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@stagira13
Created April 18, 2017 11:34
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DPC入院料のグラフ化テスト
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import random\n",
"from numpy.random import randn\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ダミーデータの作成です"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ten = randn(2000) * 300 + 2000\n",
"kalist = ['内科', '整形外科', '神経内科', '産婦人科', '耳鼻咽喉科', '泌尿器科', '外科', '皮膚科', '眼科','脳神経外科', '小児科']\n",
"months = [\"1610\",\"1611\",\"1612\",\"1701\",\"1702\",\"1703\"]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.DataFrame({'dpc_ten':ten})"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['ka'] = [random.choice(kalist) for i in range(len(df))]\n",
"df['month'] = [random.choice(months) for i in range(len(df))]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>dpc_ten</th>\n",
" <th>ka</th>\n",
" <th>month</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2062.200387</td>\n",
" <td>整形外科</td>\n",
" <td>1610</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2105.025425</td>\n",
" <td>耳鼻咽喉科</td>\n",
" <td>1701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1782.637066</td>\n",
" <td>神経内科</td>\n",
" <td>1612</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1659.748830</td>\n",
" <td>外科</td>\n",
" <td>1703</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1972.151423</td>\n",
" <td>眼科</td>\n",
" <td>1702</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dpc_ten ka month\n",
"0 2062.200387 整形外科 1610\n",
"1 2105.025425 耳鼻咽喉科 1701\n",
"2 1782.637066 神経内科 1612\n",
"3 1659.748830 外科 1703\n",
"4 1972.151423 眼科 1702"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# データを集約します。月別のみの集約と、月別・科別の集約を作ってみましょう"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_agg = df.groupby('month')['dpc_ten'].agg(['mean','std','median'])\n",
"df_agg2 = df.groupby(['month','ka'])['dpc_ten'].agg(['mean','std','median'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## まずは時系列の折れ線グラフにしてみます"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x119fc1cc0>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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i6kwkFJYAVxpj2sDzwOnW2k3GmM8DKwEPON9a2zbGXA1cb4xZAbSAU8b7JmvWbJxA03Y8\n/f196ouS+qJDfdGhvuiYinD08jyfgqZMuVz/yAVt8B3qiw71RYf6oqO/v8+b7Gvoy2siIuIoFERE\nxFEoiIiIM5ETzSIis+bWB57i4V+96JaDwCNNJ3du9LX7LeRdR+29xeeXL7+LBx/8Aa1Wi7Vr13Li\niSezYsX3eeaZ/2Lp0g8QxzG33HIzQRCwaNHBnHHGUtaseZHPfOaTxHHM2rV/YPHiJRxxxJGcdtq7\nec1rDuGpp57E930uu+yf6enpnVT7p5JCQURkHAYHG1x++ZXcf/+93HrrV7nmmq/w2GOPcsstN/Hc\nc7/l2mtvoFqtcsklF/LIIz8B4N3v/hsOPvgQVq16nC9/+d844ogjGRwc4Jhj3srZZ5/LxRdfwA9/\n+BBHH33MLH+6DoWCiGxX3nXU3sNm9TP120f77msAmDevjz322NM9bjQarFu3jnPP/QB5ntNoNHju\nud+yaNHBXH/9l7jrrjsBSJLOhSH22WdfABYu3I12uzXtbd8WCgURkXHwvC39tqfHwoW7ccUVXyAI\nApYvv4t99jFce+3VnHDCOzn88Nfx7W9/i+XL7xrHa80+hYKIyCREUchJJ72HM89cTJpm7L77Kznq\nqGN405vezFVXXcENN3yF/v6FbNgwdBm4TiDMxXDQl9fmOH0xp0N90aG+6FBfdOjLayIiMqUUCiIi\n4igURETEUSiIiIijUBAREUehICIijkJBRGQCbr/91s3K2u02J554wiy0ZuooFEREJuDf//1Lm5UV\n3/uae19I2xb6RrOIbFe+8dRdPPbiz91y4Huk2eS+hPuahQfyzr2P3+Lzq1c/y6WXfoIwDMnznEMP\nfS0bNmzg8ss/xZIl7+fiiz/Oxo0b+aM/+uNJtWMuUCiIiIzh4Yd/zP77H8D73vd+fvazx9hll125\n447bOeecj/DVr97IXnvtzeLFS3jiiVX89KePznZzJ0WhICLblXfuffywWf1MXObi+OPfxk03Xc85\n55xFX988Tj/9fe651at/w+tf/38A2H//AwjDYFrbMt10TkFEZAwrVnyfgw56DZ/73L/wxjcezY03\nXg8Uh6z23HMvVq16HIBf//pXJEk6iy2dPO0piIiMYb/9/pRlyy4iiiKyLOP97z+HF154nksuuZDz\nzruASy65kKVLF/OqV+1BpRLNdnMnRVdJneN0BcgO9UWH+qJDfdGhq6SKiMiUUiiIiIijUBAREUeh\nICIijkJBREQchYKIiDgKBRGRafbss//NWWedAcBFF32MJElmuUVbpi+viYjMAM8rvkJw0UXLZrkl\nW6dQEJHtyprbvsbGRx52y78JfNI0m9Rr9v3Za+k/8eQtPr98+V08+OAPaLVarF27lhNPPJkVK77P\nM8/8F0uXfoA4jrnllpsJgoBFiw7mjDOWsnbtH7j44gsA2HXXl7nXOvHEE7j55ttZvfpZrrrqCrIs\nY/36dXzoQx/lgAMO5OST38miRQfx7LO/4WUveznLln3aBcpMUCiIiIzD4GCDyy+/kvvvv5dbb/0q\n11zzFR577FFuueUmnnvut1x77Q1Uq1UuueRCHn74x6xc+X2OOeZYjj/+7dx//33ceeft5SsVA/wz\nzzzNmWd+kL32ejX33fcdvv3t/+CAAw7k979/jquuuoZXvKKfJUveyy9/+Qv23/+AGfucCgUR2a70\nn3jysFn9TF3mYt99DQDz5vWxxx57useNRoN169Zx7rkfIM9zGo0Gv/vdc6xevZoTTngnAIsWHdQV\nCnnZ7n6uu+5aarUaAwOb6O2dB8Auu+zCK17RD8DChbvRbren/bN1UyiIiIzDlg/heCxcuBtXXPEF\ngiBg+fK72Gcfw7PP/jc///nPePWr9+aJJ36x2Vqf/exnuOii/8urXrUnX/rSNbzwwvPT+wHGSaEg\nIjIJURRy0knv4cwzF5OmGbvv/kqOOuoYTj317/nEJy7ggQfuY/fdX9kVKsX9sce+lY9//CPMn7+A\n/v6FrF+/btjzsLUgmj66SuocpytAdqgvOtQXHeqLjqm4Suq49hSMMYcDl1lr32SMeTVwHZABq6y1\nS8s6i4HTgRhYZq292xhTA24EFgIbgNOstWsn22gREZkeY355zRhzLvBFoFoWXQ6cb609EvCNMW8z\nxuwGnAW8DvgL4JPGmAhYAjxurX0DcANwwTR8BhERmSLj+UbzU8A7upYPtdauKB8vB44BDgNWWmsT\na+0G4EngIOAI4Dtddd88Ja0WEZFpMWYoWGu/CXR/J7v7mNVGYD7QB6zvKt8ELBhRPlRXRETmqIlc\n+6j7q4N9wDqK8wXzR5S/VJb3jagrIiJz1ER+JfWnxpg3WGt/ALwVeAB4GFhmjKkAdWA/YBXwEHAc\n8Eh5v2L0l9xcf3/f2JV2EuqLDvVFh/qiQ30xdSYSCh8GvlieSP4l8HVrbW6M+TywkuLw0vnW2rYx\n5mrgemPMCqAFnDLeN9GvmBX063Yd6osO9UWH+qJjKsJR31OY47TBd6gvOtQXHeqLjqn4noL+noKI\niDgKBRERcRQKIiLiKBRERMRRKIiIiKNQEBERR6EgIiKOQkFERByFgoiIOAoFERFxFAoiIuIoFERE\nxFEoiIiIo1AQERFHoSAiIo5CQUREHIWCiIg4CgUREXEUCiIi4igURETEUSiIiIijUBAREUehICIi\njkJBREQchYKIiDgKBRERcRQKIiLiKBRERMRRKIiIiKNQEBERR6EgIiKOQkFERByFgoiIOAoFERFx\nFAoiIuIoFERExFEoiIiIE050RWPMo8D6cvEZ4FLgOiADVllrl5b1FgOnAzGwzFp792QaLCIi02dC\noWCMqQJYa4/qKrsTON9au8IYc7Ux5m3Aj4CzgEOAHmClMeZea208+aaLiMhUm+iewkFArzHmHiAA\nPgYcYq1dUT6/HHgLxV7DSmttAmwwxjwJLAIenVyzRURkOkz0nMIg8E/W2mOBJcBNgNf1/EZgPtBH\n5xATwCZgwQTfU0REptlE9xR+DTwFYK190hizluIQ0ZA+YB2wgSIcRpaPqb+/b4JN2/GoLzrUFx3q\niw71xdSZaCj8PXAgsNQY80qKgf9eY8yR1trvA28FHgAeBpYZYypAHdgPWDWeN1izZuMEm7Zj6e/v\nU1+U1Bcd6osO9UXHVITjREPhS8BXjDErKM4b/C2wFrjWGBMBvwS+bq3NjTGfB1ZSHF4631rbnnSr\nRURkWnh5ns92G0aTK/kLmgV1qC861Bcd6ouO/v4+b+xaW6cvr4mIiKNQEBERR6EgIiKOQkFERByF\ngoiIOAoFERFxFAoiIuIoFERExFEoiIiIo1AQERFHoSAiIs6E/xzndHrg6YcY2NTC93x8PDzPLx57\nHr7n4+GVy0WZh0/g+3h06gyvN7Te8Oe71x/5+p436UuIiIhsd+ZkKPzrwzfMdhOGBYrn+fijBMpm\noVOG2JjreV5Z3l3P2/x532f+6jpxKyf0Q6LyFvrRiOWQqKtsaDkKQkIvdOWBH8x2t4rIHDcnQ+F9\nh53K+g0N8jwjIyPLc7I8K5eLx1meF8vDyrKyXr75eq6sfN6tl3bVGyrvfv1R1ut63zRPSbLueqO3\nay7wPZ/QC4YHSBAReQGhH5XLIZHXFTRBWc8bCqHNy8Ig6iwHXaFUBlT3su/piOV0y/OcnM42XTzO\nyRn62cjLbblTlm+hLBtt/a76I9+n8/rZiPWH1xl9veHtG/p531r78jyn5/kqjcG4nEwN7eV3Hnue\n5/b+3X9dZX7XMiOWt3Tvdy0Pe69xrrPV+5HrjNH+oXqBNzWTPl06e4ZsHkZbCauusvkLqry4dj1x\nlhBnCUmWkGTxiOWEuCzrLBdlSVe9uGvd4eVF2UyEl+/5XXs5UVdodO8FBcNCJSrL6vUKg4Mtii12\naLCgHICKwZBiyKD4f/ggBIwYmIo6lAPR8Nfc0jpDj8t1R1ln6DWHXr9T3mnjaO0Y+lnc/L1GtKNs\nVzps0M+GtVl2PpEfctOJV076uPec3FPYEfmeDx4EbFua9+/aR2+yyzS1argsz0YESjoigGLiUcqS\nLJ1UKDWShns8V/aqxuKVf5J8+IwNN9t080ePYbO7Yet0z1CHlYPnD5+Jdr++53lEYUCW5uV7jDar\n9IfNlIce+131O+sNL3N13Hr+ZjPh7nNvnVmwP+y9utcffdbrD5s9u7ojZutbbF+5vMsudV56aWDY\nnsnwCcOIvQ0X9iP2TIaF9Gh7P531Rl1ntD2vrolFxuZ7S0OTi+52dr/XaHtGwycbnc9Q9StTsm0r\nFMTxPZ9KUKESTM3GNRFplpLkqQuU7oB52a7zeOmlwWJw3OJgPKJ8tEG3qx4e+IwYeD2fzQf44QPa\nbNMfluno7+9jDeqLqaJQkDkl8AMCAqqjBFP/rn3MS/TDLzKddNZPREQchYKIiDgKBRERcRQKIiLi\nKBRERMRRKIiIiKNQEBERR6EgIiKOQkFERByFgoiIOAoFERFxFAoiIuIoFERExFEoiIiIo1AQERFH\noSAiIo5CQUREHIWCiIg40/7nOI0xHvAvwEFAE/gHa+3T0/2+IiKy7WZiT+HtQNVa+3rgo8DlM/Ce\nIiIyATMRCkcA3wGw1v4Y+LMZeE8REZmAmQiF+cD6ruXEGKNzGSIic9BMDM4bgL7u97TWZjPwviIi\nso2m/UQz8CBwPPB1Y8yfAz8fxzpef3/f2LV2EuqLDvVFh/qiQ30xdWYiFL4JHGOMebBc/rsZeE8R\nEZkAL8/z2W6DiIjMETrhKyIijkJBREQchYKIiDgKBRERcWbit4+GMcYcDlxmrX2TMaYf+CKwCxAA\np1prnynr9QMrgQOttW1jTA24EVhI8d2H06y1a2e6/VNpon3Rtf47gL+y1r5n5ls/dSaxTcyn2Cbm\nAxHwIWvtj2blQ0yREX3xVWA3wAP2BH5orT3FGLMYOB2IgWXW2ru71t8htgmYeF9ouxjWFz3AzcCu\nQIti3Pz91t5rRvcUjDHnUvzAV8uiTwM3WmvfCFwA7FfWewtwD8UHH7IEeNxa+wbghrL+dmuSfYEx\n5rPAMooNY7s1yX44B/huWffvgC/MTKunx8i+sNa+21p7FPAO4CXgbGPMbsBZwOuAvwA+aYyJyvV3\niG0CJt0X2i46fbEYeMRaeyRwE/CRsd5vpg8fPUXxQYb8b+CPjTH3AacA/68sT4Gjgf/pquuuoQQs\nB948rS2dfpPpCyi+FLhkmts4EybTD5cD15SPI6AxrS2dfiP7YsgngCuttS8ChwErrbWJtXYD8CSw\nqKy3o2wTMLm+0HZR9oW19nMUEwWAV1GEyFbNaChYa78JJF1FewL/Y609BlgNnFfWu99a+xLDZzzd\n11DaWC5vtybZF1hrb5uhpk6ryfSDtXaDtbZljPlfFHuP581Yw6fBKH0xdMjsKOC6smjktcQ2AQvK\n9XeIbQIm1xfaLoDh20VujLkfOJPiy8RbNdsnmtcC3yoffws4dMTz3d+s676GUh+wbnqbNuO2pS92\nZNvUD8aYA4H7gPOstSunv3kz7q+Am621Q597A8MnRDviz8KWjLsvtF0M3y6stUcDbwC+MdYLz3Yo\nrACOKx+/AfjFiOe7Z8cPdtU9rlx3R7ItfbEjG3c/GGP2B24FTrHW3jszzZsR3f/Wb6Y4XDrkJ8AR\nxpiKMWYBxTmXVTPZuBm2zX2h7WJYX5xnjPnrss4AI/Y4RjPbofBh4DRjzErgWODSEc93zwqvBg4w\nxqwA/oHieNqOZFv6Yke2Lf1wKcXJt88ZY75njBlz13g70f0Z9wXcXyq01r4AfJ7it7C+C5zf/Rtp\nO6CJ9IW2i05ffBl4jzHmexQnmse89pyufSQiIs5s7ymIiMgcolAQERFHoSAiIo5CQUREHIWCiIg4\nCgUREXEUCiJTwBiz2BhzUvn4K8aYU2e7TSIToVAQmRqvp3OlV5Htlr68JjsdY8yRwMcoLhuwF3A7\nxcXE3l5WOQ44HLikrPM0cIa1do0x5hmKi6wdC/QApwIvo7iswkaKSxWfAvQCe1D8/Y9LrbVfnJEP\nJzJJ2lOQndVhwGnAARSXm37BWvta4PFy+V+BE6y1BwMPAVd1rbvGWns4xeWZz7fW3g/8B3Chtfa+\nsk61rHNlmtmFAAAA2ElEQVQ8nUsXi8x5CgXZWa2y1v7OWtsA/gA8UJY/C/wl8GNr7eqy7N8o/pbD\nkHuGXoNiL2E0dwJYa38BvHwqGy4ynRQKsrMaeRG57qtHjrwirc/wP13bLO/zUeqO9noi2w2Fgsjm\nfgL8uTHmVeXy6XT2JLYkYct/83xnuey57AC2tBGL7ExG/rbF8xRBcEf5d25/A7x3C3WHfBdYZoxZ\nN0od/TaHbDf020ciIuLo8JGIiDgKBRERcRQKIiLiKBRERMRRKIiIiKNQEBERR6EgIiKOQkFERJz/\nD7+tdkhnglUpAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119f75e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_agg.plot()\n",
"#正規分布に従うデータなので、meanとmedianが重なっちゃってますね…"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 続いて月別推移を、科別に切り分けて表示します"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_agg2.reset_index(inplace=True)\n",
"#集約したデータはMulti_indexになります。df.plot()はよしなにしてくれますが、\n",
"#手動でデータを選択するには扱いづらいので、一度フラットに戻します。"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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+Kffc81keeeQb3HLLvyOOYz74wffjeR7XXPM9vOpVtzA+foa3vOVWAIaGhtvr\nevGLf5z3v/8jHD36GLfd9nayLGNqapJ//+9/lac85bt56Ut/imuuuZbHHvsWw8P7OHLk99tBQkRW\npZikmCTSTRSTFJPkIrLZhOhB4GEAa+1Dxphx4LqO+XVgEpim2OCX169pZKR+znmztZB5b2vvB1Gr\nhed9zXq9SpJE3H777Xz84x/njjvu4IMf/CBf+MIXeO9738vRo0f5yEc+QqVS4Y1vfCMPPfTP/N3f\n/R0vfOFP8OIXv5iPf/zjfOADH2BkpI7nuezf38eXv3yc3/zN3+BJT3oSd911F5/5zN/w7Gf/ECdO\nPM77338no6Oj/OzP/iwnTz7KNddcs673cb73sNvUN9lGikmKSRumvsk2UkxSTNow9W33bDYh+kXg\nu4FbjDGHKDbmTxpjnmmt/SzwY8CngS8CR4wxIVADrgbuX88LjI3NnHNe3wt+ir4X/NQmu76515yZ\naXDVVd8BQJb5HDp0mLGxGZLEY2pqhjNnxnnFK36RPM9ZWFjga197iAcf/AY/+qMvYGxshquuMsRx\nytjYDGmacebMLJVKnbe97Q+pVqvMzc3S29vH2NgMg4ODuG4PY2MzDA3t59SpifP2rWVkpL6udrtB\nfducSz0AbSHFJMWkDVHfNkcxad0UkxSTNkR925ytikmbTYhuB95rjLmH4vrXVwDjwLvLHwN+Hfgz\na21ujHkncC/FqeJfs9ZGa638Q/ffRdqE0A2peCEVv0LFDan4IRWvUtSX5cD1cZ2duXv4uU/HOhw4\nMMrb3/5HeJ7H3XffxZOeZHjssUf553/+Ct/+7d/B1772wIql3vGOP+BNb/ptrrzyKm6//b9y6tTJ\n7X0DIpeubY1J3UoxSaRrKSYtnaOYJF1tUwmRtTYGfn6VWc9ape3tFIFh3f7sgY3dcTL0ysTJLZMn\nLyyTprLcmu9VynGZWLXLi/PC1jw3WPf1qEHg85KX/Byvec1NpGnGwYOHeM5zfpSXv/wXefObb+XT\nn/4UBw8e6lhfMf5X/+rH+I3f+A/09w8wMnKAqanJJfPhfMFFRFq2OyZdbBSTRHaXYtJSiknS7Zw8\nz3e7Dyvcf8rmp89O0kwjmmmTKI1pps1yOmqXoxXlxekszy6oDw4OgResSKRqlQpJnOE6bjk4uI6H\ni9NRtzjPcVw8x8VljXmui8tiXefYxVlZ13pdp5yHy4F9A8xNx+1EL3ADQi/YsTNo57MTp1vzPCfK\nYhpJg0acuKMwAAAgAElEQVTaLMZJc7GcNmkmTRbSor5Z1vuhSxLnuOXn6zleu+w6XjF2F+vdso3X\nUW63dxfrz9luxbo6Xsdx8dzF9V158IAifXfId+JygTzPSbKEuBySLG6X4yxeMp2kMXGeMtjfw8Jc\nQuD6+I6P7/oEXlEOXB/fDZZN+zu2A9Etl1lkeUaSpaR5Uo5T9g33cfbsHK7j4uDgOA4OTjs2t8vl\nvJ2Mo93yua1mZKSumNQddiQmbUaXf393rW9plhJl5b5q0lyyz9pMm9T7q8zORB37EMv3JzZb715w\nzO/yv+mWxKRNP5h1Oz1l1DDmbv6Dz/OcJE/LZCrqSKw6vojtL2VUfkGbNJOorG+uqJ+L57Yk0doN\nrcQodFtnwALC8ixa6IXFPC8kdIP2WbLOeZX29MrlKl54QTsKrR3AImlp0kgby8bnT2YWyvpW+5zu\nS/AvxIde8q7d7oIAj04c4/TU1NKEZEm5SFhWlNPFcpHQJOctJ9nOPHfDd7wiUSoTpNa4VQ7cAN9d\nT5vO6db8xWXGqTE+Md1OQtIsJWmPk6XTZcKS5hlJlpDmZd0qbdvz8pQ0SzrW2TmdtROgrYoL7eSo\nPHjVWXZbSVXn/FWSqlXLuO2EzHEcwsAjitOOVy76v3j8Mu/4l8X3ly+bXtautYIVy7VnL5tevjzw\nzhe8aZ2flmynV/3lr5DnrDxIuuLgbDnN8jpvyYFZB3fxAOuKg6+rLb/sIC+L9YMzvczPxfiu3z7I\nWJQ9PNfDXzIu2rTm+67XPmC4m2d+kixZkrC09juWJzFLxklEdJ428Q7F99V0/q2WH7Rdvd4tvyNF\nOQx94qiMSeXfpfOv47Dyb7X877ekjbOybtWSs9rcjtd3HH79Obes6zNYS1cmRBfKcRyC8mgoQe+W\nrnvf/l5On54myzPSPCMnI8tzsjxbNuRkeUrGeea1luM885bMP8e6yMiylKDqMjk7R9Q6Y5ZFxGlc\nnEHLYqK0SOwmGsX0VvFdn9ANFpOkdsIUthOtIHSZnJ9dksy0kqA0T9d+kVW4jkvNq1L1KwxVBqn2\nFuWqV6HqdZT9ajkuyhWvQq0cV70KoyMDnDozRZpl5Wdf/G2zPC3HWVmXlm2K8mK7ZdNZ53JFubW+\nVpt2+2z1+tY6pTu88ZNb90yM5clIza+2k4vORKNVXq3O94qy5/j09oVMTM8WSVWakOSLSdbSs02L\nyVqSpUvaNNImcTzXnr5YDiz4rr9i5yr0Qmqr1PvLdsaqlYCFRkRGTp7n5GWsPmd5SbtWuYjBrXmd\n7Yr1tuZn5Fm2+Frt+UX8zpetAwfIV78UaMnOAKyyc3L++U5HqWP2iuWcxT2WZctJN6j4IXFS/n+R\nxe3vUZpn5feutf+QX5QHcoH2trqYSPntaX/ZuF0ut3W3PV1u8+U6gmMuk7Mzi8lKEq2awFzo/7++\n47V/mtEX9rHPG17xs43lVyDV6zWmZuYX/3bZyn2Hxf2D89Wvtv9y/vokS2gu2f9Yuq+5F1ySCdF2\nal3S5OHRbY+S3sgpzfYGkEZEaUyURYuJVEcC1ZrXTDuTq2hx3rLEayFpMJVOE6XxqjtVDk6ZqFSp\nh3UO1Pa3k5RVE5gVic3i9FZd+tMT1ugL9ERsObfnP+lZpE2WnQ0JFstesHr9srJfXg65lbb6UoZi\npypdctZqaUK1MuFaHC8mW3GW0NdbpdlIl+zYFDsw/oodmvbR5NWSmVV2fi70CHKXXwLStX2T7vDO\n/+st6/6OdCb2Sw++ru+AbCvRyspkK82zdpxorTftmNfbFzI5NXfOM7Zp3nGWt3UGOM+WnA1efka5\n1T7JEhpZo2xfrPtCd9gDd/HnEYOVgcUkxV+ZtKxMZCrtm3xVvJBqmQT57sZ3r7t1u8/znP0jfUv6\nttrPbTr3+c51Vrpzbmfd4vpWWUdnKV9Zt1WUEO1RruO2z+Bsh9alcM0yeTqwf4C5qWRDN6sQ6Ra/\neN1LuvI/qu3gOE77t0jVC1xXt/4HL7KXLP4+bmd+B7fT2337Cosll9MmqyZhI8MDzM8kS5Kabvid\ndTdzOi6RXKzcvf5sFyVEsi0cp7gpReAFEPQyVKuTzGrHSERERLZOa2c9WMdZmZHhOmOp9kVkpW1P\niIwxDvD/AtcCDeDfWGu/ud2vKyKyGsUkEekmikkiu28nzhP+JFCx1v4Q8KvA23bgNUVEzkUxSUS6\niWKSyC7biYToeuBvAKy1/wB83w68pojIuSgmiUg3UUwS2WU7kRD1A1Md04kxRr9gE5HdopgkIt1E\nMUlkl+3ETRWmgXrHtGutXeseic7ISH2NJrtHfdsc9U26hGLSDlLfNqeb+yZbTjFpB6lvm9PNfdsK\nO3EE4nPADQDGmB8A/nkHXlNE5FwUk0SkmygmieyynThD9OfAjxpjPldO/8IOvKaIyLkoJolIN1FM\nEtllzmpPmxUREREREdkL9KM9ERERERHZs5QQiYiIiIjInqWESERERERE9iwlRCIiIiIismftxF3m\nljDGPB34PWvts40xI8CfAIOAB7zcWvtI2W4EuBf4bmttZIypAncCByju2X+jtXa8G/rWsfwLgRdZ\na39uK/t1IX0zxvRTfG79QAD8e2vt/97Gvv0PYBRwgKuA/2WtfZkx5ibgZiAGjlhrP9ax/E59buvu\nWzd/bsaYHuD9wBDQpNgWTmxl3/YSxaSd7Vs3b1sdyysmKSbtim6ORxfSv47lFZMUk3aibxuOSTt6\nhsgY8waKL2ilrPp94E5r7bOAW4Gry3bPAz5B8cZbXg181Vr7DOBPy/bd0jeMMe8AjlD8obbUBfbt\nl4C/Ldv+AvBH29k3a+3PWmufA7wQmABeZ4wZBV4L/CDwfOB3jTFBufyOfW4b7Fs3f243AfdZa58J\n/HfgP2xl3/YSxaRd6Vs3b1uKSYpJu6ab49EW9E8xSTFpJ/u24Zi005fMPUzxRlp+GLjCGPMp4GXA\n/yzrU+C5wNmOttcDf1OW7wZ+pIv6BsWD1V69xX3air69DfivZTkAFra5by1vBv6LtfY08P3Avdba\nxFo7DTwEXFO228nPbSN969rPzVr7hxTBEeBKisAgm6OYtPN969ptq2ynmLTBvikmbZlujkcX2j9Q\nTFpOMWmb+raZmLSjCZG19s+BpKPqKuCstfZHgaPAr5Tt/s5aO8HSbLgfmCrLM+V0t/QNa+2Ht7I/\nW9U3a+20tbZpjLmM4qjRr2xz31qno58DvK+s6vzbAcwCA+XyO/m5rbtvF8Hnlhtj/g54DcVD/WQT\nFJN2vm8XwbalmLTBvpXLKyZdoG6OR1vQP8WkDopJ29u3cvkNxaTdvqnCOPDXZfmvgacum9/51Nhp\noF6W68Dk9nZtQ33baRvqmzHmu4FPAb9irb13+7vHi4D3W2tb/ZhmaXDeib/fuay7b93+uVlrnws8\nA/joDvRtr1BM2hzFpM1TTJJz6eZ4BIpJF0IxaZv7BhuLSbudEN0D3FCWnwE8sGx+59GFz3W0vaFc\ndjttpG87bd19M8Z8F/Ah4GXW2k9uY586P48foThl3/IF4HpjTGiMGaC4lvf+bezLBfetmz83Y8yv\nGGN+vmwzx7IjKHJBFJM2RzFpm/vWzZ+bYtK26eZ4BIpJG6WYtHN923BM2u2E6JeBG40x9wL/Cvid\nZfM7M/h3AU8xxtwD/BuKawi7pW87bSN9+x2KH6T9oTHmM8aY7bqUofM1vxP4ZmvCWnsKeCfFXV3+\nFvg123HXmR2wmb518+f2HuDnjDGfofix4C9sU9/2IsWkzVFM2v6+dfPnppi0Pbo5Hm20fztNMWn7\n+9bNn9uGY5KT57v5fRUREREREdk9u32GSEREREREZNcoIRIRERERkT1LCZGIiIiIiOxZSohERERE\nRGTPUkIkIiIiIiJ7lhIiERERERHZs5QQyYYZY24yxrykLL/XGPPy3e6TiOxdikki0k0Uky4+Sohk\nM36I4mFcIiLdQDFJRLqJYtJFRg9mvcQZY54J/DrgAN8GfASYAn6ybHID8HTgt8o23wReZa0dM8Y8\nAvwpxVOee4CXA8PAh4AZ4CbgZUAv8ATgAPA71to/2ZE3JyIXHcUkEekmikkCOkO0V3w/cCPwFODV\nwClr7dOAr5bTfwz8uLX2e4DPA7d1LDtmrX068F+BX7PW/h3wV8BvWms/VbaplG1eABzZiTckIhc1\nxSQR6SaKSXucEqK94X5r7XFr7QJwBvh0Wf8Y8K+Bf7DWHi3r/j/guR3LfqK1DoqjHqv5SwBr7QPA\nvq3suIhckhSTRKSbKCbtcUqI9oZo2XTSUXaWzXMBv2O6UY7zVdqutj4RkbUoJolIN1FM2uOUEMkX\ngB8wxlxZTt/M4pGRc0lYGgw6nSsYiIish2KSiHQTxaQ94Fx/LLl0Lb+LxkmKjfsvjDEB8C3gledo\n2/K3wBFjzOQqbXSXDhHZCMUkEekmikl7kO4yJyIiIiIie5YumRMRERERkT1LCZGIiIiIiOxZSohE\nRERERGTPUkIkIiIiIiJ7lhIiERERERHZs5QQiYiIiIjInqWESERERERE9iwlRCIiIiIismcpIRIR\nERERkT1LCZGIiIiIiOxZSohERERERGTPUkIkIiIiIiJ7lhIiERERERHZs/zd7oDsHmPM84EfBvKy\nyinLn7PWfmKt+TvdXxG5tCkmichu2Yr4oxh18VJCtLcdt9beurzSGHPNOueLiGwlxSQR2S1bEX8U\noy5SumRub3M2OX+t5URENkMxSUR2y1bEH8Woi5QSor0tX7vJli4nInI+ikkislu2Io6cax2KUV1O\nCZGIiIiIiOxZSojkfHREQ0S6iWKSiOwWnf25hCkh2tvWutZV18KKyE5STBKR3bIV8Ucx6iKlu8zt\nbU82xryIlbeHfAD4yjrmi4hsJcUkEdktWxF/FKMuUk6en/tMnzHGB94DXAWEwBHgKHAX8GDZ7F3W\n2g8bY24CbgZi4Ii19mPGmCpwJ3AAmAZutNaOb9N7EZFLnGKSiHQTxSSRS8NaCdErgGustb9kjBkC\nvgy8GRiw1r69o90o8CngOqAHuBd4KvAaoG6tfYsx5iXAD1prX7ddb0ZELm2KSSLSTRSTRC4Na10y\n9yHgw2XZpTiq8VTgamPMT1Ic/Xg98P3AvdbaBJg2xjwEXAtcD7y1XP5uYMXDqkRENkAxSUS6iWKS\nyCXgvDdVsNbOW2vnjDF1ig3+N4AvAL9srX0m8E3gPwL9wFTHorPAAFDvqJ8p24mIbIpikoh0E8Uk\nkUvDmjdVMMYcBj4K3Gat/YAxZsBa29p4/wJ4J/BZlm7EdWCC4nrYekfd5Ho6led57ji6IYcIujPN\nCopJIrtKG8Iyikkiu2pLNoTzJkTlNa+fAG6x1n6mrP6EMeY11tr7gOcC/wh8EThijAmBGnA1cD/w\neeAG4L5yfM96OuU4DmNjM5t4O9tvZKSuvm2C+rY5IyP1tRvtIYpJK3X791d927hu75ssUkxaqdu/\nv+rbxnV737bCWmeIfhUYBG41xvwmxa0DXw+8wxgTASeBm621s8aYd1L8SNABfs1aGxlj3gXcYYy5\nB2gCL9uSXovIXqWYJCLdRDFJ5BJw3rvM7aK8mzNR9W3j1LfNGRmp65qI7qCYtAnq2+Z0ed8Uk7qD\nYtImqG+b0+V925KYdN6bKoiIiIiIiFzKlBCJiIiIiMieteZd5kRERERElsvznHRmhujEcaLjx4nH\nThNdMUo8eIDK4cP4dd1FXC4OSohERERE5JzyPCc5e5boxONEx08QnTxO8/hxohPHyebmlrSd6Ch7\nAwNUrji8ZAgPHsTxtfsp3UXfSBEREREhT9PiLM+J40QnTtAsz/xEJ0+QN5tLGzsOwYFRak/6ToKD\nB8n3jRLXhxn2YuYeeYTo2FGax44y/8D9zD9w/+Jynkd48BCVK64okqTDV1K54gq8/gH0bCXZLUqI\nRERERPaQLI6IT54sEp4TJ4iOP0504gTx6VPkSbK0seeTDo/QHNjPXN8wU7VhzgT9nHZ7mWrkzMxH\nzDwckz+UAmMAuM4w/b2jDD75h9lfyTmYTLGvcZb67BlqE6eITp0kOnaUGf7X4svU61SuKJKj8IrD\nVA4fJjx4CDcIdvCTkb1qrQez+sB7gKuAEDgCfA14H5AB91trbynb3gTcDMTAEWvtx4wxVeBO4ADF\n05hvtNaOb8s7EZFLnmKSyM7I8pwzkws8PjbHsbFZjo3NcXx8jj/+lR/Z7a51lW6PSenCQvv3PXPH\nHqfx+DGSkyfJJ8dxlj12JfbCMtkZ4JTXz1jQz5lggKmgj9wp78E1Ww7kwCy9VZ++npDR4R7qPSF9\nNZ8Uh1Pjc0zONHn8zByPJhngASPF0PNkuDJnMJ5hNJ7kynya0WiC4YWzpF9/gPmvP7DYKdfFO3AZ\ntSsPUz18ZXHJ3RWH8QcHdTZpm+RZRjo7Szo9RTI1RTo9TVp1mZlpFH92csjzpWVak/nqbSguu8zz\nnCzLybKMLC3KaZaRterTrD0/zfKiL1lOnpXz82J+3l4OXvjbr9+S973WGaKfB85Ya19ujBkEvgJ8\nmeKBYvcYY95ljPkJ4H8DrwWuA3qAe40xnwReDXzVWvsWY8xLgFuB121Jzy9ReZ5DlpFnGWQZ5K1y\n3q7Ll9SXdXlOIx0knc9wazVdnyuXql2JSf/zp36WjT6x7fztV/+PfDNPhcs8n8z1yP0APB+CACcI\ncIMANwzxwhC/EuJXKwTlENYquEGAE4Y4ftHOKZdrLdua5wTL5rsX781J8zwnTVOiKCVJena7O11j\nai7i8TLpOTY2y/HTM8ycOE3vwiRD8QxD0TRXxjN8TzoLKCFaZldiUp7nNKKE6fmY6bkms2MTNI4/\nTnLyBJw5RTAxRnV6nFpzdsWyc16V8coBxsMBzoQDjJfDjNdDrepTr4XUewP6e0IO9QTUe0LqPSH9\n7XJAf29IXy3A91bGg85n1uR5znwzYXKmyeRcVIxnm0zNRkzONpmcjfhSOU7SjDCLGGlOMhJNcKA5\nwYFogpFTp0lPHmf2C//Qfo04rBENj8LoIcLLr6D3qicw9MQnUB/sxVWitEKe52RzcyTTU6RTUyTT\n0+V4qp34RFPFvHxutti33EUuG7kN9s4kRB8CPlyWPSABrrPW3lPW3Q08j+IoyL3W2gSYNsY8BFwL\nXA+8taPtrevp1OdveT1ZmuO6Do5TDK4LjuPiOCweFej80pflFUcMVpteVre4jLNktNr6T/suUTNe\nJTlZlshkGXne0aYzock72nQkNEUCtPkH5X6r8z35Pm6thlutleNqMW4N1RpeZ3211jGutqedINBR\nGOkmuxKTJoK+VevPtWU45Oec62wi7Tn364CTpXhJghc18fOUIE9WbZ+WQ2PDr76M5xUJU1AmSWGZ\nQHXUueX4bNWnuRCVcTJvj5fHvOXz8zQjyzLyLCVLc/IsJS9j6OKQd8TTHPJy3fli2WkPxX/ubsdn\n/w03YC7sI+odIO8fwh8epjp6gP7LRhm+8jIGD47ged6FflpdpRElPH5mrjjrc2qaiWMnmT9xkurs\nWYaiGYbjGa6OZ/iBZAY/X7lD5PYoiVzFrsSkI6/6fYYWJtgXTbEvmqIna7L8rzPl93Cq9xCzvcMs\n9O8nHR6B/ZdRGx6gXgs53BvwXT0h/WWSU+8JCPyt/c47jkNvNaC3GnD5yLnb5XnOXCNhqkyOimSp\nyYnZiK/PNIjPnCEYP0nP5Gn2Nc5yIJpg6OSjOCcfha9ABJzA4WthPxM9+5gfOECy/zKcyw7RM7Kf\ngXqVwb4Kh2Zj5ucahIFHJfAIA5fQ9/A956Lb18nznGxhoUhoWgnO1BSNiQmaE5PEZYLD7AzO/CxO\nlp53fU3HZ96vMRvuY86rMefXirFXJXGLdKEVQfPyf5li2iF3wHUcPNfFdR1c18HzyrLn4jkurufg\ntabL+Z7bqndxXRffc3Bdt1jWc/BdF88v27tusazn4pXLrJaQb9Z5EyJr7TyAMaZOscH/OvAHHU1m\ngH6gDkx11M8CA8vqW23X1Hz8+IqdhvZ0uZ+xJIXJ88Vyu9G5dyIuxPzyCtctjpi6Ljgujuu0y7gO\nOG5xqtlxyL0AfIfcccgdtxjjkLXGFPMyzjHkkOKQtsq5U+zklGU/TwmzmEoaEWYxQRwRNucIzk4Q\npPGm3m/mumRBhSyokIdV8rBCXinGVKrlUMGt1nAqVWglVLUevGoVt6dIvCZnGpw5M02WpuRJSpak\npGlK1hqStNgJSpPilGnZLk9TsrTcAUpT8jRZ3FlK02KHKE2LHak0K3ec0iIBTdNixyhdTEDJ08Vy\nKxmt1mj2DpAO7CMf3Ic3NEStFlKr+NRCn1rFp6fiU6t4RV3FpxJ6Ogq1C3YrJr3wg3/SzU/p5vTp\naRpRynwjYa4RszDfZH52gfm5Bo35Bs35BRrzTaKFJnGjQdxokjQjkmZEGkWQxARZipen+J1Dtmw6\nT/GzhCDPCKKUoBnh5wt4eYKXpbjL/sPd6CeWUcTC3IGsTF9acTLviI+tcuc4IyB3yvblgFPEY6eM\nx04Zrx3HIYgW6J2fZrgxAeOPwiNFH3JgHDiNy1ylj6inn7x/CG94mOqBA9QPHmD4isvoPzSKF4YX\n/gfcBkmacersPMdOzXD6W48zffQ40alThNNnizM+8TTfG8/iscpR4FoPlcufQDg6SnBglPDAKMFo\nMfb6Vj8wsJftVkx69qkvAMWOabM+xNzQVTByGcHBg9Quv5z+K6/gCcP9VIKLI6l3HIe+WkBfbX2J\n0+Rsk8mz08w9dpTo2FE4dYLw7EnqU2Psm/wmTH6zfZS44YacDod4oDLIP7oVUsclcxwy3HY5dzxc\n38PxfTzPww18PN/HCzy8IMALfHzfxw99gjDAD3z8MCCoBASBT1AJCUKfSiUgqISElYBKNaAS+FQC\nlzDw1r3znjUaJNPTNM6eZX58goXxIsFJpqZIZ4oEx5ufxVuYw8uS864rdbwiqQmGliQ4c36NBb9G\n1luHvn7cep1aXw+91eJv0FvzOVgL6KsG9FZ9LhvtZ2Z6Ad9z8X2XoExKAq9IYjzPvej3ida8rsoY\ncxj4KHCbtfYDxpjf75hdByYprnvtX1Y/UdbXl7Vd0/i//W2aUUojSmjGKc0oXTLdiFp1S6fX1HH2\nZWXC1W6E5zpUA49KWAxVv1V2yXOHZpLRTCFOM6I4I05S4iQjSjLiOCXb/EmedQt9l8B3CQKvXQZI\n0pw0La65TNLiGsskzcnTBDeOi4Qpa40jwjymkhZ1laxIpCr58nYxYSOiMj9HJYs3lGhm5XC6o86h\nOIzWrWE6xWUq6GUyqHMiqDPl9zER1JkM6kwGfcRugONQJEllsOipBvTWAnoqPj21xbqe1rxqUd9T\n8Yt25bytPLqxV+xGTIIi8ehWBw5c2LM+4iRjbiFmrhEzOx8xt5AwuxAxtxAzuxAztxAz3SrPx+W8\nxTbtmJfnS5InoExmaB/4WZ7g+L5HGAZUqz7V0KMa+lTD4qBDe7rSqveolONapdW+1aY1v5iuBB6u\nu3a0mpuc4eQ3jnLmW8eZfvwkjVOnSc+O485MUp2fZv/EMZg41t65av1sfAxYCHqI+gZgYJhg/z56\nLxtl6PBBDjzxcgYuP4jf17vpv8l6vm95nnN6fI5v/cujnHzwUaYee5z41Cn8qXEGo2kG41mGVkl6\n8loPwaEnUD98OX1XHKJ68CC1g5dRPXSQoN693/NutRsxybzhl6hdcQW1QwdxuzAx35l4eRAwS2ry\nPKc5NsbcI99i4qFvMP2NR3GOPsbhsdNc2Ti1A30qDqo0yyHDKZIuXLLyQHjmeuSuS+664HjgueB6\neHGDsDFPkK08gO1S/EANin2UWb/KfDDAnFdj3q8SVfvIevrI+/rx+gcIBvupDA/RO1Cn3lvhYE9I\nX09AvTdsX/ZYq/gbPCs2dMGfTTdb66YKo8AngFustZ8pq79kjHmGtfbvgR8DPg18EThijAmBGnA1\ncD/weeAG4L5yfA/r8KLnPGnDR2PzPCdKMqI4pRmnRHFWjlOacVEfJYvllW06ppOUZpQRJSmzUcrZ\nuYgoTknL//U91ymSkXLwPZdaxSfwFuuCVhZdlpe3X1lfnLJdrPc62jvFtLc4vdqXuPO63XN9Rlme\nk6Y5adYxlAlUluUky6Y756dZzlyakjYjsoV58kajGJoN8sYCNBs4zQZEzaIcNXGjBm7UxHOLs1q0\nzqa53tKza57XLjtl2fE8HK88uut5OJ6H6y3Od5fVuZ6H43t47friiE9r7LWm222K5XrdlOP/8k0a\nJ08TjZ2G8TGGzp5heP74qp9jM+xhttrPdFjnrF9n3OvltNPDY36dOa+28jLN8wh9t33WqbbsLFRP\nxee1L71u3evaC3YrJgFdfYZoq/oWAsM9AcM9AcXHtrbitwwdZ6eaCXONhPlGwuBgjeZCTCV0qZSX\nqFRCb7G8zqRl3bKMpJEx24hZ+cuJpTo/t/4rD9N/5eFV283PLjB29CSTx04wd3KM6MwY2eQE/swE\n1YUZ6hOn8CZOwKPFjtDZcgCIvJBmTz9ZvTjDVBnZT99lowxdcRk9owfw+vtX/U3W8r9pnqZMHT/F\nqYe/xeRjj9M4cRLOnqE6e5b+qDjTsw/Y17GOOKzy/7N373GSVPX9/19V1Ze59czusj3IRSQIHlRY\nEDRGw08EgxceREVFUBIx4sKD72IkRkyCQAC/6wUVIxAJBgSEKBdRMMhGUfPVXUwEDIni5QARBRHY\n2dvc+l5Vvz+quqfnsjszvT3TNTvvpy5VdepU9ad7uj9Tn1M11bXBfXEGB+nffz/69t+H9ODzyAwO\nznimpwSUSkBp1++lJA8MdEKnctLqY/6YoaFRCsP1Q+/kaGdOaonTDQcdysBBhzIQNwXlMpVnn6G/\ny2XHtrHoapP4KhT86MqS6CqUeLn+r+ZTq1apVWv49X+1iWlQ8wniaVjzCeKrWEK/Bk37JghwggAC\nHxYQH7kAACAASURBVCeed/0KbhjE/0LKbprtmX7KmR6qXb1RgdObw8314/X3k165gu5VK+kZ6Gev\nngwHdKfo644Km3mdnfF9xkd9xufxI+r4z3QX2pWTnHAXf7NijPkH4J3Ar4gG9kPgg8BVQBr4JbDW\nWhsaY84Ezo77rbfW3mWM6QZuIirjy8C7rbWbpz/SNGESX/iaHzA42M+2rbP9qu2MpL9hl1psQalI\ndWiIyubNVLcMUW2ebtsaJbup0mmcVasJV+yF37+Kct8KSr0rGO9ewWimj0INiuUaxYpPsVyjUK5F\ny+UaxbJPzZ88mvuvn33L0j4H3WbKSdMtxc9WErQjtjAMGStUGPrdZnY8/Szjzz5HeWgLwY5teCM7\n6CqO0F8dJxvOfMmy77iUu/sJ+lfgrtwrLpgGSYUBzz32BNXNm/F2bKW7OIw3w9/0lFNZSn2rcFat\npmuf57HiBfuz6sD9yS7g5W35fE45qYly0nR7+ue+3fwgutpov30G2LZtfPYNOiCJr1tdu3LSLgui\nDtIHvQWKrTWtxBb6fvSt3UObqQ4NUR3aHP+L5oNicfpGjkNq5UrSq/OkBwcb00x+kHR+ELe3l5of\nNgqkQrnGH67ZTwcfyaCc1ILlHlsQhgyPldny7Ha2P/0MY89upjK0hWD7tuiSvOII/dUx+vyd3+qi\n4GYZ7RqgNrAKLz9I7377svrA/dn74APoGti9SyVboYIoMZSTWqDYWpPw2NqSk3RvZpEWOJ5HOp8n\nnZ/+15/121tGZ5Y2Tz6ztGWI4mOPUnzUTtvO7e4mnR+M9zvIyvwgrNlvMZ6OiCwA13FYmetiZW4f\nOGSfaev9IGD7aJktW0bZ/rtnGXt2M+WhLXT1ddP1vOcx+MLn86LnD9LTpV/VIiILSVlWpM0cx8Hr\n66O7r4/ugw6atj6oVqht3RoVTJPOMA1RefYZyk9O3ED9kHf86WKGLiKLyHNdVg90s3qgG1442GhP\n8misiMieSAWRyCJz0xkyz9uHzPOmjxiHYYg/PNwokERERERkYakgEkkQx3FIrVhBasUKug95UafD\nEREREdnj6UtQRERERERk2VJBJCIiIiIiy9acLpkzxrwS+KS19jhjzJHAPcCj8eprrLV3GGPWAmcB\nVaL763/LGNMF3AIMEn0b8xnW2q1tfxYisqwoJ4lIkigniSxtsxZExpjzgT+Hxhd/Hw181lr7uaY+\newMfAI4CeoBNxpjvAOcAP7XWXmaMORW4CDivvU9BRJYT5SQRSRLlJJGlby5niB4HTgZujpePBl5k\njHkr0ejHXwF/CGyy1taAEWPMY8ARwDHAp+LtNhB90Hfb7d9/nAd/NZcvcp67Vxw6yDuPP3in6zds\nuIf77/8hYejzzDPPccopp7Fx4w944on/Zd26D1KtVrnttq/geR5r1hzJ2WevY2hoM5/5zCeoVqts\n3bqFtWvP4ZhjjuWMM97Fy152FI8//hiu6/LJT36Wnp7etj4fkT2YchLKSSIJopyEcpIsbbP+DZG1\n9htAranpx8D51tpjgV8Dfw/0A8NNfcaAASDX1D4a91uyCoUiX/ziFzn99Pdw11138vGPf5qPfOSj\n3HPP3XzpS1/k85+/hn/8x39m8+bneOihB/jtb3/Du97151xxxdWcf/4FfP3rd8T7GeeEE97E1Vd/\nkdWr8/zHf/yow89MZOlQTpqgnCTSecpJE5STZKlq5bbbd1lr6x/eu4ArgR8w+UOcA7YTXQ+ba2rb\nMdcHyedzO1237tSXzSPc9sjlujjyyMMB2G+/QYw5hHw+xwEHPI9arcLIyDAXXPAhwjCkUCgwMrKF\no48+mmuuuYbvfvdeABwnJJ/P4boOr3710WQyGQ488Pl0dbm7fL7z0a79LATFJgtEOUk5qSWKTRaI\ncpJyUksUW+e0UhB92xhzrrX2IeB1wE+AB4H1xpgM0A0cCjwC/Ag4EXgonm6c64Mk7Vu6R0dLFItV\nAIaHi5RKVYaGRtm2bZxqNWD16kEuv/xKPM9jw4Z7OOCAQ7j88s/w5je/jVe+8lXce++/8tvfPsXQ\n0ChBELJlyxjpdJpCocLISLEtzzfJ326u2FqzpyegNlFOUk6aN8XWGuWkOVFOUk6aN8XWmnblpFYK\nonOAq4wxFeBZ4Cxr7Zgx5kpgE+AAF1hrK8aYa4CbjDEbgTLw7rZEnTDpdIpTTz2dc89di+8H7LPP\nvhx//Akcd9yfcPXVn+Pmm28gnx9kZKQ+YOQ0tnUcZ+adishcKSdNoZwk0lHKSVMoJ0nSOWEYdjqG\nmYRJrkQV2/wpttbk8zn9JkgG5aQWKLbWJDw25aRkUE5qgWJrTcJja0tO0hezioiIiIjIsqWCSERE\nREREli0VRCIiIiIismypIBIRERERkWVLBZGIiIiIiCxbKohERERERGTZmtP3EBljXgl80lp7nDHm\nhcCNQAA8Yq1dF/dZC5wFVIH11tpvGWO6gFuAQaJvYz7DWru1/U+jM+6883be/vZ3TmqrVCqcfvo7\nuOOOb3YoKpE9n3LSzJSTRDpDOWlmykmyVMx6hsgYcz7wz0A2brqC6AvFjgVcY8xbjDF7Ax8AXgW8\nEfiEMSZN9OVkP7XWvga4GbhoAZ5Dx3z5y9dPa4u+10lf0yCyUJSTdk45SWTxKSftnHKSLBVzOUP0\nOHAy0QcV4Ghr7cZ4fgPweqJRkE3W2howYox5DDgCOAb4VFPftnzQv/74PTy8+Wft2FXDywYP520H\nn7TT9U899SQf//ildHdnqVRqHH30KxgZGeGKKz7FOef8JZdddiGjo6Pst9/+bY1LRKZRTkI5SSRB\nlJNQTpKlbdYzRNbabwC1pqbmsn4U6AdywHBT+xgwMKW93ndJevDBH/OSlxzGjTfeyPvedxbHHns8\n/f0DfOhDf8Ndd93JQQcdzNVXf5G3vOVtnQ5VZI+mnBRRThJJBuWkiHKSLGVz+huiKYKm+Rywg+i6\n1/4p7dvj9tyUvnOSz+d2uu7s/LuAd811V23xF3/xZ3zxi1/kzDPPpL+/n/POOw/Xdcjnc2zZ8gyv\nfe1ryedzHHvsq8hm07uMfyF16nHnQrHJAlFOUk5qiWKTBaKcpJzUEsXWOa0URP9ljHmNtfaHwJuA\n7wMPAuuNMRmgGzgUeAT4EXAi8FA83TjzLqcbGhptIbSF873v3cchh7yUc889l69+9WtcddUXCIKA\noaFR9t57f+6//8ccfvgrePTRX1EuVzsSfz6fS9zrVqfYWrOnJ6A2UU5STpo3xdYa5aQ5UU5STpo3\nxdaaduWkVm67/WHgMmPM/UAa+Jq19jngSmAT8F2iPyasANcAhxljNgLvBy5tS9QdcOihL+a66/6J\nM844g7vv/jqnnHIaBx54EB/72MWcfPI7+P3vn2bdurV84xtfI5NJdzpckeVEOUk5SSRJlJOUk2SJ\ncaK7fSROmORKVLHNn2JrTT6f0614kkE5qQWKrTUJj005KRmUk1qg2FqT8NjakpP0xawiIiIiIrJs\nqSASEREREZFlSwWRiIiIiIgsWyqIRERERERk2VJBJCIiIiIiy5YKIhERERERWbZa+WJWAIwxPwGG\n48UngI8DNxJ9Q/Mj1tp1cb+1wFlAFVhvrf3W7gS8VD355G/49Kc/wVVXXcsll3yUCy+8lFSq5Zdf\nRKZQTpof5SSRhaWcND/KSdJJLb3TjDFZAGvt8U1tdxN90dhGY8w1xpi3AP8JfAA4CugBNhljvmOt\nre5+6EuP40S3Sr/kkvUdjkRkz6Kc1BrlJJGFoZzUGuUk6ZRWS+8jgF5jzLcBD/gocJS1dmO8fgPw\neqJRkE3W2howYox5DFgD/GR3gh6641ZGH3pwd3YxTe7lryB/ymk7Xb9hwz3cf/8PCUOfZ555jlNO\nOY2NG3/AE0/8L+vWfZBqtcptt30Fz/NYs+ZIzj57HVu3buGyyy4CYOXKVY19nXLKm/nKV+7kqaee\n5OqrP0cQBAwP7+Cv//rvOOywwznttLexZs0RPPnkb1m1ai/Wr7+8kSREZEbKScpJIkminKScJEtI\nqwVRAfi0tfZ6Y8whRB/s5nfiKNAP5Jg4XQwwBgy0+JgdVygUufnmG7n11ju5/favcu21N/Dwwz/h\nttv+haef/h3XXXcz2WyWj33sYh588Mds2vQDTjjhDZx00lv53vfu4+6774z3FL1UTzzxa8499684\n6KAXct99/8a9936Tww47nGeeeZqrr76W1avznHPOmfzylz/nJS85rHNPXCT5lJOUk0SSRDlJOUmW\nkFYLokeBxwGstY8ZY7YSne6tywE7gBGiD/zU9lnl87mdr/s/a4G184t4N+VyXRx55OEA7LffIMYc\nQj6f44ADnketVmFkZJgLLvgQYRhSKBQYHd3Kc8/9njPO+DPy+RzHHffH3HvvXeTzOVwXVq/u45BD\nXsDNN99Id3c3Y2Nj9PX1kc/nWLVqFS9+8UEAHHDA/vT0pHb5ejSba79OUGyygJSTlJPmTbHJAlJO\nUk6aN8XWOa0WRO8DDgfWGWP2Jfowf8cYc6y19gfAm4DvAw8C640xGaAbOBR4ZC4PMDQ02mJoC2N0\ntESxGF3SOzxcpFSqMjQ0yrZt41SrAatXD3L55VfieR4bNtzDAQccwn77/Yof/vA/WLVqXzZu/DHV\nqs/Q0ChBELJlyxiXXHIZl1zyfznggAO5/vpree65Z+P1QeP5l0pVduwozOn1yOdziXvd6hRba/b0\nBNRGyknKSfOi2FqjnDRnyknKSfOi2FrTrpzUakF0PXCDMWYj0fWv7wW2AtcZY9LAL4GvWWtDY8yV\nwCai858XWGsrux92sqTTKU499XTOPXctvh+wzz77cvzxJ/Ce97yPSy+9iO9//z722Wffputbo+kb\n3vAmLrzwb+jvHyCfH2R4eMek9YCuiRWZG+WkJspJIh2nnNREOUmSzgnDsNMxTLPxNw+EI6PFtuyr\n3c9v9cp+agWHnnQ3PaluulPddKWyuE7nv9Ip6RX8co3ND3xKfplSrUTJL1P2y5Rq5bitvhytK/ll\nyrUyJb9EqVZm/RvOV6ZPhnC5vn93h2JrTcJjU05KgN8NPxNu3TZGSEgQhoQEhGFISEgYEk/ry8G0\n9qCpHYj3MXUbmpbr24QwZXnSdoQM5HoojFfxHA/PcfFcj5Tj4bkebtyWclPRurg96uvhuTO0OS6u\n47al8En4Z0uxtaBdOSmRN3i/6sc3dDqEeXFw6E510ZPqjgulHrrT3fSkuuhJ9USFU1xANRdS9fkk\nFFMyIQxDqkGtqXAp7aJwaSpqmoue+jq/TC2odfopyW76y29djEeKrJcl62XIeJmJqZuZ1Jb1smQm\nLU+ZdzN4rtfpp9RWfuDjhz61oEatPg18grESO0pF3PqBT3xg4zkebhsPckSWmw/922WdDmFROThR\n/ogLpVSjaNpFUdXcHvft7e6iWg7muO0M+4kLvOnzO4vHxXU8UvGy8l10jBWEAUEY4Id+PI3m/WBq\n+8TUD+JpGBA0pgFvyP9xW+JKZEF01svfzdhoefaOc3xfOXPtOGu/kHS3w+bt2ynUihRrJQq1AoVq\nkUKtSKFa5JnxzVSD+X19QJfX1VQoddGTjoqo5gJqUlGV6m70me+BVRAGEwcvjTdYbWIaBtSCWvzm\nq/fxG2/CaF2AP2Vaa/Txp/WpBT7ZrEepXJ10xq4+MhUChE3zO2mv9yce6Zq8l3gunNrKtMec2DRe\n8kLGSoVJhU0QBvN6XescHLJelq5Ult50L3t1raIrlaXLy5JNZenyuuhKZaM+cb+JdVm6Ul3Rcnzg\nLclQqpUpVUco+5VJ761WpdwUWXdKYdU0n4kLq3oBlU1lZuyf8TKMp4bZMjJCNajFn9la4/NYL0zq\nn+nGchj3jQsYP6hRDXz8cGJ9LYjapy2H0/e3O69JVCBNLpTqBxH1Aw63af20osqduX3i4Glqe9TW\nsznL2HipkQcmj4BDMGnEPZwyWj599Lzxvyn7m3n/TaPrjW1pjNhn0ilq1aBRMLqOi4MTvw5OY95x\nHByitok+E+tcZtvexcWZ6BNv4zpO0/aTtzkx/5rdfv/L7nv9C19DqVSLf05O/F5wwKHxc3ea2h1o\nvF+atyFuj5bdpm2Ysg93or3+nqmviwd1XRxwHHK5LNt2jDWOCSYOZJuOJwJ/4iA49CeOIYLoYHfq\nsUfjmGOGfVSDMn514hikfqCcRPXcVC+i6vkr5XikUh6BH8Y/F2fa603TfGOp8fN1iLpMzDd+8jPt\nZ4bt3Cl9m+fTWY9iqYIfzFCohAFB4+c5uVhp7u83bddOb3hpewqiRF4yxxK/PKUa1CjGBVJUKBWi\naa1IsRoXUbUixfr6uG+xVqTkz6EQbJLxMo0iKZ1OUa5UJieXpuSxuwcueyrPcenyuhpFSXaWQqUr\nNVHMZKesy3jptp7x0+UpiREODY02zh5W/Aplv0LZL1MJKk3LM82Xp7cFE/P1PtUEnkl0cOKRzRQp\n1yPlpuKR2Xg5bp+2HE+zXSkKxXLjl+LkX4zRL87ol+bEL9Fpv1BDnyCY3O6HfqdfmmXr9lOvUU5K\nhiV9nLTQGmcdAn9SgbViZTebt440iqlgxuOlet6JB4Ibg8Z+Uz6aYaC4aZtpRd+kx2jefmLZccAP\nJgZigCkDJvWh4YlBGprmw8aAC4t2rDfzgFY8bTpjNrm9acBrSv+ZBq92OugV9z/lZW/ccy+ZW+rS\nbop0Jkd/Zv53vvADf+LM05QCqvlMVLE2uZjaXh7GqUQjQ158ejbtZqecLm4+zew1ruttnM6dqU/j\nVK+LV7/mN55OvQa4ebuJdSk81yW/Vz9bt40BzWfs6iMTk8/i1Ue56vOT1jiTejW118dLpvy3qX3S\nFk2nrQfz/WzZMjbvn5UsP47jkPHSZLw0ffS2dd9BGMxYWDUKqFpUfDWvr/gVerqzjUtA6gVLyk3h\nuR5pJzVje6qpwKl/XuuFTLpe4MS/dHbnEo+FPDAKGsVT8yjk5KIpaFrf3OaHPisGehgeLjbOfsw6\nOt7U5jatY8ryzqZuPQtN2QdTRvgdx2H16j42D43E8Yfx34kEhGHQOLMUxGeTGn3CMD6rNXmbiX71\nfUR/b9K8Tb09aHqcycv1fhpQk6WhflY57U4+zM335XCK2Q5FtWvtzpfNZ6uBaYVUMKnAitviK23C\npvaQgPzqfnZsm3758550CaAKooTxXI++TC99mfkfbCVhVGZnVvXk8MeT+XcTe9IHWpYu13Gjs42p\nrnltl+TP/UKqH/AA0EJqyedzDHnJfN3ql7Dp70tFpFXNl1HurhVdOarpPftYSdlWRERERESWrQU/\nQ2SMcYAvAEcAJeD91tpfL/TjiojMRDlJRJJEOUmk8xbjDNFbgay19tXA3wFXLMJjiojsjHKSiCSJ\ncpJIhy1GQXQM8G8A1tofAy9fhMcUEdkZ5SQRSRLlJJEOW4yCqB8YblquGWP0t0si0inKSSKSJMpJ\nIh22GHeZGwGa7z/tWmtn+1YmJ5+f/y2rF4tia41ik4RQTlpEiq01SY5N2k45aREpttYkObZ2WIwR\niPuBEwGMMX8E/GwRHlNEZGeUk0QkSZSTRDpsMc4QfQM4wRhzf7z8F4vwmCIiO6OcJCJJopwk0mFO\nqG+eFhERERGRZUp/tCciIiIiIsuWCiIREREREVm2VBCJiIiIiMiytRg3VZjEGPNK4JPW2uOMMXng\nn4EVgAe8x1r7RNwvD2wCDrfWVowxXcAtwCDRLSrPsNZuTUJsTdufDLzDWnt6O+PandiMMf1Er1s/\nkAb+2lr7nwsY21eBvQEHOBD4D2vtu40xa4GzgCqw3lr7rabtF+t1m3NsSX7djDE9wFeAlUCZ6LPw\nTDtjW06UkxY3tiR/tpq2V05STuqIJOej3YmvaXvlJOWkxYht3jlpUc8QGWPOJ3qDZuOmy4FbrLWv\nBS4CDo37vR74NtETrzsH+Km19jXAzXH/pMSGMeYfgPVEP6i22s3YPgR8N+77F8A/LmRs1tp3WWuP\nB04GtgPnGWP2Bj4AvAp4I/AJY0w63n7RXrd5xpbk120t8JC19ljgX4C/aWdsy4lyUkdiS/JnSzlJ\nOaljkpyP2hCfcpJy0mLGNu+ctNiXzD1O9ETq/hjY3xhzH/Bu4P/F7T7wOmBbU99jgH+L5zcAf5Kg\n2CD6HoFz2hxTO2K7Arg2nk8DxQWOre5S4Cpr7WbgD4FN1tqatXYEeAxYE/dbzNdtPrEl9nWz1n6e\nKDkCHECUGKQ1ykmLH1tiP1txP+WkecamnNQ2Sc5HuxsfKCdNpZy0QLG1kpMWtSCy1n4DqDU1HQhs\ns9aeADwF/G3c73vW2u1Mrob7geF4fjReTkpsWGvvaGc87YrNWjtirS0bY55HNGr0twscW/109PHA\njXFT888OYAwYiLdfzNdtzrEtgdctNMZ8DziX6DsspAXKSYsf2xL4bCknzTO2eHvlpN2U5HzUhviU\nk5ooJy1sbPH288pJnb6pwlbgX+P5fwWOnrK++UuSRoBcPJ8DdixsaPOKbbHNKzZjzOHAfcDfWms3\nLXx4vAP4irW2HscIk5PzYvz8dmbOsSX9dbPWvg54DfD1RYhtuVBOao1yUuuUk2RnkpyPQDlpdygn\nLXBsML+c1OmCaCNwYjz/GuDnU9Y3jy7c39T3xHjbhTSf2BbbnGMzxrwEuB14t7X2OwsYU/Pr8SdE\np+zrHgCOMcZkjDEDRNfyPrKAsex2bEl+3Ywxf2uM+bO4zzhTRlBktygntUY5aYFjS/Lrppy0YJKc\nj0A5ab6UkxYvtnnnpE4XRB8GzjDGbALeAHx8yvrmCv4a4DBjzEbg/UTXECYltsU2n9g+TvQHaZ83\nxvy7MWahLmVofswXAb+uL1hrnwOuJLqry3eBC2zTXWcWQSuxJfl1+xJwujHm34n+WPAvFii25Ug5\nqTXKSQsfW5JfN+WkhZHkfDTf+BabctLCx5bk123eOckJw06+X0VERERERDqn02eIREREREREOkYF\nkYiIiIiILFsqiEREREREZNlSQSQiIiIiIsuWCiIREREREVm2VBCJiIiIiMiypYJI5s0Ys9YYc2o8\nf4Mx5j2djklEli/lJBFJEuWkpUcFkbTi1URfxiUikgTKSSKSJMpJS4y+mHUPZ4w5Fvgo4AAHAXcC\nw8Bb4y4nAq8EPhb3+TVwtrV2yBjzBHAz0bc89wDvAVYBtwOjwFrg3UAv8AJgEPi4tfafF+XJiciS\no5wkIkminCSgM0TLxR8CZwCHAecAz1lrXwH8NF7+J+DN1tojgR8BVzdtO2StfSVwLXCBtfZ7wDeB\ni62198V9snGfk4D1i/GERGRJU04SkSRRTlrmVBAtD49Ya39vrS0CW4Dvx+1PAn8K/Nha+1Tc9kXg\ndU3bfru+D6JRj5ncDWCt/TmwVzsDF5E9knKSiCSJctIyp4JoeahMWa41zTtT1rlAqmm5FE/DGfrO\ntD8RkdkoJ4lIkignLXMqiOQB4I+MMQfEy2cxMTKyMzUmJ4NmO0sGIiJzoZwkIkminLQM7OyHJXuu\nqXfReJbow32XMSYN/BY4cyd9674LrDfG7Jihj+7SISLzoZwkIkminLQM6S5zIiIiIiKybOmSORER\nERERWbZUEImIiIiIyLKlgkhERERERJYtFUQiIiIiIrJsqSASEREREZFlSwWRiIiIiIgsWyqIRERE\nRERk2VJBJCIiIiIiy5YKIhERERERWbZUEImIiIiIyLKlgkhERERERJYtFUQiIiIiIrJsqSASERER\nEZFlK9XpAGRhGGPeCPwxEMZNTjx/v7X227Otn8s+5tpHRJaXJOUf5SgRmYvFyluSTCqI9ly/t9Ze\nNLXRGLNmjuvb2UdElpck5R/lKBGZi8XKW5JAumRuz+W0uN5ZgD4isrwkKf8oR4nIXCxW3pIEUkG0\n5wpn79KWfeysTzseX0SWpk7nn7n0UY4SkWYLmbeUbxJOBZGIiIiIiCxbKoiWr90ZxdBIh4jsjoXO\nP8pRItJuOvuzB1NBtOea7TpWXZ8vIgslSflHOUpE5mKx8pYkkO4yt+d6sTHmHUy/9ePPgf+Zw/q5\n7GOufURkeUlS/lGOEpG5WKy8JQnkhKHO9ImIiIiIyPK0yzNExpgU8CXgQCADrAeeAu4BHo27XWOt\nvcMYsxY4C6gC66213zLGdAG3AIPACHCGtXbrQjwREdnzKSeJSJIoJ4nsGXZ5hsgY815gjbX2Q8aY\nlcB/A5cCA9bazzX12xu4DzgK6AE2AUcD5wI5a+1lxphTgVdZa89bqCcjIns25SQRSRLlJJE9w2x/\nQ3Q7cEc87xKNahwNHGqMeSvR6MdfAX8IbLLW1oARY8xjwBHAMcCn4u03ANO+vVdEZB6Uk0QkSZST\nRPYAu7zLnLW2YK0dN8bkiD7wFwIPAB+21h4L/Br4e6AfGG7adAwYAHJN7aNxPxGRligniUiSKCeJ\n7BlmvcucMeb5wNeBq621txpjBqy19Q/vXcCVwA+Y/CHOAduJrofNNbXtmEtQYRiGjqM7FIqgW3VO\no5wk0lH6IEyhnCTSUW35IMx2U4W9gW8D66y1/x43f9sYc6619iHgdcBPgAeB9caYDNANHAo8AvwI\nOBF4KJ5unEtQjuMwNDTawtNZePl8TrG1QLG1Jp/Pzd5pGVFOmi7p71/FNn9Jj00mKCdNl/T3r2Kb\nv6TH1g6znSH6O2AFcJEx5mKie6n/FfAPxpgK8CxwlrV2zBhzJdEfCTrABdbaijHmGuAmY8xGoAy8\nuy1Ri8hypZwkIkminCSyB0jq9xCFSa5EFdv8KbbW5PM5XRORDMpJLVBsrUl4bMpJyaCc1ALF1pqE\nx9aWnLTLmyqIiIiIiIjsyVQQiYiIiIjIsqWCSEREREREli0VRCIiS1RC/wZURERkQYRhSKFU5emh\nMX7+m21t2++s30MkIiILKwwCglIRf2ycYHwMf3wMf3wcf3ycYCyej6fB+MRyUCzw655uvNwAkx/K\n9wAAIABJREFU3ooVpAYGSPU3zQ+swBtYQWrFAG53D/reEhERSaogCBker7BtpMSOrSOMbtnG+Nbt\nlHaMUB0ewR8fg/ExstUiPX6ZrqAMt/1TWx5bBZGISJuEYUhQKkVFy9h4XNiMETQVNI3lelETtzHH\nsz2h6+Fnu/GzPdR6V5L2q7jbd5B69pldb5dKEfb2Q18/5Ppxcv04uQHc/gG8/gG8gbiQ6usjlUrh\neQ6e6+C5bjSNl1VUiYjIfIS+T3HHMDue28bw0HYK23ZQ3L6D6sgItdExKIzhFcdJV4r0+CW6/TI5\nAmb7hqHQbd+FbrN9MWsK+BJwIJAB1gO/AG4EAuARa+26uO9a4CygCqy31n7LGNMF3AIMEn0b8xnW\n2q1ti15ElpVO5aRfb3yArb8fojY61ihswkL0j2IBpziOUyrilYs4YTCn5+LjUPayFL0shexqSm40\nX/KyFN1MNN9oy1CM56tOCqYWJavADX36akV6/SJ9tSJ98bTXL9BXK0XtYwV6h7fjMlF8BfG/alNc\n414346luxrxuxlLdjDdNx9M9lDK9lDI9OJ4XF0tx0TRlfiCXJZtyyfVkyPWkyXWnG/N98XxfdxrX\nVZHVKUEYUq74lCo+xXKNYqFMaXiEyvAIlZFRqmOjBOPjhONjhMUCp1z+kU6HnCg6TpLlKCiX8cdG\n8UfHqI2OML51O+PbdlDcPkxleAR/dJSwMIZXLJCuFMjUytSzvAfk4n9TVb00tWwP5e6VVHr78HI5\nsgP9dK9cQe+qFWQG+vFyOby+HF4uh9vd3bbnNNsZoj8Dtlhr32OMWQH8D/DfRF8ottEYc40x5i3A\nfwIfAI4CeoBNxpjvAOcAP7XWXmaMORW4CDhvtqBKz22mum1somHKwGk4vWG6aaOtc9hmSmMYRI8U\nhvEjhiGjhV5K28ejUdKwaZswbNo8jJua9lefn7JNOGUbZtlm5n1G05Et/ZTGqzjpDG4mjZPO4GQy\nuOk0TmrhTwYGYUgQhPhBNA3CaD4MQlJdacaKVTzXIeU5uK6D67R/tDkMAoJymbBcJqhU4mm5qa08\nbf14V4pCoTKxkzimSbHV553GfxptYRjiB+AHQTyNnnfND/Hj18D3Q2pBEE3DEN+v94/61Zq2afQL\nQj78BR18TNGRnPTMZz7VmHeYnDgDHEpxAVPM7tWYrxczRS/TmK9luqhlugm6enAyWTKZFJmUSzrl\nkkl78bxHNu3Sl/LIpN1GWzQfTev9057LihU9bN06Fr3X/LDx/vP9YNJ8NQjZEoQ8V/OhMIYzPoo7\nPoo3PopXGCVVHCVVHCNdHCNdGmewtIN9yrs+LiuluiikeyikexhPdTOe6mHM62bU62LU7ebpMEXV\nTVFx01SdFDXHm1bMOUBvd71AmiiS6vNTC6lcT5p0ypvtR7bHq9YCipUapYpPqVyLipl4PipuqpTH\nCtRGR/HHxwjHx6EwjlMq4JQKpMpF0pUimWqJ7qBMt1+myy+TDWukgTTQ2+knuTR0JCeJzFVYq0XH\nO5VKPC03LZcJy/E0Xh+Uy/jlMn6pTK0Uzf+6VKS0fQeMj+EUC3h+dcbHqucOiH43Fr0sY6luqt17\nEXT34NSLnP5+ulatoG/VCvoHV9G/eiVerg83nVm012Wq2Y6SbwfuiOc9oAYcZa3dGLdtAF5PNAqy\nyVpbA0aMMY8BRwDHAJ9q6nvRXIL6yVnnzPkJLLbfdTqAXdhVbIHjELgpAi+F76bw61M3Okjx3RQ1\n16Pm1KfRfNXxqLkeVTyqTvwPjwoeFcejihvNN62vOSl8x50+ig0QhnhhQDqskglqZPHpcnyy+GRD\nnwxRWyaskQ7jaVAjE9ZIBTXSQZVUEM2n/BqeXyXlV/Hif27gL9jrO19u/C89W0eZj47kpKcPew3V\ndBa6e6G7B7e3F6+3D7evl3RPN5l0mt60y4rmwqVetKTcRtGzEJeb5fM5hnLt/yUShiFBoUBteAf+\n8DC1HTuojQzj79hBbXiY2vAO0sM76BkeJhiZ2x+2hjiE6TR+KoPvpqh6aSqkKONRxKMYelScqIga\ndlIMuSmqcTFVcVPxNI2TyZDp6Sbb101Xbw/duR5yvdmdFlPd2VT7B1/C+iBGEA1+1KdBNK3VC9J4\nvhbU+9X71PvHfev9/IBUOsW2HQWK8VmbcrFMUChAMToYcUsFMtVyo5Dp9ifPDwRl9vbLeDOP/E1T\n81LUMt3U+lZR6eoh7OrF6enF6e2NLqHM5cj095Hp72/ra7iH6EhOkqUtDEPCWo2wWo3/VSiUhik9\nu31ScTKpeClHBUq9SKn/C5oKGqpVwkoFp1bBqVZxatU5X7Uwa8yOR8HropDqp5jNUvC68Lt6cHp6\n8XL9ZPr76VrZT99eK+nPr2JFfgV/0N9NNrM0BrB2WRBZawsAxpgc0Qf+o8BnmrqMAv1EZ76Gm9rH\ngIEp7fW+s/rlioMBZ2IgHiZG7eud4pF6p6lh0iB+0zbRrpzmXU5sP3XfjcH/Gc4SxEIgCCfOHNVP\nDoVhlPEI4/WEk9c1bRPNT+xr6v7rgYRNAU/u5jT1jbgEpAOfVOiTCmukQj9ers9H01TNJ1Ut0hVE\nRYc7x1+a8xECgZeK/6VxggDXr+DVatOeSavqB04lJ0XVzVJN91KND5qap1G/dLzsTVvvO+7E+yIO\nrTnG+rznumTSDmkvHqX33HjqTIzuey7p+ropy+mUSzrlkPG8uM2J21zSrkM67eE68Xtu0tlDqetU\nTnrn+g/uVtwLLZ+f7UrrVvUDz5u1l18uU9m2ner27VS2b6eyLZr6hSJBuYRfLEW/uEsl/PhfUCrj\nl8bxSyUIdv8XdjQgE32mC26a4aYiqualIJPFzXbhdXWR6unCcd3oTLYfEAQBYRASBAFBEBIGQVQQ\n+vE0Xh+G0bqgkcBDHCZyRGM6qT2eDyf6ODDjtk4ILiGpoMoLgkqjwMmGM4/GThXiEHZ34/T0R0V7\nXx/p/n4y/TmyK/rpWTlAdmCAVK6PdH+OVC5HOpfDzXRuVHap61ROGhmv0N2bbVxt4brOpPlOm09O\nCsPo6ohqzadaiwYFqrXmf35jfto6P1pfq83UPn3bSb9XwxAvrOH6Pm5Qw4unrl/DDXy8oIbn1ya1\nufGyF0xsN3XdpOWdtHlBra2vtwvxgLTXOL6pOT1Us/F8/dinab7mpghTacJ0BtLRQJOTyeJkMnjZ\nLG42S6o7SyqbpWdlP6v2GmCvgS5eONDF6hXdrOjL4nl7zs2qZ72OyhjzfODrwNXW2luNMZc3rc4B\nO4iue+2f0r49bs9N6Tur99/0KYaGRufSddHl87m2xxbGB7/1S86i6dTl5ikztMOqVT2MDBcbCdFz\nHRzXwWtKll4jYRLNuw6O70cjFZUKYbVCUIlGK8JKlaBaidur0ShFc/ukPtV426hvWKkQxNOwUsHL\npAi9NE42i5vJxNPoA+dks7jZDG6mPp+dmK/3jdvCTAbSmehsFzRdEhQ2LkGbdNlQfPlZY37S+mjd\nihXdlIvVpkuSooInm/YahU4m7eK18Y/3dqY94zh7tk7kJGBZ5aSWpHoh3wv5/RuXTcwltmiktBpd\ntlEuEZTLBKVyNDJaKkUjn6X4ctdSqTFSWu9bK5aoFUtQLOGVS2TLZaiWcaqjiTpjPG/pNG5fH17v\nSlK5Pry+PrzeyVO3r3dSm9vTgzNLngqJ/oClClHCGS4D5TmHtXDF99LViZx0+sUbdrnecZgolOLf\n+fXL1F3XmVjftK75UnbXAWfKthN9aOwn6s+k+VTaY3y8EhUv9TOftYBaEEaFix80zoZWa9H6GYVh\nNJgb1sgE0dUi0XyVdNi8PPO6vriteZ0XxoPGgU9qgX/j+jjRFTiOR7Vx5U0XtXTUVqv/c+M+jaty\n6oVKmjCVgUwGUhmcTLpxbORlM3iZeNrdRborSyadmnRpdXd64kqFTCo6pqlfbp1Nu6S8uV+1MDWX\nB5Ua27a1t6hrVbty0mw3Vdgb+Dawzlr773Hzw8aY11hrfwi8Cfg+8CCw3hiTAbqBQ4FHgB8BJwIP\nxdONyDROPaHgRCfcW5TP5xjqauFvhVwX0mlo4x+nTZWYg7YZJDk2mUw5ac/jOA5OOhro8Pr62rrv\n6Nr56JKSMC6gqoUi46MFcr0ZCuMVXM/B87xo6roTBYXjNP45TfPRFQXRYFPjsoNZ+9T/MbnfTvoM\n7reabSOVnTwrSZJO5aTXHNJPuVyNzmg2DagSBtGgaRg2zmpGfYLGYCr1tvr6+OqVoNG33hZEJ2/r\n7TRdZRObfu1KSCqYuNw9HdbIxZfCR5fG18iGNTI09Qmq0RUsfnRJfHQJfBuvKEmlcDL1Mx5dBK6H\nk0rjpFPxNB3noKblVHzGJJWKzp7U+9fPpjSW01GxUv9b7XTT9p436e++pz2TcPLs8/buZ3S4oLt5\ndshsR89/B6wALjLGXEz0M/sgcJUxJg38EviatTY0xlwJbCL6vFxgra0YY64BbjLGbCQagnr3Qj0R\nEVkWlJNkzpxUCi+VwuuZuD1AF9EwfJIHQrxsFlBBtER0JCe9esPVC/BUOsjzmq4IyUVXhWSjy7fc\nrq6mK0gyuNmuGa4ymWG+vt6bGGlO8ue+rztNcWzPuQRtqXES+k3nYVLfsEn+MCm21iQ8Ng0TJYNy\nUgsUW2sSHptyUgL86vLPhOVydElo42TCxMykybTlxt9N76zfrrd3Znmc3oFeSoE7UaBksrhdEwXK\npOIlk8Xt6lqUO+FC4j9biq0F7cpJ+mJWEZHddPv3H+fBX21u6z5fcegg7zz+4LbuU0T2DId+5MNJ\nPkBNbGwiO6OCSERkCdqw4R7uv/+HhKHPM888xymnnMbGjT/giSf+l3XrPki1WuW2276C53msWXMk\nZ5+9jqGhzXzmM5+gWq2ydesW1q49h2OOOZYzzngXL3vZUTz++GO4rssnP/lZenr0LTQiIrI8qCAS\nEdlN7zz+4I6czSkUitx8843ceuud3H77V7n22ht4+OGfcNtt/8LTT/+O6667mWw2y8c+djEPPfQA\nAO96159z5JFH8cgjP+VLX/oixxxzLIXCOCec8CbOO+98LrvsIv7jP37E6153wqI/HxERkU5QQSQi\nskS96EUGgL6+HC94wYGN+WKxyI4dOzj//A8ShiHFYpGnn/4da9YcyU03Xc8999wNQK02cdvUQw55\nEQCDg3tTqcz9NswiIiJLnQoiEZElaue3ZnUYHNybz33uH/E8jw0b7uGQQwzXXXcNb37z23jlK1/F\nvff+Kxs23DOHfYmIiOzZVBCJiOxh0ukUp556OueeuxbfD9hnn305/vgTOO64P+Hqqz/HzTffQD4/\nyMjIcLzFRDGkwkhERJabOd122xjzSuCT1trjjDFHAvcAj8arr7HW3mGMWQucRfQF2Outtd8yxnQB\ntwCDRN/GfIa1dusc4tItblug2FqT8Nh0dDoD5aQJCX//KrYWJDw25aQZKCdNSPj7V7G1IOGxLc5t\nt40x5wN/DozFTUcDn7XWfq6pz97AB4CjgB5gkzHmO8A5wE+ttZcZY04FLgLOa0fgIrI8KSeJSJIo\nJ4ksfXO5ZO5x4GTg5nj5aOBFxpi3Eo1+/BXwh8Ama20NGDHGPAYcARwDfCrebgPRB323deI7P3SL\nW5HESFxOEpFlLXE5ScdJIvMza0Fkrf2GMeYFTU0/Bv7ZWvuwMebvgL8H/hsYbuozBgwAuab2UaB/\nroHl87mdruvuyeB57T1r392T2eVj5nJd1GoVrr/+eu69915uuukmbrvtNh544AFuuOEGnnrqKe68\n806y2Swf+chHePzxRwA455yzecUrXsHDDz/M1Vdfzcknn0SpVOCd73w7RxxxBB/+8If5+c//ixNP\nPLEtz2NXz6HTFJu0QxJzUqcpttYoNmmHJOYkHSftXJI/W4qtc1q5qcJd1tr6h/cu4ErgB0z+EOeA\n7UTXw+aa2nbM9UF2da3in/7RAfzpHx0wj5B3/zFHR0sceGA0MhIEKfbd9/kMDY1Sq3kMD4+yZctW\n3vve9zVucfvznz/KmjVHcuON13PLLV8FoFAoMTQ0ShCErF69P0NDowwM7MWWLcN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DyweKcuopy0q07//iq25ev02GSPzN14+ynwDDMbAiZITpf7+0VfRDmpIYqtMZ0eWzMsdoTo7cAQ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\nRERERES61v8HsyfPlbPFJHgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11acc1ac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(4, 3, figsize=(14, 8))\n",
"plt.subplots_adjust(wspace=0.5, hspace=0.8)\n",
"for (ax, ka) in zip(axes.flatten(),df['ka'].unique()):\n",
" ax = df_agg2[df_agg2['ka'] == ka].plot(x='month',ax=ax)\n",
" ax.set_title(ka)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## うーん、フォントの設定がされていないので、日本語が□になってしまいました。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ヒストグラムで分布を見てみます。"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11bf70eb8>]], dtype=object)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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B09T8+d1Fvmf2KPm1t8KhBMBDEXFGZt4HLAXuAe4HVkXEHOBI4ARgw2RWtnXr4CGUML31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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bc7d3c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.hist()\n",
"#まずは単純な通期の分布です。"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11ee0a2b0>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x11eeac5f8>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x11f66c828>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x11f6ac080>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x11f6f8198>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x11f730828>]], dtype=object)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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OSiETR0pk8f5m0SeI6Vdaib9MiWEsMB74tne8lcAzwCnVG4rI7cCLqjrZ+/xN4GvAJsAL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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c9e2dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.hist(column='dpc_ten',by='month')\n",
"#月別の分布を見てみましょう。byを指定するだけです。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 最後に箱ひげ図を描きます。実データでは派手な外れ値が連発されて、綺麗に描画できません。\n",
"## まずは外れ値の除去を行います。"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"outlier = df['dpc_ten'].mean() + df['dpc_ten'].std() * 2"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_outed = df[df['dpc_ten'] < outlier]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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/p+1iYHt4extwXqKV5u9Y+gbwAkEQTlbH0r+7gfvD29OB3kQrnZjR/cu6BfiG\nu/8BOINwTTR37wT2Ah8K25Xb65cVp38V/fq5+70EAQ/wPoJQSZRCIsIY604tAPa7+/nA74AbwnY/\ndvcMR34qq2cSrzt1jH3D3R8rUqkTciz9c/dOd+8zsxMIRoE3FK3wmMboX/aw2ccIluKHI9+DcHhN\ntHJ8/WL3b4q8fiNm9mPg88CTSderkIivA3g6vP008NFRv8/9VmK5rTuVT9/KUV79M7NTgWeBG9y9\nLfnyCuLTwHfcPduXsdZEm+zvw6OJ3b+p8Pq5+8eBJcATSRemkIivlWANKQhenF+N+n3up+0XctrG\nXneqhPLpWzmK3T8zOxnYAix39x8Vp7wJy31dziM4tJn1C2CxmdWY2RwOr4lWTvLuX6W/fmZ2g5ld\nGbbpYdSIJAkKifi+BKwwszbgQuC2Ub/P/TRabutO5dO3cpRP/24jmFC818x2mFniw/ljkFv3vwd+\nm71zlDXRyslE+lfpr99DwBVmtoNg4vqzSReptZtERCSSRhIiIhJJISEiIpEUEiIiEkkhISIikRQS\nIiISSSEhIiKRFBIik4CZrTSzy8Pbf2dmV5W6JhFQSIhMFos4vGqtyKShL9OJ5MnM/hy4iWBZhQ8A\njxMsxvafwyZLgTOBvwnb/Bb4nLu3m9nrBAvPXQjMAq4CjiNYSqKLYCno5UAd8H6Ca5Lc5u4PFqVz\nIqNoJCEyMWcAK4BTCJbdfsvdTwd2h/e/BXzS3T8M7ATuy3lsu7ufSbCk9Xp3/zHwFHCzuz8btpkR\ntvlLDi8NLVJ0CgmRidnj7r93915gH/CTcPubwMXAz939d+G2BwiuW5H1THYfBKOIsfwQwN1/BRxf\nyMJF8qGQEJmY0Yvl5a7GOXrV3CqOvFTwofDnyBhtx9qfSMkoJEQK7xfAfzSz94X3V3F4pBFlkOhr\nzpf7Uu1SxqLelCIS3+izP/4fQTD8ILwu8RvANRFts54Dms3s7THa6OwSKRmd3SQiIpF0uElERCIp\nJEREJJJCQkREIikkREQkkkJCREQiKSRERCSSQkJERCIpJEREJNL/B1laYLu4QPvLAAAAAElFTkSu\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ac247f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = sns.boxplot(x='month',y='dpc_ten',data=df_outed)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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