZ/FkIp+r8HBfQXWVrafcx37ni2wG+tN8gUHGcV4novjmOOuVevKvfEJOzZsGFmrDQtruVweEoTD\nZ3V4o8HQyDU+jmNmZmaYnp5GGAmJ0tQPW5x30xr5kRaLRfyBVmMJtBWaZZqIoTIqk8noaWyvz9RU\nFkVIjFaA6SFHOO4qq1M54tghCmNOnV5n9+4i/a5i1+4SmVIJUpP8dJWHHjzOlRcfGL8H09KF8vk3\nXEUUHeGhxS6Oo6GB0X2kEEig2WxSKpVoNXuafN1sjifMI5jAGHqW1uv1sWkHSrsqKaWY32lTq43E\nCxBHEZnJEr3+OqXKJOurHU3wh7GSJ0kSJqcm6LXazM7O0vVr2ubQcTDNFNR5V6Y0Tcc+uZ7nkc1r\nxZtKI0x7gFJZzKGSKlcwSdIBTy3Xh14VgkqlQqPRJJUpxbLN4llNqxkVMNO0UGpAoZjDGuSIEsG+\nffs49dTZ8fd6YL7A2aUmQRDorjJ5tmuUUJI4yHPs2FHmd5WxrOx4kXdMQb1e5+ZbL+Cv7zqKlcDT\nT9U4sG9C+xkPr7IxL/YZ+OuIOzpiD4wKsDaECQAxtGQ0hpCK0rxZqQ2JZKphnxErIAgCDHL4gTY2\nOXz48HhXttn3eeL+VZLYxjRihAmJHMpfZQryKd761ts5/dQCP/zmt0JGsiO3F5k32Hv4Aj79F18h\nNDoUyxApmyfP9dm39yHe8fZbmZo8xO984E/5/b/8MDe+8AbOPfkEf/7Rz/Gmd97IS955M/FGY2wG\nc/T+NbKTPS688EL+7E8/QRxZ5IoB+/bvIop7WJbNdmvA6to2tqPY2OhwwUV6HiKKknjDZ/eVBzkw\ncyHrpsBoDChM5nEcR5/7w+8+l8vh+1pIcfmVB1ha3KAy4XKlV+DcRpd9F+wjTIzx+ep5nlYGijqn\nTunva3FxkaBaoVQqsbOaZdeuCtXqc6dpmR/4wAf+1yrf/w/HBz/4wQ+87QffhOd5qDTFskyKxcI4\nAmO0ymsQ/7xT0cgoe7Ttcl0b2zbJZFxs2xzKAh2CYIBtG7iuTRD4gDEmhg+iBMcwUUMscPQcSils\nxwahxvQXyzKIogCpJL12X0/cPYc0lbiWiWNrd/pBEGAIk8nq5HiabJommWyGUqlANpdhx65Jmp2U\nZqPNnv176Gx32NhokyaKUqVAz4+ICZkolvDcLL4fYBgp89NV6pvbdP0IYbt0hyFvCokyFIYyCAO9\nxRpdzFEUY5p6IGI7Asc1aLcGpMMuV2O5UJnI0u8HDAKDbr+JbblUq1MEoY/nhEhl0mx0kake+ugt\nmolnGxQLGUDQ88HLDeNXpJYbT0w5bNdi+kE4zqI3DAMVCfbt91hb9el1gyH2a5EYJmYywHVMEmL6\nvgHKwHYMkkSS91wMFErGtHsBcZriOHouK6VkspJnEIf0+4Lp2Vl6vQG9fosklhRzOdI4ojWIiBOJ\nkoId81V63T5SieG5BlKk+P6AfbvyYGfJZbK4bgbTFCSxXkCzxRLNZo/llTab210uumAK23THXepo\n4R8V2dG5OsLOB4OBpnWlIYNBX5+TiR5G9jt9MpkcmCa9QUIQpmS9rO7AlWAQS/woRiII4xiZGsgU\nEgS1VsC99z3F8nIbhEUYBTqDSyTIJKVSMrGDFd799lejuiEvftlVfPnvjhD1Yu5/8Lt884vLfOHT\n/x2RdTh4aDePHV8gTiUXHSzxvCv2EoYRn/vct/h3v/4z3Pm5z+BlLQxh8pKXX8+nPvFdLjpUJQ5j\n+v2QWr3Ga3/0FtaW67iuTaJS3KyJxKLV6hPF0Gj26IURUplMTE4zMVUmVQZplOrvd3OB9VqLif0V\nUiNkarIE8rwXiEwEnu3xxMkzIBQgOHvqLOeeWCOxYnZMV7GyNrOzM7TaOnHCtm1c19X/OYLuIKDT\n65IOAg7ummOiLEiTACWyNJpdvvr1e/jABz7wwX+tln3PFthXv/IVmENH9lKphGFYSJmMTRmeKQkF\naLVa45VoBEL7fkQQRMSx5gCGYUqahvqLTRK63QEMnZZGnS9SjS+AwWAw5orato1lmliGie264yJZ\nKBTI53PkXId0CJTPTk5RKOiLbzAICSNFrzcYZ8VrbiTEUUwSJ1imjVIGaZKy58Acp0+vYZpQrbhc\nePAwa7UGExMlTMsjlQrH1DaApinI5rNccHCO+voazU7MRCFHu+9r5sBw+zMaUBXLFoFvgAgxTUGl\nov1VldTdoeMaCENHVk9UqzS2e8MiB17GxDJdquU81YkywvJoNnSoYZpKbCHwHJuZikcnGBBF1nAY\nFJNInyiUTFQ8+qmg301JlWYLxLH+PFwrRtgOplmi39eLlTAUNhZZETE9WaXWDkjiUYKFTaVQIY07\n7JydYaXeGrte6ZTdCFTM1OQU260uYCLlUK2VmphCks1rjwp/ECKlFgEIYRD2uxhCYgqTlASE7sqD\nVA+88p4gUyggTFMXdkMhTINeq8tNN17IV//xJKmQeNkqM1UDpYwx/j0uAtKg2Wxru0ZhoFL9nqRM\nSMOQOIwQClSakEQhbjZHQkp30EegdyBhHGJaGaJE0uoOtBdBIvFck4Qe66sR9x09w+L6NpFUSBQy\n1tdHlCQUTZO43+Xtb3oh73jHHXi2ydyOHRx74CQXXbWbb3z9CW689SBhVONXP/iL3HjT9QwaW8SG\nyZTtcvjiSbbrXfweXHL5PmqbNWzHpb69SbFYJgg7WGxRrs6y1lzgH75ymmuevw+51cK0XdqDBMsy\nx9cbhiCRCiVMZGIOzz3B9nYTx/YI44RTjy6ymVW4yqIRR9z1pWNcdc0893zrBBfsnSZOJQsLSxQK\nBVqdNn6gO/RLLprnissOEJsFLPrUFyWBsCEJcSyTE48ssfvALjq9Js2GT+oPmJ60ueLS3czO7eH0\n04ukMiLxJUtnlzjy2OPPqcB+zyYaeK5HsahpKM1mc2zg22q1xkVVFxndARQKhXFXO+L3KaV/9n1/\nSDbX274w1L6qI29ROL99MwxjnI0+4i2OjCtGcTQjys0zMbWJiQmmpqYoFAoEQTCWLLquq/XdpdKz\ncLZOp0MUReTzecIwZHNzE8dxqNfr7NixgyAIWFpa4sEHj7Bz5y6WlpYwTXMck9Lv98cLim3bvOK2\n6zmwq0qa6siWbqczhlRAb5k6rRQ3G44/s5EhzTMjTUbBe9vb20h5/jOZnJwaG0UvLi6ytbU1/q60\nX6rLxMQE661gDNnk8/nxtnBuvorvx+Nhk23bumvIKCan8xSLFS086HbHdCPP0+dAZGZZ2GyP4Qvt\n7SvJ5hW2VWBhYYFnhuWNBBqGYbCxvj6WiY6s9Sw7xnJi6vU6tVptuHs5v3hPT09z0UUXjd878Kz/\nC2WPcf9n3seyLEo7d1N2Y9JU8vjjj4//zShefXQOjbako+ISRRG9Xg/f1476SZI86/ft7W2ajT5+\nTw7/fcp2fUC/r/2M4zgmCAKCIODhE1v8w1fP8MDxJ4Yy7HTceIzdypIu73z7rfzcT93E/K4cC+fq\nbHdd/J7CMvKkcoDlBJw9s0GSdGltCb57190sLS1RP73K1S+5luWzMVNTU0ObSJM7v3gE2405/sjq\ncHeRsGfPbqRM+fJnnyQI+ygVEznDBd1xxum3IyPy0TWaJAlBEIw/55FJ0qFdU0SNLmfOrXLPNx7m\nP/zqj3PswXUGgwGnn9Iy3927d4+vi2wmg+M4PL3U5tzSMsVikbW6Teo2abVaFMsWkj4vf82FXLAn\nixtb7J+boFw1aTV00ObCyWNUXLjyyiuZ2Fnh4msvfs517HuWRfCZT34MIexxHIfjOGO8TMca65O7\n1/PHqiSlJEGgnfrDMMW2TaIoGE/MR49RqVSGxcQbb9XSNB2rYkaQgOtq70+NdbmkxNiuHpYsLixR\nLe0Ga4CXMSG2xpEvxWIR2xaaN2o7pInB5OQkcRyzvb1NkiS0Wj1ss8Di0gKVCRcpFbZtIlXMjh07\naNbqOjvq1FmsfAHHKXLJZRfiGIpec51Dhw6RynCM3cVxjG14fPvYMg88+tS4aIxoJc9MZzBMSCKT\nStWj3eqTzWaHCQYFWs3ecEurUCgcxySONc2s3W6iFMThiIOqu0XbSBBCdxxhIigWi7TbbTI58ESG\nQbCNH3mj7xalDB2pLBWFiTzN7cHw8dvDIhhimwGV8iSNTnsYS6LVdMSCqZkctulguBHLyz5S9bHM\nAkkSYCrFzPQ0W9ubpDKj41gMj0JWezBUp2w2NtNxsRntTjS+rDtY27YxBYQype/HoEAIbXsIkDcT\nZqaqHD58iHzmfGHwbBPbsHEqJX7tVz+BVzB58x3XkPecMXVwtKhZloVwNBUsHcarjDBZxNA8PU7P\nZ5EpwSCURHGKsL0xdJXNZpH/g7r3DLYsu+o8f2cff/29z5v0mZWVmeWrVCWp5FUSUkuAsM0gJGZG\n08EQNKGZGBoChu5gpqeZATTdQOOabtC0BgQ0NMihklQyVaXyLisrTaW3L59/1x+/z97z4dx7szSf\n9FG6ES8yMjIi4517zll7rf/6G5WT5RHPvXSd9ZvDIjeLbKSCSojCYlpQuYljhPy73/kEz3/7aRr1\nKe646zY2t4YTN7Nms4yRX8G39nPy3BVKfhULxbA/4L3vex+12Slee+ZxAJIkxfYttC6ubdfeBXxv\nD//qV/8VP/oT78GyxK0sPCFZ7/e4eVUR5BluuTyZHMcH/vjASpIErUySLEXYFgiHbz72LO981110\nNtexEklroUkngGlfkGYpfsNieXkX9VKNNC2WVq+fvUxGcRhOwka15vBtxzj1+jP0NnNaTc3S0tLo\nHUoY9BRzy9Ncu3C62FOUGuxengLArzj8/X95nlZrij/49O99VyyC79kOtui+bvENx05B4xvQD2IG\nYWE2EYYpvV6AlAaZsohlcWOjKCHPi0VW0WUUo7lluZimM6G3pFLj1hvYwh1Frtg0mzVs2yNMbD7/\n+ZN0dmJsy+WxL13hz/708zz/7Cpf+spxnnniZY6/cAKVa8oVE8sysSzwvYhBVDjp+yWPGzfWCMOU\nVmuGffsOcujQfpZ2V7nn3oPYtkUSm9SnqgjDRWYGi7vnqFbqzO/dS9Lp0O/u8NwLZ7h66SrKtLm+\ntjHqHKxJYsPmzir3HWny1mP7qE83MUQ+WXCMqWemaYIWlD0I+wO0VJiWxDQttjY7hXGyUkhZrKF9\n26HZrCCELjit2kSToTU4rsWuvXUUDqkExYigrzJMFK1aifYgJEjt0VZcoJFY2gGVIDV0OwV8UERV\nu4gkgQzSTLCx1ScKFZ5fWA9W/TKliqZUtrm2usHN1cKW0jZcoqCDiaQ53WS7M0CLQso7jmz2SrC0\ntMROp5hGxti9LSxkGrK4VLxEuQQhbOJY0qwWRUeYAq2L4prnOb1Ak3V3WO9cRo34tQCpVEitObxv\nEVPvMBQGX33sJLnI0MatBmGc5WYicETxd8/z3mD1V8AKhnA4/tpJHL9EmMYMwiFxHBMEAWEYI9Oc\nJIt48onTfOErZ7i21ieiWHglsSQYxkRSkhkZVRnxK5/8Af7mz/41J555nXKlSZpZXL60SS7NEVYJ\nnU7IzfUya2trbK7ssHLtDE8+9iwf+ehP8qlf+RR62OPRL7yGFj2GnR4GRceZGxEXLw745//sY/yf\nv/ubRTxSLjEdlzSPWdtMWV/pU6+0ePa5S0RRNOGpj3HposipwtC+gE75ymOv8PkvP02QCL75rRP4\npQob/Yjjv5+idwAAIABJREFUxy8w7LYRnmYoNYa5wJnjV/F8dzJZKtOY8I8BZJoRDYbkssO5C236\nvW2mZpYIopzmzDTlegOrpLh28Qp+o8z11Yhaw6XUctnsSP7yr47z0x//EEfvWvju69j3Kgb7Uz/x\nkZFOWkxG3bHKR2uNaVsorcgzWaSqei5xHJGrYqtqCgjDkCAICIJBwU/NNbVabTI29ft9LMtiefce\ntjttlmbmGAYDDKN4Cfv9gN72Bh//mR9G6ojW1By339libnaadz9yJ+9/933cdc9hrl7qsrF1lSSW\nbG8GHL3jAK88d4rnXrjECycuYGQG9z94CM+tsb6+TqfToVYrY9sOW1tbnHztAjMLFVZWelQrJdbX\nN3Acl0atjF8qzCfkYJ1MxlxZX+euY0e4ubqGhUujWZ5cx8zMDINBn5nZGv3NLoFU2LY7ObnHXZsQ\nAsssioXnCZShGQ6SUSeXI0TBrXQsC8ex2dwakqWqWPaogu/rlcBA0N3JyUdQwtjwvOy7+L5Hvx8Q\nxUXxLh5yjVKS3btbRHGBTR84sJ9uZwCGQuqI+Smfy1c2sD0HQxSR34ZRwEGtZomd7T5ZahR4ospJ\nYomhFJWqhWXB2mYH2y7j+gKZGjSa5cJNv9en18lIZTqJBjcMA8e0sSxodyLKlTIGAqU0pmlhKDWi\nO+UTHaMQAlOVaUxZrJzqsfvgApYYQQdCYCjBYNjhA+95mK9+/RSpjjl68AC5TrBHDcK4UTAAOXqm\nxzuAscBB5YokzZiZXqLfCwljiW05BQnfEDRaDb7x7dc4fXabXhiR55IsV4VgQ2myEdbaMAb89m/8\nDywsm0Sx5NTr55lemCPPYDAMKJU90Aa2U7AUANbW10mDPm9+85t4y1vvZGN1hye/8ipLdzQIBhs8\n/o2TLCxOs+fQfsIoQCnF9WsDvv70C9x110PceWw/7c4aQezy5AtnuL4CUiguXd3k4JHbeOfbD3H+\n0g18359EO41hEpkVCRZf/8bzmEKxstZHhTFaSgzToE7MA/fv5uSFbWzAEBbzS0vcuLGBTgN27ZkH\nXbB+Vje3ceyCNmhZFr1O4TgWDBPuevA2fDui04lotpoEYZ+trW3iOMDzBLfPztBevcAHf+SH+Ku/\neoxHHvkBHn5ongMH97J3727+4I///Pt7yfXffuxn0NqYbFqFKArkeAufJQlZUmRTjfEawzAo+S6O\nbU4wr1qthtY54yDMMIxGscgJaRozGPRI4piwv0OShSgFpVKFbrdPEARUahWSNKE1VccywDEsGi2H\nuZlptMwZBAPK5RKmqJLpnFqjTntzwMrWJrcfOcClyytsrIdcu3EdpQUoiwMHdjGzOMfJE6/iOCaH\nDx/CFBbDfo+b69uUylUEUK2UMU2bC1evkWUOlpWTRhkvnrjI0dsW2NjpY1OmVisTRwmG0niug+c7\nTE+XOHXqMm65RBQFhUG4aSCsYlGX5QLDygnChCx2cEfmJFoJDJEwPz/D9naA4ztEYYgY+RUIU43U\nWYV1XibHFDhNtW7juh7dwTaDQUqujCKLbHTPaqUScTikNzCYX2oyHCQk2YBgqMnVkOnaFNvtIcMk\nouTXRliqpuy6NBs+g3hAHFtkKibLJLawsU3BwcNTbGz0kLmL5zmgLcoVg6DfZWG+xo2VbTAsciSG\nIcgcgZGmVEsmsYQkK1zHFpenGA5iTFPgejZBHNAoeQwTiaFtikWZgRYKGWfcd/cCcQ6+a9/CYvUo\nKv3QMt2z1zm7E7C+co39Sy1K5RLjxOEx93IMT0lMECZSS4aBROYGg1gRy8KrIFdi1FS4fOPpE5w4\nsU6caoI0IckkSSYRebHEUjLH8dp86l9+gqNHl9naWqffVaSZxis1uL6yju8XYX9B3ANhYzkeAgNj\nZGE43Wgws2uKsD1kWppM7xZ89rMnOXzPPSweabG93mdq+hYf3WsY3Hn0CJWawXAo+ca3XuXLT79G\nMJDMLpZxbUh6QwynimkOkcqdQAOW6ZFlOVobGEi++vi3WdizmwPzDfbM7ua9776Hf/tvf53PfPof\neceb5nj8m5fJpMF9R+bZGmbsXpxn/75pojRleXGZVMf0ejHrG71bcuQkoVSpoA2DMI5Zu3qNF5+7\nSLlq4rg2/SDl5KnX6Q1iNnfaPPvMJWaaFW4/dh8H9i6yONui3PAJejEykfyHP/9/v78L7Ad/4H1Y\nljmRsBWbfzVZtIxJ82PeYhAEhaHxKP76jYubPC+wtj17dqEQhaggSOlu99G5oNcLmJ6ps7W1Rckv\n0x9KGo0Klm3jODZaQ68fkUgYxpLjL1/g8pV1Ui3IU4NcaRYWfZJ+wD1372blxgrzi4vMztW499hh\nzl9d5+b2kAuXt3jt9as8/tTLVEom+w7s4etfew7Ptjl4YBnTdli9uUGapKg8xrEs6vUyS0uLrKx3\ncctVGtUqw16bGzeGzM6UEIaJ7ZrUKhVc35ksguq1CocOLPHEU6/SnK6RpbJIss0VWZoBBZWmNVXH\nUCDTZMRKsCeppsNhQCYThGFPxrl6vYLMi87RdR0Mw8TzLdI0Ymq6yc2VNUyzMCuGIsspVzGVqsN2\nu0+1UilCIT2Dbi8mi4ZYwgQh6AaSVBm4tkBpgWXDVN2l0vDZXAvIZE6eg2lqDAXVWmFWs7WRo8gQ\nwqJWLqFzxcx0g1zZ9PtDskyNhAyF5Z+rNUvzJbqDmExqpqdrZJlBueSTRUP27m+xud4nSRT1ustg\noCZBmuOimI1ObCPXNBsNRs0fSmkwDIJoyK79i3zz22dJlODBtxzEGDNI8qK7zFWGlBmplGRK0e4M\n6XZjYmWSKANkjmWCsDWOU+bLX32R1y9tItEoQ5JlCpRCK40yIAsly/PwP37sER66czfr6z02dnr0\nIo2wq7R3+hijgpjnOa5XIQyzQlFYbZIriSmKCPQzZ07SKLkMOl1W4z5lb4Fys0QU3aAiJb6QVFo1\nMB20ANvyCSLBCy9e4PTlFbbaOacvbRD2Q+69ex/oEm9913vY6K+ws5Nj27co+FJmhbmOzAjjFCfU\nvOPB3YinXuMn/rf/jrOvnuaP/+AL/MEf/gbDwU0cJ+EdD9/OcHsAcY5VEWxvb3D8pWuofMDM3DQn\nXzuLMN3Jtb4xuLFW9vmxd87w4R/7KZ5+6QRhv0uqNYNBzrlz17Cp8Wu/+fPcffd+hKmZGvkznz27\nxucffZLOIOOrX/vKd1Vgv2eFBplSiJGzUMERTMmyW0mc45TK+fl5tra2aDQaEyyyXC4Xiyml6PV6\nIzaCRaczpFqvsbWzzd13HkMZDpVKmW988XEcx2FqagrHsXFKPt9+6mXe8a77iYfBiErDRFJXrVeR\nUjIzPVeIGCoWnplz5M69bG3v0Kg38WsevZ2YQ4en+eQn3sPv/+lXyXVCbhhIqfmHv38Kx4L//p99\nmGC7Q3enz8HbDmJbJr5fIgwDXNNCk+M5Ng8+dBStFa88f5zl+QYRHq9f2iLfpwnSgAfuOoowTQxT\ngMwJg4iyb/PzP/suPv0Xz1GZaWBFcjKSCVHg3GmsmJtt0m63KZVKhElIEIBlZphmMXZnmaRerxDH\nGZ1OpxitHQ+MnFwKDDPGMCw2N/q4ThFpXBRkA0NDybXp7HTQRgWvXKI36DDop1Qdiz3793L61S0M\nV+GMGA2W4+Aoze5dLW5sDMGW5Gg8WxNHObZjMDNdZ2e7h9YS4VgYWow4jDae6xDHbcKoeLEsy8IU\nLplMmGn4BEHO1rbEcn2kymlNtej0N0iGawizzJULN8kSB9MUrK338LyUOKlODhkhBLlOSdOcYX+L\n4XAWxx0JX/Qo4lsJ9h6cwhIJGGUe/dJr3HOkyvLu5WIyKtexDJsslWjhMox6pJlBnht0ejuFFaVU\n1Bo1/u7zz2CaDnnukGUJli0KyEtrVFY0GPfuqfHxjz/Mn/zJ8xy+6yF+5//6Iw4f3VN0Z70ArcMJ\nM8FxHLq9kP6g2NDXwiGXLl5jeraK26gjZc7Bw/eQq5gj99yFdfYc0orxb9+D2LxAtbXIiy+8wPLh\nPdiexanX1njx+CXmlmdI0pznXzwHucHBXdPMzE7hWiViI+P066+hJDiO+R0ZdkopkrjgowpTcfD2\nXRw4vJ9zScjnPv0PvO1dR/ngR34I7CH9oYlrQmdzg4U9LarrPaqNKk8+fY33ffDN7JqfIZEGvtsi\nzpPJDmfMBsrznHKpTs9s8NTnHuXKxS4HFuvcd3SZl58+wx/9/r/GUj3MzgBvqjWRO0spOXXqIkeP\nHZvwmr+bz/dsga1WGrTb29TK3gT497xbGvdms0kcx3S725OlgRCCREpsx6ZSb3H5ykVarSbd7R1W\n16/THySEYcEy2Fh/GscuMTs7y8Hb5sjihJLnMTs3x2uvX2Df/gWUdGj3NsjbhUFGrxuTZRkXzl/B\ncRz27V/i8qUVHnzwTWytX0fLBKUsPvPZL/OOt76Vu+9fYmttg5Lf4Jf+pw9jjLbNwijxW5/6T2ij\nyjcfe4FH3vMmjh07Rrfbpdlssr6+XqjNbEikQS4TbNukvZ0wt7iLjdUbxMMBSwu7SMOMzEp56oVX\ned/734WhNSoMRx2kpF6p8qH3HuFL3zhLc3F6RHu55QaUZZKtTo9Gy2dzfUitVsNxCkgAmaNR1OoV\nwnBIlhbj3PjlqFR9qhUfQyQMVYrWoxRYnY74wC0uX13Fxcfz68SRwrYd0GBLTT+UnDu/hTJBKAul\nC7bHVC3HND2kzAmCEM/zEabCNizqtZRy2afTDxGOh2EUXWyt2mAw6LPYmuXkxRuYtg0U+n0DD5WF\nHDq4wNXrO8WhbZr4bokwHZAGIUf3LLDTabM9jHFNHy2KmBYhbBq1GptbfRRFR1SM95r1fsRti9Oc\nvXSG++r3URK3PAaSOGNza8CvffKn+e0/+hxb3YRGazdBL2QwDBgOMrxK4Z417LbJtImUKVoM6PeH\n6NzluZdPEkYpllX4CEDRRUdZTpzkuOR84G0Hefidt9FbC7lyqcf//Is/y+9/6tPcWOuz62BKHCvi\nUeTLrn172FhZHe0yTKTMcBybMEzxXJMkEuStjPWtHovLTW5uDjDyU4U8XGlu9yNuGBaWO+DijZxX\n//iL3HPvXWgUr5+8RGW6jGuXGMYZjmVwx5G9TDf90XRR4KLuSPI9Lq5JnGNbAmcnQHom2rHILINn\nXroIwqa908FSJWoVj532Jtu9Nm5rmj3NGmvrmzhND9u1CcIQdM4wDjlx8uqErSJlQV+rVqtA0VRs\n7LTpvNDHsF2iqM92aDK93ODf/MYv4IkhpuWDw4hnb0ymwg998B08/vxLXL60+l3Xse9ZiOAXfv7n\nkDKn7HmTxNc0TTAMJjSqAgYoRr/CTSfHcgtKzLNPv8rhw4fodnZQmWJtbZNBL8cwFL7nMTMzQ5ZJ\nDhzYT7+7yezMNHleqHVKlRqlUonFxSXiOOLgwdvY2dnm2rXrbGxsghYcPLifXCWUShbb22sombPR\ni7i50SaNc+68e45+b8jGaocszXnphVNMzzWwRANtDHj4wSOYtuDmSp8wDGlN23TaA2ZmZnCcMcMh\nwjQtTMPAtE12drpF4ZU+tYZm5foOXq1Ffc5lqlnm8uWrlMslrl1axbJ8ZJZj2x6z8yVKnuLqzQFK\nQ5re4nxaloXMRJEU0ZpCygytczA0lqnQxMSJGtHIXHKZF+mcjku1VmJ9bRutCn5nGA0RwkKYinqt\nzsZau6DZYNCaahKFKaWSSbnqUq3YBIFCoUCP6ToWB/bP8c533sPzr16hNbfAcBCALrxkFxfqJLFB\nkmhyXfxOSuUIw8J2FVkq6A4jgtzAs02kLMIQPRfKJZftTYnCLDK2koTdu5aJ+1vM1nzO3thBZilS\nGcgR51prSbXusL0VY4gczRuMWiiewWyY0my6WLZN2fcw3gAlyDxn/4F5Hn3sOGEasra2TaM8ulbL\npd6ss7PdwbJcwihlGAa4ns03n77MlesdBkkGSqFUjtIGSZqg8hwrlfzYD76NH3rXfuanFokGbc5e\nHdLZjjHMPr/yq/8rO+2AnfYGBgrXL2h4nU67yBUTAq0ZLTQNhKHIpWRjY5ul5Vm+/dQrrN7scuz2\nPfi2QAgLhMIwBFGc8Jef3WD3sRkO7KtiGDa9bp+77zlKuVYjGAasbGzw0IP34tkGtYrPcDgYRWKH\nxRQwYgiYpiBNJGdOXOTedx+lWnYIIoU9apbSTNFsgjIc8t5NXnjuFTrdFNfxqFSrrAwjZiszKEtw\n+NgSaWJy7vw1oMBcs0xSKnkThecbEyMMozDNf+Tdd9CszCBMA991aDRqJEkxLRcHaTEZ2bZNFCa8\nduo6/cEar7xy/Psbg/2Zn/4prl5dIckS4iQlS2IgJ00TXNef+AtobRSGJCgsy8QQJs1mk917Zhl0\nO3iOi227NBpV7rhjLzLJue++2xgO2pQ9E1RMuVrC0BRa/rJPp9+jWqsQBAUGubJynXK5WAStr6/y\n1ocfJIz6pGFI2Xcx0IRhQKfbo9eJCcIeJd+h5ZsYlseb3rGX6dkpXjp+nf/0Z3/L3NI8C9Nz1KoW\nS3t28fXHXuLFFy+RZxFLy/MTonyj3iSKYixTMOiGqDxjp93BEA6Wa9PbWSWXksMH7yJHEEcR5A6V\npku54mEZBqdPn6LZmGWqOYXM+vR64JZsgmE0YWZImaByTSYLM+40TQrXslhimz65MoolmSo2vDOz\nFYaDGMs2JpJWrXNMYeG4AkvbRIFECQPLNhGGoFL1EaSYImdzK6A/zJCjjDPHMclVxAP37+XcxRu8\ndnwHy84xrYQ4UghjyPzcHJ1+yDBMQZjkuURrKJfL7NlVo9kss7U1ROY5JqqId3ctFhabpGGEzAoP\nA8MEx7FHo2JCt6OJ8pw4TlCGwtAmpqXwyxY2FkGUgIbFpSkGw+yWuMQsCmmmc3bVStglD9cuJNnj\nF7hIfLCYm2rw9NNnyaRg/94a5WoV1wOtclRmEuQpq+sdHn/+JmcurJErMYrVMdBSY2CiREbdFPzc\nx9/Pe95TZ/fsAWKZ88rZK2x2M3IpCXVOOoClxQW212+wtjOcKOagMOaO0wzTdgqVmAatDRzXRxkZ\nhw8d5PirN7i+FRAONUeO7UVmhaFMqFJyVcJwLN7ypsPs3DzH0aP3srhYp1ausdNpY5kmrmNxzz2L\nVNwKtmOiZEa5Usa03Qlv13PAzIccvX2WatlmenaRzfUu/+Xzj3Px0pC8kzC7a4qXXzzB4q69RIHi\n5LlrvO2dd7O8q8F0c5GTZ69R8lxM3yfNFFEKN25sk8mxOtDG8+xbDlv/P1GIECa2pRkEGX7V5v77\n7qDb3ibLChWoZTmAMYEo8zxH2IKDBxs8cN8R/uOf//X3d4E9dued2MJBKIlpGDQa5Ql/0TQNlGJi\nWjKWvpqmiWUaRMEA17aoVss4jkW1ViKMBpiWgSBla6NHperTqNWIohA1eiGUUniOxdzcAkrmpFmO\niWZna5PXT13lhZeuonTKscN7sa2YLM0IgpQkKcbWklei5AtyZXHs8CxuyWNhaZZOu0g6nZqZwjIF\n3U6Pne02pmHSrHh0egOCIGIQdZifnSNJ+7RaC2xtbRTdkAWlSpkoKUardr+PwqRabpCGMWs3V3Fr\nC2xsrLO4OEu17BFHEdVSmcb8DEpAteTSahh4Cq6t9qjUS2QyAX2LCq21QZbllMtVgmGE0mCY1ogX\nq7CFpDXTwnWh24mwzCLAzxAK23BYXKwzGIZkuSbXRXdUK1VRMqdRgU5fMQwKErkQRQHyTAutJKZt\nsrLSKRJMLYm2baxMUq2XMQ2TQT9DGUUsTJpm2IbCdy2qNc31lQFBKFH5SDSjXTwnpjFVZ2sjIFOS\nLBMoI8EwbRpVmJ+dJtUpnXZGplLQBqYWGCpnYa7FcBgQp5p6o06WCqIwxtUaaRVFGMY/FlKHLNea\nhFpSLdfIsuIZLaA6zb6jSzz6pVfQjiBLYuYaLQZxSrsz4OyNDZ56+jKXV/tonZPnGoscnWfkSqPJ\ncUXMv/znP82BfSYVr0yalDl15jQ3twagNSrPC/MjBUme0Zxu8EM/+gGef+o1cpLvWPiOn3PTttnZ\n3qReq+C4glKpgkhjHnv8RUzTIRUOeSpZWiyT5SaxNvn7v3uZw7fvYXFmhmbFIpQZpjCp1ko0GxV8\nt5AhB4kmSSNatSI3rlKpINMYgQKV43hwaP8iWVq4vq2tBkQILl7ewbQSrl27wvmVDeK+ya5dBvsO\nzILWlC2TkycvcOC2OW5sbIFhs7axSbeXsb3VR8oc17UwTQPTNCaCjjEtbyxsGE++wrLANGn6FkkY\nIWU6Uh8Wace5UhgwUR9qVaQPmzjf/yyCD7z7HQhb0myWMIQil/l3OIlHUSH5HKu6xnQuKNyyhsPh\nRDoYhiGu5xbjEFCtNNnpbJNL+R1SvcLDQJFkAZmMSPt9hmFEs9nk9TOXKc3XCFKT5589w5kzm0w3\nq3i+g2FAGAaFMYmUCNMn1T5TS2VkmJKrQhpbrlS58955di0d4tqVaxiqRLUB73z4KHv2TvHcc9do\nb7XZt38ftm3TajUKK8X1PmHUx3EcwjBiGBbXpB3BA3cd5uy5k1iqxH333kEYDVi9sYnODc6fPc3s\n4gKu4xAO+rh2jVrTouxNcX1jC8Mw6Hb7hXXhG2SKE8PqEVZWiC9cPC+j3xNYtkWSZIXXqWnieja+\nl9PekhhWUYyhOADrdZt6o4TOM3Z6MWhN8bwWngfLy0063S3SxMMQaqI+csoG+3dN0+0OirwlKQiT\nEHOUGjzVrNNpB5RKFYZhjGkJ0lQihIFte3iuQX+QoXOPXMdoJZiZbWA7UColXL3UJ05z0iTHsg2k\nzKlWHOK4x3BQJCUodJF+kWp83+HYHTOsrkeMjMMm6qBhbGPbAbNzcwgYyaEZwVkS3xQ0alO8cvoC\n3YFm174Sr5y6zslTW1zfyEiIYORXUPhhFAsZM435v3/zZzi2q0lugF+rc/r8DdY7QyQmWZpOmDOF\nSKFYLl68cJMnvvkKcTIEU32HU9f4fo4pjXEc02jUCk/aJGJ1M+L9dy+ThjtsdSSH9i5iCk2WO3S3\nTeYWbIKojyEF/Sjg3NnrrFzfYGq6BkaG59l0BjF5DkmomJ6uYxiFifbyVJWSLViYrbG2vk6z2cS0\nUno9k5dOnKDfz5CWi2tXKJkhv/wvfq4IftQ2vjfNuSvr3FgfsNnp0++kBMPCBxij6DIrlcqEqz3m\nzr/x2oHJ9bueg+s6mKbFTLOKlBm1WnVUTEVRXI0iAnOsMhtTvizL4k/+7D9/n3sReA5Izc52m3Kp\n8h0m2EIIms0qjiPQOpvo+/M8Z25uEXtELtYyh1xhKIkjBDrL6XZ7aCKatfrohozoNobCdYsCmaUw\n6MfM717iwsUO//nTj2NXq/i6MOUw3DJWpcKpK9s8f/wqTzx3nG5/B5lHBIOYUkWTpxlPPnaWm6tD\n9hw4wMKuZZJBwMsvXODJx19icztCeCnDIOfitQ1krlneXWZ1O+bPP/NVnn/pBFtbHZ599kVyVRS6\na9euUavWWZiZZbrRxEgUr5y6yPTifvrRDZ546klyfOqNGomUPPT2R0j7AUk/oFKp4NqCernBHbfX\nefi+g6Rpke01xrTHYgSAOBlijmhyfslGyhjHaqB0zHBQCDU0GRiSZqVGfwjKzEniHMcuMzVdZ266\nRhhHXLuxzfX1HoYu0g1sW1MtW5DHXLyyRqk6gzOKFrNtm5lGlZZX5fpKh1SWkKqKYRdqNGTE3FQL\naSiEY5GpQjAQRwmWrdm/Zx6LlH5QeFAohlimg2Gl7Jqu0t8e0ut7aNPGEAprXLAbPmEwxLHrYAls\nzxm9tCVsJ0WpnFNntnFFUVjHlB8AoRVKuaxfW2N7ZxPTLDyHk1iicoNuL+S9770DC5tY5jz6+AVu\nrEaEKDI5QMtbBaDmuNgq4VO/8TF+6ZPvYf1Gl1JznudfvcD5i1skqYFMUyqjwMter4chBFGWk6oc\nicZwcjLRB0eOKI7uRMTgmhZCaTAynnziMs88c51Bv2gselHG//7L7+fDP/4OPvlLHyFNA3xfYFgu\n9YrNr/36j6NkTi9SdHKTv/vbF1javURjar7ANrPi+7jnyAFypZAotrd3CIIIW2hubrXJDIv1zR18\nr0KvEzLsRtheyPZ2D9s3qfkawxqyf6+kXFlj4+Z12u1NtOiyb+80tWqZoJ+R57KQqBsZQhQwk9Zy\n4orled4b7CrNyRL8Fh4LRn7L6nS8SLesQphjGQLeEOo49sfQWo+auu/u8z1bYE2jMPXIc4O1ta1b\nCqRRoR37C2hdFDOZFO19qVS+ReNSOVIXMswoikZ+BP7oiyxGh7HV4RtlitubHeb37efc2RVmpjx+\n/hffyYff/1buv2+ZH/sn9/P2txzBFx7dMGZtJ2Z9q8T5axq7Xsc0NVP1BvWq4NCBOTKVMUhTdtZ3\nqDmC5x8/jyDgoQcPstMecPFyh689+iKOXeKnf/K9fOJn3saULzj56gqvnznHgw/di5TFQm9+fn5k\nbOPjeRa272E6FYJY05heoNKqcO7EGTY6ASXb4ZlnnqG1sIgoFUVhu91HquJBObzHZ/dsCce7pWsf\nH1RpmqJygTCKQ2s4iClCkIvWTUqJ78CuhTlEbrHdHUzGL9sxMI0hs60GmzsDkqS4V8Jw0Hq0NCjl\nBFHGMBkgcBFCoZWF5bjsma5QLQl6g4QkNYjiDnmu0aQsNRr4lRLr210EhX9Dt9dGKINaycY0My5f\nWydI4olxDYakWRc0Gw3Wem20MMkyieVIklhg5X2WWw36QUqa22Raod+QW7Znd5lIGgRpQpRL9u8t\n4Qpr0s1IKZFEXN7sc/3GGrZXJsoUW5tthsNC5ZSGMYbOufeeKVSWkYajXK5MYmpQUpLJECPs8W9+\n40f42R9/J1s3d5Cyyqunr/La2Ru4jkev26dc9khyzU5vQJJroiwnkcX9c4T5hh9wTQPbuKX3h1um\n5qR1g/LwAAAgAElEQVQeX/ra7/OFr/xHvvXMCQadmDgRfOYvniB3I66fvYnqbtBNduh2u8g84O8+\n82fcuH6DzIZHv/gUP/fJT/DSM6+Sdi4TS5Ovfe11DBHy1//121xbHXLp2k3CVJIpTRpJbK9MdxAS\nKkkQSTr9Iabjc9sdSxw5dIjbDh9gdnaeO+86xk/9xEc5eeIUQggeOHo3Fy5scvzENfJcT+DBsWHO\n2MZ0DH9orUmSQkY/juJxHGfSTGBIut2RabaMiWTx/+VoDOtWEzf5cQwkGcLRmC5sd7rffR37XoUI\nPvHxj7K+sTlSCeWkaWEmHQTjGObiY1kW8chH0xCFP2gh27QnBs7jrPqiiNh0u91Jx3srurr4/7JM\nYlkO/ShEp5K1m9s898xJbKeQQl69ssnbHjrA/fcs88pL55lZbBVjjJJEfc3dx3bRbE0ThiEAUZyQ\nBW3yXo/puSoPPbjE/Xcf5kuPPs3s/CyHjy5yYP8ir7x8mrnZOZZ3zXLPA3t48aWLpHFKrztAa8n8\nwphdUGzAK5UKQRRPrndns0etPkPQW2f1+k3OrV3i3jvu4uSJ8+zft49BJ8JxCyNxYRpInbO/XuHl\nk2cxbP8NKbO3tOHAxAciTRMcx0Zmimq1QhIlhOEArQru7dhwxi8V3qxpmjIMkslG3TRNFBnLu1t0\ndmK0silVLNAOjUYZTcbb7t3HSxevoYWHHGV0OY6FaWkWlxusbAwolVzSWGFagiTJsGxBrWrj2Db9\nfo5GT+Sorq+YnathC5ed7QKHy1JJmhZZXvvmptjoBQwSicolYlQ4tTaoVFyiKGZjZQCOwsDEEAbD\n7RxJgBoxCooXu1hKLjabCFtiOw6NWgNN4WVsmYJgGPDed7+Fv/77pxDCIFfy1sEerfDvf+sXWK7D\nX37226SmZpjmXFlt49oWaZIyOzvLYDAgCAKkujWyjkde27YR3CLVC8HIg8KG0fdx5coVZqaKNNlq\nuc7uhUUGGwnf/Mqr+EuaF1++yIWViH/6oftYXtrLA285xuc+f4I7ju0ntV2yTPPSayt8+csvMj/j\nsP9Qi9gos7A8hcwNSo0Zrt68wZXLG0TZkFrVo1mvFuyesovvuKOGBsq+y/T0NJ5nYvtllnbPcfHK\nNrZp4bkmF04e54Mf+CFuXOtz+vJVwtzEHXlqFHBg8YyOs83euMQam/CPD5Vx9zl+DoWAer1O2S88\nnXMpcS0Dxytio5xRVzv28DDMYsq9dPEmw6DH2tom//DFR7+/Mdgf/tD7UbrQmc9MTxEMC5GAYRhY\npofnF9zXoD/A94sCkeWSfhhRrlZ58aUzzLSmmZpukCUxSmm2tnawPRdhmfiu9x0GG4nMiZKUKMno\n9fvIOKHX32LXniatqQZRppmdqtHu5bz6yhWGYcDb37mH2ZbDkQOH6Pf7LC/WCRNFng0ZDvuYpkGr\n2SKKMprzNcxc4Xs1ojDlrW++jTDSvPL8ZTq9hKW9Na5c2ea5J8/ilzULcw2m50scPrKLOEpp1nxc\n26Tk2filWkEY9zyGwZBMJjiezdbGOlvtPlpGdDZ7aKE5eGAP16/f5P4330dvZ6dwGksjXOFg+Q4z\nzQonL1/DNn0ymU3MSMYPcpZlNJoVcqlwBNSqVbIkIlUKwzTxSm5RSGxBo1Gj0WiwsREgbKNYWI1k\nis2aRxwl9PtpMd65ZeoND5kpXN8kTxM620PCFHSuJ9hZyRUIBEmYkyQZJb9amKSLmIpboeTDdieg\nP5QIK8exPbQRIZRiz/IuVq93sUs2g36M0glpKnEtgWs7dKMIISy0KsQBKjdoNF1mp5poBUGQYpgm\nSDANgYmgUq8w1bTpB/rWCCpslDIYhn2C9oB9e5dQQuM5doH7W8UL2pqd4bEvfpV2YmApk/lZ+PVf\n/mHuOnoXN65tYbiCjXyOlas9mlWBMbJmDIKAfhgTjwy1x8VkPHHACHccFRDPsydScdMU2FbGM9+8\nSacb0xuEzC6WWV1ZodsLeM8H3sIjjzzMb/3eZ9G5wBQZ337sVYZZiT/9D18glnDo8BTt9R7lSp27\njs1y+vh1Dtw2y+f+/iUuv36OSt3lox/9OH/67/+aaxtt/tVH9/GVL56hNT/Hgb3zDHtpIZlGjWw+\nBYsLLdIswTINZNTHlAbdzoCH753h3jsW2dmKOHN5iyjLEKaFZegRBa6gd42XVuM/vwPikgmVkkeu\nVZGZprPRgZyCBscpnm3HstGk+CWP4WCA7zi4jjNZaiml0KbBydfO4ftV+u0uKtF0ooAvf+Wx728M\nVogiHHBqrsXq5k1arTo3rl6DXNEZ9mnvBMg8m3ROQghMDGZnp3n2mVfo7ASsrq0ihDMasUeSW0vQ\nrFUmXUee5+RowsGQYa+P6/rYtuDG1TZp5CJTSaQFBi79MOH61SuUWy4nTq/zt/9wkaefukFtNmfv\nvkXSNOX8+fNYljPxjXUcgWkq2jsDdkLJ+ZUrrHV6XL2xwc7OFoeOVFld3ebUiUscvX2Rh962F9dp\n0ZyZKpzqQ0m54k2+E6UUJjm1sgd5ihgZodTrdfxKGa9SotScY25picunb/KlRx8jTCR/+f/8HaYp\niKKwUDWp4rr3H5zjRx64jd5Ol0q1QhQFk2XL2Fxne2uA51tkWcr6VpsoLbbkwAR6qdZKbG50GPQj\nLFsTRxlJrEDEo8iYDMyi46pUSmBEeKLM/oUpyoZFGBtsDVO0NHAcl7JTZt+eaXphjzA2SEd+rzJO\nEQrqFZcgCun0MoRh4boWlg0id3DtHMO2C5McNP1eQJFaAfWKh+NXidLCM2CcyeSZLg0flqfqXLm+\nSncQFJ1+HgMmJVcxXS/THkR0+jmGiCf3Y9z5K+Hy5nfcwfFnXy06yVEByCVoBJdfP8/v/uH/wofe\ncjf/4hc/zNseuIdzpzucPX+TB99+P+2eIupsYO56gLI/BZbDTm9AqpiMvGMvgzf6/ILGt01sW2Db\ntzbl498rSVL+8bHf5W/+5vfY3IkZJhG5LvP4t17G9zx+5//4d6SjsMNcw40w4wtff4bAtAik5ltP\nX+DzX36Vp169wM0bAe97ZDcvPvUED91V5wfffTe75+f5uY/9Kn/4J79INujTTZssHKwybHdY3j1N\npWqTJgqtTbQ2kRl02iGDfswwTLGFw+bqJnN1g5dfW+er3zrPQNqkaTa55uLg8CaH/9isZ5zSa7sW\nlmMyP1tj/74mzZpPtWQz3SwzO1WlWS3hOgLbMRCGg2MXyrtmYxrbgXK5SG2OoogoG6KFwePffpKX\nXzpRTIg7Ozglj5V2m+mpue+6jn3PdrAf+29+HAwDjaLVarGxts7s7DyDwQDbL+E7PpoMz/G+wzja\nL5fZ3OywsDjNW9/yJlZWVtjaWCeKImq1GoNBbzJS2fZoZFE5nZ02+/bto1Qp0WkPac0aVKol2huC\nlIhqxWFuboH9B2eYnZ/h8G37uevINF65wtUrq7iOTzgslFCeK5idnaHX6xFF0YivWzzs//WLL/Ly\nqzd58olTSKk5eGg3tx1bQKaCvbv3kiQBwTBlEAwmG/1KpYKSGbZtMxgMcD2oVD1sp0K3F5Bm6cSN\nqV6vM+z3qTTqEG6SjSKj9+6Z4fKFFXy/hmVDkkYAlCs+ou5zaPcUr569QhIrGo36d0QTC2Fhmoos\nSRCmO+oW8gmUkOc5lm2SJgops0n0iV8SYCiiMCPNUmzbwhQWli0wDE2pBNfW+4SZAq3xPI8sUzi+\nZHq6wsq1HrZbRuVQb5QIggjXUczNT41sDjWmbU46ScuGxcUKeSaIIotS2WLQj5B5itIpy7unGHSD\nUYLBLVNuwzCYnamy0x8yTCkWZqZAZhmGKDpUYUjCICZRAoHEL5dGReOWn7IwNL2tLVp1l2qrhSUE\nzkhzb1omKEU/kFy7foHNjd7kkEql4tzZiySRoFoVtPwScXQTQ9xidryRZjSeLsa/v+M4WKN/e+Mn\nyzKmp6cJh4pXXj7J+973dm6ea/PsmSusb+6QqpS/+KsvcmblBiXHG3Vuo9w6u7BqjOMYlWnWt7a5\ne2aO2aUWe/bMMdWao3vzAh/58Q/TDXtMNw38pM2BWpnPfvVFPvrW23nbI3ci/AZxrBgMI8SI7SOE\nwLUNfN8jDnNiDTc3+nTCAqZ5o8fxeFk1xliL51FMItuhKMALDQff0jRLDo5b40tf/Ca79y4hZYbr\nOgwGARqB0kV6sO1YI259YYfp2S6OVZi8f+2xJ8iyhH379gFisp9I0xTX88Ew+IfPf+H7GyL40Y98\nEGEIgqCIdk5ljiWKNt9zbbQujJATGWEaxYOY5pL1lSHkBrffvsTW+hpaSRzfwzAF1XoNYVoI08J1\nvFEEeIrtODSnWnR6XQ4dvou9h5Y5d/x1SuUyjUWDf/zqRTY2hlw4d5ZyxeL109tsrHc4eeIc99+z\nh7LrEAcDsjDnxNlLnDq3RT+MePDe29hpdzBNF9f1yVXI+mbM9EyJcq2EJQStRp1qpYRlWzz57RPc\ncewQUbJNo16iOTXNysp1dG5hiArKyKiWy2SZwhQ2jiXQCmSW0B8kI85hmUGvh1bFuKTjkLV2wNLi\nIjPNJr1ejySJSXNBkkmee+kyL51aY20npuq6rK1uMT9XJ9eChdmUMCrwUdOyyfJCNQWAoUdLyGKB\nZQpn8tBblk2t4RJHxfJqgolZGq0Vc1Mt4ignTDJyqfBcl06ni++A5zqoXNHtJ0gt8X2PTKaUKja+\n7WAKzWp7WCw7jAzbcnCEAB1Qr9ZZ34xxfZsgKFIscmnRrBVR7b3OkP4wnry8lmVTKrvML8ygBQx6\nkkzGmMIikymWJTh8cJ4wCIgSA22YmChMYaFUjKEMcs2k8BmmJos9BAkvPn+OvYf3Y9kWpgHCEGhD\nkGcRx09vYDk2lu0wDKIRluswHPZw/Qp52sG0nMkCdnyQjX0kxj4b4y5OCIFpMOns1tfXabVadLtd\nhsMACWxtDjl8ZDeLBwVf/NyLaCXxPJ9UpihZdIJz0y537F3i3Q/fzdHD0xy9fYnT51cRpsGuls9P\n/sS7MRxBrx/y7ve+l81ezHYvRCmTcq3GHlnngY/9Uy5euUpSLtPtwdzMFDINGcaSOJFkuUZYBsEw\nQRs2/eGA7iAjyxVJJL+jDoyL6htN0W9lmVk0qj7Nlg06JdUlsDW93g5PPHWG1Z7E9WCm1SJJMmxH\nsGt5Ads0aHcCtFL0gwGNWp1y2cUSgq899i1M22H//v0sLi4Wz5BS1Bp1mlNTSJVTKvv0Bz2++I/f\n52YvaZJjlWz8kochYGauxs2rm0xPTxeZTZVyAWLnxmR8sgzB8u4aMpf02p1C2WUUkc1bG1uUSjUq\njSn0MJqkD5TLZbY77YlO+vSJ08wt7eHpFzbwGn3WO20sy8YvlfnJH3w7hqE4fEBy/uwKh3Yf5dzp\nSxy8fY7m1BEsuUUnzun0ejiWxTeeOsO9dyzQ63ZxnBbCcKlYBrMtl0ZjlqkpG991+crnz/DIhw7x\nkY88yG//9meYmdrFI//kCOdfeYU89Zhu2cg8BF0ZUdAsoijFcQS1hkOS1skNa+Ik5lcabG9vkysX\nx0l48+2LCNOl3dlgK8iZkg2sxGC65HP49nneVCusAa/dbI/0/QY73W3Wt1xkXnS6Y0d+0yxQJcOw\nGA6KxWOYFN4FhihktI5dIhiko06kKKwi//+4e+8gy677vvNzzs0v9+scJmMGaQaRBBEEJhAkxSSS\nYlAOVlxbsoJ3taa9Knktel1rlbVrWYmSlktzl5Js05RE0qQIgiAEgchEnsEMJvV0Ti+HG889+8d9\n7/ZgbZfJqv0D3ls1VVNd3f36vXvu7/zO9/cNUHSLlKtVvILNcL2BV3BgZBA9XXepTk3QarUIw4yN\nIPAyVkhnSM0NubATYVgxFi7CECjto/0QryppBy6GY4HOgheTROHZkvqExvIc9nYGjGOCVCBwjIT6\nhI1bdLlyeZdSxcC0TDSZ4ffS4izreztcuNQkSvoYokixWKLf7yMJkLJMLFqYlkuShCMsVNK3unzk\nrlOcr18m8BNcK8R0XKIowbZNbLvAh997B3/90HPZOvY8pLQQtsD0nLwbB/Kk2LGDnJQy7+jyYU7q\n8uhTz/GWu2+jM+xnna006Q53EIbBykaPZjem2+3ym//s/+LMuQsYdkIa+/ixSTgcEiQxM26N9993\nklM3HebShU1OnjqFP+zz8EPnGKiAN77lLjZ3+7hVk2K1wgunX2buwALdzoAoTnAMk2fCHu1nX2Zv\nx8c0HI5eU6U96KJGSsuM7pdF5sSRJtF9fD+LGBqLIWAfEhgPprONJcEfxggzxfU0kwWLVCrQBpev\ntHnp7DniOOYXf/Z72HpwBcfSNPZ81CHJV778Aj/6E99Dp9WkVHDxREq5tohSa4RhRCeEdL3F7PRB\n5ufnWV3ZoNXsMDc3y+RsmaHfYW3jHFO1A2xuXObZ0/8/8CL4oY9/P2mqQLuoJJNqWiMSeqPRQJPt\ncKE/zAF/07SIVYYdmjJTC9m2RRQlqDRlMPRJ0hQzFSid5IVZGJJisUgQBLie5PKVVQ5fU2JiepKg\nnVD1Slx/osB1x48RhAHlchHTtCmVLRLb4vmX1nnkWxfp9AZ83/0niUNBKrOI7CMHpxEiE0RYlsXU\ndIVWI8ArmFTKDmiDxcMlOu2Ql156gQ9+/wdYW1vl1C2HWVqY5dDhwwyCrMhlrlDZZD9jUqRIKzsG\n9wbZphFFEf3eEMfJ2AYWFkkQ8MqFi5y87hp6QcLy5hC/F3HXmw5TKJXzgcjmdoNGM3PMl8a+A1EW\nb26TiQOc/fTTOM0/tzTNIrlLpRKDvn+VtR9YtqRSsbEdi63tXaI4xPdjbMfCkAaOYyPRtNsRgZ8p\n8oolj8BXSCsgtSS7zRApDAxTo0Ydl2UbLM2V6HUUQWJQKBj0uiGWDYiYqSmX5o6mPwjyhxeANOTA\noSn2dnyEoQgDgTQUgZ8Nyiplk+FQZUMlpTEtjW0VqdQcvIKFbSa02gGTU1Va7T6w38UausCFlVVm\nyw5nLm2wtDSLZZgIAVqPJt2G5PQra9h21oFGUZwFJwrB1WOR8dR7n6st8sIzhgq+/vAztGON6UgK\nTjZV94cBV7aHvPLqNlutPnEQYpomK7tbSAwQCbNzFYiLpMMtPvHLH+XD77mVqZlJlALbLGciiURx\n3c3X8Oi3znP6/DqWrTlyKMMfx+ui0WhRLpdJtcKxHLa2tvj5X/wJzp5ZZnKqTL8b0mh0SdL95Fyt\nNQUv26zGIoBx8sD4914NUcVxjEYxOzfBQr1MvWJj2w5x6rOzmfDIk1cIgiyj7qaTh7j3pnmMqMFz\nL25xbqVFkKbce/e1WGbmtFatFVnd2kUlCcUk4tqpSWpHJjly7CAXz+0xNz/J4oEJNBGnz7yKJuWr\nX3qWp1++yJlXL7OyHbF2+fR/2x1snBrYtkdjq5sdoTxNoeRgWBaLBw+QhBE6USM+a4rWKVoPsSwX\nnSgU2U0aDBLCJMYteESRwtAwUCEFx6TT7+EWC6CzFISMU5vgWoLaRJ0whLm3HOOmW49gYhMlEebQ\nYG+3R6fdpdvt4ycR1xypIXWTqakKy1c2uPueYwTBgE67jmkXiaNevmiEMDh8dBo/7PCtv13j4KEp\nrr12jpeff4nrbryGiXKVN91xAkubnLm0ysGDNo5jEsSSQZiQ6pi6UcQ0IQxTVAS2Y9Dr9bJjcKeT\nwR4jqol0DIJ2g3vufxvbrQ4TtSrt7hZJYiCExBzRV4QQJI2YQegTxBE6NfLFP2YTGIbIJclS2EC2\nSdlOpozptEICv5MXsygeUrAKTNWqbGy3mJmpksRthv2RdHM4pFKvEoQhwzArQGN+ctGZoDTnsbe3\nQ5JmpHopJKmSIBIWpqdZWV1lORRUqiXSZh90AWkZlG2HxFc025pEJHmsCYBBRLFkkyqbSAnAIU66\npH6IaxWpTxTY2vWRQmJKiTRsTEugdcrCTJHHn1uhaNlATHNvQL1cpR8koCFNY4w0wI9NLu41eeMN\ntxL0AiwhKRY90hE1S0jJrbed5PTZS5l5uRmjU0kURpjmPo56tbx1/Jm2Wi1qtRrT09P0+32+78Pv\n5QtffozLF1dYmDnFs89eoHVVWjJxgHAcwijC0oL6pE0YeAzbXX7p776dauUtmd/xIML1JAKDUkUS\nRzGGCRVbZfCXafLUU2c4dd0UharLK2d2sL0Cxw7NjjBKF78/oFB0+INPfJLr7jiW2YUOAhTG6L1J\nVCJeU2gho5YVi8Wc2371ezdNi9mpShZKaSTEaBJlEPgdvvjVSyzvXcHVLrNzk8RxzF996Ql++UdO\n8Y1HXuYnf+Qn+fRfPIR1TY1uq0WlmomLun6P+UmX+eokhmuTWhY6TtFJyOf/6iv80I+9l/MXG1Rr\nFt985FkKJkSmRSsQdNswHH7nPNjXbYF95MG/JghDpBGgU41tTfOGu++k1+thkKKiIeVyGc+zcxDa\nMLLuCkbhekCsssmgISRHjp2gPxhkRiCew4QUBEHAxMRE5tCfBCxfWaU9GDI9nGR6ospd33OSTrtH\nnMbs7vT5m28+xrXXH8F1HQ5OLPHi6XPMTt3MsUNZdr3QkmbL56nHLlKe0Nx4soQQVmaOjGBza8g1\n102zcxbuu+8giVJ0OgPuf/ddfOPrz7C92eMd7zrF5laL+sQUe7tNCoUivV4XIQTTBxfw45AkVniO\nA4ZEpQrXyjKfisUigZ/kFJ76lM3ubpONC+cwrALT09PUqx6m65IkKXEwoDiRQQSRqXFMCyUTEva5\nhUqpvMvQqYFp2mhibENSKmjavZjhIEWO9PdaWSgVULAttBREaXZkH/p9TEtgkCmJimWPbj8cmU+n\nSOFgOSlhFGF7FleubGPbHugYQ9rEQR/HKTC7MJH5tBZLqCRT/dm2JI1jFucqeJ7Nxl4XHWksy8y4\nzTLMTg/dhMHQZnI6w/SGQx+BwWTdZdiHvUa2gZAKNAnERSYmLXYaIefPN7HFPgFda021qugH8cgA\nRqIkICT+sMpjjz/DzW+6ATMyMB0LS46sDoHJSQOSgPXmgHqlgOu6+ab4/5Z2Xl2IbNtGJdBsZl64\nnf4mngzo9eFvHz2LZTkUvDLRKKretlySVDAx5ZL4Ci8a8Gu/+nG0hu2NParlEugUy9C8/OQ5brrr\nRrSG9Z1VdlYD3nDnYX7u597H577wJHFvj7XdhG9+/lGuWarT2V6nXrqXmenpPBmkUChwzRuuoxsr\nhrsdup0htu3k5P9CoZBj9VmySOajMI60N82RYxmKkmdTKrkIGWPZBn4UEaaa9k5AP9ScOGKwuS2Y\nPTjH7somh47M0O8OUYVp3v+OO2g2XuD2f/guTiYOlUTj+wlXLl9Aj+henmUyVy7mTYSwNL/+j3+Y\nv/9rf4pphRw6Ms1AGzQHBlGa5Pfgu7letzStOA5JVUwYBiQqRqkuy8vLHD58GLTA8zxKpVIug8t+\nJs519WM6izU6tlarVTY2NwnjiGHgU65Uc5/HtbU1et2ATnvI7s6AOLR48YVLHDqyyN6W4qnHz/HU\nky+yt7fHdTccxbKy4UOn0+XY8QUSlR1PSsUik1MTDIc+97ztGMOeSRQyKk4ZpaxUSXnx+Vc5dds0\n8wt1Dh6aolIp0273uPett/K2+2+m3/fpdqLMSWsk5Rw/cBvrG9nvGk01NzY26fV6eK6LbVq5wXAU\nRVnkdSAxjQpbG608BnppaQnHKtHvKuzRIAVAmAaCff31mMI0HjKMydtpmuI4DjPTU9i2AUhct4Dr\nZmotaSTMzlfwnBLd7gBG0T9Z1HjK9KyNEA57g4REKTTZZxSrHsUSlMtVdht7SLl/fIzjmGLJxrD6\nbG00iOIEy7IZR5m7BYlhCHZ2e5y/sJlJIUfFyXJS5ucn2d1UVMpzxElEq9POsL2kx5FjGVcTbSJH\n60kIg0LB49rrZ3HdInGUMAxCBOR46MzMDMOuiTSyY//4HiFgmBrcdudBnnjsNEPfJ4wyOGpcLG3L\nYGF+juLIE3bcpY4jq8fXuJCP48ozHNzM4DNAmjF33HHjCOYZJaiqzCfCtm2mZorMVhwKI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y7jw1MjmuCLPYqatFNOO1PSZeKUNw55vfSre3hutkCbOvnF7h8PFDGFaXnT3F//1nX8e1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"text/plain": [ "<matplotlib.figure.Figure at 0x7f3e63b26e10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(img)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "76c069ce-83bf-e85e-214e-81b49c05f90c" }, "outputs": [ { "data": { "text/plain": [ "(2329, 3099, 3)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "img.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "cb7f374f-1b2b-9876-91fc-259ef61cdfd8" }, "outputs": [], "source": [ "df_sub = pd.read_csv(\"../input/sample_submission.csv\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "8080dd41-f4a7-7c3b-82f9-4184a320d4cb" }, "outputs": [ { "ename": "EmptyDataError", "evalue": "No columns to parse from file", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mEmptyDataError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-7-1bbe08a25143>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"../input/train_sm/\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[0;32m 560\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[0;32m 561\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 562\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 563\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 564\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 314\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 315\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 316\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 317\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 643\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 645\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 647\u001b[0m \u001b[1;32mdef\u001b[0m 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\u001b[1;32mnot\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1212\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1213\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1215\u001b[0m \u001b[1;31m# XXX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mpandas/parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas/parser.c:5214)\u001b[1;34m()\u001b[0m\n", "\u001b[1;31mEmptyDataError\u001b[0m: No columns to parse from file" ] } ], "source": [ "df_train = pd.read_csv(\"../input/train_sm/\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "1a772645-f0b9-d609-1e45-922d69d9b5b8" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 102, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/329/329250.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "561824b5-ce54-4553-4175-ec2e6584482b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "test.csv\n", "train.csv\n", "\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "09f549ea-0cc4-5be4-56da-b8a5d8abbea4" }, "outputs": [], "source": [ "train = pd.read_csv('../input/train.csv')\n", "test = pd.read_csv('../input/test.csv')\n", "y_train = train.label.values.ravel()\n", "X_train = train.values[:,1:]\n", "X_test = test.values" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "20c91e1e-ab0b-0884-63df-65d97dbdfd99" }, "outputs": [], "source": [ "def show(array):\n", " array = array.reshape(28,28)\n", " plt.figure(figsize=(3,3))\n", " plt.imshow(array, cmap='gray', interpolation='none')\n", " plt.show()\n", " \n", "def plot_gallery(images, n_row=2, n_col=5):\n", " h = w = 28\n", " \"\"\"Helper function to plot a gallery of portraits\"\"\"\n", " plt.figure(figsize=(2 * n_col, 2 * n_row))\n", " plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)\n", " for i in range(n_row * n_col):\n", " plt.subplot(n_row, n_col, i + 1)\n", " plt.imshow(images[i].reshape((h, w)), cmap=plt.cm.gray, interpolation='none')\n", " plt.xticks(())\n", " plt.yticks(())" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "f6486e96-8866-dc1e-7c0c-ef9970f89e91" }, "outputs": [], "source": [ "def normalized_data():\n", " X = np.concatenate((X_train, X_test))\n", " x_test = X_test - X.mean(0)\n", " x_train = X_train - X.mean(0)\n", " x_test /= x_test.std(1).reshape((-1,1))\n", " x_train /= x_train.std(1).reshape((-1,1))\n", " return x_train, x_test" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "57072df7-6fce-b801-d619-0815efa41389" }, "outputs": [], "source": [ "from sklearn.decomposition import PCA, RandomizedPCA\n", "\n", "def components(n_components, random=False, show_components=True):\n", " x_train, x_test = normalized_data()\n", " pca = PCA(n_components=n_components)\n", " if random:\n", " pca = RandomPCA(n_components=n_components)\n", " # components\n", " pca.fit(x_train)\n", " components = pca.components_\n", " if show_components:\n", " plot_gallery(components, n_col=5, n_row=int(n_components / 5))\n", " x_train = pca.transform(x_train)\n", " x_test = pca.transform(x_test)\n", " return x_train, x_test" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "bb5afd71-4b6f-8026-8cc1-15a8881462c2" }, "source": [ "SVM" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "a07afeb1-451d-b851-34ba-169a30b15c95" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", " DeprecationWarning)\n" ] } ], "source": [ "from sklearn.grid_search import GridSearchCV\n", "from sklearn.svm import SVC\n", "\n", "def testCLF(clf, parameters, X):\n", " search = GridSearchCV(clf, parameters)\n", " search.fit(X, y_train)\n", " print(search.score)\n", " return search.best_estimator_" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "70853c50-d27c-1632-4bf5-4db478cc629f" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/utils/extmath.py:368: UserWarning: The number of power iterations is increased to 7 to achieve higher precision.\n", " warnings.warn(\"The number of power iterations is increased to \"\n" ] }, { "ename": "ValueError", "evalue": "X should be a square kernel matrix", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mValueError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-7-c7dd20cda422>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 6\u001b[0m }\n\u001b[0;32m 7\u001b[0m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcomponents\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mtestCLF\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\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[1;32m<ipython-input-6-8d32c7e0f2f4>\u001b[0m in \u001b[0;36mtestCLF\u001b[1;34m(clf, parameters, X)\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mtestCLF\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\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[0;32m 5\u001b[0m \u001b[0msearch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0msearch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msearch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0msearch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/sklearn/grid_search.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m 810\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 811\u001b[0m \"\"\"\n\u001b[1;32m--> 812\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mParameterGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\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 813\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 814\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/sklearn/grid_search.py\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, X, y, parameter_iterable)\u001b[0m\n\u001b[0;32m 558\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_parameters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 559\u001b[0m error_score=self.error_score)\n\u001b[1;32m--> 560\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mparameters\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mparameter_iterable\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 561\u001b[0m for train, test in cv)\n\u001b[0;32m 562\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m 752\u001b[0m \u001b[1;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 753\u001b[0m \u001b[1;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 754\u001b[1;33m \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\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 755\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 756\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m 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matrix\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1630\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtrain_indices\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1631\u001b[0m \u001b[0mX_subset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: X should be a square kernel matrix" ] }, { "data": { "image/png": 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CI0eOVDUzxOzfyAwxvzKT+85MTfqqaL+qeV7KUqWvdWQekjXz4WWO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\nJA4wOk25AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbd1ff2efd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "clf = SVC()\n", "parameters = {\n", " \"C\" : [0.5, 1.0],\n", " \"kernel\" : ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed' ],\n", " \"degree\" : [2, 3]\n", "}\n", "X = components(10)[0]\n", "testCLF(clf, parameters, X)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "8bf7194b-d2e5-1bc3-34d6-b3ae2c7177d0" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 259, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/329/329301.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "aba53a01-cc53-a04f-5cfc-5ca644de275b" }, "source": [ "Using Trueskill to compute the 2016 kitefoil rankings" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "5f9e29dd-c831-3ff9-0d70-0110ab0ce459" }, "outputs": [], "source": [ "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import numpy as np\n", "import trueskill as ts" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "b92f7a60-28c5-c550-18d9-b153fa29c020" }, "outputs": [], "source": [ "def cleanResults(raceName,raceColumns,dfResultsTemp,dfResults):\n", " for raceCol in raceColumns:\n", " \n", " dfResultsTemp.index = dfResultsTemp.index.str.replace(r\"(\\w)([A-Z])\", r\"\\1 \\2\")\n", " dfResultsTemp.index = dfResultsTemp.index.str.title()\n", " raceIndex = raceName + '-' + raceCol \n", " dfResultsTemp[raceCol] = dfResultsTemp[raceCol].astype(str)\n", " dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('\\(|\\)|DNF-|RET-|SCP-|RDG-|RCT-|DNS-[0-9]*|DNC-[0-9]*|OCS-[0-9]*','')\n", " dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('DNF',str(len(dfResults)+1))\n", " dfResultsTemp[raceCol] = pd.to_numeric(dfResultsTemp[raceCol]) \n", " dfResultsTemp[raceIndex] = dfResultsTemp[raceCol]\n", " del(dfResultsTemp[raceCol])\n", " dfResults = pd.merge(dfResults,dfResultsTemp[[raceIndex]],left_index=True,right_index=True,how='outer')\n", " \n", " return dfResults" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "e7b1546f-4bff-c8c5-699f-740b6d4aa3b7" }, "outputs": [], "source": [ "def doRating(numRaces,dfResults,dfRatings):\n", " for raceCol in range(1,numRaces+1):\n", " competed = dfRatings['Name'].isin(dfResults['Name'][dfResults['R' +str(raceCol)].notnull()])\n", " rating_group = list(zip(dfRatings['Rating'][competed].T.values.tolist()))\n", " dfRatings['Rating'][competed] = ts.rate(rating_group, ranks=dfResults['R' +str(raceCol)][competed].T.values.tolist())\n", " return pd.DataFrame(dfRatings)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "2dbb7f1b-3bd0-f7a3-2424-d90e3b2f51ef" }, "outputs": [], "source": [ "dfResults = pd.DataFrame()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "07ea7b0f-96ad-d20c-3349-446934e396c4" }, "outputs": [], "source": [ "##Load LaVentana Results\n", "dfResultsTemp = pd.read_csv('../input/20160323-LaVentana-HydrofoilProTour.csv')\n", "dfResultsTemp = dfResultsTemp.set_index(dfResultsTemp['Name'] + ' ' + dfResultsTemp['LastName'])\n", "raceColumns = ['R1','R2','R3','R4','R5','R6']\n", "dfResults = cleanResults('20160323-LaVentana-HydrofoilProTour',raceColumns,dfResultsTemp,dfResults)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "44cf6067-3517-57d3-285d-a4526a161fe9" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>20160323-LaVentana-HydrofoilProTour-R1</th>\n", " <th>20160323-LaVentana-HydrofoilProTour-R2</th>\n", " <th>20160323-LaVentana-HydrofoilProTour-R3</th>\n", " <th>20160323-LaVentana-HydrofoilProTour-R4</th>\n", " <th>20160323-LaVentana-HydrofoilProTour-R5</th>\n", " <th>20160323-LaVentana-HydrofoilProTour-R6</th>\n", " <th>20160807-SanFracisco-HydrofoilProTour-R1</th>\n", " <th>20160807-SanFracisco-HydrofoilProTour-R2</th>\n", " <th>20160807-SanFracisco-HydrofoilProTour-R3</th>\n", " <th>20160807-SanFracisco-HydrofoilProTour-R4</th>\n", " <th>...</th>\n", " <th>20160807-SanFracisco-HydrofoilProTour-R7</th>\n", " <th>20160807-SanFracisco-HydrofoilProTour-R8</th>\n", " <th>20160807-SanFracisco-HydrofoilProTour-R9</th>\n", " <th>20160807-SanFracisco-HydrofoilProTour-R10</th>\n", " <th>20160807-SanFracisco-HydrofoilProTour-R11</th>\n", " <th>20160807-SanFracisco-HydrofoilProTour-R12</th>\n", " <th>20160807-SanFracisco-HydrofoilProTour-R13</th>\n", " <th>20160807-SanFracisco-HydrofoilProTour-R14</th>\n", " <th>20160807-SanFracisco-HydrofoilProTour-R15</th>\n", " <th>20160807-SanFracisco-HydrofoilProTour-R16</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Adam Withington</th>\n", " <td>15.0</td>\n", " <td>14.0</td>\n", " <td>11.0</td>\n", " <td>14.0</td>\n", " <td>20.0</td>\n", " <td>17.0</td>\n", " <td>7.0</td>\n", " <td>5.0</td>\n", " <td>7.0</td>\n", " <td>7.0</td>\n", " <td>...</td>\n", " <td>7.0</td>\n", " <td>9.0</td>\n", " <td>7.0</td>\n", " <td>6.0</td>\n", " <td>7.0</td>\n", " <td>5.0</td>\n", " <td>6.0</td>\n", " <td>6.0</td>\n", " <td>8.0</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>Adrian Geislinger</th>\n", " <td>14.0</td>\n", " <td>10.0</td>\n", " <td>10.0</td>\n", " <td>9.0</td>\n", " <td>10.0</td>\n", " <td>7.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " 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<td>NaN</td>\n", " <td>NaN</td>\n", " <td>4.0</td>\n", " <td>3.0</td>\n", " <td>2.0</td>\n", " <td>3.0</td>\n", " <td>...</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " <td>NaN</td>\n", " <td>46.0</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " <td>2.0</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>John Gomes</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>...</td>\n", " <td>36.0</td>\n", " <td>40.0</td>\n", " <td>34.0</td>\n", " <td>33.0</td>\n", " <td>33.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>John Von Tesmar</th>\n", " <td>21.0</td>\n", " <td>18.0</td>\n", " <td>19.0</td>\n", " <td>19.0</td>\n", " <td>19.0</td>\n", " <td>15.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Johnny Heineken</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>3.0</td>\n", " <td>2.0</td>\n", " <td>...</td>\n", " <td>2.0</td>\n", " <td>2.0</td>\n", " <td>2.0</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>3.0</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>Jon Modica</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>14.0</td>\n", " <td>11.0</td>\n", " <td>16.0</td>\n", " <td>12.0</td>\n", " <td>...</td>\n", " <td>11.0</td>\n", " <td>14.0</td>\n", " <td>15.0</td>\n", " <td>8.0</td>\n", " <td>16.0</td>\n", " <td>46.0</td>\n", " <td>15.0</td>\n", " <td>46.0</td>\n", " <td>18.0</td>\n", " <td>19.0</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>Kieran Le Borgne</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>18.0</td>\n", " <td>15.0</td>\n", " <td>18.0</td>\n", " <td>18.0</td>\n", " <td>...</td>\n", " <td>14.0</td>\n", " <td>15.0</td>\n", " <td>17.0</td>\n", " <td>18.0</td>\n", " <td>20.0</td>\n", " <td>19.0</td>\n", " <td>17.0</td>\n", " <td>14.0</td>\n", " <td>21.0</td>\n", " <td>15.0</td>\n", " </tr>\n", " <tr>\n", " <th>Loic Le Meur</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>38.0</td>\n", " <td>46.0</td>\n", " <td>...</td>\n", " <td>34.0</td>\n", " <td>36.0</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>34.0</td>\n", " <td>46.0</td>\n", " <td>36.0</td>\n", " <td>35.0</td>\n", " <td>33.0</td>\n", " <td>35.0</td>\n", " </tr>\n", " <tr>\n", " <th>Mani Bisschops</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>26.0</td>\n", " <td>25.0</td>\n", " <td>23.0</td>\n", " <td>25.0</td>\n", " <td>...</td>\n", " <td>18.0</td>\n", " <td>20.0</td>\n", " <td>28.0</td>\n", " <td>22.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>31.0</td>\n", " <td>22.0</td>\n", " <td>30.0</td>\n", " <td>24.0</td>\n", " </tr>\n", " <tr>\n", " <th>Martin Dolenc</th>\n", " <td>16.0</td>\n", " <td>22.0</td>\n", " <td>16.0</td>\n", " <td>16.0</td>\n", " <td>12.0</td>\n", " <td>18.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Martin Turbil</th>\n", " <td>18.0</td>\n", " <td>19.0</td>\n", " <td>20.0</td>\n", " <td>15.0</td>\n", " <td>15.0</td>\n", " <td>13.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Marvin Baumeisterschoenian</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>12.0</td>\n", " <td>20.0</td>\n", " <td>12.0</td>\n", " <td>23.0</td>\n", " <td>...</td>\n", " <td>10.0</td>\n", " <td>10.0</td>\n", " <td>9.0</td>\n", " <td>10.0</td>\n", " <td>14.0</td>\n", " <td>27.0</td>\n", " <td>32.0</td>\n", " <td>31.0</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " </tr>\n", " <tr>\n", " <th>Matthew Taggart</th>\n", " <td>7.0</td>\n", " <td>5.0</td>\n", " <td>14.0</td>\n", " <td>8.0</td>\n", " <td>13.0</td>\n", " <td>14.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Maxime Nocher</th>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>3.0</td>\n", " <td>1.0</td>\n", " <td>5.0</td>\n", " <td>3.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Michael Gilbreath</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>29.0</td>\n", " <td>28.0</td>\n", " <td>32.0</td>\n", " <td>30.0</td>\n", " <td>...</td>\n", " <td>30.0</td>\n", " <td>31.0</td>\n", " <td>27.0</td>\n", " <td>32.0</td>\n", " <td>NaN</td>\n", " <td>28.0</td>\n", " <td>27.0</td>\n", " <td>29.0</td>\n", " <td>37.0</td>\n", " <td>31.0</td>\n", " </tr>\n", " <tr>\n", " <th>Nico Landauer</th>\n", " <td>2.0</td>\n", " <td>7.0</td>\n", " <td>2.0</td>\n", " <td>4.0</td>\n", " <td>3.0</td>\n", " <td>9.0</td>\n", " <td>6.0</td>\n", " <td>6.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>...</td>\n", " <td>6.0</td>\n", " <td>5.0</td>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>NaN</td>\n", " <td>4.0</td>\n", " <td>46.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " </tr>\n", " <tr>\n", " <th>Nico Parlier</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>2.0</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>Oliver Bridge</th>\n", " <td>4.0</td>\n", " <td>2.0</td>\n", " <td>6.0</td>\n", " <td>3.0</td>\n", " <td>4.0</td>\n", " <td>6.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Peter Grendler</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>...</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Peter Martel</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>19.0</td>\n", " <td>17.0</td>\n", " <td>25.0</td>\n", " <td>21.0</td>\n", " <td>...</td>\n", " <td>22.0</td>\n", " <td>25.0</td>\n", " <td>25.0</td>\n", " <td>25.0</td>\n", " <td>21.0</td>\n", " <td>16.0</td>\n", " <td>20.0</td>\n", " <td>10.0</td>\n", " <td>22.0</td>\n", " <td>25.0</td>\n", " </tr>\n", " <tr>\n", " <th>Riccardo Andrea Leccese</th>\n", " <td>3.0</td>\n", " <td>3.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Rikki Leccese</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3.0</td>\n", " <td>13.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Riley Gibbs</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>10.0</td>\n", " <td>9.0</td>\n", " <td>6.0</td>\n", " <td>9.0</td>\n", " <td>...</td>\n", " <td>46.0</td>\n", " <td>8.0</td>\n", " <td>6.0</td>\n", " <td>5.0</td>\n", " <td>9.0</td>\n", " <td>7.0</td>\n", " <td>12.0</td>\n", " <td>11.0</td>\n", " <td>7.0</td>\n", " <td>6.0</td>\n", " </tr>\n", " <tr>\n", " <th>Roman Lyubimtsev</th>\n", " <td>23.0</td>\n", " <td>23.0</td>\n", " <td>25.0</td>\n", " <td>25.0</td>\n", " <td>22.0</td>\n", " <td>20.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Sam Bullock</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>20.0</td>\n", " <td>19.0</td>\n", " <td>20.0</td>\n", " <td>17.0</td>\n", " <td>...</td>\n", " <td>26.0</td>\n", " <td>46.0</td>\n", " <td>20.0</td>\n", " <td>21.0</td>\n", " <td>17.0</td>\n", " <td>46.0</td>\n", " <td>14.0</td>\n", " <td>13.0</td>\n", " <td>19.0</td>\n", " <td>23.0</td>\n", " </tr>\n", " <tr>\n", " <th>Seth Besse</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>22.0</td>\n", " <td>12.0</td>\n", " <td>14.0</td>\n", " <td>11.0</td>\n", " <td>...</td>\n", " <td>12.0</td>\n", " <td>11.0</td>\n", " <td>11.0</td>\n", " <td>10.0</td>\n", " <td>11.0</td>\n", " <td>8.0</td>\n", " <td>16.0</td>\n", " <td>9.0</td>\n", " <td>13.0</td>\n", " <td>14.0</td>\n", " </tr>\n", " <tr>\n", " <th>Sonny Swords</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>46.0</td>\n", " <td>30.0</td>\n", " <td>31.0</td>\n", " <td>32.0</td>\n", " <td>...</td>\n", " <td>25.0</td>\n", " <td>29.0</td>\n", " <td>26.0</td>\n", " <td>24.0</td>\n", " <td>35.0</td>\n", " <td>24.0</td>\n", " <td>30.0</td>\n", " <td>24.0</td>\n", " <td>29.0</td>\n", " <td>29.0</td>\n", " </tr>\n", " <tr>\n", " <th>Stefaans Viljoen</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>9.0</td>\n", " <td>6.0</td>\n", " <td>...</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>4.0</td>\n", " <td>6.0</td>\n", " <td>12.0</td>\n", " <td>7.0</td>\n", " <td>5.0</td>\n", " <td>5.0</td>\n", " <td>46.0</td>\n", " </tr>\n", " <tr>\n", " <th>Steve Bodner</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>NaN</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Toni Vodisek</th>\n", " <td>10.0</td>\n", " <td>12.0</td>\n", " <td>8.0</td>\n", " <td>10.0</td>\n", " <td>8.0</td>\n", " <td>5.0</td>\n", " <td>46.0</td>\n", " <td>16.0</td>\n", " <td>11.0</td>\n", " <td>15.0</td>\n", " <td>...</td>\n", " <td>9.0</td>\n", " <td>6.0</td>\n", " <td>10.0</td>\n", " <td>9.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>5.0</td>\n", " <td>4.0</td>\n", " <td>6.0</td>\n", " <td>8.0</td>\n", " </tr>\n", " <tr>\n", " <th>Ty Reed</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>8.0</td>\n", " <td>26.0</td>\n", " <td>10.0</td>\n", " <td>46.0</td>\n", " <td>...</td>\n", " <td>13.0</td>\n", " <td>24.0</td>\n", " <td>13.0</td>\n", " <td>46.0</td>\n", " <td>26.0</td>\n", " <td>46.0</td>\n", " <td>11.0</td>\n", " <td>15.0</td>\n", " <td>11.0</td>\n", " <td>12.0</td>\n", " </tr>\n", " <tr>\n", " <th>Will Cyr</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>34.0</td>\n", " <td>29.0</td>\n", " <td>35.0</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>29.0</td>\n", " <td>33.0</td>\n", " <td>32.0</td>\n", " <td>34.0</td>\n", " <td>32.0</td>\n", " <td>NaN</td>\n", " <td>29.0</td>\n", " <td>25.0</td>\n", " <td>35.0</td>\n", " <td>30.0</td>\n", " </tr>\n", " <tr>\n", " <th>Will James</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>31.0</td>\n", " <td>22.0</td>\n", " <td>30.0</td>\n", " <td>22.0</td>\n", " <td>...</td>\n", " <td>20.0</td>\n", " <td>19.0</td>\n", " <td>14.0</td>\n", " <td>19.0</td>\n", " <td>46.0</td>\n", " <td>46.0</td>\n", " <td>37.0</td>\n", " <td>21.0</td>\n", " <td>15.0</td>\n", " <td>27.0</td>\n", " </tr>\n", " <tr>\n", " <th>William Morris</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>28.0</td>\n", " <td>27.0</td>\n", " <td>33.0</td>\n", " <td>29.0</td>\n", " <td>...</td>\n", " <td>35.0</td>\n", " <td>21.0</td>\n", " <td>23.0</td>\n", " <td>15.0</td>\n", " <td>24.0</td>\n", " <td>13.0</td>\n", " <td>22.0</td>\n", " <td>26.0</td>\n", " <td>25.0</td>\n", " <td>22.0</td>\n", " </tr>\n", " <tr>\n", " <th>Xantos Villegas</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>25.0</td>\n", " <td>8.0</td>\n", " <td>13.0</td>\n", " <td>8.0</td>\n", " <td>...</td>\n", " <td>17.0</td>\n", " <td>32.0</td>\n", " <td>18.0</td>\n", " <td>7.0</td>\n", " <td>10.0</td>\n", " <td>15.0</td>\n", " <td>9.0</td>\n", " <td>46.0</td>\n", " <td>10.0</td>\n", " <td>10.0</td>\n", " </tr>\n", " <tr>\n", " <th>Zack Marks</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>11.0</td>\n", " <td>7.0</td>\n", " <td>8.0</td>\n", " <td>10.0</td>\n", " <td>...</td>\n", " <td>4.0</td>\n", " <td>7.0</td>\n", " <td>5.0</td>\n", " <td>11.0</td>\n", " <td>8.0</td>\n", " <td>6.0</td>\n", " <td>8.0</td>\n", " <td>7.0</td>\n", " <td>11.0</td>\n", " <td>7.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>64 rows × 22 columns</p>\n", "</div>" ], "text/plain": [ " 20160323-LaVentana-HydrofoilProTour-R1 \\\n", "Adam Withington 15.0 \n", "Adrian Geislinger 14.0 \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez 9.0 \n", "Alex Caizergues NaN \n", "Amil Kabil NaN \n", "Andy Hansen NaN \n", "Anthony Goldbloom NaN \n", "Ariel Poler NaN \n", "Axel Mazella 5.0 \n", "Ben Turner NaN \n", "Benjamin Petit NaN \n", "Benni Boelli 11.0 \n", "Chip Wasson 20.0 \n", "Chris Brent NaN \n", "Craig Rawson NaN \n", "Daniela Moroz NaN \n", "Ejder Ginyol 12.0 \n", "Elena Kalinina 22.0 \n", "Felix Louis N'Jai NaN \n", "Florian Gruber 13.0 \n", "Florian Trittel 6.0 \n", "Fraser Novakowski NaN \n", "Jacob Olivier 19.0 \n", "James Johnsen 17.0 \n", "Joey Pasquali NaN \n", "John Gomes NaN \n", "John Von Tesmar 21.0 \n", "Johnny Heineken NaN \n", "Jon Modica NaN \n", "... ... \n", "Kieran Le Borgne NaN \n", "Loic Le Meur NaN \n", "Mani Bisschops NaN \n", "Martin Dolenc 16.0 \n", "Martin Turbil 18.0 \n", "Marvin Baumeisterschoenian NaN \n", "Matthew Taggart 7.0 \n", "Maxime Nocher 1.0 \n", "Michael Gilbreath NaN \n", "Nico Landauer 2.0 \n", "Nico Parlier NaN \n", "Oliver Bridge 4.0 \n", "Peter Grendler NaN \n", "Peter Martel NaN \n", "Riccardo Andrea Leccese 3.0 \n", "Rikki Leccese NaN \n", "Riley Gibbs NaN \n", "Roman Lyubimtsev 23.0 \n", "Sam Bullock NaN \n", "Seth Besse NaN \n", "Sonny Swords NaN \n", "Stefaans Viljoen NaN \n", "Steve Bodner NaN \n", "Toni Vodisek 10.0 \n", "Ty Reed NaN \n", "Will Cyr NaN \n", "Will James NaN \n", "William Morris NaN \n", "Xantos Villegas NaN \n", "Zack Marks NaN \n", "\n", " 20160323-LaVentana-HydrofoilProTour-R2 \\\n", "Adam Withington 14.0 \n", "Adrian Geislinger 10.0 \n", "Alejandro Climent Hernã¥_ Ndez 16.0 \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues NaN \n", "Amil Kabil NaN \n", "Andy Hansen NaN \n", "Anthony Goldbloom NaN \n", "Ariel Poler NaN \n", "Axel Mazella 4.0 \n", "Ben Turner NaN \n", "Benjamin Petit NaN \n", "Benni Boelli 11.0 \n", "Chip Wasson 17.0 \n", "Chris Brent NaN \n", "Craig Rawson NaN \n", "Daniela Moroz NaN \n", "Ejder Ginyol 13.0 \n", "Elena Kalinina 21.0 \n", "Felix Louis N'Jai NaN \n", "Florian Gruber 9.0 \n", "Florian Trittel 8.0 \n", "Fraser Novakowski NaN \n", "Jacob Olivier 20.0 \n", "James Johnsen 15.0 \n", "Joey Pasquali NaN \n", "John Gomes NaN \n", "John Von Tesmar 18.0 \n", "Johnny Heineken NaN \n", "Jon Modica NaN \n", "... ... \n", "Kieran Le Borgne NaN \n", "Loic Le Meur NaN \n", "Mani Bisschops NaN \n", "Martin Dolenc 22.0 \n", "Martin Turbil 19.0 \n", "Marvin Baumeisterschoenian NaN \n", "Matthew Taggart 5.0 \n", "Maxime Nocher 1.0 \n", "Michael Gilbreath NaN \n", "Nico Landauer 7.0 \n", "Nico Parlier NaN \n", "Oliver Bridge 2.0 \n", "Peter Grendler NaN \n", "Peter Martel NaN \n", "Riccardo Andrea Leccese 3.0 \n", "Rikki Leccese NaN \n", "Riley Gibbs NaN \n", "Roman Lyubimtsev 23.0 \n", "Sam Bullock NaN \n", "Seth Besse NaN \n", "Sonny Swords NaN \n", "Stefaans Viljoen NaN \n", "Steve Bodner NaN \n", "Toni Vodisek 12.0 \n", "Ty Reed NaN \n", "Will Cyr NaN \n", "Will James NaN \n", "William Morris NaN \n", "Xantos Villegas NaN \n", "Zack Marks NaN \n", "\n", " 20160323-LaVentana-HydrofoilProTour-R3 \\\n", "Adam Withington 11.0 \n", "Adrian Geislinger 10.0 \n", "Alejandro Climent Hernã¥_ Ndez 12.0 \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues NaN \n", "Amil Kabil NaN \n", "Andy Hansen NaN \n", "Anthony Goldbloom NaN \n", "Ariel Poler NaN \n", "Axel Mazella 4.0 \n", "Ben Turner NaN \n", "Benjamin Petit NaN \n", "Benni Boelli 13.0 \n", "Chip Wasson 18.0 \n", "Chris Brent NaN \n", "Craig Rawson NaN \n", "Daniela Moroz NaN \n", "Ejder Ginyol 15.0 \n", "Elena Kalinina 21.0 \n", "Felix Louis N'Jai NaN \n", "Florian Gruber 9.0 \n", "Florian Trittel 5.0 \n", "Fraser Novakowski NaN \n", "Jacob Olivier 17.0 \n", "James Johnsen 25.0 \n", "Joey Pasquali NaN \n", "John Gomes NaN \n", "John Von Tesmar 19.0 \n", "Johnny Heineken NaN \n", "Jon Modica NaN \n", "... ... \n", "Kieran Le Borgne NaN \n", "Loic Le Meur NaN \n", "Mani Bisschops NaN \n", "Martin Dolenc 16.0 \n", "Martin Turbil 20.0 \n", "Marvin Baumeisterschoenian NaN \n", "Matthew Taggart 14.0 \n", "Maxime Nocher 3.0 \n", "Michael Gilbreath NaN \n", "Nico Landauer 2.0 \n", "Nico Parlier NaN \n", "Oliver Bridge 6.0 \n", "Peter Grendler NaN \n", "Peter Martel NaN \n", "Riccardo Andrea Leccese 1.0 \n", "Rikki Leccese NaN \n", "Riley Gibbs NaN \n", "Roman Lyubimtsev 25.0 \n", "Sam Bullock NaN \n", "Seth Besse NaN \n", "Sonny Swords NaN \n", "Stefaans Viljoen NaN \n", "Steve Bodner NaN \n", "Toni Vodisek 8.0 \n", "Ty Reed NaN \n", "Will Cyr NaN \n", "Will James NaN \n", "William Morris NaN \n", "Xantos Villegas NaN \n", "Zack Marks NaN \n", "\n", " 20160323-LaVentana-HydrofoilProTour-R4 \\\n", "Adam Withington 14.0 \n", "Adrian Geislinger 9.0 \n", "Alejandro Climent Hernã¥_ Ndez 12.0 \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues NaN \n", "Amil Kabil NaN \n", "Andy Hansen NaN \n", "Anthony Goldbloom NaN \n", "Ariel Poler NaN \n", "Axel Mazella 6.0 \n", "Ben Turner NaN \n", "Benjamin Petit NaN \n", "Benni Boelli 13.0 \n", "Chip Wasson 20.0 \n", "Chris Brent NaN \n", "Craig Rawson NaN \n", "Daniela Moroz NaN \n", "Ejder Ginyol 17.0 \n", "Elena Kalinina 21.0 \n", "Felix Louis N'Jai NaN \n", "Florian Gruber 11.0 \n", "Florian Trittel 5.0 \n", "Fraser Novakowski NaN \n", "Jacob Olivier 18.0 \n", "James Johnsen 25.0 \n", "Joey Pasquali NaN \n", "John Gomes NaN \n", "John Von Tesmar 19.0 \n", "Johnny Heineken NaN \n", "Jon Modica NaN \n", "... ... \n", "Kieran Le Borgne NaN \n", "Loic Le Meur NaN \n", "Mani Bisschops NaN \n", "Martin Dolenc 16.0 \n", "Martin Turbil 15.0 \n", "Marvin Baumeisterschoenian NaN \n", "Matthew Taggart 8.0 \n", "Maxime Nocher 1.0 \n", "Michael Gilbreath NaN \n", "Nico Landauer 4.0 \n", "Nico Parlier NaN \n", "Oliver Bridge 3.0 \n", "Peter Grendler NaN \n", "Peter Martel NaN \n", "Riccardo Andrea Leccese 2.0 \n", "Rikki Leccese NaN \n", "Riley Gibbs NaN \n", "Roman Lyubimtsev 25.0 \n", "Sam Bullock NaN \n", "Seth Besse NaN \n", "Sonny Swords NaN \n", "Stefaans Viljoen NaN \n", "Steve Bodner NaN \n", "Toni Vodisek 10.0 \n", "Ty Reed NaN \n", "Will Cyr NaN \n", "Will James NaN \n", "William Morris NaN \n", "Xantos Villegas NaN \n", "Zack Marks NaN \n", "\n", " 20160323-LaVentana-HydrofoilProTour-R5 \\\n", "Adam Withington 20.0 \n", "Adrian Geislinger 10.0 \n", "Alejandro Climent Hernã¥_ Ndez 14.0 \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues NaN \n", "Amil Kabil NaN \n", "Andy Hansen NaN \n", "Anthony Goldbloom NaN \n", "Ariel Poler NaN \n", "Axel Mazella 2.0 \n", "Ben Turner NaN \n", "Benjamin Petit NaN \n", "Benni Boelli 7.0 \n", "Chip Wasson 17.0 \n", "Chris Brent NaN \n", "Craig Rawson NaN \n", "Daniela Moroz NaN \n", "Ejder Ginyol 16.0 \n", "Elena Kalinina 21.0 \n", "Felix Louis N'Jai NaN \n", "Florian Gruber 11.0 \n", "Florian Trittel 6.0 \n", "Fraser Novakowski NaN \n", "Jacob Olivier 18.0 \n", "James Johnsen 25.0 \n", "Joey Pasquali NaN \n", "John Gomes NaN \n", "John Von Tesmar 19.0 \n", "Johnny Heineken NaN \n", "Jon Modica NaN \n", "... ... \n", "Kieran Le Borgne NaN \n", "Loic Le Meur NaN \n", "Mani Bisschops NaN \n", "Martin Dolenc 12.0 \n", "Martin Turbil 15.0 \n", "Marvin Baumeisterschoenian NaN \n", "Matthew Taggart 13.0 \n", "Maxime Nocher 5.0 \n", "Michael Gilbreath NaN \n", "Nico Landauer 3.0 \n", "Nico Parlier NaN \n", "Oliver Bridge 4.0 \n", "Peter Grendler NaN \n", "Peter Martel NaN \n", "Riccardo Andrea Leccese 1.0 \n", "Rikki Leccese NaN \n", "Riley Gibbs NaN \n", "Roman Lyubimtsev 22.0 \n", "Sam Bullock NaN \n", "Seth Besse NaN \n", "Sonny Swords NaN \n", "Stefaans Viljoen NaN \n", "Steve Bodner NaN \n", "Toni Vodisek 8.0 \n", "Ty Reed NaN \n", "Will Cyr NaN \n", "Will James NaN \n", "William Morris NaN \n", "Xantos Villegas NaN \n", "Zack Marks NaN \n", "\n", " 20160323-LaVentana-HydrofoilProTour-R6 \\\n", "Adam Withington 17.0 \n", "Adrian Geislinger 7.0 \n", "Alejandro Climent Hernã¥_ Ndez 8.0 \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues NaN \n", "Amil Kabil NaN \n", "Andy Hansen NaN \n", "Anthony Goldbloom NaN \n", "Ariel Poler NaN \n", "Axel Mazella 1.0 \n", "Ben Turner NaN \n", "Benjamin Petit NaN \n", "Benni Boelli 16.0 \n", "Chip Wasson 19.0 \n", "Chris Brent NaN \n", "Craig Rawson NaN \n", "Daniela Moroz NaN \n", "Ejder Ginyol 11.0 \n", "Elena Kalinina 22.0 \n", "Felix Louis N'Jai NaN \n", "Florian Gruber 10.0 \n", "Florian Trittel 12.0 \n", "Fraser Novakowski NaN \n", "Jacob Olivier 21.0 \n", "James Johnsen 25.0 \n", "Joey Pasquali NaN \n", "John Gomes NaN \n", "John Von Tesmar 15.0 \n", "Johnny Heineken NaN \n", "Jon Modica NaN \n", "... ... \n", "Kieran Le Borgne NaN \n", "Loic Le Meur NaN \n", "Mani Bisschops NaN \n", "Martin Dolenc 18.0 \n", "Martin Turbil 13.0 \n", "Marvin Baumeisterschoenian NaN \n", "Matthew Taggart 14.0 \n", "Maxime Nocher 3.0 \n", "Michael Gilbreath NaN \n", "Nico Landauer 9.0 \n", "Nico Parlier NaN \n", "Oliver Bridge 6.0 \n", "Peter Grendler NaN \n", "Peter Martel NaN \n", "Riccardo Andrea Leccese 2.0 \n", "Rikki Leccese NaN \n", "Riley Gibbs NaN \n", "Roman Lyubimtsev 20.0 \n", "Sam Bullock NaN \n", "Seth Besse NaN \n", "Sonny Swords NaN \n", "Stefaans Viljoen NaN \n", "Steve Bodner NaN \n", "Toni Vodisek 5.0 \n", "Ty Reed NaN \n", "Will Cyr NaN \n", "Will James NaN \n", "William Morris NaN \n", "Xantos Villegas NaN \n", "Zack Marks NaN \n", "\n", " 20160807-SanFracisco-HydrofoilProTour-R1 \\\n", "Adam Withington 7.0 \n", "Adrian Geislinger NaN \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues 46.0 \n", "Amil Kabil 24.0 \n", "Andy Hansen 13.0 \n", "Anthony Goldbloom 33.0 \n", "Ariel Poler 27.0 \n", "Axel Mazella NaN \n", "Ben Turner 46.0 \n", "Benjamin Petit 17.0 \n", "Benni Boelli NaN \n", "Chip Wasson 23.0 \n", "Chris Brent 46.0 \n", "Craig Rawson 35.0 \n", "Daniela Moroz 16.0 \n", "Ejder Ginyol NaN \n", "Elena Kalinina NaN \n", "Felix Louis N'Jai 30.0 \n", "Florian Gruber NaN \n", "Florian Trittel NaN \n", "Fraser Novakowski 32.0 \n", "Jacob Olivier 9.0 \n", "James Johnsen NaN \n", "Joey Pasquali 4.0 \n", "John Gomes 46.0 \n", "John Von Tesmar NaN \n", "Johnny Heineken 1.0 \n", "Jon Modica 14.0 \n", "... ... \n", "Kieran Le Borgne 18.0 \n", "Loic Le Meur 46.0 \n", "Mani Bisschops 26.0 \n", "Martin Dolenc NaN \n", "Martin Turbil NaN \n", "Marvin Baumeisterschoenian 12.0 \n", "Matthew Taggart NaN \n", "Maxime Nocher NaN \n", "Michael Gilbreath 29.0 \n", "Nico Landauer 6.0 \n", "Nico Parlier 2.0 \n", "Oliver Bridge NaN \n", "Peter Grendler 46.0 \n", "Peter Martel 19.0 \n", "Riccardo Andrea Leccese NaN \n", "Rikki Leccese 3.0 \n", "Riley Gibbs 10.0 \n", "Roman Lyubimtsev NaN \n", "Sam Bullock 20.0 \n", "Seth Besse 22.0 \n", "Sonny Swords 46.0 \n", "Stefaans Viljoen 5.0 \n", "Steve Bodner 46.0 \n", "Toni Vodisek 46.0 \n", "Ty Reed 8.0 \n", "Will Cyr 34.0 \n", "Will James 31.0 \n", "William Morris 28.0 \n", "Xantos Villegas 25.0 \n", "Zack Marks 11.0 \n", "\n", " 20160807-SanFracisco-HydrofoilProTour-R2 \\\n", "Adam Withington 5.0 \n", "Adrian Geislinger NaN \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues 23.0 \n", "Amil Kabil 24.0 \n", "Andy Hansen 14.0 \n", "Anthony Goldbloom 33.0 \n", "Ariel Poler 46.0 \n", "Axel Mazella NaN \n", "Ben Turner 31.0 \n", "Benjamin Petit 21.0 \n", "Benni Boelli NaN \n", "Chip Wasson 46.0 \n", "Chris Brent 46.0 \n", "Craig Rawson 46.0 \n", "Daniela Moroz 46.0 \n", "Ejder Ginyol NaN \n", "Elena Kalinina NaN \n", "Felix Louis N'Jai 46.0 \n", "Florian Gruber NaN \n", "Florian Trittel NaN \n", "Fraser Novakowski 32.0 \n", "Jacob Olivier 10.0 \n", "James Johnsen NaN \n", "Joey Pasquali 3.0 \n", "John Gomes 46.0 \n", "John Von Tesmar NaN \n", "Johnny Heineken 1.0 \n", "Jon Modica 11.0 \n", "... ... \n", "Kieran Le Borgne 15.0 \n", "Loic Le Meur 46.0 \n", "Mani Bisschops 25.0 \n", "Martin Dolenc NaN \n", "Martin Turbil NaN \n", "Marvin Baumeisterschoenian 20.0 \n", "Matthew Taggart NaN \n", "Maxime Nocher NaN \n", "Michael Gilbreath 28.0 \n", "Nico Landauer 6.0 \n", "Nico Parlier 2.0 \n", "Oliver Bridge NaN \n", "Peter Grendler 46.0 \n", "Peter Martel 17.0 \n", "Riccardo Andrea Leccese NaN \n", "Rikki Leccese 13.0 \n", "Riley Gibbs 9.0 \n", "Roman Lyubimtsev NaN \n", "Sam Bullock 19.0 \n", "Seth Besse 12.0 \n", "Sonny Swords 30.0 \n", "Stefaans Viljoen 4.0 \n", "Steve Bodner 46.0 \n", "Toni Vodisek 16.0 \n", "Ty Reed 26.0 \n", "Will Cyr 29.0 \n", "Will James 22.0 \n", "William Morris 27.0 \n", "Xantos Villegas 8.0 \n", "Zack Marks 7.0 \n", "\n", " 20160807-SanFracisco-HydrofoilProTour-R3 \\\n", "Adam Withington 7.0 \n", "Adrian Geislinger NaN \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues 26.0 \n", "Amil Kabil 28.0 \n", "Andy Hansen 17.0 \n", "Anthony Goldbloom 34.0 \n", "Ariel Poler 29.0 \n", "Axel Mazella NaN \n", "Ben Turner 37.0 \n", "Benjamin Petit 27.0 \n", "Benni Boelli NaN \n", "Chip Wasson 24.0 \n", "Chris Brent 46.0 \n", "Craig Rawson 40.0 \n", "Daniela Moroz 19.0 \n", "Ejder Ginyol NaN \n", "Elena Kalinina NaN \n", "Felix Louis N'Jai 39.0 \n", "Florian Gruber NaN \n", "Florian Trittel NaN \n", "Fraser Novakowski 36.0 \n", "Jacob Olivier 15.0 \n", "James Johnsen NaN \n", "Joey Pasquali 2.0 \n", "John Gomes 46.0 \n", "John Von Tesmar NaN \n", "Johnny Heineken 3.0 \n", "Jon Modica 16.0 \n", "... ... \n", "Kieran Le Borgne 18.0 \n", "Loic Le Meur 38.0 \n", "Mani Bisschops 23.0 \n", "Martin Dolenc NaN \n", "Martin Turbil NaN \n", "Marvin Baumeisterschoenian 12.0 \n", "Matthew Taggart NaN \n", "Maxime Nocher NaN \n", "Michael Gilbreath 32.0 \n", "Nico Landauer 5.0 \n", "Nico Parlier 1.0 \n", "Oliver Bridge NaN \n", "Peter Grendler 46.0 \n", "Peter Martel 25.0 \n", "Riccardo Andrea Leccese NaN \n", "Rikki Leccese 4.0 \n", "Riley Gibbs 6.0 \n", "Roman Lyubimtsev NaN \n", "Sam Bullock 20.0 \n", "Seth Besse 14.0 \n", "Sonny Swords 31.0 \n", "Stefaans Viljoen 9.0 \n", "Steve Bodner 46.0 \n", "Toni Vodisek 11.0 \n", "Ty Reed 10.0 \n", "Will Cyr 35.0 \n", "Will James 30.0 \n", "William Morris 33.0 \n", "Xantos Villegas 13.0 \n", "Zack Marks 8.0 \n", "\n", " 20160807-SanFracisco-HydrofoilProTour-R4 \\\n", "Adam Withington 7.0 \n", "Adrian Geislinger NaN \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues 24.0 \n", "Amil Kabil 28.0 \n", "Andy Hansen 13.0 \n", "Anthony Goldbloom 36.0 \n", "Ariel Poler 27.0 \n", "Axel Mazella NaN \n", "Ben Turner 35.0 \n", "Benjamin Petit 19.0 \n", "Benni Boelli NaN \n", "Chip Wasson NaN \n", "Chris Brent 38.0 \n", "Craig Rawson 31.0 \n", "Daniela Moroz 37.0 \n", "Ejder Ginyol NaN \n", "Elena Kalinina NaN \n", "Felix Louis N'Jai 26.0 \n", "Florian Gruber NaN \n", "Florian Trittel NaN \n", "Fraser Novakowski 33.0 \n", "Jacob Olivier 14.0 \n", "James Johnsen NaN \n", "Joey Pasquali 3.0 \n", "John Gomes 46.0 \n", "John Von Tesmar NaN \n", "Johnny Heineken 2.0 \n", "Jon Modica 12.0 \n", "... ... \n", "Kieran Le Borgne 18.0 \n", "Loic Le Meur 46.0 \n", "Mani Bisschops 25.0 \n", "Martin Dolenc NaN \n", "Martin Turbil NaN \n", "Marvin Baumeisterschoenian 23.0 \n", "Matthew Taggart NaN \n", "Maxime Nocher NaN \n", "Michael Gilbreath 30.0 \n", "Nico Landauer 5.0 \n", "Nico Parlier 1.0 \n", "Oliver Bridge NaN \n", "Peter Grendler 46.0 \n", "Peter Martel 21.0 \n", "Riccardo Andrea Leccese NaN \n", "Rikki Leccese 4.0 \n", "Riley Gibbs 9.0 \n", "Roman Lyubimtsev NaN \n", "Sam Bullock 17.0 \n", "Seth Besse 11.0 \n", "Sonny Swords 32.0 \n", "Stefaans Viljoen 6.0 \n", "Steve Bodner NaN \n", "Toni Vodisek 15.0 \n", "Ty Reed 46.0 \n", "Will Cyr NaN \n", "Will James 22.0 \n", "William Morris 29.0 \n", "Xantos Villegas 8.0 \n", "Zack Marks 10.0 \n", "\n", " ... \\\n", "Adam Withington ... \n", "Adrian Geislinger ... \n", "Alejandro Climent Hernã¥_ Ndez ... \n", "Alejandro Climent Hernã¥_Ndez ... \n", "Alex Caizergues ... \n", "Amil Kabil ... \n", "Andy Hansen ... \n", "Anthony Goldbloom ... \n", "Ariel Poler ... \n", "Axel Mazella ... \n", "Ben Turner ... \n", "Benjamin Petit ... \n", "Benni Boelli ... \n", "Chip Wasson ... \n", "Chris Brent ... \n", "Craig Rawson ... \n", "Daniela Moroz ... \n", "Ejder Ginyol ... \n", "Elena Kalinina ... \n", "Felix Louis N'Jai ... \n", "Florian Gruber ... \n", "Florian Trittel ... \n", "Fraser Novakowski ... \n", "Jacob Olivier ... \n", "James Johnsen ... \n", "Joey Pasquali ... \n", "John Gomes ... \n", "John Von Tesmar ... \n", "Johnny Heineken ... \n", "Jon Modica ... \n", "... ... \n", "Kieran Le Borgne ... \n", "Loic Le Meur ... \n", "Mani Bisschops ... \n", "Martin Dolenc ... \n", "Martin Turbil ... \n", "Marvin Baumeisterschoenian ... \n", "Matthew Taggart ... \n", "Maxime Nocher ... \n", "Michael Gilbreath ... \n", "Nico Landauer ... \n", "Nico Parlier ... \n", "Oliver Bridge ... \n", "Peter Grendler ... \n", "Peter Martel ... \n", "Riccardo Andrea Leccese ... \n", "Rikki Leccese ... \n", "Riley Gibbs ... \n", "Roman Lyubimtsev ... \n", "Sam Bullock ... \n", "Seth Besse ... \n", "Sonny Swords ... \n", "Stefaans Viljoen ... \n", "Steve Bodner ... \n", "Toni Vodisek ... \n", "Ty Reed ... \n", "Will Cyr ... \n", "Will James ... \n", "William Morris ... \n", "Xantos Villegas ... \n", "Zack Marks ... \n", "\n", " 20160807-SanFracisco-HydrofoilProTour-R7 \\\n", "Adam Withington 7.0 \n", "Adrian Geislinger NaN \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues 28.0 \n", "Amil Kabil 26.0 \n", "Andy Hansen 17.0 \n", "Anthony Goldbloom 33.0 \n", "Ariel Poler 46.0 \n", "Axel Mazella NaN \n", "Ben Turner 27.0 \n", "Benjamin Petit 16.0 \n", "Benni Boelli NaN \n", "Chip Wasson 23.0 \n", "Chris Brent 46.0 \n", "Craig Rawson 32.0 \n", "Daniela Moroz 19.0 \n", "Ejder Ginyol NaN \n", "Elena Kalinina NaN \n", "Felix Louis N'Jai 21.0 \n", "Florian Gruber NaN \n", "Florian Trittel NaN \n", "Fraser Novakowski 31.0 \n", "Jacob Olivier 8.0 \n", "James Johnsen NaN \n", "Joey Pasquali 3.0 \n", "John Gomes 36.0 \n", "John Von Tesmar NaN \n", "Johnny Heineken 2.0 \n", "Jon Modica 11.0 \n", "... ... \n", "Kieran Le Borgne 14.0 \n", "Loic Le Meur 34.0 \n", "Mani Bisschops 18.0 \n", "Martin Dolenc NaN \n", "Martin Turbil NaN \n", "Marvin Baumeisterschoenian 10.0 \n", "Matthew Taggart NaN \n", "Maxime Nocher NaN \n", "Michael Gilbreath 30.0 \n", "Nico Landauer 6.0 \n", "Nico Parlier 1.0 \n", "Oliver Bridge NaN \n", "Peter Grendler 46.0 \n", "Peter Martel 22.0 \n", "Riccardo Andrea Leccese NaN \n", "Rikki Leccese NaN \n", "Riley Gibbs 46.0 \n", "Roman Lyubimtsev NaN \n", "Sam Bullock 26.0 \n", "Seth Besse 12.0 \n", "Sonny Swords 25.0 \n", "Stefaans Viljoen 5.0 \n", "Steve Bodner 46.0 \n", "Toni Vodisek 9.0 \n", "Ty Reed 13.0 \n", "Will Cyr 29.0 \n", "Will James 20.0 \n", "William Morris 35.0 \n", "Xantos Villegas 17.0 \n", "Zack Marks 4.0 \n", "\n", " 20160807-SanFracisco-HydrofoilProTour-R8 \\\n", "Adam Withington 9.0 \n", "Adrian Geislinger NaN \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues 22.0 \n", "Amil Kabil 26.0 \n", "Andy Hansen 23.0 \n", "Anthony Goldbloom 30.0 \n", "Ariel Poler 35.0 \n", "Axel Mazella NaN \n", "Ben Turner 37.0 \n", "Benjamin Petit 18.0 \n", "Benni Boelli NaN \n", "Chip Wasson 16.0 \n", "Chris Brent 39.0 \n", "Craig Rawson 28.0 \n", "Daniela Moroz 17.0 \n", "Ejder Ginyol NaN \n", "Elena Kalinina NaN \n", "Felix Louis N'Jai 27.0 \n", "Florian Gruber NaN \n", "Florian Trittel NaN \n", "Fraser Novakowski 34.0 \n", "Jacob Olivier 12.0 \n", "James Johnsen NaN \n", "Joey Pasquali 3.0 \n", "John Gomes 40.0 \n", "John Von Tesmar NaN \n", "Johnny Heineken 2.0 \n", "Jon Modica 14.0 \n", "... ... \n", "Kieran Le Borgne 15.0 \n", "Loic Le Meur 36.0 \n", "Mani Bisschops 20.0 \n", "Martin Dolenc NaN \n", "Martin Turbil NaN \n", "Marvin Baumeisterschoenian 10.0 \n", "Matthew Taggart NaN \n", "Maxime Nocher NaN \n", "Michael Gilbreath 31.0 \n", "Nico Landauer 5.0 \n", "Nico Parlier 1.0 \n", "Oliver Bridge NaN \n", "Peter Grendler 46.0 \n", "Peter Martel 25.0 \n", "Riccardo Andrea Leccese NaN \n", "Rikki Leccese NaN \n", "Riley Gibbs 8.0 \n", "Roman Lyubimtsev NaN \n", "Sam Bullock 46.0 \n", "Seth Besse 11.0 \n", "Sonny Swords 29.0 \n", "Stefaans Viljoen 4.0 \n", "Steve Bodner 46.0 \n", "Toni Vodisek 6.0 \n", "Ty Reed 24.0 \n", "Will Cyr 33.0 \n", "Will James 19.0 \n", "William Morris 21.0 \n", "Xantos Villegas 32.0 \n", "Zack Marks 7.0 \n", "\n", " 20160807-SanFracisco-HydrofoilProTour-R9 \\\n", "Adam Withington 7.0 \n", "Adrian Geislinger NaN \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues 16.0 \n", "Amil Kabil 29.0 \n", "Andy Hansen 24.0 \n", "Anthony Goldbloom 31.0 \n", "Ariel Poler 46.0 \n", "Axel Mazella NaN \n", "Ben Turner 46.0 \n", "Benjamin Petit 22.0 \n", "Benni Boelli NaN \n", "Chip Wasson 19.0 \n", "Chris Brent 33.0 \n", "Craig Rawson 36.0 \n", "Daniela Moroz 12.0 \n", "Ejder Ginyol NaN \n", "Elena Kalinina NaN \n", "Felix Louis N'Jai 35.0 \n", "Florian Gruber NaN \n", "Florian Trittel NaN \n", "Fraser Novakowski 30.0 \n", "Jacob Olivier 46.0 \n", "James Johnsen NaN \n", "Joey Pasquali NaN \n", "John Gomes 34.0 \n", "John Von Tesmar NaN \n", "Johnny Heineken 2.0 \n", "Jon Modica 15.0 \n", "... ... \n", "Kieran Le Borgne 17.0 \n", "Loic Le Meur 46.0 \n", "Mani Bisschops 28.0 \n", "Martin Dolenc NaN \n", "Martin Turbil NaN \n", "Marvin Baumeisterschoenian 9.0 \n", "Matthew Taggart NaN \n", "Maxime Nocher NaN \n", "Michael Gilbreath 27.0 \n", "Nico Landauer 3.0 \n", "Nico Parlier 1.0 \n", "Oliver Bridge NaN \n", "Peter Grendler 46.0 \n", "Peter Martel 25.0 \n", "Riccardo Andrea Leccese NaN \n", "Rikki Leccese NaN \n", "Riley Gibbs 6.0 \n", "Roman Lyubimtsev NaN \n", "Sam Bullock 20.0 \n", "Seth Besse 11.0 \n", "Sonny Swords 26.0 \n", "Stefaans Viljoen 4.0 \n", "Steve Bodner 46.0 \n", "Toni Vodisek 10.0 \n", "Ty Reed 13.0 \n", "Will Cyr 32.0 \n", "Will James 14.0 \n", "William Morris 23.0 \n", "Xantos Villegas 18.0 \n", "Zack Marks 5.0 \n", "\n", " 20160807-SanFracisco-HydrofoilProTour-R10 \\\n", "Adam Withington 6.0 \n", "Adrian Geislinger NaN \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues 17.0 \n", "Amil Kabil 27.0 \n", "Andy Hansen 46.0 \n", "Anthony Goldbloom 29.0 \n", "Ariel Poler 26.0 \n", "Axel Mazella NaN \n", "Ben Turner 46.0 \n", "Benjamin Petit 23.0 \n", "Benni Boelli NaN \n", "Chip Wasson 13.0 \n", "Chris Brent 30.0 \n", "Craig Rawson 46.0 \n", "Daniela Moroz 12.0 \n", "Ejder Ginyol NaN \n", "Elena Kalinina NaN \n", "Felix Louis N'Jai 28.0 \n", "Florian Gruber NaN \n", "Florian Trittel NaN \n", "Fraser Novakowski 31.0 \n", "Jacob Olivier 16.0 \n", "James Johnsen NaN \n", "Joey Pasquali 46.0 \n", "John Gomes 33.0 \n", "John Von Tesmar NaN \n", "Johnny Heineken 2.0 \n", "Jon Modica 8.0 \n", "... ... \n", "Kieran Le Borgne 18.0 \n", "Loic Le Meur 46.0 \n", "Mani Bisschops 22.0 \n", "Martin Dolenc NaN \n", "Martin Turbil NaN \n", "Marvin Baumeisterschoenian 10.0 \n", "Matthew Taggart NaN \n", "Maxime Nocher NaN \n", "Michael Gilbreath 32.0 \n", "Nico Landauer 3.0 \n", "Nico Parlier 1.0 \n", "Oliver Bridge NaN \n", "Peter Grendler 46.0 \n", "Peter Martel 25.0 \n", "Riccardo Andrea Leccese NaN \n", "Rikki Leccese NaN \n", "Riley Gibbs 5.0 \n", "Roman Lyubimtsev NaN \n", "Sam Bullock 21.0 \n", "Seth Besse 10.0 \n", "Sonny Swords 24.0 \n", "Stefaans Viljoen 4.0 \n", "Steve Bodner 46.0 \n", "Toni Vodisek 9.0 \n", "Ty Reed 46.0 \n", "Will Cyr 34.0 \n", "Will James 19.0 \n", "William Morris 15.0 \n", "Xantos Villegas 7.0 \n", "Zack Marks 11.0 \n", "\n", " 20160807-SanFracisco-HydrofoilProTour-R11 \\\n", "Adam Withington 7.0 \n", "Adrian Geislinger NaN \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues 13.0 \n", "Amil Kabil 27.0 \n", "Andy Hansen 15.0 \n", "Anthony Goldbloom 29.0 \n", "Ariel Poler NaN \n", "Axel Mazella NaN \n", "Ben Turner 30.0 \n", "Benjamin Petit 18.0 \n", "Benni Boelli NaN \n", "Chip Wasson 22.0 \n", "Chris Brent 31.0 \n", "Craig Rawson NaN \n", "Daniela Moroz 19.0 \n", "Ejder Ginyol NaN \n", "Elena Kalinina NaN \n", "Felix Louis N'Jai 37.0 \n", "Florian Gruber NaN \n", "Florian Trittel NaN \n", "Fraser Novakowski 36.0 \n", "Jacob Olivier 12.0 \n", "James Johnsen NaN \n", "Joey Pasquali 3.0 \n", "John Gomes 33.0 \n", "John Von Tesmar NaN \n", "Johnny Heineken 1.0 \n", "Jon Modica 16.0 \n", "... ... \n", "Kieran Le Borgne 20.0 \n", "Loic Le Meur 34.0 \n", "Mani Bisschops NaN \n", "Martin Dolenc NaN \n", "Martin Turbil NaN \n", "Marvin Baumeisterschoenian 14.0 \n", "Matthew Taggart NaN \n", "Maxime Nocher NaN \n", "Michael Gilbreath NaN \n", "Nico Landauer 4.0 \n", "Nico Parlier 2.0 \n", "Oliver Bridge NaN \n", "Peter Grendler 46.0 \n", "Peter Martel 21.0 \n", "Riccardo Andrea Leccese NaN \n", "Rikki Leccese NaN \n", "Riley Gibbs 9.0 \n", "Roman Lyubimtsev NaN \n", "Sam Bullock 17.0 \n", "Seth Besse 11.0 \n", "Sonny Swords 35.0 \n", "Stefaans Viljoen 6.0 \n", "Steve Bodner NaN \n", "Toni Vodisek 5.0 \n", "Ty Reed 26.0 \n", "Will Cyr 32.0 \n", "Will James 46.0 \n", "William Morris 24.0 \n", "Xantos Villegas 10.0 \n", "Zack Marks 8.0 \n", "\n", " 20160807-SanFracisco-HydrofoilProTour-R12 \\\n", "Adam Withington 5.0 \n", "Adrian Geislinger NaN \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues 10.0 \n", "Amil Kabil 46.0 \n", "Andy Hansen 9.0 \n", "Anthony Goldbloom 26.0 \n", "Ariel Poler 21.0 \n", "Axel Mazella NaN \n", "Ben Turner 46.0 \n", "Benjamin Petit 23.0 \n", "Benni Boelli NaN \n", "Chip Wasson 20.0 \n", "Chris Brent 25.0 \n", "Craig Rawson NaN \n", "Daniela Moroz 11.0 \n", "Ejder Ginyol NaN \n", "Elena Kalinina NaN \n", "Felix Louis N'Jai 22.0 \n", "Florian Gruber NaN \n", "Florian Trittel NaN \n", "Fraser Novakowski 46.0 \n", "Jacob Olivier 17.0 \n", "James Johnsen NaN \n", "Joey Pasquali 3.0 \n", "John Gomes NaN \n", "John Von Tesmar NaN \n", "Johnny Heineken 2.0 \n", "Jon Modica 46.0 \n", "... ... \n", "Kieran Le Borgne 19.0 \n", "Loic Le Meur 46.0 \n", "Mani Bisschops NaN \n", "Martin Dolenc NaN \n", "Martin Turbil NaN \n", "Marvin Baumeisterschoenian 27.0 \n", "Matthew Taggart NaN \n", "Maxime Nocher NaN \n", "Michael Gilbreath 28.0 \n", "Nico Landauer NaN \n", "Nico Parlier 1.0 \n", "Oliver Bridge NaN \n", "Peter Grendler NaN \n", "Peter Martel 16.0 \n", "Riccardo Andrea Leccese NaN \n", "Rikki Leccese NaN \n", "Riley Gibbs 7.0 \n", "Roman Lyubimtsev NaN \n", "Sam Bullock 46.0 \n", "Seth Besse 8.0 \n", "Sonny Swords 24.0 \n", "Stefaans Viljoen 12.0 \n", "Steve Bodner 46.0 \n", "Toni Vodisek 4.0 \n", "Ty Reed 46.0 \n", "Will Cyr NaN \n", "Will James 46.0 \n", "William Morris 13.0 \n", "Xantos Villegas 15.0 \n", "Zack Marks 6.0 \n", "\n", " 20160807-SanFracisco-HydrofoilProTour-R13 \\\n", "Adam Withington 6.0 \n", "Adrian Geislinger NaN \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues 18.0 \n", "Amil Kabil 26.0 \n", "Andy Hansen 13.0 \n", "Anthony Goldbloom 33.0 \n", "Ariel Poler 28.0 \n", "Axel Mazella NaN \n", "Ben Turner 34.0 \n", "Benjamin Petit 25.0 \n", "Benni Boelli NaN \n", "Chip Wasson 24.0 \n", "Chris Brent 39.0 \n", "Craig Rawson 35.0 \n", "Daniela Moroz 21.0 \n", "Ejder Ginyol NaN \n", "Elena Kalinina NaN \n", "Felix Louis N'Jai 38.0 \n", "Florian Gruber NaN \n", "Florian Trittel NaN \n", "Fraser Novakowski 40.0 \n", "Jacob Olivier 10.0 \n", "James Johnsen NaN \n", "Joey Pasquali 2.0 \n", "John Gomes NaN \n", "John Von Tesmar NaN \n", "Johnny Heineken 3.0 \n", "Jon Modica 15.0 \n", "... ... \n", "Kieran Le Borgne 17.0 \n", "Loic Le Meur 36.0 \n", "Mani Bisschops 31.0 \n", "Martin Dolenc NaN \n", "Martin Turbil NaN \n", "Marvin Baumeisterschoenian 32.0 \n", "Matthew Taggart NaN \n", "Maxime Nocher NaN \n", "Michael Gilbreath 27.0 \n", "Nico Landauer 4.0 \n", "Nico Parlier 1.0 \n", "Oliver Bridge NaN \n", "Peter Grendler NaN \n", "Peter Martel 20.0 \n", "Riccardo Andrea Leccese NaN \n", "Rikki Leccese NaN \n", "Riley Gibbs 12.0 \n", "Roman Lyubimtsev NaN \n", "Sam Bullock 14.0 \n", "Seth Besse 16.0 \n", "Sonny Swords 30.0 \n", "Stefaans Viljoen 7.0 \n", "Steve Bodner 46.0 \n", "Toni Vodisek 5.0 \n", "Ty Reed 11.0 \n", "Will Cyr 29.0 \n", "Will James 37.0 \n", "William Morris 22.0 \n", "Xantos Villegas 9.0 \n", "Zack Marks 8.0 \n", "\n", " 20160807-SanFracisco-HydrofoilProTour-R14 \\\n", "Adam Withington 6.0 \n", "Adrian Geislinger NaN \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues 14.0 \n", "Amil Kabil 28.0 \n", "Andy Hansen 17.0 \n", "Anthony Goldbloom 27.0 \n", "Ariel Poler 23.0 \n", "Axel Mazella NaN \n", "Ben Turner 34.0 \n", "Benjamin Petit 18.0 \n", "Benni Boelli NaN \n", "Chip Wasson 20.0 \n", "Chris Brent 33.0 \n", "Craig Rawson 36.0 \n", "Daniela Moroz 19.0 \n", "Ejder Ginyol NaN \n", "Elena Kalinina NaN \n", "Felix Louis N'Jai 46.0 \n", "Florian Gruber NaN \n", "Florian Trittel NaN \n", "Fraser Novakowski 30.0 \n", "Jacob Olivier 8.0 \n", "James Johnsen NaN \n", "Joey Pasquali 3.0 \n", "John Gomes NaN \n", "John Von Tesmar NaN \n", "Johnny Heineken 2.0 \n", "Jon Modica 46.0 \n", "... ... \n", "Kieran Le Borgne 14.0 \n", "Loic Le Meur 35.0 \n", "Mani Bisschops 22.0 \n", "Martin Dolenc NaN \n", "Martin Turbil NaN \n", "Marvin Baumeisterschoenian 31.0 \n", "Matthew Taggart NaN \n", "Maxime Nocher NaN \n", "Michael Gilbreath 29.0 \n", "Nico Landauer 46.0 \n", "Nico Parlier 1.0 \n", "Oliver Bridge NaN \n", "Peter Grendler NaN \n", "Peter Martel 10.0 \n", "Riccardo Andrea Leccese NaN \n", "Rikki Leccese NaN \n", "Riley Gibbs 11.0 \n", "Roman Lyubimtsev NaN \n", "Sam Bullock 13.0 \n", "Seth Besse 9.0 \n", "Sonny Swords 24.0 \n", "Stefaans Viljoen 5.0 \n", "Steve Bodner 46.0 \n", "Toni Vodisek 4.0 \n", "Ty Reed 15.0 \n", "Will Cyr 25.0 \n", "Will James 21.0 \n", "William Morris 26.0 \n", "Xantos Villegas 46.0 \n", "Zack Marks 7.0 \n", "\n", " 20160807-SanFracisco-HydrofoilProTour-R15 \\\n", "Adam Withington 8.0 \n", "Adrian Geislinger NaN \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues 14.0 \n", "Amil Kabil 30.0 \n", "Andy Hansen 17.0 \n", "Anthony Goldbloom 34.0 \n", "Ariel Poler 27.0 \n", "Axel Mazella NaN \n", "Ben Turner 31.0 \n", "Benjamin Petit 28.0 \n", "Benni Boelli NaN \n", "Chip Wasson 25.0 \n", "Chris Brent 38.0 \n", "Craig Rawson 38.0 \n", "Daniela Moroz 16.0 \n", "Ejder Ginyol NaN \n", "Elena Kalinina NaN \n", "Felix Louis N'Jai 46.0 \n", "Florian Gruber NaN \n", "Florian Trittel NaN \n", "Fraser Novakowski 34.0 \n", "Jacob Olivier 14.0 \n", "James Johnsen NaN \n", "Joey Pasquali 3.0 \n", "John Gomes NaN \n", "John Von Tesmar NaN \n", "Johnny Heineken 1.0 \n", "Jon Modica 18.0 \n", "... ... \n", "Kieran Le Borgne 21.0 \n", "Loic Le Meur 33.0 \n", "Mani Bisschops 30.0 \n", "Martin Dolenc NaN \n", "Martin Turbil NaN \n", "Marvin Baumeisterschoenian 46.0 \n", "Matthew Taggart NaN \n", "Maxime Nocher NaN \n", "Michael Gilbreath 37.0 \n", "Nico Landauer 4.0 \n", "Nico Parlier 2.0 \n", "Oliver Bridge NaN \n", "Peter Grendler NaN \n", "Peter Martel 22.0 \n", "Riccardo Andrea Leccese NaN \n", "Rikki Leccese NaN \n", "Riley Gibbs 7.0 \n", "Roman Lyubimtsev NaN \n", "Sam Bullock 19.0 \n", "Seth Besse 13.0 \n", "Sonny Swords 29.0 \n", "Stefaans Viljoen 5.0 \n", "Steve Bodner NaN \n", "Toni Vodisek 6.0 \n", "Ty Reed 11.0 \n", "Will Cyr 35.0 \n", "Will James 15.0 \n", "William Morris 25.0 \n", "Xantos Villegas 10.0 \n", "Zack Marks 11.0 \n", "\n", " 20160807-SanFracisco-HydrofoilProTour-R16 \n", "Adam Withington 5.0 \n", "Adrian Geislinger NaN \n", "Alejandro Climent Hernã¥_ Ndez NaN \n", "Alejandro Climent Hernã¥_Ndez NaN \n", "Alex Caizergues 13.0 \n", "Amil Kabil 26.0 \n", "Andy Hansen 18.0 \n", "Anthony Goldbloom 32.0 \n", "Ariel Poler 28.0 \n", "Axel Mazella NaN \n", "Ben Turner 34.0 \n", "Benjamin Petit 9.0 \n", "Benni Boelli NaN \n", "Chip Wasson 21.0 \n", "Chris Brent 36.0 \n", "Craig Rawson 46.0 \n", "Daniela Moroz 11.0 \n", "Ejder Ginyol NaN \n", "Elena Kalinina NaN \n", "Felix Louis N'Jai 46.0 \n", "Florian Gruber NaN \n", "Florian Trittel NaN \n", "Fraser Novakowski 33.0 \n", "Jacob Olivier 16.0 \n", "James Johnsen NaN \n", "Joey Pasquali 3.0 \n", "John Gomes NaN \n", "John Von Tesmar NaN \n", "Johnny Heineken 2.0 \n", "Jon Modica 19.0 \n", "... ... \n", "Kieran Le Borgne 15.0 \n", "Loic Le Meur 35.0 \n", "Mani Bisschops 24.0 \n", "Martin Dolenc NaN \n", "Martin Turbil NaN \n", "Marvin Baumeisterschoenian 46.0 \n", "Matthew Taggart NaN \n", "Maxime Nocher NaN \n", "Michael Gilbreath 31.0 \n", "Nico Landauer 4.0 \n", "Nico Parlier 1.0 \n", "Oliver Bridge NaN \n", "Peter Grendler NaN \n", "Peter Martel 25.0 \n", "Riccardo Andrea Leccese NaN \n", "Rikki Leccese NaN \n", "Riley Gibbs 6.0 \n", "Roman Lyubimtsev NaN \n", "Sam Bullock 23.0 \n", "Seth Besse 14.0 \n", "Sonny Swords 29.0 \n", "Stefaans Viljoen 46.0 \n", "Steve Bodner NaN \n", "Toni Vodisek 8.0 \n", "Ty Reed 12.0 \n", "Will Cyr 30.0 \n", "Will James 27.0 \n", "William Morris 22.0 \n", "Xantos Villegas 10.0 \n", "Zack Marks 7.0 \n", "\n", "[64 rows x 22 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "##Load SF results\n", "dfResultsTemp = pd.read_csv('../input/20160807-SanFracisco-HydrofoilProTour.csv')\n", "dfResultsTemp = dfResultsTemp.set_index(dfResultsTemp['Name'])\n", "raceColumns = ['R1','R2','R3','R4','R5','R6','R7','R8','R9','R10','R11','R12','R13','R14','R15','R16']\n", "dfResults = cleanResults('20160807-SanFracisco-HydrofoilProTour',raceColumns,dfResultsTemp,dfResults)\n", "dfResults " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "b5de9ec0-2853-88f2-5eed-577385c529d4" }, "outputs": [ { "ename": "NameError", "evalue": "name 'dfRatings' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mNameError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-7-f8a3a686378b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdfRatings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdfRatings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mu_minus_3sigma'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrank\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mdfRatings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'mu_minus_3sigma'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mascending\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'dfRatings' is not defined" ] } ], "source": [ "dfRatings.index = dfRatings['mu_minus_3sigma'].rank(ascending=False)\n", "dfRatings.sort('mu_minus_3sigma',ascending=False)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "f575622e-8713-7607-1460-ed40ae6597aa" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 175, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/329/329567.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "4ed55089-3b16-6d61-28e7-586c10057368" }, "source": [ "## Fluctuating demands\n", "I had the idea that demand for an item on a given week should be correlated with the demand for that product the week before. I'm sure many of you have had the same thought. \n", "\n", "In this notebook we'll see a couple examples of the change on demand for each item stocked by a client. Then we'll plot the distributions and see the trends." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "7f2cefbc-1187-a28c-f930-a4f2f997d441" }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "a0a2a634-0f2b-af46-9d45-80e8d87df832" }, "outputs": [], "source": [ "types = {'Semana':np.uint8, 'Cliente_ID':np.uint32,\n", " 'Producto_ID':np.uint16, 'Demanda_uni_equil':np.uint32}\n", "\n", "df = pd.read_csv('../input/train.csv', usecols=types.keys(),\n", " dtype=types)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2b497b24-ac02-cd2a-b84d-5277fe62c61e" }, "source": [ "Before looking at the change in demand, we should check out the distribution of item demands. It's spiked at 0 with a tail extending to 5000." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "089c9996-5193-d987-e3c2-2fa37041f160" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa5382b40b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Histogram of target variable\n", "df.Demanda_uni_equil.hist(bins=100)\n", "plt.xlabel('Demand per week')\n", "plt.ylabel('Number of clients');" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "37c438f1-b3db-1f48-e64f-433420c93f37" }, "outputs": [ { "data": { "image/png": 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G0cBTwPnuvrPU2gD+kWjRvOOI9nd4MHzuYuDjRBsR3V3tC4RYXiDac2c8cD9w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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fa51b5cd390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Log scale histogram of target variable\n", "df.Demanda_uni_equil.hist(bins=100, log=True)\n", "plt.xlabel('Demand per week')\n", "plt.ylabel('Number of clients');" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "aa3b757c-96f1-08e9-7bd4-2cc96f3b20e1" }, "outputs": [], "source": [ "demand_sorted = df.Demanda_uni_equil.sort_values(ascending=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "52f4febf-83de-f946-0b59-59a40c406ce5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "plotting 96.20 % of data\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa51b5b7518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('plotting {0:.2f} % of data'.\\\n", " format(100*(demand_sorted < 30).sum()/len(demand_sorted)))\n", "demand_sorted[demand_sorted < 30].hist(bins=30)\n", "plt.xlabel('Demand per week')\n", "plt.ylabel('Number of clients');" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "760b4b25-17a9-0994-e2bd-fd713238e737" }, "source": [ "As promised, lets look at the week-to-week changes in item demand." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "7a2d7610-bc76-1c3c-2988-93e25f07681f" }, "outputs": [], "source": [ "def demandVar(c_id, df, percent=False):\n", " ''' Get the amounts by which product demand changed\n", " week-to-week for a given set of client ids. Returned\n", " object is a pandas dataframe with NaN entries where\n", " the product was not ordered the week before or after.\n", " '''\n", " \n", " for week in range(4,10):\n", " try:\n", " \n", " vals_a = df[(df.Cliente_ID.values == c_id)&\\\n", " (df.Semana.values == week-1)].Demanda_uni_equil.values\n", " prod_a = df[(df.Cliente_ID.values == c_id)&\\\n", " (df.Semana.values == week-1)].Producto_ID.values\n", " dict_a = {p: v for p, v in zip(prod_a, vals_a)}\n", " \n", " vals_b = df[(df.Cliente_ID.values == c_id)&\\\n", " (df.Semana.values == (week))].Demanda_uni_equil.values\n", " prod_b = df[(df.Cliente_ID.values == c_id)&\\\n", " (df.Semana.values == week)].Producto_ID.values\n", " dict_b = {p: v for p, v in zip(prod_b, vals_b)}\n", " \n", " dict_merge = {}\n", " for key in np.unique(np.concatenate((prod_a, prod_b))):\n", " try:\n", " if percent:\n", " try:\n", " # Calculate percent difference\n", " dict_merge[key] = (dict_b[key].astype(int) - dict_a[key].astype(int))\\\n", " /(dict_b[key].astype(int) + dict_a[key].astype(int))\n", " except:\n", " # If dividing by zero assign 0% change\n", " dict_merge[key] = 0.0\n", " else:\n", " dict_merge[key] = dict_b[key].astype(int) - dict_a[key].astype(int)\n", " except:\n", " # The product was not on the previous form\n", " # or was removed from the current one\n", " dict_merge[key] = np.nan\n", " \n", " if week==4:\n", " df_return = pd.DataFrame({'week_3-4': list(dict_merge.values())},\n", " index=list(dict_merge.keys()))\n", " else:\n", " df_new = pd.DataFrame({'week_'+str(week-1)+'-'+str(week): list(dict_merge.values())}, \n", " index=list(dict_merge.keys()))\n", " df_return = pd.merge(df_return, df_new, how='outer', left_index=True, right_index=True)\n", " \n", " except:\n", " print('No week {}-{} data found'.\\\n", " format(week-1, week))\n", "\n", " return df_return" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "ee76246f-38cf-33e3-f0eb-d74bbfdb7371" }, "source": [ "Generating a set of client id's to look at." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "ee7d6923-ffde-c543-2e9d-90964d8272f7" }, "outputs": [ { "data": { "text/plain": [ "[15766, 2465653, 1831160, 4470398, 4708097]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c_ids = [df.Cliente_ID.values[int(i)] for i in np.linspace(0, len(df)-1, 5)]\n", "c_ids" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "4c9c8a2c-b4b1-8bdf-e2ef-92ecbf69abf9" }, "source": [ "Below we see the differences for client id=15766, where the dataframe index is the product id. NaN entries represent products that were not present on the inventory order sheet for both or one of the compared weeks." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "bae1dc32-ec19-7277-3662-0295bdfb649a" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>week_3-4</th>\n", " <th>week_4-5</th>\n", " <th>week_5-6</th>\n", " <th>week_6-7</th>\n", " <th>week_7-8</th>\n", " <th>week_8-9</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>325</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>328</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1212</th>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>-3.0</td>\n", " </tr>\n", " <tr>\n", " <th>1216</th>\n", " <td>-2.0</td>\n", " <td>1.0</td>\n", " <td>-2.0</td>\n", " <td>1.0</td>\n", " <td>3.0</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1220</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1238</th>\n", " <td>-1.0</td>\n", " <td>-2.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>-1.0</td>\n", " </tr>\n", " <tr>\n", " <th>1240</th>\n", " <td>3.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>-6.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1242</th>\n", " <td>0.0</td>\n", " <td>-1.0</td>\n", " <td>1.0</td>\n", " <td>-1.0</td>\n", " <td>-1.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1250</th>\n", " <td>-2.0</td>\n", " <td>5.0</td>\n", " <td>-7.0</td>\n", " <td>13.0</td>\n", " <td>-6.0</td>\n", " 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3.0\n", "41938 0.0 NaN NaN NaN -1.0 1.0\n", "42434 NaN NaN NaN NaN NaN NaN\n", "47336 NaN NaN NaN NaN NaN NaN" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "var = demandVar(c_id=c_ids[0], df=df)\n", "var" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2832fe7d-6b53-899c-3652-481030cff103" }, "source": [ "The `demandVar` function can also calcualte the percent differences in product demand, as seen below for client id=2465653." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "7234616a-7c00-92fb-f85c-438347febe7c" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>week_3-4</th>\n", " <th>week_4-5</th>\n", " <th>week_5-6</th>\n", " <th>week_6-7</th>\n", " <th>week_7-8</th>\n", " <th>week_8-9</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>73</th>\n", " 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" </tbody>\n", "</table>\n", "<p>70 rows × 6 columns</p>\n", "</div>" ], "text/plain": [ " week_3-4 week_4-5 week_5-6 week_6-7 week_7-8 week_8-9\n", "73 -0.200000 0.000000 NaN NaN NaN NaN\n", "972 0.000000 -0.090909 0.166667 -0.272727 0.000000 0.111111\n", "1064 0.166667 -0.555556 0.555556 -0.272727 -0.142857 0.538462\n", "1109 0.166667 -0.217391 -0.285714 0.166667 0.300000 -0.130435\n", "1125 0.272727 -0.354839 0.000000 0.473684 0.096774 -0.214286\n", "1129 0.000000 0.428571 -0.666667 0.500000 0.142857 0.000000\n", "1146 0.000000 0.066667 -0.230769 -0.250000 0.600000 -0.411765\n", "1150 -0.333333 0.111111 0.230769 -0.777778 0.714286 0.000000\n", "1160 -0.333333 0.333333 -1.000000 1.000000 0.750000 -0.272727\n", "1182 NaN NaN NaN 0.000000 -0.200000 0.600000\n", "1212 -0.400000 0.400000 0.363636 -0.363636 -0.400000 0.142857\n", "1216 -0.142857 -0.200000 0.600000 -0.230769 0.000000 0.090909\n", "1220 -0.529412 0.636364 -0.285714 -0.250000 0.076923 0.222222\n", "1230 NaN 0.000000 0.411765 -0.500000 0.111111 0.090909\n", "1232 0.333333 -0.500000 0.384615 -0.058824 -0.230769 -0.250000\n", "1238 0.230769 -0.230769 0.259259 0.028571 -0.125000 0.096774\n", "1240 -0.363636 0.176471 0.333333 -0.250000 0.272727 -0.312500\n", "1242 0.272727 -0.166667 0.230769 -0.142857 0.368421 -0.300000\n", "1250 0.500000 0.200000 0.250000 0.333333 -0.304348 -0.230769\n", "1278 0.379310 0.047619 0.120000 -0.018182 0.068966 -0.512195\n", "1284 -0.512195 0.555556 -0.014493 -0.133333 -0.019608 -0.162791\n", "1309 0.066667 0.000000 0.304348 -0.428571 0.250000 0.047619\n", "2233 -0.333333 0.428571 -0.071429 -0.238095 0.000000 0.333333\n", "3144 -0.200000 -0.333333 0.000000 0.333333 0.200000 -0.200000\n", "3631 -0.230769 0.230769 -0.333333 0.428571 0.000000 -0.333333\n", "4245 0.000000 0.000000 NaN NaN NaN 0.000000\n", "4280 -0.538462 0.000000 NaN NaN NaN 0.000000\n", "9217 0.000000 NaN NaN NaN NaN NaN\n", "30415 NaN NaN NaN NaN 0.000000 0.000000\n", "30532 0.000000 0.333333 0.200000 -0.200000 0.230769 -0.230769\n", "... ... ... ... ... ... ...\n", "32934 NaN NaN 0.000000 0.000000 0.000000 NaN\n", "33244 NaN NaN NaN 0.000000 0.000000 NaN\n", "33871 NaN NaN NaN NaN 0.000000 0.600000\n", "35141 0.000000 0.000000 NaN NaN 0.000000 0.000000\n", "35144 0.000000 NaN NaN NaN NaN NaN\n", "35305 NaN NaN NaN 0.000000 0.000000 NaN\n", "35309 NaN NaN NaN 0.000000 0.000000 NaN\n", "35453 NaN 0.000000 0.000000 NaN NaN NaN\n", "35516 NaN NaN NaN NaN 0.000000 0.600000\n", "35651 -0.100000 0.000000 -0.090909 0.230769 -0.263158 -0.120000\n", "36745 0.333333 -0.333333 0.142857 0.111111 -0.666667 0.000000\n", "36748 NaN NaN NaN NaN 0.000000 0.000000\n", "37058 0.142857 0.000000 0.333333 -0.066667 -0.076923 -0.200000\n", "37361 NaN NaN NaN 0.000000 0.000000 NaN\n", "41938 -0.111111 0.000000 0.111111 0.000000 NaN NaN\n", "43033 0.333333 -0.428571 0.000000 NaN NaN NaN\n", "43058 NaN 0.000000 0.000000 NaN 0.000000 0.333333\n", "43064 NaN NaN 0.000000 0.000000 NaN 0.000000\n", "43065 NaN 0.000000 -0.714286 0.000000 0.333333 0.333333\n", "43066 NaN NaN 0.000000 -0.333333 0.000000 NaN\n", "43068 NaN NaN NaN 0.000000 0.000000 NaN\n", "43069 -0.333333 0.333333 -0.090909 0.090909 -0.500000 0.200000\n", "43084 NaN NaN NaN 0.000000 0.000000 NaN\n", "43215 0.000000 NaN NaN NaN NaN NaN\n", "43285 -0.333333 0.333333 -0.333333 0.428571 -0.428571 -0.818182\n", "43316 NaN 0.000000 -0.500000 0.500000 0.000000 NaN\n", "46772 -0.052632 0.052632 0.000000 NaN 0.000000 -0.333333\n", "47124 0.000000 0.000000 NaN NaN NaN NaN\n", "48119 0.000000 NaN NaN NaN NaN 0.000000\n", "48417 NaN NaN NaN 0.000000 0.000000 NaN\n", "\n", "[70 rows x 6 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "var = demandVar(c_id=c_ids[1], df=df, percent=True)\n", "var" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e7d6fe12-e4b0-4f6d-e308-cbf654f685f3" }, "source": [ "Now we'll plot distributions using larger samples of data." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "3943fcc9-4db2-f413-f94a-278a576db8c9" }, "outputs": [], "source": [ "def get_vars(c_ids, percent=False):\n", " ''' Return a list of variations in the demand\n", " week-to-week on individual products for a set of clients. '''\n", " return_list = [[] for _ in range(len(c_ids))]\n", " for i, c_id in enumerate(c_ids):\n", " var = demandVar(c_id, df, percent)\n", " for col in var.columns:\n", " return_list[i] += list(var[col].dropna())\n", " \n", " return return_list" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "d397f1af-c628-058f-378c-f827f992ffcb" }, "outputs": [], "source": [ "var_list = get_vars(c_ids)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "43419b59-436b-c336-3cf9-7cbeacea332c" }, "outputs": [ { "data": { "text/plain": [ "(0, 1)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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VNzcvJGOMMXPdpGcVInIi8DjwkejfYyLy+mYHZowxZu6q5czjC8AxqvoYgIjs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\ncNjiy6MHtiAiMRF5r4gsmGT1HuC5KHE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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa51b640b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = ['blue', 'red', 'green', 'turquoise', 'brown']\n", "\n", "fig, ax = plt.subplots(1,2)\n", "plt.suptitle('Change in demand on individual poducts for 5 clients')\n", "\n", "for i in range(5):\n", " ax[0].hist(var_list[i], color=colors[i],\n", " normed=True, alpha=0.5, bins=20)\n", "ax[0].set_ylim(0,0.2)\n", "ax[0].set_xlabel('Change in demand')\n", "ax[0].set_ylabel('Normed frequency')\n", "\n", "for i in range(5):\n", " ax[1].hist(var_list[i], color=colors[i],\n", " normed=True, alpha=0.5, bins=20)\n", "ax[1].set_ylim(0,1)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "ac20fad9-9fd2-5013-518b-488ace3cf31e" }, "outputs": [], "source": [ "var_list = get_vars(c_ids, percent=True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "d1e16ccf-1b1f-95dd-1dce-7e494558566b" }, "outputs": [ { "data": { "image/png": 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PUiLiAA8yxiw5spdSSqnNbckK8FlDvAvWKRallFIDqpvyj6+IyJ8BnwamZyca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\nVv2qjY1t0+eCm4Ami2XKWw4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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa51af9e940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "colors = ['blue', 'red', 'green', 'turquoise', 'brown']\n", "\n", "fig, ax = plt.subplots(1,2)\n", "plt.suptitle('Percent change in demand on individual poducts for 5 clients')\n", "\n", "for i in range(5):\n", " ax[0].hist(var_list[i], color=colors[i],\n", " normed=True, alpha=0.5, bins=20)\n", "ax[0].set_ylim(0,2)\n", "ax[0].set_xlabel('Change in demand')\n", "ax[0].set_ylabel('Normed frequency')\n", "\n", "for i in range(5):\n", " ax[1].hist(var_list[i], color=colors[i],\n", " normed=True, alpha=0.5, bins=20)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "1aa63b0a-2941-bd97-115f-e90d0679128f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 10min 39s, sys: 1.06 s, total: 10min 40s\n", "Wall time: 10min 40s\n" ] } ], "source": [ "%%time\n", "c_ids = [df.Cliente_ID.values[int(i)] for i in np.linspace(0, len(df)-1, 100)]\n", "var_list, p_var_list = get_vars(c_ids), get_vars(c_ids, percent=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "79314886-f208-da16-23c0-e87e143059ec" }, "outputs": [ { "data": { "image/png": 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0iPjwcFaeeuC9mGw8kN8DSyVdHRF3V5WZCxwUEbMlPRe4BJgTEZskHR8R61Mc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\nxdOAq8sMKn0IFa8CftUsxjJ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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa51e05b6a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2)\n", "plt.suptitle('Change in demand on individual poducts for 100 clients')\n", "\n", "# Plot historgram for flattened list\n", "ax[0].hist([x for row in var_list for x in row], color='red',\n", " normed=True, alpha=0.5, bins=100)\n", "ax[0].set_xlim(-100,100)\n", "ax[0].set_xlabel('Change in demand')\n", "ax[0].set_ylabel('Normed frequency')\n", "\n", "ax[1].hist([x for row in var_list for x in row], color='red',\n", " normed=True, alpha=0.5, bins=500)\n", "ax[1].set_xlim(-10,10);" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "75041b4c-2c78-2d5f-9564-72ef2c6b9a9f" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa51b037b38>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "plt.suptitle('Percent change in demand on individual poducts for 100 clients')\n", "\n", "# Plot historgram for flattened list\n", "ax.hist([x for row in p_var_list for x in row], color='red',\n", " normed=True, alpha=0.5, bins=100)\n", "# ax[0].set_xlim(-100,100)\n", "ax.set_xlabel('Change in demand')\n", "ax.set_ylabel('Normed frequency');" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "8e82b252-306a-43d3-3ef5-5b784f48021f" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 409, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/329/329572.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "1a68c31d-94a4-571a-1704-07eabf4a9a20" }, "outputs": [], "source": [ "from os import path\n", "import pandas as pd\n", "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "a80fa3e4-dd54-9549-b4ee-401348220118" }, "outputs": [], "source": [ "#import files from local \n", "battles_df = pd.read_csv('../input/battles.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "00421035-2cde-a923-e571-5b0f0174dc15" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>name</th>\n", " <th>year</th>\n", " <th>battle_number</th>\n", " <th>attacker_king</th>\n", " <th>defender_king</th>\n", " <th>attacker_1</th>\n", " <th>attacker_2</th>\n", " <th>attacker_3</th>\n", " <th>attacker_4</th>\n", " <th>defender_1</th>\n", " <th>...</th>\n", " <th>major_death</th>\n", " <th>major_capture</th>\n", " <th>attacker_size</th>\n", " <th>defender_size</th>\n", " <th>attacker_commander</th>\n", " <th>defender_commander</th>\n", " <th>summer</th>\n", " <th>location</th>\n", " <th>region</th>\n", " <th>note</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Battle of the Golden Tooth</td>\n", " <td>298</td>\n", " <td>1</td>\n", " <td>Joffrey/Tommen Baratheon</td>\n", " <td>Robb Stark</td>\n", " <td>Lannister</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Tully</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>15000.0</td>\n", " <td>4000.0</td>\n", " <td>Jaime Lannister</td>\n", " <td>Clement Piper, Vance</td>\n", " <td>1.0</td>\n", " <td>Golden Tooth</td>\n", " <td>The Westerlands</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Battle at the Mummer's Ford</td>\n", " <td>298</td>\n", " <td>2</td>\n", " <td>Joffrey/Tommen Baratheon</td>\n", " <td>Robb Stark</td>\n", " <td>Lannister</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Baratheon</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " <td>120.0</td>\n", " <td>Gregor Clegane</td>\n", " <td>Beric Dondarrion</td>\n", " <td>1.0</td>\n", " <td>Mummer's Ford</td>\n", " <td>The Riverlands</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Battle of Riverrun</td>\n", " <td>298</td>\n", " <td>3</td>\n", " <td>Joffrey/Tommen Baratheon</td>\n", " <td>Robb Stark</td>\n", " <td>Lannister</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Tully</td>\n", " <td>...</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>15000.0</td>\n", " <td>10000.0</td>\n", " <td>Jaime Lannister, Andros Brax</td>\n", " <td>Edmure Tully, Tytos Blackwood</td>\n", " <td>1.0</td>\n", " <td>Riverrun</td>\n", " <td>The Riverlands</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Battle of the Green Fork</td>\n", " <td>298</td>\n", " <td>4</td>\n", " <td>Robb Stark</td>\n", " <td>Joffrey/Tommen Baratheon</td>\n", " <td>Stark</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Lannister</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>18000.0</td>\n", " <td>20000.0</td>\n", " <td>Roose Bolton, Wylis Manderly, Medger Cerwyn, H...</td>\n", " <td>Tywin Lannister, Gregor Clegane, Kevan Lannist...</td>\n", " <td>1.0</td>\n", " <td>Green Fork</td>\n", " <td>The Riverlands</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Battle of the Whispering Wood</td>\n", " <td>298</td>\n", " <td>5</td>\n", " <td>Robb Stark</td>\n", " <td>Joffrey/Tommen Baratheon</td>\n", " <td>Stark</td>\n", " <td>Tully</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Lannister</td>\n", " <td>...</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1875.0</td>\n", " <td>6000.0</td>\n", " <td>Robb Stark, Brynden Tully</td>\n", " <td>Jaime Lannister</td>\n", " <td>1.0</td>\n", " <td>Whispering Wood</td>\n", " <td>The Riverlands</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 25 columns</p>\n", "</div>" ], "text/plain": [ " name year battle_number \\\n", "0 Battle of the Golden Tooth 298 1 \n", "1 Battle at the Mummer's Ford 298 2 \n", "2 Battle of Riverrun 298 3 \n", "3 Battle of the Green Fork 298 4 \n", "4 Battle of the Whispering Wood 298 5 \n", "\n", " attacker_king defender_king attacker_1 attacker_2 \\\n", "0 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n", "1 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n", "2 Joffrey/Tommen Baratheon Robb Stark Lannister NaN \n", "3 Robb Stark Joffrey/Tommen Baratheon Stark NaN \n", "4 Robb Stark Joffrey/Tommen Baratheon Stark Tully \n", "\n", " attacker_3 attacker_4 defender_1 ... major_death major_capture \\\n", "0 NaN NaN Tully ... 1.0 0.0 \n", "1 NaN NaN Baratheon ... 1.0 0.0 \n", "2 NaN NaN Tully ... 0.0 1.0 \n", "3 NaN NaN Lannister ... 1.0 1.0 \n", "4 NaN NaN Lannister ... 1.0 1.0 \n", "\n", " attacker_size defender_size \\\n", "0 15000.0 4000.0 \n", "1 NaN 120.0 \n", "2 15000.0 10000.0 \n", "3 18000.0 20000.0 \n", "4 1875.0 6000.0 \n", "\n", " attacker_commander \\\n", "0 Jaime Lannister \n", "1 Gregor Clegane \n", "2 Jaime Lannister, Andros Brax \n", "3 Roose Bolton, Wylis Manderly, Medger Cerwyn, H... \n", "4 Robb Stark, Brynden Tully \n", "\n", " defender_commander summer location \\\n", "0 Clement Piper, Vance 1.0 Golden Tooth \n", "1 Beric Dondarrion 1.0 Mummer's Ford \n", "2 Edmure Tully, Tytos Blackwood 1.0 Riverrun \n", "3 Tywin Lannister, Gregor Clegane, Kevan Lannist... 1.0 Green Fork \n", "4 Jaime Lannister 1.0 Whispering Wood \n", "\n", " region note \n", "0 The Westerlands NaN \n", "1 The Riverlands NaN \n", "2 The Riverlands NaN \n", "3 The Riverlands NaN \n", "4 The Riverlands NaN \n", "\n", "[5 rows x 25 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "battles_df.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "e7008447-05da-df14-74fd-51de67809f7f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 38 entries, 0 to 37\n", "Data columns (total 25 columns):\n", "name 38 non-null object\n", "year 38 non-null int64\n", "battle_number 38 non-null int64\n", "attacker_king 36 non-null object\n", "defender_king 35 non-null object\n", "attacker_1 38 non-null object\n", "attacker_2 10 non-null object\n", "attacker_3 3 non-null object\n", "attacker_4 2 non-null object\n", "defender_1 37 non-null object\n", "defender_2 2 non-null object\n", "defender_3 0 non-null float64\n", "defender_4 0 non-null float64\n", "attacker_outcome 37 non-null object\n", "battle_type 37 non-null object\n", "major_death 37 non-null float64\n", "major_capture 37 non-null float64\n", "attacker_size 24 non-null float64\n", "defender_size 19 non-null float64\n", "attacker_commander 37 non-null object\n", "defender_commander 28 non-null object\n", "summer 37 non-null float64\n", "location 37 non-null object\n", "region 38 non-null object\n", "note 5 non-null object\n", "dtypes: float64(7), int64(2), object(16)\n", "memory usage: 7.5+ KB\n" ] } ], "source": [ "battles_df.info()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "888c6ce8-e56d-567c-9c5b-f8aeb8fb8194" }, "outputs": [ { "data": { "text/plain": [ "0 win\n", "1 win\n", "2 win\n", "3 loss\n", "4 win\n", "Name: attacker_outcome, dtype: object" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "battles_df.attacker_outcome.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "d961bc88-13d5-a465-fdec-f1c5589dcfeb" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fb2cf4554a8>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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HQoqK2iQdRr1MnTqV84Y/SOt2RYnGsXBuMSMvP5bu3bsnGofIr0VSVUa7AJ+7ewmAmT0K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\n5QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb2cf3aa400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x='attacker_king',data=battles_df,hue='attacker_outcome')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "8120443b-d7fa-7664-5b20-8326ac1496c1", "collapsed": true }, "outputs": [], "source": [ "#convert wins and loss to int\n", "battles_df['attacker_outcome'].replace('win',1,inplace=True)\n", "battles_df['attacker_outcome'].replace('loss',0,inplace=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "98c984bf-1518-a04d-8921-b99dc25645bb" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fb2cf44def0>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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r54lpTwNPR8QdmflI63sRcUpmfr6Nc9wCxKi6i9fR9i/HOp8kqfOqLDs9ts06SdI41M4c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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb2cf46f4e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.barplot(x='year',y='attacker_outcome', data=battles_df)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "ca582b8f-b156-3e2e-cce0-3a89df73f72d" }, "outputs": [], "source": [ "#variations on attacker_outcom based on year are very less hence dropping the column\n", "battles_df.drop('year',axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "ee3a2680-8d11-ce20-65aa-df1866d26a46", "collapsed": true }, "outputs": [], "source": [ "#name of the battle is insignificant to the outcome\n", "battles_df.drop(['name','battle_number','note'],axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "34a8f26c-feb5-3812-b24b-87fe5c59dbdb" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 38 entries, 0 to 37\n", "Data columns (total 21 columns):\n", "attacker_king 36 non-null object\n", "defender_king 35 non-null object\n", "attacker_1 38 non-null object\n", "attacker_2 10 non-null object\n", "attacker_3 3 non-null object\n", "attacker_4 2 non-null object\n", "defender_1 37 non-null object\n", "defender_2 2 non-null object\n", "defender_3 0 non-null float64\n", "defender_4 0 non-null float64\n", "attacker_outcome 37 non-null float64\n", "battle_type 37 non-null object\n", "major_death 37 non-null float64\n", "major_capture 37 non-null float64\n", "attacker_size 24 non-null float64\n", "defender_size 19 non-null float64\n", "attacker_commander 37 non-null object\n", "defender_commander 28 non-null object\n", "summer 37 non-null float64\n", "location 37 non-null object\n", "region 38 non-null object\n", "dtypes: float64(8), object(13)\n", "memory usage: 6.3+ KB\n" ] } ], "source": [ "battles_df.info()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "76c06c1c-0534-5db9-0183-4a39246a47c4" }, "outputs": [ { "data": { "text/plain": [ "0 NaN\n", "1 NaN\n", "2 NaN\n", "3 NaN\n", "4 Tully\n", "Name: attacker_2, dtype: object" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "battles_df.attacker_2.head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "893ad5b4-2b0b-f725-f1a8-7469116ef1a3" }, "outputs": [], "source": [ "pattern = r'[a-z][0-9]'\n", "test = battles_df[~battles_df.attacker_2.isnull()].attacker_2.replace(pattern,1,regex=True)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "f5166161-c82b-6ef0-1ce1-8c7f2b7cee9c", "collapsed": true }, "outputs": [], "source": [ "battles_df.attacker_2.fillna(0,inplace=True)\n", "battles_df.attacker_3.fillna(0,inplace=True)\n", "battles_df.attacker_4.fillna(0,inplace=True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "58de0816-11e9-31e8-7dac-fb32d232f764" }, "outputs": [], "source": [ "def find_attacker_allies(my_data):\n", " col2 = my_data['attacker_2']\n", " col3 = my_data['attacker_3']\n", " col4 = my_data['attacker_4']\n", " number = 0\n", " if col2 != 0:\n", " number = number+1\n", " if col3 != 0:\n", " number = number+1\n", " if col4 != 0:\n", " number = number+1\n", " \n", " return number\n", "\n", "battles_df['attacker_allies'] = battles_df.apply(find_attacker_allies,axis=1)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "90eef185-d06a-47e3-f033-821f84eab700" }, "outputs": [], "source": [ "#drop attacker_2 , attacker_3, attacker_4\n", "battles_df.drop(['attacker_2','attacker_3','attacker_4'],axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "63b51838-f926-2066-3b75-a02694721b30", "collapsed": true }, "outputs": [], "source": [ "battles_df.defender_2.fillna(0,inplace=True)\n", "battles_df.defender_3.fillna(0,inplace=True)\n", "battles_df.defender_4.fillna(0,inplace=True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "f466b9f1-5c69-1213-84e7-89cc0d7837d4", "collapsed": true }, "outputs": [], "source": [ "def find_defender_allies(my_data):\n", " col2 = my_data['defender_2']\n", " col3 = my_data['defender_3']\n", " col4 = my_data['defender_4']\n", " number = 0\n", " if col2 != 0:\n", " number = number+1\n", " if col3 != 0:\n", " number = number+1\n", " if col4 != 0:\n", " number = number+1\n", " \n", " return number\n", "\n", "battles_df['defender_allies'] = battles_df.apply(find_defender_allies,axis=1)\n", "\n", "battles_df.drop(['defender_2','defender_3','defender_4'],axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "94937f01-f5a1-2204-9b99-4b2b1f31bbf6" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>attacker_king</th>\n", " <th>defender_king</th>\n", " <th>attacker_1</th>\n", " <th>defender_1</th>\n", " <th>attacker_outcome</th>\n", " <th>battle_type</th>\n", " <th>major_death</th>\n", " <th>major_capture</th>\n", " <th>attacker_size</th>\n", " <th>defender_size</th>\n", " <th>attacker_commander</th>\n", " <th>defender_commander</th>\n", " <th>summer</th>\n", " <th>location</th>\n", " <th>region</th>\n", " <th>attacker_allies</th>\n", " <th>defender_allies</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Joffrey/Tommen Baratheon</td>\n", " <td>Robb Stark</td>\n", " <td>Lannister</td>\n", " <td>Tully</td>\n", " <td>1.0</td>\n", " <td>pitched battle</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>15000.0</td>\n", " <td>4000.0</td>\n", " <td>Jaime Lannister</td>\n", " <td>Clement Piper, Vance</td>\n", " <td>1.0</td>\n", " <td>Golden Tooth</td>\n", " <td>The Westerlands</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Joffrey/Tommen Baratheon</td>\n", " <td>Robb Stark</td>\n", " <td>Lannister</td>\n", " <td>Baratheon</td>\n", " <td>1.0</td>\n", " <td>ambush</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>NaN</td>\n", " <td>120.0</td>\n", " <td>Gregor Clegane</td>\n", " <td>Beric Dondarrion</td>\n", " <td>1.0</td>\n", " <td>Mummer's Ford</td>\n", " <td>The Riverlands</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Joffrey/Tommen Baratheon</td>\n", " <td>Robb Stark</td>\n", " <td>Lannister</td>\n", " <td>Tully</td>\n", " <td>1.0</td>\n", " <td>pitched battle</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>15000.0</td>\n", " <td>10000.0</td>\n", " <td>Jaime Lannister, Andros Brax</td>\n", " <td>Edmure Tully, Tytos Blackwood</td>\n", " <td>1.0</td>\n", " <td>Riverrun</td>\n", " <td>The Riverlands</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Robb Stark</td>\n", " <td>Joffrey/Tommen Baratheon</td>\n", " <td>Stark</td>\n", " <td>Lannister</td>\n", " <td>0.0</td>\n", " <td>pitched battle</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>18000.0</td>\n", " <td>20000.0</td>\n", " <td>Roose Bolton, Wylis Manderly, Medger Cerwyn, H...</td>\n", " <td>Tywin Lannister, Gregor Clegane, Kevan Lannist...</td>\n", " <td>1.0</td>\n", " <td>Green Fork</td>\n", " <td>The Riverlands</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Robb Stark</td>\n", " <td>Joffrey/Tommen Baratheon</td>\n", " <td>Stark</td>\n", " <td>Lannister</td>\n", " <td>1.0</td>\n", " <td>ambush</td>\n", " <td>1.0</td>\n", " <td>1.0</td>\n", " <td>1875.0</td>\n", " <td>6000.0</td>\n", " <td>Robb Stark, Brynden Tully</td>\n", " <td>Jaime Lannister</td>\n", " <td>1.0</td>\n", " <td>Whispering Wood</td>\n", " <td>The Riverlands</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " attacker_king defender_king attacker_1 defender_1 \\\n", "0 Joffrey/Tommen Baratheon Robb Stark Lannister Tully \n", "1 Joffrey/Tommen Baratheon Robb Stark Lannister Baratheon \n", "2 Joffrey/Tommen Baratheon Robb Stark Lannister Tully \n", "3 Robb Stark Joffrey/Tommen Baratheon Stark Lannister \n", "4 Robb Stark Joffrey/Tommen Baratheon Stark Lannister \n", "\n", " attacker_outcome battle_type major_death major_capture \\\n", "0 1.0 pitched battle 1.0 0.0 \n", "1 1.0 ambush 1.0 0.0 \n", "2 1.0 pitched battle 0.0 1.0 \n", "3 0.0 pitched battle 1.0 1.0 \n", "4 1.0 ambush 1.0 1.0 \n", "\n", " attacker_size defender_size \\\n", "0 15000.0 4000.0 \n", "1 NaN 120.0 \n", "2 15000.0 10000.0 \n", "3 18000.0 20000.0 \n", "4 1875.0 6000.0 \n", "\n", " attacker_commander \\\n", "0 Jaime Lannister \n", "1 Gregor Clegane \n", "2 Jaime Lannister, Andros Brax \n", "3 Roose Bolton, Wylis Manderly, Medger Cerwyn, H... \n", "4 Robb Stark, Brynden Tully \n", "\n", " defender_commander summer location \\\n", "0 Clement Piper, Vance 1.0 Golden Tooth \n", "1 Beric Dondarrion 1.0 Mummer's Ford \n", "2 Edmure Tully, Tytos Blackwood 1.0 Riverrun \n", "3 Tywin Lannister, Gregor Clegane, Kevan Lannist... 1.0 Green Fork \n", "4 Jaime Lannister 1.0 Whispering Wood \n", "\n", " region attacker_allies defender_allies \n", "0 The Westerlands 0 0 \n", "1 The Riverlands 0 0 \n", "2 The Riverlands 0 0 \n", "3 The Riverlands 0 0 \n", "4 The Riverlands 1 0 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "battles_df.head()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "_cell_guid": "b0a9738d-1e46-177f-d47b-511f7d5b2c22" }, "outputs": [], "source": [ "battles_df.attacker_king.fillna('None',inplace=True)\n", "battles_df.defender_king.fillna('None',inplace=True)\n", "battles_df.battle_type.fillna('None',inplace=True)\n", "battles_df.attacker_commander.fillna('None',inplace=True)\n", "battles_df.defender_commander.fillna('None',inplace=True)\n", "battles_df.location.fillna('None',inplace=True)\n", "battles_df.region.fillna('None',inplace=True)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "_cell_guid": "9866fce6-ce46-9efc-21a5-a106ba605df7" }, "outputs": [], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "le = LabelEncoder()\n", "battles_df.attacker_king = le.fit_transform(battles_df.attacker_king)\n", "battles_df.defender_king = le.fit_transform(battles_df.defender_king)\n", "battles_df.battle_type = le.fit_transform(battles_df.battle_type)\n", "battles_df.attacker_commander = le.fit_transform(battles_df.battle_type)\n", "battles_df.defender_commander = le.fit_transform(battles_df.battle_type)\n", "battles_df.location = le.fit_transform(battles_df.battle_type)\n", "battles_df.region = le.fit_transform(battles_df.battle_type)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "_cell_guid": "1994b33c-e376-3dd3-0637-d75efdf80ccc" }, "outputs": [], "source": [ "battles_df.drop(['attacker_1','defender_1'],axis=1,inplace=True)\n", "battles_df.fillna(0,inplace=True)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "_cell_guid": "4e356e20-618a-29f2-b09c-36655b6fc6ae" }, "outputs": [], "source": [ "X = battles_df.drop('attacker_outcome',axis=1)\n", "y = battles_df.attacker_outcome" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "_cell_guid": "3ac51fb0-f367-42b0-465d-c0c53a6fbaa5", "collapsed": true }, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "knn = KNeighborsClassifier()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "_cell_guid": "2159a236-0917-af9d-1d2b-c3874fe7cd4b" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n" ] } ], "source": [ "from sklearn.cross_validation import cross_val_score\n", "scores = cross_val_score(knn,X,y,cv=10,scoring='accuracy')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "_cell_guid": "89c37dbf-46f3-ffb9-3fb0-507c26d3865e" }, "outputs": [ { "data": { "text/plain": [ "0.84499999999999997" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores.mean()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "_cell_guid": "04df341f-ea8f-7f1f-491b-fea6b9923d51" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n" ] } ], "source": [ "k_range = range(1,15)\n", "scores_list = []\n", "for k in k_range:\n", " knn = KNeighborsClassifier(n_neighbors=k)\n", " scores = cross_val_score(knn,X,y,cv=10,scoring='accuracy')\n", " scores_list.append(scores.mean())" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "_cell_guid": "aa6df2a3-d6b4-5803-bf4a-b3ad87eda2dd" }, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "_cell_guid": "5c0cff5c-5c60-12b0-3267-b33e8de1f2e3" }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fb2ca8f1be0>]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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kzxDBUEg3gRER11EARCkrM4Mli+bRfHKAweGxqM9r7YrcB1gBICLuogCIgakr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\ngg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb2cc5f8c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(k_range,scores_list)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "_cell_guid": "49dd310b-6ebf-5504-7d4f-e46c9190bb87" }, "outputs": [ { "data": { "text/plain": [ "0.88500000000000001" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "max(scores_list)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "_cell_guid": "65457c38-b1c2-47d9-ebc6-8804ca80638e" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n" ] }, { "data": { "text/plain": [ "0.85999999999999999" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn = KNeighborsClassifier(n_neighbors=7)\n", "scores = cross_val_score(knn,X,y,cv=10,scoring='accuracy')\n", "scores.mean()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "_cell_guid": "1fb838a9-712a-360b-4aa2-14af08d5f244" }, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression\n", "logreg = LogisticRegression()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "_cell_guid": "ace7ce6e-963a-46b0-5bff-b98b6d2f1787" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:531: Warning: The least populated class in y has only 6 members, which is too few. The minimum number of labels for any class cannot be less than n_folds=10.\n", " % (min_labels, self.n_folds)), Warning)\n" ] }, { "data": { "text/plain": [ "0.85166666666666657" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores = cross_val_score(logreg,X,y,cv=10,scoring='accuracy')\n", "scores.mean()" ] } ], "metadata": { "_change_revision": 471, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/329/329676.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "b40c8937-d7e0-ee0e-0cfc-b34063e3b877" }, "source": [ "##Basic sci-kit-learn training" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "a4df9abc-dcda-4968-984b-52e83e7837a7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training...\n", "Accuracy = 0.980920314254\n" ] } ], "source": [ "import numpy as np \n", "import pandas as pd \n", "import csv\n", "from sklearn.ensemble import RandomForestClassifier\n", "\n", "def munge_data(df):\n", " '''fill in missing values and convert characters to numerical'''\n", " \n", " #transform 'Sex' column to numerical values\n", " df['Sex'] = df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n", "\n", " #fill in nans with median ages for each class\n", " median_ages = np.zeros((2,3))\n", " for i in range(0, 2):\n", " for j in range(0, 3):\n", " median_ages[i,j] = df[(df['Sex'] == i) & \\\n", " (df['Pclass'] == j+1)]['Age'].dropna().median()\n", "\n", " for i in range(0, 2):\n", " for j in range(0, 3):\n", " df.loc[ (df.Age.isnull()) & (df.Sex == i) & (df.Pclass == j+1), \\\n", " 'Age'] = median_ages[i,j]\n", "\n", " #transform 'Embarked' column to numerical\n", " df['Embarked'] = df['Embarked'].dropna().map( {'C': 1, 'S': 2, 'Q': 3} ).astype(int)\n", " \n", " #find mode of 'Embarked' column\n", " mode = df['Embarked'].dropna().mode().astype(int)\n", " \n", " #fill NaNs with mode \n", " df['Embarked'] = df['Embarked'].fillna(mode)\n", "\n", " #drop columns with strings that we can't use. \n", " df = df.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)\n", "\n", " return df.fillna(0)\n", "\n", "#import dataframe\n", "train_df = pd.read_csv('../input/train.csv', header=0)\n", "test_df = pd.read_csv('../input/test.csv', header=0)\n", "\n", "#save ids from test dataset\n", "ids = test_df['PassengerId'].values\n", "\n", "#munge data\n", "train_df = munge_data(train_df)\n", "test_df = munge_data(test_df)\n", "\n", "train_data = train_df.values\n", "test_data = test_df.values\n", "\n", "print('Training...')\n", "rf = RandomForestClassifier(n_estimators=100)\n", "rf = rf.fit( train_data[0::,1::], train_data[0::,0] )\n", "\n", "print(\"Accuracy = \", (rf.predict(train_data[0::,1::])==train_data[0::,0]).mean())\n", "\n", "#print('Predicting...')\n", "#output = forest.predict(test_data).astype(int)\n", "\n", "#predictions_file = open(\"myfirstforest.csv\", \"w\")\n", "#open_file_object = csv.writer(predictions_file)\n", "#open_file_object.writerow([\"PassengerId\",\"Survived\"])\n", "#open_file_object.writerows(zip(ids, output))\n", "#predictions_file.close()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "86a241de-f4a9-47a0-aced-d6917189b24b" }, "source": [ "##Feature importance \n", "Random forests & sci-kit-learn make it very easy to look at feature importance: " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "8dd0707d-f7a5-9ed0-0bf4-9e8da455c2c9" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/matplotlib/figure.py:397: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n", " \"matplotlib is currently using a non-GUI backend, \"\n" ] }, { "data": { "image/png": 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SJEk9MVhJkiT1xGAlSZLUE4OVJElSTwxWkiRJPTFYSZIk9cRgJUmS1BODlSRJUk8MVpIk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\nQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb0ebc01828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "f, ax = plt.subplots(figsize=(10,4))\n", "\n", "bar_placements = range(len(rf.feature_importances_))\n", "ax.bar(bar_placements, rf.feature_importances_)\n", "ax.set_title(\"Feature Importances\")\n", "ax.set_xticks([tick + .5 for tick in bar_placements])\n", "ax.set_xticklabels(train_df.columns[1::])\n", "\n", "f.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "0242b584-33f2-b408-978c-a01704743d1c" }, "source": [ "We see that sex, age, and fare are important features. Interestingly, fare is an important predictor but class is not as important as may be naively expected. " ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2a426d51-7db5-f24b-ea16-af9e2d8aa36e" }, "source": [ "##Cuseross validation \n", "To more accurately evaluate different hyperparameters, lets use k-fold validation" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "9c063eb6-15c3-6c1b-4112-2d8085aecf36" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "0.799102132435\n" ] } ], "source": [ "from sklearn import cross_validation\n", "\n", "#default is k=3\n", "scores = cross_validation.cross_val_score(rf, train_data[0::,1::], train_data[0::,0]) \n", "print(scores.mean())" ] } ], "metadata": { "_change_revision": 84, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/329/329711.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "7ebebef4-a4cb-8af8-105f-8b5a8b141e09" }, "source": [ "Let's do some exploratory data analysis on the people data set, and see if it makes sense to use any decomposition techniques. I think there could be a couple good reasons why we might want to do this:\n", "\n", "* If a lot of the characteristics of people are interdependent variables, we can consolidate them into single features so as to not muddy our final classifier inputs with repeat information.\n", "* There's potential for discovering latent features that are only evident by looking at multiple features together.\n", "* If you're like me, you might be pretty limited on computing resources and want to shrink the people data set before merging it in." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "c181ed5f-beea-4dcb-7fc5-d6393fa30099" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " people_id char_1 group_1 char_2 date char_3 char_4 \\\n", "0 ppl_100 type 2 group 17304 type 2 2021-06-29 type 5 type 5 \n", "1 ppl_100002 type 2 group 8688 type 3 2021-01-06 type 28 type 9 \n", "2 ppl_100003 type 2 group 33592 type 3 2022-06-10 type 4 type 8 \n", "3 ppl_100004 type 2 group 22593 type 3 2022-07-20 type 40 type 25 \n", "4 ppl_100006 type 2 group 6534 type 3 2022-07-27 type 40 type 25 \n", "\n", " char_5 char_6 char_7 ... char_29 char_30 char_31 char_32 char_33 \\\n", "0 type 5 type 3 type 11 ... False True True False False \n", "1 type 5 type 3 type 11 ... False True True True True \n", "2 type 5 type 2 type 5 ... False False True True True \n", "3 type 9 type 4 type 16 ... True True True True True \n", "4 type 9 type 3 type 8 ... False False True False False \n", "\n", " char_34 char_35 char_36 char_37 char_38 \n", "0 True True True False 36 \n", "1 True True True False 76 \n", "2 True False True True 99 \n", "3 True True True True 76 \n", "4 False True True False 84 \n", "\n", "[5 rows x 41 columns]\n" ] } ], "source": [ "import pandas as pd\n", "\n", "# Import the dataset\n", "df = pd.read_csv(\"../input/people.csv\")\n", "\n", "# Print the first five rows\n", "print(df.head())" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9c4c06c8-fc76-38c1-46a9-6904dd3fdc98" }, "source": [ "It looks like all the characteristics are denoted with a \"char_\" prefix. Let's use that to make sure that we don't drag along extra columns." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "7571f3b1-cdef-b1d5-3011-da3ee5348f4a" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 22, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/329/329717.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "7ebebef4-a4cb-8af8-105f-8b5a8b141e09" }, "source": [ "Let's do some exploratory data analysis on the people data set, and see if it makes sense to use any decomposition techniques. I think there could be a couple good reasons why we might want to do this:\n", "\n", "* If a lot of the characteristics of people are interdependent variables, we can consolidate them into single features so as to not muddy our final classifier inputs with repeat information.\n", "* There's potential for discovering latent features that are only evident by looking at multiple features together.\n", "* If you're like me, you might be pretty limited on computing resources and want to shrink the people data set before merging it in." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "c181ed5f-beea-4dcb-7fc5-d6393fa30099" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " people_id char_1 group_1 char_2 date char_3 char_4 \\\n", "0 ppl_100 type 2 group 17304 type 2 2021-06-29 type 5 type 5 \n", "1 ppl_100002 type 2 group 8688 type 3 2021-01-06 type 28 type 9 \n", "2 ppl_100003 type 2 group 33592 type 3 2022-06-10 type 4 type 8 \n", "3 ppl_100004 type 2 group 22593 type 3 2022-07-20 type 40 type 25 \n", "4 ppl_100006 type 2 group 6534 type 3 2022-07-27 type 40 type 25 \n", "\n", " char_5 char_6 char_7 ... char_29 char_30 char_31 char_32 char_33 \\\n", "0 type 5 type 3 type 11 ... False True True False False \n", "1 type 5 type 3 type 11 ... False True True True True \n", "2 type 5 type 2 type 5 ... False False True True True \n", "3 type 9 type 4 type 16 ... True True True True True \n", "4 type 9 type 3 type 8 ... False False True False False \n", "\n", " char_34 char_35 char_36 char_37 char_38 \n", "0 True True True False 36 \n", "1 True True True False 76 \n", "2 True False True True 99 \n", "3 True True True True 76 \n", "4 False True True False 84 \n", "\n", "[5 rows x 41 columns]\n" ] } ], "source": [ "import pandas as pd\n", "\n", "# Import the dataset\n", "df = pd.read_csv(\"../input/people.csv\")\n", "\n", "# Print the first five rows\n", "print(df.head())" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9c4c06c8-fc76-38c1-46a9-6904dd3fdc98" }, "source": [ "It looks like all the characteristics are denoted with a \"char_\" prefix. Let's use that to make sure that we don't drag along extra columns. Then we'll run a matrix of chi squared tests to get an idea of which features might be interdependent." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "7571f3b1-cdef-b1d5-3011-da3ee5348f4a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "It looks like 100.0% of the characteristics might be related to one another.\n" ] } ], "source": [ "from scipy.stats import chisquare\n", "\n", "# Create a list of characteristics\n", "chars = [i for i in df.columns.values if \"char_\" in i]\n", "\n", "# Create an empty list for appending flagged features\n", "flags = []\n", "\n", "# For each feature summarize frequencies of each other feature\n", "for feat in df[chars]:\n", " group = df[chars].groupby(feat)\n", " for otherfeat in df[chars].drop(feat, axis=1):\n", " summary = group[otherfeat].count()\n", " \n", " # Run a chi squared test on the frequencies, and check if the p-value is less than 0.05\n", " if chisquare(summary)[1] < 0.05:\n", " \n", " # If so, flag both features\n", " flags.append(feat)\n", " flags.append(otherfeat)\n", "\n", "# Remove duplicates by converting to a set at the end\n", "flags = set(flags)\n", "\n", "print(\"It looks like {}% of the characteristics might be related to one another.\".format(len(flags)/len(chars)*100))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "088f9230-6ec7-6aec-da77-014b7d487409" }, "source": [ "Wow, 100%. Hopefully someone reviewing this can highlight if I implemented those tests wrong, or if chi squared wasn't the right test of choice. At any rate, I'm going to proceed on the assumption that we should definitely be using some decomposition techniques on the data set, if only just to reduce the repeated information. Let's use the scikit-learn implementation of PCA. Some success with PCA would confirm that the chi squared tests were useful." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "1b927b29-a816-90c4-f4d7-382c28eb3d33" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 242, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/329/329725.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "aba53a01-cc53-a04f-5cfc-5ca644de275b" }, "source": [ "Using Trueskill to compute the 2016 kitefoil rankings" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "5f9e29dd-c831-3ff9-0d70-0110ab0ce459" }, "outputs": [], "source": [ "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import numpy as np\n", "import trueskill as ts" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "_cell_guid": "b92f7a60-28c5-c550-18d9-b153fa29c020" }, "outputs": [], "source": [ "def cleanResults(raceColumns,dfResultsTemp,appendScore):\n", " for raceCol in raceColumns:\n", " \n", " dfResultsTemp.index = dfResultsTemp.index.str.replace(r\"(\\w)([A-Z])\", r\"\\1 \\2\")\n", " dfResultsTemp.index = dfResultsTemp.index.str.title()\n", " dfResultsTemp.index = dfResultsTemp.index.str.replace('\\([A-Z\\ 0-9]*\\)','')\n", " dfResultsTemp.index = dfResultsTemp.index.str.strip()\n", " dfResultsTemp.index = dfResultsTemp.index.str.replace('Riccardo Andrea Leccese','Rikki Leccese')\n", " \n", " dfResultsTemp[raceCol] = dfResultsTemp[raceCol].astype(str)\n", " dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('^DNF$',str(len(dfResults)+1))\n", " dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('\\(|\\)|UFD|DNF|RET|SCP|RDG|RCT|DCT|DNS-[0-9]*|DNC-[0-9]*|OCS-[0-9]*|[0-9\\.]*DNC|-|\\/','')\n", " dfResultsTemp[raceCol] = pd.to_numeric(dfResultsTemp[raceCol]) \n", " dfResultsTemp[raceCol] = dfResultsTemp[raceCol] + appendScore\n", " \n", " return dfResultsTemp\n", " " ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "_cell_guid": "2a9f22ed-cac9-a488-8ef6-db4ae47d7e82" }, "outputs": [], "source": [ "def mergeResults(raceColumns,raceName,dfResultsTemp,dfResults):\n", " for raceCol in raceColumns:\n", " raceIndex = raceName + '-' + raceCol \n", " dfResultsTemp[raceIndex] = dfResultsTemp[raceCol]\n", " del(dfResultsTemp[raceCol])\n", " dfResults = pd.merge(dfResults,dfResultsTemp[[raceIndex]],left_index=True,right_index=True,how='outer')\n", " return dfResults" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "_cell_guid": "2dbb7f1b-3bd0-f7a3-2424-d90e3b2f51ef" }, "outputs": [ { "ename": "NameError", "evalue": "name 'pd' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-3-18c980fad491> in <module>()\n----> 1 dfResults = pd.DataFrame()\n", "NameError: name 'pd' is not defined" ] } ], "source": [ "dfResults = pd.DataFrame()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "_cell_guid": "07ea7b0f-96ad-d20c-3349-446934e396c4" }, "outputs": [ { "ename": "NameError", "evalue": "name 'pd' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-4-505102b7cbf0> in <module>()\n 3 raceColumns = ['Q2','R1','R2','R3','R4','R5','R6']\n 4 \n----> 5 dfResultsTempGold = pd.read_csv('../input/'+raceName+ '-Gold.csv')\n 6 dfResultsTempGold = dfResultsTempGold.set_index(dfResultsTempGold['Name'] + ' ' + dfResultsTempGold['LastName'])\n 7 dfResultsTempGold = cleanResults(raceColumns,dfResultsTempGold,0)\n", "NameError: name 'pd' is not defined" ] }, { "ename": "AttributeError", "evalue": "'StringMethods' object has no attribute 'trim'", "output_type": "error", "traceback": [ "", "AttributeErrorTraceback (most recent call last)", "<ipython-input-34-505102b7cbf0> in <module>()\n 5 dfResultsTempGold = pd.read_csv('../input/'+raceName+ '-Gold.csv')\n 6 dfResultsTempGold = dfResultsTempGold.set_index(dfResultsTempGold['Name'] + ' ' + dfResultsTempGold['LastName'])\n----> 7 dfResultsTempGold = cleanResults(raceColumns,dfResultsTempGold,0)\n 8 \n 9 dfResultsTempSilver = pd.read_csv('../input/'+raceName+ '-Silver.csv')\n", "<ipython-input-31-a74caa3db1c9> in cleanResults(raceColumns, dfResultsTemp, appendScore)\n 5 dfResultsTemp.index = dfResultsTemp.index.str.title()\n 6 dfResultsTemp.index = dfResultsTemp.index.str.replace('\\([A-Z\\ 0-9]*\\)','')\n----> 7 dfResultsTemp.index = dfResultsTemp.index.str.trim()\n 8 dfResultsTemp.index = dfResultsTemp.index.str.replace('Riccardo Andrea Leccese','Rikki Leccese')\n 9 \n", "AttributeError: 'StringMethods' object has no attribute 'trim'" ] } ], "source": [ "##Load LaVentana Results\n", "raceName = '20160323-LaVentana-HydrofoilProTour'\n", "raceColumns = ['Q2','R1','R2','R3','R4','R5','R6']\n", "\n", "dfResultsTempGold = pd.read_csv('../input/'+raceName+ '-Gold.csv')\n", "dfResultsTempGold = dfResultsTempGold.set_index(dfResultsTempGold['Name'] + ' ' + dfResultsTempGold['LastName'])\n", "dfResultsTempGold = cleanResults(raceColumns,dfResultsTempGold,0)\n", "\n", "dfResultsTempSilver = pd.read_csv('../input/'+raceName+ '-Silver.csv')\n", "dfResultsTempSilver = dfResultsTempSilver.set_index(dfResultsTempSilver['Name'] + ' ' + dfResultsTempSilver['LastName'])\n", "dfResultsTempSilver = cleanResults(raceColumns,dfResultsTempSilver,len(dfResultsTempGold))\n", "\n", "dfResultsTemp = dfResultsTempGold.append(dfResultsTempSilver)\n", "dfResults = mergeResults(raceColumns,raceName,dfResultsTemp,dfResults)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "_cell_guid": "753fbde0-cc3f-fe11-b7d8-a0bbf8db2e51" }, "outputs": [ { "ename": "NameError", "evalue": "name 'ker' is not defined", "output_type": "error", "traceback": [ "", "NameErrorTraceback (most recent call last)", "<ipython-input-10-8b29e68ef287> in <module>()\n----> 1 ker##Load Italy results\n 2 raceName = '20160717-Gizzeria-IKAGoldCup'\n 3 \n 4 \n 5 \n", "NameError: name 'ker' is not defined" ] }, { "ename": "AttributeError", "evalue": "'StringMethods' object has no attribute 'trim'", "output_type": "error", "traceback": [ "", "AttributeErrorTraceback (most recent call last)", "<ipython-input-35-a3c3c44d0526> in <module>()\n 7 dfResultsTempGold = pd.read_csv('../input/'+raceName+ '-Gold.csv')\n 8 dfResultsTempGold = dfResultsTempGold.set_index(dfResultsTempGold['Name'])\n----> 9 dfResultsTempGold = cleanResults(raceColumns,dfResultsTempGold,0)\n 10 \n 11 raceColumns = ['CF 2','F 1','F 2','F 3','F 4','F 5','F 6','F 8']\n", "<ipython-input-31-a74caa3db1c9> in cleanResults(raceColumns, dfResultsTemp, appendScore)\n 5 dfResultsTemp.index = dfResultsTemp.index.str.title()\n 6 dfResultsTemp.index = dfResultsTemp.index.str.replace('\\([A-Z\\ 0-9]*\\)','')\n----> 7 dfResultsTemp.index = dfResultsTemp.index.str.trim()\n 8 dfResultsTemp.index = dfResultsTemp.index.str.replace('Riccardo Andrea Leccese','Rikki Leccese')\n 9 \n", "AttributeError: 'StringMethods' object has no attribute 'trim'" ] } ], "source": [ "##Load Italy results\n", "raceName = '20160717-Gizzeria-IKAGoldCup'\n", "\n", "\n", "\n", "raceColumns = ['CF 2','F 1','F 2','F 3','F 4','F 5','F 6','F 7','F 8',\t'F 9','F 10']\n", "dfResultsTempGold = pd.read_csv('../input/'+raceName+ '-Gold.csv')\n", "dfResultsTempGold = dfResultsTempGold.set_index(dfResultsTempGold['Name'])\n", "dfResultsTempGold = cleanResults(raceColumns,dfResultsTempGold,0)\n", "\n", "raceColumns = ['CF 2','F 1','F 2','F 3','F 4','F 5','F 6','F 8']\n", "dfResultsTempSilver = pd.read_csv('../input/'+raceName+ '-Silver.csv')\n", "dfResultsTempSilver = dfResultsTempSilver.set_index(dfResultsTempSilver['Name'])\n", "dfResultsTempSilver = cleanResults(raceColumns,dfResultsTempSilver,len(dfResultsTempGold))\n", "\n", "raceColumns = ['CF 2','F 1','F 2','F 3','F 4','F 5','F 6']\n", "dfResultsTemp = dfResultsTempGold.append(dfResultsTempSilver)\n", "\n", "dfResultsTempBronze = pd.read_csv('../input/'+raceName+ '-Bronze.csv',encoding = \"ISO-8859-1\")\n", "dfResultsTempBronze = dfResultsTempBronze.set_index(dfResultsTempBronze['Name'])\n", "dfResultsTempBronze = cleanResults(raceColumns,dfResultsTempBronze,len(dfResultsTemp))\n", "\n", "dfResultsTemp = dfResultsTemp.append(dfResultsTempBronze)\n", "\n", "dfResults = mergeResults(raceColumns,raceName,dfResultsTemp,dfResults)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "_cell_guid": "44cf6067-3517-57d3-285d-a4526a161fe9" }, "outputs": [ { "ename": "AttributeError", "evalue": "'StringMethods' object has no attribute 'trim'", "output_type": "error", "traceback": [ "", "AttributeErrorTraceback (most recent call last)", "<ipython-input-36-7afa601a0238> in <module>()\n 5 raceColumns = ['R1','R2','R3','R4','R5','R6','R7','R8','R9','R10','R11','R12','R13','R14','R15','R16']\n 6 \n----> 7 dfResultsTemp = cleanResults(raceColumns,dfResultsTemp,0)\n 8 dfResults = mergeResults(raceColumns,raceName,dfResultsTemp,dfResults)\n", "<ipython-input-31-a74caa3db1c9> in cleanResults(raceColumns, dfResultsTemp, appendScore)\n 5 dfResultsTemp.index = dfResultsTemp.index.str.title()\n 6 dfResultsTemp.index = dfResultsTemp.index.str.replace('\\([A-Z\\ 0-9]*\\)','')\n----> 7 dfResultsTemp.index = dfResultsTemp.index.str.trim()\n 8 dfResultsTemp.index = dfResultsTemp.index.str.replace('Riccardo Andrea Leccese','Rikki Leccese')\n 9 \n", "AttributeError: 'StringMethods' object has no attribute 'trim'" ] } ], "source": [ "##Load SF results\n", "raceName = '20160807-SanFracisco-HydrofoilProTour'\n", "dfResultsTemp = pd.read_csv('../input/' + raceName + '.csv')\n", "dfResultsTemp = dfResultsTemp.set_index(dfResultsTemp['Name'])\n", "raceColumns = ['R1','R2','R3','R4','R5','R6','R7','R8','R9','R10','R11','R12','R13','R14','R15','R16']\n", "\n", "dfResultsTemp = cleanResults(raceColumns,dfResultsTemp,0)\n", "dfResults = mergeResults(raceColumns,raceName,dfResultsTemp,dfResults)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "_cell_guid": "5ad0ad2e-1caa-6158-0b13-6ee419b081a2" }, "outputs": [], "source": [ "def doRating(dfResults):\n", " columns = ['Name','mu_minus_3sigma','Rating']\n", " dfRatings = pd.DataFrame(columns=columns,index=dfResults.index)\n", " dfRatings['Rating'] = pd.Series(np.repeat(ts.Rating(),len(dfRatings))).T.values.tolist()\n", "\n", " for raceCol in dfResults:\n", " competed = dfRatings.index.isin(dfResults.index[dfResults[raceCol].notnull()])\n", " rating_group = list(zip(dfRatings['Rating'][competed].T.values.tolist()))\n", " dfRatings['Rating'][competed] = ts.rate(rating_group, ranks=dfResults[raceCol][competed].T.values.tolist())\n", " \n", " dfRatings = pd.DataFrame(dfRatings) #convert to dataframe\n", "\n", " dfRatings['mu_minus_3sigma'] = pd.Series(np.repeat(0.0,len(dfRatings))) #calculate mu - 3 x sigma: MSFT convention\n", "\n", " for i in range(0,len(dfRatings['Rating'])):\n", " dfRatings['mu_minus_3sigma'][i] = float(dfRatings['Rating'][i].mu) - 3 * float(dfRatings['Rating'][i].sigma) \n", " \n", " dfRatings['Name'] = dfRatings.index\n", " dfRatings.index = dfRatings['mu_minus_3sigma'].rank(ascending=False).astype(int) #set index to ranking\n", " dfRatings.index.names = ['Rank']\n", "\n", " \n", " return dfRatings.sort('mu_minus_3sigma',ascending=False) " ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "_cell_guid": "b5de9ec0-2853-88f2-5eed-577385c529d4" }, "outputs": [ { "data": { "text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Name</th>\n <th>mu_minus_3sigma</th>\n <th>Rating</th>\n </tr>\n <tr>\n <th>Rank</th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>Axel Mazella</td>\n <td>52.782910</td>\n <td>trueskill.Rating(mu=58.246, sigma=1.821)</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Maxime Nocher</td>\n <td>52.469589</td>\n <td>trueskill.Rating(mu=57.700, sigma=1.744)</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Johnny Heineken</td>\n <td>52.113835</td>\n <td>trueskill.Rating(mu=55.975, sigma=1.287)</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Nico Parlier</td>\n <td>51.281797</td>\n <td>trueskill.Rating(mu=55.237, sigma=1.318)</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Oliver Bridge</td>\n <td>49.623996</td>\n <td>trueskill.Rating(mu=54.721, sigma=1.699)</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Rikki Leccese</td>\n <td>48.102821</td>\n <td>trueskill.Rating(mu=52.039, sigma=1.312)</td>\n </tr>\n <tr>\n <th>7</th>\n <td>Florian Gruber</td>\n <td>47.702761</td>\n <td>trueskill.Rating(mu=52.657, sigma=1.651)</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Joey Pasquali</td>\n <td>46.402454</td>\n <td>trueskill.Rating(mu=50.076, sigma=1.224)</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Florian Trittel Paul</td>\n <td>46.338240</td>\n <td>trueskill.Rating(mu=51.666, sigma=1.776)</td>\n </tr>\n <tr>\n <th>10</th>\n <td>Bruno Lobo</td>\n <td>43.350173</td>\n <td>trueskill.Rating(mu=48.227, sigma=1.626)</td>\n </tr>\n <tr>\n <th>11</th>\n <td>Nico Landauer</td>\n <td>42.972590</td>\n <td>trueskill.Rating(mu=46.506, sigma=1.178)</td>\n </tr>\n <tr>\n <th>12</th>\n <td>Titouan Galea</td>\n <td>42.256709</td>\n <td>trueskill.Rating(mu=47.122, sigma=1.622)</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Adam Withington</td>\n <td>41.825411</td>\n <td>trueskill.Rating(mu=45.177, sigma=1.117)</td>\n </tr>\n <tr>\n <th>14</th>\n <td>Theo De Ramecourt</td>\n <td>41.373653</td>\n <td>trueskill.Rating(mu=46.230, sigma=1.619)</td>\n </tr>\n <tr>\n <th>15</th>\n <td>Blazej Ozog</td>\n <td>41.341783</td>\n <td>trueskill.Rating(mu=46.190, sigma=1.616)</td>\n </tr>\n <tr>\n <th>16</th>\n <td>Stefaans Viljoen</td>\n <td>40.630671</td>\n <td>trueskill.Rating(mu=44.009, sigma=1.126)</td>\n </tr>\n <tr>\n <th>17</th>\n <td>Zack Marks</td>\n <td>39.945728</td>\n <td>trueskill.Rating(mu=43.267, sigma=1.107)</td>\n </tr>\n <tr>\n <th>18</th>\n <td>Guy Bridge</td>\n <td>39.372714</td>\n <td>trueskill.Rating(mu=44.215, sigma=1.614)</td>\n </tr>\n <tr>\n <th>19</th>\n <td>Adrian Geislinger</td>\n <td>39.084042</td>\n <td>trueskill.Rating(mu=43.928, sigma=1.615)</td>\n </tr>\n <tr>\n <th>20</th>\n <td>Martin Dolenc</td>\n <td>38.406937</td>\n <td>trueskill.Rating(mu=43.242, sigma=1.612)</td>\n </tr>\n <tr>\n <th>21</th>\n <td>Toni Vodisek</td>\n <td>38.103248</td>\n <td>trueskill.Rating(mu=40.913, sigma=0.936)</td>\n </tr>\n <tr>\n <th>22</th>\n <td>Mario Calbucci</td>\n <td>37.688634</td>\n <td>trueskill.Rating(mu=42.909, sigma=1.740)</td>\n </tr>\n <tr>\n <th>23</th>\n <td>Andrea Beverino</td>\n <td>36.833056</td>\n <td>trueskill.Rating(mu=42.028, sigma=1.732)</td>\n </tr>\n <tr>\n <th>24</th>\n <td>Benni Boelli</td>\n <td>36.556093</td>\n <td>trueskill.Rating(mu=41.381, sigma=1.608)</td>\n </tr>\n <tr>\n <th>25</th>\n <td>Riley Gibbs</td>\n <td>36.126334</td>\n <td>trueskill.Rating(mu=39.430, sigma=1.101)</td>\n </tr>\n <tr>\n <th>26</th>\n <td>Theo Lhostis</td>\n <td>34.039392</td>\n <td>trueskill.Rating(mu=39.224, sigma=1.728)</td>\n </tr>\n <tr>\n <th>27</th>\n <td>Seth Besse</td>\n <td>33.961681</td>\n <td>trueskill.Rating(mu=37.227, sigma=1.088)</td>\n </tr>\n <tr>\n <th>28</th>\n <td>Denis Taradin</td>\n <td>32.820932</td>\n <td>trueskill.Rating(mu=37.633, sigma=1.604)</td>\n </tr>\n <tr>\n <th>29</th>\n <td>Jean De Falbaire</td>\n <td>32.475439</td>\n <td>trueskill.Rating(mu=37.655, sigma=1.727)</td>\n </tr>\n <tr>\n <th>30</th>\n <td>Jacob Olivier</td>\n <td>32.048910</td>\n <td>trueskill.Rating(mu=35.315, sigma=1.089)</td>\n </tr>\n <tr>\n <th>31</th>\n <td>Ejder Ginyol</td>\n <td>31.709638</td>\n <td>trueskill.Rating(mu=36.522, sigma=1.604)</td>\n </tr>\n <tr>\n <th>32</th>\n <td>Xantos Villegas</td>\n <td>31.673116</td>\n <td>trueskill.Rating(mu=34.946, sigma=1.091)</td>\n </tr>\n <tr>\n <th>33</th>\n <td>Jon Modica</td>\n <td>30.182219</td>\n <td>trueskill.Rating(mu=33.442, sigma=1.087)</td>\n </tr>\n <tr>\n <th>34</th>\n <td>Andy Hansen</td>\n <td>29.160374</td>\n <td>trueskill.Rating(mu=32.411, sigma=1.084)</td>\n </tr>\n <tr>\n <th>35</th>\n <td>Ivan Doronin</td>\n <td>28.432826</td>\n <td>trueskill.Rating(mu=33.240, sigma=1.602)</td>\n </tr>\n <tr>\n <th>36</th>\n <td>Kieran Le Borgne</td>\n <td>27.919134</td>\n <td>trueskill.Rating(mu=31.162, sigma=1.081)</td>\n </tr>\n <tr>\n <th>37</th>\n <td>Kai Calder</td>\n <td>27.784493</td>\n <td>trueskill.Rating(mu=31.034, sigma=1.083)</td>\n </tr>\n <tr>\n <th>38</th>\n <td>Ty Reed</td>\n <td>26.954240</td>\n <td>trueskill.Rating(mu=30.221, sigma=1.089)</td>\n </tr>\n <tr>\n <th>39</th>\n <td>Daniela Moroz</td>\n <td>26.773290</td>\n <td>trueskill.Rating(mu=30.020, sigma=1.082)</td>\n </tr>\n <tr>\n <th>40</th>\n <td>Alejandro Climent Hernandez</td>\n <td>25.961045</td>\n <td>trueskill.Rating(mu=30.765, sigma=1.601)</td>\n </tr>\n <tr>\n <th>41</th>\n <td>Marvin Baumeisterschoenian</td>\n <td>25.621861</td>\n <td>trueskill.Rating(mu=28.884, sigma=1.088)</td>\n </tr>\n <tr>\n <th>42</th>\n <td>Benjamin Petit</td>\n <td>25.287151</td>\n <td>trueskill.Rating(mu=28.529, sigma=1.081)</td>\n </tr>\n <tr>\n <th>43</th>\n <td>Alex Caizergues</td>\n <td>24.848694</td>\n <td>trueskill.Rating(mu=28.094, sigma=1.082)</td>\n </tr>\n <tr>\n <th>44</th>\n <td>James Johnson</td>\n <td>24.710560</td>\n <td>trueskill.Rating(mu=29.513, sigma=1.601)</td>\n </tr>\n <tr>\n <th>45</th>\n <td>Jordan Girdis</td>\n <td>24.553943</td>\n <td>trueskill.Rating(mu=27.794, sigma=1.080)</td>\n </tr>\n <tr>\n <th>46</th>\n <td>Oliver Hansen</td>\n <td>24.366688</td>\n <td>trueskill.Rating(mu=29.534, sigma=1.723)</td>\n </tr>\n <tr>\n <th>47</th>\n <td>Sam Bullock</td>\n <td>24.293982</td>\n <td>trueskill.Rating(mu=27.542, sigma=1.083)</td>\n </tr>\n <tr>\n <th>48</th>\n <td>Peter Martel</td>\n <td>24.214502</td>\n <td>trueskill.Rating(mu=27.455, sigma=1.080)</td>\n </tr>\n <tr>\n <th>49</th>\n <td>Chip Wasson</td>\n <td>23.478939</td>\n <td>trueskill.Rating(mu=26.817, sigma=1.113)</td>\n </tr>\n <tr>\n <th>50</th>\n <td>Tomek Glazik</td>\n <td>23.398876</td>\n <td>trueskill.Rating(mu=28.202, sigma=1.601)</td>\n </tr>\n <tr>\n <th>51</th>\n <td>Tomek Janiak</td>\n <td>23.015412</td>\n <td>trueskill.Rating(mu=27.817, sigma=1.601)</td>\n </tr>\n <tr>\n <th>52</th>\n <td>Alexander Bachev</td>\n <td>22.358357</td>\n <td>trueskill.Rating(mu=27.525, sigma=1.722)</td>\n </tr>\n <tr>\n <th>53</th>\n <td>Giulio Chiti</td>\n <td>22.207118</td>\n <td>trueskill.Rating(mu=27.008, sigma=1.600)</td>\n </tr>\n <tr>\n <th>54</th>\n <td>Simone Vannucci</td>\n <td>20.830259</td>\n <td>trueskill.Rating(mu=25.633, sigma=1.601)</td>\n </tr>\n <tr>\n <th>55</th>\n <td>William Morris</td>\n <td>20.523155</td>\n <td>trueskill.Rating(mu=23.763, sigma=1.080)</td>\n </tr>\n <tr>\n <th>56</th>\n <td>Mani Bisschops</td>\n <td>20.087741</td>\n <td>trueskill.Rating(mu=23.534, sigma=1.149)</td>\n </tr>\n <tr>\n <th>57</th>\n <td>Will James</td>\n <td>19.556568</td>\n <td>trueskill.Rating(mu=22.810, sigma=1.084)</td>\n </tr>\n <tr>\n <th>58</th>\n <td>Thomas Lombardo</td>\n <td>18.844386</td>\n <td>trueskill.Rating(mu=23.647, sigma=1.601)</td>\n </tr>\n <tr>\n <th>59</th>\n <td>Igor Malenko</td>\n <td>18.489974</td>\n <td>trueskill.Rating(mu=23.660, sigma=1.723)</td>\n </tr>\n <tr>\n <th>60</th>\n <td>Amil Kabil</td>\n <td>18.016213</td>\n <td>trueskill.Rating(mu=21.255, sigma=1.080)</td>\n </tr>\n <tr>\n <th>61</th>\n <td>Anthony Picard</td>\n <td>17.464017</td>\n <td>trueskill.Rating(mu=22.268, sigma=1.601)</td>\n </tr>\n <tr>\n <th>62</th>\n <td>Mike Martin</td>\n <td>17.128123</td>\n <td>trueskill.Rating(mu=24.289, sigma=2.387)</td>\n </tr>\n <tr>\n <th>63</th>\n <td>Ariel Poler</td>\n <td>16.255642</td>\n <td>trueskill.Rating(mu=19.597, sigma=1.114)</td>\n </tr>\n <tr>\n <th>64</th>\n <td>Sonny Swords</td>\n <td>16.155888</td>\n <td>trueskill.Rating(mu=19.400, sigma=1.081)</td>\n </tr>\n <tr>\n <th>65</th>\n <td>Elena Kalinina</td>\n <td>15.844955</td>\n <td>trueskill.Rating(mu=20.651, sigma=1.602)</td>\n </tr>\n <tr>\n <th>66</th>\n <td>Valerio Venturi</td>\n <td>15.226025</td>\n <td>trueskill.Rating(mu=20.036, sigma=1.603)</td>\n </tr>\n <tr>\n <th>67</th>\n <td>Gunnar Biniasch</td>\n <td>14.768092</td>\n <td>trueskill.Rating(mu=19.943, sigma=1.725)</td>\n </tr>\n <tr>\n <th>68</th>\n <td>Michael Gilbreath</td>\n <td>14.414316</td>\n <td>trueskill.Rating(mu=17.756, sigma=1.114)</td>\n </tr>\n <tr>\n <th>69</th>\n <td>Victor Bachichet</td>\n <td>13.811583</td>\n <td>trueskill.Rating(mu=19.464, sigma=1.884)</td>\n </tr>\n <tr>\n <th>70</th>\n <td>Alexia Fancelli</td>\n <td>13.284965</td>\n <td>trueskill.Rating(mu=18.097, sigma=1.604)</td>\n </tr>\n <tr>\n <th>71</th>\n <td>Will Cyr</td>\n <td>13.051766</td>\n <td>trueskill.Rating(mu=16.506, sigma=1.152)</td>\n </tr>\n <tr>\n <th>72</th>\n <td>Felix Louis N'Jai</td>\n <td>12.891101</td>\n <td>trueskill.Rating(mu=16.148, sigma=1.085)</td>\n </tr>\n <tr>\n <th>73</th>\n <td>Anthony Goldbloom</td>\n <td>12.406005</td>\n <td>trueskill.Rating(mu=15.655, sigma=1.083)</td>\n </tr>\n <tr>\n <th>74</th>\n <td>Fraser Novakowski</td>\n <td>11.569488</td>\n <td>trueskill.Rating(mu=14.824, sigma=1.085)</td>\n </tr>\n <tr>\n <th>75</th>\n <td>Jade O'Connor</td>\n <td>11.404136</td>\n <td>trueskill.Rating(mu=16.224, sigma=1.607)</td>\n </tr>\n <tr>\n <th>76</th>\n <td>Ben Turner</td>\n <td>11.306227</td>\n <td>trueskill.Rating(mu=14.559, sigma=1.084)</td>\n </tr>\n <tr>\n <th>77</th>\n <td>Alexis Delquie</td>\n <td>10.926072</td>\n <td>trueskill.Rating(mu=17.197, sigma=2.090)</td>\n </tr>\n <tr>\n <th>78</th>\n <td>Roman Lyubimtsev</td>\n <td>10.341758</td>\n <td>trueskill.Rating(mu=16.616, sigma=2.092)</td>\n </tr>\n <tr>\n <th>79</th>\n <td>Craig Rawson</td>\n <td>9.622074</td>\n <td>trueskill.Rating(mu=13.094, sigma=1.157)</td>\n </tr>\n <tr>\n <th>80</th>\n <td>Loic Le Meur</td>\n <td>9.590268</td>\n <td>trueskill.Rating(mu=12.957, sigma=1.122)</td>\n </tr>\n <tr>\n <th>81</th>\n <td>Chris Brent</td>\n <td>9.212304</td>\n <td>trueskill.Rating(mu=12.478, sigma=1.089)</td>\n </tr>\n <tr>\n <th>82</th>\n <td>Kevin Growney</td>\n <td>8.290331</td>\n <td>trueskill.Rating(mu=11.677, sigma=1.129)</td>\n </tr>\n <tr>\n <th>83</th>\n <td>Connor Bainbridge</td>\n <td>7.605003</td>\n <td>trueskill.Rating(mu=12.440, sigma=1.612)</td>\n </tr>\n <tr>\n <th>84</th>\n <td>Steve Bodner</td>\n <td>7.184998</td>\n <td>trueskill.Rating(mu=11.145, sigma=1.320)</td>\n </tr>\n <tr>\n <th>85</th>\n <td>John Gomes</td>\n <td>6.744754</td>\n <td>trueskill.Rating(mu=10.662, sigma=1.306)</td>\n </tr>\n <tr>\n <th>86</th>\n <td>Peter Grendler</td>\n <td>5.765985</td>\n <td>trueskill.Rating(mu=9.723, sigma=1.319)</td>\n </tr>\n <tr>\n <th>87</th>\n <td>Alessio Brasili</td>\n <td>5.503359</td>\n <td>trueskill.Rating(mu=10.349, sigma=1.615)</td>\n </tr>\n <tr>\n <th>88</th>\n <td>Steph Bridge</td>\n <td>5.083317</td>\n <td>trueskill.Rating(mu=9.933, sigma=1.616)</td>\n </tr>\n <tr>\n <th>89</th>\n <td>Enrico Tonon</td>\n <td>4.230926</td>\n <td>trueskill.Rating(mu=9.093, sigma=1.621)</td>\n </tr>\n <tr>\n <th>90</th>\n <td>Mateo Vieujot \u00ad Mouquet</td>\n <td>3.790752</td>\n <td>trueskill.Rating(mu=8.648, sigma=1.619)</td>\n </tr>\n <tr>\n <th>91</th>\n <td>Gina Hewson</td>\n <td>2.401493</td>\n <td>trueskill.Rating(mu=7.275, sigma=1.624)</td>\n </tr>\n <tr>\n <th>92</th>\n <td>Pierluigi Capozzi</td>\n <td>1.936233</td>\n <td>trueskill.Rating(mu=6.807, sigma=1.624)</td>\n </tr>\n <tr>\n <th>93</th>\n <td>Anastasia Akopova</td>\n <td>1.327601</td>\n <td>trueskill.Rating(mu=6.572, sigma=1.748)</td>\n </tr>\n <tr>\n <th>94</th>\n <td>Leonardo Morelli</td>\n <td>0.951235</td>\n <td>trueskill.Rating(mu=12.586, sigma=3.878)</td>\n </tr>\n <tr>\n <th>95</th>\n <td>Madis Kallas</td>\n <td>-1.262397</td>\n <td>trueskill.Rating(mu=3.656, sigma=1.639)</td>\n </tr>\n <tr>\n <th>96</th>\n <td>Catherine Dufour</td>\n <td>-1.959501</td>\n <td>trueskill.Rating(mu=2.986, sigma=1.648)</td>\n </tr>\n <tr>\n <th>97</th>\n <td>Camille Salvinien</td>\n <td>-4.835015</td>\n <td>trueskill.Rating(mu=1.023, sigma=1.953)</td>\n </tr>\n <tr>\n <th>98</th>\n <td>Enrico Leporati</td>\n <td>-5.829782</td>\n <td>trueskill.Rating(mu=-0.773, sigma=1.686)</td>\n </tr>\n <tr>\n <th>99</th>\n <td>Benjamin Geislinger</td>\n <td>-5.993792</td>\n <td>trueskill.Rating(mu=5.765, sigma=3.920)</td>\n </tr>\n <tr>\n <th>100</th>\n <td>Federico Aguilar</td>\n <td>-9.088954</td>\n <td>trueskill.Rating(mu=-2.955, sigma=2.045)</td>\n </tr>\n <tr>\n <th>101</th>\n <td>Fabio Turra</td>\n <td>-10.135260</td>\n <td>trueskill.Rating(mu=-4.394, sigma=1.914)</td>\n </tr>\n <tr>\n <th>102</th>\n <td>Eray Ozgulnar</td>\n <td>-10.788240</td>\n <td>trueskill.Rating(mu=1.294, sigma=4.028)</td>\n </tr>\n <tr>\n <th>103</th>\n <td>Alessandro Alberti</td>\n <td>-12.462986</td>\n <td>trueskill.Rating(mu=-6.054, sigma=2.136)</td>\n </tr>\n <tr>\n <th>104</th>\n <td>Andreas Messerli</td>\n <td>-12.692789</td>\n <td>trueskill.Rating(mu=-0.312, sigma=4.127)</td>\n </tr>\n <tr>\n <th>105</th>\n <td>Jose Fazio</td>\n <td>-13.408957</td>\n <td>trueskill.Rating(mu=-6.149, sigma=2.420)</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " Name mu_minus_3sigma \\\nRank \n1 Axel Mazella 52.782910 \n2 Maxime Nocher 52.469589 \n3 Johnny Heineken 52.113835 \n4 Nico Parlier 51.281797 \n5 Oliver Bridge 49.623996 \n6 Rikki Leccese 48.102821 \n7 Florian Gruber 47.702761 \n8 Joey Pasquali 46.402454 \n9 Florian Trittel Paul 46.338240 \n10 Bruno Lobo 43.350173 \n11 Nico Landauer 42.972590 \n12 Titouan Galea 42.256709 \n13 Adam Withington 41.825411 \n14 Theo De Ramecourt 41.373653 \n15 Blazej Ozog 41.341783 \n16 Stefaans Viljoen 40.630671 \n17 Zack Marks 39.945728 \n18 Guy Bridge 39.372714 \n19 Adrian Geislinger 39.084042 \n20 Martin Dolenc 38.406937 \n21 Toni Vodisek 38.103248 \n22 Mario Calbucci 37.688634 \n23 Andrea Beverino 36.833056 \n24 Benni Boelli 36.556093 \n25 Riley Gibbs 36.126334 \n26 Theo Lhostis 34.039392 \n27 Seth Besse 33.961681 \n28 Denis Taradin 32.820932 \n29 Jean De Falbaire 32.475439 \n30 Jacob Olivier 32.048910 \n31 Ejder Ginyol 31.709638 \n32 Xantos Villegas 31.673116 \n33 Jon Modica 30.182219 \n34 Andy Hansen 29.160374 \n35 Ivan Doronin 28.432826 \n36 Kieran Le Borgne 27.919134 \n37 Kai Calder 27.784493 \n38 Ty Reed 26.954240 \n39 Daniela Moroz 26.773290 \n40 Alejandro Climent Hernandez 25.961045 \n41 Marvin Baumeisterschoenian 25.621861 \n42 Benjamin Petit 25.287151 \n43 Alex Caizergues 24.848694 \n44 James Johnson 24.710560 \n45 Jordan Girdis 24.553943 \n46 Oliver Hansen 24.366688 \n47 Sam Bullock 24.293982 \n48 Peter Martel 24.214502 \n49 Chip Wasson 23.478939 \n50 Tomek Glazik 23.398876 \n51 Tomek Janiak 23.015412 \n52 Alexander Bachev 22.358357 \n53 Giulio Chiti 22.207118 \n54 Simone Vannucci 20.830259 \n55 William Morris 20.523155 \n56 Mani Bisschops 20.087741 \n57 Will James 19.556568 \n58 Thomas Lombardo 18.844386 \n59 Igor Malenko 18.489974 \n60 Amil Kabil 18.016213 \n61 Anthony Picard 17.464017 \n62 Mike Martin 17.128123 \n63 Ariel Poler 16.255642 \n64 Sonny Swords 16.155888 \n65 Elena Kalinina 15.844955 \n66 Valerio Venturi 15.226025 \n67 Gunnar Biniasch 14.768092 \n68 Michael Gilbreath 14.414316 \n69 Victor Bachichet 13.811583 \n70 Alexia Fancelli 13.284965 \n71 Will Cyr 13.051766 \n72 Felix Louis N'Jai 12.891101 \n73 Anthony Goldbloom 12.406005 \n74 Fraser Novakowski 11.569488 \n75 Jade O'Connor 11.404136 \n76 Ben Turner 11.306227 \n77 Alexis Delquie 10.926072 \n78 Roman Lyubimtsev 10.341758 \n79 Craig Rawson 9.622074 \n80 Loic Le Meur 9.590268 \n81 Chris Brent 9.212304 \n82 Kevin Growney 8.290331 \n83 Connor Bainbridge 7.605003 \n84 Steve Bodner 7.184998 \n85 John Gomes 6.744754 \n86 Peter Grendler 5.765985 \n87 Alessio Brasili 5.503359 \n88 Steph Bridge 5.083317 \n89 Enrico Tonon 4.230926 \n90 Mateo Vieujot \u00ad Mouquet 3.790752 \n91 Gina Hewson 2.401493 \n92 Pierluigi Capozzi 1.936233 \n93 Anastasia Akopova 1.327601 \n94 Leonardo Morelli 0.951235 \n95 Madis Kallas -1.262397 \n96 Catherine Dufour -1.959501 \n97 Camille Salvinien -4.835015 \n98 Enrico Leporati -5.829782 \n99 Benjamin Geislinger -5.993792 \n100 Federico Aguilar -9.088954 \n101 Fabio Turra -10.135260 \n102 Eray Ozgulnar -10.788240 \n103 Alessandro Alberti -12.462986 \n104 Andreas Messerli -12.692789 \n105 Jose Fazio -13.408957 \n\n Rating \nRank \n1 trueskill.Rating(mu=58.246, sigma=1.821) \n2 trueskill.Rating(mu=57.700, sigma=1.744) \n3 trueskill.Rating(mu=55.975, sigma=1.287) \n4 trueskill.Rating(mu=55.237, sigma=1.318) \n5 trueskill.Rating(mu=54.721, sigma=1.699) \n6 trueskill.Rating(mu=52.039, sigma=1.312) \n7 trueskill.Rating(mu=52.657, sigma=1.651) \n8 trueskill.Rating(mu=50.076, sigma=1.224) \n9 trueskill.Rating(mu=51.666, sigma=1.776) \n10 trueskill.Rating(mu=48.227, sigma=1.626) \n11 trueskill.Rating(mu=46.506, sigma=1.178) \n12 trueskill.Rating(mu=47.122, sigma=1.622) \n13 trueskill.Rating(mu=45.177, sigma=1.117) \n14 trueskill.Rating(mu=46.230, sigma=1.619) \n15 trueskill.Rating(mu=46.190, sigma=1.616) \n16 trueskill.Rating(mu=44.009, sigma=1.126) \n17 trueskill.Rating(mu=43.267, sigma=1.107) \n18 trueskill.Rating(mu=44.215, sigma=1.614) \n19 trueskill.Rating(mu=43.928, sigma=1.615) \n20 trueskill.Rating(mu=43.242, sigma=1.612) \n21 trueskill.Rating(mu=40.913, sigma=0.936) \n22 trueskill.Rating(mu=42.909, sigma=1.740) \n23 trueskill.Rating(mu=42.028, sigma=1.732) \n24 trueskill.Rating(mu=41.381, sigma=1.608) \n25 trueskill.Rating(mu=39.430, sigma=1.101) \n26 trueskill.Rating(mu=39.224, sigma=1.728) \n27 trueskill.Rating(mu=37.227, sigma=1.088) \n28 trueskill.Rating(mu=37.633, sigma=1.604) \n29 trueskill.Rating(mu=37.655, sigma=1.727) \n30 trueskill.Rating(mu=35.315, sigma=1.089) \n31 trueskill.Rating(mu=36.522, sigma=1.604) \n32 trueskill.Rating(mu=34.946, sigma=1.091) \n33 trueskill.Rating(mu=33.442, sigma=1.087) \n34 trueskill.Rating(mu=32.411, sigma=1.084) \n35 trueskill.Rating(mu=33.240, sigma=1.602) \n36 trueskill.Rating(mu=31.162, sigma=1.081) \n37 trueskill.Rating(mu=31.034, sigma=1.083) \n38 trueskill.Rating(mu=30.221, sigma=1.089) \n39 trueskill.Rating(mu=30.020, sigma=1.082) \n40 trueskill.Rating(mu=30.765, sigma=1.601) \n41 trueskill.Rating(mu=28.884, sigma=1.088) \n42 trueskill.Rating(mu=28.529, sigma=1.081) \n43 trueskill.Rating(mu=28.094, sigma=1.082) \n44 trueskill.Rating(mu=29.513, sigma=1.601) \n45 trueskill.Rating(mu=27.794, sigma=1.080) \n46 trueskill.Rating(mu=29.534, sigma=1.723) \n47 trueskill.Rating(mu=27.542, sigma=1.083) \n48 trueskill.Rating(mu=27.455, sigma=1.080) \n49 trueskill.Rating(mu=26.817, sigma=1.113) \n50 trueskill.Rating(mu=28.202, sigma=1.601) \n51 trueskill.Rating(mu=27.817, sigma=1.601) \n52 trueskill.Rating(mu=27.525, sigma=1.722) \n53 trueskill.Rating(mu=27.008, sigma=1.600) \n54 trueskill.Rating(mu=25.633, sigma=1.601) \n55 trueskill.Rating(mu=23.763, sigma=1.080) \n56 trueskill.Rating(mu=23.534, sigma=1.149) \n57 trueskill.Rating(mu=22.810, sigma=1.084) \n58 trueskill.Rating(mu=23.647, sigma=1.601) \n59 trueskill.Rating(mu=23.660, sigma=1.723) \n60 trueskill.Rating(mu=21.255, sigma=1.080) \n61 trueskill.Rating(mu=22.268, sigma=1.601) \n62 trueskill.Rating(mu=24.289, sigma=2.387) \n63 trueskill.Rating(mu=19.597, sigma=1.114) \n64 trueskill.Rating(mu=19.400, sigma=1.081) \n65 trueskill.Rating(mu=20.651, sigma=1.602) \n66 trueskill.Rating(mu=20.036, sigma=1.603) \n67 trueskill.Rating(mu=19.943, sigma=1.725) \n68 trueskill.Rating(mu=17.756, sigma=1.114) \n69 trueskill.Rating(mu=19.464, sigma=1.884) \n70 trueskill.Rating(mu=18.097, sigma=1.604) \n71 trueskill.Rating(mu=16.506, sigma=1.152) \n72 trueskill.Rating(mu=16.148, sigma=1.085) \n73 trueskill.Rating(mu=15.655, sigma=1.083) \n74 trueskill.Rating(mu=14.824, sigma=1.085) \n75 trueskill.Rating(mu=16.224, sigma=1.607) \n76 trueskill.Rating(mu=14.559, sigma=1.084) \n77 trueskill.Rating(mu=17.197, sigma=2.090) \n78 trueskill.Rating(mu=16.616, sigma=2.092) \n79 trueskill.Rating(mu=13.094, sigma=1.157) \n80 trueskill.Rating(mu=12.957, sigma=1.122) \n81 trueskill.Rating(mu=12.478, sigma=1.089) \n82 trueskill.Rating(mu=11.677, sigma=1.129) \n83 trueskill.Rating(mu=12.440, sigma=1.612) \n84 trueskill.Rating(mu=11.145, sigma=1.320) \n85 trueskill.Rating(mu=10.662, sigma=1.306) \n86 trueskill.Rating(mu=9.723, sigma=1.319) \n87 trueskill.Rating(mu=10.349, sigma=1.615) \n88 trueskill.Rating(mu=9.933, sigma=1.616) \n89 trueskill.Rating(mu=9.093, sigma=1.621) \n90 trueskill.Rating(mu=8.648, sigma=1.619) \n91 trueskill.Rating(mu=7.275, sigma=1.624) \n92 trueskill.Rating(mu=6.807, sigma=1.624) \n93 trueskill.Rating(mu=6.572, sigma=1.748) \n94 trueskill.Rating(mu=12.586, sigma=3.878) \n95 trueskill.Rating(mu=3.656, sigma=1.639) \n96 trueskill.Rating(mu=2.986, sigma=1.648) \n97 trueskill.Rating(mu=1.023, sigma=1.953) \n98 trueskill.Rating(mu=-0.773, sigma=1.686) \n99 trueskill.Rating(mu=5.765, sigma=3.920) \n100 trueskill.Rating(mu=-2.955, sigma=2.045) \n101 trueskill.Rating(mu=-4.394, sigma=1.914) \n102 trueskill.Rating(mu=1.294, sigma=4.028) \n103 trueskill.Rating(mu=-6.054, sigma=2.136) \n104 trueskill.Rating(mu=-0.312, sigma=4.127) \n105 trueskill.Rating(mu=-6.149, sigma=2.420) " }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Rating\n", "dfRatings = doRating(dfResults)\n", "pd.set_option('display.max_rows', len(dfRatings))\n", "dfRatings\n", "##look at Bruno Lobo " ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "_cell_guid": "f575622e-8713-7607-1460-ed40ae6597aa" }, "outputs": [], "source": "" }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "91ce2a10-de6d-94fd-2b8a-eef5c3f284b9" }, "outputs": [], "source": "" } ], "metadata": { "_change_revision": 305, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/329/329772.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "3aed6c65-fafb-6e95-fd43-9c1c0b9467e6" }, "source": [ "#My First Kaggle Notebook!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "99bd0efd-1a14-b3cf-b08c-2a0d7218fe75" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NationalNames.csv\n", "NationalReadMe.pdf\n", "StateNames.csv\n", "StateReadMe.pdf\n", "database.sqlite\n", "hashes.txt\n", "\n" ] } ], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n", "# For example, here's several helpful packages to load in \n", "\n", "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "from sklearn.linear_model import LinearRegression\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# Input data files are available in the \"../input/\" directory.\n", "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n", "\n", "from subprocess import check_output\n", "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n", "\n", "# Any results you write to the current directory are saved as output." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "56702dea-b8d9-d869-4aef-afd7ae774ad0" }, "outputs": [], "source": [ "df = pd.read_csv('../input/StateNames.csv')\n", "df2 = pd.read_csv('../input/NationalNames.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "e8453779-61ee-433e-46ee-6905d4754920" }, "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>Name</th>\n", " <th>Year</th>\n", " <th>Gender</th>\n", " <th>State</th>\n", " <th>Count</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>Mary</td>\n", " <td>1910</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>9</td>\n", " <td>Mary</td>\n", " <td>1911</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>15</td>\n", " <td>Mary</td>\n", " <td>1912</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>28</td>\n", " <td>Mary</td>\n", " <td>1913</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>21</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>35</td>\n", " <td>Mary</td>\n", " <td>1914</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>22</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>51</td>\n", " <td>Mary</td>\n", " <td>1915</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>23</td>\n", " </tr>\n", " <tr>\n", " <th>69</th>\n", " <td>70</td>\n", " <td>Mary</td>\n", " <td>1916</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>18</td>\n", " </tr>\n", " <tr>\n", " <th>91</th>\n", " <td>92</td>\n", " <td>Mary</td>\n", " <td>1917</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>21</td>\n", " </tr>\n", " <tr>\n", " <th>106</th>\n", " <td>107</td>\n", " <td>Mary</td>\n", " <td>1918</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>125</th>\n", " <td>126</td>\n", " <td>Mary</td>\n", " <td>1919</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>22</td>\n", " </tr>\n", " <tr>\n", " <th>152</th>\n", " <td>153</td>\n", " <td>Mary</td>\n", " <td>1920</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>38</td>\n", " </tr>\n", " <tr>\n", " <th>177</th>\n", " <td>178</td>\n", " <td>Mary</td>\n", " <td>1921</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>199</th>\n", " <td>200</td>\n", " <td>Mary</td>\n", " <td>1922</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>29</td>\n", " </tr>\n", " <tr>\n", " <th>219</th>\n", " <td>220</td>\n", " <td>Mary</td>\n", " <td>1923</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>244</th>\n", " <td>245</td>\n", " <td>Mary</td>\n", " <td>1924</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>41</td>\n", " </tr>\n", " <tr>\n", " <th>264</th>\n", " <td>265</td>\n", " <td>Mary</td>\n", " <td>1925</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>24</td>\n", " </tr>\n", " <tr>\n", " <th>292</th>\n", " <td>293</td>\n", " <td>Mary</td>\n", " <td>1926</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>39</td>\n", " </tr>\n", " <tr>\n", " <th>317</th>\n", " <td>318</td>\n", " <td>Mary</td>\n", " <td>1927</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>30</td>\n", " </tr>\n", " <tr>\n", " <th>343</th>\n", " <td>344</td>\n", " <td>Mary</td>\n", " <td>1928</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>27</td>\n", " </tr>\n", " <tr>\n", " <th>370</th>\n", " <td>371</td>\n", " <td>Mary</td>\n", " <td>1929</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>393</th>\n", " <td>394</td>\n", " <td>Mary</td>\n", " <td>1930</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>35</td>\n", " </tr>\n", " <tr>\n", " <th>422</th>\n", " <td>423</td>\n", " <td>Mary</td>\n", " <td>1931</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>41</td>\n", " </tr>\n", " <tr>\n", " <th>440</th>\n", " <td>441</td>\n", " <td>Mary</td>\n", " <td>1932</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>26</td>\n", " </tr>\n", " <tr>\n", " <th>468</th>\n", " <td>469</td>\n", " <td>Mary</td>\n", " <td>1933</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>25</td>\n", " </tr>\n", " <tr>\n", " <th>501</th>\n", " <td>502</td>\n", " <td>Mary</td>\n", " <td>1934</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>31</td>\n", " </tr>\n", " <tr>\n", " <th>526</th>\n", " <td>527</td>\n", " <td>Mary</td>\n", " <td>1935</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>29</td>\n", " </tr>\n", " <tr>\n", " <th>554</th>\n", " <td>555</td>\n", " <td>Mary</td>\n", " <td>1936</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>33</td>\n", " </tr>\n", " <tr>\n", " <th>587</th>\n", " <td>588</td>\n", " <td>Mary</td>\n", " <td>1937</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>41</td>\n", " </tr>\n", " <tr>\n", " <th>620</th>\n", " <td>621</td>\n", " <td>Mary</td>\n", " <td>1938</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>37</td>\n", " </tr>\n", " <tr>\n", " <th>654</th>\n", " <td>655</td>\n", " <td>Mary</td>\n", " <td>1939</td>\n", " <td>F</td>\n", " <td>AK</td>\n", " <td>28</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>5628885</th>\n", " <td>5628886</td>\n", " <td>Mary</td>\n", " <td>1977</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>17</td>\n", " </tr>\n", " <tr>\n", " <th>5629042</th>\n", " <td>5629043</td>\n", " <td>Mary</td>\n", " <td>1978</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>24</td>\n", " </tr>\n", " <tr>\n", " <th>5629197</th>\n", " <td>5629198</td>\n", " <td>Mary</td>\n", " <td>1979</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>31</td>\n", " </tr>\n", " <tr>\n", " <th>5629366</th>\n", " <td>5629367</td>\n", " <td>Mary</td>\n", " <td>1980</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>24</td>\n", " </tr>\n", " <tr>\n", " <th>5629556</th>\n", " <td>5629557</td>\n", " <td>Mary</td>\n", " <td>1981</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>5629755</th>\n", " <td>5629756</td>\n", " <td>Mary</td>\n", " <td>1982</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>28</td>\n", " </tr>\n", " <tr>\n", " <th>5629952</th>\n", " <td>5629953</td>\n", " <td>Mary</td>\n", " <td>1983</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>24</td>\n", " </tr>\n", " <tr>\n", " <th>5630141</th>\n", " <td>5630142</td>\n", " <td>Mary</td>\n", " <td>1984</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>17</td>\n", " </tr>\n", " <tr>\n", " <th>5630315</th>\n", " <td>5630316</td>\n", " <td>Mary</td>\n", " <td>1985</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>14</td>\n", " </tr>\n", " <tr>\n", " <th>5630462</th>\n", " <td>5630463</td>\n", " <td>Mary</td>\n", " <td>1986</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>18</td>\n", " </tr>\n", " <tr>\n", " <th>5630621</th>\n", " <td>5630622</td>\n", " <td>Mary</td>\n", " <td>1987</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>5630770</th>\n", " <td>5630771</td>\n", " <td>Mary</td>\n", " <td>1988</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>5630879</th>\n", " <td>5630880</td>\n", " <td>Mary</td>\n", " <td>1989</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>5631015</th>\n", " <td>5631016</td>\n", " <td>Mary</td>\n", " <td>1990</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>5631134</th>\n", " <td>5631135</td>\n", " <td>Mary</td>\n", " <td>1991</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>13</td>\n", " </tr>\n", " <tr>\n", " <th>5631266</th>\n", " <td>5631267</td>\n", " <td>Mary</td>\n", " <td>1992</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>11</td>\n", " </tr>\n", " <tr>\n", " <th>5631714</th>\n", " <td>5631715</td>\n", " <td>Mary</td>\n", " <td>1995</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>5631790</th>\n", " <td>5631791</td>\n", " <td>Mary</td>\n", " <td>1996</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>5631926</th>\n", " <td>5631927</td>\n", " <td>Mary</td>\n", " <td>1997</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>5632004</th>\n", " <td>5632005</td>\n", " <td>Mary</td>\n", " <td>1998</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>11</td>\n", " </tr>\n", " <tr>\n", " <th>5632183</th>\n", " <td>5632184</td>\n", " <td>Mary</td>\n", " <td>1999</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>5632224</th>\n", " <td>5632225</td>\n", " <td>Mary</td>\n", " <td>2000</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>12</td>\n", " </tr>\n", " <tr>\n", " <th>5632412</th>\n", " <td>5632413</td>\n", " <td>Mary</td>\n", " <td>2001</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>5632469</th>\n", " <td>5632470</td>\n", " <td>Mary</td>\n", " <td>2002</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>5632787</th>\n", " <td>5632788</td>\n", " <td>Mary</td>\n", " <td>2004</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>5633137</th>\n", " <td>5633138</td>\n", " <td>Mary</td>\n", " <td>2007</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>5633319</th>\n", " <td>5633320</td>\n", " <td>Mary</td>\n", " <td>2008</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>6</td>\n", " </tr>\n", " <tr>\n", " <th>5633419</th>\n", " <td>5633420</td>\n", " <td>Mary</td>\n", " <td>2009</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>5633535</th>\n", " <td>5633536</td>\n", " <td>Mary</td>\n", " <td>2010</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>5634114</th>\n", " <td>5634115</td>\n", " <td>Mary</td>\n", " <td>2014</td>\n", " <td>F</td>\n", " <td>WY</td>\n", " <td>5</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>6371 rows × 6 columns</p>\n", "</div>" ], "text/plain": [ " Id Name Year Gender State Count\n", "0 1 Mary 1910 F AK 14\n", "8 9 Mary 1911 F AK 12\n", "14 15 Mary 1912 F AK 9\n", "27 28 Mary 1913 F AK 21\n", "34 35 Mary 1914 F AK 22\n", "50 51 Mary 1915 F AK 23\n", "69 70 Mary 1916 F AK 18\n", "91 92 Mary 1917 F AK 21\n", "106 107 Mary 1918 F AK 27\n", "125 126 Mary 1919 F AK 22\n", "152 153 Mary 1920 F AK 38\n", "177 178 Mary 1921 F AK 36\n", "199 200 Mary 1922 F AK 29\n", "219 220 Mary 1923 F AK 26\n", "244 245 Mary 1924 F AK 41\n", "264 265 Mary 1925 F AK 24\n", "292 293 Mary 1926 F AK 39\n", "317 318 Mary 1927 F AK 30\n", "343 344 Mary 1928 F AK 27\n", "370 371 Mary 1929 F AK 25\n", "393 394 Mary 1930 F AK 35\n", "422 423 Mary 1931 F AK 41\n", "440 441 Mary 1932 F AK 26\n", "468 469 Mary 1933 F AK 25\n", "501 502 Mary 1934 F AK 31\n", "526 527 Mary 1935 F AK 29\n", "554 555 Mary 1936 F AK 33\n", "587 588 Mary 1937 F AK 41\n", "620 621 Mary 1938 F AK 37\n", "654 655 Mary 1939 F AK 28\n", "... ... ... ... ... ... ...\n", "5628885 5628886 Mary 1977 F WY 17\n", "5629042 5629043 Mary 1978 F WY 24\n", "5629197 5629198 Mary 1979 F WY 31\n", "5629366 5629367 Mary 1980 F WY 24\n", "5629556 5629557 Mary 1981 F WY 36\n", "5629755 5629756 Mary 1982 F WY 28\n", "5629952 5629953 Mary 1983 F WY 24\n", "5630141 5630142 Mary 1984 F WY 17\n", "5630315 5630316 Mary 1985 F WY 14\n", "5630462 5630463 Mary 1986 F WY 18\n", "5630621 5630622 Mary 1987 F WY 13\n", "5630770 5630771 Mary 1988 F WY 9\n", "5630879 5630880 Mary 1989 F WY 13\n", "5631015 5631016 Mary 1990 F WY 12\n", "5631134 5631135 Mary 1991 F WY 13\n", "5631266 5631267 Mary 1992 F WY 11\n", "5631714 5631715 Mary 1995 F WY 5\n", "5631790 5631791 Mary 1996 F WY 8\n", "5631926 5631927 Mary 1997 F WY 6\n", "5632004 5632005 Mary 1998 F WY 11\n", "5632183 5632184 Mary 1999 F WY 5\n", "5632224 5632225 Mary 2000 F WY 12\n", "5632412 5632413 Mary 2001 F WY 5\n", "5632469 5632470 Mary 2002 F WY 9\n", "5632787 5632788 Mary 2004 F WY 5\n", "5633137 5633138 Mary 2007 F WY 7\n", "5633319 5633320 Mary 2008 F WY 6\n", "5633419 5633420 Mary 2009 F WY 7\n", "5633535 5633536 Mary 2010 F WY 8\n", "5634114 5634115 Mary 2014 F WY 5\n", "\n", "[6371 rows x 6 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['Name']=='Mary']" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "42f38217-7f81-3bfa-b2eb-7146f80fb789" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "15bfff2f-58c8-1915-fff2-2ebe0a9ae3db" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 99, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/329/329777.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "7ebebef4-a4cb-8af8-105f-8b5a8b141e09" }, "source": [ "Let's do some exploratory data analysis on the people data set, and see if it makes sense to use any decomposition techniques. I think there could be a couple good reasons why we might want to do this:\n", "\n", "* If a lot of the characteristics of people are interdependent variables, we can consolidate them into single features so as to not muddy our final classifier inputs with repeat information.\n", "* There's potential for discovering latent features that are only evident by looking at multiple features together.\n", "* If you're like me, you might be pretty limited on computing resources and want to shrink the people data set before merging it in." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "c181ed5f-beea-4dcb-7fc5-d6393fa30099" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " people_id char_1 group_1 char_2 date char_3 char_4 \\\n", "0 ppl_100 type 2 group 17304 type 2 2021-06-29 type 5 type 5 \n", "1 ppl_100002 type 2 group 8688 type 3 2021-01-06 type 28 type 9 \n", "2 ppl_100003 type 2 group 33592 type 3 2022-06-10 type 4 type 8 \n", "3 ppl_100004 type 2 group 22593 type 3 2022-07-20 type 40 type 25 \n", "4 ppl_100006 type 2 group 6534 type 3 2022-07-27 type 40 type 25 \n", "\n", " char_5 char_6 char_7 ... char_29 char_30 char_31 char_32 char_33 \\\n", "0 type 5 type 3 type 11 ... False True True False False \n", "1 type 5 type 3 type 11 ... False True True True True \n", "2 type 5 type 2 type 5 ... False False True True True \n", "3 type 9 type 4 type 16 ... True True True True True \n", "4 type 9 type 3 type 8 ... False False True False False \n", "\n", " char_34 char_35 char_36 char_37 char_38 \n", "0 True True True False 36 \n", "1 True True True False 76 \n", "2 True False True True 99 \n", "3 True True True True 76 \n", "4 False True True False 84 \n", "\n", "[5 rows x 41 columns]\n" ] } ], "source": [ "import pandas as pd\n", "\n", "# Import the dataset\n", "df = pd.read_csv(\"../input/people.csv\")\n", "\n", "# Print the first five rows\n", "print(df.head())" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9c4c06c8-fc76-38c1-46a9-6904dd3fdc98" }, "source": [ "It looks like all the characteristics are denoted with a \"char_\" prefix. Let's use that to make sure that we don't drag along extra columns. Then we'll run a matrix of chi squared tests to get an idea of which features might be interdependent." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "7571f3b1-cdef-b1d5-3011-da3ee5348f4a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "It looks like 100.0% of the characteristics might be related to one another.\n" ] } ], "source": [ "from scipy.stats import chisquare\n", "\n", "# Create a list of characteristics\n", "chars = [i for i in df.columns.values if \"char_\" in i]\n", "\n", "# Create an empty list for appending flagged features\n", "flags = []\n", "\n", "# For each feature summarize frequencies of each other feature\n", "for feat in df[chars]:\n", " group = df[chars].groupby(feat)\n", " for otherfeat in df[chars].drop(feat, axis=1):\n", " summary = group[otherfeat].count()\n", " \n", " # Run a chi squared test on the frequencies, and check if the p-value is less than 0.05\n", " if chisquare(summary)[1] < 0.05:\n", " \n", " # If so, flag both features\n", " flags.append(feat)\n", " flags.append(otherfeat)\n", "\n", "# Remove duplicates by converting to a set at the end\n", "flags = set(flags)\n", "\n", "print(\"It looks like {}% of the characteristics might be related to one another.\".format(len(flags)/len(chars)*100))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "088f9230-6ec7-6aec-da77-014b7d487409" }, "source": [ "Wow, 100%. Hopefully someone reviewing this can highlight if I implemented those tests wrong, or if chi squared wasn't the right test of choice. At any rate, I'm going to proceed on the assumption that we should definitely be using some decomposition techniques on the data set, if only just to reduce the repeated information. Let's use the scikit-learn implementation of PCA. Some success with PCA would confirm that the chi squared tests were useful." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "1b927b29-a816-90c4-f4d7-382c28eb3d33" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Before PCA the full size of the characteristics is 160 features\n" ] } ], "source": [ "from sklearn.decomposition import PCA\n", "\n", "# We'll need the whole dataset to be one-hot encoded, but the booleans already are. Let's pull those out first.\n", "dums = df[chars].select_dtypes(include=[\"bool\"]).astype(float)\n", "\n", "# Now we'll join in dummies for the other characteristics\n", "dums = dums.join(pd.get_dummies(df[[i for i in chars if i not in dums.columns.values]]))\n", "\n", "print(\"Before PCA the full size of the characteristics is {} features\".format(len(dums.columns.values)))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "eb93f861-0541-c714-9a53-b58d493baaf4" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/utils/extmath.py:368: UserWarning: The number of power iterations is increased to 7 to achieve higher precision.\n", " warnings.warn(\"The number of power iterations is increased to \"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[ 0.99236635 0.00205787]\n" ] } ], "source": [ "from sklearn.decomposition import PCA\n", "\n", "# Now we're ready for PCA. Let's just look at the first two principle components first\n", "pca = PCA(n_components=2)\n", "components = pca.fit_transform(dums)\n", "\n", "print(pca.explained_variance_ratio_)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "7861f2a8-a3b8-d0e6-7902-46c276199e61" }, "source": [ "Huh, according the principle component analysis, 99.2% of what's going on in those 160 features can be captured in just one. Let's dissect the first principle component a bit." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "afe136ca-8828-fd5f-327c-2853989ee5b4" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f2128fdfc18>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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bnzHG94ckbnIG0PumiG/GGP2NXEe1tbUkJibicrkIDg72HE9NTSUnJ6fdR871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\nREREvCggi4iIiIh4+X/E0CwFj+4/8AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f21340e8d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "# build a dictionary with the names of the components\n", "components = {}\n", "index = 0\n", "for feature in dums.columns.values:\n", " components[feature] = [pca.components_[0][index]]\n", " index += 1\n", " \n", "# Exclude all but the most extreme components, because there are a lot\n", "sortedcomps = pca.components_[0]\n", "sortedcomps.sort()\n", "maxcap = sortedcomps[-3]\n", "mincap = sortedcomps[2]\n", "components = {i:x for i, x in components.items() if x >= maxcap or x <= mincap}\n", " \n", "# Convert to dataframe\n", "components = pd.DataFrame(components)\n", "\n", "# Plot the most extreme components\n", "components.plot(kind=\"bar\", figsize=(12, 4))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "60d91817-a8f2-5f67-f05a-49142abc7343" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 279, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/329/329837.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "aba53a01-cc53-a04f-5cfc-5ca644de275b" }, "source": [ "Using Trueskill to compute the 2016 kitefoil rankings\n", "Excluding DNF. At least three races" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "5f9e29dd-c831-3ff9-0d70-0110ab0ce459" }, "outputs": [ { "ename": "NameError", "evalue": "name 'dfRatings' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mNameError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-1-cd047b5b1df7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m#print wide columns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_option\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'display.max_rows'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdfRatings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'dfRatings' is not defined" ] } ], "source": [ "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import numpy as np\n", "import trueskill as ts\n", "\n", "#print wide columns\n", "pd.set_option('display.max_rows', len(dfRatings))\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "b92f7a60-28c5-c550-18d9-b153fa29c020" }, "outputs": [], "source": [ "def cleanResults(raceColumns,dfResultsTemp,appendScore):\n", " for raceCol in raceColumns:\n", " \n", " dfResultsTemp.index = dfResultsTemp.index.str.replace(r\"(\\w)([A-Z])\", r\"\\1 \\2\")\n", " dfResultsTemp.index = dfResultsTemp.index.str.title()\n", " dfResultsTemp.index = dfResultsTemp.index.str.replace('\\([A-Z\\ 0-9]*\\)','')\n", " dfResultsTemp.index = dfResultsTemp.index.str.strip()\n", " dfResultsTemp.index = dfResultsTemp.index.str.replace('Riccardo Andrea Leccese','Rikki Leccese')\n", " dfResultsTemp.index = dfResultsTemp.index.str.replace('Nicolas Parlier','Nico Parlier')\n", " dfResultsTemp.index = dfResultsTemp.index.str.replace('Alejandro Climent Hernã¥_ Ndez', 'Alejandro Climent Hernandez')\n", " dfResultsTemp.index = dfResultsTemp.index.str.replace('Alexandre Caizergues','Alex Caizergues')\n", " dfResultsTemp.index = dfResultsTemp.index.str.replace('Florian Trittel Paul','Florian Trittel')\n", " dfResultsTemp.index = dfResultsTemp.index.str.replace('Jean Guillaume Rivaud','Jean-Guillaume Rivaud')\n", " dfResultsTemp.index = dfResultsTemp.index.str.replace('^Kieran Le$','Kieran Le Borgne')\n", " dfResultsTemp.index = dfResultsTemp.index.str.replace('Marvin Baumeisterschoenian','Marvin Baumeister Schoenian') \n", " dfResultsTemp.index = dfResultsTemp.index.str.replace('Theo De Ramecourt','Theo De-Ramecourt')\n", " dfResultsTemp.index = dfResultsTemp.index.str.replace('James Johnson','James Johnsen')\n", "\n", " \n", " dfResultsTemp[raceCol] = dfResultsTemp[raceCol].astype(str)\n", " \n", " dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('D\\+D|DSQ|D\\+0|^-[A-Z0-9]*$|\\([A-Z0-9\\.-]*\\)|UFD|SCP|RDG|RCT|DCT|DNS-[0-9]*|DNC-[0-9]*|OCS-[0-9]*|[0-9\\.]*DNC|\\/','')\n", " dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('DNS','')\n", " \n", " #Count DNF or Retired as last place\n", " dfResultsTemp[raceCol] = dfResultsTemp[raceCol].str.replace('RET[0-9]*|DNF-[0-9]*|^DNF$|[0-9\\.]*DNF',str(len(dfResultsTemp)+1))\n", " \n", " \n", " dfResultsTemp[raceCol] = pd.to_numeric(dfResultsTemp[raceCol]) \n", " dfResultsTemp[raceCol] = dfResultsTemp[raceCol] + appendScore\n", " \n", " return dfResultsTemp\n", " " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "2a9f22ed-cac9-a488-8ef6-db4ae47d7e82" }, "outputs": [], "source": [ "def mergeResults(raceColumns,raceName,dfResultsTemp,dfResults):\n", " for raceCol in raceColumns:\n", " raceIndex = raceName + '-' + raceCol \n", " dfResultsTemp[raceIndex] = dfResultsTemp[raceCol]\n", " del(dfResultsTemp[raceCol])\n", " dfResults = pd.merge(dfResults,dfResultsTemp[[raceIndex]],left_index=True,right_index=True,how='outer')\n", " return dfResults" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "da590c08-1700-2647-71dd-07f34958e760" }, "outputs": [], "source": [ "def doRating(dfResults,dfRatings):\n", " \n", " #remove people who haven't completed 3 races\n", " #dfResults = dfResults[dfResults.count(axis=1) > 2]\n", " \n", " #this is hacky code to add new unique names\n", " dfRatings = pd.merge(dfRatings,dfResults,left_on=['Name'],right_index=True,how='outer')\n", " dfRatings['Name'] = dfRatings.index\n", " \n", " ratingsColumns = ['Name','mu_minus_3sigma','NumRaces','Rating']\n", " dfRatings = dfRatings[ratingsColumns]\n", "\n", " dfRatings['Rating'][dfRatings['Rating'].isnull()] = pd.Series(np.repeat(ts.Rating(),len(dfRatings['Rating'].isnull()))).T.values.tolist()\n", " \n", " print(dfRatings)\n", "\n", " \n", " #dfRatings['NumRaces'] += dfResults.count(axis=1)\n", " \n", " for raceCol in dfResults:\n", " competed = dfRatings['Name'].isin(dfResults.index[dfResults[raceCol].notnull()])\n", " #print(dfRatings['Name'])\n", " #print(dfResults.index[dfResults[raceCol].notnull()])\n", " rating_group = list(zip(dfRatings['Rating'][competed].T.values.tolist()))\n", " #print(rating_group)\n", " dfRatings['Rating'][competed] = ts.rate(rating_group, ranks=dfResults[raceCol][competed].T.values.tolist())\n", " \n", " dfRatings = pd.DataFrame(dfRatings) #convert to dataframe\n", "\n", " dfRatings['mu_minus_3sigma'] = pd.Series(np.repeat(0.0,len(dfRatings))) #calculate mu - 3 x sigma: MSFT convention\n", "\n", " for i in range(0,len(dfRatings['Rating'])):\n", " dfRatings['mu_minus_3sigma'][i] = float(dfRatings['Rating'][i].mu) - 3 * float(dfRatings['Rating'][i].sigma) \n", " \n", " dfRatings['Name'] = dfRatings.index\n", " dfRatings.index = dfRatings['mu_minus_3sigma'].rank(ascending=False).astype(int) #set index to ranking\n", " dfRatings.index.names = ['Rank']\n", "\n", " \n", " \n", " return dfRatings.sort('mu_minus_3sigma',ascending=False) " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "2dbb7f1b-3bd0-f7a3-2424-d90e3b2f51ef" }, "outputs": [], "source": [ "#initialize Ratings data frame\n", "ratingsColumns = ['Name','mu_minus_3sigma','NumRaces','Rating']\n", "dfRatings = pd.DataFrame(columns=ratingsColumns)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "07ea7b0f-96ad-d20c-3349-446934e396c4" }, "outputs": [], "source": [ "##Load LaVentana Results\n", "\n", "#initialize results data frame\n", "dfResults = pd.DataFrame()\n", "\n", "raceName = '20160323-LaVentana-HydrofoilProTour'\n", "raceColumns = ['Q1','Q2','R1','R2','R3','R4','R5','R6']\n", "\n", "dfResultsTempGold = pd.read_csv('../input/'+raceName+ '-Gold.csv')\n", "dfResultsTempGold = dfResultsTempGold.set_index(dfResultsTempGold['Name'] + ' ' + dfResultsTempGold['LastName'])\n", "dfResultsTempGold = cleanResults(raceColumns,dfResultsTempGold,0)\n", "\n", "dfResultsTempSilver = pd.read_csv('../input/'+raceName+ '-Silver.csv')\n", "dfResultsTempSilver = dfResultsTempSilver.set_index(dfResultsTempSilver['Name'] + ' ' + dfResultsTempSilver['LastName'])\n", "dfResultsTempSilver = cleanResults(raceColumns,dfResultsTempSilver,len(dfResultsTempGold))\n", "\n", "dfResultsTemp = dfResultsTempGold.append(dfResultsTempSilver)\n", "dfResults = mergeResults(raceColumns,raceName,dfResultsTemp,dfResults)\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "df058cc9-cc92-253a-c92b-f3cbfa072bd3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Name mu_minus_3sigma \\\n", "Maxime Nocher Maxime Nocher NaN \n", "Rikki Leccese Rikki Leccese NaN \n", "Axel Mazella Axel Mazella NaN \n", "Nico Landauer Nico Landauer NaN \n", "Oliver Bridge Oliver Bridge NaN \n", "Florian Trittel Florian Trittel NaN \n", "Julien Kerneur Julien Kerneur NaN \n", "Toni Vodisek Toni Vodisek NaN \n", "Alejandro Climent Hernandez Alejandro Climent Hernandez NaN \n", "Adrian Geislinger Adrian Geislinger NaN \n", "Matthew Taggart Matthew Taggart NaN \n", "Florian Gruber Florian Gruber NaN \n", "Benni Boelli Benni Boelli NaN \n", "Martin Dolenc Martin Dolenc NaN \n", "Adam Withington Adam Withington NaN \n", "Ejder Ginyol Ejder Ginyol NaN \n", "Martin Turbil Martin Turbil NaN \n", "Jacob Olivier Jacob Olivier NaN \n", "James Johnsen James Johnsen NaN \n", "John Von Tesmar John Von Tesmar NaN \n", "Chip Wasson Chip Wasson NaN \n", "Elena Kalinina Elena Kalinina NaN \n", "Roman Lyubimtsev Roman Lyubimtsev NaN \n", "Bruno Lobo Bruno Lobo NaN \n", "Riley Gibbs Riley Gibbs NaN \n", "Marvin Baumeister Schoenian Marvin Baumeister Schoenian NaN \n", "Theo Lhostis Theo Lhostis NaN \n", "Jean-Guillaume Rivaud Jean-Guillaume Rivaud NaN \n", "Benjamin Petit Benjamin Petit NaN \n", "Xantos Villegas Xantos Villegas NaN \n", "Daniela Moroz Daniela Moroz NaN \n", "Alexia Fancelli Alexia Fancelli NaN \n", "Gina Hewson Gina Hewson NaN \n", "Amil Kabil Amil Kabil NaN \n", "Cynthia Brown Cynthia Brown NaN \n", "Astrid Berz Astrid Berz NaN \n", "Catherine Dufour Catherine Dufour NaN \n", "Will James Will James NaN \n", "Andrew Mc Manus Andrew Mc Manus NaN \n", "Bitna Kim Bitna Kim NaN \n", "Mike Martin Mike Martin NaN \n", "Fred Hope Fred Hope NaN \n", "Romain Castel Romain Castel NaN \n", "James Shanahan James Shanahan NaN \n", "Camille Salvinien Camille Salvinien NaN \n", "Salim Mendez Salim Mendez NaN \n", "\n", " NumRaces Rating \n", "Maxime Nocher NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Rikki Leccese NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Axel Mazella NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Nico Landauer NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Oliver Bridge NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Florian Trittel NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Julien Kerneur NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Toni Vodisek NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Alejandro Climent Hernandez NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Adrian Geislinger NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Matthew Taggart NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Florian Gruber NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Benni Boelli NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Martin Dolenc NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Adam Withington NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Ejder Ginyol NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Martin Turbil NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Jacob Olivier NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "James Johnsen NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "John Von Tesmar NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Chip Wasson NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Elena Kalinina NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Roman Lyubimtsev NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Bruno Lobo NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Riley Gibbs NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Marvin Baumeister Schoenian NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Theo Lhostis NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Jean-Guillaume Rivaud NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Benjamin Petit NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Xantos Villegas NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Daniela Moroz NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Alexia Fancelli NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Gina Hewson NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Amil Kabil NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Cynthia Brown NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Astrid Berz NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Catherine Dufour NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Will James NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Andrew Mc Manus NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Bitna Kim NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Mike Martin NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Fred Hope NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Romain Castel NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "James Shanahan NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Camille Salvinien NaN trueskill.Rating(mu=25.000, sigma=8.333) \n", "Salim Mendez NaN trueskill.Rating(mu=25.000, sigma=8.333) \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:33: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:41: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Name</th>\n", " <th>mu_minus_3sigma</th>\n", " <th>NumRaces</th>\n", " <th>Rating</th>\n", " </tr>\n", " <tr>\n", " <th>Rank</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>Maxime Nocher</td>\n", " <td>48.551781</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=54.707, sigma=2.052)</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Rikki Leccese</td>\n", " <td>47.212044</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=52.681, sigma=1.823)</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Axel Mazella</td>\n", " <td>45.002023</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=50.365, sigma=1.788)</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Nico Landauer</td>\n", " <td>43.538362</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=48.777, sigma=1.746)</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Oliver Bridge</td>\n", " <td>41.751631</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=46.863, sigma=1.704)</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Florian Trittel</td>\n", " <td>39.590368</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=44.644, sigma=1.685)</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Julien Kerneur</td>\n", " <td>38.453240</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=43.478, sigma=1.675)</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Toni Vodisek</td>\n", " <td>35.963705</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=40.930, sigma=1.655)</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Adrian Geislinger</td>\n", " <td>34.076391</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=39.023, sigma=1.649)</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Matthew Taggart</td>\n", " <td>34.032961</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=38.979, sigma=1.649)</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Alejandro Climent Hernandez</td>\n", " <td>33.845361</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=38.795, sigma=1.650)</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>Florian Gruber</td>\n", " <td>32.380253</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=37.276, sigma=1.632)</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>Benni Boelli</td>\n", " <td>31.590181</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=36.513, sigma=1.641)</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>Martin Dolenc</td>\n", " <td>29.083314</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=33.986, sigma=1.634)</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>Adam Withington</td>\n", " <td>28.179015</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=33.077, sigma=1.633)</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>Ejder Ginyol</td>\n", " <td>27.791030</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=32.663, sigma=1.624)</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>Martin Turbil</td>\n", " <td>27.061577</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=31.955, sigma=1.631)</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>Jacob Olivier</td>\n", " <td>23.851611</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=28.742, sigma=1.630)</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>James Johnsen</td>\n", " <td>23.673887</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=28.556, sigma=1.627)</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>John Von Tesmar</td>\n", " <td>23.385160</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=28.244, sigma=1.620)</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>Chip Wasson</td>\n", " <td>23.281445</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=28.140, sigma=1.620)</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Elena Kalinina</td>\n", " <td>21.329358</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=26.218, sigma=1.630)</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>Roman Lyubimtsev</td>\n", " <td>20.669046</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=25.552, sigma=1.628)</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Bruno Lobo</td>\n", " <td>18.072165</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=22.943, sigma=1.624)</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>Riley Gibbs</td>\n", " <td>16.527035</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=21.390, sigma=1.621)</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>Marvin Baumeister Schoenian</td>\n", " <td>16.326717</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=21.197, sigma=1.624)</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>Theo Lhostis</td>\n", " <td>15.475474</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=20.358, sigma=1.627)</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>Jean-Guillaume Rivaud</td>\n", " <td>13.852260</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=18.718, sigma=1.622)</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>Benjamin Petit</td>\n", " <td>12.783243</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=17.679, sigma=1.632)</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>Daniela Moroz</td>\n", " <td>10.718552</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=15.617, sigma=1.633)</td>\n", " </tr>\n", " <tr>\n", " <th>31</th>\n", " <td>Xantos Villegas</td>\n", " <td>10.213567</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=15.079, sigma=1.622)</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>Gina Hewson</td>\n", " <td>8.246526</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=13.125, sigma=1.626)</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>Alexia Fancelli</td>\n", " <td>8.244687</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=13.122, sigma=1.626)</td>\n", " </tr>\n", " <tr>\n", " <th>34</th>\n", " <td>Amil Kabil</td>\n", " <td>7.231698</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=12.113, sigma=1.627)</td>\n", " </tr>\n", " <tr>\n", " <th>35</th>\n", " <td>Astrid Berz</td>\n", " <td>6.026642</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=10.913, sigma=1.629)</td>\n", " </tr>\n", " <tr>\n", " <th>36</th>\n", " <td>Will James</td>\n", " <td>5.848024</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=10.745, sigma=1.632)</td>\n", " </tr>\n", " <tr>\n", " <th>37</th>\n", " <td>Cynthia Brown</td>\n", " <td>5.285795</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=10.178, sigma=1.631)</td>\n", " </tr>\n", " <tr>\n", " <th>38</th>\n", " <td>Catherine Dufour</td>\n", " <td>4.689494</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=9.620, sigma=1.643)</td>\n", " </tr>\n", " <tr>\n", " <th>39</th>\n", " <td>Andrew Mc Manus</td>\n", " <td>4.551009</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=9.495, sigma=1.648)</td>\n", " </tr>\n", " <tr>\n", " <th>40</th>\n", " <td>Fred Hope</td>\n", " <td>3.080253</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=8.291, sigma=1.737)</td>\n", " </tr>\n", " <tr>\n", " <th>41</th>\n", " <td>Mike Martin</td>\n", " <td>2.597148</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=7.575, sigma=1.659)</td>\n", " </tr>\n", " <tr>\n", " <th>42</th>\n", " <td>Bitna Kim</td>\n", " <td>1.917241</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=6.857, sigma=1.647)</td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td>Romain Castel</td>\n", " <td>0.558333</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=5.546, sigma=1.663)</td>\n", " </tr>\n", " <tr>\n", " <th>44</th>\n", " <td>Salim Mendez</td>\n", " <td>-0.522236</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=4.523, sigma=1.682)</td>\n", " </tr>\n", " <tr>\n", " <th>45</th>\n", " <td>James Shanahan</td>\n", " <td>-2.304049</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=2.811, sigma=1.705)</td>\n", " </tr>\n", " <tr>\n", " <th>46</th>\n", " <td>Camille Salvinien</td>\n", " <td>-2.916689</td>\n", " <td>NaN</td>\n", " <td>trueskill.Rating(mu=2.237, sigma=1.718)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name mu_minus_3sigma NumRaces \\\n", "Rank \n", "1 Maxime Nocher 48.551781 NaN \n", "2 Rikki Leccese 47.212044 NaN \n", "3 Axel Mazella 45.002023 NaN \n", "4 Nico Landauer 43.538362 NaN \n", "5 Oliver Bridge 41.751631 NaN \n", "6 Florian Trittel 39.590368 NaN \n", "7 Julien Kerneur 38.453240 NaN \n", "8 Toni Vodisek 35.963705 NaN \n", "9 Adrian Geislinger 34.076391 NaN \n", "10 Matthew Taggart 34.032961 NaN \n", "11 Alejandro Climent Hernandez 33.845361 NaN \n", "12 Florian Gruber 32.380253 NaN \n", "13 Benni Boelli 31.590181 NaN \n", "14 Martin Dolenc 29.083314 NaN \n", "15 Adam Withington 28.179015 NaN \n", "16 Ejder Ginyol 27.791030 NaN \n", "17 Martin Turbil 27.061577 NaN \n", "18 Jacob Olivier 23.851611 NaN \n", "19 James Johnsen 23.673887 NaN \n", "20 John Von Tesmar 23.385160 NaN \n", "21 Chip Wasson 23.281445 NaN \n", "22 Elena Kalinina 21.329358 NaN \n", "23 Roman Lyubimtsev 20.669046 NaN \n", "24 Bruno Lobo 18.072165 NaN \n", "25 Riley Gibbs 16.527035 NaN \n", "26 Marvin Baumeister Schoenian 16.326717 NaN \n", "27 Theo Lhostis 15.475474 NaN \n", "28 Jean-Guillaume Rivaud 13.852260 NaN \n", "29 Benjamin Petit 12.783243 NaN \n", "30 Daniela Moroz 10.718552 NaN \n", "31 Xantos Villegas 10.213567 NaN \n", "32 Gina Hewson 8.246526 NaN \n", "33 Alexia Fancelli 8.244687 NaN \n", "34 Amil Kabil 7.231698 NaN \n", "35 Astrid Berz 6.026642 NaN \n", "36 Will James 5.848024 NaN \n", "37 Cynthia Brown 5.285795 NaN \n", "38 Catherine Dufour 4.689494 NaN \n", "39 Andrew Mc Manus 4.551009 NaN \n", "40 Fred Hope 3.080253 NaN \n", "41 Mike Martin 2.597148 NaN \n", "42 Bitna Kim 1.917241 NaN \n", "43 Romain Castel 0.558333 NaN \n", "44 Salim Mendez -0.522236 NaN \n", "45 James Shanahan -2.304049 NaN \n", "46 Camille Salvinien -2.916689 NaN \n", "\n", " Rating \n", "Rank \n", "1 trueskill.Rating(mu=54.707, sigma=2.052) \n", "2 trueskill.Rating(mu=52.681, sigma=1.823) \n", "3 trueskill.Rating(mu=50.365, sigma=1.788) \n", "4 trueskill.Rating(mu=48.777, sigma=1.746) \n", "5 trueskill.Rating(mu=46.863, sigma=1.704) \n", "6 trueskill.Rating(mu=44.644, sigma=1.685) \n", "7 trueskill.Rating(mu=43.478, sigma=1.675) \n", "8 trueskill.Rating(mu=40.930, sigma=1.655) \n", "9 trueskill.Rating(mu=39.023, sigma=1.649) \n", "10 trueskill.Rating(mu=38.979, sigma=1.649) \n", "11 trueskill.Rating(mu=38.795, sigma=1.650) \n", "12 trueskill.Rating(mu=37.276, sigma=1.632) \n", "13 trueskill.Rating(mu=36.513, sigma=1.641) \n", "14 trueskill.Rating(mu=33.986, sigma=1.634) \n", "15 trueskill.Rating(mu=33.077, sigma=1.633) \n", "16 trueskill.Rating(mu=32.663, sigma=1.624) \n", "17 trueskill.Rating(mu=31.955, sigma=1.631) \n", "18 trueskill.Rating(mu=28.742, sigma=1.630) \n", "19 trueskill.Rating(mu=28.556, sigma=1.627) \n", "20 trueskill.Rating(mu=28.244, sigma=1.620) \n", "21 trueskill.Rating(mu=28.140, sigma=1.620) \n", "22 trueskill.Rating(mu=26.218, sigma=1.630) \n", "23 trueskill.Rating(mu=25.552, sigma=1.628) \n", "24 trueskill.Rating(mu=22.943, sigma=1.624) \n", "25 trueskill.Rating(mu=21.390, sigma=1.621) \n", "26 trueskill.Rating(mu=21.197, sigma=1.624) \n", "27 trueskill.Rating(mu=20.358, sigma=1.627) \n", "28 trueskill.Rating(mu=18.718, sigma=1.622) \n", "29 trueskill.Rating(mu=17.679, sigma=1.632) \n", "30 trueskill.Rating(mu=15.617, sigma=1.633) \n", "31 trueskill.Rating(mu=15.079, sigma=1.622) \n", "32 trueskill.Rating(mu=13.125, sigma=1.626) \n", "33 trueskill.Rating(mu=13.122, sigma=1.626) \n", "34 trueskill.Rating(mu=12.113, sigma=1.627) \n", "35 trueskill.Rating(mu=10.913, sigma=1.629) \n", "36 trueskill.Rating(mu=10.745, sigma=1.632) \n", "37 trueskill.Rating(mu=10.178, sigma=1.631) \n", "38 trueskill.Rating(mu=9.620, sigma=1.643) \n", "39 trueskill.Rating(mu=9.495, sigma=1.648) \n", "40 trueskill.Rating(mu=8.291, sigma=1.737) \n", "41 trueskill.Rating(mu=7.575, sigma=1.659) \n", "42 trueskill.Rating(mu=6.857, sigma=1.647) \n", "43 trueskill.Rating(mu=5.546, sigma=1.663) \n", "44 trueskill.Rating(mu=4.523, sigma=1.682) \n", "45 trueskill.Rating(mu=2.811, sigma=1.705) \n", "46 trueskill.Rating(mu=2.237, sigma=1.718) " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create Rating\n", "dfRatings = doRating(dfResults,dfRatings)\n", "dfRatings\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "77938ae9-b2a4-c361-0638-213140223c43" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/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", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", "/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:25: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" ] } ], "source": [ "##Load France Results\n", "dfResults = pd.DataFrame()\n", "\n", "raceName = '20160516-MontPellier-IFKOSilverCup'\n", "raceColumns = ['CO 1', 'CO 2','CO 3','CO 4','CO 5','CO 6','CO 7','CO 8','CO 9','CO 10','CO 11','CO 12']\n", "dfResultsTemp = pd.read_csv('../input/'+raceName+ '.csv')\n", "\n", "for i in range(0,len(dfResultsTemp)):\n", " numNames = len(dfResultsTemp['Name'].str.split(' ')[i]) \n", " #print( dfResultsTemp['Name'].str.split(' ')[i][numNames-1] + ' ' + dfResultsTemp['Name'].str.split(' ')[i][0])\n", " dfResultsTemp['Name'][i] = dfResultsTemp['Name'].str.split(' ')[i][numNames-1] + ' ' + dfResultsTemp['Name'].str.split(' ')[i][0]\n", " \n", " \n", "dfResultsTemp = dfResultsTemp.set_index(dfResultsTemp['Name'].str.lower())\n", "\n", "dfResultsTemp = cleanResults(raceColumns,dfResultsTemp,0)\n", "\n", "#drop worst 3 races\n", "for i in (dfResultsTemp[raceColumns].isnull().sum(axis=1) < 3).index:\n", " toDelete = 3-dfResultsTemp[raceColumns][dfResultsTemp.index == i].isnull().sum(axis=1).values[0]\n", " if toDelete > 0:\n", " for j in range(1,toDelete+1):\n", " maxToDelete = dfResultsTemp[raceColumns][dfResultsTemp.index == i].idxmax(axis=1).values[0]\n", " \n", " dfResultsTemp[maxToDelete][dfResultsTemp.index == i] = np.nan \n", "\n", "dfResults = mergeResults(raceColumns,raceName,dfResultsTemp,dfResults)\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "d12113e8-2a8f-2190-c816-1525d0a084d1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Name mu_minus_3sigma NumRaces Rating\n", "Rank \n", "1 1 48.551781 NaN trueskill.Rating(mu=54.707, sigma=2.052)\n", "2 2 47.212044 NaN trueskill.Rating(mu=52.681, sigma=1.823)\n", "3 3 45.002023 NaN trueskill.Rating(mu=50.365, sigma=1.788)\n", "4 4 43.538362 NaN trueskill.Rating(mu=48.777, sigma=1.746)\n", "5 5 41.751631 NaN trueskill.Rating(mu=46.863, sigma=1.704)\n", "6 6 39.590368 NaN trueskill.Rating(mu=44.644, sigma=1.685)\n", "7 7 38.453240 NaN trueskill.Rating(mu=43.478, sigma=1.675)\n", "8 8 35.963705 NaN trueskill.Rating(mu=40.930, sigma=1.655)\n", "9 9 34.076391 NaN trueskill.Rating(mu=39.023, sigma=1.649)\n", "10 10 34.032961 NaN trueskill.Rating(mu=38.979, sigma=1.649)\n", "11 11 33.845361 NaN trueskill.Rating(mu=38.795, sigma=1.650)\n", "12 12 32.380253 NaN trueskill.Rating(mu=37.276, sigma=1.632)\n", "13 13 31.590181 NaN trueskill.Rating(mu=36.513, sigma=1.641)\n", "14 14 29.083314 NaN trueskill.Rating(mu=33.986, sigma=1.634)\n", "15 15 28.179015 NaN trueskill.Rating(mu=33.077, sigma=1.633)\n", "16 16 27.791030 NaN trueskill.Rating(mu=32.663, sigma=1.624)\n", "17 17 27.061577 NaN trueskill.Rating(mu=31.955, sigma=1.631)\n", "18 18 23.851611 NaN trueskill.Rating(mu=28.742, sigma=1.630)\n", "19 19 23.673887 NaN trueskill.Rating(mu=28.556, sigma=1.627)\n", "20 20 23.385160 NaN trueskill.Rating(mu=28.244, sigma=1.620)\n", "21 21 23.281445 NaN trueskill.Rating(mu=28.140, sigma=1.620)\n", "22 22 21.329358 NaN trueskill.Rating(mu=26.218, sigma=1.630)\n", "23 23 20.669046 NaN trueskill.Rating(mu=25.552, sigma=1.628)\n", "24 24 18.072165 NaN trueskill.Rating(mu=22.943, sigma=1.624)\n", "25 25 16.527035 NaN trueskill.Rating(mu=21.390, sigma=1.621)\n", "26 26 16.326717 NaN trueskill.Rating(mu=21.197, sigma=1.624)\n", "27 27 15.475474 NaN trueskill.Rating(mu=20.358, sigma=1.627)\n", "28 28 13.852260 NaN trueskill.Rating(mu=18.718, sigma=1.622)\n", "29 29 12.783243 NaN trueskill.Rating(mu=17.679, sigma=1.632)\n", "30 30 10.718552 NaN trueskill.Rating(mu=15.617, sigma=1.633)\n", "... ... ... ... ...\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "\n", "[77 rows x 4 columns]\n" ] }, { "ename": "ValueError", "evalue": "cannot reindex from a duplicate axis", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mValueError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-9-b95d17570779>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdfRatings\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdoRating\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdfResults\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdfRatings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m<ipython-input-4-4b39f40d153f>\u001b[0m in \u001b[0;36mdoRating\u001b[1;34m(dfResults, dfRatings)\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[0mrating_group\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdfRatings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Rating'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcompeted\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\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 25\u001b[0m \u001b[1;31m#print(rating_group)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mdfRatings\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Rating'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcompeted\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrating_group\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mranks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdfResults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mraceCol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcompeted\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\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[0m\u001b[0;32m 27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[0mdfRatings\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdfRatings\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#convert to dataframe\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 620\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 621\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 622\u001b[1;33m \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 623\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 624\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_with\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36mcheck_bool_indexer\u001b[1;34m(ax, key)\u001b[0m\n\u001b[0;32m 1796\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1797\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mequals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1798\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1799\u001b[0m \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m 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"\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_reindex_axes\u001b[1;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[0;32m 2245\u001b[0m obj = obj._reindex_with_indexers({axis: [new_index, indexer]},\n\u001b[0;32m 2246\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2247\u001b[1;33m copy=copy, allow_dups=False)\n\u001b[0m\u001b[0;32m 2248\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2249\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_reindex_with_indexers\u001b[1;34m(self, reindexers, fill_value, copy, allow_dups)\u001b[0m\n\u001b[0;32m 2339\u001b[0m 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dups\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3585\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3586\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_reindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3587\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3588\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36m_can_reindex\u001b[1;34m(self, indexer)\u001b[0m\n\u001b[0;32m 2291\u001b[0m \u001b[1;31m# trying to reindex on an axis with duplicates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2292\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2293\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"cannot reindex from a duplicate axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2294\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2295\u001b[0m def reindex(self, target, method=None, level=None, limit=None,\n", "\u001b[1;31mValueError\u001b[0m: cannot reindex from a duplicate axis" ] } ], "source": [ "dfRatings = doRating(dfResults,dfRatings)\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "753fbde0-cc3f-fe11-b7d8-a0bbf8db2e51" }, "outputs": [], "source": [ "###Load Italy results\n", "raceName = '20160717-Gizzeria-IKAGoldCup'\n", "\n", "\n", "\n", "raceColumns = ['CF 1','CF 2','F 1','F 2','F 3','F 4','F 5','F 6','F 7','F 8',\t'F 9','F 10']\n", "dfResultsTempGold = pd.read_csv('../input/'+raceName+ '-Gold.csv')\n", "dfResultsTempGold = dfResultsTempGold.set_index(dfResultsTempGold['Name'])\n", "dfResultsTempGold = cleanResults(raceColumns,dfResultsTempGold,0)\n", "\n", "raceColumns = ['CF 1','CF 2','F 1','F 2','F 3','F 4','F 5','F 6','F 8']\n", "dfResultsTempSilver = pd.read_csv('../input/'+raceName+ '-Silver.csv')\n", "dfResultsTempSilver = dfResultsTempSilver.set_index(dfResultsTempSilver['Name'])\n", "dfResultsTempSilver = cleanResults(raceColumns,dfResultsTempSilver,len(dfResultsTempGold))\n", "\n", "raceColumns = ['CF 1','CF 2','F 1','F 2','F 3','F 4','F 5','F 6']\n", "dfResultsTemp = dfResultsTempGold.append(dfResultsTempSilver)\n", "\n", "dfResultsTempBronze = pd.read_csv('../input/'+raceName+ '-Bronze.csv',encoding = \"ISO-8859-1\")\n", "dfResultsTempBronze = dfResultsTempBronze.set_index(dfResultsTempBronze['Name'])\n", "dfResultsTempBronze = cleanResults(raceColumns,dfResultsTempBronze,len(dfResultsTemp))\n", "\n", "dfResultsTemp = dfResultsTemp.append(dfResultsTempBronze)\n", "\n", "raceColumns = ['CF 1','CF 2','F 1','F 2','F 3','F 4','F 5','F 6','F 7','F 8',\t'F 9','F 10']\n", "\n", "dfResults = mergeResults(raceColumns,raceName,dfResultsTemp,dfResults)\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "c25d7954-40d4-58f7-7c7c-662ee8c95f49" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "704269ab-f13c-2a3d-e53f-b79217374082" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Name mu_minus_3sigma NumRaces Rating\n", "Rank \n", "1 1 48.551781 NaN trueskill.Rating(mu=54.707, sigma=2.052)\n", "2 2 47.212044 NaN trueskill.Rating(mu=52.681, sigma=1.823)\n", "3 3 45.002023 NaN trueskill.Rating(mu=50.365, sigma=1.788)\n", "4 4 43.538362 NaN trueskill.Rating(mu=48.777, sigma=1.746)\n", "5 5 41.751631 NaN trueskill.Rating(mu=46.863, sigma=1.704)\n", "6 6 39.590368 NaN trueskill.Rating(mu=44.644, sigma=1.685)\n", "7 7 38.453240 NaN trueskill.Rating(mu=43.478, sigma=1.675)\n", "8 8 35.963705 NaN trueskill.Rating(mu=40.930, sigma=1.655)\n", "9 9 34.076391 NaN trueskill.Rating(mu=39.023, sigma=1.649)\n", "10 10 34.032961 NaN trueskill.Rating(mu=38.979, sigma=1.649)\n", "11 11 33.845361 NaN trueskill.Rating(mu=38.795, sigma=1.650)\n", "12 12 32.380253 NaN trueskill.Rating(mu=37.276, sigma=1.632)\n", "13 13 31.590181 NaN trueskill.Rating(mu=36.513, sigma=1.641)\n", "14 14 29.083314 NaN trueskill.Rating(mu=33.986, sigma=1.634)\n", "15 15 28.179015 NaN trueskill.Rating(mu=33.077, sigma=1.633)\n", "16 16 27.791030 NaN trueskill.Rating(mu=32.663, sigma=1.624)\n", "17 17 27.061577 NaN trueskill.Rating(mu=31.955, sigma=1.631)\n", "18 18 23.851611 NaN trueskill.Rating(mu=28.742, sigma=1.630)\n", "19 19 23.673887 NaN trueskill.Rating(mu=28.556, sigma=1.627)\n", "20 20 23.385160 NaN trueskill.Rating(mu=28.244, sigma=1.620)\n", "21 21 23.281445 NaN trueskill.Rating(mu=28.140, sigma=1.620)\n", "22 22 21.329358 NaN trueskill.Rating(mu=26.218, sigma=1.630)\n", "23 23 20.669046 NaN trueskill.Rating(mu=25.552, sigma=1.628)\n", "24 24 18.072165 NaN trueskill.Rating(mu=22.943, sigma=1.624)\n", "25 25 16.527035 NaN trueskill.Rating(mu=21.390, sigma=1.621)\n", "26 26 16.326717 NaN trueskill.Rating(mu=21.197, sigma=1.624)\n", "27 27 15.475474 NaN trueskill.Rating(mu=20.358, sigma=1.627)\n", "28 28 13.852260 NaN trueskill.Rating(mu=18.718, sigma=1.622)\n", "29 29 12.783243 NaN trueskill.Rating(mu=17.679, sigma=1.632)\n", "30 30 10.718552 NaN trueskill.Rating(mu=15.617, sigma=1.633)\n", "... ... ... ... ...\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "46 46 NaN NaN trueskill.Rating(mu=25.000, sigma=8.333)\n", "\n", "[111 rows x 4 columns]\n" ] }, { "ename": "ValueError", "evalue": "cannot reindex from a duplicate axis", "output_type": "error", "traceback": [ "\u001b[1;31m\u001b[0m", "\u001b[1;31mValueError\u001b[0mTraceback (most recent call last)", "\u001b[1;32m<ipython-input-11-cddb24032f14>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#Create Rating\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdfRatings\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdoRating\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdfResults\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdfRatings\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;31m#dfRatings\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m<ipython-input-4-4b39f40d153f>\u001b[0m in \u001b[0;36mdoRating\u001b[1;34m(dfResults, dfRatings)\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[0mrating_group\u001b[0m \u001b[1;33m=\u001b[0m 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\u001b[0mranks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdfResults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mraceCol\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcompeted\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\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[0m\u001b[0;32m 27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[0mdfRatings\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdfRatings\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#convert to dataframe\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 620\u001b[0m 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1796\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1797\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mequals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1798\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1799\u001b[0m \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m 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\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\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;32m-> 2287\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2288\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2289\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mAppender\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgeneric\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_shared_docs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'fillna'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0m_shared_doc_kwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mreindex\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 2227\u001b[0m \u001b[1;31m# perform the reindex on the axes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2228\u001b[0m return self._reindex_axes(axes, level, limit, tolerance, method,\n\u001b[1;32m-> 2229\u001b[1;33m fill_value, copy).__finalize__(self)\n\u001b[0m\u001b[0;32m 2230\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2231\u001b[0m def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value,\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_reindex_axes\u001b[1;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[0;32m 2245\u001b[0m obj = obj._reindex_with_indexers({axis: [new_index, indexer]},\n\u001b[0;32m 2246\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2247\u001b[1;33m copy=copy, allow_dups=False)\n\u001b[0m\u001b[0;32m 2248\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2249\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_reindex_with_indexers\u001b[1;34m(self, reindexers, fill_value, copy, allow_dups)\u001b[0m\n\u001b[0;32m 2339\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2340\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mallow_dups\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2341\u001b[1;33m copy=copy)\n\u001b[0m\u001b[0;32m 2342\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2343\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mnew_data\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mreindex_indexer\u001b[1;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy)\u001b[0m\n\u001b[0;32m 3584\u001b[0m \u001b[1;31m# some axes don't allow reindexing with dups\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3585\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3586\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_reindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3587\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3588\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/opt/conda/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36m_can_reindex\u001b[1;34m(self, indexer)\u001b[0m\n\u001b[0;32m 2291\u001b[0m \u001b[1;31m# trying to reindex on an axis with duplicates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2292\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2293\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"cannot reindex from a duplicate axis\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2294\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2295\u001b[0m def reindex(self, target, method=None, level=None, limit=None,\n", "\u001b[1;31mValueError\u001b[0m: cannot reindex from a duplicate axis" ] } ], "source": [ "#Create Rating\n", "dfRatings = doRating(dfResults,dfRatings)\n", "#dfRatings\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "44cf6067-3517-57d3-285d-a4526a161fe9" }, "outputs": [], "source": [ "##Load SF results\n", "raceName = '20160807-SanFracisco-HydrofoilProTour'\n", "dfResultsTemp = pd.read_csv('../input/' + raceName + '.csv')\n", "dfResultsTemp = dfResultsTemp.set_index(dfResultsTemp['Name'])\n", "raceColumns = ['R1','R2','R3','R4','R5','R6','R7','R8','R9','R10','R11','R12','R13','R14','R15','R16']\n", "\n", "dfResultsTemp = cleanResults(raceColumns,dfResultsTemp,0)\n", "dfResults = mergeResults(raceColumns,raceName,dfResultsTemp,dfResults)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "3682715c-ed6e-8544-cd2d-27b27fc1a71a" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "5ad0ad2e-1caa-6158-0b13-6ee419b081a2" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "2a0e26b0-9ed7-2b03-c804-14c68d9c3ef2" }, "outputs": [], "source": [ "#pd.set_option('display.max_columns', 100)\n", "#dfResults[dfResults.index == 'Nico Parlier']" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "b5de9ec0-2853-88f2-5eed-577385c529d4" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "f575622e-8713-7607-1460-ed40ae6597aa" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "91ce2a10-de6d-94fd-2b8a-eef5c3f284b9" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "04e6cdc1-074c-bb05-8f47-48f2b4a8ccfe" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 2908, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/329/329956.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "28299261-b289-3397-3746-a5a061baa365" }, "outputs": [], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "62fd6b96-ea2e-7e83-ae93-2d8e92ad6c9f" }, "outputs": [], "source": [ "#dependencies\n", "import datetime as datetime" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "18bcaf30-5a52-2411-5442-c0cda003e044" }, "source": [ "**Looking at the data**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "ed2dc800-c433-0b21-e014-e51fd12b8545" }, "outputs": [], "source": [ "train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])\n", "test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])\n", "ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])\n", "\n", "df_train = pd.merge(train, ppl, on='people_id')\n", "df_test = pd.merge(test, ppl, on='people_id')\n", "del train, test, ppl" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "d062084d-5635-47c8-9dff-d5c4ff716e36" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start of date_x: 2022-07-17\n", " End of date_x: 2023-08-31\n", "Range of date_x: 410 days 00:00:00\n", "\n", "Start of date_y: 2020-05-18\n", " End of date_y: 2023-08-31\n", "Range of date_y: 1200 days 00:00:00\n", "\n" ] } ], "source": [ "for d in ['date_x', 'date_y']:\n", " print('Start of ' + d + ': ' + str(df_train[d].min().date()))\n", " print(' End of ' + d + ': ' + str(df_train[d].max().date()))\n", " print('Range of ' + d + ': ' + str(df_train[d].max() - df_train[d].min()) + '\\n')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "b979db16-dda4-cfdd-0ab7-66c8f03efbbd" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>people_id</th>\n", " <th>activity_id</th>\n", " <th>date_x</th>\n", " <th>activity_category</th>\n", " <th>char_1_x</th>\n", " <th>char_2_x</th>\n", " <th>char_3_x</th>\n", " <th>char_4_x</th>\n", " <th>char_5_x</th>\n", " <th>char_6_x</th>\n", " <th>...</th>\n", " <th>char_29</th>\n", " <th>char_30</th>\n", " <th>char_31</th>\n", " <th>char_32</th>\n", " <th>char_33</th>\n", " <th>char_34</th>\n", " <th>char_35</th>\n", " <th>char_36</th>\n", " <th>char_37</th>\n", " <th>char_38</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>ppl_100</td>\n", " <td>act2_1734928</td>\n", " <td>2023-08-26</td>\n", " <td>type 4</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ppl_100</td>\n", " <td>act2_2434093</td>\n", " <td>2022-09-27</td>\n", " <td>type 2</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>2 rows × 55 columns</p>\n", "</div>" ], "text/plain": [ " people_id activity_id date_x activity_category char_1_x char_2_x \\\n", "0 ppl_100 act2_1734928 2023-08-26 type 4 NaN NaN \n", "1 ppl_100 act2_2434093 2022-09-27 type 2 NaN NaN \n", "\n", " char_3_x char_4_x char_5_x char_6_x ... char_29 char_30 char_31 char_32 \\\n", "0 NaN NaN NaN NaN ... False True True False \n", "1 NaN NaN NaN NaN ... False True True False \n", "\n", " char_33 char_34 char_35 char_36 char_37 char_38 \n", "0 False True True True False 36 \n", "1 False True True True False 36 \n", "\n", "[2 rows x 55 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_train.head(2)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "31ce7dbd-b9a5-2488-90d1-027fa49c1336" }, "source": [ "**Things that needs to be fixed**\n", "\n", " - There are some dates in the future." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "56a8adda-1f92-f190-166b-db33c3e4a4e7" }, "source": [ "Test\n", "**Utility Functions**\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "0a941646-2759-04d3-77a8-dee490100103" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "c6b0e729-e7d9-ab9b-e51c-4279fc176073" }, "outputs": [], "source": [ "def data_cleanser(data, is_train):\n", " \n", "\n", " def adjust_dates(dates, diff):\n", " return dates - diff\n", " \n", " if(is_train):\n", " df_dates = data['date_x']\n", " diff = df_dates.max() - df_dates.min()\n", " diff2 = df_dates.max() - pd.Timestamp(pd.datetime.now().date())\n", " diffdays = diff + diff2\n", " data['adj_date'] = adjust_dates(data['date_x'], diffdays)\n", "\n", " \n", " #data.drop(['AnimalID','OutcomeSubtype'],axis=1, inplace=True)\n", " #data['OutcomeType'] = data['OutcomeType'].map({'Return_to_owner':4, 'Euthanasia':3, 'Adoption':0, 'Transfer':5, 'Died':2})\n", " \n", "\n", " # Convert Color to numeric classes\n", " #breed = preprocessing.LabelEncoder()\n", " #to convert into numbers\n", " #data.Breed = breed.fit_transform(data.Breed)\n", "\n", " \n", " return data.drop(['date_x'],axis=1)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "bf66f640-1f05-cef0-e740-d767e1babbae" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>people_id</th>\n", " <th>activity_id</th>\n", " <th>activity_category</th>\n", " <th>char_1_x</th>\n", " <th>char_2_x</th>\n", " <th>char_3_x</th>\n", " <th>char_4_x</th>\n", " <th>char_5_x</th>\n", " <th>char_6_x</th>\n", " <th>char_7_x</th>\n", " <th>...</th>\n", " <th>char_30</th>\n", " <th>char_31</th>\n", " <th>char_32</th>\n", " <th>char_33</th>\n", " <th>char_34</th>\n", " <th>char_35</th>\n", " <th>char_36</th>\n", " <th>char_37</th>\n", " <th>char_38</th>\n", " <th>adj_date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>ppl_100</td>\n", " <td>act2_1734928</td>\n", " <td>type 4</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " <td>2015-06-26</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ppl_100</td>\n", " <td>act2_2434093</td>\n", " <td>type 2</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " <td>2014-07-28</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>ppl_100</td>\n", " <td>act2_3404049</td>\n", " <td>type 2</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " <td>2014-07-28</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>ppl_100</td>\n", " <td>act2_3651215</td>\n", " <td>type 2</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " <td>2015-06-04</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>ppl_100</td>\n", " <td>act2_4109017</td>\n", " <td>type 2</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " <td>2015-06-26</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 55 columns</p>\n", "</div>" ], "text/plain": [ " people_id activity_id activity_category char_1_x char_2_x char_3_x \\\n", "0 ppl_100 act2_1734928 type 4 NaN NaN NaN \n", "1 ppl_100 act2_2434093 type 2 NaN NaN NaN \n", "2 ppl_100 act2_3404049 type 2 NaN NaN NaN \n", "3 ppl_100 act2_3651215 type 2 NaN NaN NaN \n", "4 ppl_100 act2_4109017 type 2 NaN NaN NaN \n", "\n", " char_4_x char_5_x char_6_x char_7_x ... char_30 char_31 char_32 \\\n", "0 NaN NaN NaN NaN ... True True False \n", "1 NaN NaN NaN NaN ... True True False \n", "2 NaN NaN NaN NaN ... True True False \n", "3 NaN NaN NaN NaN ... True True False \n", "4 NaN NaN NaN NaN ... True True False \n", "\n", " char_33 char_34 char_35 char_36 char_37 char_38 adj_date \n", "0 False True True True False 36 2015-06-26 \n", "1 False True True True False 36 2014-07-28 \n", "2 False True True True False 36 2014-07-28 \n", "3 False True True True False 36 2015-06-04 \n", "4 False True True True False 36 2015-06-26 \n", "\n", "[5 rows x 55 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_cleanser(df_train, True).head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "5adb05cf-4685-de59-a2b9-d28f61804e61" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 1071, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/330/330145.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "91e05212-752d-2239-4a7d-f04e9269ab24" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "3e6c4ed4-a43e-7b20-1557-3e346b176059" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "a00dcf77-1988-7a3d-54ca-6048ad5fc438" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "0da7ef22-05df-9149-8387-20e2dcedfc68" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "8d35cace-4211-fdeb-0460-df3edd09c1ac" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "394a6a74-801a-9a37-a8bf-5f1061185eb5" }, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "plt.style.use('ggplot')\n", "%matplotlib inline\n", "\n", "pd.options.mode.chained_assignment = None" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "206e7b0f-d771-4f9e-94b6-46114572dd74" }, "outputs": [], "source": [ "df_train = pd.read_csv(\"../input/train.csv\")\n", "df_test = pd.read_csv(\"../input/test.csv\")" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "7af4c97c-6323-cb1a-03db-8bdbb39c11ee" }, "source": [] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "1d06b4ff-bdbb-4ff2-215c-f973f9c8322b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of observations in the training set: 891 (70%)\n", "Number of observations in the test set: 418 (30%)\n" ] } ], "source": [ "n_train = df_train.shape[0]\n", "n_test = df_test.shape[0]\n", "ratio = round(n_train / (n_train + n_test),1)\n", "\n", "print(\"Number of observations in the training set: %d (%d%%)\" % (n_train, ratio*100))\n", "print(\"Number of observations in the test set: %d (%d%%)\" % (n_test, (1-ratio)*100))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "421e52c5-552a-72bf-6e5b-a96fd8616c88" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "5c48aff1-0421-0d33-0145-1c28ddf8502c" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b48f3f0e-7c0f-16a9-e5bd-a7b9eeb1ae89" }, "source": [] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "ec9f52f3-182d-9968-e4bd-5f48852044b1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 891 entries, 0 to 890\n", "Data columns (total 12 columns):\n", "PassengerId 891 non-null int64\n", "Survived 891 non-null int64\n", "Pclass 891 non-null int64\n", "Name 891 non-null object\n", "Sex 891 non-null object\n", "Age 714 non-null float64\n", "SibSp 891 non-null int64\n", "Parch 891 non-null int64\n", "Ticket 891 non-null object\n", "Fare 891 non-null float64\n", "Cabin 204 non-null object\n", "Embarked 889 non-null object\n", "dtypes: float64(2), int64(5), object(5)\n", "memory usage: 83.6+ KB\n" ] } ], "source": [ "df_train.info()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "1d37149c-bad0-9d1b-6ce7-788579927265" }, "source": [ "We have 891 observations available accros 11 features and 1 target variable (Survived). \n", "A detailed description of each variable is provided by Kaggle: https://www.kaggle.com/c/titanic/data" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "ee6b819d-6757-8f12-87bc-2156cdbc4ae2" }, "source": [] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "c07cd802-b702-80a0-105e-b521f85672ad" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>51</th>\n", " <td>52</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Nosworthy, Mr. Richard Cater</td>\n", " <td>male</td>\n", " <td>21.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>A/4. 39886</td>\n", " <td>7.8000</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>557</th>\n", " <td>558</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>Robbins, Mr. Victor</td>\n", " <td>male</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>PC 17757</td>\n", " <td>227.5250</td>\n", " <td>NaN</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>751</th>\n", " <td>752</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Moor, Master. Meier</td>\n", " <td>male</td>\n", " <td>6.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>392096</td>\n", " <td>12.4750</td>\n", " <td>E121</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>18</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>Williams, Mr. Charles Eugene</td>\n", " <td>male</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>244373</td>\n", " <td>13.0000</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>477</th>\n", " <td>478</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Lewis Richard</td>\n", " <td>male</td>\n", " <td>29.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3460</td>\n", " <td>7.0458</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>270</th>\n", " <td>271</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>Cairns, Mr. Alexander</td>\n", " <td>male</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>113798</td>\n", " <td>31.0000</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>52</th>\n", " <td>53</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Harper, Mrs. Henry Sleeper (Myna Haxtun)</td>\n", " <td>female</td>\n", " <td>49.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17572</td>\n", " <td>76.7292</td>\n", " <td>D33</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>537</th>\n", " <td>538</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>LeRoy, Miss. Bertha</td>\n", " <td>female</td>\n", " <td>30.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>PC 17761</td>\n", " <td>106.4250</td>\n", " <td>NaN</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>365</th>\n", " <td>366</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Adahl, Mr. Mauritz Nils Martin</td>\n", " <td>male</td>\n", " <td>30.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>C 7076</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>163</th>\n", " <td>164</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Calic, Mr. Jovo</td>\n", " <td>male</td>\n", " <td>17.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>315093</td>\n", " <td>8.6625</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass Name \\\n", "51 52 0 3 Nosworthy, Mr. Richard Cater \n", "557 558 0 1 Robbins, Mr. Victor \n", "751 752 1 3 Moor, Master. Meier \n", "17 18 1 2 Williams, Mr. Charles Eugene \n", "477 478 0 3 Braund, Mr. Lewis Richard \n", "270 271 0 1 Cairns, Mr. Alexander \n", "52 53 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) \n", "537 538 1 1 LeRoy, Miss. Bertha \n", "365 366 0 3 Adahl, Mr. Mauritz Nils Martin \n", "163 164 0 3 Calic, Mr. Jovo \n", "\n", " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", "51 male 21.0 0 0 A/4. 39886 7.8000 NaN S \n", "557 male NaN 0 0 PC 17757 227.5250 NaN C \n", "751 male 6.0 0 1 392096 12.4750 E121 S \n", "17 male NaN 0 0 244373 13.0000 NaN S \n", "477 male 29.0 1 0 3460 7.0458 NaN S \n", "270 male NaN 0 0 113798 31.0000 NaN S \n", "52 female 49.0 1 0 PC 17572 76.7292 D33 C \n", "537 female 30.0 0 0 PC 17761 106.4250 NaN C \n", "365 male 30.0 0 0 C 7076 7.2500 NaN S \n", "163 male 17.0 0 0 315093 8.6625 NaN S " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_train.sample(10)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "86cded04-262e-8a55-fefd-2d58a536a98b" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "988389dd-cdff-cdab-f7c5-7a52ffd16470" }, "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "d8adf5a5-8127-44ab-bb52-a0ccf60bea32" }, "outputs": [], "source": [ "df_titanic = df_train.drop([\"Ticket\", \"Cabin\", \"Name\"], axis=1)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "cef263da-afad-14fa-0f6d-c4ad09a84826" }, "outputs": [], "source": [ "df_titanic_na = df_titanic.dropna()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "7c75fe91-4e20-e822-76e3-602f6555600c" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "03669e27-d5d2-53cb-e514-5bf8d9110e4a" }, "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "720f9d95-5c6d-f872-3e18-9b4576430d6c" }, "outputs": [], "source": [ "df_titanic_na.Sex = df_titanic.Sex.map({\"female\": 0, \"male\": 1})\n", "df_titanic_na.Embarked = df_titanic.Embarked.map({\"C\": 0, \"Q\": 1, \"S\": 2})" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d29c6b32-cfcf-67ce-6621-e57c778f19f4" }, "source": [] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "ce24b5d1-e2f7-256e-fa12-90830cb3c624" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>7.2500</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>71.2833</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.9250</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>53.1000</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8.0500</td>\n", " <td>2.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass Sex Age SibSp Parch Fare Embarked\n", "0 1 0 3 1 22.0 1 0 7.2500 2.0\n", "1 2 1 1 0 38.0 1 0 71.2833 0.0\n", "2 3 1 3 0 26.0 0 0 7.9250 2.0\n", "3 4 1 1 0 35.0 1 0 53.1000 2.0\n", "4 5 0 3 1 35.0 0 0 8.0500 2.0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_titanic_na.head()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "5e47080e-df0f-fd5c-bfbe-409d8f5603c7" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "152f305c-ad68-2feb-a9ee-3b5a7f9ba392" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "97068055-abdb-8e37-bc96-78bc3f146767" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d8577c8c-32a4-3d3b-d00f-ee5f1857a211" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c4015029-a19b-7fde-6f91-332adddd7a9e" }, "source": [] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "dc044219-96de-061c-c560-1d89b9299103" }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.text.Text at 0x7ffa14a8d978>,\n", " <matplotlib.text.Text at 0x7ffa14a779e8>]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ffa14abd5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.distplot(df_titanic_na.Survived, color=\"red\", hist_kws={\"alpha\": 0.3}, kde=None)\n", "g.set_xticks([0,1])\n", "g.autoscale()\n", "g.set_xticklabels([\"Dead\", \"Survived\"])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "dd84fc38-e988-7564-a864-c19ca06422b5" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "739b07e4-efa2-50eb-4702-a6e46b3c59e1" }, "source": [] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "729af345-dd60-614a-cb92-b2bb341f7836" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa14a896a0>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ffa14a4c860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "corrmat = df_titanic_na[['Survived', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch',\n", " 'Fare', 'Embarked']].corr()\n", "\n", "f, ax = plt.subplots(figsize=(10, 7))\n", "sns.heatmap(corrmat, vmax=.8, square=True)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "5f59d5f8-e5a1-edf9-9520-681b862ec645" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "50eb32fe-30c0-2eaf-a600-4292939601a4" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "6a9661d0-9aa7-cf60-4bd9-2eebb0b4eeff" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "849fe60d-8d07-1dbc-ca5f-0f08c9ce7910" }, "source": [] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "25ac4b26-628d-1dd1-a24c-c1cb53ffcbf3" }, "outputs": [], "source": [ "survived = df_titanic_na.Survived == 1\n", "died = df_titanic_na.Survived == 0" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "449c68aa-2650-cb51-263c-bcee505390e6" }, "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "a886e639-6960-1c07-2c96-64e17e59cfe8" }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.text.Text at 0x7ffa11b60c88>,\n", " <matplotlib.text.Text at 0x7ffa11b7a518>,\n", " <matplotlib.text.Text at 0x7ffa11af7710>,\n", " <matplotlib.text.Text at 0x7ffa11afc240>,\n", " <matplotlib.text.Text at 0x7ffa11afcd30>]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1p1Wqzo6n1WqNdhUmlBlTZ4x2FSYUj88ttVotjpt13GhXo+tqhcydwIyImA78FDgTePsw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\nJFVjyEiSqjFkJEnVGDKSpGoMGUlSNf8f34fotePNnGgAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa11bda048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.distplot(df_titanic_na.Embarked, color=\"darkgreen\", hist_kws={\"alpha\": 0.3}, kde=None)\n", "g.set_xticklabels([\"Cherbourg\", \"\", \"Queenstown\", \"\", \"Southampton\"])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "112b1dbc-d7b9-61fb-6ee4-83a8a4782aee" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2982ea0a-7dd4-6c2e-89b8-c705a18bb728" }, "source": [] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "78a7eea2-f001-ca20-ca67-8d0f5d02ed02" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa11ae7b70>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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A35QJlC1bS/nX67jluRnsSt3Fiv0rmNBzAqEBoc26LSE6Kjn1JJpdpc2GLS2NoIsuwmyp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\ntUfWMjJ+JINjBjc635lbgHX+EhyHkrD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2Pyv5Lu6hPYBpuOG2UVXdh0siqViq9UwtLVVt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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa11b2ab70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(df_titanic_na[survived].Age, color=\"darkgreen\", hist_kws={\"alpha\": 0.3})\n", "sns.distplot(df_titanic_na[died].Age, color=\"darkred\", hist_kws={\"alpha\": 0.3})" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "395eeb38-057b-344d-fd8e-55d24e1266e0" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "616ce3f6-a6b2-9375-a81b-753377ae1db1" }, "source": [] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "7076a02f-7c79-3b3b-67dd-667ddb2120bb" }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x7ffa11a54b38>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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m5qOBfxzxc+8LHA18spPfU+N7wDHNz3gO8JGIeFyz783A55pzLaUOeyLi+dR/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\niKEqSVKgXr6bAAAAB0lEQVQh/x94qBCWDKbiGAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffa11a54ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.factorplot(x=\"Pclass\", y=\"Survived\", hue=\"Sex\", data=df_titanic_na,\n", " size=6, kind=\"bar\", palette=\"muted\", ci=None)\n", "g.despine(left=True)\n", "g.set_ylabels(\"Survival Probability\")\n", "g.set_xlabels(\"Passenger Class\")" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "4ae77449-1641-9e46-99cd-ea3e33b1b0fe" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e7b9a1cf-af32-4f8e-980c-b7145bb35c24" }, "source": [] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "5ff3dc4a-88ec-64e6-e863-970c508674b4" }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x7ffa11987eb8>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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tM2TmLZl5CbCuh3VJklRUL8N1EXBD2/Mbm2mSJM0oPT3nOhUWLlzInDlzJrXM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\nuSIiHgesBW5tzrP+AxsPXj6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"text/plain": [ "<matplotlib.figure.Figure at 0x7ffa11928438>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.factorplot(x=\"Embarked\", y=\"Survived\", hue=\"Pclass\", data=df_titanic_na,\n", " size=6, kind=\"bar\", palette=\"muted\", ci=None)\n", "g.despine(left=True)\n", "g.set_ylabels(\"Survival Probability\")\n", "g.set_xlabels(\"Embarkation Port\")\n", "g.set_xticklabels([\"Cherbourg\", \"Queenstown\", \"Southampton\"])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "5c58f3cf-85d4-3cd7-4151-8e1eaa43e6c3" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "428797ba-accf-a577-4d04-cb21a7b3a208" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "47960abc-9f55-e833-585e-d07f58a18d13" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c067386f-c560-94fe-bc50-5c44ba2de99e" }, "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "81d23ea4-e2d6-641d-dd6e-e50b4d257786" }, "outputs": [], "source": [ "df_titanic_ml = df_titanic.copy()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "41f14639-ecda-7699-d8b3-67718d042c2f" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2685f379-aa9f-661f-f2d3-f27ea5590b24" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c379d0b3-05db-2a1f-4302-aec511d8bfae" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "60913b69-b74d-1e95-27a1-0acf92e4f3f9" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e0be3287-c433-b810-807b-0ddf83a9c8e1" }, "source": [] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "fc88cb8e-05da-42f9-2a0a-e6d260ee183c" }, "outputs": [], "source": [ "df_titanic_ml.Embarked = df_titanic_ml.Embarked.fillna(\"Southampton\") " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "0a1db856-0a1d-c9d9-a76a-7d54e4debd0e" }, "outputs": [ { "data": { "text/plain": [ "(0, 9)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_titanic_ml[df_titanic_ml.Embarked.isnull()].shape" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "dd9b88ab-fa7e-5156-2e47-9fc6e2cb283c" }, "source": [] }, { "cell_type": "code", "execution_count": 20, "metadata": { "_cell_guid": "716af4a0-fe33-7103-86ce-47ffccb28b49" }, "outputs": [ { "data": { "text/plain": [ "(177, 9)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "null_age = df_titanic_ml.Age.isnull()\n", "df_titanic_ml[null_age].shape" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "fa05e0bc-6b48-1877-1630-5bce2fcdf620" }, "source": [] }, { "cell_type": "code", "execution_count": 21, "metadata": { "_cell_guid": "182fb9b1-706c-266c-b01d-85c26c624057" }, "outputs": [], "source": [ "df_titanic_ml = df_titanic_ml[np.invert(null_age)]" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "04049f19-7a60-8f40-7340-314556b47100" }, "source": [] }, { "cell_type": "code", "execution_count": 22, "metadata": { "_cell_guid": "b6500eee-d396-4733-126c-56f0b532a133" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 714 entries, 0 to 890\n", "Data columns (total 9 columns):\n", "PassengerId 714 non-null int64\n", "Survived 714 non-null int64\n", "Pclass 714 non-null int64\n", "Sex 714 non-null object\n", "Age 714 non-null float64\n", "SibSp 714 non-null int64\n", "Parch 714 non-null int64\n", "Fare 714 non-null float64\n", "Embarked 714 non-null object\n", "dtypes: float64(2), int64(5), object(2)\n", "memory usage: 55.8+ KB\n" ] } ], "source": [ "df_titanic_ml.info()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "af2a7934-0a69-bdd0-1e09-d7cedbcd8fd8" }, "source": [] }, { "cell_type": "code", "execution_count": 23, "metadata": { "_cell_guid": "6ac1ae5f-933f-3d96-03e4-ccffc6f02519" }, "outputs": [], "source": [ "df_titanic_ml.Sex = df_titanic.Sex.map({\"female\": 0, \"male\": 1})\n", "df_titanic_ml.Embarked = df_titanic.Embarked.map({\"C\": 0, \"Q\": 1, \"S\": 2})" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "_cell_guid": "bdf87cc9-cacc-1668-22a6-dba022b15391" }, "outputs": [], "source": [ "emb_dummies = pd.get_dummies(df_titanic_ml.Embarked, prefix=\"Embarked\")\n", "df_titanic_ml = df_titanic_ml.join(emb_dummies)\n", "df_titanic_ml.drop(\"Embarked\", axis=1, inplace=True)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9b74f495-c9ed-613f-9191-86030dfd2a7d" }, "source": [] }, { "cell_type": "code", "execution_count": 25, "metadata": { "_cell_guid": "5caa79b8-3b8d-c270-1295-a9c025645d33" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PassengerId</th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " <th>Embarked_0.0</th>\n", " <th>Embarked_1.0</th>\n", " <th>Embarked_2.0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>7.2500</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>71.2833</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>7.9250</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>53.1000</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>8.0500</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " PassengerId Survived Pclass Sex Age SibSp Parch Fare \\\n", "0 1 0 3 1 22.0 1 0 7.2500 \n", "1 2 1 1 0 38.0 1 0 71.2833 \n", "2 3 1 3 0 26.0 0 0 7.9250 \n", "3 4 1 1 0 35.0 1 0 53.1000 \n", "4 5 0 3 1 35.0 0 0 8.0500 \n", "\n", " Embarked_0.0 Embarked_1.0 Embarked_2.0 \n", "0 0.0 0.0 1.0 \n", "1 1.0 0.0 0.0 \n", "2 0.0 0.0 1.0 \n", "3 0.0 0.0 1.0 \n", "4 0.0 0.0 1.0 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_titanic_ml.head()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "8436cb6f-a0e8-a7d0-3040-368f38a36c76" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d76f130d-0405-f6e7-c1d6-c8de987b5a0f" }, "source": [] }, { "cell_type": "code", "execution_count": 26, "metadata": { "_cell_guid": "fb48668f-9fc2-e4dc-09f2-ab92296ade97" }, "outputs": [], "source": [ "age_over_18 = df_titanic_ml.Age > 18\n", "women = df_titanic_ml.Sex == 0\n", "with_parch = df_titanic_ml.Parch > 0" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "_cell_guid": "1d4d0adb-876e-4ab9-b2a4-71b53965aab2" }, "outputs": [], "source": [ "df_titanic_ml[\"is_mother\"] = 0\n", "df_titanic_ml[\"is_mother\"][women & age_over_18 & with_parch] = 1" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2a6cbb6a-8a9b-8f88-a8d1-1a6473f91031" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "8359bd04-9eb7-dec8-2fa8-5de4bfd908c1" }, "source": [] }, { "cell_type": "code", "execution_count": 28, "metadata": { "_cell_guid": "36d95660-621a-fac0-48ed-b5129f23d8ae" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "from sklearn import cross_validation, metrics\n", "from sklearn.ensemble import RandomForestClassifier" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "_cell_guid": "067878ec-6ebf-f39f-7088-787f493edd88" }, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = cross_validation.train_test_split(\n", " df_titanic_ml.drop([\"PassengerId\", \"Survived\"], axis=1), df_titanic_ml.Survived, test_size=0.3, random_state=0)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "64162dc1-7ace-a926-b8e1-731a878fd38d" }, "source": [] }, { "cell_type": "code", "execution_count": 30, "metadata": { "_cell_guid": "7d88a286-2151-d1e0-0260-6fe13155ad9e" }, "outputs": [ { "data": { "text/plain": [ "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=1000, n_jobs=-1, oob_score=False,\n", " random_state=None, verbose=0, warm_start=False)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "forest = RandomForestClassifier(n_estimators=1000, n_jobs=-1)\n", "forest.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e9e50019-13ea-cd19-c4a6-5a337db9a702" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "7fde0d04-aa02-4a16-cd9e-296d4bd139ad" }, "source": [] }, { "cell_type": "code", "execution_count": 31, "metadata": { "_cell_guid": "e315573a-d85a-2e69-a15c-974105043a99" }, "outputs": [], "source": [ "def forest_metrics(X_test, y_test, clf): \n", " \n", " f_preds = clf.predict_proba(X_test)[:, 1]\n", " f_fpr, f_tpr, _ = metrics.roc_curve(y_test, f_preds)\n", "\n", " fig, ax = plt.subplots()\n", " ax.plot(f_fpr, f_tpr)\n", " lims = [\n", " np.min([ax.get_xlim(), ax.get_ylim()]), # min of both axes\n", " np.max([ax.get_xlim(), ax.get_ylim()]), # max of both axes\n", " ]\n", "\n", " print(\"Model Accuracy: %.1f%%\" % (clf.score(X_test,y_test) * 100))\n", " print (\"Model ROC AUC: %.1f%%\" % (metrics.roc_auc_score(y_test, f_preds)*100))\n", " print(\"ROC Curve\")\n", " ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "_cell_guid": "57adb9a2-5821-486c-5f78-719ec3ac41dc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model Accuracy: 74.9%\n", "Model ROC AUC: 83.3%\n", "ROC Curve\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ffa11bda7f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "forest_metrics(X_test,y_test, forest)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "bbd93299-4d8b-9876-3741-2ded1d39d584" }, "source": [] }, { "cell_type": "code", "execution_count": 33, "metadata": { "_cell_guid": "82a16307-839a-001a-a5b0-7a7926b3939b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[99 26]\n", " [28 62]]\n" ] } ], "source": [ "print(metrics.confusion_matrix(y_test, forest.predict(X_test)))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "_cell_guid": "e2c8f840-9064-a4e3-2d78-bc88b12a615d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.78 0.79 0.79 125\n", " 1 0.70 0.69 0.70 90\n", "\n", "avg / total 0.75 0.75 0.75 215\n", "\n" ] } ], "source": [ "print(metrics.classification_report(y_test, forest.predict(X_test)))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "b38098c3-8965-bf3a-f7be-597756ba6e5b" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "42baa861-a90d-e11f-a3a5-519fb1b49828" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "87c17093-a51e-98ac-4e7d-b1276a59984c" }, "source": [] }, { "cell_type": "code", "execution_count": 35, "metadata": { "_cell_guid": "46ff4dde-191c-328b-6c03-be9f9bd925f9" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 35, "metadata": { "_cell_guid": "d261475d-1c7f-bf81-bcdf-61725339185a" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 807, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/330/330183.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "28299261-b289-3397-3746-a5a061baa365" }, "outputs": [], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "62fd6b96-ea2e-7e83-ae93-2d8e92ad6c9f" }, "outputs": [], "source": [ "#dependencies\n", "import datetime as datetime\n", "from sklearn import preprocessing\n", "import brewer2mpl" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "18bcaf30-5a52-2411-5442-c0cda003e044" }, "source": [ "**Looking at the data**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "ed2dc800-c433-0b21-e014-e51fd12b8545" }, "outputs": [], "source": [ "train = pd.read_csv('../input/act_train.csv', parse_dates=['date'])\n", "test = pd.read_csv('../input/act_test.csv', parse_dates=['date'])\n", "ppl = pd.read_csv('../input/people.csv', parse_dates=['date'])\n", "\n", "df_train = pd.merge(train, ppl, on='people_id')\n", "df_test = pd.merge(test, ppl, on='people_id')\n", "del train, test, ppl" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "d062084d-5635-47c8-9dff-d5c4ff716e36" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start of date_x: 2022-07-17\n", " End of date_x: 2023-08-31\n", "Range of date_x: 410 days 00:00:00\n", "\n", "Start of date_y: 2020-05-18\n", " End of date_y: 2023-08-31\n", "Range of date_y: 1200 days 00:00:00\n", "\n" ] } ], "source": [ "for d in ['date_x', 'date_y']:\n", " print('Start of ' + d + ': ' + str(df_train[d].min().date()))\n", " print(' End of ' + d + ': ' + str(df_train[d].max().date()))\n", " print('Range of ' + d + ': ' + str(df_train[d].max() - df_train[d].min()) + '\\n')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "b979db16-dda4-cfdd-0ab7-66c8f03efbbd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1221794 975497\n", "[0 1]\n" ] } ], "source": [ "df_train.head(2)\n", "\n", "\n", "plus = sum(df_train.loc[:, 'outcome'] == 0)\n", "minus = sum(df_train.loc[:, 'outcome'] == 1)\n", "\n", "print (plus, minus)\n", "print (df_train['outcome'].unique())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "96f49b86-98d3-40f5-a0d1-37a101388beb" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['seaborn-darkgrid', 'classic', 'seaborn-ticks', 'seaborn-notebook', 'seaborn-whitegrid', 'seaborn-white', 'seaborn-muted', 'ggplot', 'seaborn-colorblind', 'seaborn-deep', 'bmh', 'seaborn-dark', 'seaborn-talk', 'seaborn-bright', 'seaborn-pastel', 'seaborn-dark-palette', 'seaborn-poster', 'seaborn-paper', 'fivethirtyeight', 'grayscale', 'dark_background']\n" ] } ], "source": [ "set2 = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors\n", "\n", "font = {'family' : 'sans-serif',\n", " 'color' : 'teal',\n", " 'weight' : 'bold',\n", " 'size' : 18,\n", " }\n", "plt.rc('font',family='serif')\n", "plt.rc('font', size=16)\n", "plt.rc('font', weight='bold')\n", "#plt.style.use('seaborn-poster')\n", "#plt.style.use('bmh')\n", "#plt.style.use('ggplot')\n", "plt.style.use('seaborn-dark-palette')\n", "#plt.style.use('presentation')\n", "print (plt.style.available)\n", "\n", "# Get current size\n", "fig_size = plt.rcParams[\"figure.figsize\"]\n", " \n", "# Set figure width to 6 and height to 6\n", "fig_size[0] = 6\n", "fig_size[1] = 6\n", "plt.rcParams[\"figure.figsize\"] = fig_size" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "16dbf002-c92b-9f88-501d-d575afe85d47" }, "outputs": [ { "data": { "image/png": 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8IIkGg16HpJQaBAGx+EnV1NC5wdFTQ1grRWSG1zGpwaWJtAhoElWqvIkIn46M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\nlVGcfCT+lhRpLyP5IXx/AhYssPDhh3HHKV25HLB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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0c99738668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import rcParams\n", "rcParams['font.size'] = 12\n", "#print (rcParams.keys())\n", "rcParams['text.color'] = 'black'\n", "\n", "piechart = plt.pie(\n", " (minus, plus),\n", " labels=('plus', 'minus'),\n", " shadow=False,\n", " colors=('teal', 'crimson'),\n", " explode=(0.08,0.08), # space between slices \n", " startangle=90, # rotate conter-clockwise by 90 degrees\n", " autopct='%1.1f%%',# display fraction as percentages\n", ")\n", "\n", "plt.axis('equal') \n", "plt.title(\"Animal Shelter Outcome Train Data\", y=1.08,fontdict=font)\n", "plt.tight_layout()\n", "plt.savefig('TWP-Status-Groups-train.png', bbox_inches='tight')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "31ce7dbd-b9a5-2488-90d1-027fa49c1336" }, "source": [ "**Things that needs to be fixed**\n", "\n", " - There are some dates in the future." ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "56a8adda-1f92-f190-166b-db33c3e4a4e7" }, "source": [ "Test\n", "**Utility Functions**\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "0a941646-2759-04d3-77a8-dee490100103" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "c6b0e729-e7d9-ab9b-e51c-4279fc176073" }, "outputs": [], "source": [ "def data_cleanser(data, is_train):\n", " \n", "\n", " def adjust_dates(dates, diff):\n", " return dates - diff\n", " \n", " if(is_train):\n", " df_dates = data['date_x']\n", " diff = df_dates.max() - df_dates.min()\n", " diff2 = df_dates.max() - pd.Timestamp(pd.datetime.now().date())\n", " diffdays = diff + diff2\n", " data['adj_date'] = adjust_dates(data['date_x'], diffdays)\n", "\n", " \n", " #data.drop(['AnimalID','OutcomeSubtype'],axis=1, inplace=True)\n", " #data['OutcomeType'] = data['OutcomeType'].map({'Return_to_owner':4, 'Euthanasia':3, 'Adoption':0, 'Transfer':5, 'Died':2})\n", " \n", "\n", " # Convert Color to numeric classes\n", " #breed = preprocessing.LabelEncoder()\n", " #to convert into numbers\n", " #data.Breed = breed.fit_transform(data.Breed)\n", "\n", " \n", " return data.drop(['date_x'],axis=1)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "bf66f640-1f05-cef0-e740-d767e1babbae" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>people_id</th>\n", " <th>activity_id</th>\n", " <th>activity_category</th>\n", " <th>char_1_x</th>\n", " <th>char_2_x</th>\n", " <th>char_3_x</th>\n", " <th>char_4_x</th>\n", " <th>char_5_x</th>\n", " <th>char_6_x</th>\n", " <th>char_7_x</th>\n", " <th>...</th>\n", " <th>char_30</th>\n", " <th>char_31</th>\n", " <th>char_32</th>\n", " <th>char_33</th>\n", " <th>char_34</th>\n", " <th>char_35</th>\n", " <th>char_36</th>\n", " <th>char_37</th>\n", " <th>char_38</th>\n", " <th>adj_date</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>ppl_100</td>\n", " <td>act2_1734928</td>\n", " <td>type 4</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " <td>2015-06-26</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ppl_100</td>\n", " <td>act2_2434093</td>\n", " <td>type 2</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " <td>2014-07-28</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>ppl_100</td>\n", " <td>act2_3404049</td>\n", " <td>type 2</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " <td>2014-07-28</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>ppl_100</td>\n", " <td>act2_3651215</td>\n", " <td>type 2</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " <td>2015-06-04</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>ppl_100</td>\n", " <td>act2_4109017</td>\n", " <td>type 2</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>False</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>True</td>\n", " <td>False</td>\n", " <td>36</td>\n", " <td>2015-06-26</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 55 columns</p>\n", "</div>" ], "text/plain": [ " people_id activity_id activity_category char_1_x char_2_x char_3_x \\\n", "0 ppl_100 act2_1734928 type 4 NaN NaN NaN \n", "1 ppl_100 act2_2434093 type 2 NaN NaN NaN \n", "2 ppl_100 act2_3404049 type 2 NaN NaN NaN \n", "3 ppl_100 act2_3651215 type 2 NaN NaN NaN \n", "4 ppl_100 act2_4109017 type 2 NaN NaN NaN \n", "\n", " char_4_x char_5_x char_6_x char_7_x ... char_30 char_31 char_32 \\\n", "0 NaN NaN NaN NaN ... True True False \n", "1 NaN NaN NaN NaN ... True True False \n", "2 NaN NaN NaN NaN ... True True False \n", "3 NaN NaN NaN NaN ... True True False \n", "4 NaN NaN NaN NaN ... True True False \n", "\n", " char_33 char_34 char_35 char_36 char_37 char_38 adj_date \n", "0 False True True True False 36 2015-06-26 \n", "1 False True True True False 36 2014-07-28 \n", "2 False True True True False 36 2014-07-28 \n", "3 False True True True False 36 2015-06-04 \n", "4 False True True True False 36 2015-06-26 \n", "\n", "[5 rows x 55 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_cleanser(df_train, True).head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "5adb05cf-4685-de59-a2b9-d28f61804e61" }, "outputs": [], "source": [] } ], "metadata": { "_change_revision": 152, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/330/330287.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "a7d8cae9-82e9-24dd-249c-0b68700ccfce" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d5430385-f240-17d1-f7bb-ba855aa9f3f9" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "a8afd93b-46e9-5efe-08c3-94f77744b3e7" }, "outputs": [], "source": [ "import numpy as np \n", "import pandas as pd \n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "plt.rcParams['figure.figsize'] = (12.0, 6.0)\n", "plt.rcParams['axes.titlesize'] = 16\n", "plt.rcParams['axes.titleweight'] = 'bold'\n", "plt.rcParams[\"axes.labelsize\"] = 13\n", "plt.rcParams[\"axes.labelweight\"] = 'bold'\n", "plt.rcParams[\"xtick.labelsize\"] = 12\n", "plt.rcParams[\"ytick.labelsize\"] = 12\n", "sns.set_style('whitegrid')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "96abac92-440a-9e46-fd23-2a49fa0038f5" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass \\\n", "PassengerId \n", "1 0 3 \n", "2 1 1 \n", "3 1 3 \n", "4 1 1 \n", "5 0 3 \n", "\n", " Name Sex Age \\\n", "PassengerId \n", "1 Braund, Mr. Owen Harris male 22.0 \n", "2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", "3 Heikkinen, Miss. Laina female 26.0 \n", "4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", "5 Allen, Mr. William Henry male 35.0 \n", "\n", " SibSp Parch Ticket Fare Cabin Embarked \n", "PassengerId \n", "1 1 0 A/5 21171 7.2500 NaN S \n", "2 1 0 PC 17599 71.2833 C85 C \n", "3 0 0 STON/O2. 3101282 7.9250 NaN S \n", "4 1 0 113803 53.1000 C123 S \n", "5 0 0 373450 8.0500 NaN S " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('../input/train.csv', index_col='PassengerId')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "5a94b306-0c63-6998-bf02-8c232c70d809" }, "source": [] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "747c00e4-564a-76dc-d517-96f6d7827ba4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 891 entries, 1 to 891\n", "Data columns (total 11 columns):\n", "Survived 891 non-null int64\n", "Pclass 891 non-null int64\n", "Name 891 non-null object\n", "Sex 891 non-null object\n", "Age 714 non-null float64\n", "SibSp 891 non-null int64\n", "Parch 891 non-null int64\n", "Ticket 891 non-null object\n", "Fare 891 non-null float64\n", "Cabin 204 non-null object\n", "Embarked 889 non-null object\n", "dtypes: float64(2), int64(4), object(5)\n", "memory usage: 83.5+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "6709216a-9c10-e97f-ceae-8e810c59e97a" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Fare</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>183.000000</td>\n", " <td>183.000000</td>\n", " <td>183.000000</td>\n", " <td>183.000000</td>\n", " <td>183.000000</td>\n", " <td>183.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>0.672131</td>\n", " <td>1.191257</td>\n", " <td>35.674426</td>\n", " <td>0.464481</td>\n", " <td>0.475410</td>\n", " <td>78.682469</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>0.470725</td>\n", " <td>0.515187</td>\n", " <td>15.643866</td>\n", " <td>0.644159</td>\n", " <td>0.754617</td>\n", " <td>76.347843</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>0.920000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>24.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>29.700000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>36.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>57.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>47.500000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>90.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>1.000000</td>\n", " <td>3.000000</td>\n", " <td>80.000000</td>\n", " <td>3.000000</td>\n", " <td>4.000000</td>\n", " <td>512.329200</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass Age SibSp Parch Fare\n", "count 183.000000 183.000000 183.000000 183.000000 183.000000 183.000000\n", "mean 0.672131 1.191257 35.674426 0.464481 0.475410 78.682469\n", "std 0.470725 0.515187 15.643866 0.644159 0.754617 76.347843\n", "min 0.000000 1.000000 0.920000 0.000000 0.000000 0.000000\n", "25% 0.000000 1.000000 24.000000 0.000000 0.000000 29.700000\n", "50% 1.000000 1.000000 36.000000 0.000000 0.000000 57.000000\n", "75% 1.000000 1.000000 47.500000 1.000000 1.000000 90.000000\n", "max 1.000000 3.000000 80.000000 3.000000 4.000000 512.329200" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dropna().describe()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "8f12ca61-906f-4ec3-7570-74f16ea5199f" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Ticket</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>891</td>\n", " <td>891</td>\n", " <td>891</td>\n", " <td>204</td>\n", " <td>889</td>\n", " </tr>\n", " <tr>\n", " <th>unique</th>\n", " <td>891</td>\n", " <td>2</td>\n", " <td>681</td>\n", " <td>147</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>top</th>\n", " <td>Silverthorne, Mr. Spencer Victor</td>\n", " <td>male</td>\n", " <td>CA. 2343</td>\n", " <td>G6</td>\n", " <td>S</td>\n", " </tr>\n", " <tr>\n", " <th>freq</th>\n", " <td>1</td>\n", " <td>577</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>644</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Name Sex Ticket Cabin Embarked\n", "count 891 891 891 204 889\n", "unique 891 2 681 147 3\n", "top Silverthorne, Mr. Spencer Victor male CA. 2343 G6 S\n", "freq 1 577 7 4 644" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.select_dtypes(include=['object']).describe()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "904b4c16-8a56-b89d-11fa-9c5ed9063aad" }, "source": [] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "2994831c-9147-9ac5-1afe-d0005b818d45" }, "outputs": [ { "data": { "text/plain": [ "0 549\n", "1 342\n", "Name: Survived, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Survived'].value_counts()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "29807dca-1c5a-4fa2-9e2c-d1b73eaa8f0f" }, "source": [] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "56c6abd6-498e-d08b-fe03-5559e0ad2fe7" }, "outputs": [ { "data": { "text/plain": [ "1 216\n", "2 184\n", "3 491\n", "Name: Pclass, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Pclass'].value_counts(sort=False)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "99e2829b-3c64-4d23-e0d5-814186cb5cbf" }, "source": [] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "fa52d1fd-9683-e89d-9fd5-71a2eac172d0" }, "outputs": [], "source": [ "Title_Dictionary = {\n", " \"Capt\": \"Officer\", \n", " \"Col\": \"Officer\",\n", " \"Major\": \"Officer\", \n", " \"Jonkheer\": \"Royalty\", \n", " \"Don\": \"Royalty\", \n", " \"Sir\" : \"Royalty\", \n", " \"Dr\": \"Officer\",\n", " \"Rev\": \"Officer\", \n", " \"the Countess\":\"Royalty\", \n", " \"Dona\": \"Royalty\", \n", " \"Mme\": \"Mrs\",\n", " \"Mlle\": \"Miss\",\n", " \"Ms\": \"Mrs\",\n", " \"Mr\" : \"Mr\",\n", " \"Mrs\" : \"Mrs\",\n", " \"Miss\" : \"Miss\",\n", " \"Master\" : \"Master\",\n", " \"Lady\" : \"Royalty\" \n", " } \n", "\n", "df['Title'] = df['Name'].apply(lambda x: Title_Dictionary[x.split(',')[1].split('.')[0].strip()])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "16e9fb19-e054-137e-9024-399860a546c5" }, "source": [] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "2ae9f067-acf1-8fed-ef55-d2842eeb48ea" }, "outputs": [ { "data": { "text/plain": [ "NaN 177\n", " 24.0 30\n", " 22.0 27\n", " 18.0 26\n", " 30.0 25\n", " 19.0 25\n", " 28.0 25\n", " 21.0 24\n", " 25.0 23\n", " 36.0 22\n", " 29.0 20\n", " 35.0 18\n", " 32.0 18\n", " 26.0 18\n", " 27.0 18\n", " 16.0 17\n", " 31.0 17\n", " 23.0 15\n", " 33.0 15\n", " 34.0 15\n", " 20.0 15\n", "Name: Age, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Age'].value_counts(dropna=False)[:20]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "9143695e-c73f-0c9a-4d93-8d8009850854" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fbb1123c518>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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2xQsvvBArVqyIL3/5y/GLX/zCPn3A/v3748SJE/Hcc8/F448/Hps3b47XX389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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbb1123c2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['Age'].hist(bins=20)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "fe228032-a236-efec-f319-307b4d05e265" }, "source": [] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "e831e5c3-943a-6841-e4cc-50810c8483cb" }, "outputs": [ { "data": { "text/plain": [ "0 608\n", "1 209\n", "2 28\n", "3 16\n", "4 18\n", "5 5\n", "8 7\n", "Name: SibSp, dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['SibSp'].value_counts(sort=False)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d8d7a9f9-5b22-e031-4a69-3e2226f80a66" }, "source": [] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "c15461bf-2cd3-510b-644b-32d72b39f000" }, "outputs": [ { "data": { "text/plain": [ "0 678\n", "1 118\n", "2 80\n", "3 5\n", "4 4\n", "5 5\n", "6 1\n", "Name: Parch, dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Parch'].value_counts(sort=False)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "59b2cfba-ccc6-70c2-5bbb-579b49681bd3" }, "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "bad1f92f-9e90-1143-1297-b7bb374e7de8" }, "outputs": [ { "data": { "text/plain": [ "8.0500 43\n", "13.0000 42\n", "7.8958 38\n", "7.7500 34\n", "26.0000 31\n", "10.5000 24\n", "7.9250 18\n", "7.7750 16\n", "26.5500 15\n", "7.2292 15\n", "0.0000 15\n", "7.2500 13\n", "7.8542 13\n", "8.6625 13\n", "7.2250 12\n", "9.5000 9\n", "16.1000 9\n", "24.1500 8\n", "15.5000 8\n", "56.4958 7\n", "Name: Fare, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Fare'].value_counts().head(20)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "eaf1cb74-c015-0fc3-a138-178e235b5044" }, "source": [] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "5b6525c1-0399-36af-fd5b-b65a2d6dcba9" }, "outputs": [ { "data": { "text/plain": [ "male 577\n", "female 314\n", "Name: Sex, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Sex'].value_counts()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9a973f2c-b255-f30a-f9cb-ed078cfe1f9a" }, "source": [] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "8c17f764-d094-a7d8-d89a-7a230951bd30" }, "outputs": [ { "data": { "text/plain": [ "CA. 2343 7\n", "347082 7\n", "1601 7\n", "CA 2144 6\n", "3101295 6\n", "347088 6\n", "382652 5\n", "S.O.C. 14879 5\n", "4133 4\n", "17421 4\n", "19950 4\n", "113760 4\n", "LINE 4\n", "349909 4\n", "2666 4\n", "113781 4\n", "347077 4\n", "W./C. 6608 4\n", "PC 17757 4\n", "F.C.C. 13529 3\n", "Name: Ticket, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Ticket'].value_counts()[:20]" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "96fbbda4-27e4-ce98-146e-8f3ce22bc668" }, "source": [] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "23a0452e-7b27-5b8a-805c-a3e7bbdc6859" }, "outputs": [ { "data": { "text/plain": [ "NaN 687\n", "C23 C25 C27 4\n", "B96 B98 4\n", "G6 4\n", "E101 3\n", "F33 3\n", "D 3\n", "C22 C26 3\n", "F2 3\n", "B20 2\n", "E121 2\n", "E67 2\n", "C65 2\n", "D36 2\n", "C52 2\n", "E24 2\n", "B18 2\n", "B35 2\n", "B77 2\n", "D20 2\n", "Name: Cabin, dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Cabin'].value_counts(dropna=False)[:20]" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "a027e774-546b-3416-3133-a7a505e89e11" }, "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "5bd807b0-9841-af20-4931-c303f888ae6e" }, "outputs": [ { "data": { "text/plain": [ "S 644\n", "C 168\n", "Q 77\n", "NaN 2\n", "Name: Embarked, dtype: int64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Embarked'].value_counts(dropna=False)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "af6869b9-18cb-40eb-3c3e-24183d6aa28a" }, "source": [] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "0266718f-7f5a-a527-79b6-22b0a0287fd3" }, "outputs": [], "source": [ "df['Embarked'].fillna('S', inplace=True)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "8a10922a-a3d5-012a-aafd-ad31bf62d407" }, "source": [] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "9a4b7286-cbdc-f8e7-64bc-83edc0c0c5f4" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fbb0e89fac8>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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16waZTGY0RW3BggVYsWIF+vXrh9atW8PKygrOzs4YOHAgoqKiMHjwYJNqb9GiBd566y28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\nRCQhhjARkYQYwkREEvr/fnKhjKJfYUIAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbb0e243c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.factorplot(x='Sex', y='Survived', data=df, kind='bar', size=5, ci=None)\n", "plt.title('Survival Rate by Gender')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "_cell_guid": "4d5ec401-42b0-a31d-a267-2c90460e4ef4" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fbb0e858d30>" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JEyZg3bp11dbRvn17rFq1Ct27d4eVlZXRFLXg4GCsXr0a/fr1g729PSwsLKBSqTBgwADM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\nIxqIsLAwhIWFQZIkwzJ/f38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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbb0e8920f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.factorplot(x='Pclass', y='Survived', data=df, kind='bar', size=5, ci=None)\n", "plt.title('Survival Rate by Class')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "_cell_guid": "bbe89f7d-b2db-1e2c-ed8d-45f265ebebc8" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fbb0e7ffd30>" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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XNG3aNJlMJh0/ftyRpQIAANw1h/Vopaena82aNRo4cKCKFi2qoKAgtW7dWitW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\n8+bKysqSJD3xxBMKCgpS1apVVbRoUUl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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbb0e843160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.factorplot(x='Pclass', y='Survived', hue='Sex', data=df, kind='bar', size=5, aspect=1.5, ci=None)\n", "plt.title('Survival Rate by Class and Gender')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "232e3106-9e6d-279b-078a-64623cec56d0" }, "source": [] }, { "cell_type": "code", "execution_count": 22, "metadata": { "_cell_guid": "c444fc03-5f3b-7d28-4336-abf63765c317" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th>Sex</th>\n", " <th colspan=\"3\" halign=\"left\">female</th>\n", " <th colspan=\"3\" halign=\"left\">male</th>\n", " </tr>\n", " <tr>\n", " <th>Pclass</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " </tr>\n", " <tr>\n", " <th>Title</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>Master</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>5.306667</td>\n", " <td>2.258889</td>\n", " <td>5.350833</td>\n", " </tr>\n", " <tr>\n", " <th>Miss</th>\n", " <td>29.744681</td>\n", " <td>22.390625</td>\n", " <td>16.123188</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Mr</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>41.580460</td>\n", " <td>32.768293</td>\n", " <td>28.724891</td>\n", " </tr>\n", " <tr>\n", " <th>Mrs</th>\n", " <td>40.400000</td>\n", " <td>33.547619</td>\n", " <td>33.515152</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Officer</th>\n", " <td>49.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>51.125000</td>\n", " <td>42.000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Royalty</th>\n", " <td>40.500000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>42.333333</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Sex female male \n", "Pclass 1 2 3 1 2 3\n", "Title \n", "Master NaN NaN NaN 5.306667 2.258889 5.350833\n", "Miss 29.744681 22.390625 16.123188 NaN NaN NaN\n", "Mr NaN NaN NaN 41.580460 32.768293 28.724891\n", "Mrs 40.400000 33.547619 33.515152 NaN NaN NaN\n", "Officer 49.000000 NaN NaN 51.125000 42.000000 NaN\n", "Royalty 40.500000 NaN NaN 42.333333 NaN NaN" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ages_mean = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='mean')\n", "ages_mean" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "_cell_guid": "49069a64-de99-a7ea-264b-6d023e104ecf" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th>Sex</th>\n", " <th colspan=\"3\" halign=\"left\">female</th>\n", " <th colspan=\"3\" halign=\"left\">male</th>\n", " </tr>\n", " <tr>\n", " <th>Pclass</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " </tr>\n", " <tr>\n", " <th>Title</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>Master</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>5.165475</td>\n", " <td>2.342634</td>\n", " <td>3.593608</td>\n", " </tr>\n", " <tr>\n", " <th>Miss</th>\n", " <td>12.629276</td>\n", " <td>13.374708</td>\n", " <td>9.697315</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Mr</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>14.148275</td>\n", " <td>11.850977</td>\n", " <td>10.490946</td>\n", " </tr>\n", " <tr>\n", " <th>Mrs</th>\n", " <td>12.779119</td>\n", " <td>10.229566</td>\n", " <td>10.031579</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Officer</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>11.432254</td>\n", " <td>14.020393</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Royalty</th>\n", " <td>10.606602</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>5.859465</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Sex female male \n", "Pclass 1 2 3 1 2 3\n", "Title \n", "Master NaN NaN NaN 5.165475 2.342634 3.593608\n", "Miss 12.629276 13.374708 9.697315 NaN NaN NaN\n", "Mr NaN NaN NaN 14.148275 11.850977 10.490946\n", "Mrs 12.779119 10.229566 10.031579 NaN NaN NaN\n", "Officer NaN NaN NaN 11.432254 14.020393 NaN\n", "Royalty 10.606602 NaN NaN 5.859465 NaN NaN" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ages_std = df.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='std')\n", "ages_std" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "_cell_guid": "8658ce95-1cc4-385e-9d1b-a7bed4b08aa3" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Age</th>\n", " <th>Title</th>\n", " <th>Sex</th>\n", " <th>Pclass</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>6</th>\n", " <td>32</td>\n", " <td>Mr</td>\n", " <td>male</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>34</td>\n", " <td>Mr</td>\n", " <td>male</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>30</td>\n", " <td>Mrs</td>\n", " <td>female</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>23</td>\n", " <td>Mr</td>\n", " <td>male</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>19</td>\n", " <td>Miss</td>\n", " <td>female</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>30</th>\n", " <td>34</td>\n", " <td>Mr</td>\n", " <td>male</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>32</th>\n", " <td>42</td>\n", " <td>Mrs</td>\n", " <td>female</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>33</th>\n", " <td>20</td>\n", " <td>Miss</td>\n", " <td>female</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>37</th>\n", " <td>37</td>\n", " <td>Mr</td>\n", " <td>male</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>43</th>\n", " <td>32</td>\n", " <td>Mr</td>\n", " <td>male</td>\n", " <td>3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Age Title Sex Pclass\n", "PassengerId \n", "6 32 Mr male 3\n", "18 34 Mr male 2\n", "20 30 Mrs female 3\n", "27 23 Mr male 3\n", "29 19 Miss female 3\n", "30 34 Mr male 3\n", "32 42 Mrs female 1\n", "33 20 Miss female 3\n", "37 37 Mr male 3\n", "43 32 Mr male 3" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def age_guesser(person):\n", " gender = person['Sex']\n", " mean_age = ages_mean[gender].loc[person['Title'], person['Pclass']]\n", " std = ages_std[gender].loc[person['Title'], person['Pclass']]\n", " persons_age = np.random.randint(mean_age - std, mean_age + std)\n", "# persons_age = median_ages[gender].loc[person['Title'], person['Pclass']]\n", " return persons_age\n", "\n", "unknown_age = df['Age'].isnull()\n", "people_w_unknown_age = df.loc[unknown_age, [\"Age\", \"Title\", \"Sex\", \"Pclass\"]]\n", "\n", "people_w_unknown_age['Age'] = people_w_unknown_age.apply(age_guesser, axis=1)\n", "people_w_unknown_age.head(10)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "7eff1d3a-f34f-c525-ab4d-4acf4e0f02d1" }, "source": [] }, { "cell_type": "code", "execution_count": 25, "metadata": { "_cell_guid": "f2f4dc82-2874-9d8e-b29a-273ac4a6075f" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " <th>Title</th>\n", " <th>new_age</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>male</td>\n", " <td>22.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>A/5 21171</td>\n", " <td>7.2500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>Mr</td>\n", " <td>22.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>female</td>\n", " <td>38.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>PC 17599</td>\n", " <td>71.2833</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " <td>Mrs</td>\n", " <td>38.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1</td>\n", " <td>3</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>female</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>7.9250</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>Miss</td>\n", " <td>26.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>female</td>\n", " <td>35.0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>113803</td>\n", " <td>53.1000</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " <td>Mrs</td>\n", " <td>35.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>male</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>373450</td>\n", " <td>8.0500</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>Mr</td>\n", " <td>35.0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Moran, Mr. James</td>\n", " <td>male</td>\n", " <td>NaN</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>330877</td>\n", " <td>8.4583</td>\n", " <td>NaN</td>\n", " <td>Q</td>\n", " <td>Mr</td>\n", " <td>32.0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>McCarthy, Mr. Timothy J</td>\n", " <td>male</td>\n", " <td>54.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>17463</td>\n", " <td>51.8625</td>\n", " <td>E46</td>\n", " <td>S</td>\n", " <td>Mr</td>\n", " <td>54.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass \\\n", "PassengerId \n", "1 0 3 \n", "2 1 1 \n", "3 1 3 \n", "4 1 1 \n", "5 0 3 \n", "6 0 3 \n", "7 0 1 \n", "\n", " Name Sex Age \\\n", "PassengerId \n", "1 Braund, Mr. Owen Harris male 22.0 \n", "2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", "3 Heikkinen, Miss. Laina female 26.0 \n", "4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", "5 Allen, Mr. William Henry male 35.0 \n", "6 Moran, Mr. James male NaN \n", "7 McCarthy, Mr. Timothy J male 54.0 \n", "\n", " SibSp Parch Ticket Fare Cabin Embarked Title \\\n", "PassengerId \n", "1 1 0 A/5 21171 7.2500 NaN S Mr \n", "2 1 0 PC 17599 71.2833 C85 C Mrs \n", "3 0 0 STON/O2. 3101282 7.9250 NaN S Miss \n", "4 1 0 113803 53.1000 C123 S Mrs \n", "5 0 0 373450 8.0500 NaN S Mr \n", "6 0 0 330877 8.4583 NaN Q Mr \n", "7 0 0 17463 51.8625 E46 S Mr \n", "\n", " new_age \n", "PassengerId \n", "1 22.0 \n", "2 38.0 \n", "3 26.0 \n", "4 35.0 \n", "5 35.0 \n", "6 32.0 \n", "7 54.0 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "known_age = df['Age'].notnull()\n", "people_w_known_age = df.loc[known_age, [\"Age\", \"Title\", \"Sex\", \"Pclass\"]]\n", "\n", "df['new_age'] = pd.concat([people_w_known_age['Age'], people_w_unknown_age['Age']])\n", "df.head(7)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c847eede-95cd-b808-dc5d-e1c01ac0e078" }, "source": [] }, { "cell_type": "code", "execution_count": 26, "metadata": { "_cell_guid": "128e5c6c-ac18-bead-3deb-f14cce653a8d" }, "outputs": [ { "data": { "text/plain": [ "(0, 0.05)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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XLqRNmzY0bdrU+m3kr6Kjo2nYsCETJkwozrJFREREpIgkJiaSmpoKQFZWFrt2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CAAAg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\n1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yGEEEII4SYSfDcxJSUlPProoyQmJjJgwACWLl3a2FUSblZTU8Pzzz/PgAEDSEpK4vbb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kjc/2o1XL+cM1ExiXNHDqqIpzRDiZ/pQvvG43lZ99Tsk77+F1OgmadS4x11+HwjD46kCfDout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\naj799FNSU1PRNA1FUQgODmbjxo0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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbb0e7e74a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for pclass in [1, 2, 3]:\n", " plt.subplot(211)\n", " df[df['Pclass'] == pclass]['Age'].plot.kde(figsize=(12,10))\n", " plt.subplot(212)\n", " df[df['Pclass'] == pclass]['new_age'].plot.kde()\n", "plt.suptitle('Age Density by Passenger Class', size=12)\n", "\n", "plt.subplot(211)\n", "plt.xlabel('Age - before filling missing values')\n", "plt.legend(('1st Class', '2nd Class', '3rd Class'))\n", "plt.xlim(-10,90)\n", "plt.ylim(0, 0.05)\n", "\n", "plt.subplot(212)\n", "plt.xlabel('Age - values filled')\n", "plt.legend(('1st Class', '2nd Class', '3rd Class'))\n", "plt.xlim(-10,90)\n", "plt.ylim(0, 0.05)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2cac1f16-5cac-2665-b71d-a896c15b59dc" }, "source": [] }, { "cell_type": "code", "execution_count": 27, "metadata": { "_cell_guid": "a1d4ee0a-144c-5fe9-da75-cba6f2682cf8" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fbb0de87828>" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LlmHDhg0xjx+Z9//www+bkyjDk3LnzJnTZOrH3LlzYbPZoOs6PvjgAzz00EM4ePAgAoEA\nDh061KyJuABgtVoxatQo6LqO2tpa/OlPf0J9fT3Wr1+PxYsXAzA+B2PGjAEQ/bsVTt/ZvHlz0lx6\nwLjS9cILL8DlcmH16tXm8YqiYPjw4c1qMxG1PgbpRNRiqqri3XffxZw5czB69GgMHjwYV1xxBb75\n5hsIgoCLLroIp59+OgDjsv3IkSMBGAHVOeecg7Fjx8Yd3UzVhAkTYLfboeu6eWk/2YTRxsIBeTiI\nDee6h5122mlmJ6OsrAyjRo3CuHHjzNHiyEosreXWW2+Fw+EwK9fs3LkTDocD5557LgAjsBo7dix+\n8IMfmBVdGrdj4MCB5ij2X//6V5x55pkoLi7G4cOHMWTIEFx33XUQBAGHDh3C9ddfj5KSEpxzzjmY\nNWtWVNpPU3RdhyRJuPrqqzF8+HC88sorAIyR4Z/97GdRx4bLMIbbmpOTY07OTSYy1eXBBx/Ejh07\nov6LrKYSnkB6xx13mClXjz/+OEpLS/F///d/KCwsNI8NB/cTJkzA+eefD8B4jydNmoSzzjoLP/jB\nD3Dbbbfhk08+afJ97t+/PxYuXIjs7GwIgoB//vOf+OEPf4jBgwdj3LhxZsWh5iz+dN999yEvLw8A\n8Oc//xnDhw/HDTfcYC6W9POf/9xMVxk7dixyc3MBAK+++iqGDRuGa665psla6wUFBVi4cCFKS0sx\nc+ZMs3rSrFmzOJJOlAEYpBNRi91+++2YMWMGzjrrLBQWFkJRFNjtdgwaNAh33nkn/vCHP0Qd//vf\n/x7jxo1DXl4erFYrrrrqKjz66KNJS8clC2wcDgcuvvhi87hw7fRUH6dv374YMWKEefv5558flf4g\nSRKeffZZnHfeecjOzkZBQQFmzJiB//f//l/MSG6qbW58bGP5+fmYOXOmmd4RPoePP/44JkyYgLy8\nPOTm5mLSpEl46qmn4rajZ8+eeOyxx9C/f39YrdaYEozz58/H448/jhEjRiA3NxeKoqBXr14YNWoU\n7r77bpx33nkptV0URdx222249dZb0bNnT1itVgwbNgx///vf0b9//5j7hEfTw1c8rFZr0ufwer34\n73//C0EQYLFYMGHChJhjJk2aZL72cF76yJEjsWjRIpx++umwWCw47bTT8Oijj0Z9NsJXGwRBwDPP\nPIP7778fQ4YMgcPhgNVqRZ8+fXDeeefhgQceiPuZamzs2LFYtmwZZs6ciTPOOANZWVmQZRn5+fkY\nMmQIZs6ciddffz1qBdlkn5V+/fph8eLFmDJlCnr37g1ZlpGbm4tRo0bhz3/+c1Rt+ZycHLzwwgso\nKSmB3W5HQUEB7rjjDtx0001NPsfzzz+PwYMHw2q1onfv3rjrrruaLDtJRO1D0NtiKOgE1dbW4r77\n7sPnn3+O/Px83HnnnQnzJp988kksXrwYHo8HAwcOxAMPPBD3y4GIiNLrtddew4MPPghBEPD222/j\njDPOaJPnCQQC+PLLLzFq1CgzZWnVqlWYN28e/H4/ioqK8PHHH7fJc3cExcXFEAQBI0aMiMnvJ6LM\nkZETRxcsWACr1Yo1a9agrKwMs2fPxsCBA6PqzwJGfdvFixfjX//6F3r37o0nn3wSd999N9566600\ntZyIiBr7/e9/j+XLl+PQoUNmnfO2CtABY2LyzJkzIcsyevToAZfLhbq6OgiCAFmWo0pXdlUZOD5H\nRI1kXLqLx+PBihUrcPvtt8Nms6G0tBTjxo3DkiVLYo49dOgQSktL0adPHwiCgCuuuAJ79+5NQ6uJ\niCiRqqoqHD58GHl5ebjsssvwm9/8pk2fz2KxYPLkyejTpw9qa2vh8/nQu3dvXHnllfj3v/9t5qB3\nVYlStYgos2TcSPr+/fuhKIpZogswLs2tXbs25tiJEyfi/fffx/79+9GnTx+89dZbKeVSEhFR+3nk\nkUfwyCOPtNvzybLcrs/X0ezYsSPdTSCiFGRckO5yuaIWhgCM+suRyy6HnXTSSRg2bBguueQSyLKM\noqIic5U4IiIiIqKOKuOCdIfDEROQ19XVxQTugLGU9tatW7Fq1Sr06NEDS5YswY033ojly5cnrRqw\nfv36Vm83EREREVE8paWlzb5PxgXpp5xyCgKBAA4cOGCmvOzcuRMDBgyIOfbrr7/GxIkTzdq3kydP\nxm9/+1vs2bMHZ555ZtLnacnJotaxfv16nv804vlPH5779OL5Tx+e+/Ti+U+vlg4OZ9zEUbvdjvHj\nx2PhwoXweDxYt24dVq5cGbXASNjgwYPx/vvv49ixY9B1HW+//TYCgQC+//3vp6HlREREREStI+NG\n0gHggQcewH333YfRo0cjPz8fCxYsQL9+/XDkyBFMnDgRy5cvR1FREWbNmoXjx4/jyiuvhNfrRd++\nffH0008jOzs73S+BiIiIiKjFMjJIz8vLw6JFi2L29+rVCxs2bDC3LRYL7r//ftx///3t2TwiIiIi\nojaVcekuRERERERdHYN0IiIiIqIMwyCdiIiIiCjDMEgnIiIiIsowDNKJiIiIiDIMg3QiIiIiogzD\nIJ2IiIiIKMMwSCciIiIiyjAM0omIiIiIMgyDdCIiIiKiDMMgnYiIiIgowzBIJyIiIiLKMAzSiYiI\niIgyDIN0IiIiIqIMwyCdiIiIiCjDMEgnIiIiIsowDNIzhMujYu+hGrg8arqbQkRERERpJqe7AV2d\nGtDw/OItWLu9AtVOLwpybRg5qCdmTS6BIrMPRURERNQVMUhPs+cXb8H7X3xrblc7veb23KlD0tUs\nIiIiIkojDtWmkcujYu32iri3rd1ewdQXIiIioi6KQXoalVe7UO30xr2t2ulFZbW7nVtERERERJmA\nQXoaFRU4UJBri3tbQa4NhQVZ7dwiIiIiIsoEDNLTyGFXMHJQz7i3jRzUEw670s4tIiIiIqJMwImj\naTZrcgkAxK3uQh2Hy6OivNqFogIHO1dERER0whikp5kii5g7dQhu8qiorHajsCCLQV4HwhKaRERE\n1BYYpGcIh13BqX3y0t0MaiaW0CQiIqK2wKE+ohZiCU0iIiJqKxkZpNfW1mLu3LkYOnQoLrzwQixd\nujTucfPnz8fQoUMxbNgwDBs2DGeddRZKS0vbubXUVbGEJhEREbWVjEx3WbBgAaxWK9asWYOysjLM\nnj0bAwcORL9+/WKOW7Bggbl97733QhQzst9BnVC4hGa8QJ0lNImIiOhEZFxE6/F4sGLFCtx+++2w\n2WwoLS3FuHHjsGTJkqT3c7vd+OCDDzB58uR2ail1dSyhSURERG0l40bS9+/fD0VR0LdvX3NfcXEx\n1q5dm/R+K1asQPfu3TF8+PC2biKRiSU0iYiIqC1kXJDucrngcDii9mVnZ8PlciW939tvv40rr7yy\nLZtGFIMlNImIiKgtZFyQ7nA4YgLyurq6mMA90uHDh7F27Vo8/PDDKT/P+vXrW9xGOnGd9fxXl6e7\nBanprOe/I+C5Ty+e//ThuU8vnv+OJ+OC9FNOOQWBQAAHDhwwU1527tyJAQMGJLzPO++8g9LSUpx8\n8skpPw+rwKTP+vXref7TiOc/fXju04vnP3147tOL5z+9WtpByriJo3a7HePHj8fChQvh8Xiwbt06\nrFy5Mmkqy9tvv42rrrqqHVtJRERERNR2Mi5IB4AHHngAXq8Xo0ePxt13340FCxagX79+OHLkCIYN\nG4by8oacgk2bNqGiogIXX3xxGltMRERERNR6Mi7dBQDy8vKwaNGimP29evXChg0bovYNGTIEGzdu\nbK+mERERERG1uYwcSe8oXB4Vew/VcPl3IiIiImpVGTmSnunUgIbnF2+JWxtbkdnvISIiIqITwyC9\nBZ5fvAXvf/GtuV3t9Jrbc6cOSVeziIiIiKiT4LBvM7k8KtZur4h729rtFUx9ISIiIqITxiC9mcqr\nXah2euPeVu30orLa3c4tIiIiIqLOhkF6MxUVOFCQa4t7W0GuDYUFWe3cIiIiIiLqbBikN5PDrmDk\noJ5xbxs5qCccdqWdW0REREREnQ0njrbArMklABC3ugsRERER0YlikN4Ciixi7tQhuMmjorLajcKC\nLI6gExEREVGrYZB+Ahx2Baf2yUt3M4iIiIiok2FOOhERERFRhmGQTkRERESUYRikExERERFlGAbp\nREREREQZhkE6EREREVGGYZBORERERJRhGKQTEREREWUYBulERERERBmGQToRERERUYZhkE5ERERE\nlGEYpBMRERERZRgG6UREREREGYZBOhERERFRhmGQTkRERESUYRikExERERFlGAbpREREREQZhkE6\nEREREVGGycggvba2FnPnzsXQoUNx4YUXYunSpQmPPXjwIObMmYNhw4bh3HPPxRNPPNGOLSUiIiIi\nan1yuhsQz4IFC2C1WrFmzRqUlZVh9uzZGDhwIPr16xd1nKqquPnmmzFt2jQsXLgQgiBg//796Wk0\nEREREVErybiRdI/HgxUrVuD222+HzWZDaWkpxo0bhyVLlsQcu3jxYvTs2RMzZsyA1WqFxWLB6aef\nnoZWExERERG1nowL0vfv3w9FUdC3b19zX3FxMXbv3h1z7KZNm9C7d2/89Kc/xahRo3DjjTdi165d\n7dlcIiIiIqJWl3FBusvlgsPhiNqXnZ0Nl8sVc2xFRQWWL1+OGTNm4LPPPsPYsWNx6623IhAItFdz\niYiIiIhaXcblpDscjpiAvK6uLiZwBwCr1YrS0lKMGTMGADBz5kw888wz2Lt3L84444ykz7N+/frW\nazQ1G89/evH8pw/PfXrx/KcPz3168fx3PBkXpJ9yyikIBAI4cOCAmfKyc+dODBgwIObYM844Axs3\nbmzR85SWlp5QO6nl1q9fz/OfRjz/6cNzn148/+nDc59ePP/p1dIOUsalu9jtdowfPx4LFy6Ex+PB\nunXrsHLlSlx55ZUxx15xxRXYvHkz1qxZA03T8NJLL6GgoCCmCgwRERERUUeScUE6ADzwwAPwer0Y\nPXo07r77bixYsAD9+vXDkSNHMGzYMJSXlwMATj31VDz++OOYP38+Ro4ciY8++gjPPPMMZDnjLhAQ\nEREREaUsI6PZvLw8LFq0KGZ/r169sGHDhqh9F110ES666KL2ahoRERERUZvLyJF0IiIiIqKujEE6\nEREREVGGYZBORERERJRhGKQTEREREWUYBulERERERBmGQToRERERUYZhkE5ERERElGEYpBMRERER\nZRgG6UREREREGYZBOhERERFRhmGQTkRERFFcHhV7D9XA5VHT3RSiLktOdwOIiIgoM6gBDc8v3oK1\n2ytQ7fSiINeGkYN6YtbkEigyx/WI2hODdCIiIgIAPL94C97/4ltzu9rpNbfnTh2SrmYRdUnsFhMR\nERFcHhVrt1fEvW3t9gqmvhC1MwbpREREhPJqF6qd3ri3VTu9qKx2t3OLiLo2BulERESEogIHCnJt\ncW8ryLWhsCCrnVtE1LUxSCciIiI47ApGDuoZ97aRg3rCYVfauUVEXRsnjhIREREAYNbkEgCIW92F\niNoXg3QiIiICACiyiLlTh+Amj4rKajcKC7I4gk6UJgzSiYiIKIrDruDUPnnpbgZRl8acdCIiahJX\noCQial9ddiT920NHIUsiFFmELImwWS2w220QBCHdTSOiNubyqCivdqGowMFL+U3gCpREROnRZYN0\n2WIHAKg6oAaAOq8Pwao6SJIARZagyCKsFhlZdhtkucueJqJOhQFn83EFSiKi9GD0GSLLshmM6wD8\nGuDz6KiqrYGg61Bk0fwvy26F1WrlqDtRB8OAs3maWoHyJo/KKxFERG2EQXoSgiDAam1Y2CEAIBAA\nnNUeaME6yJJgBO5SaNQ9yw5JktLXYCJKiAFn86WyAiUnFxIRtQ0G6S2gKAqgGF/mGgCfBnjcGqpq\nawBdgyKLsChGyozdZuGoO1EGYMDZfOEVKOOdN65ASUTUthiktxJRFGGxWs1tVQdUFah1G6Pukggo\nsgRZEmBRZDiybEawT0TtggFn84VXoIxMEQrjCpRERG0rI2dK1dbWYu7cuRg6dCguvPBCLF26NO5x\nixcvxqBBgzBs2DAMHToUw4YNw1dffdXOrU1OURRYbXbIFjt00QJVV1DvAw5V1uGbg0dx8HAVjlRW\no+pYDZw5uXBEAAAgAElEQVR19QgEAuluMlGnxCXPW2bW5BJcMur7KMg1Uv8Kcm24ZNT3uQIlEVEb\ny8iR9AULFsBqtWLNmjUoKyvD7NmzMXDgQPTr1y/m2KFDh+LVV19NQytbThAEKBaLuR1OmfF6dBxz\n1gJaEHIo112WRVgVGXZWmSE6YVzyvPm4AiURUXpkXNTn8XiwYsUKLF++HDabDaWlpRg3bhyWLFmC\nO++8M93Na1OCIMBiaUiZCQIIBgGPquFobQ0E6FAiarsrinRCJSK7Sq3orvI6O7L2eo8YcLYcV6Ak\nImpfGRek79+/H4qioG/fvua+4uJirF27Nu7x27dvx7nnnou8vDxcccUVmDNnDkQxI7N4WkwUxdgq\nM0HAEwiNvOtaqNJMaLJqEwszdZVa0V3ldXZk6XqPGHASEVGmy7gg3eVyweFwRO3Lzs6Gy+WKOXbE\niBFYunQp+vTpg927d+P222+HLMuYNWtWezU3reKOvAeAeq8PwWP1MZNVw5Vmukqt6K7yOjsyvkdE\nRETxZdxwosPhiAnI6+rqYgJ3ADj55JPRp08fAMCAAQMwd+5cfPDBB+3Szkwmy3LcyapHqlzYvucI\nPtuwHwHVi4Dfg6Dqg6YFARh5ui6PmubWt46mamJ3ltfZkfE9IiIiSizjRtJPOeUUBAIBHDhwwEx5\n2blzJwYMGJDS/XVdT+m4bdu2tbiNHdnRWhX1fgGyYqTP6LoOLagiEFRRWeXB4vc+Rp/uNlgVCRaL\npc3qu69fv75NHjfsSLU/aU3slavXoVe+Je7tXUFbn/9UdNX3KBPOfVfG858+PPfpxfPf8WRckG63\n2zF+/HgsXLgQDz/8MMrKyrBy5Uq89tprMceuWrUKZ555Jrp37469e/fimWeewaWXXprS8wwePLi1\nm94huL0q3t/wBWpdfgBGyowkG4FQnsOCc0YMR5ZNgaqq0IIBKJIARZFgkUU4smywRtSCb6n169ej\ntLT0hB8nGZdHxZuff5SwJvYFPxjeZScMtsf5T0VXfI8y5dx3VTz/6cNzn148/+nV0g5SxqW7AMAD\nDzwAr9eL0aNH4+6778aCBQvQr18/HDlyBMOGDUN5eTkAYM2aNbjiiiswdOhQzJkzBxdffDFmz56d\n5tZntiybgpL+PeLeVtK/B7JsRlAUru8uKjYEocATkHCkyoVvDlbiuyPHUFl1HLXOOgSDwfZsfspY\nEzvz8T0iIiJKLONG0gEgLy8PixYtitnfq1cvbNiwwdz+5S9/iV/+8pft2bRO4ZofngEA2LKnCrUu\nP/IcFpT072HuT8So7W6Muqs64PfqOFZ7HJKoQ5GN0XabzQJHlr3N0mSagzWxMx/fIyIiovgyMkin\ntiXLIm64dCAme1VU13pRkGczR9CbQxAEWG2h3HYYCzK561RUVruMijKysRiTzWpBlt3W7qUxWRM7\n8/E9IiIiio9BeheWZVNaFJwnI0kSJMkOoGExpnDgLomALIk4XluPmto6OLJsUJS2D8hYEzvz8T0i\nIiKKxiCd2lxk4A4AQcGKeh9QU18HXQtCkgQosgiLLJ3wKqpEREREnQEjIUoLQRBCOe4GDYC30Sqq\niiRCCaXMWGQJWVl2SJKUvkYTERERtRMG6ZRRGq+iGgAQCABuv4aq2hoI0CFLAmRZhCwJsCoyg3ci\nIiLqdBikU4cgiiIsETXaw/nuHtUI3qEHIcsiFEmELIlm2kx75LwTERERtTYG6dShxQ3eNcDr1VHt\ndAK6BkkSIEvGyLtFkWC3Wdt0NVUiIiKiE8UgnTolQRCigncdRm131Q/UuNzQgk4jbSaU9y6JAmxW\nC2w2a7uXiiQiIiJqjEE6dTmKogChNBgdgF8DoAF1Xh+Cx+ohCkapyMi8dzsrzhAREVE7YtRBFCLL\nclQgHpn3fjQ8aVU0Jq1KojEKb7EosFktDOCJiIioVXXZyOI3f1uLvGwL8hxW5GVbkJttRZ7Dgrzs\n0LbDCkVm2gMZee9Wq83c1gBoOqAGgHqfisBxNwRdhxharEmWxFDtdyP/XVEU5r8TERFRs3TZIP1g\nRR0OViQ/xmGTo4L33FBQ3y3H2JcbCuhtli57Grs8Y6Gm6PKPAQCBIOAN6DheVw8tGIAkCZBEI31G\nCuXBWxQFVquF5SOJiIgoRpeNLnv1cKC23ge3N5DwGJc3AJc3gCNVrqSPZbVIMaPw4Z/zIoL8LJvM\nEdUupGHBpoZFm8LVZ/x+wOnxIxBwQdD1UBBvBPDMgyeizsTlUXGk2g+XR4XDzrK4RKnqshHA/J+M\nAgCogSBq6/1wuvyorfcZ/5k/N+yrd6vQEzyWzx9Epd+DyuOepM8pSyJyHZaG4N1Mt2kYpc/LtiAn\nywJRZDDf2cUbhY+XB6+E0meMSjQSsh12Bu9ElPHUgIbnF2/B2u0VqHZ68ebnH2HkoJ6YNbmE6aRE\nKejy3/SKLKFHNzt6dLMnPS6oaXC6/HCGA/fGgbzLD2foX02LH84HghqqnV5UO71Jn0sUBOQ4lKh8\n+W5Ro/QNI/SSxD90nVG8PHi/Bvh8Oo7X1wKaBkU20masFiP33RpRcpKIms/lUVFe7UJRgYMjvq3g\n+cVb8P4X35rb1U6vuT136pB0NYuow+jyQXqqJFFEfo4N+Tm2pMdpug6XR40K4J2RAb2rYb8a0BI+\nhnGMH2gibz7brjRKs2k8Sm9sWxTmPXcGgiDAYmkIxgMAAn6gpt4FXXNGp82IAmRZgs2qwGKxsP47\nUQKNR3wLcm0c8T1BLo+Ktdvjf4Gt3V6Bm9og9YWdLOpsGKS3MlEQkJNlpKycXJj4OF3X4fUFQ0F7\n9Ii8mXYTCuq9vmDCx6n3qKj3qDh0NHm7bFYpKmjPc4RSbBpVtbFbmTffERm57w3MlVd9OmpcHmjB\nOggAZElA1fE6VFYdhyyJsFktnLxKXR5HfFtfebUr4VXjaqcXldVunNonr1Wei50s6qwYpKeJIAiw\n22TYbTKKujuSHutXg3FH4p2NUm7qPWrCx/D6gvD63Kiodid9LkUWoyrX5MWZBJubbUV2lgKRwXzG\nEwQhavEmAIBkg6oroRKSfgRUFwRBN0feJRGQJBEWWeLkVer00jHi2xUUFThQkGuLG6gX5NpQWJDV\nas/FThZ1Vvz27QAsioST8rNwUn7yP2qBoBYVuDtdkZNfGwL8Opcfmh4/b14NaKiq9aKqtom8eVEw\nJsFGjMIb/0aXrMx1WCAxzSJjNZ68GlkD3u3XUFVbC13XIImALEmQJQFiKJ1GkURYQws5MZWGOqr2\nHPHtShx2BSMH9YwKnsNGDurZah0fdrKoM2OQ3onIkoiCXBsKcpvIm9d01Hv8cQP48OTXmjofnC4f\nAsH4wbym6aip86GmzgegLuFzCQCys5SoAN7nqcdR38Go9Jtch4V58xlGFEVYGk1GDQChSB5w+3Wo\nznroWtBYjVWWovLhJUmARVFgsSgcjaeM1Z4jvl3NrMklABA3DaW1sJNFnRm/ObsgYxTcilyHFd/r\nmZPwOF3X4fYGonLlG6fYhLe9/vh58zqAOreKOreK7yrrzf3r9+yKOdZulc0Um3C+fLyqNjaLxLz5\nDGBMYo3OhdcBBHRjMScE49WCN+rAC4LRqbRYFNhCo/FE6dBeI75dkSKLmDt1CG7yqFi5eh0u+MHw\nVj+f7GRRZ8ZvRkpIEAQ47AocdgW9T0p+rM8fOwm2cUBfW++DK8niUR5fAB5fAOXHkufNWxQxqjxl\nQ8pNdFUbh11hMJ9miVZkRSilpt6nInDcDUHXIYpG4B4ejTfKS1pgsSic2Eptqj1GfLsyh11Br3xL\nm3R42MmizixpkH7vvfc2+QCCIOC3v/1tqzWIOiarRUKhJQuFTeTNqwENX23YiqI+p0TXl280KbbO\n7UeCtHn4VQ1Hazw4WpN88ShJFKIWiYoXyOdlW7l4VBolXNAptCprrduLYKAegqBDEo18+HAgLwpG\nSo2iKLAoMmSZlYmoZSJHfCur3SgsyGJw14Gwk0WdVdIgffHixRAEAXooWmr8BajrOoN0ahZFFpGb\nJeG0JnIENU1HnTt+ffnGk2KDCRaPCmo6jjt9OO70JX0uQQBysizRVW0aTYINp9ywnFf7kmU5JhUm\nHMSHN4IeP4IBN3RdgyAYnTNJFCN+NkbmjUo3MiyKDEmSONmVYjjsCvOXOyB2sqizShqkT5o0yQzM\n/X4/3nvvPfTv3x8DBgzA7t27sXv3blx66aXt0lDqWkRRMAPlZHRdhyucN1/vM1aEbVzVpt6HWpcP\nfjX+4lG6DmM1WZcfiMibj8dhk0OVa6yxlW0ignybhZlk7SXeaDwQyo8HEAhNdNV1HZrHDy3ogaZp\nAHSIggBRNDpqotAQ0IuCkTevyBKU0Ci9JHEuBFEmYyeLOpukkcSjjz5q/vzggw9i+PDhePnll819\n06dPh8ORvMY3UVsSBAHZdgXZdgV9TspOeJyu6/D6g3FH4iMXkHLW++H2Jc6bd3kDcHkDOFzlStou\nq0WKGYVvHNDnZVuRZWOKRnsRBCFhQB8WNUoPwK3qCNZ7oQWDRhWb0Mi8EdyHylGa/4qwKDKr2RAR\nUatI+Ztk6dKlGDt2bNS+wsJCvPfee/j1r3/d6g0jak2CIMBulWG3prZ4VNSkV1f8SbD1bhUJ0ubh\n8wdR6feg8njyvHlZEo1689mWRvny1qh8eubNp4cgCEbAHSfo1tEQ1LvdKo7W1CM/2wKrIgB6KIde\nQERpSgF19W54vV5YLBam2xARUVIpB+kOhwPvv/8+evTogX79+mHPnj14//330b1791ZvVG1tLe67\n7z58/vnnyM/Px5133onLLrss6X1mzJiBL7/8Etu3b+eXH50QiyKhRzc7enSzJz0uqBmLRzljRuT9\n0ZNiXX5oCfLmA0EN1U5vwjq/YaIgIMehxFS16ZZjjSpPmeewQJL4+W8vgYCG1z/8Glv2VKHW5Uee\nw4KS/j1wzQ/PgByavxC5QJQ3KKO82gMt2DAZVpZESKHJsHJoIqyVo/FERF1eyt8CU6ZMwaJFi6LS\nXXRdx5QpU1q9UQsWLIDVasWaNWtQVlaG2bNnY+DAgejXr1/c4999910Eg0GmDVC7kkQR+Tk25Oc0\nsXiUrsPlUROXp3T5UFtnBPNqIH7evKbrofv6gfiL65my7UqjNJvoUfpaVwB+NcjFo1rB6x9+jU83\nHza3a11+c/uGSwfGHG9MXlUApWFSW2RteV9oImy4trwoGrnxYmSKjdCwTxJF2KwKR+aJiDqhlIP0\nefPmweFw4I033kB5eTmKioowdepU/PjHP27VBnk8HqxYsQLLly+HzWZDaWkpxo0bhyVLluDOO++M\nOb6+vh6LFi3CY489hmuuuaZV20LUGkRBQE6WkbJycmHi43Rdh8cXiMmVb5gMGw7u/fAkyZuv96io\n96g4dDTxc/1j5cewWSVj4ajGE2AbTYq1W5k3H4/bq2LLnqq4t23ZU4XJXhVZtuZXmEg2ETYIIKiH\nNkKTYWtcDSPzkXXmZbFh8qs1tGAUA3kioo4j5SBdEATcfPPNuPnmm9uyPdi/fz8URUHfvn3NfcXF\nxVi7dm3c4//whz/g+uuvb5O0G6L2JAgCsmwKsmwKevVoOm++8YTXeFVt6j1qwsfw+oLw+tyoqE6+\neJQii9HlKeNMgs3NtiI7S4HYhYL5qloPal3+uLfVuvyorvW2KEhvjqZG5gHA5dcQcNYbE19DZSnD\no/BiKG8+XNGGJSqJiDJHs5Iet23bhldffRUVFRV4/PHH8emnn2LIkCE45ZRTWq1BLpcrpmJMdnY2\nXK7Yahpbt27Fxo0bcf/99+Pw4cMxtxOlg9uroqrWgx559jYL0iyKhJO62XFSE3nzgaAWlVrjdPmw\ne993sGblRdWfr3P5oSVYPUoNaKiq9aKqtom8eVEwRuUbVbXpltMwMp+bbUGuwwKpEwSAPfLsyHNY\n4gbqeQ4LCvKSp0G1F1EUYbFYYvaHc+XDo/KAkWpjlKgMQgBi0m3CAb4oCBDCAb4oQpKMUpWiKEIU\nRV55ISJqBSkH6Zs3b8a0adOgqqox4peVhYceegiXXnopfvOb37RagxwOR0xAXldXFxO467qOhx56\nCL/61a+iFlwiSpdUJhG2N1kSUZBrQ0FuQ8BYoNRg8ODofGlN01Hv8UePxIcCeGe9DzVmkO9HIJgg\nb17TUVPnQ02dD0BdwjYJALKzFDO1Jqq6TaMcekXO3Lz5LJuCkv49onLSw0r692jzUfS2kKxEZbju\nPCIWkgKMv8XBoA+a5gF03RixByCIYqgGfXQevbGNqDKWiixD4Qg+EVEUQU8xuv3xj3+Mr776Cn36\n9MGBAwewY8cOzJo1C/v378eKFStarUEejwcjR47EsmXLzJSXu+++G0VFRVE56XV1dTjnnHNQUFAA\nAAgGgzh+/Dh69OiBhQsXorS0NOFzrF+/HlXOxGkARC2xcosTZQdiSy6e2deOC0py09Ci1qfrOv63\n2Ymd38WOqudmSci2iXD5NLi9GtTgiXecrYqALKsIh1VClk2Ewyo2/GuV4Aj9rMhCWkZvg5qOVdvq\nsL/cC5dfh8Mi4JQiG84bnAOJJTNTous6NC0ILahBhw4BuhHkhwJ7Y6Gp0DYQsS86yA/XwSciykTJ\n4tJEUh5J3759OyZMmIBu3brhH//4BwCgV69eCXPFW8put2P8+PFYuHAhHn74YZSVlWHlypV47bXX\noo7LycnBp59+am4fPnwYU6dOxeLFi5Gfn9/k8wwePLhV202p27ZtW6c7/26vin+s/CLubd8dC+K0\n/mdkzMjqiZx/t1fFKx/Hf52CIOH/bhyFLJuCV9/bEXeE+eSTstGnMLuhPGW9Dy5v4kmwPlWHTw3i\neH0wabssihhTntKcBBtRb95hV1o9mD+7xDgv1bVeFOTZkr7PnfGzn07GKH4QuqaZq8gKum5crkG4\n46aHAnsB27ZuQ0nJWUAoyG/oCBij+7qum7n6AELlMSXIsjHCz1VnW279+vUtClKodfD8p9f69etb\ndL+Ug3RRFOH1Ro+efffdd22y4ugDDzyA++67D6NHj0Z+fj4WLFiAfv364ciRI5g4cSKWL1+OoqKi\nqMmiXq8XgiCge/fuvFxK7S4TJhG2h1ReJ4CEVU/q3H5c88PTo86FGtBiVoB1Nkq5qa33oc7tR6Lr\nfn5Vw9EaD47WJF88ShKFqEWiIie+Rgb0OQ6lWXnz4Qm/1L7MxaZSJFlsEGQrACN9x/w4RWyoQTSk\n8vh1aJrf6AjoGqA3jPKLYrLSmMbEXEWRIcsyg3siapGU/7qdfvrpWLVqFU477TQAwP3334/Vq1dj\n9OjRrd6ovLw8LFq0KGZ/r169sGHDhrj36dOnD3bs2NHqbSFKRUeZRHiiUnmdze2wKLKI7nl2dM9L\nPglW03TUuWNz5iMnxYa3gwkWjwpqOo47fTju9CV9LkEAcrIs0VVt4pSnzHVYoaRpvgG1vXAKTbI0\nmnilMQFA0zQEQ5NwoWnmiH1Dqo5oBvWAkdJj7gtdAQjn6IdH9Dkxl6hraVad9BtvvNEMhN98800I\ngoA5c+a0WeOIOorOOIkwnlReZw+gTTosoiiYgXIyuq7D5Q00WZ6y1uWDX40/CVbXYawm6/IDlfVJ\nn89hk0OVa6ID+OhRegtsFq4g2pWEA2og8e9+OMAHQkF+o6wuYyRfhaZp0DUtVCDB6IAKZsoOQhN1\nBbMEqhC530z5iQ3sG++LOSJiR/i5jDSi0LYomlcSwlcLwp0IdiSITlzK3xojRozAs88+i7/97W84\ncuQIevXqhZtuugkjR45sy/a1GV2P/0eLqKWu+eEZABC3uktn0tTrTHeHRRAEZNsVZNsV9DkpO+Fx\nuq7D6w/GHYlvXIPenWTxKJc3AJc3gMNVsWViI1ktEmwKULh5fcRqsNEBfV62FVm2jr14VHuUIG0J\nt1fF0VoV7hYuMpUOqYzkR9Ib/Zt454kzJvyq0HXd6DzoOjRdC6UOacZnOKKjUH6sDt8eqkLjj3Z4\nHkDk/AAh1KkQRSGqAxI5RyB8ZaEj/64QNSXlIL2srAxjx47F2LFj27I97SbHhqhRCV2P+FsWsaHD\nuGwZ/kMQeRx0PeI+iCoDaRxnHByuP62Hfw4dK4gidACCIEaNQDCnvmOSZRE3XDoQk1OcRNhRpfI6\nO0KHRRAE2K0y7FYZRd2bXjwqst58ZHnKyIC+3q0mjIV8/iB8fqDWVZP0uWRJjC5LGbGAVGQ+fU6W\nxUyVyASZWII0Xrve3/BFRrSro2tuNR1ZsUG2JL+KFjVPAGgo9xn6V/eHy31q5hwBsz3m/wDBvASg\nm2U/w7fHu9oQ2mFWDwKM72hRFKPuJwjGUzasFSBGVBmKvoIQ/plXFehEpBykT5kyBYMGDcI111yD\nyy67DFlZWW3ZrjaXl5uT1ucPjz5ooUuYwWAQQU2HFvoX0KHp0R0E89+Ix0nldz9ZB0TXG3VSdON5\no/eHOhZmu8OdEs1oQGQHxWyQENVQIWJD9fvh9/lCdZQ7X45lV5lEmOx1drYOi0WR0KObHT2aWDwq\nGNTgdPuNFJtQAH/c6cWGrytRVeNBIGhUGoGOhMF8IKih2ulFtbOJxaMEATkOJXlVm1Adellq+2D0\n9Q+/jrp6Uuvym9s3XDow0d3aXKa2i5qvuROF40l4YSHeDQmKShnfgxp0PWh+l5vfgbpufmc2fNnq\nZicgsiMRLicKRH+/66ErCeFjwreHOxXhQT4hosHhwb3w/cMdjPD96+rdqHXWxXQuwvdt3Klg5yIz\npPxplyQJZWVlmD9/Pn73u99h4sSJuPrqq1lOrIXCvwDhXyxF6bgBTFhDYK9H/dxY5SErTu6Zi2Aw\niEBQMysnBLVwB0A3J/6Ft8Odhajt8M/mI4curwr8g5MpukqHJUySROTn2JCf0zBi+Op7O1B+zG1u\nh38lzjmzJ354zvfNFJuYkfo6I8hXAwkWj9L10H39QEXydmXblajJrrGj9Ma2RWlZnXG3V01Y0WfL\nnipMTlOKSaa2izq2tv4+iXzkuJ0KofEOxHYoGm37NAV1XpidCwDRHYxw6dJQ5yL83R3uWER2KiIn\nQIfLl0btExv2SZLEic8nIOUg/dNPP8WyZcuwZMkSbN26FW+88QbefPNNDBw4EG+99VZbtpE6iMYj\n/onIsgxFUVq1Y9L4yoSmadA0HVr4j5CmhX4GEAr2w6lHkelIum7cR9MajtG0hs6AIIjmFQCmJlFT\nkgWJO/cfxzU/PAMnFya+qqfrOjy+QMKceWe931gN1uWD15e4lny9R0W9R8Who8nba7NKjcpThlJs\nGk2KtVuj8+YztQRppraLKB1au3MRVdAoMlcpYjViXQ9ETXzWdS3UFiPwD3+FioJgXAEQxKgMgYa4\nAkYJ1FBPwbxm3+jlhNOZzCsW5rE6gIZJzrLUcCU/kyc7pxyk5+fnY9q0aZg2bRr27duHhx56CF98\n8QXLHlJGaHxloi1ooQVTAoFA3NQkPaJTEG5TpMh5C1pEh8DsHDTqDIihCWOZ+IeDUnOiQaIgCObV\niF49ms6bT6U8Zb0n8WrLXl8QXp8bFdXuhMcARtnMyMo1DpsCqyLBp8Z2FNJZgrSrlEYlykTN/V5u\nmEnQIOYKQtIDmhY54blhbgPMyc7hNRCiy6UakxGERilEkXMdgMTzFpqz5kZjzUruOnjwIJYsWYJ3\n3nkHBw8eBAAuw0xdRrjHfaI5kU3RdR2BQACqGoAaCCAYDJpXBYKaMcof3g6P+EMUIUlyRNk3ygTt\nGSRaFAkn5WfhpPzk84UCQS0qtaaqxo3yajcCAQ0uT8AM8OtcfvMqU2NqQENVrRdVtcnz5gHA4w9g\n4WsbE6TbhMpUOiyQ2iBvPt2VhogoszR3wrNZGTUyxSiFjkHkvAXVm3yRvWRSjjauu+46bNq0yXzy\n3r17Y8qUKZgyZUqLn5yIYgmC0Kx0oPDE43Bgb+T2NxqlD01C1nVAC3ihqd7QRGBEpAEZz23McTIu\nPQoN1wy5LHoLtFWQeCJlDmVJREGuDblZFny+ObYay+zJZ0GWRWiajnqPP7q+fNyqNn4EgvHz5gFj\nNdhvy+sA1CU8RgCQnaXE5suHUm4i9yly9BdsU+eiI1QaIqLOJTJ9JngCg9kpB+kbN26ELMu44IIL\ncPXVV2PMmDH8sibKAOGKB7Isw5bCwOyRghx8r3ePmP2aZgRajSf9hv8NBoPw+VUEgkEEg1oo8NdD\nP+vQdSOwhxYxmVcQEJ7QK0ldc3n01gwSW7PMYVNVT0RRQK7DilyHFd/rmTxv3u0LmDnylcfdqDjm\nRiComYtKOV1G7rzPHz9vXgdQ51ZR5246bz7LKiM32xh9P+5sWJTKbpXQ/+RumDS2PwrybLBZpNDv\nRkOloS83bMM5wwZzBJ3aTKauE0AdU8pB+p133omrrroKPXrEfrkTUcfXVJqMoiiwpdILCImsHBBO\n4YkM8nXdSL3QNCONR9d06IIAUZTMfPzOoDWDxNYqJ9iaVU8EQYDDpsBhU9C7B1B8SkHCY73+QNyR\n+PBkWGeo3rzLm3jxKLcvALcvEFUxBwA8viC27j2GrXuPAQAsihhTntLr8sO2uyqqqo3DrnSIjiOD\nv8yWqesEUMeWcpA+a9astmwHEXUyjWfLGyP9yYN8TTNKcvpVI3UnnHevmSk84Yk/Dek8xhUAIWqR\nsfAIfnjVQ2NWf3pn8WfZFJyU2/KSlK0ZWKer6onNIsNWIKNnQfK8eTWgxVSzcUak3Bx3+nC4qh4J\nUuYBGGk2R2s8OFoTnQ+6Zuf2qG1JFKIWiTLz5CPrzTusyHEoJzQBrKUY/HUMrMdPbSFpkD5w4EDM\nmDED99xzDwYOjP8hEwQB27dvj3sbEVFzhAPpEynP2bhOf0NJTi2qLn9kPn54ND9yQq5RuSe0kmBE\nqa62lmjEtDUD60yveqLIIrrn2dE9L/7iUQcqnPjt375KeP/S4kIEgroZ6DtdPgSC8SP6oKbjuNMI\n/MGWGcQAACAASURBVJMRBCAnyxJV1SYvzgJSuQ4rlFYMnhn8ZT7W46e2kjRIb2pRGiJqWy6PivJq\nF4oKHHDY+Uc+Fa05Um5U1tEQCAQbSm/qRmpOUDPy8oMRKTuabpTPlGS52ek6TY2YtmZg3dGrnjR1\nLm64pDjqNei6Dpc3gPUby3BSr+8ZK8I2KlNpbBv57fHoOuB0+eF0+YHK+qTtc9hkY7VXc1TeGI3v\nlmONCvJtluQXsxn8dQysx09tJelfiJdffhlFRUXmz0TUPtSAhucXb8Ha7RWodnpRkGvDyEE9MWty\nSauO0lFyUig3vrmVdnx+FaqqIhAwVtINhoL6oN8Lv8cNPVQHX5YbFgVqasS0tQPrjlz1pLnnQhAE\nZNsVdM+VMejU7gkfV9d1eP3BuCvAOiMXkqrzwe1LnDfv8gbg8gZwuMqV9HVYLVLUIlFGdZuGUXq3\nV2Xw1wFk+pUp6riSBukjR440f3Y4HDjzzDPbvEFEBDy/eAve/+Jbc7va6TW3504dkq5mURMiK+0A\nsakalYdzcGrfQgSDQaiqak6krXf5sWHndwj4o1MuBFHCpl0VmHR+PzjsllYNrCMntFbXelGQZ+tQ\nAV9bdDIEQYDdKsNulVHUvenFoxoH8rGTYn2od6sJyyr7/EFU+j2oPN78OspWRcLu72pQU+8zA/vs\nLEvDiovUbjr6lSnKXClPHJ0yZQoGDRqEa665BpdddhmyspJP/CGilnF5VKzdXhH3trXbK3CTR2Xq\nSwcXHqEPT6StcdfA5RMhW6IDe10LoqbOC4/bjYJsEYqgY/ol/VDv+h4qqz3olmuF3W6BsTB3y66w\nhFc07WjS3cmwKBJ6dLOjR7f4efNhwaAGp9sfPRIfr6qNyw9NSz2t1KcG8fqHu6L2iYKAHIclqnpN\nXrYVuRGj9XkOK3KzLZDbYPGorqwjX5mizJVykC5JEsrKyjB//nz87ne/w8SJE3H11Vdj8ODBbdk+\noi6nvNqFamf8lRyrnV5UVrtxap+8qP3MXe/YigocKMi1xbzvgijhpG4O9P9+z5j3dcCpxr/BYBA+\nnx/+UHpNIDQBNhDU4PKoqDzuRo/8bOTlZHWIUoPNlemdDEkSkZ9jQ35OE5WNdB0ujxoVwDtdfhx3\nelG27xiO1/lCk5mTP0Z4BD/Z4lEA4LArcYN5I2++YZ9F6RylUNtaujuN1DmlHKR/+umnWLZsGZYs\nWYKtW7fijTfewJtvvomBAwfirbfeass2EnUpiQI2ACjItaEwonwdc9c7B4ddwchBPaNSnMJGDooN\n0CNJkoSsLDuyItJrIj8Xx2o9yHNIGDagO6ZNGARRMOrTGzXqjeomoiRH5cdT+xMFATlZFuRkWXBy\nYezt7lDwl59rhSiI5kTX2kYj9OFJsTX1Pnh98RePAoyOvcuj4vDR5HnzNqvUqDylMRIfXdnGAruV\nnx8g8zuN1LGkHKTn5+dj2rRpmDZtGvbt24eHHnoIX3zxBXbs2NGW7SPqcpoTsDF3vfOYNbkEAOJ2\nuJor8nMhCAKcbg0fbz4Km31/zOdC13X4fD54fapRwUYHVDUITQ8tMqUbI/qyLLdLCUqKr3HwZ7el\nljffOGc+vB1Zh77eoyZ8DK8vCK/PjYpqd8JjAKNsZlR5ykaTYKucKpwuP7KzFObNE6Uo5SAdAA4e\nPIglS5bgnXfewcGDBwGg06wKSJRJUgnYmLveuSiyiLlTh+Amj4rKajcKC7Ja9P4193MhCAJsNlvC\nhaaMEpQB+Hx+qIFgQ7WaYKhyTdAo1StKMiQG8hnFokg4KT8LJ+Unn0MWCGpRk2CdcctT+lHn8kNL\nUI5ZDWioqvWiqjZ+qh4AvLbqU4iiYOTIx1S1iag377Ag12GBxLx56uJSDtKvu+46bNq0CYAx8tK7\nd29MmTIFU6ZMabPGEXVVqQRsLcldp8znsCsn9L619udCFEVYLBZYLJaEx+i6HhXIh4N5Y/EoI0de\nkGQoisKUiAwkSyIKcm0oyG1qRWAd9R5/3ADe2WhSbCAYv968pumoqfOhpi553rwAIDtLaRTAx47Q\n52VboMgcLKTOKeUgfePGjZBlGRdccAGuvvpqjBkzhn9sidpYsoCtObnr1HWk43MhCAIURUlYT17X\ndfj9fnh9fqiBAAKhwN0I5gFRlCAzgM94xii4FbkOK77XMyfhcbquw+0LNOTI1/vw9d4DsGfnR02K\nran3weePnzevA6hzq6hzqzh0NHm7sqxyQ558o1z5yCDfZpH4GaMOJeUg/c4778SPfvQjdO+eeCEI\nImo/JzLZkDqvTPxcCIIAq9UKq9Uac1t4FN7j9cGvGgF8IBCa2KoDYmgEnjoOQRDgsClw2BT07mHs\nc+AYBg8+PeZYrz8Qk2ITr/68K0nevNsXgNsXQPmx5HnzFkU0J8HmRgXzkaP0Fjjs7DBSZkgpSFdV\nFU8++STKysqwcOHCtm4TEaWoNScbUufRkT4XyUbhGya1+uFXg0YAH9QQDGrQBRGyrHBeVAdns8iw\nFcjo2cQVHjWgRefKh+vLN5oUW+f2I0HaPPyqhqM1HhytSb54lCQKxsh8VFUbC/IalafMybJAFBnM\nU9tJKUhXFAW9evXiAkZEGaa1JhtS59JZPhfJJrUGAgF4fX74/CqCQQ1qoGEiKwQRisXC0dBORJFF\ndM+zo3teE4tHaRrq/397dx8ddXnn///1mcz95IYEDAkoSgEbOIqEKBXUikRZgbqKVurZWkVttbuw\nR+GIq1hZ6aJrq6dbFHWPtbvWm+p3tZVska2IcrQWakjoT0skLlBjWIhQiM3N3N98fn+EjAyZ3BDI\nzCfJ83GOx8xnPslcXBlyXrl4X+8rEO2yEp9uU2x3fefjCVNftIb1RWs47fOdDEPK8x5fL5+6St95\nkBQtcdEffS53Wbp0qR5++GEtWLBAM2bM6HETEYDMOtnNhhiahvL7wm63K9duV26aLoTRaFT+QOho\n/XsiWT4TjUZlmibhfQjLsdmSJSw9MZOHRx2z+TVNV5vW9ojC0W7q5k2p1R9Rqz+ifekbKiV53faU\nevnOlfqOw6O+bF3pdp5Q0z0McX1+N6xcuVKGYeh73/teynXDMPTxxx+f8oEBANAfDodDIwq6/svB\noQOfKt9jKBLpCO/ReEKxWMfKu93hoH3kMGIYhnK9TuV6nRqr3B7vDYVjx3SuSQ3wxx4kFQjFuv0a\ngVBMgVBMTYd7PjzK5cxJs/H1uLKbXJe8bg6PGg5O6Fc2s7tCr1OspaVFK1eu1NatW1VYWKjly5fr\nG9/4Rpf7Nm7cqMcff1x/+ctf5Ha79fWvf10/+MEP5PP1fMADAGD4sdvtys/rGsji8biCwZBCkehx\nG1cN2R3UvQ93bpddblfvdfORaDztptfU9pRhtQei6i5NhSNxHYoEdeiLnuvm7Tm2o6U0PbenzPU6\nOTxqEOtzSH/++ecHchwpVq9eLZfLpW3btqmurk533HGHJk+erAkTJqTcN336dL300ksaOXKkgsGg\nHnjgAf30pz/V/fffn7GxArAWfzCqz5v9KinyDco67OEsW9+7nJwc5eb6uqynxuPxL7vOxBJHe8B3\ndJ3JsTtkt1OagC85HTkaNcKjUSN6r5tv9Ue6rMR32RTrjyjRTd18LJ5Qc2uo2zMROtkMQ3k+p5w5\ncb276//rtqtNfq5Tdg6Pspw+/4SZMWPGQI4jKRgMatOmTdq4caPcbrcqKipUWVmpqqoqLV++POXe\nkpKS5MeJREI5OTlqbGzMyDgBWEs0ltAzr3+UtqMJm7aszarfu5ycHOX6uq6eJhIJhUIdXWdiRzet\ndh7cZLN33y8ekDrq5gvz3CrM6+XwqGTdfM/tKVvaw4rGujk8yjTV0t6xAfYvLUd6fD2fx5GyAbZz\n4+uI47raOB38y1Km9Dmkr1u3rtvnli5dekoGI0kNDQ1yOBwaN25c8lpZWZmqq6vT3l9bW6s77rhD\n7e3t8ng8euqpp07ZWAAMHs+8/lFKb/Dm1lDy8ZLrp2VrWOiDwfa9s9ls8no98npTV0yPbxmZDO8c\n2IR+sBmG8rxO5XmdOr24+/tM01QoHD8a2lNX5DsPlDrU3KpgVAqF02+ClTr+JcsfjOrAX3qum3e7\nco5rT+n68jCpY+rpPS7q5k/WCYX07ib7VIZ0v9/fpaY8NzdXfn/6N01FRYVqamp06NAh/dd//ZdK\nS0tP2VgADA7+YFTVH6dvr1D98UEtDkYpfbGoofS9665lZOeJq52lM9FYx+q7KUM59HrHSTIMQx63\nXR63XSUj0+/J27lzp8455xxFovGuNfNp2lS2Bbo/PCoUjisUDuhgc8+HRznstpTONV1q5o8+l+t1\nUDffjT6H9AsuuCD5cTwe16effqovvvhC5eXlp3RAPp+vSyBva2vrdTNocXGxLrnkEi1fvly//vWv\ne32d2trakxonTg7zn11Dbf6bmiPd1mY2t4a05fc1Ki20RtvYoTb3JyvT3zsrzX9n2UwkFlc8IcXj\npuKmZBgdq+5Dzc6dO7M9hGHt+Pl3SSp2d/ynkZLkOPqfT/GEqUA4oUAoIX84rkA4IX8oIX84oUAo\nfvT/CQUiiW4Pj4rGEjrcEtLhlt7q5iWPyyafyyav2yafK0del00+t00+99GPXTZ5XbZBeXhUNBJS\n6ai8fn1un0P6Cy+8kPI4Eono1ltv1bnnntuvF+7OWWedpVgspsbGxmTJS319vSZNmtTr50ajUe3b\nt69Pr1NRUXFS40T/1dbWMv9ZNBTn3x+M6tWt76QNe0X5bl120fmWWI0dinN/sjL5vcv0/Pd3I2w4\nHD666h5PrronTEMOp3PQtonsXMlFdgzU/CcSptqDkS795dN1tYnF06f5hKmOXwBCCaml+9cyJOV6\nHV9ueu2ySv9lqY3Dbp1/nYqEgmo+1L/9kv3emu50OnXOOefot7/9rf7pn/6pv1+mC4/Ho7lz52rt\n2rVas2aN6urqtGXLFr3yyitd7v3Nb36j888/X6Wlpdq/f7/Wrl2rmTNnnrKxABgcfB6HZkwZnVLX\n3GnGlNGWCOhIbyh+7052I6zL5ZLLlXoYT2enmVA4crTTTEKxuCnDliMHhwsiS2w2Q/k+l/J9Lp0x\nuvvVYtM0FQjF0naz6dwM2/n/UKSbw6MktQWiagtE9X+H2nscl9dlTx4YdWy9/Ig819He8x3X3M4c\nS9fN9zmk33fffSmPW1pa9P7778vj6bnVUH+sWrVKK1eu1KxZs1RYWKjVq1drwoQJampq0oIFC7Rx\n40aVlJRoz549euyxx9Ta2qqCggJdeumlXTrAABgebl84VZLSBiNY21D73g3ERtjOTjPHdptJrXUf\nOqvuGHoMw5DP45DP49CY03q+NxSJpXax6aarjT/Yfd18IBxTIBzT50d6rpt3OmzJVfju2lMW5Drl\n82Rn03efQ/rrr78uwzC6HGi0cOHCUz6ogoICPfnkk12ul5aWaseOHcnHy5Yt07Jly0756wMYfBx2\nm5ZcP02Lg1Edag6ouMg7KFdhh6Oh9L3L5EZYwzC6XXUPBIIKR2KKxr9cdae3OwYDt9Mut9Ou4sKe\nD4+KxhKpm147+8sf156yLRDptm4+Ek3oL38N6i9/7fnwqBybkVyZP37j64hjNsPmeh3KOYW/HPf5\nb+s111yT8luE1+vVueeeq6uuuuqUDQYATpbP49D4sQXZHgb6YSh87z5v9ve4EfZQc2DA/4w5OTnK\ny8vVscUHpmkqFAorEAofXXGPKxajXAaDl8Nu08gCj0YW9FzRkUiYaguk7y9/fHebeDeHR8UTpr5o\nDeuL1nCPr2UYUp7XmVIvn+fO0cyJ/fsz9jmkP/LII8mPq6ur5ff7NW3aNFpHoU+O3UAFDBacXooT\nVVLkU1G+u9uNsMW9HC0/UAzDkMfjlseT2h7y2E2qkejR1pCGIafTZelaXaCvbDYjWcLSE9M05e+s\nmz/aX/74QN/Ziz4STX94lGmq4zRZf0Q6pm5+5sTT+zX2XkP6v//7v2vbtm16/PHHVVBQoPvuu0/r\n16+X1FGW8uyzz7JjG91Kt4FqfHGOpp6X4BRInJBMBmarnoAJ6xtsG2HTlcvEYjG1+4OKRKMpBzJR\nLoNAKKrDLUGNKvDI67bWe/lkGYahXI9DuR6Hxp6W2+19pmkqFImnXYk/tptNa3tEgXDspMbU69+2\nTZs2KRqNqqCgQA0NDXr99deTz/31r3/Vk08+qaeffvqkBoGhK90GqubWjutWPEkQ1pONwDzYTsCE\ntQz2jbB2u10jClI7dXT2dA+FI4ol69wJ78NFLJbQ/3vrE32057Ba/BEV+JyaOnGUvnXFV2UfZgsX\nhmHI47LL4+r+8KhOkWhcX/y1TWYw/T6V3vT6t+rAgQO6+OKLJUm///3vJUnnnXeefvazn+nmm2/W\nn/70p369MIa+oXSSILIn04GZ9y1O1lDaCNvJZrPJ6/XI602t/+0M78Fw5Jha94RkdNS6UzIzNPy/\ntz7R7z48kHzc4o8kH3973uRsDcvynI4cFea51NzzvtRu9frrT3t7u/LyOn6j/tOf/iTDMDRv3jzl\n5+dr2rRpamnpofM8hrW+bKACetJbYO6pBVd/8b7FqdK5EXawB/SedIb3kYUFKjmtUGeUjtL4M4p1\n+uh85bokhxGVkYgoHgkpHAoqGolke8g4QYFQVB/tOZz2uY/2HFYgdOp/DqNDryF95MiR+uCDD7Rr\n167kSvq0aR2rV0eOHEkGeOB4nRuo0snmBioMHtkIzLxvgZPncDg0oiBPxaMKNWZ0kUaPytP400ep\ndJRPPmdCDiMqxcOKRYIKBwOKxU6udhcD53BLUC3+9L9ctfgjam5J/zMaJ6/XkH7hhRfqz3/+s669\n9lodPnxYI0eO1HnnnSdJqqur0xlnnDHgg8Tg1LmBKh0rbqCC9WQjMPO+BQZGZ1/3EQX5Kh5VqLEl\nI3Xm2NM0/ozTVDzCLY89LruiMmNhRUJBhUMhJRLpu2ggc0YVeFTgS9+ms8DnVFFB+p/ROHm91qQv\nX75ce/bsUV1dnXJzc7VmzRoZhqEPPvhA+/fv15VXXpmJcWKQSreBanxxzqDZQIXsylanjMG+8Q8Y\nTLprD5k8lCkaUyyWUDSeUCxmyjQMORycqJopXrdDUyeOSqlJ7zR14qgh1+XFSnoN6aNHj9avfvUr\ntba2yufzJfuiV1RUaMeOHV1aNwHHSreBqv7jj2hjhz7LRmAeihv/gMEm3aFMUkeLyGAwdFx4T8g0\nbIT3AfKtK74qSWm7u2Dg9LlnUn5+fuon2u20XEKfDYWTBJE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I6QAAAIDFENIBAAAAiyGk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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbb0e7a8c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.regplot(x='new_age', y='Survived', data=df, x_bins=50, x_ci=None)\n", "plt.xlim(0, None)\n", "plt.title('Survival Rate by Age Group')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "bf800c58-3eaf-9784-2b81-21a88acb1a5b" }, "source": [] }, { "cell_type": "code", "execution_count": 28, "metadata": { "_cell_guid": "8b40b8c1-c336-a5e5-d043-a1d25f57e414" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Survived</th>\n", " <th>Pclass</th>\n", " <th>Name</th>\n", " <th>Sex</th>\n", " <th>Age</th>\n", " <th>SibSp</th>\n", " <th>Parch</th>\n", " <th>Ticket</th>\n", " <th>Fare</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " <th>Title</th>\n", " <th>new_age</th>\n", " <th>parent</th>\n", " <th>child</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>887</th>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>Montvila, Rev. Juozas</td>\n", " <td>male</td>\n", " <td>27.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>211536</td>\n", " <td>13.00</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>Officer</td>\n", " <td>27.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>888</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Graham, Miss. Margaret Edith</td>\n", " <td>female</td>\n", " <td>19.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>112053</td>\n", " <td>30.00</td>\n", " <td>B42</td>\n", " <td>S</td>\n", " <td>Miss</td>\n", " <td>19.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>889</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n", " <td>female</td>\n", " <td>NaN</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>W./C. 6607</td>\n", " <td>23.45</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>Miss</td>\n", " <td>10.0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>890</th>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Behr, Mr. Karl Howell</td>\n", " <td>male</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>111369</td>\n", " <td>30.00</td>\n", " <td>C148</td>\n", " <td>C</td>\n", " <td>Mr</td>\n", " <td>26.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>891</th>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>Dooley, Mr. Patrick</td>\n", " <td>male</td>\n", " <td>32.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>370376</td>\n", " <td>7.75</td>\n", " <td>NaN</td>\n", " <td>Q</td>\n", " <td>Mr</td>\n", " <td>32.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Survived Pclass Name \\\n", "PassengerId \n", "887 0 2 Montvila, Rev. Juozas \n", "888 1 1 Graham, Miss. Margaret Edith \n", "889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n", "890 1 1 Behr, Mr. Karl Howell \n", "891 0 3 Dooley, Mr. Patrick \n", "\n", " Sex Age SibSp Parch Ticket Fare Cabin Embarked \\\n", "PassengerId \n", "887 male 27.0 0 0 211536 13.00 NaN S \n", "888 female 19.0 0 0 112053 30.00 B42 S \n", "889 female NaN 1 2 W./C. 6607 23.45 NaN S \n", "890 male 26.0 0 0 111369 30.00 C148 C \n", "891 male 32.0 0 0 370376 7.75 NaN Q \n", "\n", " Title new_age parent child \n", "PassengerId \n", "887 Officer 27.0 0 0 \n", "888 Miss 19.0 0 0 \n", "889 Miss 10.0 0 1 \n", "890 Mr 26.0 0 0 \n", "891 Mr 32.0 0 0 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['parent'] = 0\n", "df.loc[(df.Parch > 0) & (df.new_age >= 18), 'parent'] = 1\n", "\n", "df['child'] = 0\n", "df.loc[(df.Parch > 0) & (df.new_age < 18), 'child'] = 1\n", "\n", "df.tail(5)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "_cell_guid": "f37458d5-e48d-6f73-db4f-e0740ad73bf8" }, "outputs": [ { "data": { "text/plain": [ "0 537\n", "1 161\n", "2 102\n", "3 29\n", "5 22\n", "4 15\n", "6 12\n", "10 7\n", "7 6\n", "Name: family, dtype: int64" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['family'] = df['SibSp'] + df['Parch']\n", "df['family'].value_counts()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "_cell_guid": "8894af1d-34ed-a1bd-fbea-6ae7a71c8fb9" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fbb0df3b358>" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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DBgxAx44dAVTMrfbq1QvHjh1DSkqKYZel2uzze7chQ4Zg2bJl0Ol0hjd/TRvk7jZ27Fis\nX7/eEFKVc82V2rdvD6VSifPnzyMlJQXe3t4AADc3NwD33l3qfsyaNQvbt2+HVqvF7t278frrr0Ol\nUqF3796Ii4vD5cuX0bdv3xr78PDwgI2NDUpLS/Hdd9/hu+++A1AxH9y9e3e8/PLL2Lx5My5dumSy\nJiQIAp577jmzehVFEVZWViYblSrXPu9UuZva0aNHAQCOjo6GjT11ZcCAAYYP9VWrVmHVqlWwsrJC\n27ZtkZWVVe1jsITq7nfKlCmYP3++4UPV3D1DysvL8dtvvxnN6VcSBAGenp6GfxdV9ZCTk2PYs0MU\nRWzevNloQ2nlkXVTpkzBvn37kJycjEOHDmHgwIEmtaTGNeEaBAcHIyAgAF26dEGLFi1gY2MDe3t7\ndO7cGXPmzMEXX3xhNP7zzz+Hn58fnJycYGtrizFjxmD58uU17rZW04uuUCgwcOBAw7jKfYfNvR9X\nV1f07NnTcH2/fv2Mvq5bWVnhq6++wvPPPw8HBwc0a9YMAQEBeP/996s9Iqk2RylVNa5p06Z47bXX\nDPv5Vj6HK1aswJAhQ+Dk5ITGjRtj1KhRWLVqVZV9tGzZEp9++ik6dOhg2Ihz55zj4sWLsWLFCvTs\n2RONGzeGjY0NXFxc4O3tjXnz5uH55583q/cGDRpg9uzZmDVrFlq2bAlbW1t4eXnh+++/R4cOHUxu\nU7k2XPmNxdbW1qznqfI293peNRoNPv/8c7Rv3x62trZwd3fHqlWr4OXlVeNrZe5raO7Ymo5Wq9xb\nRBRFNG7cGC+88EKNj6nS1KlTMX36dKjVari4uMDW1hZ2dnbo0KEDpk+fjn/9619Gr/HdPVTuLXOv\nXdTs7OywadMmBAcHw8PDA/b29rC3t4erqyv8/f3xySefSD5FIUj580b5+flYuHAhjhw5gqZNm2LO\nnDnVztmtXLkSERER0Ol08PDwQEhISJX/8IkeFps3b8aSJUsgCAIiIyPRqVMnuVuS3PXr1zF48GAU\nFhZi6tSpeO+99+Ru6aEn6XREaGgobG1tERcXh5SUFMyYMQMeHh4mcz27d+9GREQEfvzxR7Ru3Ror\nV67EvHnzqtyRnEhun3/+OXbv3o1Lly5BEAT4+/s/dgF86tQpzJ07F1evXoVOp4ODgwNeffVVudt6\nJEg2HaHT6RATE4Pg4GDY2dlBo9HAz88PUVFRJmMvXboEjUaDNm3aQBAEjBgxosYjZ4jkdP36deTk\n5MDJyQnDhg3Dxx9/LHdLktPr9cjOzkZZWRk8PT2xfv36h2LPg0eBZGvCmZmZsLGxMey+A1RszIiP\njzcZO3ToUOzduxeZmZlo06YNtm/fbtY8HpEcPvnkE3zyySdytyGrXr161auT6khJshDWarVQKBRG\nyxwcHKDVak3GPvHEE/Dy8sKgQYNgbW2NVq1a4fvvv5eqVSIiyUgWwgqFwiRwCwoKTIIZqDjMtfLE\nMs2bN0dUVBSmTJmC3bt317jFOTExsc77JiJ6UBqNptrrJAthNzc3w9msKqckUlNT4e7ubjL2zz//\nxNChQw1zSqNHj8ayZcuQnp6Op59+usY6NT1YIqKHjWQb5uzt7eHv74+wsDDodDokJCQgNjbW6OCB\nSp6enti7dy/y8vIgiiIiIyNRWlqKp556Sqp2iYgkIekuaiEhIVi4cCF8fHzQtGlThIaGQqlUIjc3\nF0OHDsXu3bvRqlUrTJ8+HTdv3sTIkSOh1+vh6uqK8PBww7HlRET1haQHa1haYmIipyOI6JHCw5aJ\niGTEECYikhFDmIhIRgxhIiIZMYSJiGTEECYikhFDmIhIRgxhIiIZMYSJiGTEECYikhFDmIhIRgxh\nIiIZMYSJiGTEECYikhFDmIhIRgxhIiIZMYSJiGTEECYikhFDmIhIRgxhIiIZMYSJiGTEECYikpG1\n3A1Q/VZWVoaMjAxJaimVSlhZWUlSi6iuMITJojIyMhD074lwaGlv0TqFV3QIn/QfdOzY0aJ1iOoa\nQ5gszqGlPZxaK+Rug+ihxDlhIiIZMYSJiGQk6XREfn4+Fi5ciCNHjqBp06aYM2cOhg0bZjJu8eLF\n2LFjBwRBAACUlJSgYcOGSExMlLJdIiKLkzSEQ0NDYWtri7i4OKSkpGDGjBnw8PCAUqk0GRcaGmr4\ne8GCBWjQgCvtRFT/SJZsOp0OMTExCA4Ohp2dHTQaDfz8/BAVFVXj7YqKivDLL79g9OjREnVKRCQd\nyUI4MzMTNjY2cHV1NSxTqVRIS0ur8XYxMTFwdnZGjx49LN0iEZHkJAthrVYLhcJ4NyUHBwdotdoa\nbxcZGYmRI0dasjUiItlINiesUChMAregoMAkmO+Uk5OD+Ph4fPTRR2bX4ca7h0tWVpZktZKTk1FQ\nUCBZPSJzaTSaaq+TLITd3NxQWlqK7Oxsw5REamoq3N3dq73Njh07oNFo0LZtW7Pr1PRgSXqOjo5A\nrjS1PD09ecQcPXIkm46wt7eHv78/wsLCoNPpkJCQgNjY2BqnGiIjIzFmzBipWiQikpyk+32FhIRA\nr9fDx8cH8+bNQ2hoKJRKJXJzc+Hl5YXLly8bxp44cQJXrlzBwIEDpWyRiEhSku4n7OTkhDVr1pgs\nd3FxQVJSktGy7t274/jx41K1RkQkCx4BQUQkI4YwEZGMGMJERDJiCBMRyYghTEQkI4YwEZGMGMJE\nRDJiCBMRyYghTEQkI4YwEZGMGMJERDJiCBMRyYghTEQkI4YwEZGMGMJERDJiCBMRyYghTEQkI4Yw\nEZGMGMJERDJiCBMRyYghTEQkI4YwEZGMGMJERDJiCBMRyYghTEQkI4YwEZGMGMJERDKSNITz8/MR\nGBgItVoNX19fREdHVzv24sWLmDlzJry8vNC7d2989tlnEnZKRCQNaymLhYaGwtbWFnFxcUhJScGM\nGTPg4eEBpVJpNK6kpATTpk3DpEmTEBYWBkEQkJmZKWWrRESSkGxNWKfTISYmBsHBwbCzs4NGo4Gf\nnx+ioqJMxkZERKBly5YICAiAra0tGjZsiI4dO0rVKhGRZCQL4czMTNjY2MDV1dWwTKVSIS0tzWTs\niRMn0Lp1a7zxxhvw9vbGlClTcO7cOalaJSKSjGQhrNVqoVAojJY5ODhAq9WajL1y5Qp2796NgIAA\nHDp0CH379sWsWbNQWloqVbtERJKQbE5YoVCYBG5BQYFJMAOAra0tNBoN+vTpAwB47bXXsG7dOmRk\nZKBTp0411klMTKy7pumBZWVlSVYrOTkZBQUFktUjMpdGo6n2OslC2M3NDaWlpcjOzjZMSaSmpsLd\n3d1kbKdOnXD8+PH7qlPTgyXpOTo6ArnS1PL09OS2A3rkSDYdYW9vD39/f4SFhUGn0yEhIQGxsbEY\nOXKkydgRI0bg5MmTiIuLQ3l5OTZs2IBmzZqZ7EVBRPSok3Q/4ZCQEOj1evj4+GDevHkIDQ2FUqlE\nbm4uvLy8cPnyZQBAu3btsGLFCixevBi9evXCb7/9hnXr1sHaWtI96oiILE7SVHNycsKaNWtMlru4\nuCApKclo2YABAzBgwACpWiMikgUPWyYikhFDmIhIRpxkrcfKysqQkZEhSS2lUgkrKytJahHVJwzh\neiwjIwP/XP8ynnjC3qJ1rl3T4Y0ZP3L3MKL7wBCu5554wh4urUwPiCGihwPnhImIZMQQJiKSEUOY\niEhGDGEiIhkxhImIZMQQJiKSEXdRsxAeKEFE5mAIW0hGRgZ+/XIK2jg3smidS3lFwOyNPFCC6BHF\nELagNs6N4NbSQe42iOghxjlhIiIZMYSJiGTEECYikhFDmIhIRgxhIiIZMYSJiGTEECYikhFDmIhI\nRgxhIiIZMYSJiGTEECYikhFDmIhIRgxhIiIZSRrC+fn5CAwMhFqthq+vL6Kjo6scFxERgc6dO8PL\nywtqtRpeXl44duyYlK0SEUlC0lNZhoaGwtbWFnFxcUhJScGMGTPg4eEBpVJpMlatVmPTpk1StkdE\nJDnJ1oR1Oh1iYmIQHBwMOzs7aDQa+Pn5ISoqSqoWiIgeOpKFcGZmJmxsbODq6mpYplKpkJaWVuX4\nM2fOoHfv3hg0aBDWrl2L8vJyqVolIpKMZNMRWq0WCoXCaJmDgwO0Wq3J2J49eyI6Ohpt2rRBWloa\ngoODYW1tjenTp0vVLhGRJCQLYYVCYRK4BQUFJsEMAG3btjVcdnd3R2BgIL799luzQjgxMfHBm60D\nWVlZkj25ycnJKCgoqLIHqTzMPRDJTaPRVHudZCHs5uaG0tJSZGdnG6YkUlNT4e7ubtbtRVE0a1xN\nD1ZKjo6OSD0lTS1PT88qf+jT0dER6efk7wG58vZA9DCTbE7Y3t4e/v7+CAsLg06nQ0JCAmJjYzFy\n5EiTsQcOHEBeXh6Ail8tXrduHQYMGCBVq0REkpF0P+GQkBDo9Xr4+Phg3rx5CA0NhVKpRG5uLry8\nvHD58mUAQFxcHEaMGAG1Wo2ZM2di4MCBmDFjhpStEhFJQtL9hJ2cnLBmzRqT5S4uLkhKSjL8/d57\n7+G9996TsjUiIlnwsGUiIhkxhImIZMQQJiKSUY1zwgsWLLjnHQiCgGXLltVZQ0REj5MaQzgiIgKC\nIBj20RUEweh6URQZwkRED6DGEB41apQheIuLi7Fnzx506NAB7u7uSEtLQ1paGgYPHixJo0RE9VGN\nIbx8+XLD5SVLlqBHjx7YuHGjYdnkyZOrPOyYiIjMY/aGuejoaDzxxBNGy1q0aIE9e/bUeVNERI8L\nsw/WUCgU2Lt3L5o3bw6lUon09HTs3bsXzs7OluyPiKheMzuEx40bhzVr1hhNR4iiiHHjxlmkMSKi\nx4HZIRwUFASFQoEtW7bg8uXLaNWqFcaPH49XX33Vkv0REdVrZoewIAiYNm0apk2bZsl+iIgeK7U6\nYi45ORkLFizAtGnTkJeXh8jISGRmZlqoNSKi+s/sNeGTJ09i0qRJKCkpgSAIaNSoEZYuXYrBgwfj\n448/tmSPRET1ltlrwqtWrYIoinjqqacAVJykvUePHjh27JjFmiMiqu/MDuEzZ85gyJAh6Nu3r2GZ\ni4sLrl69apHGiIgeB2aHcIMGDaDX642W/fXXXzxijojoAZgdwh07dsSBAweQkJAAAPjggw9w+PBh\ndOrUyWLNERHVd2aHcFBQEG7fvo2zZ88CALZu3QpBEDBz5kyLNUdEVN+ZvXdEz5498dVXX+Ff//oX\ncnNz4eLigqlTp6JXr16W7I+IqF4zO4RTUlLQt29fow1zRET0YMyejhg3bhzGjh2LLVu2oKioyJI9\nERE9NswOYSsrK6SkpGDx4sV47rnnEBISguTkZEv2RkRU75kdwgcPHsT7778PT09PaLVabNmyBePH\nj8eYMWMs2R8RUb1mdgg3bdoUkyZNwtatW7F79254e3tDFEXD3hJERFR7Zm+YA4CLFy8iKioKO3bs\nwMWLFwFUTFMQEdH9MTuEX375ZZw4cQJAxcncW7dujXHjxvGk7kRED8DsED5+/Disra3Rv39/vPji\ni+jTp4/hl5jNlZ+fj4ULF+LIkSNo2rQp5syZg2HDhtV4m4CAABw9ehRnzpxBgwa1OvMmEdFDz+wQ\nnjNnDsaMGYPmzZvfd7HQ0FDY2toiLi4OKSkpmDFjBjw8PKBUKqscv3PnTpSVldU67ImIHhVmr1pO\nnz79gQJYp9MhJiYGwcHBsLOzg0ajgZ+fH6KioqocX1hYiDVr1mDevHn3XZOI6GFX45qwh4cHAgIC\nMH/+fHh4eFQ5RhAEnDlz5p6FMjMzYWNjA1dXV8MylUqF+Pj4Ksd/8cUXmDhxIn/NmYjqtRrXhEVR\nhCiKRper+s8cWq3W5LSXDg4O0Gq1JmNPnz6N48ePY/LkyeY+DiKiR1KNa8IbN25Eq1atDJcfhEKh\nMAncgoICk2AWRRFLly7FokWLIAiC2SFfKTEx8YH6rCtZWVm12//vASQnJ6OgoKDKHqTyMPdAJDeN\nRlPtdTXmxJ1nSFMoFHj66afvuwk3NzeUlpYiOzvbMCWRmpoKd3d3o3GFhYVISUlBcHAwAKCsrAyi\nKOL5559HWFhYjQ8GqPnBSsnR0RGpp6Sp5enpiY4dO1bZQ/o5+XtArrw9ED3MzF5ZGzduHDp37oyX\nXnoJw4a/sagaAAAXKElEQVQNQ6NGjWpVyN7eHv7+/ggLC8NHH32ElJQUxMbGYvPmzUbjHB0dcfDg\nQcPfOTk5GD9+PCIiItC0adNa1SQiethJegKfkJAQ6PV6+Pj4YN68eQgNDYVSqURubi68vLxw+fJl\nAICzs7Phv2bNmkEQBDg7O8PaWqov+ERE0jA71Q4ePIhdu3YhKioKp0+fxpYtW7B161Z4eHhg+/bt\nZt2Hk5MT1qxZY7LcxcUFSUlJVd6mTZs2PD8FEdVbPIEPEZGMeAIfIiIZ8QQ+REQykvQEPkREZKxW\nJ/AZO3YsDyMmIqpDZm2YKykpwcqVK7F06VJL90NE9FgxK4RtbGzg4uJS6wM0iIioZmbvohYUFIR9\n+/bh0KFDKC4utmRPRESPDbPnhBcuXAhBEPDGG28YLTf3VJZERGSqVvsJ1/aMZkREVDOzQ/hBT2VJ\nRESmzA7hO09rSUREdcPsEA4PD6/2uqCgoDpphojocVOrEK7uCDmGMBHR/TE7hHv27Gm4XFZWhgsX\nLuDmzZtQq9UWaYyI6HFgdgj/8MMPRn8XFxdj2rRp6NKlS503RUT0uDD7YI27NWzYEJ6enti7d29d\n9kNE9Fgxe014wYIFRn/n5+fj0KFDsLe3r/OmiIgeF2aHcERERJU/QT969Og6b4qI6lZZWRkyMjIk\nqaVUKvljD7VgdgiPGjXKaO+IRo0aoUuXLhg+fLhFGiOiupORkYG5/45H45ZPWrTOrSsXsWIS0LFj\nR4vWqU/MDuHly5cbLsfHx0Or1aJ79+78xCN6RDRu+SSatm4vdxt0l3uG8FdffYW4uDisXr0aTk5O\nWLBgASIjIwFU/HryN998A09PT4s3SkRUH91z74iYmBjcuHEDTk5OyMzMREREBERRhCiK+Pvvv6v8\nCXsiIjLPPUM4JycHnTp1AgAcPnwYANCtWzfEx8fDw8MDp0+ftmyHRET12D1DuLCwEI6OjgCA06dP\nQxAEDB48GI0bN0b37t2Rn59v8SaJiOqre4aws7Mzjh49irNnzxrWhLt37w4AyMvLMwQ0ERHV3j1D\n2NvbG+fPn8eYMWNw/fp1ODs7o1u3bgCAlJQUPPmkZXd5ISKqz+4ZwnPmzMHTTz8NURShUCjw0Ucf\nQRAEHD16FJcuXUKPHj3MLpafn4/AwECo1Wr4+voiOjq6ynG7d+/GoEGDoNFo8Oyzz2LBggXQarXm\nPyoiokfEPXdRa9myJX7++WfcunULCoXCsF+wRqNBUlISbG1tzS4WGhoKW1tbxMXFISUlBTNmzICH\nhweUSqXROC8vL2zatAnOzs7Q6XT44IMPsGrVKixatKiWD4+I6OFm9sEajRs3Nr6htTWsrc3/iTqd\nToeYmBjs3r0bdnZ20Gg08PPzQ1RUFObMmWM0tlWrVobL5eXlsLKyQnZ2ttm1pDxEE+BhmkR0/2r1\nQ58PIjMzEzY2NnB1dTUsU6lUiI+Pr3J8YmIiZsyYgcLCQtjb22Pt2rVm18rIyMDxL7/BU85PPHDf\n95KVdw2Y/ToP0ySi+yJZCGu1WigUCqNlDg4O1c71ajQaJCQk4OrVq9iyZQtcXFxqVe8p5yegbFm7\n2xARSU2yEFYoFCaBW1BQYBLMd2vRogWee+45zJkzB9u3b79nncTERGRlZaH1A3VbO8nJySgoKDBa\nlpWVJdmTW1X9yh6k8jD3QJWvg7Mktfg6mNJoNNVeJ1kIu7m5obS0FNnZ2YYpidTUVLi7u9/ztiUl\nJbh48aJZdTQaDRwdHXHjlHRzwp6enibTEY6Ojkg9JV/9yh7Sz8nfA3Ll7YEqXoeIXGleCL4OtXPf\nv6xRW/b29vD390dYWBh0Oh0SEhIQGxuLkSNHmozduXMncv/vH8ylS5cQFhaG3r17S9UqEZFkJAth\nAAgJCYFer4ePjw/mzZuH0NBQKJVK5ObmwsvLC5cvXwYApKenY8KECVCr1XjllVfQvn17fPjhh1K2\nSkQkCcmmI4CKU19WddY1FxcXJCUlGf5+++238fbbb0vZGhGRLCRdEyYiImMMYSIiGTGEiYhkxBAm\nIpIRQ5iISEYMYSIiGTGEiYhkxBAmIpIRQ5iISEYMYSIiGTGEiYhkxBAmIpIRQ5iISEYMYSIiGTGE\niYhkxBAmIpIRQ5iISEYMYSIiGTGEiYhkxBAmIpIRQ5iISEYMYSIiGTGEiYhkxBAmIpIRQ5iISEYM\nYSIiGTGEiYhkJGkI5+fnIzAwEGq1Gr6+voiOjq5yXGRkJMaMGQONRoN+/fphxYoVKC8vl7JVIiJJ\nSBrCoaGhsLW1RVxcHFasWIElS5YgIyPDZJxer8eiRYtw9OhRbNmyBXFxcfj222+lbJWISBKShbBO\np0NMTAyCg4NhZ2cHjUYDPz8/REVFmYydMGECNBoNrK2t0aJFC4wYMQJJSUlStUpEJBnJQjgzMxM2\nNjZwdXU1LFOpVEhLS7vnbY8dOwZ3d3dLtkdEJAtrqQpptVooFAqjZQ4ODtBqtTXebtu2bUhJScHH\nH39syfaoniorK6tyystSlEolrKysJKtHjz7JQlihUJgEbkFBgUkw32n//v1YtWoVNmzYgCZNmphV\nJzExEVlZWWj9QN3WTnJyMgoKCoyWZWVlSfbkVlW/sgepPKw9ZGVlYfnx32Df0tni9XVX8jBf7Yun\nnnrK4rVqq+J1sPxzAFT/b+FxptFoqr1OshB2c3NDaWkpsrOzDVMSqamp1U4zHDhwACEhIfj666/R\noUMHs+toNBo4Ojrixinp1n48PT3RsWNHo2WOjo5IPSVf/coe0s/J3wNy5evB0dER9jkn4dC6pWw9\nPAwcHR0RkSvNC/GwPgcPK8nmhO3t7eHv74+wsDDodDokJCQgNjYWI0eONBkbFxeHuXPnYvXq1fD0\n9JSqRSIiyUm6i1pISAj0ej18fHwwb948hIaGQqlUIjc3F15eXrh8+TIAYN26ddBqtZg+fTrUajW8\nvLwwffp0KVslIpKEZNMRAODk5IQ1a9aYLHdxcTHaBW3jxo1StkVEJBsetkxEJCOGMBGRjBjCREQy\nYggTEcmIIUxEJCOGMBGRjBjCREQyYggTEcmIIUxEJCOGMBGRjBjCREQyYggTEcmIIUxEJCOGMBGR\njBjCREQyYggTEcmIIUxEJCNJf1mD6HFUVlaGjAxpfnhWqVTCyspKklpUNxjCRBaWkZGBKRs3o1EL\ny/7ic9HVK9g4ZQJ/6fgRwxAmkkCjFi3h0LqN3G3QQ4hzwkREMmIIExHJiCFMRCQjhjARkYwYwkRE\nMmIIExHJiCFMRCQjSUM4Pz8fgYGBUKvV8PX1RXR0dJXj0tLS8Nprr8Hb2xseHh5StkhEJClJQzg0\nNBS2traIi4vDihUrsGTJkioP57S2tsaQIUOwbNkyKdsjIpKcZCGs0+kQExOD4OBg2NnZQaPRwM/P\nD1FRUSZj27Vrh7Fjx6JDhw5StUdEJAvJQjgzMxM2NjZwdXU1LFOpVEhLS5OqBSKih45kIazVaqFQ\nKIyWOTg4QKvVStUCEdFDR7IT+CgUCpPALSgoMAnmB5WYmIisrCy0rtN7rVlycjIKCgqMlmVlZUn2\n5FZVv7IHqTysPUhZ/2HooebXwVnWHh5nGo2m2uskC2E3NzeUlpYiOzvbMCWRmpoKd3f3Oq2j0Wjg\n6OiIG6ekOX8rAHh6epqcPtDR0RGpp+SrX9lD+jn5e0CufD04OjoCOSelaaCmHv6Kla1+ZQ8RudK8\nENX1QFWTbDrC3t4e/v7+CAsLg06nQ0JCAmJjYzFy5MgqxxcXF6O4uBiiKBouExHVN5LuohYSEgK9\nXg8fHx/MmzcPoaGhUCqVyM3NhZeXFy5fvgwAuHTpErp27Yrhw4dDEAR07doVgwcPlrJVIiJJSHpS\ndycnJ6xZs8ZkuYuLC5KSkgx/t2nTBqmpqVK2RkQkCx62TEQkI4YwEZGMGMJERDJiCBMRyYghTEQk\nI4YwEZGMGMJERDJiCBMRyYghTEQkI4YwEZGMGMJERDJiCBMRyYghTEQkI4YwEZGMGMJERDJiCBMR\nyYghTEQkI4YwEZGMGMJERDJiCBMRyYghTEQkI4YwEZGMGMJERDJiCBMRyYghTEQkI4YwEZGMGMJE\nRDKSNITz8/MRGBgItVoNX19fREdHVzt2w4YN6NOnD3r06IFFixahpKREwk6JiKQhaQiHhobC1tYW\ncXFxWLFiBZYsWYKMjAyTcQcPHsQ333yD77//HrGxscjOzsaXX34pZatERJKQLIR1Oh1iYmIQHBwM\nOzs7aDQa+Pn5ISoqymRsZGQkxo4dC6VSCUdHRwQGBmL79u1StUpEJBnJQjgzMxM2NjZwdXU1LFOp\nVEhLSzMZm56eDpVKZTQuLy8P+fn5kvRKRCQVa6kKabVaKBQKo2UODg7QarUmY4uKiuDo6Gg0ThRF\naLVaODk5mVUvK+/agzVspqy8a2hWzXWX8oosXv9SXhFUNVx/7ZrO4j3cq0bhFcv3UFMN3ZU8i9e/\nV52iq1csXv9eNW5duWjxHipquFR7/blz5yzeAwB07NhR1vo19XA3QRRF0cK9AADOnj2LiRMn4vjx\n44Zl3333HY4dO4Z169YZjR05ciTefPNNDBo0CABw8+ZN+Pj44L///W+NIZyYmGiZ5omIHpBGo6ly\nuWRrwm5ubigtLUV2drZhSiI1NRXu7u4mYzt06IDU1FRDCKempsLZ2fmea8HVPUgiooeVZHPC9vb2\n8Pf3R1hYGHQ6HRISEhAbG4uRI0eajB01ahS2bduGjIwM5OfnY+3atRg7dqxUrRIRSUay6QigYj/h\nhQsX4siRI2jatCneffddDBkyBLm5uRg6dCh2796NVq1aAajYT/if//wnbt++jYEDB2LJkiWwsbGR\nqlUiIklIGsJERGSMhy0TEcmIIUxEJCOGMBGRjBjC/6c2JxeyhE2bNmHs2LHo0qULFixYIGltACgu\nLsaiRYvg6+sLjUaD0aNH48CBA5L3MXfuXMOJmwYNGoStW7dK3gNQcYRn165dMW/ePMlrT548GV27\ndoWXlxfUajUGDx4seQ8AsGvXLgwZMgRqtRr+/v6S7oevVqvh5eVleA46d+6Mjz76yKI1a3oPxsXF\nYfDgwVCr1QgICEBOTk7dFRZJFEVRfPvtt8W3335b1Ol0YkJCgqjRaMT09HTJ6u/bt0/cv3+/uGTJ\nEnH+/PmS1a1UVFQkfvnll2JOTo4oiqIYGxsrqtVq8dKlS5L2kZaWJur1elEURfH8+fPis88+K6ak\npEjagyiK4rRp08RXXnlFnDt3ruS1J02aJG7btk3yunc6dOiQ2L9/f/HkyZOiKIrilStXxCtXrsjS\ni1arFdVqtZiQkGDROtW9B2/cuCFqNBrxl19+EW/fvi3+z//8j/jiiy/WWV2uCaN2JxeylAEDBsDP\nz8/sw7Lrmr29PYKCguDiUnHIab9+/dC2bVukpKRI2keHDh1ga2sLABD/b8ed7OxsSXvYtWsXGjdu\nDG9vb0nr3kmUeaelL7/8EoGBgejatSsAoEWLFmjRooUsvfzyyy9wdna2+MFY1b0H9+3bB3d3d/j7\n+6Nhw4aYPXs2UlNTceHChTqpyxBG7U4u9Li4fv06srKy0KFDB8lrh4aGonv37hgyZAhatGiBvn37\nSla7sLAQq1evxvz58yWrWZUvvvgCvXv3xsSJExEfHy9p7fLyciQnJyMvLw/+/v7o168fPvzwQxQX\nF0vaR6XIyMgqD+qSSlpamtEJxezt7eHq6or09PQ6uX+GMGp3cqHHQWlpKebOnYvRo0ejXbt2ktdf\nvHgxjh8/jv/85z+GtQ+phIWF4cUXX0TLli0lq3m3uXPnYv/+/Thw4ABefPFFzJw5ExcvWv7kO5Wu\nX7+O0tJSxMTE4Mcff0RkZCTOnDmDtWvXStZDpUuXLiEhIQGjR4+WvHalu08oBtRtPjCEASgUCpMn\ntKCgwCSYHweiKGLu3Llo2LAhPvjgA9n6EAQBXl5eyM3NxY8//ihJzbNnzyIuLg4BAQGS1KtO165d\n0ahRI9jY2GDUqFHw8vKSdCOpnZ0dgIoNhM7OzmjSpAleffVVWTbURkVFwcvLC23atJG8dqVGjRqh\nsLDQaFlhYWGd5YNkJ/B5mNXm5EL13cKFC3Hz5k18/fXXsLKykrsdlJWVSTYnHB8fj0uXLqFfv34A\nKr4hlZeXIz09XdYfFRAEQdI54saNGxtOH3BnD3KIiorCzJkzZaldyd3dHREREYa/i4qKkJ2dXWdT\ndVwTRu1OLmQpZWVluH37NsrLy1FWVobi4mKUlZVJVh8AQkJCcOHCBaxbt07SKYBKN27cwO7du1FU\nVITy8nIcPHgQu3btgo+PjyT1J0yYgP379yMqKgpRUVGYMGEC+vXrh++++06S+kDFN7BDhw4ZXv8d\nO3YgISEBzz33nGQ9AMCYMWPw73//Gzdu3EB+fj42bNiA/v37S9pDUlISrl69ioEDB0pSr7r34IAB\nA5Ceno59+/ahuLgY4eHh8PDwqLOpOp474v9Ud3IhqYSHhyM8PNxojSMwMBBBQUGS1M/JyYGvry9s\nbW3RoEHFZ7MgCFi6dCmGDRsmSQ83btzAW2+9hT///BPl5eVo3bo1pkyZgnHjxklS/27h4eHIzs7G\np59+KlnNGzduYPr06bhw4QKsrKzQvn17vPXWW+jdu7dkPQAV2wU+/vhjREdHw9bWFkOGDMG7774r\n6YdzSEgIiouLsXz5cknq1fQejIuLw9KlS5Gbm4uuXbti+fLlaN26dZ3UZQgTEcmI0xFERDJiCBMR\nyYghTEQkI4YwEZGMGMJERDJiCBMRyYghTEQkI4YwPfJu376Nd999F8888wxUKpVFD+5QqVTw8PDA\nsWPHAFTs4K9SqTBlyhSL1aT6jeeOoEfejz/+iOjoaDg5OWHSpEl48sknLVYrICAAgiCYnFuB6H4x\nhOmRl56eDkEQ0L9/f7z//vsWrSXHT09R/cbpCHqkTZ48Gdu2bQNQcfJvlUqFWbNmYerUqejTpw+6\ndOkCtVqNcePGYd++fYbbRUREQKVSwdfXF19//TW8vb3h7e2NDRs24MyZMxgzZgzUajWmTp2Kq1ev\nGm6nUqmgUqkM0xF3Gz16NFQqFTZs2GBYVtmXv7+/ZZ4EeqQxhOmRNmjQICiVSgAVP400depUDBky\nBDdv3sSzzz6Ll156CT169EBycjLeeecdk5+kuXz5Mnbu3IlnnnkGf//9N5YvX46pU6fCzc0NTZs2\nxdGjR/HFF18Y3aam0zpOmjQJAPDzzz8blu3duxeCIGDMmDF19bCpHmEI0yPtlVdeQdeuXSGKIrp0\n6YL58+dj2LBhCAsLQ+fOnWFvb4927drBzs4OJSUlJj8V1KBBA/zwww8ICwuDk5MTBEGAt7c3vvji\nC7z++usQRRGnT582u59hw4bByckJ6enpOHHiBAoLC3H48GE0aNAAo0aNquuHT/UA54Sp3tm/fz9m\nz54NURQNa62Vl69fv240tnnz5mjSpAmAipOZ37p1y7Bm7eDgAAC1+hkbW1tbjB8/Ht988w22bt2K\nXr16oaSkBH369OHGPKoS14Sp3omMjIQoitBoNDh27BhOnjxp+I2wu8/ceuevh1QG9oP+osjEiRPR\noEED7NmzBz///DOnIqhGDGGqdyp/mv3cuXP4+OOPMWHCBOh0Osnqt27dGv369UNRURHi4+PRuHFj\nDBgwQLL69GhhCFO9IAiCYU129uzZ6N+/P0pLSxEXF4cJEyYYfj35zo1qd97m7vuqaUxVf9+9rHID\nnSAIGDZsmCw/F0WPBv6yBpGF9OrVCwUFBdi6dSs8PT3lboceUtwwR1THtm3bhuPHj+PWrVvo1asX\nA5hqxDVhojrm6+uLa9euoVu3blixYgVcXFzkbokeYgxhIiIZccMcEZGMGMJERDJiCBMRyYghTEQk\nI4YwEZGMGMJERDL6XyD791TGbNK7AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbb0e1e62e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.factorplot(x='family', y='Survived', data=df, kind='bar', size=5, ci=None)\n", "plt.title('Survival Rate by Family Size')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "_cell_guid": "51e1dcd3-b5d2-02f6-3d1a-246f4cb7caca" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fbafc7b9a58>" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Xh48++kjrdprzh0WPPktLS+SdT27sMPTK3d0dXbp0aeww6BEkWheWq6srSktL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\nRER8UUBERMSXv0lYiZcPVBI6AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbb0dec3128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.factorplot(x='Sex', y='Survived', data=df, kind='bar', size=5, ci=None, hue='family')\n", "plt.title('Survival Rate by Gender and Family Size')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "_cell_guid": "5855f25a-9f76-b2fb-7d34-f8465644892f" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fbb0e1b12b0>" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gYJw9exaDBg0q0NbV1RUnT55EQkIClEolDh48iOzsbDg7O+urXCIiIqqC9HbE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\np8guXLiABQsWYNOmTWjUSP2Vfpr+wKy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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbb0dee1f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.factorplot(x='Pclass', y='Survived', data=df, kind='bar', size=5, aspect=1.5, ci=None, hue='family')\n", "plt.title('Survival Rate by Class and Family Size')" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "_cell_guid": "a077ab24-0f33-1f19-c4b2-bf53186a83fb" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>family</th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>10</th>\n", " <th>All</th>\n", " </tr>\n", " <tr>\n", " <th>Sex</th>\n", " <th>Pclass</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">female</th>\n", " <th>1</th>\n", " <td>0.970588</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>0.500000</td>\n", " <td>1.0</td>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.968085</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.906250</td>\n", " <td>0.894737</td>\n", " <td>0.928571</td>\n", " <td>1.000000</td>\n", " <td>1.0</td>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.921053</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.616667</td>\n", " <td>0.517241</td>\n", " <td>0.545455</td>\n", " <td>0.833333</td>\n", " <td>0.0</td>\n", " <td>0.000000</td>\n", " <td>0.375000</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.500000</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">male</th>\n", " <th>1</th>\n", " <td>0.333333</td>\n", " <td>0.387097</td>\n", " <td>0.454545</td>\n", " <td>1.000000</td>\n", " <td>NaN</td>\n", " <td>0.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.368852</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.097222</td>\n", " <td>0.066667</td>\n", " <td>0.470588</td>\n", " <td>0.250000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>0.157407</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.121212</td>\n", " <td>0.178571</td>\n", " <td>0.320000</td>\n", " <td>0.333333</td>\n", " <td>0.0</td>\n", " <td>0.000000</td>\n", " <td>0.250000</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.135447</td>\n", " </tr>\n", " <tr>\n", " <th>All</th>\n", " <th></th>\n", " <td>0.303538</td>\n", " <td>0.552795</td>\n", " <td>0.578431</td>\n", " <td>0.724138</td>\n", " <td>0.2</td>\n", " <td>0.136364</td>\n", " <td>0.333333</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.383838</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "family 0 1 2 3 4 5 \\\n", "Sex Pclass \n", "female 1 0.970588 1.000000 1.000000 0.500000 1.0 1.000000 \n", " 2 0.906250 0.894737 0.928571 1.000000 1.0 1.000000 \n", " 3 0.616667 0.517241 0.545455 0.833333 0.0 0.000000 \n", "male 1 0.333333 0.387097 0.454545 1.000000 NaN 0.000000 \n", " 2 0.097222 0.066667 0.470588 0.250000 NaN NaN \n", " 3 0.121212 0.178571 0.320000 0.333333 0.0 0.000000 \n", "All 0.303538 0.552795 0.578431 0.724138 0.2 0.136364 \n", "\n", "family 6 7 10 All \n", "Sex Pclass \n", "female 1 NaN NaN NaN 0.968085 \n", " 2 NaN NaN NaN 0.921053 \n", " 3 0.375000 0.0 0.0 0.500000 \n", "male 1 NaN NaN NaN 0.368852 \n", " 2 NaN NaN NaN 0.157407 \n", " 3 0.250000 0.0 0.0 0.135447 \n", "All 0.333333 0.0 0.0 0.383838 " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.pivot_table('Survived', index=['Sex', 'Pclass'], columns=['family'], margins=True)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "03970846-1dc9-b1e3-06d9-33843dd2ce2e" }, "source": [] }, { "cell_type": "code", "execution_count": 34, "metadata": { "_cell_guid": "e2b8fd5a-b335-1a23-e837-918e302f67c9" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fbb0e1fa7f0>" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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VeyNQKpVYuXIlli5dCkEQoFQqX2o58fHx1a6Vat6tW7dqu4R6LSEhAXl5ebVd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\nEUkIQ5+ISEIY+kREEvL/A51hYOg029k/AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbb0e7216a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.factorplot(x='Embarked', y='Survived', data=df, kind='bar', size=5, ci=None)\n", "plt.title('Survival Rate by Embarkment Location')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "0c6b6e1e-5a2e-19a9-fd98-af540772e7ea" }, "source": [] }, { "cell_type": "code", "execution_count": 35, "metadata": { "_cell_guid": "b6e14678-d175-ec2a-a9d7-14152c08a45b" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fbafc6c1978>" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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bntc/6Ur3miVLlqhdu3a64447VLx4cd122216+OGHNW/ePIduSXXq1NFXX32l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\ne8WWfVEAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbafc789f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x='Embarked', hue='Sex', data=df)\n", "plt.title('Number of Passengers by Embarkment Location and Class')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "_cell_guid": "5504f8b4-db00-4eec-a04f-da15776f189c" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fbafc63ee48>" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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evXqRdV7psRf1I13qXrN06VL17NlTNWrUUPny5XXLLbfovvvu0/z58+26JbVo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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbafc68d048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x='Embarked', hue='Pclass', data=df)\n", "plt.title('Number of Passengers by Embarkment Location and Class')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "fac18ba3-668f-9695-138c-d59cbf898be2" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "0b1760d8-e8ea-27c6-0a8c-1b8a34c0b3bc" }, "source": [] }, { "cell_type": "code", "execution_count": 37, "metadata": { "_cell_guid": "29e0c277-9689-4f24-1e49-48c46c2c9a76" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fbafc5cc080>" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LImqRFDtSkZOTAwsLC+lSLOD2iU9JSUkGbYOCgrBnzx7k5OSga9eu2LZtm1FjpEREzeHd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\nPzz55JPYuHGjFFBM+V3DG4oREVGTLFi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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbafc63bef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.factorplot(x='Title', y='Survived', data=df, kind='bar', size=5, aspect=1.5, ci=None)\n", "plt.title('Survival Rate by Title')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "704e5211-a1fd-1629-c3d4-385ee6343876" }, "source": [] }, { "cell_type": "code", "execution_count": 38, "metadata": { "_cell_guid": "21aa163d-9d4b-ccd6-b678-5208c97c42a8" }, "outputs": [ { "data": { "text/plain": [ "[None, (1.0, 1000.0)]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ke8cHbp0OG9s7kgkY2omIiHJMJCbj2JlORGJytodiec0tfogm7XoaCCeS7R27vv7GbTPg\nKzSvMw2Nbo5sD4CIiIjSIysanl+/FzsPnIM/GEdZsRsLplfj4SWzITo4D3exQDAERXfAjOWgmqbj\nt6/tRyiafOP0pSsvxfQJ5SaciSiJoZ2IiChHPL9+LzZ/cDL1tT8YT3396PK52RqWJUmSBH8wYdom\nSq+/dxyfNnYCACZe4sPt11xmynmIuvFtORERUQ6IxGTsPHCuz9t2HjjHUpkL6LqOptZO0wJ7w/F2\nvLntBADA6xHxD7fPhN2k3u9E3fgMIyIiygHN/gj8wXift/mDcbT4oxkekXW1tHXALppTx+4PxPG/\nu+rYBQAP3jYDpcXmvDkguhBDOxERUQ6oKfOirJ9wWFbsRlVZQYZHZE3hSBQx2Zz2jrKi4flX96U+\n1bj5qvGYcRnr2CkzGNqJiIhygNcjYsH06j5vWzC9Gl6PmOERWY+iKMn2jqI57R3X/fenONGU7Mc+\ndXwZbl3IOnbKHC5EJSKyuEhMRrM/gpoyL4PZKPfwktkA0Gf3GAKaWjrgNKm947a9Z/Hu7jMAgNIi\nF755+wzYbMbO5ityAm53qaH3SfmDoZ2IyKLY3o8uJjpseHT5XDwQk9Hij6KqrIBv5Lq0tHUAdnN6\npJ9sDuL/+8thAIDDLuDhJbNQVGDsbL4sJVBdVgiHg9GM+sZnBhGRRbG9H/XH6xExoc6X7WFYRjgS\nRUwCHKLxb2ZDUQn/+co+KKoGALjni1MwodbYx15RZJQWOVFQYM6nBJQfOFVDRGRBbO9HlB5ZltHa\nEYFDNP4TB1XV8Pz6famuPVfPrcXVc+sMPoeKAhHwFRcZer+UfxjaiYgsiO39KJdFYjKOnek0/c2l\nrus4a2Id+9r/PoIjp5IbKF1W58Pdi6YYev+6rsMBGZUVrGOnwbE8hojIgrrb+/UV3Nnej6wq0+sw\nmlr8pvVjf3f3afzt49MAAF+hC99aMsvw/wdViqGurtLQ+6T8xZl2IiILYns/ykXd6zC632x2r8N4\nfv1ew8/V5u+EqjtM6cf+aWMH/vj2pwAAh92Gb981C75CYxe5SokYaqtLTRk/5SeGdiIii3p4yWzc\ndOWlqQ11yorduOnKS9nejywpk+swAsEQwnENNrvdsPvs1toRxX+u3wdN0wEAX795quELT7s7xYgm\n1OFT/mJ5DBGRRbG9H+WSdNZhGNHxJhqNoSMkQXQa394xGpex+uVPUm8wvnTlpfi7mWMMPYcis1MM\nDQ9n2omILK67vR8DO1lZ9zqMvhi1DiORkNDij5gS2FVVw/Ov7kdze3KR9+xJFbjj2omGnkPTNHic\n7BRDw8PQTkRERCNm9joMVVXR1NoJ0dX3G4OR0HUd//XWYRw64QcAjK0qxIO3z4DNwHpzXddh1yVU\nsVMMDRPLY4iIiMgQ3est+uoeMxK6ruNMsx+iy5yuSW/taMR7n5wFAPgKnXhk2Ry4ncZGJFWOoa6W\nnWJo+BjaiYjyWCQmo9kfQU2Zl+U1FpVPPyOz1mE0tfhhE42fYQeAjw6ew/r/OQoAcIl2PLpsDkr7\nKfMZLkWKoa66jJ1iaEQY2omI8lCm+2XT0OXzz6h7HYYR2to7oegO2G3GB94jjR144fUGAIAgAN+8\nYybG1RQbeg45EceYSh8cDkYuGhk+g4iI8lB3v+xu3f2yAeDR5XOzNSzLysZsN39GgwsEQwgnNIii\n0/D7Ptsaxpo/74WiJls7fuXGKZg9qcLQc8hSAlVlXrhcxo+fRh9LvpUPBAJ49NFHMW/ePFx//fV4\n/fXX+z325z//Oa655hpcccUVuO+++3D06NEMjpSIyHoy2S8718mKhtXr9uCRn76D7/zsb3jkp+9g\n9bo9kBXN1PPyZzS4VGtHEwJ7RzCO/1i7B9GEAiDZ2vGaeZcYeg5FllFW7GZrRzKMJUP7U089BZfL\nhe3bt+PZZ5/FD3/4Qxw7dqzXcZs2bcL69evxX//1X9i5cyfmzp2L733ve1kYMVF+iMRkHDvTycCQ\n49Lpl01JRu3gOdTXDn9GA0skJLR0mNPaMRKT8e9r96AjlAAA/N2MGtxpcGtHVVFQVGBHcZHX0Pul\n0c1y5TGxWAxvvfUWNm3aBLfbjfr6etxwww3YsGEDVq5c2ePYM2fOoL6+HnV1dQCA22+/Hb///e+z\nMWyinJbPtbWjUXe/7L5CoVH9svPBYLPdD8TkQUtlhvva4c+of4qioLktANFp/Ay1JKtY/fInaGqL\nAACmjS/D12+ZZugCUVVV4RZ1lJUYWxtPZLl/jU+cOAFRFDFu3LjUdVOnTsWRI0d6Hbt48WI0Njbi\nxIkTkGUZr7zyCq655ppMDpcoLxg120jWYHa/7HxhxGz3cF87/Bn1rbu1o8OEwK6qGv5z/T4cPxMA\nAFw6phjfumsWHHbjopCmaRAFhb3YyRSWm2mPRCLwent+nFRYWIhIJNLr2MrKSnzuc5/DTTfdBIfD\ngZqaGs60Ew2REbONZD1m9cvOJyOd7R7pa4c/o97ONLfDYUIvdk3T8cIbB9BwvB0AUF1WgBXLje3F\nrus6bLqEMTXGLmYl6ma50O71ensF9FAo1CvIA8Bzzz2Hffv24d1330VFRQU2bNiA++67D5s2bYLL\nNXAd3K5duwwdN1GuavJLA842bnn/I4wpNW4hGF97mXPlZcDcS8rQEVFQ6nXA7VSx95Pd2R6WpUyo\nssMf7Pv6QwcGni034rWT6Z+RlV9//s4QFDhhsxlbBKDrOv5nXwgNjTEAQKHbhpvmFeDk8U8NPY8m\nR1FZVozmMycHP5hoGCwX2sePHw9FUdDY2JgqkTl06BAmT57c69jDhw9j8eLFqKqqAgAsWbIEzzzz\nDI4ePYoZM2YMeJ76+nrjB0+UgyIxGeu2vdPvbON1n59v2Ez7rl27cua1l08b3lD/Zs8Z/nqOTL52\njGDl15+/M4jKmAa7wb3MdV3HK1uOoqGxBQBQ6BHx3XvrUVNu7AJRRYpiXG0lN0+iPhn1Ztlyod3j\n8eDGG2/EqlWr8KMf/QgNDQ3YsmUL/vjHP/Y6dubMmdi8eTNuueUWlJWVYcOGDVAUBZdeemkWRk6U\nm7pray/sF91tNNbWclHu6DKSHTz52jFGMBRBKKrCIRr/eL3+3md4e2cjAMDtsuOf7plrfGBPRDG2\ntoKBnUxnudAOAE8++SQef/xxXHXVVSgtLcVTTz2FiRMnoqmpCYsXL8amTZtQU1ODhx9+GB0dHbjj\njjsQj8cxbtw4PPfccygsLMz2/wJRTmFt7Xnc8GZ0Gu4OnnztjEw0Foc/GDeltePm7SfwxvufAQCc\nog0rls81fLdTRYrhkjHlhpf0EPVF0HVdz/YgMs3KHxESZVNkGLONQ2H1114kJuORn/Zf7vDL713P\n2VPqk9mvHSNY7fUXjyfQ3BaC6HIbft9/3dmIl99Jdp1z2G1YsXwOpo4vM/QcciKKS2rK4DC4pIfy\nj1GvPb41JKKU7tlGq4YOs3HDGxqu0f7aGapEQkJTW9CUwP7OR6cuCOwC/nHpbMMDuyLFUFddysBO\nGcXQTkTUpbsFYF9G+4Y3REaRJAlNrQE4Xcb3Yt+y6xTW/jXZFcZuE/DwnbMw47JyQ8+hSDHUVpVA\nNKEGn2ggDO1ElNOGun38QMze8MbIsVL+y8fniyzLONsSgGhCYP/vDxvxp7eTgd1mE/DQnbMwe3Kl\noeeQEwzslD38XIeIcpJZXV7MWFjIjjQ0FPn6fEkG9k5TAvuFNew2m4CH7piJuZcbH9jrqhnYKXsY\n2okoJ5nV5WUkLQAzPVbKT/n4fOkO7A6n8YH9zW0nsOHdYwCSJTEP3TnLlMBeW+VjYKesyt237EQ0\nag22fbxRpTJGLCzMxFgpf1jl+WJkaY5ZgV3Xdbz27rFUYHfYBXzrrtmGB/bkotMSOJ3G7QxNNByc\naSeinJNOl5fh9Nw2Qy6NlbIv288Xo0tz0g3s0biMtkAMFT4PCtyDv1HWdR3r/vsI3vnoFIDkJ2Tf\nvmt2VhedchdlMtuAof2xxx4b9A4EQcAzzzxj2ICIiAbT3eWlv37qVurykktjpezL9vPFyNKcRCLZ\nJWagGnZF0fCntw9j79E2BCISfF4nZk+qwD1fnAJHP28SVE3DS5sPYdveJgCAy2nHI0tnY8qlxvdh\nr6suHTSw5+saBLKeAUP7+vXrIQgCuvdfuniLXl3XGdqJKONyafv4XBorZV82ny+DleY8EJPTPn8y\nsAcHXXT6p7cPY+snZ1NfByJS6uuv3Tyt1/GyouF/v7Yfuz9tBQAUuB34v+6eiwm1xn76oAxh46R8\nXINA1jTgs/HOO+9MBXVJkvDmm29i0qRJmDx5Mo4cOYIjR47g5ptvzshAiYgulEvbx+fSWCn7svV8\nMao0JxZP4Fz74DudRuMy9h5t6/O2vUfbsCQu9yiViScU/OqVvTh0sgMAUOx14p/umYtLqooGHVO6\ndF2HrsQxtrYCNtvgs+RGvtEhGsyAof0nP/lJ6vIPf/hDzJ8/Hy+++GLquq9//evwer3mjY6IqB9m\ndHkxSy6NlbIvW88XI0pzotEYzvkjcKax02lbIIZAROrztkBEgj8QT4X2YETCc2v3oPFcCABQ7nPj\n//7yPFSVGlcupGka7JAxZkxFr8qC/mR7DQKNLmkXW73++uuorOy5Iruqqgpvvvmm4YMiIkpXLm0f\nn0tjpezL9PNlpJuLhSPRtAM7AFT4PPB5++7I4vM6UeZL3k9LRxTP/uGjVGCvqyzEP98739jArqoQ\nBRm11eVpB3aAuyhTZqXdPcbr9WLz5s2oqKjAxIkTcfToUWzevBnl5cau1CYiIqLsGG5pTigcQXsg\nnnZgB4ACt4jZkyp61LR3mz2pAgVuESeagli9bg9C0WTryUmX+PDIsjlpdZhJl6LI8LoEVJQNPc9w\nzQplUtqhfdmyZVi9enWP8hhd17Fs2TJTBkZERESZNZzSnEAwjM6wBNHpGvL57vniFADos3vMJ0da\n8dvX9kOSNQDAvMsr8Y3bZsAp2of+P9YPRZZQUijCVzz8uniuWaFMSTu0r1ixAl6vF2vXrkVzczNq\namqwfPlyfOMb3zBzfERERJRh3aU5g/F3BhGKqnCIw9t4yOGw4Ws3T8OSuAx/II4ynxsFbhH/s+sU\n/vTXT9HVvA7Xfu4S3LPocths6ZeuDEaWEqgo8aDQO7ISFq5ZoUxJO7QLgoAHH3wQDz74oJnjISIi\nohzQ0taBuCzAkcbGQ4MpcIsocIvQNB1/evswtuw6nbrtrusm4YsLxg2p1nwwUjyGMZXFcLuH/ulA\nf9J9o0M0XEPaEXX//v146aWXcO7cOTz77LPYunUr5s6di/Hjx5s0PCIiIrISXdfR1OKHojtgdxhX\nqhJPKPjtaw3YdyzZBtJht+Ebt01H/dS+F8cOV7IH++CbJhFZTdqh/ZNPPsG9994LWZYhCAIKCgrw\n9NNP4+abb8aPf/xjM8dIREREFqBpGs40t0NwuGE3sFSlrTOGX/75E5xtjQAAigpE/OPSObjMwJlr\nTdNg06W0e7ATWU3aof0Xv/gFdF3HpZdeisbGRng8HsyfPx8ffvihmeMjIiIiC5BlGWdbOuFwDrzL\n6VAdOdWB59fvS3WIGVPhxaPL5qCixLjzKLIMjxOoqqgw7D6JMi3tt5oHDhzALbfcgmuvvTZ13Zgx\nY9DS0mLKwIiIyBoiMRnHznQiEpOzPRTKkmgsjtPnjA/sW/ecwS/+a3cqsM+cWI7vfX2+oYFdlhIo\nKXSgqqK032P4HKdckPZMu81mQzzec9ev06dPc0dUIqI8JSsanl+/t89WdqKD5QWjRSAYQkdIgtNl\n4My3qmHtXz/Fu7vPpK774oJxWPKFSYZ2iJHiMVRXFKHA03f/eD7HKZekHdovv/xyvPvuu7jssssA\nAN///vfx/vvv46qrrjJtcERElD3Pr9/bY9MYfzCe+vrR5XOzNSzKoJa2DsRkDKsHe386Qwk8/+o+\nHD8TAJBccPr1m6fi72aOMewcuq5Dk+MYO6YUDkf/UYfPccolab+NXLFiBRKJBA4ePAgAWLduHQRB\nwLe//W3TBkdERNkRicnYeeBcn7ftPHCOZQR5TtM0nG5qQ0K1w+EwrsvKkcYOPPPCzlRgLy1y4bv3\n1hsa2BWt0SAVAAAgAElEQVRZhijIGFtbMWBg53Occk3aM+1XXHEFfvWrX+F3v/sdmpqaMGbMGDzw\nwANYsGCBmeMjIqIsaPZH4A/G+7zNH4yjxR9lT+o8FY8n0NwWgOgqgFGFKrqu4687G7H+f45B69ox\nafLYEjx05ywUe4e3MVNfZCmB0iJnWjuc8jlOuSbt0N7Q0IBrr722x0JUIiLKTzVlXpQVu/sMNWXF\nblSVjWwXSbKmYCgZZEWXcT/faFzGi5sOYs+nranrFi0YhyXXToTdbkzduK7rUOU4asrT3zCJz3HK\nNWm/WpYtW4alS5di7dq1iEajZo6JiIiyKBKT0eyPYN6Uyj5vXzC9mtu0myDbHUzOtfrREZYMrV8/\n2RzEM7/bmQrsLqcdD905E8uun2xYYFcVBTZdwrjaiiHtcOr1iFgwve+Nm/gcJytKe6bdbrejoaEB\nP/jBD/Bv//ZvWLx4Me6++27MnDnTzPEREVGGXNxJo7TIhbFVhYjEFPhDPTtrkHGy3cFEVVWcPecH\n7C44DDqfrut456NTeGXLUahashymrrIQD905EzXlxnWdk6UEfF4RpSUlw/r+7udyX489kdWkHdq3\nbt2KN954Axs2bMC+ffuwdu1arFu3DtOmTcMrr7xi5hiJiCgDLu6k0RFKoCOUwA3zx+KOayaiqqyA\ns48myGYHk2gsjpb2EEQD2zmGYzJefOMA9h5tS1131ewx+PIXp8Ap2g05x3DKYfoiOmx4dPlcPBCT\n0eKP8jlOlpb2W+rS0lLce++9WLduHTZt2oQrr7wSuq6nuskQEVlNtssNcslAnTR2f9o65DDDxz49\n2exg0tEZRIs/YmhgP3TCj//ntztSgd3ltOMbt83AfbdMNyywq4oC+zDKYQbi9YiYUOdjYCdLS3um\nHQBOnTqFDRs24LXXXsOpU6cAJMtmiIisJNvlBrnIqE4afOyHJhsdTDRNQ3OLH4ruMKx+XVE1bNx6\nHG99cBJ613Vjq4vwD3fMRLWBCzrlRBylxW74iodXDkOUy9IO7V/5ylewZ88eAMmPpWpra7Fs2TIs\nW7bMtMEREQ0HN0wZOqM6aVj1se9eXFtT5rXUbGqmO5jE4gmc62rnaNSU29nWMH63sQGnWsKp6xYt\nGIc7rplo2Bs1TdMANYG66hKIonV+fkSZlHZo3717NxwOB6677jrcfffdWLhwIQTBuK2GiYiMMFi5\nwQMx2VKhzSq6O2lcGLi7pdtJw4qPvdVn/o143NMVCEZwrj1sWDtHTdfxzoen8OrfjkFRNQCAr9CJ\n+xdPx/QJ5YacA0guNi302FFR3Xc3I6LRIu3QvnLlStx1112oqKgwczwAgEAggMcffxzbtm1DaWkp\nVq5ciVtvvbXPY0+dOoUf//jH2LlzJ1wuF5YuXYrvfve7po+RiKyJG6YM30g7aVjxsbfqzP+FzO5g\noigKmlo6EFfthpXDtHbG8OIbB3DkVGfqunlTKvG1m6ah0KA3GrquQ5PjqC4vhseg2nWiXJZ2aH/4\n4YfNHEcPTz31FFwuF7Zv346GhgZ861vfwrRp0zBx4sQex8myjAcffBD33nsvVq1aBUEQcOLEiYyN\nk4ishxumDN9IO2kM5bHPRLnKQDP/HzQ044FbrfGpi5kdTIKhCNoDUThdHkPWoGm6jnc/Po31/3MM\nCVkFALhddtyzaAqunFlj2CfwsiShwCWgsraCn+oTdRkwtE+bNg33338//vVf/xXTpk3r8xhBEHDg\nwAHDBhSLxfDWW29h06ZNcLvdqK+vxw033IANGzZg5cqVPY5dv349qqurcf/996euu/zyyw0bCxHl\nnkyWG+Sr7k4aw/m+wR77TJarDDTz3xlK4N/X7sZ3vzbfEmUywPAf975omobm1g7Iqg1Og7rDtHZE\n8eKmgz1m16eOL8N9N09Dmc9tyDl0XYcixVBVXowCjzH3SZQvBgztuq5D1/XU5Uw4ceIERFHEuHHj\nUtdNnToVO3fu7HXsnj17UFtbi4ceegj79u3D5Zdfjv/1v/4XgzvRKMcNU7JnsMc+k+UqA838A8C2\nvU14vmCvZcpkjBKJRNHakWzlaMT7EVXTsOWj03ht6zFIcrJ23SXaseS6SbhmXh1sBs6ue5xAXV0l\nZ9eJ+jBgaH/xxRdRU1OTupwJkUgEXm/P3dIKCwsRiUR6HXvu3Dns2LEDv/rVr3DllVfi97//PR55\n5BFs3rwZDseQulkSUR7hhinZM9Bjn+mFqgPN/Jt53mzRdR0tbR2IyTCs9/rpcyH84c2DONkcSl03\ndXwZ7r1pKipKjDlHd+16VVkRZ9eJBjBgsl2wYEHqstfrxYwZM0wfkNfr7RXQQ6FQryAPAC6XC/X1\n9Vi4cCEA4Jvf/CbWrFmDY8eOYcqUKQOeZ9euXcYNmojSlo3Xnr8546ekLhc+9k1+acCFqlve/whj\nSp2Gnn/+pTpOnnbj4OnMnjfTorE4ghEJdtE94Cz1/v3707o/WdGx89Mw9nwWRfcH7U6HgM9PL8T0\nsQ40nz6G5tMjH7ciJ+AWAV+RF63NnF0nGkja09HLli3D9OnTcc899+DWW29FQYE5i7nGjx8PRVHQ\n2NiYKpE5dOgQJk+e3OvYKVOmYPfu3cM6T319/YjGSURDt2vXLr72RrFITMa6be/0u1D1us/PN2XG\ne+YsGf/4b/+NjlAio+fNBE3T0NreibgswDFI//L9+/dj5syZg97n3iOtWPfupz1+TvOmVOLLX5wC\nX6ExXVxURYENCqrKi+F05vYbJqLBGDVZlXa1m91uR0NDA37wgx/g6quvxpNPPpn2O/ah8Hg8uPHG\nG7Fq1SrEYjF89NFH2LJlC+64445ex95+++345JNPsH37dmiahhdeeAFlZWW9uswQEdHQRGIyjp3p\nRCQmG3af3eUqfTFzkbDXI+LvZtRk/LxmCwTDOHmmHQqcgwb2dLR1xvDLlz/BL/+8NxXYS4tc+PZd\ns/GtJbMNCey6rkNOxODzOnDJmAoGdqIhSHumfevWrXjjjTewYcMG7Nu3D2vXrsW6deswbdo0vPLK\nK4YO6sknn8Tjjz+Oq666CqWlpXjqqacwceJENDU1YfHixdi0aRNqamowYcIEPPvss/jBD34Av9+P\n6dOnY82aNaxnJyIaJrO7u2RrkXA+LU6WZRnNrZ3QBRFO98jryiVZxds7G7F5+wnISnKhqSAA188f\ni9sWXga3y5h/U2UpAY9TQF0d2zgSDYegD6MtzPHjx/H000/jgw8+gCAIOHjwoBljMw0/oifKDr72\nrG/1uj19Lty86cpLDe2yEsnSIuFsndcobf5OhGPqsDZJurg8Rtd17Pm0FS+/cwTtgfOlMJfV+fDV\nG6fgkuoiQ8bMUhga7Yz6t29Ib59PnTqFDRs24LXXXsOpU6cAwJDNGoiI+pOJTXgoKZPdXYzsSZ4L\n5x2pWDyBVn8Qgt0F0Tnyme8zLWGse+cIDp3wp64rKhCx5AuTcOWsMYa0cdR1HaocR5mvAEWFJSO+\nP6LRLu1X/le+8hXs2bMHQPKFWFtbi2XLlmHZsmWmDY6IRq9MbsJDSQNtRuQPxtHij+Zk4M1l3QtN\nYxIgOkdeChOMJLBx63G898nZVFcYm03AdfWXYPHnJ6DAbcybMikRh9dt546mRAZKO7Tv3r0bDocD\n1113He6++24sXLiQL0QiMk0mN+GhpIE2IyordqOqzJyuYdS3zkAQnSEJossNcYRVJZKs4qMjYfz6\nre1ISGrq+mnjy7B80WTUVhSOcLRJsizBaddRV8VSGCKjpR3aV65ciaVLl6K8vNzM8RARZXwTHkoa\naDOiXO6ykmuisTjaOkKAzQnRNbLNhlRNwwf7mvHa1uMIhM+3vKwpL8DS6ydj5mXlhkzAqaoKaBIq\nfV54vXxzR2SGtEK7LMv4+c9/joaGBqxatcrsMRHRKMcyjezJpy4ruUaSJLT6Q5A1AaI4slKY7kWm\nr209jqa28xsWFnpE3LpwAq6eWwe7feRlZpqmQZXjKC0ugK+4csT3R0T9Syu0i6KIMWPGmLahEhHR\nhVimYayhLOYVHTY8unwuHsjxLiu5RFVVtLYHEJd1iE4XxBH2dzh4wo9X/3YMJ5uCqetEhw1zxnvw\ntdvmw2NAC8dkv/U4fEUulFZVslyWKAPSfuWuWLECzzzzDBYvXowFCxawVo2ITMMyDWOMZDFvrnZZ\nySXdi0yjCQ1OA+rWjzR2YON7x/FpY2fqOpsg4KrZY3Drwstw+uQRQwJ79yLT2rpy2GxcFE6UKWm/\neh9//HEIgoCHHnqox/WCIODAgQOGD4yIRjeWaYwcF/Nak6ZpaO8IIhyV4XR7MIyW6z0cPd2J17ce\nx6GTHT2unz+tGrddfRmquz6ZOj2y00CWEnCLAsaNKWW7Z6IsGNJb7mHsw0RENCws0xgZLua1Hk3T\n4O8IIhSTITrdcLqHP+ut6zo+bezEpm2f4fBFYX32pArcdvVlGGvQ5kiyJMHl0FFbWcRP2YmyKO3f\nGC+++KKZ4yAi6hPLNIaHi3mtQ9M0tPkDiMSVZFgfQYmKrus48Jkfb277DEdPB3rcNmtiOW5deBku\nHVM80iEDSIZ1p0NHTXkh3O4RfhxARCOW9m+OBQsWmDkOIiIyEBfzZp+qqmjzBxBLaBBdbjhdw/9k\nQ9N0fHy4BX/54CROnQv1uG3WxArc8vnxmFBrzJswhnUia0o7tD/33HP93rZixQpDBkNERMbgYt7s\nkSQJ7Z1hxBIqXG4PxBHkXklW8cH+Jry9sxGtHbEet827vBI3XzUB42qMK4NhWCeyriGF9v5aOjG0\nExFlVjptHLsX7X7Q0IzOUAIlRS5cOaOGi3kHMZQWmReKxuLoCEQgqwJEpxMj2RcpGJHwt49P428f\nn0Y4Jqeut9kELJhegxv/bhxqK43ZxVRKxOEWBYZ1IotLO7RfccUVqcuqquKzzz5DR0cH5s2bZ8rA\niIiot+G0cRQu+pv6NtwWmaFwBJ3BGFTYIIoj67N+piWMd3adwo79zVBULXW9U7Rh4Zw6LLpiHMp8\nI9slFTjfZ73AbUdVtQ+iyE9eiKwu7dD+hz/8ocfXkiThwQcfxKxZswwfFBER9W0obRwvPrYjlGDL\nxwEM5bHVdR2BYAjBcAKwibCLbgy3Y7mm6dh7tA1bPjqFw409O8EUe524rn4srplXZ0hJk6Zp0JQE\nvB4RtRXss06US4a9hN3pdGLmzJnYvHkz/uVf/sXIMRERUR+G0sYxEpOxo6E5rWMp/cdWVVV0dIYQ\nicuwOVywOz3DPmcoKmHb3rN4d/cZtAd6LhiurfRi0RXjcMX0mkE3wkqHLEnQ5DiKPTYUF1VwB1Oi\nHJR2aH/sscd6fB0IBPDee+/B4xn+LywiIkpfum0cZUXDqj9+jI5QYtBjKWmwx/Z4YwvKfG5Iig6n\nyw2Hc3hzXrqu49jpAP62+zR2H26Bop7f/0QAMHtyJa6ffwkuH1dqSLBOxGPwOG2oKvOivbwIvmJj\n6uCJKPPS/q2zfv16CILQa4OlJUuWGD4oIiLqLd02js+v34vt+/ueZb/4WErq67HVVBmaqsBX6ILo\ncgF2F5zDrFcPx2Ts2N+E9/eexdnWSI/bCtwOXDW7Fl/43CWoKBn5RJimaVDlZAlMTW0Zdy8lyhNp\nh/Y777yzx7v+goICzJo1C7fddpspAyMiop7SaeM4UJnHxcfSed2P7ZvbjkNVJACAYHPA4fTgc9Nq\nUeQd+uJPTdfx6ckOvPfJWez5tOesOgCMH1OMa+bVYf60ajhHsnq1i5RIwOkAijxO+KpYAkOUb9IO\n7T/5yU9Sl3fu3IlIJIK5c+fyHTwRUQZ1t2vsq8MJMHCZBwBcNXsMWz5epHtR6eK/r0MgGMKBk2EE\nIhJ8XidmT6rAPV+cMqT7a+2I4oP9zfhgf1OvWnWX044rplXjmnl1GFcz8p1LVVWFpkgocDtQVV3M\nLjBEeWzQ0P6rX/0K27dvx7//+7/D5/Phsccew6uvvgoA8Pl8+M1vfoOZM2eaPlAiIgJEhw2PLp+L\nB2IyWvxRVJUV9Jg1H6iEpqTIhX+6e54hCxvzQSQaQygcQyyhQnS5Ibo8uP/2eYjGZfgDcZT53Chw\npxeCYwkFHx9qwfZ9TTh6urPX7RNqi7FwTh3qp1XBPcx6+At1z6qXeF0oLqoc8f0RkfUN+pvjrbfe\ngizL8Pl8OHHiBNavX5+6rbOzE6tXr8aaNWtMHSQREfXk9Yh9LiQdqITmyhk1o74sRpIkdAYjiCUU\nCDYRdocTzosqXwrcYlphXVY07D/Whg8PnMPeo209+qoDQFGBiCum1+Dzs2tRVzXyBaCSlIBD0OFx\ncVadaDQaNLSfPXsWCxcuBAC8//77AIA5c+bg17/+Ne6//37s27fP3BESEdGQDFZCM9qoqoqOQAix\nhAJVs0F0OuFwDi/wqpqGI42d+PDAOXx8uAWxhNLjdoddwKxJFfj7mWMw47Jy2O0j+1RDkWVAV1Dg\nFlFZWQSn0zmi+yOi3DVoaA+HwygqKgIA7Nu3D4Ig4Oabb0ZxcTHmzp2Ll19+2fRBEhFR+gYroRkN\nNE1DIBhGJCZDVpNtGm0Ox7A2QOoO6h8fbsHuwy0IReVex0y8xIcrptdg/rRqFI7wsdY0DYqUQIHb\ngdJSDwo8I98BlYhy36Chvby8HDt27MDBgwdTM+1z5yZ3hmtvb08FeiLKD5GYjGZ/BDVl3lEX9LLB\nzMe7vxKafJVIJBCKxJCQVCRkDU6XG4LDheGUkKuqhsONHdh9uBV7Pu07qNdWerGgK6gb0apRSsTh\ndAjweV0oYvcXIrrIoL/KrrzySmzYsAF33XUXgGSInzNnDgCgoaEBY8eONXeERJQRsqLh+fV7+yyp\n4MJF4/HxHjlFURCORBFLKEhIKiDYITqdgN0B1zAamyUkFQ3H27HnSCv2H21D9KLSFwCoLitA/dQq\n1E+tNqROXZFlCFBR4HagZkwpO7IRUb8GDe0rV67E0aNH0dDQgMLCQvzoRz+CIAjYsWMHzpw5g5tu\nuikT4yQikz2/fm+PxYv+YDz19aPL52ZrWHmLj/fQ6bqOUDiCWFxGQlahagJEpxOC4IToGt59doYS\n2H+8DXuPtOHgCT9kRet1THdQ/9zUKtRVFo54BvzC8peyUg88LH8hojQMGtqrq6vx5z//GcFgEF6v\nNzULUF9fj48//hgu1zB/UxKRZQy0Ic/OA+fwQExmqYyB+HinLxqNIRJLICGpkBQNotMFm02EXRQx\nnDlpTddxsimIfUfbsP9YOxrPhfo8blx1EeZcXom5kytRW+kdcVBXFAWaIsPtsqPQ40Qxy1+IaIjS\nrvQrLu65CYTD4YDDMfJes0SUfQNtyOMPxtHij46q2miz8fHuXzweRzgahyRrSEgq7A4RdocDgsMB\n1zD/yQlGJBw64ceBz9rRcLy9z/p0QQAmjy3BnMmVmHt5Jcp9I69RTyTicNgAt9OOEp8b3oKSEd8n\nEY1eTN1ENOCGPGXFblSVFWRhVPmLj3eSpmmIRmOIxiXIigZJ0WCzOeAQRcCGXv3T06WoGo6dDuDg\niXYcOO7vdzbd63ZgxsQKzJpYjukTykf86YaqqlBlCU7RBrfLgaoSH3upE5FhGNqJaMANeRZMr2ap\nhsFG6+MtyzIi0WR3F0lWIas6HKITdrsI2AHnMNdgapqO0y0hHD7ZgcONHThyqjO5MLUPdZWFmDmx\nHLMmVWBCbTHstpEt+pUSCdhtOlyiHb5CJ7zeYpa9EJEpLBnaA4EAHn/8cWzbtg2lpaVYuXIlbr31\n1gG/5/7778eOHTtw4MAB2Eb4S5hoNOKGPJmV74+3pmmIRKKISwokWYUka4DNBqfTBcAGmyjCNcz3\nJrquo6ktkgrpnzZ2IBrv3ekFAAo9IqZNKMP0CeWYNr4MJUUjW4elqipURYJLtMPjcqC61MdSUSLK\nCEv+pnnqqafgcrmwfft2NDQ04Fvf+hamTZuGiRMn9nn8xo0boaoqZzeIRoAb8mRWPj3eqqoiGo0h\nIStQFK2rswu6Fo06ALtj2LPoQHJzo1Pnwjh6qhNHT3fi2OnOPuvSgeSOpBNqfZg+oRzTJ5RhbE0R\nbCNdRNq1K6nb6eBsOhFljeVCeywWw1tvvYVNmzbB7Xajvr4eN9xwAzZs2ICVK1f2Oj4cDmP16tX4\n6U9/invuuScLIybKL6NtQx4zpbNxUi493rquI5FIIBZPQFa01B9NT7ZetNkcgAA4nCP7xyUhqTjR\nFMDRU504cjqAz84EkJD7LnexCQLG1xZjyqWlmDKuFJfV+eAUz79DiMZltAViqPB5UOBO/02RlEjA\nYdPhdrEtIxFZg+VC+4kTJyCKIsaNG5e6burUqdi5c2efx//sZz/DV7/6VZSXl2dqiEREA8qHjZMU\nRUEkGoMkq1DUZDhXVB02u6NrcaUdsAPiCPcC0nQdLf4oPjsbxGdnkwH9TGsEmq73ebzNJmBcdREm\njS3BlEtLMfmSErj7aCujKBr+9PZh7D3ahkBEgs/rxOxJFbjni1Pg6ONnoGkaZCkBV9ciUpa9EJHV\nWO43UiQSgdfr7XFdYWEhIpFIr2P37duH3bt34/vf/z7Onj2bqSESEQ0olzZOuri0JRnONcBmhyg6\nIQjJ2XO7mPwzUqGohJNNwVRIP9EU7LceHQCcog2X1fowaWwJJl1Sggm1PrjSqLX509uHsfWT8/8u\nBCJS6uuv3TwNuq5DliTYBA1upwOeAhFF7J1ORBZmudDu9Xp7BfRQKNQryOu6jqeffhpPPPEEBEGA\n3s+sDBFRJllx4yRVVZFISJBkGYqqQ1FUKKoOWdGgo2dpi00EnAYNLxhJ4GRzCKeaQzjZHELjuSA6\ngokBv6es2I0JtcWY0BXUx1YVwm4f2qcT0biMvUfbel2vqTI+Pngay64dh1KfB0VlnE0notxhud9W\n48ePh6IoaGxsTJXIHDp0CJMnT+5xXDgcRkNDA77zne8ASP6jpOs6rrnmGqxatQr19fUDnmfXrl3m\n/A8Q0YDy/bXX5JcG3Dhpy/sfYUyp09Bz6roOWZaRkBSomgZNAzQ92QpR1XRAsMFmt8NuN+dXvq7r\nCMU0tAdltAYVtAYUtARkROLagN/nsANVPhE1pSKqS0RUl4oodHfPogcRbg/iYPvQx9MakBGISNA0\nFZoqA12TOja7AxHJhgNHjhv+M8gV+f76I8pnlgvtHo8HN954I1atWoUf/ehHaGhowJYtW/DHP/6x\nx3FFRUXYunVr6uuzZ89i+fLlWL9+PUpLSwc9z2Chnmi0SWfR5Ejt2rUr7197kZiMddve6XfjpOs+\nP3/Ij+/5mXIFqqpB1XQoXX+rqg5NT9aaOxwO08s74gkFZ1rDONMaxumWcOpyPNH3QtFuDruA2spC\nXFpThHE1xbi0phh1Vd4R90m/kKqqUGQJlykq3ti5HYGICrvD1eMxGe7PIB+MhtcfkRUZ9WbZcqEd\nAJ588kk8/vjjuOqqq1BaWoqnnnoKEydORFNTExYvXoxNmzahpqamx+LTeDwOQRBQXl7OPu1EQ5AP\niyYvlIk3HwMZ6sZJmqZBURQkEhIUNVlPrmk6FFWHqmlQ1fMz5clSjq6ZaBtgswE2k36LS7KK5vYI\nmtqjaG6P4GxrGGdawmgL9P0pwoUcdhvqqroDehHGVRejttILxxDLXAbTHdJFh5Dc3KjIBW9Bsh3j\nwnkTRt3mVUSU3ywZ2n0+H1avXt3r+jFjxuDjjz/u83vq6upw8OBBs4dGlHdyadHkQKz05uPhJbOh\naRp27D8LfyAKX6GIeZdX4o6FY9HU0tFVwpIsXdF1wGazw+5wdE04dI3VDtjtxiz+HEgkJqOpPYLm\n9gia26Kpy/5AHOmsFCr2OnFJVSHqKgtRV1WISyoLMabCO+Q69HRIiQSgqxAddjhF24A90/N98yoi\nGn0sGdqJKDOsuGhyuDLx5kPTNKiqCllWICvnS1W6A7iqadB1QFU0LL5qLK6fX4dgREFFaUGqR7gG\nQOgO5IaManAJSUVrZxStHTG0dMTQ2hHFOX9yBr2/TYou5rALGFNRmArol1QVorayEMVec2rDZUmC\nrqlwOAQ4HXa4nHZU+orgdKZ3vnzavIqICGBoJxrVmv2RARdNtvijObHxz3DefOi6nipNkWUFiqqm\nArimA2pXmYqmJ6/TdUAHIKQWddohCBfE7q5yFeD87LjLDZQUm/A/3Id4QkkG8lQ4Px/SA+GBO7Zc\nyOW0o6asADXlXoyp8Kb+rihxG1p/fiFN06DIEmyCDpdohyjaUVHshcvlGvF959LmVUREA2FoJxrF\nasq8KCt297tosqqsIAujSl938G5s7kCrPwjoOvSLijpa2uM4cPQs6qqKUiFc1XTomg7YbMnSFLu9\n91oYGyDYkrPhmZoRH0hcUuAPxNEejCf/DsThD8bQ3nU5GJGGdH9FBSJqyr1dfwpSAb20yGX6YlZF\nlqGpCkSHDU7RBneBCG9BKex2KzzSRETWxNBONIp5PSLmT6vCWzsae902f1qVqeUEmqal/siKCkVR\nkiFcB/TuGW69O5gnv9ZTXyf/1gFAEKBpOkqKPAhGlV6B0+d1oqSkGLpNtFQIv5Cu64gmFLR3xuEP\nxtEeiHWF8ngqqEdi6ZWxXMhX6ERlSQGqSj2oLC1AZakHVV1/e/rYRdRouq5DkhIQdB2iw9YV0u3w\nFHngdrtNPz8RUT5haCca7fpZbahrOlRVhaZp0PXkhjyarqf2RND15Bb06P4byd7guCBsd4fs5NU6\nmttD+Ox0a3fbbAg2W7LcxJb8Iwj9lF/YAAHJP0Dv0O1yAXMvr+6xA2a32ZMqUvXk2SArKgJhCZ2h\nBDrDCQTCyb87Q8nL3bcl5IFbJvbFJggoLXah3OdGZWlXOO8K6RWlHridmfsVrygKVEWG3QaIDjtE\nhw0upwMFZSXcwIiIyAD8TUqUA5IB+PyfC6/TtPO117qWXBgJnJ+x1tE7QGta8j4iMRnb9pyEIveu\neZ1/Mq8AAB9gSURBVN629yQWza+Fxy12hWuhK1gL/Yfrbhck7AvDtkN0w+nyGPCI9HbPF6cAAPYe\nbUMgIsHndWL2pIrU9UaTFQ3hqIRApGcgD3Rd7uy6HIkrwz6Hw25Duc+NsmI3ynxulPvcKO++XOyB\nr8hpWp15f7rbLNoEpGbPHXYbPIVuuN0+00triIhGK4Z2GnX6C78XBmAdgN41w9y9EFEQkFqM2P29\nuOjrC67uqq9GKiyfPz+g6Vr3qsZetyXPoafuG7oOpIKQ0PWfAAhCV4BO3nZhqD4fk/vRlfPawnGE\nJQEOsXepQiQBhOI6fL6RLwbsTzQuoy0QQ4XPM+LZcIfDhq/dPA1L4jL8gTjKfO4h3WcwksCpcyGI\nDjsiMQlN7VEIABKyilBERigqIRyTEYpICMWkQTcTGowgAMVeF0oKnfAVurpmzD0oK3angnqR1wmb\nCSE4ncddlmVoigy7XYDDngzmDocNHq8TbncR98MYRLb3CyCi/DNqQ3tLW2e2h0Bp0LsD6wAB+MKA\nO2gA7r6hO9heEIABpELwxX8GDcED6ePbrRJ3Knwe+LxOBPpYxOjzOlHmM6fuWFE0/Ontw33OijtG\n2FO9wC3C5bQjFlfQ0hFFLK4gEpcRisoIRyWEohJC0WT47g7h/mC86xMKYxR6RPgKXSgpcsFX6ERJ\n6rIrdbmowAmbLbOz0hc/7kUeO2ZMKMHd10+C2+WAo2vWXHTY4C7ywOUq4cz5EFlpvwAiyi+jNrTL\n+qj9X889F2apPgKwcNHflL4CtwiPy9FnaPe4HKbVgv/p7cM96s8DESn19ddungZd15GQVEQTCqJx\nuSt4K4h1fR296HLq60Ty64Q0slnwvriddhQWOFFUIKKowImiAicKC0QUe50oKUqGcV/XHyuFM13X\nkz3PdQ1/fOsgtu45/7h3yjZs3y+hpLgwpzbSsrJ82ayMiKyHyZVoFIvGZUQTfXclSQZgeUjBXdN1\nSJKKhKxCklUkui4nui4fOx1DU+Rkvz3V3997Fh8fbkEsoaYWt5rF7bTD6xHRGUoMOsteXCDihw//\nfVYXtA5E13XIsgxdVWC3C7DbbF2z5gKcogOe0mJICnD4dAwOZ+81Bbm2kZZV5dNmZURkPQztRKOQ\nompIyCo+O9OJYKTv0B6MyNi49Tg8LgfiUlcIvyCIX/i1JKuISypkRUvj7MF+b9F0DHnhpsfl6PpU\nIPnH4xLhdTvgcTtQ0PVpgcft6JodF1Oz5aLDjsZzQTzzuw8HH3E0WSefrdCuaRpURYGmqbAJAhx2\nATZbstbcbhNgt9vg8XnhdDr7LWdpbOnMi420rCxfNisjImtiaCfKAk3Toaha1x8d6kWXZVWDql50\njKZBUc5flpVkp5jkdcnrU0H6gjB9YciOd13W0qzf3rLrtMmPRE92m4DLx5WisEDsCuHJ8h1v19+p\nYO5O3u5xOkZUFz5QTf+FzKzvB7raJaoKBF1PzZTb7QJsAmC32+B02OFyFUIUxWHXmOf6Rlq5gI8x\nEZmJoZ3yTvdmPIqqQ+kKuuoFl5WuMKyqGpQLQm8qAGtdxyhaMihfEJ4vDtKp+7ngstxHCO95vG56\n6YdZbIIAl9Oe/CPa///27j046vre//hrN7u5bW6kEIJohAKeiEhN1nJaqWeU+It4BWHaOsdKtXVo\nxzAdpj+kg1iGzFh6gT+kiu0vvzoHQX7DIFNhzCmKQM6xQMolwMkhGn+CYuQml0ggy8Zskj1/JLvm\nssnuJnv57nefjxnGzXe/+e7nu+tHX3z2/X1/lWrv/mefn3sep9m7+3Sn9vwzzZ6is2eaVHzLZO0+\n+JnqPro44Ph33T5OTzxwa8zOJzPdrumTRwfs797bSHq9d3V1qcPjkberU1arNcAquUWpjjSlpmZF\ntZ+5I8OuGVPH9qm39pkxdSxlGxHAewwgmpI2tP+ft+rjPQSEwqvuMD0gBA+9Up2YkThy7DarP0Sn\np6b4O6b0d1NBlu74pwL994mLOnvputo9ncpMs2nSjbma/d0J/k4svmPZUiwj6iZia/9C3xyfq6JH\npikzPXD3mFjr39/dbus+P0+HV7mOVE2dmK+7pt8QsL6/+6ZTHerq7JDFoq+DeE/JSorVIrstRWlp\nGbLZbHHvxLLwsemSFLCzCSKD9xhAtFi83gRd8huBuro6rfx/sf3aH+bjC2ndf3pWTvs9tqdYerZ1\nb0/pv7/V4m+z59+n10WEtp7HKT0rszabVTZrv+PYrLL1lFP4wnXvkpHrbR5V/t9/BCwByXHYdds3\nv6EPPmlWi6tdjnSbpk8erSdm3zri1ouBHD9+XNOmTesztuH0VI8G31hys+zq7OzUxeZW/efR02r8\n9EtdcX2lPEeavnXLaD35wFSlp3a/xykpVqWl2mW32xOqb7nL7dGF5usqyM9k9TdKjPge19XVyel0\nxnsYQNKJ1NxL2pX2NHv/G6HDqFL8IbjXTV56wm6K9evg6n/svxFMTwAeJFQPFqQHvNYg+8S6x/Zw\nXWpxD1qzfdXlUe1/n/f/7GrrUO3x87KlWGNSppKZbo96WO++YVaXOjs7pK4uWXpWwq1Wi6yW7s/W\napHSs6wak5Mlmy1Faal27Tl8Roc/uiKp++ZTre3SvuOXlZ3VlPCt+xwZdi6IjDLeYwCRlrShfe3/\nvifeQwBiItSLLXurP3FJj4XZ7jGaukN3Z89daru62/VbLLJau+vsrVZLz8/dF29aLRZZfKHcapXd\nniqbzaaUlBR/iYr/jpU5A+9Y6XJ7dLhxYM29ROu+SOPOoQAQmqQN7UCyCPViy95aXO0RbXHoW+32\neDz66qs2yeuVxeuVxWodMnj7ttlsNtlSuoO31WodUSlKKHespHVf9HHnUAAID6EdSAL9L7b0XWD5\nwafNgWvdM23KcdjU3nMnTfVc+mLxrWJbule6v/65O2j33uaVtyd4W3taGNo1OseuosK8PivesRbK\nHStp3Rd93DkUAMJDaAcSWHd7yy55vV55vV6p559eb/dNjnqH7H/9X9/UvH8p0uWrbo3J6744bn31\nB/qPowMvynb+U5Emjsv2r2qPdHXbx263R7WtYTCh3rGS1n3RxZ1DASB8hHYgDF5/KO71p6tLXnnV\n3WeyOzjLYpFFkiySRb6V6e5jWHqe861Kq9fj7t/xPd93f/UK4FKvVW6r3V9KkpKS4g/YgVayXW6P\nLHaXxvbUD//yye8qK+vrEoW87DTdevMo/WTOHUpPT4viOxkf4ZS90Loveig/AoDwJW1oT8JOlwkr\nUFCW19s3KEtBQ3Lv5yR110t37zBoSLb0ec5Xb22VxWrt6UBi7QncA/8YyVD1wxXfv0M/uvaVXv3r\nf+nDT5tVe/y8Pmr6D1PWF4dT9mK3WVXx/Tv0lAFb9yU6yo8AIHxJG9qzo3dHckSYLyT72/MNEpQx\nuGD1w2+886H2158b9HmzGE7ZC637Io/yIwAIX9KG9tyc7HgPAYiJYPXDP/jyelLVFydT2Us47RRj\n3XoxmT4HAIiEpA3tQLIIVj/c+GlzUtUXJ0PZSzjtFOPVejEZPgcAiCRCO2ByweqHiyfmJ2V9sZnL\nXsJppxjv1otm/hwAIJLMc4UZgIB89cOBzJg6VmNGZQ75PKufiSVYOZTL7RnWvgCA+CK0A0lg4WPT\nNfs7Nys/p/sK7PycdM3+zs3++uFgz4fL5fbo5JkrhL44CKWd4nD2BQDEF+UxQBIIVj8cqfpibk0f\nf+G0U6T1IgAkDv4vCiQRX/2wL5D3XxHv/3y4fPXRvhDoq4+ueqs+MieAoIKVQ/X+bMPZFwAQX6y0\nA0koGiviodyaHrERTjtFWi8CQGIgtANJKBodQ6iPNo5wyp1ovQgAicGQ5TEtLS2qqKhQSUmJZs2a\nperq6oD7bdu2TfPmzZPT6dQ999yj1atXq6urK8ajBRJLtDqG+OqjA6E+Oj7CKXcaaWkUACC6DBna\nKysrlZaWptraWq1evVorV67UyZMnB+zX1tam5cuX68CBA9qyZYtqa2v12muvxWHEQOKI1oo49dEA\nAESP4UK72+3Wzp07tXjxYqWnp8vpdKqsrEzbt28fsO/jjz8up9Mpm82mgoICPfroozpy5EgcRg0k\njkisiA/W0nHhY9NV9u2blONI9R9vJK0jEVws2mvSwhMA4s9wNe2nTp2S3W5XUVGRf1txcbEOHjwY\n9HcPHTqkKVOmRHN4QMLzrYj3rmn3CbYiPtQFrFJ3rfzRjy7qqqtdOZl2ldwyhnaPURKL9pq08AQA\n4zBcaHe5XHI4HH22ZWVlyeVyDfl7W7duVUNDg37zm99Ec3iAKQTqGFJyyxjdN+NmudyeQYP7UBew\nSurz+Op1j3Yf/tx/oSMiKxoXE8fjNQAAoTFcaHc4HAMC+rVr1wYE+d527dqll156SevXr1deXl60\nhwgkvN4dQ85ebNW/7/9URz+6qN2HPx90NXWoC1j/0XBelkFey9fukZr2yAmlveZI3+9YvAYAIHSG\nC+0TJkxQR0eHmpqa/CUyjY2Ng5a9vP/++1qxYoWqqqo0efLkkF+nrq4uIuMFEt3bB79U3Ymv/6Ls\nW029eOmSHpkxyr/9XHP7oBewXrn21aDHb77appp9hzVuVHedO3MvsLb2Ln3Z2qFRWTalpw5dejLU\nZ9H//R6ucF8jnPEjfph/QOIyXGjPyMhQeXm51q5dqxdffFENDQ2qqanR5s2bB+xbW1ur5557TuvW\nrdO0adPCeh2n0xmpIQMJy+X26I/VewI+9+mFThVPnd7n7qlv7t8TMMjlZafJIunLAOE9Pydd9868\nU44Mu+rq6ph7/XxdN34p5LrxoT6L3u/3SIT6GsMZP+KD+QfER6T+smzI/6KuWLFCbW1tuuuuu7R0\n6VJVVlZq0qRJOnfunEpLS3X+/HlJ0p/+9Ce5XC4tXLhQJSUlKi0t1cKFC+M8eiBxhNP+caiWjt+5\nrVD/fFthwOdo9zg0X92473PwfdNR9Vb9oL8Ti/aaob7GcMYPAAif4VbaJSk3N1fr1q0bsH3cuHF9\nWjpu2LAhlsMCEo7L7dH5ZpcK8x0Bg5yv/eNgq6m92z+63B7d989F8nR26ehHFwe95X2gTiO+cbS1\nc/Oz3kZSNx7oYuL+n8VIBXsN6t7DF2xOAsBgDBnaAYxMqK36Qmn/GOhYJbeM0UMzJ+qGMVl9gofv\n4tYLzddVkJ+pVHtKn9/NTrfq2OljlE70COWbjonjcwM+3/tiYt/7HekQGOw1RjL+ZEP7TAAjRWgH\nTCicVn3BVlMDHWuoVo6ODLs/qK1781if373W1kXLwF7C+aZjML3f72gZ7DUiMf5kQftMACNFaAdM\nJtyShUCrqZLU9MVV5WSmDrv8gdKJ4EZyoysjSPTxxwpzAUAkENoBkxluyYIjw64bx2b3+Qo/J9Ou\nq9cD37o+WPkDpROhiUVtejQl+vhjgbkAIBII7YDJjKRkof9X+IMF9lCORelEaGJRmx5NiT7+WGAu\nAIgErn4BTGa47QCH+go/3GONZBzJylc3nqjvS6KPP5qYCwAigZV2wISGU7Iw1Ff4kpTrSFWLqz2s\n8of+48hOt2rmHTdROoGkQxkRgJEitAMmNJyShWBf4a/5xd1qve4Jq/yh/zjONv1/zfwunTKQfCgj\nAjBSlMcAJhZOyUKwr/DHjMocdvmDbxzpqfwnB8mNMiIAw8VKOwA/vsIHAMCYCO0A/PgKHwAAYyK0\nAxggFnfZBAAAoaPAFAAAADA4QjsAAABgcIR2AAAAwOAI7QAAAIDBEdoBAAAAgyO0AwAAAAZHaAcA\nAAAMjtAOJBGX26OTZ67I5fbE9RhGYJbzAAAkB26uBCQBT0eXqt6q18EPvlDz1Tbl56RrxtSxWvjY\ndNltof3dPRLHMAKznAcAILkQ2oEkUPVWvd75x2f+n5uvtvl/rvj+HTE7hhGY5TwAAMmFZSXA5Fxu\njw5+8EXA5w5+8EVI5SGROIYRmOU8AADJh9AOmNz5Zpear7YFfK75apsuNF+PyTGMwCznAQBIPoR2\nwOQK8x3Kz0kP+Fx+TroK8jNjcgwjMMt5AACSD6EdMDlHhl0zpo4N+NyMqWPlyLDH5BhGYJbzAAAk\nHy5EBZLAwsemS1LAjimxPIYRmOU8AADJhdAOJAG7zaqK79+hp9weXWi+roL8zLBXlSNxDCMwy3kA\nAJILoR1IIo4MuyaOzx30eZfbo/PNLhXmOwYNssGOkSjMch4AgORAaAfADYcAADA4QjsAbjgEAIDB\nsYQGJDluOAQAgPEZMrS3tLSooqJCJSUlmjVrlqqrqwfdd/369fre976nO++8U8uXL5fHQ8AAwsEN\nhwAAMD5DhvbKykqlpaWptrZWq1ev1sqVK3Xy5MkB+/3973/XX/7yF73++uuqqalRU1OTXn755TiM\nGEhc3HAIAADjM1xod7vd2rlzpxYvXqz09HQ5nU6VlZVp+/btA/bdtm2b5s+fr0mTJik7O1sVFRX6\n61//GodRA/Hhcnt08syVoCUsQ+3HDYeSU6j/7hidWc4DAIIx3IWop06dkt1uV1FRkX9bcXGxDh48\nOGDfEydO6L777uuz3+XLl9XS0qLcXFq5wbxC7fYS6n5PPzJNDZ9c1umLrfJ6JYtFunFMlp5+ZFo8\nTg9RZJZOQWY5DwAIleH+y+ZyueRwOPpsy8rKksvlGrDv9evXlZ2d3Wc/r9cbcF/ATHzdXny16L5u\nL1Vv1Q9rv397+7g+v9Ad2CXJ65U+v9Cqf3v7ePRPBjEV6r8TRmeW8wCAUBkutDscjgGh+9q1awOC\nvCRlZmaqtbW1z34WiyXgvoBZhNrtJdL7IfGZ5bM2y3kAQDgMVx4zYcIEdXR0qKmpyV8i09jYqClT\npgzYd/LkyWpsbNTs2bP9+33jG98IqTSmrq4usgMHYuRcc/uQ3V5q9h3WuFGpEd8vUph78RPrzzpa\nzHIe8cD8AxKX4UJ7RkaGysvLtXbtWr344otqaGhQTU2NNm/ePGDfuXPnatmyZXrkkUc0evRovfrq\nq5o/f35Ir+N0OiM9dCAmXG6P3ty/J2Boyc9J170z75Qjwx7x/SKhrq6OuRdHsfyso8ks5xFrzD8g\nPiL1l2XDlcdI0ooVK9TW1qa77rpLS5cuVWVlpSZNmqRz586ptLRU58+flyTdfffdeuaZZ7RgwQKV\nlZWpqKhIixYtivPogegKtdtLpPdD4jPLZ22W8wCAcBhupV2ScnNztW7dugHbx40bpyNHjvTZ9tRT\nT+mpp56K0cgAY1j42HRJCtg5I5r7IfGZ5bM2y3kAQKgsXq+vX0Ty4CtCmIXL7dGF5usqyM8ccnUx\n0vsNF3PPOKL9WceKWc4jFph/QHxEau4ZcqUdQGgcGXZNHB/8wutI74fEZ5bP2iznAQDBGLKmHQAA\nAMDXCO0AAACAwRHaAQAAAIMjtAMAAAAGR2gHAAAADI7QDgAAABgcoR0AAAAwOEI7AAAAYHCEdgAA\nAMDgCO0AAACAwRHaAQAAAIMjtAMAAAAGR2gHAAAADI7QDgAAABgcoR0AAAAwOEI7AAAAYHCEdgAA\nAMDgCO0AAACAwRHaAQAAAIMjtAMAAAAGR2gHAAAADI7QDgAAABgcoR0AAAAwOEI7AAAAYHCEdgAA\nAMDgCO0AAACAwRHaAQAAAIMjtAMAAAAGR2gHAAAADI7QDgAAABicoUJ7S0uLKioqVFJSolmzZqm6\nunrQfbdt26Z58+bJ6XTqnnvu0erVq9XV1RXD0QIAAACxYajQXllZqbS0NNXW1mr16tVauXKlTp48\nGXDftrY2LV++XAcOHNCWLVtUW1ur1157LcYjBgAAAKLPMKHd7XZr586dWrx4sdLT0+V0OlVWVqbt\n27cH3P/xxx+X0+mUzWZTQUGBHn30UR05ciTGowYAAACizzCh/dSpU7Lb7SoqKvJvKy4u1scffxzS\n7x86dEhTpkyJ1vAAAACAuDFMaHe5XHI4HH22ZWVlyeVyBf3drVu3qqGhQT/5yU+iNTwAAAAgbmyx\neqEnn3xShw4dksViGfBcaWmpXnjhBbW2tvbZfu3atQFBvr9du3bppZde0vr165WXlxfyeOrq6kLe\nF0DkMPeA+GH+AYkrZqF948aNQz7vdrvV2dmppqYmf4lMY2PjkCUv77//vlasWKGqqipNnjw55LE4\nnc6Q9wUAAADizTDlMRkZGSovL9fatWvldrt1+PBh1dTUaM6cOQH3r62t1XPPPac//vGPmjZtWoxH\nCwAAAMSOxev1euM9CJ+WlhY9//zz2r9/v0aNGqUlS5bowQcflCSdO3dODz30kP72t7+psLBQCxYs\n0JEjR5Samiqv1yuLxaI777xTVVVVcT4LAAAAILIMFdoBAAAADGSY8hgAAAAAgRHaAQAAAIMjtAMA\nAAAGR2gHAAAADC5mfdoTwZo1a3T06FHdeOONWrVqlVJSUuI9JMD0Wltb9fTTT+vkyZPasmVLWPdc\nADB89fX1WrVqlex2uwoKCvSHP/yB/+8BMXD58mVVVFTIbrcrJSVFa9as0ejRo4P+HivtPRobG3Xh\nwgVt2rRJEydO1LvvvhvvIQFJISMjQ1VVVbr//vvjPRQgqdxwww3asGGDNm7cqPHjx2v37t3xHhKQ\nFPLz87V582Zt3LhRc+bM0datW0P6PUJ7j6NHj2rmzJmSpLvvvltHjhyJ84iA5JCSkqJRo0bFexhA\n0hk9erRSU1MlSXa7XVYrkQCIBYvF4n/scrlC/obZdDN006ZNmj9/vm6//XYtW7asz3MtLS2qqKhQ\nSUmJZs2aperqav9zV69eVVZWliQpOztbV65ciem4gUQ33LkHYGRGOvfOnDmjffv26d57743VkAFT\nGMnca2xs1A9+8ANt2rRJt912W0ivZ7qa9rFjx+rZZ5/V3r171dbW1ue5yspKpaWlqba2Vg0NDfrZ\nz36mW2+9VZMmTVJ2drZaW1slSdeuXVNeXl48hg8krOHOPQAjM5K519raql/96lf6/e9/Tz07EKaR\nzL3i4mJt2bJF77zzjv785z+rsrIy6OuZbqX9vvvuU1lZmXJzc/tsd7vd2rlzpxYvXqz09HQ5nU6V\nlZVp+/btkqTS0lLV1tZKkvbu3avS0tKYjx1IZMOde71xg2YgfMOde52dnfrlL3+pRYsW6eabb47H\n0IGENty55/F4/PtmZWUpMzMzpNczXWgfzKlTp2S321VUVOTfVlxcrI8//tj/OD8/X0888YROnDih\n8vLyeA0VMJVgc0+SFi5cqH379unXv/61tm3bFo9hAqYTbO5VV1ervr5er776qhYsWKAdO3bEa6iA\nqQSbex9++KF+9KMf6cc//rE2bNign/70pyEd13TlMYNxuVxyOBx9tmVlZcnlcvl/Xrp0aayHBZhe\nKHOvqqoq1sMCTC/Y3JszZ47mzJkTj6EBphZs7k2fPl1vvPFG2MdNmpV2h8PRJyRI3bXr/d9UAJHF\n3APig7kHxEe05l7ShPYJEyaoo6NDTU1N/m2NjY2aMmVKHEcFmB9zD4gP5h4QH9Gae6YL7Z2dnfrq\nq6/U1dWlzs5Otbe3q7OzUxkZGSovL9fatWvldrt1+PBh1dTU8NUgECHMPSA+mHtAfMR67lm8JmvX\n8Morr+iVV17p07i+oqJCixYtUktLi55//nnt379fo0aN0pIlS/Tggw/GcbSAeTD3gPhg7gHxEeu5\nZ7rQDgAAAJiN6cpjAAAAALMhtAMAAAAGR2gHAAAADI7QDgAAABgcoR0AAAAwOEI7AAAAYHCEdgAA\nAMDgCO0AAACAwRHaAQAAAIOzxXsAAIDYefLJJ3Xo0KEB28ePH6/du3fHYUQAgFAQ2gEgyVgsFk2a\nNEkzZ870b8vLyxv28bxer7xer6xWvrwFgGghtANAErr99tu1bNmyAdtfeOEF7d+/X83Nzero6ND4\n8eM1Z84cPfvss5KkM2fOqKysTJK0YsUKbdy4UZ999pl27dqlMWPG6PXXX9f27dt1+vRp5efn6957\n79UvfvEL5eTkxPT8AMBsCO0AkGS8Xq/q6+u1atUq/7bp06fr4Ycf1ueff65vfetbys/PV0tLi957\n7z29/PLLKiws1Lx58/z7WywW/e53v1N5ebmmTZum1NRULV26VDt27NBNN92khx56SCdOnNAbb7yh\nY8eO6c0335TFYonH6QKAKRDaASAJffLJJ/rkk0/8P8+dO1cPP/yw1q5dq/fee09nz56VzWZTYWGh\nmpqatG/fvj6hXZKWL1+uH/7wh5Kk8+fPa8eOHbJYLCopKVFmZqamTp2qY8eOqaGhQYcPH9a3v/3t\nmJ4jAJgJoR0AkozFYtHcuXP129/+ts/206dPa/78+WppaRmwKn7p0qUBx5kxY4b/8dmzZ/2P3377\n7T6vZbFYdO7cuUgNHwCSEqEdACBJ2rNnj1paWpSVlaXq6moVFhbqmWee0d69e+X1egfsn5aW5n98\nww03+B9v2rRJpaWl/p+bmppUVFQU3cEDgMkR2gEgyQQK4JJUUFAgSXK5XFq1apXa29t14MCBkI5Z\nWFio+++/Xzt37tTPf/5z/8WqJ06c0PHjx/Xhhx9GZvAAkKTozwUAScZXstLf7Nmz9fTTTys3N1e1\ntbUaP368HnjggYD7B/r9NWvWaMmSJSosLNS7776rPXv2SJK/8wwAYPgs3sGWXAAAAAAYAivtAAAA\ngMER2gEAAACDI7QDAAAABkdoBwAAAAyO0A4AAAAYHKEdAAAAMDhCOwAAAGBwhHYAAADA4AjtAAAA\ngMH9D3w4Khu75ZNSAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fbafc5c5860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.regplot(x='Fare', y='Survived', data=df, x_bins=100, x_ci=None)\n", "ax.set(xscale=\"log\", xlim=(1e0, 1e3))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "91f1deee-8ed1-15dd-bd65-8431626643cb" }, "source": [ "The more you pay, the higher the chances." ] } ], "metadata": { "_change_revision": 789, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/330/330371.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "718c1569-a5a9-a828-2984-9434bb625696" }, "source": [ "**Author**: Miran T. \n", "**Last Updated**: 2016-08-14\n", "\n", "If you have any suggestions, critiques, etc. - please write them below in the comments, I would love to hear them. Thank you! \n", "If you fork/like the script, please upvote it.\n", "\n", "This is a second notebook in two-part series. Data preparation in this notebook is a result of the exploratory analysis in the first notebook.\n", "\n", " - [Part I - exploratory analysis](https://www.kaggle.com/narimiran/titanic/exploring-titanic) \n", " - [Part II - machine learning](https://www.kaggle.com/narimiran/titanic/democracy-in-ml-ensembles) (this notebook)\n", "\n", "Some decisions about choosing hyperparameter values might seem strange/illogical, based on provided grid search, but all computations were done on my local machine, where they were making sense :)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "7cb3b4be-2e9f-40cd-ecda-344091cdd254" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "347c5e91-8f2b-f66f-ec26-ccf5aef040ef" }, "outputs": [], "source": [ "import numpy as np \n", "import pandas as pd \n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "plt.rcParams['figure.figsize'] = (12.0, 9.0)\n", "plt.rcParams['axes.titlesize'] = 16\n", "plt.rcParams['axes.titleweight'] = 'bold'\n", "plt.rcParams[\"axes.labelsize\"] = 13\n", "plt.rcParams[\"axes.labelweight\"] = 'bold'\n", "plt.rcParams[\"xtick.labelsize\"] = 12\n", "plt.rcParams[\"ytick.labelsize\"] = 12\n", "sns.set_style('whitegrid')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "229feb9d-bb4d-1a7b-d444-d31853411b2a" }, "outputs": [], "source": [ "titanic_train = pd.read_csv('../input/train.csv', index_col='PassengerId')\n", "titanic_test = pd.read_csv('../input/test.csv', index_col='PassengerId')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "b9ec5269-3fca-d901-e33a-1464f31a8785" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 891 entries, 1 to 891\n", "Data columns (total 11 columns):\n", "Survived 891 non-null int64\n", "Pclass 891 non-null int64\n", "Name 891 non-null object\n", "Sex 891 non-null object\n", "Age 714 non-null float64\n", "SibSp 891 non-null int64\n", "Parch 891 non-null int64\n", "Ticket 891 non-null object\n", "Fare 891 non-null float64\n", "Cabin 204 non-null object\n", "Embarked 889 non-null object\n", "dtypes: float64(2), int64(4), object(5)\n", "memory usage: 83.5+ KB\n", "\n", "\n", "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 418 entries, 892 to 1309\n", "Data columns (total 10 columns):\n", "Pclass 418 non-null int64\n", "Name 418 non-null object\n", "Sex 418 non-null object\n", "Age 332 non-null float64\n", "SibSp 418 non-null int64\n", "Parch 418 non-null int64\n", "Ticket 418 non-null object\n", "Fare 417 non-null float64\n", "Cabin 91 non-null object\n", "Embarked 418 non-null object\n", "dtypes: float64(2), int64(3), object(5)\n", "memory usage: 35.9+ KB\n" ] } ], "source": [ "titanic_train.info()\n", "print('\\n')\n", "titanic_test.info()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "7b3c64e5-dadc-fe89-c75f-f226c3f4a89b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 1309 entries, 1 to 1309\n", "Data columns (total 11 columns):\n", "Age 1046 non-null float64\n", "Cabin 295 non-null object\n", "Embarked 1307 non-null object\n", "Fare 1308 non-null float64\n", "Name 1309 non-null object\n", "Parch 1309 non-null int64\n", "Pclass 1309 non-null int64\n", "Sex 1309 non-null object\n", "SibSp 1309 non-null int64\n", "Survived 891 non-null float64\n", "Ticket 1309 non-null object\n", "dtypes: float64(3), int64(3), object(5)\n", "memory usage: 122.7+ KB\n" ] } ], "source": [ "combined = pd.concat((titanic_train, titanic_test), axis=0)\n", "combined.info()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "69d6c9f5-bd22-4d29-51e3-15e359adfa61", "collapsed": true }, "outputs": [], "source": [ "Title_Dictionary = {\n", " \"Capt\": \"Officer\", #\n", " \"Col\": \"Officer\",\n", " \"Major\": \"Officer\", #\n", " \"Jonkheer\": \"Royalty\", ##\n", " \"Don\": \"Royalty\", #\n", " \"Sir\" : \"Royalty\", #\n", " \"Dr\": \"Officer\",\n", " \"Rev\": \"Officer\", \n", " \"the Countess\":\"Royalty\", ##\n", " \"Dona\": \"Royalty\", ##\n", " \"Mme\": \"Mrs\",\n", " \"Mlle\": \"Miss\",\n", " \"Ms\": \"Mrs\",\n", " \"Mr\" : \"Mr\",\n", " \"Mrs\" : \"Mrs\",\n", " \"Miss\" : \"Miss\",\n", " \"Master\" : \"Master\",\n", " \"Lady\" : \"Royalty\" ##\n", " } \n", "\n", "combined['Title'] = combined['Name'].apply(lambda x: Title_Dictionary[x.split(',')[1].split('.')[0].strip()])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "dc24152f-cfd3-5161-eb59-07d391eb36c0" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th>Sex</th>\n", " <th colspan=\"3\" halign=\"left\">female</th>\n", " <th colspan=\"3\" halign=\"left\">male</th>\n", " </tr>\n", " <tr>\n", " <th>Pclass</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " </tr>\n", " <tr>\n", " <th>Title</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>Master</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>6.984000</td>\n", " <td>2.757273</td>\n", " <td>6.09000</td>\n", " </tr>\n", " <tr>\n", " <th>Miss</th>\n", " <td>30.131148</td>\n", " <td>20.717083</td>\n", " <td>17.360874</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Mr</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>41.450758</td>\n", " <td>32.346715</td>\n", " <td>28.31891</td>\n", " </tr>\n", " <tr>\n", " <th>Mrs</th>\n", " <td>42.926471</td>\n", " <td>33.418182</td>\n", " <td>32.326531</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Officer</th>\n", " <td>49.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>51.090909</td>\n", " <td>40.700000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Royalty</th>\n", " <td>40.000000</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>42.333333</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Sex female male \n", "Pclass 1 2 3 1 2 3\n", "Title \n", "Master NaN NaN NaN 6.984000 2.757273 6.09000\n", "Miss 30.131148 20.717083 17.360874 NaN NaN NaN\n", "Mr NaN NaN NaN 41.450758 32.346715 28.31891\n", "Mrs 42.926471 33.418182 32.326531 NaN NaN NaN\n", "Officer 49.000000 NaN NaN 51.090909 40.700000 NaN\n", "Royalty 40.000000 NaN NaN 42.333333 NaN NaN" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ages_mean = combined.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='mean')\n", "ages_mean" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "4210d15f-403c-1655-cfde-5083e7d7babe" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr>\n", " <th>Sex</th>\n", " <th colspan=\"3\" halign=\"left\">female</th>\n", " <th colspan=\"3\" halign=\"left\">male</th>\n", " </tr>\n", " <tr>\n", " <th>Pclass</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " </tr>\n", " <tr>\n", " <th>Title</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>Master</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>4.974061</td>\n", " <td>2.723942</td>\n", " <td>4.152566</td>\n", " </tr>\n", " <tr>\n", " <th>Miss</th>\n", " <td>11.528914</td>\n", " <td>12.207963</td>\n", " <td>9.935434</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Mr</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>13.489020</td>\n", " <td>11.862038</td>\n", " <td>9.907858</td>\n", " </tr>\n", " <tr>\n", " <th>Mrs</th>\n", " <td>14.122403</td>\n", " <td>10.422005</td>\n", " <td>10.194745</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Officer</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>9.689732</td>\n", " <td>12.927576</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>Royalty</th>\n", " <td>7.549834</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>5.859465</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Sex female male \n", "Pclass 1 2 3 1 2 3\n", "Title \n", "Master NaN NaN NaN 4.974061 2.723942 4.152566\n", "Miss 11.528914 12.207963 9.935434 NaN NaN NaN\n", "Mr NaN NaN NaN 13.489020 11.862038 9.907858\n", "Mrs 14.122403 10.422005 10.194745 NaN NaN NaN\n", "Officer NaN NaN NaN 9.689732 12.927576 NaN\n", "Royalty 7.549834 NaN NaN 5.859465 NaN NaN" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ages_std = combined.pivot_table('Age', index=['Title'], columns=['Sex', 'Pclass'], aggfunc='std')\n", "ages_std" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "8bcd22f2-9034-c9f0-6945-e17530d9709d" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Age</th>\n", " <th>Cabin</th>\n", " <th>Embarked</th>\n", " <th>Fare</th>\n", " <th>Name</th>\n", " <th>Parch</th>\n", " <th>Pclass</th>\n", " <th>Sex</th>\n", " <th>SibSp</th>\n", " <th>Survived</th>\n", " <th>Ticket</th>\n", " <th>Title</th>\n", " <th>new_age</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>22.0</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>7.2500</td>\n", " <td>Braund, Mr. Owen Harris</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>male</td>\n", " <td>1</td>\n", " <td>0.0</td>\n", " <td>A/5 21171</td>\n", " <td>Mr</td>\n", " <td>22.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>38.0</td>\n", " <td>C85</td>\n", " <td>C</td>\n", " <td>71.2833</td>\n", " <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>female</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " <td>PC 17599</td>\n", " <td>Mrs</td>\n", " <td>38.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>26.0</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>7.9250</td>\n", " <td>Heikkinen, Miss. Laina</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>female</td>\n", " <td>0</td>\n", " <td>1.0</td>\n", " <td>STON/O2. 3101282</td>\n", " <td>Miss</td>\n", " <td>26.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>35.0</td>\n", " <td>C123</td>\n", " <td>S</td>\n", " <td>53.1000</td>\n", " <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>female</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " <td>113803</td>\n", " <td>Mrs</td>\n", " <td>35.0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>35.0</td>\n", " <td>NaN</td>\n", " <td>S</td>\n", " <td>8.0500</td>\n", " <td>Allen, Mr. William Henry</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>male</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " <td>373450</td>\n", " <td>Mr</td>\n", " <td>35.0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>Q</td>\n", " <td>8.4583</td>\n", " <td>Moran, Mr. James</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>male</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " <td>330877</td>\n", " <td>Mr</td>\n", " <td>35.0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>54.0</td>\n", " <td>E46</td>\n", " <td>S</td>\n", " <td>51.8625</td>\n", " <td>McCarthy, Mr. Timothy J</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>male</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " <td>17463</td>\n", " <td>Mr</td>\n", " <td>54.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Age Cabin Embarked Fare \\\n", "PassengerId \n", "1 22.0 NaN S 7.2500 \n", "2 38.0 C85 C 71.2833 \n", "3 26.0 NaN S 7.9250 \n", "4 35.0 C123 S 53.1000 \n", "5 35.0 NaN S 8.0500 \n", "6 NaN NaN Q 8.4583 \n", "7 54.0 E46 S 51.8625 \n", "\n", " Name Parch Pclass \\\n", "PassengerId \n", "1 Braund, Mr. Owen Harris 0 3 \n", "2 Cumings, Mrs. John Bradley (Florence Briggs Th... 0 1 \n", "3 Heikkinen, Miss. Laina 0 3 \n", "4 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0 1 \n", "5 Allen, Mr. William Henry 0 3 \n", "6 Moran, Mr. James 0 3 \n", "7 McCarthy, Mr. Timothy J 0 1 \n", "\n", " Sex SibSp Survived Ticket Title new_age \n", "PassengerId \n", "1 male 1 0.0 A/5 21171 Mr 22.0 \n", "2 female 1 1.0 PC 17599 Mrs 38.0 \n", "3 female 0 1.0 STON/O2. 3101282 Miss 26.0 \n", "4 female 1 1.0 113803 Mrs 35.0 \n", "5 male 0 0.0 373450 Mr 35.0 \n", "6 male 0 0.0 330877 Mr 35.0 \n", "7 male 0 0.0 17463 Mr 54.0 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def age_guesser(person):\n", " gender = person['Sex']\n", " mean_age = ages_mean[gender].loc[person['Title'], person['Pclass']]\n", " std = ages_std[gender].loc[person['Title'], person['Pclass']]\n", " persons_age = np.random.randint(mean_age - std, mean_age + std)\n", " return persons_age\n", "\n", "unknown_age = combined['Age'].isnull()\n", "people_w_unknown_age = combined.loc[unknown_age, [\"Age\", \"Title\", \"Sex\", \"Pclass\"]]\n", "\n", "people_w_unknown_age['Age'] = people_w_unknown_age.apply(age_guesser, axis=1)\n", "\n", "known_age = combined['Age'].notnull()\n", "people_w_known_age = combined.loc[known_age, [\"Age\", \"Title\", \"Sex\", \"Pclass\"]]\n", "\n", "combined['new_age'] = pd.concat((people_w_known_age['Age'], people_w_unknown_age['Age']))\n", "combined.head(7)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "40e45af8-e259-5583-f1de-6541a35bd266" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 1309 entries, 1 to 1309\n", "Data columns (total 18 columns):\n", "Age 1046 non-null float64\n", "Cabin 295 non-null object\n", "Embarked 1309 non-null object\n", "Fare 1309 non-null float64\n", "Name 1309 non-null object\n", "Parch 1309 non-null int64\n", "Pclass 1309 non-null int64\n", "Sex 1309 non-null object\n", "SibSp 1309 non-null int64\n", "Survived 891 non-null float64\n", "Ticket 1309 non-null object\n", "Title 1309 non-null object\n", "new_age 1309 non-null float64\n", "kid 1309 non-null int64\n", "parent 1309 non-null int64\n", "child 1309 non-null int64\n", "family 1309 non-null int64\n", "male 1309 non-null int64\n", "dtypes: float64(4), int64(8), object(6)\n", "memory usage: 194.3+ KB\n" ] } ], "source": [ "combined['Embarked'].fillna('S', inplace=True)\n", "combined['Fare'].fillna(value=combined['Fare'].mean(), inplace=True)\n", "\n", "combined['kid'] = 0\n", "combined.loc[combined.new_age <= 12, 'kid'] = 1\n", "\n", "combined['parent'] = 0\n", "combined.loc[(combined.Parch > 0) & (combined.new_age >= 18), 'parent'] = 1\n", "\n", "combined['child'] = 0\n", "combined.loc[(combined.Parch > 0) & (combined.new_age < 18), 'child'] = 1\n", "\n", "combined.tail(5)\n", "\n", "combined['family'] = combined['SibSp'] + combined['Parch']\n", "combined.loc[combined.family > 0, 'family'] = 1\n", "\n", "combined['male'] = (~combined['Sex'].str.contains('fe')).astype(int)\n", "\n", "combined.info()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "b2ef36a3-7b19-fdf5-d634-27d7a63a3086", "collapsed": true }, "outputs": [], "source": [ "not_needed = ['Age', 'Cabin', 'Name', 'Sex', 'Ticket', 'Parch', 'SibSp']\n", "combined.drop(not_needed, axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "cdb02b3a-d8dc-4e0d-8b5e-f6f024dabe10" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Fare</th>\n", " <th>Survived</th>\n", " <th>new_age</th>\n", " <th>kid</th>\n", " <th>parent</th>\n", " <th>child</th>\n", " <th>family</th>\n", " <th>male</th>\n", " <th>Embarked_C</th>\n", " <th>Embarked_Q</th>\n", " <th>Embarked_S</th>\n", " <th>Title_Master</th>\n", " <th>Title_Miss</th>\n", " <th>Title_Mr</th>\n", " <th>Title_Mrs</th>\n", " <th>Title_Officer</th>\n", " <th>Title_Royalty</th>\n", " <th>Pclass_1</th>\n", " <th>Pclass_2</th>\n", " <th>Pclass_3</th>\n", " </tr>\n", " <tr>\n", " <th>PassengerId</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>7.2500</td>\n", " <td>0.0</td>\n", " <td>22.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>71.2833</td>\n", " <td>1.0</td>\n", " <td>38.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>7.9250</td>\n", " <td>1.0</td>\n", " <td>26.0</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>1</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>53.1000</td>\n", " <td>1.0</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>8.0500</td>\n", " <td>0.0</td>\n", " <td>35.0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Fare Survived new_age kid parent child family male \\\n", "PassengerId \n", "1 7.2500 0.0 22.0 0 0 0 1 1 \n", "2 71.2833 1.0 38.0 0 0 0 1 0 \n", "3 7.9250 1.0 26.0 0 0 0 0 0 \n", "4 53.1000 1.0 35.0 0 0 0 1 0 \n", "5 8.0500 0.0 35.0 0 0 0 0 1 \n", "\n", " Embarked_C Embarked_Q Embarked_S Title_Master Title_Miss \\\n", "PassengerId \n", "1 0 0 1 0 0 \n", "2 1 0 0 0 0 \n", "3 0 0 1 0 1 \n", "4 0 0 1 0 0 \n", "5 0 0 1 0 0 \n", "\n", " Title_Mr Title_Mrs Title_Officer Title_Royalty Pclass_1 \\\n", "PassengerId \n", "1 1 0 0 0 0 \n", "2 0 1 0 0 1 \n", "3 0 0 0 0 0 \n", "4 0 1 0 0 1 \n", "5 1 0 0 0 0 \n", "\n", " Pclass_2 Pclass_3 \n", "PassengerId \n", "1 0 1 \n", "2 0 0 \n", "3 0 1 \n", "4 0 0 \n", "5 0 1 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "categorical = ['Embarked', 'Title', 'Pclass']\n", "\n", "for column in categorical:\n", " dummy = pd.get_dummies(combined[column], prefix=column).astype(int)\n", " combined = combined.join(dummy)\n", " combined.drop(column, axis=1, inplace=True)\n", " \n", "combined.head()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "_cell_guid": "f9287507-fc35-e116-8086-e636b162c128" }, "outputs": [ { "data": { "text/plain": [ "((891, 20), (418, 19))" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = combined.loc[:len(titanic_train), :]\n", "\n", "df_test = combined.loc[len(titanic_train)+1:, :].copy()\n", "df_test.drop('Survived', axis=1, inplace=True)\n", "\n", "df.shape, df_test.shape" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "7fbc2be6-78c3-b9f2-133e-22e86384129e" }, "outputs": [ { "data": { "image/png": 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7nJ2zPmLAoh/0tknw7+Q/N12kOFlZWZiammJjY0N2djarV6/WO4cV1FnC999/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4bExMSQlZWFr68vrVq1wtPTkxUrVhR2voODgxk4cCABAQG8/fbbdOjQgVOnTgGwfPlyfv31V+bO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\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cOY4fP07v3tq3a2/YsCGHDx8mPT0dpVJJeHg4BQUF2NnZvVRL6MkWEBAQEBAQEBD4n8HP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\nIYQQQgjh7H/Dcu1SAHLJFQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8a88212048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "corr_mat = np.corrcoef(df.values.T)\n", "\n", "ax = sns.heatmap(corr_mat, annot=True, fmt='.2f',\n", " xticklabels=df.columns, yticklabels=df.columns,\n", " )\n", "\n", "_ = (ax.set_title('Correlation Matrix'))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "36bf1248-f9ab-cf9e-dfc5-c8b4dcf988f5" }, "source": [] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "1c8382a0-0671-ae94-e65d-f0bbeb53521b", "collapsed": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n", "/opt/conda/lib/python3.5/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", " DeprecationWarning)\n" ] } ], "source": [ "from sklearn.preprocessing import RobustScaler\n", "from sklearn.feature_selection import RFECV, RFE\n", "from sklearn.cross_validation import StratifiedKFold, cross_val_score\n", "from sklearn.decomposition import KernelPCA\n", "\n", "from sklearn.grid_search import GridSearchCV\n", "from sklearn.ensemble import (RandomForestClassifier, AdaBoostClassifier, \n", " GradientBoostingClassifier, VotingClassifier)\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.svm import SVC\n", "from sklearn.pipeline import Pipeline\n", "\n", "from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve, f1_score" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "58f74b5c-6d94-ed19-ebd4-bd5752d15f4d", "collapsed": true }, "outputs": [], "source": [ "X_train = df.drop('Survived', 1)\n", "y = df['Survived']\n", "\n", "X_test_ = df_test.copy()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "576d873b-b17e-b679-3eeb-1aa3473eb043" }, "outputs": [], "source": [ "seed = 2016\n", "folds = 10\n", "scoring = 'accuracy'\n", "\n", "kfold = StratifiedKFold(y=y, n_folds=folds, random_state=seed)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9899fd48-8688-78d8-0836-a6c279b5e5d1" }, "source": [] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "1d589ac3-b8f4-5662-2afe-ade14a358453" }, "outputs": [], "source": [ "robust = RobustScaler()\n", "robust.fit(X_train[['Fare', 'new_age']])\n", "\n", "X = X_train.copy()\n", "X_test = X_test_.copy()\n", "X[['Fare', 'new_age']] = robust.transform(X_train[['Fare', 'new_age']])\n", "X_test[['Fare', 'new_age']] = robust.transform(X_test_[['Fare', 'new_age']])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "3e7650b5-8b7f-a9cb-a89f-a4886f07a4cb" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f8a340f9860>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LC4sq89a0X9yPZtJmbm4uPVZ/bpo1s+oW03sf34/mVQvNbWdkZCR9LkqlUus20YxPvT00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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8a341532e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rfecv = RFECV(estimator=RandomForestClassifier(n_estimators=50, random_state=seed), \n", " cv=kfold, scoring='accuracy')\n", "\n", "rfecv.fit(X, y)\n", "\n", "plt.plot(range(1, len(rfecv.grid_scores_)+1), rfecv.grid_scores_, 'o-')\n", "plt.title('Accuracy depending on number of features')\n", "plt.xlabel('Number of features')\n", "plt.ylabel('Accuracy')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "d526cdb7-9aa1-d84e-bbfb-7c24f0ab66ae" }, "outputs": [ { "data": { "text/plain": [ "RFE(estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=50, n_jobs=1, oob_score=False, random_state=2016,\n", " verbose=0, warm_start=False),\n", " n_features_to_select=7, step=1, verbose=0)" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rfe = RFE(estimator=RandomForestClassifier(n_estimators=50, random_state=seed), \n", " n_features_to_select=7\n", " )\n", "\n", "rfe.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "_cell_guid": "87aac6b3-b263-acb7-a6ca-2a5b68d025ce" }, "outputs": [ { "data": { "text/plain": [ "Index(['Fare', 'new_age', 'male', 'Title_Miss', 'Title_Mr', 'Pclass_1',\n", " 'Pclass_3'],\n", " dtype='object')" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_redux = X.loc[:, X.columns[rfe.ranking_ == 1].values]\n", "X_test_redux = X_test.loc[:, X_test.columns[rfe.ranking_ == 1].values]\n", "\n", "X_redux.columns" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "_cell_guid": "b3ba358d-e4e8-c1ee-e8b4-56f018960468" }, "outputs": [ { "data": { "image/png": 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kHEWuQOkKZsuWLfH9998rrNuyZQtat26t8qCIiIiISHcpXcGcP38+Jk2ahN9+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\nhDAoKTCFEEIIIYRB/R/wRurwAQfPnAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8a340cea90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "corr_mat = np.corrcoef(X_redux.values.T)\n", "\n", "ax = sns.heatmap(corr_mat, annot=True, fmt='.2f',\n", " xticklabels=X_redux.columns, yticklabels=X_redux.columns,\n", " )\n", "\n", "_ = (ax.set_title('Correlation Matrix'))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "_cell_guid": "58984be7-8ed3-e9fb-4bdd-60eb1c9f733b" }, "outputs": [ { "data": { "text/plain": [ "[mean: 0.80696, std: 0.00794, params: {'pca__kernel': 'linear', 'pca__degree': 2, 'pca__n_components': 6},\n", " mean: 0.80696, std: 0.00794, params: {'pca__kernel': 'linear', 'pca__degree': 3, 'pca__n_components': 6},\n", " mean: 0.80696, std: 0.00794, params: {'pca__kernel': 'linear', 'pca__degree': 4, 'pca__n_components': 6},\n", " mean: 0.80022, std: 0.00692, params: {'pca__kernel': 'linear', 'pca__degree': 2, 'pca__n_components': 4},\n", " mean: 0.80022, std: 0.00159, params: {'pca__kernel': 'linear', 'pca__degree': 2, 'pca__n_components': 5},\n", " mean: 0.80022, std: 0.00692, params: {'pca__kernel': 'linear', 'pca__degree': 3, 'pca__n_components': 4},\n", " mean: 0.80022, std: 0.00159, params: {'pca__kernel': 'linear', 'pca__degree': 3, 'pca__n_components': 5},\n", " mean: 0.80022, std: 0.00692, params: {'pca__kernel': 'linear', 'pca__degree': 4, 'pca__n_components': 4},\n", " mean: 0.80022, std: 0.00159, params: {'pca__kernel': 'linear', 'pca__degree': 4, 'pca__n_components': 5},\n", " mean: 0.79461, std: 0.00825, params: {'pca__kernel': 'linear', 'pca__degree': 2, 'pca__n_components': 3}]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca = KernelPCA(n_components=6, kernel='linear')\n", "model = SVC(random_state=seed)\n", "\n", "pipeline = Pipeline(\n", " [('pca', pca),\n", " ('model', model)\n", " ]\n", ")\n", "param_grid = dict(pca__n_components=[2, 3, 4, 5, 6],\n", " pca__kernel=['linear', 'rbf', 'poly'],\n", " pca__degree=[2, 3, 4]\n", " )\n", "\n", "grid = GridSearchCV(pipeline, param_grid=param_grid, n_jobs=-1)\n", "grid.fit(X, y)\n", "\n", "grid.grid_scores_.sort(key=lambda x: x[1], reverse=True)\n", "grid.grid_scores_[:10]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "_cell_guid": "9f011272-a566-7f01-ab41-06c396a18268" }, "outputs": [], "source": [ "X_pca = pca.fit_transform(X)\n", "X_test_pca = pca.transform(X_test)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "8b83dc48-f5ba-a54e-2cb6-37ef19385199" }, "source": [] }, { "cell_type": "code", "execution_count": 24, "metadata": { "_cell_guid": "43a1c5bc-2498-a271-e8e4-2bca6b8054bd", "collapsed": true }, "outputs": [], "source": [ "models = [('logreg', LogisticRegression(random_state=seed)), \n", " ('knn', KNeighborsClassifier()),\n", " ('nb', GaussianNB()),\n", " ('svm', SVC(random_state=seed)),\n", " ('rf', RandomForestClassifier(n_estimators=50, random_state=seed)), \n", " ('ada', AdaBoostClassifier(random_state=seed)),\n", " ('gbm', GradientBoostingClassifier(random_state=seed)), \n", " ]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "_cell_guid": "47049b0e-947c-ce37-42b5-b35d5ac0c3e7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " full X \n", "===========\n", "logreg:\t (0.8227) +/- (0.0324)\n", "knn:\t (0.8037) +/- (0.0384)\n", "nb:\t (0.7812) +/- (0.0362)\n", "svm:\t (0.8092) +/- (0.0298)\n", "rf:\t (0.8048) +/- (0.0468)\n", "ada:\t (0.8059) +/- (0.0334)\n", "gbm:\t (0.8262) +/- (0.0464)\n", "\n", " reduced X \n", "===========\n", "logreg:\t (0.8013) +/- (0.0319)\n", "knn:\t (0.8351) +/- (0.0408)\n", "nb:\t (0.7800) +/- (0.0244)\n", "svm:\t (0.8148) +/- (0.0345)\n", "rf:\t (0.8149) +/- (0.0442)\n", "ada:\t (0.8081) +/- (0.0438)\n", "gbm:\t (0.8250) +/- (0.0420)\n", "\n", " pca X \n", "===========\n", "logreg:\t (0.7968) +/- (0.0347)\n", "knn:\t (0.8037) +/- (0.0451)\n", "nb:\t (0.8002) +/- (0.0263)\n", "svm:\t (0.8170) +/- (0.0341)\n", "rf:\t (0.7981) +/- (0.0437)\n", "ada:\t (0.7757) +/- (0.0408)\n", "gbm:\t (0.7936) +/- (0.0374)\n" ] } ], "source": [ "training_sets = [\n", " ('full X', X),\n", " ('reduced X', X_redux),\n", " ('pca X', X_pca)\n", "]\n", "\n", "for traning_set in training_sets:\n", " print('\\n', traning_set[0], '\\n===========')\n", "\n", " for name, model in models:\n", " result = cross_val_score(model, traning_set[1], y, cv=kfold, scoring=scoring)\n", " print(\"{0}:\\t ({1:.4f}) +/- ({2:.4f})\".format(name, result.mean(), result.std()))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "bcd3ac32-a8fa-7c52-ce4f-6767c720014e" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e6120ab7-b0b7-2f85-f540-d9213ded6466" }, "source": [] }, { "cell_type": "code", "execution_count": 26, "metadata": { "_cell_guid": "2475df56-90aa-2a0c-8440-9020af6bc904" }, "outputs": [ { "data": { "text/plain": [ "[mean: 0.82267, std: 0.03242, params: {'C': 1},\n", " mean: 0.81930, std: 0.03566, params: {'C': 2},\n", " mean: 0.81930, std: 0.03988, params: {'C': 20},\n", " mean: 0.81930, std: 0.04261, params: {'C': 50},\n", " mean: 0.81930, std: 0.04261, params: {'C': 100},\n", " mean: 0.81818, std: 0.03371, params: {'C': 0.5},\n", " mean: 0.81818, std: 0.03875, params: {'C': 5},\n", " mean: 0.81818, std: 0.04145, params: {'C': 10},\n", " mean: 0.80920, std: 0.03705, params: {'C': 0.2},\n", " mean: 0.80359, std: 0.03746, params: {'C': 0.1}]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = LogisticRegression(random_state=seed)\n", "\n", "parameters = dict(\n", " C=[0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100],\n", ")\n", "\n", "grid = GridSearchCV(model, param_grid=parameters, cv=kfold, scoring=scoring, n_jobs=-1)\n", "grid.fit(X, y)\n", "\n", "grid.grid_scores_.sort(key=lambda x: x[1], reverse=True)\n", "grid.grid_scores_[:10]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "_cell_guid": "a1285254-571a-8b68-9351-c3cd550ba2e1" }, "outputs": [], "source": [ "logreg_best = grid.best_estimator_" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "c6b743f0-ebeb-2c9b-9934-073000c937ba" }, "source": [] }, { "cell_type": "code", "execution_count": 28, "metadata": { "_cell_guid": "8eac79fa-9f56-bef3-7a25-246a766c6265" }, "outputs": [ { "data": { "text/plain": [ "[mean: 0.83502, std: 0.04082, params: {'n_neighbors': 5, 'weights': 'uniform'},\n", " mean: 0.83389, std: 0.03482, params: {'n_neighbors': 6, 'weights': 'uniform'},\n", " mean: 0.82941, std: 0.04251, params: {'n_neighbors': 4, 'weights': 'uniform'},\n", " mean: 0.82828, std: 0.04250, params: {'n_neighbors': 8, 'weights': 'uniform'},\n", " mean: 0.82379, std: 0.02587, params: {'n_neighbors': 14, 'weights': 'uniform'},\n", " mean: 0.82267, std: 0.03650, params: {'n_neighbors': 10, 'weights': 'uniform'},\n", " mean: 0.82043, std: 0.03828, params: {'n_neighbors': 12, 'weights': 'uniform'},\n", " mean: 0.81033, std: 0.03176, params: {'n_neighbors': 2, 'weights': 'uniform'},\n", " mean: 0.81033, std: 0.03379, params: {'n_neighbors': 14, 'weights': 'distance'},\n", " mean: 0.80696, std: 0.03532, params: {'n_neighbors': 12, 'weights': 'distance'},\n", " mean: 0.80584, std: 0.03055, params: {'n_neighbors': 3, 'weights': 'uniform'},\n", " mean: 0.80584, std: 0.03342, params: {'n_neighbors': 20, 'weights': 'uniform'},\n", " mean: 0.80584, std: 0.03252, params: {'n_neighbors': 20, 'weights': 'distance'},\n", " mean: 0.80247, std: 0.03335, params: {'n_neighbors': 8, 'weights': 'distance'},\n", " mean: 0.80247, std: 0.03642, params: {'n_neighbors': 10, 'weights': 'distance'}]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = KNeighborsClassifier()\n", "\n", "parameters = dict(\n", " n_neighbors=[2, 3, 4, 5, 6, 8, 10, 12, 14, 20],\n", " weights=['uniform', 'distance']\n", ")\n", "\n", "grid = GridSearchCV(model, param_grid=parameters, cv=kfold, scoring=scoring, n_jobs=-1)\n", "grid.fit(X_redux, y)\n", "\n", "grid.grid_scores_.sort(key=lambda x: x[1], reverse=True)\n", "grid.grid_scores_[:15]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "_cell_guid": "10a036fd-8106-122d-f249-9e5a98de0984" }, "outputs": [], "source": [ "knn_best = Pipeline([\n", " ('rfe', rfe),\n", " ('knn', KNeighborsClassifier(n_neighbors=8)) \n", " ])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9e89a83e-70e4-c6cd-e06c-213fbbe9ce90" }, "source": [] }, { "cell_type": "code", "execution_count": 30, "metadata": { "_cell_guid": "6f7653f5-2011-028e-3793-c379497b4854" }, "outputs": [], "source": [ "nb_best = Pipeline([\n", " ('pca', pca),\n", " ('nb', GaussianNB())\n", " ])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "f8d2324b-d5c7-85f4-3e59-9333a92a4738" }, "source": [] }, { "cell_type": "code", "execution_count": 31, "metadata": { "_cell_guid": "ca92df12-fcca-d21c-50e5-8a33a7dfa3a9" }, "outputs": [ { "data": { "text/plain": [ "[mean: 0.82941, std: 0.03554, params: {'gamma': 'auto', 'C': 20},\n", " mean: 0.82828, std: 0.03197, params: {'gamma': 0.2, 'C': 10},\n", " mean: 0.82716, std: 0.03673, params: {'gamma': 0.2, 'C': 5},\n", " mean: 0.82716, std: 0.03650, params: {'gamma': 'auto', 'C': 10},\n", " mean: 0.82716, std: 0.03622, params: {'gamma': 0.1, 'C': 20},\n", " mean: 0.82716, std: 0.03241, params: {'gamma': 0.1, 'C': 50},\n", " mean: 0.82604, std: 0.03604, params: {'gamma': 0.2, 'C': 50},\n", " mean: 0.82604, std: 0.03504, params: {'gamma': 'auto', 'C': 100},\n", " mean: 0.82604, std: 0.03958, params: {'gamma': 0.1, 'C': 100},\n", " mean: 0.82492, std: 0.03487, params: {'gamma': 0.5, 'C': 5}]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = SVC(random_state=seed, probability=True)\n", "\n", "parameters = dict(\n", " C=[0.1, 0.2, 0.5, 1, 2, 5, 10, 20, 50, 100],\n", " gamma=['auto', 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1],\n", ")\n", "\n", "grid = GridSearchCV(model, param_grid=parameters, cv=kfold, scoring=scoring, n_jobs=-1)\n", "grid.fit(X_pca, y)\n", "\n", "grid.grid_scores_.sort(key=lambda x: x[1], reverse=True)\n", "grid.grid_scores_[:10]" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "_cell_guid": "e0ebe5f7-914e-b59c-177e-d103bc649a2f" }, "outputs": [ { "data": { "text/plain": [ "[mean: 0.83277, std: 0.03612, params: {'gamma': 0.15, 'C': 25},\n", " mean: 0.83165, std: 0.03309, params: {'gamma': 'auto', 'C': 15},\n", " mean: 0.83165, std: 0.03309, params: {'gamma': 0.15, 'C': 20},\n", " mean: 0.82941, std: 0.03805, params: {'gamma': 0.3, 'C': 15},\n", " mean: 0.82941, std: 0.03554, params: {'gamma': 'auto', 'C': 20},\n", " mean: 0.82828, std: 0.03497, params: {'gamma': 0.15, 'C': 10},\n", " mean: 0.82828, std: 0.03197, params: {'gamma': 0.2, 'C': 10},\n", " mean: 0.82828, std: 0.03456, params: {'gamma': 0.2, 'C': 15},\n", " mean: 0.82828, std: 0.03616, params: {'gamma': 0.15, 'C': 30},\n", " mean: 0.82828, std: 0.03692, params: {'gamma': 0.1, 'C': 70}]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = SVC(random_state=seed, probability=True)\n", "\n", "parameters = dict(\n", " C=[5, 10, 15, 20, 25, 30, 50, 70, 100],\n", " gamma=['auto', 0.08, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4],\n", ")\n", "\n", "grid = GridSearchCV(model, param_grid=parameters, cv=kfold, scoring=scoring, n_jobs=-1)\n", "grid.fit(X_pca, y)\n", "\n", "grid.grid_scores_.sort(key=lambda x: x[1], reverse=True)\n", "grid.grid_scores_[:10]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "_cell_guid": "6f06493f-f6d2-8274-a7b2-3eeba80745bf" }, "outputs": [], "source": [ "svm_best = Pipeline([\n", " ('pca', pca),\n", " ('svm', SVC(C=25, gamma=0.15, random_state=seed, probability=True))\n", " ])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "3f17e56b-f399-a621-94d0-1c865280db10" }, "source": [] }, { "cell_type": "code", "execution_count": 34, "metadata": { "_cell_guid": "e346ea12-ca93-b12b-5d76-d9b31ae7e149" }, "outputs": [ { "data": { "text/plain": [ "[mean: 0.83277, std: 0.04445, params: {'min_samples_split': 6, 'n_estimators': 100},\n", " mean: 0.83165, std: 0.04574, params: {'min_samples_split': 6, 'n_estimators': 70},\n", " mean: 0.83165, std: 0.04443, params: {'min_samples_split': 6, 'n_estimators': 90},\n", " mean: 0.83053, std: 0.04241, params: {'min_samples_split': 4, 'n_estimators': 90},\n", " mean: 0.83053, std: 0.04568, params: {'min_samples_split': 6, 'n_estimators': 150},\n", " mean: 0.82941, std: 0.04193, params: {'min_samples_split': 4, 'n_estimators': 40},\n", " mean: 0.82941, std: 0.03548, params: {'min_samples_split': 4, 'n_estimators': 60},\n", " mean: 0.82941, std: 0.03656, params: {'min_samples_split': 4, 'n_estimators': 70},\n", " mean: 0.82941, std: 0.04689, params: {'min_samples_split': 6, 'n_estimators': 50},\n", " mean: 0.82941, std: 0.04999, params: {'min_samples_split': 6, 'n_estimators': 80}]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = RandomForestClassifier(random_state=seed)\n", "\n", "parameters = dict(\n", " n_estimators=[40, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300],\n", " min_samples_split=[1, 2, 3, 4, 5, 6]\n", ")\n", "\n", "grid = GridSearchCV(model, param_grid=parameters, cv=kfold, scoring=scoring, n_jobs=-1)\n", "grid.fit(X_redux, y)\n", "\n", "grid.grid_scores_.sort(key=lambda x: x[1], reverse=True)\n", "grid.grid_scores_[:10]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "_cell_guid": "650304df-f80e-48e2-8fdf-48d2e9cca050" }, "outputs": [], "source": [ "rf_best = Pipeline([\n", " ('rfe', rfe),\n", " ('rf', RandomForestClassifier(n_estimators=300, min_samples_split=5,\n", " random_state=seed))\n", " \n", " ])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "8d3614da-6070-4ae5-90c3-a0f7f47faac9" }, "source": [] }, { "cell_type": "code", "execution_count": 36, "metadata": { "_cell_guid": "8eaf8db3-5e47-d354-8638-5390b18e8dca" }, "outputs": [ { "data": { "text/plain": [ "[mean: 0.81818, std: 0.02702, params: {'n_estimators': 100},\n", " mean: 0.81818, std: 0.02558, params: {'n_estimators': 300},\n", " mean: 0.81706, std: 0.03201, params: {'n_estimators': 125},\n", " mean: 0.81706, std: 0.02749, params: {'n_estimators': 175},\n", " mean: 0.81594, std: 0.02793, params: {'n_estimators': 150},\n", " mean: 0.81594, std: 0.02664, params: {'n_estimators': 250},\n", " mean: 0.81145, std: 0.02313, params: {'n_estimators': 75},\n", " mean: 0.81145, std: 0.02765, params: {'n_estimators': 200},\n", " mean: 0.80584, std: 0.03345, params: {'n_estimators': 50}]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = AdaBoostClassifier(random_state=seed)\n", "\n", "parameters = dict(\n", " n_estimators=[50, 75, 100, 125, 150, 175, 200, 250, 300],\n", ")\n", "\n", "grid = GridSearchCV(model, param_grid=parameters, cv=kfold, scoring=scoring, n_jobs=-1)\n", "grid.fit(X, y)\n", "\n", "grid.grid_scores_.sort(key=lambda x: x[1], reverse=True)\n", "grid.grid_scores_[:10]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "_cell_guid": "5bc0d6d6-3ca1-d8b2-915f-c4b9243935e7" }, "outputs": [ { "data": { "text/plain": [ "[mean: 0.81930, std: 0.02749, params: {'n_estimators': 120, 'learning_rate': 1},\n", " mean: 0.81818, std: 0.03602, params: {'n_estimators': 280, 'learning_rate': 0.5},\n", " mean: 0.81818, std: 0.03707, params: {'n_estimators': 300, 'learning_rate': 0.5},\n", " mean: 0.81818, std: 0.02702, params: {'n_estimators': 100, 'learning_rate': 1},\n", " mean: 0.81818, std: 0.02695, params: {'n_estimators': 240, 'learning_rate': 1},\n", " mean: 0.81818, std: 0.02558, params: {'n_estimators': 300, 'learning_rate': 1},\n", " mean: 0.81706, std: 0.03222, params: {'n_estimators': 200, 'learning_rate': 0.5},\n", " mean: 0.81706, std: 0.03141, params: {'n_estimators': 160, 'learning_rate': 1},\n", " mean: 0.81706, std: 0.02598, params: {'n_estimators': 180, 'learning_rate': 1},\n", " mean: 0.81706, std: 0.02628, params: {'n_estimators': 220, 'learning_rate': 1}]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = AdaBoostClassifier(random_state=seed)\n", "\n", "parameters = dict(\n", " n_estimators=[100, 120, 140, 160, 180, 200, 220, 240, 260, 280, 300],\n", " learning_rate=[0.01, 0.05, 0.1, 0.5, 1]\n", ")\n", "\n", "grid = GridSearchCV(model, param_grid=parameters, cv=kfold, scoring=scoring, n_jobs=-1)\n", "grid.fit(X, y)\n", "\n", "grid.grid_scores_.sort(key=lambda x: x[1], reverse=True)\n", "grid.grid_scores_[:10]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "_cell_guid": "dcd57ad9-3a74-ea03-a575-65c567218a4d" }, "outputs": [], "source": [ "ada_best = AdaBoostClassifier(n_estimators=300, random_state=seed)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "d5888dd3-3d59-f9d3-ff6c-82284f87a62c" }, "source": [] }, { "cell_type": "code", "execution_count": 39, "metadata": { "_cell_guid": "c1a300fd-dba5-ff21-0b5a-c8498f3a779d" }, "outputs": [ { "data": { "text/plain": [ "[mean: 0.83726, std: 0.03757, params: {'n_estimators': 400, 'learning_rate': 0.05},\n", " mean: 0.83502, std: 0.03891, params: {'n_estimators': 125, 'learning_rate': 0.1},\n", " mean: 0.83389, std: 0.03858, params: {'n_estimators': 150, 'learning_rate': 0.1},\n", " mean: 0.83277, std: 0.03778, params: {'n_estimators': 200, 'learning_rate': 0.1},\n", " mean: 0.83053, std: 0.04010, params: {'n_estimators': 100, 'learning_rate': 0.05},\n", " mean: 0.83053, std: 0.04268, params: {'n_estimators': 250, 'learning_rate': 0.05},\n", " mean: 0.83053, std: 0.03983, params: {'n_estimators': 150, 'learning_rate': 0.2},\n", " mean: 0.82941, std: 0.04454, params: {'n_estimators': 125, 'learning_rate': 0.05},\n", " mean: 0.82941, std: 0.03824, params: {'n_estimators': 175, 'learning_rate': 0.1},\n", " mean: 0.82828, std: 0.03789, params: {'n_estimators': 150, 'learning_rate': 0.05}]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = GradientBoostingClassifier(random_state=seed)\n", "\n", "parameters = dict(\n", " n_estimators=[50, 75, 100, 125, 150, 175, 200, 250, 300, 400],\n", " learning_rate=[0.01, 0.05, 0.1, 0.2, 0.5, 1]\n", ")\n", "\n", "grid = GridSearchCV(model, param_grid=parameters, cv=kfold, scoring=scoring, n_jobs=-1)\n", "grid.fit(X_redux, y)\n", "\n", "grid.grid_scores_.sort(key=lambda x: x[1], reverse=True)\n", "grid.grid_scores_[:10]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "_cell_guid": "f62f66a5-fbde-f105-9c58-057e059feaa6" }, "outputs": [ { "data": { "text/plain": [ "[mean: 0.83726, std: 0.03757, params: {'n_estimators': 400, 'learning_rate': 0.05, 'max_depth': 3},\n", " mean: 0.83614, std: 0.04008, params: {'n_estimators': 125, 'learning_rate': 0.1, 'max_depth': 4},\n", " mean: 0.83502, std: 0.04987, params: {'n_estimators': 50, 'learning_rate': 0.05, 'max_depth': 4},\n", " mean: 0.83502, std: 0.04711, params: {'n_estimators': 100, 'learning_rate': 0.05, 'max_depth': 4},\n", " mean: 0.83502, std: 0.03891, params: {'n_estimators': 125, 'learning_rate': 0.1, 'max_depth': 3},\n", " mean: 0.83389, std: 0.04008, params: {'n_estimators': 175, 'learning_rate': 0.05, 'max_depth': 4},\n", " mean: 0.83389, std: 0.03173, params: {'n_estimators': 250, 'learning_rate': 0.05, 'max_depth': 4},\n", " mean: 0.83389, std: 0.03858, params: {'n_estimators': 150, 'learning_rate': 0.1, 'max_depth': 3},\n", " mean: 0.83389, std: 0.03895, params: {'n_estimators': 50, 'learning_rate': 0.2, 'max_depth': 4},\n", " mean: 0.83277, std: 0.04938, params: {'n_estimators': 125, 'learning_rate': 0.05, 'max_depth': 4}]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = GradientBoostingClassifier(random_state=seed)\n", "\n", "parameters = dict(\n", " n_estimators=[50, 75, 100, 125, 150, 175, 200, 250, 300, 400],\n", " learning_rate=[0.05, 0.1, 0.2],\n", " max_depth=[1, 2, 3, 4, 5]\n", " \n", ")\n", "\n", "grid = GridSearchCV(model, param_grid=parameters, cv=kfold, scoring=scoring, n_jobs=-1)\n", "grid.fit(X_redux, y)\n", "\n", "grid.grid_scores_.sort(key=lambda x: x[1], reverse=True)\n", "grid.grid_scores_[:10]" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "_cell_guid": "1921cb4c-d9bd-d133-4bc2-88eff8eea6ba" }, "outputs": [], "source": [ "gbm_best = Pipeline([\n", " ('rfe', rfe),\n", " ('gbm', GradientBoostingClassifier(n_estimators=100, learning_rate=0.05, \n", " random_state=seed))\n", " ])" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e76d00c2-8003-902d-fc31-c79a44df4ed7" }, "source": [] }, { "cell_type": "code", "execution_count": 42, "metadata": { "_cell_guid": "243a3e04-a025-b88b-389b-644bc7f695fe" }, "outputs": [], "source": [ "estimators = [\n", " ('Logistic Regression', logreg_best),\n", " ('KNN', knn_best),\n", " ('Naive Bayes', nb_best),\n", " ('SVC', svm_best),\n", " ('Random Forest', rf_best),\n", " ('Ada boost', ada_best),\n", " ('Gradient boost', gbm_best)\n", "]" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "_cell_guid": "af3ce06b-667e-17d9-b48a-a19e38c0ee09" }, "outputs": [ { "data": { "image/png": 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HF6G1K0BbV4DWriCtXQHae4KDXfQGGHQaygttlOZZKcm3UJpvpae5l6PvNSP54+CPEwPc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\nkUgqPL+zgV+/dppwdOhspsm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T3qekpASAxurdHNgaWsOakDIBq83JpFnLcCVk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\n1vtHks8AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8a3d09fa58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,9))\n", "for name, model in estimators:\n", " model.fit(X, y)\n", " y_pred = model.predict_proba(X)[:, 1]\n", " fpr, tpr, _ = roc_curve(y, y_pred)\n", " auc_score = roc_auc_score(y, y_pred)\n", " plt.plot(fpr, tpr, label='{} ({:.4f})'.format(name, auc_score))\n", "_ = plt.legend(loc=4)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "_cell_guid": "71333be6-d7eb-3725-23e3-d03268d92c1e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logistic Regression\n", "[[486 63]\n", " [ 90 252]]\n", "\n", "KNN\n", "[[516 33]\n", " [ 87 255]]\n", "\n", "Naive Bayes\n", "[[467 82]\n", " [ 94 248]]\n", "\n", "SVC\n", "[[516 33]\n", " [ 96 246]]\n", "\n", "Random Forest\n", "[[535 14]\n", " [ 39 303]]\n", "\n", "Ada boost\n", "[[501 48]\n", " [ 64 278]]\n", "\n", "Gradient boost\n", "[[515 34]\n", " [ 77 265]]\n", "\n" ] } ], "source": [ "for name, model in estimators:\n", " model.fit(X, y)\n", " y_pred = model.predict(X)\n", " print(name)\n", " print(confusion_matrix(y, y_pred))\n", " print()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "_cell_guid": "e9e92b98-2aad-ff94-8556-c553473f0653" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logistic Regression\n", "Score: 0.7671\n", "\n", "KNN\n", "Score: 0.8095\n", "\n", "Naive Bayes\n", "Score: 0.7381\n", "\n", "SVC\n", "Score: 0.7923\n", "\n", "Random Forest\n", "Score: 0.9196\n", "\n", "Ada boost\n", "Score: 0.8323\n", "\n", "Gradient boost\n", "Score: 0.8268\n", "\n" ] } ], "source": [ "for name, model in estimators:\n", " model.fit(X, y)\n", " y_pred = model.predict(X)\n", " print(name)\n", " print('Score: {:.4f}'.format(f1_score(y, y_pred)))\n", " print()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "_cell_guid": "3cc1da22-4633-3165-a99c-e711a2f32942" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logistic Regression (0.8227) +/- (0.0324)\n", "KNN (0.8283) +/- (0.0425)\n", "Naive Bayes (0.8059) +/- (0.0374)\n", "SVC (0.8306) +/- (0.0355)\n", "Random Forest (0.8205) +/- (0.0398)\n", "Ada boost (0.8182) +/- (0.0256)\n", "Gradient boost (0.8294) +/- (0.0418)\n" ] } ], "source": [ "for name, model in estimators:\n", " result = cross_val_score(model, X, y, cv=kfold, scoring=scoring)\n", " print(\"{0:<20} ({1:.4f}) +/- ({2:.4f})\".format(name, result.mean(), result.std()))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2ba95c6f-536a-a76b-5666-fd4659ca5f6f" }, "source": [] }, { "cell_type": "code", "execution_count": 47, "metadata": { "_cell_guid": "fd2d16f9-a713-2910-c6e3-7c969383e4f6" }, "outputs": [], "source": [ "voters = [\n", " ('Random Forest', rf_best),\n", " ('Ada boost', ada_best),\n", " ('Gradient boost', gbm_best)\n", "]" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "_cell_guid": "38b094a4-7391-e0d1-9910-a1d1124a0518" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.8362) +/- (0.0454)\n" ] } ], "source": [ "voting_ensemble = VotingClassifier(voters, voting='soft')\n", "\n", "results = cross_val_score(voting_ensemble, X, y, cv=kfold, scoring=scoring)\n", "print(\"({0:.4}) +/- ({1:.4f})\".format(results.mean(), results.std()))" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "_cell_guid": "5925bc65-af84-33c5-d04c-cd861fc9cde2" }, "outputs": [], "source": [ "voting_ensemble.fit(X, y)\n", "predictions = voting_ensemble.predict(X_test)\n", "\n", "submission = pd.DataFrame()\n", "submission[\"PassengerId\"] = df_test.index\n", "submission[\"Survived\"] = predictions.astype(int)\n", "\n", "submission.to_csv(\"submission_001.csv\", index=False)" ] } ], "metadata": { "_change_revision": 578, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/330/330380.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "7ebebef4-a4cb-8af8-105f-8b5a8b141e09" }, "source": [ "Let's do some exploratory data analysis on the people data set, and see if it makes sense to use any decomposition techniques. I think there could be a couple good reasons why we might want to do this:\n", "\n", "* If a lot of the characteristics of people are interdependent variables, we can consolidate them into single features so as to not muddy our final classifier inputs with repeat information.\n", "* There's potential for discovering latent features that are only evident by looking at multiple features together.\n", "* If you're like me, you might be pretty limited on computing resources and want to shrink the people data set before merging it in." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "c181ed5f-beea-4dcb-7fc5-d6393fa30099" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " people_id char_1 group_1 char_2 date char_3 char_4 \\\n", "0 ppl_100 type 2 group 17304 type 2 2021-06-29 type 5 type 5 \n", "1 ppl_100002 type 2 group 8688 type 3 2021-01-06 type 28 type 9 \n", "2 ppl_100003 type 2 group 33592 type 3 2022-06-10 type 4 type 8 \n", "3 ppl_100004 type 2 group 22593 type 3 2022-07-20 type 40 type 25 \n", "4 ppl_100006 type 2 group 6534 type 3 2022-07-27 type 40 type 25 \n", "\n", " char_5 char_6 char_7 ... char_29 char_30 char_31 char_32 char_33 \\\n", "0 type 5 type 3 type 11 ... False True True False False \n", "1 type 5 type 3 type 11 ... False True True True True \n", "2 type 5 type 2 type 5 ... False False True True True \n", "3 type 9 type 4 type 16 ... True True True True True \n", "4 type 9 type 3 type 8 ... False False True False False \n", "\n", " char_34 char_35 char_36 char_37 char_38 \n", "0 True True True False 36 \n", "1 True True True False 76 \n", "2 True False True True 99 \n", "3 True True True True 76 \n", "4 False True True False 84 \n", "\n", "[5 rows x 41 columns]\n" ] } ], "source": [ "import pandas as pd\n", "\n", "# Import the dataset\n", "df = pd.read_csv(\"../input/people.csv\")\n", "\n", "# Print the first five rows\n", "print(df.head())" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9c4c06c8-fc76-38c1-46a9-6904dd3fdc98" }, "source": [ "It looks like all the characteristics are denoted with a \"char_\" prefix. Let's use that to make sure that we don't drag along extra columns. Then we'll run a matrix of chi squared tests to get an idea of which features might be interdependent." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "_cell_guid": "7571f3b1-cdef-b1d5-3011-da3ee5348f4a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "It looks like 100.0% of the characteristics might be related to one another.\n" ] } ], "source": [ "from scipy.stats import chisquare\n", "\n", "# Create a list of characteristics\n", "chars = [i for i in df.columns.values if \"char_\" in i]\n", "\n", "# Create an empty list for appending flagged features\n", "flags = []\n", "\n", "# For each feature summarize frequencies of each other feature\n", "for feat in df[chars]:\n", " group = df[chars].groupby(feat)\n", " for otherfeat in df[chars].drop(feat, axis=1):\n", " summary = group[otherfeat].count()\n", " \n", " # Run a chi squared test on the frequencies, and check if the p-value is less than 0.05\n", " if chisquare(summary)[1] < 0.05:\n", " \n", " # If so, flag both features\n", " flags.append(feat)\n", " flags.append(otherfeat)\n", "\n", "# Remove duplicates by converting to a set at the end\n", "flags = set(flags)\n", "\n", "print(\"It looks like {}% of the characteristics might be related to one another.\".format(len(flags)/len(chars)*100))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "088f9230-6ec7-6aec-da77-014b7d487409" }, "source": [ "Wow, 100%. Hopefully someone reviewing this can highlight if I implemented those tests wrong, or if chi squared wasn't the right test of choice. At any rate, I'm going to proceed on the assumption that we should definitely be using some decomposition techniques on the data set, if only just to reduce the repeated information. Let's use the scikit-learn implementation of PCA. Some success with PCA would confirm that the chi squared tests were useful." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "1b927b29-a816-90c4-f4d7-382c28eb3d33" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Before PCA the full size of the characteristics is 160 features\n" ] } ], "source": [ "# Convert to dummy variables\n", "dums = pd.get_dummies(df[chars])\n", "\n", "print(\"Before PCA the full size of the characteristics is {} features\".format(len(dums.columns.values)))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "eb93f861-0541-c714-9a53-b58d493baaf4" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.5/site-packages/sklearn/utils/extmath.py:368: UserWarning: The number of power iterations is increased to 7 to achieve higher precision.\n", " warnings.warn(\"The number of power iterations is increased to \"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[ 0.35559326 0.06815741]\n" ] } ], "source": [ "from sklearn.decomposition import PCA\n", "from sklearn.preprocessing import MinMaxScaler\n", "\n", "# As was suggested by dpace, let's also scale the features so that they're all in the range of 0 and 1\n", "scaledums = MinMaxScaler().fit_transform(dums)\n", "\n", "# Now we're ready for PCA. Let's just look at the first two principle components first\n", "pca = PCA(n_components=2)\n", "featurecomponents = pca.fit_transform(scaledums)\n", "\n", "print(pca.explained_variance_ratio_)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "7861f2a8-a3b8-d0e6-7902-46c276199e61" }, "source": [ "Awesome, scaling seems to fix the problem of char_38 dominating the first component and the overall variance of the dataset, thanks, dpace." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "afe136ca-8828-fd5f-327c-2853989ee5b4" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa9aa56a58>" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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BG264geeffx6A0tJSYmNjueaaa/wxubm5pKamEhERwSOPPMLzzz+Pc45169Yx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\nAFQ4i4iIiIgEQIWziIiIiEg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"text/plain": [ "<matplotlib.figure.Figure at 0x7ffa9aa56240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "# build a dictionary with the names of the components\n", "components = {}\n", "index = 0\n", "for feature in dums.columns.values:\n", " components[feature] = [pca.components_[0][index]]\n", " index += 1\n", " \n", "# Exclude all but the most extreme components, because there are a lot\n", "sortedcomps = pca.components_[0]\n", "sortedcomps.sort()\n", "maxcap = sortedcomps[-3]\n", "mincap = sortedcomps[2]\n", "components = {i:x for i, x in components.items() if x >= maxcap or x <= mincap}\n", " \n", "# Convert to dataframe\n", "components = pd.DataFrame(components)\n", "\n", "# Plot the most extreme components\n", "components.plot(kind=\"bar\", figsize=(12, 4))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "60d91817-a8f2-5f67-f05a-49142abc7343" }, "source": [ "Interesting, and plotting the most extreme contributors to the first principle component, char_38 isn't among them. So scaling down char_38 was really critical." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "8960817c-5ef3-fed5-8c50-b3cd2ec21ef5" }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7ffae4d79e80>" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dd14RWZlMxpSp234mx+jU7XK9qNCFtjuO/CuCUGJdi9XcVVfXwMRnJdfW9W4z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\nCysrK7ywsMCNRsO8r0Rsy32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jzG2hOzw8bAaVktjLtbmEs2uTKDSNRoN3d3f5+vXrfPv2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TomlRPWZlocxclGUQnVfLb1Ai9xjyf7SJYOLfpsURnWEYm6hmKxyBMTE6bDkkqleHx8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\nC1q5d2i5XIYOXLF5/TaZiOHB7nQ6rkkpdnZ2KJPJ0O3trfYcz+dzY1uzU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sZp9qxONx63mW62eM8Uw4XFxc5NnZWSvkXetTJpPh1dVVOyAYHBzk4eFhz9M9EYvu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\nEW+yO2BsxH/uZ9k5DAK/0X4ctgmlAABQDwhtcCSoVCq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"text/plain": [ "<matplotlib.figure.Figure at 0x7ffae4d79668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot the first two principle components\n", "featurecomponents = pd.DataFrame(featurecomponents, columns=[\"Principle Component 1\", \"Principle Component 2\"])\n", "df[\"Principle Component 1\"] = featurecomponents[\"Principle Component 1\"]\n", "\n", "featurecomponents.plot(kind=\"scatter\", x=\"Principle Component 1\", y=\"Principle Component 2\", figsize=(12, 12), s=1)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "f03a3efa-33ed-0b48-fd5c-f78adf7815a3" }, "source": [ "I'm curious now what the group_1 feature looks in terms of these first two principle components. I wonder if the groups were made from the characteristics, or something else. Just for curiosity's sake, let's plot a few groups." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "9ae8d615-f106-4eea-0fde-b7f510e43bb4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 26 data points in this group.\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ffae9277e80>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Add group_1 to the new data from pca\n", "featurecomponents[\"group_1\"] = df[\"group_1\"]\n", "\n", "# Get a list of groups to sample from\n", "groupslist = list(set(featurecomponents[\"group_1\"].tolist()))\n", "\n", "# Pick a group and plot\n", "group = featurecomponents[featurecomponents[\"group_1\"]==groupslist[0]]\n", "group.plot(kind=\"scatter\", x=\"Principle Component 1\", y=\"Principle Component 2\", figsize=(3, 3))\n", "print(\"There are {} data points in this group.\".format(len(group.index)))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "9993cf1b-de53-b4a9-5191-8935c7e77884" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 2 data points in this group.\n" ] }, { "data": { "image/png": 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ihDWrECesWYU4Yc0qpGcJK+kYSXdI2iSp4YwCkg6TtF7Sz9PEz6VQxMxkjjd543VKL8+w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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7ffad18c8128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Pick a group and plot\n", "group = featurecomponents[featurecomponents[\"group_1\"]==groupslist[5]]\n", "group.plot(kind=\"scatter\", x=\"Principle Component 1\", y=\"Principle Component 2\", figsize=(3, 3))\n", "print(\"There are {} data points in this group.\".format(len(group.index)))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "1b18913a-097f-fca2-2300-cc13c700b6b6" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There are 2 data points in this group.\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7ffa9aa76e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Pick a group and plot\n", "group = featurecomponents[featurecomponents[\"group_1\"]==groupslist[6]]\n", "group.plot(kind=\"scatter\", x=\"Principle Component 1\", y=\"Principle Component 2\", figsize=(3, 3))\n", "print(\"There are {} data points in this group.\".format(len(group.index)))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "fc895c0b-ee8d-de08-ab09-454cf977e25b" }, "source": [ "Plotting the first two groups with more than one data point to them, it doesn't appear that group_1 was created from clustering the characteristics. However, these first two principle components don't capture all of the information from the characteristics, and browsing through six data points is hardly conclusive.\n", "\n", "As a final question, how many principle components do I need to merge into the train/test sets if I only care about a certain amount of the explained variance?" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "b752e01e-d218-af0a-d303-ee35aa36d2aa" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "To explain 0.75 of the variance you'll need 21 components\n", "To explain 0.8 of the variance you'll need 28 components\n", "To explain 0.85 of the variance you'll need 38 components\n", "To explain 0.9 of the variance you'll need 50 components\n", "To explain 0.95 of the variance you'll need 66 components\n" ] } ], "source": [ "# Define a list of possible amounts of explained variance we might care about\n", "cares = [i/100 for i in range(75, 100, 5)]\n", "\n", "# Run the PCA with increased components until each care level is reached\n", "for i in range (20, len(dums.columns.values)):\n", " pca = PCA(n_components=i)\n", " pca.fit(scaledums)\n", " try:\n", " if pca.explained_variance_ratio_.sum() > cares[0]:\n", "\n", " # If greater, print a statement and drop the first item off the list\n", " print(\"To explain {0} of the variance you'll need {1} components\".format(cares[0], i))\n", " cares = cares[1:]\n", " except:\n", " break" ] } ], "metadata": { "_change_revision": 74, "_is_fork": 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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
0000/330/330673.ipynb
s3://data-agents/kaggle-outputs/sharded/000_00000.jsonl.gz