Created
February 22, 2016 13:02
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"# All Imports\n", | |
"import pandas as pd\n", | |
"import numpy as np\n", | |
"\n", | |
"%matplotlib inline\n", | |
"import matplotlib as mpl\n", | |
"import matplotlib.pyplot as plt\n", | |
"from ggplot import *\n", | |
"\n", | |
"import seaborn as sns\n", | |
"from scipy import stats\n", | |
"sns.set_style(\"whitegrid\")\n", | |
"mpl.rcParams[\"figure.figsize\"] = \"2, 2\"" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"2016-02-22 14:00:05 \n", | |
"\n", | |
"CPython 2.7.11\n", | |
"IPython 4.0.0\n", | |
"\n", | |
"matplotlib 1.5.1\n", | |
"seaborn 0.7.0\n", | |
"ggplot 0.6.8\n" | |
] | |
} | |
], | |
"source": [ | |
"%reload_ext watermark\n", | |
"%watermark -d -t -v -p matplotlib,seaborn,ggplot" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"df = pd.DataFrame(dict(a=list(\"abcd\"*8*3), \n", | |
" b=[\"in\", \"out\", \"null\"]*8*4, \n", | |
" x=np.random.normal(1, 1, 8*3*4), \n", | |
" y=np.random.normal(1, 1, 8*3*4)))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
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UGxuLJUuWYPbs2VixYgUKCwuRkJCAxYsXo1u3bliyZAlXtrITU6QhPDwcAoEA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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x20e983c8>" | |
] | |
}, | |
"execution_count": 3, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"# Returning a fig works, but only prints one plot when the last \n", | |
"# same code results in two plots\n", | |
"\n", | |
"name = \"in\"\n", | |
"data = df[df[\"b\"] == name]\n", | |
"gg = ggplot(data, aes(x=\"x\", y=\"y\"))\n", | |
"gg = gg + geom_point(alpha=0.8)\n", | |
"max_x, min_x = data[\"x\"].max(), data[\"x\"].min()\n", | |
"max_y, min_y = data[\"y\"].max(), data[\"y\"].min()\n", | |
"mean_y, mean_x = data[\"y\"].mean(), data[\"x\"].mean()\n", | |
"# chross at (0,0)\n", | |
"cross_df = pd.DataFrame({\"xx\":[0,0,0], \"xy\":[min_y-1,1,max_y+1],\"yx\":[max_x+1,1,min_x-1], \"yy\":[0,0,0]})\n", | |
"gg = gg + geom_line(cross_df, aes(x=\"xx\", y=\"xy\"), color=\"grey\")\n", | |
"gg = gg + geom_line(cross_df, aes(x=\"yx\", y=\"yy\"), color=\"grey\")\n", | |
"# the mean cross\n", | |
"mean_df = pd.DataFrame({\"x\":[mean_x], \"y\":[mean_y], \"label\":[\"mean\"]})\n", | |
"mean_color = \"red\"\n", | |
"gg = gg + geom_point(mean_df, aes(x=\"x\", y=\"y\"), color=mean_color, size=150, shape=\"x\")\n", | |
"gg = gg + labs(x=u\"Quality\\n(delta mean)\", \n", | |
" y=u\"Quantity\\n(delta mean)\")\n", | |
"t = \"Lots of words in two lines\\more words to fill the line...\"\n", | |
"gg = gg + ggtitle(t) \n", | |
"gg = gg + theme_matplotlib(rc={\"figure.figsize\":\"2, 2\"}, matplotlib_defaults=None)\n", | |
"\n", | |
"fig = gg.draw()\n", | |
"fig" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"# When run before seaborn, it doesn't result in a plot\n", | |
"for name in [\"in\", \"out\", \"null\" ]:\n", | |
" data = df[df[\"b\"] == name]\n", | |
" gg = ggplot(data, aes(x=\"x\", y=\"y\"))\n", | |
" gg = gg + geom_point(alpha=0.8)\n", | |
" max_x, min_x = data[\"x\"].max(), data[\"x\"].min()\n", | |
" max_y, min_y = data[\"y\"].max(), data[\"y\"].min()\n", | |
" mean_y, mean_x = data[\"y\"].mean(), data[\"x\"].mean()\n", | |
" # chross at (0,0)\n", | |
" cross_df = pd.DataFrame({\"xx\":[0,0,0], \"xy\":[min_y-1,1,max_y+1],\"yx\":[max_x+1,1,min_x-1], \"yy\":[0,0,0]})\n", | |
" gg = gg + geom_line(cross_df, aes(x=\"xx\", y=\"xy\"), color=\"grey\")\n", | |
" gg = gg + geom_line(cross_df, aes(x=\"yx\", y=\"yy\"), color=\"grey\")\n", | |
" # the mean cross\n", | |
" mean_df = pd.DataFrame({\"x\":[mean_x], \"y\":[mean_y], \"label\":[\"mean\"]})\n", | |
" mean_color = \"red\"\n", | |
" gg = gg + geom_point(mean_df, aes(x=\"x\", y=\"y\"), color=mean_color, size=150, shape=\"x\")\n", | |
" gg = gg + labs(x=u\"Quality\\n(delta mean)\", \n", | |
" y=u\"Quantity\\n(delta mean)\")\n", | |
" t = \"Lots of words in two lines\\more words to fill the line...\"\n", | |
" gg = gg + ggtitle(t) \n", | |
" gg = gg + theme_matplotlib(rc={\"figure.figsize\":\"2, 2\"}, matplotlib_defaults=None)\n", | |
" gg.draw()\n", | |
" ax = plt.gca()\n", | |
" ax.get_xaxis().get_label().set_visible(False) \n", | |
" ax.get_yaxis().get_label().set_visible(False)\n", | |
" ax.spines[\"top\"].set_visible(False) \n", | |
" ax.spines[\"bottom\"].set_visible(False) \n", | |
" ax.spines[\"right\"].set_visible(False) \n", | |
" ax.spines[\"left\"].set_visible(False) \n", | |
" ax.get_xaxis().tick_bottom() \n", | |
" ax.get_yaxis().tick_left() \n", | |
" ax.yaxis.grid(True, which='major')\n", | |
" ax.xaxis.grid(True, which='major')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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UGxuLJUuWYPbs2VixYgUKCwuRkJCAxYsXo1u3bliyZAlXtrITU6QhPDwcAoEA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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x20e983c8>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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ExqaPKbsnLygoCHfu3MGuXbvUKt/YPXyKyo8bNw45OTkqX5NIJKq3FpOpqSkk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\nEGJw/g8mCcQOfS5YgAAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x3a12128>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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xAoGBgViyZAmuXr2qUT3ff/89IiMjVT6eMYbY2FgsWbIES5cuRWlpqUbtFhYW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\n9A4lJkKI3qHERAjRO5SYCCF65/8BGgMWu54qwVMAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x1c7d0358>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x1c7f48d0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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BAAFNRwsE0HQdR5WLikoHmqaHrrMZDcHz0DQdTdfx+wO1\nz9WCHSf1uv0G0zigaQzqGUf/bjY0XWd/SRVqbbOrogT/1RQjHbJrtabplFRUN5jOpsAe/Nnr05q1\nj/hY04EOA/WCJ8EmwyOiQb/uSXy1Zi8791VIEInooijBgZf1aQDBfhjUvf2Ees3X6z4PwY4egUBN\n8JqYpgU7edR/09J19NBeFJTajh3BUFNrr5kpeHUzLu+BAA1UB6+11T0O1F7HU+DgRRFqb9blr9qB\n/6t8/vpdol2hmk5RmRt/oHmBU9clumtavRpOqo046RId1fp1TwJg+94Kpozt2aLnduB/GdEZNLej\nR6TsN1oEu0TXn2Eg2D26pLwaTT9yHzUFSE2KISs1tmGzWop0ie6o+nRLxGRU2byrrMXPDdsrYt26\ndTz22GO8/vrr4SqCEJ2ex6exK7+ywYSdhaUu7JU1LeoS3bVe77TgVyxmU+cN8s7IbDIwKDuFjbtK\nqXJ7WzToNyxB9I9//IPFixdjs3XcSQCFiCRO90E91GoHgFZUeYCSIz7faFDJSIklKy02NLtAVlqw\nS3TdtTQhRvRPY8POUjbuLG3R2LawBFF2djbPP/88t956azgOL0TE0zSdHzbsbzBg9UizNOi6TqXT\n22Cyzrpu0c7q5neJruuVVle76drFRlpiTJvPnyei3/C+aQD8sq2kdYNo/fr1jBgx4uhL1ojp06eT\nn5/fqvsUoiP5YcN+lv+8D4AdeysAQgNYNV2nrLLm0MCxu6jxNG/Szlirkaw0GxbFy5D+3UO91JIS\nLNIlWhy1gdnJ2GJMrNlSxNwWLJJ3xCB67LHHKC8v59xzz+Xcc8+lS5cux1zYo7Fx48ZOddxw64zn\n3V7nrOs6W/bWYK/ykxpvZHAP6yH/sOu3VuGu9hDQIKDpfLlqJz+uz6O8yk+504+/eR3UiLWoJMcZ\nSIkzkhxvJCXOSEq8gRhzXVd3K+BAdzvY74b9rX62kSlcr++hg/u3aPvde3a3UUmOTbXbyS/mcuLi\nDp3jsHe6kY27q/n0q1VkJje8TjRmTOMr1B4xiF577TXy8/NZvHgxV199NVlZWcycOZOpU6cGl1Y4\nBnozet/UGTZs2DEd62gEpwJp/+OGW2c87/Y855Xr8tlRtA9QKXdrZGYm0bd7coN1cPJK/FQ6D9Ru\nHG6NwvKmm9eSEyzBazcHjcOxxRz+f1T+1u2rpesRZfdsu1ncj4XL6WDUqGwSExMPecyp7GPj7hyc\npDBmzMBm7a9Z14i6devGeeedh9Fo5O233+a1117jySef5Oabb2b69OktO4N6ZJCaaMzRXB+JBh5v\ngMIyFz9tLaKiyoMvEJxl4c0vtjXr+YoCaYkxDeZPqxuDY7VIl+iOqLSkectVtLfqaheVlUmNPta/\nqxWDqvDDunzOOD4zdH9CQkKT7/lHfPUuWrSIxYsXU1JSwnnnncebb75JZmYmRUVFzJw586iDqFu3\nbrz99ttH9VzRsR3u+kg0cNf4Gp3SprlrtaiqQnpyzCFLEqSnSJfozkbTmtfJpL1ZLGZWby1HUSoa\nfbxLkoWd+6tYsmInsRYjbreLcyYNabQGBc0IojVr1nDDDTcwbty4BvdnZGRw7733HsUpCHF4+SXO\nw96OBHVrDzWcYSAYOnVLQRyJqgSXWO7bPYmu9SbtTE+OwSBdogWQnhE9H8Dq69fDQ2HZfkocOkN6\nH3m6nyMG0SOPPNLkY6effnrLSidEM3TrEheqCdXdDhdd16mo8jTSQ82Nq7ldos2GQ2aIzkqNJVW6\nRIsOqldWAt+t20/ufgdDeqcecXtpWBYRp278QXsu+qbrjUzaWftzjbd5XaJttV2i64+/yUq1kRRv\nkeuholNJjLOQHG9hX3FVs+YglCASEactF30LBLRQ4IRqOaUuCkqd+LXmXRhOsJkbXL+pq+XEx5ok\ncISolZ2VwC/bSsgvcZJ2hEYNCSLRIfn8AYrs7gZdogtKXRSXV4eWbTiSlARrKHAy601tY7Me27AF\nITqDXpnBINpd4CCt/+GvE0kQiahycNfu0QPTKS6vDtZq6oVOaUU1zRmmpiiQlhRDnFljQO/MUPBk\npMZi7YgLBwnRTjLTbJhNKrsLqziuX/xht5X/NHEIXddZuS4/osbxuGq7RH/3Sz6bdtnx1a50+vr/\ntjbr+QZVISPlwJIEmamxdE2LIz0lBpPRUDvIsV8bn4UQnYdBVeiREc/OfZVUug7fsUeCSBxiy96a\n2pH/7TuOJ9gl2hvqKFBgd4dqOg5X87pEm4xq6JpN3WDPrDQbXZKkS7QQ7S07M4Gd+yrZV1J92O0k\niMQh7FV+6q9C2trjeHRdp9zhadCUVtdTzV3jb9Y+FCUYOj3S4xk5oEsocFISrTJppxARokdGsEmu\nqPzwg7kliMQhUuONlLsPdLk82nE8mqZTWll9yPibQrsLT3O7RMeYDswukGYjMyWW/JIqyhw1dE+P\nj463nVcAABomSURBVIhmw2gWic2wouOIizERH2umuLzmsCv7ShCJQwzuYaVbt5Rmj+PxBzSKy90U\nlrob1G4K7e5mjSGA4LiDuoGemam24EwDabZGV3kc0ufIA+REkK7r6LqOqjbeLBmuZljReWSl2di2\np5yCUjfJSY3PTydBJA6hKI2P4/H6AhTW1mjqlpQusLesS3RqYr0u0fXmUYuxdu6XYiAQwO/3UdfV\nT0HHYFAxGlSMBgWDqqKoCgqgE6xtBgIaiqKgqrVf9Soyuq6jKCoGVQEUApqOpmnBT6U6oU+n9gon\nut+ADijA/uJyTEp6sAS6jqYHx175/FpwG9WAwRD8akzdjPoynkrUyUqNZduecrbvczCkX+Mfajv3\nf79olNenkbu/8sD1m9qajr2imubEjaJAenIsmfVqN1lpNjJSbFjM0T1p57HMDB4IBPD7vBhUMKjB\ngFFVBbPJgNlkxmKJQ1XVdn0Tz85KxJV/oNY6tG8W6WnJjW6raRqBQACvz4/P5wd0anMNheDfva7s\nuq4R0HR0TUfT9WBwanooRHW99jsKiqpiMBja/dxF+0hNjAFgf4mryW0kiDoxZ7Wv4fib2ms55VUe\noOSIzzcaFDJSbA2Wlg5O2hmLydgxe6g1Z2bwYOD4UNAxGhSMRhWTQcUaayY2Jr7JZrJwGNUnll7Z\nXcgrdNArM4Gpx/dscltVVVFV9ZjXIasvEAjU1gaD3wOaTijgdB0UJRRmgdpaYPB7MNQURQVFQa0N\nMwmyyJOcYAFgv93d5DYSRB2crus4XN6GU9rUfq9yN2/STrNJJTPloEk702ykJVkxRNCbanuo34Mw\nEPCTl1/KuMEpGA0qZpMBo0HFYgvWbppqvookqqIwfVz4Fl+ra+YzH3op8Ih0vba5UdPw+fzBQAsE\nCAQOqoXpweuYwetl4PN58fv9GI3y9tcerGYjVrPK/lIJog5P03XKHTWHrIFTWOrC7Wlel+gYS3DS\nTovqYXC/7qHrN9IlOsjr9ZCRZGRbrgdVAaPBwKiB3cju1iXcReuUFEUJBVlza2m6rlNSkEuXRAte\nnx9/IFBb+4KAptV+PxBiiqJiMBqj4kNFJEuINVFS0XQXbgmiKBPQNEorag7qEu2iyO7G42tel+j4\nWFODjgJ1NZ3EODOKotTOMhCZSxS3J5/XC3qg9hqOSpcu8Vxy+gi6pCQ1qylLRB5FUTAajcTGxhB7\nhG11XScQCFDjCdag6gKqLqQ0Tccf0NA0HUU1YDSZIqrZNZKYTephp9ySIIpQ/oD2/9u78+AorjsP\n4N+579HoHB3m9IGdVYxABm84UthZYQgmxEnWRWwCRWpdoWpd5cQiQJVr7Xh3VcLeCpU/Fuw4rrhY\nWC84FFvxupzY4COJCQ5mDMQEIzCIG4wQICFpprtn5u0fQkLikEbSTL/u6e/nP8EM/WtA89V7/Xvv\n4fzFrn7daWcvdOHLi51IpjLrUIuEPL3b2fRMp1UUBxC8SUs0dUsmk0Bag8/jRElJAB6P54bXyJzK\nIv30hFZwkCk8IQSSySTiCQWKmoSWTCN59VmWEID9akhZ+fmVe5BnxgwiybpborsXevZtGGi5FB9w\nAVgPG4DiiK93/c21hZ9siR6KpKbALlQUhzwIBm++1oEGl04LvPfJiX4jxnxfIGuz2eByuW46PSiE\ngKZp6IorUDUNtjxt4hmM28UgMoR4Itm/WeDqSKe1LZFRS7TdZkNZke/aoWu9B7D54XZx/no4Uskk\nRFpDwOtCacSHirIi2SWZ3nufnMDbf24GABw42grA2iNIm80Gt9sN93C6MfLIYM+YGURZ1tHVv0Ot\nZ0uby1eUjN7vdNgRLfJf22Xg6nRaWZEfTm7amRVKIg6fx4FIgQ8Bf/fo5/gxhjkw8hHNsXPtA35N\n1pQaZME7g2gYhBBo61Cve37TPdLJtCXa43L0LvjsfX5TEkBJgS/vpzJk6Dv6Ka8sGrQLyopTTMDI\nRzRjy8O97+v5mkjVBt7qi0E0ACEELvQ5Vvpc707RXYhn2BLt9zqvrb/pczxBYZgt0bkmhICmJG4Y\n/WTCqlNMIx3R9HQRsquQ+ooPsskxgwjdLdEtl+K9G3X2Tqu1XEEyfT6jPyMccPc7UronfMIBt6W7\nZWToGf2EAx5ESkuG9fdv1SmmkY5o7Ha5C2TJmNo7NRSHb+xA7WGpINKS11qi+zYNnL/YlXFLdGHY\nc8OGneUlAQR92dv2hIZHVRJwO20oCnmH1fnWdzqus0u7unFod4hZZYqJIxrKNkVNoUtJ4faqW38P\n5WUQKWoK5y5e//yma0gt0SURHwKeFO4aW9E7nVZeHIDPk5d/ZaaVSqUgUir8Xhei5ZERbdvSdzpO\nABhbUYCA32WpD2SOaCjbzrV2b3Y6Pl+DqCuh3bilTWt3S3Qm7HYbygp9N+wwEC3qbonu3mHgjhzf\nBQ1Hz+gnEvAgHMrOFjt9p99sAAJ+F55Y8NWs/NlEVtWzP+OEUQW3fI1pgqj7YKX+29q0dagZvdfl\n7G6J7rf+psSPaKEfDrZEm0Y6nUZKU+D3OlEWLcjqLtAAO76IcqH5TDucDhvuHpMHQbTm9U8HfY3H\n7bju+U33Ohy2RJubEAJJNYFIyININHcbjPL5CFF2tVyO43KHgjHRgRfemyaI+gr0bYkuuTalVhjy\nsEMtjwghoCoJBH1OVFUNr/ttKPh8hCi7Pm/unmG4vSo44OtME0QLZ0+4+hzHj5CfLdH5TFUUuByA\nz+tERfHgi08pc9cv1I04MusWJTkuXrwgu4QhczldsNttULQUDh67CJ/HgaKAwXZWEELgZz/7GZqa\nmuB2u9HQ0IBRo0YN+r5Zk2/ToTqSSVUS8LntuC0azvrzH+p2/ULdr1TZMeU+yUXRLWnKrQ+TM6rK\nsAeT7r0bv/3TcWgpge8+MAYPTxuNcNhAXXPbt2+HqqrYtGkT9u3bh8bGRqxbt07vMshAlEQcAa8j\nJw0I1N/1C3PPX85sSyqSI1phvueUoUACNqcPb+88iaDPhUceuBt+78Df17oHUSwWw8yZMwEAEydO\nxP79+/UugQyiZwQ0qjzCABqCkeyDd31nYFmEf++Uff/z7kF0JpL4pwXVg4YQICGIOjo6EAqFrhXg\ndCKdTvNkQwu5NgXHEdBwjGQfvOs7AyOOltwUSZZ1+kICb+84jsqSAL45bVxG79E9iILBIDo7O3u/\nzjSEZI2crDpiy8V9a5oCt10gHPTC5XLh9MmsX2JEYrGY7BIysmvfZXR2KX2+/gJFzswfahc5gaLb\nAOACAJtp7jubZN1zbW3tkF5//MTxHFWSG6m0wM4Dl5EWNtRN9OGv+/b0+/1b3b/uQTR58mR88MEH\nmDNnDvbu3Yu77roro/dVV1fnuLIbde+soP91Zcv2fSuJOPweB4oLQ4YdAcVisSF/SMhyMXkcF66O\niABg6sRxqK0dXtu5me47W8x0z2NGm2s5wY6/nsHlLhtm3z8G3/tmTcbv0z2I6urqsGPHDixcuBAA\n0NjYqHcJpAMhBDQ1Ab/HgWhF4Yj2gKP+uPCWjOjYmXbsPdSCorAL/7RgaD/I6v7pYLPZ8Pzzz+t9\nWdJJNo5gsJqhNh9w4S0ZTWtbHO/uOg6H3Ybvfb1yyJtD88dUygpNVeG0p1EU8g3rCAYrs+ohfJQf\n4koSb//5GLRkGrPvH4PKYu+Q/wwGEY2IqiTgddtRURKAx3Prg69yIV+O87bqIXxkfql0Gr/feQzt\nnSqm3BPFnaMiADI7/aAvBhENWc8u2EGfCxWVRdJa7/NlJMFdv8mM0mmBbbtO4MyFTtxeVYApX4kO\n+89iEFHGkpoGG5IoCPoQLpP//Ef2SCJbIzI2H5DZCCHw/u6TOHKqDRUlAXxjyugRfR4wiGhQqpKA\nx2VDaWEAft/Q539zRfZIIlsjMjYfkJkIIfDhp6fQdOISokV+PDx9HFzOkc2KMIjopvq2Xxt1DzjZ\nIwm9RmR9R15jomEAAse/vGLq52JkTkII/GnfGRxovoiSiA/zZ4wf8JyhTDGIqJ9UMomUFkfALQzf\nfi17JKHXiKzvyOvjz84CAMIBt6mfi5H5pIXAh7FT+PzYRRSFvfjWzPHwuLNzRAuDiAAAqqrAZRco\nCvkQLQ6jMMIH5oPRa0TWd6SlaKlb/h5RrqTSaWzbdQJHTrWhNOLD/Jnjh7xWaCAMIovrbb8uDsLr\n1bf92uz0GpH1HXl5rpsGYYcd5ZqW7G7RPvHlFVSUBDBv+rgb/h+OFIPIolQlAb/HjvKKQp6AanB9\nR143e0ZElCsJNYm3dxzD2dZOjC4PYc7fjx1xY8LNMIgshgFkPrKfhZE1tXeqeOujo7h0RcGdoyL4\nxpRRcORozSCDyCI0VYHHZeMZQEQ0qPOXuvDWR82IK0nU3FmKafdW5LRxiUGU5zRNhdshUFkagtvt\nll0OERncsbPteOfj40im0phZU4l77yjN+TUZRHkqqWlw2FIoKwwaahEqERnX34624g97TsFus2Hu\n18ZifFWBLtdlEOUZVVHgcQElET8Cfp/scojIBIQQ2PW3c9h98Dy8bgfmTR+H8uKAbtdnEOWJnlNQ\nb4uG+QyIiDKWSqfxQewUmo5fQjjgxvwZ4xEJ6buUg0Fkcj1dcDwFlYiGStFS+P3OYzh1vgNlhX7M\nmz4Wfq/+P8jyk8uk2IZNRCPREdfw1kdH0dqWwNiKMGbfPyYna4QywSAykVQqhXRShc/jYAAR0bC1\ntsXx1kfN6IhrqB5fjJmTqmCXuK8kg8gEuo9hsCMScCMcyn0rJRHlr1PnO/C7nc1QtTS+Vl2BSRNK\npW9uzCAyqJ5TUAM+F6LlET7/IaIRO3TiEt775CQAoG7qaNw1ulByRd346WYwPaeghgMeFBjgFFQi\nyg+fHbmAP+45DbfLjrlfG4fbyoKyS+rFIDIITVPhtKVREglw/Y/BZOtIcCJZYge/xMf7z8HnceJb\nM8ejJGKszxgGkWSaqsLlEIgWBuDjDgiGlK0jwYmGKhVvHfxFAxBC4C9N7dh75AqCXgcevr8YEU8X\nUvGuLFV4I88wzjJjEEmiKgq8LqCc5wAZnl5HghNdb96DtcN+rxACr/zvZ9h75AoqSwL4t2XTUFbo\nz2J12SOnadzCVCUBu1BRVRZCRbSYIWQC1x8+x8PoyOiEEHj1zf14a0czxlaEsfrJGYYNIYAjIt2o\nSgI+t53HMJiQXkeCE2WDEAIbfvc53vzjUYyKhvDvy6ahIGjsH3gZRDnWuwUPW7BNiwfTkZls3n4I\nv3nvMCpKAqYIIYBBlDNqIg6/lzsgEJF+/u9PR/Hfvz+IsiI/GpZNR1HYHA1QDKIsEkJAUxMI+lyo\nqCqGPUfH6hIRXe+TA+fw6m8/QyTkQcOyaSgtNFaL9kAYRFkghEBSVRD0OVFZyQAiIn01n2nDf2zc\nDafTgX/54f26niWUDdI+Mbdt24b6+npZl8+KdDqNpBqH15nCmKpilBRHGEJEpKvWtjj+9dWPEVdS\nePqxyYbZtmcopIyIGhoasGPHDtxzzz0yLj9iqWQSSGsIBz0oCHMbHiKSI5VK44X/2o0LbQksmfcV\nTL+3UnZJwyIliCZPnoy6ujps3rxZxuWHrWcbnqKQD8FgRHY5RGRxm7YdwufHLmLGxEp894E7ZJcz\nbDkNoi1btmD9+vX9fq2xsRFz587Frl27cnnprNJUBW4nuA0PERnGZ0cu4I3tTSgr9OGf/7HG1DMz\nNiGEkHHhXbt2YfPmzfj5z38+6GtjsRgutGs6VNWfpirwOAVCAS8XoRLRiNXWZr5lTywWu+XvdSlp\nvPy7L3ElnsLSfyjF6FLjrxUCbn3/pumaq66u1uU6QghoSgIhvwvNRw9jypQpulzXSGKx2JC+YfKB\nFe8ZsOZ9m+meb1Xnf/5mL9q7Unh8zt14pG6CzlVln2mCKNdSqRRESkU44EGktLsB4fgxdsARkbEc\nOnEJ7/7lOEaXh/C9B++UXU5WSAuiqVOnYurUqbIu3yupabAjhUjIy2O4icjQ0mmBl7f+FUIAy75z\nL5yO/Phh2bIjIlXpbkAoLQzAzwYEIjKB7Z+cwOGTl/H1SVX46u0lssvJGssFkZKIw+dxoKosBLfb\nLbscIqKMJFNpbNrWBLfTjh/O/zvZ5WSVJYKoZw84v8eB8soibkJKRKbzYewUWi7F8fCMcSguMM8+\ncpnI6yBKp9NIJxUE/W5UlXIHBCIyp1RaYMv7h+Cw2/CdWfnRoNBXXgZRzxY8BSEfCsJsQCAic9v1\nt7M43dKJuqmjTbWrdqbyKoh6tuApDvsRCHALHiLKD+/+5QQAYMHXb5dcSW7kRRCpigKPCygvCsLr\nNccKYyKiTFy6ksCnTedxx6gIxlSEZZeTE6YOou4teMAOOCLKW3/49BTSaYFv3DdKdik5Y8og0lQF\nHpcNlaUMICLKb3/YcxpOhw0za6pkl5IzpgoiVUnA67ajqizMTUiJKO9dvqLgi5OXce8dJSgI5u9j\nB9MEkUOouC1awAAiIsvYc+g8AGDyhDLJleSWaYKovKxIdglERLr6tOlqEN2d30GUHzvmERHloc++\nuIBIyIOxedot14NBRERkUK1tCUwYXZj3u8IwiIiIDOyOUfm/OJ9BRERkYHcyiIiISKZxlQWyS8g5\nBhERkUH5PA4UhvJ3/VAPBhERkUFVlATzvlEBYBARERlWRUlAdgm6YBARERlUaST/zh66GQYREZFB\nWeH5EMAgIiIyrAiDiIiIZIoEvbJL0AWDiIjIoDgiIiIiqQqC1jj4k0FERGRQfq81zl9jEBERGZTH\n5ZBdgi4YREREBmW35/+uCgCDiIiIJGMQERGRVE69L9jR0YHly5ejs7MTmqZh1apVqKmp0bsMIiIy\nCN2D6LXXXsO0adOwePFiNDc3o76+Hlu3btW7DCIiMgjdg2jp0qVwu7t745PJJDweayzYIiKim8tp\nEG3ZsgXr16/v92uNjY2orq5GS0sLVqxYgWeeeSaXJRARkcHZhBBC74s2NTVh+fLlWLlyJWbMmDHo\n62OxmA5VERHlVm1tbcavzcfPvVvdv+5Tc1988QV+/OMf4xe/+AUmTJiQ8fuG8g+YLbFYTMp1ZbPi\nfVvxngFr3reZ7tksdY6U7kG0Zs0aqKqKhoYGCCEQDoexdu1avcsgIiKD0D2I1q1bp/cliYjIwLig\nlYiIpGIQERGRVAwiIiKSikFERERSMYiIiEgqBhEREUnFICIiIqkYREREJBWDiIiIpGIQERGRVAwi\nIiKSikFERERSMYiIiEgqBhEREUnFICIiIqkYREREJBWDiIiIpGIQERGRVAwiIiKSikFERERSMYiI\niEgqBhEREUnFICIiIqkYREREJBWDiIiIpGIQERGRVAwiIiKSikFERERSMYiIiEgqBhEREUnFICIi\nIqmcel8wHo+jvr4e7e3tcLvdWL16NcrKyvQug4iIDEL3EdEbb7yB6upqbNy4EfPnz8evfvUrvUsg\nIiID0X1EtGTJEgghAABnzpxBQUGB3iUQEZGB5DSItmzZgvXr1/f7tcbGRlRXV2PJkiU4fPgwfv3r\nX+eyBCIiMjib6BmeSHD06FH86Ec/wrZt2wZ8XSwW06kiIqLcqq2tzeh1sVgs49eane5Tc6+88gqi\n0SgWLFgAv98Ph8Mx6Hus8o9BRNTDSp97uo+IWltbsXLlSiiKAiEE6uvrMWnSJD1LICIiA5E6NUdE\nRMQFrUREJBWDiIiIpGIQERGRVAwiIiKSikGUgSNHjuC+++6DqqqyS9FFR0cHli1bhh/84AdYuHAh\n9u7dK7uknBFC4LnnnsPChQuxePFinDx5UnZJOZdMJrFixQo8/vjjePTRR/H+++/LLklXra2tmDVr\nFpqbm2WXQlfpvo7IbDo6OvDiiy/C4/HILkU3r732GqZNm4bFixejubkZ9fX12Lp1q+yycmL79u1Q\nVRWbNm3Cvn370NjYiHXr1skuK6fefPNNFBYW4sUXX0RbWxu+/e1v48EHH5Rdli6SySSee+45eL1e\n2aVQHxwRDeLZZ5/F008/ban/uEuXLsXChQsBdH/j5nMIx2IxzJw5EwAwceJE7N+/X3JFuTd37lw8\n9dRTAIB0Og2n0zo/j77wwgv4/ve/zx3/DcY6/wMHcbN98SorKzFv3jxMmDAB+brcaqD9AFtaWrBi\nxQo888wzkqrLvY6ODoRCod6vnU4n0uk07Pb8/RnN5/MB6L73p556Cj/5yU8kV6SPrVu3ori4GNOn\nT8fLL78suxzqgwtaB/DQQw8hGo1CCIF9+/Zh4sSJ2LBhg+yydNHU1ITly5dj5cqVmDFjhuxycmb1\n6tWoqanBnDlzAACzZs3Chx9+KLcoHZw9exZPPvkkFi1ahEceeUR2ObpYtGgRbDYbAODgwYMYN24c\nXnrpJRQXF0uujCAoIw888IBQVVV2Gbo4fPiwmDNnjjh48KDsUnLunXfeEatWrRJCCLFnzx7xxBNP\nSK4o91paWsTcuXPFzp07ZZcizaJFi8TRo0dll0FXcWouQzabLW+n5663Zs0aqKqKhoYGCCEQDoex\ndu1a2WXlRF1dHXbs2NH7TKyxsVFyRbn3y1/+Eu3t7Vi3bh3Wrl0Lm82GV199FW63W3ZpuukZGZEx\ncGqOiIikyt8nskREZAoMIiIikopBREREUjGIiIhIKgYRERFJxSAiIiKpGERERCQVg4iIiKRiEJGl\nbdiwAYsWLQIA7N69Gw899BC6urokV0VkLdxZgSxvyZIlmD17NjZu3IjGxkbU1NTILonIUhhEZHmn\nTp3C/Pnz8dhjj+GnP/2p7HKILIdTc2R5p0+fRjAYxIEDB2SXQmRJDCKytM7OTjz77LN46aWX4PV6\n8frrr8suichyODVHlvb888/D4/Fg1apVOHPmDB599FFs3rwZVVVVsksjsgwGERERScWpOSIikopB\nREREUjGIiIhIKgYRERFJxSAiIiKpGERERCQVg4iIiKT6f/BUOlSmZZuJAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x2178d748>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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YUljFO0u3A5CaaKFf9xT6dU+mV3YS3TMddMtwYJHrTKIeSUSE52L4gxq1ngAV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\nflT6c38YuuCvpKFQCIsWLQIAzJs3D/v27St0CUO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IiEgoBhEREQnFICIiIqEYREREJBSD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\nIhKKQUREREL9P4uKa580u2bbAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x215186d8>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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iYurYVKaOTcXj1dmTZ2XL3jK27i9nR1Z5s8QUbtbo3zOWfj2i6Z0cTe+kKHol\nRclCjF2UJKITpOs6DpeXwjI7VTYHlTYH1hoHxeV2DpTZKSyzU1hux+H0+N+jKNC/ZwyjB3Zn9KDu\npPeT4pOic/KtdeUb6HDJdPB4vOwrqGJPrpU9DRdl27PK2ba/vNn7LGEGusdF0D0unO6x4b4/x4YT\nF20mKsJEVISJyAgjZqMm96A6kS6biGrrXfxnVTb2ehcej47Hq+PxevF6fX9u/L/D6aHe6abe6cHh\n8uBwuqlzeKi2O3G6PMCBFvcfZtLo0c1Cn5Ro0lJjSUuNpX/PGMLNneMrr6u1n9CJoK7Wht1WfQzb\n1aJqhmPa9kQ1PcaxxnUyxzjxfRw5tkB8V7W19pN6v6apDOodx6DeB6cd1Dnc5JfUkFtUQ15xDTlF\nNZRYaymusJNdeOTPYjSoREWYCDcbMBlVTEYNs1HDZNQwGVVs1VWs2rcRg6agqgqq4vu/oiioCv7n\nBvWOY8Kw5JP6bOLkKbqu68EO4mgyMzODHYIQQrSJjIyMYIcQcjpEIhJCCNF5ydAsIYQQQSWJSAgh\nRFBJIhJCCBFUkoiEEEIElSQiIYQQQRWUSS0vvfQS3377LS6Xi9/85jdcfPHFwQhDCCFECAh4Ilqz\nZg0bNmzg3Xffpba2lldffTXQIQghhAghAZ9H9MQTT6AoCnv27MFut3PnnXcybNiwQIYghBAihAS8\nRWS1Wjlw4AAvvvgieXl53HjjjXz11VeBDkMIIUSICHgiio2NZcCAARgMBvr164fZbKaiooL4+NZX\nHpUSP0KIzuB4yvt0xvNea58/4IkoIyODN954g6uvvpri4mLq6+uJi4s7pveFmszMzJCMC0I3Nonr\n+IVqbKEaF4R2bMejM3yGYxHwRDR16lTWrVvHr371K3Rd5/7775dy7kII0YUFZfj2vHnzgnFYIYQQ\nIUgmtAohhAgqSURCCCGCShKREEKIoJJEJIQQIqgkEQkhhAgqSURCCCGCKijDt4UQnduzzz7L999/\nj8Fg4O6772bkyJHNXt+0aRMPPfQQBoOBU089lZtvvhmARx99lPXr1+PxeLj00ku55JJLKC0t5Y47\n7sDtdhMTE8Njjz1GREREu8VutVqZN28eDoeDxMREHn74Ycxmc7NtHnroIdavX4/FYmHevHmHfT5x\nfKRFJIRoU9u3b2fdunW8//77PPHEEyxYsOCwbe6//36eeOIJ3n77bTZv3szOnTv5+eefycvL4913\n3+Wtt95AImYKAAAgAElEQVTi5ZdfpqamhpdffpmLLrqIN998k/T0dN5///12jf+5557jggsu4M03\n32TIkCG88847zV5fvnw52dnZfPjhhzz99NP8/e9/b9d4ugJpEQkRApYuXcqyZcuw2+1UVlZy0003\ncfbZZ7NmzRoefPBBYmJi6N27NwsWLKCuro777ruPmpoaSkpKmDNnDpdddhlz584lISGB6upq/vrX\nv3LPPfdgMBjQdZ1FixaRlJTEI488QmZmJoqicP755zN37lzuvvtujEYjBQUFlJWV8Y9//IP09HSm\nTZvGgAEDSEtL4y9/+Ys/1htuuIHa2lpqamqIiooiLS2Nv/3tb/7XMzMzmTx5MgApKSl4vV6sVqu/\nlJfNZsPlcpGamgrAlClTWLVqFVdccQVDhw7178fr9WIwGLjnnnv8jwsLC+nZsycACxcu5KKLLmLI\nkCH+9zz77LPs37+f7OxsAO677z7Gjh3bLLannnqqWTWXa665hmnTpvkfr1+/nhtvvBGA008/naee\neoqrr77a//revXuZMmUKAHFxcWiaRnl5OQkJCcf71y4aSCISIkTU19ezePFiysvLueSSSzjzzDP5\n61//yl/+8hemTZvG008/zUcffcTw4cM5//zzmTFjBiUlJcydO5fLLrsMgAsuuIDp06fz1ltvMWrU\nKO644w7Wrl1LTU0NO3bsoKCggCVLluB2u5kzZw4TJ04EIDU1lQULFvD+++/z3nvvMX/+fIqKivjk\nk0+Ijo5uFucLL7wAtF7PzWazNasfGRER0ew5u91OZGSk/3WLxUJ+fj4mkwmTyYTb7ebuu+/m17/+\nNeHh4QC43W5mzpyJ0+n0d+M1JqhDhYeHc++99xITE8Ptt9/OJ5984n+tsdblkdjtdqKiovyx1dTU\nNHs9PT2d1157jTlz5lBYWMjevXupra2VRHQSJBEJESLGjx8PQEJCAjExMZSUlFBaWsozzzzDa6+9\nhsPh4NRTT+X0009n8eLF/Pe//8ViseB2u/376Nu3LwCXXHIJL730Etdddx3R0dH8+c9/Zt++ff7E\nYTAYGDlyJHv37gV8J1eA5ORk1q9fD0B8fPxhSQh8LSK73Y7NZiMyMpKBAwc2axFFRkZit9v9j5ue\n2MF3crfZbM1ebzxOVVUVt9xyC6eccgrXX3+9fxuDwcAXX3zBTz/9xJ133nnEZHLKKacAkJaWRnl5\nebPXmraIdF1HUZTDWkSN8cXHxx8WO8DkyZPZsmULV155JWlpaQwbNuyYCjeL1kkiEiJEbNu2DYCy\nsjJsNhspKSmkpKRw++23M2XKFL799lssFguvvfYaY8aM4bLLLuPnn3/m+++/9+9DVX23fZctW8a4\nceO4+eab+eKLL/jXv/7FOeecw4cffshVV12Fy+Viw4YNXHTRRfzwww8tFh5urRjx0VpEY8eO5fHH\nH+faa6+lsLAQXdeJjY31vx4ZGYnJZCIvL4/U1FR+/PFHbr75ZhwOB9dccw3XXnst559/vn/7v//9\n75x77rlMnDiRiIgI/2c80vfYo0cPdu/eTWJiYrPXjqVFNHbsWFasWMGsWbNYsWIF48aNa/Z6dnY2\nycnJvP322xQVFXHXXXc1a+GJ4yeJSIgQUVpaytVXX43NZmP+/PkoisI999zDI488wrPPPktUVBSP\nPPIIAA8++CBffPEFUVFRGI1GnE5ns8QxYsQI7rrrLp5//nm8Xi/33HMP6enprF69mssuuwyXy8Uv\nfvELf0uoLQ0bNoyMjAx+/etf+yvsA6xevZr169dz0003MX/+fObNm4fX62XKlCmMHDmSxYsXk5+f\nz5IlS3jvvfdQFIWHH36YuXPncv/99/PPf/4TVVWZP38+0PI9IvANllizZg0mk4kHH3zwuOO/8cYb\nueuuu1iyZAlxcXEsWrQIgMcee4xzzz2XwYMH88QTT/DOO+9gNpv9n0+cuIAvFX4iQnVtkVCNC0I3\nNomrZUuXLiUrK4vbbrvtsNeCHVtrgh3XW2+9xemnn06vXr38zz377LN0796dtLS0kPzOjkewv99A\nkhaREKJDmj59OsnJycEOQ7QBSURChIDZs2cHO4QOp6Uk1DiirjMus92ZyYRWIYQQQSWJSAghRFBJ\nIhJCCBFUkoiEEEIElSQiIYQQQSWJSAghRFBJIhJCCBFUkoiEEEIElSQiIYQQQSWJSAghRFBJIhJC\nCBFUkoiEEEIElSQiIYQQQSWJSAghRFBJIhJCCBFUkoiEEEIElSQiIYQQQSWJSAghRFBJIhJCCBFU\nkoiEEEIElSQiIYQQQSWJSAghRFBJIhJCCBFUkoiEEEIElSQiIYQQQSWJSAghRFBJIhJCCBFUkoiE\nEEIElSQiIYQQQSWJSAghRFBJIhJCCBFUkoiEECJE6boe7BACQhKREEKEKIfLE+wQAkISkRBChKhq\nuzPYIQSEJCIhhAhR1TZJREIIIYJIWkRCCCGCqsruCHYIASGJSAghQlSVdM0JIYQIpsqa+mCHEBBB\nS0Tl5eVMnTqVrKysYIUghBAhrbiiNtghBERQEpHb7eb+++8nLCwsGIcXQogOQRJRO3rkkUe4/PLL\nSUxMDMbhhRCiQ5BE1E4++ugjEhISmDx5cpcpXyGEECei2u7EVtv5BywoeoCzwRVXXIGiKADs3LmT\nfv368fzzz5OQkNDqezIzMwMVnhBCtJuMjIxj3jYzM5P5b+dz9Yzu9E00t2NUgdPa5zcEOA7efPNN\n/5/nzp3LggULjpiEGh3PX2CgZGZmhmRcELqxSVzHL1RjC9W4ILRjO16myGQyMvoHO4x2FdTh240t\nIyGEEC3LLqwOdgjtLuAtoqZef/31YB5eCCFCmsmgsjevMthhtDuZ0CqEECFqYO84sgqrsNe5gh1K\nu5JEJIQQIWp4/wR0HXZkVwQ7lHYliUgIIULU0P6+gVzb9pcHOZL2JYlICCFC1JA+cRg0hQ27S4Id\nSruSRCSEECEqIszI8AHd2JdfRVllXbDDaTeSiIQQIoRNHJYMwNrtRUGOpP1IIhJCiBA2YagvEa3e\nJolICCFEECTGR9C/Zwyb95RSZeucK7ZKIhJCiBA3LSMVt0dnxYaCYIfSLiQRCSFEiDtjbCqqqvDt\nutxgh9IuJBEJIUSIi4sKY+zgRPbmV5FT1Plqz0kiEkKIDmDGhN4A/GdVdnADaQeSiIQQogM4ZVgy\n3WLD+WZtLrZOVntOEpEQQnQAmqbyy8n9qHd6WLYmJ9jhtClJREII0UGcc0ofTEaNz37MwuPxBjuc\nNiOJSAghOoioCBNnjutFSUUtP2w6EOxw2owkIiGE6EAunpaGqiosWbYbr1cPdjhtQhKREEJ0IMkJ\nFqaOTSWvuIbVWwuDHU6bkEQkhBAdzCXTB6Io8N7/dqPrHb9VJIlICCE6mNTEKE4b3ZP9B6pYubnj\n3yuSRCS6LK9X538/5/DyJ1tYv8/eafrbRdcw59whaKrCG1/uwN3BR9AZgh2AEMHyzdpcvlyVBYC9\ntpa+a3M5a2KfIEclxEFVVVWtvmYxwtSxKXyz7gCfLt/J9HE9W9wuOjoaRVHaK8Q2IYlIdFnZh9Ts\nOvSxEMF2tImrCVEaBk3h3W/243Q6MRqad3LV1tq5cOpQYmJi2jPMkyaJSHRZfZOj2b6/vNljIUKJ\nJfLIv0lLJIwe6GDdzhL2FjoYl54UoMjaliQi0WVNH+8rIpldVA0O1f9YiI5kzOBEtu4vZ8OuEob3\nTyDM3PFO6zJYQXRZqqpw1sQ+XD9zBGMHWFDV0O5HF6IlJqNGxpAknG4vmTtLgh3OCZFEJIQQHdyI\nAQlERRjZvK+MmlpnsMM5bpKIhBCig9M0lQnDkvF6ddZsKwp2OMdNEpEQQnQCg3rHER8dxq4cK+VV\n9cEO57hIIhJCiE5AVRQmDU9BB37e1rFq0EkiEkKITqJPShQpCRayDlRTVG4PdjjHTBKREEJ0Eoqi\ncMqIZAB+2lLYYQqiSiISQogA0HWdurr2v3fTo1skfVOiOVBmp6Csrt2P1xY63swn0WF5vTrr99lZ\nn7+FvsnRTB/fu83m7ni9Ot+szSW7qLrN9y3EiXK5XFTbaql3uHG4vBg06B0e1u7HnTgsmezCajJ3\nW7nyF6HfKuowicjr9aKq0oDryL5Zm8vaPTYsEV5/aZ22KjLatIBpW+9biONhr63DXltPvdOD2wtm\ncxhoGmYNdI8jIDF0iw1nUO84dudaWb2thPMmxwbkuCeqw5zZswvKKSgqp9xahdvtDnY44gS0Z5FR\nKWAqgsXj8VBZVU1hSQVZ+aWUVTlw6UY0Y5gvCQXJxGFJqAp88F0WLndoLxPRYRKROSwcNDP1bo28\noipyD5RRUmYNSJ+raBuHFhU90SKjTdcR+t/POXi9epvtW4hjUVdXT2l5JXmFZeQWWrE5FLyKCZM5\nHIMhNDqaoi1mBveKpsRaz/+OUsU72ELjGztOJrMZAJcOJdY6qKghzGTAEmEm0hIR5OhEa6aP7012\nTg6Y4/z3cU5ES91wTQuYnsy+hWiJruvU2OzU1rtwON3oaBhNJlSDAVMIn0VHDohl3wEb73+zh7Mm\n9DlsmYhQEcJf4bExGI2AETdQUeOitKKUcLNGeJiJ6ChLyC8I1ZWoqsLYARYyMkac1H5a6oZrLGAq\nRFtxu91U1dj9Aw2MJjOqasRgMgY7tGMWbtY4M6MHX/2cz7frcjnnlL7BDqlFoZkeT5CmaZjCwvEo\nJqrrvGTll1FQVE6FtQqPxxPs8EQbkW440V6cTqevy+1AGXlFVdS7NdDMmMPCO+xgqV+e2gujQWXJ\nN3tCdknxDt8iao2qqr77SkCtS6eq0IpBg3CTgajIcMwN3Xui45FuONGWXC4XVdV26hxu3LqCyWRG\nNRowBTuwNhIXZeaciX34fGUWyzPzmDEh9HoOOm0iakpRFEwNo1ccXqgps6FSTbjZgCUiDEtEeJAj\nFMdDuuHEyfB6vdjstdQ7XDhcHtweBZO5cyWfQ1185kC+Wp3DkmV7mJbRC00LrdZdaEUTICaTGYMp\nHJdupLzKQXZ+KUUlFVTX2DpMSQwhxLFzOp2UVVSSX1hOdkE5VbVeXLoR1RDmH/zUmXWLDeesCb0p\nLLezYmNBsMM5TJdMRE1pBgNGs+++UlWtl+yG+0rWymq5ryREB1ZXV09JmZXcA2Xkl1Tj8BhQDB37\nfs/JuPjMgagKfLx8X8hdcHe9v40jUFUVU8N8JbtTIbfQSn5hOWUVlTidHW/VQyG6El3XsdnsFJVa\nKSqrpsRaFxITS0NFUnwEk0b2YP+BKrbsKwt2OM10iXtEJ6LZfSUPFJTWoCk6YSaNKEs44QGoFyWE\nODJd17Hba7HVOqhzeNCMJjTNiMEU3jC1o2MrKy05qffX1dmpqjpY3uesjCRWbjrAB8t20ad78zti\n0dHRQZvuIonoGJlMzSfR6uU1lFfasNnsWCwRMl9JiABxOp1U22pxOD04Xd6G5GPC1AmvDb1e10m9\n32w2sWanFUWp9D+XGGtmw55yPvh2D7GRvmRUW2vnwqlDiYmJOanjnShJRCegcRKtrpqpsLkprSwj\n3KQRGWEmMtIS7PCE6FR0XcdeW0dtnYN6pwdPwxBrNAMmLdjRta/EpJ5tvs+xQ7x8tTqHPQfqmTq2\nW5vv/0RIIjpJmqahaeF4gAqbm7LKUsLNBqIjpfsuUGQJiM6ntiHxOFxNWz1GNKORTp572l2/njFE\nW0zszK5g4rBkws3BTwPBj6ATaUxKbqC4ohZVsRERZiAuJgpNk38+7eVkloBoTGJrNlVS4c4JeBKT\nJOrj8XiorvFNKnU43agGk694aBdo9QSaqiiMTOvGj5sOsG1/OePSk4IdkiSi9mI0+fpeHR7ILbRi\nMihYwo3EREfJ/aQ2djJLQDQmMXutg7KGZBbIybJdeR0lh8NBta2OeqcbtweMJjOKasIU1lmnlYaO\n9L7xrNlexOa9ZYwe1D3Y4cjw7UAwmcNAM2NzKGTnl1FYUkFVtQ2vNzTrPnU0J1N7LtjrGAX7+IGk\n6zo1NTaKSq3kFJRSWGbH6TU0TCoNkwu0ADIZNYb1S6DO4WZPXuXR39DOpEUUQIqiYAoLxwtU13mp\nqK7AZFAIM/u677riJLu2cDK15/omR/tbIo2PAynYx29vHo+Hqmob9U4PDmfT4dUdf2h1RzcirRsb\n95SycXcpvRKSgxqLJKIgUVXVP0+pzqVTVVBOmEkjIsxITHSkXB0eh5OpPdeYtNZs2suEUf0CXkC1\nMxZwdbvdVFbbqHd4cHn0hi43rVMOr+7IoiJMpKXGsievksLy4C4wGvBE5Ha7ueeeeygoKMDlcnHD\nDTdw5plnBjqMkKIoCuawcHSgpt5LRXVZQ1IyEBVpkYEO7agxicUbysjICPy9mc5QwPWw4dVeXxFR\nJcQXjRMwemB39uRVsjW7KqhxBPxn8umnnxIXF8ejjz5KVVUVs2bN6vKJqKnG5St0wObQsdZYMahg\nNmnERlswmeRGrgi+xuRTVFJBndODZpDh1R1RYnwEKd0sHCizk1di6zoTWs877zzOPfdcwFeOPVTW\ndw9FTcsMuXQoKKnBZIAoi5noqMggRye6Gv9y2XVO6pwebA4Fj2LC1PmLV3dqowd1p7DMzler8xk+\nsO0n0B6LgGeB8HDf2j82m41bbrmFW2+9NdAhdFiN5eor7R6s1aVEhBmJj5U5SqL9HJp8fC0fX/KR\n313n0DclmugIAyu3FHNddT1x0YG/mafoQagHXlhYyM0338wVV1zB7Nmzj7p9ZmYmZdUnV3Ops3K5\nHGiKjsmgEBFmkq47cdJ0Xae2rp56pwenW0czmGREZxsZlj6Q1OSEY9o2MzOTdXtt7RyRz568anYV\nejltWBTTR7Vf91xGRkaLzwe8RVRWVsZ1113H3/72N0455ZRjft/w4cPbMaoTs3Xr1pCKy+l0oOIl\nzGRg185tnDZlcsiNvsvMzGz1xxhMJxNXe1dHCMR3dmjLJ8loPmryCbXff1OhGpvucRzX9n16B2Yg\nS2xMJQWVBWzYX8+f5pxOWIDL/gQ8Eb344otUV1fzz3/+k+eeew5FUfjXv/4lV/JtoLFCuBuwORSy\n8sswGVTMJg1LuJkIWRK9XXTU6gher5fqGht1Djf1Tg8GoxlVlXs+XZFBUzlrfE+Wrshh2dpczp/S\nP7DHD+jRgHvvvZd777030IftcjRNwxzmSzxOL9RWOfCW12A2GTCbNKIjIzB2gvVaQkFWYTXVdicO\nlwezUSOrMHSrI7jdbqpq7NQ73Dhc3oaKBpJ8BMwY35PPV+XxyYp9nHdqP7QA1jyUIWtdhMFgAIMB\nHah3Q3VJNSpezCaDb7E/ma90wmrrXFTZfCv41js81NaF1v3Murp6aux1OJwe3A1zfNA0zPLXLZqI\nsZg4c1wvvl6dw+qthUwe2SNgx5ZE1EU1duN5aJyvVImqeDFoKkaDismoEWmJkOH1xyAi3EBMpMnf\nIooID+535na7qbHVUu9043B6UFQDBqMR1WhEOsDFkcw6YwBfr85h6Xd7OXVESsDuMctZRjTMVzrY\nN+PSwenQqaip9I3IM2qEmQxER0mrqSX9UmLYkVXR7HEg+Qca1LtwON14dAWzOQwUE0bpchPHITUx\nionDkvl5WxE7sisY2u/YRvidLElEokWK0nAyA7yA3aljPWDFoNFQfsgkS6Q3OJZ6cW09sq5xoIG9\nzoXT5cVgMqOqvmKi8o9anIzZU9P4eVsRS5fvlUQkQouvHt7BKg8VNjel1jJMRpUws4GYKEuX7cY7\nlnpxbTGyrr6+HlttPQ6n5+BAA80sC8eJNjW0XzyDesfy87YiDpTa6NG9/au4yCw1cUI0TcMUFg6a\nmXq3Rl5RFTkFpRQUlVNaXklNjY0gzJUOWSey7pDT6aTcWkWZtYasvFKKK+pweAygmTGHhUtrVLQL\nRVGYdUYaug4fr9gXkGN2zUtY0eaa3mNyeqG+1ktpZRlGTcFo1DBoCmEmIx6PJ4hRBs/R1h3SdR2H\nw4G9zoHT5cHp8uBF9Q0q0cJ8SV+IADl1RAqJ8RF8syaXOecMISayfW82SiIS7aKxijj4RuZ5PFBn\n91BaWU9WXikGg4LJoNItPqZLDIA49D7SpGHdKSuvxOHy4PHquN1eVIPRN7dL0TCYfPeVVm4qYPPO\nGqo8BUwa0aNNKzYI0RpNU5l5Wn9e/mQrX63O5tczBrfr8SQRiYBRVRWD0eS/uncDOQcqiDBrxEZH\nYDJ1zppmHo+HunoHo9NiGNonEqfLQ3mN03dPTTOgaaC1MLf4py0H+H59PnX1Tqzr8wGYPCo41ZFF\ncJSVlgTkOHV1dqqqYps9N2FILG9+pfHZD/uZPiYRo+HE/21GR0cfsStZEpEIKnNYOB6gqKIO3WsD\nXUfTFFRVwaCpqAqEmY1YIsJDuuXk8Xiora3D4XLj8ei4PV48Xh2Px4uOisFoRNN8iedYBxcUlNqO\n+Fh0fl5vYCZHm80m1uy0oiiVzZ7vn2JhW3Y1r3y2nbSeUSe079paOxdOHXrEtY4kEYmQ4Cs31LxZ\n4AE8OjjrdMoqrWiqjtGgoam+RKVpKkZNxWw2YTQa/Vdc7V2E1Ov1UltXT129E6fLg8vtxasrGE0m\nVLXhn5RGqy2dY9WzeyR78yqbPRZdS2JScFvAY9PD2J5dzc48GyMH9Wi3ATKSiETIazp0XMfXpYfX\n95/d4cVTbcPr9aAooKkKP20+wPL1eQBkbgNrZTVTRvfE69VRG5KYoiig6+j4BgqUV9ooLrU27ENF\nUX2ve7w6Xq/ekGx0PB4dFAXNcLCFY2ynhtqkEb4SK5t35jJySKr/sRCBEm0x0T81hn35VRSU2klN\nbJ+LIUlEokNTVRX1kMrtB6wuVMPBxb0KKpzoqglF9SUyD/j+0EgBXTXjxgg6uDyNGzXZxAAaJ9fC\nOV6qqjB5VE9iNCvDh8u9IREcowd2Z19+FVv2lrVbIup8d4bFMWkckbVk2S5WbirA6+08c34O7cKS\nLi0hTlxSfAQJMWFkF1ZRW98+96ykRdRFNY7IAvz3ITrLiKzGLqyCUhs9u0dKl5YQJ0FRFIb2i+eH\njQfYlWNlzODENj+GJKIuqjOPyGrs0hJCtI1BveNYtbmQHdkVjB7Uvc0HLUjXXBcl3VdCiGMVZjLQ\nv2cM1hoHpda6Nt+/tIi6KOm+EkIcjwE9Y9iTV0lWYTWJ8RFtum9JRF1UW3Rfeb06P2050CyZSQka\nITqnXklRqIpCdmE1E4clt+m+JRGJE9aZBzy0JUnYnYuu6/45ZR6Pjtvr9VXS8PjmnR38s7d5lQ2v\nzoh+J1adIBSYjBo9u1vIK7Fhq3USGdF26/1KIhInrDMPeGhLkrBb5juZN5ysG8ohNZZFcnsOPnYf\n8v/Gk3zTbfzvafhzYZGNveV7D9mmeeml5vtsfHwwgXg83kPiOnjcE/X8HVPa8BsMvD4p0eSV2Mgt\nrmnTRfMkEYkTJiVojk17Jmxd91V+cDc9OXtbvjpvetV+8MR9yLYN//cemgCaXeH7/my1VvLDrk0H\nT+CHJYVD4zjYMnB7vLT7clV77O18gK6nRzcLACXWOob2a7v9SiISJ6wzDXjw6jout6fZVXVjt4un\ntavzI5xkm564S611VNY4GsoJQV6xjde/3H7IlX2TE723ecKoratH+2Fly3EFfSKyI8jHP35aQ0Fd\nTVXQNF/NQkND7UJNU9BUtWGbhucat294renzh752tG06uviYcFRVodRa26b7lUQkTtihAx503XcS\nbtbdccjVdkmVi6wDVUe8Oncf1u3StJ/d2/oJvNVulxZaC4d0xeg68EVgSu7vyatkT17l0TdsJrQW\nFFQVBVXRMRi1gyfehhOxockJWNOav3bw/wpGTW3hRN/Se1o4ufu3aXrCP7jNvr17GJo+xP/+xv2r\nihLUlW11T8dL3E1pqkKMxUSVzdmm+5VEFGIOvRHa9AR6pG6XQ0/EOXl1VLjyW+0bb6mfvbUTuduj\n423SX96s1dBku2MuE/RDRft+iR1Iy1fOarOrdoejnqhIyyEnYqXlBNDC801PxIaG5xqX2Tg0aTTf\nvknLQFWbbaMqClu3bmX48OHB/gpbVBqhERvVvquKdlXRFhPWGgcOlwdzG1X87bSJ6ODJ/OCV9PF0\nuxzW797C1XlxcQ3bi3YfdnV+xBE0rRyzaVxt1tmyqbqt9hTS1JZOlg0na0NDt4va5Aq6xu7EWuNA\nUUBBISkhgjDNSbeE+IaT+OEn8MOu9pucwFVVYXeulYqqOhLjIxg10LeIWPPWwSHJ4TiuzkP5hC+6\nnjCTL204nAFMRJs3b2bkyJFtcrCT8cTbma1cvbfU7eJrVQTE/rbtKw0WBQ7v7jjS1XWTk/HhfeSt\nX52XFheTmtqj2dX54d0uzU/ghyWAQ+I43q6WJct2NRtk0a9HNEOTXQwfnn5C393KTQXsybUCUF5V\nT2JchIyKE52W0egryONyt1138VET0eOPP47VamXmzJnMnDmT7t27t9nBj8fu3OPtUw+Oxqtz/0m2\nlW6XQ0/ERzuBH37yb6m//OB+cnNyGJjWv/UT+CHdLoGa17J1aw3Dh6cG5FitaXm0n/WE9yfD2EVX\n0tgF35bnjKMmotdff52CggI++eQTrrvuOlJSUpg9ezbTp09vWFUzMOKjw5r1jbd6cj7sKrvxz419\n5K1fybfU7XKk0TJ79uxm+LB0/2vqCVydtxevvZABqbFH37ALamm03/btJ56IZBi76Eoa51FpatuV\nKj2me0Q9e/Zk1qxZGAwG3n33XV5//XWefPJJ5s2bx1lnndVmwRzJwpsmB+Q4xyPCrBIRFsCV0kSr\nDq1eMHFYCj9vK2yxmkFbV+fuTMPYWyKVIURT9jrfiDlLeNsNMTjqnt5//30++eQTSktLmTVrFm+/\n/TbJyckUFxcze/bsgCUiIY7k0OoFe/Mq/V1k7V3NIFSXnWirBCKVIURTVTYnEWGGwLaI1q5dyx//\n+PR0YIAAABonSURBVEcmTpzY7PmkpCTuv//+NgtEiJNx6H2ZvJIa1CbdpF3xvk1bJRC5ByYa1Tnc\n2Opc9Elu25p5R01Ejz76aKuvnXPOOW0ajOicml6Z46xj6FC9zbt2Dr1P0ysxqtkJsyvet2mrBCL3\nwIKnrDQwk6xbEhZuRqH5v9OcYl/ZpFiLht12bNNDamuPXmqp084jEqGj6ZV5XX09PbccaPOunUPv\n07R0j6iraasE0tnvgYUyr9cVlOPW1dZy+ujBxMTENHv+X5/tAmD2Gf1IS41p6a0tio6OPuLrkohE\nuwtE105L92m6+n2MtkogoXoPrCtITArO9263VRMTE9MsEbk9XjbsKScm0sTo9F5ogRy+LcTJClbX\njsfjQVEU1Da8qdqRSAIRbWnt9mKqbE4uPK1/myYhkEQkAqDplTnO6mO+Mm+8t5RfUkNKQjgT0pNQ\nFN1f/cGg+Sbiaprq78n26joKoKoqBoNvaL3b7fbXwvPqvv16vV5fKSW9oRyUy4HHVQ/4KmR7dR1d\nB00zYAjgfDkhQtUXK/cDMGNC7zbftyQi0e6aXplv3br1sIEKbrcbj9uNgm8QQ+Ok4p82F7B8XS4K\nsD9PIy4yjHMnD2iXGEsLI+ndo9thzzscDmrrfAUevV7fUhFeL6CoGE2mkJnALER72ptXyaY9ZYwa\n2I1+PY793tCxkkQk2p2u67jdbrweDy5HHR5XfUPlZwWTUcNkMRMWFn1YF5q1Nh9zWIT/cV5Z4Ov6\nmc1mzObDqzi7XC7q6h04ne4mVcm9eL2gA4qiYjAau2y3oOhc3vp6JwAXTxvYLvuXRCTalK9148Ko\nKRgNvtJKRqOG2RSO0WjEWhLdYsujJX2To9m+v7zZ41BhNBpbLXHl9XrxeDzU1ztwON24PV6cbi8e\nLxhNZmlFiQ5l674y1u0oZviABEYPap9ao5KIxAnzeDy4XU40FUxGDaNBJc5iJjw8pk1OttPH+/qi\ns4uq6Zsc7X8c6lRVRVVVjEYjTaf9eTweamy1OF0uXO6D1eMVVcNgNEqCEiHH7fHy4tItAFz9y6Ht\n9huVRCSOStd1XC4neD0Y/r+9e4+N6rrzAP69837ZY2Mb84xJFHAe7howFJYENSUxDyVRIwoEgjGb\nokRVVmpISAkJEjSVqLf5A0WVIKVNE5FELckSIqrd7abQsGpKtyGZDU4phWBqXjb4EYzted07d+bs\nH+MZbPOywTPn3pnv5y+/8P1hj+c759xzfsdmgd2WDB231wG3uzBjD06LRUHtrIqMfG8ZrFYrivxX\n7kiPxWIIhaOI6cnRU0xPHmditTtgtY7MeS9EN+PD/2nCqfM9WDC7ApUVozJ2HQYRDZCaWrP1dSO3\n2yxwOmzwjPLDZrv8cEkkBP7w2ZkBoxU2wrw5drsdRf6B03xCCIRCYYQiGuJaFDFNg93hkFQh5aML\nF8PY9fvjKCpw4l8eviej12IQ5bF4PI54TIPNqsDeN7Xm8bngct14au0Pn53Bf/25GQDS93FyafQi\nm6Io8Pm88Pm8KC8twJgSL3pDEUQ1HXqc95oos4QQePM/voKmJ7D2sW/A58nsiyAGUR4RQiCmabAo\nCbgcNvh9Dni9Nze1dupCz3Xfp5HlcjnhciVX7yXvNYUQVZP3mvS+4+VtnMqjEXKkuRtHT13CzHvK\ncX915ls6MYhymBACmqrCbgWcDiscDht8g6bYbpaRV7TJkM2pyuS9poE/70QigUhURaRvz1NMT0Ao\nFtjt3OtEw9PaGcT/nehCcYEDP1g2LSuPHwZRjtF1HQk9BsSj8DoExpYUZ+RVsllXtGWK7KlKi8UC\nr8cNr8ed/piu6wiGwoiqMUS1OCxWO6wj8CKEcldE1fH7v5wGBPCvi+9BUcGVe+gygY9Kk0uOeqKw\n2xQ47VYU+13weIpwsb0AxUWZG6Xk2oq2W9V8vgc9IQ1qLA6n3Yrm8/KnKm0224CRUygcQSgcRVSL\nQ08ATqdLYnVkNEII7D90BqGojulTinFXRVHWrs0gMpnU3h17X1cCp9OGwtIS7uCXLByJoTuYPEI5\nqsYRjshp3389/UdMsVgM3b0hRNU4ND0Bh9PFKbw8FzjWjjNtvagYU4Bv3D7ybXyuh0FkcJqqQkEi\n2QrHZoHL44DHXcDgMRiPO7n4IzUi8riN/adlt9tROir5ijcej6OnN4RwVIMaS8DucPLxlWda2oM4\n9LcL8LnteHDmbUjEsttOy9h/LXkmHo9D11TYbBY4bMnWOGX+Aji4f8Twbh/rx9+bLw543yysViuK\niwpRjOT0TE9vCJGohoga56baPBCOxvD7Q6ehKMD8WRVwO20IZXlAzyCSKN2Xre/+zq0spx4J3KR6\n83Jl8YaiKPAX+uAv7NtUG46ku4/HdMEl4jkmIQT2HTqDcFTHnG+MxdhSr5Q6GERZlEgkENNUOO0W\nOB3WEe3LNhJkr/wys1xcvKEoCnxeD3zeZAd0IQTCkWh6ibiuRqCpKhxX6U5O5vD539twrj2ISWML\nM9bQdCgYRBmW6l7gdtlQ4LGjYHSpYYJnMG5SpetRFGXAgocLZYUYP7oAoXAUqqYjqsUBxcpWRCZx\ntq0Xnx1tQ4HHjgdnTpT6vJT1IBJC4Ec/+hGOHz8Oh8OBLVu2YOLEidkuI2NSm0htVqSn23w+c9wv\n4CZVGi6HwzHgHqaqqugNRhDRdMR0wdV4BhWKxLDv0BlYFAXzZ1XA5ZA7Jsn61ffv3w9N07Br1y40\nNjaioaEB27dvz3YZI0rTVFiQgNNuhctpQ0GGNpHeSP97PFBDmDZNDOseT67c5yB5+h8kmGpFpGo6\nND15j8lqs49IZw+6ecn7QqcRUXXcXz0OY0rk3BfqL+uPiEAggLlz5wIAqqurceTIkWyXcMv0WAwi\noQNxFW5b3DAr2/rf4wmFw5j02Zlh3bfIxfscJM/gVkRCCEQiUYSjKmJ6AlosjnhCgd3h4HLxLAr8\nvQ0tHSHcPq4Q/3Tn0A6pzLSsB1EwGERBweUzWWw2GxKJhKEfiKnuBU67BU67FaOK3XC7Xfi6zYdR\nxcaZduM9HjIyRVHg8bjh6deGKB6PIxSOpBu4anoCVhtX5qV0drRf9/MutxMKhj7r0XYxis+OtsHr\nsmL2XUUIh3qv+nXhcGhYdd6qrAeRz+dDKHT5PznUEMr2yCmu6xCJWPI8HrsVHvfVN/kFAoGs1nVd\nagihcL+NaGqXserrY8SaAOPWBRi3tpGuK7UyL6rFoekCVtvNj5aMONty792Th/X1Z8+euubn1GgE\nM+8qhc/nG9L3imgJ/OmvyeB5tMaN8b7rvFB1ACdOnBjx+3s1NTVX/XjWg2j69Ok4cOAAFi5ciMOH\nD2PKlClD+ndVVVUZrizZxcBmEXA6rPB5XANeuV1NIBC45g9WhmnTBCal7xF1Yc2SuYbbB2S0n1mK\nUesCjFtbpusSQqA3GEI4oiGixWGzD73jw5EjR7LynDFcIq4O6+unTf/mNT8XCvbg/m9WwO+/8ayM\nEAL/9vZn6I30YOXCu7C8tnJYdWRa1oOotrYWBw8exPLlywEADQ0N2S4hLZFIQNdUOOwWuJw2lBeP\nzBEJsvS/xxMIBAwXQkTDoSgKCgt8KCxIPpF29/QiGFahCwV2u/x7smbyxy9a8Ocvz+PeO0qw9MGh\nvfjPpqw/6yqKgldeeSXbl02LaRoUxOF02FDoscNn4H09RJSkKAqK/IUo8gPRqIqeYDjd7YFLxK+v\nqzeKHR9+CafDimcfnwarAV+gmvfl/xD1PybB7bChtNSbXl5KRObT/7TaRCKBnt4gwtFYumErQ+ky\nIQRe/+BL9IZjeOqxKmktfG4kJ4MoHo8jrmtwOaxwO20oKBnFVThEOchisaRHSqlQCkU0xDQVQoi8\nD6WDX7bif/+anJJ75L47ZJdzTTkTRKlNpS6HDf4CJ7weec1DiSj7+odS61kPfE6BUESDpou8HClF\nVR2/2nsEdpsFP1g21dD3jE0bRKkpN4fNApfDirIyY2wqJSL5rhwphRDVYn2baJEXwfTBgSZ0dkex\n9MHJGFc2tCXespgqiHRdR0KPweVMTrnxZFIiupFkKF3eRN9/E62qxRGLi5w7DLD9Yhh7DpzAqEKn\nIVfJDWaaILIrMRT5XfB6sneOOhHlHqvVml4WDiRHTKFQGBE1Bi2W7O5g9nOXdv7nUWh6Aqsfvhdu\np/Gf5o1fYZ/RpcWySyCiHGSxWFBQ4EOq81j/AwHDUR0Wk7UcOn2+B3883II7JxbhgekTZJczJKYJ\nIiKibBh8IGAwGEIwrCKixmEzwRTe+/u/AgCsmF9p6AUK/TGIiIiuw+fzwufz9nV3CCIUUaH1baQ1\nmrNtvfiksQV3jPdj5t3lsssZMgZRnup/dlHq7CGzvHoikiHZ3aEARf7kgoeu7l5E1Tg0PWGYxQ7/\n/oevIASwvLbSVKsCGUR5qv/ZRalTWXkWEdHQWK1WlI5KLpwSQiAYDCGixtJth2QsdrjUq+KTwy2Y\nWO7DrHvHZPXat4pBlKd4dhHRyFAU5crFDqEwQhENUU2HgBX2LOxx3HfoNPS4wKJ/vt10sxsMojw1\naUxheiSUep+Gj1OcNJiiKOn7SgCgqip6ghFEVB0iHs/INeMJgf/+y2k4HVbMmzExI9fIJAZRnnpw\n5m0AMOAJlIaPU5x0I06nE2XOy01aM+GL4+1ovxjG/FkV8LrtGblGJjGI8lT/s4vo5nGKk4YjUwsa\n/vjFOQBA7SxzvqCUv8yDyMQGT2lyipOyTYvF8enfLmB0sRuVt5lz4z9HRES3gFOcJNsXx9sRjupY\nOHuSqZZs98cgIroFnOIk2f7U2AoAuH/qOMmV3DxOzRERmVQiIRA41o5Svwt3TjBvQ2iOiIiIDCoU\nvPbil3A4hObzvegNa5j9zdtMOy0HMIiIiAzLid5rfs7mjKOx6SIAoOYu8/SVuxoGERGRQdl846/5\nOT0aQePR87AoQPXk0ixWNfJ4j4iIyIQSCYHTF4KYNM4PnyfzLYQyiUFERGRCXb0q9LjA5InmXaSQ\nwiAiIjKhzm4VABhEREQkR+elKACYetl2CoOIiMiEOrujsFkV3JYDbaUYREREJhOPJ3CxR8WEMg/s\nNvM/jZv/f0BElGc6u6NICKBijE92KSOCQUREZDIdXWEAQEW5V3IlI4MbWolyFE+PzV3tXREAwKQc\nGRExiIhyFE+PzV3tXWHYrArGlLhllzIiODVHlKN4emxuiukJXOyJoqTQCWuOjHAZREQ5iqfH5qav\nuyMQAigtcskuZcRwao4oR/H02NzU3rdQodTPICIig+PpsbkptVAhl0ZEnJojIjKR9q4w7DYL/F67\n7FJGDIOIiMgktFgcXT0qyorcpj6RdTAGERGRSXReSk7LjS72SK5kZDGIiIhMInV/aPSo3Ng/lMIg\nIiIyidSKuTKOiIiISIb2rjCcdiv8XnMfDT4Yg4iIyARULY7uoIay4txaqABwHxHlqcENQYusQnZJ\nRNfVcSk5LZdrCxUABhHlqcENQe8Zb8HMGZKLIhokGo2k325tT/YKLPJZEY1GoKlRWWWNOAYR5aXB\nDUDbL8UkVUJ0bbPvKUm/ffhEsoP6/BljUeJ3AgDKSkuu+u/MhkFEeWnSmML00QgAMLood3apU+4Y\nUz46/fa5jkYUeh24Z/IE3iMiygWDG4IWWTskV0R0bT0hDW0Xw5h+1+icCyGAQUR5anBD0ECgU2I1\nRNfXdPYSAGDyhCLJlWQGl28TERnciXNdAIA7JzKIiIhIgvSIiEFEREQyNJ29hOICJ0YV5s4ZRP0x\niIiIDKyrJ4rO7igmTyzOyYUKAIOIiMjQTrZ0AwDunOCXXEnmMIiIiAzsH31BdMf43A2irC/fDgaD\neOGFFxAKhRCLxbBhwwZMnTo122UQEZnCP1qTQXQ7g2jkvPXWW5gzZw7q6+vR3NyMdevWYc+ePdku\ng4jIFE61dsPrtqOsKLcOw+sv60H05JNPwuFInqWh6zqcTme2SyAiMoWIqqO1M4SqO0pzdqECAChC\niIz1v9+9ezd27tw54GMNDQ2oqqpCR0cHnn76aWzcuBEzZly/7XEgEMhUiUREWVNTUzPkrw0EAjjb\noeJX+zowq9KHRTXm30N0rf9/RkdES5YswZIlS674+PHjx/HCCy/gxRdfvGEIpQznF5gtgUDAkHUB\nxq2NdQ2fUWszal2AsWsbDmfhWAAdmD11MmpqbpNdTsZkfWquqakJa9euxWuvvYbKyspsX56IyDT+\n0Zo8riSXV8wBEoJo69at0DQNW7ZsgRAChYWF2LZtW7bLICIyvNPne2CxKJhY7pNdSkZlPYi2b9+e\n7UsSEZnSufYgxpZ4YLdZZZeSUdzQSkRkUL1hDePKcns0BDCIiIgMbTyDiIiIZOKIiIiIpJrAICIi\nIpnGlnpll5BxDCIiIoOyWBQU5+hheP0xiIiIDGpUoQtWS+72mEthEBERGVSpP/dHQwCDiIjIsEr8\nuXv0Q38MIiIigyop4oiIiIgkKsmDhQoAg4iIyLAKPA7ZJWQFg4iIyKB8DCIiIpKpwGOXXUJWMIiI\niAyKU3NERCSVjyMiIiKSyetmEBERkUROe26fzJrCICIiMihFyf0+cwCDiIiIJGMQERGRVAwiIiKS\nikFERERSMYiIiEgqBhEREUnFICIiIqkYREREJBWDiIiIpGIQERGRVAwiIiKSikFERERSMYiIiEgq\nBhEREUnFICIiIqkYREREJBWDiIiIpGIQERGRVAwiIiKSikFERERSMYiIiEgqBhEREUnFICIiIqkY\nREREJBWDiIiIpGIQERGRVAwiIiKSikFERERSMYiIiEgqBhEREUnFICIiIqkYREREJBWDiIiIpGIQ\nERGRVAwiIiKSSloQnTx5EjNmzICmabJKICIiA5ASRMFgEK+++iqcTqeMyxMRkYFICaJNmzbh+eef\nh8vlknF5IiIyEFsmv/nu3buxc+fOAR8bN24cHn74YVRWVkIIkcnLExGRCSgiy2mwYMEClJeXQwiB\nxsZGVFdX45133rnuvwkEAlmqjogos2pqaob0dYFAYMhfa3ZZD6L+5s2bh48++gh2u11WCUREJJnU\n5duKonB6jogoz0kdEREREXFDKxERScUgIiIiqRhEREQkFYOIiIikMnwQRSIRPPPMM6irq8P3vvc9\ntLe3yy4pLRgM4vvf/z5WrVqF5cuX4/Dhw7JLGmDfvn1Yt26d7DIAAEIIbN68GcuXL0d9fT3Onj0r\nu6QBGhsbsWrVKtllpOm6jvXr12PlypVYtmwZPv74Y9klpSUSCbz88stYsWIFVq5ciaamJtklDfD1\n11/jgQceQHNzs+xSBli8eDHq6+tRX1+Pl19+WXY5hpLRzgoj4f3330dVVRWeeeYZfPjhh/jlL3+J\njRs3yi4LAPDWW29hzpw5qK+vR3NzM9atW4c9e/bILgsAsGXLFhw8eBB333237FIAAPv374emadi1\naxcaGxvR0NCA7du3yy4LAPDGG29g79698Hq9sktJ++1vf4vi4mK8+uqr6O7uxmOPPYZ58+bJLgsA\n8PHHH0NRFPzmN7/BoUOHsHXrVsP8LnVdx+bNmw3XPizV3Pntt9+WXIkxGT6IVq9end5r1NraCr/f\nL7miy5588kk4HA4AyT8AIzVxnT59Ompra/Hee+/JLgVAcpf43LlzAQDV1dU4cuSI5Iouq6iowLZt\n27B+/XrZpaQtWrQICxcuBJAcgdhsxvlTfeihh9Kh2NLSYqi/yZ/+9KdYsWIFduzYIbuUAY4dO4Zw\nOIw1a9YgHo/jueeeQ3V1teyyDMM4j25cvTddQ0MDqqqqsHr1apw4cQJvvvmm4Wrr6OjA+vXrpYzU\nrlXXokWLcOjQoazXcy3BYBAFBQXp9202GxKJBCwW+bPDtbW1aGlpkV3GAG63G0Dy5/bss8/iueee\nk1zRQBaLBRs2bMD+/fvxs5/9THY5AIA9e/agpKQE9913H37+85/LLmcAl8uFNWvWYOnSpTh16hSe\neuopfPTRR4Z4/BuCMJGTJ0+Khx56SHYZAxw7dkw88sgj4pNPPpFdyhU+/fRT8fzzz8suQwghREND\ng/jd736Xfv9b3/qWvGKu4ty5c+Lxxx+XXcYAra2tYvHixWLPnj2yS7mmzs5O8e1vf1tEIhHZpYiV\nK1eKuro6UVdXJ2bMmCGWLl0qOjs7ZZclhBBCVVURjUbT7y9ZskRcuHBBYkXGYqgR0dX84he/QHl5\nOb7zne/A4/HAarXKLimtqakJa9euxWuvvYbKykrZ5Rja9OnTceDAASxcuBCHDx/GlClTZJd0BWGg\nJiOdnZ1Ys2YNNm3ahNmzZ8suZ4C9e/eira0NTz/9NJxOJywWiyFe2b/77rvpt1etWoUf//jHKCkp\nkVjRZR988AG++uorbN68GW1tbQiFQigrK5NdlmEYPoi++93v4sUXX8Tu3bshhEBDQ4PsktK2bt0K\nTdOwZcsWCCFQWFiIbdu2yS7LkGpra3Hw4EEsX74cAAz1e0xRFEV2CWk7duxAT08Ptm/fjm3btkFR\nFLzxxhvpe5IyzZ8/Hy+99BLq6uqg6zo2btxoiLr6M9LvEgCWLFmCl156CU888QQsFgt+8pOfGCK8\njYK95oiISCpGMhERScUgIiIiqRhEREQkFYOIiIikYhAREZFUDCIiIpKKQURERFIxiIiISCoGEeW1\nd955B3V1dQCAzz//HAsWLEA4HJZcFVF+YWcFynurV6/G/Pnz8e6776KhoQFTp06VXRJRXmEQUd47\nd+4cHn30UTzxxBP44Q9/KLscorzDqTnKey0tLfD5fDh69KjsUojyEoOI8looFMKmTZvw+uuvw+Vy\n4de//rXskojyDqfmKK+98sorcDqd2LBhA1pbW7Fs2TK89957GD9+vOzSiPIGg4iIiKTi1BwREUnF\nICIiIqkYREREJBWDiIiIpGIQERGRVAwiIiKSikFERERS/T+jPiV8naeDHwAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x2187bc18>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# But the seaborn plot call also produces the ggplot ones\n", | |
"for name in [\"in\", \"out\", \"null\" ]:\n", | |
" data = df[df[\"b\"] == name]\n", | |
" g = sns.JointGrid(\"x\", \"y\", data)\n", | |
" g.plot(sns.regplot, sns.distplot, annot_func=stats.pearsonr)\n", | |
" n = len(data)\n", | |
" t = u\"(a) Headline\\n(change 'during' - 'before'; N = %s)\" % (n)\n", | |
" g.fig.suptitle(t)\n", | |
" " | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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ExqaPKbsnLygoCHfu3MGuXbvUKt/YPXyKyo8bNw45OTkqX5NIJKq3FpOpqSkk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\nEGJw/g8mCcQOfS5YgAAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x204b2908>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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xAoGBgViyZAmuXr2qUT3ff/89IiMjVT6eMYbY2FgsWbIES5cuRWlpqUbtFhYW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\n9A4lJkKI3qHERAjRO5SYCCF65/8BGgMWu54qwVMAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x204b4438>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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yRHGUmNRI0fvrlHnqr7W1NVxdXREfH9/qtunp6bC3twcADB8+HJmZmTK3Y25u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\nIoRonf8HolbGrGlSL+4AAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x208549b0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# And now the ggplot ones come out\n", | |
"for name in [\"in\", \"out\", \"null\" ]:\n", | |
" data = df[df[\"b\"] == name]\n", | |
" gg = ggplot(data, aes(x=\"x\", y=\"y\"))\n", | |
" gg = gg + geom_point(alpha=0.8)\n", | |
" max_x, min_x = data[\"x\"].max(), data[\"x\"].min()\n", | |
" max_y, min_y = data[\"y\"].max(), data[\"y\"].min()\n", | |
" mean_y, mean_x = data[\"y\"].mean(), data[\"x\"].mean()\n", | |
" # chross at (0,0)\n", | |
" cross_df = pd.DataFrame({\"xx\":[0,0,0], \"xy\":[min_y-1,1,max_y+1],\"yx\":[max_x+1,1,min_x-1], \"yy\":[0,0,0]})\n", | |
" gg = gg + geom_line(cross_df, aes(x=\"xx\", y=\"xy\"), color=\"grey\")\n", | |
" gg = gg + geom_line(cross_df, aes(x=\"yx\", y=\"yy\"), color=\"grey\")\n", | |
" # the mean cross\n", | |
" mean_df = pd.DataFrame({\"x\":[mean_x], \"y\":[mean_y], \"label\":[\"mean\"]})\n", | |
" mean_color = \"red\"\n", | |
" gg = gg + geom_point(mean_df, aes(x=\"x\", y=\"y\"), color=mean_color, size=150, shape=\"x\")\n", | |
" gg = gg + labs(x=u\"Quality\\n(delta mean)\", \n", | |
" y=u\"Quantity\\n(delta mean)\")\n", | |
" t = \"Lots of words in two lines\\more words to fill the line...\"\n", | |
" gg = gg + ggtitle(t) \n", | |
" gg = gg + theme_matplotlib(rc={\"figure.figsize\":\"2, 2\"}, matplotlib_defaults=None)\n", | |
" gg.draw()\n", | |
" ax = plt.gca()\n", | |
" ax.get_xaxis().get_label().set_visible(False) \n", | |
" ax.get_yaxis().get_label().set_visible(False)\n", | |
" ax.spines[\"top\"].set_visible(False) \n", | |
" ax.spines[\"bottom\"].set_visible(False) \n", | |
" ax.spines[\"right\"].set_visible(False) \n", | |
" ax.spines[\"left\"].set_visible(False) \n", | |
" ax.get_xaxis().tick_bottom() \n", | |
" ax.get_yaxis().tick_left() \n", | |
" ax.yaxis.grid(True, which='major')\n", | |
" ax.xaxis.grid(True, which='major')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
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UGxuLJUuWYPbs2VixYgUKCwuRkJCAxYsXo1u3bliyZAlXtrITU6QhPDwcAoEA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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x226f3d68>" | |
] | |
}, | |
"execution_count": 7, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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UGxuLJUuWYPbs2VixYgUKCwuRkJCAxYsXo1u3bliyZAlXtrITU6QhPDwcAoEA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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x226f3d68>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# And the \"return a fig\" call produces two plots\n", | |
"name = \"in\"\n", | |
"data = df[df[\"b\"] == name]\n", | |
"gg = ggplot(data, aes(x=\"x\", y=\"y\"))\n", | |
"gg = gg + geom_point(alpha=0.8)\n", | |
"max_x, min_x = data[\"x\"].max(), data[\"x\"].min()\n", | |
"max_y, min_y = data[\"y\"].max(), data[\"y\"].min()\n", | |
"mean_y, mean_x = data[\"y\"].mean(), data[\"x\"].mean()\n", | |
"# chross at (0,0)\n", | |
"cross_df = pd.DataFrame({\"xx\":[0,0,0], \"xy\":[min_y-1,1,max_y+1],\"yx\":[max_x+1,1,min_x-1], \"yy\":[0,0,0]})\n", | |
"gg = gg + geom_line(cross_df, aes(x=\"xx\", y=\"xy\"), color=\"grey\")\n", | |
"gg = gg + geom_line(cross_df, aes(x=\"yx\", y=\"yy\"), color=\"grey\")\n", | |
"# the mean cross\n", | |
"mean_df = pd.DataFrame({\"x\":[mean_x], \"y\":[mean_y], \"label\":[\"mean\"]})\n", | |
"mean_color = \"red\"\n", | |
"gg = gg + geom_point(mean_df, aes(x=\"x\", y=\"y\"), color=mean_color, size=150, shape=\"x\")\n", | |
"gg = gg + labs(x=u\"Quality\\n(delta mean)\", \n", | |
" y=u\"Quantity\\n(delta mean)\")\n", | |
"t = \"Lots of words in two lines\\more words to fill the line...\"\n", | |
"gg = gg + ggtitle(t) \n", | |
"gg = gg + theme_matplotlib(rc={\"figure.figsize\":\"2, 2\"}, matplotlib_defaults=None)\n", | |
"\n", | |
"fig = gg.draw()\n", | |
"fig" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "social capital", | |
"language": "python", | |
"name": "sc" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 2 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython2", | |
"version": "2.7.11" | |
}, | |
"toc": { | |
"toc-wrapper_display": "block", | |
"toc_cell": false, | |
"toc_number_sections": true, | |
"toc_threshold": 4 | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 0 | |
} |
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